From 8d3e59c65fd2ede819f83492ef93727b4029e09f Mon Sep 17 00:00:00 2001 From: Yuanhua Huang Date: Sun, 1 Sep 2024 08:54:55 +0800 Subject: [PATCH] rebuild with R v4.4.1 and bookdown v0.40 --- .gitignore | 1 + 00-preface.Rmd | 2 +- .../figure-html/fig2-simu-OLS-1.png | Bin 120216 -> 120092 bytes .../figure-html/fig3-diab-scatter-1.png | Bin 195931 -> 195817 bytes .../figure-html/fig4-dice-1.png | Bin 83183 -> 83197 bytes ...rants versus incidence and mortality-1.png | Bin 265056 -> 263825 bytes docs/cancer.html | 102 ++++++++---------- docs/image-digital.html | 32 ++---- docs/index.html | 34 ++---- docs/install.html | 32 ++---- docs/introClassifier.html | 48 ++------- docs/introHypoTest.html | 32 ++---- docs/introLinearReg.html | 32 ++---- docs/introR.html | 32 ++---- .../gitbook-2.6.7/css/plugin-highlight.css | 2 +- docs/pop-genetics.html | 32 ++---- docs/preface.html | 34 ++---- docs/references-1.html | 32 ++---- docs/search_index.json | 2 +- notebooks/module5-epidemi/M5-epidemiology.Rmd | 34 +++--- 20 files changed, 149 insertions(+), 334 deletions(-) diff --git a/.gitignore b/.gitignore index 58e2b32..e6a8b77 100644 --- a/.gitignore +++ b/.gitignore @@ -7,3 +7,4 @@ data/outs _book _bookdown_files +BMDatSci_files diff --git a/00-preface.Rmd b/00-preface.Rmd index 5efcd84..d53d7ee 100644 --- a/00-preface.Rmd +++ b/00-preface.Rmd @@ -1,6 +1,6 @@ # Preface {-} -This book is designed as the lecture notes and textbook for +This book is designed as the supporting textbook for [BIOF1001: Introduction to Biomedical Data Science](), an undergraduate course (Year 1) at the University of Hong Kong. diff --git a/docs/BMDatSci_files/figure-html/fig2-simu-OLS-1.png b/docs/BMDatSci_files/figure-html/fig2-simu-OLS-1.png index 3769c13e2caafc9cfbfa743bf030eb0627ce376e..786620f4f85a8d1e69848253e4a16df7616755f7 100644 GIT binary patch delta 67098 zcmY&gWmJ}3*OU~H&YO@9K_sLb>F!n<1VOqbE)b-VMx?tHDM{%Dr3FD6q@=sQgY~?h zi*+x4xZsL?X3w6PO=9uwl%m@oD%+qJ4sLF4FgCL`?;6aYmZfop%?i1 z%ITf;ZrcZi6Z*;i)jSQznBoha51cy(*c8#wX5Xjo?3Jdv>vB?bUJK(M=4pkQT`(t9 z!B}{3Zru{SB`fhr1CbkZ;`NXs#4s7Zt3E^N7;C%O8Bw5HkvDTJzqiy==DusfY5i4U z+lM;_+}E$K32wL-zLQ6X%Rgm&5oIX#;1_DOKNvkq5pZMCySs^5_++ZV!)dcm*~g&L z66fklz|jxWJ5?!#7YE$GgdaT8!RFJ)WH^cZE=7+5@3H%QdCPIz6zY22QLJ4WH>4r> zfx35NytK@9Q%_Su-g#rp(=d}i3$uDN*4Ld_If)ZBUnzAMF6!EP{p-6hD)GRHcW=`A zwm$8U2$Cs9wzhrqcUpOQTvCX_^aO=8@?G2L1H7G9fr}sFp#hBSAFkLq?|p+|Vq)^A z9es?ZmBnS&ufty%NbiEugghyq3Fb_`_nPgAAbycEH6`$JpAHU}5`6jD!*luuR?HFJ zJb(!TxYKAsn@P_20adYh)0j;0AZqbIboN46gL3OM5z7c|PImeSS0#sz)+Y?3M6f*%)#V5$imcBl>r4+aX1E1yk&!%`yG`Rlhrt ztfmJgXT09$0k%!C*?5yaGjKxIN8)0~0d=mPvr)-U8yuc)Z^M5{N4$A$zKGO#ANUXo z+_OszK9P(=@AduD()@Q!R9HRj;_8X~&J88ISOCp+_X6t<{bn(TBqb?&&M zMVfaIZes?-#;SAPn^1tK6@@>N%}{vzkk{*k&F6HP=;v$`MNFhJIwqZbf<&>_J1B}y zo-p?1kcDN$8X}I9LT65w_pY>2UxIyP)r6G8(NA5M*oPKhQpyS)n<8+dMhOGENAi`a z(y}hk4ml*InOw-YjJI&Nwu(?a$K?|1NoO#A2D#mxGTt2GN-TT+(BQmrcuOjY_8EIk z3(6>qmf?6O#_y;5yTB45)QNC74Zj|T$>fZ~tmQCU;RLy78nl1QJcmW4j`1LH4ck` zX+lFhAw+B#8AJPJ%R<$5v)uQ#KP0rILs!p|kyRoX@~2p>=lGOuaml!lA}etV3V*T? z%qRy6jR(|^x4iP4=SxaWO&Mx*v^XfZx-0lZs4{5h?Vk#|siWpw@Z9Q12CZuiTz6zn z=f751DCfw(QHnk5rh)3~>WT_b#q*~#9w7t-1i*^kAJF2bnUKTBsH;<(waaUNeFQL& zeIO*8ukUbUJo{UA)9KfDkI&q69Euk=1!TVG(BWc|6><0kTGFy3nkNfktaGcdZ|@&x ziXlm!^u#h0$vyZKvapTO#c@+hzDop~gP90V2hwb?!@r~okmg9!jo?JZu?x6u1w=+F zho^=fTa+4nq9jj^5g1kJNfWBiEE+wRfj2Qrz?%=kD_S=CKw} zg_$OHs4c z2nq}&aV8(CMsle0LP9;qe7V2IQMVRAFOQtQo4u>tq5<3AAFtWliLp(Whv2oHPs;`B ze7+&rvTW74+Wu}Izdyam;BG5&_3SvtP>ct4kP~2I`>N+FF|yL^Dy$7<-%bKgcGKOQ zT^LlcqL&4`J3A`1i{Tg{v`sY(l;cu9?!8@C)Aeo+kXTyFJ$SVo z#bnPRa$yn3ZnjbLn$>Qyat%QVH&DoZ$LL^t_S)ZjcZ||Q?JgNv+U+vDKo|4p$I9=+h zuH`$s=&nv9_=dwJ7|&1gA3dQCA8Gv5(4cW$&b_n0A2#At=Cp{qvu`TM;dGa3gZuOf zCz{k(4PtHAl03f-c=si8pBhdbo2?ekd3T|s#(g)7DTm5q20hwgvD5hcXtUSftUHP- zkPNr@%fZ@+X1ZVb&M1svDabj3ggpqW2MT5PAnB%V(5td4OG`$(sX0;jK)jRL>#N(l z60u_ZEG#Vd)N)XgxU8SL4sfwN-4UEuySV z5b#3mUMIxEG|y+A_jeH}=|CUhTc%p{MmQv-wpfMCmVzc7h8Id z$8H9_u*RUuN)u*vE1sqm9nTauM=PrJazZp%v%_tD6gjqRkJn?U`=aeSYl z1XD?n?I(-pAnb>6OXhjj6@R zsC8QD1o*W(rfpdG{pT4J9LJ(hQl3{SwP=y(P8CV!|KXdHU(~hO<=tCBEUEUZ8|x=q z)sJfS=E`j+x00}r{UQQ0-KMYX9bAX1oc#ZQ?DP2MZ0X1nN2_Nec~!j*PJ`R1iQjKo zq3i74t99KZ`I6$1jwRdbq)3Nr!oae@pWI#S8atO0J_k)bfN>l;dD?93kS~+hLv)|o+FI9CojJ@` zqEzOjtm+an*Tr9a%2{qZ^7R%@y>E53+xL`i_!nbWBF1py^Vl-YCa7L9a2k%ho743? z!%LM0x7DI#41=FYm=8rH=J&;si|(bT1~aenyZ#Ig!lEz%2&GeIfc&cmG}_HE+n3;CW5&669UtuFQ+fTS2>GfE1%9tK zdK=Zp2fdz5x7=)qF=_Fdbo1J7Z`yQ#XRh)(a&@k&oJ!>TX9w@fw&0V~Xi{PDNVT!p z!>z`#v+i$*JH3_{XpHoB9faiyb}7D`Vuh@Ce2~xrp4p5A36v&rKiaQ(BAU-^k1lU5&1b~l`ymQ@w^okscYA__x2 zxA;vv(OqsE&SJTE+puFOZ=H0RqiBHwjfd0NQ&Rn}$2Ak823zE^o0K%Ndy{694xK#f zcSy+CD7VIHK*kChS!Af2M_lQNxSZvFG`lY{zx95q&Q)1k`Jm(*kf@BY z5`khe_gHOym$QQNd3%L=Yd*i*7CPe~wq_+c&eueFru8S#Nz#8r=>R9nt9ZfN^Zdxh zyPl>hWT;FwmOgs&q}3iClhvTv`oyxv=1Sa6CGn}vd4at3fs*Up`s2vasy?YtxESmO zS137h7xVmFF*OQ>-AH^OO=x#SSwwdMpO^kGN%pfu;guS>8u}bl>2qf}b?I)*Gc2H9xeUGBt|K0-K&gn|}eO3X~sR*Sy zmyN)y%X8`KkE8^7}HtUw_2xs_VuS@ce$VIgm)@$8nw$Glr~)rH!gAf>LSn$xg@|oF~b@x zaC$z|P;15SzC+n~z9sOz#sPtd)gZhm&v7+4Qw=>v4emg_I6+oK`ZbwqCgZ{(B~pAS z;e${x2NxrS(&pHHEg|lWW881infTRwf4AU|5*Y@cUKE`jMT%SnU!ET?0%|K0cQ4w? zdbaVC9?dss*lW>?CCMdcs%R`pOgeVHfbl3ho+mdn%dUs*;%Z6TCiaExlRUn&@w=f* zsIk&k)g45*r{>|`o1j^kOU0T&y(gp^-;J zMYW(y`6`OGDNdt3mML2=8tu8;)_2u&?NZ1f0wB~PxH{3Ri_7f~Cm1;t&HUIoWxZB~YVkD)e&i)g7X6 ze!X=6Al17n|0b8_5<~^tTk-Yz`t`13uOc-{4K5K73i|DV_%Ts#ekXxhHw*U=0+-rr zw6=P(t9oi;7ViTH5K?XZ1!$C-!Hgb`Q%&c#U)?a2rEd7p?=LM0(XX$79;0Qgq^%8U zFZI2_(k#}}?BLB#m6AdEPQ?0ZrSh%)4%Wb$N4_&$<(C%}D;LM>n2PIf@h{ggAH_a= zu)}Drs~@CQsaj)iyzFbV8U=c%SwWHRVY@>c4Cgi$+TR!8dw4_WG#c3a{rxrRTz9ud ziAbpLh%-jfVxhh*)p|#w1f}r$8TyA{EVasw#sd&cmyQ~-7V@^jYgv(l_?C;=f#qF5joW;a z;X#~b<(g{J5lRJwme}-hQfR8$u2}b37!~ou4m1L$dsRS?AKtm7k701>3KcSWQ)iAR!aEn2PHT{PH2_GF*T=m&41i?-@2Ib=k#dBPaL)) z^x3FDJ>?)&d2w`=4_XY2%Sxki{u(!x%e)s~^I}vD(v_Wl34|*cX9G}FiWiEI8P!An zHi~9yltfNMgI3>4k4Red8I|4RW9=eaTiZZ#F`@sxz&n)KU}EZuq$qy*rcgcq>pP@H zCrdSZC(yKzF>sUaR*2x% zF?h{(H}S)(A&6!*wJ&N>w;D8hh2S%)WdOC2X|itu+eyFc>37TF98?y=MzTdu>CiA) zxmUqR2A_E)(n9}U^&Aaq;^-}=&H6v_@^Bq7HC zF1}yKy!kas@c0s#Hp(mp$Sv=5%gp!WDJB;U?zM;Db&+BaPlXY&ng~I3yhb=F(VLTs zsaqrX29}0%T$s7;CD!-jvzeuM-uk9IbK01!*`@MJ;d3$pY9+VzRFs6+asM4U53+fQNR&9FG!{I_Vk{%d`qN?H~G)LG6@i`BVjnxneQnGw5&p`DD zqWMQtOV>Is4?{n$^~{Hu-Bcyfxe}Soul}c#;06=HlX5>7O&9jonC}Zlz|PKgH@MpE zq9}Uk{_*)Y>Mic6BQTJg3Tjh-o+*^^eQiIy_KKh1$YBN%g2ivp!CzS`rSf7R^T7Q0 zJ4ja#YK+ejD?9YIXFi2_9&fdbjHqAcfjRfXWsg(f@5A$yiV}x2ncJon92W;LymU`W zW)(Rtb;CJe#ubFJ=-25;m__LNCeG8+e}-dP-2D13xm?H7(=!{qlOiUwQAx{0`UV-t zSG`bcEod82Y`M5}as{C~PZK$mAN_}5v^&v=K&#ChKOrs?U?-`vnIiP@@xe*{{PDpn zkF3}_`*yw>iHAOE`)K0vy?(Hi<95j_|gChyWjphjwjF)q4riM>a%9bF9``(iPH_4V~M)?Z6buFki|T6GMrfvgdQ zxQ74V8411vzmV$wqn}uFM=BW-Wcgyc;r*JFXA1!uQ1*6e^w_7@D$&Wb(@v=d@P~zp zC-r6It(wzULr#NqQckMvHO|a!YB)?|SUM{#z8GoVdHDELq;I7#SS09YhwqEFxp}!r z5v0)ndX|FNT!ZH5VkF>7smrUodc(%JC-5xW5~ci_@p&@2!F#7 z#`5nFG6#c!8)#dNH{Erp34#c0&C2nbLn#YJ9>iis_;lO^wmut z+}Yi2u}Jq0++P{U2B-$~0{{1(AtupTUXCLMXecvHkRc!XI)2UW5aL4rvYMlxR;htL zUFS7u^i+WZfHoHF;OyeYOTR*c2voq~iX<1rqWwoM{Sr}_AAdv#bRj5bHsl^|(x(@P zRzGJxk%CvOs>+0O?DSX!j?wv9xCAq`X}?3>;LTXZvN;)Hp#47kDPr4-Ko?v1eb`G$ zJ~5m;2Sk_ApBo0*M1Pd0JYKHzUuXVTari-4DBVfwYN)dKl@dKUGSLeNcWwM7)Ue2S zv4M!x1u%K2%v5@lMbPs|VY>azLhQ5QoS`afp7vOcVYN_WO+(e*@lwP4(h|D)vW>2? zht%GZ3?_X7J{L7;U2hK9aG%BUIsI52jo%!AU0Mn}+D!pXG zd7rT13?r~_Xh-Fe=S%Yku@;?pE{((V2|h;TaZ?l%VKo~R)9!#pr z?q&hIgpoBVvEgy$*$uvfSIs8kM>NCAX51#TpUxY|@k1{8_H@x<_@lNO{c>_Brke(b z(bp_ESlzfS5CcP%OC}#^D?q_RP|KBj1)4x1%Dr9qR1S;R4@2dAhJiA@ahAkxio8U{ zgLfMpc5C-a&}ALp=X{gr=l5E?DyuQI(yH6A;!>e6PX-9hZ^iFay6@w&5jms<{e23Q zUr=RM2Y@_;E*zNyZDhi6x;`-(1QpMGb$L8H5zj>u1HT4NiZ=4oo=+tCS-V#Dh9@+F z`2&IC0bcu$M_y$?Lq>u_64mv5^_iZBGpj1K_n)M$5y0}t+xk=cBC7%OhwQZs4GrT3 zY{2-meml8L>H>@34B@w&)q{G1+}QRNZ#R}luny{p`>Z*WgFFayi}eN;x~1>m%T2p5 z%`f7rQby;au|40(7z~5nFn2r)bphm3d8fx!B`xElii) z;sO+=hnhi8FYxk_2LYHZ0tu#9lbiG4@~+$4WEel)X>rZ0oC9qS1Eq){Eow>4wAz1= zDKn>z0!E>>g0_e;o+2kwb#gvOT43uHKW^{h{siRrf`*+12g)>f2P!2ZRx0CJKYL+M zgYirCaW-^jl9!6aGo_EL@e3m|F{G*9eK`Z{x6ny;)k|VApHnVCqA%fr zruzj@$X$?C%BDh|G0xD>$-zdmvAe~c1zO}I8ZRQgtoEk_2$p#Ii3^_AxC&c$8bEi?)@Opd$D+uM<# zeVKx>1vJsnvt1f03pJfea5(x^N!whaoQVRv!J*#iV{N)OKp=ilDE=FNjL6{&@$y?b zuckgbJwzB-SpH$2k!Ng$6NV!@;h{m3b5QQMEGGl26E0NE?qnbqWd$v*F5W9W<{^EN zrISZ!nfiTPA6D|TS+D(w-eX3d@(u)z21%zM{e_w%LZro|0)q;q^gO?df78s^gH)h5e~*PP;sGw3t70^dox!Ke52_-5lU3H@4MgqgkFwm+aSElF0@Zho z*j~||%*uk<4+Zu8EA70d74ksgBx-5Tk${$wF+6`Oub|TVE-x!$=}`-i!rXi0qF-)| zx2;gO#Zyr2VP{Vdh+Kz%uXTF7!@OW&xhJlQo(u){Qa~4v5DGFs6iD9?pQqGwG0jLw z4+$QQd|O!IREyTXuSWF=5a7u-bt`~P&VeUfH(t)}ys9EIcBG8McXhhIQWOTPqOOT} zd>r(_9NZX7APtqww%Tt^_Sa97nhxqkfFQCQ1@HUM7N?{2a44AIP3tIR~w zKU+0N2Cah^-f_jSh8QT?Gsj!g=WEC+ib~Q0JtOmt&U|E?&*CLoIjCLPEn zjoJJYk;u}ZSvf|%ISTNv(rQd@Sw6zn{djv;I*jmv#x5e^UWkJ1UOx#<=Q`;KkINrJ zjglW86_5VLySKl(@Cb&}NSARrDWK-&HU$K&IsD!v+lyzhUf)<)lCX&pB&-e;lDRRw z&kj^i#dCLt8okeVetzR%eo})C=&PDFg!lGMaM>9$dP@!ysF5^Y2Qdtlm^?JHcYvu% z5({)Z&H%Pck;h!KA2!bn(7ua$)o{80SBX-`218X`FCSxO2YX0>Gf?;i=?S*N0)3f% zBXBkq9Zg?{6l`1^i^%XAp`>h0k8>FwqqSAjy>y_JggNFT_l2n{(r3ixFH1oN=@RYyGR+ z?Yzn^JWqInObt}5*P~S$=ma>{Wm++`vRNz*OFSgNEl{1mKPi0j%YOd-9?wu@U>9Jt zHSJH;DNvz;N_5KLJ4sXzQU}PVsDD&EG`s4Fq2rL)M7SHoV>h46=opvdL7KYDF+qi1 z;TV+hWQ_7jcOo_oNkrQZKY@3>CyaQPAVMIVXy=Tfma4f|sMd-KOKxnPIDpMtEc}Wq z021Iu;`jQlKv9%Ktsk@IST+e$_7Mxt2<}UAO{hJAo*JnmJ5=I{{G+{sti$2BssQ0= zOPf=r_?LdvxM-|H8Vl_wKR%OAQ{j>r<}2L=2AW}2DxZ_o%_-j)3@4@M@(n%IWp1Wc z#mlmCB1{$X%xGS;P2j;^iRJfu1+46rJJ3 z{tq61T%8n|=XCWf;4bP?xplUu-{D%)ccd?)w<}*$s++Gal+257sDauEQ|$JfcKlrC zc_L!@xC|kMY}Qj^75{)x?-O+Q?p=rDEq!xD{ZB8dCcmc1*gDJkY``HP7aSIO9m{vWXvgQ(JD&ue zt7ddoaF-nH(?^o}kNRnJ59Q3x4%Tkw+_8^GkvRoFr>aZk6d7lWsbeZM&_66J@dD+0 zjBt10XmdMEOS4c-l8dyMqKwtaCK-(PVscAaGLB;fP#XT^9sIFdOn3yvdsreDzD_G& z-dfKHV*L|&h*7%X6}Hg(?poD8*3Xf_ad&r5obR8{k6QW(5&~*iwew72GB&F!HH6i| zfuc#A&*F%D@e@K6&1@#Btj8xtozL%bZuA6*OSc?G+SGl#)y1aJL;7;tDTrG6$4eb( z+>AQZj4^M@&W3d8)%y<4$O4Z~Cc{;4ym4iEJ3&_#Z#@%{GQ4Z{Lj;sy?*v`R5{NXMQl|TH3raHSA|oS9 z$<-BIQqgMJ{*PpahuyXUOaRTX?OW{L1D>@-h~-wroRfC!`VK%kL$a zU7fXlpe`s3-Hkcwj%vt!zroXNy`;n2h>2CppCpJsYsJ(cA|H#Ny@4q;QvM+Rwq(1L z4#;i2?8YMWp0_#sLHz|8r}*9aNLd$mZCSbH5cR?85Q1Z@czSU4-a<#%7#6?x`8U(Q z(%}zkN+}hGTXEZTvK`LLy#(gy@obJbyLoZj+@}Vyb$qIl;jyPm1dV0~ z_kqHi-j@9x+S5n0TF=UKtaE|durAh}EL!BRSJdEjircV3zCVT&NrKAmwY%`@p>tQs z=TATAfkXIS)zZ?kL`37W5O*3@6vfBMrBRAS0*OEec}kb{(cTGaV@8lpiG)a)OP*LCyyHNT6^xYrAQe_jp)ZnW%LBw)M^ugBnyqg?o+A9plA^?mfI)PS0^xW`gZ!5Ti%LDRrRtOyhW0JU@F3ckx|AG zs*FJ*ijmLi7LrSC_MF<7+wsjLz8FFQ<2@feit?k_A}G%>`>|td*zHGUCLKFB_B8f= z_9Y4HTVrG!A591Z859B+>vuksHmL<=xs@5w4j7VUbIor774xL>k>1=;jGKJQt*reg zqnNtkZUU}9RZg2LZSr1xBtWo~AUF9LwLO;TlOZ`;J!(TD`rxoPATkVj(4H{iqa;E> zu732+E(EUj%ge_pSypb!M9}8k!)*C84=TngcjM43Qon z3jI7zY5&QhONj0M&jAu39|Dg{6`-y1GS$P?TTHr~aO|TR@ANc#K8KcAxc(sfj?C z3*#TE1qfFL9rmYzBCSQ5wA{gFFc{w{++ne;(TmPm#J!@$bfyyb4hLwf5+5tiR4gY4*As7DBv|W;6dU^| z4GAV}zk4_CiIB%$j@%@ZF+&J0-OGS>X@Wuxy8rPfY;b}Z{puRuk`$LhrC1+zao`jU zq>g1r=Gg71$Y}7VbW`~dE0}1dkDq3%Pl{R#N)q&N>=s8W`rhdMSSYN`mlyW` z91G#szCaSBkqU2)rjZ`ZlDwk{_tC5;hJxoIx#VA_Y~+HNvrx{i1`?^JqlMNTh^?5{ z(4FhAHuEkUX|{xJLal?P60q{h)t4?@jsE$?`L^kwY-JiRzzDqvijp63L1z%;)ij@I zJRZ`ijwSuCDn#MSaD!vXGSnV;&op=dr7vy|IhEUnq@=1>y9BZDudG9xL8AhaX28GF zI_A&AVEj2;0I6mtYSau6qMV#9O!G|RXf!_jeg$G}ROn$9K^fcCxr5i|pI|x=Z$x^M z^@9xy2kKzUi@0iEeCabl?`{V0Cw0y}gfj#wzX$qX3CP6wn}-21XJWvg&^)Q{Ujlsv z#%rHZbY@Vj^`Jz*o_iAQ&C^sqWhv<2!*PUPD5USe#)ua7J`-g!Qh)x&cjOAep7EUc zJK+O=(}{Br1EB0~)XC1Dwox;$RuFzuTlt`WORE1l7&Dk4ABCVBSxi#r)GEC9f5cWw zxVD>A3X?&)d;+uTX{gWTq4sErE}oK-5(+A63j`8TLnXQ#%f!Qd;gqmbqa8$%nf+>%XlKUnOFmgy3RHMXQXar>BQN zHu?F~M~rfnWFYCF5wVoGHoZ7RyBWqoLMDpUP#7Q(65x-cUEM)>;-gO$(J$pqb1 z2!#66gaiP=jS}{L;dMBs4Tb^{O1?G_CoAzvAZrwc_u2SL`KWNX`-4rB?eSaMOxTqT zq&qt6LMTLv9+(_fZ%e`B3k=o5piJf^4vGKu6xdRHd%`H!1Y7`*8?3f7JmmE}V*Xm9 zdqa+I#AT+)p}7`+b^s6n@d)nnJBtD)Fu$;nReJwp^xssh4yqrxgAS5mYdZCLHVjJa zqC!3Bs_3!65}&JN{$^>2r=bQl&fb9T%eCAM8eU!)Zl_|1!W^Ku(WE9GbYt`XgM&9Q zuV42Ill<6uOE zxHa$=vAP^urY07N%)6BkeoYeb^{(`x+99)9P68jgsojM<9Q!|lc4L^&BlZJmaaago z3XKC{gyl64K+vZxa}`_X+c}JWPL$o#EvJ3|1ofDDknw+&Y8lEIRKX^DTr3e*zpPjy z8VAt=lE_GPm`_nEDP((&nWbCLZtvn6aqOUXb_;k#rlZ(&uke2-YJbN@o9*GBvir&< zehEOuivpTN+&xwJkvzqbWxg0MBk>^24!~gJ%ha6#&Y~(SD?e%S(O*XOK3I*O=g6Y} z%hkIIh(BEjMaiHR! z=Aw8eb3VhJC*b*C*f*iUw%FxhHEe7L^!tO=7!w)4Gm5dXaa~zWU0v2Af8-*zmrIw< z&gj)`axxddxr&>w0O1`bbOSU-m!A`=Zi0VPq|mP$TnK6oNo}G$j4CTQhw3C2$^5Dv~?`cn{69vm%}Ta&K57KmA^J?l8H06A03p)8~rdPNcsT2+Iw5?%E*nT`|a8oGRgJb2IRB<9bN5Ae+i z{Z?&9L8~Xm^sdj%<`o)2Gnt+l*)`C7 zBg3mbFrbPRWcC4ZY`NKcIHFw3lwio(_cf6GZ$Safyb{z#jtHwIx)pJt&n7@XP{;(X zMemL-h@II~JR11&Wm^aZOot{>!hdW@nPvl&Zj4rt2r&gLn?ivPSPwUDU})Zn5d*1A zvkW}kqpOK>XHKU6Q;c^S-o3}211sc8HA0m{<*uvdW|10^uSG$$X&1<*_bpxm#=X$u ze>>{%LrG=_#dQWva z$RvYvj`>cnDwONfXnsV1^nXvt*98w`94f(me^m^~X82(WL$2)$W&g>8uJ#vCd9UizVq#?UuVZ98!4Syp; zJ!AFq_NGa_1et?Gu(;BAedRd{jN)P8|4}R8aNU(D8!q@m{x~VOP;~_T01!|yGxyIq z$O%6}LxqVMP4zs09V-$$lh>iAz7Jd5|FpYreunS<>DdFpAN>fH7o3Q>P>&1Xd4XHg z^)yAG9;xl@?3~v}WeFrkKJNY;Iz=0l74lh=9!xv4J#V`cbO-0=K0AJDF#lq!g`Q3C zr+=6Wpix8OUHh>J((x~S62qb{PMvMO2UEa}D3L8Af`BJt51k@+tf zauxR_&v1pD3i(uj|Mtg}DtQXT*?Gn#Af2QBjR{o-?UiEJ*X-p%#{$7;#0eCx!!VIq zPj6%a2H$}}zn$5NuukPWwRH?iDQs4>K$RThi~kQKo8?dk5k}=$$$}Jr{Z6BF#PX+~ zLzy&-=0FlFl)yJRIk^b)E>xU_bPo|3d0}t4&;9)9!ElbOKN+8+PIJL?I~1br^sYpg zwkV5P*`K_N0kn_ik6SP0@cm~?o}u)leLkYM?cj*HNb3v^=vV)pVEFw>jh6w(`4Fq| zZ{|3Rfej=)ZdlJix=5r$3D9T6)<+9XM?*|Vn2?gaY;n$}Z4y8OgY@JgwTB`BUn zpFjHtpq+Zz2ZjHmEFOtcUVLm4>Q8X`>#CAc-cS@UG$A2DcL+xXl0jQD0VA>H*OIq0 zmkGlsOFeiudop0-$dq<5KYSZNAB@fD7LYxkX^eTwzfH~!7jK;&CpU<&GVN<|IvwC` z7?Cx-KsUTX(s&=tbMDsGq)$pB@?bejk~qlSi}e3%#-N;=^-u$5ud2TiL-ybQRD@9w zB0$}jcLiJvUNQjf;T66JPMZZtr4Iql4TRXytioX9_kbRO2$EFz;svnBqHiLJC=sgB z0wR?5KfW*$Y;8eT=srTxw{i^wSkJ|98CvTdvattGim_gs`kUk2(|%6|Y^qnLV5N-z z1Nbge28yOGTUYh_M>xw-WY9=KN{pB96;Uo{7s}D|C5wslgaQK9cG8 zU&Gvch~H<v2+-%dY;011@1d|xBoB( z9j*rw@jQACA`K$EPCwAMWisqrs}dr$z8=pm-r}_S z`1$w%VOpA@zO|<>kB;Ke2i8n|JqFX1pg1%AXv7h%a{6|mh?U(_>beW++;5g5X7OJ1QN>9;uaXp_%ZJ z&4i={(ujqfE=%3fmggUS+&#Ee^`p3}?9Z$6jmNF=KL4mn)8w*|pn7(c=e6*|A>2R! zzj{j_YM%>JTJ_+9!wq=l>3zm!_tGspkVXOTOsPx>9xS`H0!iLsdq#*%*b4^umIgU# zy7v)ryC}&lE=XB@PUczMZo1Y&ngj^>@}YTehHAFd!OZLJu(m&SqJs{%I|26S_}dXQ z!c!KI^cuZ9++VyXVmD~=xeL}xAg$R`&^I(^&p~{Gr{@GnC35K9(Tbsyr!lGmTk*ZH!vgI z5%+nq?DS;&-g!S`QS8VO;J5q8% zcWl%q_cy?&QS^xGjuiUKz$ZBTGBPNER4Gm@lFVcG8aQ8VV`E&nF#s)%fHff#MF~gv zKraC44q*Wbw(P5Y#s%O&0^z-FXb1;4gs#wuIuvFK0c+eGKfe2ZPn!tXED2_Uw3eDv#^4 zQvMze9XL4lL>LqCLo=1k%E0xhx|!(WAQiXXH;I)F=yJ0@L693?_C5v!ujN$;;!3~Y z_+y{0`Z5|gEUd>}EvFqceuIN50Oui6+$-`}nU+_;3o9ZsWnXik_|tYL9>Uq&I|tc{ zC_#@sSUwYZ1~6S557#w=;r7*YME&54x`#m&4*Z8e%X+JC<+%^zCQYuq1DmBizt#Q| zc_`nb;4|xBgT>(vpw^oTb$}F|CQPLyl>7b8>X^6!G|5RgrEDV^BWl$0jG?k-BPZD< z@oDDIsccfOrVUg!bKlo@cotqagI!j8pg#L6QHs>iN?U@xNU*_*`|DDDFR|_24@t-? ze&~z_h?kqcg6g^HNn^h=kN#)|guL?6r5s{hPDIS$DPX=?U>5@UZoaZC8nZ0Bq^vST zGuyE+&xnZ{vpqu7is8iG(A!4X!EIq4rXA|^w||n-u-Sg+bqUgSL|?a5W>g`TBldGQ!tuDML27=3&dvBkpouF(3v!_XE^dHX$=kP=UYQMMf`wHOTrZl zpnvF=6+S!D4L10CV?*{kM}b{2=7e0|n)lM*j^28)*oh&t6cDW8#Unv`yV2cNuF9=& z;;Imk2a@2R6%a+-0SWZNpe4%qtXClUEC%SQN|8pPvQR_^`2e|DiKji7&mA2np=#CU8TQOAcC zt`h8N^Jt%-lLm}KwmG{TPvgFm-oBywrW&QChE3dZ>AipZ=6yukaD55%*C46F<*qj= zcml+Bu9L)7e4uG)I?#kP$Dr+)7!eF;pt@0TNX6>OF22afBeU4#C&vnXk=DVnIOD7xhI z)f-!0F#1FDnm?jW)bDaoc2k5M^|xhB?_s2-1M*KdFt?}(+6Adt|3i2Kp?|>kecIGC9 z{on{?9km+%=;%VBuDaYry&$N7Kp-h)gG%Z}naq_Z8h!V8JdX^|@)p;jK=9=swMCQ+ zr^bFB9%L79dtjoYOMryT=*wIX6X&kt4WaQOoZU>T5CHqkVE#Rx!$x+l-wKXi!E2_W z^;(6~WNNYKMGVMOCYtGffEx@@ot{BTQJkscM|U&=m(^%+e)L}DpSO?Qgt+(Pa~N=` zMFsik^7EID!WDCTGd|j@0nyl0J<{PCz@5X&m%EjAv*dfJ$}{epfA{-Kl;skbp%4(M z6r<)&6ZCiqdTa*BU7l&YD}V4kwV5es;L+a>x`~H^iP;ITMg`EF?gdFE*1o%X?18=1 zd4NB-U!0U}`hOTAp7D8scbBlTl}DamG(5d_243Kpu6c=LzH?wOM|Q_`i@8C+3amqr zdQ<3ZR6hNjgw!(yk;Z}`wlP>O2ZLv*Y{dEBN_>hc<1la&{5_4hE1>nNrHlL9P`0$V z&+YExpG1aUILR-<&zC*j7urJrqDY3n2*G3cN^DWpIS#}6kd+GRWT<07<#(O@j1>ic zRH5v-?m;~J^>Nd6CV2hs=US$G**X}|&0MH3j?WWAEq$AHQyN~;c<|~C+3tXWcH9f% zMv47W?c1!^Gyc9(7{XSzCS>V=q5%~ohY7+Y<5gMqi18}M$34-2-^+duw1FtGr)N3f zdQNV#jcKfZr}PgN@CeB0zTffVKDe^mcG3cws0E02@u6hLP$3Ua?rL=K(vcUCizdGw zONTy474{}E`7rilbv)l9!Uo;1y0f^G{r8)gz<`sLYXsy{XT=i$ReZe#NJ)VWda5hb z=OR@Ecs#FL7RR@VG9(T7j9O5Mmb@QG^&AKGM2!~Cad-&A7i(Xx!rh1@pn$KN`$P$w zAg}|WMcy%)qCw$}OKy3LNUO00%-I%@#?x5+USTmn*+ByKOZ-6WFgpsi>&rq(Nf*T-}z<2WYjE(-fEA^OY%3|G-0P_uLFZGA@mSgw#>Y!Rh~k~5|2U#p&-PsT;LC%VWaa)6cou$vjE(dNke!+ zjJ^odu#45tI`X~A&$D@6$IU2`;9~lwmBHT>j*Q0L?5yf}pc_pPHM43~B>4_7bmMn9 z>M*~g@MRA(0EG~tQ)~bAd*zQ)JaD^vIr;hCP}6N*&tJdKFA-Hn;EuB3RF9v| zO%nx1OPqG0mmPybA~sm&X$L<}!*JoW7L+{fysGo2Iu|t;6K|>$=x;v*eFLgVnLw6$ zUC9v~1Pq~o7a(|aGh_=qHCgt{MM3p899ylhz|`^T#3MGiZ6&pvK5mg0zLO?Q{AgL^ zJSRFt`UBNiEuElGdoT`@$Ra~()a^ef_ZO>zNo0VW?>*3q_YUF;$U-urf`VZJ9UJ?} zc3=~R@x950N`r)HOfX|)xp`mU%^ED&9eW9rx?!GxiWaxgwKsxY@S?r*8!0iGJJ08x z0|iFLSNAdAr@!A-J(baO17*|0O=L4NrP0}0!v|XW`uc}y<|Dha?B*l9g2z62N1jM< zv8zs6Q{j6%S8{xm@5?9AIT_9x1hc)S~)QIohP=ms)-MFRO$ zq&M>$(Csi(tM1&Dy<#ndTe=;R6Neoc4CE>*us`U^4k`T+JRvX(3nJ162zeg;TsKbR z2uzn0+udYz!2oK{c-rH?J>CHk2p2h_VX%iEz&acin05jS)K>6NZua~@^@)QJjkNW3q5}G11y#e0fWK(BAnN;$Dhl%6*1nl8pyX+Z*o$81uzs}E%nDF zW8s$5zk=@lxxQcHT!N%8vI@=npe2b9E^mxz>{m741m1z8gdRV%&g~X?N{4Njo_-Ud zW!ic4D5+*m$E?{GZs*`2T(XbbP5R;@Ah3~#zq3Pu&f+P^bL;|o@M)T%8@_?*+8>@ zgGP1tE}ok|FaPr6?DO9HJlD_k=@g;nID_w}%``3^*y)Z3W>fi{P|juaknRX?_;{sE z_kLk?J3H-oYEBf<_R9(-rTpM^>gWtMT%_8do46>I>?jS(k&V$hArDgF1mkH^R;a03 z1YSP<%W1~9{m$X!)S-p3Z)(As-Up9_)(rlPPQx9=r8@aXl#d8+ zN9^)7)Q%C~R;Zf~m%vF;Md(Y=BA5}cc#5hWp8epIJVE98u2r}q*ra{_p^WomW7bqU z@Dj{Z-dN*txrjPk`O_< zl$2Ja;{_2hD5XUSLApb_m2RY4Qo1|7`C;7S|6A+cv+lvW@Qvr4*|TTQ9-?Tgw}#r3 z>gVfs<)DGpxa6pL){L{MJye?jog7*DcAoF)3=vGZKf^oEn&{;M-8V%*9>rUOoIRSi zsy@FjMIupiZg$ekp6+CIx$Pa_Ck2*Tewse& z@4oA-D%H?GR3jI1u$H}Im$d{SW zPH#&2dHfwS74vrl7kS1LPD=@ezmcbWBNh`O**G^`dz2S@(k&otd6OkkIpXDb!#w(&Y*E`oX>ae0worupmm(b7f-AD;U&-iZS{hdfl2H{;!2YP=I`n$z@3U1 zn*gUqVeee*B9hBC7RD}n{@da<&uLeCz8`JO#zDCFy7$t=v2iaEdH7N!mV{18*aFfW zEOqV~?F1sDO;20%{#CG_5E2%a6A`YTll^Z?^(BHama?%;gD3fik%nM)?)GS1*Ud!1 z&sP{NE}Wo2Xt=**5=B=$9z`lxjeXXo(L#@I4AYpDh)~jEgC~spbDVad3LL^4K}P8# zkm2u77>6Ao{Du^Lwl^!bo;c{5X7_|^_RYT!68^_H&h=CO-lp#vhxOTuj8+uh771T& zy?^wvq-%L-jFz;}23!7U0jHBpB$O5*_2hohn*W|>g?|5MLKN_~yHd3^8*kvq$7<$V z29yNWyd*n{d!1DZ`c&6tP}PlsVvhZjwT%sDqTV}Y577QQ`?x-4`txv6<*y?)P~<`% z9MAo|J{DRip0 zKG1(eIUE49`a#{8Mdt%dQnL4NGkacDiuX~kHBDkVQ#}P~xV_-9e(6nFaFzhaGEcyq zRqgCk4AALFVh3g%B;IfTFtDhDwU0T2Zt6BAMEB=n-LVi7K8Hu5HR&`S1Fe>`64PTp zJeIE5Np6)#RgD)*7ws-Spg0kxJ56A{9nbkf*!4B>`yTk{J-ofW7u9SmAxNH4P1o8< zRI*zf+==ze`Q|yX`|2&g+sKB1Ydgx{1it{jNd%gX#PlBJOOzANOiu<4wW!-Z`U_wH z$Gs66HEYR&K)vTWy6P~AZgUerbKX5bE3Kvyd@rZCFdG(wQ;pBg$kLboNiko~Yx zJh8xqa*$`UClB#{bYsxf*|tZ>3GKbbowp!R7r!y;OYArzRn~?YS+yavr6VG0ZG#!l z6v^jCmVZ6rARDRCqJ#Qt`IEKO?~97|uK>K({>4HQK33k2MN|z+{R8XrH~V)?3e=B8 zbzP+NGHM2F_LpLxb&8yq<(C=FnD~HIkiOO=ITUuo_fOz z2O0_%5P2ATQ0vDi{&6xxRg)E_IBjUAd7*wMemyFnN#SFF)Y7~RpNL7qwU~m3v_rX% z0&B$Ep9qo0QdtYl7aXiJ)*OhCAiDkMf6nU$n%gOnuhR~PHuYY zCTzA949xaldvp_bk&G^h>*8Ljyya>3*?;a{&?D@Qn#SHdpWE-FlSzzH54>sLPWm_n ziBv>@ChiNNrpqSYdi8o*Ah1EfQZ=nE4^P^s0uSG{!G82O5e)E%fz&!5Fxj8|8KO*<>Bs*X5O1wIm(}|o>QZz z?Rq%mgVOU9?k5KxRr84C)K#2c5BO{~I zvm8hfKn=5vi8K{4aH)SEYN62s(J z{>i%#!JCFe&ae<(y=`rhNuWN~bep}X{ub$`?d>X~kE1TTK9;&B*#^txq7>-(v$OrF zk8YkWUYlxGs%fTi+eTL$;e-mKa(iW55p+{#@6XlB{I7utkDC!b|3vBQ*=K&eScK%I zAO<<75m&Me^ONzZDWmV~Up|$r%m~Q#^PK}azDtfdT=mI%h9IWz8{TKrgFDM%oHPU! zC$d!bDZ4#edlj+oM9K~r1h@M6`OOUEv-Rg&`J+7kKqF=S_^~()b1K5@Y@S8%nephq z0S9%&N6)|!-+hIkIr9AunP0g*P_x=V=b+4(jN6iuRWmFDBCj!Pm)KM58xz)+hZ6pb zpPU(esdBbVB1+>#zVb7s0%~)9{e@xpzF+*#KbDDw+8`8k54SH&7Y5?5EsZL)$Eq8C>8y)bUO*vA3 z)s}g8C|Aaa&G#)h_bFtXFMkj?u_$_oc}mU{?d|nu6i6zeWG8#@{+Z+-2IUX$Fo23~ z7P`7Ee#iNuLIPU~do58h_PSA1n29k$Kh^@^>=q#UPhDBTRG^4avp2vuGXMB8RD{j8 zQzn>?`(zi5hZ6%u)lOTvRBwU6hs8nE?JC=DMY5BN8#d1L7yNhR8K8mQ0O~_pm}~6& zMeX$xdBEsf@+DqW7qKg4EM}178&85k@r76D4ZksPlX#cL`%`0 z_nf~!-isKJg_MV*dAL5CB#{4wCHabm-iPqW&4DvBm6S_xmsj)$|{y6^aJ<}Rro|)Ff%Ggs%%?z1wLMv zb8EHt+^x<~at<}6Kxag5K3iEEX}oT{_jG%EZ>A=g-I4M1p6Thb%WB|R5N*A;bKb!( z;g$u zi|!ws`&hnxGJC;cj)!EE=5#26_|Lu|eSiJpey!iFPa?v^yYu8k7g7gm|LYHVEPRPZ z4_f-IfB{V6z_7f%K_@%RMSw&OUU+ojK3>;dov5FIhG;i#jF#!K3ner^_e9*~*`l59 zZv&J)W6+RjHvaFygHQVwj3a}Hn_}?KVDag|sflS`5+lttUx~l&gS{`3#n4`yk?>B4vi;RRO159e z7ybz&X!WYNvV(jS#jT4kzuPZ7+(5Z)#!0_Z$z$SklyP)rEO3%b zIRB3InoGQ_STqcbqSSv7&i?*Vcg)iaVlWsO_iHc^^{bcLF)^O)XF0UGTOBjS!^Z zF3HH}@@Vy`$G)PZLFIgO=-TvA@H~_!HNf_Q7W5fV&au!4oEa`HPDlQ+Ttq8YI8xq#@wGMBa);Gcp;Qm|Pwl9DMk_bFpM?{u~Mwonk%wY;ms@g!7dZPSpXI z6lFI0#}+gl{61oDb(Q~Iuev}r)|19Lym3TeZF3)bz*Z>dfzc(JMe!^80x%BmWDX8b z(7Z%>HKf!0f0qIm;zIbRHv=c}PDO34B>7pC zZ$aTgd?AMHo#iw_3G~J7>6VXK&>DPn|l&b!~tIjD{rcwHg%J^^EXVnoS^Nd}U5)W_VY0NP*(K zL~l%>9bq>PcgW|uo4aY4+6p{fEIOBpjPjrK{0j!{p-|C*mRU;>>I3@m(cOWxB~n=P zY^GyM{oZ91!p{YJKV9S??~2KpCC>>oQuLK&pZe{jZ|e)7`Umdhp)m43Z{1qEwgT_jemYAK!!!SA zRLl46^3S_@p&DY(xg1`V#9}1!X3?3HM z^yg(C-Vtvgg%;EpB*WAZe1@1>xahpq=@<`|@&X+hZXOlgd^^O=Yvdp934`4gJw4}# zE|BQG_T)Tl9d>n>RLp&hc9dW*HB)MjPE2%H?DaY$_UlVhXT+1&b#CHgzs4fH``Y~} zF*^Fs(Nj{2;&+SI-(z;nx$dzJGD`OANP&^mv}09sGt$!1a>#>IAHmEjL;F4K(py>L?nBj*8aTNfoZ7uX{U}~pMN-@6a ztkjbPTSMJT(^uRz=_)R61tJv*>s(W1=uxEy+>B4dUVj|0djJJb0L}FmRp?B?qtj-y z#Y|hd)^B{(UIFu0sO-q?j*>%V*Suc|0W! zuoXSponsrau@PrD2m{<$bxGi+=8w$zmmj-jZx#UVF3f*f(r^-pypyeW@Wy05jtFeS>2 zd{ab0!c}0xhjQi7OK#d-3?gRdGt7I`+@}3Ri;IiBS%!GKqNl$~F>L=_=UQeyzpq2S z!G9}>Y`Lk6*K+VB_*n@lz26>yrrQr_i_J1>yy`)NAqOAOFNxfbcvLSCE4sgYf*y@+ zuA?DdZg_au0f89?_J?1wb0ne}yUJ5_SZlJs%mdLi3c?<(!Y48E9|chMPZxp>iNpT$ zo-YUJVC+BQEH&t3J%0U(80_rqM$%!LBdIm&>iJ?lgu}JFh|0w;dT~5Il;r&i&BVUa zG(<{@E4+VVXIE4AmTt`92kCpCaOAmAL!yFyQM}LYz7+XJVQ0b3cPka0sy6m!p<;2e z-cc}7e0aELmB!6$$g~5)Sen;DtLPJ!&5Xm04D$Ct1E-6hStCk9Lh=k0-bpmjWBD2< z9XL8VN>q-!CV&MpW0b4&I1GDn^51+mpItMjs_W_n9+Xshs2f85zNY{JjK}4l<8F?d zzx(xx-OV{cRrPeN;V^ULmKPlXspL7n zkoMeco#bId?0YsDPljN$nl@=PA*(qv^LpMeNEII-R;>)PzO28#)?eXGiV{BH&YHJr z$FWX~0|RE>#2}3aCap}|g(;l^54qPek95Z>$M=@^9#Qa6X&QGpl;YyEmmQFjHfw7Q zjm`jY2w=dKnW*z90XS(f!IxGhj`RBD)46B;ETI>#?@LE33x?g2=q7JQn&`JsvY(tH zTBSV0qEc=Kb=7=8GAKU{>3n}0Q3CS5{$hJ87(r&$#_$K_`Wp&s?m}x4tGFm&q9mb- z`Yy5Z;onmPAt+AX9eJqmk+SvEacJbkq~Nvm0qFvxGs;~*%e;?+kDs4G)BjYbJgor2 zNO5QDn#6dLt=zLHFPn3fRXQUN9!H%uT~)6-Pk|kf+9-pnzLM(%z z@cB&b93#0vIooXGRzDb4Cj!LhjVT_^Au&4RHPTj-ffH}>;pONulORPQU*SW&wU}NJ zBQ~9k?_cBS>FcZa2ZV8#WX)caN2{TQipoWkt0&cm9q7Y zJ7Hk>CA}BEA`!2hz^JZ-_7n_1XZ_}who-@j&l=d|pyY5@Ejwpkrf|)X4`0+ne|@c} z*iSo$YmMvD?YdcFgL)z4tji578G1m&%_bHDm2|^yi4{5p7pQhd6eWax1U>TE*APiv zbK%0iiFdwNxWDlk7CBd5@RDtq^ljYrUH)(#YtzF&S6{I!<R8lME(Q98P>yPM6J! z7<1HpWs5`n;dR}_c+Am}57HIJSR_w^lX-k{5(~j;pb>FVvnQOO26cQwv73Qaq#kIEgg*#x5(Sb#R75 z+PW!nl^dT%5>~*>0c8yU+%DOj zO=0fV$NKM63}dO_7HdAM@A~V9?!nlD>*XG0Z=65XK9gsxY(2-8SLyfoR2x$KxE!-m z!TV0=+k-EKRs>8Yh&%R2U`h*VYamsxN-}^HWMXgYuQT z(eCO)*YnFFYhd^fHC0_%T}{_F3zDpA&@MetqR^9YLQ({vXS%GU7nuxDP#m>x9 zKR$O|0fp4RropihYUHn`Ud2|j4t@yq%BbcjO-ADK(`u*7|i zW^EC325B^99$si>slLmfn4k9nPeR5aUd9|%p5}LAIX>G4zhe6D7bpRz|NYzkl5@X4 z30hyQs!P?Gdebgf`*Mh~JB!)xUsfhQBPe^ji-e8xjjRq8B8r|71JH-vQa>*285Z#b zFEhR({%rX7Ny@pi0nB^ZczfV)_&txWy1H7gVKEs%Y)@#&{D4Bw+TPwP$Me@tcrM3cRPVaI1T#W3!<-BMoDDd= zU#o2Tu!s0n9BbJNrzTdOUby5_c_~fk%yrWCsLfhsRh7-Y;?pd?t zhx`RGKPO+=XHkkUUS~aE^xQ`!zJzo8QE3$EJU{ZkONTcReaD?n%$7Zi6iPO%&$9MKOU>XdJ3{L{TiQ2cR#8zZlg(=UZHNE< zXfwq;YGeGD`@ZmRACBJpO7U>qiA>Lbd7S;FE2-+b`C|Wi`0}p#MXeh*a&IcKsVepC z`Y_+W$D&`eiDl5U1?{PaFgE?t&rg#_d~yHVdEKutj$KInZULcKCO{Q(zmQnSc|8P; zs}aE^`W4i!N>@amUK+H1if?qpMRiz7n3q1;5_0PEr^fM~jlJA#V@|K&XI|Df&Rk|0 z6vB6-K;5amecvQigM_rKx}?~%D5~JM6@bj0Kk*(+M}R7~AmaVqV}TQ%mSVHR)wY5kn($FmRqW?f?6_Z5gV6WemS zc0};7o5I-X>PHh+HW{Q);14wz0)38}hh@k=7Zs1XsHEEZh#1s0!SD=0v~IbZ!}fK{ z^e zLi5_zv5p1@dS+hW?fNshRp!4DJ(I_yU?e0pZF-*V*Et;}M+xOo!cLrLQ8lJ9F)^sY zFSq>_NqxomGMT$JT``j;^P1;H_cu$=?Ke$pqpcOQeO)~?`#y>!Jna}Brfz9ql&G1A zHjt=5X9{9oTJemcB=(ef`4#u;70QxgvXp_ZDVW}d23BCOxKf4377b_lGMKcy=dpMV z;>!N7!tq^lbuU{I6CDH%KDR9cKckdbS^dvN>!ACgBhC8xn)V zq=;4gkOStI*d zCfFL)RZsNU)r#}kbzvS(Y!z%sY(IKyPEB(9zM6IH#QfxTEvF zfAF99fci@Gd?AKwj4SC!{kj|vcOM@vCWbuDFZ>o^O1uJpOOLFTUX6wCH~4Y&A7of% zO~fuKURA(J+wb!k&l=x0Y3J~{C*dX_GBh)jjBw26gmVm)hH|WOULVGRuF=<-_So@Y zHTz9Cg+)C%LAE+De5OCd`{86M2>OfE)09(Gnp1aEWq(JJK?KVsN~gbz-VpAr-&WP?Bw*&|?3+UVqIiYyzZ1;XIS-X%i95avx;KyN88at|%#h z2*pG%eX*)sSv{KAo85)$_x4!~V=@SalE_PN{ifaq1sv4^NH@mx^#kI!2%i>}(|En~jCk?2GLipT$#4n( zJ={&OqPV~yLodABOR@CPH6E4!|Hfk%sFTOdt}7frLlqX3Z^`!V?(AaucZCenvuC`P zHd(7e%bRYR7$n+-KPVca-kZ*4S${MHcgYs2L(ojH9FrjFg zl19=C7=RBJ0_VUH(1XLm)YLyB&H4b#-jFV?(ROP0e8Xau-*{86$ zFG5o^=c-|6IuJ*ql~dXFkSjS`{acz>fi=baoaJuStV zGbea?d3|7S4ooCF$u!eD4vvh_5EBziQ%|EOE|NSww^dgs^-e9!U5Ik0|D~_*{Ub5G z2SbgkLyw<4xsk3_xD3{VwlX#$r=+pmU6zoyL#^N#ovmW@P!0wmPlz6@2lgqsftmpi z%)U2{f}q90V6q#3gx}Q5MJ{+aX!IoY+Z~JR$qNn>TheZF^2N?uW86+cT3?phT6izh z34{OHjb6!8G{amS6*)jaw*jc%-wQgeMVf>1UPRF3k}Qp=!Q|l0npaA{6!e*r7^>M6;If*jfhEen#a*z5amFlWu}QIS|I9WLe)8SSfMnk%b%z7P zZRkZNI*sAa(FCEohYh>&KqShphl^+ez8ICob8DE932$M&gRpP!>^vCYncQI(Md(9B z_U1XY!Q?x48{}?`-I)K7aboBnF?cvXfC<{QSvGPghVJpYUmq^tzg-N^dA47bVYriETd%09---Ut&;<-K@;|K`#B85x(_OmU7R2_Rf?K~ zT^wQbw?S8b5&drX&fB-r5{A-kHo!yTq+g?3N|1|)x4dgOALx?_<>AK_i`N-of>8;Y zG^Ny9++WEV`GW<2VALNOg>3VmQ+JyE&YY*qaho-ZzI)Ptp?lcrr{meIOPmJ zHh+DGIXX$0#c?SY(2Z#@kRB1-_)9NHE+fFN0%(ftWl)p&ZzjTxqt)X?Z-b6mirJ64 zU6xl2<4!cvR0{JKLXPhO4eJNqbBX5)qGv(YLW#S7c+=2B)^d9pwI zYKn?g>se15v9*BPJwfWZk5rXPjI^JsBQ(f4U*XlQo?InP#`HUZf$_b)H2biBZ9XJ& zJ9s@<<}OdtsPXeyQEgqRN5 zX>%fPbo6kG&_F%mc0k`8^hlD2X6R*mqxX**&jL9!R1x{q8)v3_`2=6DApHpMr9k| zrDq#R9d|q}pqjWC5qbOe?VtSjhtQ;XZgLFArUzcleK&aG=qRhin~suR=AB~A;MZ@f zk^Whd93+vtq7wLxj@)hsbva`_{*ywX#?Dn4QO38oBQew!DvU6q>xBh&CT}6}{%5bM zwHe^28@Il79|Dn1gn#7VmpQAg{|JYq9GgRhow7&FRqch zmnMwI7dQ5_?cqD?JxL(13%;iMnu_OxkXKk_sfXy{>y!vln)&kggtcERel&zRTYC8H z@4`}~Fag~ARtXS)Qrwn;m&y^8k9rS7kIf>bRmp>m;tI+oC5-6(quRs=jv49c=^;JL zn^I>+d;BFHyd`%#P@B+i7hv!(Jv@qieEp`!Rc1ZT;tHmXb(l*&Rp-55LEG87O`~-0 z-qi$DF7epLsC-aKR{RjtlpxKVWIt79)^I+;4O4ehJ?Pw;HDWqJ3ykH_hgx9z2o-O92lb32El~)@wkVOC?eGA0z0$z@Z*HZa>hT zgGqBY2DNd4MJ4sFXH>ScIhfZ3E@batT7|hbCiU8(A>nFXRjF*wb53Fy%>eXn>FGr; zB1x|2oLG4JuJz$)9_})C+JFRGFL2AGnU`V2)8Leide7bXMh?Tsim4G^z1^Dnf~}eK z?pGgl3WJEFI@>>s+uNRq0NUKFmZXu7n*ZdMzjx)>>e&Tr*|$~L2L|Up%`sDM)~`>K z_P5C!Fy2NFzsK51XxO~u(~mV=Fn{OuN5ok}eAjrt*n09qm6>|0gi|0)eB~)+o4?riX*N*D z;8+t2(S?~1)rz2ucf!ul7_zP$GtVKU!1bkHT0!b6WZ?cP9miW4zz`3l5T zmmIOd#4Q+rcpiLqSQM{VLh!Al5RT&XiSbNO+r`72bXsYrp`$1_t)sX zW=(@YHONIm%uG+KaaKdrkf8OofL=Q|vnok^t!s>WY4F+~CwA|Cz!BS*CNs z*$Um4ab?EUM>eh5vJc0 zW74s%m<8*WI4Dx3T|#`CqePF8oclYG$He>}Tr2h2T@Fi(M$|2J@rN61F(bXAjEx%e zt9z~7zsz+E6dkUhwEC#c&)9f)cRCE96M!nXVO;o~0o5Hr+E{d+3)-H2qH^BI^362o z!jdQY1_LW5drzFxt`_=ei_r=(?N-?yNTj_NN5ac4^k)#>I7R5rK_$+j05?SjAlffh za$F)t#MuwCVc2cYVt=)fZ8n>(m3n&5=M0&M3m-Uy_=9qV-Wg@b{mHZ|&U19t=8em* zMEy51JYv)1jG(P&%K}%yXPBaZTiyVy;n)t}jAAo?i$PlV2?LgT`YZ5kKQibmb1fF2 z_`W^-PPbKS56`OA%(Fe^g=wY2Dd$s9t(Gs^`HIE~ZvWh080N$zh8I>!)*sA zhR*x_*?rOs;HQ}=_qpDKmgNi>$FrABnla*_ri1~hI4tD#kSutlj`Kg6kg~Ot`E*uI z>UYlmUH`-tyEprw4Z*4|`6v;5bpvE`zHj!%;?MM2JXGyahk?Ptts1XACr47@4BJaK zR9csv&@Uc_6&s%G-naT(uc4hM(^K=DKnX>@XuD>YI}=9g*Hjzs$wQ;C5Bs7)Z!Pv* zCtib}zD6N`I4q_4na(=rDIY0>sB152Y)PkvO<`p3j6fSK+U;Xq;vhJQ5@s_?K<#jG z&gF@L@%z9W@&DEcVGMd_(C_wzNlCcKyc^mAemRu>1~`vA&FBg?pc6t>bhLN?VMCjw zQ7hLHoL+PV`wG*~?9_*ZJCIW3zp)fq+9w;b!$35YEnYabC z;ckTrmiZ+#4NyCSvtpY{6!Fp<6w?!r5>|aror^$e37S*)ug{?G^C(o;=$py4xFBgR zCw#0#vA(SaU1wHU_2uB`T<$ynl1dZ@8>cJzHdm_usCp-nW~RH!rVzQRs;VvX`S9jw z-p5XPI?UbwN2-#HdImYO)#I+H1X+jeff_jnm^GSDmcCN zW%nYV5Mud4Eark2`(|^!iE?4mVxyo|0d~ekIz7I}GJ;IqwUo!u&$Z)Dx=CaLdS{q*?5PZ;ZX{cA_VFXmHOn zxq95LEtjuO*r8lrj=&Q%)RyO-Y$e>I>8Z5bctQq?wfYt2WZz!8y$nQiMLYYBGzH1N zIgl3`x{(XNG54R~n#m6ohsEOC)fcFr#Q9esFwL<7B|t39c`}|4ApQj2m|sDzau-$z z2I`ZqFkWHAePf4sF_zh`tkpO7GrXB%^q2^S;3^x0ia~iRTP8x_=Vq*wCkI z7g{4b{a)D4Vi%VyR2}+uYp6RJY2=!C01+JneJUJQI`j_6&0}ElDR1qAMH>BI{so>? zz+Kv%F$SEuoae_mAv+Ug(S4By)=?1xG<~tWY<&S1R37b(9KljOeF$%3lcBm2pk)9H zA>XH=0_&~l+vzmkjnFLZd17OyWN#NrHT;q4W8P{IhrFa;mCej$KApnk`@YQg$i!`o zE+r!^Da&<{+AOUvWnuG7ro{jcsNrxx(|GggQ!bgH%P}Xz7b0!)ccy zKUYiSSnGIy6+nN3MiZ$X8uDHCy2>$FMo**@0kXq4EG&$k-$3|oUz0Hi1*ES|6 zKewdr?j7ZO-=al5~nLVajx#V8FpGrK_c!@EMkfe`Cb=rW&#tg@2VlMl0pHj-= zf(^zY*x1yP_Bo>6wG|ShwGh<1hd0?$cQAguNxP1M(FiZ_6PQ=B^RC zs~%O>4Hp)l+vmC*LEv?o`lb6vQO@n1ra%pFF@9Isx_uwjBbkStS8fRL+W&bf9NN%> z;yfVEJc0Uy)H}y}d%nFnZ)@AHcU=$4m9Xm%o~7@;y1%=0cwcsakC+6_H)`0SP*|dj zouDvh@|Es~hr^QzQDgjeB=g{VJ1bGDrcSj%u2l9xrKaf&I*tRIdiE_|Z}T>TN5!28 zOv{mhltQH^CD^6RW#?|Wn=2~H5fBg*E%~1P+bcQ_Epza~z6(TAI?JK*TPG&uYK!hX zlo&5(mXgK$_KPH?A;Sh|gQsy>WRrMh?ys!mZsw7&nHf59Y}zkY*{nMh%=do2&s$JL zYp_l9+_m?N3_>rHL^F*)q6TW(6?%e0CCYI zpDiAkdOt#0067%u&wbtpO<1iO&&z`zLHnH6F;9g`0z4 zc~F&S6A83GskOAUh=_?}Qt+}{9?CG&U&7QikMjP!ckZG9k4B14Qr(*{WWD>TGONaI zmd;?s?zE?xC8X3jM%AoWEx($6ox>**GNX8iH__8|ngt!O&`q$PL&Xj43n0zwy3bb1 zf?9I}{-Z^#$AZuf0)YM<_oM!4T8N>~k&Ln~cW;#RYND<+oHan$+0JFXU9xC*r3!&& z68y6I?x80?Buhko-mlX@bw~WY3FAvSDJfrAas%kJKEx;)$n^$*bZwhFqWYA7`8KJ) zs!IgzHaR(YxSngBfuMQpDZ}dIW7uXi2jX?P#ca^wF)jrhm6T%dfdQsN-R8bIBzA13pDa6-hWdq2pMm5v zYgT)qwzONiPZKx%IBtR{XjW0tI*OM@#8X3?%|&O#UIVQx!GZ1WOa;u zPY5}xvTk_cqI|}SbS9u}kmsa+kq$v0g+<30UCH;WSu#qVL2oh1k^E<|+ksOss=k1x zf#d57;E*l`{|}|qP~x+ZL+h76o4qgYek8N7@~?zgk00aJk%}q*fxg0VzOBH(x)4Ow z847#8pj%ZvFfb73jT%yGi#cI8-|O+U`{*<-;_M@BRx^rU&R9VG$%oFs3rG8SB66D5`kO+%%pP~8*>_fdw0GRb)>!60&2MAc49%VxP|ODC zY41}lz#QMjh$*SN%Fkk31L-SvA!h!7ElLI8BC*C5oIc$^x{^Qaga!6#*Cm&*N-RgrWY#yLyQJLCp^KN`;>qo(S^&TGkG^PLBq`OP2ghg_A zE--0m?Avj?s?fkRFi4Q~S_13a!~Hb{+x}d0ccsrVXY06ji~}*-d8jq<%9}e!Y4-2- zA?`EMzP;8bJ{@9w$_S6lf*yhFdoF4GmT>AQ?@l*JZaXhs zrlj(HA&%1|906hrZ&D+9oTLOi_2{8LryG2 zpg!g1>n(G2PHQ6J0Ifz1Jef$uT-AWo1Si|>SF+a)a$8P1)ARLy+`XUWep z#+rb8MxygXh};de8M*qLx5lI?n6JTx(k!MFX(}3er+4pnU*eW9)i)Cs3LqtXrRQpX z_@^-CYI1y~AaW&s8ALw)c#rx~Ed!l+H(Y!&I$z+42FipZDvzEvMeBmyD7(w?Cd8P{ z;ja?=GfWIuI8oLT&P%1|%P-fLU>-obHBPR&6=sbCDi|yGe9amkK7Lfdr8gL9F zxxjg@WN)!J47T)j$7cy~>VQ(y58~WRQEo~~3dQASax40@^fR29?{acD;}Fz_D*#CJ zWT2e2&>opHa4PpPX_#S`1JV1Puc;wwMtp_X&;84$|#eN%IQQb0^+38XtN=FIV z5C&SxfyuhJHxCe}w^77lS0%+lp1sYWk>+5zahUM{C?@Ff*z))^#f@fk1Lrs~Ern`n zm=80fS{m;@CxFt}V*-m0OT>|e`j;+!nh5(ePQ`QX*fvXfo4`@yCgBrRcFg5X%ygoG zxNZ%Gljm0;b%d{1$cI-g@;|wnosX#ExltfNG}bpo=W&jP%IV4XgCuy&d-Xb0o>;*P zc?LQ}M%8Z%5Z(*2ZksC^jFi)M0?{&@uc zPF-(fU#ZkZR{`QqGE1c>myaw>oi|~dn{C+5ll=!vx$j_To%rs;87Y1YIqR`M6g|NR z?D6-Kb>?McI>oeZvV0NQ6)4^}$euzb;^9*6@E2Yt*$2nPKCk^Y} z+t6K!1v1%!9=K^ZJ|QQ6WGvqyc=r2;Jh0NckqMh!a?SfXKBUJv4t?d}lv%t)B31Hu z{DpKdI2GIwF=*~wD%)#$Xy@)fQXi`0ghLW7;C%9+>ayVs`u01`j782;CV%L^r^6e&Zp*lMKy5}L#rF?F{Ny; zCz)eH8sqt=;_MXJ>l8EC=f~3qNbgVH#%%uR752oi`4Mh5`P*T|j~$ucdjbuRq7r^- z2_+-DE8wdGSs|vFvh}3sc+_Px0Xwj?w#Wbn_!UC+TRP8XvYFqxsA;^b$Qq{i=wR2c z5$iVlM?HszwT@?vTjIvj4Z0@yK5k(?e5jvkeni5xnx+rUy0BQHb1uu*$_`9EpW6@W zG@Q_vnd3lYgw-1kB%ROas^=)e_b{kMEcC!fU~*`1G$A+1zLO@2<;@g`v>V^afdu;f zJ)V+=dHOk-hAE0i!3GACpz-KPF1*^=0^P)?BPr(d^mLa0$lk=z*Fe+5-TEf)I5p5l zR%E@AY%@|hBRg~*y>d(e>1KLr_q5X8=w_z=yc<{YAeYVplX_pX9kgk28phABKNps- zCcidrA#q{H^qOs8Y^DhGR&VK=-h(+RMcCw_od8>i37k$up!k`t_^fw@8@|(@C(Qt_ zwt-GE*Zdw8It39?K)gl2lRga8am<}-KAu4MrJq24pIFMX0Ielxu38;aCQi76u1`tDY%z?MqVw^1zu*eVruC5 zXZ!JE?M3%rUfXo(OfmdA4mNNXE6XR|VVXeuEK2KapnkTv?{JVP;`Tiq(blxuc$&uh z_{XhWvmVwyvoOvfjc!?P``9D{wn!|31{%yJ>8Wns1dHzDIQP4Fb58@Mq-lym$kuIC zDL{q!>EWkLlPy(~PxFgqlVAP&&%XX$r-S@G#=7Maipj;fwY$6f5PG=*%_dqOKCG}` zSoTKgPdqqwJHswfl>5263K9eO4ch6i`7!MjoV1X=mXG-W*u+O2Du92w@E^g1oh9Jr z2(ZQ&FWnlx12Z~WivuL_6?cr|Xuw{S0p(`1Z<;uT*3y zpF~Pcot}SXju~ThCxcgdS*hC+G5oJY1P!a{%qXMOlw;O0h`!1Ks2!9EmKRt@-V9XK zHuvGXMx4Ik|DtqkKGBM4bK4zm=4Q3a&KS1?=7+;=B|*{%x>L_wnJ#_OrcAqfjT1Cx zu-cFmI2sS|z!tYN3Vj>SA2b)w1AGL2Ho;WLHAIudq6!39V8eObGZ2mp_sV8XajC;@ z0X-aQ-`1R;xhX2IEx5{%-xVg`a#DCD1}rowk}vCA2Jm2mllT zSs{V><3(R@9sUZ>F&w0|z_Q#Kxpl-)+P2VW57Pb- z4rB(R^M0eImX6SmQ=*?e!DqQ=NI_i##-HaFSovYi5-^nBA#J*%bUjSC5+afc3^y4 z1XC`7Zvv?Scf}yUPLkCiepGKzO=abP)+iKN0gJFN96Y*I{SQ~s##Vur_&crz7-DW7 z9;E#4+HcK+r;SIm_m2Z7;jx*3h9i-xs9Hc0SM8%I-+&hbDek!(v-P-6rNG_8_i!s&eh5b_7~Gf-~fm+0ua+UxNt zxEvPS>zTFQdmqO!cv`3jr?-ZYw2$9CrS0H}`uFU(3RGVnL{lE^dvD_!y^p3XSkW^5SS(hwT>kxvi83gSA7^sodnq<^zT2r)e+>3f~XV&lx{+ zPhBW{`tu&DuLdNTC+L{*{pryf{$;!GR=fk1ao$8ibs4H^t3&hnh*+vhx(?$OBxzrZ z(6l>k$$tUWXd~Dp&?qbMLJa?QJQ9(6Kq#Ddmz_VEHO=kJ{Gr?S5)1<(x7iEJJhl~#R-uwP+UstSsH%Xcml ze`=m(OmTy32p?J75UsGMk0rGoPj+QNEbXmVn#h<-yzdqY?jfk%|vE)D=J znJ+o0PzeTG>?TMXZJ?8)qT+1&)MmrGQ^1yc#?m44*U^7a{`u>F1&^DU+YwSPuCHA} zUEI~1qtjj+ z1MIzM!JwMFCq}6^X}P#ap()ha)d#>I`2=1mmz5KV(sj%IpeWXdS;qh#dPhe`)Pff> z4g+i`siITl{!#M5s|Yv;SSa4>(!lX&ForR0b>LY)SNo&)8=Z#2!ux)GCMa<~pS*1-;|c-aHt+vq{UcS~(c7laPV5{%95vd@^XyYr zN0=-GF5Y3i1sjyQS9GWXgNBA!NRzjFy;Z+nOROZ{99U`0I*JQhk+jt(fq zIBBRn6|X-xy4A@gbow=AWoRP{uS5y^ISbuIG;;L!vvKAx#=p#HI&8Zl7{$xjY2hy! zeTjr@t~^|0&-@&t_LxyfXfSmcHLBSM!+lxcLFBUgGXb?eC+p=eZ4)ZN{0c%Ha&YfY zV-1hhz8sRHOLNB89}SW1Ki+uWjgD#_I;IIiAK@e$FI$=rH@UaXQH5EZ6yL#4sGXC! zA94XZk_U&_Dz_U>sGG;Jr`;K-?s~L!c~sv+np?yAVXOOph)9soR8TShKeoO)s><~H zo_hreB@|FWN|6>&T9mNpPC-JYO9bhbpM!LK6cdj#^?~Kmx9~NuP z8hP(?-{-{M`|Ojy)-_!Tw~&v42^5j6`zOZ(W1>wSiN}v053&wNSY7fB7?F2R6zWW_xB4ks)Fm2p1Kji7kV-yAN!(R1 zraF%Ij53|b3nSUP6Z3?HT!x*xqaYjyFa(!Fx9T+L`-0gR)rp`Mc|2)S>`>Z0#RSeZSK_XbX;?A$}l)!I>dPrY^!br>UAK2$NB<7 zG1Up^I>FyGWQscc+G8Y#PQ^Ru?HT**Fv|-ceL{juwt6pRT&Dtf@dn&QX$X3}{|$P@ z@LMpT=X9#z*I3pp-O9C@OJcc{p2bri#STV|qOCXcY&V{sw&dO_eHknF*6ptE8`+;n zEoY&u(5|>ZV{vesdbeTJNbuH=D-pr6*?U`yUI2$(zP+L>hYyS9K(f%y%Nt~Tx%(Rj z2M6jwsWpR`FV%Y(W{4~VtEQBb+QsI52}?7>;FSHm0CgKAL)tXdBn*1izpbEO1d5!D2={MWFWD(`$rVj?|eBL?7H zKfx2S#D19uTpSd^LcXM|)>Zg4wICT38g)Ru1X_rI0T8VAdz5eM;Qz;gYU0BPl3JiS zW?E2G@YI3ZEHh*7MK6?PbQdaTVqxY5@x{&i%VLM4a|u>UgA#=Fr++rz!*2V9zEWKT{3`Vu zE25*Lhm|`EbwEp*_xA!a>LSzAuNs|VxfZp>0zs;fGGXrh_YwWARg}050f)@pAq?{{ zhWPis-Fv)y#IWPCul4HQE8`*Nh;o72;@B5X8HTZVR(9*87A_vlhDxkLi~6PR_P_q+c1nE0|e1(&qGU$+wlA0eIMN3 z>pPK0KinE^zC;?TYCe%Ee_y|>e63@>nN=l4ygcUt4lt*WX{oZJDK(1uUKqBCng@J_ zoEXlj0J5R_{s_o7-@7ToUvSWKp#J?Zzb@G5dw7@w8O$YvX?xIKi(<5>^Zd2)uo9&P z)Jm6>Uyit}%PuNlbh}5J(k{DAw_e?UV=~FG9{L&N8mp##Wtx5K`cxdc9@BXp&xEa2 z%ozD1&qG4I?~v+?sn8hOGu1gUn5;S~HHqVQ_#U#*SGX^fMxUrKT||9a3v}Un(3#KG za&twOxa_WB!0!H!7MW)HLy%Xy6=OQpp0GTiuKXiJkvr+?@+7qb*d#XAIP;lye@~TxqL_EKLPmE4*6OKcJ@8LytASolR!Q4f2sZNAgZAS7w+m2KB{IQwWa_G z)nb@{RRX1Xz$2Xl9@LS`FW?pyr)p_ykJIMwRV=80xLX{)eWnKI8%`U{@CV+&l+Ycd z5^R)N2cbTb9dnM1ModRK)g$JMrT`7;uHd-zI3pS-IAHNYTXgVH&5+0;a!L29yS7bX zkCwx}9bOC<0(FwsGTWP?>HITgzO+5S*1so{e}wq&s}Nda1Q`qZBz zYB^fzzGS?i-%y9JnrXaR7Ugfxj3rQ=W}dh7IF)+cy6Md)cJVl~mofE`Oq^FP>{m3b zeq}NEK8+`!^KRh2aLOYDc3Z@o`Zt@g#f~wt?~z;t1EJzij4rctV))a)swd15|473s zeqYpq+cc0MY6{eap7kery-4sDSA6MSyWMj{2^601+;?Y5+OybltU~Z;9e3?w+@+~& zHdSx3@35ROfBt6jTaMM+_Q|59XL5%?FBJ>*KwSrX$;9|KW32_0JB}yhHPsQ`KAV4R95=&yGHxFmA_Q@~V0{)HQi3L7J>C(Z?*< z$JgcYib_J>?-=HSf?6-bFPU3iiYV z_Gb&F?|UlRsrae%^val1e}_`VB=rWFli<)0JI9L2yK8i7r%7BVC5r$=HQQ8<{NTKC zfkrvylf|YcbRLyLpN02GF5MMTdlOw|JlXuOBgg!TfgbWBAb@EszF&7^@qZ=y=+UFI z;(;7QczAj5v5Wyq52|Xo=L-rIrjF566?W}19vw49+`1RUpDxK))TDA<=HBD_KFSoc zWT^tXz6)WcX8_v_r}dEm)320Vx!*6@LEcDjoC7JD?XLVE;qw>t{=d5gSIb>dRLr_D zMNCq5k*~+=I0AT}9m{M!&TU0-#*Vt>sMIS2%sfwr)9uH%!>V10j#dPaxZ5Q_HJpH# zj#kvX;zB_x7xm$MZuOg>Ams#$WEJC7^I+H$dseI1ygP$3D*B9|1J<4WO~t8!^?`EK z$uFn>4}1AlC&5KALF6DJl4(UOV~|!zsJXzwN({rw1K>svZzRk2{FpC5N49rsz`nK9 z7p~PL<=mziDxLiy$P=T9i*dg2z9c5Jr2kCp>ve*0LWlwlVI?cl&f@+|94vsFd#x|@?_2RJY@S6@T@GfT;kFD!?qLRy{6HNrlX2D7 zf~9h+-|PbaQ>qlX=!gl68*KDf&tHAue&==YH=)>+>fG2*6PtQgbB$Jd0cDZp3vvt9fAVZTDN0x~P~trLvDw?xQDf)0vgyqBJK|2Ty_2gP%rj zD6_L_K7aY=b&GQgu&{^kvhM$U6*%ZJsr7$XK>YRPsRByib{QU!5eXp9Z4V^QHKSIl z`qq`zpamyk<90egjIX(Ukevj=<@D~(HfjWAg~2|2T0v1{mMCoC^W9clO|Mm+o8$sz z+>rC|!Y*das4$~PjZNCi`%7}u3Kbtgt2nAR!mA~Q@Dpj>rLpK@XlwpX6aPjnBrqxC ztM&sakE`tMut>ya;l~*auL)xr-DiCB%N+rhMVUY8wHG3W3O?W8Z)X~toIO&6C#9*$1cz+@PcHEb7Fhf;$NgF@cPeZTq!H%_A!`JY;gH=#B`_ zmXK4oyO`+nRZl@$BDlh^0_F(uphcdOnA)+$FnAzDNQ8c1>XF!T^nfef@t+jWNyTJ-!;WLMeFm&8hQ` zw+lc0Wpse?IY|D-@I&m}8O+AjKp<}@Fh={pQYHe)S9`hYk1je-5~I^u`>>V*9oct+ zK@@ZmZjq@^)>nNPWqGoGJfPmxxT3BYv^MRQXwEYk@4aKQZ%45Zv&6{h-1ydh{>3P4 zV)NtCQr{@O*>v)%c1_7r>aZIeb09*Y@PM02d=dZhv$my#u_;t+B}xM?cS}i2gK-*W z(cfa66=2ic%GQ<`)Ypm?4y!Utst6_mDYo1GHeCuTi}tOwJ*VkniC`(~&2r*9ekUt`j!h(lq~5Px>dBwpL~;=sO;lv+8&3%%%ua7 zNa2+=r@Ax^lN4V;&WweReDnHsyhOm|xuqGHZ@2@UeD<81cQm0YN{lQr2!z};G(-@~ zpUdfK{}F!G7~+~sT&9XOGw0lD?Y4lgq+$p{mO|6UrPq6xkn!u+fv*g&yBq{wVqdAh zL|-}4Bz)z{(_p=m+QVZU&)0tv%4l+*eCT%Ekz|>jQSH&N+${Pj%7k{X;MjyM>Ww$b zeQ2n>pI47f{mysw*iTEztfq8QFCLr?YiuUFaN!-e!i)}(KU3|-)hfuVzP7QUA|C{^jl^~! z5*VGC!2``Pb`x!0_*JQzo$_=WRsf0)6Las|UL(AzcOV?_MmC>?;71g^Y=*a#c3<~? zW3Ui^5pLy}na(P54@@S){iI*SuDx`}#^t(M6B>T!jN+M{gXz++0+W-d&zHyD0+^Na zs0{7*Pj9mE%B(L@Tp7Lb3@`7JPF{B(McB2I7x+MVLfjU3b#guxD53|TcJRcAtUiPN zRMwQ=-L=w@d;{r)!BXx%=OEh4zZPXd*P|(9w!4Sn&V6Y`ayD<4$`n5x%7cX=6B!S> zuS@UlePAs-J%`FFayir|d3#^yjjqVK-3Q<0KpGOw0>s9Ii`zF;1r8O6?~e}JU$_CL%?JbZlFdnB8JrU zG3}0o8Q0vTDc+v`xhGQJe0rY$Gd62*(q&54quOTnEG%5j+~iI6#%>37Z-5f7mq3+4 z(>Ykp_)uLJHkao5V)LrCg4ks1l&^PT;rBwocPqAsomuSKH1KPrwI6D>t#TZr&OFYtaa|^ms4LeBIKSr!gw^?Qu*X}2XM&Li|efbym~+1 z+3O9<&rMBDSzh(QyD$}d0Zc6wdV6_{NNEZU08Fbj^eU z5H^bgb;&FOps*fI%lC)BO!odsBmS)LrYnD|4T7>k`do?ia$s5ObUbP+(x$+*#rMLn zA%^MUVv!HxZ!EKWdg|OKy`20==Zc~i^qM?(+r3$?fpZBXy5XHzhFZ31j{z_oM7#bx zW<6JJmIf)5Q&2J=>Sf10&#@Eq{nW7pKc}$ajB^_DJylLXKYSDhv%uk!*Bg$o|-?-X2$#+6;lw=drN_SNF@#+{#1VHiCpi=oaVdCOet)~MIL{y(27 z>G-hl*>fX_ykS zv$MnaCoNE-6kJ10YD}W3LU_UUqu9OfAotOE(nLO5LW$~IX93r_1L8NXERhP5KYP5ST)1k)sa)!p$!+6xUNE+>?HA~rzu3VS{e=qP}7%C2jecD5|+=&S?uThgB z>RjWU6^wtLg1$crj)4NZr3wCa`gs`2dgmyz51U*k8M|`Lf-_#fr%s8NL9B~0M0Al( zXAgkC6LMi$q+QM{W8;P39CykSob0P?knR>?*xM5hf3{4ULQw;Dv})UGH00ylxqG*p zsBED9EO97%>oA5m`n$!3KWD;HgJrCVGR3dqXfz!)jkJ2Q)Ke=4M4YmCsa4017l!boTDp&+N?0g& zCDcerGbZg%yAt2slUpzEc`jrj|Fj+x*`j7}l58+p$i3uF`OHA2d!hceTboDAyGP|H z-P)^5VT;bwRjtcE`KM!+cipO|Z?z3BH5;{VM@=nh#C8QcYMv->rsamQ7qyWp*G<6u zrUnine5C_Ey4&+J?l}&q1CwZkoQ&ay^ii7Vh^#krYgRaXm}-m7ecpP5X~0T{&K(Ph z_kTa+$mp=n?xr|TO@El}tj*?4X~O1B8(#S_cDV(&%$}V; zlInHNZgW*m(_y{k=d_q5gR+_i=1IcfqF;#w5ri7XR>& z^w+U%z4Xhn>Mm;?;pkMAtmJ^`y7a>v)Lz83hiR@m6}z4>Yk9~c?fLe>tBY$rp0UCj z8bxO0BbBZjjZuJy%)sMD5>YG!_W%8;!z016 zZB}R}!9GxbbV#fF(68LGzo>1=m4hzX|NlQvL*nI}WUz-7o;)Z4(!sof= zQmh(?b%*le*=YOW4nX6Eq{r6AUlkjEzx%I8@(4UX${!lYKkbW0w~|{k{Kc8)F#5fd zk?WRwQ;1Pn$j;7%F6G?Cv>nOu32TutSyo{W zsIBKfgW9}#k^|kOscQ{^)d@U?$f40Q6C&`^skwDj1qEd;ClxKNWy`s28f)kHfRBqwpEX+orabE3;u(w zu-kXZ57s%iCb=_JRXkbS^_GB-+MB#laP?a&F7TML8x1qqXdY>C|WHyDecui*9i-m%mOFCvsucI zbffbR4M;h!CX~n|5|uqss9#=3m=T@w<0&cnnj7Mz(UXQNjI2(bJg=*F@{vcC9!=B} zl2wVtXT?tkT&DM${HVoa?=dl<&%nSi>2}ResbHX_?{SO@7iKrGf8Stz)^fP6=eu|9 zV5+j7`T6=kHw1pGIfKL&rM`Ze2kDWIj&?)PBB;=nZU`z=lskSzIEs;E&VtL&AV^9s z4&)^mn96B;Df`u{S6Y_lwe45sMIQ;O(-55b*S)KGjQI!o=y-5F^66H3j}{{e9kPUn zR~~;{(sDi`q_k4b%0TA^WLKo=VpH8pn)yIVRy8xTw3ajah+(t(p(ODz(>8bc)^+yn zg+BTM7Qb+V2QU?(ZPRf-qmgLkqr8aH}!ZW0SVFn%Wf@6%)U#zM4zp0VOU;{8Cfp$LV(M|d5hI);=*fo5yAQIlQFa50u#4U3?oD8 z1cFW|?)i0K7htgDFeH(Mv3`9j($e&BZ&S0(Cf(sc5LYq&RP*U}0O>UR6sGMzG;x&6 z*biEXyQvDOA(2y7+uW@JiP%eD+5a!}VII7F z4{q5tgTX@vE+EN2F0b8ZzV>ubKxm`=uHzD&+kAHMTryfmDbINpdG=pv>{mj2eP)!u z>DL*#UG)!+NMaIkP~mjpQlAZ<{fPRiVH;s;-YY4XdxQf~#sGQTA0HM^%(Btdw)jy0 z{(Tm!4ZP$HSYK;Y+$0nZVk;96l*XWU-41ug{WiZ4i_AnlZ*3^1!IhUAJ=$MZhXh*$ zR@aAMMIa?FU(DA%`{M)symSl|i_z~d1>VGEQ7j(Rc`{EDQ96{zcC~f}i=|)h$XmMs za<+SF-{a=r%ujhaF<;9IV?rAlu#CM%lkrYI#-S>RbeIc4vpw#=V$d!4?P>2S1?l&z z5?qm+Bv|b49t_(O)7(l+O1c8m`?7}I)IpF*TlJP4gRHQBi@c(7c#urbp%9nRqBXkM zd@v@)ZdzdrL<%s4qz=9dg=lI#Lc;D4b)9rDiOhh-er{QJ zd;fh^u}}zXsXGt^C7GWSW4mb43E$B1V~XVTv;X13T0r$Mpq`-eT^pO5RiHq%#uo9b zj7jIVnNjFYR~9#{v;XHg|Bl+i88B*8udbUprb?YvH!Sq2tyy_~rhWcY@U`dtk~kuSMlv$q{9JX6|C{Dd zkqI9Q$#T3^9_QEWZ2Op#HGFiMkbltMY^5ve2t0fq*0tnp$;rSjfz>h%sOvjHcra({ z%6j>?kc>yCK!PxY_y3+(8D8*X1UKn&3#V?kw>{JRqp^;@fbK@s-gl8tY=?08LnW3^ zoIOj%YZCEUSCg*o%O=OR-d2PCmfF}#ypsOC_yH@h+F;}5RfSxyE+r-9J0Bi5?M=b` z9jGm;I;jjgK0YfdSTl|dsy0nzE6MfDBUW3!X2tXCU3I9p|AGP*r!_>^v! z+YJ;q7=Kq-El6P-f03Z=RRCN|PgxKK^g_PFC8AOQ%Ol$c1~kU`7scgQjaJ*+U#{M) zy=duRyAQ({nzk^Mwy5gL`bWT`8?}VQdT8-=mJ?ZrM$puzY4k$=SnDfx?UWhCak3IrwQJ^vp)C1v zy^R5%i8*>#xwaXF#V)~WeE33ZUmsWmY1v0Tkv5>=%!F~mVU=W}Kfex~bMSRM;*t}s zDaNVV+dM_+v?P4EGu=@)bob@m%@IEPnIoc?Dc+8MjfnV`$w6R;({7MspQfwHcn@W-MgE*xc5HTqfbJ_!7j4CH zCYBY63fIU&HFk25=Nb8Py#-ANyj`%>4{2L@-wSmPCqwsa$jd>5IVo1*)aKZ&@OW#hEO7qk{|Un%ZsB-0)*C+_%}X( z{+vZOj?Wf)=QRYJIcUv#gXD$NVR4CSx&86+l-i|BmrRGsicS*KWIb;aDkjscW&bUk zAg~W4-LcX{eE3{9Z+h59R=1b%i_dOX`?c1bA|~uT(MzP3E4{~j^-6`l%%GXC*sX0; zLPCmAmiq+ReoVSWuEOU+$wtBC6m7AoV;mZ{oyWEdKNQ7%sk9S$`6GeYX~L$Aj(|}E zUj2HK`PjY>!Z&Vc0FkHe;DGPCe1rUr-5)n5I1H+@vx!q;c)?LMklD>@I~$yg4#&*- zE2j?RB5bF}Q5o8$zTn~$wD`cP5^v9<;K@ls<@#drUaFQ16-9ij4^oTPdR9X{)42m+ zbIJ~u1kfaRL!EEE!F2iZ9V;uVIgo?<>dCREsbxnfM~apIR)Ha~8}r~1xNeo$v>ao1 za&>O3rE(f??Q;i5g_@uEo@`?1l}?K51QpYH5DzyIe=Su;`8!xjC4*hPa=pZ|Cgy{=Fgw1<(!Z}_^bncmc zhI5?XP-SH3Gba@$sA!_DYk&^8@j>(%m}|g_^uwE07N-A)Q~VFHs9A+u;dlNgFH)E> zZ@q>{*4g<*D60DE!=~ID#X&_4iarDkCBjM0u5D=)e#~Rx(_|~lSC!G_B&sYzM~Ppr z6E~>lXno{cLk3+sNqXlgM3-;u&6{VK89lylkX5cUXbG);G7qm{;>~b;Q)?HU7V5*X zMHW)u>(5!OwBi4jtYTAs-!0r$UiH8`U8;KGGMe}!f?QG|5S~g}Dbo3p!aZar>gdN| z$-ePn5vttv+sw*^O-m8cO3vlX(YQcoGu*FrY^Gx>ud6E*5XT>D$w}fSs$7Ew%=5@7 z9v26o&wNCXiYs&?8O}4X%*I%WU^m9qd~|)t`!oANc3%nW!{r=Hw;-A_tE`OR%!urY z65UrhTFEm%6UhqqE%((ZQu&n*=(|F!hD+@zD1zc|klrtZ>Mu0(*7p54ETp0Mb}3tI z-cfk;u+VICzmb_%G1S9u&*c^0yZnmdAFK#;4}4uk(9Q)hNw|CE$3twd-}F!F2U+2e zqB;R8(u~*}yolh$7Cv}f3Mp#&Zuvt0Bt6@V2hq#!_55Li)29oYq%V_hwU&F*@XC4M z68$Ka7D)}~o#!rCrGQ8J^QD2V5Fd^%`WitY!b1z&?bx}XsC61$?fR6K@L6tlU-D!N zrQH{1RqqUa8Z`PI@|ErJ zC0Ti)Iu5pUnH{!V7SjWLoHG7fYe|J(nyhY9JOcRjMFgN@eGvke9)_P62()?NmmQY8Yo?NIZsVTyT}sg@(&8nobW3__ z+L1l69~F~Re?z|HSSbCkyI0Xl;8wPg&_nMU)&l>a0HAmN{PH%tt9hsJ>5wLnY|5~Ix}uV7^Y2iE`o9eJ>=hh@_~{cqE4Usn z-{Wq~I1|rz7jZT?0PF5V$|5Gs10JOYiA0bpxlQ_mHQd@M`ij zyA;*Bzt;8ZY8r}1x@FFGv#;<;c`S!-g=@U2OTiSQJ1alS)?07-u`}x4L4sdP4}0-o z0tz}5gK|AFu{UsL$pE0Ka2qx&K%-VbJ&p5cZj*xyKR3}4Z((qUu8I1A%Xri4h}9)B zYB@pu?R2r%$~ zr z-K{?#%aH0TRbhDv&T`qh0h!7Z}uOARS&w7XB1|uX$ z?NunZ^6cJnwo9sr{oh;q7nBr7hKZ0!!Q!it$ay|ge98U9BFF7qJKQgKK6e){3YC=) zxpM7xN<6l#&YbDcBX<}b?HDL7QNT@=RhgnlrDbQEiqhyUplirN;YLJ7Wq}IdMU>c3 z5E-5g=Su~DmG(WHQa;;1$`=x5DM&r{(;JEoltv+nZmx7nYL>Z4emv53-0EH$8e&RQ zOYZM$l%Bgkx>QXr1vGY0(@f_s~T-#R$#$pW~(v6O@##+@<>_Hpi$JVkxIh%^sTDi5X z_UsSD0Lz2)6bY8lhcmn+^{RC2^s?){w{q)3+mw_v1GQ7#in@4!70~WU^3hgHpg>Fi z%&kaCTMZ%)lYX{w{h&~qLusbMi2O%deDl))F4&+igXyaf64c-KsLY@)LdL`O77(t6F8Z{b? z9mpCQ(tG1m_%c>wN#osW7t=hLu#vaN+3@LdjZh#Eh`E8{+}@EZfN8ClJvC8>Ikvc* z>Z3iam$4}pa4}QM?3Z((mP6YquUDYpsk(vdz2)aj^y7Qvx)wClk4%-qgTytFJ3w(K-a3}vvsM}|jZ#N%-9 zqzlq<%CC9xti(J+#W5!No7JCa>qGrTpT4$wzf>;H2XS;Z&)^YU}p$l+b;K zV_p^wMdCK4uY=R%qr;0sSJsbNge+B`EZns|la((cwRQ}H2MTcm;eEkZAld$t(0+vq z<{NNl8H`$@T|`G;(hm22Ev}uzR6sqUepzt{>bGfi$;YaX9?gQ$*4plQPYSL(w+D{% z2SOFAW6m1NsXAqb+EIiDl9$XRcoEoq31z3qM0zMv{0C4W0DSKXT`HQ;18ow9xQH6g zs&uCpC*!rsxO|Rk=vLh%(O1Up^1JWv;!2+RX&2cLTv20^8lA$HQ*tNPWvv0<@@Y?R z#b(b#`3HUkf4<6e3N}asL9B@2VEsjAZRpOw)z+Sirc`I;M&h&JL)@Y5u8@yW73=Bn zdDWWk98+r-iAqXIjZw~UB|IFZzwdV;J5I=X(N3SoBx^@Jg)LD-x!hZ3mOuvZHflke zc2EVbTf9qGFEX9YrjE267kKx-OZ0aoFG@kqT!2tOJG*{`KFbV>2(FtlK_mh?qeGU% zJ;4lC{q_^a&ps&GFc4O4U9v5o2}LOlZ3@;uNd(z9Ix|GoF_Gd^1kGO8Lk6BCtI*;$*~51_cH2I&YbSW7o&9 z-I5AU0`vY-MJr3+&OLVBffnM^c;DI4gDJOriBRgL^=o#HCitVB?leMsT5EK%kx|T865r7mABra%{9lQ6E+r568XUEO8#!Gzv8> zKViS9wy~l}u)i~-8JP19kAa9bYvQIQdjDdX%0OL<{MAQ2J{G+>(VNR(i_$&*IDaf8 z!zG0jWE`#QM!@Eeau4pC?7DV+1`*fw#3aF#3-?smB*{6{;hU6dZbjU)L_gTG*)($^ z-e`+R@qe5#MTbb$4ppt-oFeCaoPi`gyTF*FZTEhRHYOoUdH#oie$Z_`GBpB5%pU;! zshuS?DgjELbYNRC^C<)=w->pX@*eLK_lz%znD4hPJ$&{k@2QzJ&Wew(%;q9VUGPaP4CP7WdZy4?Gfzjp4MYMX(X_N_TrZ z!cc_6Co;`Fn5>*VXFgZ8M$eDA%#e^=+?a1=q;94qv0pVq<-@XcEd|n0OC|#aJ2XBn zKQt=zRdcAVtMGF;7Fgc@#4S8^c-J~dR6&7cnHHDGZ~9Of>^gBj`Z79VH`nI4969hr zt#_{)0VQgCPJ&uSz?eT?ZL&XN(Ckf_*5D8aTekOn9bsd|IETq$ zv3R6Hb_Q~~S!uMz5qH#2jl-q&Ag*@X`X{y5JVEm25lhZdaMAqQk=53_wGloeKjLND zzkm4co`{uY_Qj{4}k?N?A2jrsAm=aRx%WHM-!ihQQ~USow;64o|)49jq%u# z0as!|qHfn-qVMl=^vEdAnV`{PwIQJ?S(3_=5nS3*ZF!L-?d#`oT4U0&47GnS{?$k( zoCT!7J1N|DbI4x2*Ynnte+VzPXv@-CQUy-4ZV%uMDTn*pY(hdp%bxcgCSvLLH&y^o zF&moi%jqv`nDkualibYc=?!=(u79Mi$+8n^NiJj@_5!s(^z@ElwsP<_i@?qSolB)J z6PGMG_8(XoKoiBfz7rqL0$$R?sIfA_ULi?DTcQP2iGEyMz9$>x*Q}$|z9OfpEvBli z3`>Ind?8=$F4cOB)7*R!{!faF++tUZ@Kthkv<FpGF&1LBV#{Eww}c!CN2);96KD(vbvtVdhpbAEj>M5&;hkFL;4~m>uNG3 zuW4}dQu}#q&-?CJGgesv0gi2FWvp_$B^1U4@?3PzZBxbpS?)Bsj)o4ZE5zU0W_v?0 zM7|ryd((jp(@fiML%uF}NlE(mWh3L?O;53mj_@kBG^VjsIxz$MR+=!x<{H|mnVvis z$VrLogEch{u5`!m*ZidS zjJT$8=9Hgs;Ig#{U!;q@E?}w5uiu7<6_haj*(%}DjiRAzIJExZn%iYUVd7-c^4!T~ zmtIOrI{&6ZuIY(yfZsGl6kht!B*jfyHg@+{_No-`#cdWSct2(&t1ebhAYEWGco7|6 zgdn{y4WHKS#zIy1Y@3@#BEF?hM^7c{2ch3-pYKBIB5}g%!wdyo&L*i?+i!?qClpzg zP52NQ+WD?W67I5cr|Y)F65&UQpjCddI}DtnDk&2!rHN?dP2z=i$ZC6|Z?_AgJAPKo z^Q@(O$WV+o{J7TXbY`n(z|+?UlTqwQJ0oLrcfz}Z`_kgI#n6v^0S6L|o4|qIq~eDC zEe}ysC(oFNn}soZE7$*rPnFTyTlUR7kylK2BOEN1?)W0cx)8)`cT?-k87Zpk_ZsXs zLI>yF0x?lpMoK+S23Hgqg6uZo{Vbc;rOqDMYKI#Ej8){B)mxUuZC*rvJ%_!gm2&y) znFZ<*Bl#TY&$>D@!!yR7NK(>8yOg3Tl~rPSMYZTov}-(qnPO<(rPj2iR<-Zm(~>+d zvB7I%s*S2GYC4+es(6HmfXLj@U`DFGozr#_w^^)((30D)8Z3FAms|OayhtYCbF6f{ z-A|=W-)54F7$j(QN1cf)y7<>ezb9G)L86iWPC9$B&QTYSms>z;b8GV%JY@-*rxG>)ZUMnf|`4!h$&}j*X zOEjjnLS5Wz9d+cpU%LqE>+>Q5JBtd=j@8x!)%o|S6NuJ?W17($90q9YM8r*$i!$A% zOSer-Ofpm&E`72a(NF6Bv#i>|;2p6S7)LB8Iya?xHWyd>?e%kq!lgPwcy`JH=B8qH zO$^-yFEcQ(n9iNPf9p5_Iapp9sedGrylo+Mfmh4ZGrw_1WQ6qE?Y7idH%-fm)i-tv zKOWo0x$L5=5)r3y_H|N?ucI#&AugOqQ@DVhOGMBhrEj7Mk`Oeg+t{!cDCMDLr7C>A zEq46?(rm((9aC6*15z$tput#p>E!;YYz|-h_ZRWJsgvT6I+>fRXud5 zc8XB*fnPa)j%|vzT7S6v7jZIje#JAnl_vb($jOo>ocCC*+FvR;glWswZlkaRgMzxj z-Hi%#InIG$VQDv)E0kd^TrVuo{#)GjdlfP|4hQV&f)(B4g5LkIidJvwN<+V$75(g1 z?x@`@|?WGydxW*B%rX z%_iaso>sGQDAd-0kL$Wm#%&t)IkeIeJ}zZOk_Rz!`mwgsHA}*awRPN}6W%JrOj&fs z-+ikRe+Pw=+ZKQMKha6xp*u6=&pIYe`)jN2hLtyEb5XmV&8HRQ%VsvD$p>r`D0_@2 zSggPH=H2%qr@pD<@s?;{QL=gkxvdFOzrGosa&PbDQ(?<>urZ=#-dEqiU>1gj)d7UB z6n^XZ&nt-f``1x(_=K%AwBp6B*j_sOPdp6SBU8RezFqj(lcDu6QP5AhJcW9Ji%B0j z6nXL6>5J*@-Ld^GR`n+t9}222o$YR2bgBB zQQ}5Y^f6Um>RYs2&sgqAeNO`46C<7U>)N%Mw|(Atp7tt4#`UoVcY4K*94j>YpCQ1r&KDlB(lp&VXM1OM6?(WO@Bv_l3*V$Il zTd4?fR8BMzK7I#`2iUgWn4WNoy7S-nR2#mO8AGH@G~VpJkRjeCm{2VBfK{#{3yq%aE!omllcJb z$ZcQ=`l%XhlyGjz7y&S{GATs=pKmq>^Ud<7yk1h&6pPT&I*f{Wu=*~r)AuQ*%3tX; zt|>xGXU3Uq6X(V8why5CJKOAA$=qh$`FuB|sw;}L#cEMF87k=sEXpay07UeE(0kk2 z)uq~WD0G&BVz%0ldE-w30YU_G$ha@H{5jmVnkZ!*%)CD?9expJzsz%I`y(jUb7q(d zy2=xko&bZ_s_^v`CreG`K)EExw%jmFrKR%c`o~Ht!HE&ldQ!SIS(_TLl(7>XpN=5E zaux_F&CAWL)eWsDZt|F{K5*R__cZrebQs=rnn`)kougiy8e&X0Nc0lA@4HL+Y$h*ZoRRfl^S)iA*6rI;RmHJqaS8p)_3Um?1bN&cBRIzJ z#Bb_Ex+~FdG7yI1SCED;(V)Mb0pFH-t4gfx{4iX~g`TeC$e_g^KIiSaS1yWWN|k2x zHAb&oezYw7&D&cw>?YP6d>om!uO7q?xJ4#)dIN$w_{q-!)pgIu_dE-IE@)+=xpye8 zyymexM(boEmLnh%L3opTejmyRl z%vyAi-%(}DA9rJmGJrhIJ3IeHtW=ON;SrDckP>@+qtcESC!5on4bByYK3U1~ilVVp zc{Q~~O^D8LJmjKlkbEOzef?;_>P0t)z0G?0V(?awbhvX;)=TkBaD3mM@ltBVu{gSW z{q7sK3Dn3B_J#}v9egXj;Qnl{zdxKX+(b*b{7#7zdbXgoj5YG=;-9<)k@t1fYXp)mTmsk}#J{LhMfG401Al~!3sicQ?N4wK( zSFYSMWOA@nf+aAO>1yV-{}!DJas4AY#k8FiC>-P|C*)={CkZumHc0vV=HI{?nnj&n z_D)ZhbWiAX$OVV}*>}RNpQ}@A6$FkN1w48FyZG@J=v*U$6Nb+vUm+smCFg`7ymWA{Feb3U;&-E#`(UFU#NM!KfbhVU=aT*zgMpp6kSRW1% z@IN33oW;D-c8@^%y0_6Kzj?6UHjh!_!K7jS|A$_A3e`CqCd(Tn$t6Ss-r5UMs1P)} zt}CB#`(e4x<;$19W7ZzccHV~>UE%kcF$XM{VzU&R^8yI^OPi`B#J+TqiI|mfMZ{V{ z%%(sTh?#?&ya=A;mZh=bs|bxa%xW*UNV7>t1X0j+9#ZLKzyS+`apSH$#q2A@<2W!@*^n9WD>%X%{-dd$pN^N!c37S$$w#^+{e{8dE`GNG@<<^hAPpKI2r7g3JnXwSSBH!~nOH9w@ zp7LKWZ^l>(OG!xl!dakeo>KGS!f?K~B;dB3!>2!g9qEBu%;US!sJR|q`NdgHCHSm3 zp?V|AS!!u$ShAJm+C|C`Hg#CULA6?T8}@Cil|6lmj>s}HI_CH4)CUWU`~+?1 zlIm!juRcVd|A1gX-2py8pmo6 zs==31&?3~BWGWyr zy!|ch_!zlF{fXX4B*$}6kVRkku=u0Y+-5JU@%Xk>b!jG~sc>Svsad?_w>>XPL*0W%S<`DZk-7`6|l40Hg~5IIut(5VJv zZ=K$eYE*UA!J2gLyz=i-`Cm}8Kmb-GU9`lzadDi#rmLm4xKe4pm_zwdHv1tun zzXDh@RJKqdBuiQ-*$OjR+br3Y zHDwA}vt>C$Mah=oGPY8Q>{*f$BS|!(Y?q2DMIpP441Uk)-n!lIef@rae}Desm^tV3 zIp;Z_&-1*W_w#;2PenKT*YvCjeek9XOZnDUwBlOu{t3m%b?l8?yWQU~nKy#%g~~%~ zga*X_Bt7m6 zF?uQ4DA)E#xKbut#WfF2jRrdN8}Zun%Er^c_e+8h30B0RbD7q2KFx_+Tp%f1)VSq8 z31IM83@u=+P0dMnrxEPjsBiJC+EK@?nx#R5oZP1GAs7f*z!asLa7o&p~xwKAq(gaesft=mu~ zrlHB_F{f5&eyE`}J+I)~EQHc{WUU@ey%!4H{73Aaoo_?LCm%2`5Kg6<{~z#O%Jb@cb7t1~ym!t`VR0LJQYXQG1djaR*u)9(-Gp`Vh3 zZ7vPn=6v25=5<~^FJ|^BiaROv$RVlN!2Lzu9}S21q@&K!*5MfvGn7-6g& zKX~Mbu9s3#*!SV{kq6kZqWxMMtje)C!j&#cz6837bE+N%r{!j3$+Z? z59Ot{VDkHW7;%oDr7h@b1d}ByG2-}jaQ8319I#2g`VbD;qlX^D#kWUq9Qy7HC8t(m zHskeRrF5K86{1YSx!#~3`oT8@*hDaS!+nf6a*-FmgWj~{=e~dcCD^Z7g|FF%J%~iI zIRVQ4t*x!u_arwr$n&gYE+f+l1E@pxzE;8?jQsAnSn_l8>ppjESlGygobh3 zw!HyM3xfRo@E3dM)~avS$iWv6#QmqL5y+ZY#<%AldyIwdDZxvLB~>bwT$~7JXx3fy~=J)mTZ<{#8CiZD}|MM5Zn_GjP zMz6+*48^qG-Tr>jryxRcDswJQCVn8=;$_qTJJWhNfR|MLwHjf1q$xg?thG?koBy2n zVrT2n8Yj~|OMQJwtzsV8xseF5M*xh4eAnR6A=BXCV9bk&+PuN@wV;T*$NR&jXdys= zo!#i`sEl67du4R09+gylUJZS<_JE>2ggv}oDMX;U77|8NdcIU6BFd^`gOFv;gEkgj z3o??D^?|ErrUg55kJR%$qtibu4$#IgmXs(!#^C_y@_gXX-WVAZ$I%=3wjwgE185?fp)nq7ZlEICQ1KlX@mw#q;Oq zRSm!l+x0GdMNOXKpo+s^MR&tp2X}yN3vn2t5L-EKmIit{PU92sKKxf+8(B*wlAmWp zjk?(2F*uR!ZIDL#(o?3-%gg(%e|c%>)4f-!xOC|@P|uXwyg3&hsYY1`1b8B=ay6E zcpx_Clx76>2h<3Ty17U)YO&5mQ9~gV@Gxt6W< z04D;g=J0qwW=4#{t17^`3-yRsaqqCzP7b4E1d ze+5?Y)Rn6mP>Vwt3)oTY_=3}ZZk`f$Xa!oV(ifnOuqGpcZ+jE!7VbdI@L+y0%^ZZo z9%iagjWp+V2un<-O!ItzDj-__5SYFKuWBm}+8U4)!+tp zc6NdL^JtX;gnxISbBXm{Ta`1tW|nq=*$7Zvj2W(8SgfHCWBTc5Nu7msIh6H9x)!3` zKj;g6Bf!_BSH8Hra-OATaX#2uKw&(M$z<~T$tx%r18ai8F+$~J)eH#mC-KNC!es27 zBNAen4{6{!Dg{oRkdl!(R)>oGKWBxfLIo>`xvv(}w1HvFrZ%w<2HFYigLOa7*ABE2 z)Z={EFfv@wUwuDFd=xK1JFIDbJ{4lXX%)y1Xnpbf>e>tFh+W=&&xRRMEl9%Oc&Tt>y&YkbvVHk11&Dhd!T z5OA=Ds?fcTDh6z4K@mu%fFw7u5NmnZOzS!wIA!JZRZ^mpq%df3)LYjIj{MtH>Uu#z zX~p_8ORV`W0{;ayV2hdO7>X9>Dtq*~bp|oQR-P#G4UW2eS*_+$H=Y?y0|sCLg9PEa z5;Tv8UBPZJ1oVn!L%w}Ra#Ep94v0X$H%HKZF`X(HQ-0M)qovOiuU^dp6ytTx3+Ps7 z4s3_wB7G@b`ZkyrML+W1d^mY%D4%P`ecHK$;+4v2GJMqsAX$4;C!=(eC$xmz3RRBX zV<{YBtr~Obpyjkl@qF*=4}pZKh1kmD9okSD0EpyQX0lNoW)CJOIjRmLjTl_-r(f`^ zvH`Ys4t?|pMN3PoVD1GpODt#{HBjhaF$}e6-R#Wj$ywf8i`m;59QRYCOi+H3mQrNv zHLXdn(l`#AklD__d(4YMb|sycD@jt10z!#F!uaR2xJSTgO(ILgBJ|gI&QI)G1KYO z&*f{a$_Op5_;S*{qjLUOLaV%71wW%4RO%mJF}v{0T()Z}ntMEVzvjgb4&4F0jO6Y0 znAOOs_tQDok9Wvl!47Jm_2?s{on@=6z%U<;N!N}C7=UdzN3+GzlBrp@B$u{1Z1ip2 z&OCmY&iz{^)ot)Dk4M7LN~}*&VDu9@bEoS_N@E!GdeEZ!dea9cH{*hj&2eY_?>4O3JCXfAD`&uMvQnpt30S*;qp8sH;&%b45X0x&TuP>ItX192B<{yHcGpM?O~g+_|Iu%Nrvl!Qe>NXut>| z8+Cm3*w}b>9?;om20DWUQZYL~**Q4v$&gWvy2#9mFtmgda0B2@(|tw1vhd6h8G>l6 zgs~r*Qhj?jn{-kK_FDt+wCEKoKs~GoPpcB$U3Wn7d^?T+HHC#BAokH>yS}xK&V^a4 zE?|^8EjqXZXqXU44`m_D$zf1sp>O-GZ2mdC2%~CrOPq+jLm&5Gkh+?hTB}mGdRS{) zTO0pZ89BM;Jg2^#K&jSQ`px>yxaJ#RwkN&JKAU>sS#!0^<`DmxL$gV@LqgPR6=>~4 z3sxzt4eAO%>0pdCktnAN_ixK{pt`aaKE<#bZN(o&;_prWxZx35w-qzx-9dj zP1;^a0Xd=Eq;Br8@nwrgpnxV498!K7am4i@%OS4kyP%NJ9(?qz^z141yR*plSbGj))aoNh3Kd$I?Y5r&mpL&tB7g|*^g(Yl809I!jfb)QXtwNYU*whT6R`_D4^)1Uw}&4JJ=W0BMXWn zxy8EBec4(aG{2kuqJ{9xJTQ)$J>KOH%h~%tE84Z&fBKob`@S@2Ze(S!8+r0Vo_Shs z5}7ycz1g&Q$du;R>|zqRM$|BGJ*&pY#4Y^-xs~g~qRqut|Clx63Dh!A2@XC&#|C9JfUq6y*_D3xlB4nKV!U4f^{UhLW0=*7w7`G9PVwA^~LRN}Bv zZH8t1r6+w;?NW-b7ieV`T)2RvHXzJ6BVZd|R?@EP*m-4!f^IUJ9>F2F)5vMgl-3Mz zjC27`Gbw6e0#aP)?Gak@ep<#-i*u1kb7LEvcbaWl@}lCDX3g$yg$?1ZVvg$Ha>MKx)&zs#<+|!ZyAXpbNj<8QBUA>WExE50ATML}2gmS8k zU#tOWcq1|NRYxde_XlE~xG%R}a}%ka@Il)uTi515`^V26u-s?qxsQv5_Nz$<3-HKN zu$VFN$o#`!qYWh(GFa!x(`C#`(rT^gdZOJO$NGq_QNz|r&?#{R7$0RYgS79*E%%gF z!MBW`t-ol!auPE7zgLYxMmBtp|?Tt9o7BMbqqs;Vi zXTcu#CtNrYIoJoZS4KF`N0aBPL6`q}g~h|uGf?X6u($yG{>jXB3Qb6GX24R3v%vl3 zxK^<^oIP(mUF<#G#C|ZY6ND&KB@Wn)i>@lMS??|D=1ok?-dwg+MnPv-tGTd>;sqmwFrpbgdY;J{XXPXnq zB>pZCidma?j8$VQt7)i5lY!jLFr{AaCNJ=S%iJzs>0vZ%d+HIYs`Bs^g-7-fc5vnC zX$^PfL{uX6z4d4}?{nREw zB^sVRdp7Invmv7_rfN?sJCijz?ZNh7Tm=H@Md#elvS<`8z_}ztGuyE&>~k*qJ7&a5 z?nfb|w5;1VAqo=UWJ2Q3_`l=d$lpWz3mh~y!GdV01z<%N~MCIp)khJaxB8+QDxJK@}1;x(XW>siNs@W6p1r%vU9lvxhc znyPfWx&eTWmQL{T@d35eHUuJpJgd9Mi+ulq(1wL`OyGm-s&iw0V88)Vo`Y19%hTxf zh{fCT^4+d}ByVr;SRYD4LPv$Gc0n-=(i$nwUxHxOKol?aXr%s_DX|6% z$pTC0)LpR0Sr1fuwS78jA()#*%icdiA505ipLlm<{Srn4R%}PsXBhRxD)Ro|t%vb* zLW*=CuLD$ww|Zc+p&4UQ1;;B6`Gf5LxL+vn*llL^6O%SH zbbw|^!++ep>FW(Czk>DG;7sjmL+MT$-|Rq!GX%1Uc{Wv3O>fC%JjgErzPJ%)V^}LC zV#UwNy~Kmn?*l4Jt?yk3d=8<80k{f8cTo9h2l_tY*L1a8%zwE-&m?H8+C(<5{RuSd z$~*X-SK0e$88VSQJKVb#gFj_OmL^SN|HKuLKc^A}5B=0jYj3F^GZVKDgGk(JUt33q zSw4*V%S_!$v63-Tjmxjq*VjEEdtt9ic=5ARs9Nw+bhMe3Rf;VF%kvaY`-|4DAJajf zzVR&-@=By!`Kv4##3rh^Ze?eGCM?g#LEw1YQX#IMTXauHsKIZ4BOky-hr*ImFVnnr ze-1E`R^nJ+4(*QfgK1<;o=J985&#*Ev!GD*G1mz5E7ra?2F=mMNLv16%1o^d7|QTJ zI}n4Y9Bl-apIgYnmr7LqOG&b0EtJHW1L$!RWc{uVDXIQLBtb?7?k~k>VB3#z(6)ww zkWjwSHBF1RplO71T_hwVPzaae!ok_zpJTw`S%yTmZ+lhD|2h=r!Tw%2qs6(5S)eiYW&Ip8 zIw&O6clB@Q6*k6Q$#SVUD)ReTfB(5ovpM9)Jm8!6j}Y0;Nv3@Magu8|zlXMyf}}UE z%gp31u8Yvr)U?~SS7Z%fXj!TvTMUQ&9y;E&vPt(q@57HTjj)em&OJ(^{`gyr{e8}8 zhM~-p2}Qd{cH^%n6klA9+*D|hKe1fc`q3L%-!RN z+Z06B)H|1Laqk?NRoD=t$!*WFy<_(H?4HGSNNDJqNdpt1pI66VPh&O1jHXQi1^(sW zpJO3D#rCna1bCH0KH=v-d4K#yOeM4JZZT5ABwuaE@Dta=V^y>hDg0$%-yiq)5lX`d z|JQ3^+oFZ^U6YS=V)&);w>a>`c9t3c-_!g1EO+680}U@X{CUNdr?J?!-Gt7+zIbAr l_`k;b|9+^u|L?mx!R`9wdO9|~_$eFw9MCn?DbjKX{~x{V*1iA$ delta 67099 zcmZ5o1yEJn*QZ3fK}s4CB&CtKbT=rSDhSezzyWEbTco=L>6C71M3GKGx;wtjd+Pho zH*?3iqvO5j?6db;zgjoB@NQz!-A|S6P(0~2&-9FOOC;vW!uDh0$J3s*oQ~|zLphAr z0yvE@0xs=bT=H^QZ<4=6qbYtE<_f#SO5Uh#m}KRGUNVG0tNb}? z61OGMiWg5b9BSl0uL&M_ckjvLqm@k=ZzLHQ8~on%Bdt$VgJ~kA&!ii~nZEPb%<5e{ z{x$?|qiYV5W*)}X%)@6;lIRQ}R85EZ$%}}+W6KvoFTsT zO@8RbkgzKj34C6+@xrEPokOeTHw`2rRzJn=@22bDcJ@XeZT=MRlPF-%zqaLi_=6Y= z3risNxH^jKl^U~dHO4(wJ*Z{&^Bd|{g4vTL9&^26B<|T$Q%~L=(8J?VftL?E0+)9{ z**wwh0!$FXpG65;O>)HzsfxvA#Td5-P@8szjua3#D!0wtW6^mY>1A%7KYFzgNQp6; zr?!dO^G4L(iR*p{={d^aW3 zu{M$=l4O;hTYlCRMpg#hcwL@vls?qRAC?xVo}Ay^Clr(p^WVt*SkD+>fzc-X`HeY+ zklkJ{Vd#5JO0(_cuUjtmj$%`60-4VSu4R_vWxiYQklBxg@(ylZ>9?H4{QDeX5(qqI zaD+Co+?LIag0Y7>l@FOT3kmFgdPx4WmD5AfO`flP`$(250$toTR2FtBgfp04AR z(r21r_3^!nE%oXWL&S9NVLSo)BBM3-oe@0iS>z!I*L41LJq}CUD5|Y*<5GxE#M@Dg{Tp1a?aC08~1iHp)qHaKf$3Wd`)Sx9tM{?z-MKRAQ( z%x|u*FjZ^h?n^5sHuyC#eD(WmN&j_#aOdt(_l43{+4h7^zm_nGjfa7y3$4|tBk`ya zoyEPPQp+Xe->2#JOu?W-q}N?=Oz)nfw&It@fv27&8}mRW<^iTsGG9iBxeQ+#u>}2P zxY)nNCCYnQG6v_RTSpb0e0+btI&qPW#;V43khg}kvx9@WDgotIXePC0p+>FS!ApWS zhrGe}*fG=3&nEU$pSWxj$GT{JnI96lsZ>PF?}`~aN~qdZAr?UmPkJk@gF39?oPf5I zCuV5gHnTCmc=d^VU{GmrO{EUq{pk(XHtI}O;O_hX9)L^>90HH}9fP)Cja=w5XA9qo zp0Q;sry0bY_0U2L3W+L`io^n2$e5&5e0&osPB{!2y95U~k;zGT)U*cm#H z>|XIh>bMZrOrXzEz}V@l&hM$>4F8^2c)TDH1fC@bo@q8kO=IB;TeDx5sYHXdKN=yy zLY{J3Ayd`igOFxk@*#!W1&w}I2})_A(3@&e_Ik%(CXp0z3eqsOuFc6R^x4_j5oe~^ zw9ju&0$d&CwpGOG-yClS6sqmuqXtE}U0 zG$1!#snBuWT$_pw3u}T}7JaOU0?zq)vUs!a&6CeuFRq9$JxuStRDOufHs?~G@j@mQ zx4oI-U7^3l+GMThKaU!;67Vt-g#W@^*4{ zs`v5mOorVg=7>6e^=ZdGK98@+)+2(9NMxy8hJ4zoB$!AjvN(H ztQ~7A*VfnmnymZZd)j@4M9wu%*WM@Vr9(;dCbqd*L@7=9V`FFsXO7uWs`+eV-RxeU ziAP^7qx;GC5`D5C(w?MyeXVV1D4MqG3Xq64oW_r|q!kfdF$Kr)qE>`@$cO-* zaWr?}vpIHJ8sxGwTX2)WYcqR^%YfQW@%T9k=)1ckO{W%-f(4)n^w};gE#Vusvv0WV z%r!^!#xR(U9jjg|s_&2UaB>wJMS>j080ie>Owrq0rB>dO9>7^0nD^GahBj z?o3S3S6`pIGl9zzk?i^vP6EnOrH!((@_2JGriZ8J(xKkwv@62pCGk+do^(&WViP2> zjGX=zLNC)CxfU@lI#_CyX(2#Tu!hqI?VMG=J{#?oOe(MV^UKpc(2)Z7sYnDI^Qi+n z@;N=v9mmbQnFYC}?wnEUXGt9{i0`MFIHn?(qm(}Vs1v%Hi||j~@?Jo?K54!Y6&%Tq z41f0yJ%oVq?aP6r_cNRWySP(nxtD8R`_OW5y${VDWZJwWTpB#9z42#;)b+_>F%J~g z=DLrihdnssGb0zr8Kqu{ON35EcaJCAAFXS~K2)XYB0!Js))#!&S!)!A=YEOw_K6cB zs~#GeR~H6KiU=9HEn&>J2;Uk*t)$@BuPRL4yX*3wR=!LWuKC*-z#9cftY!I8AB%1^ zjH%?=M;*cdQW!nP{v34U&*<Zu3&O$2{wxJ1#avWPq9cp%dM5*kR#;XO&bY{AEV>bd{JENx-D z=-Ih*g}ykf@$It69aJmAcFxXnl%A@QfR<*jtI2ZY?vaN!M|85$`Ah@HI0!aY33_79 zRn)uR>diUt9;jwXiDvS5&}xWrcao`E%Y<~Sxa1S};V~$^K1oXhV2{-4eW`u}QQmjS zC&XK;($KXH{*r5kn7{3E53Y>k03~z@FIz)(CjyP&p*InWPP*a;p1xC(gdLKt35Byw z$2Iopb1ZKS_wRBt-hjqb?_h=gyg00*@=BFU9K=Je=eJ7A7+D0P6WAxEGK!C(Ue!mbMt`vlsOFXqtujs~QodC-Ri8&rtjFBGQ{%Wqt6Zb*hkwD~I z3040d|7S!qyKeOgzu@U^egyZtEm6-?ay?wbb>5i`i+l7;tZgX^k3ZL>GemVu&1ygA zfg2>rc#ax`t1VITF`F`7iwJ%zg4FHjn;+#RVWCpj&3I8m5yv-b<1HdYqA+3JeWBK& z2;&4=`FLglNry`)>*_|a>CNUDa`N#F?)^(*p5?#>{*nl1;sp^A`*yRAAl!B80M#v0 zF~=94=7V;xx=#v*ut>9FyH90puU@bS7sDVQl>tTL9oGBorL|9fUjIP!@-ePkEzj%q zQ4M35KbR)`%Eps32SWWn(^~bMvMYb8GN3%E# zK2l+%)P+vf#4dn86^}yt9}Sxg+>&(8HF8|n-`+w0YID~vGZH~HTCNC?hxHRV*R1TE zA^#=XKd|8xM)eeVSkP_XXraxYsb3OZBZOq8!37&YWIw-9X@BZbco=+rU&qN(C;IB%hsAS_nJOCtSV_V&{hVI zlmT>yJ9*k>i-5&u*yhkdTx)$M?xK?a((0nt((+K&3-JwB=xEg!sn2+r9JSXn*-HC+ z0^Dyi37ggcJ_4mq>cn2q4l^ z#qzX&YvDf-q*?}MeW;WwNCK*9C`W7^=&X^8ngmOzbkntWQj^4_!%N%tUtQc|36;b= zSh$dXgtN=V482fGJv?JC>_v+0H<&!d*KWmCb9LOA{fr#uc<^&zZ7dHv8z-JVq(8^< zd&x-d$V4+F;(oO5pXPPmiP*aY-j&K%70KBbO*^~3!c<19MIWk zih)@7+g#>60|5bBqRc-P9(BW`Rl`x6M09#F^zHnEKMqBPOxvHC^*p+Bwhj)Q79m~hWaDnkP%N84qxshnReo3Earn5u zys=-dWf@te?#<*!U0(fDnWOb_k;mSB7`#@KejjyfOn!~#FsVWf&Remqqa%A`S^J$m zOjnf_<7oCSXJAC`^>I5im&% zz=9-~y29Fdi>&g#Jd4bqGvZ4V%A2|u$&8xo6-7Rlq6{xenM99JAX&$AT5?Q9H%=_N zr3x@|R_3S1_8jElZqdYd4u13@WzA%uNt%5qp8EIgZU^^I~l;Qk*@42Mdr2*XGPkh#13#TVhnUUFu5j9(jEY?!sn>56T_ zTEBWuGSd|q@V?zUfaoh|=I)hpbqy5>%w%fI8;yCV5Klpl(KFz@1}yI-XrKoK@yWTm zyOU;GIH!OfO>H$}vija$UG=_2rVxj7=Q1AoAL1AUWeCRB=DP&I7H8SjfF{b{7gBOnUqxgvraIpg7yQ z6=++Sqz0g@Yuai0#uiBYJ^Q#w3Zw{Z#<0moX*^_`{L`~DM$Ko>o{3{;Q2fvOdEbF> zc27>aV;Gb*TEB3;8eRMLc8{Nnv9`{3LCiNGQYC9+=1Q0DvmF^$lEn_qzKdnl^4j|| z)W#G|{ABW!;h*oY*7P0V@apK#hkKd@5Y!N1nav$bPifi=KD+w^EhW?AY@ccJ%LI;t ze)r31^U-Y761_UStvAvk?_}ltckdWL1AG#xA%E6(9{-=U?HU>qzQ6!%zXbsCFtC7R zT*d8y*pGzd6+^6t>GJ>HCJez@0@nBd^rCP;zQuH5QfSINR+Ae3@eH!jbhG9?69!El zBui(HTH)UdqFf{?X+zTwkNCk~_w_%|AYV${h`B0ozg$om2I)1XFJ9RjgRx~fX)T;ZP zo13IDI@x#Bm{OTad&mMp))T_;R%PGboNO-uw!6E#tJ3^xdS=E7+E1qbmy~Iv$RG{; z9QaUXG0v1M06C$RJ4fZOlk?ldk`d_(GeMgr!qqk{}b`-3*p=r>nkhp4&JzT)DXK85Kt%`K6iM>f-7u2QfNwt-( zPcsFMe;|!yhvq6qNme0-v2uc*sN)0-JNVx~( zjdY9gLf6yXjQ3J6F7i0);5CZJ(TSKSzYhd@^cr1pbL8UMa^w?YKPtR7wqMfPG{bYF+876QpuiKl zI!dr-#y~+~@2%VVj1SuE9dm#jVjtNAV30U^%r!%$(IH<C-XZXyX`NJ z@a+AMCZ`}Z4;19WbI7sv?H$#Mmx>-|D+xVmA|gQjNN)>MaHe^(wzjv_&F6Jl+sYX? zTylzIW@c7tGiTtkPITku$%A=s-%C&%g-HXm3l2V3VXi# zd=~?L5NCpX@10wBBaZk6Xpw%z!fC>ug>BLu@){I`Od7D2zBm!LUBSpd<-;2(llGIx znSCfR2ST(LL!*rOG4LfmnGI1C>o+`=EbyBf+R8_j$&fdt^S~uB4d?W?hQKGpYA5IqqD|`3lP->V`ioh$E1>oVVKWHLIQ~1F1u|&UtIyE~w zUJav2l`zNS;3pJjFA?jMv}~E6!8V#8jH^2vjL)FK*OVO?CG!D@$s6j=szpo>kDQ+` z-nV-R7iTAT#I&~?d)NQJd+wK=2W>!=RHQvZlqfQ~Xurh=v2a-S)N}ee(VK4dUwv`M z+=&}`XL*Bnap*!h&PToI4V4p$`#o%^O(+7JMSHr!^wo`KPcqZHr~X4cFxWb_3}41& zOYEVpT5HN5fV`&&vu=9FW%*5k|4tVdMQ{lbVjh768e6opeSacH^0HGK^fu~ zYAW8xP_9x+VWUoER`jCetu$-EHR4ude)Jom8+jpor!*A*z^~X3Et(G|vM6E|pKYI* zbA?XPZ%_vd;zc3I0MI}PWY=LFO<%_(9;=WrGF}>|^{;Dl&CMa+cx=OU4y#OA`O6G5 zpNxG=N=Z0h!YJ%H?dDs2aCU~q@*w3UKt$TXzECN8%5tRh#=Go%a;g95wJNd-s`5Kt zWq1eNRky#@XffH_?>boonw>w8siz=?nRd}54*=-1c-ttowX>)hBb!ba(O($FPZ!xS zk(MIYNQ$(Xp1G{EV=4=$=^TaJI(- z3Pr@=`qEOCecskg!?PQm$LR3+4~fzW#2=V=~+}Wk-ToMTsCJvlZ_W@syEO*H+&A@bG}AF z^klpTbbkMdvj#=+XoO(p0moBAC-W{5@azE%#!3}*%hY=zE+#x}mrxg+p&s2Fa`BNs zI@$wazg;}KC1i@_e-43(_&n%oQunQkn^aS#68G~}vs7xV^=D{=bAQcze)DX_|6T;* z9zhVgUA-eo@Hyt&gF5v3?KFA_qcc6Tbs~AMowQOaYLfVyE!XI)ko&5y-?fC_f0D`N z6_A!gup3S3s-~Jiy|>V7`{AF1*Fui8{6^+co&CzFwUlf8L*xGv$wdudB@; zeWb!{C}Lmqmnk*x=bXM@RhHim3p{k*z~H9M27E6rC2db_qFC==oollP5TSOF-J@!F z;Af?=gbJMsy7((U349;Ee{j$qLip&@tFZ@qpWQXDPNO0@8JQ>R9L#>^hy4_gsYe%I zKHi!-T}d#ET@9f=|N1XiV8`}Bc#m$ZlqdO<|Gkfy?E8mtK1l%u{%lfo#;Z|bk)vM< zHH)Z>*HmS}@iKV<_u27R^!n3%Pr1Itp3H$2h_OrQqs-}NS68ot0bZuLyy+eyY?VEt zf%lwwPZdvI7dLIgIIR+=;m*&GEaS3Zf+As2hrh;Xl~V<~$i?*ktP4Qe(cWd2A&qVa zQa`ZCvL2{3I9mmsyme)gA&8)+8JFp_Wum~yjYPV6;xBP^m$vojQ{UmqjMxbDOnuqh za8kvU?yzN^bS^_bXVBJpln%bi2$xNpQO$NqYC`XM?LMspg-PWFQ23*sxLBj7N?u|p zdOZv=9#c76iV!h}<0c?Fx;>*U3P^(bLL>PK>YOPoUP`otKdNX5M*%S?^(=Tp0q`V% z#W`aeaREz#yYLg7O*m9+hGo zQ(Rvu%SC5=-tTSl0}#7{hdJC{awT~{3@Jbh)o*atgrj%y_WS%r%x0$EDKfI+^^fv` z&XkIZ7i>xcPO7dHf0oK;0yolM8`$ia;ZjjF5F^17N>2*3@G>7>!+j2*&`r$rq~vgl zn7nqYA835$qfEhm7S}8T6mL21aQ}+qDw=YqCpEFXijqm8V{VR9t85Vg{ui)E$ua6%CDFyN7<_w0rj(ZJ0=>13{bVxt=wZyyj6)Kwr;i`Ho7z zbZH`}EGJk-bgzd7_q$r8V7d8ct`{u0aWa+F9(i)DFkDps1al@k6A$3zZsVYam8$1q z-2%|dQerX_}>ktJh^A=o|>A<6kJ$ZS}J!=Va#Co zAEJaYqiB@qY5TW+F;UXZQG?nX_E)|HmYH^&Y<&Azf2@_r(ZAZcmo=>`{N64CF)+F8|A z4AEpM(R1#g&0BnCn!Ca)rx3mq{;jF{#TO#Y?1z_Scv7VwyGpDQ%z{Jl-V1vQTwh<< zRDHanWn{WI+Q?H=?>i|BT4p9hEau}MOym%+^1rDt?Z!GPmy2Us|DkoY>-pYtDw}#2 zPmSiDWOR|#F_b-Qklur=@;+ZH4mw@9dy5hD4u1ZJ9Qt7}A|rb=Wi69nBq;0LGRZH{l?x{_;BDfn8#tzvP38v&`K>Lz@BvPd;t zYiBA$_Z8@dpWd8U%P8#w9kmF@lV9q?P{oTc@x(yS`c`UK`px4p&qzXB7>H{G_#HtZ zO#(gr&@Fo}lh^76>f?c`Frt-fh(T$Q=^G7GI8Eiad;OMa48d_Um9^e5cZtghBQ zMXjFkG6L$;r|Jt=Zj;Z6)m>1M;;jkfAei} zXkE-~&`2!w=Gbp~IY_QU*R402e*Lb##>EfQe=}t;gr782QYvtSCrOhGG?VN}8ZwxqF_0DWRWcB6U4xKl zd1HHAZAy7k`8~WNpd`fftUrYG>m7ZTu_AZzSQLKgY}=yKor%&teO0DqnIj!Ru`bs0 z0aoa%Q`qQ!gx0u0aW#$`L5j)&M4+6p# zcuP7O#v%d7;-8gisnCxBf@ZRr>k;b7)L{zhqcJE^1Q@IPF7|CWq?Q~7ePG`it&>kN z|6-Ip!~AIp_xU(UXf{PJOj9aX^ z?8yh(f@YBBtXVnqGb-6^QJ3#dDTj@!(lk7W1{e9{9|6LZAJA6>CF6^WJ5UW>g(5qB(7~@2n6w;(f|;J1PTU~; ze2YO-5%js5WxspaZ-F|x0IZCs)K5)%Ml9jR$v5ilxdofjFuG5SjSZn3RM9jOQJJV_ zzAg|EIxu6xX%N+!ABlF6Plh{mW3>om?rKPR;!@~gG|8hbG!+8RC5X-8+;d{#C^Fo( zp}Wq5nI7-{qOD63(VQ4vekv3G>u8D0gsV9mCS=;ma)8V3hSIo9xdu`eKL=CL3s{|i zd8}XVEL+HgtRkDqbcU&xBS+YZ{t=kRKR@sf{nZPN>=714tARq`dw%N+9q-JQ?&FX+ z!o@Ksc@tX{KK)oo(S`B#*H0GLV@iaCB?ia9&vsaypW0d{G}-x)$H~G2pq!+M*K6g= zLwI-Lf-^_&y`t+H;&=Gj5xl2Bo4z;>+~~yaJ@jv>@XH`p;X+Ynit8HfnxlPoF6ewm3N4^S zyFzNkHMXxXS`B8ulEkE3qTjmw{IAC%Lxp$i5hmce}k+R+%B4|iB6`)N17!bmRGKY9C0`K zN{eC72)~($^f&{WXBQ9)olrW;K)4XOur|Lsv&@>|zm){vZ$gK|zz@sOIlnwRP%YG8 zT)`9oSy4f+OTMmF6`w!KPD?$iM!pI>;L4olcxn?lAX-uUB7O!lDewX^{aM766!t?%oHV z|1xk?KBv@1_K0ghu^K5*XCS<0RR=l@9?0Tl7dCw_)IxvQ;dBN7NF zQ+JFy`hioCwZAIu(gRAKre9n{SH8mkoEJRy3OD(Q+?Yki)32efJJ5 zd85sVGQETQE&&Pkg(*{ObplF8VDmAsYCz{(*l?< z#%K=Se`;T3%RiYK$fC8rt}X*8R})R1Po8J+n08?hux`NI zG9Tr%#PA(bjvVdMF=G9USxkUYN6iaW=!73Sv|vXnVYROJoQ4C@o)U_H^2i~a{}DoD zT0meRgOo#0=r{?arrRg0EX$lWo&rf(^?QbEFfg+Sf#M0dKw=wmu zygv~)TK=n!2*LT!LFYfz8F-?QBm(wNrMH$p?16dFt%e}aP$`~u#Gvw7Lr6Edy7%Yv z%`k|OO6vb@>K~Zt49ck*l2O1a$PpCUnQ6!)`&iklue66j%xg_97tb7A^sK!R#WCb> zr0e!OgIo~USX0}w*yPgJmg!{{Z(1Fo;VDJ}k?}#y?^l7t#aCK%j!MKkOO=Q4%e^t( z`P6b~jA;L}XF&q5-{e+I3oo>I5Bv_0n?nHU#jzFsXb&TR{n~*}6kDe5C>=(62OArE zy4Fs71=aKNv}}Ph^TGdo4lEfD1?8~&2H6SfFyki6*bVy>IZgXs&+arqJO5%yDO)*; zL#f+HK1F`b2soU6QSn~d`d^>1&(>&t~L-u36a_SV+EYnvo#m~@P$Y?2vA=^T)w)Ed)XucSlnrT zE9YJxWoNuX%Mx;x=BLYlbyCX`%T@zR%IkbdJH&-Wyq%hh&XhHt>Zool^ zW_k%CrIF^S1(mFGRltydb3od^GPXcCNtoUUr6(aB6UMNZu}~=#qtP5tJeZn!yPT zGk*z_4>rPxN?uh72^i zKv%CWspn-3)Uf}qA_z$;oV?)sX~8%_yDZ=sb|6nfrzY!3;o`FTI}$>M`ss1&176{KyaGBZF3+(0|r6x zKx@A+`W64G=d#^ip^p0H5O%;24rbCtm}RTyK38pB1B9<3h%?dXRw4Ih0pyYwHUXFG z0IDau*&zAXd{x>u;ilhG@jsd|2&)xe3IzW-f~Z=8KH!%e#KKr`pn7Q^3iXu{;2kw_t*oc;AmOx&)B2Mk~DO0_&70PProXIaG zHjkeNt-BVNE1iApGCI)x z$b|>#njO3K?BLgAEv^XC_2q$b+|F2Jg?vaT(C#yVV;7LG{y#yZ%>Xzd*w#-qhs=N5 zI8+Tw2jZfXV*9_hq=^?vfc7E62KL2xMa6B@zL;+@o5Ra%_Z-7s32w*OcfQ7jH!O|F z-U$fSufEEFsDGgY(|;$(I}R`XZho8rAJ%VQK^XPcj0R&Ft+z2KS~!r|(7Z1oC5f(r zf`ZZ#_Xv8nyFK$HTk!)Czy0zjZ`B{OIIKdo-entsv_seG6K>y7Wf9xA{V#weF{eF- zlPvUMOL02HjP6V*3>Wo%-rtP?-bm2F5X~hhevk0L5QvnGK#)FPyW)WX@SnG|Zs({# zBt)VJf9^?V?QQD2Si1s31}XUOm<+(S1=+laabjC1#G2u-8etpujfjF%vy-93D-mf8 zFQRHB40{*fouG_e%kMhs$5*}#`Tk1ntbuXDp6*#cQ&{x{vJMOW=HOrm$1tkR>Sm$- zcn7-QZU-3^2wNb(o~b7ULQAMU51_SejgZ^HFu06|C%SS=V8}}q^odIlZv)NvaT7QS zMnG@_v@k(cbBE=Hb^K#2R2M^+=a_|IkM2egiY)c8GKB4MzCK$utfQHwm_Tekwlcr0 zjD1J+ihOa;>fddn6%(j8ZODr{)j^y@bP|J^oJk8pMIsb~& zqvr^*UL@6<=t|MVJKYfh&{C<0KsaW+WV&9R{X;Na6;3HavD5UYQcL33>N!~`r3x+}r?ri@pJD-RVqHnwa(m!2I zgKR-S5a^=V0F?K{GU9_kV&H8V0ou1}I9Qt-%|Un|@q%@e45}=%_z)qO;Ey8l@yU9%x60sYH&)_LZvH;;Mt8S=^{7*0s^uh*D_6;?~T=WGm zQ-NW#@NGHB1`=!G%ImXsZlcOuQ#ssyul05vllD1M2)0^xcWE7YTWP} zDTNR+l7?MEobY5)1i0gY9S#<03A7~FqxHD`iPOu2A!_8&Qz8h0fD?2Fy1A-h8jc6k zjJFeRz>3Q}arQ7IPfh#h%UeuPg1TRVkRvrky2&yD+c415+uPg4!}fspkSOR@em+Ra z^E&Z-)(`@dOtM{bfGZy9r-(pa{Yl*D(Y$(O_7Ir$3nk~rBjb4u1Ll(5^`%=}Kp!1L zb)=FxTva4`tMTvJ<1K_6i9>1H(b?Ha?9?#gdA&`N`}(~%3xk*Rk5w!!&ruTo#&(Rf z*7uLwEGOxZyi{kc6DQ6k=(z?*(UOCqdvag{@9i$Mqnvg&JfEz5)t4pVwW<1>-nXQp zA|WGZg5jXP(;tm)4#iq!2q0-0>P-iR`n2jD8S&_3yuoQTz0GbTNT@AzNL^lDege`u zpMZcEO&Oh&k}Iz1rcLuzZfGW+8@<~p&`C)RI@fDR5NwVTBJuy^wc$5tsa#USa6z~E zHAU*=`_^}N?3zBGCj;U5{@ML8r6Bx9r373r=2UDs7<^;ZOXL&xs2t*2M-hzcB>Zfudu-KafILHr+vA9c9LAugj9 z@7)IBR~JCy22;JCnPLms>?K08_ET7B3S3?*g2k?S)OYr0u?cZ-e1kKhIZUMp`d&G=ix8=p)2z+<>b_*hO>U!@vZOAG#Qmaa@UF@mqtonG+UP%C-P)NK|B;x zAms^rQZ4L^1QY13eTPcZ`Z$PEC{BI{uf19DhXcyNSSLB7T6U0g;UYX!#x4z*#quR) zH>CwgRYfu$WblGPDq?-B5D4&nDi-g~PDl_+Ps$zshG}4WxDUZ}@axCId1fxB9^cW8`rz*0v+lTm zi*1%bi9t?*dD+T_4+FAGlC4btFG6@iliqu@{jH=zQjw2N+WGXRvg5gF^auY*@>7tr z;>kA*_KvPp#S>_@{LXqilbyblsTPeOucn|nI@aS-=IRRCN*TX2?|0G#9 zp#5VpZZY}K*6TtE1-N&{IG$WwHJG^x%+kK}+2cV{jG(9RDB%Z%-Usk4s{uRW_Zk z&!PLA;u0_i<}*V}(35xF{yahJhTW7N?q zP^13}q#Tea3)yv^ zUs6ZsU?Ay#I()SltZhQFV?Gfye&4*D0pA-iir^bDlDGErXKo0YEKU zi;1$NuX3bAU&Z{7HG={_^J3 zYsq}`NwaQ-x#tuv{vbc~DBPS zKmtj;r98g-x3LzriCrM%^`Qg1Wjr5b5EW*$$Gu2lAJ^^eKK&MdjHZw_XHI`nt_60! zSvB2DJwrj+m;QOuk{pAo@=IH5SScv*?ra1(S~CwzMj6r6G1=RS3Uh>L-q&ED3pqmd zkn#8D>_WIc%e^=g%{$Tq)n6kS)gN%*NA+z6a{y>oywANdAJIsPDzZ={ap!;}fGMbs ze0GaSw;Vb&9jGgkAQ@oQ$KivlqTcK~LHV1Gbl~~`Mq>hadE_dXHwQwfB#RbcGcVQO z#qhOj*^d7C9e_5oy%}g*s7Bt+Q`pss5-S6CLKaT!L=8Xf+a9oOn1k1t=MjN}|AyW# z#YdOUhz}k$20ts8I?lvlxyOf;+k$wO|BqbP5`eNi@ZlcdPJUowR!O;cizG${UeszK z8|c?v*%VvX^z!lZfSa!kXCN*=;b&q5fo?#}k)YrS*YiV0FlGW8vYbSYMVyuKHqujm z53E0|>vbATMFl4Sn=%~SgBTH;nw$3aa0rJ65eEq7-zJ@3Q~HGs?haPyuGM}bY|@^@ zHM#6R!kVZOfq#&O3FK5d`4E?bmkTrND_mZaYZom#d0@1MS!Q;2tz&Eq!Ut;=5EG;B zXfZAQ8)Nav!$RKN#*PPUfP%t=A)MPAPQr=2{{+@#)_l4DCGl(44BK2g_;rJh+bsLI zd^BC~AYT!8lhhk;!mVuhuOX~NfpY(Rm{A(i?aw}}d`Z|yxxBeP5di=kX=nBovl&4V z_59L~FZ3&EQA%xjZFmJyCY=#OV7{F8`bPpzj)6bR$mRNYYf>?Cc7EnO-FACLRg^(pWO~}`(`*nYL?ik0OtnjwwMQh=vwUkfQ>#(Q+of?ZZ`UU z6K>Y>-S+3UNndwU1>xbxi@f%|Z)1L-e|s#ypn5k4l)6Vygo%|>p|UV4cI6ucJf@Rd z9uB|?ljAlZnvo`$AcrZsx7fLriVgp#1>!^=YU1&a15D?3-*~X4MSx^ORT1<{X<37UPJE- zcSrNWe|+s8fwQp4-29sZkf9P78tTadl1ms`9CmYLaB=(hJ^A_bB}?KOW2A5ckz(X% zm6$n)33`UZ`gpkT1;NLjx;*U{s_i&Qe0vPot{{iocUn;D3xZsQhp4wQWZ8J*(NVAS z#RDpX)WgHdof9}S<$v^dIvgSR;2zwaKEN}Kzr?dlR9lhRuMUQRDzwz}GL>SI6`HR9&F!|xY`R*oE5hFt(m)K%2Q@h^v) zz=StFa3?LY;iX!#&Gho}vM5VpH*R}4RDRa6lUVL1Xy7XJ>_Q09r}vrQ@VW&RIaQZ( z*qM68#L?C?{0?*%8*X|=L-+M{u}+noKO4ztpeu#UI+D8&sn|P#-&GoD6gY1X7Tp*- z73q~P0F|)?KfeNoXB7N<<*75*vVjbSVlxq{#JexNcO3hW8kZy0pxH~`&5MXh17&-A zJ8Il&of|Wq$BCbxND*C=?oqe0-PXZ)D&Xm<{V4payay?%H z17Z344b{|z&Kb15H}u@zmsC-)c#F8Ir3OOTGEr@;KZj)h4s|64Lf(ELPo1hS)bG&s zsY2#qlTc0xu#z_?9sXp5n8RTJX=vEtiX+DZuD{&>ehf!*z2j{Q2*M8MDw4@$B<#}2 zuF#iV)hlkOG>J35ZvI3_osl%UO)C2n`_h#Yv#I`2=V09z3`AF~9sU|e1fPPy$|^Uc z0U0!F#UBu}f<)PDh107hw*zF*wba3=BW;497GRT~`^u&=#P3$gt0~@ zc71%3YJU+w2f6|nrU(-K!s8NnbLr>j!7k9%sX%Y`5*TEl-n@I*pd5L}RezWIny*>A zrupj2^F)t6_!67;(CAjlcP%%8+OTVnpm#>J99^;x<1%h(^ZS)It_)?47S<|1#}zzn>xcK6%jn)wE#cPi9POx$~6Ck3t* z55gro-NXk?e+k%E7z6r?jH!;HIzlqpNv1oB*2DRy?Z{Bkzz5etpQK~~(Bj7WV0s+^ zq(rGkLb|zx>7%?g1}c|t*+FeU*Tu!z&G_W&Wwp)03tz`$AGRz#1%#jhPxn+dWZ>>c zK7Jv$qWvu1t|k~2h=Rx_hbUNBU7)ZkOuy-$0rdqNoR^E9B4z9TqwN`8x2#v6 zcTKkkj=7+YAcixN&d7V}-2KWapFLR72_C|+v6X&e{Xep<0xHX`=>me32nb4tQqtWi zD4~>q2q+p)`n+(%oJEyy(4Gzke-t39cp2d!BRV%U1ecWg8{p89$bKD~bV0_OBBQ{6uK-YbPZBoV_atDZyJ zpT6I#g@Pe_>2ga7nMpL~-mZhw?^u+EgX-)M@!IAA{PqbXFK+_9dUJHIzcs#6u{FTo zckA+p6_8jfa8rR=K-RY6<8~9nfuKud@8~ZwshK{ukSW=}~8{ zAnRAABZIM`B(u66!)<6F`SU*YriWdw#cL!zgXkTes~Y9kXB4DQ@)VMJJvYy1M{u;Gf&(STJ>*IO_yD5K0-&1>^e?|At~0 zCHhv$I}C3kA~@0u zhW_PU)&3?K>iVP=V-@P_KV}})XC_}~W3*8!Iahb_5gMkz-LzGM1gairB1asTh9m>j zJi&`4z}fc3Tg8YA5OsKV)*18vJ1L+uh91NRxxHfu2Q~VCgq9AeUwYr^^u_2m&x)-$ zDi11qzR3poVX*iHR3!5@i=*E@#CJ!sz`w;;ruY)kJ_(a76!t&OxFFRKn5_g6Z}ifs2<;+wcSx7%FP*e2*gY4!98NS@ADkX#QeiU27 zCKm!q-%p^v3xFrM^7KPbPfwb^k_MI(Y^sjUPs#r-F8pQkybPiGn!}{-?=lv+7GD_| zb+mH}JLo+%M;H88_^oH>ziHbEXdik}ecB47khZr9PQ4*$kSZzApk`=c(VcsX44T{I zfxKxIyS4HYYAxh(2W%|Ucs{dMG3{%T@#I2%xwnD<15*(F2!X^H%sX_7Es3dV(hX%Z zA+TtIS56__f39bcaz0B1!r)6=lEptT_L%}1j36$$+IIvDj#E3FgVO{011tKUXs1`U z_}?MIN;iikwM@{~7CMPf4?{QBvPBz@#+n#7JUFMM)HY0;rMd)C>*+UY(Am zZsE73dGBq4rkJ9TDfNw8<2eo;BH)-5E~wl+5rOjVIoLby>6H4QUhZVIzg$lU24V{e zd*6PMC%^tAlE$<|3!z(-VE<KrOSsHpZXH=>9y^m`jLz@$B;PP)2&toneZArAJMLf zh6nj4(|7#vv>$o*DD7Q9^Ygx*9h0Cz|6t17J{J1y6jx4}?cU$jt6DY_? z$jt$|l}|gG@LuU3@Elgk$PbpOr1t5D^S==;s=#GQe#?>wKM8x%<_ zWqDsTgg)-I>xe>wK0dNejvuo7=Qh{A=6g~-yUK}L~tw(g+IcfO=_@OUHE1t zT{m5IvbfBcUAA@EO9Iw+Cc!UzAIuktA@6xX!S?5;U%-69eaC8{Xl(m;9H^DY0ZBfm z-dLtf0zAUuxL5dhDVvpk;aW1F4`g$dZh(K_ni+?$v$wFx>8s~&EldJAgPs~!m+dV+ z`XjgK6Q%Too>E@(DGEMvuYTAqrUhJQfB)wQ#5VX*D_H-5lh5XIV193IS}P%fd& z-vxOxCKOp%baZq9*Z>4w!NkVS#!xy*XBzMXI>WjiRD^x3={xNB^0lrG{Y!JHY|Zx* z5k6nc-HNlZunnaLeU9E3e(+qyeDLyGwsq0N5ZKGF-;ASCxpc zV)Z+%x`Wl><4vQIcTVk7b@!C$c0D9_fgB@0I1$j}6BlYMK+pXY>gY81`Mm<1p)vp6 z%J8X`Wnqs+8JV*D+IR>fkt-(1m`t^%G{zes24|VrTygJuy8+h7_LyPr_#;xT=+puyjdQmJbFh~j(}@!Bu*f24^vJ;3gV@+!*=! z(;te|bYQsd{$OKixb)wZjOuCNzy)dQHN;mU>G$*fOppOe^7@O*xcf`U5&yIngc)JQ zw%BN{!5Ux3B88D*_8{l3drl6Qv+)+%%X(!PYdokPzv;W;pWWxQ7a(|NaY(aTq4wqt zC)?732w>+>wo9cvXTSa{vOC^ze=h)(*hFQRpnwi$R!R8icE;7!75X>W3?jRR;$r++Rl-{kqxbid{F#9U8(n28XN$0}y zJ_Xy7tTgY%*y+8FdRaHJ%WysavYZIrA=zWfn?YTJp5+I132zn+_?a0BY$)`p{?Z%5 zJGM!L1f|s5TU7j4(n58i9h@z0jGF^u_0ENpBl=c`4qW)47CDJP{apN5(Lc68jdcf$^*rbVc)0fCS5=`VxGEl-MQc| zV>;;3mxui&UNMry^(%%slVoYU!W(xy*mWK#cg9CigJ6pWnWWJm3BS=g@^ePi@6XA!6 z6?ZU!mQSN9yM{sL^rn+$SZj_zm`wQC2`9bb!g`N&vZ_(Xh2;cMth)F2L5uK*?VS?p z7y=Lo*G}8qSKZ%7#Pxty7>g!;V~D8*brsZFF+mbDwL7Vf|!5ez>9IrCUHLfGab)yT-jt zE=>_SbVuy8p?1R$r7`00j?J~_v$YNZe}xG}dU;sXhDVQ%g&4<4#3a4L`u96$7k$Q* zbOQGte9fCS*tq4ivr6^3bZkLfQNRf2_8}sfL8YPi;_@<;Fy7<6O<9nB%BY9i(f#iZ zRfJ7ajs0&l{uxsnB^<`?e)rk~M=ee*Id^Y!#S~(k(+_`qU|N1AL~`2aP)zb@ z_Qq)L%C7RVF7yB2_KNfp2s))qebe*E{)(Y4Q8 zuzBq26$jjXuDQ~>lDV!USwmzr*-n8)QT#+iXj`Idqf++h)E@#%_Phtth7!WJ+Q*xm z8t?tjTKi(I!`9NGqxes5^(?72Aa0A-v5S0Ow2WVNxa4V1WB5si2q7!6o|KVptBH;H zk?+pQ1`fMU%PFgp7y_R{QEo6O#^k?WMi*f@2$-%&{!_d7sWFcLEy3hTX0$T26KL2jDS40EGkejB^V(@~lcYEll8!`Q zuvLl=;&(_osgk5HM-OJeEP+Dv$JgD~ zL9R<9yml7`diOKB@SU9=*l%&UNsoxv3O>I({MH29V{;x?x`u~PgQ5!<%5Ico9%X(E zN(`ri|2-|fusJ3?Q8~r0qy{{Y?d|Q&v>@Oz*bVNg7hLq-T?e`-g-<@CUCShlL!-mH zE>CpZ>d08}m22*Jiw>Gef>#(g)mSEu6j~EX1O@wcby<{`rd7I+2wJoGSDithpKFK$zb+?5c& zLTym}U}~AyVQ@D{m0K&St!t{nV0osWCSg3K+@+tK*Z4A0+@O}b3Tmc6NB~}d``UNr zKl&JtPtrG0*pv(V1hq4$U?<~6>;`>;c&}8SEz7yx_X-J^3Q6#dizCx3xf=+^CRcLP zth|Af;|sqkrApVhg~Hblt2V<_ham@3X(_#YR4y0Z%J4orh`-Ul=G_$((6e>o_CL)^Or^Z zi@#hlkaRph|*jFKP)?6w)E|1DlvMwN$C_7lNbUh`|mGtpYE7 zzX1D1bVIdi8QC2WB1Ffv4^v9wL+1+gz4g4$x1I2NlVsFUYSkWE*bB=sv_rL}@OIB7 zj0^N>zN9lMM5I^3nfX!WBSoLNfPv9jM;QLXKh0@I5cg%@-lqOLmC)j< za4@`TTYHe0n4Fw^V0$J~Sd%AbgFM=a^{E<5TO&ZI#0Vwj6DgO_XJ%8FWK2YNl_Q+3 z87wzXeLQ!a@+!0Bo@kWvjSY1zEv>6J+>$Uz)6lRk%An(^`(luOmc@8XSn^pGt@6h4 z*NO;w|0~a0Uvymm)Rla%*=|;^hhZ4 z#$c4^_h9nqr>E-*_z@}A_7!_@JX43(6B@x(t%wnJl7AQF*K?$!ok)|%t=2$2m`pj& z=kipPC(L50u+6u$3NH4HFqu-oaXGGx=$vvuZVG9;vYBD`4=8HbJVPH6i1kednScX;gII?^Aj%|72LU7Xp$s7wIyRarSDY%Uyz}t zB545aDwS(r#mi?4&;0dJ;ewd&PFnW5eXYEPdRy_#%S$y!3C_0nk-5Yk5%#%rmV@Pj zv0VCio~bP9QZ)A(Lbe4yQWANiKZ>q;9PJ%ODQJrWnfCm*LaYP2lg2O4>^pf-Zb#$$ zV1SXmke1nGYgnzf~; zv8afOi<^vpj&P80#8YWNlx1nnx-)t8HRZj1UIyCdlQF}M-&4tOl!ea^HBU63%r(S%Y zEpfP2N$T>Df`5C5dwEdz|{SQBGl>@zE`!naxsz6Lyq>W>=a zs9IM=%3~b|#7@dY&pb;bQ1m{)C#c?+RLPH9tT@#0F9hl>IPD)hf&K1)Q3%cl!_bAp ztQ~0f=UZ{(LO&^!`ZSD1^LX=L#o+s->z`0~E-R%Q$R)`h9Tpl)Rf9vCsJ^iJekzON zUL=`1Nu8&VoNK^>2lv# z;=_FX{^t8dU-T2Dhx;4Jjy_I;CQbHj~k(87ancEm(#X^M8kc7P2JD0xF zUCvzB_CfTi(VhIIf_H6rg4?V&qhC+*X%u`9`EhZekY*s-YN>(rK-L}8?tqxLnq;M8 zq^{woso})^7l2zd^(rj|fF~TP?MOE+BIH?XvblXPR_D9I0Ta-%l3+@Q#PMEiImdDP z9TBKJgwDO1nL{ZkkfQJ6_r@RTZs^AU8OA*@$pm>zGh{YZ_u!q(Uc(u zTt;IMozEj+isby$lqnb-)<4)=j`d-738`Lo*p8wJxt(;Iw_r!zhOUS#|K?rePT~q| z!tjN9pU*=@!qkhBx)xn&Ip~yp_by_TB1aFVo7H;l=E0ZiNxGY%#|%?zs`T*7gH|_& zt~|}C99A?jpM5vy#*OI3`_POi;ykN$wWn2ztD-6~3ZjkbkDP12PAe+*hdX15U`fqM zc@Bxf3-LQe<~9*?6Xn_B+&u`i%lQgdGFA=D(9r#*i|Mot$+bV8Av@{Zr6~h8_nPEm z5{#4h2CIxhWtk^d8_W*m-Bh1p?A0oKf15RXgLwTho$=K~yL)C97D4Zu8x)~8NIM`2 z24A#)MI}2ZJ5(v}-P%u|uCTY|D1%&~$7*=et|3}Z7o(Q46W9y5B2EwL>%TR|ViZY^ zWp!N~Fc*vi#X48fY!Tm|+h-kTSy)x_YL49T=dxlOSy&+0F=`g^`#i1aT)r1q(<*0a zn&mQT{QjDeEVf}cr=cM`zxllVz=NsJZyow8hIKFl&J>@fkfgpN87-(E)t@7uq2JO7 zuj{;@2*u2;939@MjvBr6_~@0@Rm2~20`!SF71;L(p*N{ZUz(pCoI*NtT2e(EKjiFs zuwA1^Mcp3F8@WI`&uJrl1C^8n7%#U`K> zO3)NcPbO)UR*WUd6QA9hMpXB-vqdi6v^p;%VG>qsDh9km}Nt+Z|)Q`CrtxN}2(bcm(ZqIZ4dM0`^zeh-x zgV%FqXM3?+T9<>3r!KNH`(`T#zRue@dl=(uKIU5H%BKdZbJb>HgA@i@z_ ztrbU+1!3*QRMY1+&}P3dRqEP6Oi6NEWsft+U&#~ROIcsd5lz1a&EJ`3EL*|ex~j9q z)*NBJmHxWZhL(#f-9yM3gVN=j zoYK~C(Ko_AST4`z@q7afG5VH!YW+b=V1cH*FQw-8tp$Ww!ixHol&*a`Tl^#!U1=mE z8e1Y2^lPy<-MZ)E(jYwS6}3ne)z6-2u9T)L@5T3Lx=YUhyZ?)#t-;e@Mi-8W1FKR*}SqStmD? zXIDcu@IFU-_Xm0dgEOm_G&(xUEX=^bf3UwF-o5S=)$H{v9Q=FEjL^ET(ot8a{0e^G zd}b|_hXp8HG3>3#`vu3ZoFTigJ-}T%Jl-3%lbBpX z@6m=g##Vmp1bnY*qI_cC<3T2OD@^`$8?~PMesI@ez%r#2Gvx`jH)mak>N+onsaZgy(t4yAL5+25K6VY_4C^FhKqeS3}dM?7ykOg zg=zHI;cfn1m$ft15~r0v(|P=RONe3|4M;p~nrryE zJz=7UZ94OH`r66Mhq?YitL)}oiV$&~KmYxCR$TULYww0=w%0Md zCVLo#b0`7*Td;z-tkB4rNbh?1^C`D6G_4HaG;K-~xCQ;2bJDlq^PnY6NMqsrigU4y zM(Ph;?L^8?79k0zk4A#@`tDh@1ZZ?jtjz6t>-87Kf4EvFEAkvS`m9~bNofKEK51b^ z#r*1NvvkIc90vNjw$Nt95y;jhI}#7UZnOjP)*OQ>N>G_97oW!eBb;6|!iRnG{e$MX zB&r}z^L;Gi;K1Vp_CC}SZbXn<27u0wa%86aH3`W7Y&H5KhmziJk}>DgJ(bQ&=`NqJ zTzbTXY1$)jUHZF+ERQ&~ru%2TQn4^WT@+!u z+~u}+h7pwzLW}Mx=n6LBHT>(DG*QpgJvRR3_?bW~iro$@kAxSwu!n{gkhDQy)k$4s z{zVqDGEO`;g#IJl{j$~%2}ijPg3$ysZZ>zW#Rbsum@Eiu|EvF%>U)iH*QE1)!$T#4_F-gAaL`@Q|2cDjoojC=2d#iSaPZZ0%gx ze1j?&Ny7AJkk?6+8L+?B+j}3`{8hhQTn?@UHB|@5&@k)NOiJ3@)lZT0ph+KYr@y_Z zX~>lIezyIdz-I1KI-flQe4oiRF5jI&GGq56!Mn?vVpjI{gcfEb3?}!~`4~ogNt&a# zw(HtL9$jL(Y>Z_bLolQt+++LmhJQU@VHvavTEIszq2_$U0((n{vGt?)#Ec0E`(7yGzB%Bv*70Tiir&} zRH=J&usClWikaYjAD*FYbC1$1-9$7r>rH#Iz}?r)1&yDhi_7BxL>B&ppf2v zzdnQS(k;(WXfVpaYfRXj;gl4} zLkQ1ZdHuHS*W(~EGV<~XQIX#%ek2LCzNY%G9Qo#Elk0N#Id$OMg1$1H)sC%;v6&CF z4{aJ&zBzgoTT;lgGDy>;+BrGhpxEeFj$a(QMzKRdzVrZaLt|j;R<7oVdxB(ka89>b zEFZGnw~JJ&0YlILFqor_($8yqYiFQgA-}T>IKG0oJZN`S)Si03LWMIwMl64S|4510 zZ|L{uSb8506{eFN1_}A7@Ys%B#tf34$$T2ceo}zW->_k^el8|~c~DCcN1{>nf$NX5 z5x1QudTPw?kvvRELoNT*iWqv!{b=aNLIn~iTptQs!r_K#Yi@p~iggbE{Yhm3`ypyptUbUq zLeDTo(t6v3Y<-nFF7C<5Z!`>l`Id;QbYc&DP$au-5QfF~_Fe-dw1GGz<*Rf5}bNjV-k5 z128GoXe|$<2QpG93m`!eJ+S-Ia(xSiPSAwuONj#(rot%21B{N+0OHO}=QtB) zOWiO0S$yPNsr{P9Xgyiqul-}_ZATQBU5{I%(N{E;3$A*SC2z^1pqS?-(h^Xj!@Jx4G z9`O@+|B&%{&QRcK)}_U{BLJ?`v` z?3?*7;3L2BzIu}DNk8n)9%&CoFDw%2v?qW2b8A-NwCUF$|$1+)foUZvm6#A|N|ZqqoGq zUotRr3dFo(#+eiC)A3m>EXDOIWW}|cnB#Duw}@7BR*(pg?wfB|q0_P^ttqdW zcNc(*%)82|oRDBRWMD~~IlWT0hLcVel??fk;o*xx$TXc7*eTAlZNQ9DYl+sI$2VUAFFL4e zyf%7nlJlFNLiPAiByUcs4ejo1P|6iwkQzD*_hZ%{IR-4^=P2bq`=G#;_`nqd8TIjhq<`i&#} z*Y>HOa!5MQMt|pyzYwPrY3Rj-#>T+x|T6&Wl(4 zczGafU>Ale)D~^rm77T_uVWbH4RZ!f1@{-=hx}k zBMi7SFlU0tHU*b6g0%AuXil6wB@@S`&*$Y)qC68D&s_M`+*k=?c{NCff*9r2Tha_T z$d`nNu5!51(g3+4kTt2zpW#UV9Y9fuBpQ|mQ9(S~G-z>p1RYHeVm?MXR)xI`WpO#( z^8314!IEW=<@Kz0@+vhueIb3USCq?e8B5vAn(|GIo2bvm<+amfTy@sdI3hICuW7316^dn-Q}9v{?*;9OcxWimic|~`7Z3h zthXC|DJow)e!doEIoyc<71DpLxQzBWc)%=WXKr=+6#`~cr-wH5^hz#=Dr`;B#gK9` zJCj30Ti|>~b9Aw9@W%*7@z#692QQQP<{oiiaw(;d9Fd3Xs-2&iqeN1}pq{Ali8OC6 zW=IUP`#3!z=Qwt0AVUyUMV8lNI$3X{Q_s^xE+wua5eEZpM$&_XE-ONhpL(wmd~8i7 zD3f?U8ta8GL8&HF9rWo48Q$KA0?O^w-fl!Hx2eFJ6h>#WCzbnk;48NpuG046#f$Z% zH+H^^iRUSnuJ@whUy~-&M-AyG_m_>?B3S+je!EUEuSv^;Y1~6jn8=h z@?7J+l$IjN(Z05#3tnn|IV?u}=muNDz^>Q(jlLEzN8Xv8h!~f`61+J)+$*hBkyy7g5qfc-;{Mh{JBxH)x0$f z^Mzi|Mb3$Cg$NH7?COt%Y_X!NA0C->7gR!vqb z5j;BT5P9|-k%=WOh}R#9^6)^3(Z=O`jKXIu_QP0NSz~`;_+EY^1xCU8ycmK(+CsQK z+Uy&An<~=&+Pu&*{(6Y+c~W{T>X!Z?bhnvr==L58 zWI!jOjC}gb=<%soz|vo8g*5(CvmnRSqMs?I#$l6qd=%$FJ0Knb02XBd1|wMtj!aE1 zx<14^VD221Gq$*xGMdQF*iV5kN$)oancf8+9`&tmDg3IliSx1Z4>*y7=owLP2rFpv zES^k^T)I>k@IJ@-VwYJwpPJFy5*3g4nCXD0)6Kcds`iCtc+iFzjz{ zGJX0gnM;4b?=$o{CmQa&(XlXT)xw%pNz~9%tv*q~l*}!?SZ!{&HD_1ZSF2R28+mnO zeRH0(cMVTioIq4ZOL8n2s+y9;%f7Sf3d#92m=|)O$Gg+FCw3VQ68pspQV9Cy7Li zP3g2ed43EvUVOe$l{d_3V+`%Kz4+E}8GUR`cs@lUH9j!s_Yrt1y&g6MiZ)M`$z$ac z*~((Y5~$qHJsy5`Hs;WCj+!~2>gg~yO^=5y5Vo#!m0>f3<_s34Np}m_`twu6iQH5Z zvHD)IFPq*ltpy7f!nuBe^XU)Ko9Viuxn*(>#`(Y<2{rx0adAL&!@AUM^mde}2S`Vus-% zfgtdZt*(wd@ZuM35gYpU{Xi$kW3?*yXpO4RL2ZaFJ&Y0&3ZGk@DhuxIbCujVk;J$& z)F4ei&EJxx*N{pu?yQ*s?OcXXXap7wt-{ugg~Zq^Jnq?N;x@h$(3Xd45)?c7$}p zWLxS{{~Is0UohN15UNIP21XjH4b;`K1`GqiObt_7TH4w43Y)DP<#D1YaTewO(f$g# zM)n{ukjbhL*a1|*4a#<pHp?P)lsTuXs+s}KeZX3MkEnILb_I_Wb9ecZh zi=HTj#Cx>dCtdqXnzG^Pg+A=(0oXF}p=mLh(GkSKkV7|iq&udor%Ebdn8E4^`Rq)P zIrm3cgspiu;GP0U0+i@95Fa6ntg?hDjn_-=c3yJWd4EOf*G>KF9)vQin5-aPC44;Y z8)|l?e=4B1pCGTH*jL_bBP&%z$1}a|2ZK;psP+X4ZHBA$X$bCm**E1|C=uUO7+uG*Le(;;y#0YxL{)6@ixi5G=Px=6}jw0}( zRIwkbd#tj4><~@JvVKf&u=;PjU6wzDLQ(50()291~cwR)#2`(N4AAql`j#3v<~v&TOXEn zn&#%|y|Olu4SEgWOK_f4|8Ny{8}Ha?p?4;bhc*w0_~uO>Y|LPpHS*>TB(*{b>vS`2 z;}ZD1L?lEdB-$(D?Ujk-Ky++kkYY|9m?0(X8C>_F`r9=z)zclsJ|5+ono+)^Y zmW9|TGcrgqH7$Aq=6vnUb8uuY@0-0^NKA{`OW}c-<6+k(U-DageWoc{Kik|uduDy# zv_)A0FXYD+;Z#?jU^{Vi&NrnUgJf}Zy><5^x##Jyt)?#b=J<`MnP14bJli)BEtN3F zXZ5-&__?>v3M9WQ~f%nUgWsg|L^n|Wn%~tqjBEM zqdrY5VVT|bXzL36BfTaTlv#IsH@=|s1?Jj5x_bF3b5>d3HrrUYkaXO~AmZ8dLC6sgk7C!It7 ztnPg=bZ?;6KzCzwr5bfZnm(unD(4ZTE~9XnmUDr7Ilul!XOV`o%_(i#+YjAu9S~8| z3d^ObhkhX6n}l>{Kx=D)hU@l&zHnlmB#~FiBy(dS$Elyx4AafxW^?o3zHYN(*HUJ( zhWL;qJQb#v-5`wOLmb%zHsckyd`Lm0k=g8J#Ju;v#Qcww@iejKZM8rl$YiRLHFltOm;@_9a0_hx#*T z_DQ2)e4`8^??0==ARpYW8RGmqxBaI&H?;z&sAzp`>I*6nX=hp9{9&JdliSQ<%``GB zjIerdQT$!S+$}Z!_zVr_>BV$$^2%cFp~PX@Df^@Jl8ypnl6mq9x)X_nfls5VYhR2R zW{!646S!@E2}~~J%1l_D3OWdzLyXEiL{GiHXSI;&oB<78+W<6=Oh4OaB*66%vTUc% z_VmZG~H>Pb5lj8?~+P=-F&-#Ofg!vBoZ0d_yepkP7GP-%J_>_&2y zB=AZ_jV#G<`S#AZ`9kEC`(%xgQp00&gf()=^P~*#UN*@@+%Vt9?)YW9j&q?FZB`2P zV$Kf+hgt3%$y?`gNb;i$KcEd&9eQ^$<08;z;BH<9S5g*{28_RIAXq5Hi{bmrkq zWsi5Yu1*@BB)lwqk|>Ha^M{=0^x0WwS_w!^bIhTMZWwR&F&PM z7sxXvfIzi=FIw?hyrSjw($~Q77Zamox*Dw>(CX6m{>T2>`catlVJ*l{9CudDoTKl8TBsc< zwY{a&IeD|~J|giO3H}`s3M5fv&rSfrpYQ;MGFX+n$7que+Fe-Pn;woC1(3J%bn(glXEUG61sTH?%&Z@=g|gv-+ld!&4JY>@X0mar z|5GHi{ZK$R?-|SMT-ymeu0>$h#!T7+)&tMBHI^mF$G(t zZBSx`*r(1pgHhazPr)aVZKpXK&h43**~cBWEJiJ#4dEvIzJpzxePm?1G@y^~Lm90S zBzuzZa_!etihZw1B~GvcY8tWFe}m#kP;QPjkKF%fQR2CPgNi@I`F6YmdzqF2*cyX2 z>~b+%9`XY7FOq@$^lx2Ve+7X{?(7%s=Q09U2V(M z;<%fHba$Q84RUfqcY|3CRoP7@ANyif#&PJrNR$(@EG!JTB9jj2M0r z7WzaUNw;PCJo>tcbTkX7w2`PK1Gnr+xebpI!+_hqfmZ?YnPA1^mmV{?>ne zi$=wi4H0+#HL^D-=ydL( zbW@H$xz~6}&oiRr?uF^7_(z-u%Iqf0&wX>|tW+bXIOzblFlXscQ|f)A!Um3Dw9xHb z2l{MvFqC-t=4}!N^5^mt+(Uu$`{;#xsPFQ@@Y;wA=K7sUyK##K50-WvMfR62MOjyF zG*^)KdYroFHp#=PHWPAy^om=mj;sO}5y$4Izg7+2a z8Df9#(zg0e@25mGRJk|uwZ^X~dsvIHk>$RKnQ6W_r(MozfET7)bcgJmu(9CJjgvSj zf&_1|R_E?MjPZrU=ehcGSyYVR3I%E2?3=iunBF}7r(jmCA9pMWwF-hXf96fwRnlK& z=<#+1Ccqijfy`~r!(g5h5+*-ovd<}!;!?ZQ4EhHs=BOS1Gt)Uc(G>=R)O9w~Twp@F z8tbvUhT=mAq#SfwF=RGaB#44fs|<>O=(9! z;hi2-j;33*vAKZ!V0P`VK@e!^t(t)W(f5M*=byc|j<`w`{hP@BCn~C-qT<^7)x6`V znC+`fp=xDn0jiar!TN;-lB4{|`Hh+m2PfN8y1j&iaw#^243|XW#A&CsUvP_cdnm*| zw`(BX$h!EhEs-#_fpgSpm2ly(9vQa8Iy$HzTA8HM(^XbJWlBQ*9-CrR=eaCaG~11< z`rNC)5$ILrgh!w)lNoyXK?yb*bJO4w5Q*KD!Rz`pK2=xS!NUaz0*Nc3#0>JN36HG3 zEsyZp>^!171;6Oe3B20;9|>^*wckgCy(H=IEz7TMXyk`+x;bQGRAYr$w0}5mx3nC4-H9f6TW|wOtl7nYli4Ya&^zd9NB=%#>4$OqZEo9 zVx?x}j`Dia&PU5+y?o~7zG69Z0h4o^`eBl2i8&BE?QcyK6brZxLpPs4e;%%a+-JPV zR@X75bjIiaBBbN|H{RPdJ_qgzSNrapJ=wlxUllmhl{p4U7;|4aj7B<}yM~}5|B+*zSke3Xe;K)p;jb}pk*7`Fi_1TcPoEJd z2EtdUnekz3wg(^X=}a+OJ)zpy(1m51uwSI)Z%GxF8#p{^wM^)fWh1lxQr_kr`gH`o zqffc*8}I?p2lw*ilycZd&&-^q8D0z8fog+O;%0+tN+%~6_N!c@Lqg^2(=aDfRKSj- z-oECO?UVI+M_hwY@B;)gN(S5(QW43dc~9IIKAZg(C^-+x8@-1UP*hcEo8!iV zFk=i>My-^c53d-?_waJmoMCJ_FJEXJ_z9Uks{0qkjl5JM`CL@?a{aq?{wS1~y@O2G zbSt!6gZ|cSk+yRhW$w$e)v)u;z>r&HtFLM%D0I@M@JTRU)O7)ngmp{aYhgamv7RyB z|C#^E4U8)Q5tpSdmF6uu^Hmwj3s|^_ zA%~Ua^R@B(h(-62+{FC?fLwqwyJYUV@IF74+fZs_%^)R2f`Pf^OnArwBq7gzsy?^j zD919;U#t7v7seQW_WV&F58z3OiHTxV^5z=LH?09+T)qDiMPCRlMWg78rcOFs|?VK!ehn$UKn0T9q16FvW@TMz>8`E zq5dkA({Eu?anu^$T!`{kU*d=Q`tuC;a#`P3RG5H3F}&9D-fM@^53}nBvwtwKnLzy`x_DHjMAj(QbF!W0alE4(6XG)-OGU zQD!6a`K2RdfXLgsFY;loklKq0rL)aqDIT4G_CzLY)76B4B0;uysCAxn_=cJxDzgPO z?VGtw7)5h^P~Z)u%-iEpYRVaVb5`u>vnVG&*afm6e;}!PxsHJU%#YG>LaVe;JbdEQvrphGSj}*I5gkLnt7yi zCEf=I7xS~sB;(!vsuu-qR^Mt6X}wY#E7hBwm3GF76ilwfxQs){JIDH$&A>&Rrz@PC zfg$%6yyCTuOXJtLt1xlZ>A)c%x#0-(0?uN$A&6XmE*rRggP#c*sYU;+*cKgWbCs!u z<+I}Te?QOhORANCyXwoDvF-7vRFoaL(|)4$&`L~*THiz?-EL?1S^E&#r`2fn7Bi%a z`o2a*7WsHqANl4=ukDjJs~7wyzsf4VFPs#>>JL_2%2Kt`nVnPO(;rG=OlRd6kKw#^ z4lafE!4Qjp)D^X8K^k!|(kn&Hb=wZ&xWIkMsh?Cyii1;WM8`f-W(H+@Gbi|$dd?SP z7u322HD|3gd=a2sWG-OvzU9>^MQ|y1dUt9*s`K+j>-b34{*3Yf$4r5i#nk;G*viCbK-2DFwFsf~R5#9+yvjhkWj%y(p& zWsXElyxd5p==?Wc7ir2Hc9i*>Q91MG;=sRRPORX2e4x!74USzAVhHsri7jusu9FI+ z=?iYb*D$gxOFK6qPZKrOyvpwj#OJxYT{=wzyA5ldtEfzL8bpzLLa#&72yk!|yM%13 z-YOtrwDHbQfAt%n(&OJLN1AU4Drrt9j4eCHLL{*krOE@&x9pBq?JPJRx(qbPg39_> z2T^-YZuIAPf+-o?kB?AfrHH|8)_zA}g1y)F2ce7cq`2iZQJ^Q@lf;rrO~C3XqT{UJkBuhj-wWJwQ8%N`103O|i;o%gy<@gBRtbr;v+dp{Kx z@P1d|CZKg!7qK8lVYHYRyb*8jw;mgJKbs41seauiKfNsn$`Toh11n|X5JQF9>SR<} z+w5g)vU0zwx!a|G=azo~z=Q%;*^unaJ`@0dQ(;JmRiMplWyo{s6*S~ClR$=O0$9oP4fOKjh%jT}I;RRST5! z^E=i1%ajrrs>uFpnkQc(PSjPC5O?H-XvfAztr#(2IM&MB;@;pQFiYwg|I=<@x(T#c$>!By8tXLI%Xv7#$;C~(5|G+i2c}(f zikkX(VXB%+E-0qolsIomdxMTqrPY zljZm(N;i7lu`A))1bYZBO~%g{K4%SY{Lp4gHHwXyZTo@GAD->+5J}u;yAWfw)SG1b zAk#70;__w8R$aNSGfR8*gwh|Ok7i_PDb*O+k`JRzHm1>5yhsn>^xi43oBafIu?pMg zDK!)dVfvXU^iUvT^e+%%n#>4I>o|x(Qyw3N=h5Mf-+a91ro&2Y?WEj_PgRP8li~zI zcV~2Yc@NX>X?0*S-=JRPrJZm=DY=F)|4aSLpJOAD5^U)S-EEj(1cLi3kihoLtSCv+ z=?cTF=nN$qb9X!p*u8R)FFFEI0qon!h=_>U>2a7xB(XI;PRjMA{`g(~Q>wW8X&s)l zUgN5~gt`E9HAYa3JE*4=(FA-v(svWd`&}eJoJmsiEvK@}USR|gbYi4FtjI_vOP`;m z6|i7_VUYU@^q}q-zXo#EVgSTHJ*)P< zl7CYWg*kvC=x%nbHL%L~<#0LdEM5UXHcak_n&2SyLJ*1l5}T1p}GEItEgR+SIEXYmvD-I zQkLHz`5YtUVOWxePeHX*nr zBei=ZvkLKb@#wyGqx*U)uUU(wS*uP^t(r7)@Bq>&rXT&NSP8!Ky1|7oBh3 z_cUt?02sWkBSPBc8QKcLUn?Gd9%EmHQSO;LG3w!B&o8e%rsG{xFdXo9QkV-53nF}2 zOc_{aID?6+^-8FJxH?_c)pf@?toOks-C=&yah?a>?KD@%^bk@0-t$Dz5DUtbnb~@#&I{uti}` z1rB~|Wrua+8zzqPKZ$cQLBHl!=0e7_EqVqd4kh=B_?tsV^4M?In;^}nzF}iqXDami zTp1jdEVU$tJur9XTZJn(t%O@zb{F4w&5VyV>z&t7BkcXhytx!lTxvGg0gbIjzze=t z)e!K@zyCi7zm2^XZF&uF)eE&a=QxN+H_6w^5dX8-)pE)<1G^#IXY)gqnim((B6v5v z@`ipG9uOMHDSX&L6iQr-VhA>Po{H70xp{H36( zF@{FT&oK6PU#|5}K_F^1$WW^h7!!iJqd(t958z|F-}TmcdC!WH_T;CKk(P-VDuyBQ zr=nr`xqaU=2nH%hOM^%wC?(RUfQXcI zqlBc0!q5zeib06PNOz}%#E=F^OLs_jBMrYj-tWDitM~U0m*?@hyff!L=j^@LUTf`} z!Llb~u91J(Ip5o^k|j(^PybMJ86FpD14=$^DJdL@gAFyXUi_^6mH0vS)7+kWzww&^G;1cNPkYMBe)4EZU9s%%zqn0xh?=5V zkzC>Oi51%H+n6v1PiF8lIx*x#K}VO3$??P8+}i3fh$a7(~RS z{Zh)thP(UFj$ERTkJLLJp_{wrGF_MUal9>m4xk+?{s1=RbWr%V8hwv=$?=we1d$TkIS+5V*gns1OIWx-X;xpX%z^nt6Fd%c_nJ_qKr;aMuO{ia+7U z7as;^lPUimCSjSW;6|`GF1^l&4F>HuMgue1x@MP9Mu7uawe>ZphG;gfSEcy;SQ1VMo{t^eu3&f*aib!W9>X+1dP7Ov_7k1$qv@X-{QHWt8f0=2miz+XkdXt zui|+hR+r`NaL)Ps#<@hTvtRX|=rN-_R|=F$U3*1buf8az+8eA0e2}?CkH#2$X&lWFsYiGg z5vuu2*o`~OdybUGC-emRM~7_zG1*UTjByR^^G-nt8m)&Zp~*rfTiaK#k;`_HNSE}u zS%#zbGSN9co6Dn98KL~aS8#zs`2vqh?KTNxHelmewOU9!esR`w8EGBM8u%!4xj0NE1C^jO+_yG zk63{aV>G^Jl>=FDkN?-I;ucG-$4G}7U;o_%r$o<3j>wJ%wMhHn52vvcc7H61XTO?R zn>;7bO4Jt0Wog!Aipr|iGV(~?dy1`}B2Gy8Q=+9TKx*%2M_P_G(Sv;S1_)5VcI*9U z3E8ccC%{<#1~aM>>U93|nVct!HSC;(| zC3B}i4I<%>8_qE1qbC;S*{yU{_!^VL3eK~i^T*g-QksZM9OuKzegq9?LJfSL&uqA* zMQNGCQa2ZIs7LNvpf(J)E*1T{=Z@bm3oghNI~y1nF~Aq;O{xBYBC2*eaMmw#Q^iPYv7xj!Y(-9r)BN-GwOx9>At1JeVyj#q%MGw%26|Z}grRio2Oz_}H9Jxw@{dF6&LBfFscT3tHrJ8%S0{ zIF^4;!k~?y2969(ozq3w+mPZU5m6o7DS4?d|Tgg95k4C!S0prJ8 z_kh;eM2RH{nc6}p$Zl@V?~XK@82#sGVPClk5=c*0?EVG0JrJ$qu*B0%n(-{#p#;$TF1<8)3=^pK}RPLM6f4cCwaP6Io!aX2oI2YL%WG}ez5u$UepY&P?GhE9M zB#mr74N>w!xtbg{Ya_vqzQv-lnQC{@zr*)%BaXpw@h>nAz@8w}&MBzOu@fL2$Em_m zO8S-VE?Bf13>3Nq^Oqpl&ao?7a(y7n@+$-RRaF^%f(=2z^77Ei&}BpqJv3q6N}P?Q z)|s5E43&kiCDk0idF9HZJ=ZH@Q%yb)JCV^Z&HahROgQhaPqj6?{~+cK$S!uG*;nXs zzFQm=T`u>5x529Ci`9vu{(Y`Z1tDq|^pj2>+?7hL2K)98F6-vsG8fnlK>Au}*>M~u z>V5Bfx~YKyI2@}XgG(mD!7wFv8BA`#vbg#ki$Z6!{vFOQZp@l6M^p7!`s!=a=79GC zw0aM7>T1^a?gUh=K6dl7;HPlOpBCg$UPRIO7EJ#Yenqkci8Jri5XA_cBDU3q%RxKG z3vUeTHxC)&J@C{yB{DZU>IK}Xv^r8+Q^fynPHZmj&CLTRj^YS!bWv?o#~U)pJ5j=_ zXFuM$%Z| z*jaF-qGVbr+^sX}gqK6k3ZC)|xtOnrKsi+(Y59& z_YlQX4#)8cEY_^`n><~@L8I;g*!o;M0rvivV^1a1G*qQI$< zW?UT^<&S4BzX%Y<4lW3=W)cJQU}IbqEdY?h@diUU>q7;aY?z_Q0_3Nsw342*Tv|Em za<8cy$Esjp{qpJv>o-v{oG2r8A?>ZajsTZZv(KYp%-@Tol_#2mDM|c1h9{iILipyM z;L-AaS-il2Qp>}6`ato9>F3?n0kPKBn+U4swvEgm_=CdG|HgxG@NLw+2EO#q_)OZW zG794InIcRS=!RWecU3^o*Ke5>>8FCLG<(h^E*N zL43USdF_5r$&HycT_@pQ(TlD*I?v+!Z)!3J5m%FE82s@lIpo7ng#ML{}Bw$1&s+66;)>$ViG7c`Nz?)s937{P!xXkgq_ zGUKPSR+eAvj_LIH$}XBA$2o7!`jOBI(wD*xS_1Ltr6mf=ZGoHT&$%3bS-F;k(gkd_ zXg9sNSKReXAV<6;sRUcBsAYi33w3T$nvY41@90}L!%;P=ASw?k zs})k^E!0&`)dW1?aD;v%-=Ms9lu!CAP!D@7Ce08`5)1S|pbm{s{m-TP+j*;IXJo!+ zrk0qLf~1-MifV@L(0;LIBziARw}=x4gQU;XLvudDq9QJjv1vr&1j?D#+s92G`VZbH zzPWWGMj=DLKDF?G!_6L}&-L{jZ6EJ)7ODx|UIQusBu`cvXIolM(uv{3AMCQPFYYWF z_}qK(zhvaEyd=L6rw6CGJuvpFvbQh-@-Xb|MQF_H12Pu`yOhjIr}YOkDQD)gFc?u7 z>0x}gC(aMfzk>}OwQ{x(7-BHjo=c>6;uB^bd!OGuEzvRh0LI5)FY5A@Pp4TIXFSI9 z?~(pZBQzvOU_M@OP(dA++`OJo|Hob>Fg=AJByJ!H48JOm@W5)2>VP2Npb=K~F~rbq zQP7X(Dk1PY6xUE6qe6+pPE&A8#b0k2i&5?v>1j1y+x;NyI9c-H)=;wn(vhLp{XG4k zix(KLje$F}Rdoqu;%`Uot==6p>#fjUInLv@Wt~bhICzfFRrr&u!*jl8T3iqQvp{e~ zvH?i3#_tWpDm-c-o0miq+c+rjAMb7;X)NCwc5AIbn%!SCN9q9X)Fmk9;o-p)DfU$v zv=?7sBNdIB8Q;R0vfca)Jr_o1DgSiYbgj!DKC6dts3=`p<6L?UY!4cu)Flne5y!qQ za05L3lZi=}Cg^Xs1zO6tU4(qUNy-USP}sNU#aWHRE=dfVoYWiOo;WFQ-1qzs2u&Ys zZ_6Xa83ZiO0k(X;syAf~`YQGM!3w^32)qOX7Av z-LXm|T@CBZ&}5m~AMIw&gf#fmiB;CkU(H)zjJ_H_x7wly_0Ml!JpWf)UBt$bF&^$@ zsW1u{s7NG&TzH9XcY-ow?xbAbP&oj&LLcTaF}`rE>wJ^j->B;feqa8OyG8#sB7Ees72sB_%ypj6YI69^7h7NgOXo*;dZ+)O`EX+d3hkxb}xAzi{Z2kC#uXgK;|uv}@$5>!9m;D&RLj*llE zm*y{}p|Ical_t9KpwS^qa?M8`H5ZcUdy39oRZLkvcx~Fl1T|&nOhiqPTGe8q_;sq` zdE9I;abE`Nxn}Jd{C6bV;F*B+A9cL=WM2f^*aO+HYL%ylqqv@4|2OVK55C{&#=oil z&M7mp3LVcRyrk8$dxpR*i;iPd?HpE!$jY0t$}VD-^q^^5ekI}x4YJ{+iV4B;3hPt0 zK;N3W0qrlWS_Bt4&Ye3KcepwROCaCf2ii5Pu?ciDYHDhj>RrXKFdI6C7kVY_`lDM4 za!^q{4t{wZw2zV4 zFuyt36fH_wk1y$e1}2?g4f#NM{J@0xH!U?htTzb|HK84C7d5!=t*~{(>8fV64?Ei^ z*cH)sT6f^* zWDI2yQa@ThGalH8TbhlB*3@e0c2a(WN^p$62$f zd@fZTS+o~xc8yd|IGM*8w-lm?mimqg$(3%%Rxd3XEJl0?zZtl&(&`X$^7Z1)T ztaX!ak?w)P4@@4$wI#(nxHBg@0@EdZk6`T#DC9FRl`L8z%~2?@tL$91-EXU1iwO~Q zKhTcRt=)WSBuVukI)cl^)iom(t6MxF3Y36H4i3*UGr68l67}ApU@_hwQWqfysOYe* zCqd->i|8w!v9~_6_L1S+D8atsr%&JiG(Yz4BXdntNqLugC_@7HM>N&3)F|&rOFcan zPG{VBC;nv`>ZJSOx_(oiz_EKl_Eon+IJ5~1Fdi>yUUYxqJ%JyFon-pG*( zlsMTjMeYaKRymL+dUm7rQ$QS|UG8lESR-o=^*$NCG`a3^$8Oz9PrjF*FYtnB>Ww?M zRq90Ft%fE#1 z`QBc(JCk$7d3~bZ2UN=!`1ttd5!%~yjRipuF4nYSR%ID=or^mP-S%3SR8ug4bw#KX zT17~4eX;LVeZ~&iM~EoesKT|vhLWrSoYq&Bc|`6>f>fK21@S!iPG3nLlbyfzKSeCx zF^Tn*Y9km82^Ut^Zp2+#TnixX-b#;IZ}u8io~QnCUDO-e_`8cGfiK7BjQ18H`ArtTENW#n$&nIBc!5F~nHCD#JDmZHqs& zc^i5pWu>D$DQ|bNgw;s$-NXay9jeIsd_2>i-dRCd2OB(v<2stk1%`GPFJF?Zl7BZc zKDp(hrwH|2ahKk>hbm(;Udg=lUqDm!JNz6;RQU^uH)Z2K&kgQ8jkV1v{@h}JVzp+T zA=zc6!?16V-7Pien~=qOihTk0#&dXz(a)+GdY~nDsb1;n{lA*ANZp>$%{!YZ0FCKP zG7y-0bc5vri(BNH?^}56z?~KTzR1bn;~2PjJR(ivKH`wVK@s$Rlq%u-dN6NP_2p`n z=ksqfCB%c11nSk#ic`iW-wAfmONE;+mO0>yr8Yk`C|9(+Fkkh8vC8sg(Tcy%!;wnc zu=?i6Rpf`O^G$xa?f`5R@PIL_j_rh(4x9PoNv0bw;y~m8gyF9Rt*OvDv~p>Vm|ZE2 zt>0>H1myZRLgcMGkXV0F*9JVN_gQr=YO%W0pY&13(F>%c;lsYmX6jl>m^;S?3a4+lZ;xC?m;_9KW)6?&d z%R|z{a$3LCN#0<3{k!CzVp(6Mx!f5$er&HNJS4TA#>_yp82`=a6|KHz@nPK>;!6=s z7PVyOA5SXG{H=+^e9-yJM&E zCX3D5r~(=gbG|ajx6ywzzTP4rr!>dm$ERgTJ`y|W4T$ApMo;~%kL z>6;m3{JeRo#XW=%T0U8SdnC+}TT2!xiYD3KkVzuJK^1*e#~^JTcQ;@VZ1Ymtf(qvT zR|2r*JqFW-dXU2cn0N<~co5pny}Tvftgym=$g|?wa*ythyofAM4Ds-(sY~lKarl&X znerAVlr2OS1syA}bj^XG6KWzYUuxqA+S#_#{Wct8uhB>}54?xgGHT=@(a}MbuGxy( zjF}HKIj5@Iu56;1gZ+bm*B|zpExa-n>?Lk&MZ_e^fvasxp5vd7{&o(a6aHu6l<|On zD~x_xM78a#7hkrRl`A3LHeII|6&1yLBO(1I(<&PTMhle!8mPh1L$BK8P%97h=9Ow> zAcy)n@60f!nT~6hogXOAE0|ljVYOd!k9W7XlXlBaQ}W!|ES+^sNKH+pe!uzir_xUn zER+j$qu}h%KL22|f0aY*#&o#p48taLTXlxegAw99M@I3i6R{|Z4R^3Se^&jZ)~3X>m=@XKU?+c^Wt4jOzymLk3Q>YdZ8YOgY=chI513}G zU1UZ6b9p2X82CS+UG~;;$@SeGA9Rs4e88+!*B4#s045_BdgGU#Rl*SHFJfnn6;7B= zZvcoB4?Byho5AqqAH^FAzg-Is-NvTbJfvLIr*Ey;Vs)F=>*``B%W{i&XlUw>%h`21 z)9~R6h`;NoJO&0&SY@46vmBb!J5G83+`(hP?4~fjd}epBaOnz!7=xW?U_KO6RaNyB zUuyFBMfSbst0QSN7vxp6kZ!zplTLEGoNy}`{bfP;FGEf!0tDbq3KC#TZUstVxA8L| zI(nm~6<}@PnCB!lT{eWh0-Q_DIvP6oOenWc#ss_nC`0;mXG!a@@fRA|JtL$)X@+@{ zJs8GMa>fyflr=R?S{eY-VCqs?xTeCEu5f!x!{C2b0fogT;mLfrU=_|hoic-3 zt@E@P!Pzh6u?Kl`E02R`djxNHefmP!p_1a|l1S1#`)a*=5v-dKKerexQe`w;rRg@Q z)IL1b3?t{`%l`>F?ys=;4nsM9Y24~*Z*$7BO#5(w^=fru$kyW3iAIVC%m0ED0EF@` zBs0blxFpy|0eNT&?uQQ_v{4f$n)>+Y5KIW&0hfa5&)^{6R;H_$&7IAU+*-=?Rz}-a zSt$O?yFumSt)7O0yWl(_QFbiuF1p?oAE3u)FucGR_yZ+74t>7ZQ4?UkGc!^p$?f5g zyV+1M4PX4cfx8iM_vwT|SbXgY(tx#u`BI{I0Wy!UDeOkzSp?B`O zF%u7h$Z3ltmxCt$Rtum!0SZ~!a&0<_w$8;H2lphS*WW*cK@f(MMk@DQh-tJAcW->l zI>ownIx;6bwk}11rnvh(4{2wd>t~ZUZv+`;`isCglB{fsf3K`BN6wQ}_N>&~Tp_ey#fyRxz1z?O2c}QWpwBUm^ey%I0i8{rt|%Q`3n!r~ob|UU{7Sjn?RgI{HSz zb^ct@g^}f5ljmF~vGUn+LbVrvFW|4G6qbcWG*t7JqGcTeI?^@7V&zfV7V14V zpTDuis7^3HA$uEU9vkCoEB95II@P9!c{u4E>3d=Ogc$d=dNI4V(b2ZqyrponR=^-z z0s?}D0{Z7)d5!5&BN(ULI?8~+z+&?^-RWv|<4@9F{+MyEeG=~uWG#h?ydAirJiy}!o7ZXCwPZX*)@gusnxj`^Prj(tBe#Edw) zWa2F{INsM!yYCE4Q&(9OdLFqIcsz7Q-6~ls-9-%9PYw~B=BSa2QG>#3UR6eHGYdE1 z)W*f5)JT%L08Ql1V`SvFkf>@kW%0??uaM~~;B=Z~Cul*-HNOGPn+s$-H-`@^E04Oe zbUG5t)UW@&mw)Z*hTC2^7CQA$jPUmF7;n`WM6CUEzI3!SL@Q2mHIl}^+%ifjT|J-X zEPn1@tTkHN13`Ix4HqPGo7=2V>a{~AkkuR{Ch_Vu+a)YuF`t7`0^Dw8h8h~C527^# zlhU63d%4XZu`T3AM|dDEqDbm5vU0k%3p}sd&c{8hkp9T^E!bVojwRsy1sU0}Z?>ft z!?^YJ^{~+z_KYQx&5P{|K8vM0U?&eV7L*3*j1^>I#$qts=2ke@molY*Qtnb6zjP;OTIv};M>aBxkM3H?P9RE2lTkb>}+bkb1O?3 zizw3*5dU*WoRg})hl?jgC&Val0%3G++xiHps4J$)V7Gl)&M8Oy=(v9ghh|q}&o@=W zL26!HLi)QKy{8C_92}f>7DkO(HmmgHiH6yF&_kXG8Y+2Hp3(`DY#6?nsL+$9dIL7q zRvJ&}Yh^Ss3UN_lb?7Y@({Edfg&kHH&%RUu_mK7NnClmhr3SXmrUtf)35}+H_L;a>8R?-JnOQuMK%5$0crEEs)5qI&=lnqETtY z{ikBs?ju-W=fc?g*LAs1h{;Qgb%v;N2%P8q+&Tj{EW7 z{-HF8`|tbu96ObF^}AATEg2jHinzCIi)hm&!*|IgM>$~h0XppJd}13acDBlPiG6kS zL9ex4#{J05$9R$*x*xyS1fse6HhcFz)F|`wYz;XX#niN#)Y(90=?Z@SftM~A`Qd`{ zqvHmVc*rv0Sn;rHrXiRn<72{j7=M2yCmB`95S|6xuj!{RKrE9_mv>$Bk=y*l_9QeW z{2@vqN+UJB78#wiK4)Z%4pnDe$)$TuwfpwEEt6J#|KVKt-wzW+gt@0E#|}s%rF_)mJdDQVm^7Nm3N=YG$h~An9VSNH>4FSfE3M`2OkTq|p{=Q{mXc*w8Lh zp^hu*kR*#u#Yj@g{V$OI`t7weTt28PjE_&@?aLUiFBy>+TNHNN9)4QN?~9v~q}=L^ zv-Zurt}cTqPvVn~L}@j}-6PnU65XTFA?ISh=bm*JGiZvQME4b0YkB&var4?7i1Ciq zJ+IHf*@P^-2s9UNecs@pXy7s#N-g{-4jL|Vbs`RQ|K1{~b5d~vu!X~3@NG#|H{y>4 zk8k#_g-0CCo^EJrD0F-Bu;R2pOA!X+vQY6wP4>lwSZCd!K(Q3BLV-ObM~~y(Pf7L- zId{MEaIvfP(q$zkCY~;%ts^*hIZ|;G$|INt(4@-MEa(ZX;O}w_9!WqUtn?||v)cV5cF3^NYiH_{Y%%zBFL+hKk|eLj7y;#*jenE@2WI$tHSONlh7GE*ad=p+@8l z|Gtf}tIPgQ>eRCO-K3B9e7Gk=ysLjC-Jss8y*0G)$QMOh(@Jqx&vG`8NU*GN`|c^v z7w(%yoQ7RBt_wqS`%Pj+0fZHmQm6P+qrou$QSFy679d`ruaBrJIKB6+T#oW*+`Chr z!;e0nbvZ8CJPTP{f#cwU)$*4@(|VG?NT3X}9)LWKz9;p<@23w3_c^igaSPPF!n8MD zM5o_}JKK5gWSEzW>YN}AYzfHH%qf!{B+H!VDfyu8%JBAWT$+3b*lvq!mW;3g#_SWYKH5B&(h(G zSt?U7a$l7R$G2Ly9s$sZyJC8 z$&jTJEBWwfcuDn7FMfqmO0s3$ zD2OKo-O*8y?2}80fq@^VR!)`zPw7p_u~F|__A9d5K}4N>b=diH;s8j@dNKwuPIiHF zYN!5vQn3dGMaqaXXUD9BA{Le%H6BAWd)mFA$cg6VKh_z|b8VdOD>6Fmco$9Co7)@K z)!sg@=|!b~*wJ~|h=>Yn%Wn-OCY@LgzJ5bt;t)`T?|Jt=-=0AA%ao?|t<3bU3bz%3 zbJ?X-d?^}1t0ZV|#iY&tTzzp?VcM;^l8NeHWdQb*R6*`GclCy%9I-Ix+sS^FnN429 z4@jp#Wr_O?X{tZS{O81!dZGsEdAMfp^tPM5rWaK5-rE`|zHQg7z8ZK(%RIOi87^to zggW#6Q_`ny-lihuk2yM3op|Qr@-Tn4EJDC?OkQK|e-+VRzo}Tj>v+yOj)zySH=1cw z^*wHSsUr|Y?=dJYAgq35`^0x?`LZ@~yo`}a2Wfn@xEuMwcZ=uQ+uw3x=;%|vFdYkZ z?}@&v^44IKz z8MwDqH-sq>yd>Mx-z5Bvz@QMt$19h~YdferUZ$uyl(G3F1U>O)<#{fb)&50%`d7tv z!QC0jV-J)xG!I;n;Z^3Kz0yg`DYihhC8%sFBtCx2H6GL*C6QYFSD2jeiv zO~vkgp zox|BoZA;1ZWXpTFPD#Br#eQ(O)kfv~BmRf$Xn)O|qddd&=g-gqudJKfUQVM7)jJD) z$=^oZAK@=rB)|P$NM0wkv#{~oepNMyBC;TO&wUF@v@V=70}}=3aH&K!H9${bJn{Vl ze$j3=HnvQNuLMNA$;;xYpT6__zD>RF!hzAX>Kr`j-A=l__x)kBwusJhmQmDrN>9{? ze8G}CO23G!rwuepk7>EmDW$7yp~QhG`*S8DQ74p>lZi(&-QXd2`he?!BaMJX985$> zvm2@G&o@)ZIfTXnI}eW%l*~pHe?<(!-f3!dtot~#m-ja zkx;eV9VXkDZ`Uw!PRrIl1T9-30>6U$U~zm zB4V-!14JszjeDHJ%xN3L{fO1o*Ac=RKX`f7tE;l>2~Vm#9+9|BE2RC}FK^)1@nELo zq_Vc&?Qhoz-B z=?+wC)Xw^o9er~!i*m{_kp1hHg~q-4Umg#LF5}!zBRuplHcGh7RLm+t=5pyS5n_@v{Yw?v`*_q-)pXE`g2R zpt5wC;`Zeyu4Nad#eVE=CE@D3INdH?72(c2t!}S?8{~BKwrI2_ z>-HW48ULgAPYntrhd2sk9a-)~CJFSIEVBfG`4{gcqe^{fdTKm+lm`JX!8)f@Hk6at z;?gNQAZ)@(o?g^X}*7LaMV9%@t?x~ndC{@ zJ7RHiBW~KcIUYLZ$J0a&9CnjmFN&z1wHA0x89EHKj2KmZdetxdtsK=wOR4fHW*p*n zs$=hcL+D6NLdmC(k}>9JB`|1#@rl9VX3jR$kYB<$lpG+7t^=@>d@&E0phJ%vFQI^> zp%ox%Zfa@)S0Hv0lb}iM8K}d=>+B8x4B(@t*eyj**FQyytCLebMq|X&>F1O>^QfH( zqb$f7l|XzoKr?mSF0b)6Kk3?6t=Cjy`qDYk!?1XU247p>XKcLDD}D2NGxI~qcz|dj z7(^|B^H6z(?l6ZVmXG(u@e(^zw!$P5{yim=2Xw9vyu`ytl!hh4kZ8WiSb ze&%n5_MyBApP&IUvXmX?CzG^}K=HMRr+$fC1;oPDJrI&7T`)e~D3Kt4!$8y4>jYVDPA%teub6~yOxZ#Zp@_Uf4{v?j5m#$ve-UO6uPZ}cRL;e+ z;^g2#ae{B)-QETbIlEZ`*5gXFsS77|xt@S-Y%=+6CNz)Zmq_U8mqJlBt)Nu++JD6Z z_%^R!y=sHHGOdvZ(QdR}=2^TovRjR}I`#!y=RZsqgoK2d_M{sv3i!|RNuvcVP0*oMtfG29a?4ZFR)fjW z3Bd@`Yst~nke>j9$r>b4^h3K~VD>XDMvZfEq{;|**KJ_Y@itT5yk`R{$#iJ-mIho) zj)QPck6sE!(4)xMRZ1SvpDrgmLA=7?-Rh~6sHE0!)h|lAIQqcUBI&q+sD7ht7I(qu zgVET5tf3*37ygAlu{t?AFIPL*=2s$5lYfu15i;T%p#nGIxq;%`x{-?j*RPj6)X@@i zUWv=8I@}fWiA}M^8k+!_D+h*zqaD@OD^T!pyGjV{$K3-lM9*Hl;1m+-K0t?uKoq!( z?hirG%jlTj!RZVRj|PNa74WCV{PgE#TVSJLD&)e+Zf0tlhB`WY9T>=)rW;Pj@7)aK zf=p;E?tHA_R#<^P%gjPSYfCX&JQN|N77-q2B$WQCD4LuONSGYy-Pr~D^S1OZj{^eA zx3luC)Onky$eei#2vbhw!(Dr+)pe@G#n1iIvr{hgPWOL6GP(iIA8wrj{AwG}P}@v< zsDu6e<1#mgEQFPc2)P(i|4A|#Tb z-e!Ql6^7s?qpg$NTj0O8v+Bt&0mRF=P2QR!^qVjQE&DLLifj;#T~b2xA?t5x#U@RJ zQ};_66OCm3F51atbZZ#m0?|MRH1-I(o&pO{84lNAydn$GZ73 zJ2Up;d#k~Gq7|5q@S@i4-W*a#PoL9}LLCWm=%^C4-u*H-O+MPcICOFS2qJ;T{e>G1 ztlDPbAyY>h;6ATX%wNFqpPP$O#TAQ(v_^=!cvqj0EYE#q=ifEri|o_NzQmv-eN2Q< zHBjLwv${H8Pw48zIxiW&>9eOFkIafMC3=dpG(}}>XkJ?6<4fjx1T_fx+>1+ke45X3 z69J|8J#NU5K^lKkN@)wdh(^$%hO#QWnB~X>?K3V=&<=^Q4UoT7TypXy@g;nB;nSi} zFo%bChmxqGmcIRSiJggH(&53ZTo(|R#N^SFV~f|{iiEgrJa-YCh>#G|MRtQk*|T43 zYbj!>wbrP-pM~J{EG-3E4YRd!GSAP*UZ3t1S2n>YqV9I6wQ_V+zVKWLN|5u@{2o(* z6y=K$(A;P42yM$sH;_%S!3RA>3iza39339u3w`O#)Gb=*4!D$OPkZx^=U2<&y9?n& zEb21Y!;sScyn8-PgdG?00w|rgB8vshO`2*i)?3Pj}mLXK`L_=^CvkEXLE*X?{_V*3%U!C4o2dl1#g^ZT zKBF{v^2-ejDZ=xWp10qI z#wQmmt1Qr0i*aLFPu~6q0Q~!Xc(+rol;X^QH?SE!KX#sG_F3!v{QTQSp`s#pM{l*? zYu1ra<3))m>-3@Is(2%=THF&~8ZPM$H|t-jHo%aJyN7&|kSGf?Mc#Zy;!C8t7G2!9 zdDb9haeRu6;_`g$N9&%JU8k}B&OhHjj(7XHm2{jLKqpPbe?A;-QffpSAj81~K$SXR z@N*5W`L$=HrpE z_|oC$kIM^{_$Q9ff9x)v_YSy0_A7^!VSO|o3u(~xc%$qByI2h#*;gjA*rEw76@VZ| z;o!2djjAEOgGo<@0-A@dVpi4MJO#YT!g41d=jj4viF@Fa(KIiei=s zS2THZTzTX~bz>c$r)+uNe9&oiS5tBCXs@iYM_|w1a_}a8Qc|yNedVDpzSz%~3D(^z z^3TkV9eMZ-i)9$V=(N2`(~0 zsp?!jcTODL&ZD?+uOY}NqL!~p-M^n;y>5#fX`!5Xe{AYRVg4=i<{lTWSX5VumfW22 zCuz*7BF>)f2}AzQ$+0D*j*eMAYA~)zbm?6;%B}DebSodVaN`e0p>Pf@CKINch^*Xs zDTh8J<)~;b$G)2j%r!u71s2GK9DTgz=I(qX{C_{17)A$CO-Q7|L4jjADoT6_`$M9T z5cUjvB)f*?#&tip?dWQ)D$jW7)}d5}qy6C}H;YGUOBiIFtOH%*x4c`Ja&EppOUs4` z0gXPj6opwTBq^@za6)w-KY4Z5>49fwf`15*qV(Y&4Ga<}$N@jo8Qq{d+WdUT+WQP* zLaMxW8lnH(RR~(S(iI~(LK)j%Ft~QXOrI*b$izJg`WMSd5>OUhRc2ya1l=I3; zQ&w@d>&B}@Id;+hqV@+=*`c~AJopLr-%nnaXgQQHcDFvy@ZhvC2Hx?nUy2>4Dn9S| zuG;5#t?MH9_a?V{TCA^Jx{l-h4jYNymFZ)?;6bbAy8RnjYv|l8x;=aTEYoPf%~1<; z$hVWP#1xyY(e##>VAtArD8%phX6R)=CGJaq_%~KHhZn0m5akgeJ#9>7Iowd&;lHhwnWH)9{p9|&i zc(ODTU)^#{Q~<)7#P*K^+6NB8%OcQc&$3ca3r{T1GJICC`7y!dS4bIpwlOu$g@p-( ztSrz$(!oqZq_XTDuyDzCEp*C^ZzEz7rZvU0QzhK*{0$7{)D7tgmGv5^rx+b`TDpsc zGp!Fz;S#)m*D_?dHRhyf?Cgmt{2?G@4ZnHPJWY5t7%$=MD!mQenlMQu(c8ZN-;7 ze`R21dxHFG#LAMHX;gunjDlSc6|8(S$tBGa@-we7OIcs_I|D07dc%uF3Ma(w86TVaECp>x!sL}BB@8#~_!T=P0qhFs-&dq^T$v~TdwO#5T2W+ur)(_;S2+vF^Fs8c)V zMf%K4gQbPYPDZi8OLZ|BL-Qmcm>4W;1v~iH+Um%6zH|_L`<55^uDz&$WUQ7Qa~~gI zAncP*dGX@|pWunUJNk73LX^8)jgg5-+S1Z8L%p7<2DxmS)b&TYcGCGdATqh6z9 zopN$Y#$Nx2T!qaohXJna+^hQ@BrY7~pv^xVlZ78!k(RZfN!#ZP4WYKz^)Rn$ylcv`6&b|8-^+u|zPOg( zg10b``d)RM?Bjw5hNCY&{xK3BEPi{>Uv}w&ocme5LhZH> zJu(SZ{H-Gjezm{5OHFETs!lFChQY6aZ+e7Q@h9bd?b>SlG{u=C>elIFjZGvmT;2?b{YT?^0 z7BK;J4v#~w9?cDSp(E!mBfgvXti^0JxElrkotyqsFjPB1<7Kx+JIqSCa-hs`TpxwO z*bS}*dyIS^Rl#zVUe62a)Ss%qt)Ng47;0Hdj&~Ym>7Br)q#gCydGZo#a{RF#2pYz8g#Xm*?Z1*^;nF*FB>%ZjwqxLi8lma9u*HrIlR2JB z%3013CgrZ4&Cn|HzK>D*Zl=h?yC(j654YxMuQpK>?=~QrCZV+v4C8uLIj*Rxsy$#Idya<4x;T_%)1x%ZQDLX{>D#@#>LG~{ z3MLJP)mfVoIv^Ozw>x?1za`CGEVYiasimqO8*MFDwLLb*U(@<8IuCEU&ZKBVGlpLd zZJZ9kJ3cxp9dYr}5bas(s2nWT_^YuJutWW5CNBXDCigdGkdgU5Pea`8Bv}Zl8MW#b ze)Sp12@c`8oxXLFT}9|K83^-R+_>gGXkhowz^sCAjukjP`R`9qFLZYtMFx-e`copi zR%m21Z+H{wM4<*J36ZLXR4pG4TdNC7o&sQu9C}3NkfKtx_!0X1O|L(laP=+ zE|NIVBE<>KFDMB8prN^a=qF`i7PC{~_fV7+~9R{YvsB?V~ z`8SD^{x=e*G@7XbK`xYs>-}8Zd$)!1Wk5hT0LKgVH-H|244kL1{u-h#qZpn;Kxg-j zc>hjmX(nNgj{Hi2_J`8*zuHA+vA9h*n{Dx4>A81L+~TN}?&@ zFCt}jD>1>L$r}%!J3UjftNbaP6`RS>VSGnm-8S%KH466xA*~8n-)w+=&xCN^lW8Rn zVEDQo?o4mCEAXa+VDDPM`tPJVIBgH%uA;!+ReX2Z+FG!nD~t`$1?H>7_UJKNm_I{I zOl$>%fyx|a6QDa1`$a?fFy!A<%ZtBLEd#uCm_Gs?S{LNCl~)D$uQzjl#}2=WYc5)= zz&R0Yk57u?g9xWvdrXD87^Z|anjMB}5C}WpYQrAU=Hdlb(-SASW`{8$0^bFX334~_ zu&KG?0tfD~ca5@>0yamIqj$LpjJ+q6tK|R0Gwh}N>+)EkMhHo-MZ~YQym-OmW)#|A z*a)xK4*8Lg2PG%Z{NmicF8Akq1|^T;>zu&fpeHma=!d<>e*-@bt`e|%($r)>)%6qS zJ;-f1OO=y1$5MiB$kJ{b->uUcEO-zo0)De~^lsd8=%_pd3*`k!?=JDhql_3W)|WFLnOv2r3()e~ zL&F?WTINIwLaMR;yt#{NNaRPDub7FwdaAj^Hapc^CquOevOY@($6q5w96hCYuNP2GtE~OY23}N(v+sD(%Nk{fNK2-^4wBG)edt`n0jrj zVZLc4vj2(yw);XF^59+iPRDnBTOE37;&8#tjZ_`2`VwsJ4mpi% zmK+`)>}^h>ja>C(U;kMXFnU$L!t2GGy?LEhlJo8VhZ}c)f!z$voBDK`M4+s7G&2|v zI5O#gi%Wr%PdC7+AwMF9-1;q3b)?(8?)R2h0n79GTu%B3{L}U58)m^Z*GW;5a42`` zRbp;1PGshiiv!wRY*W<8Ki{dj@0WhNYJmjf=eI>5VAXkR%E&&IRQx__|A0X6XnWn| zzo_U*TCQs5l{%}ptt{t&e>US=o8~?p3H^|d)|$q5M+O{6gNaUgPLU*zI(vgl41z?ChcyE`$(Tquj4hYE~9 z_IgD$oa@6-k?ME4W&H+unR`C2`H3C4%v>CHL6b{<{u?Sx>{G&j(<0}K)zoqHS9X+> z^Zs9RR~iUa`?t*)Whp7)QL-dUNQ-@+lC&WyOOGwelFGiu9783;Xvgcpv`_va!R>rEHxqEBug^34S=0WzPTs&ZV_~p#+0xN9r z0ol3<%3I&f&6RsqRb{@n9)iL)1_?a2Iu_AufQE3W5f`D*7*Nf^?1DAL)}w zRUJWwF0IEW6f$IT2wLFJ0_R{C30-MB=g5%jhmg0PzP`ZMTS<}sW~#xzKV8niqJ2Hz z%Qq@aiCrN)Ya2atM^^N}X0{^~uLOtnXcRj*0a?Y~Z^d-MjfD#?Qg@wblx};u_fCn#iZ%6t}Nz zu}fPm$Bs?nx=2ooyvoNPPCT5+24|M-iV*%TW>*&zkuaNyZE7A-IDYZDDF<)$_7ab? zKb?0m7Hrv8un5c!G=&my!fJM_brf>UOkTExA9m@k3suMpLCqIFF<;a}taI1MGL_;c z9J?p0ePT{;$L+siIljl+|JnwDY`#CJ0Da3Wx_-j_iNaRP)jEDq%ULEm$9*mO<$9r= zJOeZQO|;K^qB9oM6w9?>$nlaNJi~}`L_iasNCzZE{;-wEpPym2vabLu(chBAiZEjA zXPqB7yi2A16j1 z)?e?b@dj1&x(JVS1m4Zq@OzP`?!JA%m-x%IQ~m_PipA#6H8wWZ)YQnd6rYfX5)A1V9==AjAeC@H+ zCW<^g`O{b`KoR)*6o>8<;z1NSRp-I^(YGTAl$gjp2W8fo_tm{?4hA!g1S>rM#0hPK z;ou29O_=pYXnda)%X7~WZuqR`TG&A2Qmv=xXZ&@hkuj|zL z#a`|&s_7@6eD=c-?xi7a*WU7wK7DhVNHKwaIp5 zNUWy>-Ai^qgLn;^B&`x$GHVSSH_I_KfW7eM#8$N`j$Gpx@luU6wS zYU^Dk<5Tl$|Cl`oK<9_~K2Zd*W9PBCm{aFtE!okB&ygevlh|2&-zA7)@p0nsz6VE~ zf3t)@jHog{Cnf_HL+_8*8idh@&dDRK3~$G)18=XOCv)Lx&M*YY%3GhT{&A@Y1K9Nn zj6KAD%sj)}7)}ftTjb}k;{^-r|H~Z_w2#>Fb|71111WBMXW;zd+X$DySYA^B>xiL~4+ax5iU}ZIp zsZ$@8L#q!Oo)f;X7cLFnNh{QGaei6=XQRgNmQHKyO@J?wkma!~B=--oi}q|I0-Yir zsi>e%Co9T7d8L7CYHG@5lKj`*1VpMEc9a=4@A6mOLuKretZiTR^KKPELBS=iz4hx= zIWS#1QpDHW*=HWJkZAMcJxxkK9@GMgJ}F05Hz4T0s{+m8-*x0ADn^Bq76!}t+{&2< zoSAobaZw|9Y5q3tG6!#LQc^C|>MS2pN!GioiUWSBJcZTO+}zyVGTKG7#|#SLKU{>y--ii^EwUUiTO>Q?h2?bIO`6a6E5z@bt`` zR@Cs_E%6WYC1y#5gDW_g$u-VSWs=0SVot*}V<85@B`*T9b9R}MjiF%jK> z7%}vF>?|y3wX3h%p}j+R#t9f7B6Df?{nhO6vGdHa_RX%;dpb)qx|hCxgSwZkC&2jc z3}n=1pL&{YRUbo_N)1dCK^As*XG4&|m?W$fKuid05J@r%-gUR3;_wSurpB7}u zckuAxrXO@w25QD$UAvjzy#IY@;F3_z-YzpX`n;sJ_R{4}t~F~~Lf2{gy$pDWEg$o% z+Ao0%JP>Tv6XYO0c&(f(*ShpnjFge7heshF6{y~Y^qrZo7(_q?r}4ikTcNZYDV~n3 z2K+Uzu~Oy`aGwejEV7PEY*BTi)quR`DNsE>>Ex7I zZMBQo0u?F#oCa>m?K^iI78S-y!K57m;bG>?sT2EYgV?|>)u78xsWEJH5i$&{jV&lT zCS`&PxzW5j=tjLr5zZzEu|m-(Z91c3R7X*zK!n2X>IQIBm_bRKdgQ zpX;$2zM}wbDe5MaC@Cn&$sKjL+5sHpJVcV+6I{a_u%d*GFLY5qWHV{3>)u!n&urx7 z^$c8}zkYlZ5y~2ySJVS9@Eiyc%h;4r>GMvYItHLP#7N1pPH3&*o~rbv^Y0&5WV>P? zO1cGnI7{9xE{?j`&w`S#a0z5XG)>rwbeLV$BDj9D6p8x+V-<#3Zzf>rMx2htTVGH% z+U9p_W?^D~xO&-EH(+MTN=Qi5kH&F`)N3)5pqS5Ah0u@9UkEyr^U)c0ExNQ4c##5_ zGvrnpMjeww=BdC)B8TpF(=XT6)uEB=vks($jT<)DmHPUOd_1e=;2LtTZ?*3RY?nN( zKdOi%IK7lN?z@?4hq?pp9J%9?YD|)q)Ivz4z@($&xz>41Y=h%DJ zg)-0FRsOgI-C%FMJjoAOxLMrCgUuLR{zBFDpo7C^dKwsyiQwS(etr7s(g z!CFf~JIQPRsP#??o_8!rTe|+LpVWKx&Z^rtY2XCqgDh`jfL*O-xcw6Q$f3{7Ek?0O zW@xZ08l23VxUWKxu@!Ddiv>VJG)I%|lgnmPh_}@DRtpF(vl9K2+JmBchbK2-TzO}} z+02tHS>ELqIB&qxaz8{FNo(|-_k>GnC6#=t3L}mzR$eXU<{%bscvDkT=*v^< z8k#;Z;FRN^qIt`y%~oN1xl6qL=V4{Tha6OL2dS zOK02p`;O4WxqVP>hUk`P zu^t39=Yo9C55|+&?>~5e8d?6|A41^~Q#XbDP)Sv12e*Wteh`KSzB7fI{Z%IN+O@~R z@8+NcSFQ-X_8ZQ359tGxgtpcSn7R@F%2pXEhFYhcVY3mzy|CU_1d@PK(O=S+pfN7E zX;Z+@bGW~Gs;e9>j%ZvHo_vN6$O7jSD--F&o(Koh}lZH7dcBd%LXG9_>0+qD*BUamY1dWNcbKp zE&yS^Iz?*EeHU-$K`udx={eCQbjx?!DB*g9tX=Kz#x})@_w0w6lB6p0RReA3M7Y%LBr6FGEVu0F@Yg$=1X5H}cIhjmDM4Xhhy?`h;=;zb!R@@!B>!i%LulQM3;=u;G~Pc zVB3-ZvY&($k(5VvM$o|Pb-mDl8f7^kC!tq-qO5;%-mpwj2v2iCm-dl{MmJ>ru3=rE zNl#wxRNBi=(U=Fj;T{AYUFN0+XX`%84cGzvGN){ANOstG1LdxtwKHgTKoqNnd+4A7QYx6gzr(q*k2j-emWCma>)uJb` z7+1a?#_K=zqkYIvX^F07&LeXdg+J8FI#C?!g>%CDNblL$bPOc4k(vO-V#I`oPAsn*k_FpnHlFQdO*5^ zV-tkej&?k|@a~FJh#JZ;JV|J?aQv!nh`|~P>>AFGk8Ky>r;CS;iez^tnrv(lp~zo^ zNayVvH$*9aEKdx^7-<(cNXMx=+$8+ItsTKIiWxPs`2(mqLJsXkw4QQ~Nns9gII-*@YXd;wPH`(D@^!tqeH zAva#dK|CMa#D1rzv#(m@MvfI-v_hVq|FEaJfZDb5;maLGpX^#843_eA&6W9W3MO*^ zK7DH4r;_;5ik#05`0-Q~2t_|>4#%RH$5uH;m@=cPu3!+J%guPPb4^=(Ke>Mso0yFW z4HY2BKL%3fFf$hHXO!12)$(R|v2sj9>P$KotG%74htuQC{F(XzWFwZa)z&9YIT9&p z7gf|*^Mwq_G^g58FAzmh=i-M6@Cea$ zf2cj*mF-lg%gGN?xAxMG;ONZ452A7k3MPF5#}z_p3+pT7GF^r3k*P*~bj1=XeLJ>j z_sAV=8Ds$7!vddarFI2Gb(ccW(Osg1-+E}56wR)p~0`T_>| z<`33m(yG>H)C&@&#o!4IM*ZdfH-D`kX_-EUcl= zDJ~z)Hpz&7dZ)zya7A-fKPlsQ3qu0Kl*~m9`{PBhLmUiDx*lxCxr?<$HZ1CZIDm5< zcm*gGkrvmx7u+uTJdhIWIfCt5?aa`+s2q!ydA?azT`M9;w~y-ki<3bJkBFF*keLg? zhrU zn`;M$<*cLQC9mO2qs7(Qu60Rj!pXW4zh|qZ%|&U`ti_HxHw6N>ME@)3w;+o;XZ$-( z4S>4alf%MTpVd z(RhWCro58;?zu^ecTC9=RB$jyydig*`ym+=MvChUK&$|sabAxjEcU1raY$-$EC^}* zx>(N_*l1FyTO5D&l#O$0tlI`i@`Xcjg5bk(*PdJk>B3e(Yo5Q2mT!cpM9s-C?C*o7 z>W>j7>iBEa0cs8T`wK(q$G&6MpeB42eBgqiN9G0lfV5pS37jJnC?A$f<%k75WJ#Ok zK`E;AKcL$*dl)Za6XW$OjZ{@5!UCEDUSr!^@1d3ghrm&gGwM(BjGi921ncrN6m-pj z7@d?aYu!E;WaVcNNe^W6!)e=W;{F9We zTg{*-;5sF<-`E?XWH5O>ZvsZ?=U8tn5B{*W>)X^)vysl%=g%#ejC*0iHO8;Gehvuo zV|1mp(Kwak+uJKZ6C##P`TToZw!J1MCV2tS>SmyA&|ajnWxN0OXo+bYXqvIEV(Eid%zsC#_5 zW>uldt>ZOtY+mUfL~m&V+CbNuUkM1JB^LI)Wfiex~o^v2$(02Wp35dv@T%748p|v#2 z+Iv<;@<%t4l3;v|>Y^ws1BD^so`E!r_HujIV!9&lWp@^W368$~yo4z6#54ZwxJ%*6 z6(`!1PlW|@@^7~@yt}%A>)?x|ZC85Ow$AP5vf)8Krw*7Ooj8B{8+B}tX7gfPvX=Iv zb1KTppGQaKho%FBgoLUZ8;!k}2M4zYlv-0uOY^3W>fzXVMDf8a7%YxGcc&<>j9=uJnCGyl~Wo|${WEK^~&H5@;?*UdriXPgsoRPUge=IC+iY9!ZXT!?(N6>3&x zlf)mS0p03(aq;Yvu3zndgJCXJR<9u0+bg|sWoyd$78VwE^JTqPa!Lw8knk{J%J_aq zN5@S0Fygr__Hs5J9quD=JUiv!Hd?5MfR#fWg<&c z%(gaDOBM{(i#BZ8pk@{_}DC*5CjA)1c{?DR=Cw U3Z$2@z#sb|J;VKv_SuH~7sx|(^Z)<= diff --git a/docs/BMDatSci_files/figure-html/fig3-diab-scatter-1.png b/docs/BMDatSci_files/figure-html/fig3-diab-scatter-1.png index fcbe747f15baeb15c678eb2a09fa18f1cdb6884d..c38eeedaa964be13fc296cefbcf38d990b26d18f 100644 GIT binary patch delta 169499 zcmafbWl&sO6D<%35Zr^iLx2Fm8Qk67C3w)m4}_2e2<}dBcM0yn-62SDch@)E``w%S ze!O~Br-oD1%$&V@_wKd2d+k}cMcTPRDnBp;+zHL6Ys1azdj$^Nk|y}bQ-$)$$BSe14e~kETT+eiJ1;r^;xmGV6>3w0op=Aav9=L=F5-&9S$(8~g9aws$v-oSZlrre(AbjnUEoSsOEj030M^Z{M%>7mryfI4`joCS&@Y7Hu>Q&R}Py0Tj4Fb9w9slGuYu+`tu%OAw$)UfS zK31bM%i7105Z$Nh;Yp1~GbREoV4MVKv{!6wY_jT;n|F1`B}tV|`5dlhHSLwU;Mx;o z#Y^J#Bv#uA_B7Pgvbhah+}s#u%qj$z+)2{cGvBXS?L9`F{NzpM73^VIu8w`@_yZYm zdyA9-Mkb!LvCQ5ox*YRq9Jr_P8!$$54wC1m5xO@4d?qRlQVJ%s2b_T7sT3-m%TTk# zRTHYB5GtUW*Z_uY4Z6?-v%UM8wbs=H?3( z9+}g3#23)$Q2)6Q1fT~#Uj$zl1gEAk=bx1gqM*dwcCAf1Euth~9w8zOkQo`l)Y&iK zE8WL@Zw53}WpnuTA3jn%pn+QKY+up&$~@FCbr@8c^EGoABb#6&p}$7xg7!~PzgAII zwOZ{867E(UF=a}_jylfI&W2shqkYX30EOW9?%;uA zMKL@yIxD(=nO4euQeLi2g``KYcXSVM?#_HCmH1_#|H0GOb6VNFZsP4FcA!5LLKnP$ z0*=ty+OcsA85GQ)%ZMLlv)QX}@V8cVlKG-eiMTQ$#+J4jlpv8G0-%nV-alv2dOc6zpFY@Yf$QH^hW1bsLftxyTt+ z1LyAN_l_RHx;g#?3)Bwemfb5fZ@6AcDlRMILY~!tAOsa!y5_(1XU>qx3+-b`NsV$z zG;dr?g^feT`ykfVp*?$%uyh(^Tx$6L_$=_Z*@OnMK78mpnEx0DVyFzv4;!12roBoR zrla7yNh{%jjOWpvV`(KMm()k!1mPwnMwZq$VGsWHK^=HGINCH4#1qYEMgzE8H+Eek zo261psBC5T`3D;jBKY|UBYcyW&zU%L;<&i{+5UpQuh_8ihno^&PC~$)zk<=xNHpiA zxat>W1c+e@5x1W>gLZ#sIR9C$zm`sdY=wyb)BFfeIvtzxPALm{b(PEOn$*SwDN8*N zN^{LLSkKKQRXMPNRP)cj@HCBcW-7&Qt z7X6VH|1&%%2S@$f*yn}%+yRwSeixa3442>V-~jawJTk-rzR#|aZW;$*smZb5UxzZNjuRa4%p27?D@8)CK6z$jHY8|RvXGnqa$DG z?;~HZvWy5FCfnVo!c-Q?V^tnnl@K^ewyF4l-1a7XdqtIe7msvb<37A4negjOkD zKfj7x$fx1vGP-=FG3w=tGWpXaNDDKBTGGODyi6uIKc-5yQ>ZQ3?`vA$QHyAtt4(j0 zNK-<{$lCA;R_{9iy8iUE8SDr==2iqfY8yxwCF}mG_#oZ|Vg|*(VH%{hA@Try(Ybd4 zM3-7eleIFJTvym3j$eV8plAD&M8xH)C5&ZuVPPSwG1}MDa$^U2Zcbluoq&+AAh4Ok zs6v`7sF~2o*}0&hA&Hn}_6!C$8ul$a`2{hbf*mdr_ygXHNb8}l% zR<*2E*^C&xc-7V5BhN`XJjqpnvQHD3_jSZ5B9j*A7xN6fw;wpF(-Iel<0Hmji3+<* zxbK&ZIX{>YIId4bBNeu@Oc$NDOs5$LI|!y*8Ne$H=(bv*&%4N{#ntS+NJtduw6?J^ zb#!kgh<0~gEVrZenI7E+13_%=Bjw~V8SQH}%t%U_4s!~jOig!{W{_ZpkpsB6<9d;Q z=?e}H#vTFH<3`)O)jG$LT3M#s!wQcKI=Y%%X5Fc2*q4#ZtUbexHXWR`51^-`%K+K_ z5(DO2suny)&?tGrHYl?`tEQnUs*F5T5NX2%dH*^+J&jSQK0*F`dwbjKCS|(U-Q7(f zoB0~ybvV?f^XsF8?>*1)0{AObkdsB6|Go>p-jRK$2FEop%^cJ5k(h}CHcIeD3Ip`w z?Q(7G_~WIe_K1#jC9 za@NY|G3GHfOURt(NC5G214jR84Vl{&0r|lp6ciaAKt1FKSr5%kGno5>UUI967HJl& z*eAf-$}lu{tnb`Cah{}?WQ&Uaf+&D3Aqe5Nt}@uDyIep4p;we6NS>&7y{^A&%kb3e z$raD}*y9}7e<ed7cUdJKDl7hVt)tUQ!yB7LHGuI@h$EHZX)ocL2Xl-b)mBaq|SABC8&8oD)NL zu#?iSVppvQ|hoCK|SrO!gw=e4~<7dE0e%dI`0a6?0Wf zjL^6e3Y#tr!S@W!XM}u3=mzr-J!0j6u8LdG2**hJ(6=s}1rAB+qGie@Z=Uf#83^=g zD2w3_|Jnv%hCfp1hw>sJxyDNVPJ`{}X4Sj6B!Zq6L-NN+A=V9tV@OYm5EAFDw3U5# z?Jq6unIt)Lzu4Mb4+~jy-gfFAj;=}VIx0;Pd%-_88SRM){-{FlG(mx1{J^)*+QArGLayr{p zd>?($AG){S*{hvDV#*OWcHwE;M{maW2rEBGd~=i3Kc}E26%+Go?~SpfsRHKfUNjb5 zJe>Et_c_#oF<4!lXCFIY7?4#Y2nM8BC(2}wbu$pXiLfh)U6Fa%Uxe&^Z0|<1=G`0u zW3g4x0W;SXha>*D7M2#H7<1bG(WrI=z$}Fk$OUySj9&+1-ps5Jy|4xBaUe&t5ENo! zvw}es>N9OyN$hNkwrL%Yy@^GuycgR_b9aq+=(OuLVrt6gCOBs~sKWJIU>HHKPh?$M zn4fE8cq9u+`xmkWejM!WLjG@P@lmBA$NmmJNLl%<*C5qF zxbF1yv;^v~H&Tc^j8n?S)c81nnfwCzcRo>;M0A$R>%^p_UOhuen&D$6-}GT}(LT2h z>HQjKB6#HB;jQk|M&{OKlcAxZ{zi;PZi#PHJ>|a#n<58ME?zl*KZMY?L>9A6q_5Wm z+0l95c%7Z=NN^ABbKoilLi`dvh=$S} zv*wZ;gUgf|`5H=9Hwf7TC{yk6-$o46n<<*S$?JB)WUl@Sr%unJy`+0-h-O~J-9X3n z<&an~M&g6+@9Oh&mmHse@aTghkR`jpx zFy5(UdOJP4*A#-K4Gp}V_p=_Im8aqcmybq^q26o$gjNnB6=&L?qV!t&ic3r16&sQA zNm6=K_N`m#IJGltsH%z*6BGZoLFSZ<@guUVk1Q=N{`MXKA{F zG&Rwjc<9JOc10!2g9%!AwxVEW z7tV5IA}L95eOGd1^cz;zdms?J{t0H}PTMdw(C@VtCIA zR9L#7-W^4mFY;aymN@HUJ0A5h=%3+9BJt^U;QX&?{0!%=1s`7v{6mX{z9J;dR`Fh( zNcQPfjSxS{7bFQ_V$GD24*;K0CVi(zJgl$Q(QCe1%kgha_77ss>Xyo)E{uE<*v6bG zqaFw!ydSK>sd!cWO`h!LLX&=HU?f)9on-IRX|tJ+Baf@SmZII(n_MM#hoWrET6^+6 zRoPgpp)|YTnacC-j%6F_m+Ni*yKmf|^Xu3Ctw$vpk^^4D33Jo-S-|oULl)XD!S;8I z9HOuc#`SOQpT8VJauauSAtem7*IKauAS0B&0ftfg&~2A#@HPFNmjtm3%bCk{%vKut z*`a>3H4L8z2KuI=VCmQN%I={#=fpg=JSIbl%~ZZ@gux70OwfZC29Y|(_?gx~gjw@O z52=*cxKbiNFsjt8STIo~?>aZAx>};hL>`8PQ)r=#21!UknC|9=p^7IHRY=Q~n%l&D zV6W1Sx6c_Tq7W44w!diIGlu_lWquJ_oiVIL*Tg9orASKPZqYi`eL~biC9J`ax<4e8 zS((lAmora0-6Zr6HrD09S}_xP3#Tuppbog7AC?x!9|SG?%5Mfc9St?#rra+9i->kG z>PDNd!}|D{NngT{88N}w-9KIb7MS;CSu$lAof>i`{3Go!=yd`1$0c&grCPa4LZ00Z77xs-n_q zfvRh)-CF9U1qW)RU&`Yh76(lkaXy;?>-zJW@4I6r=#on}yVU7SI!G>yp7UI%*zp&x z`=?F;`%Sb>Pd!JPNm+aMJcZ}q@uLZd8WlrVg>{~>iF%Vl;O7om$28X?2c z=bFv^pC|Y$p_eNPYB_VJ=5+SP#^%)Xzq+LvEeNPLJw|uhSidesu*pgi+L}FCmytte zkO)SnFB=Z12JjwGQx^7!K*|$rrTD8%OTMyMrTB)GrT=s^&Yg zROLzEjrb}+AUpJJ;r1b|qOyJC3Wd|=CjEnp^QPjVc}u$;=#0s!e1>`O2P|(Ge-iIC zdBE3{7P*rSBEB$N+Vou;=d$Vg>n3ZyCz#B2GjrgIr#pSp+){@(Js>_JL|o}LWaFJ6 zq@<nCGam2&?DeG3j*S8gqW~CFaK} z-&;;D@Kn1At63OAH zQFqv%^sD*!wFfa1y=>MTeOCibUh%rfI4nkYDV+e$k)laEUkuGb&Hb&XjPH|vWwD{; zYrFVjBi6Ob{)YM-e;aC-SGSRM^iZ^Tv?)^|VnnIeGhxf{vihX+R} zkw&QeAc@H9bSPM*HB!-U|3nrcAzElWOJfHo?%sZb#Gvm#Dt7kwF(p-03)Zd!fys)M zPjA@5W*XmWlNn=R=Z5+@KUMUtn3_&I*>baq_c+oj_1v#%yI4R^+FNcCnc@uAKyxV- z3?pJm8<^Q>lCb_LE)cs|J|4o0eBq&Rr(XfeRS0PAH(}EVa(3X_ZyI?pNpGP*6=mb zR4Pc-jN|j8*elzIy=#}ZOLB<8Y`y!8*?N;SxZ%}kf?e4oMXT6-z`d#Ego|51eNyV* zW{DE|TxQXko0LO+!%{%s#kh6f;myk=8xWt$AZPU+NgP>$j3*G#yWiC`74C`^$S_P6 zGlf|BK~{()7 zgyf6=V5PBu_B)4HYGN27^1tj)_sU)};T;h&@)QiN5d;oNYSWc$UZ*{W;y>3YWWVPd zn9!m9k1LTG(ZLk)6xoP>0o;D?4<$ADtyLB4;6Ep6p9*E(##><-3S5Q6Z(Q;0`=th& zFi*E{2(Fy||G!gre%|#0Y~!_Zy4n9q|00obz%@2JucIM6bl4PMk%>*?&yb*?pCHh1 z3s?p)|8MqCFl<6?-^V(Yhzr@JJfa8qLZce;(yt!oHXm$5_oh(CHy;W_#&r!A(T@K8 z1mxxfk{rJGCI!Bc{LgA8X+l;qm8EQ`C@M>dh8abImcA{erzdJ!M-^kbf@xYN#sB#@ z0P_g(v5S$Hy8oh}iC^2dZ!Z0{RPRapnxN;P2j*#su_30BuylLJ1CMs17if+KU#J_=ztRUU zPR;DzRs+t?0DFmkW38g=Yah#!nG^|9+S`BE2t5xrYS5y=)2JeMOi@-bE9>J|TL`nH z?r9T--czM@TTtMR{JQYvhK6jZ)^dI8c4@oja{nVgK}_*T@=Vby@Bi?wujn7(5G$~U z6_^cMQhtUJ%Q`e)XmBS7-8KM@;v^Se5QzVM^ERXZ#HrH`UZ8(oW_WGx=tHM$09W@% zi-lFB-U`6Fk-^RL@ck-U?F~}?Sw?_>rs*8KKjF*R(cjvMg0)0iu4-;H`U4wAe@u)n z1G9RQ7!{S-#DptLR}>c9z`)43Q(QHiSy@?`4{R$^@nLvLaz$~Bz553ng(fl4q3vFI zxeMwTNE7NT{CXJN2+=rIX{f9$xac=jeeR>Jf|nx-YSar9yOg#fzMCb3 ziFhPJ-Rk_|o?tN(Z$v3&cDj8y&t1$p4{BY1AfZVsWOfI_x#$>#y$u{}hDHMZGf)_q zot>S@X}6UIR#^4SU^;3@X<7yixdHBgQ3IR0@5D*tbbSO zn2go0S#Sdf(-ciY)1Kre=@n_zt*BOz`67YL{Vk)+Y<++n6Aa(nUOSrw(_9$uXFcAk zg)im0i-ink!a4@!OIWN^`&p2_1wb+IleBlRk66#Ul#d%f-e;P8lE>p!#p?21e`k4K zK}#B>C>p^8vL#a$4WJn0;~6gX`N>qNm>kC4U~Ck|AWUX}7&B*EU7Z;$X!kzb+KZO@ z4dw7SZ;ob5M{^T3J^|a|3VG2j zPmf;aqtw*Y9PcBJxQ|w;8>}X1=6^U+KAcY|_Q%{PXGugh{R}51Ek{wzB;k+qFEohk`Sa>&ZJzcDw)ns;)5rCQ*fF`I_Aj!OT^SB6Jj;zj`PZRLk zUT|nt7gR`o7n&Xb2GHL)$^cvrOU09|#+@%QNmN={t944OIenMS4GVz1>5^BZzbVR$ zJ3n6x&$T(oHZP!OGCnPFhBf~iDj+wv{(g%J^dlhIpuHLT&;pZ^AohU}KSa8SF@`;(%jF4cLM=e(}z8R=OX?%~M zsk6?W;$kM(XbOUK@RG;*5ZCgLnF1s@IJom!({Nxch}OwTk(7pFPaIEQ%%SPz<&DfG z2l!CWb2W(eK6pT|5P5(BA;Y*mSmxgpLWs;0A-m}`io&-LCVF={vjxiwl-=|rW&Z8k zSxOhZa~C3kF>x|uLN7i3D`dv6`j~Z|-cw+0o4M;Oiiq0uD^~-}cd5VD7yN&hrQB6Z1ahsV^b=cwQhm-jWWRb3>KqS0z zi1(H3(~v}dA#(bva<$a#h2sG9_}pF6XMV4)x0!iEz-r_-LX+luYb9=mT_%em*Aq&Z z=dM$#QzPS%F6hJCe7jrZGN~--x?sq!DDHlL+=&)@5f~WQ|43mFjJJaZx)&M|ykqit zyxBTGY&;rIU^D%77ZPvj<<+RtT1>?KfeZ%^k8weu8D!{tW3JQW&K3l`W|IDHF_<8X zMI)E7*a&7cEiNp~j8@Xtp43}twSeJ>Laa=$ zK1xPbHr6TfdTkkv8?#wB`A;x~f+43i+T1w|Vr&I_&D<2~nNfS>G+i=SIy&7ld~|(D z7I8n%XttURaPq{p$W4bJFr>91CUe~|jF;_2J)JRl74qI+Y94$uAFzNB@*>|ZzGraQ zrulGGTIsi%r}LYZpYxI3xBZ)_P?xm&J@#*f-24#q>tD_zV`0R6*k#ffjNNO>Wzvg< z*B%?OnhY1s=i{<6UaRU2K6h^2S*p7@N29>~8Y$qL9AS0i;o%Ydnoe~k{*8Vi!p9NN z6aSZRMfFc2R-(D8z3S40Kaa@=W#3}3OBvG^&_*_h- zH8Q9kEPHGbUo{k!HUyjLG&PCQ;T9DYQDQ`h-xKk>v-+HmNd{-!U9W{tK!hlg3$=Dx z9%sNNz4=k=lke?n2uqClMfkZm4(RFes+G|$xGwJlrlVGqQ+GBozB3gH%zLVxEW1Ah z3W43fE0!?^_dduZZ7A8sCgwULZLi!G8D_ImAVx_Wqq=I>BO?t@7>u9_jinf0bsSot z5#j>WTw-lJ159`u?q$ZEG$0vw^fWmszDzbs*$O*Jzek^vqLOPA5}#$gI^=$}2#%IJ zI1CklxLUtGCZ1Xx78X{cIwi%b;nS^V<$gHhkqHqR8o~Ip%`OYKZUsNzb9&UK-+;rYoM!1k)zXdQiw$hPi3#;XJbhP5H{gkRb5h z=y%artM%P?M_}nTDvGRz@GCdNbZU!&BsFPFwmuG5;GO6B`zsnNQ4`PLpbxhT%a0n` zxDJz`r<)O(!SS^)J_Tc1z~IMYm#KN4@BIttYEt7Y`acf7?Es^Y)(}%}S+{AC>T#1a zkZE*|{$X=-XZKskT7>v`3h#Wp)g5X|d?df#dqP-bc_S$Z?SDj5nASAV+@K>;fw7q` zcAUxcG;YvzXO{AoZHCF$2{hxuQyg{Oo2Fm-Mxhcs3*`5Fq<)Hg3_AE#AI;0lOGi)N zCt$9JiPDGd#7vV}PUV34n=QdZK{{Ru#+uvT=*}a!ST#>Uy-A8%umGVA5=V1RG0PS* zGbwb+ji6Iqr?p-7D|#A4;b4nEdJAlDf_i4kEdTIYCr;XorjctteRB3-votKW@J3hPMn&{JdRPDmCt%1D3~-@h|EmI zH`8so4AM^XI1JmMqaia%x5f;38xiz+h7gO~0m6ySiwqxvNj%muZXx_>adj(SIcs~~ zrfiBzL568aDEHDZH%E38Tp{3f3Bb;GZ@rt-j6$=jSej(Hkg-)!Y+R3$#a5hUAK_O$ z(NM2bhx84o#4uULI?+HWRQT#t@^@uIR9-><*?&;5p5htK9aK)p_%aa2a=U0K#(skJ zJ{?R{G2p-KG>hI1i;0=P_G68HBL@!~gCk=3OV*DB-aNTb;M&NxhlOg7T@mh}3?b(- z@rzhf>o%ks=*NagRZjbh^F_!G;O2s7=N)Rn?G=)iK&T~kRzo^1{@E`tpKdjYmw7?c zp({kSY|&|enaWY9TP%bBWK_IyW%FPcU^v$*(8#r>3~A&O2-S1+wC|(C=24O?SzFDmz_;SO(q9xn1&@@m;w1 z#AdpvVU{!V2h9)b%Y^|VWd*+bC1Y%Ntk(6nI!25lCf%>hCq5x!{St>{%SBAG&5(pS&a3(AV>Ffl2?WT4%dhtgpBfe{Mzk_^ z-6vrn(ocKk3kbEPD(FdXKJGxG3X1ANx;z^JX$A+actHe}NwwvR?MUkE->i-i{{$M?>8#W%VnHHF`GAcqn)%lIe37BS9p z{vNTf^Rhpzecm}^gbq!@w5cvCbr+|?x3m;_flrFd?rWik2Y80w6%-^ge9zdts!or; z)_ic%L#k==q2CjOQebGpW|9xtFLA^RL#}(m^P*c>9B)!teA8A=%2 zB^M3|TqaZ+R5pfe?ShwmB$t7w=5fhBizBHcSmvU;3j}AniXpx&Y zeiL}Z!PGTDOxYhNP{3rPp}cyvtSmA|vfAiFjiIsiDM){5=dSq&#k^$MPf&hQp62D$ z_~&U2YiI5PDxRRQ-hQ<(`8TkhEIL1LwFV7NPCw$Ow;4X3;)yru)>z{(13J}T#Zvgz zzOhSn)qno{vSBmHW;-X|U|c{cNRxgknqn!a&HZ*KZz6@qImWYFRRvbGK=%`9YWJEv z4HWHZ#&C_5g1tSds+eQu%dI_vMxPUAneL-8cW^quLOZpms)3D*JDv(&l7@(zQ{Q$Q zcFALSsYg0P2q;xrS?j|A;1K&zRaLdKTU4eIHwB_d=s)+suzXGtcz5%&q^6~t6oNMB z?*t*qxSEDW?$_^c<@GT^()h_kxnKKRA#qW zenygeNMWY6udkrE3KXX!Z4FI`+Fdx977MN5a(E!~{`;Q#AVbe@i34-At?Bq{G>LZ|q|pKg8WWjSi3h1lyDx3U$xzX2bj z4|CdB^74B;Q}UR*w54`ym`WM|Y21&M{LD~W-sqi0OA|S;OcCEdMQ?;J#G}L#-XE9O zE$AFEZy5DVSOX@B`$do;U5$0VJ0)!%0d?&IXLQV>{oM8!JH;Pga3u*M5g40$5o)LV z1~citV{BG;5R$*UJCu{py6nb_$a2wA`<5Hp9jaS*kR49gkXz#kW(aN0C0Qw$tYT+x z;X{jzAiB4%{90a4Nv{O0iZp_Z*U()MJtL@v_x}@WxKYBuNi++5vdv*BL2>n1pJaI{{tv7u2jI zUi%g2?eL`e;;~@-&KP>t7;_Bz5uPm~a01i`a_n_Rhk6r8o$sJ};{DC(MleEgX*4+T zB}+$X!q=bF_Zj-WTkG!mSCmfn&8Pj095&NTIe;G3WABqXq-^z%Iu&yBDMqLx>>%cJ zzg2rt}5QgC`eP)dV%A|0GkIdZ1LhVPXdeYGphG7`Ws-O2j+;8&1qbq@{PU- zjqhtxluOV8_yz}*128j17z`}8hwkIQdY1F@Lt{V$PgnX7!sb7YkHOh(s+=1$5aW#T zL>1C#fW>x>23!Z|j$+eBg$v$FIie?XJ5n@50_FL0a#_EXR;0#^O7qwxFTR59@6`1b zBN&0<0ZUN@L%q9G#pDA}IJYctM{jyP)B2cWrXGKtL7@TMSv=dJyuF^dMsXNib>_ZS_S@b^UAqNQO zKShX*i(^1r7w3+B%r_|~dT|mHnte_G@CiO&2_dWf1;@7!%C0~!LN6LQzmVu>c5B9u z@b4!{k|My!BpKwA1hF_0jqvYxMS<*3!KZ%vY7V1ujHMJ?qvL+&YvyY?OuoJ|*>=-I zS_4x#%oM<(+(39@YS{GBoZnj+5nDzpVfI|wyz$X^C(4+0xfz9>SI#=p-MFTehw~UssKoTEoS+H z+?BgV>LdK;4Hoigf6TE)v+=u!>AO3O)$iaRRJVn06Mz>}&CIu`zrXA|732Xi9LjkA zjRri<7t{nFHrGEAV7*Q%1_%zWtJTWc$^3=;q#Ha1BJ!9CrczQ?L{Y%s&5=tDs2Ijn z%BJ7`)j}$$A$?XZK&o=+IuN0N=m1vDfB3*JtPjYf(!iUr_?)Sp66cQWAC@#wOhkOL z;iJ@jjxUdqEcju1%q*=AtACXfC#DwEdspL3o;|p2ZbVm=(`AZ|I@>zk+;JhIkyCg! zjdDK^`8}ispvFl5KN;fR9ko#jGPX!t=Ph~z!*($x_z+@9Pi9QRfheZIu9s{m3%CaY zf;r>G+qWrLIVn?72PWgTw=8cw)Xa>~M#DNcBiIE5R-lytT3e(rG_6w5yR^9Yq8s5Z z^%o&@?b$u8H6RJUch4*z3f3FL4{A5$)%2e^LgWQOem=t(X6f%b^AHMyiHD1staZJi z^L!PyRdp+>&-RM}z4bByaa6{1b@E1}szylWm1{jmPRRUO zUqbW~Ke-jQssF6#>kud}@tSlG+?sSZntDKMd*6b|9U@Q2m@X=#*0eXMlfM=^-xH>G z0q+>eC~kWc*qb|UPD8LY?0bNJ{|aoSwvm^~pcHR9oC8m3T3q*Xq|Hm*2}23gJh3vn zw2g0-`w(HEPH9WnKIUP^fa!Rop^)8{p%C#|SV+?EZ`C?hb3evhycOPgfYR{0mVh6v zrYE-&k0c#`8xf*uSvO%<0Ef;@Ks0yO4h~!fJ@q{rk3H$BsU_#l6%~;X+JVf2>cY$I zPt_7JumN>3H8nMf|9ddG-75sMnK~jdQQFGNs{atucYx>u#uf_84P&q()>4KiP#75* zvzqd@4Ga-_k81CILm;U#prmFvezK8UU-?n);zBHCyGOaBLzo5g@jNhI`Rbt}Cw6+4 zudh3E%pF&ZHV^y|M(E1FUFQHptU#--ty-{TPaDLjZDbR^STI#m5~I`-|Fx=97KQ^4 zDPzo59u@v!a$e%V`&;l07g!9vOrP15p3Z2jt|T-mDXAJ;68{AzAhoW<>fxb}cD=4O zGw=j@q2EWi?S8jdSeUabd}Li^sxgcqdu$n!<; zH30~VPFjh?ZPHZk&nie8`sQ|> zhPcF8lo_m1B76!I!bc)&U8)SI^&HwPG!fpC#T7TmUy?)%ThWSa_Mk(LBLQrRTm-yu z4C`F^4opW(6d8}+XP{}Lrw-unK85GGdnpcFF@>0;A4{%;Ch6R^rg}+{kmPIhppCno zUhPcz)VOFnlIv@zWZMV#U&UYlZfkD5Qrhw-FGtwTAihUmH}@r@_)YJtjJ=pPpp1x{ z3geG}wM}A6(~&BC_E*gPM-GWW(Kf+X^5omW>?^}U#TI1gYs&6LrW1c3Ehis}u}hT% z=l@7P+GWzSkz9L$4ieBc>=ap@(yfh_Gvo?Bu)x^WOs>0x{QvF=IwYfYv1Py+Cr(%h z%Rl_)SG`Zqq5W#!R#+zOV^&ktqrUb=J#S&$ldi5L_z2j2O+S(RduGW`XckdR!&foRPB);|bTh%V@2H8mn&_3EUnC;GjdQ$FXxR}qL^Feo8FWU@WR`X_9Oq4{?mwLo_I$x21Bfq06vys8H_ z40IO8hvUh`8IQtJjcBW83D%(QYpVdaBfG3`0|@(T-@`F+Gj+1aX4{u5>gFci&hMK( z`c76|)#2KhwqFT^p&$d0K*@^w93^=)w(0*o?o+4`Bx5{90B_b2o9pn?eqz-WKf;!1 zP3?J!J}M-H`GcdJHbGYU^8PUt%1?xa zQ0WXVlU%u{P`3%bH_aPaKKI*(?ugq0hPDaY;}Li?qoEQLNcV@J`K?xF zRa0rdGWRAz%<+{(_zrVf-r-W*)E)7dZcE)mhwmd|Onm&^^6PHh;E<54AK;1wPIteb zhZWtue+Td9S0Q%D5dQ#uE}Pa zuXemA4I2kxSe3oj$}T8-!G!3d00_6_R?@-3hzRk2Rvemm$8MzKNiftu_?7oVX9>NF zH|ogeQ6uLcPthJkf6FBiilB7#_2r-Yug@(MwT`rB8G=g>F__M!)?r!U3#l*nbLVxP zLuim_jY#Dj`%kIu9P5L*x#|MX78dn&cm&mwK&>gT6>Ylj{(xeH_IwhS-h0`pb0x}8 zAWr%S9E+Me9uwoYk!iJ-`HP5`55!^=ui_!|6hQe|`DI83#Lq-mAjdCvR>(3}jBsU+ zhy~RYYaVniYsmxNixhGiC3mVu?L2)(LDYIl2(e#gxR4gCQv858n!q`FD2YJQ<8=>c z9LP)em07C5ciW#8P6r-&Qav^kT_6p~=tcW*LEdeQKSu7tfbN-foV+b6%CBhI?+^k}}$idoUQAV#MB)`E(;7k{OheiyXNZ$`Grn`ZIDx|o z69WHZam>2E1a{WtvV!hAX*;&6Mg(Ow!lbLr`Y^BDnEp<9fxrKpgP^zCgeTo@V$BLm zzQantS@{sb&JvzN{o<~b{XGUOg`5#;&vo=nTgR9E`a~)k1 z?i)QR;OTDJ3eq=lIjET#hm`sEH@O{__~#p38B@Iu>(A#b)5owB`GvWb+$lrx*?;Q4 zMEaZG|3$?^U{m;YH(Mck1be$?3ZRhc_?HugdYGj{C8QLYY0?Wh61%@1Z}Q7VyX4W5E#-|}?R`eAJ2GhgwBgARh_yDbS#BXMn zC?4HWi8o$+k#NKw7x6yJlX7Jz^{tU`<~Pj|O!Ga*4oiLBX|x_5$e#-pDcYFJO20c0 zBPz#|mo6PPQ*05w7YNC&Ien3fcQrK%Q8TKI8Pg(y$a>b!oAd~l!+WTSkk_w( zhIZhpJH*uj`zHfR&xuGX8Gw!kEAqzgaYqrLNFI_<&6WF(XW%Y;vVRWmbiC4jKFoJC znjsvJUt1doDZa2ZJU!k$;PiO@VPvfQ{tPXB&AxkXxUKUIq2`2M@y(huLSK$i)_jOa znb~D>XA-%C%=;%4qs;GW?H}Yxguj^(ekrCXY4z>JvGAXdKvv!!_l%DQ6>)<3zLvI}%?-G~x_)kt6i$JfaxAe#D$@EZgoEY6sRlVTg{kTlKWWhu^m{cWq{YpyeM+@O&S-yl4bjz@U8 zH1l%ELGx#!MY_;c8`166QAN-0R8g$!DjdVad_ZChhN5!CP%;-hW7xqTr!&%mVctJ* zzS%4hfaouweTGD4%jyn|SlV6yT>EJYcdm&JgC(-@=g$PjXs$kB7gD-=b7y6P2003l z@-+i|`0Jpa;V|xKVM_HL`K2)*(%M$Bp6uOh7Vb+J3F;3$VT;e-Td{9UeqJfBD@}qP z*xyxj%C!J?!vYEH9j>R(-d+25G4A`%E!PGCTP#|QEnlv8=E5xtfy=S<%l!+8Nbs*E z^%q1(D1h2s3Asffd`GvK_|7hA)1FzYT!_9ABKR1(%OLv`5MPJ3jR!Me3Nt2@Y|@Xj zczGbcT%aXdz-qnzhIt8#$9%*{BIoK&{CaT7#G$xswup^HKCD2_^&nLJc)t+7*5#`0 zKwbctg@>gO7W&i+=3bX%#Zv55LiAmAyyd&(o;rQKgVuPZk{XX^QQmUtddCd1&DQwB z{+^TK<74mlxBt`Z`Ou=PKy&!NfXO>bPn#GW&8P=Y@dwHjXJ?C@3I5%L{CgcFD99W^ zhSTJJ&DmMDKe(xU?p#L;ZGh;gom62_Zl-+P0{`tCI&`-HR0d?Yb0ROpp;i?GX*;If zHleonu;=`P9^rxGKheKN8lDEk$%H}9m+IVjU7&(ec32kXmJ|%V0$~s)F9)V-{NI^E zekI_!Cny0!jw6&a)ECWzF1P?uqGUAL+)zTAeh|4*ehzp4^LzgHV+Ve}5n|4;Hyegm z45D`1-Z>L-pO^$ERLn%y)BNaJ{S3{s9`o9jU~RHwtGMmty$^9+3Q|-M5oGZx%MHH3 zXWfg~p9hEkNq=v@ivr*LE3=|0Zl|>U zmN=OAV~zg-8M-x$=g5N6y~MVI>Ro3+%c+&?u1%4Ar`k=+CF0#P%@g8BN2rJLJR9PQ zHUm3YTW9V})^smp}ct^)$n4bnq1AOg}|(jeU+Al=>l9rSsh_x*os%~BK=GjnF2d*5+gzm22!%_^0} z6eZnd)|{{J<$V80m%Uqwp5z1b|le0ZGy?3&OP|lY2DG;?4mHx%U|Eu zEwz-57Ty5P#Z~R}3G77#59;&?KArGlSX=PrdUWHIwj^u1XA?aBUbDxI1z_djNriYF zOERB`Hgv!=Er-$-6ck9Hzt2wEXhkf3<_=QC5JGtE)ht^GHx|$hG+$Q;e9F(slTnU3v?P$My8oplrVcRGAR92IXMI zR$T8U?C!A$BvC~O5VhyM-ptwBDQ(FYM&arh=+I|AH(zY_-am8P7|a7@IuNl) zf-T*kXa9}X!J@Er~eZFLfb8)vnbBnyZMsMd8Fu;XF7XIyZKL|B}a)mmbu z*Of#4`K+b+(vLt^P=kTTW)WXbzoYB9$L8^HcVYgk*51!Er1-z#6zwV8D%DM2Y_=~s zy3kRLFg)0gXVb5{pWXypFILp)7~g5O4$N1&Mzepx!1Pm=MSDCPX0c|XlIgcT&-OCp z4r>7j6r{*}Hmi^Tc+^}`(b*2iCg&Vpgn(2*lrXpBd3ogJ5g!X(lyr>uWe4o{gfG`+ zX~+5x2-#VGA$W`fYy!hcsi|XzO&c%JBRp?Xmo!kn{H@OG5!8PmzR$OQOe6c~()KcK z{(J4;z3`Ksu29Nn7N|94RB@VaaQ1v%3be#@mdxC3MvNCf<>#M9adirK9Vv39I z)=z_1aXdLrxH>Sb!10GjeVjnln)&R*w3cK<ryUX^?@pE3a-_mrymXHqVQM%hzkWi=Eyk?Ddz-af7-gT5g^vbxs2wB-2 zO05|Mga5JIEo&IGx8HVsIKDaP>ER5_H&FAtDVf<0qAh%cK<-4ZwvKGPK9#rvle1j9 z*tt1Nup#^TFL+C2(bf(o&Zh%_1@pLmc3Fk9v-9}E0#Sqx(kwNdCqzOnB^M?uriu6RWOTJ}p2eEiG+)?Y8E{ zo(k7~sr!%(vzonwbhC#O_N!>OVA}MHg5klLeYLV$wXPPXUayLGk)k6XYeOzb#;a<> zqHZ4NriQwdUIfr`&iXK6F|gq!=aLxroRXT3$&kUz0VGd!^pzGD?QWO*BpjBou8LK#ptJjkVw?51DjIFgBo$2vSP)zV6h z<*fC7ur=X&Fv&l@@FII4jy@3hISq$#b=%j!gK@T|J~cIkZy*R_BtCCKXC)EKUcX>b zi;p`C>F#NK;4CCd=A}n>JUo*Fb@9Ckpw}N)Y~`0|+a&-9-`DQM$@6D{swy2LjLWUG zUrDCM#tI)ll19AtgZ`?2lKpQJ0^FpMG&Yldax5J)2iOCmhsq*DPYC5!@h_7VR@?A0 zi_qs;1Dd?o*38MTmMR*RrKcN1Fw{!P;5`?(HJs&eqovP#<9eA~8Z`qF%&0Xczftvn z2F{F1ib-QvRi46^*YwFapx`-WYFE;iMcZh!eBra2basDra<%;C?b5v*H8}e5*~@by zYgSO|&#nlze~XuvCL(TXI^q>cNJHe|UB!ML;FGZPP@Wa?b=L(WO5_NZKLYBWXs2~& zynJvB5^9?CrUZF3jBpgZDY|=jWB3}NKsodyklL{kuckLJxZ+WiAIZPo(XhtOqcNjp z$tz&T6$l;m3_^>ca1uW%ix87uIuPV+y5|5>fT{C|k*M2+eW$Y8ln#34nWrwk4sS)s zF;5ZUuH$?Q3uC@X-;1uVQ@K_wJiuVM94Nw#PSTYq-VfCyiJ2`uDsza!BXI#1G506; zWAXwc9E)DUT)Zr%vz6w`ii*oMrgF_T)0O6!#Tpf2?!R3?Jx&EFZHvvVThtB)kRZ}_9qs8W^=+tT!J|lC7{3F@(lpKf`MmeV zyp3%hzsl>j^d%TPp-VGO7MM7+Vq%~;T@j)u79wDEICC`^lT)y_u@MtuimUN(2S@pR zBeW*uBJ?hLMu$oJ>929O1DwGjs%F1({yU1^h+M{s6V~=~r6H0R6{Zjb(+h{!pAs*w z&I<_J$xi*rrEVw`OAKt=@oV;T@rYS0MSL4w^qJCk642-gh%zVW8y9Mw;fsNfWmAcS zLu0MB#N0_D0W7iB#*CvoH8!TDTaiTb*h>aX6-}RU`~CCHSmjZN?aoT$h8z*A%D;W} z+{CDATEYkoG>nmwz*gTxY<4GZI|LMSA=sI*x#8S9(PE5EY@HC=_{n_$<9^oOa}ZP~{H^WZEm&bI^y zK*NEXj{9-FswjG^4E^mMHCJxTlE;6IDs?xt=pCA2ag|%zk{zF@rz{|L-qa+!RUvSs z)Rj`}@GOt_!fN`)eIs(-r&HeimKsvx(VNepydm5;KFW1e+2A$Cp0NzwW&ANtte)gi zP1`v3OY2g*-fB#>Wf^+9wm0ih?>;vO90mvRTaT@MpZa0DNsp7CTH6m!YC^(d^nLJsF*j(D@fsgs%gz>WOrr{%@ ze3Tw$^}f%-u!Qa8bJy>0HsFkbrByBuSsUJi*}5R^OTHw&Mrayl*4e2(a&^Y!Uep)0 zQ5WpGPczV;t9#sr9n|qs;A5jSDJ1)j}Ot5%nqS>z|)x^a3&+GjMy z-FF!9g1w>yUkX>&G6cNMhhl1c7I~P&VU`hnGG8jkM*U=z2Z4tZ0Vgmk8arO=-|C|) zRj$7PjR$oNG!LmFt?{?4peX@LW&{Bp3{K}Wrelwd4&bt#xTlL$F*n>|d4}eF;cA52 zK2W!@xfDdK%CF}-?_BAyCS{z`q`)gM!4#HmT?Apl^orX#8VO@_c%Gol*pjo}f|I4`hi6L=& zEiG$^>CTYUkq;~kQ^C z(^paQG}KfF7$-`NGL#~DP|4R+=!y|0q&R{H4F~+4FIC4k!rs=uAwlgmqj^!)LHw#y zvm7I=7v{EK?>-yO04q<^0IBuAz23)MJRk9UqUa~LM6-A394dY+AMDWyFzTpo8AW{sF5G4W12-3 zAHzXxW6#`YWZdj;7(=oXK9@`I#z0wRrLw)^E1~fdN~J|gl!EC;`I_s|@hhI?VDfm^ z1A%S=$MB1<1rG$r^oM%h6K4Tepv!HP)s5>(tg}>pPyY@m{Z~7WYb^$=36V%3*&aaw5qn7dASCyjpi#C7w z8+vMM>2iQ^$i)jgJUam_!FjF>M3-GcLoAC`G~ ze#$T~M27>Sc|kJ^wF#y0FnS!s9H2#aHjXN(ip3Dd0G%5)bCZEhKI#cqZFe0M4 zJyPQ5hY%QA#F%gc(!D}tUPi{KuSG?Y%rcUV@(lSVDaS60Dxk0e3kxtWd~W=I-d1ZZ z9A`*@;pyx0+i5lxhH1go&p#IjQZ||+nO1lO&TqLv`yoIrbBAy? zOa;wT1n>ZPO;##WuJ9;v-js09iY~(pYst(bT_(7d7~C#`7C3@-@o4K2n)uu6qI*(x zjK^mBYUtrp(Fa20Mp2BYFyq5lK9UBxoyvSei{VzlPSrSJAv0+d47c?xt1&&eKk!IS z7U-Y1K1KcD?ymM()LCyvyj)2Vma=SgRB;U;{Qo}GFNo8TkfIK9o_F@e8)g&MWY}N( z`J|n#9m|5J>@Q=Q*)MkLBkNA$&p(|fb~4NCcIq3|3oT%^@!lfa_kQqdtk-f5@$c4(V`D^#D0{?Ir5KBA)7g0n;1LE7B; znL%O38*LyQAbQ5{raz)4!-uMFT8mX1Q2hysaYe0@xOu z{g5t5csBAy%NANK{g&KRp^UUU*X6p0JSsOQl3U7Nu@MwizbclUr#fi48nBH|#bM1i{7!0wtSXp5k%;cH0P-tOViu@e_iRp@USlD z)A8mmy$|l`AeMIi^RARU^n3+JaibD9c1f^~M*2FM5QVqAsShRc)BEyt2fW_#W-}%b zY#7JV5*@qf8I&+sa`U9hcJLRkg}{|EC7f^lTkQifWB}#0b(W{gUe;b3!~B?{1}pYZ zZ|APilvYLg)9zR%@~tdB&#lhw6r!;uQ={B5YvJc3!||&N>{;$Dsn1b!6>TJfV!HU;v?v#8g3V9T^$5tNnVM9M$$>bBGOnG8E2gJpr zlm{ITwt&NBLJxGL-s91oHG&}-rt?dZ-^&l;C_bGjA!5|;H$nT6OdPd*zGxUWjb!+O z+sez??;oE^@{T{q=emn<-O$^dZbi{`Ba^_zl$C^(eQ#mKOgdv2ib53R48%vPAnN5KKBppXFTNvc0ZZB zSg&ljn@JCArTX^(S%DakxXe5i{3IpQt!x%5Lk<<+W#b8B2M#7D$2Pz@TS37PoJ4V@e53J+F*yk)Thu z=T&q0Z8vyPAC?YIqv*SwcX3uCP2@Yt-K83!)CNDQi;XlIKL4Ch35`B5iZVgGA|WL+ zXuDQ=67ftI5F>aBH+;`!aYXBTo>0PT_Lw!1{R~r|P3>68KI#PruHPws6rj_8gDv2A z62#jXSwSs}CfGCt>lG>X2=Y8C2xyiERf|Kcnw9}i@QTI7^jc(YzV=?9#>KZTq%W)+ zF#Y^7mRihftWJ;3C%9NpnD>a*V#k(jEDr9>0dB;R^CWMYW}9ldXE)T*1!Z@OW6O8V z%fDYhYkK2ZZ+1c0-g0kc^0N;7>itZeY6uG!h2MxXfgwvu>|C(g+RHf?v%y-30eg&$ ztN>f7XZVOZwqQCv+C$#AG|1O)`{)>ggMt2T_18}cFsoX(KzJ-9r{m>LlRWxbg?FDa zU?H$4etbY5GI{v;b&0llno!$-luvbjP3=l9SwpIg*H; zhLwu~6v>=I`-|NIq_ibNa1Pf~ZE2MjY#a92hiLy9g@P3~2(K3>xr$Kh{kh<9FB7r* z1{N^;ahl9$`2Kos^Vg;`K5Pu`%$n@eSyo>p_% z9ogW*Rem?uZg4ED!R5q(qYSL@UMLKq_vZr(w}Ur!$`} z+t(W5_z=X7y|j@jcLP|5l5SC=DJjtONbk z5R>FOY?uM;?rC(#1w5JEb8cvjvC*hnU}ws6GN$*P{>NYaX8SC^Dw~oX-QRw17TVB- z4-E@`xNg?0PpTN~E;mOPNH{uS@-B3On=f~HmUo4oFmXbCRKy611CJ&B%fQKs$TL51 zQ%%d4OaJQTDW#r&>7CYSx6tU$>UnMz8?Y`mb_fporG85HKBt6aczg@+he#XbsK7vn;(p$ELro%Y+d+X{P`}&4TKzh~*Yq_8*W~dHT$pT}LK`Mv zctRzm;Ux9b$WL$Tt2b?#{;Yn*2z{w~DEA?5cEPO={xwfiO}NZnTRYab9=c&?-pTSO zM7@i35d+2b*E>ar+z{>qzURWkmc;(r?Wi=YxDSivCw0v}f+z#AtKu;9ewZ8iJ-< zE6va~idE}D3q1`NSMnEnjcggu#X3joN1RNx&AilOo)&@cPU|1^8ym^a7@0YLd|bI5 zRXj4QQNK)E%sMDeqok(aMXtYrZgjOA0o~Z{> z#w@RCb8K@GCSbkNzNfGlcV{Fi*Q7{xVz1idQt|$B#`YXtj*RPV2H{?NAJzGjNyCVc zS&;#H6hMEFegAEiIIabqEzl5HgWt)DP#!wFBMfq1#8-$ie23HR)O-6_ak2v zf$S~=<v|02Iw$L!c2r1r67aKcKG~`GkWSxJNbyR~ zo$uA4tsBk4ZwAY@v7tosUOk|MWZ{ms6N?SkG zl^_j-0}yCh$peP_pAt0=A72VfkfObZr65f>T}WH!EF-*FFp^d+{i0HRH-u}RJ3p~I z7+y)YC)ILt)x)zk$gt??+IW)cI0SNCRo*5v+pKrKTxolOgtU^CQb3M?94y%JZ1@o^ zfpyiz(Tx6qGz6Vw0b<*7=TiEFR$%=xQ%wOeEJSy=8v@YB8}1SGVmB`_jf;p%G5R@X zI5OrPBzxrfL-W~Uq?kqz3p>JZ9zy&(v|`PIMZ+@|%{7w6O*Fd`ThfdGs*mU$!Hl`y z=LBmHn=zw{{i7tY%Ci4K1B`9vJzsgh7+X1x{(vuftqM2nwr8j(hvHrVIQGn<`H-d= zv#A)ZT$U0Y!F~}S)JoxR^ll-cOit65*GTrs#Qys7Xm(1I;LEY=wSF;7RxFo^v-2o8 zpI6f5!2G zBF{dAxF)yTx^;fSQADKlm6`>+qYC_HC+h=!)b}v|_OAR!WHK!$a94>0goHBr4=+~Q zmZg^m^vI>RdqTH%^h-+?<>>Q_j`m*|8WYIx1+8=iNct(2iL^TZGH@8sD?qpbvbrkW z&b=#glk%y9P+>`R6I+s74Fg)9wp(+Yyx2!ljF34*{bn8;eu8KmpUE=#v@~?6$sls# zYHSo^K9QT+BzJ7$%AK@3I2<3BJiSPcdMF^|2=Qsv^FJ@~H9PDQpn;O9xh=JSb8&!L z>jB!s`(9DM$>rOjUm zq!Y*Vpfb5gGYv|X7`Mvj9zJ&46q>DBs0M8!-&;(N;pzc0#>8-b^#LP4wXTD2Xe&I} zvshc)Y3XsIuCjRYhOCVZqQm!!ANlWW3~=iP`7*c%TY}x3GksJJDWM;3@)8w5f53kV z;djy`%Pn}KPrSiV|5o$OevXnHS6&VOG7PP57RI;ew3w22C0O2+JAid z_lOd({i`XKA*iPR{8Lw9X7VV&^(MJ+w%7-*KSOf?!@uE-%%nOa@5$ zgO2KZ4s4uZafV{$`L%3ieyUHGR=|CnTS1x8a_5!Kx!Ap;0>Ah4FR6sacWUJWQWfew zLmNyD)n{)e>;?Kc#Zyle-5E_r|wQ9BRYhG*e>4G<+CY zv~QQYtL)HcTu;19^HuF{PG7x>H9dYMd-SUX+3KH0yWksGbR5Cbx^)20G>fqE-|qQVKa{Up&a z1SvWjFfkQwDraOa9C#9EN-i99S-x)7Ush^irzqfYU>a#WidY{iUWosfOWC ze4fnm>BmsyM*=G*JOQVJ?Q;>yv!7Y&y=O^5*C?MhjgxuZcrut%>T_1q*QtB|af8uk z!LI#fr9o%tA?^zOWA*WmY8U(}TpyNS7v|>Xrhg0Z<&ly(d&YzO7F@mkmE@{ICp`c9 z?QcFQuj57XB;*u!Jj6;@r{OZs1BDLx;?4>y-t$D@n917nG-f22n$~)Mb>lSEb#IT_ z=WC$5`%2qayz9l|_H9zHM-zmgHd=7#6WTDmM_7(Bpr9bsa=XUpDJ0Pd5xCou{{zz+ z6nOCJL^=f*TmIuWoxmIVBES9lf6JxE*DD6g$iW)prV&_4G;-qq$>4fQBr>Fj3Y|%m zz`;sg{+uCo4GkGi49<^_kykzrk5iRCRdrR(d6#j%JjTVgYdT5<8deL`B^Xw2&nP*+ zlnU2va^n00AuM}OvaIy`4*v@TBF9Y0KV8yt?-48Tf1SO)g2aB}Mk6@>=fkhwsz{o|9JoTqL*%9dBW97$H2KD;}c1)Vga_ttTA6T&Q9WbWKkg7@Z~s@mUZT zZD9jx5!xpb&I}9_VH*|{kVnaTin0Aarb?1`%ZNs^dqysVJB^Xe z>fU6+mz=0ln#i-v9VfsocJoF{3V@ynB|P;@0L)b@D?3jY65? zGTachlaf+7H9j}MW!Um__?2I0$_s^Wb*0aE?YuUYI2alD=;wJM6$pRf}$S+T1d=j)FxQN2tz7FZPQhv4)@T_+rS^ZadqTekFOk=7_`W=cN04$7k$#0X}2dC}sit|6I8z2=DxC zBN_66gZNiUJq^ihP{TCJcd|0_&nXZF#P8F{{o;#| z@z6;GQ9JftZ_3B!o}MYBcVP|GDD9ERF~S!5L$^b-ef+4kqJXUCe_u@s+&2~Ro@RGU zjh!9!*X1$L=)f$llb*yZfdqM$_7R8g#SsLl9I0rsLMBdb|AAOR* zIf-}s6Ntj$z*|`SJ!AIEe_sE)6vRW?>5w5_(a4uOuy1;}tDDty^WR2>O*_B9?ndw( zt5{KQ_>7_J1)<-aU$I3zXv}lNss@1l7o8RFmJ$+sj_xhWPwAU;@}0phQr(xg&_TyR zB@xrJONobHo||aiwgA_jc(&*O(P!9|89yTR0yEwEyPBU z^50$ZxrlGmVJy)t-OR-SCRD^YE8N$M*G9^1&lo5tu*?~B975$}n{_2PXp#=0_jysH zA7^+@I7f3ebnylPiDXD+8$(CR9zC5)^;u~=aje8CAS8MJ^OsJvQuV7ZpXie z<{H&!)kW+t)`hLh2O@m+LkRl!`33cR5cSb>NyQ^!|Eep&?~sYM)?E_<;8{nvMp(Px zGm@=y&3KMf+u9y(DKyv?7H0({>JDQ^B!~8mc8$%e94Yawqsp$)neXlhoC&az)p+YN z-nkvXSge?5E&UuAn*csc38o7{s+LOHxc)b)Zh~%RJih84&<^-FYamI1QxA9WI)qf4 zW&?V4U+p%GqQuoXU^d`)t9lnwX;VLFD<2HoZH+q)UZ@83nRE~=ez)YXmX>81htTRK z-|ZF8A?Jyb{h#N+Xnl3Lc`UmB?_yRU(>G=m+ZoCE?0`dxN|fnc5MKVFbD)_{*mlMZ zotE1r{0$eEQ#QaUP!Xw?kx#K#c%UhJQ<)byIL__&h@<}dU=nQ|VEmj>YMaS{m%61d zPCesh-`*=3;)KE|=H_6c*LaZhm6g^o6y!H6)keSm_bsX-sJopx%GNb=j}2VP3tWz&n_M|t_N zRRc*ERu@=Z_X%F&6^H4>XJ;h)v;&k>2p zFt)lx?qg%_&yKNmnonz`AdfQpYwVn0xMjJ$4a;3|8>=f5*{$v@- z{ra_e(P;(@y1;LL)X}SrjKxwP#+F;=dGIHi4DwU-LG9q_urO-O>W(cwhfOYBN$n@l z*tP^DjgF3PMgh0vpz_}trrXdPK8&Q(12xO%-xV_pH{DRO6M*U5DesK!Fx3}Vd4d}# z-tZ+|kpGY%xlKa(ohKgORo(X2q1H!sr7#81*h(hr6+P}e00BGyZrlp41#>a zn(#AkojJoxWZP;oW}j>5kN zL(&QGeqZmDLP3yFHHSB^6(|HG@<4R{7eG8r@mtdecE=ekfJcy&@3x0UjAZG2Y- zVFp-$i^THiPI~VUkR*qF{66j9bni7Y>{{crLh6)dZR1||Y;S*dA=~r!zk(6COn$!B ztT1^APBCe$)xkkl1hD|bruoXG9>i}YnD4WrERq}KBYa^_zXPvB?wn0Sam~P#t|!CY zuBkvy#{DVukN-_+3ljsw3;?TQWep8UYKR*dD8Tb>+Ug`%Zj^aAc(|88Vr_b4;0|?O zKj#%RYzq1y@SoewZy0Vmtn6-fr_geXS(RG_5N%oIWVuB#P(rAU)O)|D zrrQ~YeHMk87l53^5*0abhw1FI|6*I)nY&wl!p(M2;48o*uOUj9sLOAW`Q;VXs6b!W z$SxWr6O*fYd-0^)d_4d$=QASSi+`fgkwoTvz$MKdW?^NVXoq?k@xx_z69`3x;@i{; zp$k!(K)IdoKp|z;Q+SkU8RrME?UqHgcX!){>XWHm;dx=>0gh=BR%~jtuD*dI`Ri*z zU`P#Jy0iUs@!{Upmgwkv){!(j(bov>|FLVM!km|5+IE#-QOA1EVV6%%(1a1XhlTdA z`9n#95a9%7aqBk*sV^#Ve_GSc(4t_9k5Uu2Ot{Kt2rYd2q06g7?^}<}NP06lU{qYF z8tHj*+{@?4e#Ok}F&6RVC4eqOyxwxg<1(u0^RB=!YDflq1!vW0?C9jnp{7>#$S2sn zf0mwKE#-$*r=+i*R1Xi3j)%qWP|4dN{kTOE=`ZwIcQ+Sfpl-ly{bzF1*pgbYX5JNk z=aSu>J@3cjn@`P9~v`REUSH)EBqU`t)k)ya^2Z zs+`)y#G8(%hRyAmv6$|qqMk+Zsnkek)D<;G=p;cjbk2lZ;yW4mUJ%W zg-eJ_Tq1BlYU5+Ryz*>PA9Nh|EC>U|-|}BJ+6B95tW^x#eYC+&?_|`tE%uw~Pg)qb zBVAC*^OBEc(nR-ansqLt%biAcy$4Ch(|yIp8T4;YKC6DQz5g?qbRg7 zmR(B(&FNR9>8KhW31>IyLngl>52;4@j3HO zA5hdr6Rzq`Bg#5ghhNw-f45lrb6DP{`0xDX_t)2uySqqu;%Lv2#1~c1x_?&+moXmk z3eS`ocE>AW6e-E@@^)&!1rm=7&*XVp0x*=u`p&;;RK$zMEr7~c50JmL1v2pR(s^7* z9}Q=>j}%`g5TqtKTKj$aXg&f-yMW@Kg5nfi!s}K+%58%I1tAxmcYY;5U`j79;DLAE z6{h+zn(IG-TP5B2BCEBfhqnwhqFx?(Fl>_)Ay485U^W_Tm6pfk-_?RAOE!%jf#*_z zEdVrov!_Y!uq|%|WPq380Z5L8rbENhp!WM=sk6fexRd}T&ZB(~UzrzLDh_sxo@Z|rtu$i>J;<`K{x z{3^L;1fI~sK*+bTA;#ZiyI@J@>njPn{Or2uXrUY3y;VrCc?GSjOV;(d0*sC2Rbz2(CQ5zs zI#3@a6!G3?8ArS-x{dsx)H5+%u-fkRcQ+=4%%f4I0_r~>xS$MrwE2O-c6)g_=4q6N zoGiW*Uc=-5uOHm1*N2KLMIAz9fc^?Q#rLGiHQ}b(13*boboV>Ew{Y|O4Et*NvXo0k zICt-3_?84YAo`d&;K7Of_OhRJ@^G7_=$tzNG|;>ax?Wvhgcq_3u&JzsE(x51w`7{K zt$bm`w_iSU)-?*2mUKEUGsdA3G~JYu8@E>{TRk!4PB(isCvZW{xG2mF_mf(-l<0EC zZ4?@BBxUl4`e7yhXtNyq+8_XULkPrE1%W|Up63Cl z_opp_=n`B+DQ@8&-7bt&O90W_ESw9TwuH!y#a>R3`%)?SE#Dgl0|g4}_nZK@wV~0# zs@}Iax=4i{;}r7dBuB{zB%1H5>AL5UsfUVh);sT*f&9!~f`D zVG>cE-QZ@|3{EE>Gf-m{*O0Tqt|tkdagSp@V@h`oWyt^qnjO&Bo;y)=^^9~51l3zh zEqA_N={%&sX5`1IE`+JOvXIj7L>ymiiOz_hH6e>KX2tQQpUcri%ew%r4*Sk?`W zPTmL%=i2{#s}8_oHv>EFz_p$+m&L8m0WfbizmHt;rdvO$=i&+Mrar>5CU`5xNC@T^ zhlxY$(PBCd8bL#ZM1C|W|v*{Kk!G89p~Ko^>}%n z28sCYH@v_HRUmQe{M*TKhjc?BtiR6ab1UBE3gRD+n}r$IQjYd*9}z3`~E~e>haHZPi5TQ6QU&+ zmYF;x?H8h2jdbaa4Ck0y$o&`TmqjI4J)eN^6)`P%hku5K^S8P62JEpIkUYjK@X`7Np%xqTtA!``u}T%gn{!d24wqa3U{dV4|3s>l z@^P^Z%l-xp0cOT>)o&xE9UaX?#p-S9N74kbS+}{*D!*UYEUm;iLBHV*6g(e4*Vv_W z@`=13mxxJ_9~zGWDNlR}B|SrgA9U>g1KYU7Y<=Yh;QiSe9xqT19Os}$x5`xPLEuKu z(FpuqDX@H!PMs#9V+xsnAc*`ES`lH{n=@{W_vx<}@)=HLbc9>z_HL`!!obs0l)z@S zBj<32;1qazI3rUGhOTGeqGBB^#v8_x%RH5C$_(Mn*eA?gu0E>lk+Gc5=UXgy*chcM zG`8+(FwJR833%@%*uc#_2bbeC`mVG6{7V*En~botD=_ldHP&$ zpMfvncH$=06=OQOy4hv5Qd4|RsBLnF4k6#7)YB!ZgAV;JjD>d5<|h>=etXqeotMCV z)NYSoLBAUK_KRt*0`7$|ebU))nDT>p=`V)X6i9k?)N*8uQDy4i+qG*5OIyUof62tB z$*@C8cs{Pnpr}pbln!@$O%Yq?yu6+2SeAPWFm_OmNkZ*CbXF#W5)LL~$c>KNdO&yR zzBx-zB9XvYQItc|B8U@dqW{{Xj;XLF0wID}>kBbUdwaXyxK}A|#C8Gqys)g5JOflSs_DR6BTCSY3K#3#^g~5vI)CkySIs_N310^X?tHycwwhi{REo#F{Rv&+{=lO zw0mbvt77OkC!DX7N4$G8RcPY6Ai1Gc{Oo*fN9%4RrSJB~u>h~}{F?N7Tob`od9cA5%!SiCFc&%|iJ3ITi>8&OfT4`hH z!GgFfY1ePv`<9c(mUj@&s2xgKrd4rCcD(04nWewWF1Mu;`HXt*8a>iMFt(UHH$9ntZ-I3OPOTW*xl1lG@ikGX#rM9*o6iAr+iUapf-hTO2Cs;fuP@AuQJ^p_yN zcOv$o<u*RIBe)y1y6afN02SZJA%JjqBj3-ZB;~Q5Mo=CxX=`Lhhk~!f`k- z-D&uOof)xUNNtpR%W~w|a@#0KB!%p5j^rFE&o9(>;cqO2c3(aJrM%quRKfdkmwjyw zhoS2Ru~Jcysu1QEI}z3yHH<8B-QmgzcSOzwA^ymi1b;FPGSaNqVo36!g6|HBgw(v#CtO5r!IQ+WEZP z=$706p4a2t%bUEZ12{4$*Z*QS#j@2hz;yjXrgZ=BP!tT_VqhF+lX|iBH}rf?_YGV; z?i(M!zvuE(!904OU&u36g2>C%%179dp z(&!e`-$lfp9N#%M$SrjkJcHN@8!nN_4w)D|jK&+&4l%K}KKJcXbxY@VCB~dj zSj~N?hNCZY3*CtTzYI`P@}E-quGHjX}pYX%!K2jJ}oXQVs1z|hNq(LiXec9qBU3C+y7fi~8WX{^E z-czAMxNiYxujDOhN)f@Lx_bn*AUFQ0Hd35VILM=xQGMEs^B>X^MN@y#e@ZD+xAa$? zADBFPePPV=(Drws2KARb^(iz~@h!&OHZCG(e z;6%9c#vi$1K-qdApCiHjgF(egr`2`l^3OdgXp+?8YCPM{S#csGXy&kxqm=}DEL8fZ$wgZ)DPE=r5F_vz zuk<$C+HSJ)nda&?pE?~Io9^Tj7XLr4-a4wPt&JC!5|I*-O?P*Lw9<`qgLJn@vp~AL zySr1mH{IQm(%toKJ?FgNy>|@$+5-kK=bCFi&#xYYor3&vSDnJXLi%;(sDMG)HlFv`77vg6W;_aMM``Xqo&v9&10GqvpLsk*Z&j&7-RYM-ROH z?Vu2CM#{Kj*b|(JLUV}CDLRR&it{VAyXAnZ)(1_qV-5c=<TST*9)?6AHTy z#T|Mo&-PDhkQWzpTdAe}NMg}JS1grY z7vwur^|DA^pr|OQKVM9)L4SEsZRY_^Q~>(i5Tc9IexrOZ=isw6N=|fMi{jE4M?R`OnPJ}AL8(7Q7cNxRLDkIn#3W*VV9qa-4n}33q z3vjrWUb3*Up+seTe3#I7Lw+;7V94X_RjPp)WsA$h*y7s?*^XM5KLv6ZE!GlB+|9*% z^v-^E69z~3XB_>0GnX)PF1N1@x)*TS zdcRZJK)!1lNlCKcMr2JFNr#tN^t4gIIU9;t1wg;jYeZ|gW;goEmP+y(bxq;w-9FlvkKm+1)@Hya#z6KN( z6k<(!7{XpmL=||%95knQY4wO)y?#TVeT7UcOhekpdWL9;eM!HbRryJIIrxx{&(C9V zayGBWQ;?I(QwaPHsyp4Usk$e^*RnZGp(GNNkd;N5fp{^?zS^|Ss;_rAGuksAes6z&Kd~rAMFhuL%0a#p?LNTpz8{O(yD4}r>Qj1GKUXK>(rJ zT7E_bG$>s&(~ZPLv7+9iEIiLD7k5{hGX2lOj7Z*jZRKvMghgbCyPG2eM+Ccgw5&RZ zuLLY5ADkMTA`?z9H{YXwU^b2SKgC_9zzrF`KGGZ*Y~OU}W#A(xU%83@W7n5_0_0L;4ZyvlLvVc7$-C-rYO8k}oy#=UVz2B{)j0!jBNO{vDRklp zt0+&gwf58$N?HG=>FVZa;pw zCL#B#zxb%xY{7YSf;Km(=Ed*1|3Pvif&5JW9C}xX-~nE(6653oftMoTbe@o!b_SgL zYO#0Ywp<%RugUudHDrGJsoL8$9PfQDewrGn)6K7R)Z_>A-tlW{XPk>wU#rW`(9rwGLbNPiyyz^8EKul4DOxfB1pema+@8LJPrU4 zsMvkX%qB5z!lcU%zhEQ_=lozRyW&z`?)?ntijcEc-ITZIEtY}uhib;`!xsWx z=>VVnlVFpxT?l=u3jVO?ifyrNajj-#g87uXM5OOgIZ*pZm=K=Fd)`$+lKjpld5s>P6byeVf zq(+|ESNIueT;tV%PD3}oX+ge4RbLdI)kV?)$bwop3g!*P# zgah>1M`nu&Hze$OB*j_srp0qLC1P%N;t!y}am|aw!5N}y;Y@ySuj6}&q+DnyKV4}5VjVNLMb@LPqhSQxAEmH!#Q6n$b_P*n{! zIe)q_1*2~dE_`h9N?Z5|abIDo{2Gf>zHIJ}OJ>$;R-1nGg6mhgj?^S*u|*Ea7yy1o z;Au8cjog?}!$Mf}!@^_uwQA&tf*eUWM4nT4Qp3T#oGVzdl8RP6X_r_b=kKys>y}C5uSczEgjFOhGwn$FkNMUV1fc{;b@H=*rlUX~8JKUrG@E zBnKji0dfg{w3yC7(s`<@Zc}cTYxecF9q`>)SHaMc>3m%}_9txY@MP$y44QYJw-Yr) zKS8fXC6+_fHG_-tqD4JMHinb@whj}23nkhfHwi3Hwq?a(@##;6ejm_%IHjcGh2r? z?{s;~uMWo^pCV5XEKgy#+URd=enwcVux=&VM*t5p;XJKwr*I_jfAN4nF4q7w{@{O1 z7k`&b{C%a(l3+cK$C)&NY45}MpiW>hpPF(Gtz}WyVC$4|QNk5J8_$|2kaLqb5w?=T zF`;xV&aOXYQEVy~VUiPd*`3r^^{iN3y<414{Zx63Ud?us_k?@nBk$HBKp)Z*9>M>m z=WXkOBG>F%+{0xrlKRY(K|A&NOKCajSr@B5P*J>nTh%v-GcYC)Cf&K#S0xrUUx0`E zZEAvpRAQ4`=0t7$y}(R7aFzN%>*fm1zN+v1v^|{59=TE|vtg*GC@uA3yVBw?IBOQ-?ba@e8 z`pM%_)y6*5e}om9a^yP7T`u|d0UGxs6yh4-!iME)mc6`r_k!^A^<=Q)a*ekS z3G#;gPk+BY!@7HB8;(e_H>Hk2F;lNA)#RF5-H7RGj5sa?%|wUWWuKRfE7+q~O_RUHqz=z6neyqAYn$6w z?-l1Fuj5SlB{mY3nc$@~`YOnT!?3zU_}8}ev?ZvCE?Hdj8?fo-{sB-S?~$*^3CCZi z-x&SN3H{?gKnnCKs2!emeBRs5%g1VtZ}pmVW=Zbu`|>L z(ebeMI=c$R?MZCuQj*Rk$=HyMsY%8Rt~C!>0ezToG(5ASN!WW;)38Q(5zJJBQoRM| zL!cT)EB)fmsxR0CoU6;c-Rx%{KDiu6ELo5Ug3uF0e_}4M;&uR;abg~o$vRJEek=1kg#>Fnh)vUt*zT}Qe- zZh!X-v>N#@_B+h)Z4HO6P9^MaUt1qorKZHlOq{py#vR0VyN4wPnhzOBAF?Xg)R>h^ zv+YdK-s$Z9zQ*Cu6s?`oJq9?_uLjQvA6(7s_}RS6jfR9WkhnKBn&^JN`TmDXC2IR4 z=B;=ZwEBl}{e$Qbu|Zvr?ls;jY`bUjVmk+o85e|)Y0GYL9(j<5NbVY%z)?)kr%W!3 z>#nqMWg^}925o6nE0H?h3I!n`eEa<)B(5>x*ZzTVXZd*-Ak}ZyB}C^wFu`{))Sn4a z0;~4)+a8_UJ@7C-Yke{rHTh*L;<^T{2u1nyy{F_X`0>+8SvX`Ws0|jQR#8aLIgjlm z^*5$6x%tbXdM8DK_kWnT9$Mu(F0GxJ&ql?&j{+g7DEEEyBrif}E3z9!V-OowHtlur z@C~c%?)n+PI(hWyhiIC#ABTTwFKFgAR<)^_vbP~pbw~2K@4_7>{rAw_1MwRA{5k?e z(k5yD0-9=4EdnpcP{dfAMR#Hb;{92YuUH84o`W2r+Yz_fJlx#<_ytf+_`G>E%vC%b z38f5s=ZHT9Hh8RK*~j}(-w)s+?n43?x2{~p)PVyrIS2IH&zcyhi%Uxb-L#ioq7+bj zCU+JV7Pv{s!X+P|?bqTT8s;Z*Jf?J8;o$xV%y}wK8A^V@r*QJ6akts>xv=LCRSZiP zfSy;X*?ZnjNR>p4KM}UbVDq&j2&>qSBn#nZwEGvD(0sRbaed(O^Q+~e4N$l~%oh&3 z2RP1KEgr}dCSqMKE~|r!E>^3^x1^*fA68ckCvMwkQqp9ObX&Yz{JaIT{Cb-dt62(z z4N8x#PsS~$2A1X8)4t3CZ5+Fp2dxFLZ)(XxDD#uu00)bU%pOC<3; zL?l8-)}-mCE>pFV(}|!mg?Q;vp$dtbDX0)m)cP?a)?G^T`!_ z79!z^+LOJ|7+&Ihc(S`&0E3^Ftjx>96XVI%7K`WE)neR*C6L&~NVA1q719j^A(b$T zajtcjT{#+-6dkK#Y!e8W5IAbNU_`a@EQ_`af6SEl2a9Lo(e2{_8i%F`#5_=sP_ zuQH;oUihmk6E`*kaK>_}x02hvZo4YUzE{1v*B2r^g7ilWp#Hw9`EMGk@M*Oki0W>A zaR~+E&AX$dHD?S$7<7ddUn z9>R|d%{J7^mepr+cEL?*^Nd_N=hbaa z14~jSsHUzfHWb-Qq>$1Y#|_*%HnGH?I( z&d96sZ|{vf&2h3IRH33Yxti{@h&~^56S_f?%&IPM*!gKF^wRObMY?TqzDM7bG6?nD zH&>WDq<3tAuibz|d-cn1!Wd)GmeDs0i-Rwi_|?2ifcKN(+AX%ao9`Ewq@83Q-3uf2 zeren61h0?i+xC{0%zFnEKQY1H7#MJEu^~P*Il)9<6Cu2Ocj4>!chJ8(8h;gML1v|W zGj%~#HG@=vA&=t;h)`@S!?X|yq@swD-AynJ9Cw$Vp(ICqNck&sI^4}7H9BhF`42Yv}_XVL_ok?Y1+(7+Z6;z}!dtTSGB%BlL$t z|Iak=N6Ui#v1`>!)n=`X+VgFusE2hb%c&7si}!ZN>t(8yADMm+MWM2m4feHURO1|5azf zhPAG6>4DR6RsnmDoY^qW^JrP__q@$AFx zgnp15uV@fs>!%N7o{|q%b?mX1a{@6QQzmv1IdR|-$K{)sU#HL-t+Zzb6^m6N29AAV zTHQe7$vZa41@`9-8;8hsP;iJ@{@qmw(Mb*4)_y`X^E)slK%MMqz*~qkZ$NK}nE6*~ zMN96_`7kZ27!Ps|qppJrnyLQndE4Y3h9_kgKWhx(Mz@0VK!uw~^mz4kDfe^atfRvJ z!U)z3ql(V#EZdFc=CyHvjeHIYsK+?bLR})Kl!oI7Oopvp=JFB@^`&=xLR@_i?v1n&jcRm2x-RtZ?QTtab-MT z@{fW$f0|nu*=BlEN!!BOGj}as9uXn{iTeG0S_mBDIDD}=TkA~i{kk0M&hPr6E|ScP z6{lK;h!BJKU=%~=yl>Q}*$htL zd%M{jth92C(8NISEE`MYW`^Em8-9BJQ~ahVq4GH83`@@Jml|nsUb)&30Zq3N`qVD} z2D4S}%e1Kb`0@?D^O}t&AL0H~@5cHfmc|IT5+A(7G6YGP-N~=a{+SH4MBpZ^TK5fy zVzAzk&_kZ(zrSBN8q(K^PYUb&uWzuE%H%ff#B23Wl&MJ2Elq2{ktOX1vxI{L#%F%sl~Zl>q1ZulFh=u!>6d*l<~?tLzSEFCsXj(X1KFR zho?zFQyR%(Zd^2*eZu!@7B*KzeKMs$9o*GFbj>TMILLc74wnCag{COG_lK0986rh3 z*|*{xy81uvc96GlF7ixuqWWp0QVu?6E8$Pbo~3_df!8_XB^WuxlBv56+f%ApgKxLk zjEd7q^Wa|XdY&6>!VESdHNSaumJMq}QiQkp`K5-zlxzjt7TcqDkj8 zo0XFfA=SylW-$1eAsc<{5Y8AHV*XAZlBasSggULLSl$j%Z( zPaJUJT4*(6934R!DgZc%ZRiX;En5&!C0;BxjhFVw=6;AY5fe|_O*uYJHkxVJieTCT zvP{26(t6V8{uK8n-hWjPkhcVcVS}8ta;Y!G|GNo(5fM%K0ZQB|4W@Tzmup7ku^p(- zq$|RY4hdm%R`~^nct7LI#$_;%U`cvP#piAS!L-=*+zKUS|I+Y4HIn_NonZ*!1mWm< z?-6K2k_D*giQ$3$g$BuB>IgrNnL)Mjma4{2E68_AU28ssIJc|5!$je--_q?sE;WK7 z9<7_k&(P=6DFkBlh^58Oin#ckYddx^N1WEs4qXI)X?}?a8C= zRbI45&+aD&?i&NDl(AK-8<{lW%k7FtlhFG0vcQax@SO0Wa-=w!WxR}DJ*eX(Uhit5 z%ZR|7HF|6z$VMH_v5Gwe;@^W)YtOfX?DCG_YA;CV2iyHRLEBBa0 zB3kdo<>U%t)3E}suCAty?BnJdEib9cYA+qk&1sZXRU;IjeQdt-F@|4!(}wX_bd62E zZA(0=oZ6^mi#F@TCiczkOfYAF|7ioka!ZSggE46$j^((Q%G0*y8@)^UHV0EOkn6Oa$DbPe8-azEDDViVnxk;j80jbIyiw2Qx3>m_ z=wad5Zb*Ej2!V@vRsqYts$+M@gVetQ z`+0I5o#_)0u7@!`w<0Au`I0f%`-6yx2=f<2B`DZzAqYP6t?_#2EmSQg9EU!nj2y=T zj#J;LAVZtN>h(FRKzRg5-KUuI<(xG#W#SCHbG}zA5&wapxHq8wp5qO8i>-{UEgzc7t=r82@Pl*zU z08LW9Ebp0EU-jvijr!x4r;(9^y^EI9aP!qr>3!Z^E&H*g0z7;7M>HLNhI^t8NcB)Gtt!_#RQs~h#+oG<&1T5wJ`LTRddrjCrHuw4Sn z`{Zo;!0t7s=2<&vY3EHKlM`n|3fOtM@kieXbrSaAhuf_8f*A&KlL9JuS@)UPl#0$( z`6p+$X=AH?QS}~=f*Yyi|KAhWl@g;8~?nFTK z^N$g&p4Fa)aC$(c$DGyWZz=F5k^B|vmzo~w`~dhuiLc`Pbbn+bbn^i%U!1*AxX$%x z-w|YX`*=DkvaYn9d=>DPC<+UdeI)XjNl&}1LAHRQnRmHQGBmRy-W4N^=EX7}VB!aH zX$Szw9s}w9tE}G8fq=0(FW;37-kJia65AU6>|z$+d)R|Xx5poxoR^Z-7nhZiMTvt2 zCv|+~{nV>?+NbSih8}W}MHkS`-QO5gkM;D>9T$v6P)tDL|7b2)^uCx#38x0!J9W9U z@^Ft3^L&?@nk3tv@`Zy4O{r(h=Hlj={`on8C5YOc5v+$HcWMh7gi#W-bN3kIzrMdr z2!{q7xM@>1F%!aY>qrViz<2b+zo%x$Z@@@x5&RvS?)s5f^aKyOt94>$l1RCW4U~e{ zD=`$p!%N@UT)bxOe)}Fc9cFdw2{Ov8W9p&WujPWp1C!d)32#BXpWsJPRDXP`g-l3B z`aj!;4x%^Hr%FR7M~rhI0dhe1jKLslOE)Hek~MGBP3k=tsdp=XcTk3;e)?n*L>E|; z&Ec-qA)2moTh^(F)b>a6K4q`@fK2)jG- zfEVv+FQtN|)nmcX=u@p!HMU)~;jE(~NCi5i&Fm@kdNpT0d%eO-k`ZNL$<$oH03Wyu z7{H3Aas=&mzWq}eFnxXnU5KD2{-28zaz|;v8V*8I zxlcTSJ=gsCXT6=}RmJDk^yj5kh%|C5HO4QJ@njj~UcA=EE8f_vHL`sR*=&P+QX|d=r72c^FiX3-pG! zMPCDl#-`{7?eTW6Tc}3ve$`@6<9kMV^^X2fiF*$8)gc(Ogd~nY%$y!#0;_s|@K5Yt zbM1t~%guSA>}qu@)2A%msw~$hxQcMZ%zr8!ETUIX)33wo8l^#`JmF==+ib$mD2%B!Z#SHH$cCwdxn{m5hmQ)}1pS}Mr z2f6eBijUC$KPQp@rr&(hm@cWnGMW7hc*SRI>;0zYidhr0^>RQZMJL+pVxR@5C% za5zlOlW}dI@q?<$YWJCexafAsUp`(t6as7lK>r077wfw(WuzR>_wo_yT0*k|nrAF-8Q&y>l5rTrqXtQwZ z#AmuK`Q_y@EKm7=!4ICc=Ith9cR#M&l-x&Jq2#ZdQovCWuT1ifc~P7xrL zpIYN!yt`|-mmgyMBS<2MZ7%a2sZ~1_Cr|PWcAd+ZoJ>}A53JCU)9E}fr{reez{ai% z#d#aq-1?0~fe~g(|L$$A5>Cw5&^QfCh}kIq)KT>4ea;Pl(2g`x`ztr}+o0pe$8rMv z6xNs+Q*(vrtiHkSlRGYtn-oOnrQa}TAKEG;)R{J6Wrqec*?e@;xOlsN`3`^EOd=)! zv!;n;UoSmvSTxE04^(JEXnkuKi97D)lCI(j%fsa2-pepO74JMh4-W5Q4ed-(xT^^$ zn%X<6+I*BVvtHBEGS%px7&eWB2h_OZ-

