EBSeq/0000755000175000017500000000000014147407032011345 5ustar nileshnileshEBSeq/demo/0000755000175000017500000000000014136050172012266 5ustar nileshnileshEBSeq/demo/00Index0000644000175000017500000000001414136050172013413 0ustar nileshnileshEBSeq demo EBSeq/demo/EBSeq.R0000644000175000017500000001536414136050172013361 0ustar nileshnileshlibrary(EBSeq) # 3.1 data(GeneMat) str(GeneMat) Sizes=MedianNorm(GeneMat) EBOut=EBTest(Data=GeneMat, Conditions=as.factor(rep(c("C1","C2"),each=5)),sizeFactors=Sizes, maxround=5) DEOut=GetDEResults(EBOut) str(DEOut) #3.2 data(IsoList) str(IsoList) IsoMat=IsoList$IsoMat str(IsoMat) IsoNames=IsoList$IsoNames IsosGeneNames=IsoList$IsosGeneNames IsoSizes=MedianNorm(IsoMat) NgList=GetNg(IsoNames, IsosGeneNames) IsoNgTrun=NgList$IsoformNgTrun IsoNgTrun[c(1:3,201:203,601:603)] IsoEBOut=EBTest(Data=IsoMat, NgVector=IsoNgTrun, Conditions=as.factor(rep(c("C1","C2"),each=5)),sizeFactors=IsoSizes, maxround=5) IsoDE=GetDEResults(IsoEBOut) str(IsoDE) #3.3 data(MultiGeneMat) str(MultiGeneMat) Conditions=c("C1","C1","C2","C2","C3","C3") PosParti=GetPatterns(Conditions) PosParti Parti=PosParti[-3,] Parti MultiSize=MedianNorm(MultiGeneMat) MultiOut=EBMultiTest(MultiGeneMat,NgVector=NULL,Conditions=Conditions, AllParti=Parti, sizeFactors=MultiSize, maxround=5) MultiPP=GetMultiPP(MultiOut) names(MultiPP) MultiPP$PP[1:10,] MultiPP$MAP[1:10] MultiPP$Patterns #3.4 data(IsoMultiList) IsoMultiMat=IsoMultiList[[1]] IsoNames.Multi=IsoMultiList$IsoNames IsosGeneNames.Multi=IsoMultiList$IsosGeneNames IsoMultiSize=MedianNorm(IsoMultiMat) NgList.Multi=GetNg(IsoNames.Multi, IsosGeneNames.Multi) IsoNgTrun.Multi=NgList.Multi$IsoformNgTrun Conditions=c("C1","C1","C2","C2","C3","C3","C4","C4") PosParti.4Cond=GetPatterns(Conditions) PosParti.4Cond Parti.4Cond=PosParti.4Cond[c(1,2,3,8,15),] Parti.4Cond IsoMultiOut=EBMultiTest(IsoMultiMat,NgVector=IsoNgTrun.Multi,Conditions=Conditions, AllParti=Parti.4Cond, sizeFactors=IsoMultiSize, maxround=5) IsoMultiPP=GetMultiPP(IsoMultiOut) names(MultiPP) IsoMultiPP$PP[1:10,] IsoMultiPP$MAP[1:10] IsoMultiPP$Patterns #4.1 data(GeneMat) str(GeneMat) Sizes=MedianNorm(GeneMat) EBOut=EBTest(Data=GeneMat, Conditions=as.factor(rep(c("C1","C2"),each=5)),sizeFactors=Sizes, maxround=5) DEOut=GetDEResults(EBOut) EBOut$Alpha EBOut$Beta EBOut$P GeneFC=PostFC(EBOut) str(GeneFC) par(mfrow=c(2,2)) QQP(EBOut) par(mfrow=c(2,2)) DenNHist(EBOut) PlotPostVsRawFC(EBOut,GeneFC) #4.2 data(IsoList) str(IsoList) IsoMat=IsoList$IsoMat str(IsoMat) IsoNames=IsoList$IsoNames IsosGeneNames=IsoList$IsosGeneNames IsoSizes=MedianNorm(IsoMat) NgList=GetNg(IsoNames, IsosGeneNames) IsoNgTrun=NgList$IsoformNgTrun IsoNgTrun[c(1:3,201:203,601:603)] IsoEBOut=EBTest(Data=IsoMat, NgVector=IsoNgTrun, Conditions=as.factor(rep(c("C1","C2"),each=5)),sizeFactors=IsoSizes, maxround=5) IsoDE=GetDEResults(IsoEBOut) str(IsoDE) IsoEBOut$Alpha IsoEBOut$Beta IsoEBOut$P IsoFC=PostFC(IsoEBOut) str(IsoFC) PlotPostVsRawFC(IsoEBOut,IsoFC) par(mfrow=c(2,2)) PolyFitValue=vector("list",3) for(i in 1:3) PolyFitValue[[i]]=PolyFitPlot(IsoEBOut$C1Mean[[i]], IsoEBOut$C1EstVar[[i]],5) PolyAll=PolyFitPlot(unlist(IsoEBOut$C1Mean), unlist(IsoEBOut$C1EstVar),5) lines(log10(IsoEBOut$C1Mean[[1]][PolyFitValue[[1]]$sort]), PolyFitValue[[1]]$fit[PolyFitValue[[1]]$sort],col="yellow",lwd=2) lines(log10(IsoEBOut$C1Mean[[2]][PolyFitValue[[2]]$sort]), PolyFitValue[[2]]$fit[PolyFitValue[[2]]$sort],col="pink",lwd=2) lines(log10(IsoEBOut$C1Mean[[3]][PolyFitValue[[3]]$sort]), PolyFitValue[[3]]$fit[PolyFitValue[[3]]$sort],col="green",lwd=2) legend("topleft",c("All Isoforms","Ig = 1","Ig = 2","Ig = 3"), col=c("red","yellow","pink","green"),lty=1,lwd=3,box.lwd=2) par(mfrow=c(2,3)) QQP(IsoEBOut) par(mfrow=c(2,3)) DenNHist(IsoEBOut) #4.3 data(MultiGeneMat) str(MultiGeneMat) Conditions=c("C1","C1","C2","C2","C3","C3") PosParti=GetPatterns(Conditions) PosParti PlotPattern(PosParti) Parti=PosParti[-3,] Parti MultiSize=MedianNorm(MultiGeneMat) MultiOut=EBMultiTest(MultiGeneMat,NgVector=NULL,Conditions=Conditions, AllParti=Parti, sizeFactors=MultiSize, maxround=5) MultiPP=GetMultiPP(MultiOut) names(MultiPP) MultiPP$PP[1:10,] MultiPP$MAP[1:10] MultiPP$Patterns MultiFC=GetMultiFC(MultiOut) str(MultiFC) par(mfrow=c(2,2)) DenNHist(MultiOut) par(mfrow=c(2,2)) QQP(MultiOut) #4.4 data(IsoMultiList) IsoMultiMat=IsoMultiList[[1]] IsoNames.Multi=IsoMultiList$IsoNames IsosGeneNames.Multi=IsoMultiList$IsosGeneNames IsoMultiSize=MedianNorm(IsoMultiMat) NgList.Multi=GetNg(IsoNames.Multi, IsosGeneNames.Multi) IsoNgTrun.Multi=NgList.Multi$IsoformNgTrun Conditions=c("C1","C1","C2","C2","C3","C3","C4","C4") PosParti.4Cond=GetPatterns(Conditions) PosParti.4Cond PlotPattern(PosParti.4Cond) Parti.4Cond=PosParti.4Cond[c(1,2,3,8,15),] Parti.4Cond IsoMultiOut=EBMultiTest(IsoMultiMat,NgVector=IsoNgTrun.Multi,Conditions=Conditions, AllParti=Parti.4Cond, sizeFactors=IsoMultiSize, maxround=5) IsoMultiPP=GetMultiPP(IsoMultiOut) names(MultiPP) IsoMultiPP$PP[1:10,] IsoMultiPP$MAP[1:10] IsoMultiPP$Patterns IsoMultiFC=GetMultiFC(IsoMultiOut) str(IsoMultiFC) par(mfrow=c(3,4)) DenNHist(IsoMultiOut) par(mfrow=c(3,4)) QQP(IsoMultiOut) IsoMultiFC=GetMultiFC(IsoMultiOut) #4.5 data(GeneMat) GeneMat.norep=GeneMat[,c(1,6)] Sizes.norep=MedianNorm(GeneMat.norep) EBOut.norep=EBTest(Data=GeneMat.norep, Conditions=as.factor(rep(c("C1","C2"))),sizeFactors=Sizes.norep, maxround=5) DE.norep=GetDEResults(EBOut.norep) GeneFC.norep=PostFC(EBOut.norep) #4.6 data(IsoList) IsoMat=IsoList$IsoMat IsoNames=IsoList$IsoNames IsosGeneNames=IsoList$IsosGeneNames NgList=GetNg(IsoNames, IsosGeneNames) IsoNgTrun=NgList$IsoformNgTrun IsoMat.norep=IsoMat[,c(1,6)] IsoSizes.norep=MedianNorm(IsoMat.norep) IsoEBOut.norep=EBTest(Data=IsoMat.norep, NgVector=IsoNgTrun, Conditions=as.factor(c("C1","C2")),sizeFactors=IsoSizes.norep, maxround=5) IsoDE.norep=GetDEResults(IsoEBOut.norep) IsoFC.norep=PostFC(IsoEBOut.norep) #4.7 data(MultiGeneMat) MultiGeneMat.norep=MultiGeneMat[,c(1,3,5)] Conditions=c("C1","C2","C3") PosParti=GetPatterns(Conditions) Parti=PosParti[-3,] MultiSize.norep=MedianNorm(MultiGeneMat.norep) MultiOut.norep=EBMultiTest(MultiGeneMat.norep,NgVector=NULL,Conditions=Conditions, AllParti=Parti, sizeFactors=MultiSize.norep, maxround=5) MultiPP.norep=GetMultiPP(MultiOut.norep) MultiFC.norep=GetMultiFC(MultiOut.norep) #4.8 data(IsoMultiList) IsoMultiMat=IsoMultiList[[1]] IsoNames.Multi=IsoMultiList$IsoNames IsosGeneNames.Multi=IsoMultiList$IsosGeneNames IsoMultiMat.norep=IsoMultiMat[,c(1,3,5,7)] IsoMultiSize.norep=MedianNorm(IsoMultiMat.norep) NgList.Multi=GetNg(IsoNames.Multi, IsosGeneNames.Multi) IsoNgTrun.Multi=NgList.Multi$IsoformNgTrun Conditions=c("C1","C2","C3","C4") PosParti.4Cond=GetPatterns(Conditions) PosParti.4Cond Parti.4Cond=PosParti.4Cond[c(1,2,3,8,15),] Parti.4Cond IsoMultiOut.norep=EBMultiTest(IsoMultiMat.norep,NgVector=IsoNgTrun.Multi,Conditions=Conditions, AllParti=Parti.4Cond, sizeFactors=IsoMultiSize.norep, maxround=5) IsoMultiPP.norep=GetMultiPP(IsoMultiOut.norep) IsoMultiFC.norep=GetMultiFC(IsoMultiOut.norep) # EOF EBSeq/DESCRIPTION0000644000175000017500000000231514136070500013046 0ustar nileshnileshPackage: EBSeq Type: Package Title: An R package for gene and isoform differential expression analysis of RNA-seq data Version: 1.34.0 Date: 2015-12-8 Author: Ning Leng, Christina Kendziorski Maintainer: Ning Leng Depends: blockmodeling, gplots, testthat, R (>= 3.0.0) Description: Differential Expression analysis at both gene and isoform level using RNA-seq data License: Artistic-2.0 LazyLoad: yes Collate: 'MedianNorm.R' 'GetNg.R' 'beta.mom.R' 'f0.R' 'f1.R' 'Likefun.R' 'LogN.R' 'LogNMulti.R' 'LikefunMulti.R' 'EBTest.R' 'GetPatterns.R' 'EBMultiTest.R' 'GetPP.R' 'PostFC.R' 'GetPPMat.R' 'GetMultiPP.R' 'GetMultiFC.R' 'PlotPostVsRawFC.R' 'crit_fun.R' 'DenNHist.R' 'GetNormalizedMat.R' 'PlotPattern.R' 'PolyFitPlot.R' 'QQP.R' 'QuantileNorm.R' 'RankNorm.R' 'GetDEResults.R' BuildVignettes: yes biocViews: ImmunoOncology, StatisticalMethod, DifferentialExpression, MultipleComparison, RNASeq, Sequencing git_url: https://git.bioconductor.org/packages/EBSeq git_branch: RELEASE_3_14 git_last_commit: 3398c86 git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 NeedsCompilation: no Packaged: 2021-10-26 21:21:04 UTC; biocbuild EBSeq/README.md0000644000175000017500000001170514136050172012625 0ustar nileshnilesh# EBSeq Q & A ## Read in data csv file: ``` In=read.csv("FileName", stringsAsFactors=F, row.names=1, header=T) Data=data.matrix(In) ``` txt file: ``` In=read.table("FileName", stringsAsFactors=F, row.names=1, header=T) Data=data.matrix(In) ``` check str(Data) and make sure it is a matrix instead of data frame. You may need to play around with the row.names and header option depends on how the input file was generated. ## GetDEResults() function not found You may on an earlier version of EBSeq. The GetDEResults function was introduced since version 1.7.1. The latest release version could be found at: http://www.bioconductor.org/packages/devel/bioc/html/EBSeq.html And you may check your package version by typing packageVersion("EBSeq") ## Visualizing DE genes/isoforms To generate a heatmap, you may consider the heatmap.2 function in gplots package. For example, you may run ``` heatmap.2(NormalizedMatrix[GenesOfInterest,], scale="row", trace="none", Colv=F) ``` The normalized matrix may be obtained from GetNormalizedMat() function. ## My favorite gene/isoform has NA in PP (status "NoTest") The NoTest status comes from two sources 1) Using the default parameter settings of EBMultiTest(), the function will not test on genes with more than 75% values < 10 to ensure better model fitting. To disable this filter, you may set Qtrm=1 and QtrmCut=0. 2) numerical over/underflow in R. That happens when the within condition variance is extremely large or small. I did implemented a numerical approximation step to calculate the approximated PP for these genes with over/underflow. Here I use 10^-10 to approximate the parameter p in the NB distribution for these genes (I set it to a small value since I want to cover more over/underflow genes with low within-condition variation). You may try to tune this value (to a larger value) in the approximation by setting ApproxVal in EBTest() or EBMultiTest() function. ## Can I run more than 5 iterations when running EBSeq via RSEM wrapper? Yes you may modify the script rsem-for-ebseq-find-DE under RSEM/EBSeq change line 36 ``` EBOut <- EBTest(Data = DataMat, NgVector = ngvector, Conditions = conditions, sizeFactors = Sizes, maxround = 5) ``` to ``` EBOut <- EBTest(Data = DataMat, NgVector = ngvector, Conditions = conditions, sizeFactors = Sizes, maxround = 10) ``` If you are running multiple condition analysis, you will need to change line 53: ``` MultiOut <- EBMultiTest(Data = DataMat, NgVector = ngvector, Conditions = conditions, AllParti = patterns, sizeFactors = Sizes, maxround = 5) ``` ``` MultiOut <- EBMultiTest(Data = DataMat, NgVector = ngvector, Conditions = conditions, AllParti = patterns, sizeFactors = Sizes, maxround = 10) ``` You will need to redo make after you make the changes. ## I saw a gene has significant FC but is not called as DE by EBSeq, why does that happen? EBSeq calls a gene as DE (assign high PPDE) if the across-condition variability is significantly larger than the within-condition variability. In the cases that a gene has large within-condition variation, although the FC across two conditions is large (small), the across-condition difference could still be explained by biological variation within condition. In these cases the gene/isoform will have a moderate PPDE. ## Can I look at TPMs/RPKMs/FPKMs across samples? In general, it is not appropriate to perform cross sample comparisons using TPM, FPKM or RPKM without further normalization. Instead, you may use normalized counts (It can be generated by GetNormalizedMat() function from raw count, note EBSeq testing functions takes raw counts and library size factors) Here is an example: Suppose there are 2 samples S1 and S2 from different conditions. Each has 5 genes. For simplicity, we assume each of 5 genes contains only one isoform and all genes have the same length. Assume only gene 5 is DE and the gene expressions of these 5 genes are: |Sample|g1|g2|g3|g4|g5| |---|---|---|---|---|---| |S1|10|10|10|10|10| |S2| 20 | 20 | 20 | 20 | 100 | Then the TPM/FPKM/RPKM will be (note sum TPM/FPKM/RPKM of all genes should be 10^6 ): |Sample|g1|g2|g3|g4|g5| |---|---|---|---|---|---| | S1 | 2x10^5 | 2x10^5 | 2x10^5 | 2x10^5 | 2x10^5 | | S2 | 1.1x10^5| 1.1x10^5| 1.1x10^5| 1.1x10^5| 5.6x10^5| Based on TPM/FPKM/RPKM, an investigator may conclude that the first 4 genes are down-regulated and the 5th gene is up-regulated. Then we will get 4 false positive calls. Cross-sample TPM/FPKM/RPKM comparisons will be feasible only when no hypothetical DE genes present across samples (Or when assuming the DE genes are sort of 'symmetric' regarding up and down regulation). ## RealFC vs PostFC The posterior fold change estimations will give less extreme values for low expressers. e.g. if gene1 has mean1 = 5000 and mean2 = 1000, its FC and PostFC will both be 5. If gene2 has mean1 = 5 and mean2 = 1, its FC will be 5 but its PostFC will be < 5 and closer to 1. Therefore when we sort the PostFC, gene2 will be less significant than gene1. EBSeq/man/0000755000175000017500000000000014136050172012115 5ustar nileshnileshEBSeq/man/IsoMultiList.Rd0000644000175000017500000000114714136050172015010 0ustar nileshnilesh\name{IsoMultiList} \alias{IsoMultiList} \docType{data} \title{ The simulated data for multiple condition isoform DE analysis } \description{ 'IsoMultiList' gives a set of simulated data for multiple condition isoform DE analysis. } \usage{data(IsoMultiList)} \source{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \seealso{IsoList } \examples{ data(IsoMultiList) } \keyword{datasets} EBSeq/man/GetMultiPP.Rd0000644000175000017500000000240014136050172014372 0ustar nileshnilesh\name{GetMultiPP} \alias{GetMultiPP} \title{ Posterior Probability of Each Transcript } \description{ 'GetMultiPP' generates the Posterior Probability of being each pattern of each transcript based on the EBMultiTest output. } \usage{ GetMultiPP(EBout) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{EBout}{The output of EBMultiTest function.} } \value{ \item{PP}{The poster probabilities of being each pattern.} \item{MAP}{Gives the most likely pattern.} \item{Patterns}{The Patterns.} } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \seealso{GetPPMat} \examples{ data(MultiGeneMat) MultiGeneMat.small = MultiGeneMat[201:210,] Conditions = c("C1","C1","C2","C2","C3","C3") PosParti = GetPatterns(Conditions) Parti = PosParti[-3,] MultiSize = MedianNorm(MultiGeneMat.small) MultiOut = EBMultiTest(MultiGeneMat.small, NgVector=NULL, Conditions=Conditions, AllParti=Parti, sizeFactors=MultiSize, maxround=5) MultiPP = GetMultiPP(MultiOut) } \keyword{ Posterior Probability } EBSeq/man/DenNHist.Rd0000644000175000017500000000257614136050172014072 0ustar nileshnilesh\name{DenNHist} \alias{DenNHist} \title{ Density plot to compare the empirical q's and the simulated q's from the fitted beta distribution. } \description{ 'DenNHist' gives the density plot that compares the empirical q's and the simulated q's from the fitted beta distribution. } \usage{ DenNHist(EBOut, GeneLevel = F) } \arguments{ \item{EBOut}{The output of EBTest or EBMultiTest.} \item{GeneLevel}{Indicate whether the results are from data at gene level.} } \value{ For data with n1 conditions and n2 uncertainty groups, n1*n2 plots will be generated. Each plot represents a subset of the data. The empirical estimation of q's will be represented as blue histograms and the density of the fitted beta distribution will be represented as the green line. } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \seealso{ beta.mom, QQP, EBTest, EBMultiTest } \examples{ data(GeneMat) GeneMat.small = GeneMat[c(500:1000),] Sizes = MedianNorm(GeneMat.small) EBOut = EBTest(Data = GeneMat.small, Conditions = as.factor(rep(c("C1","C2"), each=5)), sizeFactors = Sizes, maxround = 5) par(mfrow = c(2,2)) DenNHist(EBOut) } \keyword{ beta } EBSeq/man/GetMultiFC.Rd0000644000175000017500000000400714136050172014350 0ustar nileshnilesh\name{GetMultiFC} \alias{GetMultiFC} \title{ Calculate the Fold Changes for Multiple Conditions } \description{ 'GetMultiFC' calculates the Fold Changes for each pair of conditions in a multiple condition study.} \usage{ GetMultiFC(EBMultiOut, SmallNum = 0.01) } \arguments{ \item{EBMultiOut}{The output of EBMultiTest function.} \item{SmallNum}{A small number will be added for each transcript in each condition to avoid Inf and NA. Default is 0.01.} } \details{ Provide the FC (adjusted by the normalization factors) for each pair of comparisons. A small number will be added for each transcript in each condition to avoid Inf and NA. Default is set to be 0.01. } \value{ \item{FCMat}{The FC of each pair of comparison (adjusted by the normalization factors).} \item{Log2FCMat}{The log 2 FC of each pair of comparison (adjusted by the normalization factors).} \item{PostFCMat}{The posterior FC of each pair of comparison.} \item{Log2PostFCMat}{The log 2 posterior FC of each pair of comparison.} \item{CondMean}{The mean of each transcript within each condition (adjusted by the normalization factors).} \item{ConditionOrder}{The condition assignment for C1Mean, C2Mean, etc.} } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \seealso{ EBMultiTest, PostFC } \examples{ data(MultiGeneMat) MultiGeneMat.small = MultiGeneMat[201:210,] Conditions = c("C1","C1","C2","C2","C3","C3") PosParti = GetPatterns(Conditions) Parti = PosParti[-3,] MultiSize = MedianNorm(MultiGeneMat.small) MultiOut = EBMultiTest(MultiGeneMat.small, NgVector=NULL, Conditions=Conditions, AllParti=Parti, sizeFactors=MultiSize, maxround=5) MultiFC = GetMultiFC(MultiOut) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ Posterior Probability } EBSeq/man/f1.Rd0000644000175000017500000000222714136050172012715 0ustar nileshnilesh\name{f1} \alias{f1} \title{ The Prior Predictive Distribution of being DE } \description{ 'f1' gives the Prior Predictive Distribution of DE. } \usage{ f1(Input1, Input2, AlphaIn, BetaIn, EmpiricalRSP1, EmpiricalRSP2, NumOfGroup, log) } \arguments{ \item{Input1}{Expressions from Condition1.} \item{Input2}{Expressions from Condition2.} \item{AlphaIn, BetaIn, EmpiricalRSP1, EmpiricalRSP2}{The parameters estimated from last iteration of EM.} \item{NumOfGroup}{ How many transcripts within each Ng group.} \item{log}{If true, will give the log of the output.} } \value{ The function will return the prior predictive distribution values of being DE. } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \seealso{ f0 } \examples{ #f1(matrix(rnorm(100,100,1),ncol=10), # matrix(rnorm(100,100,1),ncol=10), .5, .6, # matrix(rnorm(100,200,1),ncol=10), # matrix(rnorm(100,200,1),ncol=10), 100, TRUE) } EBSeq/man/EBSeq_NingLeng-package.Rd0000644000175000017500000000226014136050172016515 0ustar nileshnilesh\name{EBSeq_NingLeng-package} \alias{EBSeq_NingLeng-package} \alias{EBSeq_NingLeng} \docType{package} \title{ EBSeq: RNA-Seq Differential Expression Analysis on both gene and isoform level } \description{ In 'EBSeq_NingLeng-package,' a Negative Binomial-beta model was built to analyze the RNASeq data. We used the empirical bayes method and EM algrithom. } \details{ \tabular{ll}{ Package: \tab EBSeq_NingLeng\cr Type: \tab Package\cr Version: \tab 1.0\cr Date: \tab 2011-06-13\cr License: \tab What license is it under?\cr LazyLoad: \tab yes\cr } } \author{ Ning Leng, Christina Kendziorski Maintainer: Ning Leng } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \keyword{ package } \seealso{ EBTest, EBMultiTest } \examples{ data(GeneMat) GeneMat.small = GeneMat[c(1:10,511:550),] Sizes = MedianNorm(GeneMat.small) EBOut = EBTest(Data=GeneMat.small, Conditions=as.factor(rep(c("C1","C2"), each=5)), sizeFactors=Sizes, maxround=5) } EBSeq/man/f0.Rd0000644000175000017500000000205114136050172012707 0ustar nileshnilesh\name{f0} \alias{f0} %- Also NEED an '\alias' for EACH other topic documented here. \title{ The Prior Predictive Distribution of being EE } \description{ 'f0' gives the Prior Predictive Distribution of being EE. } \usage{ f0(Input, AlphaIn, BetaIn, EmpiricalR, NumOfGroups, log) } \arguments{ \item{Input}{Expression Values.} \item{AlphaIn, BetaIn, EmpiricalR}{The parameters estimated from last iteration of EM.} \item{NumOfGroups}{How many transcripts within each Ng group.} \item{log}{If true, will give the log of the output.} } \value{ The function will return the prior predictive distribution values of being EE. } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \seealso{ f1 } \examples{ # #f0(matrix(rnorm(100,100,1),ncol=10), .5, .6, # matrix(rnorm(100,200,1),ncol=10), 100, TRUE) } EBSeq/man/GetPatterns.Rd0000644000175000017500000000140414136050172014643 0ustar nileshnilesh\name{GetPatterns} \alias{GetPatterns} \title{ Generate all possible patterns in a multiple condition study } \description{ 'GetPatterns' generates all possible patterns in a multiple condition study. } \usage{ GetPatterns(Conditions) } \arguments{ \item{Conditions}{The names of the Conditions in the study.} } \value{A matrix describe all possible patterns. } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \examples{ Conditions = c("C1","C1","C2","C2","C3","C3") PosParti = GetPatterns(Conditions) } EBSeq/man/PlotPostVsRawFC.Rd0000644000175000017500000000202414136050172015362 0ustar nileshnilesh\name{PlotPostVsRawFC} \alias{PlotPostVsRawFC} \title{ Plot Posterior FC vs FC } \description{ 'PlotPostVsRawFC' helps the users visualize the posterior FC vs FC in a two condition study. } \usage{ PlotPostVsRawFC(EBOut, FCOut) } \arguments{ \item{EBOut}{ The output of EBMultiTest function. } \item{FCOut}{The output of PostFC function.} } \value{ A figure shows fold change vs posterior fold change. } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \seealso{ PostFC } \examples{ data(GeneMat) GeneMat.small = GeneMat[c(500:600),] Sizes = MedianNorm(GeneMat.small) EBOut = EBTest(Data = GeneMat.small, Conditions = as.factor(rep(c("C1","C2"), each=5)), sizeFactors = Sizes, maxround = 5) FC = PostFC(EBOut) PlotPostVsRawFC(EBOut,FC) } \keyword{ Posterior Probability } EBSeq/man/GetDEResults.Rd0000644000175000017500000001165114136050172014722 0ustar nileshnilesh\name{GetDEResults} \alias{GetDEResults} \title{ Obtain Differential Expression Analysis Results in a Two-condition Test } \description{ Obtain DE analysis results in a two-condition test using the output of EBTest() } \usage{ GetDEResults(EBPrelim, FDR=0.05, Method="robust", FDRMethod="hard", Threshold_FC=0.7, Threshold_FCRatio=0.3, SmallNum=0.01) } \arguments{ \item{EBPrelim}{Output from the function EBTest().} \item{FDR}{Target FDR, defaut is 0.05.} \item{FDRMethod}{"hard" or "soft". Giving a target FDR alpha, either hard threshold and soft threshold may be used. If the hard threshold is preferred, DE transcripts are defined as the the transcripts with PP(DE) greater than (1-alpha). Using the hard threshold, any DE transcript in the list has FDR <= alpha. If the soft threshold is preferred, the DE transcripts are defined as the transcripts with PP(DE) greater than crit_fun(PPEE, alpha). Using the soft threshold, the list of DE transcripts has average FDR alpha. Based on results from our simulation studies, hard thresholds provide a better-controlled empirical FDR when sample size is relatively small(Less than 10 samples in each condition). User may consider the soft threshold when sample size is large to improve power.} \item{Method}{"robust" or "classic". Using the "robust" option, EBSeq is more robust to genes with outliers and genes with extremely small variances. Using the "classic" option, the results will be more comparable to those obtained by using the GetPPMat() function from earlier version (<= 1.7.0) of EBSeq. Default is "robust".} \item{Threshold_FC}{Threshold for the fold change (FC) statistics. The default is 0.7. The FC statistics are calculated as follows. First the posterior FC estimates are calculated using PostFC() function. The FC statistics is defined as exp(-|log posterior FC|) and therefore is always less than or equal to 1. The default threshold was selected as the optimal threshold learned from our simulation studies. By setting the threshold as 0.7, the expected FC for a DE transcript is less than 0.7 (or greater than 1/0.7=1.4). User may specify their own threshold here. A higher (less conservative) threshold may be used here when sample size is large. Our simulation results indicated that when there are more than or equal to 5 samples in each condition, a less conservative threshold will improve the power when the FDR is still well-controlled. The parameter will be ignored if Method is set as "classic".} \item{Threshold_FCRatio}{Threshold for the fold change ratio (FCRatio) statistics. The default is 0.3. The FCRatio statistics are calculated as follows. First we get another revised fold change statistic called Median-FC statistic for each transcript. For each transcript, we calculate the median of normalized expression values within each condition. The MedianFC is defined as exp(-|log((C1Median+SmallNum)/(C2Median+SmallNum))|). Note a small number is added to avoid Inf and NA. See SmallNum for more details. The FCRatio is calculated as exp(-|log(FCstatistics/MedianFC)|). Therefore it is always less than or equal to 1. The default threshold was selected as the optimal threshold learned from our simulation studies. By setting the threshold as 0.3, the FCRatio for a DE transcript is expected to be larger than 0.3. } \item{SmallNum}{When calculating the FCRatio (or Median-FC), a small number is added for each transcript in each condition to avoid Inf and NA. Default is 0.01.} } \details{ GetDEResults() function takes output from EBTest() function and output a list of DE transcripts under a target FDR. It also provides posterior probability estimates for each transcript. } \value{ \item{DEfound}{A list of DE transcripts.} \item{PPMat}{Posterior probability matrix. Transcripts are following the same order as in the input matrix. Transcripts that were filtered by magnitude (in EBTest function), FC, or FCR are assigned with NA for both PPDE and PPEE.} \item{Status}{Each transcript will be assigned with one of the following values: "DE", "EE", "Filtered: Low Expression", "Filtered: Fold Change" and "Filtered: Fold Change Ratio". Transcripts are following the same order as in the input matrix.} } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng, Yuan Li } \seealso{ EBTest } \examples{ data(GeneMat) str(GeneMat) GeneMat.small = GeneMat[c(1:10,511:550),] Sizes = MedianNorm(GeneMat.small) EBOut = EBTest(Data = GeneMat.small, Conditions = as.factor(rep(c("C1","C2"), each = 5)), sizeFactors = Sizes, maxround = 5) Out = GetDEResults(EBOut) } \keyword{ DE } \keyword{ Two condition } EBSeq/man/GetPP.Rd0000644000175000017500000000221714136050172013365 0ustar nileshnilesh\name{GetPP} \alias{GetPP} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Generate the Posterior Probability of each transcript. } \description{ 'GetPP' generates the Posterior Probability of being DE of each transcript based on the EBTest output. } \usage{ GetPP(EBout) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{EBout}{The output of EBTest function.} } \value{The poster probabilities of being DE. } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{GetPPMat } \examples{ data(GeneMat) GeneMat.small = GeneMat[c(1:10,500:550),] Sizes = MedianNorm(GeneMat.small) EBOut = EBTest(Data = GeneMat.small, Conditions = as.factor(rep(c("C1","C2"), each=5)), sizeFactors = Sizes, maxround = 5) PPDE = GetPP(EBOut) str(PPDE) head(PPDE) } \keyword{ Posterior Probability } EBSeq/man/MedianNorm.Rd0000644000175000017500000000362614136050172014444 0ustar nileshnilesh\name{MedianNorm} \alias{MedianNorm} \title{ Median Normalization } \description{ 'MedianNorm' specifies the median-by-ratio normalization function from Anders et. al., 2010. } \usage{ MedianNorm(Data, alternative = FALSE) } \arguments{ \item{Data}{The data matrix with transcripts in rows and lanes in columns.} \item{alternative}{if alternative = TRUE, the alternative version of median normalization will be applied. The alternative method is similar to median-by-ratio normalization, but can deal with the cases when all of the genes/isoforms have at least one zero counts (in which case the median-by-ratio normalization will fail). In more details, in median-by-ratio normalization (denote l_1 as libsize for sample 1 as an example, assume total S samples): hat{l_1} = median_g [ X_g1 / (X_g1*X_g2*...*X_gS)^{-S} ] (1) which estimates l_1 / (l_1 * l_2 * ... * l_S)^{-S}. Since we have the constrain that (l_1 * l_2 * ... * l_S) = 1, equation (1) estimates l_1. Note (1) could also be written as: hat{l_1} = median_g [ (X_g1/X_g1 * X_g1/X_g2 * .... * X_g1/X_gS)^{-S}] In the alternative method, we estimate l_1/l_1, l_1/l_2, ... l_1/l_S individually by taking median_g(X_g1/X_g1), median_g(X_g1/X_g2) ... Then estimate l_1 = l_1 / (l_1 * l_2 * ... * l_S)^{-S} by taking the geomean of these estimates: hat{l_1} = [ median_g(X_g1/X_g1) * median_g(X_g1/X_g2) * median_g(X_g1/X_g3) * ... * median_g(X_g1/X_gS) ] ^{-S} } } \value{The function will return a vector contains the normalization factor for each lane.} \references{ Simon Anders and Wolfgang Huber. Differential expression analysis for sequence count data. Genome Biology (2010) 11:R106 (open access) } \author{ Ning Leng } \seealso{ QuantileNorm } \examples{ data(GeneMat) Sizes = MedianNorm(GeneMat) #EBOut = EBTest(Data = GeneMat, # Conditions = as.factor(rep(c("C1","C2"), each=5)), # sizeFactors = Sizes, maxround = 5) } \keyword{ Normalization } EBSeq/man/crit_fun.Rd0000644000175000017500000000305214136050172014215 0ustar nileshnilesh\name{crit_fun} \alias{crit_fun} \title{ Calculate the soft threshold for a target FDR } \description{ 'crit_fun' calculates the soft threshold for a target FDR. } \usage{ crit_fun(PPEE, thre) } \arguments{ \item{PPEE}{The posterior probabilities of being EE.} \item{thre}{The target FDR.} } \details{ Regarding a target FDR alpha, both hard threshold and soft threshold could be used. If the hard threshold is preferred, user could simply take the transcripts with PP(DE) greater than (1-alpha). Using the hard threshold, any DE transcript in the list is with FDR <= alpha. If the soft threshold is preferred, user could take the transcripts with PP(DE) greater than crit_fun(PPEE, alpha). Using the soft threshold, the list of DE transcripts is with average FDR alpha. } \value{ The adjusted FDR threshold of target FDR. } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \examples{ data(GeneMat) GeneMat.small = GeneMat[c(1:10, 500:600),] Sizes = MedianNorm(GeneMat.small) EBOut = EBTest(Data = GeneMat.small, Conditions = as.factor(rep(c("C1","C2"), each=5)), sizeFactors = Sizes, maxround = 5) PP = GetPPMat(EBOut) DEfound = rownames(PP)[which(PP[,"PPDE"] >= 0.95)] str(DEfound) SoftThre = crit_fun(PP[,"PPEE"], 0.05) DEfound_soft = rownames(PP)[which(PP[,"PPDE"] >= SoftThre)] } \keyword{ FDR } EBSeq/man/LogNMulti.Rd0000644000175000017500000000270714136050172014264 0ustar nileshnilesh\name{LogNMulti} \alias{LogNMulti} %- Also NEED an '\alias' for EACH other topic documented here. \title{ EM algorithm for the NB-beta model in the multiple condition test } \description{ 'LogNMulti' specifies the function to run (one round of) the EM algorithm for the NB-beta model in the multiple condition test.} \usage{ LogNMulti(Input, InputSP, EmpiricalR, EmpiricalRSP, NumOfEachGroup, AlphaIn, BetaIn, PIn, NoneZeroLength, AllParti, Conditions) } \arguments{ \item{Input, InputSP}{The expressions among all the samples.} \item{NumOfEachGroup}{Number of genes in each Ng group.} \item{AlphaIn, PIn, BetaIn, EmpiricalR, EmpiricalRSP}{The parameters from the last EM step.} \item{NoneZeroLength}{Number of Ng groups.} \item{AllParti}{The patterns of interests.} \item{Conditions}{The condition assignment for each sample.} } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \examples{ # #Input = matrix(rnorm(100,100,1),ncol=10) #rownames(Input) = paste("g",1:10) #RIn = matrix(rnorm(100,200,1), ncol=10) #res = LogNMulti(Input, list(Input[,1:5], Input[,6:10]), # RIn, list(RIn[,1:5], RIn[,6:10]), 10, .6, .7, # c(.3,.7), 1, rbind(c(1,1), c(1,2)), # as.factor(rep(c("C1","C2"), each=5))) } EBSeq/man/QQP.Rd0000644000175000017500000000230614136050172013046 0ustar nileshnilesh\name{QQP} \alias{QQP} \title{ The Quantile-Quantile Plot to compare the empirical q's and simulated q's from fitted beta distribution } \description{ 'QQP' gives the Quantile-Quantile Plot to compare the empirical q's and simulated q's from fitted beta distribution. } \usage{ QQP(EBOut, GeneLevel = F) } \arguments{ \item{EBOut}{The output of EBTest or EBMultiTest. } \item{GeneLevel}{Indicate whether the results are from data at gene level.} } \value{ For data with n1 conditions and n2 uncertainty groups, n1*n2 plots will be generated. Each plot represents a subset of the data. } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \seealso{ EBTest, EBMultiTest, DenNHist } \examples{ data(GeneMat) GeneMat.small = GeneMat[c(500:1000),] Sizes = MedianNorm(GeneMat.small) EBOut = EBTest(Data = GeneMat.small, Conditions = as.factor(rep(c("C1","C2"), each=5)), sizeFactors = Sizes, maxround = 5) par(mfrow=c(2,2)) QQP(EBOut) } \keyword{ Q-Q plot } EBSeq/man/QuantileNorm.Rd0000644000175000017500000000173514136050172015030 0ustar nileshnilesh\name{QuantileNorm} \alias{QuantileNorm} \title{ Quantile Normalization } \description{ 'QuantileNorm' gives the quantile normalization. } \usage{ QuantileNorm(Data, Quantile) } \arguments{ \item{Data}{ The data matrix with transcripts in rows and lanes in columns. } \item{Quantile}{ The quantile the user wishs to use. Should be a number between 0 and 1. } } \details{ Use a quantile point to normalize the data. } \value{ The function will return a vector contains the normalization factor for each lane. % ... } \references{ Bullard, James H., et al. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC bioinformatics 11.1 (2010): 94. } \author{ Ning Leng } \seealso{ MedianNorm } \examples{ data(GeneMat) Sizes = QuantileNorm(GeneMat,.75) #EBOut = EBTest(Data = GeneMat, # Conditions = as.factor(rep(c("C1","C2"), each=5)), # sizeFactors = Sizes, maxround = 5) } \keyword{ Normalization }% __ONLY ONE__ keyword per line EBSeq/man/Likefun.Rd0000644000175000017500000000171414136050172014004 0ustar nileshnilesh\name{Likefun} \alias{Likefun} \title{ Likelihood Function of the NB-Beta Model } \description{ 'Likefun' specifies the Likelihood Function of the NB-Beta Model. } \usage{ Likefun(ParamPool, InputPool) } \arguments{ \item{ParamPool}{The parameters that will be estimated in EM.} \item{InputPool}{The control parameters that will not be estimated in EM.} } \value{The function will return the log-likelihood. } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \examples{ #x1 = c(.6,.7,.3) #Input = matrix(rnorm(100,100,1), ncol=10) #RIn = matrix(rnorm(100,200,1), ncol=10) #InputPool = list(Input[,1:5], Input[,6:10], Input, # rep(.1,100), 1, RIn, RIn[,1:5], RIn[,6:10], 100) #Likefun(x1, InputPool) } EBSeq/man/GetPPMat.Rd0000644000175000017500000000207514136050172014031 0ustar nileshnilesh\name{GetPPMat} \alias{GetPPMat} \title{ Posterior Probability of Transcripts } \description{ 'GetPPMat' generates the Posterior Probability of being each pattern of each transcript based on the EBTest output. } \usage{ GetPPMat(EBout) } \arguments{ \item{EBout}{The output of EBTest function.} } \value{The poster probabilities of being EE (first column) and DE (second column). } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \examples{ data(GeneMat) GeneMat.small = GeneMat[c(500:550),] Sizes = MedianNorm(GeneMat.small) EBOut = EBTest(Data = GeneMat.small, Conditions = as.factor(rep(c("C1","C2"), each=5)), sizeFactors = Sizes, maxround = 5) PP = GetPPMat(EBOut) str(PP) head(PP) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ Posterior Probability } EBSeq/man/IsoList.Rd0000644000175000017500000000107714136050172013777 0ustar nileshnilesh\name{IsoList} \alias{IsoList} \docType{data} \title{ The simulated data for two condition isoform DE analysis } \description{ 'IsoList' gives the simulated data for two condition isoform DE analysis. } \usage{data(IsoList)} \source{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \seealso{GeteMat} \examples{ data(IsoList) } \keyword{datasets} EBSeq/man/EBMultiTest.Rd0000644000175000017500000001307614136050172014554 0ustar nileshnilesh\name{EBMultiTest} \alias{EBMultiTest} \title{ Using EM algorithm to calculate the posterior probabilities of interested patterns in a multiple condition study } \description{ 'EBMultiTest' is built based on the assumption of NB-Beta Empirical Bayes model. It utilizes the EM algorithm to give the posterior probability of the interested patterns. } \usage{ EBMultiTest(Data, NgVector = NULL, Conditions, AllParti = NULL, sizeFactors, maxround, Pool = F, NumBin = 1000, ApproxVal=10^-10, PoolLower=.25, PoolUpper = .75, Print=T,Qtrm=1,QtrmCut=0) } \arguments{ \item{Data}{A data matrix contains expression values for each transcript (gene or isoform level). In which rows should be transcripts and columns should be samples.} \item{NgVector}{A vector indicates the uncertainty group assignment of each isoform. e.g. if we use number of isoforms in the host gene to define the uncertainty groups, suppose the isoform is in a gene with 2 isoforms, Ng of this isoform should be 2. The length of this vector should be the same as the number of rows in Data. If it's gene level data, Ngvector could be left as NULL.} \item{Conditions}{A vector indicates the condition in which each sample belongs to. } \item{AllParti}{A matrix indicates the interested patterns. Columns shoule be conditions and rows should be patterns. The matrix could be obtained by the GetPatterns function. If AllParti=NULL, all possible patterns will be used.} \item{sizeFactors}{The normalization factors. It should be a vector with lane specific numbers (the length of the vector should be the same as the number of samples, with the same order as the columns of Data).} \item{maxround}{Number of iterations. The default value is 5. Users should always check the convergency by looking at the Alpha and Beta in output. If the hyper-parameter estimations are not converged in 5 iterations, larger number is suggested.} \item{Pool}{While working without replicates, user could define the Pool = TRUE in the EBTest function to enable pooling.} \item{NumBin}{By defining NumBin = 1000, EBSeq will group the genes with similar means together into 1,000 bins.} \item{PoolLower, PoolUpper}{ With the assumption that only subset of the genes are DE in the data set, we take genes whose FC are in the PoolLower - PoolUpper quantile of the FC's as the candidate genes (default is 25\%-75\%). For each bin, the bin-wise variance estimation is defined as the median of the cross condition variance estimations of the candidate genes within that bin. We use the cross condition variance estimations for the candidate genes and the bin-wise variance estimations of the host bin for the non-candidate genes.} \item{ApproxVal}{The variances of the transcripts with mean < var will be approximated as mean/(1-ApproxVal).} \item{Print}{Whether print the elapsed-time while running the test.} \item{Qtrm, QtrmCut}{ Transcripts with Qtrm th quantile < = QtrmCut will be removed before testing. The default value is Qtrm = 1 and QtrmCut=0. By default setting, transcripts with all 0's won't be tested. } } \value{ \item{Alpha}{Fitted parameter alpha of the prior beta distribution. Rows are the values for each iteration.} \item{Beta}{Fitted parameter beta of the prior beta distribution. Rows are the values for each iteration.} \item{P, PFromZ}{The bayes estimator of following each pattern of interest. Rows are the values for each iteration.} \item{Z, PoissonZ}{The Posterior Probability of following each pattern of interest for each transcript. (Maybe not in the same order of input).} \item{RList}{The fitted values of r for each transcript.} \item{MeanList}{The mean of each transcript. (across conditions).} \item{VarList}{The variance of each transcript. (across conditions).} \item{QList}{The fitted q values of each transcript within each condition.} \item{SPMean}{The mean of each transcript within each condition (adjusted by the normalization factors).} \item{SPEstVar}{The estimated variance of each transcript within each condition (adjusted by the normalization factors).} \item{PoolVar}{The variance of each transcript (The pooled value of within condition EstVar).} \item{DataList}{A List of data that grouped with Ng and bias.} \item{PPpattern}{The Posterior Probability of following each pattern (columns) for each transcript (rows). Transcripts with expression 0 for all samples are not shown in this matrix.} \item{f}{The likelihood of likelihood of prior predictive distribution of being each pattern for each transcript. } \item{AllParti}{The matrix describe the patterns.} \item{PPpatternWith0}{The Posterior Probability of following each pattern (columns) for each transcript (rows). Transcripts with expression 0 for all samples are shown in this matrix with PP(any_pattrn)=NA.} \item{ConditionOrder}{The condition assignment for C1Mean, C2Mean, etc.} } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \seealso{ EBTest, GetMultiPP, GetMultiFC } \examples{ data(MultiGeneMat) MultiGeneMat.small = MultiGeneMat[201:210,] Conditions = c("C1","C1","C2","C2","C3","C3") PosParti = GetPatterns(Conditions) Parti = PosParti[-3,] MultiSize = MedianNorm(MultiGeneMat.small) MultiOut = EBMultiTest(MultiGeneMat.small, NgVector = NULL, Conditions = Conditions, AllParti = Parti, sizeFactors = MultiSize, maxround = 5) MultiPP = GetMultiPP(MultiOut) } \keyword{ DE } \keyword{ Multiple Condition }% __ONLY ONE__ keyword per line EBSeq/man/LikefunMulti.Rd0000644000175000017500000000214714136050172015020 0ustar nileshnilesh\name{LikefunMulti} \alias{LikefunMulti} \title{ Likelihood Function of the NB-Beta Model In Multiple Condition Test } \description{ 'LikefunMulti' specifies the Likelihood Function of the NB-Beta Model In Multiple Condition Test. } \usage{ LikefunMulti(ParamPool, InputPool) } \arguments{ \item{ParamPool}{The parameters that will be estimated in EM.} \item{InputPool}{The control parameters that will not be estimated in EM.} } \value{The function will return the log-likelihood.} \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \examples{ #x1 = c(.6,.7,.3) #Input = matrix(rnorm(100,100,1),ncol=10) #RIn = matrix(rnorm(100,200,1),ncol=10) #InputPool = list(list(Input[,1:5],Input[,6:10]), # Input, cbind(rep(.1, 10), rep(.9,10)), 1, # RIn, list(RIn[,1:5],RIn[,6:10]), # 10, rbind(c(1,1),c(1,2))) #LikefunMulti(x1, InputPool) } EBSeq/man/PlotPattern.Rd0000644000175000017500000000142214136050172014657 0ustar nileshnilesh\name{PlotPattern} \alias{PlotPattern} \title{ Visualize the patterns } \description{ 'PlotPattern' generates the visualized patterns before the multiple condition test. } \usage{ PlotPattern(Patterns) } \arguments{ \item{Patterns}{ The output of GetPatterns function. } } \value{ A heatmap to visualize the patterns of interest. } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \examples{ Conditions = c("C1","C1","C2","C2","C3","C3") Patterns = GetPatterns(Conditions) PlotPattern(Patterns) } \keyword{ patterns } EBSeq/man/PolyFitPlot.Rd0000644000175000017500000000536014136050172014635 0ustar nileshnilesh\name{PolyFitPlot} \alias{PolyFitPlot} \title{ Fit the mean-var relationship using polynomial regression } \description{ 'PolyFitPlot' fits the mean-var relationship using polynomial regression. } \usage{ PolyFitPlot(X, Y, nterms, xname = "Estimated Mean", yname = "Estimated Var", pdfname = "", xlim = c(-1,5), ylim = c(-1,7), ChangeXY = F, col = "red") } %- maybe also 'usage' for other objects documented here. \arguments{ \item{X}{ The first group of values want to be fitted by the polynomial regression (e.g Mean of the data). } \item{Y}{ The second group of values want to be fitted by the polynomial regression (e.g. variance of the data). The length of Y should be the same as the length of X. } \item{nterms}{ How many polynomial terms want to be used. } \item{xname}{ Name of the x axis. } \item{yname}{ Name of the y axis. } \item{pdfname}{ Name of the plot. } \item{xlim}{ The x limits of the plot. } \item{ylim}{ The y limits of the plot. } \item{ChangeXY}{ If ChangeXY is setted to be TRUE, X will be treated as the dependent variable and Y will be treated as the independent one. Default is FALSE. } \item{col}{ Color of the fitted line. } } \value{The PolyFitPlot function provides a smooth scatter plot of two variables and their best fitting line of polynomial regression. } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \examples{ data(IsoList) str(IsoList) IsoMat = IsoList$IsoMat IsoNames = IsoList$IsoNames IsosGeneNames = IsoList$IsosGeneNames IsoSizes = MedianNorm(IsoMat) NgList = GetNg(IsoNames, IsosGeneNames) IsoNgTrun = NgList$IsoformNgTrun #IsoEBOut = EBTest(Data = IsoMat.small, # NgVector = IsoNgTrun, # Conditions = as.factor(rep(c("C1","C2"), each=5)), # sizeFactors = IsoSizes, maxround = 5) #par(mfrow=c(2,2)) #PolyFitValue = vector("list",3) #for(i in 1:3) # PolyFitValue[[i]] = PolyFitPlot(IsoEBOut$C1Mean[[i]], # IsoEBOut$C1EstVar[[i]], 5) #PolyAll = PolyFitPlot(unlist(IsoEBOut$C1Mean), # unlist(IsoEBOut$C1EstVar), 5) #lines(log10(IsoEBOut$C1Mean[[1]][PolyFitValue[[1]]$sort]), # PolyFitValue[[1]]$fit[PolyFitValue[[1]]$sort], # col="yellow", lwd=2) #lines(log10(IsoEBOut$C1Mean[[2]][PolyFitValue[[2]]$sort]), # PolyFitValue[[2]]$fit[PolyFitValue[[2]]$sort], # col="pink", lwd=2) #lines(log10(IsoEBOut$C1Mean[[3]][PolyFitValue[[3]]$sort]), # PolyFitValue[[3]]$fit[PolyFitValue[[3]]$sort], # col="green", lwd=2) #legend("topleft",c("All Isoforms","Ng = 1","Ng = 2","Ng = 3"), # col = c("red","yellow","pink","green"), # lty=1, lwd=3, box.lwd=2) } EBSeq/man/GeneMat.Rd0000644000175000017500000000107314136050172013725 0ustar nileshnilesh\name{GeneMat} \alias{GeneMat} \docType{data} \title{ The simulated data for two condition gene DE analysis } \description{ 'GeneMat' gives the simulated data for two condition gene DE analysis. } \usage{data(GeneMat)} \source{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \seealso{ IsoList } \examples{ data(GeneMat) } \keyword{datasets} EBSeq/man/PostFC.Rd0000644000175000017500000000306514136050172013546 0ustar nileshnilesh\name{PostFC} \alias{PostFC} \title{ Calculate the posterior fold change for each transcript across conditions } \description{ 'PostFC' calculates the posterior fold change for each transcript across conditions. } \usage{ PostFC(EBoutput, SmallNum = 0.01) } \arguments{ \item{EBoutput}{ The ourput from function EBTest. } \item{SmallNum}{A small number will be added for each transcript in each condition to avoid Inf and NA. Default is 0.01.} } \value{ Provide both FC and posterior FC across two conditions. FC is calculated as (MeanC1+SmallNum)/(MeanC2+SmallNum). And Posterior FC is calculated as: # Post alpha P_a_C1 = alpha + r_C1 * n_C1 # Post beta P_b_C1 = beta + Mean_C1 * n_C1 # P_q_C1 = P_a_C1 / (P_a_C1 + P_b_C1) # Post FC = ((1-P_q_C1)/P_q_c1) / ( (1-P_q_c2)/P_q_c2) \item{PostFC}{The posterior FC across two conditions.} \item{RealFC}{The FC across two conditions (adjusted by the normalization factors).} \item{Direction}{The diretion of FC calculation.} } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \seealso{ EBTest, GetMultiFC } \examples{ data(GeneMat) GeneMat.small = GeneMat[c(500:550),] Sizes = MedianNorm(GeneMat.small) EBOut = EBTest(Data = GeneMat.small, Conditions = as.factor(rep(c("C1","C2"), each=5)), sizeFactors = Sizes, maxround = 5) FC=PostFC(EBOut) } \keyword{ Fold Change } EBSeq/man/GetNg.Rd0000644000175000017500000000316014136050172013410 0ustar nileshnilesh\name{GetNg} \alias{GetNg} \title{ Ng Vector } \description{ 'GetNg' generates the Ng vector for the isoform level data. (While using the number of isoform in the host gene to define the uncertainty groups.) } \usage{ GetNg(IsoformName, GeneName, TrunThre = 3) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{IsoformName}{A vector contains the isoform names.} \item{GeneName}{The gene names of the isoforms in IsoformNames (Should be in the same order).} \item{TrunThre}{The number of uncertainty groups the user wish to define. The default is 3.} } \value{ \item{GeneNg}{The number of isoforms that are contained in each gene. } \item{GeneNgTrun}{The truncated Ng of each gene. (The genes contain more than 3 isoforms are with Ng 3.) } \item{IsoformNg}{The Ng of each isoform.} \item{IsoformNgTrun}{The truncated Ng of each isoform (could be used to define the uncertainty group assignment).} } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \examples{ data(IsoList) IsoMat = IsoList$IsoMat IsoNames = IsoList$IsoNames IsosGeneNames = IsoList$IsosGeneNames IsoSizes = MedianNorm(IsoMat) NgList = GetNg(IsoNames, IsosGeneNames) #IsoNgTrun = NgList$IsoformNgTrun #IsoEBOut = EBTest(Data = IsoMat, NgVector = IsoNgTrun, # Conditions = as.factor(rep(c("C1","C2"), each=5)), # sizeFactors = IsoSizes, maxround = 5) } \keyword{ Ng } EBSeq/man/RankNorm.Rd0000644000175000017500000000127214136050172014135 0ustar nileshnilesh\name{RankNorm} \alias{RankNorm} \title{ Rank Normalization } \description{ 'RankNorm' gives the rank normalization. } \usage{ RankNorm(Data) } \arguments{ \item{Data}{ The data matrix with transcripts in rows and lanes in columns. } } \value{ The function will return a matrix contains the normalization factor for each lane and each transcript. } \author{ Ning Leng } \seealso{ MedianNorm, QuantileNorm } \examples{ data(GeneMat) Sizes = RankNorm(GeneMat) # Run EBSeq # EBres = EBTest(Data = GeneData, NgVector = rep(1,10^4), # Vect5End = rep(1,10^4), Vect3End = rep(1,10^4), # Conditions = as.factor(rep(c(1,2), each=5)), # sizeFactors = Sizes, maxround=5) } \keyword{ Normalization } EBSeq/man/GetNormalizedMat.Rd0000644000175000017500000000204214136050172015610 0ustar nileshnilesh\name{GetNormalizedMat} \alias{GetNormalizedMat} \title{ Calculate normalized expression matrix } \description{ 'GetNormalizedMat' calculates the normalized expression matrix. (Note: this matrix is only used for visualization etc. EBTes and EBMultiTest request *un-adjusted* expressions and normalization factors.) } \usage{ GetNormalizedMat(Data, Sizes) } \arguments{ \item{Data}{The data matrix with transcripts in rows and lanes in columns.} \item{Sizes}{A vector contains the normalization factor for each lane.} } \value{The function will return a normalized matrix.} \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \examples{ data(GeneMat) str(GeneMat) Sizes = MedianNorm(GeneMat) NormData = GetNormalizedMat(GeneMat, Sizes) } \keyword{ Normalization }% __ONLY ONE__ keyword per line EBSeq/man/LogN.Rd0000644000175000017500000000221114136050172013237 0ustar nileshnilesh\name{LogN} \alias{LogN} \title{ The function to run EM (one round) algorithm for the NB-beta model. } \description{ 'LogN' specifies the function to run (one round of) the EM algorithm for the NB-beta model. } \usage{ LogN(Input, InputSP, EmpiricalR, EmpiricalRSP, NumOfEachGroup, AlphaIn, BetaIn, PIn, NoneZeroLength) } \arguments{ \item{Input, InputSP}{The expressions among all the samples.} \item{NumOfEachGroup}{Number of genes in each Ng group.} \item{AlphaIn, PIn, BetaIn, EmpiricalR, EmpiricalRSP}{The parameters from the last EM step.} \item{NoneZeroLength}{Number of Ng groups.} } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \examples{ #Input = matrix(rnorm(100,100,1), ncol=10) #rownames(Input) = paste("g",1:10) #RIn = matrix(rnorm(100,200,1), ncol=10) #res = LogN(Input, list(Input[,1:5], Input[,6:10]), # RIn, list(RIn[,1:5], RIn[,6:10]), # 10, .6, .7, .3, 1) } EBSeq/man/EBTest.Rd0000644000175000017500000001431014136050172013531 0ustar nileshnilesh\name{EBTest} \alias{EBTest} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Using EM algorithm to calculate the posterior probabilities of being DE } \description{ Base on the assumption of NB-Beta Empirical Bayes model, the EM algorithm is used to get the posterior probability of being DE. } \usage{ EBTest(Data, NgVector = NULL, Conditions, sizeFactors, maxround, Pool = F, NumBin = 1000, ApproxVal = 10^-10, Alpha = NULL, Beta = NULL, PInput = NULL, RInput = NULL, PoolLower = .25, PoolUpper = .75, Print = T, Qtrm = 1,QtrmCut=0) } \arguments{ \item{Data}{A data matrix contains expression values for each transcript (gene or isoform level). In which rows should be transcripts and columns should be samples.} \item{NgVector}{A vector indicates the uncertainty group assignment of each isoform. e.g. if we use number of isoforms in the host gene to define the uncertainty groups, suppose the isoform is in a gene with 2 isoforms, Ng of this isoform should be 2. The length of this vector should be the same as the number of rows in Data. If it's gene level data, Ngvector could be left as NULL.} \item{Conditions}{A factor indicates the condition which each sample belongs to. } \item{sizeFactors}{The normalization factors. It should be a vector with lane specific numbers (the length of the vector should be the same as the number of samples, with the same order as the columns of Data).} \item{maxround}{Number of iterations. The default value is 5. Users should always check the convergency by looking at the Alpha and Beta in output. If the hyper-parameter estimations are not converged in 5 iterations, larger number is suggested.} \item{Pool}{While working without replicates, user could define the Pool = TRUE in the EBTest function to enable pooling.} \item{NumBin}{By defining NumBin = 1000, EBSeq will group the genes with similar means together into 1,000 bins.} \item{PoolLower, PoolUpper}{ With the assumption that only subset of the genes are DE in the data set, we take genes whose FC are in the PoolLower - PoolUpper quantile of the FC's as the candidate genes (default is 25\%-75\%). For each bin, the bin-wise variance estimation is defined as the median of the cross condition variance estimations of the candidate genes within that bin. We use the cross condition variance estimations for the candidate genes and the bin-wise variance estimations of the host bin for the non-candidate genes. } \item{ApproxVal}{The variances of the transcripts with mean < var will be approximated as mean/(1-ApproxVal). } \item{Alpha, Beta, PInput, RInput}{If the parameters are known and the user doesn't want to estimate them from the data, user could specify them here.} \item{Print}{Whether print the elapsed-time while running the test.} \item{Qtrm, QtrmCut}{ Transcripts with Qtrm th quantile < = QtrmCut will be removed before testing. The default value is Qtrm = 1 and QtrmCut=0. By default setting, transcripts with all 0's won't be tested. } } \details{For each transcript gi within condition, the model assumes: X_{gis}|mu_{gi} ~ NB (r_{gi0} * l_s, q_{gi}) q_gi|alpha, beta^N_g ~ Beta (alpha, beta^N_g) In which the l_s is the sizeFactors of samples. The function will test "H0: q_{gi}^{C1} = q_{gi}^{C2}" and "H1: q_{gi}^{C1} != q_{gi}^{C2}." } \value{ \item{Alpha}{Fitted parameter alpha of the prior beta distribution. Rows are the values for each iteration.} \item{Beta}{Fitted parameter beta of the prior beta distribution. Rows are the values for each iteration.} \item{P, PFromZ}{The bayes estimator of being DE. Rows are the values for each iteration.} \item{Z, PoissonZ}{The Posterior Probability of being DE for each transcript(Maybe not in the same order of input). } \item{RList}{The fitted values of r for each transcript.} \item{MeanList}{The mean of each transcript (across conditions).} \item{VarList}{The variance of each transcript (across conditions).} \item{QListi1}{The fitted q values of each transcript within condition 1.} \item{QListi2}{The fitted q values of each transcript within condition 2.} \item{C1Mean}{The mean of each transcript within Condition 1 (adjusted by normalization factors).} \item{C2Mean}{The mean of each transcript within Condition 2 (adjusted by normalization factors).} \item{C1EstVar}{The estimated variance of each transcript within Condition 1 (adjusted by normalization factors).} \item{C2EstVar}{The estimated variance of each transcript within Condition 2 (adjusted by normalization factors).} \item{PoolVar}{The variance of each transcript (The pooled value of within condition EstVar).} \item{DataList}{A List of data that grouped with Ng.} \item{PPDE}{The Posterior Probability of being DE for each transcript (The same order of input).} \item{f0,f1}{The likelihood of the prior predictive distribution of being EE or DE (in log scale).} \item{AllZeroIndex}{The transcript with expression 0 for all samples (which are not tested).} \item{PPMat}{A matrix contains posterior probabilities of being EE (the first column) or DE (the second column). Rows are transcripts. Transcripts with expression 0 for all samples are not shown in this matrix.} \item{PPMatWith0}{A matrix contains posterior probabilities of being EE (the first column) or DE (the second column). Rows are transcripts. Transcripts with expression 0 for all samples are shown as PP(EE) = PP(DE) = NA in this matrix. The transcript order is exactly the same as the order of the input data.} \item{ConditionOrder}{The condition assignment for C1Mean, C2Mean, etc.} \item{Conditions}{The input conditions.} \item{DataNorm}{Normalized expression matrix.} } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \seealso{ EBMultiTest, PostFC, GetPPMat } \examples{ data(GeneMat) str(GeneMat) GeneMat.small = GeneMat[c(1:10,511:550),] Sizes = MedianNorm(GeneMat.small) EBOut = EBTest(Data = GeneMat.small, Conditions = as.factor(rep(c("C1","C2"), each = 5)), sizeFactors = Sizes, maxround = 5) PP = GetPPMat(EBOut) } \keyword{ DE } \keyword{ Two condition }% __ONLY ONE__ keyword per line EBSeq/man/beta.mom.Rd0000644000175000017500000000151414136050172014107 0ustar nileshnilesh\name{beta.mom} \alias{beta.mom} \title{ Fit the beta distribution by method of moments } \description{ 'beta.mom' fits the beta distribution by method of moments. } \usage{ beta.mom(qs.in) } \arguments{ \item{qs.in}{A vector contains the numbers that are assumed to follow a beta distribution.} } \value{ \item{alpha.hat}{Returns the estimation of alpha.} \item{beta.hat}{Returns the estimation of beta.