gGE88;JvXiXe5N0%jN_QF1mWNz9NskN zFHW`}6=Br5Bn7PBetb@kvQs~t#@vlBD40-RNb;d8d$2#_Zzhpjv(U&718kNPq2lhe z+eZ6qFNV|~*$$8?F*JIhO9H>S#*YFdQ%B^zt^=Z5E{G8qglA8p!tQ*{RgGSfPdaZy zH;+=BE%%eQo;D}nM^}ICL&&Wuyu~-$&BK5L6*b+*CGi z;osvdck)iG0?R;E<)?$oNJ4b^i}V3>DjwH#Eed8wdPh*Gm6l8(soTyE_Jg-K^sXd%`D4 zd8n04JK)h?ug#tzwwBsOauo)xax=;(_rRS2@m631lQ|1^9~Kk`47>n?A;SN>9AmtC z!JrXnQMXlyMQ-}nO(rBqG5WTXoVDiRb9r8#AFzm2&WkH&C9K_y@Nr?2G=MMC+`?ku zh?5@OT@>VfdHGFJVo%4VsjV%&NxFQJx7sxHa8_KPAU|Kh(ebL^+BCHOIA%8I(o!4d z$kL{Op(3I0ymnFf$m-j`PFSp(hcBJ`@Q{9i#VD_RO{IjnrEuE|_bea@2?rY(T$3k6 z1ZY;Xng4*+yfOFrZ4I_fV2gvj7=t6Fk+scl%|6By+{>H#4R3 zY`J^#mH6daePCO=>lbFUV2cuyV#0TVUAc=JPxc50^*Alw1>^IN3Dk$8el=ZPf+CM` ze?*6L^AK`H^yL>zJwA^p{<1&wvOl^yTT|pxWZ|mV4@MlLhcataK6zC!LN=g50XgDA z=~xp1OuXqH9bRxp&}K5!YEBvvm9w02hbQ|L+l@@O5Bhtu_CxQ~r?Z{iT}7URxy429 zak`D;romB$=k}6t{_Pjil*SDE+n2`8Tez^(5zcjd0vqJ-r?&$mN1k^i9xFXJEzXtM zBrUO_0iJ^R@S+f#Xh$P<+gkv-P)|wxW9T2~k@S(64hOoB-*RX|rJ8_WIDS<2+}JoO zq4k0a8ny49T#TbP5;60!(wbI4Jbo5*PEHY3;9FpZDmY(FKtuUZUnVKw91MXs}lcO_EA~|6z=}Ww1LkW?~YB{G32XTZhSm13i2FpIK8~<0|pfP6IKMQvw3@L zb^=_`+Q~T9@j%e;?>#)7k!cSaxc#oO$Ubs9%6G@!Kawm-FX2;I84%w8v0m^RVO&1tA-1&*G3+02(-uOdvUBRyxGDDA)sTeS|LYZcpi1TQFU;^-#@O$afxaV|xoRXmdstQ$m2eC}Do0cU;g} zyXL~>NL#S~-n`5*9h38)5FlB2*JUX%pqw*`f-iH_KuLd{I~=?o5<3?|ehO%1u&Qy# z{XxdQ?WUYWrP%L|PQ`vq_fwilh7`YNB&T^jB$esxOl)9qRpxoeZQrgJvHD_i=n3_} znrGHidUQ^goEEXBc%T+@EY@t=ZWV3|k$GE71~(>SL;d>H$8f18h1V0~!{FW!|9dBK2aR8@e>nxouV3#n)Wh+DP}wM8cH@?2U_#A=lk{gnzjt zgmKC2hH<9}6(Xj)-59^?7nEVTc8N{i@<^f%xb%g${{+ir$`X+3fYuELR5;nEUhw&| z12dkl-lKNgM9}AR9Zp~$T#Fi@KY1_NCW5QmM>dv z?)}RY-`(}2q3fgUB~GhMwg#ypSDJ`5NOZ`TxuOEpN4PIoZ@SO4krnY%|DAsXco(93 z!JEdSb;Ga?dPR8%zkvkKkYE|`qdR+MlI>J9Szu>Z z1F9J6PA55nv8nQ~kd3T!g@_@g!I#Av-K5D<0>tN*;7x(max`G#ZjlyW%5sAaM<71H4qraTr<=!O3JdmQgQ z>FH>Y(Ko>l1zo7}iVqm1Swz;$bqV#=yA}cmcRI4>X4ztEo^jQ;&E=kqD|trKqibft znV44sFWEy{e>afBvdq)RI> z(5a##P&Om1dFDs_k-Sk5 zote^czwCbB+p-yZJ%DDE>M#$rn^Hjwo(4+e;$m+v(VIMeKaMHZ8tuwnv5d$iUArx< z!jLiFljyOOy=bOpY~hYR_pW|tWQT=hDKuEi_l((+PLbRM0DRJ*a3B8VdjD1mNO!3q zkaeH)w2h|<)HpPjl&H7$cp+1I4vH`kw6Co=MUtg-c+@Xl7h~6l$j_TU@BJ$w!v=`fsPC*r_M5qHHAw#hXs~!8xr06?{|F=? z1#Ya@=2V@eWxwr=YA_fBy+>1;k6LC z(nDv}F?zP`56p}hvl9}_y=^l-*VxqMaAzh&MpN=Lvu=d%li{crh-Oyl-pcFhrhn9j z+_95KVMXVT!Q&^CZ+X_-Wz=h_@uav+wjbN7ta6lyO9#3zA)t@HjKSqrdof^9EN$-b_EbP^Rc$w`8okruB{6j8(Wvp^ff*>E>{szh@&m?LkBCTU<}HFz{uZ0o@qslHRZRVXhi?iaFp z1m*9PUh>v~xD)b|g$A1wK8|tZJs%o$#&eP@ZG2M?JN$neum6W?g3NyHm^2;D>BE6S zO>Kn!kX>D!-Gp5OOV2cQqTxxzNm5-jHxRs%H7vc7E3N*DmS9+ekh?w~c%pUX<$j9r zTbh-$+1t~F6b#X3>#o@)Ta0FS{EQg&@C;w`l~ZX$w`ZYMXABVI8f5mVDDE(zz}~|0P~wq5P3} z`fv~a6UG#X42@Dy)Mjum4C*wCnbHlgt&aU7l)CUs?6hH zKy-VawRnSVm~N_YD{1ImlW`Tx9(lHE9h2t_bZL?>`hP%RO^HZerIISjA#oc_WZ@De zo#1Cjty|r1i;^?(#loc?633~ritLR%?CxKNw7$8_OP*yXc}aG-#n$-;Xa<{9O3k)- z|Ee-W-vtgcejz0%M*ervd$6F!uzaTc-1d*~Ef_pP**W!Rn1^H-TO(8fIL-|tD*f0; z7vo`+1rXy+Q8I&Jb~?I3zRt?!IacvSdQc292K!KM!Z{ssMYRsaz`)kXAj?)7?QjXi zzY+g(X(?LmiPOrv^%(kL^R6FtwTN%q74hu=Sg>qbeN103G*m`BldZX}vb5evQh_AM z3rmWFACjCPJx#4(K&ApZ z`wgM_KTtjztPp>6ifB$AF-$)a;cECOi_Yvyvt|&Z{OG}z z4{u|L+CDwa+oTLeQXgK${5}4y5~H6WLPC}bHSS*;Ky|FUiU-6NrA+h+180jdvh~2Ky|wg^E)%hhqm-J2pq)^^BI>=y0ZV zbE{hdoXE|xi~k}yW@9++Dl>`x4!K|72_J--+08x=x-M`gNRLZ1YD`2VWfMih#M<_y zG|IgIZl_+JB@6uw)1QIsEFUQL^ZbGsx4~&I1DJ&YJ)504EzBHH(Mj>dc_aia`1|t6 zmVhh=+aAe35^e89igkQdNo_ErvhO?PcT>fc5q>e@h>R25?A#GmH(Fqk1genVz8e%^ zkq-ylS8V5dZD5D2$AwH`LiPsFIPyO48WoU}G|d4e|6pt62IK=Xx}{4{t;-@YtDgl10_K&yeo{tXy> zaz7Xz$r0;v-l6e36)32D_cQv1M6bZmXguh3PwF>TaU8L?_z?ROaBZ7f#$_mnE98Y2$;-0EP zN^k-H(#v1P5q`g%a00nA{{+H9ctECFSnjd*$fo&jsUFUmtt>@$U4{2`x;NzK;S(;- z%8Xd#8R-Lxg`0yVT>i$o?s9i=z%W*jY<{x4;+-)_-bQf9z|}VH-YE7I z6=!HiU9jJnZYW`aF?!S(&&T!{79qFV?TgzMn?YC_**BqA`qmJ>^&&6;JQoP5D z70=LUh(LF@Rnl^}7-%%_OmAb2;p?wc)R;c-@}*N;nls@lrwd6K@;>5Ki#vmaRNk5# zo*W1I)b>+F#P$*EJj}J&W}m&K;*rK6)x&Y0~n`i`5z%@w#|yT^nkZi3nz8~Y*e^8Vg+pXDdb36rv0 ze{kK=l2mw|MZb#9=%6;`-R<*kI!9Vld>PSePQ$A};m?y}MTaXY+S>R*hg~~*W@Z2d z_~9hV<3_gn^aZ2m#mRZFPjPN?n+_)e*{y6=0-RD2b+O5A$mObZrtkrRl327~vXFqv zq_JOK4jzBQRNE{qwl4B8I+LBz8!fsmJA0b5%|qN-2!9edib;Dotwb^}&Yh2VT`+!P z;?KGD9$$ZVt<4XgZK#(=lncRx0?ZLAraf1}%h+Uv<53{SHkJ-PN1UJW73-(aX03DA zOJ66Z+dT2PIIM|ESjyeEMi{^3N7E9c%y-L3s}R1t9D2Yd8qU-m(^xsB5FE+(`9Zxl z@%sEa0QSAs`}gnlx3;&nT{ypAMC1BI!ZJv_iT3?y@6-IyLBz_$bWA_RPY-OL=oTC( zWfYei%qXuY&sccqI;A<(zi5swyy&3K6s$h0zd_7G9$0j0`f=2k4rvB&{BQbQGSl89*IVinSDrJy zWzZ?cwJgQIbsI(?so(B^Y64C^!SgLFh%__Fa(#9f^BWmsH81{sSu9EQZoksPAWhHb z(%n?*2Ku0Yr#X)0R8P!-**Z0hg-IeE(TIsEy=aPHdMRU^QTJpoPxexmW5hOb=(DPO zOAyFbNp96NgU3hLear7z8a&0S5&G>-&oR6(zpxPie1f{jTvt+xIr%C1Bs8!JUEX zy$-nzEL_NU;Nzx(dEzXIKjFi>4J<4p)m6RoJJZ7|!oL3~jmw{?#dC8)DrSQ!5^Nhd zWN2!=LHdnEf&^;NFdK*&VClmP!AG-HB?zTWK zex)wYZd|YL@+%l@6K{N3G*N1H;}v-4{5A6RIFL^?s!lXhE%G{wl7ho+!c*Pi=v}1I zs3q}zWI@(hegcV_^Oru{cN(Oynh+G{+WQ*;(JVhQr@mBMFAGq64>LVz*9FLQ$c0UC z6Gr*R!4KXBJ})hWd4}}9rF*m0dmDH?gb0H|H%t5dhl>^qNgKNwGr~_etBjxpvvJup z=J3r0vc#0H(21BiF7g^=9&cc?E-ujeqQ29)GVKk+3%Y>in|Uyx+1}9$!4H1%tFq*;ZdKWM8l8MyU0yi={~wJDAa=2~P;A<-K~jNVFE z<4M&e*j$kAYe5P<<2g%TEu$OciephC$1dpZKLHcbEJ1%x46>z$-^CywsF+cqhAmXi z?a3fw`u>LOP7t@bhdl34*h}}5z*0@b>Y?9tm?-F1-W5AAKhBM`6eA1^_4(7g$`G%b zI={CBo{?IFR>4cjxLuMpHb?3?bN$`$wYzzk{SV7ETZe?RY7y%~Y3$GL1*!bM0=dOB zX)#!KQusr`crljs-dM@)<0|Y;bFH7@AjU%3Fc1vkFuGP7o})8`$lzrI$&w_M7;nwB zMQ09b%)<)c)bD*=AlX>`_UMT1tzZb#l97P;QpU*ws#OcaTQim+;qj|^(XplPU@2!7 z_k&gACc?!Z{L<3OMO}I!!i z<_CwHZ|>T&xjDp0k`P7*g$)#BD6zHT6PYOmWzszxM3BDh$9yQ$x%zpXfRL8rb%MCU zH>lsf&w*(Cx{WsvEg!?uJhcR^-$sSFqi0upr0g;vp2M^YHNsxnvdaI~F$`JBFp7?_ z;>X<37Uk9Cg0aU8+4 z|7f$jSbZAYNW{TBNF>zO$)8uA)ub{Q#y)9|+O7>K^=&%IU zWB}0CS@fAPzR~563`@wRhr$C|txT4s6cN$dnVvoRl$wYT<*;UU6w8l@iEuKnPY0FP zHjAM47n}TokK26kN{QiLp>vv@v#uqXpf7!tmN@m+zI@;rK(=RR_PpZnSsLNLf!nld zqb5)H@K%GVH9aA~Y1u32$@L2UtS_4ctNiANZ~)m2+|6@Zi%lDUmq-|pg37$#e$5>` zJri*n6M>pI&Uim!MHG{H7XVFBZ^lqVNZIhf;_N(Y%Kpiv^P*`)vxxSi^joso84P5{ zK{Dnum;4jduOz^cn5J2P|FooZ4C<%*y+ew5C24=p>=U+CQX3Jcr|@O0x%US;t*D>7 z9o7{w70*_jqHAh|=5)UT2$mB%?t5=|a-_vy4z9KR*9_LIlSOycxab1GV1ZScV^o3h z5iu-{Ur!m}+eE1ltnqyM=c9@Nzbe^Y?J}b){y?iO%We+&p)35zLEXg`nTbfnZnEcU zd~J&OJ_TVIwpP;`m6L=9OTw(_EEyvLHXMM*<~k8wONIRt{5Sx#d)41FfP^m%nL#4Y zCzsThGxR)#aGNpdl}SM-qlv~rN{^&(q*VeZ&?zE;@UxpYky#oDuP!PqS7a-$kQi%C zSMHMQ#1}j-<{FN_)Uu$j%((Ff9)Lf-Vo*-$XgINvf1|?Q_Wv>UmSJ&h(b90R;O=h0 z-QC@T1`F=)4vo7LAUFgK!QI{6-GaNjesj*b_x-R@JILeaiS{JJyUy zPdsM>0mIh*&J3=w<8~qeCO{@9>a&H4=58Jda|KLO2L$dS!LH}F%?H~z8`>I$Uz5gU z1>XbY%81@`ePI6t3>wTBK2XHtq^_AGgEN--VE0Pu2(hd8JUXBbD?!3JTn4m!Pq9S} zVgGZTM$cj|!PIev6%xMVa$5rTupdXmoId*|xU5%TfiMZJ_RZV#tmRm(u&bA@-2w$TwkapOWb^`ILLbI-Uh~?PO*!rG2if zNjM@OfBuWkV(~Q1hhqWsoosglCd^LyW9L0euSwfJD6%YMEx7UYa^7i03)=6{Jz{^l zF`c9J9G0+Ky$-nvKB=465YE5!8SjSNbB4*JAji%GJajrwo&((YHLv^`g_lf#iZqBb z&}+{8p0(S@N8RM$SVp~m*tzIa2eslqu*uhkZUwgDJBwo%VZJ)vrk>W>|Ck|f;sW#P z@u0UfI#1QW?Y8h}eAZQOTy{1ApUf}CUOV{Sp5=Wgu@uFTs1ctXXEJK>-*F7qn zB)$@+N;D^&B$vd2Z|)u1(ZN6M@?e|c#XwF`90dgu5&(mTx#Uz18y=)k(tE1hx{_k( z!?_4S-neReO}tJZxM01GYq8xpPMM-bfdEGYe3_?G9yyeXD|lvIK-@-0VjjvsaR&_zcnBviP3oxc!_Z+M%1L_ME!W z8gGt&S}<3oVuH^;y!3rGR6bnoH$S6O5aUUta@Z--GkS2g+5aY5JK6XWHjqu(h|Bg% znt|Fr3v*Zj^4=(5SC3%GOkxS4rxmt{vMhMC4#=@3p(h4cwvedP*@=1m_^h2^^KDt$ z&y=;I#E?gUlPOo0&F$$GbLEFd!J-M6XpGphWZVTxYfsUqvY}kFllFva_BheimpnWI5eMRt>$O&FzRC!){hVic`?wvMfsd|=If zwe>-48QlvTrMB`40-JIR>gwL0?5R&&Rm!d$<%;sr7?(YH*cBwAkm8Hc zxNrj|ZK5{}vzDp+O@HdJVrw{ZT63$p?bvwPexS@~pEFUN;VLtcE%TCH<=yC__8QvR z5#_VOUbZ`BhmCXefne*r2^ej1o5g6ltOqPP3=H^AoIgtLjy|+j0tBb%co?E4Ch(rS z6@>m@rNk`$N&7RBjR<>2fz^(K+hLNUHgH^@zn&f%cDxCTSkZO2!Rl@ zhPLz9dm(<*y?QnD)QB7F@vQy{$=tTIIck5Q&bQWO3?**^5p|V`3nTQY^B;#v6GjeIJ#acP%wq?*D%df#} zricPMg>r4e%(+?(lloIn)7CxMLNeAllgsF@<^NezY%o&cLF|t*nRWOVRk!|=D&he< zyls8ZM@|G_L^?LHObI0lVxts|o~-75Aal~w5LTQIN%T1DwagUYWrWYv=%?#txw~=` zK59!hiW(>I(Lkm&UAyk?LAwb{V-~~DO|ukxDbsFD(eM(~A1(3gjS@2x@nmLS)4=_- zO0(_OyOwXl!l5dv`uoQbQ(50nxPLCBa@>(BnSU8^XLxGKOiiQJ@|x5|ETzP8L9^)E znoY0O)xKU2&IZ`;wQAc|`sXr`zznMfkWR`Jf1^P5BM`(JsmmG)d&40~prAs6D%mcC zfXiJPGd&FVhLFKs^QMiB->sxkSAWu~(uwB*#dmFf7pZQ79iT~s_~vqVxPheVWK&~T zYa5j7r=6iun-ODs9=|Qh;ho}#OoP`d7QeqLZ~cw~5KWmho-+i%D_M2Y^y6^%Tfe=O zg1_SW;^ex7!Rc@Fbp{yK1N4cdRI`)ndk1V(Jc8Yg0}u;77ROQi7;)rO}|Of-d<0nXNc z7zW(|N=4aSOd(WIp^gGMh5|$2)|7_bpC$EUYsQotcKF!{0U80MrTBa8M1hnK&@N+Z zI+i3fwmC!*L?kUSga5vhINx+?OV*D`7Q!fG z*k#OdMKXQ2JJ@E|`QxOi1*<^RD=}GXRZ0&i>EY+Lx6y{62bQM@OzS(mi^5@JXRL5& z4B$gH%t-cd)MHqs)YT;x~V4be%b z>Aa4hUr5B<0fo&FX}YSoDwJ1~Nz1;HUoE{$c7n>?0q$zdoA(fh~-|6<&w zh`|wbLxrDFP{7%JHTQkwu@@K7Ix)tBsqk}h_=m?bn&80${XwS=3im0D5!#S^Pd~*j z-x~s(J^4Bd<0KAnnAD5bTZck2d+__qIGi9yB3W!>D z2aDBW->vHr85w9|ku!H=fyUAg`~i7&W3~wFz3J3q`Cqh+E!E#Av8T}rJpF7H#>7wu zw(S*b7tB+1hL|2Ufh<7|ioy*1B2KgiL$>PDzrI>xgyRj&N}O^^YfxY!20o|`zR;{4 zT0<9e(2n5WQ9hqJi)iLMyGn#Ou^O~0W)~ajEL84K;HfIa>vl0N0yl>}QbsK7+Rra9 zG&VbzA-YTt;-df<<@v_p!bm^>7~C@Vqm7#8-yo1f3iZlzd*VC~ePcd?8(6EU`h3te zpQGS;J-)h69^U&lO}Q6r7Mru90X(e$V~)G#HOR)_JEE{%zt3$| zjru>CQ_mQ57Gys4K3tp+l6dXFBMF2Rud8gZpgxif*G( z4{A41IuNz9cxN}MS>|R42^>5#<)ue1l|E#^OF6D;1Tx65Adq%xv;d6T{K{vb_o=3d zRo^STHF9UVI~s#eA=T#^&z7N>c8^crCy0)jukr@o;ChY?{h)^WF1Qqm6{D|+Z;?wx zcKnvJ58cy?rt$DsN>*Tg@|%rMAGCT|2p1!?p*W3ZYQa@Mp0naJ9p2qafGw;9lUbjzpfJ(uUB;z3F*83=8Up{uo?!E^rMIXk`!>Lt=kdTt&Kac zJAfn3J#So0I5=w@st1VY@0{n|?N(DNg{Q8II#^IZV46&a(5d6CEW7uFf#<w5yo4;@g1zjivy8`wqp4_12+)iwFmG%#bRD72p-NPunUDz#wXz zmx$TlH**mA?K|@iYxzKYn2RjZgb>^l8DqbI(vFZG&YwW2Ft^iBalg=99{2=nr+%TH=R{AEkpLy3}8tnqC|!nMBde)TLo z|4rD%=QW%_VEW2&Kt6R3+B1|TUk_M9c-%T#<1)yv_jS=gt9Nul{ zSs_CjE_6xIO)E0?bJr{dQW|+Vp%Q~;VhdauxiDRuNC-;ezZqNOw@*8x^clv}id=0xJl*GpiMrKcKU}UWogM^zjiUSqg|g2$KkV zGk2SplPe}QP8O-hBU3%+8#@SB595M)DoD@5A{s&0HT0;@V?_8lQ^=)u4rY?@&)cg( zv-_n@^@+hP&M)hYNvB9s=D6kyHWO)@ecEKH{xkd&VCy(^H;kWX7|Z!`^hcieOcXa) z8k($ynH~7c1sc4U62&k7FRh72ucj$a1HGP>Dq2c`X)7J*xnDuFUDLU>?GGI zgO&1@!DlU`*J&K`04#7Jsw0a@De`m)Brx@EmLLPyh$`K)N(l!six_8095FC3>~NpA zU92NXv54eU(QJY*OqCD{H=8|IBq*I%#6dPcQp{fpB9zv|?>1e|~5uX`I8X#CoA zis!a5XPalJvH7SyZ0yuH?JK>@`YBoCy(wco6bIo~_v>^BEDyWw1<9%EGYf%K9fgf6 zVbe8Uh~{D_VdaUrh~P(#EnkEJ9R<3e=gVNN8A^tZbZV8E&GcDgLX?6ii3%pU?W@ns z+g3Q?S3MH|5eT|)Ox*6U?c0XhJBGg<~ z88r9GG9=4pxN57vS*qmc24BmJ0SX{O*-|bkBkETVUxqGZJItCTxjRtNNJ} zhzOS{0JmUj+0-IFU;}^2BaP3o5e$&P&+!`9Il`V#hcX-{KsDV!-m{G{p7!W z9}v0rPy1&|!oTtFb+@!+PXUw^4la5b* z=`*J%Vbq*`Yy@6zHaA#_pZEMu6rhA`pU^8Y6Vns~qi;wRj=^Li z&X3;|xYi52o$h6*vc$s(oWhSeiv8BBS_@^tkdOd_ofopF2!A@OKM{@N*JOdxeq>!b zj%qXm5qWEs64&Tp71n~>l!+0%Vez}a z+M-pP%Hh!In2;YWScsqV$tQ7aT<2kpo*%WADqk0$ZsgzkJ(q3`*UH!N&E2t+ukte4 z%#i?8S7JW{9=MB8#NGB&+0pmev+1}Cn0$u8h$DebC{-|u!rlGZ5J_?p;qvfAxCW=x zvkjTL>&rY6Sl{~1y&`NX1;v�WG}ATm&IK-IOt!Li5-0EZ3cfQ6_ELDD9|19YM-; zj!~7L@*Z9W7aiO$oo1G8Fy8f2idM`wDV(4T4yjt$(y~7Xb;)1JW+t*c^%z_kY(-IU z0LW0>imS=d66Y=xP}RCwffD=eyKAzg&++el+UYbL|Fga7l6SQA`4_&6wj>{3JR;}) zvAkruZ>4(9_o)$Rq;gIT&-CV*GAs!Z0iRX{v4#Oir(}%Lu=I%AyEH$czGU-CZ!ZL5iz*;@6muk~?M4hc!hSuD|5%^#?#r%s4i#WpPBr88S z1F<;>Bfj9-4QQ8B3(6S$zq0&xPM0EbJ0z{oFA--g+L#Nu1fQNZHQ8HU?;)s8ev87_ zwPv(+fp*|}(6h#=pEkWXHhW0W1~^0MEfN#EHbVLwK01}x8Na1Jiw8H1=te9HjQck# z%-Eftv5nmOM>c1F4|+W7)JADALiEj-jfWp%Q649)8s03pw`F^r;i(7GU|+y~r6S0H z;iLB0vm;5t*+yjUET*XND0|PPa?5}T0n|I~dnq~=ixuB*LBV9nW@A4M@$f6lYCg|> zI{i3A%{+FlO>WMx??@A!;+3XUP*CWj8aepo(5#Nul=JN-KiH8mMdI2f`V$jQ)qpcs z9qH{#jU&Q#Iy_8OXr&c)b8)fMqKg#c4~c7w=ueAmAFh85kQ1)Nx>Qx=kSq$*U;+EH z0Z#a1qnVKbE{M3*<3M|Jb1J0Y8b+*gjDOZLY!bh)=_>YKl%`FQts%vXyNtALll8JE z74DVVlQhWi;2rH{Nv{#DWs@?nL_wd4A0Pj&ZFFasiK8vUKdXY<#LSwCtRt(Rigwz* zy+^W8h?CT%ZX{f3tgMMdl6=uI(ihK~agUeHMO_*!Ack>ZON)Fz>Ghsp+OE{17YEIR zf?J;0Gtzo0UXZ`byjyU8SHgAUhWNQ|eDw)$fQ#^g{9tBJjX+?19pT2o6ADo= ztVbN9xUb__#G4=RUUYjNwXtr{SG6RURPXX#w##6PWFoYSg@XSLX~GW$QQg~;*#C#V z*ME)(L^_b|EFr})(iE|xd~L@XP~*ZIOTVsR8O)40xA}8m;z~QIA|~9rm$kX5viSQi zbsl7Z3z>nvy)dt;qOmSA|D`|LDk$1e_#o36Da-eMxw;bog{3MudjAs=YM%PtO^e=? zQdmpv7u!Bcy8$8%mR=0zzx?Vy1mr(7D-!toj$&>VR7SQT!KWR4=hCc_n*k>44`0LU zFLHa$eoo}-y$yv4y1F2;3Vs~fgASTS#YCjmp0rMAva*~-mi4y|4rI~Cr8=Cli4;@n zny z#kQyt+{mZ&wHXa^DVY*-#(e956_d17D0Ln4@DnnoXC>s>>)7mN*EmoKBCl*?gdCa8&cNAyYQEw39%xT?25G{fwyR1r?&jr%aASKhWCw>y84d&q)xh>=lu!zwe0j4|IX6~to_h?9C0HNSCoB3n|-ml|B7D|c4}kmhrIG%eStH$ zqQc(|Y{6^UFbUXGy>upo@&1pNh75R>>Iz`MsKhwpmHe&&bl&SB@|`u%EB+eGqai_COY1jA za=oE*zg@(93o6hHQ`NqRx8w%0$R&YBRaNb-W3IxhDR^BjrNlCXc|xmXV}zfi*>X45 z)$PheIJMz0^ZfPQqj0JOGBV3c(0#ll42XXWvn=ojDPRA98`^2GDsF3T=AmlrvW92T2eS zPp?=_op!~G_GlrXrMa~*Cj0!f$d?&@8(f4F*-d=1;&+kKoJP{RSUTEG_rp2jMbZH% zK(*c(nkQZl=Z#GlKBoKAjJ0il$~FniB^&_9s4EQ$D@Z6!xL@?vTNVP;hx|+hyZcYVx+~!{_v!D zv$1aG1V_hf3N#AaJj}?Z8RM=IPG@saOcOQi3^+a$Cy3*hNk0OWmtBAO6q_rY`x(8B z^Sg1<^uoVziWrQc;%vOI?90RfK%l%8CkMy${&>dGv=nh6D35S})pb(ffqIvUxTMZ% zzI4gC`G2Za6v!ITykX1O2_z?B)d&t4^3N-VDc8|i7>a$+MIk-%J<(8U;J;*PAZB9K z)KvQV&KnAyOfsmphWb)I4vmMG!Disl9Zap;7JdG>HDFRCJsFAy0w0>-7OTIl^qd~S z>kKIspW+su(>|Fxu_>1-b8*RMmA!^+#r>vVrRt!POm`sGZG*%MlMBuMbzW^tvmsSu zekB-s;qWu-qpp5e-xrPN$)BMQvtj_t7vFb2fKVBU-bPBeO=@XO6-_r3oxKANytZ9X z2=N!O(twc_E>hyOEC74`&^#bBqL1UJl_`1jcS?Z@$v#ThS&cLhy?RE{&Vdl*)ckx| z98Tyn-x2vPON|}{Z+mp}xYsFPkE&>{2HrC_42=}R(%_JVh4Bj=Ds%u}ye#^_at;A$ zJX7MeSQ@JTl+0G1usWbYU57A(+ zRqLyQ_o7TBN+E#;Swj>6tKfePeO|w)C%5^$(0B+f^axfhNo^g}@$vpL!cn8BU z6kOY(F8vwA@Bed3)Zl|HsCfP5k@zzP%PN*UwM(-rWQjOU>)nc#IsWn+)Wc6!{X;k{ z13+GMZ6%rlVhwA*TEXUAX0A^j5i_%L&;eh-&(vRxF!R0C1a{Blnm6Rxz_P`>MX?PxSEnp_l%fu+1v)jK?Bj2FK7qyKY1j}VnzKlF75BG2 z#4>i8j*Bxr``z=TwGyF2D+r~x&N!;$_;{OHTUC$E*xQX;~3-cm)h6Q1|cVN7H zY2YRMS0RR6(x^Xdy9y@?eIAeVZ1QeXf3J>16yQtOe1EFJK4N6rzdI7bP?7CwPYr3o zZI!yFDEY5sN5Xn%O_5-miwkAzq8dh?y6OD|>%|w-wtVZ|`}gIQ)X?whkjftfo9^cf z8OfbpXiNROsvQa@R1G!yZ_`+S7v^fjn{v4#$B>_e>>GN41rNJO>JLpeoi3f<@E=ad z%`Lpi7Dm31#{S-mil*}L+mP4F3At9EYhP0pQmeM1{-uf&xF607LFyt>t^u3{+PJ@= z!itn)%3Rgv{2K<*4`tp-plXqpk~@04VaE-{5-_~sEZlgBw=Z|e1(0%%kBd0RNMkFh zu?x`9bCaA-MJ?k0cr()|;_4kQWX4SsJEq#iYNp4L9jv>r$Mj z8+kJ{O0MMNWE1BN%R3eUJ?fYKO@S};CIx0a0z}R$22>tc%Af7Sv%ExnKsFZ4mrQIK zBih#WP5(XJR$cUt+%;vY+B7N=z zF^9{@KZsE~(A}=n(8Pn5;zv+TrW5kKcm=5mO`N{M67K7H9xNmH#Fuj)$(5@!Djc$0 zI_vbSpZdwnh82XAv7%3ZNZ{J%b}xiM)Gr}9^6rL~uO2m55Y3i6in|G3%A3G|RfDCs zGyE+QcmWL97S=vrJXN8;mE+F3z>{$byXK)nix{k-;0W|`*=Z>OJ^Tp%o(CdxBosE|F#Yn22kCgNyL z%96M9-C(pH&oZjlNrMQ;NJ!h|hmuYg0d8`;RD+H?*?-ILy&SRlUvp~@3Y3*Te9b=G1Q934_J0E*$AXYI%kgftYeF}GP`Wq>s& z20#2090Ea?xb$uPa7W%F4K=zHXk9gl_WV`5D$9u>qyr6Yx$6I(qO>4v&W57Vf4pSK z(V=@^{F7Iv`jagFauX>-l$?@_o7P1Cr@AnG*?_wGOn(my$Ql2xTIOYgup(eYl2}^o z{tFyviEt=D4To4XVOYlh_%afhXiRVNA@_#$nGk7h)z^U!A_FE+C3Jzns&Z}aHG<0GJ}uzlc4 zdLO4n9==WdBy#GZN0o77n6}DF79~BsaUsg#H!fq+rP82Zm1ds@w za+F92Dk}f&a}Y9mL51OxuSu36|M+{17rAvwNT*ZBGtGQzk~H`lY*CAVw@0I;+4`Q; z&%!tf2S=@|WeKZa5d>2eqoy7wr$I4&DoHXHHotV}z0!e~J!kR9!R*ZzNin~X+&L1s zeG7BgDp5Dw;8-)Lq}SM)-~mX4KqW7Yxel&e9{&IR!>y<=W!!4RxfC;KyE-6Q#v6FRS6D_pBQ1B* zDj#awH<bD^01LdZsz8~S=q*=r(5Hsk@>p9eiiKu)V$_joK8ITGq{nNEdF9iafjGDUgj&Os} zcEWC{h_s&G)>0msM4C-2GTPQgiCi zh-?Pbwxw*Y-6sGiMc1rbipNnK{@GnSDL49MU3+5YVif4HvIs~=heWk7eo_<=c*Bjy zxdA?d4e}hIV`65cn2YP*c@4YoYZV!XibfK4ME0l_;S@Pi!c-QLaE|xIz;v>Vb2G*o zU!FQgCi3So}?s0V`G#n78!LIsBN8o z0;;6tB`it4qu-D`1Pb$_mfSZTwMbg890A`HN{fe~He4u~{uS+nqJl&_y`SW6|F^{j z`eU5eJ_(G>P6Nn*5(<=_10ZpYgl+5#xRLW#>EzchvSk)KUn_f;Qr3aHdU2>{9j);h z85iQV!&!_>Hm<8HX4pW6?2a48+^N72^4Hxt2%86jru|wLG`0R8qq%@yt(%_EA{e`y z1SFUMBmu~A^*r-U<@ir`a#+$uU43=Qd_(a&s54jB?RyYt!hzZl=e~1FsSUF&r_^?N z)0@U-Q4RI$2e#{HAz#>p(Q@(PeHnZ%>j)%0H+4m(P>a!|6l)78knq0r4?b#EWZv^A z(cFoHWg08U8ZlEG@dPlDv?zrxWG3hed7@>h8plv`L`tne22=}g$)aSW&c~hVtkg|H zUwuf_S^opLhZrDrHv@JxQk>};gMxxE5I#H8%}%d*T{^1c%p+Vs`C!06I@J&)S~^4O zGGb$5W2*vy5K=l~1~dvWPlPa71DRJIVjn9rvoL8UFGFGwKTK|C4JF3vrOW+&e9%V| z4o+}9L+}W#KaCv-!c2C7Oer=B1kk55Ko0tMDki-QnA5T8Y+S-v;BJH_;j~QcM zs~ifW8Hg^XmROm0g#sm#naAKA-gsXHDI%pkp`c>96F+;j8D`X2Vo>%g{VvTd>H+8V zZx*$7>Gi%?xgCW2M!R%)|ZD{C3{XLG(`g`uY z_Ly`|QgsRg`5W#||8s`7Tkvp{2nv&{1sjx?j{4sU1K6w@Xt#?bBYG9^SFmaC8W0-qcG1vK_J+ zWJ1WH(}yAqT&kdif%=Ps?ug`@)w}SV_ z$Tw+!@2|mIFBVe2-zTL4;GCW>}pj8HswJTuP$-xMir$JTPH5ZNInkcZ1Q1Z)pu1k!|0N7fc<(WJig zA{(WL87_?R-ZEd4BIfsp74x1`O%EHFgCm#8)nl_3Xo9xg*6e`T!dSy zU29zL#>HGd}m6F+65Av;Mm@cb-IBL;;hCCLHE0amlKVzd*nX!V3 z!*-?wnwUlIUs5!ly>bR_9LJnxY6RLI7=gzTj9 zzrzd(H?ZQF;tw6@UsxAW@wQ_AibkiC?BvExueMQAsYice8HoJY*pz2-e^B{UfG5Wg z{_b3r^=xHRL%7tg?dt#SLb{O{M$inl#w1A9x-s|ovD|3Nf_vY!X{g6Ir)IJ z1gjYh*HLk5#s*R4<{_$0d)vYN`t#W*Qw!hc* zv&AzZZ|!P6i-c2Mr%I6A<{qq><9DFQrE&0tMe$9=Zea?5?WJ`&!^J`jaY23}h z``XhN7i149;dTN% z;K3`ChO4#I5RlP&>DNAAz{@}e(npHQ%KXNb7AdcA!6)!=I!aNA0pb8e-?!U6hSd^) z&I*o>pmg}C2+)G*OlAdJYX4XMx*h4>ZSzmJCw<*V%+2soFO#73Kn+W4Cx ztkj90`tH-vdR#qmlS$&UOr~kVPXGbsU5;T8BB~{i_xefJn}YqVvVPYw_bVU)Dx5-w zBx(a<>cKX+rH02O@edW&&)NiY)>IBIwySXEE970bSM1CW!M6SzO|E?F9md6m`;9?c zr~SK`XIH;_R@H>!BChrWNNpAWu_3OmMNQ%9hBWZx`-bzYUKs<&9IG}ipxJ-I0ef^+ zZ*H327ZI_H&p_*KgjQ@&qc;FkUQ&yWBu(?Wsy_r#J|?a_yW7_6ex@cbuDM{hAilrW z3LW*iQH+AS;D=aDYJ16I+0pU)p_qiRra(d1x_uaDGHTk8-%kgF)t`gkkc=lL(L=_; zb#JDzR*J>^YJdiT2k})fP`Y_T=*ZvAir4zPPwQ}J?BvVvteG5$i)wr4f&R)*PW>^q z{(7}`0`1^2cH~-%t5UPVKgQX(qerOQnhK!<+${W@KIX}VcrX`kebCbLlthi!N*?Zo zRd4FE^|pImPi<-!=wORqn&TRe866zCiKO(qa5~Z8NlH^n^?a_^073BM*GREQ#IG-p zGB6?}=rFtc*Z^gGJoo?blaK+9qFs(vscoQHQF4Ax4L^VU9fq@q-M4KmZQ9gQst{BiD=;Fai z?I`T7j1dD?9~uYbHE===I=S2JeKvh7SNY`7pWdD0EE^dCJ*ZIRnO!E58`SU-rf9rT&k>|ZyW}Z{420-$W z8OaXCwwX50SgzJeYN8B*f7q1ai#k!5Tao%x)2~Ru&@(mizrzBnmFh@_-FJK>Gjp@K zF+IQS;w-Owcd4QZ-*YW2?NsusZ%5&gD#~@}QPBwUbY=&$PmW22hNS6wIy6s4rHR<4 zZ)Zb&p&Y9R(g;1F@Jyk_B)!b9ssR>QT2?<8euIRveS+%%&XeFOqRtV~k+_|zRhOu& z>SbzMK4W;M(6c#~5ThTV|7q#stzlrm(ycitpD_Q^#SHO5W|o&Xj1=5m*vJW#m5V#~ z)neh-(zA)|+0++}n`13{G}#Yu`tj8$VQd;?ELO@lZ};7d1{a{f)5moSbWUT(y^dB1bqW}cNNFZPyO|{CAA>`VmFmbp zfbn0vB<})1mepvpWZFQ7hY(!Ahnd3JQx~r!T2+?+=rvS0nwC}DmJ0G;_nLxCmIEy; zjud3UMJ)MaBIrB<{RRz)`mu=Yl41UTK>9mOFg~7*MByEAMI-J)5uk%R3O?y}xdm&3 zQ`~(}I!43LkbIZMUH&~eO#Lb z^&lppWg%YFzjaR;&#^?St|z~0Um*ysa2AdiCvTZ?Tb%UC`mn*D&1YymM_8`h<>2U@ zONDju69k$_6?B~DChK)#+HtAxFs-wWnh`I`39TC^>+xRe{zFv-=!$|AmVj<4!|lu?1sKSBHVIOw_Zl+Y=R6i&@QJW^9rroPl!{`obo zWU&18g;SMR{TV_r3wa^~u;;+-*N6O~3NnF=KG%u}t_#vqsi}Pp=uJPze&H=|Q9(?< zbuW)-<(#T6skOrY>9mwTI$A=nO|{Z=TTy?Pdmxo@jF)$v)Cmjo)>jZNTv(kOA~*(kTXHBnGDR*)ZJFxc~pLAkhW%MC=M(AL1M(Nsm$Q9&A&PuuR5G zQc-F^r0PrLm6#QC>Ym~ynQT7g6il^=ZTIdUeYdzLz(h_rCY+eX)0QUnGAa-a^C#eQ^fv3L-+F3k8u!kM~4(o2y2 zT?B@3j*LW4Qt{LlMe0uY1CYm1X4OWJ>P1*lTJ-)$P;57bin`LSq&Q004sUok8S^&A zQZ>4b+AhQ*zY{V-fP)m#R+#DdI5i4;%E-dGhzvVWtrbr^CV?QN>BD${ot$Z#Qxaya z#)yV6Z+by70*&4Fbn_uJ2L9u=X5#7|Aa68bi&B)zz(H2 z_p_s{0b?XI(_K#qA``Kq8zLllvnIlDEfl2g6bcO^W6}G&A6!^3mS}jYu6XVz;>C@_ zFWv?Q{-SxeIeO_pRFjE9Hj5VQxc(ggmZ9Z!Lh{(hH2br(T_MaByJ1SHlGU!f zFdFfg+S(w35`BrOMxKdoC4JdEDEQi<65|9^%BDc-+MO*C73PRoTT~nPTVi)`M89iQ zab*>I`s<_!c8@i;pe{bdU|nNs>2W`ydW(&D_}ssq5aE*mQy{(LFJB5-*p)#oj(IhI z&U)yJr=YyNs~p(I9oo^OFcQkR5Hm{jjBd=S9};U7_ij#*`I`_+eiLVQmx-g)2%LiOuB&UKoLXwP%QWrq*fukBEXK+rr3Vh0Okq$y>9Nyt1 z-(Baz4cv%;s<*|iMAJj2x7}0W&WP9u8_*!d+v!8DRFvdz(cv@QkDpSXx>8x}=M5$% zJ9rBdDxmk}S#?kwC_Fgp>X#nu=EQ)yax7lLx}uuIcWbO;FtdtV#3z!VcNWVs|e+sd9IS8l%?w@_@kk9_mdChP?B&tuPyfcO$ zvrXGe)IbFARzmMsU_MRRKNv*cNQ9<*l@;;HjyMn!{m6=PG-Rw}QR?ZDrrHnj{;Tu` zZr$Z`G84)h5?)qqx!r>hpi)=p91p#Ku@Ti!K)CaboJmN0BbJtUp@S<*Y3+#197^3nOkYL;#+ZmBquhMDgouFm`=yL9nO=86(0H$e0kJ zw=1ZGVxUr1R1~_hvf`9dHJMa8WWh3Wm7o(SI6i}f)*BaRm_RT2>23$qwnB~94em8P zg;{iBkxu4(UmCawM)%Wi7QrrQ3RrTwy{B~pG^Sy%KO_CGZ)RJm1x&t&4??6={3!`7 z>T}`6HY{Gx%@_WL`h_sgKFH`u{I%xqtU&`s_=#xOyq1rs4TP3I{2dq?R~e^M(n_L# z7#W#~qxlQFkRTRr;-JQBUP92xL!x~{wIi3W31kj;(7Nk!dU|>J0g*H3Q}Xj0@X47n zS&A+T`sI=l=_}@djk`;CKALz5`XV8T)tNMoZ6Iu&Q9MhT?MRQf0^f=>&-ExItlXz} zFMs?+xw3OiZ1@kPd)g?*gmi74Gg7UwwNSo6;p zZ5xg41`##^lljoVtoH^%r_F8+bwZE{UANF9Bud7DTe~9{nJT@Z{0Z^drqU4&c z`e&e9Wouf{etT{98J(F!T5v)^f4?v|3xBvZR-h!@OYo4sUYT|eWF+!Q!Ctfu8(>-v zcgp0cXHuYlw18pIbun<@!Y-_>WyG$d@xXvk6x0Ws3|H*65QN-C-sZ~CI+>XS*jk36 zeNn0LaDh{W)6goIG~Z>+i;eGv`&%Hz3P99+N4+jCJpczA8rfHtl7eDR)lsNV&V2+r z5$bo|Q=zPJR|e+_aI0yX1Fwn@KQHZbV;V#*hf};^{Kn3zaxIrmhMW3 z+gc^f2$v4<%JB(bzw8p4JkGZT{!j1WIJEGG4Ab)~(g^)z zbTvKPM^lGp!5vP09AIwisILbO5WfYiz$f}!H*LkdKp!{1Y8@9{WT#?_;#qn5<~N1# z`k%ih6xi&Gv}{G$b={{hIs?UyOMG!ww?q3Nd856B$X4penQoxL$Qrra^)Gg7J-P8) z*s;Va;7zbU>oTEb#G3T1GM?(w?E|`r8sfI90Y8ZxJxVDgqt)_kWswnPS*5U)Y+NjD z_(;=Go7X8j8M?*a&CSAp=qR&TVLi`$8#^2?Vycs^;F@mgG%vq!TS`C&92mgv`JnXg z7=ED35orp`XODI}llKIfRR(*Y*~wLl0U%5yI)9<%k|?xB1K&^>=t{#tynz{1X9Mj! z4*{T4unkgiM&9@e>G8cI?M=|~bncruVlyEhw8I>-PX@ZSSFv#qB0a9z#XT;hj5NB% zphep-|2&+;|0*;meUL`;hW@v{C((fkg)(o8oe!g0V&QMz1;2ZlSlexywun%TPMMuD zkTZo$v2_8(2T;O*jutA<@N`o_hr(rhUaJ~n-E#tm2YguZN`;54#wzK)$rXcgt_E=M zlVEK*vNPR7H*rLr063 z`)O`fLVU4WfRb)W)YvU;zsX%5=G$7G_TiALbBf_Mu}{9{Co^y8bg?Ei(EOn|)35*~ ze>>R?^b1mKtsGfoxl|LM92Z`mEmxZ7QQr0!6D)@qwlzaj6*t;U5xvYt^E40}cjvSI zvIJ$$3p|ChmgsJ63Qq=1WM$y6+toWJ26m>6WwY#u zGS^iuu_f(?D*eXdanuum_r{JYo$^CVtMsY4*`OFA0kl-YrFSZ+O!s!t;&CX0+I|^z z6zjy5p2RWm7f^~q>y`9u12i;~JZA1U2DrFIZ-)NQ*}BqeH3W(|I4;XT&5 zXti$22FkEG>lyTLMS;RV!S{Y$L#Qs}Z}dFCjLkCJyUaJj0iTC`2kd1guIRT|$T>7`r zr;x7sxAH=OoT2mEdZ!a~EtISKOQi5oix;%`6j1OIwlcOrR;0_N55Cyx&=)JCXD$%| zti?OzM_fsj7{!#YdVKZ=Qe&5G=RH+dC!){8)6nX9f8ar6|2fOQydaoepO5%y6Jz@m zHes%;e76JRW6#b#rK3a(>fvm&E%@nsn=!nSL!1fVB#N_p_bd#^l~#TkSqu{DDRzj4 z{W%r%^-71nGGxC5PLu&jq6JeEoa}#n!yb3?T=PSJVMQum&u0gsAN-qYy0cEnNBn-x zq@2hNxQ4%faQU(C@ox5NZR$*mvY=6PlUx&|`elUGmx!$#65T>O=5OZ#qEmB+)(ZgG z(rh(QIaaSt!c=o!Q&!cn@L#&(sBQ4s1cV+xCWbQ%!5@a&qps!2m!pK#D_{Wl7tsEB zlE1+p^FMxGE*!1uK{|0$dp`-B?-efbt#rz%DXHLZ^H|rK$jnHO5Z9dR%)GHkbcXRGP5S?fhoG*<%xsECZn1KJ#F1A#Y{(SAvs*kZr~?|We!w!@eq z1W;Hvayw|XxH@4JgMw5rEN-zJeIhpls2r}>;Q^++43+m%ig2H9)v1PveUIMGBL7Di z@N@p<4+yCbB?w{~v_5NqNfT`O4FAU^MB;;yKxk*M(IimQ0zB-yZ<{df&=+Iy@kCUE zU~vbtkgag#B;0(yYAU6(?XEVd)aI3<#6m%;Y;0UsuwNyuj~J0l?*~Q+L9=O@LlztXWx&K6^2mB zKZhz@Y6LoJ2QUM-_O3i)SkQvOr+{dp0~p4(qSd1I-yI5UaOh32b>5Bbgt=-lYu(6= zY31ux&jK}Q|4q*pIwHpMu15GcU#rC6bvF5szOq>JSKB|2a0AmJe5yv0ng2?-&= zr$WMtqkA~gYs_?m&SG!jzR+P<`T0el_ky99=4938GTnLa+m0EEt4Ri;b;;l}drK`~ zO*a#YEsM~+SsFtF7e47D0=eh) z>(T#kd8R;i%Q9pd!a>dis4qK$cMsU69>Dky(}g1h!4I>7*8X85lD(IWi{JjVe4k?* z%9PmUS=OqLs~7 zm;7oVT4*4jLjJZ3DqAJ<;C`GIm!VsVFc+GBKqSWxHp02Li{NIFkfK3n0VSUSx9xSg zrzG9Y>ie;$`K7he#3^dkg zUYm>`h&$6rm=J0JM&!EU{yD!-GJZ}n`?syWgE7I!x}pee&;J<(a;F2=m)6xG)>B8f-tucEoxbRXe@yE;N_N=l6GCTGSNtOQ;6<3^5gs}k z9zvz7_<6{HbAsZ-1WlDOH5*^v#`}{%wj6KrXPDaBq!sw`p`8Q#+n+Aouae-n_}T!b z`K|HQ6hU>cvF(81mxEq*5B(8EDI?P6+wEP7@B8rWC1e~w`AIlU)h*d^kOOWJS0|_? z0qPnwDy@~xc}nFWux>8v^~z9ptXzAO)Zt$QfV7J~7zqi)F+{0xxk!hpB9DrudmQFA z`1K1-LWL;p$N$NoB1yNk0A7?L-=Ent#DnwB_xUcKA`i%k$xOX_G5ZqcjZGAXakkI3 zyi}Qv7=5tRT@rsSmc{GshmZWIXF~%eJ>s*Nu*c;mdXm*EkCikv2)sqgL-{xh|4iKj zDwRxdq$TiEx%eFN^7yd3rfgGJ&5y-gD(=dqjyDU%8%9yy8VODb>^GI~~>})k>k>t_@{e{%<;*%pO z(361$FPo12RgQOAKBQ%GD-6qH#e9?oH;^@f2`Z7H(zV`k&7-Pyb-JMu9;mU)0KGPp ze))r8^$v{mD;iP*Aa!fGgGRyn(3)IjIdaCy*vuhdsO1CCus^o|@JYt~YuFj10E+m9 zeJWex0E)ONSQh%-*cH8r-ozANk#m&P;eP}%0R&LhGrL{F5N5$lW315d^v9*8Arh+U z1wp}JrXQ3tDISb?Fznh;;X0yykiB=iG%U%Vgh4yF`BJbiLSsF8FVS z{z4(g!UTRGi<9_e3ly(LcWfzOiuqgbg!Btsb<^sg{!>`QfMZ$-4A4 z0?7g3(I3TjDPXPzt(ucax7uPhL=t63hDc6kj3@o=`jI9>Z!}R^O=X4w&$6_anCD8O zosid4C$#dCkM>(GE|vr_tp_3ZX97lMfn2F$7&t9*4Ar3vSv0>=${I(r36_md>5{ZB zlD03VNST$hVcbxwU(YrbL!rJixBRJUhMg^d{ewLE%O@(odh@~ zw3$Aww;i=~do3DKsPCP}^^6Bv)3-Mmh8mM13XOEH5!Eu7mhXA=gK!Ri>59btCod_0oE4si;P z=9@p(hQWT1`=O5vdSHtHGxkxoN&)MDkFI_0GbJPc3Uml_e6 z{2fe1v6PKqaOz<`s_f|6P!Q!ZG~5Q$VDe7#tWN95f+uO+4TP@z+=nE*LO;jVzB6b$ zP`jFQg$~&=b6~8h(q2_IEnSvi0Wgpu47Dy(Sm$Tl%%lsxCca{LF9BE$Gna!eJ14iV zUj1x*eLWn>eGpd*D^${{a|n z<0cMR8`_*>we=4&bHLCfxOsQ_3nD99{AGpH2!N>}K4D&{&Y4VQL1@+gDcgy%$;mp4 zsAZptPFGqAOF27=5sbq8kz4GpU}6ocVJgox(2d`)J&ilf+boB@0mPtoV_8-YeH-R( zgMwn9Vd&>I<#+DQd4aLFudxMNz3xg_K5`ZTsI5F0+ir+mGJvF}Y7UBQ>?H3aU4DNf zC$FUtU_I>Uq{GY*3$GY*34$s>qKDe!AFxU>=}mw5lvG|{&uj+OE}W?CPI|w?5JagM zjjc&726f)=INZqr2AuUVKQE6@8Ph7foRLB|=5zIP7f1S!GRCL@S2SOX#_HOd((AD* zdBRqOsNZQT2B1(YP>b6NiZa9fSE)TOKb!^y_Pl;FAkBD> zPnI0Kcdx0fbY^`$l382%kGZj7;IJ4e467EH;>^qftRo`=z0|T^f4d>~Km!7GLA>64 zc|K;iU1}ki8}up+vzsJ-trDL!oj*hrzg9Bq#q75pZUESL-*5qZ5P`?fQTecj%+hWo zlsDHx?CZt=2$S~nDBT#rd`kGO=sQkEfoce>reV<_hl}+>pZ50u`QrtmKX~CL%*nzn z>nNrAM(cU^n>o_AB!;V<5MVNmpW*ZgENm}Hc+WdQ-z(&+=_%m3!_Dq9k!lc95@-F7 z0d8&(=c_+oW)4XsyCx;!AVq!fT>9SqaSvcvw3%6v(jLYXw&NHCz`5D-=-XM(TZw)a zUk8ZV*IrHF$1qL3!yk73(!eDc$sf=@mi>X!_;4 zKhwYFDkc+>Z~Zstd`inM-s#g<1fMl>0GijiAJ8k&uNi1W!QlEhouj#C?qf&%%%7aN z-8q@=3Y2&T4n_xW5Rk-ZI4S0+PIyaXpZ_WDZXo)X7t~vVfqq&bA5xyD(Lmx5A9-`V zs5|#&sEAkV0p?awD9)Uj3a}k#yocw5iT|6<9!Rp$gn)o%?qq3xUYL+qG@_~eOR=)T zLq(OPaKt>3i!HDpkPInYbTqMa{Rp-C%qvpBHon>ikKLzBvDzA@qtxjv8N0QDbyJSb zt24!eBUa3cmw_g(rn%~#N%EDDq)UiIKgauCE zAu6T0dLt+R<`xA*04~lvOy0sIPQsu!%`*h!iEG7zfqrwr19PXhp&MS9mG<7U$isKvDP^Xv3}{viJZY3wplCeT6fC^n05?g2Cue zs8YfP$2B`u3qM0e75B@nk=qAONae-}D}YBkogKww15lxq1xM7PMrze?f_ze{AAyHt zg442plgL`{!DTkMLuTB!+W-CIA({DG)Hg}AnQ^tx=vyV$s z*mI1HtWB)^2?Ok_W=P)1SQ}BUT~=n=%C)XYH}>0xsgmD08@ay~u9w0{r?Z4X(P!mljHzv%YnKLH>c6#o%MZ?CfCEO>rDx#)C87H8*a z1v6ej#6oVKjUZpj`Rj_Twq21WBoRm$LFj=0@ZL92j`5Td0cQzqy6Vhouhj=b)FXTz zO%yfB6CdKZxHdJX^7XiVXrVvTM6TfZ7~`m~`C)L55DJuOOw+%nH!Ae&QMsrLhW<$C;! zAM}mZe#i7}ual_zRAR-&JARb|fc&j3iHhKKiRXjOcc9~?#D|u$-ofN^Ol%Ijf{d|GwX#328%T&wvsK1Z9aY3yU=k!@Br_Uqm^k}bGT_eYqUgxjJf zr)9Oge7=wDOjvl(z3fMVk)9=DZ}ZSM6BY$-mJ-)q=r^=b*%!{4kQc>x1mnV8)x$49)0PD#vtZ~;J_r~BP3Wj;qFg&?L8lZ4m?r(su^U>LeHO3_w@tP$gbTe6Yi_&txQ}8P z^I{h7E<3SUyo8(5^Spn2U+I~v$=5ede>OeE*oy<)d*BJHVhjLx+sDXV;-Q+YJ_tV< zkd(+qyb<_}GVH9WSl0GY18uUxJPzoKtA7t_SB(m>v8d;OYFLVZ(}Snp$ zu89YbXVgTFi}lzSU8b{hJ)=nklLT zUJjn@e{>l*h<|NJP&}}-z(oo019q{0WUWO1$f&H$UJFxtg9F?1_uFVXYgkdMNDf0L zuOqL*c*(WmlQn3L4Xv971x#XDr7Hul@*{8STKQTDM}EkqTgPYu4Lyp|@pRvpqR`DjM*ZTYhQUiT-Yfkz-zEkHDC{xt8y(^-pJ zssa==I1Sh!BOx2!ER~)0EDu2EuAsKJ;63w!Ovur$mRWwd{|;*&KMmWcU9JKJq#(zD zVgr;>Vb&V1jh6sr&++yF{2{3J>hlm`CVf?(>ih}t8R*iI6vVEtHeW4LF%q%@n~rIo zni(~kW4$#ymM74OUlPj=Z_oMjuD_K{xL(w(Jvug$%_UyumW9}|b`1}MxW2|L6rK%Q zEp;z=8`pO$k6OFE)n$al0m#{TGFRpUvH{CcQ?@Jjvq?w)xD<5}ei-?3@i4xB_nM+G ziMEy*ZJ`b@pcs29pa+LcznOjwn9)lOl$(_6Vgg*>ok%8umMB5K73m}wPFd_Ga@Y5@ zFfNtuIe4dT1l2L#&K|3U@?E=*Q(8qOtNN_9Q?I6;VWBA*wlS7t*C-f9K%eO+E~@_| zc)LxE9A}O>6Xd&xanx~v{N=r3+?Xs3S^Hjr0?+)8^P^zNJ-b@%oCGUTv#o#RtR&X3 ztw#I+>3_nPY65}h#C002``@=H^y9a`MiZeL)1T1}OWGi>dBxOj`4O!A1(=TxoGg+; zr^00N>%4s(BfT3yLLLkQjMt6JCmU;v16q}=kA&#Ull<~%lxP%nYzE40_<-32nn-)U z-JJwYT1`HU4rXL=;p9V%zS^5hM~7$CRh3egc%oqK(fAiFzKWJsWdjvigUsz5DGw`3 zlv-oV4cTM*%x?wZPanL95bLA^B8$50BSE_ycUBkP5f&F0d*cw;*mx}WvHXAC|#nSsjRyx zFV@&Yxwc6aq(;q%dBU-x8I7LND$C3tysehsWg z^#kJ;u341Fha=B#=4j4%)pWwxK}uqNrin|y1qmNkbi)m6IYb#-?7(SSaImv(Wn*xL zT)=Wh>8g)jI#bv>)(?K7=Bf(j;Pqp3y2*iE)!_K7i+|^k|?DlCaPb5_5HV8;`qQvM=_#gnD{?x&<6qlHP!AH zP#;2B{byhPe&X@1k|5|I<8r)yVd(E;6%s5E+b3Ocu9((D!us`1p7bMyp6YX?#JHiO zqh*`#CAHAFBE4GPhXUW#)pfze?G8hyJ zQSlX%@b-AAPy6H6a8-g#Z2n5UMvkQ=$7Vgr-RDQpk za$ilSA$+7&bD?q>1uSu*XCCu%uw~V4gHMl)NS@!vl7~8;q^G8l;!mJ67>Y#5h0s+N zZtdW2ZaXHCJnIc#ZeJa^r!r)|`3gtuVdMQVC&!|W+!v(N^={fMq$pPqweYmDu>I!K zYLQI}oBq@FW2bFF*#a;F83H$w!pM7Lm=dQQc)i=FP7?m*jA_UJP}IWcBMPdvi@?e%Ve@^s(s$NPTAze>#%)!rM`kgc|`KDq}d-{l1%|3G7k(ZQ|d zDn27>K^H4#NJzJu-62>?0#>BQA6EUF?y{(;C^ZQv{B9r*;QK?Ru9N_`QgpOy-AcNomcE`@DJi|OwzjIrZm>fH`?#BNlEi7#8~#NtwUeu!zOvXJ zOBECWjS*R|U5CwI!F=t%gV+^UuDdP{ITMrogllF{JEWo@ClzhZBCe|O zEyJi*n=q;ZH6oXhw;GEE=8taYt{IUn#136*9$w610qE=tOEfMAwOlJ`5F?j+W_WF! zoKvxE%eV}#SNT8f&fAR3(~=L4`rx`i0L_bw)5{+|&WUIHGacA0eVooS+aR49zR6E| z{=oqZ{`T!Fg)WEIemh6kc#-tM(d;9IKPlP;fm+!=gm4^DEV{ketdH1sbryZK*GTp=-`3VH8 z@{>lh1#0f422`_aRU%#Ricc6xew&6(D~570vM zZ4Qu_szX6RDH@RxmK72VL4Z<9j`9mTySf(B8xW(Dpe4bE=xDiNukg!WC+i`7jf0_~ zql0K>z6M*{*pQj&aHbNJqcM7bvFExKZYwA-dPo~!f|gD`*U{167Nb^|PgQn9Zu;P@ z1Ki*Hzm2Pc;S6|Rg9=v@8hw36P3bIT`L_1zv95~E3Z#uBE|a3C8ubO&kR2-tv`9~- z)vx;NMvUUr^y`qoONAgQ7aA2bWd;OO+>d@6yU>P;F#H(@RgvV!skwT^(Sm-syX)72 zw0d^pgIS0YnhEGyh1O}!nOva1B$fHGS3~@i2?9I1w(D_C=@vg-)gKM6JB>p?km(SUgkDQT z3@xCZ&EH;UpHZhbw=yhoxA3hRxk{3tUm$)wzOJ5zS>?jSDF0@JByxSx=WQ|$3ZST@ zlmMj}9v;B&3?uDEfUZO2nDXyf2l!z^)Nm3B_nUv9qe7rXgHRe((O1V?4QYjoP^vS- zk*J5oH0K#b{4g0{bYy_Z1fldXfJFB^nk3x!ymfKRv^GkBjr~d?sb6y~^$ju%hpRk@ zB;e;&4yFSEr?^ISmvnUUUX3pRzkmb_(>o%0`l%>o!aJMs8g`x%fKM&QmO<&}r2fmQ z!NR6S-l_A+%<+6fs7W{0g+%nnae~`Bl=|;#(i->VdeaE92fiO#xC8wkM_C(p)^fyj z^i*^XSck@aFLgo7W~5pw6oRRVA3k<`JevzLULj%_@H&((4E{w6J~h#Ra5a>Smieex zkEljpUKq01Jm##PT)TLjx*5ZUz9^HTa7=P7+`47YHk#7`>|qamz7=5N9)I@JPJtVN zulUjEgC|vX&f&|8h0IX$hP6Q|wE^u-jEy2S*t{mS$-rgPn9_aFsigarK-V~`nUJfSF1?#_iQC`N<=-zM3ku0{XYX|vF z*I8;Pn4Q)ou>n|K#qz?0LYil;Nz(D}A=DTXqeJwAu~2?5_NFuhO}~-BV213|@LSdA zNxtm{jvP*d9g}x~5B>8@3nDcz1FQ^)o5HBH2SsU)e&@vAUXcQd%on5X(_bJ!**qV8 z93FFiY1v7WZOCzpYG`duFChl?FLT*Z5sP;ThvULe3;)^UUpwG_GOAh7butFj3vsV|AaUiNqip@8>zLc_B5UJsRO9l5>xVNnCb^PO_Kjiay2dA z6Na;!0lY8&E-)C8TRgyN^7bWwpg~W(PVDB}#7lnLcNCjW`FI(2ScT93KDW;@d=x{> z`SWtzqfru;^3kkH`&G^ z3Gk6cI50gM8+f2Sh<6Xi?eoDL-iDC-lwlPh`4O-B&ubw4S3e+aF7hkzzsH{h1=PPH z<%4^toI#M2%iE~!^XV1lm!a26s~0v$Z6|lUJqX1kvz&+|*4*jC!`!~-K@kltOXql6 z?oRhv2@$jfx*nKJ^_%jKq4Ru1drVm>TeG9J_FkRnvFFRIi(I6;Eb(4n0H#N|UB`aW z2dHPB9f{$?+2^gETxMkIw+*znp^>?HyWQvIJ>+TfiHPFRscFxf`oS;eSlTAbat$H6 z$$K~?Js%<~H*=A1rSAx}4DxaNq%}t7so5tNQKcYSH=D@$%2RsI{TUSg|KXJUfG7ob zI#dzz|J)z|G_WcgyRU@;h0ftF3lHbvDn^A3wQU!xO$*Md9U|G_I24lFYt1ymk(R-x z%2kA*NCaXrI20qpR%bbJ&D|N87ZG7-e8c^E86;53KG_%1da-$Cbo-s?Sh;D!Uu#Xy zZ?C*8Rf()L^;4C7h$NkX~{ovY0aMZ};_v0gAcToU3p!O8j(RrKe)hhll5q%c|&7R;(>ee9TYbEl{$O224*+x7y zF_h}ud3RL61}#4y_@EGp|LNDDc2koQ|JO3q`B(DOxhmE0U!xNW1RXib_*h9U*IJ?o zrkqhA5;SB)IDHEGIpXg+$2qJZZPDBcy48HTE$jw$RRD-fnw4P@&vNe%`Sg~D^3hSU z5?nHyx|ti4;LiS&NGbHi_KoD5N;fkN$(+%!(~5aWW6h0W6b|WF%dj)4Uy*hdE><w7kM3{u*9a>&H7*_{GdaTV94V7 z){2(UGb|XTIb8A`%;B{P<(ABbuMA7pH;Hm;{$_gw_4lA3m*!jsWwOAJSd1|mE1VGy zM^8^+-89|I43VE$7Lrt31OlIVgt-qWN&Lu<{C7IS{+f-1N$C&%?`bF@+~YK$f&l?* z(4e3|dd-jHQPswommY1`5ep_VxF1{Ae6)IsR+a%calBz=#ZMk-V|3v5Jvk6=O%u|@ zRps2zKT1DMVz%T8*3RbKcO~lH&i}P1?ES26!#bQfOwlUP;R9$X{_5CEt+cMFD`{HE z$)})(&Q8WAd2i;kMx{W7KEWSsFL1R3?u9V)RdrBLO|DDjXk3$FM4uAu;Hk2ueKwQz zb+FfaRo@`az|_LgvFlH9T~%n zwVi4yrM-pbXF8X8{5Cp<*S{CHXI^>v{nxpRw0SmCe;A-GHUC-f{}VDhc+vLr4HL7i z?bm(@wkJ@A%(T;1B+O zF&3R;E6w?~7GU}{qGsSDy9}-k@T+2;f9=o1rj2i8jgE za!aAQe8@ju5wG)zS9m`DS7f+M1s@+$?~9^(0N#jTtgW7vEsG@C5}KbM_})@87K?&R z@>orSRxOMZ_8|6u0xb`}f<-e39$FrtD_jDl<~JG$xv?;v$#bYVzZ~$C48|+au0+c;-T3nj8th1^(!@S+DYhal&9>#9ywn8sig?cpc%^S-gEd z)0Pk4PP)ZmC+AC`as#+F8&8Krwgkk)xK>!EWNT_dxtFxX_Lh3Vn0^~0{2M%jfQU!| ziD#4d0SS-!2m!omjF%pGwtM^Ump2|2fJBIwRBsmp7_6s&0w|#v%Z;nB=hEmi$>E2K zFFKl~?!0$_d5nC()w`)m##pqbU!n21n z#^q2}4?kuxBLnElLlk6G1A&D%t!gi_11g7X4jKNeEbOLCC;nu~%;Tm|5F`2A7d}2c z3o~ErJ-0ezxb^^dF$qRY!^oY|9rE-?ASIza$eN0;Cx@Yz3H7(YtAgPoNws+H1uR zvKS@3UlgeyzYP)=lEe-%PD9b8D}(DVD}A-XFARAt4O#X*4?-apVaGvBXWrb-PE4un z!C_?D)HAI~B0h^>{khy>lyTo}C%zF8C z#rK&BW7zJ5qVTKbUY|yM&syXW3Udav0BmnX$O!clg|GnikOEL{y+8hk= zR#7x7hz6`~vhiHT@xndg%#6Px&K_uHenr(o>9s33>jZ%f441*7!U3Yc0KOb&=QG0iT}we|*LU2NUR{zao< zfp3v|D12i_M(Wn?r3DF_1OS3!qMQ`Tydh3~Cp8mD`cP&IMex~_PqMJO;U0KIvKex} zVEm;=C-?rOu`Rm6)_On9qAJx_L_~`vkcLm3 z^ewWaE&9mjga~ySAR+9qU-VGdFDvMWmA>m?1y@@it#MuBHzb|mOaW|2b~#9P=bw(T z0@%}R=uP0kPJ)0%`e17#5Y|kBNIftL@YC@dffoNV+Q0^Gd9gsyb+EJ`xmBOjY;A(dxbcD+z>HkWvf9)P1qkYB!n)Jlb# z58S_CIr82ASfOl&bb43eetLaMx|IJg-A4{!yWHv)an&D13(weW>$z_uHT?c!%P~WsV}eCa2qZ z?irOYEc$Xi5qHVG#r}cs2Zv+l-4Sl<;rr(n0GQ)<{)P8$Q{Fp`4$+SV@nO*R^>q%Y z@s9ewPenVy`r4dO5;j5}cPHXUpWxK>xuvuZ>{2!WfNLcK6+U%;H>1`Un^S?z_w(~n z8JZ__03>diq-XI-&(h*A+M(Iv+7S3$^QCn3Cy{p|dKtlZ9$^o9(0k?o>~|;Xy9O}U z8O93&i|bj;zg2AGf$)FR0f3d|0BVhaU_2yQC{} ze3=Y$4tXzqAKZ)sy+Bm70l=9oE3cl2mnw6UPY`hj0E9v-1mo!=wP87CjoM)I9PSj; zT*(m@jVYe;meSBd?T(xU@q?kg7iug$q934-*p&Nx0uOG68BN1yC^&uj)9+NP(k3@q zO6T*aq#%h-M@TI=95{@8YsWCHfZOuWlP~{)LV|vh#D6o00skpY{aZ-3J*~K!U|##; zb5;e|yh-A`Z6;Pb1b0MkcZ2-O!Nku)9j@Hr$fTWOoJYc3sQ>ej6LY&m}v6o_| z$>cOmu&pwSOL=*}*nTOGL7K%}Qd9kO1q5Gl;j5|HAv8D|`Fw@U6& zV_~wmF0by)1fnJXRzE)gO0H90Qc?8(?mT`p6qv+hjjyccjGaZ}6tyN00nd;a&YT65 z=c&Fh7#RB1tz(WWrnaR{W@!>^NyP^4=6|?Oi;qic;j-GyuPEd-E$v+ni*ngI*KBN< z_?WrsG`nQ6j4|2EPwqz(ox;TYUb8jezHd%h?pnzyLvI1PRfS(#F&pmY2JxKAFH!u_ ztoG`C>Gvyuki?h?*eo)2JeAz|KjrinvTd!dUgZ-w++h0At|BR!q7-3R!JZxd47!-~ z2BR^;t(znVrH8s*vN?|_A(XrUa5JB`THB9e)BFzZIS+fmt-1raU&hJmHYbr83PT5n*RK1Ts;YK*qrj~6kRlVc>7+bkZ zQfLu2ubZY$o(%+Za5&3^wVK&5iyxY_wwl%_JJW>tLHlT*U+ySN0s8Cls?OZA?feGr zCrl3Mz!>Q0a#^3Bua(VY_jTaQ-`2j4pT8~3$I9XRwa2+LbR`CG4DQ8n@gTR5P^Pl4*IrNN(CyWfI0+HM7&n!HLd{`@Y8Q5(L{ zsC3Ts0N6L1==}3QtmafLngW!y+wR8PFh_yu5X|HEy!ZB#5On&vcz4^Du(w-mX)d#e z9;Z3_eJe}F_;MfhPBoqfHPxAA!b=r7Iqv}d9Vbh?dneEQG_lAI#Fy169iCgmL^ELb zfv0kKg0aI3HguMOjSZ(bF5*{|A7Fqxy&2=0yDq=cWoyJ$A1ApLWn$Wu8__x+xgfjTg$GkLrY<)y z6V}yH?-|K{nr*k13o9OS_NlE7?dG@O1IW#tMJgowsE3^jq)+27fd37d#`2&b?uZeb zzjy5Z`sCZU!As-XSbih)=hM>>-*eD?6wT(tEe)HJ9|>ucVYpfaiq@Se`l`kxvGb5B9BMN z7*2Lsd(d|L1FV3Jxk2#(4`Gpk~k(^vybICO^uqe3M z5(WmO>l-rag3fMkX6sUJA?!Ite;oePmFk=I0XWDZ2#z}JLad1XZA*npa8!&*%LeRL zY2jh@{L>sGIWnPEJF{g6$v%|xn@~WG(b^*zHk3yMAaODN#+ECWD=_`jFL(|r-7bGlI?pNF5Rb?!8rbTwZ^xfA>6r1g7m6UuYZEZI z+e2+%4*9Ds#5xe%m@g_AY#*PRLG5aP{K;n=X|}GUWJq~a70ttM;$jE&k3brQJCWMq zfnrmfN@dbGMI{A9S$R1oSmFqpFI7Zi;b=YHsb|`Vop)W18@LwbmMSoN(##QbWc&@03{l}>J(2}^*yQgU8B;PG@&@+wzBkaSjKxOz zb?C=rug%8NQp)Hb*%MVICFXmbE*P-15WiF{W6cOjpZ{JAz~@%<-q7&<;Hfd!f@lFr zpd(yFVlMsWEdxmah3PCLR*tnq>UV#WsMGtU;Z{2aIx$b`&=GNv!g9c42cm))$w`sZxy6>B!&H;sIizir|LSZ1&Uh8f1yMe)=j=Ks^$tEC&E&*y zUe((>Y=c=eTGAxXUR4>$*JV6P{W4-S{TSTk%L(a|=BI-MzY5SAm9p@Ocafs}mS=q07n5DeAj?$YQAVqEd6# z1PkuySDoIjp?5TN3(?Y&|LC5J6x{US&y>a30D(%~g>lzJM zCAZKziGO#K3;f2qr}dvzkl~}i%Z{w%9ueU$wT7TCi_<7R58@GqGrv+U36}$2T0`0r zNlhLAy)SpwF$5*i->Z^s`(0N7scLf4#l~$x^&lovZPiUpGjk-%cZG2Ke6M{vNQ%5! zKx|MlMa(6u1r_4FU`l&o$6!v0RAnSO&Rc$FN}@U&QG4%n+`Sf3+>R}OtA`||R3&7x zcsG#Nst;Ob_+5=}0L;0nsbiii2^EDn*TEK)Fy;3GC?23*b^N`bLBRL_5%rCMajk9F zVWYYoRmmfoD2% zZgBy_aUSK$G0snh3%;I-J(#VgyI*V#7$5Rs>(<}v2tC*5axN*8;BTfRX zuYdBw%Z-f~l|(BP`StU=(XlfH=?@-&Q1(LESpKq^K5hN6%l%g#BPd2LYn z4X;E-AogOX)?C}xVEMlJlN%0esqFwNpcG@hz=KLCHlS|LBgD(MQ3Ow_kB`G?9}9r5 zoz1aTU=r%{v!>)U(+ThnGc1)=yBG(Qw89 zgD??c;QKbppB9E_Foqx@*GiNyEaapl_bV#f6B|ffv3tVe!GKi`;)-IS(I->%sfY~k ztR&}az)sM#JSD3S@Co`+M28^U_}tF1p(%)RXx58o;)I%18I{q;*K0!Xi0#T}7)Kqz z@2jgJSADu@O?z%axo2DX_VJrKAva|f6SE=Ex;@X~RWYT~FgkPdZb<#ei9lof=D@cIM|{mP!!n1pSLxWxbLSVbdo*k&oK(dJmmm zoe*;#fRD(*GQvZc=0>C|{lJfQePD`Ds?|Wa9iL7>V<<)S%W%CD&bd8JcV@qDFC61(%BLKC>oE<+z)P zP{9CKj2kE;xlZb@Fle}Fsn%MPEtHk3UGE{y`+rc{fAJGoJAQJJ*!I6d+}Sy z%64hf{Dp@rcWKJdtL~iKK_3(FG#%yn_vanFPXKvWc9{O}Gnl!d1cTTl@s-yqRrau( ze}0}}`GP_1dkt1)B;+D)nHYbJ zwP-%)U|H1CX8Q6BF(q52Xl07I>N>&w&CeON(M;Q?3F$k|Rqj*cAPevKgq#n#)uZ6uyC)T_x=JAzJmyu-Es zOx~Q3fnWf&uN5il^Y03OMudE;!!ADRo#PaFAid$@O#hNw6%KWaj5RdDWvjcR-Ne6J zc4TiwMa4L#CC#H&%~}y?Km4ut_(-BCcEjMgNB)RbIX7~j<11KK5J~uOFm2{+nbGcJ z$G`wcg$)qXmRdcJLVe}TwaaY%lNL)3Y<217DK8$E0GSe#Y41yhB~^}rrpS=`8t(@1 zeDvc}Ws5xLY7DQs8n|1YLX_ywN|+hx*mWhtr$y-ft$BwiagG`zKoPQoj-GY-YPqu- zH$HF!A(4zTh5|W?RKDOK`hU(%La1;?djJ(GfcD>cwlJEsDa-(u9uT6Z`uA4}D$RwZ zNvHl~2ha3POcpfZZS_vEQs(((X{Z*DlwrYwXCqhS!Z`EmNHMrlY(Ii!<=%o#RmFR5J7o9yDwl4U@r7fw4uq z*4Bmv7NL6FxBTLWKMfz7hot21T7HWbl~b%WKvBlcyj4Uuelr}BlP{Cj~wtL+}$ZOx$VH!|FWjKe>7(pu@IbW zaaZyhVRHZ-5>Q!C;I_(Vs>JK^wj+PUirG^@`W$Acw#l^Sz2@2KQ!^0Lu1gqb;PNz0^RGAbRb+$F-q^vzD z6r3>XdI`b*9YbfJL?Uv1n(Lu5>S15JvWxTLO2Kc-8}iRt&srCUrIPt=GEY0V>(tTZ z|HOtO*~7hNm{7wtLaj8P!Q$&vS_%&O7GLXvdGvs+s3i|yG{4P6${4~YPjCm1P+xqE zJ3cEA9^R|pax{G4!~8#f1q~)4(r-I5Zl^46u_*Qvi$AK;+mw<{4a!i?4dI@MFidqn z0Pav@zoo1mdXBH9rG-5}y1<`@z{G&5p@R@cb8ZL5$JsgQSq-}24{Y3212Cq0%jl|7 zL*j0n&E)(rT@MM(JIA`J;L_SY4Ny|7Kej-=9+8aI_iK-P@=H_PI`jH7EW7JDD6Dz3 zq{1NJOxq9==mhLAoXmjj(7DmGLNOu;vg0E+aew>P%rA9;N^hJ^;xq6L zRZvx(@6?-@h(T0nr*e}BJtvpAvL<&NXk+ukExqBH1608oq{%RoV!I3a{5+nOnJWeQ ztJl$a2)R%2*G}$g4X7hHJ!r3|m-AVQNU>YkTkVM1vX}fl0S^|OfQM8Vh=nDt=O7*G z83tx5NuZhl;^zTbk;qUT9UYcz3&C^TaLknbX)5&}KYsjM6Epq#*K3Dc0395H_dhVM zsJMe!?((Uk>LEC@sI4t?r--lifx3*H5dQB%^NfT9(ZB5dm#bEhW@8|?)wPBb}& zW&K#L2-FpTWnC*uHrQfU3e_W%`}qQ}3tT83{|B6}t;MYF@97#pBD2@c1cgGY+ zdejAd_#_fiv);oY&9$~Jbqa^71OZcwoXRC%xpq4`On_^W#PF(TsIH z$VBp4K;IIaZKrC}~Q;HY*kXELdKH zHvZlt+2VE&b9mxRTUhV*;(=w6#>Hsaj;cqBSEzq~Lx+>@yD?TcqDtBCOv!MVPpP(5 z>16R50}UE(H0En*ZN+*kTbufPmEA^wvraFTD9KMEzRV9?Ta?%K8^zaAbG&Pc zsIwH`roAa0Y;CpV8ybQ{`z{AYW9iEV{haE}c~UT3k_dm$ez4L-q&d9GyD`5YZ2y&s z8z-8~s>HVsv0qSuFYNOaSe}*|q#7K2boZ?e=D4Fm@((o%R^`8vk$}3Hi1M%}B__V5 zQO(I^^KStr8z^4>rqjK~f@0UzR=62A0oz)losJP93C#T{L22Mv6AL*5xktGm2>C~< zi1dJPGGXFSRHeIKA<8zh@?tFnl-BK*vkzBxt>OZv55wA#UA|is0*C>UiWvq71HMR^ zB3nDpCnApdHUgWKVD0Q0v2vtjhohNO72(eGehjdQW-iF{(P3u4nscIHYqgCA6rUT9 zoT8W4)0ETpW#m9NvpNr=#%$AoC^u+g1hfH{5UzMXtr&0XINF$Ca78MWM(Q)+Wvqa+ zyb+ zl?xP-8-D>gt17KX57#ZnOxxh-GB0z|H*A=a*uD5+gV-~K*BREv2Mdx6la`w zh$xcgi~Mqx|MAlg(c@IaLGX%rOzu{rkd|!MP|YT~`D48i>8Mfk)-?*|G3$W4ZL*LHL|zOTRDH@TI_vfhR!<-P^AHFvKv3%&(3jb zWCuSm&jP05w)Q+UWY?aSOG*!h49AiW^-l#>cY^v0+{So&ayAcfl{dnkr4ZTw%M@s? z^gY->e=z@eK3WS~*mpM0)cO(+Hw;PCE5jjOC%ddT1th=5rI+E}#gQ|ZKV27#^nGDx z_yEo3?L&vay6AGcBJ#TTh{X^sPvNjEEVS#pFEl(EYWu|7)5^2Fghg-oc7@GLYve=+ z(Jtt-gV!-b>Z@%D2Y;Xr&U^fzv20dkK>0h05I^X~WGJe32aV`DuI28pvrSaVUa?%m@PCQ_ zvVSw=jot@j)pTjQ-v0KL!lZ3?dFc&}`MbOTiKd`snD?iOctJ~0mu&5LmQvN*udX~Q zc7w|@6mrk@cNSxhy&tR{uW{8p$|Zlvc=fN3mq17Y#X?GZk>C7KrCe+*)oq3U#!kHh zcoTphg7Telm!Hd~ob_i_;=7fB!c-7f7nwG&%Tw$AT0YKI5ic(HNS=sd+dpEcvy=#6 zu3O!azn1bX7d)g6LFt*$rSO<0{h`m!{4rCm-woS4b8W@ZnNBy{Jc{%A@*eRHxY@pc z>0tx;Z7FWJNh1t2KN06c!+-emD6{bvIrvwkM7pO3e;{Lr_J^Cww1&gjfD;hp5*hZp zej&IowlH&hMrOqG61LYw4j$N2BtM_eb|bm=L?H94*}?bZn=pj{`U59}w-_|a;p~pg zFS5;Ap7<*OygN!5mdpa~xv?S86-m5Ry-HDP!%)MAka4KA)Xg8@BhI3}8K(%|<4Y@p z9~|=2A()N-!-^)KHn@GGh=@bfGXIV~}#}vwhTc4Zq;-3sw31sSOVEJc* zUVOFE8-&^odg|D81hBlnUmAnfa=thkAG40Y?RhgM)HGIX124LzhspFqkzn?#bf?L3 zk5>zi`e5|icTz8OE!7rC&FC;ZPC9=_vB2UZ$Wldc)5tOeGbUlmNA-VuYspd<8VK2! zLtCYj?DC`ZBDx^Dc)4GcjfsZV+d?c_-7cJ_HM~&aH1( zX$$$fLf&&f3l3SG|MS00FGS!UB(JXIz{tkh^25trgM)ZqCHPH+f@DH$*x@9Qcg-V^HGjU)56GfKQT<;)UMm^+N#5F&b-?(a=$!%qGE;Sm zM)b+h=DvwcEbNP|qzxePn@kAw9QAQ;SU!nNR>tYjBMv;w_`IX`5s;=pykwq-^h||e zWM(w;1x{-e>7JU9D_b#C<17&qOyoN1Tx$*TMjIrEsEAKuw zW()zS0FRr6;cW0C$K`;rPe#rul@`)}0(==UfQF7u>524Vc|BR8_Y#z~R1~9RI?OiB zP4_d@sjUp~P0upnmns%)X*#lOCI%+OSy(TRYO`{Shw}QEHv-m};E>F3QJ_J2zi@ox zt8p<&78R<}$hDltAFJ#7rTd2afo(4q=G=g=2Ahx})hC`Lc&rfnUINTBviwphm#nL3 zN!*q}ekiqN#`p;-*a0aL(m#k&Kbhly?wD;($*ZFP{}8vT-zYjReeTvFCE|IvKwJ8Q zl?RrSa`}b-p!xR$L-8?-Y z(#)(ffV3~$ldPx2{%{JOHowcecCj;vEM<4^YdZEfUeCrq>j+TuT^6;auIf)7*WURj zCpC}yUsVj~s%!9rS{%@?uw{f`eodrm;o-{1j=%?J#?dry=-Kwr2 zTQ76^SPoP-ZQ&UekaHF4w8vfK(C!ptQ>)zKSJ%Pzq@|1z`lf991+{8h-ztDGxGkK^ z?{&2`S~*q!XH@}o(XR5~W+#j7hQPiKqKaCGtkEk!Ro`}IwaQZR{gn($h)P@|R6<_QYoyDqVFgIb;i| zcj1OV=WEhZD;EY$}$5Z^^UArDFA`9cXj9 zuWjeS5_>_xdIudxwOwTq19lw(?4v(pJ8*Tx*PMXrgo!_DE(kin<8_?A2~P(@1(X+E z;|@d8%`ctRWdTf#C|xf6RsbUh;~$13m)F<|zmSuCeRuh&7_xgQv-sayLbS9{Muo^} zU3m|dkuu1%;F!jTi{I?m`kd^Oa&=^NVa|Ti6j-yQ5Fo_%^HWhTiDoc<6jCGfh(D-5 zn5GAgX-yNVs#Hz#YWVrqRq>KxL%qw56TY5h(K>$AzV*J? zOpe*=31ath)J}$jWN62MrY|(k-Y*+Rk^AE0%!T24f^l% z#MQ<+4yTfs48r5Mu2@r!oujjBSt_-lh>iQiGI%cDkqU2O_y^|kNv(>^nP1$)o>F`0 zVXL~bNGWd99~Q}2&E!{9%I3Wd>=1g4pCDR3oc-Gi{}8w}Sc*!sXE!(O+TSA@>t`vY z6#Wy=8f9Y-%;&`UwMb4&#n6{CV9&Lat|kI9ROE|$rwWEnhau+ESTipBw4;G5M+Kjv z`#>org#$jbVS9ah5(xzDK&P6-R-mw;j?BUZ!(|&@S7N^y_Zz+Nt9gOGLy}vy*~#8j zih<6wuYStRPcPi-RyZ@x(2eK{lkl$;GTEeU(ygUmg)iT3)0KX8--`8aIo^op_oQ z@{hWx@9k8V0;htNa6awG+Wz%ZM4{WtC{ui#Khr~*qEyOlf5Rr_7W_7q0jPT zSbb?2l|&F=#v~D_2#|IGATCPbgXl7;08T`VgTuaN`7w|-jr)$|nwbitk_W5)oke{l(ma$3DyZk$|?e3F);%y$pPePFk@n*8+xok0GTgBu?ayZVn7c}pDN7ACD@@r zAEvou8*?F83}+zdZ=5K=$$`N41LcO?vzNS!OQWdY)juZ|9H?kU|I{nf(7bG?{Jh1~ z4<$+mFH~=jj6BU6C&@23K6vfL^h9sJJpohNCrZLB39eu+mUF?oCQeMN|0WkvH)~#O zkYn*(+N|NOW3O1G_-U38gcHEomQ9spr&COvI0=AWvMV~@#8VAHeA@bBT7Hqom9|OH$6pDL42+R{HIVA;y5W^p6$7)p%gW7rrr;R(PD_uT>u&!r?qG*+n4HFPlw;nYtwvqr+)wF{b#tZirpdar*N78Xk z;K7)(xAL5ojCR}_*drDoCnU-lXAeZ&tHD!ci8ZLSvKNZ>YEH2&=R0I#dB zg~9p*UK@hXtzANMRW~BJu>@uUS_hd%r1K-9IoY<`@8_ad5i6-j8;Opk(|ll?Lc`a` z+5!`}Jc2(WLiSeQo*zF1dzzNU;??izF-O4(F+_GTQ=@P-}`ZE8?d-@LqRA&I*D zNf>*{FQ^c8Srl=5snQ|bN#7+CiYot1wdpn*Dj|O7A5-b1Jlx}h4#iIN*-;Non&h8p zpVz1V?5tqiix5Sd8;=Oyo*j+x zK%tx(xv0;H5L5!jZtbbh3tOag{H5zDo%h8Lk%^h$o8Z_O3+ zsSG8-K(|$SM!@sy-q3f`E24}za&91074lhmh-_eo7tQ`;Er=rk`k+7uB|>i zD|~Z5olrrhFZ^nFwGLLvIYc}79u)Ba`5_Wc&SM&<=mBpxJ6a4x&&I}qq6x>k&7MrN z@PAKhP+od!Q}FF%e(5c00E~b&hrfukhpU+zv(jq&bBFqaaxvpRI&x3sWqh z@k*-J(t&1Nkf$RnPUkEB@*PrKwc59Ah|>YlDcqQ*zno}oo;^&guT6POWx;G6&loOe z+|u$Sq_IX7sMvOSsJ(Efp&MlV9tQx^x93lF$G`G7f~RYj&%^LGl;Hn<4cgT}lNS7k z@~{O6{&!KRkaB=Y5GsdapbZuHjcF#;Uc_bohqr$4bUmukRp=V4Mc-FxSam`&fsrk{ zZJla8gX4nDQUIzEd0FPV6OOugL#V{t`1igs8q7*`C zm*E6Tb6~8EaNT9U{h(Y(sHT>?oegwYj;_2X9ii=WJw6oFo%Pa4Wdvy8^d@(YNRgUR zTeebA<+{ByB?HCyRoE-w22`*ezQUw**?uFXyTzRAdA3UVIkC+2*eA!|<;VwwIw_+8 zbBHh>ISfI?2JF>zu{;Oy3{Ai(WKne874CuJgdieo1zSJ9;>qd~{F#j50i^1J?r2_l zU5;mGCB}`VsE>*cW!qh{kN^627LPb;>wW23Y{s4I?U(tk`r|0wDwm|8 zR|SAYN;(2f?cb z=rM^*E~0kVoMLkWS06TWmXcERD9#W;mxdv=k6yP45|7 zBj2*W5!(&|g;+%)xHpKo&i?B>J+q~K5lFE?Nr-A+IiT~Yv9ekz=5A%CKb+aht0mVX z!G?AxmPLFli1ZaJ@`Kk$cz|kN>gCB>S)01RpD@L{T#&@gr~^e_Q^I|74#k>64oVLb zje8zl!Ud*D@v9nYJy zE70|P>fRv%or~{;ImkFq(ECb=r1XE|{>%qLSKW zLcL0tV48X!;)!LV3W_${Ww$clrhl3EibR(J-WsTpXy7Ou=RwS$He7WF=H}aGs)(I=@6yl?edUa?d%0;(-lp?M2MTgSHjTr@FJcpy5uD z#fY!ijX=Gl@4po70>LLsLDwuFWcu(tGoq!jRds@-s8(*7>- zH@l9K;-%_%9w}OPO_ZT22)_$n(ufs>^0Sb)vUgHu{?Xpgzylsp(yWtlnM!qkq z*88Groqk&S|BH$ODJ3f_E*dSOBuyaKG=}D{g~s#d4lk9bZn_@_&Rx!k5lY9K#?V`` z-t4ph_(TgPBcY^XXCkSi_VNqU%+>8g3O`-Wwlw-BoCG~1K zD&fj79DVwGQ!oaN>S3b)gQy+h$;xROA%3);cRO?Jm1p^F)BT8ttuzW9r9I5$uR$$M z1N@rn7==#RDC-Pu)-n)UV0Hc5AX7&qt@F4Q>8hTd;TJD_Hu-Lfa2Wrj_vG*-OGG8{ z|G0^?O!x&kEr_xM4S7YJYxU9ahj0VLvWvMd^hO*VWBIf6md=DXn&f5+q!!$l;tNle z9kB?l6@UON*q(iLgPJ1!%+x2{|F??~qquZjtPgb$y0;o$3!_Oq6p0&Ah z(u)le!~=v!-X||3?7=kul~@4b z|H#8YT{M~w|}(5!Z*8-&S7YUq}yIn-rdP`}xx{r9?t|8#w5U ziD=ReLOo5JwENVTOT=8VWCyV|=YTo;3^wVEqHU%TKVK$od~Tj{7QHaj$IEgg4|z1Q zFi-6y=onrVG=}-qEGwIQ5b~GX7`D*pbt`v_XHq;LRiZPNXAN(gHLDqTsKMM>xET11 z^%*i!f@3ND{-w`Q-fa@jf53Kl`v>9i%em&_XCBAKf|_EPqjvkR$CO4$-7 zJrxy}TY;%mNAYL9q(oxc#fIZf30bU{F}vwIMxMw+iQqk#ZQR~#^cU`|B=Rj=r2ODa z^x_YC6;&K^1c#mnW4_3HOS~b)(kRFtybxZKj z9`28h(@zT}Y-Vat%=6b|V@6-;=J}?a2+P_ge~s_#O9s|{X0LX-ax8@~F)Wh}T$V?+ zbDE3QNp-daHR5fr;^a)qE{(O2U8Z{jWo}(@U%-By06#R~52vD{qMZL0OE`jz#rKjD zE66koy=8DdZRt|Sm8VBvJ5&6__4RPu_X5jr!yn}Y<5c`ZJd|b5E6Z4HVXtv#RZZ$y z(6>bc`ulgZo7Yr`p(rVLv^!kCG-r;;mO>Sl#1+~iQ=aP@HO7*dSUzCBtHu;!KSV7WNpB>>ovx|AIIN;>gzh1aP z6sXO07-cD`$^Shx&|p5}L9UJP-<|PwJA78v2qVF^v9p;ni1dhp>dJj0ABKIX? z zwHadC_d6B_1PsCRzl>bZKicbqaSHU^TNngh^Il|kgy#{eI~@**cWSHW{D-k7le^;m zXSwU}2@edNp6RT4Pq(o0eG7!c7#jAzoG?yQ9)s1LoD7{&2-|$V!=^j>t=?L9q}7$h z95v2UBSMv{!f@2OWIx@ls;}?EeVwXz`-A^_e?EM;+9GSE2L^qeD_=K1va-Nth+>m} z_0<#LFyv=VfP#xpwnakN9luqB?*B}S@g@SRJnkbm3bh1{b27It8Q&VCM~6tfkuE6ScPl6r8vktp_)ysfj!ob`T`%I4fA;@{7uJp!r|7{m@F|k)iXs2M&(j!^S6-BnxkMN z+J@_1mto{&SE6R5uqyVW;pV=D?I&%EAH)xFM4`LDlKm8LEU5*{xQ8M3um_()c9w`r zSz!YD2RU93^@_q;uXRPwP7K^YVVz*xm^L(YU%<}eR;3$hRb~DJ*NjA$Do$as+n81z zDoO%ze2*q+zl&)%{@l(!5RX6A4ue5O>z`|X;9II znm9X|KyN5 z;#ZbXQ!`Gz@|bFs(@Yze!o*dyjKzjChU+FKpzk|xD#mQ8Ml1SDu|F?A-9KbxSl;;j z#?^?BliCpFXKN?+rLG~8s==*H+I$vbpPN2KukdfY7k|n@wRWL8e9(8{o@v5+fP*xufL>7#32<48A> zD{F<$d(_>O6&bM>E)S=qUwFc2IbptkJVaboc{-*rh?@nG@9gL0w^U#m^J+xEmA%^k zqV3ckDR#}|d#2d{5(3lE-h;dVO$bsLqnP4M* zgm3`w4`yTFwEDL%zR;wpTG{soqHRbn1b1*rmGc$a4c$DFB2z@mjgj8`GC>>~uC@Xk z)jBdvaUZn~MYZkl@d{nzian$Uahzh`@3blBWt}tSdAYL()8r&9oG`^B-_&U7Rf9B_cIDbZPrjbl&^Ec2d6GCqzZL z$LM>Wk7K(|1s2VWfnqbC_mtpGnmm_(3#yZAJZSY-5KyoSValak@M)I83&GhNR2=qf z$v{d2I~r^9wb>87I8~jaBxA_|uFb|3hu4oH?!3vIm4oiY(NV@9J&ApGXs&jKT`+Ov z+-oVau-#2fi|X(MhK$`2k)+!8YiqCvSZs)gPsd+FraxW8qgMzVj6P>NTRD2`_^%_9 zY@0t*xwskr>9fb^BHVBr6#{;O{p)HL6dgVS9Hm~vU%2>DT|M!~Rz^{ot!0|xFd__W zq9J;hL8JVzs>gy-i45^pRDGYs&!&V|>VehtI1?)4jFkv~H6f0!+Df)7R7RmT8~XxVoG!$+%LXiWEK=!j5+GLC<0?uO!Q{mX1HY)bDtE;Q}kv zLzS4RVOe-C+qVhm{IRKG*BMSvPm|QFmuiB(zunF6z@W?9=!SSc)avF`Y7n26P$T^H zz6x}{>*1aGxLE>`_c*vSVDII5h$aXPDBAar`{lVJwtu?L=Fc8T_kvsU95&A6t5*qfW1 zpTlft)87q9dG60By%f1%CrQDn2|K!ND6jV9_Wkk*EPn@yKz9k4z`IVpnQkDmP&abT zk|c4&pT}uAUdV{cffhGZp1bp2D5cyj0{>hYQih2@*a82pZ~4eSf=EvP z_1Fx(P{PWO4OWSVU0|8LpTQO#oUY!G2`h%93?>*0h9h3}YjE7xfA$=3{6!@>m~4kk zIWY!r{C=?dv+lB?1EJV=Gil5a$9bTz=3IDUU!$_p8bZII!6dp(Q|`3fc`7ge5Ic_@ zWeux2(HHzu)$BGtl*1U>zOOHwLugM03RfF>*}OMI;B(5PDya`l8Up=Ur|~Gnggjd5 zM7{+E*#NZvz=Pn3y(V-K@vW!??B}27!j*5N&F7DyZ@Q5U9H2{ z-o594#j{;_rQ*r+`gu}j{bNtjH{y%oY(_ZDPvn$^Mbx*Xtl#V^0l$5p%gQ&QjGYdO zl6af^)5b-J!I|&WW(WQLt>BcP*zX5h`17KQ6p0XxV4d{1n9!%e!|6OLT9KoLQ>a+^ zQik?}%g2Jj+KyE=u`wM6fH!0(@nTm|4*&G3*xQt6f~4~R<*jCPQJ3r|;ajaw_2UST z8JefGjl&;d`ryD3i%Q=A*iZzi;C~8QfxnU4kXr=Gf!~a9%KyRMmOy@$r6&EM6bONo zyK?T6nl%_hXELwp)mqQPyI4&ljRO`tnJZEq6EZ+J`926Z5-*VI>Dz3Z>Hj?2&m||E z3dPp=@>I();j&3Z(8D?Ij5c_tn{-_t3QvDgJZnmtHcq@;dyBTbiM}bA@Z5W;=80hv zQU(060&Y}qD8hh@9dDR+)MziXg|Gc7Vv4fdt9F^*0rbAF28-UpWT4SUTTVVWLiq{u(L9-Ouf&3 z`&!{E2FT!KKi z`-!aW3n|KK98dr~#;l%vvKBy#2(<5ZG z&uUWbZJ&jZY#b;I%KiA*=4*ee(*JA{nZjPKgsYuw%Hw2ps!oba zAUH)Rb8fyOdgq}amZDa2FVeNUixRFW0(=SS-K-E2)b%o2XCWiC2h$G%BN?HTTn(i_D!%u`OV!@$qZzGeSHYm3E8^uLLVpO97RE+Y zbgZD&PvS@WQOIvCdh)N=gordCUzeWP>Qdex6PNDERy{#fAVv;VxH!KmuBJjQ?6Bs} zu5bu(v%WxAcMP1|vWxp#;ld@w!ZluQxlrJw{eNzfDNO%`g^LpNRm^-2zKVU@l0zDW zF&xy_oT^)CsT0h~`lY#(@{nfhFcT`MrKa5`lxXMl^v0ab#x3)VWv=N#+vQTQY`z6K zy`(v2TPiV9#=aVZsNbeKN<5s> z*9F1EE!Nwn(=nx&KcMx) zLK1^7gIP$*l#(@5GtrQN)Bu_s7GjFP$GoqMR#md z+_$`lYCOl2_Lq<_Jk5S)AXwbIE)Pg5SDmRHZ(Kwv8K%9KSF&MJhgfIIB&SVvqnJ_~P9tompf9o^%zCSbn}$V98#Lx!$PL&nQC7wU(;6!SPBr!LE3> zc!aoY3~c~yUWOr+TQS+yc)sQ&^ST@IWLuraL4TWzz}oazKK^R5D_J-;u%gg0zg0P% zkeZr`2{$UA3gJ4PlnSj3e_@wZSNECnltzadY?VlBWnK&~D=SN=3`k;z??d)q>+PQL3{gcc~xakMw-3T zRe$gM@2vniy20j3cf8Hvlc|7pq2Z1- zv-&Jhj?xo$eSOi<`G|W3_Y6rFkYjaxgnUeT_Nu~9`qTagKzqZ8bzDy9o)Yvp3(4wd zJ~4)~3t%wT4Hb`G@7{yOYWTze6X4X}iozS$zdIB1%rD{QtMF$CRs&7#Qgt9o@LKzT z3u{My)w-CWfcc`WCJ4=`nRl&~<_+0ap;->N)lizdOC<+zU8$Y$^%)2KEtwO?(n{2fO?BX;R=& z!&@f#Y%ie8?#z9SJ{6;>Q0tp*?{DT6ly>U;jS>2iIz(YOeHh|mjAL()$~s_%YjTRB z?&Del_7{AsOhw(l-ka7dxH0;U_=^Uc8LUv&YnSq=;wPv&Zvg3mOL^Q9z1h{posJDM zCl`ptH37|Nc>dCaRSuAGd(eJ8UW z&m^U>!jAvz!U+)8B8w9N+ACRxchUS$J_-+2FZ?@l_?O#k%gByytoXNK@`+onZ$Fys za47rt)YemwtU^hO2;@mXH*>Q9@KmaF@JAtW<=c7(4Jf zLlh9$ejw5a@M&-I9C$3nl=C$jJ!c$@{mQ0;#ajEb1TGi2ZLQ7J6>&tEd2w11)d3Q~ zO1mkja64a2!d#zWCtf&vZMiPSG!V4m@P53aCqJr%R`c9nceJ*cvjp<{_N(q>b_th; zMkMML4cK)B21wK*@E4@M*^GOveMe^HnZRg5IdFPc5rAWl>S^ih?mes8AmoC<8J$5|Hzi9Chu~W_&-EuKCudy-m-tE-xm7Ma zdd?N@LV?IIfYpbqIg(3GeAnw)7pqY+z;gRtIu=wBu+uvMd6~PhXHMSu+#C0Cyi3@N zw{#s`6G2|s7ztK6nM-zmXxV7F#JTFJb9rvR9tOjdMHbFbg|YM~hm|{~5s&@TFS=9x zu{vltKUVa{SKEf{rchZiO$yFI09b0DQVFYeFma+E_}b-Tk;b1mmvbbr2yX&PrwUnr<$MU%{Pnl}@6^{r-|urZq^gAq5y^O5KZ=*I8-O zp#dyXG3hjh)T=}+l)4BYo+*|3GAb$GeOx;nlqvx8YJC7(k^P?QUbr&@6i1=6gjiDj zCxcw$H^DkLHw@q+F_xc1qWvWX6d_T4|EQn*|1E(|fdrxVefheI?t1(&VM@Ia)hND;CWQXY1H9xOx^Rb}ee zrMuHz|M&nvy^c!_MPQK~Cr&h3_{$BgBaxsuCyM)UkmR-ZE)MmPA1*bb5!qpX%3~$0 zL<45f37T`+AIHgHfHna&Zu;!!ftm$xn|+k49GLI{NQBEaoi0y~H0$|7+I z*nQ(6`@*HrOVEbp!$Q^Z&)0@Uk*}#tQ=j+p)LE;tAn`L$PbOdwTX2AP@M<}bQJC!x z7>AnCuqZ?q5xDGA92#~EEJHW^?voLLy0s44_rTlD^e@n*-T#lNw~DH3TiSN9Ai*KH zdvJ%~7G&ZH!QI_m2ksIaf(8w)!7bRtHMqOG`1c^wVq&%-n&;rV8Jm;f-=WTTOAtI4Z1X;zTdW_VpQGjg znf=-ULS9U!AZ05QTvBzR*rTfNaFuiB z93A8brPX_iy&r+atQF)%NKqx_e=i=dKJF>EudtQ>7g6v;SDrtpx1y4Qb)bh_MnFPL z;F8vvCFjg|El{jJon-PDQZ)lREeHdp8|M2J5DkANcpY}syw;UxTe?=xALe5{tEdvw z4xQrf!GYB<@Fey`;6?i9tJfs2Q|=Yv{`0~gQU+MVUN{R3jRoWYx^f(_7@-1ExgU#% zH=S)@AFEm){eb1f-toSAYy%GsqH=XX@Z%)BJrB=xK%~{XD|mwrKE{kOFUHIv_7>J( zKoIOU#LH+(M4U_h*h=;?E3E`#4J#X!P614lL_H-!0NGq@d8{`qy zdiNaDKd6jAH(&+c2qGT~;6uQ*mLge)52`3tHN1SfYk#SSHJK&vP+wmRk$hK=)XS-@ z(Jy`*(DpWc;Kpz0o}+Vww?*9*zPQ z39rnN8aH`z`%lX3Dl+R2{+@#MC`6`P!o$gvn=cgE)7<3P&AJbKGLjp8vHi#IhfCST zT1auXpHmMwGR(PC=25mDe1ylwO9_0ZFEqJTSV8}kCYZ*=K%O}8=zW%frp;SiJ8%b;xYbk{a@LF5MQbBE79S3WGHx6`a zNX*Qu>xNs_8U5 zTkx8PkomH~FMpTp;+x;`Y;`Jk!ULn7t1S|gNGXbgn&8+WfXP>m)?bE84yM3v`?i(q zOqJ!P%Hx?Gaw3gA186*;$E7QlBpz4-KvF1WXgrEC zri3!e9*kuhZ1z!EhV=PfwCownm~IwUMT#hu7O)-pRX!h;9>sz#V`9Cvt8A@^)Nc~A z>{cwb-HDd)n9+Lc|7#alZGi*lD)WLA^c{zRPSoS=s&?lRn40)DBA=hlHjv3mvdDU; z_`Kjx9= zf`5Hs7mb2ReZYi40YL1pH+K^>IDn5h!>X4=)S2b&0wox$tM;rbmIO=dp(A*Grk~4M zxE~&MDhhkmA7@;I5jyy>`U8g7&)?ST^!RfF-$dFT%7eEJEp}u_tvS+H5y1>==`zO0 zW~-g_itM66jOsEoXJl|sc%yxYJ1-<)n(&=D5eotq5n(SKpq8q{O4a1`ofzBeVMXdr zB<$0raR==1yi(Q(#f(oEFaf+m)xfW?zPtX(DqiN#CrHg5HyKSci+~d+!hj>U+Ca?9 zgt=aOv(icp53^#ad8Xxo;pcbN!1UFA=)%qOdIqoH-D*d3t%>;Y{+e}0SV zTP?dePtCNjY?R(de<9#~J7}+szDL5VEmlwgNoHpRJ`sf`O`{$y1Fj`(Gqo1-j9y`=d{(zv!h6;^}kcVk+Sre`h{*7N7n zbx-P?zbN;B6_ZE@*=lfz%#Lwf*U{XM@Lz_MX zKs3P%*FXw*SdARkGEMxc8dZ{ls;|Jb6RCs)P3EXnKRBJY1*f&#Hb< zxGns}G4&gq(##hA($2@V`DO%m>ytCbw#S>S){x<}xVMELo1RA#pjzY{M1&_!lzCkJ zwa2f?bdRUxWQ_LN50zEUZg*P}k}wps^nLK>cQeNJ4E1t{fpqT$z0kkQj2|KrYx_b!G%(1WEv@~L(7JlAAIdE*jF|l6DM)u?^GyBc z{@cL#SMjz~M?Tku{f*ST16+d}L5#rcLhDWPVYg7n|#1ME!zb*+i^=Ddnp3b1j(MT#@ zTP3El1Ii{U$^7Vu9tXwZyQSJHZS!JTdffHj*ZDWNT)k@IL;9GLpD$whbF zw-l*u$cwST7P@A)=c^9ycDY^Hcz+R)KYy{vgi1On;Oxn|cY~{;ZF_87JYDIlw&TOX zYdtkARsFYfIA6MWkEf~rz#^nECMMR=#_RhQk1>rjs?5}uq5G}JZ>r)-ENYM6gVwKD zY31Me$sK5SOM{TohfhfN^h)|u)!X&1%n8fUASX*et(YiguIV(+5C)`Qq7=Y?Cy)zn z-88wER?$xI;SsCGBt`(es-r&Ml}^_y@4&4=_1b1AulOKG+NScr^IGYa>bPgiy7~JoaUOFu>f7VX*BBAj zDg4Vtcy}Id&uHeOnOF*do77DJfqr>nhh_EzBhJI)Ps>DGb`56%9>prjFHC0mg)jYn zr{~^wIt$A+IZr+K7}{prUc%nK5JM40!V_xK&QC48LXM-(Wp*~O3MZb@8tNT?U+@>- zHL97p!;?Wz`6xEm@ZNPy*qYWlu+2d27`UH~W)y%HPDlE?XCo zHJ{)Gfd1C_tD$qrA(%LxWMiUbgSRikDu>O7xJp&dgxPUlszT@iO|+t2oaDyUiyGeV@M; z2dr=wE~_vgHPr>9&obd#z_fAypcqD`+vT(+r;ec_$z&Ak=r7r5e2SVumHOKB&*%t_ zN$!;UEz>uAF$ii)zJrAgzsLJ}E+&*4@b-s_a4`}`8+NXv?7#`x`fny8&vu(h-`YSz zm^u2y0#eT`;9@-{c9*o?qO6_wS8y^_`wM&flDdGOnFY@mYxM~^Ku=c*#+jMNFwgJD z-PNw{QsJa&c%a;owP|@sn62JU6GoRtQzNRQX4WGVTnRd0*A^1im4Elx|4iMMB~;kP z^SebBw}rZaPr8!ytG=vr(kUgyy6LOu;L(g=tY@q*@7Dnx*Zfq^el<13o7Q-ztTHI8Mb%fGagwADpEzq;<~Y|%7Z$iN~_Oln%%DN!~q z(?%@vFC7nxic-2muemhAER7UJnWK|Z13b%~uz7MvRyz&5QYx12#qmq0-+Rfu<3_E1 z0gsBq7)Z5OwkG&(%ANdXNeZ#gsiz%>vYH;-$_zotbRVZ3$R09+o(jq)J1Xk0mbSn$ z+|{ho&FEVdgNBygh_)}M!8Kpw%`;PtTZM;svLJRayngJ%nZa?Y6Xd=K^7LR`7y6?( z z-F8>WfZk<0F=zI&SF{w~?F382O^{d4=Ng~S_Y9AEVS5aDLZ>5bug(7IRe|rIdO77b zA$mLZTv(<2^QGi9*75Vmo(WIe!;kZq&i9Wk6^Nv2@Kak4UyKVFNE7+yC585E`Pttf z54~sOku2tWUCa#MhWooSYyj{+WjR_%S24eCm|?Sb+_S{b;gU>9R`moc+)1X&%qcV) z5#19HJC|04rB3ajx?X;LVvnHy`oR=oK&b&x^UjFg7tB(j6}Cxp@w>f(n4&71TVlDV zUH9B4tJ!h+`3R@_IY3RO6q&LG1*iwe5t8WMw_a@(LVlF!dG7Kv0Q|U$J@QIG{35

g^ia!FO`;JHF@8tE?9;0NYaTgDZ! zlZlhef}{@x)B4PX9=A;(1G%Pihn*6@B*?_6W6-Tfc=Pvnj_TbW?Hyxv^{&yYzG)*| z7c~+(@_u5uFch(t(sUN=bf^TYUnyrloxzEI1|YwivG>Gm=sb!hZOn@}6NJ7S_|d7T zAp zu+6X70^{HJ>GaW29wg{AM;wCFi+hvH{3kue27g9oXrOEGU0 zD3JZHwgUNu;VVB}Ft%#e>d_BAKON~g(=5E@iz7)(^G72(7a%E$JC8-LDoGSqOp8fQ z6`0A6T+gON(1O-kw`5#Cn0bxcHG8fHXNXPGmqART{LQF+dnohQZ5K1aYln186;D#8do| z0H~daAWO}B5xS*n;9#l5yvwYE!*OkJB>~y8eEj%Pyqpt!yEu}5(bEiQ%n%AZ`2enU zp4|6@NSnlnb-NK*>whnB(7zU#80^ah-rEEFqQxZi;sNY#GCePXcpC-sv6%7AFb~^$RqQ5vV$Pq{r>$8lD&S(oHN4E4RKw$awXdy8ASp= z*f(gum6De$)9Q9tA0*I$skA8mA0F52d>0scY z1@zKcP$3ZfYCTa~m2X(7Vlz}|Py6Xl42dvIApI8wFwY1ABLu=bvIV%eU%mxD+}tm4fLZ>3cDZYWuO9E^m;LASF-IY3<~jvD_xc$m~ez zSCK($!RL^lDCh#Z{&->xYuqh|G(bI#xSUn`#daTfQc>}zVel)I)c5}N8)SQ;V6(h8 z&#i8X(CF+$&hA| z2U8Up!6brVGgqukfGvvPzMKJ|>M({(6(h@s^l^xFlJ=Int6>~1a*J94l~}p8VvTtX zFa?RsyOu9*lml0Wcux|<{i->DWdbFS5t&DZu1y|F+7Xb#V8RziyqN$UfX?31jqDG3%v4Lq~Zi zbawV8Fr{cGM$dwNlT2}Oh7-23R&Wds2YOfVf=2{WcF{H^cgdZmpTabrRQ>UeVJ__c z+T)$EaPL1G96eS-i2b865WQ5wO$Hc;d6Tb#Kz1@t0_D#No%2>lK4MC^je#Xj0KK>0 zTHP+q$1bP?3IIEpkf!&YE;r)*P)ZgVaP^UJTYLqReyUL0nPtLAqrhb>4~_)C`b$LU z2M&}%>BvQ3=rO12ZpPi0X5iciU-E;lmw6aWT28q-1&Jf#_Zu-_=Y`shS8GEOu&nO5 zMRXs$A5=U!0V}1A^uo?x_!kCrP28*vfck7dfHsC^tkk@X8EBd)7^Xw|ubD5QB;kI4 zo)7Vw{3Zh%j`v(At+30l7uK3k`0kDB0FhH#5AP#x1rTgBAedJ04Vc0BGuNS>6xykX z^4higwisH?>+MkVtyS~13hS1}aV5*0k*vhiaJ2D!zf}T>1*QPnKfxw7sI=NsQyRyn z-CCy0atlm6F4jKqcG?eMI~t*Hgp#AX!*H2o5qk+Z7o4o)ECOtFtdP*yn~vZ*MD*`L zME@>HCPT82Bp5|tn*vnxLeMW_m`8@tMv6^CtW(`1inQOw>O+fGiHusOr>C3kZ_FY{ zx%3#pEM0YvI1qk<=jweQj51KidZDFuiF!gnfxI|cie*rN^am5B2!IcP5Aq$J9&M_Y z8U^Pd&NvGGMlNLBb@V+DWr{EaDr4!%ZBX_$qkz+(ED{o2{PwqRa#fFh2AP@w0?&eJ zm~c08*iF=eA5;eDCQFZD6sCV38O2u5xC=8pJ~WmCg}-4Y>J>nswrD0rGXUu6;}f@F zi~Nyr(F|o&sP}h}34EQ;z_DNbyQ~T-_qqgTGUFBlwWcCyH*YjPtH7ht7e-uOyk0v5Tji)3U>FrHNkwz9hr zL9xsCK0Nqmw~UFiOf1)pF%RgqBU2V3tRShjVP6nsK&VTYB2@&b%6wy{CEW-mj+lxi zok>5yjHK&A?^jnJ506u7JX&6}XOO`=jmZAy9sD;J&4GA{2*EEFs7mN+6X^n&l^6^W ziB3qggGC3KY42x+!Pk7_I+0lB5=4c>QwoHX-Uh}K(dsI2>3C{IAe1l)u)yD(ra#ys z8BCE`^GO`>0!8h7J#}DZIS?a4cek1_>~jbq6O~kPPH+H6!|En2XG7jH3|U7A13b?_ zpmThDBSJ3F-=wDlGNgUl^zE~Megmxc)xXstR0X61G_;m-uuN;kgT<1T9f?fAXD`IU zaTW^jEkF&3MZ*U1Su8;me>`bq9M;>-&>$7#t*)8Y-Ty=PO4_YqNq{r`Gg$7-fzJr% z*~=vh3JS^uj8Wn|tAJ3+h=L4c-{)&U%A#PwW zGTrXHwHA*bF%`saQeRsOM*i$XH@kby@9B}d!U=@<_IA6>r0{C}|Lc?p$>k1=0f&{* zWFy5V=g35N2K1-Ok9NiYc+=mJfv;?w#0Bg;{hQND#}6DhqJ^r+zy9;t_E4DM@hkVq z{!{+anwvzbLdcUjLP4R!W26X#a}dJL+?;01)yRql?Vp%+3oceup;eQzL{g7H1V_7nQsR9N1d{ojh6qpwB^ zcgs+94&(|I1hRum)f zF#>rH{!{cd|5fxsQPB-VfFc6NVHC%G+{t4F+|aL(Dd==l4UK;+7rLt)7ZoUZ2`-iQ zQ2{G9aKpaQkMV5a@!_r_s5LWCZFweEp`YhQNI=Ji<3Y10O?sg(|L>E-aWkplrX{GY zIRt-Qb;8PV_nSKw7JI-RcxaD=V_@ARn4d8f`50dFBgd@@!GV_@Yaje|h>H3@-`~Y` zpFkYK00SyZB(VV0S47aPR<1_$HCdv*4sao)SX6j6=+2zEC^v-#NYfdnTOthh5qmBe zaYgeKnb1-LBtp3aU>nvd?LfvrS~DWV*%afrVt_!KoPM;>%aj1LzE+X35!jnuy8Lg# z{rh1-#H=rxp$+S3q~~}fO>L*&s%Owhb--yWa&S68rgSK=1{nAeT^d9I1#t9VgVajG zu9qn2XbiJCigr2X^jLU%*8zFs7KLx2PwVJVZtm8E0X7K0LQZ!u3u^ww}^~5s+-r!xvzVKGq^Va`a<;cglkikv?tG0nrG84YY;->Y= znYF?)tDf87vMX&_Vw8u}=&l>H0w?!e2qbK#^JxbUY{A+GJ7b}B>=JVZe z=*(RZcP&UF4&nNh4#@ytz#>v439nxCz^fn8j}7nwMwhyfBWwP-(|(-b4^v~jeD>^7 zJDm~OLnGE&^WK>TLwGg%2ZFBut>N@|O-S`MIGPY8BU8zGspzRtks>B8@^(;faO_?> zTXi#P=bh5)Y(bdORqC_c?VVZ?dO~Ls5QfLVfCL;+7uCnY>_B{T=)IQ&pb$d-kAn24 zJYT$lRiP4;$yXl-bF07xUV&lqXvjpKIOWg~YDP3Zm%XQTyU`MpFlaDOTaldW-gQH2 zQ2c<>M#g+#OiDJz|G*l_VO?{-B>&tV>p6J1fy@2H*a8?LcEdM%^v9o*vJx-(fo40( zv#)MP!u-{n*!^k%oMn<|eLVG?J29^Ltor{I%wfsaH8Blq8sB{!b8O&5P$i^41%Qi) z8IY9rGQT&X%9?+9c4?!XuS-r#>(zq$*qb{zA@c)zNOMggh2u;9UJRQb7%N_|VlIIE z_gdUTaFUf=Z6~YF$wgT%SddK2+d~E&{wp-?F6oY9Lcg z2*1}c1eLcH98A)sV$aFgEsT*fAAoL`S5s5-T??N6(~6S$AVS;Ir%w|LmyuI5lI_fq zR$`@*g-Irc#uya#xf=UEkIIoQ13{l={}r6 z?w;ym1|mfRmA=2(CDHK&`qqx&6rSi(_$(-$#EOkDDxzYc1xE|??bwf1fbrVw;rQ91 zKRqVh>L0_sB{T`hi6k=M>tF$V83`4C+(rG*nLL6JMxj^kA9AdN4IB+O{S6L_F+12= zDDnLLkQHcMQg$6BI{&=~e0OSVF83|}Az;THDv&OS83W50v1a>CzZ66c3FvtSVKo9; zg_rQnYJJL^c9fWEf;a8{&BmNtLpJal972VK+&k4pIZ8~>DG*cxo&HX4IfTf@f)LSd zq&+5lNDS~IQOsv2zVP!+1cXyFz~MkESa5EZHb~p(*FsLW0p=4QTYNxXGv!qdP=k>p zmozHu^u2xro^5Bmi?m#@9pw-TG&miB@{}C@cwg8tsHv&h;Qhx6PWoyBq~iwOIo#(1 zByS}sPuB+d^di5%D3X1AFh82*(TdU)SaA?DYK5|;LD`3%ISu3;<&WMLPE4jG9ieVcn z46in{=!_2@JSc&Y55%4M$7BdXOx)+spXolo{EAHA>>!>rI0UHUi&`Se=0sSMPTy5}QmBM$X9wIDjWfB}H_hmR%}#5>8_wIr3dDZZ?Cu zxPe+8KO0Y~Js9wlFOXlFiIoj2>^_D%4yk&O9Pc$syo2ED@9?g(-IXRKRVxX|FMQDP z?j5Bb4?71N{I&2|yd;i1dh=-ady0*Zg@^-@7@yJjq#s@UY97!-b(PiC2U^}gpHR$z z)r|P%oi1r0ADo6ak#uM0ryLL4RS`J{ZbW(!7J`w5rFmjktIK(t6Bzo7%KjiIwyJEx z@3tc6vmnHrA7ww}DRZ#<)uY#`VFOU0)Y5wKyCDp{KidGUvg@`_0WEh$@D}k#9>+mi z77S{t0nx<*h+%6Ycq;&#C=x<#tZnj-BkkKaJoXoG44U*7?=1%=;lxH*~82n;*K?#e+= z*+xc2ZYJbyZw0E%KIc>F;vHz4D4&OO9qa(k)hGaS+on1tMl!hDK5{OG68$lm%mxEX zo$UW{_nuKvW!)AiAgG865=A5_6p}5H1PKZ#1w{~0Br6#tCnX*c6;ME-BuWy=BA^He z5)23yIp?4vIZ2lM*75fJzHa-z|8Kk=_Gq=KaL(C#?X~8bbFM6RGTr&Oe%rjR3_@vS*8KG>|M4j zx};YhI?-%*Juf=0C8qPEk%MSRw;yNYJ}nxBltr>jxZBc{ipX`zwVe(hyS*+BUsvf8Sd}(J{Wo-~ zoL1OB50W)m6L#Eh%1gJ(Y@YL#U_G+7Np|b9a)Lj#%n-=lVX9zck34pR8V~ z_}yIAlGje-T0ULM2E9Aa^`rLKWjs-9WjS&IfQ)@NFkjySudSD-(b~e|*UOQE)C_?d zxem5CP0bQydz&docD!CUQ+B4{lLgdMt_z=;>f!Q~*?Z_GuB$*-uXv(LVYFlz@uw2w zvkE!`(w~ItB`9NmU1uwlWiYvBYiqmu8r9RtmQ!Xi6q1$6x;u_P>z9!YR&vR|J-0l) zXWestXtsjyV}%-_Ct^BIrD-(Q`@IM3$&f>|`_%Gb1x=><%kj=JwhU7?QL@ZXmA(@e z_a0l z{h%&Q#8*7B%A$p>Bp12*X6M5`b^*{tw8yN5YHi-x1ssggJVlj)&u?!QW=TV8^hws< zZnzfJlJNxI1+;R3l-~3iD{I~;h+6Vb=WUT>3i_Fqn|z!=CR(A)Rf24nf{8g_Q+z4dQuS z;ql1}p{MAghxYtM6LcEl148>G++FWUSyyLM_PX0VBFBOmimY{j`2EG+H7Ku}FO3 z&o9@$FbCHZ0|0qXeTxsUwXG85o=gDX9fPu{nEl~c>03jEIKND; zlZm#Q#hiwW#9F=Ju!}0?EgP!3(Uc&yN~&(qEOnzp1}pscYJ}jBcmPRJC5L-!6Qxwh z6gH(8Bvi4iT?!oJS)DFeAbwA6w%{02GMyeBhgQgnT-ZQdC&U^hc7e3^4U;=4UEQa2 z3lgD|sE{2_-Uo6&al&rh7E>TrI0a33xA<4o>!>ObinvQ?v0&%~Vz^b2br44AL3(^p z&&LxLlFpo-zgvzrWX$;Jf}fIS9Y}=hLZOAHB%SpgE6I$qqRUe+@}nUM?O*%0m8p5n z7+U)d^F`ezbg~b1A0F;j8vxFVDQ85s_07DiuYQY<(3EQpe<|?-;hCO^SNh#0nhfXf z&kv(%I&zm$g%8W^F22^;QYf~u{nt3_>E(FRG-dL4Scl*09qd9f(guqr<>q^KkDMp_xx6^B(Spnyg(r*%_XXMjItpGM+75j#`6WbFaLl+E+X z%F3zz1`pm*0P%dxPa%h8t$y$&`}Tx~X%=)9Q>!kWnq#y9I@qk5@q=4~Os=ad1A{tB zj}#gyt2>!fDnrYjXYZz7S`N4XnO1NXuWUP^Q1;}B=OTZr00{&44^UlV3Vs^Ip=^AA z@9#vfaJTscDS<2T2tlzq3kf*N05qB)`<{Yx5Di$LAEEtM1^Mcv0-2cS#vK({RROoj zK7ana*kk?(2O6(*SS_pbM=l=c+_U!}O8MKH5Psy(qMWNt!7g=A!4~|zLN3*h(NUsh zj4=iXci#83st_-Em`e}U&AC*bB>@{S(`=B(ZU zUr+`f`OIp&9egbF@6X})JrSbkAf5WCanb@9v&RGAor4)AK>N>#7@ble>$sPA;R_&X zl4L=wo>!Hq`eBy^Pw6oK6I1{Igo%Un*t~4RBIoVfFDRPMF5z(+hn;kc!C`>;?5%e4KJeSq$`^_faua*KD0#_n;%-zd3>z?qoA+RwErnD*l{3xX(9HjB5 zWE)+fioZbt_Z(xL=y9dx7h2}fVetD6f+GI)tW!xSTa;!zqTzcRaX*G!5(RUBiH(a9 zGA@5LoxXsUmMHQea-&P&8>x!PF%yozH4%Q}6<--tRw}gtO2 zKVE~(M>SE}!%W)h&OfQqkR)VKsNC*yr#_9K7~=T=kiCTLJpUUy|NG5vE7*_<1yDOE z9s19LA(S~CD)i|%O1c+rZC~&)l-;^to0K+1XjbwgTA2P8;vy|%+0R~R?I9%}I8op@ zDD5?so%sVmiYMjeXCXX<9R#zOCiz`ofMmiA83QH%dihDA-Lg!R*M^r6I3zR{d9I_s)WDvkjiU$cWe9jV$>8+Sb+YNMhdC`cN18QgWVl z`Cfg1s5wkA48<;*&;#9nN+!1Zw-MB(0V?>oO(84N5#z7W{EGrw=slwbfU4`E#8T{f zPWHDz@z;Z-(S%Jil6^)$`Y!z~@Lu_3G6%}YOKt7#p8!*j!LpIkG0(z}3=RcWC;ub^ zmac8j-h21%EyDZXBWQJV9(?`!^$C&SH~vatI?RMiwR8v;ZX~vg^KPI*8reR!Ti28b zN(S<&HQE1m#ok{X3#;eoG|k6z9NGcE5)_}oiLFELuC`!W=l?*CoZpd9YL%>UEbx@S z2gOu6qweX-&PC3+D$wC9ECSfDz*g zx1ZwWClwUk;vFJaxm{!U==E7j-WKQqdZ8s=ta*5L!Pe&7yZi=Vc|ik2=lbzIzpeFz zZ!xKatx;I4)2Pq~K$&X*%w292T?Y--`NhS>A0QKtMqwaGCTQ;kj6zqJj<#<9V-mqn zA6Uo-gj*{8d|DBDLWCnd#Sx(ZYeDpYqJbp`r;kgd6J8 zUnFo8J>bY6lUTC;|L*6R5I*wXD{_JRaYO{??#^13C4~95lyiF~`-)z?ffjTnV`TcD z+7Avq+?wm>FQNUHV2^-1cgR3m?1RDpk;%!Xm;tn_0AMTkH6<~=xG&9g)V^BFadIS&FlBj%jbeh&HfIgXQDG@y8lve{O^7R@gJpd0mS0q zgUgdY(MHSv>)Gcb%@1}Wfx*!)ArU#!`R}95kr9rvv7wWm@GkH7ABUds5vjknHDR;s z80b`B2P+ zO_)MQQCDVK^%Q7%EY2glhDqL2yk3A%DkUGzr&ek6@dweH_F%Yx&|EC)lXD48}D`sX&D(A%`Oe}Gfye% zEhjUcsi`WdXMfZ_J=(Es>ARe}n$Cz;nuN;Gz-frEHzPd2@bBpBnI;$_`fKfe(dPX{uj#dRA8O>`R|_ zZ7ijZHzw7DgI<_U$UP>ISekz6G@d+ULYK0f9`mV4c=_wI=BOqsO<)Hb^04YeQmZyteGiv+jfYXSis z@rQ4(pM=~Y;QMzo)W8@x0;=E(bt*eMyAAAuQ7sve$9UZLbf6*&EUhjq(L0`JAI`D` zKnd_$Z38F0Fx@gLbZM4dOF+{F)ENvqu)?BAXW)y_bDS_DYc{y8aF8l3GqWD*5K~Ub zaTfvJvxEqeY8^uOP{hDdcK{BK=?^mGW~!hsqgahx(>hlH#}e8fxtmjS3q8Qtzrg{9 zF^VaC?|9@CtdqaPBB&=AeKLt^fL|EA_;` zk^-&=Xihh28{O_f78e(HYykRIdUXcS=(e`D z6Y=*6q!sim-;C{o>UEh=E7Zun|5|N zP)K5P%XI$__jFnu+}jf?67^Hk=-7+8C_NKqvAG{VEKo!lJsNCeF92Fe#~T7^wb&7# zL`%=8_v&W`xzN@&wA1y1H(L~`qflWP$Tv*j6i3zf0!rHFAVuq8k_1053uyf0- zuX6D6^1A-|7E}+CPeBM|uc}Ct!2F*M>39tH<=7qT;{8f|DQoL+uw-sCO}^>4m{P9b z;_?dQ%_jcqqfD*+r~Yc(tDLVmOF#cdG%*r}+<(RR3mKZIMo^;62vmy*jTW^y%6?#j z-yCoW0%M7i2IrxFLC;2=aMgCuBiA3BAd}ywQLUyF6aE$ zLF<3sgytQrdGivd;DhLil7pTi7SOQe>@NrC1ns>Y)PHo~Kk@9ZHSzY`ZSpJ@?~tQ# z4FQU$_QNSBoO|WLg9jGihWURLN3Vq733T}c|3a&+djhs^H#8;W|JN z8el;bI1UB?rmYJ1_Nch*Wr#kOz@NX~o%ye&_=*qS?uyU-x4+-+Kw5gb@_>P5M#wD; z{=Zq?Us*l_EV`Fw*WytUlG(YhC@>DT0e4wZQp{a)n;xV8Jt&d?O#_0jL#)CMNZq{; z$)b`PJa^oM@Z-#$ZV*Ebyc1&h4+2A|3P1<%47{8$knA@IWPk?yp!6)SeP+<>T4fq7w79*uDWa=&dUwPs2|9;ir+ekoa z>WSV;zjtuWzw+N}{hovnOrd=;=?*0%F*#&VnY|J)c!O#QPp+@87tg{2(iW=xWmfw0 zBNaFNle6?3Oa74o4YQ~vwcPHO+tXN(x)-4?@SqVo;z-x=uTr%JEmdzZ(I}jt5-=>r zTQw!<2Ydw_G7{33Is~?#0H|G3V1ZxG`b0vWqx}^;Fr!!;-=6c|n9LL@#Hp)C0qN@t z*o%R~T(^*D_?bWVYIQ1FCYt5b=$zl5YgF3iwAg<0+e&WBi@dVBn>6`HvRngVOuW$* zuFtuiE!tIh9;O%g|87Dd#-krj@A>DmCnaCizD+)1LM9?b7ImaWZP2hDt5c!dtOsEM>G~}N2n3((%z$V)wApoEoHXhmYY~=a7Acxr+-?#$f}%So&i_?( zlClS&ROxZ!pD}P+!InxU&#%7v=hfIHEtveD5ye-FC~VNkcdJmP5;KS2fpKpQ3UpyRG8N-No+3YHWgBRt-k7SZ)6 z%jI9=ifAa{+g?&gqa;7UFwyxUW(Ev)u4{Ti;(FgW=E44%Fa>}&J|&&y=2k|+WmkLt zkCHba9&%*^x~T@yZxk@AfFI1a%>K5+z~1@|wB4Eeo@w)BUv2nB3dFPxxJ4`@oHlc`6~)Yn9yTxtRA z!}ywwf$(9N8IgZR;aItDClz7JPwL>%Vrusn3zw0+hdYJy_I^&{C1P2szjBW(M!{199Fj)f1?2%5*98 z-!z2%TkzCZ0zdxN zM^IA3lU!(hBtY>SVg7aGE2oOT*3rAaA%-LcPtRi5Z`wlutM>wF8wKhCiQ=|SaJdhO z4^R1ptb2d1DF#UCUY<9KN7-wuXK-?+ zfgX00bH0M3W8Uu_{NTYIAtir*|Enu^BtHJD*8OiRc!rQKvxTtx{tN)JY+uqLy{lJ` z+R@gWq-iGuzCt9N>-QN(PDgFaK${PB@B7mtWF`j*N-FF0E6C>KB&MEO@Tt+mu~&P?A-IW z)xIi{V2=gV?Vn&y+KEiAbeOdK07E4f0d~fd8*xHbTOa)09{3w*Ao%m?ib1TuphGAI zKpLnZ5pYNd3p_-{!=O_xK}+I*t4Aa!(+DRHaQ{Yw&%;_fL|SbFfubdd^`7B!fQl3B zy0@L#K??Z&%P`0a8r(;(2tcnP1`v(A=*Iu&Cf~1nk7SMH<$=q6c$)yg=y+2{AN>y^ z7r;lO0x1HN3z|)mBL|5<5ReRlCL{>x)nu`rWEqOujSP@kq8!kPS zg;p^ES7{}eTNE8=mjQBny;gxLB>viLmd!r}goL8ige%0pj(>xGUL^FhiD<82qx9Jy zw~9lH0Zn7hM@i5bT!XY<1g_t=b<^iQ--g&M@EL-jq#`#n`1*DvoNimMasXT;NeRgs zgUieA%rFhi@77hIJ|=wF`ZJtx*pZzj=e2txcK7zh6wrvcc3#%I_t|AU@#cY;_d(!` z8AAyv$F2EYWAZIuXa@lGD1;MV3zZ?G zv&A5%?ok_pmRxh7Y2B9v%n&)$JTbwHlThbIq4?+T88@ii3xx6Ib82Yc8gx0Fsl{1; zZj{^k0CJihLQGE5+<%V-0Wh0-IUG+xL9zOJ7;T!O3IN!$MY~&9tT@PA1Rh66Gq$$P z_PkQud-(WOt7Cq#v2wEhD3%KwOAWfq^`Ybch?VH0|AxjJ=Oh8>dv!V*M ztm3j0vT69mTUP<$Frf$sy>&37dh_E|JyliJAAk_OGjjRNkedIOh53KikP=#wXk({% zLT@KEX2*;|+He`tpIO&o=zLz69W>^jrYlICq}>}vp`4ZZph5D$59NHsOka`O^2J-w zT(JPaE;2Hb)`c1kD<~N71!g{Q0lW|cl#t$igKr>Dyk-~`hq-1Rn&n9WL1 zQ-s{28Bj91g4b-_Q>RLV+4C%DzG5KeTnInA_@yB8*$J`*F6VCJn?x8DF}HnucJ_56 z@F!8a7cU>*qD1;ycc(+8<5M(&w1vc%HFKB(osBgElyYcoVt~CgM(dq_>>piel7MPj z6_FVFnnv+^-jwMkn8~xgii9g0BZyu-!M1C}}uV5$34B1%YE#SL%#n1{2p zm**BcAda31^GH-&&CLz1{CatJ9^LJ$NAo@V&cIx)SDN=$OX_UTE7INHpy8Z7<42;p zp4x7^s7S{GV9Xnj`tDXyA@Gzu1=zd#-?6Rq7!yA4N6k4xz>=#v3tFMo$aR#B3gA~oVaIF>cZ|^qJ!6k3$O>u??*g*M3r<|So`cV|1Ni*iTsITjglY(u@6rZ66|*;i!V{{jE;@CdR(N%X@6qX8xIbAHDuTTnYt4m?o3$ZUjx9~*Pi)ZR@MUsQjxqdV zOd9x_PXD=3kDsxgah3ebxmBbZ4-ggCggez84ZauC?BwGezI7=iH#(b%P(6L4*qup( zjq{q2sPmsAlZu-U`yubCf*ZM@tvx5xaAT_4qS%F7eH$sUF=iq3R`$(a=g5?NfLptF z3|#W^f4#mkl@^_y{UOmaeucOtba2d~a)!%mjg7Z!KDjK(kJ<^56i%qT(Sc~G;*a}3 zbi!|xE-lI6GSwbj!CRHiSo2-QTgg{<;ncCoGKF#!4~YWLJw!76)g(2pS9iSkJ#(hT zPhK>6&}y(zd}&25(sMSNt1y{f41>_sygxlS-0mj}?n}BF9C@@U-9~OV_P?9t zj_Pd_)P{ZrC791m($l%fZR&j9=1k3#Ngx5;{*V>YPSB7hnrxJV{AW|^uzJVjCuiWl z$j_(2=96)W6&`p((y8INi;8T%f4ugy778z}4O65NK!1C#Hs+^z*Z!e|YP(-RQ2y*PhJoecVaiLOwNA$<pY-FOx=c~_vPEsnWAhDgRJ@?}p1qN20$KRXDy~XK44bvD78Hn_C;>%B84{CI zD3f3IozXsMSnU?|e*#r@Fy?sC#JPmu?911VYs=BcrK_N&+A1S&Y_;wG6pTP*+b>t~ zxIbM7?NEPOZZYzl`tcN4v7-ljarj{$I8K$FrWYV4P8b@#F^kI3w!#Aukm-QCH9eFc@C*z55RrCfGBX0K0@GE&akFwN2 zL$#)0DlY4RqY>U7+(fR&)57I?Q@OrGjCPU6t6rWjGD~8_O7BSed29c(v9VLK%Qv$( zC+H%}CNI7`!3}mE{__OQSC&u;AfeY@V5+vfPgaCF%jN#XUS4b|?e%${XW^6_|N ziAim?1dmUbfBV3IovV~7Nw_y7DU8UKTY-)H@tBdNo%vY167SIPjOJ1Eu4UGF}oESI#8G4*702#~zl}a=jQE zDH~7U`ma5969gmO0rnuWb65dH=$WEWb^2-1<;#tj*t9?H*}i zYMD^RW**e(KYIvgSbSc4cj;9aZXcuj9G6r~cvSf_>t6O+-r*h1spj@dQEU`u^5NmL z?D^il9xh=Tl?2)+!grSodY_2eqGYU4{yTTyR^lIFC9lx^0sRX zwVfvYw5LBk@->sr5z8o)#ct2bXyP;S&S2bVPOg3Y7P%dxCX09vih_0y#e) zYW%Rl8OU{`&VpCM&%x7ZH`Mminx%QXZ#wB_>>Acim_Aozp}t*0xQZa6kf67#e@H)y!O^-qBnnnUV!*H`najQ4_Dv zR;E*@;7Q`>CGe{?|W4ngKCLdvGf8nGONk>?>_c0ft6jHbHHs2U5G&Qu} z5F3_`i|so;6EYc+*|7Tc5>7ms#R)I_C}sv%L%e01UF4Z)e8vH9CH01$ZKxIA!KgR3 zp;*a0$H7chXs}yIr2oB9dx@0ZQ64uAU24a)xMtNv>(KVDVFyG;A|oJEwM*Q}&#u3Q zuAQLiW41A7Xjm(q*z$>vpx<58v9aa@OW9Nyr-5gcfrb3KU&#%Z#O1WnTIeUzFXDt3 zG*lV-`7BcQo9ojrov~AK4yuF5RyzxU?Pnc}dyd6ZkMb;I!@@fZYJ)tx*GryeTRqME zT;uM0lbCet9zq}MkQr?-?{}@(W38`hC|3G7M#@|$lD;s^(WbJn*sq{cEJKgG_xMDs zb{aOW*>W{19UDjPRgsR#u*T!79Ev>b@q1wZNLL$iW?^Nwb2~QsKYb4)=CV*|;43s= zw|9D*nj7#Hbye(UwqKuhz?YsBF2I%lNJbXylTGxIZEL(>yxLIdc+Y729tRwT+1RPq zG_1Wa^7ZH$P5c;#QBsY2k&7J?9F`sKg5T^{jNG`KO{lRbCM~n4@biJ^KV9t67Td6@ zsP?SJec=?5RrVuYyKq%}ETihIUMNAT<+(ClNT~+iJEbw#+7@W`D%edw168pM9nA>) zLPU>$?qtheH9PLzPNs#!LP2w#2}kkhBa@X=1We+ z$!=p`$djxDrRwmBhKV}7MFt~DDo~;WH~^9|sWpG;igt;-oiTSXQw_CE36&rQriL#ExVF9@B5imsYFBd(^F@~T_*Ew1^`KU-LEK{ zMW1z$U2XtjD{tKs+(eqY=pl|tjA6@l%_}}-YR&GpcO0bOqX6|DK1!HtXf89)!2kMw zBiq_P)`l0mD@HnHxLg~xbM=Pvh}foiYf1Q$0YO6l3_j`tA+zUhlBiL?iwVmtw6WWe(+6oQphgpsvGZiYcDG z7p6$yKE23Pt(&jfe5qq!%rkk6fEz%%CFDE&9=3r&HuCVhAG z5F5uay-&Q|`@$z%6uFkdCn2hn9vSvb=#GD@YHDV-F+H4CjGOw!m>Is)Smu%=OLckr zuh(W@N}8;?uanWY=WwM*LifF(H&H-COZz6?-A^7WI#AO1eZw}g(k2N>ll>LkAwfJf z{Jo>Qs<+$Jyjl0MCS1m5#9IsEXG!xl=K|E~z*6Att z7>7dH`hcxSMKLb#7vu1Tez%DwZELB-xe1vHr|oi`4b?o{lgaV3T6ks5t2qB|*0wLc zhqe6&XXG)3^I82ovilKjpT;Ub>q{Axm?!B43R6#;ty5wbHxmS;m=eZAh{v6O8e_(b z*v_gZi>e({ufS>JUpF$(q0c_+N##$rY-`%EqG5gW@$s#3L-`wvQ4;zl$TwP_%jll>d0zech@|)d{!Lr_6`! zZJpcQapE{f1bv#`B!Ye)jIwVW-aAFg01u+|x_3clwJ&vgV=0d_9R8q!dDi&}Pf@McLRKkeTr}z?dsQz;bKWm( zbf3B~;v~65mD%KurL@Gxu*9b%{77-~ZF06&o6ir|F>S`UHPO1ScQ z-fw)Q;hnFn^;^uqGCo=*Y^k3ooyg%{QzCxLv8ND)u#B@iXRR6HTf1HGyASNYO|K^$ouVcK9)` zW7u)}txcnsA6-Ra7U%tUz{l-SxJ&LfH|en(vggk_(w#Q!`gO|tei&oL)}X0N(uLzq z)DC{mVVI8Pwoi*hO^0_d6K_>OyJERjO7xXT`>%)Xv!9mjo8rdKaesTF1wU|7cTs1$ z+H7$NrDUKW<0k|ty9zV`{P|+mp;{kl_eX7YT=>{)bzi*jH2*!(DlLy?n&(pE!q;5S zDYmlTZhG|jn*3geL7IJy_K9uvA}d8zsY^c-ty82WGH8gJi4wZ!ySiL#@yFh^pV=t$ zU68)=oKT~HPZEu+Ukh3qnv9upeVxopXlPxh;1j1M;I3Xk1ko27&uR6OP1E0tbvG$? zqT|4bFX?^MPj;8?-{p+-HW1FO6z!Lq&ZGOq8&RFMkBUt2X}kMME&W+su1lv9KKx=z z)%DL7dEaeLpDZ6P2pIe14BdXefQ}w&Z2A5blVk%j*@O?wb7whtufBNJ=Q>8XK8H=6 zouvE5FB*na9bGm__h#2Yl!d3~LC#!t2`f4S{og~kcr6;pnbQ@>5wZNag~vlhD)8QZV9DSkbogJMcDIfm*UH z$i${q>2g-;8c8Q@mLMxK{${+PM5NtDX5k9$XjTUdVzHr<_+~Q4?zG*o$U&n{D!Zhc zT$i_YrHh9!?HBFQ1KvTl$rtka^($W9bxYxmU&?qH&$NI1U}F5E$o+rsTEA~Zb#|R< z5i246-n&EvN)79`ZtIQ(8twZ6ZHUdy)K?pA^O10`@5#}1_>uet^Khve4B1v#yJg2_ zc#SJys~!SDOi?|{Lj|y}!qzK;>ppJSD#KVy zkmoOOReXQ7GMR27Ah=fZwb^T<%X$glG*a74S*>G>)46432bp!hIjyhqiK^eb(0ChI zC;iAy&1At<A(0{x0;o9~1 z$ErU@AI0o+777#z7fp~RD@-GP0s{F%yT1Tyjt1`$lVuZPrLz2^7$esZKbt`y+4)^u z=%XNO#zizpP@fZNcBJ;M`f!apIxFY%LGfm-kryZOw3ZHvC&iv6>zqwnYNwqEwKn7P z<4_lux#yZ6Qh5@y=|Z{AuE7~%f6qqp-kHpwH$I!;DU8FTa~T^~a&IF)nbP8KOnvaD zP3a76n)$BEah*0rL&OabZOYyN4Uu-7>&DZR-wWFGog;e&rBcO{%LMvos*=_>($mt& zWk=M;pWzamg(|3{VmohvSDPbU3*fp0@JaI#F74crg@(8#m=Dl4Nz>c)G6O9)f(i^i zb$;{ztNf7)-_>4giR&+{E!JS35F2?rqc|4z-wrjsIq0zn+<_}9snL%z0LVq{ zz83@Q%e#NLWTm;h?avfBwC@&O)9UF2Y0~)oj<3#ROgT;YR!vLlw9DQEvO~*(Uzqcd zB69T;0AQxkXzB`6UaE~tZmRWiu%1~;7CnpDVdGXri34psS}tQTU$FYqN8KK9VP*|8 z+3VK|Ma!#a+G7v(n7kZmeSkB6GtGkSopasD@*P$lB;MkQ^We~02>S#w<&8_T?<61L z8a&gh?U7CaEy{79Pg|!#OoroPx$c{3$3)%=C(bZgXjDmQt%)9ozPA#GW764{RAq#! zpId(GXW($2%YAUM`Q~4{dDy+Lji-F`9ha`K;xM%N*7NYINiX%|Y<HS^Jl4X7NY zOvO860{WQMrT1=E)j<;^*-ym1pnhtFb_jasy+6{bB-~>$9H_v_nTT3R*EeJ(bA%7|Z&^^GEin$^=1gD`ui*mG42NlsB+63Z!}j*1t6OXXCt63to#kZG%^>NbO4&j4sot>=~dfGdhz zo+)@9E?w6JBajT=OMBz09LJZx;tE_pP%wR&EkTAIOD+dYO>S$k-}*{AzvtIhXS*ob zZ<7`fT77eR!bm!QQ{_3vU5x2lALlIG` z*nHsEh|cpbGJe4pO$kC9ikBZcdB68EzID|HSB1pUKjl9{*2ybD-KA>kl23np)WsFa z5Y!nKxHa1ay3Pu{}MQb#TCi&k5ZJ7*@c#;dtIt<5{(Zt?!DhZ0&D z&3TRQePsKSgjha(_{fre>vyO11Jt(TA-1N?=dL3j4;p80x6O-cB+5|OU%Y5Bw`S#d9m|}M_v=v|nuO(1HKpdZ*6+fOtV!Z4$B4{c=YGa+ ztPw&>3zf73C5s$JLbCOB9J+S5+NR=S8Pz^ujmf%NS4wX6scX9vcPSoao3az%Zsw(6 z8H%KO=ycqgJ5~J;HGGH*wS1VCGL1cFUKcN-?<&N`a2k^+S=CI8bZNGyBa9~?l*kgl zh$g*YpRP2s$8h-ij8iF*~2ze%#+c@kOYNj$|&HOGkU_Y*|Z zH06D4Uqgs9!+UegJww$IJQdgOFSg=v_$--YI;(f9R8}ox$RABs({Y)%o;^~SpZhs( zXw4&{bl_%lAQF|j9awFVp=o+nbJ2&*sefU+*|xoAIYy0pbFuzTt^0ZE_O7k3*tn~! zHp8qfb?Db~8YsgvKb(u$GLiNB#H%x0<34%yMtXkHui{bW6WWMHH2-AlKIPf^ZYUGu zIuzHJuak!CZRoDz570GnEPsY+PWiL`I-tzm$~!M$KkljhNx$ z#%i(0*0!~I8Df(L9hPK5d$i+7Ta1th)+^7-_5Nm4evdtW`>s_NPvPt`tI1f=I@_1n zGlmDY?Wtyzwt%MPa`Lh3Q{IIFu?@u~P|=pGSqkia(qNAigHZuK8BNMe#uf?cvtUTBfL^SI==Z0Q3`~wcx<2Zeg>-odK{~8IA6eZdtNN$r-A&9srhIR?2(PM*Y#QZXc7WxHd3B}$4#9C zgHHP-X*TpqdxQ8U_4`4rTiAamLtPekQ2oJFu^}PFK(F}(uo~W>)}`pc29VOd#>uM3 zPX!z`?q0!b4l=qUlWoeV*9{yrF@xc#sBGE36>6f1wYP`j$H4!JeUu5PN#N95@7Osuw)Y8E+W ziXB!LTZNHtO>&#|KDRZdCG|um9LU_?9RWLbtsLabQaODtF}sx+OZ?B3!G zOv`dG#7SV&?KhWZ`ahmL9MfRv_;_YIKjNeLqgV1jB9VSR-Z9+ECT7Ct&7vm>^)(2t z=Ht8ciJ#$-H^Xrm@v++eTewKo`Eg^j4dM9&QT0W)5PDD9E3QFt)$W7Rk*_Cta1t5C zv9SllBCbd7UAIoWZg%XH@@yaOwG@W|1|v$Xe!*(^Rw8|TwVr>KoSFH?X!mPu7|RVL ztv~jDk|0iBACtjeU*ld}Y%j7x35qBmo)-j&%9_Cyu@*u=^dcKCplMyY*RlD`8Nh~Dr#7Mlg zdEILD6Fu)?x21LH(YOOJub+;MijCC{OFxtG!-DrTF{**WjoYb5DpLNZE-xZN9RHCV zMsUKj9wrEwKe5cU$8T?Xn~5Y1Rf@*2j2SH=k+zX1x^RT1F}!2{&89$p@eEOorD_Y> zJ*8Gj@dwohm(6=)@vhwMTpWw{siU??x6@l1?#mZH(Jpl$NXT#d0g%hBC z#4CdT*i>V2FtWkZuzQ$mI97vQfO##B>lH3VpQq5Tz^^1zEF+Ce2GJm#@M21`j;>0k zj}OG~S`Dg8Y-$e0Hb`fL{K%aI3|CHLsJJorsT@}P2A|Y!CRXrq>d7!d7;kg6MVAW? zrm*Vq%{~qmoo|%2EcJzu{+ujA2x~J7%MakwMdwP@7V+N=xvY)D}jE5cLq`>Iw*o@gGHmugNv~yxIZsbSi_B)AVS$Vz2NbZ)* z`Fo6Y>8LPV$(czjKfbe*O7AL8VK*lvGK}9<`03dc)5W=2;qvt5sqn4_B_Rw6eZ>(o zyo4E2A|?djKoTo?2I0KWHrO@-LZL4w>u`D%NzZc~scHe8;giZk{pB_Tf!GJ`rNW43W+r znUTP_orb5h-y$&~w!ylOjxZ^+B=t&TOu%aJ0*__P&=tJ7^a?%B)ar24^0c{`wBm!c z8{b?Op~E5{C+b8`w{F{lXG<@<61wzq>h}yVKD8rtD=_0pNa20aXGz+ z3g4;081dxE0jj3LZS4+yXurwmoJFuvCg1b0aSqqY^v9P)=fH>nml%Y7$7y1w#L-3G zz`%Ge?^SS-OSua?#OiDVTd+v}AQ0ED`Rra8wur`vtVH6}{fiv> zyW*;oc+;%6CLeV{bJ{z3e^uiQ{Nb{C_|z{j`kn6-8w&a4dt`czWTgZ-8gJ1mYPCY^ zuI{>FIJz8EjuFz)(Q){Aj__#op~e~vcj(z?Y)hs?=MwM$!=K>8vNz|`&pJw*MHRZU zjz&!UCDjWuQJCfBgR+9&!$VpzZngPJ?$P-Cqb*9WF+ZT!`KZ^OBgi!ch0`4N_DV_ z)HClY-eN$o>n}2~mT^ic#dMG*bKQt+q;55%BVaIF(FsOR3$S55?4DyPmp|Lp;0V$N z55(t4fGyKs-2Wr(rFqi?KuY06Nn2VS#C~r$u>(778~=8N2_ayDp5CAx;Br|C{UF2> zrz>HfzUhO@*`u| zOpe{t->P?3dd`|%ds9H4uw?c0+Q<-9rj=Enu6j>w^T3tD!8OB=Aw=IAhk=Yk@8l>R z5bdc5jS`nic)1T9`<0l;_OrG628dT`va^S!tq|x~$rlb8>_Idtv9`l;{;PUomyY^i z<6bP`wPm$=w(~mY9mZuY(~T9IHmuoc@fauLjSYEHbZqe$P6Jw-VFl{YxZ(#a;!kMZ z>IJJl!v~+~S&yq?c(7`m``My$JU3l`?LlMZ12*?%y~u`f9No5e@z{{0`03>G`E>Y&M}+^8h2EO49s#Y+*hV)hn7kWRCQxbIJF7{lc20(o~sPZ%mk+ z)&va9EUOei zq0Y=xlCZEZ!6&py)o)9ebhTO(ukwOB(I{QYoJo9jxXC=#{PDXtNBq6a)=WO9=L5O? z>OD@OBQ2sQ69Zco%7lN+OqZsq?WYTfrTaP1!ZY?U$4J4OH_;?AGlW(aY^hj!_&Hl@j~Js*x+~UYug&Oy znFn7JNmyr6$XOxy??5OojS8Dsz+34(9VJWd7j<@(pHEbDLPAe-Ne38@smxl;sCb_8 z{DCJG_hl?R_)y7GNf0aPC9;@FR?;#gQWYQKd@ScAO~iZD3LRzFwEF_S3X1c<;0#p% ztQ+b#0Ow*KG{70q5ouw7-C+Zt?T$2hVt54A(>Ye=$);3*XqP&R3^)HVLGSflt#&uu8&P)KK zrcWRP-fQaao5xQ1#H4FO`=2nJ0;;9I#ELit=-Ct%s`wM<)yJLel`hc3=UlxyvF)1* z6zx|hzrHgyG%`9a6L5r*Y=3nqDgwgm(d_^Ii1r(T=;e*FE%oo4VRixj)Zk7L@Rxq; z4a!qDe6orTrJ%Gz6*~WUA!5iuJZt-gt1qmLjjWOkrTfneRrDX80!Q?;jEs%&F;JOx zJbn)}#2;Xzo1D0L=^`sP>h^F*`J??2h4*?KGBSqfgp_^apof5&Zc{Sx-SEW&$-o>x z3DpJe-+S;V2a%YdsU0}@A@|771NH-NFG;|_s$t-{(zd>aal}5g3uHiv)&luO?r-Jp zUYZiJoI`HUbr_>?ARwiENHB}IoyBej+{r3$4l`I) zFtM#dM9m#&=R`O%jjUqlpMvK+5#(#%WrE?}(>%M)W)XY(`uUb@ke~4aD|xl+n@p$L z2P*O$rLVA{qrv(AZs+P-mysV}4z=1f`mP=QpQ#uvZS51FJ$jCg$OxAjI|^e6ULnK9 z{7c(c<7~H@5&}S&Doxr%;$a+9dh)Bx+}lqWc`&%*1lRW8nhwz5j)FOBG_d1(gt4-z z7hxpl1Qo~vIxzD6Z){JN47ff7j`)YBA3#Enf`5&7HiviX0NS&Fxd@h4l`ubI(;pC2 zZ6Fkgj4Z1Iw-Dh!XbuVmCCpdfCUj&V_&F`fyfVRlGyJ8o()m@Ktd(hEptMld`Dm7< ztn&g?@Tvv|@qi|0;>E{B8*f@OGY)ZR*#;ye9M=cJvAX|#yEKvEM-V)8kB5SW^yYr& zy;(9F#LJh!&8k}j#zXsf_Cm}*1=RkPntW>{0L%(HOvq5kkCxAb*@T0Jm9)ah=F4Nw z)Qe4kV_vrfK4aW{uxaCBGIo#P;^syNl*~SodRYMDJ){5=Mop$tlhsj{X4rdBsaS%h zQ=%88B;HS;9M0wp$-1o&bpY6viZ$V^H}WmYpk0;*_WVQZ3xktL`d13F|EIU}ex$np z|39KFm6WJxA?es$MnS6NY)F5PlO5~d&D`%%yzO;6p@I`PzoJ;hT}Nj z$Ls1E@4w*tV@Y1l>p33le!J`M`)HE$v#%HA7_p#-_Su!A0^)%&8g$++;a}OY!pU(V zAe6uHlI$D9y&!XrG~=)FZI@jmZCSp$3bqKE{{FSq3oU2Mk1>u1s4TJ8u72AE6W&km z?U5&5Sip{F?C5}ZH^BN62IohTU)Q9r zfD){6iD?t2+OYy_B=-3)hs6as`S8xDQ&&+N1+`HPO~U9e>E}E3f8W%in4|G*fMm*rYqd7tkS2DzLK|* zD6_FbXN}LzxV_4?uwp+vm}MdB((UKlj$OrK)n-Y#YO`&*zoyx7t2*qbpeaW-SK*ZK zc6B-*fB^CGTKw_hWUfEC1E81XQC9*(`0lUSWqawpKp+ydGl^aE&uf)Zy=QrfWqYMY zfKLF0{Bm$#-q0jA+|?avly$eQTnsoh>{fl5DX@m~qvpW9X)!9+WqG$#jp!`%wj(3S z35zFnIHkOo%=pr>Y@bm5lwT4i1-xA)YZOy}ZXSCG?RjLSrKzCCDY}aD_tBy8jJO^u z-e2AcWDOFQ^#Kx+BMN1^x4G^m$}rb(qz_?D!j*bURPP~MxyE1>v+duCu41f?T;our zI>-JH?C;OD;X$xG?8-gpuDqgLCESviC&F78Jt4wZy?DtX$9!$R`U;4F_w^Rz!$mA= z2W1Lui|vZL&V0aEF@}ednbeJvkRyoVN2Zj0a=1$>>{oE#_Xe@fGvPGU=bm!oP7NWK z?+ZOY;3mFbTk#LYy7LyZi?yzGSCFAT?>O6(nOe9ledkKxHO_s-^>W&x^R@)^sFCyq@M zbL`%=7^Eyq&U7ma*U2=-8+(!GpsAp_d5uOFPDH7)UAcyugPue#Kb(t>w>O&1C6l%8 zHAV5tetnoGW2vj9v>-q@;bobB`x7`L_aCJJ@ddDDN&76*dYTq8P>f*WlUxxn&(V8q6%kr z38Nyg8Io^N(b#OtCVyAnJjjb~8CR^{;)1)k zWXqmE>P4GuBRK}{s-e#qvj$>qTIqugE_WA2lqxl-O4~!jLCo3gb zsgVIqGbn?B9=DI$Uy$%s@?L6ic{ZHPwPZ>s79e9%EbGOpYMimbZY^HfPn(`)o_M6G za#2p%zCncxZ{w=-wXaIfdn#PT|L-5k5bgyhV}g1BlbspGgz(+?HJ?wFL}PVzH`(L`_q1$-_ib|8qA>6z9TG+ zihSXHIp5j80`f*rhM6S|pIM+R*f~{3n<|M)75f2amX2fP=4cM46!=!faE;_RTZ8L_ zjD9G3vTp_ zDN(kz=Er1C=(+#B3>QLfvA{A+tT&iZZYC8cM{suY20}#B3j*mWh$*D$p}NBv;B5X8 z^}%)o%whze^3Wc|xq9zW)rK!scxZ2DU3pd7O>s|o*b4?ia2{wb@KxsR9 z{w{5Ka{O1%>;kBI?AD{wn)b3=9~e`>48VP8q|*KdV4VYCDCirEF{8x$4*{3j2OzF= z3cS)`9fj%A(k*jZ+M|}tflqdBEafPdfs)_&+O^oDFll+Wy|F}6l!JF4(ksGzo>oAi z3M;FRgF_Q%)V0>d7x8}kbw6l;I`B3aQBMw@%T+j(#~6u#LkEOwnG+eYhslf$!pxwY z@mpT_S8{>Swj4w{^xo}5mD!V^9K~JxGAO7OZwZbSo}~pqd8&;J$^-yJ*k_cB6~?J& zP*zLy8VN+UZQQ)_RS1<$X=1A0#3Vn#d1VWbiuMFP)4v`x%#qCpf(hsSu;&tIE(e>3Q>qcjtg=;eKxOJ;;@b9Z7ngUg zy+QDHU{Jndd~n!*kPW|@od1YdQ_>+;DInz8b>QvfTht8!^4=PdOczyq-b$A73kVGOQx8aqIOOPx8tq^;Hs&1`q3Rthjvn9n3zAx z6JAzcN9jJ>iIMQf|9@XYh>`W>-6Soue>Iqa>&G8yJa+;Qt(7Qb<26`ixPUOG3^J+D zQB9xV9sezh4Gi1Qzoj(8MKI#`giD%N;30FbSLOWiu2f>IXc-Rz!$y7gt_YGrDs)2VUv3|r4`r>h~ z#F!;mp_yzF_e+c!yBqemd1uq4?=WrXSDb-3^J!5~$+F!E7M&t?k*8SzWj+_@_5Jac z{vxZ_d1~PYYZZI}Eui8369j|FexP27Ba1T&An;&cdVJu&oi?mnzotslBT2BvOp`L| z1tFpR_m|YzYk6>0qcXhow{1-z&%sK7gF1mXcD7SJnn|EJngW%WN%lLaZL}URnciFr zAW9Pe|8MO6F+UnY--L1IewhI$YrySP08OwDKCN}#R5X7FQw%%QOdB1ThPJ&dqvSp2 zrqTX~s3Nlm{VQ|I?qLgyJjgf7e2ptryUoE7WANODX zQtZ$zJiCNKgh3@cU3jsl6HE*9F4{Nb0&H$|xft7V9%UIJ!{9pkhQ85sgkiVhHC;#} z7fe%-byQ-RIwf?w0xGTb9&N%BJ@-u)eBdW-$P*qbua4ZK(^`?u#Z*Qw96lQk+BV&A zupq6%*$-{ZqGuv(DuzMCxZ&4vNf$)5t>o0I49ax#U@HEgA_s_kZ{Z(7_d6)JYB2TOX2T%Qj&Tp{cq5PD=02)Wjq+47DVUK2_rM`L zxg*1D`q+YS&la{TuU(7^`#m8N&nkzzDUVJ#A{%0(F|95PBku*nZ`tgz=%MED_UIig zg(hIxMtf?Sjt7O1KvA1zS}!)uoqi4zk0iUl`dsP<8TyYm6sy#53`Hp4zm_dd^_e`R z=~zraf|piLnbMD-WTVi^sp9t;>ln+)ikF!AGl!%iJ}bkiYDBXSPAoc0mIRPpo;=1Y zfeN)AoM5SDA@G{DK@cnHm|B$4yKeen6md-LU3#;6vhUOU0wbgLgy-1<(RY2H{zqeK zIaTz-y2zCI?@_j{t%0q(^~|D+m15~XH$~=^L0-^8LAu=W0aPAOSg%pmPD4c%T= z*d@58xtzf&V-Z`RdAC&CwKQt-^X%lr^@P(m1GbKX`aZ|>5Tgq!ehED1y*+G}linla zNw;-8j)L;fP59>3kfY!p_Q?I!C5yLgs#wgQJThv97;b9C%N<1-gy7n{5D8^04towb z+rRv23TK30sb6cnxcxx1gv!O>Rewwxk3o*9EWInmXXax`=`%~Hd|^oaLrr-kGD5kLxa-zsYZ(et zVtavBfmwf9i=rAcrz^f-aXP*jXd^JLLfpfAui;zbxWd)v{QSGU?I+8IfjH#B?nDawu9+J!QP?FSam36+$$Gn!Np!t2xV zyWY^KEqa^d%8Z}Gxzh=wiPVzx7h21GZm+a%zgj5$R5?Pdvf|p1w!i25?QrdL2i<

Q{yo^$Xi@LWhq+S5Xje4nSt`r+<^OZVFV`k)18POpXun_%7>8AZu>~^(4ls8CsOZMdR69xE7c=fOY9I^@7^Pf=M>)%Ok?2~tifVt7U@f5h`@2eA1`8O>z}xc5BA zP49R+jyeziKAO@LA((tW+G_Os&%{qGE-aaLyVddiRn3ttLn&GH*_acojZ3uUKiZCSYz9ieB|E(RUsr24NGB2{yL2E6B z^BZ3vY-dzblWUjP4z*3$aV3flK7l+TZ)%a1M96)q8rY;4vn*UPytz`LRK)$^b$F_8opB zO`TRCdHu!tCem1_4##G9DuZE_@?m{BlY<-{WAcR*13@m?lXp9*rG%C+oQ!2b29v4K zJipOIOa6}A9{vQzhvm#20XXpbdL}s+FY0ypL+TO_M}UBHRG}l>#TK|RGG(p@r~kB| z7;|qlm9b&_5{hW~P;}0IkSnF%4d4-L1ach_Y7Di%P(9;Mv=2dPgnO{bKm3K`)&}o6N{hz?^$?R%8$(W!24P4b8NYfnheR^6^58>4&!;4&00pD$s0QJ??X zQ}yTZw2aZ4TkmUrNy&1wJ^C*bMx|}%cb(hBzQtQoe?K{qDR7uRb4TeO7`Wj*5wPB% z@f)VMh3R6Vt8@$a4XVwL8zs%Jcl#B(on4Ies>cj<`@=;p!8QQ)g0AS5UVAai8EfFI z&DA#raE9!HuU1#neBVZH+mEdk06@P9 zs!%_s-y1e##_qF?v%|0%;3k7a}!-%(Jqi>R$b059C&wNiIet@7c^)At_ zN??HVXw3gW-%cM|WiVo6HgHM*#2&(`BcR8tKs_*I)e?~V&%O&7>ema!WMPJ_2CDBa zHVm9wAv-lfr(#@@!M8cLtxj9@BHkyYv%pM&VesWesyCHXBtLoWa8dE6OPX~+fhj3> zoe2~BR71H1PQLHuIC4~6xx-6g{MP0fw&TSXVU;9lu{WQlsdor1NCOB zL$)V^KbDKOJ+k|zE*gx3rX~enSg>H#dmsGMIv4Q#?eXHTbz@N1sYHc410Zi789KF*;)RzqFiNjjwgyb2u z*%VshwGR>A^NHWMubkJPuAVD3;48#4x&+*qQb-Zo3Zc`2l_FgumQ)qW*M<#BSs8FI zek$}}Oi(b@a~JA}E>pp*D&5yMfixGfqQ7D*1e~!7;K{8vA~5KlC?FCR2cg3N8lf1T z;r50$l-jFdsGjtr?jQ;)z1GtJ+(|6V^f|!CS$1Ihfd$|HkWpJsLa9^nL(=cmFffdB z0wRwCtt~y3kTi5P?4CrcsqUpP$t5@s?t3Gvr+qG^LcAQDTag z^3D(_*S5F!7R(=yW$On}SKg$)0_1FCJ&t+rp;$#r_zc$v^69=h+H^}Ty280kR45k^ z5mwU=n0>>kyAY=2T!~??xJJ`dmciq8#F=UND1nny zXfd5Zu9NzPyVdF-7gm~-Rl4ldxpv^sYZdEam#5BE3_g*z`FYqj$qEcJ=3Y)NN&`B1 zLa#;1VC=5e7+t7ttmVmZ{yu!U z1aCo5yQX4Q8)9FrMNpkV03P8CCE;4KK&ig$Ew(o?Ttue6rIT+8Pb)#-^3@LUn4c723W|@QSGy=D zWJeY^C!|VfYL^Qx_KN(^XK=;x?5|LJvSQ9Rk-rZKyZ1&k!+`N7ivlx~xr2 z@^c;J`q?g>W)u%`wu8A?w52snH~CT#J5938l=$<#8QNn8GO$z)YR6`)6vFR8DpU)+ zto@`6lrzdJxh$`(279ljDh$uZ22#N9kkR+aPd|j3Eul}S?(a|Wb;k4ldkfYhY}*4^ z7SvH7|IRS3v|YZ9L&iKIC~>;N^dhqfbM(jLt#K00hjJ@5r4bv2z!1s5!X{vVxIdlvuz diff --git a/docs/BMDatSci_files/figure-html/grants versus incidence and mortality-1.png b/docs/BMDatSci_files/figure-html/grants versus incidence and mortality-1.png index 19fd2014ddf15d33b1e882e1bbe8828a0f91763d..68f4493bd3a2fbf149b7258cf1deb692c481ba41 100644 GIT binary patch literal 263825 zcmeEvby$?!7B?}-AS$3BNDC^BAkyhUQc4g|LJ*{+k?xVBA~1wf(h3YnDlM%vNSBl# zNQ`th^X*p{JokS0-tX`4d3+8&XB=kUz4z+%TWf7!sHw=EK1qEN2M6c0g8WT&92`Pl z9Gnw4@Dt#jC=<;L930pg3u$RJOKCZ2TPs@!4Z8=%CUPb=CJq+H>T-YK;0Pg+n)>Fn zn&c9(AM-gbwfXg+P%qCM@P`*zsdxT8ICEf_@)*vR5UR6)nTtQ*n#l)88I1 zd?OAm;na=%b{UV8I)`hrdpy5ks6Q8RF=NB34zx8hv zZ(_`5bJXN;{YRdD%@S&68aKf?ps35!_l4cqZ6xY9+9IkIwTfdLKU-6XqfM1SvXs~U zI_#Rifea^WM$kd+kU!xx^M($L@bS`6uo!=OHXBomOp`!nj0$sih2O4Ek<5r??U0{p zNIs7j`6zzLMije402gamL2kx!${PGSk0TwqW;f zL28=|G_3eaTqRu43}~%i_=O z$DML)0L$8`~6RQ%b zC)HjtyqedOsHKQTHc5T@+ii}5$MD?EP(~69PhG?0w3E~`=5J|yj+L3^EIq9Ezl_OI=h z%@O@J_eH#Hv;)gzwGzwv9-|#EnNdIDa%>#ziFatnd^VWwe{V1?sB`Lo&>P=pc1Ww3 zfA;zpt&?7*wz{=)tKupTB~rOGtsdkjzrDw=wS&jdg5&kkwG3`{LU-@SW|QPg%cjkj z3|D+6zWtE+-u5`rA<=%+w^4tL+rhxWz@Fi8qTHh%x5ew03%SW3PEJ`w-5yT-OXn+> z80*8Q;-70iKfI+nL-Fiyq6b&qodq+`Au09CPBgr?eVFN$>tjeiudq zD{s#&_c);QTF#6IS;Q9<#{3MZY5Fl?bTaoE`}tFo)k&(*w zeYRCN*ryzFOEr0%ctU88MRZfANSI_S3%_I6yhXs*Y@%WjCrbk5x}omMQfoAdC)Dd# z^GREkx;>L&c3s!i=JGe3ubvh6F}wTjUaR;$qc|l=7<|lSSK>P3z7}90giP)#m?|sd zu!Gld9Ngy?I56-E7yO|He{gW{lJKza5c($JV_!qI1qWxo-v@<*BZZ@I^Tr)l+_@3L zxQ8+yh2{oL?m55UzVLzx9*_rTPfvnBfyspbpdh`ZPk_&OWjsfUv+>t|eg^jOQZg`@FGp8s#*P2E zBrdLDF7_+`WovhCO5v0+oqk9CU-$j%0bonif421hq7s!~k_YUY`)s!3phcPqfsP@9 zpbHgiGbsL4<1AJ%5W&PV7^k=%0-_^1(dSCS^!K(V+x_A|(lxwfRKL2(BfifZ*PMR# zT(=o6$vS_IMMdcqiz@whes#~|2O(f5MM3l z8S&noM?a;!FW+*t%2UTALbK}xja8^=taTA;y6~||H}QZxc7YCFu+QMzrKCrDGQ0_& z!65xj)#VRQmh=$Oe8Sh*V#4o?2}$!;fLYYx z8)JSXZBME)b1U6T=ki**>L?B3+C&%2*Vy-?|;M(9wxva*_(W9mktlgql6bhjMH$v&0wc1s%tXJAVD^SzQo;UyTzc(x_BhA zK7=9hjPQ@p>!Y5lCav$c#skxgL&ODQ54M{*a+NetO78?!MW=gIH?V}1N}9mi@u6dJ zx^Z+|DQ!r!oK{(B4_E9Uz{8i5FH7>3X8YW$Z4kfl)i`9WM?*E0s#DaWD+O1z>R@L! zf1d%f)zDpHKVd2)fO>M!@mwPOzR&(T`He9#z&Y=n#AC}4Ptk#y%}UF}N>r)76ef2_ zSAP4@q9JTZ)9>JHC1Uo>+;@jY6E;R%Mjd-@%cT;XyK>6hvt4}i;V$#tVNMwwp)SBl z&C!uI;q_PIzX_NQ2q-74A9O^(j&*Q6{n48zk3`22Q60_AD%}w9exY<0)haN=iM-IQ zQ<&g7{(^I{xMJQ~xh2%rXK%S-c6UfP&D){IZ+D^WYQ=Ir&1qfr4bNcVsc4JcQ-p5E zqOL~SfyvZx#ainbA|RLv=B5#s4&O1oom41~<}_9BLlbkCjD_a8Z%;<$PpnGWj=q|X zcllFTJH0O{dpQwugwxrX>H=0!vh=dG}25Z!HLjMt&esF zCuP-7im%iYoesaxd*LjWg4Zd`)2{Zn2`lpzhhc><2rq&fSB$)&Gg&Ted7>c=67@;k zZ`?$85!a=D;yz1PLR}^6e_V9w7Hg@G8a=@0bF7;-GLvlK* zCz+-Bo~iY04HfGcqTEQ0y5B6I9L@8lGBf`1PZkd>I~jSh@aESh2Zhb1<9c|0i&2J`IiX^*&uRFrBSMxp~d( z>-+O@8j=I}$D4X^#JL(Bh1W+Egu#0V2GK1*1;wRFu*A`4?C8cFO`3IZD^&<7KX@y%qFA*n!nsAm>=z=jGgGkA%+cPX@^AK*>O8`E#{s;_8S#qkh?Tz8N=H0L1zBU& z;?%3^badf$B{vuSBHqc>i4e(z^Y>l9b25s#uZ=A)JGY^8$2HPalqc$uEzYWOLcGce zVjrudt+66bI^%mtI&^OOj?aG6SFB#M9_xhrKXpQct)p8*YV)ZcaR2I_r!N;iZd1!y zyn9TYao8bW?fLCYj^BI(KNPJu;Y}Z~Vx&TRN}{%~Tey@Sdps=Pu!aSzgiMS$dhpD^ zBTG=hxzu&*YHIYAHbt6>J$5x0JXom=GexU3<|znYf0IjioMEJGWNvcV0&5>_{GQW zYn^g(cCF;`V0+9OW0kuLw{&eQoMK$QxpjXgz8VJ^vx3hjIZ~KI#I99r3|cbht1BvF z&%l#j&DSpW%6Zglv!IbF@F6BjHJaxlSJ77^3*a)8z@OoyX;J9Ynxs^kWi;;Y6zu9x zMU>)2_>nY%+?ma_FV1!UXnK{Y#^rR#@(4;-Ag;e6kb|B6DuHZE7#%mz?*icN1V?O* z7f;S$)@_C|-9F#}mGK7dxbm0FLtV%Q%83r($Z3>-TePJ>%h=!eq ztkKxN_^oml=|28y|*VVdNsAJi-rUMGw;Y&!^fJhrv@D9UCqTM;Js6j0)y$J zW47UJvV=Yhj}vcge)CwgBfp&h7MNi}Kze1J-zuF#L`_)V>@iSB>3|HcySX}lj7t!%x2IW@=L9xdbxoik=}FZ(~Z>^ zQpnAV9L(b|r+Quet+f#C`y{UpV;+@?Mc4 zPK;RLmD)U6`-K~L&igF{LEcHdyt-2k;Frv5QaG+Kff%MEOEGeBsZZDTw1v*}*7G$4 zA^lA7XAT1J40cvKAPcA7X-SKF%Tv1W96#0q4+Joh&m3#kAGeD~DdFqutTy`NARdsC z;?SRPymzD&y`=tSPoKLt*ujvOA+2MTHB9$0e(vtco9e82tcLf1@Y>Y3?lDsAF$$l%r1KLQ7m!q z2TL~|F=xlh^5Ns(5Z%Oaos7`7*-m-0ciMLQ?XS0g(STLo@JlqW1rQ)h;ZdMBOh|ez zv94>;_(Y^s_IL-MktJ6&3=dD36brke9MjXKUz6W1;UTiOR8#hDrWpi82*s1MUTd9f zrg~JIbu**!&Vv^3YvztEAk{@YLRvei7_$Vo6I|A``J>*ur`_Z5feqn#NdWVebC_il zJ>&<^9Nu!sgi#=ufT3_b2r$}!mnis|k$J{7E*?@Ro>{!v?Be@7ogv=WXZ(XrBJW+P z8#RNZ_)yuX@j9!@X5I##IE~lvMUR-PK#iY0`#hv4av)J36Ym&8T)70X&f4E`t}?sLHaWd&0PVrR);0T`xBV%o07Lpny0% z1@b9(nqrXEipocph+`XAqFIJ3A=MeI*M6O6h6n+l^ZvoipkQ0REUPto>uSYyYY=In zKQz$a%B?zZ*Ln0VwJYv%+rYW=>;nlP{PnPDW8xu4_+WNN(LjmYC2)eOTEMB=ySU5d zUg|TM+HV2#6aH;E*yYsZ%iu$?u?As(q4M-iJn8fPqs3boG4(b4#q!m*f~gowltnkn z72jX zkxN7N^w0%dCI;#P!k~5sFJL!k`D()TP7Q#(CCLsb^ye2Qhw28uqdb<^!hMS+!PQ7B z`|hokx%_s4!Qw~AED2!BbqwD;J+GLS;Qpv0n2_GpaHodhK+R|2I2@GXCot;E*<$d{ z`8d_gzLjaP&kpHLZ&sELAX7IUUbXnh9=qzHAvmQu25hZ@=t7C{egXa&Ax5t;e1>$% z&0oKjeO4_?V;$~1;?$q@-nMevRDi-!Y4IvH&w}64HT*akd8>iWMX%x|B(vQ$B$9fE zTghMqzW=G#9l|38x{d&}xF|y^)D>fy^BT&*x>h2|Aci<|3!O*DkGqi}-Id(8W5X_CrO_uVZn z|3sZVW)nVfoaaiDMW+m9>zPszS9HI5PA{@Vv;l0kA&h9N)&a&J`S!?w*Ra@utRW4= z51IFUGqghJr;>0c3UbtCy*>%w03EZn#>s?#sq*FtSqUme>NcRa|Bh^+T$%u6$r_Wb+^cY$I-RWa<>jTqlr%UBCgwuM}P^9(HK4a!*&k7WFF>BIkY&g5H$E zJ{^u0mM2xbBULxCR$k2uaawSgXTE62=AH~l;9Cnoewq?x;QeujLR%U*ud9Gd+gG%1 z9CFD}i(q5a2jd7h`Y`IA(TmGnFIW~$L`|sZZ<6Xtn~Szyl>DJ@ zv7!7)a-pB;$z32!iJn{nSOrSq)fI~9*jtdlq6OmZ5I1?0;s&W9Aaz6gGY2;+N>c`PJI@e4sFap)ezj2UqR%g zp|r}thS~?tV?Mv3@}JTT0x0DVC+re{`lPLa*mY8MA2`)&|8rD)#FCk(@`9~hhbq3cjwuN}FC8M{e z6WiHiMo-yZDPr*L!nJx&iN_3?T{^iU6 z*QaMfeR1rbGwT0xoBy)wf7xaY7bA|?Fc+iG|2~4FyrI(boQZ2kYa${EP^yUu;~ZTx zozi6tT#_z5>9^S`FC7w|903T-AzB{48MZh4rqbnfs^^h&17ByAWn64Q6P}-#%>Y&` zs=7nMem*CUCHW}t;YfpLCTi69LUvx zhkQ999f)(JKbHVlXm(lx*aT^j{!hnrso_Xs5Gp@#V3A7l1f*SQojk@Z|1WZZ=i(s# zf+^wZ!)F`IW{$TOqy90o;oPv;5YDEN=S)we(N+n3^RJKrGGh>^ z{Ok@I!f`49WN}abx#L?uqVP-a`*bW*AruU~zv$zmAl!V&#gu}vC&a(NvSca?%sC55 zEYlDh1Z((1#gDH5(Pc4&EiDbWXtq@ytmo!4-i8J2>;{bKzQz(xkr;;u@c5xxXvQ3GqKAlh*>&9A4n zOabs2#w-~vAD0o$MxKrxhVaTVU_JHNb2 zY_Rp$&d}(AK#PRSu)W+MFwEUdft)XazppH)J$vh+TNVI-(J8M~s0-rFKH}utA#yz@ zInYpOA~G4Sp;OunvX{+*ZWYng2<`jAnG*m)PS_86=Uj7xsY4kL+HxtNV7Je~w2!mK zhw!J6F=6L^KOVqgx+u9!PcF!$a4Z-V7n2yi(#&T?!ZNNHmKgQw6twQ_eJAlT-~Iyk zme?Be8|b(7*&SMIF*wM}lzp&2dDZ8a9Xa(0jqXlM^H{79+${x|Znvhkegu<$zlu9Z zQS)$&N0aWB3aFqv3iR&j0{VN05l8nNLG#abcP4486x(nDwM{g?y1U(b zBj2&yZdbCB|Vs8ueOR{rMmjD5=W zNXVpIh-L_#wLMr*BiP}m_FuKK_vp}>o;5!udoU$fr64$LC*7S3KOtjh-K{LxnnRY% ziJ6c*FhQ{O!n;PI+%k+BBitb!~%URjXtQ-e@W)pfU(A6D8yDrZ^{l|kImw|PaR z$sUHFk<3EpZZwrGlycPhkI6B3UElw~BB{VT;vyE}1-btqO7rgn7tG?qf$`Y)X$)egN3h% zgG`GJr4kZLWpok^f1(<5ooxgJ3EB7i>|rM-ooNNICOzayB1k6l$(DFCrGo?#M4TQ?yjgGH?UKlG0{M-Z zV0`&z#bRq);A{MxlJS5Y`(F`~6hG{3`vsrPiCM@6?!@Nr9Gtt>xv2+`{`}aJ#-_1Q zCgugOi544f>nqdyX|DsUv6g!rwzGlHoc2mpLU@+arsVrG{I;`OKsp#jffXoqMKue{FeK=qP&Ag%)|PJAXt__%l#9BR9U20zat1_{6N_VDaN zUfd!zM!orinL}vRDJ%uO``WWd z3}fO&ITj@gW3|(!ryD%v%A;;3S^+P7Poi*u6(t9N`jvxj?OF&c58{MC_ul)8x^xJY zO?(W9-g|eS0ZVPIk-jx7zT?u_FA9v_Y(v2iN!IBfjX4yFE`5>JnbQpYA)ZWsYd+#$y44?%XS*2EhP=IcoDaL5kdU~n;5SLU5x z6|7uFTCD=1iG5_d=S6a1X`6^8E_6O*J=FN`87qKsbT30WR8f)boY1&Gv*{q_3txK* zmBo29KEqbE(yqq?>5}K80E7uG)Cl@%&0n|E6lV0$CTn$=vyQcVFQ-%zv)#@nyI&Y% zRBIQshP*Qyok~UYl@p2z)>{R?xf<^;+ll$#uHwipP#3`CT?r}l(N*Bh6E@%wMzsVN zX6>R%`phI&Ut2v^+NRf-P=uA<9s@?-K`Sdd;-V?sJaXk-r}23N)dLE)$m{-2HO4)= zERx$zYt{H9DO;j+K7ux-XjB2HE1Kq?sTlB+n-SNwoI>Z);CE=DNm*Uta-n zn_95cyy}%9 z$f?D-?)ruO3SmJ}!Qghkld)EL3IXWY*i^e0>lVy)Iv@i=cEwcKBFW*poo;%LF z{X%na^k+Aa)HXlS{JtE&=*gn*JYd45&t&t&pZwEWJ&njt0_~@T=pB*!Wvw>uGalvB zAe&%BVGKYWBA>0LU3fa~bpWZ)>`E;E3d!t5(pDOWjn!RH-p;d#1SZ8w+Z4L=M6E`YT#x}Wz(*z0a-gX@Si;SBk}6qN>r8Aph1i6&D_?9k&${1 zO<8vFKMCunsS3jy(3S?fI?jCVo|P9NgyS~A$!;ghF||izpCGA*d8QpeTjW2nM}!r-6Vku=L}_%IBT0z-kah%i>p*@ z@r3hQdiHgdWoE7kgrr6VcVRj-8uU29Qaa_gN#p{2_d#hZK6{^<`M%HI1cTXZm8_ju zUr&&fNfrxPd^Fs%T`?exxRWSgy>r}Y1prvr%S?MD@z8~AN%f=CkJ|*ZkSyQZ7$sFR+F7`VmMg4&Fn=oI( zmwC2A1mjK&mSZ;`oyQ5L@gBEz{USz+7?CF<52m@!)Eh}EU-(WizX3UFL^lV4RYT;- z^RcO0;tVra0!*jB{sIhpNzJGCSsu~vFS;58UxEw1(i3>IDb{zWe)9Ad_+5WPq)8dRKw_w zw&&fm8YVq`g7cdtm7>)62KY!Zs-POc8U1xMy%^CJ25cmJ5Igcj9fgE4933MSJ?x=$AU8&+PSA9XL6E0p*`z$43*OBHAq>7iykFPFLb7 z^0iV>_HmP=yrKZ!hjIhxjNhi&ARbYhazs%qe{oiNXu%cOQNTr8cMU z48Pu}TYIwIxDQ{D4Y(a@FD?mLTXQ1Q@5SBZdZ9P+q%5z zO4Y0Y$&_rEap|;}%u@04vvg07y)SaB8-kWMg5E~`%Ew&@o`FzEecMBA*t~yqSCy91 zYkJVVX5b~kayhRX#-e8_8nVQYS*?`v>?;O6BB<4NNy%7?yyg1JEvsuM@@nU}k@=d= zZyBigdnq}qDkkL(Q;6U=AqD4W?c9tNN1*7k>`_n~t)%y!ukg(d)^FtpvsDKibsxiw zMn(?$6s*hdW_{d(z(<=$X#0G!d#x&8`rYCI4$%ejuA#ZmouVzq%Xm4`Y^pFA^AgRC zDmeG4aHBQzgcXO$_Ej7^&(L$WJs*wdLVWG!LaHrc^2W9197WrOy8EvQ_b0ZZH-u4f zyG^Rz>WO`#dQ3QUCNJ2~MvC29vThCQMwgtkBK86e_Kcpb8yUlvv_bHZrP1>!wtjt3 z;lqXuefj|C;~D%|>pOjOnhS3;Y`pM%@(@k;_vZ}vNe=2`k^uO6ms-$9Z$FeuoWTWN zUogab*}0&>P>`ZWbzyZ3N)Qvdvc-}8J_$yDPgHDvi!F3BelIb~!0@PO$0H$ef(7xN z)PLYY-(6#*)!-D>ax9_m#G?A1ABAA36$mib-%k3l7)eVw`FNSVB3$0w1JOaIDkgye z7+Af}P%(K8RjoGa^B0>BNdP8$g@%%_t0lx%tbSKD`B?)ti2Y9H-`>iELz_=qZ&n5v z=%W?7C%bml^E)x0CwKJbtZ6C()rA@2GpZgf5!Lc%LG4H(TT|-ZugJ&Gku2yZ4QHUC zSx_}{J$1ZhwZ7UEwp_WMZEJ$a(h4|lnRNd3N@tKC!H48iZag`?=+ue05*sB1jcpgD zp4|UI-7$_yvu?-6J!RBKn z*59d`%+qQ}H<-`Cw-(>mobe*zSlb4U>rRz7%+L7IPgmu7|M6%d#wU8Wi>O+?q4Wz> zg2i{6`%#&oIRe3b&VMxZZvM`+B*smETF54+5rYn>x{a*L-QJZg&oLHH(CPwaSET!a z%y2rN#ub5IfQ&ZrsmXm7)A6QVqhFC0afe}qE&xJJMQV53l`j4I5TtjCln|PuzSe7` zCUeUF#V2~+e4~R47BK(So_F4cda4FI+Uu`y5C<@r@9ryf*gO(Z-9Yb=g)V*J#b!j; zch`0eVBR)ttY0_x-X=7}WQDDaEl6$uu*4DEW%Nl}t=t`1G)3gWf7lfEYTaWLLRq(I zz|Jv+p8u3DHmSkYunOX`G6`$DI7&K%OU11I_M3rr0JoZ0PRLe^i=5`=daQK0t0}zL z114_d_(*AYSP--_Ftl(oQ;i7e6fD{!+l|RB5}0Oaa>GC@!8dwmt36s#6s7ZmEy-AM z6|Pt0AT5`m7EJQ0vUVEqI!+nbZ z+%c-UxHQ++r`>F6aW9>R?HfEFy8}cfAIDosn=W^RYM>kdti78aQb{xfpvDzc9)jKk zwWb{__cZ_oT;+HKg1QeEt8@jtizn(6qHp|l*ta?lhZwT+t&=3E^6?r`2#Bto^Bm6{ znm&M_QNnAH)5~s4Am`n;YO00fG$^Ba)7ecIc0pW!=W43AVf<&%(4t66rT-Uw-i#5V z{++M!oe2m@_mb)0l?XB~@7`l(*p}aT;2va$WV6z}K&E5byP9MJ!<@La%`1IcrQW=+ zu>ZbjndjF!>UX^H=0P^WY_X%e-!@UVxIbQ+2Su}WrXQAa_0M{>bh&60=)B3Y=4kei z#zq-vcp#&euEkW~YDzGtK*M5PPsiqVaV{ zdeZDg&aqEWYu|Wq8Px~sPowZH&$VQR`MWCiem8=?A{lR|-0dzNn3?mWj)Lc0&1+;D zh_U|OKgZFB2o6ttCC-M{oe1ydZ5Dm9j{G#rDjP0|N^Y`s@b7n1GGQj0F-1h*djQ49 z0aTVw1|TpUnAwPa@@~;_etUFmi_V{HaXD#6RMl|(~5OkPAwyX-+0yH zst%FBR6{KxzqB$LDZueZ@@~iSjtfeJTPQ)bF9G)Ji93R*8$dg*80(C0QL_T*TW8>U zITY`8gO;7_&G!vz=Y0{LByMP}hI8~PJG z7G5LmkfLLeAR5===1(yO$UK9|EV-QF6d=lyGvMlFW_2T z%%}lor$2u^!}yY2nGb%9{-^++47^tIKJWbFy@V+~@mi$vHu5jG1p9NcNC3qDK?tT%%%3)_8*vMkT@HvHwB$k{+)D8(kq zK(sHuxqnR`)M?F59>^{;&w#c@mcIVCNRm&+*A25Ci1d(aG}Jo+h>e&1V+4*}&_V$D zdcfF_lx4(}#&yBqHtRw?dli@$b2O>Oglq?&Y^AIkk8DzX`WV$nY+AYA_NtbTtRYhl z9kVL4&(6v6v?lD4$(1ZBabHU;pEdCA+g8dt^t_88|J1)V#!+cgzFePGvkKKU zX}l1fr&u^F zmBQ%RL~Nkq^^TYFlqJ3yVeuB%PqOY_Su@@*gw!Unqvajrsk`D?yg0PEeh3>Lb1O{M z-sDxh>ivwt&R{8}7vvbm(~(AlDFncYzOTvKU%u#`Wwu{jwV&<%N@Y*~XDkz==iG+| zQ|4zAHJc^h+4|Po%%1y?*10YQ_zW}EwQ?S2UUFj~R-s=LXN@HEzvro=kX?zXEi}3i zwk647!xMC1!bxh7R1K_lp)e=gj-A-$XG zdqu}z#OvZ-9n)lRB-_VTw%5rOxc~~ZIyrws)n?f@T4ZB25#t#U_ASil^o$>ggllzv z6v!#90_LOJE~Ajv{FC!^ecNk=t!?u?NE|3D-(T^|jvb+L^Z>15Kd0TKlFs3QmN{!Q z>WM2!z-WV(`fvv+LrjCnFz=)f=e>^6=bQGe(eaEDe~08k?cpA(L18sjz}M#SdoZnNdNA zky52v>IGHavtTYc&w!E(V0)n|I$toitX5eklEy2us8&VYA3?`jb=OBwmDi!DihQzh z)F0GY)(_6ix_AU>1v;4$D)P>or%;8Jf*L^|39`_uhPUr!t6YDd%Z6KMNLw&liZNH8 zN^1ECA&n+ox3^q?ibC?dS_>JX9;%BF2PAm!pw}6{K)obT@R$mZu1}wTlS1{K?nbQG z7R{^%gwiaUFKvR_eUt2g?2=&CIKSjw(D4;IdS?kh943NFHw}v;a?1}og@bVJ_!!J) z<*RtlaP9|P;{qLS1N(GSKIqE45=j1z(7s9Y^QPK-~ip+azXH&9)Lgw zM8w)9F`mw^6ocTwRh!E<_MmzIR5X}+zI)hbyF)?MysJ9QN{!L2+r2ZqbfM+cO$MGP zRh@((v2>y%4fDlg&e-}Q{L4$JFSPlSk-Db+{-4N~w{3kCB;QM37$PE_0gW<)=1u7M z@+8Z`tE9Q|9FwCnOzrO!UdjhEXh zd+z3vFiICGN%G;_G5{PqUd+w*``ekyvowB6p*7>- zKMiAN<}qmwC)cQI@NgL3Bll^K-H*>wdaC*GD?#%D=CsFC3Sg;Fc%6&Eu?ugj=mmZ1 zJvu;LbY$%=4TL2bJ_KDt$hi#-cf9!X9VBxF>9b^vp~O_UaktjS+C49s2dUG6j$1J@ z+U7+gchEEg@UPMrTnjOypm8dnsg7Ew74^si)K`Db)5FxBHExCeb zgfEV0_(?#M&TW4u_DanDV!Pv)EE|8aGBR3Z(!^%vW`%cuw{>JYC>iR5WK+b#{+zV; z#LfJP=cQaf>1KWF;q&G-R8Kn}EeWm+K6p;29yAzCp8NDbRqj1u;AQ0@eE;+?X(ge?8N!^Czh%uB1U$h)=Z5R zSpvx=1WAEjJKRQQj9p!cY!7McnLPQpMTiGAgS?$e))?Saz~ovCx?#;e6+-2~SOFnC zo#pyL14Dj^t#-dM-jkdt*0yfSs;xS5W-(@=GSDAm_Nf!JlCl!N{Z&ws-I`+7sL5FD zX(HPSB6|aMRt6Ya=`o||k?XF^GKS)PZ_9tYyp=c2+q$e{nH?c+vv;0ZHkm$*9uy6s zF%$lUh0SFU>qX87`pPO{Xp%i9hVQlrfevsd$f@h+VnED2v{Ja+>gY>%dlCM3am+aP z0PEv~&w>oEV{9!q`1;1oPwAcMspnN*R~8%z*T2&C;`aD5zuL(b5ZhfAy(IglVV0+l z7#TKf-z^m+8&VX~uOBq$-*2A9Ldz!W4E1;oOypHP^~w4K0!fSzO(*@0F{(H83?i}t zG<4(Z)Ss40Zl?^K#y5$&=Y3@Gv9Z+=ECc#PssUh)zkA2oc0#`6Qy<3qrhn2;QP3mD z=}&Q^i^itpVjb%j8-?3NaJ7a(UEXZFtSD;Sk5E2VN8aZqGWWiwD-Gnqd+1Y=#t*HO zaH-j3{RB|ak-|?*cb3QtdCYJ#*1fLVQ#ggdhAb_+{TBbvMyX$gI*03S{ zI~7@M+Fg#V+Hd8KwnTE|A%(5)i30h+6~&nYoxgiCATlB z?;Yp?jGT`u@}8{Qao^3c^*(x$O&pC^>XfNS_c{cIRIG#7X=wx*2922|7?Il*r@|`_ zfNyF?xdeyhfG#L2@hzI}jJlqh{p=Tmiuz1E9-xQEWVWb&nYYfCeor4XHs&Rbf?8u@ zeUOE>k4(_kDGdC*YzIFI3E2prnoa#hckQC71&g$Jt;B@l2#3?oUqI7y9$BTmUV&Sd zm**z}%7^RzzUIJaV4$RP0RA=CYu?gNKW}aSgMe)q5Ff!4=0mNPpe47(hTI>WVI1yI zerCeyrGaRIElB6lpk&)&x!lZP4IaRVC!fp&f@xzWz=-qU|+ zasje7gI2h6SAN9q&xK0CB8{)xD-F>J64fH%*+q|^ z2Xw5M82#{sp(gn0a;Hl!-hgsQ#5kBa3&?lE&J{=rf>w7Y<4o3bzmz6DL2C74KyPLm z^?JHgP?2@fGaD8mqk=imaTV`X=utsMM4imN8Wtt}Q6;OLy4$6JUq=BSxs%ZAUhvZA zDgz&AYawU0n8+OJhUV`;YF}Y<6}9qT1!^B&a9;T5^h40IOGS@>TK4oox!D4wLRKGf zws)#{yM+3(A|Mp@-${S;P6i8zsVf9Dlj%wQ3q43A_m-}MuEj{p*btoHvBj?+jCCd2 z5&z99K&D%+n!JNKEjv71N`LHUW-h%tsbF8yJ_vr};u*SSZ~c$2_L76nFw?RO%{qc- zB2P9!U!r0QcbOF`4;XB1J-PM&zUvAOMx5a?>)a{vOH06iU4H>D z`KuNFztZ1b1JsfPhxD_t{_Z~#EIkRdjS&9h{%6j4}3}xG@bt;*dK!Zi;VvV2_wck*@3+a;6I4nA89*Q{r^C`KLq>lp6QQ0 z`-dO<;{c9{@yBBxlj2W^f%Q)R|Hov;?03^5GaUgcl*XokI_17M)0x~2roit@E`Zi* z1^Wdl3otgL0(zK-rJjG4!v>~md0?ciX)F(#0f7>bQ_wvKJc7P+hw&uh0U0zW51#Nk z0ko;z_`Z9>mjU!eI|7jTjO#b{%q%7xv82#nbA#Xsh!ui0OtWAV^AP}GCHN3`aMv&CVstq9$r5HqM6E5H@+(9DJgHv}m1#uW34Lv7mKQ1LJuj4C0^euh9j zTsy|cg;3=->@DtQ4CN&0r!fb1@vU5{yqmHwAz(;?1G+&|uj4J$;a}cY%&; z-Wq$*So5R`ln$Cs?H*s7Kss|DG*T{D(_F^Vfczr3`{C~l%cFYU2scmZ5U~*LWUQE* zg9wWPg|{IE>Oj8=E*i1;PD_*W6#$?K1-i%| zp`__S9S!u>*`}mM>afRyS`Zps7o!N_&D7%Vjz!j>x~Pn=%=OA(DI+r{hRvKdz8xyg zjyk=hcE6J1j$wG{LEZmow%WdOULY&%_4>$$a0iqAwGk} zPbND(I5JyO+7NbNlFv$hsBaw)02~sQ*)8hXO6CS|I}dK_%IzsO6hau(AiA!#aCU2n zgNs>m3P8Cb6#}@RrVm}3l3rq&F_+;I`+@IAw;0*1-)T6;3{Ksa!r1y7p23b92iXA% zlYR3IIGUR*;N&TtWajW5qtMn1Bk<7%xJ{8?)s0^_1*k(Kw9ut2962a=>?y*4DhN6Z zsWf48)_HKfP+{^E?cuQuFP)6W+rj9}Lzi*TTSLw4MH8UD7@FX7uua~5Q;_EJ9vBEV zn})h$+4_inkCByXK(=8><{efIWq@sj(%GxJXaEdycL5YDZUGu6 z-rpEXgRJK+G`rFT)yd<_W^>9=6k2y5RL_1rx}OD%(Ro6rF}rGZBuUkFVD;0_OT~`N z{&&z_mDAJ^QyB(z0L)EGjT(h7g~`#sR>t=N<1HwvbsB>(sL6E=VCKx(AL^;`^*>7- zJq!%?pYOoXOq~c7Kk@@f55ZYpelj_7JEt;1#i^&E`*|UZS!fl^h44XB6Y|g;#d$H% zd+^chodEW_j~rh6@mgpyKZQs zCiJ1@36dfkyTGW&R7czKm5PF{VRFM~!(Q8wR0HTicfv{d@CZrdh|bg>Ovjg0VZZlp zNDZqV0AK8kF8r?~1VADGI`R2mK8*Mb025-C$m7}Ne_QJ)4QwVO#o=za$9<$re>V1q zVvw=>BV)hS>W^0bHV%Kx#j%C_V_>n7%O7*`TS36+`X48b4VPhm%*7vb@!LvZF`_@w z#j%3?F&D=Q0$%(v7su526J7ka8-J3KKgkFd3OZJhU*>{JLq&jZY6W0ezwY(3*jYoP z`4YF4binMD# z7M}YSenM&KNjfYn1zd?kcAGq$@g>;{Mbb-V1h@)#OgFyfV%ELI+rG^o{kK2sw&9HG zcEn7XqFgdE1N<)Ho@G4zLAVmx9Z+6nI)C(c@Gjy#89b0+3EcU^%xC%~l#szw?L3#x zkKXNX6&@{M42mN}@}R_V?(6N76zrF*hl(^!-fQ1j2*n{f{<(P?w+-`l>duGcOHz18 zKcr{z;xJEP%ryU4`#mW<5U<3nJpo+!c5WMV<(lkn%&Rn*wZ>fb+F7&T_+UTTO!4}< z#mha=X1A_5edhNPp;Bw4V|!QK+bBE~u}etdyXa?pZhqB9y~7id&!E zkC3GA8@TD|{x!3WJ=AD7G2-k(TKF68zCweM40*@f0TSi~V6Ng#!ZV zPX|_9{!O&nvtaEHuzQKvPx-<=-(vj1>)U{&NzVaGI;syzo* z99Jb1mB*eow3+DdVLaECNUnea{W9m^6-uJVQ(x@WB-c8m+XMt+1Z{J6XZx~O|BY-v zpa-qAlX)CJ2$1a(L^g{&u78kC91+UyD^4F`HB_YRaW74UF85noyr}l(_k)kl^Mm)@ zo}Q%W+6P6dI*xeJ-&~f$3sd%8t!k8zoTK~AtiNM$rhVRAY2Nq;S=V5HW#CjgOa|zc zyWaMA&7~`lRxGbu?)qV26kN#ml0%!Dg@gv&;@G<%C;Zqw_3`uHq?TvQgM@Zq`gsNO zG1(Am(7}3c4$%K&-2^Flit6_ZT;K|fjV>KLV>5JDs>8?LK3qdz)F;B_4bnaAb z|I4#QAb!(Lzo@wMk7tvCXWzLr@DBm}I|O?6jA?I%W?^xK$7bY6nM=#Z$I~f5oZH|! z1?Jza5+!}^pCF;<1T~J=s1eYSsX{OKn*zSDw|;;uo%RV|Z~cSr6cF!aUd|sYQ>c_H zJcZX@RP2qF`$(yi`bP&7>lIL+dU@)?HKD#Lr`bLV8saU`tWV17M?xo(ViHKs##H7! z{}QTlgpa()DB{*DVZ6X++9RQzu69Gf>MN<{zL}WIX;z=~(RzX@Y*35oU{A6i}l z=T^}U^ara?O9TfCK(3wKH$cTpqA1iT; ze}k_Eo_@7j^;IeQ`0Jq#kf7}9IUG>N5@{Us^)FS%AA|kiQu`KgZy}3rk=2X*V$d3D zA716PyAkoTH?sxIDASqInZ7($ZXBYU^neFUi*fJe{y_#6$Y8$W9=h^d4r>AR@m#0A zyuRi*qvo_vYrYxDd5?)hJ10ong4W!FQ||ZKE`>URUEdPv6Fd8x+Bfl_b-r`HC;7e3 zSfbQjBb;5yIG47`mMD|yw)i~z3E9@hIN^L3+=bsJ2!|+z73?TAy}RktKZLU!q75Y( z@ju>bB)X)CJDhX_|6YmR8|^f1{nC_O<(TXCdSV_FcJoPd5ggia;h;sgcVOzo@4bae z{eZM=rH#nr=I>*ChfI?YI?wtaDnf#|$>s|#<<|cQ1`q1ky+-ucBBt6xK6-(>x)i`& z>Bd(c|HClLGd_VBb%VuE{~xaB667h)N=;onW}G}@E$nP64z~s>D2P^Ay)Q?9*3oid z_@l#EHSRm@+yr1MdK5Mb?LjxW0-$=z%@=38-pUX+MQ}vr8Y%rYL_qvVA@OT`VfYVi zXM6!R5p^@kjp`oq^8F+hskmatm&cfPSQb{Rx9(WPd*ul*0eb*ap~ znc7iZske5UO{xD$_>;_l7$-&F(Eh`|)k*_pb;aS0#cF{s?B#2zMv7`K-6Ex_iv%uP zkEh$3qIr$Sv;NjZ_8a(AXkWOw&EVcqMhXtC@KUE)#ZpIfd9%Ym z46XV#uv;LYO_)(y8ti7ItN`WgfmT&LVLLu<*iFVCCk;2pg6YNChl*_}S?^)Crx{!# z+vn9&72h%SnwdtQb(tSb4Hzzod2ZC7jU(aoGaUGJ8Snjd?d(WV7n^NxL;QNaQQb*& zZ)TJpxSGJ8zex;BAl$R^VUVVTrA7EL9jh8{6?kto@Y#jqZCnbqf_;9A^Q>zV#6Yir zJ%y!g6(&C)FK}7><}9dC>9R2VuHl{D$Gl-MGb0f~G<&~zZGK2QqDnL4P8gt%zQd2k zi-aWf;b3r2Gm=a9O3ky=ujs|yOET^LL1Xpnhh*u*jM35_lZEq;EVGGEh>rI|CJo3k z#%I!_oGQR)A)^~to-M(S-L_9|J&deq&*;_Gk$UwZ z#C*tcKhnK$QT{^+cV{U1WgSSohn#)d68J0@i>emAlWHq?EY*>DtEJvEXUtHqFMn*f zxctBCAo^z=O7@|>^4|{(K6|hZUspMA-~E5q0YcaOg!`KrM)P7@oSG2p^ALPf!0#XN zzrKIcgHJ7g`1EXM3S`W0t>sHQ3veO*8yALQ%6E;F!S8Qr8i@@#+3-&F~1!UQXin`p;CoR59I; zIa<&gC$s`>Vr_8C37^~0s|Wp04A`DqRaFe!*C@2nfs_BXX8|S&t_|3@C3O8nQU~CO z>Xkb2vl&VVT6uZxf6%pBE@01}<9^&0` zRQC&7FB6yd*#^rl4IIB9cCltOQ^GFfk(^h1ue66{lyZ>Pt08c|kvQ{rFPP);a>D6Ik+n0q*DHXWkSx&$qbo$N+m5}U zb(+R9apTFK7so69XD5yAt8t|N8_c+#1A=Vbv@zN5TK4_BmFj6opA$28h!N_G<@iQ$ES~s**#m}Y=-EK@tdzJ?|euS zO0JOGZf^OYHVl|`SDBDDPJc}3;Mq9u+^@byGx z2=Ol6StV`=E7vaZ`ENr9iy8SY^c3qNojj*rGqq7ZQt&xb;V~|@XCHgodwft93bkH! zh|&AUccyp5eRgWECjEVo!2L_(-@W!)3vhoQ*aRJFNw@E5l62Z;l4&%3rTWVW?7 zFR)kWl*mT5Wme&8UOW2CtRThW(9hAT^T#P^*qGgOXvQh~V``e7G%{Sb^oDB=n%Y~w z#4iUq*x6}>($?+!L*Q|O)M7}r-1t-{o@WIBU^{qhzI116`g1Z%+J}l-Rhp79ihfI) z(Y7U`&OFM2hFqXQ>}mTBdyhzmiv{xHdM&b@+m^aoHUI7#Uh+S$EhR{aBkAu`*rK@4 z6YRraZ|8;A7vHn`6e#)3nMM~}D8x#(rHb1;V!yo>A>MrXQF23?d6pWX+b#s8khB^j z=GLbKDmS-Y^+UQo&m7S5M3>7CUB}=b&3e)i5W`UX#rF@;qFzv(<)*CYgs$AE3nr9u z{VXw9;t)OpZ8D{etNy3qx!)`BBF++OC4t@(ggBBW48zb$5dLdp?jdx*?&V@ z+$*9U{)I`N`OD;tqH$+cHc#;zKKfSX(2*)W@C(XT`OOFx3iZ)6_#=b)tIP5H=ix0} zlv&s1(xU!c;r7sC-9chu`1Shp9Um(Cq6wG&!soh>s>t5G--JN4;Lh`LxER~AcAr|^ zb#a3?dw0!OY0iqP=69kLCkNFOZE-Gge$}LrvhD#~gYY-xs-fzUzw^CV__wy8-X+o~ zBG?04;upAY1-(_5FCo-i`S4E9_4UP3>+rqrPcw-a8{z<%jI`bE6E6&dWGw{v#iyr) z&`C=4-sEB>)aurLBiy(o4TAlzMwT$TTpJlB8+>OC{8+kBj5toz;a0#a*pz>DbH&LI zCcgLOtDKHG3Zdtska12Zj8(?o+L#Q-&K%MW9{!GbVbKOgB!P%FQoUQ2wcjFD!xqk+ zRpx_oh3B5exDJ*!7Z@O! zCfE8P&8GVAZ_9iCvqf~$|7;tdy{}h$|A{ly%0xZPS}_9tm25*98sB?gT#z-dz4cbc z5=zwQ^VKc`V&+iKKk-={F;CW6Ja#Nbh~DNI)yx4dL}E4^#da7voagR=+$?Y-Ik-aH z5}!rJOp~WUt63iSTz?eFcTZ!`#o&-*~J%o@P#DaR2x#{h6EV#|YP7!_#YX+}g0auN+X(Z;&dMKDJ$as99l;iHu3? z$E%J5GArU5yc4Z07W@6bI7Nr+6}%Jj<7X?h|2?wQ>_i&wgpbnfsu-y5UJ>Z7G;*Y1 zmQWl0Yz?3nRbV6NXG?PfEXd9o!$Th`$Zu3c1XvW?s+-Y2K9W>hOb{x?$K7r-EK{q? zm4KdLEoXd=j2+df94c`*ZKX!o(F0AN@4&Jln4DkL+q zEqAXHI!PwYMSXw7yj7@&;w6AJUEcqf2!Vw)VS=P%GO#&&`7d#h5U#f6$#EU=n@#_kj97xc#;9GRW@}F5H2U66Y+u8IWYKe2 zzPY#A=4BTj#01))^v(;KdwJArP~xw=9^e)AnTIx6u{_}YL`aStjjOr2y=7|KV8Wb& zfB#T+pph#^62bk3ot}dFA2~(fC76mhVb>b`@d4|LCbhS2w&rv8S3rT;JN1mbz(T~+ zfReFT|4zUh+-IgU+|WQj9OT+KCDV(-EbdKMAHFdHyiVUXx9BS`j;yHHC}PQ^hRj=A zLdpzS!5v^GJRyC5&6@b1IzE>SRyFV5r%v_1x%?Chu2?YG4o9+H0GODPtQ9)@8BI5h z81M8P7IRxhYfo4(a+0BQ52L{9`t*dp;AJ|I{xiQ}p2n)lUgdBYpXdnF^k+e;*FW|g zS7+a|X0mPWZmnHSdb+rRoggHhEw*bkUH;w?QVswlW@W%?9VW*Z>6PDO5b+lZ8g=sj z^)361(+}uQC2vnG-33_W9qdko`QWyush1BBZ zq3-b33m5C8m}$vmCrVf^p&%cbBGj+&SvEz3e-E7+3JnS0CgfuY&%cEV0Xj4tsg(g2 zCc*5CXEPoWZ)@Ss8=?{|q^^zE@;{%iFF@_q=uP(((lg_bi|Y*e(rTI;IaKpUz2$9eO99J~;Fr ztxd=WxMiQ_e*#P?0vag>o<@wLab`Abl!~S&t0OCqesr`9HLx5DL!f6O1R& zdVb+`5kk(70h{E*wqS~EWVKVY&!CJi#Fp>~5=#`Gf->^Cd)Uv)c`+Oh%Na*Dwxf}n zQwb2_uUrr8e*%2jC-3EQlGLA=!1DW$ogXd)9=pB?QPQUt5+UK6$nXo7L?W1u;C#GI z$hO#1Jtqw;ucZM~M=c1!2F2Ix{TH_L#z`S8k<^D{r~g8mIB#?=s(DGEXp@L`!qbQbZ!0JXi*N3O&hp&-&-Ud89MXChNBtrsCU&;pp0@EKU-Y61V z2@cqRyE>h{uo)JqJrSvFsh{P_prVQ`NF!oR?Y~*QG4zTgv{_`)EbcHRD=>dor*^p* zD;X}V!w}~(-IAK=bNwxawl6$^c*#sNuY}Lnst-S4LMQocGfAN8d?-EqpM-&Xhp=mo zk4S!p{EOU!iFTm4f;WE+D3D$UbFC<4Pbf=$R-s;~(A}cctMXr)YeNcD7;BV#p5q4W zLY`Q)vWxGJ-dK3+zjp8SZ6oI+oGO+blm0<)t6#dpZxy@gX$6)dN_^Dok{fP*=PL%D zyjKnQUHD_DyhEhxt;49~rOq_t5lrQxR87TXJpt|c9h)yuQk&0qWxn(ZE}<%M&zoEv z%Zy?@c_xcFR)oK_s;GRdJf&+<$@QwSfj-X&JP|1gyrc&8WVb&X>l7;v7?*dD(yq~b zgX8sfc5CvR$gR`$4kpEsry4&pRp_E55pg1sbRAL%4~`(SFW!^!Gh9K3)8U~uA(lv@ z0l@Nc`HfW}Pkkne+0PSAULPxj92-YEwa!Jj$4pys6Yy#~6j0MYJb#Y8?d#zT8!C4Z z0H)?@`@l;Zo7Zq%;)&Y+CLoZmT8hiM4fQ^scR92;v!MLUW4xy39x4CriY`y}OaP{t zGl6{eC1gYy6hK+#3`_3U4ZSgVD1(yS@bvH?-lpy?BfI?YQf=+V6ghjr1tB*p?QN44 zfHW5hrsVq5E8NJ(jRX(Q9M=efHDv87{*(uCkBFefg3WINMTGdcO;jluJs;PmP z@Jz{AHPj>*vPcS8;P@9V>35WsI(0ZJ6pq_h@|KgO0U*=rSfV;utKgZJtoo5d@fa@=uMxNQcL!g;?VbwYDlK z1)nOl9MKE@y)f*;x*_e{`>rkMOZ@3CFwW}J&E9g%dmS~-Z78fG8ILEx;E;B>p5S|L z*>XhC#w3Zn-%4&B&kz~?=QvPq+Mt!=m`sMGl5p@^9Ji*c@hzlYRKp@W2vhC)=-~7~ ziEKOh4er${yt)OLpbe-l;>Z&;L&UXwT;WI96N;>==6)tMhEoY>ia*|ug9vSW8|P8G zE3-wC*scu+QCM;Es2KVb7okc);%x4cdAjr?KYG~NxMHDT2aAk)6f>sY=odbjpPBxa zzWPWC?m?X@#3UMBE(m?7FkVcarpo@#)#n!F`HpP~S~d|AZ0$qP;bO5`V!~0d{KKBg zKssn5#1C#|Lh1DbKm5#_VYwmOs73iuyQ$!bY~NkD3(B=zuGW~*JwMwtET*6AGE|YxgP2bJ!a7b__eVv)PK(;$or_GLCrEpYSz75f(oR5UAL}sl zbePlt+0@!p4HkfFy*<;C`X*4Fa}D?53IKyh4Wa03B9}Q)W8Y*8DGKXySQpd*`kE;( zvfmiV?njHi{guEqTe?5Ha4i&)ewBy5kGoukpCc^sF~0OYl+W(0cPiu=JYyo zL*2w{w8ne5yP1=9Zw`7-7&=!=@Tn@1(6#DG)F1(FCDJgMuvs`2VR6Ta?G}MLcPOVWl>d(_DApy6^woe2p^-~I{_iY+ z91$lwNfJKvNQQ=Ml>X=>Cj^cN{y1?Pl}-3eryrk7V$4LXBYprEP)II~UDv25C-2+Y zT-`p}1sAHlATt)c;`I|-?wmZPxUyqtX{51ui<4{$n&ak}3x@-q z<_s45*y-9Hzw=_-rTBr$O@C1)4rEjkSL-=#cAHa!ubDy+egoS?ZfWOSx=yux|i?{ zZx<(IHS}XoxAph|3zQH6T%s<~gK%ISmvn=>oHMa$2Qs!b6ry~gts;Cxidf=Ee0rQl z7@2%K8TL>EpD2;)qb#)$J&cGEKcc~2m+iZ0gZETF&LioGCDAC+_wpGKEa=9|heoC8vEz5Biu7jlhnLXh{lJQ|_o21rh zYRe$Fxzv+y0mHwopa#UP5BG6v&&CygG4!kVX>Jp;+FZ4-mo6NNw7H!O{Kj|17<>zv zhcQ&!Jz=#=6ZD4lKMhD3ySC_*Qr|@MbEbd({%F7XBCQVD*C3pcJqzv9S=hRtU1_%p zTLMYCTzs$o5(va>A8Wiu8xe@&eofF42V{~Pa?jG*X{xDdERq1ez{M4@iw(*J7dE#M zi;MHjOQ$9e+6WBF^K2Aa9g?^7m`8EDO%$7I6ODK^xCiDkO zw?Fk=o?^AXxHi|fXkSt>xr-Jpp5PQuTvjBmU9M|U?vFoq+Bv8=jWNVXapV1ALkuuC zSyu$-&r+w5tiiGRN{{d(Pn6*iKepjo;ZAC8N?qwDb1HNZM-Y!IB2W#M-Oh zO{eYv%8lJ8g%i@I4liZj`0TebR`S+6N7#ko`kzR#-iB@c>Jx6g&*G=wGn}3%Q;^@Q z^3$g!l_V@{Kjvyb67QcPX2n`?bDFl_!CjEUVR=?E(WeUpJNsnaX!PtXxr^&AGx9z<43pl$2F0V(jKsDcj)?Y9v}GeOIo7=M`%b^?8J)90Vx-@k zUVG=Daj97$1&$usZbB75dzvF_^mc<;q}b-qpbB+jy8wq}-x_iM_h=MK>1R zHT~uB&v&w9UIr(~&6UCGbGJ``H;P%Z;dbD*^)ZjJY7U2mA3vs*!;Pg~tfD$nt_Iz% zO~x~SmTIv$Ka7wm1}g}K_9<#=@KbG2&{w$@+Ci6qy`9Yrn8EnLA*uJl;-cc#FPJeX z@8*5X9iFNAy}(7WuR)S`Ju`_yj@4cAX~^trx%#*@wA?cz-1j%j%*(Rauj9bs6bgiP z%}wlyn?E4pi6_8R+%fIEXZST-Q-{rvINA%9MrJ4#{;zXJC=S9ATJ`yVI;`3=H1k=f z7*rXMxOd%e!TIEvp3$+%XB9u40Un(z51GWRe|6qJ(RvP&*ZbS0gV-5=_O8s86WMMS z#KmSR50@@vOg4rGR9;`0V^Q)gzUn&t+hRKj%rRYX=ebgg*^60q$sNS=ARYmN)?#n{ zVaELne3Fb?KS$o2TQ$((h%MprPMY9%FMRTNwCqxse-z0vw^it4|9Eryj>#%yu|rRT z2P5JGq;AP({Eh2L_{^X^zfRZD@Ek@r=G1PE?H*