} } \references{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \author{ Ning Leng } \seealso{ DenNHist, DenNHistTable } \examples{ #tmp = rbeta(5, 5, 100) #param = beta.mom(tmp) } \keyword{ beta } EBSeq/man/MultiGeneMat.Rd0000644000175000017500000000115314136050172014737 0ustar nileshnilesh\name{MultiGeneMat} \alias{MultiGeneMat} \docType{data} \title{ The simulated data for multiple condition gene DE analysis } \description{ 'MultiGeneMat' generates a set of the simulated data for multiple condition gene DE analysis. } \usage{data(MultiGeneMat)} \source{ Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013) } \seealso{ GeneMat } \examples{ data(MultiGeneMat) } \keyword{datasets} EBSeq/vignettes/0000755000175000017500000000000014136070500013347 5ustar nileshnileshEBSeq/vignettes/lengetal.bib0000755000175000017500000011726214136050172015637 0ustar nileshnilesh@manual{Rpackage, author = "{R Development Core Team}", title = "R: A language and environment for statistical computing", organization = "R Foundation for Statistical Computing", address = "Vienna, Austria", year = "2009", } @article{Newton01, author="M A Newton and Kendziorski, C M and Richmond, C S and Blattner, F R", title="On differential variability of expression ratios: Improved statistical inference about gene expression changes from microarray data", journal="Journal of Computational Biology", volume="8", pages="37--52", year="2001", } @article{Wang09, author="Wang, Z and Gerstein M and Snyder M", title="RNA-Seq: a revolutionary tool for transcriptomics", journal="Nature Reviews Genetics", volume="10", pages="57--63", year="2009", } @article{Bullard10, author="Bullard, J H and Purdom, E A and Hansen, K D and Dudoit, S", title="Evaluation of Statistical Methods for Normalization and Differential Expression in mRNA-Seq Experiments", journal="BMC Bioinformatics", volume="11", pages="94", year="2010", } @article{Hansen10, author="Hansen, K D and Brenner, S E and Dudoit, S", title="Biases in Illumina transcriptome sequencing caused by random hexamer priming", journal="Nucleic Acids Research", volume="38(12)", pages="e131", year="2010", } @article{Wang08, author="Wang, E T and Sandberg, R and Luo, S and Khrebtukova, I and Zhang, L and Mayr, C and Kingsmore, S F and Schroth, G P and Burge, C B", title="Alternative isoform regulation in human tissue transcriptomes", journal="Nature", volume="456", pages="470--476", year="2008", } @article{Oshlack09, author="Oshlack, A and Wakefield M", title="Transcript length bias in RNA-seq data confounds systems biology", journal="Biology Direct", volume="4", pages="14", year="2009", } @article{Robinson10, author="Robinson, M D and Oshlack, A", title="A scaling normalization method for differential expression analysis of RNA-seq data", journal="Genome Biology", volume="11", pages="R25", year="2010", } @article{Trapnell09, author="Trapnell, C and Pachter, L and Salzberg, S L", title="TopHat: discovering splice junctions with RNA-Seq", journal="Bioinformatics", volume="25(9)", pages="1105--1111", year="2009", } @article{Mortazavi08, author="Mortazavi, A and Williams, B A and McCue, K and Schaeffer, L and Wold, B", title="Mapping and quantifying mammalian transcriptomes by RNA-Seq", journal="Nature Methods", volume="5(1)", pages="621--628", year="2008", } @article{Trapnell10, author="Trapnell, C and Williams, B A and Pertea, G and Mortazavi, A and Kwan, G and van Baren, M J and Salzberg, S L and Wold, B J and Pachter, L", title="Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation.", journal="Nat Biotechnol", volume="28(5)", pages="211--215", year="2010", } @article{Pepke09, author="Pepke, S and Wold, B and Mortaazavi, A", title="Computation for ChIP-seq and RNA-seq studies", journal="Nat Methods", volume="6(11 Suppl)", pages="S22--32", year="2009", } @article{Langmean10, author="Langmead, B and Trapnell, C and Pop, M and Salzberg, S L", title="Ultrafast and memory-efficient alignment of short DNA sequences to the human genome", journal="Genome Biology", pages="R25", year="2010", } @article{Bloom09, author="Bloom, J S and Khan, Z and Kruglyak, L and Singh, M and Caudy, A A", title="Measuring differential gene expression by short read sequencing: quantitative comparison to 2-channel gene expression microarrays", journal="BMC Genomics", volume="10", pages="221", year="2009", } @article{Wang10, author="Wang, L and Feng, Z and Wang, X and Wang, X and Zhang, X", title="DEGseq: an R package for identifying differentially expressed genes from RNA-seq data", journal="Bioinformatics", volume="26", pages="136--138", year="2010", } @article{Feng08, author="Feng, W and Liu, Y and Wu, J and Nephew, K P and Huang, T H M and Li, L", title="A Poisson mixture model to identify changes in RNA polymerase II binding quantity using high-throughput sequencing technology", journal="BMC Genomics", volume="9(Supl 2)", pages="S23", year="2008", } @article{Marioni08, author="Marioni, J C and Mason, C E and Mane, S M and Stephens, M and Gilad, Y", title="RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays", journal="Genome Research", volume="18", pages="1509--1517", year="2008", } @article{Nagalakshmi08, author="Nagalakshmi, U and Wang, Z and Waern, K and Shou, C and Raha, D and Gerstein, M", title="The Transcriptional Landscape of the Yeast Genome Defined by RNA Sequencing", journal="Science", volume="320(5881)", pages="1344--1349", year="2008", } @article{Lu05, author="Lu, J and Tomfohr, J K and Kepler, T B", title="Identifying differential expression in multiple SAGE libraries: an overdispersed log-linear model approach", journal="BMC Bioinformatics", volume="6", pages="165", year="2005", } @article{Robinson08, author="Robinson, M D and Smyth, G K", title="Small-sample estimation of negative binomial dispersion, with applications to SAGE data", journal="Biostatistics", volume="9", pages="321--332", year="2008", } @article{Anders10, author="Anders, S and Huber, W", title="Differential expression analysis for sequence count data", journal="Genome Biology", volume="11", pages="R106", year="2010", } @article{Zheng09, author="Zheng, S and Chen, L", title="A hierarchical Bayesian model for comparing transcriptomes at the individual transcript isoform level", journal="Nucleic Acids Research", pages="1--16", year="2009", } @article{Howard10, author="Howard, B E and Heber, S", title="Towards reliable isoform quantification using RNA-SEQ data", journal="BMC Bioinformatics", volume="11(Suppl 3)", pages="S6", year="2010", } @article{Wang10b, author="Wang, X and Wu, Z and Zhang, X", title="Isoform abundance inference provides a more accurate estimation of gene expression levels in RNA-seq", journal="Journal of Bioinformatics and Computational Biology", volume="8(Suppl 1)", pages="177--192", year="2010", } @article{Richard10, author="Richard, H and Schulz, M H and Sultan, M and Nürnberger, A and Schrinner, S and Balzereit, D and Dagand, E and Rasche, A and Lehrach, H and Vingron, M and Haas, S A and Yaspo, M L", title="Prediction of alternative isoforms from exon expression levels in RNA-Seq experiments", journal="Nucleic Acids Research", volume="38(10)", pages="e112", year="2010", } @article{Datta10, author="Datta, S and Datta, S and Kim, S and Chakraborty, S and Gill, R S", title="Statistical Analyses of Next Generation Sequence Data: A Partial Overview ", journal="Journal of Proteomcs and Bioinformatics", volume="3(6)", pages="183--190", year="2010", } @article{Lee10, author="Lee, S and Seo, C H and Lim, B and Yang, J O and Oh, J and Kim, M and Lee, S and Lee, B and Kang, C and Lee, S", title="Accurate quantification of transcriptome from RNA-Seq data by effective length normalization", journal="Nucleic Acids Research", volume="39(2)", pages="e9", year="2010", } @article{Srivastava10, author="Srivastava, S and Chen, L", title="A two-parameter generalized Poisson model to improve the analysis of RNA-seq data", journal="Nucleic Acids Research", volume="38(17)", pages="e170", year="2010", } @article{Hiller09, author="Hiller, D and Jiang, H and Wu, W and Wong, W H", title="Identifiability of isoform deconvolution from junction arrays and RNA-Seq", journal="Bioinformatics", volume="25(23)", pages="3056--3059", year="2009", } @article{Wegmann08, author="Wegmann, D and Dupanloup, I and Excoffier, L", title="Width of gene expression profile drives alternative splicing", journal="PLos one", volume="3(10)", pages="e3587", year="2008", } @article{Li11, author="Li, Y and Terrel, A and Patel, J M", title="WHAM: A High-throughput Sequence Alignment Method", journal="ACM SIGMOD", volume="", pages="", year="2011", } @article{Robinson07, author="Robinson, M D and Smyth, G K", title="Moderated statistical tests for assessing differences in tag abundance", journal="Bioinformatics", volume="23(21)", pages="2881--2887", year="2007", } @article{Robinson10b, author="Robinson, M D and McCarthy, D J and Smyth, G K", title="edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.", journal="Bioinformatics", volume="26(1)", pages="139-40", year="2010", } @article{Hardcastle10, author="Hardcastle, T J and Kelly, K A", title="baySeq: empirical Bayesian methods for identifying differential expression in sequence count data", journal="BMC Bioinformatics", volume="11", pages="422", year="2010", } @article{Gao11, author="Gao, L and Fang, Z and Zhang, K and Zhi, D and Cui, X", title="Length bias correction for RNA-seq data in gene set analyses", journal="Bioinformatics", volume="27(5)", pages="662--669", year="2011", } @article{Jiang09, author="Jiang, H and Wing, W H", title="Statistical inferences for isoform expression in RNA-Seq", journal="Bioinformatics", volume="25(8)", pages="1026--1032", year="2009", } @article{Li10, author="Li, B and Ruotti, V and Stewart, R M and Thomson, J A and Dewey, C N", title="RNA-Seq gene expression estimation with read mapping uncertainty", journal="Bioinformatics", volume="26(4)", pages="493--500", year="2010", } @article{Ozsolak11, author="Ozsolak, F and Milos, P M", title="RNA sequencing: advances, challenges and opportunities", journal="Nature Reviews Genetics", volume="12", pages="87--98", year="2011", } @article{Gao10, author="Gao, D and Kim, J and Kim, H and Phang, T L and Selby, H and Tan, A C and Tong, T", title="A survey of statistical software for analysing RNA-seq data", journal="Human Genomics", volume="5(1)", pages="56--60", year="2010", } @article{Haas10, author="Haas, B J and Zody, M C", title="Advancing RNA-Seq analysis", journal="Nature Biotechnology", volume="28", pages="421--423", year="2010", } @article{Salzberg10, author="Salzberg, S L", title="Recent advances in RNA sequence analysis", journal="F1000 Biology Reports", volume="2", pages="64", year="2010", } @article{Turro09, author="Turro, E and Lewin, A and Rose, A and Dallman, M J and Richardson, S", title="MMBGX: a method for estimating expression at the isoform level and detecting differential splicing using whole-transcript Affymetrix arrays", journal="Nucleic Acids Research", volume="38(1)", pages="e4", year="2009", } @article{Bemmo08, author="Bemmo, A and Benovoy, D and Kwan, T and Gaffney, D J and Jensen, R V and Majewski, Jacek", title="Gene Expression and Isoform Variation Analysis using Affymetrix Exon Arrays", journal="BMC Genomics", volume="9", pages="529", year="2008", } @article{Xing08, author="Xing, Y and Stoilov, P and Kapur, K and Han, A and Jiang, H and Shen, S and Black, D L and Wong, W H", title="MADS: a new and improved method for analysis of differential alternative splicing by exon-tiling microarrays", journal="RNA", volume="14(8)", pages="1470--1479", year="2008", } @article{Anton08, author="Anton, M A and Gorostiaga, D and Guruceaga, E and Segura, V and Carmona-Saez, P and Pascual-Montano, A and Pio, R and Montuenga, L M and Rubio, A", title="SPACE: an algorithm to predict and quantify alternatively spliced isoforms using microarrays", journal="Genome Biology", volume="9", pages="R46", year="2008", } @article{Liu10, author="Liu, S and Lin, L and Wang, D and Xing, Y ", title="A comparison of RNA-Seq and high-density exon array for detecting differential gene expression between closely related species", journal="Nucleic Acids Research", volume="39(2)", pages="578--588", year="2010", } @article{Degner09, author="Degner, J F and Marioni, J C and Pai, A A and Pickrell, J K and Nkadori, E and Gilad, Y and Pritchard, J K", title="Effect of read-mapping biases on detecting allele-specific expression from RNA-sequencing data", journal="Bioinformatics", volume="25(24)", pages="3207--3212", year="2009", } @article{Neverov05, author="Neverov, A D and Nurtdinov, I A R N and Frishman, D and Gelfand, M S and Mironov, A A", title="Alternative splicing and protein function", journal="BMC Bioinformatics", volume="6", pages="266", year="2005", } @article{Birzele07, author="Birzele, F and Csaba, G and Zimmer, R", title="Alternative splicing and protein structure evolution", journal="Nucleic Acids Research", volume="36(2)", pages="550--558", year="2007", } @article{Jiang08, author="Jiang, H and Wing, W H", title="SeqMap: mapping massive amount of oligonucleotides to the genome", journal="Bioinformatics", volume="24(20)", pages="2395--2396", year="2008", } @article{Hansen10, author="Hansen, K and Brenner, S and Dudoit S", title="Biases in Illumina transcriptome sequencing caused by random hexamer priming.", journal="Nucleic Acids Research", volume= "38", pages= "1-7", year="2010", } @article{Zhou11, author="Zhou, Y.H. and Xia, K. and Wright, F.A.", title="A Powerful and Flexible Approach to the Analysis of RNA Sequence Count Data.", journal="Bioinformatics", volume= "27", pages= "2672-2678", year="2011", } @article{Singh11, author="Singh, D. and Orellana, C.F. and Hu, Y. and Jones, C.D. and Liu, Y. and Chiang, D.Y. and Liu, J. and Prins, JF.", title="FDM: a graph-based statistical method to detect differential transcription using RNA-seq data", journal="Bioinformatics", volume= "27", pages= "2633-2640", year="2011", } @article{Chang11, author="Chang, PL and Dunham, JP and Nuzhdin, SV and Arbeitman, MN.", title="Somatic sex-specific transcriptome differences in Drosophila revealed by whole transcriptome sequencing.", journal="BMC Genomics", volume= "12", pages= "364", year="2011", } @article{Dempster77, author="Dempster, A.P. and Laird, N.M. and Rubin, D.B.", title="Maximum Likelihood from Incomplete Data via the EM Algorithm.", journal="Journal of the Royal Statistical Society", volume= "39", pages= "1-38", year="1977", } @article{Katz10, author="Katz, Y. and Wang, E.T. and Airoldi, E.M. and Burge, C.B.", title="Analysis and design of RNA sequencing experiments for identifying isoform regulation.", journal="Nature Methods", volume= "7", pages= "1009-1015", year="2010", } @manual{edgeRV, author="Robinson, M. and McCarthy, D. and Chen, Y. and Smyth, G.K.", title="edgeR: differential expression analysis of digital gene expression data: User’s Guide", year="2014", url="http://www.bioconductor.org/packages/release/bioc/vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf", } @article{Stamm05, author="Stamm, S and Ben-Ari, S and Rafalska, I and Tang, Y and Zhang, Z and Toiber, D and Thanaraj, T A and Soreq H", title="Function of alternative splicing. ", year="2005", journal="Gene", volume= "344", pages= "1-20", } @article{Smith89, author="Smith, C W and Patton, J G and Nadal-Ginard, B", title="Alternative splicing in the control of gene expression. ", year="1989", journal="Annu Rev Genet.", volume= "23", pages= "527-77", } @article{gould09, author="Gould, M N", title="The Utility of Comparative Genetics to Inform Breast Cancer Prevention Strategies ", year="2009", journal="Genetics", volume= "183", pages= "409-412", } @article{MAQC06, author="MAQC Consortium", title="The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements.", year="2006", journal="Nature Biotechnology ", volume= "24", pages= "1151-1161", } @article{Sengupta10, author="Sengupta, S. and Ruotti, V. and Bolin, J. and Elwell, A. and Hernandez, A. and Thomson, J. and Stewart, R.", title="Highly consistent, fully representative mRNA-Seq libraries from ten nanograms of total RNA.", year="2010", journal="Biotechniques", volume= "49", pages= "898-904", } @article{Phanstiel11, author="Phanstiel, H P and Brumbaugh, J and Wenger, C D and Tian, S and Probasco,M D and Bailey, D J and Swaney, D L and Tervo, M A and Bolin, J M and Ruotti, V and Stewart, R and Thomson, J A and Coon, J J", title="Proteomic and phosphoproteomic comparison of human ES and iPS cells", year="2011", journal="Nature Methods", volume= "8", pages= "821-827", } @article{Dixon50, author="Dixon, W J", title="Analysis of extreme values", year="1950", journal="The Annals of Mathematical Statistics", volume= "21", pages= "488", } @article{Haag03, author="Haag, J D and Shepel, L A and Kolman, B D and Monson, D M and Benton, M E and Watts, K T and Waller, J L and Lopez-Guajardo,C C and Samuelson,D J and Gould, M N", title="Congenic Rats Reveal Three Independent Copenhagen Alleles within the Mcs1 Quantitative Trait Locus That Confer Resistance to Mammary Cancer", year="2003", journal="Cancer Res", volume= "63", pages= "5808", } @article{Ludwig06, author="Ludwig, T E and Levenstein, M E and Jones, J M and Berggren, W T and Mitchen, E R and Frane, J L and Crandall, L J and Daigh, C A and Conard, K R and Piekarczyk, M S and Llanas, R A and Thomson, J A.", title="Derivation of human embryonic stem cells in defined conditions.", year="2006", journal="Nat Biotechnol", volume= "24(2)", pages= "185-7", } @article{Guenther10, author="Guenther, M Gand Frampton, G M and Soldner, F and Hockemeyer, D and Mitalipova,M and Jaenisch, R and Young, R A", title="Chromatin structure and gene expression programs of human embryonic and induced pluripotent stem cells.", year="2010", journal="Cell stem Cell", volume= "7(2)", pages= "249-257", } @article{Chin09, author="Chin, MH and Mason, MJ and Xie, W and Volinia, S and Singer, M and Peterson, C and Ambartsumyan, G and Aimiuwu, O and Richter, L and Zhang, J and Khvorostov, I and Ott, V and Grunstein, M and Lavon, N and Benvenisty, N and Croce, CM and Clark, AT and Baxter, T and Pyle, AD and Teitell, MA and Pelegrini, M and Plath, K and Lowry, WE", title="Induced pluripotent stem cells and embryonic stem cells are distinguished by gene expression signatures.", year="2009", journal="Cell stem Cell", volume= "5(1)", pages= "111-123", } @article{Ohi11, author="Ohi, Y and Qin, H and Hong, C and Blouin, L and Polo, J M and Guo, T and Qi, Z and Downey, S L and Manos, P D and Rossi, D J and Yu, J and Hebrok, M and Hochedlinger, K and Costello, J F and Song, J S and Ramalho-Santos, M", title="Incomplete DNA methylation underlies a transcriptional memory of somatic cells in human iPS cells.", year="2011", journal="Nat Cell Biol.", volume= "13(5)", pages= "541-9", } @article{Bock11, author="Bock, C and Kiskinis, E and Verstappen, G and Gu, H and Boulting, G and Smith, Z D and Ziller, M and Croft, G F and Amoroso, M W and Oakley, D H and Gnirke, A and Eggan, K and Meissner, A", title="Reference Maps of human ES and iPS cell variation enable high-throughput characterization of pluripotent cell lines.", year="2011", journal="Cell.", volume= "144(3)", pages= "439-52", } @article{Trapnell12, author="Trapnell, C and Roberts, A and Goff, L and Pertea, G and Kim, D and Kelley, D R and Pimentel, H and Salzberg, S L and Rinn, J L and Pachter, L", title="Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks", year="2012", journal="Nature Protocols", volume= "7(3)", pages= "562-578", } @article{Li11b, author="Li, B and Dewey, C N", title="RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome", year="2011", journal="BMC Bioinformatics", volume= "12", pages= "323", } @article{Yang07, author="Yang, H and Churchill, G", title="Estimating p-values in small microarray experiments", year="2007", journal="Bioinformatics", volume= "23(1)", pages= "38-43", } @article{Sen10, author="Sen, GL and Reuter, JA and Webster, DE and Zhu, L and Khavari, PA", title="DNMT1 maintains progenitor function in self-renewing somatic tissue.", year="2010", journal="Nature", volume= "463(7280)", pages= "563-7", } @article{Rai06, author="Rai, K and Nadauld, LD and Chidester, S and Manos, EJ and James, SR and Karpf, AR and Cairns, BR and Jones, DA.", title="Zebra fish Dnmt1 and Suv39h1 regulate organ-specific terminal differentiation during development.", year="2006", journal="Mol Cell Biol.", volume= "26(19)", pages= "7077-85", } @article{DAiuto11, author="D'Aiuto, L and Di Maio, R and Mohan, KN and Minervini, C and Saporiti, F and Soreca, I and Greenamyre, JT and Chaillet, JR.", title="Mouse ES cells overexpressing DNMT1 produce abnormal neurons with upregulated NMDA/NR1 subunit.", year="2011", journal="Differentiation.", volume= "82(1)", pages= "9-17", } @article{Grabherr11, author="Grabherr, MG and Haas, BJ and Yassour, M and Levin, JZ and Thompson, DA and Amit, I and Adiconis, X and Fan, L and Raychowdhury, R and Zeng, Q and Chen, Z and Mauceli, E and Hacohen, N and Gnirke, A and Rhind, N and di Palma, F and Birren, BW and Nusbaum, C and Lindblad-Toh, K and Friedman, N and Regev, A", title="Full-length transcriptome assembly from RNA-Seq data without a reference genome", year="2011", journal="Nat Biotechnol.", volume= "29", pages= "644-652", } @article{Leng12, author="Leng, N. and Dawson, J.A. and Thomson, J.A and Ruotti, V and Rissman, R. A. and Smits, B.M.G and Hagg, J.D. and Gould, M.N. and Stwart, R.M. and Kendziorski, C.", title="EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments.", journal="BMI technical report, University of Wisconsin Madison", volume= "226", year="2012", } @article{Nicolae10, title={Estimation of alternative splicing isoform frequencies from RNA-Seq data}, author={Nicolae, M. and Mangul, S. and M{\u{a}}ndoiu, I. and Zelikovsky, A.}, journal={Algorithms in Bioinformatics}, pages={202--214}, year={2010}, publisher={Springer} } @article{Sandmann11, title={The head-regeneration transcriptome of the planarian Schmidtea mediterranea}, author={Sandmann, T. and Vogg, M.C. and Owlarn, S. and Boutros, M. and Bartscherer, K.}, journal={Genome Biology}, volume={12}, number={8}, pages={R76}, year={2011}, publisher={BioMed Central Ltd} } @article{Anders12, author="Anders, S. and Reyes, A. and Huber, W", title="Detecting differential usage of exons from RNA-Seq data", journal="Nature Preceedings", year="2012", } @article{Delhomme12, title={easyRNASeq: a bioconductor package for processing RNA-Seq data}, author={Delhomme, N. and Padioleau, I. and Furlong, E.E. and Steinmetz, L.M.}, journal={Bioinformatics}, year={2012}, publisher={Oxford Univ Press} } @manual{easyRNASeq12, author="Delhomme,N and Padioleau, I", title="easyRNASeq, an overview", year="2012", url="http://www.bioconductor.org/packages/devel/bioc/vignettes/easyRNASeq/inst/doc/easyRNASeq.pdf", } @manual{HTSeq, author="Anders, S", title="HTSeq: Analysing high-throughput sequencing data with Python", year="2012", url="http://www-huber.embl.de/users/anders/HTSeq/doc/overview.html", } @manual{Cuff, title="Cufflinks Transcript assembly, differential expression, and differential regulation for RNA-Seq", year="2012", url="http://cufflinks.cbcb.umd.edu/index.html", } @article{Du12, title={IQSeq: Integrated Isoform Quantification Analysis Based on Next-Generation Sequencing}, author={Du, J. and Leng, J. and Habegger, L. and Sboner, A. and McDermott, D. and Gerstein, M.}, journal={PloS one}, volume={7}, number={1}, pages={e29175}, year={2012}, publisher={Public Library of Science} } @article{Pickrell10, title={Understanding mechanisms underlying human gene expression variation with RNA sequencing}, author={Pickrell, J.K. and Marioni, J.C. and Pai, A.A. and Degner, J.F. and Engelhardt, B.E. and Nkadori, E. and Veyrieras, J.B. and Stephens, M. and Gilad, Y. and Pritchard, J.K.}, journal={Nature}, volume={464}, number={7289}, pages={768--772}, year={2010}, publisher={Nature Publishing Group} } @article{Wang09regen, title={Regeneration, repair and remembering identity: the three Rs of Hox gene expression}, author={Wang, K.C. and Helms, J.A. and Chang, H.Y.}, journal={Trends in cell biology}, volume={19}, number={6}, pages={268--275}, year={2009}, publisher={Elsevier} } @article{Chang09, title={Anatomic demarcation of cells: genes to patterns}, author={Chang, H.Y.}, journal={Science}, volume={326}, number={5957}, pages={1206--1207}, year={2009}, publisher={American Association for the Advancement of Science} } @article{Rinn06, title={Anatomic demarcation by positional variation in fibroblast gene expression programs}, author={Rinn, J.L. and Bondre, C. and Gladstone, H.B. and Brown, P.O. and Chang, H.Y.}, journal={PLoS genetics}, volume={2}, number={7}, pages={e119}, year={2006}, publisher={Public Library of Science} } @article{Blei03, title={Latent dirichlet allocation}, author={Blei, D.M. and Ng, A.Y. and Jordan, M.I.}, journal={the Journal of machine Learning research}, volume={3}, pages={993--1022}, year={2003}, publisher={JMLR. org} } @inproceedings{Andrzejewski09, title={Incorporating domain knowledge into topic modeling via Dirichlet forest priors}, author={Andrzejewski, D. and Zhu, X. and Craven, M.}, booktitle={Proceedings of the 26th Annual International Conference on Machine Learning}, pages={25--32}, year={2009}, organization={ACM} } @article{Wu10, title={Dynamic transcriptomes during neural differentiation of human embryonic stem cells revealed by short, long, and paired-end sequencing}, author={Wu, J.Q. and Habegger, L. and Noisa, P. and Szekely, A. and Qiu, C. and Hutchison, S. and Raha, D. and Egholm, M. and Lin, H. and Weissman, S. and others}, journal={Proceedings of the National Academy of Sciences}, volume={107}, number={11}, pages={5254--5259}, year={2010}, publisher={National Acad Sciences} } @article{Monaghan09, title={Microarray and cDNA sequence analysis of transcription during nerve-dependent limb regeneration}, author={Monaghan, J.R. and Epp, L.G. and Putta, S. and Page, R.B. and Walker, J.A. and Beachy, C.K. and Zhu, W. and Pao, G.M. and Verma, I.M. and Hunter, T. and others}, journal={BMC biology}, volume={7}, number={1}, pages={1}, year={2009}, publisher={BioMed Central Ltd} } @article{Malone11, title={Microarrays, deep sequencing and the true measure of the transcriptome}, author={Malone, J.H. and Oliver, B.}, journal={BMC biology}, volume={9}, number={1}, pages={34}, year={2011}, publisher={BioMed Central Ltd} } @article{Auer10, title={Statistical design and analysis of RNA sequencing data}, author={Auer, P.L. and Doerge, RW}, journal={Genetics}, volume={185}, number={2}, pages={405--416}, year={2010}, publisher={Genetics Soc America} } @article{Schubert03, title={Microarray to be used as routine clinical screen}, author={Schubert, C.M.}, journal={Nature medicine}, volume={9}, number={1}, pages={9--9}, year={2003}, publisher={Nature Publishing Group} } @article{Ramaswamy02, title={DNA microarrays in clinical oncology}, author={Ramaswamy, S. and Golub, T.R.}, journal={Journal of Clinical Oncology}, volume={20}, number={7}, pages={1932--1941}, year={2002}, publisher={American Society of Clinical Oncology} } @article{Stewart12, title={Comparative RNA-seq Analysis in the Unsequenced Axolotl: The Oncogene Burst of Expression Defines One of Three Phases of Gene Expression in the Blastema}, author={Stewart, R and Rascon,CA and Tian, S and Nie, J and Probasco, MD and Bolin, JM and Sengupta,S and Volkmer, M and Habermann, B and Tanaka, EM and Thomson, JA and Dewey, CN}, year={2012}, } @article{Grant11, title={Comparative analysis of RNA-Seq alignment algorithms and the RNA-Seq unified mapper (RUM)}, author={Grant, G.R. and Farkas, M.H. and Pizarro, A.D. and Lahens, N.F. and Schug, J. and Brunk, B.P. and Stoeckert, C.J. and Hogenesch, J.B. and Pierce, E.A.}, journal={Bioinformatics}, volume={27}, number={18}, pages={2518--2528}, year={2011}, publisher={Oxford Univ Press} } @article{Hester10, title={Comprehensive comparison of RNA--Seq alignment packages}, author={Hester, J.}, year={2010}, publisher={Citeseer} } @article{Wang10c, title={MapSplice: accurate mapping of RNA-seq reads for splice junction discovery}, author={Wang, K. and Singh, D. and Zeng, Z. and Coleman, S.J. and Huang, Y. and Savich, G.L. and He, X. and Mieczkowski, P. and Grimm, S.A. and Perou, C.M. and others}, journal={Nucleic acids research}, volume={38}, number={18}, pages={e178--e178}, year={2010}, publisher={Oxford Univ Press} } @article{Savage09, title={R/BHC: fast Bayesian hierarchical clustering for microarray data}, author={Savage, R. and Heller, K. and Xu, Y. and Ghahramani, Z. and Truman, W. and Grant, M. and Denby, K. and Wild, D.}, journal={BMC bioinformatics}, volume={10}, number={1}, pages={242}, year={2009}, publisher={BioMed Central Ltd} } @inproceedings{Heller05, title={Bayesian hierarchical clustering}, author={Heller, K.A. and Ghahramani, Z.}, booktitle={Proceedings of the 22nd international conference on Machine learning}, pages={297--304}, year={2005}, organization={ACM} } @article{Young10, title={Method Gene ontology analysis for RNA-seq: accounting for selection bias}, author={Young, M.D. and Wakefield, M.J. and Smyth, G.K. and Oshlack, A.}, year={2010}, journal={Genome Biology}, volume={11} } @article{Subramanian05, title={Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles}, author={Subramanian, A. and Tamayo, P. and Mootha, V.K. and Mukherjee, S. and Ebert, B.L. and Gillette, M.A. and Paulovich, A. and Pomeroy, S.L. and Golub, T.R. and Lander, E.S. and others}, journal={Proceedings of the National Academy of Sciences of the United States of America}, volume={102}, number={43}, pages={15545--15550}, year={2005}, publisher={National Acad Sciences} } @article{Da08, title={Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources}, author={Da Wei Huang, B.T.S. and Lempicki, R.A. and others}, journal={Nature protocols}, volume={4}, number={1}, pages={44--57}, year={2008}, publisher={Nature Publishing Group} } @article{Eng12, title={Pathway index models for construction of patient-specific risk profiles}, author={Eng, K.H. and Wang, S. and Bradley, W.H. and Rader, J.S. and Kendziorski, C.}, journal={Statistics in Medicine}, year={2012}, publisher={Wiley Online Library} } @article{WangS09, title={Hierarchically penalized Cox regression with grouped variables}, author={Wang, S. and Nan, B. and Zhu, N. and Zhu, J.}, journal={Biometrika}, volume={96}, number={2}, pages={307--322}, year={2009}, publisher={Biometrika Trust} } @article{Huang09, title={A group bridge approach for variable selection}, author={Huang, J. and Ma, S. and Xie, H. and Zhang, C.H.}, journal={Biometrika}, volume={96}, number={2}, pages={339--355}, year={2009}, publisher={Biometrika Trust} } @article{Zhou10, title={Group variable selection via a hierarchical lasso and its oracle property}, author={Zhou, N. and Zhu, J.}, journal={arXiv preprint arXiv:1006.2871}, year={2010} } @article{Segal06, title={Microarray gene expression data with linked survival phenotypes: diffuse large-B-cell lymphoma revisited}, author={Segal, M.R.}, journal={Biostatistics}, volume={7}, number={2}, pages={268--285}, year={2006}, publisher={Biometrika Trust} } @article{Binsl07, title={FluxSimulator: an R package to simulate isotopomer distributions in metabolic networks}, author={Binsl, T.W. and Mullen, K.M. and Van Stokkum, I.H.M. and Heringa, J. and Van Beek, J.H.G.M.}, journal={J. Stat. Softw}, volume={18}, number={6}, pages={1--18}, year={2007} } @article{Yuan06, title={Hidden Markov models for microarray time course data in multiple biological conditions}, author={Yuan, M. and Kendziorski, C.}, journal={Journal of the American Statistical Association}, volume={101}, number={476}, pages={1323--1332}, year={2006}, publisher={American Statistical Association} } @article{Tai06, title={A multivariate empirical Bayes statistic for replicated microarray time course data}, author={Tai, Y.C. and Speed, T.P.}, journal={The Annals of Statistics}, volume={34}, number={5}, pages={2387--2412}, year={2006}, publisher={Institute of Mathematical Statistics} } @article{Nueda07, title={Discovering gene expression patterns in time course microarray experiments by ANOVA--SCA}, author={Nueda, M.J. and Conesa, A. and Westerhuis, J.A. and Hoefsloot, H.C.J. and Smilde, A.K. and Tal{\'o}n, M. and Ferrer, A.}, journal={Bioinformatics}, volume={23}, number={14}, pages={1792--1800}, year={2007}, publisher={Oxford Univ Press} } @article{Storey05, title={Significance analysis of time course microarray experiments}, author={Storey, J.D. and Xiao, W. and Leek, J.T. and Tompkins, R.G. and Davis, R.W.}, journal={Proceedings of the National Academy of Sciences of the United States of America}, volume={102}, number={36}, pages={12837--12842}, year={2005}, publisher={National Acad Sciences} } @article{Luan03, title={Clustering of time-course gene expression data using a mixed-effects model with B-splines}, author={Luan, Y. and Li, H.}, journal={Bioinformatics}, volume={19}, number={4}, pages={474--482}, year={2003}, publisher={Oxford Univ Press} } @article{Bar03, title={Continuous representations of time-series gene expression data}, author={Bar-Joseph, Z. and Gerber, G.K. and Gifford, D.K. and Jaakkola, T.S. and Simon, I.}, journal={Journal of Computational Biology}, volume={10}, number={3-4}, pages={341--356}, year={2003}, publisher={Mary Ann Liebert, Inc.} } @article{Ma06, title={A data-driven clustering method for time course gene expression data}, author={Ma, P. and Castillo-Davis, C.I. and Zhong, W. and Liu, J.S.}, journal={Nucleic Acids Research}, volume={34}, number={4}, pages={1261--1269}, year={2006}, publisher={Oxford Univ Press} } @article{Sun11, title={Multiple testing for pattern identification, with applications to microarray time-course experiments}, author={Sun, W. and Wei, Z.}, journal={Journal of the American Statistical Association}, volume={106}, number={493}, pages={73--88}, year={2011}, publisher={Taylor \& Francis} } @article{Bertucci03, title={DNA arrays in clinical oncology: promises and challenges}, author={Bertucci, F. and Viens, P. and Tagett, R. and Nguyen, C. and Houlgatte, R. and Birnbaum, D.}, journal={Laboratory investigation}, volume={83}, number={3}, pages={305--316}, year={2003}, publisher={Nature Publishing Group} } @article{Azad06, title={Proteomics in Clinical Trials and Practice Present Uses and Future Promise}, author={Azad, N.S. and Rasool, N. and Annunziata, C.M. and Minasian, L. and Whiteley, G. and Kohn, E.C.}, journal={Molecular \& Cellular Proteomics}, volume={5}, number={10}, pages={1819--1829}, year={2006}, publisher={ASBMB} } @article{Aung10, title={Current status and future potential of somatic mutation testing from circulating free DNA in patients with solid tumours}, author={Aung, KL and Board, RE and Ellison, G. and Donald, E. and Ward, T. and Clack, G. and Ranson, M. and Hughes, A. and Newman, W. and Dive, C.}, journal={The HUGO journal}, volume={4}, number={1}, pages={11--21}, year={2010}, publisher={Springer} } @article{Glaus12, title={Identifying differentially expressed transcripts from RNA-seq data with biological variation}, author={Glaus, P. and Honkela, A. and Rattray, M.}, journal={Bioinformatics}, volume={28}, number={13}, pages={1721--1728}, year={2012}, publisher={Oxford Univ Press} } @article{Trapnell12b, title={Differential analysis of gene regulation at transcript resolution with RNA-seq}, author={Trapnell, C. and Hendrickson, D.G. and Sauvageau, M. and Goff, L. and Rinn, J.L. and Pachter, L.}, journal={Nature Biotechnology}, year={2012}, publisher={Nature Publishing Group} } @article{Leng13, title={EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments}, author={Leng, Ning and Dawson, John A and Thomson, James A and Ruotti, Victor and Rissman, Anna I and Smits, Bart MG and Haag, Jill D and Gould, Michael N and Stewart, Ron M and Kendziorski, Christina}, journal={Bioinformatics}, volume={29}, number={8}, pages={1035--1043}, year={2013}, publisher={Oxford Univ Press} } @article{Hamilton89, title={A new approach to the economic analysis of nonstationary time series and the business cycle}, author={Hamilton, James D}, journal={Econometrica: Journal of the Econometric Society}, pages={357--384}, year={1989}, publisher={JSTOR} } @article{Ailliot12, title={Markov-switching autoregressive models for wind time series}, author={Ailliot, Pierre and Monbet, Val{\'e}rie}, journal={Environmental Modelling \& Software}, volume={30}, pages={92--101}, year={2012}, publisher={Elsevier} } @article{Koehler11, title={The uniqueome: a mappability resource for short-tag sequencing}, author={Koehler, Ryan and Issac, Hadar and Cloonan, Nicole and Grimmond, Sean M}, journal={Bioinformatics}, volume={27}, number={2}, pages={272--274}, year={2011}, publisher={Oxford University Press} } @article{Derrien12, title={Fast computation and applications of genome mappability}, author={Derrien, Thomas and Estell{\'e}, Jordi and Sola, Santiago Marco and Knowles, David G and Raineri, Emanuele and Guig{\'o}, Roderic and Ribeca, Paolo}, journal={PloS one}, volume={7}, number={1}, pages={e30377}, year={2012}, publisher={Public Library of Science} } @article{Shi13, title={rSeqDiff: Detecting Differential Isoform Expression from RNA-Seq Data Using Hierarchical Likelihood Ratio Test}, author={Shi, Yang and Jiang, Hui}, journal={PloS one}, volume={8}, number={11}, pages={e79448}, year={2013}, publisher={Public Library of Science} } @inproceedings{Schluter05, title={Bayes risk minimization using metric loss functions.}, author={Schl{\"u}ter, Ralf and Scharrenbach, Thomas and Steinbiss, Volker and Ney, Hermann}, booktitle={INTERSPEECH}, pages={1449--1452}, year={2005} } @book{Berger85, title={Statistical decision theory and Bayesian analysis}, author={Berger, James O}, year={1985}, publisher={Springer} } EBSeq/vignettes/EBSeq_Vignette.Rnw0000644000175000017500000013161214136050172016652 0ustar nileshnilesh%\VignetteIndexEntry{EBSeq Vignette} \documentclass{article} \usepackage{fullpage} \usepackage{graphicx, graphics, epsfig,setspace,amsmath, amsthm} \usepackage{hyperref} \usepackage{natbib} %\usepackage{listings} \usepackage{moreverb} \begin{document} \title{EBSeq: An R package for differential expression analysis using RNA-seq data} \author{Ning Leng, John Dawson, and Christina Kendziorski} \maketitle \tableofcontents \setcounter{tocdepth}{2} \section{Introduction} EBSeq may be used to identify differentially expressed (DE) genes and isoforms in an RNA-Seq experiment. As detailed in Leng {\it et al.}, 2013 \cite{Leng13}, EBSeq is an empirical Bayesian approach that models a number of features observed in RNA-seq data. Importantly, for isoform level inference, EBSeq directly accommodates isoform expression estimation uncertainty by modeling the differential variability observed in distinct groups of isoforms. Consider Figure 1, where we have plotted variance against mean for all isoforms using RNA-Seq expression data from Leng {\it et al.}, 2013 \cite{Leng13}. Also shown is the fit within three sub-groups of isoforms defined by the number of constituent isoforms of the parent gene. An isoform of gene $g$ is assigned to the $I_g=k$ group, where $k=1,2,3$, if the total number of isoforms from gene $g$ is $k$ (the $I_g=3$ group contains all isoforms from genes having 3 or more isoforms). As shown in Figure 1, there is decreased variability in the $I_g=1$ group, but increased variability in the others, due to the relative increase in uncertainty inherent in estimating isoform expression when multiple isoforms of a given gene are present. If this structure is not accommodated, there is reduced power for identifying isoforms in the $I_g=1$ group (since the true variances in that group are lower, on average, than that derived from the full collection of isoforms) as well as increased false discoveries in the $I_g=2$ and $I_g=3$ groups (since the true variances are higher, on average, than those derived from the full collection). EBSeq directly models differential variability as a function of $I_g$ providing a powerful approach for isoform level inference. As shown in Leng {\it et al.}, 2013 \cite{Leng13}, the model is also useful for identifying DE genes. We will briefly detail the model in Section \ref{sec:model} and then describe the flow of analysis in Section \ref{sec:quickstart} for both isoform and gene-level inference. \begin{figure}[t] \centering \includegraphics[width=0.6\textwidth]{PlotExample.png} \label{fig:GouldNg} \caption{Empirical variance vs. mean for each isoform profiled in the ESCs vs iPSCs experiment detailed in the Case Study section of Leng {\it et al.}, 2013 \cite{Leng13}. A spline fit to all isoforms is shown in red with splines fit within the $I_g=1$, $I_g=2$, and $I_g=3$ isoform groups shown in yellow, pink, and green, respectively.} \end{figure} \section{Citing this software} \label{sec:cite} Please cite the following article when reporting results from the software. \noindent Leng, N., J.A. Dawson, J.A. Thomson, V. Ruotti, A.I. Rissman, B.M.G. Smits, J.D. Haag, M.N. Gould, R.M. Stewart, and C. Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments, {\it Bioinformatics}, 2013. \section{The Model} \label{sec:model} \subsection{Two conditions} \label{sec:twocondmodel} We let $X_{g_i}^{C1} = X_{g_i,1} ,X_{g_i,2}, ...,X_{g_i,S_1}$ denote data from condition 1 and $ X_{g_i}^{C2} = X_{g_i,(S_1+1)},X_{g_i,(S_1+2)},...,X_{g_i,S}$ data from condition 2. We assume that counts within condition $C$ are distributed as Negative Binomial: $X_{g_i,s}^C|r_{g_i,s}, q_{g_i}^C \sim NB(r_{g_i,s}, q_{g_i}^C)$ where \begin{equation} P(X_{g_i,s}|r_{g_i,s},q_{g_i}^C) = {X_{g_i,s}+r_{g_i,s}-1\choose X_{g_i,s}}(1-q_{g_i}^C)^{X_{g_i,s}}(q_{g_i}^C)^{r_{g_i,s}}\label{eq:01} \end{equation} \noindent and $\mu_{g_i,s}^C=r_{g_i,s} (1-q_{g_i}^C)/q_{g_i}^C$; $(\sigma_{g_i,s}^C)^2=r_{g_i,s} (1-q_{g_i}^C)/(q_{g_i}^C)^2.$ \medskip We assume a prior distribution on $q_{g_i}^C$: $q_{g_i}^C|\alpha, \beta^{I_g} \sim Beta(\alpha, \beta^{I_g})$. The hyperparameter $\alpha$ is shared by all the isoforms and $\beta^{I_g}$ is $I_g$ specific (note this is an index, not a power). We further assume that $r_{g_i,s}=r_{g_i,0} l_s$, where $r_{g_i,0}$ is an isoform specific parameter common across conditions and $r_{g_i,s}$ depends on it through the sample-specific normalization factor $l_s$. Of interest in this two group comparison is distinguishing between two cases, or what we will refer to subsequently as two patterns of expression, namely equivalent expression (EE) and differential expression (DE): \begin{center} $H_0$ (EE) : $q_{g_i}^{C1}=q_{g_i}^{C2}$ vs $H_1$ (DE) : $q_{g_i}^{C1} \neq q_{g_i}^{C2}$. \end{center} Under the null hypothesis (EE), the data $X_{g_i}^{C1,C2} = X_{g_i}^{C1}, X_{g_i}^{C2}$ arises from the prior predictive distribution $f_0^{I_g}(X_{g_i}^{C1,C2})$: %\tiny \begin{equation} f_0^{I_g}(X_{g_i}^{C1,C2})=\Bigg[\prod_{s=1}^S {X_{g_i,s}+r_{g_i,s}-1\choose X_{g_i,s}}\Bigg] \frac{Beta(\alpha+\sum_{s=1}^S r_{g_i,s}, \beta^{I_g}+\sum_{s=1}^SX_{g_i,s} )}{Beta(\alpha, \beta^{I_g})}\label{eq:05} \end{equation} %\normalsize Alternatively (in a DE scenario), $X_{g_i}^{C1,C2}$ follows the prior predictive distribution $f_1^{I_g}(X_{g_i}^{C1,C2})$: \begin{equation} f_1^{I_g}(X_{g_i}^{C1,C2})=f_0^{I_g}(X_{g_i}^{C1})f_0^{I_g}(X_{g_i}^{C2}) \label{eq:06} \end{equation} Let the latent variable $Z_{g_i}$ be defined so that $Z_{g_i} = 1$ indicates that isoform $g_i$ is DE and $Z_{g_i} = 0$ indicates isoform $g_i$ is EE, and $Z_{g_i} \sim Bernoulli(p)$. Then, the marginal distribution of $X_{g_i}^{C1,C2}$ and $Z_{g_i}$ is: \begin{equation} (1-p)f_0^{I_g}(X_{g_i}^{C1,C2}) + pf_1^{I_g}(X_{g_i}^{C1,C2})\label{eq:07} \end{equation} \noindent The posterior probability of being DE at isoform $g_i$ is obtained by Bayes' rule: \begin{equation} \frac{pf_1^{I_g}(X_{g_i}^{C1,C2})}{(1-p)f_0^{I_g}(X_{g_i}^{C1,C2}) + pf_1^{I_g}(X_{g_i}^{C1,C2})}\label{eq:08} \end{equation} %\newpage \subsection{More than two conditions} \label{sec:multicondmodel} EBSeq naturally accommodates multiple condition comparisons. For example, in a study with 3 conditions, there are K=5 possible expression patterns (P1,...,P5), or ways in which latent levels of expression may vary across conditions: \begin{align} \textrm {P1:}& \hspace{0.05in} q_{g_i}^{C1} = q_{g_i}^{C2}=q_{g_i}^{C3} \nonumber \\ \textrm {P2:}& \hspace{0.05in} q_{g_i}^{C1} = q_{g_i}^{C2} \neq q_{g_i}^{C3} \nonumber \\ \textrm {P3:}& \hspace{0.05in} q_{g_i}^{C1} = q_{g_i}^{C3} \neq q_{g_i}^{C2} \nonumber \\ \textrm {P4:}& \hspace{0.05in} q_{g_i}^{C1} \neq q_{g_i}^{C2} = q_{g_i}^{C3} \nonumber \\ \textrm {P5:}& \hspace{0.05in} q_{g_i}^{C1} \neq q_{g_i}^{C2} \neq q_{g_i}^{C3} \textrm{ and } q_{g_i}^{C1} \neq q_{g_i}^{C3} \nonumber \end{align} \noindent The prior predictive distributions for these are given, respectively, by: \begin{align} g_1^{I_g}(X_{g_i}^{C1,C2,C3}) &= f_0^{I_g}(X_{g_i}^{C1,C2,C3}) \nonumber \\ g_2^{I_g}(X_{g_i}^{C1,C2,C3}) &= f_0^{I_g}(X_{g_i}^{C1,C2})f_0^{I_g}(X_{g_i}^{C3}) \nonumber \\ g_3^{I_g}(X_{g_i}^{C1,C2,C3}) &= f_0^{I_g}(X_{g_i}^{C1,C3})f_0^{I_g}(X_{g_i}^{C2}) \nonumber \\ g_4^{I_g}(X_{g_i}^{C1,C2,C3}) &= f_0^{I_g}(X_{g_i}^{C1})f_0^{I_g}(X_{g_i}^{C2,C3}) \nonumber \\ g_5^{I_g}(X_{g_i}^{C1,C2,C3}) &= f_0^{I_g}(X_{g_i}^{C1})f_0^{I_g}(X_{g_i}^{C2})f_0^{I_g}(X_{g_i}^{C3}) \nonumber \end{align} \noindent where $f_0^{I_g}$ is the same as in equation \ref{eq:05}. Then the marginal distribution in equation \ref{eq:07} becomes: \begin{equation} \sum_{k=1}^5 p_k g_k^{I_g}(X_{g_i}^{C1,C2,C3}) \label{eq:11} \end{equation} \noindent where $\sum_{k=1}^5 p_k = 1$. Thus, the posterior probability of isoform $g_i$ coming from pattern $K$ is readily obtained by: \begin{equation} \frac{p_K g_K^{I_g}(X_{g_i}^{C1,C2,C3})}{\sum_{k=1}^5 p_k g_k^{I_g}(X_{g_i}^{C1,C2,C3})} \label{eq:12} \end{equation} \subsection{Getting a false discovery rate (FDR) controlled list of genes or isoforms} \label{sec:fdrlist} To obtain a list of DE genes with false discovery rate (FDR) controlled at $\alpha$ in an experiment comparing two biological conditions, the genes with posterior probability of being DE (PPDE) greater than 1 - $\alpha$ should be used. For example, the genes with PPDE>=0.95 make up the list of DE genes with target FDR controlled at 5\%. With more than two biological conditions, there are multiple DE patterns (see Section \ref{sec:multicondmodel}). To obtain a list of genes in a specific DE pattern with target FDR $\alpha$, a user should take the genes with posterior probability of being in that pattern greater than 1 - $\alpha$. Isoform-based lists are obtained in the same way. \newpage \section{Quick Start} \label{sec:quickstart} Before analysis can proceed, the EBSeq package must be loaded into the working space: <<>>= library(EBSeq) @ \subsection{Gene level DE analysis (two conditions)} \label{sec:startgenede} \subsubsection{Required input} \label{sec:startgenedeinput} \begin{flushleft} {\bf Data}: The object \verb+Data+ should be a $G-by-S$ matrix containing the expression values for each gene and each sample, where $G$ is the number of genes and $S$ is the number of samples. These values should exhibit raw counts, without normalization across samples. Counts of this nature may be obtained from RSEM \cite{Li11b}, Cufflinks \cite{Trapnell12}, or a similar approach. \vspace{5 mm} {\bf Conditions}: The object \verb+Conditions+ should be a Factor vector of length $S$ that indicates to which condition each sample belongs. For example, if there are two conditions and three samples in each, $S=6$ and \verb+Conditions+ may be given by \verb+as.factor(c("C1","C1","C1","C2","C2","C2"))+ \end{flushleft} \noindent The object \verb+GeneMat+ is a simulated data matrix containing 1,000 rows of genes and 10 columns of samples. The genes are named \verb+Gene_1, Gene_2 ...+ <<>>= data(GeneMat) str(GeneMat) @ \subsubsection{Library size factor} \label{sec:startgenedesize} As detailed in Section \ref{sec:model}, EBSeq requires the library size factor $l_s$ for each sample $s$. Here, $l_s$ may be obtained via the function \verb+MedianNorm+, which reproduces the median normalization approach in DESeq \citep{Anders10}. <<>>= Sizes=MedianNorm(GeneMat) @ \noindent If quantile normalization is preferred, $l_s$ may be obtained via the function \verb+QuantileNorm+. (e.g. \verb+QuantileNorm(GeneMat,.75)+ for Upper-Quantile Normalization in \cite{Bullard10}) \subsubsection{Running EBSeq on gene expression estimates} \label{sec:startgenederun} The function \verb+EBTest+ is used to detect DE genes. For gene-level data, we don't need to specify the parameter \verb+NgVector+ since there are no differences in $I_g$ structure among the different genes. Here, we simulated the first five samples to be in condition 1 and the other five in condition 2, so define: \verb+Conditions=as.factor(rep(c("C1","C2"),each=5))+ \noindent \verb+sizeFactors+ is used to define the library size factor of each sample. It could be obtained by summing up the total number of reads within each sample, Median Normalization \citep{Anders10}, scaling normalization \citep{Robinson10}, Upper-Quantile Normalization \cite{Bullard10}, or some other such approach. These in hand, we run the EM algorithm, setting the number of iterations to five via \verb+maxround=5+ for demonstration purposes. However, we note that in practice, additional iterations are usually required. Convergence should always be checked (see Section \ref{sec:detailedgenedeconverge} for details). Please note this may take several minutes: <<>>= EBOut=EBTest(Data=GeneMat, Conditions=as.factor(rep(c("C1","C2"),each=5)),sizeFactors=Sizes, maxround=5) @ \noindent The list of DE genes and the posterior probabilities of being DE are obtained as follows <<>>= EBDERes=GetDEResults(EBOut, FDR=0.05) str(EBDERes$DEfound) head(EBDERes$PPMat) str(EBDERes$Status) @ \noindent \verb+EBDERes$DEfound+ is a list of genes identified with 5\% FDR. EBSeq found 95 genes. The matrix \verb+EBDERes$PPMat+ contains two columns \verb+PPEE+ and \verb+PPDE+, corresponding to the posterior probabilities of being EE or DE for each gene. \verb+EBDERes$Status+ contains each gene's status called by EBSeq. \noindent Note the \verb+GetDEResults()+ was incorporated in EBSeq since version 1.7.1. By using the default settings, the number of genes identified in any given analysis may differ slightly from the previous version. The updated algorithm is more robust to outliers and transcripts with low variance. To obtain results that are comparable to results from earlier versions of EBSeq ($\le$ 1.7.0), a user may set \verb+Method="classic"+ in \verb+GetDEResults()+ function, or use the \verb+GetPPMat()+ function. \subsection{Isoform level DE analysis (two conditions)} \label{sec:startisode} \subsubsection{Required inputs} \label{sec:startisodeinput} \begin{flushleft} {\bf Data}: The object \verb+Data+ should be a $I-by-S$ matrix containing the expression values for each isoform and each sample, where $I$ is the number of isoforms and $S$ is the number of sample. As in the gene-level analysis, these values should exhibit raw data, without normalization across samples. \vspace{5 mm} {\bf Conditions}: The object \verb+Conditions+ should be a vector with length $S$ to indicate the condition of each sample. \vspace{5 mm} {\bf IsoformNames}: The object \verb+IsoformNames+ should be a vector with length $I$ to indicate the isoform names. \vspace{5 mm} {\bf IsosGeneNames}: The object \verb+IsosGeneNames+ should be a vector with length $I$ to indicate the gene name of each isoform. (in the same order as \verb+IsoformNames+.) \end{flushleft} \noindent \verb+IsoList+ contains 1,200 simulated isoforms. In which \verb+IsoList$IsoMat+ is a data matrix containing 1,200 rows of isoforms and 10 columns of samples; \verb+IsoList$IsoNames+ contains the isoform names; \verb+IsoList$IsosGeneNames+ contains the names of the genes the isoforms belong to. <<>>= data(IsoList) str(IsoList) IsoMat=IsoList$IsoMat str(IsoMat) IsoNames=IsoList$IsoNames IsosGeneNames=IsoList$IsosGeneNames @ \subsubsection{Library size factor} \label{sec:startisodesize} Similar to the gene-level analysis presented above, we may obtain the isoform-level library size factors via \verb+MedianNorm+: <<>>= IsoSizes=MedianNorm(IsoMat) @ \subsubsection{The $I_g$ vector} \label{sec:startisodeNg} While working on isoform level data, EBSeq fits different prior parameters for different uncertainty groups (defined as $I_g$ groups). The default setting to define the uncertainty groups consists of using the number of isoforms the host gene contains ($N_g$) for each isoform. The default settings will provide three uncertainty groups: $I_g=1$ group: Isoforms with $N_g=1$; $I_g=2$ group: Isoforms with $N_g=2$; $I_g=3$ group: Isoforms with $N_g \geq 3$. The $N_g$ and $I_g$ group assignment can be obtained using the function \verb+GetNg+. The required inputs of \verb+GetNg+ are the isoform names (\verb+IsoformNames+) and their corresponding gene names (\verb+IsosGeneNames+). <<>>= NgList=GetNg(IsoNames, IsosGeneNames) IsoNgTrun=NgList$IsoformNgTrun IsoNgTrun[c(1:3,201:203,601:603)] @ More details could be found in Section \ref{sec:detailedisode}. \subsubsection{Running EBSeq on isoform expression estimates} \label{sec:startisoderun} The \verb+EBTest+ function is also used to run EBSeq for two condition comparisons on isoform-level data. Below we use 5 iterations to demonstrate. However, as in the gene level analysis, we advise that additional iterations will likely be required in practice (see Section \ref{sec:detailedisodeconverge} for details). <<>>= IsoEBOut=EBTest(Data=IsoMat, NgVector=IsoNgTrun, Conditions=as.factor(rep(c("C1","C2"),each=5)),sizeFactors=IsoSizes, maxround=5) IsoEBDERes=GetDEResults(IsoEBOut, FDR=0.05) str(IsoEBDERes$DEfound) head(IsoEBDERes$PPMat) str(IsoEBDERes$Status) @ \noindent We see that EBSeq found 104 DE isoforms at the target FDR of 0.05. \noindent Note the \verb+GetDEResults()+ was incorporated in EBSeq since version 1.7.1. By using the default settings, the number of transcripts identified in any given analysis may differ slightly from the previous version. The updated algorithm is more robust to outliers and transcripts with low variance. To obtain results that are comparable to results from earlier versions of EBSeq ($\le$ 1.7.0), a user may set \verb+Method="classic"+ in \verb+GetDEResults()+ function, or use the \verb+GetPPMat()+ function. \subsection{Gene level DE analysis (more than two conditions)} \label{sec:startmulticond} \noindent The object \verb+MultiGeneMat+ is a matrix containing 500 simulated genes with 6 samples: the first two samples are from condition 1; the second and the third sample are from condition 2; the last two samples are from condition 3. <<>>= data(MultiGeneMat) str(MultiGeneMat) @ In analysis where the data are spread over more than two conditions, the set of possible patterns for each gene is more complicated than simply EE and DE. As noted in Section \ref{sec:model}, when we have 3 conditions, there are 5 expression patterns to consider. In the simulated data, we have 6 samples, 2 in each of 3 conditions. The function \verb+GetPatterns+ allows the user to generate all possible patterns given the conditions. For example: <<>>= Conditions=c("C1","C1","C2","C2","C3","C3") PosParti=GetPatterns(Conditions) PosParti @ \noindent where the first row means all three conditions have the same latent mean expression level; the second row means C1 and C2 have the same latent mean expression level but that of C3 is different; and the last row corresponds to the case where the three conditions all have different latent mean expression levels. The user may use all or only some of these possible patterns as an input to \verb+EBMultiTest+. For example, if we were interested in Patterns 1, 2, 4 and 5 only, we'd define: <<>>= Parti=PosParti[-3,] Parti @ Moving on to the analysis, \verb+MedianNorm+ or one of its competitors should be used to determine the normalization factors. Once this is done, the formal test is performed by \verb+EBMultiTest+. <<>>= MultiSize=MedianNorm(MultiGeneMat) MultiOut=EBMultiTest(MultiGeneMat,NgVector=NULL,Conditions=Conditions, AllParti=Parti, sizeFactors=MultiSize, maxround=5) @ \noindent The posterior probability of being in each pattern for every gene is obtained by using the function \verb+GetMultiPP+: <<>>= MultiPP=GetMultiPP(MultiOut) names(MultiPP) MultiPP$PP[1:10,] MultiPP$MAP[1:10] MultiPP$Patterns @ \noindent where \verb+MultiPP$PP+ provides the posterior probability of being in each pattern for every gene. \verb+MultiPP$MAP+ provides the most likely pattern of each gene based on the posterior probabilities. \verb+MultiPP$Patterns+ provides the details of the patterns. \subsection{Isoform level DE analysis (more than two conditions)} \label{sec:startisomulticond} \noindent Similar to \verb+IsoList+, the object \verb+IsoMultiList+ is an object containing the isoform expression estimates matrix, the isoform names, and the gene names of the isoforms' host genes. \verb+IsoMultiList$IsoMultiMat+ contains 300 simulated isoforms with 8 samples. The first two samples are from condition 1; the second and the third sample are from condition 2; the fifth and sixth sample are from condition 3; the last two samples are from condition 4. Similar to Section \ref{sec:startisode}, the function \verb+MedianNorm+ and \verb+GetNg+ could be used for normalization and calculating the $N_g$'s. <<>>= data(IsoMultiList) IsoMultiMat=IsoMultiList[[1]] IsoNames.Multi=IsoMultiList$IsoNames IsosGeneNames.Multi=IsoMultiList$IsosGeneNames IsoMultiSize=MedianNorm(IsoMultiMat) NgList.Multi=GetNg(IsoNames.Multi, IsosGeneNames.Multi) IsoNgTrun.Multi=NgList.Multi$IsoformNgTrun Conditions=c("C1","C1","C2","C2","C3","C3","C4","C4") @ Here we have 4 conditions, there are 15 expression patterns to consider. The function \verb+GetPatterns+ allows the user to generate all possible patterns given the conditions. For example: <<>>= PosParti.4Cond=GetPatterns(Conditions) PosParti.4Cond @ \noindent If we were interested in Patterns 1, 2, 3, 8 and 15 only, we'd define: <<>>= Parti.4Cond=PosParti.4Cond[c(1,2,3,8,15),] Parti.4Cond @ \noindent Moving on to the analysis, \verb+EBMultiTest+ could be used to perform the test: <<>>= IsoMultiOut=EBMultiTest(IsoMultiMat, NgVector=IsoNgTrun.Multi,Conditions=Conditions, AllParti=Parti.4Cond, sizeFactors=IsoMultiSize, maxround=5) @ \noindent The posterior probability of being in each pattern for every gene is obtained by using the function \verb+GetMultiPP+: <<>>= IsoMultiPP=GetMultiPP(IsoMultiOut) names(MultiPP) IsoMultiPP$PP[1:10,] IsoMultiPP$MAP[1:10] IsoMultiPP$Patterns @ \noindent where \verb+MultiPP$PP+ provides the posterior probability of being in each pattern for every gene. \verb+MultiPP$MAP+ provides the most likely pattern of each gene based on the posterior probabilities. \verb+MultiPP$Patterns+ provides the details of the patterns. \newpage \section{More detailed examples} \label{sec:detailed} \subsection{Gene level DE analysis (two conditions)} \label{sec:detailedgenede} \subsubsection{Running EBSeq on simulated gene expression estimates} \label{sec:detailedgenederun} EBSeq is applied as described in Section \ref{sec:startgenederun}. <>= data(GeneMat) Sizes=MedianNorm(GeneMat) EBOut=EBTest(Data=GeneMat, Conditions=as.factor(rep(c("C1","C2"),each=5)),sizeFactors=Sizes, maxround=5) EBDERes=GetDEResults(EBOut, FDR=0.05) @ <<>>= EBDERes=GetDEResults(EBOut, FDR=0.05) str(EBDERes$DEfound) head(EBDERes$PPMat) str(EBDERes$Status) @ \noindent EBSeq found 95 DE genes at a target FDR of 0.05.\\ \subsubsection{Calculating FC} \label{sec:detailedgenedefc} The function \verb+PostFC+ may be used to calculate the Fold Change (FC) of the raw data as well as the posterior FC of the normalized data. \begin{figure}[h!] \centering <>= GeneFC=PostFC(EBOut) str(GeneFC) PlotPostVsRawFC(EBOut,GeneFC) @ \caption{ FC vs. Posterior FC for 1,000 gene expression estimates} \label{fig:GeneFC} \end{figure} Figure \ref{fig:GeneFC} shows the FC vs. Posterior FC on 1,000 gene expression estimates. The genes are ranked by their cross-condition mean (adjusted by the normalization factors). The posterior FC tends to shrink genes with low expressions (small rank); in this case the differences are minor. \newpage \subsubsection{Checking convergence} \label{sec:detailedgenedeconverge} As detailed in Section \ref{sec:model}, we assume the prior distribution of $q_g^C$ is $Beta(\alpha,\beta)$. The EM algorithm is used to estimate the hyper-parameters $\alpha,\beta$ and the mixture parameter $p$. The optimized parameters at each iteration may be obtained as follows (recall we are using 5 iterations for demonstration purposes): <<>>= EBOut$Alpha EBOut$Beta EBOut$P @ In this case the differences between the 4th and 5th iterations are always less than 0.01. \subsubsection{Checking the model fit and other diagnostics} \label{sec:detailedgenedeplot} As noted in Leng {\it et al.}, 2013 \cite{Leng13}, EBSeq relies on parametric assumptions that should be checked following each analysis. The \verb+QQP+ function may be used to assess prior assumptions. In practice, \verb+QQP+ generates the Q-Q plot of the empirical $q$'s vs. the simulated $q$'s from the Beta prior distribution with estimated hyper-parameters. Figure \ref{fig:GeneQQ} shows that the data points lie on the $y=x$ line for both conditions, which indicates that the Beta prior is appropriate. \begin{figure}[h!] \centering <>= par(mfrow=c(1,2)) QQP(EBOut) @ \caption{QQ-plots for checking the assumption of a Beta prior (upper panels) as well as the model fit using data from condition 1 and condition 2 (lower panels)} \label{fig:GeneQQ} \end{figure} \newpage \noindent Likewise, the \verb+DenNHist+ function may be used to check the density plot of empirical $q$'s vs the simulated $q$'s from the fitted Beta prior distribution. Figure \ref{fig:GeneDenNHist} also shows our estimated distribution fits the data very well. \begin{figure}[h!] \centering <>= par(mfrow=c(1,2)) DenNHist(EBOut) @ \caption{Density plots for checking the model fit using data from condition 1 and condition 2} \label{fig:GeneDenNHist} \end{figure} \newpage \subsection{Isoform level DE analysis (two conditions)} \label{sec:detailedisode} \subsubsection{The $I_g$ vector} \label{sec:detailedisodeNg} Since EBSeq fits rely on $I_g$, we need to obtain the $I_g$ for each isoform. This can be done using the function \verb+GetNg+. The required inputs of \verb+GetNg+ are the isoform names (\verb+IsoformNames+) and their corresponding gene names (\verb+IsosGeneNames+), described above. In the simulated data, we assume that the isoforms in the $I_g=1$ group belong to genes \verb+Gene_1, ... , Gene_200+; The isoforms in the $I_g=2$ group belong to genes \verb+Gene_201, ..., Gene_400+; and isoforms in the $I_g=3$ group belong to \verb+Gene_401, ..., Gene_600+. <>= data(IsoList) IsoMat=IsoList$IsoMat IsoNames=IsoList$IsoNames IsosGeneNames=IsoList$IsosGeneNames NgList=GetNg(IsoNames, IsosGeneNames, TrunThre=3) @ <<>>= names(NgList) IsoNgTrun=NgList$IsoformNgTrun IsoNgTrun[c(1:3,201:203,601:603)] @ The output of \verb+GetNg+ contains 4 vectors. \verb+GeneNg+ (\verb+IsoformNg+) provides the number of isoforms $N_g$ within each gene (within each isoform's host gene). \verb+GeneNgTrun+ (\verb+IsoformNgTrun+) provides the $I_g$ group assignments. The default number of groups is 3, which means the isoforms with $N_g$ greater than 3 will be assigned to $I_g=3$ group. We use 3 in the case studies since the number of isoforms with $N_g$ larger than 3 is relatively small and the small sample size may induce poor parameter fitting if we treat them as separate groups. In practice, if there is evidence that the $N_g=4,5,6...$ groups should be treated as separate groups, a user can change \verb+TrunThre+ to define a different truncation threshold. \subsubsection{Using mappability ambiguity clusters instead of the $I_g$ vector when the gene-isoform relationship is unknown} \label{sec:detailedisodeNoNg} When working with a de-novo assembled transcriptome, in which case the gene-isoform relationship is unknown, a user can use read mapping ambiguity cluster information instead of Ng, as provided by RSEM \cite{Li11b} in the output file \verb+output_name.ngvec+. The file contains a vector with the same length as the total number of transcripts. Each transcript has been assigned to one of 3 levels (1, 2, or 3) to indicate the mapping uncertainty level of that transcript. The mapping ambiguity clusters are partitioned via a k-means algorithm on the unmapability scores that are provided by RSEM. A user can read in the mapping ambiguity cluster information using: <>= IsoNgTrun = scan(file="output_name.ngvec", what=0, sep="\n") @\\ Where \verb+"output_name.ngvec"+ is the output file obtained from RSEM function rsem-generate-ngvector. More details on using the RSEM-EBSeq pipeline on de novo assembled transcriptomes can be found at \url{http://deweylab.biostat.wisc.edu/rsem/README.html#de}. Other unmappability scores and other cluster methods (e.g. Gaussian Mixed Model) could also be used to form the uncertainty clusters. \subsubsection{Running EBSeq on simulated isoform expression estimates} \label{sec:detailedisoderun} EBSeq can be applied as described in Section \ref{sec:startisoderun}. <>= IsoSizes=MedianNorm(IsoMat) IsoEBOut=EBTest(Data=IsoMat, NgVector=IsoNgTrun, Conditions=as.factor(rep(c("C1","C2"),each=5)),sizeFactors=IsoSizes, maxround=5) IsoEBDERes=GetDEResults(IsoEBOut, FDR=0.05) @ <<>>= str(IsoEBDERes) @ \noindent We see that EBSeq found 104 DE isoforms at a target FDR of 0.05. The function \verb+PostFC+ could also be used here to calculate the Fold Change (FC) as well as the posterior FC on the normalization factor adjusted data. <<>>= IsoFC=PostFC(IsoEBOut) str(IsoFC) @ \subsubsection{Checking convergence} \label{sec:detailedisodeconverge} For isoform level data, we assume the prior distribution of $q_{gi}^C$ is $Beta(\alpha,\beta^{I_g})$. As in Section \ref{sec:detailedgenedeconverge}, the optimized parameters at each iteration may be obtained as follows (recall we are using 5 iterations for demonstration purposes): <<>>= IsoEBOut$Alpha IsoEBOut$Beta IsoEBOut$P @ Here we have 3 $\beta$'s in each iteration corresponding to $\beta^{I_g=1},\beta^{I_g=2},\beta^{I_g=3}$. We see that parameters are changing less than $10^{-2}$ or $10^{-3}$. In practice, we require changes less than $10^{-3}$ to declare convergence. \subsubsection{Checking the model fit and other diagnostics} \label{sec:detailedisodeplot} In Leng {\it et al.}, 2013\citep{Leng13}, we showed the mean-variance differences across different isoform groups on multiple data sets. In practice, if it is of interest to check differences among isoform groups defined by truncated $I_g$ (such as those shown here in Figure 1), the function \verb+PolyFitPlot+ may be used. The following code generates the three panels shown in Figure \ref{fig:IsoSimuNgEach} (if condition 2 is of interest, a user could change each \verb+C1+ to \verb+C2+.): \begin{figure}[h!] \centering <>= par(mfrow=c(2,2)) PolyFitValue=vector("list",3) for(i in 1:3) PolyFitValue[[i]]=PolyFitPlot(IsoEBOut$C1Mean[[i]], IsoEBOut$C1EstVar[[i]],5) @ \caption{ The mean-variance fitting plot for each Ng group} \label{fig:IsoSimuNgEach} \end{figure} \newpage Superimposing all $I_g$ groups using the code below will generate the figure (shown here in Figure \ref{fig:IsoSimuNg}), which is similar in structure to Figure 1: \begin{figure}[h!] \centering <>= PolyAll=PolyFitPlot(unlist(IsoEBOut$C1Mean), unlist(IsoEBOut$C1EstVar),5) lines(log10(IsoEBOut$C1Mean[[1]][PolyFitValue[[1]]$sort]), PolyFitValue[[1]]$fit[PolyFitValue[[1]]$sort],col="yellow",lwd=2) lines(log10(IsoEBOut$C1Mean[[2]][PolyFitValue[[2]]$sort]), PolyFitValue[[2]]$fit[PolyFitValue[[2]]$sort],col="pink",lwd=2) lines(log10(IsoEBOut$C1Mean[[3]][PolyFitValue[[3]]$sort]), PolyFitValue[[3]]$fit[PolyFitValue[[3]]$sort],col="green",lwd=2) legend("topleft",c("All Isoforms","Ng = 1","Ng = 2","Ng = 3"), col=c("red","yellow","pink","green"),lty=1,lwd=3,box.lwd=2) @ \caption{The mean-variance plot for each Ng group} \label{fig:IsoSimuNg} \end{figure} \newpage \noindent To generate a QQ-plot of the fitted Beta prior distribution and the $\hat{q}^C$'s within condition, a user may use the following code to generate 6 panels (as shown in Figure \ref{fig:IsoQQ}). \begin{figure}[h!] \centering <>= par(mfrow=c(2,3)) QQP(IsoEBOut) @ \caption{ QQ-plots of the fitted prior distributions within each condition and each Ig group} \label{fig:IsoQQ} \end{figure} \newpage \noindent And in order to produce the plot of the fitted Beta prior densities and the histograms of $\hat{q}^C$'s within each condition, the following may be used (it generates Figure \ref{fig:IsoDenNHist}): \begin{figure}[h] \centering <>= par(mfrow=c(2,3)) DenNHist(IsoEBOut) @ \caption{ Prior distribution fit within each condition and each Ig group. (Note only a small set of isoforms are considered here for demonstration. Better fitting should be expected while using full set of isoforms.)} \label{fig:IsoDenNHist} \end{figure} \clearpage \subsection{Gene level DE analysis (more than two conditions)} \label{sec:detailedmulticond} As described in Section \ref{sec:startmulticond}, the function \verb+GetPatterns+ allows the user to generate all possible patterns given the conditions. To visualize the patterns, the function \verb+PlotPattern+ may be used. \begin{figure}[h!] \centering <>= Conditions=c("C1","C1","C2","C2","C3","C3") PosParti=GetPatterns(Conditions) PosParti PlotPattern(PosParti) @ \caption{ All possible patterns} \label{fig:Patterns} \end{figure} \newpage \noindent If we were interested in Patterns 1, 2, 4 and 5 only, we'd define: <<>>= Parti=PosParti[-3,] Parti @ \noindent Moving on to the analysis, \verb+MedianNorm+ or one of its competitors should be used to determine the normalization factors. Once this is done, the formal test is performed by \verb+EBMultiTest+. <>= data(MultiGeneMat) MultiSize=MedianNorm(MultiGeneMat) MultiOut=EBMultiTest(MultiGeneMat, NgVector=NULL,Conditions=Conditions, AllParti=Parti, sizeFactors=MultiSize, maxround=5) @ \noindent The posterior probability of being in each pattern for every gene is obtained using the function \verb+GetMultiPP+: <<>>= MultiPP=GetMultiPP(MultiOut) names(MultiPP) MultiPP$PP[1:10,] MultiPP$MAP[1:10] MultiPP$Patterns @ \noindent where \verb+MultiPP$PP+ provides the posterior probability of being in each pattern for every gene. \verb+MultiPP$MAP+ provides the most likely pattern of each gene based on the posterior probabilities. \verb+MultiPP$Patterns+ provides the details of the patterns. The FC and posterior FC for multiple condition data can be obtained by the function \verb+GetMultiFC+: <<>>= MultiFC=GetMultiFC(MultiOut) str(MultiFC) @ \noindent To generate a QQ-plot of the fitted Beta prior distribution and the $\hat{q}^C$'s within condition, a user could also use function \verb+DenNHist+ and \verb+QQP+. \begin{figure}[h!] \centering <>= par(mfrow=c(2,2)) QQP(MultiOut) @ \caption{ QQ-plots of the fitted prior distributions within each condition and each Ig group} \label{fig:GeneMultiQQ} \end{figure} \begin{figure}[h] \centering <>= par(mfrow=c(2,2)) DenNHist(MultiOut) @ \caption{ Prior distributions fit within each condition. (Note only a small set of genes are considered here for demonstration. Better fitting should be expected while using full set of genes.)} \label{fig:GeneMultiDenNHist} \end{figure} \newpage \clearpage \newpage \subsection{Isoform level DE analysis (more than two conditions)} \label{sec:detailedisomulticond} Similar to Section \ref{sec:startmulticond}, the function \verb+GetPatterns+ allows a user to generate all possible patterns given the conditions. To visualize the patterns, the function \verb+PlotPattern+ may be used. <<>>= Conditions=c("C1","C1","C2","C2","C3","C3","C4","C4") PosParti.4Cond=GetPatterns(Conditions) PosParti.4Cond @ \newpage \begin{figure}[h!] \centering <>= PlotPattern(PosParti.4Cond) Parti.4Cond=PosParti.4Cond[c(1,2,3,8,15),] Parti.4Cond @ \caption{All possible patterns for 4 conditions} \label{fig:Patterns4Cond} \end{figure} \newpage <>= data(IsoMultiList) IsoMultiMat=IsoMultiList[[1]] IsoNames.Multi=IsoMultiList$IsoNames IsosGeneNames.Multi=IsoMultiList$IsosGeneNames IsoMultiSize=MedianNorm(IsoMultiMat) NgList.Multi=GetNg(IsoNames.Multi, IsosGeneNames.Multi) IsoNgTrun.Multi=NgList.Multi$IsoformNgTrun IsoMultiOut=EBMultiTest(IsoMultiMat,NgVector=IsoNgTrun.Multi,Conditions=Conditions, AllParti=Parti.4Cond, sizeFactors=IsoMultiSize, maxround=5) IsoMultiPP=GetMultiPP(IsoMultiOut) @ <<>>= names(MultiPP) IsoMultiPP$PP[1:10,] IsoMultiPP$MAP[1:10] IsoMultiPP$Patterns IsoMultiFC=GetMultiFC(IsoMultiOut) @ The FC and posterior FC for multiple condition data can be obtained by the function \verb+GetMultiFC+: \noindent To generate a QQ-plot of the fitted Beta prior distribution and the $\hat{q}^C$'s within condition, a user could also use the functions \verb+DenNHist+ and \verb+QQP+. \newpage \begin{figure}[h!] \centering <>= par(mfrow=c(3,4)) QQP(IsoMultiOut) @ \caption{ QQ-plots of the fitted prior distributions within each condition and Ig group. (Note only a small set of isoforms are considered here for demonstration. Better fitting should be expected while using full set of isoforms.)} \label{fig:IsoMultiQQ} \end{figure} \begin{figure}[h] \centering <>= par(mfrow=c(3,4)) DenNHist(IsoMultiOut) @ \caption{ Prior distributions fit within each condition and Ig group. (Note only a small set of isoforms are considered here for demonstration. Better fitting should be expected while using full set of isoforms.)} \label{fig:IsoMultiDenNHist} \end{figure} \clearpage \newpage \newpage \subsection{Working without replicates} When replicates are not available, it is difficult to estimate the transcript specific variance. In this case, EBSeq estimates the variance by pooling similar genes together. Specifically, we take genes with FC in the 25\% - 75\% quantile of all FC's as candidate genes. By defining \verb+NumBin = 1000+ (default in \verb+EBTest+), EBSeq will group genes with similar means into 1,000 bins. For each candidate gene, we use the across-condition variance estimate as its variance estimate. For each bin, the bin-wise variance estimation is taken to be the median of the across-condition variance estimates of the candidate genes within that bin. For each non-candidate gene, we use the bin-wise variance estimate of the host bin (the bin containing this gene) as its variance estimate. This approach works well when there are no more than 50\% DE genes in the data set. \subsubsection{Gene counts with two conditions} \label{sec:norepgenede} To generate a data set with no replicates, we take the first sample of each condition. For example, using the data from Section \ref{sec:detailedgenede}, we take sample 1 from condition 1 and sample 6 from condition 2. Functions \verb+MedianNorm+, \verb+GetDEResults+ and \verb+PostFC+ may be used on data without replicates. <<>>= data(GeneMat) GeneMat.norep=GeneMat[,c(1,6)] Sizes.norep=MedianNorm(GeneMat.norep) EBOut.norep=EBTest(Data=GeneMat.norep, Conditions=as.factor(rep(c("C1","C2"))), sizeFactors=Sizes.norep, maxround=5) EBDERes.norep=GetDEResults(EBOut.norep) GeneFC.norep=PostFC(EBOut.norep) @ \subsubsection{Isoform counts with two conditions} \label{norepisode} To generate an isoform level data set with no replicates, we also take sample 1 and sample 6 in the data we used in Section \ref{sec:detailedisode}. Example codes are shown below. <<>>= data(IsoList) IsoMat=IsoList$IsoMat IsoNames=IsoList$IsoNames IsosGeneNames=IsoList$IsosGeneNames NgList=GetNg(IsoNames, IsosGeneNames) IsoNgTrun=NgList$IsoformNgTrun IsoMat.norep=IsoMat[,c(1,6)] IsoSizes.norep=MedianNorm(IsoMat.norep) IsoEBOut.norep=EBTest(Data=IsoMat.norep, NgVector=IsoNgTrun, Conditions=as.factor(c("C1","C2")), sizeFactors=IsoSizes.norep, maxround=5) IsoEBDERes.norep=GetDEResults(IsoEBOut.norep) IsoFC.norep=PostFC(IsoEBOut.norep) @ \subsubsection{Gene counts with more than two conditions} \label{norepisode} To generate a data set with multiple conditions and no replicates, we take the first sample from each condition (sample 1, 3 and 5) in the data we used in Section \ref{sec:detailedmulticond}. Example codes are shown below. <<>>= data(MultiGeneMat) MultiGeneMat.norep=MultiGeneMat[,c(1,3,5)] Conditions=c("C1","C2","C3") PosParti=GetPatterns(Conditions) Parti=PosParti[-3,] MultiSize.norep=MedianNorm(MultiGeneMat.norep) MultiOut.norep=EBMultiTest(MultiGeneMat.norep, NgVector=NULL,Conditions=Conditions, AllParti=Parti, sizeFactors=MultiSize.norep, maxround=5) MultiPP.norep=GetMultiPP(MultiOut.norep) MultiFC.norep=GetMultiFC(MultiOut.norep) @ \subsubsection{Isoform counts with more than two conditions} \label{sec:norepmulticond} To generate an isoform level data set with multiple conditions and no replicates, we take the first sample from each condition (sample 1, 3, 5 and 7) in the data we used in Section \ref{sec:detailedisomulticond}. Example codes are shown below. <<>>= data(IsoMultiList) IsoMultiMat=IsoMultiList[[1]] IsoNames.Multi=IsoMultiList$IsoNames IsosGeneNames.Multi=IsoMultiList$IsosGeneNames IsoMultiMat.norep=IsoMultiMat[,c(1,3,5,7)] IsoMultiSize.norep=MedianNorm(IsoMultiMat.norep) NgList.Multi=GetNg(IsoNames.Multi, IsosGeneNames.Multi) IsoNgTrun.Multi=NgList.Multi$IsoformNgTrun Conditions=c("C1","C2","C3","C4") PosParti.4Cond=GetPatterns(Conditions) PosParti.4Cond Parti.4Cond=PosParti.4Cond[c(1,2,3,8,15),] Parti.4Cond IsoMultiOut.norep=EBMultiTest(IsoMultiMat.norep, NgVector=IsoNgTrun.Multi,Conditions=Conditions, AllParti=Parti.4Cond, sizeFactors=IsoMultiSize.norep, maxround=5) IsoMultiPP.norep=GetMultiPP(IsoMultiOut.norep) IsoMultiFC.norep=GetMultiFC(IsoMultiOut.norep) @ \section{EBSeq pipelines and extensions} \subsection{RSEM-EBSeq pipeline: from raw reads to differential expression analysis results} EBSeq is coupled with RSEM \cite{Li11b} as an RSEM-EBSeq pipeline which provides quantification and DE testing on both gene and isoform levels. For more details, see \url{http://deweylab.biostat.wisc.edu/rsem/README.html#de} \subsection{EBSeq interface: A user-friendly graphical interface for differetial expression analysis} EBSeq interface provides a graphical interface implementation for users who are not familiar with the R programming language. It takes .xls, .xlsx and .csv files as input. Additional packages need be downloaded; they may be found at \url{http://www.biostat.wisc.edu/~ningleng/EBSeq_Package/EBSeq_Interface/} \subsection{EBSeq Galaxy tool shed} EBSeq tool shed contains EBSeq wrappers for a local Galaxy implementation. For more details, see \url{http://www.biostat.wisc.edu/~ningleng/EBSeq_Package/EBSeq_Galaxy_toolshed/} \section{Acknowledgment} We would like to thank Haolin Xu for checking the package and proofreading the vignette. \section{News} 2014-1-30: In EBSeq 1.3.3, the default setting of EBTest function will remove low expressed genes (genes whose 75th quantile of normalized counts is less than 10) before identifying DE genes. These two thresholds can be changed in EBTest function. Because low expressed genes are disproportionately noisy, removing these genes prior to downstream analyses can improve model fitting and increase robustness (e.g. by removing outliers). 2014-5-22: In EBSeq 1.5.2, numerical approximations are implemented to deal with underflow. The underflow is likely due to large number of samples. 2015-1-29: In EBSeq 1.7.1, EBSeq incorporates a new function GetDEResults() which may be used to obtain a list of transcripts under a target FDR in a two-condition experiment. The results obtained by applying this function with its default setting will be more robust to transcripts with low variance and potential outliers. By using the default settings in this function, the number of genes identified in any given analysis may differ slightly from the previous version (1.7.0 or order). To obtain results that are comparable to results from earlier versions of EBSeq (1.7.0 or older), a user may set Method="classic" in GetDEResults() function, or use the original GetPPMat() function. The GeneDEResults() function also allows a user to modify thresholds to target genes/isoforms with a pre-specified posterior fold change. Also, in EBSeq 1.7.1, the default settings in EBTest() and EBMultiTest() function will only remove transcripts with all 0's (instead of removing transcripts with 75th quantile less than 10 in version 1.3.3-1.7.0). To obtain a list of transcripts comparable to the results generated by EBSeq version 1.3.3-1.7.0, a user may change Qtrm = 0.75 and QtrmCut = 10 when applying EBTest() or EBMultiTest() function. \section{Common Q and A} \subsection{Read in data} csv file: \verb+In=read.csv("FileName", stringsAsFactors=F, row.names=1, header=T)+ \verb+Data=data.matrix(In)+ \noindent txt file: \verb+In=read.table("FileName", stringsAsFactors=F, row.names=1, header=T)+ \verb+Data=data.matrix(In)+ \noindent Check \verb+str(Data)+ and make sure it is a matrix instead of data frame. You may need to play around with the \verb+row.names+ and \verb+header+ option depends on how the input file was generated. \subsection{GetDEResults() function not found} You may on an earlier version of EBSeq. The GetDEResults function was introduced since version 1.7.1. The latest release version could be found at: \url{http://www.bioconductor.org/packages/release/bioc/html/EBSeq.html} \noindent The latest devel version: \url{http://www.bioconductor.org/packages/devel/bioc/html/EBSeq.html} \noindent And you may check your package version by typing \verb+packageVersion("EBSeq")+. \subsection{Visualizing DE genes/isoforms} To generate a heatmap, you may consider the heatmap.2 function in gplots package. For example, you may run \verb+heatmap.2(NormalizedMatrix[GenesOfInterest,], scale="row", trace="none", Colv=F)+ The normalized matrix may be obtained from \verb+GetNormalizedMat()+ function. \subsection{My favorite gene/isoform has NA in PP (status "NoTest")} \indent The NoTest status comes from two sources: 1) In version 1.3.3-1.7.0, using the default parameter settings of EBMultiTest(), the function will not test on genes with more than 75\% values $\le$ 10 to ensure better model fitting. To disable this filter, you may set Qtrm=1 and QtrmCut=0. 2) numerical over/underflow in R. That happens when the within condition variance is extremely large or small. we did implemented a numerical approximation step to calculate the approximated PP for these genes with over/underflow. Here we use $10^{-10}$ to approximate the parameter p in the NB distribution for these genes (we set it to a small value since we want to cover more over/underflow genes with low within-condition variation). You may try to tune this value (to a larger value) in the approximation by setting \verb+ApproxVal+ in \verb+EBTest()+ or \verb+EBMultiTest()+ function. \pagebreak \bibliographystyle{plain} \bibliography{lengetal} \end{document} EBSeq/vignettes/PlotExample.png0000644000175000017500000035665714136050172016340 0ustar nileshnileshPNG  IHDRyڴk pHYsHHFk> vpAg,KIDATxyeg?s,0 0((@YV-ff%X.Ǵ\,3sEs/4,+Em2qP`aֳo}x}>9ΰ sa~_꺮 0 0 0Cmwaaazaap@0 0 0Caaf=0 0 a8gaa!  0 0 3သaaa03 0 0zaap@0 0 0Caaf=0 0 a8gaa!  0 0 3သaaa03 0 0zaap@0 0 0Caaf=0 0 a8gaa!  0 0 3သaaa03 0 0zaap@0 0 0Caaf=0 0 a8gaa!  0 0 3သaaa03 0 0zav .\p+VXbܹsΝЂGXrʕ+gϞ={l|{՟}fΜ9ś^Y{'s;緶t}ٓل> qavx<o˗/_N]A_| ,X`޼yw\}O#>8t{5qEf͚5k,2B_ګ h{=ovlzOWသa]c0>O5zzò9s̙3mѢE-}O?O4 t8績3}dګܟIf_za |?'cdxC#.W30gwI,_ӽq<>ѵ{*V}=0k0x8T_ )tmTcG5i<?0ib2#.]tR}c0D0̾@mBW*/3e=+kw2&}kU.}vfသa]@] ([W^z`CLW *Q쎫XПcae V@g!I3ʕ5UP<ޢ\e*h=0 OB:4SoQqE_ z|M ^ =XWzx]z4ʜg8gfՋ=M6 {s@t,yCv*mBO#Ч# V@WuX){063 ]UZ;R#I%5zG{6Y=*21>]Wa]@zr侠e=8j{Ctz|M {*WW4_ef}4Q뾀:B c9gn0/hw6Y{' .\pٳgϞM=Xɫ==]K]_?Wٯ' aoˮ@:trx5凍F,_ӽ9Je.7}U^#^0 2v,Y@_ʃ>Pϊ[VF@W?0+tě#CwqHUFW03 ؙs #ܱ?խ {aǮ,_ӽCuAK_Wzu ws(?aaa!2 3dz{qu8}Dow{*-2 1{k^w}#s@03 0 0zaap@0 0 0Caaf=0 0 afGxxM6mڴia֭[n 7p 7 / ]]]]]]//<ԏ}w7{~ c$d2y^xtXjHp8뺮`.|>r\nʔ)SLFhtkWH$p\t:N~߿gm^,B*JR{}ݞ{~Ϸ{)|Sgv~Vmܸ~@G^yW\.۾&N8qj˖-[lO~;<a{{ S[0b… .\xŋ|7|swضm۶m۶nݺu֩SN:ux_WRɶGY|#`s-/~(s̙c8Hgaa\(l>( ZZ[u EYqVk!P~zaaf}{gXz͞(g(JKˬYRDRK.-͛75paactu%b9/~XQ~3eƌF؆ 0 0Tm74*aISͼy1 0 0`.t]Wtu!CZ[[[eʕ+E3 0 0{T*K#3GQziny=0 0 @> ,=PuAVY2k$7JTzC"?gΜ9sx<=0 oQlS[Kueliiiှ:3f͚5kz3 0 7o.{/ro=dt)03֣e 0 ]o5c2Ra*03ng%a޼y̓؆7 0̾BbTmhnﶶr!=+Ce_\WlC"Ⱎatw'bwJ&`}DC^S2.7iVe 0 }$ )oYh]BQV䦷Py21;@ܔѐ٠쵥[P#6݂(oٲ}X[_aݣy늀quMGD!m3f?ϸn廂Ru>@3z!J߁z'8ga6^ MZEMR]C6[*1J%.#Ǒ麪*뚦TC_4 M+6MSU5qq4-/4-JźxXTUMTjk;΂t 㮻CRWzŢ㸮l[ zEA%ι1Xr0 0 Ʋmb{LD+1;yiiii)<h8C0 0 STg)_.euڢiB$Z4c8(Ţ̘#Mboܶ]-PGGWaruu+W>Xqig8ccۆs[Qjkq:Q~N|1;+9ow߲e֭]fG\C00(%m`Uy}ykjZk ^ ƣ>.K*/b>VO+^1>=nF3 xǏ?V { YYf͚5kaf` .w-D*|lt]+/>]u!8@)$7$+].'-lO[ eil5j#F;V*9V](`:m>ٿ|3 *jo2Q&ɜzYgHM - g@O@`eEXb(a.]tϟ?ƌ3fX` 駟~i0 3 4NbWi>v䑟>(t_====V*J/3Ϝ|rpY͎3nO*^S Eq}'״x\`9cz0 0Lo%[*T[هuׅWh)P*t^r۴IZL6-9EӔ-Zź_LۃAv_qđGuiZߏ4H)뗾bҩu?}p@0ͻ#[Wvbc07_{G3fܸhfᮮN]mjkz[M׬??BXnh{-CK"qgecǎ(Js1ƍ1jԘ1}v3 ei:sϝw[vmk,\J(lU4φ ;;X,aR9gN:}z]-;;=uVz$sVZ(\Ǜmf6e;z A9,z8l+q84g*uoʖ;|'kcǍC0M۽^=Y*`*Juu㯼ƿeY>;wn&s#Gر~ڵ55'r==rbsر'hx]?{9BmmO6Dч-|6կb9_䦛w;;.{co-}ac ^Q.^6[(`iav#CEc^7`fA!\Wr#x.)_}0+\1u|G&\/(BXjZyٮX,DX,ڰ/?\SҴ5kb1:3ww׵,1.-_ԤɤaBxџ=O8!= ,HC{VK(XK./WWD}i*@=F]w(MŶ\WN.!>ahZ&vmi~.nbuL*իO:X 'O^S[~ ЃAO׳|޶7lhk[vڵk׬җN8ӲBi}>*w7"%)[4d;DtTjmu]]Xloذ 3̠v`0 qc[ڵf >lZᚚ; ndNھsLSU׮6ۄS=V_啗_Gd2w7ވLh XKEb1OZs{p@0 0^䴷>]ZToRwA*g~UU|KZp;miV[3ZչsO9eڴM֮M 4? c͚SOM.`>8~m8}3 el [_Ss饦iuuB=0 0Ln(J\v\ PїCWÿYf!t8aC8\_=O \pe_vmmBOx LN[Gy~jk6򜸪N'~o=oƍS];o^:}EYWxW]E78.LS Rt:a0h>^<){.N$=,VZd htմX 0?a= 0 x\{+QXV.``ۊR)qZГiZ>iLeXKGet끀eiZ:˕JEדIB4|rpQE/y* [C7_{ kd [:ۿNlh50 :S+xp7 ?|՗_~饻oǑg)L"D{CK6 uV&\ra|NjWUC3'  +VX-1 0{uuɤ{mBM;.8Ql{ئ?͛.`Ƕ`~pNjnww,NH6{睅iNvӧ{g}3qeܹsU Ǐ?~?3 / '۶}[@=[,@8t-6M[fv==GP>>󙞞EU3pptҷ^[/!?ܷ =0;fE( y͛7˘ 5'0/|޵^ 9וa Xmǩ т\rhaz>_.сʋtgx<=D&Ti bNZE0X""0tT#e,˶_~yذD_4J$BK.kA?.֕W2?p@0`˘ 3>tڬ9s̙3f 3 _:Ncc&HraK_bI'uu~,o?shߺ_?4ۮ`'ﺫ!:\tvFݖNgja!PKڅV<to(,X`,C3{0RR[ 5U9U|A>O-Uy ~ q|"7TW Ţ"OB OzE_*6> vo+ZRQ|rW^_]wa+< /뛚FhDK}41c_ c:|~ /}R,b֝uwu/烋OW bóo<ӟr`3!7)6RDY/z4.]tϟ?1 vC`ӟ0 ;o+|3_q PW7GfbQt>\ O8;ijZ{׮E5_JѨpvTuGJ$Tz%nOax38gz;TZZQEa51MP)SƏWX,wQ/_|r3gΜ9s`x{f[[&xCҾR}θC$ DR-VE ۊ"BP8֚[D fRuÐSC,ҥ>ߣo3Mv衇Rɶ( &LH&Ǝ][?on7N?J_G^,ϙs≧N$J]Y1|Çwv|2&JJ&cnO(igO D~~S8gz%PptC|f)#,g4sx=A9lKKKKK? H4[rm{"TW@CؾZz?}D W: xYJZK;t>/b&EֲLSӨG; FOOgH.w]x:cL$t+_ٿ2{ * ~P?=@>-xhѢE `FznVf4{}~x]Vo/ K4de٦nVUR*IWx8\/<+Jh AT{Æk8`C, rϖe%h*fvBq\v"oT*2drɒ# Oɓg?3͞ade˷uȮ~i3i EishYihp]ԷmۚoZS䓚 ΜTfh9^CۗF{C*S+8gAݑ+O@G? >\0{$7G5wW`ӛ䦚7݂ f#2ӣ"*;)%F~iz*%~el{_|K%K3BAJt CשK4꺦ՕhłlTrL;ߙ87nk;/mm5S߼s;u=cŊEۦ!Hny) [^hu{B;A( >D.8Ӧ8G>L`45ʄx@ +*4£,Gۇ9gAd-E8>PѳPFcw=sVh(NkڡYw(` 8J ik鞣>keF0 ł~p7{r_zS5>+J>EK䕽.^szpy!XM |(ŲPy#K/ 0 Dkjd 뚆=łAZCI/߯;zt"˝sĉߏgz ֡}͚K/H0dAibhAp0hرFsi~+6 5MCha)B ^1Ըc -M.I7pyuF /b_iV[?~z,Ay т0 jܫ䱖 ((V)hK'Ob'#Db|BRH|B"T|>+R,{[m|"d 'T*m|"tF+>QLf>\TJ$Y3/?d|TLP'b;-;:RBld۷ӹ>3bTں5ё[&,gnM&s9|f/[::|oFeydD6mÆ=Q==t!/pB{{*Ϸ3/J%zDk]]eP~6M;VJ&pN eău. hOZOy*'Ol~ 8V=]_Z]{C%Ђyggggg'Z0Q%:bw -] ~ɧa4u'L(2kmn>6}{/[de%žrժ~W8W0gR"}MTS׬d XrCցLy_fAWlC, >-Τ2T=ȣ&af/SHoV^/yx;UUPbۖ%IT.uM6q$;,-Zuy\eQ( L!ƔxK@Nǻ[Ğ#FSU.P4U5Xh dWjlo{.:jN:_t ?G϶Y9f˖m9-"PִWʯZ(7 U|^#:ŹWJgr4M+?[7/W8uA@];v}z)T`W q(o' 1 wJz}{ ^>};#֗+ wPgz1n}n۶h Jܹ6,\xaK|[<}e7|>UM$>Xӎ?FU_}uV]?U~1*?T￿Ux & ;st3 0E_z aU.WEѪs"E(x#FPNA$PUZ*f6Ea$$员,-`-вW9#ÊdQd))E98(E vEk(.\O[DQ*& hQ5iR:uk6`}oF|k-kҤ p>a15MӐ?aU}-[t6l,g4u]=ha(}h+f8w@̌+Fw䕥3 8GVNyS=MOOD6laNoiYZ5Ey^k].iYWfQ (-RH1~p9BI:ifŬxx@a+ZJ_y1B8FGG:](%u]*J )0M]O$y+ee ՅB.ǯ-;E]j#{lذ\_}y&NcAtzԨ1c^|ߘ7PH@P*Bדɏ4O8!ִ^kkso~7mq](G`N.(Ȁרr$",KQ4MN3r)iV>?ȀD\O/O郥5vNC7=y%0 0{SL+ƍ單.L$?~Æ N_"ٸ =x .PDjj#1qM{M4S|u+ag葏GwA\NjmiӮK.tX,ɓ]nj_ƶO>KE Cx Kh7l,B<@gJn}g֮]SN?5Ҵre߫;h߃L6&Bpߗ:У`)4^!a!;8իW^*>f)00؇# 0Pei<8ӦZxepDUCT!7瞭[7n\.+e"G^iKuxo#ԧ:n}+;i%p&a%PC_=VѵxE]〞ae֮mmmm?= 7_qJ`O?Fe w zK`Od<׿>X,*JM=9cԧtg #A wdjkkkkkzPǏ?~#ze^ zU _4;are:ůr!Eyao}u'-i%X姴473vرƵ9x,t׾/o*Z2>QLJlŗZOoiBI(QҊOi x4 D5-23;{vWGsR[{!_<_?{1l{l$>ǍӴ@(Ӧ|a+eO\kzD7P$ԩ:}gг;sP~] Cy Ze}(݈ f'4ztC>X`JZDQ,vB&q؟2_^dٟwx\޽{*$7H6䜣R؁}BA`:1 qޢX)_9O|⨣f8pA*|!ve(BI(}eiZ"͊<%L>2MM%Nj-ʳ)uuٙɈ2YzQ4|^bRd2[Յ==7|ͱXvږ-7|aW]%)|4ZS眳`A:](J8&ve]vYGmO쳹܃@,b^az|i`{k(XTP!J{;ڞ?bEmV'G7g΄ 7Es5 ooz{۹?5yޗ1⢋ 9䶶K/=;wyuy;dih ? ׍ ҷ=3tp@O)vGujߞ?`:aXFhէZ*JkkW P?pt'~۱ 0Cqc0\"?<^o 9Jm} li.7R<,Cp r@nRq >e9Bf0 CU4 Uŧe,]uamOdi  ai>ρei+OR @O>YW?ydwG?:{o3'y%SL\E">i ޶˒ TC?^ MB!g Fe ޟȫ 9>{e܍* 9^,:d8 Bԃ=\ye޼3eƌ})%q c䎉U0&;q- ,a7rMrSw-z附8z9ň񅯼h1 MӴbQPI *0Tm!AH)JO0"uŁ f4Օ L X `T Jb>|__ |h`W&0 * fu=.Njht߿K.D=}Kz]#]橧2moTzV@Ӽ>HrcRrֲp]3b1I)h7ɍzLQ2_ܑnag -ݵzw(a7̎JW -iavj;[,7Ti>xzU]qBT?}Fu ۸&L_~{O8Ϳ)sN{|pc tϧ==Osα,UM$:*ַuBql[܏%PyH3R2" L} >Hј#Y$"~rUźܫnw2` =c.?XC}F13B/Ӫ!3>}^N# 0ӟ&M|k\|)?~"!|_z+nAQQEҗNF5 U71iG Lev4'OE)5 ZP xK~:}}ů=uqc+cyaѧ!;a z Rpo?`rY"Js Q(BB[x ޮ2+Jfe"/8(}R pŘ 8 &~( -bӔRq< ׎cZ:*>Q_y@ 5,ri3(=7q7Z_iS/O<կtRg?2.}}\@%/k%JuBSYW ]LA~+CR;\[i밿ewPr,ӽ?#lt$7.J+;6$\ D^E{UDDž>?W2+ǧzmHP#+|>_(8BBA=2V*.p A &Z -to-B:ϧ==`/]]٬hfK%鞮(Ĉjj~ڶ->|$!͛\NlѲ [nѫ˧giԨX߼975Ԉ5McS/~Ғ͎Oi}C?񗿌>\$RSgu饟a\sMm[o'^}Ehl!' ʯ muE#B 2i :&Rp'?Rb$%7x/{`Q,B d'kwjs{C_ߢ w-q\avf.EpW_ߟL l8qM{A{&s2s <WrfC-qCf,WCU%7tDaO)[| )PElq]4 [GWJaYZJ MYY=.+Z+AUu#{4YZ F!q&Q GyT.cfŢQkTiJ-xO=D6yذpE֬ _}SO0_ 8}Iް᳟=ئ۴iT|n=״+hh}>d)zB) `P' iyƘ4knYT#ߩDGUfe_ -+߀?b};J aYϟ?|(ν)W|a=ϮS5RrS&{ q~5X h٩8Bh[C iy[ۺuk`yqZZr9*>Ȗ\N f 씞߯idR #7oْL Q 46F~b-ԙzxƌu뺺Y@aCww.ՅB+ymp9U1c.gęDjj6o3>!n-N7ٴ)qu}bmw=twȑ554{A;"Rx -r#]KE޷[,MC[/̀zNi0 0 üر?Ç?Y]]gYs)9@Ooj1b_5-xlm+>x{p@O'/% 0ЇzWߴeOY4Z9z[YQibfPdG; +-P9 #BE,i&l0Q=hA3ZB!юY]a٢i# cGVUUꊢ(˕JTr۶B_.d sX,@ me 'Top>60MM;VG/Z?[o?t55b4|[o]w]l/)l /~_7msŋ5S1"-d><g "W[:oiӲ]xbrLzwo"ywQ:T _[tJK W?w-[1 ]Kn}eh^#P5d3t(uS ;AN*6t:r-]r#Z'٬!0M]O&s9!,.aӲ%rX,+(0EAM=.=bD|>loOEI+Q*Θ<(`]n4S)1Ҁ ǏWnod2vlmm0Hd.dL_}[jjz衻Bdbh}6WGJ.YF0! A009,du#mۖ#JYϧizp@H@bu]Q%ߕ7K)S(ww*od\nP_S}" dB&aٗNOo|]>}q.+_A|Hi^H$N8g/{;o+#F ^_0bDs`? 8߱ էk;L<3O a#<+s޵?EGR+jy^TgRh{Hm+]kK%Yit?8:(ԑo$GG(uYۖ*Xm-0Ey&%gr-ܗFURWzaIQӕd.g= Jl/yɓ-8gʔnZs{z{kjlٸQQ]E9&zTm~p[IhRJeQ,34gWxz n+N=;K޷4CBe1wUsnJtVRy*aafʇZ#HO}uhB3Mp ]#G A SО}o:>1 _?Fd2ƍ?|U`smPSd+ ȟ}l\(LnvU;O[~_Q1imt:Ғ%]t{ 8Zfzl9!>r̃}8 0 _,)jyފ\2r"FgD8(K E"KTep)BLԴ|^U 1wZ'x/n]"(@UF"9h40 UUK%u]т7 (SbE+kf?Z3-A=,ZɡGo>zl"ϧcȒV_jk9-Q/J`\0_z-+7X%9RQSsSEяTjn~DRq꺪һ"0Sh6z0l[+i8idK<1ޫކ~1  0eƶkɃ њ3?:I}tI~lo~xsM7lظ1zSGQUF];֮Or)SF&~_y%&N8q#;rUafx]zTtMڇ*Z.YJ? ۏ|hq]ET',2k-K#`\Hqh)u}>MT" #s/AH>F"l躑lQUUm0Δqq)AVVzWceӋ.'~~PS߾uam(tM *O(RS(?}}yd]]($:>i JZkjh-8T6t4U5MC/WT0[/ʃ6,⊫Fޫ2\den<=]u #p@?r61df~`R0&oŶuKL${@@XE)GtL wCoۮ /c3):n wvf2B):;LS9& ]]JA#o1~SSM߿qlB9AN34j&COEA7vw IϰaemZ.)MȲgG<W_:e?ew;(A)SOmp/P(pBww6+eK%[Q6͢^i&8 -̺n-KuI*|k_;O-!>\βzz^.R(Z[WanYQ>1Ey5kfzK.1f5fر(}a1̙3gΜyիW A>{ٳgWG@L_>g 0I_o{+iE/H#EA7.8:-FmpEdWQ$P~-n2\pr\NzƣfMӶU4i%㸮̀EAq-ZA6n9l&# |HȒX,m:lQ`&w&&\ooyEo=cƺus.]Hw c+1-W77zk"qM*zόXO(ajitWriix;Dir4x`GyQGU~/G?ZL٩ޛzmSSah(;{ӌ8֢wo0 쫬_?y㏯[7ec54lxU_ s̝;Ԛ5~}};//N=ic,2 aX/I>#GkNx/_|x afOBlz_*ф˻b< Z͉﷬M\  |%ϧ۷R1c@`z:?n\mm )|T}˖DBmm҇Jn&Nlh֯kQJ /ǦEs^:~,9N6Jiĉ]溉>o.^˚6nj͖J"92.)CBk"!\ CPx &iZc>ն-5o;g|Za=2SUèAՄ`FzӒ[xhQlo۟ _,ϙ3gΜ9x#,v dq67w8޽MD0 0P*|K/}N'ӦW_}~)0ڦy-mS+V|+/ 3<4}!>+P#pG;,=f_>p0 nz\]ykoEҁ_p WI1-DO*3u=IE*u] ?iHj|i(,4MU˂亮 yIUPȵ[i% 80TUӐfK%ǑR]Fb#FHAK2˕J`٦hT5&@ 'Lss=;.DK8lYL.!;>~|}}(D#8 /D(OW{}۶+V(Jsc[OS'_}uCC `֍/1iTV |o; U t]M3ϧN4siJYz0LS޷ 9x*)/5 XgWn\SVJ/p~Nz4Nz=3x=Vopxh`w.ͩrIn*/}]ugR  l0meڵ *(lVΣFb~mɤl~>/oO0&HDN: ql[l1%HkC+=$_>1 qnaJ뺮i>HnbYY qXOU4|JPSM?J~.\p¾rΝ;w\a*T7}xP*,[ݖxGЉ~dK"TB`˧.Zjt:4 +~5]]_SˇsUW%pԨ.+Wn4|>P]TծqoXՒkyѡǀ3"(a:`P[(P~1[o0 s]6>A 1B Re|D_hKѐ yJVԗgEm(ᰮF*U(Jq\yz4MMK&eQWxI-r M4{zr91drM30M0EId( }>_jj槩GEU~ePUP= 9իgϾ-[&Ouf?/KƎuaOQ"\QVzkmk[8ǛUuvQFFof蝆;= \Y9ѰRI畮PyR;tp<)A_s,G>^f鯪r?vv΀34l4Ьީ8G]a[}_|E3 0eiӞxb˖Cɓ<O>\?:OQ+XGNxc"s.bm{AnS|ԭ[UaY@iŢmcpCq#J];^e:2OuzQ2+m"?] 8G N)^<2pVaxc* N.ԙ[T5C/*!rd H#{(FglfK%!F`ӓˉѨoY({5|PPlTJ$::2p߰[e]]bG{OKWfMGG&B%7(ܼG '{Bu'64oys"ɓ7ظQT45bM<1y-[DY-۷zf͌7h\$^(J 0ȑ鴢n.(emڴhQ4MzŅ,=Qt}vqNLiju:;Y<%GLF\x<ƲaM20 ʐ%<׿PhI.> ,˲fɒ\nu.?Mg|aaFb}F2†-ȵS#?Msd1ˍ)тltyF_U+}]",wAF_*J(cW,c/ 򫙌ݢ5.l+0lqbYX+:og[LXfQ+$&Z0TT%4CLf2"]Hd2zKFAm$-({dFx?+4l6'|rɒdr˖YQ"`PQGU 喭[FO q]Zĝ8+!nk!GA92ZZ;zabr]G]j>If̾ ӭol_rةaaA?}$NG/[{!@b#[}fmy%_H!/F@N!AŔUrt_rrElW(.bglxd2't%h4oIULPt²:DG{ gUQH?7CrL 5|߮=0 3h~;+y`2B8R>o>MSۊeRi-P3l6E55R- ~<.\3BAdyb8 KZ?ÇG">e:az4xFbG-d.g1EZ&[[+\~PՖP~g@&NEȔO"lTBXuuђNJ]eQlySh}=[[?K/E Yӭ*ʁNZSc_\_ﺊilr׊j׿~qQ9n `"EQ1 ],67 ]ySu<D~=3~+/rXnmY>Tex\9\{|}oYFkL/z :TTuaSapD 0C(* E&v  J\! Ew|^dNu]\>-ޢXtVtLTѨU"ݺ5-7FVZV[[ Zƍ==\8l_( 4By} cv9w,="y3,0=: 0 -kÆNQZZ*ɓGDxcÆL:jTMͫuwuWPF 6nfs[ӟBK$F/p˖_9RT ƌc](yoh\v׿~%m[*G2d;;-Kh `Y62ߚP1(d)a===4-wϧi8az$E,Q-4Y(+ߢh7!f9weIkw+^#p> /<傠Gc(xؽugafڵp! kkWf`5t(ڡ"ܹsΝ {Mh}dWX^>JMWS!WT,+ "'@aR7e FkKRq= PW҃z$F P,i:E~iDM/((ǥ@ybH@6.IuB!f u^*J=lX$"DA]*9DAhIs9ڶ66FB#([Ay44Z6w…'SO]{m4zG*J,(?1\}}m sO6{O=xg>sL(65U(cENສÇGT8jrVWP`в9,Ѩ/""PUkiů9pg`es)-eoAR;/DnJys>aBmɤ`)E-d˄ p[[gmm鴐j7l"6)MMu۷Kѣckm.^>#GMs~{ŢߟL_>jT"ϼvWW"LPW|g?[s>[V21cTUQ |dBg~DB\ّ#c1UM&sbM߯빜q0߯iQ((ÖeTȤ>aP΢(i2k?ӠZzԔ^x6ƀbWXb ,#>aaB :{CVz%K,7n0AQƌQK/;V>ɖ&c__wҥEV9i`8gϞ={l,{Kf"$U 0;O3AQn"ɫи.-+d d4>Z2!TQ\Wi!xlj:],bGp]MKe.6e#3p&,fUUUˋ)a˲m8|8>v*eb6F:-4 Nrh>KL&s@4빜<\βlVΊJ bQCJ*uKG ',YL$r!=tMG;r'}<͸UZ?L_ 83gΜ9s^p… !A{.aa(3z5^ ] 5pS_y*GA G: g|TE@[CL- Bifm f_:!2Fo#/Q0ql =?T0qDBQmmaթjn].ɌWW PȲ6o3Жɓ[[ĉ +A(ֶ}45~U^Sc5k6oN$2cFKK]ڵ[$qw_2>a76-[v%775uv*J*5{q>iiBbk?\xG?z`"!ʂ"@4iq$&"+Uo(Jss(V,ә̄ кuRrS_&uܔKh:)]f5[Br㺶-Y[I_ymmRrC 2fL<ښLn3oWJcǶa*QIDAT}͛ѡѡ(T,fYvӦ _'u۾aÂHdM'*Lmm$o&6ܴ˖X fjG|lVpLS| L0+,àwu!*`4u5ƶ4WiCfCSMnP,m- lga? `rȣ;Ļ>m)4V(0AEȕi4(#/dۄ!fy#**-nVWjUw9\}m74l:bċ/}wӞ͡a<̙5jTS 'ݴ֯61]37-JT4t3o[ :?:r@o3̙3gΜյzիWI`a>*Jy "2x-V54" \ JԀA\8ÕfC!]dX@@Uu=3;| q~i* C\NI3Ѩ yAӤs 2C/v5MUi]W &Zr9˲R4'EMD|LF1a{34\7f[ˊ-+fydK%Ϻ FBPE\c׮}3Xضd2Κ6mNxRCeio~FQ[+B' ,yÛq~{O.Be!~rHY"8:ϗJRɶmB_ץKaz(}y+('55iOafPCٿEo3egX(8-M&3b7MMCa RcLg2\$r3zt]]($;Nss}}(e1w_wY>jL80 S$4X^2;pfb)4 lLP  O%7ȶlg˖D"YDkYmoYAͳY)jn;;Eq -Z]rC-kƮ.QR[ ]m[*8@`Y,nߞJx`cc$ںy(QEغ~bϋ3&\96lاN5fkmGzkt/뭭YOz`8[tvYuuuMP0GQgɒ{?cCULfܸh#A\5D">6(}LF:4H0V^|aa;l֝wUW!7KUu۶H4ɓMsҤhTQLknɒo_c/>2|_bŊ+,;w R#pG;fEv>|aw>W{'}}VzBXc8N(Ŕ>+e x D(5M)uǶ-KJ S97FF3BUlTr_ k˻D"4vZH(`=LX -KׅF "Êܰ'B.:vD>_ʊƎ WںUQ;[䆺C0a`ڴ);6*l_}ӦɓW_]1%u/(+>}x-S67/ގ+n۶-eK's۷KTND+˽>|:]ͣݻE{:]s>5BukEQI:i2RS~?ٿMUlih0o?߲::&Oƶw>Ql̚VkCz_WD|S;,""4U PV/sb8ӆ[Çׅw Z, >n?b- RV/fogEf͚5kkipOfi0 0q>+ nZ#Q7|:̩S;N2L/\Pm͚pӦ+c:gϞ={l 4 ZΝ;w\p .\p!{oP_____.T 07[:oj3z~Hh+b"O5ȗ# mKrX*A| P*ٶJO&#sF2ߜNMsDgggi==L4:z4ˢt12meᡢr54ݝ (rEe"!alVlGOr96yb-+oh0lWzz"h&g2\H ({z\. L bX,2DBL۴qx}W</Iyd^;pl 1SyMZ[{IGyM.X(;[n0lgKаa(G8(]5M< 0> &.X `YgiwG,c1ϲXiRz=JTy*z\(/Ͳt]U齊轇,X+`sчnҶ{W7sTg{ˀ%78ooR^<_/&x 3 +)_[B((<[BVhI(ӒVM3 U|屌6mJ$V.q\.* -[l#QoY#JnPDiSwvkkAܼYZZ(#8qذP77lH$2eJcc$]L&C_ok81sR)-^xalvѣc^ZZ$b1:_*G l~#'ׯ^],.Xp睎sCiܬiKpԴz_?vlcc,v֭+_2ƏFeժִ'<\EY(2K׮f?)S ^ZF$ @@6(--hkt֟1#4MzdQ46z{{:-`C> [|uG"iD6+R0h +f߯TNo%*9_`->TTv CuX,{`,Ǒ駟~iA?LhZ= 0 äR#G^](qW >g2 .bU}ѣߊ|>]SO6&MJw]JQs˒L&+nrOzaKyw^cJ 6}s} 0;^߶QSIH_ yʹ0UrlqHhdo&(Td q{zdqqIYr녂㘦nOJttXLenVT ="\JrͯRb>/vuRm,u]ۖg5$HyJ8mvX(nO8RҬmO ,O;)3[n9ᄆ_m[dЩ/˄ arqrHK xG :ǹ.UմhTQfͺ 2];VU5Me g*I%l}bYRr!ZKӦ-aWX|>YB]*+$_u)Qoh 5b+b0EedŖ 'y.ZƝ{?d?s̙3g?~Pfwa{A8Okz >E*?>>d@mtBͯBُO,rd,ckHOo Ch }F@K-@bZ,ٽL`e?T 1|e}]iG? uE)wJrݯ~xc&L7^{{?~/iӘ1Mrb/E3x BH=9>QP_~*Jyܞt9v9=Sym7RX&,[@NP반f Tww4Z*%7PED==BӌE[m2SuFoӣ;."W\Cp(X=盚R[HIJlrUO&lqM{?_Pߺ?3K$nPSlݝ`ؓN:蠺 g1"dY#Eygu]2Ŷ-{7gQ:|x< vvJEaPwlm]uL;۶㑈 0Y96L${`P|>Qp(Ȩc -KӠkǵF-Ke{V;0D架CFE9/O[\UWx/Ʃz`*2%2B*Ρ`0 (Qؔ*~S%uf5A m!r fEo"aM$r9|W2۷p9*BAU ի\#ƍ zK:zt,mt6tv2܆PȲ6n-TBy[_{m:>:uԨ^ZFP9w֑#c@`ZYҊ^ye͚WUUsoժC7Wںnj֭E}ɿm4w3Ϩj2 Ʋe_b*|>x;l܈6q/{[.YrpYӧݽm[GG,_pl;wkaLb7׿~sW^?v|bg0M]‡-`8,L0qWɭZZ#yWF~X9wNX  k4n(Aq4󕗀í-28M4: 2|&젇p)m‡:2e_i5C_96,Eh7(fEN}jO90 0LuBLX[nA(i7zi~{|>7#Դ?̙3:k5kb3Θ3GQ}wR co^{?fa>=xxH7̎a3PxﷄZjiEX@J^Us9Y| 1 "m;N8" yE8Y)I &==2w7t #驯WtwlyKY M"͖J8:m( bߤ&.03p3n!dyhУXQ4FWW:ϏWScFҊ0ִ">޺߼npxŊO|bĈ.Ӥx8wu%Aw/p@4jY{sUW]twv.^{l2kt矟H᰸ig55@2)cCCmm8L䨪i&qebpX@4F-MMx @uu,y1 ]h\ͣg8lYB|ȯUq e?bY^HR&)6tm ؇Bx:(]HvgUT# Lzw|#J-IP_y45 hAXC5d!BASmRÖe6Hw*9kkH[f=s|޲ Xܶ-0֎__ [![Zt φ }ذp۰SPA]mpt9ѣkjk{uͨQx f?ujss_i*Jkƍ--uuk_osXQ*͙b5yէR,֦(8o )SFƍFf֬Iv뭷wߍk =6|뛛;7kj"uoGmY~e)J274b5|x4i*ÑQ BA>2 YVGGcByݩIf&#݊du)t=09TU+`&TU(`P+Jx+Kື *GKB0{?;C"@S`>0 /L5z+By͚1c,?_in,E͘1bB;:m.i(Kg5zt}WJ_=lG~vwgBiv 7G/Ne諃NCl 0jHr`W޶G8 }\k v-ȵF^-uUKfitM2 ]HhGU;: $ZUuL&;OUmP?}6[,BT|>ol67B􂵅eFe:m)e[+y?X7r-2[, Prf2[(ضouzC}O!h,2 ?}~im 7`04C!y'CC 1˰ӻy 64 1>2{뚖ͦ==曯 _|tz`Y,ȥkdGl*iy~z:6(aav f2+VvwJ&>l,^/K/(Ç=™gΘ1zWϞ}x `ǿk|Bt뭟>v^]]L0/ξ|衷v?>T@OurA<avrXKp3pR+_w(KRui&n:ii <~:'1k8'FK ǩ A\[+~,">\Trpa(2x8QSB45Mlo3b1!,A6}x\RLhF"U:zt]hI&S)t.W,b2)ql۶njihǺ~:_X Hf<8g&155~6 >5~y6jTOO>_*˖կN9K_: /|l6u]Gp)b%0DMSUzQe|ĤP'Zpg¹ NI|devw}x|EK$˂v 6cB0g6}aG>gwl'uׯ_ʯ[ysWa55zܹ:}ȑK/mW\qU_=꫿gFL裗,?g׬ih;vԨ`pݺvq'N=:\^?s9nlFnhܸ#MseLץ+N]]${w-FغaXV6+P~[#G\-U~(E鞄Yg\ND_"fŲ)|׉ f}_K>TBC3}hC :iWŢaٟqC}bo6n1]V8z_34_E3?1cs2%,?_~iɒEBd .K_~ hf=g}݉EdSCsIj:t)fw> _yo2!.Z0+1"@Bu޶eK6[,(UEA+Ȥ,(&ttMƶen.W*!M}U;cBTeŶKKҼf P,&G|/&55XRQQ4.ZJ x|sx˳.eKOO:χB>aՅÖukgUbe'"H$L8F*amƍGm>O׿{lC|KGzssÇt]7M]ܯRXwAT2jTMG+ uUVUyW)T)X ʐaq бe ͑/7d+_/@oe;Sŋ/^L[XB0 7зkMx@WA9][)2,SmOTRMeՕϋP8 c&>i['TrИ fFD,& BڼYx74BqȑPںm[*E%4SUS7ذGHnD a}X戮r3ujss,˫W̌--uu/75:zux#FtthڻiK~K/Z~}>?zt(9N Koi0r&/~qhKK]]8K6tv^sw<ĝw_wJbw8} 7Lr=w\TS}?g?7q/mi(%#GR_ F]^BǛi3F">{ɳ˪˜t -c1Ҭv`F=m ]tZ fY?0#=jNxTK6{Frz roJKn6 pUaT?4@o 2W@s^^~`;~-}&^lk_{i|n$D?>{v>]R~*(V/gu!MMgqahg7߼7xȑ~SqēO~dђHPAغ/w|>]a7״pg_ggx8ӧnT[kYW^y #Flp!kvv"ǟ˅æ(J*Jhqg>3iRcckkGG*uy< O>yko~:ܖ7||p1c6oxSxW]Wp802xe L8==2䯩DdR_[|==^F}N(US##HP4s9YL8_?g.-2 ]/d x -f- e҉3:Gi꺮[.LsՊb5۷sw<%޳Ϟu]]}_/BÇL>f̦MwϘq}vi([ bFKQ,E QM*TnR)PgQ*̣lT/DQ'(jRIf= [ioOE|[ђH|yGG:](`tPṁIX^JșS<==RĂ\,fT`TJCB?DWdIkjJT.W,]J36+J%dkђrŢibOEQl6,+u;H6}7:>ɞL 쐈 XYԪ(K+XGR kP7놊KU6mjPȞu2޹?3$ZqK{}xss>vqᰪƍ&]Nx<'AWp5GOݑiʕ|p@Suu7>PyyG2ܷ)*Zꤓ.`v%7{?:jÆ>;{ٲe|ۻ1ZF(HR˲$ta, 8QájXpHɒ8+R̉e|̽^Uy0BEیGU1ؚ5eǝb{W!i y̓܁]aKȎGRk7G(ք &L|&"L_jB::Q 9[pv6H%v0ߏ)1&Z,o_[hZ!Ď8 >Öt[QxKZKJNYÇO;fmllo6m{9s„|M?=thߺwjj6lqt>(+l삂3nnִի.|ꩩSwؼyޒ~≉#njO>ٿv+*FPC EIB(8v ! C4QZE%: $:'e37a„ &zR,ذA}]D,{SYG:I/1cKN>"Vmٲgs]S{?O~kժqy=k/߼+Lٵ6'x5 ʘ0a 37a„c#XJ~xOcM*e 4 XW` H~P')4dahaB!\b&^HB(#q<&I2H~ww8HP(I&Ɉ xӧRГaq ; >&)DF===H|8$1LkkO3,}[KKnEfF1IzVQlh=D ζXilD\I,1"?f۹ "N,3la.hзuݱ3Sla%55--h{l/޸}u dLI&sryO,˲p?i=neY&~OE\t=nWV ďfBo„ &L|PH$馇jo3f^nix8'9al:t;k֜w^e9FH"qEO?WW' .hhؾaѢ©S?8+k̘#ps+*>dҳS?K.Sc}L0GB;a0a5f.s4eՑ|w`KATڛkrJ(4b@,s$D*E X,Rh͆@2T@].EA]?@X <[*c!Єrs1C; X UyI\"6 V,m0 9`i@.xTJUaO?>'/a"EDPDJ%>׋HD'Y6̞}Yv$]S~[嗭YYc|S#Gvw77|zU̲GiǣvŢ(g|gor]~.\ fg|F"q"I<jM˲ł(7NQ SZiN,0,[RmfXyg,qDzn7Hy9ivՊ\6$,\WAk{L Պ%Yh~PhD@ qGwIC?/?``gLaVM0a}Ϥ'Lxad< 'Azi{a@&ptG-ǡ)4657h<<H ÆZjȑGmmk+"@*OqG*EJSn\.B3jTqyPKK |!ԴK/}e]>_E?0#҆<8;_3GN(-zU[ݻl믣i== wo~'o Fr)..*⸺V%I E$0GQ8J &tTsrΖ@ ;lY*Bt q,.3d"z"i(ieii^p2! lK:D0CMo-Nс۟$GI9̔Ǐ>7nDo߾}vt:ꫯ; ~?y~am۶m۶X &L0uJwo"qUOHY'_w:vlgpBuWL[@^/8gď }ԜL!5/-----?o޼yA?nܸqAZXbŊ 0aķ>bǓ_JAB]>ɒ]WA a_yIh:L$PeT ahTqM3@ E3,äRdoW=F"=n!k;fHk(\<&8BV{zBDB1%}u]ӀCӚG P(4<> .xyM=!q/4}s洶vvB_D"D9K5-X|×.=!C j֭쳟RS8YSWiӜ9H0\,7x?)*F O;۲%eƌ?yΜprs)eQ=>Z5!B E 4˲, "JbMa@9h3b;Ghaj B~;Lk.㱳TECIaYF ̐[?TQ&F a9@3᨟_|_|>?C=P< ??ӦM6m:ֻo„ ?$ބ"}oqҍx0=HxY8!9N!?#@..=g,ˇ^8żo^\ dy,z,{<,?1y6^z̑#KK].U⋎Pׯ-[u# +*"V֔(j͢ESTWrV'IaEQOs-}-`4*˫VεVWtepO$A?r`yih2Ǒ06EM ˆp8H'"$A*޾4MӚI+C 6({{W'&L G+K%%_Og0x<OGGG2S3a _š=>=Nq3DzH\QH`0K& X6p8H~r` mD$ƮP(4Nlj =sE['`#$ 1A=]VQdYo8x|TӭE1:7KÀqf=+J(p_ӦY,>;gH U~h֠i%&]}uEEAD~x۶쳦./uǎ[n䓗^">oȐ3H$ThSx,9 sx#Ddyٲ믹08nذw].jZY&SXa$ B%EY,d99~h =fed)IWxE$Eeb27ζZҠ(X8֣5"ϣ(ExЂyxȊ+;:v&./邏뭨@ @1?9C?aܭ[Azwuya#F&uummv;uu~?P\\\laa1Y,)a u]9>_~˵o_[/*㸎Hct=E fi*|V&˝1eTJ׳YI,a½RmC!ޟ_y 2" (7)= ~/@;2N&$NI+o%$%:0ϱ}&L0a#:mے_⩧b1%f?<8{Yl6I<(,8t҉ʜNYkߚe~{Ϟ:wu56^嗯RY ]) 5 w9w֭~($I /\sMuum-EQ\| &uB;HZW\rJx%1p0X &L|[ =#R5B < W7hatq,y<$*ㆡ==xLGG4+OvY @$DrcIʍ)Ԅ#$妼<'jkoGV#Fl55|"݆n锛ZL5n=|y1cJJCRHI ÔWW_tуyy55 s@QǭYsE>TV~ISSSǒC\k̜9l_~Ϛ-{ݻ1W]]s3",_^_{K>[*;^}|׮],:V$.<Խ{*guյ\Q1r$ǁ ax_^!07jmoT~?\. D"t"*χعFaLeT zh:<<@LeRfq!+czRI{ox&:ykHӯ!0 ]X &L)BO<;[oݺ/JJ@gذ2>|(^XX8}-VkAm…Ӧݻf͕W" T!?GԄ _S u06P*ʛ0a⇈+pL,!BPoFI+EcOT)Q34T;CuVnP{0' 6@A[4^h"\geH@2H::4dUtcEVJ.o94$7~۷s\8miI7y A#}6a7rɴLX?&L0a(-klgCfaÆ}K.<) H@=~ғN*.^}>wAF˗76_K67 9Pk-;JH^GKΜmJW3 5aߚm%0Ɉcc„vjQ, ;Ò+t9AܲsOO2i+H,늂łɞx\Ia,iv+ )B5 bpJX'.r5-/\p8+)i`% P(N$b-Lcڵ۶ׯ[w@gg<麦͞յo־ݞH YG^޼yӧԤR6EuvuvHNptӌ_yiwWUdgϘryѨ$Qˢsxήo(+Hd@m6J5YXX6L$IHqg4/f >0A-NEo}&d:dфoЃ͑H`I@87a„ ?&\[[srfǎax"E׋:;) 31JJIZ 9R'3|Ol9t%|yC/]d|ժHdəg9 ׋RHDMn &Q' DbŊ+V@ CrY,0fLqC{uw3 VVz<6,gyy<睛7];|xWWC~QT?ysl).3J(v8)'FVEEX*+fxs;㏿7y_2++;[JKEb apϻ\YYrx6q =$ -2采Ḧ́ބ OdzdFI67$dx{aIHy QioFk 4jm]P%::R#r޽--Q\l矷')7V$wt lUIN2fF,(p8vEc;jjPt5Sn<!2bDy9WU͚׿'oFӡ_eeB'VU҂(+KQW쳳g:{vyyNXOZ|0(lY9W=e &Mz"^#z"pj<~?ԴyEEwW]7BvcS@,6uFzϼnv|&zh<̎_ &|[3]qL0aXa`k<-@zaAFqULŀrCV%a|0HV&¾0Z PX bx\GKI_yeb1Mˬ;qBKXʓuY84QUA`YOWBԩ[&SO|Yfɤ"y ~'CWmn~Ɩ-[?|0Bkw|P59N.xРsٲbχ`kI21v8,TꭷnWom]Ǝ]L)/4wxɣ0c(JQl6 Tv8vUE?yQi8EE8fSh]..Iz-*)4n*O7 bZ9iұ>&Lazʃ_|y0a⧋̊{Vt+t]w$A3.F@[{$L꺪b!pm6\+4۲;4tB+N&E8XLhṹH ۑ#m6c"X,"'|B\G|ܸR$ߴ; EJDɤ(l޽?a664x+\{mnn{99AEEv>iN$}EWW$r=۷77R99v{^#F"W_M_|;ggƌ>&O7<zzXjzvۍ!D**JK5/5k v8q+Ə5 mpX,ǏU\ D0@DRVՊD$Ms\NNVL0t()l1~!pX,hY`g mYa,QdY(pX,sTUA rv6)Ezp|zU]i,0 Te8UnI,W|?I'MٿqIM*bw;שF3w=EQ՘NQ/\8vlnwVVfelV}aM#B/ee{[ow_ee(D==(\PRm"ݡPu_46f^W>_^U[1䒵kNUVzO\'O:4V?~Ѳ'Y&DSVt6667# 'Ȳ$}YCC0\%I òwG 8]AWZ,#J c BSl6ExcMu42~,yQ$}VAy(2 ˒]@ڀ6RUSsСC6fLqqqqII^ vi0|\BG6["u|4g$X3f  5UU--e|P_e'>Fq# /ݲsV{(jU^˖L<|x"rhr/0mTFTjiv^fDY'gx> m(\{z4-]JӢ1 0'|J1 J,#T2I,+,7CH7I8zۣ_FKKkj!OKu=,1sΠANҥc U>vժ>+--,RhE"/[{sA=-+kԨXaXvƌ/p[֎ΙIy%i'/zbl@d( E#p) @ZxB904m+YG;a\ud c14\ xuM,Ð~p}5iŸBQW暽Mr&~|M|x<OGGGGGDZ&~m3W$}$@MY2 y`H`.2R!oh'1p28pfd˞NlR dj=G@x* rŲAd 4tvT0^7~$uX>*==H2 mb[[w7j;i#G{w& }4}zy CE_QɤR~#F\pf[[lC,]Qۻskm6Z{'yƍӧϞaî] l-,uvEF㳲NbQI)S^}Oܴ)Wzŋko( 'P"JY,"YY. cv\v{g'VJD"ÀP8I$R)fCxfz<Kgg(Hb<^Td$wG"Np"XLxGT7K=UhѳVA9p5lB(R "` o)~4#>AnNns=@Kΐ. <HZSb{?@J)B 䙯mk*&L8IsE)0zdR #=ƂWp&=Y6*c0([Zp'IqЏ"==hU9 mjxcSn6oUU Q99kW}}w7lY7w:"n2T 3s:H&M{h { (E,ygҤ*joǽb6guׄ 99T3zSN)..|睧Ço'U&<{I#Fȗ_ֶp!%ÆVW ի/|Ox"q[|4jrK0LMϧ`^[w٥V ]c('LJB[k^W@@?Pv7bߗի,x+2eΪڜ미F=^L0ahT L9.0a⧀X}_L\Ꜥ#"z 9U㚦3=|1D"a$mt< ɤa m!a!:DXLӼ^qF H$uO$t'2 ((pLIz`CU_X GH$M&ynv.4Zƌy?1 8@"Ǔ.:4;{ɒ,]YG>{vhow:ee}l~ޒ%vm`'}Gegr 'p KaevEnv/?え*msύG r ??~ LZ>_~>.e}|R,dq>_^ҕiMtiiNOXeUnļ(ܰZ,8cxfdgB #8w8y1`s0kmǣ=R""X@s8 ' $%9KQ0ݣH-pȦp2-%Л0ݡ߄5쐚~駟~[1vSrߞ̑e뽣0a{zKP!qG =)08I >Z#AdXىR qx`76h.W_I5$ekhDK].IF40\ssrh4mΜ_?p7wFtnhhn^߾ʦ<˫VxeEES$>0Bٙ^xÇ7;k׾~eeןvЫ||mkC₂V(QTU trzq rs]&$U$qxOY,ڊ)4٪ՕL*Eih)d2'VLq,QWβ LSh2KR8$ڡPrP#kP#{=9\>೮Ð.ڶu⼼3<|XQT.g9RC׮ݵmٲ-[jjq]-mmv{ ZyQZzCΝ[Z[RBQ݈CpW(jʔCwᆩS_xR+6n?_~y L(졎+rEtTOXA< ϋ"6$?-ѱeAPUFy^UUU$I C80VYyEE0\}I| >=WUgx\p_ +/:Ƣf\c*qص&c g\G7d՜~|U?<un5jC#WIbJrX &L|hj7ѓNz]UUQ?RTII_ߚ={`}͚i NIUߖeF 7/?p\vV2jUQ˖ȱp)q?oCdѢ:쪪I(POY&L>'Cr\.~gYYYYY<$tuuu!v E@V39`rmB5DBӠ)t=%yUD̒񱘮z8+MM`<uǞx\סo*9mJ91ZHpǝk1  IyFqMBB>IJ~8HxL  56Λww x-)#N Bgg06/5a@ [n-[#h4K$dh joᆳ[׬9U܃AQT_ꤓ.?2tjtf< +,Z7lض}ٲ37mɓn c1U]'fϞٺhz9Msϓbiy,( eeY9.+@ A\=}Yjc$;^\%Dyy:[,@$ =)TUA`aC:qiK xn|:}!UQ8%55z$lEۀ3 }v}uEi[9 ~B>ƍ7nVJ}~ofs?&L&|E=0"XH"WHt`d,E{F1}E #"q&IB\yQԄzC Uyml5'bC4,UD>R&H`!cرEE)1nmmEW>;{ȐPhAjWWtR*f 0Pp`pi.6njmD-ڴ a̞ܼmۚ5{әL٪?qƌk}D:;Q)(<7Y\tm6MK&u[_ɓjow:7߼喷ڹs8ryDIS_y޽_Cs疗eeQE! &L|?5kkc G–Ëo3@BoZX02 6y̓IBJoa<"W^ӒI"uHRoGsUE4 @$.d*UPeahF-^hRV+6,7Ep8Yd8iHoaJǍYTp@V3 bd2//쳽{/pJMPw.Υ(E1 8a*ʟ4nә#I,uuG"yys穧M;x<+WNte[TTTVREy<6$UVz0xrЂ|iv8,솆5rrl^8G> JK}>EZm6rb4mWV(Bo`UX9be٬,,|v˅|q$zvUEYFw`VUQdx\ nXyEglX,q5i)\VKQxfþ* v$EAOb&]į` *Γ$PgA E4-<3 iXeYN8݇iߏ'G i,̙nQۆ$GN1š88ad Hw 5 c GrHI=2a⧀ 6T)*r/I_jW8(&@cN<硝D:;QLEkj _`Wx; @)un?oiAW6$#'f 6 ܹF*(!OeK8|k2L8EE?~M;rdGEutzfSM; A>@I }(&SuB,|$u!<9V^0܄D>a ? ̰ƦK-!Ȝ+4#8}j07YGcS7oT^U7r(ܸ f.|p;'Lz!r۷wt@*$ 5}s׮-(0aٲ38Yz3' EÀ;W3gzo9w}ӦQE r 3 [-,JL# ~LI9L|39{w$߽Vy̟^o^zߝ׿u׭͙sE @0Pz24*PɆ4@J!;݆6xz|j7&bX,"IХw|+'O޾eyƌ_ aF8"qhN)snZ9+ } 8@8"==<& ϳA8MĤߑ9% C>0`WxٿϞ;ឞ`|ѢgͺŋoN?VoO=} u|TVVV9~܋/rP d2am+=dž|$&LME0vNQ4Mc^( a)TeYQ2 Fσ'8T?VBUDJIbBPZ|߇ǒVQmbWT#Ik<k7Cr̞h49#,UU3+X2ݿ/L<׮.u:EQQH&ud0Y^dpuʔlIڷ/H&]x\EqreҤ={ny۶5kX Ʒm+*5>DǏs8[ki"HhZvӉdV#Fg{曧O_z f۶7߼Z7'jzsr(VUxx~RYZMEVQ&L6,'"Nͦ(& q u8,t4&7VU5 òxq@X]QDbfjűcyG]iAi.bz @ ~`WUIJ&ujdRvEZ%evQ!3ݎ恳 ֐0YmvYyOvu7&Ǎ7g4#ŬzOoGH*;7?Zמj|oYI1qlq =P}LI )6=ֻ9Lʍ <}ʓҿk+2h `+^I:ͤ|"a`vSS0>aϞÇQښw/;-lnYyWV={<Dh$3TWrs[O=t%Kofy$#GZwQYTVzЂoYSl(u=\_}u}\39,\tNDQ=mfΪ(/#E>_k|O> ׵k?/~s-..(ֶ6?bDQfRϣrI,յ8uqC®]>_vպ[ɖlc,C` @Q#Gr —_*I 5 LQU8R/6$!|T }<=p" od[(Ei&5{ 9|8:i&'r틢|32&L|8fA \yR2ϔum-Q{gvU0a^\\S tJj((3gRYl6SNzGp:EGw>THk|rΜ*lYy̙w|Ywoc7ߗ 'MZf_ʕ6͟m׃xׄ &~8ꄞqrHOK&~駟~:`KĶLIJm۶m۶m3nL8Z ޱm(3ed.ǒV@$}a@$^HF*'ka3}3~ #FIm#Y1opnj(G4Te) ĵEQ jr$QdYCdX,(2D0h'q<('gYeYFs =\ rKAV,Ї"@R8DDQ(e*Ნ I.?CtyGVdWuiO>&O2SY7 ͏>$I+I|O:^̓ެʛ8` V\y5lHy"6KXX \e'i6-f=2aD&f m$Ǿ#[ßOa(J"o{HVȈ(!2 3s I G!#dFwwwvo~>W#|;㑤_?眒HoO;: 1}uʿrnne={eCjʼn@Eb/xw |UU]]~I'XxquuCC~"ˢ;e̗Fi ZHQi4'. م?{",xԞ #d$}(-<❣?>߅?Q&Br$Ptv"2't(w*yaU}]';-E>3UK2yOoԗk;eupuhnLw?M|##pw.ך*aO ?D "9"39ǃS DQ\bfH$u<5:Euua/@ EK$%)78$7W^9$@t')7qRbiZt]!.G Z,. $²2媫 X/ZeKcc5jNӿ}uSO]yemAQ-=֯zG(H>Ⱦ*E}O޶׿:WNEF׬_ǍCU-b(QrOUU;;xQE[EaHxnWYFWe)JKKOA\abHTI\y:"ȵ>]D "-[iZ1 '`{yUUgMQϚ0aO_3y+LIrLK}7 E߄x30-,MGrRAK!B qRfxd].ڡ g0:! U]Bu/5-rqxhk۾#1{w/qdGFpôi] ,|EQH4+(r{/'a|s= / ^'TRwv[o]rɞ=9v;|yy.pWbY/)C7/-͵ rl4pX,pŢ(R40`Ff8^UERi"B|^ [XO|zY|2("zL`>|o-;2gvR&~8ꄞ4<u2Vx vR ac&L|1GM<$dzғd ېau}`AO&6 }?I&r$rCQ4}p0 zzИΞ锤\Յ"dRnm6Q$]˳U;͓yy$ֶB^#)lqV5_bvuQ'):6yrA7fBU~ɒm⋎+8tWzI]Mx]T\qTWw0r!]].뭭mmEkv:UTi͚ .xƗ_3f_p׮:3q:].UtnՅ/+ϗ|T].YP(7H0zz;œ/,#;ǰHQT w(*RAhk<dv&I0Zg~)˭l6$T1AhI(F"y\8]O8I]D"LX8IBY|̷r:%YjӦS-{3|\s?T,Xpg}Ջ/]:q)LzȄ6:''_7С>ϘqI7\TT^oai4Ã+O^ áҥӦm޼t驧nG"rEO>yUQAQGQѨ<Ú.ꈲqX g:E1 Cq(J jaд$aR4EAbRaY*+Z_Ut|3fxww|8DLX,}`7XvhѢ^Bc*2ũ!EdT,EDeI/yr, ˒֖, aG#2y q0h[!J.~$8Ӈä>x$`6LDJH<p(JEŠA^?q}qLp{%IvF64ricereeYrbY,xY3 :~rYHB%IejZYYNYqÇ!-С4m›]U5p  xp8dɊ;<8R dz?\#_apa:˪+݆a@+40%{ 7r˝w=v,Jq˽#l9ܹ]`9?ºakQadC}@zɒQ &L|q =TIi7od„:,x8Ȋx,FQ,I![<  tKK0B@,TSS ̡rKdHUKKnEٳ C R>khC><(67fź:,>@0I`H㺮P%H$Hz$Lz8HR8BVA$ŀq3ǁ(<00 Jm0$ z*& KJ44̚u}o3fעc!ƃ_?bͶp!6guvW_oY9r %b^lTU{zqʲ z9+n<݇=yYSXpZZz/p$y<~OG$pY?xR'HK!+&== Xr|UM&}>EI&?$Tfl6I jŞNC5:]i#@j2R[Uy$>wXDBRd2 #ּA$9RW^y'(((*&L4iTRxڛ5Yсpӛ}W;wrC>?\ë&i)BO:o„c}g:>>H}1ʓ.1!=j|^ovw[, :nwmm[G&/;fKdgl@Y>`͚9sy>g_uպu3f@d„ax ѐ&FDZ,:>_NىaNm47<"lb9|ҜQlnED4M׃x]H|`„o (8w ~dQ~kr_w /TU=kf*'.Y2dHvvq1Euw~D1^zgo?QE^ykk#΢(<Մ &~l8xrCOv%ń G 65xw@@]7 HC} ij dbI * ǁj@ cT8,KQ`2Y_״d2Jw״T kwPH`e9>tp}WQӇ ٺWڹS׻[&F*^Z^nc~?XzÆ_}Æe˜DI}tʔnٳ9|$xsydC|K.y_ʪ(+cY'8GX;@񅌈$ $2)EIXKΔeYyU.4(^}kU@y&~`ɗX<'@Ky͛7zL0Fw>dkǿ,o~Iv3\sߚ3S<97&L$}'xcժ^HOg3KQl٣FmG/_UY3f<7B* &Lqz T>dXo w l٩40O|0g<9>d*Eb 0C*LRPGI ׬˥(<>DΟLk rl( ,3 ]^vN`k@ N$$IzϜBƽ۽{ee7v#nt4M).V~X!ִu|pݺ?4n(Ya+4;ifE6ZqM3 hKa(+ Cm<&HLeYuDϝt` Bo~S[ֆD#GW,HD"z챛o~G1~ ꭷ5oڵ^ u0|q:}]2UKJΎ:ϯjumpeݺ>jTccWe((TՖn$#.(zmf$M RcJQ^/HZ ^ى(*'V?D}>]Z[{zytŋa;xƷajVA3LGG8JAB:t:{Gx^XJ,,QdYG<ʤR(m5 j#<0$oA gpv+2 x Nj"Ǎi';*3 E%XrI,&0*[.0 CJaD/LoRLDTGCU{ӦM6mq_}W_}Ek„ ?餷2kڛn(܉3Y,7yrN?{% /@@,3'N\,.Nw::̞=rmk֜wO;?ȶmf]ks))>ӄ &~8jO?O M׃CoX@_Ɣ3YLju ]XUCf,iND"$z==T+W>~x*>#d2ujh 5 Ɖ:0*IӃ駓'uWsseoX--CpsϭZqłnewk7w{Fۗ3hвefrKsBrX,q` "N4"w>ϝ;xC͟?z/۶]vo8phR"hTR)U$ζۡg*$ [:&M;/},q4pHi ):vcxՄlx<43Ln͆}NEyNbp:RA\z#\Na;,8X :σ4J@ };(ӎapWI Fo/y8NqAy2={(r9IAǤ}el&Loº?_y, 7fO!)7q:X EtMWW$EdWU"D"3'+KUtx盚~D!]n͵vnl  2rd~g ]]gݕo!Y{t x$fΜa+ƌZ!oi-7n۶͛aLu͜y7h2^WwP$b!C;:|>9Gxz_kKJrrvD))ζ;:QeY²%%,{wCr?!CGIDq^\c $I}% "f<3LMMW:G%%㇖bIJ&Ea40b1x'kMkkOO,Ie8Hd#,ECx]8ݷZi:FxYfY!): Q©EM$Ly<g AN!|x)>A"XTG5|u9dRz4?<0@JkGtLgz<ޤܘq7nܸXo &~<Ȭz~_ ;uT3'5W@4#&H~&H4 4g„6!QncƸݢx6 ?ŋ_{xO-({s7>3{S2c9&Α s I:zzYɽP_?m'w`[z;e"7{sSN6m5oMzc&L6W=>}Wbo@CRh|fN@5 q:h c`"jMݍRjldjy.8^q C^m6gY*O~nm;7O;oTn3HD =GEEe){&Lx U _}q{l{9_WEk(ggR=>~>̙6D~޻{}a qηŢ((ՅͤǼaJ 7'N*Iݍ߂ZKFvUX _W.*FK^~SULeYS)A0 Z? %h\.E8]C4k"cWsp^bRR8K>urv49 @EED"C^$LD"6z54:ׇ7lx#F78$/U2aNM0ađ#0<縎w͚?!Db&V;c=I/Qީ~5n܂_g|>7.lժB]{r NQfocI]>ؿ`)SM#U4&L_LM0A'/?Ľ}ӿK.Zz'HgY xQR? 69DV\]I&5 '!J'>2 %\@BsР,D ӼC-@2E>#Fl(˕oC;J$ vviowa{݊HX,IJ@wQ|oU2jD⭷xC֯GG9+x =B"l;C;D"t9?>7_&Mznm~bŇ^/El"v;%7D.FQ4#@zX$m6UqH*%%.f(VR)0hsrA}twz9!iBD5l6]O$ CUj ;BoWX ״ÁV+~oW:^<xN޵] uA`dFQDBUFXHw ɾ0^Ɠ`]M^!Sƛ鑯obMGBO'i67rct@;! IDt - 9:?Th3 KсzzwA~ ama!˻wcB]km IGN,XDɒ **ZZM[a"n}>䵣C0뺮sI'eeA*OQ~ s]w_/'}KN!'C5xޢtƊaX67T*SޏL l&i0ۦ(fބ &5$|ܹ˖A* +_vYyNQキiETgX'l?ڭ;67o;'˫j/xqmߟ5~z4E G-FD*_O[78qokͽ~~v7>;8)5]G,Mgb!,Yq4iI$4?Yer6ˍ &2@Rt2:[3m[4Ð>\$<81L94S)Y-S)E F|睥KJKe?3I^:dvƹ(_dѢ`P R;No΄,45<0sCqƠA}Ԛs}o~z#2gn=IX@#RHsp44gBdunA~F*Pea`:2 Đi%d;4&kIvm"%Xn2#k&OQߛ֚93%{Lh#Lvj׶oٗ+Ae%HA \^@ W=*B$K4nǵ[==x*c"h zaÁ"РnRHZ,Qc >B>|x~>jRp0ϙsmd*?f́gwtw9De0w:)'{ӟzzt,?޲c) IHǎ-)qɳM~!{y+Vl mR[;qwmm,[RqOsMaIT+DOR^r|SU494 x8jñtCssV@ ;#BSR&p2ǕzHX MY$AÀ>/Dπ^uk>_!9@rgBAۻ+~K& 0p ;( 'DR'12[nKݠwzxkz_oi&ď&PA~?s)7mڴiӦ#_ywل &)7H$X>3Twg[v 0̺uGӴ(PrˎeyP[kdehb(K{N׿~^Dh*)'/̏2 D.0y:=).`0 4XZX n ].ABXcY8=qgsl<V8^/>')4<J1(ʲ "bc| l4t檫νk'N:rOe8hD;E4yT]F ma)V^B)1 uSz2 6 MKQ}\C1LV:NimLa`O@#)R>ĔHSEhu LR xftvF"0bȖ`EZ[15Ç w8Phb1ơC^ДݪZW +)ΝzݡPe;\}6iM]{ӣi2ztQյ"G7Ϗǝ#]vMww$BcyooGB+Nx/?[Aze/xZ_{|Vk}=GLlJ$Ps옞jsss3:^m47D==[x0vyTUU[ZY f^CQF<q]48^"yyGӝ0^(q<`B)ˢ<8R} @s$1 9fE=BX|$t<x=H$bO6Е,aJ$ݛ 0ARn@a/;X%|Ĉ1c#ֶm55}Y0ađ+gӛ0aX/keJhxI&u04 Ǣd2$^ٓמ9LR@ @twuj1XvuE"(93g<βd=U2O$t$ɤ8xF2HhZ8|Ϝ|]]Y׿!L&96pΝFy9`Ik׺u~SnW`qǽO "UUӦou@[s8eYܙ>ZIyBϋ"WxY$FiAYeSMCpWF1DUem6EA!BRzBOlhKеgKb$IZE_9,K^J1a`\{,r5Kl?=cc[oˍ7wyȄ |#=鄓p~A\KY7aCf5g}b@=;fYYԴT*YD`)jۃtB$N$R)R) 1=+bF\q#XLӠ KX0/er nRӟ%.+(yi/~q%:?XquzvNQ-HTpoϞ[o=((1c.ݸx^b+ [> ITaiQ$a M3 E~fF{g|PURʲ G EQ.h`)Wjv6_ᝒł;VU04t?4"l63 M[XJQƲ`XX;y_\dJ^ vROv|ЙޱO?/FAy,@Զ"yzxR5={)Bhi_őz2a⇋_s5\s coA|m۶m6`ȧp2)7XcbN|!"<$vԡSb::QBjdL&%W[Յʻ'=P)߹.,ԄI)$G!r}|rewܱy(-eF|Lȴl;;1rrl62qq@^#E w=N< >=VT'2lgYq[u|7 t_C)ryhu` cHd.8SWۓܛ9/Ckj̡tƌ)*Boasc)S qƏ޼yKLfS()x~Aϫx1MCMjj&NoaTbqi].TvY(-x$ òYYzxygVݎ"8fDec1MKHAi˜1>l]@ Ud KyY6;n.,qYYB-;V+eE$F`h,Km Q e&aT D*$ W*:0can[OH Z]{jН4"<:}:xLa{Et{Ģ9ѝm!)Bhkޙ""$;KMazTf:N3Y͛7o7a⇁}冢@|lT0D@ aLr'3 q4cM,hТ6 C C;qaIq8$)rI: `)7(2ztAݾsg]_x ~ӧk?1oގ`SWֆ(:=cǓOΜYT(uE]cǴi 0p N%% kW}=Mz{ yrԄ#G \dDU pHcىraө(yW-Coj$kmA]U@'(z-Qd-p Q4g< $>=IBY2i I09$٥)HMz5 ᥂@ӱhloA^)a L#[dG HI޾Xh|љ"U`)@afEL1)7$2BB3e&cބOQfZؐu^LH4wBLwȶVG`v'= q1[t 3Yϙ9>,:5f'ܾw$QG"svґ\睷oߞ=;wQ|׭?{~Gȗ0a⇋oS,cc7+?(6O?H`dlFWcٜOwq}fKhx,"8UaW{'G,-W]sw;3r8 =ӱ@{>g 4.pÒ{iW\(wssC(#O? >6w ,h D;'{fԱcb.˗/Y`3Lxr5zwHAƝbAZ3a„ &@;@ޮv.!_ٽg#r֬z.󎠾;h_y~PHë N.o(i. ViF.WY@L&1Ͷmy(d뱯|8e2NJR2Nz:ݽu˖]t]w*˿2w& |2NDh{%ۂpMc BϞ{OO }2 ?;np_[[o=[IAԩA$*IEgg<@.&/l]MSr9y:\h(y C5?e(,+.pAVg|LKq Dk<23t(IP{q# S~^Xx|1zww^>c?sXfbӸ_b/m$wgw%zu=`: A<>~[phٖ>}iu#rXXpHsp)ac#@xm[,JpӦpn@fǽ^U|U׫ikJBc1^jYQv*.-/w4m^Dp i:'#rE}~7n6~6 h>; /ٳoo{N; ӆ3ghWLw?uq{x㧟9΂MQ6mڸ1(imjJ&J65U6mjldkҦMYdzm[,Z4}jo ;w4 &L!E.0WxUݼЩݰ?Hon55q)T(HRSJy$a˥4$,;k$A:/ut /׾(& . ݒ_*˒1&n&I0\r ނqIE\ a9\0*IZ4 vnֿ9so؋\%~ϮA8Ag%7 9s̙3q$4`aYHN>'bP_=ԬZCv`+V̞ѫ \~><^۷|g>k65i3to?hѥyAX *yK?ҧL"m(AjU!SD\dy xf 8ϛBn?h[qqYap@hD Q{d|$I!4 hj:ɓS^,p=#j# /ٳ}o{N^4ygK5k^~3'Mߺ_Z#neQt֭m\NU$&4\jE,ͽX ŝŜ},INNVMΥ#0 `AB nM!Eq+HM -U[<<<\2_FEYL { {޽ JJh?`mؚ2+Ee-Uz&Isq5#1/W_VXO( wb|b&bcl+ 7{@jRUڳA )y ) ? G_p 083/xG'~}m-)^͋?_P^{ldI|_"<>}^~y/;B{Ͽ>H$z!|x`@ d-^bE)J={>e۷w\^v:,\Қo={/X}{ q;"^SBBv'{oo*~>߇+˛a~K;$_~Ylx>bĨQ^kU2x!`Gl2М| qL@?f̘1c=!9\DkyO$g29.7H\@pcqf4#^]~C4g-өi8BIb1g tj<΃ CQR)~tE8ө(t۶+V]t`I =7 B,vrܑ44Y3cwߟNbpgUhSӹq9Ng,+Vy-̚uߦi+ł4^`ZV\NZTDEỜ^%<~cv]l{+}<1[%I$Yi6PRoAtZnMcNYkZ<MS(ΆS IJŗ &¯GqC/)Vw(%K,\."m¿K0>ܰRsdYk=[y<F}X*sٚ&/!(JR.2-jA*$!sVU)B@L#\BDB e2 njm( .XU«d6k~?/VGBSt098~4i]7C!>pWTlZS3se+Wr2m/F~{acUV\ BW_iS,>i'Ga +ee+;ۣT%Eq-8'<[?-$9l~,ko ǛmwXίsxzw|0銕ρXuw-#} bSEL7+opA` yY"F.г)b-۶EL S^v;64626m^Mþ0w'\S^Vr uKPNu<@^mh>cvVxuN90|<܇{ouuVAƍ>c:TVVT䓯~͵B>cNJ Iڸ l -L0Jzee0rm1MO]]]WUUh41  Bm\F8!r:C(__H 0ЃgGhNQD!uJ ^ါ ~GD QL$g8Ђ]bKC_ۭJjA(ә { ͂o rG;!#a U(œX_z4,8`Ov.\> 梘/)>LZXڻp P@ORp u'g$4A0;w #s8xByT0 Ce&BQ,XB 8ϊWBZSË/!ٵ+/ܱ#M! ks 躮۶-+0ڷ%po>{d]wс{ALAg̞=m /LᦛAӚƍM۰! ۷S`0dL{BX FEA^NiBs8:=SQzo0^ ynLVӸ%"`}.pn+ BdTn<S,"CZ ` \Q$sv_-[As.]p^U% ~79\o2YQT]eVph+K` &X-÷_xKwmϭ՜W h('݌\׊be}.5;$(8@K\ jjJ$FsFiF" ˬBH1!|;v:.ҥ[hee.ś6x s86lg=zTUyKlC˖-x6˗w0gιn>sf۷ Bcceen7՞e|r?c '$Ȏuu~=s64k._uk<΂0:UTx<775BۚJhasfx\\.KU׮e~p[G}̉Iv˅no~NkMhl~GSo*8 s9܁_MCrB![QZbz.Գ9B.$k>dt0o t&d~/jwڷرs#.%પ(2YϙDoM\˱G6M,r뎏Ca(7W\jߢ-_S/@),{^ODB=A4;v/0g7'ԩٗ]~ 07n\h„ɓA:v+׮:[7kذE3W_ݴ^ľ5zw, u('`Z/ӄ!n]ޓ,ӟ?9p`*D]fE7"T4NUeBdRA-PP wq ZYs\`)}0t4۵ V@ =qȐӽ-[z8௿f>6ؑg͛g|^As;"l6{U2HDS' |++=ABaeǎ坵XyȻCy+n?_QĮ2 hR=k5QY`l޽^'$r@%7ՁӹawoU)Sd9矿-[v쐤C!AظqǎDSHd۶giAaG⣏>.UUƎ] Ӎt)+۲%FUUӹuk$J'0o[%7[lM@r直{0raөiJiB۶u:<*y(DP D#f2ȂcQX˷TRhT [=Jqۭ(Jq s5*Vy͗_:mNtwР#:ϫ-NIeFUE; o]4M$ dfw.2<#텵uƮEE|֊< `#?nn_j͕ؔҟ O0-}~c.W,6o)yahU+(;vi{|ӹf' z%ӂt sϸq] |EGݦ1y?bov-S{+.]'ǏgM{ٳᇟ}Z/_dzGf3g_|C-= Z/dG=z薞؞nƌ3fXzի[zF Ç>|x)xjGMsc_K`A\&w~Z@bT*ey\EUAcles9TJ!C_e9-N,[}c1o+9Ӽq^׹!Ϊa&'eˈ/}F>찗_Cym]w ¿zN?h׮{i߾SΝ1[zA(g[z.D~,Ǖ˙}Har8E,ٞn!Wi0 ŲYӄ"x<1M^h)#,.rRle啐 9d۶F"x6 }oد76w܌>ߖ-={=gWeeN׫W;ewBsSAyKn> 4MƏd2VM Tbk>J o_VvöYnYwm0GdB3Ly ˈW%)NS+I6QEg ǻ8#XKF)$Q.sfq(BŚVh>ISX}8>ӭRbvWwv- >:4hРAZzľ%4GApf-thI&zanCت &CDqxoBȰys8 r$ v ݴ{A[hojk+*< xQl^UU^Woܤۯ\i/~-+{ѣ54Mͻ#|ꩾ}++E>X _}ԨaF⊎5mPHQ>h˖tWb>XXnʕ 媯 L ڷTwrY[ 0)TUtF"4{C2kVCu4 x,+iFi:ip*΂N|Na׆, [\tδ4^Ђ{& );"ЊwXN hݹֺc1|RbV\ %E^Q$)\s~B pVz|EQDluh<{c|1y }$2u]8iuEvF5jԨ]vڵB(~ݼyĈfhMsXEih>>{?!O_znh4N;S`pԨM۰!0;\OOE`J?Ъ3)SLҳ e.ŲgV{A<[sp(0?$i\\.\.h^@3r5 $7XJF;ryI*: - @Qt*JϞm0OwG堃ڶ%N$2ev޼?2-[曫=F<ɓ37=v:Ɏ;w󡇺uc3-[KT0e9uzyIk0jZ߾:F}$R|N'Ȳ$UVrOwp//2$ÚB.«JݻWUUf z;Hq+|0r) BKF@(b 䯹WKv@<%jX4($Ii4P_i*I[lܸvׯ] =v}r,6}*̄} DE2;O-/L_| h]P@OBq [7؅`'_ziy,ޡ}ֱ@ ~΂oe;ͭav2-c-VP6iruXSs7|Ujz3`Ӧ_#6ᚽ/Oᇐ;u>_;΍Ph;yAozhBٶ g%Pr֒PR)>38 :y -MMBX;^}>Cbt4!Lą୎$(ęNA;َWڲ3&MRd;>;)SNNw^DP,^_l#2e,uxI\zIDATVOAл݊"˸ e뾧3s////?c9x)"6gY1`7'kdn׫(x˖%d扖f׮ǖ Aāe Yp,?[oϐA L04pirT \yye,K6YD|yL:u N"/uw6m|>h L2ʐӧ}{V9گ_ǎqSO%ڭX1b={NVUoߞJuS˗?a,Y^x!P__UաÓO;e!>pA>_.76b&|l,J(6x4ov؜~KUINzEYŮ7OP{<(R.];ߋ"dEXUo(X6m|>vV?uAE> ut؜Aͳ0יHRuu55l|EEOYI_pVX,RT%wx㥗; 񚫪,㌻h|/>p z~/ۏu^އYIB e |M~Q]~cu#9.-\skD8̧1d\<i@Bq;l/uu%QյkwH$@PE>PLb|QPG*wٲ͛YQk͞]S3u ={vXc?mZ"?سWĉ~kM|].'rDCe98`Z<33Eqժ&6C!{͚Hc '.(Wc6m޵k}C&Y[FNm\ ljAQIھB1.WX3 3]gG[x<`˥(.W$Yf;ܥ#5f%4fφ"SN6V$) pأPUky7\(7xmowYX$axp [+X=μ=!ɲX];DmC+UZyU87zd4oy=^o"px3nq˖޽}htZ?~͚L&4B:gy]ݠo ؗhu4ޜ$mm6YSBxBFȈ"T{>dXr0Ch =< }U}I3À\#a,NKuZJc=;X=Qwݺ5y%KbڴUFPUUo{Ӧ+ɧrs t55]{m׮GN$My[gT^.Ippy dr! z&e!Avd]% ),毂@7|QW@Z[sћ JqFf7sKf%aϭ'7W˞yK\ &s9|v X Z[ϝ]l <~nUާثڠ v8uMA! sa41A7 =䀏iL6[8Hml<眫J&5-4iFw;Ϸn2Ž+L9E> 5k1 wpc ers#|\'#8WUYF%m/p8,BpJ&YZ؍֒; ,9-pa:\uf['v7G-lvvx3*)Rs({`O pD´֧x y/@=h.[nͿW^s9Vhr9w&l^L4s9\Ȃbŗh*em]K%IAa 2Vz cQܙAxڵky 8?TˠfJtj.7jԷVW[w;vx}7eͦٻwNee/X#G~ٴi0ڵ]ty ={>_E, (N;=zTUYxWTgar0Cx鰗m׮lMKQ:w./wZ*˒W9^^ 8| ^өhj$*gYU vIЂbEQĖ$iͳ5Vw]gt[X*޷rqgynKoej_V[|p^آzÃ?[ς d#5g4_(l @96-TAF('Z$4W2a ib_y+d=iU!kl+q֭+:^ %!%=]˗oƊV;v N l*+^M۴VUywϧX6f#t}NW3f| Hy#G}ѨqDž{(:^p*u55vK(>ƍbE4j8_޶m `ݷm[ܾ=e-SF zR\+xUQt=^ tr JN쇿s] ;/F?ͰϊβA('bP9kmj7մ@z0];Ah_ůzՓ&mR^ޮ6BzEqtZ̉FwfwUscsv̘СSΝ[zvAzǮ..iNpvBd(@S>\qe::8p2 a-aNо'٬iz<\!]E@>s8R)]7M,5\=+X7v,_~|CHc, ‘G~Žoqiz<о}0r 7Ԯ]}M0s]t?L7`@ ]:x\W;ɤѡC0Z)˒Ծ}0f`%?)( e.XUWLRhOZ,by9$&tX N&PKz # |>PZ 8AJZ WþL`cupEXymxvO2\8:ZpIh(ztEK}{6AP@OرE$ߛˁ_ d2\&awyWx0D4{-[۶qy (`ךn**n+ߥKy 8]bl0 Nzr?>xk.7-RU]裷mt6x:7x}.`Ijj#LyLMS"Diϥm@jOvfuBtr>áiYt*(IUeYUFdYUT f$ \@ ZsE\i.u. $P|ɍ,[K&Vw*(z6>Hj r9.Pp |ͅBd-cGUUvsWW{hH>A6ߦͻs!q/e& Ϸm[^`G9ko{̙'KNz=y{^xaE Ԩi56E춃  Ҡ =g-)|\g%᝸4%)K}9xR)˦?>"3ʴ-W!K/}s+Ezo"ST~Uu:e4EmÆ8F<{oydpycrI_R^n6,I"0r9pUv;55[l`M$7r |H}F"n7REzU|x `?;u0Xbc}Iœ|w:a=j +LF9djckn(;w6UAN(COD3\w]^&@(pa*իڥG<޷w@A @?~] 7H \}e% >=дDسlذnڵ/X-= ס =q@R,ȵ|1i&ձM0r9>gUl4qbϫ$IB r`[Drݺ+BYhUC![ wr-s^ş2(q&:^$kI+d˝N~JU¼iWt*ן}ak֌ӿ8y?**D:*|_ /B||iybG̟ΌODkz /rݕOJY.D=YQ,/d^ء\d9Î捍+TI;x)ˢX_}};>^Sn56rz4ojkޟ~䓭[=ZQzZlРڴûuJ4A&4|Yv'h?p3cxᅂN=zb.wI^[J[oE",iFU%.gg<Ǟ&2MM$+QJ&cEy}X(W;v/>tY˖zSO9衳g? ~o~?꫏<-Vuܻ{7ߌ{ݢ(\ F",MAĮ@z^R.k-?f4dw\rÜ .A&nL4 K4fMsg.<1oD"JMS }>SQzf*@T.]<~mpƍdyȐ/dH_)/oFz7o^{ج|@멧6m**:;d,'UUΚnYVr)X`V;ۅx`<(Cʲ) !<ru3Ƈ׫i\r^(I.I« .\%4ϯp$ׇu2$Vw^ft i ӏWV Y!/d$YAPn }{au˝ X!1so(!>G=6ͽ.*˲}a۹z*E>ux֯oldjv͚ .b[#^Z;?,8ز%XOe.z۶߸1c3~u׹\(+~}啿;,q*+u3 ܴ)N r8 2˳YY~X lUU^sySQ$ ˿VU,[4I٧H041*MMD&i|.R NX%7"I?wp CE٧r(b9 kp;[e9nHvp-˒[in JUY=o",5oaVOǽ 뿊s;]G (In"[P𤴱p8m_$2w}΂Zz??_~w-ڵۺw8pú/~q:w^z2haF;w u=|l"aaOJ쬋8,4~D|Flb=Ad);};Ҋ_..e+/uMv;7>A e"6{~xX`i,# ?&(~/,qrI,T 4GGwǣrȑۏJf ? Ae2LreIǣi n9ٳg2O~F^b#?p++}>M;,Y`YtEKU9y˥(*Ilΐ9 dB!SQڟzl)nMS(n^Xî )E!;FPUk(󙇫"M պ+\scد*̸Tr=v݁#0 P=/=U`fjrgWYks;}=KwAįC=Av}w -G?XEEkkM3WQٕ+ CQ0%AA |g{UV&h5γpGzoC٩W $an7dr98 {bt({%X&cYmF~zyw t>oUW]TUy{O}{0t1Ǭ]{={Ge^ 8X$r꩏?~x_W^z۴\c )EIKE~P3s.߰VWxI`<(oVxNju9ư8cu>F(i$Qt:ߟ]uP+J2Ëp7W MY*>b墵6rk eܸ?N?>}>d =q@#<A I =Bd AG0ML&3@#P1 ? B,prt-D¹o#y<|xtT<@f?[NErIˍz.J#Ngn릙f8 U$sܭG,Oa:[!СS.]?u]R@=APuVE]w~[7}|A4:mZ2iLB~dzxà6lI8G[St_|ӱǞ|wߝsΨQ)lϞlݪ(ܹL! lٰa7|yZ{`0s!ط q\Ql8-ŷZ„Xn',7l2Ϻ=;"psBXh8C(rJ0xzozu*uQS_~ zG:J)J$kc&o jE,vV-]z??q"__߻w~_=v+e3x$I'IHIZ++S)nh9p!nUepswyhXdbevuP+IAy2*C}yc 8*wa% _Ж/&U.{c{A䦰{%xv>pCNA7G U{`_`{,0 ~#P햤HdȊ/8}s};^~9E* 3KS׭z/2d/ѣ#U'FO<ۯ fJܹ>ai輡?WAn[ Uu?i8WU}_Lҹs<ޯ'^}-89o۸q:h+=W>}otbtL"y,kŊvWT;#{ehZsyos/~<["7555-^`?67¯ tRz8(,q$0 Iq .E8v 7!'-{dwEUUd_~H?aR~M׭ݺrˆ ='IA8ן}{>Cx׊C Æ>|LSFclVdyB;$I= L;@{(4MQr9~.y3<6Ȇ2Yf32\]7 vëp TUQXy+YwQB^<x r_euX_(n~ 87|Ǿpՙ&2Y\]gȾ XЂ*VK~mx\" }^:CY MHrC & '8WU(/YJ駱zףv4jT"(\UULNjE?/YrO=5~<46tP~?x5cT*Ѵ\47mrx\}ydܸn䒡CK_-=k Bz'9\8hL =ϋǡǃ%0 ͂mZu^v/ZtYuu\s)СPPvԱyWҥ#PvmEȣBL*U[& N˵ysyyeeUa9;U Iïj`]SQ<k(r$ \S$IZ T\t@ d\#[\cH$Do߸c#aS*xtP>R4[ys5$=vWx*|yY[ :V .ͿgQxE0~FLlx fnƷ ެ/U8Ƿ f_D5APh+ZUVu~)6yvzWUA;1\-Z0&NP:c;:ti 0gΐ!uu?dH}&IpDG<ر75h_=~0uÑr{G"Rmc}A ouAغuu~e҅ ]}yo{;9[[ּgẊNso/lXZ; Sq#ADk25r ,dq)!qT:\25 %N'ϔBn>eV *;4HFsE,޲E/.cY.öm'Odjj.;^G }>C?hkŊo\nРAB!lݲܹs$rz6i v{<]lQ>$I>_Ȓ~oe/X0e_lBn60+ukU.e{<%EѺ,ZlZ`˞61'6e|~DΩVk-*1(6?o`fZ OYK~E;h:_?bݶ>Gq ZV{(n_{\$$XbAl, .MY]w'4Ynl%T,wq:EqQl(r%bgKҢE_:=aʕ~9&:ӺHr8CF\jɆL d;Ιᇝ:MBO>3,9+E1uW}>C5-L:vDc1Ms: d,b*ɘܹ|'-n;9Uşx> [st8TUq11*xUEae 8dGPpq*kjƎݾXs7nΝ}UwӦ{ΛnL/7G|؎|ժPLs&ADe#>|3f̘1}ho9G)2,_UT:oWll<#8@@-ZR?3 e9V>r˵pimzUt)̗= ;NL1/̛F6D**2drv7ǝNx|vOSXvN'xރxK\p ] |ϽhԊf1i,VSP*Yj1[5Ҝ nu9}(;w7/w:yѴρ ϒ2AP@/L2eʔ)-= CV~:Ӽdxq.g-s7o;9EL&fÆ`Uvڵرs3<܋/iǵk#AAC=q@A#ΌBn2řW;g2a./r`ȲݐŔr;{Ar\pa.7qpX~DB'J0ڼyDܿMq> ¦SO=L+R[dwzU5MX:$Yv:N:$xZKT!@7çK"?xq8#|%h9qwf N[gǏ)]lvv{3 Fe_h1 7_tۼӼ7BTKľEay aBRhC(6 4HQFQ^p5-FS)//+x4-cbN[b1Ynj_׭06/OucG>wPSxv[6ϧi,^U7F++XpnUU$(i"8ȃ0*Ÿ.(9{S):(.$az|DP<>ܮ9p) `iP kw74QK$2vy:!a_y_Ůd:\ag'x6|kG(e%^J ^ܷOL/ЪbSN:/O?=qw/_dɂ-ƥMM}W}s! =E+ϛ7o޼yCzիW.Z&L0a„ӧO> nXpY-_ 崹?CqK4A/tD._)]rSx%vݿ'}ygW bzO<O<N8 6lذA{q@Q͚5k֬Y0ܹsΝKn9- 9BAS)& ±B:Fs{45SmjN_y4t3PsقǣilrGv3DB+ܴ顇2XjhLvx45tP?xUcƈb2bO,MwϷz?Λ`mmmm׮!^ t>唿 ٚX.!OXKUphU`#N(?`]`8/vի_SM{}e,ځ}.ǫ Y% nFbW^:K;(>¯Fh~泳* yZrR3gΜ9s&Wϲë#h0rK_kLÝK-?3ϴXRY민ң,?TSiodМ[xX25k}`L?\X-6{X^g8ze#M!vWq[ xlbǴV|v{wgudsHAMZ]2G?~W6c`2lYey,+!_~e< C`>pW{4͛njj;Ø14E.9i$I4?2C(w۶<ӥK"ѯym2v9A!<φ ̘rɒ뮻6]رK=El6޾=,/dCO>+$C=_butʯlv ۱֜;A9s~U.8^Pv]"(W~BͽY~BGlNvb/B-6> v.C+`>?bWi߾SΝ?yZ>`W_:GZڴ? I&]`p"xBYbRMر^zɸO3gߵkвqxikמ}ȑ4]f;uŚIEQպ`ە ֭[[z.A;iuP( J/- Fnk@&t1%܇arp`z\lӦX ZwuTUIZsgY&]`pJEU 9uIu6ڂr.ۡȑ45]w֭<ҧu.&F"f`pÆ'Mza{BV;w}MOnݺkn>䭷^yÎ>zȐΝ;wܙ͑u'&LO>ݻ_=S%I}>9g2< a.ޙZ|w8g幸~K9/ҿ {COɎvžȯ$Ym%K-ck7_9z!G,^ȻAVWnj3f̘] F`>+5 dŨd!XO2=LFZ=^ᆱFO9 w"\_H$uxwd6#leؘHdN4oGtiGu$KڿUUafLl٤I/Ԥi^o,JF:&鴮'a^o"s:scz5W+V\j\VN:3*+۶ \L@)q(wXxdWE6khNt4ZGta8).{cqﲎ}]odZ Ӵ =X!)} o7>f}ҟAAQ>m4]>f)gϞ={]}BJȞbKQeY\.E,MM]f_x<ƍNO@*0ĐA|t]$X;vd7ݴ}VTBy Ȳ x6[׫/?4 >w8DQN>ggGA5S4Mڶ$EaB$&SL0HhdZ*IֽW9]xbڰ"S6~s{kqY0pEX[Êb[ Y!oYw,"*v=ZKA*. jժUVlz,,w׬J"L0`m+߾qa45uJqm ]?%H3t\[C=VAH[~'Vt.6ktzȐpX37aO?sϣf  $IaMd* aF`PO<W X&*ka}(iSðrX5E Ʀ+?RBv ;Ҁ{re J* H$].QddPwH\|m76jڥ&NeiH׋$Ayh*e-]QeO=&M+0{BLQEUi{ .]Fۼ+oA 4t ״˵۷Am۶kj]VD\mVI z[*ޥfwRcf`ﺪ"Iq>>A q V'Q4ί^Ϳoɒ P@OP@h0Jy0V(ЦM2짟B(a>8G#)좚͔L3^.(65=Py }/n.Hӹp:<ûb>} غu6 өiN#B[]]__Wpd 8'(("BMSUQĻ&;!8`np{`|{(X%aE73O(g?OSa>|E#wH,"`yw~Dr9c'{[B;2O*xI% ]]®ͪ<?zXLQ;n'rWF8"Ynjo6}wo˗OKfNpCC:ukYYEEK(AA{ w 屢IW4s90 /DŽrxŲTGiE8uSe0pi,Kp#2yc~Huְa䥗СCsϷdɂв}WxvV[ EB!vD~557o ϷmkӦm[KӘ[%1ywr+f}cj*AA^Z=q`B1۝Cx<;E"W]ܵk]](G!n c/pApeTu^{ClKcǎ?ys|yeO?(?9!CmM<' i>xxsb}||ָX}aA<{d#%ϐ0ɺ<2ΟُϿC;Ao]8h>tU)IP}} 빜(>qee]HRc74YLqǎEU8PL>tՂԄgaDۗ-ӴGncMA=k{Unݺu4~|CQӝ{:">ߎ۷˲aFCCuuu\0nuȎK/H! Ι*U,:|u\N3.i[dYQ9~&LC f' 'ZhMM>_}yA(=>7ߨgM>H>7Yw]u˟zG%?㌳nj9KK9bP}}]]"xpEEeeK.AA-e}gc)b匬eI%I͛GYdK|P.s8x6qy%!{o-HF;`4Cge(MM7|5l`0F'Mz_WCnDA+qߒċV!Vۚ_[#v;C;β0jq]3=o] 9+U\X%UlV{!l!z ؽҀBÇ>|իW.]8ӧO>B>h|K$@a/tzZKanAhj:U<N)SvS ԩ3fŒÆ °aG ^3f@3X˰a{ذҎJe2,wAرז  Glm,_~_2ѣG=zWnj3fӃ=}]vڵSu]׿x<=}{ /?  ul+Aw5cƌ3f@1+PB8=5k֬Y |r0rKAAѺhu=ݡ|g!~hP {uAAQ . jժUVNAAK'^=T۠hvZ]VoWmк zv('  VrmYYYYY((sh{ ctm؁߶&{dW*Zj4bOp]-}`hEVD̝;wܹ$(\q.-ľ](]`` X zш=ǁtowk[zp/RQ&7Zz ;Poע_me7SL2e -8|wш=zouҪùop\?)gXgK42еaweM_=M)Nb;20f)G,&w4bwq`_-} HCρM3K})d}Fhɕ% ڰ79&{Ǟ~d4шŁ}ohu},?(E}?w_>"]{D!ڰkzx+Sʚш=́qow[z }\_޵+$lڰkz{ze; 80n=+Z$!    b?z  ؏~⊀#'r)+SܣZhMvhľƾyo78~UJl~ =} w?(2B{&ш}}jٽn[qA>̘1cƌ>#'r!J/5|FkWvߊ$gwm}zш=ǁtowk[zpӬZjժUF5jsh>F(=]G,mD!ڰ+k@ 6W[j4bOp]-}`h=NAACEAAC=AAAP@OAA1AA~ AAC=AAAP@OAA1AA~ AAC=AAAP@OAA1AA~ Ane֬Yfjsjv3Ÿ 7o޼y~ۘ}'bz Z-=}ZB̘1cƌ{A ѣGN8N8aODDl P( ZzRkz 5Aľ̙3gΜҳ vڵkիW^nr 4hРR x>' (COA%1lذaÆ͵x ]z @=f̘1c } VagJZXW/aW= 8wܹsr Cxo)Wמ^VR:=aeJ A@=AΆ; eBp`) t!*>aD8Á bg7 s1*rWV9s{fzǏ l~WB=m(嚇s/e_3k+OND)P@O.QJ p)>~ ĥ/=T- Re)!I)!o)+`3t> oxol]) {u&ֹ횁v?/h͐ @D@(.ggہl4xku0r)s}:®#C3*]´+7U_5rг5of(.;6 ƮHn D 1qY}#]gg33\~ vKZw,BZJ}p0ǁ[R dwvO-)f Ի~ih͐=A4Xv2B@l!5~{fϱOq ~Vm޼kl„ &L.ڕ~O-fO_3m#6hY('b(_ H2@p_B={( %"ajDKޠwElcgs A ^@XzP( wB, aanlhҮܶ=^z o' ovu~O-в A{ > о[_8loʍpRY'Yah)s;KaJ [n~Op' }d)]Bz(E\kvt$D]8,ed,ړk]x%apyϭ-f_3A ('bg7ABSpyv(ptNpg)*%L,J0O.V0l?=J{O޼fثY{ tv_$R;<K rBgW?jO/śܾ2>C K.]T]` Y ϫжVŷB+~uk~-hK9߶A2A@(Sj mY ith b^:W #r7Ϥ"ċ0C<~2XүqOl(ؿ!oiAA[ =AAAP@OAA1AA~ AAC=AAAP@OAA1AA~ AAC=AAAP@OAA1AA~ AAC=AAAP@OAA1AA~&|Y%tEXtdate:create2013-06-03T14:47:08-04:00rX%tEXtdate:modify2013-06-03T14:47:08-04:00/, tEXtpdf:HiResBoundingBox504x504+0+0wtEXtpdf:VersionPDF-1.4 G:xIENDB`EBSeq/build/0000755000175000017500000000000014136070500012436 5ustar nileshnileshEBSeq/build/vignette.rds0000644000175000017500000000031314136070500014772 0ustar nileshnileshb```b`add`b2 1# 'ru N-LK-)I +GSVSA@AJ tC zּb4C]R RR@g;<E T [fN*ސ89 d Bw(,/׃ @?{49'ݣ\)%ziE@ wEBSeq/R/0000755000175000017500000000000014136050172011543 5ustar nileshnileshEBSeq/R/GetPPMat.R0000644000175000017500000000021014136050172013300 0ustar nileshnileshGetPPMat <- function(EBout){ if(!"PPMat"%in%names(EBout))stop("The input doesn't seem like an output from EBTest") PP=EBout$PPMat } EBSeq/R/PlotPostVsRawFC.R0000644000175000017500000000132514136050172014647 0ustar nileshnileshPlotPostVsRawFC<-function(EBOut,FCOut){ #library(gplots) par(fig=c(0,.8,0,1), new=F) RainbowColor=rev(redgreen(length(FCOut$PostFC))) par(oma=c(0,1,1,0),cex=1.3) plot(FCOut$PostFC,FCOut$RealFC, log="xy",col=RainbowColor[rank(unlist(EBOut$MeanList)[names(FCOut$PostFC)])], xlab="Posterior FC", ylab="FC",pch=21) abline(h=1, v=1, col="gray") #legend("topleft",col=c("green","black","red"),legend=c("Low Expression","Median Expression","High Expression"),pch=21) par(fig=c(.7,1,0,1), new=TRUE) Seq=1:ceiling(length(RainbowColor)/100) plot(c(0,10), c(1,length(RainbowColor)), type='n', bty='n', xaxt='n', xlab='Rank', ylab='') for (i in 1:length(Seq)) { rect(0,(i-1)*100,10,i*100, col=RainbowColor[(i-1)*100], border=NA) } } EBSeq/R/MedianNorm.R0000644000175000017500000000107514136050172013722 0ustar nileshnileshMedianNorm <- function(Data, alternative=FALSE){ if(ncol(Data)==1)stop("Only 1 sample!") if(!alternative){ geomeans <- exp(rowMeans(log(Data))) out <- apply(Data, 2, function(cnts) median((cnts/geomeans)[geomeans > 0]))} if(alternative){ DataMatO <- Data N <- ncol(DataMatO) DataList0 <- sapply(1:N,function(i)DataMatO[,i]/DataMatO,simplify=F) DataEachMed0 <- sapply(1:N,function(i)apply(DataList0[[i]],2,function(j)median(j[which(j>0 & j=(1-PPcut))]} else{SoftThre=crit_fun(PP[,"PPEE"],PPcut) DEfound=rownames(PP)[which(PP[,"PPDE"]>=SoftThre)]} # classic if(Method=="classic"){ Gene_status=rep("EE",dim(GeneMat)[1]) names(Gene_status)=rownames(GeneMat) Gene_status[DEfound]="DE" NoTest_genes=rownames(GeneMat)[!