>_3G4o6!H7J-m?bOm-H%UGsRw=m z(@I;8R^+QFKL&odY9rAd=C#lJclEOma5kNjIpf13yFnCIzl zlhVGaz*JYAXPB-~veK9gCl&v$)QXl`m-7tiMW-4BhYjU=34GB{)AXaL(E5*q;iZ;` zED-tVYJJ8(x?hZ1P2lFE!`FX&o^%y(bO7+Woz%#}hI|FPiZYy*Vv6OJ543Dq2?hFImgc z8FWr#-)q*pG-C;d4ZaTJZ>5a9xjMi70w^x|?;{(j@Xq#2Cm126_s~w1Z3V3++Gh62 z>9NVUb|;$Uonf%*oSE-rS?{SyXnfFe$p^b!x@>C5l}lz@7$;p6?Ks&G24W|xWKnxy zk+;E2r+nbQY7MYAlJKm0l1~#3-L*x*ZQn+Dab8ZW^=};qbhx(AX=%lCZJl@`M!Ib= z<3pwG7IfpXAJ%#J&LnxpI9O7VTeoi|8kK)POXzw$1xjS@jV6JL9Z&+x1q&YmF?iIb zocDdKUUp2(Mn`z^iDp%(fOpF5z#F3Gi(WF{RsG3RuTtQJ`LW7+HBpB)sLOu%bkJrX z=7I(gg8PUZ-eLy>9t^C6?)7CsA(h0fc}M5>kHOvbV0q-EQ}Yo`%>ID7zkdt^hBRR5 z82OYh$sS~K#7#slZc|~10@%Pq1mKsK#|Y#Eb2!6BWVYD5@N>>hpeYx_mc56X()&1C znOMpDpJ1lSWGKxZ!p=(AM6nD+1Wm9PcT(^G>FU6H#pBtCUv-DFunwp(P zL<)2lK7J#-ewu_j!r9_%HeraT`osgSA~uS`@RG{(15zYjCkwQfmFZYj=K-j@LG0ws z;Qf6Ukcv41_e4J*o6SN%V&TR;nosiQPs1&VoVbqmjU>q;;SaOTkxaz*C~YK_(lu_$ zcNa$!{@&rmI6EiAob`RYw)XwK_2Ek6mbbJ;g}cpX zTgWGD3w>P8M`~}PW5TKU{E#oJG~#Cp8;?e%;C2vZdjD{ji_+7e?FHG|eQiL7T8$L6 z5oZul7F;k^U`(5?Q=@nY!p>Z)?ys_sRX03@gLjMVq+su?&~D76+8mt`A9-b0Pxi-R zI}wEJWPLmul;Q_lI~O^^~DSH85ZfY3y4 zookyd(lTD^q^C?IN&zLeFxC{Jie!1kd{16vOsgABL<0;SE&QMfm(7TZS1*MuW+ZVuw|&C3DrNeD(KK|w|A2)`Yv(w;luq770d6?f z3Dh!F5JM-l0ANmJ_7Oz9A^j!oi`k}Q{yiyc1uB^%FpYcnqpp%FO=!5$7mU=3(mj9gw4Txdye^LwB^&BeWO`K z_+oeq1R})u56>=`2|niZ`$B`*gE(qIoRxALl9ur<#5AGPZW z8z`QqF)?${?aQiju1*W&-n?7U0w#mliIZEB%UX|LZ2u5P=VDP8*J@h}?FU>w;kj4S zQlAN3FZ^T1;tfTRZ}#t61zqL+6JiGGn@@~Q4v%_0-sszEY3OyZt`3Nrw|8q43{lqh z`~C_b{^UXW~4J1GCy zgsT*kX2TL^S%~jEx$H;#+=p^Sr zj(P5sPnTTaMscgJO7{-fuI6^@tOmw!0`&jzP`2y4NXu6}Pt}IElnyfDx0C@x_}sJm zp?!B=(mnqqXJB3Zfg$Pp#@pVPHmczaD_%jRO79!kFKE?zQME2F;-`;L&C<2l54CXu zAe-gpbepOnk!-h*^)g=g!actGqj=pRm+gG2VZil6t?C>q7uDkoes5Jw9|ilsd4(H$ zfV;hL=MjZYpA(oB!8uWa%dE-5!)S{VqHGf%Owq*1q)oKbzJMw=2 z#0BHi1O+ofG#bHlb)@n%KMtdtxFoL5>VG9Bv?e;BkN;HZ3I2Pj%*4edbt@CiD%NQr z_I*{MVIFw*z@xC<6n%w!^PJ=7z(A6GFj+QZ`@VZES>6fhJMLRzC;Cx(94~(LlKimI z+)(O%i&3?<#R$!Zo+N&Dd&go1S7%gT1jB6>ZAkz>B`IX|n|Z$qX;J>5Zr#WneB z1%-9pXO(7CoedKLHrY=WV_1s(o~DZFJt))4Q-m&56e`{y253S^T~~e!MkXl?@D+6d z{q>!Pdb(&(WEZ9v{f3p|#T5A7cOEfSaLP7y1Am6_t>~)D2Ceof;LvT#pZ{5Nzgy;z zF~MUx1SmfRujPzeiqQI#RnFMozcPts;bQQvKCB->4 z**!yciI&|yvJ7)#a>i>b@#o+A`+W5yO!=KB%}|%ZTKgEMX418vgF|oYpwQe0oYHONRZkHz zUMjJC!a5TX#d1QsC6@E0q~A?;>gAVF4d!JNA$??9l;_`5o-dTpG=*PrGR@&48*9&9Vj^_a`O>8olW|_fxwq<$l zMM-~#2>_hIHYj}mWQ8w6dv@64Q^WQA9#?<#2PE4aKxtXT7WonD#zLH*Mtp*m>gaMS zf=k}IGqEvsc=p9?vsk`iqp~fT+tMRF{czENrMYavI&xeYA6u`*JEGoj^TeVtbT2VD zP#(|IOTB*nlbb_&;k1^e@5tF`SyCUr*p0;qVh?MD)+xur=w7SZw*qW=FFD95FLq^E zMR{BPT$htsnK;6^xC7px{MOPnd!IZ1m~(|PpzNzxEm*iZCU|r0HG9MNG~$xdt6vT4 zs}FZAf(6Wf_ve!NLfCCZUTQOy8J5(Y)>ADWx<9*@%K)RuCZQnAeiid1CQA}y%T?h51IWT&j` z7ws%Dir@ESvTW-Go~Y>_!WeaP-4be2Zb_SY1M1I2Cc&a4(gJoXs_KH-1VWlRZ1c>z zffIB0{Ph{GJy{iOHEKq<=*Bdye2P@BnO1V%;%?e-eZ^gTP>Ut^#S<#)l47Dc^*pK2 zHsieGMMrzulk-GMhsyltQ=-%jtucL}6h`3FsHh?GiY-YaSyvXlh2vqrXCopfb}M$0 zkgc@ z9~2^+aRcF-o%m_ua8&%sBO zT^zR*7;Vkpc;}6$VY1~IhYy;Cf{3UeI|G1xUI6>F3OYX3zt1DHHv>9lE+p;zZ`UqNG#H3% zk4qYoc;9}uX&8ph4~Y|=)4q<`AIt0-r7An;{h#?GYEqYjMLa% zx2O^x{u52g+6EJ#{2T!42*jnNRi|sq^!MpZL~*aOIR=49B<8G6!;OHfG55d?TtD6$ zOLkC`g0!y(!g0E=zZAXT+tli^K7)&(J^?{-Fc!BAq`xgry4q**roI8c?ck{7gaQs7UIj zku4C#h`mmDR4vFv|BMWt0`4)v`mI0O`WF}bnMwEn9nh8f(%Gz^*%B){g44FtV_ao) z_F!#wgi&B5HxfB^luG;|a>ZJ%f^_V`ryHLJqUxEV6MD`Q5c;bzFcCMi#}14#D{a6R z*LEH#It~TL^-}6s@(^hovQNg3wdI`-4c2*(i3*0P(U7@ViA3Bc$K~Kb=Y)*54Mf28 z-=pmxVUsBMECm4Af4~@NIvK+{etojwW$EM@P0m8vDrhKDp(+E{j2Hj) z6A|gc_u;~o$o;nzypo8$W%9_8lCli{Iib5<_F|VlJlbkFELmY4wCf{(YX^Ts75YID zPd%=~ta>EaN#XzjBqR`EP7LF5zLS(p3G{#V*f>P7)%Sco=hlA=9`k&Bbbw@%x)AB` z6~+`kt1?>2$zbRl1belZrxpb+qyiqdGFlbn>%N75HEigw?YW7!)22`l4MJloZ~PQP zGzv`Pl(EZNBr~4+m(ay3_yPT4&j8qWOKlvw(TxGUkPGR_wGw=zcwtwZCRP9jNQtE^q<#` zEb}uZH&nqeaxw0Q*liZlSmvdrb9hfX^-h(hz8C+<(M4~6TOg6G((yy8cxI*b^$RT( zE(3<(a(|YE$j$2a;U;)Zh=_cM;0%odr?zP*+8|821T9g{h))q}QtuB4-=PtJwwD~| z=aJX2JnP>-ora_>1Rk!BD@G}mc~?F?sZ3+IVoM}41=QTU@9Nhs@t?K(HH_gra`MWd zwuHxtB0XNdnPpuog(M>I2CiK+C5&w@H?q~VB@Bd~wSRUw61It9oixy#hMil}~so?h1}S zyiJ`f{KUYky3d~*ZMctBn`bd2eGi2;wbnJ@B#{Tn;EukUU>fLbx?u8oOYI1lNwY>R zo=i(BDMbWR7zgr=&Onh>U)YI4kJ}7_{sCao>+7b%-@bE$yJ;^h^5Q$-$%TRMhzA3s zPRvnx(<>D7V^FqV*#qcFl+8IJ<^4qm>{cmU{1WufJ>8-ein(fF$fqNi5Rm4`8_B%m z?)`=L^&iO@zfG#k@rR0D%Kz78-2A1`ew>IaR$+MD6dH5Q6V~HN{xrSn+(*c$`(KkE z|7`=)F(>K?_m;!n%#`)8w!+l{S8e6Tp|jmze$-N>ida0BtKXt7z4f3uk(7xLZ}Msx zZ8vzEsqOzhSSIJPBf?z1G?6yo)dTnU1~gQud~%xt)7tS3JaMFP_RN*b| zQHe0FrXv~)MhA)GDAC@{6Vy5V5b8xiB7OlkG_uVJQ;J7SC4lhS4m-C<%V?k-GSdv) zEj(a_zk5Jiz^B3emlY)xiF2Qo4!RFd9fPfvvs}nm(?vRn3KLYRClw7ZFe_lxPbNwY zYZ!R(%RomHtWb&hW(EAY&I!G5=JE@AO<3;PfQJe?X;FktxNy|xmW!rsU5P0 zu>eWjqJ#(yu>9JBU6lxO9ipVI_{{YhcM$%lvoZ%0Zl3~y*i`XK#sTrGt3CmZ*hYHX zN6Fp!N@!#MGz(KBr)6rk(ej^+u46z%{q-i)G#}#ZxQg9J6)t762_5u;2XH`gE8>=4 z`fzfVq<>3d7MI!r0#Cuy)NkxpmXx1Li9y!`jT{b?y)&Tcn{Gy0AfzVJp@NgGOE_TH zd2!Mgo6k=L)@{mhjcjv`B_-4Hoq6U|hfHpO3pKmtEF&1A%(AEpk669+FRwe!N;Hnx z-4}p`>wx>Ca)yO=sqAB&z&aOho~=Rb6P?=A+PNIBmP->n%z%Ft3P?hKG+E1nbMPRG zMw|k8!uO*Pu`#$8{t;xTH;JINFt?=b<0nXYWDP;}a~};El5&rQlG)GKyOla->waj|wAvZfc2|^JH8? zYul#HwaigyBYX#rnRN9!ZPIq&bI*1_gmyxp9zgjwAVK%)5M%0v%u*m@8ts|ra9I)+ z$2G7%nn*uJG4^bSlF6+CoG-TMP%T1={WU-s32-ly{-FmBC&@>4T_K!OKPF?WO_N4? zSYf=a;V{!~&9(0dNLO-us<=3GEL<8viz^^mkW>H%U!VgZ`!8zfWjb@Mv(EE(vfF`j zpNq6p_k1AT{ufcT>2c|$h)VgbIF=thRoDoWtBqzL{4{Yt#g@n6QOFt5ye~>wzgwo- zVa+^)^w)=AyNt+!*80x(z?O6T4v-!7(QpFn|Hv>CxyEn9uf;V#$V($#{?vvjJ1@e8 zmyJlC|4h5LaxTE%yDC_BVMqQJEgq&agZjuvW)A`m!X#+?lfn_CJ$3lqrp-uT+8;X* z^ztm_uz3Y@0}YY{Ud87F_-~vnrzc|J`|Z8k5m$x{)K7d3XjI~i?kQU4uZ5OxOcKo7_v!9vn3vr24Bo z*K!0|ZzQYgpsQEfMNE*sMmmg91WreH5XmX>UuD6gt&pbVx8h6b3^+ZX-hH^@g>XEe zPGfc7S{PMq2$*Tn_k3A0^jHZ(8&C?0n2eOZ3$!36EXzylw?mA}o6*giupSIedRodC znA~pet_`TnbI~lwxb*8U-6HXUqs9+L#sa0a-g6Sxn+H`~bybCwa>+h9r- z&6`#iL30eM4oInA0G2*kk*$|DM`Ah_rJGN1q~hwI{x4lE0#)rF!e@7aC#m{#dAg>N z$2-6c=sNo^aMRe9e9&It2GzS`fBu6U(b6zkQ5LZ6`B#8$PXgGcu!+oMh|otx5J$<4 z#ieodK;6d&$-f(0kL|_zJ}KsJnc4!FgSM>GG&+(}4p#6~$PXP*Y0p5UL0| zh`7n%S}sjW#SPGKT$FPijzaVJjiGuKm53k^^_r+Z!nbew@pxII+`^uZ7b zoCIAKO0F|_Pzhe>7rNjnCNf)*4ZD9fEIg8qJkb*<`id>q$qcN5Bk*lSE@Ka-=VsIBXwe~LlSpK6I(BA~cY{SKxZTBmE`N>^J zrW7(1eDa08LHH4aYL@|@ROsgW_fZQ~b=f8|WV6SSqQ27^ zrRk%^lP13ThOdqR&gQUR0x+q*O zum$KOGwvexU{V3CRxo4T-G8$F4*rF>K57!d7H4l$M85fo4?!b7Aki*sqs}J!&>0rc1Z*C(5;fkWCFRCJ zy4HnhK)V%i{om>Gx?K={%vhpa`YJ81pI`a6zvZ&N-3@k|;fm__x1Ge3K7idK31ldj zZ(>;bRsnEqCf}iCYX=;Iuqa!8>!UZ;;5G;aAJhBMfn;djI|R3EuFdu8F73~rwcRy5 z%e41R0(#oHgh-7DAJHfIQNW9j;38O8jW{v$ljIO4z5%td+NHYwCjTVQ;d;Y=o{a!a zWKd^8(70v+s&q(E*TMOQDNuaDX_=ls$@DaBP?7my3;ym=1|r>taDz1bSP8mdKC!7Z z4MGYW&p;|l4h)ke{vqpPU_U}b@09kptZI#d#6>o>-=IfO=)a6J z^VAnIeHMqLYG{0Y&M^+HD_LXX)B!sk;A~osL$n$q zu}8~>Ghh)Qyg}e~^JUedLV1l_cNXr~&vDg>%Cy(n&@GAoO+v}a3voVn;3ffZdzB43339*EKHh@mj0$;i3!xCfL9hZdR06N34=p>pv}t`*z{S*a&6EUd)? zme-LxPR2Z@{Wj8OqDD&>T$Kr1gXo%Rp>{0{$WU`K|8E*mDH=bjT6J8Hr)uEOkgZ%x z6!0%(SQetlx8SwOwPZfZf<7fjh+XE8^HdAC`?<1S2l&lbzLv{D#IK6NSeURRmk&ShSV;)`rjIU$c7Vkd7 zspKoso%7#NCF**jk*fs4!N)dEmAJtF^!$!macz0X@l0nm?=0Rp9h7HuHMFu~IR%|y zDuM`NY2dmegtUbu!Xl9PG{vwho`G*$ek7?{E`D4OPvN5T?2rFc$ zBCAtJOZxjhy{liq(LdcJf}|3MJ^u{F!$k7=Qt`F<{`W$Sl(Ymm+f3bhl~rJJ;Gr~hA$w=%wpGBNc~aCQita5v zegY$dXcQT*NBw)d$0X_(@C_MgR6iS zQSpixFla-h{1)Ij)Umu>2(;$}wHkO1=L`iJTeYF=cZAz`RJ_yv9k`W^_-`-!Z8=k7 zczx|i2Vsf>-bP|exC=4>0xFj1f_pB(utq}m%aG|{Et7-WOR3SP4&izg_8UdM0kM_e z?2bbbOEbP4UMqch^~TPI*WjD0PlbK$@*V=WXww|{WYV{oRmF26*s}7PZt@Tg>%ak) zUMn3YVN=0p&B7{*^-c7rCxHGl5&3=WbWD??OjGe#&CP)B{?%of&I56PnhO_{sz8jz zAwrZ67c081kD9&;+Fa}&jkY=);mLFUX1ZJolsmcYYqQhs%OgE$f$ zAD!0cHP~0*z@GB0zS3}Aff=y_>2SppeM2e46PP0j*`E}#3n2pu$ZrvC&{kuymDiqs zfl&eHWj-PQ_~2JgT(Ftgm-b}6sWPd%4U;7uNFbt4CbHdq4LxSjtKVG+B8b8Lbe2W> za@hsZqLrk1`8GnSG^hNG$y5u%iox(qgS(p3X$PCL4GjqK%R4`{|NjSbK_x@cru~QAN-As>CPIEgt&V;!TatlSq5q!SojEYCIy+U|w1;lyw+=sq69h z@-ALwHQpDb+spT%V%|JXcVZgA96kQD_AoMMN0e^?TJR8&#v85|bXIk<`G`3C6)s-2 z-i9xn0GYlWx?)Aak(>;W0I3Uz`|&mO74rix2X0|gD~c@xA(ik#J8}XHsJjB^SwCH& zPre26<`=}r!`#3Mg9mXaEgyY9SoU(aFRe*{k#r&d=fR0bJiQApz@;JcV^?6FO9u?j zu*XcQATP~Q7URkOX6E_Rj2;(zAl;B$is(bXSP>A5w4Yd+hP+L2n+dhPU__6MBLQ0R z<$5|I%9PkQ=YoK0y-skHWc}1g?e(8yFT&ZOBXjY!b6@-Q+v9)EfjVs#v1eQz_D_fE zK{VruQv(#53%02(7AZ#^qTg{=|GWkqDX*<)f*_5?1T-pgtcvGk0+SZ{_B=A|;c7ME zp~FNst6^TCf0Rbwm1&r4y23o*p8+rAvIPjXf7*lGA=OClBUDfFB|4}NI-5?5R5pv z-f}x2N3qKt7Iui`kSr2#IFP87rxfB9`%aY1d+|j}w1}f$R_e+cN zc876~`*B7Y;Bq1!Cw2K3e+wT|Bez|>lb#@ff)P1%kByA=s(@iaTb4Dv=P1;VmNI*B ze%yDO@_1tLF(Ql$kZ#^E?1)_S7szX3l4NFW@yEi?Av9Cli)O(=K8!Eg0fX*Z+G@m=bdr3h`f0*N#-?{~xJQwLlf$|i7bl{r=Ex*UT z@yM`g+nE4pzmz=V_69!iRUjDAdZn0CIv&b;Fs>d`lW`l$?QfRb0~*tQ4gFn`c+q_r zCR-wd6_I}8#}-2-$=}w(OCWne6dBAC2ex_50j@f4u&A<;itD?)S$zj`KK< zGY&#bcx5>(W;_+3@m7115qz&wW?EyH`On}Pj7l&fAs%4irE+cVoSm2O8ZI8Ql~iXR ztfPMeFwm<~VOnG2E0eA_{QW=v1S6<35JyNDwWx{4>|nbq|KGocJ;flebNp#kOpk*0 znxw)vM6%wB-Kn{-uGhyToK2-k(gQO<(HJez$f!~)#eX)&XiE4;!(@F#e@25Cm2) z8J|`t(zUCU6+ha#qrum>vIXY8OG@g|C~ z;OI1Ec}7k^kEYIJ+i@R8Mw0^65jJ6O%u2NnF9(D%c0)R^35EMilvtzHhMBVB5RLzS zd3VBgyt;Bid`3KIc^yjnM~H&m#gsDW;+D?s&ayK14dWGSAg=W#y;~8Yh@mRMQv+zP z#y}gyTm*Ait6an%h_J8zsY@8#QCe7`*nx3OvJqGl82>K$pWYuo01@^#razNLr1-#m zxoEuK67%mpxJU|c5NVeJ+18n4Ta{udo)PFLGOw-n)h0T`{!VkVAviHU^boyvYg zL9y@Ae!-6bR3C>*ofIS1C4S?{{RD6<(oee87WA-~e2d00h!Nqg_XiGrAGV(%)JbX| zlCQpPunBH;uduU3iIUszX1GtEk@v9c&0+|=N`Ay_!rs=O% z>=_G27vJ4HjPi>Jzu`~0B8lp`iLv$p;K79l7eX#L)-XGqb^61OgIK~E;CGAh4l4$s zs1erW2o>1QIck4!Et-%+5V_=hhmX_>!?iviQne~JA^GLWawS&3eh|&7^0u!~`;NqF zH6YBoAm`0pO@5XZkjsH})`ZP9YAE!;!f&ROBILNmg1n^so;OIOuvfE8fE+qU(sG)z zbmJx@8LZ4yr-g1tOg616jc3nKUoh$19AycZWm?ZXIg~G&8EXxd<)Uz(JfTcJ1e;0? zMQ^RS29(4LL!MBso1JB8sn&SK6H~qy>ep!Qz^UVyY-`??bT1!6#ndxiS5Ys83^zr#x=*>NrN5xBFL<~S!-2d?fL>d<% z;3szqzMlB!1puZWcoh6~b7IG>MX&Dbe#%4%kqtUY6&FE_?Esv*WHqlVU6cjpEqz4$ zc!LA4?}9Gi9T{4sjxgYFo@Tr_c^9J>B@}EpO!g0e!8mh~8Pokzu)GwN62pCUgntHv z;ae>i{*>MDn1~+(juX@QleDKMaGGpYi!n@B7g1p!&=mH|0=19!HO$M@a^whLJFfYC zP~t2_P)B5^qB_Q4?29|Sq7#s?4M8IrO1oDF7HD@P@*fn-ayX7cMz*wJO*W@o46o7J zVRAK{PX{fpGg^ye=Fl+BL73pu5M~IWYOK>((my5kn6e(m_A4^qb=h%AU z`jX|l&g~y%{ke*iWrs(*GqL@-V$%3Nsg{s^%qor==@E|U4cK9$s zyHiv#{c`$M&0eVNlWZK#_a-K=1-fOgwayx|gPF!7pSU?e}Jr zWc2~$nYAVXxlocv@LuFx2EUWwFbqA2%3hF)rTenocpVP#vAMC5>v+iP#&b``6g$gk zBdnK0XzUO-u%*+S1)w#5nQ^Z6c;A|?)g<=XWx&D@-Zzf^smE~7AXW1J?}%c82+H?rBQYee&L9R& zZsa8^Aj{PzZ!!#c`-u^;-e{EC5HM=Ma`AZt6OfW(Nh2c1*q&FQyI~-~f`ht*u*fS6 z%2~37Gc^S5#|sxLR*i-)k59NY` zR?P4In?Vv)ia|(B7K!v`f1$}l5->k{hj1kq`HeA6>iPdQzc(fgpm{jy2-Lk!oCkp> zSs$DYD`qyTo~DAq5<&``ag`!5z63*zs_?&*z~O96pT??U5XR|Hv{P#>>jXU2z$KFl z<5^Oie9|AGD-2bU_DjM<-CC@HQbn#Fqg z9!Os3lp)0p#5g{IizJNj4v5PFxgt)C(03T25TWp*Kl42IPtgMHVPSEmQT*E4u_`D{ zSHxFoU{2>0f_ndx*e~*aTHs&1{IEyZ zf&aht7yrhiIk|h+yteabOe@s3cN}%=K@j(wF4!29KlT4vocWP}t99JSo-vGId4j`l;qlDDX=0 zkL&14$+fx(fsx_%Y!(S`DN;DAxg>r=SKjx8&Jv^FKbs7cd<k-k^d^N6?gANM-(&^yP{$NGp_o-kH*)jcXf zuJ;L_cgJhas1+TX?mfdKdRLrubH1y@R4#^gF=;I_;Ekv22rp;(HM9wP!ZkU}RUQTQ z*l>d%JZEiQMmY*D_g{vWSPNBA?vOrgjW=P2DX;Pw%ZsRi-@Ne1gf0ihP9w2!TbkRQ zSuV~Z<)G=PvPaZBW+=(3upH|Tp5l>fErHs-(bI#7p}U*lHLB33Y^(7AM+77)GUU(u zv8FuVY3s}upVzmd;P5ne0x0|6ktDLDZR)j+~lBI=QPlWAfYz#KW(Rf~YPP zCI>yG$iX1Vd7cQBYysYU>W1~$uOr(=R0Uu#PojP6==k*VnYQiW*74C5W3)z{X9j96 zNsf9v%tg->dg+!~m8Dx43n`entz9=LL;G z5s6PL#BE9tm-B}ezz_iK@u?aY)bd-Murrd3>wZ7dPp6VK4}^3SYAR8ws^jOO&18A9 zuvFDm$YfP{7$fq~o}V>&jAcl$oGCidW|-$vW!1T}4GB|w`SnVE4tgEFvR||5TJ8MN zxf;AC*0s8&uBoBvGuC_0r@@Weok1Km6!jciT&AaL=IRshSq-wIx^gE{KPLiKA~jNvfJUY3&UMo z8>%86v75n?&xq@&!W~JUxCJlcMa)dfsOHU(eaO`F>mWF?y}sAEMk>u2g6sSA*O;i# zWn1ox&asKOn2aiqaT9;;mn3$zL4r1m5r~TCy8s4@SLUXyY@{S{;U^fdf+firvwj&& zDGTH;iapJ8sqU7-d%S9wq;Z$^FrY7om>7lpWWZG^Nt{Vi5p5lePJy0A`vdGL8BBlh^``L7Go1ha zvOh5z2c>gDeoABBaH8Ql-LU1$^r+7zLh@=Q|9+22Dmsy#I#T3YApe>hg3(pP0BRQer~72TKQ`u{f67zA;;dR=dJn7Jhkjh} zUcWAAH(GHv-SW5>3X+esewD<5Ap(2!W!}1hcZ%JU)@=Bl;6J=Vf_dJbZKKfY_U|Wv zV!|jB^>9bLiI&R%ud@To9y=fba7?Q9CH;F$4UFqh+Q_G~o_4@`DDeAJ{rM^?Z*bXb zw!YdebU*7k1fj>QFJMCp9|VrgoSuRo-zZ4KfB1s)geT*9pm^kn`}Lz?B>%RO-m!_K zU&!aPHk`p(NjQVELg7yWkw2HEg$rDcM%%LfyQ|e}0&c z5p@+qka1U}^!MLPByrzprW_OaW;27ZuxZRdk~#{$BKM&BQ}h_3e){iEP{v~D8$Bd1 zAdHvnXYu^_<95x$(c8wGW&a*Yq6iA^#Ctr|S5d#dDss69l`C2?JAiF)6;O$uZxbf{ zy`0Sq45u8L(-dyWAlEgH1_fIaK8~5D-1}ZW;p_Dt0AXGAwRrgpi-;m&2MB`|K)3h+ zXz_rEZSRZdr&moQbm0*>iH2F>{k;tqC@chn5WS-AkH=A}axN;%lBjYr5Rdv^I($Y^ zqU@ax!k6CG>2Bj5cCgjLt&ruzgT-mi>UF6dApFmMOCLG&?_Qk?fc+!g1hhikONh66 zF_^i$n3xo9`nfP<66>-CyvlSR^^V(2FMcT%!}1D`v+zzve0nno#Vu?Nl!ihK?$7)yyJ^Zot6L=lz)Lw@Rp6haE;Na-v5O+D-vaV8Id>8RqXx zkCw)2Gp8WahD*=hc%ICzEqjNJHhGEF4+|Oh?ljM7}|K~ zrJ?xe%^*fSCJ|aLe5VJkWD$zB=z`y&qZi?Z7&Rz-bm5(op=Wj{RsZe zr%s(btz$W>IG5uz)vTNeCuk897%7AUH@DF zh!K~trnkD-iYts+35ssEFnNBw9B}V+(dhud!bjlIeWoQ|&ez%gaH`oo%PiLKyHv0K zwxq@Hiyj~+GYO@Iix9RM>4OU|)0`qiPY!?bYUD3$!b>8O?H3QP|M)fFpn81XfoAu8 z7%~B_FiqBO;kO>>)?3*02UE}aeQSq@0zW`=y-sTMjmT>Xi0~Lq)bT^%A zug{>C>3x(_x$AxD<>`G&nHNqSI;Ki$rryRA2M)%2;JoRIe$TupwA;hwhj69U$dlln zcdO&>QWD+_XIKYmK<=T7p`SRaL`F`-r*U0hIUjKKp^0#ojMU9>?f%n&W_mriHt;BV zYA%mDU-j`^8;WR=dUWphN%JF-{utjqS9U8+6nJ5He*RY%9HCu{R6uloR(9ei$L`yBO(=hM zS9IEDlyH91U7b%3n^E?x_DY5PU#kYfz#>GchtsffHbHUZN$TS+|Gw-R z^?-iquMl}_%KfQ#V3Jfea9TW!agLxxbprZ(d#hp7l;~RLQFu)_=c1eVV|Kv7ScmTW z5zTK6OqGMfDmR(g!G?NbDUu}Fy=T(vNhh&jS|5MqX zcu;ytslhLJ6{lXb44+N zBXZ3~;^Jq;o2l758O1Z9oM&r3u1IreIHNk|wOYwi%Qlz3uQpC)ckq75I5IKzZt)uf z0|>|#I*=fKtUVxQB$hV>y}TPZlD?pUG{kI>;9LPv?YrVv z5&tr@0w$&`#>pCc*tqhR@5u*hJ^jR>jqT3e6jhG=YA+O5@%jzDJ;VacJ{~G8=Y?ipid}0nP0}r{GN@lF0Vt_}FVQm6 zBcGLXf!8d`YbpQb^k+Tbg^WVc$ zmZ(U6+*ZhMk+y{@fw44#ocZ~(oma)b&(Y!76p!yN4QQDVmuF6eB<#{0@^?xe~}pIa0)Zo-We2RZX==4OJPM%MJ8QX+(3`eBQYt?G#3B0*O%AYfmf7L0;xYj&vvpZL#CUb@OMk~MB^ z=FQZfs740WcEBxN##3#6Z6UGsD^eeP6(XXR4QJ$bn5?sqQ3<*cESbw7S^HG);^2Pe zR#DZzV~YsDsO$_>_aZdxIS3yodZ?e@doc9?@7^S2W%Ks7xQL5~F>G_a^2e#!G^T=n zXfvO!L}A*KWrGtZbc1RJU=YKb>UZ0#AAPSsoU=#|j%tUCgvLTuF7Y@O8HDj9E6|I- z?fX||s#+>g6}3nLhTE+7RzcE6VI-bqOB|f+ z1Ujfue7^!Ra%bMzqz9uIv@i2pZbYg+n!h}q<4?z`%q_#D;spzqQp(ehiP|mEPkHV# zQRVX#Y=%xf;xWp*McPtVos|~iOFb&pjfJxTD>p@~o_G{T;f43!2VZ^S+(YsFu+>M-Pm79BDq!$%1gVktTO;{QBm?$*> zTWfRNe~Ru5&Qy1ZVLq~7ZprAuX)df&n=oUr{w8#87j6A*KmwAh0xqT>Z(JfSD%+nm z{@he3<~`Z0l68r}#2s$7^m@ySQdyX|yLzl7ws?cCK7J~rH~v&cS?;(NN1E<7K3I}l z-aiW|A4*7`-$Hvz^c=itVI%kaScT4dG?3j*E#sg$rx*I)q7Ym5$oa zU^+`TW|EM>yJ#wA->&-NcKVaemiK8P7Yvv~LdJ(uHuu~Jcpp4Nir=B6b{WhSW~Dph z#MXYQbEO0sriPz+uW^o3SmwyA1LpV7aSa-L}&3X?t5>!29xhM zKG=wF>+!WeytOx4zkSbA{cHSJH%1i^Tr_HEg7uOV1081dTSCbFd0MJ}X&B_7_d)H%o-)~i$a<^D!wsvHClVo~C*Pe(z-@747!4wTdQW-9QdNOz^2K5G zR~n%U44E(A-1YFmrY#wLU1EQGgbO+{55~Ax7nle4bNzAH&x)M_3E?mfQS1Ek=)A0F zo0rP+zWpog zGHzGdbKJlmURNfbFb3AolSN*hvRq=)6Z>^f{fpd-1;P@Qw*oSpEjibxqiZ%lE%|%t z!I}o6IsHmj>?rowdJO_ub;5wJAk_}apD zDJLw|;o9ua#l^FCGG6e^k-ugw!6 z+}HYf5RNxENug^KzqvbVD^Q)Um(3 zQ|-Tsa4hxNuNTe?jo&y=9?H+O{IRNuCbWpd`ZI&kt!5FCN9*k0Q^j*-i>`=e8u2g& z4vQA&YV|h1IrB|C)1gnGRxbG0#Ye=L7e?OE-3Q$GF7PXMRLFeHc*IyeRoL2I4G@lFX>Ex({+Eb zEBbqjUqqG*Jwt!w`(@K!`E~a$_P^HaQlP(P;?RxtS>*@@bi9$kJ~x6=%#$ln+{5hs z3>xI@L-WTt@8Id`$Xq&*E-u}w0-8lxwtL?x?2P0u?Vl#(_{_!Vc~wdDwbhpogVVBR zC0D>Lu1xu4J~|`W{G zpWir>ef3kcTpx}{S0HiG^#?1^4B311N_iYUY9q+z3wy5AitZByR#>`bTS!UIOFHAa<+cY*pD80VGUlM z&-l3Ox_hB`Zmay^@$K50I7;Bmx)t_7rQm0;s^7G z`Eu06saEEMFEoScnsVRhRflIoM?IH|(%qiqkrz>954QQSlOX*J848o>YURs%eAP_% zf>E@lt&hu7Pl+9sE#Yx)N`lIWEwHW3m&ua7DYFL9WQUV5_F`{3^4VX_`HW3 zp*21X;7d!k3a@O4lW&{EkLWeR0lZ9{`XT0O<|m>vQM@)j&F>m}oxf2>R5FWYE+xD^ zR@9+)pMPHUM8rPrb46z5+!aLn{|%zdiZ=BKXIYcdM?S)(KjOE~s7PK4TbL(MN{9R5Yz(45FCO<`bv7?xqHe@1QSOGn;& zr5-w&p@;Jl@xj-ZB3E&LHL&#H!gRzvV$@FN+gEeX2d6!z8s3C4Y(MbJ2PH0x+}jY} z@WbZ>l?O|t%1PG0#AJ=sIb__rkDuDthPU?$R$m_&Aw5G45!XG(8C~$}W*?4^*NkS7 z&muMg1>MmJG*9s@8wPdF^B+&;>R~6zAHbc{+Qwr?Rflk_@xwIoS+tsuRd!g z6VZgKJe_G}#;Fj-5+-X8<(H3{rYSZ;^dd>|sGiF^{q!=V-yvMvO`G;%#Eri;+j?I@ zwSQI%e>%FBb&b~eAnfhsuV9!Zp%v3aFEHc{#ko0}=B1iAcc%0%_EPg~@i2MV@+SZ&VCe9*wlo2HyG ze7%7;(`RuxK!wGdG*|JOli)kfz@nfvMb66m(|Ye7E8iojtG4!#=Mu%iXQYk%`S4he z2`V2;rnnCW5htdK!IEVRl6|KtQ^Qp18qbu>1735tV~fA*tSpAIg!0isSQaC7+s7My zsrd%PA|Kn_Wc>dY(2}@`=TI^@IERsjoH z`#yh9!p&b&M_XM=dt-U>t3vi^&XcQ_ksMV)PE2NTFou$)(z*V_Kx|&Ga7dtru}hpc z`rJ12K%h(_lguvJmFx_YN7Acl&R;B5GG&(PMw%u-nqW1fbRyprCrB!<+PHx`O|07K zyJSjQY%q>JvxkLXx#rJR)C>wMPp#fOVEtxYQj@0XZ(i8C)^sOCbj@Ft&1#gUp@`#X z-RqLM6R~ptLfGiswK`Edp_0V+dCXr}2kQV}HPc|-i8$=;6#ul+nkqI{DNAuHLu_~j z0KxLuo%`9rB;#zmM~qt?Uhmw#zqTH_lPX$*@21bV5>^*sp!iGnzINz73l=mrZ6uhh zyyK~hw}t1(BC#%Cy~-&jq5bmp3$#lBBPMwJ9GXhc?X@29r=NdL{L}y8!_m@lTr1UF zZjp;;R5Qa~3b+=YPq;X{Lf~PSFMXeL`7wfL#>Wa44r&tt@pACk5NGOfvvbP)>18RV zvh#TH4UcERlfhB^SIEi>->UV(m&$9^skI#gqOVOEWab6bt9yo96eSiW685oz7#yRd zv;{J`2i>V1Vw<W)o3u0_Pb=>T&IS)Yok=`|>caSBE zu6O9-+56`G^Ygh_|F7iDmfX0fBNlhv^4{{E?>Hy=`5m#y{iqM(C{Yul$LL1K$@@`M zVKXfg?(!<3`)M67ROW?Llm&v@#csn4sFO<2eSLCwN{R6Y^V{XzAB8YtrDfBP(9@H}}}8-Rynd$jdt#N~8v z{DgQPmMOkSB-bMn;?IpVC*diuq&@B6ZIw4!5xQY`6GKTI?Bo7+H zLYOun61V^)E0{ymE|C~Ug{5jtG8WMAhSTU;Ox(Qiy*3)fMCBJZI;7;>;+_unvg=}9 z+*4D%Vmx|Z6!+|xy7GiGIVys^EL8EKl({UGTWwwPeV2MDZwmYJ{(599yq6T-z^E3o zb75T^W(rD}1k!57QiLPVN82m6?-cF}$&sG_VsLrtNrMA1Q<&<)_bZ2X4%Jb35e`gC zs%hDLmm(--CFav6EWbKlm5mC`V`0h-L$zN-+w84$r3v>IaGwC`J!DW#FvxNC5I7%C zF(uG1&pQ(r$JV9FZmk0VYog$ZKeY8}jobGl?#dP`^586#u(V9-l+^5`f-iOL#W9cF zSrx_iX?AszQEuYJ{hs=~46p05mQ>rdNRmgRo3rHvo4^wlWePtfjnyT*|BMdj?icK3 z#Su>(YrIm%P%)`{*-uIE>jPLr%<)a8J#Kn!o@&)z=3aU`E)!<>eK_$V6?b@x=@HF+ zVD}Q{2%;2kGiK;kKVs;lnQ=-zXDTLtrp(5K6puY z+mU;RVUnJf*sv==dlqb-T@jM%BBk(4q>@oM9q`ekP{CY`I-D3SjOG1>#jX4L>EQ~M zK*=Dc-^Jd)Q3Nq6I>5i{<6V}qd*UXzTfU_=P@6MVRczIMT|DyEFVj(YqqOCVFg|3p zvbfKZE*!z`s0)<%tpVe)A_2uE#9O{osT<0)B6yY|?DsWFfyORVgy>{~K!yV!0C!=dt@Y4=l$>^sHfGh`Xhh0`8$ z=fSLN`$eHIu;iF2ujZs;E$l=k7GNe=(3TT*K}lCSfW+$BHRo zRht=mD)LrBoR2CLCl?E->n%IyGx<$;*u|*mwY#n#(2u-3KC{dzBS2#t z1$5lh*3stvk(};AgQGuN@FYXUR0xfZrjYvd{Q=m=;Y@pjy>Mi%=C7tz&l(0Pi_t*acbY{hfb z8068Pu)BFmeE$kZCe)1M`BCV_2q!H{W|~x%9820Rf%tI}C))hf$|Y5KbkOnv^Hot? zl1B7cIA5!wBAGbJcPb3S#y}DNZif(osdJ_xe3?Q~lGg}ou1ImprCsAjpNme)@Z&8V ze2L@@N!&ge|1!dz-Z}=UrYk*l8)1V_I3j*Vi&Z~HXPl>3eJ^&ZZCTXv>qTdZK;b}K z)u>XLicR+GTHmk(>akfwq9Vi&+$y?DGid611fzl&DJebOIlat50ETmZP zA5iZOPKFfqG+XaC%8j0GosJ?3VzUJU|EaLT7q(#xgf29j;WJcU0$`r-(j`cG7SQUI z3!V#%CNpn`#&$ssK1RMLn%#`78q-L;(qPK&l zs=#7_;Bxu#*4g~D@5?!|Md^a>1H)g~meurcQWQCQP=-gw9>4R+5aF3)zBlthO}{>6 zvj<;Qx5Qh)qdYAyMpI^3yd+G)Gh18Oc`_9Q;3aP+=2@agJRh1y`{+NjumuaCb?Uhdu8e7b zj;_q+4bi;MHrdaM&r2hqUrE?(skV@qiO7_GTxyk{e_p-urMPTu%KEnVn!NDdx*h#OU)cVj5=ZQ_ zZ?w;%LB+ODqE5SfXl}Ktu+==PKu*DUJmZzvvt^7}1-O_+Cj|@l=eafLNuF-1SM?ml zdlkoCytUW%h1Sz@w4iWJ?5W}s^bCp~GdArsLC(||)>8JtW!o9DOxQTXxLwo(;bQcb z^akcH-p7`Z_!JO-BY2Sr-U9r;U^XmTbMyMg1N3U5TjJZ9H}~*`_huihRBe#-H!tWT zr>xR~V6zgp0Gp-3YV)9&1yVcF|HI@UM#)~UV|yO))qVlqILz=vaoM#LdONG6^O)IL zs9R<)4j(?&DS6XEUH+>0@T0o_wogOwndH-{Yx8j zsPAV-q&t-OECm|gg@i3Yd!lm?CoS}}ypQoS=7W*v+-*$`@wNuP`kBu@=_8`$l93r4 zyqt2RV)^LK`2dve@7g1bId1G8|G3mQsVoH&f2qjXZCtMY>Q03}Wyyd1oHxY&Qe{Ih z_dX<{sG+aFoFK)7ft?%s{5>a6_IIZ05~glGbB(&H8xtor?^B;aWOQ4 zU!3A{Uc(lZPtT6Qd zsbsv7?fE{a2Va_=$~3{|yY#G?UX19fP^-Z`sbJ;W!Ta})?skY5&%@BDYT$w6)-EW% zRz#i%IcBd+Jm|f}A#YUF?m|ZVE9lK6gg$=el9x`dfWN<{(6i1+9;Tc8>BNj^s;+r% zy|!g4Azq4OIRJ7)`P!jLr_AQ&DosrG9>KtNM1<4qnKM-P)@1Q#JH0-@Gq&AI$X%3<{|1 zlLrlOMM)!X;^tA4FvfoCe$#xtz-inlj7r>(De#pB@IO%Pin{(-$cjg11EXLfXiZ49 zHUIOd3NS{A>|uQM??A?OhEYz8u<+XJMnbd13?S_UB~wdZonf&OTE%|@LkHc&5uh1Z z@a3u`IVo94@|a*-rU86y)8=a31Mg4IBE)}F#mHm*yYQ@ceoWp# zXkQgDn-O#x&v(b*tFI<8w7o47%D|H55V#Bs=iLGX01K83t{d%1Mjmz^WdM-d<@MdG zF~HWg|5v%{e~|5;A|Z|l#W-?x@J5e!Os+s~wEx19!olIJs7SUxPn$8{%6QEOX5-|% z9d|NYPM=2|4&d3mT6c=5G|Vlk%5BUg<-8vc@wpygqI7*g$PG&r((E`>*LFsG+rutT zU)y;Wdcg@Hkep9?mI!S{jjcq>nem4~Sq~8JN!k{};IIj>zIw7jxj#?BET=`kk#{jI zFJBtVCk~WIY&L+kBd_xYF03ZICU-y^_3`zKx<2AmnkyIl<B>t8@U8fBGxZF*lmMP}`EQ3;dS zQtNI0;};J?s?vA*0h<_b+Gxp7dBnXB3XrR6k6HR-Q=aJrw(o;> zlB$9cx@Squse+XVIpnjD6FdmNZMVBVWe00qRKK`Ah(gxejJ;BP&_O=-OYfd z^7Ro{1R5*5WcLE)fEe(Gg1^B_t|w5bIb&Nk=71%IwGIVW&-#}>>0w3&&yR0KO)v2q zrri&(o8!{8#7z=zwT5+eN2Xq4OZ`b*A}PM=n!itT36~i98_H7-#1^|y@Vm;7oc-@9 zOEEK?a(G6f;inOR5ut)#*9IIU&QlH=X)d?a{)S>nQ^s|8R8 zymxzQ^Y-Q<{%z*}>y6&Z`MdlQKllOSej3U$;UBLXN5O=w*_W6xo8V9NhUP=HW9nJR z^c!d}fcTs1edpDVAw3q@&oK_p(gmsSFaPc;e_tGR92CN*&n>OnRlE|31j$XgV|ZKS zPQu@8z?XA{*-ZJFN00DldJ%==d5pwQ>{3|V3XQN4EL%x#jIU7-m{5A){kl@3mfe3- zP};X2_~!k#Rf{hayuR|KB*$F&TTy0RN3tGJO5!&tTdwBi3?;!LZ-=vF~v;8mq`HA1PDf%?l@$L+fAUK=v0Mwyfx`r&${>?cB zsK3Hv{^%(KZZp|l8%#h1UUgJ+9|H@uevZ32;=D;1hMClCKSJ02{i}XIfkYw{j8z{1 z1YYR{!wK8&*DEj%YC)!pbG&Dw)&Cr8I|X1+9ov1DleePlAZk#1ER6w(8xwZ^rVG|Id|LzpE zHQHQR$}v~Ph)U%-o}oWM&prDe$}p1Id9WPbjv7aDj3T3=+}BC3lgA<48<&;-J4awL z0MSv*rr-G?b2s<~qIcHdR2S@k@^v=T(Nu}(EO6mQzaEGAD9=>0pk`Vw$rJOLNl@f`;|br)FZv#=z^my|c3bGK|7y+mmla6iSh37eQWxWH|1by+2^sC>i2sx1KsQI~qrr;ZsM$GH>R z&E992|8wjNY*>g{-(aP)`ROCxuLT-xq1RLSOMVwza5W@N7~wrPVJw_; zs26Y}62>LtJF}mDREn@da}F!(H&r04;P$;yv!}MBV4^t&!XbA0Z!j;OZ#!KB^fZyg z(VI?lNtz;2?J6gR>z85?Z~ZX@)f0g<4W2*7N5JN6z4=+=^1qj)9v89NHRH==9#VRn zBuk;)W!E#n8(odPgR1si|#i;Fa!1C zoG}dTd1TIQ{R+g@6%ce8tQv9TO!lYGkCqrnxB;}GgRE8)rY#B#j5aaz9FTzSU==R) zRgf-KCp8ZJ^e5@#h-}}Iu$;_EViz^NpMiH8$u`c5| zVKZ}ETs*VPoc8K_hL;^Blm9cDakwg@P*W~}c=3LpyBLi}tucn2qudc#Pv4K4^?@x+ z?D89Tb#3Re^2HGfC~7w=i14jZx7&-dVJPG8yEu$CFuqckC8^8xG~?s^pIFf+gVEY} zJqV}LBu4nMlN9o1*j}sp@wQ{2u+aYBv!KRA;A+hKblGo7!sIbyX@%duWqCDx9){TiClPuPLy^+F79uLnFysa7KG9SR8qhU%CSo<)W{KJ}1BI65duKSA|Lo=!^y zR?vg$3AK^CSPrb|1ktj8QyUVCKH(n1duxXjGeCgRRyTqP{wkDCWu-={&;DcK^D!Y* zcBUZ_byFtEV_^%Dcp%MoF==0Me(^*@5@RIed0IF+EeJ7fFzO|FMn*Pgp5`d0eqkaj zjW+V{$dee=EaPuZDkVOl=iClNjwe0cQsTU49qe=r1F&iL9wOASJVF{r6Kig=JGW!T zL3?xFhxd#6P1H5MP2ekBS}DIe#1E;sR_CDcO+wUH+7xle2Bs@HiO9)%yYdPA&&diY zMx&$|TJ-P3A96yfG#Y-Z#4WKZ$v^Qt>m~wo-<%q(XTuBtzYf_RW|xyK&bGr3_#Bw+mA;8Vlm$ z%ra0pK&hj7@Z^GHBon^LGE`Elp4&gpKdAsZ$?!<9tI=t~JI`gPfObS9>RL!X$p1yD zOJgAyKBE59d068ky|aCuDS<~1=xB%h+essovKT2N=X>1$k?RC(zgg3+}6Ns z2b-;lHG%)f8J4v5j|wBC|LQ3j$RMLKb<3}l9%4KQy55FIBY6DK$>%O1immXqRLdtU z*{h{p*MY2N^%VVKIRUKO`^$6>{{0m`ssPYI>wx(WaMPjFbY9|;A?igRX;U%TAwEt! z;GQRUgk!??;ERgr%MNfFSOeYd8fY7MP1K(mIH5A3I<*6Pki~v$pRW5Dm%DVpQmoK- z6dg?>s-i&eP={Hz&sK)||MTSpQOK^o^L~&!eApQJ2f@8U4+hHE@qc0mZa@M;)l3i~ zpq8m*mK+z^93-(s{o?Xixi}XoIqLqD5ATaJrxYg@mZ{ghO-O0~&)0&nK6+^eK7LI3 zyJ2^D?1j{U)#(_r1l;Cy-~ zHz4^G3S2;(zuA2#3c~pgr~yYPqvjaZJOlxYFB^GJ^1AIiGp>mqm(_n`REY47e7wNU zbKH(GYMA#=vC|iX!Phz4DvR`O@-gE!vj9rCA$5tyXPISCD>6Da{0?937Xspx2_x!}`uWHFouzYt zW+i*Up8Dm(ry?#^e9=hif|}N8+XwI_{%pHXwqa@&-Sg35>AYQHy)ho(>XbU|4gP%K z9kfd%vcM4O8-bJmw?l%KTgLEBGX?LyX;g&FlI!Bv=6HL`ZPJAVlCO+PFnUL{E_BYF zJl~&}!t3+x=ar1xj6SLLlb-e1lta!j^H%PwAyuXJpnCGD0Vk$Wh#PdS!}CaeF5KVn zlges=*i<#gn8E;@7_N2vQnZ^!P(GbRyd&Nq0+Kp4b~DGf8vmPCWhEzHmDDk4{zh5g?8$JTw7?6>@gzZ!pk|*)uM;9l(rhPNveN;O^BZj6 zeHL&z#x~_+_9rB*x`5RVwWt2D5t+VAzXpdv2VBYT&?B*bGidXJ==GFF+`FQNb`(fW zcG3I;+y#o+xmmT~gY|Nbbv6}Zv>zi;QSHAF-zVjtNUp;FleVcs1iWe9!-%K~GNJZ) zIpb`PXW?>rCm@gzimG$@1O|#mK3LLDhv#YScS`(@I%*NB{s4=d#pW>#3K0Y1k$7i! zg!&hXI~nr)Cay_c9R9f*V4plK%{pxR1#kJL3`yN3uP1!m|E1`xKoWN-I_`nJMf7lp zWx`uzI=KpOSIMNdwbh^ZLWLD+gILYgexlD~QE1A07pU8Q^WMB|9KJxm8*&gdmHBi}&0=<;ZBPRPn z%KyAL(9G99BCqwx^?JR97HKDKA^+=O#PN?%qOH z7*!%zL>b5ivtyoF)WVJn+P*H}=D+d+*&9cC3q(1~&yE@Gnves}32UwZ#l6S1_K5%P z2jZiS4bT*7Sd;8%?c4;uvE`hWJ@3bgEAoJAUq<2!Bb@K0r6len~tDRUhrcDZ; zOdY{M*beS)>0WkRP>8Zx2CfY>=&R7+m)A?|P6pWl&5yk+T)jT;(_RO?s`4#cciBqC z=R{jm=)6mRAMPG*;@5p5&kx|~M|0`tS+>GS5O-^AnKQ>X(4LcLj|pe=1bl&GA^Zg9 zBhevG%-NUUfn2yE>xlBzJE*;7Nl?wXXAL{tjy<>Y_g>U7a8Gu)kQHKe3{muvp{lCIB;hHJr(l>vx=V(eL-zUSz4jaAE9Lz7Mpml^{gQAD9XgBjk%!QTyL^ z{rMZ*e0J*Pq333G3p7k|k&b%Vp&l)p-W-%OTtaA61(jyd|AM!YW4LkDb3jy%q&mXg zgj)3UD3Tf7q*wllK~;=#!%$UCw{PSVp3Yf2Z=N<4rW7(lgiXEW%^s*iCKWE#XWX`P zx8SEmp(9Y3^f^^1#DVuoa!<*G&Y+c*18Xgg=a+c3}hDATAxIEj1K_TR;YMlK7Di)_tUZDWpo z081)t?jNpzJra^#ObK<%@PxWNS#Y$TUskE%wrQo2yvj!EHh z+~@U%f%_-`Q6_szA0bGdO?7E}fswQW+b3<(>)wi6_2q@QjmD5g{g%6cM7-xha|hOe7Ex^psq1UmcJUY2zO%8;&5}VY(w`7 zs$zp@kmO%Dg!(|n-(?UgvG3izSoI$3V5H&reEI)UoIFw&w7we%GYLtiD5%!d_h|kM z>ES!JAou|7K!p1M{+1$4=h|`yz@3<;AO3AdVEF#Iolh*8eA8&fpDL$t@Ex4Hj&_vs za}2U}I6n7SsW;0_LQU{~g6In-Alh!AYh{!TYPx1>KJ(R^q7&J(#pa!4~Ob2Pz(9OXmCO2(~Ij>H|GxaZ9{5nGr)RfWC;- zbPV>necn~vT87qJC&#YLhq2@bLQFr~ituugBPllc=-ZDI^!?xjcU`w~Avtz&gvVgY!e->wmoh1J>DK3`^jFz0GZRZF|Th`CJ9`#*6)DH`Bwyyq%t1H@F5q9_07&haWfrIFyQ+T4jhn zFy3`X%LW3>8|@v_de&`d!Kwk)?&^-#`tQixtwu1{NJrI zj_c871sYjv@cTlsVp|C^JZff53Y97AyZsBl}QbdtbAA1s%?`L)F%bLp}>hb z;MmpA9&!bXKst3UT>>@p5G2EW)36(rdUi!bvjYTo8kr(GKpHExgP~7~UJ1^vsb^)K zcox8VUeRn(w?;5G+#Ux1Ef14o(_aKzDizaQ(tK5*Z_V`oDJF4Xr&PdL zbA>bJ_P>8L;0gT^>YG`$>Lt4u->Yg(2m{`X^KKnAR~=wJO44wsXpP%`q=9V~8`Uoj zx~G1TmaoHq!C}sx$wW@(w3)7UE-_RERpZ;y!zZ9{Dc^&ncLd6ks1UUfc&kT5zn$gJE#G|na1u9?l-Hk+os9N!CysXY?Wzmoso1(f#*nYH=i;}kdn zUj6J6k{vt+y-gipiz=dWi|hV_f{Ef&KS8p>PdPEmxdBEi$%%l3K`EYDBbvgy#rwOd zK>!0*Pk~Y|cybLRvPRa6>MxiHYz=C@8VuZ4C7yloUa{A-K|nJE;qEJuK=A{g2S7-{ ztKvJL%^wg=8s7&kq9K1!;E8X}RbFP84H%`zL&M4cCqiDI`Zcq(;i?A`cImL@{V40m zd+0j*3r_ffDvSv>c9^7hbZQ-lCix4%4pyuEZN>k^Yw|E4^+?a8FQ`7sfX&oS>;2B} zFJrO{Qm#$Fg?u{6mWvMHzTArJvj6G+fO01c0gD&LtWf?1mTeoQa%45Dwg`($V9W*+7n^_Z6mH~z zxFsNw{wC;$l?mv{gP2BZ2vG=iaM6=zLFdrz*?3?0k^BL~;*Gw3y9S+!kdueJs;Y1r zj^it0G0!K6$+ZkfPsOr_`FHH^h^!NvQcAprGya95l_pz(>^l3lh@*ukDt5 zfJn9j@EBtjFFpB9_~^rQ+y7+`@+6_NtFuDggCCbWIAWMrvgGrec@n3u(eEc9UEdnI zE#KSp;Y21iznI&%CfNk*f9w`Kwi%xHO|XB2g9ftWCNzb_4nX^w|K(Mo8%I117l$zm zk?4#F8RZ+nGj*=^P11dwAOs7cB755ZJp#S{;K zRqf|XU@tANM-t9Pe2M2t*J5NSCkl#7m+-F#H@dpD)`}*Cjr}4?CCcKEr`bYT0;uo8 zAW%jQf{0@a;J(WrWu0OJe!Cw#BkA7U-b3Fn<$(T|;3Da*Po71i@PCWZCjbyxp%%@u zuo^HmLbhT@(7!PlRFTbqFUbiceh>cN8+GI{JYpFlFa832rm~}h-Vu;$q*!)uLu7jX z#g@c8hYbrIhdkKDtwjB09Ho1%2c>bT>GL8w#XtsrJzD|5jF9I`qKVLomVk+{*y}^} z$kiYIZcYC%@^`!Ud8BAX6!{uhes=BwE?QC;Trj7qrXD5qdJ(+O6=;zAPqtTXlTglq zQ8>6O_Tsdiw$z)JL!nITsi)zD5jtGaqa4~L?`ps>Vq=LFe@t$hB&WJ&4*yNP?c&mQ zbnb#jzkiDFfPUEl$PS8cYx8la&C_i^bHuR{=|jUT&Ow1#2R`i=1h;Ix-@ehFs?(JR zS?iT44ps9V0_mO7}I;Q`Fv#)bWdfXhIoe;0q*zhEdh$=WFpib4k39N(>vb!RhbSY$5DD(s2LcT6SNrj#nrXj zkhzACz714-5R~Dz1D#fn^~m z^zI^z`UtYD@nB`UW1}vZ*ZiIbHTr&lwi~d^&x7Mx?-+i4s-}tDOE|bswl!B%GG|g) z9ONu|%K9HXj4s$?(p|yb#F5x1);qo%bYTsS=#geJs(qL4ZKAlXGumhc|4J6<2yg@9 zy+X=NxdP82+~2R$2B0lh{~5> z06Y!LkcpJAW&`J|JTk`^pkeiW=}d@ve!NxW@6scSqoq-rK+(-}41{+w`uxHDuEXf5 zMuv0Sw+M=0!(gPmZ7&d@2&#o?5H-d0cj_A ze&A*Y&4iffvQp%-*X%9!6g!0wL@?)o1Bw0d&Wi?b8v(&R}qK$Gd zfHJPiE@n+oB2*K)Yn7VeP$mub_x0MrY*2xJW~SB=JQ;z&)egQ8D>i?{D`+Hi_E89_ zbJ=fA!0ZqVFl=wEnr0MWwF=#E+_kg00UfjyTu-y}=Mto>aD*%G1JES=3>AsJdyzue zNpdZ=5)tbdU^N?QeQT&PL$fDBjk3qWwBrpvkUzp(Ff|d&g9q`TOA zP-d1OPW<~J@Ju4Gz|R?YD_>4g)gqtq+IE0DK@|y_{%X{Vle~c2*gu zdE-%jgku`svrZ-A7-J>w7eE+5EEB}g4Z)Zcgzwr-y|(D@h9ZDPM@2UkE}+vo!5&oY&| ze|ibk_{>L#2uAy8wh(QwpZo<1G0<_u6~0M6123SAQ-}{D?MbdVw#A-fy?^c{7TbIj z%bo+m#_IsSQwy*?0i$M&XDp)LRz-dx{3n^ipOB%v?Vo6^+CLZ?Mf((<{iiwc+?E1q z2xENbhw;rzd9gdaj}eoYKNbK#{(mYM0qE)|9~Km71n1-=5!pAUD@vm@Gyy97*2YbJY_SdzLz3EYq!7FgCx-(ReVZJnJDHi<{|ymm z4sDJlt8;&?keR=-!h4e)jt}_rHa_jRNtUhJ+GXuRHn3_Im3sRQqodLx( z4~BSvmd;!?MB=?`s?xpaao7p4Uzvk<@jg5d;olB>C-`rr;d{`~_47|gUXpke(!m@7 z3}FxO8Xe;#zEJW1b^)Tq)F2^_IHi)sKSUeLbpZgp#SfVF;Geg^tk5{&yzl>6x{vWZ zYl|84brvjbBh%*YSHSoWGe2_$V4JEFo9OP8dr7bG4v_*)aIf<1JLc8v5fPu|U_dUIQ!V(0+M!h`{O%w!x%W;y1ekiw@+|5#} z2PId(;1LzbcApISM)YRqQAUvLLMRK6NaEhwI0+J%L6-t?cc1`7Dl`DJG{i44sguZ4 z24r=mA&3TS4yo2&B9oY8NCHfF(WRlFMoS2tFC{T+!LJ~K8wLr@NME=$PA~+e8XcS9 zQ+PObqNsSAbcr4R6OfJjgPnLv^{)07q>@Dx?{DJzYPqrn%F1BlwieAtO=hc}DeopIIsf zI7iP-W|qGE0#XvFRSCA(Fo5UAJ$fxvmX&}~u;yZv0xpRd9_W&|yF}v}5vqB!z4kO6 zt9wy%GOdmxmt!Y$$*0^3EFQa}NZhf(T~K{g|Fsw>+x+l9(DhStAdZ*2#ssw6EjGO< z87fp6vv@#26zEC)A9%Fp5R&~?Qw88-#YZ>hy|)FU5OA>$@BEhE+mu4<8A)ZVBVqtB z{%Y=(CPx6N2WshR^i4I}fL@6xlMS;nma(o&LzNn4Y)f7LxBw?4C^yBa;LT}wp(uv@ zg4)qa_g3bi+cY?QSkQ^|*$IcLQ?U7?OH*Cytf>m(jF#mW9v#*z)G2WOjfiL{>uwwL z-CV)()C^g%5#c+BOILhEk0cw>)&a$?19=FAut5KNje!-FSBs$8X%Z0h4iSb-{Ge?M#c#53wAE2Sv>}L!Usk+R!iAFJ{?z(g&yHzBDO?m+o|WGQ z-ecT?;T&^+>TvPLU*m)iLu0eE?oDL8^cXub*ai5br7H;>0ALOZMDHi~2CnO=)7_b0 zx1gXVPF%wGjryiJK0o!3Ed5VBS&s0|;VC=HQdZTM9Ng~X`}L1}L*qJc<0~jSt(p5S zNV~f+%)xvR^J2Nq;Qxa!d!c1$(^FKYkpw`L!0yE?Yh4$J@s>hk*t0Auk8s8lewRdD zRTh@IW8Lofe5=@YuY_y|!`nv4l*Be2S2X+%AWu^>&+>)P)Ft7SUfic3n#{@@1_L5!yeD09YvAnZRHwOuU`m~6uj;sAEL@OYrYXH|#3e_szW*1tT z*CBz*2Uw17NAY9XYDJa)-SQ+B$WBBj=oymjlvU0t z8_U7mN)_`1qvn5mQUaWPIW0J2Sgmy-jG*fCf$QdBN^sd}@#!*xAg|1wf(+OH*}OiY zD3EgqK^_es%8kM{aO3exkJZIy%t%yDqoX57bI&?rvwryywk!H&wk&n5Ik}R;JY~#hjuiE zS`pVPuGuH)m1vwctG%W9!$P=EUSRUaMnZRBUfA(&fm!0AI0p6%dP@1fI#;v`if1)W zI`kv*fM7}^yjUE>q@TwZ0Pf*hdxh-rzmh4qrxR55Xa~q;z>H75-nzRYwR5lrqE|LM z{0k~_fa{(JgQ$vm&`(;TaALYj{swS*&S$eZpbTY!r8-4zi~YYCPUbK~0F$K?djU<3 z^{Z5MOfGPSGxGi)!KrKpBECn#@-sMNX+=Y&vNvTdpHR`L1KRPCtIXd(VYmhod?HUP z7~}Q1rGA4_>T|l)av+2HP#M5Re`$gAhEwe)nx7c8*iTa2uC}U%h$$rDjjK^(G6)62 z6v{Wap$&cXiCo)h=}S;NW5|ysP?p==9OA^Th9R)QCGSz%^tl}Ge9BEOTmW>m>onx> zV?@gKb_Mf|{2#ZUlu^&Rl+sq12+^~}R|we)Coc&}UY{}`!@$k*fcE=c1r^GYTrk4= zEj1z4J#G+@?AjBLN0f@)njjhM_}XYRLuy7rX(t}2zDY`P^3b6Ohtyw5!{75 zA}GoA^{kd5G42S^rPgffm|;fKD{vb6Ls^o$Zyi+Zk=ftaKy=dl2|&KvNeGQ|m|-O_ zWJ?sA*e(Mwk|7MPw+ezO!}iA<0;v%c5Z|;wR~E50so-l=zfuqmDY?mr&gVtF5emSz z?^MZttCV~I8?pJDiAO>!th%psMcVWNmHn)4BBf_%L%GJX}QKeQX{eNf>)=B1?K$BDp z1QU~tnGX*xrRs=}WX81)KJtM$O>*2WJgB|p!$@+NHO`s#>_~ZOmxM<jeo{ngw>KXn?)l-D1px)E&dFci*%*o0 z`$aRp`vjrDdH-?O05%v8{IYW70?C54`tH6ew?O%dAw6BQ1VBUn6fZdiWytrtz}^wf z2l@)lmxPYoo5N0s5CgmM(kEoFB6DP(~!>WpI80UP?97`RJ6wrk6@(vfkcw3LLd0 z*S42136{~_RkHOO1nv{i_8N<&c6}ZmFi5H!FP?=j0ZdC!yWaa?srRI(g9ZMO08@cT z&QXkE`y5OWD-Y1kcsN}H-k|?BD6>zgTfI3opR*`>RfsiVy{8gK==#u$YYR}6I&icK z&8nJ{I%)zL&$C?Pl~!=-^k`7n{pf;9>D;qAmCupxpueA$5S*(1PuFP#hYD8Yrsc}P zcoYH0_zIp92u9 zjntKxov*Cdohf*MTq_!5ZvDFDS&G%t{2|OxI&&4(GIrCKdR?40K0F(%DF2Gsa2Ob% zTKFa}XDZyi1xUq549%2qH(UqLA>%zNJKiK zm_|{d#C_Ip@6+c$=5krPX|4Y317#fAVCL|C%l$%zcAM@ugSawVm^rKT>kqR~VV7r- zZ4NQBlPLqi2#IzpmRBx%g)<;ZwCDm@H!G4_x9rhR$t7GwGRV3v(^S;W!$hu(NJjws zW_jKR5Wb^`|AFz0#khp%ma?MKbF|IsIc`6ab0J~d{ zJVN{NR%Fz^pEUY-cg=mUks9I1{`#1|>8?gXBM|Ob5)S1M>bu_|rhdrPM8EZVDsX<6 z6MHlSU*kNXr_9a$98h}vMuMq?-Smiq`xHT_%C&!kJWpG@=-jE3_7by4flW?JtXDf zg11oNP}9e!YfYEM$>(yz1q%79M{LpgfCuGjr@sA0eE-+>QS6l+SF;n&!BFsM-B~`z zc9F2l#X%lx3*mn~L|h`!LStP|;bw6<>zWeXngjj#DFS|H`|NzpfuySlCvBc4i9dIO;UOdGpM zk)^(~OZe(I7z38!YIeccVDw^d%Q-t%rGP`F-_=75&}N5LoTjWbJd31Av@y$#_E( zh4kmzAR&8K9n#vqeJFGw+E03J)?&C+G{N)upjpb9Uz~Xs zIx;I!uzJ3$Xbd5iG%pd5$E@bFFHsEebh z?6!=D?cXL$nt7qMm$#t>StwIVA1?e*-{1UlmdI&j$WA{5678JET;s~#;3<8vMKCY@ z-4!*9Dv>whw6pwddzij%7655$o;$=AB2I5m_f4zjkZVe?P#;D5&D)k6C-xx>R8Uj) zp;CseFugwF*PJvzN`5_wsw73FvYkTtQyM}H&w(rURu`Aq9 zl=gO7u7)`LdI9gktG?egC86gG6c4h81I!PM0}!VxT(X2ZvIeUFxy95>qq}rmkfQZBUdqxN8KoWN9}UZ=Dglghc4MrVXk)ZC@YbN@J{%|pTJXBVr%nc-{5OQ02P( zP=7v#LxyWtOk};R-~S2W^RsaPw(e8_n;27S1|7*mltJJ;;ljsk6HykJRb?X8wEd&G zT9DYSE3Yv$0jP7#DBTmoXNj^x6$MoiI=x@9qDrVlH2p=KM7pZT);kz_1R#b8lg2#< zfhG0zC=Lj1sXuW*a6o=zJOs|Pe^jjMucTm~d>IobizaF+%%zRGF{*d5> zg$-}?00TPdaAB{pK2dn5c+qmtIr}^H;~Yn1Kci>{%TQ+<9R9Lqq`U|noNYfJqy#sb z9f-G+Z3n@AqEfzY__Si6NfaVH7{!lrFcvsP$M%6pLr6in|1AYwSfcoAW8>3Sl*3Em zJ;4PVI7JL_TC+b?)W#OFe}`UlKoT09YZWLY_Pj~+P`NeXqVQ)S*Fs6PuP750wB!zK z23<#gO&i#oK-}^87kv zKqFbX3kK0KkvmG`n8&1n(lw(Q%{LKO?2)JoGn1XI;nnz zwm|N>UD-%pS0U`cdP?#Ufu1Dtl)@>2R)z)BxU}XCYKO5Vx&cD>#T;P31W}~NX@$Y4 zvmkx4Z$z@}+7+oS7Y+PQeoHsfl3Gzoz`rnwP5#P-90#)%n7m$%qu)^lJtuplZ&m*m z;VP~)mGRFLkQAe7J8t+aR(3-y0@*%5j`h7>Q?E;4PS1zb;*0qLTy3He+epV%k$uNr zpo_P>wZ7APqkxy;+!oy$^8|~d6Och-3^xt|cYYvC(l4nFCE^H+*Ncx?sCfqZ^5b zsVzjb6BJ^0Gs=C7dpOS3unNN#Q+W3gfbO zIuA?O#i}nthi*=n)fnS4itgD8H}O>nGknFoW1lJzCZO}W70CV>kLKGS;nZh1W^whb zHb=2)x--7-NH~Pi1_`+IF#fFiW-)**t$Ai0o+09AsIA_CfpdGI8Mo!nRY10#dD3%1 z5r&^j*~S=g0*hr65n+oU&jrDpOd(Z&5?sWGPk>TQ=Hk$KfR;)bjgB2pd)85pR{4eF zK=(#F$8@TNkN?oq5G0eLS{D$KjQfKzbcLe#&}o(cmK`EmRdTO{?6Y_QOeoP4`RzY> zD?L#P@E*{S4x_}VTypZgqh^g|XV~nkbyM?xEPj+7irHV5wF$?p{=-Y~*l|nWV6bD> z!wFnVDwPc&%R$A@lOH~1lU6OMmVDPJ0tCRtSf(^d>mYra47Zhk3yh|F1xd=!5*-2K z+b_reAuJ*OIHAz>Hh!nB1C1Q@yj$+_f&LVb0mYxPA9EwD&{Cw-*pCiilW_HJN3zng z`m!Tr?hvu-dYpz^tOEQZkdk_KeP+-V3rR2P&MT-$XOxhGA>>5N>SHD*%@=yHmdAFq zhgG5@bR!}b@33z>$tCJgb1_z=Eu%zo4CG8q?41DYY7GN50DzWslGPbX6)yzeRcIPnR6|h7Kr;tW=3G_Ez!7`EH@V} zHdi+SOpz+qmifATOp)o_GwcENpAe%I=h`l@-GS8gWprrC*u#Nf%r_;nbzXteeJTKg zV#M-}r_}x`duhf=>~VaGyqYiqv;M8yiana+82|BtHk|f43KM7bPFDx$O9z}emG~aX zhNcT?2J7O-U)%=1T)9||^kuzVFrI^wM6g*7t2OV51_K|r>*KDN9yA+J`i(rfT$Z@j zMZo6I>g(YA*ctHnJhQbN$HT^3KL6?pZ0;;VHJx`K~Doy+TwtX=d~7L6!p%N@@clEQUG@NEebae8p(1`q_t${k z#~A;)DdL5!1tV$#4M#fPmE;SYzjd-hhwm`!^jC~mz9>GSPw378Y?}WBX zW(oM8E4mB<&@BQG6Gpr9)6$tbrzzv*QI=eU5jmv&b~Zq57*5$=-ACn#7k&M5K8+-| zHSjG;m!L<1U-RYf?yag7>3Leq_ZeD9DXS#`uNut{QbP8_e@%AM=pT3aH?OdzePhf! zNc;4I#py##E(VI`*$Nj9?cJCkuStDI(xOuW+i1_$5#Vm6BWI~}KK4H9C|mYBJ*mE8 zMV5nbXXUeY%D>E8Y)i5mY>c=@(J`qk`Jw$_I2Eo(ROHJS)LKe6pJ~8MVT(&B7ZI=Km73vwBjA!r=Brph)k|= z7_i0`|K6N~ImP=My58v!@j9!@}6fUi|2wUZRP~eeuxy&kNw26-_6S4#q+DH-LEySyqAJ;h>icU_4}_NIcQ+pw zLqSHs*X?$cll6H}u>ZZG9{wsovl04jXv4x_92O@VaG<3{lfLc*X&QBHg4hY^Y1{9J3dX=?3kH*t9|)fdGRfyuhvwY$amSdfAJzk*RGXK)d7fMC5_w^uW$9K|TK0kw)fCkJOf* z&oRT3dB#trc7@3{AA{(hVWy$Yc z)80yQMZnD2we5(-O2lgB8;AvuPeQD#9mL$lCyz`pBJ>bWQJ zaC62i=}SGktLFHVYR?~ws0Mud3A)3h^;QAg6$*A?-+b(A4Qp&gWXkmAg>rEzS(aVW zzL_6~#KS%9kQ=>&nozq-&QAnfHq@nh29C4yr`*+D>658qW65DD*j)s~nDZMhr4KM3 zM%X;Iplw@PWhyS5MHr|XP+`Zu-1IdoEYwEAIjEGt&eWhn_NG9WjPbk)YxuISU9!9= zvng;l*0u420||!F36!3*7L>*$?=wSlHLHDT&=pdnyZ@4=c#U2rRpr>p^WKe3J2R_O zE;zGqGrM*;yUltfN-;@?*rh)UKB7JR>QXfAEKa}2vu3o6t<#%<|G;W~B$-WQtEac} z0}4${`@&8!I6rpZtd`fxa;n>6VhYRR4doBxMpw>}13gX7<{5wBfRQ4dc5Be(ax5PX zJ`{Lng}i+33xrZF3mYE8G8CbRE3uMrE)-Z}=SpeU_veh9{H*(AGf!zbdQrswOyC68 zY+Nv+M|a8`sB;W&(Dt1)_Vf+C! z94(>v4f6WBkFOk2u3x@--hNg2hM$~slgKEQmgw-J@Ktv>rRA`}aH#?I{wAH!VA+=8 z@}3pK`2fx58>xG10B7-^keQ*1vEhcav>gl(sqJK{rbBXJ%7XaS+}An;AO;D+hTO4# zgTxcYS4E>2hLs#(m}V=o%*T0gm6X96t?i@#hkfbSr*CD1nQT`@@@=?vVS_TUG73T( z!9TkY$n5lrLS4gt=TY*w$l+qli>vmiU)@g&K6K58 zgNDNdqe8}k<75$1oe(&&y%BYKB$iJVqm(r@B8aCXw&C84I()M~lsHpU(Xik2`+Vrq zWg&6qoAdtsb>cHa#Y^%i*gzkY4d_3uCx88!Z2*;_?*BNku_x&|h zRRbcFG0J{03W|On!0dbVF@EcVuC$pAD!2vl>WNMb1mYh4h#8kYC~B0oN~QX_=2iL2 zW-%ns#Wpj!ZIOK=haVE>E1Oc)>;Qd0oSW(ag4eN99tL-&Jz%;qKGk+ok86I^xT0aXF@;cq#LW?v`XG2#x1=fuwl!M+joxB_?SXyyN#i_Ryd@)$853Owf7Uw zQ)Q1l%vN*7Yb{VbWph$SX+?5)Vmd13Iy_I4yL? zdau2+k*REnwP={DZWc0M=v{Dgcqc1i#NCnmb#-9ohM)$cdK$2Xi8fH_5{p+2HJDwi z_nbO}dC-8Ip6SF=%2v$JLpokjXuEuX)@-2Df3<08f254Z6hg}T!-`5-UzNO*kHX0G zBchR12rc`L9et_wlTS}b6RBC8k!yOyN8(0a?@%RuzH#Uo(otMu=L$?7s~*bG<=&B? zr>_<+XLpukDU)RUfpmtNfTHlFf?q94v{716^`}ZdYTj>>bw09CWO#d}pZ;SRtI5k( zaXb#pL^_V{QT$;|s0>0ZQG+&joL`boG#`rH7r(bklT+h1A97|&6njli>+k{u@356g zPwU|9dZ_gYEPvg{2?mQ;MrL^aL5R=WuG1!8k=ziARB9_kjyV`jx_F41w8Hhq-t>vb zpT+W~bP_L>vhQj0vRYA3?1kHbCkx4yeMr0yESWhY0yN_0ukA?a=-hI`(i*lPo`dhr z^ZhaFFS06I!(o<1NNNVj4inU0pPBFqrZlm>!XJZ+I4D$4WYD42Qe6}L zp~6CZ{&uO5>8HU|ruY3m<B76X z1n{@T;i0 ze1EQduR$Nn4NE4igj#fqFU8PW@|&)H>nOUb#TT^fun;s;U}CR}U;7f>`i0SCrn|l* zw$4j#YM0>f3A2>7)vM??TyKGAS9ckwyK1;%6FOgN#3= zco=uK&=5^+KWccmJ5bOT>b@0U=Kegl*5o%t+=PDPc=zXSX=DKX+yjBXzvRz_QB#K1*m;(}yDbRj1z?q!wnXAHvja-YP29SY3C3Tu~ zBZp9F*pY~*8&>ACZ|vi5sXRI-F}g59hyeORd(gawaEFm*_uWf+L?loM=k;?2L?8-}IB zIT_}N+BoE-jgkg2u2Bps2B^Mc+2tnT!ppjyq*jGOcKv0 zDc5*L6dv+!T?Z_letlPoT>V=@O)Jm1#}enAvgr8y#Jy#|S|&}o$HJ~Hsyt*P@>Kq| zE<7YK^FX5XOAz89#dEOOeF>K(oB4#%y(=fBbFjzkkuG=QY+G(i$2r>9&nK&XHhm@s~JB>FwzQ02Vq+hTg1&_K@07~5;wpK@~nzl8VgV0Qz-=e|nf zbbjHI>-0wlDRVs!1z>v~9y{)PR`D)ju84>!q_iGfxKBGzcz>QKqVc?>HO*vVPuri~ z&n0RSAdmi93_^hf82Tm^+>dD`sFHJILYT;8IAb66WxVv^pc8B1(Nie0oG#UKVEn@T zjdfNrBs@^ElmS?JN2Wf07&2$`dn3#??m|BFDlkkk#Kp<2GXOV`=TOdFk}pR4 zsAQfh(jMm}t}^50vE{tQjby1ByfjN!I=o1%@sDPgC6fg3Q#H)cZ>qVU3JFdf3G13W zeh!yPGBb8JBbu&pT)>YjJE`UKT4Ajpw$CP$oAom^-X%QKVD{7xL{{AX%Cs19CX-uM zcVL!`-#0#fyve*t8c>=^r~HLyk3r0!50H|0yF{NcBGoGcV>ZWlmPd++RFW0~k=?fV z?a;gNRH{D#h1lz!n5An;GI#u2feN~SXrVneOgenmJzwN4(?rOmik+_=@9&8`k-bvp zc0z&%m+wUUA%MZdY(1W9-kF~mHZv2(7kipgeg^yi8mG^9m5Ldoij!wIMkaioYAs^4 zqi6o=LZJz}`2@`MOa$| z(-!0%ia8r0r>n}gC@F33%2fO3{Gy4E{cga7GSMv0&oJXJ%qi1iWDXx+M)*t{;y&X{ zF6i-wNa4+ZGr~vAz8u*sl%TLjf=TAsjQ)%vU&RlwroxkdDBnwmkM0B0&cIF_8Ngq+ z8tIgI%=H!JDCQGXRcLMA;j8~3E{S%SPD1zreORgsA%S#*Z+1& z(Ah}8$OK!rk4NsX30SR-QglV*2i%2ITx-Ws?uK!V9wBSqn?{qa{o0*?g9zPcfyCcG z3QKZoK1!cAy_)67n6ak~&e^^|j#JZsNh)0Y-JB{(O_4C{eRKkQ-pLAPpyDg^6ZNof za-t?x6CsWU9$|KCru8BYxM1n`rKm`7?pZ&6UOoOs(ecjO8hQ2sqS?chC}oi{&6<^isM|bSEuB)9 zTD#Fm1%v5U$Uqx=H=N7B~of6wM8`**h-fC zA*v=?*eKJzj|llWUS_1v{zdP4%r?7IP4ZT?i-=m(cmY=G0rVDY}cBw)T5e#Ehf5q-Q&Y@^Mh$7xu%e5T9mbqdx z2(;D0$&XUWy~x1@M2$Rhzx%Cw;a+M^W%Pa?QPYmsd8wA!V-PVp;OX^Jq}22fHJ9y{ zx{pI~a=h}?{H6SdIM3g|8lR0ivKEJW@ayRNn=OQV&Ex3F{S6FS<8DSjrD4k+bYn5r zzHS`2+iO8gOCKstm*L#FS6N(gZ#J<7i5LLw`jK0GEWY>mhvh1q_e45xH+}G~LynbV zU2#gA4aizG8?;?EH6{^na$nraa#XyO#jO82HX}jm8bsEr+kK7V0@Uj(L4WrCMA?2a zuLG4=0CcE1PPq&MV--6NlsV^;8be5@peXPVP2Y&RZ% zR|MJa(I@}r=qleB@)G9R%y7t9BG3cGQard$dp~OBe}?xpwTa1DdAa(T{oJSIc?LT} zo2@P5LQEcymiByQ`m>gGsli0wWuHhW!T>xj;$y1+F2QZd`>2U| z#CdT`RaJyU+vgW=!tjOi{U?$2hIEhy~-fMOYkV; zQSr+Dq2Q1D%ywDa-X{t}3ko;Pj#1<*B@XW$o8~Y(AGnIMN8t5(t%l0Z*Ttm{Y5Wvg zK5CR8C7^bIj43T^*u7ab?!yj%KbNO4z-mZ-D|PwYfGAFJcmM2empG)BGgr>D8>%Wn0&Hr-tT&jN&^o!in<{H3~@)8}jEpYvz*K56D*)Tu&H#v95T`QQE*aZyPRU$zc zGK|`AOlUB%&vV$rbhVR7yTuXxl6^xs!2b!=9b6h(%>jR{I`zLuqi#4;V=M0-bzMG~ zCGiB%jmkx;{JH*|$yKVLhsI{{I=b$$g`YvxVY;bd4-@V?rTppmfQbbo3gx}$c%#06 zVov>Rq0E5nv?(O_`%LZ4;&)?WRQBE^(EV;*i|in*yEH|5ocM|u1F6cZo|<7o6jf@{ zVB`%NX^KKnSy_2-_B5jQO$WZBf1{b|f0q=_l(}Ns7DCp^OUf3rtBf*7BJ)mVpWfRyOF#5)rK^Ap3#=1h}Or ze><9SnB7om2^Qvk0rnWS^Y)!9_-|~dM+d_moQo8(PXr5$L{^U=VG-jr^ZvAloQ_|##d&ig zTWu6EFxSfGvVlHmrQq5%N}5Gb>ee!C?~BJ#Q}U?<#o7&}Zy9TOe3A#RHqF)disPk+ zj77_5GBe~<&Z&*-zihQo&qsl0uxV&$cHX-`6DPe zoGtMl{J7}(u9Th1)*ky-2Opn9q&p}#SnV? z?-Fl0dMzCEsdeqNMCnICSoU~-#^ZWFa$_ohnXJF<>{~);mf&1oqA5tVk%#KVY(rLk zipu_HK;es%Rc%wKFuJhyC=qK_Asc;?qWrNiM*J z)mmlSY>!NaD3eA4i;ARF0lv>CA+T9_A}_Cz4Aq_G4Khz`=zu_s^22_q1w0L5f+%_s zFMDtu!k-L$5R((h>T&4-NDmnzj9wNm`iNd{zSi1v3U~;>NEzp}fR-0dG%yP6dD8uY z5|B@Ma{Z)=+ddVu5sF@T7KyuXQ3~=$w&$TivQ_{htXdOr8H3i~H4fh&pGV`Db0s=0 zPwW_)EIpg4Wzrr#L7;(!aa=Q3tkzmu(&HDPCUWwvS8EiON?f{?77()4CA-P3CY8KTN*$EW}#OC%9${+hD{f`M71kD`&-t3W|PpOXL zg4g$@vorhz51D-vQiW`pgb5w>+O_<4V2S?rHF_qA8>M>A$)y?kvzp;#z}X~bvgZea z=lIA#=yN>|W14C9qQzqD^#Hl&LLfq_e*T~2JK1L^-wDtMh16muzkmrW+^42%(#oh! z9VZ}mr`awPN|*3I*0R?H7QKlb3cC?8{P+)Gg8T_zOcyITt|QQ@$81M(VnAm3CvP8q zGbzY#M?@t{3)4yBmAnC~4tyCU0?(Ga(~>{Hn=xUrc)f7W)qvCQKq%9SyXl5J@)KkX zMYB5|=ngLFqkznzf8j{n)-ZyAP_?xMS8o&4}1!1#Q4%O{6qyA+=v?c zMJ2CrA#(XW;_3rwNTpo#ERvXASMlYJqNZ&W2pX7<`%3H~l@gBZbSDuc(<08#DuZu;Zf z+$8Ac!bGR24?vN`#8N%|(UVh>9?GnB1la=Z-K3Ug{w~lt5+=$D(=aQO;saR;?Ldq1 zNGO7tkov`RFwTV-n|d%?7w)D90=Ml~_KMv=nlL`_^34w(sgFk$-y*DeCpk?u7;f9_&co4mxy!4=w1``Ss_LwaHt_foC zCKPKX>RN91EdPYN-{@-$TyDI$uM^;iS2xM8POwycjpsiA@Kgdtp~FtDYkyO`BFSf> z<*pmRoA)EdFC4+6O^_gl0etglKtS^44-A?F;0Iy_IOKt} zO>a`o5Nlk+ev1M*PiF_O9h8n+R4cpGLa~LZFbcb=E#(ZY=X;ovmQ=by z1f`LX?iLXN1wp!7QjqReL`vcyQc9PK5=x0u(j}>Mh?Mj@Tc7%T-``(emlw)8d*AoW znl&?P$@#%{Lo;Y1(IM_cXH_!!Oyvd#AJ)8qN+=9g0c1~cJ2xwJbeXpvBllF9)CGT0 z%4hDduQCdI0#KuS2gms}1A0Z;LD+W+qXXaDmWY! zm`*#N0#3HFn~a5utdB*VXrI7phvdWky$IV%}-Mrm)+dkYdo#*mns=Cfn7Y}#~MGs zw(r6!Cw?5&u7Rtc)WzR#@b?K@yov=9o|o%Im7c&3(_0r7SS+i_3dwvf7^LX~L!fx& zWk-w3()d{OXQ}v@9(EXX1^ohTxKLz-{$ZMiD)E8r)kfHLK4Z5_fj5fTNq{q>bMso> z0pqavw}JfP(p7%x0&7u;d7iw*TK~E{ib9yq2OXhva$btZjql*eCt%l z7!SnAN`$Ww(~IdWL}0R97IgsS6^&Le5arXI_!yi=!LWDbdY1Pr9zC)-)89XA=yq+y zBm`W8DePN%t@fT`YlUt}!GOAc-)7op_&QHT9{d8smk*%4-i&PW`-7s;iZ5E`1NfO6 zAx4rO&p}K4%@0`8DHzV>!e9B-%y@!oYu*ms_#LBrnJ^xdAxa_Wb@wemd2n!}6gy&P z9p11OD+X-o5SQD%U$&O@TTFt1P~C#^(RMq!?x#B>7+PMrcXCfpXDJ(T7mF5q5ePiw z+Pxeyacf=hy+~4~Y)bfvIFcm(@<5<8r8nm5UpKUMW8&PHB zD3fmlJqRJvOU8~DYYZR5B+Q*MTc1i%m~+`^Y?PPoU{SGgUJIq+A;o^Oy@Nr(^-uyN z*leZoo~!rhGmb}BsE(^zsg5bdYDHP3FWYwrs3i0&XS78ti~TzFTy8a4%1asAyfBw% zrox}TvM0(il(9I5f{e@m$|+YAe^B0^;ubDzsMtvscpf?6DG?qWOz&t zgSLn7P9o5zo{*)csMOCZV2IwVpWJR-rSS6uaY9WZu|*V?@DI`u#IDTtkUn!4%d z%eg)g5|UPVLD}2fX@tCuNB8dV(D{&)S4tTE71aNH77C2yGY=rW&J(Gw8B?hUPq;Me zlzp(4@9|gPGXid{ImI>69Yo>mzd((xuOVre z4sgd1nxLDVJZOs8`R3m(9o-3+J+QZCSoQl$3e1p6WRzSA~4Cko$cL{bsV} zy4GL$0ZAn2!?3QjXCNZte7ij8u4`_ba37bkfDlOd2y{Kq=iQ#Rz`kj}0RlmIz4mUB zl`kI&c8jvUgH5)j-U$Q-$O?80-!_l?0eY}l>+NvX@^*Z$gCnhF&5WcgNKFZ! z9zhFzP`Xn2&r@5($3k5^{<`6xLgY6SMtSK_)~Cq}9wbVg1l7iSK;c90z>OQr-YfYS zZ*}L@Tddu1Y{j0ppab+|#~O4^b?SYQ=4-A}96s5D(VSx3I=Wc=b~-l=RIMNvfy4bS zS92E!8|}%_KdE^S%eM2_JRDus9@vGL^C6^Hri)&39S6ow{YpqR2rov7p4;K0N_S$@X{N6yN$hF) z5(GzPcMyZADAD%Ib&qu&M{r9#BZ{BGG##@fqwyWP$|YBU*4F#R{Q31GAS_XHcC;O7 z`%L3j(_oZK{iy$#EqcMg(MKv5 zHI`nh?kB@bEjG#E&*Xm_MyiA%PmV{Wk>#^og!MN64}guA0S{2*u6*nBB0*tOk1`-e zIi=Jf$?wDeF&u$QnYCKeSaxMmn^z>Df4t;6<@z@!Lf$$T2Ata4w_!snSRy)<-@Hx7 z{SbPNiDM^miIL&f@kCl?&q(S7Fvds7l9J;q;?uzCWKF#VtuWdI154D!F8QxwaBj zR;m%I8mBNTgQwaz;~Z8buFgiXez16aEuWfVNNQY@ZccWw{xVTn-SUeid%IH0DyJsh z#k!iaX=CgIHxPXkp||N=2@*5);SU{GXMY3Sm&|BYL84c;uiL2Jfrrefj4B}sOjE#E zPm04*I-h!{SLm7vR#2FNTJ&?Dprj~Vz8=&?H9S)L<#8t^wE1kfS=T)rwga}s@N@_f zboUxWpFcpbHs)t_NFG>JbbfBdD^`K^#E#q8-PZ1~udVgZF-Q3VhTb!5D+9U$8v|hd z%S+6N*_t*MqU{%^<6Xi(ccd><6ghW`Wg{tn1EKq96NYX2e}RMxmYo6` z%_7u$*fyvLBKq{*53aa1yjMQyE1Qg|c%sOK-|vI<*ll0^iYE>_evaLD?y6WhKP;YQ z8buFks)uPxQ!%mnPd& zShQXCZPs*Q-2WUsaEZu}vAJwOf2hCL?@vvJR95ja8}flu-oabt^)o85mQWSz`qEiS zxllY)Pn{+NnI(57lioaE(rJGa^U%b7=YYef%bSa*iovA`^0)1N*%gs|3Tr{nR6IU< z{TDKYgHANP{(iKqPYzd084HKAMxpSLQhhDJ>-sRO+kTf$4~847dFZ>RB;KQHKj2@s z@vE~_>V4PX-}!GDK!@&zCJoV@rUJZ!-7aw;D9x#8{LVGA2p*4jV&oYW0}+`3{;HQ? zUPm6<9|%&9gPya07B*fiIm{UD1BZyI!CG;mkiSt>koBVO?uP8Mnp;0X@4TpL zn66ny2*^()KUpcTxH>2;8Q`yuxSg(j=mfNh|*ZHbAytXPD-VB+T-w^iY zZEyZy9miRgCTtMxa9#c1!!-@1GN=2=n4P&Ys|GJeSYNaI80LRGsatfiF2+uU0SqMUwUYb_xU3R$t+QYb*Zl$pV1!%KW%QQuSLbn$ zppWqHgXq=8eRxJ=h`pxX1#J$Oy*yTf$c>G{$QMY~{+s1bdIMe*~n`;rW*}~4pPQ&x~UZYW}HJDQkc&wQB0K-Ty>>8%KoT7~5;N zUikOW{r<}C;uxYnpy^}M)#3+eTZlYG)m`aSgI4g|qqOKRQL*t7)FAfLM=Edh4y?`! zzc_;t>9RL_)f0neTq5gF20vawVTGOXa$vlbnNB>$rw=ecB`INYA_ zA6&4)$_H`^Ngid%qaS*$F9_M$ZaDrV1$@G**@%4d?Pn(DTKn-+^EObM(SVqtPpeclCIuO7ZnEncSO3ffimdiJdR;v3^pmo$6x*Lm( zLZOFy8;pQm?7Os4!gRr`h)U}9h_D7~>-8WU1}dj_n@Y!rqdooLRQ)e@lkl9LcN24mx^#C!Y z^C807EjMkxH1@>r1~0Z{8D^ePTNA=PO2FDwQ0y|CU9nD38Cm8MS+?c_Y0aunM8>xc z<%uPA&L&dP@N3F(stAkdU+R3sWsiaRk*D4D7&a;FhP;g^w>19~CAD#zSTO6$mwYp? zIRO*3Sge{Yp5U*b#H-W2tK5-bCjh^$V6@(1|Jl|M**kql4e7r^54@Dun6K+MvU?`~ zo24PvEs|lOT*H4NsvbkJnsgPGSPKk72oE#$14@2d&yt;XYTUss5HnuYssmavfEMM` zMoOs5*T#E7vY74KsZAhCQ5g=pt#vM~=Wm6AQLKt!M4To`^Dxk+)CkWwv3jdA7{Qn2 zklt{X=~+j)#(o)C?yj_=I)!ZB0`C-t^jeSRMH=zI0VQTqaGh3;TO^}I)bj0T+(_2U zPIJ2bUh+vm0|&}hSM)y>WZa7$GR@cH^EgG7NrBXldjkwDxM2BQ#7ItS+tc#$qukMC z^V0=}k(CbwB_jZ32qT$J=o*D&UKxRdHr$91-w zznxBP$MCEP!%dfuiS%j|0tcI5|9h)M-G``P7U=QZn$V)C82L98_o1%8!s-D)u)p(( z!NI^-J_Dg0y||Nd?OFZCUh9x8NGK%!K=ZU+t8>ggg!;J5R?_Av!A>TONGPM6nPtx2 z%k|DHMq4F=`7>iB6z^g{(lkPx_f6M5J4DFzQ22dSi|%ETno)$QH(YjSx9LTkGL}(_ z1X=EVs^_m~)LZQRjL7}^7s75LEesEo_PZtD-t%uV_?xk5#?^p@;Ph*TR!jo4Py}io zU+zz+g<{$KHW1IBJqZAj+b>DgcoyP>7_`Ta%f3-Ld-r8CZT8B#A6<_qYTxbOnkc9t z1dWglj+rrvtqp9y#JT7sok2G2MtCyOMcY6%&t(lLY4DA2h+N0xL<41aRVZL(!IYulg^=-6M!rK)B_9 zbI~8R`ribFdP#!%)(#X!X&wU-9W2kJnMHGcfEz%MP0T=-eA_Ilw9Y*tKIZr=JI!bK zT=teq(d^iVv6j2DKf?Cg*1QyL_Fez_NCu=XI4&hB_uoo|q1Ajs`mIR2iqr4zJMU-b zij0IoX=y_2p6j^YsPd9RkizXdx2#@Oe@gdb`1hv&#`b?b_dkC~Mxb@F4Peffy~0nL z1Oo3GeS?-bxautErVxlnTke^aHS4ecb9kX~3KN^OHyF#-NRbHtfk=Fnq>$cP08lc{ z;;mxrudwOIi0sAWy`Toy9HFg}zvK7+{ngcc7^3#<;M=M#_?*Yj@KueDweu=ml6w3g zkk;dp0GQ92w*?&Wiz#Bon4|Z^{v-%tfFHr@sr1i7N4+F<;Tb7a8fkaFj8zKBqkPh5 zrD)3wL4t_xkJ6gokM}q;P*f9v>`IN8e^F5z<}~OIP=e>z*Pr$oP%L}^SIYc=H5KEp zKhX}cfKC4YztHq}v@~~^07*PKKAbx3rauJ*wc#xU;WRxjw;5F&D0qChDQ;%T_3a6K?CZa7#D+K7bat>82tyS(>6;SB@w(ac7E zgWq$@@Roa@;Cf@b+$$R2(TWT1i0Y%WYneK7XUw0$H#eEdDs{I zMj60(ZU%-k2G%X+5ILwB5Uu&LZ~{$#PEbWDZtjI|<%H-2Zj0ujAdV#e0G48fQAiT6 z)anTK;5-*saFEa=Ny;=j2W?4}D&PI$MhHy0l=6LNgyjk|ay;(Bzr&ak{KLl}CF*t7 z=|&|ZyRQzTN6VIEa zMw@SH@GC~lWzzqGWcYzN;8!!Kr{Dj7y!PMs_#;!`)^F~8mnZm@>%{B;^%WZ}#A2x# zQX4}!AvGBSc=9AWB-lf!_IJtxO49tX{BB-~whqW>Mm}us39gBoFDQa(M{G>NtN#H^ zq^{8jaLtd!d|eMdkS+dmy{cKD-g9bwJL|UWiFLA>5D*HTsT*Z)LF6=SfBtD%|5cfV zN)QV?Q}}GO=P>|SHbf(N1`133UX{I-VO~TTbOtfQ1ZAZM5j+EDnSZ8!w9NPfS@fm0 zCb=!*lM?}A;SwY}67Gw=5A$ACzDFgGBHe?pkY30^&|;9!|H!+`DZeZGWV-;(=q#+t z91NCK(|?I7$n;D<`^YIOGQ1MI$g`_Dg9eL!$>w)9) zxL4_wJY`tjKrjY1%~(G&mYZfX@o|)`+>Z)wvkf&?WlfNynFKHN&pKzI?^^szGxRSgP1pqEXEtjwOJ*W&7Uo!8B`-Ps&LOpF*b3^A$EcEA+X zeor~D*>+Y+hNqOvmts)>(z3MGZ@v;u22f&j-Y9;?eO=?@YTz>(e49382+d3Gyp(xQ^OmoUtQ%i0H3KaZed5?J#I;Y zN~TATMCi=rG|%+dDTpQ9NE^*GKh3JL`LDs7Tg+NlRK%*M$3>kFWPll04dO8ZuV^rSDa(t!}Ic9HPBuB5^KcBEWM5X;#CHxqwlz zCG3nYp`7vvQm4Vwn+vbfkD!BG;yH$ZR+-17O!~pk3EXxK@M@lYez4&?MKID{{O$~q z@u!@BNhiNELH7(?jAdJgM;Ax}ck%XN>ltVk^6IX&4`*Ad+Y2eH6ui z>^|{2(k{C5E?q5^0#b-~ovZa%I+Xx^UR z*8gU#WyNVoW_s9U2OwVY7+=cRE(+KG5Ve_t2io?}gJk zsn{ktuD&a&>B+i$4~@6FSI!jd4QC*e5?LH2+Q!1r5-a+GV0b+;B_nQFP{s@9sxrdm z)OfD!fIhxu?rvfK8y%)`2V$B)sxA>bo4*Z+J8IzY`0068&`S4c{E8UU_y_hRp@~tG zpI1VU^9dpNe({tyVf^#PB{29eQ=pFzaP_EF*``9^Bdy2!j)zL=B9Qs8zd|&mGA)BAUik^7?t=>#uWXZ|Cy5aBNB>I8KAv0)m(r#RxlGmlNt>tmh0m z2H!8j28ilGJ>S9Zki;s(wXH5C!8`?vaCvuj^?O+rDw^^1S9|)Py4416z`lB(SG+#P za#pO@UyLSH*qjK_ohLGsqDzEM_ZlFMBZ`7ANuUN_{}OPI3aKD1Fi5K5$Q=75;Ph~+ z83u6*s(owXb@RW1LHYT@ClRy9DUC;pOTYcZ=$~;N3ufLHsR=7G#iD1@T!V>0&GXBM zqhaU3yZ@<3gqRV9ZLE`roCO&I$naedO86U0&)|bjc*^?L1`soPL*yfBg|_PX5$akC z&0rKttrSm3T}kaC+L-_(E{qxlM0`SOz@TK=!1Q(4oemh>*MKS@y-S&us?21S<+cq- zF&JF*hl?P3L-;(gMs#G2M z&;OR$zcakD#*qr60-6CQ!#dBqA97s}=m8A#jTNwnMryb0$AEt)5BdUcuv|3eYlCCs zG1)1J&sUe>?iK1UlGt}M26jzOn;&o>iKUyow9j;;mcDC3TNml zFW(MVJ3yc)GNm}mTwnj+IwB1B-Ra38`f>9&a00^Bb905vW(wnWU3o|H?FJwY^Z_W< z^DqqSwKBO-Yte? zLu5j3WAhL?leaPpJtZb5p~GxYKv`#Hw>QKJ2y4h`fTnhpnhrs0`b9Gz=^6k>R`(!l z%3lVN)jF%$A2NV)d{bz8X#Zi2k%vN!0V6bDriI32Yw*rr`3bgkFU?;W)QgO*!PE-6 zyqR$lBEW_e2L}Bjo+pV*zAz&Ff_W&j(Qh+z#@-XlhZh6-qyevQ-)6DOx^HE~Ci|&e z?-4x$D6byD0MBA#>lPOw<{1ZltjK1)3ZxO4^%U>Jd(*HccBo&sm9RLv9xWWj4dXis z?p6SH5dk?`CBdB(eXBHtDSNq<8oLkgsVroR^3E7Cgrw4h8o(KCS`2#70JxsO>!gk8 zH*o#^mbEToOp(v2X5F3hL}kr+#aH1#+dkCb4Ir`mVplu@3pqQFa~pWqZrG7ZIrUCFbUcFAtxY~bxOlEO z=uC;<1QDUMj%{wTwqPQhizSLI^2Tw`GS62&=^}~LCJLiK+H|&|-=TWUuSqJjb&#tT zywZ=WCgJA!AI52(rI&K25!hRf)#`pte73r707{uwaa!Xr>F8XH_YJrKk!ixU%fXN5 zklh3l36*@hfmn-|e?UX;I4NqLHvsFyh?~##66y#(>O`$g^4PUm+<|Br@x_3V7?=ZP z;6AUnQum^!nOP%>-{Gdko|(wj>b_m%{?)&e0Lm3B1EbEB&@c3EXcIVn=AY{Zo$jnl zZxqCS0+#Jxy5yR~0;wlm>5vT1fx6L0?u7J=#uYxDa0R|BSVEXV;6uS>q3FWv)!-xX z!Y!pvZvH4wI;mGdj75pFg!kUb%!7FvQ55N-=g*ABjHkey^tp0>M8gqC8iuJ~J*F*~ zPq?0MduiZnh#n1&P@67Grgf@m`1ZU1se-g9&|{;T!_TU}y>Y#p1GDHXj=EZjT;?<~ za%hWYS#ms9LjOJl!LvA4)Su$%JV#6o0*LE-e-nv84S{GxoF{eKxV!9VV-418UY8o! zWv}WHKhdzDR7SmG4%Ec%a;q1Bk+{4XU)NtefF9BnZftl@A~fz_8S@h>j9h%@U#VnR zBS9arjM)qNFXyuug1dd;IAL1wld()eR5AEr&E!9IQri&go{?bn9b>XyfDC{qz29;D zUU&6r4p^fUg-{wS+%61=l25(Iq$Ev~z32Dmr3AH+GU3wcK|sgLi2shz5aqn@;Ocx@ z-hqBsTY=vc>EgK{&)!oL`l~?mh#SQSA*j82AZ_pm_sjlK(f*_gpi98!J$8`+g#H9tJtd%}pLm{Z3Up zGJ*G08V|p2U|14x)F_~lr?n%A>or7|W@Sdd=R#_S+MK~Wfuj))rdXerZuzA zesCaBbKu1SZ!+kS^C9(gIyxOAJaN=3CYiWN0Wje@73iw}PmXuTM4$!s!(+1l0!Iil zGRBH%*7INsK?Onrg(ME(b8ZS3F=BVRsT_Zt@2Vu-bp!hhbcvV(_V} zzl2t_@@4ySH7kaFafMP>(0!oEY)7Cb8aGQ@%p+&lz;y|~RT zv+RSHZV4FhE~^Uy7zl^M&A{RHmNzG&Pjq6EJD}R?q1NfXOgWNDn_l zaa(4oylbD}JIiRFh1}zy`R*ybh8QUkc;}02V1e2$P4t@}Vn0L(BK>}>BGh>TL{*5iK zXM+H@P)|x-KQds~FP-WQ!QphEB~|3=-&z0zrx0v35Baf)wmp-7^Tkj(oL7GhUDG}x zppGKWFVy`!leTN(14`GrhS+Kz@yLY3`S#-u(|q`F%hJLYv?tQxX9+ zRLvcJsW2-BD|!wf7dIglAF5E5=`h-%xUyymA7^!*9Ptbgg|evmdcZ-y3k>LDd!1cu|lDJ#vm``-ve7j&wOc5wh2cezA;r_u+fz7|~Cm!E2%FX38D z#v(?{t`HBez9R6t=zI|~v&cSH!EVb@`ray^-3N zwSH}J{i#J{jAfcQQHLLlc7>4Tg0Hv_!0ZTpPAJJ37JHSM43Z2U@JuoHlAi+=kU!U1 z#+OM(>9YRK`Dr?!H%H)JiDybs<5>_ILkk9-wF_Q^mry4+Y%rPvbRYt8^Gu}{FEOnc zp+GAaAfkh~@OQ8ANd$KRsZCL6KcP13qqXyP#z)KJ!4YyIk^6SYS`PGE1i*0443O8H zfeBVZi@t6sIZR=Yl&EpP%lQL%Gz|5{D5QjD(Ol#ernmX}oVvLWTEmI+j9aQ3;4uisL?ukt58B--Z2=&qo98!TS zUWwGL`Gp?B!HvY5KDXN2djRV}a4>iPn}0wU$zy35qV^8v7*7k8tnaqVbrpaffuL41 zNv@;ud5(KfX$Tof;a?%G{)d4KWOf0K{0PVbOP_(fzl@-C&lmmN?jfQ+`}SVHi+>1w zr13D-_yhRy=bugE5cqD@t>s&)+p>ciTMu24t(o~&@>et;V+;b(+G~z%BdTMn;6E%! zx$qfW#Lt}o6vG)JBe?Pwu|yGjT4R2|FCq)rzyo`|+9Pp1QEYfT{hN+31>^Eb+KI+Q_2AB}_*a$=_in3%f<)0e(3QiD0Fi)c? zeH3^N3$M)pipDeEk6WA*$nHpf|3>|qvD-;7j#4WkEQB;qeHwI=zximroWRSD_;XSa zI_63=nuo5b(LU1mae`q+qV6g02dR^gH0UH1eoR8kpFX?$>nZRv^s#5zUdg_H?~o`f zTg4-;!R@wcI8e71i#?YOh`0ilBYnOp|>@))>dk%676*8An4xS-PXHW&mH*N+# zX!$K_@1Ioe!;C?t>N(-6eD;~yeFZV34^;1xx33<2Z~(JM#6bTw4sFMUv<`v7@1S5? zEHyc*+UbI;WZ{$b@ScBKtS(qIveQs#E58Qd>48gSiMza^f%|g`89@NjnP#NtF~rr2 zNU?{knBhtvd`b8K$*V*a%fey)Dn6s%_Khx{d@g+93yXz782Xe*$96@T$OuBxgY30U7zCj(lu%cmnfV%03Wk0M z7-7W>#pQ>NL9369;r=&L`#w_wb|gR6C~d(I7%^_oaWVtd}p797(t%Nwd`x$*3$yef|lJ)AAMAVX-A*A2A(RRW2?A_rI*-26G zFC5&@^q!5nUm$cd^UWzNt>|{5uHeMTgG6aBb;Iyo-=);Wqw-&yG|_vNvps~Cq+7f3 zGQ_M*@!W{K2#e>Brsb&~|J^E{Y~2TvrH2p-L`g$*uMJBsJ%8Nyrf&}smoGAYDSQC^ ztH%+H{;y$8Vp4CIGyF5rLfvu`(la@CCyGLycWb!uL`afH=vLocLq8ps!j0<+MY^$J z`2wV8YL=ViSd%7+Z@9dyT`ReGLHkOpOj7$~ordwB)|*kZOmTuo#K~}8%>LB0_8snH zKayMpmLQ$4uA>Z%;Z)BO0R~B!cIEev>%~S!@kMU`qKjdl_R%60Xl*wlzSP#%^H6-BVc(3Pz3eX-CGyu0m3o8FZiFHOV!C{heTG0_RCmGF8nrj02m4MRrYtS1 zv}WenshM{izjRqu#N9vA*@TuVC4K0$si|i>sA4Fh%=Ia?^z)fb_jLS=hkb#I^j2wF zdyWcS)z3*hI;z+I6_+2LPW9}{%Vq(B+k!$vk%%X~c%SBMc@(&G zDg9XLesfUsR}MK~$0&cDhAd*$-q}muYzcaKLDHl)M~}~Q0FaV~peERNtT#oz~t2q$`Lv<2bP&1qn73oW}*K@ISO zZoX-2uQ=3)#Lm(RYj~InSmt~QSG=Cl68!`#9DBqSrULjJ&HYBQz!lXlB7b7+ci$66 zvh1bZZQB&eYhVn>ug>}OV-w(j0wK&p*vjfFo`C z@uHl?aUMk(hDk#MtRPXEm*N64vn%=*H}6{9pkUZ{Rn3k5)>U*kx>)9rt>^D}!(4l- zruoWu9OL2POj0pL>LvwJwR+KG*2>Pj`?!%GvLam#Hfnm;2PDb|5>=mT#_ryMV)@=- zJrofg0Dy|WL@6xdabIH@;E&>H#LIrq!pxd^$o=VQe9uv6me`EoC7ndYuZggrAe*c9 zeTJ2J`H{b&Ga|sl5%A`CcfzelfpDOM`_)9^J4pUf9D5z`S0%$R6AK$Ay{=W6-mTt@LDRKo z26w8D#J^+D4{0njzhd$2#!{@i!QNy!l9~auyHf$DrSA;YW4XgAj=KahM{1AazXCGl z=5Oz22dL<~EGsvfs-hKS^#HZCsUg2BImKy~s$<0dbaNn*L4m!omxSGd?~&z)E!Kl) zSPJYG`hT}w2I6I$`%dkQJ@#rR;CpIkR;OQkHlz>1+DD9m`Z3wWR1U2$X%5Sfu&Y(6 zC0Z^k#SUT-R?T?anl@m%DjR~mv|s#{vq3XLGYxaoH=fW0>nZsbjevzpzQ;4_=NWgP z)3I%M1DkL4zSYeJ*eVcJ1iOvvr=zyi^*%VXz81I-U9eBNnA-rAl!vnyA6E#Hd^MkCoVQ+;iE3|%+Kj29pjri32EFvL+3X zdGva%8kbu7E&+zclQ-447@G;MUQ}pvE3&5VQ|yJ7i9?nObL0DJwWZ}>ZM)%==k))o z{fIvC>!Xw$DF)g5l!lbNaJ3s%6La}!YF#EC!EJHNIXKUx+br7Vs>1rI=F$}u^4%1u z>wx#2Q(yAWy~3yUtrMDbKJ;`wWEY-Z`q{fQ?GkrE@8L?$$2U+HJ_a(K)rhiMsQ1{U z#bWKKLNB9Fe+HfC1%rJ=e<}{<-Q3kmirxDpQF$tF;>R^$H+*alpiB($u%GcI>`ltw z)td!L!K!cC6v@!g9Yui)1MjU?YPeV0em%*1VQ)!iNPNIEU6r}b zMA)#KB|lsdC>xKM$m1o=t)8xXHNNy-8GMN|sQ%@)tLvZU2(skb`*gd}{f5yy*0gB6 zAdMRQptU0kve2nQlgH&#Mn#tmMKcB*2R)Au_pXn(5SbK-KV?Ypf(-PyWtBw#;wAKC zB|+}da@SYk94P8Y6M-Q`Gfbl4+nxD{?b6B&3r@KO+X~fSVW@ug)hJa7KK}MyXe(#f z-|BV?hmPcdk0eJT6r(oHg0x4*F_^CxrGn0Qn-)apkrGC`S4rU8*krExsm#hhQt`aM zF42XDH@Ty7OHyz^J=>^kt(pPf^@qUGCn>=eKUWu|^S0y{+Ay)s={dc+klo-Ue&$Sy z5{V1G&3@9GP$p6#S${gr0mj*ZB7D`7b4;VM%Q*@UMZAovi4E_ZTQ(efviffMz7emY zyd>3tp!C6&d)$T5FY!i_&vO_rx0bBdpyE1mT{&r#E3Nu(YQN(BT4t%7B;?3mqxb;^ z2OoPDr7+;WNQVJ39zKRULqCBhEeJFCnlrsLTwS9)c#p$JoR#>yi* zCs^^L9!+>c#Pv4LjqoJTs5e!-#JrrxHWOu!j(s-NS%E7z9uY&zrT%Kj!9gDP<&`_+q_TAB5tOZCVM zx3znx<+3LtW%e9cWDiVNM=IU~B)Y!nGCQzcY1bpOolRM4C@(BBO2*Wuu~F5=P2{Hn zLb9$L90;EKrB|@(cNx4mL(h^Q)HZ()T3Lls1Zy5b{0?ls0+i(C!TD5nW^8RrmE1e2 zd<=HBQV#uED(0B-p{@$AHl@c{7H1V%sGrDYIQWPSxy6iGC){M`yUxZhN9-nTW{VZ# zozcDPxR$t$vb2gGqZL-Vk>z+%^T%pV`rY>1!k;}qg{|cDIJGF{?`(X=)(IXf3FMd2ze z`nRgu+*~XVJJ32e5V|NJpifPSPt20_Lh+g>Rbe=0Z^p+2mr7v2myf2UJn7DFR1$;B)?@@ z+{^KK2%n@|jTj!9thM9Uey6p3i>aBKdGK*2lDu-`Cn-8sXWcq(uZaB<^Z2XmYw8qEtY+uB zb`K3c>ybHZC%R(uUHA|kN~RM_kgIQqAEQk`*K>H*DG4L}jR~gZQ7SeKzG3`(3R`Up z%-Twu>ay>|;^th58V?1gTaQf#XVO^mUD#PisOHQL??_~=$q4SRcqT+Tt*=Szqf`+pMf2R*hd0&}E6=fxKpbB$SoD8`v* zi~%M^DGfQKJ{6VQue7(Nj_=j*>eu#uN*|$gmppfN$4u)RTKHu1e3}QLu$pnu+`yuH zTyksanJ_xd8*VoDCKjVc3)&o#X;KZUl)lT+5~^}6E8Q_SzgbXx_x;RSb`B;iwvp6Y zZh`3UM%M*As-@NL=s(iP-X0@+pZAp(vZZH9Nzk6V9^cXCXk4ED6+Y2ZYU%5-pCWsL zRaen?)E_mA?Gw$EsjLQ_HCVje$*=9R{7doe7=CRUEnxLuURp)!xxgyb23O}07^+%Gj+GRzr$WC1i z9G4|dBz&xMoo$-?`Xt0Somn-_sW^W_v zD|e;%x32HVE_#4qm@ZeD>N++ncGbo(*rLa=Meq^RO|cKf&*@>4JZK6vXL~dd{24s- z?B(_7wE>)5% z8nR)*Idp%k$~^hq8BKoO>e1J#_g*NcA9psXX8P12PF(Te-1WL zPtuHMqQ|Q^cM7d8mxd&^UY#eUG`jfqCI@q$|A0}9Vqv#-USzRO4&|4ADyAYl6ORfP zIc;f7Q%zCf8``hgAL+bnyKie|+?#PRg))h7mdJEswxkqlvakhT2&%l8519tvj z+M}?S6)BC9N`qA1WO|NqwsPxDrWTYChb3xgu;hH){dPiTWV06)ygi{ld9j#2?|j(6 zxhfE*QdABm*y6vCXrI`j;V96ibb~9pIM*=?_3mi-R->8~bm`pvg|CmBB?*OHb&g%- zZ>!p$zRknBDIccj+KMl=(FiS}D|-pCcBPkfq|T?bpqtpg?-d5azR;fMe({%RJ`)IB zaX68V^-dGONouM`%vy1mv(c-1cT&)GBm)foCD41FFY!t{d8uL@P zPGVY@EfFdhIn)AqZ=9;=nh5XiHt1-4H2z&hXaxcjQj?4M3o>5c#+?Q?yk9JQFGO(b zE@I;e)>TNI^h)y?rb&(7wEylWPcM3S_SOEVaL;SKE4PM28r;xcg)p$?d2joP0@kPa zswpXj4DY|Sx*BLspJ{u#RzPa-{Trx! zbXwBA6uz`8b0DCZhJ~K-aQpqHO)XdDv8d9nt^UyK5hSZ&yK9wlKisBz z@EP}!zAz4f1|QCA`Z7!VZ5cz_g4diuuEv!mIWiwc7-G5_Z_uQ^i#31S4HL21rTqd8 z=4ak~e3VVeY-?V5!8>Gwwy^bv+1$>U|0){0Rx3xqui|qZFb`i~F4T{1xZ7~9t1IF6 z6a@(@$%^^fJX&~Gckt@3?$Yn3LU(;HuW!*RlhDaJ?25M}%ZOsU5)+*8*h$Cj`)qYM zCb91qS`-o+DS+;nugn*}BD?QZuDgwb&tW{3vKTdph0VBb4TstAC845u&6~fw^g#;L z3afi>ziu~LO-Uybx;!x3;L{i(Q|R8fs}W(iJC;nc83J6V-M#>=h}ES0h9EQT$b5K7 zj*5ymiVlb9GC?=*92$FC*b1tD2A3WTt{S93pzI#+C?O6X=0?90PaU?BDUcc z$i3_wvAc^5gP|>F=sK;$KgbeG@~dJdb7!tG3W^|>0Y2%K-iBo~taO?_Zg%Z^kImhX~iw-|UK^MoLZ?hnI@RTKHlPrCa4Xyn^PmD~Rb9M;)8$ zW*y*)p{-AAlzVPA+*p5{bEUdr@RQTYw~NwWb{K(HbT1-#m8sV>!>h)^7xkbV1WV5a zmDG-@?R}4sjGW<+?2Z@3-KiOfC=|`|V2V74<0=VJiL|gnLf@osW8OEn883Rt zLyB(Be~8sA_3Sef?5Ie>#3%I6r8FKgb})6@_&GnJzIm>+`VEy{|FMB5Te{{`RSUC+ zg&JRRtQA;pg=kF=Y+sCy+?~l5l?XX{jdS0nx96eg>`n%Ulh5gM?3{*dMbn|V#G2AO z>#jmGf)*@~iea1od(h>tJ?lU2m#}Y+433RnZD3{d;Zv#3ZZQ6a&KXJ0rlREkuql{{ zSU^@FyX1b|6Rt%jB7Z-}mFw4)#E00$bhdchK0mJ9yWmVH{d;*-##v4#7U%soBYzv# z_FK>M{M(^$T|`j1KWHr)A1}(7Js>o5$Dh?8Fw=XcED6N$iJZ9X6mPWxg=AZe|eipYC|rHA@mu)ij2z(=iHF3 z(P_WXljVQfM+3oVjkdERgd;2E-%U4U%6Ci}$*l{sM8r+G&8@~GS-DhDZ^lF_EY*e# zYgG>nGE`+h`7heLW`rU0`;rk|#lVUjh}A0RpS8dd4;J-uC#=0++wt0OvcckFxUQbQ zVnBP?CYQ>+s4=-#*{8$rY}-2-yqnL8Jg+rg6{x`#=2akL2^PKKA$_<33UCC)vKgQ1 z*t^ZelbRH3geXkwz!7jo%fz_6T$(T1gB3NUJyuh@~K>E87+v2dDcji;~d#u{N!%&4T#Z;ycD*iVwRjVkUku+ zT?7{|7doa~?A`cyE1}O~AyedlbkS6dys)Mn8hthSQQiWVC=0701UqOk2n%9HaPY+t z(TTd4boB@Aj#oaPTl}cB81$1zlcEQcSmGgK@DK(2B$6`?h*aFz#JW7NJ%h_<4B~$L zx+_A)Y)cwdEFVme2y@S#g}JypLJJ){F_u964$loP|2lU=h8NmIV<{C|3Q5n;88_T{ zp=!OCXJGu-mMJq5o|Dkc8a*Cd&#ijD{v{sPC9`d)N%{lyi356xwNGQm>?JP@FwXDc z*BL)eacm_?MpbEPwl3nWfZVI>OU{Nh6i$@v^@Zq3gKNb1jC>?Te+H-Ll+{R&(Zef=9aw!zz5| z2U8RmDqT%tq69B@XtN7`>29wL6`*eFj1sVmHFvl&z{Q+Uz4Q9IzCJwU9XgG02^^|CFV@`>CBAZ@p+vwSnJhSfPm=rUktblFOzqi#s-OaNoUJ79lr*z<{QSVrQY%ErGzV zp7=gIaV)+3y8jQK?`;ot5M=f%+4c{VsQ6MM(=*^(u&-&Qw`GP-uWJHxOh;4Q!QF92KOMUui zxl6aaPa&&cP#gPcu}0As^_0%E>poS9rPqlyZ*t}PnrYsw@lrfAZT|dIPzOhz1zDjB zYC)M~KgDe(o)V{!DRA^>OgG#rN1ZEPBUKT!8ZF&+a!{h1J`f?DfBZg6X6_Iw=9+J^ zI_AB)7~YMVwBlY!C$I-;{Bl2kzJl!!_MsK~m7;EnMegO0)VY-k_vjV-ic5zK#PvpQ zrEBg5am3EC=Qlk`(Mp46L+%Fn~7Y+a*gjB z{lWb(=2m;>itlyF-<%xOlykAh1g6ii2>BIsa2(jO9@7~P6F2LIKQ|+H(ABoAdGpM0 z9CNy`&)G0$4s@+j4BbNA0jk})Msydi(NT~LvKd9Qy6QeXhx5#CI=2L znj{`-gqEEx7=^3?9%Ij0bX`#Rtuag%|9SqM*RQ2Y zo^AU@SuC0zCfCzB=I$|K&tE%Sn6W>!wJ3XJqcxn;Hv(s-j+y^WKi-oIDCy&tQk%;|AZZ;xB2J5{F zlt)ZiUeGiq-NNQ2b}r7SKq=_5>V96oMSnh`lO>!BQ#j|Bh5Hc6rIwwUFY2Lp)*DiA zEMHb6$}{)SaQC$q1y&SL*91?HV?@he8Fm3BdO}^BO_)*$O_Uj5#Bm!Rsiba-@1nNh z2ev{=QCds6!n9ZFjEj7Ha}gdayYreKp|= z5#O_!-pdru(!EvkI!$lh_BU1CrjiOB&YrItjQwvYi3THIvY(r}=22wNx@OgteakQp z%+J|H~&9_z3Qs(mIX zzA?Dpz&9kr!ySj-j`hWX&q1KTE+|b0o;{D39|t$@vB{+_Y`PK|1KHc(3YH8X*Qwq0 zUA530acrh{yp`wGxa((YSkCc@5dS7&8PQOyt<*}yVD{#UM0-rTWx=PJm$DZU`Ya}% zlKH^vbdazO`O;)iO=XhRP#Zpr`Dt^5G`1p zFI{^5P7u!smtN>UY~09)7vzlCQXEt$IF$Ubpz0OI6b12nrkF z{(q+4CRdP>-VydpuF4mY$;GtK>(ey)lrvXOgE|Y)t5;d{>}_ZsgQ}>evIdlLn6IKxV8+myfT?HFHBM(x2Dv|BMLFfK_NEbQ0tbl zCj|tC3kfT9xT#~hbQo`IqZ3k9S72bMN{g%g-=agf4^njS!5lPS+)MEF@4w+Q%axr@ zU?eX0xc&Qu0)iH&1?e~a^btz)b0v!Wteh`BRvd5elL#;sl=C`BWJ+u&X*Wl{cYj(T zD^!<9Bj(}E2z>ONC7B&{0f*+t-*W#QwJB%mwmLAV6i(IEekIUrJ^0A6W@?bs`vTfW z)?)fO&tvqZ!2<6K)1ceYY-YY5$r4~ZWIIw>xp;9f!cQi>&R@&Ya`3(KXmX;7c7HJ2 zE&+}Cit)A(?d>89+fau==l44LojE!@*!|V*6mj&$A}zCD@`hrlQ=g5PNnW!#T}md( z9Z+oEis6-B{O7(7uI(1_prdwn@tuvLDuv&U!zT1Kbao>g?4<&)0lZ7AkoW5NPyLiq z%v0yB#4IWOF7%6as;OpZJ{H1!>`dWwTX-4f4=f)_sg*gp+2&mkjj3f05P7_6GFB(8 zjZ`GN?#s7AC55K&sA6AMM?>Rt>J_@0okumJZK9rjG};3|svaS~sz+~pZ{{~7vNuQU ze`9KeVG3Wr0b{bpi$&A){JyIRjXFPV;}VA1%GwGXEkosfro$WFnKuu58x(QjEQM&h zvIw;Vek80Va}ejj{9QT|db6vSAONi=6Y0o>krF?pJ}_*o_wTMrn{P2|O6r&oAFc_O zyQe`{X+|}2h$eTy1nZi6dQca7J_69bU(nh%gZ;EHl(HOcrT&#N;F}SI# z7@ZNBn%PD*v{0c(_>@p0@Ig(+?nV{g9O>^KQ!c7&ZG~F74Dv)aN7U0_F%+r!&ABY% zJ*Q&RvwC(@b{!F^c#;rnx7vY$#4h?VF72Z)GkH8@l{7UWKJAEZY?#su)`Rk51(xf@ z3F5)uhM0|2Ds>_50ZAj0r$<}lo%A-wuKXTH$HcxUtCREY*g+zBI7eB2s%~dk zZKev=wv||R>CclCHNa9mWV6xh$u1{{-VT^RL|%4VRH4VSZ_V>S2y$HI-_RfOxXV>; zKMMIiaYOZv-ZDs})$=9w2V-1^cotmvCf~!ybtL7oKQNFWFtjV2X=pQZD3EMm2PC6P z{Ny@8akmExVfvauV^}o@`j?>K!OuBmpDx!w({W+)K{qbyTUU;vFP9`XYR^`tN%x|L ziC90M$mLCuZ+3J-V1{#|c{4}uGDWC1evFCORNwWd7Rbm?KR5aX_0gmXnR@vRTrZ~I z)U#q-SA|`9BjR8)-WXe1ixmc&()y74kiMT9t{<8H?}X@RQiQ`M`6Dg;UJNQl;!q zI7SuTnr~LlVO&EUu-3=yf|-Lx&ZR4GT;!^3tcKdF3z+4upY;ykrs-{0@9EoTxb{T1 ztI`P#sy>nk@N~%{)s47tsVV~@Ng?d(_Q(D%91S1|_#wy4aENLhRQu{`^^IWrq0@SvPSbkhsEkoKOG4^P35s!C-Ua>cG_@oL(v*6fNZeIwteAg^&X7i!3 zkNzl6yNQvEKVLxB!(!+q)hsl%Njsv8@|Tajt+0Fw2qw+C?+f&zKNGA`|E&~EWu(4D z@sQW5i4a3LSXW09WKD<Ppsos|qOObmIMxvHvkAF|oyi*8UuEpJz( zh&R(`^oPH>T(*h(yPa93W{OUF^=Ya}~(i2$(h<_&Tg8m{R` zkcIe>{v!Re0=8wah80d(X9#}t8RM}|ngi1GW@ZaD9{Olukk6MM`)|Xvf=?qwD`tNG zp>tH?d6`LEGqg_qqC$=POYzwYai1SInAeK$l*AV=f9$8Px%y)M>|J2uwZ>6@O4@6^ z@dh*20gA1CH+lL|$E$#X`fgvKPxc_9g2z$p@kk0P-i42S8O@9#={_!Zr>&ApXWfmB{70DL{)|M^p#@0U z+Ue^N7Cp?9aU$KM&!V1LA&Smuhn5I4R*p4`j>RBbR=(d8;;S5h&K{!4!py9!{PBfKKw4zl z7@{L{Okp{|uRxB0lvD|GRcnIgGK4*Gmc z6-1r;;eMTXavvzs2r`=qPi0<=AJ#eh3x3%8nlCGbL(b)#q=SD$|1#NbMEKTtgcZmf z?IruvYAD1jC_irPmwuq+7hS(Z-znJp@|~!phc{B#{~#fyZ@zp~SL=+YF(0+u8E|~w z_*oa(OxxgVUug}j3KAAKN;h`f`Ky>XcV~2}fmBfU3l@%Dw=+EvhO9IS{6hOnM-c;~Lg|^|DS^Zn1UMJ5ssy%<5R^>ncB;IwwC)tw+pOy2J}r z;0HX8v%D?@-{DfwLJ1gY|M?aVu_}yZV{cbDJ~SHwL2C2<(MG3|c~;wtCVIz*B_J6r z2Pk~Y{(`j?knY;QGKu$pqP1E1RD&U*0e{J&4&dGV$i8NY5esxum{Lk&H|+vL|V(Up4a!Wf+pwcorHR|+U%)t62kgZf=O*1U?Mh&-v<#WYqN+AJTI%&I zg{j@)F#wks(F<&&KI8oR1jFT%;`XIXj1dL5T!vO;cxCN+(sq=Wu1230d^<0F*`$Gf z{I~JHzxVzauL>O+pZifpWK+9Lmd@j0in>_~(+6xvQCUhlSP_yX)>%*4RX`=%uWA39 zqUlNrHU8^j#+dqg8jDk_yuHVCq8t}PZM$EdvUsYK2ATLSI{8j1bS4sO;A{{`!U+Fr zc=3O)6$?Ys?F$0cUlP=K?%<*?1!%hEfvyp+i31oDU3CB#;bPiqqf8ZF(v&Wu{L7bp zlO&}QMqn&u6gb-t2R-?$rH+hotX&>F`f=W zYmzsOtcC^mIMC)hxATLclg}0ZF3D0uBF>?#1T3O(4DBw&?_Jg~men(58zyt{rg~k^ zy+&+RpBIFOJ3Cg&OMk+za2aKTq#O`hs!sex0{ zr0v7Vz*1SQK!*-=-`dM*0sYH&Vu>lLq#f@aaP#CZ2rf0afc# zKsPrMbGpA#pg8~J`9>e**$*(M@KSst^nLi^qX~6RkEzpGT7^Hm$$_QY!U8)le5Ek{ zw)}eG)pIuM!BH-p$d@w5!(dekeRi`v-aL|s>*y8yEBI|m53pYT3Nk2FVsO4F@Y;~D zYgmmGcd}p`CbMI*-|C(qEq>R-moNo`**dd&)5@U2{d}XYfL@EI;m*hjT#Zkuq0BQ2&Sk1KykC#%kZnFk+snqWPGjK!|Fn~sFRmWB_#AXi+Ufe>xpz+ez zyXF~T@-hD9gR*58O*F>=r%a)L7tfzpi@YyoVx|~TTtk%15HfX+B^JNd2UprUj3RLE zf)k}}NG?Kh> zsRO;$AjRO~d3~a=68K4CM;$Og)WYJvbqG7y9wu-KT!Bu4ePp`=j3Iji_no_ot_rUt zI|O=t$s9JyqFXzQr=4%geZu|ou9%giaVMD>G3@^wXlGwu8ubDP!^svAc63%)w|p#z zjs&63JS6#*;md`}IH*(cM-7223fJ0_jxINB!I&gbh03(IlVHPm6^i=LDnS2tkuR`e z-P3nA4Imx|MZ4xg@#K$T1(27fl!F4w`}-~sd~BUQUVgHY-|PT1fM7(p?ERhcoS77n zxX9~=cQ%?@^=(uX(>YwaoR&MG$Br3Up0Q$zm%#Gh$1`H3p{*t6_r=3W_P=kB@`@(f zYq|Y$8X=)~V>2+#iqhN=h4KASSW=921Ah7mJkRBK$+5I!6pJd#6OV4_ZH*VLB5Z)nvd(|E zlXN08(>a^O3z=3*$f^9l-y+~s1G|11*^lZ`L^hx*&xteiKL7s7rvg!AKoolTZQl2B z_Q$^mR!tX`gP1)dzEk`+^z#E;*gekg(~*u>IlT=Zp(HeSK5SYrH~9PU3y-k~#5bgO z+pl%`2D0nyiomV-6(L#ae8gi!@N;8Gt$26a9j0$Imk~}AGYQ^}379d>VH3amE9H8Z z-pKoum{l0#dGC_zjKlWMgO>|VZdy8N(rWJ zaS>O>yWg#HugeZLAVb0G-{@qs3{`a~80$uBsD=$54ASO#x1?I0AUAM;wAR^YAu)qE z7LmGT(T{sO-7W}0L-*0EOPinUXT@bDBa1-ei{Lz$m)vKkw|{}Qm~-@gh5IRJJh2+r z0rV%Dc{-b4KjH|YeTR+0|5nkTC;(pwEBq3%mnnkwA-;EVvRggC919pKAFokl@-joh zny;c9S`R)kJkIWK64QIYz*M7aJ}B9sgnE`aj#k2D8;xgNyXO!#ciPy!wS0j3HX}Y} zn2{B8wQUrH=5Nlz4HlF#`a`TsQBMME5QiJCd7O4Cj56@1$ux?8DfMaqg}<2y&&qIB z2)wDz1?M$fxxpI(FGqE+p62`WUze7<0I+QZLQMSVa5zp^ME!+_42~%<&em%gp%+O1 zyAa|j9(^2W0LP$IGJZE68MYaNQ(=`?)VCV?X#w8Jv{S3daMWm(CIgRAg?mIo1J%Nm zs&@&Gvv0X-X{@Uq=l@nB%Ih_;C!@E}^g2w&*#m4v%3H1;lw1RmyQ^?Akk9J+I=~cQ z9KDEDM1vrLx+#I69_NGrQ-O}2J*dE*U0he{Xrn%9GK9Q*uJI7=%2N0x;?A^wFC*?X zM3%MKbja7dkyB!(x~u%}Dt!(YpfC;Mg2WtKuz- zhem&oSTl+z!ojwAV7vPNZCbYwG=Ha~sC1aANKWY5g%L7ZqQ=Hy*(N`9hAg2y)?qvm zgyKejg{1gf6~dWSH>-I?;X&m6KCd^UPnFXJ9Q(#Ha(Lm7l|vAfpEIk<=_AzM*dtqChS_P^9)yg zD{EQ!LlE>Ea)M#sHn2%BPkHtI^Ey;a=MaN0+Jwlao+CF_V(?0u*)L|wK(@iFGjj3}|Dc`thTzrBy_*G&`l(Xvbn2vQnUIUO|i zo(DT6VwDPF1H`Td@g=fA%LE)|++F$%YSE~ALBn<}*O8|v#H z2?wy(irNBYsPn?<^B;Hbe?=z2Bg=ubGzPW&d(8sl%mc6d9P|OnbEndOW^IkndjR5sy5p^@LNAIl}%*B?K@MMX|pKZH8r(HIybXfsB ziF)Q`mWpf1DZMCD4u0;)Qm3Q0qE|0#CYf@0%sdAucDbBl@uW9iKjDn8jIi0ArT0R) zmL0a~`pJ_7UmYS^^$tTEt%VfZIh-sw|9h78Sm+#{0WH$^-Q#@E%TM9eZXniw1Ux^c zZuzDlHs3(x0*Wy{r_whCaov6-A0i`-{{I%d1GDGA3KEXI4@eY)pcd!+%&`3`|0+V> zT7x-)Uj$SUgn0rxRb_qt9c$*%Gp~R^YbSH{&38YGa_}ocst!XK2ON^MHBAZJRv|Ou#fq3{+~?-Z`@Z) zk?*N*wqLv2j{V*}n9C z3>grW*_G9*fi+y1vwaEUCb0|jXUBlz_b|1*O%@oxn9nd_s%dU zJO;!2l$-X*;Go~3d?o*j8gv}r4>c`$LZ-v&I{)YHCzC<*TL<0-ee%l2i$Q>U(|L;r z^12|i|8LCv)9@?Isc8hC_MsU!^4q&sa#e_ZU4h$2TnNFF`zhxVW*+?AALp>brtv!~{H}!=$tNLX$_vLgO#lv-8xjl!fWsh7JAxhr^d}vy9$;xk>5>M1b<# zssql*rZ|j@9bSBR*LJ@wEl4UaSJ3yD~LOr5{fz%AP_alnPWf&SXG~+@cMLK z^BSH48&UD)cjF6%4t{?Ug@3bdehh#aR*J3U)zis}v9g33u3(42?E4yAZ?;a)UR&t*UaYV(D9JjAu3-U;9ht4}4bk0za*rBGBXaF8>wOqTB# z`~juhF@J5ad?niFNQT#O_ECL#(!e70qa6?cLpiJ|?6FpO{Bzfb%~^4bo#EpI3!@(b zRD`bojRNX(8`y!SDQiR#|DRLr4$82>0Sco8pDbDp)QC8p0R={@l@Uka28c7BAMpi0 zZwik4`5}s-U9e=cQC}0Qs0x!hadL#C1S_=@*h=YmuDRT2UziBvE{o?BBLTgN3%p!< z4yj2zWOANpCzLj$Jn0o-6h~Wj-at&}T7-L7i zY>9IJ_r8%^=^jx{5|8SB+_MOCo-s9;cCNVcsp4WgqyD9OKdNd#+1Q6~3RQWej2N!O zMt|nv{^>wBMbO8<2?DAtz|qdiKF;UZH8w+)#Ri2f;- z7@0|0WR!#}xrSk}jJ{$^h_x4`V_WHjb8RIrf$V>;vYytFeBv(<*OOuF0;Am? zAqOTG$lXP;Dv;G81lW4+4=~Yh1#jD0J0nr8zzivR;*OCls;N^1{qJZ^R!E{hV$?YL zwiTx}9sTtVS-!kxZCgw=+%`qGc3gDoWG7&2?@WX^{K+{0q`@+K{_@>nSl;8ArMgej zqv)8^%SGH(>S?=G&Ym>Ayfi}%Jy!V$&kh8OWN|3}8*FcKh8@WlTL?&+g%5C@bWa89r_5$E*D&y{* zO!wRjTud=>V^^c>^O*XAD$G)89;$Orq1HAGBHm zD#%Of^Tu<%39TX9E4o$7=;}Cs&sHx{4AXh%?`GlrSSL@t&tJ|g>0RnXa${aWg#ve{ za`;S^Z>{0kW5AqQ1Li1!8rvx8^I|CK2~q98an(M%3#3tGNLgW8d&gZR_7lHWvD>?1 z+NeuUMH0%v`L@ax06el)(~v}rS(Z-uTR|?yoo;y5m5{gK=+cvRzGnPnit}N{H>Mzt z=*kAP)6^~yBvxT(e=sT9pjD&{ivlclg9J1Wy}qV%PvPZTwQpWlv%K|xeu+_-v`%hd z!OsAaN!S3wPU%c&7w?ZDi8Smt0VOI6tXMcQba~rpTO}QqhE1It^l9eRj53$cB8&w} ztk%x0Z#hWK<9o?%&LU*I+zaOr^6D{AVlJch@d*_rrwWdfFk3_s47*JV``A~4yEQ$S z_%Q5#V;A!0fEO>pBuhP9#oO=fD3dN>f;7J*Se|m*7~UB74?M07QB}YO<>&&>z(>K* z&GPvc@a41DkXUF69RMbCdoSir0MbLsg?9~)Ok74%RMvbeMiI5}?brhM(H4^;x;x+w zu&kp?0X8;Aq!u+_s zZz09mNksJ&t7@u}!$NNCyxwUw>InGv1ow=O4q!%h8R43Q%KZaH4g$>_Gkc8OAY>?I z`NhxXUy+w6zd@QbkY`zUmDI%<2G%dMA)bbbFCfnFuoA%~VweY{7J}4$ z3NCLa&EU}4c#6Sp#-R@*42F5qH8^+i{ap~Z8#hizMxIFo5Kf8biY<+X(~-o&2SmQi8u zR7-JtdJHd2&lg1)^6_s_UZai#r0OY&r4Hp(H4UcEJ?he)L8$Vs^+v2DRU;gu+$GtZ zcw7MMT?1+aPv6D;sc`_L5jkAW#b{jv2b?d-8>H3|2*WuyYjlgL$ zR`Dt4HXPnTk>?tDgKvCitK7qF8cw#q0fttPwAg+%lJ7Eq(2veTEN@Wb=SkIsjf#YR zWI(6Y4a(V+R>w#M6mjyJ+N{(h(m#SO3FnV7#7Uc=i~g)&gwD*n4K2=7D0uXa0*7h| zOd3A-(dq#)K$cS=Wt2P}4z>}nM6*>0DhI}<6uZZ-g-f?>Xx@~q0#O5%{B}wQ-tD%G zsmk2|7AoqWz=4nNzttQ%wR6*?ip4+h#Zw@O5oV2v_RHw~J*jXZqyFg0uN5%df)-u* z5%I5_C6}W{@Rq(yzHgbI(J|mSr8>Mtoj!~ns=sLhei-b1s#vL%dN1CH{c0e^Ouccb zA}&VPN~8qF^8>nj{b)F~1FMj%@!ZYR5-Mj;m}K&xC@8&ULG`%>07BRA$ON=*84lT=ai$N-Azt!4kz&8nOOvzr=#?xF*J84~l_o*fU=-OOXoI#7Spt!GXaw=?%pek4Q=5;eBuJ8&E-TtQz_mWFxSMNt_U=9*zm~J- zS$?(}pXXHvyKkSusQBtCVKMmSp2Eo|TwzxJW?=*%d*d$#@-AL{M(8oC$lOJL!^pQh zUsm{Q7A1Z`m}iQDugnRZ=;LiB9j+b*Nq+x+wpQ%m@6Z~N=_n=zexGRFy>j{ZdQ%zWzYAc&8p|c*bOZuXA4=;U4Q8AT;8Uv?Iz1Jk z(^QsnL_&{E$;*!z-I(| z4QZ2=EnVRJdX6}dEPb7=1w;1l$FR877}h>bAZl_1d~;=yG~p}airZ=!SvPyLQ?r4@ z`*IdlsD}rf1gg8|EjrUQg-=vbOZq#aPie^R{!InY(0_Scs8RVK>#Rah$|Ao+)`ynu5TK3*SiGqQPBPIQ*%UySpo`e=bRrr^=hc~A(R{hP`c5p)PLKG zNW1PIs9%rjRM6^Y4^Y+k3-$U(KpD59-kadVX5f*{s@cirLAc`yL{9dQR=MftC-GK7 zgoA;p+8G74`8Ek8p3M{1@%@N{Yqmwg0F&q6o_x3~DH)2f2hP;(S|l@-_%kD1KYuba zw5rc^j7Kb;UDR;v{qksha|pqwtV_gk;~NXM12r6YteB z0!=B@9fiIIKK6%sxgKZh(pP$(ctP-F9ljiX1&*SLGgLWo{Eq6Kf4&zv1=}RQ?rw1Y z3_XN}i7g=WUa(FLA!EEFAjaer_%#DCDmx@Dw^GF?dSlc&$xX>g{-=R}^199nv0WTG zcduJ6pQq_s_NM~GO`tvqeePP&_vaxdQS?hQ+x8HGx2<3^6aj6yHmO^Lx6Fw!6@P9W zsc0h?iIj#uOts5^6nY(S~yoxf1{r)Ivi>S}}zg2w+ ztKl??HoF?ismV10$^E-r1gX;E)S^DlqJH$j@>L4Y9@E&)85UoQxsWO>sH zwV0p{ld22$F8_R5D*YQRb}M4Jyg8LiR>3Vm45>)p0amU<&-rH*^MJKkGOik8FTDm2 z#{uy&Lty_ng@G~LM_tI7+yuBv^!ZPFGAC1{x5fGX;mC1D0w4rj_fPQO0Ahg*?iNSf zMT~((1?;>U%e(rs$UO+1ijS;Vw{Ahm{}~akk3F!+CTCnbaE~?KjGnZo?S<{Vq9#0r zRRTJM=72l9&JK|E)+XUM-J}YeLR>qtOhuHnyZvjED@wG*an%hx=%g zi0!IiTVdEVjlU$pRr>V^Mv*H=_V1Q6znq;XYD`Qgx#F#`$9@`AGM2(#}|o52Y`S_2>yuR1k<0rwxw&}RPtuo6o}5}OY!x%i&SQfGe;!YiY&3-?z;y0_o-`^-mv#iLm_@1g&sIx4>fe+&2fs)L-FYh z?%O>?EZDgQu?lNhI(3+oMoE6biqdCBwnyTSqEAYt{Q5?5UC7J*4hYAan=!g9n~Q2 z;0yOh31_8rEsO4|KQz90pWLg3rk%Hy6D5f?s z{?m*LsG8UmE+Ic&qtpna=hJO3XJ{vEpk`qpVIq)FcSlXc^kG`|p%af#tK6*#K4hxo>_qh> zTA;(2J{=Aae9l8w3kkz9+E-$?E-0VSr!Cv_w=FaMTKe ze+n6Iod7IQtcQV=|Q^wOvXRN!e) zk9pS#K*Q!}n=J%eWAr1ky3bSiu3fX-ujv9#>7t?Unf!~zwj1_eV% zb;ph;aimb$5kmV=G_yk_H*q-`X8)^vP{I1^upnhegP5Ns;n+5c_{bC)2PC1#j`^L@~m!4)GdJ?d6}FoxRAcW z9v|ocHti1gTig{-@Wf3MKLjHcI?ePSx1atE-vBX52oI?uxd2UmP)>62JlltE;^E?W zdEqhm(Icw7wOFbK*w*c!5#iVV`3CPdG?^cNyDbLOQPEE$D)QNX`SZtEW)Xb0xz8+3 z@D7pvAo$gtYS^{I9Z-SL`*GV|@!wiN8LgMvFOQ~cQ$M;x-EyIn;1C$g2W^K!i3=EZ z8vw0bxD0B5Q48X=J!t5iH|-5BDLY&=w_b;yh`SK9p2U0;x{>m~0~~$7^|i9kmNXGY z4(ZVZ+?!Le%TqB;Akz?C`qmwOj8yn{jEjxu0}1M0mhUN+>)+L_aGkPmb6G!GyzPPO z?ENa3gmkkJ`a8nx!ALQ^>0{yF?#D->a2%W47w#9uyMwBbo*{(&B?J%85f#E$r120B zIuV)L2S0wOha;JQ*P`#QSr_CQVx7?L=`KU79ysq-PJFfxU@E-?lbKY}LvlL_iy19V<& zWHqtm@V~jR*P1k9E@q2Zs}OBdcL_Z0%M33_`oVxS)RQge*6+-j-eMk4~YLQLCAx40zqsUzR-le^3k(C=S!H`fh+(#AMfND8_(=%rS z8m%9{%LD$PeY?g#Ug-zcSAnd@z<}1|@8&XlNU>n&uQoweCv9$k;v?|KAAZHW98IW%!-cTU=yRtfs%^I=Wm`f zz@ZBf12hM=v#&U~Jz7DY>u3#s(R1@RX8HzVBuOPMmT`7yAjj(PDdU`iD zfdtp!>vo6I!Ia{bh%G$ea%iS^CO_+730{Mks`N{*U)W4z`IJ9x85kU2|EH5!iEau& zgdPF;6Cehfn>_rEl9_|T$SUF#QrKI+iTJS0bA$J_&AQ8lv@D`4F+eY{S#xEc9l> z4E4b4HxWZ{T9N0oOc@+Qwp1#u>=NXCuDx2rKY;8Szy5txTy3BYAFL&&y;2Ml8%&&At-btGM#{C_NrFf*IPiJ>bQQ-`~F}LDpEk*T^&YSJ;W9NNmJRk4F$%R^~eD7vjZ9 z&S~KX_uNO>R)f}cIF3W;O@-Z!UPQFXY$}>U+qiP?eKDhBct&mlfVNo?$7~4Y( zHctRiAC=%dICVIjFgHNZu3a=EC0;H;7YKhZFdxD=9%QHWC>W>U`fS3*pV7nM%*laGY! z#x^u7*VrGpsek3KU}9iac(rhiiuv_P5Sl(0qQ%*hp;~1bpW$=Z=oF{b0cq^&|BnA3 zES~M3NuP7g?elLIJzd|d&W@V8pkn(AE(?LN3!7}ld#Em_A=8+z&{Y32PVxLUoidm~ zDVD%r6#YK%pQ025jI_v49SJP_DupBlH}#UAIa|=pp_=m8ZR0Qg^SVU&3O;ldB*?i> z!#s{ywNJ=b@nmzMbJ#it7Klj7=$duN?h;~kbwJrEhAuI=3nvyL1boM%F3CAgqEoeI61?le;9x0$4ody__nKB$;NlL^M6Qc zD>?W353_b3q+}CO<)9*`em-b4XW#z_T&#BFefe2?sgHEXli$*xjGH*h(1k@m1{L*r z5YTa#0Cz*!i7ikv9tvqzU}g9mtiMB%LPo@oE2mfA$w;QPN3`38oWurDSL>&8I(e#% zZh;hV330uBb#rG-P?WDJd(-RB7iqw>UU#8(M_*R&w^s<6pIP4dUPV?@De!=No7{R3 zIz$ykdiTO3?n$o378#gW?8JS=!MbzhOP=gEYbCWX z_EM?O6tW-aMdF}EIj?!kF-n+^d;5*#Hm1=+9{k8sz>2utIs-zGAGPYa=8w13bv^f%Or479z5pE7=4kaIHt+sfcL zxa{ifC>gtkC%~i?S7QFlO+>;=rj!&hv6noX`2K6xr$e!)ee8~Kd*X;dWgPN+$o+WL zw?nMA!7XqKOp{fV!)&4y1-P7gefSLR#`} zbXWHC@V7Ut7SP7g>6oX^aYolz1rcSAv$cQU;o@%}glmsGGkcD*2+?xQl*~q@CWlEj zs5R&6?VLMK)7lF1L61Fb`8daxjoy42>`dPi>P5OZGoEB#ZzQ|ZS5b3)=g3BCpVdRN!q(&81$W!?liF`_jMd3gB(jV?2t~X#{cN}W zZwgumYJNI~_G5<4VT*VBZp*s<-D>2q%>ML0wr)bg0Q^{2Q%fW9>91`8s9a}e%?{Tl z0YL}-Uw2X&$7GQVdClF)%cuh{_NM*NmGb3Pux;_$Ij)$GD^h&x#<2eWzM9Z8zwk`_0yUn~7xm%d&jBZ`Z z#&}s!GOBD{!t3eyBrB=U-q5aMUY6xT5)pJ)Kh2$1kjf=HUU}K|e(Z54&5idUB*C4y zB5qOs({8*zzZmMo0~PfRNiUt{PTz;+X&P62PK#aV{+2!*c$Y`D$GpVC$XjOU6EH8Yr;U3k!#hM@&8s5`md%yLF?@Y9&2tF z2Tk4_STkNtog=T@DR|PwDV}|wePDUJo}gbjg!J%I`?1qREj`lTsC(#`YGb1V)-d6gY%(KlND5k za;`#;ESY|Ii-KPe>xm>*i2di~;taIk6u~lIgS=y^5wM#eBZ(M0Goi?A_(4F_bH2{_ zQsxA5d{QE-U4G`l4^Ty``^S%W0or5st4QoM@s?0ZL(VXi57kB9C4h~s!k=u?FBZuq zHTrV5411rP?M5Z3^A%$5zPBGsm>yva5MNMaqDZ?h(&TW7 zEPZ0X)3J_f%ksDWj3DFnul{_QVcd7OG(77>igniYKD&QOA#{*>#<$l*G@NkCf;)+H zarGHk2Nk=@cFn?gK*-bWh|=mf&8H~b%^w>{)^~iwes7xr)F2(95@PJ{!yu+V*p*zH z0qY2zt$l9`x1i5SPHN%pO!OKN=lBRD#}qBWFO8t914`U_^D0xSZ|qqP&D09`mW%CW z#m84C??~s7>qj0SImy^JymPec-h|Z*3C?>ickqoPZNYCd$izhsu#EZ#QmMEK`j;u+Vw46c z7Qg~s?pJ3SO6xaYmh0(vLq&WsdmeTuuHz>zpErI8wK~laIcDtCzRbiY^GE=SzPeAx zELg$b+uGE;v0%!VaFMZq+vzv|lc~j^W*)q80lezc%0hQZeqIL2QC&+&cL*`iVu=|a zB^Ay~7bIq%TtCO#nx?o7=hY9k-$gJSQ&_T!<#-^R>nrFvOyy|M$Tu=-d_G;4dLd^gQp|uL`O{9?Ffsz9f)7XR9 zc#o5PpZC9*jq9Bgd2(NdS`0fG=@H~-^2l~L6&G~2d~T*WHy;181I&PY#GN({#=r=u z&UyTNKQX(BbM6-VJ+#}Pu}Qu#+{zNH+zZsdFMKYIlR_#^jMT-5>?q=6z>am(VE+rM z)631=mceZpO$R+2&6CV3PA3Q5cFDKiGs}>>KPw`%O>__4cuyFoQ_Z%P9f&bF|Lx0w z3_BMIMm;s64!F)Zw3|UcqPV5?v8nf?dghe~nlaXiHgUlRwn;qq_udEMelnIq>g3|mZw}e(g3OA1 z@_7<^#h1VB89>2aMuE@f8m4IqjD{#Cks6P3GHNR@>aNxg1iDwoP#m+|M0W$NX8*)G z*X$=-zqF%xc5_D$D%Lt}Ri-x96!ZZLz5H2=omsyX)xb)C;a_Vi?*k3STL_@2b65tg9Nu^9e1( z79VRts(-{;OUo(XqwJ4JvT1?d6U20(9Lgg;B}&(JdY9-R2|||=m)JzD8@k?edwzi4 zxD`8y?srT`^^d@mQNhDu>ndyKiBhu-ZccX9-{-L<*59>gq6zhO&F+lsl(#<$l}tO| z(|d4lTcVxR|6`GAJPWp*Y2rXim*SAaKfVi3b~B7+nG`*(m2x*c+?M&Y|Ji4?EQc}2 zInJ8t!?0Zq{dbSJvMoa`fs1b&m9eYAejuK4Vf|dH=z?kP2gUA)A8g{x8e~DA6qrOD z8!-w&3iYzP5n5{jyo%y-Zp+NUN*zIQe6#zPLh$@;KtVI$Nff%liqxq(<~y=oZ@(4A z_99^@)4`hXtcxrns1WVK)W@YVyIdK1{+Rq@D)Y}KP`BxCeNDy|2oo&-b-ZE!#l19_ zx}q-jo2(?xq~~V4OrejX%BIN9z7bBOf@eOY;Mn!p!;Q)jt|u@prD?EkLq9CTOsN#{ ziuO@x2E$Jj`PBm&^hBk6Ndledli>Vp)~TsBqNA8}$EmnDh2rZlAP7hm95M^!5c481 zn)Do~k|_D2r#h)Cj~*=%Y6wT{-#&8F1$eYpEs2443Hn{hPJ6p@O(7vvYX_?~sI?vB0Pi+07` z;ZVC(h8fPintO+^1>O#(@)&hAGFphZI{rgeBPry{UN8DjPr2o?)^M9$tIF>zU5}gS zqYdA9_k8(hYNx`&S&DDBADKx#;*I*9S9}G>#;tsI?#3Kr;|N9A8}alTqiW-lQ$`!3 zfm@Bq&l+!*WIpQkWh~yf{$cv+_?yyYA(vMzVSc>deFgfIx_v#fo?bDDrTvmnLyb=E ztwPc6dwg|CkaU7E7V$pwQfYY9v3bGR?+$sC23o55M~{|pQQm1((aHMh36Ho^h_7SC zO>jdEp}AW!*>EFl16m*BkhIB^V22jo)?SVM6tQ=Roe8Cd|rb)llcwfn)AoXx=ri`FM1TF=$qsc=EL*D{L*}_hoGx2$NgwnNxO9~ zR$Ax+w6 zNSjvESL-{+yAzDEyW|>f(NC&Z9(Bf&uC?&;Y%yHFB0qmp+a+tf`H)$w$s*{S{4LXj z0sbz)Z_kB41e#d=b45&@ju4QxWx#s`<{``OXwoE@!NO{66oqOnZyMe=y7K7DA z%Qy>P#KR&UHAxMa7D4Eo|(8g=?8KLD;$ z$PkVV&rOfP)@bPGFwaujxPgj4oh)(v4sRL^UTwFXO99S2mP!_T|CiFj|nb=>lL_SLc$(b5^pC=dD!-Wly|ak`)^ z)_=EXjHN!s)vDHs&j;^J}mE4Y#N*pWE_#!4DbkZr(qwJV8*nAv|3l4Yh2rQror9bstS{E#z`} zI+a=9jH}kt2W(}@!CK#tJ3US6v1y0Ie~z?Tj z585{^jgO$UaDYphM+V@szK`W3g$SayS^`DWyK>4R&$>*U6IdO!GE6#U$kcigQ+OxXV8zX%Hp2jj;SlT)HI%6dFMsnnj!RPSaXSs4}c%$EvA?MVLlJ6{TR+ z-A%zhL}Py1nJXHT4Qjx1(03XXBT6fk57M#QY`En)P{zWs>Q^g>_=vGn@Ilz3n&6{z z8%HM6{9-+)-v^+0Go&`NTFgB1vVXDgz7`Fn5Snxr0ik4V`K`B3MJa8B5$(jqJiHWP z6agLF?=$U|l$9iTCKT1D^Ev{Ap{#g8az9J^t)JhdeW7SC zaxf2zBLbZ1$jKw-t<_@f)W$R^%AP?k zt99wc*8B)*V7|QJ7gT{*TTj?^qa~_FB`6gZ&ELm>CdG5dAtR@ywJSl=+$RArM6zv| zUzap8Dl3i7$T({_)RIMlG08sP{;-sQ;!w-wX9I5ZH(4_4)PzMh&HF6ZdlLSxj zOywlVHV8I|DGAoJVxQX?_e11t@UEozql+YCRQ{x1hcw|K%cEdHmVlB4m32#N4UZ$~S zK*>C&=tDu_O)!(79{1+uK(RPBkjCC;))DCi2Gh(B~&RcP%eZ-2jIR?~W+u*}NUqqk8rgVfR5*xw{dl)%HdWGT43u zOI^qJ*xH%V!~8_v@(3F{CCG@Jh!Q1}sW1-9Irr(2_(xx6JLdbHW{_sS)U2LDQ=yHm z+ejXQdBpl9UCMq-NsF%>aFuQ6@m9jMUMpod4==)2XMhP_Jg>hFvM1lF$T;eHL7xyV zYtiu|Ln8dvHe%yLngm8(XZ2`wBoXCV5G@~acEp!qU($9tvQ;K?wEK=Srz7UeEy~Fe z@z6R@2GdX8DUUMHeJr`cNv_GgrHic($-D`VZr`s;+P)u{lGXZPEuXv>KHXr3N2q>s z8A2&dE#mgjan{IUOd}cgJbyCDm;~1n`z@dkq+(040WsoR7`0LpCZA6kL`CrRlN=pi zL+(A;0<~0oG3k6FDeX<^6^mV%GhVQFZ$~y7EsDm|gJWAkr!aCZXl7(JZqLCJg(rFS ztb4}=16_OvV2&5hg7Y!j@rOD;_6LAk@ckKhYMsD5uF803uMx77c-6p4Enveb$5m1k zN5$O@>v1})#EcW74$yf<2L3XGRq5K`pA&RooyL$9KY$NsT#@pK#GkJ zw|cLjUL-u7#}(_ND)$0~+d!~?_ioBKCn%K~<$)D8;!3z-J+cif1Vp}}ub)1pF~?sv zeLf=S#ccf1nv$D``Sl4?h9`n)VvdfQ=FolZkJ!}WIFypJr9K+w_~Y-6JeN^UDU!!( ze8@!mJy^Kk29Z)xIY#JJ5D0TzzQw*%sX*9mj(g~sghcJVrb*QuF~^vUfkd2jhwg!p z$nXelPQRI|*~J){?riJ~DqpMqd`fWI53rta{Jg$nx4VnU{&l92?=}z^tsg2_RpVuV0HmPgbld+=6cMNZb!ITIXAu7+xj=!XvaUm zs>5l!%s~|!m-v12865|@XSiefktX|HuANm+qw1+&lv@;*CEpc3UHbMHbgLg~Yw2aAlIa`i$2iv2r0_40@9wC0Hl!LhpN zf(%`c`m~$84sRk{$tv|)x0~5?(;X?zwdyI}PWEfx8u=L?I-tQkMrQfFXgXLevC+Ha zPL$rz$0mT+cuACMav~Bxywj(jz<@WVt%pPXCBHoucb_LD(U<#MdhgOghWW7p3>D5p z^2>GHxpj`YBMVcY>U>JBP(JC`@B1zf3CwDSc9xsMEilXHCW$cu|V9YDLE~joMxl$DRGUE}r zg(n&d1_Q{bVo99=DegREI`((nZih0moOyD9OU|0Y7huZ1m8{MNYxRxJW(^c`j{6Xx zIwkU2-baPtjbp@f-pj(LsWQnhC=*;g1!6}WVn&Ldk3!POXrdbSE2 z6O7Z#K3gBxHz7;M9G2v(VK*gvl~y`0du5?WRm0TA7WfMhVZNEt`QNuX4_%ivy1EfYkAKz z>V~AHXT%*|vTlD02Ykq|CbyiUc>{TO4e*u?%OfNx?Oi1bXEX<^ zZ_sCK#kJR+l3AA|hGi5>pc#$49+U#VI6ywF&lPTq^b-GtI`(~}dgNP7$Mj9N(4cLh z0P{4}=@$$JR?9B#MPvWE+lYWzwl4_b>Gie8?)J3YmGhB_ZY&=0hwwrIVJRzGg^JYv z;&PQP5RVWn1E^U*BM*lAcAOt1Gd6?F<7(opXB+nPBxtrEtOj5%O8;Xr=nA^xr)lJT z2|iO2Fl+I%8{nj%g4=ot*iixg6a0+$x73cje9yL+@@828HQ3v~twW%@d1?LmGKwSl zX&b7LlI1yGNYSTQ`UUm&yYS0EXb?*r~Gx4ANbH&&cUs+0{35zr$%!TF{_SO^=aQA zH!(=Nc}wsmc+%rw6%B-xrW+zFe_ARLU{^SkHYIHN$>(>RdeK zy*smIH_GmkBGOWk2MCXC2j}fkq$#A8H1-s;{)V=_H4t#K`RWf&(1fyhLo2`YXP>RM zjQP;wh#k3Vh#x6a#rt#~7MxPS(kwTASmM4Kx1>HZ_RqcSB6G{~{5hbnz3A|(l9Wd5 z0Ya?%`GAEM8Wu3-BcAFIgDFXyecELPC2k$#e>n!d#W@5suU_MCY>Z=F<-8*jm%1jR z(Kt$^%tNO*m6e2&=S(=Z|P+XvhX zqTWA_;vSwXdm(z=`-_Y@9{FuTHW$3AjB#-oj;_}su%Fkc`7!;g>`!EM$1_U(^`D3Q zf-rP=nTMUy41_G68+;k_DaREXbM3sp4og499Fu|8;ezS&PL0j?%kGhwAtAAbCkd9H zbWcb+$JklW-k@h^km+-$c&zF_m3+T8?v2;lC7yCD>jr)P`Cw;@nj6qWZcchRVk>z$ zCLThFty)23u=jo2af~^ZJl!IF{gj7|Cw2lha}l=AtERj}8y{ZY(p*~+jp^5mImQyL z%C&1+O@A80d5z*7^>{i-)0H#1uX(LGHs-S~MoY19Ol`Uvvc@WF5Oe?u?)M5Q;7Rc6 z)OHYTw{rDOfNaE{Dmsj{-RcrwJAkups04IW$c-P?N6e56=TNT%>pC&?S7&)xIa z)v03<4=$3w^G^wKk!V|no|TJKIk~Qe@TSxSjQGV3us7xk*J1^%O4k`o`(%kY#?d_d zP-+RtTTt9?4a&LvhI!ib9X%hX{SfC_?z;jp+TI>7$LNVz;W148D+Hd)UmQqM>(3X_ zKZHwqAy?iwUyVE)#Jb(z0>!Ulb$e+XAf^XehC0H+!^bFX!;h4c`pGprAFTxX25U#} z;`>DgkC>NY^6}c76$Za0&Pg6F2yR|fNu)x@smeA!gB-DZT3WX{ms})tnf6+~9eAQpK2e z4@%umD^2crFEpdx?+Uknb6MXBwM01!0o~kyg5GS4TbTo7B=HAe4?yK&!+v^o)tXVR zG75UJ-d(J|2(muaPIb57`EE2AvL4?R({6!LIuKEd5x@!~IP-EOgSPTfBZ)a(8dS<; zU|d-1uvFP2y~_CdsZ;uWnld43bz?YI@@K#9ideSILt%X)h@v&Pd!+j}OHDj#iFQ8c z&^t1P&=TF?SNZ&$Z;X#~-5)ua>p(J&BsE5|T4d}wbwd`^5xi*WWZ4z9=JAZMQHGQW zp5j$gCZ1W{o!V77A4M=)?Lfq~D`-PEXQ3$2leCb!A0nK1N~1Ow5rZS^hO!~y>MJ*- z$v&=Fd;pUq=T$r|>WHrsq!&}6Li4zZOf|@L*7wb)qx<)L(3itGuQv{OdR|g&@itz)Aq(n9$*yn0 zlA-oxO)1^RW_h^8D|{{C=}Z{$Pr3ky!v4cK}Vh<*@mk9J2XgaLH*2AD6tga_0PKpX~q7}ZYLV1R~RD-XU zjS;{RhNiIRGGwtqtD3JQ`k3v8_BBS9I$B7WS`-a#>n-OZml|(!#%;UzUkg>nL2jn& zgf?%e98+V{%cx_0M?Q8gp63P2OzgUT4F66so|rKGdj4u=aZ(%3YXjQE7uu4zeyEBV zyIi=}i0W0EfSX)yx7X+M$1T5E%K`ySvM5&^&}C4$L@RC5 z5|*wgQA~!{=Np$wiJ2)wED?AvWg9~^23Fou0FF!$dwFjzX;x&6|GX9vp~c~b%@9TJ zSf$8yM#K)}Sg<0mC4?*jDQBW>e_v83moG_nBOjG&v|s9%(N_hNny3X~*HV5aG_{+v zO2ezaSx=n{KTb@EYrmXTH1ONkrF8a-_c>aoV~2j&UP=<`OR)`ia?uBB8<6pa8qpSf z2+ZC2=P07ZfSs-U3deM^$`f0(a{Gayh2w1!TOtk;6BGJ)7;;SZvjIrnSF7?BdmfC) zW^}}gRZR}_M7%~4lzi|ROshwnest@(8kn>(s)%&_HHw?g`a&pvI$O(;T(r41FIL^x zFoR4|yG@i>k^u1TChw-40~+2-qE4{Gy7Tra`)-5X&~vgM0!N0n2#8+@Fxx)4yUsR_ z?VIH~3J`SDZ63#Zf?|bf>by)%43KN2f8J=CExq8iz1lu69B}{26*5;jt|U^Rn{qHh z4AvwrtiP;&OD{$fB#B*ix>3*_91q2@z%DscVXq{@mJ=~d%kmPU_`s;WSM+I&an~GW zTcKvuRs(vAj<|JD+vc^>^T^w%tz7!irgRi2USQUVBdv@||vh zeew0D-><%dOga!_O-8=iA4Ktr2Lf%rM;f5MK_;*V(&vrP_C#gOk4nzk3h5akwv$BM zx_RMtT|H1+jWTW+OQFMFm}2q}fyeX;W%V^U9FDxG!lIp8Y%~RBXIz>j=2#`dDgkyS%K?XZ$^; zhw<4*xv;mh?z*R@cfEHA8oDVwHnMb%r$&?B-8JtUhp>kzR*9)w`1p_++UI@4`&ks7 zE0#uVi7hdR+T@W#;CZuU2e(%iny7dmJ>_8KWMigg)(|V8pwBm@nas6An6NYAI7~IK zPtvy;PeW4rde}LZJ*p?f6;Z`2wu7fsklT5vURM5;UrFMOlee9`F($_apo3!Efozi; z3e;n3K6Ha2ikJ50zgr6{W-Qmn>hcwjuj_?2Pp!{IEzCD>_HbZh5diAt6Jp=D4I`{a z9?ZUjd@uB!DviUf$oGpHPf?wsCb#9n*O9tvpU0`{Btn^FKyOG+ls8MFrSb+(&Hd9c zEcHrPJW_gZ@qNpiFFnq*?ET{qXyr-vNTw2_B-9Y?j=JXrA6wA71H+ybn#WlYS|Aq^ ztD;6z5~-L+c{JzZBbnIux#D$an!oL7wcBac!x)c2N>;`8hOr6Np&l1p+&@-=d_qIoTKzBCY_V_jqC1%Tl zbiiD0ogdFVM$m)C--rR#$uxzBte!{DS(yYDYRLQw)MszaMzML)kT8`5v!9l$!KjKf z5{(|-8Or*eqG zve0d~a97g&4<21EgZxs3MCCL`_$KVwu}|d7(}eLQe~G?^u}0THd*9Vq|0MG%|0TiA z8|xhrptmn7{e2~?B%wbSt1s&IU6+S?&M8y5y~m)$Zg)EbUmSn8GFPS%b$CVo@@@Is zB2zf^SP2$OVU4DoQb2iB$}UUxj#T!cZPo07b|xY?jTgcm96*qXsHg~gKY*}aJi>~f zNx1rrtfw=fqsmyKzYar?k$(?e5>+>|q}f&gO;Xr1avFs@nh~9>JEm_)8(XaIl@0yn zGN8E%evNg)+SgTtEYR$b=-L#~QsICRxbx=c&i#uW_bJI zT!{ z+@uH8pHIGN22{WY)qdmXG(1CkYhJU8b-8Fqd;IwYV~Q&oZsEk!E_8Y%&TVJC>npN5 z984n#&oTfm_%T~jGJm;$DWg4OK*4rMM&b!bxu15(MuH+T5-A`>xX<%Sv+oR*;cWz4 zi|Ns#NY#6-4M^CE9DWe#(~W&P32%aC0^SHOk|W|9Oex$nRzHlv2o}K|9-Tf{OmlHr z4T&$YCx1qVh!+@AWFpad8{$BCnvs`~7xg$`tS*;yCRzMNGU2b3Tlf(J>g9y_3H>6K zcD^IuI>GcmY@V`I+x8sl`z_X=I^P}x!?rTc;uhbqc;!|Cs+blq<8RB3A{GTdI6F)C zDyAHajbCROW_4%drVvBPPyz@|iq8j=$TiVdDU9NgybF%V}6PwHk?)ZE7GsyTn6}JRV-;d#*e< zR3H&w`Rq5iPM=6o;;dDgd44af?Q6oLLSR(4gb#&UwKaHy7ev4?D|^p0tP3hG0`}?m z4i9Scgk)MN7DkC>2)hP$g+v+!aepR(d^W~$DW8H)ZG4z^-xQn0a?;M%D{3qi46NvC zK4oBV-X!!}`%3DPGSh4v$YOC4oya~NKe&`eF`>Wd;5U5iDxN#pTjURWLUY8qTLA}Q z5bb*PW#>xCUh^1?M6ia&umTyN!|_pW_P!(4;^0Zmys^sFWQN~*}ZCuUt+ z3D4G1Hdt^gGiEcYH*3d!^Z?7)m2!t^ynOA;DeG)BCXU!lnL`UM))^e2T&KStF_rp; zj+J>MLRC6PcQBtWhi`{qo)cg~#6?@=#Sj-d)4vQcmJPtp*ZHXn(j+9)rAB#&e`%xG zao2F2SGMCxbd_9HWnuKBe+yrB6|_ z9z5sC5C;gV-6aItj0K^FodsPWZu8K$rT7i5Ba9Zy1Fqw(rrZ4xS3sbpe=dX8Q?tP> z>%7<}N3=oFlt_O?lP7#7v6UZ{BX6knAZyfhUSYUlG$eKE6eQI{R`Vj9jli<+0aN1nHJ&=m=TR{ol0fo)UZL zW_F~8e4HqtI-wLY9~TY#>Z?v^@P;K!8V#wn=aKmSkNL9A5^ldQ-5O$>wL;`k0HcC> zqR^XCca*GD@{V*PP;+X+Rr+__#-=gsql!jd+gk>{5E0aHw%oc!1q+!+lc9avw2=bZ zD94e7NGu`NB8ey5LW;4({cH*^srgWy7$sBCIOFKW zGBbp2+-m&Ewyq3(5=qVNBkXD>^ZkU$UYaeX#Lejm^;LZTXrFaL>W2nMV4D6|0>zcv zdy7h=UGJF6mz?Wr)$)$Ej{wf~1W+U6p9?X2Jtw>FFqB(Lp; z@uM`&V>#t{F)O2E=4GA!-evIB)$u)lhzJC$mQ^xBM*6?1Z+h<8TE<)Yd}3-W@{q0o zO?ahnJ_+Y%|5xI&1<0VYCyx=w?^7qKzXql^#ZIFr4)8lY9bnwG!5Yqdrwv_-k*?!I zz7k9(WVmEE0b6<|eQ)4q;O9|#vd9+^sdxy-9k`py5A}w7YIAE?XogGG)XsV&+Lud6 zuL|-^$zF7m(Z3#M=(5e`S9I}LG(hiEGCzKmlknULPLsg>t_qurB?$E;yf&gJgfOGx zpwQ~|A@=oEjtEua>Gm%^?|Tai0&0j-C96qfg(vp74f3_l=Fg51!9yq|DQhYVn-Qrq{MdINN_C$MZE4w(=iQ1ke0?oy399k(VJ2d z{XEe1qFaA)dKipVnf2~!7Bcm1m%N!`Jd8AH8qz4&8av)?@m5avW}{VXzkmKXS(z1% zq{0@Vd4wUgX!fyJZFS2v9AzeZx0~9v?!3BRJ^Vq+JPYY?)HF;s-6d{6=krazldWD& zBc+*z6x8i@+s^*JKkMqt!drP))g5o?ec>5?!YSuf* zaWsPYOXIK9RpfmlFD?j!ITz9hq?dQ8mt>i{fQ&CS#7bAZUrXqj3s0Fhn63f zu!H*(*@4AppF3XbO8p7j(|)f{&jUDWY(M&bin=+aZm&|fxR&(kj+7CB#hI@< zOozYwItvlr4s4d(l#e@d*M1I0XhY`p3NsqTB7JqhgMI}Y= zFPgtrwhNGi;9zy%$J!eb+Dk1Fn~{~GPEe04X~x18HkT+YQh4uc0^WH1MsBa1o_%{k zl~iicXlB3F!fv$dg8fkFY zu2(ojy2XF_W?cNNW$m78B!6{}GY9&hbJV+yGmkb67ZGj!6s*Fg8hljYa+}hOz`2|eJd=N`f&JWD#_*D3F^Shk6^`A}QcwSDH){Po~o;z zLpx@zsS*H6n#0$0nTwp1cYWr!_O#<-z@*o~_r82A0K>GKk!`%oOPzAkpV@DS6gUkf z097gzb%IRbDZ>S$Nmlr?bLrta}rh3?I!R zH=!tnPfQbv`t=8=A?b$u{k5Mr2lX>%Af6icTf^Ut$TT&*V`}IsPDp$eLggWq)uiB$pDim2euZ+xO0aobf&)9f)<;{3A?o-x%SA61`+fXee7(tpL-Vr_ zcfO2iXOJR%uc%n;QOU?G4_#=4#@lnqH`Xp<20cY;9eSi!y_{_pyyD+|)An7`&`ML_ z=^83F1BAsK$6x5nr|9Frva?A6jnoO-~tHLZrV^OSfuq3 zemUMBR!koGpag{tbd8TWP)#16;dP_P;3PzY9LE$q;85I%Bos!W{PK)!w}zUl)N3a=3+lfv%2 zkItQNIe2Z9i~p8(Pu;wIgE*4a&C@})380$Fh5LPeKVPu5KneCm>efia2UwHdsgH7^ z-NFlP<3bVhB*o4t0x;D9&nvXQ)z|4E{oU1YqctoD=_%SN<|*zeZMaIsa(Dj-%jeeM^qFs$ z2o-atl|75h*MG3Tj}*M6p-a$+^3s}>iUVtbHw3!K``dU~Z?*(!Y^Bc-b1c=eO7X1H zNCicO`g~bEH=+`87QIRQyS4}3)9QK%ziR1+*SyV0YEMQ~)C|8yIF;xblar_#@$?F? z9=Fk^ZNB~iuY?@~YHD@$0TE$HEvW)?a4jJ5WV!3B<9ZlKwQkGBT<5z7K;si^DEJ7* zzd~U+31J&%e#zmyvhFCQ9KKiaDV@k1eiuq4Nq6Kkf>r@(PaH%|FfHqLh1^{ z9n(9aam7^yp|aOzG&1Y5M1`L$wHDpo0mmOq01uNZt2g%x6(uW?+{w@!`v^Ni(e_p# zGf0E!6;$TqjHOoX2Q!KqIkY3+#zAbo9YnFtm?ERbNd7*oE<-&csd(PCVr=zd3oosr z`l$wu%2#k6hMc^zKjBJ$Ea7z=)HbacICB>Bt5`7|xj$p)9%|hIY7*~GsZ#E{5x&yC zz+7imnuJ52(X+_+em^m7iHtC}Lh5dhV5^zG~}PR8z%%#{-| zhGFkWUuK`9nMT;flc4vj`1=Y_jzhhariJi2;(#!0;Ll0}87F=XQnzd({+qN4FSb@<=Lc@^>@5kb;#&z1X|$n`rLCl zZ;)ZhUkf1sx%v(#CQE)D?v>=9xCtrwdaBJi#=V<1qOh$o)Lqio<){6Z zqJL@GP-!20{Kq+(_cLGd_fS!M!KP4DQsaAJ09_?QJJne0;KNM>CH^wT-kE#J<6)q? z2QjH|y5%_ZbBobj!|Kh^m|6FomxZ=+xh}!jl;NHun-FxT4Z+IGXCCj~J1cZ_DW!ig z>}Ide9sdRO&5(k2OOiTNt#;ra)w{+v0czEC-xp@k*5^5w7r0w*%wkP+^bSpl{UUE) zd#w3{gh8hxPTf|ESH0xWp<(sr*;-!o6I$>lCUW?o3gC&bQGg-ImxU9dzoSrTpHDL2 z#UhF>g~>f6k*{R#HOZ0(?kbcDf|D{t@@-1|F|MrhnR~Bc7Y+cS>U^uU_U9NcTF5`- zGv4MB5zYCp5b(}H@^k4(B1m#A|HIY`#3w@o$V-%HAr<;WNY4WU#}i11z60zc*Ns(m z;T5+rA1U#preD68$zA7fG~v(_o@D^yD7>7Hu+k2+w=&AmNfCfQC|v?rtaALT-NLLs z$@cqjtG+S8qi@g7{=u^NCyW;U*+F@V9T6EG00K2aAD|gxuJr@92_Yxxn1DYH$9oOb z{~#~MoO1!Z9troF`S-8#UtB-2?Hf-LxC1}Oq;+QS_3#A;*&-G05!u16l*QM_%Ha3zg7lx zD1X0AHx^=-WIAz=oL4qradPmsbPjHKP4 zv;TRM|I##?G(ojPhTmX-LbVAF+Ex>sdE)?Ng}4K>X8x~#0Ir1W0j!yIxB@S5g>RUH z5gTX@tH3~_4xEn3e^SQ(^_FFg=#T)O!A%G9|ClKJ_se2|9RM*0UK-bg`(8O6AE`AV z&;{Z0fovc5U+(X}zxls4-M{^U2#{*d>HfWw|MuNKjUfK7_qc)kquiR&xl{hz8~ul0 zfKS2x|DW<78VQ&H|LnW}hgvL%7gffS0S1_UG{gCg;^Jin|D@E(#do}V$+-j2(cvwJ|ltj2*5{H z0H5$BU*xZUjT!$=KWs{re^8Kcvu_XQD2X5Esu;@wddz=yh30ekr}ewB>JdE(6Ma6& z`bQ6W69R89<%QrmnUMh8GcL1JS+0i*aIopI^E}(SG; z1}Fhnu=z*uxh~)w5;*jepLF)^GbJOS{?r5Dvw2xv0(dz$I0JHLPBSD|#t1mvcZM^G zf6N1v)ZcJU?AUvqcVDQ20MYG?9~`a-5DCM4c2eIxb2P+(Kh_-XJ~C%nF$mARc^x*~ z2FRGWlf+U_l-VLD1TLu!S+6r|xa-bC1f|a{9@KziYcpPn>C<6@R*5IcsB{yYVD-j*Q z@^}PxD1hTnSLS5SKNBIX-OM&F1L}S9eVG4AUbHu55a46d>jy-)6pk;A5jCr~$}G9l zYJ7uFH!guVu>xlpNzK`cpCJ6{B7jSZSAa&*^%@5t#S(xON&XLj@;Pi@H!76cScN7Y z(g7#0c@8qAhsG368v^)LaaZFHB`zoc@EijmzFmfSD8*6i>;g;RP*oxPdN>h*l*S&Q zH{x0tOlN~W`XkD*rC7jsI`7s{)*j)M&+^;PW5h}YAMVzGkaXk#&=w~W6B(8M+QNa$ z-n;^B(>aZaLhk}D{vbOMnRfKv07i%&Nc>T12OSD$j9td*taq*7j}fxzFYAI-hY%!zyUg(n@CT z!sYTR|Mp*)XCP+QZ%kM4Jl>sDw`UNHCr%2W)XNsf! zG(IDt6Me0K>`h#4Z~d_7ffT|w$fVT!_VE?~yYB$BOMcy-9%%NCMi`A9+k?p~l%jx> zgo!J%!H@CJZczV)T5~?13%ihs@3!xL&DWUzLIWUWa|Bu5ukF6u%xni^aj^#SKU*o| zu^gki1_lOp7hvhYs+KLpsrB*wet;W7y4B=$_7!hGmFb_GpISCIc!DoQP|wucfh_Wj znE^LIJ#ClDVUhi&K&f{0V+)tj88D(A0NOl4fOuF-aG zEOvcXe0RmG&;X+U|Dnt}~X@vjv2Owm}T_?lU{!iB- z1i>&T5UEyc8KEG$Km?@!?T2(M0(A#3RmFe04gqO{n3d2u4Q7#_L#iN6E8S+=kIQD* z=KGoU{f87+uAXWD_E-!705H4L`*T&?xnl0mdVX>cL(c#-$xyBN@JLgG?VLJbef8$q zSPQ;}OJ^|Z32-oz_4;77wdKv1S0(rwb#HQhp8fi1zSibfiv68K7a!EpadUoPK3Sx& zE1>t?hKWbb^&Pctx9E!`@sG(ol6ki_Gf9Ffc>I?6#6bJ6El3fHn`gA zQ{u8SGzCD2pA4g8K$X@3e}IoOeHb+9jxFnaaf=B@QwTPqb%_csOcIyEw{C)xqruU8WlVn|9P zw%`;!t{>~cq4x$uQ!jHuLl^KFVZh2W3~ZY@;h_Fc_Dc=Cw?*$D^FC2^u26Na<6ctp zhr8=q_XBO~nXjpC`_syB8#1mhnZn0i@ zf3}i+G)piUP{qI7pZ=08kZ$mexP9~KC=VsUfL6!wS_)P2Q|~|&hQ!EN3PBZkHv{K| zEz{-ixUK&{eAOX68@B>%0aii}K+xiI-jvsEu*(Ft%feFyGRVIs3PW;$M9r%jlip9z zjdz(o%1ZpdcU*JMFz6qC=HUZxEIGpJ51&S9lldG6bXm1-Hs+f$bhEi9x{ z!VC2a`(Liz{ZK@6-4x#US(8P6!s=*q!m=ux0OG!_dJ!9uE1SeUi6pP-O%8*qf61T?wuh zvYq81f9_omtk&GS%n``ABGUDMcx|<{C}0h+&=C6tOej_D*7xlGy-|a#?4WIBc<)It zjT$2{6y&<&Nx%XJM}cl;i39p^ATxbDDqX}kpLrs}rW=@nK(3;2*Rj$90$>YH<0C+n0FJ7$5Ht2a> z7EweT=o9{W3n!{7jj%1t2;hyuh(*dJpfuAS)jZ!Gt;3}jfB4-MLc@1nOXY7thi-U_Mg>`R9>3=+)OP?VNE#wC z+7IK9=w5z=c|ng%47?&8OEqT6O=tMZAhRL z9<$qeS0UYC{-x%Jf$5=&x0=-2*oT?j(|Z6jqyT&m_(UgPuVpx5Cp8*3@(7BM&^-xWnE7Ii;Yk#eBMpgUrD|VbK(O2 z-QyrtZ_P~+A)64sn_w7Ii`?bk6|iE-$uTtl!$e(`zZIHnCYOksqoth)prE?%9hy&u z*pq3B@eh0qB+HeZ=Od*t5!;qtb!>Z`u01|@o>E@`3{CpYSTxs!ZvnIbNY1xz{8yk$ z+(w>ZD(R| zgVIs=`7(|Cfh#NCeC|NH=)RLRS(1 zNgjj62|!C__yK57qb^AyqN+gtw_j?cfYYs7^?{eV-ciZ!;VQb)mP0L(9-sElKS_-Z z=$&SLMV^moO&5Vjqw9$=9?Pd@s$WEk=^4jzi+9oRq3Y8aekEQdpUnr)DIgj#4=Ym# z6Pdx!t#<)h8*|+n`T6djOP9(k;{MU6j`#z`f$8mRUaEO=snvn2_^tq7RPoG$td-zR zJAbk}g3omhII8>78Y6(tMb*HOd1CzIst(l}Pi5RnvqnJCy` zsAdAoA*WNXnn#V2eFU!ho4mR4XS7r((F`4)`damnu$c=Kd&|kA>bK?EOjPoX3+ptXVp7-6WGe}B)59-I;(Gh_4e$8pDQIk|9(?gC{&i4q)hzND z_@YE5b;RfPWmz!X)onE8=x3=~UG~d+Tdkp2z|ar5OssirFN!m%%s*H1lC5L;NgkYxz%Kr5S8KeVJ|l$Wkz_ zH<8T@_^&HFD=w9qADbL8#anbWVU29}dVqT^RmB6{olCz)uD!LHi+Pn6-2)8bglI_? zhpA7>Q$P^;lv%e{20UcAT>3O=!`IOyVR$4sfJcIHV-=kaa%0OpP7i?A5#<`;Gqeo; z+sNeWw*uz8p(aYe&hV3JCf{y=VrvbZ_cgH6_K>U5?+m3T19#rsa`()Y`xp40s)ePW zQUgg(>L(@WTl<5zvxRmN5X__woiTwZuv3>s5}6C?!GNuhpubWrU+g;p;Im7<2~00# zmoDpb-=^<9D!I3KfYP}Q4YGtC9WD;|L(aXrzL0RQQTq2Pr$TugF3as8?~DC=F-)6? z1;@P^7lTo; zlOFW48UansWD111P1SLhF}I0L30rA+kR6+D<^AIGk9A%V?yQGzsi$i_Q6MvNVa9Io zyA@bw`Fpo0G&l;2QcC)Ez10LbesL1BYYX5+iZMv}PaeG$-)#NR;_=7f@8Z3zdV&@t zfnj#@+lKGXZ-CIF1Gg9_{_l>dLieXz?Wf_qzkxR^LiISwkEXRp*|Y8UDOW*0i{&$C z*FeBb=H>2KuC2w#EPl&)fYA2HvviI&XfyM*DGJQiUeGg{{Nf_hO(R}755@a@pNWuJ z^<$ggZ5AaaAN8ldgM2MBc0NJ6Ub=KVBYdZYQ*UI+r?`s)(4?V1D=h#u5Ljb#&JO0reQCwU>4~8=)J4ae>8j>g6%zN+g!ywkRp=#l(>!ge z{Hd00o1sm2wSz6E0}{?O1Qx*tK(2t-U7J!0SkN;i(U0>febkM8IaRXlnMt2Ng0p2~ zlI_XU$A#!Nx|r5SG9CErr1l0~42uK4M)NW$%}NvmJS0J#+YT?a4fIQt`!Y*E+J zk$fpbAlYJI4LnGzUK5pY*k_V3sXvU@JH_i%8MIbt^uZVStU}e}AAN)|m8?LJZ89Ll z34!bsM$ja=AIxS3G<*-5slZy?J~I+>1GEy>nAyPLLUq zdxtbd#G7I8|FHMo;aK-?{4gS;$SO%BvLXuEq+w)bMPw8*vbhjhS3|NI_LiBlLiQ>l z+hxy`y)%>jJ3orpalX&Qo_`ty{Tf2H}HY#F1I@pRla>>S~&8ir;&L9qCeNWM?fZBM{=vHEM2F9>Flpf_Cg zt;muam3ahaOB)GeS4A#ulveU(Z2O@~W^kG+QYy1m8z6&y?sY=Ev|zmm7@Hs#=_ijc z4NNXM`3kzPS}s+c^C-9HH0klpS+<*Kt&WdxByt6K4{6LpmRdWm?+1$B0(0}lU(}C2 zxrFu|k?I<&zMC%zYrgY+wMr$b+g=;>O+L~?)b*#8-&`lM>r0#g}z}wr5k6upVe$!!7l-8&7;=;r4+9^iI%a+3Ml*R9j5%UPWzNbSa zq+KYM-t?D6ErwULE?;f5dbHLJd)l+5OCNz2)V1xds!`9*3(k>0`r4A>#n^;CbV}1e za^rTfvea4>$mx%KP4&ZSXLGST;qQSNk6Ft1a1G24Mp1UxjtVs4sd{l!wIgoHZMKt| z3TF@CIOh&yV+N0-RMQ*$-rOwTD+t+1`9hub7kdHjk))ewZR-AJL}gBaY~p&>8`A`? zAZaa+pRw(%bQV$7ygqg-PHo#Mq?FNM7Cu1xWdoXGg-{C&TGnNJ`QgLoj9wi}|KSGq zaF0-zb;&&Bh=|l1hrS!qp$pSJxghC$KZ2WHc%C^%w-?6qc|)Xd6>75^chO??+jLKO z8%#r}I^wkW)$d8qC%p4!nOw@&WU$LrP;As*xZ_Tdoc*L$dY53J0jrDObA{l9$Wo`c z`=2hwH6HiTE!vKn8rdfahECo%SJ;Ye8@OL;z4-2O*jKU0gG1?V8?Srv0@#*y#8`9& zjK$1Eu5&$Z4DX}U)}13~+007;NaL9l-5LI)Q6SrG1E6LIiH>NdUrdu^%jU*fxXDsB zV<)FhP2*PqXQ}RHqD|A|t;Au2T4ees5x6~W2*>mK~MXPZ=S8wG?C5@^_yfk~Tnc9C# zCBU>b$jG{i(B*idAUatkDXdu3Y4Nm*ILp2xi#g}k1T#9E*9AMLG5vURH;JtK3MiMC z$c9kA48_`)Y+an{OiQk($bsHvkrVh-ysyAczDDSw54=IM>{0*2r8b9Vx7`w66_K>L z2ydQ2>^vPF@`Km0Hr5hb#;!z*^~nh*w-}lvh;V>O{}hINf($~;Tp0lt&t?& zF}E**qCe5Uo|Y&WlU~ybdTzE>yC~?n)jMPpb<-=nTB4EZQ)1JA!F3%JmJ$2GOsj9+ zCez(-V;$-$xnR=#k$7G>@A})@2TQ}3^vIh`SfF=0nXdp@fNyG6duupE8{pLpd~Cl99bQlHTl*S1BFYRR25EB-K2 zUD7>`SvtsNJvDcyKHA-1jv0GL3&8Jm$0)@_CP2kZqR@@+?%hY+xVY~ztm zIbf(vH_y>{N!^%iPaP4|fQ(Bi0>u$NHcl$?8)!-ZA8w>=`>Z1$1dL zU`kfkbv8;Dq4R@&<%hTRhVLH`8j4G2mVGVVD(N!o!M3ZYUG~tmeja)8TJFkDjQ)0t zd-!hkb0%i#M6~4(D9k96?ZL)RLu?bjN8oxgsfRH1rIt^NhFp+sRjDW*?aftaNfUn6 z#gD?<4X2wdiP(Dez%)Q4YzmF1 zYP$Ps7@zq`cD0nJCM}5-2AW&>O(q}0c}*C!3s8@{s$}eO5JSB}EAur+r|8a`&=S?8 zu2&jahLs$5keJ5*%mS`>*?lB1r+a=}<|Ez?4B2~(Qn+FPz$*@+;X8}5j6rlxW5=_q zJz5Ty*7V8>g`MMy*7vO%h!%g*wp`?@wn4#?Z~>sI)bFy^qYaTZ;FpB+6$3PV4NjEs ztFeIC@qSrkb}4=)=%kTx11&83IeK%E#UXh6WXlWXaOZao^i_65Tm}^^iBKyd05tQv z+EX;L9=Kp=?tMbQDL=8&3$9-~wv8-XKevKZ>sI}EfV}bh2QuPP-*rGZGq%yZx$xlP zlQ4UMi9Y%6Gx3o=0F?8!h(DV6a3QK|Wnr`yKq>Q+KCA1)VlJzmpR~_GZb>88B692) z^K@%QV`x*$5)wZ?h-lB9I&f5=NwwSVghM9g?e-d`Xm6~CwhVPfm(s0^v*d-V#8$@` zd9Fr_QN6wRh@_4Z`}eaG z7$9Kc9z{C)N#Acmfmxj6n^bKZxzK(Ix&xkBZO`u5Pb<_a@?N?Fz%;9cIDWP9{k{dp zYpK%)r)eT|w}C1Bg~Q*}i!Ihq(r|UW)U|%E<5xYOZY~9R&B$J$XxjiP!?JI{VxJQN znskfila^Mu8czP4Gac}tm58hl-*pHRTkI7JzRb?H%5(=3zA^xOTyVDkQeU7DX#cf% zIjq6)?x8?-b;Pd?Z}?)+VzV7snz`PQKlOC{z|+b_R8%k&vdMG$FSS^TVECR)$1zR<>cmV+ykTA?gnAzQxuvA zOs{gx(Fipn>>nYXW62d2$_oU1lUvV)rn5ewIw3=$GTLfHZ>YJ0YZwU`eG4vf4{p5p zbTFWZRgl0R(4u#zPMr#e_${1T6VM7yC!VW(`}(hRu?`=qY|Naq@m)R0!e234MS?7! zulHtyWkz^rx1s3dfH5ourgHm#OcT)?;DkP+@tAt>k@KJPRGN5@Crf2&bY%R8?3egE z!X|7jD~TGyUt{y#mS6`Zp`&b%DT zeXFkVrBa-iXfZv@ENqX$Ly#ERZ=+UC^YgWF6>qG4o3_v%fKEvJZbQ9XXBl+MnbJxy8nwtR9+)DR^zhjiV190&H(IXQKCE=CHj{s zI6?Kjr+1N2fZd{Yidb58^Lz z!9*}ampR$b^CE_IhcU+v?Y<8-?)wv@t*dq+`>WY;&QskW8jA@gUAg#=$>dDR8@N_M zintfC&{l*~eTC|WIIi8`hLFcJStBdatS3hvIWUn_xN5qNHo_;BhMb#3lE~$p6rEzt zl8tZIAU$yP-iJ;-rxmuB1}|?2M1C(*D>#^aD$*FK3)P=*&$HGSL_qQd z-kKo!qGK$pM@d8%6*0mK-=?}4qn+m;BaMia3+>GXXYvN)As2kcr29^mAtY=Oq6MTb zrMTsTUpC@sywQ8;3{iF_YFt?m@bGw*B!y;RQzUEU+sGYBR$J^HX^iGV&bKqd&>cly zOy!}G@XlC1^(_O)P>%Lu`EA++$bVU6r^C&rbv%_190nOmrkad+Ko3$Xy%wvA5%p#!!&$mr+bk6bYhD7 zh^ExM@|7&R3-uB0_-{8~+g(mAydC)+0{YXOnvJ4>-BUE2a=oaeH86NvRBJM1HSVi0 zYV5{DsOB-l^EI8v(l=X zR-CX`EET`sF#X{NT`gfG<057!QZ6L|Uw1CmJ}dy+$IsM2wkP@q(~Ege`K`Yo;| zUF2PR||1&!$tBAKU(iJa$GRI_BnUJ;5d zNLWLzv;1=!_kPqw5w^UR& z>3ziFVY6KZgfgMAc2zr*Mcx;WkQ)Fj1$K3SYx)tK0R}hdEY_RB=T{;`>`a#Q<|MPl zVh!LhjG#as&CsyWK|Wv_IRef0yab3HFiH%N-+W8cZnEE}D~WyOC8J|%l0*knKQNX) z0+s2Q^^v~bbxoSlu9{~^i{#^9?e9}j(R7$x^XUcIgv;`0Hy|mwoY-JmzTmZU>=-b@ z(ttS6c?jB0_#wKQgmw``2TxvtR?$kfc|aWscY2h%)u~Qw3*@MHx#oQ9Q`}~q86Sa# z9c<++S_!pt2~t#@Fs#P8TS zv4Q=tnWcJ`AfG?wk!z{^)-v&xE{o~8w~lK-$719$YCtWV22qi4x#kWKPU;|@L}{@Q zmrLT3vWj6#VhQ)_Z~)1q6Gs8By7%U&>cmjIzXCU-rrqK@*w^*6wn9SRz|B7!XW%V6 z0Q}}aXrkw4cqz?~9;0+P?9s!0RAC*AegYQ~*f=mK2S>(f~Q)R>|zmEMI* ze7l4wN6MxGpZ13l;ak03I@vSvac5Bv?zdpyo)CPkmdYK+6zkuPNmanl6u$5-AG@Kh zHJ0b)*c>Xp3X+xPl3D_h$%J!pPj2;(#`y)9iq?+|LpU7_6>ViAQW{d+{N~-Qt;g-! z4@Fj2n#Ay~MO3QNR1}R2#4@TX0D0W{2x0vja1xCql_yybM<$N~&oCIyk%j>8_S)&O z$dLUR$oAWQ)_5{23soL-mxeMzo`uMU>?Au%q#`&L?g zlG#bGk*fT0`NcaJZ=R>`>wIFTjk-&c%x&!Vz6Vl8{`ttXB;^Rl99wxgA;Y` zlO5^&ju@fH9PEjYMzpl4=793n-)}$ArWkzxfY`Moyv8>^O>#UIqIuEIw6xA57ecRq zU(gji72F|L-3tL(b=W;C^ekptb*tK! zVC(6k$xkv)qeaLg`V zYtm%S?sC!#&R9nDbgZK@aDRIjnuW|!+N$M_c9=Y&u*xtY{*#if>vK|lQ=ABk;gV4| z=Co$N!R`r@dC)U`%Va}-(#+mEQPY0%c(+dClCM&nqy*|jhIYieBNm5~R9Yk-8 zG)sM=rzNCvCkXUJa~4AKL-Z%lPZ8X>eyjl6&oeYI_Axa0IzAg)9fDqJhJG)ynoeBj zEfV!;d0}o?^q3@R`hE_vO1Upo5jy1-a=Chxqn1oZ=T2ILL5+u0-5jc7oY!I54U$2G zYj*c}>T_sD08_qv#KRZtMiGust_Vyvy`mM)9I=j&i%q6PQejr$OCaH#X7)@KsZf8q zJ+1awr4VV)DM3`~ZL|;*c%|@C^nEq8NFz*__RMR0y`H=ymDEa8Gg+1qMh*El2a=Z8 zYIQgDzM1p_$)Zg1v141!8y<%82Q8H?V;=8-=9E`uFS%OzA&b3%=d#>rc0CJinVN_C zPq*u%8olUySn0)@(niftiazMg#^qYynMGI=UCGN~Yb z0;@ramaW4{6Q!-($W^ewzj_o{qo1Qnc}#r@*{P_Eh92MbkK+le6g6B&dWKfY_ki3Qhr_;nd~J-WV9&K<66AeRhiExf@G}&Sr`ie97G)YWMvc9kxm%2BQ0SYg^BlD3 z;7{)6Z)r>CvBNU$>!yBVHg8vPQ2j%^kvfv;&eA>hC`rX6i{9OR3+GlO*8#)ut%Bic$uW)Y?>!`;A7EfGlKOTSd5!yIY6hB_x9i2A>(DUu=eT$`Xq4d;_ z!0i8KUiVtz`cy9mYlAUyKCsQ}QRp++18O#@jKgtJha#CG(-e~?Wplv0!1Nt8_xG*^ zx-nD3!uLAsNDSZ1++e=|(7`7)ZZMPz5zSU7yA5lBD6?7Yu{4(+#RkD3jpBVV>p(2i%0$H6Q>nHZ5v?vSH zelQGfp^o5?*2y*L>ux_4*+pE>I2lV}Wf6oDT?J^Wy*#dbmJSG@y7Y75Z@#yjz=P2* zQ^$6xw0tEIy)UJ4p~=W_Y~hZ#DaL3hQ8W8_B5m%fGp|v$S@Vr*A^Wp8z$@>aML^Yl zsZz{fk3_V&9+eBTr&yx`mHBsD$;X}Agz4RnO@kr9FAY?Px>qiAo;>80sk7Zym~==7 z9ZDt5+O`>3 z)ZN}#UAhIe0p6d6S6tHU;+1@|;U4aEJqZ6X$Mf zNbST2DdN`Ip%M;QJt*a3p(h?Zg{W`-K!TCUbW$%+7*ehB?N6gE5+c7dH&7949wOv< z$ugEbc{nAjft(h5T8_zFa!t|!>m2C&FkkQ1>3pr8{=9}^oI!B{uc%&FaiR-L91DG# zu+wB%dALgf|7F}w@7DuEB0QW|=MC>dqNo0~!tydylbm?aCQP(Y+$lWg<)oEb*DXOa zfqJu&+cT*9_Ix1TPJGR3fz4%?h@>*jZoO)xWcr%l{%l_9ig!bLKc~)oy4`2!0Lm%U zQ?&bX+(ez>>)dR;=?qtR89@}!GQS-E;Hd`lHyS`Ay_V9@lAfpU+= z5t6wmrQAuDz9KFBV{_z3NpW(9xlQ9*K#*G(Da=2aqWW6x)MN(kZXn`yQM~7KHeL4Z zwv#O*fo#5GH2owZvt@GcyoxwNHRFPyPTni??-4=f0M4U9irh}T5F)EONiO0 zk)e)^NgZ=(-EW9d(2bl~2~^i1TRWpdNeM1Jxh5Z^?NS-W zNqmIYGt@~VdQXz}39#DuAC<6|n3nF4Lb8mWzA^0uoY+L1M! zefwE{EQi`Q4l1hptq4Kex~y50=Co>J{>ZcMJHsb#O<=4oDeP{3SG$1M?C`XJ0$;`n z?OwSxlz0WynaH0melr7orJsZMC}6@X{QZ&<)uQQmdun*LMPIj^)CP2h$fqp}+f(`Cg@z;+%oAw2UWk>k@x+bh|$6$FO^LagkGs9iW`0hHDI z)^*UOBwT?u(!>#PIUaR=6LU zi@Wf%vqYkvJEk)l#^x4cySgllgj-fx>e23O4uS{bsPfiqlNRNXaR`F~fe!xh*yb$$ z3dB|_9xb##21hph9f$qMKr@j4{O`5LA#ra&vdKmk9O6)1a3CMLw<~+-lejA$I!rgQ5zA%HEIHe+Kv(RI^kqvbsFoVUV~SuXhV32ee{OQ993 z96H+Bn}L&h3-MKN#*>5+X@`ZOo(4l=+OyX}ktRKjyOdY;585^HbeVUk38H zFn z0ofE9gC_V$2=L=bBE5tSD=MJApx0?K^|@K=gZ4Ej-m!ddtj$^=X24guQS;U>;E9u_ zR*+Xwn0GCb%Su*?^SHb)@j9Ka@t$lD8mStCN*t!{JnD^*X9hRBJa!iDcP1;K&J@`B zLmM)&TS*hpLUW+qwn21R>q=MFeV%NWW`#(z0|q@DcG~z)Ph8I)T!z@+#vviRx`{a8 zi*LRE-rB`${PKkWP#H2uLLFTD9b@NoI_k_y3mD3Kfd5LAF|gtgJW&6&(c7xcO8P6*H>i$M3U{gV3%r#%!1purb)hIOJ0N~n4XugnBr@gym zm9Ja6Lv17oFd)SU4j(f7j$&k*f%a~6PZ!7))HD=>Pr@e)?7O>@^|N`tBg~HL)@=)| zN~eXmomS?DnUmSm@-DJSf4CyF!B-VV>sMT)Uo^0Z@JQJC%-+5_(YG>FL z-{Sy=Q934%F5uA0Q&bT6g8SwP0md8&?v?~-HC}kP@8dZIpvSQ0mD+$RxiwqsWcBo@ z$#@l`W6(RD+wzkL|K&*KIVjDeOuas#45km`p|%Tb(i7mUvibl{7n(BW$7Ei(zQDIcF(6liE|n&C^L5VV0(m2kVHINp)NNI9CE0L>NMNF~NIjsg&AGb%i? zl3O3M&4U<5AB@eYpq<4Lg^E@SKS#Tnb=&g&$HYVoqtgTq)_N{P2=V)L3#jy6YOOgk zF&SzUMQts4lI24OaRb4Zkc@^bWd`sB_`(C)o-V+LkCo)5nh|eJtz- z1^62)rdk5ECZ+RRTQZYnZ_`E6Z1&n(9a%>C7v8bM>@lWYd@jpr{UkL}VNcQ--!y$S zAR!FMrH&<5)9m2Z^#0;}OXrM+(ykw{-h1zaXgm+;d*VT#5?{i&$p&{ahthUkfB{HN z0M)PK8@09|GeTL&X=!8lF8aoqKOR4nq{2Udny);%005*zobd)v!OT#d7KJt43o-=1 zF6^T=pKQMssd1G-^FX$Di<0?!!N>x8e@A)s(snF>`E8z9wp?jTBytFjwvoThIo; zwmuvhnU^B=3PDDCWD;OkDE>78^iu?2a&D96_<+&X+|mtsU|=w|n^brmjCHs;q3td- z;aKL5UGdz3WETz`x+p$rEA(mrj`n9G_2J7F_xhs4$Psup_I7HZG zTnEbmLwdsR2(fm(?dJ#&C5%aGWTmwJtMRcnOO7={6W#g8dBjZG$$pj`qR9_7DP^om zX93ixlWi^+47{B@Ml|ej%kG1AK@`{B7N38Cv~Mj^M%&_HIM@?vgmPIN!`O6aJA_iJ zptO?=+5wMPTCj9opy9b2om1Q;)ia)r;VNEyzPH{4voM2+dn9}?hmJ!?7lYrCqi$~0 zCSi1>wF^rFj+Y_48sk#iYNPu{5ktRnuxUJSn(}1Bn1+L9DHLF6E;{CFUKR#gUIn#p zxZQgpmq>+o13vUYK)Nxc= zK9ZcJcEQ%VhFSFeujuA@j9qnwH&wV%s7c?I$&2|ME3X)bm9Nw?ZSh%fa-Q2-&obd{ zWqS)Wd$Wm9s6EPkG;(nFIL|HvcR_ALQ5R?(dUS({{6)jIux3+vO!{~>pV>BWxF;?+ z0$!k;YoU#ynacJr$fKH8Y#%)^8S`YU>b*qL>pg~Wh=4*JnNbd1iwSLUGz7Vd+>q1@ z_7P5Ia2G$b55NS@5d%SyogMv0l2h?EZlXfuNg<+Z(dz4fAFD4?*B}t=JWdz!_{kOL z=X-Yc;7&C0TS5!%PB8VUxW`4#YoYgsklP+*PJ=zz2S0v2iWl;~%MK~y%OJ_Y8tw8e zbZoH+>Vjjp>pn&7R*k@@6ZpYd_l|9!%`P&zJK{S|9-O(jYBwOhut9)_p|9X71tC?O zhBWN(_?gHMq?}7;gdrddI0c=H-kKWu=>TA$C-boUzdc%llz8;q^*4GFUHP_2ytiAW zT_G6)xOlI!6PADq?ga)#<^hi5)$ConF86PPu`{o%=g~+VTcs}zk~v}u+4mfrW)wV8 zQp?R_|79M2O$#XlI`eo9yN1Mn9PpicZ0|W~M)1jx9JT+a%rzFyP4bvM0&)owIy;;7 zx9j}1W*E(jJm|ZdyC?UrC;Q_QDKq$X<*J%Py9@fS*ZJ)R|32uyBl^ec`TLLmuF?Pd zHxuiz#JcAlLpWDN<{nJ-AQ0-KbD6jX+9B=MZ+*;_5p!~aqL1z%0#zS3`@pArehWFQ1 zsBy=z8n8Y*RLeS&wKsVqnYPC?fl&e zgDD*kLh20>RU_2-&RvX>o|GF6bdW3Hx)ON&nhYx^ly;3QdYuSS4$!dMy!}d3cgV49Wogv-j|r$?2irKe zYJ&pzJj2~@h_yz!byh-<54dPMc6FVC^>%UE%P$+k6v`KNe))5r>6KvmE>Yw_LWPa2 zGuKl0Swr^AFWx9CoL>yh@63v=PTET4YxWE${$x&ylf&ZjkI5i}u=|lXespa6q}P#8xfI8b@4np6KR<^wP6X~ld58{#a9x)R`)|3#2A;h7yhTCy z)a2|h6rGeH&Aojx+ckJvIlxIjr|QJfKh0qU6O4p)wT%{xM3Fe8UT#60tued+82eKY zhcP@DB>rpc-W__WlH`XZAT(~x1KjsqAyVkpZ{PUwU0mdlq7T4vglq~q#T`IH698Q@ zY2mt_ld|}=|D5?@`(LN$1KH0YiS-;nms@oxP#fa*IR3|8#KJj`xct%y=XD_Y=YWO< zS4(4AbR4PZ$c6$T<_(C5!k1juzEzi@_uOiX5?64nujT8kFbYJJf&;dw2YR3SE8B2T@DPI6maP4y@*`XfG z(!jH(?b0BXBfpC9_1&!mNUyg9+U+B59{ zok(ZD+_NKp&W0dCz+(YflDWr6u+YG40=dz`1jmJuOvr;_4S90VD+=K77}lLuxiqOh z3^!x}=SBALab*Fc+R*I>2fMPWi=6o$8aDviY|ghe>UyL9NYEG9SXUJw?R7eu+;KPE z^kX%%GQr;BWWuXAVQ_cnM}u@vEy8ACnDjlgXYc;g{91Q}kG%P1;NCMd0T`b@ozv4h zr33gmNEsb+h~)pZNPaE>gbN%Kw14FS7P^d}7kFRIDH=Tf(Liym21+cBKR@@ccv#UH z2PzW+p*XhtM27XC|pUF>a@`=eoub`cTtIcRr)E6Q`iy+ z=_xVGhdua%UyFrhM6Gr{Fv+XUF?m!G_9 z$(uMgyD-`q47S@#VXiG%g+&nBOvJELGGI-^uKJQ56ncXxJIutf*?8xKzd-v`_8Q0iG??bbCAY7vFW@sd#OlA;|^OISM>|cZJ+r#xo9}*Y}M%$U$Y$HXRhd zgXAVHaozSZ{alK*bj|{6aZ5$YD?vm2YqS`0{6pz7-+stu`>X3XCh+=O&HS+ouqjzX z*kVdIS50#l9Dukz55P{1ELSlIx)qEuw6GzJ<~Ov(IUE+nOKMrEurlVM*+w-;%$%fP zQ3!x4-jCq74416UWS#F*FCEAuoZpL$i^CMdAAqpbX7*zvI&IDXYNBfxLlKK7b02G| zv{|txf)VE(c~f%CvAa`el68s;syh(@IA9qPt1(v-|Guuolc`ukNIoOUVuer0_CL1u zT`Kw7n^-n2m*S!4vO}Q!=no8MPmioxFrb`Zba>+KTNY09A$W6_9hmpz z21ckkN{-cs z*0lOTDk{Ga@(8y-^)%dcs7klY-g7M&jNL|o#D{bNi+e>t&o3hy+rI}ia03FkbUzI# zl|DjE-1(99al?JNwK6Dol7RlH)@RVSv@?3blL)!L{fDEd zrt`{BlZ5*Y>{MRdou0R*FczbgZjCIg!gCL`wkkSZy`3-NLf;$bM6x3(1ntw5yg>_6Bx8JW*p9;tz< z6JiN#pcNPD_F<_D=J=bP%G*fZWFdFc)|WTmcUu+c^;0>3?2D2mD(sCf4Zo!MN@=c> zPB_1%LTAHXT+&&o8=%gNsD)fdWV3)lO74>Qoz|iE%awun?Va~j6^=CJLwqoWQFwbv zj7>KMUi&_fHQ*#iv8r=zL@e{*XjC|JLfeBKr)K#l;5qB+TBjZ$zmk%fFX6|(j$ zkP1r;>nZ^)wFMB>i4V2rO9OU)tQ2F96@m@Gk=u0@AVx@l74IFb4>w9vlJEW`GGNOm z4d$0r{Ifc^vd)kTF1vzVOr|L8X2%x-46DY*_{+ep_|0CS<8ko%ut8hlKrK(B2vl}k zg6KNb(u*jauYjTwNTGjm#AiHH8C) z-0NG}o&&s$w{tD~qcX1WcTU4V*e$Oetdc)HQSY|7aLiWSv6Xp2Z6Rq?tI&QLbP0CP z;e-n?7s;%K3gmyhA@U>rK6IwW1H!AM)7i9qW+CJ2Z6?>gLcL%-l-(rS_*sWs_;YowV>7>$oYE~R3RRX%I_SM=y=NAz+fS$DA#6#Rn=fo@{xGTAKeFKw zz1nk4*eo3mX^lLq7rbVjiim0@P;IU_&I3#7>>8vd19^S==tNUrLk+B>zoZFZ=)^!VEV1hooPZ9c zy4Jn8j`w$+)?X`j_g6c&CJ3-W-y6q@60&>6p;Oc;Hz@+*mtaK=D`Nrue+?>%s%k0M zj)S0*H{rRP?{T||9J7E?a0yg82WA$2TGgHBehvmyw&p$X&HwTOKmLeT=2Wbt79=Aq zdSOqO$PAbaq90Mu@GZ5ogx;>S8xCex$j%bs(1VkDS~_=%1iiGX#{*F3M)nS)0)ZGbJJSWb#-fk z>P0T*z!^a2UkqYFau3Z3hSbHQ_oc}q#aUN!q&hAiT14aMwh&QYdF zl#%2HS0u?A>Bn@erLuJ=8{VB4{yD+}R3CfGHP#&o*Wfu>Lpc=w?Y^!&UjpOkH{$W4OpSA!Rx`<=r<%-uNd zOZ>#YC04{0p#YHNcJ))P-=E<>tlt< zL_&~m_oCa&T|5Uz4m&cH5*bdkI}xH?7-f@lciG~~98ya5^8rZrV^5&pvEu)MyWq+r zQ9~fX3Y?br$n${RGc*?WBnYAEFLXm71M9TKEL_CVrp9&yA~%U7;?_JM(OF!`0})0U zFmsHMVe|)LyEJSeWZ83r@BSd8;7evLCr=e9?gSOBPQ0EE?_ZaErJl|S=T#smNi&)p zh2v%C+3rxq(2e!^h3pK~&)?@mX=OzCu{Vej&c;EUcqqtIR7=w`c@IKqmv48Zndcxi zPHclE(4c4@o^Cr(gj{wije@WT6yg^+PRR={=YR}@NshL2gp%xGAgcvJ z*c-A0w&=0_%qIXE3|%02h;5Ot;wqR1a{M#Ub-cd!038P~BLiVREAjPr8D-)zhhh61 z+H>;JAIOz7DTGbv2gER=;&VjceCa^{JO+R1Op?zaDAJB1fmEs{fE^=-BT$qcylvG5 z*QT=irZJX?S}ADWl1*QV0*S~)&V>@F6fh(jdq8XU%j&Y)!)mTjklR_^J8Q{L+z}iE z7=?AJrf0=~@FhV9V5G{fJK2;>Oaz<^Eynq|G17P2fakGDanE*9M(pw_mi37IPW;eE)#`!mGx8n_nIst8)VXAs#mG zOo^)H5%38zUccE!;V~Vvoiq(LP~9XXodMskDizOwKKe!#YHoWbsqeY zFkuZ7?~27^;YFqlw8D^Fl2N2xkYF!_XD6VvE1*7{Ulz{fVIQ6bY3UIt>^#%cEV`w> zXDp!EvUn-Tk;Ll#`M3EzYQ9_ViUXx6>84L21X5q29PV1V}i$pMLx{n z6KF@QAUe4ZQUE}zHtZ4pTBI+zp9R;7PcEFVUPSm>($n@)LMS}gxg@R%SlrXK54C~EX&ju$N3kDo zBg(sjL;pD(t9Va|l0Q;X{#5G*z?iYL?+W6NY?$iPfo^Xmlv7Zofme^<4~TlQ{Ajv8 zsV)j5!3~=1CVd5Oec%7(CB?z4?ajXN@z8H?Nos@UZpeP|>}mAHRCE^K-#Gf*dI}^@&$$0ag7k|beLHhL5@fVjZWeBA&9AH9SpkM3TnhT$t8L_Zm z9+{cx>lbAn+^Z>82#fM{P!6B&7S_hn6+{j6nDp8y-t%s8d9wS z@YxJnnJtj-i011u@aL`PZQ-rHUZ<*($T}Im<3z>SVK2L-(o~=S?R61b(UgVfWQLXZ z;>B^uep`=2$-ctI>Raa!C>T>F8B!SMmAa8@jdf@J`O?g)NPA7JS9=ipEzXKiwcQ#Un^yszjpcWHE9ugByLLfKS0)*0V zNuR^duG3vH53L-Qr`TEE*&LrY({It3{Xx=hYH_ z*+21)q&Joou3IjQGTVr6AoQxxasrA=3v~Z{>IZskyrAAN$_*v2ZbHKWD$RU7X8zE~ zCovlo2IHY9R51N@q8#VgfgR@%t%ldT^;YJ@p5G2A#LXAyA|LDV_gl*8v5TL5bmJg39S}yE66~I}%*o>+_+Yt}B z9WDo2+FJWQyN+y(B+c0_Ri7mqlbkMSR#Ozh7W9NxIOkeG6~14np0;DfRy1o6KWB@` zmAWTK1-YT$n~{HNm~|yJJ7+XU9Ny1&N7FIGP$6IMFVU29O~<(r+B-XDex%~5 zK;5mO>@i*606in_t3>3WgnYVqp+VR;D~$Q4v${wG`>l9@S6F&yIqa?iv^~M%ODrp4 zKVhR&m0OXHBcUzO3?>92V-yv^Hq6~C1{(Fv#QeQa)wJ!}vKxdZVRbSm0RMzx^f7l} zvpe>l`GHuP@!K`3-D$UWCtQW=^QPqlu{nBY&+qQKm=ZiEJFGh(Wh{#^{|uTWVO1oY z3Lwnvvb~6}V@u^W0a4GI1Z#P^hrj-G`TlWO<(AeFpBZL*U}G2M->#utGTtl(nr{N& z_WZgL(DEKxI~hPP(&HJb`&udyENKr`Ou{%T~&KDo8t&y={k!cG$A zZM}}yquw3Woe$zykiA8c*j`1hKPkBiRJ;nl9Ukd|3A}uK@`2EpuBdgsofbZ{dq+$|Iu@F`f z&@@L{$j|oxQH@_v0`|AD*CB>yuF&%*xFfY-;?+2$r45}RGtmNC52`wJ6l8L)P~6}A zejitan#Qfb`GViK{mW1=>tRaWq<d>CLe?V~P>Eg7d zTVYYk+@-U(js#np6hM04WZ=VNq;-@3TLX(^pWz+D6U7Qs!4h?~>hE*WM2DuM#j-f? z0kOA8f|(b(5FeGMJ$*ww+)FCB5zbY4(2j6G&VD$NQkP$8%ZIXEmBhLuBhe25fl=F=!whYnUAe#E6mJ2A8IBbY1u8`Uy9Yi{QcW4CZp`PHXy(US!4 zF7^NTq3kn7 zZ&JMhtrAVbOcIm7KQ4P`mQ^1VbWQJZHh2#Sh`VyCw|o=`w$adDMCWQe!nWb^>$HN~NK^Fs9PLJ$0uX9$d3yX}Gw73RHMXhp!=>buK11Xs2OJ!= z0^nP>h^(Ex4C zWM4(=5OCm3!NaK2H!pa#--3#wJFDPmgnx2b?#td>olIhhy2Fr5#A!5xrYK!)OIOAv zJpuoWqC$@)--P>dsxN~=mI9D@91+Ss_>PN7C~(^ny*Ue59j3Rc&TYPlNM}*-0e4c{ zWvNYcuIVVnAJ(C%9!8|Fi%>-yd8`xYF-{Z&Y3X;Q!Tqtbxcqj&i+vyG+0DG`KVay#8{m|Fj~w z2TgsrLD2j!8~Or|-tM<5Cr(v!19vKQ-W4Kp9rd=9(Be^axIEv*ILNuHJMQQwBEkR3Gfju=_xf^O}SSCl1e##A1+)j&Q zTHm0!ws8U4C+k<3UzdgvpJ7ncDvv9vPam`v_0gf90e>0=s(6VIfiPFRwaKdIlLb1d zzW+t%L0|r6Knwy)mQpQ;C#{CO&kR5)+o`svU=9a^AQnNIx09VkIbHV*@P8?oOf$`l z)?;>6*E@U<-kL8s6vD4aS%^RG3E~a7KCI;5NtCb zAVl>FV?PvV4bJEJKx}EQc>|5t421*DP>B?_xw%}L1llhNFjG+9qt9H);>Rhc&rvH) zR^*vU=6Pr8P1XEp4SRas`5^*UDzF6>GW{UluHy5MdhNRLi-*m5)`QJZ+ZT2Y6S;I% zT|2NH>YBAqOpsM6$3w5L#P3MA_X-9ipco6nA?x&|3eRjY!k}E z8V)SR?HdCGha)wsK`1$)J!2cs+e~{?d1Tz7bWIy54A+n;U)qsU9cX*P8FA0O+aI+{#`#XiMb>ud+$7jZi}}%AwCOQ zMkM9pRTKmhwD@qa&y3u{bsH4Emq%vcxzPu0k4)a-QPrJtr@@bS0*hKJ9Re>QOBT&% z5~4sootw9phgB3qhc}?v@%;$hyOukSqtRRIMkFOsp!3a!lMX~cCz2pFArFEQ$=AJz z;N+phN_S*`L-`A9Ompba%d*r3Hqfujol$6e49k{3;T9RtkO>F5k}+50#Q5lXtY% z1|8^K{||5P9glVU{*NO{i)5A=l}+}RiYPNOGOmUc*(;k=RA#ct$V@_HZ)Iffkv+0Q zT=x7Pr~B^xe&2VW-@m_q?#KNoxvtmiJdgE!K9A#2W7LCkd>WikZ9CvWE`zJ*LUrFv zj``<%);xqUkKEjRf8eT99JriM#N>Njk*(a}bthb1T^Mr98uz8Fe6}}LT9D}6dW1te zcmRAsL5}HIR;!{-kT040lc_faa$wIoW{aH}x68;lo8$VX3=L?AmzZSg2MGzKRT#Br zudcPpc!}>}FYX22+akd??5}!=PabD`D`85KAFnwePQgaV5df8BFnp#RSTxae$-_{c zriLPh(U7*ib>KZzr|yoF!ZYvVRh_l3w;H(GRM2owX9tany0*$t20u$&WK<4d96o(% zJike{ewc9Ls@LrJqtvtzI_F;%`;|Ft^G*Ojf_n}?Lj2e)#@#{#3;PYvKW7@lP~lhHtsGEoDwH;2rXPR_2SP z0j*_$x;FlO>IG)HP1(Ho*0@y5UwM|_sWNy~#}+-Q{q0Uwj7+dqMF^<_0{9qd5BvdifZ} z69Z3CpjY%0r0bQ<3r7t_^I3e_-w~i(&}kyxztEnn9JtLxO1#LPIK{@V>eIXhjxt^~ zq}U|dVWKw8B|f#4krOo4tdfmt*_)2gVr-rIxt^F6dYZ0G#?sfkzb$Vu_rKWu?_-{k0b~Kw^xP4dKjjexzD9ndPIXPw#cylS zjoNQD3uvqh(ZQMn>-TiOReNY3p(Cq!QnoW^?w~MIZNR2Txj~)bjdL}d!TZTPKqsNk z+I_nV?3-FD3UPm#6sknFH z#}RY`_@#>fgh_@WPZh@=vWGxZHo9N3-SGh-(CnVwa`~uYNb<&00t&6zVwSp^b$@7> zi>a@Vlgvz`60?F_K#AmnU%45Uf1wzg1Y0c_4joGe8b$o71^#(S;4M&IH>r$&InF>= zUY}ZMD9HiklCtjP8d_&e!QV^+irI+UW9ULXHZQ0YoIa9SIwdHYj)t(hjxlm7$sQeI zl<^+1pH89*l?aiw5KbRKfH@JH?41J3@VK84;A3=lt9vS2kK6k2(3BlrWK^+{r5%v3 zS^qV(GjHe3_+P5xw@uh0os3ZOWf}Abv2V(qv5lcc7Jiuw@dUSY)NII0rsBLODDops zb8i9K9N4SPKPd*?&vg|;RJVBxMbHM@`Z1ZSNmLhwRTI1JlYDx~!#PH(=Pe2I38$gs zS&2l!9v<6T#R>fSH54TVJukv%wWtxq^-U7u$a>A%V{t6xYxJ^@`I$w;Z%u_ih)7BN z7|01+$(ow(7SlR4_xSc|UYR0fGiK3Tyt1>yjO#knqLP44@;k5p+D1cu+hMldo7>Wb z0JAPxLQXJ~3)Ew5D9VCQiZ%-dG_nut`ss?*h3~kwIgMrWW-{XrW=^;ff zIP_w4Jcf!`5~K9y_euWeey>21t!w^J+GYPpUzKyaZPYIv*ddI|Wi|!RCb9+XXu;2L zgX^m8OJE|5Fc=`o1x9B;FXlGW=_JvwudUK;Gt3}7@wZEf4HFNVly#y}jA!O-G9BNUhR z!l{5hqsV0Mi;SPB$8J!JU3UfVHEtHG?=wnxMEb&AD3%qqU(0Mjvk1QgbmBXxK_z97 zS-Y+0^GpPKNs8yF__P2Imy$SI49Iq%-dFGkP5tUi&91AT`?oH&o)jwVGFOg(Fw@4G z{PvBvwD%wsv)lqiAFm2EgWtGgmPs8x8ljUwl_8<3JF6@+ zTAKu@&`P&;NW1r__Jp(vC|T3j(6HcWUnz_1V_d41$Ne|fX(OSmMeK`r=bwfjb-pEe zSta3J`|1N(TE-Kc)uR&;wVyQl@ z-75>NE2k?HngEC2o86^tJZ?qu);Gj}uSr;co@g0WUovsjEpfrOe1x2T1jtfEUe9b! z4AExGJIz%XkG=v{xEy9KuumG#^Ui+!oCHKzXg4B5rOvbjuer4fxar39M#NEXL%s4K ziz25r^~!RB1+2~pI#3dvd&+$XCPg!6M8JR5icgT~uT|TMJuL`eJR!cHJGn^@+d}YD zL^uG3qKrP$B#5>>0&YbQuy8V`~OLl-F~w zR+`J=M>G9$LX_$P4CVh9qBalaEr zplO}WoDXi>k9dH)(GY55`3kLsb7W)6^WfSFQkw`5N?WY?3JuDU1ij>;g?^n1_&JtR zsB6c~ZPc%AxGy!tI0dfK!>MwvTeGpxv;i5-?^@6GQ@E0~Cw z2HAs><#F>AvGH6Q&0<3=U%w!;IwmRg~EO<5r)EC9rtNV)sk3FpEI$5Pd`=fMl^rc&*b_a9nSk!IMKCsG+?Q)zJ3)Ul9vM;^!oQ@dl#GjA^MM#5;g4P8|eKv92I*rFlN#AAraP{9W%>6Xs6kg%MmvFP- zUq^AHiXfY2TMwqcwO=|!+p!V+NfsT$6a^mGsL6)iC_)@zVw?FRL>wTg_|#J|rn8 zg3Ta0=bI}(#$ZIa2DXgZhmH-y)#6TZq*umqcgRa782#166 z@MtYx@za5g&W3n28IMFA?XVOm7=dtfkAsL_NbMlXKgN1=H)CgV6rjLsNKW7gsjt-t zf_;qOy_oHJ2L0nop(P`E>eC%B&x%>oe~1Hm~aCoxR?k6&hX zPQ{k81egwnjm5y?JlJc6LfwAR*(a_gv1M;Xu1a85S z$0U>`*ws=DNXgHUwh-LeDu(?dv0F!UA#vU>dk&r8EQp((l%cqlutoPef^_Nu-u&Lo z*M+0dYtcx?z{ zSgmX%qCuWM0(JqhKR}=#R%ears*hZ1TCR2>3gb}k-Q>ye;5=%N)00KHl8Egn&m?>xX2s>O_PB&S9x+o+wTK2xZeb0+nb+n=raJ~qH3L>e(q(*s^@*RKlXf3q( zAT5fq`eg@Nl@r@@M9hk`PQJjTW2`?oHMrA(906sU8}`v4L9&7Enag4N)(roADNxu& z9D0Hh!;gg!QF5-q@QzX=s5t8#WPML}Ch_v_yMcw1>j163h50yz&MluCc;!Wbk#M4X z5`Ru*5}n32e#+o|yhX-G$7IzKAXagefMr*G{DVK2%Avp1%$`q6}AQS3J5Vq)4iEF$-q?H|lWAROGd zBLuw2@)-SIz8y_|{Mi;o{Qf=LK+?nrasnmT6~PbIF=5-KlLu-FQDn5I?#W7s?~7 zi)V{&&b)HKkz9rg&EnVus;`A|tz**J2SfgGlhNH7xD`8rJA8#mzryL|J3o6er(rG;r#@PObxoPXfQS@-1{6TI z^`eM^jgre@dY-21D}icUzr`ni^pQx}QB0{FX;Pw#imGE0g`p1Zryjn&F<1u+=mYL4 z3`6zL0MfiOzECwHTyNhroA{s@T}J5-XX*OrIPdvht`^X2(MZ$t8>*a^aJ1<=S_cA5 zcTtKScq#4RmLrVO(u-v9<)wbcsgLjp^Y>=^%MQ%%J(}GCp-d7;okUi; z@mAO^<_jj1z+#v+FSybJiWQE~iY{|lEQ;e!BW%6b?chezzt=g4HrvWUw0JQ13p`FR z{Hy5Rg-VJ)0%H_I%Ng9^xIrWb-A861D%{$Ywrl3;>H>$eFFiyB*m&MgZ{E^A?qS?J zTa6GPVqNjkbp?l>y9_wNM5w#xu~TpE9jgXMdscG^@+c)B6|Hw}YAHf3@|TFU!z2-j zogCIw3hn++NqmJyz3+aHO&bA@E4A{uz^|sas zG}LM#1VD;rD(C z`8fb1G-dQ+-kxw-6sZ*FgY0GxV1Rm21x28?xF-a7?mbsu5MEY^|EcXP)Q-J!pd6;> z>`RrkVTo%y0&Wf-qk4^mz|IW_%k&bO%79od{yQ2ea5N00I(6E#fuA8sOw6J0*ST{^38XONRiK_1bA zTtm*AGgm86e+%Vb+3iY^p;cNs@EErPS013>5&Q568s9i4`e}8-yt~=}A~K4)kFFYB z(L38RI25@$u@Lt)OXuGqmhwo4ILYxIncd>H)@AYtN^ym}ek=J1sC3)9Z@WbyzyCm_ z0Xc3dWzsMxarXVi_GE#y`t$i;_?CqM#~3F%ep>3i<0 zyMyq@l=RU9{}F-zouib<#U|kn8&<(&mF5Sf`EYW3~o60${TP%TQgKvFP3R&;02+(->y z^fp7a3lhq>9Ryd6MV5j%@K#Eyu{vswbQf7u3f5DE4b2$5D= zT-xV*lbM@$V$Ln@g&|qMni354N(jkHgycp-&!U2WGd*v=aQNTb-O`CCV2Vp~=yShS z{kT5ztG&)9JVX`=z&0l}8r(lC#gGUC?vfA@heFOYBmfGO82h`f3?jgmqb7yK+uNg* zM%RfioH{wh<_?n;lkJ6XZ$IGAr~zg>rsgGL{UbAYm7hRMCD~rcz+UG_?X-dVz8Zh~ z@kbcnJPiz!0@5Ul!l|``5)tH#ZWZ?;}8Tq%;M19G3TViHXc&Y`(UjZi6C~(pM#Qz zPrMHR_i*}BZ^O;Q(itqA1a8#k_OM^(8sY)G)0~+WP%{LiBI|PhGGO4*0M+a8`t+_V z*pH%|+T_!v!1Ra7k!FR+gKV!lXDj#!{qp7?^WjTkj&aWF?D`_(I;6N?3l`z|na$nf z&`Pl*tQ0OoeHW2Lf|yTe!d7pP=J|bB2yN0^Wsf>OaF6X~r06X(k_-sut&|DEUMGe5 z%=gn84S&2VM^CB78XTe!kQ)n0tPej!+!7}{ATO#mvV8zBnGaor)lW~$3|L9=wJGf( zEcDWB`=&uCW^;kSkY=t$^w%dR!Y8whkfpvVha#wbV%YrW1&r3~KW>|ZdhCRr6s5}0 zMw49|>We|u?Dh^sr=;~toegMhu|F+tZ$U(6o>@g9Ljv?b_Kny@c`{ShlY8^XNAN>p zntiV5uPQK>4V;d8ChN@u?yG~;Fu^kYpi|D_e?^5SQZ7eqv~Yxp@{sp{DAOFXDej!Z z*djqN0olT1b@A$3QQ)L|)vrGI-?tbPu8n>xQmUvG$&de$SlP0-oB_SkTI^LCGB`2_RMCqtF{98Fpc3jTD1DR_A05-Sa&>2%x|z%vNa z>pLywI>ht-PVi@>?|`G7mc6)nBk~a+x*1UWXDB)t{GsRuriy45Zxg|e@mGDX$IJRF zAs8gmM6lN|9!0-g6viEk$|0u@aIYj$5Xg^QPS0%FxsnZB^o?#TTP zz=^YB-V^~{soj6G{~|3&+>C%DG3N0n|I@ZORIoU|fLH3aS#FiJdq`@_++vCkv&}+X zv5`Xw2Z)3jkj87Dd7|4hLvaN7A=CY&7}k*n!8v6huW1)Krg{V2geeRv`MNh|YICAM z%3?*#y0>0)alh#VK?x5aJI@CHpNO_Wm4H%f8s5(}m<}e7#4E~47AF7tic;JZQ|&6( zbHsO1^-y&n6kgGJpwD-1_uhR$+a?4;D}Kn;atk9_+Ve8*zS8k{6!OmPze4(g`ndHw(pUE%F90)S2I=>tl=zdO=~`82~AzeF*`pWB2Ga8;r~Q9vM21X(^m|c zZP>{r&fdIJx-lUSU>N6Ii9d)9i2}axHyd0o8tD87~G48}c2X zL>^B`n!f#qw}y+RP&x0=h4(q>0SUu}rLIuE`Xm9lAyoP0d_lSc};*U-O)4@kgKG3wv zf1&0|Y|-IuP){`ve3C8ER~UwTEV+pG>E^x_(4roKvB3C8frk|!22R5`{p&z$)X&1= z%bzxoeJF-B=3#j%z(`8rzNy<*gT|!E2xhTgK@)}#IX%ai_-F;Xd1P+`Ckcz z0?MdU$ZwYN5tIUDpA25;`~t7z)mL)-$5?ntrT zJgg~@e%B{{1uFB2G(^&}N}65+P!cfVqe0^MA5MgU{i^y{Lq5_s-X=?Hd(-uXK17(* zd&XY-zs@}gK0m@SEY-~&WJ|nJAgVG#w>k?lD`jo)8fyJ~e^yxY;BmNO#~L?4k|yqg z_R4;j892Nw=ROtf0zInVY7js+o*oOFatD)t=cRpsX%ZmXS#e{h4{7D7E=G7^aVs>i zGS7i&<_x%}{c+@l@Op5E0ksnK=R7?i0S>NA2`!7>Tp#lXgtN7zH%Jo2c*+w!=iCR+ zS%C5~0iex^XLKS~_eH^@I3lPFvLyEibgxBHjz4~blA3myC0;`&K_cT-R*%w1=D7j0 zf4yB?JX$6dEYA~4U5kok@Q;ZokD7kTzTSHE0ft@#tfjFogN-MpXV0Ct6jqi=&vus@) zZ>Ia=K}VBa@BKhUtNHc42B@mPSHH>nvZ=2?*EiV>(06nWwSIv9HUoE9 zd7JmYGq`L{Cl6FzmN>jD>fR{W$1w-_ z_(nb&$PEHZA-zj$g=GXpVc>{=H$=XZ#0M-cIU5;BUFDwsy)Yj@7dGF&)&8fsK^54` z(vso3(ff7|B#PHzHH0GSfh~mi*AB|IsC(l35L_dWY^;TcO;`a-b2I{k!PF=RTEJ|c zNM+lEgMzRfRtdb$+l7)jp^8H1s5nPs&Vjg3&wHhtJd2oZ?Qy0AEltc8B)dzkfdy`u+0(*I~OU`m1d>chqT$ zd?tdKr$Ib>-J2sz4)(&TASWo+z_NPE=z58vO;3|4dd2_vwtrpo!eFV!1LcV7MHJMB z%kNjdVv!D&7W8>WkXE29!Z=fjXLTA$a_L|%asTo8e>bq-D*na6h~(V#92PgQ zs|0v``4o{^zDi_}g!MU9D~a1amicFG1uwqk0Kq1IkSeiwI@t?b*t?VnGglLlM9P-O%>mJn;-ErW=kHb?kwltF8LRCJ`Vr`@3zVx0rc^)zq-?36L!wwHQV^{ahqyN9(%+=r2%969E3XEm`_9lUbD zMwNQa%XZqJsEcy@n%t$hF~|z$X%}rL`v=JZofNU=?ce>Pw$MBPa2x}fVyvbT+Hz4g zZP!HB`+A#TpltNBveb`R*Eaz=j)rl&y-47FjJGXa7 z>=AI14?qwL1#mJ1H3=8UoLKQfkd%10@m1zLbgkCXA)ji&`Bk>Jv#^YWh-uK;ZUiw!xwY^ypg(e`TW4^b{*OrpnRv7(adshBu^twpdoKH0h%QVv zuQnhqbrj+b&j4VfBF&KQMcyke(@p>)h?5oo!)b6Gb)1|;63Z>DW~->te-?~xuD1H7 z&mCL*^f4_)LfwQWHnpG4Lg>}mL`fYxhuU7y60pQ6F9(28O*47HARf{&3G-G`EhCQ1 z+rgW>RbM7eEvL_CXtGOn`@9mg;^df+cfYunW58}^!A4n~sle(2+Hy5*Q#}F*C~9ua zTJD{U|A!sVQ6PjBAH(!ZM_d8T2WW>u(NQ{zb8vZ$fJNFT9@Bp!m_Hk_2~(SvkJNky z#D0$$@UNgs^Ggmw1X;0Y;dp8gBM!|pcqq0ExMkzC5hw+7=OTGINPkc3i5!C4wM{as zw9{(9Kq6P+Y;78NY7)ZCqVG{X2M&u5H7XZ$zXCplhcoLNBQpL+?Vku5^C+>MCe{!x z6(Nam^a_m_r9#CjKemByGCkcKO2P9*4*%N~k%)WX;1A~;doavb5Y0KbHaNkDy8U|r zcT@EV6$f5&oK7e%^(Pf`0pU&AmJ2gWsP{n^dLOKz!k#pM0<;dB`7yD44#ekgvD?Fk zF0z<2pC=OkRbIRehR`tZxW>8InL^@Cr6s*ZIj)1l58tg2;q-j&m-9ad;K3=0eA~rch|IsMz{S?W^}RNPZjaUo3MpF3N&R43b_IHcW8-?$K1`u zbq7&>1?$rT;t14pjGz-p87`TrSxRS!X)y$45ttj&sKMiu=?q}e_)3&|Qt=g1rF{IUx%VJU9i#lNd#akLvi(P%WDe|t$&xF70anapjyhBDwE93)q7a~P+PFQO_M z%K2ufc{MsSD?Rc5h+4hOExF7Mb3GGCB=FV=DjbDHh62@JLcEpF zIt|Im2H0g&V+lWEfI6S+Y5#F+Y{W`j&dhid0D0>!#+JTf-R$jGH-8w5540LA`9_gbcO)>4(tG|S_Lr}~Xp*nqqqLNwrcuiRp>M=2$5iNXQ*mWa98WWCjnlEtZ@e-bYZ;bCfCON#fyRes3NF?Ea2u15=t?321lEFRlHL>{s zwy2!C`Xis~j5+`45bIDvncluTH-o|J1x{66A5QCCp9h_jh|Z1N9Nru)dg41?i#ys! zqN}fL%E?fM!c|Tja{`=a>A<=@;m~?JVm(#NQ{Iku9+kesh|$nODv3`?Ih-<|7?x_1 zg_3}<1Lw#&ue8h{tV6FYvc%rz~N!BFZ z(#z=y;So;2ho+%Bkliju0@8*bvBrxaS!^g_c1hU}!6%hh1_`$%0q~uU zf!-oBf$yI89K=h%QKtEb^2vm@F1D~$ucroijkg|2k%ODnb9-z7Xqtk1nw9&>T$16f zbGC1#@P*+Vi?VCaN80o&nS|qjr28e#!vvJyR;_0tEl#Jc_fVx=E3K8h5d`T*{pqXS z-*^U1j8iE3bRod8%_l7$z16CRMd^pvhF8}Y-=ca~!0W8m8gJYIggQ-&%LGjDhu5s= zBeXq2$FjXO*fs%abVhsXTQ7^LZZ};IhDsuF37&nBPvhh~(V82wEs^^FEgklgaXsdJ z0)(oSz$9VnDo@0cZk_i`SicUiuH;Ri$@RMS=?e2Iu! zv2wk7k)d=W`36NGEyoGHXB268V)mMXz}Zk91y3wKep(y~-tp;GC267hRuAI@bATc@ ziV`m}5}r!Q!Ldlo{jQ3WZz;9rG_-~BiWX&aiR-jVprTxdN~*CPx$#}Ut{k4LE+q+! zS_7&sppJ^n@aK7w*aZ*QIT4-t#>TE-&)Way^v_#==mw`wBYqOZ<+qc7&*MQ&W)9+C z(O$+XCT|UOI5`KeE+y2(+6#A6r=gF8{ob*1Iat_cRnl?sc)D%5rY-rcK5s6u)^zx@7Njb%S;wwNtaf-cn>MR5LgK4!7?^6<4oLpN4F1 zSL&+0cIxMqm2;okuCoWTBN1^(vQFen9@nvR@2U6%cT1{=LPtH`PmMZUPnqH>$SEt! z>3zV{<$@I@=Ps1C%LSV;ij=Hc8HcQ@KGHi@-BP?UZ-@kYS0a&&TlpF-^DvnB+8Z^e z<{rz3@5gfc)#gFZA2F&ufJsX!ko9s=x4u0>ms0ZZn(IF=eoGOOWDS98*+I9Qiu%*y zWOv!F?I!dxM3F;)_|l&^3gmXE5-_~|ZD{YG9Bd)bK&`0z8^3^K4Joz2UhJ|X|J+wXtB{(C->5@LWMI_MH;(K z^cA60I{cUj;o&C-ROPu9q+{1XO>F7~eVlLWrtVpdcu+Q73NPCp=jQA>hnI^qAM>69 z+GR4|4e#cIasw4$>Kv45YZ}mmm<_uhCsrQ5wY*Bl#5W^?f|a?;tPW~jFfw%t0P7{=j1xM$Q$Ys>k+kUa06F4qiA+W9dWUAqBWncVY3mJ+W-5!^Bj z7*ONZTQra9L8NJjpXBCLF<14*Hn{VMAY*ZBh%^(6KC%kqJDsIPWiA4#7{aYmeh1wPg2&{bWm_Ex3+mjEP#+@{41Kwz4( z=4O1(6VowXU!9SwQEZ7Jj@b7>-B~kWH?}_|^j97x&0c@p?UZl=nwiE3I`Yk`%$@pY zrMp+k)~mSLO8GTYof3NN`Y)P5a=hWnIDZQb`4Ja%Y&~$}hf#DtlK>?T^u_(7WXonz zam3M4XUkcBnItH%Qz>X1ew~<=`ue39Pt_C2qYvaS4SFIS%OtBrkJ+0^z?AFi;j2!1 ze$(79V=_305;YVz#Dl7NOynOs^qx`a3DL$11Zh_a1YxwlbtRf9QeUsAtj)dYSnzJ! zJZ__|&HLc-8|mwISYLDJ_;>tChF4M|JC?FkTaw?ANtvi-cd#YABYVj<$x1+HU2soq z5zVF&*l;>cjU;ZTuj7tc6{s2)d$wtXjBoTTK#ts{azsVo`)$^j5!lMi5WHvy(Ylc8 zH8U350Bx?@M?MO&Qu4`22=!Zh^#pNG+*_eK$`#HWLK7B(p!4iHGbFKhp}CQJl07G= zVKDJU+_*ueFLaGNXJw)8ChKUZCUV~iKdjol&Yj3#DX9G^@YGsl+p`fjwwGPJ&WB|+6|sBPWu zM0YsML125hF?)MYo2$E6fmKBnvj##Mq1#cDc0G4ryiF$iW|UQ$&xK$R#SluqMRUm} z#c2l~dkT>qI>#9ajnFa{?K5I z4f}0l3Xpbqw$)!$RI2UhXCgNkbHpP3@tJx0dxeVOQC{+v89jq9e{lr8CV>37ATFn(^1x=a?Oi<_yqN9$&O5a+vwZ+ql-(=DdiL zo=S(4dov^YQ}HL?7m)$5`m$^3Sp|TCoZQCSvLq}o583;wrJU3K3Jn60^&!>QOkUz# z3t)^T?c>I)XuPe(k`vuLjv9kJp*NVKYDtjMo^;D=f58@pk~ER)6n06Keaureb==e5 zo(fg?k64~+-$?KHBE-$TYq?`9P^_e$qsd2& zebm?W8YW$8cq(({IjyNF*_CBmO6*T;LHA>DC}j;P@x!r-U!W-n<@}P>(de@A_G(Ld z$L1$puBWSL9@gMPBbx77x9_v$TxhD7*Y_3*(sl}|4}PtgqhVyDs6w4=o|k_5P>_0F%1z zf{E@2YjUPLnrHSW7yQp9Qz7K{RVA12`$bE91=f>mwkwYv;xK)GIrY{kE;dLThm!i` zlV-r1L$OzP*QM)+A&<@e#{1pwVQ{!f4LJoF@o?b`?i~-dl`t6((?ttDA`{@Nxh}?f zKam;hP{mn)BhOW9aDCvE(BXrET-0gyV|Vq9w#%!%sdGU-Q(WB9Xc;)pLwczV?#7c3 zmPa>1)EcW>W1p|Z3UXA8h%&wc6N_#_H8Bg*u=eTcPqmlNM!6b;TIrS#sQX7^Ii=ZSaTK(+4u&p&hNbwvaK5d^TR-9+s^Bo*q8< zp?}_5+&G$X3e%BdSf%yD0koK>84c-Y;|;76RJ*^AZ@2c| zn4!Z%@Y6=I%o96Us<~11bgyPGyu9ybj@xzsD;cjqB;yTMenUpa#zg7s*IvDOEqCsU zV|~!OglnER+s>MMoMpTI`9xxZoXpKpnRoqryJ>3!TW<5VO(m0W#e~1EZ8$ecIAxS{ z^%}dZiLA{Rxy?Gzxh;DU#V(XmPJHidpX5jlEuwI=AL$;j7>~{FH5wi#7vuL)FC-;O`6~zBg*30e`ysFk3d1ZGvmtCJb zosA({IPnArp8*G#Pznd{_y1O2W-!AQ4hcLfujg=*%k^Q~!}_QZstt*0VExbH$lC;UYYR6E#6&LaB!6moH}OIy zlHn-xyZmX>q*(Wh ztK|va@AU+ygA4|K$+WguU;ozi^!VZK0CA2LoDTX0klKc)=-h7bvcX9FRG%_wRKIsq z151xzUnJ7ujH@;B@oV_=6Duj9p~epWYyff}_N#a5sppyE5H{Bk~L0Gul3*8Jp*f#|ZaBP=-b(-CU0k2sDOspS2 z?-fbk-C_!-r^8H(IM+8ys2cV`)8Cff14nvMoA=Z@Qz8YjbOKNlhLKr`UPt{Y?gZ@pl#CJl~?~NpGY%t<8%>ca!=w9^IM6 zybJCeB(*U4HC%yE(1I(2qt9B{COlMZw;%-$cEFgW`vXSMAWkf-Isb|^e8yPT)&OIF zX^n3F1+#8Avq|BV+ip9y`S4FtiFD>A!uT7nO>jo^#QRmBsx20en14MfLUVKR%V@-; zhd!;S%>`~{E0y_IeXHOw(5~hN(zpif)yFkiyi3$DMC0pF!}9#$nh6K3BHm_dvGz;xG(_72@Xj>=2hUU0C0hrZb7@U41TZ+?Si?F!+Ara2#9jSMiaKV(~AJ zp)8J=JY~H3)4`jg0c$4Y!Y$72ie0){r0>Mza$$4sl`^Y_Y3p*T-8E zuc(q1VoNCp|Tpj_M>gZnPC`pL}J+cvwXw zd_G{=sjohfE+E2@1d_hKIpI(g5lXBUIpHu+uqJm=d_hDg!~*XV-KgWBS3pEW zC}neTf0Pngy>>13YMnOO&n<6w6>Ly+ow?D2^~!_=eZ8>i?`=M5di3p!vrcDmwN}n@ zIf*RO*l|BEau}i1v*upNb6Wetb?}(prEmT!m)H_|OlNSjINF3uoU11s%nuR!uQ(iL z?k!SU4`{)M25hResP4&c-eh;9WlOh|W~`GJA|}Zr$JR?OR4Wk%KVciubeY1QbMM3^ z9qq%E=`gyKb}&RsQgZ21t?G^(fr-%0?eas)hkGzu58CZ?$5zXlM|x8lS?w6ATHTT!V6xQTQrzLCa9p)DbhYS`6nNkAf)~@Z^f7#(0 zqhl>^KKO6f4Ci;_#5ZePt!SGOjapeAinR;32J@)!-?*3m8u>a0B%=jmV&xrfJDbY8 zvzMZ!kG^s%tsPqi>BeKacc9^WjCmg*ex>Af#e|2Fz^h=`>1z>}x*7D+86dkbN=#2N zP4juZtI-+X!@0FQuo&O7E$chA&v@P#nPv7unR|~@JNpxCv?ov;Ct>mb z4$4@hOP<6m9r3%5;rb!>;_Yy1X$b5D$GG0h<5sSO4eN!1g^~dr(HrY7)9*4V1^b5t zxal(^)l|x_wvoJ6VXuIpUy4H#oX1+&GdM189;1z{)ZsR#>{m>i#igZLuW~Ou@2DmG>;mo^OX6;XQQB3>te{+D}QREC{8o}=m;1WJ(j(raRnW z$|99pb~>9lqQd4aw+b(>=*smz3#cItK2%Gno~N;#v+yUQ6g++D&!;~BdXlajR?BA# zn)XW5JgFr1xT5Z->cBOl_fC)eORmjy8%duf4&JNlcYrDoH16VHF0Er1O0CuDq~yMW zt@Y??Kk$uMa=o0~!z*&L^xIdh!Hgx0`tI<@iOB@D!TgiwuIGQz0U8)UoIe$Qdm8 zgb?n5!Xsb&BXl1Xrm|)HEEe<#aRmljFYjL3>)a)((8)a4g<5@GZ`=#$jigAyRk)e& z<#6$Zbg?WNXwv_MWBk14{a}kVk-~)~>t`{*=W4*ZNQq$!|Lj9=-XG1E^*T*Xe&&xD zvPk-?GNt4JW-8-YvlzoyY8OvRQHu;;rtiu>9r#5(Vb`YeYRcLEb(h@e;c(!znxY~{ zUWLw!PGLW4zIR|uVaI;qduSh>{_X{@_@Yoe?54b=!_BShIeF`3$6~R}01tQBLr-R{ z2ljWvpXm=CsPh4#Y0(&FcPO_1Xz<&SSB>e#N>QSxqRUUyv?J{$uXtt;?V5NPA1$`n zQmv#kVvP5mRtG^?pMQFZVS^CXt9PKm zPA?BEbZ5kfB{@IbEEY2EY7n)XH;G=KDz+Z?7|jc{OQpx$^8Im_F+FuVl>cf;CaOwJ>9@nlQWAUDl3+2oryx=LM~)}l2p77vxBD|^&-^#Enis-TCa zK0OB9yolH3AerCkXXv#n!E2XKPBjfNT$78-*ouKg;Nh1=*j2selgWrcjJ*YaqULIdr5%-*fP9lqfNpa#E%xy?N#dY>w^~aXRQdpT?E1p5Z z%;q>)!DWJ7o^Dqh@wWTfa~DFY#fHhbU!D<5ja;`&`z2a#8XAnpBC&+t;O)b)2M!VO zHy^0>&?7RXEKHL*bvg!LGiRmv&QIu7v@j(X&W7@fF zvZz=zGZe7h{bM{lH;FLelOyQwy^|wfnx3tkt;jTi4Fp`v)`kWiGa4+095 z8N85Nlw-yNo~ON2P(It~><(v_OC#_u`aqKA?||fmOXB&it9f5;BWFi@38FbD7cX1- zlaVM^9_VM1k^cIRqH;f=NJW#foHZX2Ul6D!TDcIbnSq(`(D`~)`0C>i=`Y@&BriGf zMCrYE{Lu)q6S#WJ<8FGNANV@Fdo~9RKKzH@v_=$bbZTJY9q**9I=&1KtaVxkp2hOZ z2R972jegGq?U$BG%yjj|Cp?mh3mmE7PQ>)WSSa%x<8C6W<3-mUwy(rZ)djOhm9rC1 zFOAnvE}m-mQQ{1p*JA#U4mVTnCtGCVxbu5fHR7G~d#ha%Z_=O6OJsjjT)Qt|+{MpX zdh{XG(eam0!5#C#C&RszrOtX@1Ypff^9%zI zp%jDL;cxnT0CzoRZoQlVJ9j4+4o^!MK@JqkANoP+U}4^T0Kdq9U8PgZR|!>jXK~@f z@aIt3xg%tPj-b*|556n^eD$rJ|Q1f-^jVo{MO=foW)(56HlbP zPPSYj@(9x?y*N%+&4A;+eJ)nF=Zfbs%XdvLi0S9Qv^#aeA#Ti46H=P`TvOsxLlGTLSbBS=5O1RiyKgQ{-E>fY>v+t%tF8vOTp zw95M#vj=(=mnj2N63B7q1KMV+_-_3>>yu-`bKt`9r@uJ(qmv{RbyXsdQPb`#lSw?oQ0{zYgJmJrsAVFfOAP?v5$+jR->D^R@(Q z2_*|$nImLE%+I8vNrvfIn^oFK{z%*z-Vy#Nz@eG_In$Z?P_NJ?6NDv#T}d4;aPXJh zr3MOZO;>+@PoIQfGV%4)kZkk zV}&6}tY34?#UWHoB+7^8LL#7(fhj>#LKh?NJMMvvE8Lw$6C!lVTuXx4HJf1Fx*;$> z$!KK**B3M;S^p>n$F~l@f1o4@?C#=gIO$+~g<1Cpe_QbVE5k6X>GLVR2lBDsRf7F&QtiOsJN=_ZL{3LQodGS4mg|QV_nJU_%!emr>v(N81C6qNr~lm-S)% zL5S^aSL2G_dzE(zbNgw;@BB+jl(Z+Afq4%&#a^%^%#QNR=Kbhe4{D*!cn& ztMn7559D}}vukfGjqACF0xi}!kYJq1eDLphAmw;M<-MfGS|BUE#S0=@6ENm!o(=x+ zb8Bu!R`0^k|7AK1M0oH@@)X|+x1#OnhiELG1hd@u0zD!ok-P?8vyGI`chb+1!Km6< zGu(H@F5`zksx&(qZGoQ=NTmAu4@lF!0e?vbXBS@EN0+TtE_!eJ`9<(RL`@%^FC5v4 z*X66jU5EY_f*M$B|M#co90u%5LL*s;R;U(kRbjtrip1D*810(dv}IKIxxar(2!Hw5 z6bwb_kM|B#5@luCg7qj*O%E4m!DzxSkGj)ogSM!_aakNavg0!Nk2KnS@8mZuO*w$J z{y9eN)fkr*b-ROUbwPQ`kV(yfEZgp#GhE<9p=;wXpCu1O?D?DRNs6D*eyUl#-*3{3 zlm%0qk7nsT25K*?SPELF^kKzGihx0snI6ZEvKakCr1<7sD zLYtIeHg&eB3keOn|Btcvj^}!P|G;@i84a105oHTyWJC#tvdUIQNyy%Nl@ZDaiOdq& zdvB?XjAU;idt_$+u3O`D&gc94J$`?j^XQz6*ZY3m_jO;_^Ljp?SJCeE@auKrzh2e) zHId1xQ)*dv-}lk=Q^J_1z0189@+I^6VVBxrtY4pD@0P{F;UcMBB5DCJF!O@g(ziw# z&9Y(9<+l%o9^EQ1G0wT)nBDs3dM|iXo>VrvUw(e(SCMx!0_kSbO!|vW{2>}fT|X4m zN+LU4XTBR&O%ay#>#Oe44*3-XMUb)(mz}Kkr@kQFw2$izvEMzHD9MkfLwA94=Uz#b zba?Fd*+Y*csf?}|PB{8mff91V)t;84ueLa&F4)?vke^Wv)NyA0_29IDCvZl`;^bRk z)vZY!z&XxnuCFNRToKPpTsQ$XRpA_aiyzP6F6v28WY6X)!5=L7GD@B@UB*UttRbub z#CDZRH);wcpZ8};8Y`wRwAKqFbbTh-95ANeTNrA8DOx#P?QN0;z$e3ZSQdmGUd6pS z8<@Q^-6^3ECHVU*?|C4L_G|b=|7+(~y()vd z+xyLT(3X0$H_P0Coc;C^aQZm!@^w@DbxpV)IBz;Y#m_LR7&h|zr|+!|BgUQ;i!DSB zK$F`Xmj+4bPaLbqKJ!IP`}gy@FQ1~tR$V)i4QW;v@L->7Vm))p@7rZq$G!LC=Z|t{ z#t(t;-i}5nJ#6_DCqvfAkTDBJ?&cchppVG6`~Fq9SS#;AC^&~01T5+kY%%j(lUW6( zkoXsmKOc7QaZX@9Gimu)TmvSD*JKQ~ryH<~!=m`iY-Hd(HV<7b0Yj#4rLmt0A$$jm zqzLAf=w-H3kE{3lQ}Q*T`|#y?<7^0(MJ=wy6@>tsror!Lk)&Nutl8FD)DpXoPTNU$ zxZ%2=9Os^asFVjI1sZ{)D`MD@8qRAv&<8doa-H|a2k9pS81S9osyXkvvt=}{oUF)V z*b?iW0>$_^f~PblMnUtV3OQVqN@nyl{J0O`Sruih^_|KHDz8H{LS5)4D(8d2N^cFQ zHs+YEAK&zyyg(kXKJflQ?`w%8hM!+~Dy6EPi%!>vRO2JNqRxAtOpF5Jc#xl!tcEfQOw9S0CSEPN6;jq{Zbk@(b0Y-X!>ViNFF z6%wSgdh!fEe&IEyC8EIIZsdUf(`JnL&?r`DCGwN=2 z+1<7+=o&`4#~9~bad_)laP5CAwwk;Pql{MkxXVt0;A0h31Km)nN77E8RP-N!GQ9@B znHBJ=HXRguL)mY22+JYw;(pP~Zy5aE=Mo{`Fbf$-S#=Z=WuLwgu>L!42WH|d-p1;9 zx(g{_RA!1&tU9{`&~l1hiIbJnrmDrJzik5yl?~mr?)`0qyoc$93}V;YKLZ@#%^;*@+cjm0!ee9pKxzlaf`{IkHJ z@y!=l`THjQ`SFoOiiTutY2fPL9wG=Z>KjsgA?AePa3j z)V0xWLIR}ZXHNu=`-tAZ`zw@)Gb})JPl93R!B&jZq7S=jChd*H>Oxwg=ORAD3^O4L zvgV(A78SP2zyRR3{v;Y9Hp3s%E^Y6lOOQenfY^Q=nRmoG73toF_8$BDVgk^S`B^84 z{6Of8DBlOmi$YFcIxs&t}47NL9pn!6|gIHs&3@DXhPi~SUD*qwY3xyp~4Ru_Z(nS^` zEgT((JhSF{3o;HJJ6k!bliSTzOwMeYr8ZRaN zC?L;8I!fXAuT1$mX7MH!NZ#7Y7HrucC@M;eVW3ie$gWx1MXIi$S!CFmp2w<`Tv%F0 z>;(raf=RJ00Pcx`?*R4b0E(3j-NjxsQI2W*3smL*Y*m71B6OU&GFLB02U?B^yF+Wg z8#3}#hn1-WrQ~1t4()OLi97Z@0whNSCurB!((lzhtK*mXaL`?d4&1z=wc|*s-d!kV?cl)Jp-H`&8 zXkXh@Vjt*&ePjYIGI5ipH1PXhY;pu*JU}e}>`^6*E;UB2vbzhQhP!`FcL6jcJy7BF z;Xvo>9%$FE``4Ylh8KU-9Z+f9qEe`%^`Sm1pUpYqLTa#lR6F@++(D-?37-~=X$6w{ zp`&gJL#McK|J=t!`{1g)>vly6kDeBd25JFrtYSlLZ6?I$Zh)<3fd?vE8N@}87tiSq z<=8DqzhK zHJEFr121%k<6p!WNl6S|CBD7dG19-cGy2%^g6;q$HzVf z+O8U~%wwMB@MboA&l!pid=2 zLm(~@)Q=u5g(UYUwsj7V*>nv?COoXNn(b*HJ@n-7ju`Dxv|+z0-I4yYhktFO!1FjK z_)LsYx%qM)aLF`ipcru5G7`AoJ7os2PB$E=Ij>Zq%K8KUe}AGf4rH76f#+mtNyBFn z$*xr{l=iLp9$nx;9Ml#8SwcJ#@|zR_29_&e`HlWv%kk6yte!{&e0JF!!X~S~b2b!p zX}{x9nb#bJaLmV-399%5&DAv};!p4}{vr@Ox$Gxxr`qb^Kfrug zYw4~?7Z?z5M4c>N(n#XBZ|7QV|=leJMU04V5Bf!O3dP z(ZVwJU-z1Ma1w2B>O)v`9_>Fwki0zA?)P3C`_OsXKzjoIAhEtx@^~VCZ#Sq0qPo*p7J@ zehB2s3DC)fOdH=ZQWUR76!2eF{TmdRx+@~XG1DDsh7f??Q2v1VNvUu%*74$BC3NSZ zi_M(f@W0A6w|gom?(gtafj@*DIp{HY^7rC~FvZF@Bd-MU?+HkP@cab)@+yBa%t2%A%wpRWNe!RU1^KH(1tl-tAMwT+Eu6BDzMlMJI(N?u7`a#$_pE&l<}JD@3Rt5?OqqmBq2Nwo1zEs zw%6%$SAJ&ya+pQT>XIL74dp{OZ5gL3l`b$A=Wf~VCRq58STP>_3!|kagF~43RY_O& z_YWpzb_Wbc+`0V8lY_U)o$T9Il74>NNvT)}JRL=rdVK3pe<0}p3kgn+m&iln$16Lg z-PyYMf+SwJe=UtKERDkVWG<%PHxWTdWmD7z9l;oyQJtT^&pi^4c^7@JMxHB33Ri)U zJ>KR4HNFV;(Z?$;9330DJcnfesx1Rwp&zkoN!i_u=jL+-;j0>r`GDn)X9@O6)J(PQ(Rn>L$}_bZP8ejUx3VFL(`~0}$QVsBmP%7xGnuro>1K-^Qp5 zoyFGE&yym5ML&^ToU2by=x)v|4Z05JmBHiazr1+_LsZLzA(-7l5CMYw0NQT0BJbaY zR3u=tLR#@}sG6A>wp)O{b9;JdLi=a7E9>31v;TP^t1c3GJomHE(IwpHc?vHc+Z_g6vZdP z2!`zMH9y)_=2$QpdZuaggdW#ZzI4B5VyAZ_#^j&A`x$}uxkE(ZAacD9EgZ<2uWmKE zANStd*t9MfUuJW#f7?$DhMK1hc8(D4Z~Pa;jxvqW3lLkJBqDp5P)EhFg@BsoYTEZU=|9Wkeny1acb)*|^gT8fSP%_>6 zra_aEkZG?wd^$L>CMro;d>8bG|$A-4C?rr2Osx>#IX_e6$4nsRJ1y>}5mfr-)o8 zI>!4Tgp5_OHtHHp)is%)i}|k%RU8ZZMf2tAqms`TbWx)M_Uma5mkYLYxsZu}Y+nTf zkaK0(Id&h((wkvlda@;;yjMh>xMhBDb+)$`{^{RBST+2m%yn|<6ML(^%Ha;YW`Q4+ z@s6wI&YN@n*yc7rkIFy4T=E!7MlD%d9cjTIHH5|WD~RFs;Wo&H)1?j&{479a1cX=Z zDPVXCk0aQ%y`xdN1Kg6^)R)BdYJBMqT-;t(K;AdEOMn44L)Y5$;@mq9u+&IVNc5*n zyrlee8_xHkNrPsYwA%Tutk>;nw>lx@U+RSqng;#k^V*eenWBrRiTlAEB%?)5=qRDv zI?ZW)5y-g$CC~>lnrcg`@LD zHh{BPh|UWW0!)!9nYdovGlscgubLjogagZq9{q*pI`ORG^(&e2P(LP|V~Obi4?`Uk zDtU=vgj(wxKgf^j@>rlRGg-BZP6$752c)W9@L^4e4`lZM4d!c&mX=VOYSt?NwX|f1 zp4SRHZ&(FarYw9aH}1}UPd_z91s*jyFqpnj>_l*>H^bHD2;PQ}FMOv@zvC$XarX^d&F`HRQDz~WaTS=MH{$LsG#dKr-|mF&VAb0O#-_QTpH3kx&I}3Ho;=q+Ln-W#3uzUHe&V4q-%^Y?8OT&oRoUR`J*rj*bSup|~qIdybN zfG>BRz$OZjHzVB0NhfXNE6frA3bp|?;hW{iaH^kI$N_iAk$Qm&GoiULi2paj^>d9- zbn7z20ed|ig%@1mILg9>sF~gi9ir5w_%fL1)mJV`+@N#*lJwUU4>kMoDd%sW!Z~KL zTsiDJ(Ezb3esIWvg`7UABB|#}Q$JgxU4_{VbPTIaTSWbu6(GmRsBoNv)IrYr$OHQG z*FB(Sx}A{UbECWtfTV8dUygg+_lz>>OpjWU>@c^-Yq*Za1m++l9i`F8*6b{!&qPBQ zr%Jn#OmrD>M*A+9?u>dER8+xDYzUFeRy4mJfik;9dj)ap!%Apn%#wHOXR8--!`z_U z7kt70E#3d@m1hBPaV%=&gdERWWW07e^lT4AsLUlu1j0~kC2g{UZup()(FA##9Af8C zLY3&-#5Wk+ydrQ^@pFiiLih1Swu1tIlw6(hh{hm~30O9=#ODe<&WWhLd|mPRQ{Z!y z>1_k?$QGxlY)fZRadj4^{^8YFR!Cz!YqY~lLoK<+aFpLpAw2#tNPsij8O}6_jvvcE z<+=BQAG?vS{s=mfYp1t3S{F`E@TNsEoZz3*?9Xohd4pqcYOy0R6z9|uV_QCOjXm22 z-<$fj7e4{GztEcBfBaoEFmB}1N18w&JfvW6$flAWIT9E;exAHR zDpE-c!b3hVF5ZFT7Wqp->tw#x} zC}s@pVH1_2r(?!cPjzR-jH^BU{Sz`&ryhru_CiPpH;7 z6`PGI2ewDL+sVBx9_VJ#<_gV4ll%2gX;^wh31p*K{x^&LwPmEFwPWaME{MrlV1UCJ z|MGHtY2nT?c%~hH$*t$#ux}=8ohGZdz;w-zt88;vkelp&cM$)rLbE%;FU}m{9NX>B zyz_|UqF4&6;xZJw8lQpVbRRr9bs4{_98gYGJ<5Ym_GVjPZlJ6efT)~U*^Z~c=@E2S zJ)>xA+66XrL$Z$Gh}s2%|7OAax|V)tDi+64ZGuVz`_kUkMT^belcby$;YhcWFob*m z+TOWD3Ew|k9Q{%924qMq4tIj@sG};L@*7m~#>8sxx>MD1W&pd*2kuxesOw+HUIRF} zFTcJTKLK?d<@q9>!iCcVccX?9e%xZmij2LPF0n7_W>V-m#0c42WS(U8??~6UpwA6C zP^|2PSE<1T>#5P|8`Byzb-2bS9&z5iK7*%+I(x{oa!!7N>RtPr-;k$!8)NJyXW26b z*2f>hrAHbA`h&g{oF?FHYgIyAWLiAQNVuY|Y-ccb za`nCO(>tawYK(o_xqQ0e<}^&WlL`&Mv9rp@jJlx+4*uqDn&6Z98wwCf$2Wq5eC5Us zda^ekB=;K*lonCu%)M{@Y^9Yp{iYM@*)K;g`&)JhZvpUg2jE<*UGG##0Y08DN~(P2 z(bw{t{l6z}6jE<~CQ+(@2)9xbN`MJxQ=nUY+0_Ay*JaD@0$iQ5(6F~I!T)@tKTQL``$-W;<9_ro_F zBc*2t79bOqj~yAg_xJv;HN zsTnq*#*gx@21{R^lP*8v0A6AH^5j|=hp`_8r;TVbO{8S*ZRkj|Xz4%;?lzvD{oscO z#17C19E&iZA~>J{TmvQ%_^Y1Kj9)yn(tQ9{j)?2nuK~*1K@AvuF{+rpBq0RO5%ORe z0e6OY*4(_F+DAXY0yPs7#4cc^y-1R|JfHiZy*}DuiY07Nm8RWtyeW?<+OC>(V;wZi z=RB*QCt3p2tiJ_nkoAW&r^f%v#U4Krf$k78JGRUKG9RBm&CLuSAs4_6MqVA9yr{7@ zRy^NXR%BPVe9330fPb!VWFgXj>GWCMdeXIuKA6FARPPASUq5fJ69P0<(nV1@-I_0f z=Y7OPjrbB(z(Ed{A zH9wnVsYa2-8xp&*B(l`Oeu zg7TpwIwcg!uyyy(Hjbsm)4v=uH%Z(dvR^Fv)pzlTZeV@hQXD6IlJ!YxJcMOYhF$?D z!tMiEdIcgP)Cvy!zYz@=SJB#{hJt)$;C2#t_kKr!im zE&+H~#80v4Vo7Fw&(i#TL&Rfazlr9D`$r~73-M_I2F!y)fAtfY0|GU`jqLulw4-&v zBKkV@DO&i#%~0Wb;A1N&G^wnx)0qJr7(NA@`Mrvn!ra2Dp4U1Tt@}xYEKlobITfYgRtd~o{eLPklngfdZ$^_Y{6OmV08Ylv^HN@PDwKMAI1jGHVwd)UQ$6>1$Mnmf2bKyxG|JGT;#0oT859Q8LXBBi>E<4K zgEf^@)#1gB2N*D+FjoD}XJtWv&CbDPA7BdN&E*A}-bIe4jpIG9Z$82OzM52f@$(CE z?HV1p#G99jK5!qdcuIclhabaMcIv#E!pc42roUPfDF20)2LNcSD$joR*N8*hM7O5z z*lAuOl53gdIs+-_0Sqx^Xn^w9Uerznmg1jk8nkc)P?q)2fxD2fL(s^=AmImi<2pyb(mU^|MLo1xn}{C)P%C3@#87y^k?9f z$RZqmne=6M_H83H!VIC)VM}KQA#7U6?$XA@X-XdbKx8?}c#f-$RP&Z)2+Mm4KBeznU_DN}v_E zdqXZN(+;(Ss!kBf4M9s_9%QP&TpaZbwg$ZI)M3r>3EZMwpW4kj}B~K z>-9(1AyK>eg}ElT=RI5chn{h`m}bOxz^U*9RO_=nMP}{w24~E*V&dfetg*cYm1FR; z-U2SL$x&;m6?24L2rnvy{DFRzd(11z!F{uS!KU9irfM@hiFXR^fH?klP!A-=3E6k- zx(PF#2=_IS$(*wiK-2 zA@DgfujYBaV-qn9yL5JM54bw70&^R8B(NZxiDunf<0tCC*4!<>L|*=)Uf8{J4|1?o zU3UfCQE?MU%4yQ!TtK|M3-A24zCuCT*?gd(E%v&^lYs!HQvG*@@H$?0D^@~^yA+#b4X3xy5{EvFW+}!+c6t&8OZje_qtjqe@SVs_)_xPjLT!jV#g#>^2{^Wq6Vg zt^OHl#HS)?PBHrcVUh3_6Q3vXL;4L?iaAhN>k;1w?c{5tue|mmR5FdKa5xqEG~eu* zipAoFt$BapBOY0)2@6HUp}GW5DVrdrIjmsUFTW%d`luhKjMEV{Z;NuC3M6@y7o+jk zsG?kSkySCT`jn&Qdl&NrTl|LX7J>W8f#Mf{!-BVXdR!SxnM2Llt4%?9DjKPwJP{R?8N~s^pJ=@2>WR=tJ!E z3mN?oEhzA!;+!WPq^LsqipWWT6T^>gVf*$(A?tVSLXT~*y*?y)9wZLRlN7C<2W0Bo zhu#dW3hkiF6FBrl*GE@Gg$@k*wT!IiMZNSS-jJ5akv~=OLAK&w%L1aIV8GT~THl!d zPHl+&-Gow|GRpM#CmtAlKq5=;n;>ir_xCQ-5aE3!N-wkEs&J8Ac_lnIcKA>-Yg<~i z^d?5njP{pv~D+*~a3K3M#DQ=|TaOzjUTvunlbdK8(=_K`E%w zCAC{!K33)SeV%#9*b+sHBcylQGgmW;uRVbB^EmVaOICpv<+fB z%M>N$#&|;t!f@iCKco&q715Vjhp_R)X=#V8;L@3UTQ0ZZ^6egsqEMAp;-YQ_bV_#& zS4mdhUW!#ES@eb$w6wxEKKl#V0vGB|eZllg@gC^1ssmvoQZF7y(;nA5VqTZ^j{Lxf z&#E04+ZZDc=Q3r;WeJZFW zigM|_y0icG*^k2;Ak9*jKa(+qI!9g2#X&00SBc%W-{WU-R4y#7K8W6Md@1=nDmLBx zsvu3XsE>FApAK5KzZN=>1Xei`&s=C0{|Pk1kQ9I)v9=7F_hF%ZN8not(7OpC1&38) zZ<(WQ*B78?1fbE`kH2IdIcbl|CrUvpeXB#eDh(7yqeYZW zx$b9op^<2X}r0BL!+2Kj=OE2N`))WRi6sP3%zxaSx(k?lW|^KtMCw6 zwDWfv0LelQ@ZON|Qv-<4fKOc#kkfFX>@W9;2x_FWB+nHmo}f4J+WEi z@|h6k+I0(#-^cLRvf5wKjwzjb>_Qvpi?bYfAB=3O+MMKaW8FYZh!84$43eMpgTK5D zLcBn%qQP^(GdpBFMtb-x!>U(=P{8&G(nO!iwg|c=4NaY#IzLsKI%tp*U2q2*tyW&& z5Q@(E8w1;*Tr{_L+vd`m_giH$7nkd57>$N)&aZz?94x5765CYoI}6WErB$E7wnU+3;O<$@8FYpz>%3t}scKtL zp4{qRl#jh79jX9)bsa|kQDPQ})Jkm9QsT&V(OB&Bc>*|lA;agY$U{$@r+_!Ux6hOM z@%hmVY>gI}4J6HXTMO&6KaD1vP%1f=)It#dZD>)dWI(U7>E(;S=EDarA?<5W!|<14#tmWGa~*tx7Zfo{KwIAWDm@P^{llwb z?>~c>;ehd}H-|sZpI|@xd7iRNk3y86Uy8<5|6uuJ>Jth4%RK65Uti&bd)BK%D&N?1 zYn6gTy!c|AC2=AEIfbX}JMs-Xn~XQ_rdf>mD$p;zV`8%qm!)f5dfnV&zV1hISHo@C zF)xL4mz$&~BmceB(wDU{kexL-@XwUHQd{J_J#F-n^bh~xS1@&>!&;(q6T!haJdW$D zC?DRQ4jw|uq=>~)`5&#RUwy@+K)_ZfGYWVaK4ZN14gFbDNY;UX~C zx`A2PCwhpC`QQ>_kwfi1XLsfj>i6!c|4(sh4^FEHnCYHS=x`yss#!e@u9h=X6W-OA zHxmLY>rZk7*QPP!#9_j!ud~&=ahez&bbPn$D1SFk`fmO!Di73Bx_wyZ(t&Ib+3%7z zDVk*tL}SBHMp_a+%zhb6j$aB&%XHsG1_)p~CbwM0a?eWIz@rwgrCwStYI%4%{9*<4 z&OfO$d~tDEKJM_)jzU{8O}*XeJ3o)$P5r@Z0kIV^m$nwf7N|ST$Eu%ux*WLezIHl; z!urSd;%`F4pJ?iZ^~oCx6NHEG0l*Z#9qvq0_~?1$sbbhqQxeJB7?+9q)YHpl8={t6 z*HoWP#<{%xsG#2qAG(iKC7nvW<~&kQ`~o)F+6*=Qm_7i@9~iH1S-cAtrx44D16+j1 zh3yceNt6SSrd3>+7=@{%(*;O|siyY;O=dyA#jI73Gm}QE==i)3`Jy=TxuiyGY~OPh z@pR6EY;x)4ON(tvMdB&H3>RjgnPs!W@Z75hCeNH0JxXzUyIcSSBz;a~`|ZHFKfTyP zd+;?+uJdK%wdv00sB6Q4M+8QRkeG?k+7(76@MCKhX)$OwjAEZL=OmNHl+PO@?a{ao z`{e*p!+pF9g=zeAzi283baFLDd0o~(`=b)bw!aJVkeCcFXE1UkgDgYG~XU<$A zxBlV0^q-eP+mCpLb1_)oP6kyXPKwUr&WfcXuBXu+U?g&rD6oaJ$tfeg#k;FK@fGJ` zZ^J|`dofV;19^u2$Ah3Kk6|F1zYrZQhaqT9j`EoA|H4J_|G`D4r1oG@hzRv?(|;4? zZ`eGq9Ndy3iOuy?Y&dph^Ky|Z*(dsGU;ge0 z_~+yy4uZYxHAy9X!@QsaS>y>o%com7ywXd$TvAPU37Wnf6Z`ul!pqb0Aat8;_%n!Y7p)uj97G6F(Ju zTaVx}^Or{l$J}f7LOYOiJ$ws-t}->3>&wL5(*+XEr+lfmVJi7#0(kDE^t9j4M9gd2 zD4^Cki3meHD8JSikMw{ryT#{P`A6--)#TUA>>KIV4tA)LCcW2s%Lj#@S>r>Z@;gTy zJYh(HO-9QIaWcIfa$lG2dsfNTtg3ZTsei7&q(nu(q{Z0&;Pe~$N(8{3%JSe|p`PlW zDF`}XX=5}t8%b4ES^nD#px=Cj} z30~Kr86~*21yFCokQ*{wq1AGsMT{Ht+IamL4d=%k6gfRuC;y7KF7*Uz{Y(^eo%iZ@~wP7nT zR=`uBandez{LMw2)=;5h|50`FVrs*f#hOL~bGJd)Qo^%snwk|3>z3_$w+1#ABM$2=Mm*;$6Z(`_DI!h3 zwL#we%706pc{sqw_FtP2Ss+2{h%Eh-B?#x(lvDAl%O-~(i$?u)@Q`1?5v3S38Y~yC zJ$+1&*gkdaOj#rO&XV`12f_>Fbz8bCBxad6{%u zKnN^C{@wlj=NI@$B9cxi>l1^Fqw*J7#}=Nk(D%WZq3q5N)jlY}ia&wvlK?q&v$^_W zbsF&Ss3B3eftf_z(3sQkmlhhgVU%vm3CA;iK)Z$}Kee4Q`s0-jx zVJLJ==ohi3fxnM=;!B6tTGA8)OpAQz%&P%8o%%7P4_(kUj#6B}4g}9av9@7m^OF@R*G{T0TSQWVUPS0SEZMCC3D8Il{1-}}) zVu><9H|PP9d-kVr)vv@LIW*5Wbo$?*;jg;I@g){brQ;@n*^UQoY-ar8)S}Gr)t_5T z@(c#GO3*AMt@m1<)+Rt~v7YJr>=|`fLL~Ylv8!&q!oP$b|NcOd^q5gB+4z(y)zuU3 ziO~-Akjcz9WthD>bb_bLP-nPiZy_?*@R-|{p&T-9>Vh7+F{E3_4Yg23-xW~lY)B1W3b`RGpmX?r(C~Nv;RucTUKn&n zVj277HyXf)gF|i6OH8&hks@?yKfK9OQDakw;rTZ}DgM=>}%o)|;VBgfa z`{}1T$>NGOG!Iun^xcITBLKB>ZI|)GShE>$Cj6vS!Q&;ONd%jS>q>uTl*3dKBj*r^ zUyzngn&sjzb;`dFeIN(U0+wtTTcglBVtHmct*ta5all7oH4-M+EA(i$@f4w14yPqz z`6WNMmk2S*4|t{hYsz0Uke1|eg_M)9cc6ZG1TnAk(JLFt{Z_X zj|n;C*tyV=qF;YQAA8cn+3DkRmBbk^k0HwCWZ!17K&vVi`BYg>Y2Tg|~ zx#-T$rs>BapweZ6GUW~L5ysyV0xX3Ju_s+C`Au+2fx5%q#`)tw7jy-l%J6 z|KQsjr;+PUu;r;IKgrg=Lf-FH7^Q<(B;+Bb;v*+g4YX&Sei1?tok6IV-MY*z`e#+K z+=cO&XL719HLOCf`z4RTGthbN4$NTt{`Z34dscreImyH(r9&?2WLu+Z2qPaAj|Ib9UV z52J8}j8`BDybJSOo`dVeXJnVS#|HHpnmc?Onu@L z(flA5E@d~7D!4+yL)D}4_OZ(78W#5<<_JNe1S5w@OC(h_1YMJS^B{1f9w9sqXu&+= zlT%@T>PV3$9IKNBX2YW6vnie`u*V9~T$Ri3z&5ZAjeGec8~FDlVqtURXkWx-kt8Zz&NPBHOn5Z(UO6tblxgFUP51KxcmU|3*hOHxVEQUrjtq(JkiW?)_NfS|7i-UqXQHWz>&%Ez&sPm1fV7ry7> zcOKCV!iTxv+zIk--QhG;`Jk5-R!xP4v{4dx{sD)aQ*-7%XMon&*3)~s4bStS+Es(} zzzCLq73kAwC_H(RgW?!Uj|jP`mB!A5+RXJ$ibm_P9JblrnC9$-NhgoC_0NwU+J?=d z3JwFWlcJOST|Q1ZZO)MNIsiWMWZWrSw&^h3O73`|Px8ZJ#P(gWm1ZHca*c{g8i4n0 z`XBA$C!gDVoQld*`v?L z&360yS3=VLDw+AH5$sg~-}g7Huf7SPj}>~94O_nvIix`*_>^Bx_$?ANE)lK1R}y|< z(i-n$IL{{pldAi^A*li^=aOGk;Re(*-LS;Qh^UHG2Vh|G0GQN~D|7w5TQ-;XnT~+B zP8I_2AbBC4!!*DQPS>Kr8$gK&U(&n?nSv1j z-e8^;)nf8!7TnajK(2*^oE!o9IND8(3_RaymZx3o)iyHbNLX|!wj>)$&&0%blmm#uY#BwhLuM`T3e2f8)ZhZ(X zMnaaU?bW3>cUuMt|I2_a=PwzIgK9~mVG#`f0+=9MLDikw!#$BrM znYv)G9x)XXpU)s=6e9USMWhG6AuGmZqYgDlXjEA$j6j0%aHHpOqD;c!?CzkhGwu=? zzr{CiQtCEuepSp(xLpZ>(kEYVrS(ZA&~@g^6(aIo_=G@pLGJglko>qfu^>z@6et0y z1J!4Ed1^qJk{;&J1;aRV(M1>*_H}hkpcm>M)D~gnRQG8hb|TxkdE+3AVK*df^n3du zc?pI_sX_WC{V+Wh`eL$X9X3ff-`u{SHKQC}8bEf{8Eh?jtThWAGCAZ@celnbrAuFe z;aWC)39&=2l=T(truz1W8o=;j2*()zE0<4Cy&v)lDROqDfiP@4+|9t>P*XK8rV|Zp zYZOVTM-YMAy$_WU1vpC?&cL$Jxz--{UIFcd3hd9|W%5^R>DM16MKM|LN2ROrzwzAj z@#4Z4TGS_8BOr4f(_s3pQtdA^MZZ%|ANvH_Z$50`-c?T@QuF15b$!4KydN~(pU>ai z=F!?EVowfvn;2M4toxeRrwm31n6-~LzSM(-lYtu{ii7*V*Asc=jXp929U_Y{O+M1Ns+SeA1F0; zQ74g5?vm?{lOss5rGxN7C~rASoq6852!o;@W$BA;OKw5x|CTb~Ly9Npel)Z1`P5me zCU|laXNbv+2kB*d+`=!}*e*67xvC;Jt{5{VhOtyeB-eQQ7FT>yg^5cX%V~-gO+oPaEm!yiSwGgTPobQ>0svG|;zRQ!Ilq41Etn zAvL(*-nAyOYDt}v{sJU-Vm($j2zhAiziuR)?Lum{-uC$-Ttf06-hCyiNdZWyLO-z) zxAb1~l@$S7PgYZG4H~0w8JgDHhwRo zJWZP_~t5 zx{GyroYcFk1<-X$O9t{8;Tdm}&Q0L&mng-(i)DL}Ivh_XPjk`9?gxj7+GUM)2^K3! zeOwpbip*zTAJ!iBnY`aEXAwyqP9?jy>Seo{Z|E`%g{+-lmCCm4ic5MsTZ9J|29KUi zWRp{^YI*Kt+EeP;w?5wjln+mX^8q#v(ck}ae?`Brt5M?HshXm_u z<_v{ENn!HWhe9b`<_69d4Nt2z(O|DUj^ZUmFyT>Q93Aawa6Q#c{#B z&8Bt_K+g1owO~fL?NwYV8PQ?6sXM9hgYKHG@6MuVr8OW}_CQk`x06@yJ9Ne$R^7sV zQWigJA@93t=J{}ODu|lp{Rz*@)*A4l)7>oNxm2N-g!kiU?Dn`s(!!$t#=b_!lN)O+ z>c;AM4|Ks+TQRpWRpP%Zh>hc$CX#g+%W-43{6v-Yd&Z|2odCSLZCVgHF$ln2{8k?ooDQwKj zDY%*UnPLY4U}M`?U|2Kz5GDH z@P~l_r$%vGBX%lHdDgRUSCnsNs#(5biYRrnj^8fDvMCi5?+LTUqjeK0#G0)z3;Lwq zF<1%`=sKAqqLlj)cP}sXe%x>*H+0RXbAvSm4ftRGx}p}Vdcdu0eUQht3+l%ipqra@ zs$bsU_%X?z8(GOxh=X)Bcot+Tq`5bI&kw%%7OJ%971Y#xF&(RX?0PJCaU?{CBe6hB zk&GDO1!M?*wjgfWE+!lkp|^^ttW}!0r&lgIO=xF)BOs;$O{EF5F0NC1a66@7SHs9! z`}FtZ18n{m<_h1Az5ST#5M>LNuZK=&3|!lY8fL;u9Xz_R9Xom`i7L%Sb4c)}>02({ zdhuR%t?5fYhW70lrZwNsaD(Rv|F+w1ALX=IH+TJNoYZS3=DL~`(}`8P`Mi_(aI~25 zuzOr_<0AXc@g#d)4Q~m-#J!fWd-I@a6H#RRg%%}E$uj=4JbV91hk3u@zX?7VBM$qM z4`@uZWKK!N4#Z7efj8;!g9R%UD;RT)z{w7Kfav;K$1SaVBejCa80YGS<>$?U(`Qg7 zSN47=#j9=!^Q9{E{g0;(3a|*?t6(o$D4<~dS_S(&?g8sYpdo8LvF6%rTUqdQD0UEb zYVYOiSojtZ&@nxn(g|QMCj--4&5Qx30dzTlW8J0uMJW9Q4oG2u~(W(D^m(ss_$}>6Qds%P2nfrc|nHb6H>YY z9YO*V*iKbZVgvfJxsp<1iV{=DkFl7Vt0mGro|;VIym_AxbTzD9?^LQ3GMEhF)jMG< z7_K_m3<9Lnz-B5ao1skBa&C${UQ11Us3l((wvI9cT4u`(Z46x(MElot%Tl$2!6 z&%OhoiB?`7Eyy*&^%4CZT;V6;qW>Fjh$N+B@7?Y8Q!(Lmi=#0fh%6`g#%k|AU={@9 z0}7Qu(v5FYk3OvZBax#dKfo4f2I=a`Y};<#5|#jF)f6UexJs)rsUP~qCBTsFbE+W7 zzPQ?@li*F)O14E-@Nge&*M#600n3D^z)uo&Eb-DJ#=VYM`336ln7kQHd9cz5=NnUp zLqgjcx?Tpirs1vIbZXd!uQ@XGFDV1rw2xX%*a}d5ikp7BuA5*ek3%84ud?`g@r{i5 zX)=~Fa7aMH5+!hN2xwla?u-@{0Nsc*$|XeoHxW@!4wLBIgMv?%+52p{eeLFjm%Yjnj4A4^=(i4jIox zg*|)&7)CCzGphz%HjTdo))1uo0}KZ}g>SlSft`pq**Kk|S$V?iruzhNF8V-AH+&UG zz8@)b5i*bp{ypikC||FdY$0%1dT7`WXl&oYcZav)lQ1MC7ZTnQ`=@{`&DPsc?yMO@ zd`oa(hLNsUn&w?!wk}$L{=LY8Y^H;1B2Mb?Yl(n-uw`c|6kNW1>}lXxkX02RdvX)a zm6q5d7rqCzvJ5K&t6ooR*nW6HWF?J-AdIQ`CjY+&0t@4L3awK{*=oeJ%v;*)@$p0c z&nQSgJ022FiFbY%?BxT%U}yhS%YZSuz>S_CttH3C^7i&+nx&eYYV)^A zc|c0`<|&j2n&9)sx0dQfhq|9Lg(Lo!@6I~f6K*KEcSDsmC+%#S}JN=tOJ=3^OJqC$t08JT#d@)@+&GzqWW<{M1MBtncnK=eij}R42LC_Qy;VTf zTlWPD$UY*77$8cC7?gk#A|)t@A|gn4C`h-|ra{3LMMb(hr8{guLb^eu8#kMdJD2AG ze#i5_5BKf9p~U{hT64`g=9pvn#5M2Dr4ei<^BZsoueWh}CU%mHr7d2-ta(L2oQ7R4 zfibf65&xYxx}m;95Ai-OC}1KTLqx=Btgl4CS%QL+-vS!Ba9{m#BrQHoMIe1=x>pHl z8=I;i)Ukb62({0bn2;=@=_?SjE$=hIGtIvs`U(v;9`fmw$N*}PYd_$=$HGhP z1OQ|@*J&kYT*=b}pFh=GRwi_JNC;e69VcL?$!-o~ z3w_XaFkhY}ZyDz5={}5+Yl8Mq zZ#)hlhQjY*6fmHEza$thbjec(qfK?XC8TgURQ{0EaKcV4%04Bo+nDzUWY}(jb_VC^ zG=>^DL#}26MH^BZR_chg02_JIIo1f)Gjrt$466;p8GvO>MJ5BnQzFJN_ft(K^N&nO zY~NmVSZS4ks&f(TnV`6DR$EPnCHOG;h_z+zb=ew@JXRu)@=kU6J3h^?tR zUqE_4Q;gso>F#9PT}EwMC^vxjhLfiE9s0cA1$1jI}AcMkI+$Z=4cSVr3H=5{cqeix!>;lR}n)<>_WPiV1C!D z%61Y0!=KHptA(s@l6`4-2W*RDz_h4T6PN8%*F8R&>}2xnT>pJt3M2_**ws~{t>tv- z*8v-mKw7_E{ghqEx?chfg{4<%114two}7QNO8Vsg)=cQr8DA3+_NfZ0Wj>+xP_v07 zO$?%0b^?xo6nzOyj#YT158-O+Tr#ATJVO6D%oqb;BWcndF0w++L}+-9CA-1+c^M#X zPnSGgji00Z@%PeH^M z!8X7kr~A@FfK}kKZ{df}OjFVL4&BpY+Ncy`P~BAmK53P*GTMuq7xd3p>lhM(l{_Y9A~jYkAj!2!F^3K$PeBBY zLbCrV+T7c_^25Q!VbKla*sHG|hK{;xfQr~8`;owZy#n~Brz2eAk6mx+J((C!+l?uL zz{HKOJjQJqkw`M|3@&4PIhL1|gC46MfL@P_!~rHrf#F$e+;hS|-t69)V`2nt&-Od* z{bJ(@;kOf3f+ZRyH+Czij~N7xV8J998IW{;aNb%Jp2soJYB7OM)6EaQv%J@`8z5mp zQsLKrUYP)uwtqbdl2w(64`E_N$BHmjuQ^O1m?DWE4bb?s58s_5TT~8P)EpW@aVi)e zp*thGa0Tui&#DRkc>s)dkcY|}s>z)g?d25l%L8#2V4~;o!RZW6H#b2Yl-Imb?wd(8 z^m(l@N++Ir|8WJEKoi$$Uw%dx!JNZBC}u6751Rv2l7UgocsjAo)|&`C>c=rDiV<{Q z8l>(77C;J2IR$zK=;h>tUg#v)7}(?_0Yt{3mifWSK$S0X&mXs{|C~Tv%<$XCsH6vE z35Ii^_+gHS0-#~33QfIk@|>(%!RpssI$@5U6P{bwxmTqRx`Vl($&^2@0;nc%Z*0;G z$KiCC!WMnvh&W`B5BqO+((3nyoja2 zSv0{7b!wfL-7*}@zuc+#JAj`=rCCS2{YnQgyaDFG73eC?L!=yx$^9SD25J3k_aR;2 zS_e6e`-^sL1e^xdRUvRFLRwJvAX1?Y9a!_##o2ktO;_FHcDty6BB6q=<^`Kr)Fcao z@^p6!tI{8(gQwX(U6K*A0{JszfgIulpU&NTY1tt236zl0WY&+@&~vhM1{k-y*wmWG ze{0o#A@ICrzJbs~FlC$h1GM6KrK*bNYe{NF&g_X`stmf+V5T*utmQ7=2Aa-udwpb`1q zlv&v@ZSC`ceR8s$ASWbPw|s>??pk2~P~nqoj&F9bYV4c((yNw%iw^LZVuFudcrY&La`1_!^6W3sXwnTx%E?v z?w(+t8Wh(z6U;kdooS5hiNLkwBgbu+!9BcDu7kA9p`)A&8@dRm1_S$0Je{2?_^ zLd{4($NA3Y`Ce5h<=+iQ9wkHR=|5|8>3}L^Di?)L28qglhj}NlnC5-0ta2e&JOpnZ zHP(g|DT5Tbye}UH1t+9?QDM-t^~>9T#*G+);SDC)mR%DaIqcsBktWlw>GxZw1_99q z)s;K>{Svs#HrHd;;(i#szt4qi7-5|iG$M|VdASE+-1nT{E^YI#d*~1nmAl1$2~Z*e z0WPWh(k@l$H;?HzU#w?@Skz>?1a2n<<^JByT6oD4CkNs?TPMtCQ2^0i3BNIpyrloSHAUEB1o()Koey5p10IIJha;m`VQ(5wtTo z{bdc@r#eyFd6GAQYxCJ|E_M>i46z602L^;MYL|~Bo(~!NtGcrCE%La9N3Wpe(h>k} z#1;!u+|EiBK05@>S|&+adkUcjDQ9 zTrOQ(q*3L@HN*sEomqk1TGXfkgF4MH-N+KgHoU!zHlMT}1{knM+bm0U6TUB|) zk?6MareO%){%N@xq9E(rP@%_tk4RQZzKtgCo{zrL@CzEli`Wc&@e)4%`Hx-ed5VgK z=0~If>-pyECFRh}00B8YWe}Th^m7MQt6Ze|8R^Slf}pc8iYpJcpYT4+`YKKqO|jEk zl@mft9k*MyazwSGexs9R0Q{&TjVuscd@=?e07A}SMUx6^_??L0vJ`hADxPHg^C#0beqWD$HjbQHN!a}qaSxvq=6`k*LUVjF<|UQS2|-Twptzc=Nu36KQsLQW%$)7^${cw&07 z{vXdDE`5d>Qp)fH1>Bmta2%JK=0i`x8A62XF!QmkJ?G)>iKLMs=XaStee2WiUZ=|Y zn~V3iE8VLD2cpX{8-34mAbf_(aUj8syFdL8Z#|;&a0u|361bIQOu=T*-})wjXFt0b zLH?rl3X|Osut8)-T;MhWT~!5WuIUP*!z~i{e1=cu-qnnBSO`-33SXpvmDE%Zj(l?w z{F8=y_H&9+^!2arV!1f1^Y-SYV9;$A0oF)MtTc6FrbQ+?>o`I0O9Q_5mSTO(X57a( zyDvT0)aWAqui8%xXOl!Xvr2ApU8oP^deUk73*cZ+lLt8dEsThDfOYEOGm9&5et*VX z!&}{oiHR6R{T`yI=edRrg}`%bLk{26tLTHxzeQc*97z@%1p1j~u=Ir)=ts37_aDUh z;5ju10p=CeaFEng6XsvHLP-2|giw?CJf^*VM@+^Wz9XuYFNBBtmdZdawgs3imS={> z4;zMj_ZmIJvjwD(USg!}bf4`ofbOBqu@1AIVu3VcP}{opUP1-zu`GJtRYmB9!Einj z+flGIO6b2b3Uyf~3=4FbBb5)}(J{V}5piF^j}N~dC8X2eRT{a|%Fl~`b4zY+?1BA^ z+^-*b--|jaHp9O_6$*~gQ&eYOhk>|D;}g}b{pCmM={T8*dS17s3&J9FSCsW znH?TWO!;Q`l6UP2dj1qg&I`ARV}UZR{z_4Px_>neQl!Aro3Ztg$+rf{x>132mvh-I zomG3TPs~l;Z7eYSM+U{8D<0zUfpk*4i;OZvNGxaig&l4h>cIKX@W-2XAjVDfAY0*1 zhy;#VAx~mKiNf6Se23!&EzBnXN$gP@<=U|f7USAJcx4@;>$A`LB6CvGVHa}bU$<%x-}R=$f_^+L22PE8R2<$G)&rXeFk=}k=#{db*@s25o;|hLXK+nv|^9#rren4{SAmEwk0Rj!Ev*+daA#9^9dd;_`Z zR&78S?$Z3~zS7y-wN5HI-WKHV2xTE~4*44E8^Pwm26r&o#av}cMaMM<+)Z0BY2H+% z#ow%2gW{JE4Lg}i5fb9u>g2w((xy%gocIxtpo=Uj6q~=l@0gdT0lkjh8Z_s+XBnI) z&NLQ(A!YH6cV8RV`8BY*oP-q4;!9w@yCNM5zuR+u-@gyP=l=KVsU)f2aqh6l{3XU} z;&+yrACukS%K8oTgf@TBZ@;HRC;fw5Q+fGHV$yHLrKE02zEjAm1Ura|g|pDv&Tlk( z$)(dn*{59ID6QVoio85hGa+4k<5fm_|Cgb)>N2dzpi(roPn|*!`4WUW*9zB*y)KZ# z#Py{U6_cLcfXRt@h{)Ol7EX@sas`Pm4DRP&|B6zcQJiv2GZG77 zQEo3Tt*!EY2(ifJ1!c5u{mR$4<^xN0A`2nOSr#^^)UY24G(?t5nS+#>X<;;yGr*Ia zRhovbQya>&$0{!VlMQ)(-Tx(fK72h^?%56Uie&9bfI-t~Z>m;YCI2|ioM;$N^u(bQbqKOX5Ibn|UHfbgU2;>*!I+FpBCj%6j3qH=ccT&zM z`Kd@oHd-H@fA%(U`PLlh&G$7@oj*2XN_F zEPr*9^85kiQ~t;LTcE!E!$Q@VTzn03Zw=@w6l-~<)7Yyo@up-|;a_;=CA9)qc8I9t zJMJX=xfBmuAP;GJ6v6RcQ2PU#0(Yvvmv|4R*Tof6iTzQr$k7o)^ruU+@d=F{A|3~# zU9i6?@S!b8&f$6MWlnbMHn`l3nIl;R0x4hjDV#s=V}=SHQTc%jzxI8EBNVu2aLP&-y(Dl0GXQ* zfbA-zQ&mo@RD7BJ@ww=%HJlm1cQxo35Mn)-5@T}=_T?uugFacMpz-V_F5zoY1_UjpJ>;q7nJ=)H{QsStvTzc8CDhbRyWMl zG`JbYeU_d`M16P1_k|C+DAk|UZV>k*B~BhT%Z>>PAx*d~odiGyZIar1zGRM+$fv_~ z;nPD^O2MiXvZ)F)bBK8y-M=W1hgeQ#G@JtJf_)x45H*&wErp@{`3q`4^6of-^nf{! z+l~!%MX2bfjZ!eY33rY4V zL_^p?1DH@VcCM7@TJFK@AzBkh!IE^X#qUyfNpcO0Lb7w-K9V3uTht9huk!$vAvJ+6x}qc>29G%**;Iz0RiM*r zPX4I5rwO@i0GB$!xiEw5$+waZZ$zlz=#j)FIiVYDy`UlDmOICTvd#&+!icVg5=7P>TKTUq6?7Ek+b)l)_ZuZjpRp$op*9CAFOw&4l<1dyp%*vlQl38$)t>iZkEZH`G^9oL`IxJ}5Cs zg?ZEbNI}G#FVE_13;+dfNaUV$G3m2Sc1+(Sv{}$Bf<2hwfsb;UmU5}fl2K7U9&*?W z0AGt1f)4O2|0EiX6tNS>v1f!O#W0Q|Z=A8p$rCvoeCg?QyxG|u;on7^;AAok&~hqS z#;&Zm#*&Y#JapMiRQ^e|K3?#+B5R#rb=c-9DKA#MXeqG)NN}tA$7J^t1qEE!Ilfu9 zGTwmW$)swUnP8$>Tzq#pOqliDR4y?Qe1asx_JHUBGQ8<3gihlZj0c}Ywxr1ezLYFd z1xC$dc|eV?;N9(zGEnm^xu<#bI?cz=Wa}{1SCIDE9vF|_wnxSnk*;ks$>j5Ka-P(k zlC3a?NVNWZEXnmIUifJ?#rPi^^eNG9LM^&<>`|a->X-GtANM)9nO#G>i3hW~dyP&V zC(-E@xpKSH*-~4prm+Yd4Dy?G@+`DYKkL=5?GbkY+mBsSwCh92OB;yY4XN`#eTSt6 ze?o(H^5OWmXy5T!R|s^2r0XWp{0_5F=p7cm1FdPVX+ZFCeJl0+r0XC&+*xx*7>?(9 znc_c)f1alS0o7wX6(=huzHs6Nc;^IOvH!UZR8Rs0YF;FPqwY(LS(OWV-xlm_4{$0H zEx#;+lE&N6VGQWhPFCi?d3{z#DNmo<7(HDObh`BO7C~mdK<|}!p3}5a8YmTxfzM-} zAV)fBJ+?x_vgiBEj>-!nA+LZ_LqocP__Lk!%oZQ+s(#B6-{`M0-g%DUHM0bCk;!tmM)HzL9c;ghoJ-Eupre8& zmDpiQYW$va4PUD&NbOy-rBz9gtAGl~1QDjlEZFLWK$53Xvpf=_VTpD*YT~e){$jdR zxgsT4ryxUB&jCPkT9D%fshH`W9smgo%10p`r&TxW==n8@F@flBSRO=DgqGhJmd}*L zgnY+&v$oR#mpx7KL--dyPfDSn)9PD5s1abLZ)%r7!8_yq9-*&zo0h%x+$Xl8*5)IA{(-Rpo2s%vBYp%dm0ccUL!{ThPNSxhCfNr|G6Pnc8TCCWZ+}w9 zn$xU1!H6*DeYG^rg1*GTfr0!5fWK`o)FKyPN&<3Zq;6{u7CE|B{`|ga%4iS}NwWAj zC{uKy^9e>+ehjTO`=NZRq#G)lICIQ%$_%BtAIM<)&Qk~%Szz8(bTp$`TFr9|V#sG? z@|zURP#-B}r?fVh3DcL2nq}#w%Sq$-6ulfIKbj1%`3UEKY)%rQ6GZPVCLd&kDXATg z&?YRbh>9QJ%T|wzhdj6w(*LfSh?!^MSP7$^^qC%tK)35n6r0Bd%N5USCL73eH7#!x zfD_zaql6w*po3CaxS@u%Mx=lX-_9vOIlK4PL$`|;MJ7+Q}9RwkTWHM6l1IX)SU8Fpwor>N5Zion2D@%AuS>}2jnlMoI; zSSSo2V+mMdb|N}t>?CSk=MRMZiNG`V?Shue@ytcO1C|80&oC=(1v_qSk9nO>I!tW; zff7;%5!ez?(}?WcZjt?~t+(}0d4L1Y<%GRN?6u!u#?6TI+yB8ICG5KxJzW0V|NZxG zh?zhfi*NC7`1b*_^{JcR@Vt0{Ma$^&P&tWyLOGP@6R`d>0hLDDaAr_zSJwy0b8H@m z5xT@M+cwp486C>dBiBG5+U5UN{4CkPzyMOz*9FbC?0=@{P$P0` z9Qx{ckTbL(RsTkr>#?A1gF*-DX4nHmK_8c>;IR4GjC!8vm5FXF$vQ)2B12Khn&{f} z_SR)&C^ZShJDk=K3c5(=7ffY}GUp<+U?eI9wpVF2<0g{$`{NRG@!aoB#Oij|Mn6*w zvTzwsnQjS00ywFXBR4(|&5$U=A}-kO9BT9YNi4(rhXnvWi#uod^^ zWr3RJ-wzbFhnLX?O8Bn>(5C^3+6M)8wage)kJ`hem}D12q5Nb581FQ|nYO>MqeK+U zkIcaGBNxo82HHQ7Zr<;2A=>i;yiI0{*VfLIAIQre3yl)9&p|Gz4ozjP2;mAX?{+F; zxAD}x6nMB5cEx|!EAacI;*8xu9x$PRsn-?AC~t;^VRs!_ZfXGb%*rd@I{nn{Iz{VRCC{ z9w9n#^4fjcRi!gD*G4SeS2UXTqdE^@2)m}q9v?W)>XF=v<#Uz)y3^x;PBVLcPOIEi z480o|mOkAYjnHP)jY9N&vh0&8Hd7^h@##YgGMCgdbT2gN?NzvO-eBv+1~Se7wO~2T zr{fNs6nSWDMS&Y;otso>N}QE#ZN~R$=cV)bv-sFgH);-raNk z_McBY;y%e<0!}7sZjI}=ARV!IGwg_zb)RSf<9s%AsROuFb~;O|`VBSpFk7-*d6i-F zZ!nr-c$uU#jR9apsrucAQU*mFkvo2CgKRximWwD#*MI!#qYwxq>eD7BZAh@9+hN`4 zFksen<_hn|oBa)7WK+sf)P5^OWlHG+Ym7B<3UDfy00qF8`JZJ>(|`HT1$qOTG7<-s zry_@} z6d1`aeRvqyEbl~oT&b@82fZ!g4IBuZx2cOWWF6pK-le1m$i}Y0XD`*nDbRQjFg63P z1bdHPef@a-hg|Gcs$Y$4#C%)b-kJ}SJUV1dR*(^_RCqsV$p3h4jj#nsfRFGWIlQ@i zHp2>fCjDd*&m2GY2GEflKP3m%-radn%ea1VDcZmS3?$I3Ahb}t@2jCK6uc2~k<9iU zp@|G#*;h!O3JJAAu~Go2=+XYtl&xmZ!zg4sdo4QeF<2M@laRse$O)S??B40Z@w6iDKpHZTY{kNSpFLO# zE+<9%n6Cr+ zaMxZ*fcbnBmKT{uyNmJws34E7M}0SHhwu$I!uQZUEKGYx8xHdqtO`O`?GR@@dPVW zoLRNdu`)&+4UGqTTEw3lMe2ilSD&zcklfrl5mv7n<-|P&=rHhk&mrj*oY^hRR^A|Xo21L?J^5K+;vrZZ zrT^LllXZ&RtW!NC?L+g+oz~t_E?!x8|Zv_Ie zh`p$F$>f%_CzO?+K*8?(%WWFSUJXd6%#P%4ZO!eg{HO*O`3ToM+_iPg{Q0+keM-a? zv^nJi{Qb91!T;Jz@PD5Dr=SgcPRF&D{`Xz>UqAiuCFEU9b^-#2|Bp}Ixh_667EP2(msv^_j5V+JXOO4r5Eo(URoI1rbeZ zfb>yKzvp|kt#^evm0{j_))~sU2{;n-pn`7!HlsdJ?y`~j55%Nb^Bo}X{uyHIZG^Z( zU9ASi^90D(<*l!+00%_)Nbo#a`6kD5>ye5vP7mzS|zt&#)c7j))bLsJROXwPS&^s^2+l!#ho^R*~vq|j`){gM&FdgPCd=$5K zKLCb$^zyoFq>IhWw#uFDox!aRhpnUz16x2sIVeAwFV1{NzXkpiMimO84epXBH-vK?XDMf~`%JwuP$EE2qQF}3 ze5`N1oX2*y=J|oR$sb9BTsMpN>q<29hLXDt)*7Web|Md6Xf`y!Kh}Ow-n{eQ7Lzvw zAj6rDktPpzbwR=TV6yL6-q8Nvngz#C&H#s?lXu#}O@VFr`x*qo{b`bH&%$Ra+W&(E zb44DW#S4##u){r?z zzv_?`I$&}G(~-P-Vg0#1lEX^{6!H1lK{W?Bg@)BW|3{euWOsP9jm>Nq?Ud^L6yuPJIhHY+w!Qw6sAYGd~u)5f?bub@|m+~}d3np?IuX_*W6@iOEa z5F}p63GAf5BNw(h_SOc-g`H8C9;E;e!6`S3XzK`IOfQGiQXd$(nPgqBK|Qqx2DOR< z`%`YztD0x5{`Kq!r6rQP0~^)+vqp|R2%lj9->8+=vURG`2L_w>_uG;i|CJi0B=b?# zp7>Y%vNf0f{0@SE8Hj2l%3J5#TfTgtj>?-JWD#=dD!7L6lgF@K+pYBmw#8&{4jaw#(xpdO5G0M&3F97XVMk-< zAsZhQJb)i50sgu<)cI)+>3KK!G0l&Tsn4`l$2;{SFT5CEYvimkQH-m#72k<9@Gac~ zVniop!^~*3{kBps{baEV*&Dw~xvm1(e3c&+!IcOZw`$onE2XVDKwul}m~x0j%~_BX zF7%yQof1F)=NlsMT!iiMgVw<1saHNB%UuY&t^MR#T`&TQ*e=E_1}88YXg0Lx^GXZi zfx~C^8{l%*z$V^?pAwdyEq`k#qVOS|DUkQ+XkFjVKDpJP3NJ&jBFwktz99ygDjyLL z%{_y2qy>nQO9+NbyA$}3fgAKw&$Oob{u*}Oj`4rLyPE)~u>QsIxWB=5hum=X&5xwN z4D3#F^$0wL%snl?pEN+`{V4LOnYFj>Jx?xwTGZor{C@=>LM z&MZjh>DDV@lyf9P=Z5;8uq#EUIjAD!{(RaIZ&1m3l3K96QBihpnN*3R=*k37fGcQf zxH(j-tluSN=GnYp53y^%G_{~Rb91P2`jbTGy(N)7D_bFJd*OZ5f;eowLA)ru0Wi_> z*QH+M|7&YW1J_;e7IfhEYdl8;XTT9KQShhpp_TPIB=fq`uJ@YqWfd9O2 z`Jg>FGzNDyGCHq;cjE``R9qY%Xq(QP8e|2on<8?Fq0NCiUS-a@@lixm_+LlGFr zzqX$eS;D!u4qAO;SPiDlNw3iGr3zk=LR4Hx=kM%JMxz9qgA~)|`ob9~yWm)JrT4wI z{dl&&`g^LVsx4%+m~bR?4(hp7=z;Xy{M!2b*yRrMyzYZvaS*@?bW^=5zBSz4bzlPE z-?}U(m>sllQ8UC0Q|_s$2XqagJEJEJl3s-Q=0hzz2>@5EEl(DlNcNz>RcMR6nv?cO z32c6{*gK+8vke*Ol%R>&UORa)XbGg=z@bTtQsAWscOs)&Sq8|KR8R3 zl=+GJNN?o)Ze3RZsSG$&j%Y4H0yv3%9%B9J@Ii)iB13O(0r|`6hIbiAQ)zwxC}svy zT@7FhPeQ@@)vj3oATq>O*|;L2)^tFPpLGSg79&XxRMlj|CaL{qxlM`XJ42+S(@e#ub zH=-wi^kMy;$=9NB1r7Bgz7D^H?c!NWCph7AAy90dD&3TGx%W!PQ~{RHmOa08{Epp+ z1qeF;cnZ|}e0d6EE-6`varlm;yjOex&FiOE{ab6v4Via=f7jwU#mbNaHt$_XbO>eM1WH<~AKifg*B>?axY?F#$T1JNPIVvg?HzT~qcNHxx);~5a*&*(HO1z#iV zLoFkR8I_$sJLeTjQI;{^0tU6Wf;c72#X2;W3N#u;QvOSZX7f6EB0sjkQ_)buB*Nz|?YDw_!>qTp1!QE4yu39wj{uIeWM*m$w% zM389l3^1ZKKsxN5%4f~A8__=Fu2Es7V}tuyVbDu9F8DcAW%siKk><*;zb__9a!Moj zD}Gfbm;3Jv)J|EYv5N&b{2;?BzDju}AAr}mp6dU4--J9w82D&tFz#YlI)%`2T(V|yet2k#=xJ0 zb9-5G5tuL;`^>FBz4+(VxhHxWXdGn)BICQ3lVxo=rYb@}0C%N=2K2Gk>K2uuLxIRk z<&cK0rF;*x0;>>%u8TJ66mX1An3u#x)4gckd}bb&o&d==JM_bfO03KC?CJGFD!lG!SR=pm$_2W)E{ z)ojhfMcv)f3=VFcRoC{`8=auJEfA;(177?TmPUtfeR}-%Acy+hw1FwwzQxkZ>Q(-&s{QAoC=5c_04r!T*#;XJK6WMu(r&I_dQIJyE!C>HKwj+0D zxbSDHDL#5`(2tx1wEk(nh$uTe9PM8@zV(-jm{=~DV02y{jYkTesJ^(nr^4^><)CVv|8oIPmvgHiem76o`;Yfu_L($S+KJ7JxwNfci9tVwz?<*lwBxXxco;W2y9ILog_D!Ijs+#mz=CJQaany3M)zb z;5HU{j|p5Vb_R<$=ceB>wdh4a4X~_Jp>|KxI%s}bgykH}NoW9{mfam1-pCXbr4yza ztR!^oCpz4&^17$o0{?|SlE5#@uvFTygAJ;dH5Ehr1i~(hRtj}(cTT=a7Z{vVl9Liq zZR+HgUxw|T3VRYU*DrO@*=a#@Fv{ZA*0h`48zPg9C2|7r1-fSs;fqOq5YGYw-Em97`N^1fMO3$Fiahn>B(5?Hr@og?E#YqCcSElgD zo}W7GHcWE7n1*lbsGx;y>1&@}3ggR%CnJ2XaDgK6{j z>wgg>?1;SQT#3fq`PcS{<}DTN!F-#)I=>O9zaTp(aI#T**gb94RkEFI>y<>z5I1z% zWzEz+I>yW@SLU{se_|E-SoY0ID($Ec{x~S$g(CHLunQr+aXPfma_(6Z0D0&nJyTX( z9$;}63d0KI9F-qoI44hL`N%@w_`Epz^Qq8QD2c|#RzW^ICgvvgkRGs5TB0tI{c7o# z%cLVdiYv^2oq0A3x-hD6a=N;@LS25Q`sNRJ4APZ39{{5%5?7@l=3L+}PC745W1{pv zjsb|u_OLfQVem@<HwnG;@?2U+D9!i#@|cN(B*Y(@9y!ZK?6kUi{rKtCEOuFgzwV=|SqJUM>Q5ww$q zyx2APOB%>3ITKanUK5FZoT$Q>5$2b?P_SD#A%ppJyzZwEU$#sTpb_y5uDemkY-MXI z%P1DC7z)3S7h(HsOX5yG_qxXx%HK;OpAg+XVZ|o^4-H^R4qx&ODv~*qwgV&|gc`*t z@GmonhYt7hSoM`_wo0|~%`Fo%+-<`_2PS^Nv67M-P6JImX=-%OPKK7YEF^yJOEMSJ zrU0zXH!IGA7z@hFr@UX0P^x$;=5C@0`xq2;#vDF!;l9Q>1whFbXU# zDEr=`Itoj4o$EH+y2Ue?HiET5$gHguiQar$wJ6eZM`_p0#tp=&_Q$Yr2y> zFPODNk#9E+o|pw$;GGm|1HK zwYjWJqz#X6eLNUJPt(bK$28MDkMCs>b}u_B7l|r#$J?{w#|t)~PnXY74nq~Orb38w z&~%n@@Zgo)$yY_$#nS)mnHzua8O7DQS1JK~o2d!pbTIZR7+w8R>oif~1mXTeEl#qI zTv_FpxyKH4eDBP*)Sb%r5||}Af3@#+wB^o%kxF}c7ef5*-Mh6W%xL*cDBXRg+#kMvsgs@IM&?G{@gnzs9_3mcRb9~JZ;vuY!$ZJhheHu5TVjBO z?tPJ!ah(kl7{E&leh(2*=Z?q{Y0{qArw2%4PVj|W_U#xC>)%5>62&kELs`}$pPGWD zaRu}crswm@)@txUj|&z4GOI5Reh^dc*Y!Z3E7z&nSp_G>?j%C^LoXq(pw(H-sif!q z?7Z~wJoJFcb{9?Tu|_J!pf>Vy*a{wG%y?unIbfCf7z?X`$^)h!ZR6q#jS|ENK2K6 zEU8w_4wLXQ)1E_2k4uhCKG;h)AKtwPJwcTQO<}cTl_$@ z?9lZ51K4~ z%6{C?d>hjK8@)^H0%}q+8h0NT1={?n`mAaqTazFk$%YxbLgVu$O$ovmf{2oo!`>Si zb?EyOT_d%*d{xk$yggYjl&It=NByy{;K1CwkqacdPWN?O_?04q4}#4aI&O_neU0&( zzO7w=so0%$y_N*3LM$W`u@XBHd)yLTn#s?QFQvxN_J5OygT5;c#x33hz<#hHK)DEI{p>(6{>Pl8L1Ih#dGOXx8Enhw23>nPM@X- zUs;RLBjDjPe&amt6GJq$yxVl~hvM-y?WTI)tC@0p`Q#Dx*BMUl%Up#cQCHNu3Zc~f zHW%>?#4fwM`8~DwPWdD;v`C#DU1miS;}#?>R|YXd?|-Q($^hi^cE;D6s3f2}_uc9# z$t3>qy;oj|oz31r)v>Myp^nHK%0sU2L&-xwXiHqUvuX#E* z%MM7BSXew8Fm@6BZge&63Gs4(zL~?p8XBB6fu^W%hsTb{;pygvZk)Q7tXOkG05p=& z*q9AoPO(o90{abFS^oWYJYppuuM16PCoGZLdZ$H)3q0jx?gn`?wKqyNLARW3ZWKTp z&ms*CS{*#}@QjQ9w81pbk#StwyEz9-7-Eg{^)7|It1GL6@un(QW+Iq4w5Uvs$&6g1 zek0Eq`;%r;vdjSque^LM4IquUhke3^y%8K6VEFl+GqOVl^0p^ffo93(1V$_)Cw=s4 zOfr4ayqWIU(}ZCvb8ku6`i&Yl`AqY4$ImlWDy6EEMMfy(l|3DQwQqc}iaOOb(fn#< zc5BJJV{xuy%0|Ju23hHEe2TxDq~$uYLhSVlQ1}&%#s!}`J^Wl~3}oC#eDV>0d|U=f zU%AE%m3yz6G>$1F)gw%O_+l2V!t5N71=5$E%foVaIdj9Ta?(CuKeAqDAR6XE3vi~{ zIsmUW?+;S>G+z&zvS{E(QWWB!xa=D+ukTinoWb?RZQaBZUwRu{cUPYt` z&-f6VW+Fl0%GqSwBztv%Z+|fw~%njWUN;Rf=OHZ%AJ! z1%zT-C9jgkjQcc0yL|lYI1X6^O(!RH4KjbK{&_xWZ4($L3Lt>lFVc#FV>=TilFI>aZ>=QbgwJAm=#@axO zP^Vqt+x$a^2>p$4Df1O|uPJo9GqV!X!{_Vj?C)3Q~I1wP#v|QiepD z$St}PTK%HjumRYWxMe$_u_2z}OR8kudjXUKvqKN(S}r+vgEHYb2g0c@Rm&%gTbAIJ zsn3Bikm=7k7Nzu0XgtQ8tLf_P%8F*PmT|%a?PAif&sUdyUEn-O%S)J7lAm_M(>x29 z%Zja2j;f^f;_Pwj8P+IK`hiKNY-^OsmKcj()lLHcibU#rUfHC3e36CmdG>wah(jm`_q~5I zH>^`UZ0un>7!AZ)Dy)xb8EW0aR5tUdix2t6qjn`m8cyHE85s)`Las$2pg+-0*i zd1KmX%}t3w<^APx>|()TS}fx<5It8@6J)Yfc58meQwqdZ9jIp?1( z)3J=mT!hANX6)MV8vXU0RED0T1R*9$RnoC2iRt(VZ?WaQFR(>25#jF%$$s9 zJ4s@088g-q4?CSz9dvA8Sn-wY6#6lF$mDr%Zb0v?frX93=}(~qz@Bt|x@4a2Y;N6r zG;pIJduy5;VzlSGgFqD^P!TI>cp0^3JB;zL+mUv9M|qFjv>p;^$rsv9!>C&~HZ8S=--6ZHDd zqhXiZnA=o$7ghwmS>=yj)1K~fykOQ`;k@^_%a>;`}Fp-$h4Vu3&i;AJPN1 zLBT2-A}4vX(}!qURbaDwe;ji}{3wmrc|^^qK%jl97B-d>5!o>)0*)^W^*&hZSiyVi z=j-_>$k)eI3){K%-Vv*Cx&GC3tWB2STzBia<~`4=aA{d-HPCOE_6lFVmgt*8l(;N3 z3lpb-UeQRwE%>tFoS`mYF!lgD7DU;Z!^F!|Vc&0Vg|z_cTZJFSsM@7SyjXB*JwJOM z*E6i;Brb}JEYYW2ReGj_al#kffi@Q?nDV3)^f&)dpKh)Wz5BOlOkHrJaC2JZh1ZG! zGEO8h7oj+wO62bkJh|c^l+6*J`!H_z!Xm=0(eLmM_sxJARzNbT$XOL5HE#%WJkuy4fCoTaL8k@V->DQ11q4zDF5U zW@(okEsk+|cSk|ueF_DGaGQoXa)P9_QFy;%!tx!;p$=%Ycy;DnCsaB?zOV{(;tnwP zqw7lJXIp`3Bs;s7)V=GoP%l?@e4Xcm8OLd%7>(<^Ss|#PF1eSvsDz%va^oS+uuY@*?B3 zDk&{Cm{#&6wB(eu)N<2COsw$ksHy0Py8#!6J?2_IVr2k8%xrsIKonWx4GfW6Fb8_) zl~SLytWL6_5C4Z*WV}cuF(!3n+DA+;7z8EKT__1)SFzQ> z!8Yyl#iAoSvFLfCN5{ZNA1!Gl5I@qkD zO$TbE(|Rhd7A7XviJuZ8|Cq3Ecd={n&$VJ~xWgd+eBHNsvTlauK$Q&jRu%q^!aJic z3?0-)2>+_F7Jluk!12lT%_dt z@S&$l`9J+1mU=7@m#!PT!_oB9$%nZt@?+^>OS`LJPuyDq@BlkEiGbV!YYFJ=RsGQZ|(Yxbe*~speM!c6zLb4 zh@`cqQN}`wf;60{NuD-I^Eb@?NX4ZU{Jcl8L|>Y2(ZHG*pA`Ob%5)-rPvy`zyLqBa zWJVL+Q{T{4f)vBX@$@E11Z54-eXN=G=EZ)wD8f9~zVumi{kvOFROn|wqbGf|7S2ys z4ZDU#PaY@~nd3>kRtSxB5Ae`?b$v3^vAL%51Jq0k~Qk&R9YE-qrEiyG<`DC?*8lE z>19Ay1{YKZew+DY?1sit{_pQN=D!5=t?EMQDE|c$zUy9+Qp1en` zJ>tVzD@$-iWLI~Tt_9^s(l0)>{)H|} zKXnPtJ-1bvmV*h}mz_wrAaSgcs-shKcpt}TTm*l(%E*#Lbvk~u^wZm?p-JYEt!ft* zjif}mkP$fhA ztv$|Ld>&Js2qIak{il?U7$KL=}U_@SeD!Igves7GB4S* zvza+pR+9GSBLCnDadOtqYroNg&|NG5WF$ zRR1aUsguu`{po7zETMt>bAqNZNtm2=hm>q;@}uJ$-pP{hh;9o*)n_4m(%rwzBZf1n za37R$%vvg0qsHK2@74&ScoB9Q%QhvwHy?3mE@bIdzfs8}23&$Vqz*g7s3FJhfjWKQ zpVyTmf8?~Tn9)7!T)UUffo1KnbqVT7GG13^$2wGjl&cz$=SfvsB`xfFQ1H}GZbO`?wwXK#(MFVP;!o0 zX~hvswP{#Ehy9m!5@yr88YVu{a6fN&_2u8Mr*2qgTH1+o8IOA?RzRE-yBc3*@&uFi zFhHKz_4*Njul-lG6d&R<_YhxY6%)wLurWG1zvN<>2jUkY(3d}~l6+MUBXV55O0H(G z{_4w;hif7VX3JM6Zl&Q^uc~Q|KVA-IS8-GQO>kE13=#W5|2Nr7yB2A6OQZNjUWH{S z89_UBIqKzt$!sjAX5)rD6V;NWRw^no1#tUw1HoggIft_8T(sT=(K&omPGOUbvCu{-Iyd%W5o-HN8d0;zLIZ_>kbCO} zV}&BFv;e;Ext%y@78l`?pp@#RE;K-Uhesy-=)HlopYIx0f^Vz8b~$hhz>3-%S}7|n zIeMdfb$PYQ?3{L^+DU!SoYRz>$L2LIv_91DG+Sa>I8&QDDbE3&DVdBUmuFdQjjl6P zDFD@2Q(PMnFQkP5M>VjTj% zn$u{?J2+r8T$DQJ|7q^K+&J1OhXdz`gwvcsX97RS*l99^E9wnP2 zpAp?QMCI~8N97|wY3!D;ZB&#QUxtBHc&3QdQ0YEu?=!^iu4gB>{YSg_MwapQRlE-^DJEwWMi_|= z)Gr>~a5#QWO2gpdfwfZ#-vJ!8R_c<|ldCPud_mC3l5fa-?XL$W=zn!%v_7Be>CQF* z4*~BTK8_haJESRAEG{=BC(O*0<|Eokj4iI$yqUg6lhSpsxGv@52jx#-ttdfx%ZL{tL z;-di_=5*|YyT;9NPig6aw~K@6jOyJF)2}sa5@gOYuwQhQUBk@%c#pC+Zi2vQ(ZvH) z1)*6DY91kAkK`^P?Tb@)HVrFOpZolaaUe17=0 zZHssSsMH-YVP|j{DTg-YNe2LwbJ;G>X@0uQMe~jeHqUR4*K>yY)B9i4_Imb{;| zzA;0^a!oT<3Wm&Z9u-sVh{QZ&(X;L4V=poFYN3lRk6DkfcP;A~(qEON-T7cIlBIT1 zKWii`=0TU-b;Oq97mez>rV9(v<(}Mr1JaiRVm_%P@!k=&jq4?&KFnZi00nG{)#t(www0Ol^H`pdw z$^eb;OG<8gDdw(Set7wV(xJYmK2>;Ii5c=!TalMC+zsIkugZm~b%#!hojIGkp@!8a zNCXl&Oy3$J?|Uwk+Ss+sLd{Gpt*jBSYovl#O6^+SAi3J!V6D9Z)yw!9%u#OU7;rc} zeemqaJ?P(YnfRY|Y|xY?=c}E(mNh%l3a{f_gXUg=rDf%u32d$D@|7tvO24q~z|AIl zyvOJcbgr5evZWqHm%O_KR?W2lY<2%$O|H2Cns&WkJzxlNmDhoyX+M3X-vgQoJh8KH zt+vSIe5ieE(*hLcBvy+>4^V1;$!NXRO%!0O_rszy`DQrp5B(zHK78LI2X02IKuNl9 z5%U@3pZ0vd?qOa1oI>5cRBc@tCLvn=Sk zwO5jA$!}-J?F&;{mqhm$-kkv%_wEe6j5WKAIdyDACmlu6v#=>fn~KGkzB95Xs^5kb zMSttQt(#^Z>jt)%II`)6&UCGE=Of|efgj&d|DuAW@Wa@4I?{IlWCuZ zn;CybG+;|jiJ$7Z%Q_}?-o7MO#kOeV>1jTfq;Lfu)N}t4UstlQOLyjsimvG%Cwx!L z?XPs)pJBbsu6CQs&F#B6olNFaeY9GUP~Wnr2mA!JRG-44H$59^+J2zqVI*$v&zq0s zp>Etk*GYIxsIg014P~H*OjdAh+VqFk)EhpUqQqAzyd7^67@X!kp;=2jeXIPm-$V3k z`u#u=o#U7R(1zamwg=U*fQ^_sJaF^jl>H6p_kA79^>atpb8iA7NS*i1=858)vtRYt zEc@;idjc$N%C`$Eo5i~aL;DrrsC7^KH6_=@R<(9zTy49lk`;ZNVv?rn=5pqu)jANG zE+OP@-2(PnKfivp`enup#ii}dp>1OHpuU7s=bVH(aoKXtFn3A)(!#vjZ0F+h=Ju;Z znrEg&XG-FhRy$n2JnpZTw!K~NUmUSGv5~=>H6wY0JpVoyhguV1*mnBr_@m`wZh0xO z`72E+L06}jmmt9omjoKC&mp#r3F(^Ph5DDi`R)-65kC5mBLUIHW5y^c#y*3%oXa`Pef3>E3=uVW7ybc~>8NQHF*EkV z!b>$zdts&@oK@L$dlRpipOY07%xcrn^YlPX$dsLJ63TGmI4G+*0p5}c1&na<#*l+n zwhgKUU%^|ksfQuG$|a1nk18i;_!3j!qLEO1z&2SAvvJMf9&vfd)>=}z^B~EFT<`_I1E2HQ;IS?CIpJJyt-E6+D zCO5y9ED&44JCvVb2k`kvjVDc;-L1z|i0nherF?rnNrC3m8IseF)8Ow~WSCbwg1 zC!Y{QSS>CiVA-rAfshI0`H5{0ya&lsVGGEl8cuno1Ip~uFnG6pi4D2Gqv?2yp#GVm z&)>BPI7)WWSN3}7Af7)QpZ#_QE-9sCQBi+tiUI4w&0 zBeK7_4Dj0>+TJ9KnA%?ZAJ=f5aB`_A^rC+7=?n)GdVH@%^mS%V`rpUPmmNr8(VM+Y zuxsaqaa@Kib^h*QCgiwEFCvPx4b2C@(~dwKs@?*l50kyIqt;JQgA0P)mHkhqL_jo| zQAt70JUP|ejos{e_z~d#cGzo--AuQ)zcJKWBo!9dlq?te%;q?2P{{Vvg`AexsV99hp+FghK&!70G)ZGOK zI_>@UqcCUhP<%Y;$k9vnpRV(7|96MNxcOhNLp~vdOpQSQ+}@)lfBZCdER3CJ2nRMw z{kNa}IrR=2c;-{@yst_8$2<48o5g!aiu7NP@;-T3p7;i`F z`4m!+VvKa}Z|4tqbNPE$_nFnrnWk9f&dhpyRbC51N_A zJ09!(vRwjq%)IW;HGBkgrghCj_IRT&gr+jzDtF^dlK_!C3jIL=4$dny=jkKB>e&^* z$^*(6T~KKn>L4psNZ92KDu(UKK-x&^0g{dnvkh^kPbwJ^;Z}(%wX~*s;0I9G?REaz zFV#cMH5o5oRPTjISF4$%&c)Yz-<{ka0n9j8Aj0V1_g-dO8- zj&uD>AeKR|vFql{2dty^+1d}POYhII6Vq27i@D*W3rz}}Q!G3X5U^IIX#T)_|M986zrA0EL;}=rr{Pfw3ndX@>}}B|{RB48jZ+fOD4s zXoutYnkRTp&BHbl3I45-(C!@sG;o#k%!kftkcP2H!fK@#i#dt05${G1=HXUS@EeyJ$bA?o6>YvZ9>Q_st)HiFsj}D+c7WF>y+!%Hks^W+le4pU` z220_D)Xo4X;+y+n0yTQsYwgJdoNP_$+f^o=v{Li8pkMauS;?2}*QwH4J386PmX{|* ztZ~Q*T0q6O04ysGw+!P2jO!B$_x1<_IH;v)?_rJjJG`@Kz>0)_|MbQ`n(9PPLXGNK zDT&W9$u)aLjy$eJh+@rPO`KRSVSs% zAgnJtSul4lVDq@lyE1!GW@90|Vx!uiq2FhF3voDTg6`N8%OvHgszwkFId0C@olO_= zxcrLw252Kc#>8stJNQii_;}y3`Z3;SD<9U3*8vD`P9IdITt_yRE4Is|CQ}oA3f&oY zKhTR_fy-|KSt~RkaM6g7>2m9J9gENIRGAusuk|Ak=kcHUh}vah4FYBPO3EH)<^Wcg zowx@KFmF zU*fMXq8SqtcL_cChzPQar!OVm^%u>Q zNSqvS>^sb>(+!E^D!me9_N_JdRXL@;u9ooOI&&O|1hs**NJ0?Qb!>xYsyRP!5)RkxuE0IGyAqs~Rhtii! zqFSYYu6n=MLbSgV+=M|2A(9(1St~8C`m7a=<)~@~6GW{23SLw2MF1=z3c(U#My&a6 zHzKV<9;x{ZHTkTo9g9xDXtsV;s5}_UJ*E`Xesq?({XOwCl}I;hHjaMjUQ(}fyUIX) z$7WtuqffH=s60hgiFMP?{ zMihDNIR^n|(*w2^Cd^pD=LfxkM-UBN0WUkwY$;&U`=YBm%T{2DY(Kr5zebCMxqALk zy=(F-{en`C?8OTllVeG;2NegjG0Yo{qAi6}G4&|mA?(pgnpLJMd6v)>@LtP)yp%MlRhWl}L~kqcYl*j!V~XM?3Kyq) z+mZGLx&>ha=Y6`zOme#p*=$zs;sOxlsLg7|CsRl4f#DzPo7Xe6=TAVF&m_(ratOwB z)#;}ZFYLaA6`?>HnZcdwuEnR5aN%Sag^!4})8uPyEC$|h>v2^@WMAQOJ{EcKLgf7TuSwbu{IOQMFv7*YKKLRqfK$RDfs$b-_7 zNxX(Z=hW@1W=~T{o7x#i-F7)a8e3&*)El91!ot5N^5Yu4&(+VmTc@~&%=V8s(T|C7 zjs|s3+D;is(A6QdR$I#Uu+krka-11cZHKZf8A=5<+=X%R4omLl?#hcJO{gDUyMGlV zcMc>F_FVdAZ7bpYbyQtPG31s;WZnnsj7Uh;8T%B4OfmBEmy2!qpF_euC_@u(75+Si z*+KP*m0YAz;T^rT^u=r(xmzfQlsgcOEFfjUDcS>Z-bXRseJ&t+3F}hjv#pv+s8i*F zT^f?Afp8gmfoqPT^;V0U@kfxO5XG0+w}XdFrj@whH=x!~=MA{@`PIx1pUVbVVAUM= z*q-jJI}Zcgxb}_AUv0U6cotXQ-a)9-UHme^pH~yFb-w&wlJ-fSxQZ^nEdMF(h>H`W zbw>0HlfviBj`OAZ;oaKm=$`+0kJ~-9*4<^ol5_Bj`#zH&i%~PXxx(MYk=C;(*gwm^ z6(J;=`4iR^4@esA=ED|b%Jcu$blaC2j#SAFZ`jJ)%l!=Jno<@bJbNJ-Xg~11yq?H8 z@5;d5^mPNx*ViboGzUwj)Zw%F`IP0y9u}X`P&;4;X)|u>&_~2xV?fHL9NhyLC5A96hrFk<32w(g zNmlax&6Dn^seZDOW>{)-<-)P?cV?NSD|&j(2EZZG0tUsn&u8iDWy6MJ-WRvGwCMNo zXfjn8l^#~x>#aZLu;ywMBHdAoX^xZW>xV1}-Q78-hOL%e`Ff8X(97LV?zw&bo~4FZ zn2s45;Qyw(M?I~v6hfTDEY--`^mWCF_@d&c zT=x8j|L9_LFcawQ>W)^3y?svnM6ge>YcriYUpY|xuRVCZ!MpDkl#q=`2@Bhf1f)2# zF;XEvZd@}f*O}9VfECRuq1lA;ArdNsXi)fyhV@$96>hO8}ttm<@#RRye9qSH`)dmYzCc{G|uQMx6wE3 z9o`Z|K57)8qhdciSTgjkXUrLn*vQ?p4{OnkX%D-Mcj?aj+DqS2Y&dW`g;X|h;_!D5 z_Awjdq&bG6M1|%*1&8&egwgDn5B21ISr~7`2}7FSpjgbz(A_Vnc=)JB${jR!di6V? zr&~5d54+Um@Q>`pbw=#b-2PU{GV8?*8(+Qkuh7K6iKu3UbyR2T)p0i?p|hq@T``t7 zpmQXyBJPF#yB2NE+%qxlFUDe*v9yI?2@#}MGqItze23}b2Y>Fdpf<;WIiJ$5W8SIp z;cM44COtIyu6o=AfroCK$TD!=F8AIoKi1jw4NR+)VXmUCN0LElS}h0QxgfnHq`d<# zu`kH&8&3=&)H0l3YVl;3@VC?S6^PpiG#DR2GQfbPt{=E#1!OtFd-~Hu!4cVW(9a$@3;fW5jVwEAzF{r~Mg#F7o>R>RHcDRSs3%xQG5I<|Qx$$L68DgUQyxu{A;Hp&I3C-0EeV-8-OUdj4}0;O)U;lrhxu=Bk!nJ5^_oHS_$c9I`Bz}LI!MqW&dGU@5Wd(* zV?IMX5YZumF;Cey(tqEHsX%>$FQ5{E3%G3rw=7`Or!M-z%$q3CF_zh+JM!mCmrFUm zKJn0NBN1_3Wv*2ju%E24)8U5wf9Azs`@hNhJ87beoS{?Fs&ccJ@(0%NY{3J!0SZ|B zjAmi;7Vr>9H-!v~HK9`M=f`kng-$W@s;)U?YB1TA;7H}P(%i1j4L=y2Q;}m6dEgXP z?7U}DD6@V)g>{vI^2a=#Aj65aQP_hn{n&$&oue}U7$YR$kdCZ&$Tjk%H`}*jrJRzl zP-ZN=G_E3?2h3FwgC|H`oi}P+=cUns#)UrdOWvz(+Z%%8HQ1-C^Bg}`lBb-jfU3Me zJyh1H3!vbjpp6GNpOV!r{WfM;JzAG0$T!{dUwuE+Y~{}8Dv*5#{UH`=f>D?i-xyZ5 zkyaie6tr!qjuG{9r(T;0JEhViD?;skm3(Es$H=R_dur^MQF#0CH`$Uqdd!9(l!dd! zfC6?QZt6`9OQ&9kzznDfSc1^l`yjqeK$`UD!d6(K?X{=DL*PMfY|yDIij>a$3uUl( zFSP2n#GdMI(k?oe*Sj8Q6qss!N|Z!W z(cqEB`xWxwXwwATE|IM)$cNC!I`Y#$&p=fE>JjF2q}l4T*NgNc{%#QoApa0IV&E;5JN9DE zuVIpI3nQQ_M{sQK7KXBc;8=ueUd$j-zhIf#I0s%nMGsbA(pP}`5E?JX=0^J?VKrGd z$eQO;jTz64n|fv;8`$Y|_h;swK6d?)`tR|8_eFA8VmuSPz0HUGSw1@=A*GNZLAKA@ z{2K_sloT+HJyF{x`jL&QZ-*GWF7@2}?hf_9L5y%5O9#^^)M(Cm?$Twwpc2-o#B6sB zkvoG}hmCnVps#uJDU80KA-TD)xq~Y0wZ{RuYd~AY(o}a#mIig&gvpC9LY^Bd3HeJ%^PT^}ToJs7 z#tB{hOOIwPo_LWmsL7Matb&BwDUy?du3YR%w5^2|Or*C!Wl~(P z`$xHx>}D{+}<raEb~GnoBk&VC@5m`c}lKvLEVGo(WCYT)=k` zQFEq^xF)wk*3@m?eEgwf^NoQyx6&>NZ*GW@7Ifw?_0uS0;J#MkX|)Opo+iK&Pr4uq ztZq>-EY>FP2hY|#kYp=aIH`)Xw9*UPb_;t>>n43#Wu^O9W*1Mok>O(-qFi# zMWyU0&$vWb_7?}y=?d=n49S%5>zXs!yxNT*?V%6)R7jt%vG|lbL*AT*^C#$1&#+ah z&63s05U(o#tl?cL4R>@XL!lEKLn=<>e#s9yV5DZ1llQQm-0` zKmjIA=*KpP8BXooG8{id6C9kPbrH5=3Xu+&K_oitU4}QZHF}V#L6bTxHMfT}e@_=x z7wFrp>+)nndj0syAl=b~=z&PyJBXZ{6F3iz<;!Bkaq~h_XQeC)(VUbb2JuAC-3=$7 zTn_V)8H;i1f1|Sc0%`cEfZnNZ>g4EZ4YR#7wZ&Q_^wV4%G^AKg9Hs0&eq3`+JHK4Z zubeGRAB4_d5>Q>ZfuzoCsy>kyXs^g>!>7Np(UeO2q@*#CK~V(Q(IFY-M>gkH_(%7r zH_qwPSJo@WWP4-lQRX2es6V>EsU*-)&-ldMd6i8wEm5$!``cat7W8`Q8~xK|l!%yWwGnW_<|a)znynmXjyKqxE5{%HM6Ev;XU zwMc3{LjmHp-H4QZve)(&ebaf|+#<;En4SP!$Lov5S-Iqk)#SC-f-JW0Pp~gw*wbmZr2)WdC7zvF$7?nhUt7#h| z=BhaVrsf|v&5^Q|P_lqV81N+zh=5vX3L$NJ3ns~Qfid91Cibo!I;m`d(vQe@EQ>js z`(~>r4#|naO^h4RTccs9c9)&hJCgD2L43}1rllF@UI-vF@K0;=IL3-E2Pe+7o_t`0(FNHPn?6p;uN1fF`|!enQ| zkHJti{k)gfxv2;t6QA{VXT+HaYBv~N*!j%7xQxh|Sb*_^1!#ia94@e0g<^Ha`^{r39{Rj&+p>e4ZzY6rcN14aW3 z4?!9bdkR0~dAmLF+(RaFDy$#fIrGQJs&wpyeLed^{_UgW3?kuR~J zBNks1(ivR{!QR)|gciu)7j1LvxWwD6Ss^b{k)C^Zgs0AS#Btv;yb_mnM~Czss_Ds5 zJgjf$S92sP_b(+!qq0Y5!1`47LVZToHC26>JCmQ{(htUP4*(`FD)h7t0%6mSf-(3} zpp8dK0yRUS+l220?b4`O4{v|z8H2~#*Z4eX0TO06?t*%C{Sl*-<26U;Z0k~1`+hU(e|45YgzsSdQWrc zapLvNw=C?sPlhGFkC%F*CF9?B(kISP+03IJ?K$q4@Hshk_1UWZ}0Dy|r6 z45}A{mjJ&jP5VC{2MzI@y_SVnFb%x&-o+dVT6hpucw&=-(J;^r%983h)2kK4gmPtm+R@Y=QTxFkWadj44#TdA%b~9xy?-QxOb@k(UA7;o zq((tDsiiOunXDrF?kEsModsTW9=3p1z+S!0bn|RI%?556y5Ylo`3q3a-82ZC0Rl%2 z3plEx<2!hD1#Z9qgW3r?V#)STmw#Qlydfd4ZA^q5h zc#+x?XZAv?F1TK0Dl;!62U~+1$vlxD3Hp6$InI>`R79>)_|+YwK^=w?bE7`?)%|2<6$aFm{0gYy(fjLQO%? z_bEwmZ{1&?N4=D6{@UdstL-pUyGw!nHW~*w5&sgOr#(v~7O!3MBXZGpDAy%d^#(h_ zU%@IgTi#8oR(5<$FJ)~3e@2qqvd2WCe*r>HT-%v0S(XEDCqQoE@}%L6f;V~o4NcwZ zz0r@N=iB6J%m~(DAg;)4ToJg)(-fZ)oeOT7xOODrhwpur03a zXV7@sak8T!*yH|7CAxeetw7UiE9e%#mfbk;HlU4AC%|sGt*SiD+`&;T!)X0RBB~rV zrenu10#$8TFr?mRVq`Y#{vSV@pCR-e>JCHYQulGtxu1-`lQk{AL}K`I*!sdV22$&$ zjD)A9Al~BeQlAzdZPEQ&t9@xS_DaTsF9}bJA)610PU?CEjk#NMpIax8(Q{M5!1sfF zO*vN@$l1e=*|odWOWr}M)^`eyu7FMIV7`0~wv6Yyt+vl7(r}&c5cvX+L{&F# zLx~Gm8Bc5orpIigKY#JK(h&JYEhuoNtu_B`mt?K=chiz*s)qT@l)YMyNzImGp(UYY z#!LGv5dd_kMl&Mf8C0`->2z7pib!ldBa_oI;xC$Y*MlWLL37H)Z(&eF!&8G&JO7sE zlrfg{iEkF2i-2RNLZFX_&ilo+$1KNO*Fd>g9RIv~F~pQF+ko%OmyyV1Xi3{83cJ#4 zly?-qnK*66nvs;};}(A9;fawwlf6m;>Gbd_o6)3@u2;UW!Dg*mi)V=(`iFBs!Yu+% z1A6;+Ob}S=i8_LFcu&4L<0#>R{Ox__AT_IGaE@16q3#f7~Wv+G-5CfM~n z%{2*hZ0@`EM5PhY5d2y?uj=&fkpH!y*<}S9+ic;3!&qzYSrWYwSsD3AHa6HwjBn9w zb6B{s4DR4#A!!z!pxS)5{ii7o1Wc%=NeBp+h6p1~=e3uht|Dh;D^dHEE8gNdKVG@Y zg&@LVv&C^#%ALK zEfcj~*I{3}Rp#_RyE!|kl+3&Q0r#p2u~o~hL(11OZC%Nt((wHO62F4LUu6j~V372M z4wA#N`9KV#)+Y`JK;WE(|I$}$$)bj0+X>&duF42};eKZ^X3F@a?dy;L20eo5lCP-T zYXMeZjiuAZH1E8-c`x0*59yM9<*w0Ji&jecd?(XeZ~7Y?d1ED-t7)8~I3nRDL#S@wXUCnDeTq3v3U!idLx6}XICimd1^Q> z4q~l>Z!7->W>L_8qt2s`5>(#+o(^ds0zHD;a>ks$ViRmy{S}2lGnG0vepY$0qpj@r zO$qeNdnB&Vyl6sNT7c>H(N#dRz~o>Rhr9N@BR`M5Pdn?5$1TL+{DnSfy2{H%{~av8 z%X>eW78L}WW_{s1M3c475%*3V z=OnV~!lMOlTFYLLtKKx!y&4{fd=&F)x_z=v$<-AU-f9u9HB_)0Pasrjg$@wE@B z=zFU|08NGE9%WTj^WZjw;QqpL0PKtT;XhIh=n#koI$U5IQ#Ck*gRvs9ZDi`N)eI%$ z(QtDOf@p!G#}QN4JjLKw)1c62qqd5;Ttx%2(+@WW**@i{jqS&aeu!|P=B@@4uq;IH zF~_K6$uGBHPg2LA`D+Bh2ggd5KnyU9xkA9!oQZuIC{4nM z=WG#VZ?B&d&AarNkmgOMXB00#lv&adlJhuYuxviLbgNxO0`nfREJUPT7hY7|9?3AO z@Jxnr%P9Duk&PZWbtNdU=Jh;%7qoa|F0i2_WsIPXOYL=R5NHA-AAJygIGg{U^o^7F zMDI0P30yXQ6*3LWfmeFvYlxqhoNj9dyT8sbSqf?JYDNN^MYHt0vIOhWW$L|a33FyS zRbv;X$Q#0qwh^4f24f?@Rl|P3c%K74ZfFmP*_<2xhiY`fS*TXS$sTt?RX%V)EeQv@ zceFuheLv!0^U)HUu3(()X>0|Q&7pyFpq+peR#IYXh%?Ifdmo^uYpDg}>~K{98BZ9? zwPI2=bJ2Us0Xv4PR=ob=zGz}`D)SkmgPREU61sshVx)*j$|LWZe8hC)J9hBdqsZTl z0gdy<2%uLlJDq#tSy z*B<$&J(TKCh@cWZ8~dP|XoTWehO21gtpc^o7rm_q$%u$?zO`U7)HA(E+gGs<4roF! z9m6HVV6WnhK-3z+{wK~seDch<+y+6X28@$Kv>jd;3=XJ1Uq%4jh~^gJ6%(EE^g#LR z6ZbW~oD~*4VBq}dN&_{qCJ7=;e429!RM<^BKDa^3F99>h$iT!+?%G0E^vEBg1_d3X z$hGrAo>?`yh=WIPy%5FZz!#8H5Q01@OBWQDqi;3{6hx3vJr91{0ZrwCy_@Q*vj|dh zl1J%upPHKDH{t{O#-KYjJUBH36WV@Xmq}}>A}p?kQ}6_W61~p2-w&jMUQ2^k*(huP z4y)&l3W~k8B7oE)SUYU|v^LzV!(DGwbc? z9CZH&V9+oQgd#fR1{sw3(`rd}j)C`;xr(h2J@^9E{6WnIvaAe<$x2xlzk+1Ie96#e z^TKqmXghBss1;fxUN!~#5(JptYF3>BpP(~fi}*PtGO$@YWa6HBaJtmcGZZE;h6Bbo zispzCW3YebgIOfM%)hS1XQa81KBp}qK@SSiM%s}E#!L5qlkdY+5@3_Wxu-fdAT=TA zPkyNv0V=!;{TuLMiMDx^5qDXm8L5Gej43L(t_X%_mwY|>17!)Tzhuz>>T}#iiUeS} zviVSxRq`VWt_#%fVSnRKy`!aQn_~hHSjW{?xAK+Gl*sI~kf9mcBNu`-`Q%`c=n8qk z$A}`}vO&>*5JA*V!uEQ}vMvhZI2#$#sd0ypGYS(Z41Id>`;=uw9Qr-gOT^A${9brX zL6bx(ac1!e1WjnLA2X1`kZsNSJo@_fT6cvHvzy73hIFt*CYlsM;&%tf={YOMN%tyh zmupCELJ=APpx3)u&&OS>3%Rq<^6C3g1G*Xcjer_ubGGZj+o423`L6q<-uju}QSUTGkCsHVuiU^pyega0C4e9V1<4@!9lmnv|w8LE~?b(eO2(Iy6C= z#T1m1@wD99Ji|Q`_I#YF-pC~?_U8|@iDmAF_aE+Q3i@_=m_5U>7}Us5TgPV`*LWRs z=QA#o`G7yqwS#klM5C~C#>DHs$FIa`<{?I2324gH=Z;|PGz##_f~guE0mhDce8e}v z&lc4i4aloE47uykq}ba-lA|OsV9^pcqqmN?>24lax%5yQw_II}^y>#QJIeWHi=U-M zr~HntfY#?FMT|tSV;vcnfT0j{Z<-sk0`0rtdUxJqSsH+41#j_V3_!y`NdyQ-dSRB zS)bm6xY4aG&Dbb~uv{3iFO%cj^?MP*g?L{?U?2PIZU0Fh^LFDF0;Qz9dfto(*crOs zlv#%V?Y?<`K$vGP{k^=uGTFapI6D@bnj=B+9YHA`zW?=j{(1sG?RH?wW1s&469i0mtPw{~jdr<>>?d$U-0g(`3G{aLp zw^6c*F`vyX)XX$)3OhJgpRezSe{;8yxX);dxK7kMfpPpyZ4pe9I*Dv4 zzx`GC^#B7IPS(uegSw#r!fECW9el#aOG6=I{24iHOf51^0$FiN%-xm#yS~LTBUW`o z{>q^RJl>R}c%>UL?2ggjpOYu6{}^@($h&4)P*09A{b)(N%nZ{nuXyTsa#^CzSlzSY z;*8U~Ou^0?j`W^a=H#RPZqA>?nj02w?G^DFB%E~iQB5G7BBO_mjk9yT_t<2nf3IlC z9?*i&v=Bh81ZWlXE-B`(eFLr%%7!cA z&mJU@IyLe*@2&5+XePcQ8hkDWd4ILxZafCK72}>eden-oDZ1*Vw^jXn}cubSoz^KcTz#W3x%}rB&1B zONOhy6W@MFd~bW4?3nB@>er}0cFobi(ZGS>akAW_9{0r?RttG=Kb)MhjJY?Q{FhEY zml&(bQ}NHWpH1#6&zyVqH}OMm4;R6#vj{5v@)M}{b|zWg*?v^-9?#ik7xaFYye}Jl zg2f9tvB-y~WIORU^Bu_xOiyjfJ^r3wN4ogj6(7ulH#}ap79%k7GVr<~K`Hs!-v7dA zP}RM;i!)3j$X%&zOY*-VaBfZ5&m@Lt918HJUmVDK>U_KFhMhejre3{4}=g2SX6X0>`)!d3EWuiKV)5J&=Xi~vw zAI?aW|Kv6$F_U&!)ccxzgX-z>bWCM|IG=c>ciUQm&(F>HR|^rDKi?&x<9w9W5pfUh z-eHpRgW=%f6Ol9i#}9A{ZXS=iJ}H9#_JyN+8PAf#Hvan0&%i!jN(LX_kE1Ir^XC72 zB@T{Z9`=_1vb6iRq+q2?r{2;0*LDAT09X>upC$d@QHk0=#S{OV$83(%pk=x#fsP@Z zpbHsqJ1G8C?F?2h;2|V480Ul@0^%b%(dSCR^nGo8YyXP_Dc5n|BKuTLAMt(WxbFP3 z=Z5WYY4-WEEJ_LyEXwrX`BlA+9|Xr=!<^{)5PHN}6>*&X>YqjLXeb~?!EGsd2%H3q z1F!%3^}k9%$|)S2F@cVRzy7Bnfv07x(bVKstjG^q7vI<-6R9Iu`lT01?vpCS4MucCc|4q zGZ;jGnd7eCmVxUj#s+Mb+S z_A_&Cb}HUZX?L;OmqyfOjj{3#UrmgR{#)Hz0L&XK+54M{*@)XpN3hxA!MW=g|H?V}1N}0gj@ug#N zzIn7=DNRVUoL5;d9=@@I02fd0c6o}QG~4H|ng)p*{l=kdJ!;Bn)SaT1U8y+A)dxGX z1^Wz`EmU`@!-ScT0P@K}$8(9u2fq93lsCu30Ox#ilaDP!;<*mYY<7AUR-(%Er7(Fz zy0^CvEgIaGJmWrWD;cwA?y);GnzS+EI_lJOPcDu4>{U_=uXgcGlU-)5VNMyCP#55& z`shfT@Op&!Zvtij0?G;N2OklzV;yWyfArSLBhhh!*Ti09l|}`=UnrYJwh9b!A})06 z6eYQhzu;Ufshqb_YzedT-CIV@?hXm3`#9G6?=F;cS1vcup3+s_@Cp&0inZJ&C3HU) zbrq@(OqQA()>_XH1HnwPFpIi;c*X2qO3`gJro;pPA3d=q2C~V7sy@WD!NE$5+VF>dL9WWRt^vwk?-EZXET?!1{0>IHTrI|WhD^>B zjOJvoy5Nz-tKmk);d-#L+EuP=c+3~|<*QA-GEYLLw~22K4{W?sOE;4QJ3>FTIa(PJ zIjeq3VwIlgbmRlx3umwtym8JVox871Sdp(J94myuxKT7XVw9-Pw{q#r6R31Z)F*Mi zRZUM1p+e~5=O0aj)qhF0d;jT#WpgCkcK6(Lp=EcM`*foD$(alH#V#FD$4U0^g=?BR z`_%^~)+|TYDVaNz40016?{HKe82DZNGq7&?Pp*{ABBFw-ZUXatozB!l@uqC`pwfW8=Ih|Bf z%+vi&*Lk&uiFFK7ZKTCKXckb6<#}C|mH7B4izk+yj6B)6^J|lX!sc2WY>$sELYb7P z_eYnucjhxVM+PW`1m8mhR!c@FzMznb?a3%g^i zrV}3Kq<+DgZ~UK!Z%I#jO?ad!@N48VXxLKOY;HXNC$MWiHMNTMUR^XWovp=r`OWO> z`|}BEk^^|hi+Xs(xmuk&uZ}2)Pt-#&h;9KYC@xKgC5}F0M>ozGf3FrZSHYL-=udaF99<2PBX8cBXw;*Ug4_VTR|_BFSgOQOYF{BE>>C++FpHc_&vVLM4;VKXCib$tdQrHnzO%(uU3(S4&q?oM=F_xF{zG@hT>X zeXN$Y!HPKfjNc*Y(0LgVybeN4J{P=2Jc3{#CtBT`79prjom; zbxfUc{6oGv@Y|anUwln;C|a*0n?7L0NQs10qOPc0xQrgVJuKg_h6SsHOpGu+qO71J zOHj$V)OD9TE%s`gJZxP5<0)fL2D zrKM_{5428dlZXiBs;tXOeE3?PCj>}^S1%zkDExB0C3Sl$o|~(5?1|;U_Ovqai;q3l zI_2c-TPYL4@|e}es&*Id>e^L0$GLuU@BU1}od6lL!p|o;Qkg@=u2*ghS}_->$}3{` zz>`tK*De;}GU~lq*vJ%Qf{9U%<+;RF+;3zFT!sSpH=Hsp3VmChoLaq{*2Ci*yXsRB zg+vj41g+q;tme8GXS;tiMP#XPIUlk-ipmX$>#qppV5hHIAjb-y?i$eVLg4NMN9>Fj zPtIW0ZHKblKi~qD@d56*>X*wyUdRE;i4Nq*ZImFg_V7a-Mr4e@{yZ_%kFLQG54#N6 zpu3X~oCNR`_#$Pf^l#| zj9B57+B{kJP|U=KQgOG!QAi#;+T#s#mJ?lUR}FWmO9g0&)49D z^fMu!IS9Zr*jep?ERep_kQVp3P36jS{8&p|5Wq-2bE;i`+%6uYfTyps+86+XKO`rI z(VuYAK2nOW%j7`J7ehW}AdScu*B=hKi^hASA>pVPefvhE~DHc#pVodQ<8 zI}&k$YPrREE_~FzHte<98gpLTBs@CQ6{3r5oF8oIwiWN>l|xM~lQ+!DDSq@(EP3w- zOE)eFXUEF&;p1Nu--5YKMrqn^r@r1hWw-t2*X3U{VAVJL63c4|1jtHw6zB~Tf}TsP z>v}96F*&sZ?!jk7=~Xqu!yTr?<6l*b>*><3EohhU6xmynYn^On zdeoftGoy(vgO=}W=Z-BP^(9=wi}q4+=1J}+xNI&KjQZ@J@<_k~HiYLT0luG{<1Cx# zAwPKLaF;_TjDolX422s&fYAoLMB&fOtkZ4@iI6(+%;L`Glswq!4E4D&6A)q=t$nqA z)EtuHLuI4E?W``Jc@uQv6fWwE9tl^W3O{?!c}P#>K%&Y(9go$B9HP36Z%q*_=FdYI zBz>Ow-Z1W2G?&~RaN3o)15yRhIkfeaasmcqUy{hz%RJ zdz?DDHzkN(>sD^3-^I!`qsl2b@IZpQ4BlX&O3RWSN#hPxvP-~rz1$cuPxgv|0^*ER z$fw+IibK@MD;`-Qj%{FxW*M%A)?~6?|FxePVmQ&9&ktsXJ9ZVzvKpgzxhrqjfJh7d z0Y!f|ulm44=h3^gu7t;J182{(4XoRxRAt|tZjgBK zS`L`TD01YMz8SOI1w>m8IYqi9Ymv^ z`<*%Brk_+1{#2}SNa5Z}i@CwF8-l<0Hi)ZS3*;M0OXo{QCB!j%t6wt<2Z6O5!{Fol zRZ?Qzk`2+^$LS>W!R}&vo8H5x=el@N7rPtO5A)R8OZLrd>8Ud3wJ*2qKndV;I+uQX zN@>WR9=d=lBtTt27}W0Q4eaJDUu~ow=>W)EQtW|3e|}+lsBT2}702>huH9uxax>D% z(Ym`bSI{mnSn>#wEdflqj^XR4=jGFrJRVhs5YoFD?$k0IsQ4}%hl5i51V+8NTMRzA zAE%nxx3UcO*&)68nw_l!$kdICTO&TQ$F6*62uRh(fUUhFx=?DoUx;^Fh|zltk0C>H z^VeTxpVi3H+C;jHIQM10x2xJV6FBFju*i+gvxx5N8h)INzKf!B)vJ67$!vEmnWWy~ zQZgBV?|-UspYTY5ZomO8F3FG!b;VibzJhYFu9avCh#^iIV>53<+j^}-vm};k@IVH5 zC$Y8WE0$#VWvYhhi7xWV`*Y?6`HHWuU$H5lFSZ;kwA9zucnZKRDN(Sc`3&Lbhsrv0 z2M$>&^hyhN`%eiVZ^(i+5e@`>j_B72?h{d0rBcq|PM|{Dk+5L&9`pWMx};*v0}sn9 zKappS*@Q=u;I-0Z*(pQSdb$k672U6&(~B$-ZvdNZh)=v#=ZMcA{pQGk*Rt4ytRWr5 z4_OcV&MFCAf0c3UO(Uh2P0?4?Y@CF2>QgV<*TU>pBaUr~2QFksMAm!MX3vSv zr1(SWw#Fg#Wb+{dY%6YhWa{K8+$Io$UBCe)t`uD=8Fp;3_Q+7Z9`h>{qP+GV1ih(6 zy*eB%EKjO=N2+gOt-Oji{FLA@&wTNat+otE;9CnpewrF%;PY|koTfBzUfh67+gCMi z9&*W0gJ5IS7vlstY7(Pu^x{g_3zj6ItQaSWUyKTuQkeDyQ4=!uo2073=AxZ96@S