(rownames(GeneMat)%in%rownames(PP))] Gene_status[NoTest_genes]="Filtered: Low Expression" PPMatWith0=EBPrelim$PPMatWith0 PPMatWith0[NoTest_genes,]=c(NA,NA) return(list(DEfound=DEfound,PPMat=PPMatWith0,Status=Gene_status)) } else{ ###Post_Foldchange PostFoldChange=PostFC(EBPrelim) PPFC=PostFoldChange$PostFC OldPPFC=PPFC[DEfound] OldPPFC[which(OldPPFC>1)]=1/OldPPFC[which(OldPPFC>1)] FilterFC=names(OldPPFC)[which(OldPPFC>Threshold_FC)] ###New Fold Change NewFC1=apply(matrix(GeneMat[DEfound,which(Conditions==Levels[[1]])]+SmallNum, nrow=length(DEfound)),1,median) NewFC2=apply(matrix(GeneMat[DEfound,which(Conditions==Levels[[2]])]+SmallNum, nrow=length(DEfound)),1,median) NewFC=NewFC1/NewFC2 NewFC[which(NewFC>1)]=1/NewFC[which(NewFC>1)] ###FC Ratio FCRatio=NewFC/OldPPFC FCRatio[which(OldPPFC 0) { out <- 1 - sort(PPEE)[index] } if (index == 0) { out <- 1 } names(out) <- NULL return(out) } EBSeq/R/QQP.R0000644000175000017500000000274414136050172012336 0ustar nileshnileshQQP <- function(EBOut, GeneLevel=F){ if(!"Alpha"%in%names(EBOut))stop("The input doesn't seem like an output from EBTest/EBMultiTest") maxround=nrow(EBOut$Alpha) AlphaResult=EBOut$Alpha[maxround,] BetaResult=EBOut$Beta[maxround,] # Multi if(!is.null(EBOut$PPpattern)){ QList=EBOut$QList for(i in 1:length(EBOut$QList)){ for(j in 1:length(EBOut$QList[[i]])){ if(GeneLevel==F)Main=paste("Ig",i,"C",j) if(GeneLevel==T)Main=paste("Gene","C",j) tmpSize=length(QList[[i]][[j]][QList[[i]][[j]]<1 & !is.na(QList[[i]][[j]])]) rdpts=rbeta(tmpSize,AlphaResult,BetaResult[i]) qqplot(QList[[i]][[j]][QList[[i]][[j]]<1], rdpts,xlab="estimated q's", ylab="simulated q's from fitted beta", main=Main, xlim=c(0,1),ylim=c(0,1)) fit=lm(sort(rdpts)~sort(QList[[i]][[j]][QList[[i]][[j]]<1 & !is.na(QList[[i]][[j]])])) abline(fit,col="red") } } } if(is.null(EBOut$PPpattern)){ for(con in 1:2){ if(con==1)QList=EBOut$QList1 if(con==2)QList=EBOut$QList2 for (i in 1:length(BetaResult)){ if(GeneLevel==F)Main=paste("Ig",i,"C",con) if(GeneLevel==T)Main=paste("Gene","C",con) tmpSize=length(QList[[i]][QList[[i]]<1 & !is.na(QList[[i]])]) rdpts=rbeta(tmpSize,AlphaResult,BetaResult[i]) qqplot(QList[[i]][QList[[i]]<1], rdpts,xlab="estimated q's", ylab="simulated q's from fitted beta", main=Main, xlim=c(0,1),ylim=c(0,1)) fit=lm(sort(rdpts)~sort(QList[[i]][QList[[i]]<1 & !is.na(QList[[i]])])) abline(fit,col="red") } }} } EBSeq/R/PostFC.R0000644000175000017500000000307414136050172013030 0ustar nileshnileshPostFC=function(EBoutput, SmallNum=.01) { if(!"C1Mean"%in%names(EBoutput)) stop("The input doesn't seem like an output from EBTest") GeneRealMeanC1=unlist(EBoutput$C1Mean) GeneRealMeanC2=unlist(EBoutput$C2Mean) GeneRealMeanC1Plus=GeneRealMeanC1+SmallNum GeneRealMeanC2Plus=GeneRealMeanC2+SmallNum GeneRealMean=(GeneRealMeanC1+GeneRealMeanC2)/2 GeneRealFC=GeneRealMeanC1Plus/GeneRealMeanC2Plus GeneR=unlist(EBoutput$RList) GeneR[GeneR<=0 | is.na(GeneR)]=GeneRealMean[GeneR<=0 | is.na(GeneR)]*.99/.01 GeneAlpha=EBoutput[[1]][nrow(EBoutput[[1]]),] GeneBeta=unlist(sapply(1:length(EBoutput$C1Mean),function(i)rep(EBoutput[[2]][nrow(EBoutput[[1]]),i],length(EBoutput$C1Mean[[i]])))) GeneBeta=as.vector(GeneBeta) # Post alpha P_a_C1= alpha + r_C1 * n_C1 # Post beta P_b_C1= beta + Mean_C1 * n_C1 # P_q_C1= P_a_C1/ (P_a_C1 + P_b_C1) # Post FC = ((1-P_q_C1)/P_q_c1) /( (1-P_q_c2)/P_q_c2) nC1=sum(EBoutput$Conditions==levels(EBoutput$Conditions)[1]) nC2=sum(EBoutput$Conditions==levels(EBoutput$Conditions)[2]) GenePostAlphaC1=GeneAlpha+nC1*GeneR GenePostAlphaC2=GeneAlpha+nC2*GeneR GenePostBetaC1=GeneBeta+nC1*GeneRealMeanC1 GenePostBetaC2=GeneBeta+nC2*GeneRealMeanC2 GenePostQC1=GenePostAlphaC1/(GenePostAlphaC1+GenePostBetaC1) GenePostQC2=GenePostAlphaC2/(GenePostAlphaC2+GenePostBetaC2) GenePostFC=((1-GenePostQC1)/(1-GenePostQC2))*(GenePostQC2/GenePostQC1) Out=list(PostFC=GenePostFC[rownames(EBoutput$PPMat)], RealFC=GeneRealFC[rownames(EBoutput$PPMat)], Direction=paste(EBoutput$ConditionOrder[[1]],"Over", EBoutput$ConditionOrder[[2]]) ) } EBSeq/R/LogN.R0000644000175000017500000000451114136050172012526 0ustar nileshnileshLogN <- function(Input, InputSP, EmpiricalR, EmpiricalRSP, NumOfEachGroup, AlphaIn, BetaIn, PIn, NoneZeroLength) { #2 condition case (skip the loop then maybe run faster? Code multi condition cases later) #For each gene (m rows of Input---m genes) #Save each gene's F0, F1 for further likelihood calculation. #Get F0 for EE F0Log=f0(Input, AlphaIn, BetaIn, EmpiricalR, NumOfEachGroup, log=T) #Get F1 for DE F1Log=f1(InputSP[[1]], InputSP[[2]], AlphaIn, BetaIn, EmpiricalRSP[[1]],EmpiricalRSP[[2]], NumOfEachGroup, log=T) #Get z #Use data.list in logfunction F0LogMdf=F0Log+600 F1LogMdf=F1Log+600 F0Mdf=exp(F0LogMdf) F1Mdf=exp(F1LogMdf) z.list=PIn*F1Mdf/(PIn*F1Mdf+(1-PIn)*F0Mdf) zNaNName=names(z.list)[is.na(z.list)] zGood=which(!is.na(z.list)) if(length(zGood)==0){ #Min=min(min(F0Log[which(F0Log!=-Inf)]), # min(F1Log[which(F1Log!=-Inf)])) tmpMat=cbind(F0Log,F1Log) tmpMean=apply(tmpMat,1,mean) F0LogMdf=F0Log-tmpMean F1LogMdf=F1Log-tmpMean F0Mdf=exp(F0LogMdf) F1Mdf=exp(F1LogMdf) z.list=PIn*F1Mdf/(PIn*F1Mdf+(1-PIn)*F0Mdf) zNaNName=names(z.list)[is.na(z.list)] zGood=which(!is.na(z.list)) } ###Update P #PFromZ=sapply(1:NoneZeroLength,function(i) sum(z.list[[i]])/length(z.list[[i]])) PFromZ=sum(z.list[zGood])/length(z.list[zGood]) F0Good=F0Log[zGood] F1Good=F1Log[zGood] ### MLE Part #### # Since we dont wanna update p and Z in this step # Each Ng for one row NumGroupVector=rep(c(1:NoneZeroLength),NumOfEachGroup) NumGroupVector.zGood=NumGroupVector[zGood] NumOfEachGroup.zGood=tapply(NumGroupVector.zGood,NumGroupVector.zGood,length) StartValue=c(AlphaIn, BetaIn,PIn) Result<-optim(StartValue,Likefun,InputPool=list(InputSP[[1]][zGood,],InputSP[[2]][zGood,],Input[zGood,],z.list[zGood], NoneZeroLength,EmpiricalR[zGood, ],EmpiricalRSP[[1]][zGood,], EmpiricalRSP[[2]][zGood,], NumOfEachGroup.zGood)) #LikeOutput=Likelihood( StartValue, Input , InputSP , PNEW.list, z.list) AlphaNew= Result$par[1] BetaNew=Result$par[2:(1+NoneZeroLength)] PNew=Result$par[2+NoneZeroLength] ## Output=list(AlphaNew=AlphaNew,BetaNew=BetaNew,PNew=PNew,ZNew.list=z.list,PFromZ=PFromZ, zGood=zGood, zNaNName=zNaNName,F0Out=F0Good, F1Out=F1Good) Output } EBSeq/R/QuantileNorm.R0000644000175000017500000000040614136050172014304 0ustar nileshnilesh QuantileNorm=function(Data, Quantile){ if(ncol(Data)==1)stop("Only 1 sample!") QtilePt=apply(Data, 2, function(i)quantile(i, Quantile)) # Size= QtilePt * prod(QtilePt) ^ (-1/ncol(Data)) Size=10^(log10(QtilePt)-sum(log10(QtilePt))*(1/ncol(Data)) ) Size } EBSeq/R/GetPatterns.R0000644000175000017500000000064214136050172014130 0ustar nileshnileshGetPatterns<-function(Conditions){ if(!is.factor(Conditions))Conditions=as.factor(Conditions) NumCond=nlevels(Conditions) if(NumCond<3)stop("Less than 3 conditions!") CondLevels=levels(Conditions) AllPartiList=sapply(1:NumCond,function(i)nkpartitions(NumCond,i)) AllParti=do.call(rbind,AllPartiList) colnames(AllParti)=CondLevels rownames(AllParti)=paste("Pattern",1:nrow(AllParti),sep="") AllParti } EBSeq/R/Likefun.R0000644000175000017500000000137314136050172013267 0ustar nileshnileshLikefun <- function(ParamPool, InputPool) { NoneZeroLength=InputPool[[5]] AlphaIn=ParamPool[1] BetaIn=ParamPool[2:(1+NoneZeroLength)] PIn=ParamPool[2+NoneZeroLength] ZIn=InputPool[[4]] Input=InputPool[[3]] Input1=matrix(InputPool[[1]],nrow=nrow(Input)) Input2=matrix(InputPool[[2]],nrow=nrow(Input)) RIn=InputPool[[6]] RInSP1=matrix(InputPool[[7]],nrow=nrow(Input)) RInSP2=matrix(InputPool[[8]],nrow=nrow(Input)) NumIn=InputPool[[9]] ##Function here #LikelihoodFunction<- function(NoneZeroLength){ F0=f0(Input, AlphaIn, BetaIn, RIn, NumIn, log=T) F1=f1(Input1, Input2, AlphaIn, BetaIn, RInSP1,RInSP2, NumIn, log=T) F0[F0==Inf]=min(!is.na(F0[F0!=Inf])) F1[F1==Inf]=min(!is.na(F1[F1!=Inf])) -sum((1-ZIn)*F0+ (1-ZIn)* log(1-PIn) + ZIn*F1 + ZIn*log(PIn)) } EBSeq/R/GetMultiPP.R0000644000175000017500000000072014136050172013657 0ustar nileshnileshGetMultiPP <- function(EBout){ if(!"PPpattern"%in%names(EBout))stop("The input doesn't seem like an output from EBMultiTest") PP=EBout$PPpattern UnderFlow=which(is.na(rowSums(PP))) if(length(UnderFlow)!=0)Good=c(1:nrow(PP))[-UnderFlow] else Good=c(1:nrow(PP)) MAP=rep(NA,nrow(PP)) names(MAP)=rownames(PP) MAP[Good]=colnames(PP)[apply(PP[Good,],1,which.max)] MAP[UnderFlow]="NoTest" AllParti=EBout$AllParti out=list(PP=PP, MAP=MAP,Patterns=AllParti) } EBSeq/R/PolyFitPlot.R0000644000175000017500000000260014136050172014111 0ustar nileshnileshPolyFitPlot <- function(X , Y , nterms , xname="Estimated Mean", yname="Estimated Var", pdfname="", xlim=c(-1,5), ylim=c(-1,7), ChangeXY=F,col="red"){ b=rep(NA,nterms) logX=matrix(rep(X, nterms),ncol=nterms, byrow=T) for (i in 1:nterms) logX[,i]=(log10(X))^i colnames(logX)=paste("logmu^",c(1:nterms)) rownames(logX)=names(X) NotUse=c(names(X)[X==0],names(Y)[Y==0],names(X)[rowMeans(logX)==-Inf],names(X)[rowMeans(logX)==Inf]) Use=names(X[!names(X)%in%NotUse]) Lm=lm(log10(Y[Use])~logX[Use,1:nterms]) b=summary(Lm)$coefficients[2:(nterms+1),1] d=summary(Lm)$coefficients[1,1] bvec=matrix(rep(b,length(X)),ncol=nterms,byrow=T) fit=rowSums(logX*bvec)+d main2=NULL if (ChangeXY==T){ X.plot=log10(Y) Y.plot=log10(X) fit.X.plot=fit fit.Y.plot=log10(X) } else{ X.plot=log10(X) Y.plot=log10(Y) fit.X.plot=log10(X) fit.Y.plot=fit } for (i in 1:nterms) main2=paste(main2,round(b[i],2),"*log(",xname,")^",i,"+") main=pdfname smoothScatter(X.plot, Y.plot ,main=main,xlim=xlim,ylim=ylim,xlab=xname,ylab=yname,axes=F) axis(1,at=seq(xlim[1],xlim[2],by=1), 10^seq(xlim[1],xlim[2],by=1)) axis(2,at=seq(ylim[1],ylim[2],by=2), 10^seq(ylim[1],ylim[2],by=2)) Sortit=order(fit.X.plot) lines(fit.X.plot[Sortit],fit.Y.plot[Sortit],col=col,lwd=3) output=list(b=b,d=d,lm=Lm,fit=fit,sort=Sortit) names(output$b)=paste(xname,"^",c(1:length(output$b))) output } EBSeq/R/EBTest.R0000644000175000017500000005617714136050172013034 0ustar nileshnileshEBTest <- function(Data,NgVector=NULL,Conditions, sizeFactors, maxround, Pool=F, NumBin=1000,ApproxVal=10^-10, Alpha=NULL, Beta=NULL,PInput=NULL,RInput=NULL,PoolLower=.25, PoolUpper=.75,Print=T, Qtrm=1,QtrmCut=0) { expect_is(sizeFactors, c("numeric","integer")) expect_is(maxround, c("numeric","integer")) if(!is.factor(Conditions))Conditions=as.factor(Conditions) if(is.null(rownames(Data)))stop("Please add gene/isoform names to the data matrix") if(!is.matrix(Data))stop("The input Data is not a matrix") if(length(Conditions)!=ncol(Data))stop("The number of conditions is not the same as the number of samples! ") if(nlevels(Conditions)>2)stop("More than 2 conditions! Please use EBMultiTest() function") if(nlevels(Conditions)<2)stop("Less than 2 conditions - Please check your input") if(length(sizeFactors)!=length(Data) & length(sizeFactors)!=ncol(Data)) stop("The number of library size factors is not the same as the number of samples!") Conditions=as.factor(Conditions) Vect5End=Vect3End=CI=CIthre=tau=NULL Dataraw=Data #Normalized DataNorm=GetNormalizedMat(Data, sizeFactors) expect_is(DataNorm, "matrix") Levels=levels(as.factor(Conditions)) # Dixon Statistics # library(outliers) # normalized matrix for each condition # matC=sapply(1:length(Levels),function(i)DataNorm[,which(Conditions==Levels[i])]) # run dixon test for each isoform within condition # DixonP=sapply(1:length(matC),function(j) # apply(DataNorm,1,function(i){ # if(mean(i)==0)out=NA # else out=dixon.test(i)$p.value # out})) QuantileFor0=apply(DataNorm,1,function(i)quantile(i,Qtrm)) AllZeroNames=which(QuantileFor0<=QtrmCut) NotAllZeroNames=which(QuantileFor0>QtrmCut) if(length(AllZeroNames)>0 & Print==T) cat(paste0("Removing transcripts with ",Qtrm*100, " th quantile < = ",QtrmCut," \n", length(NotAllZeroNames)," transcripts will be tested\n")) if(length(NotAllZeroNames)==0)stop("0 transcript passed") Data=Data[NotAllZeroNames,] if(!is.null(NgVector))NgVector=NgVector[NotAllZeroNames] if(length(sizeFactors)!=ncol(Data))sizeFactors=sizeFactors[NotAllZeroNames,] if(is.null(NgVector))NgVector=rep(1,nrow(Data)) #Rename Them IsoNamesIn=rownames(Data) Names=paste("I",c(1:dim(Data)[1]),sep="") names(IsoNamesIn)=Names rownames(Data)=paste("I",c(1:dim(Data)[1]),sep="") names(NgVector)=paste("I",c(1:dim(Data)[1]),sep="") if(length(sizeFactors)==length(Data)){ rownames(sizeFactors)=rownames(Data) colnames(sizeFactors)=Conditions } NumOfNg=nlevels(as.factor(NgVector)) NameList=sapply(1:NumOfNg,function(i)Names[NgVector==i],simplify=F) names(NameList)=paste("Ng",c(1:NumOfNg),sep="") NotNone=NULL for (i in 1:NumOfNg) { if (length(NameList[[i]])!=0) NotNone=c(NotNone,names(NameList)[i]) } NameList=NameList[NotNone] NoneZeroLength=length(NameList) DataList=vector("list",NoneZeroLength) DataList=sapply(1:NoneZeroLength , function(i) Data[NameList[[i]],],simplify=F) names(DataList)=names(NameList) NumEachGroup=sapply(1:NoneZeroLength , function(i)dim(DataList[[i]])[1]) # Unlist DataList.unlist=do.call(rbind, DataList) # Divide by SampleSize factor if(length(sizeFactors)==ncol(Data)) DataList.unlist.dvd=t(t( DataList.unlist)/sizeFactors) if(length(sizeFactors)==length(Data)) DataList.unlist.dvd=DataList.unlist/sizeFactors MeanList=rowMeans(DataList.unlist.dvd) ############### # Input R ############### if (!is.null(RInput)){ RNoZero=RInput[NotAllZeroNames] names(RNoZero)=rownames(Data) RNoZero.order=RNoZero[rownames(DataList.unlist)] if(length(sizeFactors)==ncol(Data)){ RMat= outer(RNoZero.order, sizeFactors) } if(length(sizeFactors)==length(Data)){ RMat= RNoZero.order* sizeFactors } DataListSP=vector("list",nlevels(Conditions)) RMatSP=vector("list",nlevels(Conditions)) for (lv in 1:nlevels(Conditions)){ DataListSP[[lv]]= matrix(DataList.unlist[,Conditions==levels(Conditions)[lv]],nrow=dim(DataList.unlist)[1]) rownames(DataListSP[[lv]])=rownames(DataList.unlist) RMatSP[[lv]]= matrix(RMat[,Conditions==levels(Conditions)[lv]],nrow=dim(RMat)[1]) rownames(RMatSP[[lv]])=rownames(RMat) } F0Log=f0(Input=DataList.unlist, AlphaIn=Alpha, BetaIn=Beta, EmpiricalR=RMat, NumOfGroups=NumEachGroup, log=T) F1Log=f1(Input1=DataListSP[[1]], Input2=DataListSP[[2]], AlphaIn=Alpha, BetaIn=Beta, EmpiricalRSP1=RMatSP[[1]], EmpiricalRSP2=RMatSP[[2]], NumOfGroup=NumEachGroup, log=T) F0LogMdf=F0Log+600 F1LogMdf=F1Log+600 F0Mdf=exp(F0LogMdf) F1Mdf=exp(F1LogMdf) if(!is.null(PInput)){ z.list=PInput*F1Mdf/(PInput*F1Mdf+(1-PInput)*F0Mdf) PIn=PInput } if(is.null(PInput)){ PIn=.5 PInput=rep(NULL,maxround) for(i in 1:maxround){ z.list=PIn*F1Mdf/(PIn*F1Mdf+(1-PIn)*F0Mdf) zNaNName=names(z.list)[is.na(z.list)] zGood=which(!is.na(z.list)) PIn=sum(z.list[zGood])/length(z.list[zGood]) PInput[i]=PIn } zNaNName=names(z.list)[is.na(z.list)] if(length(zNaNName)!=0){ PNotIn=rep(1-ApproxVal,length(zNaNName)) MeanList.NotIn=MeanList[zNaNName] R.NotIn.raw=MeanList.NotIn*PNotIn/(1-PNotIn) if(length(sizeFactors)==ncol(Data)) R.NotIn=outer(R.NotIn.raw,sizeFactors) if(length(sizeFactors)==length(Data)) R.NotIn=R.NotIn.raw*sizeFactors[zNaNName,] R.NotIn1=matrix(R.NotIn[,Conditions==levels(Conditions)[1]],nrow=nrow(R.NotIn)) R.NotIn2=matrix(R.NotIn[,Conditions==levels(Conditions)[2]],nrow=nrow(R.NotIn)) NumOfEachGroupNA=sapply(1:NoneZeroLength, function(i)sum(zNaNName%in%rownames(DataList[[i]]))) F0LogNA=f0(matrix(DataList.unlist[zNaNName,],ncol=ncol(DataList.unlist)), Alpha, Beta, R.NotIn, NumOfEachGroupNA, log=T) F1LogNA=f1(matrix(DataListSP[[1]][zNaNName,],ncol=ncol(DataListSP[[1]])), matrix(DataListSP[[2]][zNaNName,],ncol=ncol(DataListSP[[2]])), Alpha, Beta, R.NotIn1,R.NotIn2, NumOfEachGroupNA, log=T) F0LogMdfNA=F0LogNA+600 F1LogMdfNA=F1LogNA+600 F0MdfNA=exp(F0LogMdfNA) F1MdfNA=exp(F1LogMdfNA) z.list.NotIn=PIn*F1MdfNA/(PIn*F1MdfNA+(1-PIn)*F0MdfNA) z.list[zNaNName]=z.list.NotIn F0Log[zNaNName]=F0LogNA F1Log[zNaNName]=F1LogNA } } RealName.Z.output=z.list RealName.F0=F0Log RealName.F1=F1Log names(RealName.Z.output)=IsoNamesIn names(RealName.F0)=IsoNamesIn names(RealName.F1)=IsoNamesIn output=list(Alpha=Alpha,Beta=Beta,P=PInput, Z=RealName.Z.output, PPDE=RealName.Z.output,f0=RealName.F0, f1=RealName.F1) return(output) } # Get FC and VarPool for pooling - Only works on 2 conditions if(ncol(Data)==2){ DataforPoolSP.dvd1=matrix(DataList.unlist.dvd[,Conditions==levels(Conditions)[1]],nrow=dim(DataList.unlist)[1]) DataforPoolSP.dvd2=matrix(DataList.unlist.dvd[,Conditions==levels(Conditions)[2]],nrow=dim(DataList.unlist)[1]) MeanforPoolSP.dvd1=rowMeans(DataforPoolSP.dvd1) MeanforPoolSP.dvd2=rowMeans(DataforPoolSP.dvd2) FCforPool=MeanforPoolSP.dvd1/MeanforPoolSP.dvd2 names(FCforPool)=rownames(Data) FC_Use=which(FCforPool>=quantile(FCforPool[!is.na(FCforPool)],PoolLower) & FCforPool<=quantile(FCforPool[!is.na(FCforPool)],PoolUpper)) Var_FC_Use=apply( DataList.unlist.dvd[FC_Use,],1,var ) Mean_FC_Use=(MeanforPoolSP.dvd1[FC_Use]+MeanforPoolSP.dvd2[FC_Use])/2 MeanforPool=(MeanforPoolSP.dvd1+MeanforPoolSP.dvd2)/2 FC_Use2=which(Var_FC_Use>=Mean_FC_Use) Var_FC_Use2=Var_FC_Use[FC_Use2] Mean_FC_Use2=Mean_FC_Use[FC_Use2] Phi=mean((Var_FC_Use2-Mean_FC_Use2)/Mean_FC_Use2^2) VarEst= MeanforPool*(1+MeanforPool*Phi) if(Print==T)message(paste("No Replicate - estimate phi",round(Phi,5), "\n")) names(VarEst)=names(MeanforPoolSP.dvd1)= names(MeanforPoolSP.dvd2)=rownames(DataList.unlist.dvd) } #DataListSP Here also unlist.. Only two lists DataListSP=vector("list",nlevels(Conditions)) DataListSP.dvd=vector("list",nlevels(Conditions)) SizeFSP=DataListSP MeanSP=DataListSP VarSP=DataListSP GetPSP=DataListSP RSP=DataListSP CISP=DataListSP tauSP=DataListSP NumSampleEachCon=rep(NULL,nlevels(Conditions)) for (lv in 1:nlevels(Conditions)){ DataListSP[[lv]]= matrix(DataList.unlist[,Conditions==levels(Conditions)[lv]],nrow=dim(DataList.unlist)[1]) rownames(DataListSP[[lv]])=rownames(DataList.unlist) DataListSP.dvd[[lv]]= matrix(DataList.unlist.dvd[,Conditions==levels(Conditions)[lv]],nrow=dim(DataList.unlist.dvd)[1]) NumSampleEachCon[lv]=ncol(DataListSP[[lv]]) if(ncol(DataListSP[[lv]])==1 & !is.null(CI)){ CISP[[lv]]=matrix(CI[,Conditions==levels(Conditions)[lv]],nrow=dim(DataList.unlist.dvd)[1]) tauSP[[lv]]=matrix(tau[,Conditions==levels(Conditions)[lv]],nrow=dim(DataList.unlist.dvd)[1]) } # no matter sizeFactors is a vector or a matrix. Matrix should be columns are the normalization factors # may input one for each if(length(sizeFactors)==ncol(Data))SizeFSP[[lv]]=sizeFactors[Conditions==levels(Conditions)[lv]] if(length(sizeFactors)==length(Data))SizeFSP[[lv]]=sizeFactors[,Conditions==levels(Conditions)[lv]] MeanSP[[lv]]=rowMeans(DataListSP.dvd[[lv]]) names(MeanSP[[lv]])=rownames(DataListSP[[lv]]) if(length(sizeFactors)==ncol(Data))PrePareVar=sapply(1:ncol( DataListSP[[lv]]),function(i)( DataListSP[[lv]][,i]- SizeFSP[[lv]][i]*MeanSP[[lv]])^2 /SizeFSP[[lv]][i]) if(length(sizeFactors)==length(Data))PrePareVar=sapply(1:ncol( DataListSP[[lv]]),function(i)( DataListSP[[lv]][,i]- SizeFSP[[lv]][,i]*MeanSP[[lv]])^2 /SizeFSP[[lv]][,i]) if(ncol(DataListSP[[lv]])==1 & !is.null(CI)) VarSP[[lv]]=as.vector(((DataListSP[[lv]]/tauSP[[lv]]) * CISP[[lv]]/(CIthre*2))^2) if(ncol(DataListSP[[lv]])!=1){ VarSP[[lv]]=rowSums(PrePareVar)/ncol( DataListSP[[lv]]) names(MeanSP[[lv]])=rownames(DataList.unlist) names(VarSP[[lv]])=rownames(DataList.unlist) GetPSP[[lv]]=MeanSP[[lv]]/VarSP[[lv]] RSP[[lv]]=MeanSP[[lv]]*GetPSP[[lv]]/(1-GetPSP[[lv]]) } } VarList=apply(DataList.unlist.dvd, 1, var) if(ncol(Data)==2){ PoolVar=VarEst VarSP[[1]]=VarSP[[2]]=VarEst GetPSP[[1]]=MeanSP[[1]]/VarEst GetPSP[[2]]=MeanSP[[2]]/VarEst } if(!ncol(Data)==2){ CondWithRep=which(NumSampleEachCon>1) VarCondWithRep=do.call(cbind,VarSP[CondWithRep]) PoolVar=rowMeans(VarCondWithRep) } GetP=MeanList/PoolVar EmpiricalRList=MeanList*GetP/(1-GetP) EmpiricalRList[EmpiricalRList==Inf] =max(EmpiricalRList[EmpiricalRList!=Inf]) ##################### if(ncol(Data)!=2){ Varcbind=do.call(cbind,VarSP) VarrowMin=apply(Varcbind,1,min) } if(ncol(Data)==2){ Varcbind=VarEst VarrowMin=VarEst VarSP[[1]]=VarSP[[2]]=VarEst names(MeanSP[[1]])=names(VarSP[[1]]) names(MeanSP[[2]])=names(VarSP[[2]]) } # # GoodData=names(MeanList)[EmpiricalRList>0 & VarrowMin!=0 & EmpiricalRList!=Inf & !is.na(VarrowMin) & !is.na(EmpiricalRList)] NotIn=names(MeanList)[EmpiricalRList<=0 | VarrowMin==0 | EmpiricalRList==Inf | is.na(VarrowMin) | is.na(EmpiricalRList)] #print(paste("ZeroVar",sum(VarrowMin==0), "InfR", length(which(EmpiricalRList==Inf)), "Poi", length(which(EmpiricalRList<0)), "")) EmpiricalRList.NotIn=EmpiricalRList[NotIn] EmpiricalRList.Good=EmpiricalRList[GoodData] EmpiricalRList.Good[EmpiricalRList.Good<1]=1+EmpiricalRList.Good[EmpiricalRList.Good<1] if(length(sizeFactors)==ncol(Data)){ EmpiricalRList.Good.mat= outer(EmpiricalRList.Good, sizeFactors) EmpiricalRList.mat= outer(EmpiricalRList, sizeFactors) } if(length(sizeFactors)==length(Data)){ EmpiricalRList.Good.mat=EmpiricalRList.Good* sizeFactors[GoodData,] EmpiricalRList.mat=EmpiricalRList* sizeFactors } # Only Use Data has Good q's DataList.In=sapply(1:NoneZeroLength, function(i)DataList[[i]][GoodData[GoodData%in%rownames(DataList[[i]])],],simplify=F) DataList.NotIn=sapply(1:NoneZeroLength, function(i)DataList[[i]][NotIn[NotIn%in%rownames(DataList[[i]])],],simplify=F) DataListIn.unlist=do.call(rbind, DataList.In) DataListNotIn.unlist=do.call(rbind, DataList.NotIn) DataListSPIn=vector("list",nlevels(Conditions)) DataListSPNotIn=vector("list",nlevels(Conditions)) EmpiricalRList.Good.mat.SP=EmpiricalRList.mat.SP=vector("list",nlevels(Conditions)) for (lv in 1:nlevels(Conditions)){ DataListSPIn[[lv]]= matrix(DataListIn.unlist[,Conditions==levels(Conditions)[lv]],nrow=dim(DataListIn.unlist)[1]) if(length(NotIn)>0){ DataListSPNotIn[[lv]]= matrix(DataListNotIn.unlist[,Conditions==levels(Conditions)[lv]],nrow=dim(DataListNotIn.unlist)[1]) rownames(DataListSPNotIn[[lv]])=rownames(DataListNotIn.unlist) } rownames(DataListSPIn[[lv]])=rownames(DataListIn.unlist) EmpiricalRList.Good.mat.SP[[lv]]=matrix(EmpiricalRList.Good.mat[,Conditions==levels(Conditions)[lv]],nrow=dim(EmpiricalRList.Good.mat)[1]) EmpiricalRList.mat.SP[[lv]]=matrix(EmpiricalRList.mat[,Conditions==levels(Conditions)[lv]],nrow=dim(EmpiricalRList.mat)[1]) } NumOfEachGroupIn=sapply(1:NoneZeroLength, function(i)max(0,dim(DataList.In[[i]])[1])) NumOfEachGroupNotIn=sapply(1:NoneZeroLength, function(i)max(0,dim(DataList.NotIn[[i]])[1])) ################# # For output ################# RealName.EmpiricalRList=sapply(1:NoneZeroLength,function(i)EmpiricalRList[names(EmpiricalRList)%in%NameList[[i]]], simplify=F) RealName.MeanList=sapply(1:NoneZeroLength,function(i)MeanList[names(MeanList)%in%NameList[[i]]], simplify=F) RealName.C1MeanList=sapply(1:NoneZeroLength,function(i)MeanSP[[1]][names(MeanSP[[1]])%in%NameList[[i]]], simplify=F) RealName.C2MeanList=sapply(1:NoneZeroLength,function(i)MeanSP[[2]][names(MeanSP[[2]])%in%NameList[[i]]], simplify=F) RealName.C1VarList=sapply(1:NoneZeroLength,function(i)VarSP[[1]][names(VarSP[[1]])%in%NameList[[i]]], simplify=F) RealName.C2VarList=sapply(1:NoneZeroLength,function(i)VarSP[[2]][names(VarSP[[2]])%in%NameList[[i]]], simplify=F) RealName.DataList=sapply(1:NoneZeroLength,function(i)DataList[[i]][rownames(DataList[[i]])%in%NameList[[i]],], simplify=F) RealName.VarList=sapply(1:NoneZeroLength,function(i)VarList[names(VarList)%in%NameList[[i]]], simplify=F) RealName.PoolVarList=sapply(1:NoneZeroLength,function(i)PoolVar[names(PoolVar)%in%NameList[[i]]], simplify=F) RealName.QList1=sapply(1:NoneZeroLength,function(i)GetPSP[[1]][names(GetPSP[[1]])%in%NameList[[i]]], simplify=F) RealName.QList2=sapply(1:NoneZeroLength,function(i)GetPSP[[2]][names(GetPSP[[2]])%in%NameList[[i]]], simplify=F) if(is.null(unlist(RealName.QList1)))RealName.QList1=RealName.QList2 if(is.null(unlist(RealName.QList2)))RealName.QList2=RealName.QList1 if(is.null(unlist(RealName.C1VarList)))RealName.C1VarList=RealName.C2VarList if(is.null(unlist(RealName.C2VarList)))RealName.C2VarList=RealName.C1VarList #browser() for (i in 1:NoneZeroLength){ tmp=NameList[[i]] names=IsoNamesIn[tmp] RealName.MeanList[[i]]=RealName.MeanList[[i]][NameList[[i]]] RealName.VarList[[i]]=RealName.VarList[[i]][NameList[[i]]] RealName.QList1[[i]]=RealName.QList1[[i]][NameList[[i]]] RealName.QList2[[i]]=RealName.QList2[[i]][NameList[[i]]] RealName.EmpiricalRList[[i]]=RealName.EmpiricalRList[[i]][NameList[[i]]] RealName.C1MeanList[[i]]=RealName.C1MeanList[[i]][NameList[[i]]] RealName.C2MeanList[[i]]=RealName.C2MeanList[[i]][NameList[[i]]] RealName.PoolVarList[[i]]=RealName.PoolVarList[[i]][NameList[[i]]] RealName.C1VarList[[i]]=RealName.C1VarList[[i]][NameList[[i]]] RealName.C2VarList[[i]]=RealName.C2VarList[[i]][NameList[[i]]] RealName.DataList[[i]]=RealName.DataList[[i]][NameList[[i]],] names(RealName.MeanList[[i]])=names names(RealName.VarList[[i]])=names if(ncol(DataListSP[[1]])!=1){ names(RealName.QList1[[i]])=names names(RealName.C1VarList[[i]])=names } if(ncol(DataListSP[[2]])!=1){ names(RealName.QList2[[i]])=names names(RealName.C2VarList[[i]])=names } names(RealName.EmpiricalRList[[i]])=names names(RealName.C1MeanList[[i]])=names names(RealName.C2MeanList[[i]])=names names(RealName.PoolVarList[[i]])=names rownames(RealName.DataList[[i]])=names } ##################### # If Don need EM ##################### if(!is.null(Alpha)&!is.null(Beta)){ F0Log=f0(Input=DataList.unlist, AlphaIn=Alpha, BetaIn=Beta, EmpiricalR=EmpiricalRList.mat, NumOfGroups=NumEachGroup, log=T) F1Log=f1(Input1=DataListSP[[1]], Input2=DataListSP[[2]], AlphaIn=Alpha, BetaIn=Beta, EmpiricalRSP1=EmpiricalRList.mat.SP[[1]], EmpiricalRSP2=EmpiricalRList.mat.SP[[2]], NumOfGroup=NumEachGroup, log=T) F0LogMdf=F0Log+600 F1LogMdf=F1Log+600 F0Mdf=exp(F0LogMdf) F1Mdf=exp(F1LogMdf) if(!is.null(PInput)){ z.list=PInput*F1Mdf/(PInput*F1Mdf+(1-PInput)*F0Mdf) PIn=PInput } if(is.null(PInput)){ PIn=.5 PInput=rep(NULL,maxround) for(i in 1:maxround){ z.list=PIn*F1Mdf/(PIn*F1Mdf+(1-PIn)*F0Mdf) zNaNName=names(z.list)[is.na(z.list)] zGood=which(!is.na(z.list)) PIn=sum(z.list[zGood])/length(z.list[zGood]) PInput[i]=PIn } zNaNName=names(z.list)[is.na(z.list)] if(length(zNaNName)!=0){ PNotIn=rep(1-ApproxVal,length(zNaNName)) MeanList.NotIn=MeanList[zNaNName] R.NotIn.raw=MeanList.NotIn*PNotIn/(1-PNotIn) if(length(sizeFactors)==ncol(Data)) R.NotIn=outer(R.NotIn.raw,sizeFactors) if(length(sizeFactors)==length(Data)) R.NotIn=R.NotIn.raw*sizeFactors[zNaNName,] R.NotIn1=matrix(R.NotIn[,Conditions==levels(Conditions)[1]],nrow=nrow(R.NotIn)) R.NotIn2=matrix(R.NotIn[,Conditions==levels(Conditions)[2]],nrow=nrow(R.NotIn)) NumOfEachGroupNA=sapply(1:NoneZeroLength, function(i)sum(zNaNName%in%rownames(DataList[[i]]))) F0LogNA=f0(matrix(DataList.unlist[zNaNName,], ncol=ncol(DataList.unlist)), Alpha, Beta, R.NotIn, NumOfEachGroupNA, log=T) F1LogNA=f1(matrix(DataListSP[[1]][zNaNName,],ncol=ncol(DataListSP[[1]])), matrix(DataListSP[[2]][zNaNName,],ncol=ncol(DataListSP[[2]])), Alpha, Beta, R.NotIn1,R.NotIn2, NumOfEachGroupNA, log=T) F0LogMdfNA=F0LogNA+600 F1LogMdfNA=F1LogNA+600 F0MdfNA=exp(F0LogMdfNA) F1MdfNA=exp(F1LogMdfNA) z.list.NotIn=PIn*F1MdfNA/(PIn*F1MdfNA+(1-PIn)*F0MdfNA) z.list[zNaNName]=z.list.NotIn F0Log[zNaNName]=F0LogNA F1Log[zNaNName]=F1LogNA } } RealName.Z.output=z.list RealName.F0=F0Log RealName.F1=F1Log names(RealName.Z.output)=IsoNamesIn names(RealName.F0)=IsoNamesIn names(RealName.F1)=IsoNamesIn output=list(Alpha=Alpha,Beta=Beta,P=PInput, Z=RealName.Z.output, RList=RealName.EmpiricalRList, MeanList=RealName.MeanList, VarList=RealName.VarList, QList1=RealName.QList1, QList2=RealName.QList2, C1Mean=RealName.C1MeanList, C2Mean=RealName.C2MeanList, C1EstVar=RealName.C1VarList, C2EstVar=RealName.C2VarList, PoolVar=RealName.PoolVarList , DataList=RealName.DataList, PPDE=RealName.Z.output,f0=RealName.F0, f1=RealName.F1) return(output) } ##################### #Initialize SigIn & ... ##################### AlphaIn=0.5 BetaIn=rep(0.5,NoneZeroLength) PIn=0.5 ##################### # EM ##################### UpdateAlpha=NULL UpdateBeta=NULL UpdateP=NULL UpdatePFromZ=NULL Timeperround=NULL for (times in 1:maxround){ temptime1=proc.time() UpdateOutput=suppressWarnings(LogN(DataListIn.unlist,DataListSPIn, EmpiricalRList.Good.mat ,EmpiricalRList.Good.mat.SP, NumOfEachGroupIn, AlphaIn, BetaIn, PIn, NoneZeroLength)) message(paste("iteration", times, "done \n",sep=" ")) AlphaIn=UpdateOutput$AlphaNew BetaIn=UpdateOutput$BetaNew PIn=UpdateOutput$PNew PFromZ=UpdateOutput$PFromZ F0Out=UpdateOutput$F0Out F1Out=UpdateOutput$F1Out UpdateAlpha=rbind(UpdateAlpha,AlphaIn) UpdateBeta=rbind(UpdateBeta,BetaIn) UpdateP=rbind(UpdateP,PIn) UpdatePFromZ=rbind(UpdatePFromZ,PFromZ) temptime2=proc.time() Timeperround=c(Timeperround,temptime2[3]-temptime1[3]) message(paste("time" ,round(Timeperround[times],2),"\n",sep=" ")) Z.output=UpdateOutput$ZNew.list[!is.na(UpdateOutput$ZNew.list)] Z.NA.Names=UpdateOutput$zNaNName } #Remove this } after testing!! # if (times!=1){ # if((UpdateAlpha[times]-UpdateAlpha[times-1])^2+UpdateBeta[times]-UpdateBeta[times-1])^2+UpdateR[times]-UpdateR[times-1])^2+UpdateP[times]-UpdateP[times-1])^2<=10^(-6)){ # Result=list(Sig=SigIn, Miu=MiuIn, Tau=TauIn) # break # } # } #} ##########Change Names############ ## Only z are for Good Ones GoodData=GoodData[!GoodData%in%Z.NA.Names] IsoNamesIn.Good=IsoNamesIn[GoodData] RealName.Z.output=Z.output RealName.F0=F0Out RealName.F1=F1Out names(RealName.Z.output)=IsoNamesIn.Good names(RealName.F0)=IsoNamesIn.Good names(RealName.F1)=IsoNamesIn.Good #########posterior part for other data set here later############ AllNA=unique(c(Z.NA.Names,NotIn)) z.list.NotIn=NULL AllF0=c(RealName.F0) AllF1=c(RealName.F1) AllZ=RealName.Z.output if (length(AllNA)>0){ Ng.NA=NgVector[AllNA] AllNA.Ngorder=AllNA[order(Ng.NA)] NumOfEachGroupNA=rep(0,NoneZeroLength) NumOfEachGroupNA.tmp=tapply(Ng.NA,Ng.NA,length) names(NumOfEachGroupNA)=c(1:NoneZeroLength) NumOfEachGroupNA[names(NumOfEachGroupNA.tmp)]=NumOfEachGroupNA.tmp PNotIn=rep(1-ApproxVal,length(AllNA.Ngorder)) MeanList.NotIn=MeanList[AllNA.Ngorder] R.NotIn.raw=MeanList.NotIn*PNotIn/(1-PNotIn) if(length(sizeFactors)==ncol(Data)) R.NotIn=outer(R.NotIn.raw,sizeFactors) if(length(sizeFactors)==length(Data)) R.NotIn=R.NotIn.raw*sizeFactors[names(R.NotIn.raw),] R.NotIn1=matrix(R.NotIn[,Conditions==levels(Conditions)[1]],nrow=nrow(R.NotIn)) R.NotIn2=matrix(R.NotIn[,Conditions==levels(Conditions)[2]],nrow=nrow(R.NotIn)) DataListNotIn.unlistWithZ=matrix(DataList.unlist[AllNA.Ngorder,],nrow=length(AllNA.Ngorder)) DataListSPNotInWithZ=vector("list",nlevels(Conditions)) for (lv in 1:nlevels(Conditions)) DataListSPNotInWithZ[[lv]] = matrix(DataListSP[[lv]][AllNA.Ngorder,],nrow=length(AllNA.Ngorder)) F0Log=f0(DataListNotIn.unlistWithZ, AlphaIn, BetaIn, R.NotIn, NumOfEachGroupNA, log=T) F1Log=f1(DataListSPNotInWithZ[[1]], DataListSPNotInWithZ[[2]], AlphaIn, BetaIn, R.NotIn1,R.NotIn2, NumOfEachGroupNA, log=T) F0LogMdf=F0Log+600 F1LogMdf=F1Log+600 F0Mdf=exp(F0LogMdf) F1Mdf=exp(F1LogMdf) z.list.NotIn=PIn*F1Mdf/(PIn*F1Mdf+(1-PIn)*F0Mdf) # names(z.list.NotIn)=IsoNamesIn.Good=IsoNamesIn[which(Names%in%NotIn)] names(z.list.NotIn)=IsoNamesIn[AllNA.Ngorder] AllZ=c(RealName.Z.output,z.list.NotIn) AllZ=AllZ[IsoNamesIn] AllZ[is.na(AllZ)]=0 F0.NotIn=F0Log F1.NotIn=F1Log names(F0.NotIn)=IsoNamesIn[names(F0Log)] names(F1.NotIn)=IsoNamesIn[names(F1Log)] AllF0=c(RealName.F0,F0.NotIn) AllF1=c(RealName.F1,F1.NotIn) AllF0=AllF0[IsoNamesIn] AllF1=AllF1[IsoNamesIn] AllF0[is.na(AllF0)]=0 AllF1[is.na(AllF1)]=0 } PPMatNZ=cbind(1-AllZ,AllZ) colnames(PPMatNZ)=c("PPEE","PPDE") rownames(UpdateAlpha)=paste("iter",1:nrow(UpdateAlpha),sep="") rownames(UpdateBeta)=paste("iter",1:nrow(UpdateBeta),sep="") rownames(UpdateP)=paste("iter",1:nrow(UpdateP),sep="") rownames(UpdatePFromZ)=paste("iter",1:nrow(UpdatePFromZ),sep="") colnames(UpdateBeta)=paste("Ng",1:ncol(UpdateBeta),sep="") CondOut=levels(Conditions) names(CondOut)=paste("Condition",c(1:length(CondOut)),sep="") PPMat=matrix(NA,ncol=2,nrow=nrow(Dataraw)) rownames(PPMat)=rownames(Dataraw) colnames(PPMat)=c("PPEE","PPDE") if(is.null(AllZeroNames))PPMat=PPMatNZ if(!is.null(AllZeroNames))PPMat[names(NotAllZeroNames),]=PPMatNZ[names(NotAllZeroNames),] #############Result############################ Result=list(Alpha=UpdateAlpha,Beta=UpdateBeta,P=UpdateP, PFromZ=UpdatePFromZ, Z=RealName.Z.output,PoissonZ=z.list.NotIn, RList=RealName.EmpiricalRList, MeanList=RealName.MeanList, VarList=RealName.VarList, QList1=RealName.QList1, QList2=RealName.QList2, C1Mean=RealName.C1MeanList, C2Mean=RealName.C2MeanList,C1EstVar=RealName.C1VarList, C2EstVar=RealName.C2VarList, PoolVar=RealName.PoolVarList , DataList=RealName.DataList,PPDE=AllZ,f0=AllF0, f1=AllF1, AllZeroIndex=AllZeroNames,PPMat=PPMatNZ, PPMatWith0=PPMat, ConditionOrder=CondOut, Conditions=Conditions, DataNorm=DataNorm) } EBSeq/R/GetMultiFC.R0000644000175000017500000000426614136050172013641 0ustar nileshnileshGetMultiFC=function(EBMultiOut,SmallNum=.01){ if(!"PPpattern"%in%names(EBMultiOut))stop("The input doesn't seem like an output from EBMultiTest") NumNgGroup=length(EBMultiOut$DataList) OutNames=rownames(EBMultiOut$PPMat) NumCondition=length(EBMultiOut$SPMean[[1]]) ConditionNames=colnames(EBMultiOut$AllParti) CondMeans=sapply(1:NumCondition, function(i){ if (NumNgGroup==1) out=EBMultiOut$SPMean[[1]][[i]][OutNames] if(NumNgGroup>1) out=unlist(sapply(1:NumNgGroup, function(j)EBMultiOut$SPMean[[j]][[i]]))[OutNames] out} ) colnames(CondMeans)=ConditionNames CondMeansPlus=CondMeans+SmallNum GeneRealMean=rowMeans(CondMeans) GeneR=unlist(EBMultiOut$RList) GeneR[GeneR<=0 | is.na(GeneR)]=GeneRealMean[GeneR<=0 | is.na(GeneR)]*.99/.01 GeneAlpha=EBMultiOut[[1]][nrow(EBMultiOut[[1]]),] GeneBeta=unlist(sapply(1:length(EBMultiOut$DataList), function(i)rep(EBMultiOut[[2]][nrow(EBMultiOut[[1]]),i], nrow(EBMultiOut$DataList[[i]])))) GeneBeta=as.vector(GeneBeta) FCMat=PostFCMat=matrix(0,ncol=choose(NumCondition,2),nrow=length(OutNames)) rownames(FCMat)=rownames(PostFCMat)=OutNames k=1 ColNames=rep(NA,choose(NumCondition,2)) for(i in 1:(NumCondition-1)){ for(j in (i+1):NumCondition) { ColNames[k]=paste(ConditionNames[i],"Over",ConditionNames[j],sep="") FCMat[,k]=CondMeansPlus[,i]/CondMeansPlus[,j] nC1=sum(EBMultiOut$ConditionOrder==ConditionNames[i]) nC2=sum(EBMultiOut$ConditionOrder==ConditionNames[j]) GenePostAlphaC1=GeneAlpha+nC1*GeneR GenePostAlphaC2=GeneAlpha+nC2*GeneR GenePostBetaC1=GeneBeta+nC1*CondMeans[,i] GenePostBetaC2=GeneBeta+nC2*CondMeans[,j] GenePostQC1=GenePostAlphaC1/(GenePostAlphaC1+GenePostBetaC1) GenePostQC2=GenePostAlphaC2/(GenePostAlphaC2+GenePostBetaC2) GenePostFC=((1-GenePostQC1)/(1-GenePostQC2))*(GenePostQC2/GenePostQC1) PostFCMat[,k]= GenePostFC k=k+1 } } colnames(FCMat)=colnames(PostFCMat)=ColNames Log2FCMat=log2(FCMat) Log2PostFCMat=log2(PostFCMat) Out=list(FCMat=FCMat,Log2FCMat=Log2FCMat, PostFCMat=PostFCMat, Log2PostFCMat=Log2PostFCMat, CondMeans=CondMeans, ConditionOrder=EBMultiOut$ConditionOrder) } EBSeq/R/f0.R0000644000175000017500000000172114136050172012174 0ustar nileshnileshf0 <- function(Input, AlphaIn, BetaIn, EmpiricalR, NumOfGroups, log) { BetaVect=do.call(c,sapply(1:length(BetaIn),function(i)rep(BetaIn[i],NumOfGroups[i]),simplify=F)) SampleNum=dim(Input)[2] #Product part ChooseParam1=round(Input+EmpiricalR-1) roundInput=round(Input) EachChoose0=matrix(sapply(1:SampleNum, function(i)lchoose(ChooseParam1[,i], roundInput[,i])),ncol=SampleNum) # numerical approximation to rescue -Inf ones NoNegInfMin=min(EachChoose0[which(EachChoose0!=-Inf)]) NoPosInfMax=max(EachChoose0[which(EachChoose0!=Inf)]) EachChoose=EachChoose0 EachChoose[which(EachChoose0==-Inf, arr.ind=T)]=NoNegInfMin EachChoose[which(EachChoose0==Inf, arr.ind=T)]=NoPosInfMax SumEachIso=rowSums(Input) param1=AlphaIn + rowSums(EmpiricalR) param2=BetaVect + SumEachIso LogConst=rowSums(EachChoose)+lbeta(param1, param2)-lbeta(AlphaIn, BetaVect) if (log==F) FinalResult=exp(LogConst) if (log==T) FinalResult=LogConst FinalResult } EBSeq/R/LogNMulti.R0000644000175000017500000000546514136050172013552 0ustar nileshnileshLogNMulti <- function(Input, InputSP, EmpiricalR, EmpiricalRSP, NumOfEachGroup, AlphaIn, BetaIn, PIn, NoneZeroLength, AllParti, Conditions) { #For each gene (m rows of Input---m genes) #Save each gene's F0, F1 for further likelihood calculation. FList=sapply(1:nrow(AllParti),function(i)sapply(1:nlevels(as.factor(AllParti[i,])), function(j)f0(do.call(cbind,InputSP[AllParti[i,]==j]),AlphaIn, BetaIn, do.call(cbind,EmpiricalRSP[AllParti[i,]==j]), NumOfEachGroup, log=T)), simplify=F) FPartiLog=sapply(FList,rowSums) FMat=exp(FPartiLog+600) rownames(FMat)=rownames(FPartiLog)=rownames(Input) #Get z #Use data.list in logfunction PInMat=matrix(rep(1,nrow(Input)),ncol=1)%*%matrix(PIn,nrow=1) FmultiP=FMat*PInMat Denom=rowSums(FmultiP) ZEach=apply(FmultiP,2,function(i)i/Denom) zNaNName1=names(Denom)[is.na(Denom)] # other NAs in LikeFun LF=ZEach*(log(FmultiP)) zNaNMore=rownames(LF)[which(is.na(rowSums(LF)))] zNaNName=unique(c(zNaNName1,zNaNMore)) zGood=which(!rownames(LF)%in%zNaNName) if(length(zGood)==0){ #Min=min(min(F0Log[which(F0Log!=-Inf)]), # min(F1Log[which(F1Log!=-Inf)])) tmpMat=FPartiLog tmpMean=apply(tmpMat,1,mean) FLogMdf=FPartiLog-tmpMean FMdf=exp(FLogMdf) FmultiPMdf=FMdf*PInMat DenomMdf=rowSums(FmultiPMdf) ZEach=apply(FmultiPMdf,2,function(i)i/DenomMdf) zNaNName1Mdf=names(DenomMdf)[is.na(DenomMdf)] # other NAs in LikeFun LFMdf=ZEach*(log(FmultiPMdf)) zNaNMoreMdf=rownames(LFMdf)[which(is.na(rowSums(LFMdf)))] zNaNNameMdf=unique(c(zNaNName1Mdf,zNaNMoreMdf)) zGood=which(!rownames(LFMdf)%in%zNaNNameMdf) } ZEachGood=ZEach[zGood,] ###Update P PFromZ=colSums(ZEach[zGood,])/length(zGood) FGood=FPartiLog[zGood,] ### MLE Part #### # Since we dont wanna update p and Z in this step # Each Ng for one row NumGroupVector=rep(c(1:NoneZeroLength),NumOfEachGroup) NumGroupVector.zGood=NumGroupVector[zGood] NumOfEachGroup.zGood=tapply(NumGroupVector.zGood,NumGroupVector.zGood,length) StartValue=c(AlphaIn, BetaIn,PIn[-1]) InputSPGood=sapply(1:length(InputSP),function(i)InputSP[[i]][zGood,],simplify=F) EmpiricalRSPGood=sapply(1:length(EmpiricalRSP),function(i)EmpiricalRSP[[i]][zGood,],simplify=F) Result<-optim(StartValue,LikefunMulti,InputPool=list(InputSPGood,Input[zGood,],ZEach[zGood,], NoneZeroLength,EmpiricalR[zGood, ],EmpiricalRSPGood, NumOfEachGroup.zGood, AllParti)) AlphaNew= Result$par[1] BetaNew=Result$par[2:(1+NoneZeroLength)] PNewNo1=Result$par[(2+NoneZeroLength):length(Result$par)] PNew=c(1-sum(PNewNo1),PNewNo1) ## Output=list(AlphaNew=AlphaNew,BetaNew=BetaNew,PNew=PNew,ZEachNew=ZEach, ZEachGood=ZEachGood, PFromZ=PFromZ, zGood=zGood, zNaNName=zNaNName,FGood=FGood) Output } EBSeq/R/PlotPattern.R0000644000175000017500000000024214136050172014140 0ustar nileshnileshPlotPattern<-function(Patterns){ par(oma=c(3,3,3,3)) PatternCol=rev(rainbow(ncol(Patterns))) heatmap(Patterns,col=PatternCol,Colv=NA,Rowv=NA,scale="none") } EBSeq/R/GetNormalizedMat.R0000644000175000017500000000044514136050172015077 0ustar nileshnileshGetNormalizedMat<-function(Data, Sizes){ if(length(Sizes)!=length(Data) & length(Sizes)!=ncol(Data)) stop("The number of library size factors is not the same as the number of samples!") if(length(Sizes)==length(Data))Out=Data/Sizes if(length(Sizes)==ncol(Data))Out=t(t(Data)/Sizes) Out} EBSeq/R/f1.R0000644000175000017500000000046314136050172012177 0ustar nileshnileshf1 <- function(Input1, Input2, AlphaIn, BetaIn, EmpiricalRSP1,EmpiricalRSP2,NumOfGroup, log){ F0.1=f0(Input1, AlphaIn, BetaIn, EmpiricalRSP1, NumOfGroup, log=log) F0.2=f0(Input2, AlphaIn, BetaIn, EmpiricalRSP2, NumOfGroup, log=log) if (log==F) Result=F0.1*F0.2 if (log==T) Result=F0.1+F0.2 Result } EBSeq/R/GetNg.R0000644000175000017500000000111314136050172012666 0ustar nileshnileshGetNg<- function(IsoformName, GeneName, TrunThre=3){ if(length(IsoformName)!=length(GeneName))stop("The length of IsoformName is not the same as the length of GeneName") GeneNg = tapply(IsoformName, GeneName, length) if(max(GeneNg)TrunThre]=TrunThre IsoformNgTrun=IsoformNg IsoformNgTrun[IsoformNgTrun>TrunThre]=TrunThre out=list( GeneNg=GeneNg, GeneNgTrun=GeneNgTrun, IsoformNg=IsoformNg, IsoformNgTrun=IsoformNgTrun) } EBSeq/R/DenNHist.R0000644000175000017500000000307214136050172013344 0ustar nileshnileshDenNHist <- function(EBOut, GeneLevel=F) { if(!"Alpha"%in%names(EBOut))stop("The input doesn't seem like an output from EBTest/EBMultiTest") maxround=nrow(EBOut$Alpha) Alpha=EBOut$Alpha[maxround,] Beta=EBOut$Beta[maxround,] # Multi if(!is.null(EBOut$PPpattern)){ QList=EBOut$QList for(i in 1:length(EBOut$QList)){ for(j in 1:length(EBOut$QList[[i]])){ if(GeneLevel==F)Main=paste("Ig",i,"C",j) if(GeneLevel==T)Main=paste("Gene","C",j) hist(QList[[i]][[j]][QList[[i]][[j]]<.98&QList[[i]][[j]]>0], prob=T,col="blue",breaks=100, main=Main, xlim=c(0,1),xlab=paste("Q alpha=",round(Alpha,2), " beta=",round(Beta[i],2),sep="")) tmpSize=length(QList[[i]][[j]][QList[[i]][[j]]<.98]) tmpseq=seq(0.001,1,length=1000) ll=tmpseq lines(ll,dbeta(ll,Alpha,Beta[i]),col="green",lwd=2) legend("topright",c("Data","Fitted density"),col=c("blue","green"),lwd=2) } } } if(is.null(EBOut$PPpattern)){ for(con in 1:2){ if(con==1)QList=EBOut$QList1 if(con==2)QList=EBOut$QList2 if(!is.list(QList)) QList=list(QList) for (i in 1:length(QList)){ if(GeneLevel==F)Main=paste("Ig",i,"C",con) if(GeneLevel==T)Main=paste("Gene","C",con) hist(QList[[i]][QList[[i]]<.98&QList[[i]]>0], prob=T,col="blue",breaks=100, main=Main, xlim=c(0,1),xlab=paste("Q alpha=",round(Alpha,2), " beta=",round(Beta[i],2),sep="")) tmpSize=length(QList[[i]][QList[[i]]<.98]) tmpseq=seq(0.001,1,length=1000) ll=tmpseq lines(ll,dbeta(ll,Alpha,Beta[i]),col="green",lwd=2) legend("topright",c("Data","Fitted density"),col=c("blue","green"),lwd=2) }} } } EBSeq/R/LikefunMulti.R0000644000175000017500000000153114136050172014276 0ustar nileshnileshLikefunMulti <- function(ParamPool, InputPool) { NoneZeroLength=InputPool[[4]] AlphaIn=ParamPool[1] BetaIn=ParamPool[2:(1+NoneZeroLength)] PIn=ParamPool[(2+NoneZeroLength):length(ParamPool)] PInAll=c(1-sum(PIn),PIn) ZIn=InputPool[[3]] Input=InputPool[[2]] InputSP=InputPool[[1]] RIn=InputPool[[5]] RInSP=InputPool[[6]] NumIn=InputPool[[7]] AllParti=InputPool[[8]] PInMat=matrix(rep(1,nrow(Input)),ncol=1)%*%matrix(PInAll,nrow=1) ##Function here FList=sapply(1:nrow(AllParti),function(i)sapply(1:nlevels(as.factor(AllParti[i,])), function(j)f0(do.call(cbind,InputSP[AllParti[i,]==j]),AlphaIn, BetaIn, do.call(cbind,RInSP[AllParti[i,]==j]), NumIn, log=T)), simplify=F) FPartiLog=sapply(FList,rowSums) #FMat=exp(FPartiLog) FMat=FPartiLog -sum(ZIn*(FMat+log(PInMat))) } EBSeq/R/GetPP.R0000644000175000017500000000020314136050172012640 0ustar nileshnileshGetPP <- function(EBout){ if(!"PPDE"%in%names(EBout))stop("The input doesn't seem like an output from EBTest") PP=EBout$PPDE } EBSeq/R/beta.mom.R0000644000175000017500000000026314136050172013371 0ustar nileshnileshbeta.mom <- function(qs.in){ xbar<-mean(qs.in) s2<-var(qs.in) term<-(xbar*(1-xbar))/s2 alpha.hat<-xbar*(term-1) beta.hat<-(1-xbar)*(term-1) return(c(alpha.hat,beta.hat)) } EBSeq/R/EBMultiTest.R0000644000175000017500000004405214136050172014034 0ustar nileshnileshEBMultiTest <- function(Data,NgVector=NULL,Conditions,AllParti=NULL, sizeFactors, maxround, Pool=F, NumBin=1000, ApproxVal=10^-10,PoolLower=.25, PoolUpper=.75,Print=T,Qtrm=1,QtrmCut=0) { expect_is(sizeFactors, c("numeric","integer")) expect_is(maxround, c("numeric","integer")) if(!is.factor(Conditions))Conditions=as.factor(Conditions) if(is.null(rownames(Data)))stop("Please add gene/isoform names to the data matrix") if(!is.matrix(Data))stop("The input Data is not a matrix") if(length(Conditions)!=ncol(Data))stop("The number of conditions is not the same as the number of samples! ") if(nlevels(Conditions)==2)stop("Only 2 conditions - Please use EBTest() function") if(nlevels(Conditions)<2)stop("Less than 2 conditions - Please check your input") if(length(sizeFactors)!=length(Data) & length(sizeFactors)!=ncol(Data)) stop("The number of library size factors is not the same as the number of samples!") tau=CI=CIthre=NULL Dataraw=Data #Normalized DataNorm=GetNormalizedMat(Data, sizeFactors) QuantileFor0=apply(DataNorm,1,function(i)quantile(i,Qtrm)) AllZeroNames=which(QuantileFor0<=QtrmCut) NotAllZeroNames=which(QuantileFor0>QtrmCut) if(length(AllZeroNames)>0 & Print==T) cat(paste0("Removing transcripts with ",Qtrm*100, " th quantile < = ",QtrmCut," \n", length(NotAllZeroNames)," transcripts will be tested \n")) if(length(NotAllZeroNames)==0)stop("0 transcript passed") Data=Data[NotAllZeroNames,] if(!is.null(NgVector))NgVector=NgVector[NotAllZeroNames] if(is.null(NgVector))NgVector=rep(1,nrow(Data)) if(length(sizeFactors)!=ncol(Data))sizeFactors=sizeFactors[NotAllZeroNames,] #ReNameThem IsoNamesIn=rownames(Data) Names=paste("I",c(1:dim(Data)[1]),sep="") names(IsoNamesIn)=Names rownames(Data)=paste("I",c(1:dim(Data)[1]),sep="") names(NgVector)=paste("I",c(1:dim(Data)[1]),sep="") # If PossibleCond==NULL, use all combinations NumCond=nlevels(Conditions) CondLevels=levels(Conditions) #library(blockmodeling) if(is.null(AllParti)){ AllPartiList=sapply(1:NumCond,function(i)nkpartitions(NumCond,i)) AllParti=do.call(rbind,AllPartiList) colnames(AllParti)=CondLevels rownames(AllParti)=paste("Pattern",1:nrow(AllParti),sep="") } if(length(sizeFactors)==length(Data)){ rownames(sizeFactors)=rownames(Data) colnames(sizeFactors)=Conditions } NoneZeroLength=nlevels(as.factor(NgVector)) NameList=sapply(1:NoneZeroLength,function(i)names(NgVector)[NgVector==i],simplify=F) DataList=sapply(1:NoneZeroLength , function(i) Data[NameList[[i]],],simplify=F) names(DataList)=names(NameList) NumEachGroup=sapply(1:NoneZeroLength , function(i)dim(DataList)[i]) # Unlist DataList.unlist=do.call(rbind, DataList) # Divide by SampleSize factor if(length(sizeFactors)==ncol(Data)) DataList.unlist.dvd=t(t( DataList.unlist)/sizeFactors) if(length(sizeFactors)==length(Data)) DataList.unlist.dvd=DataList.unlist/sizeFactors # Pool or Not if(Pool==T){ DataforPoolSP.dvd=MeanforPoolSP.dvd=vector("list",NumCond) for(lv in 1:NumCond){ DataforPoolSP.dvd[[lv]]=matrix(DataList.unlist.dvd[,Conditions==levels(Conditions)[lv]],nrow=dim(DataList.unlist)[1]) MeanforPoolSP.dvd[[lv]]=rowMeans(DataforPoolSP.dvd[[lv]]) } MeanforPool.dvd=rowMeans(DataList.unlist.dvd) NumInBin=floor(dim(DataList.unlist)[1]/NumBin) StartSeq=c(0:(NumBin-1))*NumInBin+1 EndSeq=c(StartSeq[-1]-1,dim(DataList.unlist)[1]) MeanforPool.dvd.Sort=sort(MeanforPool.dvd,decreasing=T) MeanforPool.dvd.Order=order(MeanforPool.dvd,decreasing=T) PoolGroups=sapply(1:NumBin,function(i)(names(MeanforPool.dvd.Sort)[StartSeq[i]:EndSeq[i]]),simplify=F) #FCforPool=MeanforPoolSP.dvd1/MeanforPoolSP.dvd2 # Use GeoMean of every two-group partition Parti2=nkpartitions(NumCond,2) FCForPoolList=sapply(1:nrow(Parti2),function(i)rowMeans(do.call(cbind, MeanforPoolSP.dvd[Parti2[i,]==1]))/ rowMeans(do.call(cbind,MeanforPoolSP.dvd[Parti2[i,]==2])), simplify=F) FCForPoolMat=do.call(cbind,FCForPoolList) FCforPool=apply(FCForPoolMat,1,function(i)exp(mean(log(i)))) names(FCforPool)=names(MeanforPool.dvd) FC_Use=names(FCforPool)[which(FCforPool>=quantile(FCforPool[!is.na(FCforPool)],PoolLower) & FCforPool<=quantile(FCforPool[!is.na(FCforPool)],PoolUpper))] PoolGroupVar=sapply(1:NumBin,function(i)(mean(apply(matrix(DataList.unlist[PoolGroups[[i]][PoolGroups[[i]]%in%FC_Use],],ncol=ncol(DataList.unlist)),1,var)))) PoolGroupVarInList=sapply(1:NumBin,function(i)(rep(PoolGroupVar[i],length(PoolGroups[[i]]))),simplify=F) PoolGroupVarVector=unlist(PoolGroupVarInList) VarPool=PoolGroupVarVector[MeanforPool.dvd.Order] names(VarPool)=names(MeanforPool.dvd) } DataListSP=vector("list",nlevels(Conditions)) DataListSP.dvd=vector("list",nlevels(Conditions)) SizeFSP=DataListSP MeanSP=DataListSP VarSP=DataListSP GetPSP=DataListSP RSP=DataListSP CISP=DataListSP tauSP=DataListSP NumEachCondLevel=summary(Conditions) if(Pool==F & is.null(CI)) CondLevelsUse=CondLevels[NumEachCondLevel>1] if(Pool==T | !is.null(CI)) CondLevelsUse=CondLevels NumCondUse=length(CondLevelsUse) for (lv in 1:nlevels(Conditions)){ DataListSP[[lv]]= matrix(DataList.unlist[,Conditions==levels(Conditions)[lv]],nrow=dim(DataList.unlist)[1]) rownames(DataListSP[[lv]])=rownames(DataList.unlist) DataListSP.dvd[[lv]]= matrix(DataList.unlist.dvd[,Conditions==levels(Conditions)[lv]],nrow=dim(DataList.unlist.dvd)[1]) if(ncol(DataListSP[[lv]])==1 & Pool==F & !is.null(CI)){ CISP[[lv]]=matrix(CI[,Conditions==levels(Conditions)[lv]],nrow=dim(DataList.unlist.dvd)[1]) tauSP[[lv]]=matrix(tau[,Conditions==levels(Conditions)[lv]],nrow=dim(DataList.unlist.dvd)[1]) } # no matter sizeFactors is a vector or a matrix. Matrix should be columns are the normalization factors # may input one for each if(length(sizeFactors)==ncol(Data))SizeFSP[[lv]]=sizeFactors[Conditions==levels(Conditions)[lv]] if(length(sizeFactors)==length(Data))SizeFSP[[lv]]=sizeFactors[,Conditions==levels(Conditions)[lv]] MeanSP[[lv]]=rowMeans(DataListSP.dvd[[lv]]) names(MeanSP[[lv]])=rownames(DataListSP[[lv]]) if(length(sizeFactors)==ncol(Data))PrePareVar=sapply(1:ncol( DataListSP[[lv]]),function(i)( DataListSP[[lv]][,i]- SizeFSP[[lv]][i]*MeanSP[[lv]])^2 /SizeFSP[[lv]][i]) if(length(sizeFactors)==length(Data))PrePareVar=sapply(1:ncol( DataListSP[[lv]]),function(i)( DataListSP[[lv]][,i]- SizeFSP[[lv]][,i]*MeanSP[[lv]])^2 /SizeFSP[[lv]][,i]) if(ncol(DataListSP[[lv]])==1 & Pool==F & !is.null(CI)) VarSP[[lv]]=as.vector(((DataListSP[[lv]]/tauSP[[lv]]) * CISP[[lv]]/(CIthre*2))^2) if( Pool==T){ VarSP[[lv]]=VarPool } if(ncol(DataListSP[[lv]])!=1){ VarSP[[lv]]=rowSums(PrePareVar)/ncol( DataListSP[[lv]]) names(VarSP[[lv]])=rownames(DataList.unlist) GetPSP[[lv]]=MeanSP[[lv]]/VarSP[[lv]] RSP[[lv]]=MeanSP[[lv]]*GetPSP[[lv]]/(1-GetPSP[[lv]]) } names(MeanSP[[lv]])=rownames(DataList.unlist) } # Get Empirical R # POOL R??? MeanList=rowMeans(DataList.unlist.dvd) VarList=apply(DataList.unlist.dvd, 1, var) if(NumCondUse!=0){ Varcbind=do.call(cbind,VarSP[CondLevels%in%CondLevelsUse]) PoolVarSpeedUp_MDFPoi_NoNormVarList=rowMeans(Varcbind) VarrowMin=apply(Varcbind,1,min) } if(NumCondUse==0) { NumFCgp=choose(NumCond,2) FC_Use_tmp=vector("list",NumFCgp) aa=1 for(k1 in 1:(NumCond-1)){ for(k2 in (k1+1):NumCond){ FCforPool=DataList.unlist.dvd[,k1]/DataList.unlist.dvd[,k2] names(FCforPool)=rownames(DataList.unlist.dvd) FC_Use_tmp[[aa]]=names(FCforPool)[which(FCforPool>=quantile(FCforPool[!is.na(FCforPool)],.25) & FCforPool<=quantile(FCforPool[!is.na(FCforPool)],.75))] aa=aa+1 }} FC_Use=Reduce(intersect,FC_Use_tmp) if(length(FC_Use)==0){ All_candi=unlist(FC_Use_tmp) FC_Use=names(table(All_candi))[1:3] } Var_FC_Use=apply( DataList.unlist.dvd[FC_Use,],1,var ) MeanforPool=apply( DataList.unlist.dvd,1,mean ) Mean_FC_Use=apply( DataList.unlist.dvd[FC_Use,],1,mean ) FC_Use2=which(Var_FC_Use>=Mean_FC_Use) Var_FC_Use2=Var_FC_Use[FC_Use2] Mean_FC_Use2=Mean_FC_Use[FC_Use2] Phi=mean((Var_FC_Use2-Mean_FC_Use2)/Mean_FC_Use2^2) VarEst= MeanforPool*(1+MeanforPool*Phi) if(Print==T)message(paste("No Replicate - estimate phi",round(Phi,5), "\n")) Varcbind=VarEst PoolVarSpeedUp_MDFPoi_NoNormVarList=VarEst VarrowMin=VarEst } GetP=MeanList/PoolVarSpeedUp_MDFPoi_NoNormVarList EmpiricalRList=MeanList*GetP/(1-GetP) # sep #Rcb=cbind(RSP[[1]],RSP[[2]]) #Rbest=apply(Rcb,1,function(i)max(i[!is.na(i) & i!=Inf])) EmpiricalRList[EmpiricalRList==Inf] =max(EmpiricalRList[EmpiricalRList!=Inf]) # fine # GoodData=names(MeanList)[EmpiricalRList>0 & VarrowMin!=0 & EmpiricalRList!=Inf & !is.na(VarrowMin) & !is.na(EmpiricalRList)] NotIn=names(MeanList)[EmpiricalRList<=0 | VarrowMin==0 | EmpiricalRList==Inf | is.na(VarrowMin) | is.na(EmpiricalRList)] #NotIn.BestR=Rbest[NotIn.raw] #NotIn.fix=NotIn.BestR[which(NotIn.BestR>0)] #EmpiricalRList[names(NotIn.fix)]=NotIn.fix #print(paste("ZeroVar",sum(VarrowMin==0), "InfR", length(which(EmpiricalRList==Inf)), "Poi", length(which(EmpiricalRList<0)), "")) #GoodData=c(GoodData.raw,names(NotIn.fix)) #NotIn=NotIn.raw[!NotIn.raw%in%names(NotIn.fix)] EmpiricalRList.NotIn=EmpiricalRList[NotIn] EmpiricalRList.Good=EmpiricalRList[GoodData] EmpiricalRList.Good[EmpiricalRList.Good<1]=1+EmpiricalRList.Good[EmpiricalRList.Good<1] if(length(sizeFactors)==ncol(Data)) EmpiricalRList.Good.mat= outer(EmpiricalRList.Good, sizeFactors) if(length(sizeFactors)==length(Data)) EmpiricalRList.Good.mat=EmpiricalRList.Good* sizeFactors[GoodData,] # Only Use Data has Good q's DataList.In=sapply(1:NoneZeroLength, function(i)DataList[[i]][GoodData[GoodData%in%rownames(DataList[[i]])],],simplify=F) DataList.NotIn=sapply(1:NoneZeroLength, function(i)DataList[[i]][NotIn[NotIn%in%rownames(DataList[[i]])],],simplify=F) DataListIn.unlist=do.call(rbind, DataList.In) DataListNotIn.unlist=do.call(rbind, DataList.NotIn) DataListSPIn=vector("list",nlevels(Conditions)) DataListSPNotIn=vector("list",nlevels(Conditions)) EmpiricalRList.Good.mat.SP=vector("list",nlevels(Conditions)) for (lv in 1:nlevels(Conditions)){ DataListSPIn[[lv]]= matrix(DataListIn.unlist[,Conditions==levels(Conditions)[lv]],nrow=dim(DataListIn.unlist)[1]) if(length(NotIn)>0) DataListSPNotIn[[lv]]= matrix(DataListNotIn.unlist[,Conditions==levels(Conditions)[lv]],nrow=dim(DataListNotIn.unlist)[1]) rownames(DataListSPIn[[lv]])=rownames(DataListIn.unlist) if(length(NotIn)>0)rownames(DataListSPNotIn[[lv]])=rownames(DataListNotIn.unlist) EmpiricalRList.Good.mat.SP[[lv]]=matrix(EmpiricalRList.Good.mat[,Conditions==levels(Conditions)[lv]],nrow=dim(EmpiricalRList.Good.mat)[1]) } NumOfEachGroupIn=sapply(1:NoneZeroLength, function(i)max(0,dim(DataList.In[[i]])[1])) NumOfEachGroupNotIn=sapply(1:NoneZeroLength, function(i)max(0,dim(DataList.NotIn[[i]])[1])) #Initialize SigIn & ... AlphaIn=0.5 BetaIn=rep(0.5,NoneZeroLength) PIn=rep(1/nrow(AllParti),nrow(AllParti)) ####use while to make an infinity round? UpdateAlpha=NULL UpdateBeta=NULL UpdateP=NULL UpdatePFromZ=NULL Timeperround=NULL for (times in 1:maxround){ temptime1=proc.time() UpdateOutput=suppressWarnings(LogNMulti(DataListIn.unlist,DataListSPIn, EmpiricalRList.Good.mat ,EmpiricalRList.Good.mat.SP, NumOfEachGroupIn, AlphaIn, BetaIn, PIn, NoneZeroLength, AllParti,Conditions)) message(paste("iteration", times, "done \n",sep=" ")) AlphaIn=UpdateOutput$AlphaNew BetaIn=UpdateOutput$BetaNew PIn=UpdateOutput$PNew PFromZ=UpdateOutput$PFromZ FOut=UpdateOutput$FGood UpdateAlpha=rbind(UpdateAlpha,AlphaIn) UpdateBeta=rbind(UpdateBeta,BetaIn) UpdateP=rbind(UpdateP,PIn) UpdatePFromZ=rbind(UpdatePFromZ,PFromZ) temptime2=proc.time() Timeperround=c(Timeperround,temptime2[3]-temptime1[3]) message(paste("time" ,round(Timeperround[times],2),"\n",sep=" ")) Z.output=UpdateOutput$ZEachGood Z.NA.Names=UpdateOutput$zNaNName } #Remove this } after testing!! # if (times!=1){ # if((UpdateAlpha[times]-UpdateAlpha[times-1])^2+UpdateBeta[times]-UpdateBeta[times-1])^2+UpdateR[times]-UpdateR[times-1])^2+UpdateP[times]-UpdateP[times-1])^2<=10^(-6)){ # Result=list(Sig=SigIn, Miu=MiuIn, Tau=TauIn) # break # } # } #} ##########Change Names############ ## Only z are for Good Ones ## Others are for ALL Data GoodData=GoodData[!GoodData%in%Z.NA.Names] IsoNamesIn.Good=as.vector(IsoNamesIn[GoodData]) RealName.Z.output=Z.output RealName.F=FOut rownames(RealName.Z.output)=IsoNamesIn.Good rownames(RealName.F)=IsoNamesIn.Good RealName.EmpiricalRList=sapply(1:NoneZeroLength,function(i)EmpiricalRList[names(EmpiricalRList)%in%NameList[[i]]], simplify=F) RealName.MeanList=sapply(1:NoneZeroLength,function(i)MeanList[names(MeanList)%in%NameList[[i]]], simplify=F) RealName.SPMeanList=sapply(1:NoneZeroLength,function(i)sapply(1:length(MeanSP), function(j)MeanSP[[j]][names(MeanSP[[j]])%in%NameList[[i]]],simplify=F), simplify=F) RealName.SPVarList=sapply(1:NoneZeroLength,function(i)sapply(1:length(VarSP), function(j)VarSP[[j]][names(VarSP[[j]])%in%NameList[[i]]],simplify=F), simplify=F) RealName.DataList=sapply(1:NoneZeroLength,function(i)DataList[[i]][rownames(DataList[[i]])%in%NameList[[i]],], simplify=F) RealName.VarList=sapply(1:NoneZeroLength,function(i)VarList[names(VarList)%in%NameList[[i]]], simplify=F) RealName.PoolVarList=sapply(1:NoneZeroLength,function(i)PoolVarSpeedUp_MDFPoi_NoNormVarList[names(PoolVarSpeedUp_MDFPoi_NoNormVarList)%in%NameList[[i]]], simplify=F) RealName.QList=sapply(1:NoneZeroLength,function(i)sapply(1:length(GetPSP), function(j)GetPSP[[j]][names(GetPSP[[j]])%in%NameList[[i]]],simplify=F), simplify=F) for (i in 1:NoneZeroLength){ tmp=NameList[[i]] Names=IsoNamesIn[tmp] RealName.MeanList[[i]]=RealName.MeanList[[i]][NameList[[i]]] RealName.VarList[[i]]=RealName.VarList[[i]][NameList[[i]]] for(j in 1:NumCond){ RealName.SPMeanList[[i]][[j]]=RealName.SPMeanList[[i]][[j]][NameList[[i]]] if(!is.null(RealName.QList[[i]][[j]])){ RealName.QList[[i]][[j]]=RealName.QList[[i]][[j]][NameList[[i]]] RealName.SPVarList[[i]][[j]]=RealName.SPVarList[[i]][[j]][NameList[[i]]] names(RealName.QList[[i]][[j]])=Names names(RealName.SPVarList[[i]][[j]])=Names } names(RealName.SPMeanList[[i]][[j]])=Names } RealName.EmpiricalRList[[i]]=RealName.EmpiricalRList[[i]][NameList[[i]]] RealName.PoolVarList[[i]]=RealName.PoolVarList[[i]][NameList[[i]]] RealName.DataList[[i]]=RealName.DataList[[i]][NameList[[i]],] names(RealName.MeanList[[i]])=Names names(RealName.VarList[[i]])=Names names(RealName.EmpiricalRList[[i]])=Names names(RealName.PoolVarList[[i]])=Names rownames(RealName.DataList[[i]])=Names } #########posterior part for other data set here later############ AllNA=unique(c(Z.NA.Names,NotIn)) AllZ=NULL AllF=NULL if(length(AllNA)==0){ AllZ=RealName.Z.output[IsoNamesIn,] AllF=RealName.F[IsoNamesIn,] } ZEachNA=NULL if (length(AllNA)>0){ Ng.NA=NgVector[AllNA] AllNA.Ngorder=AllNA[order(Ng.NA)] NumOfEachGroupNA=rep(0,NoneZeroLength) NumOfEachGroupNA.tmp=tapply(Ng.NA,Ng.NA,length) names(NumOfEachGroupNA)=c(1:NoneZeroLength) NumOfEachGroupNA[names(NumOfEachGroupNA.tmp)]=NumOfEachGroupNA.tmp PNotIn=rep(1-ApproxVal,length(AllNA.Ngorder)) MeanList.NotIn=MeanList[AllNA.Ngorder] R.NotIn.raw=MeanList.NotIn*PNotIn/(1-PNotIn) if(length(sizeFactors)==ncol(Data)) R.NotIn=matrix(outer(R.NotIn.raw,sizeFactors),nrow=length(AllNA.Ngorder)) if(length(sizeFactors)==length(Data)) R.NotIn=matrix(R.NotIn.raw*sizeFactors[names(R.NotIn.raw),],nrow=length(AllNA.Ngorder)) DataListNotIn.unlistWithZ=matrix(DataList.unlist[AllNA.Ngorder,], nrow=length(AllNA.Ngorder)) rownames(DataListNotIn.unlistWithZ)=AllNA.Ngorder DataListSPNotInWithZ=vector("list",nlevels(Conditions)) RListSPNotInWithZ=vector("list",nlevels(Conditions)) for (lv in 1:nlevels(Conditions)) { DataListSPNotInWithZ[[lv]] = matrix(DataListSP[[lv]][AllNA.Ngorder,],nrow=length(AllNA.Ngorder)) RListSPNotInWithZ[[lv]]=matrix(R.NotIn[,Conditions==levels(Conditions)[lv]],nrow=length(AllNA.Ngorder)) } FListNA=sapply(1:nrow(AllParti),function(i)sapply(1:nlevels(as.factor(AllParti[i,])), function(j)f0(do.call(cbind, DataListSPNotInWithZ[AllParti[i,]==j]),AlphaIn, BetaIn, do.call(cbind,RListSPNotInWithZ[AllParti[i,]==j]), NumOfEachGroupNA, log=T)), simplify=F) for(ii in 1:length(FListNA)) FListNA[[ii]]=matrix(FListNA[[ii]],nrow=length(AllNA.Ngorder)) FPartiLogNA=matrix(sapply(FListNA,rowSums),nrow=length(AllNA.Ngorder)) FMatNA=exp(FPartiLogNA+600) rownames(FMatNA)=rownames(DataListNotIn.unlistWithZ) PMatNA=matrix(rep(1,nrow(DataListNotIn.unlistWithZ)),ncol=1)%*%matrix(PIn,nrow=1) FmultiPNA=matrix(FMatNA*PMatNA,nrow=length(AllNA.Ngorder)) DenomNA=rowSums(FmultiPNA) ZEachNA=matrix(apply(FmultiPNA,2,function(i)i/DenomNA),nrow=length(AllNA.Ngorder)) rownames(ZEachNA)=IsoNamesIn[AllNA.Ngorder] AllZ=rbind(RealName.Z.output,ZEachNA) AllZ=AllZ[IsoNamesIn,] F.NotIn=FPartiLogNA rownames(F.NotIn)=IsoNamesIn[rownames(FMatNA)] AllF=rbind(RealName.F,F.NotIn) AllF=AllF[IsoNamesIn,] } colnames(AllZ)=rownames(AllParti) colnames(AllF)=rownames(AllParti) rownames(UpdateAlpha)=paste("iter",1:nrow(UpdateAlpha),sep="") rownames(UpdateBeta)=paste("iter",1:nrow(UpdateBeta),sep="") rownames(UpdateP)=paste("iter",1:nrow(UpdateP),sep="") rownames(UpdatePFromZ)=paste("iter",1:nrow(UpdatePFromZ),sep="") colnames(UpdateBeta)=paste("Ng",1:ncol(UpdateBeta),sep="") CondOut=levels(Conditions) names(CondOut)=paste("Condition",c(1:length(CondOut)),sep="") AllZWith0=matrix(NA,ncol=ncol(AllZ),nrow=nrow(Dataraw)) rownames(AllZWith0)=rownames(Dataraw) colnames(AllZWith0)=colnames(AllZ) if(is.null(AllZeroNames))AllZWith0=AllZ if(!is.null(AllZeroNames))AllZWith0[names(NotAllZeroNames),]=AllZ[names(NotAllZeroNames),] #############Result############################ Result=list(Alpha=UpdateAlpha,Beta=UpdateBeta,P=UpdateP,PFromZ=UpdatePFromZ, Z=RealName.Z.output,PoissonZ=ZEachNA, RList=RealName.EmpiricalRList, MeanList=RealName.MeanList, VarList=RealName.VarList, QList=RealName.QList, SPMean=RealName.SPMeanList, SPEstVar=RealName.SPVarList, PoolVar=RealName.PoolVarList , DataList=RealName.DataList,PPpattern=AllZ,f=AllF, AllParti=AllParti, PPMat=AllZ,PPMatWith0=AllZWith0, ConditionOrder=CondOut) } EBSeq/R/RankNorm.R0000644000175000017500000000046414136050172013421 0ustar nileshnilesh RankNorm=function(Data){ if(ncol(Data)==1)stop("Only 1 sample!") RankData=apply(Data, 2, rank) SortData=apply(Data, 2, sort) SortMean=rowMeans(SortData) SortMean[SortMean==0]=1 NormMatrix=sapply(1:ncol(Data), function(i)Data[,i]/(SortMean[RankData[,i]])) NormMatrix[NormMatrix==0]=1 NormMatrix } EBSeq/NEWS0000644000175000017500000000673014136050172012047 0ustar nileshnilesh CHANGES IN VERSION 1.11.1 ------------------------ o Fixed a bug in EBTest() which may cause error when performing isoform DE testing 1 sample vs. multiple samples. CHANGES IN VERSION 1.9.3 ------------------------ o Correct typos in GetDEResults help file. o Include an alternative method for normalization. The alternative method is similar to median-by-ratio normalization, but can deal with the cases when all of the genes/isoforms have at least one zero counts (in which case the median-by-ratio normalization will fail). This alternative method is developed for single-cell RNA-seq analysis where the dataset always contains a large amount of zeros. CHANGES IN VERSION 1.9.2 ------------------------ o Fixed a bug which may cause error when input a matrix to the sizeFactors parameter CHANGES IN VERSION 1.9.1 ------------------------ o Added Q&A seqction in vignette to address common questions CHANGES IN VERSION 1.7.1 ------------------------ o In EBSeq 1.7.1, EBSeq incorporates a new function GetDEResults() which may be used to obtain a list of transcripts under a target FDR in a two-condition experiment. The results obtained by applying this function with its default setting will be more robust to transcripts with low variance and potential outliers. By using the default settings in this function, the number of genes identified in any given analysis may differ slightly from the previous version (1.7.0 or order). To obtain results that are comparable to results from earlier versions of EBSeq (1.7.0 or older), a user may set Method="classic" in GetDEResults() function, or use the original GetPPMat() function. The GeneDEResults() function also allows a user to modify thresholds to target genes/isoforms with a pre-specified posterior fold change. o Also, in EBSeq 1.7.1, the default settings in EBTest() and EBMultiTest() function will only remove transcripts with all 0's (instead of removing transcripts with 75th quantile less than 10 in version 1.3.3-1.7.0). To obtain a list of transcripts comparable to the results generated by EBSeq version 1.3.3-1.7.0, a user may change Qtrm = 0.75 and QtrmCut = 10 when applying EBTest() or EBMultiTest() function. CHANGES IN VERSION 1.5.4 ------------------------ o An extra numerical approximation step is implemented in EBMultiTest() function to avoid underflow. The underflow is likely due to large number of samples. A bug in EBMultiTest() is fixed. The bug will cause error when there is exactly 1 gene/isoform that needs numerical approximation. CHANGES IN VERSION 1.5.3 ------------------------- BUG FIXES o Fixed a bug that may generate NA FC estimates when there are no replicates. CHANGES IN VERSION 1.5.2 ------------------------ NEW FEATURES o An extra numerical approximation step is implemented in EBTest() function to avoid underflow. The underflow is likely due to large number of samples. CHANGES IN VERSION 1.3.3 ------------------------ NEW FEATURES o In EBSeq 1.3.3, the default setting of EBTest function will remove low expressed genes (genes whose 75th quantile of normalized counts is less than 10) before identifying DE genes. These two thresholds can be changed in EBTest function. Because low expressed genes are disproportionately noisy, removing these genes prior to downstream analyses can improve model fitting and increase robustness (e.g. by removing outliers). EBSeq/inst/0000755000175000017500000000000014136070500012314 5ustar nileshnileshEBSeq/inst/doc/0000755000175000017500000000000014136070500013061 5ustar nileshnileshEBSeq/inst/doc/EBSeq_Vignette.pdf0000644000175000017500000454005014136070500016370 0ustar nileshnilesh%PDF-1.5 % 150 0 obj << /Length 1590 /Filter /FlateDecode >> stream xZKsGWp\f)KTdrsXJPB,cfɎ,qX1={aա[2JZꜴL(Zy! ke򱂟>gYr̷ ()m'gm^O᪰T&'8k¯=x|ʤFI5jאeSMnQb,r%hw޶s"WTIQH3:ůSӢ=loVbWhIaBXRD _HnÀƃZ]_8~4Jc^ac e>@Yx)oK۠DH]Ha$X [PƟ۩r={9RqIK#q 4Jx܇(xLI_)ճe5v*3Oh&)lj]vQ48-2Y@#TY4~]jX&Y?ZV+~ YӡN?<>L"^Ӡf>1^dm/ hR:0<. AvBVs0N- n(n!X-h+*PAǓ~9/`K "uj5r#;ދFuMP0,o!s:}bJ5+ Y΢6u)H~p!YûdnN5s&ySWn5>![:bok8N/Yf1XKK{.'u4nKcfD_Y᫾`ՐD^3rW߸jVe阔R i֡%qcI+";wNb3ޑMUs22EOApCg?!ߣ50X}k~t#;ǽ1Gq)qOWx2NidOB>nc^.mq~ǒ1O |3 0 jʟtxnΚqpotuj;A)"A03GA"k9Qڣ:ݴ&NWz֭&nX#FwNU pkZ79Ur+kXgjFp^;XxssNh%рP.'Z~b/<|p=<pm:tRҙr@Iͥ%ё_) ΣZd^V% Xծf U6)ao-V^Ө_YSe~ qu17Cf:trd;Ɲ}pӑmgfE g^s)=QVy5uIp0t|"J\m.}x9˖.}O@KyFծ$x|gD@sRy!&YO/9HÐ}#&aAƹ<AWK9-4X6ڗZւ̪}Ph#ₘI_g2FțwՑܜMy)b7: Xѱ7l9 5YƦX{[JfU+KpXGM|cTnB[lB~ŖowzqV͛]~؊t: E/:/_jE. endstream endobj 203 0 obj << /Length 3350 /Filter /FlateDecode >> stream x[[۶~􉚉I>uR{\g'}HZM$q+JFv⤓q%C\;G_>u*]\VJRej}sVI3w6i?1&y ;Ӟ Z-/nKV?R"x?`Z*1v׳,K+mg-ͫ\hg1㊀q㞡(wY]Slq "s e,<H KHWYL-ٯbܻ4A6wò^HsOx+n۽IMjX؊jr!2QWGXZ0 W柙#--k:Vq :=:r$N@Ac{(d Byi7CSC mi^[ o7ڃ &b1q8w5#(??G6Eu/2>[y^ϝI *k1hflEdiNk z. YpP~ae=0j=` M G9iY@i1@RkVŲFeLo>Vt67ZX nH uSVp(EZo؝V'j"P$S``43Edn{p?`,W^/yhKU󢑼D%ik\w]L#eŜKf?@vp#2/8JiH[xKӣGȲW%s^:1!ԲHH#0iל ; \ SZټ!01YqAx7$>8Na%Jwe p>c&Beh1ЬgRVAQv¬#jG {x~EP#~Хٛy^$o}#HqHV/t~1x(L=ݟӳә3w[^liEBIdp{&86] ȫloj5;Xj\둇5MLy,U\,L@.dؔVvOd`ҹ'r#I:[XZ'3lxppv ?` O7̿攂׊]Ui^,nXBSW4 wm%x%'z F3۠i=T>Xsi0<˓oP^1ۊIƬ;^@ 85Si9SYVq@aYy ,n}b./de E6@zG<1IӼLR)Yj(ULH] ͜g#x0 0WxwfaU(H)C?ky1B.[€vb!e,ӱ2N0QeU<(b/*O&*:]-4aIF P|Q/3Ew\0}p$L@apo8D("J)|3 !. rpg" 6s>a ,`/!r&V\M>ӷJ8gBwiհ*0~6bn(tD܇r|[KcX=t D8Et^ƹR]LPiD&R>4*n))yAEFu8*/ap[[a1a/aH|Dz("-I{:!@}-ީ+|'A'2'2*w%Dt90/4R~=pMfqHйGWyHpC^eeEktnEunF%C>fdzU>N=*iT=*({ u,uO5/"؍Nj~/8CտcHmC4;+S& A"'`NaLs@*NEH vqSkqra̤X'i=8^*oZz9;erpi|/E+P'OiǪA Ҳ3!.T* Tr9qgG"O:s5U,yIu$Qԝ(Rr󁜟yͭR\DSN Nyco#?p1.\Agd,&OYܙQL1t1+)9H NȏacouPzܾh\F'&v*TNxj_yf)owYpW;uhY$g-(hx} ה^CkD'W)/v.]GC;# `B;q:11~ӂ:DLhI_ tk }]Mo9|1u@YQ/wW{m/0 ' b9N(֊X {AD~vԄx Ǟ-lLN#X5IC&ά/&[ҾX9v= pwn(vdZ,lQ4seP} endstream endobj 2 0 obj << /Type /ObjStm /N 100 /First 812 /Length 2128 /Filter /FlateDecode >> stream xڽYnH}WyA0'Фx~OQ-_YP hQ>}TuUuS KZ#c)$@RTh I?/"%I)?Sd IHʑvT$=#)Ik2?2Qd[^wrό"$"H8P 0 [S0x2 9,N~zF- M!zOK.3<"!!D9BnDQܬn[YG+'–\R>VOJ?٧*V> stream x]oݿB.b]8 z".zYJ"ɥ3Cr"pDqřp$zUD)3f$Czd/&]P8bPJ;891jH wsg2PƺTK[MX6,p~;lpnr«`?6Q3]xcziK~: ^B/<1pymMk 2~z]l=oh d lbyݍ$pL1q5/ 4֋2$'im N!"LJ_E]*Q3 "|d+[d+]' z%|)" ءByT[W9O`}p?ʯ& ?n(u']|_Í2?σnË sl" 7f,d۰?A@X)wD%D"+]0^QVo >tLP0 FGnEj LY8}g̠ʧeIBI.Sb.g6xO$U4%&2#>w-d (9xxQ;B9E/y91v!HBnNx d{Va0 ) Y`81k^ fKYh.2B*m@@I̙vu q5sՒTs3l4:&a A`E]iB0QXzYIU~տr\RlѼ.ym"$[g‏YYKtY7L\ӏvcBlDԬpخ]ٕ!6))LI3@r)x|t/Ɂ$ُ3x aΫqqD K[ d\qe71|𢿄N-X wi"H~9Gk2cLQQrZr+sTVM4\d e8?ו>f((oE <ѹdBUJ넎7tf_ srCR, 2#L1r\~ hphxh,P΂Jͥ 1褛7\6 ϏW={{:1p7Xc#1ii}o 8 YRyK_dޅVm\;rRE?e!饂6&n\:|) J!D쒉ƚUSZ!>XǚZ<]6T4BմƐexf&}{HʞNVSB@Ү|vol@9ky'eN|}JTH>`b`#{A#~]hx=DDAas_ 6 ɤ?w-$fC^4F'ai/iT:ul6^Tu_nzţTm[F, IA(ŀaJ%KN?V ߞv%c|/?b%t];w0%T8VDЗf{/: d\ye/rPD/ڄ/*e@aogR@G$яd;O#)jwx67A}u[mBtՂ92'7RRɕDBvJtwtw5 ƺօYEEPq+xi>8Ɵ> >> stream xyeg?s,0 0((@YV-ff%X.Ǵ\,3sEs/4,+Em2qP`aֳo}x}>9ΰ sa~_꺮 0 0 0Cmwaaazaap@0 0 0Caaf=0 0 a8gaa!  0 0 3သaaa03 0 0zaap@0 0 0Caaf=0 0 a8gaa!  0 0 3သaaa03 0 0zaap@0 0 0Caaf=0 0 a8gaa!  0 0 3သaaa03 0 0zaap@0 0 0Caaf=0 0 a8gaa!  0 0 3သaaa03 0 0zav .\p+VXbܹsΝЂGXrʕ+gϞ={l|{՟}fΜ9ś^Y{'s;緶t}ٓل> qavx<o˗/_N]A_| ,X`޼yw\}O#>8t{5qEf͚5k,2B_ګ h{=ovlzOWသa]c0>O5zzò9s̙3mѢE-}O?O4 t8績3}dګܟIf_za |?'cdxC#.W30gwI,_ӽq<>ѵ{*V}=0k0x8T_ )tmTcG5i<?0ib2#.]tR}c0D0̾@mBW*/3e=+kw2&}kU.}vfသa]@] ([W^z`CLW *Q쎫XПcae V@g!I3ʕ5UP<ޢ\e*h=0 OB:4SoQqE_ z|M ^ =XWzx]z4ʜg8gfՋ=M6 {s@t,yCv*mBO#Ч# V@WuX){063 ]UZ;R#I%5zG{6Y=*21>]Wa]@zr侠e=8j{Ctz|M {*WW4_ef}4Q뾀:B c9gn0/hw6Y{' .\pٳgϞM=Xɫ==]K]_?Wٯ' aoˮ@:trx5凍F,_ӽ9Je.7}U^#^0 2v,Y@_ʃ>Pϊ[VF@W?0+tě#CwqHUFW03 ؙs #ܱ?խ {aǮ,_ӽCuAK_Wzu ws(?aaa!2 3dz{qu8}Dow{*-2 1{k^w}#s@03 0 0zaap@0 0 0Caaf=0 0 afGxxM6mڴia֭[n 7p 7 / ]]]]]]//<ԏ}w7{~ c$d2y^xtXjHp8뺮`.|>r\nʔ)SLFhtkWH$p\t:N~߿gm^,B*JR{}ݞ{~Ϸ{)|Sgv~Vmܸ~@G^yW\.۾&N8qj˖-[lO~;<a{{ S[0b… .\xŋ|7|swضm۶m۶nݺu֩SN:ux_WRɶGY|#`s-/~(s̙c8Hgaa\(l>( ZZ[u EYqVk!P~zaaf}{gXz͞(g(JKˬYRDRK.-͛75paactu%b9/~XQ~3eƌF؆ 0 0Tm74*aISͼy1 0 0`.t]Wtu!CZ[[[eʕ+E3 0 0{T*K#3GQziny=0 0 @> ,=PuAVY2k$7JTzC"?gΜ9sx<=0 oQlS[Kueliiiှ:3f͚5kz3 0 7o.{/ro=dt)03֣e 0 ]o5c2Ra*03ng%a޼y̓؆7 0̾BbTmhnﶶr!=+Ce_\WlC"Ⱎatw'bwJ&`}DC^S2.7iVe 0 }$ )oYh]BQV䦷Py21;@ܔѐ٠쵥[P#6݂(oٲ}X[_aݣy늀quMGD!m3f?ϸn廂Ru>@3z!J߁z'8ga6^ MZEMR]C6[*1J%.#Ǒ麪*뚦TC_4 M+6MSU5qq4-/4-JźxXTUMTjk;΂t 㮻CRWzŢ㸮l[ zEA%ι1Xr0 0 Ʋmb{LD+1;yiiii)<h8C0 0 STg)_.euڢiB$Z4c8(Ţ̘#Mboܶ]-PGGWaruu+W>Xqig8ccۆs[Qjkq:Q~N|1;+9ow߲e֭]fG\C00(%m`Uy}ykjZk ^ ƣ>.K*/b>VO+^1>=nF3 xǏ?V { YYf͚5kaf` .w-D*|lt]+/>]u!8@)$7$+].'-lO[ eil5j#F;V*9V](`:m>ٿ|3 *jo2Q&ɜzYgHM - g@O@`eEXb(a.]tϟ?ƌ3fX` 駟~i0 3 4NbWi>v䑟>(t_====V*J/3Ϝ|rpY͎3nO*^S Eq}'״x\`9cz0 0Lo%[*T[هuׅWh)P*t^r۴IZL6-9EӔ-Zź_LۃAv_qđGuiZߏ4H)뗾bҩu?}p@0ͻ#[Wvbc07_{G3fܸhfᮮN]mjkz[M׬??BXnh{-CK"qgecǎ(Js1ƍ1jԘ1}v3 ei:sϝw[vmk,\J(lU4φ ;;X,aR9gN:}z]-;;=uVz$sVZ(\Ǜmf6e;z A9,z8l+q84g*uoʖ;|'kcǍC0M۽^=Y*`*Juu㯼ƿeY>;wn&s#Gر~ڵ55'r==rbsر'hx]?{9BmmO6Dч-|6կb9_䦛w;;.{co-}ac ^Q.^6[(`iav#CEc^7`fA!\Wr#x.)_}0+\1u|G&\/(BXjZyٮX,DX,ڰ/?\SҴ5kb1:3ww׵,1.-_ԤɤaBxџ=O8!= ,HC{VK(XK./WWD}i*@=F]w(MŶ\WN.!>ahZ&vmi~.nbuL*իO:X 'O^S[~ ЃAO׳|޶7lhk[vڵk׬җN8ӲBi}>*w7"%)[4d;DtTjmu]]Xloذ 3̠v`0 qc[ڵf >lZᚚ; ndNھsLSU׮6ۄS=V_啗_Gd2w7ވLh XKEb1OZs{p@0 0^䴷>]ZToRwA*g~UU|KZp;miV[3ZչsO9eڴM֮M 4? c͚SOM.`>8~m8}3 el [_Ss饦iuuB=0 0Ln(J\v\ PїCWÿYf!t8aC8\_=O \pe_vmmBOx LN[Gy~jk6򜸪N'~o=oƍS];o^:}EYWxW]E78.LS Rt:a0h>^<){.N$=,VZd htմX 0?a= 0 x\{+QXV.``ۊR)qZГiZ>iLeXKGet끀eiZ:˕JEדIB4|rpQE/y* [C7_{ kd [:ۿNlh50 :S+xp7 ?|՗_~饻oǑg)L"D{CK6 uV&\ra|NjWUC3'  +VX-1 0{uuɤ{mBM;.8Ql{ئ?͛.`Ƕ`~pNjnww,NH6{睅iNvӧ{g}3qeܹsU Ǐ?~?3 / '۶}[@=[,@8t-6M[fv==GP>>󙞞EU3pptҷ^[/!?ܷ =0;fE( y͛7˘ 5'0/|޵^ 9וa Xmǩ т\rhaz>_.сʋtgx<=D&Ti bNZE0X""0tT#e,˶_~yذD_4J$BK.kA?.֕W2?p@0`˘ 3>tڬ9s̙3f 3 _:Ncc&HraK_bI'uu~,o?shߺ_?4ۮ`'ﺫ!:\tvFݖNgja!PKڅV<to(,X`,C3{0RR[ 5U9U|A>O-Uy ~ q|"7TW Ţ"OB OzE_*6> vo+ZRQ|rW^_]wa+< /뛚FhDK}41c_ c:|~ /}R,b֝uwu/烋OW bóo<ӟr`3!7)6RDY/z4.]tϟ?1 vC`ӟ0 ;o+|3_q PW7GfbQt>\ O8;ijZ{׮E5_JѨpvTuGJ$Tz%nOax38gz;TZZQEa51MP)SƏWX,wQ/_|r3gΜ9s`x{f[[&xCҾR}θC$ DR-VE ۊ"BP8֚[D fRuÐSC,ҥ>ߣo3Mv衇Rɶ( &LH&Ǝ][?on7N?J_G^,ϙs≧N$J]Y1|Çwv|2&JJ&cnO(igO D~~S8gz%PptC|f)#,g4sx=A9lKKKKK? H4[rm{"TW@CؾZz?}D W: xYJZK;t>/b&EֲLSӨG; FOOgH.w]x:cL$t+_ٿ2{ * ~P?=@>-xhѢE `FznVf4{}~x]Vo/ K4de٦nVUR*IWx8\/<+Jh AT{Æk8`C, rϖe%h*fvBq\v"oT*2drɒ# Oɓg?3͞ade˷uȮ~i3i EishYihp]ԷmۚoZS䓚 ΜTfh9^CۗF{C*S+8gAݑ+O@G? >\0{$7G5wW`ӛ䦚7݂ f#2ӣ"*;)%F~iz*%~el{_|K%K3BAJt CשK4꺦ՕhłlTrL;ߙ87nk;/mm5S߼s;u=cŊEۦ!Hny) [^hu{B;A( >D.8Ӧ8G>L`45ʄx@ +*4£,Gۇ9gAd-E8>PѳPFcw=sVh(NkڡYw(` 8J ik鞣>keF0 ł~p7{r_zS5>+J>EK䕽.^szpy!XM |(ŲPy#K/ 0 Dkjd 뚆=łAZCI/߯;zt"˝sĉߏgz ֡}͚K/H0dAibhAp0hرFsi~+6 5MCha)B ^1Ըc -M.I7pyuF /b_iV[?~z,Ay т0 jܫ䱖 ((V)hK'Ob'#Db|BRH|B"T|>+R,{[m|"d 'T*m|"tF+>QLf>\TJ$Y3/?d|TLP'b;-;:RBld۷ӹ>3bTں5ё[&,gnM&s9|f/[::|oFeydD6mÆ=Q==t!/pB{{*Ϸ3/J%zDk]]eP~6M;VJ&pN eău. hOZOy*'Ol~ 8V=]_Z]{C%Ђyggggg'Z0Q%:bw -] ~ɧa4u'L(2kmn>6}{/[de%žrժ~W8W0gR"}MTS׬d XrCցLy_fAWlC, >-Τ2T=ȣ&af/SHoV^/yx;UUPbۖ%IT.uM6q$;,-Zuy\eQ( L!ƔxK@Nǻ[Ğ#FSU.P4U5Xh dWjlo{.:jN:_t ?G϶Y9f˖m9-"PִWʯZ(7 U|^#:ŹWJgr4M+?[7/W8uA@];v}z)T`W q(o' 1 wJz}{ ^>};#֗+ wPgz1n}n۶h Jܹ6,\xaK|[<}e7|>UM$>Xӎ?FU_}uV]?U~1*?T￿Ux & ;st3 0E_z aU.WEѪs"E(x#FPNA$PUZ*f6Ea$$员,-`-вW9#ÊdQd))E98(E vEk(.\O[DQ*& hQ5iR:uk6`}oF|k-kҤ p>a15MӐ?aU}-[t6l,g4u]=ha(}h+f8w@̌+Fw䕥3 8GVNyS=MOOD6laNoiYZ5Ey^k].iYWfQ (-RH1~p9BI:ifŬxx@a+ZJ_y1B8FGG:](%u]*J )0M]O$y+ee ՅB.ǯ-;E]j#{lذ\_}y&NcAtzԨ1c^|ߘ7PH@P*Bדɏ4O8!ִ^kkso~7mq](G`N.(Ȁרr$",KQ4MN3r)iV>?ȀD\O/O郥5vNC7=y%0 0{SL+ƍ單.L$?~Æ N_"ٸ =x .PDjj#1qM{M4S|u+ag葏GwA\NjmiӮK.tX,ɓ]nj_ƶO>KE Cx Kh7l,B<@gJn}g֮]SN?5Ҵre߫;h߃L6&Bpߗ:У`)4^!a!;8իW^*>f)00؇# 0Pei<8ӦZxepDUCT!7瞭[7n\.+e"G^iKuxo#ԧ:n}+;i%p&a%PC_=VѵxE]〞ae֮mmmm?= 7_qJ`O?Fe w zK`Od<׿>X,*JM=9cԧtg #A wdjkkkkkzPǏ?~#ze^ zU _4;are:ůr!Eyao}u'-i%X姴473vرƵ9x,t׾/o*Z2>QLJlŗZOoiBI(QҊOi x4 D5-23;{vWGsR[{!_<_?{1l{l$>ǍӴ@(Ӧ|a+eO\kzD7P$ԩ:}gг;sP~] Cy Ze}(݈ f'4ztC>X`JZDQ,vB&q؟2_^dٟwx\޽{*$7H6䜣R؁}BA`:1 qޢX)_9O|⨣f8pA*|!ve(BI(}eiZ"͊<%L>2MM%Nj-ʳ)uuٙɈ2YzQ4|^bRd2[Յ==7|ͱXvږ-7|aW]%)|4ZS眳`A:](J8&ve]vYGmO쳹܃@,b^az|i`{k(XTP!J{;ڞ?bEmV'G7g΄ 7Es5 ooz{۹?5yޗ1⢋ 9䶶K/=;wyuy;dih ? ׍ ҷ=3tp@O)vGujߞ?`:aXFhէZ*JkkW P?pt'~۱ 0Cqc0\"?<^o 9Jm} li.7R<,Cp r@nRq >e9Bf0 CU4 Uŧe,]uamOdi  ai>ρei+OR @O>YW?ydwG?:{o3'y%SL\E">i ޶˒ TC?^ MB!g Fe ޟȫ 9>{e܍* 9^,:d8 Bԃ=\ye޼3eƌ})%q c䎉U0&;q- ,a7rMrSw-z附8z9ň񅯼h1 MӴbQPI *0Tm!AH)JO0"uŁ f4Օ L X `T Jb>|__ |h`W&0 * fu=.Njht߿K.D=}Kz]#]橧2moTzV@Ӽ>HrcRrֲp]3b1I)h7ɍzLQ2_ܑnag -ݵzw(a7̎JW -iavj;[,7Ti>xzU]qBT?}Fu ۸&L_~{O8Ϳ)sN{|pc tϧ==Osα,UM$:*ַuBql[܏%PyH3R2" L} >Hј#Y$"~rUźܫnw2` =c.?XC}F13B/Ӫ!3>}^N# 0ӟ&M|k\|)?~"!|_z+nAQQEҗNF5 U71iG Lev4'OE)5 ZP xK~:}}ů=uqc+cyaѧ!;a z Rpo?`rY"Js Q(BB[x ޮ2+Jfe"/8(}R pŘ 8 &~( -bӔRq< ׎cZ:*>Q_y@ 5,ri3(=7q7Z_iS/O<կtRg?2.}}\@%/k%JuBSYW ]LA~+CR;\[i밿ewPr,ӽ?#lt$7.J+;6$\ D^E{UDDž>?W2+ǧzmHP#+|>_(8BBA=2V*.p A &Z -to-B:ϧ==`/]]٬hfK%鞮(Ĉjj~ڶ->|$!͛\NlѲ [nѫ˧giԨX߼975Ԉ5McS/~Ғ͎Oi}C?񗿌>\$RSgu饟a\sMm[o'^}Ehl!' ʯ muE#B 2i :&Rp'?Rb$%7x/{`Q,B d'kwjs{C_ߢ w-q\avf.EpW_ߟL l8qM{A{&s2s <WrfC-qCf,WCU%7tDaO)[| )PElq]4 [GWJaYZJ MYY=.+Z+AUu#{4YZ F!q&Q GyT.cfŢQkTiJ-xO=D6yذpE֬ _}SO0_ 8}Iް᳟=ئ۴iT|n=״+hh}>d)zB) `P' iyƘ4knYT#ߩDGUfe_ -+߀?b};J aYϟ?|(ν)W|a=ϮS5RrS&{ q~5X h٩8Bh[C iy[ۺuk`yqZZr9*>Ȗ\N f 씞߯idR #7oْL Q 46F~b-ԙzxƌu뺺Y@aCww.ՅB+ymp9U1c.gęDjj6o3>!n-N7ٴ)qu}bmw=twȑ554{A;"Rx -r#]KE޷[,MC[/̀zNi0 0 üر?Ç?Y]]gYs)9@Ooj1b_5-xlm+>x{p@O'/% 0ЇzWߴeOY4Z9z[YQibfPdG; +-P9 #BE,i&l0Q=hA3ZB!юY]a٢i# cGVUUꊢ(˕JTr۶B_.d sX,@ me 'Top>60MM;VG/Z?[o?t55b4|[o]w]l/)l /~_7msŋ5S1"-d><g "W[:oiӲ]xbrLzwo"ywQ:T _[tJK W?w-[1 ]Kn}eh^#P5d3t(uS ;AN*6t:r-]r#Z'٬!0M]O&s9!,.aӲ%rX,+(0EAM=.=bD|>loOEI+Q*Θ<(`]n4S)1Ҁ ǏWnod2vlmm0Hd.dL_}[jjz衻Bdbh}6WGJ.YF0! A009,du#mۖ#JYϧizp@H@bu]Q%ߕ7K)S(ww*od\nP_S}" dB&aٗNOo|]>}q.+_A|Hi^H$N8g/{;o+#F ^_0bDs`? 8߱ էk;L<3O a#<+s޵?EGR+jy^TgRh{Hm+]kK%Yit?8:(ԑo$GG(uYۖ*Xm-0Ey&%gr-ܗFURWzaIQӕd.g= Jl/yɓ-8gʔnZs{z{kjlٸQQ]E9&zTm~p[IhRJeQ,34gWxz n+N=;K޷4CBe1wUsnJtVRy*aafʇZ#HO}uhB3Mp ]#G A SО}o:>1 _?Fd2ƍ?|U`smPSd+ ȟ}l\(LnvU;O[~_Q1imt:Ғ%]t{ 8Zfzl9!>r̃}8 0 _,)jyފ\2r"FgD8(K E"KTep)BLԴ|^U 1wZ'x/n]"(@UF"9h40 UUK%u]т7 (SbE+kf?Z3-A=,ZɡGo>zl"ϧcȒV_jk9-Q/J`\0_z-+7X%9RQSsSEяTjn~DRq꺪һ"0Sh6z0l[+i8idK<1ޫކ~1  0eƶkɃ њ3?:I}tI~lo~xsM7lظ1zSGQUF];֮Or)SF&~_y%&N8q#;rUafx]zTtMڇ*Z.YJ? ۏ|hq]ET',2k-K#`\Hqh)u}>MT" #s/AH>F"l躑lQUUm0Δqq)AVVzWceӋ.'~~PS߾uam(tM *O(RS(?}}yd]]($:>i JZkjh-8T6t4U5MC/WT0[/ʃ6,⊫Fޫ2\den<=]u #p@?r61df~`R0&oŶuKL${@@XE)GtL wCoۮ /c3):n wvf2B):;LS9& ]]JA#o1~SSM߿qlB9AN34j&COEA7vw IϰaemZ.)MȲgG<W_:e?ew;(A)SOmp/P(pBww6+eK%[Q6͢^i&8 -̺n-KuI*|k_;O-!>\βzz^.R(Z[WanYQ>1Ey5kfzK.1f5fر(}a1̙3gΜyիW A>{ٳgWG@L_>g 0I_o{+iE/H#EA7.8:-FmpEdWQ$P~-n2\pr\NzƣfMӶU4i%㸮̀EAq-ZA6n9l&# |HȒX,m:lQ`&w&&\ooyEo=cƺus.]Hw c+1-W77zk"qM*zόXO(ajitWriix;Dir4x`GyQGU~/G?ZL٩ޛzmSSah(;{ӌ8֢wo0 쫬_?y㏯[7ec54lxU_ s̝;Ԛ5~}};//N=ic,2 aX/I>#GkNx/_|x afOBlz_*ф˻b< Z͉﷬M\  |%ϧ۷R1c@`z:?n\mm )|T}˖DBmm҇Jn&Nlh֯kQJ /ǦEs^:~,9N6Jiĉ]溉>o.^˚6nj͖J"92.)CBk"!\ CPx &iZc>ն-5o;g|Za=2SUèAՄ`FzӒ[xhQlo۟ _,ϙ3gΜ9x#,v dq67w8޽MD0 0P*|K/}N'ӦW_}~)0ڦy-mS+V|+/ 3<4}!>+P#pG;,=f_>p0 nz\]ykoEҁ_p WI1-DO*3u=IE*u] ?iHj|i(,4MU˂亮 yIUPȵ[i% 80TUӐfK%ǑR]Fb#FHAK2˕J`٦hT5&@ 'Lss=;.DK8lYL.!;>~|}}(D#8 /D(OW{}۶+V(Jsc[OS'_}uCC `֍/1iTV |o; U t]M3ϧN4siJYz0LS޷ 9x*)/5 XgWn\SVJ/p~Nz4Nz=3x=Vopxh`w.ͩrIn*/}]ugR  l0meڵ *(lVΣFb~mɤl~>/oO0&HDN: ql[l1%HkC+=$_>1 qnaJ뺮i>HnbYY qXOU4|JPSM?J~.\p¾rΝ;w\a*T7}xP*,[ݖxGЉ~dK"TB`˧.Zjt:4 +~5]]_SˇsUW%pԨ.+Wn4|>P]TծqoXՒkyѡǀ3"(a:`P[(P~1[o0 s]6>A 1B Re|D_hKѐ yJVԗgEm(ᰮF*U(Jq\yz4MMK&eQWxI-r M4{zr91drM30M0EId( }>_jj槩GEU~ePUP= 9իgϾ-[&Ouf?/KƎuaOQ"\QVzkmk[8ǛUuvQFFof蝆;= \Y9ѰRI畮PyR;tp<)A_s,G>^f鯪r?vv΀34l4Ьީ8G]a[}_|E3 0eiӞxb˖Cɓ<O>\?:OQ+XGNxc"s.bm{AnS|ԭ[UaY@iŢmcpCq#J];^e:2OuzQ2+m"?] 8G N)^<2pVaxc* N.ԙ[T5C/*!rd H#{(FglfK%!F`ӓˉѨoY({5|PPlTJ$::2p߰[e]]bG{OKWfMGG&B%7(ܼG '{Bu'64oys"ɓ7ظQT45bM<1y-[DY-۷zf͌7h\$^(J 0ȑ鴢n.(emڴhQ4MzŅ,=Qt}vqNLiju:;Y<%GLF\x<ƲaM20 ʐ%<׿PhI.> ,˲fɒ\nu.?Mg|aaFb}F2†-ȵS#?Msd1ˍ)тltyF_U+}]",wAF_*J(cW,c/ 򫙌ݢ5.l+0lqbYX+:og[LXfQ+$&Z0TT%4CLf2"]Hd2zKFAm$-({dFx?+4l6'|rɒdr˖YQ"`PQGU 喭[FO q]Zĝ8+!nk!GA92ZZ;zabr]G]j>If̾ ӭol_rةaaA?}$NG/[{!@b#[}fmy%_H!/F@N!AŔUrt_rrElW(.bglxd2't%h4oIULPt²:DG{ gUQH?7CrL 5|߮=0 3h~;+y`2B8R>o>MSۊeRi-P3l6E55R- ~<.\3BAdyb8 KZ?ÇG">e:az4xFbG-d.g1EZ&[[+\~PՖP~g@&NEȔO"lTBXuuђNJ]eQlySh}=[[?K/E Yӭ*ʁNZSc_\_ﺊilr׊j׿~qQ9n `"EQ1 ],67 ]ySu<D~=3~+/rXnmY>Tex\9\{|}oYFkL/z :TTuaSapD 0C(* E&v  J\! Ew|^dNu]\>-ޢXtVtLTѨU"ݺ5-7FVZV[[ Zƍ==\8l_( 4By} cv9w,="y3,0=: 0 -kÆNQZZ*ɓGDxcÆL:jTMͫuwuWPF 6nfs[ӟBK$F/p˖_9RT ƌc](yoh\v׿~%m[*G2d;;-Kh `Y62ߚP1(d)a===4-wϧi8az$E,Q-4Y(+ߢh7!f9weIkw+^#p> /<傠Gc(xؽugafڵp! kkWf`5t(ڡ"ܹsΝ {Mh}dWX^>JMWS!WT,+ "'@aR7e FkKRq= PW҃z$F P,i:E~iDM/((ǥ@ybH@6.IuB!f u^*J=lX$"DA]*9DAhIs9ڶ66FB#([Ay44Z6w…'SO]{m4zG*J,(?1\}}m sO6{O=xg>sL(65U(cENສÇGT8jrVWP`в9,Ѩ/""PUkiů9pg`es)-eoAR;/DnJys>aBmɤ`)E-d˄ p[[gmm鴐j7l"6)MMu۷Kѣckm.^>#GMs~{ŢߟL_>jT"ϼvWW"LPW|g?[s>[V21cTUQ |dBg~DB\ّ#c1UM&sbM߯빜q0߯iQ((ÖeTȤ>aP΢(i2k?ӠZzԔ^x6ƀbWXb ,#>aaB :{CVz%K,7n0AQƌQK/;V>ɖ&c__wҥEV9i`8gϞ={l,{Kf"$U 0;O3AQn"ɫи.-+d d4>Z2!TQ\Wi!xlj:],bGp]MKe.6e#3p&,fUUUˋ)a˲m8|8>v*eb6F:-4 Nrh>KL&s@4빜<\βlVΊJ bQCJ*uKG ',YL$r!=tMG;r'}<͸UZ?L_ 83gΜ9s^p… !A{.aa(3z5^ ] 5pS_y*GA G: g|TE@[CL- Bifm f_:!2Fo#/Q0ql =?T0qDBQmmaթjn].ɌWW PȲ6o3Жɓ[[ĉ +A(ֶ}45~U^Sc5k6oN$2cFKK]ڵ[$qw_2>a76-[v%775uv*J*5{q>iiBbk?\xG?z`"!ʂ"@4iq$&"+Uo(Jss(V,ә̄ кuRrS_&uܔKh:)]f5[Br㺶-Y[I_ymmRrC 2fL<ښLn3oWJcǶ}͛ѡѡ(T,fYvӦ _'u۾aÂHdM'*Lmm$o&6ܴ˖X fjG|lVpLS| L0+,àwu!*`4u5ƶ4WiCfCSMnP,m- lga? `rȣ;Ļ>m)4V(0AEȕi4(#/dۄ!fy#**-nVWjUw9\}m74l:bċ/}wӞ͡a<̙5jTS 'ݴ֯61]37-JT4t3o[ :?:r@o3̙3gΜյzիWI`a>*Jy "2x-V54" \ JԀA\8ÕfC!]dX@@Uu=3;| q~i* C\NI3Ѩ yAӤs 2C/v5MUi]W &Zr9˲R4'EMD|LF1a{34\7f[ˊ-+fydK%Ϻ FBPE\c׮}3Xضd2Κ6mNxRCeio~FQ[+B' ,yÛq~{O.Be!~rHY"8:ϗJRɶmB_ץKaz(}y+('55iOafPCٿEo3egX(8-M&3b7MMCa RcLg2\$r3zt]]($;Nss}}(e1w_wY>jL80 S$4X^2;pfb)4 lLP  O%7ȶlg˖D"YDkYmoYAͳY)jn;;Eq -Z]rC-kƮ.QR[ ]m[*8@`Y,nߞJx`cc$ںy(QEغ~bϋ3&\96lاN5fkmGzkt/뭭YOz`8[tvYuuuMP0GQgɒ{?cCULfܸh#A\5D">6(}LF:4H0V^|aa;l֝wUW!7KUu۶H4ɓMsҤhTQLknɒo_c/>2|_bŊ+,;w R#pG;fEv>|aw>W{'}}VzBXc8N(Ŕ>+e x D(5M)uǶ-KJ S97FF3BUlTr_ k˻D"4vZH(`=LX -KׅF "Êܰ'B.:vD>_ʊƎ WںUQ;[䆺C0a`ڴ);6*l_}ӦɓW_]1%u/(+>}x-S67/ގ+n۶-eK's۷KTND+˽>|:]ͣݻE{:]s>5BukEQI:i2RS~?ٿMUlih0o?߲::&Oƶw>Ql̚VkCz_WD|S;,""4U PV/sb8ӆ[Çׅw Z, >n?b- RV/fogEf͚5kkipOfi0 0q>+ nZ#Q7|:̩S;N2L/\Pm͚pӦ+c:gϞ={l 4 ZΝ;w\p .\p!{oP_____.T 07[:oj3z~Hh+b"O5ȗ# mKrX*A| P*ٶJO&#sF2ߜNMsDgggi==L4:z4ˢt12meᡢr54ݝ (rEe"!alVlGOr96yb-+oh0lWzz"h&g2\H ({z\. L bX,2DBL۴qx}W</Iyd^;pl 1SyMZ[{IGyM.X(;[n0lgKаa(G8(]5M< 0> &.X `YgiwG,c1ϲXiRz=JTy*z\(/Ͳt]U齊轇,X+`sчnҶ{W7sTg{ˀ%78ooR^<_/&x 3 +)_[B((<[BVhI(ӒVM3 U|屌6mJ$V.q\.* -[l#QoY#JnPDiSwvkkAܼYZZ(#8qذP77lH$2eJcc$]L&C_ok81sR)-^xalvѣc^ZZ$b1:_*G l~#'ׯ^],.Xp睎sCiܬiKpԴz_?vlcc,v֭+_2ƏFeժִ'<\EY(2K׮f?)S ^ZF$ @@6(--hkt֟1#4MzdQ46z{{:-`C> [|uG"iD6+R0h +f߯TNo%*9_`->TTv CuX,{`,Ǒ駟~iA?LhZ= 0 äR#G^](qW >g2 .bU}ѣߊ|>]SO6&MJw]JQs˒L&+nrOzaKyw^cJ 6}s} 0;^߶QSIH_ yʹ0UrlqHhdo&(Td q{zdqqIYr녂㘦nOJttXLenVT ="\JrͯRb>/vuRm,u]ۖg5$HyJ8mvX(nO8RҬmO ,O;)3[n9ᄆ_m[dЩ/˄ arqrHK xG :ǹ.UմhTQfͺ 2];VU5Me g*I%l}bYRr!ZKӦ-aWX|>YB]*+$_u)Qoh 5b+b0EedŖ 'y.ZƝ{?d?s̙3g?~Pfwa{A8Okz >E*?>>d@mtBͯBُO,rd,ckHOo Ch }F@K-@bZ,ٽL`e?T 1|e}]iG? uE)wJrݯ~xc&L7^{{?~/iӘ1Mrb/E3x BH=9>QP_~*Jyܞt9v9=Sym7RX&,[@NP반f Tww4Z*%7PED==BӌE[m2SuFoӣ;."W\Cp(X=盚R[HIJlrUO&lqM{?_Pߺ?3K$nPSlݝ`ؓN:蠺 g1"dY#Eygu]2Ŷ-{7gQ:|x< vvJEaPwlm]uL;۶㑈 0Y96L${`P|>Qp(Ȩc -KӠkǵF-Ke{V;0D架CFE9/O[\UWx/Ʃz`*2%2B*Ρ`0 (Qؔ*~S%uf5A m!r fEo"aM$r9|W2۷p9*BAU ի\#ƍ zK:zt,mt6tv2܆PȲ6n-TBy[_{m:>:uԨ^ZFP9w֑#c@`ZYҊ^ye͚WUUsoժC7Wںnj֭E}ɿm4w3Ϩj2 Ʋe_b*|>x;l܈6q/{[.YrpYӧݽm[GG,_pl;wkaLb7׿~sW^?v|bg0M]‡-`8,L0qWɭZZ#yWF~X9wNX  k4n(Aq4󕗀í-28M4: 2|&젇p)m‡:2e_i5C_96,Eh7(fEN}jO90 0LuBLX[nA(i7zi~{|>7#Դ?̙3:k5kb3Θ3GQ}wR co^{?fa>=xxH7̎a3PxﷄZjiEX@J^Us9Y| 1 "m;N8" yE8Y)I &==2w7t #驯WtwlyKY M"͖J8:m( bߤ&.03p3n!dyhУXQ4FWW:ϏWScFҊ0ִ">޺߼npxŊO|bĈ.Ӥx8wu%Aw/p@4jY{sUW]twv.^{l2kt矟H᰸ig55@2)cCCmm8L䨪i&qebpX@4F-MMx @uu,y1 ]h\ͣg8lYB|ȯUq e?bY^HR&)6tm ؇Bx:(]HvgUT# Lzw|#J-IP_y45 hAXC5d!BASmRÖe6Hw*9kkH[f=s|޲ Xܶ-0֎__ [![Zt φ }ذp۰SPA]mpt9ѣkjk{uͨQx f?ujss_i*Jkƍ--uuk_osXQ*͙b5yէR,֦(8o )SFƍFf֬Iv뭷wߍk =6|뛛;7kj"uoGmY~e)J274b5|x4i*ÑQ BA>2 YVGGcByݩIf&#݊du)t=09TU+`&TU(`P+Jx+Kື *GKB0{?;C"@S`>0 /L5z+By͚1c,?_in,E͘1bB;:m.i(Kg5zt}WJ_=lG~vwgBiv 7G/Ne諃NCl 0jHr`W޶G8 }\k v-ȵF^-uUKfitM2 ]HhGU;: $ZUuL&;OUmP?}6[,BT|>ol67B􂵅eFe:m)e[+y?X7r-2[, Prf2[(ضouzC}O!h,2 ?}~im 7`04C!y'CC 1˰ӻy 64 1>2{뚖ͦ==曯 _|tz`Y,ȥkdGl*iy~z:6(aav f2+VvwJ&>l,^/K/(Ç=™gΘ1zWϞ}x `ǿk|Bt뭟>v^]]L0/ξ|衷v?>T@OurA<avrXKp3pR+_w(KRui&n:ii <~:'1k8'FK ǩ A\[+~,">\Trpa(2x8QSB45Mlo3b1!,A6}x\RLhF"U:zt]hI&S)t.W,b2)ql۶njihǺ~:_X Hf<8g&155~6 >5~y6jTOO>_*˖կN9K_: /|l6u]Gp)b%0DMSUzQe|ĤP'Zpg¹ NI|devw}x|EK$˂v 6cB0g6}aG>gwl'uׯ_ʯ[ysWa55zܹ:}ȑK/mW\qU_=꫿gFL裗,?g׬ih;vԨ`pݺvq'N=:\^?s9nlFnhܸ#MseLץ+N]]${w-FغaXV6+P~[#G\-U~(E鞄Yg\ND_"fŲ)|׉ f}_K>TBC3}hC :iWŢaٟqC}bo6n1]V8z_34_E3?1cs2%,?_~iɒEBd .K_~ hf=g}݉EdSCsIj:t)fw> _yo2!.Z0+1"@Bu޶eK6[,(UEA+Ȥ,(&ttMƶen.W*!M}U;cBTeŶKKҼf P,&G|/&55XRQQ4.ZJ x|sx˳.eKOO:χB>aՅÖukgUbe'"H$L8F*amƍGm>O׿{lC|KGzssÇt]7M]ܯRXwAT2jTMG+ uUVUyW)T)X ʐaq бe ͑/7d+_/@oe;Sŋ/^L[XB0 7зkMx@WA9][)2,SmOTRMeՕϋP8 c&>i['TrИ fFD,& BڼYx74BqȑPںm[*E%4SUS7ذGHnD a}X戮r3ujss,˫W̌--uu/75:zux#FtthڻiK~K/Z~}>?zt(9N Koi0r&/~qhKK]]8K6tv^sw<ĝw_wJbw8} 7Lr=w\TS}?g?7q/mi(%#GR_ F]^BǛi3F">{ɳ˪˜t -c1Ҭv`F=m ]tZ fY?0#=jNxTK6{Frz roJKn6 pUaT?4@o 2W@s^^~`;~-}&^lk_{i|n$D?>{v>]R~*(V/gu!MMgqahg7߼7xȑ~SqēO~dђHPAغ/w|>]a7״pg_ggx8ӧnT[kYW^y #Flp!kvv"ǟ˅æ(J*Jhqg>3iRcckkGG*uy< O>yko~:ܖ7||p1c6oxSxW]Wp802xe L8==2䯩DdR_[|==^F}N(US##HP4s9YL8_?g.-2 ]/d x -f- e҉3:Gi꺮[.LsՊb5۷sw<%޳Ϟu]]}_/BÇL>f̦MwϘq}vi([ bFKQ,E QM*TnR)PgQ*̣lT/DQ'(jRIf= [ioOE|[ђH|yGG:](`tPṁIX^JșS<==RĂ\,fT`TJCB?DWdIkjJT.W,]J36+J%dkђrŢibOEQl6,+u;H6}7:>ɞL 쐈 XYԪ(K+XGR kP7놊KU6mjPȞu2޹?3$ZqK{}xss>vqᰪƍ&]Nx<'AWp5GOݑiʕ|p@Suu7>PyyG2ܷ)*Zꤓ.`v%7{?:jÆ>;{ٲe|ۻ1ZF(HR˲$ta, 8QájXpHɒ8+R̉e|̽^Uy0BEیGU1ؚ5eǝb{W!i y̓܁]aKȎGRk7G(ք &L|&"L_jB::Q 9[pv6H%v0ߏ)1&Z,o_[hZ!Ď8 >Öt[QxKZKJNYÇO;fmllo6m{9s„|M?=thߺwjj6lqt>(+l삂3nnִի.|ꩩSwؼyޒ~≉#njO>ٿv+*FPC EIB(8v ! C4QZE%: $:'e37a„ &zR,ذA}]D,{SYG:I/1cKN>"Vmٲgs]S{?O~kժqy=k/߼+Lٵ6'x5 ʘ0a 37a„c#XJ~xOcM*e 4 XW` H~P')4dahaB!\b&^HB(#q<&I2H~ww8HP(I&Ɉ xӧRГaq ; >&)DF===H|8$1LkkO3,}[KKnEfF1IzVQlh=D ζXilD\I,1"?f۹ "N,3la.hзuݱ3Sla%55--h{l/޸}u dLI&sryO,˲p?i=neY&~OE\t=nWV ďfBo„ &L|PH$馇jo3f^nix8'9al:t;k֜w^e9FH"qEO?WW' .hhؾaѢ©S?8+k̘#ps+*>dҳS?K.Sc}L0GB;a0a5f.s4eՑ|w`KATڛkrJ(4b@,s$D*E X,Rh͆@2T@].EA]?@X <[*c!Єrs1C; X UyI\"6 V,m0 9`i@.xTJUaO?>'/a"EDPDJ%>׋HD'Y6̞}Yv$]S~[嗭YYc|S#Gvw77|zU̲GiǣvŢ(g|gor]~.\ fg|F"q"I<jM˲ł(7NQ SZiN,0,[RmfXyg,qDzn7Hy9ivՊ\6$,\WAk{L Պ%Yh~PhD@ qGwIC?/?``gLaVM0a}Ϥ'Lxad< 'Azi{a@&ptG-ǡ)4657h<<H ÆZjȑGmmk+"@*OqG*EJSn\.B3jTqyPKK |!ԴK/}e]>_E?0#҆<8;_3GN(-zU[ݻl믣i== wo~'o Fr)..*⸺V%I E$0GQ8J &tTsrΖ@ ;lY*Bt q,.3d"z"i(ieii^p2! lK:D0CMo-Nс۟$GI9̔Ǐ>7nDo߾}vt:ꫯ; ~?y~am۶m۶X &L0uJwo"qUOHY'_w:vlgpBuWL[@^/8gď }ԜL!5/-----?o޼yA?nܸqAZXbŊ 0aķ>bǓ_JAB]>ɒ]WA a_yIh:L$PeT ahTqM3@ E3,äRdoW=F"=n!k;fHk(\<&8BV{zBDB1%}u]ӀCӚG P(4<> .xyM=!q/4}s洶vvB_D"D9K5-X|×.=!C j֭쳟RS8YSWiӜ9H0\,7x?)*F O;۲%eƌ?yΜprs)eQ=>Z5!B E 4˲, "JbMa@9h3b;Ghaj B~;Lk.㱳TECIaYF ̐[?TQ&F a9@3᨟_|_|>?C=P< ??ӦM6m:ֻo„ ?$ބ"}oqҍx0=HxY8!9N!?#@..=g,ˇ^8żo^\ dy,z,{<,?1y6^z̑#KK].U⋎Pׯ-[u# +*"V֔(j͢ESTWrV'IaEQOs-}-`4*˫VεVWtepO$A?r`yih2Ǒ06EM ˆp8H'"$A*޾4MӚI+C 6({{W'&L G+K%%_Og0x<OGGG2S3a _š=>=Nq3DzH\QH`0K& X6p8H~r` mD$ƮP(4Nlj =sE['`#$ 1A=]VQdYo8x|TӭE1:7KÀqf=+J(p_ӦY,>;gH U~h֠i%&]}uEEAD~x۶쳦./uǎ[n䓗^">oȐ3H$ThSx,9 sx#Ddyٲ믹08nذw].jZY&SXa$ B%EY,d99~h =fed)IWxE$Eeb27ζZҠ(X8֣5"ϣ(ExЂyxȊ+;:v&./邏뭨@ @1?9C?aܭ[Azwuya#F&uummv;uu~?P\\\laa1Y,)a u]9>_~˵o_[/*㸎Hct=E fi*|V&˝1eTJ׳YI,a½RmC!ޟ_y 2" (7)= ~/@;2N&$NI+o%$%:0ϱ}&L0a#:mے_⩧b1%f?<8{Yl6I<(,8t҉ʜNYkߚe~{Ϟ:wu56^嗯RY ]) 5 w9w֭~($I /\sMuum-EQ\| &uB;HZW\rJx%1p0X &L|[ =#R5B < W7hatq,y<$*ㆡ==xLGG4+OvY @$DrcIʍ)Ԅ#$妼<'jkoGV#Fl55|"݆n锛ZL5n=|y1cJJCRHI ÔWW_tуyy55 s@QǭYsE>TV~ISSSǒC\k̜9l_~Ϛ-{ݻ1W]]s3",_^_{K>[*;^}|׮],:V$.<Խ{*guյ\Q1r$ǁ ax_^!07jmoT~?\. D"t"*χعFaLeT zh:<<@LeRfq!+czRI{ox&:ykHӯ!0 ]X &L)BO<;[oݺ/JJ@gذ2>|(^XX8}-VkAm…Ӧݻf͕W" T!?GԄ _S u06P*ʛ0a⇈+pL,!BPoFI+EcOT)Q34T;CuVnP{0' 6@A[4^h"\geH@2H::4dUtcEVJ.o94$7~۷s\8miI7y A#}6a7rɴLX?&L0a(-klgCfaÆ}K.<) H@=~ғN*.^}>wAF˗76_K67 9Pk-;JH^GKΜmJW3 5aߚm%0Ɉcc„vjQ, ;Ò+t9AܲsOO2i+H,늂łɞx\Ia,iv+ )B5 bpJX'.r5-/\p8+)i`% P(N$b-Lcڵ۶ׯ[w@gg<麦͞յo־ݞH YG^޼yӧԤR6EuvuvHNptӌ_yiwWUdgϘryѨ$Qˢsxήo(+Hd@m6J5YXX6L$IHqg4/f >0A-NEo}&d:dфoЃ͑H`I@87a„ ?&\[[srfǎax"E׋:;) 31JJIZ 9R'3|Ol9t%|yC/]d|ժHdəg9 ׋RHDMn &Q' DbŊ+V@ CrY,0fLqC{uw3 VVz<6,gyy<睛7];|xWWC~QT?ysl).3J(v8)'FVEEX*+fxs;㏿7y_2++;[JKEb apϻ\YYrx6q =$ -2采Ḧ́ބ OdzdFI67$dx{aIHy QioFk 4jm]P%::R#r޽--Q\l矷')7V$wt lUIN2fF,(p8vEc;jjPt5Sn<!2bDy9WU͚׿'oFӡ_eeB'VU҂(+KQW쳳g:{vyyNXOZ|0(lY9W=e &Mz"^#z"pj<~?ԴyEEwW]7BvcS@,6uFzϼnv|&zh<̎_ &|[3]qL0aXa`k<-@zaAFqULŀrCV%a|0HV&¾0Z PX bx\GKI_yeb1Mˬ;qBKXʓuY84QUA`YOWBԩ[&SO|Yfɤ"y ~'CWmn~Ɩ-[?|0Bkw|P59N.xРsٲbχ`kI21v8,TꭷnWom]Ǝ]L)/4wxɣ0c(JQl6 Tv8vUE?yQi8EE8fSh]..Iz-*)4n*O7 bZ9iұ>&Lazʃ_|y0a⧋̊{Vt+t]w$A3.F@[{$L꺪b!pm6\+4۲;4tB+N&E8XLhṹH ۑ#m6c"X,"'|B\G|ܸR$ߴ; EJDɤ(l޽?a664x+\{mnn{99AEEv>iN$}EWW$r=۷77R99v{^#F"W_M_|;ggƌ>&O7<zzXjzvۍ!D**JK5/5k v8q+Ə5 mpX,ǏU\ D0@DRVՊD$Ms\NNVL0t()l1~!pX,hY`g mYa,QdY(pX,sTUA rv6)Ezp|zU]i,0 Te8UnI,W|?I'MٿqIM*bw;שF3w=EQ՘NQ/\8vlnwVVfelV}aM#B/ee{[ow_ee(D==(\PRm"ݡPu_46f^W>_^U[1䒵kNUVzO\'O:4V?~Ѳ'Y&DSVt6667# 'Ȳ$}YCC0\%I òwG 8]AWZ,#J c BSl6ExcMu42~,yQ$}VAy(2 ˒]@ڀ6RUSsСC6fLqqqqII^ vi0|\BG6["u|4g$X3f  5UU--e|P_e'>Fq# /ݲsV{(jU^˖L<|x"rhr/0mTFTjiv^fDY'gx> m(\{z4-]JӢ1 0'|J1 J,#T2I,+,7CH7I8zۣ_FKKkj!OKu=,1sΠANҥc U>vժ>+--,RhE"/[{sA=-+kԨXaXvƌ/p[֎ΙIy%i'/zbl@d( E#p) @ZxB904m+YG;a\ud c14\ xuM,Ð~p}5iŸBQW暽Mr&~|M|x<OGGGGGDZ&~m3W$}$@MY2 y`H`.2R!oh'1p28pfd˞NlR dj=G@x* rŲAd 4tvT0^7~$uX>*==H2 mb[[w7j;i#G{w& }4}zy CE_QɤR~#F\pf[[lC,]Qۻskm6Z{'yƍӧϞaî] l-,uvEF㳲NbQI)S^}Oܴ)Wzŋko( 'P"JY,"YY. cv\v{g'VJD"ÀP8I$R)fCxfz<Kgg(Hb<^Td$wG"Np"XLxGT7K=UhѳVA9p5lB(R "` o)~4#>AnNns=@Kΐ. <HZSb{?@J)B 䙯mk*&L8IsE)0zdR #=ƂWp&=Y6*c0([Zp'IqЏ"==hU9 mjxcSn6oUU Q99kW}}w7lY7w:"n2T 3s:H&M{h { (E,ygҤ*joǽb6guׄ 99T3zSN)..|睧Ço'U&<{I#Fȗ_ֶp!%ÆVW ի/|Ox"q[|4jrK0LMϧ`^[w٥V ]c('LJB[k^W@@?Pv7bߗի,x+2eΪڜ미F=^L0ahT L9.0a⧀X}_L\Ꜥ#"z 9U㚦3=|1D"a$mt< ɤa m!a!:DXLӼ^qF H$uO$t'2 ((pLIz`CU_X GH$M&ynv.4Zƌy?1 8@"Ǔ.:4;{ɒ,]YG>{vhow:ee}l~ޒ%vm`'}Gegr 'p KaevEnv/?え*msύG r ??~ LZ>_~>.e}|R,dq>_^ҕiMtiiNOXeUnļ(ܰZ,8cxfdgB #8w8y1`s0kmǣ=R""X@s8 ' $%9KQ0ݣH-pȦp2-%Л0ݡ߄5쐚~駟~[1vSrߞ̑e뽣0a{zKP!qG =)08I >Z#AdXىR qx`76h.W_I5$ekhDK].IF40\ssrh4mΜ_?p7wFtnhhn^߾ʦ<˫VxeEES$>0Bٙ^xÇ7;k׾~eeןvЫ||mkC₂V(QTU trzq rs]&$U$qxOY,ڊ)4٪ՕL*Eih)d2'VLq,QWβ LSh2KR8$ڡPrP#kP#{=9\>೮Ð.ڶu⼼3<|XQT.g9RC׮ݵmٲ-[jjq]-mmv{ ZyQZzCΝ[Z[RBQ݈CpW(jʔCwᆩS_xR+6n?_~y L(졎+rEtTOXA< ϋ"6$?-ѱeAPUFy^UUU$I C80VYyEE0\}I| >=WUgx\p_ +/:Ƣf\c*qص&c g\G7d՜~|U?<un5jC#WIbJrX &L|hj7ѓNz]UUQ?RTII_ߚ={`}͚i NIUߖeF 7/?p\vV2jUQ˖ȱp)q?oCdѢ:쪪I(POY&L>'Cr\.~gYYYYY<$tuuu!v E@V39`rmB5DBӠ)t=%yUD̒񱘮z8+MM`<uǞx\סo*9mJ91ZHpǝk1  IyFqMBB>IJ~8HxL  56Λww x-)#N Bgg06/5a@ [n-[#h4K$dh joᆳ[׬9U܃AQT_ꤓ.?2tjtf< +,Z7lض}ٲ37mɓn c1U]'fϞٺhz9Msϓbiy,( eeY9.+@ A\=}Yjc$;^\%Dyy:[,@$ =)TUA`aC:qiK xn|:}!UQ8%55z$lEۀ3 }v}uEi[9 ~B>ƍ7nVJ}~ofs?&L&|E=0"XH"WHt`d,E{F1}E #"q&IB\yQԄzC Uyml5'bC4,UD>R&H`!cرEE)1nmmEW>;{ȐPhAjWWtR*f 0Pp`pi.6njmD-ڴ a̞ܼmۚ5{әL٪?qƌk}D:;Q)(<7Y\tm6MK&u[_ɓjow:7߼喷ڹs8ryDIS_y޽_Cs疗eeQE! &L|?5kkc G–Ëo3@BoZX02 6y̓IBJoa<"W^ӒI"uHRoGsUE4 @$.d*UPeahF-^hRV+6,7Ep8Yd8iHoaJǍYTp@V3 bd2//쳽{/pJMPw.Υ(E1 8a*ʟ4nә#I,uuG"yys穧M;x<+WNte[TTTVREy<6$UVz0xrЂ|iv8,솆5rrl^8G> JK}>EZm6rb4mWV(Bo`UX9be٬,,|v˅|q$zvUEYFw`VUQdx\ nXyEglX,q5i)\VKQxfþ* v$EAOb&]į` *Γ$PgA E4-<3 iXeYN8݇iߏ'G i,̙nQۆ$GN1š88ad Hw 5 c GrHI=2a⧀ 6T)*r/I_jW8(&@cN<硝D:;QLEkj _`Wx; @)un?oiAW6$#'f 6 ܹF*(!OeK8|k2L8EE?~M;rdGEutzfSM; A>@I }(&SuB,|$u!<9V^0܄D>a ? ̰ƦK-!Ȝ+4#8}j07YGcS7oT^U7r(ܸ f.|p;'Lz!r۷wt@*$ 5}s׮-(0aٲ38Yz3' EÀ;W3gzo9w}ӦQE r 3 [-,JL# ~LI9L|39{w$߽Vy̟^o^zߝ׿u׭͙sE @0Pz24*PɆ4@J!;݆6xz|j7&bX,"IХw|+'O޾eyƌ_ aF8"qhN)snZ9+ } 8@8"==<& ϳA8MĤߑ9% C>0`WxٿϞ;ឞ`|ѢgͺŋoN?VoO=} u|TVVV9~܋/rP d2am+=dž|$&LME0vNQ4Mc^( a)TeYQ2 Fσ'8T?VBUDJIbBPZ|߇ǒVQmbWT#Ik<k7Cr̞h49#,UU3+X2ݿ/L<׮.u:EQQH&ud0Y^dpuʔlIڷ/H&]x\EqreҤ={ny۶5kX Ʒm+*5>DǏs8[ki"HhZvӉdV#Fg{曧O_z f۶7߼Z7'jzsr(VUxx~RYZMEVQ&L6,'"Nͦ(& q u8,t4&7VU5 òxq@X]QDbfjűcyG]iAi.bz @ ~`WUIJ&ujdRvEZ%evQ!3ݎ恳 ֐0YmvYyOvu7&Ǎ7g4#ŬzOoGH*;7?Zמj|oYI1qlq =P}LI )6=ֻ9Lʍ <}ʓҿk+2h `+^I:ͤ|"a`vSS0>aϞÇQښw/;-lnYyWV={<Dh$3TWrs[O=t%Kofy$#GZwQYTVzЂoYSl(u=\_}u}\39,\tNDQ=mfΪ(/#E>_k|O> ׵k?/~s-..(ֶ6?bDQfRϣrI,յ8uqC®]>_vպ[ɖlc,C` @Q#Gr —_*I 5 LQU8R/6$!|T }<=p" od[(Ei&5{ 9|8:i&'r틢|32&L|8fA \yR2ϔum-Q{gvU0a^\\S tJj((3gRYl6SNzGp:EGw>THk|rΜ*lYy̙w|Ywoc7ߗ 'MZf_ʕ6͟m׃xׄ &~8ꄞqrHOK&~駟~:`KĶLIJm۶m۶m3nL8Z ޱm(3ed.ǒV@$}a@$^HF*'ka3}3~ #FIm#Y1opnj(G4Te) ĵEQ jr$QdYCdX,(2D0h'q<('gYeYFs =\ rKAV,Ї"@R8DDQ(e*Ნ I.?CtyGVdWuiO>&O2SY7 ͏>$I+I|O:^̓ެʛ8` V\y5lHy"6KXX \e'i6-f=2aD&f m$Ǿ#[ßOa(J"o{HVȈ(!2 3s I G!#dFwwwvo~>W#|;㑤_?眒HoO;: 1}uʿrnne={eCjʼn@Eb/xw |UU]]~I'XxquuCC~"ˢ;e̗Fi ZHQi4'. م?{",xԞ #d$}(-<❣?>߅?Q&Br$Ptv"2't(w*yaU}]';-E>3UK2yOoԗk;eupuhnLw?M|##pw.ך*aO ?D "9"39ǃS DQ\bfH$u<5:Euua/@ EK$%)78$7W^9$@t')7qRbiZt]!.G Z,. $²2媫 X/ZeKcc5jNӿ}uSO]yemAQ-=֯zG(H>Ⱦ*E}O޶׿:WNEF׬_ǍCU-b(QrOUU;;xQE[EaHxnWYFWe)JKKOA\abHTI\y:"ȵ>]D "-[iZ1 '`{yUUgMQϚ0aO_3y+LIrLK}7 E߄x30-,MGrRAK!B qRfxd].ڡ g0:! U]Bu/5-rqxhk۾#1{w/qdGFpôi] ,|EQH4+(r{/'a|s= / ^'TRwv[o]rɞ=9v;|yy.pWbY/)C7/-͵ rl4pX,pŢ(R40`Ff8^UERi"B|^ [XO|zY|2("zL`>|o-;2gvR&~8ꄞ4<u2Vx vR ac&L|1GM<$dzғd ېau}`AO&6 }?I&r$rCQ4}p0 zzИΞ锤\Յ"dRnm6Q$]˳U;͓yy$ֶB^#)lqV5_bvuQ'):6yrA7fBU~ɒm⋎+8tWzI]Mx]T\qTWw0r!]].뭭mmEkv:UTi͚ .xƗ_3f_p׮:3q:].UtnՅ/+ϗ|T].YP(7H0zz;œ/,#;ǰHQT w(*RAhk<dv&I0Zg~)˭l6$T1AhI(F"y\8]O8I]D"LX8IBY|̷r:%YjӦS-{3|\s?T,Xpg}Ջ/]:q)LzȄ6:''_7С>ϘqI7\TT^oai4Ã+O^ áҥӦm޼t驧nG"rEO>yUQAQGQѨ<Ú.ꈲqX g:E1 Cq(J jaд$aR4EAbRaY*+Z_Ut|3fxww|8DLX,}`7XvhѢ^Bc*2ũ!EdT,EDeI/yr, ˒֖, aG#2y q0h[!J.~$8Ӈä>x$`6LDJH<p(JEŠA^?q}qLp{%IvF64ricereeYrbY,xY3 :~rYHB%IejZYYNYqÇ!-С4m›]U5p  xp8dɊ;<8R dz?\#_apa:˪+݆a@+40%{ 7r˝w=v,Jq˽#l9ܹ]`9?ºakQadC}@zɒQ &L|q =TIi7od„:,x8Ȋx,FQ,I![<  tKK0B@,TSS ̡rKdHUKKnEٳ C R>khC><(67fź:,>@0I`H㺮P%H$Hz$Lz8HR8BVA$ŀq3ǁ(<00 Jm0$ z*& KJ44̚u}o3fעc!ƃ_?bͶp!6guvW_oY9r %b^lTU{zqʲ z9+n<݇=yYSXpZZz/p$y<~OG$pY?xR'HK!+&== Xr|UM&}>EI&?$Tfl6I jŞNC5:]i#@j2R[Uy$>wXDBRd2 #ּA$9RW^y'(((*&L4iTRxڛ5Yсpӛ}W;wrC>?\ë&i)BO:o„c}g:>>H}1ʓ.1!=j|^ovw[, :nwmm[G&/;fKdgl@Y>`͚9sy>g_uպu3f@d„ax ѐ&FDZ,:>_NىaNm47<"lb9|ҜQlnED4M׃x]H|`„o (8w ~dQ~kr_w /TU=kf*'.Y2dHvvq1Euw~D1^zgo?QE^ykk#΢(<Մ &~l8xrCOv%ń G 65xw@@]7 HC} ij dbI * ǁj@ cT8,KQ`2Y_״d2Jw״T kwPH`e9>tp}WQӇ ٺWڹS׻[&F*^Z^nc~?XzÆ_}Æe˜DI}tʔnٳ9|$xsydC|K.y_ʪ(+cY'8GX;@񅌈$ $2)EIXKΔeYyU.4(^}kU@y&~`ɗX<'@Ky͛7zL0Fw>dkǿ,o~Iv3\sߚ3S<97&L$}'xcժ^HOg3KQl٣FmG/_UY3f<7B* &Lqz T>dXo w l٩40O|0g<9>d*Eb 0C*LRPGI ׬˥(<>DΟLk rl( ,3 ]^vN`k@ N$$IzϜBƽ۽{ee7v#nt4M).V~X!ִu|pݺ?4n(Ya+4;ifE6ZqM3 hKa(+ Cm<&HLeYuDϝt` Bo~S[ֆD#GW,HD"z챛o~G1~ ꭷ5oڵ^ u0|q:}]2UKJΎ:ϯjumpeݺ>jTccWe((TՖn$#.(zmf$M RcJQ^/HZ ^ى(*'V?D}>]Z[{zytŋa;xƷajVA3LGG8JAB:t:{Gx^XJ,,QdYG<ʤR(m5 j#<0$oA gpv+2 x Nj"Ǎi';*3 E%XrI,&0*[.0 CJaD/LoRLDTGCU{ӦM6mq_}W_}Ek„ ?餷2kڛn(܉3Y,7yrN?{% /@@,3'N\,.Nw::̞=rmk֜wO;?ȶmf]ks))>ӄ &~8jO?O M׃CoX@_Ɣ3YLju ]XUCf,iND"$z==T+W>~x*>#d2ujh 5 Ɖ:0*IӃ駓'uWsseoX--CpsϭZqłnewk7w{Fۗ3hвefrKsBrX,q` "N4"w>ϝ;xC͟?z/۶]vo8phR"hTR)U$ζۡg*$ [:&M;/},q4pHi ):vcxՄlx<43Ln͆}NEyNbp:RA\z#\Na;,8X :σ4J@ };(ӎapWI Fo/y8NqAy2={(r9IAǤ}el&Loº?_y, 7fO!)7q:X EtMWW$EdWU"D"3'+KUtx盚~D!]n͵vnl  2rd~g ]]gݕo!Y{t x$fΜa+ƌZ!oi-7n۶͛aLu͜y7h2^WwP$b!C;:|>9Gxz_kKJrrvD))ζ;:QeY²%%,{wCr?!CGIDq^\c $I}% "f<3LMMW:G%%㇖bIJ&Ea40b1x'kMkkOO,Ie8Hd#,ECx]8ݷZi:FxYfY!): Q©EM$Ly<g AN!|x)>A"XTG5|u9dRz4?<0@JkGtLgz<ޤܘq7nܸXo &~<Ȭz~_ ;uT3'5W@4#&H~&H4 4g„6!QncƸݢx6 ?ŋ_{xO-({s7>3{S2c9&Α s I:zzYɽP_?m'w`[z;e"7{sSN6m5oMzc&L6W=>}Wbo@CRh|fN@5 q:h c`"jMݍRjldjy.8^q C^m6gY*O~nm;7O;oTn3HD =GEEe){&Lx U _}q{l{9_WEk(ggR=>~>̙6D~޻{}a qηŢ((ՅͤǼaJ 7'N*Iݍ߂ZKFvUX _W.*FK^~SULeYS)A0 Z? %h\.E8]C4k"cWsp^bRR8K>urv49 @EED"C^$LD"6z54:ׇ7lx#F78$/U2aNM0ađ#0<縎w͚?!Db&V;c=I/Qީ~5n܂_g|>7.lժB]{r NQfocI]>ؿ`)SM#U4&L_LM0A'/?Ľ}ӿK.Zz'HgY xQR? 69DV\]I&5 '!J'>2 %\@BsР,D ӼC-@2E>#Fl(˕oC;J$ vviowa{݊HX,IJ@wQ|oU2jD⭷xC֯GG9+x =B"l;C;D"t9?>7_&Mznm~bŇ^/El"v;%7D.FQ4#@zX$m6UqH*%%.f(VR)0hsrA}twz9!iBD5l6]O$ CUj ;BoWX ״ÁV+~oW:^<xN޵] uA`dFQDBUFXHw ɾ0^Ɠ`]M^!Sƛ鑯obMGBO'i67rct@;! IDt - 9:?Th3 KсzzwA~ ama!˻wcB]km IGN,XDɒ **ZZM[a"n}>䵣C0뺮sI'eeA*OQ~ s]w_/'}KN!'C5xޢtƊaX67T*SޏL l&i0ۦ(fބ &5$|ܹ˖A* +_vYyNQキiETgX'l?ڭ;67o;'˫j/xqmߟ5~z4E G-FD*_O[78qokͽ~~v7>;8)5]G,Mgb!,Yq4iI$4?Yer6ˍ &2@Rt2:[3m[4Ð>\$<81L94S)Y-S)E F|睥KJKe?3I^:dvƹ(_dѢ`P R;No΄,45<0sCqƠA}Ԛs}o~z#2gn=IX@#RHsp44gBdunA~F*Pea`:2 Đi%d;4&kIvm"%Xn2#k&OQߛ֚93%{Lh#Lvj׶oٗ+Ae%HA \^@ W=*B$K4nǵ[==x*c"h zaÁ"РnRHZ,Qc >B>|x~>jRp0ϙsmd*?f́gwtw9De0w:)'{ӟzzt,?޲c) IHǎ-)qɳM~!{y+Vl mR[;qwmm,[RqOsMaIT+DOR^r|SU494 x8jñtCssV@ ;#BSR&p2ǕzHX MY$AÀ>/Dπ^uk>_!9@rgBAۻ+~K& 0p ;( 'DR'12[nKݠwzxkz_oi&ď&PA~?s)7mڴiӦ#_ywل &)7H$X>3Twg[v 0̺uGӴ(PrˎeyP[kdehb(K{N׿~^Dh*)'/̏2 D.0y:=).`0 4XZX n ].ABXcY8=qgsl<V8^/>')4<J1(ʲ "bc| l4t檫νk'N:rOe8hD;E4yT]F ma)V^B)1 uSz2 6 MKQ}\C1LV:NimLa`O@#)R>ĔHSEhu LR xftvF"0bȖ`EZ[15Ç w8Phb1ơC^ДݪZW +)ΝzݡPe;\}6iM]{ӣi2ztQյ"G7Ϗǝ#]vMww$BcyooGB+Nx/?[Aze/xZ_{|Vk}=GLlJ$Ps옞jsss3:^m47D==[x0vyTUU[ZY f^CQF<q]48^"yyGӝ0^(q<`B)ˢ<8R} @s$1 9fE=BX|$t<x=H$bO6Е,aJ$ݛ 0ARn@a/;X%|Ĉ1c#ֶm55}Y0ađ+gӛ0aX/keJhxI&u04 Ǣd2$^ٓמ9LR@ @twuj1XvuE"(93g<βd=U2O$t$ɤ8xF2HhZ8|Ϝ|]]Y׿!L&96pΝFy9`Ik׺u~SnW`qǽO "UUӦou@[s8eYܙ>ZIyBϋ"WxY$FiAYeSMCpWF1DUem6EA!BRzBOlhKеgKb$IZE_9,K^J1a`\{,r5Kl?=cc[oˍ7wyȄ |#=鄓p~A\KY7aCf5g}b@=;fYYԴT*YD`)jۃtB$N$R)R) 1=+bF\q#XLӠ KX0/er nRӟ%.+(yi/~q%:?XquzvNQ-HTpoϞ[o=((1c.ݸx^b+ [> ITaiQ$a M3 E~fF{g|PURʲ G EQ.h`)Wjv6_ᝒł;VU04t?4"l63 M[XJQƲ`XX;y_\dJ^ vROv|ЙޱO?/FAy,@Զ"yzxR5={)Bhi_őz2a⇋_s5\s coA|m۶m6`ȧp2)7XcbN|!"<$vԡSb::QBjdL&%W[Յʻ'=P)߹.,ԄI)$G!r}|rewܱy(-eF|Lȴl;;1rrl62qq@^#E w=N< >=VT'2lgYq[u|7 t_C)ryhu` cHd.8SWۓܛ9/Ckj̡tƌ)*Boasc)S qƏ޼yKLfS()x~Aϫx1MCMjj&NoaTbqi].TvY(-x$ òYYzxygVݎ"8fDec1MKHAi˜1>l]@ Ud KyY6;n.,qYYB-;V+eE$F`h,Km Q e&aT D*$ W*:0can[OH Z]{jН4"<:}:xLa{Et{Ģ9ѝm!)Bhkޙ""$;KMazTf:N3Y͛7o7a⇁}冢@|lT0D@ aLr'3 q4cM,hТ6 C C;qaIq8$)rI: `)7(2ztAݾsg]_x ~ӧk?1oގ`SWֆ(:=cǓOΜYT(uE]cǴi 0p N%% kW}=Mz{ yrԄ#G \dDU pHcىraө(yW-Coj$kmA]U@'(z-Qd-p Q4g< $>=IBY2i I09$٥)HMz5 ᥂@ӱhloA^)a L#[dG HI޾Xh|љ"U`)@afEL1)7$2BB3e&cބOQfZؐu^LH4wBLwȶVG`v'= q1[t 3Yϙ9>,:5f'ܾw$QG"svґ\睷oߞ=;wQ|׭?{~Gȗ0a⇋oS,cc7+?(6O?H`dlFWcٜOwq}fKhx,"8UaW{'G,-W]sw;3r8 =ӱ@{>g 4.pÒ{iW\(wssC(#O? >6w ,h D;'{fԱcb.˗/Y`3Lxr5zwHAƝbAZ3a„ &@;@ޮv.!_ٽg#r֬z.󎠾;h_y~PHë N.o(i. ViF.WY@L&1Ͷmy(d뱯|8e2NJR2Nz:ݽu˖]t]w*˿2w& |2NDh{%ۂpMc BϞ{OO }2 ?;np_[[o=[IAԩA$*IEgg<@.&/l]MSr9y:\h(y C5?e(,+.pAVg|LKq Dk<23t(IP{q# S~^Xx|1zww^>c?sXfbӸ_b/m$wgw%zu=`: A<>~[phٖ>}iu#rXXpHsp)ac#@xm[,JpӦpn@fǽ^U|U׫ikJBc1^jYQv*.-/w4m^Dp i:'#rE}~7n6~6 h>; /ٳoo{N; ӆ3ghWLw?uq{x㧟9΂MQ6mڸ1(imjJ&J65U6mjldkҦMYdzm[,Z4}jo ;w4 &L!E.0WxUݼЩݰ?Hon55q)T(HRSJy$a˥4$,;k$A:/ut /׾(& . ݒ_*˒1&n&I0\r ނqIE\ a9\0*IZ4 vnֿ9so؋\%~ϮA8Ag%7 9s̙3q$4`aYHN>'bP_=ԬZCv`+V̞ѫ \~><^۷|g>k65i3to?hѥyAX *yK?ҧL"m(AjU!SD\dy xf 8ϛBn?h[qqYap@hD Q{d|$I!4 hj:ɓS^,p=#j# /ٳ}o{N^4ygK5k^~3'Mߺ_Z#neQt֭m\NU$&4\jE,ͽX ŝŜ},INNVMΥ#0 `AB nM!Eq+HM -U[<<<\2_FEYL { {޽ JJh?`mؚ2+Ee-Uz&Isq5#1/W_VXO( wb|b&bcl+ 7{@jRUڳA )y ) ? G_p 083/xG'~}m-)^͋?_P^{ldI|_"<>}^~y/;B{Ͽ>H$z!|x`@ d-^bE)J={>e۷w\^v:,\Қo={/X}{ q;"^SBBv'{oo*~>߇+˛a~K;$_~Ylx>bĨQ^kU2x!`Gl2М| qL@?f̘1c=!9\DkyO$g29.7H\@pcqf4#^]~C4g-өi8BIb1g tj<΃ CQR)~tE8ө(t۶+V]t`I =7 B,vrܑ44Y3cwߟNbpgUhSӹq9Ng,+Vy-̚uߦi+ł4^`ZV\NZTDEỜ^%<~cv]l{+}<1[%I$Yi6PRoAtZnMcNYkZ<MS(ΆS IJŗ &¯GqC/)Vw(%K,\."m¿K0>ܰRsdYk=[y<F}X*sٚ&/!(JR.2-jA*$!sVU)B@L#\BDB e2 njm( .XU«d6k~?/VGBSt098~4i]7C!>pWTlZS3se+Wr2m/F~{acUV\ BW_iS,>i'Ga +ee+;ۣT%Eq-8'<[?-$9l~,ko ǛmwXίsxzw|0銕ρXuw-#} bSEL7+opA` yY"F.г)b-۶EL S^v;64626m^Mþ0w'\S^Vr uKPNu<@^mh>cvVxuN90|<܇{ouuVAƍ>c:TVVT䓯~͵B>cNJ Iڸ l -L0Jzee0rm1MO]]]WUUh41  Bm\F8!r:C(__H 0ЃgGhNQD!uJ ^ါ ~GD QL$g8Ђ]bKC_ۭJjA(ә { ͂o rG;!#a U(œX_z4,8`Ov.\> 梘/)>LZXڻp P@ORp u'g$4A0;w #s8xByT0 Ce&BQ,XB 8ϊWBZSË/!ٵ+/ܱ#M! ks 躮۶-+0ڷ%po>{d]wс{ALAg̞=m /LᦛAӚƍM۰! ۷S`0dL{BX FEA^NiBs8:=SQzo0^ ynLVӸ%"`}.pn+ BdTn<S,"CZ ` \Q$sv_-[As.]p^U% ~79\o2YQT]eVph+K` &X-÷_xKwmϭ՜W h('݌\׊be}.5;$(8@K\ jjJ$FsFiF" ˬBH1!|;v:.ҥ[hee.ś6x s86lg=zTUyKlC˖-x6˗w0gιn>sf۷ Bcceen7՞e|r?c '$Ȏuu~=s64k._uk<΂0:UTx<775BۚJhasfx\\.KU׮e~p[G}̉Iv˅no~NkMhl~GSo*8 s9܁_MCrB![QZbz.Գ9B.$k>dt0o t&d~/jwڷرs#.%પ(2YϙDoM\˱G6M,r뎏Ca(7W\jߢ-_S/@),{^ODB=A4;v/0g7'ԩٗ]~ 07n\h„ɓA:v+׮:[7kذE3W_ݴ^ľ5zw, u('`Z/ӄ!n]ޓ,ӟ?9p`*D]fE7"T4NUeBdRA-PP wq ZYs\`)}0t4۵ V@ =qȐӽ-[z8௿f>6ؑg͛g|^As;"l6{U2HDS' |++=ABaeǎ坵XyȻCy+n?_QĮ2 hR=k5QY`l޽^'$r@%7ՁӹawoU)Sd9矿-[v쐤C!AظqǎDSHd۶giAaG⣏>.UUƎ] Ӎt)+۲%FUUӹuk$J'0o[%7[lM@r直{0raөiJiB۶u:<*y(DP D#f2ȂcQX˷TRhT [=Jqۭ(Jq s5*Vy͗_:mNtwР#:ϫ-NIeFUE; o]4M$ dfw.2<#텵uƮEE|֊< `#?nn_j͕ؔҟ O0-}~c.W,6o)yahU+(;vi{|ӹf' z%ӂt sϸq] |EGݦ1y?bov-S{+.]'ǏgM{ٳᇟ}Z/_dzGf3g_|C-= Z/dG=z薞؞nƌ3fXzի[zF Ç>|x)xjGMsc_K`A\&w~Z@bT*ey\EUAcles9TJ!C_e9-N,[}c1o+9Ӽq^׹!Ϊa&'eˈ/}F>찗_Cym]w ¿zN?h׮{i߾SΝ1[zA(g[z.D~,Ǖ˙}Har8E,ٞn!Wi0 ŲYӄ"x<1M^h)#,.rRle啐 9d۶F"x6 }oد76w܌>ߖ-={=gWeeN׫W;ewBsSAyKn> 4MƏd2VM Tbk>J o_VvöYnYwm0GdB3Ly ˈW%)NS+I6QEg ǻ8#XKF)$Q.sfq(BŚVh>ISX}8>ӭRbvWwv- >:4hРAZzľ%4GApf-thI&zanCت &CDqxoBȰys8 r$ v ݴ{A[hojk+*< xQl^UU^Woܤۯ\i/~-+{ѣ54Mͻ#|ꩾ}++E>X _}ԨaF⊎5mPHQ>h˖tWb>XXnʕ 媯 L ڷTwrY[ 0)TUtF"4{C2kVCu4 x,+iFi:ip*΂N|Na׆, [\tδ4^Ђ{& );"ЊwXN hݹֺc1|RbV\ %E^Q$)\s~B pVz|EQDluh<{c|1y }$2u]8iuEvF5jԨ]vڵB(~ݼyĈfhMsXEih>>{?!O_znh4N;S`pԨM۰!0;\OOE`J?Ъ3)SLҳ e.ŲgV{A<[sp(0?$i\\.\.h^@3r5 $7XJF;ryI*: - @Qt*JϞm0OwG堃ڶ%N$2ev޼?2-[曫=F<ɓ37=v:Ɏ;w󡇺uc3-[KT0e9uzyIk0jZ߾:F}$R|N'Ȳ$UVrOwp//2$ÚB.«JݻWUUf z;Hq+|0r) BKF@(b 䯹WKv@<%jX4($Ii4P_i*I[lܸvׯ] =v}r,6}*̄} DE2;O-/L_| h]P@OBq [7؅`'_ziy,ޡ}ֱ@ ~΂oe;ͭav2-c-VP6iruXSs7|Ujz3`Ӧ_#6ᚽ/Oᇐ;u>_;΍Ph;yAozhBٶ g%Pr֒PR)>38 :y -MMBX;^}>Cbt4!Lą୎$(ęNA;َWڲ3&MRd;>;)SNNw^DP,^_l#2e,uxIVOAл݊"˸ e뾧3s////?c9x)"6gY1`7'kdn׫(x˖%d扖f׮ǖ Aāe Yp,?[oϐA L04pirT \yye,K6YD|yL:u N"/uw6m|>h L2ʐӧ}{V9گ_ǎqSO%ڭX1b={NVUoߞJuS˗?a,Y^x!P__UաÓO;e!>pA>_.76b&|l,J(6x4ov؜~KUINzEYŮ7OP{<(R.];ߋ"dEXUo(X6m|>vV?uAE> ut؜Aͳ0יHRuu55l|EEOYI_pVX,RT%wx㥗; 񚫪,㌻h|/>p z~/ۏu^އYIB e |M~Q]~cu#9.-\skD8̧1d\<i@Bq;l/uu%QյkwH$@PE>PLb|QPG*wٲ͛YQk͞]S3u ={vXc?mZ"?سWĉ~kM|].'rDCe98`Z<33Eqժ&6C!{͚Hc '.(Wc6m޵k}C&Y[FNm\ ljAQIھB1.WX3 3]gG[x<`˥(.W$Yf;ܥ#5f%4fφ"SN6V$) pأPUky7\(7xmowYX$axp [+X=μ=!ɲX];DmC+UZyU87zd4oy=^o"px3nq˖޽}htZ?~͚L&4B:gy]ݠo ؗhu4ޜ$mm6YSBxBFȈ"T{>dXr0Ch =< }U}I3À\#a,NKuZJc=;X=Qwݺ5y%KbڴUFPUUo{Ӧ+ɧrs t55]{m׮GN$My[gT^.Ippy dr! z&e!Avd]% ),毂@7|QW@Z[sћ JqFf7sKf%aϭ'7W˞yK\ &s9|v X Z[ϝ]l <~nUާثڠ v8uMA! sa41A7 =䀏iL6[8Hml<眫J&5-4iFw;Ϸn2Ž+L9E> 5k1 wpc ers#|\'#8WUYF%m/p8,BpJ&YZ؍֒; ,9-pa:\uf['v7G-lvvx3*)Rs({`O pD´֧x y/@=h.[nͿW^s9Vhr9w&l^L4s9\Ȃbŗh*em]K%IAa 2Vz cQܙAxڵky 8?TˠfJtj.7jԷVW[w;vx}7eͦٻwNee/X#G~ٴi0ڵ]ty ={>_E, (N;=zTUYxWTgar0Cx鰗m׮lMKQ:w./wZ*˒W9^^ 8| ^өhj$*gYU vIЂbEQĖ$iͳ5Vw]gt[X*޷rqgynKoej_V[|p^آzÃ?[ς d#5g4_(l @96-TAF('Z$4W2a ib_y+d=iU!kl+q֭+:^ %!%=]˗oƊV;v N l*+^M۴VUywϧX6f#t}NW3f| Hy#G}ѨqDž{(:^p*u55vK(>ƍbE4j8_޶m `ݷm[ܾ=e-SF zR\+xUQt=^ tr JN쇿s] ;/F?ͰϊβA('bP9kmj7մ@z0];Ah_ůzՓ&mR^ޮ6BzEqtZ̉FwfwUscsv̘СSΝ[zvAzǮ..iNpvBd(@S>\qe::8p2 a-aNо'٬iz<\!]E@>s8R)]7M,5\=+X7v,_~|CHc, ‘G~Žoqiz<о}0r 7Ԯ]}M0s]t?L7`@ ]:x\W;ɤѡC0Z)˒Ծ}0f`%?)( e.XUWLRhOZ,by9$&tX N&PKz # |>PZ 8AJZ WþL`cupEXymxvO2\8:ZpIh(ztEK}{6AP@OرE$ߛˁ_ d2\&awyWx0D4{-[۶qy (`ךn**n+ߥKy 8]bl0 Nzr?>xk.7-RU]裷mt6x:7x}.`Ijj#LyLMS"Diϥm@jOvfuBtr>áiYt*(IUeYUFdYUT f$ \@ ZsE\i.u. $P|ɍ,[K&Vw*(z6>Hj r9.Pp |ͅBd-cGUUvsWW{hH>A6ߦͻs!q/e& Ϸm[^`G9ko{̙'KNz=y{^xaE Ԩi56E춃  Ҡ =g-)|\g%᝸4%)K}9xR)˦?>"3ʴ-W!K/}s+Ezo"ST~Uu:e4EmÆ8F<{oydpycrI_R^n6,I"0r9pUv;55[l`M$7r |H}F"n7REzU|x `?;u0Xbc}Iœ|w:a=j +LF9djckn(;w6UAN(COD3\w]^&@(pa*իڥG<޷w@A @?~] 7H \}e% >=дDسlذnڵ/X-= ס =q@R,ȵ|1i&ձM0r9>gUl4qbϫ$IB r`[Drݺ+BYhUC![ wr-s^ş2(q&:^$kI+d˝N~JU¼iWt*ן}ak֌ӿ8y?**D:*|_ /B||iybG̟ΌODkz /rݕOJY.D=YQ,/d^ء\d9Î捍+TI;x)ˢX_}};>^Sn56rz4ojkޟ~䓭[=ZQzZlРڴûuJ4A&4|Yv'h?p3cxᅂN=zb.wI^[J[oE",iFU%.gg<Ǟ&2MM$+QJ&cEy}X(W;v/>tY˖zSO9衳g? ~o~?꫏<-Vuܻ{7ߌ{ݢ(\ F",MAĮ@z^R.k-?f4dw\rÜ .A&nL4 K4fMsg.<1oD"JMS }>SQzf*@T.]<~mpƍdyȐ/dH_)/oFz7o^{ج|@멧6m**:;d,'UUΚnYVr)X`V;ۅx`<(Cʲ) !<ru3Ƈ׫i\r^(I.I« .\%4ϯp$ׇu2$Vw^ft i ӏWV Y!/d$YAPn }{au˝ X!1so(!>G=6ͽ.*˲}a۹z*E>ux֯oldjv͚ .b[#^Z;?,8ز%XOe.z۶߸1c3~u׹\(+~}啿;,q*+u3 ܴ)N r8 2˳YY~X lUU^sySQ$ ˿VU,[4I٧H041*MMD&i|.R NX%7"I?wp CE٧r(b9 kp;[e9nHvp-˒[in JUY=o",5oaVOǽ 뿊s;]G (In"[P𤴱p8m_$2w}΂Zz??_~w-ڵۺw8pú/~q:w^z2haF;w u=|l"aaOJ쬋8,4~D|Flb=Ad);};Ҋ_..e+/uMv;7>A e"6{~xX`i,# ?&(~/,qrI,T 4GGwǣrȑۏJf ? Ae2LreIǣi n9ٳg2O~F^b#?p++}>M;,Y`YtEKU9y˥(*Ilΐ9 dB!SQڟzl)nMS(n^Xî )E!;FPUk(󙇫"M պ+\scد*̸Tr=v݁#0 P=/=U`fjrgWYks;}=KwAįC=Av}w -G?XEEkkM3WQٕ+ CQ0%AA |g{UV&h5γpGzoC٩W $an7dr98 {bt({%X&cYmF~zyw t>oUW]TUy{O}{0t1Ǭ]{={Ge^ 8X$r꩏?~x_W^z۴\c )EIKE~P3s.߰VWxI`<(oVxNju9ư8cu>F(i$Qt:ߟ]uP+J2Ëp7W MY*>b墵6rk eܸ?N?>}>d =q@#<A I =Bd AG0ML&3@#P1 ? B,prt-D¹o#y<|xtT<@f?[NErIˍz.J#Ngn릙f8 U$sܭG,Oa:[!СS.]?u]R@=APuVE]w~[7}|A4:mZ2iLB~dzxà6lI8G[St_|ӱǞ|wߝsΨQ)lϞlݪ(ܹL! lٰa7|yZ{`0s!ط q\Ql8-ŷZ„Xn',7l2Ϻ=;"psBXh8C(rJ0xzozu*uQS_~ zG:J)J$kc&o jE,vV-]z??q"__߻w~_=v+e3x$I'IHIZ++S)nh9p!nUepswyhXdbevuP+IAy2*C}yc 8*wa% _Ж/&U.{c{A䦰{%xv>pCNA7G U{`_`{,0 ~#P햤HdȊ/8}s};^~9E* 3KS׭z/2d/ѣ#U'FO<ۯ fJܹ>ai輡?WAn[ Uu?i8WU}_Lҹs<ޯ'^}-89o۸q:h+=W>}otbtL"y,kŊvWT;#{ehZsyos/~<["7555-^`?67¯ tRz8(,q$0 Iq .E8v 7!'-{dwEUUd_~H?aR~M׭ݺrˆ ='IA8ן}{>Cx׊C Æ>|LSFclVdyB;$I= L;@{(4MQr9~.y3<6Ȇ2Yf32\]7 vëp TUQXy+YwQB^<x r_euX_(n~ 87|Ǿpՙ&2Y\]gȾ XЂ*VK~mx\" }^:CY MHrC & '8WU(/YJ駱zףv4jT"(\UULNjE?/YrO=5~<46tP~?x5cT*Ѵ\47mrx\}ydܸn䒡CK_-=k Bz'9\8hL =ϋǡǃ%0 ͂mZu^v/ZtYuu\s)СPPvԱyWҥ#PvmEȣBL*U[& N˵ysyyeeUa9;U Iïj`]SQ<k(r$ \S$IZ T\t@ d\#[\cH$Do߸c#aS*xtP>R4[ys5$=vWx*|yY[ :V .ͿgQxE0~FLlx fnƷ ެ/U8Ƿ f_D5APh+ZUVu~)6yvzWUA;1\-Z0&NP:c;:ti 0gΐ!uu?dH}&IpDG<ر75h_=~0uÑr{G"Rmc}A ouAغuu~e҅ ]}yo{;9[[ּgẊNso/lXZ; Sq#ADk25r ,dq)!qT:\25 %N'ϔBn>eV *;4HFsE,޲E/.cY.öm'Odjj.;^G }>C?hkŊo\nРAB!lݲܹs$rz6i v{<]lQ>$I>_Ȓ~oe/X0e_lBn60+ukU.e{<%EѺ,ZlZ`˞61'6e|~DΩVk-*1(6?o`fZ OYK~E;h:_?bݶ>Gq ZV{(n_{\$$XbAl, .MY]w'4Ynl%T,wq:EqQl(r%bgKҢE_:=aʕ~9&:ӺHr8CF\jɆL d;Ιᇝ:MBO>3,9+E1uW}>C5-L:vDc1Ms: d,b*ɘܹ|'-n;9Uşx> [st8TUq11*xUEae 8dGPpq*kjƎݾXs7nΝ}UwӦ{ΛnL/7G|؎|ժPLs&ADe#>|3f̘1}ho9G)2,_UT:oWll<#8@@-ZR?3 e9V>r˵pimzUt)̗= ;NL1/̛F6D**2drv7ǝNx|vOSXvN'xރxK\p ] |ϽhԊf1i,VSP*Yj1[5Ҝ nu9}(;w7/w:yѴρ ϒ2AP@/L2eʔ)-= CV~:Ӽdxq.g-s7o;9EL&fÆ`Uvڵرs3<܋/iǵk#AAC=q@A#ΌBn2řW;g2a./r`ȲݐŔr;{Ar\pa.7qpX~DB'J0ڼyDܿMq> ¦SO=L+R[dwzU5MX:$Yv:N:$xZKT!@7çK"?xq8#|%h9qwf N[gǏ)]lvv{3 Fe_h1 7_tۼӼ7BTKľEay aBRhC(6 4HQFQ^p5-FS)//+x4-cbN[b1Ynj_׭06/OucG>wPSxv[6ϧi,^U7F++XpnUU$(i"8ȃ0*Ÿ.(9{S):(.$az|DP<>ܮ9p) `iP kw74QK$2vy:!a_y_Ůd:\ag'x6|kG(e%^J ^ܷOL/ЪbSN:/O?=qw/_dɂ-ƥMM}W}s! =E+ϛ7o޼yCzիW.Z&L0a„ӧO> nXpY-_ 崹?CqK4A/tD._)]rSx%vݿ'}ygW bzO<O<N8 6lذA{q@Q͚5k֬Y0ܹsΝKn9- 9BAS)& ±B:Fs{45SmjN_y4t3PsقǣilrGv3DB+ܴ顇2XjhLvx45tP?xUcƈb2bO,MwϷz?Λ`mmmm׮!^ t>唿 ٚX.!OXKUphU`#N(?`]`8/vի_SM{}e,ځ}.ǫ Y% nFbW^:K;(>¯Fh~泳* yZrR3gΜ9s&Wϲë#h0rK_kLÝK-?3ϴXRY민ң,?TSiodМ[xX25k}`L?\X-6{X^g8ze#M!vWq[ xlbǴV|v{wgudsHAMZ]2G?~W6c`2lYey,+!_~e< C`>pW{4͛njj;Ø14E.9i$I4?2C(w۶<ӥK"ѯym2v9A!<φ ̘rɒ뮻6]رK=El6޾=,/dCO>+$C=_butʯlv ۱֜;A9s~U.8^Pv]"(W~BͽY~BGlNvb/B-6> v.C+`>?bWi߾SΝ?yZ>`W_:GZڴ? I&]`p"xBYbRMر^zɸO3gߵkвqxikמ}ȑ4]f;uŚIEQպ`ە ֭[[z.A;iuP( J/- Fnk@&t1%܇arp`z\lӦX ZwuTUIZsgY&]`pJEU 9uIu6ڂr.ۡȑ45]w֭<ҧu.&F"f`pÆ'Mza{BV;w}MOnݺkn>䭷^yÎ>zȐΝ;wܙ͑u'&LO>ݻ_=S%I}>9g2< a.ޙZ|w8g幸~K9/ҿ {COɎvžȯ$Ym%K-ck7_9z!G,^ȻAVWnj3f̘] F`>+5 dŨd!XO2=LFZ=^ᆱFO9 w"\_H$uxwd6#leؘHdN4oGtiGu$KڿUUafLl٤I/Ԥi^o,JF:&鴮'a^o"s:scz5W+V\j\VN:3*+۶ \L@)q(wXxdWE6khNt4ZGta8).{cqﲎ}]odZ Ӵ =X!)} o7>f}ҟAAQ>m4]>f)gϞ={]}BJȞbKQeY\.E,MM]f_x<ƍNO@*0ĐA|t]$X;vd7ݴ}VTBy Ȳ x6[׫/?4 >w8DQN>ggGA5S4Mڶ$EaB$&SL0HhdZ*IֽW9]xbڰ"S6~s{kqY0pEX[Êb[ Y!oYw,"*v=ZKA*. jժUVlz,,w׬J"L0`m+߾qa45uJqm ]?%H3t\[C=VAH[~'Vt.6ktzȐpX37aO?sϣf  $IaMd* aF`PO<W X&*ka}(iSðrX5E Ʀ+?RBv ;Ҁ{re J* H$].QddPwH\|m76jڥ&NeiH׋$Ayh*e-]QeO=&M+0{BLQEUi{ .]Fۼ+oA 4t ״˵۷Am۶kj]VD\mVI z[*ޥfwRcf`ﺪ"Iq>>A q V'Q4ί^Ϳoɒ P@OP@h0Jy0V(ЦM2짟B(a>8G#)좚͔L3^.(65=Py }/n.Hӹp:<ûb>} غu6 өiN#B[]]__Wpd 8'(("BMSUQĻ&;!8`np{`|{(X%aE73O(g?OSa>|E#wH,"`yw~Dr9c'{[B;2O*xI% ]]®ͪ<?zXLQ;n'rWF8"Ynjo6}wo˗OKfNpCC:ukYYEEK(AA{ w 屢IW4s90 /DŽrxŲTGiE8uSe0pi,Kp#2yc~Huְa䥗СCsϷdɂв}WxvV[ EB!vD~557o ϷmkӦm[KӘ[%1ywr+f}cj*AA^Z=q`B1۝Cx<;E"W]ܵk]](G!n c/pApeTu^{ClKcǎ?ys|yeO?(?9!CmM<' i>xxsb}||ָX}aA<{d#%ϐ0ɺ<2ΟُϿC;Ao]8h>tU)IP}} 빜(>qee]HRc74YLqǎEU8PL>tՂԄgaDۗ-ӴGncMA=k{Unݺu4~|CQӝ{:">ߎ۷˲aFCCuuu\0nuȎK/H! Ι*U,:|u\N3.i[dYQ9~&LC f' 'ZhMM>_}yA(=>7ߨgM>H>7Yw]u˟zG%?㌳nj9KK9bP}}]]"xpEEeeK.AA-e}gc)b匬eI%I͛GYdK|P.s8x6qy%!{o-HF;`4Cge(MM7|5l`0F'Mz_WCnDA+qߒċV!Vۚ_[#v;C;β0jq]3=o] 9+U\X%UlV{!l!z ؽҀBÇ>|իW.]8ӧO>B>h|K$@a/tzZKanAhj:U<N)SvS ԩ3fŒÆ °aG ^3f@3X˰a{ذҎJe2,wAرז  Glm,_~_2ѣG=zWnj3fӃ=}]vڵSu]׿x<=}{ /?  ul+Aw5cƌ3f@1+PB8=5k֬Y |r0rKAAѺhu=ݡ|g!~hP {uAAQ . jժUVNAAK'^=T۠hvZ]VoWmк zv('  VrmYYYYY((sh{ ctm؁߶&{dW*Zj4bOp]-}`hEVD̝;wܹ$(\q.-ľ](]`` X zш=ǁtowk[zp/RQ&7Zz ;Poע_me7SL2e -8|wш=zouҪùop\?)gXgK42еaweM_=M)Nb;20f)G,&w4bwq`_-} HCρM3K})d}Fhɕ% ڰ79&{Ǟ~d4шŁ}ohu},?(E}?w_>"]{D!ڰkzx+Sʚш=́qow[z }\_޵+$lڰkz{ze; 80n=+Z$!    b?z  ؏~⊀#'r)+SܣZhMvhľƾyo78~UJl~ =} w?(2B{&ш}}jٽn[qA>̘1cƌ>#'r!J/5|FkWvߊ$gwm}zш=ǁtowk[zpӬZjժUF5jsh>F(=]G,mD!ڰ+k@ 6W[j4bOp]-}`h=NAACEAAC=AAAP@OAA1AA~ AAC=AAAP@OAA1AA~ AAC=AAAP@OAA1AA~ Ane֬Yfjsjv3Ÿ 7o޼y~ۘ}'bz Z-=}ZB̘1cƌ{A ѣGN8N8aODDl P( ZzRkz 5Aľ̙3gΜҳ vڵkիW^nr 4hРR x>' (COA%1lذaÆ͵x ]z @=f̘1c } VagJZXW/aW= 8wܹsr Cxo)Wמ^VR:=aeJ A@=AΆ; eBp`) t!*>aD8Á bg7 s1*rWV9s{fzǏ l~WB=m(嚇s/e_3k+OND)P@O.QJ p)>~ ĥ/=T- Re)!I)!o)+`3t> oxol]) {u&ֹ횁v?/h͐ @D@(.ggہl4xku0r)s}:®#C3*]´+7U_5rг5of(.;6 ƮHn D 1qY}#]gg33\~ vKZw,BZJ}p0ǁ[R dwvO-)f Ի~ih͐=A4Xv2B@l!5~{fϱOq ~Vm޼kl„ &L.ڕ~O-fO_3m#6hY('b(_ H2@p_B={( %"ajDKޠwElcgs A ^@XzP( wB, aanlhҮܶ=^z o' ovu~O-в A{ > о[_8loʍpRY'Yah)s;KaJ [n~Op' }d)]Bz(E\kvt$D]8,ed,ړk]x%apyϭ-f_3A ('bg7ABSpyv(ptNpg)*%L,J0O.V0l?=J{O޼fثY{ tv_$R;<K rBgW?jO/śܾ2>C K.]T]` Y ϫжVŷB+~uk~-hK9߶A2A@(Sj mY ith b^:W #r7Ϥ"ċ0C<~2XүqOl(ؿ!oiAA[ =AAAP@OAA1AA~ AAC=AAAP@OAA1AA~ AAC=AAAP@OAA1AA~ AAC=AAAP@OAA1AA~&| endstream endobj 228 0 obj << /Length 2984 /Filter /FlateDecode >> stream x\KS#+:E,*1=an8;{ bb̘z%Z՝W}YYYG{. Cgoߏ4{^(WH,H"SE 2vh*!TE*lGpm NtLU$\kdAͯn;B2T {KpS#,ӈ}Kt7L }kB1AkMDMpJ5@++eA\ hO E[V$IVApd2ɲeu%P65-$sc{jfS?P=2dPS1}=Zh)QgH7ir Ӊi,u-PD[yF BN j%5LAߧD'MAY++!u*6* Wmzk&gLiDjjj")x/J.jSr9 Js(M $笺QCQ1w2J=o3C}LI樂""Mǜe2,u̡]jZVN8x iܴ볛[y>,0*X a^l @h-o%4%=E23}8S1-X!aі9wZC [|W01(Qi{G t+%Д0OV6FݗA\K1t¬~́PL}2BVz$KpB y$ox xy  ~_gxGpCbCopcr;ȩv9[ZAx ϑO#dxI`%0\FƏo+7gi]fA*$ :'#P%7׈#B [ \Aoȥi[n!]݅t1 & 4n="̄tK5d2_C ge,u)%{O!> Ub1@Qj? s.άUF =9}ۇG pJy"2Q.X9EJq Uq^1~0LD Om @rVapu,t҃LP`(xcGW9/$jE65׆#)j$7 =`/L/$DN0gK16D ~CDTuE<0g142 @\%\~|,139Wjh-Hz|? \S!Rxz\x F ~w%nW_|Hw?y#} 9b=>1C-M/\ 5<(8e*MpeL5St8_}ˡ } e8fO*-( 3Gu-fdx# IrA0a J0<>Lw1dLS8\I8 M18F`R, o<|IFN1$ixCmȫMRQ^2+aRx.yOs!=p@}oJ=<8xw2QLISU:Sh&k^61Q_./ F$UaUNF獪j*AmԸS&h5gGs3(} j*vjƨěSS4j*_>S& ک K] ]64ىZ?e:}Lz)n^(SLxcBNӂRcoqyj\1M݃xqK;̎_4ee F 7S+d"N\Q>vْ%J.\8qZ YGInRДE^=EZ c㕕#Cf'0eof+n:Ozx=y'މqHPѱXjwlԧYb,>q!DY][m|ⷱ/1_Fl99)ZouwTjT}yXe9`| \f; Sq/0|]kXae2:1pV;)Tb>)ciwJ"ڜ?#0 z ;i8@Lgk%fDZT\R*;nh9tЕCw b%iWXӫ՝JyY| ~㬥qJRwZMR>-VMBMIP"sðe|&*+Sy4wר m\8vrDvhr(čU7WaK9DsQ+;{]<_=Hg lAi愺F,^cf%֖#> kxW\ƦdK4/XN;ߗ1S$ (QUKOmQ}MG΍+Ҳؼ#ˆwbDre#}h+ dmod γ endstream endobj 238 0 obj << /Length 983 /Filter /FlateDecode >> stream xڍVKs0WŒ=]e80 :iI}HܴlǷH2FFQ),WeXG?b%"VI* endstream endobj 251 0 obj << /Length 3067 /Filter /FlateDecode >> stream xZYo~B&tmkׁ+m^l!#1->uv)|FanwL: §,pk!ό2V,&݂>CE-0=4݁~d߸\(YE+<]lFtpbDe1nIndDG4ܴs#Г6cwqM BCI}Ӣ~j^h;=#CUoд;Q9OW8bϛ5 XޖͶ43-34oؓ|V]oH` F_XV!"2ɭdFq Up-AQ^GM[o0*H Ƕ|D~ǀn9쾺e9lŦeL5NrI⍎AJ}nɟojuƿz).;ӈV;z*_hπ^袤 ߼w(gwI>FMGQ%9`czAgW0ّˆ)tOr6_Ԟj1Ykv[?m+^Y11Q.$#C;&wn9sN2]%^8yKYk$7xs(i3S1N+ LXK.FL9K#TF,JՂ&UWQ(E͠lk.X$|BHoa2ȣ2K:<}[O󝂹Ͷ.?ol_B$-g4f̎,*/ѦSƺ-wEH"8=(L:࢓O8Y+FûʰV+iu|m c8 _R~L+ lf/TW@J8&0|t y=N!}1i4ႍ $t١ > stream xZ[۸~ϯ-i\DihvEnZ$( Zv__ )J=tD#{՛eŕQWF&"nvny-Gz+ipF~okr?[jh]e@nw=Vn8$G{]n7UM`ɫ3HfmߙU3=T8Fg@ռPB6%!b(Nm6!JWG:,1E8G/jnQ8CiWh! HaEneq k 9 nҖT[o*]`8Ѝz>^He z JJ㑡ő}<=c S?&ǑpԣCMkW]cߒ0aQSvvn'4, Y$,渻.mhGK$NEn N9m!ko=qrjyfcF=j̑&h6vt~5` DA8E&6TtsR3r #QpXcFZέ8& d.͸rR8d:k!򳡣,C䷍l4gc]Z"{ T}D-P @B)#wsc} \Eᛥ 1tyox!7-dfbJ6$IJ)Lq!=5d> O[b%<fz ϳNwNzѻEF NN6ztpÎ.Бa).i "e+-*.-T976,| a[޼DKW&GsxUȾۓޤv3 쬩C!Bңqr>v¦,f]i[xSbrh pԻ]`3G۵@AHc ]%0ݒj*2Sa^ !Ƌ[\#TB*n| CզF$F*%|Q04\ x/҃jڍ7': \T9-LAͶʛV谡c ߁IաYφn;pJv ڵNratm'M-Xr Kf~D~6ApMwp;\~Xl $rYXlj'X;e|Kw(SB|>GuDzHs@(W@ vT crl&o00oآ\UݗwB VE\NDz+N+W!w.Od:Dk$`HSB!li9"=*|VF$Y&1IvB&y>xb7Tόp `7[ ahύ-$%[Lzcm*4Q~gSw*2w*A-~ Dد?j3ISz.ڑ'886'DdY<1ȎQ"A&?o;>d}":60_:臭OKc/֫!C"js"d``ދw/ R endstream endobj 269 0 obj << /Length 2028 /Filter /FlateDecode >> stream xnF"b9 7!!QCbDB8mȡHJv r8ym6=?$dAx0 =HT&gjgw6n`899#,xl/dx kó0T[Aexba OV MY`k#j `ȏR5t#g&5Oa8YPp\N DZEVQ}37DI_ׄW@i00000[5XY\"ak:5論F:Vw(7ZWY:GwO ^FKÙNh ޗh-pPSc3 >b7djx h=lފɗ&Vv6xj4 ŀ_] $J(fxԄ䲭y ٽi9gM\m]E:Сd3z1H7W~aI3e#@am6;aph}dze$ (sf9LHv.CyPCwl֕O+`fr!أLJq-b$Qԝo˸|-7O¼pROfּ P}aĺzuoDMaP Լ\BWPwY̖*Pw9ATM /Rs"I#( 6uyf (/ۼb6l8 $\hsh RVey5'Swsǡ#e IS_\D"2__DћPGmvTL. =Y$uVA$}u`$PJcΘ l06G*V~Ɓ0>+R: | ,&'?<3v®uXpI.V0ۥH%S$gfb}^87 KQ#Ȃ4;T|>;Ϯ9skUL3^`vP@T>{6r$Tzʃp'MhuyEz':6w\D勶-_:W,\aBA `5ғ endstream endobj 274 0 obj << /Length 2149 /Filter /FlateDecode >> stream x]o6`oFQ$guMmWb+r%'iw_hYݤhxwqyHGe/ֽW xi04:Z 3hAԁesQZ*Td_;Ox7нP4-$,X摻kkO}{"ko m.{x){]ʸɓ5hRwOƾ80-}@e*s /Fq0KYue"e"sG&C-cEϥɽ_|YKʁ΅;MVq]߸Evgs/uo?aV11SGBhIOƒh8884sZTyJOzE3Mշ\G֡(f.?FLY8+]PSVY>dٻ8ڿ8j[0!%ZCxō8kYSf뀏'Զ\@[iECƱboh:^ve\n57iߗ9HB0Fd߈L u {g\\o#ooJ:ʧK'TM3|T~(H+5p%VOI$lLq;fl*D( )"+ A {x 9u nH _PdzЯI>CX6 т"9YC3SVUxr MLbsW9>'*u"B)'⭕Iw[琮b&z#~> stream xX[o6~0X)Q̀aYKW Jb+ݯy.vb@S"??4Ad(yORaNɻ뙚u/~͕ rcpх.lQ+mm`*Ӂ @Q 5G%Y&fQuD&FǕy?C]jd5ѽƩQlk-nۭrr.d+^,SAJ)RlhU'3ʻ珶݁nu{6t?k@Z _$fZa_l4_bGs]lh$2ъh_J\;RG14v>>޸ WפmYRlND/M5ʠBvZy)!^w8MBܲryk'z;(0¬`8&9Oqm+*s5ctZm־t鳕S:Mwgoy&CMᒵ@!Ob Tq=S ~s&mμ r_Up J.Mqٽ|8xIk@$?T/#Ybla9uio.s}]\y[rUb!hأ<DV]RgU돕=r3tKv:?u'Hkn-P>DEKS`&'z~2tlt+_wܯ$}>U\W[|BǕNxq%gڴWĵ؜J{,EMm'6qե)\,QkIUMo4(hsPQ_+rFyO?:С#&&?~.Ӹ_ZRfn]AJY ˜Wq#;V ;XJ w~A}(+ڎIdiowLy&U*@(2XE.E9 RӜ\I~Nc1z6% gGՆث!a):!1^PCс2 4%Y똙pNd Sa]FN!Хc8 -K,5LE&@CfP8Wt7'r?~d0S2Q5'̿0p7ɿE>GǚFHvzD'm"'DQaRu%Ƴ&Wx KF:K]p&"ւypz8YSfF39%r<.X$d&x{(Q: [Et/afGCsBa#11U4IuhҁVyCsI*GƟddzUTZX tvX( wR7:HK'@L%5V#4Q:1mZ,PJġneH-sf\gS{V6OС`}/? s2A㎠9|N9l:BemejY< g endstream endobj 283 0 obj << /Length 1789 /Filter /FlateDecode >> stream xڽYێ6}WAZoH6MM@A^kwݮ兵6peS}% C_e餈T$L$q|9y47S}xV\Knçg O O38dE'3)@TJ^@_ASv &37H0 `"Ypx 9$O{g E=U'}-?"Eq cDcTHr7SEb›Liia-#*Og26M;_bfjYRy6waZW5VuB26\{«8¶~ 8F%8)X=ba]4Fk TtZzXMT';agNE%=]'k+T.k`z]WMPǹuN#w?RJ/C6a]&* ڛ rLH.$IٰˊhM3,+W|I]JV7UGCb LgXn?q]oMV(M"U.h2pyA !5)eCt=E'CIUilt8 1P"Ę_hYaBCnxNjĴtXj9@֑Q,;∕xU*k\x3X[j]pA(2eDZ-t4T&\[` p{eu, Y*гgT]pBa&Qysl̛64E$+4yHI ${-5\ f_EرSr`QPa(:;PXeG(qƻw[5qvz)4Q$=_G DPɣI-Xڲq%r確!aYnT`ΧM)ԋ*Sŵ^[W^cNG3Q&:3s7Y_ˈP/RGᐎcHtߚ%7)bm ?o[^-/XSWȂͭXr7y݂]6b[SuSGF;dq\G87tv,,ֺU[L=rkƐR6`Wfy'uÙ5Oq]^WR&ʻ%0Rx=?C{~<3[nco>j~q8+c=~hlc[@Z9dv./T$O,NUܮ]6a _{nFDK\:=GV:5N>vʥ<Ϝmt&*5©#5Ro)ÁaM$NmZ8X4}z̈,vG&0 4WK4bqǣ;Zhi1|T2mw|:qwD|gp&-Ym_߽C&S4th־^C عExE,g}|wvkؕ$:p KSkseV#tnxyN#?y}C endstream endobj 287 0 obj << /Length 1685 /Filter /FlateDecode >> stream xYKo7WA"m2 $FR@Zzq@e[-4cȝj%5==Zqf%MOF15 4hJ `z=0HƇ#:YzTX,-,][:t6 KW ؆Z[brhKh] rt9y@J #^X`DXg/[y(A4-UK-:f%d YB/ld@ CxЊ0%jR>2~v!ގ&zZ6ܮΛ|um3Dea j%= )8Z+Rh]`|4a*Z6s􏏁ϡx>=οEظ7"M LԲ'p`n d@RS8ɦAYC6tE)?C v3%p ڗw-pQO, vlT@DM45rt:"e".JAR1ޤ~rtU͝J^&Bmϡw1rcR+1 ag7~ onp_ləyucXɋJ?;W)VB&(/??hێ>ԚX(AH% &Q~]UpኡֱYA>Zz Ń X.WH+[SZQ7=%%=G@K᷶V=#Fm<=;^r]a⷇+O m 9Te ;c;TM{?#D%baT~x3V+0/ό,h`S`ACHO^ "<DZiY͆F *0D$׍1eBB7UGC&|@|Ai`e(B$WA 1z"fi‘y@%2Q%PL#TfCbhV 0,|#/wR,Y] ٵ{EOW[B)V_T>\YsxH._j_Bo} %_%ԗx(5g^mt{Nk/8I_lF.jo99N;'w.lrzs4sN:n9{EFTAAI!I7ˆtF] #ppK7|4,a P1y`K饃Vu6Ҽ.vRBCbBp=smҕ}H׽7kYͰUz)5m˲ P_=NOOH endstream endobj 294 0 obj << /Length 1349 /Filter /FlateDecode >> stream xXKs6Wh<=ɒVf:4cf:D=QrH*N}(j6@ X,v?.X<}7Q2Jhbt48A8/w:I)ymVF$6?`㾝DjB?cT[p(r%ݰ=  pXXoXo,bHS+)žX7߫/goi|`څ,4<fO$YR2wlMv? ޞ#Su ɡr=nDH#Dg18V`Ϻ'1@3Lwk]Zt͟rxrej%A@:Hp8Gow΁d۸9]~-N8Ȟ> ΜXC1wE`W32… ˒̵ ?\Ϥ엁13Sa)ĥlJG'rWB OH U4Lb}VF0JϠVtv W؜Z!n9R;qm$o #vɃ#8UNRvD{͆遞;\N۩>~ezU~y(J] nHm;J>rFe_QCfE%pqV4edg^n<א V?Aba |V4u`W5 %nI\Ĭj]%) ̕-̏3֡$f\ endstream endobj 209 0 obj << /Type /ObjStm /N 100 /First 875 /Length 2177 /Filter /FlateDecode >> stream xڽZێ }c)c_d b~5fll>T}jTU*<$U#MB 4H)sZ1P1Z0=xL)ϷН C@ΤK\Mp@On݃&kԠTKL`Up,A3m}e(yòx+۝rJ>S!8SB.0!;er¤RLprp]flS9{xcbS`yeH|`WCw,.fpRhM&Op& 8i@Y 9XT ^B5|ՉDDťV(7 \0fK)@0, ;6E!րL<"2xB4#L2=7KOswB#=tɄ|¤ٓ'lp_Ow~lz6"a.%^ahI \ O7w/9 ?קJLX5|$?ZNf:],)63r(D^)Q{WDW$I4fTPD\\NI#V{4$Q$⬧y$uz-G%ANOZ+^LHV<UO CT;$Xh\gFby8CXY֢<Sz(ITC=W%OJcVUHVEEi,yBE'0E$]9V?N4DI'$qrR8Iߋ60<`alԒ Bsh<%Q5`.FJu>,gy2$]1brh"i˫K(nuuKc3_П-E?Mx7 X$_xo߆F~uy1?[Tx;Mysuwqq34(é->]^<= Geƽٲv.' 7 a&I]ž F)#"蛔,$证!i:<aN@I >r 6`+E]~7vAR$υQN9(9UU z.swJ|S'{_kRJNd/iMaaCšSt(˦\0Ml`S7]Au''Fr'|6d*}[9u__}[LRn)+p?o5z\R֣G_u=);{?cw_ԏKӱi m87ӸW o/~[.| 9 [d2j8hwbv.Di0<:J}5.<]7&P, ׹ Q$|DgՋOSjQ._ r#JHd^K0F㪼pZN y%tQ ػwNHih{*G5-qs4^-d8(CsC^2.+QQGjb\8ۍ ^tRT_Xܒx2hרSvϧjG,yM%[~%79}U i"өUpekճ){+ IJo%$~R2 l;J۸JlmAIQv"u [P=5>u깰2%\+ޕ;/(r7a8Gs~|oJVLRD>/~kNnl,pxM!Z '򄕟Y(WߓL N 2Hw%EmvB-th_]5'(jp/~u-w~W!N#j۠Y{I endstream endobj 302 0 obj << /Length 1104 /Filter /FlateDecode >> stream xWKo7W,VEY%7EN\H_KkQXRw^܇6Xp8f}5=eR4e2]&$^[6Oޤ'36zV>o@Adr9l. W ?Lzvm'֪rnpZtEb$]}6E:3^R*N336__3vT5H#Y+7 ,7gޓKb@,?WԅP]&.jCC@8giis"+Z# w}Xjλ'\V 3B\g&m 2IG}/A㺽gȘ c;9xk)ǽOӑjh\LfH[)o؍~hb̬ _CFJET()ąʬ„h<5ɊpUޣYFFJ{9`КĨ*9<Loaȝ#y0wF(/ W(sAӵ-@T^߭vpY^|CZbHfVʄ{$J xnH%6@a 8 Z#$3%wm E;qvF0Y[-m^]F&]0} mR3N< hj -m?e2@= 䳨#RP|WISsmoGfmyp]Ov!93Bј jTmg y<L endstream endobj 290 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpgqSfj7/Rbuild3bcc0d7ef148ab/EBSeq/vignettes/EBSeq_Vignette-022.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 304 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 305 0 R>> /ExtGState << >>/ColorSpace << /sRGB 306 0 R >>>> /Length 60217 /Filter /FlateDecode >> stream x}mU~b?ASK !hCH@볪v\;?8>5w?O?Ϭ_)B?__?+/l??Ͽǟ?1oz3j}`r*ů^PƟ寚9⼀6U񦶤ڀ׸Aؼ~?~ym=9v¾I_0?+_) +6$^@X[1ϺƵE=/#=?R_eIc|͹0R?&U J-)R/Dokb^ rm@k\tI Kߞa~C>1 }Z^PYВi}.$s N_s8/wZ14ޗ̯7Mk_ڢC.r0x-׻> _n_u+֍] _Sbu_ڠK.^rX}*}K_o&6AyI)zjxNoL} b/CM%7[&wbW5>ֻAh9|iO6׾Ԝ/ _{CfS.^r0v8ן+Ca^_Kѷhbݍ?y=;EnL[x_†hk]}]tI K>Ő¾3ڕ 'Q*F+h&N| 2N{ɍ_GxZ6uZc;8-R/+f%kb^5}}gmڀrW_&]pa_Zb_;kZ_{[uv< i_]7^A{mݏ&%y=J H;ͺ6 yX _7eP2O ag- r럵HGI;5޸>7[{CX&r#?~akkگO/n`m_sׇ}1@a.(^Aia~MQjdﴮM:%7~釷4vnoƌ ==2j Y[piKtk.{OaKZ|׎k"bN$MZ;~NmmyamY"RײӢfKR^r &={-&j޶ޙ;}vCK}}`%/ @0M[ޏum!xOf>N O|aq6 e<׷"Qd:=+,̈7H1Q}m)yO K! `-muQ?m>qlzϰu]дmќ{_Gt,d镵S |-KZ[VΒ i){/G6z $w]tI Kn>>ôu Ra6^w6gT-{Tkb6;ʭi]roZpÍЎ6V]Q nuXjZd-Q_]tKnaѱ2<$Ou RK y+tˑW^[_ր8oܟސ~6 }:ZjSi: Cn鼀펴p]tI KniޟlʲI=׶ԗ̰2oAvGxamCy-Juw[|m:<k3G Ժs |xU-??Z;lkN{}VhM$mW[iPg5obXz+7 o"yw)x//OTz[ROLۺCk//͊оeGz&b_0y<CkGjum)yOw?]$cv ;|dpz&9^p>ޥwYw֬/vW%x//>VG|6:Z48gIR޷Vņ̽8onD^)yO ӧg>\F^M%77<1Խ3ܘͻ㷕}vA/]yPG44t]VN{}>̔4s}-{2YIAC672jm@ym))x/`[?4L_cYlҌzYC1^AICxuڥrY#8rKM;[Z5NTWbԶW_!޸Z[{C8=tKnGxytbm2wӥ^ov0!aۦ%j?ݡn1a6_3o:\8%x/iS+7/:N|@ϪN/x+h8/kڤK.^rkɢSdtd ^ yܤy/yWX-ڀSٜN{ͷ w oYO Y+4!œ>aK3jmֲim@Bs{| .X8hAWKhbZ՝{ͷz+@_IwHLޟk RGa{A@Kix1ڀJCN\=z_ !Mbr,3-7!HTia!$E( Aڧsu:%73O>lV!ٙH!L}bҗճ_{G+> JXo Z;C&r'~VbHYp gTs3<WP0Q_!kN{}~!?iTJf>1}&ŗ.E233^B{m51A/_V-G@rGm Ca*<> !؉2/e,&|hxe ؼA1 e_bqaOJa~PiAi@L++[cwR//D&@kݲ-:)759' *BfyOz90&_dѵ`;WP;m qiv;m&r[{)\?>lf DA,N/=Tb*N.ڀ/c\K+6<3+#&!s) s .]joIF!/`V;1 ƻdkmMJ !Ӥ3nvS9/FAOڗԮXL g&jI\=徼 K^4}:$>k9/da KLG9y_:6 xH]r{aGu_LӲ<Yd&9$L ą{KPbt{}g>-Aߨ$)'4t8h+KLE+xsF0kW\tKN p3$!7MJ$_>#/ nc~Mڀ׸蔔ܗ_D9Rbf>"Gx史~޻a["]mMwK /ntI KnӔOgwmB6y1 b`%T=6 sIkCv퍑Xc5x 'f"b"6$&J; x*5.k/~7xm%x/9?YyQ4G jdt:sK(*FUT0=6A)7;N_^QarD+eBe7ꩼ w,IxSE^:Ӟ1]R^rP~Ute@oLC"KAwRoJ$ˎ>0C&$/1ZۣTvT>WxKGQ}Y!_n>vPLTa^vdVHu:")2~) xF.1p@:Zvz٥O ٍ#BkԻ%WrBpv>Jpv8WyCYl씙J )a (ǮJ ?~m)x/yZ'j_ a:xOQ!=3|T"Bg%x//Mzqn rԦ< Ì\D}K$ ucDp΀WPSa6{śvTk'aصA\m8>jq*ciBӂq ?W1 Fxס1mm@:-B3$%o#v4T'LBj DY\Iԝwx gY.leZmm@{jr[&tCNBV 00SpuZy"+ {Dk6?x׷Tlm@WVg$L{$7Ä78#k %wԼWPVua*wD K ڢS.r;#EL6LRؠwp^Hx'ϖ+08ŻvT ]r3}oU5?@f^B|Џ{67AvO-^@X[@$]~bk/|p= gxS扻g~ϝ3{̼,w4m13+ /<%%uGEYj)ȇgkI~n"x amubyNf* }zFۡm(v$}L$TM&+]ֲEAyp:FrG3~ʥalzDӤ mpU=Vg3r-+X(aAu9{D5UWrLR̎CYvԌ""1 =3ppM:%o6ZwbO*Re9R6o`t{(On[UV{i ;ܔwOi%m. ]tK+F'Yq;+Ovfs-ڮ9&:\n%T ƻ]v'k5*D08x [6gq]3 iIŀ kֵ16- SxsX)f=n+, f Ȍ@gʶ/ (ɵqm`L.<_r_?%'0) ea$^BݲA!=y#.-蔋~i&o66O ҹ ×hZlpBw u%T0LcQlڤۃ߼[IL1f*li|KksN')֦YʤGs-$W vtJJScPN>1cJ+ f}iT/.AJ>y\r#vi :;v ѰpwB & 4c{w֦թkN{ɭď49!Y,avmNֆ}Y/l$6+ȫ 8 ъ%x/r,Q-B* ۽E\3Ax*cGH[jQaމwBkGrڤS.r_az'6;mlboek3w9Ng)־N87kwX)yO?u)s0܅RJ][EGk]j S=7 "ƵE\=VeǸD6W0Dm#ƲѽOn)$}J{I\7/Z۽vJ6-e[<.ʊ`[r#ߛVį y U7-$ex)yOcĉl0/LK]2<,@+W  -=}cvGz$qm@{8J_ڿzc /hf[$ȼmq߫-S$[qzy 81aN7H׵\妯~OdhcG^ Hpdm kW \;x?^^+;妟snb@%k I> jZgCeZ۰Z@7KZtMS-X֎]ӵI\=K >1[0wߊm(HRJ 0&/lg Hϰ a$xoU{ II( i^Cb]l%Kk˶ؘ=Cڀt?SRr+s^: ]Y}`b g}#|9ؘKw@X[yc]Zʴi/`=Vc>km~T$= _r/$^gpv{*(3$fxjm@Rsz;"$ܚO16wZEi.=^RMJԷ4CzSaLvatee Z~NО?" ċݏ:tESDg2©mR-)H2S%=$VO^ZڤS,}Zܣ@OL&1h;}i.HEN/]^j  /(Y ]!/yZ|5+Is+I[/!w80{5(X%1wQ tq`Y ܴ?OB=Dd$3XlI uO40E'\Qy1Ӌܪ"|_"&latQy10&}C#_C!a&"-f@9=a}v%d Z- =o].r_]?޼;|~/%O^T/e8=#"cPt0aCo1UAvB.r_'~zL~K\=yGbuO´}Ne;4`eO3%NݍmhuO6锋ܿu2,/1ۀkL[\t}GHEfQna v{YEA.!r&`o0t(7\r7!ի3`D]C@Yq8O13;aQA!)Y3fAbN6cF&r?!i01g8Rֻ44Am2>-jnOpf7ސ- 2obv:")#'j^aPv3ix{K{wdA^,0Aak2- crm[;O/Fw#lS0"2CaȌrpBAsy po1XM.ӧ|,$0zgjz,^2yIY񆎣>HG -OȉZaenDf=sQVµcf -{iN bV#" O7cڀ׸蔋__lfhNĔmyϺ'|%lZ0EoZcQk^:qr_=J~Y;L-]1јUӋ"UԎ0=g:a/xÐ1۫ y0&WY|}kkIv{aih.-XNG#UmF91a¹߃ t.-}?( ޤH?=| 2Xp:w(̰y'Dk'-\i\=徢ko0M1-+Bi Fe,e0<52h2ZYd=tJJSk/}uO1m- xe9>f=uwD9KZlC,9ҳ]IRg|ȭ?Lnbbؖ YCw4 ڶ/;]ޅbkkU:_DJJ2#TX 0[LQ]`4y mm@p:%miܗϻ?4n7i^S'?e=z'|YOėT/)?/XMLQQ%ayWdPӬIrJuzHޮ2qfvEkGfkGe輧ԼG?ͨݑ`Q.R+oa d&4d*3Qޢ2%Ԙ l:%%G5˜ʥ՟LU[ Q|\\ /_1*cBkՁٵA\侴Gwd>1hVb~,U&ڛ V/ 4G&W:]r~vYC&pZ 5AǟNeKZDB?Q;X6C=,*D(3\ٷ4WPM$Ûހ\;xV^[tJM<v:co[ s<-OnLtD~ 羓&d1jt;V='-UI.ά"igiwvEMUES.r+G[:lq2 s 2 4#Ce3^AQ0ђ[YZaOF\v!!yȔh},o*ԝ^pStTW2R?WANG&x 1G%\~0a6K#vz|b6K.3"$vIC=&ԿZbD̾v-:")ݿv̜f MUfp* ;zA`y EYEx@A3^P.^rA4&@5aܮfbۜP3O P5OiXI )=fY+?]*YDh1*C~aNgZvѫd1pCkN{}M;*/g(D*f-+z;L=FB5^AmZb'GmRUM/5N{}- k^󞣲{ੰqf(Li ۚ :G:v=q*]묮ZǯԎ:4iNorpm+0 ڀ,݄\}JvDk(9 1)iNAp E¤ڀ,&ҜNI{_yr*mRN=\1d2"q#AAax~ VJՆOɦ#1<r_Ql]_;F^fdE }X|ʤk].H&qm%mSnUX~>-޲l}DŽdAԜ^--;6L1#b,v 8^D_]#-kDd^t=]#ֶ<|@?FdA{pzT!uH}aiDtM=T_6Y0 æUkQ=WxR٥%R {Snr ^E cZ]ݨ v?Yݬ x ~3Cl-f,H; NoJ5!UC̉ {01.M=Sin:z6ն2Y.v6:xK/)7-~Q^at-A:y>d:xam=qfIfOoF;;C_=maգ)°^5N/:m#E@^B~01xSƵE\EEz. J4vf\fwɊV\@U jQ]aJk7o4s} A\VrE>\=3OͲ[ISw [ŵPUv =eSȒVϕ]֩Lkk*JVh&A9Ta)kPFaV&ohXLhVת[n)3dR3 Sd04yam;[=dgԋ Hϐ^\RN('*}'(I\bPibX c'?9,<->v` #,̔aڐe?G s*@f=MyY=GXe3޵rOS?Vnт.]2 ӎ0ޮWSafUYLVV#EKYKuNwSVL.;VBgdt PyoMmUT=ǵ锋K?FhBsE3J4vRv}:l:6x Uڄ26oRiLkӵ[3=ChRFEH5GإT;l>ɦ 4^Affe]M0[3YwY/e)}`ܰe-WA 6!;ԛ .QgfӔymѫe}ܚ1lh6xkR yolUҙ ,h=<71#b ҈1YD095]8N1;/;ee*]fs:Jp8r[(Ь51I9YVj)0@^Y6Yq}DVٴYL$:ri4_r ?MV~ k3 ӛ 7a 0N[\V`yc+BgxuC)jmByU.yO/[ ubӴ.zL:ƽ!LAVBr:Kx쉘$hG0:+!tʭ{Q#j< 3X j! {Zt.gWLB2^xEͧg^AC+SY6><4qZ, :YPʞ`G+B&0D75 !ﳂF8l1+R:.ȢA!oam펊T UMaɾf}wKJSoiK_ ]0zfKY,PVvts~u3p^NtzJR|.r1ӳq$♩|f[.q\"oCAI(aD #.XEĪY;==Cj&=W7 Kk˒׵I\=:F&{\GgX1/f(eW&Kc!Q5qS:1s9tq%g=}ڙ5'Gĵ-*}HvMޏtJJS&ݰl R?؄ٺeA{xjo`$C.rٗ= S1:aUK'ZUiVWU؇P;mY"-PkriKM6!dU)g_s__whXl;P7awɵ rzo֭x֗bCHoihQAG4L,a :G_?vmуz҉%kV W)e}YCSġ$H'>c"\Qqy6imV''+"xTW=hF,zm#jq{ʭz4PZ! UymӳuE?=fVmm@jƩ}S' M~㴘҈2.NߛuZqZ8|?8b#aӂ=.7Sdc*Y`={j7}>bpy(+Z*eF ^ڊӓ&Zt!D)4YqQl&J)HQ>LP-hm8_@ 6 x%rm<4Kj60Ux'!@< E҉J]! &mwڀ׸6 Ƞe>zD'β}6-o))RMgdgA#\a*+-Nr3(7} E[ul0&k0Vc;;I):)=]a]$S(O3ʚ OaM+kL+o5k̏k^r}}Ş5i>1CCV3MRd@ ӑ-^B0EΎƸ]tu%ko6ar!b^9j#[/cj-swGUa x_2sxَkNI{⇾iHU##N7fB ml_ L _hy 3 :Ρo!wףaX\܄5 G]Rr:a fuWVۉ/}u4kS5s6T%ǯv/3W,fEVU_}#d=CQmM})HI)2Ӫ)yOtBn z=FIZd ̊x [ OniG/?vmѳꀘ3rkOz)#ɽ^yprf2zˢh5(x0ah=ڀd1 % |Kd/*6 XSzJ=5? 3Lþ5%w[%%)yŞ&5bNehd)H]Mlk!W6KO>jFnn7!u!٧?LCNX8y8}n6m2$Q;`kwX)yOyZ؝xp[y#c}p_Yjʉ|g^e7;k ;))yO47W { i&[ef HН%5LFTˮv*f<qn;Gܿw.Za,ry3ܧt;mq~Ͳ{D`$x/vj(HOeEDX;oIko}M>(<[Dsܿgt)JȈps$)Q#R/XR#{Բ)fž H8pr1uθNcN@e,ۅ N,̖9נ^ˣoJDbt%nr Wk65/iL U\O١two{ tAM *Vn%R  C7 [0&W|mώcZXN#lA,gYpH`OOe?˔W~͌ikCny?4l~'DAgL)? 1Ay#9bm䬇uOlU4g3S)ha=*Ag O)t צ}D1 yy$/%o} yY(6 &m-E*8?^#sYgdWW qŠ'3kN{}W?23 9ؖwDS)kp:5,+X}j@ش6!Űh%K>б|N׺-AТ+6S]spENU&rp֑e*hiӎ"ƌWA]%YܗW͋ٲ`1 jMڦ0[5{Rb*a |m锫j&{dۗw̳/XmKBۃٗנtf_NzȒ񆩶z$}r_Y˝Ala&6"?L DO5PIkg7dQ!8\=V_~wx}b3jK|zfS׶} 3TF*f}uZQj8!+{ /Dǔ-?O;fz/hs!w%a',M#Yնf20&WPҷ)C}-ras'dyKJ΃ɡoy_ 3ǰ=LVFr߱*5ԯ-FtsHP9؃4_sr,R>aCnzZӽ\/թcD&J暕;Tfֶxn?^(4'-i*YrI^]O}B=fVi2$H4:)%iɪ[it̙`_Q'* İuJĴԇ㡖s:/k"c:oBZ K cr%|ܿgAW4aLw]oeLav6I m&ѡ[2^: 6NG[>|~ĕS% 3 )x n7X]eտCd~i>{uUE9λ(H;z,NgP[G.ڀOtESnj?Ğ2:VyqGg[WA ӧ<͚xeo#iXZES߼L%c oiE{40 oRvȸ6钋yj~Lc'DIDTovw6R,2JwG;A]_d9g;KCni>œuKBt弐NDW#T,oa#\qrtCC ːlA3Y~iib QspNgrs/!A[FF@,<~M-u?8Y]*Ch.)yO/obz y3'S{0қ0,NyO"qHk7^ӋuYO/FPbH K19vYsWcJz8:9$"_;G8i[ܷW$v\YO|>bvkO np1Zhv rxm ] #}kPG{A@`%ĉ"oG^kN{|ywh%[ކh葈^[dxLB3~]O)fXw-3p\ag:ﰆwȪQuOw8߾v}ǧ3!=Q ~00y$̉ rCs:|3'sY9 kyrLW ۄ sAy+;1r߬~yy:f cOv.yO/z˲k֣Ϋ+:\f f2hm\dA꒒~6-#Lm6+z)c#<ͤu?_wen5[N r-)bHni wӋFl 4D:= |?1lS˶x!EG.arPseecVٵg#GTWv CUjbS:ڢSRS#][W+HRԝγ^+՝2Rc!쐌m?kb8U]f4kr]{TI9-29Tфi,02=] &L7-%7z*x_}ˉ Mhs3»V.gF;u`a;x t{g17qoT6U3T<`F8R$p ;٤=b0&C~0N2\mptD l0>!˂6LQL\kN{}>4V-DXage](4ީIy@AG[ZڤYyOy\=hF`8$CgƉ*M싓־D q@SR޽S1CO  N== Ġ\y^ڀ2ӕQ!ُ!=n[`U2 |G27A:#JŘWIE¨spR9lm@?H.u>^14\ ðH3Go`s14ݑjgSԐX.E_;yw7Yq>侞ay?#? >W?tz3xi)ly^rdx6LS)_Nv?D)qpfd_VfZ$ GFqTsͨvؤٶ͌Zy 1^fu7{ỷMEGJ vޮo]֚M}&>;FX;d{!kWt k['g5T@l-I6Wul~21|R5G7CF) :2i3#sw k#fR4nS~hmB8Ҳ@\V%|y"ϛ9OAK07Ww8(}ڀ_ڝ^r_zϻdVg##F,^AX[?i[YYtIl|& r>zcz FϘ!Y8}"gzjdĆTA^jk2`8rUMuk䀘{Z.4M˅ 87+1-צi]a/N#iFg qF>1 c5UA7^?Cd<k0O6 'aLS"03l ,r}{>%;tulmנuO9Dxh!Hcsx^=v{ρ#<`Cd<91{:CdD rӋgtv<-uQmLMD^ ҬTPGr[Z#Ey`]pSfwEk*DݴjPݫo!])Oo[WAb0g 6*ڥݑ ز֖NpU!imBi4`B;x/[laI671 _ݤIU<\9UT&5]̶6 }")UiwY;42K=ժ iӋz)PִdVϵdMUE0&WдD3d צf~?P{ vBtC,.ynf͒̾~c_aA'LŹ4a clL($VZϜamX"imB렠h.WLO[mbsibHtIidr&,ηa $?Ã7 sk=F"Pf'#i=d F)[9t xPk=: U<םxxzK[ZηzK[4]oiz{Kf>rM MLQU@ )HAONWX栵'6ޕ_T= b4aL)7OP[-ryžv)3d:<{_kIEyHӃ:Q?p,}84)yfa3 p;; O4y;Sݺ%eMaV9;,[͋QL9?C5Kvٽ4A/°ܓK?lZ]\d^buo!>UIMdS3)͌bRCsҨf5=gEӫZ3[?~꩐5wbvI]x0tvNf;lm@(N]s\n>;ּ#ٚ2k^ M*^GuCƻrfsoN%UD;u N/dL)e!G3`ՏA rc(S-^li,0sj`.d[t To=$>k'ivm҃·r9j9zvEQgS@ea"mVP1{Mۢؽoajr*HaNF腦]́<_^پNfɻ܊+rSm.g N}mQnimn,rYd'~vzU\.7;$ǜiQ_$W2Z Ի( A:ҨN13ec$X/k 9= xCVB{ 9y؞0u8UEf T\6\b iwpzCHֱVso:v~TZ];*)a˕5!'j߻5. SdFxA}*W}Ϡ;]\Rrߙ{Ϸ'tchNx욕&C;td*xWu`>Ajqv2]>|&Y~_`%N yOLFB^kgڢk3/|׼e~82_r:g2`mEOx 8Ƌ7;;!e7F ^̊R3򸰇ֽbAi0iU d[:9 AN/yv6{°oDb[޺ - { #@O=={6锋ܿ>cwaK{+vuM&iPAiQa-;v,^kڢS.^r_ovm*NCLUa`Gd1:Y8T·c 02Kvׯwr)ji>kMs {NAŬ AX%,CWX6e+%<喿 yun!wچ;{rӘ:x+KGG :c9vOzCe$)zfWAa}فU3pS_+JFrZsl]lY-[ +{ٓ?D->/V a_XKnz8-[|{ [ʷx¥X]-U߼lL6 aLJxzE\ a2s;z$b%L5UeCLMH0C.^r!־ < 2!omXuiqˌ ~RgMQ4/Yf%ʉs v>y`?do~YYpz'ա'1Og6{4u.WP}ۇ⦆X${/hvN5fE idf*-Llm[N[vWd],Zlj=azo1,4<Dl.s`.Odo<侼YW_zW_9^}1[_ addw_p`ܴ-][^8;EchA@j&PsÜJYk/l锫h}eT(eSc GwO6g([6:e׳xwjmB w"\o?c&4v obWqEJNaGY^rN^˝^/*C+ifM1*dYAC'af&@Dǹ<c~}4!ϰ$!5ce7kM^{tx6jڦQe\Jrߑ%syͯIx:&v՗]Qن_m ei޼6A(<}r{G!PoUc.Q4+p|IF9c>>)zҒ}Ҫ9a[r{ʭV39'a<lc}A$j 34wGM/v1X{zO=Ӱ<`f1'֦Ooygy0!0 h>ڢg鉪?UUk}bI37lځMhKxnz?SnS[)yOKT<Np(wb 3FcO 8p GL;pL{ENlKz|'L>2#U:4OUPéV{FCQ4ZrY?8=YFQy}ywY3Osd2a;Z;hV\5^eWt4U}pI{ʭgiAgߕ?״f496u?dvy7Ra7<īuNb廓C"|20G/&@gv{$;GIot}!#|H1 f>i@lR*gצڨ& !}aBzJ; q@]#~ erDh&wI\?Y-Ok!i~}} jHRCZ8kH}oziP=]OVY"ľSbA-^q=~ofh^Mx!"rLVm7_zE4*0pЛU^JߑO-q3GjOhsp|9`ݬ9({(dR µ2#CЅȢB,*KڏDl n_x?ThS!~O85یDnq^ Sޱ<%x#iN= ە#q 13kqY%E+Gԅ v&KubВ()7="Q㍉}<y!Q^@s5i~1 ^2 WE*׆'T/jKC/=UV3C 9 Ɠxڗu!UW#`$:/z]ȍ#+v_Jz,wV[.G3O65wܕF#=|.\;ߘ]ñ33=,(uQO'%Rkѕ?VD=)[DQB ivABW/܀1RȎZacUX'&ݐbkX ݂c1):>TGo8lw݀})v)ɗnq" X9@ajt{Z]{}EatlG3I9lp0?åTKy|J'C6~h Zy>t*Ȉ`g5!]yG^Bjb9RH7,,^;ɋg<**k^ =,VkT6<NIwypCY}*)jħI.v_zאȗݼᇑ71"oMlMM\; ˒7QX~M̞̐7z#Ɨ?%3J/Y{/3Zý0{TW"%孷B8slLkFCSV k]=?^qcj*T)Z YRuF2i)HlT®н쾙!@[q'Sd1w,aުZSِ'汴vHSJad\Yǻ)"j"rRZWy!1"P*\vF#~y)}wZ] zwϑGq$|iKl-UwC `6v2KY9^%hZ%FX#HE'EZ5R.ХTHtnγX;X;> ݗKԇM(&xwQN3= tef]Cax)>;iW^vUgh#?F `/55"!WJJM{¶itTIMDQ iwS0N\6_0`|g['&[P+}"/kC≟.^v{=~gTKY[(/g8-k#Mҍ'ktc朶 Tѱ iaDd.g 릤oX(l7ob{}WX&|(Nv  iG/';0d?2VkmWep/YP?)S!.3INḆw\ :VKEd]\'Abn=T huEV7M<q]~ BE!P_ZXd_u%%gѠ| :j3B>YjE`9mfa-n!dHԽG9alReؾxхkC罇iW^vߵ&WMtR\{NXʶt +'VڐIV]{}3媑C5GJlT-W?E鐔//]JɑON,K(5$y# xnQM$$#|g ,*a̞TJ= %EuatOl}hp僻pHfpp 0ie#54b,gc>vK!s{̠XؓnDyomB*!`K]͞쭚nZIcWX2%zL#pXK)-{ޙ%&|Cl>/Ţ$vg'¢Z%瑧.e#hG.^`YgnھeHMDmAZuhqn-| sY'Er퐄6Y]=Ɖ74{qmi3hA x,J@nX盅Dl׆D4PKve1[f;vk+xUP4͞ A!A)B{`ʼnq9Q*JwFp/#tĸZ)gvrPX/߰1;ͫ~&D<Z(i/yJ]JRk7j4]뼍Y^v_y_z:OA#{x IIwD)9nUt9ktoꍴ_v#kެiB!C*/:ޣx ߙ6A[ g/Ʉ"(98~d;z/#/)z_84{-Jy}?j6Xő7Mͨ6_b"ukbvg8;aZ^;+_ +?~25/S=7QO%3n Н66ɺh(1R [. y,6 #Q-wR<) yNI94= Q<2uS`OxK>ZVC FP"ϳѼ;yҨ IF d:|6atO'*?0xL刵'\OХXF"EڎOZ;;ֆ|7]%3!4YQ6_jOUOIF$ iZ#RsNl(۝wZ}v`֎s`o[fUAp\; mwqK\p^3{}c/}GsSRxy~m%u3t/ 5޲.D1e7|/%@=Gvx" %EA ]Hy-"ϙִvHDuî./=UnѪV_cy5R}vǭ9x8zԅԅHյ E>O?]xLnܞڷі@려(|CϊS"t&A려7rvm7S&&1R֊7ӉtTeuIR6_gcw}h[+׆*J\9쾫~Vv_[[zzs$t؞knA'WvH[T.OYCPL]XRX9GO +X#.,`3) BWRvz#hnEl_t4Юнw?؟f܁`#|2z˷[s] :t,8͎BaR6)im]*.Wt9B*ݧE&]܎\{A(2\;Y1#^Σ5k6ltO&% Kc?.+O2)I.ڴ-%1J[橵C/^îԇa}WFAkOΨ) GʧyBmI]Hi4j\vO<)}-~쥝uMRUG\%8Y'GcSWRr-waORo=$XAsڍN}U9RɋS@'~ޞʂgKR\6:*JķV.Vv[g9dOr){MGgA ۧ2(ۛB h]q}G~rТ3-Y~GOw^y<2j-ă7;>Ff]f3o~JFA))]#v52qu}1Y~wOO2=tZ(#Xx~M|8h1$dS-聺Rv"62#Pdf\(];GH.X[ԘWbέ۞ob~/]iUK =KΉ"쎭PRg}Ѭ D@`? DPZmu{04cӇ!SfwJ0_.X;#3:8?; k|6ii^v|?YvmfxzE1X~*u"úM7{/a~VB+gi0" h91y>k#=k_WA^_5)薳ȍt/[ k) @uGBG0YhbrocRTvH|Sྕ]zKx%"ϧ?\ b]4IrqM!amޅ ڐb_2;i{HgGsчzmJw9Y:n$H|lch]m/<7`DU^,DRy)JEXal祵/l.~w&s8C `<+%Oȶtԟ`$G  Sΐy{8ߌ3= <iy؁TK- - G;3 YvH|JMkۅ3D~.;nm;A2Ǖ))T+)9^JI2ʿl·]{8Nc~{kF*+W4vJy,7E\STק+vZfXT n=}aPf1Rsu T۷apyrm~CT,W+CR.rvm嗖2;2}WZ#2MFυEȊgԕ4H jMQ8lî˶_C$]=>STy|z8;"xwfڧ y/j߰cmPl q .iSWR1 Zh$I (h4v76@l)/{K"N;M]IKKKDq!HaWv#bKgo aoճz>ջu!