bZlb=ss_JC)ej;k zHHB3MHsn5V9`gmLs((s12%r={oUl&<>XW_(V%JIKec)7U0?tzNjmu}(9LWp(41tk+ z$L+_h{F-q>4n{A8TLK{-Ka(EIE@+cqhK~YhD%xo3c_}d~G9da`t~0rQu`SeR?JatH zI=P)aZj|)FyiOJ!h@8%W$mvl_CKW^rU>93(jT?gRSOJCRG(i0VY7Ku71=EQN210|w+;&kCq z5U`IsvPZ|akd^?sv@h}7!@9d;mHC$s9hU$XPTwpeJzW34KLz`^XSyK7D%F)b_?Mgi zk8jU}`N8a;GwT0xoBy)ve_3WN7b8q;n2XW(e{aF*wxPoF+==T)?}SGaAeEDo#yPrX zI;G1QxFlVBGVZZeT|Oi{B^(f#L$o4sGkkCOb(QO>G_ND)25x7SWn64Q6P}-#%>-5~ zrlv!}VLmsXl$Qp=ITAXIVS$Rd^E1xUNnI(dv+{vYH5&xOJN z!dJl2Cz@?6p8+xTtMO|Ya8(5aug~5V1l+<;&z5;(_NFBZ*#2Tsh*h~g@C#XEhJ8EO zC`sov-jhG~M66N9cqbZoAf{GH%$_9>J(?dg8!o6tR3w{Pz6(9ET3Z$H&A&nh$c#at z^0WJB2*;@ekcGDXbEh}{#F3ZZ_v%=sK`0n{f6>?Xj&SoK7gGzzo{;EeCL)uOYC2`jAbZ&?>{b#@i_&}`oHYR;P*O#8a1 zeTaMt858zv@5ci<%oZh==_v)7?i>q7<)svce%kr$X#C8phNVVdbqZT|_P&$(T5NwI z@{!mY^B?H5^W7a)g}1$WB;rrWKqsUO7@(5K`9 zQq+8y@o0)x=`gUFmMvT*3XtpVZudP{xdT(FqxQn{GwH)?A9W zoR|s817qauslI2i#V-D*1R9@I8>2!LAyOm~5GR9R0V=Je0eDi7#L;{FY;xn1PtsiG z`0j$h>FxwHI`?X`0FPIFmx472whFTBL`6ZmPbi_>>KpDWKdd!_l}@+7tAek^-s2UK zrZ5RbBbbHG-fSvgDC4LP7?Wf0zOny$Sdub9)&c!eydDP6FhT2{giglFepkW4W93p&G z9AsK-NTtwtYNL~Aq9@96H`qo%kdX7B*8%_Jxh+79R}vs4LcR?VVQ*pa8J$@4u->ib z_UW|y#$B~#6o`I1QCdoo&bvRsfXN7bk_?i`0*WQxEa_l@BoXIF6R+1%yj>Ex&LF=r z7fh_!tXynu3wni@TRI-Nj>}DJ5ar39*FC}iZh?75grb1C5Otcp~25H@Ik_U-X5M^ z$ctO1#R=F9t^v!<(o%AM?h)JQASJ51q~9pW!`+G@p~UGyCn25N7<~QuR|&6Vw(`M@ z5f6Q%PF1bNEoVbhgG5Ds{7`&iWo@u=)#KGl^_AxgK5o_ZM4PS^6LbT@Md+M# z$(2TCK0006dy67WTDWneeo<+@;rWiIj98u`LZZ?oLfOY{fBL!P z-uJl#oiH~K8~49=wjGpyT%oX#F)ZEj&53N~#m{72%JbdNI@sK4WDdQkdd^DFr?)+4 z#4s*Vlw(n{C|)ytdK%>^R}ph7*&29ZZHb}*R-_yN>Q@fBHR~X(JeU&#-M>DN*QJ9i zZQ@}-^!|1C8L-qgY8hL@;ybRLeWJkV&7um2$g)rUXv{sA?AjY$lQqrI7wW|%3FY%2 z{U~aRszWN1j1U7lVI0O=pt96+Ms8mE#eEW3Unrtexi-;gus}zOheLMwCWEWNx+3rV zs$kVJ!g>`5P5dLfJ#VrLOWVXO31Rc0>tV)!&sYPDqk9?3p^A&`=7h!rn9T+;U-;Tn zsV&c=@ffyh6m~ryN|!z#1t3gVky`Li8~*y8rf?$@+w9e0&U)60z1%WM%yv7Q?0!+4 zQJsD88sh$JY#KFjKPMCwthWk&b2HvwwigSySIv=As49TVyAoRDt9yqxU)X>{7}*k1 zl)Z~A?KPKJeP#VvVVhoULLR^D-WV|YjvCpqQJ2h!ETUK5bsC?CQ$IY%7JVbYxz@O6 zmql{BX{`p2EOkqi&R5X342>)Vbw#rR(mHFZ{hUR5-`NQdNE8M1chV*I(bmW(W~T2E zkT=S_(y_3s%v$%7xhOs?e}Gv@Y`HF*jHcyPou}#-PRar@kVN|o@V53;U~Z^=_;m}2 z+f+iN=FOH12OnjbzDo4o7*OOxw0{Avu-d$UdMa7H0bVdj7JTJoJHT$OFVw~2@JTF& z4D1eSH)mRY0wpGpx8c@ASu|*8-I`cy&pF}8j3UCFw#{Tco1Y1TKvlrjyHP!Kj zAg30W`WqMaD}@C`1w-2XPsUs4-w8y=$EVr9Shr-Z*ZHuFqh#Ei?i*s`&hpy9S3A@= zZZWp2T;L~P%k{Z&yo{~)Y)G7IMrET!4Godb5Kz|J`J$#pMOC3TF0b<9r&YKJey=5inVm?KM@qP^vYh`^xOvNIXi^GqC2F?6g}sQG(}dw@D&)U&R?)W0t$E_$)(y9}6e=`-0r384J6)<7$=lXUUZLhO#ngYs5ej~UO3 zX^>4YA~6P_4pG3?(k?um@G6kpcXlP7e}!yzB4sO`!`500l(+LO<3Vz_JvY4Nz0)Ws z@v?Gb8iXZt>kQqZ{C9Z?oacfp6UUQtA;70z_!k^<-ymgVGkJ@F6S)`U^z&UhaxnWm zTlD)Ely_@x*>8e4$7Mqdxf-+?SJkwuY(UYD4g4oh{z&HDTZyT*9yDmNyOq~!5*@AQ z*pzLb_>-_qA$nqdGH^sD=8>z(7yD*(W{QEt{FiHj~`ODQ1xsMEwe$R(TAhZI%f zHF8d&oW+@DTQr<(C*0Wv>*a1?9u&1hnW0du-^EHt=XQ$NH1jFyK5)z8$r=ja&N`GB zRGgTo{p@wwtzfpKjLVDJ8!Vyyfz|Vij<+)vsrtLnysFn#gFP;?){8hnMg73_Tljv2 zFZ1n$2*#Zmtj2CVIu8q>^%=Kw`yxgTAGu9I8A5x5>1#Ck?V@*r1t`R*5#1aFRt-_U zosUo35@(pX8fZ4%{|hkuN^UW|&+>?Vf6>h#5JM;cq7Wo?CrfDj-HCm6iQ(D!3e4KM zWo*4oC0EMkMd{q*sjcjyMZe%J1yPB!h)BY~2?v%PxI-ea*|Un0r~q$BVE#%eV#Udl zC;4%+V{2&~is|n2&lYq+NED%mpQCg{?WxrZQZ`YQ8lCc znqFFG)J%JN1?M+Qt3+w=4Db+Qh*cVYq_@A9J4^`Ki+~y7pD^yRj5L3j7Tqj?ANWqz zm}d5kYVey@|AC@)tAg-qUUtD11lg-MwP%<$l|eOtGq!&;qXgberIyj)&YP&O;QTV3 z`ZX1^hAV&wcM|Qkt2BIMMSFTx=$ATTPw(|rA2_>w0p*`zr$-Z@BHAq>7iN({NmuG6 z(qAR0bc>MtNhXC}C}my=R3QcR+e`b)p}q>sqEu?yDAviW+_Fc}NZimqk6{xiwK;Wv z_|-=J+LP_ZeWC@~ztXtyeT>2ps6I7Ao7yN2e%c8a$cui)lNvnk`_GcVEJ ztR}if8fmm1|^8eTsh*1KHnsxzb7g;zr`218^4zrWng$zyyKaaJi!A0 zP989Dp;yZoVLdoSy&O;IH?gR?=YLKx%o+rk>u)A~S&XD5oPE8`BM6r__ds-zrHn~p z00vg?GgM4oL)NH_`u@e{OBRU9S)rvO>}m$!TTJmZ+NBXF%=fPL8^i_OHmt--#mlCoN}? zp?Po(Vm)oVcD13#41c+5J;%-zo~;ph-YVt%tCh}Re}WHhN!@vJzoOG75=w0q;IwvK zRC@L|MaKN_REIx|3d;B~8nkn4K#BXhG*dN5K_xxuzfp({gLJ9@9b3!SOB%v&RJ#k3 z+U8zo#OYh6@#vp{nM{My-o(KM}sooPvoy8w-lZEhn59aw!2QJuHFD_fCkES{v%11zmO%l?{eHOFvrd^|7krqjZVU#WaLd`_#cH0#$|N0W7ck)yanxne* z)ksB#G~mT2dfoz~gA11U0joXld<^xJ4R|!yBVh0Ye0;y%2z2;70$zin_sm9@z3^r; zqU+VNTLYN4EgNh9=H8nmR9trW%GiR`_75wV;4Y(Y%4*f_$f6lMpXi5e(N_&^Mj@n4 zn;QOErm*v$3dAPWxKOJgE-RO?u}`3)gS%GF>TkauXa{hssnvvRjkw4uUarRqSGt-a zOFZ$#jhr4S><$ZpRtAO^PG;&6A)UfS2SmFurDYP+3~gRGh$Z+&?{Bro%8Md(Ua+MY z%dZmY6+22xWf26eyD%FbKv;+96oV5$b^o0I+`D^pr}WB>*+9;EGW6 zCa5*-SbMAiD1e*e5eVu&T&mU;@F|&SNQ%Ar*J0o4JQ2i@oo}6F!Bvmfh(kei?UL_w z`q1DO#`h;6B;8x4gI6NhqN00`nPL0(#zT)_djy-c?ga`Rv#+Zuw)mJ6cei<^PboE6 z^cM9!5H0uWucvv(n`jYi8^RVpy8CStdH34K%k!XUw$Aj!O0MDAR}Eb*+B*82oWACyf&??&M=O{;#4_3V zh7&z`&LZd7C#bb=JfxiZ1I?#VqAjnrw+!=IN)G-vgZmMT_foaGO9p1>^uj|TlOx30(7F?m-Mr1BPu3BiMp>LC7+!aijDQ3*zvDy!z z_&AW-%Gm$}rUNq@u}|JDI?Zp7j&0EeP%Q4JX|Z4O*_@bqC8Tl40&jw6D+gz%rxtH+ zBVKvu#Yl$6lT08xHFhVq-F=R5c6_2pmax#3O5ld_anA8(u&9pGb{fa~7_#05f4XFd zZ-1d#KSAyQE{*@!MD4s5!w25_nXlAM+fdXMugWKz)-W^b(gjSYZt_oS)@eDF%qV{2 zRZDIiVu2}C9U;H8A_Y0X@kjFS#q*8}N<>;JK(#Lc_8ZANg2s_R7p+dBMgq!!^{4;=IN(J`t7^&c<{f zz2=sns(S@4j7?tYgo+D9TTLTVotmwiU|_*|n=s zU6a;Ytxo0a-e*~(YNTSfN!<|V79rk4|Z^Z^NkX>XMZ$~0(gpr z!%88Xo=wCSDqin+D^6MAnG=?5as8y|{wizE`-PCkG=8+AV?1qFJewDGG0z`v%VS}U zsotC9#;w`U9PA8{Qg}g$VLTOWG?+>Noap=7{Qc!i9@*ymCDr>mJ`qZL`ak2D7`^5` zpv;(`P1J6derM}lZ!>=$Fk0`n80b69RNu;Zn0d*KfmnrpO`J8FFhJW&=T1%)rmo27 zLim;>hb>R=fhi~P83r2Jz^aGA^i6WXb**oqEVj7)s5=vY{MuAAPLe$Gl*yuX)3kE* z?k2s-LG4mh`c9p%PG&I(oqcf%pNR4@U?ln6?NTULNUFRbqb<4hiR`r2kJuf4WLL2B znN5%*t$`RU-?GN7CWx|vFIh=Yg69QA6dvM6tG~NHqK+=3kypmWi~lZ#Q==Mw-_RWd z^H-6NAvO}YLP%G7Rv|i^VX;$`tZkv_T|VUPkUXM*2B!{|L?=}b(3qeWY!Z-1WP!@) zrv6^p@fXRuxOc}i1rdVn<0{*$x0QJS3bZ~se^c3Z*)LXPV>KD$6&U_4-00MdKbeGE zO+gIEDXjzNW81DE5jOmj^K`x2YelVX^F0U{l$GzV_~*orP&;{oREY_DDih_9@HN0sT>?$TMc||KEEeZ zJ2io1e>U+iqzZ)99pbp#qX%$#%Ee1L8)(P^@^RNIKBeeKVDyd9ku!u1r{E5JmctjR z*>;+s3Ub_6S{B00x8B&u?!j4zJA0sEENHI4pZd$$cLRu-5Bickl%TlO4E&Mo)s-0~ zxEMKgnw4H~m9`~w>3IgETp-&EWzqS@e9;T0tJt0M6nGS^RH8>zti1} z7u%wpRfABPMT@0PP`huEJ&;`z%pT{L)B+t}VWan#0K{P`sBp`$Br31spi?*)cHh@v zHoHK{XNGe>_&OKpa2w!X=|t@7zh8d#tf9ui7d51mLW>V-(&ami8wUsI=9UYB@ALo! zG9V(>E{XASiI5K_3aQ>)zPSh01E8Y8)briLKHHsh)Xlrf!>lwI-TK}8!%G)hNN+Ll zJgM#^42`D~9YM{PjJaUzi$q^uN`1LlFd40D))(-Ja(Ua%_m1Ry$qPfoYK%`=Ebcq)TY$ktFnQH>RyfFDuI>(%%$hvzB{jL{ecqR z<7PY?P5HKH{PZ?aTf=kKWw>_%CByh4XlEP9r6w2|&3JD%-9YOa`~cy%4H|yB=EAHa zt?-*ZqoK-C@l{)&F&x5+VMd6t+YT(gzI1SEe%ju9>9IN@Pr?&#^Ac=2ws6JhvkzXw z&1;oCduvG;sf&;#`|xcU0FE6m=4Sf>?w$G&Z4*30k{)a3{P}O<^?iTw0J2&m@Alit z+h5GPxP6v`b{5+Vr;GC?dq3(MBlz-<@xj+uvAFg&c%@Ug*Vmo zg1!x&9iT2cvZh4~VM&H2pi2lbx1r{Nn|Qv1Y_2e4mVz;igc>K|?%G(p*JX=fRXWgd zD@Jj#dC|xNG|d3~tLz2WLfj~5oGM_dr_pIeKJoe%kqHNpNNG;d46G;K2~eyr&1Uf+(vs;3#YaGMQzm zUD>zdO^3%zSPdlBow6-Qti8X-zFY!R9PPQ(%u@uux^>R|0p|j=S;kLRD`Yf_pF0#qGKs{aq6ZzFoeX~D-KoTQF+ev?OjQTY_ zgNSS(E#3G!&8MZ(d#MAb@JwU0eU1!1Hnuu~Wk8=u4FIh1cJI5`P2BGI)QhpX6_D~% z6!ge(2AsRuMQdAnsh;(V?VWqYL@KC3UEUn~>=+uHk5E2VPuc4(GWWi=D;?y)duUQg zV-sry92z!Re*t7{wD1#C-g+l`l_b!}T!0Ru&0~NM5zPtQWl8pO6hPQ2EYrciX}MmP zHAH26r>2Nc*W%c!JFEO9xowHBmuDd_nD1i$^qp@D6jC8l=E3$somIT84#7GIKhx9? z#wa9I7nG5DAFmp`fp~{nrL^gk*Rf2CG9!YK^)w)GCM_nYUyEr4`dI>;)z}!{VbAB* zt~k`U)ym(euq^UI%0ZzdDV?ayz-zgF-PwP2-t<5W^abVfe`pt5{X%J_iP9WiBhN#X z-n*>2cc2F_az5&4ZCUr@zMEs~ee@xRBo?>KIZK}IRVY3}zMiOFLoL`ac+5P>h|<0! zji~AX_@;KGYe;x5=z_8q-=gi#tnaDa&v`K@ug}Ee33_-;XNwz_dF$=y_w+$yV}8mg zs5K@x1Y7#}$^>tbVu;?$cJLz+kd5%I-PB+7&@7%>uuM2f%2X- zst0bDNO(@WC}qF6n3r{%*HJ+B-4t6hIkUPkXoWj_^+){vT$mJowDAoGg&{gY;yMIT zPVwXCfgLNRMnAmpkrVuMdDEqrZbCUEd>qW21r|7C=L)0*L94s7aTe>@UrLjaB(-`e z@M~5&&3cAZaIsDCGg}rRqry4Rah2#@GVzs?;z#7@#z zkHVL}+zfo6t%Z`=aw2P}8=AiZsePp_H*)2_3e-MP;d$Yo(M22qJyZ__XuauxO%&a_9 zy`JEi$dgUbmnh$It=t-!4-B@Zp4|F>Uv(u1BW$?bCT~jo@)Gc0+>j)Q-#1o2YVrOL zH?AT5`29Q!M!v=!o3#J%0eHkDE_&2cdg;p0$tD_!_|L`Or3+$|b zvY%wtf3?E@SNeOXfm)K_kbVx<-~C5|Wu$<%F#=lk|HVn5P0;+nH*43TtN)GL5M@%H z08@U>`o8)hBJv;K2fif;n$G_a><_{IMaKVwgpuH$?7*G{@E^qPkF*`D{(m6eAA{{v3Sxx{IN@r6;l6vgTbiVBdQ{WFI z7eH(E9ft)eOE5O01bUcj5E1L->mJ;@BZV)^HzCy5uX%>uOJ^}y?g~0F6 z)C|5IMu|1{#A{BlEe?ZidFTv7a>p_xRWZ@|g#qk#fO?_6n8;l$XHS4}WJ`9o6$juJM!&kqBL! zj2Clv6ah20-B3R&G#qpTv}3oc1jpivbU`zvwJyroMd8aL0UaBy1=FyxbRh>gGa)x& z_3#_;a4-`#oF4TJv=}>m;YFoF9q8QPpb^Ut_2=Wi2lQjWDFcJg3xZ>gjP6e{tpwImkz59>wLI5-& zK^OTWq%=LKqk-N!yVSI39ri>}3qphAV&oybnMT~hsn`Zo7ZvdoxgrdfGP82y*eot4 zwnN3)QRf${kD;-j!&lBE0NI)5)W>E!JZGTeRQ}>%J7PZ%2gnpod2}Nc??OY|#ipP$ zuu*nfV6KgRY8E2_&N@Lyswv$8qX&jMU^Fa0#Q{Hr3^eMmp(GD@=RsNDTRHLjHkdDt zJ=kvdZF|p^R2MJ-@f10K+}iDl!*41Ttd=$Y5(rXL`gjt(rlGZgxm#E=dJ_b;x14T+Q%rZ zVNZ;aN|^qSFF9)UALxSyag4nLeR2$HMZ`I)u8I&o70hmP4lheZr8h~+5B zf`0}tKl#@A;gQ*r(uA-B(*jn?Lw)Ob2;h+LtZq@SRtk53+j(BIsoI`mL&AkY4WjFM z3um{6I5?Okw-A&Y(jb5fYWmQnE&WO&Gv+#6>M-#A=oBN{_4}w}%z*Ts6vi&V@HBSR zIM^OgnBtpX;L+Se$FkK+o2&T0>F zf}=9FAVM$3C7U^mQ)6=%d zuJK}ELQSkrkQLk72gN+5K3a~SR19>5h@+L12( zS=b+nLB{TnjQv)tKU(?QIQ%ge#}@LBfyG8Hf6T>i1p%Y$f1EfrT*m)nF8-K{-&O*P z5&elSjuqsOxj0r3@ZpcSIHty*=;F8C_>+wMNk*_x(6NI2G8fcpN&_zJzl!lm!Yq3Fm`}A0i#}fbQpxvN zvVE4{wQbvWde=MIvu(T0y{diHp69X@?$J-!Wt=w5%UgBW-@zZqd2uN|zzzUQ>T)BrH%MVCps!QM!Bh7kw*1CPB_vu*Gt zf!!q%|F+-J&*#7VR;%9|nlS%nr=_#u6=gu z@x}*-@1F@E6#EA{J$XZZ`ZRGE4ErNBM?SX4?b3G6Q~vkbzcYdt3$f;(!Y&2pOaLkT zJ%^uWw$>$ZgjVM_v*yS<`R45lYkT0>yL)#>gD>zk`{Cf30KDE292)#cg0i+h{r6j7 z#C7KZfsAIoK9u}kc$g|~0wyhzaW>-ja!iQ-7C*5BPWnP>R01?O>PwQiZgUKG9&>-S zZ*{go^@Eb361zp#7g?Hl*T5iL4yShhEseYfT;R0MfVqGH5$wLe6Q#6>?GLCl60C!N zlkZIuY})spZ(#pT?AG8;N{n;oYeZca?x&8FySheJ-`09>2>0KK*0psg{?PfsSZ-vr z!o5IK#Pyg7;KQ3_@SslLkbY_GgK*9ev4A~(z7t^+{(C=L#DA;f%q_c_^yTUgwwSfX zxv#r?|NOL!Y_nZ4USPeM18{!<^t#n@B#Qo~j1+FCBH;6kgybCE?;ZUe58nCxYV*eL zg)r{nf6jze*?)hsx87$k*peXpSfo;BFyFMr2?K@ydV`ViCb$NKd$N0_uA)0*30Adrke{Rj?48_&kIC( zzxHmgPG7O=%^sX)SI=yW=F&CF#LRJGxg)i68SHnZU3A*F$s84T%t}i+u;m{G(|g?8 zb6OEtQZTyVJ}iOdKBz1MLs+!9lZ&7rIM3?Kp`LlUz^v8L!{uiWo7}c;iWE-k-S%b! zLQ=thu)Ki?h_x|G@W_?lrh@Ve(Bh{s?L_Pw{P39+7}YKhE`F;l*TLb|dM~xHFv4+) zR)A6qGu4(jB&-K)P@2l?zrdJ8Nh3IKa<1;Q%Sn6x-}m2UG=pBmmmWy>j~Bg%%mpX) z01zfZb`1# z=J4Yey$`Qq9|S4<8U=XPP8+f3t$%PG;>%@pz72LAIA?APL$++XEs+BpV5!;H{>jB| zdu1};cBJfbRKurYl;bh?z=wAb1AB~Hng5~GH=s4swBha^+kYusIPtB^C+x>+-KY1! z-1iEgXk%}VE}rNQa^F(ZJgmu zbq?)(lNXxl2M4=$BX)hci4)@kS$ZH9UUU+w;r5ch5YLa>Tlm^tr{7@9*K& zFeU6_123sN@dlEFR%FShFla*bwYP_y;Eap ziWkhQHd0_dmS~{XXMkGEG`%qVr|d@ok&KZ~ai{*xO(JG^keKY}{-heNFHZ@BGcd$OI#piVCY^6&cC^b~a!4;+zjdu@YrHPb zg#NeO)m;IiFuqdgsB*lH%j99eVc~T0{KKkr1xwwctWnvK0L94b=4}Znl+`Y4Dha^y z_becrH$fA43hLdxx6f`o>S4v!2WlQyt6P4p`ZI1fJ zfZDot73N?!;HQL=8o*_5t-RrWoECCkiCf{dz9>PlaUe0imaj%8CiF-GYX|7TUN@> z-s3qR_3i(AUeEJ-`7J+R?)!7U-{)NCI@fiMId`4^Rtn@@&TnVd{^w)<`R7CEI-$_$&W=(;-&J-~6@39Cq*DE4!-1vOqZgt3y>2+Pke>2u`T1e_m@KMkXMALi- z8s~2P)Z772*Yx(vR3fX=$t&}HuQD#U4E4@yvi1qFYiCJY&faN%#w_jgnRDj=#^$|z zEaTtDN4mfIt6K;T+RFb6_bT(jX+0Ddg0y3Lba_zdzek34ha?4`ko%!rnZUnqz9+*k zL`eYfO+0())DBD+!KG>ZA}DtF8+`)rI!sXZ1Pzi#9KGoEvHHN>)3N~==kK*9pp&_3 z&ka_rNLMqJl7}l}ad4AoQv_g=)D}EO!G?p)j8U)pT&xJ)bVXE|D%MJ{`{MYtcySn@ zH;o5uDr{oJ@$F}~8!H2Jm!{A55r}CXM;n&MNB3m6mP;CUn1%5Fy~K6-LFVMP0`{rmt9Fn0LQd#I zq1XLaB=D@tZ(K2uB4(17c>Gb-&J2kl8}t2_?8XLokQa&t&s^eYD*0d5OsI!&4&iHe zH~!bnzjuda6aVfv6x}6x49rL~qZf02QJh;Q&=R(;$#2m{16|rH6Ke{HH2~p?%|yd&$ALJ zckOswp1{4$Wcjl8FsFO@2qI(gg!sO96b^AkWhppT;9nu5OndxZV~UT|HY5JK>5ZxK z1JsQNdt0dng@zws8f9K|Hu+gqH5Mn6u}^uMvb$ceV*p3m88(%CqxS*ihMKo(IT?|0 z6N3p~?aUsyjcL}>L&YKXgO!Yep%eN@v28OX7A$bCvKwsC6mNZ)46cGpAGM z@IJk$228?d0c=?Z#u{Y^mjx-!S0(aZEcU<|7J}*SaYKh?wnz-XI=-#732Sner2k^V zZ1{1+%uhY?y8j<^wMj6i!Ip(|5ExFBCU>GTcL#aQy3YZ6`*|yZi+CT8fa@{k*}jrHGGIdRaa+w;A_> z`O8!uJ=LX#qoBe1z_+}2kwKU^c8~s?7qVe^)>HdJRYzfi{{POs&Yv**;^;iT_J7GZ zp$4b97)w?96D|>r#M5mkjO^fyehRW-fEcD>fV+&x9Kg~Z&z$=6W5(qI-F%AMi|-$t zO;6!75^RrtW8D;*z6o1oG!};{DSH*9*hM-gagx*IQ6kFLP{h?>PFO5`c^YXoUE(j{ zjt!y8x$-dcoL#guY%a60wR_w7rW(fHYop(8xD)WFOr%g6?&lJkm&47I{Nngwv7~+K zLAiamhAxfmJtpa{N5;#EX+(OZUFo7uY4IID;Z)gV{>fguN5Xsm-V+)9@qr>*l#9s! zmt}M_;Q*nnNl`+8gK>7e-6ejAQgO7sg7AI7IOk@ap+WA0f3`M_!P z%JiFf#vVHvEspy~d5?R>gW571`Z~6LFpL66q}s}70v9}e!4$nakP_`+f_%*jl*k)+ zxq^UKQb60+0WXsxiIfxD^*OT5ipQ*~h}XC*;Y5tsXIgIO1NCg#z#kwIdpO;h+Bx#( z)-*@}ds>-anO3fv4usZV+#q1UxtW)tkm98WxSYZ7GvMk zdSSDqe%H~U)^-Fc#gZvMdW~gPrHGrvHtV$f8KO>{P~)#VK0Gcy$%qiJ7GB;WoA^Jq zI#Tb*ra0gG@4~X&!M{6-+J>1{YlY(kC)FLzk`+WyxF@MtEZ*KRP)DkI9Gd0? zgv${#p;psb2Wc4TYxfC7hc8dUAL6yr@kdWA)c|hI7{qxW3>puL)tB0Diwe zJlL}Xbo`Po7?i%EV+VL14~8oornvn?47r1ILs}(PeAhmT=bKf#4)p<*lsOkAbDwlc z#Be;qf9>Z-`uM?TJ3+S6Q9`HOh+WZ_T`P{@W?hDqHw|)2y?oiEJ@@Hw(xvzrJwQ#< zU$y1F%>hj6rJKnPK73;iGjdEzZhm>P^z6+Q6nny{KlfH@zxr3gf05sU@;LY24#aj} z{u_kIP9RrVtE1BCg2?PZ+0&<3Xue@6-Dm8fG=J?pcy5uG>MS*{zIzQx;iVRdi0QLb z$~whKd`4P1wasEl3V0IByv@iXgn5t{IvEf|C+o7^b*J2ORmww-D+10i}z@lniXtSs@5XFAm;qp zAGKsq%C_`5H|(NWHTe|XR)lVw9u`&CW_pB~gAZc}gw@N`qcv+|azMK#u1ljS1SZ9skrWcAr!b!deRNV;9-oP+5h5y%XM>rRfp645+QI zI*;E@!>o;m-eZAAqiSZ}J4*qsNWPaZ{gamlj;K@q)vr;ce}Fgh(T6j*e}T8MIJ(pf z>un?vZLv;*;ulnSD7EG~86FjlUnxtNF2RyYZUB0fT@<&29OI)Jpo_5&Z+;5EJF!r{kZrrTs zd(ZaiJ`9nhTvl^;BAyny-V#L~RC39DENbK8wPu`3KQF!zmv_cklZE>#OhxxRjud0v zf;+5|PG7j0z?v)1$ zqoI2P5C=+@imtsPCMBpxbz0+(Sn44ss}VR%IdPKXr}QVv%J0J!7dq?~x%3}KWhe8; zVS9oVaZ%5>@0D>G8#`OS`K>Ez)9*~^$|No)apz|142373k)@(gBgG-4;vOfabWPIZ zElmhF?lcZwq~_68<~1zhj`AiV8gFT-!b|Nk)}c^ym*x&3_ot1K1qx!yU>$atYz>R7q%&@$0b7hO9-bs^jJllb9gP?NdKy8?&E$8+B!`x+b!yox>fOBxo^I!Zs?1 zRqBy)P}y_=uF^WfU9Nl8OPc zI%+r)Py@WTe|0@NyUor~CEd~FB8?Lm!-;lUY-ur)bnQH;Ycm`W1V=DW2o76*$=b&+ z|Ch|*j_))7A4`g!;Uv3*TY6DYaHIzD4I~PfiM;{6v4O{3<1F_*;_BR}vN!hKOkQtl zo%#hoKskH+tIcot5HD;lK`Kzv#d@c4dwINR&JY9DHcI7O()&Kq04N11I5I?RRX=KX z46QE=mYWI|Y+A3lwOVBynm4}NSOaH5rD?uKa;WZ)=2zv^n3%{$DM?Y>11(LTu`98s z*5D6>asUlenrD3fzoZ+2GZ_Z2e#L`H61I>0DI3w-9LsV7YH4j4hp(J21+esJ6%@>p zuYf_)O|Ap5kqR9H!>`Oi+t(1D1nCo40S2-&;S}Spgi=s4*wd6%QBn@mn#t;zY^@|Zeyzfe>4&0> za744(@tcObHj#=Bt>AMA9#p0DNtLpQr}Ew6kl(7UviDq!|A?_K50WNVB;!8xD>+8K-V zLKxrxUA>f}6nW}?Wg)nU#`{)3pb8ycg*kaQ&sZ-g6+z6r zh|dm-p02j~*oZtB^?J2YTp4iZ*R|oAfz2zwWRXbr|JQ}6fox7Y`YW>TUjSr5m-2>8 zXlwviqx;g3+?L$vg^q%Fwaf60hwBW4xikW%?*SI@zoEoFn81nfxU$io-OhI&x|f7v9H zrq@>!e$`D*GDBS9qAx|Ot&FymSSX&52a#zxWcz7P;3+H1VH+O~sW3rp6oSBs^!|S$k64eO#W$qNbuT+yhYHx4PGF8=)1~!t1%WK6@5uPY-*p z0my_ctf7sBwC#8JRO8P`J_(e%vyg_kOjcpII{VGDWp@D$segNEP>G*Z%V$)4JKA=^ zhrIXcCDJP=m|NtlJz?isC-@wmzJ1~JD*Zu-?0q^B#S}pc-qYugN%8I*Gd*dU$ zq{rmlHl1o;9cv{qx9|Mm2&x_MX*1S2(FX->xylZ}K6w=RS-9>kpI=`)zS?MR7GbyV z(r;kgAv?rIBq@{N>6KW@ynzYod4Lw@7H6Q0xc1uta1jRp;d4IA8E~28&bkNB zF+A)s9bX(MQ|eqMh465j@_jO2syyqxREh*J5y?CD7Yj&w{?JKX+Kza=35yZF1k`dj zUGK7-PrrHCh`Tml(jZuRee}((=e$M}f=q;V{PgH^)E%1KmgE;D=oCya1rfT`(Q}C$ zmkIeJ8AKiPT+^=A#Y{CF1R$}{p@%1oR=A{YGrfLBf!_+_i1dMOFAQ9@P@hsEo7`us z5@{o{E2eIl4_9%8ULo$5)Uv{kpheA0;2P|a`us#k*Nk6?*)&+E4${tM71n{#VWiJ@s-pzCpJmpr;$VxKj| ziJoC?wD+2OOe9;V7kNHj5%qv&S#D7|&9s_Q-m3qp&d)|(x|AT-Ldi2Ua?e;?`%BXs z<&ZFT8B606!hpa$m@|dO+)lueT`J=nzcxyQE5xr)M8iMh%@3U_{c!H23Y|m*g1^*> z2zSm^495}7v+|v${_DZ!f`o; z-Qmi@*P?Qw@C88Z)3EV23EpYJMSF~ETPHJj)NjnxDaNo3zj6Hw3{`@b>?SeS<`3zM zZggj5T8GO~HNP^eMi|eV0JJiDY_H)(`?P3B@VT5}@OA1hHc*kaR7QurdY-%!iR3Rf zq(_2}wk!YppcSzi>`S@V0J8Q^*`jCL;uyR<=743N?zam5wrm7Qw&75#$t@K|gS}|a zx-jcb%uJOPTN;%@;g8g(b+_7>9?DCTkC#hreB#XhR?wb)i49#!DXbAy+7V67C#`yK zN`!F(mX-)Gt)2bgFoyB#yFEq!&DI9jKQ=t8-5B35mQDRD%v5uI?(8=rOe#2yb*D{A zScYvp%{HwO7DmfJvA2Noi!Vy|*w3z54H@l!e4J zFw-Tho!j?-=DbL#jOtF|$Y-JB44KCXbVj0f2GSd~y@JGoC@Spow4NOo**gmZ%A_!h z#^?FM(|FnRn}a&%K2BbB>G#- zc{#V}3&#&GWBj@#d&XgN?jefqQ*qiho>1AhynghHt#K!t=%hFx58Y&We%Zo@d;9V0 zo4psCdzuRB0}CKR;oW5zBKH9jYGDUiOM96oZ0b>O2s*e;E9Vm{ zn)NXeVfl)|hi9LSAdr7wWlC_+2PBOac#ex3d21AG2)rNR)bykSS=r#OVDyPnTbdOF z6{#=)N#UL4o7cA~1)m_CQ5^GQV9_mlp_SyKQ1^q|XvOn3FioLip1(c`43^u|-c52C zu6k8uQr@<;T+;yE7YnMG1X29Bua!1wkjI%ui`Rn8N-KJbRfc%ToHH7A~g|&dRUVS$+j^hKHE7| z7-0V4J=gnzZyw@$-#YNk7GKHl;QfADDnUn-N^WBhJwz?WyI&O2m@XQY65kfg)ELnL z#J$3dKF;VZ&E@pwih+mE`^xD%Zca5RBMlUj@*4(>r($ndzu$sJp5*fj1@@wZpl#60 ze#6b61|;rD0b6NVDV6P_J37#HXS>Xz`>gdQZL!7#MVpbPrcV5uTp!Hls69nath(>Z`Jjr)_=4lkbzszi^`J&U$e6 zlGI{IoD1bJLYzdbiD)=pX&-Qwt&iKNHhMckpb6)!(RqK`{qR>j;a4>fPJB5mYEw&G z+t-e>7j5>0&*rP|_lZIKq=rwjS-}9so@K@6WqsqOx>^5(WPeK=f_KFMY@h2?|5-ky z&_qHJ#f@F$Oj2lVv$Uslwg@k3s!akrcIshg?%xK#{22t|?Qo_3Uu?n`$DS(ax@rxu zM!NN5iCOh0+vbs(%i5Gb+sgg7TwT5zSNH({9Op5|l>Ss0bT0C&yH7h4?g(JD@;-sw z)Fauzns$sypC4F8W7>@;ZZcVZS3EsWv!^@An;nNI7VqJ;s6gBbenSbBxM3%kvvBE| zrh0~K&Doros6a}vU;4G4U}w6wDdHj%Kh18&XRGw!{cVr%WS4C4X@l)!ge04cKF(6TNIEiVhQXLv1%J?C@i%{LL3xSTQK2EE(@Mkn=OB# zD6>#&uKV+CE(jgLf=zjzhqS~TDXiE>I5ra|nIpe~^RmCOI4XXbJ^#sPOXsQd+xLWl zu>P*>=e3o-Pnj3JP9+SK(=Xztm#}ne*brLF77B0#J(RzW4{gm@W*lS2U6|3%xs6Tv zh_yw#@G(|f_nRMTkSvie78yjcv15Nx;AgOL(LGYjtd`K6?6Tf5HDQjhW5kk>fR*$@ zhj?-gR$+4TlPMA|{hsbf{#vD3q~Aa^9k*xOu1ND+XJqnsIB#vi;9-;Bx9s(~Z+6DIt5SWJ@f1W!hY>R=4Nre8V>^@kIw#FIsy`mVe1ymQ@{e5x z*Y`gn%AX+9uA{^;vp#n83>(>E_gs?n(bghgePE%`M~V)1>yZJedr^296e2YFHbvHz z_n_4jDPt;k7BqQpEZ@CRppcHLmZ9c?Tg4MIhfIsuz zzqX(t`#`0If5WbRAEuHH<#}-Z&`f*hOI)S$e;JFNOa_5eS+|<9kQ@xGNvu2?h5Ps< zH2wYUFG?~XYC^*>&oZbBu``615)F9#)@QCt`mW|d4}#0uWSDafCy`AhS`Cr5K=av# z-{gSiEv!C1cs4A;FmL&bqpTsc&8u=BuGam~VN4ax6q{}g{nm%Wm-qwpon{Tb4>T`` z#8X}%#Vw(>#da3Ybeq~W0ZQ`u?`lNb!g&T6Oi~hP9UN(>D((>sY2HT-nDTc&I)7Yt~{B6I5YHmg?C%{-z4~2wNVTPaZ$dyy%FUS>&th&n_o!|*sEEn&!7u~&) z-Jh1B{1C^FW?1%Q$FK4sib-(-oa5T~6i6<*XEd%BofIvzzYg4SB2=f1GSdxnBbgW=K>H-|Ah{#s^2G9gaebmjt~+i_0E|%5yYnz@!=s9DCelg(Z2?2E`h4C zl!8&(r?ioheuQXhdL2MVWxINKTWDoLa!b*b+@v-(e>Ghf)2&wY&|7GQFR*61VLVjR zu%hlw@io<+i6~Ti57>Ew!M3cu?=Jq4LiR(7;Ns$kFW>EtBRTmDR|*@bLFLdl0}XBS z{vPD|!C#ebGbeti=qCDEu9+~Z?>b+squd@7iW7d&7#$09SE2~01ff5g3bmZ^xKTZ* z1wsv8-W#>HmWlng2mlo@vKY!|u;`x^)F0a0*^?YAzk8-34=uvMhcilK6D2;W49l+OP3 zTC7ujrtKD|w%meOLEW);`GzSkk_M9H*APiH0lxBlIXxrE*4miNc9L}Ams45&K3dFD zx8IKz_21#{cGR!vo|o@->>^ptMeZ>PqHMxDb%q-%M|agQIxYx z08=BMP8QIy~f^$&@t zt(JJ%-4|9{`AEajI0@G^^x2NC^-(k1J((Fj?ZXHOwr$q^u535F^NB1xJ1>RBpC=4|bD0U*8#^oL3J z6x_}ex+E?(U-$+PvFqyQuv;g9NgN^F($CaWzkBjym{2qkAX-dC`l~R6qj#v-ZLD63 zsal1F5Ze3)bK!X|!&R#=NqkUnPCUqsgjv?SP8c}!Q`23K^sHnNf-!=G;aM4f9j^ye ztSl^OP+q-9pn7Rm_{IUWlTM_loGktgNKU=8(G;&=vZWqN)!BR`81=@pVu=pasl0tp z3>lW!@LVS8t3~$rsmS?JWn}>_%=^h}sS#rmI#_Nam@igU45HxZ+(1wueK_sTdOTte7k6qqdAa5%>s2= zcgtOx9trU+oM>42_iolFo=FDwoxyjIFL^&UEW5LoLsKT(KzzOj@VXv;2uhv{mh241@I54H~6kj?ev7TEh=8IQV&m>!_&= z#Q`XjUgm|^@$ZJzzw;c2X%{R0+&&_quW37`5PHy7xjEGPV0YuC4lFo#qO6rb&+D%< zcRarORJGuVHR zRkcwoOy>I2J6HyMjQIc%o#zyW7OIYu!$5{Km?r%EdKnR9669n_VV)!mx*8n zc50;wH+f8tfZk?oL3zup;%4rS&qPcs&mm6VC(uGt-fd};ArB1~x`SSTp{)l7U;U|Q z`o?{)f@<-3>;}cJy-5kH`T(udBW8iPTa_?fxa*&^bT$OZ2xnLJy|a6M$QFJ%nKeo# zJ++9saK-VrBGCM=U^W=!H7H+|zI(If^Fj#@IUHCx9$>YoZrFxilZ8f zJIqzZ?q_pm5(p;IYs=N8JW0!{kjw<;5bVqevXx#Xqq zB7zB(T_Dz**4ji${`Bu}z1_G*YfG2YQc2QXCtdskf-fN&qP4E*+wlaKr$kZ$v@D*+ zZqjuce-sxwFRjWt@#ES%<4?W`r?gbn3-{E-``%@VI`#Q<5pjJyPe^=4iK(Sl?aS?_ z+auDfR--R#eDZ8bR`O-VRJpuW$AiTbQlId6|dG?$}xv z<;%3KYRw}eO53D&M`Pq`{oIW*K*O%lrG86yOES@7LR7VhoFV@CWm(_m@wP(YVENVW zuU?M#Eh|X&kZY`K@gaBbNTaRyt9xq}Je_QGfw}*QN#3Ma_cri?(rs(#pH@I2`^mZY zZ+MTjR9e*Bc6fCQe$Efn+Ox%zA~A-UV*-J@>nk1SJ9M9ypo6-{gYNn>v^iVu`+=}( zq5@r(5zfgpWoWwAgmiA@1y;bOWm|%M;yQ9SSvyDJI~?t0C?Z1K6}v@99uigv-YA{(``NRqXQRF5hR9JMq87Tlg1N)7&Yvn6ln1jrj%`@^K!r zLhHNH(N`mhY#c{eLks7v0!kKVp@rlj^;FMCuGF`Gkyt_iszB!G@;%f*(cI>Et87*z zbHFlsOo5^QeY#swlRkk1(T?8r0qH%)A{3nlt*ycHc62BWp<(rSYebpy*MO<|uFPp? zr4KP4>k8eAt4Wy=@;)n`FPY7b!h2Co|u*<=zo(#HoZRFbgWzvjDH%f+HNzNo{ zLtod53D_kRt9S#XK2rT0 z#oGKQTiM8_iU;}XJf}Od&R)9Dev?{AFD^slIe*ei5J~B^mUuW6%^G*vm`icJV3?_Y z1G)@*Y+{;1>lJ*e-p%^Rpv}2&otLp=+QNJbVm+Z$Sfvs$)!&PtZNy|>BSxB}=kKF% z34L$A$!e*USYeBAMA!T)@4%jS04A0csq zC4Mbn{yE3f#TUuFJF|)DFQ~;P8jq*n0;GHxoyYyrxQo!7i&-wne4Ox`@na76M~;@5 z_b@Dr7Z6xY+m8)z`Id%rg!Ugpi46H>#LY#O#&w6*8TO+5+pYq z_7Eq=;&0EdWcHai+kIr1VnUQ@&e2U4dozZ(nBo5a#^oF~6EUm|chn(nlmfq<)6V0bNp!Klt%qIF-7+Ef8@FTR{SQgn8vm^Z7RZ`(JJ!{^^aXW#c^AFk!s+t6`7$ z`41D9dHVd4!zv{SMg~*V8ol&eJW{3w*~jdgMW3K+lNRp0J50QWBi&A3gPrRQRk>^? z?SOr)(bEz5ZEDo0N4nG8(#Lj{7H;fXH3dyYe9Iv^aTgIOiaY;l#&uP(x|O5K<`N<8 zo^Y}44OY_)<_R2GOM(t|cRlPCJrMRMZq{ie zJ=(+T<)99oS($B%Y*GWd&4ItSLL-OnvEhp2aH}{~tzm3s`snTYFK#WLo3~>-9l2ya zpQPg7-X=`CV2ztkHkqn8V!JNTjc9fnaopo!WZmIY<|#2_Y7Ah>hi#iGUBlA8t2o6A z9SZwy0i`~;9R}vV+c-Q?b}tH}c`uLdExC-?uY}U{i!GVK5HMG;XgN54 zZEPkBGIVzPaRfxJ`}xCSWj8s?0+Ps;?qwm=oWdI!kj`=MVyxVtpYQFjm-_bW`G zS`~GL_D2IFK$42+l5VvD%tGTuCn%qU5b;YYEIwdGc^)>f3>EW9zY?Up z=5W4Jb;5Pdqd;n#E3q^trPd13HExs5GtpNuwD=Vo7gX@9?U0%fKQ!bXD>tWXxE=+e z^&e}FydtpE9M>t_cyt~kTv4P!fi4Hn)hEh{N-d9Nsnj(jW2)e9uy4$LdkBq@`IlbC zj~>xIBsg`Va}*qki0kCBG`B-81~FpPaEFD`7WR&!Qtikv_NV$PU$`;6V1ADx^_`$t zZXRioF>1Pe<}q_Dr3NQ=#g&B#cgDoUzd$<#ngnuqxc}?l;YGX-XYOBR6Z`}M^y|>w zobwaa(JXX76XSA>%~?yekLKb|h;SN#MFeOn1C{5)C|Xz3FPFH3W2ku8 zo?eRYBRe%vg_^f6#CY*#z1ldvd(T#R&88PfGKr2cCK*39=z{!!r2=pr0BX1yK+kOu zaf)%SrBh+y%=SH~w&U$!;n01en=QpPAi|lVk;MZi5!oot$Wk`+Q9jKq@%taA8waGd zec^6)bY$COMhb4h;>8YVPY9&_KAVz6?L-c`Sd)w&8*y|G)d`Zy-MtCzR-vZ~n@jIA z$-Io#_O`%8tDdpIpVat%XV~Q>{+-mpN~hHFc|(n1k!GUzpj~}@uATvO1A4e3#yQTW z@1YnkiF6sQsnpDtwQ%e!n$6!=;iTFm+oBUi2P1SX!q|qxeY?ymZ^-rMjU(?JpAuX& zKF|*IMDjokS<8rNrDHN!yHtwb{G^Zj9TPH%)H5K)#kX8%VQgw6XQ_ocb?y_1kbY?> z<2le!4_3QnyRJbq9u7SXHYTKEg#?iuGlC-jmC~97a@EOhA^g=Fy_BL;)i=K=&O?c! zIkMt28rKx2UF|xW2dbdgK?igMUuCEShKxHJ zl5KiD7diR#9MYbRd8`j_^tPE zLpP20x8uLKUE};KvI3q2ET-giG5a)&yfJ-Q#>f5&VQKz_VAL`083jR z=m>Z~b>6?KQ&qd(b6K~{M#anMWapnto5zU;O7p{;Tff#5q6E^wT-z%x9e>PUiXi%t zG${)$d!$kB^_1AC11;;yH}IcmLpG`Qg)J`O2QcptnUgE!dqZM&yoq6c?7Tgdeg36f^9#2x5m@#2h zKqk^4MyDmk4}sk>TT#P~rHoq}U_Z}J- zxePaj6p`Eu*Fud47;9aPgpL0ZLcK;ra)7!rH!*Nq6gF!03v_qc2vV@zDP3@dM3;L| zibU3z!^hwwFHmV*JRGd=(t2h4_Xs#4N1*cj=Fvg;Hm6bJF_=q9iN- z9l_w50MgXL)L5I=X!Sb3jb4shK=bx)ydFMR1-VZb<9)Jbt-_N(Qn;@w3V1y@bs6fl2z4mp^MQvoW$Mta zMjT+YK6oCsG8ho%u|tIsZfVGj%19mx@As0RPX&Z&GKA$NVsLNIk?wRD7r%WSwdw)%s9 zU`Fo0GDf{9mW@0pb}|AKCO#095vA?rc<8q{# zC2jH(CJ{st5UcQ-n>{_n9|dhOJ_HQMGRu9Z(SGtlPE?~qS7O{fsXcy|aPRukme@%Mj!lut7) z0UW?@TA`NZH^<|Z96_sm?*puTWB~(dIgNq^UsT;2$8Wr&;TbpoXgf|~7lk5B)`pJG z{$wlfpR>r~fq;@vT$Z<19^X}@%GSY2dFM6POOUVex>Ipt-f)-mc`nFK5!cN_JyC-> z7@A=skes`nfBZ^7XD4$A*uJk6YlgH|MjGZnfB7uB$r_8q;Im!aj&pFJ5@Fo8ikxvs z6^PL`@=&er(w#tP+JVW9UiU8HxR2~t2RSji+9oqnX`b;d{T!82hcUfPB&RWf=M48k z$oMq^G>1Hh^w~cc`YZqx^j@XAvmF%m_!$D=Le9m%$#*Mrzm zdz4sHSVyFF|KvytXrx+A2BiH{+~31$4rsc3HyP@!=#BjwU$+_ekT`a5%Ll^hdk9yV z-Oyg|eSz2S2I0jk1Q^2`HKnMa!<}E5Dtu+ZHzY%Ea=8EOIz0=XN8aEs?XXbJDE)#1 z#j8=f2KbwkA@TzPAy8Mp6c=jAP}CG2Y0*d(e3gQYI6t7Tb!Ez(wT*M6K>@6LTyUX_ z!`475Mu`~>xec=wt{0-7vmTS9kkY>UOp!cS{a?NmhZEk7wOH4mhz>$%wH)ZX^Z?!E z3v3ULM~7R(!JR>vkZ~OVfXrl6r^u~C+P&x0znxZ2^<}f1p(!@I+nXliU!|ETWiUBP zIKYhe?18)lfix^TdyRC@DRSMRGeiC9=O7J|{@IUpDffjz+8Ku%`%TY%IyOyzd;1|! zutZ6%QTK4scBKY~RZjg{!Tj5W?V!yD5G;;8;!jpWh!VGp@>cruO7ZUlMr-o0Ikp}m zxkv~HyisQ%_&fww*a1WA%-i&qp{ z3G*8oNks1xp;7dPwK|+u78jZ#;RjuE7AdB;x=+0qHq960eIf@B zQ*YW0Vs5Y07IY6hm&X;*UcaF@)c1|38h89IfcUO>gLC--?pB@wT5Z0m*&9N->20`h zuWechN14Q&(;8d7Kw5hnS!nqcX>|^b@>i>~4C0XO_V5OmZBK&14j#tf1(OF5?e$Rw zFR@Nl-umMhEl?Sc6aVghD%{}NQt~UUlmAn${;7CEa1^0o#A2@SZjld05Xe4&ma}M{ z7~v z6GZvM@DH+V5UkJlYr8Yn;L_B&XAsYu^ADwoxul!^ZszT;@$^mw9arb*+fQO6GG^#i zZ|JMs>7O5NJ9|e6pU32N-uM(>)Y0Ck_n5=j_rNWSk8Pnr)liqKj!%%Jq()5*gJ1`7 zsj!^n8@h`t5{{!A)~Kf~!_k*4Q53`lOM>Ymr$vxA)fj_iKWHg|fd+p96Tx^pIvE<& zUtL%KV=dKKwD2(k@&FxZxSmc5A3ujpd?d}n6a_AwAI<}%3IGWOOHbuNw#bvk=2iyH z^5Rxw^@wMTDW4d4aZCCE+!k&Wc_lmZ;p$?&rLKXhx}x<&P`0$MF4?awm=)mFAy04s zvYL4ha4i*-`DS)uF+lNdpu#Y6HCx8t5T*$gGE+r5Ibq7{VQoYnND#42t@O@*ZXhRLp|FQXTS@o*!Kw zLZP6qljoo$Oa_d>bY#Wv;kY7S=m9QyT=#`+rTc#_ zY9j|<(HdDQW6Z6I^lwIp5AGqC_Rq_=tb|9n(E6NM)!jN+O4e_n`MQ(iXDtv0#SJrh zZ|01oCyoJWc&Ts;Z}5Z^Q}Ij0HVx9Dn6D?39Lc!v9H>KH?|p|=OAJ4$Ot$L;)^%_U zXU|BCACN>}BqU%@#R{@&JQoD?9d^bG)5@2J%qRI9iH~oUFq)X$HT&anHt!`%lf3n; zEokNWz6uvmG`wIsIJJF(&GBUOeFWAYf8AAXtO z9%Cn?!1gH$$#=8qu(}{|XTcavEqxqKdZMwY8y!@#+F>4+ngJ?pU<(Ky_30&OnNK52 zh`!aYSpRwX@b3>emcar&i=DF{PNeXszLX!LXml$EYKMJ@z!=YazV8Zx@Rd77V7LOy zIh^s;(`A=g!OTpCaVIq_#!M0_#9n(3Uax(_u#F`MCjzj>r^k)IS1G-+O8`tiV}3KV zHusc92(3rXk6*(we22boQfMgfD$>>t%KYk;cHUVsaj?axlVdje%)Y&rF7Sli!J4`% zX~$y)$T{FJDX+ZtfP;0DHDo{-ARg(FB6JNBi7Q#lNF3UKHlV?_s1 zD1rD#)fetbJ`m3LsMj(grJ(!maer+hlKJJZIe(WKaH4HkmsGezA=k`Rpr^dR^P`n1 z#ep6^B6aexsm733RM#Op+hPW&xEa@#(VHn476nz`;DY&iRFK$OPe*uZJvH-Rz z)}_UQYwhq+HLEwa9THVtnddz>u*&~$R1#Sx&d9A0Akb9oxd!==Ypr$(NAchlwd2n1 zyNOC1U_l9?c0ufV0LoUuukaoqFZ1V2Phq^s#5scD+r`-$_H5W^IL z6dhlJrgqMcKKG(^6A@oWf!=dMbGdiTv>M~kbI^c)uo9@Ax?jC8uP!I&0E{wD1Mwh< zD^xkZi&f%|VB{8tV!h}0_3#) zuFi55RT@<(_>fW{O_{UEAuJh4KLXi?qrX_rFpe?(*~swDP>DjMv1YvX z#2HE~g94|{y_(3XS-_-;pg>YeZh1kZB3Rp)6O=R6^wI}N$CvJ^jNFM$(Tb>g9K}G+ z@ZGL88K$k{*Uf?R0!xcPrVpY_)M-?d#d#=7b9Q5>v)zyWmbM6b2}J4t&uee2?6>4| z=OZF&v_|G{5Iv(q+DFQgXrNRgeh`Y^`q}leB79OzG89t}DXmAlbi<Q}X48F5Xvu@1~BMZg=qk4++hr_^;2ff-w%iM*4qli4b;8 z$o=oP-etk*%F{+If4U8$c5r33VR+a{ixk|y)GtdS4%slhM_$+ipp`YC3J>GyKwQ!6 zFuqhD>kN%J@T!T(Q!s=&G*x7K0_FAHqX zLbc1GIFzY@P!H&WI(8j5^68&hg;Ay`VY){E+01W$QMtECo+U^&HPn`@RUv%-_!%q0 zGrmt|ECv(}Ss)JBuh_vBWZkGvZ^;j4K}9HvX#+Fu;}J-8{FSKE$Q!ak@eH&l9~{Gy z|9rFgGFG1+1iB>0Z!dRT=sctVq3{FB+|1}!KF-(20S3}gpYSd8BsPRsD})J!K~Wlo zf~y$h&FA0U^tI#X!gt|2kL~%V;E@lTU&0Y!C@*?ThN2v#1axzv^ zhd#n_a3z8W@>=(Wf*d%{oX!#*d~HYRE@)YcIV0iDa*g$`)I9GrcI`uiuIb9WXav9i z`PKr=+rV#L)h{v@`MBG9;-Ne{BCb`han8WejcXS1BS2=7Wd%Le3}t&Qs8W;H?WD?P1-=!gA5*d`u45F#ga3p2)55A z46Vrp2S)v>fcjO!umM=AWDr!fthq~y{rxyt&~(VuKjO#icc2hF{$wXZuq@=;o78`h zA^)3&iQuu6mqv1q@xoPznYY(^f}qUQ1y_v;OnsBiGQ5BMc65_NypCeSno)@96h6lP z&(|zk{@J>)+WiMOllSDI3xUih!?xvU{c0?kvaY}a9Vo{^w@XCcna1QKLb&c<<1kiG zChCbxxyLd8E*^(%WxkRysjs!k?f8|WsmLU5&m`!*H7>LHB=-MPp&q~XECLEWR>n<@_nWEx_^8c_~tSdnp`_>&>$OC?QcBXrpUo9 z6(V0ku-l(6d1rDTs1dJ&%Vua6a?XcF^a)!htObF;KafWbcVFx5@`~R8kiq9UBR0Vg z_~IOoWaa^c{2y?DKNRpHJWdcrAu)#$<_91Jd?P*QNF1002yy5KtZDl;6}&t9Tw!Ah zSUIiN<{C>zAKp~COB;(H&yWBcTRfnyW9Q=^5RBw-HL?({TDT-1VBq5qWO=*zL@rxX zqRXGu4S_))>v_o<A^8gqDgsr;%4v_I5?4GIQ*1#AUsk5CCBb7J!m$N3;VHh`Es=)R2pw&NPf;K zR2vOfwpEupFs_0_$_0S2HYCg4!7^0qyQPt6clo91nnEbAuB{?|%O@a``ml!eFPlT2 z4Jc5|Xl76xo`3C6gkZ*dIAr$R>Qg~(rBSV)GL7!_VUnS9&+41BX!G^TPsz~0{wLTf z$uIJ&ev>!!WLyg#0Po-(d0bLU-J}YA->V2$>KaGss|8pb*;5P_C!WAbPRRTTK)Cg9 z*5^_$n7TQVk}~f%mwOf}ANxWydF%ylXw^MX=g$f{I$r$*RTD#YzT-9^&MQlREX)V; zkMIG|(ZXq(dL;)$r;Td#2hCubG%MJC>75M>0IQa1-V`hVir}idMRg-Sp%%DqQV=Ix zmLImp)LWlGXSa#;Ug;1*tt66$&rp46Z^c88xNDRQk2 z1NkH+j?W%bn6Y_zjD1kY8f=Q^(jtrhdiA-~XQsyBlb92lSKvmNaA*A& z-pe>|+vNT>hMDpEC&7MGHD;e&cS#3l-~gp zfSO%JnoXy3OENn?7TOP-vDNzaK2EKEuf4o?={-VilJX8EpfF*%{pt#--Xb_l%0|lT zfE;s88_NE{Ie^DVvIAlvY+K75Y)tSYCXkTTo@p>x4MW-A26uk7udx|qYD~Za{ULbO zZcGnR)s&1h7DD7;wP=rzg_<3gXZN%sRM9iLFBYJO+?N}0;rX6C&@8pN$2$R-VF^Ll z+%{aaud0vczyM5R#gxu}y6GwIafpji;FnR*<+RCw-bPtq(QFkANw&yTW|BE!8@EIll$H5tZ`<4h=c#?jcXhAJ^g^)Yd z(I`|2_1}JO8%K}w`@`5Yulve|-1+}2HiCITq+qxz5T}>W#zm*oQ2!5(5&#em{j-T0 z3kJC7F*&@!3k>Sln{5Xwdj3f>nQrEj!u5MtM0B~&Zg&#wFmf7YC538YPCUUzqwKt! zsaHgPeYa5$4c`#ue&8)jNus9x2hdh@_Skbq#TWwN+V%@fabFQxJO*5K4hlSN3*q?h z(BHq;eXuHfqy;m7PrxE|eh`%PLN<5_HQM8QW-mFLAK!=h$|dzNCHN~(e2sc~vCXjN%WLX16_|K>oFSPxiUbD!rUtDzcgf_9v#c?HDUKPpLWlnk zMUzOkAFccM_u9?U)2_#Hh*g`_yQe?^_-tL@&_05zsn7M#PRfR-!7pMmb$b<;` zk(DyB>DLJHPe0}<8%Zbz$tB6W6P*0V@XaS>9&l9{u^)^S2}b-#2LA<)MRJ~flm5t) z$lCH|+{*$WJw_scIT|7<^-7ez!5P5yzhy|_Z4~^PHAxYVpPn{|~^b zeq){mVq?|bWtIAdg{GmE9~7l8G~ZpNDkMJm0i4I5x3Oumb*e_(wg^GuRvx^65v*R= zB8mAP#l3){A5Xdl?E_^&&>Nxr80K}s;{@=N$-zYBsY8<2VAhsD-a!k;!;(t7@QeI+ zPsYK`uyP13;+79)g!t*x>V!DwT9x&ZP%noEgtv@67!f>{PaDBe$DbRoSyQGTt_b)3 zJs6s5b(Q{#xAfTl5P1JfU$$t64~U-!-2pA5v8KFRlElfuu`b=%AjTzU zVBGYx8hAa`&l=d>OvyMWGJ?1Z-D0qB@aLyxxr`uYA>UuTZ}cI%X^mFk_6crZogJc7 z;Fyw2|EVtCT1pFz-lC_I)hp9i?G0FFV+i@%!2$wauw--UTKj^ z(g#lOf zTUkCn1R3EhGMjnHt*Zh94xJxMrFFm7$|j1RtN)g#7WUeGR52As>oQtOd`>C6$_$i3 zz{eWzKCRsNk5-tAp9);?k#AplOobH2%pxlT3ls!zp`7Ug6Cf2_*YAF;+HNm+J_Cdy z@^UZv4uf(L;=7t&wJJ*`m|yv($!5^Y2B3UR1T&U4NRoM5)meRZLj_qdn_(YfE~NVt z>||pA`k-Uur1&2@CMwnd5)F^D0L*EDNe}QB@4qb899F;nw_=8QDz5pHM=Lf`cDV8U zMusgEz%`^lM7V|s9kUs=)M|hQ+zUR7w5`wz%EXazT26^jw9_{YTtA(Fj`#g^?*qz@ zuSu0Cg+L--u~s@-1$#6mN~|esw8vDeB%9$RKawKVZ$9t_y5V~!Ed2P}_<^p>80+SU z6*6>byPCgQi1ECict;{8F@W)_+pvgiAUrLg%-u1u{{5kG1)2}+E98Z)3Z&k=mlpmc z?z@&5e#d$Dk49>y({+H!PYts`rnim&SYO4*`*+KL>><(@`MtF{Z}}ifybi5{VIw<8 zf=1{)n)Iu^T#H?ze*IhO2IewB9MDb;8(P7*J0b@|XAbqEWAdij)xVb0+W(B(B!i4_ ziGyeAluPxUYO-kr*4~4U2I3$(%uNU_!WyW&OM*>_cEb_kgl*BFD9=`Y`O9ow1IJJ; zOF(o>pA3rTGa$Asu1XR1dTj4XGl}m$MHDo(;YtP<%3p_;w=9+kkSjA`pcM>+e|Q5d zybyKn-Ph&KYroJ``qbGf4nD*i=$C&omctr?*K$eD9mJ2EP-y*dG1@>Ipm)in=r*3kGvOfd38G^ zqYj$3T)X%w74h$uu-~OC(0gy09VRyYzMP&KEqELc^jxuZn@BhxA+(jB%^6Tgtn9-t zbKq^72!NUX!Bm(hEG!qY8x#9UJl74c@eS}p?_-inTkl0sm6PnYl|9hol`oWZoW9N* zEaQM}iz#>my4LJ3>P{1XK;qWA>MPU43bBN8j6L$1V_`)!Xm5H!|5#OtI!FHhB_1mC zp&ER4sJH^0Qd=~9ru>y%^^vBM#f_Ta8*Bxe_B}aIjV<_RL96-0EUd@RX+xK*qLd_? zN~&NI?EtInBF!-oJV-DJY}ftDc4H> zrEHRYguyr}@QmoWMxB?0%i^S9p+uS+K3EszFipRdA~7h;f%iq;B1f2E;_ivc&;VV>p}~2&Hblq;yY-X(-EHE zFnDZw18((ZRMG06y)l1)aI!rgH8xT1Q(4PDe>%J*xNeZaijBE?QPQ-2JAE$isN25w zjP8z8+!&i^g{Z~a#uw2`3mqNF_ZHYJg}gtN%;=Dfo6ynk^zqq zx#i$k`eZe3Q*HH;KLmAhX&jEc(Y*3adMSu&4|9P1gcKGxo)O_}Hh+(5_iPUb*J00{~5pJ{WVnWA{x2j>f5 zfQjP5!;U1A3YoSFL70{w0BT+i3B6%^XMd@;rFydg|0C3}%9l2ybq@d)5#IknHpR|q z{N75HR&zAc26stD*-D=75a}JjWf$Lm@?zq`T^3hkUcd?7ug|C0yFpC4!8y+MX?)6- ztO|UZg%tOIdoe~RIyM>O7nb$(f{5w@dxBoWu_(CeaV))4yHAf(=NGOc4MYH1k_=tF zx+!PvNR5V?Jyf=6pV}|0MG=AzCt&Km5G;oGxaRxDhcKT~HR&Vs6F;2%M^v@IihRyW zDY)H@Y#H&z_FVAnNx~weG2i|%8KZs(=|#U}^@gep z3J72G3O*1wTRGcdQ1{xH$;u}U{P&{mBAIy-1u{R`E^2x;&cC5&s zE+QJ@k-ESc>hHH-9EfUV#P-fhbfk*+9*6Zx^jE~^ecZ-gsqTx22HJ`*aI?`M5aLP4nf@25cChR)ME74Cm#)g;%hxI=q4a(H~OvElPxOp77E z9o1U>{HdH|Wy!va2js9IzY%y}31$EEuDasOCL1{pt}uj2mJza$=?{{2(ANtg*l zguSvyQPb9-J^vAZF5Jc7Ir%wtm6dc*=}aUoh4R{3Ly`OJjvXX0IIq< z51qbC=f4Agb39QXWVne3K!f~&+a|ADyS(J}i5&vZUIpEtjCP7{6Hfs0fpA!(Db$#6zLhk_srS5h(1ss99s zT>l_o|KEtE!_0*3pz}bDP!V}g>QWf#0C^6=jxzuSChld6`2t3_=z#X^&r>7MD2!IJ zjzZ+*depiXDlzBt;8uSb)6*nk!#B$jBNvev_vZ)Ggw?^vUZ*f{kh7kRmRxs;te->w)v2keREw%lF(@e#;v>%b){YHz*9>K}aiS@ry+SFn;ep|E0>H<8_n_jZpMuf?R$ ztD9ZEd*6!v07}P!(cdr@qg5X3*DuGzw*jkDk>9M*Vz2R(26q)IA>uv&<52qg5E!dF zQ5yLa2ESxKkM8Pt7)fWTZX;G2y)ck0!z|mOoAz{pF60aJ?w2GCKhOfxqU#=B^e4{_i`b3`8(>l?Me4c--X`^RGTQN~$Yb2IKm|!11NV^S+n& zuTO#@wKLKddIRb6Cla_J9L|D$huO}z`%MaF6o1}OU=M4+?hbSm)|ZZvK{%imw%yF> zN-!gE1mw3=6806`nuryWYGjGvnJyi3y0tP)ZJDZZ#qtpRS*O5)psY%%V?>MjGEXgN z2ZE_OOggzYh)(cABja{XPbSXzW?cZe{$($>=k$;EqKA{M(Hy+yM-uRw?Ic!RAO879 z;+O(HQ|<%IqIAfW?Fl1qL`NG$50Uqt!xd-Q&fGR=a0Jc=OhzgyuFjL4$_8|$lHRtY{Yz* zFP1!n+Og~lgvT*?eBN1y*CN;Pj-iiqN8I{v>qPG#>ciFj3CDGj0XAnuexH7VRGSZw z`>FEDNDgRsvL`&Mer>kEC**Q9PUXUB&Jje{fF`r^jJ6x)Y^3B!GeHq9(@=by6ikrJEX z9_W2Kj;nqs(u&7h%D0z@sT<4sXL!z2i$Vq|gQzle#Pm4e05XA13$A&qc?Un|9my#P zAbItV2QxPR{^>T_d5oEWaU(!S9gV;iCK$tapO=D-{a%5jmxbZZC;wmH+GG4vyj3`$ zV4-H7tR*qG2nsk0Qa#jf{4It4N!3fo`{(8lJ>0VxRhPAqtH3mP9`Zcs{WX#+)9)3H zVU!zHW8L7c_Exu+Q@3C5kpuFw0^cgi?$k~j1p?uV&#UX`>yzn>Otkx_Ei%7JP3nXZ z%g#H8n7NVHYUf{Bzuu35qEOLM){d)INUu%Nv1+KL`(lG^Doo2$$4lD2f#e(ns#;b+(72VisX^pI{) z(w4a(pB1OEmJ8+ZB zX2!Q(T`}x(Z6p*O@(HvZ*$*U8c5S5X_9zosTmfrD8+R&Vv7=pn4H?ar#x3>mXOq)u z;{%2;vLs87qEj>bl&);wQr9Dy8yCG41qtl-b4XynX|*^org2D6!?c-VO@Mjr4#CXw zw4gLp_6DnLF`Gt61UFGKIOt8wAcGltfqvkDxPbFPCAEO|H> zn>6=G>e@%)T^j&9_`P2~O(uN2sOKnLpNu8vO{Kg|8V6q2?~gtH@Q=-+WtwY(viZWZ zMxgJm+JSfE0)0_fhI>FI0HP8@1qIiu$$MXIt33?Ia2FVQ4eF}W3hGaYk5_*%?g>zJ zUwILCKN0Sh64ovp1RKhF&6^I@#*`-kT;Z>97R-JPNV-IQ#it#Xd>6MICYosA^mHcq z1Z?K+XCNwwg|xE-tWHU(sJ2wAby1f^^#%GiyN)Em=)wkL>Dd^Ce5>@)ykBoJ3QRRP zMj%H11fbmb*PJ`v&LfAwr=5Djf}SStJ}dfm2)2}Se=KiE0;|)o!l)l^Z}+MJSozt$ zsgE-6I7YQ&5>Eec4VHm+Qhk|h`xQ`V=K(_(C7r5laW5H*_6zQ?fKsVN-6@bca!a4} zbpHw7BL%pD97?i8lN_}e%d+AuldNA}+a2ZRKGLU^&+H*Je(~!0oW68Msm|T=OvyzO zgMMhQeUC|UF*Dc(J0gWA=AtRQNk;~Xay=V;Xbo*q@jb=a4q|>o`Rgt0hIvnsVkBml zl@K$+2xdKQt?d?0(U|unQ&on5;ct7i7K63BN&@xLig@>{Tv1)R2rngi*Z8(IN5gGw z7L7x&{Cl{{kG=g;*4&YX>zX-sy%lr?uDth)1(X{hH;)IYi>pP~zz9fsrUHKbebb5m z0-k0#k7kiTdqb9`O_DZCQE1MgY_ot}y02+OU25FcJK^mEr_@?d%AKpk=hFBOcV=4f91)((+E1u zZeL)8t%JwvocevUDzT@*%(}NngawT45|qhOJukdy>&Ws*x364!OHux{LU8`3qnTgW zOp06bOny=O3OuJNNXYRgGb8B;jzdH&K=XPwc&4f+3BrtvBlKv zkOJrP;GqoK#n@2>LDP`VAJ=F{H=Eib<7jWCEeKK)*}_VaI<�+Ucg2;oII|M_5{U zdtBryJ$_uu*F`}6d3F+*?r@?dhD$jNIGjGmer+vvJu) z zxZMfL;tHO%gk<0SNX4v`uF*E5Kq36Hb~O#7*!_?lHg~~a+9y%emL()K z)+*n-4r5p+9NkS7ceUmv`uzPwZRUNBo0o()BJ^Vd_Ei+OekzZqhgvV}1xDk3 zijQ;YkPKa;dePg_68yDg23@cb{$I8))ZLo5cwZK0h)U-QOrZ;{i6wbq72C3&Jh0~C zSq{jim(PqOB|#tZ|Iij9dl~I|J-uTS=?^<93?XK9LY%`JKdQoF?T@NxbWP6tCw5s` zf=P3Ale*(j(b1>cbT;1&{$bvB9nV`mm5&SV#~CH2z#hZ`gMKAnXiX$*1CsjE(21qK z--iAgDVf+XO$z)V6Ymd}*&CUKH&C_Z^q}vc-@8Ud-MY*y*irF|C0kJC)HFqq;f31q znZ0pGkIXVFoJb}z4{6F*g=$re5~kT9$Dt}rVaGSu>%+p9R?ALQ{vcs%I zK=0=8z<8lS6QRPtNRrO-D>O8D%f#B|+gRaaC?Cfjf?5N$UUEG1K|iFphegi>G`qxR z2)^7c7_0C%{zv!Kkuj#(-gkSdxjpKPZi9Sv+&%d>9pa1!iGRAD@7g%CU!vi=d`qJ% z#y|0yGrf+blg*v3eSZ6y6Hdo{0Vp$X!#HfBvQ+J$pEtI%RDG4Nf zsaFr0@(izM2y0tLKT#z`FBRZAG9GH?-Ng?3L@Xb{s4_AR!lTt|JGCx5DAB!s5~JpE zw{6@xmNtQ2{r1DWPt5DJGf;QDh!$3x=RN%zRnZ5ugDBo_bl4FyhkiXz+CP9fExDy=5@Yvt3L0DYibR^sAIfpOx#$sZvKyi(NI)W*x{HNMGJuw=ijt_Zw$eQJnH2h)mG!{GXvHw@%{g5vxG+BIg;iQO+zRnvezG3;@4+7#tYGGAh}S>os2_BA9? zTeSZ&qkwUKs)uJrq9AGnmq3}Z_Q{RKknSuh{=1eZpr^XE3IpLV+QEPog$E-v;I@&r zZGye#WqG|}!MON$ujod;s9Q|;2dZb(BS(eM{S(cdbvF6P{mI7yL)=l-Vs7Fk zwN>!K*k65E^MYtco1MJRuqwwXtw>6;O3InVHQj5(>fOD28zhA9)J~qgP!#h&^`xA? zu|(C9m1UXXEAsvXHpC3CACA3{ekZ(6-?e0s)=MvPLpdY*tAK?e*)gU6UZAJ4D>{0T z*RTyRunYG+2o+@-tDiqs8FQk3PE1oQl5A;NRBlQzcnfKrJXtr(WNXjnV{uWLH$~BA zTUaP5Nq1&bm@JbrTuvXz>dR-Mkp|pWaIq*&FRwqbXh+>hy(O_u|CV%SK$4Vq!rs`X5ife7^VWO4N!k&_ z3v;D!{~Kr6)%-10|Ji{ZwL=NlMxt%;MjE`1tOA+e?Y^C^-McNmDec#v4f4;6@G$7f z>47z7+^ev*E1uitX2$|D#Nx#!@8jNe-;%rU+3soS9&eO*@=UNUa^dp^eO2?i1USLQ zjVz4M-*@N~Y$ni3m5S<{Ar6zu-|jnoGHW_3J+o7HWZdJ49lC?xz2D-Fb3|P2Jn@lo z=83)s@r)t7Kmf_G&y>r3ck4C3eu#d*0YHF*xiD^J!*1jx+yarcOTWUc!$NJ zCoWKmTO{Lew-~&H0fip=w~Cqt!JTi$OH}#e^>jpoo}EnuM3^(S;l9I$$t?mh_|8|` zdzu03x23ZE+$rKluaVnkox$ltGA?I@&-!6#9lfOXfLGQn01!mkUQD~Z%VXb(g0U* zaZg+v-|v1SF)Cm-O&T|>->3067js^K>J9G(pYOGFM}Yy4()x~ko!|;=tXY(3T7oZ| zW8unFJmlzNba#CBG<%)`4mXmN!fa%J**mbxa>_tJ1*~PtLK1D&zL^uQoqSdLps-$J zqiAR}!eOLeAohWoc87K!$JO^)qWabL9vwV%L{ax2Z z6Y7&2V-8}U22}}+8I6tg=KA&W1v1<>l2kqN7DEy>{`|}REp6|`eXjgBsy1xR>E9Ul ztitz=zxVh(UZ2%V?6Jeb5?Iz0sGjj3;}+;YtP1gtTv1wIFAqJz3?u!;AtU4&+vZNS zL7Zqdt%8%}jh0~6wvCITLNa=2AMq4uG$Ku$q)skd9`J8#m;@lwl z;~&0!^C4rZ13cgTXjjR3%1T?6N(Z2a0 zZoMD-1G|r`!IGp_h;SwbY5LE|(2a4Mop6_T9nC0jrP?`sjCyO_OKC)wsOmaC%Nr$~ z2~Iqfp?wswAXk^Vpdm7Ia#pdBe(P|OO)Cv&&!f}~u`n++{{B!{8;mXy=<+hn{0(@A zUd8;bthwWdlYxusK6c4Zte5X2FdI@1NCK5%w zz*ze5>607R;=m@#l%29il3CpZhh#}9o{4~FNH&i&+0SXUbrc)@`kyNbNPOlCk^n0j zsz8Bb^ERBi_n{hcU3^ryusu@ue%K=?-UT1^B-TOR{0ID`sqvyBB{m*+x`bk#spPezW6)7oe499ZsG1-)a%k> zS52J{F_o;bIMnL|N`876*7TV%Uov>>=XEKw6H|fc+IpgPv)khoxc+0~g=?qsF2N(_ zYTx!c626emU!A~D)@xb~pL^9-7j6)AirI{Bvsw47OPl|v-~1TrflJFUHa_qt?7rzm zcRx)eaF<5OCbFh$rf?OM{silA-`Vci7k;G>p65%7#ZIY;5cpjFHj*^qVzXbZy6xx` zqYd8(J7uYnO=;C1;F^A3l6kUa<5}JH><4;V(n*bu#Qv*0I^N-d!D$%i+<8b*WK^jq$Ij~H5uNr8b=$2xl-1`)s5>n4 zcLnr4`5Jnf)%T5w60OEBKtI1W(=I$UJA6TIkvzeUC(BRzQ?9eIA;YNPI~B_;8AqR< zm*0ws`w1C+=6nR8SDXruq9?+VTG1%xDa~TIpf_cRHIg;d$auh#@dmuiC{N-a10W2| zD$F@ZWwpusqfp|A>FTH=_?=L65B5I3{2|EPgc=!18|Q95rB^U)^SRT;@!egA?_z%k zh?$in#u{9Dm7y69`3Xj`yJm8Pm)N_EyQAB1u%olWS_heH7oEc;QeOfoqFnre75SxY zaDP^nrnb`Sb}0z0o2>eDXPUoNa)EoJMfS7ush~{zAPq|(RoBbg)5J&86sEEH`$tbZ zQ9D@NVo>If&5Pul$wwM0qS+h;D1?@jnCG3>C35Nulg84@5&;cF?1xfJdLNdBq z1KCgM(}&~4(RxoVcjU!r@P(2Rg!joIuq$qhkSw7X_Z){@=o9u!QQg)R%7XHCQ}J)p&jxd zR>7`*^_hEgbd$;J!pDIGA`~r`CY;I5L(!9%uFpTEn(4kkmt=pEB}L?&+FAADcc=?D zMv3Pe+Q#TB+yJ5Z{s<;(ING}mk1>Tadxl@O>74hLB=;u9lbdE}@_nM@iWCZfB&6%LJ_#*f0r zEDB_wtX_JjHzpUMfNNIBomM|$To^60-T5M%Dgn)gf}irysw_pD1CAH9q*=Vr>Tu97 z!NS#Mx@-MOpJ$jln~K>M3AwQvgU6~29}ZK+@H7>*Mmh`EaDEFiSJ!U~(O9O^ocLUh zJ>zn?ITdZ-D7IuPOFn+bo9tO@JF7saa5DSjBal zoSv|Z*OdObAyJ_y&IIwwm)nwrk@?vEDdDgAFMHrn;69AjB0n5fMOLR2V7M&IU98NY zA1OWUbA64OsvD;ji{w#|ASGT{5C)NFI+v7aYcE~KXL8jGL*>%GPjqIEvkc`XS=7{0 zn=Mlv!|<1RI`~a)3L9|{J66d|RaNzf2E9lM4|AT@etW0G*CYMMyD*w=yP;u>jj7G% zdn&a-vEQiYZPdRTGoU0ar5|O%xFTo=hvGUrR%C%qePHrPp1q8kcXMOv6g+p-{4hpB z1Ij}`!WF81rld9m@ovWsJX_LxdB?wxoLSusXEXbyGFCIXxb#!5FJ12y&IeS;@X`e1 z{SVYJ*!7p+l`79C8le`>;FYUm!OVM8_+9DC%SL5e&9sA#^Eq*!f|FOA`>7RJ{canx zPU`tAW`!F#(2*NZ)78jL^QTEUFVfm{PA=ljH3}Mm1Y}g2k~ewckIES|I)81^^LKL&%O*AX?V4gj#!^tGrEMt1A-M2 zLv5*4lYa-HOU4r~OpX-A+)+o3WT%_q;c-6u>Ci|>dcqS={E6qZ=rD52S2hL>m7^dS z=p@7o&O%|Yz5Wh?1L;VZVhAm1>UHbE8^qckEboH6=lP9{3K=q_^=*(nw|HLG5X zHi3ZLaJRb*k#SIfdQzi<2i6QV1>MUI%iIVjOlQt?eP-jxtp-j)x5+^4SCyDMRWR)z zMT=otRSy$kUUjfDuMG~C=p+1;8^aVE>6~x{Q>xdIBl}7HDWUj^=~P4HWwIzNyLr~v$x|WtS@|WQT~Pi!*yk2uFEV` zYL8NX{y7Wy$cTN#D_7Lw847ta(^aKi3j90&%|-qUz_#U?)@=x4f6etbo_dofmUPv+ z&C^gmaET4(dQ#th`zjh^@Q#qtkwj{!Sb#C%=bMRL4X>G237y`X@-&$UJkUzha;rOI z)wPP@AgY*YDBQ#&7fqWQldp^(a|msxZW*ixq{v5m_U(23tm z7tSk3je$~75S`D8zcS_gYUC0{XKW#xF~*Wr>Ks*VGVXp|mTH~y#q(0&?TW>KCOe6IP`+Q|K*z$K2xdX8J-Y&`;;l5y1C!FzYKINu5 zGIJCN58(Jq^uerPJ|o~3bFJ#2#EoQeh!vOKnE@v3(9Y8ZLZZ9oF(z9Ng{Zz32lhI_ zT&3CItK6+j+IrQmr)zaPBzce1V=)xr-sh|ppVWpGB7xZZJ07z`wMTsYq&kTw^|CZqAaWTs8fp}kId=>I!Vhof3k6b$UHMB>z4ix{@m+3SNXLPN>$m{c6qRApLZvw=$2j6?_2f^ zw;b^D6TCcikAd{zDaF$i_ZK6A{MCt@s)L7FAx3p79s`QngZwHSi}nKD6eVWTPlTrE zRd6>>al^^dt&%n)X5-OT5#laC?6{$>A~8=% zV=H4enJP_e(BoJeL({*9@1)#v+K|z|_aa>RWXAq9DCgytP3ogEce@)4yFDI1{6wTu zSf|oFe>fh`SrlXojW03K??gQR@mrPFP-wK>dm*Fx_(r+v4f2Y}me0odY2Wz1@H}q$ ze1{B%e0vpIG3i&MISMmK(w?EAfyHz|~!SCcVzj0O1L2%hb=UwMjboP(fi z&sHT|zWC0JfqK40+TQucsBSR4UTqQJ;GGx=2rbCO6ZA$y_Jd)tp0l%&-Df2aXS?E5SZ_SvfFoB! z^bq4kz_^?ZK^$PCG}%d2Ijty$;PsJpKTUUt0-=4(xrC+#x$v*zEWcRY3R%(Bi0(Kk<`#V8Z?#QwJe}mP{ z=O{dSJs2LBvMx@3iQ5>|%=OLLkTdZ%Dd@3hEWS{9&)F6?&#I5p3E}}No}5#j7=H0m*Nt1=C6Q9oZihjb2!=jqM+P_Sh&SvoG#H)ZIE#d3aZR~ z7%!IZf2>e6U_w*n@z669e%K+99E+BsdG#Fd{}VOIfCEC(G9-shA3w;ViCd`|&v9!F zXQ-My$;Bz^1W6SiV?JgdI7r_)2 z(%ZkMQLe#Me+)#&_AS$U)1$MO6%3!JI`>(ukAv^nhn0KfTdxU6>~b+or7vMCIBlAW z4Yd;LsWy^6@8A#HW$Ed5RPZ7dS2Pc3(^skyPi-!mW~uvi+fN}9bIT`^2{m%1)sjDr z*|E(x#DLYavM}ssavZ}`6eyp)*ycP47UsQC#P9H=-x9j1Q*f9tDn0^++90^UI#9OprCppGq)lj1a#*`<3We4u8(uYa64e6K_~kkO$=jH zZ!0m{w8TIJQhD$)TDy_#zW@~?JI5R_^Zr%7ddp4e3X!%;$1sC>7(!UebJWy&CgD7< zy6`4Asx3f4C@FCYKSlY|EY*GyG_8@9Jeu{z0l0w*f^kzmY=gY3CUms|Um874H2W+C zZnolwqyM}$MUDRB+swlb6B@dV;gPS&sa=bd9r#pjv%Jj}x1)<=dZQ!?VvizdiOPNG zbL*|bvibYvXze8vw{qqaq^Cm2>HLQySMDxeOaABQeevdB9YKEY9WxMb6F0^vddUGI z`=T}uil&(RBBS0(TZZYw$PSeGS@y&oqzKh1AFSeWZej~QDn_t)K_@J;c;-9%nR3Ca z*UlDqfH=1%Oy}l>dr)At>JyJvwfb4|_u&k$2UFKumT>lbk;t)#`f1NdN0piD9FweL zGg+yIe+t$9XcSLVTmv@PU$7OjaU!Yx-H&)cJi6Vft~MP zK_t+5RyR>Q^1&s5CV_;L1MT`qhR=4}z5~6uQx2~wFECrbN`;R6&l^FaN0_XcK!9_g zMC%~t(_sT}crK6)jpGt~HQiEV+vxJu@tYAd)(pwTjAjA48pn6`h22kC*O7_HL&x2= z%fzTb=uQ=kz6>__0ZIP9k9KI-GAQ*%#M~S=$@3#4ci(^bxD5PXI>B!~HAV+8QCF0s zl56)_?#o*TVBERD>x;{hshU-W{*R#(u@_4+`BNSO*TDsH3}}fPYT|PjS~kpni(Mij z$hb77_nY7~tO_dCqsO`)gVALeq@zw#2cni~pPE_q>!1GI&i6_I0Vy$d!S&mbvb^k! zT(j@7mC^HqQMM(dM1>}04s0EN z>k!U`zA-&Q>jx!&8|rhibln6_d<6R827}dyJd$!?=0xJ&{%ua@R1W>GmPb#Z#Dm{5`>ok{jF?uW z1N;=)c`FxR&uc4c(y*ftEMRm^8e`my`$B6efHiZF?!7L!z`xziDLk1J{Q)b6JXJ_w zF}NBWaZI%SeQw2oz3V|0(_IrVXs?89qS7@%X5cxmenC1~_l_8h83v3NP-i9mN{bxX?gV4L`u`K=Xyco*OQrS`!0*C!!3Kaav!mQAgnEq?h%$r#?fu%}G) ze1nAQgo@|dPaq>jD&wXUfgQCSh|T}b8PCtHtQt<{teoE-P>$|$8j!xX&t%jw<61f2u%q~xVV^=kjG$H;UdO#&lsaWA08c++B3Ote+k3_ncKSUSy!GF-q?M5QFt1Ir_u?_cO_| zGCKeF&O_l|F)(0qQe<{K<2upfKG$71K3=Pa_oq?T}aW%l^8yL&r z*{fdVrU?YX74OFHOJukQP;1XWxO4IRjN=53$iYFNE}YGyCE5UfUGWUeaol^h5a#gyb86vcWGUBrl3BDP#FEdL!$3V913QAYC|H}B zr?#8|Zub+mHAnMyS)mj~Ub7z*MxHZbAe6Hbe=y0&x!db~Uc0a>1xRHhj@7h~3%tSg zb`034THDUc7{jDbgaeW#TmO4x+liWG0x0pkA1=9;wOxc?v1DE zZv6evGv6am`JV3`R__qb?#6_S1j3S|yuJE5ZG=-21JU8ejWW9n$8Eb%I7!|~7Ghu8 z1fT4DzE7hzpQFAY+j{!)w0?rUoQ0D#m=HRHrqmTinfElSH|K$2>@M~a@!xYkB)cx) zd+g%=;|3)n&FcbvQrpNG_*W}jtUi}jaB`uE`C=1LO%53O`{JvP7zYy?fOzvKM~cmK z>zx58F5M9nvAvCk5mhjNmeD!wTyKD+!BCb>(3+RS64G9T4zEF;PcIGDE_U^pl#%;Z zFc(0+mJnGp1eG<*%y-u2MVnm>Kz&vXWX!!HnGY0CXlREJ>as%*5k&MU^)SLy;O|ue z(c&ISo%llP3{p+7X&W;%GBp!{Q;(SrVs^go)yf>6MDQIL2yx4c2+#3P9@Avm1Zj&z z&Z37MLaR%yf<1_}Z9Sowm(7>G4%Tf0RS*|33->-Z8ek}^p+9qjQVc%dpLQUVfbHx>(!uB=F&H<}{7NY>*Tsfr z5Q04CN8R(!z^;UXTNx!X(wgNa2!?G**Xi*nF$54^9#IRc(0Asan1QOa_Em)n>IxDJjhsc$u83401xNi=er#WH1*q|RbBd5aS8-zQaxw= ze?evO{-F)cO)rcAJu|{G=nwnE%YinecrwkyH8h1NKcliYV*x)ULr_6~0HWOenk@L?t4W-a8=UxD4rwHeF zKgsRCQ_q1Izttm2-IY`u0}>iJ4^4My9s(>E%M2!B$#d(8LVJ*`N& zQ|-jhjpa~b#Ju;1dH&~n&9%T_@ZHrYgWFY!xX&6JKfs<#wJjZfn_%)|=#megt=OyVx5Pg8PCyAK+`?%agJ<^jc!K{s5pb zhfX#8)_oN$v|>2JV$OGaFVX!>j$Cj7E64ma=vUda3OObi-JgIBSHtabM%VM4`R5`{ z`s2iOS;NP2XEwGECKPc%%)0bJX@wDceo>2LA$1m&6D(hU!;0eV3=lX`MH5KfOLIDs zR9fDX9W%O*zg@A9X;F3O`B?*cu&-{c70I1Nys4HS-kF`J6Q@50z5?&K=7_~-V+=l2 zAkwDWBEwDXaWWntS}v`I#F-p&#YzC-)RkUKw-+;mh48D;qm|@#AfgG-dYQFwsHHw4 zngtol_;I)w*6QrlL4#gjP<&|6i#X8q4nU;69Xj{%%X*Q-tl<}kTlAHokL zjid!LEAcp3$CuIPCti(<|7;ic4e9w^`5XLtS?XT>uzHvF&`J|AvRzRHQTk7SH&;Ll z+h3g4JA8g2n&6!LxgfF%BtwwrSQVqpGiig&tyZRUi1)!6G^8_U5M<9>OTG#dAz#;_ zN@cUw+N1Bje-HcoXUqoF0pXndWqW)N)8&|=A0Hn`jyB2+`Fk^c}GETr(jqLh1(%YcCh2+M$PDaqy!AMR+G zTJK`iEaTas(v??YA2>#qM5Vzb`*0M?9{VT*Br89FC3wx&Tg-nBMGZgd->V}_N{UnA zZ&d`gV4fvaAl6r$t38~Kc$9L3e-(CutEOPhAf4F^kcC}x-x>?Jg0sdgvl3~rskeKn zbGPuI?ukOBmq|=V!K5rQj0)o8s&weFRJ&+u$qoYh2pEROcU?A4Pmrt zbX>N3(-YFtfB|qk@_6}(*7FfhickxzNLiY$e~{%QrYrI8%EFsYL=(COK|V|6UQL+s z)k6Po+uONX=T|aBHi{`gh48&Fm_C+~k|(+M-WIJuMaSyHL8E#5_x3{r1oiv0j#kDu z8q_3CDw~8D#!vZ@22*LQ6O5jBi+U>T7W*hELS1I6vJq zB%%}hKH>!vjF48T2x_vJ6|-fvqD@tQ*EFr^rX>Hh+E!p6PSuhgoSL;JSZ zjaSb@#hZ^>S)a4f<42m)4MH7k)Klo>O-@yJB(d_%mTvj^zhMoz?w2nH!aAY~&@D)* z1R|S)!O71`>9VWAa@oI+FM~Y(+0_9YTI#@^xiG+Ml8=X2Z2knOe*9 zjK8E$({0?zD3VV??6LaaBS1qKh=jMU8^4Pn3(UKY*D<{90mKnlfKt{O9@DrGtS4@? z1bx0JZkdy&wH8Wg=m4XK0;o1SdF*M{UZ*+&=gvYkg~XmU4&7tvO`;p3I=mPq`Nq){ zTX_j8ahdV|$JAAYWwmu{y1N9VyHlhQknR?wOBzXOq`L$}T0%s+q`Nz%yO9zBk(M*p zuK(QZE8UOZT62v###=*6&+!2iirI?w2F_uX;{P29K%HqIr1C?SxF4V)tH-Ugof$$w z0U2oJQ@D1ze;*w+W$3F{exYg3jE*4bC@ka(ybyb%<;>9%T3(DQBBxRwAdgN4YYq&@ zD@2{oDI{D7Qo7=V4w&@aet?xrwEWc3jQ~B%Y6cS{`y=WxQ+v?$viI)K{B!J3U_oJO zMhOMaTX=EI621T0&d>hZxbkkeu9@x<)_vUL2-ThQ)7W{)~@vYuQK7Ge=>Q%wxe znL+_CDbG@qQ6W1RZO0*K@3DJ%+qyaiEH*NsfN{uuLP%@%IA#;H^8BdLN)AtU{I@@L zqT<6hFjs_(vH#O%ila=^z=3~6^WkD`3G)!wALzOf1x7qaVEHMP7I^ZPX%0LyafIEn z4zZG9$(&MLYmj!cg9xqprIj53`Bvmds(Pn5x)$%G^AIN`^;f{vN%nU8ZYLhqclnf* z$YAR@+<^1cuV@JhwmT^QbP4%Nw>Y!j3FAIz0A8SfI$i=4x5L@dQ!;7NG#EOfS{H7z zFVH?vLVC4g&?3ms$a!?QUOx%qwX-c zh~XWJ2>S|98DWi#gq#1#fiyt7*q~9aMRIWtEQS@q+>z$zAc@ZzO5Y~AC>Lq!qIp#b zM*lG7))Z**gE)E@`Xpz{7B4FN~wxmAmAB zbw39Q*JM?G!ese+DPI=*a#~Y$@LE6kGB}J4dM97pz*tiLi*;V^zJFiXqywfN*czAM z2(XGlBJN+EO)4R|kQIoYA_GPdeWXH(&RNA!;DctqT^9)nRX<%Kg6yAUD+bwrw8Y&b}^f+@0nm?%@IRR6b zL29A;X9H!7b(dD2gAe}|MYUKI9N&6y0`=L(R_n45IMr2IP-Gx5P~=Uc7H z;NZn9gQW}&ARJ->@?iYlpbHy~R(c}iwHquDShD~uf0hmi+NtX}DrK&a;lCV=p`D98 zsLDT~7JJK+DADx_l-U{{t^kmsnKaGd;!k&JC(I@O1K}g)-X#l*KCZh@~kOj9z2M0BU&}1~rbrPU}D@O5xsS zitra<`J>8R^>F;m8H z{w%XhAk^!P12AMU`0#`L+DwZiQ^I`5K=#J%#5l6{eS47h^mUK=;E_P|JL@*ki;_)^ zeVU3BWlH zKoj@_ywSQ-p0}YG-M(7?756FJ)M3sabS!>2GgA-Xb{d@zj0r~+w75#2;%Hchb5)xR z2a{aSF*w_L&F8@GwMerDmbH{E8+xql=sK~6c1D;4avPfmot1fZKgw>zUzy@EhnnRO`RVFUNq=*LoTU_@&0u5bvJ2_rMswV=7w_Zu@- z36uH-DzcS5_Uala4K4FhQOg}t)6ODEqiMYT4xlH|!RohJ76|j@&~**W3IEk~sUsU)tKb?m1et^Ok{-Z`FX%Eg$w-IIOLB?}Ld$Ag1Uk?8 z&cfHPdLa)fzyIBpZ-$!Je}!KhHuw_6M zAnozT1#7-r;4S~WNpO_&pQ+c}0TL4bzES80Z_+ve6QG`uuoiX|C>bMAR^3X z$ghdq=e$p)x5=kiD=nTsCXm5P@O2Wfh0-7*)bxO#yX@_DUj8U(Qg{>XP#HUHMg$90 zvJS9Beu^X0rg1~XHN}Ha2fg2hU#c~vY2zZzD%%;PzPbn`4I*nq^ty&oSu)vqBj_S9 z6mH8hijajdMy7Iph z51MNN*x83T!ChixZn_AwA0XS9;Z}+3(|@rLfOAH6;xinjiQ55-W+Gochoh$ii%+r$ zW$}dMC@8_aS%;0x;k7fh0Kj+C=E2AH6O!l)PpbnSZsyvT!6)D-_+9L39X^Tv4uGVi zX+8jI1lV8pk{@VBNzz)jLHl(I!tiVae?XmQ#`lZkrMWSQB?0em3m_8CvDvh%LxiIT zfR#FZCb+I*df#X!KwhkQfU6-ipnN`#`F{)r0bwARmU$jWRsHEb)EdF_;PH}Fm@Tn2iC(P- z*R_K3G9#FM7zdW0HG_%hx{_ zTRIvnfep$lx(;kBE(zawv70X$V9?JfRz%Etfw3NBqDlY%zM*M}bs+zA2a|ft?Dc?` z7l4^li>Y|N{D*SRL;#d?##1|*i$S+$9-!`N_1gv@c`lyA(vCLeXVdLWb6iF>>Ky;o0`{Os~G?{+RX(G|}En0jplcQxXXt$5>B6E10!H(I_n0?4)j z{BFS`j_()XGK)S~@!X2VdR0?;ihBj#KG~&YCCI6JX(H)a@gJfqnInV)A*#!u(LDMA za^Efi*4G5ulA=u{24tsRLmqeCf5vn`5ZH#d}FHaAJw=_e1jxQq^y5e3G)x^GJTid@X{{lJE_15`S zwrj?Oh8JHn^;^vED*NkaoFu?Pt4ztREuF`x@xzatfsSJtcv`iQ35d)={IrhMTVGj# zYZ@>VnztAj}zM(~aO)t$_Z+ku45%eu9QoF}FO1(ctP>JpTGkg#mrwgXWc4S0p)Ifo9ee ziK@mp>~`Vv4A527c2}|4i6At@Ly%aO)Axzo-MjcVkkcO)9DG|e;L))?aCxz9(hE~Q zF#K5s1Q=0)Qcpq){B{C z`6rm13&?+hj^dT2j-mU^r!An>sl0E=cAJvNmB4XX1uXsvSh-`xEW+9POG9xjO>3dO z5ey(JAZOjS`Q2%M1uy6sZ!-WpNo&ZYfDHzCm9m%LPV81;l^iRf4)-3+|5(U$F6~h6 zun%Z1V{oI$Py}f%ZjX3&PXw^C|L*TRk>j}~eG9X7K)nxp9p zE*m7HbzR=36cvt0ldY#lk?{rJ`&+42ssR`u=Z91R_1KrOc0fIfkUx&X0AP#bFMF?9(1 z#wowrrdB&lUU6gDY=cpG${hs06jJ}pZ3e_eDO;TD{IqkhHfAd&sujbUzy|opjaa+F zMP{Jzl7Z`CXNK1TmW?W%Dgqk_OFeo95gVzY`=TIdQI^A`O={p#_=Yb>$_5UUJGTPq z8I-%=7$_@Cf1%)PfTbaF!noMPLD+wFY(V*x0X^wobL3BD)cgduVtAJ!I7_V%K=kWP zK-S`(W}f>)U72W}B?i^&|Iz4b#6Y@07^Yt^+eHGiz&MX3d}MOy2U;5J=3l@F0Ab6@ zAstX`QG(5?3J-$Vx3fHpwD$@kY(TB z3s5+-Qx*;|_3OH$=Vu+6HW7Ff$#Vt3)e(7lL8A9Th{#-HYv51w9hDcqU^-_TsY>WW z{sCzB@}zM7l>p$8u}3a}SKOQEEgA`bB*QtTBi0}g8#zA@1Oo?8BA@VwB|ZHXeg){6 zY93!N0<(v!{%0fCpb!4AB=9aI8)U?D$3h64WcrA1A&A!}>Xl4@WwUmfsV#;h8MkqY^z-Lb_7r6K)KQ$IZYbFn%}@6cieE-jtn(H@xcI@aRTzYDXt zCgj7_q+V;-vy6RrVW`o9Xzp+Zb{;sD-v1*>$=S!HQnp^ce(e1fTvTDoir+|AbNbNJ za2|7+h|g#J8uCZUlDMGWH;HXeYfkBpPr@FiLu>}j%EGCqivF9BOMofYZq3|a|8^Ka zjA^?8AyZk-U!=^|#pyTUXl?UBD`7L-UDxNt0*0IAx=Y~BrPJXQw!y!)iOC!R(H4Q3 zJUUbwM@ zX8gVZUI0OdXsX24111{QV8Ad}=-w1#!>Xf)KQ=*WaPT@$P9K>;ec) z|0<9;O$t$!d0PS`gM2a+i`qJRy}xObFor7^F^UXQo8-p8x}xFKDZtmX?380tc0pZU z?j)8SfD+1p3mah0YqeWNYihbaPO6KE+@Uf#x5<)mRW}M&k`w*Eyo)y)U>97EMymVkfzh~(mRhbP!3bJrs$PrpmH7Be5GHp( z$vTZK%HPjVjR>l0L?3c3vP@J^8X0k3=+@BhfHXvS#WyN^f5BCN{->^7b{V!T6#$^z zLX~InI7DFnF8eyAKyv780`wAN-Yq)VvtJROXp9T6ej|x9Itw!c$zyM{EI=3*QJ}aK zY|}Pq4Y^Rapf)=XBSAUr{^iGa@o;-K%i!waoCC}Icb{iGRblOIjZUy%{y7?cr6a8bd)87+>=<1RoN${{al*HaHV^6;B$7YP7W; zIsZgr6t~Xc>te58o{Fo&&Kn9r#oxZegnL5h0lx7E;~(juysxVF}q8oYZ7RkEb$pj_83x7KAks#7Y?H( zc*^DFee(9@!uomFJf-J4tobCbBe4z>VW!Ivv;5ib+MvijX6Ty+ z;-lTSX$j1ue;VMg!2RJ4_!{L^Ml`$$kuB5TTESHv@)J9irrq{qqp5QIHGpZ=rD*=S zluz1M%>8fQZfy(Rh1~&b!u=gIdo=@MbmfzLQr?B=tiHTNe`XQ5kJPi}GzM}jUwmOP z`JMg!%ijZefVfDZ5OefTk^)k(OtZz8c4d~Mr_IDO$kP^@n0v3Ou}l^vb}qrZ#Jy?S zCRRS}pScBFB(x_KuGZho>x(=8;|TCmb%_h*da4DS8U@wb1z>2?SN4LW21 zr{pOVgSh&2a^4i*Dk@u!WjW-vTpu}}3%?Hkm5XUESDUWxk!v>vLwZ7Jty1E2uMndI zqLP+r!6phL|1sFhqfxqR*9KNpw@hRnz;s?nF|^ELNW&6EYvz|HEFe%4-y%?kh%D8H z#rLs7k23=LK*?hJ7UIVDonZFt!0NQBujJDuDR=D%dWl5a&>A54elT}$gRuC0AP5lh z09v0}!E#6w`_Faj4~pgoN~2gkQXW~rjIgihA9z7htT`~Pd|KIlu*?n-%KzsAR|to( z;SGkcN`jZ&K1S*T(x-K)VP|a2FV_Hq8 zlsrNYIjr@Ec8dXY+Lb#%Bc%O00VMMKlt`F!{*3NVnuRKT2n}$gbofXSmRQYtqg{jm#1J zqbooH^6#C1>`XJBvCx5uNoX19qIVoEf5v$MZg`=lrFg($+aNd}2peJbx^cyCNKmcqL7JWiV0WxN^9g6WKpUO@?GjsE56F~?DkqDY zp)(e^2|XrfC$_JI{-ublA+QDxSnCjNshlamEuMHvD0|A?PnLArfc5X^jRTj4J8{R! z`*gADQ8O?xk)_#C6-U4pkzB)#0 zK0~3tJALig&oVHf%y%XWe>RJF)NMq;R93o_5S_5n&#Yvtc*mM>`tGb6)9?zQX%i3|`jzlUrp>Cg?bU%~LKGRTcr zy`LKH*<5z$U3)V2@%GDlvf-U`5q|&=Ih+=d_2-KH$9}wkLK@~+la@KB4nlMOY}2~0 z;Sxjn-P58uqTqcHAi`$0!6RWffw{j;aKP!8`^oAGinhQK)yhxdFqi5@iS-_QRX2c4 z*ljz}bD4maXBP!v-G%Iaw#*X&zxcq4*||bhN?D`zP@X>yb1;-kr5|+&#xRI zU>~4p^}oMgf-7zrD7nmvBo(>i`W$OM)90iyLASvVu$jUM^xy6v*}7=w_vjnkhCyAo z&PgB8*3B_1AmD~^G?w1sLI@NY>50g|n`LL&dVpg+u`Lv2#7G4b{~FZr=4Kg3$IO@o zjA0{uVhqAXB&B)V)%MbJ$7slE6Y((WOv>r~V%MhFr1ss8S*}Xi5Z=tqm20D{mJG^v zG`NDUX>^*-2E?}p=rPec-%DZc+0OLA>|RQX$^4<+t*Abb2%r)ZLtb7&z{ek51r1%) zW2&{PyB*%Ok_L(5Ztx(r$RJJp7(J_nik4u$rs^Yq{{MX}7!j^{9PE7`L=i+5H%1`~^J+tRf9-iyn>1}8`Ags6s zD^kkuPq+H#e1<%8Jes=xp~CNfc4OB)*3bpP>NnsBa{uNOLn2#15U#A?9t^tvE&Sn( zZxkkso*zIS{U81S$SKn;=@lGyqe)B#OuzH86I~ZhABfU_^z73ykF*J>S=GK(paQAp z3%?Bv34JGkp6Xj$-GxIH&U^iqn?h(?w_IBBGN24Q9LPlT+i0W($3CT7vu#U6w3Uw! zT;p3r9ii~>kH5r9U8{H?fT{2U;;7DKaYwVhNe(Y18J4J)upulxS) zd^~Q=;Zv&=Qj=^v#jK%)D+ot7NB_Yd{-*>xHoOCf745MWeD&(De{r{#T8NzY4PiiSUZz(*j;B6x;h4su{Ur>@zxgJOH#$bTuwsB$l{rj2qbb0C#z-i zKtSn(3?`*;D(*Dnw(q&o^g{+0034Ub8^BdLuqVEbhW$h1arE!)Xq2^*0-YdU~-HiA?;YckA;abCgL7=AdB(Uu4IWY z+T&*t{F)%t40>thDElDhqu{blC5%==?oNMnXq*-^?oWNMFh_wko|($brFYR;8?BfK8RRypTb8LI6u3R;Zk@3#l24@`0Fa_Y=e5 zoX>;=@`BiG1DGvGKW$yNm@30P^>Mn31J0Ra@V}C|vB$U=@x4j6TS&Kr=_c5g_28l) zOZ&5VBtXF?kpBBpMc6;5`=%8Tl`*&@oA~`ENgyk}90kXw4ibnuQ&cajR^Q=>-w19; z>;oO39ayH|kEx|@LsJeS0t#DXLfnRdpG(ObU(Y2OLxFrHdjJB}4a#Mb zF2mPVN)|rn-ECOj@)a|g^#}K-rBGgbb8iEfw6Qg}uYD@&{>FvEq0cDXkJ?Jcg?CQ+ zP4+t+_u@{J8yYSQz66M}0J_noMWTIVXN%9gv+V~75xfb$i23anja)rPwQ-&2I zJlm=nYmg=&D~Y9Y=2U((h)~K%{ULo6gmpY+4cBt|g3Npfs)Uk0CLGcI3DAU)Q;PXE zFc&6tVfSxADb6YbY9v!c=LZx1AlhB|g14+l*713fO5tpM#N_be;1_|!%^ zRYvr;^9b5F$e>}URk!#Q__fSjRr^9UEQ`olcN%_fvf5kHH#T^W2V)GpM8{SMa9@c%PG_sX0;&QU!=MO8c{P1%*mr8KUb39!ouU##Pa?V4~#S;eWVccD`k@&~$FJUu++-2X;w0|i|`PKSDq+58ywUz;d&nNthyNz*her*rv4V}0{a{f3k#Lf_GE~TH1 z>o3n1)>rq=PqHpgH|zX>*+N&}sEDsK{S>@#hf7<)U*pG%{?_dhzC5}DP4CM>$N3F- zYP{salhx}-Zw_lwao5v5na^SlS3q~7d@H(;z_I1C+05np;TPCiMC13A&Nt;fDxj$M zJg7Yz!m)GWf600FnfYTT>)BAeG8Hkh%^C52k-cJiwx5qdyX-q{?)By?s&_9Pqmpm8 zAK}}J$plnNc^gr4SPHAxsY<=WRimw5Ag+GNqMiCVDwYX@`UaViw~%?#wNSvd`q-tl zSRX>r?H3$#o?+}yk|VM|XN7H0p1F0f%)n-z2<)3vUxie?KfA3>s0#51NP-qTT&>8% zR`W#3;XH4r?B1TQ;B_u#Hm!hiNw3J(HzKC4c@1Mds*-U%U3Oiqa{=8B%;+q(>RmIC zSbQnc>2rZdao)({=C3id=f|<7Me^5xR7RyXeBN$Q&OOduk^E@%Qe#i51@e^PiJT90 zl5WEMW}!r_;Ic7428#2bOJd@ymf}bVT#UQUOmWXX>2-stdAhmdb8Jw$;<>OUG;xzm zFzGzrRl^sihT@_{Ma)GpZjBXw#3zvAyn3A+Gg;f+8Qv{mur+-#QOSYl&+D`zI?TV3 zmV=tPD2ACn6N(e`e6c10U+hI$S-p7NPqe@{WQ-~;s%c~$BcUi<1<#Cxv z*sr5&Dcx`k^GyRNC?yM<@$F~9nsG)u%ZAsW=n^)4n&ScU(Z+iOJkUeSWqKbJMS_e7 zI>2F+Bwic=GB9$Mts&m?sb$k^h1p1?r)I`^RD^TWcYkn z5quiBh&U%TWJFol$}_>7fcwfQtYEV(KrxmLJ;n%&nOP_*^!dj^k>l=nksOoCN~zr; zPn8lq3(7w<$#>{-_H^|`qt=X%;us2Zj=!sGG$8AcI`L}EL72?>s@jfC z6l694fCT@rv?k89n}$uj#HgB~GgvgE6--p9G;LS7eT@+@%Rn`YC&g6gN(Y=1v#2O)K+GXTU2TRpn5$t$hS?!b&NU%L^GX@0>Y5(#DDcgBw@>7a-e zM5S!6QhO(UF}R5d>IRs-H9m9SG+5d3*!(~jT02rAyW)0fq)WCg@C@l%odF;2eTaD3 z_(NA7COTpx$7b()&8|y3dXRbIw?onk{-Eb33CvTGIVez}tT;{^1rAEw84XN*>seHt z={;KG3i@V6NbE}dr06{gg~?=!7osZy@!5!K!-uOld8>u&OE&8>153wbhrn({gqMwC zem7Eu0V_%?0`LmsXDAd7e~q0OfiFKk?07$320s;*?Bl6BDyOjh;adT=;a+gc!(s=n zh^?B$*=FHN-XViq_A~=0FTl^4(iP2eb*8h&kgbM{>>3TvM=*Mw`6`yIT#c4u4uR`!r88M5rvt^W_iR}s-438fjO;rH^#3ll9N`?52DU7T zk~Tvamx*LD9eyMfgb*dWR+)Rcat37W^kAvPR^~HWAjVJ+LL6#1QobrZMcZOY3g**% zy@h4sPqpDzz&<81ZB=aP3d6gPjnA_V)`J1U(Y>dXr|YkVOYGi6=1|%N`&<89>wIiV3qsx80rJ`* zOz7o@$~m_oyJX#lgd!Z^C~3s@xkj8kBP!Eq-zsr#seQt*J=oxp_L!g>?S1UX@0Nbk z$JwK%u$;9O0%6@`2Ou&%77Kqu+K_Cd^A{Vo-1pja+;tBa^kW3;v$Rw$Q_00Of|;1W zz7%1&TCs;=@}h_LQum=SmwgJ?y-gAZq>XKVhz?+0tnN=S zGAq+rF%}nq#i81G*i~Nbpc^f@5Fjs5kS-&eco8>c&RWvHJk4 zwsr}V*zl&#eT+Y*WsjFNV#mcBepJQ>^MPZv+wf}#=nm198Cvn=x=!7SLn?=Z5qwx^ zMT>A-iqDDo&ZWO?3M}*v%b`G**O{&pYQ!4@>IMKt^TaUy&Jn0D1E(e%Q%FYiFc zE}p=VQ$&b6Eecs@S7tuo;BERJ^^p-cb*1B8Y#q|PZaI~)=JB>vrZ!29G$fnK>iyPd z=>j8*S!kbAC#gnuh|5&&q;j*Q^&8Bt3{EO}U+X}A4PzBPJjDhdCGt;j>Didmetg~5 zcg~T?>yH`S!{6ws85_WSfI~%6A11KbeP0Ju9z7XKi@W@EdzSaiV*-UZV|H;>j_-$< zRBfeR+k$1sbDJvbN}rhhW#50hQ^i{6Y0~C{Wjiv%c~3~e1u0l{(yF`0COm6X)k37H z^J4vh*R4<3izGWzx;lZA#$d~>f=k=7KsMm^9Mnq1gu-`+6Kcp@qF6l7zlP@vI)Vma z`BA6>_Qt@j!Ss~H(ymvGbD$%Nwl`>%n!H&ns?;8UibVea20` z%wBh1cp@MB5K(Vuxf;f{BNbBy*T?#1r4a zuw_3ql3o{e)szzlD*k<3D8(}5_6j&+xL0#}f<(NQgJ{{tV6gfx{;Du9aXPLarz&sM zWZewCjs`k@o#6z&I2vl`u+VhsPQOC=@ydZ&fXg-O!&IasdB=uc#!c(HW4}y9Lol`8 z8I0Hr>|~7U7SdHx2pVpuZ;d5*>N^9~K+jdxO`WI9FpWJ+pIwTbjpXY6 z-jr;;y**M^lX633}2yvibeo1m_lPW50INtd|nDt z;kbxa=O5;PN>VX1-rHU5QN%fVs3Xv(kL?AIE|z zyzn!x*f>6fk6&=xH}dJT#;M8I2pxRV$Yk67dWz2DZXV1}*gCeElEnVFkddMwU=kgn zV4)(QN`PHCc+2#il9lU$>p#575IlmqJ2$X8gAhI7mQOd9eJ@zcZ&vq@v&olM(pHN1 z794yo)Qix0$WCVpCRi$85GbtU+#}OZdfPQEWE2)gFH1Qs^k|GQB0LBdsSl{j%98Y=4F zL7tchr}ikD7aHnwG*l`>;{sbTgZ_NNG$ZJEH0mW3z?&&SQ})$bM#LzH$@cwt19&aT za|(}!ZdVl*VyGx+1G(3L<3mD3GO>8%F$GKzBh!aFeIpJHqjEi*^-IF@4ZSU*eChY9 za_R7fuPxr6y2b_2PE?^p1hIhr_UQ%yUsaLJs)M(s9v=}Kcf8cW_%d`L5d)T&ScOUr zn>MHEp9W}=d! zR&jevh8lVm1?9Gzs1$xuMLi_0L@=7+Fve0{ySWeRN(&lStISGw8)49Bsf3r77~s*n zYb2i%Q^UUm+ns_v=aAJgkWmrKKhRYhCRjgZVSFs$ltak5sDVuKxLT+!-&b>(emlqu zfszC5j4F;vh~XQp-1kc-a5dWTwVi^OK?=KFQwqPeCu$gGo~<2Oi9_civ3vWZL`6T# z712qb`tZ2!F`Vy2kAmu3Ew?BhoC0Y+%BDAu-6G@Ye8YK+2_&kf-V=5tw&x>g^nU0O z;*F>`JC%QCi;r;SPzE06%?mqZrV~1BMVym&RE<_aA#mE=;Tp$XzR`^3e|mRxEq=oG z5hEaSD)EL$PNO^z1lWT}WykYCxpurDb&_@E_ju8D`o7viT?tK7~1#nHIX(EYgz32+xRM7!2EXPe%Q~ z_dX&8l(?GY>ck4s9}EjnN)mjKEz#4>*sRQY@@&GzSDn3Z5zoE@tQgzN!4~#m51e7l zSn*OA85lLJMz{vDL`RGxj>;Re=<*KMvcJ7Om~Vy&@S%{+vJMe(9xOLr~%ZUBogGz$4&d<0ZK1|Be{w#*>Jcf1H zwg+7hE>3&}S9~EHw_fTLZS#HEG409y*we0mvjA|$pJ~I*wsva;|HhPg6G$ru#Ge}U z3Y6)~-z+HirGapf!Q2_! z4H-X=ydXXN>`87px9GYw6@rcuF(+t!44?~Z1SMzgCth~m&p!JG&(|+XOe=hts$G~P6Kf5 z9d6R=+`aTc;Tad)BO{#MZ0%}>Q=AjrO?}QTME}cG&gWw~9I@gI+ljGV1~)OumR&!J zh@u`&2BCfS3wMfd4qJmSJ+*@4V?UI!%WS#Uy`D`ZGw-y~imSCquc~jdS2oS$qb$cJ z=Ar_6VRKYmO0_4#&>KFd3AoHf)iMj!DI&q`gxw@3M$b~1Ikk%uuSKE4kr5cFzXAl0 zYI3{C+fs=bRi;9}ot~^y&bMmJ9Qn62=GFAFZ^3!_`_292Ui5Lzyq9)92~u#1nTE_JDk4E z)$}xOrn)P$7GVjAyDTDr=4@QSWyV0{jzS(HO_Q}ZlETZgs+DH9~wqI+oFOeJm^Doo;+7_$-jyB<`l1o=hdgS z2(daJQWTlgDjGJ?*vD!@@}DbtN}BzO-$EY8lX;MG39c4#_!i}vO&-G?IRWLJF!D$p z>1J)p;c4{1P7*`-8U@#BV zvw78pK96Fl{o>QCq)h?0#Fv?LOY^2UN`5Rj&u{C9=(!ki&(SPn@pj|S5`9#=@$isv z?L=GbQI*-QExG}Ko1)DJs9Xw`QhFD0KqVwu!*Wf8sPzn~p0(1sJ8-{$b&~}5uoC$w z!UlVGmd$I_g@5HUdB4L_qsG$9pH**$t5QCd>M2$mgVR&snrlc)eCuhdB==omjc_*Q zH94#-h!+Y7=S3vw%Kq9xYnS{PM&`TLurPgz!?QgUVmRMndBMOrG+YIx?}P3k<|s0_ zeF|dUB^t9SIFV}fXeNC@umqCNw%%=4z6+^j-5YOl*$hm%S;c2Y%%u^jLzTMzkxtc3 z(Bte=c6?jk!WVMqT|=eDjQ!H)`c-FV|31nSS~w=s)1T4g99(q$xu2HRMlMn>L9zTe zAQ30988O0e?FcyyN!_E_TWa2sq~R;@>tHOo=UHk?dR}xHcYEX{NAHmK)l)TdEhslR zOwE(@4yk(*pa^0PwX=22V^}h*=8P25qLeB}l0UxcH}BNz-IhKj+v4%M9ia@TOwI=m z>TH?!xn*v`Qm+G%pNsYKqK(TkS^U6OKc1T0J-aE{6=9xaBjX*i_8fPK*n8B-mRv9) zUDN_*ltcIAvJvzG?r|Vo~Lv9h_vFu7og#jdEX zBRS`s0LnVGqN5NIJ`)U}u|F<%7Cgt!b?b@3>11n+L}d(inhD9|I<=A1^#NRmbsXhQ zx5hi1u#n>1GTj`Tpp{vXDRs%m1!d%@=!Qe$8-O97C}tq2dsDnH3;)sO<`~6Jo~`OM zJAk@BiCC3E5>*f?C$6+6MzAA!e4Hk$7esmE=MPUW{a#N`;!7ZOa*fjm>0H6lVvKTC z(=e7xA641yI4Tc>a&i9NWY?z5Woq1A*B;UD!eGQ9&5L+-{B6;=pq(USWEa+y0ypFb zJgwyIhO+_lb$yFQPm6CpjoQ9#B%Q^HQrG6YMgBv zDW#GSH8uz@cp3WL!qb%W9sDE7wnWEAZ7_&AGX(~jpKS}X-YA^41&PiRPf}3FE89fe zA?{eq;+!!hpWzH0xosx{dyXt7!U!>T{4|@F=IZ>Z5j~}KdIDuGnJDlvJRx{;c@NO3 z8Ls6J4;kjA>>fYItK}#;_HV#??v>fB6rYQF|~{ zugm|q1{;*KkNOFFUUCU?dTDGb4*r2u$;t1eBeEYM+Dnu&po7Vt3T^I_eTyq#Na-)= z3=)m-`h*1;gBBY+C)=2kYcsj_5Km3M*;Pj<9w^Lz&O3WU6&)VL)$%gV`EjqibfA4< zbX_%*{LNSNnN9PH+sl&*hbWKQtf3W!1~&HL6KthxzbKbFDk4YrgVKi0C~rfNN_~SG z{JiNIp6Z`fnu9u}Rrr~nj8VPT5=7Q7SARHb%zKapGDvn@w@Xs{Uq-oSeK9B7OH5u1EH)hVr6}0wefW_Rto50OCI)zU z8vS&uO^AcfO2L*qYLY#`x!d>Z+xKcVQW+5-dSYDJ<-4?BZtmyZNAT#jpOjX0NFxl- zYE9~h6#iZahcm(Hexq>R#D>vBOl1>)-8~l}VIo1W&hj?lyJM)XBYhlbYM1#twr{l*VB|Sr(4Nx$vR*YY|+7zt!J*2XtVr z{phN!*)xluPrJN$MCDID?|6Rhg=zSr?5QcA|9Ostl80&WDa(ic+A};vwWXl(&!X9| zjIvbNu-rs)Abc`BdGRUSLwG@yW-!${hRHF15caze8hQT+FUp5SAKP0Z6K|zP*c!N+ znHf2k6#C2R>EUEMys)~G&Tnq#d`K0^aJr>Z=!x<5y?*hGc2PS1lr}OGL-NEIpD+(q zOHI%>ZA$k{>uSD#&TyPMWZ3=`6H>(-pPE9y`u52l=m@~|H76-h2n`?J$F&KP2D$;)2e`E-`W**qAeNBS@k{n}?@QVmKE zct4t{sEeX*&f#Tq^l+U1RH#K@US0kEKoc30*Y4wgyF*-@Y&%>b2p0w;X?i>M(|+Nj zX9E!zLJ2>$RE=1S>foU{2!fJd02(n~=Due0-w)+JSADN=ySVUse(!%P{31uc=7Z1_ z^9Y{piW9*>56)a@KobVsv?%`1z9XQRs0D7c-KQuz^d^0w$~Ma~nH3B5riKn{f_*Ew zzfYax*EQy-GF_5+Z`K0E>0+4A?vWfC3EycW$tL<(ikQ%taLf>2h>*rqsCV@ZnVkx- zu4~b#OQLPQD>^9xt-0@(&#&*f7oOv3Q>uCRL?9l!hUi2zxs!>Pgw_|+{L$-8nrkHU zzAJLQ7-k}exEOLTR2df=TV)Q7*N9yAiXja_Azd8lj54=O)AT%68&Tg%T-%=63S)Do@1P`5w1w2FI4M7t?6I^{?|IR+HMIvx zJ!2x*R9+;v_s`$@+aRnzRL{o#){m%zu;$j$HNOj~OGDlrp# z(14CWnpd@;xavHEi!=BNF@3e4biC*kmq`|Ogst+k0&mjkk&^tdYr^k1t3Z5o1?FJM zX}K=U{nt^5Na$?(15V^SHF|$Kx%%7ym0~SQ(9Km^g^a0G9FXNZ$vo773ySCPb6b4!}r0>&dCs^P-f97u2S`F2)9h@RLvP!D4 zQDY_f(~}j*FmrEk_#(;Z;K6Y-cMGt0MZ~hk-2`5$`5JZ^5UCL_Wtw$zUa(%T8oybv zT#P$)@#-`Bno$Vz`?cVA0r#f}>vP`@iEc+1QZ9l+oKNgEFR!N~0?yy^!RfxbfNUHA za5Mh#_Sb*jM;&Ru`sSb$!&kl?%Kp=zdPs-OhiRrLB#wj2cpJA*HJQbDm=85JGv?jB zLgZ=gQSShjKeC=uK5lhrm3jXGF}=O3M3)`^BF>Vf$ns-?PsEHp?X9CJ0>wYVe^~ z5zRFIu}`e9n#Mcq{Mdr9Uq;YNQJxVJB`S@ zSoyQuFBgna#+tqwl<0%0X7n8HUsSE-5n4T~U;5ZcKXvGFXH|!p3&OtF8Djfj$F5ff zC-4+S0PIU9Hp5*3#}GBH1v`^0@YVCbUU2l%Ezav+jh>U`A-vhyLEJA$u#%MNFG@sJ z?QtD*nH=monay{XRzY;&w%6ykG%rVh1069hsAo%(8WTH7dL}HkblWHXGWk?E2EEsn z!jd`QUOziL<>Tu&2y9BWcRsO~k=r{%Yp>de9eDls9u|>a*b(lK+sRtU{LYY~a0XLG zO4*m{I$sE8drJj#!gBouU206?xAfPRy3fDWdT2eukoOF6g~`5NEVtb6%0P0IN%sst z^G%2R)3@a?jyYDNBpo*^80dms@$s2@qY9q9V}I5r97jT1JPhAg28gT&z@tIJP?k*; z+=T(148set_vs#Bj3~um=SLa$Pq#%Ced4&>&;@orm29AIVLvgA7%)!!K$*(D9;xb? z-#{@N-D1VYuOc$58+i+O!{TZZPMs1}-2#wv%A+|!27OgZ54G!>k>aUsL$xWTGO$V! zOq!YfxgYoWKLdxmI(KhZoX8U}w(){G2=e;S(O*kSBrUZNh|*nuVd^)|mMqosYoD#f zHOY;e{NNYvpm-Ar|{il z$;!;k-lZ}^_9!K9q$uNezns%K=ll8n)#-7B_v`h1-s8Hj>$)wsuq0Pf^a&5N#x4DB z5!(;APItElB#L3zx;Tg+?u%Ek0qPGVE@Ld zh=UW3pbk^D-IJYw(-@)B%UZ;NfxJ&cjQze`Ve3^ObIQb{G4v#n59OA};gH7(xrnLD zfJeYSlaee6RHqIYA(z>uQ>!J0UDqG!#NR;G;9}+b6WOB3d;+3_1+OrqRX33bX(S%- zEr4+vLH>K<#og0NLB>Y+ex-)lli9l@lEurHH3@%F#R+DL)^6cYn-M(x{tlnuHBDEk z;1$_LL2z5ZuM!<9GfPN@=k9V$k$)ceEF$Pxd&?MsR-Z=0mMqqJ6bftPc;9XwRU?Dt z|DxLm{$*AE2%Dph1gzlhKs?8~be?}Q^<%t3(!_*kz5tRfqx*QRO51COqC`TEPf--= z%<(L$(t!h7TJ_yW(HFNn;^n9>&x6LCB;T5_jPo&QNV&d(tk{C?OzwSll*I8>0@IaL z<>q_9xx?K$7~5p3?J|}kIxEZu>6+z1ecD83x(8qu<$)O6?Vydk5cwoBV6dr!cr)Mg zHm)1?N1q!QU;IS0E`EM02xgeKZUZ7qNRgl4N?)Vzl%&lXiJv80$rVjTq+0yp&icnD zclchWWPN%V6K^e9$Cc;V?>2-OtK)42yL2)C%AgAhM}l~GtBh;|$7KvwA%dSYY{8ck zD@XdByJEzyTze?!gSMtdaTf2z!_!I$3G`Wx0FSs(Fy1kZQNQ@m4Y=6p`eTC&FA<9B`o3O)Z7%I8m8EL(hWqXd&_u5nWP_maEeycETv(NO!g$n;sW1Hufoge3rX zAxeaV$r{3{={-H1bCTp-ug6L}M?T&d>nCqdnrp^{p&mWkjJo~^4dxi!ta7{FPOXl1 zu@!0mHwenTPu6a_4Mp8V)CS1XvR#8^SueN4kD?1IRFZouiPIlPi%5o;Y`Rac2r$&b za*0sW{iS`U(ezA@Bv}Xe+D~LS;1E%)v@i8eLj;##cZPWhtpxzYJSmROHh*;=i}Ju6 z%u>RD*VV8+!AJrB#32Y{bPv1*pKTJQv?+Z@%oHv5r?I#%jY2Vn>vp3tSvG+RW!?V* z@N|x~sv}pU=rcS(rMG11ava~^Ye3+QyX1lLmbOvs9cnz!DdB_3p2@f4lXRPJU(HTQ zm_a@Pe33pA8ro{^V{PAX5DhcUm*9glq9!Xg`RoR`+!@3iYYExphQgbv;lV;kUbY8U z984&+uiu_6$KD!&@4!UH=L+;-z1LQ6DdZ(|5O3yR(_g_}@5wSm(|9=!tfyDIXbiUN z!x1NfA=%$wAFdocWqos&3{Cci_%^i+j>e&D516S?uYyT48qT%jw*+9Yt9D*|-&R!T zN*;Daq#zax>MTQDFkqw_JO8Q2IldnOu2Q_lm`2Bb8 zt$QiYLzhPkXP|!dX?^$>`XlrVorpVjA?!Vr+AuqLhv!u=aNj|bc@FwmnddQ8`=GQi zb(gYyMUH{FcI@a!k{I>C`!}epwa=M?C-i&PfS$8AG)DH3qY57mPRAIz8|i%c5zg)H zeQmKZ0~_#I8#SRK408ih7_p7mpFokj_7yz1w5m6ykgsn*kwve=WOwvZht?e`52yYS z56*iG@PFn}w;6B{Jpde&QKi!*)`*P+30OK6@&rh|zV+jw1QvZbhHj(A{;34ln>rVr z5(h2NBG~uKiH-Y3uEc6*qK-0Y9g8m)k9k-KnMS&hZJ+$baz)M`~ z>3Ny^2R^%4J!Tu)0l)Np0OAvKzB&DU{LXWh_nBVfCY|67@O*X%hOueBk~J0yv(SQDdF3IDqDNXPUhRd91h8xJI)kc5V)nV6M;!n7PuyyfA^#_iZe%o z4rXmOVBE0&X>>INO|l-{9&oh7=ryvxcIo)6&e!CNUm)FZf9M?&c}@50=O!a)tr{y~ zSyec&7k;$}L}ggrMqV1kh8gUJpd6ff4CkgtTR^!hrAC9YiHsx5twRvZqzb?1urE_K z4qK=Be*Z*{FoXm@TuZK{cl$rDiyJF$;Mm5wCzo8;V4$Gk8_sEs#}Cv8o=>o|zsheJYuUmBY z+kW)q)gHkt_cSa$rvanjf~TK=ok~{@YXOJ*v$MWuhpPleh66d!qlz2g>w`jwbt6lUTw4i1 zNbJ>V{S_Nh4e~1@v3De-ojHOoU&WJVWXpTpseS~ZVPlzZoOz`A zx0@aMuPR@?sceZP^ZxD!uaVlT5$y5T_b1h#7Y8z)y!LsNK`h2>YvF-%A!4Z1xLt2c z*%HjEBZ)<0HNVva5*fAuE23{C67v|ur<*}jU^H7i;UJEacy$)0xIw#J;B>)QnCW=l zaJ8dt7UB$FN*lcN+|I=q0S1`K)42dlg9bi$D5$wb%;9<206TSpl5yjL&t~7r$I%K8 zj0pU8P5gaPasoyyi%oKsH0xy)^EKby^;D}o`3ka;Ur-so8_fnl&!(5&+xg9dUzL9z z1{~2BOvu)yFktaFjeVYhXliq?xAi>DDI5!xt5ZA+L0NhB&9LAd#wULrdAVCCX%MI^ z0I&SYxkjKdD!$H{{0~B-q+RiUCT`MbLv0(K=TK z33sXswA}vmu1Na{@rT}}zmf%#Gc=`5!+1MZgW8X500{fCAT>6_0a|LOp=Lt%* z-k*6JZd@bf`_Lx*YzKJL(14Hs;rhj0Q!jap{m)R%Cvy33ZQtUXB4wCPYf9gM9?vSh z%7^vuIGT#Qx&fh`bDFX3FxRK5oK+@(FS!%}UR^?2keoEdpYYX9DtP81HY4i~LaxS> ztIZ>I120rDcLwVJ{yryv7{s+-e{_qgFT@83t`T~K)*ikKRaM~ARmv&HPB;(d9J;u{ zsYFQ^leI41WRWhb;m7K$WAf2RSxwawqcNDmU(tR4={*G$;i8(Bgcpx4244MJpT$o6#Ql|}6GSNP1v`6LUHzd=lf?0-s< zrpLPSHqe?&e9S=q=0~_l`cH}lD>BfxX~2*JX{eLl(d~Ex1A%jGF}rEARjLdq(>9qm z3rB?(#;U=J3s>4wO;?QOTCF(L+4OU;ULnQHL_fmKY8BkRhpWb0{Jgt^9VAQ?WTa&> zHjz2-h6R{NluqV?XvZrag0NQQZ%xct^1E7JEra>&`j@VjguH53p}j>3WiMt6;}0UV zMi+xziWf8$f1>lM0VG3e z8X_K>gqW3D^lkJ>#P%BOOuOv^LqyXVsaOGmm00I9mfw&xfH)CJK-9t>IbVKotB7{( z<6l{uAB&=Cu0)=U%;_L_M*Pt1_NX%LHG^zV8kSfdR7gp{T)^=?eNDZxt)!}Hy6QS) z#4AtptuXTu>G)q&Fl0SPl<4_mapy_F6;<}NirFNfjbBb+$2VK$3%*32Fcr+TMTj;+ zSEFm8619ujX5l&?xu{*Ce?32nmD=B=@D8q0@j@vXydk(W$4MW*2wTYPul z%Ekpy2|Pg3>D&CAWqy8M@X1#Vs1b-BST>fXCPdag$ftCfUJXs@7jv4fOIk7>JXm5p zzB2F?`VD2f`f6w8s8XM&_=bFVF?@9yKX`6xMrWD>cX#&yXve0vb3*jBB$b8g+@mP! znL3)T9klrU2C31upC9ak<{90;)5DPTmBtMu7(dX*J*9wTylvE_wMQ80pp(f8qS+b&dqeJ=qS}?V%(mk=>j&G$TsC&whONr6z(U_ZGf== z5Miz3io`<2Q90}|gcmZ0=PjSI=X)-&E%neq+-7%Xa!YngwtUL>o?Dj)3e4MC zyh!{LDC8YQjcn+rpohcPgJ!EBcB=^HUd(?KY`ZX7y>Gl|{_#LI|Y# zUj&eTIlZ(yQ04p<#_9***Cfr3L!FWgU~Jp0PbQxepX}#C`MLtOoUPY7O-%M!%1{`+ z3={7|#8F>uXQH%1dW9Og54&&0yp=5B$tzk?Z{7U!;ckPQZ0hkd>IQ^8xXmXnTlPNf z3ZGUjD>~r#X8^A5Qztfh)e>E@i1ehNn5~w2?vwx_rw7yQo#|q#_#y9e>hHHf90vW? zDg4Eg4j5_e{w^~mu=Y_%Bb52f&`202cP?D#uY40|b_3GndS%Z!CrS%-#w>6<3%c;@ z`{yT({_iFv)7*y6buU9*cMAN3A5$^cmyeEJwYt^Re+S$OFiDNpkq4-4cSV6M0+9!4 zQQzu!=dg+Fgm8Lw*~{FZxGN|$&}eh_v5(u&{ImA%=sjZ83GOS!Ifa=hND`cXE00pcl61GT{_~&l>T`Cp7cMx z_c)3S?-x|CsL&t=X&<+@^lC;5MCz7vRMG8E-voH0xQWOYHvy{M-~5pF2zdsh0^ z(KlSlDr||B>%mwc0&qHsE3(3d5;5M-e#B6SYa;8NaujINE<@$2pW)iNUvq2Ae3&QA z?>^M^!_6ugGF;EQ%BVtywA8X{8d<-Z0WRmE9y|;`rG&W#Br|4)%e`T`M@0m<6%Twc zrd_I2&D${x4%4mfm%I+Qn3w(;)0Tl^)bercxQsZYox{ue!WEnSC(=Uo&`2gnZbD#a z+L}}+rEfw+k2r8LHTrn{1|%Uh%MxsB3>SczJLve5B&F@?Mfbn6?x%~W2CRlJGfufB z|GtgC(xVtwAx2u|eN1g)k5GBg{>Hh0CS3hv+OHdgAEEhVM^BtHxjO*ugN3|;rW3zN z%A*5SF56+rX3$L znOz@_p7;WkT{^NHd6U^!z6))~yyikVL#Y&&&sX%G)1*w3uAribYcKrtIWXDDb;ysZ z$@V?$+@TGV$?J@Rnd?iYMi9)z2$bi1^zmLg^A(8_+4z`#jCOQ-19DGyh9++;qHbH@ zAbmYjEHd4-AH+h?2Z3kiH~2#r7Qs~WkPo^J3_c!>RO9Q_D2JT51Qehr!xCWf^d63i@JJST_XUa@j^WdylVG~L4@M<)# z_6Cv2kWQ)b@WgtMd}D|l7wW^z_ImeZ&F(>O0%OHbh7|$AL#V+`i18=IJESI4+P`oa_`OAQVjmo<8+Szum~J`mT~Zc3!gwTZt-k|r3bO1&qmU8 zS{s0oJoCGO+hk#agw`l_uDcGfAxl|i(Y&0)1man4R)=YX>sn2J*zK#s0`fI4#ee_%YVUJa_yf_Zx9^F z6UZY!ICw`A2c;j@PX3ypEUo{6N{%?rwP6)1kHMV)(dq1+t-r631Qs;<49&}4tV#%G zT4Z_3b4cj2F&vn|bIX}d-F7ZfMqP0oA{KW zv8)`@R~JTgD0(}D6bPUYTv~ttMvS22=R=o#zve=Lt?J;Y&+p0LYRB5a4TaY79piU( zrbOpADDxIzou#QPp` z)ih{bQf1)<*nY(w?Lj#OII3t(jzz9*G`F+(yE!4mt`tMS{ z|4$wMrWwT)!NMrm;}a^`!?OqP=!?O?93Tva?~)RyuvsL*Ky`zrY zj#CJ68WX!wpmmQ0I$*wmHGWu01Kr!~<=M;Mkh(}tk$Ik>o!CptUob8(@NV`zpG_z` zlIA+W?~2U&D_SK;h}D2NSov>!>8}>^B_Y=GybP2TshqYWR4$EK`>Z&K*2JJnJtY;2 z4As z<-ANWa@F&scV5Co2CFesx7-riP@5?BG!(K7##Y7W)6K$%95cYxbNpni$(Z_{`KKqa zTCr`45@4&^?$@53jLJx^TpY%zF7L94GorAQ>}z{}on` zJRL9}v{x5R0|I*qOEUU3vS9Ok`n!c~l*l3Q^Bi%grBlh@T7Xq<=Fjwl+pvq^8dQU) zOnrnpFOAAd=jl#`OpaWA`5bx3^7DP>TNo;z3GMV28~MsrG2bXH5|{bVMk{JEhZ{ol-Jm`#LBHLAnH^w0#0D!c?S6lDemX7EaF4W~H(grfrz88wN__cuwNHdWE->IF)DpFMDrQ|rF0 zoZPR!Gwmk_9J1mk-&ZQk^GC{FjFbkfj8}PZxo(-RR#2>ecyWWho9Db4G2R(3vNJ0E zFF|{$bmO8+dR4>Q`X%(w9QvU0(016MV2G*Ng&7kaW_v@bU-B3tYB(!9MHjffm|qJF zT@zyHdIKIs6abF+i2dsE39G#{V1vXqhx<5!BwZ3+l&#IQeZ6MU^NL$&7@34Sqdot5 z!*WlU0ijiCVwKGykqo!{5l&W8l;w*3+hL+Kmb@t`}wEwj)z6Y z$OC#wE1O25I1kTU!{d&@;u-mzYF-6`CTMuWq5f7CSb7x`#*{}0z~G@9Oj8^pivc!j zF1{FTnxWlc03eD2brvO)z!V;>^JA*sny7S-i)|0Yl;T-*g04ayc!eo!M@ofudb{Nks6%Ltp4vGK`Kgtl-gC>6FrC+ zZ^~s4fcE5jv6cPmq~x=bF%sIwH=FU1#_+K_Ia3%d!{O?}Z}<|<>bf%B(=Q*IFBGD? z2PgDf?U`5B{*;NBLB}`!GHq&PRW3h{Ba7U*%UjuwCHAu#Qc~%*_W*Yh5oh*oy-7%W zV#oN=HVT&?{B!`8r48`+h+vD5Zp@KRYm!-pFNk+S@W6&0yvkpAVX;YbpOnB#+iGgkdyvK0@VzDkE-7+uib!t+_(SLf^-!yjyVaf z@^=lBTc2O!yg&Mye)Vx-fhIqNOVvC(3T|veLxQhxNfCGiHz%R$OJadk z?IqV3Q^d#63MZ)T{MC1}wJ>!|Ha-0$D-3`r#*gJN=?tQ~4Rz1M@U#nI&n;5|1Ya79 zDR3p2vG%BB+~|qH1ucYhDnUqGkz3pSLj#+Src(Dm{`p1zDb`P3hJzB0;QbNS=0m0q zSz5&B@TEFok)9~*rNX(2k*?4O8RJ$AM#&&z!N3x>3A{q{YJEB$JR%yH(5OaSfFPPy zwnY8s=mcF?h7M-fcg=ssOF7WLiHxe<_;^fV`JMgNWRZ3*%ZlsFDkeVN<7h+5I>3pn zPm!9HMQQHSPAIxdJG^1qW#XuJGsQgk*>-I4I|qo*oj|!heRcTbC4l!&=Deq_dXVW; zCpa{x_y2Y}wK#)!@b}p1e;cMZ@~EzX=`!Z7lKATKqSwfBZQp3=wgS{jGLlzo7_q>o z^@U1V=L_mJdh(BE-11e1iz3- z4dDM%%gV6}N6=I_8kPkS6l7iY*+z&FrOuk^+}(+bAJkEwp>`E(hOuR-L)|Z`qr2c- z|3d${i|%*5gqhxDhE>vwK+aOMEav5#KmJq0|IhWipky6Pz;VU&7<#XyL!Y&|<0Ef> zGy4PT!&98GDLR=ih+gb=_63lZSvG&CChBnSw(D!fp_tRU|SD~X%kZ#XMKJ^m$Q(qes{_n z*gn~3jbJ8H33G0o4$0FWUjK~(BSGs(4$j>Xn*>%cMx#p{y37Jmy(@5x}xzg$(F) z4aqec&USEgc9jEc5Ow+12jVmngxE$3hOW9jj}nKMg_*4c3{>}SeE9||5K%Cu2(Ug7 z8IiC3q2}c?)<5P`3V4@&vN#$YPaa@H6rVe25}vDx-{Pg3-FDIn*c%Zxl(1seW*~P6f$v{hq_*QRph0s=iXb}VM5rrBlXoVA$^wmjQVg8K2!cNA z68dU1su2(!0VneDb>M_p(OkS;9B$50ifLmh#H4Um3P=s59%(;qWTI-W(5SupSvz6* zEFdI^fGeNn$njb9y|Zyk5{ozlgU~AW8>&BxLqUXfqT_MMm(M7l<<@_B_%HsPE^Ww2Dj$FRW$LN$N6{Wik3%(kA1Cy?bu|AQZV9F_I^gG#- zHS&M29Q+1rfKHey(@*9{GRXS-7$eLgsDJavkcQ3hdiuMx^nYnRaCs?k=B?0{Ve>_k zoHHEJA9*3Q{eYE%XL29~>=~qgK>A`uXq?CNFB%MwgruA%|0K1fXbAL*LVv-;NrG~d}Yg%8{H5vY$Ec_&ZV_+L0>om z@VEoXQOZPy%aL`SG*u0Dzoq+LwNU=bND%c_G+=%w|q+ z!As;jwG0Iw&tsyE^Px%D%r~ce$Xw4Vh%7!|J^2=LH!cU#9}-p z+^KM55SVY^x2dU|VH=`Sy3e{pIDY!*^`AEizh}^S^1$AXcAzpSDZP4MBw(xl;$xHQ=me0TOC_$i_lcvPv?s@ zeD~~9>=@#FSU|>}O^gZZiIld!ao!XWvn--4tIy8bQLq1ofgeM~OFEDDQgPTCb5hQ2 zU+2GNje{@FOO|7CkL5l=z_(hw!5QrqjmUKkrftF;4ikkRdgA*)i6ihF7Q0%)qUne5 znDuc5Y_jcGj@b!q-Ub=(jqbe!5#~v#dmrBpCy5?Rsif{rAmlz};vLg8Ej2rFdtJ+DkZkQ_2WhIoD+|L;)jrD)xk z$h=VeB>t^~@l#V4Q{0^1Ws?A4jEGKAZ?2T5L_cw!L)}_Hl*s3{5^`;O=GnUOuvO#JkHU!=( z1<|5owE)wbr4lbE#VYb^0S93L@dBsoPu9F(qCeb^k7!Bah_5Eo!<*E9JObhQw623d+LHXfF@3_fj+0J(8MgrgcNDVmYQS+5MoHYy%%4!( zjzA^uZVX#5&)qAQl|4C->j)jToHk`P5f1-tBOKY|1J7EV?(H*9y1Zrt?#titvFOP; zdaM5C06H+>puWV`^C(RY)r9%^a`>=GLm6uGzF0TNzacbEMNk~t!3bb3vN*Twjysa) zmQQ@@4LvK3sMx=Y{Z>ISz(#7RU4GUIch*sW9L_<(Ax3=f;vnw}E=DCS+oDTkZVY|7 zIDcQ3k@$9wN6r+`tdoKBW|bEdNlsxmcM3OPg+j#BaPmn*L_IR`>ICw+d378#>70<_Mc4;L z2fLvs7DdWzO`#cN`Je{ecgK`BMA$#X6hAjDGq)R^-J0_*U3MHvQ-wqY#_%5NX61g3db^Dl6^?xIRk6t-g}16c*ZH9+)$p;fkg6Ly+V0i zb8t915MW5h(%aC3q4?SC-Q4<<0A@iN1P@5{F7{r?bruir(+bopgMSe1c5~Zc0R;Rv z?2pU;4V#&WkiJM;m-1trhB|MRl3^#(=)9k#u-t?WfxG|Fqo12rN89OWs5!5W{UTN^>pb&gr`|VaClNjrV?3AF5v8e`!K8m= z%RQigsooDr7k?IjppKt?``OcP=YRNZ@Fz0_{bCNp;2U`tLs9d9GJE&Y%{%JLrk!Tt zlF9ETfXqk{n(GIsSO<`{KIml3Z>aBWAbPxJDCtWNvacnnVkune@KgjkQRw;AtEM*# zLbV4}+F$>-k9*^eBJ&C_<~a#zP;$>I?9(R1qBs%oI;X=2c9f392;BH~3{AqT{Ge-8 zeUc9^ln-)`UoYynCkeQmNtL2k&_~&hxN4Nx@|7ci4BtrYo#2aHU!LVT{^F@;^!epj z%-2hqum(+7>AK|8IFB8<-ovU*r*Ki0`(II_{Lo&x%=o?IXARMv?`#X$_{8VFw_#=bBs9Xzy!#w`XjbcWjh>VR zkw|_ISWxS5<%>!%W<>@R*5}Den;uC&Dzu`ne5gD=|ctMv_N@A7O#FK10ca z)djo%HWBj(n1lEcok6h-SPzY#vze&_c48}qLdn5`H&>}CZ2K#wK$BfMQkcuHw+?PZ(uMWDlZ~(IhqH88S z;=Hg(sDTO5xJVGt2#Y9i^M_W*qt%l?G*0d--)2}xZ;s%cl538 z_-NU@*%LZ_9u=P;&jfc0B)7kR%*3BQTY(pHo0g80LE5<28w|zmEsV6iQnq1i>HTT^ z`J-??{1|u!yw|vMqCEQi`Cd{qwm|D@(arNBgo5+lunrbo+cHX&1uGxz=17LqWs5Ju zc|xU~_BU`3_%78+BtofVZD`b1l;xo&Y_xtKn4|V%PVW^EQsV*)Af1Jz32r^xNg*TV zE3X|R(3Rkr9Jnm?S%s!)xp~=Kb>z3!Az1@ljMK4UAI_s``=3C8_k>BKDJ`8WJ`4ta zY!wbgvI67z1lK#Vyw@9lEuY*$hf_GV_seAV+UhbCjUKUBtG1HbiW=?#x$;G{SE(mU zt`YT(TkS*lI`_+B8&J!tEzHH2%Wh;D{9K3g@O$08I zzBkz{dp(Hc&!9SxPHYFX^=*_3( zzw#?KpXyuV8bb?zW6~*v`JzX~;11dozDQesNzUln{Fiqy5>!pr>7dQI^Af4?*nU6X z=z2W^tPkS`P@UAstR`}vqO#9VBVW0*02rE9&%-oKR{f6-PvVlkB(W&<^60vL&+@UR zhAnfSs1)gBd~eIglGGA;feyqlF;YZTs;}h5wOje20w*M5$tmgrPmkDGEe}cRc4CD( zN9nu1gPvI3{@G9cc)IKozC=>gGW9$#pEvm5>`@?|Z;;O1Ar=-5=QeH8YLW?Onq3o?TQHKgYt{gaD zRceG~7Oft4enH$p2fAah4CHZ2Ag zX%XbDueNrI(4NxSk6b>ZJ#xcjk)7J}jzfK>uPCR+eN~>fS^T@AMoGqoM(QTx70+wK zCJe-O+anw6ILAD!raZG|(@zr0MqE48ou&IOpVQrXjXU6e)tU+}`pJ=qkt>y4?FBk$ zvgqY_v9jg;&hu@$1g#N?AIWAZ)%hE*_2(@@wqiEzEKRoe zu-D(oxMw08KYA2>@2`Y^qQ;7}w5@5!IyLDsW;S&ix4iJXF@R_(t_tzLAZVBNa&uq0 zk{fiDLpWdb!6j;wb+B%FX4$3`_3B9tl&0r`!nSR%WRw^7_BlqWaYhfbkj@Wv*yoso z$WF8cpL`SH%?z1x{JzC^umhdfvu44Z>d;XR-&n4wGtYpIjkhe_89_QBJWfut5EnfZ z^_(Uq{DjLK`BpkoKHnE(DK491*%fvp>ymyQHW=I&%$TT&o@sm>GP3d@Xrh z$hC}}H}nptE#Yf&^8`?`d>R0ed8e$#P~V#wglWKvT|naRE@q$^Fs{w#_3S~wTURkB zy!EkQ^4({7@tj<^@igBh!g{Koni~_-~Ub`Blx@=FF*i)3*sq1+T_V1B|wy2(?1b+64o1bM&{CYvBaV-~##p%C&6)xN^~>d)8&R}IL5 zUfL?j^qe26^mRR@U%nIl{GKv*-dcu0T8s(XK22>bFOI2s`ATbz!;tn#hs=cPIxzM_ z2izjQxamjmGtHlty~e`K`$OKwQloX+?;h$GD|B=9*O|!8&sVfJu)|I+#k4G6-2U{U zpP8{d+oo(E}gs0#)2~xSEDyrU-!X>$;X%chzijjVlha}U`t2&+(*lD zuSi;PlXe|`fB|7@uOv*ZUbv*^*d1c$vh`Fc+dDQ2C=>4oom{?nxuT`xb6aU@sD)5Q zKK2AI@2^n#)zxtlP6yM}))t3LD=CmWoz)HIm>9;+aP_yeP!%cMEfyF^dRo5W44hzJ znPJzlMNEz9KAN)=$VLTbClDPpC-c>#H>1DyLQmNc$j>x}v zRGm*3jd_!WmIC+2Te@xfXPksn%l^%*gtmOxIlvm{k2-x>Ph4_;F59>cmyv_H>z#6n zcA=|u3-F$?rz*XaBpKSvXc59>b|(~6x!g}qE57dl&;2rc*#4bHB$C7UQ&jyz2Ia_y zn}cH57DlX6?3tXetGHUdM*1$X7ieN0hCf~Cf2bfv@J_YE0%jY3SXkV#um=4Y%x)N{c)b-rurz;0fu;!Kz6(b4952_u^hgMs!9g zF=mME+YIcfl_lqe`(+i#w4KQ)&fWW^l{yy<*ywO7zp|dDW_>tFE*WRacUhsOj=i%?40%R`9hfm@!o-eT++5Cd&&Je^s@L@f;s%Q2>D0Ztlycj3PlY&u}4*M z5`|nC@5deqMT(TJv>MX_gXE-g*ki=XjsyPY(3DF8x{vF}0DFpv|7$_nXQ_Ks)Afxh z4=$E;aUuK9*erSo=OHh!NiuFidt^A_vw4@j*h&TWWU&74N2BUbHBu>0XMp_f9$%uW;<&xmA0; z*yzrbC36=sW%9f2X=H?S#%?*W?D*?G2c;z8gs6$dwbw8|${k;kW$~+>!~9b6eKYrt zqlt+10Sh^AvTS0#+BXlG9-a34Yq6IKo7aT-?b@n*hTC`HG|kj)PEbR$I6?h7)6~fo z4~~{J6w&9i;O% z)r$`vnBuABE2?Pzg}STuz~pP{`Q%nS5feU_&vm9!tBvwPW>Kk}(U)^>@b;c(uB^;r zH=gglHlQh6iCQPRnn9bA`QvvGLEL8`5x#R;mW|?jUAf%(XndX7Kc3*VJ?$yW2A?mA z#zoth7jU^f?a6H@MS^SM1gY`p7sYWz;sq(fF>G8kKELF$sOng@^dzl(`C?1>N5;^= znFJ<+Y5kgcA6PhV5T$OBK|!;Z(H`b&J_phs3evN@ic7MMmIAnLwLjSz>i@z#6IGa2 zdowLCd2JSxyk$pu*hI~IUup&n)f;sjvG3Hl(y1s0{GOg0Sd^h94xLQSf9rRh+@PI# z#Ef%cg<=yMy+{*WA?Kr%Lb1-o8@s>j*j78-gZ=7}%?2snvSmoGNN~M0U?QiL-KN?r zO&KD8JUg>$;AiEm_2;B=sXP|h3g{Mjc1*4&uuSVO0u1|+YAHc>n%#j1CTWfmMvE+F zcVPl9avbTV5J2-5Jm(OIUh1Q56U>sGETqZos?A$!9;S60+BQ&M@?6FUPdv3C)Stm# zaQQ8tuo%IsGGQiy!Lqd-%66JL{ah~ZyA5u$dkj8wtmCrpR}v!Klt>hEu2qZhP`fTP zawmA|aaY&r4$!7P!-!vTGfL-UDE%H+#$RYE*taxXP1(*h^@;JU$cPj1K!OmAuq?-v%-LK; zHaz%#_*B>rw{K1oap)L7*~An-niOZha5%1xX+C%_YHR!O7Ym|4UNq5n+UjkP!3lH^ z@IPQb4rz7PDO?^!c#8HuLfwoh($`Bf(Zsc()X&(y(i&NZig%t%Os8kWk7qBsEPT&T zeiIbE=Z>DdB_kF!;KsX4XiaGwo6xprXC0E7b?%pYDTjYIB4o%DLfr@oX0!+ENA#zf z#Iydlt*`GV=3P@NAvkRbvMTnPis5H7e6c)30#td%=I+|LpzoYa%h4w7@bIu z1im%p{6+`Yq1F!lz{leKi+vK;FVLHCl|eVqU*Fv-gihd{dS`i>UdXz~4aSkrn5jEr zJ)$-%hw){bDPBVJn)EWMp1;KORz{a&D{{&)lL$ObA~HrTux10i=iM6wP)c!*3qcO7 z+IqGdbs5)oi_f1 zy}ta;S)M@ExCfV?T0YiH@pC6cy$y7iRhKn*hLuspgk5kbK_p73GeXYymP;GY`Li6C z)d-`93M~mijZ)QCAg36x&?{UsqTTnLcixZ5zD)66%BFCWN*f1(i<%1PGDN5Q^+Ya z!WGLo)TMV#0yVhui?Y>N9^IYJ$CIkY#N6Tzc{djGDyEXds(Ol+KP&=Hn*z# zIR+G&n@dyBG#506p4gca+fY2)AHP6LiqkVAf>Uy^@_?Y6;M6UHQ>03?cNJ8ea-VNm zR$(>#@3F&!W2dq!)v-h=&}{q-r$S!BY#D!rg5i136J*`giqX5`8%nM1d$0FC08dbB zT0t@<$WJn$kL|HBrulg_`t6JAhxYz-)t5Rkadk7-^Dr)SyU^jCTO+HCS+O&U-{C02 zVia7U(_&kh4{m1XSZ)u^4`+nUvuv0b z*X=pY^44_|hW4~-;xctz-lZCy(W{_xeYpdlwFul@+S%Ea2YSlxt1ABI zHR9MbKO?YcUd#A>AoF02;oaOsxXOB%&I-XQB==5X2iYc*atcWCmPpjSAsw2td}Ch0 zriAVFteZ(&-cSwG+-Z-HMy)Kxx={DKdq5Tcwb=vm(lEb%PpnSdcZ7sHW0?6`)>G9vfxIheGR;c*MH)J?wf z817SuP2#Dbw4oomSCWprZaqi;`@<(w5wghrs_Vd1kma3iY<{#;w)*IE{OKg>pwN2} zvDz8eGj9^2$`=g?>4gM*c*;7r90j#ew5-!=(t!X>KAls?@iEbrQyUhbD$f-2!KB>_fhi$?);HCuPlE}MT4vA9D9@+DyvhKTzVlcPzTkKYSMb(Eusnln zS^JmF#d>k0MhDx~rQU;mlZy2$q{(D=N#rl`|7kN5brKG5EGG)v8UG zZ=_HGvD0(@68CKfaA|q-GR<46f^N;sOhl*0gE$xbgXpfmJ#*DO?;fON4NeFEL$CJx zUm~>Jm+~fliW|ciJ%@RdAwvr1Ne*p9M85cDFmr7#S>4*hn=0dIrr9aJ@SX2orW-p+ z(cs-a%Qwx3&4c2X4g+ z4hGClxc8M_Z|+noXh;1&rmi~>%l2*Cqs%h1N63yc3K$Y*@4Swa*WiY$aUQZXEi# zkvXvYBm|vyY($Z)yhWxw45W2unqMnG#~&{&<7oFph6!C3c!*NdPj-kYDmf4f7?;8&~yc4 z`0VBS-qyFF`Rngr5Ar)RR;@U5eO-b3ExX!=;(NZLYlTzT&)$+A=6R$k&8#<1pxbEJ zk7{Gpfc`0f&ZXOasnoHZFzL)~MRxWG-#^kJ#^xtv#x?e&nlKJ(ks$RjYL7ynDv)%Y z=6jD(7qVa9FkX)?XthZWwHtEIcOC?hW{Th%oFB>SkR>ihS zta}Ru6Y~8|olv9j!?$I*n@XMA^5<~2JneB3|3MI1LNRcu^W{YAo2C3*BtRzOy>s0J z)iTzppfoz|k-LwD=i)4RbKM*8*^3|5e7}l`BWT7Nn#2_0^43R3oV+e(^mSF?-rhdb z8hd#g5P*E^0Fuo+*f1n!7E%0G#BxSj{M8|e>|75nGhRahGdFHSE#Io~mzJe0 zeBF!VxS~b$}-p){pOd3N4lk2rRwK$W$3*LSFrQQYZCD`QA7JiM4hHitD%K; zZi=$%!fo1^p**}iKNnw1yLC_Avba}K&59(m zw|1J}NS2A6r1tlE494(_em{8mGyJc@OR^v%V}sI6 z#F9r9Lf;3ql!8kcEoIMO*ZS-l$4fote&KWUK{hW@SAVSL<3$a6c!pw+(s~*JP*2oK z@BZY)3-30Oo3`gzSs!`_lGeZ9kT$yc>YR(j$?YKb#MQWKt~YZ4>8WhckUGwct!@agV@f@)u*z1+D~578&g+50fqj8)?nN ze->!AQ_`1r?LBsv7p6k+Dpb-bSt_cO{*6J%jD#hYaE`#)p0}Uv8Y^ z0T!6g>L-_FY&%{xEe`vz9P?Gx=E3c_Y8b^*aSq|E+#w-5WXsK+#PN? zpb;YV-h(;lUvc{*_KK??QrxyK?k`B^?5fru|hJmBlHTrl-6&a%wH`$4JTs z=u>QB(?=%t@2b#JEs_ zkHgyWmxr`z9Jp|5-A zU9fXywWDgsf@N$5{OK*p2&cTPBwVgWorDfs(#byfVf>i*t?5*7*-iS1gEqnYXP=`9 zt-pNWvDNp*S}}C><)h1muN8O<&lO#>S^hJ~czUIiU?qb}i=v4mui1k&l^?2ZNX*x-}&SFL_s6|Kr-6$Rx+^)t-D!tq@m}BYQA*-e{mk z^hsnh$p>n_ez$WQXBTk?{5Wv0mc(vJIZ!gky?N$qXEO6TtXlp#g>h0$vEMV6*^nul zM;Gbqie)blw^(*R3VQWQerp45_FxSB7T@Wo`i#fY-Q&&=laV~{C`apE%qtpdO#IlS z%|%_OSIk6lmO2cVOMqiG7&mw3cB#aK-Exgj3hA@#Hn*S`?8@hyZj}M?)%E4*ivzOr z-4;1tfvF|s$`|FM&NY;JWN|q;&$S@E=c$P++P$QCmk#u$ASZJdd&1EU=7|X{C0>Ix z`&o0--=$FGbg77&W()_G5-?c{I7pYwMT(a_7A@~1_iL~E;LlAwZt;`A!KilOu4vb9 zX~~~Yzp8%oXv1#i>%~SVM%SgJTI^By)6%7*nXws>XD~!TYg#V8SJ#x{`CH<+1Z9(a zqSzRsg%@nmP}8k1dS+Zv=1Q-x6zKj+)U@!M`e&MX${}o{Q5pMY>H%Dinx9T#xtF}B z4(RpE3cMDGX-q%hFVmEbj2`gEqvR(<1JOfbGph;>2n=0|j7_;`vI)@}jpkwa%3W8S zPHjMg8=7)CcQW!_#v=KdfWu0yP2f_nmb>7Yo7^WlANw9HoKgk#x|4ao(6z-i5Z}>y z?svOzu1JG^-y8C}n!nJapJQ08oqgO;~l)3p3wHLLZfgt_G1{PKqD+u)1<&&rRp zKQrgX?>$h}u@*VVLX9Xt!QV2E2tYtah7Ld{Y%QH zZ^#bw{Vbu|MkQl|W>Y?B?LQdaqbepRQg_}u`m6DXkyt(8XaoAuNF4WIFs{o+N9WJ7GN!JaL;Y@6PBFGZc8L;rupleWF&8 z%J_|iELbCHwaCTO;+II2xGhg2L-Ac~{)vz&+kRK3m(<2ksui0~e2m51XuLAh=6wkU z9-ZVsG@)q5p}v@f+00U;&_+yr_ATl86l+42(j0$z18_{V(=e z)9q&|Uv~~g?44-;B-m6t?eTQ0+JBFCKkOf-+1-7UWBne=A^6W>+;0n`FYxW7wHK^} zX2o5_+=BkjZmdS%3n7Df-OjcytfsE}RQmw^34`Ms>Vwad==EorG#Va=ev99tWY9c# zGkNulVDI1<><5ow2%@y7h5a5YoUJW57b?zkaXK-ar&Fo#LhHu!!i_-exX9|;N;7DA z)7ZLPI58aFg`bN=O|>`%X$Q1D&2v77Y9FAfXQRp4nZJ?~l1O5=|NOEM+w3b@hMl!0 zji$U++myjnGYw|T^@z3hg`fFvUj^1*?3B6vpjca{LpVe(f<7_&atr?Zni)}K%vAK@ zv8U`{x<24feN~4CqQds9k3_&=)39~<^f7hec z^(05PbbB;q3@97o9 zYH`0!fvzN!SONq6+>v4b4sboEg>duTVRC-a_kMI=p?8?M8w?$PmX2R73ffTODT#hn zZ;AC``)@23&QB_S(O)p>%I|rzVPN3#Rn?qVxT&P~IOnLZ4^{-v)sfZpjlMSNs`*bT zunWV-Z2Cm|cB=3Bo0Hlmk+SDTyLV|*WAC5y$@S&cA-fn%`;G1pdNO8p1-mfgsAXZixt1O(>uF5?AwlIHRJ(0(qWsZ;Xg#5Rv^#&q=QmZMx4j z6pX7*h!exi}9_g_-(X2)31_4FWc|U)jy5g zy6qjGdyC`zJ!EvO>8?Ln<57EzplHW$G)9d6gxo@mH?k5b>aGKZYMt^?#2kE|`i*&^ zkL+9{^d8W5Db$9D>sd`+SFCjwImHWK8hRC0$cUb{MaS@$$mM!?e*l5{bAvxy`qXri z9NS1EpUvZ|V9Ho2YDm;Pn|7TA=Yv(&BSZ5;s0Shsd9UGkKIP_JtKw&_O1~V0x)6~? zpJFf{wdM;ld&Xzn8~mD(2s?w$U7mS9p=aV=oi!|0wmI_48PeWclH%U8h_;(LF31du zsqGCTuQPb87y_HCZReD+?z)&tqcLNFIDoJRVP;+E&_T6)$Ms=tOsmZ@(R&Hs436JG^!T7%yGL=qbFPw zKA91kQ6(YXEh{_$q#y!wJ5Q~JtgbhEyGHfrf16>+7cstmJ}F6%T{M3|`5^ep!Eu3X zx=%wPOOVZ&$mPR;3`2rv&e;>D6Ctpgf`2DOcC%E>-&nq*kmcY?Il+cWRmGQEq%w50 zA^bSwu~Ly5ymqU2m|>}Oo}(@>0!oui@3dI)u(Z;{)S}mmA8yPjU>y=xkr*%_TW&7FvL66ejg<;7}n20Gn*{^+@iCc+-$6F zG=5#6KrxGx7TSM4>2kq%=2fy#*OO$ti>PnXXL+K>Uug7xH!nG3fEsn#rD=Jx%xELS1#DsIhYh?QWlTg(KDEv!CTO6+P5M5y zZTyGTVubLjsLATmA59(IdQ&K7dF&ESSKjGN*Xg@kX)#9l-DrB9(@Jy}p-CA1OttTK z>HoMZ#}sPx=^?s?cx!Ns`vtBlBUOd;nBKKWGuGEv11uU?H4bvE*y!&m&<;f{0s7<;{CQu|M!CL-)u~9nt+2z^)@1K1a^O_omXW_KK5(_E+ya~_Bl)+*`B$&k>z^K zd_A1J(J3$&Nu7{cLz@$VT!_;Wu8MN11@N=Yh*|o)`JgM%>HnE@hDGxp#p zjT7_GW@!xRQ>)l}stm8oUs;SN``PIs&DQGf(`|OFYHN6LOxY;mG4a?XOZjKh@g+dZ zC!6Ha9BfnWaGpYD+E$MWX;pw^oM{LeRYBW7Vub9i(8^I&;nAj*`HyW#?M{Gc{tl9-0hNm%?ON7-CZ?y2iGPN=CD-g z8rqVVnlRInWY30NBx^Bk81M3;;%w-D5z%}shq5y@8_T;R&( z%MYUI2dA8SM(p03F*fmJ%z#wyiPX19ar3>T(x_c0WJF4SZ@PK8eizS_1_Ntt`6j5M zxXgeIdgd_57)d2Zwp2WdU&N2<|6Eq8H8|p9+^Dc7))KaGB<^`z(V3WUzyX&P8EHmO`JY3ZjAVRe4YVRc?`q9~oh^v$PiP#t z=j&U3sW3e51Vo=Ew8ZFQ$$J*Q1KN@h{%?pH$~hJp%RsvI{b2wZIH{PP<4~ZlOJau9 zT2Nw0OX3k|U{&~>T!*&i;3kfsL`(RSo~BpU@N-HZW4uRa$^D z#HwKM(H7WLUSuMb>7E;(iI?SgG~gDoBh*bTTOHy}R!4WGblD7TY9AN?&ck-{%#Vy2 zK3-A3hZa>jx<-iCI7q(#%E=R!ZwzhJt8RsAAyo8o^-CM}o$$ZZ(i8krL%a8L?0CDZ z9j@C*#JXAjgN7Y5*YF>|^u^B@41N#os)=T?AjTMBJH#R%(l-Vg^*rvm&SLS`Q09Bo z9#k-5q2TJWA}979c^yhgD1u0Oe+1Y8R(>R=7O#Z!*Pl3r?)a2hE(s!@;M;=Umg-aM z<4px!p&L1LwCmfZorcWs9X^!VWZN!G+c#r6Zp>#|!HdH48I?`$*}Fk7XC%S4D)!YY zOrV`32KG3LzIS}qtG<4=R}gtU_fha<{)AH47d2hIvam;)!mq? zXp&II;owdGxP zy@rpgQ-AJ}kbQIiBEpc)K0X_3UpexFWomqf%(i10W~yKJ)&i|^^<&6o#(i+UyBPirLDU^npy7}GTDWZPh~5CN{@&6=LqbO&sWJ;{XVNaC!Ry=F-6CC4mm^) zoEVAVVAECo&1RX@MU;OS-uDRXGIa@wAUn-vz*c+xq0=U8;!r~Vn+?xo)nvu}(T~ro zeFyhQzI}6V`xHp;mZ|ULa@i(l8u-{WJB#`!6=NQ{R1dJ4FbTPjKj`A&p)>J6Ae}ck zCLZWVgx^dQWm|Cv;v<-;cKKWwB9ZHn{F4Mxpi2~{cl6qAWEckh?C$lE-{D5qpv&n< z+x?Wgs6$|)^SV_z?5=h0;c0(!N55`UJoVsdh#RF3se75_FRcv5adBDS@0883vwBMm zqFg|&z@9=#YWM204kwM`_l5NMy!%P1&lj7MAMM#)!%9RJb9O`WiJL^H7~ii-GL9WM z?u#?f$uaZmB2v`5A@0l7H3?&wciYFP$P&+@!x{M{X#jdMx1(#RhG{{u(lPq3*DEmS z$+R`w%>}CefE9p&d3`N5R2Nb=t05jFj!~AW&`dNV-o0g@uTug00=YAa^Aa(O@|`S< z^qF&k59rALTxJdos{2!_s?jjMWOD}YSJ~|(jbb`QcH~l!VBBEwz84xH@ayo^ixr@B z;0fHFekTXCybs2?Ua$i=c#+PX4E>U~yMsX=4}4CPq#73Ztq?hb$ZNp#RH&eG_Y=%G zA|^yZjG?OG*(bLhaALU5Aoqm_BxI~7{3(gLad3)#K0HLetu9Qwhk!h;fY>0u__~g% zNNs-ZR6lP6W^tYh;&bR^?>)0y^R(vx%6i7sz#Ski z(rGX(2y*D<;8gvY)T2~=aaJSTubnpnLe$LVdqjU8PlbseRYY zp{ZpWLoyj_oGqigllAb%1vyDE)kcyJXviOefOHwFed8{w$XeI~V&4iZgPfta0>O`v zDQ61e0He4^+>qzp_$l}*avxS6^epZ7Um&_&1Hu+-t*?03T}v*rv2uH9-OOAC7Zt%n z(z`-Otsapi^Xgc$V0oFhd|@Jc?>wP0)}|SK!c?B#pHued8~+oxZey*z>j$ZRUw=XT zR{)8Y0cpZK-D;+}TaM=I9f3C16^NZY_X$TCmQIEUq#p&~UKy=HNZYl{1D(#;{V5k_eaN#}Lf%n&!g2k)E@tGaA*9*cJ#IEY>HG8eiF#q?Wc(LB0t>mrWwfs{$ zBA*}m>QRzPq#~eqg9H}JUsSM6JOT$;#2{r0Rx1(<#@r)`&L2}pe3P4pk3&3K1DZZr zjXKP1RTngL9s3H(R>9tA8YhJER;bUnf4|sgR1S?Emqo0!hC_PIA~zM*=~_ozroyBa z4q!lN;+774na)yJ>=ESfP&3%Zn9w?cmXEc41i~4gC@!U#+ai;gi}8Ejxx$Z{Oooe6 z>aKrYdLohs6R!(d#3E7Y^a;F49&uqT9h8EkFuUU<`t=vS#lYFW7G1c|_2m}k$ttSA z3xqFk?%St~jd|=ORzgMNTegbC1HMk!wN7+7Kr~K|Ru+B#-*0up_JN(W)_N~GmvNt~ zmBqkRMdk8auyIF@BO6d>^u;^|iUdMbjlg!LU25VB1ubxaoisj%*T^*`g7i7N;z)l8 zr77P~;fr)`!-CH>9ffy)lGV6a3R`e%m5M9!A%~X)0~2|oewIv=!@O(EF9 zHb3ySDxY~}4eVz6u2NFHJ)<#BvNqFw%$CIH; z83}h;b-wg4PP+p`lWw^Z*3J%4Cx{8b2;Ar?5elY;8AOpyYW)B_e>T7P24cA%q#pi| zFh3ga#{PRX;4B%CFnTWq<8dPkDnSJeNgw5mLodtmzqk(V&j@VCn!Z+|dtPG?@ft@( z-=z|^RIdr1fL06@Sq+EB<;}$_)RZ40|;7 zya{FaFfXuK1W=xMyVv~xq%YV%DTjSI&NWRH@vZx7?afmuL8%nj0;4q?UZmGrn)J-q z)LagDVcJQ@{xEdt!_$ASNfNIZ6Mfo!P42K7mStDKK*k9bh}c{&Zq!^HzP!OBeSHj8 z-guQ-WD`LNN%Do=9dK14BG%fyyWJ=|(C%9K+)ClaV(i_YRnDT5BbJd*+e<@^WA6%Y zkIxK(0|Ap~F?aIm-H63gxEGBd!b>4L4%x>6S^0W-nCwS%VY7VASF8y%;s-`GS&|;-hvYZ- z8G&jue$~>=^LI=J9RAb@r?)qVq>dz__TwgOU9unSAwtm}sh=Kb-E2eFXd9duK&#Iz zXuE;FX)WXO1gZP4tc(DM^)0#1=O5+!7akPSaJj>q|5EXR3d}->kpRWvYHZ3UHV=vm z+M4C`O zV!h~US>|4om~6|_^bq&lTg*HD`79&X+4|T^UWH9a=Y~_VLAlsODAb>9w)+~Gc-1Nnz(lDBg^^KWI?)Qc+SohIR;LD!=8i>}9*f2mb(K_|;;iVW9m?H$3@6I861*65(Cc8k+L?AAVp( zWShFlQ>1TO!F@!5@$VTWd9Xmwk_XoDD=)T_Hra!h*^j-3I8S zhWQ4+f5|R%Y<;{k8N?E>EOR@{(lAcc-UtLzniVjpK|{Xb zYrz(-uRen9SXQmYxo)O5BzKjrmdOc=b_DdIN6RjW` zU;_9Tmqcy~h0C?@MkZl?({XIWvc8;#Rxc%%@ng3m=a_ z57HUrb-Pyj+Q-b3?XX9=_ydI3)`UU)Gb8E~>^;;YEMr4U!QH4$3UgP(v;*>JJM`EK zrg3+FhP$kt8)(_4{Py{S(&?FUqeuE4&!EM{aR4!Ah_1~uI>Y{S<;mgPjTDlqO9mJ2 z;0XgE^;6NeYuPntu5nYkze<2xvI4E>Q`C#gpi@yk46?2xkR)wh+eh%nJh(~mzL`Sj zcKcYW!KK81_e+%$1L9je#E3@Uu5V`;CqEdQl1t(g@0Ju@)LPFpONDUCCNL{oE)?d@LX_B^fO3fcy@7xoXE`T_KdGm0*l0 zUQYiud0du0sNr=jVyjt`n83oo8cdgxh)B8$w@?|g?o39(pBG(YT1)#p!Z;la6p54r zuoaqTOLX{dn@c|$CNXJK0}(Psbf z<0Xn3A&Ni!5@f^pL{>`-G*y~kJN_>Y69`~wx1AU^Il)QPlqHig$e`II#kjy^tk#fe z10sSPaaD^wiLeD_4c&Fe$N>>CRp_ zZ}-o1e#g`>28t`I)>Z2*Yw}HRxZ^-3y;Er`J@%pHR6KxS{j{ZNh` zCqlSW>^BxYj_KOkL}Ss*?aCI<28y=yu{*k_e~S_89F_@ljAY7r5~$8`hv6>8yBy7? z_&PFZI8{p`NTB)*pNAM=Z%7q*ucQ* zte?>X^_AuWS5Y`3xcM)YSML=K1{zVCmbo68#V9$@gwhpl|N5es|1Z0sB%PTNrGfkE z$KSJ|H@bwfsM%=e!t8ANVa}CX{IW5bJNFQ3I;{~XoEfTo2aF~mMB5^RqdPNvL_B@b zwZ)jGe24ft1rznxO+J(GQl<)Qlu2WA{);8_vXb-w{uSgFqj3*@#in?`$nPZk9WLp* zZf)%a3O_`YD{`d*T23BREn9*f{W9Fz6{s7&R?_92cbbEM=JY`2e-YcKK1`Bp_eMLa z6V3$R86S6~q6BxP64gN;MN}kn@Ho;6sK?ua8pkJaTYF~_r&n>SHyJ1%g{rS{;>{hH zRHILGPaPU#*O}u84$r0E@fwq_7U+Zfk@gYg)Y4Nnn(2k|uL0WN8TD#VVK0YW3+AQs zRiBwdK2hqqq*&AdPq~})uD0RZ?1p#orxyY7*624B4@ z1fAasD4Yjos3(!2xB_vV3Iy~Kp~-@cGM$$4_fZP||B{a(7Cj(H716a%WCPiGBUb@9 ztrguS9EIw3QR8^BTPbKE)#Mq6GUc@2+?O=yIy!;$=Xm#c7X97g>7(4m3w^Vn`__*N z9i?D858EkRQB^EGua*Vfvyhei@akKL8!qB4=UuT{5Cf4o%qo3#NB_eb8ylP01Z>_!MeUfZ`IFiRsc=F(_V~p@G28IKPidumVi+teoePIQRc*#9*WvvCDCj zxOiKgnlrxU_CViv4bGT4JVecG1<|uUAn!!l7BE0@MvD4N*p>qlRqzVKYp;ZXM5Z-J zB`>9sB^if{4kHc^WKE#G|D5LWyU6bY4K`G|Vj5sm8&^QgU=;_Q2jw|mHdgFKk{AumwKbyPoZ=dHKBKoB=SMTFn zt9+4Gylx!)q!1~hq{a|8D1Pt-ZWWN69>;kyq>_n_ocDI>0PN0M`U+5rh*y$?F3bFx_tF%SNA-BhYr?VfXAHoVph06BPV zyjb+ch<+)UE6*08GB)1A5Y-};<_*|z0bxK}MEx$38#8VelIK%M-sv(#)aR2rKCiye zLIgX*U;OwFkOJ@{td6fr*wi$cCT0I5uxAz?*t#W%uQ-6k&$FQvEjTgZoi?Ef6cPA1$LZjAI{!PVA2jh4TIh!0H|x%pD>L!FQCbq zkXFEH9^uT+xy$-kK@e=*osrCX=OkPIs2#D*s8?Nuawb1c$m2ZUIK+kP9I=#OV!pW? zz8gksi(u*^8TZpb?D0#?vRBE5gUTsZSB`1>8{!Ri6f@ z$WHCP>CsjYOFu0G|{+>2wZ{sAD5;5XMn%Sppj_F0ha$4}sXH39-vb3k3M{Gc8xI{Rh?G@zH}ctTT# zpj)LQ>oqN_%G5UW6JB5OZ{?Ln1-9IKaM~IhFp|E&aAE9L=^Nq1;a9)&^)3G;Bz_t+ zd^}flgC}$nF*|guH2paX8wxvMo=nF1_GkiR&VG|PrEB0sk06?8>J#jvY*76?$W65v zZ9R|c&rcBxHG8gcN4%^{CCUCES;GR0VaD%mCTk6v`{Dm9M2UPfHoRah@Z%6*s=7%9 z)^Np$NAVFrsa%d&)k69P1DIdw>< z7te}uklzW6>e3MBI;Es|PvBXL+#g8zE9-Rsk*874=Ee`XLX(!PeSn3Wvw}aTZnq@8^ z>HKGVk%BHqY_@-FOOf{f9`XvYkJGvr>Q2j3bg3Z#*@vr$iVImwFV&~Lln+^CodLAm*126d2LTOXBKdf7Mel> z{VR%=9;aClDm79fMnqn^U;a&3&*ezUi$d+=PVyI{v?%@j{jt4!QP zDBB%KhhKoz=XPOHrsy~)!@5fT!=)hnK*0t)mfm~C8>eU+RWx4E375nYA{QFe4s86N z`igu1gzh1=O|WO;g*~7Qfy+0(kG9@i^5eVNCI&VMrn5HNvdmfIxw_G7pLi?6bh`y& zlux6x76R#pJUH9qOoQ1k%^?sn3K47s)`Fs4`8-IQ=Vge+qiaABhEmr-ZD&3$Lg@V+ zjtVurG=kZP>WK}qLezID>V%ygXqSGDu;qw){f-}-4u~~ln6%!y_JM}X^t4!0#YSKE zQm?x;BAudiGn%>S2NP9I-y)CByKo;xnf@logvF{`E=T8lN$Yt~xFVxhmbc?B^**@x z??ft)z(W()U>>L8O*|$b{?UPF@4~J(S-Kb!#)d{+yiKu z^Firm#^U1HGxx}vml8Li_YUyTEMP_)5fbAmJ4x>0uJSo?uzL zC{4s{Yf?K=RqM3Mr)#4h{Vxj&l8~He(#n?Bd1ttS(1eBAAoy+4F(tc4>H)=S+^3=R zV#%Za0z)D_+uV{L_QJ!qG72^AoY|k?t1Z#c82Llb_;Y@=QIcirUZ3BtdBQ+d(~|ik zVoP}xXvvntQV*2;MTo0dJ99Tq%Jm%(tp`31-=8oQdu27}^rtxz9w|2aa#`|bz(AH7 zzNM2S{$E%u*&N{{`2~C&qN?-pUV#*6j|fVu&zS$vW5MkH!R+|`2UM=?!j}|>pbD(g zpHBc0#Bbs-c9EhEy}Juj8M{8Rsim>d*2@@@WR0Lz?lqG1sTacB>NIE2K;Wl1`)g%I zC(eGVV@SD`=lka1Ur|-<>%CiR&$0)MNNi7>&X-~h=cij}Y#dOyOX>|Y7gT7YC*i_7 zK&;s4zn_;*KEiWgISG_LANw673**lABO1O0--53(PE*{U!qL$| z#2Qy%nxj%$Ucl|L&~Wku8Bi5>%N2dObN%DV8W<|X>CihGJt_kk)S2{d!c%+=z!iuG zOuETQ)+T6Y9kLg0L61fHr%HjpRG~7kbO%`iszen3I=7The1uydxlu=du+u5j6N5B? zWXp5<-^5PfD_KAsq;4`Df_xAHOEC%ZP~tZtSP#2i1K5hckhwKXV8k#9GF>+`r49in z+brtkzQ;%3&_@P8B4%g3yC|^5k$ZZUofsL9^TpcN&f#tM>xbJulS2uc@qI$9m^*$q z{`!StY4OHmf_iH};%h9xQ-01J0E~{#?PF6u97kA1wuCTG({ulN2Qsn8_dm3#zlC@g zs6)q`7%N?NDL|@W2I7FHm|jSqr$an`(zgb=1jl9^K2YJ?+k|?7R{TzYM5X_}?!q{= zkRYV+5Jm>RwE4Oq;z0OF{l9Rp`C5Zqkv_<9($#;~~HTAlQ#`k+7BonGhpI?=9=0}? z>!tER!gb8UT?a^AqipACwbfmT(=X5!LhTos^zALE&W8;*x8eSd+yU#r`cH%`W%Y6> z6ygKvCv3};gtQgT0Kces2YL8B&to|aJqXu8vh24P$f*$vu)FY=?lsgKyo;2x;0sc- z5@AMs1q|OBY@ZFsXZJ;BGaPw#w2vv&*d0k<#d$=4bW{>!poPXBr~GNm6-2HYHHT1E zsI@s-JeFVZV{{DubeF(>`UV#@&7m*OC{j7RGmc~ps9h0#^66|2`;{7(6G9nDGd{xCgX!!=TH%QUj5piTLS;i*PZ`NWeR_e4QBEv= z7wGbvhheTn@xefU9HLmhdSBMV(>Ef`=noN#jFIQ?d1E6y_ap*Zdg`PhGzV({F&v;6 zU-u3uur}OLFaCR73G9)EIvJl4r@#+K<_;+aJ^FU9F&})Yy>t@#r5n#+PFn%Q@N`wT ze@Tz=Smmh!BZ&))zK3Dch>kf)^~M1PA54QVyUF@4{Z|hdbTL63IFeD``~)^kt%;zl z%5+9=7=t-=38Y}HfwmGHd=4}1zY=HO4NGo*pFR^4c~d?VOo-Eke|1zL1wxA(f#!V* zrinEL4E7pt7%2$u9+y7izn}B3QRupY1#=QJXFxF7W~{*La0Mtk3Z9EM|8-p?7}cmc zm_CzNd5pQQiaP@lr~<|oJPV_6mnXMjSF2!|n!f%?4BP4b>N<<}8S|5$oLw?E+5i_q zVde6e*>C2M!96&!?)|V*Hmiv`|{($MReHe((YXC zyKjvm)wVQlm#}uR3k7n()x~|N^!$vXinUsF?%<||oI$;9DOg5mQn{mm$Bx`$PwbTy zSG{-I9y+jNERUZ+kj)_ZNfkhc9-<)u-mZ)r`eawlnWYs~0? z(>27%@UB`GUuzyA1;TVw1V(HH5(DEgkop^w`H=zj!Eg`p>4_UbNm{-a96>cPKH+Le zf>xc6!xNF*ZmmfNk^a}$p-b`eEzx?B%!nw?`n@1(c^Z1T#THPn#Ot5~;FnyWAda_( zj52oO#|~Sn}5#10YE9e+LE zsk+^8z*H$Ibq(xfuO|qYCD_R!xJD(w-fajF$h28F;wR6@Cq{3oW|l#!OovNrE6ER` z7PFOPw-JiR`iX9x5PpJ(Al*5P%ml!9J&Tdys;p@HZ+O1`uK?BmsXQNB#AFe@Z|om z#fVNs9k=YeA{VyJmtD8fJrML*_y)vMPkJVP2k3s|Xn-(nrV-tM*rcoanzMDuo}Ll- z^H->mrW--LuEP^Q)u{sb6h-2~w$y&-jolPFWxiU zi&Ve<3vqZpLVu9;&-4HWXH~)xQQSh49v>SOngMT}185un%N9A+TTeAKbT z;b-${0w?fE5kWP8CM!_o5L6ktS%$(i`DeWj%T!LFGj9*kdvc7wueOUDf~Ymc%R3wy zuFTl$w~kDF>~}d$f7M6G_-di)3``^`dE`a zprcW6?*LIEWLoJdlm7Pvj95^=!ziQFUIdCm?cT`~@*(^oSrR4^QNyH-pJI z3JZzgNgx(zdf4?>PItc_8VOky0;b3TT4Lvw`4A)b_Si^a3B{0KkL`$;!ItHy5tW1?%CI#K=>k8reVLi z_MpXm7xYEKirDA2eyGgT45eW)je*vBY_!%Q>RJe@HcLHx<#b>I^$=v&PvG4T9$W%V9VogqAjL^;zftRHiP^G2r1DPcLz2#MD(gnQF6UeW*VdtS{&E7 z$~xAOxkMX~C5!`S^wk>NR-rw2f?`y&fRno?BGu8s;#!OMvDwl4R&&M!gJqh}LdB`p zIS&TT$Iqd&;HXXEIZQpw+NS;fA77dR!*ox0D%K+A%MlDtiVJq~V|IX#KlFIPc%UXa zD>=sW^<(*rcm5}`LHLMB2T^0UfXUdy>kVG#g(7c+0^p%K=sZ+LNWToF;nKWQk+86y z(j@zrj`6oWrN*JI^oUo%TP_~gAE81*gE*D~Ppy*0F>-mXo$;v;mZa9pQwzm;>18w& z3^y#pmJ+W-SLJ@a12#)2zjXh>>(5uHgz`YE_trGopaal6%aIw{t*CEfOJgsod*z~6 zO(<5W^{1|P>^jn1^}~H6{}zUg@;lH&6{mej%1)y*9_C=^NB(kv%lPsDZ(6MZU3*1d z=K8aT`of7y=)-r?`cS2lggOE3>17|koU#A--CVb5BZQX6OWXsoKN*JE0oC>yLzM`G zDxV7k4QpU#IzlG$afk1b_F!!5LLo8Z!KCR?{TfoEgp0=)T5~r#2%l%)-YVK=LajI% zp%+4XiTz|iDWJM6)&|hkfOs&%L=Zl`dvyqzL8P5@OBSKuE6gei`w=!MK%-}O! zoKN3eqle_{^nF^}{SrGq5KC?D9aj6=0MiG*aSXoR)sM&AosfHIc2fX(=hc1EddNmL z4Z&n_4M$o{IUXzsE;_@`^3~oJn9>PSB_X25d7l{qO0}Av;*5y^Te}=mg5Xlmoj=BD z<0ZZA=9jX?M_iU+y7Z`xiiRSG0sOLY(2L@64zF^;e2b3NXgew7P@N3(xPSB zQjk;}wt?37s|o)D9)n_>m|pb8js1C+GWYSbp{VpY6 zBy23DerAyNyMWrN;J80BS9V#{+`ZER)!hoYb7s80d#@nRc)7{Wk5CA7Yga`#7+BN- z8OPZTnpFzc=CBWV9``|M`li>=QT=)z@JAd=8gF#G|Fur6YbK5~v03($H7?Bx96dU9 znt#gPFVDZ+ej5QbQ4D}N`5$3qu6_)5C>qZ=;@aT{)Ys{WV~-X#LtrwYW0+7io;r!` zXSMoLvxePjlW(i^=UB%XXh?>G^|@J(*4M|FhNd03am6Y^VNTssLwcIXIhy2WPvey zr#n-$TLj|$?Qge9s?DjFdH=&9^5xj>Eudg+Q8%mN<7kxYPdzXgcW~TaChZ5}2j=W3 z%O^5=J)hy|!j0g!XMX403vvt-{+>N3%*UVOg8np%?OvWRKj93h{Ct>ceYy+Np%u-D zvDV5-e_8b}51``Z0>ZCMZB@nE4J?al%U{$}R647dHP_`Z%Z-~Z)VYtgYV9WqBS(zM zI(nx#(*@mTK59%!tR}{FMcju|?^a}plvt;xjuj}zIH=)mp27G!dlEoIr#(Lq{IQE^F*+80kYN@>D~EgvLj2o|a|ZPWQ3 zfMZC!^E)Z!)s-5hGt1c61!Lxdu0+eORRB$Jfd$sR=2hAI6S}SLtvb2t0GySFGsJ3q z{izW%wC?@EN8AS~l^86tBYf04-kK!7R!8x|oMJ<-0T~Am()48Q=DDb`Xd`{o8FUrl{`L_=C}WtM1yRzd{5RN*Me%0_ARxC zW*KzsBiO93+U2R%bx=1cdEyZsR$54H6-0wVZv>ju+m1@+GX@MA61QsSQ$sWo=SJhj z^OWbLljlet37xcRchz9#N)T;UoR=v~^>_##NP|MowpdcH-dud@zvGkNF1W-u^m#X) zF;+^*?OCT)((WU!7^Leu90glWP7(P5hL6f7V{7`aYI=U)ue+4_s-V})xu|U%EH9m7 z;Ujy3G?mSP`uTNUbw7>gacZbc#UNamkjml}yM@NrGwLMPFs0 zwUAKY-uynUPz2DfI(XGLCui&dV$KQ4&Fmw6vq5BuUj7RNLwfnu4DEM?6CiCnuKY`w zuO)Z^{*)q((NxY|lwh@DA|}n**$^{3N-~r}i?PZgtD=V>AzyK_nzL&rBh%g`k{~vL09Tru%u5r`dAR!G(2uMqJBdrpmLxYqw%+TE_ zAzcb8-JlK)LkbAe!q6e$%rJDww{V|*&OYBc=i1+Y-#=Uz%rLXode{4|wVvm`f44K3 zOjx8#h%@C@#;^ea!aN>d-cMhvWW6^O1v0>x74ht<99tFyo{#J%Pxa&+rvqJ|eJ+T9 zw=ivf+Ys)poLwfLP9{(qenXy+fyA61-YO)oecEysyoYKCZo;|sfc(DY#TX+U7{lJ79@`8LvVVrIa5j5oNmnVc zxa)%I!;lOB4YYUHHnStNYHUss*QT}5`oZwn3#gL4tuFmAB?^;&0qGiu+241l0s#4p z?zj0b;R<-qvm_lO51`@*D?=ba6iq>;LK~-~Bm!nmbZLUVs1<3UD-o!N5Y7&sx z-^u~zPY0Jzt0ia91x_cD)Jr27TGJP?lpIjCuY^&3Kmo+GJ62&ye)*WE?z%9YRGiPa zPt$&aRl8&9XT!DR6g#HZwj-ki`|cU<&9!%YHw%M;@>-Bak3=-q1ZJ zt!(i~2B4teYzNexDlJRm*?671UpB=X3xz^_2GWoNTV$J)BS&n+O)q+7;S7emX^Jft83;};=)X?yW%q+ zi~r;Ww`>#uXCdqqNY#RErtrQO0`Evqp6zFk*{Ox>`Obuyekk2iUeR{y~KA$!}eATp<8!_xpp z%q^Q%WXLG$Hbxi9nmDe>Dy&`!l$bLNLG!KrFKe#hM-U&3n+1V;Hm{Gw zn_}TC&MFtN-1p+G4bhZB@{G9d{E7iUTl4Z6E#rF_Wz6VIhwV0 z6HI27x5hcMKXcpMQ}|f{D>7XMa6vvBgq-~P#sMF4ohl~OX*}u2?J0wf16rcIBjag- z0hNV8vi_LaTzL^rk7swnM$lv5IJd~qq16eKEY^Qly5c2z$tu8sG$a}pI*QW)oP(;h zB`KrN01F!z&X=g0bw)!j?S!C!cotrnJsB|yo4DS%cJs!CahW)tKx|$5YXFWvW4!VH zZAO7dPlm8>PujS;Di8LNTb93E#6Q% zpcb5pXQtHb5CB{VG_JnR_lLA~dvsUy zXTJ$0d|XU1tENe;aGWM-yvJraQUnCy-+MLA&v=J2*4t>>!lH}Ng;EG+_7puqEcrKB&|Jd*yNn4X+% zkebqXczps^i+vRB+ zqeoiV%2Hr)yc&MqRSze$V|bUiEi*2}awz+Cg)+weUf>AOz-2w*C=ZnV35c=gMpLK7 zDhfSX#{ly!AU5W^%^ju_AI zszHvBSk2IxLP(yOCGqqTI?U~1pnk59So3+$35R-%#)E-d$+dDHO@v9h7jTwFlQg&Vu%d3usOk6EpTwhrgnOrD3$r|VBaq?7 zHO#9XlaX|k@UA$nT%Py%<~=lr%;gHS%Mf7F>pL8v7ps1s*Vv=xf~aOu*lt|z>@G5W zZH*SfSe%6#krBcOkj5Q>A!~N>)o+!Q&MdGvkB=?OI&Uds^%6j;v7(eCA%D$T3SAXhJzZ4=5uGHr63KI5|r1WEIn-q5`Lo72L9oYR)2Vdr)aO zI{FK;*2Q6RM_R0sjOpWw+9&*QMBfUPe1=1LVMkQ>^Q+o_nfWN8y5-hmE_0IAY)3E2 z$C!!y-Au;~8(SXp$W>$7#a~BQT9_{ecuMP(303FFo^%8OrQ~kv>^*`zze zU(Y=QG)I=jYR8<8%Mf9VK8-2dmmI}HN<2BuCb8|_70i^XLJx&H_xXD8lk0Hp^NumDy zu6r^m8?sJGk}jQBk8-~*q{alorFw+ax^Q3#+N}bcUXNI@i-4aZ;=z6^J@HcE;Sl@q~c0L@yu-d#D-&N!( z;GO=`K~Uu#$L5=?>ydSXD0e;R+xBc~NY0$~KX7#R|QTCk1w~EXJ$_ilK0a zLhM4DLa)LgXq!RbG*KqLU^EASB7veQ>^&msBJsnx$2g%oU_ToLrI3 zmRP1k#^nI>MuUcylQ#1x(+>wOPeK8?=|L0#f`qW5G7=n_-MRpz9X;W#P^E;0A8u&L z$aN%!b^(+LUe9dBq3`N`PWlOu}5naNPWzMu8ZqXXF?tJ14BN%S_CWF9GAmt*KW zHv+hQBIW@(wz)H>fVdMjCsI$w}FjEez+7OAF zL50b?TT%S3K11&+Siz;``w-`h=BRm{c8sZOQ_{Cr*ay+%4#DU@_5x&go?%QWJefp3 zq$Lb{tyIi-u287I%!%@kal?mecSO+J)XBrxn9i9R6i<{@vtrgru)WWC3*DB3PR1he zH_XGsGerz8EZh40pd@Ysa~~jr8`^d)hOyh^2R%F_Qt#XI)-DUY$;gRMxbzzloC9JZ z&#RY~*hDQ~=;&$OIH6vu&77#&;UP()T|uhvoAya^!h&LkMou3FM>p8wwRe3G8rS(f zAX8fCK;S?r^w@2{)Rj!)lI4P7Jq@B?|7XO1ELAEq?dG_LULt~gp9b_}i*#{WXgl== zUGu37gNwBRb7Q!fW7%5B5|L8nzBLp5S{k6S0r z7&`~OH1kH|$(1k?g7Oq(_vkZBi>&o&aKKw7{no=75{dZ7B5#KVPA5JN28#2^beJ^V zZxPb?ZUf>>tp8TYv1u)43xBW`7sXM=Y8smBBb&%MtEvoyOT6B=IXlVPyxGs$UZP+$ zW8mTsJ-0XgQpFi>UpSv_@R8kAXf-6Cd{uiPI}sW<1ty z_!y+d#zyq(*lE%rdmuT|U$_JI+kqL9Qrr{^*Db-kc*|xv{0yCeqY*h`M#Ffuaz1F9 ztwepk!cM_ur4v0%scbI_O*+r3s;}@#YFH(`UD6#}&WuZ`}{E|9J=f2*@~j zTLYUi!ZY7AWl4_gR3B6<5*}-IqdSO7*~-H&p;s4T-QHxvj&;I8*g`1c#q(l!RLc`oSTgYjEg_qZ-Hv&99L!bpoW-N#(l2<^DM1STytAj2D%te*MnI)V9E!PZ@d17)1ficoKU3Eg6Ytvu1ES>USB1z!`n{Gn?!)!I*_ikD6ief*Ke`<1 zPa?na+)m+Pd|xWT;Wo;$Hy)}iuCP7eu|o}(-^@&D&&xWHb^C<*IQd?JU8Igq6@QO* z=p_ndY_{7_)2xiiB>40|ZO&0c9;eirpzB^~Y_AMP6l;kN(tfbPslnsnIx{nIC;#B! zQS(1p03pki2zF}K{%VPS>T^pPFr<742h_xP-<5VXOo?GPNY|M+bST2XCzoQ&%)+2y zMunix42Phs0QiZfG8qMGUK-2GYluvlnn)RPheWP=0ur*P7p;tkLgNta5dDE|J= zD3?CVGLpaUf{BuS^;dSpB)#|LMaD-As{5I2M&?j*4N`;FIdw+rYK6VG0p<(?;QT6* zWFgoQE;}@8KShP6hoGD=!DB%*liV^NJ(0@4BoqKFFhm5=|%VTH1%6KWUj++=Z8EIXFpa>EwB;w?APGFuo z2S|5v>N;n3j%e`05la_69&FRJz_;%)-DjJ4&bpw zcIz>QuXMW}H)?oOD$l#En`~uoQ6K(_A80eC5tMyfeZ4C@##glGqM#~|{G?_-ibZbs z;~$N#$20XGv9g7QuM$I=;i$Cjro^{k<2-L&0x@6-TEAH%U_yC`8*HO!$sch+iT4=` zW*?HoPm17;z@0U~pffWt1-j|sqxqBjkB-WhkvhZ6Z#u9N1gnF+RH#!U9g)BTZ&1@hcg?uZ2)zkeg$fSb}3r$GG=m#X%^c!iq>Y-mhDmhLQ{$Q6V!+0n~ z5&6x=DvO(+yGi&IUA0vCY}N%=h+Ito_8?e_><1)=&Q1IZ4=pF}#M4y=R+ft6pew`I zv6W-#Qrp5xKv&86*z~7I9X(DVT_y!nnGh?)l+hoAdc=Op!UC7)*vF@HZ-Z-}cbX20 zC)7=%NaEzu0y-OwPjEV z(o^)U-A@TGA?Z($qB$B(I2(seCqq>XPx_v2NHqmL&kle@9u92XCw>y^(e%Y8dSuu# z@T}0za5>O!tj>aX+z51LI^5jShmQ9@91a?fNp5a|X~%jm&egv0=E?uZ#b)gW8QTnj z?@*@j$GV@KT-_0%u6@3o#Dj_l$aVztqGo(>MbziWSlVNaE}AY>o3Q$o9q$6bM~9hb z4J6)OcO_lRwPtzGA7TRBd<2eU5Pz4H)Y851#)rQG1J*T; zT#xm@3m0#3>)zp5-iq3}1M%XiBn9jl&!ZG2P$Bhcudk{|kEEPEIrFI};n&a;tBrfZ z8pTs~cui1#xLx}<^F_K$hCa&FRCB84M1?1JS2u|iqQdWl`8hBGpQZ-o^{*VTAq4me3wN~NB^SK$ckn&KEwOu}J+8`FccX9Zo^ zW!-6u7)A&H&R5uiFf8dX?C-VBvWor*hl~ge*wNR~SoF*>?F#N6wPjR%dICSSl5+Y_ zsNSbQl04QtP+-dWy#Iynf$g%}w%W~_&mXH-4w*jFYK-lk1Lt)_KAR?7+fd{Hq{Qe9 z&7R2^-A8Pfew1+Z$&V*&coojZP@)jJCl+z~gc?dDT&d)(Zn zf1f>0H+dw5ko$Et(0@*0v-;>biye`coAZ;nKcB8#veTj%ooHj9>;KTm0#u+jj805N zvyiTihrGy#OH4yB+tAuN+jJiEyOGYpMGHb)%huJJ^<(C*qP#(2JG5rDX+LHEyxWzv z*@lYeqf0#U+k5#V9Fh;jq%KlF4?&FKK|4&_D*Z>jU(!5I1InYM+f59&UaXf}SVoGO z-CX^7X!f=Mj(GIq)HpWhGy#OOt-h^n{Bk)$e$TTF_7lZ2I2KSP={BF6_Yfh~B-O(- z$cb3mzbYMsu)hF5(i)KTFL~Lv)gG5`0+VKaI0K0ugKRB_;UZE+`tsQYLxdq#P;|V{ zcJCL-S~lP1UY?&1FF;68ua-w)n%6R?@Hul4n0y5}#W%J#B29-yh_%!QP~~7+Um4|4 z>=#T33&CQ);1W=UWG%hda_rza(k%)Nc~^>*N8@ov9g@%#?Fo!BOZJAuRd5=(CPM2C z-sHF?wEpm0HXS(M;r=Z9KIrSORky1w+A)pqgtAnDVem=rz^^L~pWwu;Z%*8?;N}U5 ztzqP+J-<@(-Av?UtLcoS-E8-vm<~N(A%Z;HQCZ1|YKh-I%f&qqwH$Q(4}-Vl4%>lZ z$?jyVy8>4Q5}Ljlb{i_cq}?*M0-ut7r8yhf(4f{^LsXJ4{SLIv95uRbVSk-%VS|)M z;aQCFU5HLK?^(;jc2WAR>|3t%>!r3nKOPhvRwcIuj*MSFw=)f%w9ho;Q#h|mlvJ2y zyuZH5>##FXGDZx#q}i3jEudSL>cn?6trSiH(}Ar5i@Yz`aTelkCn0oOCT-;)>xJjg zgxCg|8eCvdv^}jIO|-7JZ>-V$iP{bi<7pP60@ukb2XnJq2I96XuNJUP1eiC4HQK`^ zp1c;K5Q(ryVwhbg*0MVb2g_}#t&1&NjzFS5t8`!q{^I12X1&9EFV4>K{d$g?`o_DX zdurf;d)uuN-nW93)B_|48Go`7EUK4CXP4CVT)?>?8cGO?6({g4*DLl-te-X%WPNqs zT?w#jG~PT4hMO}qAEu8F$D+NKWQuVhQsW%XU#f(;UdnB}tCnW96Gs+9ohJg0CQmhp zR^BxBn8PBO9Ph{7Sr@SD`Bc`pxBISDop3noOgf$=k|0=c^~`f=JGiLj3%W&ylNHl& zo2^TkvEPWLq)rtH0a}zXZ#m*w-IQhpA%$y0RHZw|EFns18@-V8Ng=GvIW^#m z@}4nhN|k(6{mJzq=uUEH_LVUmYTR8OXj9{siQ?t*SX>7WV+Me=I_7!IgCdLj*m@(g zuZb$qSjWnV9?c!^cqEjyXX-8DJ zbwfWiQ#j@WKAal<@njw#J~PWFHW;?SiPm8lNV89c*NoxcJbgm{h~FxVEqb6wdd9K@6ztDv`CzW?+JFrrRdr7WghW1|_~k5A*sL92dhI z22e{%=X8Y#Y&a*cmniKdmUcm}B+ba4}tp4{2`XTst>a*8+ar;?+xw9V&!ua-75@&52 zKc;y|^wL+I|0-!KI^JOYC=1@+;?0k5;oXzkV6-zXY~CVB@Zl+BB{;qY@hG_`)1_-)6~my@@pHc9KLPA(xGz5FMbvJO2u$hDd$kM z;zt>aYVqQkLcooLGmk0L>{mtmFpFsLxOcp`Z?$hk9FU-3*c1CLCg|X8EUxTwtEwI9 z5{6Gc>s?yqM(5}fj6MiAQLW2YX=s555yrGU!q5lP7SA)@$;KiiGN8Pd6F1IN*wxW^hU@bZFGxAp&!x2_dc-3gkSJIGa zSm;MF)xzFXYMUUTXu71#YI<)D{9z6#@9BcGU@M>uh|&~Fj8elN)VA2lMU`wQVZKO8 zq!Z|gG13!EtVg|ptv~A>nCuHI-sCP?8t3EeD0-$I*lElFOmS7!o=|$hB{+ZsfS5hD zkXX0uI3HXv1o{eky0SF<+iKpm2Vp!jmDm9JSa#)# z_vU~PyONhC15Dee+`=J%ylmnch69SbpH_C;r(rwqs>BeO+3}!v>?+Z1fMr?$Y|rLi zr535Jgqb)&U1r{#+HOos!W4;bCvJU}n22hIf0KVC@>|G(K7OV9jRq`qYiahlh;rr9 zlKuO-6w)icXxxRYY#Sdg1uZ-4t;iD!icGNc&|sl``uLn86G-ss9*^40x1!iQ6(AqB z$QUtA5*e40=$V|&XjFp}Zz)A9M)tF^40Es>(B3{If*;vLBsE;?ez)1WrNwvZ|8jIf(eSzq>3Vy;vOs+zyQ1H3`j;bcUMACW@v#lZ z@`kK8tO7!gy(-JDF<)_7$1;{2B|#*w@T2CjbM;;usxi78a_FlnjA-&iQktpFelnp! zWgvaCMN{mDNfKQYLc(UUO<>m-))`=x$hZltux<3UcWr2j22LB#=KaBj%Kp97BCQk5 zwytz{G?X}H#o)|Lxqx>duMILJg&-__z#b4OWYkC9HY*!a-D`xwNQIli{}i>XLQ$aR z+n$wr4#+$8MWakzKA1}%VR0LTzy7$;naH#To z95)Us)B9B1cQ(#T0QK4s;0guxdDdj%8$vbX6^f^SiDL35GAutWCwROqoI=lEU=0%} zsA1@2Wbj)^Ib;lTVd?H$cN7DFM3F)4hm*S(Hx!o1D?xVAq6wso^>=unS7}|Pf zf2(3;Cno0W;YMlju)-1$>_n>^L@QRk(NQf1^s5SnMDKZ7E=v)(Cx(#}(G=Q32=j;- zLiP!>E`wH*HZw%L{ zH-7qR;#Z5e(>eddeC4B*E^=&%7Az%U!j#lIx?BbD4mG5NeRq-GlfBmJSxw*sUFc?& z#Thp4z^B((?eEGRQx3JybcT{*39r8-LsHs%hZ~gNt;dTVu3@gooF9~n?0p1lsTtxE>~KR13AzR@(c}{3 zu9O_M+-FajrhE(ks}F8dsFokk-L9^AvPm2!i}NM!#zw#mkqZ4o(&qz~5e*Fcz;BJA zFJqQ+M|guFR-Uw6`kZB&Q#4di(QxoC%Y2jTxj_=bS^$(3KS<)@Og>ys@Bm^*6@3OE z>AYY?KBc3@46=%#o4wD4$4M;*k9lg1n|kKX1e33f_0890VS+Z6^n85lV)=}$!=J&1 z20QvE%)9qMKk|)M%5K|Py?@Su7gsp)}Y zB_l_9F4=5g@mX-vT>w}xqS~!WpWvkIvn(F@J<8I4a@g8jC+AMyagjkNtv%`E5&lw_ z#XTS(cm4WK!=!{~^>>ffiCCfQ@+Q4j{1nFJ?rLM(4|@kQ@D}ce#euB-C5Ky;fEtQ^PCj+l^V2NPHE58JF6=|*+W{UCiN6R) z@CwgnZAq0);_YqQmH>Tg=du?qj9$-j&M>R)%YE4=48b(PscrzzOgaZC?Yc!vSnzXJ z#C1K$DH5wO>-z#Asm@-J1j0C0m>tj2$-*HKv%EcDj#w#E6Y+b#W?v$;>|f>mi8g&n z@QJ+xo5%E=%gTWA$*5aDloa%t0==A=a`rOm15k6{H3sa5eVpCFUbh-XW+=Jsm8cJ! zqMe&Xp)mMf#?Ca&^o9gEU7Zzqi@l+XyV1et;v{-`Wwof+V7U(it%HcBSwE1)y0E&z z%cT8a|HR78RnX{Ha|ScWO23dxsLMmo<-cXah3^C$UY6TXblY)S z+vf?v1{)ApVe$PGq-SF8E476UAxG-*DMN26c|GGZO2m>l4QfX*ItWw@IR0!__a2x# z1io#aIeVAAy#}JpQG((|^A@0o)$Elnt%seR3;)*GPfl?0ZDk!tZCoT<-I`FPFI0G4 zzONy(s@3a-t*az!vOWyTJ;5+pSK%FK5mx1VkwJMmCr0B5ARauqRGGl>!r*iWh^1Ucw02&*u0HBQE~3Mfxyi_t z*m*JtOdMt`36!nFtXJcuCD~bNPYj}wvVH4FK(|SzEPp}z2V7K~od-CW!&k`5v+L+k z8cA0tN}S#t5Z$wwi8~4E1YBnmD&(YCP43+{oJWMphDZmifV0(@#p*03MrJ>nO7nrAFNdBxVfhvw19EbyF{(m*TI*Z;o2Q&>WTwf(gfzfQBfOy!nmow` z`az*i6qPRUbSFiVz?K~tVz`m~kv~)Lqe=y}n)(S2ZTxYW<#2l~^Lv7h6o)nxadjz= zra5S>Z!s~N(H=%jSn`5`wCzo`AZ4&&7V<^28+abtURa2zPYY^1@SE{)LhLHF*~N-K zy(%2n9&Z){?hWGI@B{HeT8v|JU`2useDXV@q3e*o2uv&H z$W-k91wgV-7wmA|8!!O1URiexXdH}uoq->cqXE#Yp0}w5tn$TExe8pGG`UR9({q|W zB9HJ6hV`H1B@ajzJlvB&D2T@Peq~Ep3jh!G|9Hii_dU8z&Tbq+i#i!FZOpqPOc_QjHL?WR1)Y3o$c|Q(XPC#Rx1YOVD1s5 zQ)jJy-AO`eI5L<}=7dSTF-T^>_9U~*4CundzXywb|4GB%AWMUHBtm=`d!p7UGL5!@ z6>MYc1W^UMuB%PK9X5O@0-v%7_FvCo`vEZCHKSgf&ZHOk${h@~8f!gE;nv)tinh-2&fN5PS12!^3;I?<|B8wAoe! z-tq;1H$nBp2Y-f6maj!Et#;h(9DTLe3-?nRaFM@)C8{= zb}I78FZ=iNQ4*c15t=7-0l*kzpV3J)EC6X72#%FcI_#-@Pl5>hzFl~Y%zC7C*RA9k ziU=NWvhYk+#l#8(X4mTHb9y5tPf}!8Eeq&thkR~C^lL6twA%i}C|t3B&XPyb+OsE_ zfIhg94!YH5SA$`ayD6*0T)tyz4nr~h-&}0`N1>KtS_3jef;j07PzRTOVg){Vs69`5 zL;Ew={+Qp758c;tmhYv|y+8RpKtBHB{wXlCeg~g^UZERdPlCUD_!&Of_9T{k)jY@c zjh6D~71-CgfD+@tS8v|Sfp!aim;mD3qlC2g(0l6`MQ8o5Cs}KM)K-eC6{--O6slQ~ z3DH44l=?+i?+B{afDvWyNeh)Kz+7MSWuV>4o<-2W#;diCHQYZ#K8Rfqs^?h#9Ju<> zm774n8@-t|-!q0=LeMdm3RuIIUs>uplDc=t9}3#g>E`{ zF7bs6d`?y_X2n_>yg5hi4P9+X37`i=cSt!eR=Z>#T_l9Jyp~Jv74fLqDFnuq)194WzSm4$}=@{d+B9X`31c#xz0}geqz}Nw{gVa8#D6? zW|J2l{qc1CecyfnBn*i!T6Ha`xYrJa{t-3zQcS*ZAs`)ekf(wn>zV0y1fS~tvIz#S z+V%&?-h(SIbfI5wkRD>W*~`_&+M?Pk)(g`LCeo)1rA$RP`eV9|xSSBw|p zziu9~(UmMg-&Uqc$Z855E$`%Lb;FkDW@fMhJ$S5Z#}u4V%U(9X&W85OW`Spp!N-v-A#l%JH^Z!aPi)EOcgBO z%rt}eZ{DSyDy1R-vcG@dD4|xWy)QRZ(?XTQV;(rK&T)IdQjB>+KM~>Po;K*xwxVK4 zl~`CMGTd%S=>6upPfh3iQ!0xmPD-r!t{}DFihn3gtOyYEEmMmL+4E$#OQ_;TQ?TPE zh|`z{iW%?&f|%l`iYp?34cY{%=G7k)SP9b z_Pl|=0(?koe+RLY4h{T79F2)v)=FQ=RwaUI!q4;TKCX~WQfPt0RCXD?S0MvU+wx;c zzvti-I9ppH`h$Y6YzhH-y@5d=!E1z~4ZI$%DU+-=!PHSZPW~!R)h(k9Z|giFw!uL# z+YI&88!Kpv%>ki>q3ltuW4~i~5#DYk%!O)`WrVN=#POm7NSIbW!>B*t{C#>1e0qy7 zJ{xRkh0gpO00^IA5EZ9nWXC0zYk7`Ot6w}`m(vcn&im|U=ammsxq3ND3K1=t)iwyu ziRvrX9&fj5#xv25PqO+v9}UFb7vYj{1QNVQf)a~rk9K;V_}7kfar)~gBVSl`b)yp| zQ`}Tp&XL-;I#@Y+zhdtQjZvd$(_nWE!%%i2xc2fhB+f2^iOAlKfhj|Rhhe23q5(;{ z>B4Sgi|kYkOHk6xjnSCckyA?-;~77X5RArG@7@wvt2UF~VzZ2AT&}iDv@&Cy3z$#* zQo$QqeF3O9&jEu<`>=hUYZgyV6)2HBht*8kfrN&?fLYkcI{18t|N3_t*OnuIq`SCB z8h((y@)GLj=?O=e%sK>L?l4`0C5N{mYM=JnSsw6wuO^vs{^2k#blQ&ZVI)6pM4L8_ zaAp28Owcd%w9zk)w-Pe*k#4Qy^h}3D!_;kykhXDQy2cqr&nff>8+t^KP9Ei0eiF^9 zUgg+R+@B~f+_4d49T;;eTXSYGV3pWK^)8Fw=~l$0V*EA!L8zG*Js#5I*5Yi$gtvOj z369zub=#n(4~~!QQv7@ThZ&ZKPJ&z9-ZBf{)zHEoj|`0Qn*az>bYLm4>`drwFxX1_ z*=yuWCHaVad%$xd8LNZD$lHNS-HXdV;A#{40~; zN3Y*A^F@<%2FrX0uY4QBoUjyR+J;Saq?P%_Jj1KMa!&#I`;VCHuc>RB0iH{G9hZoU zS&ALym}C|n_6Rs>;4VcF#;IAl{Vw*eO*P%*Bw^O!mtgBN=k=&nZg=U17WR?fs!qqZ z;x3oJ`@B3iZG<7p>QXTD!m{JAz4G|2xCwe;VraGuqsGRY1-ueA48+$kWVG{$7_#jH zw#8M6LLb!Q8Xc65dH}ol6n2{qV}>z76E!A|qN%i1RK!4!WkZM7I!XG-SL`inStJp! zisf$ai>HUs8eZZ}UTs5G-?DJ=Ek#37Si$F>d=&eO9_6RR{V)ycRYqmaM9C|bpeUTC zmE`-}#_vW2sIzCBbeqqIWz{?sA$D>H6`9VihXHrFH#xzI*#Pfe?UpcwbH9vXl)#d0 z_&MIDZq{Z9%Oe0=Yt?{fE$}Q0Xm$zRT`|WWk9yO*(2B84q=I4oft^Oxl!fsfJ5EgJ z@S0=L+~PeqV;bItaO)q#8lh34Jves0%vQ2J9%pKYeH*>a+~-;;exj}oP$tT`P1Hht ziRE`UspH-lGiLCgbtUIcVS)rsyy7c$$V6NQiwz7z$yOGYsKFG(^IM}@m`k8%P*RyT zx_MWaqeq)X{I5N)(pBwF*M?mo?7NA9GW)4e3fmT$VdP|?n%VKMT#8q80IFsos_EA* z9u81#oTwEEe7kDk>RNPPv<<*0P_f{c1+kQk09m3cVJJaabOD#>s|@^){U3zn=?NL} zAW$9{OS!4;dVs@-BG<~XaBIIh$ zd{`nhmh~T0zZ^72zJ9Mk59uM~KH_7w6D{Nk4%zUozYboPa#}s83SvS%fT_sqpFdVQ zCR$pX807IY8(#}!<&*Aq0sNi(wx%<+Ph6oBzl6*A=$R=N3L(@fl*69N^fRUE3$N@X z7V!86lI>`^HcEm^Df+Lsncm@Y7+abG#J_@Q0A-`cNZG7GaHPSu{D;EMok1G7@k>Wc z0@#PAT+WM=k9x4166kPATCQX7{5olO2r>{THIjq!;1SxZ+N)=&`p2JJfgMYq$pKd) zSH?IkU7F1m>zoD|Hf&bw(GuDZ^hu9VXa$HF>TU zkIt$XGjEpc%&u&_w|%wjFczambl!fy3cZ@X_wX^pKRGM^AfQD6TRgK;VFU;a*j9QO zWdz#@_BsJW`1kYF4qfM#Q)LfO+Ak>8PYB}4c4pKVAB^bj_~f!iE5UiY!RUGQ-F$NM z9q6pxHxa-}rr-x-aF9e`z5wg)wZ|`6{5gCZ8kO=e{RhyiA43%E24*X4x91VReM=+M z#a+#0^JOcEC7(QQAKDs&US77!k54otdHmPPxptBvce<=@Re0fEI2K?oN`O>J=z z2C_jG;(;x0>8T#Rr3@(*8?vx{m~;PZ2>LO6vWO*xxVOEcv8r}HHe1>N{Rn?JUZN0o z3vH0@+PaDyNd1Emj9_H&4{yo4E z%mAb>A_(u}4S?HtPsuZqPjhHAN2Aq%$tUU(1$gjrHU$}O!>M}CGyWsK>!1J9NAROJq4X#!HdhoN>i6~U z0&!ts1OWbkQ^@#2~0$VYGhjYucKLy+@O(k5<*nzM{ z%md7y*ESzn{@>g9UwipKU%>)+8_pd6^Y8y0!2kJ*KMpW_0k&+@{|V9im!14?d;G7z z&~)YgMN(n@_r3VPHZ6=rNs(xUg+cRQfWQCxasU0${^u*WbzA?z*!Z8@{lDx{vBvG# zYTy|y{pTm#o?8Fr7fsA6ssC%c|F_-Sxy@Djofh^~;@^Mve{D7}EM))n+rK}=|9=k4 z%1xsmtECam?)m`y{x*-HXX?EnoYHUt>n@DwCr30wSEPO%1h6NPhLn9WQxNOENGvYd zr+SL2I-zXZ6d9a6niNBLhgX-?Kwoflzprq9`S-k=!uW3;KNFv4Ax{1+Eftk_F>xpq zG5+Td9?UQW+?Ll%4{o6*3Zfb{|M9Nk3EVHf2M+|EH}M<=riSek8r+N&-q%q6`w?t( zxF2seL7$9nM%qO-6aVoqgXM}`$DPpBW?NBFrjhop<@SIwog9(h(Za-B)9pm!j z=;OB!1)dW^0qMQn)fvic+-x0|RHhn7<+(G#Zr1GdY$jzKvz?OXSw)7J%iP4hmVndO z^G%NF5`^j6Gq@E`KXHgV{SX>hfI%>G-h76??h5AU>?sd<@qyzZrlQ;JLU<&R0dwc4kbUt6?J0Izh`!D6e1LzoZF z3t}rC@EuUKR$1+U;>OQ_Kkl!-`$+nv89q?R&P57>A#f= zI2runv)ABZnjEJ191Hj54WD^S)sIS(@eRj;Wab9Dk*uae4a*xsu~!p{M_%tnhBNuD z-zs@#WGR+uXTCSCe$JH)D)KLAz%eg&oGLe}G8JBU6aF$$?{}q1?ZfE`fJMgm$R*Vx zF<5^X3X>>u>Coaz=NAqlE(GLF?IE5BhC{= z*3M!HZO0Gu0Ec94FMR@W!?@__%N0OLkO8Dd_NeYGw|`~w=%f6k!F3Od5a&q!8-Qz> ziJXA7-wLpmUfmp&{@LyXVi`4XpWR#nM2<~>KqYK8<%{R)W=Yu6wK0@lz$jJw<<|o* zY*5|!$5-3I=ZBjj4r5Fa6Q8{t9N`^7-|>dR8fPhU{hzo*5#8AU@JQdnL?0}C5c2!| z;vDc^lJ+=$DBw!EEB@eVs?M(vZwxx8?~uGRAg@@*Qhb-B06t^IXwUm;s`)TPSjS!t?9UM`fw=kwWagPqBB@=Jkq7raSE2W zG9$AE#ByURQV>A9GI*bC0GKNAZDBCd@-sk)!wS*M4-LVQ_W5P2&c9Q|h2Hvx)`tyh zt>D6v7`e&ij#8ykgl%A&Me;#^->QJ){iAkiV!7fO-}laReu9nMP(Y-JfWz~%T0~z3 zU_Y+IZDO8aIsfXtYb4snJ?HSW$*Y$60+6D;Rhqb&rvG^7YFq_48SH?gWAs6fIY%zQ z7pea-wy1lq-bPL4*are2UCSKC3&Za4($3|ld;IT9%fBu+z&(p}D5s>U(*Lpq{px%-Qdt^Ol5qz!&JR?(kIW;@aW|1*VZ>%}e#(CW)BiaF{%hrp-1J0=CG<(@7RkQ^{?&HaVB`&MSTQlcD^#Kt1i9$F zgP^x2n;fT#i>S^|)4Hn68f|Y6Y{gUFTTXglhG+woci6K^;~`*W=$_(GB&r1nu@QQ^oh}sW!N8p#a z%r{I{o3|>D;1fFhjs40Lcb)+(ou(@?3$W>W40kXd;&{JN{kNk>q#VjyOst$~ z!|`uNFNzHqX!hdtd;eyX3D^uMfPtoqd@%6;;ro?B$ySsVxwZc7DOt>hqfOJNBvsyt zV_ap*4iM3E1A%-RqmDp`<6=v7K9Ec{`Cf}U#IA{8a|=LrW%MV|4alwclF8^i1lG^M z%cYF6Jjrt^dzHv8U~L0>=4Yi>*NBj}z}l>~Pi)~~gWwtiu7R7!vlef0iD?H<4i?L9 z&x8^cd@10Gx(tk3TQyq?%20e@H*|bAYAbyj7=&S9!LkFE?*<6k{*zf_x!CKUrVqdD zaC`!WZS=*L&&JSrYSCU7RW=n&iI68EI&XI5sR*K!MLah&&01d<`~;}kMwQ0Z*`K}8 zQXbSXh^g$Wu=z8<2|L^uM}2!P9|G85tpaJE_b5IFNl7$504&DID2VBZu*h1p*LuN5ji!sfyhNiaJWt&3^8xLmo<07jM_ za1FS<20Km^hcN+IF;9T857W`@JS_yw`q`KJN$mlrS-=%_zH)2&+iA3k2ZzYrl{N!7OyaV+Ux(nx43BLUDv|PWaaMBSt&();;e(y&f2P_ul?JY=s4(-q?`(~{Ft#Yq- zeD~giHQ7olwhSJO%W#3>0_EFiLa8{SI}Zjsf-Xn(fq7|nwDaBbhoJ`UB@TA(C2)d| z^h6P_Z-HdCEda<;?Dgy6N!|QdRkI=PI@=*C2C-MEOp@-30D4+@>U*wWYIAo|d!D3> z?|$lE|L|`o`HE+iT)>PIHT$*DT$xBOtgM~+z;5RJ4sWWHnPaR}M=+ zmHo9R8bH};0!I?PIr?q8PnW4fAN?KQsW(^L8Nds&Z@E?5LygL`8=Pk!dXWK8*|zf# z(F;86+TowN(zzZIMK)eNSHq@?dBv|guYJ#tru55f29j?PJuq4OnrVQKX>7ArM;RuG z71-JzHne7c5cwGZX4!o&RonV_Az?^&i6~u81S+}Y**A~BB`XB)vZdaVk5=;z+dBc) zV3~RA>oH#mI3uXvLnH^=4f=qw%+o8${|+~Pyb+LY!VD}XI@%vRfoLYJn?KhDb-}MM zy}Vw^aMMmmd}^8Z_X#?aDtNC}#oWdJnb-0flNiWM(_(jSas#$dp2*DaeN+|MN~*Z^ z1Mjss?&vRm2$qF551ZG8(DCtKK*+0@U9lYymF%{CnIrCO+{e(y`&hz*gUarHQP*uG zoHRfl)ScfIw|#boj`~jedVi_!dlNug;Yu$8aNp5YL}9Oi?iT?ggF-3>w=H--na8kV`}a4>7DS;!2KiqlZjK% z)Ll#$y}z(tGYR(G@$Bj10>lRNftSQ?+cnd+jMk&~0+vY)L`wkkbIfmwRN^NEn*OsX z+zV}%6xXYcvqpeiqQ&PL$-60QW(Ittq6qe3rV;oc3>7Y8lj?PqJRGz0WVqsf=VsnIR*4&xp`8GNQV{mLxS1U|ZUj`-yV4Oe!2*N~=0_ z$i)hK#Y9}RT2;|ly20k;CV@(Uy>;P}u`BDQvCogHPX()|6EnUk-LY)4l}Ng45~EbD>WPB;!OH;*FH@i9eCulF)>F>;}UvuKVK`q;3W>M7mfh6DIhw z4p+TluYcb(`m#)=rz6iTYJ4(XFIREf`9|R7`!x^O1Eg>q0UZlu=#P^O)-h$BOCEn6 zqiJ5SHbbK&opGO2sJ!NGoK$Gd63j($s(Z9!r$Qv=4!SVoWp4)i#tiYiJcf;gnyCB9 zttpxltSKP+VEO%mS8VOFe&TW5x+{id^nOaA*YZtzbBRbzJ5ytJvYy~p%&DpQ4>i5f zgG-iEArz)}@oL^)`YJ(FsNZ~{F*k_Qm-d^v1+4Q(S`eXYD6SA5>~kDhbOMStUclzY z_JuNoQ%%N@wKaT}*R(z;I`n00o1--oy<>*lxKk(;e?}VZ_>f8$YKid^iH$`guk0Mx zmq;c>JYBhenIO@SyUy}W`e}<3YqOT9_>(M@#ql?Bm_w|&GULrk)*83{#O zm%#iEO0`DPW`cXFiSdk)wj`a4R#mdEvSAa0WKoG^5QiAJGzxahrDgp$jCcODiI1DD zgDa@Ce0%NWCy|aoT@Oldp_;wl&Tw&=2+)~)*E_f(;Hh%NrK9nqDsN?VHTyREgeT6o z>a6}pOl^9H8PxK#(+ygyI*0EB=82V0k9P3z^kpA!SPSe!n}HmU8)usD?IdN+zhCIC zvbpf>jdBO?(wo=1m(xS<1k7%WTIbo|2GHNS7Q~^ixircCWYO-+&y&B_JYbZ{WckCd zHOJG;i3!#%ATs{;}xf0eP?%v&R`!%Jr{aKCHdnq!8kvIlqSrO54VfI zblTo~p}-v}{kbm-w#t}l;;9JdO;KScQqVCYGPD;+iG6Z%Qt7~D%@W*{*dLxZdL@-| z-H2b&GZA48mV2KvR91?Yi<2r@Qe^sN`=KGz-mlJXf5<; zA+kSV$#waPaTj;qj`23XTuF|ddfsO$EunID`J8x%0Po8y+M7W0h5?A1Un)3Cs+20wao=IWicnvL4Y&okYAKXCqg z?8e4%QEBQn-KL$@A8U+yA@TO&Rr}>1#wPSPY<%t}%;c;((6MU@VtvD^6wo5`pUE`4 zWIMC?oW8(?R;{~XVZ6DP*=Q|m9;U|G9#07qXIx*N;o2@oCQmlSNuQl})Z>3~JF(ox zwai1!_lq_ot2-)`jazE%dkH$2Ty-W z`SOWn9c!tDnyp~LH%CQHRIA%Iq&A3C{3Np?6%urD7`8D-Iy5ne=SeiX`E9b-hSIb_ z!a3}R1ioH6^h$gme{PieODP!`eQe)&{-|#bu;!|@#mQR1?yh1zwjDd(VK+qLVuW{o zeaZl};aK6HS>zQJHL)OaZ5q2RubY_VuP=m+9kW2}j%ngrd(jpxOhsjjCt`E&A19K`NhLJ*#k-20$C$I+86 zNS=O>xbE8lvx0*OW^_^^^lMR`jlhn7F@M)+$HG6Rl`wfiJuqT=Q>M<-q3qNHfGLI{ z8uJBl-s^z3qokRZ?Hg&N-i%N~;2j#G}B9AL|W819jOVh-TXCk-M zwYWY#xHDqXbjye=KaIpC3EVbk?1ZU;u}rpNwb>oLh6|i+O2(%yAut2Xz_=81BzrX^+wJSJ{>{>F zlW31qY*(TlWvInRIU>Un=a=P0F(zLZqI|`^j#)LV-MzjOvmBltv7DO3V!RfJZ{S!z zt($>M!V(vR14&t`^Vl3A>VtxAc}KQ+RJuuTSNf#z*$+d&TJ1s>TKZZ#f zVWc>Tl%Jutm^zMOGhguS4h8Q_&fJ7>=leIohAj!)IkNsKb=O57IDtZi&cV7da`vg& zU+%Pf^vz_ln(Em{Qgp4CM@WD9QvT8=fI_~HI4u9@)d}Cc>P7BDsYKks_5+T)IKY`S zD5q)c1(UT*saHFa}hy0z>?KsZ1x_X-yUBEx9`(>H<$)c8}qA{s=R zA%P6iUreXzQ0sXe-fq6)l`dYrotz^Baro+zQ>D*!LA;LeONHI}EF=Y(6CmWu8CnEI zxtBSljQIjuqavbPnFg(lk@M|+amj%1awHhGC0>ehvMyzn%L~)Sdb*jK^&)QcPRJ)C z?5(8s`+jwt1_8QP%kdn}UEp>BzZO)`n`_N3hb})nv79NmZh23AzH#)P>9K8AR{}qe z$xQL9>O0GB5p#h1PQ3H8G{KOi0CeJ1_o*9z7-&uFsGP%VNmOc%PL!({`yR{OTXMWvmMLS*)*T~$YU8fLoGP%rCPgN>85;=O@*w4^hmW2)F zEcfQHBKNw7J}h^nZH)*UukDZ5#@(5LyxGBAre_L*7qN~v=+l8DGmagMmiZn#3z$iu ze|kbmY#Fq>^5}z=c+%)fs-El^gp|4JE7+HbJ_+WvV6xvjBHsh}dN9E~W9OG!DfL3+#Ma@PPA)qu z8dQkv-VVD|_37zvizHm;Q-eQ)_%SIpKGxH<#AsLgUOHwZxZb{Hq)){)JZeo*NTYQ! zF8RuctYdxcXt%<%t2z#B_ngXBOy*Vba&TF~J40*t1vk^_d zZofs53lM5qelC*vOK1YtQHgQL4`eEWrDx6IH#>i)@qRMe?(&^a)498d!K8^=>Kp#p^1xJQ+Thmh zc-c~I*|@59Gsm@sOOwBX5KV`0vN`ltQ!xHQPGPPCD9c`||Gd>71J#$hM>}@eM6kTB z@@E;b%TBbHXpbdH%|lac8*D4g1EmD^E`{*^gzW$|^HLHq;`S@Sc*GapVxCLEa+<58 z(@E0Jxf4EE@;wwhj{fzKt?TQco?Pv6;7~}3wSha7 z3*PD)g@Vj|tYu%{IASbPng==AcZ`MAj~*p5PJ{DI3Ljs?;|XgkZ|5}Vmq0;@9*{%# zj`EuZSa*}-dF&`}&Sa{kYP$s%-j3`10PnAf`Pa&F%wwaApE(B?F|hicyIG908%A`T zJ@L?eG%n2krd8F?Sz%FTM8ZT&SmJmX=Dl_TG8xvsP*74q8Rs`E_d zW42NN5vyR@qITB;%sF5j@6EHl>1H$2*ZZ_NcwtdwWcEI!A$80yK*NhktKLb*LnKCu z$&;%@;{QkyBWr9A15?$rvn|_v&|&=x*Gj=oCjRJA&PLxKX?UTXylBgI?fC1PdS9?_ zqD5EKpehnsZ`_nevfv$L!#&X}*>x#8^ktNY=jCSfp@|Z#uucTrmE`KxW_-D}F|1Z# z@7AX(2n8mkqDKW|XD>{*d zUj)~?stwxf2HSW!0%D-^LBNsKAkSu+FAY!mT4j~*WUD&uhi$Xv>F)NNc(EehK^BO{ z+1Xh;)_1a-l=%uKtj?WeQf1tdVjFkN(rJ;WI5~gLU8vrFU?*KSn01^8uNTVYX&iYB zO>AybQE}{|+MCRCc$*tIJN=W%IN zVSqqXt#_Yd3_Hg}ty_)xbq%i3_o~ znMhT4r6_ANC+B0RAO38xwsGdKFyzg5@P{NZ7%x(0q?%uZb=Ti-y6OH1PYr}oWn(1* zF~U{N(mu+?@PkY@>~#STz@Bcle^iwmHA~db*#Yrw9zF16^O-v4Q)Yz(Be5~{S6hR5 z($CeYS!sWg*YrkB&_5(jzJx8yApWuawAr(C{Ze-l3$OrjuXCk@o;aj`}U* zYF|g-L8Iu}OB*yk?+++ij{ky6N|)mqu`L^eT^C91)@&CceYcc~y!PyHWW4|%Oexm7 z^7J7RLYuGi*hqzOk>crH*U2Xsxcd%t zXQzgBo8UCMEqD&<3%aIPxWaa7cVeKR`paBwGP*62L4pu$#NR?9hYCj@;;^Y+MM z%7d=)1WH?^WI<{~LUO+ULx1;H&)xP_smu4sWp}ZXO7}Me1KQVkQq{Nh+x0~j2uupr ziyT(xcr0{23wVa|+PVuQ=0oNcd)J*rT!1MrI2Cf{j?}Hn0wh#kcxkO&^eAira}hj@ zj+K+Y;K94eN(Wg=zUCmtSI(9Bd}tJw$#(1z{h>G#(c+zeO)_kznfn=`bM0DDwA6Yc z7iC+#TzL!6BB^WT$z2;;Sr@qZjBQ|KeqfaQ8QV>Q$LGPuI*E%{>p(iF^W6V}WRPp0 z#G9%w%W{l21E2j&{R1MBxU`b8*$3EW9@R>W@5Yh*T!D=cDLz`1^I+vC%D*xr{#=$M z#C?mN`}Q1@_gt#SKh_&h+<|JRhe4%f-~TRP;!^ooLTPLC?kkIH|MZtKw{f5d_kp(F z?w<;y=sLUx%n(cAD!g<5P=rI@B8fPV{obE%)bpR7iH8oVb#){RbWi{3FNNTVCC7)*i0NpGHFu_ZljKibn1`?i}R|tylT2YgSGo4K7LrpD0i2T_F|U< zl8za0PKe5-V;*e+wV?t+KAP_-P`Ca}6mfu?`@XOg5t^zP>zxdJaSq+&{!U22em__$ zt3kf|rOxONSP-K6SF`3xKuzhD!3l+LO-Wf&VSGx;nMMpSeK0-1KNS`?T`YLR?}|hpK{3pxNI!-%7q=X5tcEpaX)P1<1@_PD2PQ#am$Q z22v12hQV_fcX3cl^7cW{DA}iyVL%I26kqLT+KDu@{KvZtK)%UfR>_n=I7XBxD<8;y zZ^F&cG`VI)j%MEbjI|2m*gQM&=tAzzk+Si1{~|o0FQd2hs!@Wtx1#W_4wKQBCW+F z>+7Nlw*ghv%GB>mKWYw@P#sn8cZ>YSX)Q5u-Qx*i(d^$H0HaFPEIqsmwZPh_8$n#A ztZPxmFXCIQC9&F)A1qEHq@i^6u5TRPHihYZ@9T0UPvzCN5|D2~{#it3*!$6XVxl!c z^pFM^{KK-vYnm`f0~6aq>K>fobCY?^8xp}+SQftEvpr2DkkJh;NCqO-J08oC625KB z0|(#E0$0pCr@s&26)oZ)P?${T3RFm#?+~lE4-?e`zQDNzn$}L8(RWaUIG12SMx>zd zrT*&t3UH;KCEnC3T#=_Ut}rNO{|RHs27OvDQLw+fBUT&myx?kc7TJ~g(q_K4L?w0( z>xtXEdNm3O-$^HWaxCG0m9hil{1~azxrhBCuWss+IK>SNc#9p=O;46H2Y47(NUpjEG)e zzTLS=oqSg_o;p9GVLSfvl0x1b`-y`B=M|QPMl>B;p zXci@}%~PNt3=e6*>?WJbm(*d%2@zdlt2rh65pp7Q!~phbOe4pKDOApA6MRLI^Oi3{ z@5K8--rxIDueI?|bDT7nZO!;t2ZU|iFMV0)-2e+#+Ja%pfkZi|$3mhSdS_0uC~K>E z^NgH^qMJKuBRSJCgi|tRr${2tJ|R3fId+utT_TOjK>`YTXo(9Afx_#Eln`YG7>+wB zjBj;RzbcXluy@hemw;S#A%pyr*fCYRTxro{B=3Ex*T}oEKu`+c3o>~9BvNiKoYEIa zJe*}|8_~BJH#!g81CKsVZiuxlPiYpq8tug|sq_(W(D0SGwt3PD^m@fa4aaZ~B^+pJ zv6N3%GZqnlN|zL+l0=}b`}DE(fsnv>VO^QV!SNg3i2r+;{Rt&EUwTZ?e^Y?mI$B~< z-$t#^aak2%fYn;QwmxqOnUDmftgdlmpi$z%n`!k15*bl%+a>N=J*|Sd>a_AS?|6U_ zlTmZSfD$K)eG!_UxS*zy{MMq;K8N3)j`~329=!_>JCVFqLM5Ej>`RS99Q(9BrBHWI zBKd(~Hm6mOz;ilN9VxNX$s(R)v8pnYPf*tsz5*NLOA8T9f_&-vdZ7be@`YhiLTk8}ysrk~ z5?1SpmP<3%{z9RiGLydcTf`S62onzUPI)YfPuAy-9dSY348+02<~vEBU3lN?CB#lA z!q6+Ohb9eMUutH==6X~#96IYjn93+LBE?}y_SvUZ?{tb_AEgD6OX8c`A{d5e65|)I zwQ;Z$HCo>`1Fo3_{6hI!+il0Xtuf1n{O93MyviD0vDzDLHojtzF9+*lWaahFc3w&D z3`!%ixj)l5nEiOkhr1)y;dfk)gu-}MDF;fPN~drLnWCUY%2^p@RdAY zN;%Zh(Kyq%?(+0pPGEoclxcXsgiDwwW%cza7k66J*rTNSr!X|<;SZi(ij=W2HQqw6 zwohaeA)BbCZ!lQiVyPb_zUm4e&^V{v60J%PJEEEnx z9UW~G9-Po4>u$-tb!h=C(U^`dGS~V#s@dy#<+hx>77 zBB$>w_n+pm(7qvx)$PHl_!Upf+Mp{qw0(9hyp1hN_hDmW66&#{w=usC>-P?O!iU(O zRL|xD>GgUWP4BffPrG-sYeUeXX6W@Ut)WDjBj52z!u9T&2gGNjx(`iXkvPg_(DHt^ zx2|xElmJ4Xr1AuA{XQUP5S2HRBsBMQZo^!K;qGMeME%Uri$I*Vs<~(+gyHg-q#uu8a-#)J(uZ+6;ipuyVw#Sp zobEW7G@(=!WvHrjqdvA4OIL)Rbhr`!BujK?TyDuEMx6*HL$kf|16A32w)}an4GO47 z&V&?k8oo?pB0gm}sJm^JsGoq06O+oaFts;J9+U4>Qr58qZW3|5!lc z0mV3($TkapTtoAKMVOOee*iuDIF6<~jjFIXv)K1^W@)9iK8m2Zs$ zXm07Ft3v3SSwqBConWbV*}$pQEt2Yw^LU;T=Jws_n3E+0nH4Y@>U5#o@}6sS!R-M{ zc*#4Vx#iA>+m%zz3`<{5Ii7GWP>dp-$|6dJV9GizLNSVS2zq$fn{h<2U6m`-y?!2L*CUt`y=OjPcYnpH^fz1p6e!`(zjAxr(rz)Q_?yR~ z>(R6io)&98=?^^m8vG|DjRV_WrQVXp7xdOYqGhf3L&kDTE>r2XG9mj6S5=(x?5Q6Y^NxP)$=2 z9W$UyQvH@b_|$fxt2AWhod%km?-63~Aatufg&tpDR&Us*$8F!$i@e|G+^VBfXQ*3r=1k%HAnmV&TM`-`T;Q#JLm@bDt>l6`!rCgwV9M0 zFNEgnDvMT$@U!q0spVx?7PYw`oUU1;`7^sa>;~h#{-FSld+wN4Cz7S`lEk51z_;vh z^7XDLv#&JcXky2Z8yBi6ZF@9O{z;?LLFK+Rf$Zh_Ut1bWA)l36jm?voBRZ1>2>M){ z;AW*!5X$<)IBwe;NFnd^fw)52Cd`($)Z7vOoo5D~>;b^aOucTRHsKUSc5zM@2UJ~^}) zLONeyGslFy0q$)yg}?Lu+TrSvhqOIGV#}_4-x#7tgWVc0DE*lqbgi2LH??sTV=^+d z7Su65BJ(vzei}jP&AN*yP9o5EDTUoLq8Tj+rEc$oUpVL_zm3Zdn zjn;v4pgSUJ1W)b^aa5~fXRB`FI5a7Qy2b}A@|fj56~wPK!d;KC#kpV~8%WtwXxX6V z{Rqg;Nu94f88jljDNXq|R%uYi5`f_j;&|q%#@VzH+zZV`HToJkMP^T3-#r459)5Hh zACn7B()Aj@ zNQiC%k&~OUIA{yWr$s5upDHMwqr@h3Jy(39;gU=TfIK<0vp@1?%J|PsQ?!*asMhh# z*MM^RiOXwu2LKZhnMzYwST#XeTcsjBF>d5SakzK0&E9qP$4Ulge@iX69xiUgRTh6M z@>Ly@s6ZL7ujl>>kXgBv5Po{?L&{6Ug}r#Q<-tg-Vo$v1+nw34zmUhmx18jXhBumd z66?hKVhJH`9)7m&4G9t5Pt1gt6ciPCDrldooel6~aMC7T^iO#-9 z(BoP%z0so^stVbvvzgOs^e66l12i>|5hAs``EX=O%!BQ<=2)gqDX~Z#= zI1Hw=0t^2nGokS(kBpZTJhBLCZlh{H9z%4M?26G?q9CaG);(PJl-hKFDO#Ngc=(LV`AO4=l?L?>UEYgn2ShPpOTDhm?Ey0Fal@9UZR^0W)cvBTN}n&F zeMMxm>X)~_?cbzUTQZEyIS0Thki!&M&GXFJilh&Iy2o_?tJW8;QU1?#6Rg zz=D~3Pvp3ArZc<0+kAk0tyhYF6wo`1H~bIHo@YvT$cuoc!)$ir%%VvSK3l%b1*wp+m#5oa(I|`A13n=!3!<@C^tCh>%-L1jrBohHoSr_i z)!n}}a{KNgpY@5CUN6;BG@pr00IU-*I~q7vL1>)ayu)6CE-djWUDipS$rcWP0bh1r zGNS&8ziX)JwOs(cbMHIxzB*FaW2i+=nq-~mT&^*c4r0&5(J)yx+H0iJ{>pSK=iVAQ zU0}^HXtfBjnwSD5AgPi1@R{@Ra*P5n_czj7Tovgm>}sgmgE&{NqR&3NWHlz^M-uw; z2S6WgW6rJP{?<We}Q16zmr# z?qBgdE7+I6G~Qfpcg6gHVjqpOb2*m$0e)n|L6JfJzUZjrCBe3q(sPDZNp|Y*u08Wv zta<6f`Qb~aDStAxHvc?JtV=8X@YCkOar4CZccI4ebQWWf&Rw3A7u7Ghlxfs?oT$z# zF1hXfD_6b{7|(Ss;W{_vmP5^(m4`9&0Hw`O=m?ik}9(tD{dzuNWzo2&kqo z`kzDo>2yX)Y%X5>8{_xMKtNKfAd3%SD>V5ef-CwoBkaxb5AsPtojScZr^%opBdz~K zNEzQj0A_v}W0wb5Z50RO{(!mCOf)T*l#Xf=t$$HKdg8uNf*<)7T>CI-@CXHW1CLjm zWBQ|L>!4ZtC{7h|p}U4w3B={OcB}}n1%5m_%>SmU?7Uv@>}bf7*6g_8kkTZNGw8_( z1$D;^OC!zL~=IcTI;(-Q^3>J?u}5)1|Ntb6D=(397kqJK?cTtZZW=xqU!L>s1C+ z>b3Az4e9k$EI*+sAUOV&Vat0BB(*`|+yl(I<_1ydVVO7{u}nqEe5o@Wg=%u(KS**{N&R}FVhfb`ZuzsSGRhwa4g4TIzc`)Mk7}+%8kC^VM?a85U$)0`bG?6? zHA>X`WVb#%P^9hk2~}s*L=F(paC~hE+;<}j?d#}COVo$&Nm+fq5ShQdKEd9fE)``> zlO!q?c)9eYvLs!B#N>_6_i-_P{bQd3v&RM{!|W#*<|()nG;Uid8zx+8dTW5PH*yS1 z^~%jOsdyKCIa*`F^V*>*7U`}k&K$Q>Ka3^q$fJ* zV|KQaZMA`iRgRquOP+mV*qxA>HWA5wbi&9PiPTrHK*sCtN+;DJu*YZ{94q_#4-bSyEugZsDUBjgSkXhrs) zG@iN(w5zdsR615dXm8iOfU3x5k%U($Ls|~vxZ6!MM|PbjG{`9j+y&Ir&OfkiGhN_y zrEd~S_QGPi#GGk4cn?4=**%}5Vr-rqJSxGT2DPZAcXqWfORHB|%B$R?07CM)|?#dV!;OHYoK07Nr;qIx6tFl?c3dx4_<+`sKAwkRl8? zcQ35AQ4%KrgCpx@AbEHBa_WZh4TW!`eiXm`{ck_62;BoTxlT(qar9vG>ZG5=Ida_h zXq!>`VsKNt#frcAL`8=qus+3(u+xO&UOWN>VHk8MFO9}G3#L&yL(lCP#*(y>5Apx{ zj^ZpVXu7h7=oaL@1_cHpNPkTP{FP*GU;hij#kH zL&!U!M`5kK?1$1@HPS*4_=pAKWq}5vYK6fqhmA$;b1K!}dFYMN|L(Co5eYfvA#E^5 zvdL-%*J^OYXq?g1P?#nj|8(rXTzU^PCdBnYr(g`fN&Mf}*m(`-389FINs#wH*7*-J zP6dT&is&Zly%NEH*$r@Tu#g)a__XJ^_wVetPq@CY&8{-C% zy-!M~D)5!W+q|nV`AHCg4@8&)+LNfE)p?G?`1oGVPB^RPycWJZ67Bj|%YrQot?5r> zWrsk3?jKCm4Qe2}9H+ARC#DSs)>VYSK~F0zup6rW*Y5_U3o8sdOwE5}#ZVER0NeNP zjsCl$y8+tY?$-ai_h>P(3s5L3fgwH&QA}90DD^J7fq}I}*Z^g|1JG+S>3!rC{_ltR z?%@W^spDRHkoT%!`CX9oh4y+iXbWUPnr<%Nud0ZT3u9#0si{aJ9yZ!vT54>ax)A*d zN9FCbNf%|d5N>l#A#VBofB*Ff*ByyA7#QZ zM}h(y(pw)ooxe|xs-Sd%x_znDmO+8^By`~{eu20y5Ea>WYL82^YdQWipiw$?`ijxp zv9W%k>LwD7&(iVtjlKm?@Pc+d<2u+_syO6T%Dp{h;yT=IaLl$rkYUZ-|9UHhJ3}#jR z=3@u!I|x&z>66Cw&Vq-wO>!00)qX&c5d%Z)`<(VKM)wgRWTt0ty6{0n_YNq@y|UvQ ztu>nfH(iFp`L#+qxBYw7Ru}-FOe}t0L9TU;y8b}jM?ukj!@lFF=@r2;;C7%1OndgeezOBe7zLBLCVDez5?&<5_UtR?pnDs}*nQ${iL@?|?Mp-w=WF?(I z>fZhys>g0hYL6lBOCvsaZvVHxK561Y_jgGnqZjMVcgzG_tX$a=SOY!hw?mxr^6zfn) z{V}=Ga;6fCZ=e3Q4CsgmNK9DimArt8T6jvYzp!9ore83|q3_Z9v}~GA?dvbc_b*=D zj|DB%KDka&G+-^!bB^R#u@x?q#nFL3pqAWp5&QR8s#qGHcRik?0x+lttTLHKN_N0d97-ua z;|Ek6;b?VeOHO+PJ&UBkhzAW0DosJA{Zsy#1le0qCXiBdTx@lqaR6bfA0Zu(v@3L` zXuU|QBj3B3Kh}AJ6@BPazqsqhIcMN=B9y;$4O4(2QZ>FFRLZrA2b~*XQ1fB}g7n34 zr`6#Ai+;ytk_T!x_U&x3ii-zZp&~-%nQ&4**0MGL7AOWNbQfrH1@s!IOxcAE_HXgm zCv7SuA3O4$xb+YgXmj~)t}ly&+0llcEP)ClB{Y@oj%A5;ZA-55r4_H*Se>8uB&-Zg z8MKaB|#0D;dJE743)dy1lhp?-UdT3ndHZ;r#uo z*OeBG6a>hMQQg6ua7vJkp{4-YKKj`bVrlLuCq#G-#UufR!OMJ%jepq(AJJ0Q7_d=Q zfWJjsw7r!#p55wHk#Ti);PVq%unjqjZ7MN5Ie_?1KNoOZt3YIVUW;+t{$@nZ9v-SW(Ez}FRP!n?kWTDC5^C6! ztM6hm0P;EGC!pIby@RsAMhPj~=%@hNI|NSa6##51P{N>>48HOP*rFBSlnoA9s};Bp zp05BqHT%Z7Pi77X=Dh6K$k2#6;18`bdTS%wkNz-L%eMP921_s86>)Uj<3})XGX@a4 z3tDoq60eZDiF)99uKj(y^GR5ZN*Cn}S2sY}LD`)F#veR;&8pgU;$tCVEiIs>4BUW?)1Vy5rfbms}znhcVX${qb;Bi&!wNdEqs{GNLH%pwfl>uC zYEB^lY892vm?`|7KKx-e{R6i1+c;@81#6IE&xD9Ur%x_)Z~A} zJHLEE#3vk!*aM2;pW~~==t564-xgh&n1f14eP^=V!3jik%%Og!B|gHN_Xl@pmTCAn z_7%(|b!e?`9t7&r2Gd25wSq3qjLHF!MjHka>9VO#B`H$Tcmj)N@kxe<7T~Z5F4Az| z>Ria`&NAiVqC9kdQv0_QF9H{LNaD~Zsa8Jyrt^)r>{3py=M)0(#@vQYC9@_!i}yt+ zrBi+*$ajEX+eZ0GB2bvYn}fIGz!brL1V$I)%SH;}`H60xX^J2G6|m()wmRy`9uKjE zkqg3S4HTXLh>p`6*NxIQ>9^N~`+&)ZowB%#NG{7nl|kX-U4hRB4iRuiJpf&~!KwDq zvhm}63{z7_y3cI&+=Kl+`UJ=`1L`hXUOXZu1&i{2pJnLJuN|2~8bbkiNKZ4qj=OIbMhtQX9(fqm{L;89ox!MjyP$QxE7#hva2g?%nqogcc9s<+ zh@7AWl{VFW(V$V+y&u?dednbZcOLu_sDqYjYtVpkP`X#_j5!DJs)rXwUyfy5&Ly)i zPw|wbo_+np>Rso(!n3OduxIFe!q9cL7}6o!w-(|cG_@$FwB@c}NJZcvtm4APEB_}Gz2?mg+MK$|z+UC`VP`|nf zop}`4(}b*`+PMLZbDFlZDBkS{TBhxFl&bme^P+S4@Q$3Zt6nqEfJHnk<5kIs?Au^u z-=^K=VZz>Ej@YIHW^8M{(s|S0E-5TQMaSz5ij8bgOYsM)o`+4}#6lwUD_1L|zo4{v zOL{90>gW#ZR!<5Fz*#^8Ds0&21*^dPH~hhPtXsR!U~3^H;ZDNI0+0Q|$PHYXZKmU; zHDdcg_a4H68%-s{{OqF3;mFWTIQh`pjygIC3Jer!^9s^8c`dC|cqr*#GFLvDJN#`L-zTC}gH8wD{%;@DuWxAg$ zXdaf?_Ne?K@;4W7p=L$SF~oy!XvO{C>W6~10Tc9~NGYcSFxS-4z0Q{kwkP0Kxg0VEK&@jnW51bg>EW`FXykt}8 zCw`^(_zfg|l@J)lKs=#?ZS^=}>X(i$yc~;ba1IuWLRP3OkMG{v-Z^weW8w}89h!x< znjr|pq}<}bVIY1?^l7W^L2ok7n;@?4NA}7t?T9OBPtWEBszcNNs?EDYaIb!AHn6gl;5`s(pwYE~6jATI>+7*BE z-1~2Q=aWw?+KuBYF~v@Zb=1XA3MGaR41m@yL38rzenj<{70rKfjP}OX{;v@CJOIE4 zMNcZtC{W=rT!?5yu&DyH_c}$;RNcr@uvGp0Z7F0)?j0Vx9fSJ|&yktI6ox== znFFt1jkI);Fv-7$dRR3iF{juG_NW1v*gg0%$E-|6cSWZEurn?-kRsRBcz3|2MqhIQ zjG_`~l@|b~xcm(<%8;G0cb@^Nt!#7khtr(U(;}@6R1|54*o zIv#Q6E$t10-y(UROh}FN{XlXIo3KLS9-_>E0ZhJ>@oZ1)bc9rGwACEGr7g13rSbd- zDYa?gYAk61FBnQlUDdV08KfF_U&$HvR~Z6`?6Sj(8F85x@fJ#_=Cox#B6|ttYiiw4 zYkxhmJz^t(R%~egPXnG}$VK-}G8+Aq;GO^utQD|Eko=(KqpHfacRnCubxwT((LVwm zlNY@=-p?SqIH<)mRuK9Nxr?+|gR@Oji@8bCSl0^>!`DO*VYqFTJ3c0y#Sbj9IcQr# zl$o?ATH--wYk2Hl;(jW7!Wm<+7z;h@^PAyWMP8lgmxFMJ5%+LzzNSBi1m9xkWy z){~?ASHXb;^r_fQ>^J9Be4O58r-9RX84H!V3^Oq=)_>mw!zaXRJzW~Mr4&9-UWl}y z>12Q$>l{crKb>)ZkR0W~Wzs_rOyrjxADJKssm}8Jk8Abe!NI8u`ZE~i5(%lc*)0~k zI&vrt--&?j*#vUbfxNaK$6VF(4m^Nk5J_18t%?EA>&Z;*ZS&#>v~3DG`Lfuo%$iwY zyfB>IBcDRe!#?6KHIEaVfR%YDyIB@OC`swAQ z&7YWmY&+d`zYUQ&26V)1sq~>7)QlbS7;nG#F(oJ8-zoVd5ogAdxc(zwMBm+!m_?g(S;tgp>hj5-s9t!Io6Q*tKA` zKpHRZFGk`E8|p_R;S{4KQ*j+oW!Y`SZ>tum(XyUstgd$$<)~6GoXRcVlw?>3_hv z8w_avhVbEQyOR39cALvxLa?`Y>4ody2=Wmt7T9M17nn}N9$E_nAa6uzoPIb8EKFud zsr(Tk=2v;Sy(UM}zjh=ms9L{yNzNsOG=4T$@@jm+9*4BJGpQbT3j|+F!%(>p1XzHg zb;qrJb_ZdFjI;f{=gvZV-0oj*b5_Ec;9mWEF#rlJuA+JGEz&hMWg4p}{Hr8bIC8;2op<)qAmBNYHyqd3^b+B6qIAW+ORYw()^=XvLtC!Y4> z<8Ihb1}~og^LLKwcSwMbIKsA=-QYM9ifG~kfT^BNqZsfY%4zzoD5dplX4Tx5r-&#% zGe};-6mIM0)db2C#1Pbk@+v5&>uHGYGx5>IaqvDJl`m@lnJ|ExjSk~9)}tR7l^IG? z2UEpv@d;uehz&nb&tSsF6-E~oT2JT|{4fMRv_>fdytqh12}3)pym;evCeXh4n5An{Zr`0rT8~e*>P&RMz&OZFz64k&W%; zJl?eRkbcw4mNWdLJ(l6YJvo8f{G)_aXlD#eENtArerSns#xOe?c0!W+zy1mGpU|{e zSjBJNGhF{~-?Q`0E@USNF+P6wixm62OYL9$y$~8B`#9(Qzqz0NKcbR(0@vhxAj{$Q zU+(>nzkP#?i=hx1d*tsQaQ6!hWZ;?yrQI(O{rAOyzl}JKi8*ky<@LG$y&Ojm<6^WA zh&7)7+eO{IChl!&DvY~sL6`qwIj+Jr$@mz8g#P#H-o(asX1XQ!{C_V8Cp}#A37xRt zKdi1Bym(rJs=a&t+Yb>3aj=~YXQTCw|L@ghB!U+&BuVeEkd34*+fhY zPS!r)+Sa)vFB_|bb8;`riDekrG7aLpa{M<(kBz%<+@*~s%KFGGG)~2W z=st}nC2wYJ6w=sh5q3vjiPrej=Y#j>MQ8eA$DwVn{&PQcwDBX&j{1P{NkHSV0SXjV zn}K!^iZp-s%s;TN=3tp&-*p-2Nvt2+%2_GT{*paJ*y1f@W0)PO1Z91?IKWV87N@(p zB!P?=q??Y5Av(Lv+rL`JF<({&#Q+pPbi3;NE9r7$IulH~YZ(&-j+`gJ6EBZPC&-p`6QMNzc zy*Se~@~xDx;ni~j$}XM03Nt9Sk2p*wWvMZ&1S@cC5naEUoY`eC4u#J8s{@*jwD;Xp z`|x&8z49ru?e<(xmc2K=_n|;8LKMdt=|WF$xP~7CwUNu46ADE)fyT4UQ9l7%SFb?u z`x&1o!bh^~15q<7(5;JawUFjtyxn&abHsuHtN0aCFlrjPS@rtx`d+fw@WFY#NM7IQ zFYlLdEC!3;0Cx=LIbAf)>b2pC=nk7-h53>(m40+_!lzU@RBbx%k0XWEnZk9GGbmIW zG;Evb`$=BWm%mf9NUr}eXT%`0P?5gP`>bH0;+xS!Ue7dD^uKOz%OsjA$KDT9JOeUd z$PDM$E{@5>G=EjWJ0eWGJC9$YcvJlt?>mAQe_8r_xj6Q>F&@C&u4%#>m$ql1suJ>O zIj=v4zNm^ZAsXo{U9|>%81O8nlGo;Q%Pj&8-1(gwUJVB3cqyJ>ovoK z;o24m&GEg#KYomY`(gpGxFploB>pfvVaP^v}(IZi5w1KmgMK2 z7RmL!JUo%%wfl_s)^K{ZC5zsX?JmRK^{@?8A`}ie!@QT}6JHO)2@0|MG4TEN;&?Ni zao%)h&MC7a_>M6AVFber3X#psX*IqAP{EQP#bbqWca<~r3O>4R3SkoYa(lK@p9(E({Xwz!PN3)*~*|h?LO||Q` zSfH%N*}b*OMfOaf-adTc1jg(g1=TloyfgWHEtWj}KrLfW4g->s?4{8eD42$~ z7XVdi*gaa)u{o^t&1(jgoHu*7+Dr>}Fn8s|g}sBb`{Dfo7d1+ZLfAP_?p%mlcpy_1wx0TtxQSjvig>^_isLP+&gvegWwo`xb?wx zZ^d^%M2O(nTVp%`LS=m%?c+>n2R5{EKVv`k?gYOO_2$4KL3(2qElsNw^#@#s3@}*xT-++~^vZvcyzA$+338fEzh0G$Z;H+_Q#C92V zEo4oAREU)5Sw|~nn7-T`s2iq~`K4Z9oq4&ZU@En`2?SKts?sW`J))lwpy-8XdMy|p zml0t+8uaA4862fMd+r5=Z{gNrazGpBri@r%?6Asu4|qEcySX8b2eSk5_LsBd%k2N~ zt7z#HV1|k7KWP0GVH|TsdlO*TVuLh9;SJCoQ4WGe3xa5Z(-$LDOSLLSp+zhXWKjiK zJ{4odoAzglwtkGRNhhPh;A5fe-rc9eaK_)*rugr^;r(r22-wx+`o?e@KF0F@!`@qe zWw|!*!+$=aFIp&ySW}dtnC;9a+s7c_Ba4+5c+f)4dMWJ-m^h{#m zk@CSm9rQ1+g8nv23w`o(tpWERSK}XVp$5!kHqJ+hzhCL!esHxv0o+;p)%f1OkIcXR zpig%Orw6Z`{{Q=tlS)8yYBjvEIsW%$__rUTo&o!Jn8f#ApY-=X_@DyBq`iPum(0Jt z_}?$$N{)wD!XkX>{}v6M`TtwAU)O2xaQ)v&`}>~25C3;P{}FEe|G=hgv>KIVMj*34 zsS@?>9bH9|#w3(L`GoEzb26DP15i<>Y|l=6B&}ZY@sG%-aX+4O2J`prJPR)QurSZ@*nGF;mshB~UA`Xk8@+IgsTHsJoL zeRe2MutoZ6?0yQ3%O(Z$l@ox$(#AwmK1_4>THg^JA^XGqDoTN?P%c5f4`5-hF>crw8z-_m@n&}7MwuKC-;#vS51%fEOEd$^~ z3;-m7iaac~&weT;qt~W8wvMYiIi5CgnBf;Z`HyEY)F>foLl8~)lP zR^<7;cc88bdT-kaA=yjk?J4be+dj+%lbk`137EZCSLi17+F`&`duxb-5swh1q$Wkr zh#7V~2-TFVLxqGb5TS;nq6dT<>3X5p)dX~+{wA`y3+shI>*h3Owy7okq%-+k$CZ1bl_@FtrTZ6X4D^_H@Xy!|_ z>v`fj^A!|k%e*uKE(|=56(>|;(5z$(&p2poZRJF!gVA68uSsn5#Xo}E>nocjat;!! zg$I*!hI|Y%Ta|0-ilA_)+js{glHIk(PrbKQi=BXa)fw}q4(%n=5crJ72>cQssrf*-b`06(|A6OPz6+4W9M+g{E(&^H=*p9_&dhZ995 zpSRe_CeB9~zl8qPVGko8w=K1%Idh7#4a2{gty{>{2m>6KY6d;iWFRzmIel42I2=U= zOVWY^PxduK+}-|3Ld)MCDrGh^fXx}*u7l1i1~4yL1_qhTDwgrTg@)Mcs?Y>2v<5}u z7)*Sm4U>8{iXc8vxDK7BnKr>t2Ta|2j%NYKHO?qC_dHAS)&gT{u)6U_(Y!$q%5dr- z(2a(yK)3a!%rqw!ZBGX6T2dE~bY`7u^ds=ff+Lp!Xm@nVVxF3$|6se8gURP@vcvO* z$682|)4v?btu_8!7eWr7mjMO5@o3JnXx0`D?gWoNicDgcKT@TI5#0UbT3Kg4p^M5_ z>)F{uioJO36m!t)F>ujkO1w7JeA}bk#kUBkt>t-PaqjdN?XLUq{dgf_Imd193$ZL`%#6ZdOzBQXyc_Ld_WN_3L^7o4P);^ zs>RNn87+7|U{)Yb!tHkCV{wFO9wW=Auk^VD<}FVaEvl$PyEEZ?kJ!JbxSO$vgw3r} zD5NyJeJ1s>OxhqRzn`Zqs=ON=n1a#TVf1tMm8Ty4&@sr>^BOQ(sulpt50cIzMAy{a zMRz6x8q_+2xF`gbtG{;R*-|T@sgN zRLY5W*&VHn*56a8E-~eUpR+RSj(eaNHhz(k5f7LBtW_X#f<41YP`1HnkF7{=YO3LJ z{u)3g;J|Q(?e2!|s-x&jtfwqqSE9A=Ay-PZI2o`uJD|(bp$Um!2M% zm4L;0v;jsf(*00V5|2NNmS`idV3m0^@*;+s10SOOCFc!UDqgI!<6H5^w5a(^t(^CP|e^TY^0c$k+~z#YgL#?(--HxKi`U$&r-9CVuDtT_X2$OrzIo`2E=#`LvN z)v{B7z{=ORzCyYeC)S)*4HIH5w^w_t@);>8rTxQZo02r{%b{$X-g7%$Y$Rp+V&=AL6D9kIFYVtSKI*HbL5lBH za=m51tc4%yf>dXM$#Fa5Hoa-L^*Wlyn_4;6x-*PxRZb;LIF`wbaT>;hSdNgc(Kf3|b7Z45u*=jkzY{q0xUMPjKxzKR+dd=>Hr zA1=7aQK5w8=}~ai9(|7Xqn+04s0IF&ow)NQhpYXl*DA3 zw+JCNL}wDa&=NP4&(&J6uz2LN`4^@b+5PaP+vt{LXEU#D6`URLJziK<2?m*fbYs4(d+h5}BL_0^W%;z`!$0gC$up{hAgU+((}EAdqDO^kSzq#Y)&$)OOy8Yte8C#AtETCc|l{Y%Qho4o-5 z7_3c7=#8~VsWa*MBwGWo@}sZ}z2 zkfSDySg`<_Ncylj$To~bt)$;8Yxd`x37eWjc^Uge;Uk+=xC0Uv)Mlq}Os8{vv8C+N zR!3+Bx{W_)hCEJOMwP-oMlSd+!|&ce!-aMO#GStNt!?7VA>0Yc_a{DegfNjSg2e!# za-}SK@|F3qo9m@KC1O zPiU&#fIF1aCf+B(Uc3dJ79xeT!8%|rP7>mC%gDHAS3Guv!uoaBNTIDkVgqPL5c`w8 zXAvP}em9Z%Ik{Q_TH*_TXdYeF0A#0TUVSfszH_7Wz?jf3W`baw@V(7h6M#(DHW4m# z#~rDzbS#8pr;d%jVm~3~gVhn$K`{lJ%B70SaqP8LG4$pnZ=*fU@coKc3uj|tglt9P zLj$1k<)GfLeoAfSA+v|O|FsL}U@e#MU2~MhE^{^`vjcg+mYFiOhp!(f)Us1ezzkIu zb=K5&p^c`1g(aMMty(rJ5l(TRD68AzHHU`mHDiW&X&AVSA$nvKbJjO`o0L{$&q}@- z{vhP84^#NjiCJDP-TB``*zp5E5}FEUOQD@y3lQFFbcU)Jxn-ZIpR-_Zw-~kz;v++@ zx(rkHOF*IhW6yDf$sx1DJaUEfho934Mz;gd0{ffLLRH?$$wkOD%Jxy=x)bu>^uZ~G z?7}M{3KzhmqtTZ}gSW<`10>6YtEehS$-H#F%ng=BET8dR2-UQ?YY)iYQc2w;Q|a~t zXSUH&Dn1+IRKC$8C~T6i!2_WEMX`MjHdlZ+ceIpOR_uM*$yiDt#no)KV0Y9c~Xpb^gt1~5Gkue4d72F zU{uSu!WN%T4C4N^i>s9j&#%^pwLz4p!Rq$8>9cX$-A8q0uJA{>xe`bAsV!acUH5=a zLtoz~l5J{0hSDl+P?u*CMW7A)deXFu6^cSwkvl}py+J-PyzNE&yu}?hkI{gsI}Ue_ zxVzM{S4p=6!rek%c$}_AB;XXSX%e$VSi#uN2DXAMQ}O z+#Gle-cJHGIntu&r$rvyXV%Sb?SemD>J({=UlZ;7DzhW2v=h)aRx&9iOAVVQ5Hh^^ z!2KxKSK^g&TXDdCa}H=MrG80uLd$>3mq)&&6!;a&Ss}*WPFS0zkP2e#J-l`{;8vq1{R>p=9|R zf2fyq`8;4o2HtLZF+G<3(S82=RuCw}LKrMWU=e-665lm?wF3p(1=&!A^**B=s*_+! zb(1JkGSp7JubPE=J}mtjCGpf23QeGv@Sfr6Ex+eYNtZkV{I$#i>C>sFgV3Rt!)x;8 zN_`BuURhoU1ny{*sZ+}43RImS?n0x_rPwdb<Lf6)|naa)H4dceSWFZ|X8BKC1$|isQ$hk$;dJC-CfXY@bS!C4#(oPJ(ip}6kVX3rIR1@iC0X=LLmt1j${i%^*xyzisM(EuGI z_E|F{7gBotE!eA(E0EZBoG6WKTj18D6%njj=Ao(d)+(Q;Zfx6mzlDe_ha*)YH|Ns> z|Lt(#4y1mciE&zPqs8I#zLSY8I^1E`aL^d2EXGUh`y-dsdtQfd+%!5{@Av!u&y`YC z0l%MzHT<0-`|I<6F-OAL2MKX(DGRCpzzzTSyQn-oypq;sMap0Q?w`-vdu8}lo(w#* zdwe?V-+%qhmou5xEhRpv@mo9$}k@3 zL7-qV9^AZ4Wa(-6YD<@00~y5QsBJpvXj_v%*M}qJNk;iGsipL2YaV<HWkNv(uuBRzJ*b$DemHt4$M2VPfl5eF$FG(%F zdhc65I#>YlxB>LMVWU>8-DF-X@x;bVSkoCbGY53kT_?!A?(s1YQXwe^qlX*v(r}3#@L{j>5#KhJ zjBw}1H#z5KtnR|Ell%8koRo|jqKl$+`1z46&2j=_4w(yBJKeqv*!_`rtw{ZK;OOZf z-5fM);F-r7lr+fZ*8#`(;4_*Xv!1YdwSoU?3dBnjaB^i~5!E{j zl(Sd|2uj*ajOm1<#t6%MqUYV6mO6Z_K@b)=|`Wtl4g*Wb>!nvvA|gcLpyvyVlnQR7r|{k-f(&V&laH2(PAPm{KG0vszoK@4xo|N z91yc^clU7s+89knv8sdbS8b3v#Vw?^j~$mwnah>+0Tmujk!`im2%yX?HRW5mA37{kun4U0->%aa41uy@&=E4Gpmkf^7%Th;1)-&)z zQlbS$NGc^hz+y&k0knZW0??)GRWp*S?QGAw#3KRA*r_Y`B{pC*NJxm9VS>Ub+Ag+6 zP^M7bq~*6Whh}IRn}@4!f%Q2}-T@O%Tk5#`->Bz6(}Xy<#i<_ZNe1|^8B<-w`(}0% zr?w$FjBQm`X8|eXIdol74oU{`8INt>|K-Sjc{gOcC=Oan;_x{0TpN6PyqV2;-ggO% zqwJ!*ZpRo(2r;^qD?Gq+#!0S~VFboeWdKM9IS{(Znd&$S@k7fAgVOyJ$-M`RPT}HR zGaubao7$m=gHg1MOF;Fa9?~Nk^I&Eo5b-)Wd(-c?#lmrAB^eLGF>jiI{1hk3rcFR= zMdm`?oUt$?qu5|&NkE~Rh@e@#vm$zGmYQO2pH5X>5O_Bme3TOdgBG#`t(nF=T|sOa zu1i!2!;LR;FQ5oK|K}&EX%1B>h(zMR5E?8fnnWXcRS1(kc!w7FlgK{sY6UYeqepg- z|K69UTO`dWJ=CUVl%nALUq#uk4bEh0Ou!vb6Bx&~u?pd=WL%xJBU~bK4LhU*!%$_Q zft$(llWdT^alqt7`eVou@y9DUnM+*|E!+wOnJ7Xa3FC2!rx( zL)PWQblM;!&{&tp4P6+oA&3?gf3N`3DF$$c-#(n<_Q!zY^QVF53_PC~ch#kECa8TG zm-sZtMNk69x9a6Uf5L^;_Oyaxt{>MA?xI(8_kzUVsN=GQiig zsecw?Ej<9yrVPU4hWh$p4@;50u3|Op4{Z8D$yN3sPF28pNPXJFp^t55jDPcT1||ja zXY1X0dUE6Hs8bJ8o|)!Phv=H>{AFm1N>IYc6oOqF>Tv`WQXvUxGZO6mU;8JxjfV(S zd!TZ0>qHpKpAM{vFJTTku@7Pz+0^6GxH=$h+gMY z%&o;IR1^i{r*u-Ox$ZwtG!^8}|69C=kSZ*#eV}mbz7n{8`oQUzpae`rP+v#C)O2NgMS zs0!^+`@`C7koRUpSODCp!Ds{Yu?Y?Q%irJ8f$JfMavb@mg#ewycjwDwPLswbv<`s# zKVy;IK6w~Yr>w}{8>v*V`_;j0psv({EB}(0i_+$_C_Eq z(O8e6P}6_6_gL56L**LXdz^&+Du^HVcwUfqz8xj`xyv$r+%=WL3)8Ox^bXQLDBIHX zGr9mKZvj`@W=KN`YwZlzx7URzL&wk+^2^+sF5AnDG?ybGaKFRvm;qYq(S|!?I}PD$ zF13)Xcqx7%{K^AKqt0en)JWF6@%Mk(nSY+_aZ}gC6&!!}yA~BE7TJ+iofgarBj9O< zjqabha^={W7tN5YZ-GXVZ;->W2ILh7T1KATGuIr~=FstvL-@)-O-Sd;*+xw~=!|u1 ze%Nt7vgs2wOu911G9XcPJ*1dPU`{2$ujv9( z(-_2$nt*J>WQXS%ZW6@4?3C8`JH}^jem29)Jk!|N_@W3gjlloq8C%iwI0Dlg*RJg% zpb5G72aBq=qWJbh3BYTF{l;dWKr`!e6#*+vNlYHC&$ZM5%v#xUH545r8Gi!Dg-GXH zmjNCUwPSS$08(Gd4?v=h>GU$9+|XIDO3GYv$*#CS_oKt+S>Y&2z}JcR3E>mR zyl&rCJG{e{Vh1?*7Rmx76*@A^B2l=m?10^$#IODF@so<7g6Wgy8Rogvu{1|{O?WcvZ^N75|;B9qdZPN z5f@E5@VRhQ>yoSV=ten`wI@Y;wq24Tpy)Tkfr(0i-{_rgiZg;T%04JZ9&Ftkg6V~aIYUO#TT+XC3Q{ogF=yk0;LHm61NHCC6g8 z8F-Iw)(9D{&a+7PHC@eo+_jH29h5Zj2>NS6vZN)(i-vu)3wu6r4>-!717oS=rYIwd z2f7-yo!u(LPu496ZFV*^Lqhd(-M^G5P2P>VgI9vY=)5o%u?2)1&2$j}(qwRQM0BsL zK6DM!n+KmDg(UJmwEUpPsrf{O_**o?uNmNawQ2TV>Y7kd1eTtYtd(5=4}uEYJlOF& z{AX>!5@x9>^$1ae8s8x9^+Q9>fTMrxhdnv+#*j3ZLgh|Voj1D=e)kiG>59!rL1SHl zOArI?xMERa6tj~L(4JKj8k0?+OeH$wUEmv9z-CU3=5Pd|Gd(E<@8T4hhVAJB)`2bH z#X^9~76bAg6F|huq70H6SpOD8=_BHD2^H9&^dtm@xfky6{BCNaC?IOlmu}_XL6JiR zF)I@DZ4~cr79(j!T$OW)g z*pG~XmwAgU;eP0DwY5}6yOSPj$YYES{G!EV_anzxU?Oi zDv)cvYJ+}k1}8ousop=C}=yQrHeLEHBCL-Va3P2&!H1UE`OJZKc!{ zi=5Vs#yiZCA1^*F|=ZT=OHO}gVDIC#W3;vZ`PlS)VB6XPwNZ&AZ@GfCsr_&TiWOrp&Mj6m+ zFNq;YKA+q^%MPvZ<{9Ivy_O(qinr5O`a^ZK#NnEF;E5jL7~Gu;lqbsSS2!+LiA43` zBL;u;UPp8<#Eg(_P%-n2Dnu(HUk2vgaP5bFmm=QPI*vPe;H(wk94%zQndI&TebSwg z&wp3){L;V6H{dB9#UUt1(TzZA!9umYO_L3tfEa4nHvuS~m0E7e8Ux8YsT#q(Xx4QC z=mhRUw`$wi5}?(Wtsx!qL$mVhMfMxj0R8)eWzyOHxkC^j#2-Rz$$%?C9N-pKO*nsh zm56{pC%^BLzlMuva@cX!p}%IAm0=9Y09Qa(B62;u?)+Y*UB(?a(tPjkC!KOdLNz3- ztDA;G50&QY1k{RGO|>sTfN1R6?2th7sYh=H ztLpnU+e-uCKtxR3WQ82N%nIWL{Izqq@-~o%kYvhoa zT7sbAUV3c;sFUCoAipMOgiZc&B~f^5N&;rh4%)P1Oq)*5tcMnFJ+Oz~-!14`cq;o2 zw#F?Z}Z$5*!2(z+lNRpbY~Pb;Y+sOQtA*zfJO!hGVeUU zNyfOtstpiQGs8Cj4`$BVU$}J)N9%r|=pJbd%!PIgFqH4D)Kc0W;*CBrs=W7(8n0c$ zO2)uH-%fQHu^>SBnTX%IySGv$-(g?lxF&vb!uB$wD%tpxc)ZPEfP%45Qknrgn2PO% zKQkQGfx=1-I92kWppv#$CSd(D013l8I*`nhgvP2yrW#At{V)r)89eUY2)|Y)w22=K zz$9=w*>j{u;vrtU`C)`fh*j0@BV;>lLW2IZcMj;nR*@Kj7}Q3v8nv@r@mKamBEdJH zUqkVFbqz|}@s0%Dk?eN-D+ZfJ9H18CItaty0!Sk-Wjc!3B2@0CX7*|2k3gsT5Q?lE zI$-R;qJJth<@>~!@e;?ii^PkTM=z(0-tQzkJ;pq`aZVPQ5^5BAlCofol+MsR=L5?t zSqDYVWzBGhB>!VL{?~3#ibhT14(uc5zf9l?UGXD}HhX8|V18td)}@SB!fD6hZn88( z&h~YH18`ynK(lg_S*LBP4?F>UaT)Q%asBc~GwISH9F2#-OOX5g4(;)l`y3n`-^GSA z{;+b5vaZh4we2&;4bWEcG+2HJ7vq~zx!+8|Vm;a79z#;cUPZ;|anP6rRVE`*h?&Xf zWe5@7gqN&eLrNH37Rzaf{dLvg7V_PbOFWBCIu;Q*@z~Bjj0QW|63QWf8GMV2)QTL| z5KLEZ)pFPD1d}!$;J1`*%)NKmjc;O~Q|;zs~b61#+Z&umx1C zK}cVp0ZF61tPC=NEzfn|2F7vvh{aE9RQ!IG|JpZs6+9(k9BpK|As+ld_^wL`>jFux z35a^64qr+aMq1t^6Q5i;aeu1UG>&tn{6}Vxm{5{AC5Q{8HyjW$2eGs(J*b}Pkwhnd z+|m_AU`UgitI`3CRK#>Kg1P|KWf9qgP)dYJMBg)@GFhF*`1-%KqMF&r)B4!HEam7e z8SdU7u3qaFL&aEsRw&6+>2;`N<$r7=`pdQbZEqX*?NtiW*l92lO(9(*nkWPuktIR` zH8i8py|V?noWJ#QC|`64TCWSD^~$tCDQo*}Sh z&#Hg8IRIKXX{2_shhr*LF$PAPKYJUoxM`&y>F`RS?lP?zgdI}rajPO`2XTtQ3mG6$ z1}JZ<`v+U5;v8pDMIm29e)ON^qx!6k+3UJvtTNE6)(KkUM&`e&?27jM4o3Qod$Gy% z&;QoWZ=)PJvT7RuhTepz>Uvwl8-*UFvQ>JW)ny7n2^{945~OxTNkytH6#TY;OAG9C z0ckvL4U|a=x{Q@;tkzumEKQ_Q`Sm!3hWYg|?qtvDdw|ETt#y|X)eLe#!i@4QujShb zwxj=hr~h&?01w8$FV9 z9U0z&FhK}GRFnGPHE8x3Pe5aLCVK*@T=$fTT+RbR%ir%T&NVbmp|lN{3jEuL3MY0!xhs%BYk6=rkPXK1C}gv24fZ9HBX2^ETjvJ)gnxofp9h3m_1dKuJ-3m5RNL_V>U~S z@SS~WU#Xym5>V6fL+j3-?*ZN~9Hkj4H`83UnN5H}XY_D>(?@2t$_jl2CiY6SFN>uO z8^prA046j4d+XJ)plY-Sj4TzApD1nMxrY&jvkA+`iP>3%W1t1VIhNcXFHN%az@ExE zOlPVfudm=}`dat&Q;0?QQ1v_JyEm}koGg&YMz`OzJ!3!1l<$b-WDkh+TYz#89(otY zQwiaVAS$cCe3*hT@Li`V;PlK4h_&L=oTiNoAbopHJ7X6#VKRU#4MVa=z9352235o_ zo@)Rbgemts$;3B$ixsbxl;e5U77#5gCjv(7dI6^^8EeVGqzI8 z<(|k%rNMRWUQF-(NJad%!t1O>4*`|Auof+5xagce^UUEPRn#@WvEi7&wb(3Fpua1i z*IjM-kL1r^2TWf*O1t|mZ-^cMqxGuki=4(C=}R@*J6D`QOcs{-a;=otIoXfaaky#( zvVrUZ5D_xk%PWO*!So+gfHZae7UtJ-P_Q(tPcug;%P(FpXcVyz~dB^~#F zmyU{b3Yd!eqq!gRub~Mnz+p|utq-P|=fDQ}>aG{6I>U8Xu>4u9+OEbnq@sm6H+EOo z`&@YIG6bNb%P6ky0yESCl*X%#rHNOb%l@8uLnm?*e2PRoeCUKyRewf-d`T1#MJjasVVnAgN1 zA5@b=PRr~Zpp`+o<%f+$dQT#c%Ao!8K3Zk-W@8XUJKB!9Pzfmd6H_g%7SP!?vb_PRP+CP4f z`f(u~RYtrvb`JC*VJO2v^&%30VGD|@_I;_asIV-s>A2^q+iG1*Yf($M=4u*Uy`?!jebKqxOlC{5!3Z-Vvj4HHb^)s zw%1h#oOI{tOyccFj>LO{F~|tiv~b*H>j1ZOb$0WG2+_APmMRXSrWL{$Vm^J}e$_`< z^_?tanA8s+qB-Ms1A4n?Ks7$Sk@kKN^T#@r+no{ngDkL)6IR++GCyoM@4oKCoZ2yCLt!;7CjU7}Ve1L? z;;twYUIELhW^w?IQA{dR%O~oPeC;##eie<$kRUG<9{_!K0|BMLeP2>KCV`EUG~5c( zN+g8_HL-|Ruj6QuW?WJK!4{gPs`BGCmX`v^l~0AndJ>Z-@%Wl|cV>4-Y4R(aeOFHs z@#1v`h7&nZyY_q1*k4Qw$bC_>--1EnWcznmpU)gOtyvqrU2c*HdDxkT4>>pi=U1OJ z#7|Mx!XZbx<_vZ>Jar`(vBA52QnC#KddHjdHpXcZQe{Icf5*6mH>0+P&t>mB>-@v7 zWEQna+N&$ldNqA9WNg^iv8m<*^PMB7sD;!js9Z)AJ__2?ok&QkCfhXH5VT(!|0F^x zzIk2_`{wLRT67Qbu`3+$ z>`_w#h2f$`9y^C+EqAwaqFAI9YwrEdUJj=-%pOKNki)&rrQM{SGhyC|q79m&Ylo7- z$vvofHVuhP1snB8DH`{lov2o(7P%{~1`J$HeIb=V21CVavZ5Bmx=g)NvaMb7Z7guGE*uW$`Ylrx+z!8XCxP24Jor%W2C{p zsu%sXD_rU}WY*$Ng6_3QNY`WG-oDP_689 znyl~#Ovb_@>St?J(2b2fTc4l90@2fLG+|!C^MDwq$V9>_`GeWZe?a&i;(i5o@vXXv z6uvP))1UlDjFy~zKQ)z^qW}*gFi`H>&!#{@vH=87HQfy$)Z!O^m|+F#b6M@puJ&fY z-usjIGvC8Pr^l#q`1|-r&mWRZdk?dPLWVD^KwzPq^LC;PN?OOhZ`YSLY3xJF zp)mr{p;_%M%ICa3gE4xhy#}$m*v_GQ(QOMhVv#bdN=rxlaYVMdy^OTf)57FXFP;OSm?X0C3h{PZYgewhtiFW$$s~ ze{#A#Wz96eA@XG2&Lg4CyPck+iH`%wHvKm}Au#6Xmn#>jf!$GWxLo(D-LM;5q#f z-GT=29++*`{GZJv>poyle6};k3O}mc5hV6oJehSNpP!oKqA{hzA}7a+myy1sKAk!@ zkx^IUN$EFd0%LgQHGtxhDGPIg=rP4_MP0gnas!ai4nNFYHr&zbr ze)47=W=NHCA9md;$mC6b{yPh=?+WXcC5E3ZKgwrVp}e#zP=->NXve0$N*dRrez>s- z)m;9Bjp@KQ1TAo`1l9KW3HgMyiBbbixu*C4)i#atBQiq7qTXklQ!uERWb^q*|ISMG zonRJajsqiB*vcrAkBQd&5D*!P47;C+wc!xJR+cD=7@LLrLo!#Puaa&i!qiZo>U{P) z_LJCbm{1yi)K5Sl!X#T>9S=oBf+`mEln%JrojFjX5N|yr{`}dZ?mCoeLZI6yK{-L$?2rrYS% zx19WHkX4w&It?k+x;Bc#^JaqeGQ;~$L3Pe-#bbfgw+bTeo5h)wyo=b63@%kZvv3P{ znOC#}RwV{|c!)8d9XxW_j2s(NtW)knPW1F?+8qNM$+$6^go~8z5l9Jn)MA(9ZA9N4 zviA3Dg8Jb-44kcVuOT+t4Jf?onw?;{yT(9Ic1+Tp@mTQ7zEO!L{tF6{?hEJXn$EsF z&mqx#R)nc;L0RJcT?2XVX66SUyd(=Q1}*j%FBduNt_sxWuD-E~zv}q5VXWu#YL`(; zVVmt+D=@*?*fCce{4+SXgu*y@Bv0VS7lgRa;-$ZR8pMq{pGwdTL+mVd%>ZXI9CL=b zA5$wZaD}?5Y+9c=fvaiEFMC$#33Cd;lljWvqa%Xq0zE-!B5J97X?-ML{--%&?Td~3 z3equH^FSEum7k@BE%(Ad)_->VgNrtyzgGGHrXk-3COkbafLO@+YP)ASWu#OZ(m(OU z9c4Qk&AeLhoB`Wzd6Ss<0kd+l*V_A#E(}dkYR@jIvv9(&)>^yQ*z2D}QkUBH-SJZ* zW{s|p(bm7s_pzm*Kz@?6VX@OY%|2|B_7%x#F26FNxOtCfx1ETcN~;gwnkt_*SKkez z>9NNK%I44j@mYSDeYQU3+6S|StK2=8Izq;0guEiJK9ZH6X%HuVw5)?Dd;e#u_V;Go zjhLG17X^=W-O4cQE6C-G4;AMch(Cn&{K&j&=_}4})(~}+a^ltiIM?Und@&6Hn-!S8Wn0C??=`+VgZ`K%T(>K1 zlh?5u$HKNZw7jPIt1_Zz(~IsTy5kg_z|yk-LAo}3>Ug2fj)0^=@9z3oL%Bl?>UgM1 zHHx`RQ0!@4eBM>tC4YJIrmy0c;($X|0c5?(yN~Qh#ppMj_+a3uM0={Hd%smvaYU}T zlE)hC^+4fY4uo1 zn+ba78Us#UJ%2KqhPCn&t7cvf z?=k6fhc?Nbg&9k04rfo&9_kypmi)1rkNcA2hFgNa0H`vrJGqDu^E{SLOSij3_l@Ft zYVnSJu|3w-`=N6;2*9}-*tKh$EiepX5!Kex^l21s+#BZi8>6nzM^%{5;NrwVm0O9L2H+CXMw+2eoa3G(tLGym^*<*Y?GGmz&}@NFBDZ&2Cq> z{SWttl8w?Z_OwZbz8c&4R@}GamN~agxGEt?-ADJ|&HrUdBs_$x@yxGW_!gA6AfebH zg|VoxKS^dpbFTG#TkiIiU0e+V0i30(K)+eR?zu79=JBOhB<}^67c5!Klpeqw=u!&|vBUcQ zUgrY&4E4vt*Skm>AJr3|o6)xNE-k7`(~g^chCifw-nC_yN>X)hL0Sw-Pu}RvyKgjp zIpWk-MlOe=9I3duRD5;)V9BKRmo%a5)0~DmUN{?CPw(CoXOZW-YxnVaXcgbk$cX@n zxn+^$Nn1@GAX|dh-F#i@&FvmPvs$fLPEB7b`jA?2QETRbEfwxs@z(ll9@m$7p;9CK z>vdmt`83p4q152&G|jDhk=y;}4t3KuY|YI{HVmrYn+g140!gK5+t*`GUtiFf8V_1o z1_Tk1^6gpvf9~R02_4E=T*4J0JS{Jk#V@Mi7bag*3~|x1!WOnZ)-gwZ~=aY8Z^s7(NX4V;r={{rZJ})~03k%i<6>n_MPemeMfK{)(nY zYl_&_&xJ6aZ4@&O&o_b@qnvW(mz@Zf3+Kp9!YE7ydd_yVX>bT_r_Pa=hK=1e*1Cze zSb(WJEMle=bhKsmSUNci|2Z5l=v^%tv!YMoTZRiqaGBKBw2qdaW?M?YXgS(I@{s)NO}7C2;|D3U z2vq?rXu6u5rE=(t9mTv43w3CN5^w)E<$@E#oSbZISWGTo|*SJgr#dRGd^t0N68Lye-T<6&XOf7a7K!T zPiOBs`M1hG(6ZmZ`qD0WHp4khVr{NqP>(B+CpDBJq>p8-@kVeuCbwON?O?S%CO50@ zupX_Vcf~DVv5ZB*~W5< zr<_A$?E_+Dhk7rp4|^}?KX-`15u*IJdo3LGb-xa#xkByS+Y;5Dviq2RU6QZ(cu)l& z3bnpQvN1s?e~$S3^BD_#eWgz~-@uJ|GXwdEdWH<8B<1?UoY+SnD6VuAG$x$?XbDL_ zS!<=Y#{$iAN5~hy9F7QeZ!1Vom3=dR<7-~a*F{dD#r;XDeM$59r;CL!mn*No6ROyjs|UK zHZ6NUN~o=xl_K^lOds4y#jJbvzH!6jy5j%jsoKFJ`&h$`8H0OIv_v1ky|ObtW>%Qq zI{5x+_Q?8&ek066a5qb%v13PS-rk!0(nzMoB>b?{A_xCb9+g%awzi?ud z=GGX|YRa}7ET9{q1=U}m_7PVBauhTwb|m+G=Ty|C3GX0wT^f>TJC67En41zkpxb* zs#pgM<|>TcHsFF$<%mi>xOUvKcM1(nm*b+t7F%a~YYpgzSX8dCN{Y0J@yop}{%~Iaw z+GBH%Wxels+nG~*cUhW;L4zGY5$PZMapHhV0Rb_M@dB6NkQc-Iq|d|<6P(dIXuccz zF*H^605)}1B0PB}{{D`@i*E0Ek=q9xeT(FVXue%|wZXS4k?=YC+&|(7v}9*U@w(3{ zc=M9LuynlYYY9CWS1`d${8pDnrPtEehR$A+eZ$k_IH-z^+bNR-NN5>Sd37XOiBRpewvdL(`v=L5jfo6wyYI=K z1m96~%lrwgJuKGk%ZAdb&zWwLKl~ATZXuPbc%d&yht=%#u05lz5&t{BKw*18ee)BfXEd!pQYI>wtcr^2(nd>L$t}%%g;4{S~ z`Fa>)pZbaYam8Gp9wdXVPqTf6DZpr35S*D88I%u>R>b<+tLUpdOHYBJ%sn<#PlWT1 zo#eItAQQ>k>YcVw?Z<^l+8BDk^Hw7VOCyY0HK$&@WIDM?PppS2u69sbcaN@XxD{Vu z0pXIh=W?WizZ@p7I!Q33!~rUE2;O-ITU`zLc3fr1Dg$-!V*Q>$0}9&6d_lX;`7T2BMp zzfpK0z=GvK2?BNZ_uVtN zXStOLG%HISV;!Jjyr-!6^q=7+d{m5V$?r77QF89UN8|TFNKXwLln{;su z9ZW7|H-V{|tV7-vGdW|WZ^pvL{HCfo2_jn@aKgvdK+@ut)d!6=s`!aj+S_XGzKQ2A zh;B&OSh+z*P=UQ*WnK@pfcrt_}T zsi4x-25P6vsqKIS=w%<_YZ)t)iQsC>G*VeC7axmcW+N*uEKuK`t2R7Kd>m6zO$qkC zuS|ju6eODUYQYV!uU0*KS_F!4x5b*PDt%wT>~mwop>=x{;L-Ngr+);to9=J!XWfC06A~A5%X*-km(JX%TLuPJb3eDm^MM2{E5@7 zuZL5q=)N3DHmFN_M)tQ+L9+fni+UDEe>bM-y=cOl%FNq4n$H@Q$HniC)kpR^+}@sZ z-pvy{ab8`|1!RT`)=r?E?1a8-6V*$n5hv$<$pC4lX;Q6e2Y6^3t8(NM{S~g&4 z-pj@~flO3G;)dVdcM(oWN=YFYEe2V|r2==&j!_tH`uYonSx-)rztE0dXoE7ISx1nW z&Cb^PjBDHtW@dtEkbTcC>`3BJ^O#3rXfCtxBALjqx$_2hS#zFX}&=3Ykl5?6W`Pa~%NW*K>G1yCco5TvFK%XSZ3}oa#`W zEo2`(w*ic^3OwC$hW;XbEZXI~FPg=pCyYxqPs@c2arvqAb< z&TmA^m!m)%HTq1w>@d$Qo5WPl564avl01?9@c$V5?s%;C_kZs0%4p~|%LrwQqHKkd zLdf1kqU_ymt0YMjLS&REd+(KOWrnPb-1gqv?|Liebk6yFACKQ3=N!lB-1mFDUf1io zp4apFykugCVfr$@4hzm*C*!scHs+x@_>S`szVvxrq}ryc`_4)K`jqB7MTQlG_KMy z9nuSRW!8syXr31yjPx~G3hLa*lKVSY)?7sSaL88*IAWJ;2MHCY=`Ya->_;yi4>3tp zV;{yP+s6?6^g8vdBVI&@sb@U+sY;4=r)KuOVCmM_?rXDWjx=|id{QwB)Q0jYcJyhu zCNge(?4a~ei#Ul zpyV}u0BgAFUKY^6Sp~yFatWN{IsOqt=yeE!3hIvduC ztobdrm9xLT9#>8$azBmO-VGEb`BsZ(G_zQL1hzPLk-{#U;y$+Ti#V$16s}*=xbDvf zu?tFOqn~QyMG9EI1)o>PHPi-}DwsiS2gh<$zDQg`Qrcz26rf~kcgN9_qv}LIXuDzD zC^^;p3ls=OZERq3QP3kx@sBqG7h>~7>;-YzJ1KuYto>1qpsF-@A*D`*@-VS(Apq&;Owl z+~6C8zW3=Oq~Kz6n1F+`-##i$feJo*ZDFJv=C)5kdCZC2y$=RpxhvMTJOpIN@uy!r zJ;X$dY?(h+0{qzW6#3rTh7=g|+@7q(UkIYam*Y*%t1m{iLzrZN!VW$+;WX*FC zF++tSKq4?-82&nq4D%qhC>i3$H~wOc3T9WyNH?r5pb6K?F+1n=;38PWOd<_^Yo2^z z*XbRbl`iA?FZoh+Ly6Vj8Sp&qn9@LO8GGt8e9B40L^c1K&6p5()&4(rq-P7W0`s88 z%_9a-LB@s8J&;ZO%EUDmb5>OV1(U5E%T>X;%?v`Y*Tl2-Uw7qcz-aaLLdSQo9++@K zCbQ_U6Nnal=83{vac6QroWH|?9gaAL#9uxV^+Jbam1NDf8 zCPAgVdoY=G6xf@>1oObdRAdn{bENso=QypMsQCUVEu;eN zka!D&ZhMm%`va5)QcB;~q2KvKHA(#zoJ_gkzVyu^Z{R>T(2=zt#hd4JR{elACo;8r zDE|)P9%3mNR-2>!u-|a(GQ)>|KM%2sXpq@Dgo|J^oUJ~%^ne?4AYx3smesEiM*@pn zDTVCk?|dj$t{>Q5PcXT9Qw!{OfncxsyiP2xDPnR4@tv<-6nx&mqsV*lvh$8#1{oW@q!u`&!(bTi&x5y|V zMm+W9+>-dUE_wVtD*9k@edmXp?|K#(ddfSsUe&(2d`|#;Oxd+8R>e5wrR3$qe5||naN1!N3hlTd_UVGAA=+p+* z&)8HPlgbOXOJ1zl!xn1}qlnt#XWe*k`w)W{bBF`^=hkS#m{!~cZDPWhf0ZHh`%wTl zg}`j3_5#Q&po{ZPB}u&*PB{StB%1h2`DiPJiL4aTUkm)0B3B$?l0u`?UF5m&y{G!T z_hq~L(6b2rR1$9+j?iS=kp`}lz9*;W0NNsINOCz#wDnG#w+??52xaJMm^A5R{pxIlT3V}p}cb04hmaR9vCR%h?#YHU#C?8<3KwFQ&6&IVG}z#%>f zwlf(JNsQLMWI8%QNSw(wy7PPIzCE1=iCooW3e+%M_abZ#LjObX1FvxdO>Xjt^qlOm zVLI2v=KJ1k-hUrt9%6E^S@2hZ=0jZ32m0c#;806r8yr`Dv2I}nTBwGF|-RKH%$@8B8`BKE@D!wpiZp5t9P=BD%G ze&lbZ$cK*iKuQ`eWS^k&QLz#cPo){~&Sc*@`fHyAw4o0iqSF8R`hu~jc*DLbth}F)o*dpPx=rOUO3t$UY7g?C75T;Argy($-J$&Du z^&d|u4tYxVA$@=DCE`@~>WmgWCC+`UD?2f91-^(vJ&I`WP`G^%AB$xrTZCF-f*BF! zA~jNmp^YFPF$D3+2fRAF2XxJK6c`^`FQ^jvt24jgyA;Ex8opTvtrC`QNL6&mC7<~k zuhIH;T*VP_C-;1=e|4rl^+fgF+CWxvzzg(KWxV!F_y8!!k42&S-mx5GC)&rRm2M!( z*!FSXUfg>{A^>LJNnqS-!8cEp4`gE<)Qo)kc|#H1ooxp`E5HQkJeL3Lfl^P$e*W-ETcj!DotYTJY@Q~`2|sN zjAsl@8ILHsYVUe$&Y&-!H=z+_8$pYrWRc7%9T}w% z7RH;k=UVEGkSC1@!K5KXGRoaoqA2(sR(m0Q<`i-o^@RgDt>*j@SH~csD{PgEv@(@0 zGX~jHZHXa>?WZc7O3DRq5C6H3TAqs?=k76xWtGZyn9>vm3JFpMWdiZ50<4(~F5LU} z1p73YL#+!;)#Y2I<$6sfq6V+bAE=(>^d=6zd~;`Z^j5xVslo36BYWBkTA}EHXhXkJ zi{65U&zDz@t2;p%mO7Mr5YmIj2wn+D?|_34V?X|lZFuMIQ;!R`%F#$}&aTsX0L6B! zsc$ktC6T$u0jSBD`L-hk>-V}rmO$?svJTmz>m+cvvSGqP7w8!fu|c@jyVMLKb$etSe0@`8JRRr&-SBP0 zku0K+b6Lbhx4dLB)O<1r%7;8=S|PhMKrWfXhQE3J84tUVovzNmy)e?~Se~Nlc`thu zi0vcW*FUJGMiL)Q4qr(v-R_EX8fROut+t!_(c7C>&1eri$pUwF1KM|Ao*ndXA=+K{ z^wb0{aAHj}D z`bg6P6P9+u`2qy|5fiH#2Ji2Hd_=IMMG!;gf~$&O_jx@}Av2^~>Q-zz54@*(gl=^u zTr30A1c63FEo4IaroYa_7t7Mx05^ofZ^+_>c}x8k%iuva5a))Kfc1K ztYlFWBQK{Y0Z3WqJAh>S(zxL`L%<&%&4o? zdM{0`#ENU#++?5c0iyRzi7l9D-e@84K6;YwKFth7&xVf-$GOL1<*&hB`+Y3HC4yKO zb0iq=wjBOmeA`A3)yAZG_=Jkclkt#3(S|(ySPoCb^Mzqf?dJ{xX#?tZqvPXQE`o~F z3J@O=k(YK>&LEjPB4el2TnB8yNAQTz1tndBu=0d>190#UM)c1qA0-qxa?ky$8uzvV zi$cl=mgpjmH7^TUoXh>ySP4JA-~a6k!z`q~%oo(#N`XK9^4?|K;3*(S&ZWOMhKI&W z7(4Hn4B1(jpvKk1o(89*N5p~jp8@$|8=%XEh&Os<=5;EBbFQtC5ECA(bRQHO?L}By z%8V`Oh-N|?tAJ!0D_3>o&wb9bTlpY1UI%VO#r)0$=>v|SAyWwccJ^g*l=6tO+-peT zub+B{4@UY+^z9Y5o&{)Fq7etP=_QaMeE$;0t8-Cb zWx6%q{|%fo;)S>Q$*KzAEOCG?V0h8f?50@&b+9=ZKfncDZ;R*ob@9=s2~Tf0p6Og?5~OU{@7Z!%0Wwu*;1rlcR+O7<+971w z581N7%~e>B=|tr0c>!Oc6QUO5js1L>A7Q?ZtZQVMynxywy2-T#Zfvgvz(Y&>ioI~g z3u(V1z`ghDFqvrkiT3M0+PAh-&mk{U3or7}{NpCZkr@Ff@ZO{2Ljz!7+aRu}W zFVS`PW6T+uuo4X_i|D!#Zp(0f%Nzi|LRCNh9aE#daXv$`J)m#u=_`~DW;YrlXoRsL z$;=b-<&XgJ-fbf<<_iJ-*Z93nE2l7e=3Z>fj`~ zH$v9_ws5_ySQLV&GxR{c^i$3ayt6uCuc*xL8+i8TdSgZ7ONddTbPLWiq4f*239Ehu z2T4hNC^h+9*X^`QT=J)2h^zw`!*n6X`FglF+|F*V z7q1nM7lF1SNB273~s!!qDu-F)5sQZFu_#E&ET zD<|T44bR}JUdhf8I(z|QE=Hxj$^#E|9Vs;l)s?-(PGArzp+!;INn1Uzy)- zrb>t=@!%a`KX>)dg|Ztc*in!vq=QVM>g!#|aoY44s_4hnS^yht6FnHk696VI{?+?ZE9As_+jLQ^ZseMH< z+tt~DN>}N1C_EJDXOYSQ>b`zW=&&90-y)-GLI29rkr)>}L_m)hLSx#1{`Xrasky8V zJo9vx|=zrou-Ed=6 zl+uE62GZw-k_b*Y=PwXvh535`4(kHMj`!B+GX!z^ou$!N!@ZAu-)~&-*ROs@wg5LY z;=nM`{LY*DJ49zc<6G-Gj91b4Qqh2&V2c(J?_#Y|H&MGkRuO%GDsu1RHfO2!?mfdx z>@+65bbFmcEAI2J6&H;r=mDof)pu79JI38@jq40zQm(n>{ z>6?rcTHza|x34Rn{QIJ#^WdT>DK*yja!vY?-Rc1p$Ko5Hy%;FJkMOfqOWj;2dJA*e zKcEswgeS(V6uq^Uvynh3uTS|bO7DrEtYSCGlSe9&Jh`NU84r!e{FO0J+uvy4@r%@ZRacEH3P~;BK;gLzegc&dV%hiF%m9mM z2dF5tn(dJo4|KX@9#hye1Zbeiw(~VI?<|9mFW=Lj>RdUjq)3fG{VbB2V<@8Tlo=C| zpWEDY$Ls#!oIMT4qbsYt5xTwiyBa4H(GsGFt|oWi-w%raT#zZWQE;jUEQy5D??$#* zxoES_3vOh|-)H!Tl5rgsptOPA`m8<)57objMw|($?8%19sH0#P@`?xSBe)0EOV}0T=zoVL{`c-8E)O|*lC2M!Y zZwNrCA4CG5I`T4EPolN|{2w2xAq^iY!_C?H*M|-fAeM^P-y}i&`76;IV)g_sojRPM z4{P;6n3)3^uznOYJEIo~PCM^(094sR&1wAj_mv?Dq#(RVLC4y#vwM$S@HIR;WX2wr}Z4#k1H2SVdEPeR>4oEy9P<4ZPguE<3Y8Jsy zUHRaIY+-0hRO9XjK?|vn;EFrpt+$xJzd-C51{R1!sNEhGFJ^)HfFRs%s{?CtC9+{W zd@ie2rmb-|N|yGzkWZMcz>erU?7^w0Y5;eGsjieA`7!|g80P4WQV^b2k|l^nTJKQ$ zAHx0d3My~Ft>h85UfTP54SD_XZy))r2e0tUi;i{!lX>CG8n_)~9_PKI?oTH=AC&nU z8oCx#2Cm*_;pW>L+(kJdoEk4pl*Zx2ue>4_^XAgVByS<;x|5*$f^B%aQ3}|$%}$6n z(Vv(#lVr4PMdnMP<0EWzC?dif?7|(>rf5&-9eubjCy}~$nH8szc@LdH&oHeI;dZP> zYA&T~qx*>|zQbg>-oBA5pD$O7;Cqog5r?)Ml>+Mnu_Gj-E$@B(=tR4|X2EH(j0_Sg zOA2E|^E@mAS4#jFpUG6R?rn2GF-gSm8XnYt*;mGy_e2(dH}LYjDg#*46jVG()H}fK zy$4WMWbhd+KEsjEWxNj|zYep0L&b!C`r4!X692t#tTp%|NEIH!@SlW&gn*PDZ5Mi= zo9)>53?tvMV77*#6UNMb{O^4g#ZK+KCRgzLynD9_c%Fbg3z4{Us3hEm6iI6HjK^{B z-Rq>cM?Gb=hQb9+yExv)VYq#i_`p|4BL1#fkRQo}@zMPTQW3o@NO#4#>5P0lU`N8I zn7eb#wfKi&RsrLwDy?r17e!m`z7C0d>}F_U_t3yNXDNi~D-Wv>uGm5y(Qd@t{ltBq zKZ>2T-U!)iH-aeX1z0k~P&G(|?7bc2l;2HqYLP5$ZTy_W-Z?1kR|Xc128n8^4kMSQ z^L{1)WAHs6h1{XtxR-xfA5N@HP~D&r2t1KGElx( z=BPig<={)NMV3J+V*=a2_As*C5x3^~H%)M)?p_WfJ}zp-c85zVhtu%wx%UD(um6*c zzJx0dL|?ThxGm%fh)Fk$DBQo205xWuy!RZE+Ob-rAnaz9xNaO6p%t`=1sf-e-L@YR zYCK=kpEdh_mP0A-nQ*YkgW%VAzrlmOCjzhI2_EWf)WfV(0c->Hpq=0bu*y(+6RF3b zOLzU_6jB2^;fy~%=TMEB=t%Wfkv)!la>VKAzS1|sjycdD$Xm;`?E4|)Fc})i8G3&A zeFt%QUOpmtdX+lXc^P;r!P6cB!v9_fv4>$HWaX3Piv}PqfS^mrfW;iB4>h2NFbVr1 z3r@9a^t&QoDH`iX;M{QeoPS~(6u2qRPrl`~sBH2?TI1l%%G!~lma41elNHD!pM&Ia zuldZ!tH%6Q458H&a#_^8_#^q6PJvxIROp>x8D)uds}Sr1Dej3 z6)AT27!~xWkxtPY-5sT`VZ8BKkIS)tMSC@-H6*(L1z6&}n zVYTK!71iDHUdFukCDStK%(?&}O9HPH>?lx`DW?Y>S9INvJGeOiNC)!fB6e)uQd%rv zxpC*%qT0cw(xBUHq3;4_UV6GcIUEY|hU||DF%QN*?Lh9|D{!G5yo$RKwHM~#+bM8F zw;4?3j)dM?>-Oe?Jd2ggP~`c$FR%Bj+`j|E%XCaJ_V&+VnkzEwY~;vY*?GEGRHF9d z%1Uy5P!B>BDYzQ}ZCl%FN^afBs#60R0|PD#8*STgxNjM(Tr{jYG}5^YuF3_jK3@yy z#xJ`pI)UX=Zyr<2E*1mr8R$=1fNjE|xJ_`E$d?op$N$~igEIxuj^#OTAsuJliJf>z zP|_YreH!~$G!molCFsYGuuwZ)U1bYqw8C?0Khlm~+DuE<%58uy$Z(M?QylQ$P-)yG zl$3O$-&QL7VEc7K32gy{)WdT805q^{OGmeVCihc%n_8jl(~Nm$1T=vtK-Wb~;t%7V zihwUkZv+B=A;V$K;g;o0+yDSdhPsP8>dywLh3DeYYlKHyC0Q8JIJlHg(}mTV;*wn^`in;ox$quJJeT`U|?n4ahU;k zp5LeO#0CTm>fQ+G(&M2tZio>-T@-H76R?FX(G6_Zpf=k(HoxM~add&h)I9{$b{=zQ z`6mwq2~-|OIyvSvw@SeeOfGbp+AO!%p*_=Bp5=BT(x09^I)^lO z=vj6P9*Y-#%!yvY$h<_kFX7kspv8ZlS?~nIq#zSqNYQA2rj8|LN|B~A2QktYEVy@9 z*9EZ|5*drw4u1)?CsJbd*YGK+^Wu=_^7()f`^=25TAp;}9)I%uLKVB%Ak0RxW;+@ zPe9>t^Rz782bYlOdz%XWJC=G)spD#e#Ea5S3ps0IimmUQJ>T1wH{2O+MhNcNYf@C_F8vTFQ`k!k4o z%*;=%A7pBgK4jAT){FS$8BaCcPq(x(@A!4ASQuidSqz$8Kio)aYW7(SmZhD08PW|d zgU8MXQl_W)4}T13#bnhwAMd}l4=N2C6i_KjL}B8J#jADEls9G& z9t3tepjRYIS0JK9p0f(EE(htCAj3|+*|{{TVyCMzfP@*QyB3D-1Q<1RUJ!BbT?SH- zdagxpMAPx*vMm5Sd#K=CHV2qH2QiM-%(a-x+1kd@P7>$d8qmo1G&&LAa5LY=sPuYQ zF)XMl&(*|;3aA6#!~g0gZee&>T`b+Vdn~q?`fa7>lUWHC%!0A zWuLOnfk?u9PJ>RjZl@3^3oI^=6WNs)p~0rz46bAbke=w$&8Z!PQl8{Ip>NAJO|Ds| zL6qD2T?G~=!|9%UIeH4>wljyJ$uZJ%R~|A4YGH>YTEN<|6?(Km`rO2g&E&K2N<73U zFimgYK$>4jIcQ0ttN(x9hbJc!;FRwv`AFmJkvjes*r10qzqs%Up>4w?SQPL|ewr;$ zc8UkX0bOVwT#1o|I6e?h>Z^jRmS; zr-ei5?E{mDu=76#R~J`1k_6V{-R<+w3+!TqYzBQzPa(ygEcXRl&?gl|if z$`)9Y=uk4o&uPF3K*dN^iKdde|0DE>Apcv#% zyK##>jWy=%qRD>%?ES}}ihe>;P0CE4+6`F+ZleIukcZogc=rJKf=s@!6Hdhb`Efd6 zjyZsw?>kU2ERdinw4OrfaJ%rU#`O=I&UkvskTu5N4# zxF>d!2B*0PkrwRNT(~?wo1trtvJzInc`v^qV^;`i<+KaPj?w`<*zN8?q{{}vSqI@P z;7y67X)4$0%0O;>rg_eq=g)OPb^(7Y${G%`f}K;ZFPIKbZ!Nh$nf6|CuR{nxV9vrh z-dbSCvF<(!^Ws?NOInjZL$DTApM7m%XejcpvMwVSqE(J==b^9=bRQ&2VUGr$l5=!H zEWHB*lpa9lAm4C#0QgZ^@GP5PG)si>Rh0zgboiPc!jDc?jLkNX!N$4xi8?J>F+|{8 z!;`L$PPNASo5oqQ(%1o^=M7#j&=k~4%sP3fFWd`SIPyU=JAu>>*V~;cz2IU zcEheWWTHfGWq6ROb?5YYp8=$o)wV*F{$(H}=-IIW>apfi(Wd|8c8UxNRm7s?*WWXk zw|INdDPK!N;0;OVsJKQ}VmhCTPvN;2hku5^J~^BT%>l~q3b&$|4#<#MbY*?4Z`0C$ zN5*ljNGH!KhV(w}_=ImvtzEqL4TM?XI{LaclYX){LRPQxrdkw}jyIFctGx8m;mSH!gX3v@;5XDOxT9#^+jAaomS7+{8NkZ8$3<@vCn1P&3lW>4IkWfS3tG z>|27n)(P%dNLmTtk!lFvCNFSUT4p=8ppW)SUwMCt>hh)8_2Nh zLiF-);xiuU{rv=k(D-LYTSavW!dvufl+FiV;{{%fjqkXN=ee-_@dzHt5+CEIVu5ve z21~haTTYZ3EbZxLtbwEBzNJV{6+k=l3unAMN+5IX*;Z={oW+fM2IYC7M^FE0*I=X zSn-AVrk4lUA}pw~UH+yzrf~jOvHU)Wx=ucE{}Y6`3e%n|VO~;L|9NT8w`Z#^eul#h z0Tqof37--EYe(w3>XmR4rRVKPD-4M?0K?lDs@TzDKRN)%1djjQQRd@?rO=QJ=};rh z1B$W1)6#)jHgr~A&5&pbMC93la+5F;B1v({+~@6Q<&0uLvbsD5fCvE-ekxL1|x{hx5Ub^w!DXQfcH zgDVi1yA>3! zI`>X^g{l@RSGL{WKt|+7)VRZ}(*o0uk4h|*DVloY;K)s_FkhCGw#qiM?2?~u+)EVKLD zRuI35=p?O#56r)|l8HoO`d0eUR`rD78euxMez2-5+|igM{&)tmU6Qzt489$C~@3ZN@bryquenAljkPW~IgTt=Y(B}gq8BKIZ(IK~BHPE?_)b(H zC4C}~{c-OFxyB&MfiKH_kA?rf#lyF{z>Ig7z-o4z_?4d&FJviv$~eW$Xat?Cu|JmsB8 z3Qi?CpdTrsw}5NWprsY{5gb`7u?IOG<&`tB77cb2(FFm>)qSm?d ztgc>XAY*AQXv{g;6+1%_Tq~ow7J>8Oo^ikmIukQRu;hpgcIR5oNPOU^D9lwT-?}(Q zAst$jX%rbfRi&r=Gv?&K56>%rg}g~QVEA_{7O81S??WuqS=AwY2O-W^uob$wq2&wg z5p%$?GHhp)jd?_mAS2E#*IB}@DC3q6hojXAGcg~7_wm01!__8{nJbR0G`ea(RX^Mb zlaQ97^5yet4F15WWYVMV0Bb0`eB$xf1?3|S+`o_!;^HS_a);X8++s4e)`@Bp721e= zC$Z#+Ee#WAeYx!yC5DF%swvvXhorcY@RGDMh;ow}pW2}1p?D(nFOM;GgPQ8~$kbHI zi}LVlB2zlQ6xQ``ZwXyX7kitH9f7RC+^nV=x}OT;#S|^mq_S%3x@}Zj$&oe3G zjfcqcZ6F3`LRu&=ZHE(NYm~qqr+AOJPz-xLbcpnw9}TYIknb>QJ?G=(fND{I4zH#ab-pAgN;GVa?hda0?6xnDXxyB^n*(i~?k6A);8$1bIBw4f^$ z;i5~PT#(QVSFfd3(XGjtrFl1DTvtL_B0@C|IUbE!d`65HdOI88f$WRn?FmT6bZ;QO7xL>wKb7`VS-YbyZNW?bW?PKL@)c7EN-g<2PLTIor1lmfr_q8 zpy4CA{w4rm9?rQW`oP@S@&nFm$_vUW*CsbPe^_JXSxhlbObSlPICYA2 zyndl8H%(zCglS^475J>;nZ9I0DMd-S;dY%fh;{k8)h@{YN1akB20cuw=oTY~V+z~5UoAB!H(FTbS!_Fjgv z5X#HXU%$h{9wZ5{0@PR2QMr%B^%8LgBqqht?AYg&ryCM7<{z-Ld?-2{%%KwVVb>yX z%S0-&5Fo-Lw!Rcg%-!${rgxwPY8Ckcb00@e!`D}@TpnHIN18r*@f-82gT{=mOiEHk zkU>n&$E{BdNhVFa!?F)$p{H6%mN)0faE0aCruYFAOV>uZo~;=qU#xZu{i(jy1_t}A zuhf=cwjX9v%92baY7VXMZY_~Pe0H;__;vjDkGMb_%Lmhlx##g23XE@tzpC${5x-9i zGKfog^b$0USQHqlWkKUf@CY(?s9^yQ*@m7k8F$;XNgA0wV}SKw7P2mF&OS^er6p2f z4D6~)oF4E#nFB~q%Qx>whrL|BJ#;TdxW~=_S3^>!iCuRGcAfTjIr4FabO|r^p+wyT zZcF_oZfgR-(lDo@XI_>hV)Er+_A51+M%GutH@A*zZ*4bIF>OP?r2EW)AeQ(X*FeWK?w-e9#(FNT0`^C6;y2V<4PGHq z%ER)4;H_Z;wDEdUCh=e3*n>k`40F1rrGdvh$mKSmUQPq2&lXqJ)2A%~-8#m~`u5TG z^s1_Y`t;jzTSzij5PPXT0#BYk`rkoT0C0d??7$ezEE9-q_{+L(jGxX_O)I7~kP&g;(>NfowE!6+hBy z7rtMDfqx;w$29V1C7Nl1Xg-b|s_@^LF3t-YI%XZhaCh?UllH?j#uT0;9v^n2mN!Vucd%8oD_(9w;cl)Ll>BEib z0#%BNu91+6Pb<%VTNX7S7Fq$ZE_gdwrXzQ>@+#O~=+d~Z zyKWGl%}T9243_U~v+p+xc-&tEQ>c$_U>b=qrtiARE8jjJz4-6?0}pgl&@Ki(rVByM z<=a5KO?j-kiMTzleH$j(MYdz9@d+s~=XIUNm`2)|2c(W>0Abt$JfgFxQ@A}$q>iSN z)2R}BoL;>~lcHN(keCaSCP7OBiHpKCn;v4)KsD8glO)4v7=~5k5Od{~?JCYhogEY^ zceoNbU+J@3?%b9&Ks2B_q+4_WR1@JdwCtVoN0GPC|@rBu{_^_nl~OC65^=j=r89bVFDweG># zwXuzP-~nAd`85UJ-&sF>A301B-d4$Z zP3;#SY#{+D^w|XU-={~5BsVqD-q*`sJ9*yf``G9fM zGibkranpIfJLR>Z>V)3f(k`&OJ4@SPjVpiURk|;wY~$uPU!hGm>_0OT2(Fj$hA-oJ z-bW)6AQ1H0dIQ8UUhZ9$DD#V<8_SJ<$R2~PjRtDIz#J%kC8=p0MHKSSpvVO@gZ&Oim~Q>!;V!Is4$8P2lum+0 z_b(52Lj!D3eFXtErJ6N@{Fi|7%Ih`KrvCCA?tI%Gno0Cx0zoqqXNpAZRk)h(!SiYd zR6`ghNo%|MyuXFaym=rpXb{`S4-@5HhNqU}`O^rtsDzMAItM-O4*BK)tgYUUWJj>b zph4h0nX{I^DnVBL`>+Hj&>kL$01$bi+i=$yUt;?L0o2 znkM~PaC=j*)2s@KG*X;yHQJrwQgkMqsN;48LigH@pC!n;4@81)-)AVvs_mEH1TVyH zBq@LJq95042RS_b?X2Z3mkR0B=t?y3EY#972i3m~tbF}Y^(eE*GVlUn z@l!JTRspgjnx!X$Hq;ni8%Y!L0JSKDSB3G-6+`R_Oj&QHjRGD3_y;zdZ(O2t8f9>X z&ib2?*hk7=UA-Z`@g@5Ddx8MA%@trfjbD^0;9HkPJW-j`l>o!#UBNzC?hhVi zdyH{oVxO?|uGoRe?p0?P6+hUXp$a&dKBF=%^kqcKdT}f&WJ{qoD(ryFR8E4;w*O$^fkOGwc2C$CyN^CMH!)RHrV+uw5T}(){Za< zlDb9u%UJqfERJiJP`BpzevA%vhJ0WPN-_i3ZH|;@et`^VHz=O(>VSm&@2h! zD@%++v1&OV-3!3qn-4?IhpO(S3O#LkJCjhM?7y%< zENBeQ0v)<{M30!Cj9rs%Wc%%r>^8>^y>ARLdC+~~3Mvp8`AwDiouROU_Uj5tJ0V{))05=bxNGGj!%hOWQQHC@lAhI zgct@6M&F8q!Zik`pmZBe$yGOrkzizQR_4Y!)fegv=5I${@r@ZM*?dAekTL6!;aW$U z8(oR-w)2szOu-quHI8dki89Ad)SC*usx{U8u{eLW1GVqmi`fY+c3+{VrZJB$G5-fB z*$12{v%IHYVy+Q#s;AKtti*YrBC>z{izHV|O2A#x2@=-;sk0&&Y!dnV+CYUI$nrNJ z+#u8Hx)YpzN(sueZp9#|ve;G9fR2y4L@N;qcVomdupfwp)R4Ww+qR$TUesjXB7ZTE zT(>{wI&s%Z(rUel_;*>Oam|CKOymB|FvS6aL$pHY)kWQ5_+J$n4Uyvk&D|{{#)w{| z|G|%2d&cJ#^^9`*LV?BGjk@m9k-IbkR{CG=u&W)B{~J>O6WQLqKp{-oJ^|WPB185I zs5+_lG`c)qj|#yTT-uy7f(p5VoMQnd>rlCp37o+Ww12qy`G`P2UN%qpLfb5+O9 zQ$W|fI(r8sYmiLJNpVNlR zhWtY{`S)@|Y7K?AfFsM0@w3P{q7X12n&J7kbb;d%7oq%1=S_Cl6X;XSWG5D27FZTS zO2g^R0PkG~B!gT-HX!jtBw&sMVN6x*Gtm{D+glryU-5Q2d>cf8Iop~KOaP4)#ZhS- zcMyW-FGB~j6F}x9m$msF(@N@pbLswia-NIyVg#d$c}Jcx9wvZ7r-}|cbc`Q^^#Gy@ z%i*h)BuL3L#ODcwL1bE15>VofR6uX#mdqmQ#$OP>YS6FKU5OQX%7f876hxDZG* zH|S~+^A1Fxics+}9{_%uXwU)#-$3xvoBxwCLvk%xEbNK#N4_{6Cz61b7ey~moHx;j zCXKYexKTeVOn8$kgk+|-1_7T$N_ebJ96FA?)OL(QgI@7#eoKq@XNsgrw&8d%15@~R zpmHS{HB_{O0I_w<*_;X?BL1Ra?jeS-Xjsir_whoO$@@3&ii_R4j%&k(m-`v~f{u03 z&?Ps9T-q-EX(`bJ(*2l#4hOL?t*?iOKht(yP(0QD$%>#4kVDttN64u?Ob=`^J7M!vA?^R}t<%K}ZVkLl3%Hn4Z-8Nk0|hu73D0 zSFIXR6vhX9xhN3Ufh#Z_9Khl!tjHf)O0H%UfXkWhPD=AWewqFd>LRz>0A=k`ulp!X$7F0b%P4vf=N>uxHT$2Fm8CRgp3Va2Bu75tq7QXwL>yK z8A_G8d&!;>$JFy}-Xn&RInWQ;0BrV#J2zr7(GABeaT~WEg8~`IK78eGuGjAl=>H5zLqq^k+v22o8$;1#b*+@{kfd26IH!ic#O{lS#}6AK1G1^JE6feg z7y>?_w^x52T`3M;<(T+UEIep=Ku`YhpSA+O*Di3IbFL>JPo@u`MFYhKBO42jQmTMY z>?#P5IEVxv&o7KtfPr>D*iYOOO`am^k{Y7Hdn@bTHPUWV&>j{FI3gN7CPl;*AcDAS zK>=2aS7Y#I|PXd4G3Bg^rd-j+c1rv-7-mXd(?KcxVWnv4Of8<~7> zJb24L093299s&O7zZPnZ!Y)6;ca8DIlkiW_l^UIQFdns|;`{4Q{Joe+SjpfRlNfks z9fwgbx}Poz*@49>A`pZ!)M#9pivO?wsRoPE{E}-ordT}y*7;cwCPzd_@%{>Te{cVQ zLI}yWD_tXtUl6S*V$nqN5OL2hNT|r)AE}H&B+1AQK3odqf3C#^XsCyOY`xX< zzn*4v6MQhSU%(L_zumoRSoO^MGZ%^|MfKS&?2Zr)#MCNlONzjtauQ1 z;;x$F@5|f^SYliN1T<78M*g&=J7@3+^!=Z13IHsGc&F4@Gs^z0th&wYiMYxEjy-mu z4(eyJX@7NZ7Ign8!SogZfVpZ@!dM;?jIb3FNLHq$RhmLHvTo;A`|NUdWY_v9)d}RIzg(uKO>KCn= za$&Y(L*EcBitres=tRaUay_~awhu|rmm$o4r|5!uajjzUrBsRTZ#R`_denNMH87jI zq0^|{hOpGYZbwmp_xZmv_Fp$0!2rcmgZ4ezCE}cnXJw!hnL;$eARTE(gw#lFcjw89 zrN;w57lI`9^m2qb0$oKn1+bEmRt1SarSv8M96ZH~g4CDXijXWpT-4nVsx`!y>N})0 zZ{id68vd#u_meic_CQ7uk()}PI|T(g(o#8i7*xItNW%1gtW!`{lX$F=-DdJcLOPJi zC!z1qea{UMwStv}YoZMiJqdGkzyMf7#==2Z1BZ@OA+Xev!61uS{!}Muo+DP8Vf4vu zJ(oGJH(+JD40VMDq*$MOzrz7AQqd>k^%u)3fE#~nYYKX8$Q%dkIGc`#gjhY~cOt?t zE@z&`7&-xeFQC7EEPF=M6`|ue`_af`HdkpgEdLU{T(NaF%zrl~Cn*Le||@X3BYy1cC2caPFBy zr9!+++8MyV2wWa>aCx~F>l7)pPMf%3o_WJ&!f$bdCwKuTD7Y(o8YBVn_L0!@tnGd0 z-YYk4*}KI#fpW#0?IdEN_rJn!h*$%0Sse#61OKJKG-?VDL<2$jQZ6z!19Giyn1wv7 zTX&P0TMWze=u9x*z_J<3Reeak2~~m!_+Sbzs&ep=&^tv{j6$S%C&(!7fubTClG4gI z@pQ_FD>fe0$+)dfi0 z!$BX3v~;IeVv1rRp~7^i3I-W=0_S-WJX!2wvMX;PA{Q{8=sELU09*+SFT^0}1^~Ap zLM7DlIsf|XEeR6OchRKxnxhoYjn3@<-_Lz^2do!<1nk^+yR>5@n7st&JOV`;!xl^f z(1Urn=cv#qJtE(GXb7Wc{lFJX$^jfx5RGCoAPQMi7f^CFY^8KDmq&>@IY)MA{;B+?qc17#H$w#hZtK z&Pq)@BB^ZHhD?o50(K{}xEiEB8Y}{)z`(6bdBg3HDEp(D=-ow&9)T$#Zf#NZ5SDhB zUv*L~?j&M>NUfwWTY4DR_liHO=&=znX~v!A&-6nd48dq;L%sr0_7S>G)iGLCtWPP3fg{7?1Y>C7%z&&=4) zbnQUA`rKtaW83g!#3iz0BBFRjv(c$N^M`YkZM#P4(q<&0^6r4C&oFZL_DnI|lzqVD*@vao_3QuLk}PflLZ7 z1I@MHxyW2Aqkb{vc&d$iXywxCdv=ngAVqa?jZ(=^KYY0gYE<;n^gZDb)x3YuUNeWV zz`)ppGLy{@-TtH<`b&)J@~KPqKr=;$Ak3rv?&KuMve8MigBmxE+b^`ujcmy8Ro5mM zK{pJoZPXdb#YlV-o`xAeg< zX}f>Z>t&6(SqtQ+k9UXCo?1lQoE6G4xpX2dsVo^)xV}{Y;Y&?^Lpl83W&Gh;9?Ksh zj{AJObil5PT5z-J^3nWoq4Vo`mVG4}SCzai8%^Qy(I3Na*ugZ{br%Q2 z6a`enbamWS&57aH2!8dxQdM+~qfAyDmY^a7EK_ze+jze#jU$-kDBI(%qJS}tcU2iIKcF)Z%)%7$N& z19an0vqFuIb4C-dzm`#B^M_n{T(OXYcB3cX)*S3L1%-INb|i|;!wB13kY5ny#01qK zcI*g?J>MF5lGFP4aQg2B*I44J^tiQv&#%Ox#edSJi z`)yS>pnLaqJPB^l6u-k=oSCb;K#cStoR7PFn^B$_(R}xojey=mECUs>!^9C<*exYo z{XD1R%7m1uHxB*-Z1+F^CMJ$q-HFlJKlZ_e&KGs^gi2h#J~F(LA2kEwHHQVWdx-PO z6c|s|w!9u*SDCxoVWe?9L*%@xj$96*rCRERE-pn98T`m=^{29L286I(i#_JstcZJj zvM_|1-$gK$S4!-Z!AN7&ObODe-hlA@&O!H--sRSOsbs2KlF+r8g5Npnfh;|knlNdx z?gTDDEUo3VshoE2WX#PZH>p>t+W9S0H^yGR3kJ<*1=Q=4iY%s)eX}i)Nz6)4XFDqS zDr#`h)99WrSC=f^SNt~o79^u1B}GO~--^nJQcNQUC4DLOKg=bfa+aLHMPsIhiT2|z zFbXUB1bd0Q>Of29m4|ICpE?o?SC92?Hu@`Ww>2*we7$mUaYiZYN0v!*Ido6*OcFJo z|Lgg|zmjQsO3>mh54$ThjY5OoflbGr;kxGmS3r2pEr$B~fT7rb@QUr7ge$~&95u0n zZN#pTHGC;A(kxH-P@Pa;R^<@Lu>N^<#wf<^n{(XxQl}k*V8B^A!H$hKk!YetLF|-V z@Bwvkz5Ooa0nb#=ORIQZ^Gj(9xk?~;BKU;)hDkjlOXKHGw)lT^U3Eaz+qQ<0QG_EF zp_C{FAz*+ANEn0y(w! zau1&VLkpU$WuZ$KxlH<@b{usTLt1cf-o16bz*5^!Q(utAqDegzbibFnN6mwXMJee% z^;;OWl_Cvp=gdfjtg#!PyGP^m%q)4$rF1k@PCCzWD0bj`?8Cx$tYS706!PWENfr%` z$NTfP#P=@L(oDtCu-@byii{c4`3GdykcVew-@*qLe1o4`a^pq@%=tu}v zjt3`o+_mQiYA<}Noko)-H~Ht}xzkz=L!oUMwd8E6jwVA_Udiam%o9<(wOQMvabzMl zD+28*TXaHcGM&oatJ^5;w_B2VUx3kjLk06L7~xGv%5!#ZHoyV-^>nA4i7li%og+c_4|YW@DUq+{_?lfPh4%Tq@o@g zcAsq2>sB7B%unb8smIKGPy9r{$OX;X z2|D~W(BFBzqH!?3h+8U|-Dcn}5Cz)#@HPZxDQO zR1&^ykF6>sukW(zipaW^;TK)Xh#~HKN-*HO6!vaW0K7tX%6+DEpLT@&6G3!Q#o1_F z14dAV3lE>4;!nl?kCip~_fc=-35JO=6LBJQ%mfLXiPW*t+DRhsnfIi-GI+npMnu)p zMxNG_ISP=!G_#+q7j)~W)=`_*=wljR~v)ckc=a0_Es*$GmOA= z-mSdD$Fg0Ee<+&v6wFkaPXn%j#y-^s^_nNMua(w8o3nyVak;PNhwhG=w8`3v8_@~j zGM~GGe2g!bWdk-BxL|)GdmvFW_4ecTbdf>mY@CXu+Sr(dTAs9scn`ryVZ&oPib5^# z@<5X!eR#*`EAz+k9iH-@x+r2fk(jRghb)iM{JB4bK-BUvFF``?2LN{uajnH+6j(^E z*qX1~F)MQ!oRyvOd+Kqz>gxsXw!4QhZ<#^^PjGUK!=%etov9)Dgx2~rl|3+{D#VRO z>HJ;sAk&_Wx+z^wD@JlRZex|6qB1Igb1LrV*Ir<=_4m&;GHK((`~zHHMeA8rQ9-OBJ_I}1LeaUG+4ij9>E zXrx?F9@qls1{R7Ha;HrjWJGhVC`OxT2g8KP5EBf*b-|pAvm%Fd#SQ^DuC@S$d5&%4 z)rbAdoiRSaEYyW#_?K3szmoO7FtkLTRVMu>E&ugjr-ehMce!Xmq~#8fzU(I^TuMj5 z!4y7lgFimp=!09`H4f#J5;iirPQTYi@7w#iFmI2A_C$%;u_(7hNz!n0By+!Lra3V} z=jQsPGLSp_`-i9PP>K%szf++8fmru&gdX>Q(i$Jw>`78l1f$acYwyY)LE z8Mdb33`D@&pAkSk7QkDnBqFm)F%%NP#yjWONWgL)(Pm8}>JNDl! z=-18@3L_)1266xos~!z~*&<92NHAWL-$_P0NI#bd1nemJXc8SgPH@mPP{pSx49jx} zy{b35(*BbNGjuvsJET9QaZn1+sTeAK`HBg-iC%y<-P8UTbFu;^Z5dw$))(*O!xNE= zeGNUAK*gtV3=;r8+}&9GBG@6Ok!;>YtckU!NKr_!fN{xE2|`VNeirO1C^PAT=&YV; zs>ptOVZwgUj1R2iW1X7@Y_Ma3zH%j=GZF@0(vkXvad5VzF+YoR;{ImG<=F@MH4XzX zIdcJ9%$1!gV#F$xyuX9xhG(H+Gy6ivN>tIXb+psoB0U~S92()U_Q-C8w6@7D4% zG&Jvni=1(**U_u4rDVy~Zd4pufI7a(Y8L*8a^0ot&>_-bl?i_&9^3}ph~zx`imw+` zsGm(;xf}@7Mme=s-*Kad0#oDmk?5@};Q5;+_mUI=#MJ2cWR3^CT%MTh$Imnp#{s`R zKMT+iT1z#&LsRv2e|C)ye-Yoi1~TOal~)sJ_&Il_itK>OuVdl+eSop?_5#4YIp$&9 zG(nDaqOegYQ8phQw!Yy+P8VTpA>*PH@rL3rsf-wR7$(mx$r0fMeR=lUlQEuf=aK60 zNPJ$`g-Q`hJ`bZ2vF!C1cnW<~D{hTo^C})&vk3ocV_DD*`On@YaY2h~WUW2dJUV}0 zL@s}!jH7B&k4OJ}!?SKN;mf^%&Ar9&YSxx%V||cjy@`z9m!SKv@i-<9Vggaztp}8Z z_)Z18E>IKHQAwhr{u);@31DYhV!bQ;e4bD;rif(KK<8dY_);2|XR>Sv>tJBB`GUv@ z)i;>ogzK3ky0|fO+@^vQ2Zuvrr*zy9`!Nbh`>>aX^YlGjH$%&IxQVZA{~A8F zaZpSnG!~i^z8qRzY7{dvj2)b+Qp}Q;wIb#LpIITUDh3j{+!;!_i9y|2$Xq{tk{;Hq zY+Z&^4Y9>Kh_$eepmAo-oopDnb9SlT{Q${4%b-0NA|2;sxgZ;O`s%Ngh=BvH@f+#S z_l0{MKOW1oKLet<9L6k=xpzw9jW*yr zbB(R&oCCjeMJ>zNM}Iz;j|UhCXvI%mZF)d>=4w}r!*G^88LtUN-EKdFREX8;mC5p z0ppM3`@uju#CweZSDMl|wRBVEC*?C+Svw0pVKH0SLA5L6^cCK`vUp|K>}v z`xyv=s6|yTGZ z`w^G|>6p$dA!slj98T&dh}vE`$4`Yap9jf_-hA(4!Y`CTmc|jpzy5nMz>}18Au#1U z?8hiAL{xRL_vIsxNKiN04cfF@|5nHf2kWN|@XmLR!agTJ@+1pJXGK)L-Tvdd z+O22La2*Gv&UPr3Xs7O8!arA30m%kbUS1!6UirtK01__=dFv4-ZJjJ3C=`sKr2zIA z{6L3LxO?QU!fH#z{AdHAL`hGQzp@RRt6+q$N4=Ya@1K2zYuF8m81J_NgX(JTK%JmVnVn$S{-E`Ehdxj! zvLQ9qrNNFQV+~Lx`Rm^f9ra=!a3-->2TWQq>W5h)MgaA}5Z+)5NK110!%8pEVnt=%6ZS(Lx zZu`Y5^`KrBbaQiOl92{dtSr8xtO*S?xLdim3muI!1-7LMv|&4b70;*>aK&PlmgSE% z()Wabu%|;BUzxFQ5d@19p@^La>Aps!H=!K@DmvEoXm;^2ihV@>Geq0()RwSPw;d&2 z6Jubwqw(;@1LT;1Fz*Byr|M^uax&TIevcXhz&}kzSrXh#TN#5{qX>Gya*Uw&JR|G z$FE~82y3J!mDs94$?D18Bjd#n47N9z&cPb7cIdydw@-_a0^fJEKhHgby;c`cDyN2? zYny+iwr3 z1L|Ci+W>R$KA0eR*x%oNlG_XSJ(if@soP|p5b!}D;5rL?aU(J)} zO`Z*l(1xMafACS4g#6TjP>&Wdc8&y~)FXysY2iyC3B$g9*kkzakL^c*`R9WG?goAlsp95RL6#N7bXHq6l)=gW!J*PX0nr)CB=dJ6O3mhw%PwK(;*OP`T8JwtFxkBLuOL;10ZoQ%IQK<0mx}S=eVU4b*4dFo4Ia?ULsnuA3dPX&cW4xk zzJXe3mg60*mPo^S+`$qs+o1;GJ>BGaz!8Hz*$&0S9Y8N$wrV4hTOcWy zlG~qUP2Z0D3~@pb$B`f{8aHh}`-S#3UK615QLT+-|H=@-ZeTRJoE|Y-zQVo2%w<;4 zP#G17yiDl-Xf?3M20ED3X6>rYIwK_CQwe=`A(~W86P_Z5|GvlMxcV^`$Zkj(F zYfb-xNN|9?->{rH+}#C{r{i%N#2y5oKl~CwY5>JS2Er}KPj6=(Z;zok0>p?6h|XYb z;A=MLUr~Oryhn5el902I6;VvBCjR?AFt~b zye=`8!&#^0*a`Z%<6Zmmlo45#d$!vHjx4;jE^zWA$D_K#m?YQTIVovSsUe0(DmfJnr? zzhYTIdwO#fOTQt4M|)?P<=Ic%F8VWAC+OAPv$jFJHfZEhe!Tkg&Gv@*=M%$qLQKhZ zOq#773AFI*;gGoty!3o=?wHw@?R&=+P}RIV5005<@DIVn=mni+gADerys@T%LBqo2 z_YX5>{-dOSyu@?zq1t^{zezjf1>4~OHfV>MF99%sC?99o^cg60fxCl#^`#k!VQVqB z?K5aU7U}YV>f}p0v$CVWIy{JW6)?dnBum3=Z6OTOJ>a93&9im|+s=lE)jq0d;{lY*~K0 z6+C&$YtU9Dd~R*dRdxGqZhjK_j*>r{?iGw>A84vb4B9~A=PT{_TRdHaBBS7R?eeN;m%b6go+=kAS^k!V$+i4e~R*VZHir-B;#fUvA%ul*`OH=bH^zHed1fmzaq| z(r)~<&iEY(N>CQoEo7$O-0_&w2zjx2g#?&&tljM3tzERHF3Edt__G@UU^1nU%^A+nG=|1<+|>IW{ufsW+Fi&Tpd zI|;FIhGBrNEh)F3+7Kkx8i6P70p_JEE8QE$Fl$h$$lXa*0L39f;Nq22!y7?63;bj_ zk!l7i)e$ocwP<>Go&g1>!11SVIo8y`s{^KMDjXr4Fl#NB+}u38Ht$SK6nAW8#|b(< zk&snVy{-%d#4vOLv3u1-xx~P@yQmY9o$P_o#z_BGagb6fHHT=M-9G=+vzM!w=b zS}Jk!%z-eWbK=$7i30Zm2V{fVj=HY!Q&#zGvXRe_`xqRHck`{rNUVOoJYSW9=1<8$ z2BYU3e{0Tzh&_kTeS|iw{%X75Ud&G0j}a z=0+bndIp;A^@B=ynkAh@t7KGXJw{F zuBX6qj0Vh^urJz!E_*wqFX}Z)xNbIjSXrQ$wJw}AAO1_BKy)l>(87skry!4=#w zMEc)bA%1fflSAM@a_$z9nA2H9fs}CJ-0IR9XUI#C+SaV*X9uMAsd0w??upInRj7zq z=f{#(!Au&Mf9AZr09ClVp(=2>(shHi zny{-686&}JHb(e5g*$tdervvU@(`=yZb=Qgc+J5)!SzMPS^UI6zz<<=vcAZ^$Pk{^ zz3p4S^3Q4D zM_JrQj0!@NFuh2CW(;blv%1T1Z7`QdsO^>l%UB zyNe3Z=Eer#bhZA54oi|cghD&&`($83k-3ncz6@;b{Pf~Fkeor7-8^`0sew9wXBO%KFg}Ei zuOXBK=@5AG*7P${%h7-g$rXEDK!uu$ot>#x(I7PB^X23znt&>iPI`i>GB(`zy;{V( ztKXnbL6#~OxOVWpYJ*o{gha%~;x|s*@JWFM+rVDDI$cxgTFph02k0rl@RtT631)gq zA9mWgKPnoE3Usx!-8>yPmzdCHDy9QD5%$iP1yZ%CKx5U++XIw8TR%(S_UH8De0>(S24oVVqf zZMaxw;VoJ)KcPsFXVDZCX5^#Ln^hA4A#FnthU*?)l9jq#0k}jb60AJE(!8XASlj>~ zL@|u)J{9DpTexr=m}^a7F|crMwfh24FZvW@YUGR_`y=l@dZ)!(3r-He|2DQr89oiP zVyyTaDg)USGGy88g-ccNriwCWYyka;st!^jzi8YL<86O9Zvr7qcwa^8VNk&=1>mXA z=zxQKn|`C5>b=Yn3am+Ea)bwOIo9sOfTW_SGT{IahjIz5;cL=qzd##xMDwX9nP0I$ z;ItyaA83Zqt$o@=rnhYT>qif9ol=Gu+!>?}ID{2*4`-ba)msbP^_k}~W}-bN&MSdK zx8H_@bJ`I(uZhX*`W5|g@7Y3o`jJ%Iy`TqApo(!cel!9yk@tH_aN|2a!KCdH#-KjR zJ=m!ucfUAcmi!TsdHlNghm_c#hOva9SHY!fj3wJ*4j@i-LrUsK$@=i<}mKhH9( zB;-NZF*?ba-dd(ddZpbpNyxPws$-u9QoToI5PnA3&ppXbA~gh|rNbv=|A1sd z-zjSbrLnfX6z)5_02C`QKy2{(j>H>Av;rjZ<|PI`(f#IgfwjlS$!2m~Wf_sw255k? zuRv~R1iRnZL&Neax6pEMv?8}8Qrk;fnCicR>}lhv*a&E=&$TTpzr9U9*y!*D(i98k zlD+)1()B?l0Up=PDWCcywkR75*j~gM-@Kt$!)sxm8GGQ}6}}9z>i_1rOnR7?1nhbf zdelci?kpemqKoWpkR;9QgCg$Ga1+AH>;&6+&O8x9Ta9ZC!sp$4YM+s9p;h~&gqYnR z0MFSpb}R!N`?fz}uHV-^(_40zARu$bs{4YInMx95f~XHC zB4Ceefk#36D20H2$uZ;ZU4I?w!Ia$CpD4kWTE`oI7W>{eR~IVTD9(=57u=%BB1!{R zGL%O+7VVMn-u`w(Pp@)JSlfF{U;#!jbT~+Rvs6NH6GR`Tqf26jh)u;KzE!&I_ zsLGQ*gcT#yIMhAozO8PV1@giSd91W z>iC3k6w_viyTk$;Kv;LJz8b=`h!h{Vzf3}y{#Q?Cvlx62?VP8$$#V-tK6xD8vY^3m zElzG5=wjH5J;0wWp5a7Kf1}(Q&-1b{_udLHC6L!t|4G_t}^xzjNOiBtu1{d8pA-n5+WRZegc zB*?d*IYBii@NCNYGHC+6(hUa*_hQv+dDG)|CZJ)wza zxFt)~f*FfpW|m#s0eD<@cP|6$R-h#rZ0g?huPX%TP~htoLltlhFrG&w+GIx~0a9mR zyLDkfmN^!RAUzX>q)RMZ>9U&fmyn>&cz+EY%1veu*G$Fh864u+li~WatDq#{pe-0i zmRiPva=QTyr=HilHel-9*DTk|b}{*V8U5M?UZ~S};Q(cypU~8gPma|qQEo!dEB5=V zIYw!0RrI(r%#HSTNM6fWKlm9GwLx{pa9_XFi3fxzA?FG3W!GqPe_#0h2~7e_4n^g< zqs$r_X~=Q78p};Fhy*e9*?VfjYfJtny5wh_lz7t<7t@J|=<}S_DVVP)Vjx9+a+fCB z0t67^>cQ=kA){5@-l<-aJ2wl)NDQbeJ3tDH27ESX8WYGHZtM`yDch$ciZgWYpL5vCabFgSOTPet0jP{0 zT&_a32|2+Y4j4syVD*cn;>-Osi4t06rHE@?$7e`14wdXE@K`Np)>(eeqv2aPwY~H; zG|92|xuzGvkMb;CFJL4_pi#_@%@Sc|m3D-Fs2apW4QoyeZ$?b=aIrdoKX(E~tik)+ zxFNmHz!?%^2rij5k~V*`arYCwAgVJS^Gpd6K72e>hA^)rJipIZm96tzRWKiNDIUKq zz4%3n@fhcX(&FG#3&E4%_V>@3*@qY64cBW-<-k z#x}^Ps7Bq4iNqrHGA7e>zm2-=ATwnQ_~y&%U)KLy2mWt3EL55lKrjLmXI>2giAQqg zw)!3%*hJs=H}yxVct=iKd!O>QigDY#bn**0i%T2zgB8fg7J%}5$V4_1W|G_;Dw7#> zZmmd|7W}a=TwG2G5wyH6>-l0)K=7tC$bxS3a1gTxrc~WJTlm92|4684_nBR$MYZX7 zw)W2EpZyoeW5|TkT5jaJ;h*2KdEx&oR`}9=1XbL2R@)HYe^&9KyIDQ(=&Rgd$lNo0u+bt!?|Sz%om;MuzKsw2gq;>2laj{@&HG`)6hvCir^!efVO~oJ{jLo zkf5W9ziO&EX) z%zKsvd9B5`LDBpeR8^@ks@x8cWcpkLAlg~CKI}wev*|!6g02?=xbH)adh6rd=`FtU zv@8Qh)dp%@APfQ2bI$$b8#|5#aP>{gIN@WyIau=Z1=FE|3hh}7p)j($p>YYBqXffB)z^YYZ@ zLK1Q!?wel^TpsEbLs_Lbcy#G8*p0w!Ckg}SEA>j3QByRfrxtBv45{E~*7-FQ-|lId*? zJgg&`p8!m4ff+U`%fjp*@XRuR*xG9U^&e`{=EeT{IiUlv7had#sqZ5KtqgvcGltykL~}l@cHCaGh4D^c zHb@6vvNih{g+vn}pDYNhzj*sG5tSzRG0g)9@??&}>@W=dW(8Z+U9V+L*}1vd%XD!a z0vq76<}(z;7L7*^Qt%$yp-MG`sCE^33r$vO8g;C=IE2r#XvyVCjSMDFP`radJw zrCMXl&}U62qvHiCYM^Z0Yx&Gx%P5R5jX~E_t~JIO@ztL*zrU zPh^;HV^Z5cfV~C>1)(kD`JZQ?l<%#(^krlMTApK|LHJCmvUwR$?d7oCt!G$I<`0z3`9_yd|r-lx%2s}0|Ut@H8R^-wS;U>+I=KCEN591Ik4d!|h6iP`Se zY`?XAGH@H$C@K5>N?;>9a3|Uhz#bgVcglDHGshl{z@2O}=>A5rNXKuNF|jPawO`LE z^t=*he=xIfvV8*`P0+@5;5rZC`H|>fD+X_FY~I86ZV)cg|1F?n_ z=++7+)B(V`g3RQT^2gHvC}FuM`x7SaG7%CD5%?C8yU1Z9-C8ZKOvv+2Zr%^xgeuf1 z4+lh_)5aTr%m-ZRJ^&Un(X|)R5w*U$qzELJYXMR0|1O{Y-nrzzdLre#l=q%(5~|p9 za^Wz|7@q_GM&#Zx*C>iNLNDIjBK2$=SsxsS9LS4Sl6g1GRvUx4+XJnzq20h4k*fD+ zSC!oiRXB@-Wu7yF6RR;m#XMVyX76x-);gu^EFH zoQcr9jDHhPSJxMJ8qrB^`U4}IFJ$xRfcT^iLO(&IF`S3hdQg7^^f8r@=!HT8%o4gE zKL42>60d)S3@>^N^gZH{u?*s?-KUGw$j=gAae%qpyJ}xBo4@~i4D6s{z*>yc3@p=6 zpT}yS+Z8<&4I}AS5wwp?Nq$-VwxmWNL&~>zTPZiMj%gAzWVQ}+W%Q_BhD08se5#T9 zALLWK###QDjhzo%{>SDKfUaeg=&Jk}uLe6o)Ub`--?$q3T81p(?58>^Z{=%SM-4Ig zT|!wQs($N`J6myPb8&25&_Blp4s$|?AV;V$nD(##u}Zh!6?}p(#gsf=>KthK^U?qN zTSJ8)@nV-YIkoHIzrJDn0=GUrR)K@)meT@{)&F?d|N2%YX^3^C4eeh`xBmFg=k@F7 zglCh|sIb3uuaSAyaf*2~AKP}4Fgh+unB%?5( zGgG*SV)O0d3dqYEW{QD%)B+Cm`oIEqyp{yoXsy5o0Bw%j`n&#j=bs|tjah@H-vKM|!4tRAbn#9oPW)@6fMqs+r%gLRN!Nk?sS zNG|;Px>~mBu3LM!ObeFr(nxie`DJZD=@bEpq2af)kWJi6f9c})o(HmgI$K2u%sN#7 zZ*Uk?k3dp6fTlGcPShOHi-4}!LrK}&Bp&ISmao_5X@kc8tpJ_)9RJzw&y~!O2>iV0 zx%R6A2Ph5+IOOc1<81(#*UtcBQ1~HV;5dT^kjyoii|i={5Pe?Sk(K?|Gy} z3NK6i=eYi#RGEEca&Sww<&{-tay`)B%7VxgWkGcVEB4#BN(eFPL%VnS;{}q~;Bxa6 zRAYcB7m`07)NO~QZ(PnN2TgaM?CV>l zA+&SeK(#FU^bK4qvNi%05ieEB0zjt5+3g>CYn{$GUdqhgjk zpsAVw-I!QozxmBm1czah&d`&Imkzt);hCzMX>dWO%!}6Ki2esur9mH(USA4YAU}WrKs+OR97Ky;6E4@g+cKJ^5!=Icsw9&RS7fnOK*!4^B!za!B zFex(%bw;+DaqbBRisa4nX8Y;jULIsh6O=xe)NFAPY}EcuH}|v5ow^N5I>=xLS=2y& z_G^O%C`nRP9rFeBETE3L7&t86iupn#p#AfzsO8i{G;;|eYUB?DyWT^qr_I7U4Ng;3 zp{juvml41(KE`NRSP=;A$t+uqYavyVzQ(0)06#iy>tPv6FxtadD6w^y60Dnn@W9ob zd#H5)x~uelU3J9QEElFro8V-k&Ga4=SzbV5^(ip%(+U~+7_=m^;d@qY?hhVZ|26ot zwRZnraDADu;0h?KAFusv#0pui0mPMwblKs#MxPc9-(RStVd;xzo!!Uh6oQ&$ zjMONjfQ-=!vkGHvhRK!#&d5eHt!u&!HD-}0sicilcG!R5|6hwq$N`R=DfhY}5lwXh z^7r7*sy$VA+8X5D?^eGvx`9s!*B(I)+Zmf~M2dnM(4oMfs0rCrj=TOq9rAuVKIm*D z!lW^hF4k(7+r7B}09}A05Jdh)Mxh<+aorTG1=|3$rc3w8P6I7SE9458zy)zK1qOr8 zHYYv(@IQ)UuiJ2i%sVSR`^{D`p&t5hKsROR%lV(==sF9x*Mtmplq82{QS;X(FlR1+ znqK!z-4w&FB1IRh5@QPw@xNboO2fOe+x> zp!KpY#_ySceiXN8&^u>%jdqg?oVx=1{eNc!hm-(t@|KlPs;}UT60cCipNWXjQ5dpO z7wa0-*(=9*d6m-r#~RSTH0C<PvxJc~t~L=c>Op>isk^&me-#gJ3W z_7S*w^z36AMfudqsgi*y@W`mTSGl1GGu4Imbx?gg%Bl}C{VlOy-QNPB!kO3NarPy) znsLJVdpFn_Dz;PRYfSLORP;VVlrf-L%!yVhUZt~UsvdWoaI7eC#87AI-_-RN(6{2w= z*KC9+!)E8|T#Tl{f)}HZ)=Jp_nE!EcafXGto>-T4*I1Kamdp;?2TfqLH=$ec1&zi+ zlbkML2Fkx&?L^no+H#cWKs&BH%MLnGO<+3S6ZAfe0trk)@p)b&QbM72Z66YG>I77( zmHjSUT|-jA^bPC#K&KFEWY^!S7{rtrxMA=DW{wb3WI}*BG#^g9m^n0!k7}5#IV5N{g+6H>m8~6w<{GooKu@PV_6L-EG_;c$J zV6Kw@m*#qjMav~_QI|*bA+GO0t3QQrqM#lxOr{Y%5FKRpf`97cL3UyDMc#=yxVHOn zQe;8fG#)I^^O*({NgW%CkS;5dFBtsstQuNFJ#7*f=w<;uy$`blZaJQlk5QcoB#z?_ zXSd=R?$Z9Uu^GYOc;=a}KuU8wA_XY05kR#_SwR&Jax~ZlMr#Y7IyDDOc?BDI=(ZZC zTPqIAJX=^UUupX7Npo!pr5SlSH^w1wkM|!2BiArG+dl7k;L^0f5`T#6c*k$A3ZYT% zZrG9wh4j1{r(kNow|JBVkPe|J8I0duxjSKh+1AEp5F|32Sv7$g1vEpGb7`h5s((D{9VAeA_hDb0>Ly|Xl%_Hn$)2MG zB8~Po;9Lk8OF=emoE*l+2?CpmuEHo=k2OCeI|l`X32+p|Okbmd3h~Jh z(~1t+f1;CKfceC+tp`&^gagKMljKS&B*cb>k6JK{6 zRQ&pR`+|3v75PSFySe|dl0#h}4`L5Bxu5ow3(U-sc^u<~2$`b+0}F5g=djPsBOdJv z?j2*SUH(3%PsWSI9b|?{01#wAr5fn0jj&rhG2Q?Y9q$KN%PrOhLp5WzTdku$yo>CG zy`T;qt6?{w(-qPE*+P>d9(C=50eEgwQ;Cxdx>$?mkz^G@ESpqRnx-(n@3OzImni|0 zd)^^E#emYQcO78UQE0Wf*>eU=SVL;~VB}}y6_~F$*an>E@9s(o%C(T7P!?V6AAu}6 zxAqkEP`nJgcR8Bi%sT|l(h9M76(mOUEA5DUTmm9qon_rRe7(W1Z9SdZ-4wJ`lpuxG z{a&h-Y=rd$TC6d3;csdF_L@AyNKg^Y5Lkk&4p$Z?sEECJOG`>^j$RW|3*J9B&K~2XkAZiY z3GcR3DI0}-l8^n0GLsQkNuCHrJ>W%OLDVWmBG-=u%+1M%*-lDNW zx$a!`(d>Th85#n5UW*ugo%_a=naMmqXML4~8I?KPxMi0B`$W9#4B*!a)D6dcgRysV zOFGugGZ42*Ub7U^O)!b~*EE1qP3uZ0@=2G}dhQ2#@yU$c18Tp$md(E+BqE6s@-7L| zS|J@wr3m;?$Mg~)1Z7|g+=tc9%rg$NW-ajM>C@Tpnwn5k>C<<;mjzqyUDi|QYAqg7 zucZO4!i~EepK|fZ5r|;x`hXpgY6bnw6uHMLY#Zg+1?tZ~WS&CrTodUjJ-c&5{ZkYcZJzuW4onb7U^2)!;Ju{KhV*UyP zjaK*e_kcOwZ3cP9La<9zR32x{fePlpyA`F&`>^%9*F`qOVC#KD$??oLQiq}2Gca6g z?5}S2oYYRlglnT|@QPpmdHEL>6FJCnwRr$Ss>j}SB35@HZkrBwNp0!f4Zm_0+hxT-uqx&v6B-{V&Ie);gxm=ceW>Rzs9_16+q2)zD5tPW0AMXtpL z{iv1&l;N}fS<85&YXE$W=dGbt*_B7+vJd7;v-IN&J~Z3#|Lzv>bU8&wXM=s#(MdAd9t6Zd40K$MPY7 zjqN3BubcC#i_*fC*j-8L8aQVf@qWD|$W`B*cDM|p-oh?+0a5;UC7%-4a0EuOj+!aj zrB0c>JdXr-iil4f5p;3@5>@Y!jc0jAHfq4Bz13i)JOy7C^JEPtRB(RJOKGXy;@a<~ z*49=Qs;32p9QQanW4N!3ZU8%7F0ELH`tT(iQ$MH2^D@;txD7|UK?C%o29b#DC4m2D!!Bl^uuwI<|g##9AC!`b)MRGk(X-$0?%qvw455SNO?x6~DtqS&zNWKuBogm1M3+Y!TT46}4&5+QcO%+{hg7o_t#Ee_-3 zI4h8CS0ZFO_%x)+k5YGh86uRAK^0>bo5U&~qm5uBMvsf{>CBIH_nCtYxH3a%zldra zpy&2^`}stl*_RjR2=*R*>- zyBia^iF^PFuH3p0C*&~BvuI*Is+)%e{!b@2+ zI7B4g<-t6@$>2NSj2K%%Ix#JohW;z|{{D;0E3w6~)V`Jw|Jq=3c+hF@y*Bdww)&ro zZj+kQQ@5Y8FiLaI_AS?7e5OziNFGi*1u0n@_pL(~)hj~a3{Pvk!xp4&qRe;PP{ip} z7p7(Tttq5!R3FbTwDTFhQ}K1lj%9tIJn7qk-w@krDDWb_Wdw6$VrdBvh>a8oI{2vc zFdm*G8=JIjyKu%@y^Gd{e&U2_YHjvIb&Gp^CyHD|cJ7p=;%f&S>ti>A^qp=Pjom@z z={oUL1dG3#i`<+YNr;rwEw3{=7v5=%DZAz-qM_H2E}%s{Mozr*t|3(L6s0m0xE}MY zIn{k{SX(mpzZu~J^ndxF4QSKN?~b;LuK|qTvN91dpo>-I2~dB zCZs*y{E=6=#4sApc6ym#57RNu^X#dCk=W%B)^}XLw-n2a2>S`h)W^!>!(|TO$nEE| zi`OdI3ns5{KkY^8f+EvD6^{HEJGDRI2mE=vy+u`;_k5%Ua$DPhaYDpiA)#>=u?;+*S_?Z`-KIOy)jALb{D&>a&w)4 zV^1lTKWg;=aq#q`gZ+%GK^Y{0CM=!;rGQv=Q=RyOhQF+LmzQLgK=cc!x zyM96?lEX(?X13R zNgt8G^ZkTrL?qvrMl;=m@c+GO<9lU|mtsIOmEiT?5g}Wi?#?-QCyODhe&FCtFT=i(b`Bu!v!Fw93mEo|8#CnP0Q(<%Dv#9*#==r3(#xd<=JgQpiIL= zJnDOGM{Ji>tV09#EPhq77CWVfS4!42ZbZCa$Y0$Q*?yQ|-4!s5_;jL@53H<4>9RU; zQaEpa6-9fh@r)Fpv#K=x{OJafNlm-xfq$Lyif=IcUpxE1n}dtk&NCWrbshFp$0y%f#+g&>ip>Gb$=c(VQjq+-aU)<2Y^(jDC4;)z?1F-B-piHeh-6QA?H+LE;a`x` zRwLBvd_!I)@ZH!9l|!UNyUt-EUwB!efLnF-V6SBVM3$43jqnDQC~KbgIj`>}iSt!l z;f<9;(-Qo9=PteCS$LuAH@b2Dt8sD>e)!O99xbU)8|tY+DqEaK(B0GAO=@L1X5o%^!D(3Yyaz-pm6g*h#_sfU_&}{Wle!a#44f)D}o+){1?#5jLOd z*YT+b5x$@OOm59i40E=i$;$}hrfruaqGoNIg}*@2a7?xF{?ONg{s34nQJnP&?G#K43y}-mj&71rp_4%y*eG(b*n+gq!G$Wo;N{xb-H&?F zp!hGiS8{%09`Kk>wV0VGNZUjm`7Ax+5*YH1vU8`c_BGkYRNdPlXMK%7NsrZs|MCW? zCwzTp|Ec|HbJ6f8-eR};4T-dN!(vUE)cIt)+A5CBndLiA*vMUPCoS66 zCt>hHnjAAFTGo85HF8lBhtc3K+nE9H_7P-6C##Np4U9Qk0p^#~Po9p~%Mgik^&=6u zv^x1AiZq0UdddN$E0=gP;JoYr+udPIY0-tU4>xY`CCJ!Pr_v@4IywQep8zRaZCuKw zYb7+cG6*yPN^V)epp@mk9P4{lfWXV-yXpN+gMxR#8I<3s6$fD*x z2qTbwj-Uf={88RC;-VCJ!Mq6Xi0jiAb$lc5@y5q$Q?dOw%&81-W9teDQbdwi9H)T$ zqepENzdm?7$o0MR(l;#$!uK64)IW#ag&M10h^!AUgw4l$cjoOEW>oUsFBC%RPCNJa zix2I7ePb$-hF?|#*#$O7*7d@X(8<36seFfhLg4-kDXchG`OOGJMha}5W$7@0xt8&B zwuUg`6u*INdx}Mq3+Dq?3YZQG-;~9TtWAj&d|57t-@!|nJU9`iq!q&B{zys2ZWe)D zfYc5%Je`mMv^3%i_t!n2h32qFQ5=eaT^@hu;BvMvdHA)Y z8aLFZ5>${P-9HtlkR~zo3U}yEROz}mO}iF%8FNj9v73%uDU^M&d{|@HC~)`-(AnAm zGnH*haHY_c@%j?TTBk-i_5jXD;|yg;CL$ERHvfbHOn=g#4$}{NT19*eKhiW{ zcB!eI=Ft`ENpGY&=M+7R|Y>o;G%?p;J%&12R?rJgyUOj*Xh zS!;PRA=qU&aXWsPc`-gv3)rQl+NvhGgDnmIh)8eydhO+O)!0G{UlL;8+XqaGPB=E% zCotrSh_LE9+3+A^&6HW-_>gJ+jG~-$Ek*`t2=e;YP4E7$c|r+m>MNi+@ifFOHH>ds zU_zDBgglk1OtG`3x?n-2!Az1SG~rN~aEJ^xX3n-@K0!Ukw!?Iz4eF3HPf{6)>l z^xfEw+v;=snTBK0P*p8>*Kgb_5IC7%xcqkXZ2zo7Cm)s4kylG0;_bf=u9cafNIFTT zCm(Pl!+E@bAcei;=RJZJaZ4YT0~1GtGN}SIE2UQALXrrz=Zc|`nB<)IP@R#(w@!)i zRD?#^q?RnPw+}LSDmG?2k=_*u#c>N(aPDm#u?kjp&iV)7?-#J5Qb>Z?(_*l?1Z;Fr zr_oAsq_7aMYRDkkhaAVjkY%NUsU*(vgls`|e%AAaw+jLjaWu@L7&fav#IQS`?bLF$XY9}Bmnp*B3W9-TeZkwu_ zxKcePn_1ZA*}ThSBaWtRSK-Yln}Ht&jL{qBC(@laJ0=JSWAKH8TQ2r6p3OvF4)Sxx zrb)j69jCfyd-_eoL^kkgkGe8P@Ke+6P9V3D6ln49Ai(2VmrHHN@CJn<=VKmezI@h{<#AaT`V6&kI%-L^`!s7o=9ej)a|HCf zW+~eNaPE^eW+_aT-R%jsw=mzcUp-luBqolja|Xs)N1+kEH>vqxrel-Jy#2j|LhzGy&@a3ft8FnDy!d^Nq|Fd^WZC6HC6Z?>Y-zGO_PWU7 zOTf~x-$zx8Py>HOsh0O`EE2Hd7g>(#F-7CbdrmLsigOZ-I(9W$6~IQcRf7HKFerAc zvtmDeNij-DGIsNlX%HwL;o=DLzal#(1Lr7Cf@h(N zOPob_VPgUlYbYm7JNkHT)w{g26OTC{5bDYIMI|TIgDk;M==rEjXaw zJlV#$OW;RlvQ@!MevNs)yixLe?_6nn@$i%R%gL{qyK-~d%7!-7zmA3&Z2`?h`^4}7 zyUH7g=zk-Iut?ALougOvo$I2_=qU3TFssct(6od*|`z-qk_oyy=H7O1!yQZS#ch(<9Tn-sf45 z(Vwh%<`Pb9r*oMIOCcj9G!UO%H5~YM?Cbe_{`?KQcb!8+NyAC&mQqrZN=iUNPL}{$ z&;b?-4WgA~Aj17$d+!}j_20&iBPtpyp(xVS5wbHf<5NP!IrfgSv$K*UB^pL%Az8=H z%CVAt?Cg-jF^;`A-|MYAb${;9{ktE(KfjO1x4)#*Ip@7zuj_hV&yi}N9yEsg5y#Q+ zOr112qj0U1!*sn1xXQ&Nk5tiAqb!HA$UgELD>o-Xb$);k!V|AbUFH2R6g*H9ehS zbSs4^AD%k9HHGA&h9aO$nqbL=QlRH@M?!zJK5DKDLyaAgxN><8UHjuIKJS=@?+4X5 z!|~jMx(?TaDk`6HcG0pG=8UvQnyhLtMS81DXTF)N>GO%4v`RJ4l@|G<<(Fj_r?~sn zYd)qVw*rZQ9&THE@pUQVQt3(TmkQO0GS2oFBL-PdZsu0(*tz$KuKl-W?7b&eN^z+& z0g;0Wxi;nJRNP57$&2o1i9s+AZM`8{{4LC*jKR{|800OfGdF1imLI{ajlfrtjZ)7% zx~s$e`TEdhZ6>#4n^S+`KHgV%l}==O+4wO}UxM9}&(a*ElD?_uP3K4JpjRT&x+D#OHv`5!d7Y~y+0WTc>^*j=(e2yK@yaL(Pp#b1 z4OdkWchPuRnqrZS2-PH`UK>HQMx83#Z8Qm6!X9(fgX0vNCpi=q2x6B9Y(WkTeI(TN zoGU`=EM(<}r+Om;r81S-D;6D~2d_hdjJZt9l%ZCTVX0*tq!2XNLG9 z4}%%ANx)kb75HB_<|D5F$>D3RYg~ApQ2xIEuPdqR+pE=S`Rv}WG7Ac>K#d1f-w*8> zrM%gr(q1>OLR)aZ2xXJReaE#LveeEGO*;q7CZBVkFU#2^O(2*SCWbG7;fC@hC`-E4 zUWWE%6ckXje1=P%G%a7sWS|%ugL~3^?+e2-#9qx&n|?@{^!@8crsE-Ijh+WPRY+Db zIbS)^MX{?Qs}uaTd5N^{Jdy%FU(i(*F9L*&@PScemxA3?M-|Z|No=`@&e9-zz{koW zX8kIyopR%gA=+ZG#6Gk8i7%{i7W8K7Yu1=EuGpxC8pw+qMLn?hb(_+zUs>!dAWA$# zoRobWLgN|tf9myxpW7xIhE%$Ibl=tpPPWI}HtiCN!@4)Hktxx##?R=&z_8~?|3-AU zK?t^WmV#bO(HEVupIr8&xL`|&Yf(U^;E~Xx+{G>{u|@HAART?jhdEcNUtYV@v1dJz z+X}W)OXX>TpLx!k^9tu{qOnzNo=JH-?Rg^PDee|XyLO_?U8ZYn1?%rKE^oX&k8vJX zUK2fR%Us$E50F&Ux$LWwY`Hf@#xCEli6xh(%YS*2OcLRnk66p1? z&7!(y{yqeh+4eL%W;fKCKXe2T=ewe#C7yN?DI{;529IXKq@-Q85E3z?X)8wBl!!(f z#=)X7rb>x)dlMg4geq6H91;+9TRV~-iu=^YR|x~?rIH(ibHx0+$|o~A3ZmaF5%MBt@4JawIqTNiHl;Ac&zcW*6?F3l5(ewq1eS zEPQG5ok4Q2{Ru@mQhan?kd$pk!=Ot(=)eY{2$#>(KvS^|voskzw&C!`$aE$(_VRee zrTMzm+KCdB>Dq;6*Xx4Li~aUjJ0Ko)z=LnkHMy^2NbT($lmBc@q$mGlhS9G z?4_IZsnzb3uc%yOQZc>v`u%*JD7$LGjiX5r1GFna3uAPs^OtMq@-7kmmtd=tV-z!w#u+IGN91k95^@d2;X9~Il-1{rHMK%hk5udTv|rv#PS@CTBpPT)P+tS%R>482p#-2aC=L%MMvBpA zYxQf=9AKsQut9B(WFlG0sui_u0_L^SmxmYOs1B!a3w9M8p!U$BaCN#e!s>0@#x}T^ zgD*k7h^HcsOA(GxXe;|1U9;wt7F5xe9a<5Vnzn*FG}$4e@q;Qq<5N!Cc(&fymCMR^ zR`4B_a{J4d>Vixd|Lt0RAWtAkB{$JE;Q^*WW6JXK#?#Kmt3j8PwG|VSwCv-t%+{tB z3E{5T_wuv|jyelBMeO)%j0u`FN;UyTXKe)#x;zNzaQsmqur5PAUVC}5+wen*`Sl>` z!QdC-uJq=i8YE_EyiC|)^%z@ac4}KnT=dHZ-JGt*5aS}+O=uS)xYgIV{j$TaD>}AN z5&hNO6D3_oJAtRb_arXd5g66!j4txj&hM#+GBAJ@b9knFx$zas0=JAyU7GL$Jq$M7 z9Xkba)$QF_-Zl%hGg2K6GlE63G_g?}Q*LbSh)b4H+rTAAU&s|H>Js0)d{R?evza3M zy{g9LK$^DvE4VBTAZh8+OKU5}3o*AI$1zji5U?)HMOC!S$Dl%a&?KYz<=zHwU)+CF za8eQFVZ*kGka$wRNSCs|ALUkw>__rj_ccU~1~TDq5;7`IxC5>HNJWgwxx=IESQ3Jw zq0M=3x_%c;$gw>5zSkuUc#Qb|#9kjNT-z9|Hs|{ZxTqF_V&rXtaB#8KLd;9Jgk9(7 z7UiNxf+}xL8uIV=+8D$eGnV~khl@wuBp;L4m3&G8a-H>%!s+9g>`5r|5dgEFAv_$* zJPR%*eJ6e%^H{aow->m&WVyqKohmcfcN&6V`?fh?8a{}Rn6Xt^rFl|M2N+Y$=$xZ8 zOMNijSMw&9(IQK!ZTCXu$Lle(>o7m564Ex3o?FiB->iW8Sw%n7L8~j|xGH|!Gv!`V zx=8b0O)kdF8?n1a#yk3G{W~Vy4Rb!XlxMK}(!B1XTkwcXv3t>p&wE7Js5|)Av4dqU zu#>NkJ=C?JpDl5=-)ljk=Vi~K{^7K7Wsd#y*vA2`u0m_COG**ZqxlQfq8F9#E`WRF zP@7S{kzXgryj1&8|DAB)TT}IQc$*n4NH7L}%h`Bd&2s^93*dPEB@$TV`bymOh!=q& zKBmnY)Z3Njv9?~kIjakL!VX6~9}yPfEYrte{C7ibuy@$@7PF|$wD}%sv2ay#ykt~& zjeE{eSO@g)OqtZrKHBucDL5hw967@rm*o#r=K^-Wr-?ci<|3HYIBBu|I*U3lZ_;k{ z)3xCr87T^e4tw?JD;EwOVmQ|u+8TXK<4z|VW4@$&!{M-^z|{~7r@}@kCoa+aIpo71 zP)uTmi)p9a?LbLDIa!PrKT7Gew(fCVSER~!dV4^yJRFZDQOIyIKJ_y56Y$e%RMy4- zl~Iq+WvBGC!=ColNKaYTrien7jVaD!w7M|lk=WtsxnGDi`kfOLjv4H^-Cx*M%W)RhG+EZcNr3jf*!KrJ3~(hsN`Vq+ z7#6=a+K?kN|Ll;C1zYop7kMg;U*D2-JEKh!@Y+4(S%$)uV&_u%&n1qUyF-UV z+2uDAZf?bD!pwc#5ZyP*LUw!ydax@)(9j`)BzG0(L_D6Y6~tAo4`r}N^tg?nvF4*) zL(%n32+#c9%TGCFPdhO#)k#$=g%; zF0pzhbWeeLv107yRm(j5IemiKn`FMiV;WaGuip>FM|(ld!CN?^HY{p-T%RR)#yPq; z?{iMWpY=RS6DmR3L%G@m<-nW%fpP(AJw60=pA&f9i$wr7QU+bhGk!|-U90?wZYvqC zq@V5Ryi*nbu(30cNFHXbXi2jtNGc1YtZi5nOjl&E=X9LBm$od>W2cNa@3L(7{FtNJ zLp5#Q-kt#ofGkg9<`Ze3p;XrSkrV$tJ&H~nWJ!Dbu-t9W85vcMt=cDWH>lR0O{>fe zQ_|A?5q#|2SfR6|BExw9(saLJPL27ZPMT>7F*gN&18wkHxr*@kW}IR2dRyuKoYf9; z8sS09THQ&b*w63y9Gb)8V!xB5Jux-2`JD~bzRs{NFVK4IRi#h$pyGC1=-!ozXcPF@jmVlk~D{wezMWR{jm`vUnCjTqAjj^k~SPWNai5Y8}<1wfgd)M zK)|qgxd5uv>N6&>PB8{Cb)w94CwAxXOV#Zf*boRf-pu*7jOdER6d+~0f< z&j^!T=5nvRZbkgUktFGb&Gp_*3d?U=0y3>L0%(fD#f*N|eEh<|tm0(r{JZ*Q19G(F zD9?EQ+lA#8hk--AcoB0Kyf-dneq=2Pl`?+mE?4%GvnLem$pSa=%fqi0iL7uI=auT> zd~XgfE|rGNJVPMM&!Y8~dD3M=_c6LG`!0=o z7y{rs@Mm}+=IddbG$b*wzI|Xp&@*>*st_%=G-uN^)YN0>`kHZ%G2QHgK$EZAld=v* zP2;4L9PS)4fzkOcy4H#uZhnVkc0)~_Bf_b<89|onvCN=4$pcF_zZ8;sB7=f zyyhZOR+#T;)f2{hjz8|`m_Fk-zQP&OQ07iCw(p{$)#ETE?rw6gOMiWb<>$=73`6Ps zIV8&ld=G%6P9)4-> zeDdT0d$>iF#3To(J&0!%iCcFlJ`x{=+66M67PraUnwC+=R*G%2Uw&xcZ1Z=7t>BNT~1nS)RhlWW9W;B46LK`$p^%*g-ve>SwR4JI9#{sRXS zkBXye!6pjV-`^Tc4V;UzjI>QGYdw<1K#xIUh3lEj=%2-Oi zXPNk+cjaIKI7g_KaKr6N{Gd7ZJAgo(3NHA0y%4{D`~08t6ul!mLIVDisQBk4K>ju1 zESU<4YQTJ~RZJku@aZBJoKI}iv80vxG zuK)Qe57U!y&G`ghj_v%ToB!u8AWm^{=pU}v-@dh?JksHu-R0l&4{X!FeV+u9!DIjX zRoJKEDW)Ho#~#xzK^jdh>=Nn!`cEDVCmO+v6sBn54C(P>210OGjo58PiNb?9e<*5A zwea~lmu+=*;7vWhkbSY;B%>UK;;>uPf(?aiF-y*`LwI%Y2yjIO0pU9pz|4I!50-pX zdw9xEj_CmkWRbc=H9-37%Cl0FYLEn1TFZvlAS_J8@wyvSQYQU`OoY`C2iluK*dGKy zfmn5$_PxX>%H+&?A!C@qIyM8fiwii~@bFl89e-DCGRVOc z5ckYM=t-nvYsuSS=WPa2Qn1;4M>-Inxx03JVDo@>A_R(iVP6Eaf*b_0?NbEjZGhqq z>9J+0K<%Bq&ZF!+`kx<-og6waia4RtvH{(VAG9E{`_Di15r6jyR6?~7WAxn^Tk{1- zX=i1=A14#sT>v10F?c+1ltV5rW4Bt3KJ69h69=Yx?VZ0TMrgE5WVh?Y0qLyd{zau( za#{3-3-EIST)NA?H+lZZHV7SnEz>6zv4vqW7vy+ttJb|#txjvK}{sHs@aYS#deTy#D5hO3*IB1`BZdUzMP^ z3uB=3j)W1l)@o50M#DP*6eJLC{^1(>_S5kp)+taiQqX<^9u^@pBt}=|T@po68i^>t zWPYew%0HgRE82Q>3AbOa{R8j$k5_FiGq=vcqwSzj_Iug?Z_nCEPI%=2^}(at{w05V zP=EjR0}X(l!siVB_vgQ&M|Oe%+iN?M%-Vx^XKgYm3SQziDs9kQ&h(lf^H@+TI$;~ixeTqx&Ug3P$<9a=O%i(vcQ`?PN^ki-LKqW5Y8PR2>kg#D^S>Z zD?nay@p7c?DZGiKqkpL29#E=r8ZS-#Ks%O& z#lQM8i+uwc40RkZ!{dN(VI1kUzy((r(-})ycabOakQxXIj)JnS)2>;*4y~#k#9ar@ zE_EPNHbJ5j5eo@K;)moOB5d$XAe?%~)eMO=`+T(Yvf<7Q+cU80lsI@nx~mY{_hzte z5H~Q36lFE%<+7hmD*MUpeD?xxX*e(pMpgrER^2}-Tfm}Unl-QM< zW&$LB$p}|bRV9UhbX%i2WR)`prm;HVBRG#4LvYo;z8UqstXMk(5c&;5^aj@!aRwN< z%7bU{?Vs>?P1$2~zsrCYRu4Y&*c-_*<6OewO5(KKj@{PY&s5>I%S0IJs-Whv1$V+w zAfw;b+I0|rLp;b4(CXZpC18rzLeis-{<#@Jfp-ycpZ9a0;`9Kk;80kQ8xw34^nqT* zBep&l5dO_Pv#xvo^s>i8L>I6%X!F=1jSx-hV1JtYVh7}7Ew}krVA2Rp6mHTJctu|n z401#nRDt=vF=Cn34AFPCP)0sZy#i|h64qPlGeqIEcF}0%# z1>pEI2&bYRHRQh199auo;v;2*%p-~_on=4}GDcz(kXZo_q`)jT-cd7t=ejRBxxg#$ z;%|L!)MKgyvXBQ4=+g!G4M1S!Qk%2jhw#(5x`bGa*&Z0?l%tbDca(u-EP|U7BlDYf z(~}n=%VEpxnXAou18g=>W9p(qGJ(x19TLU^XCOiZ8&W7SSpNpxoN+mc;;1Pe2Rc6Vot&;te6X(+dXnjd`Jn%}QN9-&+ z=xjK?A#c)!8w!mgd zpP5qp(4Htf3nV*-8uN0Mi@i6-pgyn+#sA<0jM=LgGEhqYvQOJO*Bcd>Zy)=uH$mX^RvDXeO2W!``}|04W(FL+s~I=U{xV$cBu9GZpa4F+U?#TJKN zvNkY>X`y4~U&ZM0Qr>8%Co_EMA7<#NO67j-yd)kBY@d#N;>+q2YqdRmaLBi&F+U?yFDl=WsD0^#A63)Ii*OmWtG|+KtzOX?Bl-gxOLB;z26&BulwoS z3EQ4&qK3YeT9`#nXGMx!k60I8M8Ja0vkovcPgv)<%fUUaKHKi*^-%DR>t$Cq`vEIc zPxizy2+>jp`*-#Cl-$)<6v&7xtB@~rkyvk4$Jb2%7h<}qvGG+_#}-J&C$>5LSXdDa{EicwEa>*!PUaL4-FAdg~I0 z*<~$hLHTq;BcW)HcID%_soMErfVfqScY=b>B&WmRnI7lzg`J0kM5E36AlQSbv0?f# zM2=9-s=I@Y%_`{r%Z}e$iuikqFTK`&c5T&K85DCnt_N{bfri=9t98{0ClQ+(yt`HE zJCtiY#Luch_7L`FIjlgiwY(xIKdivb!%5n8>cRDRj?#WBXVW-Kb2C>-imsCtg~UkQ z3|f)?DVTmd4`0}9p`C+=Z4CED^-Maol0yG%mDvIrAn({we%F$R6vm(dx2C3ke|lAFeMaC9}t>1?r6ZGly*b>V zJNmh;be>Hv_TKD38kO^WNK8gBZHuJl<&pPP%dV;KBF?tnf4%B@IWR6GXZ}DRbX7X~ zj!PFXCla%cGEi$$M~gwEam9WLe}{afqMaDbw=ZNFzYCry87gRNPf@!pDXh{tPYXo) z8?G2ZJs_`Bw#rS@SSMpIoG{^trVQX1*t&mkZbs^sWJ2yOwC^6`?jWAy+SH`5+1vLB zniW}gw4~|k25{UG68N0T1GmiLv-U9x*nX*JVe%9XG7H?U-Q2M%stwqS`$uEN1qg0t z)8O1dk%TH2j0rjy85x?RoXlJDoXbO;lAQZZW%ZC7-x$p_qT8 zs58G>dAsSl%El;2!tSJg?DGk1Z&*5Jv@*tOIdeM|7yEQkkKvChlCtF?DJka^Oy@pc z+bpeWc;T)M8t86KW`X-hn+a#4UbEty5@hu*Prv0@>VH#{caXq4vJhmHw`W);3-t}L zbLY|fMA9xKBaqCe&<=g*PzCi1U9&jbQ)5Cj1m5Y@3Ofg~BD1M6gZLMG_onoCATL7I z`W=-Kv#fRfou2BU+i~NE+5CDrbq=z6iO0=eS}x#}W-{#)8fu8Nnt)Kr+)-1f1iS&W zt79oFLJ6bSq%Kb{VGV6phaI4)n7`vc{hu>JLUu!UNPR#bBmM9q&aU`b-}c#A6j8 z&tL5<6)GyMp|o|h~~o06Y=OqAtYWr|)OD`+$l-(4);OD^;L zn8`QjVKbpe4g=HH+qKqPPwcI3{gVTyr+{jd$Jm5Ku_c1lJ>*=4LUzjYCKo@qk9H$p z^xVRR(wGxd`8I_5^U=OGVS1Gz35PtudxCL&MBfwi)jJJkhivQb?D}YluGYU5gTC-` zawyg(Q+pv?HC7o?0R3>6&;m`MeHKr@feln7q(8)op*eSfmNYWyo-3$+Y7rs`j=2c> zQ?1zaKI?UV$&rU?-|{ct4-sd1_nK-7(plLYBRQcT?FofeUP*DqM3rmF9E)r7r=6sLi1UgT78b$Df;15iNT2RaFp8+Bwx>>*PH?Llicr!> z)zo>}9(EczM`w@Hy6Cq0sPw);+)9&b3&B=^hCh1pnmtbeIOI12_xjQ665>O(X(W$3 z29pRlC=d8J{#Xe*p|&P~IC-0~U{i7SdtDXFCt19Y^=XhM-BhEU1F5aAU8h&Wn){2l zqGHH0Y^Kk=N%B}M#~JFuw1eX#tieA}Y*tO8k^|d{dhsX}IRF8)#DhI?lm4^Nm&k@n z_4+q#KDl=E6~(K=OHkZ)%S&Z{DuC9;Gu$hbJG=pT`a&0i37vcPLf930kO?F`2;0C> zKqN>bh1D)|4%$)&?a{=O9lP8umqwpPPU^0&EsesWzvMcrAxOsHsibXzol&KxOgMR^ zU+$c@WPPYs(XMp|**CD7+7}d#nxY5lUQ19e2P!U)=0;g6<#t&LFFF7KG||djdQJGlM)YtzV0$m{JRH~C;@6d~=wmC>bxl-^$0A`iZ+zr-oOjL0wO@%$mS!%Mh=G1Jb(5@dBGZ~$Eb&_P3%0Oo zf#rKutauY_GgnJ#I_f%W0)-=sM}q;2bNTh~k<1wOGwy>}&9FX`4`+YZvP1+zwHNf4 z?x5c6hak{A8TMrVQh+3Ck>*Nc39+tp;+m|vqL-50W<&c4d-dAJYWc0=hpK! zy;^OiPiCsb^sKyF7V6*QxDU{IG}J8)`F~roEzQ-CRTv&hiuLrQbe<064uq%KywKRgc8_gy*_x++V|jTRS(l#HICtK_j@qq%15N1DP6!fFo}i8_ z59EEN`CUv{sQ{@YPC6=99oU9B#Dn%gDM9c)A#}9y9;VAY({CBr0d+vsQe!bh)U`@p zFZvKnllC>wsTDHlwm{PAdu=(lWGCatH;V08AICT5orr_F;f>-Z#7i(XlbUJFc=?`lCe-y|2<1Qy>@s~B z_)wxx^YgG17jioc_GAK~hP(!r@T$`^XJ`~_@)|@}Ywi6KIg|nCS@-S2LrX84XKD2Q zj3nRTs>Rux`_B6mT`%5PNm^d*-JEc)U22ZpxCNFk;ns}KB(l_mksra}K2!y<$wF6S zQqACU8fQYZBa#fgs28VOHD7c6@Mbom6^wPhDo=f?+#B?57Z0JKef|J4ApQPL(qH6G*vd%I6}# zLWRIiN~+GpqvAn1rqOwT2+mHrZ+7)Wflt=6eD&j;^YEyIBlgGhsHjn24ZVbilThhh zBr0ea<~s~GvWPx?Y7_RzcjoME(&I>ed^h4RgbWBMN6=14gR+bIOd`Pv+lWQ8wVT#+ zZZ$!K`FklQWXrBPreR8=t4Wv`VzxL-&;0e8wEPL7W0Qy!@)p4U-mmlG{a!; ztch*62Ir->O65UJPo@nUQ2W|B&j+5ZS^$$!yV3)9_CQ3l=W27Q74gh9iQ;J2GcUsR zHwm+OJ~j-+x`Yj^O*z1#wcuu8=)5@b#sNBkI6`8J7bML4yO2l~o+?{BJiv4CpBg3@ z^$dz6ocOMZ@@2X#bYurcAbCz|8H`shLPMzF#ao-eL^gtYknO$ofGS^puF*Q_#dN5t z{xhdA@_2`X*>@qTFiUDwkqXc;cGF;~y zEZGteNG^iOs0L0veu*dc@`g@HA2ctHUG<1_Bb*W_`HIlpI!Q<1HCJSb@sLz$u zetfzG^wH#+83`u-kWm>)qRIo$`?AY&uT1`0x?p?=8$k#~JqUXCuXfv(w`1Ev@PW7> zGW^^BSEA2Mg6wg7>o~gmxC|wLo>6$paKA9|twQulU9Q$_jF}hC4dZ1A`<@&WI(`+x zwTD~skB~q!!gX_2ddJ=i%z<$c3|9fmRgUn*Y1aAIb z1OIk~w*UGHd8Li_qd%6*?SK2d%WVC$4A(y*k}(m;3Gc{23~|vcIpguFW}c1FX`4J~ zi%#4C`BE+04P=uYy)^Y{$E@s)liMfVgUB)kn=8HBUr>V51gat~UXSK)D;EKr7+D9U zb=b@20)P8NG;+kPotZ{JXYmlfK*v_1p?>C%+XT%9gG)(U*h0g>fG6fAkAI>ovb%4A zeMn6n6voNF?b!A6xL#^RVTjTf5b7rWMik%$#&7J~o~l%|p6nb-#ISSZzY@B1YdcPX zmByoZjlekw`3XFrW_E}jyrjLX5%dKf_FJ)o-hO{O!MEt5DDE1cGJsj>QOa(ZOc;ae zb|MYH^H)_)AgJ;b(S|X{t16n%?W+oBlhK3qby{Y%fc5YC8GiBzy8uToFS;@&{Kp~x z?F{|=s|p+G%E=UrcfcD75)ZIw+_LPF7|Uu_km=!ML*lHA!70xY2Sda-3D2FzU@9{R zmIi#xu2UY%fbqzIfnD8x;W0f1KGT6dqm@PA4u^qNLYajXOc?kebil#zLt=Qr(iF6B z;jqi(tAm(`58Qa3=^m5gRR2(h$j0O%MSBp?KMuffV;G@Y*Fm?y_BPR26PJM_=93-t>98Jl6%UCR5Q@p;QIrMJ5%itM%ggoLkiNBu1QN7%_ zWCKGuTPJ8j8~l<39gnuaRwpPl`HSM>;Y(H&k-4!l3UN?hKQ0k-?!g!$wL1flxKQx1 zR$(FF`dqUrm#;x@HM=AX*K9q6{|^j5ip;b3?E_H%ve>G$*0)Hfz&% z)SiW#H5@^|5r?j05QIJm#SR~^m~06=(kh`Q-5;$v{P0owgaS}DeT8j>e&Pn90I8sY z(Aj?GH8{&13%b~uc`$Ri1(_RTF?k{@u8DG&t^2M7tIj`;8IRioh`oC)xPuQrmVb40 zn>+CH&WI8xUG0B`EGR0B9E5V6<5P!2M zf}lH|&XR$kKl7bb>Z+DAHET(ITDEn3=X00A@uPCA)F#^KsOAriXxCxYTg+N}zaZ3s z3?yFSUYbSMt~moR^F@Cai9A<`r10HGPuK$poB_+gDI?b)dw`xQLKb*LVN@)9)wYH` zw~WNy$`1=UrW$aC^t_--}GS?RISOgs)A$R?kK`g>V9QIqF;;7MmJeqyq{Zf)%f z9wHPUtT4vzeg}~UlXxNuljAax7jKqSW@2D6u1klE@(I?(e*ZA1nu2l8z{ zjq}ObZ5pZNQ;3g^h6-y5i1KP-`eydgTuT4vsJp)l$C?F8uki^sT?XyrwhJd1{ z5n)}YdSz*pA=gPFNjG~8on(6SjJKrLt=QK^FguGj%P53#XEgG9aF}9X#@G;!t&&85iN39G$^$-Ra;ubQ((fn0 zpYx?7JEC>x;USoI@h^DfQgtZ{R$*{q)dA=7XQJDZY(kuEl{n8rwa$&?7P9rKTs^`6EPe}wpAt5LYA1rE~(`hVGq@sXvQYgU`gd?t*evm3H^S9RZ}GRZm8TJ0?Bw~EST9M}J$f#QzMYm+2Bwr_o*IpiYT+rtwljMmyJu;+ zd;(0n5+(L%x_GiJz_NPo0<*A#d81D*Vx!zek*WDMl>)(OkC``PQqN|kqu(M)yn1;v z$J@x%Qk1gf+#$gcGX|)?LRCA>4UvP&_p`b&9Oo`HGxl;7raI_$NR#b5EF%r!QM0Ob*mHcfQf%X(a*Y9Vy~wOwF4O$dAv%@UEkZ3der`|FB@bs` z#5^-7HvwvSt>_wkS#>-``k}6gSWh?Qn!cqdO!2}GnOs*y+MpYF(Sof~`&bY~Kus^@ zL8m`RMM#14GMj|`!FZZ3988M{sb;6y*uDb2%2t0#_WdnksuJu@VnB}9%R6f>P?zeJ z5;tBa9D|{qmSDGsX_|q+^%RpxFn?Mqk8T#|zUvuomEX^k6K)vunnslM#@D!U|IP;N z6qc(KqHc@@+@a6rGxacFbD`<$OeL>#TkIJcOf;&3$@t53kqtk&iGo>$cCwC!_IqX1 ztM6X(Oh+G}?~08!dzxbXytS{~LF#z1sQ}%Fl+)A|W8hyf>sz66cDrKAqe_UdhEe}f zzg=UV&PS(>go3zu2Pa@AH=;qgddXBGsjZZ%x-cPsKVNu6!qm>=F(}S$ICG~`IxHd8fAHR=nG{GXHW^QiPqi$o)tov>$fT9 zIbxVBWRJFF`e~@JC7?f4oK)Bk!@_oy=4`8}hLPmFW>bWSJ=Xl!cXmAKGxqvQu?1gOUojK*H zqx&_i#xCU!LX4K)kt=I-rV(-h1DDa#@zhW!%mh?8*p6Awm^`j9xK3+M|KNNjwSoc=ulL5h{!EpK z63dnopn+n<6bZH~l;4e1FO#hg)!5~}_|YbF{Xm#0u3?X8*Qd`0_*IyGEK`+*H7 zz>UspcZjtBDcR>o@}l5$I8liIJWVKkbhY3QT@%tQRjETa6H$E?&IN&^5h!?p`HCRK z)Khm=EuGeGi@nZ#y57J5_zMzOuYE7#_z#_HsXrzpp7E|j)PqJ$ z`N%?D*WsG-A?>3os$NCHV^l{K!d|7437Mn>JiGo zttzV?`t6p#r(7h^mQMgJ~pOt^e0ukW0PSmJ5$t}iQ5z&j`I9~ z_s40S-d@en>`^}iTpJaSPxp_mEN`Htw81o}wvavRU3(hO23lKW65Q!!4EM6#sXAo8 zD`h5jqh((vN*^i=eID1PsipU4A~84k@ioX64uF(#YVn41uDtHiWbW@!;_1$~rvEB6 z{#EmyMG%I7=lrX{dUx7W=2#t^NFX92Wy>iGXed%CWUzij;}B{PNKOYtrzO5NOa2HG zA)K|F8BjxPEJ_%08OMz2`VNWJzrjhG!>DY(=2_IQ9a1ryY})83`cvGV=^6oGr`ajZ zYAk^H+vS9{LSVDW!CX$NeHNCOU9)`rca6M|@(4{*11wic%e_YS@+snr4MFyYJNEy2 zLu9dNIZcXtkb3Q}a5rfZj{*u_D5Ez!IS;Hh~+j~eQ?o*ql)p*5xD}Us@zNcB? zm>N}P2q79dXX`xQf57b6kAWyIciNWHT9Al;k1O36DUo1SxqpFoNyOx`{_@PI7?eGJ z%$^A7E&xoRhniWP#^IsJI&Y85ZK7qX& zY1j*kfXNEm)ex%*h%-&ioVY&NQAqV0g^e&rKo=9Mw9G1c=j~^B)ayQb?dhkdXeQdI zQ@1VyHwz^ex|q}ufcakYNuNzwL~J7+--Hl%o;d*2u&aNEcdX&xB0a?KgXUmpNCpMx zz+(#>xnJ95_~LS#2%N{WMT9aK$V_N#_{0GlA~oCeEYgOD0xXbs2Qk22ZZF<2a&>e? zsNcv46RMfpH&bP_ck4hyWSM8l0Yyo@F|<_@YtXZXA-uRj@F;eeZJGJ3+OhK$RRn7p z0o)!gnl492;lD!xDw0sP0emJu4b4e7m4k2>-nGtRjy)pKkP@-2)DVA8hRE73-cPCc za2$e-m5$0t6o!Lbjt!yXa3M6lS%f{HoP@c5ii^*@qcKHlQ)R;i*nYmwK1z-JhgA-W z-2E<&aAFsAHX%1*fGJB^wPrfSj*r7SIvR>%nbMc6B2c^y!UTmIS*dIOn!&a1%lZr+ zdv&lm55$dZbaG8?mjGJ}0V@t5Iq7poUJy90O@VOQRjRundBl-sGEiB;y1xxD^pfGn z-AIV2hIWFHH_D=4^U8bfCizS-G5lzcoln(}xuXqe2cIb#vG5H64~S|zNc3q%)xzxD zOhI^@H&;u5)P9GeNh|vgjHZPVLQssMV=}c)PJQim#sN<|_%Z50Ffif6k+Hs?ce2kU zG91XhgJIJ}&%t2$jRBQQ$ z6_fKYi)v1brw879W|;XMqmbPlMbZ?X4#`ptvlPNi^gJ@N9RspLs@E)Z7CQQ^_y#~R z7;AeW0~K%@i-(JH+iUty!00f7WcH~ev&sZgGKgT{0eWqyAb1{Yd0{Q*M%Dq9xMR0t z456aqe#A~&WbQL1O-l7!?7Yn!d_{XPu{=q`d-&{EVCynaa^az)RCkQqxrrxB7|)m73L!5A6Pba_N!N6?;&vbmRN8Ami3#TG*!&zL8-QpwPkrXtU9e&7qZ10m~xHyH*X;quVW>zIZId+egU8^v4xfbOqe}nH8SlS9!I)?Vh-% z2Oa!HBxJm>Sp=*WuU&Wg8@#W0fiz5o_PYQ4A8+{c{lybLB`C#c5z^>PlW+aK(HJ2& z4YF)a{^d>ot1u!ZH^qLk$n7!a@4hRA6g#%Sc@f^}XVi`4bIX4IDN@`1sGyVL^o>EE zYh`}_|DQi4s3V^A_WdlYg_bZ;J}O`t!Iv`}vKcOc4bYgJaGg+R%T$ r)Z177ur@s5|L;xzZ#bvlH%V_O(=a|iY#dGkf38U@TuGJGd-#6 Chapter 6 Cancer genomics and epidemiology | Biomedical Data Science - introduction with case studies - + @@ -23,7 +23,7 @@ - + @@ -286,30 +286,14 @@

  • 6.1.2 Statistical analysis
  • 6.1.3 Literature search
  • -
  • 6.2 Case study 2: Cancer Epidemiology
  • -
  • 6.3 1. Scenario
  • -
  • 6.4 2. Hong Kong population +
  • 6.2 Case study 2: Cancer Epidemiology
  • -
  • 6.5 3. Cancer registry data -
  • -
  • 6.6 4. Existing cancer funding and publication data -
  • -
  • 6.7 5. Open disucssion
  • 7 Population Genetics and Diseases -
    -

    6.5.1 Download cancer registry data

    +
    +

    6.2.3.1 Download cancer registry data

    HKCancer<- read.table("https://github.com/StatBiomed/BMDS-book/raw/main/notebooks/module5-epidemi/HK_cancer_incidence_mortality_1990-2020.txt",
                           sep = "\t", header = TRUE, stringsAsFactor=FALSE)
     HKCancer
    @@ -803,8 +786,8 @@ 

    6.5.1 Download cancer registry da #> 25 0 0 #> [ reached 'max' / getOption("max.print") -- omitted 23 rows ]

    -
    -

    6.5.2 3a. Visualise changes in incidence and mortality

    +
    +

    6.2.3.2 a. Visualise changes in incidence and mortality

    # first plot incidence for each cancer type
     
     #Import the necessary packages and libraries
    @@ -864,8 +847,8 @@ 

    6.5.2 3a. Visualise changes in in do.call('grid.arrange',c(p,ncol=3,nrow=12))

    -
    -

    6.5.3 3b. Calculate cancer risk and mortality rate

    +
    +

    6.2.3.3 b. Calculate cancer risk and mortality rate

    To calculate disease risk we need to calculated the number of new cases over the number of persons at risk over a specific time period. We have the incidence for each decade and can estimate the number of persons at risk based on the population in 1995, 2005 and 2015.

    # cancer risk calculation
     
    @@ -938,8 +921,8 @@ 

    6.5.3 3b. Calculate cancer risk a do.call('grid.arrange',c(p,ncol=3,nrow=12))

    -
    -

    6.5.4 3c. Mortality-incidence ratio

    +
    +

    6.2.3.4 c. Mortality-incidence ratio

    Cancer research can be focused on improving cancer outcomes in a number of ways. For example cancer prevention research that seeks to reduce cancer incidence which would also ultimately reduce cancer mortality. Another area is cancer therapy which would not affect incidence but seeks to reduce mortality, or at least prolong survival. We don’t go into survival analysis in this tutorial, but a way to get an idea whether treatment is improving by looking at the mortality-incidence ratio.

    
     HKCancer_inc <- HKCancer[HKCancer$Type=='incidence',]
    @@ -972,8 +955,8 @@ 

    6.5.4 3c. Mortality-incidence rat do.call('grid.arrange',c(p,ncol=3,nrow=12))

    -
    -

    6.5.5 3d. Paired t-test on mortality-incidence ratio change

    +
    +

    6.2.3.5 d. Paired t-test on mortality-incidence ratio change

    In general, across the different cancer types is cancer treatment improving? We can use a paired t-test comparing the mortality-incidence ratio of cancers from the 1990-1999 period with the 2010-2019 period.

    
     #First sum up all incidence and mortality data for each cancer type across age and sex
    @@ -1058,8 +1041,8 @@ 

    6.5.5 3d. Paired t-test on mortal #> mean difference #> 0.017148

    -
    -

    6.5.6 3e. Childhood versus elderly cancers

    +
    +

    6.2.3.6 e. Childhood versus elderly cancers

    Although it is clear that the incidence of cancer is typically higher in the elderly, some cancers affect children as well. What cancer types disproportionate affect children? For each cancer type, compare the proportion of 0-19 versus 65+ incidence against the 0-19 versus 65+ incidence for all other cancer types.

    # Examine the proportion of childhood 
     
    @@ -1133,8 +1116,8 @@ 

    6.5.6 3e. Childhood versus elderl #> 37 Total 1 -4.121323

    -
    -

    6.6 4. Existing cancer funding and publication data

    +
    +

    6.2.4 Existing cancer funding and publication data

    The Hong Kong government established in Health and Medical Research Fund (HMRF) in 2011 to specifically provide research funding for health and medical research in Hong Kong. Since 2016 over 370 projects in the category of Cancer has been funded for a total of ~$400 M dollars. A list of all funded projects can be found on the Health Bureau webpage. You would like to use this data to see if there is any association between previous project funding and the epidemiology of cancers in Hong Kong.

    We can also do a similar thing with publications and ask if the research publications in Hong Kong have been aligned with the incidence and mortality. We can obtain this data from PubMed using the following terms: (“Hong Kong”[Affiliation]) AND (neoplasms[MeSH Terms])

    The data has been predownloaded as the Pubmed API via R is a bit slow.

    @@ -1144,8 +1127,8 @@

    6.6 4. Existing cancer funding an
  • What are the main cancer types being researched in Hong Kong?
  • Is there any correlation between funding and cancer incidence and mortality?
  • -
    -

    6.6.1 Download HMRF grants and Pubmed data

    +
    +

    6.2.4.1 Download HMRF grants and Pubmed data

    
     HMRF<- read.delim("https://github.com/StatBiomed/BMDS-book/raw/main/notebooks/module5-epidemi/HMRF_cancer_grants.txt", sep = "\t", header = TRUE, stringsAsFactor=FALSE, check.names = TRUE)
     #HMRF
    @@ -1156,8 +1139,8 @@ 

    6.6.1 Download HMRF grants and Pu hist(pubmed$Publication.Year)

    -
    -

    6.6.2 Make word cloud for grants

    +
    +

    6.2.4.2 Make word cloud for grants

    @@ -1201,8 +1184,8 @@

    6.6.2 Make word cloud for grants<

    -
    -

    6.6.3 Make compare grant funding with incidence and mortality

    +
    +

    6.2.4.3 Make compare grant funding with incidence and mortality

    
     if (!require("ggrepel")) install.packages("ggrepel")
     if (!require("ggplot2")) install.packages("gglot2")
    @@ -1275,10 +1258,11 @@ 

    6.6.3 Make compare grant funding

    -
    -

    6.7 5. Open disucssion

    +
    +

    6.2.5 Open disucssion

    As a group discuss what cancer type would be most worthy of funding in Hong Kong. Statistics and figures should be used to support your decision. If possible also discuss other data/analyses that can be performed and/or other diseases that are also in need of funding in Hong Kong.

    +
    diff --git a/docs/image-digital.html b/docs/image-digital.html index df0ab36..cef132b 100644 --- a/docs/image-digital.html +++ b/docs/image-digital.html @@ -6,7 +6,7 @@ Chapter 5 Medical Image and Digital Health | Biomedical Data Science - introduction with case studies - + @@ -23,7 +23,7 @@ - + @@ -286,30 +286,14 @@
  • 6.1.2 Statistical analysis
  • 6.1.3 Literature search
  • -
  • 6.2 Case study 2: Cancer Epidemiology
  • -
  • 6.3 1. Scenario
  • -
  • 6.4 2. Hong Kong population +
  • 6.2 Case study 2: Cancer Epidemiology
  • -
  • 6.5 3. Cancer registry data -
  • -
  • 6.6 4. Existing cancer funding and publication data -
  • -
  • 6.7 5. Open disucssion
  • 7 Population Genetics and Diseases
      diff --git a/docs/index.html b/docs/index.html index 71bc443..dbb39e4 100644 --- a/docs/index.html +++ b/docs/index.html @@ -6,7 +6,7 @@ Biomedical Data Science - introduction with case studies - + @@ -23,7 +23,7 @@ - + @@ -286,30 +286,14 @@
    • 6.1.2 Statistical analysis
    • 6.1.3 Literature search
  • -
  • 6.2 Case study 2: Cancer Epidemiology
  • -
  • 6.3 1. Scenario
  • -
  • 6.4 2. Hong Kong population +
  • 6.2 Case study 2: Cancer Epidemiology
  • -
  • 6.5 3. Cancer registry data -
  • -
  • 6.6 4. Existing cancer funding and publication data -
  • -
  • 6.7 5. Open disucssion
  • 7 Population Genetics and Diseases
      @@ -363,7 +347,7 @@

      Welcome

      diff --git a/docs/install.html b/docs/install.html index f0bd1ef..d1bf6eb 100644 --- a/docs/install.html +++ b/docs/install.html @@ -6,7 +6,7 @@ Appendix A: Install R & RStudio | Biomedical Data Science - introduction with case studies - + @@ -23,7 +23,7 @@ - + @@ -286,30 +286,14 @@
    • 6.1.2 Statistical analysis
    • 6.1.3 Literature search
  • -
  • 6.2 Case study 2: Cancer Epidemiology
  • -
  • 6.3 1. Scenario
  • -
  • 6.4 2. Hong Kong population +
  • 6.2 Case study 2: Cancer Epidemiology
  • -
  • 6.5 3. Cancer registry data -
  • -
  • 6.6 4. Existing cancer funding and publication data -
  • -
  • 6.7 5. Open disucssion
  • 7 Population Genetics and Diseases
      diff --git a/docs/introClassifier.html b/docs/introClassifier.html index e2c6c29..5d5b326 100644 --- a/docs/introClassifier.html +++ b/docs/introClassifier.html @@ -6,7 +6,7 @@ Chapter 4 Introduction to Classification | Biomedical Data Science - introduction with case studies - + @@ -23,7 +23,7 @@ - + @@ -286,30 +286,14 @@
    • 6.1.2 Statistical analysis
    • 6.1.3 Literature search
  • -
  • 6.2 Case study 2: Cancer Epidemiology
  • -
  • 6.3 1. Scenario
  • -
  • 6.4 2. Hong Kong population +
  • 6.2 Case study 2: Cancer Epidemiology
  • -
  • 6.5 3. Cancer registry data -
  • -
  • 6.6 4. Existing cancer funding and publication data -
  • -
  • 6.7 5. Open disucssion
  • 7 Population Genetics and Diseases
      @@ -851,19 +835,9 @@

      4.4.2 ROC curve # Calculate AUC from the graph AUC_val = calc_auc(g)$AUC -#> Warning: The following aesthetics were dropped during statistical transformation: m, d -#> ℹ This can happen when ggplot fails to infer the correct grouping structure in -#> the data. -#> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical -#> variable into a factor? - -# Display the plot -g + annotate("text", x=0.8, y=0.1, label=paste("AUC =", round(AUC_val, 4))) -#> Warning: The following aesthetics were dropped during statistical transformation: m, d -#> ℹ This can happen when ggplot fails to infer the correct grouping structure in -#> the data. -#> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical -#> variable into a factor? + +# Display the plot +g + annotate("text", x=0.8, y=0.1, label=paste("AUC =", round(AUC_val, 4)))

      diff --git a/docs/introHypoTest.html b/docs/introHypoTest.html index 137cb64..6e8a5a9 100644 --- a/docs/introHypoTest.html +++ b/docs/introHypoTest.html @@ -6,7 +6,7 @@ Chapter 2 Introduction to Hypothesis testing | Biomedical Data Science - introduction with case studies - + @@ -23,7 +23,7 @@ - + @@ -286,30 +286,14 @@
    • 6.1.2 Statistical analysis
    • 6.1.3 Literature search
  • -
  • 6.2 Case study 2: Cancer Epidemiology
  • -
  • 6.3 1. Scenario
  • -
  • 6.4 2. Hong Kong population +
  • 6.2 Case study 2: Cancer Epidemiology
  • -
  • 6.5 3. Cancer registry data -
  • -
  • 6.6 4. Existing cancer funding and publication data -
  • -
  • 6.7 5. Open disucssion
  • 7 Population Genetics and Diseases
      diff --git a/docs/introLinearReg.html b/docs/introLinearReg.html index f00e7d3..80a0f22 100644 --- a/docs/introLinearReg.html +++ b/docs/introLinearReg.html @@ -6,7 +6,7 @@ Chapter 3 Introduction to Linear Regression | Biomedical Data Science - introduction with case studies - + @@ -23,7 +23,7 @@ - + @@ -286,30 +286,14 @@
    • 6.1.2 Statistical analysis
    • 6.1.3 Literature search
  • -
  • 6.2 Case study 2: Cancer Epidemiology
  • -
  • 6.3 1. Scenario
  • -
  • 6.4 2. Hong Kong population +
  • 6.2 Case study 2: Cancer Epidemiology
  • -
  • 6.5 3. Cancer registry data -
  • -
  • 6.6 4. Existing cancer funding and publication data -
  • -
  • 6.7 5. Open disucssion
  • 7 Population Genetics and Diseases
      diff --git a/docs/introR.html b/docs/introR.html index f4c2ced..84de194 100644 --- a/docs/introR.html +++ b/docs/introR.html @@ -6,7 +6,7 @@ Chapter 1 Introduction to R programming | Biomedical Data Science - introduction with case studies - + @@ -23,7 +23,7 @@ - + @@ -286,30 +286,14 @@
    • 6.1.2 Statistical analysis
    • 6.1.3 Literature search
  • -
  • 6.2 Case study 2: Cancer Epidemiology
  • -
  • 6.3 1. Scenario
  • -
  • 6.4 2. Hong Kong population +
  • 6.2 Case study 2: Cancer Epidemiology
  • -
  • 6.5 3. Cancer registry data -
  • -
  • 6.6 4. Existing cancer funding and publication data -
  • -
  • 6.7 5. Open disucssion
  • 7 Population Genetics and Diseases
      diff --git a/docs/libs/gitbook-2.6.7/css/plugin-highlight.css b/docs/libs/gitbook-2.6.7/css/plugin-highlight.css index 2aabd3d..02c0189 100644 --- a/docs/libs/gitbook-2.6.7/css/plugin-highlight.css +++ b/docs/libs/gitbook-2.6.7/css/plugin-highlight.css @@ -133,7 +133,7 @@ .book.color-theme-1 .book-body .page-wrapper .page-inner section.normal code { /* -Orginal Style from ethanschoonover.com/solarized (c) Jeremy Hull +Original Style from ethanschoonover.com/solarized (c) Jeremy Hull */ /* Solarized Green */ diff --git a/docs/pop-genetics.html b/docs/pop-genetics.html index 89cf4cf..9e6d8a8 100644 --- a/docs/pop-genetics.html +++ b/docs/pop-genetics.html @@ -6,7 +6,7 @@ Chapter 7 Population Genetics and Diseases | Biomedical Data Science - introduction with case studies - + @@ -23,7 +23,7 @@ - + @@ -286,30 +286,14 @@
    • 6.1.2 Statistical analysis
    • 6.1.3 Literature search
  • -
  • 6.2 Case study 2: Cancer Epidemiology
  • -
  • 6.3 1. Scenario
  • -
  • 6.4 2. Hong Kong population +
  • 6.2 Case study 2: Cancer Epidemiology
  • -
  • 6.5 3. Cancer registry data -
  • -
  • 6.6 4. Existing cancer funding and publication data -
  • -
  • 6.7 5. Open disucssion
  • 7 Population Genetics and Diseases
      diff --git a/docs/preface.html b/docs/preface.html index a168367..7dfe551 100644 --- a/docs/preface.html +++ b/docs/preface.html @@ -6,7 +6,7 @@ Preface | Biomedical Data Science - introduction with case studies - + @@ -23,7 +23,7 @@ - + @@ -286,30 +286,14 @@
    • 6.1.2 Statistical analysis
    • 6.1.3 Literature search
  • -
  • 6.2 Case study 2: Cancer Epidemiology
  • -
  • 6.3 1. Scenario
  • -
  • 6.4 2. Hong Kong population +
  • 6.2 Case study 2: Cancer Epidemiology
  • -
  • 6.5 3. Cancer registry data -
  • -
  • 6.6 4. Existing cancer funding and publication data -
  • -
  • 6.7 5. Open disucssion
  • 7 Population Genetics and Diseases
      @@ -362,7 +346,7 @@

      Preface

      -

      This book is designed as the lecture notes and textbook for +

      This book is designed as the supporting textbook for BIOF1001: Introduction to Biomedical Data Science, an undergraduate course (Year 1) at the University of Hong Kong.