ݽ#* 4sĭʍ;zWvduEsRԽL`*0>7r틺=HVewݵvr&; ,>M= T5s3І'Ll4κ TXVndxq]5$Ɖ%YjL(Efu-Gc:]tiCi &u{>le7v̋z`qX#A%Y`a/N=,ճȜD X\hz]{ٍ|.f3cKE|Þhb%:` ڇ'^MA*%? Z?nxmb*_{@Wj4<+kr1.DugdM&%M.iNq^Dn@u,Jfσx Yz@7 O7`|6~~iS7FèbUPʙk+P@nd?ή* ۥ"}ۍ;("z-fʪ>O_lRcŻuK 覸-bJdHve430ZHb.aFb@uOW%nހ!hyo#26ifiR[Rф}qmEQU{zIIQٕ]z([tlb1ቍE؅,˙you~ɵ!1R;Dxk1$.žOtMe[|~XDߝ-c=6uڐ}]{~O=* y 3 M4?}??ߐM.;ͫ_, ߇/>vltO1 4o4F#OܡSR*tSWBQ'[::i|A GqeFč\&JbTϷIYRqeWc#Bb&d#G_v_Qa OsFݳ1%<ԕtt'vH'Gh7O,w#ޡ,*ȧ}%9 ej-$I4Nyt,Hr-i7v_9kkuou<K赁s{Xu׆$wjve74QJVopWmAdT93u%Y(͉C^3iN̞.lYHS(16+(.8F)Ґ}#}ڢQ' v#ҵ7&~.)8dD=0Q0Q󕅩D5}baj =G}&S5U9F6%];DfTnR[KgPZ#%VvKLXQQ.-Xٵ]eI'7zLs+؊ϑHcgR:Q#oq`ȑ|W^v f_('HdQ RJ| vBƦHES7L7a~)YbMi|>!TrfF?7Z ь`ĥb92c 0$|6a)tOI'{q eS'+cmJQF]=nB8=~7 Q~ {%w1xF62 EaO(UփzVsmb1ꈩڅX;$zm]=&wNŎT=ȍsfpɎsQɤE]IHlB#V+v_>͏hϑjALJzGlQWRrp'Bag->h)Kv\ЯHT *ָhdD[ kϛP@Rji8Ykg1O*I`m7/xΘEwouڍ;ٳS_HOӠ.X[9kk]ܨ!Q<e7oR ##.}{i;öYK~/ٿ'uoɲ{f4_J&GEwyi/ fXA]ɵ̉bo;y~㣵wz.sƭ!ܬK{x_Cbk̇H9vHygse6yE1z}!F!;2+](WYU^#jW֠bPR hy>򯕗_kkzDy)<`o\[]mf~k퐔ɞo:3_v_)}­dv%TphjPe;@PIadʴ}O$'&j$j>{{?jn!/|RR#?D !_gcvAk ZFvYUA6*4q7馪pd`UP{ߢϓduΆ3 RV`w9=A&&&sk.^;S([vUgݗhѠ<y;Z= V+u%MF4҃s)}WDX9_/qMLw<Ɖ0Y#9.t#M/y0:#paX{8s#/c?K,v4R2F&#s߳9Hm)tOOS^θifS8()>9 6u) QE CyX:ϗW$X c2~U))Ee':|F]I^Fzx^ACyv%Ptޞ42I\=0(3˔[BnOr R.Td+]5ȊV''JC`4@mYB$6IY6$bR|%kJ/qߣu+ck)q#N`hW?g۸>!!=Dl)tOo.wzkAq 0kkU?"+Pb/}, )F]\{ڍFEd7ڦ@h-9>X;F_53' чNg,1sje+*wMw|F;~:&?G4#rܵqG4hL/[}3)9umGEq@ Setp*% t$$sW;,Y+_uMM :֡z+SykS+8]ͺf+n[+t/ΔWij6O0)@5P(օTLώ pP{_vy@*lfϞ]@=gUg״`g @`dϮmǟ]>xTЙv`: LD+rDR2T,̈oȒ#6M~Owhs E~iR@Ҷ.$H,J@m? G'Ɩ#9P§IթK)/>b_;C⾄jƑmݗ_oGw~𙺒C:{m;di|JjqFq; >6.DH)>󰫓%v_wǬfچr"+bmkbm?@m}鈵#2bm#֦T]kżG y:!,$[|P3SRVZ#mŢMr. y|6ad —w#vZ}\zSh['\&+Җ¦+w_Öθvx!P]u<\ć{n|#OyKnEth3~xwu޻G0{46 nP}EU|ʭfKŎʨZ;$b9kveyi V嗒,v|lf"BL˺f3zPSG̊Ni6ybZ/H.'%ЅmQy ۺ&O|8 F!q/dni~y7RwDX'*.uS+ QJ)֖/Lԃ7s1LЍBl ۧ +y/q$_|{)*y>q~ܛtSgg(" -ٵcK8 b-{AV)= L4HfT o1Jrȑ!Ao$ ߿Z,`5GZV=-pUEx|W|'\k+>s& 6$EWiv0z,wQgш-[GP\-Uᝆ[F/sm϶ c5ٌ%R2e'OVcӀ"6q:uA%!DH:iu/]PfEtśU{GfSq+i8W_߾!K7Mrk#(.XШt4?EHmW?;M/EG52`Q?=_!^ǕRnУɮнnO?:Qw"{+\3%~C)x'KQ}QwKlG3> Śnv9؂aw".^@eY}r vRy7M-zI Ə{e7wҭfBD5軚(iw&lsl aĶ>׆0ԝlW^v_EifZu,2)M]5?K7lZ>nԽfo2ϓ߀rG}!0`vI]HGE7}) ܋uO:?Tײe;ZQ׸Oӌr.Du%ex4f<[nDm [DGiebLLT4] ڕޘeޘWI(I)ZdNH{8?T.*F>!#3 Pt#𥍸YS()>9Ybmkw~@'Sk|#/Gt.)̑B9dJ[4 1y#9WsOQͻT_Nx~~,]JID)vD <тqvat/|Wv,|1y>+ebq T7+4:0VgFZ=_Ů&Cﱶ#Ɵ()]9}L'17ڇsR1xy{}?OfJtgs.#~>y9n}k3~,0d5d3%Z (t<bm3~~Z;$a1a np&ǕHb W&]G?}qZ"{pyy%zyy |vb4zJ74j,U "7D6{)o^щh&(9& "d9&e7 RfH j!ZH5-w`GN/io%K7UU1_jx; B6I43iE U0ބ*5MӃSiE J Hۺ=Ϋ+~'?w<+b+3ߑ36l%(=잯DwH^{DطuH#kL o2R\瑩jQ܈oV#pdqٮۊeە"'v}#~v_/ꏸ/ ԀDJyoi- }f]{/fH;8o}<2|uM?}= G֢`jƉ) ۘ}6׆k=_v_KU#P7n#w+KEN]ICUtӌf@˩q]{ۍwTݞw%GpI%EƈKqj?~gڐf/9n~N'7XP}_{R)'ϯRCow;!FVOTeEėǰi}xWf̼n)Y(WA1\_;r-a`u+:= 禚9aW%v#;nc#R)b +i"[*p16?;On񿝇pkZٟkC#ȮнOܼ :RpA)7 u)Q{pAgJy9-N%oc/txhsKaTt/uf爗爗<{}gH]:wx~8K ivZ aoI<ھ0i{K"]\Jօt`b1lv ̄4K;CAx@ }88I7!=ńl2l 7'd^}Ğ{lJzBwZ1bYXgcvA:`3}S72|xd1a <H 'gW*9Yʩ hۋi]+oLzl;M(f&peLd4"خ|]OW&%vm~boȈW&g7J KNօ׆´ l(`e*zl$;t]5q&JU7Fn1{J}=e {ɲv-v֐Qڵ4X,%][T$2][/o^ZHfsXY^F}Enj[0#-DhjdnDVT"=|ީ[{jLϷ6t%e!])|n?}٘-fQȞ9NezeʑTK)3,$I7&BAiqKQXSht!<4讨 !_gcve嵽OOӉӝ+kSKڤNȃYԥkc nSi}ޮv F_v#=F*ٽv;g6%LެHXpY*f3k Ei^Qݵ(v &:7&GŎeחŮ$ёdt܀d+-I֦]=fo9`.zmG`hD}! %fsY܇[bmFZ;+~OӺ6q6Ǽ$#|<()9Ĵ'u)ڊy8%>jK'!9w->nm#S)K)ybm<&~6b^ʑ^vR=Z^[C30UlA>պ-+^[a{W _vSI]6:NF|[` `dNbRn_>Xى{}ۛ,=G&q)ω[z0`;?Qq鉵}^q>]_K?`t4O]UP%nؿwǾ98X+@ LK'*| m@+3ʐ3Ycr& dݔ-ߦtڔ6;CE!Jrڐc;ai^vuO{".JWe֝+b8ڔᵋa E<nG7`-⹬YJ|J|cUkʙ")KWX Wv%bl%oȎD֑o[Ξbd6&%2tZWo1eo`X/~4538nm8pto4X[wf`gAh Jf%76#~d߰=c2B``2O9l8ܦk {%XT8-xD7;~pOohF WJ= /F1^hD[7b!lӭX&,33L\!p/n]Hݬ;Z(|쾫~r e}٧P_ i7 ˖;)TaӺt?!'G+too8~ieu"L*W(̞_,,[F%lX„`=)Jy|/$Ҋ*x&">e-J٦.ttb/]ﴙ-ko|d;/yZ|C⮤]9OUy*-{LK #Lx4}`Ēv`:'cS,Εdg0'$=I# <.X[*@t?n!7,󕕐%o 2f$Jhṷ<4Y #9 54Kő ԥdlFfmtvwu(//GߵM|vcLƳ3#D_gsγ#n 7b)[79 $1*ditPNSy򖱷FT e/HbI%I7Q%?󰫳v7ܿK|Yq% 6ﴩzN|tSPs.'GdW>1I#FEiN^s"*VX::mrO~@v)P5Z^ TDtKBI{G|{NMXJ7cgɬ?H`kz K01.VDSfZiq qܧ(Z#Ǩy$AZX~ fw]=wV]S$B=?VKxat|mHW,6ykQ_?cm#N| eyRR7y^̿X'܈LD5pabb %PqTnEuOd3v!3XQz}NM-1_ 7!$D<-+t/w=/-F!Ty(Ff2,p=uJJ~u@7ei퓱pW\+to~3זYqu=23=j_!OStZ|mUYvAOמ=G.X!t;/<؃A]Jp &#;>F}ħٖBrtRU؞‡c<;I%ݮ$e;/K7MRM×ώDnOnV~ZtR˨A=HrduY&ӟ>!.u21vH|1Δ(Q^bF6lAI11%jdA ZIlklTƳWo|qNK }$KsُR} bT||g]==1X C{ON,A6% :\3C6$B^0f#`P_))Ұ8u)|+ytӈ.{)N$i/ =..Ǹ8]I+xO۪]$'JXyЫF\Rd&eu}%*!,.染qUd,ƶTy1@?'f@}ٍ(ޞ YCiV<0=QBr93u%Mcŷ*ϡеvHyv%X#^b-Ow;:zh92iRs w96[D=+nO%}pmbzD9p [,nn{ۮ{-[+D ;3=9 .J*D5V3*kxK{+]&} ++eT,RV#u~t!{)zL'ƵC< n_;gT\L]ySWRrG**݄>c9rFڶV_j!䦘7Gx5aNJDfFwp⸊ƘF#6Ϊ!gcvAyw|c,D `q1bL8dr\ހF%ɜKnU>0Q]xoO){ٵШε!4Dѽf,g;޼2`gh"Gy2"ЦLL,G>oOkZ;$[G_##|ڍK0F묰[BIٓΑ') J6RМ"} +ʅyX +&^Ho6vPҭ{*-eqUb!7Dg:tFיR_v_H_҄ʭ*4:# zb[mÓu!](x?I/^VXq'ݐŸT(*RgpS=66mX ٱ3 *֨{}?z:2 #U:,E^Q=9KG@B8ZمA:{Ƚ;è/gFJJݹ`=0kE`f6DS*GdW`oN7-νQU)çItrܞ=Am"+3{: XRs$ACt^;f&cvUx:n܀ά8KM_Jчzy~1vҩJ%D:k(,9KaK&o53DUubp @oJBlw-€%Ey$eH`2ҌerTv7.&\5vTWBIQ$K)V`,ݠ1! 󕌶5vw4S!1w@g vԟUpSTJVp[4R/!&2:Wo~#n?-O92b‡Q W_BYڭ ;F7Qvq|63K|ڵ4>K6 sZNƱFIȑ-B&cw6) QFbϑ⇝؂\)qwL->19cgΥo<4K}_\s7|Fx=Am;F6~V_[j!򜞏.u!; G:ˣ3rKkugsvAyoRD5LKcWfvmyg׎[e،ٵɵ]5eϸ]Ckϙ]^_}rpfNF:T.BX]yvj ݜϿđ,P7 \[”cݸ=oh`^{Q cXpѡMHE(82hԷtY!)=߉e嵽PO;Iiwl${Lj[=gfڅkZa>0-*ٗ;U=oONY\ 'q\h <DҲ]=pz{Z$5 OZ`I;4z)qr1W\"G}JdGJ :M|6iW^v_Ď6Z$C+4'C끧|Ukdh+to5k0F6?^Tξ8ZYtn2J莿8j{Gy;П§E 0-P{~^0:$W*FmtO:oLW#ak[:%Y< v׫:#t;*J=f7;Ijč:%a1';sb;os@$,F|b7uvh^&!XیaˎXR>D+hN2+iV&KCkeMvee>ЗgW2VWymdfXq6w^2d(z>8pp LScv>^G4Lxe8h"rTcI|blMkt0/ll/›FҪqU7ڔG(qq[a$G,s`eukiH vL..\~pP; \ɡiu/G`v<~OJ3vGNYVMqU6nGyvAw/Jj3iLt'QgU9dJKJ}?8z{}4 'WeZ?-\vџo=Q' %>*+jrm#>+t/5Ne#p:;eIIȬ[jڊO]u-qmḪͦy7(ɚdaumJ- ]IɬkGN\_ٜB~U _:C*[S7U* v_ 87vzԀ/)ɳZ_dRwkTF@'Ύ˕A/$[e7֐>IRS5Bxَ B*Rb1B572CuOm "0{+/ծ{ft"jEG m_=Зᷢv xKԮkyԮ,mtOo͛XRGՃ&ƚ̸49i\F[%'s?Hdҗ˒}<<ڗXyn>/;.wz)zg&xQ4 ݑ5Iݔ!|'=͎VMŽtyA,( 5=d:[f`@-&q6Jg.8=J6')S\15b')ҙfG Fڀj<`*U i'+ٹ'2 $ %ڌ24!#rJyzA9KO)2(}W8!dՉ܂8>Rhp%mW"dE7i퐸O$ce7뀿qv WP>2Sε#CBJ#rspw_5BIE&av9`mIٵt4vYӊ=51ԅμyY7ݡ2͢ԪiW!?/GX @2C٤Btƌ #Rv6)F>lUiuaGI]{o,t軃Y̮J5{ǭx({ĵD_KEMk]=}iX^"=5F"ǧBI{G|t;k׫j.X Li#v#-vA\[JB߰vGܘy=薎;XҍklgLjJv_;N3S(7[ڐs/En_?:y Y ٹBb,^chlҌ٥dZ{(&lYxMl(F:T|4nO`ЧKT5x$]>W} Zzb@Hf[wsǁdꟑȽ= - fE86$~{ X ݸ>wSz=[կ&Q.7 pHr7Rmukw>+%a1[ŰQ-,Ƭ5lsY)q~twyV[)ZKOfHIQ–QK9e*rH6{m_YuSd&~osQ–i퇭k?4ޢ<2 OÖK硛Ȗ*%zf?nͬ\$*:'DڐА(q@ܲ`[]@4 rڐЪe'OvehvrV|6atwOt4SREn?%L]RְZs#T(L,_ۍ&>{+D+b;.L btil\]`o~uegOѵ\ޒyl}fL,wORpNDؐBh4EEݓXKcpgՐ;>*G*yG1?xQ`wU\yK}p-݀B_eUMc)]s$|K1gL5F(e)>`X,ߒb6[weR w5V\ߟ[6bn7OZJl" oeޙMRpcy&ZJlb]ujw_U%m|{\7QxQB8KAR-qhΣMsr =}^sz4E-Vҭآ\gn_%e ҭ(t1`| l]{Bwnts!*%!(7JJQDj<*%`Bn7- t{*1K"`g=n;%JrYtW3wZ{OXEYA৩'?sZOͤOYAt'?M&)bPindv=[{,GKpm!&_}Tvgm-c d6\775z &F nWRfIܢ9t9!NIa`u%Y=J\$8b7'UŔ3SɛP~ĤBO;[URWR` ["s&2ݳݯ*_ѳd! 0(ٲvI]IO!oIcJ}6.rvO `=D]75٩Qee8I|A0[ 7z򮬍W1x`,vyE%Xta&i]*_yŨb7V'>ḌplX=*ф}zGwsl?um&n(qnWa]F䧹p_ƶɈ=]-6 4a{lv@3qcl_Ld7bؙ$;P_[ᦡ+1|+pi]YI-gE9϶m> aÖj1`kd&j8OKfEj*%F`˺7JҺJFҡcR%8, ݈=h{ < !% [P~YL'luc*]LqCvoԋ&>`*-Ǐj('\ʤD?1ؿǤ iSn${Sϖg٥" n)n?F'1.nCwʂyڂ#.Fw^Ti"7]>!h8-J.Q2`w!&?Cimi+u礤ueJrݧs㝟v7TxñX n38))6"EHw|?,Ý^ ZbwI O-";;; zڒtCu7- >-vd9*d_tF ;Dױk# HU˺K[=xVpvRU< WntwqwA/D(fwU%]0t JR: .e6dd=@m=ۍYC[ Ӽ-]5-zQQr9;=[.Э dW|Svә cq.eK:~4;1)mYYA~X=(Ӛ]:gC++Hh3\wDO5iQ_ %Eu;[6WllD}ێUUNϛ}ΰfu˷O^ -O s)S9yќ.Z9M7ycu8Ȯ}_4eۚ!J_r ~PУڡ+)z\#Q&qEO@@$LlkF:XŁ% H?SҼ ޟ}]{^!sk$a1LӪp6T&;knD?wbB̻G4!$_-ec5='[ =ix̖b!#e3HV"l-!>]S܅nSa" cC71)JvnF;:1d837|G5HI.Ob& OduV1*]UnKk8h96w 0)&=NV׃dKcUf#RBT/v"3WG0yv]Gi(>4YDس:0"]7dktl]Q 1Ƶ䮱%[Wh~:Dc2ى: pWL }Crsa4*nkA1iYS ]JE8[pc_ g8JLLrOw~=e&e'QU"{޳,bzhYk"`fŗ,R׈!%^ϥ )ΥD\Z|1ut0;#زK˸ oC|llDVZEK)vJc[~f%NϧTh=2(h3aḆPu_.VlI6Ac~6aW"Rƙf>:,]U`2Y:Y+`qk¬j;ȍ?2`'|m^og'zγ]L~-+RMY;/I&6;>kURYz-0a_%@$Mϥ}YilЅ}P: }Og S| ?%W+.|nnԥj%eU&I$/F ?^>iU+mPaqj6y:F|u!FB{E|+y6.=3l;侀+򞀙\_+xkhʵ-"'?ֆ iT2&XbK)̖Hr`0.`aP[i>p]n5Jn[x^P@ |Α#u)z< Aԗ6I1U^pkN+ .i:h^acU9Q im%5ڽKjJx^JS0vӳ0+Dӂ_*9Fc_JىEv%͖c'|Q-H1Cz Kj|G)ְt'bY9ԝkXN钭D5, `` y;~#nG0΀A%"B? Ttng!W'b IS޶<1\>7z_~O9wۯ͖~y}|&o!]oؽo$UL7>ĭ7Hy/>|unx/cX؄@ogonvk׮?B{,(kǟ}x ͵ӏ+ɵk?Uέ!L~Ǐq뷺V7b?ĭB7q7=?l_D_>h/~u~_BxT?B%_q_oFP̹%鶖>Q9/\A9(nv'xeOYӿ|so_~_%#KbOwu|-c_ͺܗH\2W^Ca.z)/o?NǪ@Au:7/z]xS(k0yiY_28E&o۳YlW˿B9Vc%k%}~\Ek > ~Z8x={;בOod"{:>x㷏ciwc9>ǭ#{a锇:>餇:= t@^Qi[u [>:9,o^_ endstream endobj 308 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xwTSϽ7PkhRH H.*1 J"6DTpDQ2(C"QDqpId߼y͛~kg}ֺLX Xňg` lpBF|،l *?Y"1P\8=W%Oɘ4M0J"Y2Vs,[|e92<se'9`2&ctI@o|N6(.sSdl-c(2-yH_/XZ.$&\SM07#1ؙYrfYym";8980m-m(]v^DW~ emi]P`/u}q|^R,g+\Kk)/C_|Rax8t1C^7nfzDp 柇u$/ED˦L L[B@ٹЖX!@~(* {d+} G͋љς}WL$cGD2QZ4 E@@A(q`1D `'u46ptc48.`R0) @Rt CXCP%CBH@Rf[(t CQhz#0 Zl`O828.p|O×X ?:0FBx$ !i@ڐH[EE1PL ⢖V6QP>U(j MFkt,:.FW8c1L&ӎ9ƌaX: rbl1 {{{;}#tp8_\8"Ey.,X%%Gщ1-9ҀKl.oo/O$&'=JvMޞxǥ{=Vs\x ‰N柜>ucKz=s/ol|ϝ?y ^d]ps~:;/;]7|WpQoH!ɻVsnYs}ҽ~4] =>=:`;cܱ'?e~!ańD#G&}'/?^xI֓?+\wx20;5\ӯ_etWf^Qs-mw3+?~O~ endstream endobj 314 0 obj << /Length 1159 /Filter /FlateDecode >> stream xڽWKoFWP `m].-ТhkT7F-!r]k)R)z˝7?aP20? ;(Vtp<yۡn@yفd TÇoYt }r#kTitA 5Xm!f [R'o4<*(Z~-H Q ?e ޝϟOc(5--ųFΩt(cW738)7Vpwq buqW9?);};R|7=n:};zB7xvXXZ\_!־n ۴>G+ ]6nfMy?8rQw2l^"-ɝ1ek:Zۗn8F]{OklVudu4Q AƼdSj/s p~ZP8L':N`!~j9\ѩ]=!vd9EEKrL-)1}.V}DI3:r|]c*SנB{@ӁZ2nj-GO{UZmdCj $^?˷˔r LP &..S.k{ASmX*zoYňPkNIn4QM6f'lbJhgq7T>wNÐ+ Z/KL2Srjނwld -G}&I)Dsd5I:4:ODnS_~ZP-I8m\|wm/7,g)V<AcZoR@nfsCKs>6kƪα^鿎tlGL5APD^mJRIP2a~+Ozl͜*+U G`-錕ؔclR^I:Yv FoTNZ<|d*Lef`h/4 "Q9.)Ib Kb,;9-m:6>pn*!Y`:uyu;8i endstream endobj 321 0 obj << /Length 716 /Filter /FlateDecode >> stream xڕUMo0 W6UK%{v`ur[wpc)i4?Dr%"YtIeҀlA2Ot[#d"Aރ(ޒk5LJQ=30Aq r2ك(<*e,e%l]ZB[/%1-%y?q23 N2U+Q8Ħ#L&ҬPu22aZF@(-KPd޴zr1*[cf~A톥au9o~s!ytH}pI :zCVabHr65M%Ac,Ց{}v|doFع yՑ# {~p>xصɱPW `ԿJo|K`eR%BOhK7~񩜢 endstream endobj 311 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpgqSfj7/Rbuild3bcc0d7ef148ab/EBSeq/vignettes/EBSeq_Vignette-024.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 323 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 324 0 R/F3 325 0 R>> /ExtGState << >>/ColorSpace << /sRGB 326 0 R >>>> /Length 87827 /Filter /FlateDecode >> stream xԽ-;ӞW`Cj؀#ىi@v`r8]g`b?_/'?ڟ\?){˟??I)y?/W?g?'?ן5)ht]?`Ya/O,ۋw?/? s~T!7^xTՍupH|ݗt>T$>B]~JF6q?w)s9y-⦺q upDsNg^\x yLkTsrLPsRyMU7u#N>%$Mu`g③#U;w"^筠^;ȟ|l/IS_ Vw&񪪛SaO "ZCu`gbk X?omĻnG}KP^\ uKK_|/?oޑ?}_E'ߔx-⩺yC\=sYl/^t>[&?X)֊~"ުqu >:EW^t>wSk+zx[q> gEA\=x?*u祽&2H\S:%lǐGhw%nӬG↻o|l/I\qħ_g P~GRT']/q{HijNG;Jw}+5nqkOͣM\=$r,^\&Ձ:OrJ<ʼ&1o[ȍ^ŵkO{fRϵyp-⡺IF=N>Z[u`w˯<_¯X"^PYݽԨSWk:0@3qn̓p]_tVKS5RܡZEuJq=L9zKU<|8f<Jg&i'EUbfԼbJߝZKu`x" އ?Չ>1#F߇8_:w)zW[9#>E'upD oqJE UT1jPx{1jtC53KR]zzv8S:-Fk]^+gb̉w˜'x;0&5G[̓o|rV14ڻF1@#qe_9T'|JwAOc.[h-޼33;c /:^h">Fs*{&(t>I~ƽk,k=TLJUu#6ƿ-{$ NC%{&t>ODgEni~+Η83_9qΦW1vxV1@E+uDkS2ZSu눭|x,!VrPFfCYu=ԉk#q5u {&1୉ :%6?Fx+Gճ.K,{"q35g?`F gq |Fߘ'xv;Gfݦ<aO?]pMb~u>G!K<7qiK7qKgz.ZEud4dNjZy2u#dXO舍EKQ>\n:NJC ZKu&A#q- ~{qbc_kl33B>:s5sF5{U]0P|:FfǼeD7ú-'bDm/Zp-⥺`w1h&q~C|k/As9M9Tk`g↾ϸOg<9"ʗ+)Guv$;k7õꂁ:{x8%5bbFK1.׊T𞠃l/rӓA$Y%]pM9T3P<#VǨx:s|=\x.xVf4׊N-3:{A>wo9TkcW2P#P<*ZѩBPIYc NJf'3'xag  1<_13 CU?36hYCsuK6W<#>#>?&C%^gy^:\/Ϙ^B osYEi ؊^=xNb>Q&q|[5K3µ;C%^WE (=(%{V:gn=מ'g??W)'b䦤/Ɣ=\<ޝ] FiRCFD߼#kwmDHc p76)X<ʅcd{ss+3oZMGvƹxoD~>D\f?Wᯍ [U^+r)tx,Ml{w;WbuP\~0CbގBx"kbg &_|8ȱ[nl"W,)Epy#}}]bx11+Fh 7f PIM>Y^{NJdkē3̟+Xh:WjZĝ=\"7 +C(:%y ;?fⵈ'C%^7WP@dɋ$F\m:{#w:hV0QPmɋ\;մϪ:0kOw5?=3nAϤT^8S1ÞN҉="ns:}x3ƯVk"ĕi|9=6d{YZw,yxjachZăPʕy >ǘp}yL< &nhdjd^+f)[9b'\Fyq∷V1] 9 PFkq|8< sf&O<|8zlOh/bE<(̞o$]wy8+^ly[u`Ak+5S8Y,͚>}! CUYȒNL~ U8kZy.qx{7Q)bYAqrX32yZȇJ:v~;/l<;R׵Mx?~Cx?~1^ nxk* > {ϔ?TU:v ƸgΊM9U ]x\=Zv9Ÿ".tx\AʑO¹hqWBEx<xC7טq^?U9y.q?1#Ҕ?>zkWz)ko3W9rèӸoe^ ft^>)&yſ `c+&t‹S1dE_zTpj5#CՓ~w鼈Ы?е^%&,ht Y=څ7^ 6b0D*}y q<>w(;ᯡ?y bToq)~n"5^/M"zq/΋hX>(Y9!,p@RX00Ax|k3|D`5 ׷EԶt^ȋ /} 0cH ̈>P A|E&q-쩺Md_{9UWi!+ꂧdޙ$ >dz`-c3$]:/ŎqCCLɜE&I3"'ԃŽAbej1PfJU&;KeX,DR;3HC6F ZM&qMl8H煼gA,bK=U0IHM62'sKzR&sc@uhx<$hխ6oZEO|*gt|A;d"/A/KN '}3DA%$8E;ҽ-ex馈 ǫt0M8a7[S h-pBOB4ST;W6qM1):_&y(f=&oȩp*`oNesߤʸv¥t0M>0)r>fGjV 'UޮL;a}M$1Iwɓ%nڐp]jԓ@D3]u6xM=) %@; ^.D]JQޡSA kIJhSsE¿e~J-Aӵq$d- 6>5K,DW`?&7J勝fo;_W.Q ŴX%~_qPftq؃بO= ުNņYn3 DSGj5 :g3OkCLS@*x۠5kˌr,n7!p@48e5~GUR5 h/;J 6 $4M&~fgWo3n񇝀3xFgJ]b%v כ`ApeT b5L0~x% _mpw7>[9ӐcZ^N}^| }v^7W(OOm; <[cg6a59s~yaHtf4hX>d|4dS چUVQ6el8|LVG8޳2ݽZ7y*ߞC!~^yZka,Xùp/-  QJqQA!=llfGlSj\],t_MbŵMhM=?-k=GWh:MJ7N6` qGp '6 kG]s[/:9d1Mybh -鳳yYqvA~qmCzWXX[@b'B~Wh*GV9N bC&= ߮Az?VY^\YWQ&U5ٟ[ȎWe4ݵk/=*|l^,V]F!;Rb0{ShKk{ ԃ*> & bi<;@xj A<9Cɵo@z͎dA'G45iE 7h1_=զbz 8^PzS4,8tVO=Q œEcUz‘f~bp= ok{jQ dj{*⨻_±#/Ճfˈxo[7𑸛^0>3osj_ڃ!Oq^I,礂~_[,șۂ߶ (x]GJ ΍|4\d-c31[c {"l5 }.u =̶Y}4D]XcUm~+c]^q!Aݵ/6($?R'KEX}BY~%ZWe5~Xϩ "0cxb>Hp6y1m[u-S%[DOP]?]>($M@h\ 꽬Ư,¯/>;MD3SmsV?_N10guE{>VG/1>N]^ͫg~dS/E"ſ}[g$=V3@93ʥlAяz(Hq4'rW"O]"?G`8Pؗj%G!8d$^m 1X<_^p$rOQn Y4pG6N#$DGfjn'kw^PP9k(Qm,@ sK >;MX%~; 3"p7 )|E*tVi&X%~۵Ϸ!g #w)Tsm$= w-%Mג&s]N@Ũ\NZ-U ? _ 0j?˃?/Y|>}~YY~Z#(QW{fn?We~?}D#|6o䃚fόZOgʼnLާ\kY/>:3 c1¼EGҎځ Qh8@zxKp-iNxn< ok382 h\gIBt5ԋe!Y-% ^֋GG2#*#c[*G)TAdJ0fU54{Y/~]`dd`xځ QX]՝Yүz1q &<.܂.t6=ob.(L,k|u{ Zt&Ǫ4Q~lIhAݵ_QMM^n ZtUc(U+įODPz#,.U^z/GwAp8$@ۅ.Yԣ0c0m+ 94CG';8Skz'`тu( >Έ^ D` A AUO4 [Pj'k7|R‡#3OAx⌣l`T%62ߥVOhI/.gӷ~IG>VdWcOH/)AUcfSNV!x;cUJAx+b^D9xcrc|Z3]i/꽬Xиρ U7~z "NzX|ջ4{4[5cgYY*@d+7s96|7k6iƮQbA#H/1 ħǎz/_x&SNa3^SNjq( Yx˩PZM% ^V/ LVD:zfOk~zDZp69!(t\"@= 4 b"h&dkIOmO`D )&i}J~w킣 = 7Vqg_xZ%Maz?^t$t2>Ek溃KUW4[ #k5;(4z/o=c|$|xcdW<G~ePM_1VAg0uwtqsHPGXPo-8W!0/גrY?=VaddxA쉵KOaATpqxOԒFg7~.-z۵_/~^_xT0.^aU#T'(]G $($E=Q[5zM}eow.uy[^s`A ߅z YN{kzI|^V4t).ړ [%?zgKS5Wm!gSC% ^֋;u :<8V튵fQC,hpZy m4X%~bTLJ5o W%| jq| ( /saժbtOԒsc} GA5}~o/գ:gƟ/{Y/>9`f Ҕ ZB= 7tƂ'Ͳzgмg1꽬oxQ| n*AڈJpF 5Nr@|&l{Y/>޸4448 |ǢH\\Xt N@_9Pc4lUc࿣nP"WSqr'lZ0J= u3Baoyۮj5MJX'wKXUDXң0(aAt !6,4g^? gRHp* >`-鷜XrPb*Z:f5>ng3g` ,&wNR~|QuY08%-V=Tj <}%995g(õ8zҭL)ėik&Oc/ghO. !'|ڍ/(L?`D J'֕hΝIWehO `s/YE&l_]= rKV!4{Y_0\[Q"^:3Cfݵ8z= j̳B ~[% Z_c>m<{~H|l.WetU.>Zl$vl'|@ G"dy߈=D}fpfy~7Yg_/?^|T|UABp)A3ڍWPV?ۃ߱P0l{2zw-i_Ͳ,Őjg sVmM_řpԫ+koJ ># 1$lӧ٧RkVєx(Xq (* yCy@Sr-^|*)84Q3STԣ‡;[RUɵăj|Zjj/H=aZg8D! Г`AK-j2CJ{Yd(h2 `%8@'!i7 r7[5T\Xޟ Oŝ9t{">ł'5 JqQ/>5&OH[Weu]c9+Wnz]Qpw{{1@$>T{Y?ωVkb'>:(x>VzrkN%\Uo40o#&f[9#'S >zfa+K#I~v-iScU9o. T;w4M*X VN_+rU¯%M⋰Gט=lEA6И6#ljTI(rU"^l/~➬%MZ~E/0J46vE?݌O`%J'TQ7l?)f4꽬-$`C;=ava4PFГPtR QV!fגF*|E)K>n(XPORf6{fc5|)_c!Yzc{I{`I9P*Ȍh54meɭ۵]=oLV_$3*Ɵ/>W ~'~}oh8YqbNT V/OjY/1j};Α]K9*| rɂƭ{;v37z΁vYOBL<`q78C̭*|x5JjC|`ED ^2Јh= KYMNp0 꽬O8zbVm=4hZObOsWex㪟4̟wƼsşF VRU[zs-wCܮrWf7>狿z93^VOqNDb"݆ >= M bC2בrޭ˵j7rx]x[s,I'%hTIhTMBoh33ozYx x++c _@H y+ iFx=V&Tkͅ7Qh' N`7D;Â͗]V/l&Xw <`GvYGU㌩'7TfWMkIS3Gת1ŏ1eŋ0,Z8` PBOB: "A[zH_Wv-i`?Vd\|4LXq-AS+#z֚U[jy3kz`l؏տ7(tv,P .K>*X/$tMTBd7TxHbu3>4vy N*|LN{$Y?0Y#WAdoM|7g?яxQ-ZI*Uߧ{>Z҄+G$l%*t 68GzQ rFhf7b軵8|axFܴdrk*XbIZRAgLc A۵LUn = S9D*>?kI36§'' 6x ?7 o*X= >GCyn[}GB-iUwO :ӟ$TͿ V֋l|ZZ f*~?J<ϩ  _#Dߡy{g ?d-iB~uAVx h= S*3ШW;;:$4>JI?D#R $h UTIp^z*ڡ4*7~ ƂJ&1VM 2CORbֻd-iBc(~u7i!g `)'az‚8zҟ`+'hm/VcgHSckb|'A VѻdsX6c1[M6/7RCUXxDΌ"4ljG*,COtq3UoٵSY_QT+1b&qug7;gmAx&$,8ۏz5j|Kq՞M3xD}ylA9->0d r#kq$ܙM z5aAwf{@4{^cG>ċt$ŇC=Sp: ӯ9l'l.&X/>dLY3У‡jd*oory?V>=!^,<euVg؂P}B ;t'V=!DpBjEAK>mI6]wS?#Փjlz(-*|q=D2 2sFږ ڊ;(TPSIbMC›|ct~n}`֝^0kQ>]aU |X%~9ܞ ?/WQ¯"= ?[_X p; fů+|窂^|2=΍5!  ̋:XEM^GV VZ;CB~Wxc/<,t2Og P)IQ[i!9΃sw[@ Fo_f0{枬 -[%>;eE^! X9h"H ,(Nęht'!)=5W'7.* MզX{kw?O6oa=3'bc $$GPNz|`Wush3@YPW^]kq7$=,Lx},Z׳ܺ xH_+d&sT%ȍ,yԣ{b4n( XoR,s* f𸑬őԣ<y {[X/>.<[mxv|w7f𸑬mCBσRpmMv?\ǪԋFOl=7׶XłrV=Q@}^|8jo/ə)3OǍdmYPcϘ*sf,==V^L3^b1GM/vj$ +B~ShGX%~h98!=zMQJ(;BЂ_W]SnPYp!14ԅӳi^MCeAG>[I@jhs_]N˹3@^]qΫG<.xuh?U W>Voh)"`o׶HOW@4áV!hŬ@4rcU,=.|u9T }]{o]NҫU*MJcU="1~\U0JugxUDP /D^c%i\X *~Dh/+€%Ir-6PBu rԋ]ֲ=غ`w}onQИ- >!nEZԣ< b(ڇX{'m_m+$4ŹԣP᥂8Lz1%ho@48eSSTx\?\zu+|‚84Xzqhf'8R4EHU/ O3*TGp,T<Z|zsdE`v33_xY7~)g~wy)Tqwfo֋7,t';b#Hxsp!kzcXd^,j3s7ܮnmc,+tEǝzc1Zv}bYky bb#H6^|Q7`bDM4?~h* cnSWpf΅Er=(D'rZ{4{Y˅]*TAYpfB#= ]*/Z+{z-꽬otջG3Έo)AsZnG= >7E*K/tWZ@e5~O@3?%_NN=%Ÿ⣻fN "G+$AkM= 7)c.BMmw{4{YTϧN9#l<C@Pz|n jyW"Cq޳hC{Y/>q=$ܕ~ځzC^C)\z񛧋Očx(>63V ouX~^Ư/գwo/{Y$΢UAd.|XEmGoX1J#ځ8( @js@t|^VWt' `oݶ-?7I#\;0ÆyX/|YmWz^֋ߞP >1:2VCQ)*X{DZ 'jI/u?Of4Уp3Y jsǦz^V7K ɼSo7 ;jH= 7 H%[mY-f w꽬Groh/:M'*yK9];߀P4[V!h54rcM6t|eJ \'|QyDf\Ky`qOVWTe8? <^0kՐz| FyNO[m>qd-icA f5$[ ViqKkq!Ǔjkz`dꋿ(3=4,G%o֋O`#BIJT88gF=*|{iIikX%: wcr/ޣ,q kq *9c6`ƶUb7jza A)*oS;/.VN.cf s_SWe%> D[q</{qu߃GF̲YSDBz0_wTz/aGr$vI=  &-Gawz/+%(L'ߘao rkK= \6Gv8K[kAC~~cWx"6Z|Mz(hqڤ`t3kAC^M>_xÆkq~flK.EkÉV!hlȅvX>~zDN>O[vBσڥ>mӻ[ BВSr-hd*';"|/;iq#kẈGߚc4ZRW|=)fs转h}DbWtU ϮG!;6}Uo:j|Yw0 %<~kW$PBÂX`xQ[5Z@epU(W0(A~u(duY?Cg*גz/+'w, .Ezgyt"CznؘCDAb*-9mג&)*d{>iZq( rdk##= "zm3U%J}F]GNq8Ȋ2Eݵ8#zn3 ph0"YVӳbkIao6&ң0IȂH^|JV3 yOԒz/^fAtp[3>`c$ ETb^Nj^nz/'fS`2+,ȶ&)W*9$ TC^BZΦ=+a̫^|W0f[qp)oGd( uIhԋtNYh%гx1N\'F.Ur5F(ڒir-i^|tͯn] 3@Ry Г0> K#V!qkԒ3:_h ? T.N: _^$Uh·Ui:z 6GbD= P_ z| pVضٗ 7N{Y/>FƳV,$3*AYzDfzgUkIM6%om+<344|$, WV|7UoM>9֑c/kE;]aHҎ:"3t Ÿ*8z`U+~ԥ\ 8, j?X0k LH)&K`'5KUw4۳=N2Z =ZAe͓~~[;¯*Źw[><>V ZM&/y;;N6wܝ\|wu/~ >A[酠`WIGV?N!>ҷV[%^V㳿c< '~:bwc4\*q<(*銤,*dv6/e#$h8Tc_(tCcwmk0*|E~8f9"+8 `ۃ )aԓQ81V!4b4lO8ѥGt0J ZOBł$N A+ɵIEc^#QA@;p8 15]~V-V_11kMz}kn?\Kb%~uYPGQF;◡}ГWay^,ϱj'ឬ%MR*|qʦbA@[Wҹ%`gPUA7*-zdNua7KUg u?cZ=wD Q XKU3ĵփ?l7>X/>F~^J ?,kXԵX9' rY/BV=QKb5>x [x4;ϞGa= i<gVxxXOF9,`4qF73= y6E=${*QVgbtZg-?O+cw*Ejd/cEOf-2jr6aAtҋmXY '! 4j7r q'# kSng,('4/9}ۺz< /pfȈܹs NAOBV5AJk/54jM ˆ3~zHΔ 7r=/8 bgctcwV+D󦿥Gz1`?7"SuY` bǿbcojg9**|x@ۏ* w0k+lUp) Fݓ{'&}?Y*p˹Q,l|q5ldmՋ=9 2rWsץф zv<,hϑ# ?,ГoCջ+Į҄+ɂȔXau25w:Rqdkk$]@OBW Zx"z_zg*`c?Xظ = >HIFzc=geG_֋_0rGA-)3v#Gq#I0 ԤW+f޳2H`sD>X?9 kE(L+Y0"133 Z$QɝP* "G AI /VwtmX*̌#aӵHI `8l ZH*d75YDL 휄e2q k(A7)/X›|cUO]҃0: Ylu'wf-ϫ%>3W%5ѣ$OuJx'_a/VOt}Fϳ.Di !e0& Ԧ# 66&ңV1&) Aӵj)`&g-k{G!`izcs,M»衴r^VK\vl|Rpv'c9X#3z|ԝ b"3CV7Z%MVnc q >WE~nM{EC0(T5 32 zgY?B{Y/>z~D1=8RF?/ Ÿũ5ԣPGJWeL=s{Y3[O?8UK /b/= P>W^P)J}9׶TGui՘yٵ#2Gy‚Hi5;_We%~i?O&%"xۤJLE(Z7XPGZ}7jr-i?3h*E?uNGy΂خ-Lbu Ÿ790|D{8zP,5;kx0=QK转]_;+6F|5K&܋xe|L=QkYѳw?G8Aoy|THjhNzZC%Mc?V]ʞl _BBOrҫlGԛ?oF~ՙ_ 꽬?Kgt㉂i*"S;0|[Y2a-RGa fAB6/% ^֋#OgN E (xO :?_홂Ztňgoҫ[ϯfo  9X*.H!Z|jX.Y% W8,fA+,葈i9N Vee@C/c%bri< ]Dd ݘ@b؀QCWLHfrE$_]ݧ%:sͥ2Gez iu2!%do`ũm#8>F=qraTXN[~en-BX>k_<~+V%fVgTgʜRq*[b\[ZĴ*{Jjo^DFBz$vjs1<15OKHFdUv|GP!ӥ8>F='5݋ۖU\J4X}[.>8 s]#qtʔb:bk%%ȰuO'%P&>r9b pDy0JKX%YwNםUiRƒ^9 2MO=HkHC)z ?(l{*.%t_ҫN鑈1xzWau' 8`^1<հ~5K%GJ ccm znh%LJr1X$|2tѵ)zG""=|I)P/c G@b$!ǥ8{H=ś ۚVBP洺th [%Y=Ƹ5n+爭>UR=⋐`ʕދ*ItO_98/:9<3HL8U\ 4X?w_GۥJ5Y%V}%Fk b '<ڵ6#'+˥[D>[9@-2VvyO\J |ƃg*<m;MgMaR'?kK]9zq;<ͥ9M=dQ8ynȜR)g׉SNo BiҡO")Z26M%a.jm:"ecp pkYP\]Kw+i/iSzm_o{åD5q}h`1.oҋH2~#BKkbuy*{6."Q ~B)]Y^Dhݣ+Afqf<ŸM,Ssq$Ƣ-B>%8\өsAX>mZf.%΍D%٥nFVr|a \^.|XnX>}WW',RB-ٕh<#f8d-;VNG"bC,jz8Ǯv^S)~v:X}@}a1g"<5:$aHċ㊯^i09ғ]J#bl.H/-B>%x\)?%l[ng2nẔWGK&:GmFǃG]yk:LrHW uݑ lLv݇փ=YrqjP ^4)M-VRKgŝ3зR2[Y0dJcAumQ0tji9:[ +,RBܬS]Pu* M,{Fz8tMv颒H뙽Lp@Kdh%pٵH]N*sQ`%O`J ,ץ=6N1C 9y)X!b7bt6*Pbc^ G^<-ZK\rBU, vuS/6B9rsm$b^&WrEʇrf`Y^\ثjM s5? Ӌmdrs*h~mw-8-{qH2k2Fba#IVbi~@v/,صW0pZZ^FXpu!ya]HGu2 ^-Pdo7iK|/vdūJmTu(//αk˥ ۠4mbdMJ2Zx!kM-6C$]R%vp6f>hqmW*F|%FEx5&J~$%UtU{ĩ{ $mW:@@ugó&'TKEǰ#.ݡK9y6(cCXPGm.jv PGZPU{]oYGi>FW\Q2vг !@7폺CE1"`sbh-jMQ]hBdpo75!]RU`~۠~^gP9V"%ǣ%zrpK8~%۩W`,dY~{;7T`]Ede Qz0ʱB#4 ԭv՚]h6]tP1eX8|_KC_u|+"[-QGp1`~$6|%]g&=C W)$GKj(#휎MuMJI[ٸS:;,ӏ*ݽovn#,'fHIKKj,f8ލ6/q]tquq  T'N˰!VfyCd煦{-;Y2ƻ=JAcSRq˱BNαm}Ж&IMֆ4]4I$xsyH&E{'m|ڧ.)9- zM.7E݌;|hLz:*wف~|Rǎc;tI{4vvm9q-Lk-_,H+*9|X'EX].q9xH N6)1E݌[w|G,v'gPc5-Vܲ+l㯗j]-ҭ\2mR>y>!U|AGrбR' R vݥγLZ pۤrTr"n jWpiU qg_*![](ږ/cz9Amt@9n]m<KЗ ٢i˷ښn[yˮ`zYM33DJ݌;|a soq^|uIGr:^M*']%d[pf9E·ubD[|Y+vfuIy:Δ^ϻ!&q/Ks_ӱ2-,,#Rl&躤Z,S4Jh%)6mwe× ~,Qeΐݗ},mR 9q/ɶ|E=Wte]fWr_n3jόܔ ]kn|Y-U4s5d_3[.O -e.r* -9rn\L_ԚFG Hy4Bnw.m߭rNK}]o͸ gW=p>4xl| ".eyK(Z)I7_8lwvw^vRT8{u3~!r;|ltwH^(ܲK*UJڕNkd,W8{-_w?BᜉU6[lxX/"_ZE?IT:Fqq4Z%f)a$݌;|(DUh!X/؎,G /"꒚S\EDs'-瞭q/ԆGbW>QG?|X.tEM2ݴa%݌;|1{v8+s0- };˖ZAW^V_e(ŭ \͸×^tblp+ c6}3W%_juy-ȭAW5!qZ3u~Eɗ:BN\C=Mwgo;T. yD/ JXEg`+u5U$M9qQ7/聾oϪbюYo ]Qޝs]cFͲ"t3c9298[`-1ZsdqpWs`1G^9I7_4> eְ|V|.U=1R[8Su}KGwO#pQ7_,KO8]B*a)"#FqytEYȬ@lRk5p-emvKh/ߞseXF7߯)Kt׊ ]Q%ڨ[&w }Y!'3d[5q.l+|k.XG,zAy&}O-_.kxo{z+޵}Z J 2嵪==]Q_ı0RU<:%'.~/>_1p*)a]MQy$i钂m2%p'l eKN[%ٖ/6?ڔ+(+#"}.F`[ j\@|A =|A)h/x؅9|$. `}A-/uXF۠ܗr"Œ;|GWT) ŰvڮʖƖw8 U|>r.(ؖ/ Ά͸×WspURZ[)!R.ŝaC[fn/ ͸×lq>e"5(}RB qto[m|UyQ*6\j|_?oD[*!r]P-_fu7mPs#ʉwr9n8/t;0=zʱH'o۠Sr7]:(cajA +*Ju0캤JԚXo5}X6({ȁKwhQk|a2Ɔ˛wK,tz_cPmDOĭՑd[ u}1N|jvU%_=Q,*1bpb]liV6(O7 /}7h8`xkm/ٳ>Wn G-_v _*Ч= }%عsp.]%_n 9qQ7_lz9k8ڦRg!>×2P4#VO՘c`ݚrbK qţAJوmX;Kr_FȻE|w8l Q6(e\;|ǚ89BEwJ; l ,!qAwO8C+pIîPŢbޚũ˶en \4/l!X/6EɗCm̶|Y Z j5] p5E -ٖ/5|9h 6"`(~rۓP|U4q)]nvU4OhƝ|:[-.nˆQ12.6.MMw); .{.fvW5ZAyTB~1.YꎩUQ>!'}&Ɯ}yotߋ:f/o<5",{F7N;(bϒ/;f_3/Lfeb>{QPoA[ ]R-_&j;r}WسQN͸=j֑əU᫗c+3^F"Bbx蒂m59xÖeQ\Zm_.sFFi!+Qqy]iUT99{;6ƦQ7]9tf"-cƸ*ʻ 'Kʤ7S'KY ;>Wwӗ3nC/wC]Qq:9.M% /w䘣×}5cћ0zpw(bP +*2P休I#)%.~_z8xĭ w//f7ꊊS {[S,[r Ǐg)4}r_;2=octJwܐt?p/#Fw_6Z Xa ~/k75tE|_[|Glɗ" =mr%K}ՠ7E hkR x) tE-)鎫~f-9qA|h堾2;hc-'A1CY8uE5xc^o ۠ [ ×37IqtY%_M=AoA5_"^ulȅ  :|I34i!JZywx_ĩ6.$xTeC.\|9ӻLB\cx wD/;Sk[J[Je;{Wِ t?p/ _RgřX)Fk3ݒm.sI!nUTCَ p5xZ-_8]:kfkm?rS 0uE Ϗgj[Q#F t?pOT1j^#^|x{AaTMj붲]#zcِ #;|yُ9qmtkXXE/78-]RWkҭk%_Vsp5E#v_Z=wAFT;&V;nq-`ܞev]Rs0JnD Gr].\$eljSo>Y!];#>qsWe;gUِ z%OՙDq `97wҗr;HMq(Zَ=*{l K)o"~tJr"ݩh5_6B3CBOwmWl]n]Q 9^ґ./uPV(q/ <#v1+*fəá.ÈB'a{yz-9qQ7_,}_8@X_&7C_qp ɗ݇&]Rq8]ijȉ|h{l9 u56(՚[Br uA|yv݉: M`ْ{; ]ހaOhqxOL%xO\d g^d:;-E!۠|C\͸×^4㚵օV#[ B.AI7]]w~dڞq2˖q/Fw0Ft;qz7R7=钪уs6ԝbE+W%݌[4WR._MPޚwȷE%;)alZa{N -9qQ7_^fdQ^~p3GCH/7uIc/κmu%vz 9qQ7_b_3,[Q<{3Ei>HT/|n;nWٔf-_^67w9hY`a=[н,IT_s-v_eSN\޿d}(=ڋqju6!r_vj uI & Pkh+TN͸%2^,;+=S/O]R3Vjې+OClw18XxyqG(ntI͘wz0ߧ}ݔI7_6z[ܗZ_Ћ/(rnȯj钚~HPwgٔu3nbGޗTBW[mK0Z Q}a]}vKw1>q)6V|D.^+jM 5Qv;NT6E݌;|y9UqZHEk^~t]..-hgywM9qA|YD]V}]3o#I>lYVD FmP/z;ٖ/<-Eˌgҗ~'YK K#߶mPW9B|aqr6v֍o܃//"|F@ꒂm2}j yDKwݯ={mtˬ!oRT)AF]_N=˦`{)&-9qQ7/ص!.M`lqc(Y~tIl|͗ʹ gn%E݌;|ޚ2g3X۟>:~kʷJ5Sl otSt.fውYg!kjKj!ߪ8wAE=Akܗ򦑬/J׾ q^mz .<#,]Rq8x/@֌vsȻNnƝ|+|MZ DPyw@Htv-v}v*rnlu8]~KGET$Ǎ% DK}KcReSN\C3l˗w~ĝ>;H}Ey/{B~lV!]RF ;e;H=rn-_p?Ʊ2tbg'nwgRr_.Yf;+v䡲jI|\?F-/Xz^?~-_zܞr]P'eٶB}R eKN͸×W_j']CJU%/sz 螭MQ欔t3[~KqpXj;5jR~)ȧ]%]Qq8N/S`!mu 9qQ7_\֘1Gօ<| U}"vQQ\E 61Aْu3nbTFدvv)o/MSWT'NdeHzH$݌;|}/1@wgBǗ|"H]QWũ3ůT+,rn񅜉u ޥ͞nd"ߵ+HqGz:mxm2Y+2QƳG\p´k%zCWTٺt+/mreKg#_g[ڵ70^yo֍6&S>jG]Qq۲8[*ݎ]-lʉ 8L8vo#$N[|"⣮i*r]cʦ;|pEޒVXe~hZ3Bw+*^p56%r墾8[QnW޻AFV]C@WTdUҵێU6k:FW 0ڭ//Rk|F-n.vv5ˆ\;r%_B3Eִg H/'x=M.qp?tq2On pAwx{9zʉv\BluE^6zC\U7reљwqߋ=jPq4|9+tEm?.βs}&%_upA|D]}rlƀՎҿGjROtI} MUk_m򱫺\;|su =آwru-l]K+n|B1cY:UQ^knf =A7^xD_t6)Zȉ|](VG8٩i {)fRՐW\T8E[Iɗ6BN\E$.>%qlAuZq"]1Bn6.8/ҩ^MJrn,w0dlq^sZ|1(tjÐhvۤ!'.fEkVRd^4X ~t9!7Klg_-tv˦q}ISg v}5D w G]R)c[,\`T6E݌[ C{p=j3!)k'5E%=k#'nA$J%6,×֬řN}^ygҔ_mꒊhQf;[[1IJeSN\͸/xzW|BO}v.ı]vk)'.fF*Q6Or_?~ЗlX? |VZ)I~v˲)'_i'{GűKAײqn5]H@WTgbE]BmxX6E݌[*'sFie&[su]+*^qg^[7Jlʉwr?bs*)ha},Ho;l򑵆q˗{ȩҶkZ+YeȤKĩ?q 閅uy|%_Wʸ×55 r*^\U/6cԑ/Av|ITDNb_AɗWDzdw}VBR^mj/=˗W#]Qq[Zϳ]˖[5򅜎ANmنugz1H۞V/er.In+]mv+l77WeC.\|AkQnjce;QN D@]Rq8nCH-H pAw墵&_^^rB tIh/S텭&-[].\Ce%K\|Qi^|w;3 IJ}wʆ\ny_FI)eGmAuI-PǑl9qQ7_GZP+L(5+ ,uI-_grGGoF{-_||[\/1(ʳ\~R֮&q0n | J,!2q/}/DPm%%_^v.Hqm6(ʖHq/⮿5@(ѭַ+x! JrR亠`MF j6(Y{ȉt?_PG4eglݛQҗ+uIS/ ۠׻!.fA}/_⵿6o[ߗ^.BtE-T[n:ܐt3nb}zS=Rv({qp_Z鶋T6eu_, {û%vB/oĠA]Q_ım1[l}Elʗ~;|(]؃}uU}zx]. Wqln:ʦ;|y@}`:ѵ7+!uEžqq[.۱o\eSN\-_X'G K-r\{!]Qq887;|zw$r:nl Py7,%8Cm|5%_PNwwT л.J̊vE=u{lʅZ-_]u_8c!k8(5')RWTZ#9vf;|`|e= g_"Ծ%tKQn.; IJ]ҞW;](*vElq1$AJx<ɐs/$p]IL]~m/j+Kퟯ~l_Yz\ ?_~T'taؘۖgG oێE_Z_?dg?ZvkkulvY?g|JnLvп?__|G_?WZ 8c;D;@U #u6|.p{=ؤz$E zUW>U"*겯z+mk;XPlٸђ Rz,MN=C]W=[a*߮2)e{{N/7ܶֈ`ptݸ$v]sv*>^ecjSZVeBJ4X1y_$`Im,HLM<}Vf*z>xMq7{*)ig*͉>~g~j~eGKXu'k0xuO;w=Xd{l_Wblݎyl%Kmg:HL6 ޙ^@txLeBJ4Xiuf};o$%o˪ß ӳ3ڴ#LeBJ4?f'%ƀK?K{g ij^ ßk7+~8l` w=Ah.7\b ]~go2nm&Wblb^FC -7Sz-%^W(r' %%{[XnM[‡֍%Qqm GaOPkZک2!%e»% _tYե*GWVZ%8/|.u{~%o'ovY>& a߸w^7mbkyX4h_g?#? R_OM, &,R'րM3[٥I7NebB~MGX@ /OX|82|tqս߽)ova&@/c>)NMn:[W|GmER'V#H}%I++R |]\:=/'R> J=E1v4SהfQ=f#E; \:f1Z8@oh4ٝßdԃO%:|KGT=XxMthfs>"c;v; UKJ4X~Er8js; YߒP}b05@ Bh?$L >[~=-|=zko}ߺBlolښS|dĭpWRY:հm$۩XWϼ( sI81u'vX0waŭ2)%eXmDt睷³ǥv:Uz *_ջzY`[Xu j{{1EK畜,z,RLL*%%2[Ƕ?. \:X8s{C[!JJxG L0^:OK"$6ZSX| }HO` I S'|e>Nz,gII=&$|b oFß;|>79o_w_|+1=aM$RCS+@i1"d?٥FB_ DJDեd`*ݥFz koQV{Vo׺y$~w|?@XU7ƹs\Z6'րDac= 4tYH?]Zei:=5XMwGohH{@ p6KoqV+>K Czh㕶bM;dUfX%@<\eUz jr =*c8 $m./ [V1DVA.êcv!2基H]NXZ~OޖU{GG%u7ni$[R[>ܥ9|_n՜j{>C/aM-J^vf: _]jHDťڤ0 MSau]2>zGr[$t%%ź:G 86"KX|Kw8W<۰@Rb&=-ֶB %ֶP&UKkoc}YBC/ns:cssIxj;,C/a 0iήpB@; 'VSF eR 4KX ܨ@1l1\|q37TwĚ[ۢyXg#XKm^zYxVKLP&@S5 T6 Z: JSgFZ%{ª÷@1:s7AaҶpSm3ǩexϺ+|>k;ê?c) ]jɥAc"V돽V=xRUJoYzc(\jV9 <8a5T:¹.~C/a|$fX: j ᳫ_"G8jwu4KX| L<6I½NH1_(xjZ\N 5|t<~itSoZWm fB }OXXfnz koOLr|.Ԏ3` 35M10uv)P/aM.w-yOQBD:- P97a5NKLJOY*/jc+7 cet!=iw#z]kepǥY`VPDueLaǶ-"sg>ELIRZH_68!XU6ԦXq+3|&9ܛV}0ۈ;]KnGV}ATaGGIgZC/G/@y$¾.oHĊQ7puᄑ$>:%ԹzpY·b-BE{UAxXH43ô 7v]j^5PF`{Wɽqq@(V\Zq=ɂ7K&?qҳi7Wl6=;*U\o?pKI+X֘y0Z/>t3I-rt b#9ӥZ__W~OG[Rp>F+#vTA(1bnwhwLBV:C/=qto*kbcݖ JsI}V vKCFshYn7vI^rW\Ǥ>RU<=ZV 5*nwO\:5bزxNXz$PB,CV ,¡~V5%c{_=V[#X!?v֔ NXXJcw u)ZޟJ\5=t uNZZ 5GigL}+E%s!u }N*f : A?vTuz qҦK`ׯ"1Y1}?Tͦp杔wX "* =&yt|OH.CKVtԖք` ` z@ЌAda㹻D !\(uHNt0K;S:' K`ij[Wc8!?v9t\aٚ4Re3yV'ưiK}ruiaض$%MN=z0O0.ౚ]ZH- k{yv}4"ڋW\T:X'# 0AQGgrR^p!?miǀ"ӖY$q0m61T.ʫ5>$]`ZX;Ҽ3! {>R/90`>s†x|c!?>oUQ00iӋc.Zf ֌A=1հY^"}֘T*_Α.C8'A)R OT"* %8uo חi5t-||z1݊Wj%UgCh!Džҽ:|8,6sA8RTW2+*9 D >> J0]/ẤR{Ybnr ;0"I`" RWJǓs>T>[x rA"8X!xϦyD9)jԃs/jS<81r].@$ ظRcV:7x-QZruI>1(L B0qi7O o%ax1`[FN$gKFf{A5{[!}~JIfD07G }רKW/P%U# S-{Iql)C$Ҫ?}>:Vr0)ybR59M]b~6FF󫙿%tƋ8Śxq_ z"Š.d\ދH ͛7Pgҡ-m-Z7\74@]R-ZNtU*#UzRN' tA|"oz^H.&9l> WtOϭ'tH6=,!֌}ڮc(9=UtUC$qiTcƏd"1kqTOQD6 |.UPr*JӽWo+$dW)*fv{ƣo$k_Kr*y^ԋ(n;,,ŧ8Չ`n,3ˆ[@WT U<ךl]tw+v,\Ǚ6+;1-˓߂]Q,[HOu`i==XXn`"XNXXR3>W%w`E̙r{,. %ՍPr6oA]rfqFu+Fm|ӯKwXs'F3K V9lZT 9HWT[ Jђ횗zV.亂,Shy}тª,u-[5GrMX8I7`\* n.QO?_CW@HӽrmX$wr7c;MnosdkAgd߮؞NXǢѬ*|eX˟+j/NG ~>j?I7-,El8 ❶ cibK r\*]Qqٰ8[ԭ{6CNKu [ طq,=D˔!,O7hc'T~!FP[!w,u` f,/|.Qe+*>k&kvv|LMT5KXGr/^3>XX6;tXI,5fFP+>mQe X K]~BNK-,y ,񍱅'=~j3rHqx o mHުt 2ػEy\;K(qO s@͐ӭJX2130w{r,pO`۱ CuA9rz ݏ ފpbޱwE9r ,]RXfp6(a%RmaMkݟĩU`/ 3}ܐkWITZYMꖥXʪ6ץKj~NXNZ_NBmZ_X˟Kܼ01j.;/}|)wk_/@NզۛEkǓE (2\++x9\-[gdXvxt̹&ybbCP^Y.]0)--, 骮[ztv'W`|u(yPE5tE)ޓK"*~[rUd[X:ϥ?ݱVغ[kvo5l-,hA ˜!_ߍ,V(2\¥+*DSA9rUgדma\?b$WCMSʱ_ۀƄ85L ~אӯl;V"hr" $.VO3qێqwj ۠RLq ֏g.x}ef3]~;njqX開#ǰ]R;5~[rݏ`e}/biKKJnD,gd,gb7#t?~ ފu^J;r=]~l>붅V7ܰ]ҧ)j : c~b#cI1?0濡XRO,7F@I˧ܱpV~0'ObICIJZ˜,_,q.9CwPSչwTS>zdؒWQv7o]qBw‚Ytĺsԅ였ʕ{oC]Q#,q*Ǥ۔/Ew~%ێoNWͮ vcY.vոtEi@^4Cd' 9lӱpʒr=TS+j8-kŹWڮ!_-e۱0JXVʋ9+Y!~%9اnXNW;O4DW9 (*֕*5Wk. tE)aِqd;}!(PBw¨U\cD9۰CWԌ)> N_G"o7%t?~',w‚Y;{[ 丏Y[ Um梒l8Xև?Uyy}]Xlf9Q} 3R;ڎH} ݏ߁j+n5%gޯGMӯ!TR+jyv%{{nXZ{' FM%g|W]RczW]~Kb#g`ϼS۔ӯ-v,,O䬽Z<y\BWԊJ9^]gbXvȷbA^.֒mǂOmvGi5݊ ۰CW|qpIt}Q农~Mkɶt̹-1fVv\!qg=r7g y20Zg`͚>Փ ʱ7&+jw,ہnŎ=mMXy_,_Kvߐ׫,nvÌ%`#t?~ ފf޹sJ yü ]Q)!tU*s!\.XzG~NX k;w7)VD钪q8ߚOeT~K]].Ok];}f'DYx㸈r,7Y(tIۏJzX3v,k ,xwv-+^3lBΫ U ۠ˉh~Qw`zG-oFձrb9r _|]Q%F8hGA m._io83'=tCf.>X:Ę?~',̵bh3OEMlX{底ݗ J{\~e;8|$M~ӆ<\Ոa%Uc W{SdXMrׂd۱pZihIp\[c!3%Uc;u]Uac9._~MS}<_؁eqʕqîtIU|)[zզmPQ'Cw`}}!TU#֗3DyChK*Nհ JXr:D-, +tU'>σY- niVNU|Vŝv钊O; wخugWlvd۱ *} S^VA/֑P>P.bTATac._UgG-,v;61O"-Y)j#ܶ}\Tz29q[PO'ې/fXM [aM(DzC>P.Ѣ8;a-vݥl]]t ݏ S2g; M <7v,Cs&v=ܓ;_<++ZUc!#KǺONӺo]Z+t?~',X[<eK 3r\p+]R#ik=w5Ì+t?~_,u'"{;;":w,nE7cIœOBw‚οB.8Ld\#钊O=Z鹺ݏ a cVAD{\+9n.ԣHN d;05{W~,+y`٘yVwQ,5NKe^:3l7/~yN fk%W?`d%<>x*uI7/9 ⻖Qe?Vj5tg'+ŪH_,߰߰|꿱zVېկ1%5;!Je\~Aw`ɜ,.֪P>STZYG'L._Cd?:OGQ*EރhTQ|"z.ԣHyD4&BhTn;0ͱ48ޮvo| uIə;3.qZf̐ӯ7&2ӟx.cU,e7Cos9IԊ[Ϊ +T5X׸1O,k}\c^/{*c^c|,crfr/NXaOBw|eNQe|b/v g)^mPe~-ɶLMv]Xu5Y5^yϤNXO>&՝Hw<c!ȁKjG}tar_Yz_OJ$BH]RBq}X&|h=~Q7 61^Vӟs͋A9r"]R'FRN붩~E ˭!/ExoJe{'s.jVx^TFՈr"]R)%g)BlH-9Z:mr77w Ϩ\U}u/pPTfmcH=Nqה/h'r^tT޷= a]x-j=IBNO8ɶcac`LW=sw\wy\c!uBA Sf5O*M`[vaqstAD-ݶ ug#nDw sIT3OT]6(aa{n~',Xvc}O="khqIT!!c:%lCncrn[XUx{s|tU^: 3**GXK%UcǞxV6(a-薐l KOk{x7_[EKrܲ%]R7q𽶈Zn7.=aOW |MmR6m fgn6rEw‚խ;w=FЇݜNc"9 ݭU;qݨt%ʱJHXc%'ێûn7!N<;n;a[1z`an:,rn*>*9N=JT>emS퍿MySDuc!ZYsPKI򍌅i8YJݪ}VD ˮ!_MQd[Xxnjja/ߵUQ3PzWSI cf&晽΢i;>X(lj4r:6e'տ3X +.c-c7B+up,}.>.-M}x'fĹ71㰧_{ $5K*Ðӵxߒ~t߁bu1^.F{*;(k-t]Ri׎bd:BN߬lkYt 㹜[in#ƣ-gpƇ#N7ߪI7X", ;)*cNӒjCj9ya[7t[EnqCNgn9ke89 j{h5-yz :L{5]?~', LE~Pa['!ǩ钊ӱ?&lW{M7̤"V'8 pIױxeKݮ=X{>Xf~',xvz46ɮ(?O5#Z$g̭$.f~JrxW`-dvZynq϶tIX+ZJ_C;v,p}#UyflPmZQU%gh\ Dy_Nqnnj5]UXf/i%q34l<.~{~wGFgBU&KPkJT#lemܡUFmagc3Sa+*ꓑP/RJԊ]r&1vb/fdW_IQVW[s5{ҽ|ànWUf]QJLprB6</r,7W\+AX_auSi9C7d۱` y=W{(~jn0]R+Vrr&HKcSj֛K~',=!X_<6.t„XZ9aB,zI7: 9Gxh_kXN/"[ZQ!gbŠ(a5Cuq~,qp¹h2 ,|WQ_EgcހTW8Ugۓn; dWwT*AvI 9:ӥ{2( c{&q~{ p$w‚x[uE9Ѻܶc3t`xa l2 &Umߑs)S!%_ga֑>vlt]'eԈDKXvt __FH Z_ޞHu+\_:U2麯OogƝNsd[Xi70\ *Z#uSUyoyw~x%_~c;$ݿ2CP9]שg'l(~[rUt>'ێeo*ē_ z;8:e ":]Eac!_~',XL~(U~ęċmutN, ~5sc~ސӯ'N8] w*ʱ\qu:귰#~Q7Ŏ`&bl3C'cqYj)]Wc1WHzb~k ߊ;](1_]ש9;WA9rU;?mذ_,QcS~gsjϰo>]ƺȬWQMe$ێek9]1œ{P(21_]שڅsbgXNWQ 3maa1bO)W j:^/fY<_:{uNQfIoe=3f³1'/G=GdWS ݷmPrVu Վ5cMy-ʱLqWui9ndWGGrNWU'JU'sN4T__'*Zo/TDcSMs*f٫S_o{Բn;lD)xaXa,Z9TD1 1LU F'.97)ɶX-9>rz#^?X^"qu:Ȁkɴ]rd[X.x' Xyeoo5QbquUpNS$ZdFoaYYz g_ZU.uS]sb{Kk=_uާDDm!n8:eӼ2,;!1pODoT Y/S?Su/S95f#ne'Ēl KX8BV}e,O}Lue2^X%,}e,u~,܍lŖw*\ש%9vub;E c.ޞ\w‚ח@9-Nͼ0R3t֗{D ,!_K; S~9[ܗtuXe|=Tszk-ش#noSTd,d,b9eԌpݯ o1oHb%L~ZQ-S+|vϧ ׏߁erݞe?]Wvs>S+ƙYGLO=qr5?uc`Y`Y `Y,,^bNߘ7ުݟE9&Nme$Ι>o {nmTGLu2J 9ZZ_v-,G`eY5wX_62R]w,.bglfORM:2~X*"MDz/U完_,욱]b5cTKKXX ,7X8^^l= J’2WYܱh,;i*2']GX4^maEgO;rf~KU#$ǍauE%9S|])sCcDzX$9UJXLcSnqNUXz]bбAX?X``,ba^),f,ɶ̹5JT ?짬4mPޭ]V?T4=vɽ~U?d[X.x+JkTyzfYlCש}4sB]r B_4]*0_+MTxI[TWWs'ۼVl׸yza_~',x)3DX/V?XKKX8de|·GV>yyTInuowNG uvbX8WWaIb, ꋥ̕eRWR׿X {h,ޠXl8roʻ湳Z,w=h;aZ\q[E}.B4Bשs4ulI%5Tjɶ.Č9[;OnQ^[! Ԍ9C;&-^Wke$2oxnj1_vf=UZ;"+[]?.cft33~{__q)*WV$Ǎau*E U:v-.l W8~/.u~sB/A)S9u*\>~NX"x.{~\z)m;0v͛r=[d[XXwbV|PwKRBݎ=I:U89ɶc>%ӚW=D{H,!)KjE5mh&m=tsr,K09ɶ\[ͱi{VOOPjP+vՁl|*odi9moax+`9=V\w)WK*ECgKyWJQUf5d s-߳ Ww Q%bbhUP~ǣo~E^l;cYvbwE 9n;.k3kOr,Wkꎓl x8~Zp Q3>(ǩzSBwS0jm3u~^wk;TbٺJxF素vTlə@ JXɶX|OydD'B\>gN]R~ws+Kwi7R7gu¯og̳cyUP‰;/kX$+k7Iw(Y1'g9qGSFrcNCtNX0|kثފktr,-f}T+7>lr,Sl ƜZcxxVߓQ| ;h`MIN;=m-*߮;aoe|UV;E9r.ݩ%,)\d#(q8Ǽ;2/Ɩ/V]Mai Jrb{MXv$,X."4꤫iP]tAt?\u~',ox ,9\_mquT#0d\T;;X8kGlf,q,6X^6XVXVŲj2JbY ǷNX8loWd跋r,5g[J ٮ;7N6(2BNX.Fkz.FwWc!IA*=δE[ve|dITK2{ixjXkh~6({qgtIUP,_tݿ {Vȋ;,c٘Y16RS 钪3/*۠|/~I7Xnrkc',69XzXzkya}ܚ7Qmk*(eW钪. ΃WU1urEw`iO,w(a+_%UnINA"mP^w!_E':Z-,tEMoŠ7*%zH);CF~[rEw`nw,+@dgۆ(a= uIը.Oź-l[U3l YMp_7+^Mϲ%.;rxtEolϛ{N9*Gl`O̫Rב,2.iwrU.-Dz1BTŢjON,Kݢ ԨVC^m2w`^VNx /+*^GFB]R=2jr*g|]vMyUڝl mdnE-K=|,7p.E/Í{!_Mgjowfj`џ{S!PԈMrl׼JZu߁o ,OsΊWQI 9O)]R###^BonoWn;a>9 +:X~]Zܩ'9۔.>9'KT%r<.`vQ;9x%X:f&ְn!&9BrtJԌr:NQacj<䎓rub'28s&j wh|9 钚Q}&cς iEM9:4mrWcgoj7nbXVqtIͨӱbP'MR3~Q7Xތ%Ίyh|)WtI>%rxu٧4nE8gt X@`[5.u~r湎tO{۔ӯ&ێ؁u[j߶ݨok!uVNnGm{ƽ&wEw`|Wl;;-\F|lXNqtI犜[lsM9n;s^xR_,|=ITU gz$J,vU,#>l7N܌evu,˸u,f,GX0Xd{oM^6Wh>;'NتEҞPlN55zF-,t,N_+*ʣk߫.xu@5 *wHN,C98! eU)Pk]NTdGl۠VBNziN-,V?8wEvKwUިQVHRpOXe;G 3RM H|QXzqtbltAw|lt~ MoaюWF1o!JXZ 9֥ YB JX 9=n;k%8(Jx.QI9nUTk%9#Q55l 3דma̴z{lɱ.]v SmP^w![Ul;C m'3Ò\TZ9[XFl;β+5.gYڮqV_dԮ]Ó*WkJqz8׭0QGl= Ĺ;JwXS(\^хK]Q{GűC[v]'/U_7_րD 5WtQWԈ8nt`n')_` /|Y <ӷr,QWetkd嚨vSʹx.cb_/n?oSGkx+K[Kx\f 9ꪦd[X#·Nܤ#{ީ+jw emr';F~,r,{vsPy*ߑ*ZQ<7PWԊ)^$#׫8X*;Xvuws٢ P^mYٕ8Sk_U gw V5&xjsx젋:PF (tEmމN=JθWM,G߭NXXQ {2{~ܱVF2O2/F~NXXLpCz{CT|%G>uEōE, uwMZEF-,f<}Y!(eOJkK (u֊>f~NX09yj: VQ'uIcA֛nkZuS~``K,(,f,X6sUW:ݮ8x⌀8M@]R&gc jot Nuk?^9ų_q,QOrܒB]R-,֟CݍN-oS.On_EO:?9c$XǷ7y^GɻeHwMyDsX9TDk9r+n:Qys'ȡv8QKPKȐ^5Ϝ6]q3wΝrEw`٘yf͹ 9!G%;Vbt]P-,JU'lRn܉];f]SʝZIfJ:8.Q>Fȗ).7?ݳ%JX8,{9婷j[ru)5lR7X6feYˌd;Cܨ Ul= 2 9=n;{܂5>U/*ؾUr.( 'ݳ0Xrz~4okdskK-Q"_ɷ:{qlX'h\$RSf,{I3Wߝ+JXlQl M=[\Q^I/!oWmaaȻmTG}۱5l/VʷUMKDι.z6+ 9w-qMbgv钪%NАݰ ʱw]J-,/-Jr͝X q䨾KTS;]boSN, a;o:.o}K%qpt v/imu ~5{Ue AM]RO~ӰD]lvoSNvZ-,cLWJEBy1ON؟-ٮ1ߦ~5l;?]P{o[/#ʱەKjx8]Dpk<\yM, }9NUc`~W~;quI kinyf/f[QcY|\,g“M܎&wp䠃R gKЈ<̐/f<5֗ѫ0~tI8CWl7?˓b99ncmm(뵷"X(r.8tqjJ۔ӯ*M#v 9[+CO᝶bqtIX1⣖lw~r5}d[X* QE6?x)ǗLKjU\fv Q,P<Úl Xql`ztQ݈oIzw3%Gt"t2Q!^= qVz/"9(v]}sZy$Rw( 0ooSNj-,Xt.Ix{'%,/򿾞8=;ɿ)_~GMju67|f"JXݒ7ΡyQZ9{rd<yc~+}芊(Aj̱D 2}g*f's_mz{MTZ1$o35Xqq68lr,=+WsdI+vci.(q]k>_&Xfȧb^~`wlzUXtY~džrzc+t;l;~103OQ?X.(v, X&ekځWbn6)2\| ]PX#]W6)DzCNO,k}1 _Y}f]ER[tWMJX TQ߁`湁ׅߛMVR\h[XfYkuMwz)t?~Aibw4+^[T%oG*lFbdzB>IB㷰̣yK T<º. ]QW plҵ mO %ݏ߁er]ebL0z "xZ.qق/yIWT|gK|uۤe^R~,s,#30X;:)a6^+*v[NlMwkFCw`~%9b`)r]lJWS07*nr#FD8[X!!s031V3ä|.ovXÈS!Temy`%9eWD6zU ț]vmDz206)DzCNK_{ 9qlGϲNHWTlGq:n׫ncw`i8is5~D}:6I h.oVb;OkBnMwgt?~1r:Wy||m7ݶlf;[mR^{k._XNUqX1J?ެR/Qb Q5tE5CUEIS_*h;Ln Q[e[ϤtE]䠫Iv%𷋼;aA Rb\mvИc9!oZmf{*NDdDъgݏ߁eyl'sS ri tEHjYmG$߆\~U+v,XzckH{KUU>tEu?*IJo`Y#}%r">%qxs} ##r)e.q承'rk9V~Qq޵G4;#j@]nM._зę[QXf;fJ&-[Q+k UKX*xXzXf`߾ XXIXXj~,3"^VoJh >:tE )A[]ז]?]՟Cw`9sufD`z-۾6򁨙C1iknz~rTI?ɶܦOL l$?Qlv$o uI=0冷mAqYf[_(fkÌvՠgPniK*^ / ;TU=jIlwµ O0{}#l bNrrյГno98 3ocǫA#R܎@9T$N? sdnU s=6۝|9>3gak ;!Kj8C4CGTp/×/S1{[bXQ>Tdctu&ݑ_e7O _8ʏcU&-8/Vk(׉9wi"w8ZiJگgKV/3ܗY/Sg$g`؆q[:w8VfiO{wn'_RaO>KjdqGo1,rEl[p~[4V*]mY: )Aa;z]cG _徰Mt(^H]Dɗ/]>Tdgڹ)b5x8+"6۝|YjsЯfu繵+[4$bI+[pՆ'<5nP9"6L#~Z Jߴvw5=hܱ|}窤@,[rZJl;|UAZC[Qǭ|+!űȂ-LĥDl-9-]:nYwA ~V-)|aAAv>Bݠ^KWf/Pїe2(ܦ cX}Y)] /v"g]j*ZQEn9'ɹ[u7-Klw2ꬵ8l7Sm|-%7,s,AS>#9t?eKNsK|ۃiQ rMՉL/QG1YAE [~]Q-9zq@ϣAjVu/R~S{ԻkNY0t/kvNl oV¤ܗr; XR5Z9k"]tr_Ni jAk#ZZz R["VTsűD5}4Uڴ.bŖh.Oo8|bD8(oXQW:YbeJW:z 9/Ԥ}٪៙ &T\(rJ(o8BkTkֿqM9"6tʩ"j"Jf+^ 7u8Ȳ)]f×e/ƙv#Nm·64_: of8\&Y~ ^*rEl;|JtBދNK|;*70+8 _؊F5v7Y6崫ia&eRq/fמ/[Kɾ-_Jyr`k]=wʦv/+/cLgA-G{$qZsO)o3`E |űmD {\7 "8Z{wGD>f1K 3`EE>Xqz'ݑVe7-/×ܷb7+x D| m [*uuv;|h:,>Dz`ظ *7#CB7({}t;eU-|Yg×%qaF*{U+`Em_6V%_+v[,Wׇ wP3m7 !t˗dքnPjSBNK˓t˗|}Gwq.(r(oe t˗Ѫn޸4trZ ݋gTQmں^|+բtbIA|Y'# J'F:ZQ;/{bbqCUeu͒M{nP5tkc%?xNeD^*-t-!oVP%v7b9JwIGџ'oይowAN-'}*-*xBnO K ۛco ݠ<o._>~G5g&lJ[ 9VHNNA6AbqRAɗy\N} VK""AxּBniڄ^98D-,MK7([.b_v/1HckSkB:vH$vBXQO1pQj.;1lձؗu(8l]^_7BlW$9Չ)SڏZ_m'}c~W߱b=O孹 RN;G (rN-#fgp[ܗr XR‰`mX\gX6 a$ :0v?h_"1ih%U#/l9 Ay \v=+%er$e:5bz{wg@ܗ򁸔XRow_krXtk=. [yP~;'rqTܐb^Ilҋ(O4.`_vӗfio/OkXe/fE(՚(@O,kث`G\wqM9"6茢8e}-JK@O,xYltt(li-@L!xm]#}!%5|F"N=za[F#ʦv/vIξ$-Ym)Dw1^XR3`X")Mb +iP6۝|9+%|A6c^nÍ{ '%hĒWK;X<ʦ6K}19wlf(o=X=>q:֜!u7SٔӮtgk]ֿM8^-r Kj)sq֔vN[Vٔ.bAϳw1r!68 p B/K,_BWc[*rEl[T!P%ޯfoy/|muL_ᗨIwf*v/ '5~DJ9=%3xqUvl%l;|qd3s6 VF%]|5\%ԊdZ˦v W[}j#Bu0"!{r{b5BB*6uuv G-_u]lSk%%_ 9%"Z܊=-^v]@7([kȫb޳qu{qVf֍䞽/}Js{’"Zց>kɲ%]f 7xfZUvj~)rM ǂqMf`+v:@m!f×ݗ _ڊ]i&1Z퉬pE|8"Wuȥ%KIKEϤ= rfNG=M 8ǂ{PVY䴔lw=_F|ieN~7_&W}״}/=o_ Nm{XI'XKu>YѾB*[XROPЯ[O後.bəZ_b!G^PKB,bKNh1b#]MlwrCmޫ8 g&1'o3#ȳ(,37r젾c^̍eSNv/w>Ԧ(Rd|gOZP>bq)މ7)K;,ʦv_]9,|fܾ֜LFgV3AޑXQ{ q{t)XvW{6-_b9"v<-(Bz>e9"6۝|9ә^qc P։܎|c;>8xY؆>2˖v_S.hqϹ6tG;/<&:"49 mL-rAlau{w3sGoɸsG|9XLEx嚜}`ƞtWms /ښ._iUڇE-yUF8xۖ[B7(>!]"'/'4ػxmeXEɗXQ&XJl;VQySrxt˗Fk̑"!ZMQbEE|q ېu[dWI9*Zw_I|quCUGC(HX/-j=xN M9e|Y{/LޗƏbcNU]Yuc|!o [n8 A?Q+>%_n/$駚$lCH-Yvu/M4q_#cFSU Kj' Tʆ\v;|8c{%cǣ /kJnͱęnW| ؗ]',nwuzٻf'g>D6;>,Q.| K+FܐxeGܐgْRb˝S9Ǣ QV fbIYvد/=C7()fi)x;8__X?0~&Jݸ;_Ēq@d#˺A,#ؗۃcˎpFvʿlQSOldْ;|:zrԪ\\+Ռ*ȇ]V wBw Xq/KiY;4{V/5|OQ^k> V ;,ݕtRo.Kߡov/>KG=zkVfe/7/ߐ39؁ JX<5}L⾰}o.ꧬmǙaټXQ%1pe>vd6uHtv;|{nCfNo8b<"QܞVT f&v43u[ò)]ľ/ؽ^i? ;%_=Q0+ޫ8xcWnP.`_v/fK<$+b]p~cq~eHQ8x-q9t/qewn/~<ָ~HXF }I*b/ m̜-ֱ%}:ɻ́%5t8+,nJwә*r929M_}!Hy !ɱcIEmqsmh .`_v/wcyTϷ! K*ހ87;ހPِ.`_v/b/m6[F{TE_u/;|c ؗ=4m3qmZ[PJƲ[jHǂnb؉3=-˦\;v?gawh~[ܗr̐.X,,گteS.K}M_=mgكDw{ XL>zp*z/苤;zEΰlwbcYXa(za Q+H>-۴M!Eǭ+ti=nb#gAڅXh#l [)‚nR1>؉bg/xC{=S1ou-1ȗل%3"Q`5AWIXKlw[Vpc Y]^kq촍 3|[ ޲gX<TQ^kJz5bIEJqՀROݠ맆C|^r;dXRZNDjꞑךeKNn2]cflqm=$(rc ɗ K8AX;㺍N 9"6ww92$as/0 K2qZ"U6崋lwbWgaX8p{)J_ވ|##Tg)*rEl|KRO/ _Ɠ}15bSX#ʦ}iŞB9A/}//7Z%5V8r[5r.JrEl;| kx~KqXk 'j3RY\~WpkOdq$VTz>>{lC/xt틉X8eP\~v+8(:XInP.`_v/wmW_+wP@`PIy.vKXQ 8EŮuK%_9ewPuVAKk5_'v)(,rHw: )fw] &c=k藺Yѫ+**c'8y;*li/ rã~f@m溧FDĚ/kĊBjhzQ7GVvxewO{L; q}/C}%]|8`OKli/×rbr6'.PPC@q0N3ؗ]|=m{[ܗXQÿ87֒tس`ِˮ蓒5@ P^qg< j;ó"+jFq.'U6 ؗɗf<퉋}ZЎ\"+ZĊ\ 5 ݑ@ewvZ`_v'_o(w<;|Xy]RC{zĊbq(Y6kmI|h%|1n4ܙ9<-7)rcoB+v/L;#O ^ g;e/ ^mW/1ӱ'-yDK)yCXP-_:3m_A}lϜ;|xNrVqo>Vq%JmAǂn؍]zؗF>8Z(gb,(5+xYNXPЍ&gw>t,e7}Mjm18ۚZOStGrTR#gco؍\iԽ#˖v _6!퉋sT X%)Ywc ɧEP’3l3$v "u8Ȳ%]f 9cKq{ctĎX:e]mk\q׳XYuP-9z4ҍ[=S=8xmcM1TdݝIlǎ# uR.bˆa{r_zfjkS#oIY6崋lwZtmǴI)rϴH ’j.NU"aq__IWُVNf 4wFɗ#o%_DrT@W-9;@lʫj-_|EǸΨcw񥭗oP hSq%V9'"6͗~;ė/RVUXVPN[ݪq:1ټ}5br/t;~r&_ur_Zinǖ>3Ne6B} mM<)G9 +*^gc=]7)z6#v/ɕ{P i_A..G+j{%qluݤs>1{n5xpnY3*vKkZsD,$|٫‚nݭ&nnRz6i)/×/}pȅ堗UךeׄE堖b0\7).I|1}Àsc4*Zz4ﱵn59nRSԐR`_vtȩX};ivhHR^kj:+ }p̂%nnRG.a_v/uHũU ꉗuAJc_ɲ%fK"~enpekǷF/w"u,(/ V|eKNKv/~٢\{Z(rMɧ܄%U=Щ|h#"6-_)'8xiq!^; Oxg!UH",Ȍ#EbCOdaْ.bK8úe9[[IZRN%G^aI荜jʄ-YYٔ.b=m~fDday06Z1]hOo[TY8btX`ٔ.bɗe^8xb!c{ xςFr$Tg!ޤCw,X䴋lwreYrk1Y Jw ,D{JACNKv'_xQ.~ǔVLwЭ%`k‚*q7A/ u%f×/]\:,<JY^XROS7` >˖v /ݚi3s}PEP&_;z A$x<@ZlAHݠK!]f5[3oTFk:4s[jfǒj~R [!JXٔ.b j8ãbd%_nG~j_A%,B+.>QELl|bm;Sawt׮qwa2NdXQqW^֖t^M9"6=\8vu{֌-m8+ʽ8qt[;"6-_mj/ PX u/\~EVTl2Ty>'ųɨli/×tז}47J%VqVpPw=*rn늨lTAg-jc+<ƚ(˦v;|)Oo?b (5N+帎"8 *N6ʦv/+}r&:T=׭>P.[ 4wsc\֯hr~ov/w{Ƴ+j(ET -ϛ+*qIDaB8u8)]_{=`v񬦝KqRڳCĒqROp$B7()e)//Tس<θgj&$yœ n=mVKm/ݠGkN +r껏I~11C+*S[-tVƲ)]ľ/VPO=\n7^U!fv^}iM[[< m) ?3[OZ5l]g=huD5^K*^Xw;حZ*O$GOS/|vw KR#sݏ+U6 ؗZSEz=e\@{1ʻQ~ZPSE#.9UT6[3f7}qDcޟd6~o|Kf '؊_ɇx%XT6崋l|A6}m\voP'BMVXRqX_w̥;NliWWߒnbN/'}n8-w )bQX҄iP~_eSNv/w6M.qFԑq=nw6)Im’7AfDbܢorliTtէN cH44EKH9K*\sS6Lwř+-9"6-_ZsS#`?w^xtD=nx +*N#b˕'Y䴋l|zL iJ12bEE q؆"˖v[bo~F{Hjqdž3uAkĊjj#2 [0g{6%ǹSaIqo ;g{T6崋lwGsXܕW{߯VxOr,3;Vki\wvM9"6ؓ 1rӔtB"J1ɡəXXP=2ۉO@{d.`ْ=-W7 '{DfwBx(b)_zd`v~ Jrhov/ :8AE9KuZaiF~)ߞ}U}]I7(b崋lwrG;9C/;2|XRoGbRAQU q /8scv@?q?_<Cbn>QqOـǏ$e{;d]rI\2ɗv9$!}ßQ>~G{ҏ //?~Oq-=m-tř i7l;$/ ? :}BH/`yEx?T:1}4>R84˝|6IJ cՔ;^ % 6SSy} S?|X/QˌOZւ5һN׿_}=٢X{Uo~V{ߢܩ"ˏwRߟ/~_dlٽުo]~t -S?S+~ endstream endobj 328 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xwTSϽ7PkhRH H.*1 J"6DTpDQ2(C"QDqpId߼y͛~kg}ֺLX Xňg` lpBF|،l *?Y"1P\8=W%Oɘ4M0J"Y2Vs,[|e92<se'9`2&ctI@o|N6(.sSdl-c(2-yH_/XZ.$&\SM07#1ؙYrfYym";8980m-m(]v^DW~ emi]P`/u}q|^R,g+\Kk)/C_|Rax8t1C^7nfzDp 柇u$/ED˦L L[B@ٹЖX!@~(* {d+} G͋љς}WL$cGD2QZ4 E@@A(q`1D `'u46ptc48.`R0) @Rt CXCP%CBH@Rf[(t CQhz#0 Zl`O828.p|O×X ?:0FBx$ !i@ڐH[EE1PL ⢖V6QP>U(j MFkt,:.FW8c1L&ӎ9ƌaX: rbl1 {{{;}#tp8_\8"Ey.,X%%Gщ1-9ҀKl.oo/O$&'=JvMޞxǥ{=Vs\x ‰N柜>ucKz=s/ol|ϝ?y ^d]ps~:;/;]7|WpQoH!ɻVsnYs}ҽ~4] =>=:`;cܱ'?e~!ańD#G&}'/?^xI֓?+\wx20;5\ӯ_etWf^Qs-mw3+?~O~ endstream endobj 332 0 obj << /Length 1281 /Filter /FlateDecode >> stream xڭXKo6WЃD IQ"=lS􁢾uBQ eɉ$5|"s3]ݕ6qYmcRȚ_YB5,$yz, H%~$SJs ,35o@?-@V߁'<0)/(Uҹ$WF8]$HT֖#ҧW&mF_Y$q"".񎍙7dfyjȌa U 94alv-<6?\2'/Gs/|nOZ5Oqe|i4o k#|sMȵh^5r/h5z0f"eQi,S~`5E:dqyK!T#㷀"Q@Ú7dFl$ Fz~Ǩū‚Rk=JE; v:@C&V2È@0x%@'W4+Gjfsg %ξ8r?n%-ԷRJxFؼj={x̦m.4^Ti\ jl9zMatإg5g2Bk}l ,E7fT?Pfڬm<ه(_;|InޱലCDFHWv+GK %uN``n/\,B !)u6lC1S1Q ~i]cߛBT3TTp\5gSja!e c*H To7!'O 6* `-}!!t~f+4ظy!\hl/~LKh]*F02 C|:<Xq[ܷ?wR'EW~Wfq 9QUIrST:S{?LW R?8u;u!9'M<mˤ$ꙧFVCEhdT ?(Lyfz}s̡Xg'%پ䒀kK~1/EK!m PS.s۶d~Z9hS5*ͽ"lHm=rK{cP7Fݾzv_X J!zTyMJqq:>x믑W~w{!Yg}^{>g:QHn{G}%9\eP<g7Y7 endstream endobj 318 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpgqSfj7/Rbuild3bcc0d7ef148ab/EBSeq/vignettes/EBSeq_Vignette-025.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 334 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 335 0 R/F3 336 0 R>> /ExtGState << >>/ColorSpace << /sRGB 337 0 R >>>> /Length 13302 /Filter /FlateDecode >> stream xˎ-9v)rh"xh"2,n@ÃmK8-X _V]UjiPuwb#Erg}o񭼗:G?7ow?~O2nro$z-}o?xO$S~}5oׯ_Y}{HOҾ=xo)7vq?__[yWV6}ۊ~{߼eˢ9ߛʫϫ>cϴףׇg Ú?ǻ^)by/PK{rww旻?8勵>f[]}'}^cqy^uu?.?<'^|_EG~͛r[ko>u:诠XDouђX?K_{]{x/y?o;/ۤ⋷>/B޿b]8UAh «WwU6/XЙ}^y/c^hZ|:eVW;{L?Bz~ N`WE2ˋ}ɮO_ߏ]O3Ϋdmu^V=w'?_O~CmjY4@x9~v;yݎ>ϼ,1˾zcY\ONǗ''1s<<z'[ϵړ%t<:e_\,^YdExt?q|?}~{xˀWZ߾]fsx1nt+-qmdTv="N1n7?46:]O^asGbsch}|m۵vR >sSo01p}ͫ4kɶxv|o[,fݺ|~r--VMBk,fvˈ6/aC}1rA$"ˤ b0<"hKٿE0 6la=Dn3yK߮ p w}8>z#|w YE^{۱f?=jHW~mua܃} ٿؚWOB,[ 05C7g"7d exd k"b5*JS@dI8ǫj(؄!7UaEٿEU#oVZ=h#+6"S1u3)d$?h#eyEe3r| dvWָ]o"FI )75n`5b2 )W cf~x ߦe x"6)iY.rḨLfd~a'ކ،)//"_ ?2Vٿ؏0:Qhe1 ¶=&AٿNyW*Ȇ$~w~wKi38C]rSzϯ}H3@5AR]b_}Qǽcމ{[6/z;ƼE2oqQxV[.|_KfNlbkTd.f3+ i_8|XĶy]ES[]xq_g\؊!!K^d*,9C` DuUawj\lFfmEf?0^X8~&ZOpw쳉'&q2ؕO֕}w"Vn&5{~EKd )s1*HC/\ElCAA*›D_S1KZAT1>BҤd9ʒ ~>}/zua}`iD}OO8 FV@Gdj >|&Q)eQ)u(@Gg9~G@(kAw5P(!;_Adۮu}ACd7X4\[~N~q5!2Mȩ~St+⫣sƹW5|8|u"kʭlQYhʾ;TIܾ7q*_PD. 6?(ez#薺@#[Ȟб_E;NE)_D+tfmumVM~%D}uqA|: TM틐ٷΤY54T #夘i e?@}#FpfR{1A.U @VY%~.?4P/ dz*†ưP3]mE?ƊRFxJM.KVC|IDUÅӈڴT=SI**GoXgCqЪXˬ@u|}&7Vbnx1L˂aFiFA J x+ E֋h>\'y~ )J>/+]UU(Js`V ;IΓ[v1jCa=0̰fCJ#-\va&bү {̭bw6=Ww:\,0߹F{,y0<%W L{K 'a /fc#5L53U̼U31{ ct.{ :iz=R-mIuǔ=Fc#1ΑtZH{L٣k Ξs==B65P.KjGW9a)|°J{pagd}ggӍn<=Z-{أ"-\& L{4Miy1S黼٣ey9Gr^VT&f`&zC}a^J_s,YL{x nۧ~̴GiU9R7XK \^5:yCG#01ecVv {\VyF`!^vH=|p0*P-y*L{4On l/_Fԧu x&?gUC{J(/l[=ҟJPU? n[dcxTdl`J=?@ 0'qJdNge0_a:0þ]+x%g2w_xYQ꿵Hd#ˡK2ܾ!{,om^S_( ڦ#)J&+ߏw3ߏ9x'3o3ߏ!Y |%{|%{сkT%qБC|-{|-{?a~r~[~[YXK2޳٣g;Gw/叁iL{aY?X-(}ֲ\?r"QdpR9c/_ɴGվDnJ$e} |*{4 dE=hoү]dڣ}L{Tϗ/w/:B, Ba=9~$wߒi?! oi/D=7)g2^ܿ]==Sk2Q^#9 { $ 9=?PX^0L,8&8oC4Gya}+rx29t=y+}o%{l/W_/Ocٿ=hQ!r,#xP%GrU-)L{xE1w21+`fK|IɴG'ЅܒүCd#E#̲X%ٟ'ҏ[21~rt*r'P'y%;_ CɴG'hxSk2 > JɴG?9f*!d~M|&{D'l9p*=Z_-//Gc2dڣ%tJ?GF/J=j~.{v'5_=j!{|S k2QCt 9FGkW2Q\;WF'(p9i߇ɱ[ {j%dS9ݨdnV7?&Cdأ/+ mop7Y[Dd3hy$-0PYHJ_Agdc?|Q YnXKO7eFdce}=z6/tza2951UaM21=3aPQo1VF=r`pð0_nsTa͞(.a00=h;Qm}2)}˘@[a`$(L{W=kˉo{qɴGO{]Ƌ/g|9',x ؚdWic=r|35lt٣=]hn i1Qc^Da'U;h}ieXORNkv3iO1if^J/Nn3Qw~/vf5a`W0]~ Zhr>$,Lg,l,ryEXn&#}8 ^S~"{?1ONdr>#Ѹcal_M:2\[O{8 sJ_ wH߻!7>~N\fuMy6#烰0Kvg^0eb\_!{d{ W\6oR5ӣ=?!{#,,/eǵeiJ_W \viB7t=9dfB \F[O{|=}3.ckO٣fyG"`~aҷQ?Osv/גa9o\}Henee1Q-٣_Q sKA >̰G7/w\ffأX,Gٙ_3,`tr"a'L3,_.+=GpDQdzKΗ"{aj_ʹGoڅ0aX|R0=Płưf\r>b)dQ_raq`M^\ra)d{ Elq!a?L{8QjaDP-N2,iH)b{>rȥnvK-hRۻ{y14m/{;(жa^%NR}WK.CW>b)޾rŐeopqXŐsU|,ٳ`rDu2b1ݰLɠhux1ϤnrAI!Ǜ#<bY8+zfj,g8Gqgd:70F]NòݓQ]ˉGc01Boy5? ^_F`v1"-k+CO.cM9)rr{T_]Ɏmq8j0e̝Q!Sߜ>ElW͵mЦ Tߎf{bš4!JMeןto~#F?܂`[?x:״?Zz%Ũ9>\oQp}G;wƇ/wm?B.42gޅd~T'e1F =@?|S_4U} l+?)`Bouy}ퟫwoa~wh)^QQ˳ʞ6j{r5nj{P@ȓhpWד Uz~ p'ٛ/9:Yn,W4Yq˽7=Ysp^WW8I7|V)/K{G'Y$sY{KY|QֳޗI~K~jy'_' |3 IKI˝I~VD:|>=+G#mb{LO"|fXF׳Im<}XD\lO"|/$$h"|Y$Ub?=^=S{_Ԫz?E|ȽEO"|_.'/\O"|_O"|w}MEiHV9YI˝I3דߗ;ߗg'|$b}{(%3D\O"|_O"|YY<>s?%FX+y|NC=$eZ#_l'|y~rFS=Occ˳']p@㑻?y!ƓA|rAo\xxSu?S=Ӟ|kz<ȟo18 ZiRE@21S !r>ߥC)R0N bth 15l27\`-R!ȷ'Co.-I7Sy:/Iq U}qjyC&i/nB^S΁Tw a<]C.1F=GoZwgQ/`#Sw)S@&0`#,G߻KiGGQP BGԶa?O gH?hޘ =9p㸖F~P[.\@pfأ|ߙu%VeK'@pf S v[ K t\ȈbC!ԱSn3Хe Dzoi9p L1;ƂCs. tzv-%hQ~m0QnKwL|J{n.sH|-ʝLoH~僁.MŐ+H΁I>+Бʇ󔙛.:.+a2H=7T-(.GK RnO](L{LBJ![ǧ/y}Imuci¹~hi%{̴ߒ=1ܕ~i!SmiK+{Dzm\}#e[WQcNL{XH$3=uo`ڣ=ѳkಶj1e|2wɹn=!1xi_ȿ،5Un GS u!ɸ_JiO2FBp*a7ZejكLM>o$\/TYL{!a7}dR+/uy| ^vSRqp7#3Fp[&a7gDDW txJKn;=|sgf=$/JCn=ot?1^j\i!{d{)S <ڧ_o>Gn.SH-)6͡k7ٞ){_O LoKmfzF{X\%U8Ư3aa=ݧB}=Ò=,wxLH[i-{tZdN3O |n#|j,:fqF#{QpSnqpPiGg'>"H^4mioXb^S{P7fڣhSJSeSeh>UHU٣h,OY>M([=sX執&{X/$iజ/aI$ַ9$E;_jaaQnRϛ6lo^?KL, sJsW21ۦ›~K^J_}벇߰]ڧJMF7dOS-tXȇXې=<>J_d%ި d eܦ1^ScfaUq,E[򮍃#릍s?L{?ڴqp9R`ch46π/!53mb )a$6iS7Nvsk2ѳ٣h8)aL{XLKGPɴG#\B )|)"aL{|?m-߯JU٣ih~88|h9`ŰPW$춒frHm$ou'Oߒj;\2Q\BG=>hOJ%]B>| Á{c!.fأ_gHJbm얄7_cdШ %a7V+$ZC}nJ?Zrˣ6ԋH=VGm+ˣ6P„jsЇP86{9KXq8ǑL{XH, g[x*}O}ڿ-{[>Ҟ8s21ҞG񀂧>i#{d"E+|g|Up1W_umV ?:{[ԇi-8Ϯ{uOFbp]KٵqIY<=x#v[.Ots<ѵq0M髾wWי<=c6vgW}:{= L{4;b͡)$L{du6CGTm~vmE!9CX.!,P_SzIKBt8k~m5N5ޔ8$vuZO),$'nR% nShxԞs@X^DXY^uOЙ>vR.O!:pXnyJ8n%_o ?:'rjgt5sHN>a PXRn_ t!ޞr)Lw̡#+}C'J/_W*ǞBuʏN:'r>輟pKNЉ?y#l?)^S%tuԟP6*}CDkEd n'o u3aGk<2tO8,4Фp]rHN @)\P_3B%!:CEcey9@ CEΗ -˫ ?0tP,: (r!;6)dPg ms]Osyՙ@QP Ɋwyձ@Q\zDSW :(JW @Qܾ96t:P ہԾ&(lUBt'IN㩡3Zη)BwBw :':(Y(1BwMkBwBw<,tLjꠠoB'ZP:(ipU,twBwC:(mNd $ʘ:(ed 9$tדW5uPP )!=WS4uPPiOrD'p`r6&'B!!;S\O:(MSAAm=:(|)L>S?3s ぷT> jAA퀫W:uPPe?P^2:(e4uPPDxKN}ꠠ]ߧ JKU i)g{+ɴ?xs!W)ԗL{xf頠o:-s M3gCUnpa/Tsm[)遼j[_+\uPP i eyB}~Ts>m頠>:(ftPbs7PsRAAeAA1-弄jΗ-T3I|1>oS}%T~ @Eڧ]ROKߴuPPP*(}?[Uσi c-ԧhuPPxJv'[:(ףּJ *@W*m)ԧ#m7w -3=:(l{[%xuBkE 63:(K}+޿̿S:( 5실=r>I`|o€aT{_2^s+޿|AAex> stream xwTSϽ7PkhRH H.*1 J"6DTpDQ2(C"QDqpId߼y͛~kg}ֺLX Xňg` lpBF|،l *?Y"1P\8=W%Oɘ4M0J"Y2Vs,[|e92<se'9`2&ctI@o|N6(.sSdl-c(2-yH_/XZ.$&\SM07#1ؙYrfYym";8980m-m(]v^DW~ emi]P`/u}q|^R,g+\Kk)/C_|Rax8t1C^7nfzDp 柇u$/ED˦L L[B@ٹЖX!@~(* {d+} G͋љς}WL$cGD2QZ4 E@@A(q`1D `'u46ptc48.`R0) @Rt CXCP%CBH@Rf[(t CQhz#0 Zl`O828.p|O×X ?:0FBx$ !i@ڐH[EE1PL ⢖V6QP>U(j MFkt,:.FW8c1L&ӎ9ƌaX: rbl1 {{{;}#tp8_\8"Ey.,X%%Gщ1-9ҀKl.oo/O$&'=JvMޞxǥ{=Vs\x ‰N柜>ucKz=s/ol|ϝ?y ^d]ps~:;/;]7|WpQoH!ɻVsnYs}ҽ~4] =>=:`;cܱ'?e~!ańD#G&}'/?^xI֓?+\wx20;5\ӯ_etWf^Qs-mw3+?~O~ endstream endobj 345 0 obj << /Length 3152 /Filter /FlateDecode >> stream xZo_!zju4RIl)j'6ꎒ";}s˚QbSX]Ó|0Y-O+ecّ[: ;)VhM4 Ei'DUdQ }6*2~G Ⲩ,\07} Ȥim͔K$(p67+B(kUѢaA $1mE&L50ld3#&ɢ ͖9龚ԷpS/LV{Ϣ.OWVA&u FC&e"/?ւ4ˁagQ05~ֵ(MýbQ}ȲȔ|}p}fQDyY4d59pPUM#,)#a(hhƩu %jeΘd3lwF4d33k"cE= eL43Zku7j Lg ƒ$4)pzJF"2͓ɢ6e9y)gGIO 5.iig)#Dr*6eiY>P<#tl7 U3X}R3|V(F>#bljB1nTLZYLVvMARM-vTmc4~TRCj)^H^6b-G]^FI00&Jq Vdث IW0KCl,Vc w|o  MմJehg{ Ii d2s*ܤFΚvRXw%$QGCW؆uT@7@C%$^ YRfq${1r]iĚrD(_HȆ`1 @gIIYHmmBey;tp#߼"/~y)i qeR%m֪-mC&}V˶vȍZ :HN@;Ke$JT3qqUkZ+9` qs?$:..,~I,bʭH=螐.Q 81dQP}wSז)ءCNs.R޿f&F%3-/ ǐ*ccSD$ uWxs(+~m͆ H_:rnVŚK>$BZ`#;5y&v:j_p/|:' )FnN}QCCFyV>g~22O:/ir0;OJc^b h IVLK3ՄkxHP>W*5HI糳F hoSKd2#zt!5z1pL+;s# \mUTxR->^d$W>qX-shNh1Amc_.[@nU4Anv)4_r~OP^o ${Y)({<33NJ\/VZFR >߱`qϻnu:7j{DZlzDr$M3]Jh$+kڀ]Kh'y$7p|>aXlDj5 I{ITw=*5+<0J >-l{s__o-y;߻TX-5q.08&5kFҡYAqŅe5{d Y9 6oSgLab8`*LL[#J'VޖZވ} s4~ĹBsJumQ&Ύ@n~!T6}Z-ܵg:LZMnq7YA?u*@p(—O>AZIs#:{7NtS8.*QFH[ObtBDW 560AVT\BȞTJʪLl#g+E<*ss0ax+ \O+̂t޿Cf4Z?){_nۑU!i$$?Agq8І_yem@ו/8T2l7A$McC)OЌ%a]u0֭Ȓ$eg%/H hod![N2+z.o:<'2DRPeB/ ޛHLCr?\¥9o[Xs&yz}2=w4,y}k"YhD!?AOrURtVn ʟ(t!I L_f`iixbiXr;^oYɐ#9nLA0k nQEҝZ .CZ۽a9t =Ә>fE>#w+ N(gRFEQȑ |_lMt$ endstream endobj 354 0 obj << /Length 2374 /Filter /FlateDecode >> stream xێ_!Jфs']@".Hcժٕ68p6Pzrx̙sze5UL\]OD]b״rE;.v4S] /W氆Mg,oaC^w;NH~fl%D4?25 J3gMq h#1sDnwW2l{e7'$w3:eː>op~ w&=D 8٪VLkU{h [އL#WkC{]`,.ndR&.}I[3UqP͎X!8iS>QK ަ3ci7MvJ1ZcyYb9d߂%~v +ә7xd;p]| ϸ0t ?#eBjºǘ-Gq*881RHi t3 L rOײ;G6cl* :^7@ n%ê6$7†+<_2s1-9Gyy\~{64?f灹lYʡD(Li.$QIU?&QHԷbœs%N8\ ZD ..3+WItI#]`$p{%x Ā?C(8+?JJ}CAPZF-@\yi j~;G3WPE[:ۋ *OAүRNĩtK|bP*!, ʔ5O 7y1m'FZ\!SzR;y G_9EiNجaNynduo5C]^o5rC NA(U0bX]/.rkD&{LOH?耭v뛣IYYV˞P@I"4eT?-mdpPe1/\ԄL85F7L9Ըqj1.HjNӾיcjrc3ÔY* sOֺ75G)' ˫30ayug{i|4|뻮bdJ-%vA;eFmLwhP:t˿HRk &798i'qz!`%qYr$A(4S AZT*_d R!ڑ$  ۨ L7(TiߧpzP ގm9V1/Ěr#~w=!NYv[[e~+yN)CTk̘5CYv v,sX׼8M[.0T2?Ț-_ڰZPe۳X렼{"9\rmJ?es6g}fe5V汾TBг c f{n6`OϨv%+)v"(6rfiH)(”b(b@/[ti7.x8!8Rه|t+ Ŋ&쎧Ƶ<͸D, >Iu<|KlI%SKHHa[hp<:Rdwߥg^ y^ӍguTQ٤ް&y/ z,,pbXk{^&k28(m6%ޑ;t'ףCk8j` U+e\YA>$`zdB^h#"M!״_)_\b{y4%:/,Jjcx|YQnYRpa4h,U/bWat.(9+vKmw鹘6mWj][Y$nU:$ f ݱI,ŕYx~TҔ!b_43JډvT/0s7SGLC] ?T} p1hPPi0h8L#CA\+Ӕ_>{u쿧i endstream endobj 363 0 obj << /Length 637 /Filter /FlateDecode >> stream xڕTK0+UF%&vG-H[VE{!BX3J[Gydzpj DfaY=Ho#1|րjP) 0/dy}b1vH *pO>0'ϴ.W_yCWP͎(7Xj *܄%F6>~^9 _zH^Ӟx)|`8|Z8_clҩ}ծ\wܛzdR:a)넖ޯp,Q{۵f7GQ3(ܸH c6BgiD[dFIp=GY>[e8J=y׼tO$eޢn׮+˗AeSc v&ENcXE>o:띷 <_6=élX@[b̈&o$EQהͩ Oa{\YúKD!0 ?qY}_CN[HyF=' pu> q[9s+(5h2L} /1RҭrzZžq ˫],G2Kt$.xv.Akn endstream endobj 351 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpgqSfj7/Rbuild3bcc0d7ef148ab/EBSeq/vignettes/EBSeq_Vignette-033.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 365 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 366 0 R>> /ExtGState << >>/ColorSpace << /sRGB 367 0 R >>>> /Length 227619 /Filter /FlateDecode >> stream xˎ+k^~]jJ /噙]n( Ít09oRNI{\yXOF_?˟ǟ˟ׯӟ?ÿ7o/󿾝l?럿~y{Oqz;}y~/?W_9W?x>~:}_9zۧw}?|t~?o|88~x*2y7!{c?/x4/7_篋}y;|wZ!lj﷗~||ߟ|z3q"?ϟ~7v/L|~~3}L}=9v$??󝘟ǟ>>|v h_~w}_?:rվ֯_Zwx?_}}.Z;{;ܾGx_Z۩}߿>ϯ@}l_~~߿>ԏw׾W'hk~}׏wߗ>߿>9־lik hkr~9}ș~;>&{]!g {S{;uY J;q{o#߿~[xӾ֟XyՏvCZ{~}??׾֟~,>z~k_s~,:{~,>>~~,:cs}K?^X\˗nn̈́o_s~'l/iؗ-z7>/ekNflSVdB79}f|(ϊsمo'lW+`l/˒t( eyLz eN02 esLoD hXA&~3?M749۠&Gcn4ho4d&?M7nit&g4l?M749X74itO͟?M74iM7nit&jO͟l4iϷl4iO͟l&?M7nit&?M74itO͟?M7z,?M6n4dO?M6ni\7ni\7niXZ=O?}O͟?M74itO͟Z=濚oi UEsvyykLӜ~'l/i^hO7ο{ooW4qB79Q=lWI\O5/wVDLvwi_O~Ows]cIoG|m_26~̋e߾{~<7ȄnL߮Ȅns~we7+`mbτn4litOjtO͟l4iO͟mtO n4lc5dO?M6n4d&g4iO͟l&?M7nitv<ښnit&?M7nil4iO͟l4dO?M6n474itO͟?M74is is icoiX?M7z,?M6n4dO?M6niXjo׼]uߕ3ٱN3قo&?8s3Q67[}(~Y߮Ȅnve_|Mo0Q61cLVEy0aO\]u*lw"N濮`(D xb󟉲O+?M6͟GieV6/mOZiO+tV6ieƴlcZ͟g6n?l4ZiO+?M6ZiO+t󿴶tV6ie?lO;oml4ZiO+t󿴶tV6ie?lOZiO+tV6iecqiO+tV6ie?lz6iesO+?OO+?OZ͟O+?M7iecq/m4gxvy{jgmL?/<`ʙ(͟'6LzglmZͿ??vg?~7a?߻M.0alcC(;yH&tǷk'+3nU  0Qv|?τ4伂;~witOjtO;~4iɎO;~WtO 4b5dǧO?Mv|4dǧ鎟&4iɎO;~&;>Mw|itǧ鎟v<ښitǧ鎟&;>Mw|i;~4iɎO;~4dǧO?Mv|4w4itO;>Mw4is is iciX;>Mwz,?Mv|4dǧO?Mv|iX;j?/Hw\N?w3Qr~Bw|J f>FO`v7oxbτeǷ+2a;So_n&|׻??_0u_ݶ.d6ς 2a?W4,OX`l4F&oOJį7&t&g4hL͟W͟l4dO?M6n4lmt&g&?M7nit&?M749۠O?M6n4d_74itO͟?M7tO͟?M74itO5dO?M6n4d&?M7nitVžO͟l4iO?kO?kO?}Obicoit&?M7nitObW?M7~\^yL_&Y~v<Y~τo>F|ey&?6; IL^Nsx͕LNdB7D&|~ەLOȄn&oVdls^gf&?l4=ۘV6Ziz4O+?M7ie?lOZ͟gOӳieV6m\ZiO+tV6ie?l4=ۘV6ie?lOOZ͟O+?xtV6ie?lOOZ͟O+?M7ie?lOZ͟Vm?lOZ͟F͟V5?lz,n??lz,n?M7ieV6n?l4͟Vm󿴶l׫?/nff},LoeozL_~Dy=)o3aϣlf6ʙs.yY]LȄmzL97STdT05p42Q} "L_WrMͥg_sdHz}/LTS0QsFCfH*n*j3T0R1HMbZiE*h*ӊTL+RAӳiE*hzv4HŴ"4=;&4iE*TL+R1HMbZ M+R1HMbZiE*h*֤R1HŴ"4iE*VR1HŴ"4iE*T\Z JŴ"ӊTT*VR1HMbZiE*h*ӊTL+RAS&4iE*TL+R1HMbZ JŴ"ӊTT*VVM*hXܤbZ Z=7TL+R1HMbZiE*h*ӊT걸Iť5TÝ./M*r&TP݉"zLv˄ /ldPwU`= bۧAdO`I/ڈL >4yG&/>5']~n& ^J2~1s+yL0-d] * Zw#'5 /+l)F<2~[X/L\ϻ_iL  y~a0Qt(џf=Q꺞L_ؕ@Lg0~pkP_ۻ2~aD >+\ UE}-~axr OE&/hi4 EM~&L449g_/i54 M"M~ASH_9M~&~AS__~&~AS__/hidzƭ__/hi4 Eji4 EM~&~ASH_//h44//h44 M"M~&~AS__/hiX~V4z,vH_//h44 EZ=_~~a,/w&_? =jM+Ra3Q~ '3Q~/D ljLTmk&T00RQ4d&\*I0(pY?*kդLTLB^D1 {6G&$D1i$c2a&I`BM5h&IؕXLIׇjfuI`BMbZ1 ĴbӊI$VLAӊI!{6QTC&joj~"Kn\OV*"1J<u(R!Mgx&^0ۄrg~a"'/O {}BBh(~!ʶMghn7l/꫖ţWƪbF}qm>[&L5fL5v<ښL5vaaXl:䙨ۄ[G &:=]bDy}Bf&uȻjru6aWwl:}BxYG} !wl:SPX{ [wPa 똉bӊujZ_M+1XMϯ.YMcZi:hjӊuL+ASVWӊuL+AkW:렩u\ZZǴbӊu:V;uygi:hjӊuL+AS븴f4i:렩uL+1XMcZZǴbӊu:Vqi:hjӊuL+ASVcZZǴb4i:렩uL+1Xu걸YǴbz,nASVcZZǴbӊu:cqKkAcŪXM#M9'frG&:LG(agL3Lu>f{SՐgnE5s0a7ׄA|kM5Hhqgj0QTCOOZ5xB5xB52k` ` Ȅc6TCvɄ]QDQ;bLjT5L&gR4U FI檑&ASՠjjT5hi4U49jT5ڙTQ Fji4U FMU0j3̢4U4Q MU#MTc5W4Q MU#MTASHՠjjT5hi4U Fji4U FMU&ASHՠjT5D5h4Uz,vHbW FZ=jjT5hi4U FMU#]5VsHc\T&ZSm:d9H&Dk~!F„T*OPH;J֊I0&&a{LT톉b.6*9>qJ&'*į*a≻Jx⮒L͕ ۯWZdLBbmMžOIJk$LIȻlnzG&$fL>Ϙj4549IyMM&&AII$hjib45 DMM"M΃hj45vTL&&&DMM&&&ASIbiMM"II&&&ASII$hjib4541 MM"ML&ASHXM"ML&ASHI$$hjib45 DMM&VniX&ASHb741 MM"ML&ASHIcjnir,~$hjir̡Ilaor̡3lME!M944T ~3Qg(m(P%g0Q!ԞC U9oT8{{oۯU{JvLS2&\ 4Lggړ$2J0|3/J nO9 W+K>J gDQ{?m•@+veE 2aJomSr\&~LT ̄J&g&4U*AJ&J@S%T hi4U493T ڙIQ*A(ji4U*A(MJ؊34UvVJ؊T hi4U*A(M MJ@S%H%T D Vs%H%T D h4U4Q*A(MJ&J@S%c+AZ=T ؕ MJ@S%H%T D hiXJ+A߀ӊ3(4=L+J0(M9ӊT %VJ`Ov3.*ML%݄)Ηw3Q޳s^־׆}7&<{{ilO\5 ymxL6l*m+۽4bln튂LۄoklWelkWy`&l'40iJ{:|LfB=ivEA&O*y:Wׯ;.2aJw\LdBϛv&Wg U*AT ̄J@S%H3\ D h4U4Q*M MJ&g&4U*AZ;3)J@S%H%X͕ MJ@S%H%T [QvVJ؊T [Q4Q*M MJ@S%H%T hi4U*A(ji4U*A(MJ&J@S%H%T D h4Uz,v%HbW*AZ=T hi4U*A(M ] Vs%HcD(M M94U*Ash4U49T D h4U4U{rLT%+ (J7 US W}Lm]]%DQ{?kj!*L%0`(Wd&T {&g~w7D&L tg(=+L̿V~1|i><_ L0J৤))M &}8*+U jiB & /Lȹ ͜( irƳCM&@S}>>T䌇@S}Hkg>Thi4CZ;,>Tڙ`CZ;,&@S}>>Thi4Շ4M!M@S}H}X!M@S}H}>TDhi4CMv<\VŮi4CM&@S}Hbׇ\X|~0M9ӊ>L+@cδӊ>3MaZчiEhzn~ W50Q s΄3>ɍ rvE&T7L>vD&L L<L<3>DчOs~L<a&>P}t2Q3]r6Ef=M1o4}>_ID1&Qc&(RAӳKkRASVbZ JŴ"ӊTT*Ѵ"ӊTQiE*h*֤R1HŴ"4iE**vXbZ Z;kR1H5VTL+R1HMbZiE*h*ӊTT*VR1HŴ"4KkRASVbZ JŴ"ӊTT*TL+R1HMbZiE*hc&V0QF3q<½LCY#m0f(/~v&ISYI;J([lg¼ş U]y9 L0}47xKb{X&[#LoY(bw6dBŞ2Ʉ{ ^ {-j:Lvd¼',hbw0d`¼eo*+>ʃcӼܽj&TVOPsD&\Vb2q<늇Z EV>+s:0&gsTVh*+i"+4J Me%Mh*+4v6Wd&J Me&BSYr&BSYTVh*+iJ Me&BSYTV깙 Me%MdBSYIYYe%MdBSYIYTVDVh*+i"+4J Mev<\VDVh*+4z,vYIYTVDVh*+44JZ=沒&[q& V(b)EV02bW0QdgeE9Yŀ 4-;Y&ػ(dB6xah~˜41Q=U&سI(]57Ʉ9~Jʹj3fBm`o . 2b3?PYρ]O}SU`9ddlFb¯gfFbkJ#&ؘ1eFaElǙ8=;)̄M}FlPI\lDlh*644 M&MĆb&g4MZ;K,bCSIY&MĆbCSITlҎg[SITlDlh*644\lDlh*644 M&MĆb&bCSؤTlh*6i"6ؤTlh*6i"64M M&MĆbCSITlҎxM MņbV.6i"64M Mņb&bCSIb\lX|~Qb2El, g`&\lʞʄ>u }ElwPk(bC3pѫp)*6ᙨbL2ij3nu36SL }GUߑtUq`Bm[VdŞxÄJ"&TVbfe'^oDYW&+ʊL<%+3Qdgsd2ʴ"+4iEVYL+BӳiEVY*+ӊTV. MeeZiEVh*+ӊL+B;n MeeZiEVh*+ӊL+BSY&+4iEVYL+2 MeeZʴ"+ӊTVYVdriMVh*+ӊL+BSYVdeZʴ"+4iEVYL+2 x5YL+2 TVYVd2ʴ"+4iEVhXdҚЎzjuZ9^b+rՈ\3RxE9SHuxF\X؁Z]د3b[Ոҋ6M+v>yXM,#}NFʳp)_.Ӈg}om9\HsFpW#HXHs8qNFԑ}H \GܾQGHg8҃HHrx$Α2{GbGb)N;W,hDtG"#%#ݑHHDwD;$#ݑigs$;R9Ҋő͑HDwDs$;)xb莔hDtG"#%#ݑHH+GJ4G"#ݑ͑HDwDs$;R9莔hDtG"#%#X)DtGJ4G"#ݑ͑HHDw$;R9莔x4K2b92uݑ~w;\IjDWG* <#Mg{)[Iʈ;Ҍ,?H#͗4Vi}xzz$FH?|ⷆ<#\a'yz_HFid(ʈ:ަĈ;_Hq$yu$yiFܑ|a89QG_3Rieq{Qܑ)4#ő#%L3Ls$G/#H F#e 1GwJq7Ri}M ޏ#-~y#C>;be?#HU#="#]#%=#1RY#E}mTmgy!AFʓ"NF 0낡ח!锑߿MXFٯ^2r<{)w3w [ebm(N%W,6h6Dt" % ݆nCfCDD;$ ݆d!PЊņ͆nCDD!m(x6h6Dt" % ݆nCfC+J4" ݆͆nCDD!Pm6h6Dt" % Xl(l6DtJ4" ݆͆nCfCD!Pm6xaؐ^0!߾)6^O_FʷuLx߮3qz_N׻:.PQZຏwΏǼa#nCD^gVm(ѯrS̈M|oZ X>_fCz"# Qҋ)672RlH޷jxʗn"gؐ7Ոڐ##nCv*bD!m(l6DtJ4" %ک$m6XO% ݆͆V,6h6Dt" % ݆nCǓDD!m(l6DtJ4ZPm6h6Dt" % ݆͆nCDD!m(lhbCfCD!Pm6h6DtJ4" ݆͆nCD % ݆nC^l(l6DtJ4" ݆͆nC^lhbC~@?;fCɌgQZv#ņ1Q{)sd)6wdDmH/5bؐ>##zYٿfؐ^iHbvgvEόYbCwMɌ mhZԈ!uFԆ<! bCdDmHebCzIKF̆)H!yƌ?3R[nus뷲?ڞ\3ϭf=ʈ>5rQ܆>N:_])_2)q0l %#jCz#ņJ+Fuf |ߏIĊM\11rjcу"FL)w\3R$J )O͈JT7=1#&QoF\=F\a13R$J~ÈK_ɕ(}65#E߈ua%ʮKbH#.Qo$#&Qos -޾ZlMU[|M|.1RtI3ň?,#K~.qἦ2Wa҃Kl&F.}m7]_Hѥ~QuIFT.7'Sc%]ƈT7F\E 1▕hD,[Vb=mEtJ4ZXVY-薕hEt"e%DD,[-+,EtJ4ZXVY-薕hEt"e%eݲͲnYDD,[-+,kbYfYD,[VY-薕hEtJ4"eݲͲnYDe%eݲnYfYDD,[-+,EtJ4"e%ze%?ղԌbYzΘ,=3eXB,0#fY` Fe]ҍ!#jY,e]EU+u>FZ)1j{NFL g}ZfDjg_VZ9@2RJodm,#VncZ)j.U+FyxF~#E&FZ)AeDj/v;Aތe%e"7ܲÛQ޾4zϟi*iͲje2R,K bYu9=~ah"g,Zm#ŲZփ#I,+#jYtF̲TXU)O\e^1j9y.ƈэ,R7##%X,ьFFt#K4##KGYэFhFFt##%X,ьFFt#K4##ȈndfdD72YэFhFb1D32э,ьFFt#K4##%ȈndfdD72YiȈndD7D32YэFhFFt##%Ȉnd+#K܍yfY9bDH1rb9#Q#+'3FNgč3bFaьҌEz#+ #jdz-T#jdE\1#{FVT5b3Fq#3FmSȈ?7͏#gteϵb+iFtĈYH3nrA#G9K,F6?ۭ{7bdצك@12P}FXF_Fb̌O#[w=/#jd^~qkMጸ=z],F2R5[)FHҌlC[cďfdiY5~׮ K<&?eg]߶OݮH)\bQUp)>eWl#ŧ요}D}ʮ|FO3){t6Ҟyn#ѷ'9?oe#[|js^FOwSGޏOgw}h޵k¶(}>bMn#.Qz6wx>VQq9;*n#E (rqGo#.QjIԣ"QzI>bn#E62"GLn#.Qm%J/HGTW0H(}D%~uzpnठJT?[yF=D](E-DQ$j*Q[Tc=/zj_$j*Q{Ezj_$j"Q[TڢJEE=DmQ%j"Q[TڢJEE=D](E-DQ$j*Q[TڣHU(E-DQ$j*Q[TDl.Q{ڢJU(E=DmQ%j*Q{ڢJU(E=DmQ%GpM~$jb(&6$臫M.QDM4Gs".%#]ţ%*ňP9fCm9bbChfC㤮]GyΆyƆf9lgl2lH.F 90641_m64o͆sȹHk)64?\w: 5jۈУbCP}xJoF8;i?5#nC fmGqk.njGԆvmmùnf:QMbCv]6RlȮĞ@H!jQ,l#ņmِs#nCkyΆfm ݆&6l64fCzmXO Ml6D'݆&6"֓nCD͆&6" Ml64mhb!fC ݆&6l6tՆnC Ml6Dtlhb!fCD͆&6" Ml64x"jCD͆&6" Ml6Dtlhb!fC ݆&6" Ml6tՆfC!PnCDD;\݆nCv" % ݆nCfC ݆͆ņ )#\7H9rFԆ^{g^lfC1}mvKHـ__\׶ކyƆyyʆi6d0~I % ٥K_>~(YFLmyW 1+2b a1 w*n#EԚ2 W;1 w#@-uץEۿMYHQ MFT"FQ F\MV, h Dt"%]@)Pb=n DtJM@)P) h Dt"%]M@DWDS +(hŢ@@DW +P) h DtJ4"]M@DWįE( O]M@@DW +P) h DtJ4"X( h++(WDW +P@@DW +P)Ħ@DWDS )UqHE)#xOF~=<mggʈy>f)޳6vaxϼB~lޓy*1ҼǞc<}ğc4زq+1[21Ra=fBHQ {> d>Fٳ@>*Uk>8?+K9?y{mHߑp1HQ }n]#@m(7r1Bz)%Ȉ+~FL:F0(#@m(^THQ {w}.bb(hŢ@@DW +P) XO%@DWzZ XO%]@@DW +P)( DtJ4"]MV, h Dt"%]@@DWDS +( DtJĈ)K,#Gz)1Z#*F/] ͈REiDtsib3'b=D'ݜ&6s"֓nN9I|7'fN9ݜ&6s̉4ib3͜nN9MlDtsjND7͜&6s"9Ml4ib3'fN9ݜ&6s̉O9ݜ&6s̉4ib3͜nN9MlDts̉4%Vs"9_^9ݜpEts"9%DtsJ)̉DtsJ4s̉hDtsJts3޻K2RI{)ȈbN˷+fFܜ1sZ{H1'&#jNH1']`)&@^GԜ;v2#ŜfG{ױ}Dިl)3ut3W1RI"#jN͜_ŮF9)fxJd~G_"Q {R͈J>3j4Ziw#&Q~+!#E|D݇QwF\NFDc2n`DrQߜ0R$to?ÈK?;#*Qz#E*4ҫ2޵FD^eD%Jg$D(K%*7JM.QԾI%*7J4"D]M.QDD(KTI%hEt"D%DX$*$EtJ4"D]M.Q&QD(KTI%x.G(KT.QDD;\].Qv"D%D].Q&QD]M.Q(FDI,#ET9#*Q;Dso#E􂩌DK?CB^>($#&Q$F\$FD(j4RbHQFTµi%-"#*QjwZ|E.z(v|%}}M֣u>G$#FD=sz3g$*9=#4sz6s˲qszMk3'ƊbN*F9l_J9a2RIΈiH1'}D#ŜMQs[)fK5͜2dL3ĈS͉XO9%֓fND7z̉XO9%9ݜnNfND7'S)̉DtsJ4s"9ݜ͜V,hDts"9%9ݜnNfND7D3')̉DtsJF9f15'4csra/2ʈ_H1'}2yFԜtg#'UOz##Kz#Ŝ2r<%|_ܜq`Qsk)W1W?=(E|.M}i$e(`*#*Qz#En?F\.茨Dc)fD%j{V`HH(R8#&Q~#.Q#FD띗GzpvQ%ʮʈK 3ňKTI%XOD%S&QDzj$XOD%D].Q&QD(KTI%*$EtJ4"D]MV,hEt"D%D].Q&QDD(K%*$EtJ#Q3R$jx7#E1$W3R$JcHT9gD%j~D^V(ۏ("QzTFT{inH1R$J^Q3'F:H1T3btU}.Řץf%O3H%}r|FT\]nj1RtIs/#KzciThե'uiF%F~#Mxw1'#EYRQ]gI1FeDuԌ.q]zti~T֯p>EcF\گQ]*4]* ňR1] 5#KjͩH&Q/qVh3ԊX׀VZjbS+b]Z]&6Ԋj5ĦVDWMVZMljEtjbS+%V"ZMlj5jbSMVZ]&6Ԋj5ĦVZ]&6Ԋj5jbSMVZMljEtԊj5%V"Z}]^Z]pEt"Z%jEtJ*ԊjEtJ4ԊjhjEtJTғ0FZ)t1Rj2#V N[qw'0RJS+ #EZ)j0RJ/ʈok绛)jd~J|F̲QOVuD1rOb=V*=9f3RJ/#Vco\Ns7գRRJk3jgՃiOݝW{'BFQ+FZe+`Sw#V*F\7zMGFLN sH{Inj`j] _#V]k*#VZ=8/ij6W+1FL5RˈŲqJA,[Vb]eݲr,+,EtJ4"eݲͲnYfYD,[VY-薕hbD,[-+,EtJ4"e%eݲnYfYD,[V$sŲͲnYDD,[VY-薕hEt"e%eݲͲnY+J4:]WͲnYv"eݲpEt"e%薕hEt"e%eMlEtJ4"e%e#ŲԌbY_QLFec3)gQ;)&ɈY2eň[ў,]dH,_)7eD-Klc\b.K^z #^.Qҷd!#ŽԊ2r<%y9.32|<]^;{`\^rGVFTfג C.EpSieOc2{b䙻)¥(p%ee1z,kn<q{31wgXWFԲoq%X3aFԲݽ+0#ŲQҷ+ΈZ#Ų0U2ʈŲqJA,[Vb]eݲr,+,EtJ4"eݲͲnYfYD,[VY-薕hbD,[-+,EtJ4"e%eݲnYfYD,[VqŲ`,[VY-+,EtJ4"eݲͲnYfYDe%e ݲ&6"jbͲ~,kb,&6"eMlSvwim,벅[pl(T mv$TYoτͲݲݲ&lE5a,W,k*Ͳ|4,ZVNXYͦSe?r1,6ᜪoR1jwrfY:[;kү{,R,SyŲ囼S)RQҥwTe[VS!*C}$7bYϏǽ× e1re{}^?~+fY >+ͲtNX*jYJ,]JKXe'"k?n[V3*6[HuY\ߘ WpTLW>Tp zՄǞ |Ul5ϑpgmy*E9ӷ B^pTp1QEJ.zЅk&\ ׄM-C.B pM؄Ѕk&\6"tᚰ  ׄM&lE5a pp}U]&l5a.B pM؄Ѕk&\.\6ᚰ  ׄM&lED5a.B ppM؄k&\.\6ᚰ  ׄM]&lV"4ᚱ~.Bv"t"tJh+B.Bv"tJhEE•Єk&\.\ M]~_2[+•gkQiµw*\TpBnOxSQ] ^pTp-uZO޾"\sɇ?.kUpAT\05e^QRRKw;OEK7RiekMSqᲩWT\|F"\ ?<IUmK?*p~WlKT)uW{~^Xs/]qNRKWRqRQҙT{&~T{^zh^~{ O*^Rչ?-{9QqT{CT̽NW^>:u5Cw~bTܽǭŨ{߿uR^:Js/T䖡wFWz#܋+ah܋݋+WBs/Bw^^^ ͽݽݽ{WBs/Bw/Bw^^^ ͽݽ{{{%4"t"tJU̽ ?^ ?^: <u/UFrRK*Ž㱗܋Wz#܋+ahEH4JhEE܋݋+WBs/Bw/Bw^^^ ͽVX+WBs/Bw/Bw^^ ͽݽݽ{{{%|dWBs/Bw/Bw^^ ͽݽݽ{{{%4"tJhE^ ݽݽ&lE藫 {M܋/W6_&lE5as {{ {M܋k^^6y+#Ri#:bQm&r>RێT{!N>Jq-[ƂXS)5_O{Q1.k-c˷Ic?sctRkXsM;J۝k%*^_RV4|{no˗{<$#5"|ѽ^S1J؄揥e0W .MEKAR)ux75҅p WZ??* g)um¥F:\QQҹpT\#p.gp>p$M,' WCsS1TpDMXG^*\m2(y֟NŌ-gHފ97i{{ڄ&lF6a M<=mi6O#tOyڄ>ii6Oy{ڄ&lF6a4B M<=mi6O#|z{ڄ&lF6a4B M<=mi6O#tOy{ڄ>ii??5OۡzP=miW(P= rC+OۡzӮPSwFvbd;T#ۡP vFC5+#ۡȮPljdW(FC52B7+4#yOҌЍ,YB32B72B7v"t#KhFFFFFЌlfdnd `J12ۿ+ndUQ#Cw72uU~,:Z?VXjF=Utʮ#sނP9gTc>WZ+rʙ-ܕ"gU19ӣvqڮrAș r"x*CsW̶r>G vlFWYzь,YB32B72B7fdnd %4#[a1fdndnd %4##t##t#KhFFFFFy@bd %4##t##t#KhFFFFFЌЍ, %t#)܍lfdnd6#ل&lFF藫 M،lfdndnd6##t#YLZJp{I]Ul]q#kCݩ#ZaTtbF\vōL&OH'MRi/Y|J12=aFw#+ByHRyUl])F6.6fuWʜwT|hdT9NٌL'FV$n*.SwQsvz79gӔ!U_FQsSid!Rm}S)vkʶtz=r}6e[o5SSEêll6e)+ۄM&lF6aS6BW MؔЕm¦l6e#|*+ۄM&lFU]&l6aS6BW MؔЕm¦ll6e %4e#te#teKhFFʖ.Wl M]]MؔЕ-)+[+lǮeo*ڭ6JQ6]Aŕo S1es3RN#*l9T\t]qe{+l>aUR)K64eS1LŔaJQ6ʦ']S6ͥRM-+߲<$iSeSHŔͧQqev(.aME)ۗP-es斗ʳ%,v]7OSLE=MmJ4Ti:7+Z3hʬ5*m֚lyU?kmU>_4*t1(i\.4ctW|K*iv<4R04;oW\S|m4_4z|ݕi:͊{O*i*SRfjG(Nڣb%Yk>+[pe;lΉTTT簾llQ6- RO;7e?i?y>Xl%+lQ6߻!S6<*l>JQ6;L<4ˣRMHEm 樸8*EtE*lhJQsT|ZzVbIŔДңb֖Ϧ,roeW:*t.ޭcъ^N*.} M]]%4[a&}.}.} M]]%4#t#tKhGGҗy"} M]]%4#t#tKhGGҗФХ/IK %4{|4#tKhGGҗФХХ/]H ]%4雰IK_B>BߟOGtT/KW+җJRO7NEot܂Q)ҧs#Zĵn^+*}JROە"}jfOT2^{oc?O;9&P)RQSROJ?ET Wa*?*먔Yvw]*zP.[p;n/ZûNKVT>!6!OQ)[&Pq;~cNJ?OdROSqs2LEOQ)[QqK|ےw>]̚ }TUA'b:/JBT U$4+4RPE2[!„fnn XaBBBBB„fnn &4+$t+$t+L<0a„fnn &4+$t+$t+LhVHVЬЭp  u+Y![ 'lVHV8a NجЭpf6+$t+v+Y![ _©eW Xr*mm3C啵|* uW*j‰JY{['&T<| M?KlSQ,?Sqd ?o H -ӻB!aFD_ai*M9ROR)K)SQӅRgUT^4sIgR)R)׻!FMRQɴT*[]hO|UylTړ>6 <_B3Syڿ'SS)ҧfbfw{7e[S)W1S1󽔩`*MMU)秤WOH_!ͩTTwM NI Wo*M]&l7a>B MؤХ3G7a Mؤo&}.}6#t雰I߄M]&l7a>!Mؤo&}.}6#t雰I߄M]&l7a>B Mؤ3Gh77M]%4#t#tKhGҗФХХ/I߄M]%yT^9J>gY|~J;†TY/Y7s?JeO'ҧQ)ҧ<[*:cU^e+罦$%SoK?ݖ1?=/ зeSAe n˿uiT\}*K*CvLUЍ9Um}*~&:)+UAuUi* S R)*8_]kMu^*F|T eb}4Kn*8ã5;YU_e^}iݾbXm+TzRP0LŬЧR)VNXmOEwyǫRП/bVx+tV{ebVHVx>} G*nH*BzJ·}PJ5Ƌ΋fT ƾ uT *n>ɒJB=$B0J?ۘfʋ| &4+$t+$t+LhVHVHVЬp &4+$t+$t+LhVHVЬЭЭ0Y![![a&4+$t+$t+LhVHVЬЭЭ0Y![![aBBB„fn+,VЬ%Y![aBBBBB„fnn &4+Y![aBBB„nvŤRPRq+d s^0B}"LŭRpZFZB_{s%souF'ܮJQA_Efb*,FnWz<: mWA8WAH.Iyq~6]4S1ɯT rP)*g*竮JUW=VϽigT^+HL*HRTP/U]lU/|v~|k4S2w|ъ$T~Υgy?_ޟm*|T|1?*IJbkJQÇ\QATmyT0i*3 eE!^*gמb*x *x4jT\}B@* T g*Mm>D*WA* M ] ]   &4\aQ M ] ]  &4$t$tLh*H*H*y`â M ] ]  &4$t$tLh*H*H*TU0 &tw*8aSABW NT/6 M ]'l*8aSABWaW  NTPUPS)*8_?_dRTpݸRHEUPVR)*[USqD=nb*+ q1SA߿OmTTgn6'`#>*Ŧ*3 RQ]%T TL~O`˚JQK* 2 T T > ~~3\s{ ǡ*hQy* WSyM RyER RӿRPg$R)V8df|6FJB[y3S1+lY)VxT+Lh*.NEO'%Sq;}65+dHyߟL=bxP)w5ӥͩج@_(BUp*؞SQ<|cE}TLpNaT@"S .xRTPwCLYʙ&TLʙ&V{J  Nq&.aDB Nq&6A$tA " M'lH8a >wXq&6A$tA " M'lH8a  Nq&aDB?&. M ] ]   &4A$tALhHH肘q&. M ] T TD*Ea`*&ȑJD6J5{['WL>*Eu /ѡǨdb*ݯWy+:R)sWSQW}+Z[+iR\Q'5PqW;T$-'R\Q,ME]qwZLX^?nB*J+\Q'sE̐JsE[Le˩+E*6T^YAفTή[Z+R)xWԕT+^bB*_Bm볩lCKŵ'[w9{RqmtL=RQb+6^GT\x JF◊ANhx+T⊧lsEӘ.R\qm⊇{f%WLH]]1""bBsWLhHH讘\]]1"bBsEBwEBwń抄 ;,\]]1"bBsEBwEBwń抄  ++bBsſUsEBwń抄   ++&4W$tW$tWLh8asEBwń抄 :Jq>1uEUG>ME]QgQqWtI\џ SqW,T|fbBD*E}-DTTu "wTL8":J NRQJDUTo徏JD2wۏJD]0H1JEQ~Jw[✊ NfB66>J;V8|iTlGج-+Y~7 OXX+LhVx:Rq+ٹ:R)V{Rq+t^LyߞPq+ fSQ+\<܋T }T pm*n[aBwݬPwIŬyJBHXa٬0h)Vx5+eۮV$+<3dT &4+\a„fnn &4+$t+$t+LhVHVHVyb &4+$t+$t+LhVHVHVЬЭ0ad+\a„n. 'lVHV8a Nجpfn6+$t+Y 'lVHV8aBBB}FFY=R\ϼ8bT[a*f>JBZ>R T|/JDmOt2"t 2=*.JD-rcYigTs4AiT ڴT\?O[RQ-R)XБ nHw￷_ ۼ\oķ3."oWm^_]A >R*DH.SQAdA#A\#rT\m;D*Mmы8&6A$tA M ]?*.6A " M'lH8aDB Nq&6A$| " M'lH8aDB Nq&6A$tA " M?*&}6A$tALhHH肘1 " bBDBDBĄ&6A$tALhH肘ж%,VHX^GSQ+T^RP/Tf(G*n>E-B_MŭlPy MV.*u S)*^.}k4< Sػ7VTPGT Zn*EuST_ڎOT\}D*>yJQpu\|kB/f^9Woߏ?K*HLEUPRTP"R)*_qL*Ri ?ORTP7]LTm~+q{ъ Χ5@yƊ ||} ʚw{)*PUo?^Qy_gQӯEk*fMUW.}%e#teKl >{Z&g6LRlc[**gmFǍwșrU/e])rf_ٚ0,ٕ"g)ޮ9|_V%9KӨ9ӛJ*ElrfF,Yg#KX4L-ka:݈JѰe+]PCaY^9hߵ7%< ^6K/u?2͇Jq/2*Žo^vWܽ+^odms9㮸{:|6ZCOu˪4H-iݕ^qĮ{ͽl])e[mJqu@^WEK8R)u5:{|7*ŽTR{Qq"tJo${%4"t"tJhEE܋+aa*E0"tJWo簾avrU1 sR40=(㪸we4 ӥϑ yީ)8( 4̎ڕavU1 ; .WE5loFh.?R40T ;YkF7:wmU%49#T9+G=-y{{ZB4BFx{Z~#V<=--%4O#tO#tOKhF螖<==-y{{ZB4Bii+,=ߥ{ڄ&l6a4B M<=mii6Oy{ڿ{ڄ&lF6a M?J3;y6TMMǿ+EV>7"gqG**gaY'O+GE=0VO HYcm*&l6a7B jo6{#7m&lF|{jono6{ۄ&l6a7B Mmfono6{ %|$t{#t{KhFF- %4{#t{KhFF-'t{#t{Kh+,F-a7}*Gٛ RQ{ bovT4{~Sy86{FIFJ7݀&hMRQ{SbZ;} >kM^9BP{S^ٛߕS)6/F~=CuT*jo:J7:*'{O*{ ߪ5 u>*}lz8C*ʖDieT_<'QyERQifQyERQ)ʦwe[˭]>3TT*E$uuiʦSS1e3Kw(|*1O_qe;*)Qqeǜ>΂KŔ7ƠRM@RMWʦ|P)ʦQ)6_a.Uȵ*\*lsT^Q6*l M]]%gE]۳ll _BQlll M]%4e#te#teKhFʖДЕmE ZS6BWlll M]]%4e#te#teKh6aS6BWll M]]%4e#teKhㄮll c|E]%4e#4eKh%T9&g3x<~W6L\*M%g[lKo}]]'2_+E|oTL|5*>k͗RqOͮSQOL[ bHcT49Q)R1{sRMgQ)KYSQ{yWڼ7ETTNJ !MTt_)N7)Vt)< mH9Nwz]:~-t[*t3vd0t3&ZO8N>NENXJs%8?2N\ot~3*tZJq:=x4s:ۦTTt7Z*t;@yT.s:?,Jq:@*tf_*t|UiNRq񗜎;]Bs:Bw:Bwtt ^q:Bw8;]ۖKhNGNGNМН.9;;]Bs:Bw:Bwtt VX.9盧9;]Bs:Bw:Bwttt %49;]Bs:Bwttt %qBw:Bw1tt %t'xTJq:>HFTTt t+RNNV9R*NXT\|aS*&r\WwSDΏ"w9R)"w9Q)"s**rz*"i~~޳.r~(d**r~DNg,r o6W{QigveRyT򨘲} T /W<-4=X{oHxNK74OM=PqO;}O nӸ̎^QOӝ,sQMTM*TOq.{AxME=MOM]iTtv@[=-XIYڲ*jo%4#tKo%rE]>ߠ찈\B9B9B&r.r M]]%4#tKh"G""r ]?MDEn&r6#t܄M]&l"G"7a t܄M]&l"7a9B MDEn&r1>a gXEn&r.r6#t܄M]?"rkam*E%_FcT/xo/*ETTj3$2"iȵ *&r>+EXz5wwM"q*Et=n*&r>큊\3ѩTTMNIEENWQ)"٨ӭR1C%+7"r"^3+&r>yoU5bsNŜ.φaSQ؍ܷ8]+G巼hҜq}R*x~+PN\RN,NSQ;q6{^1yTc *t~2*t:ߝ;?qӭi,lNJqMKŜ_NQq3{4/N+ze+zӵǻT|Bު4ӭRQӵTuT Nu@9VBT* R鷈U&lGoMؤgU]&l7a>B Mؤo&}.}6雰IK߄M]&lV#4yݤХ/IKK_B>B>B&}.} M]]MؤХ/IK_B>B>B&}.}.} M]8KK_B_a>B&}.} M]]鵖J>]a@H Ať)tK9>GH+rR1l=ϔU)皕ufw|6D*vߚƾ޷_r= F~:?*ROgP)zRQCTֱ3rsld ˪4ө2Ryz?7y}cEuϴTL4ImT^QTm*Y˩Y#HX*V輘Yk-bT0U)Vc">ܬPSq+t^RP/bV3A* >K>>BEŭY W i%BGL*na`*jbob* ua*jDxU6*j/oP)Vx7+q}a*jޗJBI* M$SQ+T^o2&7 >:XaBBBBB„fn &4+$t+LhVHVb |&4+$t+$t+LhVHVHVЬЭ0Y![![aB &4+$t+LhVHVHVЬЭЭ0Y![aB't+$t+Lh+,VHVЬЭ0Y![![aB¹߬JBjPq+<_B53bZwIEPcSq+3RpM[+fthUZ۽b ]߂b;H)O^%=UT}ROGR)~eCVLEOɐJ?-FKջvz0/Af+gYRUt>*ESQSK;5޳MLMTT\!G**}>Mťq0>INťksŤSqT\u$'b绞P)ҧ띩TTtӗUiҷrqLTi"TtTTt""}SLťRyER鷈E]["}.} o|vX/IKK_B>B&}.}.} M]]%4#t[a.}>PMؤV6雰IK߄M&lG7a>B MؤХ_إo&}.}6#t雰I߄M]&l7a>B M&lGa MؤХo&}6#|r?bz*~TPy8ĩ鵑Tp*nz:3=&I?bz>17 YB;;ǭWEӛ+sRQ[4_oRֈ|M/7=ȞJ1sw*jzs'1o??fz:/NLEMOXfzS/oNMNMyh*t,WνHy8r2=֨cmN9*Mr;JTTt~""}DJN5]&}:m6S)whwkҷng6.iҧG1Qq;}k8/N(|r*nzʹcl1ӵO+ S1mW^{KEMO7Sqi-f|z>bzkĀS)w/gfzT`5 jz6#|a5=B7 MLMofznz6ӛބ&lG7a3=B7 }}bW#tKhGG覗LMM/^B3=B7=B7fz6#tKhG覗LMM/^B3=B71NG覗>WXLM/^B3=B7=B7fzybzzO*PqJ1=Qqs9LLo Y>bzkzS5L»|K>XOEOS)wTk_niHZ8{GaLnROP)71n/TVKM S-'r]*ϯ{Q)г ~+[ǿ|;z?0'a*:JRɁo7K[ok :ϚJ?=*gKs߂Oi)R)ʘ?Hw\7Ӎ>R1=[VBDROW"bw7әT}}IEpo%7>$_B?B?B %4#tKhG |O6#tKhGG/_B?B?B6#tKhG/_B?B1NG>WX/_B?B?BAzNȐJ?}DB7*tL.S^=jnHaJ?ҘJ񿟋ʙ*tTT-?2.ROgP) S1s2\JNhTA*lWT|pQyRyz7q8>1_KxGO}RO}[/>;+GIBJCINRO$NEgNT3TIusLEOgQqR1ȫRW"ҷOPH?0ROq0߿=Ȧ秠P)ҧ8J.+bw[P{+R1;ƨ o e*&oegC*oe3ToJ1Ȅfnn AAA&4$t$tLhI r Au$&l9a3HB7 AN rfn6$俰 'lIC AN rf6$t$ 'lIV$ 'l9a3HB7 A5r* ˕_R *ATA霊${'U ~VA6Ys* u>%b~*ns7D*ALs95r7b-R 8S1lwSql8:bE:AgO:Y)S1Ө3R,lIt|Ϸ7WֺRiFyS9A|JMr^m^Qԭԝ}s}~yjc[*u+9b(b3H{qB TAyW O 'th,rJ t1+ydlA/R ON TAF? fcf J1H=dcK*j8b뿯#fE:R p pO pS ߒhToɺA~VAAN rfn6$t 'l9a3HB7 AAN 3I( rjW(AP wC5+ܡAP rjW(b;TB1A^ IvC5+ܡ P rjW(C5A^$ P rjW(C5A^AkWAs|+]16JF iP]#T+jb[`JqE{"+zUQWw+/WrUm])畬߸=쪨+}ծWb%„frTTϼ+:erW .WEUpsϻRT&3 \k*F73*}eq+gJ;\ΫV8֟KV+ ʷ ΄WŭڮRp^ʏy[]PC]q+T^BWg]?Ϋgόv᮸WOQig3+x;rmWΉWEmKçk-#J?[}U̕]qSe*cѮ3eܕvUqCۡWPoW(C+ۡPo;TB]І?>G%4#t#tKhG/߄%! _B?B?B _B?B } "C׻v"4K"PEN)sU.WEEΞJ9Q)"g9=dW\trU1MwxULt]q*.r mN.w]ߵ<}sL΍J;z .;=YbW%Wy(WN/ٕwj ])zF]-;WU*ElBݮSWK=YrUQot;NܕtJ{])Ng3w8-x*6b :Qya߮"}])g*Jg|.V}rc?*Elr5WŤKnW"}Jaؤ϶͹*&}l>}MHع**}q|>A*MlC*rToU%NE]%4#t#tKhGGҗФХ/IK %tw 7a>B MؤХo&}6#քM]&l7a>Ba MؤХo&}6#t雰I߄M]&lG7a }U&lG7a>B MؤХo&}6#j&}6#TGnҤo~SQӇTS*E0Ix+EXz[_S)ҷk:^1Ӌwťe>Sqy}>fͻ=T\KZY{UWL'NťOߕ"}l@*ELťOOC*Crʮ5Pbҧ{.K["+E RQw> ])gu\wx__Q˩+8R4;5Ó+/*zHm])R~4/Wom4ǽw5+%690?yWKf/>1?=eW颏T<ۿyJBTC>d8(uYGoMo6߄&lG7a?B }~~ʚ%4#t#tKhGG/_B %4#tKhGG/_B?B?B+,G/_B?B?Brۡ_B\%tqV4bVQqcm)L5r6 KT9bISqT|O?n+zc;*\Q)wljҧlGHe75xkSUyi_**}kUߏX=: yWEN#"}qI>'TT 'bSw6;>RO"7IEVH_~TХ/IK_B>B>B&}.}.} M]H_B_o&}.} M]]%4#tKhGGҗФo&}.} M]%4#t#tKhGҗФХХ/I %4#tKhGGҗФХХ/\vҗ.W.}.} }=KҗKmGҤoF?u1mr "}vޮcEJ|}_CťA~+&}윊I_B7=ߴ03=;Sqs8wW|ҪsLLg-R)9{OHy7])b+ӳ3wMϩ+\s+*>>=&aW\\MRQTQ)7|ċ7=zMI]**}nJ駓6 D*twA*K?HN NGRO1/?݋J?Ga_?;K+KR?ʳ%t3wHe*&}K/Z^Q[,hؤOןP)ҧ\R14T)K~ER"}R/au*GҗФХ/IKK_B>B>B&}.} M]VX/K߿wn Mؤo&}.}6雰IK߄M]&l7a>Ba MؤХo&}6#t雰I߄M]&lG7a }U&lG7a>B MؤХo&}6#j&}6#y}-}-BYkhc~G**} WQ)ҧ{WƇS)W4J_9}ǟ>C'?