      This book is not aimed to be a comprehensive textbook, but rather more diff --git a/docs/references-1.html b/docs/references-1.html index d640b91..c58c7e7 100644 --- a/docs/references-1.html +++ b/docs/references-1.html @@ -6,7 +6,7 @@ References | Biomedical Data Science - introduction with case studies - + @@ -23,7 +23,7 @@ - + @@ -286,30 +286,14 @@

    • 6.1.2 Statistical analysis
    • 6.1.3 Literature search
  • -
  • 6.2 Case study 2: Cancer Epidemiology
  • -
  • 6.3 1. Scenario
  • -
  • 6.4 2. Hong Kong population +
  • 6.2 Case study 2: Cancer Epidemiology
  • -
  • 6.5 3. Cancer registry data -
  • -
  • 6.6 4. Existing cancer funding and publication data -
  • -
  • 6.7 5. Open disucssion
  • 7 Population Genetics and Diseases
      diff --git a/docs/search_index.json b/docs/search_index.json index 174cf04..ffcde6b 100644 --- a/docs/search_index.json +++ b/docs/search_index.json @@ -1 +1 @@ -[["index.html", "Biomedical Data Science - introduction with case studies Welcome", " Biomedical Data Science - introduction with case studies BIOF1001 teaching team 2024-08-29 Welcome Welcome to the book Biomedical Data Science - an introduction with case studies. Most contents are demonstrated with R programming language. This book is designed as a collection of R Markdown notebooks, as supplementary to the lecture notes for the course BIOF1001: Introduction to Biomedical Data Science, an undergraduate course (Year 1) at the University of Hong Kong. Note: Most contents may be only updated before or right after the lectures, so please refer to the updated version. GitHub Repository: you can find the source files on StatBiomed/BMDS-book and the way to re-build this book. "],["preface.html", "Preface Introduction for readers Other reference books Acknowledgements", " Preface This book is designed as the lecture notes and textbook for BIOF1001: Introduction to Biomedical Data Science, an undergraduate course (Year 1) at the University of Hong Kong. This book is not aimed to be a comprehensive textbook, but rather more Rmarkdown notebooks as supplementary to lecture notes so that students can reproduce the teaching contents more easily. Introduction for readers What you will learn from this course/book In part I, you will find a general introduction to data science (by Dr YH Huang): Basic programming and visualisation skills: R scripts for the quantitative methods and data visualisation. Quantitative methods: t-test, correlation analysis, clustering, linear regression, linear classification. Gain familiarity with common databases in the biomedical domain. Introduce ethical, legal, social and technological issues related to biomedical data sciences. Introduce good practice in managing a data science project and communicate results to key stakeholders. In part II, you will experience data types in four different biomedical topics, which will be illustrated with both introduction and cases that are suitable for problem-based learning format: Medical imaging and digital health, by Dr Joshua Ho and Dr Rachel Kwan Cancer genomics and epidemiology, by Dr David Shih and Dr Jason Wong Population genetics and diseases, by Dr Clara Tang and Dr Yuanhua Huang What we recommend you do while reading this book To enhance the knowledge and skills learned from this book, we recommend that readers Read the materials/slides provided in each module Practice quantitative skills by solving problems using R Other reference books Besides this online book as a collection of R materials for the teaching contents, we also recommend the following online books as reference: Introduction to Data Science: Data Wrangling and Visualization with R by Rafeal A. Irizarry Advanced Data Science: Statistics and Prediction Algorithms Through Case Studies by Rafeal A. Irizarry Acknowledgements We thank all teachers and student helpers contributing to this course across all years, including 2022: Dr Lequan Yu and Dr Carlos Wong 2022 & 2023: Dr Asif Javed, Dr Tommy Lam, and Dr Kathy Leung Student helpers: Mr Mingze Gao, Ms Fangxin Cai, and Mr Hoi Man Chung. "],["introR.html", "Chapter 1 Introduction to R programming 1.1 Data types 1.2 Data structures 1.3 Read and write files (tables) 1.4 Functions and Packages 1.5 Flow Control 1.6 Plotting 1.7 Scientific computating 1.8 Exercises", " Chapter 1 Introduction to R programming This notebook collects the scripts used for teaching in BIOF1001 for Introduction to R (1 hour teaching). You can get this Rmd file on Moodle or here (right-click and “save link as” to download). R is a programming language, particularly popular for its power in statistical computing, elegant graphics, and also genomic data analysis. It is a free and open-source software, with active support and development from the community. Additionally, R is relatively easy to get started for scientific computing. Note To learn and practice R programming, you need to install R and RStudio on your computer. You can follow the instructions for installation in the Appendix A chapter. 1.1 Data types In R language setting (similar to some other programming languages), there are a few commonly used data types that are predefined in the built-in environment. You can use the class() and typeof() functions to check the class and data type of any variable. In total, R has five data types: Numeric Integers Complex Logical Characters 1.1.1 nemeric (or double) The numeric is for numeric values, as the most common and the default data type. The numeric datatype saves values in double precision (double number of bytes in memory), so the type is also double. x <- c(1.0, 2.0, 5.0, 7.0) x #> [1] 1 2 5 7 class(x) #> [1] "numeric" typeof(x) #> [1] "double" 1.1.2 integer The integer is another data type used for the set of all integers. You can use the capital ‘L’ notation as a suffix to specify a particular value as the integer data type. Also, you can convert a value into an integer type using the as.integer() function. y <- c(1L, 2L, 5L, 7L) y #> [1] 1 2 5 7 class(y) #> [1] "integer" typeof(y) #> [1] "integer" # Assign a integer value to y y = 5 # is y an integer? print(is.integer(y)) #> [1] FALSE 1.1.3 logical In R, the logical data type takes either a value of true or false. A logical value is often generated when comparing variables. z <- c(TRUE, TRUE, TRUE, FALSE) z #> [1] TRUE TRUE TRUE FALSE typeof(z) #> [1] "logical" 1.1.4 character In R, the character is a data type where you have all the alphabets and special characters. It stores character values or strings. Strings in R can contain alphabets, numbers, and symbols. The character type is usually denoted by wrapping the value inside single or double inverted commas. w <- c("aa", "bb", "5", "7") w #> [1] "aa" "bb" "5" "7" typeof(w) #> [1] "character" 1.1.5 Memeory usage Each data type is explicitly defined, especially the size of the memory. When initializing a certain data type, there are a few small bytes used to store basic information. Let’s look at the empty values. object.size(numeric()) #> 48 bytes object.size(integer()) #> 48 bytes object.size(logical()) #> 48 bytes object.size(character()) #> 48 bytes To illustrate, let’s use 1000 elements below to show the memory size usage for each data type. As we will see, integer and logical use 4 bytes per element, which is only half of the memory usage by double (numeric) and character of 8 bytes. object.size(rep(1, 1000)) #> 8048 bytes object.size(rep(1L, 1000)) #> 4048 bytes object.size(rep(TRUE, 1000)) #> 4048 bytes object.size(rep("aa", 1000)) #> 8104 bytes 1.2 Data structures Data structure is one of the most important features in programming. It involves how the data is organised and can be accessed and modified. Using an appropriate data structure may largely improve computing efficiency. 1.2.1 Vector Vector is a basic data structure in R. It contains elements in the same data type (no matter double, integer, character or others). You can check the data type by using typeof() function and the length of the vector by length() function. Since a vector has elements of the same type, this function will try and coerce elements to the same type, if they are different. Coercion is from lower to higher types, i.e., from logical to integer to double to a character. See more introduction here. x <- c(1, 2, 5, 7) x #> [1] 1 2 5 7 typeof(x) #> [1] "double" x <- rep(3, 5) x #> [1] 3 3 3 3 3 typeof(x) #> [1] "double" x <- 1:12 # integer x #> [1] 1 2 3 4 5 6 7 8 9 10 11 12 typeof(x) #> [1] "integer" 1.2.2 Matrix Matrix is a two-dimensional data structure. It is in principle built based on vector but has more convenient built-in functions for computation. It has rows and columns, both of which can also have names. To check the dimensions, you can use the dim() function. See more introduction here. A <- matrix(1:12, nrow=3) A #> [,1] [,2] [,3] [,4] #> [1,] 1 4 7 10 #> [2,] 2 5 8 11 #> [3,] 3 6 9 12 B <- matrix(1:12, nrow=3, byrow=TRUE) B #> [,1] [,2] [,3] [,4] #> [1,] 1 2 3 4 #> [2,] 5 6 7 8 #> [3,] 9 10 11 12 colnames(A) <- c("C1","C2","C3","C4") rownames(A) <- c("R1","R2","R3") A #> C1 C2 C3 C4 #> R1 1 4 7 10 #> R2 2 5 8 11 #> R3 3 6 9 12 1.2.2.1 Index Vector and Matrix To index vector, you can use logical or integer, or the element name if it has. Note, when using an integer for indexing, the index starts from 1 in R, unlike most programming languages where the index starts from 0. We can also use negative integers to return all elements except those specified. But we cannot mix positive and negative integers while indexing and real numbers if used are truncated to integers. N.B., when using logical as the index, be careful if the index length is different from the vector. x <- 1:12 x[3] #> [1] 3 x[2:5] #> [1] 2 3 4 5 x[c(2, 5, 6)] # index with integer #> [1] 2 5 6 x[c(TRUE, FALSE, FALSE, TRUE)] # index with logical value #> [1] 1 4 5 8 9 12 Now, let’s index a matrix. It is very similar to vector, but it has both rows and columns. A <- matrix(1:12, nrow=3) colnames(A) <- c("C1","C2","C3","C4") rownames(A) <- c("R1","R2","R3") A #> C1 C2 C3 C4 #> R1 1 4 7 10 #> R2 2 5 8 11 #> R3 3 6 9 12 A[1, 2] #> [1] 4 A[1, "C2"] #> [1] 4 A[1, c(2, 3)] #> C2 C3 #> 4 7 A[1:2, c(2, 3)] #> C2 C3 #> R1 4 7 #> R2 5 8 Single row or column matrix will become a vector, unless using drop=FALSE A[1, 2:4] #> C2 C3 C4 #> 4 7 10 dim(A[1, 2:4]) #> NULL A[1, 2:4, drop=FALSE] #> C2 C3 C4 #> R1 4 7 10 dim(A[1, 2:4, drop=FALSE]) #> [1] 1 3 1.2.2.2 Modify values A[1, 2:4] <- c(-3, -5, 20) A #> C1 C2 C3 C4 #> R1 1 -3 -5 20 #> R2 2 5 8 11 #> R3 3 6 9 12 1.2.3 List Different from vector that has all elements in the same data type, the list data structure can have components of mixed data types. More broadly, a list can contain a list of any data structure: value, vector, matrix, etc. We can use str() function to view the structure of a list (or any object). x <- list(2.5, TRUE, 1:3) x #> [[1]] #> [1] 2.5 #> #> [[2]] #> [1] TRUE #> #> [[3]] #> [1] 1 2 3 str(x) #> List of 3 #> $ : num 2.5 #> $ : logi TRUE #> $ : int [1:3] 1 2 3 We can also have a name for each element: x <- list("a" = 2.5, "b" = TRUE, "c" = 1:3) x #> $a #> [1] 2.5 #> #> $b #> [1] TRUE #> #> $c #> [1] 1 2 3 str(x) #> List of 3 #> $ a: num 2.5 #> $ b: logi TRUE #> $ c: int [1:3] 1 2 3 1.2.3.1 Indexing list Different from vector and matrix, for a list, you need to use double-layer square brackets, either by numeric index or name. Alternatively, you can also use $ symbol with the name. x[[3]] #> [1] 1 2 3 x[["c"]] #> [1] 1 2 3 x$c #> [1] 1 2 3 1.2.4 Data Frame Data frame is widely used for rectangular data, where each column has the same data type (vector) but different columns can have different data types (like Excel) As you guess, the data frame is a special type of list: A list of vectors with the same length. df <- data.frame("SN" = 1:2, "Age" = c(21,15), "Name" = c("John","Dora")) df #> SN Age Name #> 1 1 21 John #> 2 2 15 Dora df$Age[2] #> [1] 15 # View(df) 1.2.5 Factor vs vector For vector, if it only contains pre-defined values (which can have specified orders), you may consider using factor. Factor is a data structure used for fields that takes only predefined, finite number of values (categorical data) The order of predefined values can be specified, instead of alphabetic by default x = c("single", "married", "married", "single") # vector x #> [1] "single" "married" "married" "single" class(x) #> [1] "character" typeof(x) #> [1] "character" y = factor(c("single", "married", "married", "single")) y #> [1] single married married single #> Levels: married single class(y) #> [1] "factor" typeof(y) #> [1] "integer" 1.2.5.1 Change the order of factor levels z <- factor(c("single", "married", "married", "single") , levels=c("single", "married")) z #> [1] single married married single #> Levels: single married 1.2.5.2 Smaller memory as categorical data type x = c("single", "married", "married", "single") # vector y = factor(c("single", "married", "married", "single")) object.size(rep(x, 1000)) #> 32160 bytes object.size(rep(y, 1000)) #> 16560 bytes 1.3 Read and write files (tables) Besides operating in the R environment, we also want to read data from files or write results into files. For tables, R has convenient built-in function read.table() and write.table(). 1.3.1 Read file We can use read.table() function to read tables, e.g., in comma separated values (csv) or tab separated values (tsv) formats. See full manuals: help(read.table) or ?read.table or its online manual help("read.table") ?read.table Here, let’s read an example file. Data is available on Moodle and on the github repository df = read.table("./SRP029880.colData.tsv", sep="\\t") df #> source_name group #> CASE_1 metastasized cancer CASE #> CASE_2 metastasized cancer CASE #> CASE_3 metastasized cancer CASE #> CASE_4 metastasized cancer CASE #> CASE_5 metastasized cancer CASE #> CTRL_1 normal colon CTRL #> CTRL_2 normal colon CTRL #> CTRL_3 normal colon CTRL #> CTRL_4 normal colon CTRL #> CTRL_5 normal colon CTRL 1.3.2 Write file The above file is loaded as a data frame. Here, let’s add an extra column to indicate if the sample is frozen or not, then save it into a file. df$frozen <- c(1, 1, 0, 0, 0, 1, 1, 0, 0, 0) write.table(df, "./SRP029880.colData.add_frozen.tsv", sep="\\t", quote=FALSE) 1.4 Functions and Packages As experienced above, we have used function multiple times, e.g., read.table and typeof. As one more example mean() is a function here and it is from the base package x <- 4:10 mean(x) #> [1] 7 base::mean(x) #> [1] 7 Generally speaking, A function is a set of statements organized together to perform a specific task. Many lines of codes are packed into one function & it’s reusable. A function can be written in the same R file and loaded, or it can be distributed as part of a package. For using such functions, we need to install the corresponding package and load it. 1.4.1 Install packages It depends on where the package is stored. Please refers to the documentation of the specific package you want to install and use. CRAN (the Comprehensive R Archive Network): main platform For example: install.packages(\"ggplot2\") Bioconductor: primarily for biology related packages For example: BiocManager::install(\"DESeq2\") As an example, we can install the powerful plotting package ggplot2 from CRAN. #install.packages("ggplot2") 1.4.2 Apply function repeatly We may often want to use a certain function for multiple times, e.g., calculate the sample mean for many genes. There are multiple ways to achieve it, e.g., via a for loop. Here, we will introduce apply and its variants for this purpose. See more introductions here. 1.4.2.1 apply, lapply, sapply and vapply In short, the apply() function and its variants apply a certain function to each element of a vector, a matrix, or a list. 1.4.2.1.1 apply for matrix The apply(X, MARGIN, FUN) function works for matrix (or array) for rows or columns. For example, calculating the median of each column: my.matrx <- matrix(1:15, nrow = 5, ncol = 3) my.matrx #> [,1] [,2] [,3] #> [1,] 1 6 11 #> [2,] 2 7 12 #> [3,] 3 8 13 #> [4,] 4 9 14 #> [5,] 5 10 15 apply(my.matrx, 2, median) #> [1] 3 8 13 1.4.2.1.2 lapply, sapply, and vapply for list or vector The above apply function requires MARGIN, hence won’t work for vector or list. There are a few variants to support lists or vectors for different purposes. From the manual, we can find out the arguments for these three functions: lapply(X, FUN, …) sapply(X, FUN, …, simplify = TRUE, USE.NAMES = TRUE) vapply(X, FUN, FUN.VALUE, …, USE.NAMES = TRUE) Let’s look at examples. The lapply works for vector and list and returns a list: A<-c(1:9) B<-c(1:12) C<-c(1:15) my.lst<-list(A,B,C) lapply(my.lst, median) #> [[1]] #> [1] 5 #> #> [[2]] #> [1] 6.5 #> #> [[3]] #> [1] 8 If you want the return as a vector, you can use sapply() for simplified output: sapply(my.lst, median) #> [1] 5.0 6.5 8.0 If you want to check the data type of the return, you can further use the vapply function. An error will be raised if the data type does not match. Note, the FUN.VALUE argument takes the value as a template, so the use of numeric(1) or any numeric value, e.g., 3 is the same. vapply(my.lst, median, numeric(1)) #> [1] 5.0 6.5 8.0 vapply(my.lst, median, 3) #> [1] 5.0 6.5 8.0 # try this # vapply(my.lst, median, character(1)) 1.4.3 Pattern match Matching patterns between two vectors is a very common task, for example matching the gene names of two files or matching students IDs in two courses. Two commonly used functions for pattern match are match() and %in%, see the match() documentation: match returns a vector of the positions of (first) matches of its first argument in its second. %in% is a more intuitive interface as a binary operator, which returns a logical vector indicating if there is a match or not for its left operand. They work for any data type: numeric and character 1:6 %in% c(1,3,5,9) #> [1] TRUE FALSE TRUE FALSE TRUE FALSE match(c(3, 4, 5), c(5, 3, 1, 9)) #> [1] 2 NA 1 idx = match(c(3, 4, 5), c(5, 3, 1, 9)) c(5, 3, 1, 9)[idx] #> [1] 3 NA 5 1.5 Flow Control 1.5.1 Logical operator The common logical operators are shown in the following table. Operator Description Associativity ! Logical NOT Left to right & Element-wise logical AND Left to right && Logical AND Left to right | Element-wise logical OR Left to right || Logical OR Left to right A few notes: Zero is considered FALSE and non-zero numbers are taken as TRUE. Operators & and | perform element-wise operation, returning length of the longer operand. For operators && and ||, it depends on the R version. In R<=4.2.3, it examines only the first element of the operands, returning a single value, while in R=4.3.1, && and || only compare single values and will return errors if input vectors. x <- c(TRUE, FALSE, 0, 6) y <- c(FALSE, TRUE, FALSE, TRUE) !x #> [1] FALSE TRUE TRUE FALSE x & y #> [1] FALSE FALSE FALSE TRUE x | y #> [1] TRUE TRUE FALSE TRUE # try it yourself # x && y # x || y 1.5.2 if-else statements The if-else statement allows us to control when and how particular parts of our code are executed. # In Q10 in Section 1.8 we have x_mean and x_sd x_mean = 1.7 x_sd = 2.9 if ( (x_mean > 0.1) && (x_sd < 2.0) ) { trend = "increase" } else{ trend = "not_increase" } print(trend) #> [1] "not_increase" x_mean = 1.7 x_st = 2.9 trend = "not_increase" if ( (x_mean > 0.1) && (x_sd < 2.0) ) { trend = "increase" } print(trend) #> [1] "not_increase" 1.5.3 for-loop Loops, including for loop and while loop are used in programming to repeat a specific block of code, commonly with if-else statements. For example, calculate the sum from 1 to 10: sum_val = 0 for (val in seq(1, 10)) { # print(val) sum_val = sum_val + val } print(sum_val) #> [1] 55 Or detect the differential expressed genes. Note, NA value may exist in the padj column: is_DE = rep(FALSE, nrow(df_DEG)) for (i in 1:nrow(df_DEG)) { if (df_DEG$padj[i] <= 0.05) { is_DE[i] = TRUE } } print(sum(is_DE)) 1.6 Plotting 1.6.1 datasets Let’s use a built-in dataset for illustration: iris (4 flower features in 3 plants) head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.2 setosa #> 2 4.9 3.0 1.4 0.2 setosa #> 3 4.7 3.2 1.3 0.2 setosa #> 4 4.6 3.1 1.5 0.2 setosa #> 5 5.0 3.6 1.4 0.2 setosa #> 6 5.4 3.9 1.7 0.4 setosa summary(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width #> Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100 #> 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300 #> Median :5.800 Median :3.000 Median :4.350 Median :1.300 #> Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199 #> 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800 #> Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500 #> Species #> setosa :50 #> versicolor:50 #> virginica :50 #> #> #> There are two common ways of plotting: the built-in plotting functions the ggplot format 1.6.2 Basic plotting 1.6.2.1 Histogram hist(iris$Sepal.Length) 1.6.2.2 Scatter plot plot(x=iris$Sepal.Length, y=iris$Sepal.Width) 1.6.2.3 boxplot x1 <- iris$Sepal.Length[iris$Species == "setosa"] x2 <- iris$Sepal.Length[iris$Species == "versicolor"] x3 <- iris$Sepal.Length[iris$Species == "virginica"] boxplot(x1, x2, x3) 1.6.3 ggplot2 See more instructions: http://www.sthda.com/english/wiki/ggplot2-essentials 1.6.3.1 Install and load package if (!requireNamespace("ggplot2", quietly = TRUE)) install.packages("ggplot2") library(ggplot2) 1.6.3.2 Histogram ggplot(iris, aes(x=Sepal.Length)) + geom_histogram(bins = 8) 1.6.3.3 Scatter plot ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width)) + geom_point() 1.6.3.4 Box plot ggplot(iris, aes(x=Species, y=Sepal.Length)) + geom_boxplot() 1.7 Scientific computating 1.7.1 Orders of operators See lecture slides. If you are not sure about a certain ordering, use brackets! 5 * 2 > 4 #> [1] TRUE 5 * (2 > 4) #> [1] 0 1.7.2 Functions for statistics More theory and practice to come in next session 1.7.3 Correlation cor(iris$Sepal.Length, iris$Petal.Length) #> [1] 0.8717538 cor.test(iris$Sepal.Length, iris$Petal.Length) #> #> Pearson's product-moment correlation #> #> data: iris$Sepal.Length and iris$Petal.Length #> t = 21.646, df = 148, p-value < 2.2e-16 #> alternative hypothesis: true correlation is not equal to 0 #> 95 percent confidence interval: #> 0.8270363 0.9055080 #> sample estimates: #> cor #> 0.8717538 1.7.4 Hypothesis testing (t test) x1 <- iris$Sepal.Length[iris$Species == "setosa"] x2 <- iris$Sepal.Length[iris$Species == "versicolor"] x3 <- iris$Sepal.Length[iris$Species == "virginica"] t.test(x2, x3) #> #> Welch Two Sample t-test #> #> data: x2 and x3 #> t = -5.6292, df = 94.025, p-value = 1.866e-07 #> alternative hypothesis: true difference in means is not equal to 0 #> 95 percent confidence interval: #> -0.8819731 -0.4220269 #> sample estimates: #> mean of x mean of y #> 5.936 6.588 1.7.5 Regression fit <- lm(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width, data=iris) summary(fit) # show results #> #> Call: #> lm(formula = Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width, #> data = iris) #> #> Residuals: #> Min 1Q Median 3Q Max #> -0.82816 -0.21989 0.01875 0.19709 0.84570 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 1.85600 0.25078 7.401 9.85e-12 *** #> Sepal.Width 0.65084 0.06665 9.765 < 2e-16 *** #> Petal.Length 0.70913 0.05672 12.502 < 2e-16 *** #> Petal.Width -0.55648 0.12755 -4.363 2.41e-05 *** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Residual standard error: 0.3145 on 146 degrees of freedom #> Multiple R-squared: 0.8586, Adjusted R-squared: 0.8557 #> F-statistic: 295.5 on 3 and 146 DF, p-value: < 2.2e-16 This means the fitted regression is: Sepal.Length ~ 1.856 + 0.65*Sepal.Width + 0.709*Petal.Length - 0.556*Petal.Width We can check how good the regression is by plotting it out y_pred <- fit$coefficients[1] + fit$coefficients[2] * iris$Sepal.Width + fit$coefficients[3] * iris$Petal.Length + fit$coefficients[4] * iris$Petal.Width cor(iris$Sepal.Length, y_pred) #> [1] 0.926613 plot(iris$Sepal.Length, y_pred) 1.7.6 Resource links This notebook adapts contents from these resources: https://www.datamentor.io/r-programming/ https://www.geeksforgeeks.org/r-data-types/ https://data-flair.training/blogs/r-data-types/ http://www.sthda.com/english/wiki/ggplot2-essentials 1.7.7 Coding styling Elegant styling is crucial for maintenance and collaboration. Here is a highly recommended guideline: http://adv-r.had.co.nz/Style.html 1.8 Exercises This list of exercises will serve as a 2-hour demonstration in the BIOF1001 course. For other learners, you can go through it as homework. The expected time is around two hours if you have read the R materials carefully together with trying them with your own R environment. Note, before you get started, please make sure that you are familiar with the panels in RStudio. You may watch this YouTube Video or check this RStudio cheatsheet. 1.8.1 Part 1. Basics (~40min) Q1: We have 25 students in BIOF1001, to store the final marks (0 to 100 with a precision of 0.1), what data type will we use? Q2: For the grades (A+ to F), what data type and data structure can be used to keep the memory minimal? Q3: Make a matrix with name my_matrix, shape of 5 rows and 2 columns and values from 3 to 12. The first row is 3 and 4. Hint: for making a vector from 3 to 12, you may use seq() or :. Q4: Based on Q3, add the row names to Day1 to Day5 and column names to Lunch and Dinner. Q5: Based on Q4, get a matrix with shape of 3x1 and values of 6, 8, 10 from the matrix my_matrix. Q6: What will you get for my_matrix[c(TRUE, FALSE, FALSE, TRUE), ]? Hint: think of recycling if the index length is different from the query dimension (Over-flexibility comes with a price of wrong use). Q7: If you have two vectors the BIOF1001 marks and grades and one character for teaching performance \"good\", and you want to store them into one variable, which data structure will you use? Q8: Now, on your Desktop folder, make a subfolder with name R_exercises and download this file of differentailly expressed genes results Diff_Expression_results.tsv to the folder. Check your current work directory by getwd() function and change the work directory to the folder you just created. Hint: you may use setwd() to change the work directory or use the Session button of RStudio. Q9: Related to Q8, use the read.table() function to load the file into a data frame with the variable name df_DEG. Hint: You may consider using the full path or just the file name if it’s in the same work directory. Please keep header=TRUE for the argument. Think how to find help page for a certain function. Q10: Can you calculate the mean and standard deviation of the log2FoldChange? If the mean >0.1 and the standard deviation < 3, set the trend variable as “increase”, otherwise “not_increase”. What will happen if you add this trend variable to the data frame, and why? Hint: use mean() and st() functions for calculating mean and standard deviation. 1.8.2 Part 2. Making plotting (~40min) Q11: Keep use the df_DEG from part 1 Q9. Now, make a histogram of the log2FoldChange with both basic plotting function hist() and ggplot2. Q12: Make a plot with x-axis of log10(baseMean) and y-axis of log2FoldChange. Please try both the basic plot() function and ggplot2. Q13: Now, manipulate the dataframe by adding two columns: Add a column log2FC_clip for clipping log2FoldChange to [-5, +5] Add a column is_DE for padj < 0.05 Q14: Try the summary() function with the above df_DEG data frame, and also table() function for the is_DE column. Q15: Based on ggplot2, add the color by the newly added column is_DE. Q16: Set the colors to “red” and “grey”, and make it in the order of TRUE and FALSE. Hint: use factor and set the levels parameter. Q17: Save the generated figure into “My_DEG_results.pdf”. Use the ggsave() function. Please set width = 5, height = 4. You are expected a figure like this DEG figure If you want to change labels on x-axis or y-axis and font size, etc., you can simply Google and find examples. 1.8.3 Part 3. For loop and repeating processing (~40min) Q18: Load the following table from this file on GitHub and View it in RStudio. It contains expressoon of 619 transcription factors from 7 Nasopharyngeal carcinoma (NPC) samples and 3 nasopharyngeal lymphatic hyperplasia (NLH) samples. https://github.com/StatBiomed/NegabinGLM/blob/main/data/NPC_NLH-Tcell-donorX.tsv df_NPC = read.table(\"https://github.com/StatBiomed/NegabinGLM/raw/main/data/NPC_NLH-Tcell-donorX.tsv\", sep=\"\\t\", header=TRUE) Q19: Extract the column 5 (HES5) to 623 (LEK4) and make it into a matrix TF_mat Hint: how to index a data frame like index a matrix. Q20: perform normalization. Divide the TF_mat by the df_NPC$total_counts and multiply by 1000000, and assign it to a new matrix named TF_mat_norm. You may further consider transformation by log1p(), i.e., log(TF_mat_norm + 1). Q21: calculate the log fold change on the first gene TP73 in TF_mat_norm between NPC (row 1 to 7) and NLH (row 8 to 10) and perform t-test return the p value and log fold change. Q22: perform t-test on the all gene in TF_mat_norm between NPC (row 1 to 7) and NLH (row 8 to 10). Hint: think of for loop or apply() function. "],["introHypoTest.html", "Chapter 2 Introduction to Hypothesis testing 2.1 Hypothesis testing and p value 2.2 Permutation test 2.3 t test 2.4 GLM test 2.5 Multiple testing", " Chapter 2 Introduction to Hypothesis testing 2.1 Hypothesis testing and p value How surprising is my result? Calculating a p-value There are many circumstances where we simply want to check whether an observation looks like it is compatible with the null hypothesis, \\(H_{0}\\). Having decided on a significance level \\(\\alpha\\) and whether the situation warrants a one-tailed or a two-tailed test, we can use the cdf of the null distribution to calculate a p-value for the observation. Acknowledgement: examples are from Dr John Pinney link here 2.1.1 Example 1: probability of rolling a six? Your arch-nemesis Blofeld always seems to win at ludo, and you have started to suspect him of using a loaded die. You observe the following outcomes from 100 rolls of his die: data = c(6, 1, 5, 6, 2, 6, 4, 3, 4, 6, 1, 2, 5, 6, 6, 3, 6, 2, 6, 4, 6, 2, 5, 4, 2, 3, 3, 6, 6, 1, 2, 5, 6, 4, 6, 2, 1, 3, 6, 5, 4, 5, 6, 3, 6, 6, 1, 4, 6, 6, 6, 6, 6, 2, 3, 1, 6, 4, 3, 6, 2, 4, 6, 6, 6, 5, 6, 2, 1, 6, 6, 4, 3, 6, 5, 6, 6, 2, 6, 3, 6, 6, 1, 4, 6, 4, 2, 6, 6, 5, 2, 6, 6, 4, 3, 1, 6, 6, 5, 5) Do you have enough evidence to confront him? # We will work with the binomial distribution for the observed number of sixes # Write down the hypotheses # H0: p = 1/6 # H1: p > 1/6 # choose a significance level # alpha = 0.01 # number of sixes # number of trials stat_k = sum(data == 6) trials = length(data) print(paste("number of sixes:", stat_k)) #> [1] "number of sixes: 43" print(paste("number of trials:", trials)) #> [1] "number of trials: 100" # test statistic: number of sixes out of 100 trials # null distribution: dbinom(x, size=100, prob=1/6) # calculate p value p_val = 1 - pbinom(stat_k - 1, size=trials, prob=1/6) print(paste("Observed statistic is", stat_k)) #> [1] "Observed statistic is 43" print(paste("p value is", p_val)) #> [1] "p value is 5.43908695860296e-10" 2.1.1.1 Visualize the null distribution and the test statistic # plot the probability mass function of null distribution x = seq(0, 101) pmf = dbinom(x, size=100, prob=1/6) df = data.frame(x=x, pmf=pmf, extreme=(x >= stat_k)) library(ggplot2) ggplot(df, aes(x=x)) + geom_point(aes(y=pmf, color=extreme)) + scale_color_manual(values=c("black", "red")) + xlab('Number of sixes') + ylab('Probability Mass Function') + ggtitle('Distribution of n_six under the null hypothesis') 2.2 Permutation test 2.2.1 Example 2: difference in birth weight The birth weights of babies (in kg) have been measured for a sample of mothers split into two categories: nonsmoking and heavy smoking. The two categories are measured independently from each other. Both come from normal distributions The two groups are assumed to have the same unknown variance. data_heavysmoking = c(3.18, 2.84, 2.90, 3.27, 3.85, 3.52, 3.23, 2.76, 3.60, 3.75, 3.59, 3.63, 2.38, 2.34, 2.44) data_nonsmoking = c(3.99, 3.79, 3.60, 3.73, 3.21, 3.60, 4.08, 3.61, 3.83, 3.31, 4.13, 3.26, 3.54) We want to know whether there is a significant difference in mean birth weight between the two categories. # Write down the hypotheses # H0: there is no difference in mean birth weight between groups: d == 0 # H1: there is a difference, d != 0 # choose a significance level # alpha = 0.05 # Define test statistic: difference of group mean stat_mu = mean(data_heavysmoking) - mean(data_nonsmoking) stat_mu #> [1] -0.5156923 2.2.2 Null distribution approximated by resampling #' Simple function to generate permutation distribution get_permutation_null <- function(x1, x2, n_permute=1000) { n1 = length(x1) n2 = length(x2) # pool data sets x_pool = c(x1, x2) null_distr = rep(0, n_permute) for (i in seq(n_permute)) { # split idx = sample(n1 + n2, size=n1) x1_perm = x_pool[idx] x2_perm = x_pool[-idx] # calculate test statistic null_distr[i] = mean(x1_perm) - mean(x2_perm) } return(null_distr) } set.seed(1) perm_null = get_permutation_null(data_heavysmoking, data_nonsmoking) We can plot the histogram of the null distribution obtained by resampling. We can also add line(s) for the values as extreme as observed statistic mu, where we can consider one side or both side as extreme values. df_perm = data.frame(perm_null = perm_null) ggplot(df_perm, aes(x=perm_null)) + geom_histogram(bins=20) + geom_vline(xintercept=stat_mu, linetype="dashed", color="tomato") + geom_vline(xintercept=-stat_mu, linetype="dashed", color="tomato") + xlab('Difference of group mean') + ylab('Resampling frequency') + ggtitle('Distribution of mu under the null hypothesis') ## Two tailed p value p_two_tailed = mean(abs(perm_null) >= abs(stat_mu)) p_one_tailed = mean(perm_null < stat_mu) print(paste("Two tailed p value:", round(p_two_tailed, 5))) #> [1] "Two tailed p value: 0.003" print(paste("One (left) tailed p value:", round(p_one_tailed, 5))) #> [1] "One (left) tailed p value: 0.002" 2.3 t test 2.3.1 Derivation of t distribution Null distribution approximated by \\(t\\) distribution We use the t test to assess whether two samples taken from normal distributions have significantly different means. The test statistic follows a Student’s t-distribution, provided that the variances of the two groups are equal. Other variants of the t-test are applicable under different conditions. The test statistic is \\[ t = \\frac{\\bar{X}_{1} - \\bar{X}_{2}}{s_p \\cdot \\sqrt{\\frac{1}{n_{1}} + \\frac{1}{n_{2}}}} \\] where \\[ s_p = \\sqrt{\\frac{(n_1 - 1)s_1^2 + (n_2 - 1)s_2^2}{n_1 + n_2 - 2}} \\] is an estimator of the pooled standard deviation. Under the null hypothesis of equal means, the statistic follows a Student’s t-distribution with \\((n_{1} + n_{2} - 2)\\) degrees of freedom. # Same test statistic: difference of group mean stat_t = mean(data_heavysmoking) - mean(data_nonsmoking) stat_t #> [1] -0.5156923 Calculate parameters for approximate t distribution n_ns = length(data_nonsmoking) n_hs = length(data_heavysmoking) s_ns = sd(data_nonsmoking) # degree of freedom: n-1 s_hs = sd(data_heavysmoking) # the pooled standard deviation sp = sqrt(((n_ns - 1)*s_ns**2 + (n_hs - 1)*s_hs**2) / (n_ns + n_hs - 2)) print(paste0("Pooled standard deviation:", sp)) #> [1] "Pooled standard deviation:0.428057812829366" my_std = sp * sqrt(1/n_ns + 1/n_hs) print(paste("Estimated standard error of mean difference:", my_std)) #> [1] "Estimated standard error of mean difference: 0.162204962956089" stat_t_scaled = stat_t / my_std print(paste("Rescaled t statistic:", stat_t_scaled)) #> [1] "Rescaled t statistic: -3.17926343494134" print(paste("degree of freedom", n_hs+n_ns-2)) #> [1] "degree of freedom 26" Here, we focusing the standardized \\(t\\) distribution, namely the variance=1, so let’s re-scale the test statistic by dividing the standard error my_std. xx = seq(-4.5, 4.5, 0.05) xx_pdf = dt(xx, df=n_hs+n_ns-2) df_t_dist = data.frame(x=xx, pdf=xx_pdf) ggplot(df_t_dist, aes(x=x)) + geom_line(aes(y=pdf)) + geom_vline(xintercept=stat_t_scaled, linetype="dashed", color="tomato") + geom_vline(xintercept=-stat_t_scaled, linetype="dashed", color="tomato") + xlab('Difference of group mean') + ylab('PDF approximated by t distr.') + ggtitle('Distribution of t under the null hypothesis') # Note, we used multiply 2 just because the t distribution is symmetric, # otherwise, we need calculate both side and add them. pval_t_twoside = pt(stat_t_scaled, df=n_hs+n_ns-2) * 2 print(paste('t-test p value (two-tailed):', round(pval_t_twoside, 6))) #> [1] "t-test p value (two-tailed): 0.003793" 2.3.2 Direct use of t.test() In course and most of your future analyses, you can directly use the built-in t.test() function. # Note, we assumed the variance in both groups are the same, # we so need to set var.equal = TRUE t.test(data_nonsmoking, data_heavysmoking, var.equal = TRUE) #> #> Two Sample t-test #> #> data: data_nonsmoking and data_heavysmoking #> t = 3.1793, df = 26, p-value = 0.003793 #> alternative hypothesis: true difference in means is not equal to 0 #> 95 percent confidence interval: #> 0.1822752 0.8491094 #> sample estimates: #> mean of x mean of y #> 3.667692 3.152000 2.4 GLM test We can also perform t-test in a Generalised linear model (GLM) setting to test if a coefficient is zero or not. Here, we simply use the marketing dataset as an example. # Install datarium library if you haven't if (!requireNamespace("datarium", quietly = TRUE)) { install.packages("datarium") } library(datarium) # Load data: then we will have a data.frame with name marketing data(marketing) head(marketing) #> youtube facebook newspaper sales #> 1 276.12 45.36 83.04 26.52 #> 2 53.40 47.16 54.12 12.48 #> 3 20.64 55.08 83.16 11.16 #> 4 181.80 49.56 70.20 22.20 #> 5 216.96 12.96 70.08 15.48 #> 6 10.44 58.68 90.00 8.64 ggplot(marketing, aes(x=newspaper, y=sales)) + geom_point() + geom_smooth(method=lm) #> `geom_smooth()` using formula = 'y ~ x' # Fit linear regression res.lm <- lm(sales ~ newspaper, data = marketing) # We can check the test via the summary() function summary(res.lm) #> #> Call: #> lm(formula = sales ~ newspaper, data = marketing) #> #> Residuals: #> Min 1Q Median 3Q Max #> -13.473 -4.065 -1.007 4.207 15.330 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 14.82169 0.74570 19.88 < 2e-16 *** #> newspaper 0.05469 0.01658 3.30 0.00115 ** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Residual standard error: 6.111 on 198 degrees of freedom #> Multiple R-squared: 0.05212, Adjusted R-squared: 0.04733 #> F-statistic: 10.89 on 1 and 198 DF, p-value: 0.001148 glm_t_val = summary(res.lm)$coefficients["newspaper", "t value"] xx = seq(-5, 5, 0.01) yy = dt(xx, 198) df_ttest <- data.frame(x=xx, PDF=yy) ggplot(df_ttest, aes(x=x, y=PDF)) + geom_line() + geom_vline(xintercept = glm_t_val, linetype="dashed", color="tomato") + geom_vline(xintercept = -glm_t_val, linetype="dashed", color='tomato') 2.5 Multiple testing Hypothetical null distribution. Feel feel to try any null distribution, examples below ## Example null distributions # t, normal or anything. we use chi-squared distribution as an example x_random = rchisq(n=1000, df=3) any_null_dist = dchisq(x_random, df=3) pvals_null = 1 - pchisq(x_random, df=3) 2.5.1 Null distribution (of test statistic) # Null distribution of test statistic hist(x_random) 2.5.2 Null distribution of p value # Null distribution of test statistic hist(pvals_null) 2.5.3 Minimal p values in 10 tests # We use matrix to group 100 trials into a column # We then use apply() to calculate min value for each column pval_null_mtx = matrix(pvals_null, nrow=10) p_min_in10 = apply(pval_null_mtx, MARGIN=2, FUN=min) hist(p_min_in10) print(paste('Proportion of tests with min(p) < 0.05:', mean(p_min_in10 < 0.05))) #> [1] "Proportion of tests with min(p) < 0.05: 0.43" 2.5.4 Homework Make a simulation of score: group A and B B follows normal(mean=0, std=1); A follows normal(mean=0.1, std=1) Generate 100 samples for each group, and do a t test, is difference significant? Please use set.seed(0) beforehand. Try 3) again but general 3,00 samples this time, later 1,000 samples. What do you find? Think the relation between power and sample size. set.seed(0) n_sample = 100 # change this value to 1000 and 10000 xB = rnorm(n_sample) xA = rnorm(n_sample, mean=0.1) t.test(xA, xB, var.equal = TRUE) #> #> Two Sample t-test #> #> data: xA and xB #> t = 0.24294, df = 198, p-value = 0.8083 #> alternative hypothesis: true difference in means is not equal to 0 #> 95 percent confidence interval: #> -0.2261882 0.2897482 #> sample estimates: #> mean of x mean of y #> 0.05444844 0.02266845 "],["introLinearReg.html", "Chapter 3 Introduction to Linear Regression 3.1 Linear Regression Using Simulated Data 3.2 Least Squares Using Simulated Data 3.3 Diagnostic check of a fitted regression model 3.4 Simple Linear Regression with lm function 3.5 Multiple Regression with lm function", " Chapter 3 Introduction to Linear Regression Acknowledgements: this chapter is adapted and updated from the materials originally produced by STAT1005 teaching team, especially Prof. Jeff Yao. 3.1 Linear Regression Using Simulated Data Let’s first simulate some data and look at how the predicted values (Ye) differ from the actual value (Y). 3.1.1 Simulating data: For X, we generate 100 normally distributed random numbers with mean 1.5 and standard deviation 2.5. For predicted value Ye, we assume an intercept (α) of 2 and a slope (β) of 0.3 and we write \\(Y_e = 2 + 0.3 x\\) Later, we will estimate the values of α and β using the least squares method and see how that changes the efficacy of the model. Though we estimate \\(Y_e = \\alpha + \\beta X\\), in reality Y is rarely perfectly linear. It usually has an error component or residual: \\(Y = \\alpha + \\beta X + R\\), where R is a random variable and is assumed to be normally distributed. Therefore for the actual value Y, we add a residual term (res), a random variable distributed normally with mean 0 and a standard deviation of 0.5. The following cell shows the code snippet to generate these numbers and convert these three columns in a data frame. Read through the code carefully and run the cell to output a sample of our simulated data. # Fix seed: each run gives the same random numbers so the same outputs. # Commenting out this line would read similar but different outputs at each run. # Try it out! set.seed(0) # Generate data X = 2.5 * rnorm(100) + 1.5 # Array of 100 values with mean = 1.5, stddev = 2.5 ypred = 2 + 0.3 * X # Prediction of Y, assuming a = 2, b = 0.3 res = 0.5 * rnorm(100) # Generate 100 residual terms yact = 2 + 0.3 * X + res # Actual values of Y # Create dataframe to store our X, ypred, and yact values df = data.frame('X' = X, 'ypred' = ypred, 'yact' = yact) # Show the first six rows of our dataframe head(df) #> X ypred yact #> 1 4.6573857 3.397216 3.788145 #> 2 0.6844166 2.205325 1.816937 #> 3 4.8244982 3.447349 3.139354 #> 4 4.6810733 3.404322 3.427612 #> 5 2.5366036 2.760981 2.195788 #> 6 -2.3498751 1.295037 1.583397 Now let’s plot both the actual output (yact) and predicted output (ypred) against the input variable (X) to see what the difference between yact and ypred is, and therefore, to see how accurately the proposed equation (ypred = 2 + 0.3 * X) has been able to predict the value of the output: # You can use basic plotting functions # plot(x=df$X, y=df$yact, col="red") # lines(x=df$X, y=df$ypred, col="darkgreen") # But let's use ggplot2 for higher flexibility library(ggplot2) ggplot(df, aes(X)) + # basic graphical object geom_point(aes(y=yact), colour="black") + # first layer geom_line(aes(y=ypred), colour="darkgreen") + # second layer ggtitle('Actual vs Predicted values from the dummy dataset') 3.1.2 Model efficacy How do we know the values we calculate for α and β are giving us a good model? We can explain the total variability in our model with the Total Sum of Squares or SST: \\[SST = \\sum_{i=1}^n\\Bigl(\\text{yact}_i - \\text{yavg}\\Bigr)^2, \\qquad\\qquad \\text{yavg}=\\frac1n \\sum_{i=1}^n \\text{yact}_i\\] Mathematically, we have \\[ \\sum_{i=1}^n\\Bigl(\\text{yact}_i - \\text{yavg}\\Bigr)^2 = \\sum_{i=1}^n\\Bigl(\\text{ypred}_i -\\text{yavg} \\Bigr)^2 + \\sum_{i=1}^n\\Bigl(\\text{yact}_i - \\text{ypred}_i\\Bigr)^2\\] The identity reads as Sum of Squares Total = Sum of Squares Regression + Sum of Squares Error, or simply , SST = SSR + SSE. The Regression Sum of Squares or SSR measures the variation of the regression/predicted values, and the Sum of Squares Error SSE the variation between the actual and the predicted values. An alternative saying is that SSR is the difference explained by the model, SSE is the difference not explained by the model and is random, and SST is the total error. Note, we often use SSE (Sum of Squares Error) and SSD (Sum of Squares Difference) interchangeably. 3.1.3 R-Squared The higher the ratio of SSR to SST, the better the model is. This ratio is quantified by the coefficient of determination (also known as R2 or R-squared): \\[ R^2= \\frac{SSR}{SST}\\] Since \\(SST= SSR+SSE\\), \\(\\qquad 0\\le R^2\\le 1\\). The closer it is to 1, the better the model. Note that there are many other factors that we need to analyse before we can conclude a linear regression model is effective, but a high \\(R^2\\) is a pretty good indicator. Let’s see what the value of \\(R^2\\) is for our simulated dataset. # Calculate the mean of Y ymean = mean(df$yact) print(paste('Mean of Y =', ymean)) # paste brings a white space by default #> [1] "Mean of Y = 2.44422555811815" # Calculate SSR and SST df['SSR'] = (df['ypred'] - ymean)**2 df['SST'] = (df['yact'] - ymean)**2 SSR = sum(df['SSR']) SST = sum(df['SST']) # Calculate R-squared R2 = SSR / SST print(paste('R2 =', R2)) #> [1] "R2 = 0.583160943681119" The value of \\(R^2=0.583\\) suggests that ypred provides a decent prediction of the yact. We have randomly assumed some values for \\(\\alpha\\) and \\(\\beta\\), but these may or may not be the best values. In the next step, we will use the least sum of square method to calculate the optimum value for \\(\\alpha\\) and \\(\\beta\\) to see if there is an improvement in \\(R^2\\). To get started on the next step, open the notebook called 02-linearReg-02.Rmd. 3.2 Least Squares Using Simulated Data Now, using our simulated data from the previous step, let’s estimate the optimum values of our variable coefficients, \\(\\alpha\\) and \\(\\beta\\). Using the predictor variable, X, and the output variable, yact, we will calculate the values of \\(\\alpha\\) and \\(\\beta\\) using the Least Squares method described in the lecture. The cell below creates the same dataframe as previously. Run the cell to get started! set.seed(0) # Generate data X = 2.5 * rnorm(100) + 1.5 # Array of 100 values with mean = 1.5, stddev = 2.5 ypred = 2 + 0.3 * X # Prediction of Y, assuming a = 2, b = 0.3 res = 0.5 * rnorm(100) # Generate 100 residual terms yact = 2 + 0.3 * X + res # Actual values of Y # Create dataframe to store our X, ypred, and yact values df = data.frame('X' = X, 'ypred' = ypred, 'yact' = yact) Just to reiterate, here are the formulas for \\(\\alpha\\) and \\(\\beta\\) again: \\[\\hat\\beta=\\frac{\\sum_{i=1}^n(X_i-\\bar X)(Y_i-\\bar Y)}{\\sum_{i=1}^n(X_i-\\bar X)^2}=\\frac{\\text{cov}(X,Y)}{\\text{var}(X)}\\] \\[\\hat\\alpha=\\bar Y-\\hat\\beta * \\bar X\\] To calculate these coefficients, we will create a few more columns in our df data frame. We need to calculate xmean and ymean to calculate the covariance of X and Y (xycov) and the variance of X (xvar) before we can work out the values for alpha and beta. # Calculate the mean of X and Y xmean = mean(X) ymean = mean(yact) # Calculate the terms needed for the numator and denominator of beta df['xycov'] = (df['X'] - xmean) * (df['yact'] - ymean) df['xvar'] = (df['X'] - xmean)**2 # Calculate beta and alpha beta = sum(df['xycov']) / sum(df['xvar']) alpha = ymean - (beta * xmean) print(paste('alpha =', alpha, ';', 'beta =', beta)) #> [1] "alpha = 1.93401265576322 ; beta = 0.327758955833308" As we can see, the values are only a little different from what we had assumed earlier. Let’s see how the value of \\(R^2\\) changes if we use the new values of \\(\\alpha\\) and \\(\\beta\\). The equation for the new model can be written as: \\[ y=1.934 + 0.328 * x \\] Let’s create a new column in df to accommodate the values generated by this equation and call this ypred2, and calculate the new \\(R^2\\). # Create new column to store new predictions df['ypred2'] = alpha + beta * df['X'] # Calculate new SSR with new predictions of Y. # Note that SST remains the same since yact and ymean do not change. df['SSR2'] = (df['ypred2'] - ymean)**2 df['SST'] = (df['yact'] - ymean)**2 SSR2 = sum(df['SSR2']) SST = sum(df['SST']) # Calculate new R2 R2_2 = SSR2 / SST print(paste('New R2 =', R2_2)) #> [1] "New R2 = 0.69524214766491" The new value of \\(R^2= 0.695\\) shows a slight improvement from the previous value of \\(R^2=0.583\\) (obtained with \\(\\alpha=2,~\\beta=0.3\\)). Let’s also plot our new prediction model against the actual values and our earlier assumed model, just to get a better visual understanding. library(ggplot2) # Put color into aes ggplot(df, aes(X)) + # basic graphical object geom_point(aes(y=yact), colour="black") + # first layer geom_line(aes(y=ypred, colour="Guess")) + # second layer geom_line(aes(y=ypred2, colour="OLS")) + # third layer scale_colour_manual(name="Models", values = c("Guess"="darkgreen", "OLS"="red")) + ggtitle('Actual vs Predicted with guessed parameters vs Predicted with calculated parameters') As we can see, the ypred2 and ypred are more or less overlapping since the respective values of ɑ and β are not very different. Next, we will explore other methods of determining model efficacy by using the notebook called 02-linearReg-03.Rmd. 3.3 Diagnostic check of a fitted regression model Apart from the \\(R^2\\) statistic, there are other statistics and parameters that you need to look at in order to determine if the model is efficient. We will discuss some commonly used statistics – Residual Standard Errors, \\(p\\)-values, and \\(F\\)-statistics. 3.3.1 Residual Standard Errors (RSE) RSE is a common statistic used to calculate the accuracy of values predicted by a model. It is an estimate of the variance of the error term, res. For a simple linear regression model, RSE is defined as: \\[ RSE^2 = \\frac{SSE}{n-2} = \\frac1{n-2} \\sum_{i=1}^n \\Bigl(\\text{yact}_i - \\text{ypred}_i \\Bigr)^2. \\] In general, \\[ RSE^2 = \\frac{SSE}{n-p-1} = \\frac1{n-p-1} \\sum_{i=1}^n \\Bigl(\\text{yact}_i - \\text{ypred}_i \\Bigr)^2. \\] where \\(p\\) is the number of predictor variables in a model where we have more than one predictor variables. A multiple linear regression model is a linear regression model with multiple predictors, written as \\[ Y_e = \\alpha +\\beta_1 * X_1 +\\cdots +\\beta_p X_p. \\] As you see, the parameters and predictors are subscripted from 1 up to the number of predictors \\(p\\). In multiple regression, the value of RSE generally decreases as we add variables that are more significant predictors of the output variable. Using our simulated data from the previous steps, the following code snippet shows how the RSE for a model can be calculated: set.seed(0) # Generate data X = 2.5 * rnorm(100) + 1.5 # Array of 100 values with mean = 1.5, stddev = 2.5 res = 0.5 * rnorm(100) # Generate 100 residual terms yact = 2 + 0.3 * X + res # Actual values of Y # Create dataframe to store our X, ypred, and yact values df = data.frame('X' = X, 'yact' = yact) # Calculate the mean of X and Y xmean = mean(X) ymean = mean(yact) # Calculate the terms needed for the numator and denominator of beta df['xycov'] = (df['X'] - xmean) * (df['yact'] - ymean) df['xvar'] = (df['X'] - xmean)**2 # Calculate beta and alpha beta = sum(df['xycov']) / sum(df['xvar']) alpha = ymean - (beta * xmean) print(paste('alpha =', alpha, ';', 'beta =', beta)) #> [1] "alpha = 1.93401265576322 ; beta = 0.327758955833308" # Store predictions as in previous step df['ypred'] = alpha + beta * df['X'] # Show first five rows of dataframe head(df) #> X yact xycov xvar ypred #> 1 4.6573857 3.788145 4.1671116 9.6144310 3.460513 #> 2 0.6844166 1.816937 0.5471556 0.7608280 2.158336 #> 3 4.8244982 3.139354 2.2715611 10.6786935 3.515285 #> 4 4.6810733 3.427612 3.0724952 9.7618890 3.468276 #> 5 2.5366036 2.195788 -0.2434518 0.9602676 2.765407 #> 6 -2.3498751 1.583397 3.3628671 15.2611034 1.163820 # Calculate SSE df['SSE'] = (df['yact'] - df['ypred'])**2 SSE = sum(df['SSE']) # Calculate RSE RSE = sqrt(SSE / 98) # n = 100 print(paste('RSE =', RSE)) #> [1] "RSE = 0.481279277134956" The value of RSE comes out to be 0.48. As you might have guessed, the smaller the residual standard errors, the better the model is. The benchmark to compare this to is the mean of the actual values, yact. As shown previously, this value is ymean = 2.54. In plain English, this means we observe an error of 0.48 over 2.44 - approximately 19.69%. error = RSE / ymean print(paste('Mean Y =', ymean)) #> [1] "Mean Y = 2.44422555811815" print(paste('Error =', error)) #> [1] "Error = 0.196904608716023" 3.3.2 p-values The calculation of \\(\\alpha\\) and \\(\\beta\\) are estimates, not exact calculations. Whether their values are significant or not needs to be tested using a hypothesis test. In the equation, \\(Y = \\alpha + \\beta X\\), if we set \\(\\beta=0\\), there will be no relation between \\(Y\\) and \\(X\\). Therefore, the hypothesis tests whether the value of \\(\\beta\\) is non-zero or not. \\[\\begin{align*} \\text{Null hypothesis}~ H_0~:~ \\beta=0, & \\quad \\text{versus} \\\\ \\text{Alternative hypothesis}~ H_1~:~ \\beta\\ne 0.& \\end{align*} \\] Whenever a regression task is performed and \\(\\beta\\) is calculated, there will be an accompanying p-value corresponding to this hypothesis test. We will not go through how this is calculated in this course (you can learn more here), since it is calculated automatically by ready-made methods in R. If the p-value is less than a chosen significance level (e.g. 0.05) then the null hypothesis that \\(\\beta = 0\\) is rejected and \\(\\beta\\) is said to be significant and non-zero. In the case of multiple linear regression, the p-value associated with each \\(\\beta_k\\) can be used to weed out insignificant predictors from the model. The higher the p-value for \\(\\beta_k\\), the less significant \\(X_k\\) is to the model. 3.3.3 F-statistics In a multiple regression model, apart from testing the significance of individual variables by checking the p-values, it is also necessary to check whether, as a group all the predictors are significant. This can be done using the following hypothesis: \\[\\begin{align*} \\text{Null hypothesis}~ H_0~:~ & \\beta_1=\\beta_2=\\cdots=\\beta_p=0, \\quad \\text{versus} \\\\ \\text{Alternative hypothesis}~ H_1~:~& \\text{at least one of the} ~\\beta_k's ~ \\text{is non zero}. \\end{align*} \\] The statistic that is used to test this hypothesis is called the F-statistic and is defined as follows: \\[ F\\text{-statistic} = \\text{Fisher statistic}= \\frac{ (SST-SSE)/p}{ SSE/(n-p-1)} \\] where \\(n\\) = number of rows (sample points) in the dataset and \\(p\\) = number of predictor variables in the model. There is a \\(p\\)-value that is associated with this \\(F\\)-statistic. If the \\(p\\)-value is smaller than the chosen significance level, the null hypothesis can be rejected. It is important to look at the F-statistic because: p-values are about individual relationships between predictors and the outcome variable. However, one predictor’s relationship with the output might be impacted by the presence of other variables. When the number of predictors in the model is very large and all the \\(\\beta_i\\) are very close to zero, the individual p-values associated with the predictors might give very small values so we might incorrectly conclude that there is a relationship between the predictors and the outcome. 3.4 Simple Linear Regression with lm function There are a few R packages, e.g., the built-in stat package have a lm (linear model) function to fit linear regression very easy - much easier than implementing from scratch like we did in the last lesson. See more details in the lm manual. We will start with the datarium library which contain the advertising data. # Install datarium library if you haven't if (!requireNamespace("datarium", quietly = TRUE)) { install.packages("datarium") } library(datarium) # Load data: then we will have a data.frame with name marketing data(marketing) head(marketing) #> youtube facebook newspaper sales #> 1 276.12 45.36 83.04 26.52 #> 2 53.40 47.16 54.12 12.48 #> 3 20.64 55.08 83.16 11.16 #> 4 181.80 49.56 70.20 22.20 #> 5 216.96 12.96 70.08 15.48 #> 6 10.44 58.68 90.00 8.64 We can also check summary statistics of each column summary(marketing) #> youtube facebook newspaper sales #> Min. : 0.84 Min. : 0.00 Min. : 0.36 Min. : 1.92 #> 1st Qu.: 89.25 1st Qu.:11.97 1st Qu.: 15.30 1st Qu.:12.45 #> Median :179.70 Median :27.48 Median : 30.90 Median :15.48 #> Mean :176.45 Mean :27.92 Mean : 36.66 Mean :16.83 #> 3rd Qu.:262.59 3rd Qu.:43.83 3rd Qu.: 54.12 3rd Qu.:20.88 #> Max. :355.68 Max. :59.52 Max. :136.80 Max. :32.40 This dataset contains data about the advertising budget spent on YouTub, Radio, and Newspapers for a particular product and the resulting sales. We expect a positive correlation between such advertising costs and sales. Let’s start with YouTub advertising costs to create a simple linear regression model. First let’s plot the variables to get a better sense of their relationship: # Create scatter plot library(ggplot2) ggplot(marketing, aes(x=youtube, y=sales)) + geom_point(colour="black") + ggtitle('YouTube vs Sales') As YouTube advertisement cost increases, sales also increase – they are positively correlated! Now with the linear model lm function, let’s create a line of best fit using the least sum of square method. # Fit linear regression # By default it include an incepter, so it is equvialent to add "+ 1" # res.lm <- lm(sales ~ youtube + 1, data = marketing) res.lm <- lm(sales ~ youtube, data = marketing) In the above code, we used lm to fit our simple linear regression model. This takes the formula y ~ X, where X is the predictor variable (YouTube advertising costs) and y is the output variable (Sales). Then, this function will return fitted model via a ordinary least squares (OLS) method. The res.lm is a list, you can get the it attributes by e.g., res.lm$coefficients res.lm$coefficients #> (Intercept) youtube #> 8.43911226 0.04753664 In the notation that we have been using, \\(\\alpha\\) is the intercept and \\(\\beta\\) is the slope i.e.: \\(\\alpha = 8.439, \\quad \\beta = 0.048\\) Thus, the equation for the model will be: \\(\\text{Sales} = 8.439 + 0.048*\\text{YouTube}\\) Let’s also check an indicator of the model efficacy, R2. Luckily, summary function can calculate it from the lm output and gives us a ready-made method for doing this so we don’t need to code all the math ourselves: res_summary = summary(res.lm) # Again, res_summary is also a list res_summary$r.squared #> [1] 0.6118751 We can also take a look at the model summary by writing this snippet: # Print out the summary summary(res.lm) #> #> Call: #> lm(formula = sales ~ youtube, data = marketing) #> #> Residuals: #> Min 1Q Median 3Q Max #> -10.0632 -2.3454 -0.2295 2.4805 8.6548 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 8.439112 0.549412 15.36 <2e-16 *** #> youtube 0.047537 0.002691 17.67 <2e-16 *** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Residual standard error: 3.91 on 198 degrees of freedom #> Multiple R-squared: 0.6119, Adjusted R-squared: 0.6099 #> F-statistic: 312.1 on 1 and 198 DF, p-value: < 2.2e-16 There is a lot here. Of these results, we have discussed: R-squared F-statistic Prob (F-statistic) - this is the p-value of the F-statistic Intercept coef - this is alpha YouTub coef - this is beta for predictor YouTub P>|t| - this is the p-value for our coefficients Now that we’ve fit a simple regression model, we can try to predict the values of sales based on the equation we just derived! sales_pred = predict(res.lm, newdata = marketing[c('youtube')]) marketing['sales_pred'] = sales_pred The predict fucntion predicts sales value for each row based on the model equation using YouTub costs. This is the equivalent of manually typing out our equation: sales_pred = 8.439 + 0.048*(advert['youtube']). We can visualise our regression model by plotting sales_pred against the YouTube advertising costs to find the line of best fit: library(ggplot2) ggplot(marketing, aes(x=youtube)) + geom_point(aes(y=sales), colour="black") + geom_line(aes(y=sales_pred), colour="red") + ggtitle('YouTube vs Sales') In the next step, we will add more features as predictors and see whether it improves our model. Go to the the notebook called 02-linearReg-05.Rmd. 3.5 Multiple Regression with lm function A multiple linear regression is simply a linear regression that involves more than one predictor variable. It is represented as: \\[\\qquad Y_e = \\alpha + \\beta_1*X_1 + \\beta_2*X_2 + \\dots + \\beta_p*X_p\\] Each βi will be estimated using the least sum of squares method. The data set is \\[ \\begin{array} {~~} Y_1, & X_1^{(1)}, & \\ldots, & X_p^{(1)} \\\\ Y_2, & X_1^{(2)}, & \\ldots, & X_p^{(2)} \\\\ \\vdots & \\vdots & \\vdots & \\vdots \\\\ Y_n, & X_1^{(n)}, & \\ldots, & X_p^{(n)} \\end{array} \\] For each sample \\(i\\), the predicted value by the model is: \\(\\qquad Y_{i,e} = \\alpha + \\beta_1*X_1^{(i)} + \\beta_2*X_2^{(i)} + \\dots + \\beta_p*X_p^{(i)}\\) Define the sum of squares \\[ S(\\alpha,\\beta_1,\\ldots,\\beta_p) = \\sum_{i=1}^n \\left\\{ Y_i -Y_{i,e}\\right\\}^2 =\\sum_{i=1}^n \\left\\{ Y_i -\\left( \\alpha + \\beta_1*X_1^{(i)} + \\beta_2*X_2^{(i)} + \\dots + \\beta_p*X_p^{(i)}\\right)\\right\\}^2 \\] Least squares estimators: solve \\[ \\frac{\\partial S(\\alpha,\\beta_1,\\ldots,\\beta_p)}{\\partial \\alpha}=0,\\quad \\frac{\\partial S (\\alpha,\\beta_1,\\ldots,\\beta_p)}{\\partial \\beta_1}=0,\\quad \\ldots,\\quad \\frac{\\partial S (\\alpha,\\beta_1,\\ldots,\\beta_p)}{\\partial \\beta_p}=0. \\] to obtain the least squares estimators of the parameters \\[ \\hat\\alpha, \\hat\\beta_1,\\ldots,\\hat\\beta_p. \\] Note that be definition, \\[ SSE = S(\\hat\\alpha, \\hat\\beta_1,\\ldots,\\hat\\beta_p). \\] In other words, the fitted SSE (sum of squares error) is the minimized value of the sum squares with the estimated values of the parameters. The more varibles, the smaller the \\(R^2\\) Consider two regression models \\(\\quad ~ Y_e = \\alpha + \\beta_1*X_1\\) \\(\\quad \\tilde Y_e = \\alpha + \\beta_1*X_1 + \\beta_2*X_2\\) The model (II) has one more input variable \\(X_2\\). The \\(SSE_I\\) of Model (I) is the minimum of \\[ S_I(\\alpha,\\beta_1) = \\sum_{i=1}^n \\left\\{ Y_i -\\left( \\alpha + \\beta_1*X_1^{(i)} \\right)\\right\\}^2 \\] over all possible values of \\((\\alpha,\\beta_1)\\). The \\(SSE_{II}\\) of Model (II) is the minimum of \\[ S_{II}(\\alpha,\\beta_1,\\beta_2) = \\sum_{i=1}^n \\left\\{ Y_i -\\left( \\alpha + \\beta_1*X_1^{(i)} +\\beta_2*X_2^{(i)} \\right)\\right\\}^2. \\] over all possible values of \\((\\alpha,\\beta_1,\\beta_2)\\). Because \\(\\quad S_I(\\alpha,\\beta_1) = S_{II}(\\alpha,\\beta_1,\\beta_2=0 )\\), we find that \\(SSE_{II}\\le SSE_I\\), so \\[ R^2_{II} = SST - SSE_{II} \\ge SST - SSE_{I} = R^2_{I}. \\] With this simple dataset of three predictor variables, there can be seven possible models: Sales ~ YouTube Sales ~ Newspaper Sales ~ Facebook Sales ~ YouTube + Facebook Sales ~ YouTube + Newspaper Sales ~ Newspaper + Facebook Sales ~ YouTube + Facebook + Newspaper Generally, if there are p possible predictor variables, there can be (2p - 1) possible models – this can get large very quickly! Thankfully, there are a few guidelines to filter some of these and then navigate towards the most efficient one. Keep variables with low p-values and eliminate ones with high p-values Keep variables that increase the value of adjusted-R2 – this penalizes the model for adding insignificant variables and increases when we add significant variables. It is calculated by: \\[ R^2_{adj} = 1- (1-R^2) \\frac{n-1}{n-p-1}\\] Based on these guidelines, there are two approaches to select the predictor variables in the final model: Forward selection: start with a null model (no predictors), then add predictors one by one. If the p-value for the variable is small enough and the value of the adjusted-R2 goes up, the predictor is included in the model. Otherwise, it is not included. Backward selection: starts with a model that has all the possible predictors and discard some of them. If the p-value of a predictor variable is large and adjusted-R2 is lower when removed, it is discarded from the model. Otherwise, it remains a part of the model. Many statistical programs give us an option to select from these approaches while implementing multiple linear regression. For now, let’s manually add a few variables and see how it changes the model parameters and efficacy. First, add the newspaper variable to the model: library(datarium) data(marketing) head(marketing) #> youtube facebook newspaper sales #> 1 276.12 45.36 83.04 26.52 #> 2 53.40 47.16 54.12 12.48 #> 3 20.64 55.08 83.16 11.16 #> 4 181.80 49.56 70.20 22.20 #> 5 216.96 12.96 70.08 15.48 #> 6 10.44 58.68 90.00 8.64 res_lm2 = lm(sales ~ youtube + newspaper, data=marketing) summary(res_lm2) #> #> Call: #> lm(formula = sales ~ youtube + newspaper, data = marketing) #> #> Residuals: #> Min 1Q Median 3Q Max #> -10.3477 -2.0815 -0.1138 2.2711 10.1415 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 6.929938 0.630405 10.993 < 2e-16 *** #> youtube 0.046901 0.002581 18.173 < 2e-16 *** #> newspaper 0.044219 0.010174 4.346 2.22e-05 *** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Residual standard error: 3.745 on 197 degrees of freedom #> Multiple R-squared: 0.6458, Adjusted R-squared: 0.6422 #> F-statistic: 179.6 on 2 and 197 DF, p-value: < 2.2e-16 As you see, the p-values for the coefficients are very small, suggesting that all the estimates are significant. The equation for this model will be: \\[ \\text{Sales} = 6.93+0.046* \\text{YouTube} + 0.044 * \\text{Newspaper}\\] The values of R2 and adjusted R2 are 0.646 and 0.642, which is just a minor improvement from before (0.612 and 0.610, respectively). Similarly for RSE (3.745). Only a small decrease in RSE and error… Let’s take a closer look at the summary above. The Adj-R2 increases slightly, but the F-statistic decreases (from 312.1 to 179.6), as does the associated p-value. This suggests that adding newspaper didn’t improve the model significantly. Let’s try adding facebook instead: # Initialise and fit new model with TV and Radio as predictors # model3 = smf.ols('Sales ~ TV + Radio', data=advert).fit() # print(model3.summary()) res_lm3 = lm(sales ~ youtube + facebook, data=marketing) summary(res_lm3) #> #> Call: #> lm(formula = sales ~ youtube + facebook, data = marketing) #> #> Residuals: #> Min 1Q Median 3Q Max #> -10.5572 -1.0502 0.2906 1.4049 3.3994 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 3.50532 0.35339 9.919 <2e-16 *** #> youtube 0.04575 0.00139 32.909 <2e-16 *** #> facebook 0.18799 0.00804 23.382 <2e-16 *** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Residual standard error: 2.018 on 197 degrees of freedom #> Multiple R-squared: 0.8972, Adjusted R-squared: 0.8962 #> F-statistic: 859.6 on 2 and 197 DF, p-value: < 2.2e-16 This gives us the model: \\[ \\text{Sales} = 3.51+0.046* \\text{YouTube} + 0.188 * \\text{Facebook}\\] The adjusted R2 value has improved considerably, as did the RSE and F-statistic, indicating an efficient model. Thus, we can conclude that facebook is a great addition to the model. YouTube and facebook advertising costs together are able to predict sales well. But, can we improve it a bit further by combining all three predictor variables? Try it out: see if you can figure out how to do this on your own! # Initialise and fit new model with TV, Newspaper, and Radio as predictors # Print summary of regression results # Calculate RSE - don't forget that the number of predictors p is now 3 You should get the equation: \\[ \\text{Sales} = 3.53+0.046*\\text{YouTube} -0.001*\\text{Newspaper} +0.188*\\text{Facebook}\\] You should also find that: RSE increases slightly, the coefficient for newspaper is negative, and the F-statistic decreases considerably from 859.6 to 570.3. All these suggest that the model actually became less efficient on addition of newspaper. Why? This step shows clearly that adding one more input variable Newspaper in Model 3 does not lead to any improvement. "],["introClassifier.html", "Chapter 4 Introduction to Classification 4.1 Visualise logistic and logit functions 4.2 Logistic regression on Diabetes 4.3 Cross-validation 4.4 More assessment metrics", " Chapter 4 Introduction to Classification 4.1 Visualise logistic and logit functions In this chapter, we will focus on logistic regression for classification. Let’s first look at what logistic and logit function look like. 4.1.1 Logistic function Let’s write our first function logestic() as follows. # Write your first function logistic <- function(y) { exp(y) / (1 + exp(y)) } # Try it with different values: logistic(0.1) #> [1] 0.5249792 logistic(c(-3, -2, 0.5, 3, 5)) #> [1] 0.04742587 0.11920292 0.62245933 0.95257413 0.99330715 This is the equivalent to the built-in plogis() function in the stat package for the logistic distribution: plogis(0.1) #> [1] 0.5249792 plogis(c(-3, -2, 0.5, 3, 5)) #> [1] 0.04742587 0.11920292 0.62245933 0.95257413 0.99330715 4.1.2 Logit function Now, let look at the logistic’s inverse function logit(), and let’s define it manually. Note, this function only support input between 0 and 1. # Write your first function logit <- function(x) { log(x / (1 - x)) } # Try it with different values: logit(0.4) #> [1] -0.4054651 logit(c(0.2, 0.3, 0.5, 0.7, 0.9)) #> [1] -1.3862944 -0.8472979 0.0000000 0.8472979 2.1972246 logit(c(-1, 2, 0.4)) #> Warning in log(x/(1 - x)): NaNs produced #> [1] NaN NaN -0.4054651 Again, the built-in stat package’s logistic distribution has an equivalent function qlogis(), though with a different name. qlogis(0.4) #> [1] -0.4054651 qlogis(c(0.2, 0.3, 0.5, 0.7, 0.9)) #> [1] -1.3862944 -0.8472979 0.0000000 0.8472979 2.1972246 qlogis(c(-1, 2, 0.4)) #> Warning in qlogis(c(-1, 2, 0.4)): NaNs produced #> [1] NaN NaN -0.4054651 4.1.3 Visualise the distribution Logisitc function # You can use seq() function to generate a vector # Check how to use it by help(seq) or ?seq x = seq(-7, 7, 0.3) df = data.frame('x'=x, 'logistic'=plogis(x)) # You can plot by plot function # plot(x=df$x, y=df$logistic, type='o') # Or ggplot2 library(ggplot2) ggplot(df, aes(x=x, y=logistic)) + geom_point() + geom_line() Logit function x = seq(0.001, 0.999, 0.01) df = data.frame('x'=x, 'logit'=qlogis(x)) ggplot(df, aes(x=x, y=logit)) + geom_point() + geom_line() 4.2 Logistic regression on Diabetes 4.2.1 Load Pima Indians Diabetes Database This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage. The datasets consist of several medical predictor (independent) variables and one target (dependent) variable, Outcome. Independent variables include the number of pregnancies the patient has had, their BMI, insulin level, age, and so on. Acknowledgement: This notebook is adapted and updated from STAT1005. # Install the mlbench library for loading the datasets if (!requireNamespace("mlbench", quietly = TRUE)) { install.packages("mlbench") } # Load data library(mlbench) data(PimaIndiansDiabetes) # Check the first few lines dim(PimaIndiansDiabetes) #> [1] 768 9 head(PimaIndiansDiabetes) #> pregnant glucose pressure triceps insulin mass pedigree age diabetes #> 1 6 148 72 35 0 33.6 0.627 50 pos #> 2 1 85 66 29 0 26.6 0.351 31 neg #> 3 8 183 64 0 0 23.3 0.672 32 pos #> 4 1 89 66 23 94 28.1 0.167 21 neg #> 5 0 137 40 35 168 43.1 2.288 33 pos #> 6 5 116 74 0 0 25.6 0.201 30 neg Now, let’s check two potential features: glucose and age, colored by the diabetes labels. library(ggplot2) ggplot(data=PimaIndiansDiabetes, aes(x=glucose, y=age)) + geom_point(aes(color=diabetes)) Before we start fit models, let’s split the data into training and test sets in a 4:1 ratio. Let define it manually, though there are functions to do it automatically. set.seed(0) idx_train = sample(nrow(PimaIndiansDiabetes), size=0.75*nrow(PimaIndiansDiabetes), replace = FALSE) df_train = PimaIndiansDiabetes[idx_train, ] df_test = PimaIndiansDiabetes[-idx_train, ] # recall the meaning of negative symbol 4.2.2 Fit logistic regression In logistic regression, the predicted probability to be class 1 is: \\[P(y=1|X, W) = \\sigma(w_0, x_1 * w_1 + ... + x_p * w_p)\\] where the \\(\\sigma()\\) denotes the logistic function. In R, the built-in package stats already have functions to fit generalised linear model (GLM), including logistic regression, a type of GML. Here, let’s start with the whole dataset to fit a logistic regression. Note, we will specify the model family as binomial, as the likelihood we are using in logistic regression is a Bernoulli likelihood, a special case of binomial likelihood when the total trial n=1. # Define formula in different ways # my_formula = as.formula(diabetes ~ glucose + age) # my_formula = as.formula(paste(colnames(PimaIndiansDiabetes)[1:8], collapse= " + ")) # my_formula = as.formula(diabetes ~ .) # Fit logistic regression glm_res <- glm(diabetes ~ ., data=df_train, family = binomial) # We can use the logLik() function to obtain the log likelihood logLik(glm_res) #> 'log Lik.' -281.9041 (df=9) We can use summary() function to see more details about the model fitting. summary(glm_res) #> #> Call: #> glm(formula = diabetes ~ ., family = binomial, data = df_train) #> #> Coefficients: #> Estimate Std. Error z value Pr(>|z|) #> (Intercept) -8.044602 0.826981 -9.728 < 2e-16 *** #> pregnant 0.130418 0.036080 3.615 0.000301 *** #> glucose 0.032196 0.004021 8.007 1.18e-15 *** #> pressure -0.017158 0.006103 -2.811 0.004934 ** #> triceps -0.003425 0.007659 -0.447 0.654752 #> insulin -0.001238 0.001060 -1.169 0.242599 #> mass 0.104029 0.018119 5.741 9.39e-09 *** #> pedigree 0.911030 0.344362 2.646 0.008156 ** #> age 0.012980 0.010497 1.237 0.216267 #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> (Dispersion parameter for binomial family taken to be 1) #> #> Null deviance: 756.83 on 575 degrees of freedom #> Residual deviance: 563.81 on 567 degrees of freedom #> AIC: 581.81 #> #> Number of Fisher Scoring iterations: 5 4.2.3 Assess on test data Now, we can evaluate the accuracy of the model on the 25% test data. # Train the full model on the training data glm_train <- glm(diabetes ~ ., data=df_train, family = binomial) # Predict the probability of being diabeties on test data # We can also set a threshold, e.g., 0.5 for the predicted label pred_prob = predict(glm_train, df_test, type = "response") pred_label = pred_prob >= 0.5 # Observed label obse_label = df_test$diabetes == 'pos' # Calculate the accuracy on test data # think how accuracy is defined # we can use (TN + TP) / (TN + TP + FN + FP) # we can also directly compare the proportion of correctness accuracy = mean(pred_label == obse_label) print(paste("Accuracy on test set:", accuracy)) #> [1] "Accuracy on test set: 0.796875" 4.2.4 Model selection and diagnosis 4.2.4.1 Model2: New feature set by removing triceps # Train the full model on the training data glm_mod2 <- glm(diabetes ~ pregnant + glucose + pressure + insulin + mass + pedigree + age, data=df_train, family = binomial) logLik(glm_mod2) #> 'log Lik.' -282.0038 (df=8) summary(glm_mod2) #> #> Call: #> glm(formula = diabetes ~ pregnant + glucose + pressure + insulin + #> mass + pedigree + age, family = binomial, data = df_train) #> #> Coefficients: #> Estimate Std. Error z value Pr(>|z|) #> (Intercept) -8.0317567 0.8251403 -9.734 < 2e-16 *** #> pregnant 0.1308094 0.0361230 3.621 0.000293 *** #> glucose 0.0324606 0.0039854 8.145 3.80e-16 *** #> pressure -0.0175651 0.0060269 -2.914 0.003563 ** #> insulin -0.0014402 0.0009593 -1.501 0.133291 #> mass 0.1018155 0.0173811 5.858 4.69e-09 *** #> pedigree 0.9000134 0.3428652 2.625 0.008665 ** #> age 0.0131238 0.0105147 1.248 0.211982 #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> (Dispersion parameter for binomial family taken to be 1) #> #> Null deviance: 756.83 on 575 degrees of freedom #> Residual deviance: 564.01 on 568 degrees of freedom #> AIC: 580.01 #> #> Number of Fisher Scoring iterations: 5 # Predict the probability of being diabeties on test data # We can also set a threshold, e.g., 0.5 for the predicted label pred_prob2 = predict(glm_mod2, df_test, type = "response") pred_label2 = pred_prob2 >= 0.5 accuracy2 = mean(pred_label2 == obse_label) print(paste("Accuracy on test set with model2:", accuracy2)) #> [1] "Accuracy on test set with model2: 0.807291666666667" 4.2.4.2 Model3: New feature set by removing triceps and insulin # Train the full model on the training data glm_mod3 <- glm(diabetes ~ pregnant + glucose + pressure + mass + pedigree + age, data=df_train, family = binomial) logLik(glm_mod3) #> 'log Lik.' -283.1342 (df=7) summary(glm_mod3) #> #> Call: #> glm(formula = diabetes ~ pregnant + glucose + pressure + mass + #> pedigree + age, family = binomial, data = df_train) #> #> Coefficients: #> Estimate Std. Error z value Pr(>|z|) #> (Intercept) -7.797803 0.802287 -9.719 < 2e-16 *** #> pregnant 0.130990 0.035957 3.643 0.00027 *** #> glucose 0.030661 0.003755 8.164 3.23e-16 *** #> pressure -0.017847 0.005953 -2.998 0.00272 ** #> mass 0.097356 0.016969 5.737 9.61e-09 *** #> pedigree 0.824150 0.338299 2.436 0.01484 * #> age 0.015134 0.010426 1.452 0.14663 #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> (Dispersion parameter for binomial family taken to be 1) #> #> Null deviance: 756.83 on 575 degrees of freedom #> Residual deviance: 566.27 on 569 degrees of freedom #> AIC: 580.27 #> #> Number of Fisher Scoring iterations: 5 # Predict the probability of being diabeties on test data # We can also set a threshold, e.g., 0.5 for the predicted label pred_prob3 = predict(glm_mod3, df_test, type = "response") pred_label3 = pred_prob3 >= 0.5 accuracy3 = mean(pred_label3 == obse_label) print(paste("Accuracy on test set with model3:", accuracy3)) #> [1] "Accuracy on test set with model3: 0.786458333333333" 4.3 Cross-validation In last section, we split the whole dataset into 75% for training and 25% for testing. However, when the dataset is small, the test set may not be big enough and introduce high variance on the assessment. One way to reduce this variance in assessment is performing cross-validation, where we split the data into K folds and use K-1 folds for training and the remaining fold for testing. This procedure will be repeated for fold 1 to fold K as testing fold and all folds will be aggregated for joint assessment. K is usually taken 3, 5 or 10. In extreme case that K=n_sample, we call it leave-one-out cross-validation (LOOCV). Let’s load the dataset (again) first. # Load data library(mlbench) data(PimaIndiansDiabetes) Besides implement the cross-validation from scratch, there are packages supporting it well, including caret package. We will install it and use it for cross-validation here. # Install the caret library for cross-validation if (!requireNamespace("caret", quietly = TRUE)) { install.packages("caret") } library(caret) # Define training control # We also want to have savePredictions=TRUE & classProbs=TRUE set.seed(0) my_trControl <- trainControl(method = "cv", number = 5, classProbs = TRUE, savePredictions = TRUE) # Train the model cv_model <- train(diabetes ~ ., data = PimaIndiansDiabetes, method = "glm", family=binomial(), trControl = my_trControl) # Summarize the results print(cv_model) #> Generalized Linear Model #> #> 768 samples #> 8 predictor #> 2 classes: 'neg', 'pos' #> #> No pre-processing #> Resampling: Cross-Validated (5 fold) #> Summary of sample sizes: 615, 614, 615, 614, 614 #> Resampling results: #> #> Accuracy Kappa #> 0.7708344 0.4695353 We can also access to detailed prediction results after concatenating the K folds: head(cv_model$pred) #> pred obs neg pos rowIndex parameter Resample #> 1 neg neg 0.9656694 0.03433058 4 none Fold1 #> 2 neg neg 0.8581071 0.14189290 6 none Fold1 #> 3 neg pos 0.9508306 0.04916940 7 none Fold1 #> 4 neg pos 0.6541361 0.34586388 17 none Fold1 #> 5 neg pos 0.7675666 0.23243342 20 none Fold1 #> 6 neg pos 0.6132685 0.38673152 26 none Fold1 We can double check the accuracy: CV_acc = mean(cv_model$pred$pred == cv_model$pred$obs) print(paste("Accuracy via 5-fold cross-validation", CV_acc)) #> [1] "Accuracy via 5-fold cross-validation 0.770833333333333" 4.4 More assessment metrics 4.4.1 Two types of error In the above sections, we used the accuracy to perform model diagnosis, either only on one testing dataset or aggregating cross multiple folds in cross- validation. Accuracy is a widely used metric for model evaluation, on the averaged error rate. However, this metric still have limitations when assessing the model performance, especially the following two: When the samples are highly imbalance, high accuracy may not mean a good model. For example, for a sample with 990 negative samples and 10 positive samples, a simple model by predicting for all sample as negative will give an accuracy of 0.99. Thus, for highly imbalanced samples, we should be careful when interpreting the accuracy. In many scenarios, our tolerance on false positive errors and false negative errors may be different and we want to know both for a certain model. They are often called as type I and II errors: Type I error: false positive (rate) Type II error: false negative (rate) - a joke way to remember what type II mean Negative has two stripes. Here, we use the diabetes dataset and their cross-validation results above to illustrate the two types of errors and the corresponding model performance evaluation. # Let's start to define the values for the confusion matrix first # Recall what the difference between & vs && # Read more: https://stat.ethz.ch/R-manual/R-devel/library/base/html/Logic.html TP = sum((cv_model$pred$obs == 'pos') & (cv_model$pred$pred == 'pos')) FN = sum((cv_model$pred$obs == 'pos') & (cv_model$pred$pred == 'neg')) FP = sum((cv_model$pred$obs == 'neg') & (cv_model$pred$pred == 'pos')) TN = sum((cv_model$pred$obs == 'neg') & (cv_model$pred$pred == 'neg')) print(paste('TP, FN, FP, TN:', TP, FN, FP, TN)) #> [1] "TP, FN, FP, TN: 151 117 59 441" We can also use the table() function to get the whole confusion matrix. Read more about the table function for counting the frequency of each element. A similar way is the confusionMatrix() in caret package. # Calculate confusion matrix confusion_mtx = table(cv_model$pred[, c("obs", "pred")]) confusion_mtx #> pred #> obs neg pos #> neg 441 59 #> pos 117 151 # similar function confusionMatrix # conf_mat = confusionMatrix(cv_model$pred$pred, cv_model$pred$obs) # conf_mat$table We can also plot out the confusion matrix # Change to data.frame before using ggplot confusion_df = as.data.frame(confusion_mtx) ggplot(confusion_df, aes(pred, obs, fill= Freq)) + geom_tile() + geom_text(aes(label=Freq)) + scale_fill_gradient(low="white", high="darkgreen") Also the false positive rate, false negative rate and true negative rate. Note, the denominator is always the number of observed samples with the same label, namely they are a constant for a specific dataset. FPR = FP / sum(cv_model$pred$obs == 'neg') FNR = FN / sum(cv_model$pred$obs == 'pos') TPR = TP / sum(cv_model$pred$obs == 'pos') print(paste("False positive rate:", FPR)) #> [1] "False positive rate: 0.118" print(paste("False negative rate:", FNR)) #> [1] "False negative rate: 0.436567164179104" print(paste("True positive rate:", TPR)) #> [1] "True positive rate: 0.563432835820896" 4.4.2 ROC curve In the above assessment, we only used \\(P>0.5\\) to denote predicted label as positive. We can imagine if we a lower cutoff lower, we will have more false positives and fewer false negatives. Indeed, in different scenarios, people may choose different level of cutoff for their tolerance of different types of errors. Let’s try cutoff \\(P>0.4\\). Think what will you expect. # Original confusion matrix table(cv_model$pred[, c("obs", "pred")]) #> pred #> obs neg pos #> neg 441 59 #> pos 117 151 # New confusion matrix with cutoff 0.4 cv_model$pred$pred_new = as.integer(cv_model$pred$pos >= 0.4) table(cv_model$pred[, c("obs", "pred_new")]) #> pred_new #> obs 0 1 #> neg 408 92 #> pos 89 179 Therefore, we may want to assess the model performance by varying the cutoffs and obtain a more systematic assessment. Actually, the Receiver operating characteristic (ROC) curve is what you need. It presents the TPR (sensitivity) vs the FPR (i.e., 1 - TNR or 1 - specificity) when varying the cutoffs. In order to achieve this, we can calculate FPR and TPR manually by varying the cutoff through a for loop. Read more about for loop and you may try write your own and here is an example from the cardelino package. For simplicity, let use an existing tool implemented in the plotROC package: plotROC::geom_roc() that is compatible with ggplot2. # Install the plotROC library for plotting ROC curve if (!requireNamespace("plotROC", quietly = TRUE)) { install.packages("plotROC") } library(ggplot2) library(plotROC) # You can set the n.cuts to show the cutoffs on the curve g = ggplot(cv_model$pred, aes(m = pos, d = as.integer(obs=='pos'))) + geom_roc(n.cuts=7, hjust = -0.4, vjust = 1.5) + coord_equal() + ggtitle("ROC curve") # Calculate AUC from the graph AUC_val = calc_auc(g)$AUC #> Warning: The following aesthetics were dropped during statistical transformation: m, d #> ℹ This can happen when ggplot fails to infer the correct grouping structure in #> the data. #> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical #> variable into a factor? # Display the plot g + annotate("text", x=0.8, y=0.1, label=paste("AUC =", round(AUC_val, 4))) #> Warning: The following aesthetics were dropped during statistical transformation: m, d #> ℹ This can happen when ggplot fails to infer the correct grouping structure in #> the data. #> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical #> variable into a factor? 4.4.3 Homework Now, try another model with removing triceps and plot the ROC curve and calculate the AUC score. Is it higher or lower than using the full features? "],["image-digital.html", "Chapter 5 Medical Image and Digital Health", " Chapter 5 Medical Image and Digital Health Contents to be added. "],["cancer.html", "Chapter 6 Cancer genomics and epidemiology 6.1 Case study 1: analysis of cBioportal mutation data 6.2 Case study 2: Cancer Epidemiology 6.3 1. Scenario 6.4 2. Hong Kong population 6.5 3. Cancer registry data 6.6 4. Existing cancer funding and publication data 6.7 5. Open disucssion", " Chapter 6 Cancer genomics and epidemiology 6.1 Case study 1: analysis of cBioportal mutation data Non-small cell lung cancer is a deadly disease, and we would like to identify mutations in genes that drive its formation and progression. George et al. (2015) has sequenced 120 small cell lung cancer samples and this data is available from cBioportal. The original data is in the MAF, and we have selected a subset of the columns, normalized the column names, and made the data available as an RDS file. In this exercise, we will explore the mutation data and examine genes that are frequently mutated in non-small cell lung cancer samples. We will implement a simple method for identifying candidate tumour suppressors based on loss-of-function mutations. 6.1.1 Exploratory analysis Q1. Load the data named mutations_sclc_ucologne_2015.rds into R. Examine it using head. In this data.frame, each row is a mutation detected in a particular gene from a particular sample in the study. For more information on the columns, see the MAF specification. ?readRDS ?head Q2. Identify the top 10 most frequently mutated genes. # calculate count frequencies using table ?table # freqs <- ??? # sort the frequency vector in decreasing order ?sort # freqs <- ??? # select the first 10 genes in the freqs # freqs.top <- ??? # obtain the gene names # genes <- names(freqs.top); # print(freqs.top) Q3. Tabulate the count frequencies of variant_class. What proportion of variants are Silent or inside an Intron? ?table Q4. A variant class value frame_shift_del appears to be misspelt due to case sensitivity. Correct this error. # x$variant_class[x$variant_class == "frame_shift_del"] <- ???; Q5. To help us simplify the variant classes into loss-of-function or neutral, let us import the mutation_effects.tsv data. ?read.table # Hint: You need to set the `header` and `sep` arguments Q6. Define a new column in x that converts the variant classes to effects. ?match Q7. Create a subset of the data for the most frequently mutated genes, and tabulate the mutation count frequencies of variant classes for each gene. # x.sub <- x[???, ] # order the factor levels based on `genes`, # which was previously sorted in decreasing order of frequency # x.sub$gene <- factor(x.sub$gene, levels=genes); ?table Q8. Determine the number of mutations in each effect category, across all genes. Save the result in a variable named overall.counts. # overall.counts <- table(???); # overall.counts Q9. Tabulate the mutation count frequencies for each gene and effect. Save the results in a variable named gene.counts. Look up the frequently mutated genes in this matrix. # gene.counts <- table(???, ???); # gene.counts[???, ] 6.1.2 Statistical analysis Here, we implemented a simple statistical test for assessing whether a gene is significantly frequently targeted by loss-of-function mutations, based on the Fisher’s exact test. # Test whether a query gene has significantly more loss-of-function # mutations compared with other genes. # Requires global variables `gene.counts` and `overall.counts` lof_test <- function(gene) { # Construct the following contingency table: # neutral lof # other genes a b # query gene c d cols <- c("neutral", "loss_of_function"); gene.counts.sel <- gene.counts[gene, cols]; ct <- matrix( c( overall.counts[cols] - gene.counts.sel, gene.counts.sel ), byrow=TRUE, ncol=2 ); # Test whether the lof mutations are greater in frequency compared # to neutral mutations in the query gene, in comparison to all other genes fisher.test(ct, alternative="greater") } Q10. Subset the gene.counts table for the TP53 gene. Run lof_test on this gene. # gene.counts[???, ] # lof_test(???) Q11. Now, test to see if TTN and MUC16 are significantly frequently targeted by loss-of-function mutations. # similar to above Q12. Apply the loss-of-function test to all frequently mutated genes. ?lapply Q13. Extract odds ratio and p-values from the test results. # odds.ratios <- vapply(hs, function(h) h$estimate, 0); # ps <- vapply(hs, function(h) ???, 0); Q14. Since we tested many genes, we need to adjust for multiple hypothesis testing so that we can control the false discovery rate. So, adjust the p-values to obtain q-values. ?p.adjust Q15. Construct a results data.frame with gene names, odds ratio, p-values, and q-values that summarize the loss-of-function test results. # res <- data.frame( # gene = ???, # odds_ratio = ???, # p = ???, # q = ??? # ); ?order #res <- res[order(???), ]; Q16. Identify the significant genes from the results data.frame at a false discovery rate of 5% (i.e. q-value threshold of 0.05). Q17. Look up the significant genes in the gene.counts matrix. 6.1.3 Literature search Q18. Do the significant genes appear to be involved in cancer? What general roles do they play? Q19. How do these genes contribute specifically to the formation or progression of small cell lung cancer? Q20. What are some possible ways of identifying oncogenes that are activated by mutations or other genomic alterations? 6.2 Case study 2: Cancer Epidemiology by Dr Jason Wong Date: 6-11-2023 The RMarkdown notebook to run your own code can be downloaded here 6.3 1. Scenario You are a grants officer working for the Hong Kong Health Bureau. The HK government has recently announced new special funding in cancer research to be administered by the Bureau. You are tasked with coming up with a proposal for distribution of funding to specific cancer types that are in most need of research. Discussion points: What is important to consider when selecting a cancer type in need of research? What type of data is required? 6.4 2. Hong Kong population You are aware that generally cancer is disease that affects the elderly more than the young. You decide to first take a closer look at the structure of the population of Hong Kong. Historic population of Hong Kong can be obtained from the Census and Statistics Department. An abridged version of the full historic population of Hong Kong is provided here containing the population of Hong Kong from 1965, 1975, 1985, 1995, 2005, 2015 and 2022 categorised by sex and age (0-19, 20-44, 45-64, 65+). Discussion points: What is the trend in Hong Kong’s population over the past ~60 years? What is the best way to visualise this data? 6.4.1 Download population data HKPop<- read.table("https://github.com/StatBiomed/BMDS-book/raw/main/notebooks/module5-epidemi/HK_population_1965-2022.txt", sep = "\\t", header = TRUE, stringsAsFactor=FALSE, check.names = FALSE) HKPop #> Sex Age 1965 1975 1985 1995 2005 2015 2022 #> 1 male 0-19 901.9 989.7 909.0 835.6 719.0 613.8 530.6 #> 2 male 20-44 607.4 795.6 1210.2 1363.7 1263.7 1146.9 1039.2 #> 3 male 45-64 266.5 414.3 525.9 615.7 896.6 1084.9 1046.2 #> 4 male 65+ 42.2 84.6 170.5 269.3 384.7 520.0 713.6 #> 5 female 0-19 865.6 938.8 840.3 780.7 684.1 575.0 502.1 #> 6 female 20-44 539.3 685.6 1093.7 1423.6 1530.4 1531.8 1338.7 #> 7 female 45-64 286.3 400.7 470.7 535.0 884.7 1224.3 1314.7 #> 8 female 65+ 88.7 152.3 235.9 332.5 450.0 594.6 806.5 6.4.2 Format data for plotting Here we convert the original dataframe into simplified format for ggplot2. male<-data.frame(year = as.numeric(colnames(HKPop[1,3:9])), `0-19` = as.numeric(HKPop[1,3:9]), `20-44` = as.numeric(HKPop[2,3:9]), `45-64` = as.numeric(HKPop[3,3:9]), `65+` = as.numeric(HKPop[4,3:9]), check.names = FALSE) female<-data.frame(year = as.numeric(colnames(HKPop[1,3:9])), `0-19` = as.numeric(HKPop[5,3:9]), `20-44` = as.numeric(HKPop[6,3:9]), `45-64` = as.numeric(HKPop[7,3:9]), `65+` = as.numeric(HKPop[8,3:9]), check.names = FALSE) if (!require("tidyverse")) install.packages("tidyverse") male<-as_tibble(male) %>% select(year,`0-19`,`20-44`,`45-64`,`65+`) %>% gather (key="age",value="population ('000s)",-year) female<-as_tibble(female) %>% select(year,`0-19`,`20-44`,`45-64`,`65+`) %>% gather (key="age",value="population ('000s)",-year) male #> # A tibble: 28 × 3 #> year age `population ('000s)` #> <dbl> <chr> <dbl> #> 1 1965 0-19 902. #> 2 1975 0-19 990. #> 3 1985 0-19 909 #> 4 1995 0-19 836. #> 5 2005 0-19 719 #> 6 2015 0-19 614. #> 7 2022 0-19 531. #> 8 1965 20-44 607. #> 9 1975 20-44 796. #> 10 1985 20-44 1210. #> # ℹ 18 more rows female #> # A tibble: 28 × 3 #> year age `population ('000s)` #> <dbl> <chr> <dbl> #> 1 1965 0-19 866. #> 2 1975 0-19 939. #> 3 1985 0-19 840. #> 4 1995 0-19 781. #> 5 2005 0-19 684. #> 6 2015 0-19 575 #> 7 2022 0-19 502. #> 8 1965 20-44 539. #> 9 1975 20-44 686. #> 10 1985 20-44 1094. #> # ℹ 18 more rows 6.4.3 Plotting population data Uses ggplot2 and gridExtra to make line plot of male and female population data side-by-side. if (!require("ggplot2")) install.packages("gglot2") if (!require("gridExtra")) install.packages("gridExtra") library(ggplot2) library(gridExtra) #Import the necessary packages and libraries pmale<-ggplot(male,aes(x=year,y=`population ('000s)`,group=age))+ geom_line(aes(color=age))+ geom_point(aes(color=age))+ scale_color_brewer(palette="Spectral")+ theme_classic()+ ylim(0,1600)+ theme(legend.position="none")+ scale_x_continuous(breaks = seq(1965, 2022, by = 10))+ ggtitle("male") pfemale<-ggplot(female,aes(x=year,y=`population ('000s)`,group=age))+ geom_line(aes(color=age))+ geom_point(aes(color=age))+ scale_color_brewer(palette="Spectral")+ theme_classic()+ ylim(0,1600)+ theme(legend.position="right",axis.title.y = element_blank())+ scale_x_continuous(breaks = seq(1965, 2022, by = 10))+ ggtitle("female") grid.arrange(pmale,pfemale,ncol=2,widths=c(3,3.75)) 6.5 3. Cancer registry data It is clear that Hong Kong has an aging population, thus cancer incidence would also likely increase. To examine cancer incidence and mortality in Hong Kong you obtain data from the Hong Kong Cancer Registry, which is maintained by the Hospital Authority. Cancer incidence data was summarised for the last three decades (1990-1999, 2000-2009 and 2010-2019). Discussion points: Has incidence been increasing for most cancers? How about mortality? How has cancer risk and mortality rate changed in the past 3 decades? Has the incidence-to-mortality ratio been decreasing generally? Is it statistically significant? Which cancer type has the highest incidence in children (0-19) when compared with the elderly (65+). Is this statistically significantly different to incidence of children versus elderly cancers in general? 6.5.1 Download cancer registry data HKCancer<- read.table("https://github.com/StatBiomed/BMDS-book/raw/main/notebooks/module5-epidemi/HK_cancer_incidence_mortality_1990-2020.txt", sep = "\\t", header = TRUE, stringsAsFactor=FALSE) HKCancer #> Type Sex Age Year Biliary Bladder Brain Breast Cervix #> 1 incidence male 0-19 1990-1999 0 6 212 0 0 #> 2 incidence male 20-44 1990-1999 36 165 368 6 0 #> 3 incidence male 45-64 1990-1999 322 1266 393 28 0 #> 4 incidence male 65+ 1990-1999 830 2920 318 35 0 #> 5 incidence male 0-19 2000-2009 0 2 206 0 0 #> 6 incidence male 20-44 2000-2009 31 103 267 9 0 #> 7 incidence male 45-64 2000-2009 297 900 372 52 0 #> 8 incidence male 65+ 2000-2009 1134 3103 299 96 0 #> 9 incidence male 0-19 2010-2019 0 2 155 0 0 #> 10 incidence male 20-44 2010-2019 28 18 276 14 0 #> 11 incidence male 45-64 2010-2019 530 648 511 72 0 #> 12 incidence male 65+ 2010-2019 1568 2399 380 107 0 #> 13 incidence female 0-19 1990-1999 0 2 149 6 0 #> 14 incidence female 20-44 1990-1999 52 62 283 4228 1263 #> 15 incidence female 45-64 1990-1999 246 246 238 5311 1839 #> 16 incidence female 65+ 1990-1999 909 1199 284 4122 1574 #> 17 incidence female 0-19 2000-2009 0 0 132 4 4 #> 18 incidence female 20-44 2000-2009 34 39 223 5887 1204 #> 19 incidence female 45-64 2000-2009 287 179 265 11833 1676 #> 20 incidence female 65+ 2000-2009 1190 1090 237 5774 1329 #> 21 incidence female 0-19 2010-2019 0 2 119 1 0 #> 22 incidence female 20-44 2010-2019 36 17 223 6658 1294 #> 23 incidence female 45-64 2010-2019 452 188 394 22096 2288 #> 24 incidence female 65+ 2010-2019 1500 872 283 10337 1269 #> 25 mortality male 0-19 1990-1999 0 0 76 0 0 #> Colorectum Eye Hodgkin.lymphoma Kaposi Kidney Larynx Leukaemia Liver Lung #> 1 15 29 35 0 24 1 356 58 6 #> 2 1087 8 64 0 141 86 509 1741 1036 #> 3 4644 9 47 0 530 824 453 5702 8420 #> 4 8058 21 41 0 721 1265 623 4806 15152 #> 5 5 38 32 0 20 0 329 33 3 #> 6 957 8 119 9 221 37 437 1116 772 #> 7 6418 18 77 4 1071 658 642 6015 7909 #> 8 13352 13 77 8 1275 1125 864 5808 18870 #> 9 6 26 53 0 31 0 338 24 2 #> 10 1005 10 163 25 293 29 401 659 541 #> 11 10034 28 87 18 1978 684 1033 6500 9523 #> 12 18003 15 114 12 1977 1043 1381 6716 20588 #> 13 2 33 10 0 25 0 296 37 7 #> 14 932 11 61 0 98 13 393 353 597 #> 15 3166 9 19 0 256 59 312 1093 2629 #> 16 7728 16 33 0 503 130 534 2244 8428 #> 17 5 14 30 0 15 0 219 13 5 #> 18 938 12 122 2 120 5 405 212 625 #> 19 4430 10 33 0 482 37 469 1041 3397 #> 20 10810 8 35 0 830 85 723 2637 9517 #> 21 5 14 32 0 19 0 237 21 0 #> 22 1057 9 174 0 135 7 443 168 647 #> 23 7200 16 58 4 887 47 786 1168 6312 #> 24 13119 20 58 5 1203 84 913 3007 10872 #> 25 10 3 0 0 2 0 120 36 1 #> Melanoma Mesothelioma Multiple.myeloma Nasal Nasopharynx #> 1 3 0 1 4 33 #> 2 50 0 43 53 3049 #> 3 82 0 210 109 3724 #> 4 107 0 421 120 1212 #> 5 1 0 1 7 16 #> 6 41 5 38 38 2114 #> 7 98 34 326 161 3750 #> 8 137 77 674 137 1177 #> 9 0 0 0 3 21 #> 10 47 4 26 41 1339 #> 11 186 51 525 174 3653 #> 12 195 154 910 154 1207 #> 13 2 0 1 1 13 #> 14 44 0 19 31 1380 #> 15 58 0 150 57 1157 #> 16 102 0 417 93 536 #> 17 2 0 0 5 6 #> 18 41 8 21 36 942 #> 19 102 12 185 74 1187 #> 20 118 14 559 92 465 #> 21 3 0 0 6 6 #> 22 70 10 24 37 583 #> 23 128 34 399 105 1145 #> 24 179 24 636 102 389 #> 25 2 0 0 0 6 #> Non.Hodgkin.lymphoma Non.melanoma.skin Oesophagus Oral Ovary Pancreas Penis #> 1 137 8 0 18 0 4 2 #> 2 596 211 158 324 0 106 19 #> 3 997 613 1873 1165 0 514 78 #> 4 1210 968 2203 1055 0 887 184 #> 5 111 4 0 6 0 1 0 #> 6 454 220 82 312 0 71 18 #> 7 1216 917 1528 1354 0 780 71 #> 8 1756 1835 2126 1416 0 1440 199 #> 9 106 1 0 9 0 1 0 #> 10 487 291 54 304 0 109 21 #> 11 1910 1782 1321 1941 0 1402 154 #> 12 2633 2975 1982 1813 0 2302 279 #> 13 75 2 0 21 77 2 0 #> 14 444 156 48 241 854 60 0 #> 15 594 374 254 364 970 290 0 #> 16 1120 1249 761 485 776 875 0 #> 17 43 4 1 20 108 0 0 #> 18 461 182 13 254 1299 62 0 #> 19 888 580 190 454 1846 461 0 #> 20 1409 2476 725 768 891 1329 0 #> 21 57 4 0 9 84 3 0 #> 22 475 303 16 259 1466 92 0 #> 23 1561 1168 174 920 3166 923 0 #> 24 1883 3268 624 1091 1128 2101 0 #> 25 25 0 0 1 0 3 0 #> Placenta Prostate Sarcoma Small.intestine Stomach Testis Thymus Thyroid #> 1 0 0 137 0 5 61 12 22 #> 2 0 10 320 31 429 275 71 250 #> 3 0 481 281 102 1990 80 70 288 #> 4 0 3073 261 131 3634 129 59 194 #> 5 0 0 118 0 6 57 16 21 #> 6 0 6 307 34 331 395 96 333 #> 7 0 1588 367 113 1954 65 137 447 #> 8 0 8676 316 163 4256 44 94 278 #> 9 0 1 110 1 2 53 23 17 #> 10 0 18 299 46 192 587 68 496 #> 11 0 4128 527 293 2158 100 137 834 #> 12 0 14703 507 336 4720 15 85 424 #> 13 1 0 128 1 1 0 3 80 #> 14 13 0 283 22 492 0 38 1294 #> 15 0 0 232 53 826 0 38 788 #> 16 0 0 232 116 2319 0 38 479 #> 17 0 0 105 0 2 0 1 81 #> 18 8 0 322 24 379 0 64 1659 #> 19 3 0 300 80 971 0 116 1570 #> 20 0 0 247 153 2512 0 65 595 #> 21 0 0 86 1 0 0 5 103 #> 22 12 0 330 38 318 0 30 2291 #> 23 3 0 509 221 1653 0 81 3200 #> 24 0 0 351 251 2809 0 67 940 #> 25 0 0 41 1 0 2 7 1 #> Uterus Vulva #> 1 0 0 #> 2 0 0 #> 3 0 0 #> 4 0 0 #> 5 0 0 #> 6 0 0 #> 7 0 0 #> 8 0 0 #> 9 0 0 #> 10 0 0 #> 11 0 0 #> 12 0 0 #> 13 2 1 #> 14 478 49 #> 15 1362 96 #> 16 729 235 #> 17 2 2 #> 18 821 44 #> 19 3071 147 #> 20 1130 336 #> 21 3 1 #> 22 1165 57 #> 23 6582 235 #> 24 1864 486 #> 25 0 0 #> [ reached 'max' / getOption("max.print") -- omitted 23 rows ] 6.5.2 3a. Visualise changes in incidence and mortality # first plot incidence for each cancer type #Import the necessary packages and libraries if (!require("ggplot2")) install.packages("gglot2") if (!require("gridExtra")) install.packages("gridExtra") if (!require("ggpubr")) install.packages("ggpubr") library(ggplot2) library(gridExtra) library(ggpubr) HKCancer_inc <- HKCancer[HKCancer$Type=='incidence',] p<-list() for (i in 1:(ncol(HKCancer_inc)-4)){ p[[i]]<-ggplot(HKCancer_inc,aes_string(fill=names(HKCancer_inc)[3],x=names(HKCancer_inc)[4], y=names(HKCancer_inc)[i+4],group=names(HKCancer_inc)[3]))+ geom_bar(position="dodge",stat="identity")+ facet_wrap(~Sex) + theme_classic()+ scale_fill_brewer(palette="Spectral")+ theme(legend.position="none")+ theme(text = element_text(size = 10))+ ggtitle(names(HKCancer_inc)[i+4])+ ylab("Incidence") } do.call('grid.arrange',c(p,ncol=3,nrow=12)) # now plot mortality for each cancer type #Import the necessary packages and libraries if (!require("ggplot2")) install.packages("gglot2") if (!require("gridExtra")) install.packages("gridExtra") if (!require("ggpubr")) install.packages("ggpubr") if (!require("dplyr")) install.packages("dplyr") library(ggplot2) library(gridExtra) library(ggpubr) library(dplyr) HKCancer_mort <- HKCancer[HKCancer$Type=='mortality',] p<-list() #(ncol(HKCancer_inc)-4) for (i in 1:(ncol(HKCancer_mort)-4)){ p[[i]]<-ggplot(HKCancer_mort,aes_string(fill=names(HKCancer_mort)[3],x=names(HKCancer_mort)[4], y=names(HKCancer_mort)[i+4],group=names(HKCancer_mort)[3]))+ geom_bar(position="dodge",stat="identity")+ facet_wrap(~Sex) + theme_classic()+ scale_fill_brewer(palette="Spectral")+ theme(legend.position="none")+ theme(text = element_text(size = 10))+ ggtitle(names(HKCancer_mort)[i+4])+ ylab("Mortality") } do.call('grid.arrange',c(p,ncol=3,nrow=12)) 6.5.3 3b. Calculate cancer risk and mortality rate To calculate disease risk we need to calculated the number of new cases over the number of persons at risk over a specific time period. We have the incidence for each decade and can estimate the number of persons at risk based on the population in 1995, 2005 and 2015. # cancer risk calculation HKCancer_inc <- HKCancer[HKCancer$Type=='incidence',] HKCancer_inc_risk <- HKCancer_inc[,1:5] risk <- function(x,age,sex,year){ if (is.integer(x)){ pop_n <- HKPop %>% filter(Sex==sex & Age==age) if (year == "1990-1999"){ return (as.double(x)/as.double(pop_n$`1995`)) } else if (year == "2000-2009") {return (as.double(x)/as.double(pop_n$`2005`))} else { return (as.double(x)/as.double(pop_n$`2015`)) } } return (x) } for (i in 5:ncol(HKCancer_inc)){ HKCancer_inc_risk[names(HKCancer_inc)[i]] <- mapply(risk,HKCancer_inc[,i],HKCancer_inc[,3],HKCancer_inc[,2],HKCancer_inc[,4]) } #visualise cancer risk p<-list() for (i in 1:(ncol(HKCancer_inc_risk)-4)){ p[[i]]<-ggplot(HKCancer_inc_risk,aes_string(fill=names(HKCancer_inc_risk)[3],x=names(HKCancer_inc_risk)[4], y=names(HKCancer_inc_risk)[i+4],group=names(HKCancer_inc_risk)[3]))+ geom_bar(position="dodge",stat="identity")+ facet_wrap(~Sex) + theme_classic()+ scale_fill_brewer(palette="Spectral")+ theme(legend.position="none")+ theme(text = element_text(size = 10))+ ggtitle(names(HKCancer_inc)[i+4])+ ylab("incidence per 1000") } do.call('grid.arrange',c(p,ncol=3,nrow=12)) # mortality rate calculation HKCancer_mort <- HKCancer[HKCancer$Type=='mortality',] HKCancer_mort_risk <- HKCancer_mort[,1:5] risk <- function(x,age,sex,year){ if (is.integer(x)){ pop_n <- HKPop %>% filter(Sex==sex & Age==age) if (year == "1990-1999"){ return (as.double(x)/as.double(pop_n$`1995`)) } else if (year == "2000-2009") {return (as.double(x)/as.double(pop_n$`2005`))} else { return (as.double(x)/as.double(pop_n$`2015`)) } } return (x) } for (i in 5:ncol(HKCancer_mort)){ HKCancer_mort_risk[names(HKCancer_mort)[i]] <- mapply(risk,HKCancer_mort[,i],HKCancer_mort[,3],HKCancer_mort[,2],HKCancer_mort[,4]) } #visualise mortality rate p<-list() for (i in 1:(ncol(HKCancer_mort_risk)-4)){ p[[i]]<-ggplot(HKCancer_mort_risk,aes_string(fill=names(HKCancer_mort_risk)[3],x=names(HKCancer_mort_risk)[4], y=names(HKCancer_mort_risk)[i+4],group=names(HKCancer_mort_risk)[3]))+ geom_bar(position="dodge",stat="identity")+ facet_wrap(~Sex) + theme_classic()+ scale_fill_brewer(palette="Spectral")+ theme(legend.position="none")+ theme(text = element_text(size = 10))+ ggtitle(names(HKCancer_inc)[i+4])+ ylab("mortality per 1000") } do.call('grid.arrange',c(p,ncol=3,nrow=12)) 6.5.4 3c. Mortality-incidence ratio Cancer research can be focused on improving cancer outcomes in a number of ways. For example cancer prevention research that seeks to reduce cancer incidence which would also ultimately reduce cancer mortality. Another area is cancer therapy which would not affect incidence but seeks to reduce mortality, or at least prolong survival. We don’t go into survival analysis in this tutorial, but a way to get an idea whether treatment is improving by looking at the mortality-incidence ratio. HKCancer_inc <- HKCancer[HKCancer$Type=='incidence',] HKCancer_mort <- HKCancer[HKCancer$Type=='mortality',] HKCancer_mort_inc <- HKCancer_mort[,1:5] risk <- function(mort,inc){ if (inc == 0){ return (0) } return (as.double(mort)/as.double(inc)) } for (i in 5:ncol(HKCancer_mort)){ HKCancer_mort_inc[names(HKCancer_mort)[i]] <- mapply(risk,HKCancer_mort[,i],HKCancer_inc[,i]) } #visualise mortality incidence ratio p<-list() for (i in 1:(ncol(HKCancer_mort_inc)-4)){ p[[i]]<-ggplot(HKCancer_mort_inc,aes_string(fill=names(HKCancer_mort_inc)[3],x=names(HKCancer_mort_inc)[4], y=names(HKCancer_mort_inc)[i+4],group=names(HKCancer_mort_inc)[3]))+ geom_bar(position="dodge",stat="identity")+ facet_wrap(~Sex) + theme_classic()+ scale_fill_brewer(palette="Spectral")+ theme(legend.position="none")+ theme(text = element_text(size = 10))+ ggtitle(names(HKCancer_inc)[i+4])+ ylab("mortality-incidence ratio") } do.call('grid.arrange',c(p,ncol=3,nrow=12)) 6.5.5 3d. Paired t-test on mortality-incidence ratio change In general, across the different cancer types is cancer treatment improving? We can use a paired t-test comparing the mortality-incidence ratio of cancers from the 1990-1999 period with the 2010-2019 period. #First sum up all incidence and mortality data for each cancer type across age and sex HKCancer_inc_sum <- aggregate(HKCancer_inc[,-(1:4)],list(HKCancer_inc$Year),FUN=sum) HKCancer_mort_sum <- aggregate(HKCancer_mort[,-(1:4)],list(HKCancer_mort$Year),FUN=sum) HKCancer_mort_inc_year <- data.frame(Year=HKCancer_mort_sum[,1]) risk <- function(mort,inc){ return (as.double(mort)/as.double(inc)) } for (i in 2:ncol(HKCancer_mort_sum)){ HKCancer_mort_inc_year[names(HKCancer_mort_sum)[i]] <- mapply(risk,HKCancer_mort_sum[,i],HKCancer_inc_sum[,i]) } HKCancer_mort_inc_year_m <-data.frame(`1990-1999` = as.numeric(HKCancer_mort_inc_year[1,2:37]), `2000-2009` = as.numeric(HKCancer_mort_inc_year[2,2:37]), `2010-2019` = as.numeric(HKCancer_mort_inc_year[3,2:37]), check.names = FALSE) HKCancer_mort_inc_year_m$Cancer <- names(HKCancer_mort_inc_year)[-1] HKCancer_mort_inc_year_t<-as_tibble(HKCancer_mort_inc_year_m) %>% select(`Cancer`,`1990-1999`,`2000-2009`,`2010-2019`)%>% gather (key="Year",value="MIR",-Cancer) plot<-ggplot(HKCancer_mort_inc_year_t,aes(x=Year,y=MIR, color=Year))+ geom_boxplot(na.rm=T) + theme_classic()+ scale_color_brewer(palette="Dark2")+ geom_jitter(shape=16, position=position_jitter(0.2),na.rm=T)+ ylab("mortality-incidence ratio")+ ylim(0,1.2) + geom_signif(comparisons = list(c("1990-1999", "2010-2019")), map_signif_level=F, test= "t.test",test.args = list(paired = TRUE), na.rm = T, y_position = c(1.1, 1.3)) + geom_signif(comparisons = list(c("1990-1999", "2000-2009")), map_signif_level=F, test= "t.test",test.args = list(paired = TRUE), na.rm = T) + geom_signif(comparisons = list(c("2000-2009", "2010-2019")), map_signif_level=F, test= "t.test",test.args = list(paired = TRUE), na.rm = T) plot p<-list() p[[1]]<-t.test(HKCancer_mort_inc_year_m$`1990-1999`,HKCancer_mort_inc_year_m$`2000-2009`,paired=TRUE,alternative_m = "two.sided") p[[2]]<-t.test(HKCancer_mort_inc_year_m$`1990-1999`,HKCancer_mort_inc_year_m$`2010-2019`,paired=TRUE,alternative_m = "two.sided") p[[3]]<-t.test(HKCancer_mort_inc_year_m$`2000-2009`,HKCancer_mort_inc_year_m$`2010-2019`,paired=TRUE,alternative_m = "two.sided") p #> [[1]] #> #> Paired t-test #> #> data: HKCancer_mort_inc_year_m$`1990-1999` and HKCancer_mort_inc_year_m$`2000-2009` #> t = 0.79695, df = 33, p-value = 0.4312 #> alternative hypothesis: true mean difference is not equal to 0 #> 95 percent confidence interval: #> -0.01415536 0.03238636 #> sample estimates: #> mean difference #> 0.009115498 #> #> #> [[2]] #> #> Paired t-test #> #> data: HKCancer_mort_inc_year_m$`1990-1999` and HKCancer_mort_inc_year_m$`2010-2019` #> t = 2.1861, df = 33, p-value = 0.036 #> alternative hypothesis: true mean difference is not equal to 0 #> 95 percent confidence interval: #> 0.002055217 0.057209924 #> sample estimates: #> mean difference #> 0.02963257 #> #> #> [[3]] #> #> Paired t-test #> #> data: HKCancer_mort_inc_year_m$`2000-2009` and HKCancer_mort_inc_year_m$`2010-2019` #> t = 1.5413, df = 35, p-value = 0.1322 #> alternative hypothesis: true mean difference is not equal to 0 #> 95 percent confidence interval: #> -0.005438019 0.039734021 #> sample estimates: #> mean difference #> 0.017148 6.5.6 3e. Childhood versus elderly cancers Although it is clear that the incidence of cancer is typically higher in the elderly, some cancers affect children as well. What cancer types disproportionate affect children? For each cancer type, compare the proportion of 0-19 versus 65+ incidence against the 0-19 versus 65+ incidence for all other cancer types. # Examine the proportion of childhood HKCancer_inc <- HKCancer[HKCancer$Type=='incidence',] HKCancer_inc_age_sum <- aggregate(HKCancer_inc[,-(1:4)],list(HKCancer_inc$Age),FUN=sum) HKCancer_inc_age_sum$Total<- rowSums(HKCancer_inc_age_sum[,-1]) HKCancer_inc_age_sum_csq <- data.frame(cancer=names(HKCancer_inc_age_sum[,2:ncol(HKCancer_inc_age_sum)])) pval <- list() ratio <- list() for (i in 2:(ncol(HKCancer_inc_age_sum))){ val = Map('-',HKCancer_inc_age_sum$Total,HKCancer_inc_age_sum[,i]) dat <- data.frame(cancer=HKCancer_inc_age_sum[c(1,4),i], other =c(val[[1]],val[[2]])) pval <- append(pval,fisher.test(dat)$p.val) ratio<- append(ratio,log(as.double(dat[1,1]+0.1)/as.double(dat[2,1]+0.1))) } HKCancer_inc_age_sum_csq$pval <- pval HKCancer_inc_age_sum_csq$ratio <- ratio plot_child<-ggplot(HKCancer_inc_age_sum_csq[-37,],aes(x=reorder(cancer,-as.numeric(ratio)),y=as.numeric(ratio)),fill=cancer)+ geom_bar(stat="identity",fill="red")+ geom_hline(yintercept=-4.12132318942113, linetype="dashed", color = "black", linewidth=0.5)+ theme_classic()+ scale_fill_brewer(palette="Spectral")+ theme(legend.position="none")+ #theme(text = element_text(size = 10))+ theme(axis.title.x=element_blank())+ theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + ylab("log((child+0.1)/(elderly+0.1)") plot_child HKCancer_inc_age_sum_csq #> cancer pval ratio #> 1 Biliary 1.21618e-216 -11.17481 #> 2 Bladder 9.601084e-314 -6.711128 #> 3 Brain 0 -0.615666 #> 4 Breast 0 -7.519824 #> 5 Cervix 1.635299e-126 -6.925188 #> 6 Colorectum 0 -7.531208 #> 7 Eye 2.085337e-112 0.5039276 #> 8 Hodgkin.lymphoma 3.658856e-81 -0.6227962 #> 9 Kaposi 0.41662 -5.525453 #> 10 Kidney 1.560619e-71 -3.882371 #> 11 Larynx 1.963055e-113 -8.129416 #> 12 Leukaemia 0 -1.043172 #> 13 Liver 0 -4.909033 #> 14 Lung 0 -8.191896 #> 15 Melanoma 6.246412e-15 -4.324192 #> 16 Mesothelioma 5.396388e-09 -7.897668 #> 17 Multiple.myeloma 6.126613e-106 -7.062026 #> 18 Nasal 0.0001285609 -3.286427 #> 19 Nasopharynx 4.628193e-74 -3.95948 #> 20 Non.Hodgkin.lymphoma 4.513518e-11 -2.940272 #> 21 Non.melanoma.skin 0 -6.315107 #> 22 Oesophagus 1.377432e-251 -8.943186 #> 23 Oral 5.100139e-108 -4.379029 #> 24 Ovary 0.0002847234 -2.34054 #> 25 Pancreas 3.11451e-246 -6.690686 #> 26 Penis 3.023792e-18 -5.753479 #> 27 Placenta 0.07013259 2.397895 #> 28 Prostate 0 -10.08778 #> 29 Sarcoma 9.774701e-213 -1.028899 #> 30 Small.intestine 1.479593e-31 -5.916202 #> 31 Stomach 0 -7.137096 #> 32 Testis 1.871184e-97 -0.09472556 #> 33 Thymus 6.748934e-06 -1.915502 #> 34 Thyroid 7.821825e-09 -2.194891 #> 35 Uterus 1.457974e-105 -6.262217 #> 36 Vulva 1.521831e-27 -5.552298 #> 37 Total 1 -4.121323 6.6 4. Existing cancer funding and publication data The Hong Kong government established in Health and Medical Research Fund (HMRF) in 2011 to specifically provide research funding for health and medical research in Hong Kong. Since 2016 over 370 projects in the category of Cancer has been funded for a total of ~$400 M dollars. A list of all funded projects can be found on the Health Bureau webpage. You would like to use this data to see if there is any association between previous project funding and the epidemiology of cancers in Hong Kong. We can also do a similar thing with publications and ask if the research publications in Hong Kong have been aligned with the incidence and mortality. We can obtain this data from PubMed using the following terms: (“Hong Kong”[Affiliation]) AND (neoplasms[MeSH Terms]) The data has been predownloaded as the Pubmed API via R is a bit slow. Discussion points: Why has research publications increased dramatically in recent years? Is there something unsual with the dataset? What are the main cancer types being researched in Hong Kong? Is there any correlation between funding and cancer incidence and mortality? 6.6.1 Download HMRF grants and Pubmed data HMRF<- read.delim("https://github.com/StatBiomed/BMDS-book/raw/main/notebooks/module5-epidemi/HMRF_cancer_grants.txt", sep = "\\t", header = TRUE, stringsAsFactor=FALSE, check.names = TRUE) #HMRF pubmed<- read.delim("https://github.com/StatBiomed/BMDS-book/raw/main/notebooks/module5-epidemi/pubmed__neoplasm_hongkong.txt", sep = "\\t", header = TRUE, stringsAsFactor=FALSE, check.names = TRUE) #pubmed hist(pubmed$Publication.Year) 6.6.2 Make word cloud for grants 6.6.3 Make compare grant funding with incidence and mortality if (!require("ggrepel")) install.packages("ggrepel") if (!require("ggplot2")) install.packages("gglot2") if (!require("gridExtra")) install.packages("gridExtra") library(ggrepel) library(ggplot2) library(gridExtra) grantpmsum<- read.delim("https://github.com/StatBiomed/BMDS-book/raw/main/notebooks/module5-epidemi/Grants_pubmed_summary.txt", sep = "\\t", header = TRUE, stringsAsFactor=FALSE, check.names = TRUE) grantpmsum #> Cancer Grants Pubmed #> 1 Biliary 0 72 #> 2 Bladder 0 141 #> 3 Brain 2 294 #> 4 Breast 34 1147 #> 5 Cervix 9 408 #> 6 Colorectum 40 857 #> 7 Eye 2 27 #> 8 Hodgkin lymphoma 0 21 #> 9 Kaposi 0 6 #> 10 Kidney 0 133 #> 11 Larynx 0 8 #> 12 Leukaemia 18 515 #> 13 Liver 97 2077 #> 14 Lung 22 773 #> 15 Melanoma 0 99 #> 16 Mesothelioma 6 58 #> 17 Multiple myeloma 0 104 #> 18 Nasal 0 14 #> 19 Nasopharynx 24 1113 #> 20 Non-Hodgkin lymphoma 0 55 #> 21 Skin 0 32 #> 22 Oesophagus 7 404 #> 23 Oral 5 229 #> 24 Ovary 14 354 #> 25 Pancreas 2 141 #> 26 Penis 0 7 #> 27 Placenta 0 9 #> 28 Prostate 6 322 #> 29 Sarcoma 2 216 #> 30 Small intestine 0 11 #> 31 Stomach 5 455 #> 32 Testis 0 18 #> 33 Thymus 0 3 #> 34 Thyroid 2 231 #> 35 Uterus 0 72 #> 36 Vulva 0 8 HKCancer_inc <- HKCancer[HKCancer$Type=='incidence',] HKCancer_inc_sum <- data.frame(incidence=colSums(HKCancer_inc[,-(1:4)])) HKCancer_mort <- HKCancer[HKCancer$Type=='mortality',] HKCancer_mort_sum <- data.frame(mortality=colSums(HKCancer_inc[,-(1:4)])) HKCancer_compare <-data.frame(cancer=grantpmsum$Cancer, grants=grantpmsum$Grants, pubmed=grantpmsum$Pubmed, incidence=colSums(HKCancer_inc[,-(1:4)]), mortality=colSums(HKCancer_mort[,-(1:4)])) gp<-ggplot(HKCancer_compare, aes(y=grants, x=pubmed)) + geom_point() + scale_color_brewer(palette="Dark2") + theme_classic() + geom_label_repel(data=HKCancer_compare, aes(label=cancer), nudge_x = 2, size = 3.5,label.size = NA ,min.segment.length =unit(0, 'lines'), max.overlaps = 45, box.padding = 0.4) + stat_cor(method = "pearson", label.x = 3, label.y = 90) pinc<-ggplot(HKCancer_compare, aes(y=pubmed, x=incidence)) + geom_point() + scale_color_brewer(palette="Dark2") + theme_classic() + geom_label_repel(data=HKCancer_compare, aes(label=cancer), nudge_x = 2, size = 3.5,label.size = NA ,min.segment.length =unit(0, 'lines'), max.overlaps = 15, box.padding = 0.5)+ stat_cor(method = "pearson", label.x = 40000, label.y = 0) pmor<-ggplot(HKCancer_compare, aes(y=pubmed, x=mortality)) + geom_point() + scale_color_brewer(palette="Dark2") + theme_classic() + geom_label_repel(data=HKCancer_compare, aes(label=cancer), nudge_x = 2, size = 3.5,label.size = NA ,min.segment.length =unit(0, 'lines'), max.overlaps = 15, box.padding = 0.5) + stat_cor(method = "pearson", label.x = 40000, label.y = 0) ginc<-ggplot(HKCancer_compare, aes(y=grants, x=incidence)) + geom_point() + scale_color_brewer(palette="Dark2") + theme_classic() + geom_label_repel(data=HKCancer_compare, aes(label=cancer), nudge_x = 2, size = 3.5,label.size = NA ,min.segment.length =unit(0, 'lines'), max.overlaps = 40, box.padding = 0.4) + stat_cor(method = "pearson", label.x = 40000, label.y = 0) gmor<-ggplot(HKCancer_compare, aes(y=grants, x=mortality)) + geom_point() + scale_color_brewer(palette="Dark2") + theme_classic() + geom_label_repel(data=HKCancer_compare, aes(label=cancer), nudge_x = 2, size = 3.5,label.size = NA ,min.segment.length =unit(0, 'lines'), max.overlaps = 40, box.padding = 0.4) + stat_cor(method = "pearson", label.x = 40000, label.y = 0) grid.arrange(gp,ginc,gmor,pinc, pmor, ncol=3) 6.7 5. Open disucssion As a group discuss what cancer type would be most worthy of funding in Hong Kong. Statistics and figures should be used to support your decision. If possible also discuss other data/analyses that can be performed and/or other diseases that are also in need of funding in Hong Kong. "],["pop-genetics.html", "Chapter 7 Population Genetics and Diseases 7.1 Case study 1: Heritability and human traits", " Chapter 7 Population Genetics and Diseases 7.1 Case study 1: Heritability and human traits 7.1.1 Part 1 Scenario: You are a researcher working on a twin study on cardiovascular traits to assess the genetic and environmental contribution relevant to metabolism and cardiovascular disease risk. You have recruited a cohort of volunteer adult twins of the same ancestry. The volunteers have undergone a series of baseline clinical evaluations and performed genotyping on a panel of single nucleotide polymorphisms that may be associated with the traits. 7.1.1.1 Questions for Discussion Q1. Besides the clinical measurements, what data do you need to collect from the subjects? Answers: Sex Age Other confounding factors, e.g. BMI, blood pressure, smoking status, etc. Q2. How is genotype data represented for statistical genetic analysis? Answers: Allele: 0/1, 1/2, A/C, etc Genotype: 0 0, 0 1, 1 0, 1 1 Genotype probabilities: P(0/0)=0, P(0/1)=1, P(1/1)=0 Genotype dosage: 0/1/2, 0.678 (continuous from 0-1 or 0-2) Q3. How can you test for association between genotypes and phenotypes (binary and quantitative)? Answers: Allelic chi-square test Fisher’s exact test Linear/Logistic regression Linear mixed model 7.1.1.2 Hands-on exercise : Association test Now, you are given a dataset of age- and sex-matched twin cohort with two cardiovascular phenotypes and 5 quantitative trait loci (QTL). Data set and template notebook are available on Moodle (recommended) and also on this GitHub Repo. The information for columns: zygosity: 1 for monozygotic (MZ) and 2 for dizygotic (DZ) twin T1QTL_A[1-5] and T2QTL_A[1-5]: 5 quantitative loci (A1-A5) in additive coding for Twin 1 (T1) and Twin 2 (T2) respectively The same 5 QTL (D1-D5) in dominance coding for T1 and T2 Phenotype scores of T1 and T2 for the two quantitative cardiovascular traits Download the data dataTwin.dat to your working directory. Start the RStudio program and set the working directory. dataTwin <- read.table("dataTwin2023.dat",h=T) Exploratory analysis A1-5: The QTLs are biallelic with two alleles A and a. The genotypes aa, Aa, and AA are coded additively as 0 (aa), 1 (Aa) and 2 (AA). D1-5: The genotypes aa, Aa, and AA are coded as 0 (aa), 1 (Aa) and 0 (AA). Q1. How many MZ and DZ volunteers are there? Answers: 1000 MZ and 1000 DZ Q2. How are the genotypes represented? Answers: Dosage/Count of non-reference allele : 0, 1, and 2 Q3. Are the QTL independent of each other? Answers: Yes. The pairwise correlations are low (<0.2). Q4. Are there outliers in phenotypes? Answers: Yes. T2 individual 1303 has phenotype score (-4.21) being 4 SD below the mean. table(dataTwin$zygosity) # Q1: shows number of MZ and DZ twin pairs #> #> 1 2 #> 1000 1000 table(dataTwin$T1QTL_A1) # Q2: shows the distribution of QTL_A1 #> #> 0 1 2 #> 474 1021 505 table(dataTwin$T1QTL_D1) # Q2: shows the distribution of QTL_D1 #> #> 0 1 #> 979 1021 table(dataTwin$T1QTL_A1, dataTwin$T1QTL_D1) # Q2: shows the distribution of QTL_A1 in relation to QTL_D1 #> #> 0 1 #> 0 474 0 #> 1 0 1021 #> 2 505 0 cor(dataTwin[,2:11]) # Q3: shows the correlation between QTL_As #> T1QTL_A1 T1QTL_A2 T1QTL_A3 T1QTL_A4 T1QTL_A5 #> T1QTL_A1 1.00000000 -0.005470340 0.021705688 0.01940408 0.016278190 #> T1QTL_A2 -0.00547034 1.000000000 0.017344822 -0.01421677 -0.008678746 #> T1QTL_A3 0.02170569 0.017344822 1.000000000 0.01335711 -0.036751338 #> T1QTL_A4 0.01940408 -0.014216767 0.013357109 1.00000000 0.074899996 #> T1QTL_A5 0.01627819 -0.008678746 -0.036751338 0.07490000 1.000000000 #> T2QTL_A1 0.53243815 0.004201635 -0.013909013 0.03252724 0.020081970 #> T2QTL_A2 -0.04561174 0.464131160 -0.005044127 0.01324172 -0.003012277 #> T2QTL_A3 0.03316574 -0.003552831 0.521253656 0.02045423 0.009081830 #> T2QTL_A4 0.03271254 -0.033419904 0.020422583 0.48641289 0.019247531 #> T2QTL_A5 -0.01285323 0.030413269 -0.045121964 0.08288145 0.457962222 #> T2QTL_A1 T2QTL_A2 T2QTL_A3 T2QTL_A4 T2QTL_A5 #> T1QTL_A1 0.532438150 -0.045611740 0.033165736 0.032712539 -0.01285323 #> T1QTL_A2 0.004201635 0.464131160 -0.003552831 -0.033419904 0.03041327 #> T1QTL_A3 -0.013909013 -0.005044127 0.521253656 0.020422583 -0.04512196 #> T1QTL_A4 0.032527239 0.013241725 0.020454234 0.486412895 0.08288145 #> T1QTL_A5 0.020081970 -0.003012277 0.009081830 0.019247531 0.45796222 #> T2QTL_A1 1.000000000 0.006179257 -0.013129314 0.048294183 -0.01325839 #> T2QTL_A2 0.006179257 1.000000000 -0.020860987 0.002164782 -0.01131418 #> T2QTL_A3 -0.013129314 -0.020860987 1.000000000 -0.010583797 -0.02101270 #> T2QTL_A4 0.048294183 0.002164782 -0.010583797 1.000000000 0.04350925 #> T2QTL_A5 -0.013258394 -0.011314179 -0.021012699 0.043509251 1.00000000 cor(dataTwin[,2:11])>0.2 #> T1QTL_A1 T1QTL_A2 T1QTL_A3 T1QTL_A4 T1QTL_A5 T2QTL_A1 T2QTL_A2 #> T1QTL_A1 TRUE FALSE FALSE FALSE FALSE TRUE FALSE #> T1QTL_A2 FALSE TRUE FALSE FALSE FALSE FALSE TRUE #> T1QTL_A3 FALSE FALSE TRUE FALSE FALSE FALSE FALSE #> T1QTL_A4 FALSE FALSE FALSE TRUE FALSE FALSE FALSE #> T1QTL_A5 FALSE FALSE FALSE FALSE TRUE FALSE FALSE #> T2QTL_A1 TRUE FALSE FALSE FALSE FALSE TRUE FALSE #> T2QTL_A2 FALSE TRUE FALSE FALSE FALSE FALSE TRUE #> T2QTL_A3 FALSE FALSE TRUE FALSE FALSE FALSE FALSE #> T2QTL_A4 FALSE FALSE FALSE TRUE FALSE FALSE FALSE #> T2QTL_A5 FALSE FALSE FALSE FALSE TRUE FALSE FALSE #> T2QTL_A3 T2QTL_A4 T2QTL_A5 #> T1QTL_A1 FALSE FALSE FALSE #> T1QTL_A2 FALSE FALSE FALSE #> T1QTL_A3 TRUE FALSE FALSE #> T1QTL_A4 FALSE TRUE FALSE #> T1QTL_A5 FALSE FALSE TRUE #> T2QTL_A1 FALSE FALSE FALSE #> T2QTL_A2 FALSE FALSE FALSE #> T2QTL_A3 TRUE FALSE FALSE #> T2QTL_A4 FALSE TRUE FALSE #> T2QTL_A5 FALSE FALSE TRUE apply(dataTwin[22:25],2,function(x){ any(x < (mean(x) - 4*sd(x))) }) # Q4: any outlier < 4 SD from the mean for the two quantitative phenotypes #> pheno1_T1 pheno1_T2 pheno2_T1 pheno2_T2 #> FALSE FALSE FALSE TRUE apply(dataTwin[22:25],2,function(x){ any(x > (mean(x) + 4*sd(x))) }) # Q4: any outlier > 4 SD from the mean for the two quantitative phenotypes #> pheno1_T1 pheno1_T2 pheno2_T1 pheno2_T2 #> FALSE FALSE FALSE FALSE # remove the phenotype score of the outlier (T2) for the phenotype 2 (pheno2_T2) outlier<- which(dataTwin$pheno2_T2 < (mean(dataTwin$pheno2_T2) - 4*sd(dataTwin$pheno2_T2) )) outlier #> [1] 1303 dataTwin$pheno2_T2[outlier] #> [1] -4.21 dataTwin$pheno2_T2[outlier] <- NA Association test Test for association between QTL and pheno1 for T1 Regress pheno1_T1 on T1QTL_A1 to estimate the proportion of variance explained (R2). Model: pheno1_T1 = b0 + b1* T1QTL_A1 + e Calculate the conditional mean of phenotype (i.e. phenotypic mean conditional genotype) If the relationship between the QTL and the phenotype is perfectly linear, the regression line should pass through the conditional means (c_means), and the differences between the conditional means should be about equal. Q5. What are the values of b0, b1? Is QTL1 significant associated with the phenotype at alpha<0.01 (multiple testing of 5 loci)? Answers: b0 = 4.1464 b1 = 0.9180 QTL1 is significantly associated with the phenotype with \\(P = 1.02\\times 10^{-13}\\) Q6. What is the proportion of phenotypic variance explained? Answers: Proportion of phenotypic variance explained = 0.027 linA1 <- lm(pheno1_T1~T1QTL_A1, data=dataTwin) summary(linA1) #> #> Call: #> lm(formula = pheno1_T1 ~ T1QTL_A1, data = dataTwin) #> #> Residuals: #> Min 1Q Median 3Q Max #> -15.1225 -2.4435 0.1105 2.7775 12.2555 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 4.1464 0.1511 27.438 < 2e-16 *** #> T1QTL_A1 0.9180 0.1226 7.491 1.02e-13 *** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Residual standard error: 3.834 on 1998 degrees of freedom #> Multiple R-squared: 0.02732, Adjusted R-squared: 0.02683 #> F-statistic: 56.11 on 1 and 1998 DF, p-value: 1.022e-13 summary(linA1)$r.squared # proportion of explained variance by additive component #> [1] 0.02731675 c_means <- by(dataTwin$pheno1_T1,dataTwin$T1QTL_A1,mean) plot(dataTwin$pheno1_T1 ~ dataTwin$T1QTL_A1, col='grey', ylim=c(3,7)) lines(c(0,1,2), c_means, type="p", col=6, lwd=8) lines(sort(dataTwin$T1QTL_A1),sort(linA1$fitted.values), type='b', col="dark green", lwd=3) To test for the non-linearity, we can use the dominance coding of the QTL and add the dominance term to the regression model. Model: pheno1_T1 = b0 + b1* T1QTL_A1 + b2* T1QTL_D1 + e Repeat for T2. Q7. Why can’t we analyse T1 and T2 together? Answers: As T1 and T2 are biologically related as MZ or DZ twins, the genotypes of the QTLs are not independent. Treating the genotypes of T1 and T2 as independent observations will introduce bias. Q8. Is there a dominance effect? Answers: Yes. The model with dominance provides a better goodness of fit (lower p-value) linAD1 <- lm(pheno1_T1 ~ T1QTL_A1 + T1QTL_D1, data=dataTwin) summary(linAD1) # results lm(phenoT1~T1QTL_A1+T1QTL_D1) #> #> Call: #> lm(formula = pheno1_T1 ~ T1QTL_A1 + T1QTL_D1, data = dataTwin) #> #> Residuals: #> Min 1Q Median 3Q Max #> -14.7524 -2.5131 0.0586 2.8215 11.8894 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 3.7522 0.1753 21.405 < 2e-16 *** #> T1QTL_A1 0.9301 0.1220 7.622 3.83e-14 *** #> T1QTL_D1 0.7483 0.1708 4.382 1.24e-05 *** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Residual standard error: 3.816 on 1997 degrees of freedom #> Multiple R-squared: 0.03658, Adjusted R-squared: 0.03562 #> F-statistic: 37.91 on 2 and 1997 DF, p-value: < 2.2e-16 plot(dataTwin$pheno1_T1 ~ dataTwin$T1QTL_A1, col='grey', ylim=c(3,7)) abline(linA1, lwd=3) lines(c(0,1,2), c_means, type='p', col=6, lwd=8) lines(sort(dataTwin$T1QTL_A1),sort(linA1$fitted.values), type='b', col="dark green", lwd=3) lines(sort(dataTwin$T1QTL_A1),sort(linAD1$fitted.values), type='b', col="blue", lwd=3) Q9. Repeat for the other 4 QTL and determine which QTL shows strongest association with the phenotype T1 Answers: QTL3 with \\(P = 7.77 \\times 10^{-25}\\) for model with dominance allQTL_A_T1 <- 2:6 cpheno1_T1 <- which(colnames(dataTwin)=="pheno1_T1") ## Additive cbind(lapply(allQTL_A_T1,function(x){ fstat<- summary(lm(pheno1_T1 ~ ., data=dataTwin[,c(x,cpheno1_T1)]))$fstatistic; pf(fstat[1],fstat[2],fstat[3],lower.tail = F) })) #> [,1] #> [1,] 1.021942e-13 #> [2,] 8.329416e-15 #> [3,] 1.007527e-13 #> [4,] 4.523758e-18 #> [5,] 8.207842e-13 ## Dominance cbind(lapply(allQTL_A_T1,function(x){ fstat<- summary(lm(pheno1_T1 ~ ., data=dataTwin[,c(x,x+10,cpheno1_T1)]))$fstatistic; pf(fstat[1],fstat[2],fstat[3],lower.tail = F) })) #> [,1] #> [1,] 6.907834e-17 #> [2,] 2.166957e-22 #> [3,] 7.771588e-25 #> [4,] 8.124437e-25 #> [5,] 4.312127e-21 #Q9: QTL3 shows the strongest association with P=7.771588e-25 linAD3 <- lm(pheno1_T1 ~ T1QTL_A3 + T1QTL_D3, data=dataTwin) summary(linAD3) # results lm(phenoT1~T1QTL_A1+T1QTL_D1) #> #> Call: #> lm(formula = pheno1_T1 ~ T1QTL_A3 + T1QTL_D3, data = dataTwin) #> #> Residuals: #> Min 1Q Median 3Q Max #> -14.4988 -2.5701 0.1843 2.6991 11.5974 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 3.5437 0.1684 21.038 < 2e-16 *** #> T1QTL_A3 0.9076 0.1181 7.683 2.43e-14 *** #> T1QTL_D3 1.2714 0.1692 7.515 8.55e-14 *** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Residual standard error: 3.782 on 1997 degrees of freedom #> Multiple R-squared: 0.05408, Adjusted R-squared: 0.05313 #> F-statistic: 57.09 on 2 and 1997 DF, p-value: < 2.2e-16 If the subjects with top 5% of the phenotype score are considered as cases, perform case-control association test for most significant SNP (from Q9) and interpret the result. Q10. What are the odds ratio, p-value, and 95% confidence interval (CI)? Answers: Odds ratio is 3.40 and the 95% CI is (1.65, 7.03) quant05 <- quantile(c(dataTwin$pheno1_T1,dataTwin$pheno1_T2),seq(0,1,0.05)) dataTwin$CaseT1 <- as.numeric(dataTwin$pheno1_T1>quant05[20]) dataTwin$CaseT2 <- as.numeric(dataTwin$pheno1_T2>quant05[20]) logisticAD1 <- summary(glm(CaseT1 ~ T1QTL_A3 + T1QTL_D3, data=dataTwin, family="binomial")) exp(logisticAD1$coefficients[2,1]) # odds ratio #> [1] 3.404757 exp(logisticAD1$coefficients[2,1]-1.96*logisticAD1$coefficients[2,2]) # lower 95% confidence interval #> [1] 1.648816 exp(logisticAD1$coefficients[2,1]+1.96*logisticAD1$coefficients[2,2]) # upper 95% confidence interval #> [1] 7.030723 7.1.2 Part 2 Scenario: You are asked to estimate the additive genetic variance, dominance genetic variance and/or shared environmental variance using regression-based method and a classical twin design. \\[\\begin{align*} \\text{For ADE model : }~ & \\sigma^{2}_{P} = \\sigma^{2}_{A} + \\sigma^{2}_{D} + \\sigma^{2}_{E}\\\\ \\text{For ACE model : }~ & \\sigma^{2}_{P} = \\sigma^{2}_{A} + \\sigma^{2}_{C} + \\sigma^{2}_{E}, \\quad \\text{where} \\\\ \\sigma^{2}_{P} & \\text{ is the phenotypic variance}, \\\\ \\sigma^{2}_{A} & \\text{ is additive genetic variance}, \\\\ \\sigma^{2}_{D} & \\text{ is dominance genetic variance}, \\\\ \\sigma^{2}_{C} & \\text{ is shared environmental variance, and} \\\\ \\sigma^{2}_{E} & \\text{ is unshared environmental variance.} \\end{align*}\\] For ADE model, \\[\\begin{align*} cov(MZ) = cor(MZ) & = rMZ = \\sigma^{2}_{A} + \\sigma^{2}_{D} \\\\ cov(DZ) = cor(DZ) & = rDZ = 0.5 * \\sigma^{2}_{A} + 0.25 * \\sigma^{2}_{D} \\quad \\text{ , where} \\\\ \\end{align*}\\] the coefficients 1/2 and 1/4 are based on quantitative genetic theory (Mather & Jinks, 1971). By solving the unknowns, the Falconer’s equations for the ADE model: \\[\\begin{align*} \\sigma^{2}_{A} & = 4*rDZ - rMZ \\\\ \\sigma^{2}_{D} & = 2*rMZ - 4*rDZ \\\\ \\sigma^{2}_{E} & = 1 - \\sigma^{2}_{A} - \\sigma^{2}_{D} \\\\ \\end{align*}\\] For ACE model, \\[\\begin{align*} cov(MZ) = cor(MZ) & = rMZ= \\sigma^{2}_{A} + \\sigma^{2}_{C} \\\\ cov(DZ) = cor(DZ) & = rDZ = 0.5 * \\sigma^{2}_{A} + \\sigma^{2}_{C} \\quad \\text{ , where} \\\\ \\end{align*}\\] By solving the unknowns, the Falconer’s equations for the ACE model: \\[\\begin{align*} \\sigma^{2}_{A} & = 2*(rMZ - rDZ) \\\\ \\sigma^{2}_{C} & = 2*rDZ - rMZ \\\\ \\sigma^{2}_{E} & = 1 - \\sigma^{2}_{A} - \\sigma^{2}_{C} = 1 - rMZ \\end{align*}\\] 7.1.2.1 Questions for discussions : Q1. What is missing heritability of common traits in the era of genome-wide association analysis (GWAS)? Answers: Missing heritability refers to the discrepancy between twin/family-estimated heritability and the amount of variance explained by disease/trait-associated loci identified in GWAS Q2. What are the potential sources of missing heritability? Answers: Large number of variants with small effect not reaching GWAS significance due to inadequate power of GWAS Poor detection of rarer disease/trait-associated variants by genotyping arrays Structural variants poorly captured by genotyping arrays Low power to detect gene–gene interactions Underestimation of shared environment among relatives in twin/family-based studies Suggested reading: Manolio TA, Collins FS, Cox NJ, et al. Nature. 2009 ;461(7265):747-753. doi:10.1038/nature08494 7.1.2.2 Hands-on exercise : variance explained using regression-based method Q1. What is the variance of the phenotype? Q2. Compute the explained variance attributable to the additive genetic component of the QTL with strongest association in Part 1. Q3. Compute the explained variance attributable to the dominance genetic component of the QTL with strongest association in Part 1. R2 from the regression represents the proportion of phenotypic variance explained; thus the raw explained variance component is R2 times the variance of the phenotype (var_pheno). Answers The proportion of explained variance are 0.0273 (additive) and 0.0541 (total: additive + dominance). As the predictors are uncorrelated, the proportion of explained variance by dominance = 0.0541 - 0.0273 = 0.0267 Given the phenotypic variance of 15.102, then Total genetic: 0.0541*15.102 = 0.8168 Additive genetic: 0.0273*15.102 = 0.4128 Dominance genetic: 0.0267*15.102 = 0.4040 var_pheno <- var(dataTwin$pheno1_T1) # the variance of the phenotype var_pheno #> [1] 15.10257 linAD3 <- lm(pheno1_T1 ~ T1QTL_A3 + T1QTL_D3, data=dataTwin) linA3 <- lm(pheno1_T1 ~ T1QTL_A3, data=dataTwin) summary(linAD3)$r.squared # proportion of explained variance by total genetic component #> [1] 0.05408025 summary(linA3)$r.squared # proportion of explained variance by additive component #> [1] 0.02733034 summary(linAD3)$r.squared*var_pheno # (raw) variance component of total genetic component #> [1] 0.8167509 summary(linA3)$r.squared*var_pheno # (raw) variance component of additive genetic component #> [1] 0.4127585 (summary(linAD3)$r.squared-summary(linA3)$r.squared)*var_pheno # (raw) variance component of dominance genetic component #> [1] 0.4039924 Q4. Estimate the variance explained by all the QTL using linear regression. Answers Proportion of variance explained by all 5 QTLs with dominance = 0.23 and the total variance explained = 3.52. # compute for all 5 QTL linAD5=(lm(pheno1_T1 ~ T1QTL_A1 + T1QTL_A2 + T1QTL_A3 + T1QTL_A4 + T1QTL_A5 + T1QTL_D1 + T1QTL_D2 + T1QTL_D3 + T1QTL_D4 + T1QTL_D5, data=dataTwin)) summary(linAD5)$r.squared # proportion of explained variance by total genetic component #> [1] 0.2330307 summary(linAD5)$r.squared*var_pheno # (raw) variance component of total genetic component #> [1] 3.519363 7.1.2.3 Hands-on exercise : variance explained using a classical twin design. Based on our regression results, we have estimates of the total genetic variance as well as the A and D components for phenotype 1. In practice, it is impossible to know all the variants associated with any polygenic trait. Given rMZ > 2*rDZ, we can use Falconer’s formula based on ADE model to estimate the A (additive genetic) and D (dominance) variance with the classical twin design for phenotype 1 without genotypes. Q5. Compute rMZ and rDZ. Answers rMZ = 0.5434 rDZ = 0.1904 Q6. Estimate the proportion of additive and dominance genetic variances using the Falconer’s equations for the ADE model. Answers \\(\\sigma^{2}_{A} = 0.2181\\) \\(\\sigma^{2}_{D} = 0.3253\\) \\(\\sigma^{2}_{E} = 0.4566\\) dataMZ = dataTwin[dataTwin$zygosity==1, c('pheno1_T1', 'pheno1_T2')] # MZ data frame dataDZ = dataTwin[dataTwin$zygosity==2, c('pheno1_T1', 'pheno1_T2')] # DZ data frame rMZ=cor(dataMZ)[2,1] # element 2,1 in the MZ correlation matrix rDZ=cor(dataDZ)[2,1] # element 2,1 in the DZ correlation matrix rMZ rDZ sA2 = 4*rDZ - rMZ sD2 = 2*rMZ - 4*rDZ sE2 = 1 - sA2 - sD2 print(c(sA2, sD2, sE2)) Similarly, for phenotype 2, we can estimate the proportion of additive and/or dominance genetic variances as well as shared environmental variance using the Falconer’s formula. Q7. Which model (ACE or ADE) should be considered for phenotype 2? Answers ACE as rMZ < 2*rDZ Q8. Estimate the proportion of A, C/D and E variance components for phenotype 2. Answers \\(\\sigma^{2}_{A} = 0.3526\\) \\(\\sigma^{2}_{C} = 0.1610\\) \\(\\sigma^{2}_{E} = 0.4864\\) dataMZ = dataTwin[dataTwin$zygosity==1, c('pheno2_T1', 'pheno2_T2')] # MZ data frame dataDZ = dataTwin[dataTwin$zygosity==2, c('pheno2_T1', 'pheno2_T2')] # DZ data frame rMZ=cor(dataMZ, use="complete.obs")[2,1] # element 2,1 in the MZ correlation matrix rDZ=cor(dataDZ, use="complete.obs")[2,1] # element 2,1 in the DZ correlation matrix rMZ rDZ sA2 = 2*(rMZ - rDZ) sC2 = 2*rDZ - rMZ sE2 = 1 - rMZ print(c(sA2, sC2, sE2)) 7.1.3 References Evans DM, Gillespie NA, Martin NG. Biometrical genetics. Biol Psychol. 2002 Oct;61(1-2):33-51. doi: 10.1016/s0301-0511(02)00051-0. PMID: 12385668. [Review article] Falconer, D.S. and Mackay, T.F.C. (1996) Introduction to Quantitative Genetics. 4th Edition, Addison Wesley Longman, Harlow. [Most classical; a lot of online version] Neale, B., Ferreira, M., Medland, S., & Posthuma, D. (Eds.). (2007). Statistical Genetics: Gene Mapping Through Linkage and Association (1st ed.). Taylor & Francis. https://doi.org/10.1201/9780203967201 [chapter on biometrical genetics; can be borrowed from HKU lib] https://ibg.colorado.edu/cdrom2020/dolan/biometricalGenetics/biom_gen_2020.pdf [Course material of the Boulder IBG workshop co-organized by top statistical geneticists] "],["install.html", "Appendix A: Install R & RStudio A.1 Install R (>=4.3.1) A.2 Install RStudio A.3 Use R inside RStudio A4. Cloud computing", " Appendix A: Install R & RStudio This manual covers the installation of both R and RStudio for three different operating systems: Windows, macOS and Ubuntu. You only need to follow the one that you are using on your computer. Differences between R and RStudio R is the backbone of R programming. Once R is installed, you can use it via its built-in R Console (self-contained), terminal or any third-party integrated development environment (IDE), e.g., RStudio. RStudio is a multi-faceted and user-friendly IDE that can make R programming and data analysis in one place and easy to manage. We recommend using RStudio and only demonstrate with it, while you are free to use any other alternative. Acknowledgements This manual is adapted and updated from the materials produced by Xiunan Fang and other team members in Dr Joshua Ho’s lab. A.1 Install R (>=4.3.1) R on Windows Open an internet browser and go to https://cran.r-project.org/. Click on the Download R for Windows link at the top of the page. Choose the base and then Click on the Download R 4.4.1 for Windows link at the top of the page (or a new version if this manual is outdated). Once the download is finished, you will obtain a file named R-4.4.1-win.exe or similar depending on the version that you download. Most of the time, you will likely want to go with the defaults, so click the button Next until the process is complete. R on macOS Open an internet browser and go to https://cran.r-project.org/. Click on the Download R for macOS link at the top of the page. Click on the file containing the latest version of R under the Latest release. Save the R-4.4.1-**.pkg file, double-click it to open, and follow the installation instructions. Note, there are two versions of the .pkg installation file according to the CPU model: Intel Macs (Intel-based) or M1/M2 Macs (ARM-based). Please choose accordingly. R on Linux (Ubuntu) As commonly used in Ubuntu, prior to installing R, let us update the system package index and upgrade all our installed packages using the following two commands: sudo apt update sudo apt -y upgrade After that, all that you have to do is run the following in the command line to install base R. sudo apt -y install r-base A.2 Install RStudio Now that R is installed, you need to download and install RStudio. The installation of RStudio is more straightforward and very similar across the three Operating Systems. Go to https://posit.co/download/rstudio-desktop/#download. We are using `RStudio Desktop Free version. Click on the right file for your OS (e.g., .exe file for Windows or .dmg for MacOS) The installation process is very straightforward as the figure below. A.3 Use R inside RStudio R studio RStudio is a very powerful IDE and provides many useful tools through a four-pane workspace. By default, the four panels are placed as follows (you can also change the setting for your own preference): Top-left panel: Your scripts of the R codes, script is good to keep a record of your work and also convenient for command execution. You can create a new script by File –> New –> R Script Bottom-left panel: R console for R commands, where you actually run the R codes. Top-right panel: Workspace tab: All the data(more specifically, R objects) you have created in the Workspace and all previous commands you previously ran in the History. Bottom-right panel: Files in your working directory(you probably should also set your working directory) in Files, and the plots you have created in Plots. Set working directory Create a folder named “biof_Rdir” in your preferred directory Create a “data” folder in the “biof_Rdir” From RStudio, use the menu to change your working directory under Session > Set Working Directory > Choose Directory Choose the directory to “biof_Rdir” Or you can type in the console: setwd("/yourdirectory/biof_Rdir") For Windows, the command might look like : setwd("c:/yourdirectory/biof_Rdir") Some general knowledge R is case-sensitive Type enter to run R code in the console pane Ctrl-enter or Cmd-return if the code is in the scripts pane. Comments come after # will not be treated as codes R has some pre-loaded data sets, to see the list of pre-loaded data, type data() In R, a function is an object, a basic syntax of an R function looks like something below: function_name <- function(arg_1, arg_2, ...) { actual function codes } For example: my_average <- function(x){ sum(x)/length(x) } my_average(c(1, 4, 5, 7)) #> [1] 4.25 R contains a lot of built-in functions, you can use ? or help() to see the documentation of a function, there are also a lot of external libraries with specific functions. To use a library, we do: install.packages('package_name') library(package_name) Install packages There are several packages used in this workshop, in the R console, type: install.packages('ggplot2') install.packages('pheatmap') install.packages('aod') A4. Cloud computing In case you have limited computing power, you can still use cloud computing to finish this course. There can be multiple options and here we mainly recommend RStudio cloud (https://posit.cloud; previously known as https://rstudio.cloud/). You can explore directly from their website. "],["references-1.html", "References", " References "]] +[["index.html", "Biomedical Data Science - introduction with case studies Welcome", " Biomedical Data Science - introduction with case studies BIOF1001 teaching team 2024-09-01 Welcome Welcome to the book Biomedical Data Science - an introduction with case studies. Most contents are demonstrated with R programming language. This book is designed as a collection of R Markdown notebooks, as supplementary to the lecture notes for the course BIOF1001: Introduction to Biomedical Data Science, an undergraduate course (Year 1) at the University of Hong Kong. Note: Most contents may be only updated before or right after the lectures, so please refer to the updated version. GitHub Repository: you can find the source files on StatBiomed/BMDS-book and the way to re-build this book. "],["preface.html", "Preface Introduction for readers Other reference books Acknowledgements", " Preface This book is designed as the supporting textbook for BIOF1001: Introduction to Biomedical Data Science, an undergraduate course (Year 1) at the University of Hong Kong. This book is not aimed to be a comprehensive textbook, but rather more Rmarkdown notebooks as supplementary to lecture notes so that students can reproduce the teaching contents more easily. Introduction for readers What you will learn from this course/book In part I, you will find a general introduction to data science (by Dr YH Huang): Basic programming and visualisation skills: R scripts for the quantitative methods and data visualisation. Quantitative methods: t-test, correlation analysis, clustering, linear regression, linear classification. Gain familiarity with common databases in the biomedical domain. Introduce ethical, legal, social and technological issues related to biomedical data sciences. Introduce good practice in managing a data science project and communicate results to key stakeholders. In part II, you will experience data types in four different biomedical topics, which will be illustrated with both introduction and cases that are suitable for problem-based learning format: Medical imaging and digital health, by Dr Joshua Ho and Dr Rachel Kwan Cancer genomics and epidemiology, by Dr David Shih and Dr Jason Wong Population genetics and diseases, by Dr Clara Tang and Dr Yuanhua Huang What we recommend you do while reading this book To enhance the knowledge and skills learned from this book, we recommend that readers Read the materials/slides provided in each module Practice quantitative skills by solving problems using R Other reference books Besides this online book as a collection of R materials for the teaching contents, we also recommend the following online books as reference: Introduction to Data Science: Data Wrangling and Visualization with R by Rafeal A. Irizarry Advanced Data Science: Statistics and Prediction Algorithms Through Case Studies by Rafeal A. Irizarry Acknowledgements We thank all teachers and student helpers contributing to this course across all years, including 2022: Dr Lequan Yu and Dr Carlos Wong 2022 & 2023: Dr Asif Javed, Dr Tommy Lam, and Dr Kathy Leung Student helpers: Mr Mingze Gao, Ms Fangxin Cai, and Mr Hoi Man Chung. "],["introR.html", "Chapter 1 Introduction to R programming 1.1 Data types 1.2 Data structures 1.3 Read and write files (tables) 1.4 Functions and Packages 1.5 Flow Control 1.6 Plotting 1.7 Scientific computating 1.8 Exercises", " Chapter 1 Introduction to R programming This notebook collects the scripts used for teaching in BIOF1001 for Introduction to R (1 hour teaching). You can get this Rmd file on Moodle or here (right-click and “save link as” to download). R is a programming language, particularly popular for its power in statistical computing, elegant graphics, and also genomic data analysis. It is a free and open-source software, with active support and development from the community. Additionally, R is relatively easy to get started for scientific computing. Note To learn and practice R programming, you need to install R and RStudio on your computer. You can follow the instructions for installation in the Appendix A chapter. 1.1 Data types In R language setting (similar to some other programming languages), there are a few commonly used data types that are predefined in the built-in environment. You can use the class() and typeof() functions to check the class and data type of any variable. In total, R has five data types: Numeric Integers Complex Logical Characters 1.1.1 nemeric (or double) The numeric is for numeric values, as the most common and the default data type. The numeric datatype saves values in double precision (double number of bytes in memory), so the type is also double. x <- c(1.0, 2.0, 5.0, 7.0) x #> [1] 1 2 5 7 class(x) #> [1] "numeric" typeof(x) #> [1] "double" 1.1.2 integer The integer is another data type used for the set of all integers. You can use the capital ‘L’ notation as a suffix to specify a particular value as the integer data type. Also, you can convert a value into an integer type using the as.integer() function. y <- c(1L, 2L, 5L, 7L) y #> [1] 1 2 5 7 class(y) #> [1] "integer" typeof(y) #> [1] "integer" # Assign a integer value to y y = 5 # is y an integer? print(is.integer(y)) #> [1] FALSE 1.1.3 logical In R, the logical data type takes either a value of true or false. A logical value is often generated when comparing variables. z <- c(TRUE, TRUE, TRUE, FALSE) z #> [1] TRUE TRUE TRUE FALSE typeof(z) #> [1] "logical" 1.1.4 character In R, the character is a data type where you have all the alphabets and special characters. It stores character values or strings. Strings in R can contain alphabets, numbers, and symbols. The character type is usually denoted by wrapping the value inside single or double inverted commas. w <- c("aa", "bb", "5", "7") w #> [1] "aa" "bb" "5" "7" typeof(w) #> [1] "character" 1.1.5 Memeory usage Each data type is explicitly defined, especially the size of the memory. When initializing a certain data type, there are a few small bytes used to store basic information. Let’s look at the empty values. object.size(numeric()) #> 48 bytes object.size(integer()) #> 48 bytes object.size(logical()) #> 48 bytes object.size(character()) #> 48 bytes To illustrate, let’s use 1000 elements below to show the memory size usage for each data type. As we will see, integer and logical use 4 bytes per element, which is only half of the memory usage by double (numeric) and character of 8 bytes. object.size(rep(1, 1000)) #> 8048 bytes object.size(rep(1L, 1000)) #> 4048 bytes object.size(rep(TRUE, 1000)) #> 4048 bytes object.size(rep("aa", 1000)) #> 8104 bytes 1.2 Data structures Data structure is one of the most important features in programming. It involves how the data is organised and can be accessed and modified. Using an appropriate data structure may largely improve computing efficiency. 1.2.1 Vector Vector is a basic data structure in R. It contains elements in the same data type (no matter double, integer, character or others). You can check the data type by using typeof() function and the length of the vector by length() function. Since a vector has elements of the same type, this function will try and coerce elements to the same type, if they are different. Coercion is from lower to higher types, i.e., from logical to integer to double to a character. See more introduction here. x <- c(1, 2, 5, 7) x #> [1] 1 2 5 7 typeof(x) #> [1] "double" x <- rep(3, 5) x #> [1] 3 3 3 3 3 typeof(x) #> [1] "double" x <- 1:12 # integer x #> [1] 1 2 3 4 5 6 7 8 9 10 11 12 typeof(x) #> [1] "integer" 1.2.2 Matrix Matrix is a two-dimensional data structure. It is in principle built based on vector but has more convenient built-in functions for computation. It has rows and columns, both of which can also have names. To check the dimensions, you can use the dim() function. See more introduction here. A <- matrix(1:12, nrow=3) A #> [,1] [,2] [,3] [,4] #> [1,] 1 4 7 10 #> [2,] 2 5 8 11 #> [3,] 3 6 9 12 B <- matrix(1:12, nrow=3, byrow=TRUE) B #> [,1] [,2] [,3] [,4] #> [1,] 1 2 3 4 #> [2,] 5 6 7 8 #> [3,] 9 10 11 12 colnames(A) <- c("C1","C2","C3","C4") rownames(A) <- c("R1","R2","R3") A #> C1 C2 C3 C4 #> R1 1 4 7 10 #> R2 2 5 8 11 #> R3 3 6 9 12 1.2.2.1 Index Vector and Matrix To index vector, you can use logical or integer, or the element name if it has. Note, when using an integer for indexing, the index starts from 1 in R, unlike most programming languages where the index starts from 0. We can also use negative integers to return all elements except those specified. But we cannot mix positive and negative integers while indexing and real numbers if used are truncated to integers. N.B., when using logical as the index, be careful if the index length is different from the vector. x <- 1:12 x[3] #> [1] 3 x[2:5] #> [1] 2 3 4 5 x[c(2, 5, 6)] # index with integer #> [1] 2 5 6 x[c(TRUE, FALSE, FALSE, TRUE)] # index with logical value #> [1] 1 4 5 8 9 12 Now, let’s index a matrix. It is very similar to vector, but it has both rows and columns. A <- matrix(1:12, nrow=3) colnames(A) <- c("C1","C2","C3","C4") rownames(A) <- c("R1","R2","R3") A #> C1 C2 C3 C4 #> R1 1 4 7 10 #> R2 2 5 8 11 #> R3 3 6 9 12 A[1, 2] #> [1] 4 A[1, "C2"] #> [1] 4 A[1, c(2, 3)] #> C2 C3 #> 4 7 A[1:2, c(2, 3)] #> C2 C3 #> R1 4 7 #> R2 5 8 Single row or column matrix will become a vector, unless using drop=FALSE A[1, 2:4] #> C2 C3 C4 #> 4 7 10 dim(A[1, 2:4]) #> NULL A[1, 2:4, drop=FALSE] #> C2 C3 C4 #> R1 4 7 10 dim(A[1, 2:4, drop=FALSE]) #> [1] 1 3 1.2.2.2 Modify values A[1, 2:4] <- c(-3, -5, 20) A #> C1 C2 C3 C4 #> R1 1 -3 -5 20 #> R2 2 5 8 11 #> R3 3 6 9 12 1.2.3 List Different from vector that has all elements in the same data type, the list data structure can have components of mixed data types. More broadly, a list can contain a list of any data structure: value, vector, matrix, etc. We can use str() function to view the structure of a list (or any object). x <- list(2.5, TRUE, 1:3) x #> [[1]] #> [1] 2.5 #> #> [[2]] #> [1] TRUE #> #> [[3]] #> [1] 1 2 3 str(x) #> List of 3 #> $ : num 2.5 #> $ : logi TRUE #> $ : int [1:3] 1 2 3 We can also have a name for each element: x <- list("a" = 2.5, "b" = TRUE, "c" = 1:3) x #> $a #> [1] 2.5 #> #> $b #> [1] TRUE #> #> $c #> [1] 1 2 3 str(x) #> List of 3 #> $ a: num 2.5 #> $ b: logi TRUE #> $ c: int [1:3] 1 2 3 1.2.3.1 Indexing list Different from vector and matrix, for a list, you need to use double-layer square brackets, either by numeric index or name. Alternatively, you can also use $ symbol with the name. x[[3]] #> [1] 1 2 3 x[["c"]] #> [1] 1 2 3 x$c #> [1] 1 2 3 1.2.4 Data Frame Data frame is widely used for rectangular data, where each column has the same data type (vector) but different columns can have different data types (like Excel) As you guess, the data frame is a special type of list: A list of vectors with the same length. df <- data.frame("SN" = 1:2, "Age" = c(21,15), "Name" = c("John","Dora")) df #> SN Age Name #> 1 1 21 John #> 2 2 15 Dora df$Age[2] #> [1] 15 # View(df) 1.2.5 Factor vs vector For vector, if it only contains pre-defined values (which can have specified orders), you may consider using factor. Factor is a data structure used for fields that takes only predefined, finite number of values (categorical data) The order of predefined values can be specified, instead of alphabetic by default x = c("single", "married", "married", "single") # vector x #> [1] "single" "married" "married" "single" class(x) #> [1] "character" typeof(x) #> [1] "character" y = factor(c("single", "married", "married", "single")) y #> [1] single married married single #> Levels: married single class(y) #> [1] "factor" typeof(y) #> [1] "integer" 1.2.5.1 Change the order of factor levels z <- factor(c("single", "married", "married", "single") , levels=c("single", "married")) z #> [1] single married married single #> Levels: single married 1.2.5.2 Smaller memory as categorical data type x = c("single", "married", "married", "single") # vector y = factor(c("single", "married", "married", "single")) object.size(rep(x, 1000)) #> 32160 bytes object.size(rep(y, 1000)) #> 16560 bytes 1.3 Read and write files (tables) Besides operating in the R environment, we also want to read data from files or write results into files. For tables, R has convenient built-in function read.table() and write.table(). 1.3.1 Read file We can use read.table() function to read tables, e.g., in comma separated values (csv) or tab separated values (tsv) formats. See full manuals: help(read.table) or ?read.table or its online manual help("read.table") ?read.table Here, let’s read an example file. Data is available on Moodle and on the github repository df = read.table("./SRP029880.colData.tsv", sep="\\t") df #> source_name group #> CASE_1 metastasized cancer CASE #> CASE_2 metastasized cancer CASE #> CASE_3 metastasized cancer CASE #> CASE_4 metastasized cancer CASE #> CASE_5 metastasized cancer CASE #> CTRL_1 normal colon CTRL #> CTRL_2 normal colon CTRL #> CTRL_3 normal colon CTRL #> CTRL_4 normal colon CTRL #> CTRL_5 normal colon CTRL 1.3.2 Write file The above file is loaded as a data frame. Here, let’s add an extra column to indicate if the sample is frozen or not, then save it into a file. df$frozen <- c(1, 1, 0, 0, 0, 1, 1, 0, 0, 0) write.table(df, "./SRP029880.colData.add_frozen.tsv", sep="\\t", quote=FALSE) 1.4 Functions and Packages As experienced above, we have used function multiple times, e.g., read.table and typeof. As one more example mean() is a function here and it is from the base package x <- 4:10 mean(x) #> [1] 7 base::mean(x) #> [1] 7 Generally speaking, A function is a set of statements organized together to perform a specific task. Many lines of codes are packed into one function & it’s reusable. A function can be written in the same R file and loaded, or it can be distributed as part of a package. For using such functions, we need to install the corresponding package and load it. 1.4.1 Install packages It depends on where the package is stored. Please refers to the documentation of the specific package you want to install and use. CRAN (the Comprehensive R Archive Network): main platform For example: install.packages(\"ggplot2\") Bioconductor: primarily for biology related packages For example: BiocManager::install(\"DESeq2\") As an example, we can install the powerful plotting package ggplot2 from CRAN. #install.packages("ggplot2") 1.4.2 Apply function repeatly We may often want to use a certain function for multiple times, e.g., calculate the sample mean for many genes. There are multiple ways to achieve it, e.g., via a for loop. Here, we will introduce apply and its variants for this purpose. See more introductions here. 1.4.2.1 apply, lapply, sapply and vapply In short, the apply() function and its variants apply a certain function to each element of a vector, a matrix, or a list. 1.4.2.1.1 apply for matrix The apply(X, MARGIN, FUN) function works for matrix (or array) for rows or columns. For example, calculating the median of each column: my.matrx <- matrix(1:15, nrow = 5, ncol = 3) my.matrx #> [,1] [,2] [,3] #> [1,] 1 6 11 #> [2,] 2 7 12 #> [3,] 3 8 13 #> [4,] 4 9 14 #> [5,] 5 10 15 apply(my.matrx, 2, median) #> [1] 3 8 13 1.4.2.1.2 lapply, sapply, and vapply for list or vector The above apply function requires MARGIN, hence won’t work for vector or list. There are a few variants to support lists or vectors for different purposes. From the manual, we can find out the arguments for these three functions: lapply(X, FUN, …) sapply(X, FUN, …, simplify = TRUE, USE.NAMES = TRUE) vapply(X, FUN, FUN.VALUE, …, USE.NAMES = TRUE) Let’s look at examples. The lapply works for vector and list and returns a list: A<-c(1:9) B<-c(1:12) C<-c(1:15) my.lst<-list(A,B,C) lapply(my.lst, median) #> [[1]] #> [1] 5 #> #> [[2]] #> [1] 6.5 #> #> [[3]] #> [1] 8 If you want the return as a vector, you can use sapply() for simplified output: sapply(my.lst, median) #> [1] 5.0 6.5 8.0 If you want to check the data type of the return, you can further use the vapply function. An error will be raised if the data type does not match. Note, the FUN.VALUE argument takes the value as a template, so the use of numeric(1) or any numeric value, e.g., 3 is the same. vapply(my.lst, median, numeric(1)) #> [1] 5.0 6.5 8.0 vapply(my.lst, median, 3) #> [1] 5.0 6.5 8.0 # try this # vapply(my.lst, median, character(1)) 1.4.3 Pattern match Matching patterns between two vectors is a very common task, for example matching the gene names of two files or matching students IDs in two courses. Two commonly used functions for pattern match are match() and %in%, see the match() documentation: match returns a vector of the positions of (first) matches of its first argument in its second. %in% is a more intuitive interface as a binary operator, which returns a logical vector indicating if there is a match or not for its left operand. They work for any data type: numeric and character 1:6 %in% c(1,3,5,9) #> [1] TRUE FALSE TRUE FALSE TRUE FALSE match(c(3, 4, 5), c(5, 3, 1, 9)) #> [1] 2 NA 1 idx = match(c(3, 4, 5), c(5, 3, 1, 9)) c(5, 3, 1, 9)[idx] #> [1] 3 NA 5 1.5 Flow Control 1.5.1 Logical operator The common logical operators are shown in the following table. Operator Description Associativity ! Logical NOT Left to right & Element-wise logical AND Left to right && Logical AND Left to right | Element-wise logical OR Left to right || Logical OR Left to right A few notes: Zero is considered FALSE and non-zero numbers are taken as TRUE. Operators & and | perform element-wise operation, returning length of the longer operand. For operators && and ||, it depends on the R version. In R<=4.2.3, it examines only the first element of the operands, returning a single value, while in R=4.3.1, && and || only compare single values and will return errors if input vectors. x <- c(TRUE, FALSE, 0, 6) y <- c(FALSE, TRUE, FALSE, TRUE) !x #> [1] FALSE TRUE TRUE FALSE x & y #> [1] FALSE FALSE FALSE TRUE x | y #> [1] TRUE TRUE FALSE TRUE # try it yourself # x && y # x || y 1.5.2 if-else statements The if-else statement allows us to control when and how particular parts of our code are executed. # In Q10 in Section 1.8 we have x_mean and x_sd x_mean = 1.7 x_sd = 2.9 if ( (x_mean > 0.1) && (x_sd < 2.0) ) { trend = "increase" } else{ trend = "not_increase" } print(trend) #> [1] "not_increase" x_mean = 1.7 x_st = 2.9 trend = "not_increase" if ( (x_mean > 0.1) && (x_sd < 2.0) ) { trend = "increase" } print(trend) #> [1] "not_increase" 1.5.3 for-loop Loops, including for loop and while loop are used in programming to repeat a specific block of code, commonly with if-else statements. For example, calculate the sum from 1 to 10: sum_val = 0 for (val in seq(1, 10)) { # print(val) sum_val = sum_val + val } print(sum_val) #> [1] 55 Or detect the differential expressed genes. Note, NA value may exist in the padj column: is_DE = rep(FALSE, nrow(df_DEG)) for (i in 1:nrow(df_DEG)) { if (df_DEG$padj[i] <= 0.05) { is_DE[i] = TRUE } } print(sum(is_DE)) 1.6 Plotting 1.6.1 datasets Let’s use a built-in dataset for illustration: iris (4 flower features in 3 plants) head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.2 setosa #> 2 4.9 3.0 1.4 0.2 setosa #> 3 4.7 3.2 1.3 0.2 setosa #> 4 4.6 3.1 1.5 0.2 setosa #> 5 5.0 3.6 1.4 0.2 setosa #> 6 5.4 3.9 1.7 0.4 setosa summary(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width #> Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100 #> 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300 #> Median :5.800 Median :3.000 Median :4.350 Median :1.300 #> Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199 #> 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800 #> Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500 #> Species #> setosa :50 #> versicolor:50 #> virginica :50 #> #> #> There are two common ways of plotting: the built-in plotting functions the ggplot format 1.6.2 Basic plotting 1.6.2.1 Histogram hist(iris$Sepal.Length) 1.6.2.2 Scatter plot plot(x=iris$Sepal.Length, y=iris$Sepal.Width) 1.6.2.3 boxplot x1 <- iris$Sepal.Length[iris$Species == "setosa"] x2 <- iris$Sepal.Length[iris$Species == "versicolor"] x3 <- iris$Sepal.Length[iris$Species == "virginica"] boxplot(x1, x2, x3) 1.6.3 ggplot2 See more instructions: http://www.sthda.com/english/wiki/ggplot2-essentials 1.6.3.1 Install and load package if (!requireNamespace("ggplot2", quietly = TRUE)) install.packages("ggplot2") library(ggplot2) 1.6.3.2 Histogram ggplot(iris, aes(x=Sepal.Length)) + geom_histogram(bins = 8) 1.6.3.3 Scatter plot ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width)) + geom_point() 1.6.3.4 Box plot ggplot(iris, aes(x=Species, y=Sepal.Length)) + geom_boxplot() 1.7 Scientific computating 1.7.1 Orders of operators See lecture slides. If you are not sure about a certain ordering, use brackets! 5 * 2 > 4 #> [1] TRUE 5 * (2 > 4) #> [1] 0 1.7.2 Functions for statistics More theory and practice to come in next session 1.7.3 Correlation cor(iris$Sepal.Length, iris$Petal.Length) #> [1] 0.8717538 cor.test(iris$Sepal.Length, iris$Petal.Length) #> #> Pearson's product-moment correlation #> #> data: iris$Sepal.Length and iris$Petal.Length #> t = 21.646, df = 148, p-value < 2.2e-16 #> alternative hypothesis: true correlation is not equal to 0 #> 95 percent confidence interval: #> 0.8270363 0.9055080 #> sample estimates: #> cor #> 0.8717538 1.7.4 Hypothesis testing (t test) x1 <- iris$Sepal.Length[iris$Species == "setosa"] x2 <- iris$Sepal.Length[iris$Species == "versicolor"] x3 <- iris$Sepal.Length[iris$Species == "virginica"] t.test(x2, x3) #> #> Welch Two Sample t-test #> #> data: x2 and x3 #> t = -5.6292, df = 94.025, p-value = 1.866e-07 #> alternative hypothesis: true difference in means is not equal to 0 #> 95 percent confidence interval: #> -0.8819731 -0.4220269 #> sample estimates: #> mean of x mean of y #> 5.936 6.588 1.7.5 Regression fit <- lm(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width, data=iris) summary(fit) # show results #> #> Call: #> lm(formula = Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width, #> data = iris) #> #> Residuals: #> Min 1Q Median 3Q Max #> -0.82816 -0.21989 0.01875 0.19709 0.84570 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 1.85600 0.25078 7.401 9.85e-12 *** #> Sepal.Width 0.65084 0.06665 9.765 < 2e-16 *** #> Petal.Length 0.70913 0.05672 12.502 < 2e-16 *** #> Petal.Width -0.55648 0.12755 -4.363 2.41e-05 *** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Residual standard error: 0.3145 on 146 degrees of freedom #> Multiple R-squared: 0.8586, Adjusted R-squared: 0.8557 #> F-statistic: 295.5 on 3 and 146 DF, p-value: < 2.2e-16 This means the fitted regression is: Sepal.Length ~ 1.856 + 0.65*Sepal.Width + 0.709*Petal.Length - 0.556*Petal.Width We can check how good the regression is by plotting it out y_pred <- fit$coefficients[1] + fit$coefficients[2] * iris$Sepal.Width + fit$coefficients[3] * iris$Petal.Length + fit$coefficients[4] * iris$Petal.Width cor(iris$Sepal.Length, y_pred) #> [1] 0.926613 plot(iris$Sepal.Length, y_pred) 1.7.6 Resource links This notebook adapts contents from these resources: https://www.datamentor.io/r-programming/ https://www.geeksforgeeks.org/r-data-types/ https://data-flair.training/blogs/r-data-types/ http://www.sthda.com/english/wiki/ggplot2-essentials 1.7.7 Coding styling Elegant styling is crucial for maintenance and collaboration. Here is a highly recommended guideline: http://adv-r.had.co.nz/Style.html 1.8 Exercises This list of exercises will serve as a 2-hour demonstration in the BIOF1001 course. For other learners, you can go through it as homework. The expected time is around two hours if you have read the R materials carefully together with trying them with your own R environment. Note, before you get started, please make sure that you are familiar with the panels in RStudio. You may watch this YouTube Video or check this RStudio cheatsheet. 1.8.1 Part 1. Basics (~40min) Q1: We have 25 students in BIOF1001, to store the final marks (0 to 100 with a precision of 0.1), what data type will we use? Q2: For the grades (A+ to F), what data type and data structure can be used to keep the memory minimal? Q3: Make a matrix with name my_matrix, shape of 5 rows and 2 columns and values from 3 to 12. The first row is 3 and 4. Hint: for making a vector from 3 to 12, you may use seq() or :. Q4: Based on Q3, add the row names to Day1 to Day5 and column names to Lunch and Dinner. Q5: Based on Q4, get a matrix with shape of 3x1 and values of 6, 8, 10 from the matrix my_matrix. Q6: What will you get for my_matrix[c(TRUE, FALSE, FALSE, TRUE), ]? Hint: think of recycling if the index length is different from the query dimension (Over-flexibility comes with a price of wrong use). Q7: If you have two vectors the BIOF1001 marks and grades and one character for teaching performance \"good\", and you want to store them into one variable, which data structure will you use? Q8: Now, on your Desktop folder, make a subfolder with name R_exercises and download this file of differentailly expressed genes results Diff_Expression_results.tsv to the folder. Check your current work directory by getwd() function and change the work directory to the folder you just created. Hint: you may use setwd() to change the work directory or use the Session button of RStudio. Q9: Related to Q8, use the read.table() function to load the file into a data frame with the variable name df_DEG. Hint: You may consider using the full path or just the file name if it’s in the same work directory. Please keep header=TRUE for the argument. Think how to find help page for a certain function. Q10: Can you calculate the mean and standard deviation of the log2FoldChange? If the mean >0.1 and the standard deviation < 3, set the trend variable as “increase”, otherwise “not_increase”. What will happen if you add this trend variable to the data frame, and why? Hint: use mean() and st() functions for calculating mean and standard deviation. 1.8.2 Part 2. Making plotting (~40min) Q11: Keep use the df_DEG from part 1 Q9. Now, make a histogram of the log2FoldChange with both basic plotting function hist() and ggplot2. Q12: Make a plot with x-axis of log10(baseMean) and y-axis of log2FoldChange. Please try both the basic plot() function and ggplot2. Q13: Now, manipulate the dataframe by adding two columns: Add a column log2FC_clip for clipping log2FoldChange to [-5, +5] Add a column is_DE for padj < 0.05 Q14: Try the summary() function with the above df_DEG data frame, and also table() function for the is_DE column. Q15: Based on ggplot2, add the color by the newly added column is_DE. Q16: Set the colors to “red” and “grey”, and make it in the order of TRUE and FALSE. Hint: use factor and set the levels parameter. Q17: Save the generated figure into “My_DEG_results.pdf”. Use the ggsave() function. Please set width = 5, height = 4. You are expected a figure like this DEG figure If you want to change labels on x-axis or y-axis and font size, etc., you can simply Google and find examples. 1.8.3 Part 3. For loop and repeating processing (~40min) Q18: Load the following table from this file on GitHub and View it in RStudio. It contains expressoon of 619 transcription factors from 7 Nasopharyngeal carcinoma (NPC) samples and 3 nasopharyngeal lymphatic hyperplasia (NLH) samples. https://github.com/StatBiomed/NegabinGLM/blob/main/data/NPC_NLH-Tcell-donorX.tsv df_NPC = read.table(\"https://github.com/StatBiomed/NegabinGLM/raw/main/data/NPC_NLH-Tcell-donorX.tsv\", sep=\"\\t\", header=TRUE) Q19: Extract the column 5 (HES5) to 623 (LEK4) and make it into a matrix TF_mat Hint: how to index a data frame like index a matrix. Q20: perform normalization. Divide the TF_mat by the df_NPC$total_counts and multiply by 1000000, and assign it to a new matrix named TF_mat_norm. You may further consider transformation by log1p(), i.e., log(TF_mat_norm + 1). Q21: calculate the log fold change on the first gene TP73 in TF_mat_norm between NPC (row 1 to 7) and NLH (row 8 to 10) and perform t-test return the p value and log fold change. Q22: perform t-test on the all gene in TF_mat_norm between NPC (row 1 to 7) and NLH (row 8 to 10). Hint: think of for loop or apply() function. "],["introHypoTest.html", "Chapter 2 Introduction to Hypothesis testing 2.1 Hypothesis testing and p value 2.2 Permutation test 2.3 t test 2.4 GLM test 2.5 Multiple testing", " Chapter 2 Introduction to Hypothesis testing 2.1 Hypothesis testing and p value How surprising is my result? Calculating a p-value There are many circumstances where we simply want to check whether an observation looks like it is compatible with the null hypothesis, \\(H_{0}\\). Having decided on a significance level \\(\\alpha\\) and whether the situation warrants a one-tailed or a two-tailed test, we can use the cdf of the null distribution to calculate a p-value for the observation. Acknowledgement: examples are from Dr John Pinney link here 2.1.1 Example 1: probability of rolling a six? Your arch-nemesis Blofeld always seems to win at ludo, and you have started to suspect him of using a loaded die. You observe the following outcomes from 100 rolls of his die: data = c(6, 1, 5, 6, 2, 6, 4, 3, 4, 6, 1, 2, 5, 6, 6, 3, 6, 2, 6, 4, 6, 2, 5, 4, 2, 3, 3, 6, 6, 1, 2, 5, 6, 4, 6, 2, 1, 3, 6, 5, 4, 5, 6, 3, 6, 6, 1, 4, 6, 6, 6, 6, 6, 2, 3, 1, 6, 4, 3, 6, 2, 4, 6, 6, 6, 5, 6, 2, 1, 6, 6, 4, 3, 6, 5, 6, 6, 2, 6, 3, 6, 6, 1, 4, 6, 4, 2, 6, 6, 5, 2, 6, 6, 4, 3, 1, 6, 6, 5, 5) Do you have enough evidence to confront him? # We will work with the binomial distribution for the observed number of sixes # Write down the hypotheses # H0: p = 1/6 # H1: p > 1/6 # choose a significance level # alpha = 0.01 # number of sixes # number of trials stat_k = sum(data == 6) trials = length(data) print(paste("number of sixes:", stat_k)) #> [1] "number of sixes: 43" print(paste("number of trials:", trials)) #> [1] "number of trials: 100" # test statistic: number of sixes out of 100 trials # null distribution: dbinom(x, size=100, prob=1/6) # calculate p value p_val = 1 - pbinom(stat_k - 1, size=trials, prob=1/6) print(paste("Observed statistic is", stat_k)) #> [1] "Observed statistic is 43" print(paste("p value is", p_val)) #> [1] "p value is 5.43908695860296e-10" 2.1.1.1 Visualize the null distribution and the test statistic # plot the probability mass function of null distribution x = seq(0, 101) pmf = dbinom(x, size=100, prob=1/6) df = data.frame(x=x, pmf=pmf, extreme=(x >= stat_k)) library(ggplot2) ggplot(df, aes(x=x)) + geom_point(aes(y=pmf, color=extreme)) + scale_color_manual(values=c("black", "red")) + xlab('Number of sixes') + ylab('Probability Mass Function') + ggtitle('Distribution of n_six under the null hypothesis') 2.2 Permutation test 2.2.1 Example 2: difference in birth weight The birth weights of babies (in kg) have been measured for a sample of mothers split into two categories: nonsmoking and heavy smoking. The two categories are measured independently from each other. Both come from normal distributions The two groups are assumed to have the same unknown variance. data_heavysmoking = c(3.18, 2.84, 2.90, 3.27, 3.85, 3.52, 3.23, 2.76, 3.60, 3.75, 3.59, 3.63, 2.38, 2.34, 2.44) data_nonsmoking = c(3.99, 3.79, 3.60, 3.73, 3.21, 3.60, 4.08, 3.61, 3.83, 3.31, 4.13, 3.26, 3.54) We want to know whether there is a significant difference in mean birth weight between the two categories. # Write down the hypotheses # H0: there is no difference in mean birth weight between groups: d == 0 # H1: there is a difference, d != 0 # choose a significance level # alpha = 0.05 # Define test statistic: difference of group mean stat_mu = mean(data_heavysmoking) - mean(data_nonsmoking) stat_mu #> [1] -0.5156923 2.2.2 Null distribution approximated by resampling #' Simple function to generate permutation distribution get_permutation_null <- function(x1, x2, n_permute=1000) { n1 = length(x1) n2 = length(x2) # pool data sets x_pool = c(x1, x2) null_distr = rep(0, n_permute) for (i in seq(n_permute)) { # split idx = sample(n1 + n2, size=n1) x1_perm = x_pool[idx] x2_perm = x_pool[-idx] # calculate test statistic null_distr[i] = mean(x1_perm) - mean(x2_perm) } return(null_distr) } set.seed(1) perm_null = get_permutation_null(data_heavysmoking, data_nonsmoking) We can plot the histogram of the null distribution obtained by resampling. We can also add line(s) for the values as extreme as observed statistic mu, where we can consider one side or both side as extreme values. df_perm = data.frame(perm_null = perm_null) ggplot(df_perm, aes(x=perm_null)) + geom_histogram(bins=20) + geom_vline(xintercept=stat_mu, linetype="dashed", color="tomato") + geom_vline(xintercept=-stat_mu, linetype="dashed", color="tomato") + xlab('Difference of group mean') + ylab('Resampling frequency') + ggtitle('Distribution of mu under the null hypothesis') ## Two tailed p value p_two_tailed = mean(abs(perm_null) >= abs(stat_mu)) p_one_tailed = mean(perm_null < stat_mu) print(paste("Two tailed p value:", round(p_two_tailed, 5))) #> [1] "Two tailed p value: 0.003" print(paste("One (left) tailed p value:", round(p_one_tailed, 5))) #> [1] "One (left) tailed p value: 0.002" 2.3 t test 2.3.1 Derivation of t distribution Null distribution approximated by \\(t\\) distribution We use the t test to assess whether two samples taken from normal distributions have significantly different means. The test statistic follows a Student’s t-distribution, provided that the variances of the two groups are equal. Other variants of the t-test are applicable under different conditions. The test statistic is \\[ t = \\frac{\\bar{X}_{1} - \\bar{X}_{2}}{s_p \\cdot \\sqrt{\\frac{1}{n_{1}} + \\frac{1}{n_{2}}}} \\] where \\[ s_p = \\sqrt{\\frac{(n_1 - 1)s_1^2 + (n_2 - 1)s_2^2}{n_1 + n_2 - 2}} \\] is an estimator of the pooled standard deviation. Under the null hypothesis of equal means, the statistic follows a Student’s t-distribution with \\((n_{1} + n_{2} - 2)\\) degrees of freedom. # Same test statistic: difference of group mean stat_t = mean(data_heavysmoking) - mean(data_nonsmoking) stat_t #> [1] -0.5156923 Calculate parameters for approximate t distribution n_ns = length(data_nonsmoking) n_hs = length(data_heavysmoking) s_ns = sd(data_nonsmoking) # degree of freedom: n-1 s_hs = sd(data_heavysmoking) # the pooled standard deviation sp = sqrt(((n_ns - 1)*s_ns**2 + (n_hs - 1)*s_hs**2) / (n_ns + n_hs - 2)) print(paste0("Pooled standard deviation:", sp)) #> [1] "Pooled standard deviation:0.428057812829366" my_std = sp * sqrt(1/n_ns + 1/n_hs) print(paste("Estimated standard error of mean difference:", my_std)) #> [1] "Estimated standard error of mean difference: 0.162204962956089" stat_t_scaled = stat_t / my_std print(paste("Rescaled t statistic:", stat_t_scaled)) #> [1] "Rescaled t statistic: -3.17926343494134" print(paste("degree of freedom", n_hs+n_ns-2)) #> [1] "degree of freedom 26" Here, we focusing the standardized \\(t\\) distribution, namely the variance=1, so let’s re-scale the test statistic by dividing the standard error my_std. xx = seq(-4.5, 4.5, 0.05) xx_pdf = dt(xx, df=n_hs+n_ns-2) df_t_dist = data.frame(x=xx, pdf=xx_pdf) ggplot(df_t_dist, aes(x=x)) + geom_line(aes(y=pdf)) + geom_vline(xintercept=stat_t_scaled, linetype="dashed", color="tomato") + geom_vline(xintercept=-stat_t_scaled, linetype="dashed", color="tomato") + xlab('Difference of group mean') + ylab('PDF approximated by t distr.') + ggtitle('Distribution of t under the null hypothesis') # Note, we used multiply 2 just because the t distribution is symmetric, # otherwise, we need calculate both side and add them. pval_t_twoside = pt(stat_t_scaled, df=n_hs+n_ns-2) * 2 print(paste('t-test p value (two-tailed):', round(pval_t_twoside, 6))) #> [1] "t-test p value (two-tailed): 0.003793" 2.3.2 Direct use of t.test() In course and most of your future analyses, you can directly use the built-in t.test() function. # Note, we assumed the variance in both groups are the same, # we so need to set var.equal = TRUE t.test(data_nonsmoking, data_heavysmoking, var.equal = TRUE) #> #> Two Sample t-test #> #> data: data_nonsmoking and data_heavysmoking #> t = 3.1793, df = 26, p-value = 0.003793 #> alternative hypothesis: true difference in means is not equal to 0 #> 95 percent confidence interval: #> 0.1822752 0.8491094 #> sample estimates: #> mean of x mean of y #> 3.667692 3.152000 2.4 GLM test We can also perform t-test in a Generalised linear model (GLM) setting to test if a coefficient is zero or not. Here, we simply use the marketing dataset as an example. # Install datarium library if you haven't if (!requireNamespace("datarium", quietly = TRUE)) { install.packages("datarium") } library(datarium) # Load data: then we will have a data.frame with name marketing data(marketing) head(marketing) #> youtube facebook newspaper sales #> 1 276.12 45.36 83.04 26.52 #> 2 53.40 47.16 54.12 12.48 #> 3 20.64 55.08 83.16 11.16 #> 4 181.80 49.56 70.20 22.20 #> 5 216.96 12.96 70.08 15.48 #> 6 10.44 58.68 90.00 8.64 ggplot(marketing, aes(x=newspaper, y=sales)) + geom_point() + geom_smooth(method=lm) #> `geom_smooth()` using formula = 'y ~ x' # Fit linear regression res.lm <- lm(sales ~ newspaper, data = marketing) # We can check the test via the summary() function summary(res.lm) #> #> Call: #> lm(formula = sales ~ newspaper, data = marketing) #> #> Residuals: #> Min 1Q Median 3Q Max #> -13.473 -4.065 -1.007 4.207 15.330 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 14.82169 0.74570 19.88 < 2e-16 *** #> newspaper 0.05469 0.01658 3.30 0.00115 ** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Residual standard error: 6.111 on 198 degrees of freedom #> Multiple R-squared: 0.05212, Adjusted R-squared: 0.04733 #> F-statistic: 10.89 on 1 and 198 DF, p-value: 0.001148 glm_t_val = summary(res.lm)$coefficients["newspaper", "t value"] xx = seq(-5, 5, 0.01) yy = dt(xx, 198) df_ttest <- data.frame(x=xx, PDF=yy) ggplot(df_ttest, aes(x=x, y=PDF)) + geom_line() + geom_vline(xintercept = glm_t_val, linetype="dashed", color="tomato") + geom_vline(xintercept = -glm_t_val, linetype="dashed", color='tomato') 2.5 Multiple testing Hypothetical null distribution. Feel feel to try any null distribution, examples below ## Example null distributions # t, normal or anything. we use chi-squared distribution as an example x_random = rchisq(n=1000, df=3) any_null_dist = dchisq(x_random, df=3) pvals_null = 1 - pchisq(x_random, df=3) 2.5.1 Null distribution (of test statistic) # Null distribution of test statistic hist(x_random) 2.5.2 Null distribution of p value # Null distribution of test statistic hist(pvals_null) 2.5.3 Minimal p values in 10 tests # We use matrix to group 100 trials into a column # We then use apply() to calculate min value for each column pval_null_mtx = matrix(pvals_null, nrow=10) p_min_in10 = apply(pval_null_mtx, MARGIN=2, FUN=min) hist(p_min_in10) print(paste('Proportion of tests with min(p) < 0.05:', mean(p_min_in10 < 0.05))) #> [1] "Proportion of tests with min(p) < 0.05: 0.43" 2.5.4 Homework Make a simulation of score: group A and B B follows normal(mean=0, std=1); A follows normal(mean=0.1, std=1) Generate 100 samples for each group, and do a t test, is difference significant? Please use set.seed(0) beforehand. Try 3) again but general 3,00 samples this time, later 1,000 samples. What do you find? Think the relation between power and sample size. set.seed(0) n_sample = 100 # change this value to 1000 and 10000 xB = rnorm(n_sample) xA = rnorm(n_sample, mean=0.1) t.test(xA, xB, var.equal = TRUE) #> #> Two Sample t-test #> #> data: xA and xB #> t = 0.24294, df = 198, p-value = 0.8083 #> alternative hypothesis: true difference in means is not equal to 0 #> 95 percent confidence interval: #> -0.2261882 0.2897482 #> sample estimates: #> mean of x mean of y #> 0.05444844 0.02266845 "],["introLinearReg.html", "Chapter 3 Introduction to Linear Regression 3.1 Linear Regression Using Simulated Data 3.2 Least Squares Using Simulated Data 3.3 Diagnostic check of a fitted regression model 3.4 Simple Linear Regression with lm function 3.5 Multiple Regression with lm function", " Chapter 3 Introduction to Linear Regression Acknowledgements: this chapter is adapted and updated from the materials originally produced by STAT1005 teaching team, especially Prof. Jeff Yao. 3.1 Linear Regression Using Simulated Data Let’s first simulate some data and look at how the predicted values (Ye) differ from the actual value (Y). 3.1.1 Simulating data: For X, we generate 100 normally distributed random numbers with mean 1.5 and standard deviation 2.5. For predicted value Ye, we assume an intercept (α) of 2 and a slope (β) of 0.3 and we write \\(Y_e = 2 + 0.3 x\\) Later, we will estimate the values of α and β using the least squares method and see how that changes the efficacy of the model. Though we estimate \\(Y_e = \\alpha + \\beta X\\), in reality Y is rarely perfectly linear. It usually has an error component or residual: \\(Y = \\alpha + \\beta X + R\\), where R is a random variable and is assumed to be normally distributed. Therefore for the actual value Y, we add a residual term (res), a random variable distributed normally with mean 0 and a standard deviation of 0.5. The following cell shows the code snippet to generate these numbers and convert these three columns in a data frame. Read through the code carefully and run the cell to output a sample of our simulated data. # Fix seed: each run gives the same random numbers so the same outputs. # Commenting out this line would read similar but different outputs at each run. # Try it out! set.seed(0) # Generate data X = 2.5 * rnorm(100) + 1.5 # Array of 100 values with mean = 1.5, stddev = 2.5 ypred = 2 + 0.3 * X # Prediction of Y, assuming a = 2, b = 0.3 res = 0.5 * rnorm(100) # Generate 100 residual terms yact = 2 + 0.3 * X + res # Actual values of Y # Create dataframe to store our X, ypred, and yact values df = data.frame('X' = X, 'ypred' = ypred, 'yact' = yact) # Show the first six rows of our dataframe head(df) #> X ypred yact #> 1 4.6573857 3.397216 3.788145 #> 2 0.6844166 2.205325 1.816937 #> 3 4.8244982 3.447349 3.139354 #> 4 4.6810733 3.404322 3.427612 #> 5 2.5366036 2.760981 2.195788 #> 6 -2.3498751 1.295037 1.583397 Now let’s plot both the actual output (yact) and predicted output (ypred) against the input variable (X) to see what the difference between yact and ypred is, and therefore, to see how accurately the proposed equation (ypred = 2 + 0.3 * X) has been able to predict the value of the output: # You can use basic plotting functions # plot(x=df$X, y=df$yact, col="red") # lines(x=df$X, y=df$ypred, col="darkgreen") # But let's use ggplot2 for higher flexibility library(ggplot2) ggplot(df, aes(X)) + # basic graphical object geom_point(aes(y=yact), colour="black") + # first layer geom_line(aes(y=ypred), colour="darkgreen") + # second layer ggtitle('Actual vs Predicted values from the dummy dataset') 3.1.2 Model efficacy How do we know the values we calculate for α and β are giving us a good model? We can explain the total variability in our model with the Total Sum of Squares or SST: \\[SST = \\sum_{i=1}^n\\Bigl(\\text{yact}_i - \\text{yavg}\\Bigr)^2, \\qquad\\qquad \\text{yavg}=\\frac1n \\sum_{i=1}^n \\text{yact}_i\\] Mathematically, we have \\[ \\sum_{i=1}^n\\Bigl(\\text{yact}_i - \\text{yavg}\\Bigr)^2 = \\sum_{i=1}^n\\Bigl(\\text{ypred}_i -\\text{yavg} \\Bigr)^2 + \\sum_{i=1}^n\\Bigl(\\text{yact}_i - \\text{ypred}_i\\Bigr)^2\\] The identity reads as Sum of Squares Total = Sum of Squares Regression + Sum of Squares Error, or simply , SST = SSR + SSE. The Regression Sum of Squares or SSR measures the variation of the regression/predicted values, and the Sum of Squares Error SSE the variation between the actual and the predicted values. An alternative saying is that SSR is the difference explained by the model, SSE is the difference not explained by the model and is random, and SST is the total error. Note, we often use SSE (Sum of Squares Error) and SSD (Sum of Squares Difference) interchangeably. 3.1.3 R-Squared The higher the ratio of SSR to SST, the better the model is. This ratio is quantified by the coefficient of determination (also known as R2 or R-squared): \\[ R^2= \\frac{SSR}{SST}\\] Since \\(SST= SSR+SSE\\), \\(\\qquad 0\\le R^2\\le 1\\). The closer it is to 1, the better the model. Note that there are many other factors that we need to analyse before we can conclude a linear regression model is effective, but a high \\(R^2\\) is a pretty good indicator. Let’s see what the value of \\(R^2\\) is for our simulated dataset. # Calculate the mean of Y ymean = mean(df$yact) print(paste('Mean of Y =', ymean)) # paste brings a white space by default #> [1] "Mean of Y = 2.44422555811815" # Calculate SSR and SST df['SSR'] = (df['ypred'] - ymean)**2 df['SST'] = (df['yact'] - ymean)**2 SSR = sum(df['SSR']) SST = sum(df['SST']) # Calculate R-squared R2 = SSR / SST print(paste('R2 =', R2)) #> [1] "R2 = 0.583160943681119" The value of \\(R^2=0.583\\) suggests that ypred provides a decent prediction of the yact. We have randomly assumed some values for \\(\\alpha\\) and \\(\\beta\\), but these may or may not be the best values. In the next step, we will use the least sum of square method to calculate the optimum value for \\(\\alpha\\) and \\(\\beta\\) to see if there is an improvement in \\(R^2\\). To get started on the next step, open the notebook called 02-linearReg-02.Rmd. 3.2 Least Squares Using Simulated Data Now, using our simulated data from the previous step, let’s estimate the optimum values of our variable coefficients, \\(\\alpha\\) and \\(\\beta\\). Using the predictor variable, X, and the output variable, yact, we will calculate the values of \\(\\alpha\\) and \\(\\beta\\) using the Least Squares method described in the lecture. The cell below creates the same dataframe as previously. Run the cell to get started! set.seed(0) # Generate data X = 2.5 * rnorm(100) + 1.5 # Array of 100 values with mean = 1.5, stddev = 2.5 ypred = 2 + 0.3 * X # Prediction of Y, assuming a = 2, b = 0.3 res = 0.5 * rnorm(100) # Generate 100 residual terms yact = 2 + 0.3 * X + res # Actual values of Y # Create dataframe to store our X, ypred, and yact values df = data.frame('X' = X, 'ypred' = ypred, 'yact' = yact) Just to reiterate, here are the formulas for \\(\\alpha\\) and \\(\\beta\\) again: \\[\\hat\\beta=\\frac{\\sum_{i=1}^n(X_i-\\bar X)(Y_i-\\bar Y)}{\\sum_{i=1}^n(X_i-\\bar X)^2}=\\frac{\\text{cov}(X,Y)}{\\text{var}(X)}\\] \\[\\hat\\alpha=\\bar Y-\\hat\\beta * \\bar X\\] To calculate these coefficients, we will create a few more columns in our df data frame. We need to calculate xmean and ymean to calculate the covariance of X and Y (xycov) and the variance of X (xvar) before we can work out the values for alpha and beta. # Calculate the mean of X and Y xmean = mean(X) ymean = mean(yact) # Calculate the terms needed for the numator and denominator of beta df['xycov'] = (df['X'] - xmean) * (df['yact'] - ymean) df['xvar'] = (df['X'] - xmean)**2 # Calculate beta and alpha beta = sum(df['xycov']) / sum(df['xvar']) alpha = ymean - (beta * xmean) print(paste('alpha =', alpha, ';', 'beta =', beta)) #> [1] "alpha = 1.93401265576322 ; beta = 0.327758955833308" As we can see, the values are only a little different from what we had assumed earlier. Let’s see how the value of \\(R^2\\) changes if we use the new values of \\(\\alpha\\) and \\(\\beta\\). The equation for the new model can be written as: \\[ y=1.934 + 0.328 * x \\] Let’s create a new column in df to accommodate the values generated by this equation and call this ypred2, and calculate the new \\(R^2\\). # Create new column to store new predictions df['ypred2'] = alpha + beta * df['X'] # Calculate new SSR with new predictions of Y. # Note that SST remains the same since yact and ymean do not change. df['SSR2'] = (df['ypred2'] - ymean)**2 df['SST'] = (df['yact'] - ymean)**2 SSR2 = sum(df['SSR2']) SST = sum(df['SST']) # Calculate new R2 R2_2 = SSR2 / SST print(paste('New R2 =', R2_2)) #> [1] "New R2 = 0.69524214766491" The new value of \\(R^2= 0.695\\) shows a slight improvement from the previous value of \\(R^2=0.583\\) (obtained with \\(\\alpha=2,~\\beta=0.3\\)). Let’s also plot our new prediction model against the actual values and our earlier assumed model, just to get a better visual understanding. library(ggplot2) # Put color into aes ggplot(df, aes(X)) + # basic graphical object geom_point(aes(y=yact), colour="black") + # first layer geom_line(aes(y=ypred, colour="Guess")) + # second layer geom_line(aes(y=ypred2, colour="OLS")) + # third layer scale_colour_manual(name="Models", values = c("Guess"="darkgreen", "OLS"="red")) + ggtitle('Actual vs Predicted with guessed parameters vs Predicted with calculated parameters') As we can see, the ypred2 and ypred are more or less overlapping since the respective values of ɑ and β are not very different. Next, we will explore other methods of determining model efficacy by using the notebook called 02-linearReg-03.Rmd. 3.3 Diagnostic check of a fitted regression model Apart from the \\(R^2\\) statistic, there are other statistics and parameters that you need to look at in order to determine if the model is efficient. We will discuss some commonly used statistics – Residual Standard Errors, \\(p\\)-values, and \\(F\\)-statistics. 3.3.1 Residual Standard Errors (RSE) RSE is a common statistic used to calculate the accuracy of values predicted by a model. It is an estimate of the variance of the error term, res. For a simple linear regression model, RSE is defined as: \\[ RSE^2 = \\frac{SSE}{n-2} = \\frac1{n-2} \\sum_{i=1}^n \\Bigl(\\text{yact}_i - \\text{ypred}_i \\Bigr)^2. \\] In general, \\[ RSE^2 = \\frac{SSE}{n-p-1} = \\frac1{n-p-1} \\sum_{i=1}^n \\Bigl(\\text{yact}_i - \\text{ypred}_i \\Bigr)^2. \\] where \\(p\\) is the number of predictor variables in a model where we have more than one predictor variables. A multiple linear regression model is a linear regression model with multiple predictors, written as \\[ Y_e = \\alpha +\\beta_1 * X_1 +\\cdots +\\beta_p X_p. \\] As you see, the parameters and predictors are subscripted from 1 up to the number of predictors \\(p\\). In multiple regression, the value of RSE generally decreases as we add variables that are more significant predictors of the output variable. Using our simulated data from the previous steps, the following code snippet shows how the RSE for a model can be calculated: set.seed(0) # Generate data X = 2.5 * rnorm(100) + 1.5 # Array of 100 values with mean = 1.5, stddev = 2.5 res = 0.5 * rnorm(100) # Generate 100 residual terms yact = 2 + 0.3 * X + res # Actual values of Y # Create dataframe to store our X, ypred, and yact values df = data.frame('X' = X, 'yact' = yact) # Calculate the mean of X and Y xmean = mean(X) ymean = mean(yact) # Calculate the terms needed for the numator and denominator of beta df['xycov'] = (df['X'] - xmean) * (df['yact'] - ymean) df['xvar'] = (df['X'] - xmean)**2 # Calculate beta and alpha beta = sum(df['xycov']) / sum(df['xvar']) alpha = ymean - (beta * xmean) print(paste('alpha =', alpha, ';', 'beta =', beta)) #> [1] "alpha = 1.93401265576322 ; beta = 0.327758955833308" # Store predictions as in previous step df['ypred'] = alpha + beta * df['X'] # Show first five rows of dataframe head(df) #> X yact xycov xvar ypred #> 1 4.6573857 3.788145 4.1671116 9.6144310 3.460513 #> 2 0.6844166 1.816937 0.5471556 0.7608280 2.158336 #> 3 4.8244982 3.139354 2.2715611 10.6786935 3.515285 #> 4 4.6810733 3.427612 3.0724952 9.7618890 3.468276 #> 5 2.5366036 2.195788 -0.2434518 0.9602676 2.765407 #> 6 -2.3498751 1.583397 3.3628671 15.2611034 1.163820 # Calculate SSE df['SSE'] = (df['yact'] - df['ypred'])**2 SSE = sum(df['SSE']) # Calculate RSE RSE = sqrt(SSE / 98) # n = 100 print(paste('RSE =', RSE)) #> [1] "RSE = 0.481279277134956" The value of RSE comes out to be 0.48. As you might have guessed, the smaller the residual standard errors, the better the model is. The benchmark to compare this to is the mean of the actual values, yact. As shown previously, this value is ymean = 2.54. In plain English, this means we observe an error of 0.48 over 2.44 - approximately 19.69%. error = RSE / ymean print(paste('Mean Y =', ymean)) #> [1] "Mean Y = 2.44422555811815" print(paste('Error =', error)) #> [1] "Error = 0.196904608716023" 3.3.2 p-values The calculation of \\(\\alpha\\) and \\(\\beta\\) are estimates, not exact calculations. Whether their values are significant or not needs to be tested using a hypothesis test. In the equation, \\(Y = \\alpha + \\beta X\\), if we set \\(\\beta=0\\), there will be no relation between \\(Y\\) and \\(X\\). Therefore, the hypothesis tests whether the value of \\(\\beta\\) is non-zero or not. \\[\\begin{align*} \\text{Null hypothesis}~ H_0~:~ \\beta=0, & \\quad \\text{versus} \\\\ \\text{Alternative hypothesis}~ H_1~:~ \\beta\\ne 0.& \\end{align*} \\] Whenever a regression task is performed and \\(\\beta\\) is calculated, there will be an accompanying p-value corresponding to this hypothesis test. We will not go through how this is calculated in this course (you can learn more here), since it is calculated automatically by ready-made methods in R. If the p-value is less than a chosen significance level (e.g. 0.05) then the null hypothesis that \\(\\beta = 0\\) is rejected and \\(\\beta\\) is said to be significant and non-zero. In the case of multiple linear regression, the p-value associated with each \\(\\beta_k\\) can be used to weed out insignificant predictors from the model. The higher the p-value for \\(\\beta_k\\), the less significant \\(X_k\\) is to the model. 3.3.3 F-statistics In a multiple regression model, apart from testing the significance of individual variables by checking the p-values, it is also necessary to check whether, as a group all the predictors are significant. This can be done using the following hypothesis: \\[\\begin{align*} \\text{Null hypothesis}~ H_0~:~ & \\beta_1=\\beta_2=\\cdots=\\beta_p=0, \\quad \\text{versus} \\\\ \\text{Alternative hypothesis}~ H_1~:~& \\text{at least one of the} ~\\beta_k's ~ \\text{is non zero}. \\end{align*} \\] The statistic that is used to test this hypothesis is called the F-statistic and is defined as follows: \\[ F\\text{-statistic} = \\text{Fisher statistic}= \\frac{ (SST-SSE)/p}{ SSE/(n-p-1)} \\] where \\(n\\) = number of rows (sample points) in the dataset and \\(p\\) = number of predictor variables in the model. There is a \\(p\\)-value that is associated with this \\(F\\)-statistic. If the \\(p\\)-value is smaller than the chosen significance level, the null hypothesis can be rejected. It is important to look at the F-statistic because: p-values are about individual relationships between predictors and the outcome variable. However, one predictor’s relationship with the output might be impacted by the presence of other variables. When the number of predictors in the model is very large and all the \\(\\beta_i\\) are very close to zero, the individual p-values associated with the predictors might give very small values so we might incorrectly conclude that there is a relationship between the predictors and the outcome. 3.4 Simple Linear Regression with lm function There are a few R packages, e.g., the built-in stat package have a lm (linear model) function to fit linear regression very easy - much easier than implementing from scratch like we did in the last lesson. See more details in the lm manual. We will start with the datarium library which contain the advertising data. # Install datarium library if you haven't if (!requireNamespace("datarium", quietly = TRUE)) { install.packages("datarium") } library(datarium) # Load data: then we will have a data.frame with name marketing data(marketing) head(marketing) #> youtube facebook newspaper sales #> 1 276.12 45.36 83.04 26.52 #> 2 53.40 47.16 54.12 12.48 #> 3 20.64 55.08 83.16 11.16 #> 4 181.80 49.56 70.20 22.20 #> 5 216.96 12.96 70.08 15.48 #> 6 10.44 58.68 90.00 8.64 We can also check summary statistics of each column summary(marketing) #> youtube facebook newspaper sales #> Min. : 0.84 Min. : 0.00 Min. : 0.36 Min. : 1.92 #> 1st Qu.: 89.25 1st Qu.:11.97 1st Qu.: 15.30 1st Qu.:12.45 #> Median :179.70 Median :27.48 Median : 30.90 Median :15.48 #> Mean :176.45 Mean :27.92 Mean : 36.66 Mean :16.83 #> 3rd Qu.:262.59 3rd Qu.:43.83 3rd Qu.: 54.12 3rd Qu.:20.88 #> Max. :355.68 Max. :59.52 Max. :136.80 Max. :32.40 This dataset contains data about the advertising budget spent on YouTub, Radio, and Newspapers for a particular product and the resulting sales. We expect a positive correlation between such advertising costs and sales. Let’s start with YouTub advertising costs to create a simple linear regression model. First let’s plot the variables to get a better sense of their relationship: # Create scatter plot library(ggplot2) ggplot(marketing, aes(x=youtube, y=sales)) + geom_point(colour="black") + ggtitle('YouTube vs Sales') As YouTube advertisement cost increases, sales also increase – they are positively correlated! Now with the linear model lm function, let’s create a line of best fit using the least sum of square method. # Fit linear regression # By default it include an incepter, so it is equvialent to add "+ 1" # res.lm <- lm(sales ~ youtube + 1, data = marketing) res.lm <- lm(sales ~ youtube, data = marketing) In the above code, we used lm to fit our simple linear regression model. This takes the formula y ~ X, where X is the predictor variable (YouTube advertising costs) and y is the output variable (Sales). Then, this function will return fitted model via a ordinary least squares (OLS) method. The res.lm is a list, you can get the it attributes by e.g., res.lm$coefficients res.lm$coefficients #> (Intercept) youtube #> 8.43911226 0.04753664 In the notation that we have been using, \\(\\alpha\\) is the intercept and \\(\\beta\\) is the slope i.e.: \\(\\alpha = 8.439, \\quad \\beta = 0.048\\) Thus, the equation for the model will be: \\(\\text{Sales} = 8.439 + 0.048*\\text{YouTube}\\) Let’s also check an indicator of the model efficacy, R2. Luckily, summary function can calculate it from the lm output and gives us a ready-made method for doing this so we don’t need to code all the math ourselves: res_summary = summary(res.lm) # Again, res_summary is also a list res_summary$r.squared #> [1] 0.6118751 We can also take a look at the model summary by writing this snippet: # Print out the summary summary(res.lm) #> #> Call: #> lm(formula = sales ~ youtube, data = marketing) #> #> Residuals: #> Min 1Q Median 3Q Max #> -10.0632 -2.3454 -0.2295 2.4805 8.6548 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 8.439112 0.549412 15.36 <2e-16 *** #> youtube 0.047537 0.002691 17.67 <2e-16 *** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Residual standard error: 3.91 on 198 degrees of freedom #> Multiple R-squared: 0.6119, Adjusted R-squared: 0.6099 #> F-statistic: 312.1 on 1 and 198 DF, p-value: < 2.2e-16 There is a lot here. Of these results, we have discussed: R-squared F-statistic Prob (F-statistic) - this is the p-value of the F-statistic Intercept coef - this is alpha YouTub coef - this is beta for predictor YouTub P>|t| - this is the p-value for our coefficients Now that we’ve fit a simple regression model, we can try to predict the values of sales based on the equation we just derived! sales_pred = predict(res.lm, newdata = marketing[c('youtube')]) marketing['sales_pred'] = sales_pred The predict fucntion predicts sales value for each row based on the model equation using YouTub costs. This is the equivalent of manually typing out our equation: sales_pred = 8.439 + 0.048*(advert['youtube']). We can visualise our regression model by plotting sales_pred against the YouTube advertising costs to find the line of best fit: library(ggplot2) ggplot(marketing, aes(x=youtube)) + geom_point(aes(y=sales), colour="black") + geom_line(aes(y=sales_pred), colour="red") + ggtitle('YouTube vs Sales') In the next step, we will add more features as predictors and see whether it improves our model. Go to the the notebook called 02-linearReg-05.Rmd. 3.5 Multiple Regression with lm function A multiple linear regression is simply a linear regression that involves more than one predictor variable. It is represented as: \\[\\qquad Y_e = \\alpha + \\beta_1*X_1 + \\beta_2*X_2 + \\dots + \\beta_p*X_p\\] Each βi will be estimated using the least sum of squares method. The data set is \\[ \\begin{array} {~~} Y_1, & X_1^{(1)}, & \\ldots, & X_p^{(1)} \\\\ Y_2, & X_1^{(2)}, & \\ldots, & X_p^{(2)} \\\\ \\vdots & \\vdots & \\vdots & \\vdots \\\\ Y_n, & X_1^{(n)}, & \\ldots, & X_p^{(n)} \\end{array} \\] For each sample \\(i\\), the predicted value by the model is: \\(\\qquad Y_{i,e} = \\alpha + \\beta_1*X_1^{(i)} + \\beta_2*X_2^{(i)} + \\dots + \\beta_p*X_p^{(i)}\\) Define the sum of squares \\[ S(\\alpha,\\beta_1,\\ldots,\\beta_p) = \\sum_{i=1}^n \\left\\{ Y_i -Y_{i,e}\\right\\}^2 =\\sum_{i=1}^n \\left\\{ Y_i -\\left( \\alpha + \\beta_1*X_1^{(i)} + \\beta_2*X_2^{(i)} + \\dots + \\beta_p*X_p^{(i)}\\right)\\right\\}^2 \\] Least squares estimators: solve \\[ \\frac{\\partial S(\\alpha,\\beta_1,\\ldots,\\beta_p)}{\\partial \\alpha}=0,\\quad \\frac{\\partial S (\\alpha,\\beta_1,\\ldots,\\beta_p)}{\\partial \\beta_1}=0,\\quad \\ldots,\\quad \\frac{\\partial S (\\alpha,\\beta_1,\\ldots,\\beta_p)}{\\partial \\beta_p}=0. \\] to obtain the least squares estimators of the parameters \\[ \\hat\\alpha, \\hat\\beta_1,\\ldots,\\hat\\beta_p. \\] Note that be definition, \\[ SSE = S(\\hat\\alpha, \\hat\\beta_1,\\ldots,\\hat\\beta_p). \\] In other words, the fitted SSE (sum of squares error) is the minimized value of the sum squares with the estimated values of the parameters. The more varibles, the smaller the \\(R^2\\) Consider two regression models \\(\\quad ~ Y_e = \\alpha + \\beta_1*X_1\\) \\(\\quad \\tilde Y_e = \\alpha + \\beta_1*X_1 + \\beta_2*X_2\\) The model (II) has one more input variable \\(X_2\\). The \\(SSE_I\\) of Model (I) is the minimum of \\[ S_I(\\alpha,\\beta_1) = \\sum_{i=1}^n \\left\\{ Y_i -\\left( \\alpha + \\beta_1*X_1^{(i)} \\right)\\right\\}^2 \\] over all possible values of \\((\\alpha,\\beta_1)\\). The \\(SSE_{II}\\) of Model (II) is the minimum of \\[ S_{II}(\\alpha,\\beta_1,\\beta_2) = \\sum_{i=1}^n \\left\\{ Y_i -\\left( \\alpha + \\beta_1*X_1^{(i)} +\\beta_2*X_2^{(i)} \\right)\\right\\}^2. \\] over all possible values of \\((\\alpha,\\beta_1,\\beta_2)\\). Because \\(\\quad S_I(\\alpha,\\beta_1) = S_{II}(\\alpha,\\beta_1,\\beta_2=0 )\\), we find that \\(SSE_{II}\\le SSE_I\\), so \\[ R^2_{II} = SST - SSE_{II} \\ge SST - SSE_{I} = R^2_{I}. \\] With this simple dataset of three predictor variables, there can be seven possible models: Sales ~ YouTube Sales ~ Newspaper Sales ~ Facebook Sales ~ YouTube + Facebook Sales ~ YouTube + Newspaper Sales ~ Newspaper + Facebook Sales ~ YouTube + Facebook + Newspaper Generally, if there are p possible predictor variables, there can be (2p - 1) possible models – this can get large very quickly! Thankfully, there are a few guidelines to filter some of these and then navigate towards the most efficient one. Keep variables with low p-values and eliminate ones with high p-values Keep variables that increase the value of adjusted-R2 – this penalizes the model for adding insignificant variables and increases when we add significant variables. It is calculated by: \\[ R^2_{adj} = 1- (1-R^2) \\frac{n-1}{n-p-1}\\] Based on these guidelines, there are two approaches to select the predictor variables in the final model: Forward selection: start with a null model (no predictors), then add predictors one by one. If the p-value for the variable is small enough and the value of the adjusted-R2 goes up, the predictor is included in the model. Otherwise, it is not included. Backward selection: starts with a model that has all the possible predictors and discard some of them. If the p-value of a predictor variable is large and adjusted-R2 is lower when removed, it is discarded from the model. Otherwise, it remains a part of the model. Many statistical programs give us an option to select from these approaches while implementing multiple linear regression. For now, let’s manually add a few variables and see how it changes the model parameters and efficacy. First, add the newspaper variable to the model: library(datarium) data(marketing) head(marketing) #> youtube facebook newspaper sales #> 1 276.12 45.36 83.04 26.52 #> 2 53.40 47.16 54.12 12.48 #> 3 20.64 55.08 83.16 11.16 #> 4 181.80 49.56 70.20 22.20 #> 5 216.96 12.96 70.08 15.48 #> 6 10.44 58.68 90.00 8.64 res_lm2 = lm(sales ~ youtube + newspaper, data=marketing) summary(res_lm2) #> #> Call: #> lm(formula = sales ~ youtube + newspaper, data = marketing) #> #> Residuals: #> Min 1Q Median 3Q Max #> -10.3477 -2.0815 -0.1138 2.2711 10.1415 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 6.929938 0.630405 10.993 < 2e-16 *** #> youtube 0.046901 0.002581 18.173 < 2e-16 *** #> newspaper 0.044219 0.010174 4.346 2.22e-05 *** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Residual standard error: 3.745 on 197 degrees of freedom #> Multiple R-squared: 0.6458, Adjusted R-squared: 0.6422 #> F-statistic: 179.6 on 2 and 197 DF, p-value: < 2.2e-16 As you see, the p-values for the coefficients are very small, suggesting that all the estimates are significant. The equation for this model will be: \\[ \\text{Sales} = 6.93+0.046* \\text{YouTube} + 0.044 * \\text{Newspaper}\\] The values of R2 and adjusted R2 are 0.646 and 0.642, which is just a minor improvement from before (0.612 and 0.610, respectively). Similarly for RSE (3.745). Only a small decrease in RSE and error… Let’s take a closer look at the summary above. The Adj-R2 increases slightly, but the F-statistic decreases (from 312.1 to 179.6), as does the associated p-value. This suggests that adding newspaper didn’t improve the model significantly. Let’s try adding facebook instead: # Initialise and fit new model with TV and Radio as predictors # model3 = smf.ols('Sales ~ TV + Radio', data=advert).fit() # print(model3.summary()) res_lm3 = lm(sales ~ youtube + facebook, data=marketing) summary(res_lm3) #> #> Call: #> lm(formula = sales ~ youtube + facebook, data = marketing) #> #> Residuals: #> Min 1Q Median 3Q Max #> -10.5572 -1.0502 0.2906 1.4049 3.3994 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 3.50532 0.35339 9.919 <2e-16 *** #> youtube 0.04575 0.00139 32.909 <2e-16 *** #> facebook 0.18799 0.00804 23.382 <2e-16 *** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Residual standard error: 2.018 on 197 degrees of freedom #> Multiple R-squared: 0.8972, Adjusted R-squared: 0.8962 #> F-statistic: 859.6 on 2 and 197 DF, p-value: < 2.2e-16 This gives us the model: \\[ \\text{Sales} = 3.51+0.046* \\text{YouTube} + 0.188 * \\text{Facebook}\\] The adjusted R2 value has improved considerably, as did the RSE and F-statistic, indicating an efficient model. Thus, we can conclude that facebook is a great addition to the model. YouTube and facebook advertising costs together are able to predict sales well. But, can we improve it a bit further by combining all three predictor variables? Try it out: see if you can figure out how to do this on your own! # Initialise and fit new model with TV, Newspaper, and Radio as predictors # Print summary of regression results # Calculate RSE - don't forget that the number of predictors p is now 3 You should get the equation: \\[ \\text{Sales} = 3.53+0.046*\\text{YouTube} -0.001*\\text{Newspaper} +0.188*\\text{Facebook}\\] You should also find that: RSE increases slightly, the coefficient for newspaper is negative, and the F-statistic decreases considerably from 859.6 to 570.3. All these suggest that the model actually became less efficient on addition of newspaper. Why? This step shows clearly that adding one more input variable Newspaper in Model 3 does not lead to any improvement. "],["introClassifier.html", "Chapter 4 Introduction to Classification 4.1 Visualise logistic and logit functions 4.2 Logistic regression on Diabetes 4.3 Cross-validation 4.4 More assessment metrics", " Chapter 4 Introduction to Classification 4.1 Visualise logistic and logit functions In this chapter, we will focus on logistic regression for classification. Let’s first look at what logistic and logit function look like. 4.1.1 Logistic function Let’s write our first function logestic() as follows. # Write your first function logistic <- function(y) { exp(y) / (1 + exp(y)) } # Try it with different values: logistic(0.1) #> [1] 0.5249792 logistic(c(-3, -2, 0.5, 3, 5)) #> [1] 0.04742587 0.11920292 0.62245933 0.95257413 0.99330715 This is the equivalent to the built-in plogis() function in the stat package for the logistic distribution: plogis(0.1) #> [1] 0.5249792 plogis(c(-3, -2, 0.5, 3, 5)) #> [1] 0.04742587 0.11920292 0.62245933 0.95257413 0.99330715 4.1.2 Logit function Now, let look at the logistic’s inverse function logit(), and let’s define it manually. Note, this function only support input between 0 and 1. # Write your first function logit <- function(x) { log(x / (1 - x)) } # Try it with different values: logit(0.4) #> [1] -0.4054651 logit(c(0.2, 0.3, 0.5, 0.7, 0.9)) #> [1] -1.3862944 -0.8472979 0.0000000 0.8472979 2.1972246 logit(c(-1, 2, 0.4)) #> Warning in log(x/(1 - x)): NaNs produced #> [1] NaN NaN -0.4054651 Again, the built-in stat package’s logistic distribution has an equivalent function qlogis(), though with a different name. qlogis(0.4) #> [1] -0.4054651 qlogis(c(0.2, 0.3, 0.5, 0.7, 0.9)) #> [1] -1.3862944 -0.8472979 0.0000000 0.8472979 2.1972246 qlogis(c(-1, 2, 0.4)) #> Warning in qlogis(c(-1, 2, 0.4)): NaNs produced #> [1] NaN NaN -0.4054651 4.1.3 Visualise the distribution Logisitc function # You can use seq() function to generate a vector # Check how to use it by help(seq) or ?seq x = seq(-7, 7, 0.3) df = data.frame('x'=x, 'logistic'=plogis(x)) # You can plot by plot function # plot(x=df$x, y=df$logistic, type='o') # Or ggplot2 library(ggplot2) ggplot(df, aes(x=x, y=logistic)) + geom_point() + geom_line() Logit function x = seq(0.001, 0.999, 0.01) df = data.frame('x'=x, 'logit'=qlogis(x)) ggplot(df, aes(x=x, y=logit)) + geom_point() + geom_line() 4.2 Logistic regression on Diabetes 4.2.1 Load Pima Indians Diabetes Database This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage. The datasets consist of several medical predictor (independent) variables and one target (dependent) variable, Outcome. Independent variables include the number of pregnancies the patient has had, their BMI, insulin level, age, and so on. Acknowledgement: This notebook is adapted and updated from STAT1005. # Install the mlbench library for loading the datasets if (!requireNamespace("mlbench", quietly = TRUE)) { install.packages("mlbench") } # Load data library(mlbench) data(PimaIndiansDiabetes) # Check the first few lines dim(PimaIndiansDiabetes) #> [1] 768 9 head(PimaIndiansDiabetes) #> pregnant glucose pressure triceps insulin mass pedigree age diabetes #> 1 6 148 72 35 0 33.6 0.627 50 pos #> 2 1 85 66 29 0 26.6 0.351 31 neg #> 3 8 183 64 0 0 23.3 0.672 32 pos #> 4 1 89 66 23 94 28.1 0.167 21 neg #> 5 0 137 40 35 168 43.1 2.288 33 pos #> 6 5 116 74 0 0 25.6 0.201 30 neg Now, let’s check two potential features: glucose and age, colored by the diabetes labels. library(ggplot2) ggplot(data=PimaIndiansDiabetes, aes(x=glucose, y=age)) + geom_point(aes(color=diabetes)) Before we start fit models, let’s split the data into training and test sets in a 4:1 ratio. Let define it manually, though there are functions to do it automatically. set.seed(0) idx_train = sample(nrow(PimaIndiansDiabetes), size=0.75*nrow(PimaIndiansDiabetes), replace = FALSE) df_train = PimaIndiansDiabetes[idx_train, ] df_test = PimaIndiansDiabetes[-idx_train, ] # recall the meaning of negative symbol 4.2.2 Fit logistic regression In logistic regression, the predicted probability to be class 1 is: \\[P(y=1|X, W) = \\sigma(w_0, x_1 * w_1 + ... + x_p * w_p)\\] where the \\(\\sigma()\\) denotes the logistic function. In R, the built-in package stats already have functions to fit generalised linear model (GLM), including logistic regression, a type of GML. Here, let’s start with the whole dataset to fit a logistic regression. Note, we will specify the model family as binomial, as the likelihood we are using in logistic regression is a Bernoulli likelihood, a special case of binomial likelihood when the total trial n=1. # Define formula in different ways # my_formula = as.formula(diabetes ~ glucose + age) # my_formula = as.formula(paste(colnames(PimaIndiansDiabetes)[1:8], collapse= " + ")) # my_formula = as.formula(diabetes ~ .) # Fit logistic regression glm_res <- glm(diabetes ~ ., data=df_train, family = binomial) # We can use the logLik() function to obtain the log likelihood logLik(glm_res) #> 'log Lik.' -281.9041 (df=9) We can use summary() function to see more details about the model fitting. summary(glm_res) #> #> Call: #> glm(formula = diabetes ~ ., family = binomial, data = df_train) #> #> Coefficients: #> Estimate Std. Error z value Pr(>|z|) #> (Intercept) -8.044602 0.826981 -9.728 < 2e-16 *** #> pregnant 0.130418 0.036080 3.615 0.000301 *** #> glucose 0.032196 0.004021 8.007 1.18e-15 *** #> pressure -0.017158 0.006103 -2.811 0.004934 ** #> triceps -0.003425 0.007659 -0.447 0.654752 #> insulin -0.001238 0.001060 -1.169 0.242599 #> mass 0.104029 0.018119 5.741 9.39e-09 *** #> pedigree 0.911030 0.344362 2.646 0.008156 ** #> age 0.012980 0.010497 1.237 0.216267 #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> (Dispersion parameter for binomial family taken to be 1) #> #> Null deviance: 756.83 on 575 degrees of freedom #> Residual deviance: 563.81 on 567 degrees of freedom #> AIC: 581.81 #> #> Number of Fisher Scoring iterations: 5 4.2.3 Assess on test data Now, we can evaluate the accuracy of the model on the 25% test data. # Train the full model on the training data glm_train <- glm(diabetes ~ ., data=df_train, family = binomial) # Predict the probability of being diabeties on test data # We can also set a threshold, e.g., 0.5 for the predicted label pred_prob = predict(glm_train, df_test, type = "response") pred_label = pred_prob >= 0.5 # Observed label obse_label = df_test$diabetes == 'pos' # Calculate the accuracy on test data # think how accuracy is defined # we can use (TN + TP) / (TN + TP + FN + FP) # we can also directly compare the proportion of correctness accuracy = mean(pred_label == obse_label) print(paste("Accuracy on test set:", accuracy)) #> [1] "Accuracy on test set: 0.796875" 4.2.4 Model selection and diagnosis 4.2.4.1 Model2: New feature set by removing triceps # Train the full model on the training data glm_mod2 <- glm(diabetes ~ pregnant + glucose + pressure + insulin + mass + pedigree + age, data=df_train, family = binomial) logLik(glm_mod2) #> 'log Lik.' -282.0038 (df=8) summary(glm_mod2) #> #> Call: #> glm(formula = diabetes ~ pregnant + glucose + pressure + insulin + #> mass + pedigree + age, family = binomial, data = df_train) #> #> Coefficients: #> Estimate Std. Error z value Pr(>|z|) #> (Intercept) -8.0317567 0.8251403 -9.734 < 2e-16 *** #> pregnant 0.1308094 0.0361230 3.621 0.000293 *** #> glucose 0.0324606 0.0039854 8.145 3.80e-16 *** #> pressure -0.0175651 0.0060269 -2.914 0.003563 ** #> insulin -0.0014402 0.0009593 -1.501 0.133291 #> mass 0.1018155 0.0173811 5.858 4.69e-09 *** #> pedigree 0.9000134 0.3428652 2.625 0.008665 ** #> age 0.0131238 0.0105147 1.248 0.211982 #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> (Dispersion parameter for binomial family taken to be 1) #> #> Null deviance: 756.83 on 575 degrees of freedom #> Residual deviance: 564.01 on 568 degrees of freedom #> AIC: 580.01 #> #> Number of Fisher Scoring iterations: 5 # Predict the probability of being diabeties on test data # We can also set a threshold, e.g., 0.5 for the predicted label pred_prob2 = predict(glm_mod2, df_test, type = "response") pred_label2 = pred_prob2 >= 0.5 accuracy2 = mean(pred_label2 == obse_label) print(paste("Accuracy on test set with model2:", accuracy2)) #> [1] "Accuracy on test set with model2: 0.807291666666667" 4.2.4.2 Model3: New feature set by removing triceps and insulin # Train the full model on the training data glm_mod3 <- glm(diabetes ~ pregnant + glucose + pressure + mass + pedigree + age, data=df_train, family = binomial) logLik(glm_mod3) #> 'log Lik.' -283.1342 (df=7) summary(glm_mod3) #> #> Call: #> glm(formula = diabetes ~ pregnant + glucose + pressure + mass + #> pedigree + age, family = binomial, data = df_train) #> #> Coefficients: #> Estimate Std. Error z value Pr(>|z|) #> (Intercept) -7.797803 0.802287 -9.719 < 2e-16 *** #> pregnant 0.130990 0.035957 3.643 0.00027 *** #> glucose 0.030661 0.003755 8.164 3.23e-16 *** #> pressure -0.017847 0.005953 -2.998 0.00272 ** #> mass 0.097356 0.016969 5.737 9.61e-09 *** #> pedigree 0.824150 0.338299 2.436 0.01484 * #> age 0.015134 0.010426 1.452 0.14663 #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> (Dispersion parameter for binomial family taken to be 1) #> #> Null deviance: 756.83 on 575 degrees of freedom #> Residual deviance: 566.27 on 569 degrees of freedom #> AIC: 580.27 #> #> Number of Fisher Scoring iterations: 5 # Predict the probability of being diabeties on test data # We can also set a threshold, e.g., 0.5 for the predicted label pred_prob3 = predict(glm_mod3, df_test, type = "response") pred_label3 = pred_prob3 >= 0.5 accuracy3 = mean(pred_label3 == obse_label) print(paste("Accuracy on test set with model3:", accuracy3)) #> [1] "Accuracy on test set with model3: 0.786458333333333" 4.3 Cross-validation In last section, we split the whole dataset into 75% for training and 25% for testing. However, when the dataset is small, the test set may not be big enough and introduce high variance on the assessment. One way to reduce this variance in assessment is performing cross-validation, where we split the data into K folds and use K-1 folds for training and the remaining fold for testing. This procedure will be repeated for fold 1 to fold K as testing fold and all folds will be aggregated for joint assessment. K is usually taken 3, 5 or 10. In extreme case that K=n_sample, we call it leave-one-out cross-validation (LOOCV). Let’s load the dataset (again) first. # Load data library(mlbench) data(PimaIndiansDiabetes) Besides implement the cross-validation from scratch, there are packages supporting it well, including caret package. We will install it and use it for cross-validation here. # Install the caret library for cross-validation if (!requireNamespace("caret", quietly = TRUE)) { install.packages("caret") } library(caret) # Define training control # We also want to have savePredictions=TRUE & classProbs=TRUE set.seed(0) my_trControl <- trainControl(method = "cv", number = 5, classProbs = TRUE, savePredictions = TRUE) # Train the model cv_model <- train(diabetes ~ ., data = PimaIndiansDiabetes, method = "glm", family=binomial(), trControl = my_trControl) # Summarize the results print(cv_model) #> Generalized Linear Model #> #> 768 samples #> 8 predictor #> 2 classes: 'neg', 'pos' #> #> No pre-processing #> Resampling: Cross-Validated (5 fold) #> Summary of sample sizes: 615, 614, 615, 614, 614 #> Resampling results: #> #> Accuracy Kappa #> 0.7708344 0.4695353 We can also access to detailed prediction results after concatenating the K folds: head(cv_model$pred) #> pred obs neg pos rowIndex parameter Resample #> 1 neg neg 0.9656694 0.03433058 4 none Fold1 #> 2 neg neg 0.8581071 0.14189290 6 none Fold1 #> 3 neg pos 0.9508306 0.04916940 7 none Fold1 #> 4 neg pos 0.6541361 0.34586388 17 none Fold1 #> 5 neg pos 0.7675666 0.23243342 20 none Fold1 #> 6 neg pos 0.6132685 0.38673152 26 none Fold1 We can double check the accuracy: CV_acc = mean(cv_model$pred$pred == cv_model$pred$obs) print(paste("Accuracy via 5-fold cross-validation", CV_acc)) #> [1] "Accuracy via 5-fold cross-validation 0.770833333333333" 4.4 More assessment metrics 4.4.1 Two types of error In the above sections, we used the accuracy to perform model diagnosis, either only on one testing dataset or aggregating cross multiple folds in cross- validation. Accuracy is a widely used metric for model evaluation, on the averaged error rate. However, this metric still have limitations when assessing the model performance, especially the following two: When the samples are highly imbalance, high accuracy may not mean a good model. For example, for a sample with 990 negative samples and 10 positive samples, a simple model by predicting for all sample as negative will give an accuracy of 0.99. Thus, for highly imbalanced samples, we should be careful when interpreting the accuracy. In many scenarios, our tolerance on false positive errors and false negative errors may be different and we want to know both for a certain model. They are often called as type I and II errors: Type I error: false positive (rate) Type II error: false negative (rate) - a joke way to remember what type II mean Negative has two stripes. Here, we use the diabetes dataset and their cross-validation results above to illustrate the two types of errors and the corresponding model performance evaluation. # Let's start to define the values for the confusion matrix first # Recall what the difference between & vs && # Read more: https://stat.ethz.ch/R-manual/R-devel/library/base/html/Logic.html TP = sum((cv_model$pred$obs == 'pos') & (cv_model$pred$pred == 'pos')) FN = sum((cv_model$pred$obs == 'pos') & (cv_model$pred$pred == 'neg')) FP = sum((cv_model$pred$obs == 'neg') & (cv_model$pred$pred == 'pos')) TN = sum((cv_model$pred$obs == 'neg') & (cv_model$pred$pred == 'neg')) print(paste('TP, FN, FP, TN:', TP, FN, FP, TN)) #> [1] "TP, FN, FP, TN: 151 117 59 441" We can also use the table() function to get the whole confusion matrix. Read more about the table function for counting the frequency of each element. A similar way is the confusionMatrix() in caret package. # Calculate confusion matrix confusion_mtx = table(cv_model$pred[, c("obs", "pred")]) confusion_mtx #> pred #> obs neg pos #> neg 441 59 #> pos 117 151 # similar function confusionMatrix # conf_mat = confusionMatrix(cv_model$pred$pred, cv_model$pred$obs) # conf_mat$table We can also plot out the confusion matrix # Change to data.frame before using ggplot confusion_df = as.data.frame(confusion_mtx) ggplot(confusion_df, aes(pred, obs, fill= Freq)) + geom_tile() + geom_text(aes(label=Freq)) + scale_fill_gradient(low="white", high="darkgreen") Also the false positive rate, false negative rate and true negative rate. Note, the denominator is always the number of observed samples with the same label, namely they are a constant for a specific dataset. FPR = FP / sum(cv_model$pred$obs == 'neg') FNR = FN / sum(cv_model$pred$obs == 'pos') TPR = TP / sum(cv_model$pred$obs == 'pos') print(paste("False positive rate:", FPR)) #> [1] "False positive rate: 0.118" print(paste("False negative rate:", FNR)) #> [1] "False negative rate: 0.436567164179104" print(paste("True positive rate:", TPR)) #> [1] "True positive rate: 0.563432835820896" 4.4.2 ROC curve In the above assessment, we only used \\(P>0.5\\) to denote predicted label as positive. We can imagine if we a lower cutoff lower, we will have more false positives and fewer false negatives. Indeed, in different scenarios, people may choose different level of cutoff for their tolerance of different types of errors. Let’s try cutoff \\(P>0.4\\). Think what will you expect. # Original confusion matrix table(cv_model$pred[, c("obs", "pred")]) #> pred #> obs neg pos #> neg 441 59 #> pos 117 151 # New confusion matrix with cutoff 0.4 cv_model$pred$pred_new = as.integer(cv_model$pred$pos >= 0.4) table(cv_model$pred[, c("obs", "pred_new")]) #> pred_new #> obs 0 1 #> neg 408 92 #> pos 89 179 Therefore, we may want to assess the model performance by varying the cutoffs and obtain a more systematic assessment. Actually, the Receiver operating characteristic (ROC) curve is what you need. It presents the TPR (sensitivity) vs the FPR (i.e., 1 - TNR or 1 - specificity) when varying the cutoffs. In order to achieve this, we can calculate FPR and TPR manually by varying the cutoff through a for loop. Read more about for loop and you may try write your own and here is an example from the cardelino package. For simplicity, let use an existing tool implemented in the plotROC package: plotROC::geom_roc() that is compatible with ggplot2. # Install the plotROC library for plotting ROC curve if (!requireNamespace("plotROC", quietly = TRUE)) { install.packages("plotROC") } library(ggplot2) library(plotROC) # You can set the n.cuts to show the cutoffs on the curve g = ggplot(cv_model$pred, aes(m = pos, d = as.integer(obs=='pos'))) + geom_roc(n.cuts=7, hjust = -0.4, vjust = 1.5) + coord_equal() + ggtitle("ROC curve") # Calculate AUC from the graph AUC_val = calc_auc(g)$AUC # Display the plot g + annotate("text", x=0.8, y=0.1, label=paste("AUC =", round(AUC_val, 4))) 4.4.3 Homework Now, try another model with removing triceps and plot the ROC curve and calculate the AUC score. Is it higher or lower than using the full features? "],["image-digital.html", "Chapter 5 Medical Image and Digital Health", " Chapter 5 Medical Image and Digital Health Contents to be added. "],["cancer.html", "Chapter 6 Cancer genomics and epidemiology 6.1 Case study 1: analysis of cBioportal mutation data 6.2 Case study 2: Cancer Epidemiology", " Chapter 6 Cancer genomics and epidemiology 6.1 Case study 1: analysis of cBioportal mutation data Non-small cell lung cancer is a deadly disease, and we would like to identify mutations in genes that drive its formation and progression. George et al. (2015) has sequenced 120 small cell lung cancer samples and this data is available from cBioportal. The original data is in the MAF, and we have selected a subset of the columns, normalized the column names, and made the data available as an RDS file. In this exercise, we will explore the mutation data and examine genes that are frequently mutated in non-small cell lung cancer samples. We will implement a simple method for identifying candidate tumour suppressors based on loss-of-function mutations. 6.1.1 Exploratory analysis Q1. Load the data named mutations_sclc_ucologne_2015.rds into R. Examine it using head. In this data.frame, each row is a mutation detected in a particular gene from a particular sample in the study. For more information on the columns, see the MAF specification. ?readRDS ?head Q2. Identify the top 10 most frequently mutated genes. # calculate count frequencies using table ?table # freqs <- ??? # sort the frequency vector in decreasing order ?sort # freqs <- ??? # select the first 10 genes in the freqs # freqs.top <- ??? # obtain the gene names # genes <- names(freqs.top); # print(freqs.top) Q3. Tabulate the count frequencies of variant_class. What proportion of variants are Silent or inside an Intron? ?table Q4. A variant class value frame_shift_del appears to be misspelt due to case sensitivity. Correct this error. # x$variant_class[x$variant_class == "frame_shift_del"] <- ???; Q5. To help us simplify the variant classes into loss-of-function or neutral, let us import the mutation_effects.tsv data. ?read.table # Hint: You need to set the `header` and `sep` arguments Q6. Define a new column in x that converts the variant classes to effects. ?match Q7. Create a subset of the data for the most frequently mutated genes, and tabulate the mutation count frequencies of variant classes for each gene. # x.sub <- x[???, ] # order the factor levels based on `genes`, # which was previously sorted in decreasing order of frequency # x.sub$gene <- factor(x.sub$gene, levels=genes); ?table Q8. Determine the number of mutations in each effect category, across all genes. Save the result in a variable named overall.counts. # overall.counts <- table(???); # overall.counts Q9. Tabulate the mutation count frequencies for each gene and effect. Save the results in a variable named gene.counts. Look up the frequently mutated genes in this matrix. # gene.counts <- table(???, ???); # gene.counts[???, ] 6.1.2 Statistical analysis Here, we implemented a simple statistical test for assessing whether a gene is significantly frequently targeted by loss-of-function mutations, based on the Fisher’s exact test. # Test whether a query gene has significantly more loss-of-function # mutations compared with other genes. # Requires global variables `gene.counts` and `overall.counts` lof_test <- function(gene) { # Construct the following contingency table: # neutral lof # other genes a b # query gene c d cols <- c("neutral", "loss_of_function"); gene.counts.sel <- gene.counts[gene, cols]; ct <- matrix( c( overall.counts[cols] - gene.counts.sel, gene.counts.sel ), byrow=TRUE, ncol=2 ); # Test whether the lof mutations are greater in frequency compared # to neutral mutations in the query gene, in comparison to all other genes fisher.test(ct, alternative="greater") } Q10. Subset the gene.counts table for the TP53 gene. Run lof_test on this gene. # gene.counts[???, ] # lof_test(???) Q11. Now, test to see if TTN and MUC16 are significantly frequently targeted by loss-of-function mutations. # similar to above Q12. Apply the loss-of-function test to all frequently mutated genes. ?lapply Q13. Extract odds ratio and p-values from the test results. # odds.ratios <- vapply(hs, function(h) h$estimate, 0); # ps <- vapply(hs, function(h) ???, 0); Q14. Since we tested many genes, we need to adjust for multiple hypothesis testing so that we can control the false discovery rate. So, adjust the p-values to obtain q-values. ?p.adjust Q15. Construct a results data.frame with gene names, odds ratio, p-values, and q-values that summarize the loss-of-function test results. # res <- data.frame( # gene = ???, # odds_ratio = ???, # p = ???, # q = ??? # ); ?order #res <- res[order(???), ]; Q16. Identify the significant genes from the results data.frame at a false discovery rate of 5% (i.e. q-value threshold of 0.05). Q17. Look up the significant genes in the gene.counts matrix. 6.1.3 Literature search Q18. Do the significant genes appear to be involved in cancer? What general roles do they play? Q19. How do these genes contribute specifically to the formation or progression of small cell lung cancer? Q20. What are some possible ways of identifying oncogenes that are activated by mutations or other genomic alterations? 6.2 Case study 2: Cancer Epidemiology by Dr Jason Wong Date: 6-11-2023 The RMarkdown notebook to run your own code can be downloaded here 6.2.1 Scenario You are a grants officer working for the Hong Kong Health Bureau. The HK government has recently announced new special funding in cancer research to be administered by the Bureau. You are tasked with coming up with a proposal for distribution of funding to specific cancer types that are in most need of research. Discussion points: What is important to consider when selecting a cancer type in need of research? What type of data is required? 6.2.2 Hong Kong population You are aware that generally cancer is disease that affects the elderly more than the young. You decide to first take a closer look at the structure of the population of Hong Kong. Historic population of Hong Kong can be obtained from the Census and Statistics Department. An abridged version of the full historic population of Hong Kong is provided here containing the population of Hong Kong from 1965, 1975, 1985, 1995, 2005, 2015 and 2022 categorised by sex and age (0-19, 20-44, 45-64, 65+). Discussion points: What is the trend in Hong Kong’s population over the past ~60 years? What is the best way to visualise this data? 6.2.2.1 Download population data HKPop<- read.table("https://github.com/StatBiomed/BMDS-book/raw/main/notebooks/module5-epidemi/HK_population_1965-2022.txt", sep = "\\t", header = TRUE, stringsAsFactor=FALSE, check.names = FALSE) HKPop #> Sex Age 1965 1975 1985 1995 2005 2015 2022 #> 1 male 0-19 901.9 989.7 909.0 835.6 719.0 613.8 530.6 #> 2 male 20-44 607.4 795.6 1210.2 1363.7 1263.7 1146.9 1039.2 #> 3 male 45-64 266.5 414.3 525.9 615.7 896.6 1084.9 1046.2 #> 4 male 65+ 42.2 84.6 170.5 269.3 384.7 520.0 713.6 #> 5 female 0-19 865.6 938.8 840.3 780.7 684.1 575.0 502.1 #> 6 female 20-44 539.3 685.6 1093.7 1423.6 1530.4 1531.8 1338.7 #> 7 female 45-64 286.3 400.7 470.7 535.0 884.7 1224.3 1314.7 #> 8 female 65+ 88.7 152.3 235.9 332.5 450.0 594.6 806.5 6.2.2.2 Format data for plotting Here we convert the original dataframe into simplified format for ggplot2. male<-data.frame(year = as.numeric(colnames(HKPop[1,3:9])), `0-19` = as.numeric(HKPop[1,3:9]), `20-44` = as.numeric(HKPop[2,3:9]), `45-64` = as.numeric(HKPop[3,3:9]), `65+` = as.numeric(HKPop[4,3:9]), check.names = FALSE) female<-data.frame(year = as.numeric(colnames(HKPop[1,3:9])), `0-19` = as.numeric(HKPop[5,3:9]), `20-44` = as.numeric(HKPop[6,3:9]), `45-64` = as.numeric(HKPop[7,3:9]), `65+` = as.numeric(HKPop[8,3:9]), check.names = FALSE) if (!require("tidyverse")) install.packages("tidyverse") male<-as_tibble(male) %>% select(year,`0-19`,`20-44`,`45-64`,`65+`) %>% gather (key="age",value="population ('000s)",-year) female<-as_tibble(female) %>% select(year,`0-19`,`20-44`,`45-64`,`65+`) %>% gather (key="age",value="population ('000s)",-year) male #> # A tibble: 28 × 3 #> year age `population ('000s)` #> <dbl> <chr> <dbl> #> 1 1965 0-19 902. #> 2 1975 0-19 990. #> 3 1985 0-19 909 #> 4 1995 0-19 836. #> 5 2005 0-19 719 #> 6 2015 0-19 614. #> 7 2022 0-19 531. #> 8 1965 20-44 607. #> 9 1975 20-44 796. #> 10 1985 20-44 1210. #> # ℹ 18 more rows female #> # A tibble: 28 × 3 #> year age `population ('000s)` #> <dbl> <chr> <dbl> #> 1 1965 0-19 866. #> 2 1975 0-19 939. #> 3 1985 0-19 840. #> 4 1995 0-19 781. #> 5 2005 0-19 684. #> 6 2015 0-19 575 #> 7 2022 0-19 502. #> 8 1965 20-44 539. #> 9 1975 20-44 686. #> 10 1985 20-44 1094. #> # ℹ 18 more rows 6.2.2.3 Plotting population data Uses ggplot2 and gridExtra to make line plot of male and female population data side-by-side. if (!require("ggplot2")) install.packages("gglot2") if (!require("gridExtra")) install.packages("gridExtra") library(ggplot2) library(gridExtra) #Import the necessary packages and libraries pmale<-ggplot(male,aes(x=year,y=`population ('000s)`,group=age))+ geom_line(aes(color=age))+ geom_point(aes(color=age))+ scale_color_brewer(palette="Spectral")+ theme_classic()+ ylim(0,1600)+ theme(legend.position="none")+ scale_x_continuous(breaks = seq(1965, 2022, by = 10))+ ggtitle("male") pfemale<-ggplot(female,aes(x=year,y=`population ('000s)`,group=age))+ geom_line(aes(color=age))+ geom_point(aes(color=age))+ scale_color_brewer(palette="Spectral")+ theme_classic()+ ylim(0,1600)+ theme(legend.position="right",axis.title.y = element_blank())+ scale_x_continuous(breaks = seq(1965, 2022, by = 10))+ ggtitle("female") grid.arrange(pmale,pfemale,ncol=2,widths=c(3,3.75)) 6.2.3 Cancer registry data It is clear that Hong Kong has an aging population, thus cancer incidence would also likely increase. To examine cancer incidence and mortality in Hong Kong you obtain data from the Hong Kong Cancer Registry, which is maintained by the Hospital Authority. Cancer incidence data was summarised for the last three decades (1990-1999, 2000-2009 and 2010-2019). Discussion points: Has incidence been increasing for most cancers? How about mortality? How has cancer risk and mortality rate changed in the past 3 decades? Has the incidence-to-mortality ratio been decreasing generally? Is it statistically significant? Which cancer type has the highest incidence in children (0-19) when compared with the elderly (65+). Is this statistically significantly different to incidence of children versus elderly cancers in general? 6.2.3.1 Download cancer registry data HKCancer<- read.table("https://github.com/StatBiomed/BMDS-book/raw/main/notebooks/module5-epidemi/HK_cancer_incidence_mortality_1990-2020.txt", sep = "\\t", header = TRUE, stringsAsFactor=FALSE) HKCancer #> Type Sex Age Year Biliary Bladder Brain Breast Cervix #> 1 incidence male 0-19 1990-1999 0 6 212 0 0 #> 2 incidence male 20-44 1990-1999 36 165 368 6 0 #> 3 incidence male 45-64 1990-1999 322 1266 393 28 0 #> 4 incidence male 65+ 1990-1999 830 2920 318 35 0 #> 5 incidence male 0-19 2000-2009 0 2 206 0 0 #> 6 incidence male 20-44 2000-2009 31 103 267 9 0 #> 7 incidence male 45-64 2000-2009 297 900 372 52 0 #> 8 incidence male 65+ 2000-2009 1134 3103 299 96 0 #> 9 incidence male 0-19 2010-2019 0 2 155 0 0 #> 10 incidence male 20-44 2010-2019 28 18 276 14 0 #> 11 incidence male 45-64 2010-2019 530 648 511 72 0 #> 12 incidence male 65+ 2010-2019 1568 2399 380 107 0 #> 13 incidence female 0-19 1990-1999 0 2 149 6 0 #> 14 incidence female 20-44 1990-1999 52 62 283 4228 1263 #> 15 incidence female 45-64 1990-1999 246 246 238 5311 1839 #> 16 incidence female 65+ 1990-1999 909 1199 284 4122 1574 #> 17 incidence female 0-19 2000-2009 0 0 132 4 4 #> 18 incidence female 20-44 2000-2009 34 39 223 5887 1204 #> 19 incidence female 45-64 2000-2009 287 179 265 11833 1676 #> 20 incidence female 65+ 2000-2009 1190 1090 237 5774 1329 #> 21 incidence female 0-19 2010-2019 0 2 119 1 0 #> 22 incidence female 20-44 2010-2019 36 17 223 6658 1294 #> 23 incidence female 45-64 2010-2019 452 188 394 22096 2288 #> 24 incidence female 65+ 2010-2019 1500 872 283 10337 1269 #> 25 mortality male 0-19 1990-1999 0 0 76 0 0 #> Colorectum Eye Hodgkin.lymphoma Kaposi Kidney Larynx Leukaemia Liver Lung #> 1 15 29 35 0 24 1 356 58 6 #> 2 1087 8 64 0 141 86 509 1741 1036 #> 3 4644 9 47 0 530 824 453 5702 8420 #> 4 8058 21 41 0 721 1265 623 4806 15152 #> 5 5 38 32 0 20 0 329 33 3 #> 6 957 8 119 9 221 37 437 1116 772 #> 7 6418 18 77 4 1071 658 642 6015 7909 #> 8 13352 13 77 8 1275 1125 864 5808 18870 #> 9 6 26 53 0 31 0 338 24 2 #> 10 1005 10 163 25 293 29 401 659 541 #> 11 10034 28 87 18 1978 684 1033 6500 9523 #> 12 18003 15 114 12 1977 1043 1381 6716 20588 #> 13 2 33 10 0 25 0 296 37 7 #> 14 932 11 61 0 98 13 393 353 597 #> 15 3166 9 19 0 256 59 312 1093 2629 #> 16 7728 16 33 0 503 130 534 2244 8428 #> 17 5 14 30 0 15 0 219 13 5 #> 18 938 12 122 2 120 5 405 212 625 #> 19 4430 10 33 0 482 37 469 1041 3397 #> 20 10810 8 35 0 830 85 723 2637 9517 #> 21 5 14 32 0 19 0 237 21 0 #> 22 1057 9 174 0 135 7 443 168 647 #> 23 7200 16 58 4 887 47 786 1168 6312 #> 24 13119 20 58 5 1203 84 913 3007 10872 #> 25 10 3 0 0 2 0 120 36 1 #> Melanoma Mesothelioma Multiple.myeloma Nasal Nasopharynx #> 1 3 0 1 4 33 #> 2 50 0 43 53 3049 #> 3 82 0 210 109 3724 #> 4 107 0 421 120 1212 #> 5 1 0 1 7 16 #> 6 41 5 38 38 2114 #> 7 98 34 326 161 3750 #> 8 137 77 674 137 1177 #> 9 0 0 0 3 21 #> 10 47 4 26 41 1339 #> 11 186 51 525 174 3653 #> 12 195 154 910 154 1207 #> 13 2 0 1 1 13 #> 14 44 0 19 31 1380 #> 15 58 0 150 57 1157 #> 16 102 0 417 93 536 #> 17 2 0 0 5 6 #> 18 41 8 21 36 942 #> 19 102 12 185 74 1187 #> 20 118 14 559 92 465 #> 21 3 0 0 6 6 #> 22 70 10 24 37 583 #> 23 128 34 399 105 1145 #> 24 179 24 636 102 389 #> 25 2 0 0 0 6 #> Non.Hodgkin.lymphoma Non.melanoma.skin Oesophagus Oral Ovary Pancreas Penis #> 1 137 8 0 18 0 4 2 #> 2 596 211 158 324 0 106 19 #> 3 997 613 1873 1165 0 514 78 #> 4 1210 968 2203 1055 0 887 184 #> 5 111 4 0 6 0 1 0 #> 6 454 220 82 312 0 71 18 #> 7 1216 917 1528 1354 0 780 71 #> 8 1756 1835 2126 1416 0 1440 199 #> 9 106 1 0 9 0 1 0 #> 10 487 291 54 304 0 109 21 #> 11 1910 1782 1321 1941 0 1402 154 #> 12 2633 2975 1982 1813 0 2302 279 #> 13 75 2 0 21 77 2 0 #> 14 444 156 48 241 854 60 0 #> 15 594 374 254 364 970 290 0 #> 16 1120 1249 761 485 776 875 0 #> 17 43 4 1 20 108 0 0 #> 18 461 182 13 254 1299 62 0 #> 19 888 580 190 454 1846 461 0 #> 20 1409 2476 725 768 891 1329 0 #> 21 57 4 0 9 84 3 0 #> 22 475 303 16 259 1466 92 0 #> 23 1561 1168 174 920 3166 923 0 #> 24 1883 3268 624 1091 1128 2101 0 #> 25 25 0 0 1 0 3 0 #> Placenta Prostate Sarcoma Small.intestine Stomach Testis Thymus Thyroid #> 1 0 0 137 0 5 61 12 22 #> 2 0 10 320 31 429 275 71 250 #> 3 0 481 281 102 1990 80 70 288 #> 4 0 3073 261 131 3634 129 59 194 #> 5 0 0 118 0 6 57 16 21 #> 6 0 6 307 34 331 395 96 333 #> 7 0 1588 367 113 1954 65 137 447 #> 8 0 8676 316 163 4256 44 94 278 #> 9 0 1 110 1 2 53 23 17 #> 10 0 18 299 46 192 587 68 496 #> 11 0 4128 527 293 2158 100 137 834 #> 12 0 14703 507 336 4720 15 85 424 #> 13 1 0 128 1 1 0 3 80 #> 14 13 0 283 22 492 0 38 1294 #> 15 0 0 232 53 826 0 38 788 #> 16 0 0 232 116 2319 0 38 479 #> 17 0 0 105 0 2 0 1 81 #> 18 8 0 322 24 379 0 64 1659 #> 19 3 0 300 80 971 0 116 1570 #> 20 0 0 247 153 2512 0 65 595 #> 21 0 0 86 1 0 0 5 103 #> 22 12 0 330 38 318 0 30 2291 #> 23 3 0 509 221 1653 0 81 3200 #> 24 0 0 351 251 2809 0 67 940 #> 25 0 0 41 1 0 2 7 1 #> Uterus Vulva #> 1 0 0 #> 2 0 0 #> 3 0 0 #> 4 0 0 #> 5 0 0 #> 6 0 0 #> 7 0 0 #> 8 0 0 #> 9 0 0 #> 10 0 0 #> 11 0 0 #> 12 0 0 #> 13 2 1 #> 14 478 49 #> 15 1362 96 #> 16 729 235 #> 17 2 2 #> 18 821 44 #> 19 3071 147 #> 20 1130 336 #> 21 3 1 #> 22 1165 57 #> 23 6582 235 #> 24 1864 486 #> 25 0 0 #> [ reached 'max' / getOption("max.print") -- omitted 23 rows ] 6.2.3.2 a. Visualise changes in incidence and mortality # first plot incidence for each cancer type #Import the necessary packages and libraries if (!require("ggplot2")) install.packages("gglot2") if (!require("gridExtra")) install.packages("gridExtra") if (!require("ggpubr")) install.packages("ggpubr") library(ggplot2) library(gridExtra) library(ggpubr) HKCancer_inc <- HKCancer[HKCancer$Type=='incidence',] p<-list() for (i in 1:(ncol(HKCancer_inc)-4)){ p[[i]]<-ggplot(HKCancer_inc,aes_string(fill=names(HKCancer_inc)[3],x=names(HKCancer_inc)[4], y=names(HKCancer_inc)[i+4],group=names(HKCancer_inc)[3]))+ geom_bar(position="dodge",stat="identity")+ facet_wrap(~Sex) + theme_classic()+ scale_fill_brewer(palette="Spectral")+ theme(legend.position="none")+ theme(text = element_text(size = 10))+ ggtitle(names(HKCancer_inc)[i+4])+ ylab("Incidence") } do.call('grid.arrange',c(p,ncol=3,nrow=12)) # now plot mortality for each cancer type #Import the necessary packages and libraries if (!require("ggplot2")) install.packages("gglot2") if (!require("gridExtra")) install.packages("gridExtra") if (!require("ggpubr")) install.packages("ggpubr") if (!require("dplyr")) install.packages("dplyr") library(ggplot2) library(gridExtra) library(ggpubr) library(dplyr) HKCancer_mort <- HKCancer[HKCancer$Type=='mortality',] p<-list() #(ncol(HKCancer_inc)-4) for (i in 1:(ncol(HKCancer_mort)-4)){ p[[i]]<-ggplot(HKCancer_mort,aes_string(fill=names(HKCancer_mort)[3],x=names(HKCancer_mort)[4], y=names(HKCancer_mort)[i+4],group=names(HKCancer_mort)[3]))+ geom_bar(position="dodge",stat="identity")+ facet_wrap(~Sex) + theme_classic()+ scale_fill_brewer(palette="Spectral")+ theme(legend.position="none")+ theme(text = element_text(size = 10))+ ggtitle(names(HKCancer_mort)[i+4])+ ylab("Mortality") } do.call('grid.arrange',c(p,ncol=3,nrow=12)) 6.2.3.3 b. Calculate cancer risk and mortality rate To calculate disease risk we need to calculated the number of new cases over the number of persons at risk over a specific time period. We have the incidence for each decade and can estimate the number of persons at risk based on the population in 1995, 2005 and 2015. # cancer risk calculation HKCancer_inc <- HKCancer[HKCancer$Type=='incidence',] HKCancer_inc_risk <- HKCancer_inc[,1:5] risk <- function(x,age,sex,year){ if (is.integer(x)){ pop_n <- HKPop %>% filter(Sex==sex & Age==age) if (year == "1990-1999"){ return (as.double(x)/as.double(pop_n$`1995`)) } else if (year == "2000-2009") {return (as.double(x)/as.double(pop_n$`2005`))} else { return (as.double(x)/as.double(pop_n$`2015`)) } } return (x) } for (i in 5:ncol(HKCancer_inc)){ HKCancer_inc_risk[names(HKCancer_inc)[i]] <- mapply(risk,HKCancer_inc[,i],HKCancer_inc[,3],HKCancer_inc[,2],HKCancer_inc[,4]) } #visualise cancer risk p<-list() for (i in 1:(ncol(HKCancer_inc_risk)-4)){ p[[i]]<-ggplot(HKCancer_inc_risk,aes_string(fill=names(HKCancer_inc_risk)[3],x=names(HKCancer_inc_risk)[4], y=names(HKCancer_inc_risk)[i+4],group=names(HKCancer_inc_risk)[3]))+ geom_bar(position="dodge",stat="identity")+ facet_wrap(~Sex) + theme_classic()+ scale_fill_brewer(palette="Spectral")+ theme(legend.position="none")+ theme(text = element_text(size = 10))+ ggtitle(names(HKCancer_inc)[i+4])+ ylab("incidence per 1000") } do.call('grid.arrange',c(p,ncol=3,nrow=12)) # mortality rate calculation HKCancer_mort <- HKCancer[HKCancer$Type=='mortality',] HKCancer_mort_risk <- HKCancer_mort[,1:5] risk <- function(x,age,sex,year){ if (is.integer(x)){ pop_n <- HKPop %>% filter(Sex==sex & Age==age) if (year == "1990-1999"){ return (as.double(x)/as.double(pop_n$`1995`)) } else if (year == "2000-2009") {return (as.double(x)/as.double(pop_n$`2005`))} else { return (as.double(x)/as.double(pop_n$`2015`)) } } return (x) } for (i in 5:ncol(HKCancer_mort)){ HKCancer_mort_risk[names(HKCancer_mort)[i]] <- mapply(risk,HKCancer_mort[,i],HKCancer_mort[,3],HKCancer_mort[,2],HKCancer_mort[,4]) } #visualise mortality rate p<-list() for (i in 1:(ncol(HKCancer_mort_risk)-4)){ p[[i]]<-ggplot(HKCancer_mort_risk,aes_string(fill=names(HKCancer_mort_risk)[3],x=names(HKCancer_mort_risk)[4], y=names(HKCancer_mort_risk)[i+4],group=names(HKCancer_mort_risk)[3]))+ geom_bar(position="dodge",stat="identity")+ facet_wrap(~Sex) + theme_classic()+ scale_fill_brewer(palette="Spectral")+ theme(legend.position="none")+ theme(text = element_text(size = 10))+ ggtitle(names(HKCancer_inc)[i+4])+ ylab("mortality per 1000") } do.call('grid.arrange',c(p,ncol=3,nrow=12)) 6.2.3.4 c. Mortality-incidence ratio Cancer research can be focused on improving cancer outcomes in a number of ways. For example cancer prevention research that seeks to reduce cancer incidence which would also ultimately reduce cancer mortality. Another area is cancer therapy which would not affect incidence but seeks to reduce mortality, or at least prolong survival. We don’t go into survival analysis in this tutorial, but a way to get an idea whether treatment is improving by looking at the mortality-incidence ratio. HKCancer_inc <- HKCancer[HKCancer$Type=='incidence',] HKCancer_mort <- HKCancer[HKCancer$Type=='mortality',] HKCancer_mort_inc <- HKCancer_mort[,1:5] risk <- function(mort,inc){ if (inc == 0){ return (0) } return (as.double(mort)/as.double(inc)) } for (i in 5:ncol(HKCancer_mort)){ HKCancer_mort_inc[names(HKCancer_mort)[i]] <- mapply(risk,HKCancer_mort[,i],HKCancer_inc[,i]) } #visualise mortality incidence ratio p<-list() for (i in 1:(ncol(HKCancer_mort_inc)-4)){ p[[i]]<-ggplot(HKCancer_mort_inc,aes_string(fill=names(HKCancer_mort_inc)[3],x=names(HKCancer_mort_inc)[4], y=names(HKCancer_mort_inc)[i+4],group=names(HKCancer_mort_inc)[3]))+ geom_bar(position="dodge",stat="identity")+ facet_wrap(~Sex) + theme_classic()+ scale_fill_brewer(palette="Spectral")+ theme(legend.position="none")+ theme(text = element_text(size = 10))+ ggtitle(names(HKCancer_inc)[i+4])+ ylab("mortality-incidence ratio") } do.call('grid.arrange',c(p,ncol=3,nrow=12)) 6.2.3.5 d. Paired t-test on mortality-incidence ratio change In general, across the different cancer types is cancer treatment improving? We can use a paired t-test comparing the mortality-incidence ratio of cancers from the 1990-1999 period with the 2010-2019 period. #First sum up all incidence and mortality data for each cancer type across age and sex HKCancer_inc_sum <- aggregate(HKCancer_inc[,-(1:4)],list(HKCancer_inc$Year),FUN=sum) HKCancer_mort_sum <- aggregate(HKCancer_mort[,-(1:4)],list(HKCancer_mort$Year),FUN=sum) HKCancer_mort_inc_year <- data.frame(Year=HKCancer_mort_sum[,1]) risk <- function(mort,inc){ return (as.double(mort)/as.double(inc)) } for (i in 2:ncol(HKCancer_mort_sum)){ HKCancer_mort_inc_year[names(HKCancer_mort_sum)[i]] <- mapply(risk,HKCancer_mort_sum[,i],HKCancer_inc_sum[,i]) } HKCancer_mort_inc_year_m <-data.frame(`1990-1999` = as.numeric(HKCancer_mort_inc_year[1,2:37]), `2000-2009` = as.numeric(HKCancer_mort_inc_year[2,2:37]), `2010-2019` = as.numeric(HKCancer_mort_inc_year[3,2:37]), check.names = FALSE) HKCancer_mort_inc_year_m$Cancer <- names(HKCancer_mort_inc_year)[-1] HKCancer_mort_inc_year_t<-as_tibble(HKCancer_mort_inc_year_m) %>% select(`Cancer`,`1990-1999`,`2000-2009`,`2010-2019`)%>% gather (key="Year",value="MIR",-Cancer) plot<-ggplot(HKCancer_mort_inc_year_t,aes(x=Year,y=MIR, color=Year))+ geom_boxplot(na.rm=T) + theme_classic()+ scale_color_brewer(palette="Dark2")+ geom_jitter(shape=16, position=position_jitter(0.2),na.rm=T)+ ylab("mortality-incidence ratio")+ ylim(0,1.2) + geom_signif(comparisons = list(c("1990-1999", "2010-2019")), map_signif_level=F, test= "t.test",test.args = list(paired = TRUE), na.rm = T, y_position = c(1.1, 1.3)) + geom_signif(comparisons = list(c("1990-1999", "2000-2009")), map_signif_level=F, test= "t.test",test.args = list(paired = TRUE), na.rm = T) + geom_signif(comparisons = list(c("2000-2009", "2010-2019")), map_signif_level=F, test= "t.test",test.args = list(paired = TRUE), na.rm = T) plot p<-list() p[[1]]<-t.test(HKCancer_mort_inc_year_m$`1990-1999`,HKCancer_mort_inc_year_m$`2000-2009`,paired=TRUE,alternative_m = "two.sided") p[[2]]<-t.test(HKCancer_mort_inc_year_m$`1990-1999`,HKCancer_mort_inc_year_m$`2010-2019`,paired=TRUE,alternative_m = "two.sided") p[[3]]<-t.test(HKCancer_mort_inc_year_m$`2000-2009`,HKCancer_mort_inc_year_m$`2010-2019`,paired=TRUE,alternative_m = "two.sided") p #> [[1]] #> #> Paired t-test #> #> data: HKCancer_mort_inc_year_m$`1990-1999` and HKCancer_mort_inc_year_m$`2000-2009` #> t = 0.79695, df = 33, p-value = 0.4312 #> alternative hypothesis: true mean difference is not equal to 0 #> 95 percent confidence interval: #> -0.01415536 0.03238636 #> sample estimates: #> mean difference #> 0.009115498 #> #> #> [[2]] #> #> Paired t-test #> #> data: HKCancer_mort_inc_year_m$`1990-1999` and HKCancer_mort_inc_year_m$`2010-2019` #> t = 2.1861, df = 33, p-value = 0.036 #> alternative hypothesis: true mean difference is not equal to 0 #> 95 percent confidence interval: #> 0.002055217 0.057209924 #> sample estimates: #> mean difference #> 0.02963257 #> #> #> [[3]] #> #> Paired t-test #> #> data: HKCancer_mort_inc_year_m$`2000-2009` and HKCancer_mort_inc_year_m$`2010-2019` #> t = 1.5413, df = 35, p-value = 0.1322 #> alternative hypothesis: true mean difference is not equal to 0 #> 95 percent confidence interval: #> -0.005438019 0.039734021 #> sample estimates: #> mean difference #> 0.017148 6.2.3.6 e. Childhood versus elderly cancers Although it is clear that the incidence of cancer is typically higher in the elderly, some cancers affect children as well. What cancer types disproportionate affect children? For each cancer type, compare the proportion of 0-19 versus 65+ incidence against the 0-19 versus 65+ incidence for all other cancer types. # Examine the proportion of childhood HKCancer_inc <- HKCancer[HKCancer$Type=='incidence',] HKCancer_inc_age_sum <- aggregate(HKCancer_inc[,-(1:4)],list(HKCancer_inc$Age),FUN=sum) HKCancer_inc_age_sum$Total<- rowSums(HKCancer_inc_age_sum[,-1]) HKCancer_inc_age_sum_csq <- data.frame(cancer=names(HKCancer_inc_age_sum[,2:ncol(HKCancer_inc_age_sum)])) pval <- list() ratio <- list() for (i in 2:(ncol(HKCancer_inc_age_sum))){ val = Map('-',HKCancer_inc_age_sum$Total,HKCancer_inc_age_sum[,i]) dat <- data.frame(cancer=HKCancer_inc_age_sum[c(1,4),i], other =c(val[[1]],val[[2]])) pval <- append(pval,fisher.test(dat)$p.val) ratio<- append(ratio,log(as.double(dat[1,1]+0.1)/as.double(dat[2,1]+0.1))) } HKCancer_inc_age_sum_csq$pval <- pval HKCancer_inc_age_sum_csq$ratio <- ratio plot_child<-ggplot(HKCancer_inc_age_sum_csq[-37,],aes(x=reorder(cancer,-as.numeric(ratio)),y=as.numeric(ratio)),fill=cancer)+ geom_bar(stat="identity",fill="red")+ geom_hline(yintercept=-4.12132318942113, linetype="dashed", color = "black", linewidth=0.5)+ theme_classic()+ scale_fill_brewer(palette="Spectral")+ theme(legend.position="none")+ #theme(text = element_text(size = 10))+ theme(axis.title.x=element_blank())+ theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + ylab("log((child+0.1)/(elderly+0.1)") plot_child HKCancer_inc_age_sum_csq #> cancer pval ratio #> 1 Biliary 1.21618e-216 -11.17481 #> 2 Bladder 9.601084e-314 -6.711128 #> 3 Brain 0 -0.615666 #> 4 Breast 0 -7.519824 #> 5 Cervix 1.635299e-126 -6.925188 #> 6 Colorectum 0 -7.531208 #> 7 Eye 2.085337e-112 0.5039276 #> 8 Hodgkin.lymphoma 3.658856e-81 -0.6227962 #> 9 Kaposi 0.41662 -5.525453 #> 10 Kidney 1.560619e-71 -3.882371 #> 11 Larynx 1.963055e-113 -8.129416 #> 12 Leukaemia 0 -1.043172 #> 13 Liver 0 -4.909033 #> 14 Lung 0 -8.191896 #> 15 Melanoma 6.246412e-15 -4.324192 #> 16 Mesothelioma 5.396388e-09 -7.897668 #> 17 Multiple.myeloma 6.126613e-106 -7.062026 #> 18 Nasal 0.0001285609 -3.286427 #> 19 Nasopharynx 4.628193e-74 -3.95948 #> 20 Non.Hodgkin.lymphoma 4.513518e-11 -2.940272 #> 21 Non.melanoma.skin 0 -6.315107 #> 22 Oesophagus 1.377432e-251 -8.943186 #> 23 Oral 5.100139e-108 -4.379029 #> 24 Ovary 0.0002847234 -2.34054 #> 25 Pancreas 3.11451e-246 -6.690686 #> 26 Penis 3.023792e-18 -5.753479 #> 27 Placenta 0.07013259 2.397895 #> 28 Prostate 0 -10.08778 #> 29 Sarcoma 9.774701e-213 -1.028899 #> 30 Small.intestine 1.479593e-31 -5.916202 #> 31 Stomach 0 -7.137096 #> 32 Testis 1.871184e-97 -0.09472556 #> 33 Thymus 6.748934e-06 -1.915502 #> 34 Thyroid 7.821825e-09 -2.194891 #> 35 Uterus 1.457974e-105 -6.262217 #> 36 Vulva 1.521831e-27 -5.552298 #> 37 Total 1 -4.121323 6.2.4 Existing cancer funding and publication data The Hong Kong government established in Health and Medical Research Fund (HMRF) in 2011 to specifically provide research funding for health and medical research in Hong Kong. Since 2016 over 370 projects in the category of Cancer has been funded for a total of ~$400 M dollars. A list of all funded projects can be found on the Health Bureau webpage. You would like to use this data to see if there is any association between previous project funding and the epidemiology of cancers in Hong Kong. We can also do a similar thing with publications and ask if the research publications in Hong Kong have been aligned with the incidence and mortality. We can obtain this data from PubMed using the following terms: (“Hong Kong”[Affiliation]) AND (neoplasms[MeSH Terms]) The data has been predownloaded as the Pubmed API via R is a bit slow. Discussion points: Why has research publications increased dramatically in recent years? Is there something unsual with the dataset? What are the main cancer types being researched in Hong Kong? Is there any correlation between funding and cancer incidence and mortality? 6.2.4.1 Download HMRF grants and Pubmed data HMRF<- read.delim("https://github.com/StatBiomed/BMDS-book/raw/main/notebooks/module5-epidemi/HMRF_cancer_grants.txt", sep = "\\t", header = TRUE, stringsAsFactor=FALSE, check.names = TRUE) #HMRF pubmed<- read.delim("https://github.com/StatBiomed/BMDS-book/raw/main/notebooks/module5-epidemi/pubmed__neoplasm_hongkong.txt", sep = "\\t", header = TRUE, stringsAsFactor=FALSE, check.names = TRUE) #pubmed hist(pubmed$Publication.Year) 6.2.4.2 Make word cloud for grants 6.2.4.3 Make compare grant funding with incidence and mortality if (!require("ggrepel")) install.packages("ggrepel") if (!require("ggplot2")) install.packages("gglot2") if (!require("gridExtra")) install.packages("gridExtra") library(ggrepel) library(ggplot2) library(gridExtra) grantpmsum<- read.delim("https://github.com/StatBiomed/BMDS-book/raw/main/notebooks/module5-epidemi/Grants_pubmed_summary.txt", sep = "\\t", header = TRUE, stringsAsFactor=FALSE, check.names = TRUE) grantpmsum #> Cancer Grants Pubmed #> 1 Biliary 0 72 #> 2 Bladder 0 141 #> 3 Brain 2 294 #> 4 Breast 34 1147 #> 5 Cervix 9 408 #> 6 Colorectum 40 857 #> 7 Eye 2 27 #> 8 Hodgkin lymphoma 0 21 #> 9 Kaposi 0 6 #> 10 Kidney 0 133 #> 11 Larynx 0 8 #> 12 Leukaemia 18 515 #> 13 Liver 97 2077 #> 14 Lung 22 773 #> 15 Melanoma 0 99 #> 16 Mesothelioma 6 58 #> 17 Multiple myeloma 0 104 #> 18 Nasal 0 14 #> 19 Nasopharynx 24 1113 #> 20 Non-Hodgkin lymphoma 0 55 #> 21 Skin 0 32 #> 22 Oesophagus 7 404 #> 23 Oral 5 229 #> 24 Ovary 14 354 #> 25 Pancreas 2 141 #> 26 Penis 0 7 #> 27 Placenta 0 9 #> 28 Prostate 6 322 #> 29 Sarcoma 2 216 #> 30 Small intestine 0 11 #> 31 Stomach 5 455 #> 32 Testis 0 18 #> 33 Thymus 0 3 #> 34 Thyroid 2 231 #> 35 Uterus 0 72 #> 36 Vulva 0 8 HKCancer_inc <- HKCancer[HKCancer$Type=='incidence',] HKCancer_inc_sum <- data.frame(incidence=colSums(HKCancer_inc[,-(1:4)])) HKCancer_mort <- HKCancer[HKCancer$Type=='mortality',] HKCancer_mort_sum <- data.frame(mortality=colSums(HKCancer_inc[,-(1:4)])) HKCancer_compare <-data.frame(cancer=grantpmsum$Cancer, grants=grantpmsum$Grants, pubmed=grantpmsum$Pubmed, incidence=colSums(HKCancer_inc[,-(1:4)]), mortality=colSums(HKCancer_mort[,-(1:4)])) gp<-ggplot(HKCancer_compare, aes(y=grants, x=pubmed)) + geom_point() + scale_color_brewer(palette="Dark2") + theme_classic() + geom_label_repel(data=HKCancer_compare, aes(label=cancer), nudge_x = 2, size = 3.5,label.size = NA ,min.segment.length =unit(0, 'lines'), max.overlaps = 45, box.padding = 0.4) + stat_cor(method = "pearson", label.x = 3, label.y = 90) pinc<-ggplot(HKCancer_compare, aes(y=pubmed, x=incidence)) + geom_point() + scale_color_brewer(palette="Dark2") + theme_classic() + geom_label_repel(data=HKCancer_compare, aes(label=cancer), nudge_x = 2, size = 3.5,label.size = NA ,min.segment.length =unit(0, 'lines'), max.overlaps = 15, box.padding = 0.5)+ stat_cor(method = "pearson", label.x = 40000, label.y = 0) pmor<-ggplot(HKCancer_compare, aes(y=pubmed, x=mortality)) + geom_point() + scale_color_brewer(palette="Dark2") + theme_classic() + geom_label_repel(data=HKCancer_compare, aes(label=cancer), nudge_x = 2, size = 3.5,label.size = NA ,min.segment.length =unit(0, 'lines'), max.overlaps = 15, box.padding = 0.5) + stat_cor(method = "pearson", label.x = 40000, label.y = 0) ginc<-ggplot(HKCancer_compare, aes(y=grants, x=incidence)) + geom_point() + scale_color_brewer(palette="Dark2") + theme_classic() + geom_label_repel(data=HKCancer_compare, aes(label=cancer), nudge_x = 2, size = 3.5,label.size = NA ,min.segment.length =unit(0, 'lines'), max.overlaps = 40, box.padding = 0.4) + stat_cor(method = "pearson", label.x = 40000, label.y = 0) gmor<-ggplot(HKCancer_compare, aes(y=grants, x=mortality)) + geom_point() + scale_color_brewer(palette="Dark2") + theme_classic() + geom_label_repel(data=HKCancer_compare, aes(label=cancer), nudge_x = 2, size = 3.5,label.size = NA ,min.segment.length =unit(0, 'lines'), max.overlaps = 40, box.padding = 0.4) + stat_cor(method = "pearson", label.x = 40000, label.y = 0) grid.arrange(gp,ginc,gmor,pinc, pmor, ncol=3) 6.2.5 Open disucssion As a group discuss what cancer type would be most worthy of funding in Hong Kong. Statistics and figures should be used to support your decision. If possible also discuss other data/analyses that can be performed and/or other diseases that are also in need of funding in Hong Kong. "],["pop-genetics.html", "Chapter 7 Population Genetics and Diseases 7.1 Case study 1: Heritability and human traits", " Chapter 7 Population Genetics and Diseases 7.1 Case study 1: Heritability and human traits 7.1.1 Part 1 Scenario: You are a researcher working on a twin study on cardiovascular traits to assess the genetic and environmental contribution relevant to metabolism and cardiovascular disease risk. You have recruited a cohort of volunteer adult twins of the same ancestry. The volunteers have undergone a series of baseline clinical evaluations and performed genotyping on a panel of single nucleotide polymorphisms that may be associated with the traits. 7.1.1.1 Questions for Discussion Q1. Besides the clinical measurements, what data do you need to collect from the subjects? Answers: Sex Age Other confounding factors, e.g. BMI, blood pressure, smoking status, etc. Q2. How is genotype data represented for statistical genetic analysis? Answers: Allele: 0/1, 1/2, A/C, etc Genotype: 0 0, 0 1, 1 0, 1 1 Genotype probabilities: P(0/0)=0, P(0/1)=1, P(1/1)=0 Genotype dosage: 0/1/2, 0.678 (continuous from 0-1 or 0-2) Q3. How can you test for association between genotypes and phenotypes (binary and quantitative)? Answers: Allelic chi-square test Fisher’s exact test Linear/Logistic regression Linear mixed model 7.1.1.2 Hands-on exercise : Association test Now, you are given a dataset of age- and sex-matched twin cohort with two cardiovascular phenotypes and 5 quantitative trait loci (QTL). Data set and template notebook are available on Moodle (recommended) and also on this GitHub Repo. The information for columns: zygosity: 1 for monozygotic (MZ) and 2 for dizygotic (DZ) twin T1QTL_A[1-5] and T2QTL_A[1-5]: 5 quantitative loci (A1-A5) in additive coding for Twin 1 (T1) and Twin 2 (T2) respectively The same 5 QTL (D1-D5) in dominance coding for T1 and T2 Phenotype scores of T1 and T2 for the two quantitative cardiovascular traits Download the data dataTwin.dat to your working directory. Start the RStudio program and set the working directory. dataTwin <- read.table("dataTwin2023.dat",h=T) Exploratory analysis A1-5: The QTLs are biallelic with two alleles A and a. The genotypes aa, Aa, and AA are coded additively as 0 (aa), 1 (Aa) and 2 (AA). D1-5: The genotypes aa, Aa, and AA are coded as 0 (aa), 1 (Aa) and 0 (AA). Q1. How many MZ and DZ volunteers are there? Answers: 1000 MZ and 1000 DZ Q2. How are the genotypes represented? Answers: Dosage/Count of non-reference allele : 0, 1, and 2 Q3. Are the QTL independent of each other? Answers: Yes. The pairwise correlations are low (<0.2). Q4. Are there outliers in phenotypes? Answers: Yes. T2 individual 1303 has phenotype score (-4.21) being 4 SD below the mean. table(dataTwin$zygosity) # Q1: shows number of MZ and DZ twin pairs #> #> 1 2 #> 1000 1000 table(dataTwin$T1QTL_A1) # Q2: shows the distribution of QTL_A1 #> #> 0 1 2 #> 474 1021 505 table(dataTwin$T1QTL_D1) # Q2: shows the distribution of QTL_D1 #> #> 0 1 #> 979 1021 table(dataTwin$T1QTL_A1, dataTwin$T1QTL_D1) # Q2: shows the distribution of QTL_A1 in relation to QTL_D1 #> #> 0 1 #> 0 474 0 #> 1 0 1021 #> 2 505 0 cor(dataTwin[,2:11]) # Q3: shows the correlation between QTL_As #> T1QTL_A1 T1QTL_A2 T1QTL_A3 T1QTL_A4 T1QTL_A5 #> T1QTL_A1 1.00000000 -0.005470340 0.021705688 0.01940408 0.016278190 #> T1QTL_A2 -0.00547034 1.000000000 0.017344822 -0.01421677 -0.008678746 #> T1QTL_A3 0.02170569 0.017344822 1.000000000 0.01335711 -0.036751338 #> T1QTL_A4 0.01940408 -0.014216767 0.013357109 1.00000000 0.074899996 #> T1QTL_A5 0.01627819 -0.008678746 -0.036751338 0.07490000 1.000000000 #> T2QTL_A1 0.53243815 0.004201635 -0.013909013 0.03252724 0.020081970 #> T2QTL_A2 -0.04561174 0.464131160 -0.005044127 0.01324172 -0.003012277 #> T2QTL_A3 0.03316574 -0.003552831 0.521253656 0.02045423 0.009081830 #> T2QTL_A4 0.03271254 -0.033419904 0.020422583 0.48641289 0.019247531 #> T2QTL_A5 -0.01285323 0.030413269 -0.045121964 0.08288145 0.457962222 #> T2QTL_A1 T2QTL_A2 T2QTL_A3 T2QTL_A4 T2QTL_A5 #> T1QTL_A1 0.532438150 -0.045611740 0.033165736 0.032712539 -0.01285323 #> T1QTL_A2 0.004201635 0.464131160 -0.003552831 -0.033419904 0.03041327 #> T1QTL_A3 -0.013909013 -0.005044127 0.521253656 0.020422583 -0.04512196 #> T1QTL_A4 0.032527239 0.013241725 0.020454234 0.486412895 0.08288145 #> T1QTL_A5 0.020081970 -0.003012277 0.009081830 0.019247531 0.45796222 #> T2QTL_A1 1.000000000 0.006179257 -0.013129314 0.048294183 -0.01325839 #> T2QTL_A2 0.006179257 1.000000000 -0.020860987 0.002164782 -0.01131418 #> T2QTL_A3 -0.013129314 -0.020860987 1.000000000 -0.010583797 -0.02101270 #> T2QTL_A4 0.048294183 0.002164782 -0.010583797 1.000000000 0.04350925 #> T2QTL_A5 -0.013258394 -0.011314179 -0.021012699 0.043509251 1.00000000 cor(dataTwin[,2:11])>0.2 #> T1QTL_A1 T1QTL_A2 T1QTL_A3 T1QTL_A4 T1QTL_A5 T2QTL_A1 T2QTL_A2 #> T1QTL_A1 TRUE FALSE FALSE FALSE FALSE TRUE FALSE #> T1QTL_A2 FALSE TRUE FALSE FALSE FALSE FALSE TRUE #> T1QTL_A3 FALSE FALSE TRUE FALSE FALSE FALSE FALSE #> T1QTL_A4 FALSE FALSE FALSE TRUE FALSE FALSE FALSE #> T1QTL_A5 FALSE FALSE FALSE FALSE TRUE FALSE FALSE #> T2QTL_A1 TRUE FALSE FALSE FALSE FALSE TRUE FALSE #> T2QTL_A2 FALSE TRUE FALSE FALSE FALSE FALSE TRUE #> T2QTL_A3 FALSE FALSE TRUE FALSE FALSE FALSE FALSE #> T2QTL_A4 FALSE FALSE FALSE TRUE FALSE FALSE FALSE #> T2QTL_A5 FALSE FALSE FALSE FALSE TRUE FALSE FALSE #> T2QTL_A3 T2QTL_A4 T2QTL_A5 #> T1QTL_A1 FALSE FALSE FALSE #> T1QTL_A2 FALSE FALSE FALSE #> T1QTL_A3 TRUE FALSE FALSE #> T1QTL_A4 FALSE TRUE FALSE #> T1QTL_A5 FALSE FALSE TRUE #> T2QTL_A1 FALSE FALSE FALSE #> T2QTL_A2 FALSE FALSE FALSE #> T2QTL_A3 TRUE FALSE FALSE #> T2QTL_A4 FALSE TRUE FALSE #> T2QTL_A5 FALSE FALSE TRUE apply(dataTwin[22:25],2,function(x){ any(x < (mean(x) - 4*sd(x))) }) # Q4: any outlier < 4 SD from the mean for the two quantitative phenotypes #> pheno1_T1 pheno1_T2 pheno2_T1 pheno2_T2 #> FALSE FALSE FALSE TRUE apply(dataTwin[22:25],2,function(x){ any(x > (mean(x) + 4*sd(x))) }) # Q4: any outlier > 4 SD from the mean for the two quantitative phenotypes #> pheno1_T1 pheno1_T2 pheno2_T1 pheno2_T2 #> FALSE FALSE FALSE FALSE # remove the phenotype score of the outlier (T2) for the phenotype 2 (pheno2_T2) outlier<- which(dataTwin$pheno2_T2 < (mean(dataTwin$pheno2_T2) - 4*sd(dataTwin$pheno2_T2) )) outlier #> [1] 1303 dataTwin$pheno2_T2[outlier] #> [1] -4.21 dataTwin$pheno2_T2[outlier] <- NA Association test Test for association between QTL and pheno1 for T1 Regress pheno1_T1 on T1QTL_A1 to estimate the proportion of variance explained (R2). Model: pheno1_T1 = b0 + b1* T1QTL_A1 + e Calculate the conditional mean of phenotype (i.e. phenotypic mean conditional genotype) If the relationship between the QTL and the phenotype is perfectly linear, the regression line should pass through the conditional means (c_means), and the differences between the conditional means should be about equal. Q5. What are the values of b0, b1? Is QTL1 significant associated with the phenotype at alpha<0.01 (multiple testing of 5 loci)? Answers: b0 = 4.1464 b1 = 0.9180 QTL1 is significantly associated with the phenotype with \\(P = 1.02\\times 10^{-13}\\) Q6. What is the proportion of phenotypic variance explained? Answers: Proportion of phenotypic variance explained = 0.027 linA1 <- lm(pheno1_T1~T1QTL_A1, data=dataTwin) summary(linA1) #> #> Call: #> lm(formula = pheno1_T1 ~ T1QTL_A1, data = dataTwin) #> #> Residuals: #> Min 1Q Median 3Q Max #> -15.1225 -2.4435 0.1105 2.7775 12.2555 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 4.1464 0.1511 27.438 < 2e-16 *** #> T1QTL_A1 0.9180 0.1226 7.491 1.02e-13 *** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Residual standard error: 3.834 on 1998 degrees of freedom #> Multiple R-squared: 0.02732, Adjusted R-squared: 0.02683 #> F-statistic: 56.11 on 1 and 1998 DF, p-value: 1.022e-13 summary(linA1)$r.squared # proportion of explained variance by additive component #> [1] 0.02731675 c_means <- by(dataTwin$pheno1_T1,dataTwin$T1QTL_A1,mean) plot(dataTwin$pheno1_T1 ~ dataTwin$T1QTL_A1, col='grey', ylim=c(3,7)) lines(c(0,1,2), c_means, type="p", col=6, lwd=8) lines(sort(dataTwin$T1QTL_A1),sort(linA1$fitted.values), type='b', col="dark green", lwd=3) To test for the non-linearity, we can use the dominance coding of the QTL and add the dominance term to the regression model. Model: pheno1_T1 = b0 + b1* T1QTL_A1 + b2* T1QTL_D1 + e Repeat for T2. Q7. Why can’t we analyse T1 and T2 together? Answers: As T1 and T2 are biologically related as MZ or DZ twins, the genotypes of the QTLs are not independent. Treating the genotypes of T1 and T2 as independent observations will introduce bias. Q8. Is there a dominance effect? Answers: Yes. The model with dominance provides a better goodness of fit (lower p-value) linAD1 <- lm(pheno1_T1 ~ T1QTL_A1 + T1QTL_D1, data=dataTwin) summary(linAD1) # results lm(phenoT1~T1QTL_A1+T1QTL_D1) #> #> Call: #> lm(formula = pheno1_T1 ~ T1QTL_A1 + T1QTL_D1, data = dataTwin) #> #> Residuals: #> Min 1Q Median 3Q Max #> -14.7524 -2.5131 0.0586 2.8215 11.8894 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 3.7522 0.1753 21.405 < 2e-16 *** #> T1QTL_A1 0.9301 0.1220 7.622 3.83e-14 *** #> T1QTL_D1 0.7483 0.1708 4.382 1.24e-05 *** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Residual standard error: 3.816 on 1997 degrees of freedom #> Multiple R-squared: 0.03658, Adjusted R-squared: 0.03562 #> F-statistic: 37.91 on 2 and 1997 DF, p-value: < 2.2e-16 plot(dataTwin$pheno1_T1 ~ dataTwin$T1QTL_A1, col='grey', ylim=c(3,7)) abline(linA1, lwd=3) lines(c(0,1,2), c_means, type='p', col=6, lwd=8) lines(sort(dataTwin$T1QTL_A1),sort(linA1$fitted.values), type='b', col="dark green", lwd=3) lines(sort(dataTwin$T1QTL_A1),sort(linAD1$fitted.values), type='b', col="blue", lwd=3) Q9. Repeat for the other 4 QTL and determine which QTL shows strongest association with the phenotype T1 Answers: QTL3 with \\(P = 7.77 \\times 10^{-25}\\) for model with dominance allQTL_A_T1 <- 2:6 cpheno1_T1 <- which(colnames(dataTwin)=="pheno1_T1") ## Additive cbind(lapply(allQTL_A_T1,function(x){ fstat<- summary(lm(pheno1_T1 ~ ., data=dataTwin[,c(x,cpheno1_T1)]))$fstatistic; pf(fstat[1],fstat[2],fstat[3],lower.tail = F) })) #> [,1] #> [1,] 1.021942e-13 #> [2,] 8.329416e-15 #> [3,] 1.007527e-13 #> [4,] 4.523758e-18 #> [5,] 8.207842e-13 ## Dominance cbind(lapply(allQTL_A_T1,function(x){ fstat<- summary(lm(pheno1_T1 ~ ., data=dataTwin[,c(x,x+10,cpheno1_T1)]))$fstatistic; pf(fstat[1],fstat[2],fstat[3],lower.tail = F) })) #> [,1] #> [1,] 6.907834e-17 #> [2,] 2.166957e-22 #> [3,] 7.771588e-25 #> [4,] 8.124437e-25 #> [5,] 4.312127e-21 #Q9: QTL3 shows the strongest association with P=7.771588e-25 linAD3 <- lm(pheno1_T1 ~ T1QTL_A3 + T1QTL_D3, data=dataTwin) summary(linAD3) # results lm(phenoT1~T1QTL_A1+T1QTL_D1) #> #> Call: #> lm(formula = pheno1_T1 ~ T1QTL_A3 + T1QTL_D3, data = dataTwin) #> #> Residuals: #> Min 1Q Median 3Q Max #> -14.4988 -2.5701 0.1843 2.6991 11.5974 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 3.5437 0.1684 21.038 < 2e-16 *** #> T1QTL_A3 0.9076 0.1181 7.683 2.43e-14 *** #> T1QTL_D3 1.2714 0.1692 7.515 8.55e-14 *** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Residual standard error: 3.782 on 1997 degrees of freedom #> Multiple R-squared: 0.05408, Adjusted R-squared: 0.05313 #> F-statistic: 57.09 on 2 and 1997 DF, p-value: < 2.2e-16 If the subjects with top 5% of the phenotype score are considered as cases, perform case-control association test for most significant SNP (from Q9) and interpret the result. Q10. What are the odds ratio, p-value, and 95% confidence interval (CI)? Answers: Odds ratio is 3.40 and the 95% CI is (1.65, 7.03) quant05 <- quantile(c(dataTwin$pheno1_T1,dataTwin$pheno1_T2),seq(0,1,0.05)) dataTwin$CaseT1 <- as.numeric(dataTwin$pheno1_T1>quant05[20]) dataTwin$CaseT2 <- as.numeric(dataTwin$pheno1_T2>quant05[20]) logisticAD1 <- summary(glm(CaseT1 ~ T1QTL_A3 + T1QTL_D3, data=dataTwin, family="binomial")) exp(logisticAD1$coefficients[2,1]) # odds ratio #> [1] 3.404757 exp(logisticAD1$coefficients[2,1]-1.96*logisticAD1$coefficients[2,2]) # lower 95% confidence interval #> [1] 1.648816 exp(logisticAD1$coefficients[2,1]+1.96*logisticAD1$coefficients[2,2]) # upper 95% confidence interval #> [1] 7.030723 7.1.2 Part 2 Scenario: You are asked to estimate the additive genetic variance, dominance genetic variance and/or shared environmental variance using regression-based method and a classical twin design. \\[\\begin{align*} \\text{For ADE model : }~ & \\sigma^{2}_{P} = \\sigma^{2}_{A} + \\sigma^{2}_{D} + \\sigma^{2}_{E}\\\\ \\text{For ACE model : }~ & \\sigma^{2}_{P} = \\sigma^{2}_{A} + \\sigma^{2}_{C} + \\sigma^{2}_{E}, \\quad \\text{where} \\\\ \\sigma^{2}_{P} & \\text{ is the phenotypic variance}, \\\\ \\sigma^{2}_{A} & \\text{ is additive genetic variance}, \\\\ \\sigma^{2}_{D} & \\text{ is dominance genetic variance}, \\\\ \\sigma^{2}_{C} & \\text{ is shared environmental variance, and} \\\\ \\sigma^{2}_{E} & \\text{ is unshared environmental variance.} \\end{align*}\\] For ADE model, \\[\\begin{align*} cov(MZ) = cor(MZ) & = rMZ = \\sigma^{2}_{A} + \\sigma^{2}_{D} \\\\ cov(DZ) = cor(DZ) & = rDZ = 0.5 * \\sigma^{2}_{A} + 0.25 * \\sigma^{2}_{D} \\quad \\text{ , where} \\\\ \\end{align*}\\] the coefficients 1/2 and 1/4 are based on quantitative genetic theory (Mather & Jinks, 1971). By solving the unknowns, the Falconer’s equations for the ADE model: \\[\\begin{align*} \\sigma^{2}_{A} & = 4*rDZ - rMZ \\\\ \\sigma^{2}_{D} & = 2*rMZ - 4*rDZ \\\\ \\sigma^{2}_{E} & = 1 - \\sigma^{2}_{A} - \\sigma^{2}_{D} \\\\ \\end{align*}\\] For ACE model, \\[\\begin{align*} cov(MZ) = cor(MZ) & = rMZ= \\sigma^{2}_{A} + \\sigma^{2}_{C} \\\\ cov(DZ) = cor(DZ) & = rDZ = 0.5 * \\sigma^{2}_{A} + \\sigma^{2}_{C} \\quad \\text{ , where} \\\\ \\end{align*}\\] By solving the unknowns, the Falconer’s equations for the ACE model: \\[\\begin{align*} \\sigma^{2}_{A} & = 2*(rMZ - rDZ) \\\\ \\sigma^{2}_{C} & = 2*rDZ - rMZ \\\\ \\sigma^{2}_{E} & = 1 - \\sigma^{2}_{A} - \\sigma^{2}_{C} = 1 - rMZ \\end{align*}\\] 7.1.2.1 Questions for discussions : Q1. What is missing heritability of common traits in the era of genome-wide association analysis (GWAS)? Answers: Missing heritability refers to the discrepancy between twin/family-estimated heritability and the amount of variance explained by disease/trait-associated loci identified in GWAS Q2. What are the potential sources of missing heritability? Answers: Large number of variants with small effect not reaching GWAS significance due to inadequate power of GWAS Poor detection of rarer disease/trait-associated variants by genotyping arrays Structural variants poorly captured by genotyping arrays Low power to detect gene–gene interactions Underestimation of shared environment among relatives in twin/family-based studies Suggested reading: Manolio TA, Collins FS, Cox NJ, et al. Nature. 2009 ;461(7265):747-753. doi:10.1038/nature08494 7.1.2.2 Hands-on exercise : variance explained using regression-based method Q1. What is the variance of the phenotype? Q2. Compute the explained variance attributable to the additive genetic component of the QTL with strongest association in Part 1. Q3. Compute the explained variance attributable to the dominance genetic component of the QTL with strongest association in Part 1. R2 from the regression represents the proportion of phenotypic variance explained; thus the raw explained variance component is R2 times the variance of the phenotype (var_pheno). Answers The proportion of explained variance are 0.0273 (additive) and 0.0541 (total: additive + dominance). As the predictors are uncorrelated, the proportion of explained variance by dominance = 0.0541 - 0.0273 = 0.0267 Given the phenotypic variance of 15.102, then Total genetic: 0.0541*15.102 = 0.8168 Additive genetic: 0.0273*15.102 = 0.4128 Dominance genetic: 0.0267*15.102 = 0.4040 var_pheno <- var(dataTwin$pheno1_T1) # the variance of the phenotype var_pheno #> [1] 15.10257 linAD3 <- lm(pheno1_T1 ~ T1QTL_A3 + T1QTL_D3, data=dataTwin) linA3 <- lm(pheno1_T1 ~ T1QTL_A3, data=dataTwin) summary(linAD3)$r.squared # proportion of explained variance by total genetic component #> [1] 0.05408025 summary(linA3)$r.squared # proportion of explained variance by additive component #> [1] 0.02733034 summary(linAD3)$r.squared*var_pheno # (raw) variance component of total genetic component #> [1] 0.8167509 summary(linA3)$r.squared*var_pheno # (raw) variance component of additive genetic component #> [1] 0.4127585 (summary(linAD3)$r.squared-summary(linA3)$r.squared)*var_pheno # (raw) variance component of dominance genetic component #> [1] 0.4039924 Q4. Estimate the variance explained by all the QTL using linear regression. Answers Proportion of variance explained by all 5 QTLs with dominance = 0.23 and the total variance explained = 3.52. # compute for all 5 QTL linAD5=(lm(pheno1_T1 ~ T1QTL_A1 + T1QTL_A2 + T1QTL_A3 + T1QTL_A4 + T1QTL_A5 + T1QTL_D1 + T1QTL_D2 + T1QTL_D3 + T1QTL_D4 + T1QTL_D5, data=dataTwin)) summary(linAD5)$r.squared # proportion of explained variance by total genetic component #> [1] 0.2330307 summary(linAD5)$r.squared*var_pheno # (raw) variance component of total genetic component #> [1] 3.519363 7.1.2.3 Hands-on exercise : variance explained using a classical twin design. Based on our regression results, we have estimates of the total genetic variance as well as the A and D components for phenotype 1. In practice, it is impossible to know all the variants associated with any polygenic trait. Given rMZ > 2*rDZ, we can use Falconer’s formula based on ADE model to estimate the A (additive genetic) and D (dominance) variance with the classical twin design for phenotype 1 without genotypes. Q5. Compute rMZ and rDZ. Answers rMZ = 0.5434 rDZ = 0.1904 Q6. Estimate the proportion of additive and dominance genetic variances using the Falconer’s equations for the ADE model. Answers \\(\\sigma^{2}_{A} = 0.2181\\) \\(\\sigma^{2}_{D} = 0.3253\\) \\(\\sigma^{2}_{E} = 0.4566\\) dataMZ = dataTwin[dataTwin$zygosity==1, c('pheno1_T1', 'pheno1_T2')] # MZ data frame dataDZ = dataTwin[dataTwin$zygosity==2, c('pheno1_T1', 'pheno1_T2')] # DZ data frame rMZ=cor(dataMZ)[2,1] # element 2,1 in the MZ correlation matrix rDZ=cor(dataDZ)[2,1] # element 2,1 in the DZ correlation matrix rMZ rDZ sA2 = 4*rDZ - rMZ sD2 = 2*rMZ - 4*rDZ sE2 = 1 - sA2 - sD2 print(c(sA2, sD2, sE2)) Similarly, for phenotype 2, we can estimate the proportion of additive and/or dominance genetic variances as well as shared environmental variance using the Falconer’s formula. Q7. Which model (ACE or ADE) should be considered for phenotype 2? Answers ACE as rMZ < 2*rDZ Q8. Estimate the proportion of A, C/D and E variance components for phenotype 2. Answers \\(\\sigma^{2}_{A} = 0.3526\\) \\(\\sigma^{2}_{C} = 0.1610\\) \\(\\sigma^{2}_{E} = 0.4864\\) dataMZ = dataTwin[dataTwin$zygosity==1, c('pheno2_T1', 'pheno2_T2')] # MZ data frame dataDZ = dataTwin[dataTwin$zygosity==2, c('pheno2_T1', 'pheno2_T2')] # DZ data frame rMZ=cor(dataMZ, use="complete.obs")[2,1] # element 2,1 in the MZ correlation matrix rDZ=cor(dataDZ, use="complete.obs")[2,1] # element 2,1 in the DZ correlation matrix rMZ rDZ sA2 = 2*(rMZ - rDZ) sC2 = 2*rDZ - rMZ sE2 = 1 - rMZ print(c(sA2, sC2, sE2)) 7.1.3 References Evans DM, Gillespie NA, Martin NG. Biometrical genetics. Biol Psychol. 2002 Oct;61(1-2):33-51. doi: 10.1016/s0301-0511(02)00051-0. PMID: 12385668. [Review article] Falconer, D.S. and Mackay, T.F.C. (1996) Introduction to Quantitative Genetics. 4th Edition, Addison Wesley Longman, Harlow. [Most classical; a lot of online version] Neale, B., Ferreira, M., Medland, S., & Posthuma, D. (Eds.). (2007). Statistical Genetics: Gene Mapping Through Linkage and Association (1st ed.). Taylor & Francis. https://doi.org/10.1201/9780203967201 [chapter on biometrical genetics; can be borrowed from HKU lib] https://ibg.colorado.edu/cdrom2020/dolan/biometricalGenetics/biom_gen_2020.pdf [Course material of the Boulder IBG workshop co-organized by top statistical geneticists] "],["install.html", "Appendix A: Install R & RStudio A.1 Install R (>=4.3.1) A.2 Install RStudio A.3 Use R inside RStudio A4. Cloud computing", " Appendix A: Install R & RStudio This manual covers the installation of both R and RStudio for three different operating systems: Windows, macOS and Ubuntu. You only need to follow the one that you are using on your computer. Differences between R and RStudio R is the backbone of R programming. Once R is installed, you can use it via its built-in R Console (self-contained), terminal or any third-party integrated development environment (IDE), e.g., RStudio. RStudio is a multi-faceted and user-friendly IDE that can make R programming and data analysis in one place and easy to manage. We recommend using RStudio and only demonstrate with it, while you are free to use any other alternative. Acknowledgements This manual is adapted and updated from the materials produced by Xiunan Fang and other team members in Dr Joshua Ho’s lab. A.1 Install R (>=4.3.1) R on Windows Open an internet browser and go to https://cran.r-project.org/. Click on the Download R for Windows link at the top of the page. Choose the base and then Click on the Download R 4.4.1 for Windows link at the top of the page (or a new version if this manual is outdated). Once the download is finished, you will obtain a file named R-4.4.1-win.exe or similar depending on the version that you download. Most of the time, you will likely want to go with the defaults, so click the button Next until the process is complete. R on macOS Open an internet browser and go to https://cran.r-project.org/. Click on the Download R for macOS link at the top of the page. Click on the file containing the latest version of R under the Latest release. Save the R-4.4.1-**.pkg file, double-click it to open, and follow the installation instructions. Note, there are two versions of the .pkg installation file according to the CPU model: Intel Macs (Intel-based) or M1/M2 Macs (ARM-based). Please choose accordingly. R on Linux (Ubuntu) As commonly used in Ubuntu, prior to installing R, let us update the system package index and upgrade all our installed packages using the following two commands: sudo apt update sudo apt -y upgrade After that, all that you have to do is run the following in the command line to install base R. sudo apt -y install r-base A.2 Install RStudio Now that R is installed, you need to download and install RStudio. The installation of RStudio is more straightforward and very similar across the three Operating Systems. Go to https://posit.co/download/rstudio-desktop/#download. We are using `RStudio Desktop Free version. Click on the right file for your OS (e.g., .exe file for Windows or .dmg for MacOS) The installation process is very straightforward as the figure below. A.3 Use R inside RStudio R studio RStudio is a very powerful IDE and provides many useful tools through a four-pane workspace. By default, the four panels are placed as follows (you can also change the setting for your own preference): Top-left panel: Your scripts of the R codes, script is good to keep a record of your work and also convenient for command execution. You can create a new script by File –> New –> R Script Bottom-left panel: R console for R commands, where you actually run the R codes. Top-right panel: Workspace tab: All the data(more specifically, R objects) you have created in the Workspace and all previous commands you previously ran in the History. Bottom-right panel: Files in your working directory(you probably should also set your working directory) in Files, and the plots you have created in Plots. Set working directory Create a folder named “biof_Rdir” in your preferred directory Create a “data” folder in the “biof_Rdir” From RStudio, use the menu to change your working directory under Session > Set Working Directory > Choose Directory Choose the directory to “biof_Rdir” Or you can type in the console: setwd("/yourdirectory/biof_Rdir") For Windows, the command might look like : setwd("c:/yourdirectory/biof_Rdir") Some general knowledge R is case-sensitive Type enter to run R code in the console pane Ctrl-enter or Cmd-return if the code is in the scripts pane. Comments come after # will not be treated as codes R has some pre-loaded data sets, to see the list of pre-loaded data, type data() In R, a function is an object, a basic syntax of an R function looks like something below: function_name <- function(arg_1, arg_2, ...) { actual function codes } For example: my_average <- function(x){ sum(x)/length(x) } my_average(c(1, 4, 5, 7)) #> [1] 4.25 R contains a lot of built-in functions, you can use ? or help() to see the documentation of a function, there are also a lot of external libraries with specific functions. To use a library, we do: install.packages('package_name') library(package_name) Install packages There are several packages used in this workshop, in the R console, type: install.packages('ggplot2') install.packages('pheatmap') install.packages('aod') A4. Cloud computing In case you have limited computing power, you can still use cloud computing to finish this course. There can be multiple options and here we mainly recommend RStudio cloud (https://posit.cloud; previously known as https://rstudio.cloud/). You can explore directly from their website. "],["references-1.html", "References", " References "]] diff --git a/notebooks/module5-epidemi/M5-epidemiology.Rmd b/notebooks/module5-epidemi/M5-epidemiology.Rmd index c7731a4..d43b788 100644 --- a/notebooks/module5-epidemi/M5-epidemiology.Rmd +++ b/notebooks/module5-epidemi/M5-epidemiology.Rmd @@ -11,7 +11,7 @@ knitr::opts_chunk$set(warning = FALSE, message = FALSE) ``` -## 1. Scenario +### Scenario You are a grants officer working for the Hong Kong Health Bureau. The HK government has recently announced new special funding in cancer research to be administered by the Bureau. You are tasked with coming up with a proposal for distribution of funding to specific cancer types that are in most need of research. @@ -21,7 +21,7 @@ You are a grants officer working for the Hong Kong Health Bureau. The HK governm - What type of data is required? -## 2. Hong Kong population +### Hong Kong population You are aware that generally cancer is disease that affects the elderly more than the young. You decide to first take a closer look at the structure of the population of Hong Kong. @@ -35,7 +35,7 @@ An abridged version of the full historic population of Hong Kong is provided her - What is the best way to visualise this data? -### Download population data +#### Download population data ```{r loadpopulation} HKPop<- read.table("https://github.com/StatBiomed/BMDS-book/raw/main/notebooks/module5-epidemi/HK_population_1965-2022.txt", @@ -43,7 +43,7 @@ HKPop<- read.table("https://github.com/StatBiomed/BMDS-book/raw/main/notebooks/m HKPop ``` -### Format data for plotting +#### Format data for plotting Here we convert the original dataframe into simplified format for ggplot2. @@ -72,7 +72,7 @@ male female ``` -### Plotting population data +#### Plotting population data Uses ggplot2 and gridExtra to make line plot of male and female population data side-by-side. @@ -104,7 +104,7 @@ pfemale<-ggplot(female,aes(x=year,y=`population ('000s)`,group=age))+ grid.arrange(pmale,pfemale,ncol=2,widths=c(3,3.75)) ``` -## 3. Cancer registry data +### Cancer registry data It is clear that Hong Kong has an aging population, thus cancer incidence would also likely increase. To examine cancer incidence and mortality in Hong Kong you obtain data from the [Hong Kong Cancer Registry](https://www3.ha.org.hk/cancereg/), which is maintained by the Hospital Authority. @@ -120,7 +120,7 @@ Cancer incidence data was summarised for the last three decades (1990-1999, 2000 - Which cancer type has the highest incidence in children (0-19) when compared with the elderly (65+). Is this statistically significantly different to incidence of children versus elderly cancers in general? -### Download cancer registry data +#### Download cancer registry data ```{r load cancer data} HKCancer<- read.table("https://github.com/StatBiomed/BMDS-book/raw/main/notebooks/module5-epidemi/HK_cancer_incidence_mortality_1990-2020.txt", @@ -128,7 +128,7 @@ HKCancer<- read.table("https://github.com/StatBiomed/BMDS-book/raw/main/notebook HKCancer ``` -### 3a. Visualise changes in incidence and mortality +#### a. Visualise changes in incidence and mortality ```{r incidence, fig.height=20, fig.width=14} # first plot incidence for each cancer type @@ -194,7 +194,7 @@ do.call('grid.arrange',c(p,ncol=3,nrow=12)) ``` -### 3b. Calculate cancer risk and mortality rate +#### b. Calculate cancer risk and mortality rate To calculate disease risk we need to calculated the number of new cases over the number of persons at risk over a specific time period. We have the incidence for each decade and can estimate the number of persons at risk based on the population in 1995, 2005 and 2015. @@ -281,7 +281,7 @@ do.call('grid.arrange',c(p,ncol=3,nrow=12)) ``` -### 3c. Mortality-incidence ratio +#### c. Mortality-incidence ratio Cancer research can be focused on improving cancer outcomes in a number of ways. For example cancer prevention research that seeks to reduce cancer incidence which would also ultimately reduce cancer mortality. Another area is cancer therapy which would not affect incidence but seeks to reduce mortality, or at least prolong survival. We don't go into survival analysis in this tutorial, but a way to get an idea whether treatment is improving by looking at the mortality-incidence ratio. @@ -322,7 +322,7 @@ do.call('grid.arrange',c(p,ncol=3,nrow=12)) ``` -### 3d. Paired t-test on mortality-incidence ratio change +#### d. Paired t-test on mortality-incidence ratio change In general, across the different cancer types is cancer treatment improving? We can use a paired t-test comparing the mortality-incidence ratio of cancers from the 1990-1999 period with the 2010-2019 period. @@ -371,7 +371,7 @@ p ``` -### 3e. Childhood versus elderly cancers +#### e. Childhood versus elderly cancers Although it is clear that the incidence of cancer is typically higher in the elderly, some cancers affect children as well. What cancer types disproportionate affect children? For each cancer type, compare the proportion of 0-19 versus 65+ incidence against the 0-19 versus 65+ incidence for all other cancer types. @@ -409,7 +409,7 @@ plot_child HKCancer_inc_age_sum_csq ``` -## 4. Existing cancer funding and publication data +### Existing cancer funding and publication data The Hong Kong government established in Health and Medical Research Fund (HMRF) in 2011 to specifically provide research funding for health and medical research in Hong Kong. Since 2016 over 370 projects in the category of Cancer has been funded for a total of \~\$400 M dollars. A list of all funded projects can be found on the Health Bureau [webpage](https://rfs1.healthbureau.gov.hk/search/#/fundedsearch/basicsearch?lang=en). You would like to use this data to see if there is any association between previous project funding and the epidemiology of cancers in Hong Kong. @@ -423,7 +423,7 @@ The data has been predownloaded as the Pubmed API via R is a bit slow. - What are the main cancer types being researched in Hong Kong? - Is there any correlation between funding and cancer incidence and mortality? -### Download HMRF grants and Pubmed data +#### Download HMRF grants and Pubmed data ```{r load HMRF} @@ -437,7 +437,7 @@ hist(pubmed$Publication.Year) ``` -### Make word cloud for grants +#### Make word cloud for grants @@ -490,7 +490,7 @@ hist(pubmed$Publication.Year) -### Make compare grant funding with incidence and mortality +#### Make compare grant funding with incidence and mortality ```{r grants versus incidence and mortality, fig.height=8, fig.width=10} @@ -528,6 +528,6 @@ grid.arrange(gp,ginc,gmor,pinc, pmor, ncol=3) ``` -## 5. Open disucssion +### Open disucssion As a group discuss what cancer type would be most worthy of funding in Hong Kong. Statistics and figures should be used to support your decision. If possible also discuss other data/analyses that can be performed and/or other diseases that are also in need of funding in Hong Kong.
  • 4bxB&mi9ZFK3`qmwt!bHDEEEXP;U;V|9p~McgTksnOyV$5+$L~qIhsVjD z+WSRo?y7-`EX;)^AbAyTBo}az)OfYs{FH9)MZkv7uHoAi-r0AbQO;`0*1x2RdQ*Gh zQcS!*<_nLHe%Aa3OnIcE!stY}!@;8x76?(f9cA=-s((?ANEla5Rg3BMElF~oDI{1# zM)YEX*xPp3N8{=va>D*?qQG~;Y2Ol&`Er|Yh)t4Ej9l_S=hx+wZx1>Y6R~7>FFR)D zcFH(@HnVfZDfKQcF4s>8md;OqJ<(yPWeGP*9PZCI{N(2E>ZM3_Byt-X)b4{arepG@9VW)JLpvNAWxqZpn$nnA#AsT?eaylzi!Zr* z+W49q+{hO|bh9T+4uv{t{b}SQ^;%#CPvqIXBZess7Q(@Y91c~uZ7HeqtB=*TWpEAl z*r$To!FL3y`iT<^cpJXjK#Ak|x>zu+Jf=&-?FI!?wPzxn+Zi0@BF$h6|7Z{oyOD`n z4R%da8=4PKPJfinK8Y?tN^?-@jzp%tl1Oh1Lb@l=%u2yLu}4=TKN5y{MG)9%rTJK{ z_}uCaau_zd$lCA1da;V=Il_ysgp={e;ZN@-rfv%Lr07lOv70qqChlzl$-q!M*z}{+ z^La08ESM$V+AMwXEUnOuT@PUsN*jZnyxr5y`r7VWb}yQeSYBMAWdTRk{GL*=#|Mu+ zP6i8DO{+n|?u}bhdu=B_5&IGEf7XTXL!(2VA@;+yN3VZq`%)h3-Luec*jT=_Gm<=` z_Eq7--Ej)}BV&js4jUP(V=xW*Y2SCW=&HFm*8$x6fGsnW{!!B#*Yj#w+hEmb{^zbQ zM7+J3TcK&jc7#Z_3Dt4}iUSDb;!03&xEMpg-rVc}O7w=&;Ol6{4)gPgn69CGdkHx= z-e?YMP?!lgRu?#|Z$s8bGhF@)W$v}X9L#*3%{9#SeUDlclyc6i**u`tCT~m>``P$w z6wio4BZa|as8dbz@`=SCWRi}FPmYv#l1u5;D}w4bJL`R!Wp;~>5bKOcAPqbWxtV~( zvCj$~e?A-Plht{6Zb^-&k8u)rW+i=ugJ*yV)qjNj9Z{3%sMFg@&4FFJ6kLM3~#o&fa|Y}$PdHA*S#{2ubG(6(41^x!)!UJ9cQi!Ep43yX_4 zu2JChEBH=?l0V8`Jq8z3re;qIaG;^jeFDi6mBhFVF>CX5>jB@#$R!dz!lTC0Xw3iE z)qO_34poRIU|hN9jFK8Si0EQ%^0OU^!s2m^{$9TO)t$}~VWDZyq809ZPmY!vKCt1P zFK4+Z%(a$HWUok_OVG#&A{#o?6}gbDSf=xwm)GiJODk7iqKP>cKzyd5l^^3s)^_x1 z7U56NHGkr(L=$kk{VOC{-~donlMIL>C2%QI3dxuZaS)B8JESE#a@i$dXocAJGpdc8+ii>CYPlB2Yq-qyRiQEvZ!|t5 zzO<6j3}{(oJq%sEi+7Hlv z0QP&xmz-0Fz+#zhs}VeDZ6_`nvw|CPq$1Dnd2?O2)!`e@&T zp2^z`Hv)8WKa0sr|^~wq9eu^=Kv%uVzvqIjna5ZjW%cbZn(Nl#Jl;fKNTFEB#U=@`y1icf}87UbeEM!5@5S$w_uDpYs9bT@8kWf zVQWmIn|KK|6R(`}KHySJ6oSee0sUY2xis(^C9LmZyrKzlVCz3Rq*P!*TAS9Q{m_d- z__s)0*bjQnL4Mb;NdD_Jl$UU%wtu+GXrS%;;RG?W3N{buXr6b8v>J%NVw9*^LNo2{ z)z0nn?P(>3C?>bGk?LG@NL@SOnKNE&{N!!gh24nV9^6eSXQEbQc)R8T8p7lD>H`Qy zPmHuM(x$vw;R91^xH)0$!G~lo%7Nq@%wcf)~x?4ACuDB+h)mjglYSh|=!x zX4;q^n!y5RJL_AaW!f|R@QE{h%3oSNYf(rzkzB+P=~H`fbwQSq9V6HfaoZDNvFYBW z@t7 zn=KDst*Z|bSf&f*x9>11>|v;fun2&eT@6*S#B08)wpZ^Rg!Nw2epp;5~6n4`Q05dfk@_R%s%v zks1EedJ29*9|HWJO*{}77lNN~dRTmjTQdbb!M6~Y%Gj#cu>xQLfeSlt_+5 z$^&sMjB&kLCl#$YUqqihClcD~vm6N{+#?!vhmJ}fhBENK#~(Q>Ofulbur-q+QJb5T zt@dzlya@|3qN9Eims_2Mh)kj%PNtfeIyMXOq8FmPs{P-<)ZWzAXL^GBkZ$*ac z`;7g75~F3zlHW3Rv3TJ}YHyTRFZ5Lte4_hqSh7-7F=S@SlgVb!De>Mvvl`oONJ0q^ z$)B*U-~6pkvV;+APTT%jj+Xq8VFLuJT~K`XAg}N_jxT4*P_Ifso-MDng=H7<9%s?K z8)DQnMHY_eD>(d#;M4}+emMQa=g-G+;%dHB)c({UHB>{|US6YER8tW{UY{X4wg5sT zjobQko5cpMeUz@(<6VIORjoDVmTD_BhmL3S$H%U-6mGR&-Ce( z*xtt(x%pyoZk@g2KbMU@ zbhjmV{|`cqHEdPQ@xAF8n{`zPyX?xT#aR)@q8kE_TAsO~Fp{C=n>j{)vgb*#?M~dw zxb3PzszT*jyULFZ*JG)`@R;GI`a%q{@b-ztnaagJHtbzI03l>?+wAzr`0y&jTm0)^ z%7&v&l5p^mGW)QTX1m*4?I4lw3-@V?Q6iOV#LZ+`5>aQeIL|uiXUg#y2jhahjfY*; z*Rq-HB;LcPim#2mu89e!dqjfi4Rz``k+t(%`K(^Z6#zH!dt_bPAnF{r5VSi(L9z!j3o z4a!jj*e>am%zz8BMUYstED>&-?RpTI)J1^ciTCX|?XRnclf!ko6xVOf_}C97wBc2M z&IM=K2U6n>j;BMysvlBfGS3`Vbhw}b8$RS29~9}9ix^;1{jJl*=WQErCV2WQ1ihNg zJWm|S@ist#*N(kGpSZ+Cbz}WRo`O?6`LbLjgk$Na4|{J}M8wI;sbtm%5P_NucC72oc} zPn}LB7dPG=N-9FzU+wBC?!%l*K|35CME~wAsd=}q})%bRla1HMqiunZzYz8v9Y>SMWOgz(wzh!}n z73_otI6~wR2LI5P%gqR@xGq{cDSIL8iHas9Cuc7zGYD|MwBbF0JN}}!-NsVh2*!~r^nVB-#1#u!|9M@%Ag`_e^w?kA0K%Ne zM8v3)mJ?)aA`)!^92Va#RI=HU2N05R&a!C#nKM!;@?szou$H0)h)-@e#Zu_ggk`vR zK(#!Q{e9uh<3#c1vwOFKJdQ@Rj89_3x51(Fc{qM3_l{JoS|Q@MrJn6{B_4+dXIn9Z&$*6qPmiebY- zN`psFz*hUedqDr}NpB(05u1#6b*9NsNC07O;AiRI;kY|C1fzY%yW7P$oHtGLW~)fO zoY>SdaZ--aDo`iBqo!Fl_?h~{c!lE|;AHFZiJI#DvgN8NHgVnPm4Q8p-baPfp*NVG z8jj?jfhC5P^hY~KQycx?v22eavuP;eHr5=w4MjGPNN$pc%vxPh zdGweeh*>ISO6|n;B6&{9eK-@>TGgg?1vfo@BK*5S&%=>cjyIMYg#(~JMRTFdf7+Du zdjCyb&Sh0ldc^zvHL+(+z-K3;<0koyeMx#EmkbU(c?2zy>J#!kMrXX=T?1W|j$B0_*y8?oa7v_haTyOm39tPwS}*s+xPnA=RH$tR}CcY8SbT9bP@ zD@~JCFFr%p@>$>>oXapr?3D%NujNlZPfy1C3B#Y)BTlFnAuL1=jbJnBcP_+Nx#DNH zNVNhIqdI*ev;XBjl;Zp{EwR!mdXd0eoJUFHV`cG*hqcRaDh8}V$>>2&oq#g74sD&+ z)3lgo=Z4{CPPU0J70QU$tZ=ed!@mK;+;Y|b+m`;~UGM<|uDr5FGRCXK)bRJe@E}2_ zpxFghdPNj6mdPtT%N7@LimJ`)cS8EapVjeXh7ynvlLwfiNZOk%l=dLOhRB!Mra2Mu z`N41ufaAmo_hLG0%!9GHLhAmr4Q27s;t@^!EmS>&^^%NVB|M8KaMj-5mZPYQ32U!r@quel%cNuC z3DkAsRU6i^ql3m;PvfZ(D_6r;$1hTFHQf((fY{R6EZ}Fabj;M{foV)gniSj;OQ)=s zZ5v3lh{$$+4Dw30!y@0KiaIlhPXNuu@XF<}i2N*SOm?H*h|ocaP|9FgLB?H-B{CvN zcS{izqF+*ZZmkL(H>6+LeB`{Uvn!hF>D$Eqsg1%6xsRr{v66l=CDmj@4WH+;x<8LTMIw6=SJ&yVE@ zwe{a_8QS|Ka$)ZJ?2R)KPC63`XxAaCWg?Zf$Yk1o9ESVyi;Q?A0=x2wDeu>e8>0+9Iu4`F%S{NL1oNjWDk6H8A z6tN0QNhtDg^W)C~u7}?o3B%8V<}>U1fOY@-xQ3m5`GgP6CyEiHLUemSRKHw==&Y5nH0AKS9{MQ$`PGn&HtIYLOCBmD9@^a6=^!_kTK(`X2w768|AIvO-q z?m7oPh8o1vc^2X6=UN{-m_ad)Pfm<7f}8=lhQjL46$msFZoN9mJA4%@2(9ozq8nJI zr}AfY^93QFo%Y)GlM4E)q1_Qp~X2ZX5}tjAQMuc*agzJH%t z^j{-4i-;plvSj{qi9W%Jj2*@)uYa`n&b%WZrqYc(@9rrl z#Cevq1vYse{2QvH>1Nn081XpYd{qmH9*57jh5o(>@|f=z)}a$#rW+MUUmo+GME||I z8S>-iQ`TJIhc^x1EPw|dUE9Wq3uSof&34m66DqJ)nW7n_G>Bdeo#~nBEX-tQ1^1H+ zQuM9Y=jV+#Gq_QzuZHQ&C7n|y)?IXPw@P1ifj#Cy9EkB4^{ zKgo&DF@UtL9#fRY)^9d<6r?!gt-dnfI2eU@IT&e;l1G4=>hq-KS6}e2^My^&Bly4y#nb`KJa*yDJxzoA zAE%(fb_UZ@7eP7&b51_j8q4-(kp>5zKuK>{OQ1D7K(c$tcmm$u=#kzQ$NZ)_>H9CXpz|k3P(@^iLI-}uM(}84PnE!@cmow2KfrRfC1LL++^l%Le;CLiC z^A1PoJ+WSQ+j8{7pOs@iG7zrPHO5`tyX;jT0$-RrEt(gEfRCqr_QYpUNPe=hTY3w@ zcrtqd_=VD(dzD}A6Dp9_1U}r}us_=!qH_>4y8f4*M?&z@I0H<5FBIdAOWvoaLGWwS~7BZWu#dk{D{cZ{d zRE7Aw`Hma5_yQJxO1PJe99)=BRA*fX#Vb) zt*Y&r_5HIpTK6feM|*a7U*Wse$TlT~in0D*ylPL2aaJacWUL>4Lw@tHVvBA_yGc6X z!_JE%{At{^lS`XO5$YhN=t8I2pZVGTW;fuPM;%@M2W1s>lrH87PW1R7J}chboE2%z zuGQl--_jK>fLH>1fBK7h_qqV+02 z5tRj8{{D9QXEp6iVHf_Ez|1Y|Yy7tOf`BfwLxpa+o!*Sb60$wzd?kfnNG_`6-wUbw z+kQ#e<}%V<_+6ifQ1Vocyp zlL8c|jKFnbO+R%AHOZYwB>r00~b{3(DnwZyYX#$COlgWLHhfi;s>oE=2Xy zJ!PB8lNO2e0m8sDQoHAVrGh*1rGK-JIsgb}5kd3OU8`{Y=tysntd6*ylK8M#^(esi zIMG+zBZ`N~SG5DUc-thl-A#QP9Y)|`Lu>MdC-GeLsO3>BLa!QenDc6U29rFc^S4on z&hjEB%O?5OZ%4aJVeZ+5oLKc9&Lbj$y8)J{2_(G0XcL>^e!XSs^=x_^z)MS94p+~D*y!?y*{ievX3j_Ae`uJn>$MKiR@Cp)WLY+=UhU{lp!LT|Z_?594#8cik z&snC2am({ug{4pUCXcC7XwuQ6SYh03Jhd|5V)n7lI`14oXD(iOap#X8_5ZkIwPy^g zLY7mdkcwa{q!na>Sc#%F7|QH`XjF&^sSpFa1*yR|Cryg~ii3wswIl7KA&Oxa8M(^u z)M__ze!o0NHC1;-X3?B%3VY2)2QObYKS}+llc7H!#F;~<f# z4;#rb4j~7q;nGIC89-2iQ4hfJO&YMpn+jaQu>MdWPaLe5V!+1;W?f3FiYBd-WXR(e{*W(u%B0h(K2;)nb(X2xjH(#ci*S7|n)cmwFwc6Gwig&St;IO}V^^WL)=5jMt0Q9)& zaveD_Z!xAUuzosRna~(k*yEChIH7`X-6rCsc}Ja?T3OV49`{oYlHUAc{vks?bv^Q~ z;PU61nT%SP|oZZfsUEP?5+vg8OOGc^Rv=g@fXwi1+QDNASzp-6}MJ!5Rxo4_$<0 z6~HR6fpr#Xp6z54^aWw5k0)$>gFxq@<09dk;2Tq$-<_qU*HazGIo!77+&I91gt0*| z367~DCC)z3?Xa-Z(H;7{V@(D zr&M9ym+l(&ATTG5v6XyPhNAE?;F608@OOSOX2Q8LP=5?q$n_oH|IvgCtQpj8u` zrs5)?V!rTpW35A56@|Not!M4!Db&uJ1F0!dG)Q*{P!NP@C^j^(li3>Rd7W7Xa%}MG zyVu8@Y?56a=Jh@I`Z!b7kAl*u20s15H*R@;FSkZQb7Bhlv+$Snl&$kqACqV57~+{} zCXp|EJEPm@v~_B8=%B|6s2>_4>a%kM(*!@Yj2m}6wj&x4?Bxv{9+ zJS!?ERi;w)5{TymNXD_pg2O85I{dQyJ_azKvWm(&F5&hUs!b2eKFa_o z?9iEoXzv}kjc5d7B(*jTVu!4@MCb1p+5XK@-NSuy%vD|A$T1_jf`(t;u@xX=i4jt^Pz0T$O0|(o*AF-h;gR zxXZ5STQb)-{w+haybvk%>u>(VghM{-=EQmD4oi9-$TU;*vc=uqzdhCg=Mtvmd<6 z9D1y&#*KO%E$tx&m8q)#1Qz_`Ct$0F7>@g0he|Vq|1J!CazyH~GNIqS5NYqwQO%z) z0I#ISZlEP;h_=e>h-uLQCBxVw-sFaS>A1e6ky_G8xpcf4wi)jNwQ|tpE>fY$ARf&n z^Lm`JWb_yN;Z39`Rz|FHG(Sh=6|sfyP;=UC>GfBhIN4#7_A_hPbK2{Vs0S-!N6c{tjVD>m%ro8x3Ko zOl_qeC%v#;n^aK?%{N$;V$&a3WctgF>$C8L7-W$&GnSje!hUULiK&os9Sp~2C~?7i(fufv#(qx*aZozGl+_g%42V&ypxaE!r=Ky ze=^7m@e^@E8@SXHSqm<^a{}(I)9VluuOAPuu3$$1%_{=-d<3KN>Z0?taeqi4-k;fqY+{nvo zS}VDiO@-v4*y71%PFn0kou7AwXQ2MWX1q{7#1l;_sJ`hJ3(@eMn13bg!M+q$5CSCC z>q3KOD{GrZ>*-&3Mj9LSyAV867Md^mDD%w}caUwff62&Unh%k8D0gV&Eg8(Q054iD z@jp>-P_eSuhjoc`2s12f5}E>zPxM@V@|ayeUX@Jtk9zJ!88BJHunwOhv!cyyH3PHF1R#k>A-5MMG%ESAYLwV=JpPRZs=0*yF3oTZ?qGmg#+9$_!92%J>> zXf_ar(!~25z2Mz&)BE(-rlK@qAK<$hX?_}gK#h?w`7Vdv?@2CK{H<6Vp~;S1ufv74 zjof;ftNZ9SbVVSOU7F_lgnN~2WtM2?lXOlCf!trqRHe?_qd_571s8!+OUqgG;m11M z7YCuo#h7!kJ2dpG!5BqI4Bo${3@aqk=cLoOnCagrtAK!LC}7~nz=^vhdWYL+fw}hf ztw2qk+(F0G8+ad+;~QO^P4vTAzSKZ?q=Qmyq-?~poAkJ6HkFsEa2%z zl#8x!#S9I&{*BwLfQvA7AW-=}>;Z3p-uOodE*5(Hl7AKK!4G^+wHB6+Z&cFdiw3E< z8C;rq3jT7`0lM~TyVUN#x(?Q3zv8V0y(I#>r0PzdlsGMB=pg_H4C7D_c#Uop$ zbGK8n^(tI?+WFl0qGPH#e>;P|n);3kByQQO6;Wmaa=9Kp$2gV<|F(whLGdvz zcNS(OSxMEWzrdubm*cm4L)M5Dbe&@GR$E-#eEn23wKPn`AY3gxon`i|21}ac%9#kD;nOJ@|Z;rVZxBYF;ypufQdP=YT;Mn{%i+{(D z*Rs8Q#+>^UQ=RMYV2es|;ml@hE@8ohx+oh9j|Xt*l`uL=I9c!;TA(Q-q`(Y9l|R;a zBG>N}{(s>4dANa5>p0n2*a=T#&XRbbPe@p?yI)!}-n0nEU z)oPyiK?D!m=Cy5TCx;O|QdlklS@V4>_0%aQKQz;%;vmB=&yHQQFX9F5Z0-tCVDILU zoAh9{JtcniRE?Y$@T?8<2L&QV1brV3^Q`1JN}BHlE#5~4%wM}^r$E`%SV_D5>HaVo z(_oHt%2$(KM_uimdJmuyahvWqI*L;^UDf3z_CnZp^A>p=NDAH^`wC;mpZ<`X8e_77$V4Z!n)hPNpn`K_1NQifW#1DO@Rq)fapMfbpKDCMl!yu&HRXN zKF&KF&DP-AXs!N+<8FKt{Tugm4ORNY3oyw>tBVav%;xVWI;HP5AqhsrylJV>%bYOM zXx2Z1#eqQ37#6|ZrmjQ8 z%K7H1DbA=e576moemoBzyfkgQkLaO&|F$MjE_r_$d2@?b68xfWlnI1CIOYnt=Xc0< zqT3SE-=Q$;SIvrrgGS7I?V`^5UZ>E96usfD_ZWAL^b_&+(Z2?rvHi=`bF`oN@&C)z z8yoL3GO`G=89bo>LbT;VZZ0k#PQ(^<;sE|LZ#bDGNKtLRXoNqnvh#OBkvsNc%qc2Y z{|D7s-dK)mFba$vMiD&j-|-=S<@}<;cmYbJ4yEariu=?K^^}f2RZ-E*{(3DK;@7d{ zD89$Wo8y}&uS4q9RY6nl9HpNw~+ z=E3ANj5d#)!&ca4kNtb-5(zcv9!^8aCZ_M z26qVV?oM!bf_re7h5!MAyIV*gNJ4OTcXxLS!QIa6efIfp&QljNcQf5pU0wCA^{pb5 zgqx5W)tOl9*i*q>sga0?>EKE~=MQv}qmiQ8@@EkJkliC2H~HtRyD%1>+bLo+J4XpS ziwU?Q+i%cMA?6d@-ubfJ!;q+ zruo7(y$NgA;v=z2{;!ui6Zn?@a&qZqPdXLzb^LWQ0It{>Mk0~_(fOQkgg68~CK;}_QKxYVO-E_az z5EJ8tAvg_zL65tUdPbSul#TgyvTUEz3ftwS^C+H)x*E>OM82F4x^Zc&bxmFHP3k^! z#Q6Z(!9FST+}s24>Jx8Tx;K6*QziaWOEJPC8eP4Cbm;ci?U(Z>C$(;=0Pk-~u)vQH zt23Sk@3=0J?_$I5E92`O&)Mloj=8}E501KpA!%Nw^^M;V*k4^;_=Ei`5`!Z_Igy!D zx7JYH4tc$XBl^oZq9kVfh4VdS{69t+4=|(iCo1u%sNyLd;(uIR#74s^WmMwBKjk!T z?!=qm>m6}%`4xO~&aP(W;D(>Z1g2D5 z(p8*tnWEWQso`3mtYA@h)YoG+sB6UB7O(k-+0C_fDl5}%~ML}y_D?zmW{Fu40r zsJ<0&u~Y7o`VCU9dv5etbv|*&5Dp_JqWx!Ln@}S|w zsMGG%r0x6}cyA#8348>+8`E|!>hV*^P6F-rDM!99tSX~{L9dDq&4`0%khk%hpQ_fS zeo(OL9M)DUw4vmX^RG!So*u*fYcftS1&Q7+q*?Z(Y<}0CW|2!F`#J}DY+L|;cq=bh zeM`~Ws@ql80Jmd~N4`Zchnece_OLj`T_e!oOC52UT+F(i1Fb(*WljG`3q!FWIU*sr zZH>@MjRzh>`oxcu4_6TLenl-cho6x|&0CRg4>7HZy|m0tM(+Z_%xoL6rJVn3D}JP< zCDZ8T+984wdr(wo->vy+_*wZ$PoOt{SBp^u!4NBW)}NY|T0EFtmvZ9L zkAq!!hmJYH@N|GyqnziODa9nD-Cl%7Ow3hY@Z8KQ+0TdPnJiVdY1Oq$??#!Ne&TZ; zxowImneaCeb&}%`E8_s62&_rz{k9$PC2)Jg-vG(cdODBHrd^LFNS_@Ss#7{xrOl)_ z+gW4R(?v@x-O4DYsR^(fOY}RMe${Kh&CU3L;&eBW{Vk_|W0k0Jx2|Oe+#182`$FA9 zTrHC1z65fLUSIN@?GGcV)l;CC4(z?vd|tiGP@&EoaVl}9j7>9VZ^lq$3f3n@ht$y=W@aAe(Z0cJND%W zLx8y_A20I*bI%|2O@6tvReY$BfYN^6bhlMmK8!9f>DfJc*7CX7Cm{@2`@Y`QvN)YW z))rPV^Old#em&0S2_Z{`(&@E7F zy?%v$$z);?1o4R*)UXzcZ5Dw-9h|9D-W*Q^T?9Bek^*6Pwt# z3!2|rv85fSX-J;3CJn8NF+iexewNR#j4PwL67dfVA0R+a2@ci19*3|LV9j$Fs_HWv z8_0$Of2`Shv3?o*ZzQpAJf_R+L`bv0CR&u97j zB1t=+Bq~iSds;vJ7GZgtN0<^3p#m| zT>4uw-xitw)sN9YTQT8(R23gqI{yM%WDsv6n|)dtnN_6q^0!@F+A{^c#wM zGm+wt5Q9ThO+liju}Os(=GNu?jUzOuh3vJ-rrog*&Y`3SNuzQrQfx=tWp-S7TZ(~i zv!o;aonkK#HC~+hj%u>BoTDPm(wIP@X-H1uBXKBs+MDsG$wV^?-q!Mhh}1sb&~*r& zl4iAtYW?fx`;4CNrPyUv%KLPpcoGe{+<(8SP~lqURVx0Jp6|8(opPo%O7AE784J1n zTDHwG>^^L&kGJ#pDI_tNuEbo7o+N`1C@SVhZ|+J^ecWe0-riJZU3 z?KZy8_8PANmuFv<9eDSmv^^AxsBP-=WtqHeyUwq)?d*3t zQ-5aK1EkorvwkMRAH-EYmnTZ5uXLD=jD>u>8 zhL#oXYG?E&oa%?lOt%}V@x;22;(NTNFF?GQ7KEs@yI~+*B5C7T<3u+%5q8n;iq_KW zR#B63gFc5A@38fN)uXEYns7|AK@^lEwZYQ~UXQ!<0AHmPa)UPIr?VaIp$fV@T4Usb z&4c?`$@Wg~!sQo^xdI^eUstm@AO)4QpMUlzxBmCeov6S;c18M7)A84QY(bpOfjNw{ z3-e%EBDNvLyRuQe5oM6<@6)l4B6Lk=wfi}L(1uwA!Sj^XX__j#nT-1W{$CgYL2Rw$a)%hO zq2{SI*hOH`{4mV9ze?sNBadGJ4Me%p-)SK8X$&!g&)}N98i|!i`AAPKzM$C6{9K1y zy@_#CCb|Bx-6qxH)sjB4mFVF-C$;HwyKVg?t8Kn-YHusR)UUE0?Y0qX17#@Df~~t|T@2zhVmE0hDik%)k9@pZDK1ko*SrB^Fcd#%J_Y=N^Zot5Xq) z;zE@$bzXm0VA^hFa4Cv3VkIz@>>EHSTk_BoaDdJE&)#@j(V_aEJ~Cabp{OV+Bt8vubBqRa!mFc4Yx5DM?@$r zu!r_33oeb_zX`w>JJw>?Ha^E3b zk)0fthj$z3Eqxmp;7g>m)LQm+YBtfOOfbeSY{_B9jV>-p+qPceNVq)K>^+%B60v9L zzxWgpAByqF)K^Q03;)BZyRqT{D?Vy;e*g51e7vy=We<`EV>J>MQ?s!}zpjp-pREvy z6GO`Hyn|q^h_gtC#l(B5wj;|&y@VTj zuFP(AC{DxleH@wWaU81-5KDbeBVu^R{-~0ncYB*xM>9&`*jpNt*oKU>me)R@KaS6i z`Oi#LYq_`ALz}LiQ!BeQ&)zfh@iEsH_&8W(LW;r%c-7+uW3$W zh*z4uLA|mQ zYUUl=Z=)}<#O(z|Al1JZ9^4LO2rAR*CO!5V=|uE822GZ*bHaV`e@$E&K2y7qWO>vT zw(3~_oU%=NvGHt;=YH)?GuvmnWoOmvXQ^?J%~WRkrdkGYYHscxOJg5ic9ymXj54|e zJi$-OYj{XZH-6@82`@a5gHC)MIpl3(&i2Gq+D-ioeg0Q1mt_02kGWGhZCJ%)sIP#W zQlVu|(~ZODnPF@!cD!r%-lu3bJq87_mOBhZv05H7f*Be4pkopetKYGW65)bc|B`t) z4CtK%Fl)%QPyVZU2(FR?yAN9^EcpnZQ-1!YGLONhSXKAS>g4`v;sZ9#m)X_@Dc&Y34};AU`UGxY z<1Tjz0A*@ru6c{m~|J(^~P2A36E^E)K^#_8VSP{nq&7nFxP*j`n612<*$kHXRSz%@7S zE3tr6i>>JAlZ0Mx)P6_N$!i(sR*bc8W49pzep-HZ*!j25t8DTU9B<6;%W%4@qYs*% z6b)gs1IH&9L|%m>)l_|RZ5SCgtZ>^jU(nZCRXMrw6Wb|vA=Q-hfng?Un0Cp(94erZ zgd@%_N$k!>X&11yloE2Aq7Un`zz0i{A7rK3|4y?E-te%u5<{y4FOH@(xCw+_?k91$ zU1@&S4=}B0LVmJNuxq#dH;h^S*vK0{U3j)?bNbjCBc;FYe-b2CN7f38K;8~#`(9nG zxn*5fFd78AT*5JX#tXNIztW@~knSz;33AkQ__jb-j?M2nGqes~!oM#@&agc`szSzua*r$W9@A_sDkgm+OkVga zficx!lOKxnvc)1IGhX?W1s=V^S;edDQ%BORa|I{-vpd8o24Fenw`)w$=RuZD{C0~; z!i0C$?83;Kdsqin{v0lK5u$)+)fAORXXHncC1Ch;B1ap)wlZv6&+ELG%bXx|>a{zY zSstd#?Y7}_Fhr(ABT^FOd1FHqElSrovTpID!W+K>8{Ns@_r_a!9>u}(@j&}2WDxIL z=N}c6Eg@cqSm2)&l-J3^KB^MSjo!vNPZ1OBjRrl=iVwfFES$?jTXou_8ijG+Co3XuOJK1ut zHjm!V_?jahjO((Dw$ZUqP<$&2WjB44dQ6FdBY`mma5;ZxE!6*XaB(^1;}hc2(Dr%M z&s&QxfD@_`6p_ywF1vgQ0YxVE^~nkZ+_|>X{&~b&3be6fB?sUC8~&kF13e3G{ws_+ zS47Un5)TwTz7HVl=c$-zF`}DS`Wxrr~@VD6T zt$hfhkuZqHY5-%xKHheF5@P9tFSiN-S*3tG+&b2NB)-I@) zWJlnBr@`mO`r@*q!uj9cjLaq!a+5;8A$yyvZuQCrbiqgFdN`*NP|Cx*=ce_VNMSaL%e}qx0z>U!{&0>a}Ux`2-iSD7`WnB=WyaX$1^5K{9(Z? z#kW3;L6N|f=rP(2GS6-60N3kMe4J8CQHZc`aByRPOg-uGQ3!fB_GKbG!yxVp9Z5vj z<7HlK-7sSGt8?^V!r4Phd7CQR$ISu?5Dd9d{oC6DYGOBXir9G01Aid4L}L)rqF2~@ ztd^6ix*$Y)yR3BNu&c3fyo2Ioqp746btAbA-{-~=)=n$gu0eFLt-$Gcq<`=~@N~&vs`kE&Cs2xWX+>7~dnKYyCrGw{o@N zZ6jfHj-UtKLMbh*C9uf+9^MwE85wIg*vYULAk4xP$MtiRfh(CgLwEF92CA?9f-tO4FNm&g9Y5cUI1bAwRR9dh(tYLk`D>8YLg!?-kM$1zad0hJQ zkdLUWfx1uqal!DC=%PNyuJn6nUv4#eCN_u<=@{X0MSsKP0)F6=hL$X+4PV!8zi*!Q z(v)c!^b$4JD1TIIVXTa(*iho#Xl|jDWfqxnZ`b!D&X)XA=-Ua;#1*vGYH8|vTN_8g zh^oqM%@ZX3US3zJd>U6|XXx*MsP^#aI)!lIIU-q>qHcQVRp*c^JILeBCY?7`|6XaK z<+nf4P+PtS6nKx)vv~a``!L>#g}OVl)rqLI*-?(w;{nfmN&#$_zr0oxwCVywlZ60^!g+4)kVZmEF zkAgQp+x8|hAfLVQp{&8M!jWx@?rwF5iJOEpkb2r?L*_1M@Q&>OPR!0qtH80f*c zV>?!*;ekzt_Xpx61_{tlE!&*>8x&ku%#_&xP}>{dK9v$=^_yLfhTAr`ayasJ6e5JG zr1LiV>`j7T8+T=*<1hIXYu!()!TwaHdsVAfd%N}tT&!O}BGVCN5BRWS$nZJUKS=+# z9#%)*kpf?>aiWObmfuMv5{1E2SfcigeXciV??H+S9_pt^+SrTHH)sf;<^@QU;#^wv zHDnhxw+Z zd_pF^N}}i_IggMXJFu3bD4)XO%DdX)LR&V2&_L5mkiJd13Ox6=PY5@`p7oRk9HE?N z3Db{QOP$S?`P|Dm5_{%;qy+j(zi$LLLv6Z@G~zMWQ$nNV<5ev(U$q5>xh)`zyHkOK z*2IfM1~G9Ll;^WtJ!XfkStM~4~89#~#ImM<{^DdE(x`DO?gyX!+E z@mi88IhV$fNDF>JF+lyeq^8#Ykz{ck1-pNBJ^+@IbF5oIva6z%YIEKc0fu6|cS`A-7g9V@?)D=M9WmZ_^sotAxwuwzA`U?rvKjcPqP{QitP4Ygyn2 zpK3V@dB%A!dD!>aJEd;?{cnaT@ltDj7|-V;k{TKsC!?CG1_6iMvL^Fx?70mJxwvGd z&0t@;(4Luk@6(i=GgzpXeRvJ32e$>iLw^wkmqyp&yFUpFOTWI^PRX5>E$ME!pZc-s zt~8xN#K+zh3w@r0i|ua$k8nl`8Nsjsul3g%zXl|tImkwb3VV;mT(S5MV@PW1#MDeEwmbNa*L&FBGxYaxKwY zdU%QG4>4uTUhnG`Jq6>)9Cwg%;*_i@XJH*Z+wAQ9)@IXNO zUCG@~3&gEWu)K>}-+qX1rN_QC0nEL?UGw@>%dJnR(Of{T zG5>3Zwm=!T-+RRRP9Z!iHxPNu8+PGhU#84U`*dm+*ug&dfm#*`K@^gT_o653t;bG8 zisCZy41Ng#4>6tD{8F0^`{;M(KG+7lt^r+pOu~>diHjt&;`HN!d9K0d9B5ey{Vn;7 z#iTuv%4?lDI+t)akKU09f|@KnRK&>C5VILph-`ni2z}(_Xxfejw)M%Az<%_$v6v#>=_Ud@a>x)6|xX7&v876$a1@z;^ z(#-9u2oe{6oCnW!U#MU&6sx!EH@z!b7hIz(c8fXJQlkAH0i)P8$*&tzce#tuO~w5Q zoViI?S62DE-!gF+sP+u6>8~=4RaZeLdlqdeI;{0Z7+83zVR@WK!WwMxnQVv_qUh(Tc_hyjf26d!3h(-&c<=36& zvN8iUcF{Spf`-@t>G&s#b8V~_UiQu(^9rY`iJ|(RzTd63Kl$n;0_W28G2i5T2A6%8 zJ2)Msdbt2)R#sjHB6u}L3i2NeO{|}X5h#wDIrnou zUj@7~of}t}niLvV@M%Zh?)@^|`W=?W&X(0gUA!PRQlF0lc*PtTw*%WV-d}L=+RehX zPj-=|duAVAzApHS&sjv)=n5cOoB;V04LwU`MljjodC~rM$vOHH{`ZbCy~&@Uv-e^Vw8JZux&-UP>~_+hlbi%qHj?Ak0-@9R zUh4N6-HPGANp*+3{1&k-MoZ;U zSnxV$&$pbr*B|TiVB$-nP3`D6`F=+eV-2X*z3(@54)XReXt;W2=PjRh^k6cKp*^Ni zijf@uMjw0i=WmPMp$1W4SB4c;>V6Gb>hqcU7`+Jx-6zyD|KdhA z3L`*#X2EHxSxA_Bn|qQ*uqQGNTrDJ%gK^-kzz^_lT&oc#bByfEW;OI9dJ*y-hHDD# zv)5|}MXf8g3cvs4;6iS55|_)gcee7Dz3kGf)g)uMI{;zIuhD_FP2X}5 zd7a#OiZXmjTiE+I;yBSv28^6f=IWFhDAN?8-M9{oPh>v|-|R&OmmRx0r1}0zd`EYG zs`V0MPJLZ!6v))A=yqJujpe5cC_6 zoV{ZOLl~oB^vAy5Vpxi41Qe;{4=sAF^8Uo|`MvFYnXLSJM1*=*L#9bsXv8$`w>tFu zO2g}UT15cYzgy5_Uy`|B*PwLM_fRIWZR;s}_8s4dc01X7yLV?GUfK()p9?7>d3V1v zGUIr6fMNBmkINhyTTLao&8@dOEl2#AEvtU?M|EUXhf*SHrhs2i{r*1ZzUVrm>&0=mc>#{9(IRs-nf5K~F)8V^E{QBcIwEje} z{D{?a2^^<~=oVM85!eRLxzO8Z+GmeM)#hl*(N~W{|C}DUFLQ)Ap?Vf(CfE=paEf4> zq6@t)22=PJ0D4-SVY2oA7XSRz$TSWPw85*{t5a;yI&ZF>0zZNWldG3K>yR?=Nw-(k8Sa!CVs~Iq;6{gQscwpi(bu6;oZ0tv5#SB=fD&j!BGthO5813z2E9y z);9K7M@;S_xo4=WX_m>!3xX7$mof#EBfmQTIV=<27T=lo1xpC9Db+qUr9o&jyi>zp z4wwVV-Z>X(H3H_H9Mc-mG0}EI1trzKgC=vnkq%sY-wZbwwz74hac9T@+&JphuZ4@J zrL+IuGN}GDp#%>NZE=-(TbX%qPvG;5RnR|{X0!3&vz8*dUGiOWNrqq|fpIyRPzvqh zuC&jgU=u-Z-Xw*HO99PzJW`|yP~@J5R9RhQX z$0RO2r9lvOxAZkQax~M)(4b-*#iMRB0VRnh6@Wwt!%Y1>l0*IU|WjI&dPj}efBpO7X>uW1k z!a%LahcykHu(~(?Erg&19|w=ZV~u+Mfnx(Z!R-^)zvr#a@*zIgCfD|Al^6a*eD%JB zmL?~U!#LeW1SP$#*RbLK@ebD3@d+N2Xm#I7_pL$UoD%=cKmW*Vn4ps3{|!v2xxjx? z2DkoPYdSI`g3V!xfN;c$=t+3hzMqzA<%)A8t+@@G;Q1p5?8Zo(Wk< zvE#=%(x2nk?m9d@nOVDP`z!-ZSM4@=)JTq!80r-LQ*dChbr4*EO1H!Av6$7J{@9A|6hP#AQjHTxl`Qyxl5!Q$PPl3ssm)!M-!%%=$`uWMS zq<(L_f{3M_(8PUbnh$*~01%nVLHa04X2bbRS92%~Ei3~*bT!|QW6}Ih620`q%|tU^ z=yYEv6Pq(?Si`tI3&!7_7nM7HVx7i^2SF^szbd(Q77ISnT>j0MXct@+k}H1+ri+*J~3by&>1mSx(?eJzqz4Lm&Ed9A!9 zcXcP7&pFF#X&yi`OL!|_gKeft#rWY<2EIZ9RWEoDHT5Y+>~TzVIWOQPR&-fV$wuj` zJoEcb-Zu(g1muF?VLrht1cBYhI$#&Lg$`d{GfO1YT-4NV|BTShJU)9hH#c>a>6$ma z0M|z%m1H5K(S(ff0o@MspG^7m1{wE%A2fBVT>`92SKDN;@!z+Ryun*$GtJ(ciOtcJsx8zDEyxlvAVA? zo#@GBUgl;L+&?45V;qGgW(%2qLc&sH*dG|qrGUAp6FN<$>9esN{(wR*<{bAm7XWyj zQlEB9Yz7U}mB!Ya4G{$clO(f*ASc&}{M#)3!c9W1gm(gH?q6B5H0{)S=i%Wl=JEQ6 zr#L$?>=TSbEjvGAR->%{Rh14pT%M-W9gU?PCgpd{pJLmsCu|>VQWQRVJS>nx?JE^g z`_v8y*?kHfhR->Y%{W6z9`T$9I2p zE<&8fBx#ba$RlaAUUEPii;><-omnGN>WtFkj;TU3V?}% zQ0x|1RoDzzN)(bj@%Nd1%6y+DSzJK7oS-f}#9+$3Hysu^y!op_qTh=U|>W zc&rFPOJs{agUNzLxtXF;OBId3IubNpA9-iYQ{ZpEV(N((7X+un0MSHCZQ7qe3K1Pe z1&yhoffVSj+D6oq;Vqaa?palmgijuXHmA%V`b11KyTCvRN`wI^5P^mjEwL}&gE%3t zobfj9MukkKVdi)ec5h*kG4t2px%GAJeTv?Xiz{*6t@=yu@qezca@G$Bn**{>_|pAT zMo1=HRD(y97F}fkfdp?|Mb?f^am^%n*Ot~8&ft+ZE~BHU*&kk&vi+L2jCI$Kc^WMJ zb@o7cDI+jF^ac9LK{HD(CQgD(?4$xr%V0P*Nt>Rp?)z=4oYvE_pqB8e{!$o8Q8A~_ z!Z<(Q`1#Xgi~EPnOZwXj2PyLj!ehJ6A6E>TooT|E)e_|c^iH8;!K6qhhz)V>B?yBr zZ^VU4M7tm+rdYmGdmH-8zpMqU~&_B4>eaLtE{AKoXd~Qqgc(ZvAd|vPr65^mf$nBQgMK52 zB4ow>*j_m7LrLuW>PL7)4hvEtB1cSD?l=Xpiynf73n)Eq{^$Pr=~? zVM3kmLGlv}>zz{$42QMIkiF_m-7_LWh=Rc#1{VaNcSR#YAkduZw+orV*U&l` zek5!)FyCwtBt(z_l90P$j%vor_wLV3G~^6mTk;gM62Q)X`Q&D4&GKNZ&B&*NB+1tC zyu;t&Y>ylM4iW-C@a{unr8r5Yj#;K~Z9(3tUY)Ywgv%#T#?fYrX$02QBm0t z-nM`gXzAyH;GYRkaOGZ|d*}udgQ3-pTow4h&21KpJtWj^9TC!R!NS^k3X=m)p|THY z4{wY%GeX|>CgvsEgYBUoFlGhS-~(#UFCzOsW2_57+8)AcQxirwR%@$Ppozb!4UFUR z`8Dw9AOizg<1gl*3AV^_n!j}a46-cv)$U&CXE;edNt8F7xYY5S;wS~V=8~^Y>W@*v z_6vIj$>tF*n2!-6m*1pQ+0(=Ywd12zBbMCCQ^gr18Wy(O(~!Am%6kfdzaM|#w{J1w zu+-#4WM)?OLn^rCex7pajcic&5q|@9+)X*8?3s3=J%d0+H~p`O>Q*EvvS7DYjaG&T{l;%Q=}-ZJjAxhSX!ih7)>b;|zLD-|&VWbdYW9vW2Nk zP;nyN6DkR)Sa&*DCl(>>C< zpCRNum$)EkPXBPlQsrX(!+4@wAv0F!Taq%>lX%1d)loY^DJ9sBhdTnXG?^?uc~?}*chl=N>p_j&eH zP~RT&5@wIKJgsf_(fg#wy*y};3n2h?C}_e9=VsUWpY}Dwyq<~C!pCqW@`&7{N@{Y! zLZ&CAOP^<{aX%wQije0?HG7v8_CEHoDDbVyxeD}+)E&Ui~3`dmhzs|yC!gH-t>iyOupT` zK6dA1ui+ww0JSqOf9P@HksXV>0;*g@!ePKz70=jm&zlVcUOjmRd2ns7?6_-H`quWN z(}17LEC_Xd9HUm-sasfwMYxfv^^L zafvt;FCFt%wW`>gBYC5ZlD=my{c?4-20UMQgW|IP_>d!kUv@$jK=eX8$=!<_gKnyz zY&y1aIF{KX1esuYo+(Y?94WOCSK4oC__iwKhE4xPohzGdG z#WnFvxbIE-CwD5G7pNZHgKARpFl$CRNmtim*58napJMb-pO~)vR`6Qv@V`g}wV!T0 z%i4Z6`BeBgb5Jwo?R^4s)-GokZP4ut;8#IV<#ziwyuWyDEoX-zB!o1$hN*eS^=js{FKkuxQ5^Ogz#1hN}V>%qr&~aG-wxX)5SZzoo zdHO$_#rze?!a1=jf=4{S^_cVL*IY1T;6KUq`+5ACd1*c9_ z3J#*J5b;t94fK$Z6^1{spX;1cdJ<`IBWD*(b$2qnxL+-l-UhK}QZAifkOqojeR$ia z$BWT*j65cV*Ba^@?c%sXmACVU)r*OGUPn&7oi*e$2!1s)4T-D_2U}q@6PRYy7l5%Z z?~;ZqNgpYpq_Aim6Rw_?jjJ8Wg%vLx7lS+V`0hYf9&qG0^_N4%aU!;y9BapdqOP*qkod%+~56N zZG#WaPeisT(KOH{0TiwE|~bns2{PI{Hsi+UIE)vQ3buD8!#!hFXa+apqXL8A}0Hx za`qVR#5zkhTvWot&!CY)_EvS4FDQJt?{N81Xv{zpN%;vXZ(`u1|81)7C}wW5Kjqoc zjQUWV7O4`amp&#M-6h2-eJW8s{1R3YhGL@OucCRd?U{Qwd;U{b8|jn1z!}fy!q+AM z-J!+(^qPtpot#G{Xq%k79EqR2(y~5e@r)jWcAJz%`|K;LTIRgpk;-a^EWJk} z$GVXQ(X53gCGziPQ{PA&FHCLDG1vY9gx{;;UF~vgUrcJR%QaGtL>kk!Th?l|HVvr> z9NOUqWzm^D#%K~1;BMbxRGfs3V0HX*ct2`V+*O9X7jByAGr=oqmvlp&6{yK~WAgbP zUCKBaq-r%$nM0uAfmS2l>S*sfINrkny>kctxcuj}lea$5jTQ>we+?i+@rf4)1!c`6 z{DMUSg&=H7G!edst0Be@s<)S!DE%tZ;ZCqqipk$am>>R89c*)}DWY|jr;Uk`Tp8CA z*L)_O6}&2QU&X-g<8z}K?(U-na==Xg@L;BS4$)D4Nl~sQG01_~Vv#C)raVQ@{z~P6 zH^`p_lY6s$Y~9g71pz}ee=sRNiAbyb3E_^CSg!9K_^VZ-CrsAXU%(pT*EX3j)Uekd z*IxtK_8+NJnk>K6Ya~i3O*_5mntaIp=6=75lvM}$K@^w`d>%>zwQIM$ICGGX_8^OE zO3A{?!qOMRH@$rKavvIF&T=o=PO zTA^?q_M@Mh5$q7^w;la#S;{Q-SfxuIZ8>($iS88!3aQZ^2EOQrV@(G9XCH+?OGXe| zPz`Fc;{DBmy)5}uzmz#v<&#`NxMRCj3eWfS0x50+|57-~m!aQ#6~1Mm<l-P^HCMpoN9G>>TNa-Maw4^N(xz?5U0!>o;waI1HvVif#33#cj@MJJ)N zD?6FOJKs&u@&{mWnmFxZYaQ%IQ-Qn6 zU$y2jR`2veL!n!5%}8X#`SAz(4Kg=R$M&O)KWfhCH`%y^j|jZ(EwS3H9wL1gZ~h|i z**V728uMDM=xJTUZsZ7CM;dZB+V!~3-_E6_&0?iUY?jM@=V=i-eLnMFi|?Gmj$ycw zyjyL7gvPard3^-D{|`}b9o5Dkb$bVQDbP}!qNTVygi?xoaWC%fkijYL?(W5{cqs&T zcZ$0^lsCWUdGEa|eP&M zHYe;D0d6fz2Fn?yb*DMj{w)a*Ij`QdyB6JASEob{vwN2O`GuWr5-mY(szP~%6hTlr z>~_WIOb@9vN^ibC-;BSX38vTY5fXd;MItkF@+AP@eT0*Ls-xq5#*&ym=n0?S{AfI8 zyX)!PH>`DWf2-Dg;1aMVvz_Am#bFpSUu(6OGR{2l#cuQBf~s(N^JH+^#wY<5oe=39s zRC2<*f+${)q=lSBT=7dk%y}%qWy?cS-CKYS@)DKJ6ZYRt4bWGu3tJFvqYBQ(;iNo} z{6%wkl zUL-xgf6_JI1L3_SwgF!Yux@10g3jSIP;3j;-y+|`UTbi19?o-%8 z?w-tLBdpAuTro(|cF-bTwlh8P0`~ScjLfTe;0sUOS2IKfvK5aU68w?h`G57|E)FbG z2)g{Mp-W4qVSqtdt^WIbNP#UMf78AA9=pXnaXtra>B& z5XAbj5ZdM@ThQyM)4v)*4P|y;r>E{nrS?;&X#^h>32)45VVTT1F22R1_Xxk<#F8+H zZq0pZ0t9K0a?AV@NA_f3^Fh&0)-}${D{vA$t^P<^zVrhOAiXqu;#@sq?P{4_av*Ra z0WTs&N^o16{(atRTbdfZOcW`D?HxopegTAPw~k#RyGAM(x6bu=?^pd?%jExTAi73n zi``B2p`p5|c|dqCd%Enn4nZhV&3kP-g?KOwXgf<{W;1xR@JkJuFG}{0HwO46)B7~Z zPZCSUTT4INv+^$ANPN3DS$%PO^ZCVNu2cB3yH3NzFj{f$13Tc~ut2U*)07vfM#E?nJyUaN@qkY#DhI9v51h&Ufb%X5iXbVJ7@Qgjyp4 zfEF)wBG*k;C5RGXgLd9tVeL9sWB=fT7APRPbxb^f#O`{3o_hHJe3LXIvg;Wl68)P? zB>Trbf!lnY2@9cF7MPFZ4_PWY_d58&x-6suU(|Vd8Dnl0EJcyDp3i4g6L&pAH9Q4P zO@7zm`3Y}r>-wzQEPv`L7>mA%=QNiFmdV^N5z3Yei{~|%Vm&~P{+-}Jhu?j%abO?` zG)JXS5%*7f^|X3Ej#~0@#Kb&Bfmwtp^zT-V%ke_(@8AaEI?vN-JW3@FQf+<|5ZAR@ zcc&sRn3rAjz1tl!2t-9D;3735>CTJRi4y)lB*Q+3^T%85W_7Bw4o0y~C}0jI)^-^T z#z<-^UY>M^mK(;A>s=(nzYDyt8rWzKl-w3{*cRc0cjI#=0Ov$>cIbYFW%7V943~)H z&j<(HkrsUVe|N2 z$NkZ0XQswJPGEaCAqnzn8qH?p92Zoi!lq1TnZ5`gg$@YX%f7dLmpgB@^dl&lrJ5an z5p)fY=-SVX9fwEy_a)85{}*F^_r;TR*;Vr7kWwKEDt+mRk^Vq4I0=)mVxN8Yd=PgZwu0b=)Wk z?ZOKD=qOx#NWYE86HlM$Q~6MJr@pp*n>eED+@D*_Y9C3s=Y`rN%tutXYs5xU?upRr zO&rnK^QSXYPr3<2KuP_tE#6YSsECKL@g zD8*BCA7kT4ish4}xB7Rs)E>Xa%%XxV=S2N~sb-7$7$D>P{>qR!j0;xSKy^aL@R7H* zD3J#|bdkUJ8cTU+xPU16{upmr1oqROLX%v{lUkh8E}W*>0Z-&-JIj~~rV`M`ukwQ$ z-N(r%bQ|-h!pRc+2t~D*Y5UeE5ar8b_XjP{cCGYyhO*Fin+H99E1gDJkyBTru^-~o z+3~NY(iTX|Zghg0?-h()pFN5klbf#9nn%7G7leR5!ZL;A=T2tcq@eDyG}Z?5E9K@@ z?UH)gq%n@WDQmq8kmC)bhxc6wj-ZaMjq51C&Dn_`k-4WGQFHL1 z7vcLCRA&p4`yyYRfY$lVQg6&F9d?<5Gg0?K>1wu`&J`4wr*;^15T;n`?_ z<1L7x5Y=bGz(3W1&)gFAF!*iF*6i@?5DiV0+4v$|0$_=Dd8WL{8Tvx)v@T|IL>EKi zU`n7iNn?B;Wi%j_nIIoXZJ7hiR-#w*i9gpl$dGWTg+>ZkZ+^AulSB;RlO6 zByAu)Y1h>1@3w+=+6X-;m#)EB5M0W~#j1=V@c9x0&nl$me((yz@FTZZCH(Kth!ddh z^8v5&T4hU*Lz)LhFo9JJ;zlej0+^lKD4v?ln;<>J8!orVP=gH}?C@+^QnA(eVL*D; z-;G-f1?$%i#{O@t?mizf5P6wEZbz$QQ6uo{4nA|hFs|y*4!KOT~+Uv)m>L1|3)Io7G@Y``})f-_YwSY`o zfdSr^;(jG`$&@nMl5GsbAKIfX9{cRMl}c?$zUI@k#m z^p^talbzwbexpQw!7n-0APf$9A`5Lg1yQ}fDF#J;6Cl!&KmX2CgXSUMG*L+P z&RRC|ZG-_IroW}Lgx-%~3X++e1Jr&J^-1y5#pXxd1^Xb^d{{8aq^Ugt>CQM9pRLuS zw!r{R3!YLSU~3u$a!0~%${O9G!(r{asinnf)565xV$dL=1TTA`XpkvPy!3Vh1*Eb) z%1d+m_i92x$Ptq>@UrYArS}}RkIU$3>zpKd_)4-AmsHd^LYo@!-=M|_|0@m36SkqXD&f>~ z8nm3-(D$T3lrjhuD3zzd2vxY!M<+o$FoFMfL7}ZS)=_fpqC??bcu}Zr?-5xi9IPkYoz< z`sAt{&iY-c)trB6$0fUf8POLdKfm0u@oWiX?O@xSu97Q9HH*$!;d@`S&C*}ORghxN z^jnz3Ybi&x5=2M#b9iDL&CmG6ePw4*84XS@APDk9f76nYE2LpYZf56D=$bRe*B{wfLxqu9YFY_P-}zld|p`Q+mxub%=>WZTBl0 zyZ$K_r4{$O62D{;&ws2kc&?lBn>}mSkFfOfBCg13lg+R0WU9>Al}M?`nFcxNbX}`i zcpyYJO?-_!VD+h!CSz_!BwW#FbE(lqbGTw{whNsz@2LO+LRn+TXbnhJi6oUr_&_8P zjW`e-o&X$zDh;b+&4FjetcG#%nhTw$`WylqFAK9qIf1d@)v==a{SCX;$4L;D(N*bf ziYVj4^~XM%qpu~#h}%zi5^z5K`kt*Hhlwl#oAPq*{;==m%O!bCzQs~X_igch>CeA) z)w-s#2!P#xlN50yBQj6o&3fB%fh*{B66)i?t$1t-u|r9jP!|}~mlR905dH*bTe!?8 zb~tHP>b2RAreZ2Vin#HokA4sxHvB1%ncM1DQXPq<;@&-`?XO><5Xui0it)!ghvor= z4a*B6j`87pdj{{Dc&xKM%kgs!M+1qIW>(}tr1gLR0i~g2Y;d9Ha6T~K@YAB>)Me26HBHey~RaucorFT^;<)rCo(BzjfZT)}!sR-5U zO5+3m{#*FN@QM8OBy`srADk=OWUFqZU|Ms`*0Akyy?og5n6Q_fz^UGOGLSKZ`I@@^ zLr-v<@^~2wF-PHn(+=eGMdX?CwbMfA4;g%0<9fF1Zw1EbWlPRuxqidf3xe~?!7EL@ zW_2=ZxA@kvVXxWtDN8v-;(?V!$^!>74N+AfLjW~a2+dpz5~jf58JX#}&E-j6vA>sx z=Nm-HcTx(wd?36XaLVlF>q6O}C!24XR3CUQDlUkQ{!w|L{yT0)2y})od7KpMd#lYBK8s0vwQ%VRvSXQ z{yuYv~3A0rt7jK?=D%4AL zc+;-*Fns~ScMo;EaMi;67s(oA`-Fgzn<9v+JAlL|9wCJ zTCi=I{Nh$&QM?lz`L41N2BqpJ`P#e9M(c=QoJaoa=EEw`*ys& z=;jO0WSIwKJLVtCkeJ`y=gE_}DwSC0mN6XN7fh3!!4=(0?=@`7>kOK)FT_!&QX63Ks8N$^b~j;OS+J>)oZ9G<*V1IS;Tylyd6S^a&U@ZEIbczy5BbM^X!*4 zqUtGgm =p$>U2ywMB_H)HP2X8B>9J6jCL*Le~e&j+!kZx&eCx?Hqxt8OgQCgkze8w^Fqo7{HK5=>wX}3r6AR$W)vEa|T8tK87Q5 z1cpj^O;C{!tNkhA>sRGM8$T89Ub~uJjY&Q4`24)m3+~Y-jhZx$dhZy7E|hN=P~= zpmcamMF?KJWX(E~#o{zWH~p)&t7C|l8&8pOgo4A|ZzXn5>XAbKKvtoDq^6WQj>0UP zGl-u5L{x!ceoWq&ECyk|VJYqlRC+tb(c=NrxX79Mk1AKHVeZ z+uS$8-%`p`SVWD=G@O7nA!+Ikh(@Jh=RDB&ci(}ZOEV`s+>N&) zXutW5rPU#BkG~TbpQ*r@>MVz(ei5>2jqVwYdLaRZ}vg zNP|WKu$g09?1%w#?%r4e&A&@TtoeKH`IK!S%2IXOyrthbd*;!y>$KnJw$Dz0r9*B? zylz9cmhaX<4+Tl#Z&3ylnqMa;B%iTMPBFFtNhp*l_+r5Wh60#Atl4kP>$BQHMY+d& z_!oI5#&7Z7E2OP=oqw~evshz4wok{F!LosSCp+BE!OcAdxoK=4Ro%kk9n4;;3^4^n zCN|AZYP_^;Y8fnJ@;!$!$#qpax812h7tG=-++bsi(5_Ua(uZTu%IJWvwd2M;zp$k( z`s1PB>t;3`Z!L$}o!jJ|g}lis2E!6`c@wB5PT*Xgm zn`fF5dDe)MnbOHA$A2Y5GRyd3uzI&lL6?A0C#H%9q7jdw$ndUeJt*ck=}n_!5%kT| zKLaC{$Zv+ddF!a--~PY~@QuP0@P0;afNU6#2EJtfekOr+DuUdPl^&Ld2f+Hau-^B9bi&nH#F~;gDzWP(R*n?NIaQc7 zYiuaAyq6)O=;c;07`opJ6D|6$teC?xqMXQ5^gi71t}&h2Ewe=N0W4z;G}i6vq-d`m z`)p|_HHuw|2)UN8&mBl+@O2CwFXNb`9~mrSLguOh@vAEFF|5;==siv!RGyNmrE$SN zPNUw@|CRWNq5!|GS*Ibq`m6CJVk@e4n+q!bQXBp2m^O0b+vU?vMGhY3mG>Tkn7Boz zTgXIzyGb~oaJ4Ieo4XbIHz0YVxet4CL+PSN6OMEIQz8Fk#`obOshLHsg?1&KG4iS* zdgSBWl2}`DC{{v~cjtzVw_uJ|8ZQES)56&E zyDeTV<3NC`hvXnMBS8w>VV_;Yr? z@>}NXvW=|o<~CwFue+k{-@JAHu7gjwkHLl7^%4>ZAU;+g)`*nlnm+#fRKzlKg!=BC zuW4PqM*TaCi9GT8^mou`GDiCkw&OEGcQRD z;^}oVVc@D{;Q85GJ$sNE39E+WGhY|?i-3N_@vg_>!tv2w3GfRU#XnXm{k^-`{ zrhJFvTd~bz6*1ah(o;j~FirwO-5b1rb?U?UhjA*S_~G<@g!40d*@_vYY*?4F7%HBa zzr!J=2_xb(otvsqq!Dl5zvQh4k;);S?>iA|HC5mGX@%Aj^FidOHkhB$crecOpiv<+G}q2^MwJm|Xao(&9IvB5PvwXXD8KGC zlh-6R{s9*>lCCc_MWfg){uDCjCJhJ4@N4RPPdVb+dsI;z_O9DQg9B=o7|+j(Q>+j; z!oCj1Sd97%3Z`aUkDJKWTUeBqAzuGH*m_0l*q`=xdd8J;{hu9|jXoZTR`a(#2kRex zfyi3?rNAi6$gAt~H}U;|5WyGkidNjT!dmJ0Ze=H{X!RrBL5ym>@IZcEQ7BBkL>dFh z(DW%U+#NYlnG=p{fuk2_$#WEH_SeS7A&L7J_Y%6TH}U{wXW@tD1j|S0uoLop8)i4+ zkIXK#ES2M;GKUYfU7eYkp{HRlCR`Fe#Sh?=yM=YyKn*b2P6+@v;p<)Wj`*t*egfY+ z9p)(fawg^kUqxvciV&uKIAw082Tg#ua0A;~Yvc?+2#l;|8{dT&+$Dwt+ugt15!A%P zfk!3EVIst2lgsyhf`vtyt=&acTwW-26RV~mE)5UVh@*)yGV}%+xCLYWP3|*UTD_lh0NTi;YsUbBlN%P`d+(TfW)L$fwa+ zTF8a0Ye&~a$zTg%AE3^`qhhEyhZ~amGhMpf$qwuskRNzZFW0vU0MKroTwE!XNRfv=~wV!gjWv$kH(5iw* z^2mm_78op=r`||8;N15Wf9eqz=>7m6iDJa^J6&zl;E1(O1q*>W0tU;m6~G)IXEj#c zX$!#s14+t+n~6#rs4LS@me|I7SDY#oduhK8*0|A@%+=Db7$?SfGzTsmYNa{wG9l0zZ_EQeAZT zZfsJ-(tx@|I?hKhy~>Lj;iEh@h&B})?8WRi*1M5GTd5#+^J8%N6rCrv3bB8JETShVJD1_D>Z$TCfxrEMhkKy0q!Cz}BuH5B%{||weMryG z)9;qz6owtqny{f4eppYQpqETD_>a`LaDudy zM`Z5}9LE3;QM*H~GT4mPPd_3$^B(a`qi?tGg+q9BLm_MBPyErRDD25vEKi=u#zVgj z0OeSNR-9wr_ZU`?Z65??Iq1Wi2X}bzqGT-3vVEO#<2Zck&QLW@(J~g+{-_0~RjES2 z+?7Ykx>);`CRs@BtbgZZWUD}6RDn|&mZt|%MJU`k5a5~M{M@4XtypjrX@_HzS}*hY zuX_Vp`c5y~`dFX6Jiu4H9(lXH^84r`Fk(=zRQ-Ne8Nu&ypOmgV4$S)>g;RUyWPk|n z)r_2d-c-J+HGQjy43+s?)0hl%#aY+1o}s{+5f-8rUvSj+6+m1d5Z&!I1q~*pBtG<6qQ*6#iI^$>$fUjNBAm%kauzC4*;a4RBrOdeydYWV|?N8IOYohioyGxzc*Y7qZdQR~P?6NYGNUOb&y8R*CmV%s#-7fcqxn5 zV8tz-Jfm$w1Q@pDqGI{cos%p*M}%VZf1)m~eKYO2*;m7e#r(k1Brevon?X_b zNgYN1(j4ZCT>p8NOO`FNLL)6JhzWgu>g#8}ylJiBjjXle0#~VU!mI_t^#5Zmxb?*X zMSWL(<9m`6fFoA%}gYcsGoWd^XRuq zy$sMD)I_PM{rfYdTNJ(~zrRra>tkBP>l`aY%aU7r=SytGX%7m)eZkh@-D4Vb)4J=1 zj2~=U!%=$ceq{=OvMztcjq;uIu~Eagtm! znA1yqsq2NgNIn&3d|A1Sj@_B>Hg$fDQzx-7Q8&pAV}}_pO5E>N1{YEZ^JX& zaENz3%KGtmWTL-A7Q&)}J^9Li_PT1nv}1vs5HsKl*w-gq{IQ#%-bOYB&mK!mo~S#R zUp#L;1R$ijf@I=J5+HBb`djk_RI_|FKW;hDd?RbXz!n7taRs*4-TZq6K75X{CphII z$E&$mfgGL4IJYzXlgkg6-tbKIy&q;?20{4EiN2g0$lE09s8>0VR{V-8D_RZr6cBcd zcmA3@A)ab|rqdjJPd?-hTW#pN~bZ897R0ft*2o-dAYyI|}iaB0P*mC7G_K zWKtsW+Qp`?zp6|Bd8{=hd=q0^=qRL0hSbo-HSVhdza-V{7%)ShG0r)?`WF$62lt5l zp1h&3F61F=7Y}kA>TWYmrqjnhkq~PI+*6W{Zwzo8+NH!nc9RSNJ(Nc3ybpOlx*57z zP6oa}M>|LSkvvd03WSg(zc`Yb-pEeUQ>1+48p`yzN>AC1kX-FO>!AMt>YyR}c{MrS zFiF(?#t6D~D8TWP>n$$AhWM1_taum@IZXZY!!yLSNME6k&3>0?2KwY2mwB&xQG=TQ zA`!2esp~W^D?%0n%=Z>k`%9{b2&HI?sz&hEJ?f$5+mOXXXNWjYnmVCwsPHI3$3)6s zL3dv%N2kbi1b1x1itNxXYqL6rG*bFI%a8Xi7dWLtD^P}=mD0SsFCctNANc!>7e6#` zjuM{_bdpTo8lTt$I|J2Y8N?ZzIBREI>Rqk(E}l*7DP0JF#fXz94diOB%4u~8hk!&t zkvIE3+LiK$94F@7852Fd)OOYGg(TV0#<%EQ|6KZ~z&Y&MO@Szgo!{tPB^B*+4E%#u+me*Z}g_I#OxIoM5zwmJPgO>7i6zg?9K&yR~+nt{xi z@+Ba^`xxLd3w=3N<8*-zAy-Ut>R?v%k z5nq_dm;c`Jz?S<{VJUAkYjDwi&e8S3$1M9pC-l8K3C?3tt6Ljl(|5@8Wyq*Rk0QsP z+w!MaN9xgt(tm!`Gj`We@;c!Hfz!YNpL$NP@S1u=QxU|xL&e$^KKe-G zO6nOv0Jo#N1+D&|{0n*3<8%3;(*R>Z!Rt^~xS3V`KNlHhF2cJ?R!uDawsW0-3IFhk zO;wryd{KsPeX9mkrk5ocpea2CP^jO=Fk6c}`W_$9SN80#{f{06EKLb-&W!kJMT&JJ zMO@$@L^8Bq!KrEhMD2$dpG8;6ATCP$n;L&%_e$1F-scCN{&|Eu@* zmUHJFz`YOpA}mCq+QJiCXWZ>1Utx3uK=cYlti<5wjc!4uR2tg4q$hE7?0rnpMzw66 z=&h#n;=B7SaJE2F?HQIGBfcl7o{{rFsMe``38KUgQP)@go&OPxb0R^V2+7+~5h#9r zKRdE4${Wm(>ER7IzW*(St*Fej@Kt=Pj*-KCV2*ttOw#&~%9*QfziPUx4$o08pfC>t z30sAG(ONIEiV4Q;GBot-mkDK4XlRe!3Tcv)T?IAS*LMwxUU6ZQxw!^c-Z%|Oc6E8U z=graG(5?HCDT;pSzNY3wz4!vM&3r7`g{qRX#6550tBloRw`|WnsCo2ySsaL4H(oJ9 zagRuIf_U9o|FoA4lB~5%%DUhKCFpok1vyhc%^1>=M|Y&zmN%0u1XrWVhZ`gPI zNyu0mrhP+~#MSA-VSofIngEL7c!HDNuX;2onE3b7Sx)LFjw25jK@DJ$?Yd$8PR^u2 zKRwn03pfSY5Z&od7uQEdq6kwp#f&B=wQ%uUByOW(ViuH_+@QY?uW#^&rJ zIU=yQ<`%S`=3g*p^<-p$)}JR>QYA0sK&kHHg8W*4+Wq!?J`qgX189#89X7Umn4~KY zAFQ=S59C&U%I2bxd;zL>M!iaOp~jrOj2BM~9s|BMXP+Fmm#npJJsQllur7yLV@~go zE{FHrQrlsXDKMMN)qe%hc(d5q26!2C)gw=rTu-t~9TjHv--qM0xNgllvt_sBnQJaG z(@=!{*nS+-h8IPex;5`X>aAwseasU2!Kif(NQqb7rw*XwSTl^ed;3+hA@oU{Fle7= zjDz<0sP!#OuR(I~4*K{LIo;1Rfh5Q7Y=~uoKkpXN-k0PCO`(6oe+@^1V*>Tg!)IQx z)3onK(EQ1k#M&pKmqD>zd)iCJkV;JkUSBwQ+A^Ku6St^DpuTIwOCtL8hh%#!fML}b zb~NpM?trO^ySeIitAKxxVmExe{3K|ZD|qL3+)hrz<-k1o*LhBIAh%U;u1{qPVtm1__gJs3#~3o!02Oa23$kgZA#ThXbs(#-sI_Pq5y&y9`g;*gD<)_7d?0 zLi*B4wA=Sw68LX9$PO6+l`l$5rsU2;(4{dk9P;iePt9rV-cjEhS~lFH;(pBr6R{&8 z6dPY#@Qy>6D3!2Li3 zv#yxHPI;wi{7XD3FWtlniaqxG_mRo}CdbX~?EYh>3g~Dw3c`A`)5D<A8Y%7Ah7S>r}g)m^UAVg#pElGl-UHO0cDd;=PzviM{#2P zYE@3NcjMSg3}tBepZaw!0?MDhX2g}*F$A{?HujF{I5i>2S}ax(vri?A3|FO`l00e; zJcQA|=o=`uANZue1WlH_6Q9(n2azy@W{0z%B-r|t6zR!mxBFe#EdZ9)PGB}WxMXj8 zo8d1k4UULUsl`aF^@)~Q+?P2y`%EYI#mygkMqKxsHhC2K_HgML?Z^*jIwwpl+NS>#zV#^VpdduvILS4dXagIC-*|8!oO+<#&^Fw%cEPIT1T zE|W>fuzBCfK1J>g`zsTV%IODJDg%Lt3 zI=>khOkfip1FOs_Q^~xIV7=~kQPC5oq7IYs`G2|!9bEQ<(h2Au&O;flQVL9DF&P*q z^LeaEx%WF7(!_3bvTqt+D_tuPN*{R7I#gBcM(&Q7p(b3%o$MVH`3LB%SH{of6|}0V zTlCg_7^Z3PV5l|wn#=NYZ$O_DA}BiP-}lc29>V{u0r|VeTI0gC-pq#k4M-~k%7nED zb_~%`*t#C5OLmqKt5}3sg}8kH+{*~rQJeeZ(Kdb~CcMW-J9Tr&ccvPT99zZ^BtqVL zk9V^_$1@^01{u4sE``!KJe9_A%=W&2z9>tTwA~sAJD}l#OSn>e-WBX4LQ&;5c<-D) z&et~y7&!}Bz|wr3|Gtu){d6tAV%*clF}yk2YPVEQz_FUUMM@8jiqrX;&6lSdxrf`o z|C%m}B0d{A`<;Vh6XvgH@SornTh-baR96>lK#XULU!_MCRg(52gk>tdy@8i|a<0nh zQskKU%{~LbWWdm`WRL-!O&i*io?P$OvWG!HE#bXLdIA6F3CxTn{m@{#r=~r)=o~~I zF3yi4Q&yp$KsWBnq&|-`m4djRYpYUY2S$Gl&e?r9J@sRwI=sr7+|t|?hl^# zO?-{2hZ^_Xvb~JB+g(a!x<)s^xkUf;X!~eC!U&n{i{~>%yVpEQ&tcI>8N94A!o>N! z_3L!}_4Cw(j9hO8f3FK^|GcH@L=#~5bU07z@SGa-CgqO@;mINc$>dxHilFr_mcMGG zeYJ-;@urXLUeWYrd2gx)$r8V?>#v}>YDU!N{;iJP7|VwgScAS{-7!T#4R5JMEck#g zvU&*o1aqI2gK{W^Th3N{HIO{ZKqHQ}&Fg3)La7k2fsA}c+{qsQ9>p(RJ)n9!v24oi z&Ntr9=8Jcpe(O5);QUaCbnEVB(zu>{bCJ+CVwn{_x5b>Ci)#}^+iqRL*@f*&xj*N& zw`XK#5ie8lI_qb^lFRSDht{>ct$b6IsY>Nd_I{qY*d)4k&h3maIJys(|0Xb$Y1zqb zANTCipXuXhl=wQ2PqGvK2uy7liNx)gfjYTM`5y&@2AT;T=J;;9MLZ1`8C62p=`YLg zi!7sIZYs6S)X3F3>FY>~D#8e@&TW@8zxCw*LBb}&^UGTCS-i==we#8@uz4Mtoz+G@n)T-|KmaJ48qY9;iZy?b=s7qg zEq2Q9*vXf`pLeW5mdWDN`75Lj=vrU`_MX)HnTejuv5R;7-%L7q(XOV}$FrCb%*UAC zSGbX*L7IGTofkSH7Ytmz^o1P!8uAP})}4M!=X2vh57*^LS%E3OuRINn!?#q}(IFw? zm#ovps6FjgQ@}GELJ)XE5Sm5y*-`G*a!a$-vQU&$bas1DcFp{|h~SU2j2Io}t2$aE ztlbP0Le}^Q;;)~C{Qf6Ijt|WQX65LT0Ue>F*uMVLKC-L)&relYE$8N0yFvYH_pYl? zlH{FvE6xcfr2Qdo6>?S883Vs_#m(EH4v*LeOOyjxP(YeDMBSb9GWB;HJntnVQ8uU( zT~PI{1f4K;tsF`J?Td%CUo}nCdr(gLQ~Te)RM~~HA>-1a*`i+;oDq1NDp%_`|C6<6t*T01Y2je zesw$FiEr{ka_SHw_9&NV0v`l|jjO|^$7A#MGE>%P7G9@h@w_h`mDk3#P-cWVZBl_h ztR1>AC5PYeHsj@-Q^S!uS_70;*yM*$^9I3#+SV7X<+xb4LNdar0f`CRj;9meqU@Il zT;fMU#juYLR*)WwnDl0NIq}fpIfBcwmr-iG&+AQd{*nO*njeuf((5=VM3wQ(vuVo? zIGym|he13A_PMq^wkjOz(Do9!UQK|{zyd*CbwA+-&ojeyfd#Gs@Y+{u^dDvy$H(qO z1)I4nW-Q7Fi=!B2Q!!blK1gSJX{+0nQZxAd%X`EEtxlssd{3?rRbC}YH)e^x-EArkm_ToFb{W1mw?j1@(-ChW2O zn<=s#n}pasQc2~D9k_UybobZcY@06zN-AbT{=MX&U=K$87yq`z3jZ()&S|q-CvQD{ zNy4MM4^@Rn%(mqCLMA@`7Wm~pZ^wTRaE!`0zjD zS23;CmA?Y&{Bz_;z!7Msj~yBld@_aMIP+YOMQy_7vCpC%a|yJ*`c)$0E(Nuk^6e&1 z8O1stXGXsNMb}m=* z|7RIn5JB_XHBrI9sSRX{bB3D_zX^{@NqAdLV6M^hkvJTpMPu z2~7rVZ;*dnGhCv$AUm2LT~a-@4p--nMD9ZiyvIirc$vfqe57WZ@uDn&9NJ98b#AeT+Y=!v3Vs$s_YGDwzRZVJEz}9jjYJ~tC8%Fi3 zroSV@HSLmz2?@?;J^qy#-bi&2b0e?#{(SkmA+^|qh$((|6E07O765-!#t%5puHdai zxOKzY#ip)uBp2V7asMDuX7r>w7s96%tmgt=MWAj)tA{#P#t9N%Q<9-&6o+i$!s!D! zw6x${u~D9p+!IR-HG79dCX^Ri9D#c5M3PI57u_3@^N90oDn`(d`~h|(iq&~z?knE6 zG)*|ArjkRmA?8G|xOm{yJ|GhPQ2O5X>#}OE11^t7v6x^caY@KyG$K_h^lRk6*eJ|e zCU|1Tce4K9>6NtH47(S3W-H_Y|Df35XT~`-aVimjAlaD7r)rv(r5(5Fz{9>^7j`G- z3411LZA&8dG!Om)qmRt@u0mG#lW3oxMUh63ZjmnOoyNz`(rM=yv}fD&e?mdd)@b1d$04*DuTNkXJ;h?3`)!BXd#-M{T z=VUoF#UC0?63tDN&3E-#9Ty`FR#YlMtNNS$a#0&eUdC@lHs6={*sU&sef|qU0K3+2 zcQbRv+cN8sN8L^8x1mx7IeU5f4+&{-QwXR%2+s^TI&t5o$j9BYNa{y$gbq@r{n+V| zSsjYWNbel8jZVnqc}>wv;{D*G(rONtG|l( z5^V5;;}m(#p|QR{ogEFYsw-IQ`PBZ%w`GlgDz!DAvFe1Qv2k2qgncl3?R93|!zcGt ziXRu2LlF*DH{OOo$I8;2dZ|w6w zrhyGpCpx4ZVYcfuEmRoW&QRZfft?Zv+rOBK3x(!No&^rQGSwxl3mG_{<)us5SmIe2 z7iLtt3Y-7ciM}mzUwpbue_sm`am7Gx8b3Zl|LMtyA$OzFMvOO>ga>qsnnY3T&ng$| z=@FO`0%zp>G|S@p4^kNn-s9&jxOOL9s2L;FKhCmI^X8>RA(#CiX{+`tbckW&L0UKv zQ9_))I6S_CP55!fqMk(_5;UqFJf~^v&5;iLGAyVm&Q@dikLeK29=3OYdcEK5TtmC9 zt9gLqxyVPE`1j@9H(SD#g_m6i@h#83#e97=-^9|0w;{Xs5Gq_r8<^OU$M&{5T0oEa zF`hQjksD7+U@3ZEbA|g8^ z8dEu3N^U!%R}``d`nliC zor2pDBl_XTA(dI>`<_mELZ`_Fx3-%!H8(W_bJ$whu`pL|8+w`bK!CfG&a|CL{`Zz|3* zZOc$|=)^Dc`xCdYF^$(c^@loW_vw>EeNI6Y1TO5g3QugLdh&7ICmbc~MRjp9cH8rZ z5Yrp%Y-~Kzm$b*3T)5s>3wE-*(FoPj_fumye6iIL_}=S2<{xiGH*jbNjZ)kfAHjm1 zT`&c2C=APKf^CIlu((!{9dw0N#A0aUZQ1K_S2&jNvJ|7-Z`1P+)-W1Cu-@q@v4ENg zl|MzeiMoe^Pf`<8tMM9&5>z*5i%0S9QZ+e7e>XApQ;X3KE; z+15HzJ<=pquTtAMby1JbiL`l45r5nj)XZjAOpPOMq{w}gs&V)(PkbI_?<;vL9fu<_ zAWn&yB6SzNLZ0LerUVJz)X8i52FpbHP^PU%S z0Ykp9o3v#%%S!+Whh+!dPD6HVWl1ae9Zh-K>7{;NCW%sojBh)gvX9IX}H6vhQY#6*C7g^JVU zo4t~NKz`5E)io1)DUE`STqk{g1MkV|HyNHf-!(sRLI*~jPj;j03{pz63XJLWIN~USV=E5=+=z-H9u!Ehp6Kz zORFP>HpsEqm2%vdJ*NsgKiq;Gb5nEn+j>6_l0Gl=>?!}4eAn(HE`~?QtHgCaV9^bG zM%q*<4!;u*tF|U=fdecj!R<@6t<6k2nts>^m1AR80v&0uq}r+BRBfP#g?nBu{=7v*?bSl*t3w>SP&xE_Hj*XHG7 zkmCsTh7d2@-!w27f48*clh?FIo7jp(*ANB)#cT>STK}`3>{Obe*6`WSE+)$mnw!M| znj5@ezede4jm|M$ii#OD@IrA%jGL_`kn_>@FB&h5D-!-8sI-#8Y-4jKUh|n16bM}5 zbiYC4t(SvKNU$qroejO5=}K$MxQB9E%y_Z8YI9ky`twQ#QEx?2?~I?1^4soijyT;E zYr6msrR{|oCY7&}Y#9g1huka%1r$EORuGBP!Qu^Bv{Q)Zp=Yg^4R9+j_X(^Rj13fSI zf?KaTv`r*#XJ+jsF?4A&jM$phH!CQtJ))LEpFv8Esl0lRY|h@k`Jnwlgng`P+J@t+ zkF{d$y=g;It{$(~aN*x}`U&wB2aKF#V}I+)VC7!sy|;`?0kmvFaeoR`2B01_qXHj_ z6F%#n1#RvMOe){&e!tNcwAS+WTNOG^`DmO>?XU`czR*-we77Q~{(!kgu8@=kQe2}O z*c~=|C7#0fmSXE`j%seV9@0j3US%^$E3ze9%cw!a4eXAouh*KEf_#HgwE3$NB9kv1l&fBnAx(pvZH`0_~F4wV!< zHYYp29eqvu&~jfzU!PVFk}Ldom*bfReEG3au5!)bO-L_ipjqnW6$dDzDaMgNjH~ZU z(eZ;UQ_&};DPLm(2L6%R!9>OSItNvH%j$8jGHz|}eS0G$IwaTwJPgX4`Q*+eeeR9e-$rf3){y=*=8up)O5@ zHHjS@1hXhty!gP}2`c%jy&Xk^SnG4@=Z-R5F2$tsZ_|@rsn;e78{5F$kvJy9L&v{u z19}eYx%k8W-LrI@11xP^@m58G1tVqiP7dDhK=(I&Z*HCml{By4iT&^fOw;8HMv%^= z7*-xg4BbZ_zVOtWdfAef69rl<*z^fCU|$QoB>d}OS#i4twWdy4plhF%G z$|_k^tsgym_VV$pfW-N%KBq%>Q z^ZSjBonVEuqB(wE%Zj(fTm8C@I|3UsyN4RDDV^`xPBHpLAQQN+5J!exx6Q(g@)zaP zJC{Nq+XpB9SapikC5yG{+iQ4FyO{VeCrs0t1T~)V)qlXw&gRJqWnV2fz`o1Z5<8Z+ z`Pzuy258T6y$4spT_>%YQu`3&x|J9GZLbWHPYIEI7^pVi*ex>tw7+?U5nDc6+4DXn zd*onwerqO2(5X?|Wmj?*%wjY|k7n!^$TKM8K>W&L6m>KcD|*APrBbeO-qJtT9Gm3n ztn)7HEqm7e-~4D+k+_WvlALiNkBX!u&5HA#$VRFAx(|J~xPHHPrkziGn#}f`+B*>x zmDv9oM$CdIc$G1v&fn>2tF$XDEx-R0&BbBfJacC(p78V{eu(Y<)A$4%sBPjg38%;~ zm?J!x4wgm3FD70(*3VJxzT?T(sOj}M-79GKpKe5+9g{37Q&M#acV$L~q)$uP2unZc z2Oq(Vq)`v3n9!2KIAd3;oCQ7+CrM?>xy;8b3(~1XFv*U?VOb`fkvNU+s1mKl&+|T6 ziQ`4J<2RJgCR&;U+D+S_P&UnLm>nuI8peOl%>aUWFOZ&3KAR}3K=FTy%t4+*J12qZ zavcBe4Q>Ck!)rStJ~HEeFUiBN(#a(BGV_|qgJRj3?+W~)wRjG6LTknufmg#SJl30^ zZhrUuN!KQFAWHDQ*dVw0TRKJS!2roV>R2!MYMa81e+(Z(R9)uNQ6bvUA^=qd(wElOZfWP0Jl zT4zJ_T?aR@yT_7*Qk~71Mycy~S9AK(GS=!JO1uBQKkuJ7UGH>soO|eh#p1-&h7HEP zGe2r0lwy8U7piC95Y$;rEBf^3sUhMgRo^1R{z*6K4BvF$3=&(F6d6Y!`-}Sy4@(f( zPDkR2{Yc}5yo!oI8#4^sfFr2IIO-61k6qz$Oz}#!2RGVrY| z(@+w=0Qz{Sb5+c4Q_o2;u$9~Sna1WJu-|<=3j3D*P&y{3NWqY$yp3N`Q1=|smm}lg zpkpc9SH9vO2&IirymcNhbhDdumF^F@k0nr1Q-3$q0Shq=wVK~}uAh(pwIBzV#?2)I z2eNl4EkxD3a|CYNp7pE0-;rgJc~f?`+&m4(ZWk@Ymv6k{;bok3tf!BQ4p~D@Gtu@A zl32e$w3U$rZErYydbd-%X5%TInykUXZB`6gV1vc~d#2SNIJ)}rgD2Hnd1Pq3!nYx} zRo7VTHfxo*Uss9k9M6y2AMvVt&0`Wu#&|yKaIcenY3x-M`6Lldo2q$AYV#6;w*@A*gX+J?Bu&Wxd47EcBk!7yR(bn^tNBhKZ=AeRUwZMq9x61hbr$BeLAM5 z6t8N1Xhl)>yA4yB>*7VoJekRAP2evWPxV)0WR75J9-AnSou5Ca@4(zB|8^D5CKJb4 z+j+s|im$lJRgU16E0~ie!x| z@X(*>@J$&{O2J+0PVqN~HuEAov_xGIgPwa7E*$5ErsAjSK{_MBrs%fHIv?#PegTCF zf`p!UM+aB1)>(xVU)ASs6h>~EH-_VXiTI!tpV$*2q^{%|? z5!A2v7K?CAU$c})4CgNG_=(%RlRNXyWvKB;!nh5TW_Z}-Fs3R(s`bbHqNEfrI|vY; z*_3GGa2A=~<>orv^mJp#um4hD;g}-osKj=%;UjMisFhW{QO(&_9)xHJZD828) zINT?B#z@Lx#H51|bH{WF32xI#N_R=}nD*>*%E6pl0@fr+==ezosH=%%twb$wwDXwQ z+ImKdZBu|rpmhvtI{cL7bm%|N%&b1ng6}(c+z?SP9^Ud~&`V$9zCfFW{}-}BDM$vl zsf5?L2yv5Tx7OT2auZu}vzEnBcGF^KQL}4A@9fuJZIME^=)2r%LDzMh4Pu+qsYYq6 zaaN4)&S%tw;JRRH!P@#5rfs$;MHGQ3H>vhk4 zloA7HY7urCw>;8y3%dxC9$s`z7&-Y?lWH^!bDkL_4fV-woFtvNE3B^DrOq$k{GeQH zjr6Cl+E&#)a#9McO(7wkJUkGran_33YuaJTPEO)?Ya*p`YdR6=7A7pNx6D?YtzO*;pTGiSfvU)fs=&y>$ME(j0K(3#$wacv=563#_@I=4ZG zUg;Bz-WW9xh03OMp|2Tv(8?Wq8D)#{)R@EF1vg#kDNT$~~1-2j{pD*zQb04K-7 z)At%mg3jGyatGlwSLb8&E2#86`;y!isAGBu;l0=^gE=v2LvZ#RI8$YjB!tyuAvC=0 zPEfL#mwU{wA-Em($!b}Q>M&d!lEcKYWN@S=tgKAey6yCG@I<8Awz%!~iFfDb**%L& z^-Jv&-t0KTk?zB!Zit_5HWQe3uMZc*(;^Ok^~IZZddcU{M0isBwOYPK7cdd4An)+ol!$wlagm8wWAs%?zL5hO*{!O!coc?Z9%r>|14dtrZ zSoMf`9ZQBRoAMs`bpQD1<-%iB8jiL>|E|1+E8(&>PJz-PA`v?@=;-Pq){T)SqJRz2 zv((@o!V{+!`Kz4?og+$h^ivRL$7A}^7E3q+BT=&-8r=$SFKYUbbvx}RTjEL_Jr+9Y zDYM9af!{}bomHwJhY`N~;4`o83|XJW3zxHHzwaEV$s5!dm4Zp<^~vQ@#$)eaP=+-5 zq@1H%bWATAP6fH+zjY$&wVQ1n`~HDUchh0C*l@Vs>*SwmcINv3U_ z;61%_`ra1pb)ft0px{&9P23PNr68|F42O2b_X49Q`}ok3*9570ncI#D8#D8dr1n=4 zPvOTRnVe-471nI!33y|SDSGBYJ;&^sJ#WFJnDwUk#9OJOOJ0t#q!?OwhAOvERM?`E zu2W1w7iZk53FJ3?UgMNF| zNk?~_GMhNx{f&vSGo63VJyRXzVLTo^C`4~>=s|5}*}-k85xFAcZm05>`%~%$a>-SK zJ!395Ucsp~wNeLSdzh-{FrRIzAJv?jgj&<8ZMK)>gf20gXgI~=9}GVEgx;(MZfnSqabH}b$+>4>W{}Qn(#;f(-qbey?gAEF^?zVk)aD&7!jzpG1qD2rT2UwHO%Mx}anv>IM zxxYmVWn-NToma^xi`DfmD2nbyT8|btX(26)6NOzk%R}&DwnpIOQuqv?so|QB3Bs9d zUIi&;*fyLEcZ;2@zCRA6M^uXLj~dz3F&$BZE#&6m=>Sm1fqhxVA$5ITPmMITJPWvL zSN51`@(x?foi#WGuLOfNZs}dvaSN3`dE5_XadGiGuB%r^MBR6bPc|CRxDuvaamL3g z${oG8Y$}_^QMSqN^z&o3!00{tGYlVLOJP!U<_-&;(waj2{3A??NvqYhMLJdeJc3{W zURS!cls@xj3K_Gb z%ZZr*!xVEd2}fMPk$9%0@jm7{7_GI zhr;h_Okr8a!-m@wIM;p{_*~w3x^#pNF`d`$E6h03^2$g+>OJp9e=0pQA}UVDYYvOu zNcX)JA72=pn*Ckp@e=HJE%_#FJNl&VVFR9C7V^ zHJ=g5xWue-XyXj$YZ_hN@R0dHW-zVusNtA0?~0E^2DwJHV~*F}kb2>IqL5SRi(nd6 z89dfti3A=r5P53*gi5Wn=5%marJRS1==IkE*p9*#MwOX zvAdVZ|DZ)-!g23Fog=e{Dv;ZpO4nYAVf5r+IXfh`2Rxb-nzXt>A~cuEUF z3EiK`CkB)M|N2`925%vFq-$pPG|KVpL9*wYhzE+!+%GJd+JSG5Nk2$@8%XDF#z^sX z96ue^n6Fu8_3N8I0nu2coe^dNWjpDvhq1fQE|KyL2cVdZA0BCiNmtyMHEJrl_;!u+ zTIphvNB2TcvMm7IQahLJLPAs{0{pB+sOi{!1 zGz38RJ>x(qRjBy6>({;7VIsRtm=7TZGc$8>ylws1G~&b>gg4}4qt^G)raKE=&R`lR z+N0Q}jMr@pG%q5Kgq*gCoMbu+|)Q?nb z36YLJK`^rMh$8xq(iZ&q*QO$L3`KBg6gN_sG@R`_ptMmoPN?HwThksJyk1fv3OSzj z3XF=okE=pq@Sm5q*PYXEl|gDbqT>*(TBJAS^hc z1k47h^!EmzYUgCbm`66k$4YtO^Nf$cIC%W>r^{_y<$L?%^UE;hBhN%gCg}%Ol^+bwc*Edo?+wt*tkvR9rW!Mqu47mZqqKrT2vI+QtFLkCaPUQXFTTpwc$2^q#IA%9UI$A5aL&3)au%cXwL6 zbH`i49pCdDI$A7b=%erc$Vy$bm+GhyC~-tkyFYH}+)T_rTJ40<3F?00cZI@jbF9p? zW)E0WIfzme<7&FULWS1__}>Qmrif*Pw=K?7V&TXzJu+y7d2F>%6(}SM+1y4yBN0sh zLHq8EGdwT~|MeGdE#rm7MQsob*;k$Njq&JQQ!F8Y@;6{%dG~8E{jBh;Z%P7>;F=5o z{D~Can5!Bt_h+oj>)7sg#q)S z*_v(B9>|oBD1?n{r00xY^RcJsee~}68MB~K>(!Cjr?c-YZ_;G^E<}wp?bkT6x|P%4 zfa9k#Nf7l8PL^_w9H60%mH7q)H#B|_vPE2^6} zCaVTax)YXLsdgvKo?RTzBlYSRKcwBc#@1VDH=FL4+^#)o$-hdID3dg5zkL%6C!CB~ zdQ-(?cf_O+{qv}l0=~>pY|=&Jari4x_Z4TusXf1G03EqY-4I{N$nxuLGx7U4*Ivpq zR59V@ti|h4Go?I!894}9u)oSxFG|wRXe1=S!eP~upikI+A}2ky&NETQ^zv2z_JnQ2 z6c0iBkFa~zla+Ryqu(s2wl+z9KHV6taVa`^^~_(vF>!xnNw>zisyZY5L7EaXI|%cQ zd%}K-bkB>*;kWp#9tDpS{Nfyx*CPu(j%;7Vyxu;XwF?41PsISiwCv0ByI!Gebk$K) zH7*bG(=L=%0iAh2Q4GhzX;+kJ>XwdR8bhHGmf#@)|C|yQ+%U6x(xo$ovej4N7nZko zKHX}&RkXJ)EPk87r~gK;Rlj@P20*q`q<}LT;TBB8JDK}>`}=xC8yI&!xK2YyYgFA` zB{g^)w$bJc)KuZTrX{?Roi=H@x@45`$H4m9=EN>N*qd{cai@KyV2DD|95Y-%fXUes zk2;Na`bMnj1B1`sO@Re6af0x`L4LZ6r|wbe%0!|U#)$o;ym)TK6EE-@)3jvRL|94uQDd$Mn0^uv?cRt&} zTk4#f3;S~BehS-+1xcC=Ff=zl?IvZLQyR1?s;o( ziUxA#w%TSdQG`3=wB@Xw99U9veY;T%zoc)kP`^xzlX~Jk!aVS8Bi2Q zw$VeAfQlw;&R|B0Trih<(51st^bQ5vwU?Gy7vp;cRYpXZ7?kw{Uny&pNLc6O6POPd z+03!7N%`tcA4r+(i;z<;F}>KN7ymlCp@H}oAN#L4|8sZkaIhR;js@9Xau0{~D-5ew z#-8}Vd zu1ln>;#4AH5;;6xr10P@bSGl8JQn8WMlW%#U-lt~J?h`^9OiGp2*)R90i*UZsU5ZL zOT_suicTC+Yq_w2JLtQ*oR#cM+9SLn((pwa=UTP_7OoHtpOqOdE-s(pHymgJP}y`z zVVCuCkM*iH`8fM;1P?0dAD&(s5x^)esTA25&f#h&Oa7cO5UYqNPII!7xQHG4a#h(d zlT{9Hby?l+#$HBHW2VNvnohH^$8d2?i3q}JKyB7{_PnvM^A(fDq}RCrWlFh^tlq=m zc5uO_m<8X;42V6u9B%Q;ZAevptUp6`>S}+T`<^`(1(L6iiVgSGi;&yP)lKSJ_gOyI)Ew2 z-5R2H(>Nbc#67^|aHX093TBY&8Q{A_al#3_bJK|^+24u3DAY!AD!)Tst@{;lD<6p0 zFylTK<)37U$tM6;?0tU2Q^xNB4pbA4?Nds5lk4q2Cx(w>XD8hj6W4bt(0qTc?_UoK zr(%G`)v9JpDC?RV?ByY82vBBycO5HgWCEZ1Uw`RddLk|^Ko?rRhvQFU6 z@V(DT@RiuFBQ;E~%z)BRuH#nz8Igp{dX4`!1|Z6+Q1ajvpFd$7&l~`~9*_jvYE~cq zD1xg%c`+$@4y0VoyVer(bqr2H2t2qJhNbkdiQ`f%Qxd*S1TK~@obej}k5u^o`+eE} zdwpi+v34o>I zF01AM;tJ0rd(=9BoeDta0&KGUZR_@;Cl-N9o;n`~iKse{gNl`*ya%078pZfIPzl8u zvN7Y}GxI@dWW5M{(es4?nIR+aiTg*3$rJo&Pa~knvT2%b4#o+cXI{r4E4{FmnF+#`B03S3V;n;q~v6qTN9Q>I)j^u}e% zDdr3if0Uy5S$Qv?At|}z>SR@!wiC#Hjm=2FTAk>h z%+g<&C422_PF4fT`912ScBeZ?8wJ#A&1{IURp*z-(J?VpsPhDe?h=qX86^?fY^-$B zdSv%o@|KWeHqmLTjS>X3TFu4ziFKnlR!JUswlm?So~qd!*4FLibxtFCSldhIzI4YD zr1w@?>F)inyUYZ4*%29&`1hEE5TTA;3k8s70_r@-#?}6eAYKq6fu<52p(!`*Ny;3K zr3gDe>Vd|QBYKt~iG&1*Pz?=7wB2_2RJYSDT&TJL(!GX@$xyxa!36r8PgxSz-oe2^ z_Xw%9_v)SMo=VCdNPm=rKbT(Vj5PwW2MO+zGhE$;e79~cTz1k9us%q5IXz<48=K^v z+fb?l$OiJgn~~72buA%!|H5>-#3=A=MZKXkmfN(tMLwR-32K@F&^iiILAJ8d>?MHn zR8bain{*c63lX?0Bz*9nSpwr`R>5k8aj57yhy0!DWu{HAv0BPEnGl{Ee+Y!_I^1z3 z?);$PVrqYNY!>dqTBF4mt$sWgp=i~c@`MxL4d|b$!lj+X9uxr?Q{`}q!y?qp>-0=L z7N|s1i>>8|?Hsm-aCkPGdLhry9)M0>TzP<$)?iMG_b?+9#xc4JD@!{o`_bAqc5SLL zF7sYMpe009?*WN9?s;$|Mej9;`z&oIwjKuOM z?(RoOgW#@<6ZFM&YX~i-;*}=FZN?aaeK;K?srWiwLVvb3_*q9fgAo_m`Fj%uyRTY_ ztPFXBdFn+#ccKg6<%A}z-Z`8bGGfWqohPs&wAk@JDRk(UW1Q0;p(sYJi9{n#p8G~y z994l-_75Q_3xvZ8uM=k~=Amm?Vxs4Vvo=+9R8Cg&>Lad<^Z(1uW61X5wV-R=$~xQur?@})9Oxe%7ianK^s%9+Nc9{b&z*r*s5vpb z;2H2j*-TBcc5ScM9ONOhTTfrRpRA##zAFN~n{&1(eo+E!v}#^ZP*5#H=2S{*O-lMi zjo;0hem|?)oLz+xN^TRKq5ofSY|MV`f~DdU>5ujQ4AXioSA-Cs{0nuLjsYk>00x=pv;W|4oIn!q)MBJi z_w5mYT=lu*e!U#cNiSvC6bcI=s_RZ0iYcK(2|~h@_O5x$^xCkSVX$T;}EW;(|(`w*8Y!@(u#u zztOLsk^!bQp1Ml<_gKGL3YfvL(gKZ=yY}L%3U(Ny9m&wpSb)nlLfZn>L&k3n-#u_& z?OtLq-sL+#Z@4(KrAkN;bjZ}6!|Z|d#0@hh0gl|3CBr~klgk`b-EO~ZA#y?2 zWeTDgTb%nDaqr-)gtyQXWR0fgUd2h|R*KUmUk=az_voZZ`|&h|_1_B>2``qrq>`4n zvG4poQ}pSs1g%Vqf&$*>NfV$6KH=QYQcO;I_el=PImEsf*Mb0I)JDI)qgt!L;zk-I zX?}LRx6tCy?I1*_L?pA^W{ zMJ}m^osd0H;6Z=fT`lWz?ub(4u3}?R$}xDWQGK>Qu?9kbOJ2A5#pw?w;DgC5OP3{s z(am4Y_d1L=4pU5l0fP+(wX3Baaz{}hW{{6TR*)W2D^b*=f~9h`gQkW!^gpKqx4=vU z(%i;h+r|DK17(Q8hHX3@p->@+P@%z{?fx%_BnQL23d+bay0VBM_d zGVB%%-An@Uu%r$|=faFRAgGKr&=)KVE|M2lE$X#m*M0h_cGYJDW>=2#it5L$)1nK- zdNGvH=6{BMN&UDee-I#kK4hLXD)#u#B^U~Dcr-uzy7u{WFTq(g_+8d@sK~9+^>vnE z%OeLHXf%`Dml>uj;}zEN`l~iEoVqK%AL{)xDJ|IL#G7kOwCI7N+XER@>mmt5pfxAG zE)=okkli|ECc)b2>(C;Nsmj#EkdUghkqfIWkR($^XDS^ExPr+**8M0=K3g@-{1BY# z_Z0-j@>?S+Q&VXJKssB2U(fW*OeqLTFg2epP=BiKDysZV=&{*^M}%vS4DKYC*q44r zY?gX2n0O_keH%ozs@X&1{|C|yL-2AKho#{@NEH7II}hId3Gt(Gqf!OTdzEfG3z?kr zJz^*MmIce&2tKo3hUINbrd?9;(_RI^d2#?~aUEGRS=F9-Q5nAR7FC+BSAeVn0rv-z zNwrbQM@m#}v&>>B*BLCrnfTojM1(7j6vt)f73>2^0BZzPae}lQdEuK3VrQ9Qb3j$2 zxz(o1QK;a;O{(3x8DAnfG9i|f+*!}#<*dvxv?>ZD;;Y5woyikls|QORUsgNd`RxsW zU%uQ_nODP%;1W?@JN*_BoOZ%WhW z(STMF1>`!VYq{kzngR|*qD&16cHC6yLvN)BKQcvBSdIOLXH{DdKJll#5h%+6cpWNL7%{;M+8p@lBSW3ZuZaZE46Ciii%x6UXKeqxV>8&@ATUQ07 z$*=zfXcxFkdW zjqIh(e2xVc)YZB3BIiGtc#h>{xiM9P>WbxlP>Lu1E3Ep*NxGeImR#&_CaLX?%IE*< zT6~~RvJ_mig+y+dPk${?{D}njmxis@Tl+=OS;ru)UMR)kU^4t`RaF%qsLk*j)tCf4 z_+MhhTf&5>7tT}GL!a@Vf=gyji5&*_y#`OYG%Ffji_`C-;k17T?9UzdO9NRalt>J3 z_x&SuiD2*yRtc&@<$3T7(#sky>i#MG4i+8=hPer=SO2F9_TPDbPrG$_+U5loiGj*p U-xD+|4E#txSCA}xYT)~S0G4klp8x;= delta 169654 zcmZsCWmHz%_ca~TAc%B#i-e@4G)Q-M2}s8wR9d9FySr2A?v{}5N4nuX-22!2`|yr& zJ~$kVXV=yVod-Hz*Z{`alBV^!Y`^0G{pTh3@4t`w_mZeO{uTQAg%ybZ9{` zXl$fA_oo$ zJF}cr_wAof{Coz`aKasma0_ldo9mmgXGYFwuPd$!7ZlT|KS)WDrzXS~mjQd{wwD_o`YUA2@Tv5@uw$Fl8X_Nz0to2L!-B9fk;MsT1&`v<#bNr=$>>7DQnd z<(jStp_i^p@)~1)Lqu|S2i#S3b;Fr{_wbms?wP8o;pOJ%Gv3bpR-rS=KERa_J)r94 z4S)|A7?2h{dy#rT41K*6w_?xU_(iGQ(?gx`BE%HNQGiBs)!N$HxG}kPPy3{o^!-3H zmQVOSX z1|3VLQ>e5q!p#z+u&IheX@DAHO&EuD=prNRN~iHG@u`Ic>8P%ERqcTTYl6;uDjvt( zFJkE-e6B}S*MU?o$NwpgrN7EYD2CHLsFNS3r@?2(9mKoHrA&c@52+Y zA!ta0WCjK>^>*_lDt9rNt$NBboo-Dx@ZC40K3N2V3;l8HPMTbYm zhWQQ(#-aFkZf@>)j#RWrNcwWG20uO3FC|M$fWAw?o=pScf5wOMW5appek+$Np?ZL- z(%NW-LG67jmocn?0Yhd$2BTlibB(4TH)|Ae^8A{neD_%EB9a3mJ{Ixp?yM|(;8#ZX z0sS*@N$*f$92k9RdVY1%y@i4k{`+;f!3{OLMkjVwTD;y*0;aV>vxtycgJF{qVNrN)qmoCt6GHc~d1xa^yBDSQJFyVyBXpn`W!uvb@<^7R%sv4DKI9q_t%;fkW9~`9cwB_rkV9Lt_H} z?Y!R=twH4uVfOOLjg_P&CX}BKg~2A%frqU9bq>;Oc|L>=1%?b$8;;DkXXaSg{=XH0 zOCvi%=HdNSpr%~){RQQ#N;N$?M3_ohgPP&IV$W=w!lr?UcXHGM*!w$3rhc>2me0Zc z-gyCoDSRsd|B3*d#(%Sb##BUZI6dj-H7U4Z-)J^oIQwR*_%y-=a>}>(?8az#DYc~7 z#LUK)=R=#`=mP^&Eu+DW(QGC(fV)+5*A=Q+Dy5{-_6Gg;5CcLaU*ES#VKOq=lc!Fv z&oA=6!_v=}=vOt^DbnXAiQf9jn?OdRUtfqT2Pz?fL*yavJiZRz{gvTF1_krKp9I+| z0sp6k7byxcF*zS+(=iu#ZFp)^o4>s?*Ab!&BlU*!6Qc%xGvaYYyu_TtQ4tGCMvR{r ze>(tY%pH|K{OG>%VW0voj<2Ws>Uyl;R}OcNMTJWx8aQMfvW9W9=MM4z942_G!Y$Z4 zDLlbiW$nRHLf1hhLS>Z7$~cYl>w~Iqz7f4Lk2da+rJxcHneB{lpr-Sqdic7+Z3NH} zE62-?szWkWhf6m;G}A}7?<1a^ouh=JSlW;pJCSspx9}Sh2`DLvlTLFh#&*7?2$V*h zxW0zn8M@<-^63Ci6gvH zw-r@=c%nI?nVnSut+F;D(8y68G%?vZ&A%6n1#|RMrz+hAVSX1iYeb#`i~8d;oM|Sz zsna%hHO5}3gA=;L!{0xL-&yIm3(Fd}YQGS&l^#;1F zA&J1dxTR)+*d_$FQ&9MQ zf~c!C94+MWbTzePJ9(NpQQ^FhgLV>6mYYpM)Ap2MTt*xyBb$l-~T)EOnzC}--ieX*q<33O9_8%ZHY|Hd|M_N?&v`E zz#ZiUcpVfuv+&%r$<9jO; z_TBRf26-q#MENw6ux_;_z9LGX1K9!9^C-L`Pj$}ip}#gkC;Gcly6yw z*)7Tg60y01{cdeNE3is3 z@e-Fx2liSC29d>9{Zg9)Lel-$TAMs>tmg$skA&T<=By#{$*`g6Rq3hSx5-@g*`)~1 zHb|T8|hRf&m4o+PdT=39J z6>j5)Wy*?;wa2P{V)$~1EFw&jC1tZ_K+~>DBl!4!`7u2|A$m0X(HNq@T-TJVirk-( z%QWzBS%ag6g68BQwSoUTJyDprbD?j+`H3DaA7R3jmRQ&s4ZSH1bRV;^Nfk^93IHzW z<{Ofd6eN_H^ch5O)(+CEuwTb_M?>dUK8Vl6ppJb%SkgLFRu)>PzqIliGjKN2PQfw2 zQhs2y;Kj%dI}dc$MQ(F*e;tU+XutBJn(KL?>wNS}gnyF9nCnxu8o_-1x9H-B`8F0w zt49n^-wf;*@xOfrNQPv-ti;CIKwaRLZJivehsR1z;@vVw6G9!gLdR=!Sq$)uu_{H` z43p{TA~`hk`Rt)cNg2ZtMI_aaIXb8vkP`8JdA5%Hz@HV{>nWi8^uR*Bz-Tjv{0BaC zjKK|Q*=e^*Sp1-lei6RR?1-Hnl<)w6&-Ax3}xVXCv5kCZw z#X<~o_6~VrN`i4=D0%tnNKn)ShmA6gh&Ah|3GX^KOaz9rZTPX@$xx~TW_ermzwHFg zfAaD^3pUu!7E+;9K2L=_>~*~Px4=^%W$>W<#SPeBg}1MeH{_*|-A@@R7dNJPO&>oQ zKR<$Y{Yor(q>@atHQ`b)=P^)OnX9y&Q2FbKVK3AHMiQfsCbUN|#K2ulUTtimcN-0< zX~}b43Dyyy<9)w8w2WrRyM_GmNQgPe{;$7b9s+eSDpjPe zEi_vr{xEWz2fgXSF)0)KEQXTC05*Of1}h%k3#+}m++6V(!XKTd{#han&y*wx25S;_ z2j1V|S-_-2eLe^zep^w4Riq9o z7+Y5H&$ps!l=oHJsd_&Jr#bGqK#Yxr_V68tQcCT9c=}^CgnaIc7ICvnQ~sv#qN?HL z07og_&EgK$Tk$cbi)Bx@sSgZt-_I@;`EkrVJ>Jynx&=$ZsRHRHcY9Tg%&A(eEeSv8 zms)uuR-I0d0QPKu`QDVINcl@N(j+z>cEzU|2XR6VLgpxz-cii1jA-7zaW;tyqgIo; zqvChAA9~rmB#%C#hZL#+gV$D|u&yCdnle1G>DawZ#?ByZ7?HTq>M*#lu+Z0^Xm9vb zgQ{D!w6xZod<}_WozCzb(Fx`A@@P0_XCs4O@dKeN`PO}kRips6=ONJ`bb!&Rd*z+o zAf#Ty$*KCp?vv0+WOg5~7u+j*Dkss_A36oi2pEQ2v}~vDVqRzy{mK%$?(RByezM`= zx<+&nv%DTmwdoFc&hqJopMHJGjR1L;p7F^d+CGJW8A)KZ`A__dt+F-ise!L$`zqHG zLqOk9hV~cK9C+~qsiDmp4ZEn=t?CShP3l3=>isA|o?v zSZ9qKVeG!b?j;B+UlYHYosJ5IBzFDuVj_NJ_WpKsgwoYe0ZTBod(weu&;`AH z?@CW;g!jD2=6QK@O-_*AmeX91UN4(5wjuy2&$-M(3}P{W8{BXrK!7=x_ANh+WxRJI z)}QKCRaZApAm!87;A-m|oF^=4jeX^It=_pupJ1KSEoiTPpw(%ebgo;CS^{$0wA3Z zcj;wTjmCPYlC>}HiH3)SJmc7)CPop+n~576y51U}T9&Z8$a_ZfE?Ml%oLyEd_#NBj zkCpOJM*%Z4^0vCVI{n4)`JSuC_`=-8(LhcL^ir~|RqZ4iL4~TTX6tD>4+KK}SC1qO z4f2Hw?7Zs9$ye7wh5LNOU%0#$SpgOnH8@2qnT;e=GhvuJzHVCdO7pj4BQL7x7%(`K zu6`vaq*$M)%1ax6fAv~|vEyorByrNeR_RxD_d>iNFOtwfAG`8gmib_DSk`0&nk<=-rH67ux;z zss}{rhYiXqo5c~5?