_%CϷwW~;u Xqݩ,$*~* ^;S)ҷ^3X~^"}!5J>kGsVJ߬ב+7GQQiTS*t!v*jzzbbz:G7߽ϩy*l).fz^*>wWڜ'ҜT|N_mWPQӳӀwޚ\36{t RKzט]3©9 *wAP;ڗnwyջ~}Vۡw6#t݄M&lzGz7aӻ Mn¦wa;BӻOhzGz.]B;B׻www M&lzGz.]B;B;B׻ww M]]ޭ]B;B׻www M]]Elw rEzGzPn_Wّ##A*E֖(73訔ɨ^*wzӻ/:[(NwA_qT= ݨ3;5uKnNsӥWT`*tU t:Jq:P+wZ9;bz8ݺvΝNEN%RiNgC8#NEN痭K{異NuӉt[zH啹wT^qTt.t;u:=Je6v[_?z*h+R)N^*t:]oU>hqUT|X*t N2-SLENw RNw LE0:kzG˩\*EtA we7<]VE|kRLL#60?B󿷲Q*oe?u*jQS1A$tA$tALhH肘qE ~|`DBĄ&.. M ] ]  &4A$tA$tALh8aDBĄ&. M ] ]   &4A$tALhHH肘qE ]  &4A$tA$tALhHH肘B1] ] ] dUiG񦢂kpAԇT\}TTui*D;kr>aNEa-nR\QT+,T|MR\QJsE[4WTM]a+,*SQWԛW*s/PLKo^?q럊.vR\QnR)SR1Wt[zn*:J;PJ7VQNT6^6S)ڨSQmTʙTڙ618?R)ڨtOh᯲hO^"F}xBh&_{o <\|A>A:* .1 +IL r  R p3 6W{oe2b59b4ORdR3g̷T^T+ + +WW&4$t\aʄ散+ +WWW&4$t$tLh^I^мнн2y+ +WW&4$t$tLh^I^I^мн2y%{%{eB$tLh^I^мнн2y%{%{eBW&4$t$tLGJʵ~yWE*+u&╺c]*敾JJeA*~:p*OT+Y~ J·J9DJJTtB=JJ·y+Wɤބ2.M2Ykʏ{EeRJIBg<:%3ӛ@d!X*fJ1H]Jd$*jE>+ TlΤtCȤCdKyAT*dN*25yAAz;TŜ϶bhcaP*|U69Xt*E NhNLŴwˠRQS)XQQmHE R9%7E3H8DC ҉%5HUiIE  ّbLR)28JȤNPyev*ߟ3Ix.RK6L2I2y|L?/A&tm<6*ڨIŵ']R)ڨqT6sHjc4m6iRqm< 6Dm^ q*7>%'A~f*TAfA9TA*[7s mK*d*fnn AAdB3ȿfn AAA&4$tLhII rfn AAA&A $dB3HB7Ȅfnn WX 2$dB3HB7HB7Ȅfnn / AAA&"UJ&)76Ri7'|P)pnHjb:*5H:ISQTATڪejT|r2J3)nT g,};JfU^2ToT^1H*̃L mT6ހMONh@h%h*0F$Ih䑊iKƪ4mTKEQ'QqmfT\}Y*JRQ,F%]*LN%𯼢TL:-ZZ RY5>/ }Zi*ff ]O7iF]HNhn3jjyЩ6/HŴKIW\4.UW]RQWԧTO@sE)uEH*>2u喷T+>T^qT  ++&4W$tW\aqń'lH8as ++N\q抄6W$tW 'lH8asEBw +N\]q6W$!Ʉ 'l8asEBwϰ 'lH8as ++N\q抄ϗ 'l8asEB?}f_]r>}-?}ftagf4u[]An>S-S)X`*2=}Ǿ~–J9}羥O*chONTR\Q$3 DOvիTt}iG*˝m,>Flxz)6 *t*ʢ1T6NT6TTuԪmlBGŴ9T6Q*EuTT׷766k/OŴѧR)Xj6Nm,:HE hTAN* ui*:RJbnTӷPRDHETW+uT+u.>WNIP)^Y|q*>;t)ٰp*%NSLsfUbE1}_rQ@*6RSwLES3RpbRqŜ)M1 ]1'lI9aSϰ*&)*&+fBSLBWLBW̄ M1 ]1bbb&4Ŝ)&+fBSLBW̄ M1 ]1 ]1bb&4$t$tLh¢ M1 ]1bbb&4$t$tL|)N芙3l[z\*JQ53nK׳G*"UL]BCTT1a5 9b.TKJ/(d*z,[!Q)gh#TRͪtv*:+6WLk SLO(I<;U6[TT1ȟeK7*Eh]߂kRpULOƄ䍫bz+׏{UUv3iv UQW4gەv2W?^ۧ_qmBWA σ)Al]y Ꮹ UQSvsU m}ṽTLڪ2i+E&m])2i;ˤy+E&|>.*&:cWL؃5ݷ*";TܡLPe E&w2I2y..]&'l2I29a LLNdr&.6$tM& ]&]&'l2I29aIB LNder&6$t$M&'l2I2V$M& ]&'l29aIB LNdR$M&'l2I2dr~L**宸L6̜JIۃ媨L.X֗L&O5FsTL&Ne 5ܕ" Rd_KYd Rd_W&4$t$tL^>y4TLEr]nIxפ^@_J}oP)^Fx岪۬T+Ҩ\#^QTW8{nWzU+)4L2y|L~7M&b2bLe)ˤOLEί١ b: A[y@Ᏻ R23HoL R@*f~۶* uCͫbI3]0OR R'4SqtH 9Q) m6+n>1 b_5H;YfW^;rW^;zFuTT?hF[Q+Em[ѫ'ktZ*E(o]~Rg3TA~|]Mشw*ͦvήmӦtJF} ^C9VmȺP666&4m$tmLhHڸ¢ M~66&4m$tm$tmLhHHژде1i#k#kcB 66&4m$tmLhHHژдее1 _]666&4m\aFBƄ M ] ]666&.kcBFBFBƄ\}^PVie* T6r*E츫ڨ`*mMFTLqP*_BsEX*xJqE+ۨh>b`7+PQ*jT6*DS)ڨn̓ԍw"b<2*57ӛ_ 2k[(ڨRQmTLh.xOŴMv6;tUߧ8M,TUw %W@4,lOZc3|R4&qfUVs Q+mmD6*݉m6m}n6FD6 mtsC$FtE6mHjԈۨt"yTW{Wt<vO ^C__[޸'#^Qw ^X'Fvm>}>G+;"+g"+1QhIH'o 90}fQ' wmWݘ&^Q(&>gH3vtQ)"нb &t &tX0yE{ł+L^^`W[0yE{ł+L^^EWtI+ 1xʊ<%5SV$P1  },ho`0}{v0}{On0}{%'70_"HL_PqӗdE"[WtuE%ANu N/xc"q+NoN/ΊZgw[wz~ Dюd#'/ o[D-IL_qӧwx}DE"nKcO6{QӧWq[Qq &b6'-Ej$:- ڭW$XA#VPq+K+NW VP;V"6$c'bVGq+wQ+ V `YD Y*`P|q+7$+3\+6&VpgM+Hĭ`d &+t+X0YA`8 n0ZAYÿ  )*Э Э@@@  4+t+t+8Ь`dnhVVVpYA[A[fnhVa@  4+t+t+8Ь Э Эסx[fnn  4 ڻhFFn W@7r`ԸNDW"ȩT%zSבdtz3W#707]LJ$7UR1 m!,\kzwK0r{K0rt"f+F/]'zӵIFNlNV{ӕD{{tcK"#NDݛ.#M{ {FDܽᛈ7ߤ7Dܽk[/so#I7S{m!eCeSE$X6}"jv^tMλ)^[V"iA[F-+eӽh˦ꟈ;$vIMD̽7DܽZ9"H7 HpoDԽD{ۙ&b m70ގ97ހހ{{{h m77 &t6@so@wo@woͽݽݽ 4t6@so {{h m77ހހݽ 4tt6[{{{h m76\ݽͽ}qJ{͜}lAaߠy/yfAsY@sYZ}q@&JDD$L."Do"j8DREM_=3Q>GH0Qrq"fn|"3Qߠ9'_G$8'-Ds 3sNHpN&bWOqf;'D9MIs sݭ&b Б#G_v>9&i;LDF$8'r;'m"zΨ :89 I3S䜀 &T09';9L Ω`rN@wNs*НfT09';99LΩ`rN@wNs*НS䜀 &T09'; FT09';99LΩ`rN@wNs*u(НS &t蜀 &T09';9L Ω`rN@ &T09'`s*Нss*S䜀 &T09'`Уsn,;'T )hk&)L+S0[ )^ĩۥoЮ1UsD]y|ao )L+q* uNf;'D94D96snO&&b)ɰsB<]Dĝ_f5sNK_϶HpNɟ1甌GE9%V䜀i٥Z׉G}ꉸG%z1䫊GJR"#)D<0y$tMD^'^tIIo4Hv{]FEG _Qlj3'3'3y#qT0y##L '#m0z$MO[AM is{${摀iy${${ &t4<=@H@H@H### 4t4<==@H GGh iy${${摀# 4tt4SlE"2~N?/c"evN˨1!y;y!z,Q/H2yp|Lļ н н@2@2D7x y%/t/3Цq@2@2mt/t/3Цq@2 4/S0y{eeh^^^fy{eeei н@2@2 4/t/t/3u(^fy{{e/3м н н@2@2@2 ^^f`h^a2 4/t/t/30 z2@2〞 н8'/3м 0x]E$yND"̛H2Z &^F ^FD"eq/h"et/+"TD ݑeeO:{7D˰>QF-7YD 4['a̓_B$D*1[ = `ktD^dkpLDmyVmevN (eA70S3 e`&bfyJ L``ڔd`n`ڄ fM؀n`n`ڄ ff` &t3 @30@70@70 4t3 @30 hff`n`n` 4tt3 L``hf6\ U20@704 ff`n`n`=q@Of`Гd`&v>$ V'j`עf"}"e`*ZC&ɟhy"~ ='~A"O|#"O:?QӑO:OD.t 6 ^2'8'@'՟=OO>""O\;ND!'-5y%'O$O))d0؏cyj4@s@wd0] ?dwQ0 Ou &wQ0 Ou &wQ0 Ou &wtwQ0]]Cv('(E. &wtwQ0 ]Lb]L`r@w(E. &w|&w`rp.. &wQ0 ]L`r@ &wQ0 `((E.. &wQ0 9> ]〞E.q@`r8gwtwQ@ߵw#y14 T*Cp  !t"BHp [ǠOOC*~[$8$?cH"q{ILĮ>IT$99aYpWqw&[:9Mr ;)M@$Oڢc(0Oڢcc(C &P09;1-: FI[ccchS;;6%cchS;c(@s @w 111 4tt0@s @w @w 14 a9;ccc: t0І+;;6\5 a9;;cch8\%t0Ccccha`Гc=9;q@Oa9;;ccHr 4Hr I&AtMDC{D76Ay&l#H p'b6lBxynM{|&= &UDM{MyS%M6amBF?oلYډc@cO {nL蝽`Sgzg ^0uvSg/:;ЇLC|ف>L`@6;{ف ^0uL`@ !`Sgzg`@Sg/:;;{ ^0uvwL蝽`Sgzg`@Sg/:;;{ z8^0uv`sg/:;0蹳L`@Sg/:;;{فy@`<^0uv`cgzg/:{ف ^0uvw%v%v;;0豳{;N6A$tPhgո"nDBg׋p&]!K:{b"wKhzD4D·z=UGRL~/e:?L~P~~~~2XPP j@&~a~A(~A(~A j@ j_Pt`A(Ӂ/?//~A(~A(~A j@~~~~~?`C8~z j8~~~??`н/~At(~A j@ j_PJ_P手?`x_P={)+GD]ް"[{D[^xכ<"u oceEB{Ȧ{kȊx<xW@${3=@=@~~~?}Zzh{6zzh6Laa@~~~?==@=@ Ch~{{Zzzh~~~~?==8~?0z@〞@@@~~?0z@@@@~~?0O\Bz'.zEV5Hu'^:{GH"k%~ow-]^ 6w-]~~KvGDݮtEBW#ґyEvs~St`St`St`@S/=~_0{}:P0}:P0}:P0{S/=Ї ~_0{L`@S/=ЧSt`Sz`@S/=} ~~_0{L`Sz`@S/=} ~z8~_0{`s/=0L`@S/=}y@`<~_0{`cz/} ~_0{%%=0x"2GD~N}*D;~OC[KS@Kk~ >݌#\kM6c~[-/1._턐=@=@~~~?}Zzh{6zzh6Laa@~~~?==@=@ Ch~{{Zzzh~~~~?==8~?0z@〞@@@~~?0z@@@@~~?0O\Bz'.z}?{"5돈{_O${f}"~93kDߟTͷ'c|%yzߟmQ/~o~y{]RE~=@@@@L~{6zzh{6zh$=@&~~?=@@@@~0Zzzh~{{Zo@@@z〞=8~~?0~~~?==@@=<~~?0~~~?==@@%{y=<~?0ەDB/.t{fH5qA ̢Xx?ׄ3}cV_#Ow[ #TK _#XХmyS x?iRZzqX"utJ{${Snϑw ~@ ~@ ~@ ~_0{uL`@L`@L`@Ld ~arL`@S/=} ~@>(}7=} ~~_0L`Sz/}7=} ~~_0〞=0L ~z@S/=} ~~_0{`c/=0L ~{_;t Kto0O\by@`~oWOD}_)?VuDCZ_>EBu;x?UQgnOy_]3}zNǞ^n?#uR=OꜟDgN{~?_t~Mt~{{ ~~?=@=@a~ Czh~{{t~MZo@@@~~?==@@@7 ~{{=q@O~`SzZzzh~{y@~`CzZzzh~{y=SHxX?D%s${U~zSd.t${"T=@=@~~~?}Zzh{6zzh6Laa@~~~?==@=@ Ch~{{Zzzh~~~~?==8~?0z@〞@@@~~?0z@@@@~~?0O\Bz'.zDR6˛Oyh)s'}E>ֵ?"o\ >'^&N'Nk|5i}6z.:o+DBMzxnOw &`jS_05}/<Л| &&LMM`j@ &_05yS/<}@,|> n06LMM`jSz/|>LM && &_05y7LMM`j@oS/<Л| && &_05y7LMq@M`j8&_05y`sz/| &_05y7|y@M`j<&&_05LM`j@oSKlSKlS=6y`IHhzKDҕvùh:ZDB.uú|{HZ͛7=^'w&*Bj@omzzh?Л<Л@ZzzhM`j@oMM~ @o@omzh"Л<Л@&&?К<Л@k@o@oM~ @o@o7 M~5y7y7ZzhMMM~5y7y7&?К<Л<Л@k@o@o〞8&&?0zj&&&?К<Л<&&?0zh&&&?К<Лf&:XM~N$zo#M޿Ɵ6yޙ;{ M^^S_WgZh?ٖD5yAxM}ջOeSzh{6zzhZ/=@@m:~~?Ц@@m:~?ІI{6L6 ~{Zzzh6zzha~~~?=@@@~0Zzz={q@O~`Sh~{{={y@~`Ch~{{K@%{y@~`=~Oio ܛ՚Y?nO$:lOȉ=tg>"H]"0_Z։~HNc"[H~Mt~MZzzh`@=@@&00{Zzzh~Mt~~~~?==@@~~~?}Zzzh~`S={q@O~{{Zz={y@~{{Zz'.=y@ODB'^'}M^gy{_O$OJ`"/Ѿ Y?lOO{`"zO{:H}=D_>{6"O%^zG=mʼnNïWG}$Wם~?_~?cu9oӜΧi>_m׏yFϭ=<[$oCw>V%}<^lE>?gy?={>n_slc^a}c3Toω:ni렷'~B9$;OSMF?_c+{<Ͻ pv$lާ_ޟϮ{{\{yU[krklmz!Okfo!/YGh?} iϧ< l&jle|9ևߚޟ^бd_Ů >\}|}'Y=}+s=&[}wXޞgX^ԟMnr؆v3tI7?ޅrZ>7.G=}< ^U=$y؟}k;cu)/=r9koO$Xj)w:{6{Mŷ*g߳>#j{mo|9G/I^)YNrN\L5%٧sq|}ߞCzԒwqvÔ $/mXCu.^w \۱L{ vaa{ަɖKUP*-,M69'DzcK*V _tYnUw?ȋ&=Q Wr69l^},>I8{Vž?^&Sg%OX6jɟrޓ۫ry{REGsUw[~>yk4Ϸ)Imv{ISWyo|]k:^oo`!~/%_ǿw};_W?wmu ߧ___?O_[k%߳igqZ?xyW^nޞg~N >x}x7Ug}߶2~@ߞMooP[>w[>u߾ϐsuOs:kw@?u2-mr?>\8q)\ >>=N/=}>9ަ8imb"7+8>+ǷW5܏x?|{k?>񭞟ۙ[P?RSϿVuE;q|B;;V_M2[8>q=[io?FtZm.{LJIԿ^G>9>i֯loC мTvLq?>-NxL?rmT2AsTm h{&mjqmx~_=G9>~Tq|b!?qw:;>9>Or?WMǿ+*V?_Xأekvr=}Vt̲~| wwXǷ>^|;}?>m:^8^/f_8s o6u|Ǘ^O}3v?1{1;qCa0oǷϽz<^ߟﱟc_yv|;q{k[oݽ&ST{?N~r|z8y;=y?9jvu^s1eo}>ŏ\9>sǷWkMy;3z<,^pX﯋u;Ͽ|nu)ߟn_ֿxwn;v?:|ue98j~Z[W? M u?~~_n%gÚݏp?ԏMT.=[>O>M>ǔ]>8_7xf>7R:l7v|0㱾s4o\Q4_V|z~veZqu_W? |gκo+= Y/M?v?ǿ_o(v_ۤoյ|Y~+ϟ_~_yז^?ޜ6}~D9"wgzufy]0C}z5zDXa]S"6`"e?b"~K=G²KŷU"D49"L֫~D@|G/Ao~B|_G9"ozÚ$xx1[,m7]pE9U Nz ctȂf^`uxba7Yu؁ ?{ïᡗB;zwS恟29d;(Sou;$ՁgtU&r/%Rx<)`x:p['8)`n/$u/qRX 3Io$uؙ^>w&I3)v&ΤrڙKq^.}gyRxL /IcgvƯTۿHHvDBg=;\N?#V ]6~s}n?#?"ٽ 9vuϗ2uvu@/ʔ;[3w:{GBghgwvwvwvwvzzgzgzgzgh蝽`@@ >N@@@ Cgh}uvwvwvwvwف>:;;;;;;;;@@@@ Cgx;;;;;;;@@@@@7 }uvwvwvwvwففف>:;;;;;;;;@@@@@zgo:;;8)L}`>:{فففIa@@@0tvwCgokIZ;:~7짚p3:ܽn"wwvo"ˍ &:\?엞Fnǧ:{{j~"'DBg[#3;;;;;;;@;= Bgzgzghgwvwvwvwvzzgzgzgo:@@@@@~\KZgzgzgh}uvwvwvwf}uvwvwvwZl}uvwv {gzgZgzgzgzgzgh蝽a>uZ9;{فIa@@@0uvwvwvw>0O Cgzgzgzg'7 ן;~ޑٵnwc jwξ[Bgť%t^ηDV ?X]-zrX{3:{T~Xg,W$tvzZgo5yD"'~nA"Lj}ED$Bm@ ς _ @ @ mMMMMhmmim]h6a &&&&& 4tttpZBMh66a @ KلfnnnnMMMM`P MM}G&& 4tttt0lmB`nnMMMMMh6ᴮ:6tt0؄fNkK @ @ @ &&&&& |t0ۄqlmmm@ @ @ @ @ &4 6666a`ttt00O MMhl0:ВMI6K?g$UDMN.?#&``Կ 6A-`t?d^o-zrЛCLmµ^H 5x^4Б`gB8 A5 n)@C&DyP15*?$`H0u""nyy(@dnnnn ttt0fnnnny8}+<<<< 4t0fnnnnyyyyhknk=3kfnnnnyyhyfafnnn0@3@7@7@7@7_'<4 afnnnnyh<) a`t0;Fqv|}GZ@u DY@u Q$cc[ L'yk?"<OKy@ؑ`ypA$?\?~Fy~H0y0YA$Dy0YA@@3@7yyyh @dnnn0e݀@3@7@7@7@7<<4 afnnn0𚡛fnnnnyyh@3@7@7@7@7<<<<4 afnnnnNyh@3@7@7@7@7<<<<< 40yR<) a0><m]l"z@$z]چ~F'B}J5!XO#zI5'Dh>GG |) "G!DDG 4tQ06GGGG >>>aGGG\׭>>>>bGGG 4tttt1|}}}D#^3tttt1|}}}}@@ h>>>>>b-h>>>>>bS#t1|}}}}@@@@@G4 >>>>b`ttt10O h|09xW mdZ;֝s"G b>{$"H$|2xznd `z:"F 6A ~I"&ڄyœ l^A$]A@m@ @ MMMhmmmm@8nnn0lmmmm@ @ @ @ @ &&4 666aۄfnnn0ۄfnnnnMMhlmm@ @ @ @ @ &&&&4 6aۄfnnnnNMhlm@ @ @ @ @ &&&&& 40ۄyRlmmm<) 66a `B?M[7v$لИ+lBx{̿F&"">u7_#~Kp Hcx[޹Hp PHp __ϑד7LFEc-sh$n-G$VlX*|nA$0*>¯D^t#Nk@$a "#*|}D#0GL>CO# &Q0G})|D# &## &Q0GL>>`(|D## &# &Q0GL>`(|D# &# (|D# &tQ0GL>`@`(|}D# &Q0GGL>``@(|D# &tQ0GL>`)#6}D## &Q0GL>>`(|D##6}D# &Q0`FQ0GL>'GL>bGt^}}M$:~E0"GX#l#>ғXto >ƊBW}ZGEN9>b8h#~K{#z\X"GX#lGD}D_ҫ$.?ߖG5+X7|Ċx@ h|Ă#T2,>bA XP}гG,>>G,>bA XP}XP}Ă#TG,>G,>bA XP}XP}Ă#Tt G,>bA x@ XP}Ă#T#TtG<G,>bA x@ XP}Ă##P|Ă#TG<G,>bA x)@@ x@ XP}Ă#T#TG,>GG,>bA x<)tG,>}Ă#W a}N|ĕgfD[;K3|6R"GbGD} sEwrE+||\!|ޟ|]kC$ߒ|Ο|ĹuϥF+|8"#tҊae<"#tIŊaw"Y#yGq1|}D###tttt1ІG 4tttt1|}}}}@@ h>>>>>bG f>>>>b##tt1|}}}}@@@@ h>>>>>bS#t1|}}}}@@@@@G4 >>>>b`ttt10O h|0]o#l?B"GWDqᇏ 򎏘 aUSMp2y{nvSU\iS ;snNu?Ey-<]cW$tuEy9W?#<#̃.iɊЧQ;;恈fn &tt06<<<4 afnnnnyyh@3@7@7@7@7<<<<4 a5C7@7@7@7<<<<< 4t0fnnnnyyyyh@3@7@7@7@7<<<<< |2t0fnnnnyyyyyha0@7@7@70yRC`<}_>=?lhDyovK$PO yʌn+y {4`i4܉dE<m}<=P[yPA$97`2'٬bEyЕHQe=?=cHz`"'TQKtH5Sr-G?fu|/!|nUB$tEXo׏Xi "GGI>B"#z b>")(,--@n)n)n)n)ڀtKtKtK0Xf)n)n)n)n)h薢a@@RRRRR 4KtKtKtK0X RRRR 4KtKtKtKtK1,-E`)n)n)h薢aRRRRR 4KtKtKtKtK1u"=-E`)n)hbYRRRR ̓`)n)n)n)Ia@ N)"Z #RD1KqK򉸥"6>#j)tF"oIcEKFK0K&zrMKQO/{,n?#RzϿ%Yz[8T5bH5f2Hz`)$"n)| DR.1DR*|:]e)$KQRyRL,-Ed)n)n)ڀtKtKtKtK1$[ [ [R 4KtKtKtKtK1,----@@ hbY [ [ [R fbY [ [ [ [f)n)KtKtK1,----@@@@ hbY [ [ [ [n)KtK1,----@@@@@R4 b`KtKtKtK10O h,0X ߽h)t$K}[ `)Tl BO,ފrER !,. vЍ6b"'$KaHh=o;|)?4bc"j)t`)"RD[H͟,ExHH4[ "R?ʁYb"" ". &wtwtw1І) 6L]]]4 b .....h@s@w@w@w@w]]]]4 b5Cw@w@w@w]]]]] 4wtw0 .....h@s@w@w@w@w]]]]] |^tw0 .....hap@w@w@w0 yRE.]ڊW0 6#]/"]Qw) wz~".tO"]wIwT$݋.P!E}8sVXy]T=wEtD]l/w=|DwQOKr)U$W?er .v>]aЭ,$wtWwuGECwZϼl4 yc*Gd? &Q0t(Gd?6Gd? &Q0ُ~~L`(Gd?6Gd? &tQ0ُ~L`@(Gd?6a(Gd?n? &Q0ُ~L``(Gd? &Q0ۏ~L``(Gd?n? &Q0ُ~L:A(Gd? &Q0ُ~~L`((Gd? &tQ0ُ~L`<)``}^}~:ӑ`?6Dw1!~y=H:`?t"~b"~y=#[ZIחHvID~<-v H0%rH0%7ۂߦH0%'zrM D/!Lmsڑ+D)ыpSR?%]BMo;5%z`J>Cv`J}+Dܔ+D)ѽ'odݾd\,`JSr$)lJlD~ߔqStStStS2E 4StStStStS2L M M M M@3%@7% )))hd fdfJnJStStS2L M M M M@3%@7%@7%@7% )hdnJStS2L M M M M@3%@7%@7%@7%@7%͔4 dyRL MI`JlJ'dJl"fJ'bfEkV$8͊mɊ'bHp"=kyi#znZ'Z NnP"o!ȡ}]#Du`?#yYw"xfE܉ε+F+NDYDLHp"FeE܉}Ăa;YDlI[GgDLHr"N$,8Ys"~5ϊ)RVI5+Ve]PʂjUTtbUTZժ,VŪ,VeA* UYPUYP ЭʂjUTbUTZժ,VŪ,VeA* UUykjUTZժe'bHp"uQDhНННHDhNNNNNd9;݉݉ 4't't't't'2МНННHD^3t't't't'2МНННН@s"@w" hNNNNNd9;;; E%9;;;DDDDDցD't'2МНННН@s"@w"@w"@w"@w"͉4 NNNNd9;;;;yRНHDDۼ#9݉uDYDԉƳ+ND7;XD+~N$8ۮy= Hp"z%DT t$8WkgL1'2A$MΊE$Q $a[Hvmaݔ쇮;ُOF|"~.(+ݿgEP%;*L„;;ۏ6~~~4 cۏf?n?n?n?n?h@@@@@~~~~4 c5C@@@~~~~~ 4t0ۏf?n?n?n?n?hXIhcۏ~~ 4tttt1@ hc`t0`7O`߳I}Owa{HvqwY`?tî+쇮< t$TW䝝Cs "do NĶ}Dԉ6+NTa3H( NDם ND6MDB$8 }"DfX+^3u"v۝INĮ!H]C$X6H*W|QϾnU!\KܵU8&W"n`n`n`n`ha00@70@70 4ttt0 4tttt3 L``n`n`ha00c-Jfd 4tttt30N擁ha00@70@70@70 4tt06AHa00@70@70wn"WD b#H00z``l)}}$۪LD L=K$Wyyg d`exyjH2yqD$xH2Q/ d2^Fq/ENy^FMD1H2mz!遹 m /BimD˨&nK$xU9DK2q{q/t/(ekj?0߱=Ɍ;D m{Nxƚ2BDwDs ].Cth.Ct\.Cth.Ct!e2e2DwL29e2Dw2Dw ].3\.1 ].3\.Ct!e&e2Dwe&ƃ2Dw ]f ].Ct!L|?P.1 ]f ].Ct!L4!e2Dw2e2Dw2Dw ].3\.Ct\.S2e2+3I!&A`,Ig$0'ϋQ`,&A`mɿIW CthCt!]`.0M`.0]`.0M`.0D f Ctf#G 3Ct!]`&]`:!]`&]`.0D D 1xXCt!L4!]`.0D$]`:!L4!]`.0D| 3c 3Ct!]`&]`.0]`=t Ct! fO5fFgw0r&A`E4?ݥZ0 $~LKOgDwer>0L(0 S__}+A`ӃfgƜW˨0 .'0 .91 .26sȉIpy9~wM80qoODѻ31qqe.]Ip՟5^LerQHg\gRNOb\ ].Cth.Ct!e2e2e2e2Dw ]f ].Ct\f#Gw ].3\.Ct!e&e:!e&e2DwDs ].1x\.Ct!L4!e2Dw*e:!L4!e2Dw2Dw ].3\cp ].3\.Ct!e&e2e.2sYQ]fDseɗܵ`&o;2rL>h>L|ۥloLޟ@k& $ݟhvMyޟ2$|12s{շ%|LԋNLȥT3 ֢Ĭ;gƜ|-I`NŞ?&0vg7-s}N.R1qIlV2)~]O\}52v!2k.cתIr_&2̇~LeĴ 'Iv*&٩dbN$;ebIv.;TLS1N$;DIv*&٩d9&٩dbIv.;TLS1N$;DIv1N$;]v*&٩dbIv.;TLS13F!í,;TLS1ebIv*&٩d~313F٩dS1N$;TLC Yv*&٩db3F٩dbIv.;TLS1N$;DIv*&y(;]vߎ$;]v.;$;L\v&&Av#fdGOgdG/`dGn2ye~1$Ɏ(3?ɕsk\ɕ+\ع5ᴇRgˎ;ӿe~(@'.;wz2S@IwOsf ;gӯe维+$;tL(;]璯{f'0 $Ȏ|$ّ;޼LTv53 #w"I#&Avԏ\]v.;DDN ;DDeCtٙhCt!]v:ٙeCt!L4!]v.;D&;DAv.;D&;De3dCt!t 31n%!]v.;Mv.;DegDAv.;Mv.;DegeCt!L4dCt!L4!]v.;D&;DN ;DZAv.;Db\ʎ|Le#Kfd(&Av3L\v>'OwY3 31 #˼&Ivu.Lȝtg{IǍ3 p&3 S}@sKSǎ~?ƒEL{; e?{A2IU?~'=_S?/{W=8~|գ~~8|='='?/{Na襜L4iY]K4'ޓ%Eѷ4;0 ޣD$y+3{ѽCthCtCthCt!{={=D{O=9{=D=D{ѽ3Ѽ1xѽ3ѼCt!{&{=D{&ƃ=D{ѽgyѽCt!L|?1xѽgyѽCt!L4!{=D={=D=D{ѽ3ѼCt1g{=D3yfwar{\&WI~Pk.]p$x<%=Mv&g Gb>?g& `8zB`8 2L(0q/ޟn&f8_vt&p#g4*~w? S?y{? SʥJ0"&n8_uǍopߪ?wݏW1 wN6`8ze&n8i{3 I0|FL ˚Q)bwԙ5$&f8IMvK11Ig.]ҕoL̆ҕo=Q1J^4DtG"#ݑ&#ݑHDw&>rtG"#ݑH͑HDw$;i9cp$;i9DtG"#M4G"#ݑH#MwɑHDw$;Ds$;DtG~`5cp$;xDtG"#M4G"#ݑHDwH#ݑHDwHDw$;4DtG9H18DLw[ my&~E9Ir^Ρ*+W1IdNfr弡?ݥ\9oGIt$L~e&3薉DץӿB%=ui&KzQp_e)&WbtDatIwR9蒺OM.}:'K#? Hus[?oIpsםYtMMɑjgNx;K:HL#d_vxh$Gӓ9HFE}-/?9RIccb>!l&KjbL.HLL1 $Lܜs?91qs"9M4s"9ݜnND7fND7'ͩc0ݜnND7'D3'͉DtshDts̉DtshDts"9ݜnN͜nND7'S`N`2'͉4̉Dts"9ݜ&Mts̉4̉Dts"9ݜ&9ݜnND7'D3nND7'D3'͉DtshDts"9u Dts?%s̉Dts"^5)=II))?I)m&S֪r?LO姾S 6L'ɧg>UϺ|e>}ጉLԧd&ΘRc|J?rI)#O)i1I>eՓK>}>5|>Et"Oݧ&OݧSD&>rt"OݧSͧSD)}j}c)}j}>Et"OM4"OݧSOMɧSD)D)}>Et~6}c)D)}>Eth>Et"OݧSͧ:"OݧSͧSD)}j}>1}ɧ:"OݧLS?1?crftA$|ٖɕ?I)OZLOo3qȘOuKNjO#:ݧF&SѾA&*&^JzcO0 >Sx^(o&GLO)i1S3QҳKO{LO{LOrGq>ɧOqױ>0RI)?Kk&S~ Sz.S. S_蹙OYLܧ\ǘO;|Sz^Lܧ˞Sv1aO.)D)}>Eth>Et"Oݧ:}>Et"OM4"OݧSDSDSDSD)}>5|>Et"Ou >514&"OݧSͧSD)}jDSͧSD)}j}>Et"OM4|>Et"OM4"OݧSDSD)TSD (TSD)18)}$T@?|3S_$TDk^;HLOA)ז vfb>u_q0 >?^DI)I+SLO_a|J?ɕ{}D}Jo$'ne}JIO^|%L)&I2&A¹~$J/cI$Hԧ]I%Oeu$7JTރ`%Oe0 EZD$HTݛ$b$J? I^zDęʯI(KD(K%EthEt"D]:%Et"DM4"D].QD&QDA.QD&QD(K%5$Et"Du 51&"D].QM.QD(K%jDA.QM.QD(K%jI%Et"DM4$Et"DM4"D].QD&QD(KT Q$QQ*&$b7~pz>&A&Ami&AoLޟMMD=z$/vDWD#Ko?vc&.Q\Xg&.Q~;器DSQ==M\%%O|a$JOJI(pDқ3 &I.Qv;s&$&A6TLDe~LDs5qs sODy(?'I˙D<%:yw1 DwND1 &.Q~4 QzD$ӨzrQj$b(KT$QDULU1I%bI*&z(QGI*&$b(KT$QDULU1I%bgU1IT$QDI*&$b(KT$QDULQ0KT$QDULEt$bI*&"MLQ*&"DULU1IT$QD]*&$bI.Q%bI*&"DULU1IT$QD]*&$DUt|&$Et"&;%D%JOg$J/c$]!$HTk^ 3q3y>fM%'A7qbBL\ξ$Qz&Av Qz6(=I(4D&A&AӘ{?>ljI; QzUMDunLDWL=3{1SLO`wLܧ2?&䪙OL)'-&SLܧNo= >$A}Ϻd>m?qw:f>0q:}Jb|Jo$ԇ݋~&SzrSzrUO.)D)}>Eth>Et"Oݧ:}>Et"OM4"OݧSDSDSDSD)}>5|>Et"Ou >514&"OݧSͧSD)}jDSͧSD)}j}>Et"OM4|>Et"OM4"OݧSDSD)TSDSDէVTZOm)y&5q;I)n`MOه9m'5 >e/YSv0$CLgOWO|-B5q|/2&S#Sv&ΔZ gJI:<+* mgZS?KI);jMOiI)!m"V Qk$ eܷ&.Qzz6Q~>> Qv>֚D$HT8_q0O7w&&Q]k&.Qz1D$H]1ML5 _ AI>ֹoOQ<؏]%Jߴ&*Qr`ؚ\(&W$jMTVTڢHԊ*Q+DJEVTZQ%jE(K9DJԊ*Q[ZQ%jEUVTڢHԊ*QDUVTڢHԊ*Q+DJEVTZQ%jE(KaUVTZQ%j"Q+DJԊ*Q[|?4EtZQ%j"Q+DJԊ*Q[ZQ%jEUVTڢH%jEUVTb~JԊ*Q+DmQ$jEU.Q+D}>_&$Et"&z(Q3QS$Hc$\(݇&.QIOk2 oKoK|wML&sRcrŜfsykǼL9K6|\:o9}ȭ9Q=fNL`NjD3s\(&Aξ QgbAmb|%ʽI(.&.Q= 7&*QʺL\5q3'|'AԻD]D%nѵ&A45q4oMyׇ$Qvߚ\D7DrJ5S+ՊjEthjEt"Z]:ՊjEt"ZM4"Z]VDWVDWAVDWVDW+Պj5ԊjEt"Zu j51J&"Z]VMVDW+jaDWAVMVDW+jՊjEt"ZM4ԊjEt"ZM4"Z]VDWVDW+UǠVDWzHj1ՊjE jU/t齾CnN&AR\E&AZkڙ?},߿}MZ]LZ;m1Ije&4J4Ήj3:SjLLԧt&>zSzO1I>Uqݧ\d'w>e $%m%C4h4tibp$5دb#/&.FNYL\"B&I"mbbwܾnI#vM\nD%(Fv6Q1}I,(?g1^HLD1G2111:}b)%uiuDt]ti#G%u4tDt]".]&.]:]".]&.]KD%D%u1xȗtDt]".M4]".]KDץ[]:]".MEIKD%D%uDt]h1uDt]_]".]KKKDץAKI:]".]Wti&KҌI%5 $蒾$me3y\%^ _ؓKZ~&4J g?)1 b+9X^߱|M+D1bNL5_9}$'I0')6sC$US&Sp}HoO~F juWje7_V= j66UjTOb&AݙZLZg6IP+ߚ\z+Vvmbj1 jG7һ0 j~rLTWWSVܲ11:}1N&cbЎэc0ȈndD72D32эFFt#hFFt#FFt#hFFt##ȈndȈndD72Y`dk22эF6ьFFt##&4Nt#F61%##ȈndȈndD72эlY`dD72эlэFFt##MȈndnddȈnd+F672{$bL_o1 F#׎3yKP#ӷ#(bF A>gꝉ`d.=+F䊑e1r t~s\{ax'nd?br^U5fdLҧx1 F33S61q#? }'~g[}Z\>_V-ws׿~M&}֣=$}$$j4`d#F'ČsWa> vM¾&ndl`5 F׮DLOeF'A${DL0i&𙨑5ⱟ##˯sWS˼ld5#cFFt#l#G72эF6ьFFt##&:##&ȈndD72D32эF1xFFt##M4##ȈndD7:##M4##ȈndD7fdD72эF6ьc02эF6ьFFt##&C`dD7Ȉnd?ϧddȈndD3hO5Ds&\Ͳ&ZD{!DM4sZ1D%}$钽etɮJdt.\ѥTL.|Rߚ]0 at|j&K7}&A\%&I&KNE3Q]R$at~^nt:O.aݭ^%~.շ6JYۭ+Yߑ[}C&Aa%ʥ'A>IK3QRdx$HTvNzqר?r(=k&*QsG%Oqe$'._ Qx5(='I+)JޫK4 (?y&&QjLD)1 e7g$J>c$*o821Jx$*|*yWOZM|jEt"Z]&Z]VDW+DS+UǠVDW+DS+ՊjEthjEt"Z]:%Z]VDWP2ՊjEt%Zu jEthjEt"Z]VMVDW+jUǠVDW+jՊjEt"ZM4"Z]:"&tVgjU1UŤVZbRI*&ԊO5ZULjQWԪ&AR &I^LZ .L犻V빿bAT&AVL\3QӨZ&Q±~MZ1QS UVmj*xRMZCVOvZ%oV鍔ZcLZDJoX8cjr2 jIP+*s&VeOV+;KIR+\S$cRI*&ԊjU1UŤVZULjEtZULjU1ժbRI*&ԊjU1UŤVZ=cT+b☉$t&Vz#Vz9"V]LLY=QJ5ݲnYD,[D,[-Et˚hEt,Et˚hEt"eݲnYͲnYD,[V`Yf,[-51`&"eݲnY9eu Et˚hEt"eݲnYͲnYD,[-kYV`YD,[-kY-Et"eM4"eݲ:"e>7&,Et"eM[-Et˚hO5D,[V`Yղ`YzZ&nYᜃ$X^ $Xj&jYaLܲ8.᯷]rN,/&nY~~LԲo_Leq1gI|&ɲD&pIc1|3;덬R1r `YzydYvL̲\˪`Ye6g,$YU8QRb$X-OrZލ[;6,?5[g!2X3L̲LܲJ˞0qy œ^?~`Y9L̲vLܲ}&>ֽ_]b,E_Lܲfg3 H$XV~q-N%˚CnYD,[-kY-Et"eM4"eu Et"eM4"eݲnYDfYD,[-c3Y-Et˚0eݲnYD3YV`YDfYD,[-5,Et"eݲ&eu Et"eݲ&eݲnYD,[D,[-c,[&,Et"eM[-Et˚hO5D,[V`YnYLe]*җLe\LeuSfb}^3`Y6,/&e9,[ߜ&ѲvwgmD-KɕO!drLe)1 '&e1Ie0$XV}co?1Z1sLT:LjUGxM j7acJu&VncL\hcL\׳/ZdL\ ~ޏSЫbj`ߓV<~3njՌzSiP+Z1q9ZřLZ)t1 j'k$Y$$ՓKj5]VDW+DS+ՊjEthjEtԊjEthjEt"Z]VMVDW+UǠVdR+Պj51J&"Z]VdRAVMVDW+jՊjEt"ZM4ԊjEt"ZM4"Z]VDWVDW+UǠVDWϧVZ]VDWTCt"Z]VjEtj”IP+}$~ W+A&AN[3QZELZ)t1qWܜ뱟cZ~כ#&V?4$򋐘қW3q8$^ʡIP+ &AřXLՄvnVϿw5~pVI%YL̲Rc,KM$X-l&fYLܲaLeՏ*]qx: ',ˑ[W=`~z|姹1qUein31soe`Y'eqiuxX&ҷfuۥ I)YޜI,}Ize!sD,[-5,Et"eݲ&eݲ:"eݲ&eݲnYD,[D,[-1Xx,Et"eMɲnYD,[x,c,[D,[-Et˚hEt"eݲnYͲ:"eݲnYͲnYD,[-kY-1XQ-y@,薵Z֊jY+emQjVTZQ-kEղ(O5+eEtڢZC\`Yv֚˲3$XVgtMܲmei$Xh~MeSX-z$XVsƿ$X] M̲k,ˮI,gMܲ]k,J}Ûie}_$XI,;kMܲ45 eܷMS,5I59=gb,ZKT2ZW+=lM\akN [VDժG-ʿVikjg ~šZ}֟81R[V_ s (Vvkjκ&Aƶ&VgOA]5q͍'ol_MTVzuw$De5ikbu*+\6&apYp5Q9[QlEUζ(rlEUζ(rي*g+rEUVT9[Qrx8lEUVT9b< rي*g+m19#rEUVT9[QlE-rي*g+mQrي*g+mQlEUVT9[Ql"g+rFt9[Jr1rFt9hO5D3r6ўj.gDA& &I gI3{uMٽeʙHfLn&.gzƚ\3&Wl&3&A=rf&A~RqMٙk4,ff$hXY_^0&aD0k5li5c0k5li5Ft #M4 #]ÈaMIÈaD0kx4Ft #MI: #M4 #]ÈaDװaD0k564c0k564Ft #]&]Èa?U{G 4Ft #Mk5Ft hO5D0kXǠa]a hL1 Vߕ{L¶5 cr<2&A(k4>kͿyd5+$h]F&Apo&av[5q [I05 f7RZ0?e&arMcyO\'&?Vɕ{m{brȘ8&A1]$Ã?]Qu*&9;+~.gNL\N.ggGS3=%I39&.gm319֟:&rV|  gz5q9oo=`5q9;LVbrv:Y[]ϾAθEBv?=aV nLٍ$șb g%a1q9#]Έ.gMΈ.g]Έ.gMΈ.gD3lrFt9lbqLT*&r05 rVuHt?$ 4Ft #Mk5Ft hO5D0kXǠah5фkbrŲfe2r+Y`L6_$X'ѲZK+'nY_Le-pfb&nYhI?0`Y磘1q;1q{.mwFZVp2c?q뷘қ91 wFI,=f&)zz`Yz}'+$XV}$XV}_aUA{}7 K?L:7I 6WM6wk_!}ܫi}X$W=a~vg4&^~D&^w?ǘ{& 9c4{ј{i= u:"K?`hM{fL{酝L{嗄^\3&^IטMLһ3y4d= Fte#M4e#]وlDWlDW6+ѕcP7)ѕFte}]وlDW7)[ǠlDW7)ѕFtehFte#]وlM:e#]وlMوlDW6+ѕm)ѕ1(ѕ땨l]وlDWTCte#]وlFtem=]وl+$)[M7*[:re*5On_td&AuQ1W6ίO!8NT>]O\̧#$*[c~򘸲k>'l&lL\ĕ+_,([hy31eKeF +dAO٧)nU6 &Iٞ>^䊲1IfC19Uz:Nɩ&֞{ +[߯_ W`O9NNbgv俞${{Ԙ{߆8&joz&`oz&`oz,7dۛY27&/=E7?' ­ۘSdDPp&*r#1"r= "Gt#M4#].rD&rD9Ec09E"Gt].rD09\ rD09E"Gth"Gt#].rM:#].rM.rD9EnE"1EH"1E"GthO5D9E"7ўj.rDA&S E"G "gvDX;dD?ë?w`Z$IsffD_Fqb"$2$9Nܽ"9.zwM\LA&.r~~9gDc"痰2 "ף2 "ד$rzvLTR&.r~(t3&W{cr~D]\9&"5&"8Tʏqr"rs\v+L)2I"ggpDEN?Il93 "EϹ9w?&.r<y"$>L;&A0e"^\o Eo19JMyL9=I9&A+(r3QS]db"wjE."sLLY3y7LLH2q#&D&ƃD?ob<(OGt#&ƃ&ƃD?oGt#M4Gt#M4#DD?_DWID?D{!DTCt#u 7ўjD?cO_ DO/eOOcOOc.:{AR&&.}nL\]gbL\xL\vZI>es_5>I>=ɉI>I[[ n#}| W&w8ɕsK{]\?ҷX&I#&SJ_8uZѷ;2 ҧ1q;Oҧ7J~\ >>I0qS$In& 8ĥ߭dOR`OFJ_+Iĥ I>X&AdUيL,xԱ$SOdOqI>2"}L\.}D&}D>Kѥc;IѥGt].}D;I_ }D;IѥGthGt#].}M:#].}M.}D>KѥoIѥ1Hѥ9I1HѥGthO5D>Kѥ7ўj.}DA&S ѥGLgW3QӷV{L+A&A ᙘm&AD&&}&(o&wΔL\f&wzE3wzw%L\^{=IzDN/dN^kϹL}Lҧ$03;Z+crwlY&WbQ?Ifz'2q?sÿX;4&Al@&wY/?A֜Lޝ<)[[oדw~IL22q7ǘUL\~#;{N7eNo ?_<;5B&AÞ$3 z_~G/+zzGthzGt#]:]wD׻:zGtu zGt]wD׻wD;z7c;z71v#]wMwD׻Aw^һAwD;D{!]wD׻TCt#u z7ўjwD;bл-$蝞7ջB]I&AO&w~LـL5`!;NM:L\~E==q*;Lq 7=gyL~U×'1 I2==Ha$$~,`zz=&]1lj`z L293295Ça&~=9a0dr*[&wgw;|wkyLm2gzF?rI;wgwJ3y ;蝟o/2oŠw;ˑI;}/Iл~ԗA$I;q5wj3QS#dbzwz\zX^bE&.}ߥ#|ĔL\ NIDWAxT*Ht$ NI;$ NIDWADSA U*Hth*1 U*Ht\ ]DWDWA`ǠDWWNI;$ ]*Ht$ ]'S U*1D{! ]I,|&IPAD&Ao=?L\b&~'™ mlW5*42wO+ QgbO`}>LBL HIBj&jzJ`zA& C+ zЯZa=h7 V$X B&n~L v+l'M$+TcP/^fPl8=OmIēJē%~rF9e/Q2 ^$7 o~ O 3QA, '$z1DE)Dd$ATt&5XϿ͍}?AֱL\ J e-weD= / m_\{ ꛗL 껾= ;3qA<{oDQg}DuJ&A b:Kb&+2qW$+v 81'W$+~rE"]81'W\81'W$+DwE"]qbDwE"]qb> HtW$+'+++ʮ+&W\brE?TLX1b+brŊ1"џj*&W\brŇRIp{Ip@5 ^y$ޜIpŠLyak~ם]q Go ^a]og\_zȚW3Wgb$ba⮸αϗ#$~lLT#&_`?bE$$b&抉j3]/f\0h1\Qo8uE=kIpE޻'M'+ Ĭ `g H$XC-Yaty&fkP{f?1+ y<' Ϟ΂W91q+K=+gbVSdVD&n~A6]Mꙓ31+;93q+{ˮf0Z-BTI“ 4H& h$YadVLVX1YadxbŠ +&+$lbBbnBջ& f+kbV81q6QW2kb*H ^Wp$b;c5 zg2I;yMNO[Xwv>ŚkNZa>sm$]wn&w ~kz,IлzBJ;H5 zgglI;S5qԹ⚸)m;E5 zg2Ig+I:+PpMɉD\ }g>%5q;GgMTNAkxOAL=)-Kױ&.}kҷ?=W\Ξqt~Ӌĥnv8Kd%J5 ?lcON\ }!$H]VMLĥOOW\ }v7$Hy6Q3ĤKq[і 1鑣kc5Qb`+A# $A WT\Q rE5-Aj+AnQ j+An1~Aj[\Q rE5HndA nD7ȉTCt$A nIt r= nD3ȉ/ўj& ' M_Mt高Gt雨g&5 /ۢ/Y^6QZ@ۧ*I>Wb>s$$]N$"M5q[S8I?=f.W^?Hmbwc[v&{GI?; kMDw\z@rGI?;;nMz='12kb71HݏrMk88>a&A=̓$Iɏl>MꚘM MO?yM\gbw]7=.o:|-nz7=={}Ms^V&nzY0=Χ?̂qJ.tł V&k⦧&nzΚӳ D-hM)62 $aO靼(Or$1 `z0bzL&dzD7=Mob=sp&*}v3S7fؓ }3ynzd_&.}~J1 }'?Iy}4I_ ;HޙvM{PL\2mb/svx. /~ip?1$ekOO+Iy΁I>^D˨$}z }'}]I_L$Hk$}};yM\ ;9 $}LH;IѥGtu Gt].}D;IѥGth1HѥGthGt#].}M.}DA.}TI:#].}Gt#]&S ѥ1HD{!].}DTCt#].}M.}].}.}L;LuLg$H^2]r&.}rI>= 88>5qb>łͪĥODaDOI>=:gOqI>=p&*}#LH_O.I+ĥ؎I¿nw$}' }g_K>92q1+ę3D{OOC'OgOgﳎj~b; r&k'a3 w?gł w9g.E LJd&>Wr%2z "vmW0q띙22 :98?%C& $\?&Ay?71'71'#Ay?7c?7Gt#&nϯ!_D?o=D?D{!:hO5D?7ўjD?ob?on$|`O/eO`O&A g$Hc6I}(Ϛ;aJҧ'Ĥo$Hĥoĥ4Iv_ A~S92 _c01;yO?yM\ݙgҧ$HK$}7p5q{O/iJO$H^l$I_>PK'21wAyD&DWA Upb&|613q䬐z l'd*iUЏc 'x~ ԫ fwdTY: 'Q0&AU$]kYL '<+%L ֳ? =2 *xMK*k2G=H ?I&Z뙨շvoe7m;#_lj8$bI?%Þ__&n;yQOOdNd'?&D?ob<(O1ob<(OGt#&ƃD?o_D?oGt#M4#:#ݟu Gt#&D?D{!:hO5D?7ўjD?ob?ozfO/$fOɐI~uyu_DOɐI~m^1q W'I>i>N#K;L!?g0q\x!C&~M{2 $]H$DOӫ{O!I?&3}>:L3} S2drz11KdT% }g?fz+޽G) rL)=2qs7 rzD&.r4vɛ3qs$fb"$^Gܓ rLQ˾bIN~#'fDNI9},_AH&A+L\wp{DE_9^.kaD_o=o&AN^Y';y~Ifwc"Gt#]&$r]&$rD9Enb&A‹ /5jDz#92 rVM},5qUV2= "DEN/a"o3q2dDwayLDϮ`D&A2ӫޘkf"W>^$گ'IjD&.r 2jD.p 9=I9\\♸U4νfӻX8ɺuνI=~\&uOZA[7U|#<5L3%?SA[&A$_&AT♨7,j$IS}>&.}g'3q虸%OK_z&A㙨K }zI> p$[KDO?I> }?x$$\$高bI;K3F高ULW1I_$}xbI*&#=cI*&高W1I_$}ULGt高bgWѥ'I_ }D>KѥoIѥGt#MKѥcTCt#].}Gt#]&]*&#]&}.$_L= SdOqp&*}z>KKI>cKOE31sd9aI>^K0qYJ_| }L\t&ALm31'$I^$H$Hީm&.}v9&WcOIշ~xX#3T昜A昜4n6yIHϋg ;O ed$2<z>!??/x&[~ef=Iwl?1I?=|&MOgeL&=gb$H -I@/~0%~wx.IҧabI>;y/\>&.}D>KѥobKѥoI_ }D>KѥoIѥGt#M4#]:#=I:#].}M.}D>Kѥo=].}MKѥGthO5D>Kѥ7ѤW1Iѥ7$I_.9HTS$taLO吉_ܓdzʀ3Q!dL|x&pm? W/™0 oW_K00 '1 ;5=,&:$O@e矋07矏2bzLOI2=:&섺]4&w8;ə}AΥ`p=9|>Gc0 cRoɸG9&~ ??I?|&ħ~}'~2z=qO)L)L931Sk/ӵSedG DEA??I?>I?'3Q0 ןHr89I?I?=9Gtʓu GtʓD<Gth1GthGt#D?:#D?o=MGthO5D?7W17ўj&}SP`LOI0=C&}Ҟ$Kg1qϢcLO|$JfLOo+>5=y97=I0==߉I0>v85==K_ۓdz;I0==I2=p&7=&WLcLϼɹn&'w:&gw#&w:&w{;|MϷŹcvx^w埛A^ߑ#vnzOg2 2 8$05=}ѓdz/K0z_~IgYkX2=e@&nzg^^"wz!wm=IzD<;IлK_{%W N0 zw 1q#]w!uһAw!u;z71R'#]wM:#]w!u;z7zGtz/nEջUVTۢ݊w+ޭzfEջUw[UVT[QnEջ-S͊w+ޭzEVTnEջU(O5+ޭzbһn5 zgM\{51qMGy0 N/:T[t5ӭIp:5 Ng&2mbNoIp:;]oMهIp:=~DΎ $8ҏIp:jwM3'y_rz sqNgI&5tkrtK)ntkfM\>dz"wSDMg0I"gI9;YzM7DN] rxkD.]Uh"b&AN~{I9{sI~d|'e<95 "ga^9wMzm0o-I9y{YDEnEUVTbLv[QEnEU("܊*rD]>$r].rD&rD9E"7ўj.rDA&S E"Gt#ME"Gth"GtD"GthO5D9DnK{ivy9=ícx~6vyZK&'^I4f&ԳO[zZp5~i7&iU9{LfƮ{ۼM~=K4;|MӈiN~eO:zl$x]&'_O;{<>=lM7fIO3k$xZ_uM}a={=FtOru FtOrӈiD 7y=FtOh1x=FtOrӈiD 7x=c4{/hu FtO#&ӈiD4{D{!:OhO5D4{=6ўjiD4{=my=b4{=m=ӈiD4{D{!ӈ6L=||K4K+CDf[TI Z8Ps{z32nWwE=6in7+f➦WPi:y+i~K%x~AM=7Tty^1O)TtuiݬtQ"gTYW\Ψ\F劜Q"gT\tDZrר9NSIrf+Pg%ș]vVrf g<>q!z_LA}7?4M%xZm5Xok6y~F=gW%yZܾ.2+ivUY ߌR V[+Hr})XWtNOU}T ~y^YD 8ŢfUoߺȕG=z>A\YJT*}S\QA*WTJPA$4NTpO*8 6TPלmIWLYL>~ὟTPWR *)WS4яJBBJ* un BFJB_dd*`O+q+B:JK g kR!$Y"&`T. $+4^JBE*W+qЭЭЭ8OVHVHVHVءYa ;`nnn|BB ޶7s vhVHVHVHVHV֡[![a ;C [![![![av!t+$t+$t+$t+ЬЭp ;C [![![![av!t+$t+$ V1V%Y.YO/JBŬ:/R VX3bV쟇ŭϳsg%X.V,ש]1+ԭgŭ#gdPq+#`uZvvzTvzT +T^PQ++Nx.:z׺1P>bv#WG #:ʅHZ_gkve \QA*I+Tpm ]1tHTPW%;TЮs;+z*hWA_K%HTPJP0~9㹢*HUP ?+Au+[*AI?ls ֿQ;>bA}/ſ\bV >zT\Kꮘ ogUwTPgCR *H%>B" U*hH v%Փ    vI ] ] ];40 `yTUUUCV&aRAB?Ԍ0 0 fIGTpIy$ 'TTkN* }lQq fb*NgGUпJTPPSq T\!]Q 8**Aa:._䠂V銪`̈́;l&OU0AyT\$JPTT$Jϐ8*KZ0Y|&+h#t+o} vhVHVHVHVHV֡[![a ;C [![![![av!t+$t+$t+$t+ЬЭp ;C [![![![av!t+$t+$ V媒P'vEPw\Py[/PO+ԯV9_JS VX gT r*nT z]1+P VXV+yT }E*n,CCŬGT*/Rq+eT7m<[++>Y![![![af+$t+$t+$t+0v + VHV}%+0X![![![afnnnnwVCBBBBBBBBPCVHVHVHVءY![vhBBBBBBBPCVHVH>dоZ~S V1R VdRq+]HvŬ׉P V{9R V~T=TW]1+di;y*n>* uG*n>V[wŬy[/PT }1* ]1+mӨU+T|U%YΑ*WTVVxXGe5s8z^A+ W% ^ +R z8 SvEQQ/]$T\}*0 ὍC͡'H%Sq/O%^+&>J#pq/\ ]1A T\}4DO <" b y NIgCR SAԋt<~ 6R X'UIDA]QA}>Io|D*tjcWTaj#?_ M%X~=>1EX?9b+99+#?kJIK?'C*ꬠ~+[J?*t!@<4T:U<(GGGa#t0c{%0_wUC?B?B?B?BPCGGGסuhB?B?B?BPCGGh7C_:4МC9B_ڛ.M{ɐTukN0]Q{}{UIT8KyTս=\+jo5᷸9Qq{Pq{7c+jo:ۛ/VɸMGUInF%`}}%{+_P C%ٛRWv$Ge߫xQ"rT(HŝóuD.IIu-6Dn "3bSģDNS ";RqsꨑDNݏJwϹl/9^UU䮘9;-^\=XK"詸u"}WoT;+lJT,)*AfDm^*5Gel0GeFs]sK,ENK9C<*AK9Es*.rWr9]K%-DNvEENQo7@%x rX99]KEJ9E<*.rxT->^9Q "7>t؞DοDN7"_av9 KD$rvV\ná/m7+.rvV}DΦJ9VWC7v{<ßDξDn-o{g%mcWXf͊ Uf"qDn*r3TPEn*r3T`C*:dVbY "go͊h7+A"gf%m7+Al֬$3ģT"GUEEåJ7[W3-4Vvm2&CdoTdo2%{ʟ+F劽QboT,9~${[|&{dos}uY f`Om^읕`o10`oٽ.idouɪYboT:donononoU< FFFa{#t{0ah-6do#L6dono#L6do#L6do]Fm 00fFmFmޞa&{a7B?Ԍ00fFm޶000àlݬ$eKM+6*IIٶJ\* k6*הelcT"eVetTT9*lLX9+[iYq95FT=dq^yXI4t gaL=*AlYq9e|{ELQ r9IvTJYpֽ,3*l,粝}nTyڨ\4*W*VRIǫKgYIK^k,yZeTeOiQ & Rg%x-H+fX:+l^*\yJZZ福۱f Rg嚧J&Oa48͞6i#L6ii[=mFArӺFʬ\F:R*W֑vE=XS]* j*j@`{J%x_a3sPq9MUNv{! R r38PJ3S rںbr>ހ5T~;.gctᥛl}G9T2Ȯ]ؒz_WӪJsFxe'+==\YI%~O raYI;xuE̶StM)>|k"S FfOR F52{sV5 #W^dd ;52ʕ9gT`dddyx0ȨidduӶYIFvld]s#rȨu:4#0YyЍЍЍ

문}_}oﯟcŤd.A%H >A*At&^WTƓOe>])[ }Aw!I*hWT O*AtE4 }vYqk]QSo _P 7n/:S }+*}5ӶF%H]{V)Q1->f\tV\J0åغrJ]ßLo<]ӫJ2=`7=@3=b&2P gS H%.;銚<>2 3LrHuŤ }.}.}.}w>B>B>B;HK_A]q?I_A]]]:4#t#t#t#t|xХХ }uhGGGGסIKuhB>B>B>BPCGGWaM]]]]:4#t#t#t#"}]Q/U_zRqocOoQ'QqjrXQ{Rq9T\hƦuJi}W+!8HQ2J/[[$}:y+*A2T\|tNť3ҷoOSEOB%H }->M*At罪#tsҤW-I϶OَJ>!J%HCoDyx8TT]?旊K_JmcIťW%IϧGS ҧfFťoס^fzuc/ _Q QrES*8SW=o S슚.LOSTޣLO*jzG| w:@*t>UJp:lW|tJ{T{ئ}Tnf]Q T霯H%[-ItC*A[aWT?*.}>J>݅K_уKPWTt> }6*It[8*AcT*֮`z{Lop&Srx&@PJZ{kLO+V5LỌLOJX{S]tvW|E. }v> O%}𼿍/t}U>I~ H%Hⷄ{S_Io'Hťg6Rqsc~y>K;>݆J<gR ҧ_S"}TH_WL* GGGa~#t#t#t0UХﱝx$0HKKK_&}.}.}.}.}UC>B>B>B>BM]]]]:4#ta>B>BPCGGGסj]]]* סIKKKK_&}.}.}.}Il|W\lj }:*I~&T\|QpWT W*A̍J>. }zJ%H~>W@%H }5ـO'V%Iп$}:>OORq󭜨9vŤ6ۡOqKO[Q ҧ[uŤc<#>{2RqK,H*⮨տOH%n=H%nC%b qbWa?B?B?B<_yP[oFoFoFo0F#ta&a&#ta&a&#tagoF5#L7#L7~a&a-G7#L7#LG7#L7#T~$Te9ԅgST2 G/J@Tq*>?v$:<3 җԒI%N>ߖK җUTL||*.}*A)KϏҗ|J^J>ݰ*IJ@O7J^PK_2QIrTL:4ޭ_лգ.p器ޭ^ARQ >*wcUCsm宨9Tb*AtN;w:+AcH.yV z̈zGhzzK*wQ zWNntE kJޭ4*IEzQFnIF0ލ0ލ0aRGaһ-z7B׻NzWa;B;B;B׻M]]]]:<;t#t0]wwwwwuhzGz7¤wwwڡC;UC;B;B;B;B׻M]]]ލ;rX}W\J;5ª${R zFH%蝂_WTtb*w] wzF%^U띃_WL,JлgNSq0]:܅w?׻cNJ;5B*A]1Q%;_xGn 5X1_-΍*I8[J>^XItbWTtw:J;5*i=Gj஘j*&}F*t>fU)vLřJ0W|2OS9R 7:<KŀSEM'VSq[ޱ`zw%U :Ƈ| gWL| '>I*A>eT\|>Jř\>ݔJʼnl%I_A]]]: }.}.}.}w>B }m2Хo*}3T雡JPo*}3T雡JPХoEf7CPoEf7CPoEfWaPoP3CPo*}{(Po*}.}{(7CPo*}{(7CPo&}³uhz7CuM: f\WdbV͊ۛϛ`ouvj+jo5 ̊ۃ#P]絢FM7+Y xl-K7Yq{y{E̊ۛ7Y fYq{2s硢f$`o& TtYq{ӽf%؛*+jo6kVJ76+mVH+joନ0C07:*ތڛqݬ^MlY 6>eY{yx%{e{Eͮ 2+l ެ{[䒽ٖ yp&-Jc#MuV~K7{3z78+l{a?NJ)/ٛm43+mV.^{#t{ fy@6CPmmjono3t{{do{#t{#t{#t{C7B uhFFFF֡ono#LFF֡j:C [:4{#t{#t{#t{#t{C7B { y{EMJ7=v7RrV`1*][}LM|{Sf%؛-ڝߵNQI"g]yXž, rv Y "߳Dn+&ryVADNG8T\tY "`d "7nRI"gg%]tV\t^QSvDNJ9E<*i:S'*i6VW|6voW_FŜ$r2J9[WTl[P_zyY fW+iŬ$OVř%OyZ:̃iiiiAn4B }niO#tO#tO#tO<====C4B uhFFFF֡y{{{{Zii#LFF֡j:C {{{Z:4O#tO#tO#tO#tO<====C4B &On=+iFpT=OS<ŝiv]QO+J4G%x]cV*TO>.޿$gm񬸜y+ g:J3]O3/oVپ4.1+Aξt}Yq9)m]Q9a3 g:hJ36Sq9T *.g>q˙OLiJ3Q eЙ^Tu\Í*ۯ=J$O=f%x➦u+i_ŻE%}WI{uܫ^^^^hp/Bw/Bw/Bw`4Wݽ|q%0W^^^^^{{UܫCs/Bw/Bw/Bw/Bwͽݽݽݽݽ:4"tar/Bw/BwPCEEEաjݽݽݽ* աW^^^^^{{U܋݋WhEEhաYVVZZu>E>UaZ8n+QVT+jElZQVTVV{WTtOn*VwUZ42*AƨR늩O0jF*VT\|Vi]1 &T\ƨҕTZ̳+njŮTrŲ$˲}Te-`Yn&Te) S=֢[3ZPSrkv_5O$+*a'q6c&T=7x銯~GÞT~re^S6͠rŧb>Ua)B)B)B1Ty|}Sɧ$Fa&a)B&a&a)S#L>ѧݧF|jɧF|jɧݧF|jɧF|jɧݧF|fa&"CO0O0jF|jɧF|j OO0O0OO0O0OO0Fa&"ta&a&"ta&a- :|9J0'ݎڜI/s+3dה7'T㣮bp{sM91{1ϼ_*trUWԜ+q*fN3T])ب#%b{oS s G 5*tw$s4&H:;lIeU%9N:RЪQ PhTE%9/úQIsD-8'9dTҜT]^]9_.DtIkR kbhT.-A>0_wtIwt>2J>??N$+{SW=+\%*K[uiIFtiI/.0.!_ԥ&]¨K#t]>].UtuuuC%B%B%B%Bץχ]]* ԡRKKKKK..0Rv!t]"t]"t]"t]5KKK]tuuuuC%B%B%B%BץM]* DDԡRKKKK]àK)KT.dÅ]8lTR>\RJ%]>@%R}K%AuRvyڰ.U.B0'g4*k9pc7'_ IJ0'Ű9ȚJ0'B%r7'Ϋ*|ZW̜ʅTܜ|V7''7*WH%?>^+ `N:׏J0'S*WH%HnWC%HuUDQqZ.D鶄T\V$Q:iKo&E%HT[cE%J/L%H<"I^͆ʕDr}CUԧҌu*VVi<GWΤtTܽݽݽݽ:̃^^"tz~ZD0W^^^^^? :t"t0W^^^^^{{{{{uhE5^^^{{{{{uhBw/Bw/Bw {uhEEEEաW^^"t"t܋݋݋݋ݫCs/Bw/Bw/Bw {uap/26^㬘m{^zC*t{ETܽs>(KѦ{nku/P|ߒ4><G%h그Ե K*a.{T\\ rT\|QfWTt0B%haOUu%{DT{=lk$*^(JfWԽԊ{9PqrK˭+Nl*ɫk }+^>7Jpp\ ^2qK/K/Uur}*{^TKEC%^ħ+^jKU%^W9*I  SJ{]p #4 G˙OqJba6c̽^9OTVoT\VOZ3#*Atf g`x߇?̵+qY*| `ds22"J02J0q6Y=Ƈ^TO+ndT->҃sT-5]Q#ӵUIF5oXs$uSa-ӰwX@%6#[9# r%xJ3p~ӼiO#tO#tO#tO<====C@"tO#tO0xZiiiiiuhF6iiiuh0B4B4B uhFFFF֡y{{{{ZiiO#tO#tO<====C4B4B4B ua4=c{_m{σJ-A|W6)*l.ST\܌ɮ4L*l~;*lBT\ٖ%(WyP~${i`UIx^.[Wި{2{ӯE\7*i~m&_9_*oeR `N[Sq{s7${yyrR siNT:!KuqtNw0)fp:AW΃ܿ-At1wLһųE?nw3TPnw3T?:TPnw3TPnw3TP w3TPnw3TPnw3T#tCѻPnw3TCѻPnw3TCѻPnw{(z7CջPnw{(z7CջPn9Lz'g%}g7+A+YqӑzgVJл:YYqәz3 }uN34НNw+tzYqәf2Ί;g%8Mz+tNӄf%8Mns[݅ rJ"1+.ryVș̊^d)ϊ\no1;܅`o?4 xAG{HmL-K7Y xi\Y:+iO4ẽb_Jš9ٛMț`odo^1{S썏yRq{/+gmum[T+g s^*noM1+nޖM7 &ٛ\S"[:4{#t{#t{#t{#t{Aס[:4{#t{#t{#t{#t{C7B&{#t{#t{C7B7B7B uhFFFF֡ono{#t{#t{C7B7B7B ualN1+ޔ{SM+fowM_{S+*+fojPw!a7=ڛR HMg=J77+no:yo9P {*non(T|nG%NWL䜐1n9^pV\k[`T:p˻DNM$rxIeTnmJT.ֶWTT%RPCz3>bWR0+A -AV r:uzV\jGDNݯ+*rv&*IS,+Qg+QPmu7T,Y ʶNʶxF~KP6_*ArP%eI* FFF֡}(uxЕЕ luhFFFF֡)+uhFFFF֡)+++[A:4e#te#te#te#teДЕЕЕЕCS6BW uhFFFF֡)+++[A:|0)l:+AsT)Q ʦ0U6}dV>ʦ@fŕMΊ+O]UAů{#4{[߅ r]QSDNђJ9[4+.r>DΖ9e*.r.T\|HLEn[ȭ$r:ᆊ.9qIE\T\V/0GT-^cVKp:*W+Nr:*W*q朡tΓWU WUt98;9s_Ob"a7NJ7EA*no@;-|]MޔyoDnqD ":nT\e/]1[9_*As95{ma&a&#ta&a&#<LuDnI0ȍ0ȍ0ȍ0ȍ0=,r#L"7$r.r#L"7$r#L"7$r.r#L"7$r#L"Q]FDnIFDnI]FDnIFDnI]FDn ȍ0ȍ0ȍ0ȍ0ȍ0F#|0>ܨ$s=\`o5ެ{ (8*foe-e#]0YQ rbr̊YQ rb9g̗ w,q˨sQq9KéQq9KU gG,əNlT&9rJ,ި$91\F%p9TǮ$YF~oT\kaVҚoʕnT٤T*AVgn{E=v.|\ْ5+[Q ؘ*--ͨuKp<5'OәTTZ*KC@wT|[%y}nc* FFF֡y{{{{Z* ֡y{{{{Ziiiii0y{{ZiiiiiUI2B%.튪JAJP+]J%$jxĒZA*A,$]yUI]Q͢.S tRV* ]9'Ju*VR2'+>E%o ><) G{Z݅ QZ :IEUD)R^n0Tܧ`Ouh>G}RqZmO^ɧS&ի)VUO~*W| wOU|}}}C)B)B)B)BSOOU|C)B)B)B)Bͧݧݧݧݧ:4"ta)B)Bͧݧݧݧݧ:4"t"t"t0TSSSSSOOOOOuh>E>Ua)B)Bͧݧݧݧݧ:4"t"t"t0TOOOOuh>E>E>E>E>աTOm>E%uE}JIJ)%-*첰|ʮ@5+S`]1bTܧΨOu'$ʗv$jZ$QWTDQI+m2TWtwơ$ pL6R 3*AԈO9防*i  Q&IܩDuxŜsZ<ɜbt?]߯`Nt"`NA0'99V%sZ9)Pqs0.-KT.-^I/ݤK;tiqHVYKE+tI* DDDԡRCDTaХM]]]]:4]"t]"t]"t]"t]tuiI]]:&RKKKK]tuuuuC%B%B%B%BץM]* DDԡRKKKK]C%B%B%BץM]]]]:4]"t]0aХ,tu&LuEua E%蒂KJ%5,*AtcWTt&Ky%$sƿ%O椛Q ssxT9T9*nN>J0'R c6*t0ٕ$Q6 J(*.Q>%+&Q>D-SJTOvTO`]QdF_EZQ jE>E>E>E>cC)B Ouh>E>E>E>E>աTSS#L>E>E>աTSSSS|}}}}C)B)B)B)Bͧݧ* >E>E>աTSSSSC)B)B)Bͧݧݧݧݧ:4"t0a9Wt>+TRbաSKG֕#uhbԡ<"4zϯPeG١dgTC%Ȏ!K%\Y=ʎnL%ȎNdGGT耋J倮O0٩J]B%Ȏ2MW\vlI ;;dgс+g|cc%ȎЩ5p#-$;6+:i[(kR ޳xWY=a>R*;f)-^=HM>ѱOU+*I`S⫼oy}8/]IJ3mJįcEf{-ren +Q  0+*0ri 0g?θ1fܨptg%pG,՚e:hpŁ&Pz;\r\2222tx]]2th.C.C.C.C.ӡ th.C.C.C.C.ӡ Le:4!t!t!t!t\]]]]CsBw th.C.C.C.C.ӡ Le:|]]]]CsBwBwBwBwee* .C.C.ӡ L2222]JrQ9lC>TJזF}4*i%E^ uN%쾣߻R .gсwϧiq 2:1*eV̯PFPQ V*.0)TCM1^ϱ-A`VfN#鮨VҞ-Wv GP W!yWZ^e_cpT$Ye~KoqY+rp| 0*n8n]QGTԀ@pd GYrpol8[ gpF gppF gpF gpt&a2-C3d8#L3d8#LC3d8#L3d8#LC3d800000la4B7&a2&a2B7&a2&a2B7&h8#L3d8n8#L3d8#L3d8n8#L3d8#Lp9L3d8#L3d8n8#L3d8#L3d8n8#LpF gppF gpF gppF gpF g TT%튩éaNWz,*p`T5VAԨ$!<֨ # N r-VBu fQ [P 3^G@2~<[-+f8}F%8#5ux*Ψ$=awE gT0\y}ZzsY35u[MIkt(yT\kP1Y<ԇ ZSq|HZSǼKrZ3C՚PfEkfZ3C՚PfEkfZCZ3C՚h UkfZ3C՚h UkfZ3CBך=t~J3+Aklάg%hFYq/3+5:Vլ~:+׵fe`V{JpۢeVʥY .3ef?~p.c3xfܚYG"\Ƹb,^anmZӡL.0s(Å xF 0uVtF9+A`zǃz[K3@.x$0cȓ 0.0.0.0thH.0.0CBBBBM`]`FCBBBBM`]`]`]`* ӡ L&0.0.0.0.0TCBBBBM`]`]`]`* #.0.0.0.0thCSaBBM`]`]`]`]`:4!t!t!t0L*0<(0TY cP 0$6T\`4+*0zN%L}}EP C%Mwu|͚Eªg%h׻Z%'v1{v-k+5q\`ldѬ '͊kQ5u,?^WLk6fskV0iJ"qRmP 9dgqMSHSqAvtk6*Iv߯,~^0+}%٩0N&;.;.;.;.;?:t!t٩0N&;.;.;.;.;thC3$;.;.;thCCCSaMv]v]v]v]v:4!t!t!t!tde ;.;.;thCCCSa9t!t!t!tdeeeeCB thCCCCӡNAv:tJC%Ȏb ;AT feGT씃*T興Jwu|dGǐUID%N~]ұWZ:uJ۩vV.\)jVҕ Pz+*;:ˎ^'lV(Q HGe)+*;j.UIA%-dgFN8\fehd?%0dGJ[;+Av1dGR";۩w thCCCCCB thCCCCӡN&;.;#LCCӡN&;.;.;.;deeeeCBBBBMv]v* CCӡN&;.;.;.;CBBBMv]v]v]v]v:4!t٩0N&;.;.;.;.;TdC1|: ;ATQ D%ȎFwEeGٹUYRq ~㮮Ox4*AvtZ$;Mؖ äZ$ٱ}\feG^1fT'*.;zݞY c{BJ::P`WTvid5ˎ5+.;~A)ЗdgqM8'Y|X$yS*Avb*;Ksq q/Ik* ZCZCZCZӡi k k k kM]k]k* Zӡi k k k kM55555֌0i k kM55555TCBBBBךMk]k]k]k]k:4!t0h k kM55555TG]k]k]k]k:4!t!t!t!tде 555thZCZCZCZSaКUkrV(5cr8ϥFOp*Ak4+44 su=ڞqJS_} 7`v}?u)X6~MAjs3{,}>o#wnnvg}w>N/5|l^?Ghyݟ<^>)z>i+/aίϷ:J߹9G=ݡz~BjNE9áZu~7y}m?k*AF{v[콾;m|ķ?ۛd5yV!}|߆5㸶9H/0}#=Z~~i~o[(6}{1 l痾?ps#h=Jcay+e{;^[^vxn{m{۽'}ކ ʫs|=|~S{?{N޷xhjm| D89>N?>=۽=ٵW|f??}]Ϛ)y~m^C&/[(g<~W ֹoV[v~Fv8|o({j޷vV!kX+_s&yzz(>ίPyS.;no C<.j{Aj{QqH2y'9h>u{GϓOq2~gr{::ĽuOlǢilǸuO<؟gO?Ql~0~=㾭{c:9>N'҈yhyC8ZZnc[ܤ9,݆1n"wv{ [YFN͟n1޹^שwݾ vcU=nl&?&pZ0ۼ]6G?ny^:~Nj/˅nq~>ϿoInWnNc@1>;39lO<V޸mHݮ ݶ(ǟyݮ 8Sv]~_}s"|kn}u{?7w?3g;kc{(yN8nq#x;61odoܾ}3}c|=-Nfmޮa[[wFo=v-1\~=~{ݞO{1^?m{qVn\Gz{a c |?ϳsW#޷SѺ}}ۛ+mۼ]??-y^}w~m߲n}=[=>Y>oC>c\0^ov?׈~3oWޝ+wnĿU|yg&w?mϳ*c}H۟w͋6juϗ{T=7wsk!;og~x.~ {=n||?]Ǖfoe-x*^5i$Z9!8"|ǚ<$3kv-Ǘ)9}s|w}o 9~W9>}v{:~qg|wӞd8q5g{ l_b#߬1?~ r: 7rѹ)ɧ}s|9qc~qk?r?=8)?_Y~ƪp?;}ݿq7JCFYv\73G,gs^nw~|s?n?ksȭqy?s=V{p?:>|?}\u۸q|~1]}c;[fM?6ȸ_g￧hY>N}8/ڋSk셗/^8 u|Y++7z#xiǝGD~9߿k$WY<>iuO>^oy=nn9_ק>ɴo'O ~ͳgl?< :tۗya{,8s>=?zSڋnYE~|>du_oy=_Nwx?fL8_gz9ӟs|$O:fkgyԏ]ҿ73}|N7O9jH_n|x~tٿjBt{y[Fr}=;^ͅe?t)>>_sr?>]^뻮z 8>9y_y^X|~Va+N=>`5Kol]q?8~=;'O9Q,{qY9r|s/9n__r>>5Ig |kj6gOzǧu|{|\w7V>po㼟Ʃ\?kLuZ3ǧ:>ߦO7]ϜSߏïy}gڛ 붙/io5ןV08s| l~V~ھrȓ+߅yqtIz>!miǹ/~/Wa܇ㄧtY޺-]8>zsg\Lu[8q:^^9>oKF{9fS]97%>u^ER^.y{/x}ߗۺ_Xv{^ty/q^z?k}oU^>2nms9g,%ZZYJ_k7ϴ?O?m2}OoAה roYqF˸z;=??Uw@d߁UfkfA ߿Lz7%pˤqC%'p~A+ .3injb׀̇Nie/} 3%b渣] @?j薙~觜 Rrj:W;s 2c9S ]25 s1%-Vݧs&l+S` 11S u.CL1C̍vK;-#}Xح Mʾ3CR:2øzs*d5 e|+Vnje@7v(ws hFis yAZ0iFisAZ0o$sAZ0/FR0isAZ0o$MnAZ0iAZ0iP)I ӂ3L fäi ӂ0)aZ0ô`0)aZ0ô`i8C)G?/o K}%@)C@)m! hӐG 9{%(,PH̲`~_2]]ތѥ$i:H논H -z#i:H -7:H -z#):H -79H -H -^äiq 5La0ôiq z0-aZ\yq z0-aZ\ôv5Lkäiq5LaR\ôiq 0)aZ\ôiq=Lka^\ô֚R\ôiq z0-aZ\ôR\yq 0)aZ\ôiq=Lk0-aZ\ôZR\ôiq=Lk0-aZ\ôv5La 5t-hŵlG{uW z~>Jq: v9^F^^\k5@)e?< $;ŵ,O,N3(ŵ00-e s>j<V;H -^ȋ덤:H -7:H -z#)z!/7:H -z#i:H덤:HJq=Lk0-aZ\ô& 3Lk0-Iq u0-Iq 5Laz>-aZ\ôiq=Lka^\ô&5Lk0-aZ\5Lkäiq5La.5Lk0-Iq 5La.u0-aZ\5Lkäiq 5La.5Lkäiq 5La 5La 5e-ۊk&MRh{K׫|]k@k Z(ŵ<'ŵ@+e hq-c :}._\)m3>o>-el8R\K5N~_\OYq du׃ދk +AV\zoAV\Yq z{q du׃ދk +AV\<Yq z{q du^\offV\offV\?{üכYq^\offV\ô̊{/7z3+7n|V\offV\of6fV\?{e3+7z3+7޻YqכYq`MfV\ô̊kʹכYqכYq`fV\offV\?XkiqכYq`fV\offV\?{qכYqכYq`z3+7ދͬ̊ͬ̊mכYqVp-7zR\˞֗c+/u32,|kJq5J6R\[58V\_KJqny< ZvIyNQw~:fJq-K(ŵ#2@+߷'Њ>(w@Z -z!/7:H -H -:H덤:H논H -:H덤y:H -7:H -*0)aZ\ôiq z40-aZ\ô&5Lka^\ô&5Lk0-iq 0)aZ\yq z0-aZ\ôiq=Lk0-aZ\u0-f0-aZ\ô&5Lk0-a^\ôiq=Lk0-aZ\5Lk0-ֻ0-aZ\5Lk0-6܋k0-6܋kװR\`[@Nhm2L;Hٺzhs-AtJqAN&Pk@+mXqm ZƑ(ŵ,F׀fJqmxg Xq}g; vAZ\iqW; %wAVtޛN'E{+oY)_IZo$~AZ/FRi~AZo$] -#)R>L |0-I>L |äi? |äi[=0-aZô&>L 0/aZ>L |0-I>L aRôU(>L |0-I>L aRô>L aRôi0-aZôi?ޥi0-aZôi?^ôi?^ô?xi(VUߞ 'V!3MB)_ϯ/$ ߌ _h/'P |3 ?uXo (=O:R x܌-`2V#Spx 4q`dG1$PtyL'e$$ÉT2ppa@ ",@2$a# A*T2,a# A*T2dRɰtST2dRɰH A**aHJJJJaAddd&!%L%0 0 0 0 n|*`*`*`*ddsS0L$L%L%L%L%0 0 0 0 D2T2dd&aHJJJa/!%L%L%0 0 0 0 D2T2T2T2T2 kw 0 0 D2T2T2T2T2 mKJJa wSsɠI{@\v&P$,_G"g;! `30rOt90pY_Ϻ~M'Hu.^'Ptwͅ׀|[g @ae mE'CI|>0V[`T'CI `"LX쀎Y0a@ '[ɐ! dRa! dR!H%CJ!H%CJD2dRɰP D2T2T2T2T2  L%L%L%0 0 0 a.`*ddddvSSSS0L$L%CKJa"`*`*`*`*dddd&!%L%0 0 0 0 0 D2T2T2T2 SaHJJJa"`*`*`*`*ֻHJJa"`*`*`*`*6%L%L%ðچddɰn\;{M2d >  ۣ(a]|SJE>.Nd >DeݵN/,\%z{쀪s zб `kJ$A]z(a]K{(!u\=07p`; pZ]^>%W8V-;lHE=oʍz `rcT=Hq `"@=$a#QAT=,a#QAT=zRt^T=zR UA*aamzzz&!L0Q0U0U0Un|```zzsS0LLLLL0Q0U0U0UD=T=zz&aa`\=T=T= SSS0LLLLLðzSS0LLLLLðچzzzVpW0Uz Ir^V!l>XL=H.= )Pú._ρV'ry;CQ/4bL=na]/H>ݥKU쀩 Wj-ꁷjC(VvU_竵@QJtS dL= `cC(f$Pԃp *(\EM=U=$zH@FT=zXFT=zRa#zRa#QAT=,T0Q0U0U0U0UäSSS0LLLCa```T=T=T=T= Saa```zzsS0LLLLL0Q0U0U0UD=T=zzz&aa.aa wSS0``mj) djEߐ)SW;v@Ճ z8KDy z8)W M=,sYEĽt| zRQ EE=HՃS*7p+lNtzm+pk&A&%PԃhlzM|-3e(\EE=ezU.hA\E>P zHC U zHC UAT=l$ UAT=l$!HCz&at[```zzzsS0LLLLð[=a`\=T= SSSS0LLLL0Q0Ua`zzzzz&a0W0U0UD=T=T=T= SSSS0E=T=T= SSSS0``6Lszr1lzXgh<T=V Ǘ@Q#gM˯suԑc@Q M=ظP`C=(ap~z 0p+r~i4`$AwLՃA;#T$Pԃm!+PԃΔU*(!#[^]EtHE=P`U$PԃMH!UzRa!WzR!HC!HCD=zRPQD=T=T=T=T= n LLL0Q0U0Ua`zzzzvSSSS0LLCa````zzzz&!L0Q0U0U0U0UD=T=T=T= Saa````ֻa````6LLðچzz}nK!CzH@Q}O;!Xݐ3$v@Ճ HՃ\=\=;`ᴚJo]#W.2$W:6dT=ؚ `6$W?ot$Pԃm5M=5;`A$PC/5`+b$PԃWd>P |0p!0p,w@D oi"JVF"/T^Ryb#Ryb#A*/T^,T00000äSySySy1LLLE ʋa"/`*/`*/`*/T^T^T^T^ ySyb ʋa"/`*/`*/`*/sySy1LLLLL0000D^T^&b ʋa.b ʋa wySySy1./`*/`_-^.-7E^ AS6&S6 7A"&/STq}_\22 kh~rf07C5 0B5mKhI2\SHi 14,>Jh Yk 6a%{=TSȞ!'M! Ihbݡ_4ޅA)ޕkTS z    ;:   544Ek{4f)63M`]LSlfb3)63Mi jLS<ػ4f)63M`z>4f)Wi jLS<{p34f)ciLSlf޻V4Ńw63MiLSlf5f)63Mi{Ilfb3xwMiLSlf5f)63MiLSXU[0m/( 0@Q6cTixpva/hJeҰ%0&J#f` ]ДlJ( v y%2S"(JÆ_$Py8<;{( w @Q@p(Je$hJ#HqGEiQQwTFv(;*J㎊RqGEiQQPU0UXQXQ0-VbEi,VLbEi,VƝ5XQ0UXQXQXQ0UqgMi,VLbEi,VbEi,VLbEi,VbEiTi,VƝ5XQ0UXQXQ0UXQSXQw֔bEi,VLbEi,VbEiTi,VbEi,VbEiZ]bEi,VLbEi,VbEi,VMi,VbEijޔbEi,VLSơ4llxEiX+ ?S@paI(JCVh P6] e)tW( 4d MihC+u2/S덱kҸ4w)wp#`v@Ҩ'J# TqxD׀*̇'Ұ\ !(JCvK*z,JCHEiyu aSPhJ}C 0J# HF* UA4Til$ UA4Til$J#HF*&JJJJJct`4`4`4ҀҀsS1LLLLư[=* * * *a4`4\iTi SSSS1LLLL0Q0Ua4`4ҀҀҀҀ&JJJJc( *0W0U0UDiTiTiTi SSSS1EiTiTi SSSS14`4`46ܕL( }ěҐ `! A adLi8\ir\igӗs>_4d Ҝ)4lBMi3d#]`%؁_+Ҹ[i$ДYq|i4a_v@TIJ#4w\4dg ҈cה9\i9M( ڲ4lHEiI( Y-@QyҐMU'JCHMiH46J#HF* UI(HF* URJc4ҀҀҀҀ&!* * *a4`4`4\iTi SSS1VϧJJJJc( *0W0UDiTiTiTiTi SSS1LLF+ *a4`4`4`4`4ҀҀҀ&JJ#̕LL0Q0U0U0UDiTiTiTiTi kwQ0U0UDiTiTiTiTi m+ * *a wSsO) @Q6=W5WZ) @Q?h4T$PM?X4Jyr ;JƁ$P H( @xbHv@ē~Fi$((>P 4q(J֞Y4́) + ۣ@QYE_JCҰ)w a %J6nJ_O4O_U6%4lXG4>eTi\8T8R!  2)RdHg+HUI U%* RUdJ*****&,aJ`J`J\T USUSUSU2Vϧd0W%0U%DTTTT USUSUSU2LT LUIaJ`J`J`J`J****&$U LU LU0Q%0U%0U%0U%DTTTT kwQ%0U%0U%DTTTT ma wUSU+{VT*խ$PT]`PUb $઄'yaǗ3*-!pUb)khDۣ@Ħ$PTH@Q%%v@%7(s$ UrUvkt$*I.4CpUV_JTٛ\I[:uL[I@Q%6.W%TI{7URۇJ̭$PT/YJL쀫1 *9I1 @9qI1 RZ%HFUTVYhaU`U`U`U`UI ZZZehjj0*0*DTTT VVVV&ZZ%̵ L0*0*0*0*0*DTTT SZZehjjjjaU`U`U`UVV sSS2L L L L0*0*0*0*0*Z] L L0*0*0*0*0*jZZZeXm]TV^~nZŶ(H Tϻ(ZŦ'PJ;`ZE$ZELh̿y] *}Vѵv@Ihv$*kEPb$vsӓ$vs_֥1sdv@͆Jia b09PRO^y!lV  *PꏱsoV[Js @Q*P@= r/念 85b&"PL$ʉW(ŌK.X (A*P6%HBE SSSS2L:_0(0(0(DTT@@&eحO L L L0(0(a.P`*P@@@@@&e 0(0(DTTTT SSS2L LJ   a"P`*P`*P`*P@@@@@Z"P`*P`*P@@@@@Vp(0(0(jV nB(֩M[ 0(<\hTXq@(N.PtREChֺ(qpmEqIf(:T&(tp>$"Pl@5xp?RNw@@*:lLOhVYf3yZVv }{/5u3p0ڶ7]yV1uUte\2W bCeVi>4R[Uj״JmEV2 V9فM*[L|M`6zW0+o)B)5 ]3Lt Lu Lu Lu Lu0TTT ]S]S]faz>5050505DTׄ&fak`k\T ]S]S]S]S]3Lt Lu Lu Lu0505ak`k`k&f050505DTTTT ma w]S]+ƺ?wtL0]*bƺ 1cwČIC(bƖUHôEz;b*K|&bFl$bF$bFGA쀊sE̘Hw@3 &f@E I(3[ cjEw]lrp٢v&-:UjTLe@-S.[t\[G-62"[l"S.[t(EG3/@-h!;/+&PdKmla 2rpӓ@-Wrpӳ`D$I@BE SSSS3Lo0U00U00U0DTT&    fحO L L L0Q00U0a```&    f(*0W00U0Z(***a``````` sSS3L L L L0Q00U00U00U00U0Z_(**a`````````6 L L̰چFXe HJP*Lhjs4*=FE.8*Z@*Vѧ bdUtV9v Vх}pshZEJuXĝ$Pdوl1]xlљ^; %ȖuӼ\osI l9x] i-2k d.[t՘\l1&[T$Pd-@-d w]l@-oE b:'"[L쀎l_S0 FMMxPc&L`* f(****a}&   & L0Q00U00U00U0n|```````~tQ0a```&    f(*0W00U0DTTTT SSS3L LL+**a```````& & L L0Q00U00U00U00U0j   fXm]TL܋PW0 S0`G(?U0Gt #Q #`dMP#32+[Lc`d9R@Q02؆@Q02؆GL෻( F W0fT#+$,5+Pt0505050505äS]S]S]3Lt Lu LuMak`k`k`kTTTT k}k\T ]S]S]S]S]3Lt Lu Lu Lu0505ak`k&f050505DTTT ]S]S]S]S]3LtMak`k`k`k`k6u Lu LuͰچ9kT5]>@5֫5oL&Pt\hz1k$Fb'Pt̮"ຆCuTפV~p]h}XEȒ2P]cE|Eذ1s@5&SV{kY}+31z z[EW[L<\̨JŌT s01ӯ&fovF۷珵HPv+`bFq.fT$PČ1@3>]Lo mM35kbM>3 | fVa"f`*f`*f`*f`*fIbbbf0303DTTT 󩘁zĒ&bbbbbfa"f`*f\T 1S1S1S1S13L L L L0303a.f`*f`*f&bbbbbf030303DTTTT ma w1S1S1sz.fT} fhb*f$PČiE̜ޗI[~x*fdaE̤Gp83Z$PČQ;bF&Č̈JUQvyhvb&"fdAE̘&H5&;\Üg3Nv/wG=\|U^kI ?HQ0pӿ`t|: e }( &uy w`L$ ߜQ/@Q0fw@9\ cswLkJ*\tGB  505E(P3L:080808DTTℹā&gحO%L%L%L%ΰ/'%L%0808080808DTTT SgHJJJJa"q`*q`*q`*qZ8a.q`*q`*qāāā&gH080808DTTTT {o7SSS3.q`*q`*qp& 8AEX*&q׶.q_@8f}(Ǻ #&$uw@%u( &q;:?* PFr^>"qdEl~P~=*F$IH~M⬚׀Js2 >(g]ϗJ?ա-0OjGSE6Kz4# 6pw(j\ s^?몺f*1g][=@Q;6+W;v@Վڑ ڱݱ#)O@ՎL@S; j\#҅O9"""ht!a`` s S 4L4L5L5L5а[=j j j ja/_4Pk ja````&(5L50@0@0@0@0@DTTT k\TT  S S S 4L4L5L5L5L50@a`` L5L5L5аچ:xT}0 4ȅt@$ЄG@'g~-|@$Є.@ [:5&|R!b('ǯ %N c2`:ULZOFG$ЄevDM^)T\1Kx;`GLJټ yM6m0cpᣫc%POᣆ("|NU(Gv"PՓ@>T=A<1C@>fmx>ү§Di >äSSS3LLLO  a"|`*|`*|`*|TTTT k"|\T SSSS3LLLL0>0>a.|`*|&g 0>0>0>DTTT SSSS3LO   a"|`*|`*|`*|`*|6SSS3.|`*|`*|q'ȄHFl} q`Nm^¾u,lz^vOJt9Lz%QH>8 BsT^@[JS$Ж0W` hǬ8& %P0qA c("qLJ/ <0*F1%P$NıEp(&@8:GlLVB@86(8|^3'"q֯Ie QQ*q2Đ{pMKKP$Z0(%P$[*ql$T!$Ή(Ǵ$ 8äSSS3L$L%L%NKJa"q`*q`*q`*qTTTT k="q\T k"q`*q`*q`*qāāā&'%L%0808080808DTTT SgHJJJa"q`*q`*q`*q`*q sSS3L$L%L%L%L%ΰ^f*q`*q`*q6%L%L%Ο"m1'H%NJ@$9 |~ 4cJܿth ߏZ36Nw '9 |0*F%NE؈2t'AXhFmJP%P;6@[XFJ @%(s$PԎy\=cz(jӮvԎPSqEt1@Q;Q@Q;fp#(j6(Mb|#m@Q;3g]vlIک}vvZ S;G"̄G0 Td CDTT&rrrrhحOLLLа/r(Lа/rrrrh!!!ʡa"`*\T 9S9S9S9S94LLLL0C0Ca.`*`*&rrrrrhȡ0C0C0CDTTTT {GlrrrhXm]TTo.T R94Amqeyt@C[(rȞX@*3W 9ŤK(r(N(r(kz5M(gǺC* |U˳C 49$p.t\P94]1 @>( Y`Yy_ PQ;vtSE2 %_vEԎڱy c )+_^]`hTzb ѽ(j|vtPHڏvl^pvۯ}@U;4C/T&j'WSE 2E "QNt#h]#E>"a 0S$DTT&hحO5L5L5L5а֗/(5L5а֟/hh j j ja`\T  S S S S 4L4L5L5L50@0@a``&hh0@0@0@DTTTT {slhh0@0@h;*(H;HMiWhh}k?qN=P4@@N9Y  d2(hu6>ck~ZOi{h /,}Tݑˡ!`@C˜'9!'@% $Pкϧme4zFs~̚2ZC.׀*#+|JExh& ) WFZjȍ=ʨT27tRtR}2r)#i:?le2i+ДV)#F(#[sDQF6FDQF6FDQFH2RɴMx\2)C*LZ IUYM/-VLbE/-VbE/n|E/-VbE/-VU/YKkuBKXKSXKXK0KtgM/-VLbE/-VbE/-VLbE/-VbE/T/-Vҝ5XKSXKXK0KXKXK0KwbE/-VLbE/-VbE/-VYKXK0KwbE/-zv]/^ Rzi#i6>K |` |vG`Qm 4B5zI&b$TR QFR%^ځ_2J]X@E$Z&l)TDR}#M$ QF2Hf|m %@I("i}\V/"}^u yT$~£("FJ%"E$I60"m%"IG%"I'%"IH?"td.tn.t08&"I%"Iƭ@I$T$ƴ$]ƏDI6"DI6"lIH2YEy"iWD$SOk"IDR.`*HHHHvSSSS4UE$HH*"`*`*`*HHHH&" ")ELE0I0I0I0I0ID$T$T$T$ S" " "i$$$a"`*`*`*`*H sSS4LDLELELELEҰf*`*`*H sS3t]$L$L$L$ zo6@I~"dT|u-[&𯵌N)2z^hkZF~~߯eDkP$c$所$PI"} تE8C>'(zIf%'^Z.M^ZWi}JחJKtZrKL$}.6"D("IFU%D|WTD %#"pdD("IVi$SUHQU\${IT$;rIDRY*$gHHʘ-nDINHqR;"IDI2)OK6@Kf(ziu}\_ࣗVkȥP~?\l"\:S["ԃIt1^tZktN&pZkL:ي^\:2\:NJ~\:NX/E JI'暏I'^ׄK']b't>$tw¥~$T:t(4&'DN&yOdIN#ҩh*vSSSS5EEj```&* **ULU0QQ0UQ0UQ0UQ0UQDETETETE S* * *j(((a```` sSS5LTLULULULU԰f`` sSS} R**HUFl5%=nSEE0C(*ʞ&PT=MU>/UQp*bp\Eeg^PeETlplU)>IGk@T9~٪n*h%>T | vׂ*2L%Y%Q $˔pd[;N:\/N hzɦP?l5(z9M/ٚ;Q )E/ɾ%R$^qS$^){;RYUdBDKJDB0K0K0K0Kn|```0K0KZPLLL0K0K0K0KD/T/^^&z z z z zi%%%a`\/T/T/ SSS4LLLLL0Ka``^^^^^ޗLLL0Ka``z)HR KI^J%$ ZI^_E/ٰ^2@KvJ%"W<^ &`z-'PR HJ˛.zVE/-uu]X+PPW@Kt6N:Ygl*VLKMϑ@/퀎Z?ׯF =\^SQAmӺ2lo5UOTT}{MEJUTNY;*R%PT\E}.{h4ME0eKpe ps W(U0MEƠ(YK}'\E"QTɫIT$\Eپ;QT,ZEE3IyE(O("a*ȫIvSASASASA5UEP*"`*`*`*& *L0T0T0T0T0TDPTPTPTP ASA j* * * a"`*`*`*`* sASASA5LLLLLհf*`*`* sASASAu7.TP RA4AMPI""ے@TfV *3Z;ʌV.C *a@Tg * *[@T֛OV*֗ \P\P鐬2MPTj(ʦ&JG[$}&j}9=W) Ae3hJ$&JA@+~@czkX(] .Ӻ3^_~ME-LUNMUQ(*J'PTɮ\E[;`*JXԎ%*O(ۚ0s I%HRyE(yEUTo2yE([bDQQ#QU-9 UQyEE*j6v%H:Ļ* * * aW * * * a(*LհViASASASA5LLLL0T0Ta.`*& j* 0T0T0TDPTPTPTP ASASASASA5LU * * a"`*`*`*`*{6SASASA5LU * * *HU TIThJ& AeFk2@T6x>"l`.eמ 2@TQ6T:F+"lҕr(,I*3Z ]MPTfpAso(J!W;`J\%Ph@Ά\%P I*(;ʆ\%su\md TUT(J'p&JJ*\P*i;$[EPٲ RUP_vT>$JcpAe[DT1jJ!QU%8TJ{$TP߾.Iځꂪuꪠ'$j]ӏ T0T0Tnob3T0T0T0TZQU * a( j* * * a"`*\PTP ASASASASA5LLLL0T0Ta.`*`*& j0T0T0TDPTPTPTPTP {l j0T0T0TU TA*6f#A@TH S *UEEX e w@U-_@QQBFEE٣U |@E%PT ZOTTTEEЂ ըv@U@QQ6#lJEEH(;*&%PT 2wLg*TA&@EEUTTEE϶dlL:@\:رt2uM:It2ME¥"I"$I7 ҩ7 U:ټv.xw(p餻LBmBH[Td*&jw5]/\z0K^vW\;.B# jX= smSm5E[T[T[T[ mSmSmSm5LLUk+ja````& *̵LL0V0V0V0VD[T[T[T[T[ m jh+j+j+j+ja=T[T[T[ m z墨 Rm*HFl5m%(ʚL[T։VOIh+ %P(k+Y+lzAhI;#Hh+M[J;T meuYt]! ;j]}z҅u(6ZK.Ih+!s%Pٍ2M[Vhs%PF[;]Ͽ8U\ YT[7b TJL[ RAe!(tZ.t TAeJk&Ti(ʔ T: ~j:'*$ႊq]DT6&s*,‹"Q O *`$ʨ(~8 VU=$Q6,`umˬHꔢ\qTq kJQ\0U\0U\0U\DqTqTqTq S k(.*.*.*.*a```₩ sSS5LLLL0Q\0U\0U\0U\0U\Dq₩₩& k{k3U\0U\0U\Dq₩₩7Tq Rŵ4A@&@qU:&C*"īT8E>T | vS(*O)^EPƯ/kT6i:o%T *+0A**ʆ\%*JSQB{EEYݙnrSQ6o)WQ1Z e l 4e+f/.*TTEE 촾F[oLj#TuAeN-2TE[NKhmUԷNBHµIµnHµq+uyIR5 V&ߴn`KhJ*흄j+]$\[n/$\[$I$QPmeImueImU;}U[އ1MfU: fࢍjaǿEfW(a0U a0a0aDTTT S: :l0000a```Æ sSS6LtLuLuLu0a0a0a0a0aZ00a````Æ6SSS6LtX00:,HuX a R:,HuB6#/kR@|%ėtvŗ,K6&c>P@_RN$ | V/X;.@e W e(_jppX݄{( \| Q^;`KOŗR@_6al2S*lW,ɞ+&\ *6GP_}k ߈HLq2UfIҁa$\fK,4Ie}Sˬ~i2Kw<$2KppC޾%Lf8/Ef-Ef0K,}Fe%,{GȬh[7='̲$̲gSIeHYCZI9.&Ѫ W\vI4eRIkG:00a): : :l00a0aDTTTT SSS6LtLuX00a```ÆETT SSSS6W00a\TL>}f&63#){og63)YLԋ={۱N5&(nLVz$T;QXt;Q).ŏIOPA&w-T˒[;QllőEuU{$~;q'Et('FoG{vi׈j8wf{r7l@qO"hBnC5rM=eIfh^ fhn mzn&໡hn9&໥hny&{nɺf໮hn f{]΁&h w 7Tq7@Ww_0Wwa\mf.]0Wwu55a6k$NKUEɜ(NV tQgwwB% tNI'+K:܋Dt#aFK%ݿއqu Sw:GQwZ{&a8˥턫;Eɂi;`'SDQw2QDQwn)vB՝ʾ$ev;WZqu'Z-nGL,E[]z2s".lDГ٨OzLHz;zY_ݞD\$BLĄ-9z6o"E.Gk:Hz!geY+:SmH=ՆD&l~SfEqǨǿ$z.'ϟw|_)BO)BO`"MR =sqw]HGąЅЅZe5tt7ЄЅ^`z@zM]]]] 4ttt7ЄЅ^`z@z@zM]]] 4tttt7vhBBBBBo{s = = & sB/̅f&\腹 sXzYSB/DzU za zN'hDz3/sW z/?$S =[y'ЋZjBONIQEejh%Q?yz!_pg3w….lpǼԒ_ zȖ=\E̹pgKՑhBOf>&l(BO%DzXN 0"d#{4L4fKi>=t%Q4ߺt[FZEw\t;oF\L$R46$R44%Vo| &϶\k>JA5? i>\M5D"yV"EO}cA~,9_cwح[Oj>|{E :~MW$R4*K"?q2q"t "t@a"M~|LDqtt7^M]] 4tX45@|@|@|@|@|M]]] 4tX455@|@|@|@|M]]]] 4X455@|@|@|@|@|߻]]] 4X455ROE so3|a\[hͬ sV4>yKh>mwB5(OD$Vۉ/T &&Q4p瓇Vh>$|L4f.lɢpN9\y͔DzZ%B?&BV{$Tx]׏:6 zNW=[Pn! zN"(BO`Eɖo7݉2nOa߻a'ӱy;aﰎu}[7A_^ݦV5{\ڭo] d=~Qͷ㺧ES3H5$R4Z"E~O|ZD\[5\[D\}i>fH5D"\&&R4/yA51|H|al=эiq+)O?)Oe"t0DL72DS3H|:fH|`D\23G555Z{5t(֙$S!غX fj#|鵪ШcYf 2) 琙oBb>|29d曐!3Cf>|9dS!3Cf 29ds7!1Cf>|2MḨ00Cf>|9ds琙!3߄C 29d曐Oa`>|n|ʘ1)c댘O32f>e|.5ʘ%M|xE2^Gu#0 |K7p;5`54^PǔX|t@ѱ|ʈ:#;2;8vC@w8CP@wHX ; ;:pLw<߯&Hwu_i;\bٍ@w}jC*NR#T ݩqtF;oF;Bjdz+{L݄v?B@:$JWx+{קH3\anHYz' î0m;RHGgL[Z+tY+t#+t<F:,x*?Z~B:\ #w8k%ãty*tpTlUaqD:|(?D:|s!#CF SB:t&$sH0 CF 29dsH琑nBB:t2MHH琑Na@:t&$sH琑!#݄t29dsH7!!€t2MHH琑!#CF:t9'dsH琑nBB:29LH߀;"+KHҽt^YBW%/H/5,!+KHG{Ԫi05HXH;M;ta4:]gs#!lCC_Sjz%8Z xz'@̗̇n>^|̇+̇3=`̇sV1oF`>\֫F`>O|5^|@ 0j 0j$9}j\c>50߫U#2_bZa00_`êwꫮ/O {8@ gH ̇3\ ̗?{k @bc0%L 3k*1ov'QO/= 3 3j/e*|y7T%1,H\ G0UJb UaWab 9d01+Wab>|&01_*L琙|&01Cf |&01Cf U|*LWab U!3+Wab 9d01_*LWab>C U|^ad UW|ʘ1)c댘O32f>e|.5ʘpHG0 d>A5  jg@}I`m`cJTV#1D d>O+̇̇;tf]5S#1_{x6Ը|`ۻ|a5ݝtn#0w2@Ù5j#?̇`̇0 d>A5`5b_5 ^;\a>50_52U$|JM9U& ̇2J`>AWȩ̺w t%0b+|S!c]a&)Ī_TaB;my~+Qf>DWT~|,90V+SA碮ˣ|ɵ+8B̷a >-C&AL :dTH!D2 :dt$8!C&AL'$t$0C&AL :dt$IpB"AL2 :dHPa AL'$t$I!C& @4C&AL * $I!HP2&AeL *cT$I@ѥFD|%O "#c˴ ˃ ?Q|TxhI51Iv$5FK 6V߽w#o2Ux_ ?{_ ƿQuh X7ra5!F?4F?5ۍ§ƕj\jd#ɛǹfR(_ $~J AJ o, O /VvIOZqIp $J A\( 2`A/? Oj NItI$H$xkq`Zrg;"$S] $㦴fw$HTxW ^$'ή$gFL!`=F0 N :dt$8!C&A2 NH$I!C&AL :dt$IpB"AL  :dt$8!C&AL'$t$I!C& t$IpB"AL2 :d}x:!C&AL * $I!`T$IP`gDʘ1 *c,`gtQ$H3ƿ zF?Awd|HjCJT#0R|n `L =F=1F=\ = [z8V#>W#F= "uenWWz8zNMz0X =$F=sοz0Xz[7hѨWP UMezιoz.޿ϕz$ɌSAɇbb+pCW*S!c3vA8@[z<#וzy*z@•zx"+ z_~G>~J=p*?ZxCWuWQX0m.qTp+p*x7Wc\ Ao{c!2_0EW2MHOa?9ds!߄29d!€2MH!Cƿ 29dsoB?29d!C?&|N!߄ 9ds O2ƿ1)cSWYO2ƿhufոt9RF]ga.#7@CS#Ƭӝw.]QwV#ncpo7pLw<f&o}j6`6e=l kAl k`6%Mj69j0,5!u S#nF`{)˩جox4pڏpnpue5F;z%~ 뙹\a6sHl6!ٯ04=W4Wjs3M@ޏdhj 5 ÝB@>/?S+ԦM͕d8ٕd{>   @?E ýL\ @Gp3Yf *SA Cs#0+d\!6sw9A\abS!ۄ'C&6Ll&$bs搉!ۄDlbs搉mB"6Ll2MH搉!C&6Ll) 搉!ۄDl29dbs6uB&6Ll&@l"=&6gHlΐ؜!M 3$6eLl9Cbs m28Cbs m28Cbs MY"6[L0nbQhLl uHl4t#F 6:5 &6ō@l4؞8 &6}~F 6/F"6:wDlǷhnLlUs$g`bo bF'XĆwnb# -Cu#[e/7=a[ l֕-+]N3ɺv2#3t\"]}_\>MkeCwy,th$Pt볏 ͟x;A&|U~C0ᓍ0ݡuJ|]Kt/kaY$}8Nx־Bt;t%i_W!fow#sإ?ӿHw ;ܹǍ@w4ύ@wNnzn ѹn"tG{ι莖N@ ;\FZOHw4 ݡu%~?}|+ۥMezp]ك\قTpnn>Wt0+;niJtG_ ]}j>ω@w^+LwyQ7~u3+1{7k+Lw}SACsnsMtJ;Ծ iаNy¿k\ѹէ乼h.7+тG'N?BW5>`_ G(t%y+;*?WLse_klg7V6$+{c*] hrWz8|` z|CPӕć&tHPlѕ+| C 2:d>t|8!CC2:d>!CĊ:d>t|!ć :d>t|8!CĊ2N> !C >t| |P2Έ1*c>T|XYΈ1*c>T|2Cėʘ;ˑ2Cėʘ+K|Wn08j>DpT#!|#5HI8SiP}sn0;-j5yFCOKs|c2ܸWԸW4>wP+|FC؋ύć4ȇac27zjD> !+qm+Q] |ʞ7V·ʎ_Bp]?J$>SA>y>|^Wwȉq+ٕ̇tOWdOCǩs)WqOBW39W};IxTyWF|GW+>ĩ02RNг/Ю0] |Gq Tu#U #"R0DهN%bbTs1*~n5NH1+  '$t0CH @:dtrBH  2@:dRaH  '$t!C ߇2@:dt9! 2@^ 1@*cR2He R2He @*cT3He  1@Vv %j jQ!*p@4LQ#".V#j\An0*ҳz5IT#"T#"ht{ Pg^fPNu#סr:{4SזqC/5?^^}i *$]7H5*Jͩ *GW*Y] "Pҕ4q*oGtʜDW+ y¿>t 3e¢+Q|y"Rҕy_C|> J<1%>-Y]a>}_B|ț˺|ȏT |&>xS!>|ԗ{3r~q=+̇ϺxZ]p3 |OO<뱧|Xb]1JCtMWy%+̇{*ć+q[HUⶐ>SA>I>Dt%a+|o^ۛk SC 25* 萩!SD25:dj!SCFL'$jT!SC 25:djt萩qL25NHԨ0PCFL~FeLʘ15vFԨQS25vFԨQS2Έ15*cjTQ2FeLʘ+Cj<2BEevPC!],PC5@WPZtn}iBzFº] ehۺPHPXU5֠'!ON u<=T#NfQZF;^7;J;<=@w8a;J;m*Hw}Cq%^ueGwa+StN}se;oBWSe|GW3S3=]atH$8S?mфx+|F+SA]a}$cZ\a7S!;~0\a9\acbtS5Vx+?>͕VxGiWy* >{롳+z 9J~mCF A<Y@oZxvIQ# $ ,2@DDT# `ԖK\@:F&;$ȻvHQ@Ȏj0 j}A$ȇ$ȋ$XHxw;"AM&A^%J swJ A 0$A W "Hs% )T6$(+{7tI!CĊ'$>T!C 2:d>t|p 2:d>PaĊ^Äʘ1*c>P2Cė>P2Cė*c>T|3Cėʘ1V3Cėʘ1vF| .j>j0z, D5J CU#!>`!!X|D!~! C!̇|.pW~{. [no`>dFC2C^:JC<C^ !ջ`*ć,>Dq%!r%!Rʎ>r]21N=.s\bmSaRaܖrFnse;-5ַ\Rc̷哼F~_vԸ+Q'ԈG gI˨|jeS!jW[]_w?Q#>qļLXw%Pc]|E>5a0S!jc<jc\aj\5_ Siw15s_U5jg1 :9<΍Ԉs]ajdt%PƦ˾jăH\IH ]IX]BS;5Ԩ0PCFL5:djt萩qBFL25:djQaFL'$jt萩!SC o &djt萩qBF25:Dj,bjt Rd@ΐ!5:CjT8P3FgHΐ'jt Rd@ΐ!5:CjT8P3FgHΐ'덳@tt#P#]HsX}/ FЍ@nj|i58 F. F) F\+4 FL7qi'7q4iLl FR#R#MT9*HԻ+L] (T5֎] HF:+L[W5vr]RyQQtrF;?4+[j<*D>ﬓ] H~vlJF4+Z>ciQAj̷+iFW5H4萩CC;DjojCmI;X$3 H: HOFHx18SsdO$.wwW#$.;*t%$-* $ncٕu}~H.+ ý~U yz+ q"T i%vWvU?xiY+:"ᣲd?W˪owv஌nM]?5-5sKonXF|Q+*[jlƠU t6l5'U Hv S#uThV%P#,w|Ơ5b05LkL+|tFnw?Q#^ӕ@y,] Sf1#5滆H$S#M Rccj:\Ȫ$0C @V +LYa @V!+Ya @:d0d +LYaH&0d 2@&5>R2 1@*cTYY 1@*cT2He  ;#TRde ;#TRdg@EHtS#$  HDN5@@ Hgi59j05njsԈ8Fz=;4qyx75ޕnjąL jĽ+`jj05]E55GF S#߇\aj=Ɏ Q#Е@xە@xF^9FJƚQy>A+[j\pHq&yz+[j\8m*>+x&tYL@Wx䟚7[j<ӕ-5ʖ}&j!6a{(\lѕQA(oV{|8Bo4UK5R|C'(D B!v牻mB8X^ C!JB^ C!?o Ba}qp3JPȫV\ P{S?*alB][@v%Af]0ud(7C 5(t!C 2:d(!CCB  '$(T!CC 2:d(tP萡p  2:d(PaB  ^tPC2Be *c(TP*c(TP3Be  1vFPPC2vFPPC2 ]B5 ;K$H45 u$Y?F5CW# ΢ 7$/5 A3 /V#  ?:WIewIGjj XHg T# pyTmnq0 2<$"]adEt% *Tyf+Lo/;# GHѕ@D:QeK鸲%!\at%A$T~O~7;\˕ \ٟ~-Wf}ȿdD w|?BC:}+qoWHWQ]a>p &a}_=-1CJC\k=C~۩N[s%!C^0C~J?ah||t%!n8Cm] |ky+q&W~|09]CW2NH|!C 2:d>t|pBC2:d>!CĊ'|!C >t|ns*c>T|3Cėʘ1V3Cėʘ1vF|P2Έ1*c>T|XYΈ1*c>T|2 vF$xd8Or}S# <2Ŀ#CkpF`>E f>^F`>\ d>|3`.jŻAը̇u IN=\aY0kWxǩ gWx+|2f>Pl*|f>"J`>Jb> Wv̷T2nR/+ HxC-Օ¿ m~)3\2¯l ]!f[WS0ѕ||nũ >M?~9F~tY+|8Е|8P|Lb>|5d> 3o 3sT 2Eˮ0񁋮ky~Jb>X8f>;Jb>Ҡ%s!3߄|29d曐!3Cf>|&$S!3Cf 29ds琙o 29d曐Oa`>|au`>e|ʘ1uF̧O32fuF̧O32fΈ1)cS12f>e|ʘ* 12f>e|ʘ:#ŞO32fh2Θ* t^dtWr5/jGj0@Yjt#85WnLC5:cӔuK5ұONHǻNI0zfWY}'p+W|C #J@:ik׾}ꗰt'_KB =%!N* "R+\ HWၼSׂrJ@}ՇuO3v%v lnA0WtTeOwRgl?ooY= =~-;X=$UWNAGtAFŷ,=J= ޿տ\.| SA@MaJ%)* _5^]&"@~v%>2 ^Ek\ q\  +p*z0;bxk*&W$H#Lp0`T2:d>t|pBC2:d>!CĊ'|!C >t|ºʘ1*c>P2Cė>P2Cė*c>T|3Cėʘ1V3Cėʘ1vF|P2Cė*c>T|XYΘbFCQj>م >Ǧj0LHj>|kI̠jTSQ7 "] %]aTdt%".VvQr*|J@EtHWFESv՜i?SAT]aT] ;}PJ@며SAT)TͮTD$q%_!T0!+5]2ϕ=m .s\{pҕ-.ǾL%.;H…#]Aj(SA(|sW9(|7%(|&(ɂ$(_ǯ}$\a(*P/_v' Pɂ(ɂS!(ɂ(H=W 7xT CW$(Dt%@aPH9|P8ҕćđ\é:d>t|!ć :d>t|8!CĊ2N~3!CĊ* |!P2CėH2Cė>P2Cė*c>T|3Cėʘ1V3Cėʘ1vF|P2Cė*c>T|XYΘJFC.j>DpT#!>|C53Z5yh5y/nK5y'5"V#!TLjWy+q+̇<7T^C^JCGWYB|JCGW~[ćxW !`0C^J+Rϓu%!T~LW*"Rr]l,|*'干G3ϩG,iWg=͜ޖ̞s]R㢝lqIWvԸ. R#Nbu%P# +qBW51NS!jâ\ p}[.W@H7F S*Q|5iljԏm 54FW5~'ÞD8F碓S!jݡ!S#Q#]ԈWS55E55EH8P#P#rqF{ҨԈGèT FjDjqTN8#D5Ua9ƕ0SO{q%Pc765 Q#oJFF>ŕ@x_7FIWՕ@jR\>c=`*HE+L{V7Ϗǹȫ]ajl-~x-l$j$jӮ\FWPT6Ԩo=5.US↮lW[eCΟd Х+[T\ҕ-*;Q橺PpQޕ?ل-+qDU*j2ѕATyDW*fTԳ'/|,ހ?T;^ÜPjvQq7&JoOQvQ4NPq3L+] CD|Q19]Cũ*:dTtȨQ!℄ *:dTtȨ8!CFE2*N~k3!CFE** Q!ä 1**cTTƨ2FEe+ 2FEe;#TTƨQQbg1**cT,bg1**cTPQ2FEe;#TTƨQ T,!ć(Kj7 # (, $Cn ";HPo2RDlfN/ԕ+]xٕ@x8+WfN$J AJ AXR#C]a9SAߘھ/ oإJ"AO ڙ J zo?ѕ@x+q&T~)i|*hW x\q*_ʕه\"AW$x߾7L: :ܒ`f+[}lIpQĩd\H7et% 2+LUc} nd $] $XGұTq+Qy"A\ H' ?'tI/\O#G"Apdx p% >U%nHHW "NIq%`gw#Hp*D2 :dt$8!@2 NH$I!C&ALL$I!D  :dt$< :Ct$ Ip2 AgHΐ! *c H3$AgH :Ct$ Ip2 AgHΐ! *c H3$AgH :Ct$ Id@ΐ! *c;CsF23b#k3;g5tGiHt#7/!5"VGv%-J;zޕ v%]c ѽ@WPtt绳/; pu7r%]z0|?ol;-viMx-H]aõ] tGk?!-?Cܕ=ݝv#t\wTLw]-2GЛpKwgʖsʆxڕ-Gw%M'J;ZRTλ设`nuz.Dw4˹+Lwx8QAHi/ڧ ҕ@wZr t u;wx;*Dw+LwxHz~4_ץJ;3xTh@] tGr ]Hw"ut!ۭ@w"utw@w"ut!]HwG~k3!]Hw"!НC:d tNӝ2Έ1)cStWYΈ1)cStѝ2;eLwʘ:#StN]e:#StN]gDwʘ1)cStѝ2;eLwNӝ2;eLwʘ:k2;eDw]AnԸtj\Ajd}Z*G:W +WΕt4uHkW& B s;+q|GJ]W{G9J8J8\!ەq`*q<Εq(x0.]a| xj+ o,7AWչ$xh] OO.52X*<3 'Թ6J7X ە+67 o29dx0,%xsmB7 o29dxveB7 o&$xS!ÛCנ;27e o:#xSM[e:#xSM[goޔ1)cxMÛ27e oxMÛ27e o)cxSM[goޔ1U37e oޔ1)cx댮%ޔ1)o; G jxuRGٺξ՛1*pB+\axkHL͏]+ oO]o:B#823t9nCDT%p +q?fL8g8\a;UWG^ +P\ G;u9g8gM9ֺ8O-ǭ3\r23o*[ɕ-ǝ~r\Ww;TՕӱ]ώ;{ѕ+78:+ WWW^W9y 鐖ˠ p+p08vBW&Dm\ 63]a>=&M9g8'8-'ot%ppc4 q q%qou%p\_㸩9ds9!s܄*qC8q9ds9!s܄7129ds7!qq2=p28eqʘ:#S9Ns\e:#S9Ns\gqʘ1)c8Ns28eq8Ns28eq)cS9Ns\gqʘ1U838eqʘ1)c댮%ʘ1)KW@jjl 'ʪ9=*q+pe+q5x0ͭ>0ٹ] TJ8K%\6 o:JWcv>s+r8Wce@Wh+[x[Lv*x[וo>3 W'd7:Wѡ;] J7v[,=*qCs%ps%pʝ r(ߕqKq0ҕ+Ji'SaV_ &qJ8r9#,~u#pn{@!xTL?[?? `QW '$(tP萡!CC o&d(tP萡p|!CC 1*c(TPA2Be  + PA2Be  ;#(TPPCag 1*c(,@ag 1*c(쌠PC2Be  ;#(TP*c(TPPCag 1*`(  oǯ~HPrc*ҋ] PQSp%@!>uPudW y!_Ip9SAA+Lڕ@u>S!dEtIg$=Dv%٧!߫\aXW 0 SAe0 2_U ^Hį=o$A'͕@8y̕DdHp5Wv3Sul)_y"& x|T4֕= [D֕@Ty+Q] $XWO![ C W "N$J"x*M$r] $Hs =U.s r$۹HпW v5R 1~$AGW {&$(D UPH(tpBB  2:d(jBB  '̷A 2:d(|ގ 1*c(쌠PC2Be (P 1*c(쌠PC2Be *c(TP*c(TP3Be  1*c(쌠PC2vFPPC2Be *c(TP,A!]ẁPW^5]8FBũz52oB ŕ/AW"G7 -`\y%1TTqy+PcW;U*C¨ȳ+]aT;.WR?¨x_ﳮ쐩kʮ0*|? ^ Vr%".sQ y.ˀ|.+}kgs%⿺Fj/" "ov%b}[zF'TDt%b}TZC%"̜JGTDs%"J:NyfWߚk%Tt%"ɟ "MMt%"N@v *PqŐPO ./Q*n.|o߄xT7u* 7޴ NR RyT <*W*"]NQ'@Pҕ{F%bʨxx\BEW+*:dTtȨQ!b2*:dTtȨ8!€2*>_1**cTTƨ2FEe+  1**cTTƨ2FEe;#TTƨQQbe;#TTƨQQbg1**cTTƨ2FEeTPQ2FEe;#TTƨQQY@E\uQjT ^8 ":J@EܟFEKWyDWY;BWḢ>yx !7&!.q%!U | 띂+̇|?2CC} !/vP{دH9x-'=">{}X}1Wa<,~/mޗć8CQQćxֱ+)B|+]a>dVrd|j] |:\ ̇jhpg Nؕt\G-K˔] |[ Ns]2'ѕ8Еh0N-Еx+y+?<=ws%I|=8Pm.t+W$ !>t%!.p%F-Wxx*ćǕ!aCܕܕć{ȇr]a>!CĊ'|隐!C QĊ_>2Cėʘ;#>T|Pae;#>T|Pagćʘ1*c>P2Cė>P2Cė*c>T|Pagćʘ1V3Cėʘ1*c>P2CeẆ4Qć|jD>īTq+qJCѕ8ҕ8 ]!e w?_B|<=$BZW `iU8ڕ}*ݕx C!s+ |T~)Ѯ0j7u*W eW yr+ v{*;+ L(\a(d A!ϙt%@!Ҏ+ Q0] P`]a(dٚ A!+< ~8Օª\:Hĕ?;L3|~cpǔ~p(ĭaP6\a(?\a(0껟-Bm}Pm6tK|-\ {N8.= J8/֕qFUorm. /9nB_!+t%[xU&61^|@by U Ć]ab U ĶFbˣVkVDl*LVa" UM̄*LVa"6$[*dbyvbSĦM[gDlʘؔ1)cb,[gDlʘؔ1)cb댈M2&6eLl)cbSĦ@l)cbSĦ3"6eLlʘؔ1)cb댈M2&uFĦM2&6eLl)cbSĦӔuFl,ل@6!Q؄^ &dޚ"rWIW,6EW&D Ct%Pi\ J0| S30ҲTĕ@ag GӮ YqSzW¼%PH'=,d0s)+LaHWDa~0A Q+La<Ε@aؕ@an0Q+SA î Í\ ]Q\a l# O/x&+6@2\rJ2|J2<1ܕd:Y+I2̕+@J2<ÕUjVpN+d& Csg,;W&D  d+ d̄ çQS! cSs%u%qВ bq%.~ fd,m`jS!6sl!Cf O`3f&7<29D69C6sl l2`3gfΐ͜!)c6 3d3gf9C6sl l2`3gfΐ͜!)c6 3d3gf9C6sl dfΐ͜!)c6 3d3gfΐ&6sl dfΐ͜!) lvf"ul!YfGW/YWC !A،u%+=^8Qv%S;*f_W+ج+`Qׂ;zw% ]f] lFw9] lF{b3 J`3糴L{f=M7a_lS!4#KNlF8f3t+h&PWb3Z+،uv?+hWWd3:׵+h\`\W֕f2O6|1$68ra?Bl<|,i Or<=R;Wض?7l+LlrGu%.] Ħ2Ćֺ8*&6W[WmFWu![Hl"a9bbC$#7GLl"uVwؔ1)cbS2&6eLlʘ* 2&6eLlʘ:#bSĦM[gDlʘؔ1)cb,[gDlʘؔ1)cb댈M2&6eLlʘ:#bSĦ@l)cbSĦM[gDlʘؔ1)cb댈M2&MH搉!C&6LlU!.^vl6;M Yϕd8P%\ @Fo+u%@in]IK,qQׂt%gg]2W`mf"2q^ƹB@a6slto*6(`C4i۠i2}Ӕ1)cLSƘa24ei* a24ei:#LSƘ1McZgiӔ1)cL,`ZgiӔ1)cL0Mc24ei:#LSƘ1i)cLSƘ1McZgiӔ1)cL0Mc2ƴMH1!cC4iU!cC4i9dLsxTi$Lmˎ b\ Fyv%-'\؃k3Ⱥ Ѻ0MKW09*iLv0* Ӑ\ ئBfLlWx+pz+iSALuyLCs%`1t%`mDTp֕+ЬT s\aL}Jkb44P+vJh||s r\ 26ĴΑJ4J41Wm~ 9BJgLh0$ TLLÃ0 czT4N<J4:6+6τioSyǴ0͕Dl[͕@lrm*)C7 o[o2^!ޔ1)cxS27e o* 27e o:#xSM[g4RM[e:#xSM[goޔ1)cxS27e oxMÛ27e o:#xSM[goޔ1UmB7 o29dx"29dx!Û+J7|J7eM  wp%K]axCJ7nju%J7e*oxWJ7Y?X7ޮƕox57+ /r%N|s%_ c*olux\s0; Ć5"6/Wؾ~|'w%ڃ+Bվ2ﭷJ wsm Wʕ嘮$b#r%WS|5WWS%W{r\BlSab˟Dl@l% >r%[&O\ Ć] ĦIT|R9ٕ@l8+Ε@lrSAb d#ʹW@lr\!䛣@l2Mo9dbsv@lʘؔ1)cb댈M2&6eLlb댈M2&6eLl)cbSĦ3"6eLlʘؔ1U3"6eLlʘؔ1uFĦM2&6eLl)cbSVY Έؔ1)cbSĦ3"6eLlʘؔ1uFĦM[e&$bs搉!C& *␉!C& 29BlDb煮bTp4WܩbӅױ*t_ eUΕ@l8v%*o_g|NJG>I8T &X_n*oUo<Εo_ w( f Εodu$xU@ M%2xsm|!ÛC&xSM[goޔ1)cx,[goޔ1)cxMÛ27e o)cxS᭲o)cxS37e oޔ1)cxMÛ2uFMÛ27e o)cxS37e o*K6!C7 o2MHW o2MHxS G&SAxpxã\ax}\ V7x-<7~R + oސW MM~7ߍLVJ7չ 7A ŻKBw01s2m&6Wv@l7-ϭ /vOpmp3ޘW޶%й]9s*o(:Te|ʘ* 12f>e|ʘ:#ŞO3_g|ʘ1)c,0_g|ʘ1)c댘O32f>e|ʘ:#Ş|)cŞO3_g|ʘ1)c댘O32fMH琙!3Cf>|9ds琙oBb>|c0U.1+px0d>:{ F[L^ ]acLt%0.BWؕ|SA90:Udt:+xTx!W\WDW]aBz3]as7S!noCyVX]ay0T#WvK`G#c~uoBf̷lJ`><[Z|ViپSACs%0_4֕|b*|ٮ|fmdmLp\U/eJ`>\J`>TWvc|ו|Y+To>&o3(̗ %S琙)cŞ3b>e|ʘ1U3b>e|ʘ1uF̧O32fΈ1)cSWY`Έ1)cS12f>e|ʘ1uF̧O3_e:#ŞO32fΈ1)cS12f>e|%曐!3Cf>|&$s琙!3߄|^a>U"6Wх{*7a=n@ Еz8OЕzȆ0DW<\Еzzːv*z?+n͠*xz+ zi*zxwJzWitA:rA\ac OqA?twR\a۾f> 3*xҪ+|eb21۽/p+pfT?\asH7a=9+W@Oz|ؕz՟+ hTP\ k] {-W@o*z8[@5|xm>ATUI\aفC Zp%JǙ0CSW0gSy a2Еzo͠<T6w@=zq!9Cs73=gz1M A3!9Cs73=gz1M A3!9Cs Ao2/\veҬ4A;.vJR)@2P5x}9{3X4d{==zxrGNУu$Oo =t B`G#| zGУ:9Z^zIG0]'aZBztyAлd"އ3Bϑ3B#(Ы>=1zb,X5#'BO bA5#'BO f$X艱c׌ =1zb,׌ =1zb,c'BO f$X艱+^3zb,X艱c׌ =1zb,c'BXz3dgBϐ! I3dgBoBz, I5Du7!K a}]Cqxۄ}ޑ988Wb7:Š;ߏq$(6yFg|EPs$(6IGbq:-AHPlS+v) ֆA4f^>=Iaq$h3T3ڌ#A ;VU=x#AѢ[kNJrpV;S8]GYPtj3)mGtA +I$mgm6#:͘#,،9d-uHi}Hi#A;2Ӈ?4t32 "Aew$4o7Q3B2 we^#A}،'8dڇ?4WzKitFGL {t$4yyFXէ O4GXL[&41ib,X5#&2Me˴bA5#&2Me˴f$XLc֌d41ib,ӊ֌d41ib,ӚLc&2Me˴f$XL+dZ3ib,XLc֌d41ib,ӚLc&2XiL3df2͐e!˴ IL3df2mBi, Y f2mBi, YLpKi$F{͑ п9d4TvN$nC._~5ue49i,a4|9d#,qp,hGYhu#bgdGLCgHi(:!6CH+y#E6C;mHfهW,ioj/]MH*l3ˑ3ˑ3kFPzyAI #,>m̑Izс ~))K/C^(NAzc%ҫI/1^b,Xz ҫI/1^b,Xz521^b,Xz5#%KKbAz5#%KKf$Xzc%ҫI/1^b,ՌK/1^b,Xz5#%KKf$Xz*ׄ$ Yz2dekB^, Yz!K/C^Az!K/C^,&Uu$I/d$E;0-h3z %#AoDS$-r"Nב wQGHdMJ>GHY}( GXN99!/^jHPVh9ˑ8Aek  ʊN^ב:#AYI_TVƑp9#x,&}99HStʯ5LJ4ԇW5i(t a 5o_#, ፴PБ37:Tȩa9U);.rԄACř_P|P#ACщ:42#< oۓZ I*φ4ԌR䔆r5!k J5k(1PHCc %XPHCc %i(1PbXC5# %J5kbAC5# %J5kfXCc %i(1Pb Ռ4k(1PbXC5# %J5kfXC*4Ԅ YC2d ejBP YC4!k(CPAC4!k(CP&NHPh! 5! '$yH=j wmA4!K  Y Є>A~Aś=+#gd"d#A`!=U~`H=x#A`Qsq=ő {9rf}#&D!H(Άǵ9 0(:)hR@S~8 G-( Iqa8e=/͑HP@hF~Y1dcȲg8'#d!˞ d!C=,{&|=,{ ȞAكo9dI${ꎜ9(9d*֑tbm:\ {e:NwF~zt1 WF GXEAK#,{ʞO?\H=6Y ,) Z ZϟH:dFPP  ZZk ZOZKaѢCGa Zঃ9s#g#gΌZ^}H:h9uI#ZGkf'ZGkbA4>1:buX4##ZGkfuX눱cS,hfuX눱cӌk1:buX4##ZGNuc#ZGkfuX눱cӌk1:Œ֙!kC:u YLHZǐ!kC:1dcZG0hC:1dcZǐ΄{O:u Y֙0NIcZg8'cZǐ!k ,!käuhpFP#A"A~:xԕ#A`p$hl38ZYXˣj,s13j.3Z#AR$h>#uGt/A%s$h&ёשt/A}xt/A6qЧoZP@| eEbmHڇF8kB<3q$-zӶn>Ǒtzm~,q̉Iguy3;7?֑:rf #Iq G_Vfe~H=Ǒ${j*!{9#{cO01=b,{X4}A1=b,{X c#Ʋ1=b,{X4##ƲGe˞bA4##ƲGe˞f${Xc#Ʋ1=b,{ӌd1=b,{X4##ƲGe˞f${X)dτ${ Y1dcȲgB=,{ Y왐d!C=A왐d!C=,{&}1dcȲǐeτqrOG0C= >C=,{ YLg${ Yك;t3Ǒ {/Ee?٣DP#cI#,{9²W 9d G"=Pr9+{f7pGBHC bW8CXC~Cdy>άrЄiχAكF${G*>l΂G٭ O'33r$'LI <#>I5r$)qJmC$ H+f( H+bA5C1V@bX5#$ H+fXcT,(fXcԌ+ 1V@bX5#$ HPc$ H+fXcԌ+ 1V@Œ!+ CV@ YMH Ȑ!+ CV@2dd H0( CV@2dd ȐЄ{OV@ Y0NI d h8'd Ȑ!+ ,!+ 3 hFP!G=MER-PpZ#GX^}sGǗ9 Ep!?; r$( \!HP@2gMp$( $‘*GH}({Ё8djGYY4#>=> Q@3 "Iq$( 4-Ww4!ɞ ʞNH:h9szyM&ȑu9Zl·9-i3g:u>:#ZGkf'ZGkbA4>1:buX4##ZGkfuX눱cS,hfuX눱cӌk1:buX4##ZGNuc#ZGkfuX눱cӌk1:Œ֙!kC:u YLHZǐ!kC:1dcZG0hC:1dcZǐ΄{O:u Y֙0NIcZg8'cZǐ!k ,!käuzFP r$h5PupOӑu?;#uvMڬC28;ΓDH:xیM::\f GX R$h^?H:x#ae:AكG9d GHLΧ'zfe#ieQ:ieșøf.¦/·t:hø&5<5Fٓ'3gȞ({i,dOC= 3*",{n9d!GƎC~AC'?H=X#,{|BJ#,{ێٽ#w&/ۑ {k RGf֑ {t^ϑ${萴A\ZgB8(:«upyIGXh^:$:)iΧ}֡IZHXCACGu$ȞX=>MI:dχa=_ {Rw$Ȟz~H=t2WeFH=f${>Ldu {XcӌXcS,Ȟf(ƲGe˞f${Xcӌd1=b,{ӌd1=b,{c#ƲGe˞f${X)dO3=b,{Xcӌd1=b,{c#ƲX=1dcȲǐe!˞ I1dcȲgB=,{ YcȲgB=,{ Ypɲǐe!C== { YL'${ Y1d3a1dcd߯!?H=I#AWe*%GYGy.Rr$U#A¦Wd#,{B9dݑ {h+<Ñ {9d^U~FH0كBcFHpve{G,a>G>|nՑ><>#𿎰b;HP@Hy+KP@MITR@?l"I/$S hkP@bXjF{bX*P39c$ HP3R@bXjF H+ 1V@łjF H+ 1V@Hc$ HP3R@bX ) 1V@bXjF H+ 1V@HcT,) I2dd ȐЄ Y2d4!) CV@2d4!) CV@ YMdd Ȑ!+ `P@&|R@ Y0I2:.#8t0[Gv_o$p:=aÿ;+ǿuWh݊hV^3#ArWۑuRi^aj G93#Aࠍq$:SG:Ҍ^Wt$$pdhIg9ܺ"QI 18b,p 18b,pӌvXcӌvXcӌ 18b,pӌ 18b,pc#G f$pX)N38b,pXcӌ 18b,pc#X81dcǐ! I1dcgB8,p YcgB8,p Ypǐ! C8=  p YL'$p Y1d3a1dcH 3=AlD! RAHP*(f GRAqHP*tR^g XǑT9!nIJE/ JE_UԷHP*f HP*ȑTPB9 J!zV*V*TX79J=،ݴ&SJJ#gK$BU J#iM #AAq3sy  :rF8 KGR_SJESkb9T9TOJ+1V*bT+1V*bTҌvXRcҌvXRcҌvXRcR,(fTXRcҌ+1V*bTX4#"JEJTRc"JE+f4WRc"J)1V*bT%2!)CV*T YR!+CV*T&$bJŐ`P*T&$bJŐ!+ |T YR1d2aܓR JŐʄqOJŐ!+CV*Y>)CV*T&3}R*AR0IR1LJ8S ZGRA HP*ha Jwg e+˔'MP$(V*v?8JR*la§p$(mH(GR*CzV*T& . z:K#Ir /mp$]> gH8U[$(:uGHL(ƒf}7t#Ar$PDё O>|9e?on'6QM)yR#1'b,OX4}+1'b,OX V<cy"R<cy"N<cy"X'H<cy"1'b,OXC',O&3}'A<0I<1 r$-3#Aoq$-yG8owIäI84 ّIt.AM{ r5 $|(׌&ak>#|D<1dyEtFP$y ّ Oh2GXQF3}#Ap { OMn%%ǑpB^\DH'z?q$37-7o5NGȓǨA<cy"[<cy"X'hJ1'hJ1'hwJ1'ł C,.&3}A\0I\0$q1!+ IQdĄ&~<0"& k+P8VfҍaY (Ф8B2 D퀲$|N,5jFP0`YS$ӑ 03H x#,x#, ``:>H x&G` 4#,hA"NsH >Cm`c ƂAC3c ƂAC Ѯ 1 b,n 1 b,. 1 b,Ќ 1 b,ў 1 b,X04# ƂAC `c ƂA f$X0`cЌ 1 Œ`! C , Y0LH! C `0d`ȂA0C `0d`ȂÄ{O , Y0`0NI0`Ȃa8'`Ȃ! ,! C ƙ>   Y0Lg$ Y0`0d0! C , Y0LA0C^`k&$=`5\'|r7 8*?*GBG%1#XQ,(*?W~>q#\ V~?Ă#}GBӉ8'%y]d<!0a™3IՎ"Xp$T~<‘3r7oȕ8/*!W *!W~C\'|\ OgTC7?aS7oȕߐ+T r7?aS ߐ+!W r7oȕB\ SUˑPV~\Hh  OH:Ï3U~>υ#\ʏϕg-*?.]tGΜʿ~_-fF ERGW~^ȉ?pV~3fX'=3fXͰq v̰a7?a7o2 0o +Wɠa7o2Ͱa7o2fXͰq *V~3fXͰO +V~3A7o_,T+C 7Pbo!V+C\bA 7+ >7+C/'P 7q!VXb8ˇ+C/gP 7q!VXbA 7+ i?T~C 7Pbo!V+P@#>E%p$U~ ,AGBs7"\awG¯_БPOk;*?]y%) :"A'Wg},ý\ }ё 褎9#c "@E1h?AE1łhFb,XhFb,XhFb,X(D@3b,XhF{b,XcЌD1łhF"@E1b,c "@E@3b,XK"`B, Y0d0!C, YLH"E!  YLH"E!C>Y0d`"`8' D! D!C,&|, YLg$0d0a0d`"E$ Y0d0a "E! I0d`"`B, ψGd \I!(=*GΔ{Gp+ HZ_ߙ?w${lrMݑp,3sz}}=qNj}?]ّP?K(t GB'E:=GΕ=]",=]",=]",=]",=]"r2tHTtx%"R%"'~O4K~O~O4K~O4K~O4K~O4K~O.i.9=]",=]",=]",=]",=]",=]"R,=^"a*{DdTxrKD6L~|A.{DdTxȆ S%"rKD6L~l/qr~l/0=^"a*{D Y>=^"a*{DdTxȆ S%"rA~\t SE8r 1N.r'L~OA†~q'laSahv8L#Iكuο-; {h֦{ 6CA|pa&[ b/7K%|Yg~K*t)y ] v1.h>.ƅ] {P؛T.ƅ] f4qa.ƅMb\Ÿqa/ {3*b\ŸqaoF3v1.b\Ÿ7.ƅ] {P؛Qa.ƅ] f4Wqa.ƅv1.b\؋>!vC.\ ra !vC.\'nȅݐ `(\'nȅݐ ! |\ ra7>aSa ݐ qOݐ !vC.Y>vC.\'3}*삡ra0ra7nȅ}B*\ ra0N nȅݐ T ra7>!vC.\ ra0+쎜)쎜8bNw$v:[GBa; ;\H(WyuIؑP!m# EBai˰" 6~.T}oe(#g :viBa.ƅ] {3Ÿqa^,f4qa.ƅMb\ŸqaoFSv1.b\؋ތ v1.b\؛-ƅ] v1.ͨqa^,fTŸqa.ƅb\ŸqaoF] bOHݐ !vC.\'nȅݐ ! ra7. ! ra7nȅ}='vC.\ O'TCa7>aSa7nȅݐ qOݐ ! L `(\'}*\ ra !vC.\'~*삡ra7>!vC.\ OHݐ !vC.\'; #g #g ~awLawLaw$vZH(X?g}%\GBa*șș># {vvP OgT ra7>!vC.\ OTCa7nȅ}B*\ ra !vC.\ OwvGvG,,$ޑ$ǒxGN\y#v\zH({Fw].a#cw$v\EHZ^Baϳ^%./Ko/ }lBa.ƅ] {3Ÿqa^,f4qa.ƅMb\ŸqaoFSv1.b\؋ތ v1.b\؛-ƅ] v1.ͨqa^,fTŸqa.ƅb\ŸqaoF] bOHݐ !vC.\'nȅݐ ! ra7. ! ra7nȅ}='vC.\ O'TCa7>aSa7nȅݐ qOݐ ! L `(\'}*\ ra !vC.\'~*삡ra7>!vC.\ OHݐ !vC.\'; #?Dp9sFNH:|Kw$vYGBaǓ9 ;;#. ' 'õ}Pر}+ ;(tҹ1X‘31>\qa.ƅb\Ÿqa/ {3Ÿqaތq1.b\Ÿ7)\ v1.BaoF] v1.h.ƅ] fTŸqa/ {3*b\Ÿqaތj1.b\Ÿ7.ƅ] {T'nȅݐ !vC.Ra7nȅݐ T ra ݐ T ra7> !vC.\'{*삡ra0Nra7nȅ}8˧nȅݐ qO]0vC.>vC.\ OHݐ !vC.i?vP ra !vC.\'nȅݐ !vC.Ɲ\NWs$vQ~F*zGUGyGBa w$v'w$\䌼ﺤߋ #~GBat/ZK\?^Tѽ8B~?ӿ_#w??u_ez?/=mxG+v<G؋cct}TI>?: F _=&o7cŕWmtG(Om=8v؏Q9sv|d{{M>j{l@siXn7!>ǣ,1e Rq=>rc}ߒkH?A=ʱ0Ƕ}Vxox{{v~{:@j{R˭`o[QުF]~g}lʮշ(&Kz:7xROc?a۴0eYo^o/捏I׮: OT2 /б9o6bcWtoiǃ}cVi/qz,CS A<ާu|oooxYo<׆ _ _cֿ9pwv|?suoKcwq-_?͟?>?_ώ1;| _ğ1uwoxuLC bS^H_au5`&=O_^{l1x?mαsߴᣏ~9ۈ+n#6Qh6?^gCc}>F: }g2^->FVPc^mT`7ؤ Ux֔ 4ޭX6![#8Qtz{ۿszֶ/c^?O7pcQ=ͣjx]Q[͖_\*wSգ:Gc&ZVF[n!ߛr ocC_ƞ֭ѱﱕ˨cϺZ^cOmH?:|(`5w?=j&գZ'k:ݹr\wcYͣɻ^}hRGGѵne5ZjŷrʳWcgaZ.F[[=3;1FGU뛳hRѽv{O^jFKWnu~:=%EkT+u뵪SFǭףwYhʾ~VvH>O]V͛>Co_nS~y]ox]޿}|GWnQQU+HV 2<\oV~yxx:Kֱ}|:^mrx }i ;C_Pm|]۾Vv<Oa>t_?=~= >=W|}]<~₾{\=~^5{S%^uU{|Ձc߭~љW`(kHj֧}RKU.72~$6/Kfտ7x2xzz0-=4{|qݾg^e\]Mw{z|z j?nD֏owޮ&5cWVƋ’~akܷʝߛcl,|nQn~>0괜~tt~X?+6k7k0J?o(}Ic]~x~d-2ҙD=Njkk|׹K=9|ԏuK5=v~)k~}ƫORq=li\kۮ=WG}q}~ǛK_m6icĖ~~ڷ렮zcƋQkkrC̟*163}6K?Z?mfƷo5u*ooW9vo:ws{w?:Pُg{V%^ O_ϧ~!Y7O=/~zվ|7<}}I]'@]|[tK[}糱'=nJ}axXiQ{_4|~]zW{;'Հ5? \j,~__w>JUrk|0Ƌƛ~}ZY=?ڋǷ5_?oz~%?}ָ?m5Gĵ,uÕ_XyK_{lŵ}W-qoc>7gZ}}~oָ 1~x.jx|]eٞ?u[z}~ƺ7]Hݯ7/OSw^~xzexy~X_Rku]z7ץdVMsWZ~zo\]|µ'ihz٩,k{h>1~yZӸwv/mo>?x~ꘆ:߭y=ޟ\mͼEc|񘿖scLUcckr]gZ[{[e눩zwUgӪo>2ƛwݞa_cۿ}/?7=n߯W1,ja,k~ӥU.5^/Sc̮}߽;Ƌn_H-oi_{:yOoWmctWitQS߷go04?4Ҋe1)roG~??Mt4k_?u˧WUZ%|?cNwS\~w>}S;zE߾>VozV6O}?UGM}5|rµM~uy>5g|ߴ|zz':\Q7&_vmz>}M kϻ^xqj2<}Mn~ھn> stream xwTSϽ7PkhRH H.*1 J"6DTpDQ2(C"QDqpId߼y͛~kg}ֺLX Xňg` lpBF|،l *?Y"1P\8=W%Oɘ4M0J"Y2Vs,[|e92<se'9`2&ctI@o|N6(.sSdl-c(2-yH_/XZ.$&\SM07#1ؙYrfYym";8980m-m(]v^DW~ emi]P`/u}q|^R,g+\Kk)/C_|Rax8t1C^7nfzDp 柇u$/ED˦L L[B@ٹЖX!@~(* {d+} G͋љς}WL$cGD2QZ4 E@@A(q`1D `'u46ptc48.`R0) @Rt CXCP%CBH@Rf[(t CQhz#0 Zl`O828.p|O×X ?:0FBx$ !i@ڐH[EE1PL ⢖V6QP>U(j MFkt,:.FW8c1L&ӎ9ƌaX: rbl1 {{{;}#tp8_\8"Ey.,X%%Gщ1-9ҀKl.oo/O$&'=JvMޞxǥ{=Vs\x ‰N柜>ucKz=s/ol|ϝ?y ^d]ps~:;/;]7|WpQoH!ɻVsnYs}ҽ~4] =>=:`;cܱ'?e~!ańD#G&}'/?^xI֓?+\wx20;5\ӯ_etWf^Qs-mw3+?~O~ endstream endobj 375 0 obj << /Length 832 /Filter /FlateDecode >> stream xWKAbۻ+Xh _™\ir<5kE7nf0ʰveeRséN8U:ޔht--.f$ܞD*1IdUGIJV@-odfն$W $²w\ t0R=7/ |I"zw(1!ӫd,~1H^Pދ}c2hrZI`YnRiݥvCje&&Gݐ$2YQ0  9EގG#>'EN[2>.e!y.D ]{bud>4$Q{Y4U[|LMTN2:+gXh}'Os TQ`Dt >:+~ʹQj.ᑋ8bJ =&ٙɌ0}`]aX 03vF {fMY0wa!f觭tP`.ZHMĂIE0ËLV%HF6;pg( m2qnp^k jkVX-ZJ;;W|ix> /ExtGState << >>/ColorSpace << /sRGB 379 0 R >>>> /Length 88461 /Filter /FlateDecode >> stream xn$m]w*% {`xh PRs`xcebDz{ooۿo;:Noӿ0c?Nq~x7_з}_?~O8_?}qu]_y>x?}}j_?n_5=58~\2x~ .k#?/>׾ >>?} '|ϋp[M?c}ϯ}58?■?98OtOc}֟|\v~G-i|gq}{d!o}y]Wv)GvK];_|.].]n]>ǻ{~ zx>ǻwww~u;ykKi={\{{}~?}}y?ϏΧΧKx;ti/>}_s۽_{|Q?˩˹˵???Kk翤=gx=N==n=/>gxW=_֏|Z?%ix/4{6GyM.ׄ粻<_wyk|EW?[xn=_Տ|V?[xn=_Տ|?gxn=_Տ|V?[xnfkg=gxn=_Տ|V?[xSxsxn=_|t3Q~:o}i۠O_':a?>|:`r:}0=hGu.}||Q~>Ny>(Ge~T5rT>_߶mP'#m(GY|ܿvrT YQ9͎ivTN2Mig9jQ9͎ivTQ9͎ivTN2MivTNrLӣr쨜fGe쨜fG4;*QG4;*Q9͎4=*Q9͎ivTN2MivTNrQ9͎ivTNrizTNrLӣr쨜fGe쨜fG4;*QG4;*Q9͎4=*Q9͎ivTN2MivTNrizTNr쨜fGe쨜fG4;*fG4;*Q9͎fG4;*QG4;*Q9͎4=*Q9͎ivTN2MivTNrizTNr쨜fGe쨜fG4;*fG4;*οzT |[O |;u:_QN߃rTc0>?*יc Qy~u*=Qy¦zTCgGN_nQwsaYQy< ?*4=`NӃ9M`NӃ9M4=O9M4=`NӃ4?`NӃ9M`NӃ9M4=`>4=`NӃj`NӃ9M4=`>4=`NӃ4?`NӃ9M`NӃ9M4=`>4=`NӃ4?`NӃ9M4=O9M4=`>4=`NӃ9M`NӃ9M4=O9M4=`NӃj`NӃ9M4=O9M4=`>4=`NӃ9M`NӃ9M4=O9M4=`NӃ4?`NӃ9M`NӃ9M4?۵ow;OgP\޾g%=Z~0z}|WX~@or0_[|~w  \wש[\P ij(/\.gs fG'`>̢ׅXV(lf,!^H`G,OCs#lr+נ|,.]gQs+ŽE9/޾_΢gB',RnQ\Q0r7E;*8PE ,0,0 e2M`3M @S)Ls)ДhӜh4e2M`3M @S @S)ДhӜh4e2jh4e2M`3M @S @S)Д9Дh4e24g2M 0 @S)Д9Дh4ei4e2M 0 @S)Ls)Дh4e Дh4ei4e2M`3M @S)Ls)ДhӜh4e2M`3M @S @S) sV4X?3( `?A~KϠ0נ]d_?Bcu*P.aW?X= ~t, e,>c8 gt~0Ǣ0WϢ0YI`Qpbx$;58;5( `p1v,0 e2M`3M @S)Ls)ДhӜh4e2M`3M @S @S)ДhӜh4e2q)Дh4ei4e2M`3M @S @S)ДhӜh4e24g2M @S @S)Д9Дh4e24g2M 0 @S)ДV+ @S)Д9Дh4ei4e2M 0 @S)Ls)Дh4ei4e2M`3M @+=ޟic:??c͠k/7 'o3rU^EauMY/9EaUX_ϼ0]Ր3=>rsĎ8;C( pEa, ,,0H3X(8jdaff@SHH3HH3HH3H)$$$$$   h i i i i 4444i i i i i 4444BBABABAM!! ! ! ! fff@SHH3HH3HH3HH3H)$$$$    h i i i 44444i i i i 4444BBABABABAM!! ! ! ffff@SHH3HH3HH3H)$$$Hlϗ%ϗ<@7oͤǟ&'}6q zISD2LH$DH$D сdb с@2 с@Bt !:L,@Bt !:HVl@Bt !:H$ H$DH$D$D с@2 с@Bt X@Bt !:H& !:H$ H$D$D сdb с@Bt Y с@Bt X@Bt !:L,@Bt !:H$ H$DH$D сdb с@2 с@B,@ϯRgyCϢϷ?54$z/Z4 fq(@Y4 1UE@?Huj/ā|78\qϽɫҀ4 qSa@מ̤-69;}7L WL țl$DIIyk@2P䐙4 ;b4 ǀ01 gn&0LM0qL!:S&L!:S)DǔS)DcĂ)Dc 12` 1BtLYa 1BtL!:L,BtL!:S&L!:S) S)DcĂ)Dc 1ebc 1BtLX0BtL!:L,BtL!:S) S)DǔS)Dc 1eņ)Dc 1ebc 12` 1BtL!:L,BtL!:S&L!:S)DǔS)DcĂ)Dc `0E3)v)vDdQ01o’E{x))vk Ôl2&vO&&u{n:Ia%tӯ?