`zycqxjO&h3grOVh5jJFwEpv1@s8L^z6pK@%)nS^_xU3zO-W(mRO1KA z_sTm5KfK-u2t_bqxvgTQMrNsEzO#Y3ij_N9j~EA$)_rtS&!YG4Hi!J-y!G z#0y5CJQn{OEMigfDlyo>F@Sq$YxXB$hVUAj$uy@vRAh$@K4{`h6LGg#XKp~xlK%v;x(UK~6Da^0%?PZuJ=6B``J~Ab`9_^^&%=HKQ zvH^Pz;&(C3BqPg%{NcRL{YD)2D>UK;2-M%s=4nG#3qrmv_iBBi+AkG)5T=OwGPvRQ zg~3NUb=kIP&@2tx-qR7wA*JBM>rf8>jE$Sfj$%)%JFvr`dllCz0-0-@^u&z=`9G>O z6imTe45B8qKU@Tp4=$?hI^=UhQ3f&gcvshP_xt4s`!UD4f^gvpSDF4Fl?nlg1N`Tu z+yS_qbs97s{k|s8nu1b_e(x;mYO*dyx7~jk!rg33_B`j^ynl4Z1{UCoJBHYal-;xh zQNeA~fLpkFJnn3+xz}qM)yrT^KF;XWki>6=_e%pg{Dq1C>;LM0MyT+_0B+KtcD)@r zjyYH0dAP50tjks$G1052{K3vJNnw6Ixu`Qbv02>8 z397gPN{+zac&dZH1lXWuOmhXdR0WJ%;LC`$jXwKoZaYkBFR$@fVKodNzk_WDC=Wb+=*`5f%-l0dLLWf4m2zDxAfB&~!24P_eu@P_0XzEE$(>?pM z{r?jDu~d-n9`XPFi?E>JKH_}9#j`xYQ)Y}isc6IcuxF|VwdGAolG>{K`EwJ&jP|nr zW*IbWpKYzx30s`x%d#P+tSJ>h)erzlyl@yce%UD>pNRQ2J~3AlJPfiN6pV!ksShg2 z9}GuE1V^NNF{>lDUGZyUWMoE%1pXdpXX^V;UN@vhC#QS$PqplF8BRap9BHXPKc^HA zic#P8S!fT1MjSbGmQTpp&HN(kH|xZLV1q;W6mhW~(3*)crj-_e-RT@OZ_;2E+5Z>b zvtdH_LL){owv$;z;QIC0(A*x=A}|MrFCg)l5eu)u6AV4yqQRM0;Gx%Gw+S_c+1;IT zm|_pXr|+Fi@Atr>0LxKian6)J*3$k#FIbfv9pC+$j*Wav97vqm>~jDG1CU|r!f-tR zUWXmna;ZEVw8?pFRYqU^Skc#QJ#foYIbRtgP0hyL0i*@8g;2zz_$L$P?dv zdn+anY}3 ztbN&`ApwE&32Bk?uIPj|E#EVH!C49*X?T`=fA%MGo|wlH+|TRlDaYaSn7e+-e-iv_ zZrADWBB{m)ZhJg8_>Q>Nr(EMpZvIEBz{s%i)~wP={?MJL+459P#C1aD0k0cVqjcvw zRzgOQvDw^(vOMMX4m;Hf>*)wF3A;**FR>GbT8mIHJmf=C&Fsk_A>C%cWnYsEnv$fX z`Z*V&vuvU?u_TGOUGU>mo0N^o&w38!BZ8+Z3qyGh?l5y2B9Z9TuU_8I?jOy7w}P7P zG|*2=yaF#c!6UvA+~1q_2DyP3#WQ+RitA!P_E~SZoVj^CPgI-)ac`3q=pNUWLZHjGv?keILaT1Jo%l8oQOh4n0((54W$^D_&0K`A}~zzI$-yQ z9vlw9BGfa72emr^k(^lb-A>8!LZR)G;rz49Q`8<`G;P8+@D;hii2MNK#6wchvT)U8 zfbqBz_b9}BMT$*CQ0MOpjl630(-#p<_iq<=huH7DwDe$8NnD?c>wR;vHN}1xCt>RM z$xi$9Ep|A_3eH%e z-Ra&W=(B+o?1~Dcp@I>N67YW#$ExV+b&G(F{WlQ_leZFYHhH5K6Nw!U1Q9j3RSJZYO8 zlp&*u8aq4Br1)s{TiB(ole5e2+iy_3_}erV#_{`&Ffcwv_3XdCuka)GRYRNK(J7^m z$K~374?xr8y(kY+g^srkP2Z*WakPx!-*!~mlacAGvNY~^Q;`|k(av*}RDQKPxb=CD zz@sEI@>gI4PZqwc?^|*b8^mm_~NeNSXIU+TTO8)mJ z{GJ+wLkQY`XS(L=tDGH}`9?)O=!B-;o`BkU6bj;5J&ZV)G*+CNGkmN3Ij6p*iFEmY zTl(0^Pfb%n-~nIY{}$IJ;AdD!-_*myDdw*JHnZ$T2Ohp@fcCv%Yp;a;wU|+GrT_oz zIvfocV{C*rfBWD;urRM)y`t;28t0G7M%;j7-cM-Jf7%*dUfvA?{w@$wv?3Apd)4TL zNjFkDR<&OAfS3pP^OF{oY(`y39Eq|YPx@(H7c&bBr9ix3*Lpr-jaH7~K|MDx%gMpA4iSto?`=u4rE%}G_jQ7AB9=^fRIPYTg!TzlM z2BN>E1`6(nlrX=Lva)h){InIC`82zdx~VC3s`tgb3aGf@+y|S{u0eJrqG=ZXW}qNV z7>W!zQ;bo`K}Jhm>*V}&cdtrDy&xf07OG?kdpTDZ^jf`h8R>^BQRy2y`a~`5Sl~ts z)fw%j^hI_AHJIenxGFFcgt1=%78`6HmI`PB%9?U~hs0?=MBhhQ8Y+>e#-$g_lAqdi z!2DS_6w)U@VX(@*!{irpVB)r`CE1MoKsU(rhjF83{6C! zp}myXF}C*uzOKt>d^L`sRMurq)M$uKNRNrbd-y7uC6OQ9mnFbuMb(rsV`&Bnt$ubA zt8OY8=;-BGDy5Q;l7si%(;m5N%wdKTF8f@4GDGo648cSGcoJj6`1t4t4o-#e1gjiU z*ror(1~e-S?A*Sxo+0~%!N={*==MV3w_%}BSggiuNmh=18_nQ_t%(uVT(=$u*lF$a znZw$cNn8>PTiR1o#X^}l@jp-VEd-@C;DlXs_EFeO{%Cc}X{o=zfv6fIV}xEg8252m zoy1Q-?i?(=yOJjNhQx^k1kVkZyB%DJpA?WA)UNSxC?^$Um|*FQA-jB9fT3uo`in%7 z%g6T|k0kQ43d&{#MCp9K8-7GX4R8ckK%cgKt9UY(ZB|PQe-fW7i(DE%sKOzBeb{kq z+_{Cct0U-YX-NklTt}R;=w%Ud*<_sW&rT``-RHg#32#H;tCEvgnG} zDH^kOUDy}Ymm<|>!|&Ie0EGSQruH!V=_1tunYhbEd!Ys3@jljLienkF;(bXhey>8Kg)&L@_r%0H>H5m+*-MhM6QDN{fjqNPm+N zZ!M_7IFBSuW2C;tiKH^nt&Msg&#ypZzAdPf)@`>x!#q&zwE4_R`vZxRqYYI|)!h~B zJf4s!&Ba~p>H8c+Zcjfg7aMiG8Qj5w!#k89NDD z^~Qe!SuM+DrTK^wsGIZTOX8Vu!Z7Cf5mL{jg-X4@9;0q5*&q02cCQE z=Ehr06)2@uEu&D#3SO-Sd|b4xk4zQ!($}s445051?SX8f#NZ86L=rbL=abjIR_`VB zjLz?Vv-9z3FXaUFY^S=CFzYr=Rms``Gf-ANgRu~+$ok7EKr%xNQ&`?s5|m!lk#kdD0O?xB018=|GwX(A zFOY~y`G_SIK4Mka7)FF{r<=l8%B-nHl;5yem+k{9a1$4&K4ry6@1Ap2(}o--<_DQ9 z@Fg!Ooa@eLy35v?r{~p$vTnjGYnw3slfr7SH2NvF!B|tCEf0V6ZnLw=TGSx(wwU_x z2w%ZvhO7hb%GpkK1i)|~osj0VHI(`Dr_RmIe)&^g59ye1;?`?UdmEJC`U9DEj1SV1 zeLzDJiAeOp_xFmY`trrW{ALk4Mofq}_6~x`{z+q#~#l^)39w_vHkQcicLU+R8i4u8(GXL4K zCuDrC(pca&G~V3Pv-y2{2@%f+ve=jyy2aL3A$|8vT&;!=8wo84rWicV|kqaUrazI2f|3!u&@K>+j$su=rXJ zQ=CSQk;*AFLdXhx?b`2MsO1Z~$Z*H!>H%z-^TYa;3%58u&%@d!v4v5gM;%Lfemn7Z z=Ul%4$4)eEy4wLx5Uvu)1wawV-B=L*mvAX$JKndAPFcG_K?y@iiM>_PR!7-7an#*Z z;^9Kt5wJ5$-4<_s&AZR2*@{>$cr?CBJK6w&iETt8K#FE_8S zIK;1>M_V3P&z7A(SI(cBota^9kQE@jVCidHE7a1|oR|A~&OA$+js;sI1kh!p{m;?k zL8lk0kT-wtcQWXqG;o9uwb?f3_guHTnV%3UKC3IQ_j(M2DFwkBzOQh;3`w>{{Ya}r zOfAGOYMg>>zQ=U0PJ`@NzD}w|E_g*#O^t^1_2N{avXtI~tdX{~*SLbIhffd&VALE-iF&cA>pBMYv`9+Lnf=&~_|3-xj&Pl&ty>o41Thnp%j2aZNOc#J zYBYls1$Yxk`g+1%uFq51zLQm8$C3w5lUL{l*TQe&+wa7KH9Zb0dl?S8P`JOjl^je( z0ATxz(uVUdrGtXGm+R}upL=gQtA71qNL^ty&7k02^disv^QeDU{S>2bk?OlRr(-zX zYP0aQVLFQ3pl}Z+l@rq4^tp?}#lr0DfT-wb0p%X*@o%!^vqlupWKL+XNJJAaV}Osy z)EQrJkZRNG``*|taCV2PtVM@F-A$|O)3Y8oF|)Abxu0&0&zq;qNfJB9(5eU*+)&mM zE3ZJhx zqRz4CRO)tVTYG%8ax;Jq9{dx(=}7L_IOAk^H*s|9JmqTxS*j%JgcCbZmNQaBc!jm@ zVN9`s_LAdMS<8v^O|Q_y+jQ@XM9DiM5GjFo(mAP1DmeG$(A2|V&By*bmO3!>44SW& zXl|uX$eCx7Z*uj5<@RjjbsU4&kUh5&%^8-9_Ub^zAnFtFZjIXG_jkl-s_c+F%SYTG zGuCmg|3I>@a5>UP`4rg^a?$=OW6zFzaZVf|zbDk~!i%JVPH2?i+bu%eOCnjw+FlTp z@%>IPg_?Sb*I8={(OBpRu1etcW8XuF;$DKY@YHQ6`cvRW`+gSqRYt5K(!MmKTLT<( zCt)2hdl>0`ARk`w(Re`&vz(O`WW85o1GHkX%D1`La^|tNj7p>K*2GSh| z=pRDPHE5R$+g!%pB1X1|`Qk=uQ~{%p5a2Nz%+gBq42ckPur`7PWj|7OpuX&#_ZKJv z`l=m26~^;6lXYEOL3R}F^3 z%FeQW5(ifm+Lc~PJ}XcHEuRWaIS^LOAMIXRIF*Yfd|>L>0M806JQN8;!oO!EO(m9a_%As@L3_i2rlcv4 zY1TsHBXTkNCDt_RE%QZQls=!m-5Un+-2^HFpD?ULi}*Ae(p%vcVNxRAFp0&c zN79;O*vwZbCp(SD#z@CPqQYU7!yKh=n3?5vr)5}MpI5k2v|YtjCxZUaCKprjkqD z4-P*DJ28ZGu<(HEL4B9S17Z{v;s`B+xe7xPrSsDIWr{-DZ>?8rp;W<9T__i!tZj-R z!KSZcMDSFP+s>{ucgTuqa(xkKH`sGw+9spPgV$P$MbotfWZ;$wH9&RDAg3?@``lon$ z82rD5SBCE|c)7~`Z)~qDKo6bJ~%iP+IQCLxf8Xm}53y^f|E`*&OIG__9 zS%17367o}P!@60z&^VTsoUY10Dp)e`^GQa`|&oxsNzm~to0 zsXhHuB((TqY@l|(aAf=~d_O%WBW3sqD?`RF!?YuO)w%;IQ{7tssJ6$iOs362kc(I` zjZ_?3XZL)_`C5n-sW-u6MOm8OlCsRcmUiLrZq`JM$r~y@8D1`T%pGrwm;1)Zp522e zW?nE6{3k&a%cUBX{#(Fkx&p;Wszc99sx=)^QCM9N|D1uqZ=a-~tN1xY3JW4;z<+-> zX?gUsQMz@Fp58xmiup&nmGd1Y>o+L@4%|B_`DY*Z3iIDT6G|!B4vcx<>oh8mf8YWF zlIXp2dd85Dkev}eNX~wFyI$fN>snq=Yf)dL=^!5V=7(?6jq%C(5AD%ECW_-4`jCN)HBMT-~R_co)$oWNVy z+s{@?9z2ho1JF+3v7>3xnVFd@Cwb4PO5ipE75tgioopHm>~0NhpU0l_`7wRX&J$#h z$p@AdUf;I9vS7=C+u)J;eJXu%641>GTCmju#$@7#HCl42%XCExS@|Vt+IG~86^kH_ zCPcRixficSXk6TbR$!+?BZJ9YcE1cyVXboLS_lj|0DI^rlE-sos(6i_|0w6dB|A^8jncW|_-R8cu>6dzxH zm*$armxd+R#fJqyEhd6J_Dsd z6-Zhn9_#wD69js(rdaeZm^4I@>5+1m^G`!u>7W-Pgo{b#piHw z#v5WtYX%`HL>T8jVk4#M`rff-qiLlexg))9e{xj+XA<_Sz^<@alr9{-4^$l~+chW} z%WMmUimCw|;d9&aRv0Nuh{G!ftoVq|LeY|8LeeLe62T4*X5}gPDj?ha6xgCq#Zpq{$1{~~l;wSKtWedk{~Am5 z@v2wI15VC?;`(Ym0vEATDw<{-B;ukV>)=oIJZhAlUNgY%TU3FQ$D)(`N>mt>F(GtJ zg{!2>&qR%opLD;}oX+tDc)VSaR&x%U3RA;sd(s8D3tl6D#f1z z2=c@`Te-Ms;*HMKTF*|f`W&N(qsIA2y{A&WBNFs#py+7({R2e+RKxXeE+c@ufG7IQ z|Lw&um7DC4?~bTl%3Min`DZvJ86xEV2pD5XKg6dEDanLB{f0s#97!leBFSL)wD_^i z?&kZ2*iu#vf1f1765~FY33v1DeBm;X!K|($SC-l7A14V-DzE>8{{}#s=LyZTRPmRzz17 zGw*)AboJ_n!tRUFcloqEUnkzzXuovxb?L;4CtZamXzgTJOAZQkj9^SlUuFl`JQo=g zrx_iD>+P0`+_*w83r9-!83J9``=|^V}l+%;?QlK)z(wrL}1YAPkSj(e2@Fx zLRTjFtAHY>8d8b>iDRX;!IhRpuWMOG^KIQrQR>=*WdrGlz>q> zD22=kN$imShu85#p*%}=t47LrGMg@fJAVwz3!Iq0@KuX{@Lt1A{q<$)*8~Of5usYf zCyiU_uA?X^$fY&}BAF1l{3~v!ctH+QIi>eX`d?o2DH{C!4-;a#|EuC`3{1vbbi^hj zS^=^K79kIoH8>NK9V5PQ2)R`Id*{Mwbrvd44*nRge}0Z*Z}5yP5R+-u8r%`&dJ~a5 zo-Sv$v-hI6i(8wdS7w&yg$g^OQMC-?^*xeLUcG@pkt z!%T9#+R0$D4fYvf=*u^P_4IWi;*8gM`SLA>?+u?}x+|bF=6GLuTyw`ij6qRGq9KbH zP4YPNfDgU!Z=n8`1YIaX3>tq8@!&GYXkorK27WWXk!`0aU~p-usxGws1)l=4`~12! z%WdBI#E);4-u98FMy1R2?xViOta|@^M;*o$gH$Bjw(#vDKN(|y9b3~_s#SI!kglKO z_Ic1?8q$8*AubXVNRt|UajHigyZQX0%g17h4I5+Mpe}VE}#S#OeE` zp0%OU0K^GmKok)+_B--MMaRX)oYF@@lx0VqR_5e(%2{-#Oi79G461b7<;}3F1MoG6 zR#{^j5%$rKUgHT(#do&b`ohGLq=c_JUz%2ruN@ojH2fqw@#cRO)an3?uAR%>34Hoj zguxGd^3|st5tQeHowcduWxJNIP7$_u5kj=IfaI>Hu`y+BO%1*y(auK;n0_zv_+IF} z;^N}MR|ARMqL+Bo9x_kv`iaTO*d^Qfud1Z1t+KWs-K~Vr}jI_if2R)txdhoZKiGl{RwnwD(gB z5{F)4(pTKBCP?MF%*OOgCgb&`;YmqJHP0nq!h#NXMYii}U5u*@joHCRAy^G5WRYV0 zRkqB~Rw~S&`+4ZaRY1070C?|J&Wxg9uOPqW`bQM46K z+A=LShFs@K>#^y=^*H&*^4931NV>NunM&DH}=XF$>cD%Z?`a-8YcfLmwF;;P7z*9^Z+s2lemapw|+vf@# zXdc*Z5~H1Ap=3!qez|#Er(bTHJZT*?dI<-E#DsMi;u-CPBuEZH}&E902$GpYP3gU@_%DVCkJf*+{%A&AX!lSMo#Ce zw_lu>yE|J}I_m=B)SpZnE$Y2fCy!S9J4eJz-3k!&f%^N*D#uOepi2)XP=4wX1og;D zAZ$$ZzpN3&0}bq-fU1|NQ=iKaQ8mlvlB-CZDCnBu@RMX@u#EqrF)#GNrtifZ&J^86 zerj`J|4Xe3T!k@(Yj{}bQn-UVvQ-j1^x8T7L56~&&dgkk_BhMUG0e+Drpc+Tbk|tr z(ufzedBp!b6|({zHWFw81b+o%D8D)KZ@}47ebO6B68@Q3#-LY)mJAUhWColt@@Y-C z2ny5FRve|XNHmrBLe^tPt+p`^Fa5kJ&Xj(Ls8~OTG$M$=phG9>O8&A1pd0zq5OidDq;>P%0HMxFDm7AJBc7UsU86GhNyGyHc-%L za8!OWu%7;wwai2>6gfWiHkF#&t1_=RI1{Zcfmpf}&S%QKt;E`PTFbdT_O?=dh4)bu z^=syeK_)pS8yUE{2>cd19RC&ZPqDs1+)P#;y|<{U?fuXgV~SxFO9^lulx72GD;nIb z>l507Grd5>gLs_p@@ei}*+h}o0{u&*i{JJO% zxT5)J0}I;ps*&~xz9nmfu`PTeSBy!KU8BxYosivzD8zMRpL!wWfwkPYaO9+kLGo7v zrQ*N}p+H`JOInkswi?7$%>H`$0_7Hfr%@c8WtONRYTWWa=Yw|hz2I}?MEYahc~T-W zobJSi*Yc}tYGl~LTMaWL$;QlK5}C~8M~B9AM#*_KO*nRs(`R&?HBi3R&KK~|OF5+m znwnd(g!+?xKuTxzXoC+tzNDFv?aAmxpVrhW$n3_a7L*exVhp_$4D?!OYQt}X)za@ENQ#Vl7o&X zY)JME39bwFBvPqUsta8o$IoPZPKlG%)fbYN@OW2D4!b*&8BjPj`tgeQVESU{YMflW zB!QIb4L&YRAye9;=NVE{L&GpIZzWTSs-YDx_BBAlG% zM868BImSpwN`Deg=Xq#`v&2LB7c2ikp#KMRpPuEwA}TFzsz(;qePKF*THK{l_YrKA zM!W#b$0(4*5<7Ih9@CZsa<;ZLrAs6Ik+Pgo`TgkUlpzw0#Z5`+qastm?yx(7Gh7R{ zkc{Wm!9A4)jK~=VWMeO2q?o+)1S&rHe9t7Gsi~PDXF?yyEez`{pG30)12Y zr`B0&Vr<0@Cnxo1f|S&n#$kE%`cQ<^ZAt`_NvR=pJa3rnIZaW$V-vH7WqG5wl6v>1 zE8MRq_q~qF&EIPJuu_FyVBICVa-?tf3j!nozxJY$K7cU9!qOOLn&-{DQ#oV*bmBoj zcTl_ec#WD*$Js<0!A(nMB*Pmbs$Ym3ZYgQ$k~lZV4PyR&cMqd%SCBTh%8`)o8o7}? zdIdOe*nYxu@MA)tP;64aXt_64c(xYG%8-`oCyY+QpRnwGsY}3VfjK=hGhrMd07zV} z06@mt(^K_7@$-dNv75V*+h%!SOos3d0LGfvord=|H9}kukGrCUb|&7*r$E*6C|Fcw=0}nlapR)LR}7$n{O3)zlGgR z`LW8b)HzQIX^N}faqnI@SLs~-yjvfw4n~_rs^r zozN5dsM+vxdAWSh%h=A-ND1S3vKeFMaIxSaF@Luzo$C@)sC6(H$z%H>b-5VZ?z#;i z7U&*T#H_1gYcsgETIeLHWy54+@Z+mHJOF<;!t@17X1wj3?1?X(4vk)H`*Krj^3|@c zEA3;~axVszo+-xALmk>3AMkyz_6b4x@N3rwKD1cZUot#_9xzp(`3Y;lE7Iyum220_ z?~Z^jqSvn0%PsEG^Td2E7RvzQKf>L=UF!}DxU_--3alEDdFeEY`;drug92}gN`c+M z(f%sN8Vh&*euJX*{SL(oE>C0cVZHER3rAVgB5V5+e{*$#io#|@JNv!wf@GLzFce~K zgXBUYiEbiaWPamBOs-V-7RL3y1AaCp?|HSbiJsS%FWO%l3IMflbjgdcu+AjWW1;`; z*zNH7RXoeULq|%fboG!I6%E*0Ry}>rP&qO2e)u+n?7s|-AMD$8?U}2%NvBpl>w!QJB z0m4o)37>8ZEe76;d_3z-$aM4FhrD(hPftGYb)08p8jIyBvj~e}w*TG^0_0DsP_bP3 zvcH6{Y)E~sGEd4jBS2j*nbR^VZjBE1e=ZTF;)rs>{u@zGsUq0LK8Mw=PN@e2OAQR6TC|q`J6bAC7wC@{2lLX5pnCekcA=)wJXFv+&20_Y19UCL)OzF% z->L*e{Jq#@n3Hf_gYU}4C)Y=F*tblIM@#^@)?XR%+Dvh?980fN+B=mb3%DwX;eN;o zL);0!t{~mpS^+%M>Vl;#-nO-t2@ETS8>7`_k&&WrUUOd=ycYIy2erq{^iII@hu-U6*`sB|4 z$JJNI1<`F?({L{Nsjj;;tY>2N9gvM{A#^`#YSb{^<|u{Y zGUfXCK;d(K!4tTgF(72@lLJgmP03T!%h3LZ`?>a2ICt{xopH7~@!Z0`Y^qAJ1nZ<$ zWtGqV><%FtXSNAnX>lCVyt77q7hZaxp|PpC(2z7y^mw%mnnL;L$iz49ET2_!C`f_j zo6Kx-d;^&Swm4sh$D!>V106mt0XE+;d;SI9n#g7%j{|_|@_AG7c7BbH(Rp&*j+STD zbQ6Hey#j$o#b;cCO~=HP2ePf4r$}5jzMel^EsujHGW>hL{@JU+Z+jxSQWi}n*N)jd ztYbo7nBS(218O<3Yt{Wwe#6pY9!v@qO<_Z`$5`FsV!u2uj`MF^{^Xj^`5?&E2eYx9-b6OzF%X6qlZm6x*ekA&kmp{7!Dlmi5l$?ip&sV| z_Um{(z&<^@=l%6cW5>sTB=F#-3Nn~&V?0z$ZAQKZrC%p!_?lD6_8&l8r!zBK`!^X!xYN_qA7EC^%g5)Y1bfK>;ayZjFRgHtagLgW}4MCNtCVsI+GsIOo!y)0z75!I~DkkQS`SFM5 zXT5t}^(W|@rW-h8|7EUFi2|q1-_Z|#HxoQI(PMLK1!lKfhtg*DjtZxxI%Sy|EtBWxcF_17N6UU6POFfG-sbjV0+FMIa5&>)S>m)AnP%qZSVTHnkveSwQZvZ4 zI5FmB&gCQ;8sIPW_i(M(b?`R%ki7W*DS_njb@i#$X#3~Qb6W=#mnYg@-(;6s9{crl z?{CS5C~ox`wDcfX^7quo+y1V>^LJt|Zzw&J2FEuxx$I2LmfIoyc9E+j0)#P7Y^fn;N@ zt2IcY^wC0Ine+oSl2;%g_@;eQi4H~)4T-*k87xLw+^ySYI1tkH{9diAh(Ls&Kvz_| zF?zx^2EckB9Jsd*I<3FC5qE+Dko`4&3c)JoC@kol6@ND@UfYPfzh3C%I$nESV82v2 zjymMn?9e&S}EOYz5mC+zN=>fRiEluof1fmWq zuWx66iRh+Z<~orLj|+yN%}gLiwOqbSRv&6kG`$x{>v)3SxI<&(1mEt#?!JxE>u^af z4tV-5yWc{Y>x$Udj>&R0qPNko!Aj;xjTk5bfdVh~;NbUQGo6>_DgF8L=Q>w?z0aR4 z2g=LK^MCvZ5(oQ6<^b^Qtn*J&lZC-t{)@xks%`T#!un&#N`6qP2yaRU*Ks+!^Dnr3 zve%p_dR96vF7wo>9&eZ?=?S zHCU&o(4|fbyrFwpJaB5^Q07z_KgOv^Ru_Mgo8mAB>i9!$+-=GN!$Eqs?FotzkmO9( zq^{JTSsr9#iJuZJ8@OdS&O*pHMy5bTVoiZDXUacV|80Jk zDFzV$O=?Eq9Z)L1i#cLUO4x)AVA;WsuwSz8Tg6ybjVXyV-w zM1=zow;%!2WM?erTR)p!u0onT=q&~5_FI2#4{WG$jufaAetQKEQp$zG3AiGNv?-t( zff$&r%!}Tztmo!mh3PnTEQzH|z5JsNm!8kGL+}%=zP`nbfH1|>*1T2oVqPoCT4*KP z5V!AP)1Bq8{h*)$@CKGMue#x1x(M9nXK5U<)GKPo!#_Z6pj> zi4}MhMGE_jaVrurJ%{_%7S@emsltVC@*0tW9BRV>T4q}-%}@91F~m=!)r;IgVBX|q zXyfBPcPgCc-AF4rS;|-0w^9qgTRaT ziBeZmh5M@`p04HIKLCl&SX?KsVAdHYOl6C~;h zt`P>m#m6+Rwd%gK|MminU>@Wx1Rl)#FM*&gbkr-n&jr^^*VorrLOBdP@u>wxA3K_@ zt1Yr`-)*3gd-icxS>vJd42zKmdJDHz5yfVss_S+M6$?G(&vuK`Le^8?5$%DO2P5hc_qhbp972 z6HcFoi3XZ9rKcE@V;_8Pii3|$QEk^>-_d5Sl^sduj!Dda5Ohu(aDzhjOnofYY#(1& z6)UROVP_xHtjr*y@8c1feUrj3QPOLFRu2mDP(9H;qaD$1y)zLIz!N>lbGXX_os(k4 ziWM(kvAG0dmA~pFUU?_&Kse6!O`tzDvI#N%?E!$rG#}5wG2Rc!pVHV}(>*E$7_kt; zK_SYd*M-R5^AYYpS#WV~-g~UlY?SJfdF5I+=q92*%`ZR3F*e`ZYLpowegZj1(pSW8 z`z#7vn?MXgLUj5aNQY7O8-PW|8R6MV5e50K&JzlXh&*D={-w0&6^YPb$y>N? zgmAy$_?<$080N}@czf&jSHWHN&UBty%}nw-KLqhUYh`q0IMt_~-G91-vfR(9E?@El zRev3Xke&$X-P{4>^(TAt5(*El{Y#GX7T@Bg1iW*%&Ga*?^DhAq6dK`Lb{B=_5wF=_ zH(zt6W)bLISLzGyt?#(UW>P|Yzhi+JAmyi=r4E83AY)v?#`opLO=c(~QlID=anM_a z6L_)G;{2UD(y!3hJ#QkZz176><7S{hp2mOAMf4zZsQu4+tH~$~lxw-P)^K!8nOUE^ zt8@V|vZg^T6)DO~`q72ugRgr>NpU!X>o2`esUkS?yFYT>MOME0f!eqUJ-J@*$>-+D zxr=V4%2kc0kKdpjIW)-|QOzX!2AE>AJo&7d_r8LQ73-V`<{ne`HPb{pkAo1+Rvi6t zkxplb)G3(`0jpt9nKVC_${om-`PQk|9zY{4A3_%7+HANBRyr>=n>`y(8MRaO0|{+| zP7m+4g5E?Ne|mYA)}9LcxK~o2$_#FCvq^AF@WN&Cz1S`;L{1AfeV&*#7vOs<$HhPFr+%`#XIT|$gRvAca!U8ibUUiW2ve+cM045sY53b{FL9FUGW`u zS-A;5^z%4hvE)hkXYsLW$+WFfLC8zKc3ZXL zX}tOfSrNnZk=!ybp=QdAqt}OuvJ_bvQduxm^XE*ez$4o<4Z#Wg|Fp>=U_f;ypMdWI z*=?!wOU75yyEdmqg(gXKe42J=A%Sw@M(;pE&%5`wm=@PTYcn3WQF^6;EeoY&M+~jLc45-@h};U?!Ss>*1MN=m z_sfNn!M!vaytgw6ZP~w<5GQF=vawIjvSZ|tG~7WbuFp|L(&EcGiQ_x{0O=QsQbC5; z$mR;4S8i@@Kvz#WAX-m)&BKg^WBgmmT`|5}I#{^MaQrW<04~BViWaGN-wnE=FS?8K z%`)J~xC}M?K5WgLWE6yKJ33hs^B^Pjv|S-&$aanMRBe50QoJ)=6pYVf64d!QSeqFn zpeb%6%Pzo4MHfTfmGDt$+_y}9Kin%=k-HCwlTK!rUri&(@bq&tQ5qYk{@zT*fFBb@ z*wOt4L$0gw{d(zesfLMdGBS02E)BT(4q{e zmRz$b9T<>}CKuO`ojijY>_M&2mRTI7#k#~xAJ0=z?Xm?o{}{t?q|YEx3<5;$={kW4 zk4<00yl5C{eO5Ve8~rbG0hYP)EuPn3rTNP|u%=xj)D|iAGwK$~m8UtmmMa_x-G4L> zKGYS1)ZEz1x0msq40n28L+Pr1)p$j7$e%TVzBlCb@x*k73Ux7{vEuk!YDB3L#BK9-IkQl+d^qZa+IkO@Q- z$X4)KzwPrAF-7fDav7A#`F-eqREpj7$|OT3Juqe>t&X7C{;EG)`^3J>tt)^0XuEy@ z9P{ucl^!!ATfGjs9L80qw z=(MdVVIguibADQ?WRTM>VebUN=j8Z1|ObdiCOXtjq z@^`I`pn)nL-u++g*i@bUjP`)#qN`Q-fLHBEGd=`Be6nu6`z`NRROP=E6 z4}=tb@G!LB`EW^j=n6%9RB*NJ1B<*33MeinK3DkH)$w*FPntZ$J*1a@zsUFpN8xUi$l}qjg3vbMCKDSh?8k_p(Eq=PyC2?$8b!MLQ$~ zaLYmgoI@rylr{FD4z-b$rP<>1NQCW!%|sz`j~m#Wh9}0vx%xxsiN~7L)BJ}GrC4-` z-8#u8V0l9JYJWydlSi|zI;YfxJ~ak;)>ppdD$hy4f?w<|1Ncqjg^ z;XpJC`{N%{nNr@IJ`CP!(!ZSD;F}dtxlf z&Z8^%wjsCV5NU9tel5SYb6RQ^4J~@;|KOtHUp`EitoiM1!HKuQIZ(UfZCS$r2KH(@ z4^Gx3MXw$?Kaie4Xs)sxbYXKXQ-Ia)!HJ}*6HqX;RvU1JprMqXtL6(mrI! zl}&sL7Q*Bfgw`#P9&x7Cs-Oh`!y4MxKHM?N+pYnAe?ou zp?un(%$Cecb)^*miOxvdc$>Ion&`r!WN*FkNI@k&9O_#kdq^k~A>oLxl%Uydk9UwY z7ufB`v!+)b?v0|KulnF>;7b<9VHZulb&S#R3cl{DxLqD+XwIzo=ld$2S*1=6XN3BY z0KocbS6k-EV=WsoZg4?I%Dyi(syOlMg+T!mZ<5toLDEuB%1dWaBPKA^wWxM{!}Bu-PR zVG>WfrB`2Sw)m2}cn=}`t`TvE6W_|q8HjtJ@aij#KXd(E$BEIeKPD`()Y%APvY%!fQZh+V5lF`T8 zeRL({HiZ{ei}g<91Xc|qF;oh<&}jUMwKHc+mKC27(#84ogJ*S~_2mn%LBnL5xc6@% z!m|tpxeBK$3wdaUA`J_+@~%_#=6IZ$d2)x-7e)=%mY^%*#Im@|c1>X2SU5^YzNQ&c zNU_@SD=5hC7?;u~8OJ)?071q9vZA5zBG-n<^sqSa)=J9=wA=kM zIn%Zf@E9GDjCCi@E?4CBZ=45B+M+vV!LMrz2!SqB!!^yDZk;tJ`@so%RU>Hhzd|6A zdJVouf6WaO$)Acs|4uFAY{q1$q~W&-j6LzZHL5Kw78gAq zq`DcdS!Fs@lWN8}Ita4w@^2Om!n(>KcJ;llzn0m$)&XU85|R)+J$EPe7x3JaW*Plb z`AmWeQhQUE+?Qo-EDrdfI-OGJ#V!r$-QsI;RdD#D&{$oTAlCI}fE2{}P@wW6lbf#B zkSQ*=UI!As1xqKx$T`cJRk+);v?p1xD?S#Z2P8DOoxU`X~5~H&K%as*`)>F1IQ5fM!L+cw{JJP z4Q}kwg?rsHer^%^xT$)tED?2op{jgyTWEixkPQBf55>Oz6?cwtqjoB*p}%B}BNsH=EZzOs3UX+7mDac<;xQV! zTnhW!we@DkjM~_EmOE{S&=sc_mNX?5*B|nJuDb(qHEe<5zLTCb;L(K_j7a;8nPFi_ z5Cm;vy63~5&Nv8*78G8w8QXHo;Qeh&{rUdn%%DZ?o9+j#tRRuccV3nz8yT#~GWG)v zmWAQ>tIdR4Ws`fYF27182}Mo_V+|AAxa6CRKXPS~?zES3< zE+PK$vmsa70SEJFIC7W3{;;y7Cg|E~KR+jwXV0BK8Rtall@Q5zd|-IC=0_$V>l`Ap z%=MAngLCl8efK_hZ(PlfEblCSHZ(&cD%Mr^%wT+5s16bN`)J|z_s35U7pl%E z3g54Yd@~{|(?p8b0x#MuF!F5AHaxBYN# zoby>B$fG^WH|9`}5`mS$?{ue%)cd|&Ipf)Zbv&qm=zil#47SABfk*eU*)R4W*z`eK z8i_%pLNf8P$>YxP8CrR!kBEoiiR?|EDZ??fG7zuy;Tg(;9lrpQ2trYp*0p3|P6Y~I z3OMh9fq@P@6CWV*LvC?Pm_$n}SC=EvqGNSc6nSqjuKD`OUMZ?MCPiPZ<%A6{9UXPW zz0|~yGi_nRN)y(dFJ_~51-A@)Rxij>-q~q+^34T;&WMmj-S{yT!AY}(k1Z(%3r}`r zo^K&0?TqlMi?&HmeZc+ZUHmgq<8yx;%^Tj|2?pDXPQda-7zp1UDs>F87<5HZ_vnM! zO!*q;b0+%CZ_1WUzS>8pLUz2AHrdGkBF=sjShS=lm97~)jRbzdHI#(pWEs$wQ~Jx& zH6evKMP{BMJZ|A8O)h5IE6}!kgS)@5fVKu+&OKu3I;-rMUr!*CP;Q@r;$&i-BGHgj z0q~tPFum8zb^84^_0}MxNGme9J7J)LH8YX-dp3on$opiRM0*l%EhEf9gS4^S(Q+G| zcsKgqEM$il&z250aaxG1gUs4RUIIkbd*~veqSh*&uV?PcrTJy%FZdZlh%#YeVJEt_ z-0Y&%hD*ma&$GE;I@-B}gmh*vp30qw0bNUF)n%dGdAFmzCB7~c=&GGY0}WC{bjjCD zuNxZ>bxtuPC7b;4G2gz$Bt2gWwMas)vN`%ZJ@2OD0N(6zxGz2T)yPP7EF#-3)%fS= zz;{j0t0A>Bka?BUblA~}lGTcYgp`?)q$NSgObhiIR~U_mmt1)RWV;>bqjw^Z0#?J3 zg107;5IH1vpuvIQKr$EFGn`(tTTqx@RyMZPpmvRkj7B~Ypk^XLAC_lb(OPfSSl#{} zXDc5C%3aPpi=<5j`Gglos9gnhi+~uaXNGV4AJM=3J;O%E2syP6^@%_GQ0kj|5+b^X zMtX*qOJ9^2&l#lTf^UFj>WOM{FL84cx3coq@3APiRV&D=^yX#|&|QGTS6t?Y-kGtA zlY=|hUMbx!*Rj6ytV&^vpsA*~wO`mJI|W9tZz*gCD&}>?C64#f!~^Ws$d5;`uUUS@ z7no;Xb$$?%NcvO}3-+xn1iCK5#h_lJKx|oar&l@ZP7d|PP~8E?NxTjP#~xr6C3vUw z;lm2(hk&u<)p$N8BiMb7jHL@D>xIY+N!bFk7U4Aub@?)n8dHcx5GCPbNifYjW20Qr z;Ib8u?z&tDaBcerC9Pd!bjOVGTJwTWO@IK0@cN}N&C+Mi zrd5hF9Z8BP2x(IVfeYnZuTS-kh-vp||2xP;FONa>m7)|-?Jg+;LrBE{*M0Br^NYkpHpY1Q=^IY#dG?A3 zY0uti6C$_xF+QGEJ&LNePE@9$PqURAmT8K@4~WnK?kfR0-sYEjMMFAq@qJAbJRAZj z#r^##+~Ot+BY34hd9bvwD8;E>6m$F?ViL;MRVXkHK++ z#7SO@+yu1U65Kk*{pY>G(05Qko?Grc?GEbdRB4S748_Kyb=7+!bk&~eV-B-x37__w z#^|BSv&5B^kDi^SjZ=!?`y=Uw;zLc*mB7s>ZjI1pyxHBCmkE5_vxFEQ+dQtO!CKWn zKm3*iBlNN{H4RAMMa0qFhGxJ+yhKw-g;3r4?KK8pm`Hs*cK!XOuDf~0r;OtE+S$b8 zmTYP2xZ^`gkvnr(m*czOmn+;)Z+~e!Bd+WUzbROek`8$6FM;7hO8@fDjG4ehxG53G z6c)PKz;XsHMD#${Bhic3Xo{yuIq6HFcaj&_4GhRLZ+yJnd&{dG>x_VF^=GR6`RFI| z5MpEmNRaH4+boQ!@SV_3rmt%H=twbeM}s~mCnR{0jT+8`B7gsKv;(Ul#fzIr z-&zyd*^p#u?Vjc1m&ETWh(FV|8C2P8Y{VD>0e{4d5GE znv;WQ)*>WLO(e|8?6t&+Vx{3m=Z)yO_@jm%vHR34ibD;vYP1Mixnj?7J4=0SJ$w?g zvKnob9;0h$SIqIwqC)>@``M;?{xR$+_sBFFXCy)UCpBGDS6V~Y#sTY51G0Pn}S z&YXtnk7o)5ou=AB**7X^1LgbnzEpH=3-pm;ny?znmh#^1y-R#vDt}{-9uLExZ@Qvo z|3RzjJH7xkEYoeM1mBpIf3>y953I>b;c+1Jf|q;V(jVZtyYgwOMZ^W~!XQxHcNqGP zcG%FcE#jx0qHN4VC*1lJo#K70VQX4x%@3dE#5nNkReXit`UI*&Bu3{RMGZCP11yJo z?`jH1H&yS~Mz?gUZd6G#D`>8W2T%970UdYNjddzbX9V|PrZhp5BR{ibxuA94S30ua zeQnHnE^Ps4XS-r>(0=j4J+LFObgFID%?}pWVCw#Yso@yP{c_e=QB{?yVhwDNV8bYC z&@@$4VB3z`Wt5iM{O~7FEn|7!oBm9g=U4Z3rd(->9RXC8;D@=Yk=pXOLV=a|(4VN+ z7p>4yUfQL_O&}@J+hO=?pe`dLRod+^*YZZb@ITt#z|=V0n66-U!Y>#h`*pRGw}zphHut4{>wF%S_|*h(0HO5CnC;*JwJ6P{u2CokU{A;5I*xq!r-RnTUFPs@4L-v@Ua z>}R6_U>?0%+D%L#-A(X!vi%c&Qewo0$tlQ<&o4*d#D*2lLRW;aSSZQx#X87sdMZ2! z@SKh^KYfuUr9>d~eBel!2q@Jb%?8MtLkxhOLn3Pv+{gz-d*0w=H)ip|l*W-U%8u<= zneOgC(^}&-`YFxv5{itB40ICakrmgXJ=O>b!M_v!KVxWzNzD|~!JZSMlJg}%B|-lX zx)y~fAo9K3h;G0;h6VBf(|TpCw%q9pR)>d;yUgTAejxYG8M@^Q@!f~8Z-;gn5(jK@ z&_3nScBy7x0&;9mv|2ZHnSI{~Img0jwToe31iycdB-3 zRn3E3)R`IW*9$lQE`D}P-2qI=p&rErPW(Fy#NhPtv-O|)CYXqJvb$s(IRr0sF>wuI zdLo_vEWL_%UQdA|AFQlD{ksVNWD9W+h=|BaO4!waksF<1`gF3%2JBW+81kD^(${3G z!_<_D${I+6!9eg^6Zl#qP!Fo1!5AMF*^f@$9fc1|86#V;+AhQWxW)WpNgqm9_zXr3 zV$vPg2KIk;IWQ;jZ;^v_Ih7+yv31>h6c9D-l=^Y0q(CwU&P~!J#3-(<*3N#v5!AKo zIIvjB3ZCyRRFxt6?LmvU@MDA6&n0>rxR3SGimkP?eO`Ml*BH8x_V+V(z5gfvvMqrS zF3O8hB=3|-Pp|~|OL$!ohJNN_4pBe`bs)^|vbCtZ--#gs^q@v%b*B=I+u}dJgcMV> z*|*|%c=_#g22Dy;CovhNkag_ySzT5QdM*`3Z|*wGpJxC+(RCq}=>%FY{=aD@xdKRs ztib1pMUi6+GehD^-!2s&9Pj-k2vpbG7u}m%0CZA zfSC~EF*5nT?+tFt-&^Jyw77-nkOdF-eUd~*3U4;JFk`LzmHC4cBW+m(ZMf1o z?WQIRKrZ;gsaKjs)8i`UN=u%B(AV|6n+I`1(R+z&M%sA%_j^l0-3GWaAI>4(PT^n& zyjY7&K1`( zBW(E|4<9jW*AjzeSXZThT6)n5_@^eM9V{Y)M7fN~18;lm7dW|jt$m4XEnheTvrF@- zPg9b=BfhG^uOe;~nV*~Ub0?x=K$Ut-LBtB8gqr1M!b@MQ8T)TL@ThXPqc7a78PGpL3UY~Q-JIzIXf2ChAX?yO%#d96{|oN>(=FEpb? zKc40#OkK%Wd z18iAWnXB6vPwh5$pQaaJ;<$9;al*fb^o)6pFN&QgEo`C4ZOE90%{LtjV#mxAR}*-8 zZg8KkRgCEV?%3HCGBt%xPBXUnWp|DD&t3>Ther4s;THtHU{wm+KmSdME)PX5o+$XB zHpezvsP(>vEvx&@JOt!f-gag1QZiYxu-3by5#5~*gWr){FnyFpmsUc~2$#9pv}Ov> z#RHF{sf{6Hwq*Ysckg4&6`JGK&Hh@pe^VAcG9X=oN!&vrGIK$y#{g|aPgz<$XzllL zqIdO+8ibeU7m(x>u=3cO5>;!v<5AQC7oQ4Db?hBlXFtF@ll(i_bQ&mmcb0ow1Ga|6 zF{JLFADP$8*JygBtlw*+77Y!JDSxVE&3e-UwY^<)qW(|76I!^jMpl8{O=&swQxjg+ zt`|8k*VY$L}VJrQ|4+MFTz9cCxk2=GE z8VK!pxPa((bqM|~y#g|)4x@bAHN8K!S~ z-f@2h669iY>@PPjyK!ab=C-9h-5b}ObP;%5nm-^j`D9-ZuigGPV;+M!22dIvzA1g~ zRItG*^Vl>UyXjcfzAIB|@lAyWCpp5qr_Sd|e$wJJw9(64Qcs7@nD&r^hq^K`Tkd{& z^505Tm=A`qCjGaJ++YaZ`4`cwwTe~wnb6fRdA!i5@-wn$!*1kjM20gFfKt$$ogl2JZ(fY7<3#l8l zr~c>+x`wWTi73uH1p<&BWIrx<3^tEm#bAlEZ|b@`oz zS#v3Z?a0?=Kc+z*mf805ui4~L@eu%Da6cg-G6|4;BXDu3J3VV{urMF7kAOvP<#E#1 zN^fx6HLQ7b=CDs?;#c)xG#&6-=yv!N{Ydrs5`YjulMtibE2q@2g{0X_YsoIG$ zG1dlktt$3v0CfeC7sH_N@eLQUk$eI=muyYOKhMqlDlFvcRlp)`q2v;gbU&H<0D71T zl6!`1J(oK^2+}Nko%wqp^O6%F6!IiADYye$hp#D#Ztr^)Jjqh*wntt&A5>2JJ;>)N zU4OB(449gl%BZLi(Wh}qcm#!m7qcZ7Oqk7vmD-<+|GN#gz%+nj8XvAKkIsDEg?fT> z28K3$i9@jHA84WJ5rJxT+r*4d9;!WS<*|UXoreyX!*2m3Z&zBYZ^1r$%Xb~-j*bYZ z9|k9w%!jkEx4GUsI)@AV)Mspsbz_9tZ`WZN;<)fP{XGht|O4BGmUlYDyGP&C+l$em{8U!?_#Ia9 z=N0;NRDpD^byQrcV>DEpI~T;TkhUR#s*nv=zt1k-J^- zMOz8yTJi$75O`EVZiqC`+x+8azGypG(+f*wwQ<9lDBEMHM*k-;lBBD%d97uKq5y=@ ziPDe4e#E$|CCL7MCbQCAEh_NH%?yGN*jQ+?L%WxFd!KC-<$V66XiYX6B$w*t@*oj) zyk>_mq0X$#_>L`!x*lH=k{ zhk~?BYC8K8V0~Wm%OvpX=6Om0V46qa9lJew!+CME%$Uj1FVylZ5$ka^^*HeVv4+tX4{1NeMKYtIOC!;^Fus5KdqCZ^%In)5lRH~X5?qp-u(JzHCLm|$H9?g~H1g`u`da^<66G_o-8 zAf~1WKo1QLz6TP`uVb1)0Q3!#skyl?*o%;yY1VCpT8-i7>DBhQP(c?E@F((p0TkMX z5&|LTMukNZ=g;K%6x7{O;Td>C_xvMa4DV*(W18Z(2+Rj8F~zDDm;sU!+{5{`2OdS~ zRoE#if*E9X^mK^3qfTa&j4CP@*+6`nt!|hEFBnKn?qwZzl1Dx#%7xIS)Mg2h^T`{# zmCyH@3B`jok50=TTtQ2z6XY7T4d3k^=7oBW#3D9~{&s8SZxWbPu9 zOPsb@ct^vp%k9pceg))RA^Hf1oQ(7sOnhSfmEd<}vlK^Z4Iv;bYQa%+tSX=l?_Z;a z_y=hsO@TST2DTO^lvkNjHvMR=l04UWnf4@%hghv~I!hHP^pMjh7L^wwoLF^yij-gwCKQ?c?kmI>AV+KR*l- zrYny6*+oe0dAR~DN87cpPaq{6cxA6)o;g{+S z*%Kj7H%#rw_C|S(f8L1FeT0Er>0z5Ut|i&&dGN{XkgJc2i#cjA7vyx!%QEqJI&P-1 zhQ@gBM(AUwMcm(SO+Amc1cp*R1py>$;oyiC{QT)(YqtgE;s#pCp4vKt{8Dcm#kp!L zlI-m47Vsi>D2l+gwztlpWBLlHOn}iK&u?rbG8#yL1amUT;n7n7MBFMD__VnG&ZQA@ zNVG>a>&;EyjQsaio*Skcv~=((3S`M~SbvIudqiyY0mT$uZ;isir>Ko9f0|zYhfmQ1 zE%La6^kYztwv-JGW#nG} z;&QaySn2k=KZu(xOjAJNIBn(Lsa-5(c)$JSOFDnNbZ`x$!??P9)~O=&<+h{t77ok4 zTvpK_uXK~WD;`{D5uo&PX9YKb=lUA~#MU;w{{v^r>SDtXhT`~ww})pnh}+bj9MctMU$nUrAra5cx1d57ST7=70((i(0ldj)!WJXy1Ml0YA*cbR)>$m zAfhDd=*Y>=#pOGmZ&|4hJ8Op-4xmedkao!O+o`!ZB**$sZ#Y69&9;x{SPnp(>o+CX zFZ!+n@dvVs6<`d{^2|;D9UgwlBHf+iqh%tAFyfJXxNL}mg3=1X2-F{K3F+i7Ck!G{Dc|JLD;W-^bhjLvUcAPb# z-BgFhA|l@C&9R=LqgG`m)vov7Pb4j%_rbsLv6`SvPMn*G4r}Vx-_)eV{;0~SMUQZv z(Vd;4JE6MJW8rrMc!)3_V$`t-|1Y%j>#XPo7p@7N zue@=ZY~@p6EC~%Xe&H9*KVB`tZtufLN<%@*Pk(0y>?i~12qRP_eYUGowPzv#BI+L- zEA4o4RNn)3KBK6@zbyWF>pD`awbB*Hy;bpQr4^TER9`^tD3Y&!1tEl%eM(Z9=Ve}? zF^yW3Y#>vx-{pGf7qpOp;x$ZbxkRLN@7Nzy>D2Zz`refF8Mujn&j8wMyKd66*q)TG zRGh&WAPFgAldfeRz9P9xPP3lbQZlTt#ipE9fT&x}UWl_YGjGpzKiLd>DBQR0hGvGV zSNkeV@#_6+Z9XT}zGXIK=0%hnmkh?#vp)?4aF1FMC9Qj+x-v!jn|3gRszu#{K)-lx zi1i32e{RdA1IH}Yl#4OC7Jq$}AA>-JDTq0`Lffk~RlB?p|CQ!ghpvjcSkZuX;s^ny zV}I4Ms6w^kH#m)TbHxE+oWV7(&@V&E5r*~~vVdZLDuc>ut$f&P^ zEivJPKGX-F8rd7VJ*%_P8Cz#c|7~&WfA?CAHz{4}Ir%~&2SiTMbZ%6oF>m`Dh$^55 z>V##VZ1jB&jDv7_L6IS$NFumj_uzRRSVcZ$!H=j$AD6LH2C%>E-n@$9O?6OGsTxF* ziWKxqbqNG9qc^@gzbv{AH2q=e`FS!ld8Y0k>!Xf;o42t`J+Zc~!>yNQrCV7*ZWZwq zBw8D^jo8P!D%^VHTkQADY4NHrlDjb!O<2@GzozJ<*S-3rk~=D2`Um zRjVNr={;AduAHf{i9Bt0{S}XIB;7YDUNdF_kKEnFVcRa!VJUXjdQ3EsHZ=)0^RC#P zsY01SpSy2w33@X$N8en+JQk~mzpam4Jxa-L7DUQ@0Nnf+9}lKq>#WDmw}xvkANusj zV`MKI@NURmSu)HmGe@i+neDbvwx(piJAEq{FnxiqbsjL6A?AkOJpj$^*kd=jY_i}! zx`@o4b7v2We07~xa?a<^_*(E>Gt4t4e6k1mUZT5T#U+ejk8THbL-s*TI|EGCAaufh ziA6y80|gnz?@%SMd47LolyYy%w9PNcqM+BNb?uUf5kpo5ax;h4n$wyC<00zv{ozB1f+kT|A{T#lC(N{6hnQ?K4{fn2 zh}sMbH*n|MNKTgO65uqPk~u5>Kr|XerE(3rb*0RQ2({xeJ~SG zUGV_h$<5QIA+e!aEzzo-WKCYUIMpQ3K;{z0(U zg1w6KBodN%$Ar4$p@MPQWZLVk z@raab5xTMu-R}J$y5^eB3k2VxOseP#mn=)Fzv)`jaz6h>TG1by7-;YmO?y*k@pjZW zZCVBjf2*w$D;t7Wemc#ik+b_o2s=nk|LpPb`;;a0xN%y~>C0Y0giC9Cj$) zZf$P#JrjGu;w~A=l%YXHAC;y7_IFR2G8oYPuzvwiVP#BRNOpj>KAi3kG!di#1&8eg z_Fia0!fml%1fup)ytiMlzK$wlCNs*mG8I4iwXL?JRAfgk#I>3!Aw3w5Se-73OT$(` z$drMvBRw}=;S?DqyECIBA8JqYEBsTtJ4zrLE&i4=ynw6lha%;lv*It;3z70#xlmpE z@FPgziCn4ZfKBrbHz=^Pz%gL{)d|IRI=c4c>DwUfAzQ@<9tP@cheEL-4o{Xi-$`{j zA-Mj)$bZn=5!&a>b&gGo>2KzWCn7xkQ;R<x{+Rn%qZUzHPg{I9P3&or>-xQ z0~HVQRF-qVt33L32zAVEKOkU?{45sonS7lTsm`+D^W-C9dw`MHpA$M^qfyZtO!fnR zT!SEcx40_8VtkadZ z{AT};s<#ZQa{Io9>25ZSbT`u7-O^nmA&qq0bcYhs-3>~qbV_%3cXz+rbB@2~|MCH@ zz2SnjZdS}W#+YN0#7)n*47o<uNrraa8KE zI!O39u?wQJBo^6Zgl#;;SLbbjW1-xJglj5kvJ8 zOCaJ$n_!@FQyd7@P}+ zH2EM+CU+yFuqPOmg&KlW)r%l}uCg{18@6`nBR)06qu6DK$+5VYabP+r9l5HXPG0)AyxP-h zx-p$TVazxQNXV!pg)ZGq-VBQA3QOpSq4$UHyfX?+8PJmbWp-zGpg~{JQOSs9ue&yU zrDEhBIcZt{j2ON?TZv4RIB8*_nV2kB?_-hMl2zr)yX)LqU#<%+o7s{kx5S zpvTl$+N~&oQImX^00`0=!gADf>EQLOxH&TPjrjzt#~SrLucY%DA4-UDV|ojc8ju(lli zoQnRouPn!6v1LJ*r|!k9kSB%lAuZZGF!BpxDK%ge=YC<7U&dvndUGDKcr7Y2>ic%f zO8?*mTuh`bIC5{-y`!~^8e7ziDWOX?Zi>S`{qfsygr)N(-|zk3$PZ%>2Bl3hY8x)r zeX*j+BUF8TD&f4U9Jr_P!BCXN_wfI=E+3EPlne73{3LXMh<`>djMxGgKR807@W8jn zJ-nbyK_DxKaiA+N9-f}UL_*KF`cMb?|Su4(=j>ekre6iHnC2lB$JcC3!TIt zCEZohbD1vIUgVWR=bJM%hPmGK`~j}T;QAKz@0S2MY?GhCY#V9(s7b2Hj=uMg`-q#w zLN$-ouSo5@SHq6Tfew{JvO&VOT_hP3g5RzoEc3=(SA(5p&+venFHtTx24k}tL9O}b z@5$)~7Drkjh#6IgJeh{P_iph$uf|*Sqog+on8WKBAugxs6kqMlV5uHBW>w9Vd-x~B z^*HSdjxs7x?(8i1bUOF$GXlv)O0xGG@tfm;qew1kpx2QMwK*nLIcP>=6-; zDUG0JWf(XPWkH`zyd6eiII#5kY;S8jrI=a3FcHew*)~1H5>C@%j|a-}Z=?{y(aKAC z%gLpC&@Fq^74~j(gf(QYnjaKzt;k2K3`dK!>@Pm&LDnC$Mr3T8mGKqoqbkN1WQm-Qb_Dpl*RDBTK1=g_ z(3`~O7Sd5~`offKUUU5zo6h~zzM5cm=jdW}Rs-oMbZ?1nk0FI#VlUU4J|%rcThgCM z43Rw>b*Na~zfB4d{sQrL5b6KwXKL6tb+uLFwX*%Mb5QMwy5u%6i5mx;3y;{ZN z;_(~D_l`@6=0vib#mPwP4PXng^%WU2#Ah1h!sE`zxyR#7QC~?X6+SEy-xSnKHy1l@vl&0X-Pi81pHg>%AVF<+gr8)z8UZ z#g*0NuwCHbU&7%nx|b{Ocu=CGH=j!m`~uC@81?yBfH*+l<-;P=L4}P6J&BHVXdnH& zm+=;?^_AEy)z{aAt$e(NzPf=ZyZg*#$Dar!TJLTL67%$L(!m^zyJR%axY7-Hv5iz< zccH2(n)6Xs4~&i{G?F}z^qZ5DKAI7DLDABm6->FhLLvUx8b0W75;q#WivKO*#)#;r zUsJuP`e{(o>9t@AloIaCY)R#4-*tI&eT}@5-Gg84PS9#K*x6G^VHc)qFt@Obd^`uh z=~-E`BRwan(H(`zLmc-xHQcX}^K#QwY34xqUQE${4rf)!>)C@HvE;pp%QsP2*~NBL zWa+3SWn`TG_zt2zpV#kdj$hHq}VzMGU(o|n=^q>jKwwR^4i_@9UR#6}n!Sz8CaYU{(l*#lY*?daizmW(tY zK0-4K2S0!C+|*>VTR_;`U-M;Xx1^uEjjd%wmMFZ#uLT#`PD1@lu zy!7n}M0|T~EQ7OoDmsm~Jm1^C(EbB%f^G2oAW)$- zPMP@NN_bFLW*8vZrBVAJrftweij3H?p*cBu&G_*ykO}}1G()V(Wu|WXqDLNGbY&Mj zrT-FDIFQ(L%oZDTWK6H2fRp@fGv-SAD0gE!Yizc^(Uss84{fAAkrVZUjY04clUj+S z+Eg7tYNCj0ib?(I0`7-tQxbM1LlQi2!`(4BE^tFP-3TE>kh$0({ur$*VJeuoC9q(|BTENs-YFPDKeZMqzYQ&aBQ>m|=6@uO{$u+Ec{b-R z^=ek8qt3E%%*WwtokUz#ebKRg?vEOTDtnud=e=io3obgQ){u4RnfFB!X~w_9+?`%; zV~N9*nO0Y=rjFY2)4b15nKF#tj5NiAW-ovT=&&^eKY3lQ-f95;P(F%?n(-_pX@GL# zv6m3~YCKLP;Bolwop{_WY_n0za40#5doN~<*9SqFh4ogrp!9e(yCg*BfGD8%N87yP z>dz4t(_4xwjo1&9?@&}P)2k)Goo(>pKT1ZsIP$j#uZPlc*CAVC)X>wC2^)_S5JLkR zd_2nif0|$W1dxur<#qVWsr*U#0%S2n*Wa#-ykZsUDd5n`Q+R=hu(N;}TErf~=Ek2h zmf53tSQ@JS9)&(og8F=Nyps1{6iw%A&`{tV4<6`-BCdyf>3_X9d+xUFKINP7t9gEu zb85U1Q43cr@H@NA!(n6r8V3D096HOZ!+(X0R_+bo(IJUC-+;XOD-ZXs3*@U>uuocxhwf4OKRO9KE?Gc+%n_yZ?MWWc_1Q&`GFODvF_zMY zCGFpE^Plr2m+Fu707dCygFt93{x+AS1w+a^FIP@$4l5){rf3)oEL*YS2CiPlgbRtn z+<=qd(!DQb-970;&wcAK2P9M+n1KaVB9Ml%k{;}Qj~x~VQu=%FcM+rnKfI_u z3mFXsiTo?SCToW(!SxC_{7Vx2SsWM`Y->=s@`^q=g;LIvj};$HY)bEh=5`S?-~i%R zNy^kg(JVLKW1SdXYnwu(!_~0KGqWURS{l2!`qiz;bEoNw>$~{2g)8}ssdt1#M()N` z=w85pCQ9GjO(N%>Ivv@d1zy=EXRMr~woST|L8yX>wq96?O4w$v<5N>!GEJvYmYF>K znN6G*?h*uesc!&*5Xd=BNs4+wB)3t}HnIoufWte;0?w%l8iWs2jSuY;UWt#K~DeBlb(C1cSl zY&b=JNQzJzHVTya4y`?@X1cOl#;e3#I`?5d7zje_)I=}b4ihjGFzRE8KBxDxf@el= z5f!?dEpv{B8|Hw4pT5B~qXG)Vw<52E+M-fh1% zt@*MFAldm`K~dKQ1n{_>@AN;=f^+Qd=EJR)${Pdf{iGr}^?o_KmZTX8#{GBk@fzCH zuAto#7ResO(x6S99{SID&cr4C0wfb&SfR#d^mf$?kI@r8d~g16&16_GQ_mAIk}*vv z|9jAX77?I^>6oNl!apkT+?@E{VmA*@m4PLc<41WHZHoXcykI}lCBC4#w(fpH*b<(^ zHp~yIG~Sak)mfHWQY_WrEN;3`25VW&$dn&637<8FB&?@~E9X7sBOy+m*jc51j7fhx z8hXFIkE8j4?TdS6Vn#7{OT1uxA`NWyul|++JlHZ|?=U3#ij$d348N}jNy@YAPQzzD z@1`)Oh_E7LV6Dk%cZ;Dag0T(41e9000mV!H&t!(cKo}~A$w=1!+4x^5)>3##1ta7l z&3^v4xai<=GU0MBTb=$fKTsC2lJ_x=1cnkRcjbfe&(6p4yyJ){Y5?n_tbC{( zG9H5*|B3Y<;;qk0V+vgvq_7+!CE?mCZ!NM$puLNFn@UTGoiSW&vx5pRs zU3{h#ykD7;WZ(E`2lhbGvi#Ht_a>-lRR$d$u(YBK0StoOfyu-x`B_(=YBGUbGHJMv z$?%H~zpjw72MoeH*CXrJVT*jwynM;4d&@6NHi8_?c|I zEClN0&=J989`uDgjvd-p&rIXg4ffMf@$ZNybo5s%I1|T`84piwIECNLSo?k2IzQ16 zi`3fV0W1a(gOb&@ZOPc;i}1IZOUsDKEE79XFodI*I39VdeTP9+*bTMVjv39My%!}K z*J`X=K3XNXz71+~*3bV^xzUu6U9981wNELm3pVwE9P-eg(jDbB#XCi9S@aAi)^T7& zb_7^P2(8-TBdDTb!M3OH_KgA~dZZ%5^}t$C10BiL?QIaU_MSzQ64cb_)$HOTQ9A0D z79z}EtJ1B*x2&x7{pyn^AIIJ808DLtdzkIB&OS{+bHAIkl?ek#5W0s*R#ILvC$?wm|Wj~iV2(0|NF$CPG z6;pXsSQwSwxF;tD(sH3j%b*1)Z@g-`YvEo2*B>15?vO(GL5ErvMH0}xIzm$pNw$;w zb1H4=s_f24Y)ES(1RNcXTWouURRA$7I1~)(p0;ea&B#(N&(~kh>t;6q^~^Opn1vMV zZcWsI9CK4{EL5rsOx|J_pNa5#0-a-1PwNH}zcjN<0!npecIV*IgV&NnNb+vYiEtS) z5+wA$kMDksj}JV=-w$3jyGmD#*;gwu(gbfnnb>ynQtN?~(^R_8D=rsiIjhimoX|5) zsIkxHcTb;|Y!WIqe&`aMr4Kl>c_LoMBXYHJ5EyHWapb+}iij>Jm!Vzh0?3w$<|$R;drM0RaI23gq?_7FqVLE9L_f=7C9LC2kG+SqCR=oaMXXz;@csm0|) zP?hNPt!l@Wz~*mSQVEI~9!zbz)4tO4XKb3z0-Rz5)_thV;_GLC{|VJ%;U_GLW*;N~ zg7OUGFIL2!3mD=&G5o!C+yLf}r)T%!y%AYJE+0O?F)1n`uGoSMVt%>#JG}~zxtdgH z9XV)Q6Y%1)K&kB1>>98ljZ`-_bnTpwum5;-teC55riju_ zVF3u0d1x_N-^~EWpE}Ab?P_q0p+2o~BW6#9B7fr`w?2avD>!&N0Z~g?8w_T0 zX58Kgqnu97bbRS>U%(!VxUI+&PvOuNKI*UxU98&27r+ zQ7iRGBOo$tJZd81^pR1u+c3B@rDxRzMW_ZQPVUM-uc3*Jq?o8j{PYYWw=(r%X)lD+ z^A22}S1K}p7|ngPv>e>|j>9iLnN}&k8b9NBGfC0;5<|HC^k6^8s1X_Ij9(|3X_9N< zHXaq>TKN|$3LWRcqFz{^k}h8_K7Pv`5d$RW6u28m$GJEKpYKFdY8>m_Z|G#ZE0{Vu z|JYV8pCS;LVkj&*6Q*Hng-Mh`Ctc-t-v5&q{HJ^l$A`oUJ1MOSh~xh$o#@H!wfbCB zfiJYZaYzgDgwZkU8H0^m{@a z@7Kg7isEL2wu5fLGEJ^8T6K-@Y$y8Fn^oc#j)g(hr`4rF$@&ZJB4fi?ptpdY5fn9K zkrb))`sJ)9S55w`yL&tNt)>TGRRq^ zf`gTPV0YUyY_vdIv={L*uRUt%aOz((0{iY(fS?Om_L`+9uEp(DEP9&YjT0)6-zw1r z`R}yZKK#jN2*@q}UzFdC6hKN^8AwCm^c_xW!kAbDlTit60WA{I((h5JzsG)0GVMsA zmY(^vg;J5S*mu(!^y{i|y8fFc*}A=0fHBE;?-PeD?@OBsqaT5+N&U=+hRE@LZ6u0Wt z#5Gatd`}Tiw|G%}RjMogB3Rb3K7c5Vs%5$WECMHRdB0dFfjQRfmpQesRvWi5H$fpI zf5hv>*&0SNlJ{lTn1iLYpgYUn22wAYt_W31_!1tBl8a#aJGQJ*NP{KD`M5G%EP_|K zEUkszbiF4s_|Ad<5HH}z=|2M|z{>vr)fbrLXNZwNn#~b*n@5VxNK9lM-Nx94sJzcF zI#lM*FimVj9t*{pt?cqa(-y2%p({+^Au#F(Jxq*iY&m_8T!<&-A5?6+i!-}s+i8s! zH_k<~(LnG#AFa3!MX!km{d~=#_XGq2BX`VR#)a*zynYd?b7)O%4O;)r5rX5vCm4`h z(C_(|Q#656Q^*;2=pgu3I_NC)W9kE-S|vFleheeU^+dk9?DV`h4W#m=lUrb{KV z)%7>rv8G@ahuMJipF98n$I}$xV@3+=ssBIT4a^U61-uXXnJq=%l;^>ePTP^!>Y2|c z<4YfJNwp@wb>M|TCM8{m-Ap_D8&X>^PFh_+TK%faoL}nrdd=0I^ILrRcbQ)Oxkn#D z9+E-2D$-NMUMB(F?UrHhrD*f|A69=85HZA(XCM=wlSBCT|Nb;vfE6_x51+2)^ez?r zEr~`_DlRrHRUjjew%V}lnUkeO04=qq+Jht2+Z_u%84cfybTs6sJQVGx&nw8B6m-z;E(t}H+IT^Bk%f6 zrcYek8Gq49I$Ch9?W~pYpHv(CcDN~2ImaZqVeYPaYd;L*#H3kDujnMP!dH45N*gX^VGU%|^_&CiX>^y}ii8#db8dL`=%8IHQC9tUmpEJ1i!2QMyDJSrl2EYV}XLVl6;3yz`xGoM&4k2{5zQFe0D zel{#%`7d2alfwr-Dr~A0n#BKiAGCX5ZhoOOuI#B_^TN)_62 z7=ra&It}97hE3R?F}_nMtsuL`_nENkkF)YvVHO3g$5`(>u&lBlt`O4LRWtsP6n6l$k_$ZsBmK1v4 z=gQvu(*c!#^(s`AnRSOgh6r9J%nai!+yITu&1h#0#2;e~UC4*9?s2e=unXr0ALYU) z1ye{c=!*f2Vb`_W2KE&xM*E(sd_nP=x>syU?g;B=Yu1E<^`=|WC_*huV3N%gV@pZ z_4PZdoReltY(Hgar9JRbQ^OJx5p5>6&!sO%Mr}6FO~8S*VWRLWc;6MJbu!YBIGK5c z{6BNvyqn$@y|w!ZBye$Zu8D82Kg(J2dO#=&H2f2pUS71byOT5(*-TE_G2Sc_*&PFp zO{+*O@6}_~y+%b%uQ*W*b0p5C*$lW=n!IUWj-iBB@E~E0u^O$)zW=K5A&Y@~DO@%8 z0&Rm!aitB@jgpa#df8iQ%Zbt)eTvQWX;dyvV4 z`sYp9`BbNMM4>?Bm~=w{$E71U-*kZ2KC+`0qYu?fB5nq36%<@6*VePglZqStpij&* z%xgcPiec#g+BVk0X8d8vD)&XNQi5bf&wWL~r zpMOhbLNpSrI!2ueEN_SDlmOE@XO3?Ml3>TkLCv|Qe|Aoji?|LKuBZe8puVXK4j^CN ze1ef`g!X+lxopY>X$R3bFeunDM~0y`3DyxU{qTeqk7z5Vk398U~ijQ%Fr^>}UCI;MTJX zD-!8SFrbRdO}IN(<(2*h%PAw_mf7%CUMT`X-t3B`xlyOKf&xTViu&R8F?<5boYRmr z#^osNL-VSt6}DbMgrqn1j6Z9b`wa@h^A8U@Ri8zwA<8M-YA7p!XvMqXb}ULN`ONG~ zZ?%>YL+H;qT?kYzwUIc)=*;%JQB6W`B3}5vXEu0^TLnTyFy3m+wi|)ynwhal%U&&K zxtrkdRxpP%vmhHTN8ooeJUdw?i$*)1P8M@+)1Aa7>niaKDLkka)UG@Vsvxs$Y#@JS z_tAQCzW_BAI_E*HI&qGE@nhGG57HjrY6)avuDOvY&nPpz42v9VaZX)4_1{2Z?T~aj zU4rEmVXIBLNncnL1L6`f9vHmhUw$-@=skbPQ0(YQ^9$Z|7xuynU5MR4j#$kq31Oyt z@zTda3NvQ@9h80+*GU^av-JCl6kHYL;fsBB9|K%UmG+-)PGF_vII@?Cy7GqKCVJPP z8t2vQ+HLiFyO@#~Bqs|T%^ziw#Wq;mzQXq1x1ITI0svK<#m`k3uk&|ddDkt!-6brO z(b>}u07E|(pL{k_MJCS28nw%i5AT+MJms^2+pKcYjt?wi4jZRmL5ODcX@Y#re@+Zh zfEn@zLQ_G}!VCNbF*+d*8T|SU-Vr8iqL3(Mtj`kCd-Sn$v9{cN>=S-HE^!kq8;Lw@ zVOeAEJBTNn#}C;^3nPL=pOy_xUYy^Yt3$T*QVC*Eyn(~4%>BL4|G8;;MRO2~^g&Mu z?D6gdNYt;XoH#}RLp3oy&moFT)NJuwtA;8vI3lG6gs>uA zhb6ehWnrCG44g5nK(*p)v&g|7mLTu*E#v)*$`v;XjUVuiWb0XRmU>#I($#czDP$Ks z3i{E;1^%_v9o+i)KE(%-S?)i-FE{gd_Cv=aa$18msZu}M2dWBTe~X$@+B59tqM(TE z#7(twR6kA4d|?;ipg$R6&L3c%!Nlp^|Jk{+m^t*xFY3)Q1wb}^8At=3fl5S=z0aYooOf_!foUte3V;P)TcZKb-s2v=k{896EKpye`pipfh2d% zi}WlXeQ<8@{afqVNSve+)(Mb^-|~rU^w8tG+xv2jr%G|BXFCI8r8;-V^c#A1h9EPY zZ}haHwb{=SDlq)%MpUXtw*OQCfW=~9MH|bv4A%cHUNEQ31M{@m3;WSS$(eyzUIG29 zy(daGQIO0QndjWm8UQk;BKzVN?x4~xU1|vyFk9VS8 z$w94HPZI^JnqIqXjM{?{&dOlEb#fZ_XFSRt@>rh5@J)l5v%_U{o0DaYUTLFx!|Ahk zUCD26PKY#Noy`6`Ykw&41;gOqE%$%cA4asO@8`J>?{=rDVdG%wT+uuOaPOHvQzgX$ zH(#{3JqRlcDtdc=wb~pIDp?b0#6s6^>d+(CZw6?UYExRlmW3_clvfrnT;&t&+KeJ< zlfs!*ggo0UDh4)NmEC(~GVXL_2OySgm*1oz8N4O^r`|1^3hXx^OVb?oKe)gIstjSw zX2OOEvDmhZ>jL6fy-73}Sa>L@j52tjY7t|J7(J&lK#5?!f|&x3;JK)-*XA)5)q!sn2LfkU1l407T{RGeoZL=tSYa>b) zhj~V|Z*`fBtqapL_g5FnnRYrBS*}$z14^~%uyU|;STq=s?v)yAJ<`ePzqt9I`j>EA zsN|gwjO7eo^@ggx#P6EHC7W{vZ6-8G1*3{7#7wDt_Y z&+!=*j#{+#ms#2IC^(3?QpY^&bAlp@o4wH+yRZxwtxPiO*Hn|=6g0P7oNbUm`iZqz zQOw^`OcAq+r&!dzvd;!ol>j1T1(hJ;g#N>$ySa?iLw z;l*pdF#~4m%4^X>pJX-nrc+}$h?VpX0M4mfzCx%0%l9r$r$O-yeG zqg)0j->qji3{gsa0h}N% zAji>Q;+4QguqSCYQ2ifL1P*)-3{VFApT$K^c6K=MmREqI#!-EK8Sz#3m1N7SGW3{% zKIBE;KCD&W9$MSJ>OJ$G(;~Y&K%!Ii3-U$K8tU^Hxy4of3AazvtBqLe)Oz?SrP}3s zKQeG{(lWF%Mt_m!RDCBWm0s}dO@+4`@xaTL~ZS3E&!OwpCz#fd&Ktgb? ziP1y|9g|3?tLk9~VM*(FQ+D?@ykiFZPmu}VtFN6tO}ZN|ch`Y}>pEoC2^R&;GL$AV zl!L0X_dcLAAe7Y%3ULY-lmrC50uxb^|DKS%hr7-Tm-Y@0@a3~X%ADu2-SElL z(E;#3s;XG_G@CAL0nYj_hy0@L(IlkvdjV5RODM|K)~Q3Z4G0B=g<{SZ?E{mQm6ayi zKjd9($HR|jR7Hvl3PjD#&24!!@+%i7mZ7Wb>N)~m{AzwJs|HTm%wFAgTKsYo6*m{! zbIc~{QSQs|T$41#?w$+xc;&Ktgo^&<9zas9hK|H=(rM<%1Q@_n$#to({$gAHK3rK- zSWQZJ&acdoX7VABfTeH0mGj!*gd|lLm4_eirYtZ?-31OeN`jp|7 zw_@C8L8Tq9TSXpr3zq9zlPB8a`u8e(s}8>$>rzgs7w0R*3gW`M-K3TcCB5ZmFp}-j zmJeN4=_UqB1>zGSb}cPB-)3~aVOd;WglBE2>m+$P>ibv&0X$Z8U$AtYAxA(OF_e@l zh{Rsvo&Q|*-^|dWrd4T321O{oS`t!Vn(leSy3XVB;v$E1@bKh>#12=@-6(T7_SL<<9q$f!4}YW1{X*)F z)tW=z>WS3cm0#Hbrq=#sL&o52=qw8lRZHhLD|LQuc2n$sCN%WIQLw5#CA{mrSOC~? zi+Ek8#tK(-+w{=ur_CEgZyKx!(2hsr_StGw+)qL!rKbvIxVC08itQGaV>Yq31$25C#lS7+9 z@WJFqWKp@9@3m`v;GW{_uksoM-2oLo+`*Xc;x-5`j78m$TY6|X8L~L}v|fP7&JAKw zHO4MydWF8hOwf}x9^w~r&yZF#OFzfgs=@c5L$FIagku|#u}S@UJ)*~O=w$Dq9;#(} z9`&gQ2<#T;Ag-wb&LpMcFHNCxDKR{%r1u8a{)Z-6rTJI2pfXjr!SxFL^1;n_qNNV< zohEc-G7b>KhgOOOCO=Jf>hpnShe&-PcH(A{%?fo=WQyT;pI}V{Zhg+yf|}oP9Ie75 zjtZlCUD;o7k0OsE8GUXTtiD)b{$;n&j5u`K0__F*AeC>;T17wE?gf@uBeHE9D3RdgOTl#R{V9+r7}I@u!AkfppD8an=k}!?HzRk1Krs<`whC)#qYtAM zpBX+`%IP8X1~8&U6|aBsV|+T1WVRKrrf?XPIp0gipS=hK8@I&>w80GOJNT1W9(kmH zcZQmCjBZi`{Ac6s_V$8BrlycLirrg&cbz>_OR>QV@z>j*HoCWG*PwFoer>TMnaMPR zRVM8k#6EqhYf>^w|5F(Tr0ccsa3PKcFKUwrA-Eqep_M9!sa}JK+rU1#{=V0#`2Zvc zrqjcqT&%1s>~B04oC)FWA4*8{-fN-(tNzbaiiO;<+s) z=8u+08e!+V+M>Y1xZbo~h}Yh{f9dafxmI`8TD=Sf_P&w2x2C{ElJ0MNRvH6p55#g? z2^U@Srx9t#pk0E$>;+om)^T=-@HC@G(3wRwzv1{0c>S|F8bd6`*fszLlzT3-8Gd91 ze_U_q%=h%O29(vY9}^H=1S7)v1FP7O5w8b&oBE`E!aY8Fy1;g&GqW z2j}A^_h-JZTN0~6d#N&k-#!8ZQ@OsI_29yApu8$zm6#u^28mPbmLxjcQ~EP+9i&R^ zSU}$aktzfVaaxcW%!)`YlrrL`#vKBet0X%JX=5fx#suA~HMXYX(D|Jqo8Nu9b9QrNFxA-{^23|mpi$g(@nY6ee$VbBeNpZL&qp3Dp4aBD!+I+r~GHHX!oSMQ(DZtI!9@1 z7)tadvGashg>4x=d_uzZi>L3M^c^plf+E!2D*Q4->7B%?tL8Z;Cc4%KI^h26FmKAm zA3tBmsbM67L_%NyXqblWt&Zy5OscxL8-z{bF0>R850yFEm;CDTpkTc0gky=zLDu#Y zX!!0)DiQ02(@dFI=$<{~kny^gQ&Yc02ewCauf>-6ox^qOrSD+uF(^jy%`z#t%i|s! z+JncU^i>)-&3pG8elQxVX>Opj=%KCs%_kC)Nb#v4ftVNY13c=G=rY7A<{ebqzk27re$~Jj-txd_D=Gs_ zS66cSvDBtOq&LvipvR6nS7cGP4&=D;I(Mvwt&6GKD}RDTOw^(-*h)HKJGPG4g>zja zNZS$38YDp^rBN;AK8t-~-t zr5Hg{)-zX&6y4oOOPB^Kh|Z#tVOQ+Zw?o1e?Re3TL(aAvBr&Sbs`8xts+9Bb;~SSC z>*GZk(2ayQLiIGeb_U1ar-Sh)EenO9u4U}MCfn$4v(p|fp? z^f@>};|-1y0ls|s{N74~rdRD(^xIAC-<;8}pNkLi7#4n3QF8i7%oj*s?Fq8|HkNBW ztXjvO6{)>&Kk#4F_5p5l-JAopFIQR*BK#UM=-f{W~_R>+XB9B zAlf4~;lHC(v=cV-Dr5pmV*`)m%;mT7QKcTc1-reXR+D_vB)WCvbmb>X?4X%L-xJI1 z$S)Mo#4Ow>7$)2F+U5K#tqSYyRm*WZ_xn3qd4%8{yps5MUev#EL=2`Q4r2z3ppJuk z#Lp|jkM_UI^1r4Vc%fGzRdo-3?xuTi8BTdq12Eeksk0llO9sK0DE|1_zl~YCSrw6h zgTn=uI9@OzR(~U>k&{~+8PeFwR2U+{!pbFh-LjRA_aXctxOW%*e*1n5eWf&L`{yNc zdyqKuwC+e9o3uO=t}NF8@}QZ15fu#tVz}P7fnb=Lk`ncj-f%5^MQwJi(vk1fA85x~ z?0^bx)M`jBO3IlB(~74%pMNJ5ZV%op((bQL$N!R*CMbU_+Hps;D1w{h2mOPwxk0G3 ziZJe44NOR4;gaYSeSuP9lM8sPn6ZP`&_YcKItMIFC@I;rLT?Pzi~5DPqAd7uvx(VQ zncp}v=^~M|G$Xi)U9;CTQ31{T)PxCPlhZ18XQ;Gm40JH={fe1Pq6>lloQQIN^G3ZJuA;`95FVb*FE}kil=VCh4&~m z-@uP>pU!8wEO0Ah3kH^*4U8Df9@tR6b?bd=-j}UvW2E}e!IQ6}LTbRaI;FcFA#btb z^*m1+>-^quGuC0=l(=gp19RNuUt$)47EFPfQBg#JDlj4R&A*D0p;vBf@Uno2$WYl9?S@tB>m`1c{OUh z+1lr8aJ!lIAPQ|V?8BWD4!oX;+9;%e0mOYj;vle)3-w@G{+OZWrt5V|3!XL2jB#VN zy1@<#xvEJ?de=*}AkXg_m;Jan1BnH8Bb}KRFAHfSl@S#9S|LvEO#Kql3091Cl_GU# za1vL$e+AuxtOXto9P?9HEJx%LI`|w~4ZhWowgKkie4u>1!i-^M<-kGbdRqT?Ep3_x z5I>5*E|14Vv=9|f#c4oj#v^S zs@4c;^vpa@EbQFZ-s*r~Iy>u{g5=7=h6%;~uE(zU5-z;0#XLNnCg9j~PPwO{M%JW?507+(A#m`Ug%@6^}pzS z66p*2V2!}Jd-7-3RaihBj`S|@CRY4C%8oP_fDt@E@0lip%jFVR z{@A1>m0!fF*Hda$fW2VZ>{bv$4{S6}Y}Yy-vt1TG)v)SABCwcUBS?u&jneNe&&qo7OOU8VjsX)i&?x$4z_N8v6GNP0T`8QPo5y9mtsKuY&xyZ#m2@v%#+BR)YuOxJoUwR@pi6!88iaYUw?K_pDiIn=QraJZA_$e6Zw~y?dw01D zv^-W%J~YlEMzb?Duy=5e1atB2GGRa=!VGu@(kO6D-UBL1+A6Lp+6Vht`zL(6o*#ae z+50(^{5%!9U2^~Z-JY2*$qgsh5%3NAmSn*01cAzfEA0DpYIl4kuFZ4YOMeWj3_l#k zJnm9kc+ZQxkk&O(f44?maI5MKZ+CP^3-IQBwrxIs7Ppf4c9U6U@~d`tc(%Xg#n<`7 zfi|#+Ib8>VNhCTD8SfB|$PWI6#3eA?ZoVy>WNuYV#ocXOIa*zLZ0Wn{C2$#?#=zfF zhJ_T&f3#p_IY%Z$x-jwF{xCmcUx?x}H*OnEWSNhm7{4n{fD&nWP)BrqV|g{mKUhLdeuaIep!zO@GFY*KNllr83WlB}6J9{Q%d)dp#d_gvN3SmhZ%A1v1F zlZ)sxW*73<1sh5-!0!G$0S*oZ5HSc*hfua9A*m7q{UM>m{R#aO(25OcM7`Nft=xDc zc>1QzJ2fmW-_L(qqjic#1OG%U+K73kd;>;KEOg73t$pZ%PI%`XTl5r)A8`>XsbW>; z<4t^iV;F@LZ?^>nC(k)1e-_mj2#dGl{qG6PV|!u|8W`gwd0F5A;J5iXRrj6VT$(`q z)i+_La4kGpxe5mViV{LD{n}>7o=qMFj)y7PnL2OLZl4Je(dRvp1dLsj&o=WE(7&;< z(_5_CD(c-3e!sPB!0`P&x#lscfXO6Cw~2~SX&T5pT-D+&#(#_bF*}r~mU`8UK<^pS zW}A1tjb^R5*D4cT0q7clw(yaBJwBFh>oJ4Hg}E^Ek$l_^frQ6B#1ZHaWFf#@C9FsZ zUW#rQ?LGoo6)O{Uo!deUDX~EgMU6MpF~2ECBHrJB8auRYABaORO zcd3Q8%*0JzYk zL)ZpCL9QM&QUh0w6pQk7GJ@Z4K6Y%z#d|M%Z;3vtQyb`p+_(|K=1Qd;e?u=gW_3Re z;x@ty;Y_ul*z>Z@-?jT?PAy2#>Z`&bgkUE|N>fy(vCfuSPCGRTo`ShdFonYko-x_! zAnRvn><^k0WVFySBucHC#U=qcfPYDK98#7?wBTtKIlL@{W~YV4R#>n7T1mcZ#27mI z&A8GXiT7^%m`CsW{K#%h0+DqpEK!k-SNG;-IVZ=>ew-%R&QYZ3o=Yzz4Wi{!+{$ z99x@=a?IZjTIB8ByF$a2SAAc(tvb5dCFfscQF>Y)+NJ?;JFt;LhiuQcUJ-RCWDfJpn#l*SHVev29YuC%Jhm$e%b7lz-D-r}+Tx12 z{^VqM93HMeav3%E=8Q4Xl=0yggROm_JJnLS=P~D%yOH3N!lK{LkH*R5%#gIeFISl* z1$AW1Hntb@h4(+nkX7)fchswn$;k+FZq5cYSItWy4;H?qMxL}p5m%7ILtyh*UvR9Z zT4FEqD$1P&7+!__>PL0vV)wfi?p_>jJ)_>VYo({o@(s{|uCp4oCv3Xk?Jn?-6f;v! zh0#O^lnkS~g}r?ZYIbPj8)3?$fvW6*?uan1C+& za6d#dr=b|?mv_p!Lg^rN`;@hWKZ|u})PgS7_5GCtPyM{bJ>mOziCJS40MmBd$Y-BT zAYV}y@Lp8zI;}nBR?2m$AjSo+nd}qlTN%=W9ZHnSvsP9!pP@o26st+x*At$tPMzY7 zs+n(Iq}|-k4W{WO+YcnN+ts!UWmdmSO|gLaSQc!BeEK}dG^4uzkEpi_h+}QKMq%&} zEV#S7LvRT$0fM``6C4_s0KtO02MzA-?(QDkUC-?Oz572`b1{9<{b<#yRcmRM-j}8| zAgLTp7P7(NMRlaHJI^98mEvW;9&`ZuqT0HlFlYjrVP9h3-Hs@l4X<6Tx*qbWZOpcx zX9_jQFyEm;@t8(8K?uA0MlS*&lizBf&KGO#`YC(?F+@f!w{|l|<@$}6D8?1c@=|2-Q^ zqv0^6tM6T~$_UZ*((?-bkO{cr40RICHKtg6w|xr&(f`Dg*#8*8vOp{>mjCc`WoSvj zVnVi0SepT6V~xZt`w)AcfF%ExecGC+!Q;h@H+$-+<`c6$Z>!bLJ`Z4`= zy9mRUdc_%oMQq7IA79W0W&4veN{vFUGyYNjZ%sfbG1&y%IlU6qe4FP)Z{uHqMcF>) zh$o$<@O5W=M}MimVu_;cW+e)>P^mHt{uh&04t)f9*aUZb6-1wr;e_W>Y$R_5Z=pSM z+Qtmy_5$-GX|jvGuoHqEX+@-*T2=7%+;xRFOfft>*LVNa6g@M65EGx zWnbPSp9iIex^8g%(B58hWw%$|w`YB$LdV4g39Z$^-8tQcGyVi0;~IPpw=~u**P@gY zXk#Z;?*;>Qp15~LQTdqpf6+BK4mmVNw=Zn=^LzL5DS24Nq!t0eQ^01!a`y|am;S+- z8kS+LYhntAPj1iRyeZu9D|GEQkJ6@C8?m;F#_#SQT2PjlZctp$xPIv28fS^>aT~VV zQIzq%2)2_>^aR8cs(_AOe(dZwk0&^b3}38~k-_iMgUTkU$>3?=R}t#oAqfZ!s9u`f zSM(O+(9xj57ZuP60=3G|8eTaL#jC&3yI(mXvi5sz1-kM7Y0veh7-;#}VI0MX9X9Wc zJ>WdC$&vbCTN7@oh7cG)O)Ts3qhmnQt7}G)l2UP7AYjyg4BS9x{fYSRS{s~6T#ZkM zVH1wNZywuYZ@qer{G0!&uSBZo^2-~ZHl3uA{x|%jgkwOxYBII)RGXQ=n)b4OV(OEm zBpnzBnHmcsTu6C4}K8S1USd_uqJ-=H7dQ*1PLN zpfq`MWJilu5kUc+i{zW@mFq5XCHvDbNEG#@7J%exawT7Pjv=0~r=x@Km7YQTbjd{* z1G3H39QwkY?Igu%jsH2Er_$b-bE$2Z^w)WQ*VDz!@20@GIK51oA(afbLCcFpc*Z%} ze4KZmI3Hcc&L%^vLA85~KT35~@RhYh$ZS zo>QwDj{0^vc8%+GC7f@{zVJvnssVAs*oSIabD94#NjpMBciFC#cN%>1$g=kXOZNJC z3wL$j=@-bwI?oyn8gwHW+~!4B2#d=5WK6!PkvEuJo?pZLI=whCzh6&zzExKp!k_XK@yL83MxCQnefT0X2rY%LIrgKMAoSl6>u52XK*)U0LgogMrVD(n0fpXqanutPpQQ+W8j>7)bQs#R5-I`k|$)5_pd0h!y3Ne_2)h8 zO(peH%x1sG$|p<+9FpS*K(n!OMe3bV7SxhC*xAcq{L})Y#G>CA_WNtr z>IZIx{K@>G!YbBw2LHYJBd+{CY~!CKE(=CZb<)kORgvlGbHei) z4?NW56ZU{1YpB{J^?V1>%DHOlC&cj##jR~4m6rZt_rOlIWvRx(L{a$}W9bYPZOj&~ z7K1V!*bU>Hlo}cxB}=TRxC!`e1cK((S$i(`O*a_pinOiLS*ZSo%>bAxn-$Av>TiiE z!4h5^Sm8P*0-yo)>@$Wl8pCe;T1Ec`(B@DA<~P8|89Pd|RStQao8;<;-x96rzHvzE zPuDm1k1T%_oPm@un9LrVe>Sr^sKw<2z?@;b)0Q$`OUVQtP$^ zl$&y4vJ-($o$P_`2L2lr8Uq>>N+==+N|A1C4Dv3@GTgUqDyr|1rR z0*igWB~U9tn~1HC{Ky;RHUDYJO8%v9SKFtsxWnIhq6nVK(9NBbR#-(T&$Zp6%~qmqDcpUp z8-)tpiH}Mz>3yK6^D<}oS=jJ z#vK(>=DjaUNn5pl09{Qve;n`3%wwe1E59t=yC~D{2XoSh+y0Vc1i!ffT9&jAJ8h3$ z5kB?Gj30M|%Bm!azfokz`@;6(cfx(nTiDduyrkoYU&B*Cd3{$OW#6TL{OTp3b_)z7 z7hBm-UFPyX9pxi@+f(DNKYk?B3gVdfQy%U?)2;DRPCNfJs*L!!_x5~R9Iv9>+~nv) z?numqUW&J##5p@9hj?vLbJME}9`SxG$c1|nP=rg?;@4vpod^ioCJVqCT|KXvAlvmf1x*f-nWc0%rd{%;;yvth5l#0e-v>ndg#}xk3FrmACrNfWJvpnT4)wP%q5mGih{DM z#c~wZ1_Vbnte_7XT7Uc{NmtYL;L^NA#XMF)BOO*%bsq8)b4x%sJDVi@b>r9$_lQ3f zoRPG4n*ma>{3}$g%3r@cwqBp>Q7SDRD+Me~`I(ZAo}9}W^p8`Xtx0j=_-hY*Gli3f zBsCJ+8uXt7Y@fWHT3pisr4vH~ZZ)~4Lf;NHO^%lvY+@p%OPc}oyIxeiiSPYT$ zAoS(|vFnTSb*R~#YXL48yrzaNud=RgR}U)Ao`i(me)mr5#nQvlj&%KZrGc3D$Qmar z?~jVs-JMvZ3-#sF$NnO>IdcAa(OVM9ZY6{< zC>qZ5$`rM6BZVvF;ewcGCLV#=?DhVJ>hp4UH+I^#hLWBPv)lc&NQOt(r`_ZIl$m0R zCuHTB^x�Jl8*fzLKjko_1eOi&f?o^jEzZ3@uYJd{unN_<=Is zCnI%~+v8vU*rIl6oMNP0`XauMF&idVv?p;dRD&)>W9O3Jefu2Qh&x+_nWQu|HNo8! zS2XbXK@jbM4~>g233b*B)3yz367*_i+17vBxGY6{9xmR2zu;`cdcG`Yd|V+_z>AAf zxA_;?D(Pmim&{Ta5rwy12lr%l8+ww0Xn8{++Rb$at5saAG3zJhA60ilJ!nQHy*2b9 zX6R&$(&FMAF1dZ@;L3?VcsLo;zXC~iQS1_3)5EOqrZVnywxDKT&+x8VpOKsHPJ=za zg}EP_qiRvB+ry~M;p~K*)Xhr4OX&gqY9JS>nQx)n%|9b9+yNtB^ z?CL!;d8~VQp*nl}gvW)RS_fCED?T(P(H%SCNmb%)Q_S|6ly`lcK_O4ST7_~gG|6Xa&)P{Yaq}-i|RA}LZ@WyAoLu{*MrbaJ4AVG((J{}5v6CNuq(0M=Zou@X| z%0~r&hKx)>$<`w}KgchKvd|8zMCkonJeT02q{~?K>dLomu|Z5oEJP)#=;EEurArF$ ztm(YclIV}O2j^gX1>Q3wF=7vB4djJ7vs_RMu~0W8*=xqBAWNk#;k z79!}Z0!?Aj1?}yb?ySw&9@S)7*->xBby4d_yb#+gQVApv?l}k5AzG;sRgwwn&0Yst zVA%MrOYo@24U0LBKwC*d?z0wH0Cs01zwZZ-4xNCMZp z(*B5|es8FdMa`H&ReDUo;Th`c8SiT4M=+Wmgw79dU)V>}bQLs|b@jT3eWR^ru&S{P zWvUX&%;Ik$yPuwB6e1!dWAMA2ki!7;FRA?{e;%^WBqf2i z$W;BU;MkuIF*lUNuOI|s1=X5<omy$!TX!asRt0n`@sbvNITpB&`MF$~- z%*b+It3$BfEypfq1AZH{=N7@wc=Z6PFyeKsv-qNX7BN`oG-Chflra8*>j`z zpbckBtC|eG*8@nle*nk@Uxv7aVI~B{G8@*v)(XvWkdaK%;DoW$q6N%Ho&TDl{=a(5flj=HWPYcQ#hVk^cJ0*ziljK_} z6BnE(V(#Vpy)C?K8=FJCsWaq677OO%O?+;lVrGPRw*d{$8UhL{4w9Lj0YmfIg|8up zmWyrKNm*sy$VN-g!?KgUzOm_eGx&x!X{O~Xove$xF=sGAXb@eg?PXtl6`k?xjHW~q zWGwuvxwci2Zg;;BfBF|#5JnvYps&=RZi5ee6CdclFV=D6~S=C8Lw^w$JXewlpPrL{+J8-86s-&kKMYAwdbu{>V@=q zLnjsaN;i8vCkdzhVr$eBsy*8lPCLqq6I>Or_DgdF_o`w zWx*@!+zmEGNtKK`iVqjrD^nBbwwgzeiVMzD5c1e@OZd?ierEP-Qse#1pckZMw{F); zyXHfxR1V8ey55(4&3j&3!PpoL=n6al>Cczl&{oX1F!G;YZFU9T9(H~RJyQRh4*hQr zs6rEH_%S}sw0(FSGOTrxJ;5_k+uC>+9X}A^RofY-287m|R$^XutJ@KjuZu|N$AWAd ze2gOmABVP5uwKvZbZ<6bcd4I`{R>wXMDp(pj-Bb0-8YDhLrwaQRsaezs@MW5Mb$WXtDSESAK{nO) zXui^ZND`vmr5-5S&W%dDH07IJ$IhG)43Xd2^103XQ6h4sLMbq(FE1si6O2%AW*|hA ze~|-+aTPz33WWXfoZxaqBzoH@^V>;W^FkU@9H&?retG*TqGF*LYdIA3f5RZ$k2=T) z`lTrUvV{Wt#=+ZXJk=^67sueXSHyRxcU9Z!z%ejYg7Cc}34Wi*t*BMVTQ@R-efpIFBV-sF&V zAR)-%yW>prsM$Sxawl(Uu;IEHd0<5t_Cb#iPTm0T`odsYI8JcsF00C~(LjD@12`Q4 zyhgkalAbdfFkfo-6kFA#%(xy|z%~=Cw6E#}6~s0LDkM+$*(5yZ${3q(ZUS|Xz}bB5;q_=FaX5G0=x|>#%xtt0g9<&9Xt{=a{Oyk> z9&?Z0X)HS4Xlub3kg+jk6F_%-#A{3WE3DSpJX2j(S2hit$PxLIhFG6mvuzWwEP4FR zaH9*_QB~J9#mn+6X_9fFf;I5+U?}PMXL!$A&Z)9~h76Qx)>u4V?UgK~znbQX55}dJ z3y$ELE6bU9lXTWcf4L&|*P-mY$}uB@LrL`&>P z3d+130&4U$pDRtLb68Sw?ptUrH0v7m~= z-;&|CbGi_bH74PEehNEn*20|6K7o7O(BP{GOVVs?hg_UY#6(} zw}A4sMKcjyIjyA!vrR{B-HX$clU~v}89B2d(*&=0`8bo*5z7Z%PC~7qmF%h%g%lMX zlVr2%TAf>@UM^ZmvbI+A@Rh(7_TQ9dm)RLox&q(t?tW4nq6L4?BUIaJ$wk+SRm;ap zRZ>V_UxzF|j15EFs~3AFDl5x{ntDrIR7a&I2KL=L1bbNJ_aO5{3f>kgY9@KhKuQCP^Y$i7 zIo~cuRSHu!Et`QMwyR5{lJNkmYN=YTv?ee|mp^60#;3lzEXWr*V94CRl`-het&j2bZH45*C{ChhJX< zgg>KIHgJ<<&=#>)gc5m5g`f7I2<3w<&_l-spHCJyzz(B1jcpIx5yLejOf3wFZ0Okx zbW^y_VS1Gz7>IxRfBXYP{ug~3z(Kw{Qh!|xke^^d_PhM^c}`*0V<)NZy`94`px{bJ zs4BL^aAjJGsRH<+&hcacB@gryk0E?&5ROcm)_>wOc za5#*6I9`5!SPu~BL#-tNLp9jgt_*g98;in7zqQ#dgT_X|9MpgA5HOA#3z8(V4QmQu zHoDGmX5z38T~@Ac_v{Hh{_WbS!)I^hpHyKnDU>_{DDOLWs{D(KrCa@Aj-O~(Q7j4z zzDpf4Vt;(Rm5xe`YlICYmKPWJ?*n(aK^`K0)tAXT0tSafws-Y+=BAp=Ij+qJuh$%oN4{IaQ*8@ zryc;&G94uRMDU_xwjH`f5At|8)?~K`fke=P+CIKpVn0}TFedw^xbOecYC7+7r9mPS z-?s(sGWo!u_h}|kCH(7cQqS*&BW8u6Y=7B5*(}!7R=xgx?)OL=*$%~T(mSNy3t-^O zBmIp1PF#VKr5)*ktqUloj*n5iQ>h>&yM58}IRldnKQQPPM)O+ZXSqSbU^D%UW|P)0 z4?HYt(eq8jd%9`Z=O!gc%2~`N?4$m*;^vR8K(TR2|NT;RZ~|R17Xk~Y7Ge5bnsY3V zr6C9p&HP0(5CIvFzP_MG zdJinS4K4ln+*PCweJ7GaU_8c-L#bHo$8T5aI-Hi4=In&nTEBUIN0cp8Uq6+a3FQK@ zVOUUk@8PaVTYG#2#=WX|$!z((PsJWO>Q0L!SNJRuZ33N`LwLW-sgJnkRI#qQU8J+hrv@qJ#~{f zA4%Hg)of^y10eonZ@xV4!gXO3XZ)yg|&l>Rb+m)}1Z5d&JaV)hodmH=(mizYs+J)~Gi>CHA9601Nh_Ss?@R6TE zVqQWL0*6(KG16m&g;dM6mgxV)`n8_7M;e9T075YMWfv+ztxN;WiwXO`14wwD1cGdA zYDomyg02eIuveIGR5`OMzB~M(v-WGUbn+C5itsmDQ{m;Y!Bn5+9RQc5`a^J8ZYOFve{On|6MZs(?ZU|7N>e-x0~m2=1*_KqRl>BhV)rbr|8 zy|cPsqJ==|Q69t`BrusZZlUZQaWJ&?b3P2XkD);Wh^u-_B&rHKzN>< zr2^sWTW8zdvh^X~#v}6Ufus=GJiMvxB#PjJaMQjj{yeJueW=5H!-t{l|D`3akdZkBv@Nb>yE9qc;h0bZSi|6SZBq{ZbXQ zd|w5x+AsJ$*1a>*Q)jXgdi2#YM01+1XX50z#Ji#GyHpfK(Jy?y;746!bcLUHHBCxE zrC^4Hq)73D$Vr8EjPWQqb}XC3i39J4;HQCILCFga+?=G76opUZhoxuV&tvZ&&4+^D zDn1ayV(>iSToHSDr_0OD>7F;>2d&uO*G=|JR;r?;7%lXGR^MF@uoB!M^FC@!TnGKlLTq4lBeCG7MHtZR6mUsfo07b<4MPp!6T z-{!CwF^kA|wi!xflNOwghG0$?m|YjS=W3WjW|%DLF63Jz7zt*{h_T)APnCSc1ZQ+U z&^jA|2Yi3qM?S}Oz1fj$?6c&9HN z9q8JQa1CaqyHM6bV4$0Fg3n&Xp27?y8i4Wh4io`FC&X5@=mU_hI3AaBi+x9Q~La2WpkijmB6D zl{zLm3|f^vd9TB!IS7;aK|8WzD6+T790ha35xV9_h_CAwKx5Bz(lD)+YRI>U0F#93HMWJ=I?8I*T2nXdL-ESWi~dW zqPrljxPetkl5<->V7ZvAKD@ztfypS1!6q=&mNDvEFYJL7+OLZ|f4@ZtLZn{kj`1xqT z);eH64QW!R{O{DDTVEFx#@i(dZGd#`n+uP;Tr9}u1Xt;{T8J8UB%sgiE-lfpWFgi`k|> zb!Ah4*fqC<-S&r_NH6SfCXQ_q_()t3x%ops!||bFGWDmjOJ2wA-rAU~!-|5w^AWJV02|}hV%@H7$+_(Jwz)c zfSO2^BW*hNOry7JB`RuAv91g`Mr*w&6DNW}CY3ip_#p=_p(idzO_;6O zm~sCJ=~Td=HD|CC|No2om;52JUTnT#utBa+>n<*r(DO7UA*g;Kms*&~hI)4Fc+U)H zwjQ@z&$Sn6{Mf3y5E)a2ItL_sY*lOZyX{Dll!cx|MrjsOZk>oh_#{+17<~EkT!QbD zj=;Es|C7yQ}t4$hP@NCxq?k!p7AQ-hmBlcZmieUs-?B$QykLc#Nq*zOxzx<~}I}HOFHu-s1w7%Eu2RQ)T0Gzu-Ii}o2LxQ6r7 z_ynnLa+_LxNhtJ=uE@*6a`qAw^*WM%&n2E@O`4kEk2rlZn-*GnDMnf^5Km8Q6xlwp^S53SOxx@E?UcyL@+)MDG;QIQGi=tf&Y@J|x@tX6sd zqU@SC=8*2$(C^4Ap?w*SLit@C*C`kHB#-5*W~fv>a3zO2hzC8*+ixnLLqQIX4N=9w z6ODu6MEd_;m>UH6n}*Wi){1Kdcq5J_7weOt(scz#FdQ>MCn6V?6X$SZ^O?e{tE*TX zaag07=llUj*ohaWPSlMS((73c8NsT*@HIvCzl%S&Cw#PiwsN>5_<)_1{OR%oZOCAq zVSGVdb0OW*PX0vVFN_%q2IvFn{Q94w@wbX!6+U9~a0nO!UyWLu+$W4>OS*UD4Tjhj zF>oF^E+VatPjjYEbMxqimB;FX(4i$@{1d>e7YilTrC@>#EmdB+$2f5?N=qUDANewt zl<6(S@L%xjM`92l?5>3l|HJe{aiL^D$wXU{j+xBwM+IGZG(&i~1A+0Ya{vj*mSLl~s7MIvlkqyqCeL5*++3rX ztB=Q}dd!dG{39--U!|&g)W5n(fz1FMKc@RIRUN{8U!9KN zUYr&0>XW@qA6UEYR2umDyIrurDO6Tw2)F`hX@liIx=1;q>3+4AcYXzj(#14L`PN26 zyLzZn)Sx*cZrue7dA`>YgjOW)EE#J-R2fqYyHJoxUD~BCiC>F&ABJQ5`ttkWG1dzIy2>5z zG-oDtYDzem<+t21W1Ir~M=FL7`%rB?rk#+=4TxIAzJKq@Rbl-GECqTJGBK+3=Syim zaj?E4iQf-Rrn1K!Q!8XUTa}>|NL+%eeJptJItWJB)lXYKvT1jZBySy&u8jYX!kU8p zj=f@5hn?*9Tn!BT0=f+BOh2b;qX<@f$V(Lw%!&s23o9gwu>rO}{YLh3x3`Usj*ojp zz%4}(+vL<}G@WYV%(ea+U_WZwAeEvoPNhnQH_?hGDL{1mP*igoljEU5%JnLRw4;*$4eCqbmwcm-h`~}QE<9w$+VU3?hOL> zt?UZzam2&~+75l}GkV~|*WRL^jMiq+bAg51pfq*&)dId#zH$#vn3@)tP4%uWomm)M zesX$%9b5`;{nC-PB=H;A8yYr(7nCx7yMD~At;LuN@DzEy;7-Xg*pk83=3nosDgY({ z=mSgQKbOSCeKP%d5Z&%v^SNe+#aXgPr)PTllNAc=?JqYySdB|ceRd=TtZK(x!olGlb#S7uJ zujumdXa$n9wNj+0`$_M?@o&5lC(mYnc{!sw!ZWZc>}lhy`4bA|Auz3=tu13W&&%ex zhvU094WF>p7hxyEV9^ae^d*gavdAU=EmPXA8lxC=TmC8#tCg&4$& z7HpnVAs%;VPf&We+SPV8sfBPcPZY)QuO6oiah3#`Kncd^m7`nNQA%B{X2d^|Q3iQdtq*@v6@l5X9uPoGU=*$8rtVmWraZ^#*nlmZ${PwQo49+4SV zX;Q6WT`5n0agQ@o70ERDrA$lQ3=?BpTuAh#+Y%%~-N(W&7~&r>hi!be>AMlX?%FU` z&~bBd`S>NvNdHt!bA`43*QJ{tUgb@mp3fk_jGk?YHe17H7lk@fyKDN3JOyGpNg9(% z_8~1Y`1!iaA?V?^sHmnG`Jp_Lm`@<`XBk;qX#|S@t}J+vCw!xjdsZv6 zpyK#ii_yxeCOVzN=w;xtU^qG*6=5<@ulVruG~%wuv%Hh58~QQsFQWkTRL)Rvqn6AT zt;%g&B#;Sf#3U$+=w^k>;11O7^nqzHnEcliOr$W*W-{D&Wf#pZ#RVI@kKU%ij| zAz^#ee4h{3_tl;%?Z5W?icbwhPi40KomXSnpkyB~Cf?Tu=D|$9{Y>7+Qe2LvW6opd z;+hON;4*_-{&k;HGYNKT4W5Y$qjF+Ep!yh1ce$%ZID~t*h zig;{w5cj0@8qPy?l*z46Jz@PPhF%8t%Ot(%OkwPMZ#*ru3Mx6KxonLx6S%vkp97_* zEam%w;jS2-LYIHJ3MFYm#pJV{H=(zr7C^%#7Tutlqn(uWtw6noPn(+)B%jR>s^;mdvXziP7?_~oYpXR3T|x;aoC8|f7HIXIwBS=5Kr|7R)0z~qsI8# zm~sqr#Y}W8t;m7HR(f$Y=QrObRQ>MQg}%=elt6%D z+qp>^lK{pwCsbqpXcZJgn3*%o*4Y^L_JG2KT3LA~p z<+k|BGR=d;4S??|iJpXA11;z2*I_8*ILzA~$4GSU7rx!03d(X15V3C)KK^8wL&*iJ z!r`34Ya{}8It`g`^5ktdxHBdg{LWKUu%l^ zjP!nAzC?Xb-FC)%N9YWi;KJE8y(I&mdvLY8;|*rloyD@Ld$Rdthf5h7So~dD|&_F>)>ZM+J^MJ}AhfN+R?JIKJnwvwt!UP{bh0#-qegW4sZ{eN~z|C07 z>a!T)Mn5%nUiUR;iZNfGz4%;3Tll61HeWFa3Z41-yN4`i=hqOgUOXciS3HuWK5enS z{lnjEA~Euo>W5?*%kPwSp{qA*g~ zg2`;~nI`&Rrpqqch3E=QKGyR5UQpOP06Uau)k*pt?pPX}A*?{OS&ncuJ3k}$jM@WG zT}u%|yem5@!41W}Xy`lguYt?yN6x3r4NXZooQ)t`XRT)vkHYPNw^{4@rGxd#Opt7n z;qz(C-MR{)?#=t@EVUUg^7`2;T5ayQ0uM>S#W_HUQp<+^MLzy|f4xbJ70pnyMH4;6 zdBTnXZxtb7Ge#MiQ|~TUucol{c?XEbh%&s5A#&|NU;ZXxLiTLf?Qd&Lq|Y0~E2N0Q zh3GIgkY@P@`ElrN>26iU_W3Y>8ILX$Xt`>be|6!joryF-8FZNE&{0ijK?F(cjbL)u zLwb&-*4^N7G)I}ppNph|a&3Ng8Ge)4>SGjuuNAwFfBr-CI8xtxivxXN}pr`iq-?M9(G9)2IH2Jq-w@x^K7J@qh2ozW!AwP`2?~ z9#9m82c-y;aCbbg`v>*X{&zx0qJjSDYW8{4)V+$Y>C6A9sOB!qV!xu=_qwj-bh=)! zPW8H1`J#R?*b&$K7g!NvD$+8`t8ThGBb{imj%+ol(?*o4*Us@U`SOMgvXyi>{`?F; zuaI(E)!QBSb=jUtUSl2c4rH=xg4r!sB`jS}h!%ueu?>}+Z(pM7)6jbh&DXqSycQ7u z+=T`dW%?oM_t;B1$tcCEKZ9Yi6H4!kS@~+YrBlaH(VpVy@=77<&T?9rTU#Z%*vd!KqU2VxhDw zv%;zKyti_nUB+UuvqH~MA;pEYL?!Z``R{1U>*8koS1VS<{Runo6;2+?25>*h<$es10vb7T#D;(p~0>blM461Pm8| z@rg)M(6rkCs<*V(KUtbT2DpogRA;h+6)B7_r4(tTA-;F5S4};tD3v$4Tp z4zKq1%<=J{^(C~|+a{l?bNBtx5gjnHm$@=0QsWhg@RceaDgqn5Nk^s?T&1)CE!V0} z?Ne8pRQjaRX3)Jt7J-%7N9$4}x0yk4v>evpeuyQKEIZ-^bUM1xdehJOQ_TCvw&_ zQitZKH*2hNk3Hd)6w{f{t=D%wiycRG)a)S?w zf?r5YZls8PD`Mm%2?>kp22Cu$SczCGMlQUdw|R!~0kb|q6PRoJ%~PXf@1uOGT$fl< zX#?-Qw*FUP{VYzPN4cH3;!jlCW+nogEhJT&SvT>I_ja%qBliys%vfNqm=$2!dwJz4dmhmzAGZmH( zr&JNMTy5cZA46TF6iavCkmG_*{0RN?ZYcf^0k6W$nzL)b=+|Xwd|9lOTt5>-Rgr30 zS|v|@P$$CoI+qVvTxEmYREiXK*ks0`oqwrjRia=RC&?JTX{NYL(9z{1T9wWyXFH=# zkm4kb1zq0)lZpReGT??S6nG8Xhyf^MWr_djbOv&07g-KukJ0>}C7vk2PN9!Y4ro%N z&cM!fc)05Op1KlE$x<;Ac1u6SlG|GmEyv%Ohe{7m>{Q+eSi$e43N5%Vqv2bAS0xKM z2IwXrplW=k} zh4FD7rXXkjp-sAcP)<@sO`V^Ebnr!Yt=WNyF^J2Y&WTj2u2f_eXGkgRS*S>!^$H-f z%M0qX9l@GG!?Qg#8S;fm%{`pPki+WWuL*&$HhH(2cj+m=Fl9R1nK=xdDZ=|SFRwA! z1Y-r*B0)CL5lr993;edr7HhYQEk zD1~aUUj;3O%==+MQ<&`hE^d6nCOeCS z#4sptCCG(Rqyh(Qx#3%9gaSSo8(vg z&?lQn+^7_C_p9p=;763#t#QV2i;oe zmqBL|HIy>P{?ouZgz{7sDM}v%Ve9)afI-*9s)R$rGufqE%?Ui%xsN8${)6`<@nzbp3ZF|5o>ayJU(Gbd}d`{g#wPS zHK6vjlelJS#Q~Ejd30>fT~=4YF>+ z$(O+gYztqgw>)!Y~9@GpVWZkNzV zTOOZ?R_+dB9;v=K{Z2YwP1AqrHLyh5|fvCPYm=;r;zM(QgGfuU(G^|6k-$#7^%U@x~| zoR!JyYjliQZv+P_SURNa4UNm9wJFU2k}GMacyW3jn|>-iQrU#Z)Oha}tV#swr%AAQ z1og-w6bzI*iQ^V*H+VNi`<0kMqOS zBv12f21U2+sAt=SQ4-XS{l6KoF0KsB!ho4y@O?yGuAEKNJDF>E80{;r^!o>ct`Ua%ivfoBwAfw`kOOlchO_^KsOc{jFL1_lFSD4R8i_G=> z8lqFc3mgWxFB@Ti4KRkOQB9MI7%rWd&=;@@igN9`yCT(_@nTk4@34ZQBfiC}euLEN zRYIk-!$oTJz#D)Qq0V!yxjj?AT#7#KnGk(?WRdE-fD(w7c6(ArvTYX{T}2f3q(%+$yfJe zXZ7wAw~-t}lzim`jtvNwdqopExEE7MN2%r7${{08vWH_S+I+XP5hO`9Xj-Lguj-KT zGB*q3(otqJf_|K095o#)VyTrY=bdb9HwU>ktcF9cssk;C>uBJz_Uzr&O?G9-~pAPtJx%761qT%hJ6MS*& zeA6d-CEOXa;&0zIbHrwW`O^x!$2sor(pt&EH}LVk^*rYOR&BV!tO+g3hBer9;uF2- zjPsw=mw)zoVjb8JX!Dks*-)wlHle1?4>wP;T04!C7U8N<$4QVY(RWBl({~d!(@1^|7#VSlwO3w8;SZ{GH7|a%e=1H4 z%(XDroUpkyexMTuH=vqpffNyUcnOf51Mz^)|2hNY*&&3U)B`bl)YT7i;gN!ZXSW53RlA8Na{BJ;kw zQTaWr)$}UXri;?DhWq|3cYDQ7h0Ue`e75b{ZZpMb*!9j`(`@Z{kca2+$BHYBX7Mmz z4GjR(n45BT+8t_Pv(}#8QR~Hd`eCWcT((rROXgs+1ww1uuB0N5qRxy8TK1JNZ7`5H zj{m%BCbHM{Mfz%Il96s3GE~|5{@j2C0{0x2xjZvDAum%eAD_*XLf|r<8}i7&&%XiU zM?QH&MM6%wBFL_h03~R{Ws~_VG%>kb_8MX*+`nbDTryZv#!&N|OX_SM@Gu(_lTd$_v-> zB0DVJd@Yg)9vcLUi42tcR*UQD3MV~Y%}Z_26wG=2zW?{#h~{A{BSB=y_rdrVn{3S4QVpiAWGME#wPV-Mw# zPpHc$D^G7w&yfy`AG!fv&cwFXS)}d`M;)r$WzsxZeK3=+8_naReDFz^B|QEpQ&w|M zEY`sM<;5-yE>SPjLf+c?FZUn63AgQuAfIc5J&B zbh(@5eaD%)p~1uINA;q#02~8Tc)~}$D-kCR3NN($o&7U{l~DHTUZtNDJcDbzLLOKf z4qCr;E3s&9m%;I>&ybE`Y@S5EY8s!QYz9gh7>G8cspifx6oi2#@U~C)Kn>RV)tMhhUM%$^R4AwbdQ~Qa zpMQkoK1DDF9V+_8Y7vrukFZ_8)|dT3d!><*Cw2YDU#}?fWu%zJN;MpA%lrA4bqu^} z!8MbJVBBs-{2oK`hZTDwSVR?g!WN=iLFX!gg*V!i9pSBfWGf=7_P3?V_Lj8LWw3Lj za0V+N%vAa<#O6F~@23<*`{Y2ok!agMniPtTnQq%<+g+)_vmt%67bf2=Bp&K)WH>?F z1V0{`U?g8HR{j0b&&akAEiAH4>vaiG3b1jmsJe8^vC>r9bUo_AJVO|gc%Tm(b@9=e zw_SyA)vg>No&hP5Ee>BXjk|I^xQk!~S~|}+#2@&O)GuQ{;jNH0F=0ZKKvw);H^wj_ zsnIcJWmj&43IgRpHAm;DFl;5l2PyC`d@9<~0_KR(%8O?|rLpk(3m-x)sgIvTJJhw7 zMBp$$sjNu!Ea!am;1hA5LH^NGXs_R|p9hW*VONlBYlLM4iVi!Ee*KKf^jI=-ZCv=> z7$p7=B}&5lZK3$k&ldvZBd-jl*|hb!(Y@$Kei0%RA{PHIgoQHn88Ty$<0NEL**5&q zrO`0GM&>C#Kk%N%sz3wPl?T2 zN29=c#jEb`7;CiI^m^8V{OGlXK*))atmf#Wxan#-=b@<-fLB#X6)9RYvH*kTqK7Ic zwP5>Wp#aYnPVhrD9cZo{FZ+0BcjVgLHe2=KiBPLa-q478@r_;v&Y90Zo#YwGCW|@D zu(VNACPg^e5^L+L1H!;Zm6N?;nXkH$YTo~BHOwIIg#v59Kb(Z=Fo z^@t*D1DiIE)(q4jmutLasb>wvS4!Qg zAp~l=tmaxD{x2Fg9VJxID3@!BXzu%qEm3JR^Xd>CCd}i5c27gYQRWuYPQ}Zd`n4U- z5-fgiF`%`ZE2Sx&ngfRuN@$N>S;h&6dX7I(GVapvf=gYOCByXlcP@u4uso=lY@sUo>ETj;VHPsntDjnLi|4gP25Y>yLJTAfLU@)( z1WNM`lDNlSg&7YI)zM3I6%&w;wye1%(MK6iq*)W@6`poBjw;wyRgsTY{g?3bL5n1<@A2t@`5DP7xEMH23OYMp){PbJ)OoBk$8VNLX zR)p1zh6*v-q+~`XTf4F=j-sYwa$#0*saOx_#V-;wZu*yAXD1AaEwb^G9hctC?|$ulTPd+wI|z+Q7sK|rQ$ z*+rM2ivtMP+EgPvDa>Pwsp%H?e;q6Lq6wg=MjTJC(qr_DhrwcKWW#dgMP}576z!<~ zK^x};3z=sWROsS=NzAp7vlfwa6P%c+n7HDB$J1aLVy`yH|J6iDua5OBB1aNU8N}la z{4Fd6qWq;BHUBo4D-{NU2967$RAsYuTh?#O|Ht%!GQl_y9dl-iknuoWs4~v7+}lpn zgu1{LiQxd1_GdOJSyjrw2=H~#pHMgv&YAgQN~Bs=&_0S+rbMt>k)fhEf==;BiQv1w z=QnfkCB|4BT>B{STN9NJ(cDozi7G%)cS5~y^R*H-?vvBj^U1()WRSP=pk{P~jv)5j z+_W;16t@qbm`n&Orqf2L(%h~y(D~UtuQGk!K=m65sA8wWD`Eb)42vm`w$48?=T!p* ze)`tQ^e4sXMS&Csmvb|T#jrYy!J*)#@eCBq`|wX%%^cIza3QA&>9v%8vNOQQU&w7l zdAlFW7TW{Oijyi^L>ck9@e>NuKmi`5#&M^F4?TZ}uQY7Jd{eW6kZ=uO7Ft;B9A3lK0T#|tCz$_Pm9Ui8)%HDwrE#L$fNF}fO z5^*T#*>vOJoeya^{o;plqhn8UtLMOu+W0z<2*qiMvo5d-$~E^kLa`1X33{7MhqmBy zu(`wE0(aB1IbD4n@0JN9Ed&~otc=;jxcEcqP6IMCY;t9lK;AJGE|Vf_>ce1jUG5UM zm?U|)8->fWr;#*7H+aTVWwWY=bI2%}ZM{3P$O=#ORe|x;06#CZg(rdq4l0R~zGG;o z{L+hJQ9(;-nD?B5DCXONul(}q#niEgwb#421GsFRr^)2a26$btM( zp6lB)*XhE(;kRukg`CfE?)~B*BBcoFdip&zoQCcDMYHXvDDEVPhyQx=KyU;^e@Uqs zqfOXDLfdcaFyr8##T3fpGIMi%(AxSTYOoxb2wVu=WF`TDXmB6~hT`T$uf^u}D0Liky4O+=Mf3`vcOCxb%;*epY%L2sqt?fH zpkKc7^?5kgyO)??ef+oj)%-^7SD6v3B|5oVQ*9J`#%DeS@MxT#JUj(ltYc+KlV zR~|({M$%f8W63#BOmTN%u;P1RvXYyKzhcZw*lb zFJKv?RHRwH+f|2bAT;w~{TT6MJM!Y*z8V$B^g^!eh-P6ZT1h3PARG4DN>glp6IBq* zvr#;FhtoLO`=J>*^h~>6K*clN$Y}AW$F*R=M8qu}scc{9Bjlg*`I@_&Gm6oUKqk0f zo!tWC@|L4g7x!XQn>k(C#~bR6u5i<|_{qkrQ1AXzCO?;x)#$sjk_`nTvd3|r2iD*VL+A?Ms?hP;FGhi_jQd$;=&jD`O!QIje7xlA~T2`;zU2tVUnbGpHh zH>QW8|3zW`2q;)9k*7E8;88-rhK{nLOsf};9fatPEJ6M6DhTc;f(TBNsrv*#rX_kv-R zPBRZLuahargftw8JuUnaI4L%ADT454C?`|>V>73OPiS0P8m?vU?&o9x_Q!RcNi*`B z0)C)WW|#=K_24B8=T}hSv?bA+rSVnz9IQQmvY)brg~jl=r)lk}5zFDpQ4fcXb-3AB zmoYjMCgwPdBuUw>AMWofrg-a@K?`Fcw^dba_O$eLhoEq20wl5U3#|W5;>%luJyJvF zntm0cZs%&xG&{_T((cT#kZ6L1Xn$ z%|~kobe5q~Bz)X)+?{=6$j(*YX=OvW6#eY#*E{Vu4}UJRsVv5W!KF1l)r8FCT2vEqurBbafQMt$Fyb zn=|#1bV99uK%;rF@vBz?{o&zZ?q#k7+c>R881E76FOZW6RB}nW(8#dc6+j6g;%h^4 z$w84n<0!$OAD2TMV+yHFWJ?TEp<}`u2AHLQFd<%6*fE}WIJ!`qv_jd*pAm99E{$yQ z_ksIE?G9v=T3%uNy(lZnyjSMiHmzioqTe_s(yQY4GnS zbWg=4xBfVosIaMTyeEDXN=n#lA5U{plP&fihzeZ8Cyv%|Qp{Ez^OOPslcLVXv!4wB z%tzV)c^b-jiCjcA(K>V8ZSqflb}HJ79{UT}46h;2)TLrgxjwI@7#(4IqmTo3RzHJ< zTWrpVh$r`ECzqwDM8C#26e|{LYFw9=n=8amQM+10hrZJzgh)@UfMy0W)L*xUeX&Vt z2;OH3XqT>cLT@edI7}e!WM$X|N%Jj0)(_?=84ETJikQm!`b#chWga2VV4*I7*Qeu~ z`!ih+WMgxb<>JJ}EWmsN{7P8s2FZ@3X51DD_86UZv<9i!m$FA$D5Hx(Q49N?T8Go_ z9+X$Cub;+!`4~W^uI(_1TKqnmYw$aGCj{mDmX5u=6CMBBlI)Lf*)mXa!7jJJCuSuF zID6^fwvfw9rm4%R2_w(1tt20;iv+SxybQf-Yw>vkQ{M%NpW*rCP!>KH44GQq*S(E--$s63Jirgj;hx`pa4=L z*9^$aBundd;2l^#RU}xP@5f-tm5}=$H>x!Md+)SM{C-uulD~B)3_$ye5I_7jT1mdH znE7#P2JBBJ9%x2GIw3@A5tUw0=8zR0-c82f3%3=qm+P~fM_WFfh3>EMhpy~Vyol2Y z3Zx7ZWDU`4kUwaeidZ0C>XYQg2EDH2;OzY3JeVxjhGaIVKug`3Y58%Sz~Ni}GWSsL zTetyj_rdJduNcRCQh?c@2l6O*Zy)>Z-oT@XO&Tqii5h`*N)LOez~t1XzR0%t{R+HL zFetSYUG}7<^e|%ge!ul@(u;WQW*0ujgj17mRhlVE8|VO0muuDTc;9^>WhrMa7#~Js zgTL_~F<>H15-;~4yPWj+!LfOM-?fUzO$;O@*#9+{o|E}72gt5$H&$EmSKmEU)S2o# z^elfu0l7JHSME5h@N4_E!DEy}o z@5j#iHYCMd~*5+s& zeS5s;oWzQuHDV;*fPZ&BXTFNRE^KsKRLjj11YUhJ;1Ix#vhMMRyEqBGjX`0Zo_Hwo z)H}|AXZD8M`eLJy0Ar+cJDW8*nKlzeZ)C&KkeA*1cQA&YAGwcQx`8%J-X=4uAj6Nt zlCNvGiPgL!(K=qq5U9f*L=16{>uvF0F!_r!v+HnpIq+YVHEfi$lyp8sQPW@D!0Mh+39x?3+nfRwF% z!(I#*bo)CpFguqH_Q4aJjItlQzsMxKn9uBeX7c;n;J@BD$4s=EjGs%4$#=yh3T$)X z`*O~=%Ohj%fT2+es%*1P;Rz1vfzmfT`uWO<#C|V`2r#K}91%L5Pyo24_!|rYJ;8+_ zVIX#L3qwSimaytZrihn~f6zrX5}SQP%0|Ft%?3GBdBv~`?^pob^hP?$igj=D2A%4z zJn@L@rWeJ(F4Qjbzpb|Bc?=f+7EP?{_3g(9T)#(OID8n3MZlhqS+B=!EXat%9q9w2 zlaQn&8thkryG$2( zX-;*GkYkn7AkbMbe>*y0J9kyx#%1z;%Z*$0*!>9&gqSWu@I%53n*Z^Nv225lgL7}G z3tt=unUNxA&GfS!+d`4UZR8JAP9=l-{j2jW)!aPmRS^nMYN~J|qCHdQ^En2z?yQ^_ z2|MLIWMI`A+Fr6edIlbT2F3qpxBB7-V%f-q<$L}ako3X9vE|Gf$;M5s%0QV&j84F> zOPyH1iGkPV4ztW;yEo({gf39vU^!T6k%2sq-nlEgu0M@3F_3zv&HCu8tpv+>{9X|9 z>M%!5`cGL6b?CAb+uEGhl7aq!?)ih^&7-=hw?6`wg_&iKBg)yTI}=SCk<7085+e}) z!E@%li>HFi)n67|b{dX1s86*WRw993|CE;o+Lg~Pm*D0ueTz{@KgJ$sPxio?Nc-s#e~l>Ns0ISd}~Z#FXX4CL8ZOfJ`b!2*9ufkjVV1@rQ7%eqY7 zle>29iXpZ?yv3!tT9HgFd_HS$M(i}Lm&>_kf^Oo=Qj1f3^E2D=+WvdCr}4LMXU zJ9Szv?aqIi|It~hAeYlDt>S;&0m$G1Om2%p3;0Xfx{as>LAvt8uV_wft(6C*4H+QdB3W7-3N`c46jn6mySq_ZS zj+77v^s7tj*W7lXmX`gr?+vK=Z987*bypBNe}!1F&$5X4{)@DuQyv`LRnj50fy?@d zr@6F;<#8-wIW!TILuttCypWhRn!ZVZFjH5}sCCk10Fh~6(CD-60bJ(?RaNz>pYaMm z83U%`cP<6CxGF|y>uLU@nZKX|ATy!_x{cJ=zl0D4#1>p62Hy>HZ|Dbfv6o>SuA`I) zRI(j3Pnw$)QWVQHIp0SF`UbD}5p)_w&9u}P558UATW1O)Q)6p;O>`*iMZJIvA}9Jl zS<1K(QW)t2EiY5kkx+H)B#apvEULt0&e?Aa)x#Xz<>d)c4U(ju9W{V2?dlV*)RJmr z--~wXtgrjqH%2ZxeczRBi*`B&HqfK1?s}evm9` zUL)8Y?1@$FuuT-2gRXVsZ?BFSFKcMDzqUa6v7^#@P8UxZvjPD#v@`!*>8H9FFth>+ zmc%4I73{^x=YhtjC^Z1S1C-D0w%d?jHFLx#-(I%+plxn?D<8cap$6FKYT=EK^5W3w zbo-E8IH?hWPJ zCZxZaH=$@gqFhKHYLiVw+xH$c_k^1c+6zk;OF`o2?I>VB5*Nk3 zq0LbbAC1K^j9M}}R7q)HQR$(%iaUILBY(^I)!#_2RZCI!ODf4mBQZ`gtYDE>C3T zvobr{clVx2i|~4zm0y%svWUpKBLOQCQC3-HqLX0=n7HQQJ%|iDoH!r6;Irdsen4f2 z%C;-ck5tcLjzg$%yG-?nPb&ZNsqnC&u(|oBu*f}#+?;UvG5A|5LF`D)9d`a^?{5ZS$by$_QSWNrb6q1117 zGr;@wDc1**J_Px$Yq80L0qRnQn`e1A$V@s>-=W4ZbFcZ0lC&T^`Pb!k8Wf;JpP!!} za&5@|@}-R}-qR=&tcmp<2^o2i$LjczTL;+HsY`;LXLLDxPTPnR()d)9xcD%)ATGh9 zIXlp4{0WM-Y{+{LaVeMz4tku77iRZ>61R0nE>ASxJwKWiYIJ+8~ddj?0s3%g8M}Nb^<`7 zuS=Q?KDVSFE2u)Cb$^k#U}VSQ`r1&#)YEBEOp#^2qWJ5h>1RVh&xIr`rR)j$!9l(P zg4BiX<>NA9D%;X3rK1re*IEkLxQ)RHHs|loStK^qthy(g;>a7z--lIM8Ad09F+IWG zY%3~qYhIrZsF#NG-I>gTz8~augL?q^FZ7q45C~9ukXtTwQpdlSzVaW_Lk*i+aZH`&h_*r#r9RyXq@^S z3TpmND{`^-P1$vsjEX*i-kP~(zj27TG#woL(w2U8cX#K&)UfrcJeLgUFaqNIgeeZGM=uC31n4zivj;pzJLJa_GA%7YD(G>)C)hQ_Au*I& zGH8AT3nsW0N?b|e*FcZkySt(>ee5u`kKS$J!oRB#5fKXp#CVjYxTD}8WD*j5LqJ|B z8Lb`xN+EJubi~%iGsarq3}Bm~6JMDWRZ(3X*4AtrsArK>Aip*n}TB9(QWn~c z_t(EF7%l6Yn&Q{#2(qoZ0~(2THMtu_=2NRTLrvlbNvpby37{2%;Q^UatNi-3ut3Jk zasS~XRg`eu+0+lE2|mpFTlb$EKb{;OhBm>^U+sH;tR*VjMSs<%9G|~HkkZlN7d~Ru z$m@vD?5g93+%7=#%7F)88<>)KNG0qwLt5X6I~>S^@@hbCaTLrf(^?lzvmY=Jyx;~$ zOK9r9-j+Qh&(#SffC1WaK67z#?GVyY8S)E!^sDCcburoEFzGC+i;X`h)6pf;PUH&l zCr_m?F;g-vT^$_|`x7sW(_HcVkV%GA0P4j-s>i^D@;yLFIpd;i;oJRNa{pIAq=VJ} z&KD9g^#Vc^wac|MK#5MQBAw8szmu#DkM`L`6;2ouc}tLPo69+-R@XfgpJCA8mE(&rJ?#S) zL+<5Qru0WXqiytJ**6Y_ENc$gv%PA?1@oC@{oFl|EvDV&)?n*);$xu*`$?+vC!}gW zJyHEjdb2SAOZ)_|Z)6DzeVyQKJxt9KP}h`G-=_aP9CT{}S2H2fSSlUBO!FGN?(NZ5 zmVSpAO^wwebEFILwd`4+0JDmi!=VK4{uhxuEN>aO77 zL4g_!w6L*z35wV}uKI_L)>^+sJv*}gOHQ(Hi4jKJC&#Q=-eIDHgZm(RR5H%Ki-Gm0 z?i4ksI8Stl{xvlf3(L!pHAnjB>h^Vb?Y4uYC4RYVlTK|4A5;g{(CtCL4*)_@tmsL(M_4Fx*J@x%Vfp8QfS@(H z{I7^e#c_W@lKm}F>8WzOWAMK$Hc}%4N$;0^IsBc;+r$uAc z&QH;SV-DHHtR$C@MhrKKogH&(j8D@aE*h|Lo4HLGgcvx;(L1tvJR@3ae&tfNWj=3A zeN_43s&tnht^b1`OE`wPp2=OfD0fgcS-@EL9%*V#OS@2~Y<8e4NMR`+D<)nIX^s)OMRzQAMDfBTw zTzgzpFq2fUSwW?$L>@(*Tks9o3YfDrtWTrue_hc75g6#7>6Lh7@zFA)s{_woLG82H&(!otold3va=4O>swfZAR$8EsUw<0>_3SF4Wq!P=0Zgf0 zCD;E)z(D-9=!?)v6a4cR{Eb||!Wy?fB*W=>p-wMt;my4M6cm%hTp0x=M~6aB|HKA- zpl;6gW&spyQ*@h}vMCovndCHb4^74)k={)NAGsvcAJEJ_{`~CWw#BybDYNHs;N{&| z#yoS>9tX~yVZP^_i=o|>4QYApP%zygPhNX{K&>--&wlsVHtusiOxhHb%PR6lpBN+r zd}`ICk|2>5DwUTt+NqV}k`+eI zeFqIND5~3LxWezE{q^UE%im$=slwCb*&>vx|c;!{d&>myiWEy&AkC7S-`P!gStn2@}VAx{EiQdk-XwGam zR7t+{`2>Gq{Z{fa2C9;|y;wQHNQr>9Q)fcgIzl>64sU@~w_&;2|KzK$D*&~&R779uhKuLSMXj!E-JD@=2b+|b-blR50%2k@ zg=O;Ck<(ubQ9P=RPIuakHQ+nl*Py4Oip4#KVi6yi6ij_WA!nwYR=s}Om|)k=JF57k zET?T&$VfbM^d)G>z)$(Bv7WSvrx8%0<)3&bvZXpRsJYRcwV_R@wdb+&^%eB2?!pD_ zDb3!mivPDThrwX5#2aClMUr1Ai(kT%iF+ishp;zBREj>E^pGhiS@FDDqkG666`>3= zVn5=va#Q*q;eIWsPrS=wNLV6fW1^*J&Kxu87dLJaN=X~XfvXY%)=dk0{uHRVH=D-) z^lX9W!`n}(K)wL9R^XJrOB?I46ezmq#!_sc-UC5epXH zV4M8sD8c~!vy|`_G!p+Q?Vw3Fq!3wjm3hRHu=$Z;@S!k0Lr!yZaC6R{JAoL~ER?~@ z?feV6$$-Mz%h%-is* z2^WpHRBKq?T0E{Qc+BQKq@Y<*sR!-)*An;2mjF(zy%}hPa75Ox@vL>?n@7`zni=sn zwCTtO5l%agTKtFX(r7PZkg1Je^FI~-ZZU^=iu0sm4jy;(SC_Q z;<6UnofoR)BtjVc0aspZ6WMgD>Nw2j`>N~o5V#h%uQZK~dRnR??Ycw`#?Gfqwq#FB z8xv(eTff7?dlS|9?rzrbW}2wz5>|{)(FL1tL412}vV9rBXfukB`#4*R436@f1pnn0+hKVI?R=h!RLdQ*A^n z-@K^k#X6_iqT$IgPDJQ3 z;^Rg8`_YA&p`oEoWeP-RMjf6=yTqhEL3la4xD+uMWOp1;$zAvKh-_oqCY_y~rL;R! zRYEJqX+ZwUbSGV#nnJz9TG`AIVMEjX08wpgOv+>e84n2~$yVM*qAy2Nb89&Q>ul&Z z0ZmF9Vn(YF2uBg(XAJu{N&eGWY35mQbes&5tZcj)F+f2 z2KB{1s#Zb$W}lp8f)M?SxghflG5Qo4yo2$O%H%BHW?0hNiqumXhv(> zjHgxala7|AhraY1AodP7V&E&nhp^y}n4sljdhrY;Cj{r9q-Dx>1GXB^j*q{#p?ynG zS5iXn*KrIdjv<8Yqxx;)F{@LL@i&g~7Xt0kLkO2&Jfr^_v9se9)~ITeiQ17E9+=;& zUcX|}l-WsV%q5l_2P`1+dN*eIPL)US^+nLlQ<7PZ$YoGtzE|4V#W)S78w-9gB+#}3 zL+3q{aG{S|ZV{UI!P)YWTxUNXYVtsOM3>ZlFKNooIyuYrI?&qj&DplPh8IKYblrEM z<@=8I1f63SXc(&L{`RFb!nfgPMOk>QuD5SS@W)si^BBLyZQwjxFnfTh4ab%|;=}jm zw+DI^Tld7$MaSe&s~DiIf;S`(f%mH6(F>MdMGk-0?8WavLx8B)uve5I^JZX^`Szhr z8J>S0JiV2c&RjS{Djqkc;{{tPK^$7w93_+xoPPCJW1C22lgpt%S#0@2YJv_T-H|gU z5y*^5}(ZUcC~ocZlM4W@!rD=Ejw6LlkE3TN&kDs{4GIzEDw-#4#}&y^^~BA zk8q=?FLgd}bKjkPiytzhC1F0x%^>o$3wtr1Gp)}Z=ia|Gg13M->vgCY+mCJt04M~T zHyM9jyKnTXO^3GCz40%>*q+_L2U}|j7Gl*qpDhb7bv{wZ=udcDWX%7JO@3}R8@}Dv zGu=(Qe+RTk1RLTXCVz-tn$)s^O|vcHwVs~Kf4`mN?1{-PXInVgkjtW^j24^525*!Z ze|!VvW3#p6C*Kl_&DcLt3#`mY0D5qu-n9ZR_-`9Z1UJN3AH)p(!L)XOnnDKp$KgUx z5Y%|JdrlpK@)H_!KcG_DrP@GQ22MY`qVKpP4h{~5Nx_45yQCTj8ptjrp|}6v!eB^r z1;I&Sg%D>M2gUJ~9vvA|;>|nTgJ$3*O<)KU9|v~E_&2plfQE_8V>z~WP=xW0tzBu` z5w*_}EdlFbjT#%kS&Ky~OG(P@D(lR6Di0XRiT6=xh*#Qk+jHthJklAsvcv123FGr) z_BJ(H-W_zYr1#G;m7imTv-L;>$Bg^2^1QCJ(|lGqL1^{ zJ>54USM^e2fOp8HjZm9q(mGLn#Bh=OY;)GcVOO~ZC5Zqq1rQsvX}GK3_hOw-w)=Kk zMOG9K`QI5GoYw1qhjoA+b=!VE$7839q$kK0_n@j79-)P%>xHWG68OJJo2rPGuu^gVQc1E9EtbB@^R6|e*@p`~@KJ!%K z7uTrjDEEwZKA%M*(eu0Lmd>h)shdZBUtN&*r(|&AzPfj+K0uQag%r|mb+P7reHpmc zkcIYOGIS^yBzHgW#<@f+7Z*1T?kf9maz3*vQiPrI&H=GkSy7d)7&7Rzf3_xO4}1o> zivAvRP(kNc++4DS|HZE$uEY}ZglbG_-@}TUHcO&52{kIH=zk+e)tJL;nPbI#f|%j-^i*|rPPTP{RI1Cj zP;43*oEIW~N#DTi?h%OGuS-L`DZkSyn-FI?N;on(BWUDM6|*Gs2F+(=jicV-hFIfX zmY0yGT>#x%`cZ#{dHi!QMX&$i*@TI8VHewW=4+D}Lgp{RoGkI^am?sK8l^6;J{(Ow zY_jF=&wOP>r{SX#(LXAF}&7)9>m|N(16gJ8Y<|o>bP@M{riIZi}}X`Rg-xBr&|IR zLQ05OLDRHkHmCn4i6yx&2^Dq^TVe7rBCX!&i9|ucr+trc)Fiz*cRbUCa!ENYXe$>e zIWJGo%O0S0KFaG(7P2Vmo=Z*kTfVVvY8Za`eKKVE$fFUXvRYoWK&LcLK?r{K!<44W zO|UGqivJsd=es8A;lAqRv^<{G?}`plNa^y(iKm8`I?zoTGyE4<4p-29|3CkHT!@d| zBfL@}oiRou^>Y#Plw68_sJqiLM|{!*IB18$|z(T{9%IlR4-R^c%)*)^7KF(!k5CPFtsDYEI&U zbOR@2u9{Nz1#LN58q^)JA9>)uvv9U4ZFder5mI6++x^t9_;Lbch zzyER0cEN(v86|uZaFqRDF!lFQYXSR!%|yalfGn@rj;$@(owsdqKPr5Miqm;T3%`6? z#4xNJiK&Q(rM{DFrz+q7a$Py%NbABAahv&bU zp$~+qU*^8kkslii7&WykU5b~+S8UNFT7O@4OgG@E!^Ym*on)uj%W4=SPEG!^mo}z3 zU5Eogc;nE}8LY&BI$`;%cE`N~9BY9yIkgV9t-9K0es_ORDU&%Xa&NBK#gswBXSLm-lVM7mZq$8WFU}~4r8_4V^;5nu7I8ql1%@*> zuIrZ@Lu9EKL2bLA(Iw*RPelU!o(IxgG1sk?xi3gM5Bs%AgLqL#zGv0!&9Q#?*y`1T~RnXJ7YLV zy-=Pf7#{}xQ@hB*T&JO4$6HMqmM(q_p=u@y;gu?$S>tYaLILi!?U}H~Ng?cz|0`FO z17EG0#pDQzODHH6xgeUo^!1mKPRM4lu3EcQJB?h|M185@1}vb&xQc}ZJM#)cmicy^ zApp&i>xil3#t!Lo-g@AuUPPwjT4z_Q)%z&p4^!7_lShRko&-&!#h~Km)|wl5Wcx8= z5AtyYC3|g7D%GP0RJt@)(QDZbJ$q5tf`692D()t!?F5GPn)ks9IymK z&q_#<45p77#L{p%4q;MwYHY&z_j*h-7~3l@bowOVN}$| zulN1@pJ8bRL(z@#2y*p>NhtqRGqQ5Sg|*sRsDu3r>hlf_i?vNJxuNkaRY4>sV$fH0 zeGZwbWQ6BA>4Vhcs}KMp(@M}08eo{W2qhAnKAP8ZQKg@FwZl7%lG}j2Z9!E3jwsPr zJm#8`=6JwC+S`lm1oz?@wJC+gvv)poM+pCr-&KKCaD-cn^fBg@4GTWTE~ibqJDzpt z{xh%SXjT@XlDn4FE;vVPutYJ8Nz$gfc53q1r7x-tsw~O%NhrlYWu+>t!aqzlA4fZ3 zS$=Fn*?Bqay^ds^>Zt{XaF8(!Mb3IZ$RkTJAg?=#gO4BaeV!&hJNYwA?8cwlZ>ZIK zH59H`DJ#%Vp9Qi=@vaay@1wEz6*U%9it%^25SqU0>FLpy@fK>y%8t#tK7uSHxDbZ6 zu@(%pw||3IfBh#Zb+BAfL}UxcRk5eac#(dHBQ*4ievNJuoj$NhDN@+#p0yMpmt+!q z<$^B7en)#X{$VaLB^h{3<@z;;SovFu~n9884QKJDoll`N~63WI!Sv zd*Y$|e)|?A3oDYyKqjTF7KD!VeHKpRn_d!Ux<{V=#HaO`(w7}R z%%>H@Fa^nJNpL+$MkP1h*llps%m0UwiUDdo`D|5gR5M!X@sG@~8 z{%TGbHzp7+-1O#lG)#rJQu7R{zXFolZR|t5=jKFz2-8M_*J(z;;3J-JY#Imso)IX^ zb*H+1m@vQB15MT4n)-az%kdL+WxC4Gr2Mc3b0JQ|5K3k z2hhn#x&HmQ=KI-QLZeValMnOrVf66FV-fxuJ&#X+R__IBeIvrn!QojL?_1x+?>N*q zkBkR+b3hcO#QG@5u{OKwk-}FxFWlXY5@umKTl|xJ;FmtxuLlg`oiPnp;Zs%T(T|NlBYZ>mX?*&Mk@>53H+QWt z)bjx>?ALK1(gX)2&0;?k_^fys=pDG!uPH$fV6uyONwo%Ny054^`^Ui<%&q2WVYFYI zPKoGY;1cb97>x+_y;QBFlKiIp(}IofulQ8~ zmyZPvmQaE?z9Bdib#mfY;K`6z(?O+6G)?YM`}hG!3Se{5UK+>C@bVd)!spk$ zvC0~J(`I z%6!@2aGZ&JQAS%5U@_vqrM_jz=C1}`Nnb+!kubWUh~xop?dAHfIl5Hdo1p!lRAlPU zrb5!Z^KpQbKTw*)_GDEyd@jBMs?mjhKk2o{@aRwOxXt;ZxVu;z{o)q9Z@&$E$l)xn zEA4JtNl#1ZZ84EUt(Q{Q;T3URA1?enA@E*v=Z53cTSoPr=o`FVD}IkD&5lJW#iv1y z+R%(n4F@0s+&nuk?!A#)87jr6VO9z($LTxqzwdE4tAMMCbukmJRA`kbUX^}#s<#!E$#6fxJ=jh ztkWfuoUePZi=V;`hV7YptJ)at&>}~;#Fgr19jk&ZShz;-2#}UQaef%^EM1*HfV9%Q z`C=OuXOUkKI*WGy`+P)PAV?C1$lmx@mig~~3l0i`j0~l|-2(j+vHL$CZ-I(U-IA|$ z(akhvObWk&##Pi$KS~g4qLSDP_R()oMceEP$0r)mV*`OZza!*`?c~UU(Mqap$KQKw z4&s9{#ovl|zs~~oeZ0I>wX;_yDQ)D#T1Gatc*VC$=hFA%I58*_vo0FFDy{TU8O|3z zngy4B7I`N>eBKOdZPaDU^K}_9m{ww}d2h-B(zhj@a;Y1~wF>0y{;oB>JGA~PB^&3q z6#rFWLwSV?b+lXh6+Tte$|AqPyoSl|Yp&{x;8nwXFAwDOHbPavo(DP4`B z7(Q$9@pwbCYUZNJMd6Ty1FQXKjLXo>{+9D6WlsOotq*)pHQ3t8?fo)k`e8K{9H=6+P0=DQ`1WdWKt#hY{^n}Odg;< zLQOox*(e=3{;S55b1KTU3 z3UX!`+9yIBZwez!yi)b57$D#@ppM76$9O4NLSlJL5Xzm@;!~z*m&Xo7kvD#A2W{8r zU07}qwpkoES`u<}HSD=o*?dZmQa_{(7 z;b4DL=9*6Dw$-RNLDJJJ4c+L803aFcpEm0nEnxC7V(pXuvASP+p(B-dPnvper&GGzbEIdPqGol?+23YU1o)!XD&cF+C|=gSe0 zy%T#RJ<>qyE2n2;6$u@9R*1~N?7-oUPIOmoZ_ErzWh4$t*x8%lXPKkku8Hyhv~`6o zfC^^8e|Ef}J5VDmM;n-A8GMrV6BY#81M)3BelPT~=ZR#yJ^_@-g~ms_$Z&`BVlHmI z)|iwOX_7z|oWyLw4|_t(&4Q7}vT_sR;^KogtPDe1U|(gAv1sVX*Mn7z&Uu9I($f!X z!8-T<*-hU0!`c0KnhoW>Dpi*h1VSys(MpXF9$&9OW4YmdTfpyTw;_1)1JKHtvZwC4 z2d>I|jcg%#px%tJk^j=%j+LES1q%mGc_ZEZdF>de4IZaz<&($e33`cWXTS+4;lEoDTs7=mRv)H}b z5_-abjq4>6DuV%^4Rhq;;bIeI`=vo~*7$tk@ib>auf!*z7q1Wyqr-phmJHmt_MB@* z`L6d{PWbg(lWel6c^R2hg=Pmt1RS`c1m@V#ndSL?6?xG`?24y-a3pbQq!3xZg?8?! zPx{^bq7~^DX|&@1Lf3Z=7!_3zP7HfaD~wRfg}Kg751FE@ZSp~TpT;=UPLJhxcM{(S zui=cLY(6ed!apCrx1G+wi@qxmi-D2`vQ3lB)Is8$CVBv6RVr7VxIz;}@hG`3^pLZpJBBr-R&G@pLmk ztAxC*tpP_ahvonrkahVkA#Je*S{;F!A470JT=9at^;z7QM^#uV_c|%AM1X)Zr!%`H zX^D65N7VF#Q$CkiIYh*0+?|oBqaQ{6x=q7a5``duP>2SlOi)5O4t}WcM_A|X&l`7S z8KDvEGp&}7{r0Q-jK>8sw18d{azU}}BGXWvK+nZ!I+}5S>Pte)K|A-K^RLK4G8=S|g<3Y?*}jz6`$9#RsSMyl4EM9sPjg;g~i_rzy*(7htL=R!dxAOze7hsDO>KHv+ z*f5v+inKUcQKcQ}M`wRpW9IZ65;t@|P&XeBO9A<^SNN{?krwlaRT)VIhM85tzf;-) z^CM{1DGMla>i3y>`SH8EX)UzQ)y(vqw<7VBTOGO?KVYzx+mg7N}upc_)XRP4bA%*jn{ ziYZgXSki?uTuO>l>U=`+z)<>0>0y$9#!sr2zZ`H~M1C5^c6PVw5dY=O{uEhOOl~XD)8Qwcz-uJ zsSu&K`^brws0nxX8L4E7RsM^>bnULlxr`WSAmOMlL;i0sE9BV+)fWqli@;9A+GHhu zO{9dFv3)=`=6_MDvZ(t}6t|Jdoqi0mYUfGMDEVi!)INavXQr}=%x-ev;`L7IE3irB z#>NRqDTe0I)^Pr6aTsgOW~fcs_L~Ix-0#dn3}g!uki^~Jq^^0W)>=cV2p_Q*^$yXg zERP$Db3)fDA93KpEC39|zDu}=k`^QnqNE3V{#(*T@_H?5IP!N}-^WR(R_;Bc%1?Ay zb{vJ(3iy8Th(^Z#(Ka;Z@DS`HZPg{FhgB^;PJ70 zXQf?U4ZSz4yZQW2rewRf%m;wLy^L_dHyBOKF{AJ9u(^op0ElANz8S4kg6G6YE<^S` zoS&elA3}zps_`iNnMw%}i>dwgbYaVdXrajK3?p$l-eoMxL?i9m!uPZ?xn1)KswLeL z0{gx8F$~_T+=*9vEEg4gNcEV9uZ6GRT*}x`pO1H9MZ)iwGrQzxd}-JT%3Vp~(*ME; z1Y$IyfWO#?Phk6F1qi(}cSO6(Ab)e80m1sjj-;w2&@t93Igz?A1m^`)exB@Z4rA!` z4T#}QeP=>Wrc?K;B9vo}2XxJ?d}hA~zAJuWawoD*_o>|}ZFdb04#%6*N_v}Jo0gSR z=W&@YAvwWL_h5t$|Gj(!gGk^7^j);!0h$6*Cub*F%Y@F6cpZ$X%08scHLs++Oh9F{ zuab2ql;i3fZ_NR_e5U~eL-%}sG;)mg{22a@O}c#N)Pe{^{ihqHGpDy0JokmXa*~pi z>QdQC)H{0JKQY8?`4An)O5)*@A+MYDagLsz9*uyfwP&rBD*PP^hgn`Kp*uefFgPx; zxM>iCNz`I7FHRckt{PL{k% zcc;m=T6MG)dtoS>haQkUVjQvbfVB@Ji^MM<)Hm{&_gqeq$A5tyQNzfDnU^9OR~S$u zB#|!6kk?JpQvXAGpo7E*h(rcs{yPM9%&{7BrCI%PD;bwY;MoEsE-%h^;9mzbzo&-0 zQfdwIG2;1#vdA6>xl?jHn=reqcytXck533bG=`AcJV4%r?D+xuF@+Ch-Zs-In&EP} z)R&=?{`{|koqVbHJbV$f0#dk4ix>#D?|bG|NPZOfD(=;FZDq(#m6S^K5&4z5PQ#vU z=rL@s3X$&o0-8Bl4LZwNkq=S!zf!p*zti*BB!=)&0kHMAAUv^+!ZyXY(3d!cX?i`7hVw?_ zhxPUQ&hbyLjg?vpjh$buDavVy8zBiVsBP4v@Xy^cqQnuan29KPL?rryA}BD%LAe!v zc3DA{>PP$u*GK&e7bS36n!N{@6(aIEiEp%?ia~RXIG!}^|gt}@6^qLg;7cBq(g$fCwjl=)y4mhY9MaygIod#C+ zQpV`v`SB&{X13BEu2ajR)(2-1u~zGwD;aqA;M;QKA30L7+Z;6~(R*br!f?W=A0S!Q zVo_Gj0V6X$(>q_JoYUb#zFT>yKBwQU{dPmz+f_o2iXTO8l;RIS_3FB?Qa9|<7Gb$9 z2v=&GJRrVL6z}-a43J$t7IZueZ2NrQguNw0ja#A7YC0V9Y81W?ev_2bBu_l{Au$Ef zRz$Ra+ZSn*C1d|jVWtQJzP?2UFKFnWqhIPS9gj`bURW@3@iZeE>79FF+JNc>g!)oU zf_9b2jxRKfX0dz<^TC@_#K}HC3%sD8(M9gFQPXI^lZxs-+HQ6@TjVVq{PePAGrlLo-k?IX3wPfLAT@B zG<>#qkcd)d7oE+A0h3OsjTod?zJF9i*v>kvK;1lcN*xR6xuoF)K;r_GCynU;Vb~;JfrdjT;K_v^`4Y@?%A1N-a06?zA4DUOQ{b zJpM)C7_uhIU0Lwea%bT2Kll+7l7g>)`+AIFWU?p3`45{cJ<`M&O*!QD&UiY0mgI-R zGuuW=#PhMc)Vf|sm3YjQT>bs>L>*&}RtRv#8`tqO?3qmGksxLP)u69C@(DyYrQx2^ z)c)VOH3;_H?@Jj(8s^7{3spe)76G{s&g*#Z9ynw0(xd=S-2x=7OSI%pN|e!k&Ev+P zV<6Sxgc)~W|CU)fNLiet-HeP8Y(gHDQCyyD9P`Gb!Xq5Fcde__n$E?tyoR8^y4(J& zqi!Up4jlNc6%#xc7nz;REeltNJ}tO*itg|_)iT1tBa}~apXccbzLdcHl>w}?&*9#o zl zW`50b*w{+7e0txL<+&Go9ITxY-3Sk6YWiRZ6h>XBj~Loz(dI06U69m9VFvN}@U>2r zu(m3Q?&6gCKp#*X6_F5M9zNk{Sak}q8~8{6qdf8=CE9p^AcflBwPWXU7DO5vL?V+-0uAZP2fbVLhz3vbT?YK?Qn< z4VROblivMD#nd7`z#&_$v04nWu}Ot1Efkej4jI|?)f;^cTYJ`lk58#4N=`V<3-f!d zK~e~AMovd!T-^L^OvjL;2BzfIRi~ zjAn_IMJW4=p*8-}>9Fi4dv|ASXL_I^^6zu2wcP2$ANG$g?~dJ~H|orFPx3?1L`ZK| z`e>}Nv61I1tVYL&ED(# zaGdXd+|8SI~YZiX}tw;5GKw`cmc^8b~RZU z+uDlMGDOVIr>$l&clHbCbUgg?ad8tw6$=sj(qx^pxNLvc-CPBkX<5rt0ob0P#hvee^jXu{!1ek4 zl|)}}HGI=orYGNdxYxt<0NTZb`ucGmLf#aod71`uRH_v?2ixKk67ok-`e{Yi<0##fXMH!@JR(d%)n zBJ08FsccFo-RB0p7BF=A4{TUiSb^svTs1#Aaao zZ)KRQNcY&v@+xx(rv6F0)Du(kBdiWpS336S+QS;2Xt$1@?i3$kdpx5U{LlMB>hqNm z-?(9cU~{Gkd63Xe=x&U;gFVGfIzefC%D9H`-rhh_#aH4|)`PH)%v82H49W5$;fTXA z6r{xq-pvKxydPi!x_sBX-t^AB%2j33Uu!Q=G)%|X4@2QcwnV@lu6*AHC^51xTTya` zxYQXKza+9jFnLQ(@Z5l(p$o%C=iFRVN)7;gHwo+$`&^X;+MYZM`v$FJj(18#g7rwB zzCtm<qlTjV=ft-7Vd~Xo@u!NoHlMQf(w`~P5aoDZFhB22FyKRi zu;;cLPy2pW`xV=pOr#Xe&ZWW4#)jUIascBxV-P2GZw@6WlmLD&=tFW?&=3VC;03ah zK0Tq93Muc-&di87WUP?F=0Jj!WqpltAP)XtNCJBDku&7Ek%K~O-OzevI(^YH8K?1H zjK#S*>#fK@y4otQ9Jtu{Xq(3&xZ^H$WHlm;R1h@&Yq-3>S3=%Fve`v4$m%j)V#NOB z4fd_>-Us<-jUWc&QMnr%+aE+&z-MBQq2Kw-H z>F&e84T%xtW1b#jhywIA>|7CuduVW?UR=aUdj16adma8J1-(M5Hn5TKiEnvXoc`t$ z@^){$ouz^cS_vgCg;QUF1M~Hw7f(9a9=Bj0BIDkH1txLe#BDAa3S@8vbOlIK9y`05 ztNz_p>Vl_Z))PS88P5Dk&g6kKqI*J^;}|=xI#aw#*K{m`5J$)@<%nF=iFb3IN8X!Z zgX}Iq$^)VK;X@psty?H2bs&abfO{uj0g`FxY*-pRxrN^bble)hX$<){%!y(F+tjJFzGu+LC|!O z*pYaZ1b{?>SF)d(p{Jw4dP%vDlhzeLR$cjJxpMEV@=~R`Z!m&^V$?F>yF3ruaKk;6 z%y4>B%#QudgAufEf4X9dZ0ggp0>J2O06(#$1M zd+w~qKe0zSZTmGX^3P04g2K3OLTSrW6YJNmfjYJ7IbF-asrPNd$)RZqhhZ11K|77# z^Q$uuUFS9tcxvS1f8aR1ulqNbHrjqPuq-qO*b%%oZYh3Y(MCo)o8&$t<)_g#g?$Qp zb^_J%g#ZdPq{8LT$G8(VXJm*g#$p-*Ki&5ZY`H$7k|-xWY;{l*_GNr0enz?Rv-u+* z3S`^(!rSb>ay>!Hx0ntz1XKZ!2zIASjN7Og7o8BlKfit(4+BSI2yJx4eNH};dx}td z7n-dQf=4HR=Vf1on1|gD&`G}Tha?(&dsC-w{dgrWU)I~z$!q3w)!q1a+-|svBhE^@rs>o^k?aY$Cd4n%Ptw2^r{DB{l_uh=FM`)w5<$ffL8PT`eTXG z`7#hF*F`KL-=HJ&fguTdtVr5*eO%Ck@8=fjV=V>idcX^~dg$$>@kPJ-(Ue}e;|O@; z$x^-e<$R%Lc^RD@0XLTdy{zt5>9JM-;ti`xd^gwq=6*(QG{ddp(~{7kL0jYGdy`qTh?h73GKk=#DV*4DU*QJ}(Be7~AIIw!{#gz8)JM3V+LdW}p zNEbS}y3V24e)m&VJDma}HJHMKa5JoSm~-x*SY>7=bcqGGM!+giie*z4iu_GlHUmDx z@)H2c?U}|pzV+>YDD_#m1izcf}q6K*lqGjCp($$Y04@ltTY+d|}eH}34mRuzwB_51e8t-WJvh~DCH>+m- zNxFI3tLsmetg5BC`C(&1a95c_=7IS6gG1{})}3n8y9n;Bp&cLDL9y=g6VS zLsne^=n5H#7kYygo{@Rjl&>_a==TIuM0bH3e=Bcxo$U^NFnVv)6=?q`j{1Yz890{Q zIt6wPiB7z|F^h=d_ytXl-gCBa^e7b_gNLJjazhfYrTRj>|7sGR7h+OIuXB0*wZ6EX zJaJs;ID(+q`qK$nvW=Zh4hA8li(ILH_oSbI3?2>(^NzgnFN^Z^Z@mB4$&XpS|8YbN zPACH|x5l|wM>)iJs4{1j?{XqQIBbH*ean{GXQS^lek9e^gbFGIhcoMUWjga7Rr~`J z2j@%2F6*aO6dMutTnc#!;fytp8zlC}bA`hZ-BfY)`za`OKX+(nHhYjD^r7pF#fN0A zhMOZ&dnM?{i{;WYhZujI4)STHRVXX>8vj{u*eISc%UXgoxNX$AMtc+oxVtrU$^i6 zn?-^?S0g3pM=ktomoA(o$O`+vA9WuzO4W#v&R~7^%jq;){)kcl9d#WM@bj~aiX$Sn zCREYkLrf4O?PhK(##wV+$xri|eSF&fJVYmhXcJLK!21Q3;*>&Ok?}>zPE!Px{9sP6 zm<>C!{J5z^v}tauW1R%0vLt)TMqtG*viN7$W|y^gx_P;~9h4_kT_qK{1(=3Pc@jBq_!{!9ZJ5dA=;8V4 z%rQe)z-C8?I?N}G5TTF_t9hBO+$KEXw5~?piRo8ahm)!1M4|nXyUEjC^t_q`#_7*8 z(GtiK*dVkno0NzAZM^S3hh{>UondG%~R&$QGykMy4Vkk<|RZ!yIRLKW!f!k3oc zaZFuj%BGB*SC(Kt57tnr4%a6GG*g;Z(nuH$BMnl4e%954-BfpwWi(2?gNfz!j z;Qk02xbtL`0*U{p!1wN05g`K8)ij}jSFz`4MTf)qE^WhhuBX4LJS8PERR{WBnx(?u ze4=IxuEh=!hNWcHVrZ$J8G~*(Sin7m&dvrVgFFeM#as)lT+6a^Jq#z1O6tUOZlcO8 zj(gTsMtmBbKihN2Xl4UUeO}%{9tCP9&7y9#?p(_KjrLkm#;49>UGa;=HJW*WON66y z;^pT_t?W^UB}eA5={~)jlV_?qU0dhAaV>^%b3c|7h?-i5i4iNOW~KPVtO^t4XA2s& z@l}D8+o9-Rf4v|af6o}+oMQBU|5_s01ELMx7Lk46WJF2k4odLASBxVg$?ggJ-COi? z&7y3K)bu$bu%lUJf3zhk3azwG;{H{<=UkL?F)CWN`AoA$WODt4&1yZmI#Ea2Oo!cr zk%w?HqFa)ac&&~|qXAq0$A;gLMt4qBS-{%Kyk1$Up&8d{r4TCdmK_+1Yxm&M3(wH3 zh!PTzAjbPB8L)U8&emwy6nS>a&-$U#2+_KAbF{+l{8}D&uGDA2PHI)bnHM%qMIeM z$K9+I!B49)04my|YzSzKbfoH9GHnjWva?^lDGIgCTIAa|7q-Mw2{a`~EmH$S1uWii zQ-vY)DpkQVIxT`X&C+T(7|$BUtfw^PEHc?TH>~3nfKs)K#P$RUY!+2C!HP{EeLDmq zb9eh)lkF)%%^&PR;U#XWp-(Yu-&BQWQGO~jH(;M;*EShwH_AmF_1?R(W$VO5N9=ED z$4%dIMd=F2k6N(J+YAkX;>3X;tj(P7k7}%-429o7SRFo1;J-3$S9W33A>&aDd zL=npmAZ67fCT)1494hEX6gVP6ev@Tzu>UOkKampXK;(n60hEpyp0}WVq)<{}_6s6v z+X8vvTVi&WN0j?adFIyyc)sg1632(yDV!sChAQ98l9u_*j_|XyiI65ID*vXC=rc1q z$~tAs?zc-Y(A51UJ~2zU{+ENp;-K3o#$Q zij&grKI|lG1?(PGcFGWJ90r{$d$$If*TSq;&@dwZJQnu1+`4}}tU;xr`%~%ELT%7~ zS*9+!hQMxaGy6Ig^Cp3zEvJ0)_83MKG`>}wfQ?r4Hv=rRHHyaTcF$A@)V=8Ra%9oH zHJr%M28wVJd&?cpH-4{N`k?_SO;P%Cz4Xcc=MW6e%aE8Adketu4oG-J7(mS6f}ktv&ay0`aHjH zorbO+@KSQ+_y)5#tDt`d^@z`9Qq=F&;)c_DeOA%Ve_0Z+LkqeFCQ1HMjZe$FV-m2T zor3L1#S$Q{QJv1n5IV7U`!aGqi{#b5t!N>s6mv-C-q@`_U@ya4Ldp0 z_uqx4*rEdHz-Ppl?p@k_B?NG5(>L5YtXF_LsTNjQC5jNk17rlP^82vlg*0fZ?%`tH zNbBPLVLKI5H7d1!du9+@Bv*;PRdw3N%$M04e9p$Mh*bz z^@Gn&D}-7Id%!dY6QiWm;jKkQwiQKG$KfAW4?4~V(~oWPR1&ns=Hug$VP+QsC>M`< z6h<>>D?w9zkZNpP=he?oizp~-oeJBmT@FTDt~YJ>QR(Hi0uHQ?xGCgMvIXO88@+R} z=_xj>HE+rx+q5HU!n#OD)tFEI{y=y#l5{i%vn_R?}cfz?K$2P>B^>*4+`RnOGFhib@32PhbuX)*t*|p9> z>9ktys6F@f7pVzJH5)o1Ru?T+sU&^BjDDAX-dcQK_ScxI{yYk4J_eE?P3&u?2%@&@ z`nQcEa5PWc3;Wthy;adGXcV0d=G;0I#PKsYa(P9b=Ml%J%R zo3lr1d^P^FV)$h`NvQ{G8JDP}~m0U}hX$&2GGx;13w{d+w~(y`ewTuvfBbpRyAfStXh=IUj770znFQDLOlsR5!fP-oBEa=tOmk z0lGCF1WCWw;0#hpP<}ClhjnN*pK6E9)|^jtgA^*ef5w*tz9504-Jt}dhno-HAi8)y z7DEc#<3LH5c{|gUb~&BxJMLhPj43=d66lvDtZVVH2Rkk^afr|w68H(26xV+``Nit( zSFSQr1PBUUGj|%nwa4TJU;Mh@>Px1hN&oCQqlrM>BpTCZswCQPcEbT-;KbdR%$Gsv zo3CU(?GidaIl$4EIX@vDPAs3U#jf88%E|Bg?xm)xN)ZR}Z68Yin}mwRxYU zvF^`3gyoPYd*+M_0tlCP7lUUfJzS!}Mk)9uOzBI6N0B|?k&^)#>zA_p1jJ2dFhv&v z*r4KmUi-B-UbIA#()Oa{JAK!OgG$3#V18NHV}lz4waB3RQCUg1d{B0u8dhw-41Who zkW(<#&~C-ks+tH6ue4OkmC=@OqQ#S-JVkt2kf?%{1}zhhV+X!LSTBTBMJ|I!GOgU| zk5)}?_j$H{R}QQI62Kv5WK41Wksvo_iSiFoLi03QoHEtw`q3OzvQ-jDCx^8 zI|Q-nS!KUik-iA*{}`5TRl)`bKxcD>3-ta!X@H=?@87VDF9J$!K?ZpnsU!mW+6mVO z;{!pGt_4tn49@J@Lg^Oe?6se`Zr&R3d|-#)cC&X}EcQcB7VQRH-=KFIFHNd4%yL%< zJqY6~nCs2$xVR;PTIAnLqs6&UGRIDrzrBkOe5mRTtpFw}oq*Nc$Ui1i*N&q$X&j;v z_Ku?qj^p?3rKNEMzqiq^d|Y>k1d9dS{NZ7Kx>%h$*KC2Dp@DL!N@RFZh zWX~Uz<>=g9wtH)|(Jq2~`rYuCX;UgyJjS;pCjC!sBGH-xhRT{g{CMl3DEzEink3=5 zq!*O-dLcguCj$tR!JhP&H{sARum$Ky6&m6O9^%b(PC6R+>);g({~Eo;3ZP53rM(eh zg8QbaMzPCtgJze?hhG$rniIZCcd-yV)D=RI!R>;#yv)lyGtvvuwGS#^dDT1`oL=R< zp)aK{7ctdR%$QYwYW@tNT#n9koQj;8G@GuNG9n=0<_YvQ;`hS*<4rO2_B_fKa|6lZ z#$rcB+&uFvr+c&*b!Il&?XSRbSYazJ24B~l2Axj|gdEdq^SM*9V_t*3jV|eRZSRb# z+BzW^_5;>Q=Dl7wZ+qFSL7oGTv+q@3+||8ua|kuBwJ@!-zF2O{z}TLwiE{G023A1W zXlUO7UWY4{_?k$S_yj0tB!m0}Hn|i6CI|3Cyo$;)np7wk2Qu8W0dXnmKlzhkFd?~@ zxC39)e)J+nQ{Mw`^ozpo;Xly)+~_Zy5A)^>Zwwr0)4;KZx|Xeh`Dt;BjAve}rQ5u! zT8;8|)lY~Mg;vXy`&qI{`*|PY9i7)7k$R5+cBrZ#;TlZMg|4r8<`7!!864>~f86aF z5x=xS0Q6qv@o5kh2hF=9ZbcrQ{Oz$A{4ZtImpnI8@X^O+{@-GR_oxWd`1rQyAU!4 zsGh;XMDh1s2tW^0TafPRS1Y^3s}eN$T_@eY!4UsKMga}t$lHDXXh`2LAq37n`-wk> zm!7yTF_w)&j^h$OSH<5lOsOrZS_z;Q-9J@=LLsac%)byR*nvV8ty@VB$LF&CrlO1! ze~Njlte5|S-c6poV%Jt(0(igqS{-hRoU-HIw~4|y{h)F99w82tJz#%wvT3-sUU@FB z;HiZf*Jvx_SQW!OU&W9AsCNCsKkg_6iMN2jWW@CD+(Xt8A3>|u`vmg?^eT55O z9N(%uKU_x9tG-HdQ8#lti6%!h%gJg`W8~}3whdtlaF=W9xS5KyYEq|ml9m1n6L zk%O!0>}aqlgMKIfLQg^NAw2WTCF7)RYq*q|*6ns`i1T0ljMt0^=$=1gi8(3t|8bqf zU%-UE9?zEnH+A$L;X9|M9FkSEG1&NG^hW z!i!KVB$qL(T31wZ0E0VlahEPq@%nxIj0-CO7>V(i1yJZ+7QzFB2qHzVB1fe@-S$|o zs{gqHcu@kwJ^?Osyk9h`yIs~Clf$=WEH5|C4>cm}d-x`~imgW5e*A9smoOpDw9>Rb zo{&gL`hMK_o!TR>(Vf|Sr7n$2lr3gC(%I#h!-J(U@#c=IQ(@$8NFpaOSso0~+-xjW z;)PYDq4`<8fjL`g-KueGo`H8@MnSxig33eKg5JtXpMmp1E#iumgR1I zh52Kv48CR;c{GA~i054^cN(WxjTM-J7V-pWiS`Zun}Kt%q(5;?6rtgF>yqmszjC}c zv_VU#V8a`sghfhYu~QDBXo)?NZyH5}Cts9)oCT1jFBTRvH3rZRqTaXzDJEX@QR)wy z`O_tB@i*QoszGq;iSbvV!doD4?0D^D58a?Jf5PzUGI2~oDDjP&2~4+<1@8^pYdB#5G|AG~;9#<{GO=#Qy?QIe3cTK}>jbS{kqDn%z%7 zP81mud|avzT+9mN^xoRq+B^L_ilm_IK><=@hcDTf@n+jcqyP_dF?YU2@979Q2B@@* z83zW}dmvFeS;>rX!!ECx+c~kro1UH^*H8$QLQVrm8x353>!p-uCx5DTzr^c2R{_Ky zXb%>NzV-{CCda`?{t)YW&Ew_{5F^hZOXwEOlrbesquKgez+e2z{lC@P{_q>^~_7Fa<}Jasi&`8=Ex3W=f911J@)Ed_m^|Dt^yC6$<0ME67U zI=|*uQQg$Zt>SXhuV_=3yWe?sh(9z%gL^B(FE!|sD;7G|6TS71>oI)YNkIM_c z^dC;=H6g{=m;^GqD#a+$*RIMMr~8U*Tg=D&lHc*<7b|+ipkuP^4ugKYacggs+=F=7 zE(zZlj{q+DmnI~J$=7#V5HJ|BvLGk=ZBuZ-1 zbK5<6tB%R%i*j{IuNOBZ(hwbwH-G0H-HLDLQ!|bCs%moXNYBPoig(@5>r5YCx`5LT zS7P|G7CWk9F#8M934N-{u>>{hVska~$C$pYoHo{xCw>?r>O_NliEITOWg5AlDm6QrH(vrB^gzWiIt1$# zjJrqQpe@U(X&H0Xu%D)6jZLSo04F|Eu`+S&S(&Af|Hwu%2<#h-xcG;VssGq$Pa*~c zsFuV~SGBZanYCL9(m0+gEr0|Rw`roFXlZ?$)|}iozKr}`H&Q#;0WWyAIH4#E;nuE$ zib31x29hvugEYugxqRXYN?Wi+AWHL z7UJS^!P!=~dsSS}RtdL0HnhOKF6t%8Kjdr%jRsPg0Fj+;{bb4mxZ%j5W7>b})Ha?CZ2{;?+? zJ4i+FH4)HdNXgoAEZ=X!)l1VU-XSbQd|3%!b7t=}H(t3(l6V1&gizI{IZL~Xgsa(CwxdH#Y>lOOZ6+HT1yq|rPuY3FA0*)kFVWQq7fPovpC&YkfW zrhGE|bug1U2-|OUo|#&|UN%Ff{W1Wp4?c9dyV4qRLjH=(EXekhey{X%Dgn^Q>C*%; zC#(nW%K`p_%;u+sYcXL&Vi^m~A#eKoY(;qZf98j&3*q@V@V^|oT)b5uFX5Wc?j{u1 zr@!{Fu5%U-u1(5?rw{NMH=gQrsNN&#v?OtDKq|%+O9dZjA|w}>UmCbDvuD_8hAUVP zaA#wlzAPl69CQ3Qs4Odq9O4>_N-A*!4y;mPV+V0)irZ^k+&1&`4OuT zE)HpbVGQ%}Hx04B;Wr>6lX#9OgEogT?^5-<2ZeNE2gFn}s0ZTtc@Pv5FNXhuj?UsZ z?n>-}yqVjIq)9de>#55hfb_t~n1uF&zL~p#z4hhs zNmX@MMUd49_TMXa8u4gy4ySxuxSbdb{p$&^=>8{E2DbkrsO%C{dw}bvs+9 z)f6u^htiXYHDH=9(NFwMSW1mWgcBFyjuVt%;_5^ZwJHd%&hm&s|D~ey%U7ixWT=3c z+fyD$_tVdwteG~0iYMk71kpVnFIXU+mTA$&RUp*_<+<}l$DT2+GozrXU~+9O{Yo%W zNWlpdAppvNiwuJl)e%K zDQ^y|*l5BcHF%5?MoSd_1IkBn*ebVL*CD|A3d=OC^8c7R%b>cNC0qj`NN{%v8VK(0 zt_iLif`;JkEL?)?#wEDBySqamxVt;S?*7jC?z#8Zu3A;QYOR@`nVzTL?uM6V(>gLU zp8gcKsGLPHxG8S3?F&};&M~QJWB&`)&HfiTO&rK7WnJfs1i$DIs%YT|1D-xaK49+< ztIcSP96mniaz=hG8}pqw1|8@ey)D&Lm6a{N5*qk=;OQ|1&!z^c?d3*#mzx!!CMCSz zv0+dZ`YAY1wJm4ce9j%p+1J1*AGKr|?&dSQ3ZxAK4lJFv=!umZY64%jAbLaU^EKhH zFquZn0dPnG;XNMjw?_ydJiY5xFDBFS(mdL5m!ONM8;LeHb6#ts3xKY46Q$G-*d2fg zP?!wBE9Y6n`KM?NS!OXu3}KsYC<08%PhZ{;+7unnV(|Y9U*!qFD9|^#oKn%G84^nR zU47{P$>Jo>kM-%Fx}yr{q7drT|?V%Ou_QQH~CC7HZO>Ycb z;}DXI?3!Kc&tsQaIU>CB7eMx}W5=$ev}N1B0v?6#81W_2{$Px+t<7`#@gj7SGeGf9 zerK5!2bwaUP#Bg9{*IS8LOQLSa?CFh86t+#Qhp0>Bdg2G*+*x{$MdFy=d1La$Raq& zVMCWv@P+eBY)5zN(Xl7an@;FD+V^jCnW52oHd0UbPlxlKi1OL}ZmH@I^@&o;oD_Y`HOTF zM254)tN)HIb~zDZyx5?V?_(8JWrSFH$KOOJfE1=$cMapPahA-<{jKM+)M;*#EI^(8 zHjV>w&a#j|S3_k)NsmQCis$Ub$OYSrK+5WDL;d(*1}XYNxA@6kh0d5kg!OX5<4k+x z?=_jZ4Gz3Z+o@ATqi?|XVV@H`@;jF?5e@|TsmpOq^e4i)Z<0~UH*x8Mx1xXAZqB>Z7TVhpi!KbiS!hkdv^pKQqN zy{^s7gkc%#Mf&h3G3IF<}qQfU6alu_}9>o_Y8uGcyyl zmVNP$>FW7%#@SJcZljX=twL6Z^JtGQ<@918kJ~+hYZauwN} z{8VvVLGmR!Np@2j!mB^8lO{e@dXjkR!=O7tY=jiU(xnX=>r{KAm1$)e; z=A3cyb9#yjyz-uyposScTZP9(BPI_N+alFUGJVa13&XV3_h6Wyo$hF~x)Ekp7b8Iy z(>i;LtA&Axk)Q3!%?DS$5)E87sP%gZh6=xjtX+RdydIMmcQ#w1g#tfpz9llMvQ845 z$HHpoIKTwn3p497F{3EiXPD!S)z1#6T?ewgG^quW~s^o3~ZYUH6b_mc{760L}1( zdb0R2C-Gb!0c5SfzHe+bMc&S{iK2D+T#G4EgeDK2gQwF9m+5aGsc_qyHYq1V8mv(< z*G3b67-?5_l@Z1d9|H82vGCK zm1s!r8KA>%SFU+zSMXFZ3-9q)MZC-n6RV8FKf}N5qoG?)y{L$g{iUe;klZV-a*aQ( zW#s+k@*!8HddGUyz84dkNq4rP1m`44n1JG{7)V%jf4>oMaFnX{E?)zwE}pATouSw)=RA!oI2ls_LT>gI7y zl0s!Pp$VWdUmgaOcuIaEDtVS6xl>1;`e)WN9zG1btq16FcWALCou2A(9@X#{&@v1* zFEsxsRP8JfHn`O5hnoKie9ijc*CClLu)ui3NK@hx%NxS(ViWF{ye}N*`|WAM3D@|I*hb-xGp<1|-xErt0U#R1w#^oH^PRo= z?S(jD@%K4%Aylj9FFogslgk>bYa0#am>%#{#Q|Ox%czQ)G(<9$=Zppv^Y%|DOy2lJ zo0MKW!>%g{leNA+-6F59=Jwo|bytd}ElN%wjfJ z)gyd4_!gL9hd!X;1wc_1n?vs=ZgeNbYp^IkdlS)_07ixl0)%sCakr|%owZC6U2TKjsn{}Hav}GSDzg#sR zVfv#+JU+49W#n-?&dWCXWUOrsGPL#I9{)Axom5|yx-CZk0wC~EW+Qwtk5EJX|Au~o z1!l8xedfp9aEjM_{K`&N#sS~T&g}Kl;dVV#(k5JkmCJ|*?t=Iz_$M03N6DH}2u!y4 zIajo67bk{4Yqe&Ya}VBJ+bO60=t=D?QqHNto(*+vMoCNP(7;@PnkSld|iwOb=_h)2?Ye&l*KId<@%b2Y$n ztT<^ObXujRvLjK+O>xir;l?y5r8 zqDQnZg@|W#&dh(8=6^n0gcZYZOajdwD_pxCPhSX3sseIKqNqHtYYb_Fi{H^s!bv|GW-zqAT?IC)M{Dvs z9MOyVYo@jBG5Zb6<-db5H>%)Ej%i*wvp-Ax;pIMm%n1~9AN{kld?JFyhSuTI&q4kQ z`7UhW-w70l0%^!vZS4%iS2APB|8BazIFcvQcAd$mO|>!Cm7FYK@JM}i@7qhwlzuaL zt0t?a1ShE>w_&fo#F#3+us@;V|q~Z&x}@4FGEA{%8b4S16vDjNH0~gR$IV<`QT>-+xB8K%v3t@|5@S}galZ> z#k}DPfk%$G@%;CoP*!gGZ7ka6;otl2q4`A9)n1LJT5BhsA|hu%IqA+%lRVmj>a&Nw z1d{f{kS2ZI4d=mQ8$l7QNX57^+o9#(X@s+BtN0F!-;9TTmx^!AqNvzQ8s9~oD3Y5r zTQ=c^Da%EEZA@O?N-NxYUw?)mhf5a46>&FK#=iW2%MKhATM&!8D@?^P^Eh>#IZV{Q z=R7+#+YGA(%wz>+6}d?(@xzU)jN6EKE0GQI7%sl6@-}p%+>zA_5Qj}iN!H(cT)?kwD`lr$VefmYLWcE|sXkEiH26vlOO*o+Rc+-|TBk-0? zdq~qkcGWK(ne-~=E49xUW1^nOC>0pU0|*J4r4z$;ThIz;aP{Ks&0(}Z%tVy*g<@c) z2}piUlfAJ+mv*Q1ui)3unsqa*Uj^&^(0$SqAVxzm_4KQ-`T@=Q$4XbJ^T37S@bOmp zuJ@cqNwz<24(sxJqigrBFJ+Y9Xwx*CKB`aVt+u_4MD)8 z(soR_XZernh^1<2sY#B!QJUk<(Z}0-4(UTCOc>B!7Pt*GzMZ_k=t>zhrq1n=Vs2#` z^re%CZlU>Tz$H-{vJehLMKH1j0j(m+|8L_Tf%WTKw4ZT)Omq+9%9x7C{pBO0%Ov1tH?7;5|ZLXGK&gI`)Xj7Z~kz9*Zowt#iH=-!U+s-~`2m2XpvB zkz^$9-BCG=TE`WY*N$f*!%{imT_}cpt{S&$al6|Py)y63;3jXWtI)8qwBbl}aV2a% zMU_RaxnC>`21?HBkYmaD*QJ8Hk#0+krT**;0f%R$6$wGosQGMK?*42j_pzP4P0B5V zr^|Buoq|3QYVV#L5}sJrQi+oqLZ*J7E8%~}aUXE!Z8*-gnuR#NHW$0eZ>R%4bdd9c zKRM(cW)15kZ)LwEj9xtO?#jZ7Bc>}D?w=<(S}yoX!YeLDw3|F!NMG4lFbsvS&lA9k z{3GZeJhW&wBk}tt)>c+%Hg28Cl%Re{1nUV`;l5t9vY_X;U z=+8N>6<}Q}uRx6n53Kz0U&pEbV;&B?30(q>`)4(o8NAL?wz??SxUQMOm$Ir<+S-Y( zD^3PZVNR<~pBwxps|5=GdbrW)uYI9qt|yC^p^*IQ;`?m!WM1Dm96B+}3uz)AEW2+m z3~@!-sf9>kk5D5FX3tI^dH6gwUT+`qMlEijgK*{Z)1UvsOr-2O;&--U?t6KW`SeB`3I0RgmFOmIIF&8B#BS8sA)D@s^FHy@qn@+H zyvzz%j#pDZHK*M-8@?8@;H5fWz53gB-VGt$z!$Twg8v4Lk4Odi>cUou^#V7a`>kaY zCvjj-AMDo+Y+Ux>)HessyuE+O%0o(X%9>qx2)m45_*n) zTv_R?z;`T4DKi{?jPTLpF7s=PwxOW4>BMg-{6#uKeUEoH;wx@3DFFBha|XuSCvK$A z3!gRXqg!cG{^U_#rzaS;(F%7x43%NJGKr_nr~5$8H}JkVo^DLk-!d6bEZt(;jAUS3 zG$JjKN(5%mY)mArb}%y0TEk+pfh4hTX=xV$gfJU$D1dGatj*KSFE{=~GvOWfSSksU zVu>py-{Kf^#kdZo0yZNF(pW^FAFOxRk!DEq2oN7oBf2$v5^D(Zw7E}A%M_RS{O*Qh`Yu#9y5%$(qpDmzdI8Fk2wx!>$L97p)L0Q6e6j0mBtN4FhaPZmy^ZYLZ#8xs$KK}MHIUNyfv{!v2)p4ISHwvF4)!5{P%! zQXSyl2057kzC(q%Ks*KPyIa<9Ft^Q3`k_GY<+5y;m|-@B;X}Ugm$ag#{Quks|Lb&C zqfa3uL=tf@mr;>EKrZkF2+T1YneRctIF)U%JxHK1@*S*;qMs9~lrZhkZXS;!_*8_! z-~Sq4@5ztl_)}fYe4dk)xpPiK=uBrLS9}J@g*ks=qcI_(d+5<9piMFS;wU7?6ln|k zUG4|o_G$5!agfRF6FGaYgBF@)LW~eE!9K*p(7VS;dwIV4xjT~V2WdRv-bJX@a*KML zE|)Aml^Jp0Y1k$umD3Z6%D0s{cx!}Tl^m7IZuAa^yzP|R7j}{Hd3CW%GKDKOrj#eZ z`$J?ljrQd^exMSm>@SdcW|!w48jFCshGIdRnZ@nnbuBO4FRyHsr{t4$ zp}8EqjLPR+!oTlsjWYH|7f`5VKNJoGEYV2$AgqOSMvNO7-rR+EXbK~lQtGmi6jtfD z4=sAdgoP?Bma_hp4QqO|y3OaXE`@+CO5%4vd38-4hGP^F=JZk%^d7nFDx?%}zgAlg z9Uf(FIVg&=28;Rg^I)`W%`ePA#&d4={4|s1$QZpg1o^YaeRVJLf`If+R7Yoky-mqL z+BfjsfxJfpso2U`ZFK!;Oyaru_&kA|HhZsRs4p=;D@`4q@Y3kdP%0K1g0p~4(TTdL zr)DGo1O572UE(0(%HU5)->dwd&!>Z&%mORcCJY_QLU5Col|*J4bb3py(x9OBw9C~g z6ov=?Pzhm!54U)9cY2Itj*H+N|&ey<!cY%sgU4T^(En-YO&A>8CSw+_ zbb&tuD$exhSm%pfk@;S<9wJ=Qq4#pweNHBqBufSA!n{-Tov@b{AeFO_GtXeIQLq#$ zn0%;lgqAdZ1HRN@5KktrLRpV_`aP1VaMCxxbdX6K#a5!$&K$V2jUo!zT@2u?NxxpP z-L#Oq%Br@FkEDyHsm(OXj1Mg9dqA;Sjmu4v@PW-?-kL4g*E?2+5#%-SCIBi8&&)e* zSSRLPcAjnq7`5e>k6G6{pI-SL`d{05X{a^`R-9+Yb@2Vu!wX$^ z`LVi5N}q`c8v^Rm#iq``kClJblr5_hu z&XNRizcChUzaMHGjGpJxnBk;D`$B>UAupfEVAYVPFk$Y4z+TVLB!Iz>i^}h;BAcAt z2hzeZPza=-DU>&+arJ6-FfYs$y0E5Y=`(#)dTcv#Oh-SykNuH|+vVWHLW?DZ%V&-+)O*>JeZmgnlNIX=O7!BDR$WNzgV9e{fSGN}gU}DtPZl*M4tA z?na=?PI=){_BxG5jQ~*0VqPl@WWGzP@T=IqKrIJOO)vMuppOURuBS>8F{!^<>y(iC zY-$&KUAkKjH+82fP>TklAs8O=Z5I=QiOt!t56OKyDkz{~(LR5^G3@ z5OeA+(f6`JV>vWVB!6ke!F`%-w5!4o(dpn%9;5^IC}LS$@FZN@Go#p7vNO-B&xw2cH$P$@=u9dX;SPupAe zGyITpuxnMEVv_;uNzY*QXmh-)l|esLeT{BDFJ)KLBGBO8T1T|kQ9bu9Fl5842_|#Y z`OuEm?B5FK7%~EliQmHZwhkv3j;Jjl`z8tO>r$O72hBF_sbB`O=@O_ zIfSG_epkj%d5$`%FPZn(0x$$gOV&#zRvKo#h}T@~c~C=${F?;=H#h7b+NC~&Jm;%X zMBU70?b03(B>!y`5Bw?CQ<$LW{3(64LnPvV>_+v3z4HsZAu zN=QA(2Q8;ch1#>;B~#9VTpO&HT5T=!h>xd736D((mzNO4S`l~BGXgg*d7_Nh5gi$h zD+4th=z=8j5*I3)m6kR~#jnz?`7+?StYcW(8pFUONr^M*&v?^dfL)#IG?y2;BJf|s`N%7 zo&E2jA_=*J?=EwXut9nf{|mlvcwue^w&Lub1~2CVI+@{=3-iyHmh>*?;t6F9C9xNA z`y`jpJzT?Z`KRe@KQ8n#cW%f)9eUI^ble8}&~G-bF%;?Zp|(hu6`+&4<=iLn8)X)+ zIAtGmQ17SDv?gSRw;XWsB$^8c{&9hilUg{wWv}}Y6a%a{VJ>x3&-k3!RLF;tdZD*+ zT@a2&8Qh`P-s#}y&Yecx+0f@&yBZo(d*JTHqbJ<~aI88h3rdR`uZwRhdnhh{cK)vN z=M#ViZKy&Qn>VH)Y(i+CCO;i%CzR^)`p5x z%K)CYya$-m+NKu_wr%LRcvZk!WbJazm%4N&@6JT2u2vAZr94P8Z9%)8I;XPkxgF~s zLzWDhW5&!}0#KgY)GflewTg0Jept{CVH$YFkKe8mO_F0dnf8^0Na*$dVSXeqKcN^& z8suvKUvNPXW(khZEoPSNHG|Lurxuvh8bD*75R;_DxIpj8f5?y7D40_*YHSH0s~9yg zZIh$Lq>BHu<=a|xyjHuk$L^lAse^s0u7WP#?VMv?n8sBF5i0(zPH4l%Tx=Lm)bUn0 zdW^rANpUgEI_8S#a<~;oD4)`uK0YreZ5LR^5qrxDXN^@h^M4>jo>V0X@B$u_CxrCf z5YlY_3_YSQj7z>F|1STLFUe-9|L;3oRx!clAp72@^t5W_RjiHEKmOx6pRS{c)ujWPv8N$HJBW%B(E|~bc{Vi z?vbA)=EWqqans@*94Q(auOxS{(C+hh21IGoM%I2b0T!O^bTc%6YOm`quDqTPf)O_Zk`d>}?X1rur0jn17>TT0SG^%)G<256U9}WrKFGFSu;a zcGX^sq|?%G+?$np?ve-pKFxrbtySGKFfNwBp#A9D_w*r<>Hos;5C3JLO8kf8|2Ncx zECL$`1i-ofNK}0_!EixyE$NN#4jz-8HRB7GPYM|polkLE+W8&8pgQCfL!92zqISxz zUYJ1xpYLji3JZZ1hs7xQHSEUmiQTvs9{LFa$hEkezwL<*6W(>d z*lcN9E%(Iwdg(f6oogoo2cP_#y*eB#jYlj2pHh!_d~Vt3ZK*-|x4Ie2#61oTFU~a= z7uOkc(}1+P;2Rnyn=3YD@lOX_rAfBv7pMf>-i}{ALZ#F6ikvcnh*;)5%Q~4)omW{B zr*U91_X7s2w?+^WW&g4N5C7QzhX}c_|Jgr;Bwb!&afl5w7GtOJ4C4X_h59k4A7Pit_z7`aSK3Tvus%}lNar)`)?O;Y?110;;&sVr4Z^dgd<+A!SBylp0pv1rX| zYR*^c{9a|#Sl8KOnYx%U{oyILG9R%R^`}~RE=Y0O$Fpw zOM&siY_S7Je-`YuQU3-wkz-ZehRi^;-?Esj3Cz;0EyB5pdDi~#8o2E^Ph`aBzO>)g zk0knC>u48ATq4*0?1tt@9^BzzTlu1OndO{RT5xcC=+;cX^Kte$^(GtxY`W(@rYv^3 zLU_--5F8@T_NyynPoJxsby)!I?)PTUOkT+|GYj!&RRUn-gP+P|NX?HjHmQ#+T^L_r z51`Hs=LP;}BsXw=RHS#t`{~Crs26?$h+;G?7ck35Eft-^SUEq`q$s3k4Pcp7azQUm zPa~zA$}BG{XR4ORL3rjhPTN|&RI~$8 zz$kVUD4|BQkppD}}Mt%`f?SV{Yrkj*Vx9<~8Cz zvxztfZ*f~SlSJx{=%_3zTJg;?r)}9evY5A2j&b`4^bpurA5QnwYPX5Y+1$ZV3^3^w z)d5zn)c(OIDKX4G8>c-h%~QGHUQB&E0K{5?X2Qh>BDww|L%FzpJaaX)^z@N??~o91c|%>V%Z#> zdQW(8BRc;Z@=vf<=%IS9xm3X%d*h-;zR@1BX|{SvtJ%Vitm@Om72xrwD3EpHFvZI@^kIG6ntJenQTn>RZwom<4m%!%WtQ!iAfrV zmWp-|rUjZvX^qtC9yaRi{^_dGiBl38pR6!N4ixI=oE-?I2Y1fQn0( z;yu4}UjIIDIy`8}`*MR%f>u1>? zFc3X*6Kj8|mW2w$nfw{`56zygrw<~4r9g-xy~gL@S8a+$6LXtYPw_!oou8-vdeT;E zVn~7Bg|_em5}g$C*1<$jsDwQE0T=B`}%qz_IggvfvPoSSinSu%mQM(-F0mUfk;efT6OAGya9RKrq)>%T% z)BO*AC!$fzT+>7EcTqZ`FZ)k2-I32L$ zjrieRtLyH>%Z=VC+@{aFp(f2J5-`@K+N6{5nGm~Kx&p6{!a$aS^-!WIUeaiMQFI1B z^XETDCM-+1P{kR6A=W3o&Rf0SGYmIfr)_vIUyd0;McUD`?u;^*(en!y#urunG}66Q z3@;GS4SrAl2?-}I?Iz(Fil{4PkQdnR-71hmm&>Kz5gEhN>0EHIvPAjoNzJttzUw16 z=W59k9%V(Dw(Pk|#Hgx3^wVd+!=tc^>QI}E^yQAGIAv({~bTEFK zd5DbH2~XtM4Vgeu?B}pOGwb3?BDP^PF{xhSPCAY;(J128Hpw52*0FZ3;6Bk2QZg<* zf7cc2M3mtC#UlQuO>xASm{S21Tiy*M|9C1|!LgG**R4R<1)$=$jBQ*qVhpI7;2p;dHPWLnT?} zU8)nk!IZOMVh^#zClLLQ~$HlC^*^g*irT52<&k=!-g_x%zAQBX&tO-497el&$rH5>>!MW z*VC?E??YTTsS?3v(_>7sKVkfaCWpmcQqi&+6e zQYKi=tLSqR?)AdAijEl{1yUg|2~;QJ2pDj2lkJP506=f-Vl^A8>s>Lhhz6(9H*xl@ zHyGL|>rVINLJ0lgcp9zs@|O$|_bp}?pCV=PsT1}SAsG(XsEi5nLUE+r`{bm1)Xw#o zTv2HtH7^AG4p@Q4oD9}E?ur^QrokrSW6Va7n*NvR+^$XPvC;8lvX$}7>sXUZh<-v? zcjIsz08i7}RjSVb=%HJn=YdPqn2o|6p0h+nH$NxR(61gGj%S2H%zsiZ+Qq*akoBGy zyps1;t4RL`lV>ALVcwTfh{z*XsTg4Yc(B6auOlSxRIgLJrfjRo)%H7bl9i9X0#DrZbGb5?P!$myQY*r~c;z{BH2XO^A z>rkXVxOasN{*uz_2~dP&6nk0;v=NA^SY`4b`Z7bq{-+5G^YU*s@AntALl1FYyD zBgI3^+FH>3%ZA+x6~}Jvb1|~XYiJavh7uUqP}hFl)X9HCrAW*2JREJ*Xiz5pB&OhqE-ueYVptdY7 z--pl*{`@zu|2AT24y@F!M=8V;sT;<-7fiwknh5A7@(!6vr#_u zG&gr-72#oAU-?obhCzb5iH8S&!R~csM!6)OT7zUVx7faj- zYwL8&dudz8+paviE5fQpoNm4DhxCX`2Yc`Gz3J~21;QD%g0_A!e7@&Vaz?1k&yvoZ zfAV|?xMVQ0O&94Txnwc2B>_0KVpRleXo#i)7g~ZJ=O+#MJy2YFup0bB+tgR6E{I3U(xEY-yEjCSnc9qQ^x8YgV;XK>3 zCCW;_FEge*2)l2!USa6~kL1ze;d4Z9DVsk}3dp4btwM(mM_HK8$L)tO`b}74vw+qp8+Ls*e ziEL-uPJ3+IXg0xs!JLdmu8;?j3vl9aSYTcjdo#-p9WpXV7LP7qgoI<=%-^OP@4lr4 zr&$^Iv(T3pK_{0b_`6fL*kQ{V+_8PY<(JD9c1#a**@-ywxQ$o{?5=jF^NRFvKjHUK zBbr5aHB90O9Y;7(G<=S^sPww|cBvc4N-jEj0sn}wv)%xZ#*v-cCMu!-kwP?PdhH*m zzXFq0;H=uRBcjz+lvXAlybrGuV1Lsifi`;7{hTz?UoN;!fyxFV+NU{JHO_cBbmJ7L z?WlH)KB+;-u=_Y9Gtgjl((es>mZEZ5SV&!WlQGuBi@5cCyq`|190{#$n+lK2deJ!$ z6&HTBLKX=uE@5PM-yY{}4GVWU@+=!qo$5ty_*`e1kH@{G!=WwJMFxl#02VuTeTxkC zTJMZ0!=1bcx1af2?}yo?9eb%bLj13AylAIeoAo3iOlyg~zg8Y2yT4Zj;w54uK|>_J zS~7dnxkkQw&{EW392k!XLddhSmi&W(B7BJbh1BhQkTp_&I|xZ#NLM#~H9jRD)R43K zoz8I1+%dOdP;KOUC3@|c*6^dcvh0K>Ov`POOpqqN>e<%c0+C#Brv$jla{GS%ktCAu z`R13-HFQIXk{nJb+W;IVR?rw3EG{$yV`N4oPwsoSQfJ?~hp~b3ROw`p@bIK?+f|tY z5Wt97@-kBm(y)l#A(eWC}Zh)J~$&oz;cz|qi1)_&kAf}d8D5n_kCEVpb?$sj4TxJ{%x zdZxq8r*Q)As@5a!&N%cvwEl=n@@FxGwTg(4R9|r3R~a9T^*3+CJi%{~3vMTc=AQ=? zVOmQx1#h5+F071_+(V@|hQ0;LX?^}0Wie-*aDMuSR#?zZMi(;6uHz~U$?YWJG(c2K(`lk??zxHi? zQ04T^cGQLU=c5gEYb(CWMzWWejJGJ!J-J(h+RI~97x(?|0%iuklpc+PoJSQwGFn&q*b?^sxVm$AE0%k6rMm@MKjh zu)T}O9_`cObqQDJo@hh3499g?e~Y|PK$;PeNfS(VTx^S7YwLBN>xSs?ruVPL9 zz60#`l@L4sLW()<=e^aM15wh8Z$9TFG0=CXTWnr&S~q`du$V3@-1g`Ox$vE<^sO8b zNHe<=&xx32`>!W^jse%Kq=H7_&+n8)Ku#~ug>g_AnHb(H_)i}lr~h`zb!Or&*6Q|* zuGL%PEu(`v)WXxgm-)FJP~}uix~-0~8h_P*DBR1(J$T8I5N3@R?N`W=MUr7FL}fJK zxoziE%B+kDZ#4v-A{q#ybBegupD*(2umDsRq6Mx zAyFpzrWrAY;_JdfHgN*%9_cQ!jrZ%KX3Z(3Z!AD;9YNB=(-v!SpB{DWia|b>@!ROK zs|72;=~5Nh=oB&!--#%RsZo{vD&k?V7%}+4GJ_>6Bwb=g`X&>WGJx(;n3u?3gYqULr)V`CqxF zFN9R8MZWtVDMY>1;?+jOqGqb?;t|M;g&Hx zjGMaC%AWiwYgA_Xn}+tE`YW!KE_t-wFN}fiHYf8~U6Zr= zJJy?0CVvG+;~9;?`a&;DS=Ks0R_@+{&49jAF?&`Q4q~+7H66DY+Cs>Ou9Zle|L6t( zBgrR&fW%Y4yMRICAMq?CEFKL7BlT-g(b2h*irc+?N85fYVXAeuoM8}CP2YbpekRNpYvuc+{kH(YgCWCrY%1|_QzcmX&f6r}b(q#pJoS-ce3 zRB$`+*g9S~48dI7LcLai4tdw;EzYUu!E6z|)Wvn;f99X=R3x-tV-K<$Ywgw};;sr$ z6Zm>iKFagcW=ZNr+u5d?3|{9w>8nMPp;^1ZQ=5G8`tOU9j6G^_k zUIEiclgX<+vdv%hX9{{Dj z0oI0|DZL3dn>Jy>+0!Uqu9aDDmsF1mdp&NWc+OlFn*#S<#_d}NUYFzD(q&zQJrC`F zG6?=-2=9;}jw5Q>t6KSgRMil}fc6)uX<1U0hv;@o+enGL++z7og3IKKt*%NMCw`#} zR`CYo$!eh0j4P#i$g5BR@cDn!`C~jW);CfWTq+k$KHwgT49dPS#4SY-=7K7dX{!$r z`im%Yc_#gS7p8|>RLU`%UKiCV{C?`-g0dBho{3pIZCf*1aO77A$mifgwdMg#K z{FrW{hDjyuHHLppy?pJ+uvA4Xuh}l+9XRI0z|M_vVAyDPCGenr!%CgW^n%2R#Kd!^cds@c zLv$6Frb6Jn9GQ~bdzx{tS0+rh+eetPd>8>%uRyBHEs1mXf-I!5daX(()_Wb=W4mQq zsaCBCC9Z|vqxM+PN4Fr`!p?++JwqQicVEGUiwrfn)&!e>_u)?fY?~L509Ymj{V(2( z4=EpJHXbWsU0(XTF9Zdwf{{H=9#!!}2E$Ql0CrQ<$K;EnU$&r5@M+_N8% zW~1i=pk4BZ@AO90lJ^ZcWWvLbYO1}+U=R3WqHL&G-Kr$^)2-9$pL)XUkCm*eRC-*T z4cxx&sbJpr`(_Y1D2pTqF?CX?a`C}$!|jGsx1hM3&Qwa`5iEUKC!Z#$U8(QRuMQia z2_qU>JCnrwVJuY%2xk#mucW(nAPm&dT-2O&7{=yWw}6aWc_z1dL-tGQKWFSZlW5~1 z)7CL^x&Nfn2{uzXw>V4@Pw5kYzqk1l(>xO~UMtkS+CIBhl7h7NF2-53FS*@ZSGi4j z{C5d`SSI^qdh2@n`V@}@T=R`#I(&_joHM9#<#nPm1-RVHjXjkb_mxr#vMbbi5-wQ`^a z$*%j)xM#mnK#;)evg?S$zcYhBgy35iw&DZ6+pt_v@Mw^<>BQo1O*s4f0z{C06nDNF z0tS-bgnk4@6d~O26cr^v5(aP5_j!M5s^2_gS7!@n>zYP#H_9x`FX-qyC_EV$OUdQh zhcO@o0m*=0I$MGE_m7w8%_t9iF%@B~#`(uv@|+HRB`7Wy)w1!_AB_4x=`XB)LW~K0 zlvu#)xF)@vy;i|nJTzN5_>&=o6&Z<{0dTE0=o5yy{=??evhpjA2h#N8AGF^yD#alf zPq`238^(Ghh0;nzCY_BkHP%O#Y+XE@v|JrAlr!K-yZ?;JXMH!jSYZ>>qRouQGD*?+ z4a7GFAC-0{FPyZ%)y~vG9=cMaB|I-wNibvhHuLe++U>I2+hPB1u0XS;-9X6+(j!kj zDahL3Wy71mnXmp&+}i6_3O=#5z9sNVS!VWvq*PfN#HcX4?BP5tEEntTk`@ zuG4pSNR_s{#ytOp@hsYyzP-II#~*gj^Tw-fIvu<h?rv>`1mcqpg< zETK&>aJ{NkV$A#xSoi-gSdXJi_c1&;IZ1F65Mm+X z^VLZtp82GQIt;j_11tO}gD#{VAG@z&TTwU-pnDC2k5 z(P8XBDiQtQ{T_DPDB!-Zg7(i!8HS<`QB^>zw#*L~pR;hxv_=tK5FwE>SZ-v^}2I z6Cs%@G=>M^k5}?OeXUZMVtQU=C4Q))!M+7z7jCJG1RjF|#_D&^q>W6hG+ud_R@bog zs;{aVq;Byo)KDul=-{$-Vka# zeEGp~e^$ks(kDXnGjim}xU%Bu8wW_ePNO8)3dfbc3Ed)h7l%fVtbSWjObgtwxz5 zM1ebX034>C$VLFHV06e24orOlwE|bL1iGK-2J8de3AzL+?RKgPnm@eU*t@M&SbR@- z32dhkx!H6jo%hscS!q6$Wn$T^tz#+R0evl=W4y;&KdD?{)edp|3LMt^BPhlQ9if@ zFW(S@m;iE4EY5n&EDl*CEhDR&&{?FatViF%x}rMtU9 z1nCgz?gr`Z?gsgm``P<@kNL5VHUDPrnCm|4^1&v&?=Aftzi^YTy;d%aY1%bMDr2|~ zt`1U>HA?rgd5W@O5v;Sr*auz~ueq11VQnq?g09Ih37mg0?DoSv7nunR!ksWOlMxtg z56Gj!rYG)~s?P~yrsU6+_jUlJxn9ANZm4VDs6M|SV07=Y(jj3&%Bi4wp7dlAA*O26 zI#?dRkHRk{m~jy|dQoHP3=;h@W398ih;4i#hm+D`hU1QuWJjE%Sb)e}=uKyi5Jebq zIz+oZsCvdpoY(fNWDzBHoLA<3;e*&ywf%#vtbc0^IVVVH)&}kr&1Q|NOMx(W0Z}jr zKXzu`O_H68qCIrWxL?-h25$^qMDR#PbmrT_=wdT$O_}(*V@#%cnPZ``uyM&9bXOAB z{wziy_b!jIV#; z-A>=Vd`vR2&F-yEGSD5*r72+dR6r1je|sz(FtHcR_xuq9s&Q7R*BxJ~2UFS~J}SHv z;$aMx*idkpR~il7(_-G&Sst5RZn|f0z<~^^S#6-7O~{ajwgKFLWrSY_IbnUpd_)N- zR@OU49(+%ck~fc2x#UrV>r;hzxkNmayG)Es0FG}1>2=us0DhRJr-DQfDiWN2mjLS8 zFcrn)KhwnMRu3Hb8~bFLPRiwtAaQhs8Kk$i92|FNBOk1Si3by~L$a|%)2NYPt%Jcg(^gbJ|$eOqtW zOs*oemk&wVZZrvxi@rq;yOV50ChbwiKhOmzac2_p<|@dQ{Lre`!0Gh?+qqsk(v$y` zar5lfhx~MaS6Ht0CvGjuwKv_~}!@bN3(&ln%EZsOua-!9AJ$}0d}Y@AF?nUD1OW(yuMe! zWHCIk_R^))#n{e)?z!MMfo3qZ0SKPku5%=Ua!Ed(m%C(I@H`vbc?w$P5ZM(0e#Z$t62u{RLz1rBcpDfnyn`JB z-T%{~Bt354mb`aI6V=Wr^C~ z&58L@>>VLMeK_D`YQsF|dON@*R2=QeG0^+6_z$<%mmjTrVpaS2OvY`OCHVo;NNvEJpjGM|`#0+2nqnoVpadzmtnTG5L%s zkjNs`=qk0-uJ2t>eX|#pIyXP8IS}P`IyQKfPYu}1_eDM5)_ zd(mkM|NRcj)Xya$jOmORQXIyx8Ly?XQ;R4>-YubnIj zad4a#PgK~W5|SI7%MA*vdBC@O{YD`)@U`4wMan)*4d%90PW?}LFG`1{%qA)y&g}A^ z01aFq>Nh-z%ACkke&b83Mmp*J%S9{lPagx_JB10>sx4~x10MI&iaI1~Wj-_=Dt?Lq&vmvQrHUj@E^+Aq z(PrLb`!sdzr8g(25cJ*_q<*_>uphTMYEe8wuYft9M2?z(sI(#NGazm>82|DDF$&Zk znJ9f8imKr?{Jdhmv}DRFc=^|ES#dG6y(uC4f&^0W((_pUK<4#UPi(F7c!WCt9!02vn^n`mK(#J2BKz^O$GmvOdy|#`Q)+<#@+$ zw#(>zF_+dKV!CcGO7z0hrsuoC-`-u&_r*ZT#l9XHynK1Nkec5uW82=T8t(uk`5Cz{ zjS2sN&Ko>r`}nd$nkz+p?~tKhA0y--4g;`Q88QQG#5e!zs?-AY;&*ia!%#)ZMPcc4 z{Zye3O?w|nGJjdh?VkTdk^G`LB-K zQa62B<$X6|CRm*!=L3+vXUKVw0N-l7j&ceIJK^LYMNTa-gbE=B}tam`+;0TKjLDy#eo%k zEW6FTL`A`NhhIJ`=c2C%uZyHK=Toq6{NR6~Yriv+B8$;+WdzWJ=~ZuP(<4bRWDsX? zMy#$WS$B0cgf}7stIk)LN|^@fjKK+oZ>Sh5w43av zF-S{xA;oKz5j^0Ttsefva-Wm0!KgCvn;K7AIfQ+d_n%R^hh;l{gLPo<^6lG0Qf(Ex zSs6R}Q1}XiQ-Jm$t#a$E0c@djT?~TA4?ZjK7;{xd!aJ%Bi}bSofAPvOp7eed?BpYC z5HCyoIg^%2dgD8PnSJf)?<`(mJ(<@M%9s3)-qzE#Y*JaU``sL zRC=sm<_E(&BRL^@%2Rh-Tx{Hpu=|QWg~+!&kUay|jJGVw|DfvSY(7%H#44~5V6ixz z!$^iAe%^X&6?OCO%1zQpE^=g$x7ZRX$Z-4Y_P2u9@0rq^j;%ROc5Q*`^tsmCu*Y=S zAwU@;0n$^x%9P!LkV)Ia&y2kYgVSj&q-_)zda7Tm#1k%9`r))mZ!uFd^e}LuQdkXg zMo}YP?{%ICL^HKsK{0;PVe)R8o|MSe(m$2;CMFzrC~M6QHjs~oTXidMed3A{5V)lD z0m<6%Fku*OlYPrqRs|zEB#X`NE>Pp+&R=hFyeyULtRHXp{V;9q_ttzEoBzq|t>}F) z8J`u$`k3^BZp#rGJCxe=!H#`tBduiS4^J4jbkJzsS0U&onDU=hEy(qMu+D6qu@)Q4d;+-&EPuxtD z;sAzuu<1 ztex|8SNWzKNJe$zb;Ez<`^R=YrCE}3HB5JPRNARCw)o;PIP=?sXyEU;bR{L(nN6r6 zH2+pa_du2q2g>4BXlRjY>(ug^UdNtG=908HsnsW;N!xY2fqIo#{xbnsn}Nt7MrOW! zGog1e?jqolUx$FVEu$^-_c17DD+kyc- z3ohx?+?d4g?6$u_WxJVwan}07vK33M(=Xuwn-@f9WeY;BafIQK(A5MwwwZ5|It61L zUEioLdTZY$SeA!esm@uds8=PlE*-k&v~Zopjjddh!{>8<(EB*kj~4DEu_Erw*5b#p zzuWA2&0H=>e>zIh$y*1O>}cY+E7zUs#~#4ySFKx%4D(P|4ly*|@p zHB<16tF5pc<1XRw=f@Zh0te?S^G~4n5oZym%xwb}P0mjCh%so_mMP{drc^F!o!+ z>TQKjfjC5bIN+1_`Vupr{*$L8O6~xGy?MqaW+|!Z3(w9DB6>!MiCfaop905O@<_(5{R34OCPzv(0lHQ9v3B zcEO6Y#1=Bov7Qcs%Q`7WoAOMP-b7aDm=nQns^K#iSh80CLm$n32x!cs0J7HxK1gnr zPH)qJuA|~DW<^6q;z#xL9Ph#Him>s*5DiM^Ox9KZfD&dj6&EgNwzWr(H*bXB7s$r9 za=7`tT~b1>TI?NV)G9q8uX(}sF^!LGuhIXZC= z7?N<)6S1}c(JXMTSrrgY`w)eog963W8^O{kDC%9!BL49Q0E1| zbxLjE3~1vQeE-7fbl{VUpT0YKJlK5Ke4MDQ-BQ^h3t)3`X%b)7-th zi&7GhFW4Vv`HlvGf5pBqj0Q$unXj*nu^XZHmTo_f&|z8}B(%se+dzkG^m+^wUg_3cHxG;lXotj!F5?vMWP z4RA;h0Q6fwf4_O&$<2#pQ9cPSdOraBX`m;~K#B6QDt@Lva?f~yMBAs-VRR2i-(cs@ z@`l2ZkH}!Dba5&?l-K(DX&@}I8dgdN0h{1RSmj7nJ$2{eJyG=P^+e*sPSU<-N+7f% z&8EQa#Kr0sm4pQOc524ev=E3bwoYdj(JK%T4^VrE9_v2Q-D;&1?Bn5_Pg z3^6=$A-$QTBXo2cF1FY-RiC`Bs=41s_Bx5i5x@7JuG(82LWjAmJ;QlqgF)Yzkg}>H zy<=8Az2ko^jgdu^$dMUl8s3GYNsT++w23w_^hA z+mn4ShM?#99lgXP%l`bI!P=O_6vA}%AVVryuk-4BcBqc$6r=5QJCtq{4_+#fG)}RS z77xqr;-;GPy1sS4FVKf$ZBcI4+h%9Y*v;2=nqWw5o5v>{gqYCEl^V(E#NS%xKuXtv zal*(8Q~$aNj4X2}UX-z(BQW5SUCrGX3+@w16BHvDT}vWsH?G?Y`xkCPDrCi&zz+}3 zveuv9hJ%WUq`tO-m#5zAms_yD9Z4IxIS*}}09RKovddVG4GT6Je40C5E^AXVy9LV(7;c}Kl#pveoBLgBYBN-kPI0n9ohm z4T8fVDmBnOkhFY&G-Ij}lY~0b$GzMH6X-OELwgtwD5zs!jLSkxM_>FQ*+|SVXAz<) zyuhx1FE|Jw{t3d?Yrr*m;g1?~n92AYX*V7*u@u<$LhOmM8;5DMmj$fV?)DRU#C&o+ zY}M|u4!C<%V4Ggr&v6HkTF#T)DfD_nE-F;tMndxV`QhWu=Hd7(9u!o9jfKoD!woEY z9tNKsK_;ho3fT5VXb%D3c{T!6Ya?lA^v)PKgd4qdXH|+Z)U>m^5uZN9xrni2_0S}d zUY)3K7@@}$84x?*2_n))ygDLiWkl+Mui8RLC((h-mzB7cnSrqbJ?J$R9&kT`W|bUP zKL6hKKo1UHn)iywNKK{iAS)^xi>!YF=K;~hX8 zm*fcwbEC`E+L4sM0Biqp6Ok^ceLsyp`D7rHYpLb$tmR>FTaRN$TT#0Pb4Z(F&ZHZ6 zLw&sk3#XzX5w6;UE=BM&+I}|1`o0fRo$(Rpqw$RLqCakkzC{lrvGeHhT77<+8-}F5 z3yoEGq#&{Zm{`EA!hqA6cP|+`q!SSR=b@!O(}skB;TaaUfBa`+{K%Tph7Gw<9eJTl zve2mh5?XTzqRJo8;EY3V`@pcK~WScMo8)XR7F(WOD@Hq%P71BDTQtzA=9k3;JQ##MS5Q_FwZzX{6rJY!A z)>Nb8jKDe4xa2WK3KmfMv_ox^0`iSFP)Hw6Kb-9-Dm8$fMwHmuKO~zJ#xz?N;@Yl&t|jnz zdi6+om|5>P`%H)jhd?AbzGRxqgap(92-lr%Q9X&YT~`!aoS9n|37A^&;WyasrDh{#5g+_P9F@7hmh^N`}sT>yzhGT_C=%P$+x1~k+ zN>uRE%wD(ERtbm?lwKA^4WVLlDk6eXX@5txCuXR~qV%mL4QrqcS!=o{**G_|)L|vL zoaht46tZu{Ak1gCx3Q~8?AB-yQ%`xY|0Jrs_Xic*E=+?lJP>3MfGfT%`r+%&2=595 zuK&jZ9>cQYB-rnBdO`@`n5beO8fqw?dH>HV{^zx4lBxiusBHE9JzkPe*M;!jvRhOp z>;PK-4)lg@y!lE}1jy7tIMn-`aDac~GCNL%OaTAfyBpsR%Y$YhsiqR~X;Wx)o%k2+ zl_~SGf<3tM1K&&m;v`4(uOCX}8&RT@e38TdQ=&jl1){>R;^$0>7tRwAq*UoDG8b$m zh5#gneEC+~=K}A$&kGyGKay4)GQwk2S99~wp9xplraga?*tw^m`na>L#f_-iOBR*A z5dF()71!Vg6FzENizGeR2TXa3x>)ZKrl*nSu1yz~CnoZU)ru8su@eR6zUiWWWxbYV z5w5F7+mrqmN+L>+mO5kbf-+Xf08(lNhD&oWJBFOQ^|)c}+aPwA-V&94SzMzE9b}SU zIP|O*OWVxo6XS?3l}>WqXUe@)eP&3GUP*j*zUjFfME+=LQmw)yJICDX6*ZP) z{P~OzeoE_dR1Mn;Xl~5n%xO->s@owIXJRWxz- z{Kx}naP*)MVJsQZ*U%>9w^yV3~b(Lj!1=h4|wT7 zz;R?j1k@Bm^g!5M56W;4%txTT!MchOirglpd{gLgmyNP`tw$cqi@&z&^~U9MQ~YZd z$Z^GYj24q(TQG}?>Sc)m+wWB!^J1#P${4Zg;vK8O68mTP%_U6{52d~H6&f>w*vwi> z$8Z$tkvc1;aU|I&V7Yo34>ot8>%C!Xrb|!&`)VtM-AI z*gEAPW}r#q*?(g1fI6FG$Zy|y#>Mr7+^#bqj@fJRA-`k}mYpiN*h{ZOo~2wocqFU* z{7pE8{Je?E1Nz29l6Wb>4AbWKWce~i;OKp1Zp1ph85s$H9(Z$S;v?*ls@{4-k(Q;U zNchW#x!#ORuy2RFNZ%@*rB0T-!?$D3Ova`4fkij8^uP?RmonEXaT1@r%TM4)kMw$gUZ8S z0FHI!_Snd8+*cj)*cSMpyD(23loxap{6gc{AA* zT43|%>b&^X^#r$l;`~Neio*6zQ`9Ap3-{=ZpF-nJo*qp=Y9Yk94jbT$e4AMefe0a- zJw&RFr_}I8H3Bb>iYVH7&O*Qd%*rSZ>Gh0nXVgM~;w?zi{KBK_F5}Jkyo)&3J2`dU zW1Ru^*7T1$RT`-Ud@tf&M$UYf7FZ>KZpUkyezdlT36AJYqXCfb%_P<%d_PM~eFC@7 zKq6ur8kL`L)t7RoSL$e%&wgRJPQ5FPSxqbr+aXMCgIIUlpTi;Pcux?doFnA-4{374 zSpRDl*5|9EAOF8sUN`{}R#aTQ^Eg7JVJsLL>MKE2Jk@Q`=q2L=mH}|PVSkMs<&}MN zMkSj@trfBUI+ANnjj!agEaMfN+M3;j#@NICMeurHcG@T?LF}UFW-}n7VA<%G-bH?8 zq$F9}*;RPXgonO8ZVy)Qn%{_r><<80Nj*;XU3sl`y1YZdvefwk1n}lHj%j?x-<}~$ zRuV~%xD4myv7Nkrr~xWEkc&zZ9q9b}aAJ4IN}9+~orK`G#y&zsX=IwG%f`#h*oAMu zZNlqW?JRX3s-G~H-G(fK2GB|0A&6Y)mVJ_eVwhUyA~{{sGbe@*+;#DmAWLEZ158!6 zUc%+gI+vy9R3KIT`BLJvx37Ay+ir|AN)rgzrmtC4#BFt1!N7eaE4YGpRhM2g;oF}r zIJkF5X=$!F*XRB(S?~oZ2`r?>i5fx|giI>|+5_prZ?SC%CQKyGxD zdYT0gY{|#vpSfSU>0Eu}e=s{PVz7a)HbZu-^2R6dw!tTUmYEefko=nKR$nD5>1~i! z?I^L6>m{VEos{k8`H9~Wn>(CcwDgn~r5DJB=U!7KfOp_7bB z7_&RdR9nvb_V&c8nfVi)M)~}i9C4*FU+=n^c)#}m zmw8{Zxlw)bUw+>WIz%CShc2S7^B=hOBdl-)q65qa56foH4MP$;=3eSaaIip63s&|E zBE_1y6eiD1^nq#iRV|v7cXw1s7-!RN_KmLod9>ZqNOTJ9$Qs?I`#N278=&)v`ERpr z>NmdUcfnNDzwqTJE)N@9H3n}c7D7wpmn;ouXf%y8(g(VpolyUNtlJ0~-Th-{+Y(IZ zl-`TSleji#CplnjpMwYKpW5V>zE6*2obaZ&yN<`LS~Wbkk8NX^tm%+z*jC>c4H^1l zhs+7fsDmfD+h(FmwYF6{XMjn2D;}g!5W6mvLom|3lj0FhF!`vQ>Tj$N%0#jyJPH8uX?A+noSdPS}?RC@#12{gGumZ#%Q( zk05Zytu%B~m6PwUw9FR5He7sFG5-@)p2c)tWHpJhhNptVow?r0r?{9PClyR}$S^*k zjpp2=cMB|ICBwsD7^+dQOh@Q1UWa33mOWZXw4+ihHW5=&S7shHFMHefmB(y%F8_2d zuv;e26rR1lxh>TslpSaLYem;x|g-+=McC5b<}m(bYTE53)CfJblX>=Ry$abDQG z<9PEpU)4X3?7@OUIm;l5RN>@H`G42$20$4hT3b8qJd&9Fer?#Jg3=hreezc8&k(Ed z7Ihg&g!gObOn@12 z8{heHv(N%7I4W}#4rc9x) zO>cSfql%z^3$Z){VW#tFuI)nlJ?B6MLK%lgtj_!Ez-Grv{>|cfz;zsJe~jm*^(D); zY1xgJ@pAK*!7ogfQ>T2zk%SA6b;t5~+jc(mP1Kcx^ivgS#QTQBxibS+&aVSEo15<* z&--)QhdIzC{jw3TleTNWv{ZQ?Y9@G^dmhBLcQ+w#o5>|V<9;CGV1mqv6KfVH2vVJT zVw#=$i`!W7_xl(oVBGUxx>=_*4iwk3NXBcW&d>i62yK#Nkcbvd6Rh&^93WtSsi1CO z20JIxPNujwPAXg}J)?}$bEIC^Wv*Y6@5MZn~UT>u9lvVH!7k+4v)*jRk~l*MXJNRrMf zYjhYoh(mUoL&C9ktM#vrxfvz?KSBgc4Dx1tM>%?&FGzekzO7EUKx;eeJSprJjU+h{ngt z(?wqcE`cncBGP_9QC>(s%yiENsR&7@rIx^vpYtsUXC%QSTtPtI$Qi8{@wj!DTN5@p zdCYEF%iZPkFy&Mwnv}kma6)$c@;N&=VO-#xuB=Tvc`=LXh;U<*hr%8mXhUPeT$*y; zzuoDP07C$Nu7n@7Q;#z9^R6RCSfRUfY#zf>=3`S&dMxJZ5*5wbe_h4{E9Bq2@A#ZL zX=XvUj*LQ}tLxXjd~MS=XpD1XG+0*{L3d4kzvMzU&Ze>Kbotia`@@4`k zJK4CD$6!-ytfs4D=EgH%6ZN(K*A#Z(m>Yzl5;aPsvDS83uDBWyo1{6qPW1gx4;E^y zj?KS@swMl!ki-tzKMRR!xn&u1D){1tGN~=MU6ff()FBx4ZS2m-}CEZDI4vohof`M`sBiO?ay@{ zJ+6N_SQk-{AeXRx)gS)WuoUrOy_i%jB65g1pyWe@gvv_h@DJpyzCiD0p`kOVSC)(0 zkvEz+5r+f@CpG5a z5ur|qNlr@~A}5D(VxO7~Chp|1BrHs;qh+0^=3k% zNNCE=eJg`JfY$*>V)5>0&`ex5ogzkpO%qF`Fvl!#B+;_zt`31hw%wBzcL*M0VQF z_e;$Zfe7-0L`xmS-#a$anv-k46N>TAkzTZ%gtML@!I#FB;>wL>(?apSAq9!h%58?Z z4D6q$)b&Rv$8OAdXs(q>3wZe2t-$h`u9M&c_~!>nN!V|fqkK;1TR6^lCYPUO&aAfw z2K9|2p;Tx{&^XW;HdicW_Y|<*95`!9Z&3B;)ufK~oi&vVoIW+c0%zWPWiueaN3dpu+59;$WS4lHO*9T{v zUpMwS2g7S^WX%QN8{nc2u7A!TOVvcQQ;s|lBtj{1PD6WebEkm*yq+UmZ01I!z1JB4 zlk7&<(f6S6M}13WT?vC&l~d!?6Z_>AXc@wx)+0Z#nW&n*#s?khT&`Y1?V-_TpgBBx z&y@Z-N&BUiu8)RNyDr+UE3j3I1t>~Ro*J;;5msa-h4F$qRP-omhSM$_hG*!ad~<78 zHM+a7_LqS)c9ro%>4EhIA`9-#neX#D9zpA1@f_HP^G~mZTxa_kwBXyPhQDosfF6`bYo~UF(G&lM#5yo zhvc50EH_n6K;N}NJSd@cUDdin7)Hl!reG}l);sREuXSb*mas)p~JGKWl{gx&jzl2w2Mj?wb#l$lL zLorI#IwbVGOdI#hSgfz<5l@oNNPL6JTTO{fl`A(sI>hHF-X1QGB;o_$n(V}gHRK$4 zj{MO=@1tZ$PXIRVn>kZXSIPR&18bPVv51v!i`(?vc8;*UG7_?6+ z%FLjG6=esJ(V-Y>TcU~!Ba8N*8+^F>9Br?ey@t*z&g3Y2t{OXU;3HM zxldO<61sHpErB+t!ef5!-V4ocIU|v;)12BFbV+CN0f(&NE;BU~W5hWWedNQSf8=C( z&G(f?^U}|&)g++vfR!36|F{XoT)Nm#$02KLNU)P@8|sre86YxXCK??CyK;HNz>fDa z3#DZXbs$Ta?@T@*CIEF-sAnZs3&2e({)ny4<>3}zi{tb7(`c=&*k}AcvoncrIcjaQ z%ye}7k|=1g^{4h8fO+ZH@s?L%W0~BuQHa%esMu$8B{$oEJ-|$Kz3Hwg`!16a_6$dY zkhYzQtBD;?rGN<5-mjvIW!BfT;t^FyC45iV7J2A!}a)4pdV2qtI&$|-i~we zxD9!G-!Z8L>RJMRq(~Ojh?JAHh&J$Tz~QWumqM%CzSasz@U0m4H#;!pwXg#y z+x--GGzEk3Uh7lr3MYyqvuN)%lp&V#NqPO2MWmo7=4*)%f>X`99_l0ZsC$7*p%fBN zVQk7mtApG&wGE#iPDh7x6SPU}9CP;v^Ky3(8Zjt=)Vgs6_N0~Lz z#6iLX)zcWAx~3C=>gTy+$M9;Q^I}3kT4bHY^&{0Fbr4y0u2v`CfJ-s-zr=uuI{F`eAwc-Xy&t#IoOWmkV9};A3Z1i{Vqttf0{V5~1INDr)Ckk{k*g%l=kO8POjr|-1(h`WLe;551 zX9;?PNK`Ojx^5_y9!38_TCv5iE-Erm5e&+L>Dzxpf_+RH${(jdl_VU3UUkHYw`j!9x( z`dXCO_U^%0(syo$fT$_bP`(Q(Z9wA3h>n!gqjmS1F>L>$gT9;D6k)p1eB^)m*w4Z( z3k{^@)&_;<*%yfL+TgMI&J9{t7-e}B>p!8hrsXbc?>2s zmLX-C>yNS|F&yLg=aClbZyl^FEWm2>JBT(}?Imwd{Pm+09gAf>TkbmNjVdmF5X!?T za+0*YaA!-!ibDDNqBu~}Bk2DC+iVTzp8{-`t#|AbO*F$1R$ImF%MxTT1dg_3qJoxI zv3Yj!k&;sVp+rYUzRR;gZc$&R_pVYw!T7r+=6V0ZgYmy}fN?Rtv;$dKj9HaE_fX0S zOblZ;;BTjb$>}UoJ!$;WYf4E{2Ww5S2a6&TrjS$+V&&Jf|6SCj;xPWu+4Lb?=q_x!wXWYdoo-?GwU13wi@NJr4;BET*1c+(x5Qi@C~<@7ZfVby+wLnRHoB~Z9v)VeZ|MI)tSDe9l_)m_y156Fag!7IcJPds)DM8|MpFo zUiQ1HVnkaq{3PMl6HnEn-v&LoBM2cVRJ^tM($*^{e(H<1w@!X*KeBCh*l!f)?Xn>0 zQ|cKF(&SJ3Jk->9?!TvB1DL5}I`=eW@(coieF$o|;3tYnIR|0CDeCu`)O16sBU%ay zITVgJzE*Upg(PsIQTiC!P8h;eLB82k(F%EIRFK(MTCboj1-(XPXyfmd zsMc2#g*F-S$sF6Q5Vk&=z!;{UFk}tp6Fy;{5ZMh0eL^O|OI;>&IHR}z5y_JhlK6GM zqM#fRlopLcis~JJ$c=NYbqaI8E2`j+rU43%+{LVC=`LT3=On7;1i7HXWF6IqN&UXK zv&9CtXe7-3sw192*-^|Kx@dvwx%FzATGnsjJTL*3|> zv@og+EZO;pa4PQYWA*#b+e<07@3F%7rjp19Q$2<^pYb7|4rojch^g;Y$?xM=3#5dI zZSg&e$crT|&ve{ne6)@xxg+2X2MHRsQO!}fxhbay6c?%qgcbgE&@&O(Qu;Xowf?4w zWWV&Cy3Zr$bG8@8{#i zT3HlmXJY#dseYIARER7VBbbsJwDCzt@}NT(suufzSC@Snk+o50hhKvjIsY5WfUZ?1 zXz?Y}U?lwFv(j?e-S6lVi&U?Lu2o46>Dh=M$zHub~ncfiG>bQX|d)*dAjRqGIdm;>2Z_CuXE89#bQPzDz|J_Zrs-6F4G^il&5+8v>@LfVtgYFf zOjxx80q~Gd+a#osi^5+`N6yFcWA33GK{DH2+JHt+80jYWE%%UtO>dY>nP=R2+c;wG zi=0;KuJdJ&4w8&~+>HZaF8-f4tHdp!mvdEu!aojcq~9uXJ5?TQ9p`C)aOJWoHoKTo!YqP*;%OAz%MfL!1U%Jne^hG?7R5fj$r6i0u&9ydDDn@F8>n zUngwlBIvxU-&~PF?Yh+9$Q;}=xxqNX(n&*7C65I=hGVO)_!U%Gxd%zDKQ zb-0EkZ92O%%WqMA zd0$_?V_K0yPl-C|hFZ@ctD|`LOJxBL)pm_>s92sFyS%QWa+u@P;_q$=5y*gDqJG($ z_sQ?WC}UKE4MFfcW>zST7ydQgx>kp*@X)gE$rq{ipXm91neb>(h`*c3F!(hn$r5se zfF`wCISAPw5)Q9Qm#!uu-0PL?WmOtYVx2Ao=P#A(i@dRqJkPK7ID1>PYMilNhIyUbRYA|9B{6GZ9~%$&1S!=$#M)UDWD)>Lq=#Hat&5WV^5O(6_)lBbj#( zwrEa)IK$2Lc%6Qs(o-fnJ*M41=xg5t;j?ddn%O+sw%wck23BW2rqZi-KWJjTeEUWr zeBtU-E-k0?c1bWbSq(vf|Lv?<$d3D z1=|a^i=B!@<$2S{L8Wwe)7QgyMT8yvmA5~RF?&?Tp~=@b?rhh92PP_dUkN|TauL-# z8rO>K3BjNBqbm<{=QJHp35t6@a9WtYk8nya{H7b)d*^K^d3@#)$&Zo-UQp~x$*_)D z!&ih-_`QDj$KtgcDP!2*(u3r}PV<&qzIe6YV^7e%iV>;5@)03n88S)h8d6>!-iv(1 z9h>oHF`lEN{F6)y-0e2M$A=14Jtp;?rOW&gEL6>5PWy+8!#_!I{B5=Q111U50>UEh zYS@ye3#WR@q}xPvE?GGfcQ{=xB4@cJO~J^HaRvM)9@7;~y`(qc*C2XiX=CtHG|sUc zLk=Mp^Ubx;a}ipm1o+^uel_vQ*HO=*@@Za-)77ezAuriCpm5cj%Cc(b4lmT1M$U1E*2{WUV@E0?@FZn>@A$!WDkRLnmv86fC1Xd?T{IT0L zj~Daz7+|lsq;H##tYGz+<;05dZXHa$9Dqh#tTByLTsr(g^l1|>m$_6em>$-`Q_FPx z$>XPlrq<+A&M0NpJVhmyNY&LP!Nhpaap8a*!IHjRhV38DD(IwR_D8((;^8Zu^)3fI zb(AAfVh_Ou9H=-CVnw;FBK^ zr`scXk>sLMS&&_Z$0?7ZY4C8=_Izt2F8ew{OC>dzcQ$gBSQ@FH<(AkoVzI(ftj(Rm z>Vh}c9tWzc3N&xL*nk^YW{hbP`nI-gms$j80P^PY&Sf_i=B3uRR~>Aq4F7uphEyA3 z&`=;dFzzp8A`t3_V1z?eNwi5FWzgD>_hRJ{cOVA+xN`mtL}P!FX|aITRi#_yK0t#n zsvydD$9Fb_b_w-_nLg>0XID>MF8`PALTIh)PR<#GO!@mdxvMI(jO@vy8;O<;!yn~4 z<`r%Y737o9KjAKaIoBLhr>cSg$fPQd7?UCPrSE8UUtI~?n ztik+|V3DuhOT`{V+UJt61|;mZXkx75tvr^4a{75WImvrWuGS=Le%M&X{m)!?K&`>!CY?SGdp!qG26rYx>+c)|6T%%y-?lhg_#FdvDwtnv(6`>C3K5r%UJg`R- zvHYw@2}K#;M-BxTV7wL#mtgFIu!X z6oNZExRl})cX!toEAH-6+>5)PynBD=e6xOJGRfq}lY7>zb?MM#zL`NKra=2z*%+_G zLrQRd$vql%;MU-S{zhn{X!7nnHeAXd zfWc|Pq>Ifpa!=~2xB9%blTKs2(5L)2T1{sB1bXbw#ppN4T#*6&x0kN(1=HlB37AqC zziqpdl&0f@a6DK&;U6ZJ@cP_zcMcQUIV1&J4kJ3o!-Q zfk+p7*ZTi$6%)}kF+$f|%#L(P6~=^1Z^K~-T{IF#U(}JEO8<<4)OJ@gTHMT2@L{Px zhL?AP-`Jjo@I|U_tE3v|hCk6~#tLD!=TElz`3R;a?)@%V4x=)cX#@awsZhtr=@QYp zyg<{w*^85sfSjgM#z|`8`OO^fz(QHoMg^0Lo=c>ue$=2K&78{_wG%TTLp)^)n_jt? zeq)+pir~2k#&oM$2v*W|(U|ZJ>MqKrmf;soyMAoq-zJ$Qu_wp93p1D(kOi_~el5V4 z`t>>VRBmKcnk>3aA`viDs8^UyKJq?)yg7GB6~k%FB4ORWty!vq~Ql%mU~R1B8j(hZ6SA$uUhR4E{QxN z5WeH)8&7<~iZPDj746w;`AmmI(^Oi%+NEj#bDPS`dO6b_^?88F_h3g#cXf{M;RW3< zC<(N{EJH>!vu#-Fc^xWkSx6Mg49M~8*k<`wglf3r3KPr*IWoL9OoxfGJl4$GL zRC<(Yr5{Z;-KjEPc%-9*L7m29iovI~F~sM;&7xA4+!?Ewi6FWD1bg2PZ#AKJp~LIn z-)o+LV10exbKrd_?-F&SG|^!aHu45sC3s2JmK`9dM z{q%M`5qEa%+mR+8Hsc%I$C8#y8u;rY4u z8OS@mY%QO#we%3ks|>o%hLqTI-)*=RJ|~WUeD?v#6&Lsm_nETekIy4@JrqjOyrO8m zSmxL=;nmMb9NSKNJ#i=e!b6@a)g-HZW`D!$$zdauBCsC z{3d@pu3!r6F@+*+xu;}48$LxPBmbyxn1I6r3i#60br8pkq`u7?C?c}X1TZU5ylO=^ z%KwI|mPI};BdKuQOR;cf{JjanGMUZfy)Teb)=d`Cq|JKUxSV!e=nIUj*(lXf_C^=ku8}Em5nN8)nt%Fv z-h6{7;B!XK|NWHu#+Z&UqFu(fXb$gnPLd)C2873Iu&@l}wW%*Df5a6PI)^Xd&pcy< z7XA37tpusINKXwtO=C%;abH-hF|eNdW(9Oy^Z1gxq|IDCd`G~>B4?ezvwWk}jL(6+ zUxnSN+hfH&WmvZS{QsDdsgnNC9To4V_$Xq2wbqzM8J=+;T?o8cZ^- z;y&#zQuoPUn8i0A4ps$mzkfpe=T_{)Hw)JM+kXy-HR3j!3frr0)C0G70Q!Jf1Z#t3 z?9`mI;KPabd*-RU;kD15U*B}R*LiY13-gA^oYYYng>ou`uUhuM3{%%z<-u2j3Nt3J zu2SMa?7<}XB%|kqy0_tye{~rCto}~0u(W<_P8iCx&XkNKGy7poimq5H#)MYjjgijk zDZrF7n&yb+!&f!1?r#3{1Vm6rA|w=cZkUI+-iHM7*nK8F_=!u=IlEm_q|i9BY1A!4 zCg*+sh2!fQDT)c$2f5SYG2MgXsJxDP6O?{(G5DQkIfK}Z#Y zA;u=AV&NTshVP5(msQyLClK3zq&tCsi2<&CdfH<9FeVGlS?`e>9_Z8^^v3g(nxP16 zq?R4z!OS(tA32M~4&wEiMGVVBz8-$#2WN@D{uRqe4Dnhy-K8;qDA^b}+m;fV8~qJt zuSwLjw8M-+=Q8j9(nSBVsHNc>89C*8SwUG`N84!O-n)#t-XB$AIw^ykO;@s0>@Sj! z1>E@ut$FeGbNbWz0kzoBq=`!XQbSas77MTv^~Bg9 zqW`KJ2`UbmC`ATxW=RkR2tB&I zC@nQ1q)17v86uf;BMZXRsRiLozoQd`)P?3)H4}k!30)ELK=5?w&b0ytL;>laNi4PE zT_&=>-z;S2|()Xp9dZ1d& zfwe>Qk~dh{-v7jAS8e`p%s|0j0xp(+`<=%g+GlgLs_Rh$%lFtZmTQ~I7$d0 zSMQMDJ5W}cygx2;hg(QTXiooVXl8gW{uqFL9GW2qFkt}B%#Y@C#B1+ z@W9E;Hc&)y$}Yw8vu*^!Q_)l#@lZiMT7*3l_&a^y1qx48*w{Q=xRnY(mLMZ{S*T`b zCdbj66}V204ixEW(v+HBnTFp(HqrSOqH8t!wynrw&s>M5tCmM^GZ~rdbwTudH@6^0 zvOnqE5T_@~k>G3L8Gs2#szcEnG~v5kU-;EW8&VJnf4w13FbDkqC-q+n5DH>&mDj(? zbe_Zl6@kUDtvfeD-N4yGP?USG3N1HDd9^q$+6(-#+eNV%9>c|rW4Op=0z4R5H=L(_ zQ_Dc#^8*Gs=grl-2_bKFh<1$CkoYu*$HgFa#~lYk5aPNK?MOX>1}{TkCG*JrKW@auElBw z`5rYLs^Z1a>{iu7`rO8$YE+VFsnFk;y2Y++)Iel2`kU!CvkxZj-iK8fUo~ibkK(IS zs&69b_%{@nIx4t@a=&*hxJ_f}s0&z;^AMZ_VRSFc?3uoZh$RC{jc}dL=Wq8MKnhm# z=K;no>QZw;zr3;5Id6Kluytx$Ez_sR=xj?EQzX7Dg(C;`llo|PO^`NXH%zCnHe6BL z*jKe;LGr64NM>l`f+dPP*dk%Gmd53jNMpX%f?>iMCVcH)L^ZZzf7pCM2>7+nrL>&R zl&L9Ub)|PUHJv8VVUl7WvFV|kJk;UG1Mpw+g`%JXup7~TYdsnM2jcz*otf=1OTRhE za@uJ|g-%O9O`YsEF2Ovo?sJIhI*uP5laUEa@H&KKG+(LwuXt*z2!`mLNEEWfFjlb6 z@Z9*~qEo@f_>10+niEh5p`;fRQ`xf^r$nphx*1(!zdZ7WPnjyV?cQ?=Q5PZqa;vq zvphj{?*O-P_o(ikrCB7BCr-&+(c*JEZ*E`(n2?teF zz5Y~6)x1GA?FaCNrhkhc?IQnp3^odC(mq~_)2 z^pEsed)&2rG!aq?P!NCPfz0E31l+{uDq@Y6C(3PpS~zZfk|$YV(Gn%hruarW`+8v^ zl}5UgY`VJd{KL=NFqhtMd_jRN(Uc#}cEa`QGLvstFRp}x?N=^Xz7ET+T;Fi2oi)M$ zU2)$DhiJp}s&`vB}Yu>iB>dCaIb~>%^jlKbDJY(|i6Dli*4u zNUNxM4&y+(=ge2tm7O6t;vVVBgguJckczfj<4s7hVjx=1ZBu@OfuRg^?a)FQ`tuGJ%gt6{Gm?9wd! zm!9}hAB<5(Yl*=TRSFlCt;WQw{!vgey96(Am->=HqcA@%E}+mhu~AHale?>OjhPs5 z08FPozmI;s5x;C8AfG_^!c)p##G&nquj?rEV0&%wJjgjdz`*p^aa++W1Q>z#MA_Do z-5etZC>K2u@Pb4r$$VAZ4LL|RG5uGUpyMLuML!RY>)*Q4npD5KL)_=KJ<2O}Dx%&B z4(jchwOJb+aM40AOzq@joSUP{vPu9&5_s9nfI8+5$)W@yX(d6&RH0~%t4oy>5%D+a zuAk%SvI0=I`INqq4SI}fY^boqvPiuinL`E?9`lIuNil##=Qmud!RMcp znwF&@jRwz4CPn(IRh4VhL>D|R5gZLd|3Xq?%(nraq<=pkyd?K~=?0IOXmEV!;Ll7l zdaXtF-XwpIhNO>7fRFt9&*I~8nTRl=$t!8cO=ff3%BDTr5QXaDjyg?R{ad$Kt2~mv?|GC%{#h*U(8Di(Us;&A z3WW=ShS z=UAHSM^@N*_XH_h@-Iy&?Xk$dxiumehxpAF^ z6P)BlE}P9wIo!MB%)EFx)nOUxL7bdzXW8gY&N&hY9Lw0sMU>OF<@QGYDMvidT^W+xpfW3(#}NC0)z&{M|Ql?LhueV z$zuhwqZ7?rssLM0yXbqR%2t(@Ux&B6wz^t8zj=N%{5(Uh9O&0J^VU~lb#Qs*guWcq zO#NJWj?$>QhKZTjmj9Kz1{-F$a{mjQN>S5dXr)D(Ni0k9RaW#?%<LuoyPZLA4a!!mAW<=U=p!8WXWnD{@~E)RCZ!gbfaDaEnbB zV&C9x4`tR!aOFd-Q77RFZi_ilh<)&6n?!TiM-NY}nWd!jTVy!(T(x6;?q|?<3(_d? zv6;PPxyLotHp`jm_VU&mY(lSi2Hm!M=5yO>5mZTg-W=O67?Pv|L&vt)j_Z@kf@M5r zuN9ji5pW8^q}%f)s@#2FqP3RT|CfVNp+RweM$4D~k^Jrd&KK<7^&K9jO_DXAARkAY z7zumJZsa}!=T>n2iC6LLC)et>Z|h>Fj^~OyaRjDkDPDtZd6dJ{*7LbJ+wYmWFBzO? z^vZ8$0K8y>OdCRj@t8OJG&?CHJ=H-m51UJJklY4mBTAaGO@)A zw)j(pJo7Q^p`Nl4-api0+@d`5n54SjXLHY5&RCT%1U1{{%8J>uuyhke;@?Z%a%x6k zCwf#zHML;8be?eNl+9y+PHF#q(AFBPLa(@%2cn@D`NAa+B6o_>B+DAEWto{yk_Cj> z)QTselu(@x~YR+lWf6zas<(D=1E-`VZ$*GGNOKIbV-P5HA z*QCYs0|^Q0X#HaUC&d&-9mu)}y`XncexcH3Y?m;j2NWE3CON&G8)l-?#OZd*j9Mp3 zui{Ud2TqQmwA{iHhRFT^fZ`vxwI^sh+htqNqj47|R&RwOVG0l^-yLz;&V$I2DcxvV zl7RgdHMdu#Fxu<5NWXOpA$qOl?N-Fs*2zfWy_Ni0e_lix4&H_de)O) z$DO>hO0@9ueRQ_i!`){bNh0D2iqIL4_bbu9CFlR|WA;&l=)Xb^;%9k25uxD!f6G}B z67Dqi^J+lVj5DWE_2PpVBfvOmmZ7=Fcg^1YGZH*<9kA&nO4c2mPH2HW#{G zNO1VVlk94#K@T@Ua+OaU!XKwJ&xmVr`b@UY%c>fJ+&dx-T_T%FUT1`m_I8)VM3)te z_{@AybKey#=6dv3ci8@AbE!tqW@Nb>nwypD2i|L)H;O6F8?gO8bRtX)77Ifyz3m=lVdI1}{BCBT~Ys#YARhJF+94@|C_rc-FssLLgq5c5lU&?It z0nI&U=cw7{PZvzXL+Q5I9jGR6#8L}!I;UsSTPm#xcmpva?`V>aOxq4qyZV%^AAWpy zmyaf;3a=B@P+iKD#p$?HbJ@)Oyy*F@;OuvtuCsWFRrsH@VD;9(pR2*A1$VD^@Ea935l0YhWo zYs^r`z3GfYw2ZHBX!xVb?ITTi7>ykUehS;gyM^KXaY{vxCfvWPlt91LE_dZDw-8ZC zg-eHK(LS&W$-uVb$-Ce-~>tQtkE9t>ot1ZTYZY&L@mH$c(al zv%q50=l8$AkHBxMc|O8y^(s>nq!<-CaCEx-bhvXII!b3FbphVkTncQcQt0z*QP z0&id^j4Pes02J-#Xohz?LYBu<6rU__E4u`32xc_Db!^!UF4fzctRIsDVhMBCN2XzL z(2;)!)E#G$MRiVOr$!iVJkV2vxlcth z4@g_p@t+&8{#Y{s*AwjlClC*kvaH|w91CGNbXQ`&f1Bnh9RA+N@x6A5&pqrW{0ZEa zWn>}S?N7vqkxrmElotD?+!lMgg_;aNqS{y=#bkmis7<(q|X#Ccjd zHH8vt8d*+;AdR~bK%uMp{C3D9?j(Mu6xy-H`Zh_iQt!LL6w-*=0`d@isLGd~XOA8SCTC@llzMBVj<^ctXpjXWMOeCj;a7h0PF5fs8LL*ODG7Fzpsv4VIjB*WU^`b(0 zqn3v42lecNCpH$=2+3u+D(oW#g~iSr#=imc(Eyi-{Cp0zPw1U{oH@nBaz&17exYvlUWGNPVE;jNRU|*G0(ulb^W(he0rL7aS<9iq$@yDMV(P(^ zI@6mSEnMRKLouciNVrc&S#0&%wAfA84@C?WCpVBKhRfA7njY~5T2@VV0|ncLgP05iP+3G zv5x^^Hvw|YzY!7*C|k+k@3Eabfl-^|Hc}BhB{bRs4lkYLI+&(3+`1n3FSrZ$ej( zMnr(Wm$E@c9q2(HcBAP;!7*qQPaU4OJwseE3ys4w_>F_)WM>>jw?E8J`pna^Eh#si zF<(&bMR`u2zP}#QH~EBUO(Rq;^ALJN8so;kN7mcCO$-q7u-J9!@s(;up)eU=@hM7} z<-YZJ$M@}McXwBWBD|(;;=}>SdXsKktNcI0O1nZ448M~_mg@=jfYjukngwgyRBaQh zlz(3G5Jb>1!=M|qMHuXdnC*|hG8k-zjW%g~azp%Ei@2?bi`lvRw5`nVCo{;JRgDG; ztznLr0j*H5?2I6=+7?U-viqN~!e!rgNoebZHbT|2H3&ONhfz9SflT-uZrN=17NZ>G zC>kViCIWXc2dPX^xC0xh?p%;L zbkIhJF!lO#R#M=jRBtDA&k`ZU5U<*qF4T|HT&=rQ1-jBOsSy*qRfRUP7}qd)$4u!+ z6md!!9Ijs}rgQ1G>6f==B_aVLqvzS_seA)?10O3f`EtaoWx(#BD8>SE(7#S!KSp{5 zD~>!YXUbWqYxdeUSS8>9l&=N1FNEA);xB-ABSX^01@{@lR>n|`BlI12BgL}+CDjA6 z|0OzJ2F)R`j_S8dfU)_TC7Xwdl9A0$sMr^CU2o7^$P{y;?wxMfW7pk>beVzKj=WHvg)}j4MeE1$m8@DFyY_F$`TM=6 z(A%vd#iWO)@PgdteWFq})m=;Fs5L6fZtnd(+h1A0*1qo>la!s>EQ=3?tXI^T#j*@Az4P;DSQX@Tk*`o*dB7Rk!eUCA*U=qz}`@x4xZ*RQpz4_M^c-!*hPmdXc1$1)6{rx{_>FV)Kltl~b z-5?+WE>$=d|23SKd$2gw1nm#ubN3BPcM8!J=S_@G^ZtS)1=RjtgiwQIo`)_c)GMI6 zaLcEt{>JkyNSyS>eMa}V`PvmBGdPhg{y~ORGc7b@b7R34rcEgQYSQr-IaS@zw}Cb^ zO}IhAZ36(jl1zT>Q7?H&sal8mUPAlYMw}^ zwhay0X;V;+-II=vM?Xp`I-lrziU+VX2f4rS9%L)Eo=f3c6Wx&5Z|_Ui!}4m~bt|Gn4d2axM& z&bXOl>{%8_YjYZJm+KZaX9p?{6Yg@u%UuH{12iJ51Fsiy*Otbq^elXfXx7;q-$#_kN6B6Kok0ko2Ws#Ms!s zZA3pu2}&51#jkkmEj|ln$BgJBdK}G_in-t6FVfuSy~cV$*4>`yY{W?xTYmc1_wD{h zs3$m)BwS_{N+hgiM;3?yyqT$Cx;osTy2v;d1mpCwESl~(e-nAS{?2Q*8=Ibp^N1Vm zF+Im%8?VOXc)b-p81R1B%>M5cgmd5TNy~W42=(7?sXAt{*1r-u>e`Fru2`T?5cTZb zi*s)<+I&p)*>z~L`@-$CgR1BlzH){d$t7i&%LSxY&_2 ztA#*!Es?n2#IAq^n2gx?Tp}1Ic8ukEl4y9mjP>RQpAI7#YWHWwk#{%Inoss9>X-H~ zU)WmZX*Nlw&;m1qf*^YT(_?R8&Be7xHSdoaYc8=0HO^aUh%g<@do`zhtk8ios$$sUQ`;i0Ws}SgcbIcn{&jg0be(8_KY)Nb+-XG)JL_4e$=H_C*K*= zNFM#AOWe0p6kMI#m#--OkP`9Q$mXkK#jU#|8B@0L@`!Vnw{>_6V{kT(I<83;Wo)Za zi(lGL<&)_^jMSqth}lw)6-{!hzzd}D=H_QPQ-)Uk8m7@3v=B#}$Kxs|G3%DF#M|vq zzi)mr^f|zv;`=(uk~_^kDWiZ!)jU4RagnO-V*-ndC6PbJ6G06fS9f{?MA_!6WPpS~ z`!h>{qBipQUS1Z8Y}4O+1S|J_QOBhS_wia$;wAc~EFY1mi>+iqJUdLkq2r^ufwk7( zT|YS!CA~bfUgY99-KW{l$Ed$G5$p@seM++bOO30>Q%-imN=s_ zw*d$g_`ba9?}t9mAWT8{Ed^Z8#O~*<)EVa%Jne4{5ODXO{>=-FM2fm^(eOKAagCg- zMbdmrAntprCLuz*boi_vdQ|`5fd<+}=0WH)YR@@n`<_BB>GIS;zi(rj^zR!>h+Q!qz)7XQ+8A^-lOSU5L~beq&rZn@^eU{b0^P zVxRxp<{J17v03LkpJoS_>kn_AXKCVS(MBE9=pWXOg~rnSWx#6Lo=2Ge{DN!>tyI;Jsyzk zA8i{6L~uu5&lAFsk#rz7yH=R&C;LgyHI(jtnVPg4A+y?j+Rl^&ErgH(DZr0ms!EC{ zUzY*{rCxUEF2PLy<7I66o%(q-a_+NKoO-&q%3L@-@0v6 zk6#Q~9dMQ-jvmy$Tf3t$1wR%Jk$^I9=3SHvdDtgT^tn@JIy%|yij&DulB4y{QM>-x zbdP~^n6n!Kk-nq|qsQSaEEm-O8mx2U^E9uDIE|cU*;oV}Kfjm*b%J7L@arFNVImoPD$FpaXNRGW_Y%RA)S2C%1s?T7V;?;d3BA%bl zh(*&5+R>qgdK+qw2lydJ)fQxz7K&#I97_ZqSnbBG^s^^R zlJ)GcZq$6M%VO)HmyO8#RWE#r(yGPHXUB(6i|S+VxH{XsG+Ee&%Nj&}&P0&YdpJ{R zAoVy=C62n+>}6`)5U*}}B21yQm(*US*pjfTkfw6LA^Zfkw6}j6cbo0lpUUX!1XaR; z>m4sm{xQ6!fpux&dxvNJPT5x5rFNHgV?%FFlG;#iJ!F$i+I+G4CEMO`E0OtGfth;M zs;O)-=pELK_z6#Oq=uVBppY&G%x-7V!jKAR?qZ;p0qY@e!9{lD6nniScfp#cI>!RJ zGZl_jT1%l#b7MtT+hK;} z(+q*L`+B5$yRIt_8htvJAEFN@50i{MF^+L2Z7wRQa}hSr48LGwR+m#4vh z7qokxeQx3j`}U-0g}xk3tgAaOjMI>}Ff1PM|4RWr5+94CVuc_lTr@wOOY5yvzwF?O z>#|;*m4%azwZ)7H-YhX021W|Hvc@WpkjJFTlCWmQizMkh!F;taQlJ=^ z<_3vHr{)?A>fWGDDCK*gR@7**Mq9vYH0p%05@q^@aw@+3t3n+Yij)Y|*Qc;)OTU;r z9beNaZ;)#noHy#9#(K;CjEpHR^j3_h=%wWTJRwwpTJ%0B!KvJbwZ+Bvy+ISM?!$!c z@zc@>x~6MFr9uQ z8Y_%pAvE1Glaug$UNX0`-@_d5Q1?CbZtYt|RP1xq{ZWTWc38);r}z2$FOE|cxEU@*539giEBy@ZzP$`S5& zl4NclA@@5@OrH#3s$O>NeUJa*6!w<0-R!e%{qEOkiH4UkE@S&7x&Qlc4;%USa>e0i zFDxafK*7Nl*jaWfw07x*4qNB6Z9DdQnbbLCI)1wkiqN4htn>3PVBlIl^491ApkC^J z&OsA*CbC%MxVc{WUNO8J=^SC=qlKi{`F$`_xykK4-hR4@S3pyHQW|g->F@#9DAv!ktlW=Pzj}S;gAd*e zH-&U0oYXvD9Pze$0S66UM_L9hAA#p=*YvdXNcboy%H1O&f4kXihYeQVkxsPe#7f4t zq4`w)3(Gro!TVS?H)6a*3njA@GGMn zGsro)-CR1l&s=;!z0H}>Cu8|vWk$i)LD|}!07+Hlmhp&oZ%0yN&92Vivy23hAWhoz zI`o~lyiW!!5b)SlM)1P~2Dnriu31rH&X<0RBbtB5#C9(m32S{$sNAYzW4HYV8$ZlN zU7cCC5vJdi2t}#L@FFM+;^Ev{5w0EolUjkxHN2O6GZbj(`#i>Ca9`}&x+ubHwuf=y z=9#_pl=}1^&FKgm`Qif<1eAOs)}h(xOKop->EYLXJPuyo|6wwM;r~lrIXu{r5aqW0 zWm?0|_cx5xpzRx9NiT2EJJG$?fUwQI%smp|s&kXsgw2p?fwkjBY#dP9j~SCg7c5)TAp3MiZVL&V2M>@12mPBQe|VbN*ln7__?p zb;4-M<1o`juWdJ#pnbM|cYx!qf^NuzE~D^TllbF#NnyB@sMBcTxCV~*#KwFtsc zPbJ6hU5w!7RAnEQ*6P)>wwJoL(*4+4#nCh@yuU9XI#2U)ZJtW`_m%Fww35~b;lcD9 zbz`h6Nx$Wy30YE5dTrHNBRvfDQS`Wh*2`VLwxGadXeQY_OzT3cJme#@csV(q^p!Z6 ziQrBEF7${1f1JuDU(tJ`nnG<))y9Rr{ma+^mBD%y;l}f$3-0bwugKn~P;-ut|2=t) z{{V+F@}8GCss6~U_f1qLzcOb!tsei`K1}NUuUHcmTXsB?YS061CbS(`{>mL;;(N|i z#hNczX$PY3VQZgp$x&K#@R3l=z3P%eIESHKpN+k>Qn0@1%M6iV8ouSj_)$`B^}BM`bDVgRZr+L?QvL^*y0$K2dALtvVvVDjCAA)pFJu& zHGYGRU-_VBVxlUTUI%y;>B)ZiI|Fe6U$%QpS;>11W>!77#Ccl%Rg0&?JKs&#z0i_Pkj&kN*PMoycr8S9lUrI%&fOUOpoM_X-Hs_|IXb2kX7k&&@PGNghz z>fiU?^zFauvg1k2{+Lzc;MkCctTOmd2xp%#pSd%Y!RbO6@CQ%c1(6lwbVdvaGAVe1 z3!hsW$JbbNEI05EMPUXZlRU8Kwyk|*+_}+2+w08J9Z)KVPBuE(y6fuY^*i5ZsrHhw z0!D9&WA)~=!jZOxO=tNoJ|J3kJV$NmI>GX3DtiPIlJ)}hE+D&{uf?m=Hx1BED)Dmz zGsgotap>8fInAG4tcHl@ISjhMvqe5PnT*ucU9`*zXJ9CO^gpH6gKxycqa8CYvgrurPI9XQc!ceiUOkEk0?;LdH49Myd%U`x0(2OPr~JNPKlSoY00{L@zbWi#O=&n+X1 z(N+rk@8`!mRdVmrp$Dzbx&|)zm)0_Z7KNiRum(!0jXC$$`KJ#ouW<@W2{19IR*}5; z(R>LzN$9x-fn~>CK@w9Zoh;VLZ?|&|RRczllinjslRp*~toNyoxaq%&qW2*>Vr#v2?eHcy?1`Lq;8bTOSOv)S9uBCEJYSv$# zqc|*5p`(V(q8a>6Dth=kucP_2E%`Nokh`|5X$yfL6vH~I9Tn@Mf~K8YX{AZ#KbMkK z=jl5Sji)KDeiFCAA^T5#woBxm()Du@z06-cYd`fs6|&nDyOtd@kB%L%la*hewmG*A zans2!CzEI!cZp{fw#*PQ#h2G;ba)lN;_3zzx1|yAbIf;KxHKgG@uXpgg-ioqzdVkK zS$+?0oERP0rK`oxH1L_GnCBsCmhY3SNsRI z=k8X14*R+C+#2h>*ma9X3(?Tk;jm|oqCFZoBw4|SS+9!zbZT8Hhw_)>e3T11MH4f9 zuh4HS8|#*qeAKT3KBaF)%4k>se0DKp_;#O}MGBn$t1Rxw_#>GI&P#QL%(iVG#@)K- zyY~C$Q4Y2**mK71Ex_Pce&~#BFwz^meKam)I)8Kmlq)S1qv!S7<26H@)yV&RPBB+M z2P?^Oy(Q$U7(wvs1yAvcstIeg8(ZNCJ!!L=lcaibEkr`Iaz~?jf#%{$r+eLF6aY zep!=y4VxX2vI4hDr(S(?q>ctrJVop%1K6CknLDiA}H$;SLw5Y*9J{i#t)Y zS&Wl5F|Vv~lf^w4J_C4Sx|S$~ar}Ra{{(DR4RN;Z;^{hnTP7C^?TpQf3$Ez6+GdF3 zdmf>DLl@TTeHA%RUGEVupH<*9n8K-{zZ&%*z!5Gbxe5hXV1|9f+~ixm2q`N6F~0^w zlk?b2A)TcfhoaF&_$cC#6@%D`ej94{J9e)9uW_V2#J8i^J%HdpNKWO)jkhKmY(WSh zY720fX*GRz=pN`}e87l|98$d&XK@|nK*7r&GPFNJKegH+DaMftVyz0)RW%F)O&c<= ztbf`psfLU20VrGo3*K}w7WY#EiM9~ZB(2WKycMyRVy5?Zbm6V?uEM^*cm^>GThZSy ze8&W`PZo{4sR7@OUYIlkU60l-zM&9J?snNd0w84l7m*!^E1=A=8|C=2w zaHfk|F4HqL-(qA$0ht+Y;YWK&<6`y4L2Z)DOaT&;4d7c2hPGmk@|^rO%bal;{KQL2 zj>Ie?Yd^Xo-yJ0=Ws=um+?J7e0qYXSc(8Ls`JyM@(NTg#4t#|*3k97ag4b*G=itPi z&(rTOa36+WOy!YjnS+T%1U-YakN*bq*u@Og@=^yW`hgzo%m<7HC+a~v{x!-e<`bIf zWbycqfKKSW1N|YsWE!dS#S13OH2rSi*&)_Xqyy$Y`aQ@6e$VNz!@YeiE{czJ_?qj? zK^_7(+gno4yCC<+4Thp&lfclyjn9NKCf!}ZFBKE6X@1_nvRgkbz=WA-y; z%-W}`ma^M_ab;^?D2xLDJ{qz06k3Uy4_Dqfz;CyI32)MPI`t*p9LFi}cvF73Y6C5?2SO`fpucIn>O)am=#6FX3J2lkB0;j#2?!+22A%$y zrZsx^e1yi8Sa|Z^KBNBMy{ivnnGQVB`sr|k!u1WUBkQ{E`Ny^ISke;D>i^yIvyQE+ zG~D2_Z!SiitWe-@jzu0aW)D?S!kcD#4wly}9Q{19;(Tm|@FOkgpTLICmhRDGR* zTSZ~99Zk&UlfMIqETKXQi;#)N28*2i0@u&clCBUmk$>rGs?gWeid;{u0m9l56AO2A z$Xzdt#9nGw1dEsGI7wEsqtF|L8R_X)VuJ3Fr4ggBdu#_9;JSp~at-1vVA-bQ5W^QH zrSGSsca%`na|2o>B+_sA0OV0ch$(i^=-dIPK>iJe@J*xI4~G)kf;~;OZfCvJEt6 z6o?-svOG-X8P#3@Qfcz-_El1OQ>*rz{`_V=;P4SvGo1)|P_2pt1YKo`I=d$&LWY8B z%59LQaxuWn*aK=jAs-9^V)#Fr&B`p?Send^FG4MEabf9nbbo9~bBf}rOUjZm25j*B z0tJ}Oi#G3Eg}bbkKqFl?qMAl}gg)Usq>FulDq5Jgd3k;Geu+zI6jwI0(CS!n%dGDx zMqiNA{+5gSKU}Q<9@cg!C(?pOCV|(8rMAeMkeJc0Bm8QyBa_ex(Ka>EI$eYJ zV$%El$o5)*#VTMR!SzEt1qMetzq$`8lN-j^> z3nQs~C4Fj~i*8LYgCQ*c?lk2B_R8t##>D-f&8z(^UH9zBeda9h|Md3NQBihX`@=X$ zsig1#3Mc~7N-8<1w1f=Z2uPQ7&47pj0@BhbNOyOL(j_(2(A`}_d-YB$ z7Oce$-1munu6^xm-)A54w?|+D6v?}mmK5&V zkC`-t&?&QA&yg3}_vF1P9a4WZuPEyTCuvAJNoc2!c~v2Ycm2V>FhAF#g4oGkxEz)Z zG9^Whnth>v@`#r{62bOPVkouq+mMSuzGTv!0Y}31_eNIKgIx49OT27TJ?=E|Z)P{N+d25Y7#yh%dGJpRN_PFcu6P z4CY6}f}p)`KZ*LD7tky~i+Y*6$oZ8l>Z7DlEAlVqz>)U{JAo?d3u}VlC0Lp)LUX(Z za!Dae0X&B}eR3=njd)X`c#Mz z9nadxwuj%vF6}J}?^3ewLWDR7&$;Y`*$lKw;P0;7%MrlEd4m?;qldiw>R{bAJ~4zk zFq1O6H+9;nZzRtHW~Yb_Vseulc_c-N_cNyR0%CuMbe~=nXSqnu{bPe0(QSyEh*b z&(fcC`McG~E4@iOTz8F{C}~WCEc&&g*uJdM&a&UaIB~OFx!X7Q~YNT8H_hoyg z;PhUBU@y+kLgqrR;0?Yo(<7`6?mwT6q>JBN;#(LMW>O}E%ujbtu8A{QhD{Dte# za$Ru7xcse)e9*i_QGU?nHd)%rgEC!U{(;&sqWCHUaaddjyU@e$i~5$?r5Km@0~a(c zy_vG_zYIOr5@)YA`d~Ilvwy|sokmU2>|8yYb+m6iloEyg5&iYWd?RZEs48~Gjmld| z=^xweg7{e1E^e+jlATX^XJYdW*ch5))hBoH9b3%oBJo2Kv>_Np>PUj#`~;J48yiH9*bkngrNBm1s{u~Lgo>^NJ)yFHXRr9%~w6IAUD(5m_T z&1GHgR#eV8i@2(EhqJDgRzP`n-V15B-07P9i$i)Dw|354!*4GV+#0J#4e1`YWpm3I-`!x%6*WkRzHwnT{H_M&a>HxLXeHpGe1iu?aDmtZ z_ixW>3`EoK7!3#IVlnyh2TZ~tl3vpi^^1`Z=N(l1?%E3>@u%{R4dNJf37BvM)xIvoIogx%W*Ss+rH?=eEPs zTAfQjidi?V5Ef%HjAt0Ez+l73?~SrrR$O4)wQ$96$a^!qCVg%uEAsAD21=&kk;Dr9 zx@I0FW*(M#i+(|rVpHE;)`EG(zgmsi1GO>#;_yNI+1unG>Aq>9Jfyk+D^7c=dx@~p z_`sCGV-}eb3Ax@U7Q@RwkF&eQ!DhS%5P6}8uji@Ya^|xhro}yZq1qArCEZs~SIdd^ zf6r^q7Q{0^h(|-eC&(y>q-hgZ$CO03I}MfidzvSPPLd$|HF!^QM&!?G@2WcctdDJFxekQ7Hai5EDHwaxzmsB6GIIKUW2k0dv_skb?q+Ly zc_4&NXf5-i@sQ$v^N~8z$wS}$necT^Ob)JjfY|nzXOrG7y!AqxkQ1&1ZHsgZp>gfR zGkvrXb0>>#p-u6%HY-_o+0}^X8=feW8H!5RhXQW9hUqsnG$3xN>Tc!+S7}|)Ply8R z6ZDurZC`A31j=aHWt zf0!tuy1{o)Qu_R4G?F&5WZ$y1<=jutGlEmLyz1678X-*O4|AOAkC_@Z{j2OeO=TPG zNC}XI)&K2%jhsIWz8zCZbMs{pYMqwsgV;sdSah^UX^*Y*lrXuduFB;l?PE}I5-LjG9fV~5!gMK!*`i@#`4VJBaIMsCKa zZzJw1W*R0)rE}lR@7sdfK}43X$&vdWGx)d)_SO7K^!RpP8*KC);GM)W#kv^koqf)0 zg70-%k`SLDu!t9QYaEfAl*1a{dkL=cwFfs9rHvz7b5!qiX5WZyv!#HMWUiT2N$wme zmirBQy+x=r&*~xu>>R(Ms1zWOM{wC^5~@en`f2ZR`nmXcTnM-tksduh?8VXYsee7| zyXm*oA5Y9Lsh-z5f*Hi2%MUR486*nI@fsI#Nqc%7(rFEPS$8{jY~cyJQr!-54YZor zM-uQEb77Zlhd&&^J8(X8`_10|XSazI^!y$u0FByNK^HQj=#f(vI6sIxNnCzeXmorI z_4)f#oZu}wm%Vz#r0d0%D?<-fYp+uN$kx#D>zKUaL};VAX)$5;YW}DfS4zywU{Xjw z7yixot(PH=PwSO?d-oL?`nZ)B)ZazBR!tb4N?8(0%ytnim*~$3{aUv4NEdO|l&FJ5 z@)ri{&!cEZt0dh^UyYLVY1w40oSy2kXIe2?$ZhW-XYog_8}_q{;sq^Qk|a2)g;U_; z>ulIkc%GF8_#CUad)VOZpW*Z3IoV5oV@zidHPSJsju*M@e^6Bv5=UHbwQi|&YALq# z`8lGZ(<^m0vg(~n%;0|CO^OOm&lhZ?@;C` zW%>P4zJ4_tI1InkZ~5mFPJeLWm-SA0*mSz>l3ufHQOhE;OWArz8@pK$ju3v{_}f*S zqQE?eDo95>Msr4;kyzd(uRN~9+;EKCE|6tSVxclb&QM2WBhaz`pzf#X-M{y`+y~x$ z!0Bx~jIF>}QkwkVt$rw&mt3;CWBR1~xSd!2;I-(&K+OfW!rCv^k1li&Tn3&$*u)RV zCPmyR)qlGDKBO~^?mE0Pp9v~ePS9|!;0?UXxhuw3+CDgvJ!Dhq6)=GaETR~+T#IJw zq08$)Yjj}oecXhZybk0E=;Hjccx-PUmF#NfE@t#pNqKdr56+-E;;;h_etS6jq1BW=)e7Rpkpo7{<##)JA%P=-LgehjN+`3ItkIsPVnvV3K3 zjx=6lRL#D^{%y!-R^+c%4x1U3tk3Djayw*wg(|b!Res;sEygZYb7>DzP zfH3M~udgT91^9RvrLrOv9itG!y&}H!?Vmd5d-(j%*IE3$bbsZCIJ}S`O$h6xU3*GS zooOZzVY{dp6X&DP>8o{pzwRh*nYS*#Q^AB02L;k)zDTyq7pwZ;<)&)4F>QIR_#S;t z)UAO@)d#N^c*?x`_U~RLICL2lznV3=BFkAvnxrA9vc0mx1d?bvCEc5lXZNVM@@S5} z9djJ&go!NQ`xO{G8B=I{=TrFUo_{68T0{Uz;l zF^`|%=*2IhN*Pqz!3K4U^sDKtsLBDXS4#g9`>_H@xoBC}I8k99Ob9;`y+au(iD}hw zZJToAT#S&oVruepY+2}_OP==q?fh>%`vQN~Xoe;rBe|MRXWT|JImJw(Jn3E2>ebgu z?8z1vsZwo8yZ)pjFUPo>4Dw~jVURx$0;Ag2oib?_%(Tk%uSW$_)vvv-Kpx~1L{x0O zG*`Q^)|tY2k2mKh#5Ao;DZh=$w=AM6iSXM(C@@RydF;HszL5boREge~v2Uz`1q(G9`Mg=x9j3%V0@o zK`~}UY-OI}McP-187e)M2!&I^PQ1V5t3)dwRN}3Zn9!o;#-UpGM&&x~x>}5z5P~+eKfaWlKtUP-iWX(_^4$9ma5y^7Z@< zPOSJ*+WRsy8#`)#m9#M7*FZnFq)wY{ptyiWD#WTkWId#{{$g+TP&iZ_|>LS zG?#w@%GSe;ctO-@w4?nR_1$&Xi6H-}LfmlCVD}AAGJK=P0Uv)^+E1Hix7O_TYiX#1 z6Yh~Tz2u5T*I2xtEq01buzM6~@gtt;^Dolg&R!$3bGM+^&r+#2PuiJmUT+ihA;)f$ z$uKir3mLlyQl8XN5RyodS&j+H59^)vLu8KbcTjgvp3z8mHd!@D6X0E2erTL}w!tk6 z-bkDGPnf84K0e7h^1o`?VrI();=7zg?ZvxJUKsCfn@DM+WsVs(KDkNsI6XA-~dzrBX`LzNfcoMIBn> zovBn%)PeXp`2-y!M+3rfQ4aq#(L(7~j>TW0KJM7X&9I#H3NOlI(pJJ;gGH@{tVu}x zOJwSU75P5Xm28A2&ALMm0ZWHChu8}X+hCNP6=#fk?beo^lXP__5b%0Jk{DP=L+?Ra zT+kYG4$KWbo=T5pzbyr-0ZpnD&bXQL8IDvO+8@%$pI1J&F*pgZN3u$@TC2Ym$cbhM zKG$h^SNi<2vxv#n?5VNB*|az&kaq8tnsen_EXddr>%kZ78QW|)WIXv8S>%Vh%HAtl zw7{r_N{bs9y{pjM>TO{iZqm!bdO_8q@VU}{T%Z)UyO%_IF+cwr!f{ZeZo(ob%TCA5 zk$Ao5adN-KUgYT#_Qy?Hs(2Ky!n`2tOPw|!*unIeO8Q8!q@`|L29ziDLM6!XL)P{3NXbzjje~R2paa%#DVJ!s@O$c(o@o%q;yI9MBDOfgKd zY>!}lJsTw0E)%H0V8uVHxp%f8SRZrj8C*HpW7#vSip z=0wOj${6kH?i3zQL^f>btu%#?LJ2rj$jlP;LZe>Xv2)x-ja8FBNY4`9n7uHXl#uv( zKafaG^?nmOwEgakwq>rG)lGT|T(sp))^94Alnz!e3e=zJz$r?X*aKuFaSa?K=qPY; zs2cp$BGgQ1-??vYwxVQt+q#}JcZZqOExNx78~5y~UG%aIi#v6UDxpwLPIc(5S{}2j z?~3>F)|N|i{Yt}UZg|p=n|j#3M2uP1+gan(>vp}|u>MM?v`4J)7VeEV+mKzI(jtsr zaQP|2{h4s6%HU+;EYyP%c1|&ln0oDcNbaZpZ9CWBDREy?8-;F6RUtXbe7LBPU1t7O zji5cX&Hg~Z=9PQpMaxbVEcbz5LqOOog8c1^34Ca|Jcn5aWw9Yg=P4zpuDk-Ss$8t4 z==r9hK@#jm>4k!q3Q36x#(l_+Q(W*bOQsj2wH=nAcAOef9zv~7Z?mk=Ey5ESYE>Pw zNxA$|$8?`98@0cN%4ru|J$L=o;AYo!HLBP#dLAZ0>D)5Ui2B^g}0cUL4nYb)LGxR%3`MftUv?#1_{xH{jLk1l3Uy zLq-{kxkNRuxe?7N+kBBaaUwmxf%}MFAZmOhO?4E2A%5sVhwac8Ifm)K51p5ZngrGg_!fBK|*2g*>ZKAp7&FMuOxac zwQ~JS=?~*?v=<3xJeAlI%WzyXI~Z~c#dTh}su<_Dc*uM>Zs2Gy@0l}rM<5%?s6$&Z zL?{wW5bWUcKy23HHqz)ZVSA(t;Twk1^G%BEG82>oiSr~C2|;(E_MHXT{_i9e347}x zOg6m~p{U*HEz(m6sfeB2Fj>v$sk{2_pa#-QbTd2LkvKlf_C}&`qo*G&O3+@#r>wnd zN@Q-`CD=3A+^X91;KX2cDfv_AVprX6KK;Fe5-WNUJC7>#VBOQ781n^}!-;S3jP9rQ z`t_(sIYFX*T<8T@Pqf|cfo4MT#+9M`QhD(&X3l*5*t*5KMDl9Scyo6lsc#h4IyTeN zd0uK7$lC*VxHFzU6+HLu&Pz}?_1>x>SV>=0(BEp3)Gw%iGbLJ5gD9TDOOKzbVb|PA z>WLnSuoE2q!9Y?a>UK^?QX?MUTTrLhw+A=0(#ke5W{q|geWbq)JqgaDPpO|gOp$O3 zo)>l*{b@HeR;^QCzWHVmS(~NCA?PM{e(uxk;$?q!*Yq8He~N$1#DJS9VYMqx(Ck?w zN?po$(CpbftH}_Ni5g@tNN}Pcx~~t)3Y`>>KI?W5O`aO%7NO7z@#>^hcj zAWlV5i`}SZ^i?!yuRq_H8$B}!6-Ozq#|=} z``+)1I5mygcWjb)k}0lJ(O818Z)nL$)Y0^N^6q#{u8yGa=~h#UV9yt9tY#z2G0)W? zbZecC0!^B#H1CF`3XY08q4@)sB)+y7=K8y)=|0`L6UF@GBCMXcr=I1O86WdyM%a^k zOlnYR#fPWOL(gYxcg}BThv|jvdkmGv?#Ama@>dD`o)uUXjd#4A+vE*nlbS-2<^?=P zR^HkVm8L6H%T})}6vgf>lRr*N8XU(HVq`zS{QlG?hphGvoVBHZU`nkt#$R(jI-2t1 z6ciM6(qraSg{Y!Z*C?lmoLrJ7mquBK725TXz9;*uW+nc%aGTcv4H_^CtP;uDrX5WPV}K`}?6*aI_H?N{;e@K$q`)9^{0#1ksv>Gb_Aq>@jM3rvm?_aR2Wap5^ScdBg6){(ci z1>#(gM(YACQNSu5#RlrL$=73bx*&mcAwYvV(#POmoDyu_iOL;un; zUI;5fG$fe~+8%CTHn@eYxp%2?JJl&IIzvp77J)hyl{c*`?UocTTa0WkE5gR1SXTB7zZ$(7OVINK$bX$06 zwA3nk%juTel&>u&;3oT%^6ieXG8@{eME~gD<4`L838)r1L`@?Uu!Yf``HuAujKL3Z z#dS)|U!)>8Cu%V#XnjvkTVZ}g62DcH&II_gihYh~-!Uk8wDLL>CcH=08Vgw}vFNw{ z{Y^4Yr<$3muc7bPcP_mu&DEjY_1zxva%$dJ6r-nYEJ-ETX$3U1)pK(k7rXt3w*NFd z`tukRCdu=Zft^{R?SSmA_Z&4y~83cJdeZ;?ojZ&K%j62*>n=9BWnlXnQ8p`yEcdh%;X25$NCktGv((Ii|8c=HEK zt^q9h&JKr^IEjwX{+TNx{M1kgPA{JjH3e>T;~gkNL?rO! zM>tgtR4hL|{+U}%&~j?emHUtWw44y1f(7*b(VZd2KQ|2-4}X5)FQf zi?8~n;b48tW^1Z(C`XI8dcACnEXo+2r+2Vc+%tN5ylc~+rZAMIK)c%e)h_{gvaDWe zIoRu!2~semM5HJ32CpD6IcmlmV<0# z)vo!a)+2pYF6)-*@vr56@TBb=3q^_$5H%3ONbqkOQXVNswbv>eWJ!h4t&^+_W}8*J zZn~gKttYd6Y!@!1JBhP8|a1w2Gj5p8c4}mw3=7l4Pqo?VzHxSZ0KEU7Tx+LRn2$sUqnmqo5atSmC6ho zp(u?nSFh7ljOFT-SLvK>xoy@S#?~=Pr>lw?q=&Jbd>NO9_|C!beV7+jEz0`dms;gj zHfL$%Z`3_=-kiXA+s6qxXJL8L=EGT(Tyha?*9mV*GF*Y3>@6Fgz|**O_^pOMu{)fd z?B{Ca>xa(Q3^7uSOC6a7zIl>=AO^utuHlCglPTR_3v9 zS-n4`V{^PS?=qxiR;ix)J5r8G$qBi^=mT$qQ9Sq^LTdRnS5*}fvFh4pJwoHVL-rTH zWrXO(>F!XKvlXp?^_SZYKS?)wEi>K0OM?~kcY8#3pVRV~$fU|-detd1Db4weqa+}{ zWCRbcnt!Y)LK&BmAlM@UxvE+R!tSxKwBSDN5#AILJ{)(vgaV3dOm}?XC-^xA^%J<_ zv;gfqa?elD^}*P{8_2A=nxX#1(-&dwoXNIppu@bl;@PUKC&nXTO!B&0h+GA!Pk&9n zN$L|`O7?qhFfefD4JP*B40Sjut&C_RZ|l4bBe@^y^RDn{Z1Q{o`AK!FxB|_G-Xz;D zdI+6oPDby55OztwiO}U_DxJ`NE>wCn!F6J}eWy>JJEm-(HL?etIk(+13w);>v+^Fx zdf0kkF0U@!Zo*RSo71R8`me!e?ta?Zs@=~36h&cT7pIOIpO;X?W|EPg&>k;@a-Jri z!t}lO)vBl8(OP4wobx0XlO_BDM2=|fYNm${*GBp+2eZ`7b)5ahH!5bwKl2m5If;;? zON!P3EstsN!MU>LPo-ZV2 zZTkkCyy;%xjZwOf5yxH$a*2dbryKB1d;u78Jo@-u&HuZ6bd{h(?QcQ52@%`!lTvw% zIUqzY4Nzy!;~td2z5K%eiS+Fwra(S#C?MO`CPo-hL+81Z(V2PWPv74t1w6aw0EE+b zi3;7eUzY1*CY!>b$#TERzkLs5v7Y333h|x6<(oj|(Oi4}<-ylh0+rLm3Zy52EvXDQ zj-JvWR298YklHhPqL?#~@q*Dcu6=P;b*I8_n-~YCLIxIl#BdRBcryuN$8N@`bU?^C zw||bP6Q7>>Cl4Sm4JhBf_YX^%`-hEB2DQaComK|goPQw>-ICmLFQ1X2=?fgp*yBZ1 zyzyWb)1gC71v;-D35dFCwd#&)4xzL(gClNp2)&&`oPa_VRJT4n?d4NgyBfTy;pTqI zTxSfgUIJARIP_?B$WLgj3mPi z$J@RS?;(?Ob-#5_EQ6$Mhh6=dOaHz5jMBj+4ztabhD7B-jue3JqQdh?o?J4J0DAyA1L`Si=*RVxd+D~PNh*pl)QZL=XQ zoM3yIE^xAKT}3DXX<+Jl7?;h7go5C6d9|)sesbF;fav$dad3rMloG8cy$=(dmwNB) zu$Ras7vl0enoibvD`3|6+D5ip=!9GvS*j8YlwUY*vy=h-h83DV>d(~Xo3q`ALs+U6ZA;q9fpams4_eWOo&SwqQr-Ev5#0iAdjbqeelkMzuCh8bR(GqNJs;)N*kc?b zIwTcDbMw%9*uYMHM-MnPQ#I*>K)5Lh14Ns;`*oP}o?XK^k*cP^#XJ3h6?6F`nFfk2aVic1r(6+<;;0DxqPkl*SRdDhqwLD&Cs!I`=i=3_I*H0+G za;DSxc)j+BgX(yG>Di%5_12uh-=DjU-vzLai|-RP&SB+vM64AnD!|RnotLOnW?dLW zD;SF`v;~g*@3ntSsyokk9PvP4@!V(CHRgAbF-%UuaI^$vA>egne~76-U7QO5dwCm9b+T$B zTPL3$mOjAN5=@fu77By2Ps=K)ng&s2jHHy?jJ*O*Qh)qau~|>q>8V$_LV}R<%uq4p zGH5XbO*{Z)Fh=}{#mo%=nCjx}rTm)t6^PjZRCN@yqa9-W5(i3sr?=YGPNN&GkMiWq ztBKu`$yJTuqbpDX5bjz+i>P!!qvcxC@aB9nABIiuR8Cm zZm}eJ_3?E|{u982I%m#(G+DR->@fx`I-V5M7W`8a90fMF%P^w{edX%40x|ECd!;>p zvx&?f1*A~?p9FI-gIJ=OsG{UP!VHxEqMC-DDd4KkyfTL?gB`f41EbFjawwirKYpFM zdey)pG~*G}g82dJ+>4ga{4@4*+P1rr=)P*Uc4=1a@j~1sH4~ou{B)dqIF_W1MSoTQ zjSj3&8lFF*V^=$aP9PUViPTeH_{)trOmddfEE5RVdM|hATsL z-q}@zm5rs&=PlN#1384!>@ygMpK? zdisl0Mgzy$kzUlJ1dH1K5nbrAJ~{-L9M!Gsv}JG5RnarxStK&dkgPB+&4Pxrzbb7p zRWgl(JbUkGZ3}?hAQdG!Df=HPgd0HQV;KS#_J-UISi80%2nRRb|1t@7TugAS>KQ|h z{b^qdeF*T=GWr1acGM$n<(yodnL{hvJP0l{OTc0a5W8}V9`phZ z);y02AnXi`9SUDbFMs@VjU>bR5;VrB)9a!eN1!(eRqui!iCi*4sQrnVqbb*+cTmaN zOj+z|lK=h_A%XQs(Jy`Ev8hhsOMk$$IxZuP+rlnRhZ?!srdC5ag#hGrs$JFz=ZQQE zJP&QLKpeniMRDqA7r*NIAy5pog<&9IV5%;mW@RXswkmM|@+fGfgSU zmde1ZQ0I~at`1}(#({vbiCPaqou+snjF>sB-grDmqOq~tEzE6;0h-!+u<%B~L*r&m zeSQ7$6UXkyEp*5uBjG8ZD>FkDjj6KX)=Rx#xwdGlC2BoDI^rC=S&nn%>d(B_A^fa> z<)y3*>DW0-pofFFYPOzuE`5EU15Sr8%y#?qU%iY0VJ{~_-zE5;Ys>fTe*u9F#MipN z2cX3O$y|V29QE9+UDWmi@mK$VQ1RV9@rz@n>Vr`$8^ByZ0IG)eK<#`n4lZ<^aBnGX zKb;BWUOV0Gaa~1X$0nr$GT^qhwt4LJlv!egd09xp5+u+*4#pjOM&chkS_665W!#}f za%Uy@OyN@4NbI^9OM+9_di}*YSH89rPzJlM-neBBQeQ54 zRzn=+6&C$zTz{d)3yhw60;INS3u6*+-!Vadb6)6-arsFq{mg7p?7xiOl&#^fWYhMJ z?td(t0Rz+bSpF`vg^++dlcUy4(Yij0di@vYhxKELqMo`_il!PJ%{y{QUOOMJ)9|(8 z-XRuRVnE;YK0iGcF!}*Q(>MWr29;*J#D=Ixo;~Lj^Hvdap4U);IM267rd5dnc{?q6 zdFgjbE}$i4$TN@XwW1E)+ur(^iPs&R8x<=%c{M#^*c?Zd^7FxLjkry*i=~v1DnoN8 z{ZhC6#-8lFs&?mX)U-d<8enLyIQ^H75cg&M`H7WA*-{ONX~9B8{k>e1b1peY=>zPa zivayg$dsuZm~hDkZ`Vo&Qu?>2!(V>eSoIg+@WW%2`t-qxUZWQ*z77B2Z(IjY-(7R) zKpsuwp}!%BFJmXqwZfM1ol8IL?5k10E2IZ>t2EmSIeJh_^wQIW4qmcJ)}yS{DU`)p zMJf#?+%#LOIPJFAR%7T?L77H@>QjRI2@cKFX_PsI$EXYJ=BdNEOl}j}nfol@!vA=SY>U6JK%}2fQ zIqrkOdA+tp6e8N4erkVAdh&dl81?(9W7)Y!0GjTs=(yzHtVLO1=|BGKF_qhSc%`lvMBRxSBFz zECRz9l^NDEOSc8$wSODw%R~9$fF!p$%PuVp!Rr{=0=VJhl zP(9Y|h~i{C46h`=4dJx)G~g~>P(5@`8t|V``cz`nbd^>dSrcb1%iGLu?g7{!<>gfUb`rlDv2cu-l)PY3(yPDQv->x8mO2k#< zwZAgR{@U9je}H2{jMe9hwF-m7`e>=GBQATO{BNv!az9HKjkxE5wV@Ce@o}Eo zl7b*+PCNRfP{g|qWL1uyWhgB@Dx3$AQyEpbNbBV zL5sSWyf`l>QtXc9@8I0oKiTQvK4T`ZI};#`ryP8_SI~5$_)?$J_u7_3YwzY&Zv?D4 z0!=X#dp=BqE(VLhcIUnVgW7mfu+kF<>OXez{}`U$REgLrnPrfwlGRKTob>81ee=9R zqBUTNYhTiNGf~h^U&gi=fRiLN3@~GvM^b6p(UwsjT`$FL#~+n*PnzoGs`Gz*@vQ`a z*Y2#n%W~f@1>IDebcMKA^{4aP>p<9iSn5wi)ohA}Nng_X%NQ&dar^YIL}@zrRGl=f zH3NY86*WlT_S}(me!m)K`@7K(cfb(~=DOgNM7-+P_dle6M7<1u#a_SNOlwR6F-5sO!-0oRW-2?xU9mp6F^UfcfH>BU5fL=J+9JzP-hqk~>V-5p%j zoe>5Wy~z@xXDthA*@HmCYogFSFb%@I1Ci!`_)>&F;7diyrb+)?`&Poz4gdG z`TAcE8ZKRY?49%9()6W(*j~!DjPr-Sb**n;t|b^CCoK(GUh4tM*YE^j5NMU1Y*c<- z(R(_NR<5e0yJ!WGMlN0Zp^(K9uo*~@+|%j7E=aOCaf(M8%!`0??Z)#4yeIH%COuX; zFZJZ-yt!DQBuG`yg~{JFcFkg1i`39x*3bcBw}Re8B!%1HaMEwg5r5j^KH6Y&CB+80 z8#lSs{2iEREHqm#EB#ei2+DRo>nn`U?-Z!9>t!$GHRzX|iH zLygZSpCE|%Dbtds^>}kb=*1_1s5)Dwfl4j_a{fl$MLHx++bmD3xS3Ps&@reub7S2y zAuF{|II`S%b!emTJkvD&O+4_D)+Qr8$OGQ~pHjaYdoP(`&}!z!#_weyc$a}MF%=+| zu2!9~L$Vtdx)X#k3mUo({-Pk3_J>xEO1&avz@~n^%J)08`Rcx`srx=^Py^#|%JY99 z%?d~R|GH4}M?mG&)v#7rOHq&T7_V`6L`3hHCAv444()M@Un;Xs$Nc0c@9B}|KuQk) zxYad}*h1*l4YsQ^DwK|Muz@~w0q-%V9nNN$(U3yNexwChQ}yW%s>5b`=9mAQq|qd3 zRBZfGW_luz_}Ces#*B?UuexGK3XGa`r}zYpGEc!Nxu1wm^z9pQ(+CnMlm{IE(?s;I zZD##(sY%Sj0g74K&5Yjjks8l&)aNHlED-mOGGAx!ufD-0!beIqIs8lVfu^ntGsn^9 z>b!-5{ev4k3h-R6XilA1pkHZL54HMsHirwFaGwqespN_bh83sElH+AwOY%O5A?mX% zV#JjUmjzOWgD(ODLe_RZtx)22%4h?0g_3dQ{qP+O!pNqLE1Bt4 zg>0oSK~`lbTO+^1M)R@_e_;F1lVk1xS{UPyc%SJXD~||kSWZvcI7pTC?-o7O^#pWc zEzYhE5M#?u`7Xlzj~)?MjTWa`dKtr&~0 zKQ~>uk>5h-wW8$PJ3g*U3&QTWLQr2k{9jsdIWi2zxaK&qBa0z@jDO#_L5ZUG`6}qQ z3r;UEE3F_Je&h^YTX3}LDU=Hjr1dgi7xDg+p<@9qec4IiKG8pUu81r!Ln`woW`E+_ ha%U`f9fb1e!jp{p^^}X{0t9@>N-93fdt&(Je*lx0w>kg- diff --git a/docs/BMDatSci_files/figure-html/fig4-dice-1.png b/docs/BMDatSci_files/figure-html/fig4-dice-1.png index 3c811f8ae415b07ecd9900c667c877d746e02293..669004027c7743d88960fb7a28bdb36da6564f33 100644 GIT binary patch delta 48243 zcmcG$Wn5M3)(5&sMMO|aP(mpMflY}lNhPI}k`PeomX=%#QBhDp7Tp5UAzc!Rf^>s` zgwowzcTCSY_dR=`5BJmkpg)lnGoELRe~r2Pn&|K~(Z?(^%;m0>`xaeUU4$uDG^J9~ zL$i_`@Oagz%AcLQ%*=VxT`G9wbfWy(Pz2w!pG~P z%_<@Sj#I)4@*`gDBF=M>3L3FNMs#?D6f3koD=SZQ@*lrASg9j-*jmuR%^GB=v002( zS4}iWv>V#`x?nD2QTQjB|L-q~4tx?Cl%@~^sfK{)ri>H{j}TvB?E5>Gu0k`_PS@$R z!|l=L=OmO9hR1sm8#A4CZnVw+{r*3n^n{7pq3i77XC>j_DE{j&=66Kr1PL~Ne+zKfS$R}$KmKw?+s!@CBQMo*lY_4hiLKpQ9ICSHF0mQV_fd%txg z+~GRBjG5%*^XJbMpZ5B9bllCGQ%CODf1L}wcY*q&7z*t$AuqS04!3-+N8uDTC#&1~ zR2bD&wXcJFa*S@QeTNIu@Y1DH?9nychJ`%A{k(Xbge4P;SvzBQ53RvdpD{ChF+r68 z6#llVq(qO~Qu(x~CAM#|>q7v}$5MnaLp|*duG;b{=F>VkoIZs)J{`YtuuK3=6knhb z{ZN^4V@Sc%)yEn7l`e_H=xyJ>uNEwJl;npvv3cD~GLzl4Nq(C_!3yua;RpgUSrncs zk>E+R?av!@e?7!z8`c=K#fx!8@tcI)3FB1g&D3NwxichTxK?D*a~sY8^Y@eIz#Ae+ zJlx-oczZ!OdLbQiFwVqp*>j}=i}opeg~In?3zoomi!YF5%QX%E*Xl*q4q+sA5w8Q@ ztC(j{Wjs`3v$op6)cmvD@q@!;liJ6JQjK9;_p?2t#Re0le6{C4QvSK{IIWU%tScJG z%^Lgc%XZ_&5Bo!j;{$cA9NkInAe&2?nHq$WMg)(Ua6YeTNS?CQGCLdC-=o8==Frd{ zDAaD9?y#%gmHkpz=fy7{!4s$2U{3+Bq$Q!i!O`4}BZ|WDg;0|rOPdIF`;8 z4mp$f16YJKdemFE&Ow?}D11iJ4x$kwPRhXSjePzH<_2aD~CBMx!DtZ3-5*arM< zAyj6L5{~_?pzY5ef=^*#Fbc=|BfqA>NxcCMg9`JEnxG8%b#n*fWjX+`GXM^ zC+<}dH830_Yd-8laN-V}LzUQ@J>(!SV6l40(zDH*Fl|T<66f^V+juvffEiIHyN(OC zJG;3uM4UoCfgFqF#Vu)htm6$qC#c|sv@G%t$Zu?iD>TKA zc8sC~EkcH3DjcT+;k4?K`J{&9pEDp|LWji$;ftS;6e}Njb(V$IsQyg|i`*I2O!X7U z*O6#4OA#cNl!W4t1#%LFZ*YnNwRtY$9TOfMi3C2O14d2OUTf^TSexyC;id;*r3^f@8_gOYQJMDG_9K|~-%*ktb z42-ML(oA1{g))I`H*{XyVdvQHL{6GC zhevHk3t8QPwaw6=M>=wM#Fud!`eF0#`ZPm-r_G=k27XnaEc=!|sXxFr(WV=1shJ{w z7D{dj6}PSic~EhB%$eI6y!t$b)t~IWcYgU9iScx}?p;KAtu=DU7~(EDP+{;0f0A4j zbJvLX+R_=QaGn>_beL}A?oLyUpyAdnTTqO-p5wGUqJQS%jfW+?x<&GXMHb^~a`%rm z^DvE(d`2x>Qoh7dsB~f~=kKyvO7WsP8z0zMS-%pp93L)Yn$r@zIiV7VyN<$r#>VB7>(bAG^Ty5Lt@+#{ z+#N*XB4Rd6)c?~TDdeT`$R5o71Vq2j#!j%!VtR*FK+MinNJV$na6(j-J$ce~YLv`*@-~VU$o- z=%z-YL|k)gjIfNaZ`)_p;n5TYN;x6J<b9A2Q2wg{mjW85hc!$Ox^7b7)#!Z2GdJbV`osZn|V=>sR(=S0Ai3C(YB>6*M6Y z-K;%6^``gn(cxs}a&@5#Tg*1L3DqlTsvH!LcFtr$Rao|Nb{3f`pY1a*7nUK&z*MuH*gsPx-j^Um{1pPv!s-2UXD$_X(*B7$ z@l}{FU%n-NeBkJ4NFUj4FJi<=Khv!pK%;!Udjcaco%C6qOhwn|zU9ktvji_w_hU=3 zxWr0;hi!gOtFnjNH_@E|`r8lVwp)cqos6Azhc>EU^9O3WQU=aTE9YpC!{{d+p+3&&M3@ zU46bul%1e4BTPZ`KPO9ZQUfpU@aG&+mXWIEeFxV(;0ia-JEiW-$W*plnI?xrXO6L* zsK~CP3S^Qnpv8=9I_-Ro^pD$4obKm2tb-?liqtOF$l(f5GYW39@G;cB3P1+hWc1BK;3D zX{M#@a{MclD;wq)^wBS$dF$HG_i-00`=+4B!-Yu7MuLKv%mti!v&&^E^ch8;@6J0yoo#tg3a>iyRS6d)y{s`D> zH)+4VzG|tJnqFVFj#0MlwwQTG-_mcz3(n>ZHAj2ROR220>qJV#=|U?xmF5-2VkLI0 z^9tXbd2-UGyO%cH#pBM7?0xA}B@=v_>AN$hv_bzGGxOe0>0ycUd>>!n)kpgGM}dV+9*nHbzb#rh3;lGWDu2|WC4A2d&HI3PU2A8~9oL!ED&vt}HXRT7SL zwi)D?`_{Y7W)B>NgO~Wqe+I0tcQdjaeA+zbuoF{{HyIOEtfGuhJC;zLcfItlA8|z% z-NX~ix9H7QN|W*_vS!Z5(^IadXk#2PvUypQO7jb|TAd@s=-dy17ozWuHn@!X_PX30 zEq+T+$gPlJKX<&Z!^091Oa6TNi-9xi@S&v5EbXf>~RQFOuxFH{tt-mXF z==1BwmDk*5`xX|(nQIF-P>n&EU$-0^gKT|MN2s4LUN}i{Oxq#Rw(B;3+}>G1 z;EL(c*>~IK#j6%RoOk=yryFl#jXo;ny^G0nW?b(yjQ3wJi$*(^Z>4M&o8w2fbtXAx zPBeuzx3rAZU@!tK6)I{rZ^`E{^Si?gX47J2m9n0tRu@XG2Ohh#&o2jVpaXRTnJa4L zWM!lIZfw#Fd)?M~XJdsLi4-})WC?kMS2|j7&wHZlpV7qa>Nf*{GU-lLP7?5#&0eKH z*{gcRVdDFxN|!}`fv_n5gMSiODC>mjSeN^y&oK7D&U4Hc>h(Ld{Z&FYdel{;BJXUt zqZJQcQ*yLS0X9{v@jkpr@;JeB?e*-73y0^k{6cNFg?#(kJYJ26SN1ta%5tMwv0Jh( z_j?RIsONu*pyiYuxVG5{G%6#3j1GF@l~@mSjZ5hci(PCj=*jIxzx1g@r4&k-RUW10i3N6})!Ux= z&=FdVofy$h-%qpL@Y-{VK$Ft)ghD}xwrLk#e>0;P5N`)S(X?+JR0=7;(-^aY+pTSt z$J&_rnY|B+5!tNN5?1;+ugVU7X+hmSS|>6M)nzJ%-1uE!I?2#r4=t zXUJ&p@)NCNMg&f_!&p+(pyxAi7DCNEZaVHn&D`Sy4*li98heYTEd z%43}v@=zhV9Ph(DQ$3KXzTzfs%uBCUotPIT+00#Kds|qwHE889*K?KfXV24E>IqTr zgR*PdA8SQPZG6vcr`MfeEe&DT9|QPy&1SIZ9YT9n&tKCZ^S5MAaqLvBJ6=yXE+dqQ z+xDoi_VU&CX`E@$`P>p|Y93@Doa<%}}K?cN-{{%oslw?&_968HV>W!Iin<4fY( zV~x9u>B+b8H|BbqW9Mpp2uv^A#$n`SDHr@{xH*pxrX`e_wJD>WZanj2^8OLnyw@vY zO^&tPTZJFZDT;;)f9p;TyN(JCVD@Cv-&w@1$T5Y`kiD6!$YTRqGj;c^b%RFsBAQF< z!8de7+X|z4LRc4Ej_3JCnR24wcv|qh8x_JWbuI65NYJrNYgf z$GaQdstg19^j4rJzDjNa;NB7$(MFd5x65f^+v#(^$I8aRYEIf}X}kD-%V}YqR3$pf zyb6a&|GHb_PtdPJ+mtcMb%N{-A-`><#hThK^!FIG&LBe0%ndRvS6k&eZYNjN18%fI z9jvmJf}{1F@t|gMi34UzcBK_J=00Fj&2TYg2tv@gK8~GsJcq1O4W8!#*K6b%47<6$ zK@bp?weq(#){rOw;^;0u`^n;sUm}+Qjj8DE&pABZ-BYj14h<(Yk za&)L&tQYEn=`?i0&mW4wNH8qx=G7$XGG)&d_L$vSwZnr-C&64QetLVkQeh;g-yq5e`|0C$HGz%nUq<+wu9xOr#f~w$k6-fkTvK`*VE*jKZE`VV zd>0ZoJ9I;HzEOh`aYpaFuy}v>^YX9NC+r5Z>c}k<@B2}7SgSghRLgx5QDyEe79-G5 zw<&g-?`u7K(BnKEl|EQ(l@@dH8-1gbC@L30=@I!|OI7yKDI}z0Z*c{V>@t7mGPPDrn=(C09549Wg&!e5v zok>OzDG|f$LuBoJMY<1#l0+#;OKFf{E$=%jbM^g>)y*2}%h59{xu2t}nWQS6q7$a; z#GbiB#qiVRU}u#bmDyjIn-TRMeMWl2b~ayMk)W6fbC55R5Ge9qCq>t(GEnS&VX(|` z=q)pf9@_&%TKN`08{=cLdA_&<3j%F5XLPhGo)pffl?L_-qnKT9o|dT-3!!;xhA z$Z@0jLWIg}Hd^iu=Z#}5^NnaSp%`mSwI|pW2s$efGu-B~8~L*%}iDSvo=B8nc;HBhO-CP)KlL8$LQ~z zf~tpm`jYD!2tpaKrU*}*Xa<~>0u3l>AOYkv=b}oGs6#kZ87=yA(yGeeOzCzMxZL9d%LM^GD}bYA&tspae3^S5VN=T|M4y#~6Qob(UvHM+#Ax zal?M}7eQLE;urIM_H3fK-vGyO(?uRoV2d}DbTkp7wu&m&Zt+Qi4rp6|c0QK^#rV$Z z?{CU_s&QyGy;57DW{?M+2^F~DLO`_*W%AyUx^6RQY#LNLKXn85ewvnx)edh+K$0A! zIq6F0`KgoMb1y+)_j3Z!da=1;rA44UauaA-g|!(GqQbKVVROJz=soOse>+u7Ia<)( zq94@9)(RJl-OsZ8X7RX_3eC9oB4(c2J}8Ut?}0RBp*qzP#jx0A116lnyk?eG7?9(e zTeDwa3AeffDt%B=2nrFE=okhcYR!6$?5ItCSju$7+q6zwBI{OE??M|=XaaPi5-XA@ zSrBw*9TZQOdBOQWl^g2(N4IGvMBBbzdj?OBAaO8pt~V>jg$%rkK!%&_!nQwyKl`4z zIXGDD<^v zdNL-V=mn0Mf$wAtN>AW*zn3r3&l1WVCi(ZfQWVC2wBsK2QQ{J9iEdCBB6p!!3WO*e zBIZ7M@)7Y&=KAw^qYtBmthD4j6oJyZtxdcGgP{?c8H14@n)4&>oh>c%$*=0nQuv93 zsR-?lsNACFdGNy+o2q2taW1n`v48>biB5+^Nj@Wz9CuIxu@c7{LVFfb$3rDU?$o^T z_rH_AKk*4sv4w+A|FJPA8gULs2W}*!q`2?j1L0#zaC+P) zo2V_iQ{OfoyjlFxxh=Lc=9?r?r;gkPaN{Zo0n$`&k9fe916?`=)=P-Em;3B+Woe3X zj24KWrWI8QXy-dqtuY)8feg6IGXCG+-}Ym5p6hWLrk(z?d9D+F1v3rq{%Nc6`nM(^ zM4tJ*J&GA6ycs~t%L4|Wz|e^^7q2MCTn~fw&{SqWu2kyQfQLIGzbE|hs zOtB;7j&h)a8bupsYx6}6UAdR^TxYb>HLrOK1Sy3=(^g|x0f@PY7?jQ4P$>eaG4gZx z@87>qs*`fpO;w45>ZS2Ie{X5H644q>4M(fJK7ic8*Vp|PzA60Za6h2_2Vy0c7%1IL zN=h>K1Xzc6`fl^Lig@20lc2_?k{ZnV6x{ z&5bgsj7?x&v@RtkG9!MAIkPJmU{gY7Z5MTl%-4q6#o4DmlN0w)xZTgw6nHU7&p=x!a;h}+YIwDWk1cHYcj6-jYeo5 zz7mVe4>x{Y606ERbCwS9cY%P=x*9Ngg#k9q(E(rlecxo>Pb<#` zN-cXc&(H5P%Yv&3nwJnk9w%1T4~&P&k004@bVdCprMkilv`b+B7_?$_Ip4H@a}Ekz zMe%8j&jdGgMvICnkEBFtF=i=^yg1%^t?*E{Id9dK7{tlYtQ zk-<^(>Q@-o?>M>sh5{TTnPKX)=a>)X@A+i8pm6B%Q3GkQ4Fcu^1s`TwG<7#f2Sgl( zv+naErq@7&#Q!U`gK{%O`eRc&;x58=mae;ZU;sL9jn@z4;`Bjd0 zxYCu@>mE{_Y+vyNi+k-C#+a0*L?t#@8ukc*IF#G}SkV2MXQU8UyKSV?t%*6($ExXlJQ)iy7A+)kFa??x(G zE>OjD;(;KG20H15C33B?;_Ju@)~mqMTrH z^8;+wvy*KXw*K*#UVCFB+ytZokHxu;yn`;-RBYA%*v1nHHbsWzYu)*MQz$^KGGSgj zI+}r=k1Y{{nzH6@O^`g!c)UEW5kY36nFO$)?QnVB5B>uGkDRGTo|yiLaJ-~4KWNxE8XRjdyT5Fp5uh`F9u+blzRBo zkO)90pYF>u44k0;L^LpZC{-VB4QU4&)BfGVluRU@DNEQZEu)EhEtb1Bfm4m<6Df{2 z^+88u-Or0QmR6Q`+U|P&w#fXVs*yW%zO%|tyR-AG=!}BsK{LU((m04yeRUR zU&8qczN-vJwu7tK^rDt9#&ufZ`yxbCa%5+*>Co3Hm8JS-*1>bNt~V`1)UL2QW(%Bm z6S1E_S0wI32f7kc$m`PqK>bs3-<&hSm~IDD z2RZ+Jt@%t``UsVKT{!v7Rk(aZH|%sPlUzYb>DdQnJ^_X_Ps7ZFLVi{3^ma%5Fo3t~ z`JxPJlI@3px1w#LC(=K6T)O@8JJRAkwibB$?G0Wpy0`5wnLRW1uM}pS{AqqOTu4Z( zfuaX9vjQ7PhC?m?PG^#g@>XPEcj7%$e^-2Z!PQbEhSShuyq9qc-?MS|f^W8jrHGy4Qu+{{N(s$&fG$y297!2XW8&|#L*9{PI}f3Gt5$a~@7WqDj#g8sdZC6uFJmN7v%$??KmmZqUli(1F z;VY*h)3U5jYZEjCQ~qh|I2ni-$to5;7o%V_#x^+j!ZvEiOA3H&+IR%~^CV=O2 ze{$(R=P62RIJ&Lg5!4%vV@+r&@x_} zW)jEzB)m2JRzau6+bc@MG4%j~9My(bYJ++b?9l`VKk|*lD%4d?1GZV%Mfl7ixUt_ z4w{8Xw9I-#m5R4U*^@`3dQI<6G6$RE?|I!csz3eu&6`lJ`?>PqBC+XLI8E)f94!qP z$tQ?cc^+=|udOxnVw$1zKNg)q`~)9UC8LQ)EdTtzl9M|ZQNsl$0bs`r8oSGq!&Qwm zb+ha!e7gF(L@*S`jFtRNI&;2C1frG)q$j>p_o_6A8x)$p-A$ZZJYOxWhTa z+G*Hd>dsrgAr%UoD9}x0dEK-eb9|J!Cn7o5TbRd$iy04w}%lK1;T)It+FcyvUyHXV{ zi=2R`E7NkF10j6W2<_3k-FuqMmzXRBIzD8Z^${J^aiOc+OVFpYyQ|XGuJgVcFANPh zSFa)tBjV9oEss3i;)4;tH{N~jDv}Fazp4$g)E{TN609S4XvStiIMAyeh zx96X%+r}kBOq*xONH>2DYIwiNHgs80OF!q1fkLyKZ>)#$SMctd`K(Pg2ULS`C`L)R zI9gM4aJ0Xi8gP)QnI&ylPT?>a5@F~)Ae~rf(mdsPqZw8)6{b<1S$V~>ap&kn$lUjP z4f=8)RM<-ON`mL@4%IKOPdZ!I7?MVZ?Sj=c>bMJDd}MqF*j>TE)+-lOA#vGUk^5<* zOK}apsnR;g-=Ci{(CYFpL{NSiYCTw#aS`n#?@x90w_1NLHNzJNP!H_mRd9@l?^)eZ z8p*%d5rtG?6$yt2+yhNnrt}a{DnHbWZX0j7Noq;bBoXe}Ac|A55>`vT!~})Cp-lVf zk?i_~Z8(W7WUArqxwY2pO1k%K%Q?7LdN`&eZi21Fd7Nqz7fow|L=A*mq`mhSg+%}~ zn-4=`NDmjB#EDr9nPlIgqpmyru(MneiqQ|r9(MQtSw?p|r*S*jx@5kq>%XP zIz49fH^V;;5Ovp*Xw#+lvIRL48Ebi?_Yo*ASXPA+9IElw{AlOPtZ%h%o{)%B9m&=a zXp9%D94K;IH=WkM20MgGh47vj(dR7DoVor@r}Xcj=tO#?Lo(8ERPpik z5yy_PN>hA>3)14HJSC5qSaG>eYe z0$)$u5_vL)^@PwPPpQNHmW_aMii&$mVXYOf6$3y2B@e}U)=4B1p?EVX&%8yIlHu?@ z)UDo_K0E`^o)joIdR(n)@7qaUzI=HCi09eGbB4v1ns#SC@Ib|;RKhZmN)N}K{+<;w z*t};DC%VdM)js#5eyb()Dh@HRTZ$>Es6ro-frT15!&UNIl_UV->Bp-AduTIBwmdSJeY{{Wb}N7LaN{ zlE{rn9gqd2QZ-Z8V}It#-vB6X$?Bxu1CME&*LPyBYn>$X96^kTn~a}^ar5J-?X?NZVfP^Hf3eIU7b<=P?00+ ze#*pEiEub0YON@&gx|KpI~{Zh3KPTjyv61vX*Bct5p{FD?`kBX(gX1HgZ#PuZ+me$ff!ql#e&~h3O&QGOOuy~rRyxUBC>Go@-zL0cQmG28u8}ef(hr?LsT7*P)XI%jF8VZ{d(73J}tg zDzeN*#u%t=0T4l3;6k#$K6m4M(;zba`)9H{$UCMqZVcn<9%fbxIk#S%I>*S!11$#O zzVvne)Lc!7fYRsK6nb?H>RQFH-QstsnGo>r(p4^4UOk1K)8B*vxKG(Pdu&(fQNXqH zX84JajxVxswj*~1a6|-Tg`ys&v$LmG+!VypC~E<2OZx<59DxWVBqZ;^0r+PB3)*J% zjS<)z%DAa?b_zf!6QGs9-rl|8lZExR$6d@|!XzG!YK0%%lN4AfG;VwxoqK$BFA0+f6K!!rKYhd%y;7-;X*xzL{+-`2hTg zR-&&W#T-=qtl3<&t2*Kd-CZj4*%Lkm*)RTK26DB858;xB`rr5T+Y{nLYmQ=DBDi=H zB={Nz`yir*jNk2wq#53h&e2Jf#OoXt#_7FwB{p~Z2xgD$=+{5vB6Pr?RKyLyd0D^n zwVAz9@tf7T6jnDm!Bfbb%l(}^Y&%^du(&V)6_MQ_%}4_rF$=FbSW`s0k!tN*dLe#- z;ytG){37y|7CU;Kf>v4P+ZGT#n?W_{9;{k*)=a{&d()eTfj{WKzMWYd=Zf>pq(3=Q zZxh~QHPeoZ&!oX1xl1*Sntf^e-US&e;<@Ep*Mkhs3v&ulocs$ffgll*L!q6?au?1p zkaxfxMP+^d{8^lDmYkF9nc7cM?eXZcymv+0J0APE65HP;fo4%9iM|8t>wTYpZIg;B)?l9V2-mJV{aTrqrH=n#C{gV}&jew)-DYWlZBdHr z%)!!x0N~l*ZE;kZ?u?^`+i=YKWlww*f)5U|`l1ONA+vHAJF+!pRpKi|qGoP>1?QH< zFXQ^2@RrKgZa4+{g9#n>%kKF4O>XB{zvJ-QX-=Qq@vA*H zObiqq;`6mecnJcM!geFqka$T0?OMuXFyH<)bAns3A)-5jTtp;p1kCVf;Po4A z@26Q%b7K$jkbOY%`>G`E&QpAc8^{C^9jiX_VTN9tnr=tC(>7IwP(cUW#Z_Lypc@To zqaKhGbBOu=`_q|iiB4r82dbPYEDbawfQo7e&kGB3K`J2{>^Y$+wnTU5oiLMrF_BZ+FdRb8rh3y&TM7a zfFsI=n!{Z@0drHdCzW89C`jRj4MJ#GDJYO(B7m{l#HY_cq!4uM<|L)L77P((tKKZv z>Gh}1%wb6Wgc{{@9=W-x3OLyf>Z%k7;_b2bA-|ylN_#mtU4Ja^LHT9C{o9kWC``M!Y~Oiiq~mo7440zEYtOd|y_2y<>f{Xk*p z5=wUHn#QqaE+P(6c3k7LW}JJ z{|8t|5Y#AHwDa6V3C~T05i6qb?1lPk0POm_g+WWBLLD>^25RdgTbP7{HZT9H}x+DHPuA_j2pd(QV_p^YP1`rDSx7&RMY{3E~xQiS$v0)JA6_5s-$ zLoagx<@>5y$}5Bvsimnb;+Sih1(Ezfix9oA?KQZvE)SNW>kEHw1N~L8*s`}}3nAUW zs!hOw;fG1%ZkrK%)Sn=PAXZY8$U1@O2*N$73VEN_{1v+`d(t2Efe$tPBc5CDI$(#l z$atM>;Ki#DnS8PU?l6RF`0^lJ({a;v={v|HN-+Qfrn*XyLB7o@kL?QUb^z6aR((pg zV2MGD!DXNUkxgh>DVUJd3;LxQY&|6pwGR%9`pZ11%&>WAYJmf#b_xHE$6Wb$Jcikd zeS2njKrxKdWH1ZI@Z!ab@**+t6ivOrS@=)7$J`V zc&=?k8zNYK~yMFgz zy`AB+s?006{@_yT43^k%L5xtYR-@y-d0W`|jS_Y%W7eySTVYQgdfexwy0xg3g<-iMMOd_=w+R`Klyo|#?VOxo1 z%CW&WWB#u3KB1Nm=94@DJrX~m?*kHiK_EFYY6yzJ1Tf_S%(1tK*Ip5_&5=ZBK@5bN zZ5X~F(j~u&qG2B9KCm- z zME9#b-H;hbWuf^WWr2?*kfS<@&!!l@F?SuwUgGjBs4+ic@G^%7i*Htj!&4ugE@Tyc zwc3Rb5EXyv566qGAuX~Q>KoVV$lil(?EWAD`QN{v%E~c5q@XOfmAF>iR>q`)-a0#w zbE9?6A-b?U4?gsCN;2zuO=vo7EV6H2Wzp~d*FyRCvSubm>YDozJa8boSt;@_smm-Z zMrA4Usi4&dsKlmx0H&CGQwMp11HQJTgy7!q0A6W7eEa(lv*c1wReA>xdnnKP;_|@f z3n00j4?xqV$UPWpc%Z%>?alKJRAOB75dIHCtgPjZ)8Vk2z5`8op&))V{T-eY!b*I!>M%rwt`UBN%!F8>h0$Mgq1*6n3Y5X3S8;E=CX`E)hOY2im_ ztZHE*B1%X)P$LhGmo*{RTLupRQS?=IJPwxkb!h=`+UhIW2ZD5vHf7~gtSF|dUwSv1Sr{y$Op z3J;iX+|Y)B%%yu=m=im{P9XXkJk5k!@+7M>QrRp^1|*JmRoyl^<&Xgv2$RfuTtsdq zqQJvyL-6X-Kszx!o<;Q`;4g!?mLZVNVw-TCqm;6jPwj{jn_Jsl6tWCuNsqcLb1DP! zYoS-}-8w~c?FtfQ1qGoIs)`Ay+>eQnYr+Ms>Fr|C0_+1OBAy3jW4-Tii=QrHwovJ9 zJP_9929_HNdUupkN92+PfL2H~=6wfss^!fZ^w$tXO410kfjU6$153%>S#Z=3dA!K~ zRKAdp*hT#@WW)o8N#4QIjTuNOzVNBC3#Q#WnbX_Y^Kh)K@nS=NF1{p*C?TWA0$*O> z7ET$AvtqrYx$1H@UMNA>>Iu!>GU#T_lfo|ckahQIeTfd@+0#)3fQ25V` z3AXkS$&}NdJ^L$CtOC#+<7k-X{RZL>P>>Gsbqb8%fw98eMwzAq3o3-o9s5yN0oZJ?>3jXI=;=+B_YJg(m+?=^!<9uaqHRt4 z+H^ZF!na`mzek$UKWZJ}8*C1adG^gR6wy%7RAeAw%?jDg)svPGJxFErp$#fazxo{8 zj!#dR1927vT>~_^eCg7qkejZy8G5C;NN6PFx{Yo^d#ew!3~s1VA@OO>I&!3@!(syU-t^2(D=Y5j+&8 zHG($*ndjQuUgm{(`E`L$HLLX(*eQ$dzP|-tFgvK*p+*gXuZ6bEkf1X}D?&j>+3{q# z_=n_N`}5?OOxl_LyaZUhU!3xLnP)&EGKRFs6m=CILj7P#k_?X~H-E&f`qK)oLhnYU zU!Oxv8bi#XCZe~kdqe9$l1JtxX2=r=vS0yqRe89QoI2&d?Da$xLBBLlf)LaQ##L1A z0t^*U#aWvm>|nH(gZjF4B-J(TyTmZne}25X;_S18aKdE-YtjXuM;?=6)*5ZXF0lv5 zhz*MUYkTqs1PBa2f3LO=We)k618_X1Zb8n<)VlqAmIKrY<2c9{Vrtu1tt^(A((u-2?O zTp0=Z5}Y^_igW^?6(2M*)YL$L9Cc}A_^rj3c^umL=c4IN#QS3nLrxUbS$(iC z7~Y-)1rK5xF%d?|F)v`)kP_u=RA&lwmFConLG2dL%80NJ3j&a7Q9jSleiS2c0(k?m z01|X#uBx*q1mZe!&f~nSPn<+3;w;+wHWJaMCp@Q*B==#_6o8Wwuf$rav!i*y;;e^B=QiDeK`79Iptdr0x>K57H@HKL}vjlUH( zhKhcUj&8Qh52Uxo(5vbxFk;~_R*To*@+X3S4dwIo9K6S=C6aFq=;qDAXqdm4?a9C) z3SEI|t7S=o1jYbXpF^fy<&U9OP>dj?tf&70nV2fyp8a1$H&{RRDV$+1mG_A?81*V%H2wUt_S9 z5JdtWP?C&*Q6vUD*gC3@P&ed(QYs1J+)ztN1sVilTkoj)+G>QkkB^AQjO zr8U}ZEoD8IP{(^Z&Q)n;J->kwhs>$KO*8>unzY!V)B~-<-E;WZXj!~ACB1<7bOK4r zB~FEVXYUdcytYQ{4-Dk-TCTSfR$$Na4lM=BZM8?i!RtEatYW;N{-LtnLvvQI7 zN_bg5I(E8ddP`$Spxb^XYIg2uX}#9zJ~V~7s@~q-v@RrSJa6>{S+{F>n8RjvH4f!y zRuK`At?D%Q@GLvz>2l_4g)kuC5{+ckj$ncYk=7RL5&@iF1B|s2_q-qXd72jU0zXUz z3&-DmK?~}7RbF$A`EW%sLrO&OeUr`;++OhX7^FrbnUZbBloJ=9U|SL-Mxj(94?K{S zmDPTPJgp!pVmF9hsEOTvv+D>NP~*QJ0i<^K?nB;rSNiV4+d{uGO2@E zsZ=_Ho?-4J@skxX^Fw#9(jv;Fai&UQ7s*}a9gEI=OXAAX)DZ&p07sn)FLk2hSux>a z2-4_*X<%h|^LX~d3P@O`5g)D232HR9wUsn#-xau3fi*vkEXn0xO)EdTa> zJW`QL5rwQWLda^^iE?urp+ZL4WRJ2h-jzs*JL9&Ky|=Q9=#K2{mAz$Wf6vSFJfF|| z-SheL_s8!arS5TEuj@6=^Ei*=IAs^ErkQ|Nr{^xL3q23Y1$<70N#nhg8r)*@8B?D$ zsYI$c7d>U3#+!k}vWX@)%v%K6)AQU`f7~x}F5m9gYavMqZIayU8=MrV7Q&>OAk&(W zF4tW*_Wg6VT<5#DsQkG`@DswQ=_i(*drHVpC zX^9XC*}*JzNzOmGv%L-kkZz8KEn2p%+5EtDiTAs;b<;Vcqms+BpXSfdr!P|7*Cd+A zSb*)}EyE|#PO@w6sa`Wz6q_uPKOlN>P-$YReB4+0);FI7psZ6x#q$f+{byQT32kAE zEn$tS&9MQ`UH3$`q*oGMx0-$XrWD+nvbUrg?|x_$SUy8Ua-Pb^kHpm0HrKuC4X;Za z(=Q+j=Lk4EJF~o4VT{uvE=(MWT3h*aYZo=BnQI$f-F?4VbYnG#RXtLgXENclr zMIlmDe78~CQZSA$-M>W3SO&RAZeQ5&ydr~U+&mIfHcxIQ;R&&Q$Qd7b;&#m0P4eoG z>&XLZf*Rt{_X1zfRLc1wRzoXO3lU<+-VQlYWQ^@ z4p9qSw8%UV2yhUD_s4XnVpPPbgj8C7M~#|qh=}z0(~Ivcj-Ly%H`pHAVrdx^-NES9 z)GPlMqw#9C`r|(^yr?HWUTuCn7YVjTDrWnqj^7*iyosTdjC{#U7c7=b9YD&6r ziV2BcXmr}KSYn`Ln&DKqV^8cFec(rOoG4lKL%(+>^4@95VXpyC64wwUs)OjZLBdSp z!i8#U1{0^0vkv-}3JNYRB|9?2gyyaA76zADE7EFtCFNBZi>J} zk=;AQRk?$#J&3!*FIol={4i@wv1M;9#d7#u>Mvpi1%>>u5R|O(Q&B22oVt1LoxLbU z|G?A@)>p2F9Fp~zz81T#TP@r@HLnv<6QJEBW5-+9elQCbq`bwOYZ@*vgA~rJE@N!L zBT>mlsq3NM*M9fZmYIq-x~sSKN-FtQ4Ou3R$||{Q*v4@S_m?F!R>VYxd3Bvi!FWkp zH)`PS$UGWQYHdZ=Lo3F3+Fj^o2&|=*pn~L82zMW%>colyljl8WwqK%!4E?)YDpLT~ z;h%ovOqLP;0P)Ga67dPFFRJ_t05Iz4+@uW;q^nLZY{!mVvz($DS*7%txR|Y`9C&fc zU3`@kyR|kq_fR~28FZgHCWG2GpAISg5I~}zsH$;)*G&>1z%AyUF|+ZKHyK9pwoSf! zuhgf$&;cZ5fsyTUvma?8kZ;~WkzBMI4zz%DY$5PHdUb0Z zYEIqu(LIqNtDK9hb(_p3eu9Juw}eqrjF^5FR4~c5i293#oxBVljV@jvK9cA*ZCU`; z0?_3fy3S)E0(Jn|eVFaat=9D(0C9)F$jUnzRP2NjTpE6n^OZGMpq48&>v=BaFUNqZ zWL5{Vz&=q$u$w~JQt`e(vn|bTP;6P<)v+IM$fYoCb!pUmKj7=^_0LEj7i&$5+&zzu zZaQm3xv4snh{IH3*tg8$0rS{Ah^ZHPWk%o>V%gNb!qu1r&;bS>r z1T>sn0y5OGp8T81pnrSm%rSZh-8l8t@lt)PQgvHuV51Y4zle+ z2e2}$pqP+!C!T?5)WS8pd@W)Ul@ou^9hUIe{(aOJD_&|3WF7%z*9|pBD>r#HEfjGK z)xEJry}bN_ipdd;AQ9#QDOF$2cj+Iqy<-wnDfnBD&;cCeK^+_tbSWriBXxa2J?+^E z5joH+GTALbK1zTy#bO53x;-&0bawHVxhA{QthfQKU*+9{kTCOoZ>X2k-s$G80ni_G z#a!Q+P|^K(N2=HeDzAC(Ahms9XJ^;<;cSgIR~*GS@)(ustOpK+MGWf_iQ7zzy}7fc zy7hM0im3k#7Z(?0e}`%xPcsl~77nWRf6$w^+L^o!n=|lePVe|j+ZIa!gUmv2TD1li zUViq(e9UV zU*zQtMTzmK7aGL(c}U>3VbXo0QtgidLk_S110DSbw_gzD`@hS6` z&I&jKC6xR5<`@#D_l^3#HVY^+i%`@k7k6qtdR6ANFYvK zo6V^{uhj)SjN9t5-~N7W`!`a0fUMbRLG4i}1rdohj}3r#@^*Gkk<)Mrqx>M9@-mXZ ze=dAase(hFx9&2`eQe!lbW3TV=p{~p_akdY35qfTBiH+Gm^@Al?hE`}wYky3y*`tX z|5vRfkcjY?A4xZhyhfdJ|2{96PaTr;WY^EmZ-4>-mlwo~niXyzfz^ct?bxut4xc|N zDBp&FCk#>!G771s)?dW`{D=cY*-~li)O`CNtFJXQn}4r~W+x1Xy8s1@Q1|uz`)H-G zjh_nCGylXagn6Wh3zA$tH_)*>b+kSGzVZswe^Yj)$!*BoOfQ(@-sPMjQ-JF&DHtK8 zM?+XrY4SG*j$6tcNAu16%k5{05V_+zFX-T2XuJTHB&aEivjG;8I65|l0r9jUq~vx` zcUM63*}StgzJH7{{3@g9Nn+Z5(Tvn9g6iw{?;($~KHdVeg$;olX!^F)D7x?!3Sy%u zmk%Cp7;O{%KgN@5e|(t-4t*ez7;p_AQy>L?8pl03k4@g+@PrJ(sx!}V=HB3^vRk-M zuc(-H;oJJ2@{^xkAVyU(8Cr#0qxxV0sbkz2MVrS+jr){A#o$EAOhl(#PRL_TsDi+Z zam^03v^?v98@B7}D;F*JLoZl4pE z+a}|C6B0rlbR%K)g89F2KU{X-QL^*Y*FXwzA%Ye6rj_O#K+d%!a-OvsFHfEz4muj; zGM)NJIiAo2dyO})qJ!lW3UWc1Mp6G06zj*+8AH%ZW_5x^OUEZpuw%`T@}uD~M5&q( zj>H{CwU5hyMj19tKHv$M;04f(p zN6rFlL8V?(DrH{A$6WtXDxUP5`$|h5WT)LZkDj5_!r-tl4$#}?fP07{)Qdtc9=ik! z{GU^flv(8#3_0zI0p=kOFO(*=`yM5a9`>;z6MGE0U0<3omh(j7N8TLp@69s$gq}NL zhtCsf{D1HdqK2w0`xnITC`z}EB6Sy+l0zcO#TW+HKW~hT-6pXrx+x$U?00sUeal|$X-vZe= zL3&Az2$fEOhiQr1_F^5`&ri~Sx}Fb_zB%B?`WsxbNk3>jgkm&66+LkZFh$q^Kg7+> z&c>~t{d2)ciIKrTB@Y;C!aQL)Bp@g_XSDG1lGye_vUs=$Jjp$=x9=vuW{fT~j(9v^tUs=H4 zy(x!XL0&!>a=sU^nf^I>YM!C{_M~hmfVq^qLs_Ns!2n#mbO;BNC5Opo7DI8aKQHzq z9~?y^i5!|xy^%9TXI#Btlh}nVWTCQB@8#&IAzsWm4;wA=W5Mb0pO55ZHgW1=$-_&i zW)0LCpv4G6_O=F>z?O|bZ(YW+>EPrp6+EzP4<@vBU5{R}(UH}o&+qTo;Q8i&5gR%b z!*X!||Dk-+ExrI6KV;w6{_jUYTq`k#ElJ(QoIT@-*YNPLdF;eMqY6_={9eHa{@rq& zU8g8rzT_mnk@qHre1*+uGPPo~y^_f5JD-%BKE5mAxb|$a{`aIpvSjx_Nu{JCS%Kt+ zMeS|9+AR;uM~~jMLq;bd;+H5DcbMebL8Rbt^SW^;9*=hwboR$ahK91IrpDa;?!VYc zs&%ZX0LriNCbX?#{C${5-btb=u$vbaE-x)5fMmacGnK}Vc=KQu0I|W5k=!NEu(sKr zZDA!OJz;>$%J-PK&sIHIpNSwDJG1u}; zuIhB4xQy&W8n|~udZZ9t2=^asVA>~@rJ<8Iq^0#Tx?vq{FMw$oF;6rexE~T44-Oy+ z9S#Q?n21QE$xoj-QxBDVBj8klEczBkQE|T_ob#v}C1$;&k30O&34_j`H*skER4}4x zu}%sAt86WZg#k-+#>av4)??H_A3aG?oj8<*Jn)NUXpe-y&m|ml5DCl4pOj7=#3cF} z%;MrYjo?&t6hgf>_p^0*P%Wgm!dujBMMdQv+=?c}0Lw`=VkmFqgq6KKIt}h&Ato`- z?CQd`f5us`TFE%z$oN?jb?Awo6ih6%Z1L#j*5vy@Z=JALXyF2hhb>0&ZH)1C9S)QU z574r5mo1vxX&+`PqM`nCmylw<17{q&ymEe27S-2=hSwLzOrTecy4=!L4 zj!+@=c(>1#0+QMJSLIMe2o4RUX%SLK{bX@9uTCO5a9?HXm+L3AynyyC52@bgfJ%@V zp?lo_G35Ufvfe)mL^rmK4{B;^sKxE0ABq3nl!5t;9QuS(i#6|ck{vsDO(m;r>06vF z2M0$z5NQ+OB)K6m!%z9ohk`yM&eZQp6T( zs6g-i>tCnsX@oiWC^3ZH6;;j?(Hy{64gt>x6>#4^g27;d!2CcvKw{GrBQO7I=Mb~> zKPJHbe2DXgQSCS>mM_b!N>ttiP@DK%rCf5N1o7CRSAKp*P>2TPyV7Ui);n09Jj~t9%>@5KREfMI-O!!++lL zRdO4O|2Y6o9lfMmrU4O)9?M4R_sKbGl=*~mS)OOA{>?gS|1-S0s1b-^O5JZ7q6e?~ z{y_x?#EI^4z$7k0RB80pMScH}`P+}G70d>YLE7>6 zMY4Z8j&N_e12=14?!O*ZO25R)#lEBgml>WGo}q2&AoJr4s)&zkTka-_j#kgVqgY?lAN4@CE3h8aZ4{ZgM0?FM7iO2kmtjG4(=Eb}`}Z>-sgM zX6-3K(fuf$hJzzH%|vnQABQda+R_7IRrf`&3(D-v`lcat*V1WiIP$M;8{HEMae_DZ zw{=opcq%{GzPq78=Fzww$ZYQY|K*KzY5%!Yl5O(S)YJqBT;P`LkVD>R*nS$3x%7r^ z@iZ1D;T`JVkQo^P85W9TbcCj+VoN6H(I7jB$m^jZe36rr^G-CAKg-E1bh9o4BZ?&` zX!h2io)L+#YJnc=XA>FsUpi%B*klW8OFa)zYCZ%%6#>A-j*&yAX|w*I7y>sfBdZ(&%M*A8;GB(G^vN43LUJmIiB{{vH)d7#k?Q zl0la8XYUEA<8TmW5;++AqR7daQ_}y+^+N=!inc!N{skT~{{PkQ$Lk`R84#aWWImZHg?6rqbzdWM z&4YMZaJEEU=AktSc$zY}GT8ymkJY^40zvjfGT9V#xSZimh}$0$rfm4z(Eq zr1NSq8)Hrg}m(5#yR=bJ(;?`_Cjfg$357FiDfesk4wj0ppA1q6}z04Pg@A zn7K^pWqQoy0`D%@uaa!xSCVG}GTj9g+YY*kovu`g1JxF;#ony&A9HXq@!1 z_fk{3F1s=a?CM#nLV73#M;u6GR#DsEae)a*E8<|3A_nz@CGY{lC@BS?@}$FHFb4ST z$;_L725J9uR)wD=cHxtqPYyq+GZ*m!MYhGM+J%XzF8(w!dL9OQ5*G zZOJ@nhVuB#k=vdmDNLsiqp|@qH}p9MdKhxT3(p{d2PkeVJ^Rdz-kR4hlDzzUBUo&x zBD5Z;T#0~xw}S1_^^ws}Nk!$tL2*9T`Coa1j9h`*vwu za(9se)p75GR)DP-pR@koQv?2no+2q$M7&4<4lGN{%hhRT3QpbnbLsr= zMnKAj#I4J111;PT3{}=FK7e1C3hpwaS2&FZsP^If7S1?Z2~|wz_5P>TX$mNxbU%3d z&yO@|Pgl=-FSsnvKO|{wYnyXF84GebLVf*m1oRiu;H})Y`#AiHn^ia@Fz$@^nXFS9 z;7TBqJ5A&XXo{@R@et(CXxAt7KzEoaru77|1Tk$C{~LHoPFA9onf2|HaxM8ONsQyM^PKu?HAnqshVEJ1~DKMk*WRfXY*K7Hrn z{|&vKl!oa+%6#j>$y85?*^7Pg6f!Qb2&*%d#7Xp}kvIstA-Jr|(VrS9FNKC}7tnp+ zKjZuFi;3eR`EbPaZc3}MH2F55Xs39PfA5v*UQ!So^uhjVS4(Sxmf@?eqx^R;DbcT+ zn3$+X8;Eq5Ip+fdLkbOuuMc@1Bvz^?{r8jnUv_jn8{DW?aKSUFR4L#WT*&kJ*LZqy zQKERUA*5)io&+^2=582!PNjr4A;Um~Vh9XAaOoR9N>Ftd*aiw=BGfRblT6Sv2TtJs z{n3IVg*=#^8*%D!P?tt2cF&PH+x9!RUqEU^S`%FL?j@r+E{@54C8&I1?r+P_)!PKB z#@a4ZiA0hVXyxn}osb;bSE8lq_@9uhSK3L4YN*o+@Xf?%RsgJWzkxwP^_FBaFf$TG zleUv>#B~f$qb4ht#t_h(9}nMlm`+Mgt_SM$Ts25P(qGMnRi$X9% zPrRU_S>+#K8>X52LYMdx*y1QpSK1RtTDj!PFW=7jB7 zL2(ieC!#5&r!akIqFCVIDIWu`f`S5{P%EIn3D^u(3A9aC*Q`t}qL?f!H637~>llmu zbN7VJlk_(1K;G|V7)ay~p~>IJpWgJ+E|awJ$X4&5bjN#Q;zceS;F;boFm>@Rs8m%| zJ&(dvW3RZgPP!}J_=EE(1-T$)T+pjKl8;DMER@PGfkja!L_-!~TNPXLp7Op{(o7NyiL~kHdNIt0<1FRh?aX+S=>Eg`B&# z!~1(SzsV4cXIr6wI0YSf!kS%wIQ_Hy%Ni;b{4wFORTgKv!o^hq%*0Rnh=Nw8N-ZUc z4MRK#F=Zq@S8uOEKs~#H5Y4eVA|S>V+DYC*S$diF z%{PvOWS?IsUynhRylX_@=_MQtS$HG+*VUL8o2=&UK0&zO8JBIJA7n&;ej9l?&?YZD zD@zGdt*l=MQo>I}3~e8syuq+@%(SJ+w4lRv5I@y6#8^CtAJa{WwbbaXKv>jT4^y1~l7Tav2cxq9Y#yu8*Ik1;wYS&S3lz*_Dcl z#%Fk-&5nrnR3N;UE)^X4<`9Esvi$*fQw$HM)p3mGh>wQA-UnbtC=M7MmCv)~8mPNeiBBHX?>I=0>sBVERm=5F z#qWi$cv{+*Pgoi!jpFC#c$Yh^eOZ%A91~>>*6*K7X6BzV#7xz4wp}yp^O`5nFYx0X znNxF+ulZRdTCwe%7uwX4+cA>MCE4#|F%RsPS-UeLzm&{JSmq86XDGkh9h@u!b2 zb1WNi5K7nOay|CiS7%+FwyK?fuIRk?R@EQ=^}x-bwJADw5F?_aTA(jbZzQSqNnc=# zr3g_nf3?wvU**)m8q;Rt_*Wl%wmo_w>F{{zN3Z!U{Ko8+8LKROc4ctE<6>6)PC^|2 zM&#wg-c!3V^D_Xy#Mu$sh8!p#k9a%^h}T<2ahEO*o5#Vokgn;r5@F6K9hxSG+?*7h zCEV#2p&R5Z#Vy6C7>8EOTI&QbjeB0&A*S(4Oy2n*zI5iuY!Djot#A>?tCVFjY}H%zy(9Ip z$1$`FS0uT5NpB~&(xO|y8OSByShYY*^|<%r&|l=UW3xTUsFSw^eZ?L!b;g(dmR0H8 zmB@#y48>QoSA9D+nG>A2pe~VoHT?onn${FM?JtP;zM@41u;R4~q&7FD`~}s}w~(E+S(O?$gg=T^T6l0Lo2%LKh3;^6 zZ!!=;zCtE$iRg*0LS5UqSb4Mmo$n3tYeo8Bj_&ur##s^F}!oJO?(W1ZLKIfwDV`&)O7!*V}-)+p-%KGPrxH zgQ^of*NYq-y5Kau*dL=~8HHL8dTN~j(h^EOe@%Z? zNr=6lmk-GBx?o)x>Gnf}4o5e=?XZ=G(JAN4V?5nDS3%>S3Q+mLhq)XiGqhj~326_M zq!vGxbla3XaO(W^5x$@@L2>>>w~fMfy&O67%8h`X#nd)xhCVI3TiVRX^^sbi_H=Fx zyO%cdvs~3=OyNpUn&YeQ5;q3$$(XetxCAH3<&jS{>I?5G75Z<-zG+vKG^gfF)lpb- zjkTD@zWvM_v(?qH+nFBxM9&NxAt$`*pxtQ1o|NB|)0XIFq#seKVP0vZ&$aXEZu=%P zq5PK{;mWE7mi^J3KY{fnQqUFN*c{F-zqo6Rd_MaayNbnfN?PPO%D-0IC6tTt(~|QP z-me_D-JGQ27qyEC5160zor)5)jDDM4xmdF@-iy>aGYPlmc&)H!kgBaZkT$uF&2faHXf9YQl|2c^y2Ukuw6C5O%~*SEE!UGr z9lRMEXo(fciL>V*e00bdJuY6qdrfI$4N*m6)}nn=g)n*!LqxSxVgyFXM)pwQjo_l+ zx~W@H%GmMSAFd`H)WUm{Ck#{vy6A5Xa3tYg<7yqECJs2xT^$H^QCPrfF?ozqwcSbN zkDl|LLI$1n1sux}e*CkuAJ3c!o2}HIhU=$prJ+xrxYtwT!}}n&)}iNNAWrkJf9m`o zQi}72opF@suTA7~cprB>dRAX1joqC)< zvT1^)ui&F^8|6;SxqVeMqoa2rqA1i=UjTEQ=g@5img`}!g8-metuahej;xfMF1yE@ zwEPki;ETaoMX2ZS<*t7=0`HGDkbt$Gdh1T6y?XWO1-jOt4+XAsJ&_si4@(|Cg3j*P~k8ViTEeeVp~xO z7QI$8r>~~)Z9MxyupniNiAj@Cj}Vg3D?XB2y{r+li} zOlH3nS+5*Wc(w7mD_qz8W^hz^gGnzv4r5~`&_<86EyV2f=(ie&c)`!cei`fVg^w~yH>rq{Kv)a=4J8EsP$fn36F8>p=!H2*dx+0 z^_(##9BC-YO6;f{T&`K!b-~!_Pj`K)aR};Xk5E`*b#4ocnZtF5IvbdGOD}m-gv^qK z^~z+`syA7vP4AAx4#0_fe;`hctv6#tcCIIaIzwYxVuPZzA1mA|#&acUq*P(8hC89k zLbR~mW#M3cW@+c{YHj<^%LH2cpoDNshnvgMNa5VG)kOYsF2cA}VCZ>&f*Wss_9a-E zUuSrP`Na+NH!agM4GieIU;(j=AHCr}HD!`GFvv(_jWzDSXrn*v9vq$3u|?OWnp!x& zBN=_-v9-ZB24^qDVYa<(Ka;JMZ_|%LzLP_cVYFjrgu#T?{dIlMT+q31Ph1m0tHyd$J_V&VIU7U%7(Pq**DxBnZ8#6z7 zDkj`x<3~+_ig~4eRD%~IPlQ~9qkii_OJi|`w(!?CUv)3EsmAk1ySy@tTun{cd1ox; zt2wyKxytAyXO3TRd~n-fdO*=GYugVQU7s;{nHXUyu;;uRme7+6gAbl(+Q1;Q!o9C$EyJ z?A%F}WL=%b?mSA7{1z2&Qgn<#q+ORdn+z-d?M&LFTE%Lsykdtxt$~LMvT<5*dMZ$J zhbTqzbhIkoEXu5UXC{X&+?$sl%#hVY`p(r|3Vx6NqEa^C{4soZT*bcSgfcal{CQ^MB5ZMtFTj^(sGL{{1{@s|}%qk~f zU7<^-gE(eRSkMZ#YFtb>o zqo&+BzKYK{MAledTdl2vkCqwFyo#wNbjp8Qn;UR86;+D-!p<3No9%11D3EGInOX5A z)xe-+)fmH%u~ghPv#s6eXcETg?(p~i67h%l7t4_B@eXs#P^nUrPS-ISqi(tUAh~O{ z(x|VriapRAFOzO<+mp+$Sb9gbA<#jQ{qg=M2>N~&!}Xat%^eR`S5Zj{P^)a-{@NJD zuSlmWepl6FuirXaKVKZv8IB#UDB5^8{~+qgiZvmlH?fA5SQQ(L@e^#U;N0P}{)HT9 z`9P=iQNjlMlJPwLDR#Za@PyCrqLH$Wn!B5nRBd{D?1hS|b;Z{lSFAhy)#fx)w3Pq# zW@Y~+?4EFxzz;^7UzsCiykZHNhK*4y$Fn~Uus@9YSz$@jsf=5ZU1pv8bUUF4b8CYp zxnlQw#V(y|QksjoZEg3}#67dm$ochI!Nj0i!xPtjci8Hli5S%wzeI(b(o=kLt=hf! z68{mR*6$NaxT!)#yD!DRJDN(?xNts=g=Er$Q|>bp(XIFG6yvdy6JGjU0>0l@O6T9) z8n=J5G-JHSk9WV_FEMrFP0!=qIZ;1>RpED+UK~;#bdxyvreYm@*a__k6Y> za6Nl<=+0)8W~=G6Vz7R&Wuwg?Jo_7oD=*gN#Brt>JB@Urk`t1Ho0_=n0>aWJd01CV z#YoiV2Kz>s;F5Hy|2?%qk;a+xxU!~j*W~W3h`D}c$*oK^eUFJmzOr8SuIX@+3V4n! zkGiQ8hx6`P=9lXkrvDDGPeM2alv3HrG~8M=a0QKvqLSh&INK(j@vS;HK8wv3>*!z+ z#|$+coJF?5iKX8(gB!-mzx_Viy^BfcFR$`AgMI{|^B}PSU+67d!&A+BMS~mE_-8k0 zmyH#kMJ6evVeGchhk4L+ihNvN$NsVkMaplQzP`kO?2*ZNy0rI!sT9$i8y0?oc`=_vgWfhCqLUp zqlE0$*-EI9BCWdLG9UCM(ai*!w+?Z00Ut(~Dfp z%yQzxG|lzCXtm_Ox2oJ((;$DGJ;Fj%()pYpX_HdqkfN3@j`Rw4Kt-UH zj6cdUN$n~fQjOQU=9uJ%x#;(?ubE_w^@#h3y|?HC7sN4Y)8z`@KGa7rV_Q%B=GW`w zDcjfF7etv%2F7NtR}244GRb2#$2QuDGUg7+V2wkL88Bokactq`F;`+mn`o29I;q?j zkkS(y6>FI@#{z378fy*LWTOSK?h_+rPsWoM-1S69++WUkQHgIg*{;GGJ5lKV>K=Ar zgG~ziZu3iV;JL7)$oGlFnyAc@PIr?Eim3dhox(Bt^o5zDi{V6U-%xzv9%<+*mYs{P)wvF`c&a1uMB4|-CIkvF- zYFTL5#=~^x*<+!1Nnwnk${yyuU>Hj)8!obJ``0SAvuhqw4mXS}bO8qd7d4NcA7p^kpJAa?LaF8>QTkLiMUlzBw~3I?rR z(G@M7V}6~vbkRbG^_QZ8v@0Z|)|D#N!*DxTykS5ipGs;_IQFxE`TqOtUiQ#vBwwsa zSk7~gJiwoducU0;Ola#yb}53|+gu*4Mt3)AK8>zY_GM)k*Eng~9hH)Pks9t?av+W<3dc{z_^|zPpI|q`6-*SzS&MW`SVz#%>)tVlh*Mcp@Ol; z>;q4Ml#5gkZ?^ptU+IEeEx)gQDGW+goT1fzS{9p^IoyZp zpx9{6q#aN38?Cci>;WI>viY*_nD+|J$+y2xAt{9CMG|FLNwueawzprn63>W7t~PkM zds4DV96A9PuthyIO9Ya?zTN5iK<+F)8I*c79hwQhlbA+rG=@^s8tn` zxKss6tVsK@r%6+nA4v(7vYfh>EN)T}g1^=4TO54xb|P=tFX_k}vUR&Xrx}ybBM1~s zC-KRf>QBmx^Ca-v3;7w<0nqqiOUSOjaoUnC>Y!pv)K$wLWm4BnCfnWK%KW3EVMPOi zwiMl08OnVfTgr#!ZA7Z0>z_VzjF31hqu}A;QDv^=>exWFSDQyGzCYkb1N)TURDZsta6>{4T$&1U5-?oB@eavHv|vfYyuDF_}KV&T_e+f z4L?~m_>^{onU^wdx*Y&qS4pLEzU0c`V^1lOklpEP)df5}Q&UroaYvE*;`?|rE+O;7 zz&9D#UB0UH^2?^$(5mX)a>ptFx)1V5V<|f8Jy$Q?Q%u_8hPr=c`K(T?`E1ncOtx+r zMcZ@r^L$9zN_LD#`#AbYezC0_ok)*B@|$ zFk_NdPlQ5o___U*drD14Z9a%IBRyk44w3;4gIx)kYYR{y=LQ{TuuaE1mHI{}pn++} z0iA50jt>l6wB=qt*#q$lrNAdyV-7D$@!bH+lFfehNt11)WF`v-uA4>Dl!b0W0OW?D zB?Tt!@pAUSn{58o{SqN&*Qz3IieCO~Cu?>~Jp59yQpM3j*gnQOIIc7Dv}qiZOry>4pnKK05!_vLi8T27M32VDOw#7TGR4YozIOz4t`dmeJeP^Kg;1_4C zei1jD@m9LAm;ALs`tjSCmCeQ)R{qntlD9SXSmR1&`_1oOC9yxD63#(vyFBUz;;t^< z4G<)3Kk*j?u$*FDbl_=RTd2g({z0~PF0{xdl=;3tHOgDJ`2Gz$BHHBS0=_Otq#)dQQkr>GU3uZ>7N#^Ux3+_gGoPz_6gGG26S}m0Po^m4I_KQmeC3)rbTJSo zXo=r6{wQc&jI z`KP9Q{}dA*;hJ@C>G8u8jfR&VupZJ0B%@R7cuNSjUrh1jpQ_n#R|ARsx^S#Y6oiJ>hP9i2ZcBTE{3i3KM-Y05rp}A(mb&>Dh_5 zD@X3j-Kh_Ka<5Tr#eco9w!pArbUoz*!_(=maCRM*?vYw21H0O8Z-u>j$tf9vhE1_g z_x$5UG!+v0bwC+wymv)~A>Y74UyFI8Zav4*ZO^`^B|MK~TQ-lcl+_8#5yMRDH;(v* zyBqN|u2f|EHH=lbGH$1zo?~KV7j)kob`Bm~cIpjSK$Ie{Bvop2b%*KEh$T_Egcpq; z2_)3y-QJq_yoX+%+enAeHw}lt(e>#mdL{hkl_Z5aEpGSBFU34fCxg!CYhwZ>E}H9A zK$iG*1@BK-c@!tovtDvyG`6oE=~!Hk^(SN~Xc|W!rBS=FKEgUOa40@DQfj%)vA5Q+ zV^zv=#XgeXI$-Bque`@@+cmpdZC1_uipLn+Z0qDV%gJS@5BU<#i52lP^z}3n7Tc~4 zbY4m74(=>2&Ky~gq;HeGAX|KAH!7;gfj+FlA;_0tFN_oHd+PDU_yKeP1TFFh!tp)p9;1JZftl*S0c6h|_)mV56lF_f*Te;FfWHt=Xb0*@PZAJGbIua`Lo@qhLR?ASXk8v< zXMKZ@G-rq&LZA+J0o8#vL4FDWNSyeI&#yQdV9wC<+A73Clxi!Fk`f zH1~>9!y}X^o5*Hej)_@u+fD-8(>I9-V zZPuzzQ8QVRW#Tej6bEix0=C04A7`>kXP=-FuXqzbREj$wcuV+{GXkuUF2Qf88m81; z1m$@^75T~5=tIZ3o*(QIh2s6X)A$URNUDCT>Ge<=#bNWj^%&Rr+`-!}f$HTVH;P|2 zx~DmcCfb_D=kB`~cANAbbkM5%7<|K;Dz4W^@oNl}t=D3$4uVY0#JW#JQn3K&!X_q@K^2>&K#*pAXxaD0e{oZ8}nft zaB5l0UimyQe45kM=sc?%cYtbM3kjoRndpIoabgoi_!xa;|-U@q0?Nt6~+XxPJ z+}W?f%k74y(@9MAwT8jmcy^+ot8OWy6q9@}jCVOD_W(3r#;bEBVwNu93cg1?d4HOr zUvp_IR=+O!ca6Hf$K?rayIez4Y%PKP^Y<%NYtd9Veu8LQjUi>;3-Jd1%*(CL9(IE=z zN0^{Tluxd(8@w?r>Mhx2->7xid`S4v#OkX^{9Ob;_iV0IJdvM`t=l%@G@G(%;PMG=mv@g)=*QegIS@ zc3zaV9qtZ4s(fugBZh~~m_BtUvdknf`MwalK{$Xl!6uZQo6cNf({fa8d~5crCfGUF znG()m$6g-Q_Nj{m!5G3g)_q33ghErFax$X*`(lzy_v zzq>>w^9ytOn-yDQ`z@qJtlLRBFB2l91(jzEizElaPw?~WxP}<^d!+kL$q@vPFqgSc zKp|t?FkHsJj^NfveiVyMT7de-S_^>Yu`YP1bO2A4J$SpVDR{Q;-nVPi=K2=%(ZP>< zxGxi?r1MYoBH=~bYjD9m@*TqLNuo&#LADL_V$Iif9&OH>EOaljjX%_DoIHip3iVk~ zkhiB=U`xs|S7+3IVmNx{WPQ7D7xT~c%*~Zb=SP2y5>&AJzNz^zxUEDnW2n8z-%uHE zNO2bMC}rp5GbdC@7 zEGl~EHb-j=&$rE)jnCqPT?u;_uhjbwD?}Y0gwKv7kmb&%vtx3*joar26rY55;Tjfv z)b%+0&ED1;ZZ>MT7_`oPfck{1fq{T!S9rsf8mczlnFQaD(zuMaXKEjf>9w0@Wb8eM zH859@K?6=|{fLYBrvp|U)PZWP0nstx^6M?~5kf(IrzRpdLTVibF1sedB2YvCpZT$T zBoyXOzu2<4nnh9JTB%X8GPdqXu6wYcM^t~(83)luw_;Q5gR&X5AdIoGa?C@6Vkq$i zVa8>cXW*73+G4`7gZL(2M*$Q5nJXd)XX#3jK3!XCvfQSCy-kuH6IC0nYMCB+AP(+e zhY;l)IjZe}KOH{m;58L#n7?NjE#LH-*UJ436sGv`5=F6``8-Mkg$k z%Vx;Zvoh)&Q?!~6OpmlGA+Xm)O$SPBrtCa&0v>zs*40h+e4(J!Fs%7fI`qqKWL?9u zh>s{E1z@Jvye0G7(s)7{$uPH5uUlUjM6#0>mClX(X;dCbsr=H+>KDVG9v1f)@+_`o z$x&fjd_;sf)rknVOL`7S2^elvOH9gBg-7_1s|#uV?CZU(!r*;hNeXv6V#s!+MB^h{ zz+kG-yJV9|@B&)%E{PKLV2jT;bot`R9xTj4n=-lnoi`tsb;M;Qe>B^aepBcD&Iqez zeP^3tp_O2vY4E+`=#u_+%lAdumZmLF9AZSony=j1(k*UrSxdJE~*hq?&!8Po(3FqE9cPXh) zOu171Da;7o?x#$ZSV}7_yRF$1;{=VSaPk5P+T_vKSkBg69>>)zLa-w z!yi~}@W29l&HBk3|Y8NzH z@sieaTYMoNsfII(_&N(sA@Clp`Q*Yu_F!kyLy{(_+S{COwIVPHLeTEDmAZc5raWrW zXwP?QQ;}E|H(&0H@~EoJr1PE4mTzlZeq%~mf+Wj z|C6X_{Xr5_3z=J|gN~XKw|^`QV9zg9=A5Uw7<<5Ry_NOJii@7p1alevdOcO?Iv20| z3lh^WT*qvEJ!5F6&Oy59&SIk1#HJ%1PFfS?IpHnAf9Bp8ajU}AO0byXerL(G*rN6% z|B1YrB|_6f`xYi9m(;kT{9Z5i@Lys844;W8V*Y*E2jOp7YUqmz2KK#^rW#hxqvlN^wun zq^sE;HqBVWj@rVXqsCY-CaHL`njf@r717C}5u1E;vJ06V5YYQX0ORr0ch7IAksR4c z)wo&VK9+PfoJ!Hz;@=EJZA8U=3`D|2$QyNi`R8pa3|>AYw_cxR*{9cx1Hk$Q6=VyE zgA_Z?oP^Qs*A2WRQen9rAUglXzVUjpxv@+-U>8AE&~x=XIL{Zgp+;j6|I@XsfOyg? zjq0q3Ly8-);h!8v`%Z&wxOCe1Rz19@s0#X5l@FpFMM78}-SmDg&I3|6W_}V?Dg%Da zZxN?2Uc7j6C;5e&tKu5T@~yZGDd1YJ=ci_K!kg)dH+81mtVIxm3j*;VMELw!dSQa< zIAlQ#M1wm$Ab9Q~do2e7He(PuNEny=C55US$e4RT1oNIp-W45l~jqQpb`n_GCSB(1_MNQ+6jo4 zte_OUb@^e{0gTePTQJ@131ffH`wu}_oA;>t@Ye1m)%(CuF!UP~4LE}e7r;WmNF3b8 zX-S9P81+5jA>VH}LBcAf3Cc83p8=W)Vh+pByocz0l1LH1bwqs_5t8A1o^`f$mYp5j ze~pce5ddi8M#m4SY-Zb)Cmjd=bB#P5oLybNLFe0r%=)LvVSk+=mANZb$p@A~THOF&jY3nm<6TdT zDwwzh6{`-Q*mc&aA1;6-XSC=TX#98Ijy+ztxv==fc)i81LO52T!4M54tRdXvPCOU0!1M_lQ8X+h75- zn?wbOgE!D8M0#k2xf-~De<=yuk7K9$ON)z745sQH)n!&;BZg_82Mm6WjG{V!dJeE^ z$}Q#{j7Idu7SVqD*}F;J4HJ9g*w_JC9B>o5LwHB@HlZ zZR;4WY;;4Lc>ER1eKE)tXi{=@&@CVQb89_(r%s6jv@fIg5Yz+kI_Mz!aV+#f0#M1H zumc-0vk&^7Ee-DN-UlVac~A@R2rh$*Lf1M9v_hYTx3tOD2>4BEJUV+2mW2S(gnb80 zLewE@(0BC}+2H4=Y1xOfPx(|x0c&~At6GX@#>)@2S8i=>MYZKdY1d=TDn#pZejWCb zCm$Uh{R+fnizPwz3n3dG84W`3h982+^utY0L8<;0<*Zkzw^S&!80*ci*L7QzMIC98 zs~+Oy;H>a&jS@u$gH;y4{UY|7B%K_ShE4&e#O-zg?^FP1@&DJ`nLk3g_ivnu!e}{1 za%jOR`@SZ!w5Sxa?}K8HB`Je!H+5Pi3Sn%MBnBxY#+D?<*!QhL$u?unzJK5M>FGJo zU-12{QDNr(Ebr}lU3H3pO%pC2OAR|BCZ+?(b`PZf${h%!YSGd$s+QPrH-ceKR~8hN z0g{Tm+GnV)?7Qx={!X3KtSn>`dvq$^XTl;ts14^!69P#8k47Y7T3?1$X3zbHA^oa) zO`rQIgsdYXB7N2>tg71zR;)TVrXAZ;p!i51WlQ2cF{FWck04Ffx!vW>B7Y}B{ zegt=I94Li`W6W*eE-VA_Cskwy+6(9)_yOBjrV;+aB2FlZIW zCTO8Uc_q{QH+_(i+v5%z{p7s zKRT_q2KtkjbSx8*1ITd$=Aj430davp!nI7k?>Wsf##WJd$+Dr<>QuxdQjD zzI3{8x+TT8uzxuIRf+SO*QrxUZ+7QG^G`i=k0i=6rNObX>1z-rIDR1vK>>N~9cB)H zeZJsl_Cq&3YvxdBD^_&P*v??APLtK?>3Kit9}~K*mkwe&xLiO-FfC{}W?OxnKdk3f zGvhgkdfS6TKbG5OZcGkxxIOp)-_MOBF5^2~ot1rS_(6_IPp>ohktjo%*54@Ew`c=?xBNFx0Yc(}_cT5Qk8 zRx$=&p^bu_!~)$O5-7@NxQ-B}`)N&z4GJ+AcVVpcwW?|}=BEcVXNzm{Z`9HDH$Kvm zYpS`tc!H0aMf^etlM9~KB-PJH7;@`=^E|W9;5C!8MFm3h>GPOn3}g0W?8Z=)_ru6A zEM6+ye4cRCUT`Z6cJ4Un3m1om09O-dhlMU#^V*=6W=|DR7_z}*;mR6nPJD+Q#8iJf zKfS`nv2pcI>Oe6pw`EBK){}s=p4vt_ZhU)BE?cy@R!=Xwe?9UWl=p3Ylnv%Su7(Wp zm7XP)Vt#(0r8(c+;X%1FQ$EC66&*TcN8>R8Ky}oo&U*_WtkD0`cpet}F=jnwW$!Tq z{`HC0mtizer94|Hd8NswuWVus`+2^4^5HJN&F;Dwv3;Eq*t`FHJA*}Phq+xWrv&q#ETAbI zjg^;Rmfgl5cr6w$!7PLT{QpVEHNv>D8S#AFx?tAt5_`1l^I6a_@RZ3+FV?f+e0XRZ z`t%Qh7Ok=AToCl9dLaPKrw7BvE%ckcTu}~psb!xf9ENw;$BO+xoxLn`;OSzVn(u47 zgInrD@x&w5sx0S4-T*%VnE|DY%(RZ5&&V*?B?}=uq9ZF7^%_E+Y$6bj=CSW#lG;z$ zIQGOTmj*v}{jYz(6joIW{F;R49)JmkI7-T6KNeKxD;T2V1WXUgHHZg+015=38_G8g zf_UG;QM()jk&AR+k(Wb31O;OY?cm_c`5;{QvP)$!#AQE-yGZ^RFuX}a22R+q07OsG zV-)mzXDz!S7Jpngs~au~N1!DO#01}(KrEL~Iy%%7AA2vHE!u_@H|ij_7F{Zs2;xbq z@SB*mi`U8S&O@gc&e#L^S{Y>vff7!4>ucNlF#ui^K&aObylH#bk8c3Xn9=XoY`{Vl zBFXT!S0iTH`CTItXyYvhTDj#^pfNbc+WKeizU3O&4-oIwjfIkR=m8Qp4`;}ouKMH7 z%Huj9Mx5K%tl$MhkA?P?7`x{Xj-{tS$kT?p=~jT|?UD~Y^Huu65NB7u9ay$#lfhS- z8<{z{l1?(2JdyF@F*QIsIqBxj?)%6GC-$)(-wspr6&+O|I!F6uUrvswo`~|hZuV!0 zRuY#0lhx=|OIor0#tzT*<=I8JgwOKi)EjJ!=0dR4qS_B(YXiFtbT>D{?w)+?2ZC-Z z5Ur<@=_4A@4G4hOsRRh9<_UWbc(i9279<~q&6^mKM_$mbE?YwS86s*NXRm;|3g_LT zojwtr_EAAe=MI6bJv-#Ct5LJDo!J+l=WW(i=p}_ZkEZ|C{aDKjbB9HX@co}()YwL% z8_Ue%8U=f<*ZZjWyjA<~2AHGD?Np_e-n`N0?{>yEquE+7U;H#`@UZD?7WGC{v})S@ z&KC=!VU`AqV*4im19`xi+ga#g-3P}$mZFiI$tq{a9-4=pnUGXFCUROp@8?rbONs_2 zM}{%F+uA3;T}BgFc9JS4?VIV@FbYbyW`D)P7Sfs7S6z(~B6!#c@Y3x6@9z)-OJR;a zp8l^CyM1@4i8vlCstQ50Qbj!Kc@>4Mg6^vzSTPx%@h{=5Ni-8LiJPEyh4ZH%wHkXI z4%NaAGwYQUkeK&^&djR063PyZE)@Vl{Fe%^n)3+I~@1Q^5EBspeb zM18Tlsw)5t;U@Fyq$OPV#ZB*qHePPldlo9y39)*s@b`w4i*oE4a$8MKmg2!35~+Z z!i3GXQAiZufiqGsun|1boD%JaZB4+Nm^J<$Sb?UoT@{qHG@>`UNhIzN>es_4is)C1`cQ0ZORQ z$9>;@?E9WAjky%KpnJhga?spOJ_`;{M>9DDugD;Jdn%S}K+|q~I!xH#6;2_uje715 zkBcLllK=9n+)pqs)d>7oOnpurXiG%azqg9s@*6|j)30FSmQyDDLB()90$F!3nFh*u zfYm{s?yy3d-%$9KzHeAKki+Q3B+G(Yy-!>@DGJW|bOccRdc^{lpiG>Ll|9q71TxZe z8+*mKkFnd&)a>#^!CM)xv3}_`q@z~5YJ5+$M>A5Bu2;6avF^+&qSIiWXU`{khXS6mGc+bB_4WQ>`pQN zulzYwN=A#vZz)!Wl^OZ@#XQ#N*>TK`;3~MGJpk>{6GJU>0?D)*4qelJCl>4mpy{H zsZnQQ`9%87h{B~9L87Ii&&yKBH1`X5UDts&C=t+{)ybP>OZ{#DpS%{`5_gkRilIVu z9|J0pPG8^`ubP)qqgKtq)Tir{o`@Uup?9S^_;sZuK`K!QF51g9RHohbXiBieSR%$U zD_dtn^tRDU09>?C0(-K`fB0^ps&F4jTZnn)w3K4uCaAddKp#BNCyP9e91`O)waiTr zryl6Yp1-8qjM6pTd{$vRhiWs}!%u%xwJRSeroxkNq+RF=>Nxuz=a@tOW6wA9Q`g?L z>8q_sTI2Hbk`*rKy%(8V{-5<4tz%zTV_*D>5S6hL6VFSc2;+mq*V;BLzadTy#27** zGE8OaP?~^piCCACI%PZ0%!LJj08lh|N-q>k&u2oOsYHIE42o(o@(`u^Y~Q?aGBf!` z5ea`j{@!y@iFuUCxL5%R=g0K1cVx17uLf|nqZvkrJlgh84pA1?R8`HK*m+5nHwQn2 zigi_ME`U@q4XEEqmg=KFjv=FoT(}wl4aIT9M_;-JwAFMQ*_JM^6V1`sr+4*Qvaoko zC$D0%U2xAmrvS7uwho7rb?;TxE-?4t_uyA>((PSL<7_q%EKk8^-0cwS($j=WQ^1M? zMUcaYs`8kJRsuLWnx`ksKZwuuOOz-CdqTlu|A6|tW0N{+$Q}sEe%&S3CLx>#OPV`> zjZ_}N7QOBs!6dfCg-d@J$(M+7i}lHo=fVlJpd_25bMax)k^7_=4Pa>(wCHS+sa#}l zV^N)3oJkktJU#bOHm>h7sIZ)Fb9Aj^kaH<7Zl4@-3%7#>>`PgF)eZLs(dB9zrbl-C6fdS(qPW6 z$YZ>VXw_r_;H?W`0O-gApU=Palz!0f^3T+?bZ1wrcI37`&YU&JMYctFk?Y&H)<-q3 z^vy|$<`*{k4v{Fx*Wc?|zXcKSq7@KiZ$|H zrCpTr-y+Fht;vgNIF|qU(1ds()Vh9I|L2FOMcZd!KG)1`8}(=LtkC;&w{7;l5+bm6 zrRh8uRUxzAlMfT#$DvJIxxsz3)9G9K86=#q2OKaql=Y}w!cMImtlu@(CKM{9x;A^q z@>GMm{K~pCiXYaU3#U9EB}WzX%%gZ z_ssT~aP7%~$-^D_`QqO#o-?Im+AjSm9*cLon%Fyb07CwSDy~ny=8^uuH4cv9!L)H) z72(E3VN%42HWxy_8l3M)z7}X3JE+Obi>OKs3d=tH9BGoClrWJoF{#7hPXF_yf$lWa zx9!RtWBksfJEdO~=Q!Zx#P*qS5bz2um*e7XBox((9)8X;*`As(COYekro|I_9Xr>? zf}GGYr@T3LL3U5Ssbc3SKx8)(*Tz(1@Z?m+wV)^0J9DPCv+|uX?98eUo7Q;S2sS(&u>4sM0^YhMfA={05CDX8<(!(AQf^kDh#>|M$OG z<*n~SI-P%b;xHTmm6Hzp@#`STxH%NXh zciDhw>qb8pP2%V-mx6%x{$~{R7M+{7kzX;MfPoP+h9opRJot07nmqnsc=jNk?wXxS zivN5=(piV&ddK$_r9+P8salqzvzEQUcc8tY44OAAP6-dzl5;uk{y8W7=T0!k=E3w|91AkB?<8Fq!&=H z?bHr6u@sKEJQemhig%ARA>irjB*MSvhmu63TElavrmB`-SQ-q=)uON4Q!3ws%)11R z_MphDW~XYA*xD~5=6fV;b0a0eqJm!DRTQ&w{O@^s*Y#UJ}|H&c7O!vVKHzB&Z=#<$TPAj^t9F zk7xe9qxrZ5kZlE@MUU1M7DF=I#`d06HA5nvSwY6AvT_@CnN_ly0R^!luH7f zaIB1z4}6VxJNS?p1-EVz3FVO~#9d=|8Taj+i~8%k49<2LocruDvUx{3D~uxingYmR z4>Y#Jqye99jmB+Yrj_5En%xK2TEoC$+#CYF2z1{LzigtAMn22#9P^};QxE9wAW(_F z_wbopo_foPoB#5O4X%f7ZQ1G6UaV&S_3{7Cf{v;Kf@UtzI9ew|=jwHC)<-9G=syk? z!X&XwDeZDD)htmxz^8Ez)!Q%j?Nu}>bQVQX6G+xnHv$cQ7mK6?GN(n)mDHCIP^q}B zjl5=4pk-l~fF2S%ca3BI?%Q)vR7vjCQyPD} z+PDtgN$PR46(F&D5*sue37cPcBY)?ao3F^ae>@y<{PK!?q;q}TSCRAdfr^TTB(<)T z7wwbCtjJgXf1gMm8|-APg2+!27%7is?u&BfOBuw)K8xEyz!a1}vq19H&G34`S zlh~QGtDRS3!Yq`EW;a$pyMOqcN_%eCDR)7Ckev! z91g*&F;o1wE~r3H^K^ulf!!R1ni4Qo+@e9<7=mi^FA~yC$iSb~#OMDf z<@3S>t6u?9jHP#l@(vK$mhv?X+cKCKg#iyKEvi zUHb}i#?My1K`S!6eM%Z>d0K#oh$pDK)}S|le>efHwQIJmLVd)OC_4_+pBb)2m()Xq zn$EwpUy5?fY)m+7&fN+^bPyh(%`4K!M&bcA} z=j#lWP#E8A+`%!Yq&tKsV}BsvIpzi)PdT|{c8~U*Hq3H(R&lMumtlEo{QhHt#<4zS zU4BtiqKUan1Lk#o2h5?0&B}MY2J^q=o<{C@V|!2N;-x=$V&ct-r$}48A(66cK1J<) zV-#P^*YpEeM`3qG0Rd+k%}y3b%lk9tIhn~*?BRiSnho<+R#_!PqO3QEQx9JOV5bL* zfP`8Rm`!igywZAPKnl08w_ANU+W2?hVkjyG8Q}Kc+s%Kd*sT}}X@yX0kwSI^m3tE zx)>iJ$cCf48Ila69CIw*evH^4f4KI?(Zcr8M&Cd@N!qrw8(H+Wi+)bY)HOMnTUaP! zXWau9t^fJWW;B^G+em@Gqpj)fq|2;h7`s@3)6B6^^KbXQUp&dI!OXM4Qz65|#3ZhB q`h>}U-VL*X#x}GN^~YPx)($p2mm8r~?IHXdCLL}4)2~n3-}*o7(ldGh delta 48397 zcmcG$Wk6M1_dQH1h@jFSr4rIo5|Rqi-5{XSE!`YKMNp|DEg>mLNK0dZbhm&K(jnda z=k`8v@9%lPzF)jv;q0^b+H=i0<``qH<51kSP~7+LEKv+s8R`_!%-Ysi8n5ygG_+c^ z)vE$1Sc9(L!y&s!hEvNR=Xgdf6HlQ?0bS^di=}Z}Zr`)Z!*b2x8*@#P{5OY_Ij>kH zVLq2%g7U+)hbb;}sP9VYQ@%89cnkpn4S0`3LfXQ}*k?lM{Ub{)yAqXl6=K-rYur~D zP3nD5oG+uwNHB3ON@D!?kH7$Ox%U`v^A_~d5`X4$pTojqxG0Au8%`5NC+7Xk-;HY$ zby}wse?#hIy9gz}yE>liQTze}ekA8u(?a{*xq{)k-zfqEvCmc$`ilr! z&3ub;UhHR`Q|7q#zn>ytv^q2Y^*P1t`p?hKo4=HMiZ`a%;Q!+uehvGo{qh(t-RMUm z7Pphi}K!tK?*C4daM8Unk%!XDpu;CJt3`# z)>FdE76lt*@C$OzSYwrryLrlE751BX;xU*0zLv+x2N<5_A5ij;F! z7hS)9aO!P9q4XR4>t2ib=o?-=#Z2vR|w{cwAU z<+7jy|A_kOUbBQ*|66reOgAjcOPDxVifNL8SiZv~dJ(T`r2ZDaa8r0UkR?CdU5iT* z^J%THADcm|ibqws%qh&a#$>6zxgE`}Td23s?^EV~d{8|2faOp0qIXhAuH^=-V0KFd zpOJs`=>7Bo>SRAB-=Ms;m~QNLo>neiK+~Bo;%J^y3Ot1Xsm0o~er)5Z-VKruWRZ zOZfQszjKRm&yhwR?Qb5sa-e?{P|#su_2AlF2&mr~wdE>plx?_gxAgfbA^OdokS3ic zerTy=3jBa(xZ-YnSQz?EF$I?+R)uB2&f28OXr<%Y;1$?cg+?{ZEGn-f&D&xViaHVn z(A@QVw@b`grgntfk$XTwZQ|T89ftkz`!~(@lspd~ms$5UxhBxxa?gK3FEKG~nv3RX zq@@1B^vI;9(YDDF)+1rh>eFNV68DwS52J1V=we3-sz%emLu!lxoospZU1|XvGwRw0 zpU*^GR*^w|O2)tycu0paa3+@1aInl}y6L4;dE7B_|0Hn#@>emc{g1u-e)Q*R`h0)H zhVF5oBy+<5zKiiT9JAV@gSxMiO*`+)fbkAGFqxbS1B;GeSe)P}UFP}B-tXz+n+&3& z_Z%i_S!+DjV#(Nb;;UITG9$B!VC`hl(gdTgB$E)g3>(Z8pgvBQvGMmN^tTop!sSm`j%mg>6^s`Of@XGM9u%0(6)ZJ{SV z%|Z1O5%N`(jk1`j*l8*E9QW3LM)R27ZHc-T!ZMKi=SvBZFJ+5N!&EagY;8e5fB?bd z#>ETp#=q-^L@Pc$CRWSwAkKDYn5 z`;Y_}UDa6YwV7$w5=~4)^SYhs!?Bk8#pUrD!KS`@A0B_yTBaY4rA$&|*EyR$Op<90p>)XS`KHoSB3kOD zs^L&%4bQ`~d$LJVc&cW-4MF~a3f{zVmu}(fOx4trl%zWE?K~QB-(fq|WRY^wgZYfz{vJY))$dW` zH6BTRkS0lAa@tSU8*v#`&%8w)Y*~*~*eB^1-K#3W!NqN9L9@(GeSID+V5`Hbp8i=A&iSy|rF0aemf)*)b5lEl@q%cVwQE%P$QU7%k4XHk@p8 zQ}J267!iN;Ly`nzARwJ+#41J}%^X@^_W1GR`1>=5YYp6~eme>LHvMuFe!E6qQ=u#~ zYYnFrL{XQn-TfAMW9W$c+U?@SfNaVavSFNxtPry`X%u%+Q>N#zDbRdUOI2pK5{HcK-#?# zsNO*|u&g@gyfnz!^CQh=U2|8^;rp<9$B@sX*FRQ+QW{`QnDz{w|8_3Re=vO&sZCZDfOayz^@Bd0--peI< zQ3=IA1$E-ZYE)g8n}MkU}25qFC9?32a7A$?RU&)>*HkI8!Ib2gF&pESVGmtks zTokBDXk+t8t0t=9qrtzk}Oz#wkC$s8|ru1H!${IVA}r?d_jpkuaMiM{yPr4 zK#F#b3T@9}&vGA>S>aV#?2n03?_^4il5X1D#1^2M!fDxUhYAJW71eHp%~vVl5wZ|IS!5!yzgn}FZ`AV3R?m>}Ma+??{gVJm~+p_OT>4E(s zQ27%b&l8%#(-iT#{8HlES%9MWP&6jIBaO8U0xd>4>1GRTwVAn8)J^Me2i7u5*57zi zp1ni|WRlrgaoi1lojCheN@(s!&V)`ljWDB-(C~|oxl0HTQNmD*D@fOWIq3PC8eJUZ zK`qy>_mceOM^ZMeZz5loibU#v>6-YNV?{#gbuo)x3|;c<5PDe=W?2Vjkv~g$GhZoA${-o%>Pi_(6aF z!(+^TRr=S@&)nMXdnR#uqzUETvHlG?w=qYlMF+3Mv$GfIUa3kP_wQef{T~>L5g0)7 z?AfzO00N)B$DR4*dapA+?92iGV7d&tgCDiuUS+ke6^tGnj!FEvw?CJfG|^z7$};aT zR#C8d#hBZ6*QHKn>*0`?-kjf3K_!iT^5D&5gKQuA z4)G2eH(mZ`c}COa^E@pD-w$1nwIt7e9iXan9n6oqCM%M@AkrC3g!OKG((U!*c~fzb z4>d-|7TuU;0$=qsBi&w~OLkixu}l@1=9%X(RgC3u688Q1=?Sw?9;V*$&z2OinvtT^ z+j^6Y2l7u&Fx2CIUV$7TN!C5mRbc#HWGB;|$zzwzxb{P^lx0|iaoJ(5#Z4Pw|Dz8J zcMID@lzsw$aN{wS2~mBKJM4AA+kd5(Ub8r;rn+lfX{}Q~nHpUZHk)OdZFAI;T)dOW zWmu6f+%D22BI~?IeFIyg5VE!?w=&CM$m%^h&mWk4?!nZn1Go@gtGCp@Hb<>A zFeN%)psikMz3Z7=r`W94t;&A;&Yg~%gwcyH45@FpaUIAvzbmR|OW-vpKEAtcE~+uN zS^NTBTuXw|4xIEq?C2@mo$yiJnw58=c->cgTY$*4BfJp*(dd$(_xW#VTj`?BZ`UHE zC(Q;|>d($eTE3vDt>J-U#j_elXIa9gZ{PpnRgGyu_27_cb9h3ggvVUjB5v8BQNZ@a0Ng3z=%|sebq3MO-jE4uWc(GQUp?GX`I&k*3gya*=AB?OvD^=x zS=(E_qshiyIeecCCxs4j1J9_sT{KA!HNN)yg7{tKwYgtHsMvD$ zhMa~QG-sKLaXnJ=7>g}>X)fS+#?mqDtH%ucbMN=qclXy|b(&&hPMu;MGz&L49-j&u zc>GwsAYvXe zH8abk3f6S&8Ncv~Y^37MkCzG4r1D|F>?l+0Qz(Bw`D-Snz%3Z(_VNDL-$zEp7OUpyzD+|q)F3KtW zk}Bl%$u5Q8IvUEVsE(xcN0FVmD-T`l~P-y5bOT*J6KkZW*#!f z<+{b4$r&#FjFrwNyprY5_ZJ7=bqL;vHdvG{b71^)4~OE4L4hgd2ZBc^gE6uFneo;X zd;$U&5BX*{{yO95+tx22NQoI~m-^U5Fnga}y4K#l4xc8b{UE5CB0A_0S0r+6xG@mh zs6wCJJ3^%MZ3Edcj!JWX_f*8nuHf2Zha&&CMgO`bqkFAgCHY*R) z>Q1@S9M`vkZylrLCXY~RN4kyPvEFA7TyiB+Nl(eUcW-yn4$QBL}$VF~&64-Qy?JWS?i-Lnb3;>ULHlPT15?pE%GqlYDTIy!4k^-xRm z2LQbCJB`}1?it-*N<(9&pfqt8rfa zF6>bgz3e_`B65%#Rg_xGIC5FZg0N3wiXX#jF~ZS!a}_|wX!4%WXJeOp@vW!9I$m3Y3F?y*fCnWEbfbH5(XguroR{x z(z#}f{ktx*erU52cN za^rfN~*J{?T*c zl>k)%06YuB0)z4|=oz6U>ECr{mqhWmwWk%SEq#u+hY-Zb{2}+Z5c@Z;qdh16_xA(6 z$Vbo6uBwMzkaJ*?s&2eM%`azA>A(rVj&GVr?=3ayfRfx?dg{~DIf+vz{4kNVUDuHV z%wjBbPef@@0#8RbU7Z8Xy}90lo-RvV$<5g|ieuOCZ7ke#@w1zAUAHQV(I3M|P&nxU zMD#*{+gSD9r|sWZuiKsAbIG%+w0~L0Z#$@T)8V_7Lml8RUW<0hv%$=&ZcBq5QFX#j zQ_^wgRSWixHoMw1RmIH1dngeC>!*;h#QQwWoN)c2)y+|CeUdHUI@aD!x7_w!{fOQi zQ^OsE8NL!(^NWS||Hms6@&4%=`bBt=K`$9z}EBy ziKA`?t4hnS<*nbMt|{-N`jgn08L%Angk;KKXH>7+wj|%G6xO;n#zSUX(-}qv=^=}+ zwz;2a3lf9#{)Rc<$0z}Y!cCq7e3TLsBjXplLH%jgzgW;XU2aT5R2o5lp^>5g@wP5W zS|5Ivz$FzHncl)RgL4#Ic35!;TXe0>xDs{ryLn^WV{#!~bhDkWx6tTTDa~>}#^&LV4{8e9bSs7GD4MTK^%{?5|4UTX$Bqi+<9Z8g&S3)$s^z-ujB?cOI}> zLLB@=IcT|s@BAncB~xb(*44ZH8Oya!%3^~rI!nyk!ovIRyUh*qe-BwB3V0{eY#T4= zn4&+3O7Wb+--kxd3>Y5q3ty_eHZ7=b+z+sl)MyrX#C7mFE0u9R{sKUW7@%c%bb&sSqw16mS%iy6`b}JjJ_xSm=*9(WSNh3vLf7wIqQ;77SS>4B~_EE-W5 zP1ZmWtUWn`{W?jo5P5B@d-K#Xm&$u^ohR_X_?vgp+^MQ!`}&i_C%I0SlrQ?Me>|() z$aQ5+dCpVVeL22`q1^kpY0f+GM%?i34`m;>Jt@Mi#fw;pE4iJ6UC#vP@H4A{a<7+^Pah936t>S%deF{)z>7h0+S?Q6&IUR;HJca}$(_w?VYT|u8& zuHDQ@_2=a)H!Ka+KXgsM$|>RyxYO z#pZNA%RH&UslR595KjD`bSd6mZh(8pP?e^uk_us`^&>&lo9E2t))Dt_1*MJ-9y(o^ zptPRS0RB_C^7I@T8{^@RoY4^x{Ai0Eee5CFki)YhdQ$QoHdg&_quRu6zbiR5KX@b_ zO5~ldpR`uH89pCRoOmg3Bu#`Mp362sgHO6V#ia++WxLN%OsB7~K%>&ZX!G-%OD?o> zl|ZUm=!|m<*bc_u&m&4)60Jhh4FMa$ml50Pu=9z4``KWR&mr^}GT5I*RzCrgkgvE- zh7e9DV!cNByIFJ?7?K$B(o$N37;oJLhRPmtcIK8>+mBbfS@;a(X)_&2ODAgQ%2=j{ zsrG!dZ;qgM#7grPTS`o%%A1yyUW=t0E9X}`Y+*@_-&}|%NtDTxsazT+5h2juQe|~L zBel{bVL!^RU+Y;+rKc02PC)n;p<2^(_ep_iBS*>6U>ahlQPcE0{F05J6G%v-_Uun5 zxuzdHcDL*Nz_G|l4*4yMH{M%4vgRj8`^<`(&1T&^WgEXbd{p1}-dkUl=roNo7|SoB z?xqoXlvWD_fJHuD$D;v$OIr3OBlTd&*$FRtp=pnISW6707Smf?^{n_W_m6&_eEx2L zA6CT~$#Q)WXwW4?Ky0W^v!z%^8fJrKQQJ78WeFfwdJ^i(q*Lv_IYnL|5E-tmU%oDq zq!UWaR4WqWUOp7)%ZmQkNO9v7VKHrVH=zi%3>Fx6P$nkISev6387f6ws>zdm<$~Xc zT#F&^$oE~Xnrr7hMd)E>poiV@hQ$YTvJ_&>nqE*@^uD>}u__dl3W)|G(nUsnw->e1 z9#7dh%58OX5en?=*|ty;mP~lM^pRBfGPklCp#P#-ROzgthfGK|Y9`7;u8p34DvMoa z^#IKo!gV#(-(axNXsMSgnbz1+b`6*!sNQ#6WA}h#i@*O?QAP*YOiE7AbBuBruH9); zz142E`arB9G+oaZAe+7c(4twYz6dPEDI|L{U`NqE%V^LOA(WSqyq;~TrWRQXBtVJF zoXWyjWzk4cpt?~GawAg6L*fsMpP2{hTS+HJC8+Gt&)clfHCiv_YUY>$Z)8ygMJk-Z z8dhNj6rv4KU%mmw+`jzVeVk@a3f&V*&Njn&-{;IcRGt@Bz;V%jR2{!aL$e%-G|r~_ z6e)f?`la=@LtJqy`N)@Y!I%EN^j-iXue6$8#PwTQj?;7#*NOo!Mo`_>UQ!D|_C$fU_C4b~EmrxYn+4YnOMykxsCKpP`-QM( z{FL51%lf6p@9#@_{Ws_Y{tCJjOoLnvrUV|-r1(?N1Kyu$IC;izWJHCSdj;TldkrFO5h-2XsV~m%WKR-_+ zq;}J8_=ombo=yRx3nDx{Ee2LGJ|LGm=h?6_hw)J}|0egJ^M@OAUECskqM$O-h+)k8 ztkuoo`NL<`3@Yqc7y5GIU^mJG7mTQK*<*)DR8Q1;IWG_MfLQX3M#NQ4BS*!4$j^xm z(HI0JAydGD_r1RfoUcxi@e0EU!ri?u_53}hQD7iY>IaM0n<-B+QtkHc#bv=x*CY>` zMl2*^crL19j#&muIL|z@?gd_Bv9}ELp!jV-cES~wZL!LO`TEy@e~v-8W5^RUhsgTJ ztzW^!5&uH+!EH%r%827qk(uq8`Q%$3Gk|!q_rHdbxm3L*W>SdX_B-6Evg>-qisrW; z)nZn8ef>EFcPw-lH4p%oo{^yfGr1l?LhAX+AjYYgtrUeQM|yr;DPp}DvKLB=R8z%! z3k)kMmlm3UolTio;ckV+rp-;%7`UT`N-ZqgDbv?cK zOnG!uF*k4{7G9?($Km!INQOe)0P@%C^mupn(-Q(N%0zo%?=7p)34)QVUO$7Lw1;nQ zKY3VJO@vRSiBCuv1KN4g{mjm+QXqoSBl<;*{)jFup4Os2LVqaGk{r$LH zQ72#TK`a!@<>4kQv{VWyYJ=Q!rF_T?o|)$=Fzy$acaXH*{CJEpL~!Wdv!YK>uMf&vDT6bI4`Jdb7;^55(bZ1RA{Grb8~Q0H9J z0xJydnhuuPhlh-ET@t6YnXuu1b)|B5iEh5oJ3cJG;^Pbb)(aVklv!x!(x=aJ0Iw~L zc5A*CAsYRv!0Pg!yS$2T^`p;dz0c+oC}y)&b15V%owV0y+qfI}E0G;UV%V3jL>P9r zE8u0<@!}&aBsIY0YG@TCgEN4mO|NPPfqcrP>d-$Q6loE+#rQzo?M{A??z>hSCT1{`nkLe+z16(A`7D;x5+5)>eF zhP|2QMhKO5@mbLnDTp^|%fjCD$#0!eOF*_Otc;d-h@bpTiAOIvo^5(5RRf!c!-OVQy#y2W=5USZTk#;wigjv9TwEuM;kXq@}fq6e+qZ+9c zebzT{+5ND*UP?CBj4^Pi8?P$9FManVH4~5*PLWnadyq?ztZxo31%xK*TQSOL5%b=9 z)NhIjP5?Ah*+mq?{<(R(wFy$kB_9_DlSLvrQN4#rF~_A{;(RW%LaBymTqwt1&v`F` z2vopiJD88yOZbc}H-B}wxzx6OuX2$wb*9efbJ#uTZP%pr&}zPHKbW5&Yx>Sc&`XS~ zB0V%3(%j1eg`ic%NQ}1w5B<7FnZ&&p1`Covqr*UBrI6aY1_I9^J4w1!UAb*4mOyyR zZD(t^6<);}et^x82ch#~WE4{TX;Q#Eu+aS`*s5EL*#gYbD7+_}0L|Ncrrc^+=NgG_&&{)T82p7vmJ;Zmr(~!eTsEz@7yUHzmm@eCE#5`!axe#LY*a7RBdX%q-C>?Cd&qZ7!Ya4T$(H&r~L8S+<}Fbe~w+EKzaI z9O{cx3Cj%Zf~RZonDk>g+*v`J`0sx;3+#mYZk|FvM__{?P+_NAU|9T6X6_EL-^kqg zZrH5f7Wyva*xRoMUl5;n|HW#^{iOv#C8+3pNr23=p)4tpQ2#vpn*io{YH=5{Syyg@ zLC4Ft3RVU!V1q0G*u{hLi?;-xYt*DJ=_Mx8Qq$4-tD@a#y5H#*C64?M zcpA&BoanRYSuGX(c68i0al3y?G!0cN}VTqX2hd^~nyoWHuZ zR)1KCf7AHGqn{~72fJ0Bt^U8KWTuHzeFj&z5@keOezohVW=IV|lbV(H|KO=yMv{uyr$aAFJHf}m!#68rVb$+s;0ZkY~iIU_a z@>m|eGD_kFyDGi3ZYfhz~i4D)wEQMuUiWv`M;KtK|0pgTJl2YR0NWh?VlW=XXG zUG>HLIg(|8gfx>mOGFb1DX-aJa=a5YWbC7yt(xkM!Ct|D!f^Ru5;(Bi;Yu{&$zI(C{9xU0p<31GX0388+Pj za=XcyvF0D%Jg_|(Nj4w%H+(I(x@&uHHa91Q*VNfYtOLu93z`qU=@6US={0dGc9}BM zp45Hpn%OU`KB1d75YH97PBf@6VP|gH_P@OXtU6CZD&$(X`d4kv^}hSxjk~I7bp%nU zxL^|NuXB*b#Az?Ofp=@8nI84*ZmNmbELjWa-1q8zN7@y#f(OVs^tz=N$YO&qu;eu* zQ48K8&tF>vMB`HLy3h#NysF(EH2k&uU~3fi0vZuxM~>6iOlkia?hIadAmM*G>Ueuc zp~AY4xibkhSU3;NsK9pp!6JK=*JgGbkBFCs9rxV6SefPSuZ|>Tj-uO7&Sl3M(bm=Hwl?|USKjvg1Q+0Ql}ylV z8g)vW%aUsECa_N6TXlb#Q~-6Ch7Y}va}IwU#X0B5C|^>Cmu8?;3I($E#z8m4A5OJU8ztC30&z zhFw=W#3*gAND{hG(=?P{?12*N31`~clda?8O`nH<_~ipgSc&JsCe*w;+k)C{`RY4& zU@TWNusC@g;r8`KZvb*PrmKEsmSM$jg6f zjJzfy!sCpU5@E04D%28UW%~jBcd=YXHdrYK^e^0#hVri`ODXUGslf@}uxAVWc6TrW zrAfO@oF_AvY+WjK}1HVvoFZV)nOAuzC;%o7m zsuW#iXO_e0-!FlFEDB_w#NX##e_ii52Xsw2mDVX2~?ZC5iH?%9jIwf(km*L{}*Ogr@od0v>?TX7W;SG6G{{=>W&=K;-Fn46@iDr}k zU0UqhHD3T4tJNQ_-s(X~907^@=oHsg$2qyGqwjaVDAnuzlgWW&>p+3RY(d@1CXS6? zl#5sYbD@9HaFXA#LWwa3K57cg3p-ATBHaWN2SK03r1V&HBsvW$7NW?`kzT*goYBJ*DaI;7S3*Q_`)`oyUq-7#Tw)H4 zJgF|k-ItbQ;Vp7j%x!QQ`YoZLltj0?QW$}U+}%L9QQfqQ$A4UX8C?Q>Afn|UrEU!s zjJ6`!@_5~eA>qEP7hy2pV)vUa`I*8W=!p6|t@tPcSb%PDVpycF~1$E^YMV*QOVd>CqpfYyB3M^Ts< z6Ngw?66KJin#vUw6@{35TH<*!G8N-G@1>4Rius&JI$uZ+2F8%HM#`);Uz`=&?$>1Q zeqsGW)DAJr{yo4N$q{26j}h2dtvi9GbAv|`>`{KNX`()c}2T5i`b{s)2oCq5YS1G+HPR;CIQQgwPH z<^_J_26jO{)=SzCgD_4WGeN=M|gWQ7##pfUa{+TI$ga* z?mX9N-)t>(7U|gW@8kFq1%NEqhE6M>#*fs()nFcY+QWq~0r(8{SU8f??B9Mv?@Epz zX$EFO<`LODae;R*|Nr_SF?=UDemz!3nJOJ8!yRk>{S?nC5l`-wJ5S{RJ7$4lp#<0f zPr*nMq+4Zzb*@K@tOHM>GU)?2V+4j64Ga7Y5aKLg_xg&cX~YuAiJ6Kpp+w-`uJ%~t z(l0UNAryJ^Bj7yc4asxl9M=(-EP&%4nTZ_PuTqGwR^X36wBz8lh+E!k36wF?W&;S1 zNlRl01t$$8cLhjCOkkw%1+;^3B9$vy;;^=qw~8g%B5b|&dB7*u`{vHs&EEU-;CJ9O z@!O687o=|Cy(*wS4|2W0qo`2rzQTrdnpC&?+uGD<(N|K)P={NxBESYmUYUz6aFVT5{FA%?ua; zXONrJUH#;s=gO?zn|l5SsU`ATzgGRo`=`t0C6{xW%FGnqu8|&Bu71v8q`>Hs&6x3~ z1qE#JR-RZQgH_CQbA*rU5n>IW`Vv|Kt-5u0TA|e*blgbqP+V6Bi>zyynwlX0$u~121 zm^$_!#e#*^mIM_YCD|QzU;fsHpS;(eV6%~ImI~+30z-x2Zj`*%iEsGIuB}ulZ>r~1 zs1KJ3Gyr`h-k^GE6d8-f9Yb5}rau$|RZCxd?+13=3a~>KpPZB#X@s3KsWeJ}$8?_W zzJkzaibgd$u6S+GGkoVj>%GO|AaK-@Ufsj8p72xc_1*hjC+IyFo|#gq7BuJ(+0C@cRMI~FqWe$}O_$Dz zy|plnR!L_k=Tu7zI1lYBGDRTlyZ7E|9Bh=b=!kSS)WD~&%zIR1pO3Tl)+%11j+quj z#JQa}EzXx?Iij1|s0AJD9F&^ZGd-Cj?^C5go`^*yQ1_(VjptK&6>A)CnP!`~+z(D%E}SmLfWMfIMi zH(%{O=U!bi{tERvwx93hnumA8Ore6L{m^46P7xmBuY7^0GIV0^=q)*4ii7dl0Aj+4Z;53Swa}X4rQY9T-Y#9oR@A=XLbBSuocx-0kU};|Pi8L-D;&o8U!2tF8z~#ti z&+xo<$30|fzJLXMAn8u-bHq$7|Dzd8-MZI%=%5CD7WZ~f%0ThUl>}Bz<^@o)6Kq+o zU%&p*VIOi>deLy01QW+79ma99#L1Pu994>!Sj{=}cb0(*_f`2=jL?hs{jXotFDz>= z805Kqtu^NN2Y-!1J)qI8!QOBE5U4)<+lzKM;c@K+Wvl+BuR2H8 z=QZHYb5xRv{ccd#au|)4I_h+6^_v=$SzQ5EEUY6InK~eK#N@i@Rw?^Px`3K^OLKYi zQ~c_h2j{Bm;>C-9Jj*<$O&`8L?|j(@{1({S!mK>@=8a|wT6H9OK`R)Y*;#@+?hs#! zpC9OakKSoWVO3AyOG@goQ{^1g zH6ki9(Y>9c((^Td3(0+3OG91Xr@^~pO2eWx*VD5B4P{~xIx(A(7N`-fv5cX_DMhI(eDrt08uD*x%AmP1|UoMQiYXyxpxwB*lnTlx7p zG?VMkdr=&x^bCZq1>kLyRCsm$3ZNQ``4*5{s=qrQA8dD2@4PoIF$--IpNlD~KTyc$ z=z&-G-AlKTtC<}`#NoS8aN+a?J#Ifd1O7xidzamTym#B+=wJt;vKLbZSw_@d1~yd( z{L8{h4y)fe1W!ac1y6+7-n3CD?3LQjI5e!AWn{KIPDPzmEu1U5qe?`@`(<{bEry+? z+-4y9?c3`M2oo4|_d;}MikKmPu$CfpbxdGu;+n(jkMUFv(kd~N--kXPp=5~YliTNa zCsA>rl!e83FAY=KOOaU?v;syW<+`UgT5iiSsE6B=R~&}MOd+!`p?C|{4zR}b;oNp3 zN&#*?%q3zG0CJ-KjN1#yMSlm_Ee{uecKQiEoJ64s5WbYq%Ws)c@PxBb zvUH)l`$B>50{mSL4Utg{sx|E~zmp>`bl8gHuB6W?OBy=Hyd!boDE)X+C-z8Pl5f2f zn#9DyQ`U)Uw>Emm;G3tq3_wO_9|9|_%gE19N+Cgx2h$+{S(0vbTjqAXnwrFdMv&#S zf3h4J!EfM$YRT-;szLh1lLAN7(srHNuNsnf^MCrpZ>}Dqa{sqeq>w934|(#W1y-|t zP579u*RCT7mmsWuQptg7f-0@&Bk&Lx{YaLOWuc0Eh9$D^S^V&8JNqT>Bt~wsBKgSZ z8QPxffC%YOQ0xF~n5y)Ba5Bw>5i|W1VV7(~hGqEy3|nHLfuD#_0!`*P|51TeV!3wN zAwT#sTrtc!Pxk8OCpUH~jU@=n6hMpmx^Fo!+NpbbN$&UL2=}%=b2As`$!6x1L+&<5 zdA7Ks|74SWY`^XNnyz1=Xj|q9{3Lbr6cr=d^`S1>n{cV_Vi#|&MDB+%%j1{n0@%