ݤɺ><+(MTZ2)l7ämPCQ29;c3q6᳘KQN?&M&z&6)l"oM 76Y6/&rM&MLdD%&&-LI&&&&DgDeDeDeDeIII $Q$Q$Q$QلllllO8$*$*$*$*MMMM&&&&DgDeDeDeDeIII $Q$Q$Q$QلllllBt6IT6IT6IT6IT6!:$*$*$*MMMMM&6IT6IT6IT6!:$*$*$*MMMMM&&&&DgDeDeDeDeIII $Q$Q$= t䜷~Eay, ح,M=gf\>mܸg;PϢ\=mhf-:ȽCLY'||O ߨƓݤ`3@O0ei)sw:qZLϢ`:BCݯa }-)vc, =rGK&G0Ie&S"Lݟc 0q6Kp646̕MI46Yr1)l2W`&MJgMڣ>68vXDIa{kL 0)lW09&39&LM&DgM&Dg dba لl2 لlBt6!:L,lBt6!:MVllBt6!:M& M&DgM&Dg&Dg لl2 لlBt6X؄lBt6!:M&6!:M& M&Dg&Dg dba لlBt6Y لlBt6X؄lBt6!:L,lBt6!:M& M&DgM&Dg dba لl2 لlBt6ǷE|lW̢^?&,MZ֢ gh&~e, -Hp6+c֢?(.[ar&GAad}u&3iנ?FʺAL9ە.L ,q޳c?qb(ds?qbQͤȤ|__ k%Yi=`{SL0L %.LdbRD%ш<酉ɟrLrQ&44DGDEDEDE#M"M"M"M" ё&Q&Q&QfbADEDEDEDE#M"M"M" ё&Q&Q&QHHHHHCtITITIT!:$*$*$*$*iiii44444DGDEDEDE#M"M"M"M"Ă4444DGDEDEDE#M"M"M"M" ё&Q&Q&QHHHHHCtITITIT!:$*$*$L5ymHY8$Y`Qyi54N4>*, (‘kѐFo^ɤ EIA{sL إ3L*Ҙ0i}M`-Q֚IC{LΎ4/( Ҹ1)HcoIA_iLi!QjeH34L w3)H3sw?4^& i!1Q bbH(I4QatCMEe`nv,nfY8U?p, ƪYTɤЍ^F(B7|&Gޓ(-6yK8r%LҬ/3Oz.xvS&i 2qq bHeRFoˤ EԔ#4^cXC{M&iY8nofRNͤ ^m94vU&i.L *(ͤ"C:L B7D fb tCtXtCt!:ݬtCt!:n&!:nN7 nN7DnN7D fb t3 tCt!:L,tCt!:n&!:nN7DnN7DB7D tb t3 tCtXtCt!:n&!:nN7 nN7DB7D fb ^~ztgQF_Ϣ"Y8|1ǟEhn?gnI, ̕ J [p=vx23otL [etOlaf]_Igi݀u7zO<}I{o&gPn^q՛I{bR&N7΍L ÂO?OI -L ̃FcOэjӍߝ5F7L (3)t?/`RSc.abtӀM"&:tN0&ώ?/~ jULXŧTG)?vAQ&FL"8& ι^k$d7qQ/ʤ]IL L Lj̙b-ˤS&?|庝DcV;ʤS&HPPPPI(QI(QI(QIhb!D%D%D%D%!PPPI(QI(QI(QI$$$$$DtJTJTJT": %* %* %* %* NBJBJBJBJBD'D%D%D%!PPPPBBJBJBJBD'D%D%D%!PPPPI(QI(QI(QI$$$$$DtJTJTJT": %* %* % ?{f̓fVFg-,n, l~vfQ"4̢Ѝ=gnnL T2k vf(} B7zaH&nL (19B73iW(09;ؓz2)cW&e%Sy ḳ8ę纾,놳yn!&yϙGCgRް+g}LyrL (&ͤ1O6V.o3[:<߷yL (&1)̣Ĥ\㣘ĤbL*1ic^Ĥz#3?D3CtYCt!:L,Ct!:&!:? ?DĂ?DgbCtXCt!:L,Ct!:? ?Dǟ?Dgņ?Dgb3Ct!:L,Ct!:&!:?Dǟ?DĂ?DuR/3gϴ?v-Y[X8b, سX8ע=6?f2)W1qG0i^GNIc*v+QL(09aVoKGlI{4Q&?LJ1)7{ǟ_X}P&?L#&8[1);LßyM GL⣙2)Ĥ^FʤI40ic^Ĥy#3?D3CtYCt!:L,Ct!:&!:? ?DĂ?DgbCtXCt!:L,Ct!:? ?Dǟ?Dgņ?Dgb3Ct!:L,Ct!:&!:?Dǟ?DĂ?Drj3gϴ?zs-Y (s (?̞E{3Y47ˤB9&?\[uM <~3)0) 19rffRgOzeRZE,c,Ƥ0cgIa{x&< u70hę13>(g3_7g1Obu}}C;x(C1qq6bGͤW_7tC&:~:/ Йb}u~֛w.ϥ_P4:FL gR@G8Q-:zL**aL t:D:DAg:DA3ACtЙX@Ct!:L,Ct!:t: t:Dt:DAgbA3ACt!:L,Ct!:t&!:t:Dt:Dt:D:DA3ACtЙX@Ct!:t&!:t: t:D6W:48:,d@G>I۟u;Ywy<֡i":L E8Ledrv01ɤR{@ $vց>G=e-yk,?41)(eꋠQ&:zKa&:Dk& t^M+ÝOLݿCL p\sthб2)ce⠣z2icOΤ]I';Y?Й2QJTrbPPPP4@QBQBQBQBѡ(Q(Q(QPPPPDt(JT(JT(JT(JT(":%*%*%*E E E EDDDDD"CQBQBQBѡ(Q(Q(Q(QhbDDD"CQBQBQBѡ(Q(Q(Q(QPPPPDt(JT(JT(JT(JT(":%*%*%*ۣiE5(kqfѠHĢ@=EKdžE"{$,Ɂ2)P7&&>E?~(brfr/H(RaRZdh}L  -+>7G[4(Jl$m$BBLD&ҸLiF2q@4eRHȞd^|I!߄ V_Hſ'?Z_+ ~; ɭaRxbRHpjͤ@ѡPDt(ZAѡPDt(":M,PDt(":&(":E ED"CED"Cѡhb"CѡPDt(XPDt(":M,PDt(":E EDED"CѡhED"Cѡhb"CѡP4@ѡPDt(":M,PDt(":&(":EDED"CED"CѡA4iE k8$ Ģ@=EkaEzyZT(2)PdwndRHhL Qs~R-H19pX&ncRo39`r>pE#P19E3i1q>:s6XjGu#H&~yMB~L 5(L[psiϥPc$W_ɅJjzPm}Sz5?7jzϗʷ[jˢ85eQL 5L'&HL5ٳ2)4*I&)&63iͯԤŤQBMSL5N3qj*:EtZEt":@M,Et":@&":@P @PD(PD(jb(EtXEt":@M,Et":@P @PDPD(jPD(jb(5Et":@M,Et":@&":@PDPD(PD("4iP hVYj[fq^s;h8f?^cS_3)Ԥ1)8w1)doĤQ7&&:ɁN3id?(v]=äQFM 3iԤ:ŤQ BMFL 5kM@_JM/o.JM[tTznⷳҋJzG%w(&^-t[uIC%{DS&ԡTZ.!JzJㄙ4TZ0qTrbRP$™IA%u4T*Ť9JPL*C1id?F!TbDtT":*PDtT":*&T":*J *JDGJDG%QibA%Q4QDtT":*M,DtT":*&T":*JDGJDG%ĂJDG%Q訴bC%Q4QDtTXPDtT":*&T":*J *JDG%ĂJDG%QibA%QtzVPDST)*M;J,*nᨤ(dOEeQPiZ,**%Jvʤ2)d]aPiΎ@L}&m엪?֙4T E29w2ˤA&*L *]1$tɁ29JTZ)JOظKL[_ɏ_JMr:mQ2)d:ŤP^%IVT&NMJjzwPT&*LL 5%WrW5ٳ2)dWIeԤ?LĩIIh&O2)_YTj2ʤPDV&FM'*b`(KM,,@bUbUbUbѱ*Q*Q*QXXXXEtJTJTJTJT":V%*V%*V%*VUUUUDǪDŪDŪDŪD*cUbUbUbѱ*Q*Q*Q*QjbDŪDŪD*cUbUbUbѱ*Q*Q*Q*QXXXXEtJTJTJTJT":V%*V%*V%*VKU9VMsZV͢`ռ.UhXegѰJʢ`)GX5s.7vdX}?)X,?c>ۅI*y/LV;2)XЈv,fRĤ`יO˛eR̤`LV)1)X¤amaUXt7)X&4U본]IMxO*x& & \3)Xe7gRj/&9LV7Է42bRJ}kMo1q":V1qšXEt":aM,Et":a&":aNXD'NXD',BXD', 5 EtšXEt":a&":aNX aNXD',֊NXD',BXD', kb!, EtšXEt":aM,Et":aNX aNXD'NXD',j4'iNXӜV;sCŢ=Js?Ző[-Laُ²gRɡ[Z2)%ՋI!,pk&Tz1qr(c嗻0)P6FXz#Ť ]rYN&A29]42kʤp:orm`o:|_~5p}i8\eR.eA0}E& T3qo`/,`f3&nIC0qL er2Q#:%*%*%*`````DGDEDEDE0#X"X"X"X",Q,Q,QFtKTKTKT#:%*%*%*%*M,FtKTKTKT#:%*%*%*%*`````DGDEDEDE0#X"X"X#Z!4Gi`V;`(fgĢ!vvEAy|}_wEE0{L ]3)feR.!`RLoLFIA0=1)fOaRL쏽g GW"7KԘ1)fWgRL݌IA0l&͘437c܌!Xx,C_nBmL̈fD̈fD3fD3`6`Ft0X`Ft0#:&0#:̈f ̈fD3fD3lb3`Ft0[`Ft0X`Ft0#:M,`Ft0#:̈f ̈fD̈fD3lb3`6`Ft0_Ms0`6lfi, kY0'(`f7j̢>8fs.?}83}q&f~:Iso׫ݤ8E L 0scrv0'eRL/c`W0)`fDɤ_ä^*fҞd}=?_XIr]f_.׏ΥjI336&^|fjlLL̜8si` 2)`,̯cRT4fjl3)`Loշf~YL - 6^?I ' 0i`69? 1)`Ƥ L*1)`ƤY}XL/4čm&`6`Ft0X`Ft0#:&0#:̈f ̈fD3fD3lb3`Ft0X`Ft0#:M,`Ft0#:̈f+60#:̈f ̈fD̈fD3lb3`6`Ft0#:M,`Ft0#:&0#:̈fϯsif̦9V~x8#`6fzc, ", {&m-*O ̍I3}EϤ=>I3s>93nJDNI3=6ͤ^`1)`fʤf6& ͤ%ͻ&̖> ~}oDžs} kƷgRLI3u)&G0cb`4ł`z`/>`Jm3)!9ʤ G0w3&fE5G0}_&`LL ]L8Y3 L W!G0}[_?6GLIE0{Z&k`zk`bE0uB0&`DG0#Ă`DG0#lbA0#FtXFt#:M,Ft#:` `DG`DG0#lbA0#6 Ft#:Ft#:M,Ft#:&#:`DG`DG0#Ă`DG0#6 FtXFt#:=N 9Ms!EA0}4`mu,ZT{9w_k3q̤!ޠ¤ >>IA,k\ q9ÏL鍩LΎ`zbnƤ e̯IC03IA0}BG0IA04[7{w SaҞ5f.Uc3)4F4ۢ!l)1qr cR`K-I-萉yɎ!R>cR`Ka˯Ig?Zغ}淠3)w2qr cⰥɤ`֫l hOÖ?lM*lOؚ[eR`e1iU_2:l;((l cEtXEt":wM,Et":w] w]D箉]D.sѹkb.sѹ5pѹEt":wM,Et":w&":w]Dw]D箉]D.s]D.sѹ5pѹEtXEt":w&":w] w]D k{>MESVK~(Ǣp@bQ.gQ˞Kˢp,wKL w`eR~NI.C5&j8wk8wq{B2)5gunPI.L]s[|L w CjNyǝ1ieΤp]VI<>m׻dR$I.{LR2)e\I.3]&dLM&dL˕`uI}}ً_B7,D^_ k0;ט40{5[켠~?0S߿fڙ8]?|w|1q0Scˤ]oI3wI38fLL WLE0fRI3g& Kfre&,D3YYY,Q,Q,Q,Q````Ft0KT0KT0KT0KT0#:%*%*%*̈f f f fDDDDD̈f f f fDDDDD3YYY,Q,Q,Q,Q````Ft0KT0KT0Kt0|*`6l4 Y40h`f/fQLm`f?׫r{ `9&̔fl^`9&PG4fjlL UhL=20[aqA aT0۝2q0#>o_X3{R&J& 0S`R.)ˤr frC`1)7T1) fch[,`~\q]bM3WN&fA\uh`,Ǥ I_ TfRLl#fjlLL"L^}+m`vʛ~EA&f%)`7IS5ựI3{C&f/^GT0/p*70/*אf|mm2Zq4õ/ Τ@!Gt#:M,Gt#:&#:qDqD8CqD8C!7@!GtX Gt#:VlGt#:&#:q qD8CqD8C!nb8C!GtX Gt#:M,Gt#:Kiq9ĭvf ^\̢@E8} 5qn-*m ScR 9s8L͍CȤ@!B&G I@LkL6LڍL c3)Wh1)gO7ʤ@^!NNc nnd7)g϶!/vc .vc h& I8U5&T՘[yϥ]϶V^/ŹQbw_B7 ~3q{T I8{& 8Ľ+ ,oǛCރCL Ĺ1)go/Cyy37׋ۿ }2)q#Й4ScR _E:5!I\ tX9b'xlBȤL镊L %L 酚L\fxߌs*zL\ӫ8fRxs.zL z8BgxΟ\9Iṹo[&GL FnȤ"L)2)<_v}m^nG4{x餡юhWTSByD9~&=3qSy7e?s;3<>=yNE/s%}L #g3q yN̤ܼ#`& 43̤7s憙(s3ѫ8fRPS!DGtKTKTKT#:%*%*%*%*| | | | | %*%*%*| | | | |DDDD>____с/Q/Q/Qwh7́o4Ҝ9Ms[ns#<Ǥ|I9yL =t;fRxn Cq&Gޖ!_5svS&nnͤ=eΎn3)< Iu5o_X9=&sz -s>o+^2q̤MqI9{W&:fRyN.ͭ9&Gxn&t-\ ϝ7 #{K(<7s*zL 5zScRxNw&8Ͻ-wv/hh3q{oyE0q&!L\;f`RNr& مL ) 2q ScRNIC;u>&A;&v 툎vDG;ĂvDG;nbA;юhGtXЎhGt#:M,hGt#:툎v+6#:툎v 툎vDG툎vDG;nbA;юh7юhGt#:M,hGt#:&#:툊v{MюhGShGS)hv %*%@L?L2ih'[ɤ=&vRIA;6=G;~&m[| J&  av[,<78s*zLϭvSo&S瞫KnI9%9}sv6Ϥ]I9(svb&x]&ˤ;P|' 4{ Sۢ\bRϭ?oZWy<Ȥq&Q8M8Τ~&s(L^|l<~lϽ7=6WKùxSy #O=+s۹n5xsN`Lϝ/*%3yNˤݡˤ<&<Dy<<<<JC;}NI Qq݄&,hGhGhGhGh7aA;BG;BG;BG ::::ڭ݄&,hGhGhGhGh7aA;BG;BG;BG ::::MXЎююn‚vvvvUMh7dv++h7dv9MhvvT ٮT*ɘ~*쨰Ttl*Z* t K¯SRn'گhmsRNکvS)h7-SihgRqV5]=R)h)S)h~L(rT pv:7v0v:GNMȉTLMMک Qig.GPzAۿR(Om*|]_c֟z~}%wUbByQl((wziӕS]Sq@*TT1By:">*^rm*tT */g?;7.g?=M#~M*T!g?B*lY0~&w*̎T A!|\/yNzqB*~.T 2^*~>3~]X*\T Rqs)( *؏Ja?fS Je.~~?n +s}TzTa ˚y}4 T¤R0P9J@uUixx%W b }=>@@1_L`CSq 72t}W 8iMT.*zH*u *up* LT +bc b! 0&T T?# HHH8a@B@B@B ::::NX0111p‚+lHHH8a@B@B@B ::::NX0111p‚ $t $t $t ` c c a00a`2@2d &3 $s ܡa w/~ݕ~r+p]q=Si7u/YrGROւ슳ߌӱNgλgOwų+~IЮR)gzwB슳IZ/c튳,cvg?])^]W)L+d)ܕ~aTwܕ~ROvߟ"ʙ.+'CY>9,}(%< GEݕ|\O-k+d])'wŁPOT-B>|WiP*l?}}yՀ϶ؕ| Ju1?|߮]i44{ |Wn~靈+}z&+|>S+~;TSc;4 ܡa *0p;4 ܡa`B0p whC01p;4 ܡa`B0p whC00b whC;4 ܡa whP1p;4 ܡa`B0poz`d9N9N8cd+kHHH0>0PV J@ R0PJ@9m,~E奸-I +Wn\*e])8+E/kVR)T R)~H1p* e]q Kh{c @'ǩ4 dhÉR0P~é4 \wa~]{Xb_@e*#8:yL!4 \u;~/^G oF刁3zU1p~|! #߷73ۻ~H`T T?R0yS)(3R0PL`+@ޕ?71L`mb+T ~ϸ84 }w`ϓGv1V J@Y-+654 pv`\^w``(RX!**9R1 <86rD8 rpHpHp8aCBCBCBCB :::NXp    ',pHpHpHp8aCBCBCBCB :::NXp8$t8$t8$t8uyp2's8\Y's8p2Õ58$t8$t8$,pSphT ujT 5Tʖʻr8v/8G.;JJCYJCF*e8-wlJCQhWm!8g=@'"8ږ8tphKvЭJCUJCkW nbph{JkNܕ6E!3^ j*89Ɖr2N\;7NSi N Az9䢊10X*G"b[*G"nHJG-RqVʥRVMRqX:Oq¿1BN}׷[У#JGʩ4zA2*,=%FXTNxTʃ}S9 8=OArGz/LvzJ Si_m T]+zÞ4z\o3c~8=~=~_DS6RQs˹Sqz#Sqzg~;?or?P`*uT|T4TH*uB%ݞRqz<\y5zT =ډT=R$*mT =JcSX*=.㽝cΩ$4RS60x9aKBKBKBKB6$t$t$t%%%/ / / / /',xIxIxIx9aKBKBKBKB ^:^:^:^NXmO/'sre/'sr2/Wr‚6O+*H}R)xT ^UT^U*/)6UT H*RqUT H5.KKSi wT$uT Hfܺq545tT HrRH90ǵR@RhSq|ْ׀@VPR) n˺8H48HTHJp*$w!M*$uFJI;c$rzs*ΑN H_&$o+Wf*G&S04٩4ʔ*T S&gßXcJA0nR)L3T)/Cy_/'VzX_5V6^R7L/q=+w*ΔDc)d0/hL;>RRG7Rsz^_cJ݉;”T S'gJeh*)@}eKřҟS)Lٯ`+S*N1b(”n2eLYo*S[2g_z{f, )lN%%%% ^:^:^:^NXr‚$t$t$t%%%%/ / / /',xIxIxIx^ /'sr2˕r2/'s\YKBKBKB ^:^:^:^N/nvؘ>S~NEArcJԋ **HУj%B:IУVnOѣ^S)ʰ\^*u{H*uc*u{H*HT}NJG;5Bo.;ڷ S) zTRQJGJG=Z#B:$FУiR)CУLѣBц4>qIUyFTwxDƛKR9"GdMEFuI*uJAҨ_˼!Ꮹ!žuU6dTʜJCF]Nő.XY}}_lZL+Kʩ82ƣB##o놌#RRQ)#ӓ#JAF]>8&GFL*us* _JAF}O cTȨT 2"cmsd<9>ǩz$tz$tz$tz$tz\aGBGBGB =:=:=:=NXqBNNNz$tz$tz$tz$tz###ㄅ   ]~~+HHH8####d*=&TzLHP1cBGB 2P91qbBÄ;Ąm:>ESiD(CS)DhKSiD(gRp_OáRPIeJ!BOKMKKJ!jv@*NzA*M4"4ULʼnPg S)Dhwϩ"4U҈%5Sq"E"iT ";N4"T TDx5T#=u1tUSyпr d* e4WYLJCLf*M4Sq8d*RR|0] <C]NnQááMpۤP)paCsyxád*:iJyZ:O.;RÆ@=;@݄$mTY͞_/`c 'o?w5V0sg퍫`ʰbN` /g+r |Bh* uҐJ~+1J668T` @B@B6 $t $t $t ` c c c    ',HHH8a@B@B@B@B :::NX0111W0p2's \Y's 1p25 $t $t $t ` c c c _d $t $t $t `  R0P R)hRi8@w[L1pB9e?2R0PG 8ކTE2@T}cKLa94@C*mT ~ i*u֙J-+>0[*u2vJ@Z`Tx{1cT~n+g \߯?l5|Zq8PpG"ԯLK>i!BT Zl*̣Tu0y*'cf%B݆2Ncῃ|5"UTy>f~ݾ'B_0B냙g׷Fsά/~f4"TUԈ߈PSq"AF*׻*R)DHmH_RPG "TڈT!'DHʼnЉЉp   ',DHDHDHD8a!BB'BB'BB'BB' :::NXЉЉЉЉpBNNN"$t"$t"$T"|[!!!dNdJdJdJdJ"LDP0!aB%„J  *&T"LDHDP0aB%BB'„0D"LRiDhS)DhvRжߡRP.Rs^CLʼnPU1By< Bso.*RЦSq"R)Dh;(RiDhR9Mb*MS)DURPwPLL>J!B㑩/]2@"LLITDx>*g"]*G"\ Kx_ߔ &<¡~ g8o WrC=f84kLpxmpR)ph'IpSq8gaXhpxphC89?NRPpO'Sq8g8T yn*%aCT  Tu4u5P*\T;2H#SQ8LpPp *&T8$t8LpP0!aBÄ *:&T8LpP0aBÄ  *&T8LpHpP0aB Np8d++p8d9Np8$t8$t8$t8!KAp8$|T,4NBT,4R!ȩ8NR)phVRy0[HsT ~9*888-ة8ZRq8Щ8}Sq8485n80q{%78TkRPJCƩ48;w*:JCYGJCGJC0&:(ol5ǨFpTpX?#o!K8.Pg8i/#^S9m6jT ڡDT88 S)ph;CP)p S)ph Sq82_e>~O^~u@*z`L*J*_ =JC۠12mwmJC=%/YJC&/:RІT8!T ːb*?ƓTWؐБББq‚d$td$td$td$td ######    ',HHH8aAFBGFBGFBGv'sd̑q2Gƕd̑q2G'sd\YCFBGFBGFBG 2:2:2:2NXFHH8aAFBGFBGFBG 2>AF*[9cR8c✨4Nة8'J*#DȩNtR8_05Ndfs_?3 'o*ueT'ζ=SS)hˤS)8Ϗ~[&JD[&Mr.R)Tp"'H9q   ',HHH8aDBDBDBDB ':':':'NX89999q‰ΉΉΉN$tN$8E}4N9q2ɜWV8q2ɜ'sN9qe   ',HHH8aD—2s"s"s℅   ',H4NO{*횂JDrʓSyp~r*moT '4T'*Qyb*Ɖ;lp(>RP M[#Ï:ZL7XJZ#C:Pg 8ҩ8]*mTt* JCӦp[2RgJC;~"3^WBS9u!*'˚8FsT Qy"jT 6\_b Ja6?>?@N)LBP)phߧpsT :2nT zk*Zu7C_<*z+*rP9}%4'rrG!Ry"'!RyT '>!pTq"#'(ʑEp2i*GNNȉwR8QJgY?AMpo@pὩq˶R8QJn^9QWVS)CT '.T '.S)8R)ASqN^{)ONeSi-WW/R8c6Y}ƉnrJz9ц4N5Tp"6N$tN$tN$tNp"s"s"s℅    ',HHH8aDBDBDBDB ':':':'NX899P9׻s"r"r"rdΉdʉdʉdʉdʉNLP91r"sbBĄʉ  *'&TNLH蜘P91rbBDBĄ81 Sy9Yds*mY)N|ɖC~J8ԩCT쒘JDT 'ڎ@T'ڒT 'u*Ή8'‰+|*ΉzL*m:#DIpMPi8\@✨NWS)hSh S)hJą~Žx]Uʑ,D ̩9bO`[*gNĄ:P088\Ɵן8 S9¡pxTpxT ڴc*v* mT@T 5RОp}]NI)5iR%pK48\E\T]l>]+mD*EKs55[]T Y.8b*_"968Lp8aÄ *:&T8LpP0aBÄ  *&T8LpHpP0aBÄ  *&T8$t8LpP0۩9Np8 Np8d9!!!   ',pHRP#t8$t8$t8!!! '$v.sJ<8^%ǫP9rrҎWS)p8cksUltJCF*x^)pxWT19uW 8P*NSiphS8bfoJ  .w-Tu#ìݍ^ipHM;RVRЎDJC,L:L:fR9r Q9ͫbTpx㺩(6krNL;HxDFwxī@R9r‰=sM rD}9.TNxvLpdJqʔJDH*Ή7ƉƉvf5ƉJD}D91S9FNdܹ*'Ώwv~~uC*Ή(]d4N<\/VN׾mqP*,AJpN&Lq JDH*u0q9 HNő,N d֩(S6jxIxIx9aKBKBKBKB ^:^:^:^NXr‚$t$t$t)r2/'s\Y/'sr2˕5$t$t$t%%%/ _z$t$t$t%RR?©\sۏk[ZR/zAI*/y|[٤RrnG18^:Q)xiKoR)xi+թ4R)xi[ xytJK&g[R7Lc*ARJ׵R2K/T ^( KRgJK2Kݐ-:Ez orTxy2#^^7K列׵<˩/Iѩ8^^&IIWOqJ!MZ/K(iRHSPRqҌ.=yTRHSt*GҼ+(#iކIYTTBWr$yשY3o84}mXJ!M]DNpR)iORH6ҦRHסRHӎNlmb%y*?J~\ ir{))B T iJT iRiYo/;iNiRq$t$tҜ&&&&愅4 4 4 4',IIII9a!MB'MB'MB' i:i:i*i~?'M2%M2%M2%ɜ4ɔ4ɔ4ɔ4ɔ4'+PI3fB%MB'̈́J 4*i:i&|%TLPII3fB%̈́JN iH*4-BJ!MJ!M]ȞJ!MHImJ!Y*}RHS(LIS0'⤩47HCVaoB,杩8^g*/m{*/mGT ^9$V ^v^%%SixZMu*/퀞T ^wxޙJKN* /GR9MaR9uq*g'.NՐ%R֐іRF]qPF-S)?2>@2>+21d %SqdGT2b^qUdW  w HE~SWUdwo"7?0<EƄ-SQz$tzLP1cBGBDŽJ *=:=&TzLP1#cBDŽJ  *=&TzL{B9=N8 =N8dN9=###ㄅ   ',HR#tz$tz$tz###ㄅ =8~* Y*m3B:F_M*N?_LQLУ^Ll9_޾Rq.RzS)8y]Oܦ)mGT HڲT HaNlR@vJmT$C!?2{8Hvx*$m%IsI L IS) i6SqԕTHaR) i+]S9uT yqr,_*vL7#rT S>aJ*Ɣ;4qBREoZ(LIxdJLy-MȔ7٤rbq*)T SKh})my*)oF) T S"r*))_rO*ΔN]x{kLiwR6RRNřsRqCS)L&T SSCT RrN6oȅ)ԕ)U6wG)=XeJͩ4췠Δ|_T% ^:^:^:^:^NXr‚$t$t$t%%%d9^Nx9^Nx9d+kxIxIxIx9aKBKBKB ^/ / /',xIxIxIx9aK‚1@=FRRJKJKR#ߝdR)x94?K=JK] %z.h8^5K^*)rxtR)xB<:J७ORL~ߗR)xLSixiS)xT ^1 ԉUxi S)xIrci|]_0/JK۩JK٤RK儗cR9~rTxyN*G=W"_v¼DRyBT7IxU]RY(v}_Kӿ3ʑ?usSi9 .'RsٱR+<\f5JSiigRs/;~a?snŸ:ISФs}''VSLӑtN'?v;ԉS*Ο>qJ𧭄O4 *?~?Rޑ>O*O焅? ? ? ? ?',III9aOBOBOBOB :::NX}h9dΟ9dΟ9N9:::NXsŸ/eABOBOB :::NX Riiڨ4'*?ULS?8ԡR*?Y4*?T?wXIԯ@gйB/*(Bz%8F:IRHe3 T iڎ4;'*4XT i] 'MCs*4툇T ii ?RHs5HӇҴs44A+*4u*Bʉ4nr&͛R9DTỵr$N/y0*=/pJH7r\L~<*Os*ΫZR9B:y]* tt&<]AISgҼm@HR)iSi<'~\B֧,Wg#l]58is4<!P*4uq'_#Mp8B<ʥƥ$ż|_~\4un*4 9^c4L4纇'ͷo(D΍J%M;4BHJ!MMRHtҬ43Ҥ9a!MB'MB'MB'MB' i:i:i:iNXHIIIIsBNNN$t$t$TߣBdJdJdJ9i)i)i)i)iNVH3fB%̈́JN 4*i&T$tL2KPI3&fB%̈́J 4 4iyg* /c%˄) CS)Li4 MŘ2aIT $wWzLȘ ;kh;ȳ%sxKőQ׉Rq}I9V)hǕRvĢҐJőȳ4dzR)heSiȨ'ȨSqdIT 2ڝl*z:vW#nmJAFT 2cP9"m:3#2^R9"2S9"2JJwd*GdZY*gd$:WL4e*  27i_dL匌m7S9"u22@CF!և khd\sZR)8߯ ugu829822T 21t5VpӐQWͧR%SqdShGȨS)c4d) * }FGzSQd$tdLȘP1"cBEFBGƄ *2:2&TdLȘP1"##cBEƄ  *2&TdLuaېq2G'sd\YA'sd̑q2Gƕ5d$td$td$td ###### _Dd$td$td$td ###!wp8whCg vGGhC hROJ>RVSig1RϞ Ro.;gT F8IT FMRqsRfR/R)7[u۵RO告NDRT 8 R)gTwSvR9ߝocߪO |NR9v:Hxc*G.rQ9yY<I3,*8S4T ~v*~Yf*lQT* ~v@O*lCT T'T{gGo~~~~~#t#t#t߄&,GGG?~6o2gɜVVo2gɜ&soe&,GGG7aa?—2߄&,GGGG7aa?Bg?Bg?Bg ::::MXφ043)O)RώIJa8–T پCT}D~[oǷ?~NLgۨD9g?Ui7T| mJa?E*~>p8~:=HU[CwzMeJaKdJ째HTw[;ʑnPC~SS9mʑn83&ڨTwk7ғé6BJ MQ)sT ' o"4;4;4[X ͢v>oi T C4sSqsR>pH>)J>[/QKS)7"5ROuJo1ߗP)7ϔ.8RMvҀ_WQ@*||*qr6*,8NXЉЉЉpBNNNN"$t"$t"$t"!!!ׯF9ND8 ND8dN9!!!ᄅ   ',DHRR"t"$t"$t"!!!ᄅ    ',DHDHDHD8a!BB'BB'BB'BB' g!FK R)D0SiDh[Rж$LmIJ!9d}aI3Nጫ}bKgI~]Mʼn;T DSiD[ƧRPWQ)Dh;2D;T ڡT !BT֕J!BJ#B;b!'B,'BW*:҈vdLʑo>6HH7ʙߤr$9?8NXH)<_0'KR!Bv0K*(k*w1O*woGSRPPq"+?}~׻8!BJ&J!O[`*c%BJ*e/W6a+KH|?MSDh)RV7Rp>"4URPU1Bx*gT ^:F"1MS1",ӎp(e*ʉ  *'&TNLP991rbBĄʉΉ *'&tN\'sN9q2ĕN9q2ɜ'sN\YDBDBDB ':':':'NX8DHH8aDBDBDB ':':':':'NX8999q‰ΉΉΉΉN$tN$,hOR)h4NTbJD[@JDH*Ή:HqIp^RqNyݾ0D] ˨qy_R8QJu+B*Ή: JD+JD] JD5R8QsHqJDI9N\uu}WN\k-jJ9Q[R8q3Ɖ<5Ɖ&S9r]Ũ8TNxL4^xY0F6LУ:T=QƩzTRN?=EF 4zQT =ڤs*NУ_JG}^3'RKQ"Nq*G]JG[~zўծJGX1%\oYT = $F#_L\^*=ΎDR \#ǝE~RQJGJ*U+ش I 9aIBIBIBIB H:H:H:HNX@AAA9HN 9 HN 9d9H$$$$ $ $ $', IR"t$t$t$$$$ $ $ $ $', I I I 9aIBIBIBIB H:H6M*$TH5s%s{JA JIR@VlS) ]LAү38HbJI[4>IsDP) wLT HڦQTHڪT$0TäR@VlS) ~LNP) IAR?@ҖLR@R8HT HXSi VF-M@r_BzTߥRRюJQQ'z_%˯Sqz:y8=š4z엄m'T|~+FPi~wRQJzsSQJzUQzH8aGBGBGBGB =:=:=:=NX}gɜ'T$qe'szq2ɜWqBNNNz$|)U:=:=:=NXqBNNNNz$tz$tz$tz####ㄅ  #B:>9FNУj%B:qI|Sqz%4zTRQ/zCqRqzkUO8=~߬zzV =.`![УmӣCѣj%Bv\M*N曊\/$Fvnv*N>CУj%Bq*}JG;6BvM*N>tFУT=*|Q)3wT,_6zR*F;,hbȸ‰Vy*׫=^_m&JDKR8QR9qrJD1'H 'NqS)hR8Q4N4NR)u*#‰VI9ѝs?bR8QOpL۵kqN\_'X‰L~*T 'L NԽ4N7,U R9|=D*ΉΉN$tN$tN$tN$tNp"s"s"s℅   ݾp"r"r"rdΉdʉdʉdʉdʉNLP91r"sbBĄʉ  *'&TNLH蜘P91rbBDBĄʉ *'&TN$tNLP91r"sbBĄʉ *':'&TNL8Q8Q>4N4Lp}:R82‰vJDCK*-SqNԅ)N/N˩4N?NNܩ8'T '‰:/sR8vLp%‰L7-.hJDL91SgYw8'^{̻UN8n* XR)~3+XDp✨  *3갩N4K,Z,P+OKCo"`w* e3'ȘdL,ʆ -ri* m;\_!=OJ8E3!ᓩ!"Z/.r# !#aDTusT 'MdTRQ]JAF2RQ)s* mF*m+T 2T 2ھShS)CeȍucT 2KRqdKdT 2*eRqd"*:I ╊#S&GFI M Q).I MFA - M maG!_R36oe+THbT H*Rqt THAR7S HVAr@i$T H*$mP2vtl@1T1c*$T6[?* KR1ܡ wXqޱgWҐܐ)s* m2GF=(0_e*SihdQ:mQ*uJA~_БI.IőR)ȨSiد"+2?82yܩd|T2T2ێfɑx֐qBEƆT   ',HHH8aGBGBGBǯ_э'szq2Ǖzq2ɜ'sz\YGBGBGB =:=:=:=NXTEHH8aGBGBGB =:=:=:=:=NXqBNNNNz$tz$4zܡ!c‰Ɖ;t8$48"J%"RpFoDHyf eũt""҈P6O#R)DhGcRЎRPONʼnP7jOʼnPwLkJ!BK*N>ZIʼnׯQ)DC)S)D2T }2'BzD 8]NN1Q)D9wӈNMʼn-f*"TRP" F*"҈4"<|/uJ!B۩<"B*ΆN#R)DMTSq"kK1PS)hPi8.*ڂT +^*gvѮ+ڎ4 aT<@*O00 *:&T LP110b`By N8cd++8cd9N $t $t $t ` K111p‚ $t $t $t $t ` c c c    ',HHH8a@B@B@B@'8r*XJ@[OJ@نz* uP06\ʃcPR)8+[YZ)z-:F,;'DHᇬDaKhhkS)h;=R03Q)89؏~ S)86c__Z@J@2@R02ly4 \_Ǐv.vn* ]*@T]Ru2#:@]KE1p~;t#|`*Sy|T XS).N7/Sy| jdh;|6H_TS)g#==SR)gK)R)gK\R)7?3RqA* l@0|a*೙B* |1T[nJ8q ˼QyAW6*cu]*Jq}A7dT1JEFՉJdwR)Ɣ_JaJE7*)U^T Zm (S HR) MT*H?G˚*HcD*$!+$Si yx<H*+$T!@UV<+L>ߗa]) )[@r~5瑻R@RF⯂.JI1]qKan I@RJIܕbR@R sW;H$w@r;4L CHA׵C$'sAre$'sAr2$W@AAAr$|)`:H:H:HNX@AAAr$t$t$t$$$$$ $ $ $', I I I I@>ʦ@R?0\~E T HX*$]) )KwA6IG7*;~IS\JI& 9aIYk+gIݕJSi LAK( )c 9zEAo ]qR) wT$]J8Hr+K~+f*$miW$m8H:R) 22=+nT HTģR@Rǧ@Rٓ?zW/w+}v*@JI9xWH.R1_M\j:2*2g* eS])ȸ>3اd4d^Si(CJAFY+a%r">`JC~=[_6Vd**jnP6^A qPih"T 2gHőББq‚^[2N8#d++8#d92Nȸd$td$td$td #KQБББq‚d$td$td$td$td ######    ',HHH8aAFBGFBGFBGF‚2JCFRQ/JAF}Hő+,ߠRq.n.I lM+2d7RQ-o* :JAF}$M / [q`MFnI{ie˪o;kn $WhQABesNEn֝ꑵSX=UJى:a}7dyc)zDF=tJ)iI;# N4VSX=R; Y2Aw KFQJ#Y:%#;V4USX2rER:ɈU\Ni$#zId)wJ#aHFTJ$#?4ݻ4)ե4;哪G|"’ѐ$!r⛽Չ0lu"AN|s~藎PٝI͢R>҉NtwJaDhsNdF'V4:qhubSӉNit"LSM-F'y_^'c0։oGzN4dh:QщjLd4$YY|&գG1V(QգG1V:hѐգ!GF=z4dhQQ3*CV Y= 6ѐգ!GCVz4dhѐգ!GF=z4dhQQ Y=z4d(بGCV Y= 6ѐգ!GCVz3RZHOќҩG*tJSWJԣ V@)zh:SH=ސ$c`am;u"a9u":u"at:wt"JK4:S2Y)ND3d4JFvJ)h*Ӊ8\q D։\։煃NwJQ:u!DjyNRH )i;u"4:/D.rJqStNa:q ԮӉ9:N|]:fu:AW;0,3s4*dNi$:! ɭ^߭:o~'$()O]tJL{'QS|RNH׫VHWVHBtB\wBSXHp4B / on[!ُ3Z!QTm4Bf!GCVhبGN!v@ٳf($PHc!iB  I3b D!B2! @($QH I` D!BҐd @($ YH D!B2! @($QH D!B2d IC($QH4d!B2d S:!IՒId$taJJ#$zVHU?)Ia!34µ33)\ ]yIi囯Kh}4ͻ4k%ZդDF^UuJ'/7)(/yI%Ii%ռ&toF^b:Ѥ46Ii% uJ#/4),/q,/q|RX^b04UN~pvIayUHIay6)Dѝ KUF^R]S:y +)$/ohwєTFSb`RMIj/)$FSR`RMI jJ5L kJlސ֔8u9)$NSnYK]uRMIhJOJ)),>st6%є!)SMIf3)Lt,dhRM_2[MISʝiJ铔FS7LB4co5%,!֔)ޛ|pHRHih?%)GϺ%lO1֟b?5SO1֟b?uӐ!OC֟4diӐ`? ge? Yl!OC֟?iӐ!OC֟4diӐ`? Y4diSџ? Yl!OC֟? ;y?O2N!yNtu NVi)4oKF^4ͻtEyyCҔ7zĻ4UҨGr G[TJ";ayu I&@XϚFHFUJ'$.)Ԥ$R!r),$q4d!"Ia!I 'uQ80)ԤEړ ITNa!),$Y{:$*d馔FHb_RXH*d倫NHRI_R!IQB%SXH㤰AtBr?;`+$BR!v)DUNH|4B?d;!_{J#$)T^I 7E7dNi㻏ЩGNic#NpJibxRH3jͭQiS:LLlyNH3y}ԏgNiwd ƸhJ܆h I1b,$5BR I1b,$uBҐ! IC4d!iBҐ`#$ gX,$ YHl! IC,$!iBҐ! IC4d!iBҐ`#$ YH4d!iBR,$ YHl! IC,$ !Y7 C":3aSy)D[!Nij,щ:4 qzw N6DxC։ Y' v:W4:]SH'ްc"D#FډIi!:!N4P(BtJ)"*Otڔ&Q4?) EHe(B4Ji!S"Ħ^IaE)NaE;Q\4V8<)OgRXnq +BvJi! *k`Naq]XSJ#),Y9o~[) e@tnNa7@'9w7) RCt2K@4qN!xCCd?_)h?4H)~)4Ii>t k7#4/~nZJ),޽K#}F𽹯kjD|"? >1|b,Xk >1|b,X! >C|,gȂϐ! >F( Y3d'>C|, Y 6ϐ! >C|,gȂϐ! >F3dgȂϐ`# Y3d'>C|, Y lT!W!빂nȕ Y6~h9҈8fR2ZN3lUJ'r: j?TN!i9CkJ qJ#װx)\ê~4r Sݻt ~9QnXFVyNQS4 :ۛϔs$|tM+71Vn&M+71Vn:f͐!+7Fr3dfMQn3*CVn Y 6͐!+7CVnr3df͐!+7Fr3dfMQn Yr3d&(7CVn Y 6͐!+7CVnf͐!+r3df͐!W 6r͐ Yr 35ær բR)hԳ tJP:hB-fh34yN!avCRc(nug\FStJ#),x^R ɓH0"FaeSXB)~4 USXqeSXqgCc;Fq;.4 g9%!Kw`o>BSwƾ)C FqJP UJK9Qchj̰Qco>uW)07g+̰)3|45)1F3d5fLBEզ&:ffHR:aFi7gVaSaZ)0Ғ3l(F0㙾Naa=ݻt 3rNi:6| ̞[  33fb,P03CafLf(Q03da,Y 3Cf3 @f(Q0 Da,! @f(Q03da,Y 3Cf(Q0 Daf,Y @f,Q0 Da̐Y @f(a,Y @f,Q0 Daf,Y Nmݯp+ȱ%+;5.)nԒs !@]E0)82[҈-rN鳤 IiMpJ'H%>O -츗[8IJ#h@Nl$),P%V`#h94b Ib Ii4bJR>[JX4bݻKQIituBtJ-ƺ$nH +e IKT`3XR>RNDK%-FKlOJd9RTpRo!zs*z.z]:-՟[-ԛR~h4ZZzc-%ZJTFKc-%ZJTNK2d-eZJR YKlÐ!k)CR2d-eZʐ` YK2d-eZJR YKl!k)CR YK 6Zʐ!k)CR2d-eZʐ!k)FK2d-eZ` YK2d-eZJR YKl!k)CR8ѐ'3j2$tCJXYe#CG Y vSQ9J(PsJP9QMhҩ&*fKJPh9QM44)pSՄS/Ҩ&ZNiTw+IJtJRX5a SՄSªS%6tf'%P8rJ#pPN@ANR:ER>PND@9Ph SzwAURz+rJ Ya6lNl ؂ NHl9_IiiL @TX7$YuFKuEI'e (Cď \SfwͶ:+zsnoTL@=iN@c%X#X@c%X' Y@2d%(CP, Y@ 6pFaaʐ! (F@2deJP, Y@2d%(CP, Y@ 6ʐ! (CP,eʐ! (F@2deʐ`# Y@2dUP, Y@2d%(CP, Y@ 6ʐ! (CP2deʐ! (F@2deJ_2TTBN*rJ#C)JXuF*rJ#P]9JI%TWNaĵXNaSR WJJ#v)JԷ8)TvIi 24R mROH%,p K%yN*"qJ#P^TJ+C9JޥJwi([6%GZ>RJ7]SX*6R/I%PNR9;}NaBR\nj؞9޾ w_P)BR7l:?tԭ"ͥN@wJ'&)(|" (1Pb,X@k (1Pb,X@! (CP,eʐ! (F@(, Y@2d%(CP, Y@ 6ʐ! (CP,eʐ! (F@2deʐ`# Y@2d%(CP, Y@l! (CP, vʐ! (CP,eʐ! (F@2deJP, Y@2d%(CP, Y@ 6ʐa'v)BgN@R 6 SՄB)jBN5rJPh9QM[_=^Ҩ&Z4)؁)QMl?)B|яTR:ՄdNi:CQ;PBIi9SBaRX mu<)%T m~~^J9SB$(!2lIiлwiЛ'6|QR:+)Mћ?oNPOR>ʔ&%E.)(nIi2M=)]WM'h7r(cQ$ƢHEQF(cQ$ƢHEQN(2dQdȢHE, Y(lDbE!"CE(2dQdȢȐE`# Y(2dQdȢHE, Y(lD!"CE, Y 6ȐE!"CE(2dQdȢȐE!"F(2dQdȢ`' Y(2dQdȢHE, Y(lD!"CE,QdȢȐE!"CE(2dQdȢȐE`# Y(2dQdȢHE(:4ݒS:QDnI)(n(!ON!%tFrJS%VJ)B+F Q;4Jj(!*,rJ()()IiJ+SBz]:})>z;}4CVR>GJ*PR9(R|0-)TzswRoI tIijzTҭ@GSВHڼ?H'JV?l8 /H%1Jb,X*kK%1Jb,X*뤒!K%CJ,dRɐ!K%F*(! Y*T2d$H%CJ, Y* 6Rɐ!K%CJ,dRɐ!K%F*T2ddRɐ`# Y*T2d$H%CJ, Y*Tl!K%CJ, vRɐ!K%CJ,dRɐ!K%F*T2ddRIJ, Y*T2d$H%CJ, Y* 6Rɐ!K%CJ,dHR醤nH(QBnǐ G:䀜,3rJ#t'i|- r . RJ't7xRCBGNiQ;UJ+th4B;:waةxF0S%)4[ҩ4$NiMw=Q7udxmSu]uH)aFݠq')"t=Su5vNa{rnЎ:Q7oFݰv 9V7\FݠTQJn;Q7D/)~IDQ7Ni ztiƔR:uaҨ0Ni zywvFUptkՍN-Fݠ!qJnTo3oҩ*Nau MyQ7fnPݘcuc Ս3T7b Du&Ս!@T7nQ i&M CV7nQ1du&M @T7nQ Duc&M @T7n Y DuƐM @T7nQ Du&Ս`nQ DuƐM @T7n Y DuƐM @T7nQ Du&Ս!@T7nQ1du&M CV7nQ DucتN@NRuC:Ii MrJn$UkGOJnh$Fݐ JJn^)nnH憍QWRCM'0|됔FT4:$:NiF-KFT͋ۛ߿Kc$|P_NY㨍pIJ#ilNҐqI K\k)A嗔FPXRXD4OtJ'ihaǤ4Fz’y4SIcCVP=hRI_>4_I14b,iXk$K14b,iX$!KC4,iIcȒƐ%!KF87dIcȒƐ%`#i Y1dI#HC4,i Yl$!KC4,iIcȒƐ%!KC41dIcȒƐ%`#i Y1dIcȒF4,i Y)IC4,i Yl$!KC4,iIcȒƐ%!KF1dIcȒƐ%`#i Y1dI#HC4,i Yl$!KC4,iIcȒƐ%!KC4?Ii$ $4uH:J$ U$4x4<p K),i R:Icȕ4X{FݠrJngS:uCҨЕV7XFݠ"P'+b%Q7;Ҩ*cI V6G׼iIaI)IN$͛48q())=Ii$ zZ4!H'NaI J:/on R4+҈͒:\RZҟ>/ND8r5u-XxcR/b,^XxcR/,^ Yxlċ!C/,^b8@ݐŋ!C/x1dbŐŋ`#^ Yx1dbE/,^ Yxlċ!C/,^ Y6Őŋ!C/x1dbŐŋ!Fx1db`'^ Yx1dbE/,^ Yxlċ!C/,^bŐŋ!C/x1dbŐŋ`#^ Yx1dbE/,^ Yxlċ!C/,^ YFOJ#^8 Y,Sł)Xn2Őda״S#42}S 42;)zR2Na8Ol,pJ#SpnSHtb)bfNi b4;9Q,o~N/qJX޽KXP8Q,XAR,Lyۦ$4Ee㔷`n|2)Ȕ[)f(SPLcb2 e3)bL D2%e!˔@)(SQL i2%eJ C)(SQL1d2%eJ ʔ@),SQL Db2%eJ ʔ@)(S YL D2ŐeJ ʔ@)(SQL D2%e`#SQL D2ŐeJ ʔ@)(S YL D2ŐeJ ʔ@)(SQL D2%e!˔@)(SQL1d2%eJ C)(SQL Db2冤Mn$!  !J@(={&) OFz'IJ#=ȓ8I%r4$)IacؤVI @h 'd \'4C)T$Ò *aJJ7itRPovzSRfWƛ߹[S:ж4z#Q7.c!zCFFoc!zCFNo0dazC7 YolaC7 Yo6zÐ! C0dazÐ! Fo0dazC7 Yo0d! C7 Yo6zÐ! C7azÐ! XazÐ! C0dazÐ`7 Yo0d! C7 Yol! C7azÐ! C0dazÐ`7 Yo0dazC7 Yol! C7 YolU$PFoqJ7Ј(8pJ7h)7xFo`ߤ4zSApMV$o Ioܰ4#)]$t"SAe*Ii\4K.䡤lDƻЉ t)NiD'(҉ Fd9[IDdcD݈ 1b,2XdkD 1b,2XdD! C,2a"ÐE! Fd85da"ÐE`#2 Yd0d!؈ C,2 YdlD! C,2a"ÐE! C0da"ÐE`#2 Yd0da"C,2 Yd(؉ C,2 YdlD! C,2a"ÐE! Fd0da"ÐE`#2 Yd0d!؈ C,2 YdlD! C,2a"ÐE! C0da"ÐE`#2 Yd0da"`/2`OR:AND4"݇R:)Ƞ4"uSAt҉ |XOIiƦ' o)ޠUzCԱ6)7؈8X Fo#QJ7X9ovY8Zc!zCFFoc!zCFNo0dazC7 YolaC7 Yo6zÐ! C0dazÐ! Fo0dazC7 Yo0d! C7 Yo6zÐ! C7azÐ!덂0dazÐ! Fo0dazC7 Yol! C7 Yo6zÐ! C0dazÐ! Fo0dazC7 Yo0d! C7 Yo6zÐ! C7 6-KEND4ňSeb),;9)$'nhT*Na ahZR)@yFCP/t&88A) 4ht Ni4 hBo>B!|NC`aS H)4룜hZ+)h1"9 a 5j1f!PC0C !h@!QCD "5!k!QC0d "5D jC!QCD a"5D j@!QCD 5D j@! YCD "5!k@!QC6"5D j@! YCD 5D j@! YCD "5!k@!QCD "5D jC!QC0d "5D j@!QCD a"5D j@! H$njS TّF-I D0$D$pC. V8*).Ij!)tSZ]ۤ| .H ) []$Zx.HJ lR>ND$]jy%]0?a X.c]Pb X.c]P Y.lt!C ]`8Ґu!C.0d]`Ⱥu` Y.0d]`Ⱥ@ Y.lt!C Y6u!C.0d]`Ⱥu!F.0d]`Ⱥ` Y.0d]`Ⱥ@ Y.lt!C ]`Ⱥu!C.0d]`Ⱥu` Y.0d]`Ⱥ@ Y.lt!C Y6u!C.0d]`Ⱥu!낂.0d]`Ⱥu !+CVnȃ}C`R> B<)]T;SM}R>-f_^b]) 48w /F:NL\HJ7qpJhBR ۟9I`BR>Q_)1VbXk+1VbX!+CV` !+F8d4d` ` Y0d (CV Yl!+CV` !+CV0d` ` Y0d` @Q Y()CV Yl!+CV` !+F0d` ` Y0d (CV Yl!+CV` !+CV0d` ` Y0d` ` Y0d (CV Y6 !+CVNuKJBRX`tI@??MaFAR&L)R(ꅐ~/ | #upHJׄ*. OqJ3ǞI0$$`S7x_`_b<~noȃ}C`_`ߐ<l3 yoȃ}C 6}C`ߐ`ߐ<7!! yoȃ}foȃ}C`ߐ`ߐ<7`37! yoȃ}foȃ}C``77! yoȃ}foȃ}C`_`ߐ<l<7! y/ yoȃ}C 6}C`ߐ<l<7!! yoȃ}C 6}C`ߐ`ߐ<7! v}C`ߐ`ߐ<7`37! yo`_)`FANi8)`K R>6nH:Y:r>5KG3r`_]:"~_Z")`-I)ґS]cS>;??_#*_>KiQ\sUGWesz -O/﹏YwGys:1Lsi[ɝyesO!qơ+ l#櫱!|޴ZRU_pv ~QB{nZsmtG(Hg9v:*~l^"?>Nlv|pYye{wc<_9LjkyNטJ.|e Olu=u܃|=v "RFٞ%#9u<+]>Vw4tC>-{^ꟻ{>}yW->:?o_p~qҙjY{?Z]wo8v+@~W"=kJ~_Mǎ6sۯ-72^3< ?RCUj:vd7\O @cS$izK !ֺ;A22?P\пJs%~_xby&7/Sc{57!@ kzk8t*|?/s^o~.Lr >~g=8Rb YyHy+.Lth|c_WC>F/yg<\ዏwz_ׯs5HwsTq-1z!O$Kxlנzu6T_c;¯;|q//bH}7;qp:5o>k_cxG^'I*'ZTyR<;r,Rr?㱽4]q<+땿_j@`u>>x?[_KV9l?~N_is<+^64D^{=tssk?kdF8XNzG :'>x#c3Oǫ*XM>o7+>R&Ϸx89_>sy8B? 4=b]/Ϻᓗuur?6_OO#V%_cJ%XKqXOk8/0?3u:9^^XǧGs}~C(sψ~~dz$~3U9OkOMxU9f/Żq|?<_G3ZX:Fq)ZO}^_K׿3U_Cu=zu>.}g\Ǟ;ֈ|Tϵ׊x8渎/W,>UwoxoXOަzg^oPŇ!qڿ|5aǣk>u~Xzkokb._+VHt>q\_z9}8O5|ה݈y:)3 5zE>벱9;k7q7.#*^t}߷a*xW|%W@#>|L|U|'#?1&wwuyq߯V]YYE>׻&e9Uri{lq}-x][KӞ_qYk_euGkjzh{xQ^_$5qO҈x?t41~<^xcCQCPO]y>xL}/cOq.of ]9N__Gץɯ.'G<&3ާ&~_%^_Tݺ=ʿ~ANSz>xx (#4m|SVtYb6^OgڟP}R}V;zK^Z*gjRc`\5A摿OwMx˪sxI|{W|*>]d]VWuW'N>|c\+qٝ;>bmSo==uc3yq<<|,xs<) #I-g.ϻϸ>.?2>uE4{͚P-#޷?~d~IYWG|*xN|=SM+ޟwRq\x~.ǙƕׯAxr\\uM|MyK7 㲪[#0X>{w'Yە=/_fA~\yJx~>Qtz[Emleֱu?4o^'G{#~cd?;Ut9Ӟ߮#>'}È/+?VL[7f>qZgœZ^x V(oX\x1O_^g||Oq]ozWk^{\22k==^/MZf}eާKG6F2H*e+OWEח/y:ݱ+S|R7M<ϸ kK9/mnKR')べ:>r {,2jD4z?*8|h|zIi,u#\X+?Qׯ9xtxO5mSb|_S7lLYg9#U[s>T%ϻzܙ9~lijϼHr\s_uO嫾Ż-wm9.~[_ySu[5I9lk aub>}t3b}+7mo~\_{kݿ?ij5Y5mT>ǫ 6Z>OVO8|>\poxwӭ\KmxU?6|w3Kk=Xj}?%Gg7_~z=oEWƷw.:]SxKߡڞت@Z/Ļ*kKiy_q>ϩxbޙK*;{Ueee\&P|mⱽ?KV5ZrS|͒S{^{u|,S~ϚVc_ouX|-[2>_F|5^k<ƿKjL6|{mj|R_A\ gU(N~߮񥿟}_YRx'ƿi+ՁUsb*>~~O՛m=z~n%F$wx]rBW󫋯?&({^r:şoOX\M//5{Ê׍{Ň650Aq[?K|M~yujx&iV|6k0 ?kvk|V ySG>M5#NM_P|::e^'M}cXw #>~cy~y1U[*^5>]g_7uX&Wx ?~~iX1b=\s_zPUXxl~K?Uo[xkG:p*<,Q|ħ^k|^(-bLz+_}1`aUx\V7'>K0fsm<53ty|t'Oy>]1YħG:>}^F|w^Gj#~dWnW[xvy?IGI:{۵}_gŵ/Q0]5r|(^7c4^r׃O1띵?lX\[Ov]c\omgMwO<~q|s9G7q]O_hӧX>o=A=*xfx~֮/z?2uSw7p>z//WkFܲjOhvmy{]}WqCr~osyDcV8*S]xϺq?;xTe__U:|{Oz= hi]6W^:]SϷܟ:rqyxܟ9.W\5zW9>.==Rpi-uHT?sUHSx exOW~R}Z]~5kG<g./qәݫ F-ȯ{^zw|q|)Wg~OSN#Cq~_xO?OY4q`&.qszؾyԍМOWNjms)_4mu#2Y\,פitע{ċ:MxׅpFu\&u}h9>BkҒ*S?ђ~HV;ֺ~./U~ AV#|=~ݏ,K8g5-dQ}ˢOc{-y>ǒq٫0ocUWw,w5R?_]w]_dIuAY%bͬc͒nxeVf89:ĖYZ|)gSfIjV1f͍YwGzVݑzϒ;b<+ȬJ;r,or׋X jYz==ݵcֻgx{ >3Qլ" &,2?u]S5x㻊2W{uReejy̢K8S%+YUUj,)UBR5:v=U5KV9fmwfJP]TqW].fS7=2s=UaO9\XJ둪lX~jLwէ2ͱ*~4.OBO*O?%Ȗok婮-CÖY [llR5k 0N_-(2~GÿGŎc=}:=c}=У=UGG{r=J3KǣXBwQ1kxj.c1{Q/2;&]H2j;oU6(f%"cr}īΔ'wg՝IYIwg:'~LNJδ$γL;n餪N;Iu9uxOꜚNJtF]:jBwgTҥrɌi*ΥݝK=2;ytyu"GVNԙ'wɝ)ӝ{N qw\ҹS,Kʣ:pgmMIw=SsNI'qt&*v@wbte݉11Ӊ1Y1}Y1YqIDu2=s' -ih߾yr~ĵ=ypğ/=MO:g:?NNH;w ͝NswwzS'O#-ԹpeL:DSݱ:HWAu;tSxu;:g>΁pǙNxG:s't~;.vxg:霜Nkw'+wZ1RyNk{wZiȝ2Kg6;-sZL3 ʝV3ٚTT:yUg@w:sO]g*iܹL;w:m4{Fw:s[3[.'rw:t%dN\uwJs'ǫShOu[ gwgIY͝trOV* 5iy9䔙Ԕ1h:5݉irTڧRR~ouR,NS騜O:!3:{sQr& J?t9yLēJtމ3LT¦<33P:ݸОN/uYfSgsL_:IKԒ1E:d&A:@:N):,锲s;UI*qs#U$̰tIgty2/AqN1Ӄ;u/GY9d.8\cc}uxSI"3[_$w[-39b)Bܝ"!w wx;CL0NW:3‘wg[':dU*i*ΙJLesfvryTڦrTeTyfJ=3Sɛ]}r6Ts*Ys=I%kST~:׏/gpܒڜܫݫyu9ݫ˭Yͫ˭y֬}zՉz;e5ˬWVY=.|zܙjngducozF{dΖՐ:[fu=eζf2ƖHZg5;{Θ|^SVG;WG;~^ (gg~~{{\?aYt7VvN^]\GeLVvjdg?Fv.YIKj5Yܫaޫ^=,翬վݼWY+5 Y{#^^3c>tjJWղ<[^-kk$~Z]ɫqb}+euMu=V=L4P5{ΫO]z+um^]L'={^`oKՃթ<ͽZԵw{u{5~&(՞{-x8՞R՞ҭ+;m>[V3kδg.Ɣg`Y^]lO]gěV;>g^Fӫ)Y3ͬtVKZz͚׫ՓRՒάՑRՑijWqQ1#uZͨJ .Njz6T,K]VbvǧᵚwQy!ڊA<ڕ/U/x2-ϨNdwҍ%|V׉33ZY='?zNe<9yfs2.y&r՛ZΑg^-簣j98ޞqy-^ 2xK*>{u%=A)ۤGVmzjdj6׏lLzt֤kԁzu{ F3;WLǫLY=ɫLӫ&Vk&3ͯj=^jk39xZyC^M̔n_xu _&_\O˔Ս˴du2Wo2LZ5k;~Gz)#:˔٪Y}^-«d^×t\5ku?_Rku.TlZJrV7j =kjz}VgY$I5e g8֬65V{Fp<^3<3j"I=s}xj"s^My]!j"s?2g6WL֫/eJ%*ScQݽsy59ǛWxus1x98Ɯ-1/-]tRxϖX|̓[5ǫ h59'z59wyWwx9yvkNCnK_ ܽ=5%˫O̹js@|}XSᚐ5н:W_5)'>ktOgHd/quμgY"UCd{\Y|,3P ~L^dz:CVj g^HwGjfJ7~mV_Y}!ݮWOQyZ]5 u{lZᚌ<4/K,yF{ =3ZzW7XWMk4fG?];57j0܍w\)^-`SᚄC_WO25SWP#5>j@\#%~Mɼ^wӿ5[ݍ{5u_2엻i<|ww7#56]CY455G|晦|o^-ܳtݝ=5~w3|ݍ=?{VJ7+̏pM]ӕo_jF<qwYmwC_?-5;=׻FJ׻O35>kƏNՍ|2nL&{:oUkww^ӭݿUJ?{nw7R7CL͏kTS/u|>ij).Z-tͼiw#uMϒnwwIxQ˻{y>CFzf晹InkVt7;GGk{wtw5 tjhRCtwwmwsNC9?9o9|u>tsݑt?r6}[U_wFw75݌6n:3+݅.wwEwަW7?Tn9 e~w'tn˫ۮ#n~w -wv;hm#|>vt衁BJO!)uޛuJ %08R8J7| V#CnGV=^BfҦk`YrZeb>2wIMZSuml-e U&9&]kl)Sd暡Ue"2C#3DY^jr)TJ7iuM]kn8Un^uR:k~HB?-a}@mJPEoq=F1i@ءyz15Owzzky9mRVt캞kiMlC4-j9Zz)J}j٬Q} zfe+ߥske\+Z$j}9KCB:_P\AWChAͮh=O}u9/ŕĿpE ]݊jkZtjp _ӐբXƠyvrCj!5V'\Ի֥o/[߸Ō`ޞ?Zon|n:ArSqS2%\s8(KGQ8G p-iQ_|6nݮ?݅zgwq 1̏Cu`J=n0e קEC9??a0btvWx!̿6s=>b f ;Z endstream endobj 381 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xwTSϽ7PkhRH H.*1 J"6DTpDQ2(C"QDqpId߼y͛~kg}ֺLX Xňg` lpBF|،l *?Y"1P\8=W%Oɘ4M0J"Y2Vs,[|e92<se'9`2&ctI@o|N6(.sSdl-c(2-yH_/XZ.$&\SM07#1ؙYrfYym";8980m-m(]v^DW~ emi]P`/u}q|^R,g+\Kk)/C_|Rax8t1C^7nfzDp 柇u$/ED˦L L[B@ٹЖX!@~(* {d+} G͋љς}WL$cGD2QZ4 E@@A(q`1D `'u46ptc48.`R0) @Rt CXCP%CBH@Rf[(t CQhz#0 Zl`O828.p|O×X ?:0FBx$ !i@ڐH[EE1PL ⢖V6QP>U(j MFkt,:.FW8c1L&ӎ9ƌaX: rbl1 {{{;}#tp8_\8"Ey.,X%%Gщ1-9ҀKl.oo/O$&'=JvMޞxǥ{=Vs\x ‰N柜>ucKz=s/ol|ϝ?y ^d]ps~:;/;]7|WpQoH!ɻVsnYs}ҽ~4] =>=:`;cܱ'?e~!ańD#G&}'/?^xI֓?+\wx20;5\ӯ_etWf^Qs-mw3+?~O~ endstream endobj 387 0 obj << /Length 590 /Filter /FlateDecode >> stream xmT]0|W `Zi/R;)oVB"%!N]ہxw=;fJf kX+ [mUJͅ ~Rt ^D)vlC>ꑢڇdZ}gR,<4&YP0\$cJ$L2)UŴ,-Gׅly[<?1g>I:GD>)4w0U)^ ^Q&˵֩}rm9*|,0Tm=CqዐE9xtK?kThP};|8`3, +קͿ+Tla3M#m认 c؀U 7=rq)t8^+]{@nd?71^e@"!n~?WK,5v <Lb+ Ff )'UMi[fxeCJ(F#Փ4 !zNfA!ri;?Ɛ 8?a!O1G+ĵ>!RK#!߬D{O1t@SBp 9/g/ endstream endobj 372 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpgqSfj7/Rbuild3bcc0d7ef148ab/EBSeq/vignettes/EBSeq_Vignette-035.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 389 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 390 0 R/F3 391 0 R>> /ExtGState << >>/ColorSpace << /sRGB 392 0 R >>>> /Length 115795 /Filter /FlateDecode >> stream xKv=r6~řE5$p v+@jyXk*}dm{Nb_寿?~kϯ2گ6VW.W_ǯ?˿~w-[>e_|}c_5b"KΟ_sPROXjޘzS NJ z7?;-` _V@H4\]J KzR wc hъ[QnuKۯQ]J%|c,T*.%%[FJmkV?O{>.JucWROX/Spw uRJxл~9|0>0ְAm \z P*3>3(:)%%pTOGJxл>jO7\:rVwKz(/:⼞X7m֨ǩY!ӥÝT'-wT*?uR 4Ի:CmCw1*SN(Z&JV l n?r3?TDp@ׂP?Kc2<zgL*R+k(r=e'~"򆔎_+ +m:T#HͥD%yu== )dؒ#m nqX͂Фfq+SQ_"߂LHi-鑰R 2;㱪T Kz7Vgn ~\:z$Tч5 ?#4Kb>ʗGDMC:l)/L59Le-x.]?GJ%|t'K5⎤|GukH-cFt!8ߒKY('#Xz锧R#)Hz7V直rzw|3F-А~bz$TŸлj&.kbuSQiwALuԂB鑰ROȧJ/!~1 n7 fW߳Kiv('X!KM>X'D+7 ɰ~_ֺ~Mjk I?#QI('>^u.%X~?D0~DGn6>M8b3{T#4 "c?:b3Y \\{'j+bÑ9@>uB:+|bu;1 NO˔+9z U^[z7V~_ 埢˚38ίPb|,=V*n+=,sXj, A+Oɘ~,=M1=Zm4B=bѓF JnK2[vPKٞX>b2:c6[ALu3zhyĨ SF>MeXed-P&N#IODW,F;NzFKm>X'D8EV#1lu)V[!CpHO*e U?Ks X~VnI˴72K18D0!2,g}X0Q=:Ny6eg,?uaK5B]r) Jhcq*>'EriF4=S\ wRrR);Nwcu}E k3gZALi1'b$c^?J=b{`u,}16eԏ+"ԡKQOڋ1O#q W3[.6Gcz@cNQbϩ҂4鉰R>E ޞ{羱:F˂~fC,6K )-> *cEZU*ERIܹ>:ž20>02 QD>-="K(alM[T?gz;˦qR`˚Sљ5@\bHT7VR 1X~k/9dI̖[:SZ/I\zR>t]dmg6Z͖l3[ZSz P*t3tz7"[C7`|`~֯Һ V d>ãw'y(޴"33sWeݶ9nt2wջ-$@LUvǩ7'ck:뤔wcuzWuٴECOV=(9޴E:K:)%j-sau|wALg.Z.z$TƤVrY*.Xz_7}ßoo kBH*orRsvjIr_YT~(ݫ;Ҋ!߼qԝU}5Ku)PW|G 6!w2]^mi2಩7m㙥Nߖf AJ Έ#-4HQDh<&bGzÉ) WR S*bRލ᳝>[ 4g~wf=^#Bo]]b2 *:vAYPe:g!u['T EKz7ր>zx36 TDpif#}HF2wEY"˶]7ixhag}I:riG" ]0Ɩ*BRލ4!i),Ԓ#QR2ᤗ*EwۥjV=4Q9[iC6t?sGt{@o')hMg^K5kr)@Ѥ[FbkXvOw[TS:G)i/Pœ_R\V?Ђ<#F7[b\Ԛu7=MbĨ)J:_Ewc%%GӾ"8PNۣ}]1Bz"ȃ0hn8~_HbeolRQ&%zvY6_NH~ຟXX}1&٤U1i)0>)J!Rr )@JvOT$P `?RR#/#a@z]r'/Kz7ր_ïc+ >m,BKs<\ÄuBRzb +-ZmSGۧ!F=V*O][?Biuv]lj#n"J;N=wICڵ:!%X R\ClDL-@*ImBz$'ɘ M㋡ ލwǕL1ɟҍz$b#i{[؈A n֫/6i>E۶βnƎ5kSu?(IuX?K~ޟX ԧV # *zDqL1H$_qFg5\:3z*o ~G<{ \#/#aĈZMzRލz']#+7ECwnD=ItXG>JA'Kz7V m+aϸ:}b=t \#HdO\al% #Yj )@{7F[h 0Nh NHCODN(=;$RoNHz7V?w :Ҁlm ȟBODQP S^ 넔hwc%|hʃb48Iv̀Pb|-=Umb !GlސfYgf hhwcuh/;~ &k'c ? &eWؓq&=Wk4lm1h$LClԼ?D4mQO"=s*51;&{`u6/Mg@IKۃ+zsrJ- : ãJݖSB/h5o.bskFz_SշX1X~o'cm^l AgD).UAODZU."BczCo7dw?pERt (p #16bymڴ:!4 ?NU}1egΈE;oiE< =~J k8y)ꧩT'I'ր2G[NeiSZmz$v|ĀݱQGlo)|w;2ʶ=aIS.S#1q JZw׻:cG5W< 흧"\C4EN@=Kި'kAneh=#m;7OB="h~1, qBb:0NHYZ)j|Ք]I~qi;OѪJ|8Z&RI| ׻:ػ&V3Ҧ~փ3;d$\+y q0uCX5lۃ:ti?!:fa9Y* ]Jxл|'Y"z6H~m.gJ䎳c´9гaX* _.54һ/+b|j v$I%gJDv'EZ&T9h[AoR `F+ItH11P d$_ nO,n5C !ۥ"=EY`z uW]ufGnfw#irHHd1+f3z7VQoKюC^S? ty 'a2},Yww׳XEܜ@D5uy$&JzŢz7V~"g?cC,c ]jGJ%|cT[Q8Hph*p_f@#5*F\?zîx. 8#҅ z$|M ;!=;R.5NHz7VoAq$1-,9uED.PeTGzRQ\V}_ ۾|~|ٰ:twB@o]?!GIiO?K|XOԚ3j}ǥ?ҍzNh0>' BK z7VS9EFYm?H$HaE3fJAhwcug&˖ٖÖ!Ǵo >0$N]mH tC \ 4Ի:&a/quLJ !ӥvZz$TV󮮗 tMuBJ4л9N>,RRR8$i1m/z"<-F&^:JMzMuBJ4л]PFIIzD"`wD≑ƈ)\fc=:F]ѬCP;Eut&L0\b:eXjHufFR}JNκdtVe3"f H SbqTjhBcz5ciKF5oqZRD=~+n#z֌*Zu#JvG 1VLq ]Z1ARӳH_|~a;ŘOiO' |F HoaGg{T*շT1X|´؍iEHriH,#0>SK!'U\J4л:Tbإi7?5͂2XJSa2ΐ_>*7NJz7V/<-bS8/ bɥv\z$0R?:)XK}]1i,YM!!ALt/K=~ʅl',5{Rލa 4mQ_lD !LåvKz$^8ٞ^K,5pI)PZq`|svro?EFzO BKz7VjȂol7=4x"WN݃Hz΄l RTos+@C+%읇sV5iw)#7H ?7J툽H ]òҳāJmX:!%X ScL6 b:JsbJ_6Rލe Xz^ֳ^!Lۥ:#=dJZNHz7Vi1*-=nQ-73TJ7:3brzV,&Hz7V·Ԫyذ>6]8ds99EMQpn?,I'VL)10l8eu4tsj>qvuI%ݥVtO^6(Y}{-8o69v7@lXN =# (]R/HJE۪Dݒuoܲ<+αNmOwR@<'^w]REtp]e'zݔuoa˙{t[g}&Aw^ɱ"]RM/96%]?e-\nʉ7nb+^9KN 6-SQq~ xd:r$ڲuSN\Խq- sز>O6l9˦ރJn%^ tlOzݔאe[Dm 9'Vktے 9@WT"H#2^7^䄿z!9˼Gv5>5\@]R~9֝\\ݕj)'.޸Ö^پoݕ8M/m˒Н-cGK*D8}֌_UvC[r[rOUcuB\9×ϙ%o6K*ؠs,*nwTv{Z[r^wݷeG9"{M 麤s~/{ݔuoaɕ ,92_ԮVY];#ɻ.)29Wnʉ7nbߏÏ8X jڱ}shB֢.)?;#10P1^PEwr2m9b:坥@dˉ$.(-[NS*l9uKNԽq˖nf^g9v;~ismsKm&.) Ja/ IwSC*,?uK\ҽq˖_ps,ŔqȠNW%wd.O4:.+/;-[}bc˿ͷb[߆#~ b>qrI`!'֩$C~s9lg#OB#ۿ?mZR)^ﯿ{+_xmgzn%ݺ>3Ⱥl{L~%K횚ARv>lr|+]6"c492p>k/+?ʿ (Js+?鿦\ "ř+㡠Z嶔kݖbxN"A-#DJeazز-aKaK{B嶬iز~2ZOHcw1-a9.(m^-=ݶ7˖Öݶ嶕s[j~Rmn/BNaKi˂}=VD-# ~PܛAɖBNK^-p_TjqpʃjY۲Cv[u]dV9nɉ7-#vFyhQ_%[o/}|eKu,{[r";laԑmF%cߜF'WYBp.}>یFu5c8y_pI}(8y/QJ%W/*qDc̕Y^6(5ꖜ+3޸/[}XfmQn 't9VaI⢺w_AF)lQ%|=vJ+lY!_%uI7Q/1=ꖼ p_`h1^ XCZ}Y۲CEx!ś!*{xP%eVoG` ^vC[9]ΖfW4ҍ, Qn 9pITVbTѫ nr[FȗfvQ^-h1=V+Q%e ~seEwcm(e|a.| ڞtu<=$-9pI}ق1^*vcdxMgVJ: {\[clPn9pI4ݎж㥠'K|CgʥK*ё;6bzfv̘Q\ҽq-\]yLaU8('ZqN+I 钊ct$E<\w2ɕ[ [Z9q%ż`^Le0^Zu$ݚ<ܚpV m!Tsz[໡A 'z̕(>|ûPw#kAɖBN\Խq_02gg {u%wG,1^}p](ΦQÖ~ز--X[蒺6Xt{mK/o[zm;lL,al?l[ÖÖa|[zޒHrMQerZ-7rndݞטכxv&%};peee h =F(>|'i+xq2Ͷu\˃vO/ܗ-C=-Cllh "[~#uEɊqD)֬rQO:W{h>D-p]Q3aNN~xXuS>p5c+t-_"*l$Nn8s"miY_/[{?5ʄUUQnKvN9AWT Pdp%]ꦜ{iaK-aa~ز-;ladbsu5y}O%m.?X*S+^Kw7g:RȇU^FPk-,'-qF[|E2\JWTdmt4ϥxWt{lA}Wl9g3;ܿMl;aBWT,NF|u"{7tpT-wزR.#A-Qȷ௘.ʿ|9ˌ} 7˖Öe=l?li[ÖY-3toܗ-s/W|͜ܖoQWT\LwjK#/23UKeN(hVON;Ix_7d,Ӻ# 4S9إqfwy<'휎b~Snnʧvzli?li[Ö򶅿me--%[WbK&9ksgضclw)RU#"Q>uE%KyP/aKF}$~;Wѵc(_)_T8ǭ*Qv\5SYyC7wq;vfN(e|'F[N}Ɩc(;*}*J#_s%!Fzz!I!_T`W *^8y-UPY&m).I^/_T'T;!݂S[c:quiKHlጰ Oiy[{ܖ]9er-ǹy/rnʉ ܗ-Z-~h71pvg#5~N.ʿ:{u)uyV^ /?V>E-t]Qc Q(u/[tW"1S5C亢q*'8u[4x~h-\YWv.<r]QOco]3%SԱ^/[ƍ~h^Qi|[\+n8UPb).^s= ܗ-\}' }!mi.Y>{]T^#NVQvOfs<Rؒ--aK~R--aK[Bl鶥--ax2~2}k!Ö=߶p~re"iE`n-D-{߶p-ymUڲ e zE|@ 9ŝd6s~Tff췕N svd^Ծ/FBnqj|Zǧ2^\T}1qW6>RȯY#Sy2_ÖYy.NLDtgftY8麢8MQeТzbj9Sk^/[0#_eaijQrj.(sV{mͅYN種0ÏMά2~Q;kt! _Cla;\060Dy-W麢8igtJc-ַ~_L|3sun( a ə٠اwQ|u89]Q#r906 ]ls5Ɔy-I+|P{Fa>L$tE ?)]q݆(){dlʙ_F%>/p--Ygו3|"[Pn yA8@.j@]|7Te=r!]ʦp%>ÖÖ>l?l[Öpue|l?;Vତz#d2C\EyC^ը[49V/].}[c4xR03*]Е򊹉U3N3u'b3 bݔun5XG\UNf~0WWÐsk@ߧ=}Bn+:{ljc}U V[2C^ϩ[ݻT{E˖dڽ2]EyCn\Tt'޳`3޻`ݔu/[v#|@2ohe;TdɫF37ЯeO`ꦜÖ޶~maƋۖn[VzLG@=lʢЖU^F+~I+Uu.#rJgQXuLzTEe7mmXzJܖLuIf/u/[>rqsdQD ?G]R)Jp\MA-Zl͕;labdQtvQw+yES) 32.*s[MZinݢu/[+ypޟ8L*JH^s*{Sn FuI5AYQvDV]bOC71;F{ܢ#ɸ+7۴~ 9ݜ]xPu'Z9to?m)o[E-=ݶ\+xmL[[Ö [Ö}87̌MY}L֡WM? д&#tNd<нq-;1cF[)o5$Ep8wʲ}3+o1ç˖4͕9E-= %')+'p>OVFLBޱB.~:͘Yvyugvf [xm W𴥶-&w[zm{nK_-Ж+C[-}pe-sܶpO[xbreG綬t۲mo[x0;ÖTA/ Oـ~%gt꒺VnXUh5J \Խq_ b=lAܢ;v +v(z2*|ў_Wg]ƒ{ޝ4A8-@GMQw%St /C*p-kmYeۖ1޶7Ön[Z{m@tj}v:-N;-/ # (]8xzȻ~zQq] <3آ煋[ 9qUewR[n =|-:d#֥nWTb~a.Pf e([ shތZ%[@KSwhEq8znSmYkȋ;l/$Ddfe-$RGm!9M(3PyG-nU*_Öq}L};qvvcbYV钺^!k؟IJȉk g"+= VܳK$8WA]R# "cEBݦjF \Խq~OLŲK~lQ>ȶ"r:2]9Qf5EM;;lɬo- ѫŪZnA=r|MCCC!Jhx;Nwط.[2`.|H!Nj%59g۱i{[ʐ9: 3wZz%FmKNKDߣx;SuSN\]5p-)-lalOxњE ]!uʳSQu۵YxjȫUm钺nM3|.w}Z7oJwRђ9~9 jwSr|Z# UmQîU*3\4c>մ#r[v`Z'g"=Ey.i^ wB/ڲ=Tڂ~P(w"q꒲K ˝C7fsjț<|/ܗ-V8Q%:uoJWOKjM }vcU r:r ;lZm\QR1T嶬/uQ+N,ereFV٠|穄{lowc KCZeQn7KjLN7GsA-5M3/U^+=GP D3mNr|Iu;g꺋/y0j.Z!'R޸e Ws--ݶeۖn[Fz2mJ-\^r_}5o_;+KtN\f?jtFG K|{.eD3J@ݒHl✖-۰2E-= .{f%gd+t1ߩ{fH{[ b]t͇?$(fjt X2"w/Erȉc}yDI٦, x;tI{LuԐ3 /V5":an~[oH|<]uŻK( Ntt\ҽq-BɊξ.]ntPn溤,G^|~ 9pI 9Xr KE۵&J/*Evk!:P;c;l'{?&m]w嵥^5>'S6+Xr[Vȭ.4rr]b_X{-{lYoNKV~(1ZȷeK* $U^6(e|h e c`0'l+)m!+[%#LN ~+ ΋H>|W_e 9k374W+Ru}moZ(? \VRT RJ#~抜xL(U>{]TVK;'v=NquSN\Խq-: 9@x^t)sTV9 %݄[sg—lÖ whGHgqgp%Evi}ݭfݔV߸()@K'P_ݢNqJ 颊frM5Vpp$>q_n|~u߬?x,rn%B)fs6;*;)'_)p--aXzµeK-޶k2F-ܶX/e=lY[[Öu|e :Q/ܲWwq*kl+qq_ 7&+w%ٺ6J+t&l$c-ƺ竺tÖ+:y(e ޢ.) Ե:^񺋲mJ;lLMky uI5 ;^_r]<O(oq׬/0%2w%)Hjz9xF`}1"#.rlj34X7,;ewq Rαtݪ٦vQFSp-˄-.-Wlu9 s)u89aل/2q2ֱSg!.f%[-ѤkZ7dzpαo -_nSg O9J.)s ,]\FT}1r;li} X=c^%oEX`*ۿruSN\Mt΅mhNmNθ=eͯRsKjm6Qi6Ez-Eeh-a2<-g1G(o?PԈy 6[{MkEw2ybg*hDӚ(bb㴝tI 9Ǿ\n)'.޸Öə|-x8w`eL۲BpFt(UyF=-{lfqlh=>v`[فPbr;lS,&evQe֐7ٚ.jF~iĦ~,uO릜{-{S8ǣmpmEq~[&[K jFK7miTvyuwy;liU і smd()b꒚!*+uSN\C9tuml=|QS̐wdߩKjsfւ>Kcʺ)'.޸ÖI^s[&G3G$~z%[ y4tI͘1؟.쮻5^7El)XQL}9KQ GVxEɖCޱsL]R3"_r jvE4;Uh yhx}sֆ 3F*#r .+!g`ѓv=2re nm1%JS2BKXPn f;,Q#6U{n7n7gĊ^(%x7A^ƹ8).GVrG wy'xKiMLcH;񹒜;֡I={faa v3vcjqUC+7GGδ^P6)e(o e br.\ˡwܖqvW :ZglRn 9R0*lefÚǶ(ew'SNMF9DJls Dƫ_(2wȇ|p%=3P;U#H{[2)njs0ɰ2[K|Rg5wcKzlmw/|RwɆQtmEU6)ڴc&V;l\cklX[5ܦ%69rlRfh9toܗ-<=~μڝ^-QeG'hNE=~2ؓ9ѕT6)@Wlyw™q%jQ)&% DO̕ԝ9칊t=fߙA wB?U–bb13Mp]Rs<.WkEelZ 9qy</[z)ȧ7"n ԐOHNy\MwKer[fȉ+ie c\=7yruy&O?%uFSљ1r;ljomhinl5ѼCnS*U%Mrˢ^kWV$/[Їe4th5r5 %%_u9g>CXL_spA^^ L- ꒪&/;NJuSN\Խq-!-x8"-(_S%U#7,c;q:([țk//ܗ-὆-^bzûfRDH!)uI[:'*Drht|#M;l5-Z)2?==5{ [^{$9kq5.#Yw\{[+p[6D^4{^)ߘשKKABRHA}MyW[g.KqVp|cI]R-v1ⓜ*DϺ)M{{[/6ywNGp".xوYCWTl8Xt1k]u릜{[v< 9gZ=̢d.`muEu?#R ˮqfzZW;p˖)X-x\79~(^\F$tEhcҵoyxS \Խq- |B^ra.ͤn$gŒtEqp>G<ò=rZOnha9jf7Po9CWi'¤efC^pA;ljź!g7|*E-9ËBWu86-.*{ĺu Jvnٲy#K"ǦS_Pn,:;^_X21˲).>p- +ya{JG\^E.ȬKEMMb,).>p-m2Ѷ 㳣BdWK*N5MɲAɖ^].\< y-9am=KNlhɜE ]!G&]U~A88I݄,FW!'R޸Ö49xjGط.g]_0{^rp-eτ-PEdK/!# */,bJT٠dK!'R޸/[w؂^؋aer[zț~%x d/۲CNK^-8ƖS%t1K*y3랠{٠ߏtoas0X>X3>ǐܖrҎʁ{O gegS왻xLհ  @@zː wr<.e8$7;y ->l۱<|vv-H{~ٙ 5Y?$KZ$R-gwbΖӳɧ^z n\ZPjьkt%qjğxo5؂ IEF}{^W&zQ6X )WW d3һZJ9dIUzՂ+w%Pwg=|E٨cy|\u氵4xM<݂SOX%0Ȓ'ŪgO(SW[=d,̫sC=(ayrOZ%5}Nf u$;S/F<&2vOT5*ZqlYP6!IP lF1sóŧ^z;_:{yZJw1}KsNI ><$ɂBkp^3}/->5]zKcYx 7Q1>*2*@ NJN4Qз4`yg3%,c9R6X6XN .\e# ;q|ɒJVȖgt5}FWA ld‚r66-6Eon;/Qv닳i{ .(QIAi^KQo2eX&X+PN?gؙ,!XKJ&ݮslMӔ^z 9yz=ψ<(,Ssdq7(}8zQ6fc--Uc ~4_NɊ6ՂMӔ&;A|E٨chu,ec\,霔aY2#YQƾZlz58L^z Q%[]ZpA'$jj/Q x{eZK:{f l #.^z;Sy }3;Pǟ?S )iw*MVTI_M }'|6ԋ~^۱ < [w HJڝ1>$YQ [U }'/|6+:۽o~Cv2[P y^p%[W|D(+zk99>Y/ {-,[?F(aybcKT5W-X zz6 ގBsقrv ;;3Ӗ,)?H-IO,ҳ^`YoXUo# ˳sB~Yz6eK/K=eP%2,yɒfUMEU>ld/K/ٖaiڦP *Ur1.ɒ;ԂBJgwK/^z;ւ7 G=>Pɘ%_*3)'Lf !Yݡ=|ŸHԛX ✥[B-r1ytvuUc,n `[dQyz6ԫ XVG[7,Ő`M¡#BJX,tRJWRj{Eler4:m\_r}M,ۂjbw=ڝ5|ZI]"Gޘ瞍b=ַWiS/F nX^{ye4^chV[pUdqUn% z/c8ݗd-sV?YΕcy0YRv_`$e J>[|E٨cSR)x{e&Qji>bJ j'NCrUW]׳ŧ^z;[~=c|>Ίʹ5qz D4b[-5! DI4_Ϧ7ɒB_o(Ol}y%9B۱[R_T6& fV|RS/.`WG,E,cyre_ɖ ^{"Yǧ&YRųjY7lAU.~->5쥷cYjTOL?@ c3_aRVgԂⅨY>[|KoDz,N ,xZq}nZ6Pe4dEe[ԒW#Y6(S/ajS ZybYVzR?ae%^-,NVgMP/^z;w8ZpQEglQn6cףdkY%N_}'l]Ӕ^z ^]94~;"f鏝Q=(%1% .$٬8(}9?靏۱<>B=̟z6lz痬Љ6,fY$ɞ],VgO(T!?cK:t;bʒjQK5kl^_z;35؂Cj*N%,;cΥ,[pdq܃.f'S/Fe׆s)<9gs?ɒ[ -2|䛦G6X!l- []M쭮MB,)YZ#e]}7YgO(,g؂z'#Rdz rdI"j)av+M>l[Xjy-·8͠7w(>mg,giZe-,js{BY7Pr,7RʒUUˀFYQJ=|E٨cxP C Vx;)UdƎ7f7t!sS/F˙Y|ZWN}cO.}cn}c%|C,Ԃ> ,leގٗђTsCmvڂ_dZdK쥷6ce-8硶c=OF#(iXbcŖ_xfȾgY^zKK61\46TP \,.ѥlM>l۱,Ԃ)yt%Y{q\k&,t>ҳɧ^z; |]#ڎOlbbN&'HT؅Zν&OlB}g]'|V-dpڹ6dp9aل%UQKхb&;ţgOF-,t#hz?YBP<|'YRݲhjNVb=z6ԫ-ގ匡n5j\|stxUIT-8UǭYz6ԋQoau'|ɒfH~o?D&zQ6XjAs;׆ v:RşXY)KjJ !ϠWþԳɧ^z v e>ZPܰ¹ag(Ӯ9a,e>Zs%;z6ԋQoa&-;C-8K+JH ;K|ZN&[ (w}|ԋQoaYC1hGtGM.(h(+^xjDن#w%le/3qy< Y_>PY1?NI)ެk-8v%,ϳɗ^-[tgyJW̫{I%{>5;[M>l۱0B-}nGtvg%KjedO ճɧ^z /+royأ|KoaALwTG-tDLA ˓g~.eEU[Ԃ5$ 5z6 ގ}0_O8y.QKcHTd %]fֳ^&GV /c?@jy"X%_XKTb7(ai԰oz8~/,_Xڅe| ˺Be~Q0Zގe7ű|ao,m^Xօe}qol|ijX\^^8c9…&8zy/z/'aXh ^o۱p}᎜]2,x]k-'!فNz6tipΎeBE=ӻ(R_0C6,mOd٘mճɗKH]4PL]=`ifm5ۅ7ѬaPv eގe7U#Ȣ v~X˾e7Q#U#~K~a/,w>X`"JXFr>,KKH.,~ qai,R.,E}cd] ʰ Kӥ}̮K`=68a]aG6]h- ՚پi}Q&?`/mn-aD[?X]X˺ `Y)d/?XZ`KIKIo,3Wk&K }|F,F,;`<8a})dž(ҜoXZnuIc9y_$wyyaLSeX˩],)mXͷicbP2}d|i͸ގel>^62v[aO% e`&{`|6h}= ݻѵ'%c}NUeXz FaS-eՈe}a LֱƲc<2;_j rcVC1P"J9a/&;.ʰLKS^z;\XrXvXv`}a,²#XvXrXr ,aXܘx n,U3N)K } ˄ !Zߠ 04]:X˼ K`za,߽Yߠ 44u,7`ձ`X+7V䶥M6,mܒžAe|i KoR6fX ;+&XFe%5Rk9#َ4Xc78(R_uзa76MvLGߠ 44`qa,uзao;҅e_XˎXxŲ?X`Y$XPi2,e^XK6٧|"[ߠ 2~_z;XjX f[#rݩ m*b9ba6&`aNXjX\`kag'ֆpjX)SE1q OVexa01KoB|اNૢT(WslY:`Z%,e_.z~a/,2?Xe_X˾ u0{u|e2,\ҙoÂol6;Am|idގc >^:Z|o`-b^|.YP[X29Lѵ[ߠ6KӥX˸ K`za,‚kic 44u,7]ا?Ey/d!ڝ_ecOзa76MuZߠ 64]ve`=ð w^1VDŨ&Iɬ,È9?֌/M!{]<61y,*FUů( }csl>Xƅew[`?`<=#uY#cdA[Vzbl}{٤M4]\ory1ļd^,X̋uvütj؅lr.UX]i|i K.,2>Xƅe]X˾ K`]^z,"Sv,hN]tk9ï틲**\ ʰ,S/^zK ee73t TʺM.,탥]XKoB6ZgwDz-VUo%_+Y)zՒ4r$S/^z,僥\X@-f(òe} ˊX\sR.,Rzai.,?Xe^X˼̗ ǧa~eyE"󫬀ԝJ^QMdDϕU~`|f3N̼q'sgxuXd O<<߾WN%`2S;EP5Kyc .,僥XK3XL(~ⶏ}~ucgTұjY!Cn9oòO({X>`9>~m8C*oMu[ߠoh3BKŪ+Ɩ\3쿃JΌKъº+y,o˳IYfT<^z,/3s|_fWb_UU~%P`2^N{,nrO!3Sڸ +$}0KoW?f)Vѥ9|%Gʢ1aW7EbS)A,j:|wǢ+3_!@%X&V[ 4$XKԈe}a/,^.aO;`ad:Yg~g$cod-,n߭oP;j3;`hؒ#1,-E,- UJVn%XxJu.X=! D*+`*x23:3m=fB<ck^z,T$<|;w,+beۖ=|eɶt>7/,²?X`MҢJu K`;`Y,‚Y,B%/,_Xڅe| ˺/F|G)veX󫬳[U K>''ŬZ/ ~^xNeXq~d!֧]1ٱQMsLIvwBO92N5%S":Ɣe5,a&~MaR#ʪ+J)F]|Z oHEe ,c^%).V5ɭ|4 #> ]ބ@->aVޭc2ZL7(2Of;`W1fF(E|ÒKĒ0ٱUKC{z-gOm㥷caf8`%b%be]Xօe} ˊX~,D,ܿK}il f=\ t~(caԴ70[[ p~=: -I7(alV1KQ _+;VzdUU+"k>3oɢ0= [{^z,T7]*]S7me2Ivl٭/t~RDw̗ y^w~/Q1c/~jCYRcl2fi1Ǽ}3$6۱0ta2/,2?X慅s8 ->2,wn 30^&0^X=ͷ+*/%gpIv23E٨㡳/2, Vc,²/,X6gl)c%_ڬߘ]YD'FOЛe3:l;`a(|j2>XڅlO|ggtstr~a}03RUOd! At$c+PzgW wr(kudxX4-meLNJLWv~yKm:}i_-BgIgetk|tVxHf;~w؛(R+؁^q䳳+92,IgX=Z*jZ #IJa H۩Qզ*\#Z#ZXj۱0=ַ豰jڳ}r~ӬUir٤}@s0t(X˸ K`)ra),R.,e\XƅeF|,U06Ƌx:9Pa\y3 }dѶ۱l~>rO{^bBvh8i΁*4Zg>ܚ JXf𨩗Vzª_Sy^ ޞyPsya,2_z,xY52"3_XNaVU msNl*|:gahz-,[c2ØDXʒ*ke|a/(aa+1RKٱeډ7gʢz Ra6,X]w3U$7Q"'?cKULXJXBXJXtVo,/,^X jcCWVTQO%URU~yYu樭U-U1%a2.,²XS"Ɣ֫İzeUM_,e^Xe~ ˼W`/|=z̺chk2,M+gT`.[agZQlxvގ%Yjڅ묁-ʰL7x{mP#>nK}4cgpG:+]}fTtwDYF8Mvm[޷]cN<0K?XlX_veXˎXF~c>rkrO6ca$`aъ/,e^Xe~ ˼KQX!`b Wǹ\x^40 K~a/L5hEN{Lh~B3XWv٦q-Htʅ>!և2cX,3Tex>?2, eJ/ܿ5&X\UlևzQ60 w&(Nd=ՍMՙ3slWM^W z; f|k"3JioX1(T8}}>!ƛG'>l;`at~7ܓ0+Vg4oXVXV}cYe*ҫka"w^BX뤄&" +eIϨ4a#3*ӽpvfъ( ;`a%EBOѫ!fSB&h@Uv;LWeXeW\oˆVqᗦr2]wrYR5ЇfJ gh[Y/z^>]fѕ;/Mt$Kz~.38g&za' *7aF WjEge fV|U:Eʣd%iݪVµR뇯tBp٨w•wˆS<0[zI h/eI5h3loY_z;XfXX˼ `yao,e`ѽo(yz`0 C,Mjw٢3XOb-a/x-]v~>3t5,MX'+i|6ԫ>b,/,_Xe~ ˺ua/-]yYl!Ƚ-zYgoE>+5a S/ u4,$=71'3H,R>XʅvF:>+2Us \Br ׳EY:z˶lU} +h[Ew%-YC1Ks0py]&h%@ K#6Ӿx׾k-z;ƶ0-3Ya]̒%<J}hEuʸȘ/XK K`ғ˒j^m֭n]Xօe} z0x~&/];lDY8˩bº@1(2UCKre;ṍs_XKt1/AVu٪XlS\oaxaKWW5Cu3Q!2W5"jٚ^*-P 1Je%@ 1V1ੋ6;`% fYzqpȊ#ିw5|tEhq%on^ Pzu﮷ci8kv澅.Yt M Ԉ9m֭ɚu(a,_? 7"0Y_2F;Z)vEnq3j ]]62?Xx&Eά drS"lSi/3׏׹4;;`,/,%_X% [ K ,;0RYs=;yG[liG̹b260}۱G,pniK,=ba cSYفߝDa9b1ްhoeXW׹w¨N+bT|FW3[X__XK+ ,C#}R~Pa7MpƋݯSIJR2]rCvAJYՎ]Xꅥ~Tk} dmёEʒ^ŖHz V|ӺO/#;`=3ޓߝUYԕ&WŪg=JjJf?urq٨wȽ$y@1 Dd;d󇬀([vLsUh/X ~< coXe^XK .Ma7,%v,k/1˙_ p ]S"`ː)]}S,̌e`iL|tyV&3u;5}ܐޮ3O^Ӻ~߲ KVɋϤڣE]-;3tONMߑ5 ]6߆[Hkl;`UoZuee,ef<kXaYΧ^C*vf K`~a,/\6XNXqoϛYaZmz\ yl,JXҾQcesehjzՐg;r_Clc3{^C>]vE;F3Pwy²/,0[X"Zv[v.|UuKd"`쐷mՐg˝ ̢T,nylkh0 Wr^ɚD].;ymKz{츪cBUKx¸dr,3S -fd$H;yeViUPP+sU X0ANHU$-3rMy՝*ʰԪ|]^/V˿eͧqai,巰 `.X˸ z;Ƅ }yV̘Q'(Kjyvp(_)صl;`زuZIh @%eI*a^ $}|eUFwY7Vi2[5+1!om?SvȂ()oM+NcpߒuKmo,\Rb%M4%8#b=b "eގaެ>5\Rb2,Yʹ.Z2e}VM2le ձ+ɫ-]T?5g@-ަjZ77(2Or?XxcF,G,Ԝ}dI-_hG ̉e7O,}XhgǂD7|C Q9߱v3m V%r>]oˆoӚd4IaYΟ;MYR=xL|5t ;uY\]w,mQm2DŽ]r@-SUc밾A|5ur0] +*K\(R?1Rv&ìjL7a t~1K+seX)FV♁ZYa0v{ B1Ko2M_cFz֒(ò?bPTpe"2Lm} u!R7U;z,ܶdd5VDY4"9bTMͲlps2,kD ;`|3nLוkuQ1z'ĊA٩lό̚&:oPe:*ǵKololS7fL(2?ʔ GcݶA KOίʸIt,².,e]XVb0v+ʈe`kB?nBJ0 ˼/F/4Dň*e2f1? 97U^z,션B l32J,.Q2'ʒZSQs4a}~U x-,M1!)E^/Vg"Y-%bi*ɘҴ\Ef<E)ey?CpCeN r)7i':~MVhQ{Uv(*bX̫ZV=]=TD ehƒ cjMN,MDm/K-+zq+;x"ҠΟgv촬b}2]7(wY񨺿Q/2/,2?Xe^X +ee~ IbwB89h"ueԴ_$eAp#[jzsD^^z;r_,e\Xƅe| ˸7uXm=BaY饷ciM ʌ(#{9FJv N٥XօeX0>c*!ʰ4/?]Z-K>p$aYoT۱0J|my&wv[a_(Ŵ*H&蚭oP™.2RKoB$eFz:JmK}QvޒN6^B`dkS}0cbL P ]Ϣ v{K ҡѧkAʜ_zzaR/,?Xe^X˼̗ >MfwPzeUrJ 7-ilR>Xڅl;`oxo/җoR9xal#W}j}o8v,cuCdc?&K*DLV4NiOof.R:_z,GW\yH?r x*$AEWu2%CXW^e_XF0,WX~c3bxcΉ-_z;fm+[Fd {Pf/D(;>'OiX#b̗0RKoBnYٿ1Z%VD|ewœQkꙋx5#HB,`%kn[qIW+\?zXΧ^V-z,cY#KKy׈ -TYmA/ #HcKAn5"UyS׻mTvOq+ԇN6Vf5]Q$ Xoœcz*O] +*Fʾnh&aՒJd@-, +]'SUz0kG5ύ@e݊̌i7oP2"/cX</ |C%,;#AYRᖭmtdV1>ߑv2crO}p;GWtSOwBw8Gsy6֠m\Bt`+@/\w m򾅅R9KGD:8̚۰0.R\63jV\qޒXXZIPowcÍX/˒*gl痪caen2nKf5xaCzW෤YmoQ 3)S[D Ru, z,|}ij}Hd K5~K r )G:?1\6Xh[M/[rO#Fn+GW T^nּ5|v֙sl;`/ke`~X]yuk7YQbj1Z5}զg']6?Xꅥ^XKdEXe~ |Xg-1+;t"L=~Uq&R2_m7;_Þw4-My'vC̻jtm&+꧖\ıȱ<ޖb*+v,c k=7AoQҧ[wYQf=X43280xt<;`h sW79y4Qw5'bZl-cz}77 b*G>dE5Q 9ݪshwNI-z;=XXN'-TSO N0TEv"[orn|Ep٨w4-]2k_BD]Ɋjq}M3/^;&?wW>2,-!YQͲj9WlǾkԫj/3>Mh7¯XvX,,2.,e^Utl*s Ʋ0^v2YQݢDj9VzRKiQ^z,%KBXjbk 휐k7YQ=.6-yc|v.ںM۱4vߖN8jھy?M;S91|l?ђ^z;Ι~>Tʚ^[eTBVT+sM&JXp>jʥ ga:Glo] K7k7YQݪANdg_Pk?eu۱ k7Ɲ u2,dEvjx#w-}U9=oa_jQhCe^2~$@V԰ yLDE>|ujXle;ʌ(<kCGRKтaȩkOY-A|śގ+Besb~7OTOEm ]ɊSQr6lSq39j*w=1Wz ҫ-̻JoY` [?HP2,ԫb`E{%Xr}c9dRkbdBoX8K%K/e/ϖ F*Naݜo>5,zmLe5ԋ1w|v>?["zFH&+jXաZynx/jZEaiΧ^v,7[7,WIbsICKOaUMjMmeQ]Ɨ^E!X*}jX*~Nk;wDV& +R Ւ*i}7leS +*KnVY+4тa#@-dA}Xq!\oLV -csuYK_.&+OEUKW.,ܮ/v,\Ϧc\︿ D28 YQdtɔ}Je9a|c۱p 3\'/IY p>n_aY;sd-[ñCuf2wbϻёIVr:e^hǝ˚u,n y`Qʆ<7~cY%~zҗi/OGWR-{Y^V]ev,ePXE,DEM<7dfi2?Xֆyݲ\/^z~a/,2?Xe_X˾ގeBkagyOʊ'h&vFZp{#}oQL;`V F#3҇(FO1$ٮu__irgގe&ZĂfQ2 iH Em&L_Uk- ,+`oY"p>n.飫(^rVKݓcY)\{쥷0|}/ ;חc︾+חL֩'B]" e1Uv,ZaОs9yQfa%#շLح/{9Y~rx|;`Jn3S݈nZ$7&:`ajEۓ^e~k>ގVs,tc/"+]*ZX/ɒZ[-)kcYߠ,V/6cigydlGJk#; ,yXH& [W}G&_zA;`5|쟖ւ7A_LQ;"HYR~׀Zf=꺪>|e~XzzcJO,5_X˼ `yao,Ȩ/೺ql|GG׆oNQwW#P2ToPe_zM՜ގe`^ߣ[ r~E>īrQ]4YC9QL۱ВزIvh۵%JXPd$Qʮ!/-Q"q^Sގ;o[a$ʬ|.*?UZ k+2}kjVs7|N׬J.>n,)?%E-8IԭoPƗ^S;]?˸ra)R.,>XƟuZ&fH#t˗o_թ,#-Ȣ[4l+nV&{M,K;Ɩ*="XvXh@ j8 #3W>o׋Qmgܱe'ee7MWo,Z"ZXjǝ;q{ -;X?p_+X+9pMM+yMg-& ?W}G:k5]Bz;Ϳ1 _Y+q6"$QqPM+N[Vʪ g:۱>>_GWFVTmMQW#n[沾AY\d8R6X,b{Ă2(aMOwjjY=~{|jd’ٶ-ϊ _UD ǒtk5]\0.re:,(X=OL k!dBÚ/5˿1Zg|U_ Ks~ǩ'XQu[ߠ r~~X^Kkq~U }vO[5dI%@ڈ7E9Csgm·^z,C% ƛ>ҕ,@U~~/Z>շa`0.tyla7,>X2gfZ7KzI6X:WǸ:`YZ,r$l1~L "ɹ1glWQxWMvݓaLzkv=gO` `hM;؝׳߇|(YR~Zp-eז)kѱ|E٨cYAFsRy6fLP6,gFs5}~h_eގ6\P6j90TJǩ6#VâqzgO(9DXRPC B;Z_k{p8M]hw,ESf}Hr>l۱d+?"UN r>N8,)?L-@5ӵ[koeގ`RR-sٻ?9+FuK5֎l7mʮo~]Y9c4vV0 Ks ǂ;3D#%ʰ w|}FӐMVeׯz%cCK,#Q"QXFXVXV}cYeq[ʇz,Xv~$"\mJe{X0S>k"OLKz#G8n%U<̖,2!T2],{v,ìb{V5>[e_q[<üeXJaP}'U[YE٨wB;8; {pK(R?ގ0 ;R]wo[&zQ6X&52ӚG,'>D3͒%U֚-Q6)VЛ( S,dwjqK`,㦸't6%U3 ß(;[pdq1X˚eގey۰*;uuYXtMɒnNe@fvٌJ =|E٨w/J{F99q3@yIIT $[*v|GO Kq>>E} meʴAmQEU?Ϲ%ռZ-Ukc&zQ6Xh4__6gaZoǓE:~@Ee4+ق $˙S 9j[t٨4}eKW1ց,KTT$O-1X>(FKoǢd ,R`SPpO6(iwqdVNJVϙ\o'G&iE饷chͰ&;6kq258oJ=̖vNlOy!59zQ6X&ڊ ˙"^g ;&UXWSX6uy +Xe cdIujB`*r=}($S/E\^& !"txm}4\'pr2E٨weC/3ח9E _%aKCTu*]+^ \6-,f[ [6O}ᩈ#9iU]l4,/KձKU7UE{liDR{wleގ+-y:U-Z߲EYj+js*(m,n1ň5R}yfꈙwPk#@ ay\+gC}JקFXDq,}';@n٥%`U3\.[y]R/eyV.=HVό˔jFLbui աY->۱֊>_)R'}p81bdłYAЂHՒ۸oK{! \<u" ٸKT8}ߚMԈ%v%[JQw­:+u/}71e`g=7ѝ%}Vלlۚl4p3puͯ;Ұʯ]֐=s+Yej$Kj-K޴Y#SW-p_}v-9vOWd$'n;T-u_jj3^6;vG)k%/;}}/9}QKnw_˱/ǾPd_r} /(cu䜼8'ȭ9y *iYh%_,,T"pG_6k%tՍ;dJ}1] G%se;橥]=+Ǩ V\E]+~|8G8'!+j=q X(ag=l|:#kx_Vw__}Ke/Ԫpjnw/K>JѩoD,;y nvO׈z')doїβ} Ve_&%te8#jeEM?P Kk5*K}v;![ї|QѲ(ns]TĺQ HnXwĺQܸ/ݯϿYZXzWLaT/1u2kenW_0ӎ`$hI»3;}3Ⲣ֔JiՒҪ{}r}Y%1+X+woD$v &g|Q+J :dE/cv[}Yzw9 kJҰKICqYl)+jyarBYúT7.t.ߴ/ї mJ,ٚ7`mZjJYOpdEŭ)L;wY&pQh/ї}k}s2KN`#}4dE-߿7!kX׽]l< F3k7£$xkw$װd;}G(V43)M9˾pG_ֺ5(e;H J}9^Hn-A%lͺo-m/Kv C%,|Lk C[cRޗ|"x+yM%6@(kjVݤԗU/}Ꮎd >Sg$gbWʊ }<$ko:nRd_/#{_ FU`sn,{wE;._Lu,m(1%ks|r׎z;$}X?Z1+ɚm}+OeQޗ|\Ml,b=>Ɛ(nR .ɾp_}A{ dkèS|\M,~J_{ח'M d_/DJ'^[2҃(غ,zrngM^[^|?B3܈apud 3iEW_1")[LQ1j6VǢQ&|i}' =g h/- >%ٛUB UC{Ceݚ!ԿpG_FT9y`T5RU[[ȯq&K* ,FY[#&{| we).J)/&yJS9)HT%<[!kݏ[&n'뺓U'oI*AU{~'F[}ኑ=/O! qOo+Fxv\Ή%_% ej$M~i/W_f]b>]B>#KITU$h˦wUa-µ |*^Rw_jѪ`'O*G}K}K]_/_Gl{Zʐpf͋e|]Tu1, Y Twv1e%doW_}׾M.`Q#ɳdt5Y-XYwuH3亿k޸$>ƒz;(jX9taɠ,:?K9G:m~DS/V\0Ӏea|$${Z㮺#ϻ&({V_\-~ "J*N)[c,u|;R86KB/1z*!P,K*fpʺƓ(.V}V%q5MkYeW*kUElg6E՗ae-~/w1+:0:lkU6ڝ/;nmi/Zu_%C_dV~%R";JgM~L/.gktuBQ:M{H"YR=N$eDmHS}x)2/'}R_z/O٩њ/u7?{UM!{㎾p Kp۠õ9-OlnZ"GJJ_I%յ W_0*j|/Bg9: eIEndt!v!I­4k%2Qɒa@-,TLöl<);?e`m.\v3S >y%KjwJ:p^wsLM~>2 g Ƈ)w̩cәdIEj ýda)v)({1Fjx9S5'S0:ȁYv%UUÔMTuȨʶ'):pG_l_TE:'}͑=Eم"JÅ ]Dieoї5OI&QBWV-eI]',A&u&(i[:Jkj&їjc]]upG2EуlHԊ7͒ d u5Ew+t~`jqN{}_x%u@ e>֝*({㎾kU.NZ}lA/)l% u/  {$/ 1bc[K=3V_Ƹjy)m5})X||[57y.(kh)YP[ Y7(&՗$#y[ð6̢ѕ[F&,dC^MBG7KfpEOᗠ( .tZRMP rP6!/N7mOqG_L ǒϤN_,#n!/%U`urH3 | p8P@y_N5}\V4[{[/+doۨc (YAy_|)+*cI eӂSdbdo˒b*@맽cͰ>Xl&$t ;M>qQ;0S45gu^@YPBGi}Gd+9e@Wݠԗ/\}V_6~}*Rߘr4)G4`GÓ"JD# J}m\[yI_/lO9T@ (3ݒ|벤k%*ĔMAjne@)m*ӰK.oeIe*MX@Y7(?in webї/'ف`nD_vȒS%BYXw,>l[|JYV_f&*, z( f>E"[J2*;'F/Y#sXEP kB|Ky/G1=#Ê&?k7|Q "3 qfBc'YR}{ToffSTwv;&({5~t0!|R_fXg YR ޤDYnmO\qG_j֜K|TeP= ((K3kĭi5:B{/_搽qG_rژy ~GYR=TµpUVmO\qG_6!ZǨ:N2&E4&壦vb ꎘj| g}׌Ql:7w (K ~OYRol,lj;7nN _$(}Y;eIExXB-ˮ'.޸/oRgI*ۀa:QBw JdIXXR`)YuGxM>qQ}xw{_:mu aQH 0ŧ"J,Zs;"m򉋲7nryJ_%~y]bg\TT J؃j|;R?PU 4ڱ"\}ݗUzw9UE]܎'.޸/3򿨤f'VQ&Jj >ITD'W %[N'.޸/ T, P A/}JCe 1% /Mv}[}>J1pX01p>G3jZqP)YRݽT "*nU件TKAtƧ nlPo/r:!>BwIT3> dCuWWm򉋲7nk5Qg‘qk~*5ݳu,~FYR=[Ub]R {s=mVZpG_V*1*\2W>$JlO>CITķT %#;"m򉋲7/ˣ<Ƅf]a߰PT<*R)YPސ-aןK# ߱ǾrHk1-D 'cydIWaoϺQeoW_fϻdwYC5o,+~C֩I]3x}jgB}j28003-qɄ"J:f&Vu7'U2螅J]} '#@ydžV!eE V \$=SV]WṞUsK3WЩ>dE ?S y%Xi;bm /їٵ}%g׫(,#(eac^yʶo-pG_6]dsmӞل=x쌏 YX$G5_V;f6nZT싛qQ}̤J{gZ9OU箖"nJvI '.޸/uf9o*cpIƭT3/-0SOxrR_C| N苕gJ7n*A41&.ȟ1 %՞*Kٌ;G-~w<n&l/,)$LX rJL(%rNRKTvkJ87Pޙ;ɶZV}9:)ޗol&LqQ}̤{*WޮTH4"EG,&'.޸8fyJUnDtwD_p!/WNTϨ6Sto_}{U R@!"xz#G^U@Ex^6n n)~j !ފ/=\ wůx(K*nZd.֝Um/\0nt'Q%zXk #~7dIU?R 6.E՝^'.޸՗o[J/pZ_ξf-G˒3q\[#ʏ&~}шޗd*~gRؓ?xf 7/u+dȓ/>nNl]Q Cm!J}9{Cq)YROk,{ꎬj|G9Y+CRNl>3:PBw6dI͘X24"3ƯHccG{ weL>*g\pذR"v­Cf|],adqReoїF_ֈQl~>C|`%5J<ϣaSeo v(٘7Яȅ,4)558S[)[..qm\qG_<*AmCeˌdIe߅o) uҼb򉋲751ji@/go(>PITS [PCuR_ >qQƭ{׽$ +zt G"J'dNnMSeo Η2.T Ʃq((on&KĨa 2K6!Yux6Ew R9V\5[ JV"/OYR5v[}M'g2VFiZW;ї_AvZZ.\m%tgN.~}*odᷧïOmO\q/T#h΂^cZ䐲bR H}LmO\}Ꮎ ('=Yk4JpF_ʊj(Zjރڮ)Fr苭gvsL[TեwP˙"{FYQO8TE;CmO\틛;]%x) * #eEMU Jcx6n>u=>lmɖ΂]/eEX%ȪH:4S.JO d_/v3uK\НCWe%\W4e+Xw ?;MpA;2?-U͍ ǠO{V_E@Wz)[̇uR_tpA;>{zS!Ur6#FdAnJp-Ւ nP =)d_ٗ#=J-k^ VՍՠKB>"IT*IRo1eoW_Ncܩ gC Bf[S%U[%El~UmO\q/iPI|K?L{( \~˷ J]نξgUeo&TTntXĖni#AׂȔJ.kw$]>Kd-el'#({V_<\"3eIX+YkdᆬWeo&b%03( .L@ ӱ x)|ū ,L~Ķ'.޸Dm󾩄YS#zaNQ2Q,ZJ`D'.޸՗J qn Զ{Rde9 cK#N@*od{'&({V_Ɛr dcc=XaaZRbKR>ZcH%^dyᱚ6Cɸ­,ÒhXvIێZ ^; ZdEů%Rx랐7Ҕ)˞%)X?OGgAnV%v K,dAuK"=E|%y^E mOnF= M~վp/Aiy:ZXv?!J,1X$A/e;'1~ve\q/`;V^2`]-ò(7\l)a%5dwY u7wm򉋲7n#@k%D7am (bo\,)K!yO{]m򉋲7n.~.%fd?-)usKs?aˊs$吵s/żmi_O__/~˳/xſyJO_Ͽ~_N`bVSa W!/o }ŰmZ/_b- oї[?~ۿ[Jås>~J3o??[ #Kˌ$/%a;:߬f{oE h0Af z/j}_D_W/[ϯ4JWzY]H?V"##^i!@zݲ?Xz|eohm{g<(TU$X5vQ>ъ~H?Жkƣ_OlS_!}vkeڵgϟ?8;U7_}8oDl|a>?eW2C w٪=@g;fdm$>"Mffq(e\u@)c2-2>*,MS͸٩[iwT39 E JɶIB}YCl <`̅?mvOBRce=j;g"[O¶\q-dV!>=ZDjͧ³~% ..gpRv*/kζ\AmI[fG|ԟ7gE< lA<{1Җ2 y10@G΁-I=jO kYngsMhRƮ)>\q1 Dnh]5wx_a@H-ieyW0@lGܭu53~!Y6, et9t=o2vW+x #Ɨ}TTn#_14~y4/ 7e:_b1WWt+r}#qK F4}n7lBfpL Z%Sxxc>G\qZ_ќ֚E,<yKgm)6ol <`\#V1ff!.&>IYMMhҖ2Ll <`̅4fvd48Dܒx .2P_0@爗<&"m7d#݃ijsw# 3'~x C-giR㙈xQ2sswĘԣ#z04w|_itwFBw쟛EU³W02xnhm(SmG}ߧxB< #Ɗ11i̋ӵH@\= tNk\5Vz 6@0ef]mZM4;ڢvQZY_S80@爛 }v'/YVoiLdQ8jЫkumn9nj|yf-|yg_쿮/ y*|yeYzt:ƶ5$ssX㦏eQ1lvY+C\(zOZ_Y2݇u-׹_i4t%m1.ՒָVl gі(!ZҦSzPJ˶kԷWy#bL -WR veg)9eGb^q U19ۦnR{#><qd.|VYX@%fwE$5@yqSϞ*؂uBK2>G<+1gvaÀmlQkd.|~m xo6 l riȳ=srˉ5CwHy2>G싒5Ŵ,,KmOCUW|1<lK6e|WotxANԇgY x@GĶ{9_g7,ހFpBxg2xNnQ4ۙ28nj"oh/|㎡HKi9 gy4(ppq dB}x78p|ؾ(@Wٞ2N%³W,e\|bQ&{D}1J|d.|5kYtgZts 1BFڪ/~t& #^}-ftj;gk狚TuRƂœyB>`$ hJCZ9εl>Ia H[kX#WU mڕz̍/g+Pv!n]ۅx|!^u!^_0̍<Цa\ּnw@wm>7y-e,+桭p$o k;oPkx&|B>7i-e g, &s kK?=L׵1?vs2|3fcL5U׏V|0̍W|A =:ΪeI3GG9PM+b}L+PNE³ol O(̔m5 t&mj{Zh|Uij,1ZFڲSƢ7{e41̍/LGln~7k:,xe2|\G| M_kLy_ #nE1y$e[l"T2| =K XuJ5^0̍zqf͕l> Vy 4LL爧ǶЛ:٣O[\=<[ g 2lho}M &s ~tGsVi󙈟<; VҖ2òN><_`27>G|˭4-֫yv$,'dЋYq-M6׿\'(s Tz&j+؟x* AϨOBkX3nXS{&>ofl?I7z&+*]8xṆ2xF}Dm\J1cg"^41̍/㛯Q U/eu>k-{&gG3_0(óOd27>G1m!xwuMLmE^Xe >Y}xfaga2_ >k l4Tֵ<%O&{F}D  Ӿ(sYiǔ -)<پ"˂7 gH)E)3B=6gWZ^37Ϥc-\7w3<ywD<' v 2cL>k \181'f3>-w@;S_vB91. 91s\K| Uv!99hC _ mg(s#b6t7҅zy_6҉g>?sv tSp ևgM<`H#̔tv_j[}^e"]2xפj>G16OC ?ƙLcM QSLS*0Gą=3T8%c)gOJ:t:KC-!K^Wsyݍt[5Ga9a0|ZAn[/j7,J<مIgT{IeİLSt|Z%{ҍsg>YLϼ6i=C:<`l <`|@1hxwk<ȳ S&[ C"tڪml+cvU\BS1>">4g ^zn6v;<:5VsiDdu>Mt^qڵ3/Ykg&sfeke'H\CsR/_ I׼b~+Ud2x$+}ʚcg)S<`1SL;_ǐZ~"#.t2lLj4>KOUN#޶20No| gb gEB/۪I2>"nHo:p}v/  =%iKMJi8rys'we>HDܳxvGO" 9Veƶr}sg2uG&fiEuBIjBcE*wJ)W:i%cyK7ߌfc tI[& zg!nd.|D<"tc=o&g>Iڤ)祛'=Vcg\I/~爇k"kvV|;}kf2iPZh<`0,'LgYcyxaw!礵 Z%L;3luO|&gq2|S4ȕi']o)Ea̍?J pIB t;Xd崰,ē[ΐ᳧q=O2Q%,a̍/ۻHNog fAҖ22>< q'M &ssAHI|Y_T/ssfA_2k!YbҦjwj2T^|!CϞ:%̄^XK"M &sss@ax %ֵ6L$C~!gOBgaIv΀{J-!~þS"=hR /yd8cH0!dEllhbHu|B&'m{,fM;aɳۡsQҘ cĬٖĀ爻`!]~֨rPa|>k/̗2x.d d{nؖ`27>G<OZXkgl_"iPE𽁌}/+ 22m#q|fXI7.C YijDsNFWP\ _]wޞUNa(u|-o- <̳D܇xtN:XW1ƻ}E>o)y}5Zl,t}ݠ's'4i >-2>G=,ćI/<,9xgJ(.i(L;f}~j̶\3&mnFn/-e=\<&m'i9?yjx`̅?:ݷy|'g"~tK,$ey!iC̒?|&- 2>"7A NE&3,xMV>e@# dYG0P爟p'b6Ʃ[lP#p'yvA<'bF ȬEꧾy2>"kI f_TLĻ7 =dtzd<ج><1PL bu4|p-R.|'@||GyI'޿Jai/!^sϷc)i-ҰDe٭[9~ &d>kd.| CCüv5♈<L"~(m8V: -vfC6 ,-e4\ؒ4mDn9 ðX5 K#E޶wiww̓0l <`̅6|~f=Äx&!Ϭes׍BҍɞP}~+m 9jV,ғwΜLx&G%o/ ͑4oeJY;]0@G`W:]U!lDu8V`0@lwM'…sO:]y׬'= LEC09 ^g"9IP7gjuYx2/)jMȭ|D >U'sà؆.luȟ3$y$u>SDۖ 6ەEhl/-9~G](<:L䫊gH I|Y1Zq>_ݢEVpXȒ# '4dƏAgQZPR*q6aopl,veĒ 2s2~2֤Mڴ1VfH8 !1٫oZ+gūKdCLDx}ÿ\܍oqFWA@Wi&4sJDѼLxIj3noIUͳ"+Σ$ZdɑF{I̟mj\܍frad4M0%Br$ *+l0JhX>U-\*xpo4?-qRr$/ri#iHht{n_5o+gAibΙ^-% kvZVK j!Ys r7VVg u ^XY鷟b/KKg"b ,U+t՚USV 4& p$9֖ *b6z5-kcv|#1T`Wg%g>h5U n_~J'.XJwG[ k,%GݒU`^rٌ5K9UYn[kّ+"!ùʑ /#9PYh wc%|GydX< CdYKq0>IZ2sr7VﶞzWU,T.b:Or }W9K2*h wc% eTyh.lk-ZNx&'bHVAU6%ΓJ?  Iv q[d Z3ɑڧBJJ[ڴ1V AW&͞of=z.aε)9Y GƐdB` _ S#{|21\;*OQ/%pIƭ1g3-hΑsOS=%K*i UI4EYDTݠWL.޸՗n>eKCYɃbAߍmV\ɒ2ty V5v]&({V_AK,fX^6s%qYRT{er~L^wE&({f_;v]rlk9)k/S-:K%BSޯ^7(5({㎾ ?wI3c{5w2ý3ǾM<:d[%pJĺ=Ftd({>}G'3GNؤrӹ;mssה["hv?6k@J?!gR/?8c6--pEnTœٍ*ܸs2MqAזl4f(θyrI />:?sB+ٟ&&JZrȞ?!+9[SSls_I?\߉s2!9G~'E>λ\_?KD,M' G˭oȫ ?͵I*:5LKwZ`ʁ&7ZlƔRXMA [hz4:C ~~?~WAs`/=q'Rew1g ʇCvMrYk$)u~c[,QA3SRj'!:w1d FGr PfE!P65#zTN*`*04Q]g-*Yk $h(wa+*a 1x2i͹-k&A8䥜]ITY^j\܅oahS P9b w * D9KV6m ykGxI:ARH; չNMDف ,޹,j:;P .Z+3yq!yd/ډ@J̆Mrg7 dpUsu4' E (7yß.wa {邏?X$pb*R _Zۮ&@?HL0qNJ6 ?l R8v9 Z=Ұ$1iooP^\ W4 sCD=,S)gqUW .|_qo `*5a^m 4^c~. 4H8Ű##b11P^5J$3pLoagvȲ<3!,2Kk]/?:SÒVYj||D1|bdlC,9p˹OH3>BrHf9MrCppA[U!it=,9wq5)ο p.ު#Mr;WX/1?Wf0@MkhpݹQDbg3j-6]X~oc8 ? Sd1o kuwRI.Pzo Q1]c{vp7Cf _tDonmy{36 6҂sr"dgK(ZBA .S|r|1xD~A|\UAվKVO6v=܅ode MV*7]X,SĀ1oㆿrtGΕ59]X3F邿Oc-bL @NP>ܻ~{c|k;9v ٹI9MA6UP'94"[A.waGCeؐCKFpy/\r-(NQm `5L*mCpMOn %Mm4>f0}Ukr_iƉD ~._,L-̚/~c&\mVL/J-lv2\.o_\zGܐc#kAos7D}% ,|k-̂҆: ?o߿~o;]t4 >3LŪd n GP) ,U\Z m۹m+/*7۩j2i5I8UB@AR 9V^6Q8 :}?/9}'CK Wha$DAo(^kQsI.PaӆdM$tQ@nD+cvG• .|lmc/hC1G"sͱAry}l~mG"sjX/3خ[/JBt(8A0lǘ~o..s c]/ W׺^WRZpQm+: *"c}hFnaA\ǭ\n Y@̎G&|/o&NWna9~0S }X\kG:϶L#MOn.9O$ '>5iUs4<(W}SHP.rښ9OA/ ɄF|lh>O.|؞Z\cUM: Un}AtwXep6f *$w{7րM?f}Dm'͡5qy^9U`O\f^X/xO++UU$v7&pdcչg3^s l@ ?E ɹ)f]Uek>6W60a Mغɹj bx ϥWN~e9j^Ç&~E+m -8wNA JWAX@mD6Ty<7րKNILfF6ހ?p V :]s%Z/AKR`oE Xʑ D"r+PݘZ 2/-}_7u 7v ^ksM_oo6 Ǿ%+#9&ɑ`*Л/gb89Mh5|j<ưNn,tj"PP`tl6oo^k ~or_ 7vo7  }_7zQZcw:ѭ3B6\# s+^6Q |·aTj_+-K=Wɑ|‚z)0kKŇ'foo|bH%oi$~l#>THDsy=W<00RɄo?l1Ifcp.9m,U̵/{_vztviW-t~{ upܚ✮b6UX>-ai@A pׅ?Kp̡)N^;H2kG'Z /*,n{ğ"1`HqIG3aj*a(apE3? &N|&%J< 0zZ{/Zܓ܅o ]JRi@r=* n (KiHgFrm|2.'5~Zv0FEmɢ(G",Z,Ђ!a\<1l6N{zcÊ鑇s25I8*$G÷`MpD-5.?ip(p  IQι݉o놿.HqZv4C&zG-đJBIܓ&ITQ Ln"^k]j}o刷_ g7.Er$|mppkaӀvwa ،π-aӎ{"9ӭl,ZSl;7ԆtUOeGxyJ\=n(B9Vpb!k4Mr'ίz/ ^/ 7}w ~g.Ucoc3PZCaY܇@BrL-@L -qexc˖ l 4np}LyXI8-kTfD\Z UGNfֱ^_ ڵG'L9)Gb 6 lH>Vm\^Bӹ@3X>>6<8 ȃr$Vv$\ƶ*0V8H|j$.EܓHWʂ) T ~8wn=7SŷM$~wgnG`"|GV7Mk ~ /k~_s)SI\fzQw1z⻑pM, >63<:U+?t fCP,S2 -#@CƯ%h%Qzt >VN<$׊8Z\qʁZoGᮓ!ktopoxLSH$.EF\ O9=b|`U+i7 ~#I!A]TZX-ȁŅs92emX~ cBRmzRV"[|8Z?$Z}%]r7 >Ԯ{YXm$sBQDCH0Z$,~[X0E&6^r n.sw^\B9ɣΊr.h"枊BF܍52taowPѾUe"0H$XE̡4oQ9ͻ[)2X/X߷\$s-~HخXAخd]p[\܍5cio3/x'`>[8Z'fʌz:&)+#'wRz4d nQ&Kt.CDgΫ;cU XễYd6H8b H0[A7_Zg"7 iZ|h_+OnSjNxh-/|O Wqt:րh- md3 Wds ?DRut̅\2ѸxUqr7ր_gC53 dRsEyv8҈rS ĥJo JijMF,*ε HD[SKCra.hNX>3OTT#9/|KDN-KI/^ M@&z2>"w83J5\ !}\X`-uVouu X>QPɵĥȈ[l_Ycy{p_T^$$q,ߦ.ReD&#QĵțY4°>jDX/Y }z4LggᾨkѶ%WiT8R ~׭cbA} 1GM\K=$99V]$&aڽ|YctRd ^)q##xuFKέPD,HcyΑl3t4X,脰z!@rn儻qX9r `x8n*(VW?;׾`U}c5rI;^I8..$)@5sTIx;۾4VD  lr~Wfh.58ր7Le܈͆ ?k!%- Rt{NGehF8]c?3K)3pOWDciK&˚X/ ABgsngBGHlŸ?7|xw #j!Vmv(Qr3S%Pkxf3Mc/m_7u_ ]k_ \kycd+,2mG\j$2UZ(j hO⑼䑀c$GVJzUiTP-!ۖ+-&SHDW, tl\* }oo?7n?WAo$\ε@qQ`!kM]X~ACC܈ _έzEY=UA.A=BIY=fc_o|fR<[ Dhuk,$WiBۇe\_)_*@X ".)u;U,qp}VL3ER} qb! 0ہptofLL|+l GV^;"sͼzS]v (5/*f i/g G#> Qꕂk9h!TdF]SweN][D5ݑ<,Z`=J֊Vj3iOeokZzCc6ŵSY큏YQ~+ǿ|Mn#V6;ݮ!gF?ToߕMb9Aq\ ٮ岓Y>F=R*WМv5= υ82dx|f˵-fAPi0#]^Vs%քod{k7ctfxP0"^kSޘ1Ø Ƃc yFm`(>#y=Os&]i6S28!8-3̛g&0];,fL;qB v])SS=YrpIǢZ ĉ,r!^t'k]9±`|Yمǂ%w^1@]Tn3Gj)i!-1(ہX04*TjZ5}4 u'4{u7.{I#g`Auܙ\ Y5 'emQh֎88v.jckyk·VF\N?O? [-YI%^kokaV3snD>&N[ Š`- &x1l\ >|%քoMh)q?Z׵v XLέ{s;ZWIw-h~ g O*J|\hE͵/MTˆ o|kA3uJ{tpGǹAy7 ޙfAXƂ pNB\sȅx%VcX$'$x6>B>Š* 8JbܜJrքoSetL:eD 4wLNvaŎQLl@|:T^ZL5 :~GoY,k Z\kK^AXuO;E~ǂv51G" M3|<1OIk_: [M:XZgaɄPf |q5W O .yc#! ;KQ!`jڿYٛZ/kO֨߯k0lAر  v8pttv37𷽩,FW lrgNb;!,`!@ZӪל#v5[?M,8r>̋g_2յAHQ|P>:#פ4:>Fxu1uhs yRco8Jnokna&\1 :^5߬!|,]|3NiLnt{yMjAKWgMޟTplmt4lru#d:qs;in &mzinkR8,5kL.gi.Z^k.-Pk}mQ#j]~ ߾Ȳ+ǂNHN;|v^vgCK.D[ؑZW5\0ANqoךAvN8]\{n-jV+wTvva{&|NkK׎yNC ٭wjZ]bM3?3?337aQPO#w=_׎vSi_X~Mتu~]\SZ]b eQ7sk3Syå5Ggw>Z3`=Qܗ^\k4K*˟TPucOW(x\kgB`ӝT !/AO.&|k3>wRжqLeD<Gv ּt;37'gZlj>ZAWj;{-1fsce.hXȩ7;n7Ě7c˦ʹP#j\ex &=ö\;9]winŅz cB|ibn.NCn4EQg |i$f] Aͥ&9d!.W|BZnX. Zeddim/ xuQP ssc"8kAC~}0{Ưo]0-ډS\5~<Jܶ\I{Bڳx{WBpکf kW^:A`meK E?xj-T*| :n*tw7pBϨ"vv".mo9nS4+xv-h<CDŽ - ?\k] (AA vZkdԂv7zC`(K?]{?D;P4,őPk׬2v5VX?5X>@>BEZd>J4aWՋb%y)䬁B3~7^ Q5id!d!;=V(hÎ Pk~kVyڱƘ51p/ e fZ?crei-";i |Lf׬rJƿlyn+ֶsB-UA)n0W:\qe1K+ oքo݉ǶP08H rNDA+E-I򥫠#Fjmٗ޶kACӺ/`m_H3ɂNI:yT1>@-C\3f-ko{VG.i1I jצVB; &Î ִC l v51y5qot8>, \9Y:BL))/)(0E; SG;//=ZgSpZ.~L`:~7#ƌ cHYoXNH`<:(5@>Ob e/6cc3݌D;9,h|˰4VCʎQ댹5I-hΚHx*MLrsA $x2 ,KvS>Ў?\F͚A.Cp`FgI1l>[.mʨu&f\Z,5˹| +"zuy/3"Ϭ_VzA^]pbi5nb>; __V Ÿ"NSg!XcR5[[Iֱg :Ö{촓pbBHSwl4[Ö55by>sSÉ 3vԧ6>'Z΁] xw>K} pЎju|{'Ek7᣿ >ğexEAل*mX{zZ!PX">Ӫ81wk/NALUه}5Ae"hS UZ˓P 3]XAE+h4;YXoȞG=H PW ?)J-zT<+yPkQϣkK]b 4󨠣Y్ma\ bz"jd)GvGZtkR %Vv,?_=_=៞|៸9.> jǎPӧ?QBe1 8-ZTke1I-hhX؛:{['3CsSCGZ\k Ah'eWIFAkr 37k%SohhcG,A 4y G)>]b>^|O H b{ e<w\㲛r| ]SB}R7Tׂv51')~gk[kV[q]4,&Wi\c9NӴsVEli"ls$oյRvtT"`gg)֦I-hhKwko67voյ%;#|*(Omz-|*Ħ OaVĘ=[n41Z `bQ|ZUv Y b\ bZi`QA:Gv؄&f/kN/t~1E{I~2ύ< ֳ݄̒N%Ԃv57zu|4GOߍdZ 0` ^=4K ?7dR]k8>N,t'[ Q뒏Sפ4+Ow8مnw|p 7ƧHұG-|zd`[ӣ] %|.iiG?lX?kJ+<5u' _jC­Yqk -DmTp31A7A9bvq`5#*Cw*,kyGAiӎBw$jǵF#w3o]  #mKBt ߎE_SAhomi=7ߴ10Y!ǂo VxMi7;]Ys=ō?skO\369Mj?¹nc{_DrN.&|jHʄ@7ߦPcl Er kA.t/noKAӭB[ѴP}& ^D,z0p|UӾ3>A=惧0Q>+Z'M'׵Z9۔ zK/ѵ9O8.}jߌp\Z]Ap9xް(Wv5rOAG$X8ox}jߌ5ؽ֔!wkˬ}eGN bݵ$iG[e;= >Ψ;T-s5I}J' u M0.߮siGju|rnWKUZ.g>߫aAL}z(twȱv߬HE]jU2kok#^m  b\hn ;hG!,n$d5r۵Ƈ}g cw<;mQm8_x(twJY 6bImZW4eVЌ  B7,ek' _ٿzZ= -i͊D@h_U˦ 2]8ztcn4^Mc8kwk+%~y{57//ZUvFQmNf3z|kBKu&|Zo>#,0U7bqmvZKֵv2YkJ Nc9M{i^_7߸T`m>dd'1=cMv':!XZlxMj {UY;&&!;3~(.;Ȑ5]b}No,ru Hkn%7kv3%_(aka۵YBc~SkNפ4K·bE -t beҫ``;KT݃C&]b s86V /vT{LnFZk6+ vHa^kR %g3`ʟvlƸSe8x(òhױnGݳxMjI#jN>\Y']k'-t$؁5T2Iפq~7;< ,~)kZ{ea\K:kR5߬?;G:U bzZ)fm/zw;CR#P;NC*|:O*X畿ph~nݶf?Z7݄N?פ4K?(A݄g+_ z0}Gvv4CIoy^v5𗽏;S? Jv]v`Y gZ'Ԃv57~0&Ȯ4  &1$hwgc4K ~fYr0E7pEew8VaIa vU_Wljw բtϞpm{LEmWg}SG<@v 7vb k!ـe~Q>JB8< 6fܱT/Sk5;7݁}wB-xZ muC:0oV DyǓxN\~= >@9{n 뼯$q=HTR*1O0}R|xz!m\j4WxN)-o )eKE܋b7k봺du zJz&4?AnK)6ʥ-N`| ]|AǍ,>7[0 H KߊHRlDUI~5U*3*K:iK؝\9n G}n8Jj>OUIń`]bkSrq/ҥ;{^չ?R")z%IE}!Ɍh^nH_mh+a^oyYwJ׵'m3wKC^bf=vu'A{JsGz6~V*%mU3_]M=`bb%~]&W/~ ^|>uA[IST`l-g5[6[Ҭ/n ?)q%9Ih$:ΟmHUlT=ݳ^\ fn˵Y^b By>\9(m'Roy vvxwЋ _q/f +]:Iо}xmc:A[JU2̑-[V=`kC/.~q{h5U=;Ʈ%T=v]JbQɰEڷ;rum/nK$+$L%Oӟ!It-%X^ڐ M=h^’_b^6!I[.ϒwimq/7Ԝ"u/Վn\b>~I|E;IZ>qW ]}kSO.f^gzQoͭIsN-p*-6@kSO.ft/ǮJP,ծ$*YXك%儞\KCpǽjJF zƇz+zޫRھ\%s[=}M=hu/~r-&@3s%z8Qz,hՃ+;I%8S8Suymq_q/o8~/|OvlQr9uܢ,I+AVR>fxݐ~[q`rQ*3Ai=R6>H{ *jd|_ IG7[susUb9>\!g-=J{ nk~mḘ{WM/*J>{Ps1$\붔$mwTwzxmḘ{i?'æ7ڈ>yg˖R>%F}M=h^&ڴU1# 5ͻ9u\NhK)RUqVak1]w}2ߋ #Bf95zK3Juׯb\ܺ;ĮΘJ#Hmf#p)mxu[Jc*W"nյ'W3VSo]cV1=8Yhu7?Uצ\q/ tèIJym~0'$Ym)qG*aG[F}dQ=jp^젔Ÿ\3f8Ǽ#.l"Z%8,DgUݑckSO.f{Ă~>Exg8%і|_,]Eʬl]zr6s^>Xn\yX7-ơl)瓽ļZnsDT]zr6s^4 JZ;-88jiaN -%m1l!7%$mֽ:|J%H80Ys# It\i+xdIe[º!7;Ŏps=3h8qعzDCd+yO%؁%[;.1ൡl^EQ ~aPw\]OExvnk[~m/{67}d7p&yυ[ْI×|J,+m-Ib޾~q^FA^>:VԐDn'-t\Yvh=z &Н]mɧ!]lz6s^y? %8tH HS,(z|IJ(y~ϯؼnH =du/^o X%@xgLCtwJ$-[J+଻v`][bGe=WWl Y{(zv*x}JdVɬFkSO.fn ftpDaWd !7#\^b#Tw|Aצ\{9h#)d h ![X){y3'zQJSR 4ɀ}ίkSO.~q^ۣ KT;;?'%moQ sd=6Oπy 5.O|k+K`SEwi+)>| LvkC/.~q^&9{P j̊ /"JufK)"]* NsU`um~1_ܺЎ%Hʞvq%G4z;Rm)YA*%`, IsmḘ{ixJ p&dfQ;طdtPR^[zr6s^g- |DkG5pzȄk">.- *'h-@ul൥')m3 T2+tPӒ(H_wmnHo =Ii^vhG;K3[v%n___A\?8b^[7deYh~Q gOlؠ=1zA3Qi+E%Dys{tmḘ{WYr+<ځ]/R~qǽ,+Sߓ{7 |Hjjm+l%h(m}KuCҽzr;gpUbYY+-If葋@[IWn,85Rݐ'my/˚âyJlf]@mm(=_|dU};m^wUצ\ܺ|t%6<ҳYwW|ukܖXJllmQݐt/ϵ'm3s/ӯ/T~/?Xo~_׿?-6ۨc_˷^?z~)ڷ]O1ݾLzjS }~˿wÿo?_/ ۤ~~G?+ӊm|cZLۈ$&mnf|RLm-Pцu_ K܍q!ţymaȯoK-q_G~ x}f^da{?zgXxfI2}ޭ4 v붍? Gvro!^~;q5 WK__*q_ǿ۟ӜZ_?;ooo~տz귧5?wս+h~U>?3oxf@7ҧengAx7iiGW?dgKZ(5% 2+f Vth1N\hQpZktЕ:'ZAׄ4ˬ? J?oW_oA|֪ -iY~}׌BǵY&?$|ukƯg>FPӻ%ӎBC63]}|ʁkBKeք mUmKyӏ5Z;vx\X[v?;oeL73וzו;В5 }`2|vCkK?3'jZВv57~57;7;7~5ڰ\vRNboowZg;}#-L| @ [PpZiG3QAH?/]>&Ü57~-8'7 ^ĢkBK[`ٹBӎ 5 EvOdX`~{㏌eYz㯌2x㷌2~{㷌VkBK_%ƿfƿ첣}7=oքotFeD|NOךkvX;I;;CV`tMhI*,2> ̄ Ԅ S/⟚O9?&| 7L<OG?7GhG+]vPU&]fMhb?׵(d+ӎB )ٙS7uMhIx㷌2~{mUvK6*he_}&| |s]u'~)8~u?T*ZkBKeքo}I5wS#ЕE3Ҽ֦f]Z.&|[ބoKC|qUz7ǟPQbZc]Z4*|wo<AgSy,yx?lq;]'"^kAF&iꆜ5Fo6bVmeQ 촧Vgng~C)5%MӋ ځ 9EõgϨ=OZv7TM[tMhIzn7{5^y<,u`lv^RY-n-ثU+JkekoTv8Z5uRY%ƙpxnQ0"$("5)8p֥oeg _]kf}?d?$Z-׌_5o  >//(8pm\Cљ7Ʃ DOH:]߬ :d?$V^k8C@zfM3?3덿237|8rKS>3X8vPIs,/ @q;wAAmKU7kóڢ/6P,AoETAEAWtc\trքIxxNh<v ;}tȑy-Af޾Y_/>~ ~ '㟌'oք0nL0Yr;ǿAVG]7~]v5#><lV})V\9!'v: 6^k whaռNuք+㯌?-㷌-_[P\Z #%Fpn/sT0;Rش!,Z[ 3pa:~g8'[sXo#x/4^\0{Xsʰ)dDX߬ 0$ &.T,`3H@ڥU#cQg=Z?0~2xgf w7uݟعʞ5׌_5oքotah#䉕Sش.ugxh3G-4:<Ǥ% 2kG0%-ĚlcpƢõn3?R]->XFX[Gv5K]cm/߮ڛDž! xtKA&}&͠O*蔑"eiK}enיIDngVMВuքUIӓm( gQpZ_>p?lߵ7k¿ktiS[K/vFQ#Zy&>2f |dxukxu ] ]۹!hu.F1eCw;k·.G0ƚg'QOqmg^ aؙtu.՗4ˬ y='㟌l&yBѠ;oK%^YھYc'a6{4x $^kUDaT:K7k‡C9l2jme<S6fiH!x;%Z{L G+ _՟T0 _A6駶CqC Az֮&r;k#.9.|[릃?E9#c0;Q|kAj rgM`=υ7=6ٍB.u6]DGnB=C zm8P7 _O~RfmwK8.y?:ͥITo556]W7+=VY~Lv_ ]9&Œ>Ɖ W{<}T`KI߬og"0u9޶~{㷈g֯Z5~߬ _?`aό?l0h!ծ-%9l:nƿo4CΚ?`;H/3je{F-/k%g\4w SrS_f(Y37BQ,]@yQcg?ٮF3tצvTkZ&]bM2~KOƿo? ierXbkD` !Xu&+1k:Eפ0hoք?IKODwiuly ꎚX&S?XAXѝt&vC |"*ٯLΚ؏9ϰfGXe§\ oVY'l9 X5sn]!!'As;IYڏԖkr7kG6#GɯOp R;8m! RŦ1⢽5#Y02s?Q;4 aER`6`xLNid2~K o2oW_W_>o7bh?><d?$Y-㷌i|&$' ?h_KOduY>SӞ"7w{q/=^]Oۊ a&A͜n74$m O]b}W+kP7WI |.()x៑fMXƨ; [sg{Q38n徧AӺntH=O=Zg]9 5OsRQI4?Zz8t=vF=b`tOK 0B ~x慟IP7캹vR pK嵦@LePi]bM30tnv<8co}VGhs;$q5m!_yH~o߬XsOg*zWLv;;=L̙D$U^<k8\hp?n;o`L! : "`2czTfhQY_oGoG_oO?o7>OYrvpu _]+i11 .vp [!<*}dyMy#8߿k·..Fu n{9=(xvp^!&+v'^( Y7k*m]=IkŽ`6w;߻+g;2?3?36֙Hf\~Ƿ -/ tX܇B_-rsLLn73/Oq-hV>(bŢEQ\'7'-3f))L 3: ֺ ׺gR`mwhϵQPμEL륲.?<8g'gQȡrj6Cf>a4W_߬ ^&U/|?5?D8B_JCjڙ߬9_><zYD7Ǯk\qu+ntx\[5cZ:@:J5w:J!U agG y%H:NT;M*fW-b[ vpsPags;눾%dd9 kG>T< >ˤC&%+{k]m|[uv̎@[#*MrclЭNd`keCȎYr][_߬}%ךgg =; F\o⏌?!XW㞌3~o8&O*J`1ޟz!8uye!E23p{ 5G2pҹ]bMJ |,~RD2fCl@ LK ^5h%ldֶS‰`.x૩GBᬁ?pm3@"J!Xb0^p۴~:o_3~h' T63756Ji1v)x֎ufS[vk2|VK xugKn/'3)>䴗G_o#XvtJ8߬ p |٭a/eī1}DvNΜ9sXq:EI&b&|Db+*:NиS]k@AZIUv4V\[h>5[gV[ru3)_i_5៚Oό?UN.?-㷌-㷌?+㯌+oքI#r*(8peB M .;ĨbOaDRp7kGx-5+x{?NO־#Mg/8txi7I8%_PX@SH^oi-);'7S[vV?kAaZgM,tzPpZ 2#Zp1(8r-<鬁}9.>JL Nd e􇒦9]bM3~O133M,m? D*O{Eõqw!ǑGvOgV\[q|Y=~mOQ ˣ>5BHN{i'ju|d.5M=0cGLeyo?a8u't*/k/^W[.VǷgNMuAt|Bu-hX><'^!㲸֎4 U*cfܶTlN*߬ 3l>}kLĽp?z]%VϙK+2:iӉ_P'k·9N_U.5)$1Xcd7=qS:bߵ7k뽣ŇR x֡˵#v:>⑝[% ]bMş(XE!NյFM8OX!y={:c E<Q3Yi|Nـ#l }6\q)yS6)f¿5OU5eDv+2r.nX>-XQ*~-Gs|$%qh<jxSbN[Udk1lcV՗Z8=P >xLV 4 ) M)Uʼ-yQņ̫e5cz1p2;)8~wԘBf,8!@[5'߮mLQ85cg,<"{IN{gg^L,NE/Z5:M}%<.w~>?i1R,qk\$^RdRd9 1Czmi yuJf 3PHigƯW[{1-hxjsoքoMĤJZ5N>)#Ef󽅵O-Cv3;/uf Ũ;w \kWnr(Ia>:U+F )2rR^۵¯s L)/E^zo>~22Ixl|Lc֙sݬV86.]j5(`Q1$ vk_?12:4¸=OXnPcFA WڑN͵Y_o^QW+qGg4"ؑ22fMNTдqwMٞ8h),BHŅR/[QK L?\v5XP'2Io[oӔm*eCak٫2 )_.v=_^+rW1:3r_uքqL5NqYBrK jLQP4a- kAS ku|[$m(չޠIkm[<+>Vkn^kǤ۵a5Yn0o`0@n־!YZ)yAgt:󸻶e7k·dExZalqY\kIDz[h<,-pF#վv5ÃfϩSHN{ifƿSNPk]~&5O*hNW!o3vj/F}LM̱i2Xexb ֢Z;UvjeН0y/gk9~>}'W) }Rpe1N2Q, I+txFGߑ?b㟌2yc3I(1a6JK%t:E;.&|0J'P8u!Nk'7 {ZvES&Ckݨ&|hJ<$Ѹ($('uy0etVrYq55`{ #'sYwMgf̂Qki{sdkf<9 6Ҟ?Ŗ4Ch1-FeZnZԴSA:>')<姵 %~? _]+:/]܎kASu~&|O,2xAC:)E ?R-Xx9ս˵񰾳>V GA[X|/.;bC/g:.g/b&|9.Rw lv-!$wbEěkACĚ1Go;g 繥QȾ'j1"{*GsƖ SyfP-ږ`AhѠn˦׵]Xeh~\ ">Nx?h/g}vշ\ §&RY{LY{پB Mq; iҀUDnWe9iCOmu͚{oo+ݚ)eCsYG 5Uk'|M ;K`(s\8'P^^|JuZ4;kF < ';W^>8]ivѣ@kMgL5NlvïxNPpqC葔>K Ι7 {oƿoƇzf?Y)K7҆=FC˵!}is2 Gy.rƌsa~_ i?W%=Κ7?`ȵhy4m:nZ;MvOe3HbTtIkAӴY>F4,{9iW(d7;{sO-NƢ'qXIöWiϗaY)d GmM3 VݮiD4mm5͚𭭔0 _33< VvVBCǑ"ۅRctVK ߚl- tM^nZnˆIf(S wE`Iw]bMST0uʶGLEkyrhaD1wN#WvĚgncZ8Z`PfF6@&Y~:<,N3n]rVf(Z>9K?[3/ޜi  |yJcNr5=4K u h_Om[EX a4ܮk4]8k·]>M`L \k'B+ WuO4K_5IK^{ޥJ!/s4@'[4]j߬ ڊ(X5 u ea~[kbbM\ֳ͚HZm=ΫB&CAHsk ƴں׵ט ߚc Ov R+fƿ?Qk5 .ܠ`a50Xp8ƞTaF  Mk'> ~B Nzy~.~ fْ#mm֎cĨ~ն׭xfE1 9@Aib'$E~"y=;yuKEI~p[\^r?i?Z<8 ŷn[rSzl1N=3np.䵲9i+'V:\:8k8Y~%J׆rD)=]g]s^u[;~.ה;߾}Gw}.JDzc _JvlM/51:9iX>g36:3rI8cGj"X:N<8s=u?&XoCPnrm4).[z; Yy0f8DlYy@ro KX|p.*l\Cw(AF.~q#|pӥ@3wƱ&' 3{3w_ϟg3~g?ŸO;ßki}%bDz/>ؖO!TH:?"0A@hA|j~BIŶnJĊ`4|zS`ӝ[] 4KX~g8*q`0?㨒%,B6כ8e[^}sd࠶O uY=:%'{7ï?w 'wKX|kpS_X$-7:4sa"; z> m:1^'߾_;hu{\`gFX̑R˼/=`i[7v>5(. R+Cwd#Y _.$\{m,zw ߫/.j530yW.hҍ(:-p^( !z k`Wb``[mp RL d"KMυzq ?<,,W.ݺyYv-)LtV\DJxۮgy6ޟl"GǓ͞_&&ڈ1H;A83yz^g&gJxg|b qL[ot.,=ў\qd\?Kb~k?ntc_3}Bǥv_zAzK g:u21h?&8s[I8R{Az7ǬGLeh&fKot2yؖ.n;bzcVß.R>&c:"K/qLR]oiKŴ=^X|,zeaEß.D2Z0-ē!2? 3_7!7') ~y>:4}Hd[pOa9+Zz7{Id.#iEP nx|bM'_cS8K'+R3a*Uf\~MKXK{]4|鮚mpӥCzFuKg[\?XAygĚ̯ȸzR _6^N mYbTf!ǚ̧s]q-3>3]z;}}X*u~b.*Xhm_ F ZPDc]q4tuwu7 "L@=Ň.jhݥc X3|c>MqA|K7QG bHn*RR/aMD?n?%"z$oCcDqg^/܂yGj_&ߒ3#Cz ]H!R{drٮٗ'6/[/I:.LǮRA8RxW.c|8e)(MYLj^/Q:9ꑈtd0-;nKwK35׺`4o?>Gz$7m<@y \ 45/ׇ~*=В &BTJ -NIO1T?|%FS{q>ճf'+=.,5G*7౷5fs@zߎ=S.鑈r(8,wzO _Mg}V$}%̖tD&x] z$;dT-iG.>k?g[鵕ÙqO:,H4wȨ/PǮ S KX|,nm+WmI~Xtkr@4BFAľ)5,}n$ 3h/KD3dXby`}+Kc~K00/:y#g&ÖHxzE;~KFNF-X~ﴷÇ#ÙXBJ"aVw $q^/ xG:ٺWb 5#,D(1N0֕r踮 0mz,IiX>(Í͛,OťTf*G Mdnlk7|\<'צ`NN= ,cG=람fWvgZ)5F+1vI!n0ƜR˝F=ɷ yM\̚}kY@ެX|kѬ摛 V6v*0U9D7攔h|jf C!pۥh6 .䫖KM^ƈxQ9>&30ǭ+ {$ܘO6jhE #avKϭ(g0&[ 7gOl>S1Z f V6 \ >"Zof8Jr,$|.:.mt?qGqkc5׫ B"RJ4X>|~y5c`J%! K*SpXF\ ?O[E7~ TEӥͶ> Sf2l:ѯ2ր7ݗ` gBRK./(yaİNH=0ǚ{3M >!7bPj$c=ᅦo}wW.''مy@\\j ¥b vIl6]aM׮c >_|c"2W~%%f,p坰|,ZtuxUY{suigOjۉH+GF13:|dݷw<1&\[zJþ{1j/z~SWt9$%X|=Cܺ< rmk8;gR1HsdtXx<:$u%^vd7׳UJM 3ks4]v߱;1><7H~0NwH"|Z9b{2EY^+*kccF§Nɰg%,,5}NJz kohr _2IT]Lg^evi=X|uٛ \yEqSEZYz f82` :,J&uR 4KX|q5:OH"uh,1=*=sp庴pkWbŰiWؑo[$t=(1jVsïj7:XcM(K|?F:?\:ؓL4d ^Gl1t6#ގKf܁c vp :sA@u;ݯuih{54bb.ܰ*CR{@zFw=қ^cB'sO8{4߼9 GgL:@DNB2[z:R#'ohSJ%jbFV!!tН^ˉchv+CpSw5v}¾o#L$|*.67[GG嬀M%76w^š[WO|L/OZ|Mz V6)XF=^ꈃ!tNŃUm7w|?O7?){ Ehj#U;dl>pť="D+yxGuR 4KX~RIe7W+ 7ץ4Mz Vl1qz#GK+Csb þ^@[DxXݮb0&bpRX&٨|,NU\K­v="=q znJ+3HUn05[Fyह~'oNl,tH)\ @o{igS Waٵ1JQ~Sz cy57%o\n̛K-\MMk{uR؟X>` '츬M齱1:dSj'Joq1t1MD([E.Kct /,&M߼OZ]4E4'vSR1IicbOIP88_%K bžo!I]֝R9fSR>|tk3Ϲ~Kj@D 2% K[^}$((cޙ97ĖRg);Y/1 WdO?LçQ|kTgI=!s$'{?'րQ{2pnK'.9Z !LI Dⲃ#139֮c qaA,6ػJ_ZSz NX4͗+`\E?߇K-@Xlϕb qUU-2.& [X#f |.*.t)N'bO`lҗBMȋ Jf+XKjKM*d' 3wO0úfEDrd$u=ß?oaRO >| d'㈥Oe'|nϯz koƴTL[ÆŴ5 ?\jngJu×ݣO Ѧc Zԣo M êK-@^xSDAXG1θvuK7{p >fzD% N—ӥw9"ͣ 1NQ8L`9J%?tˬ:#G GK,9)=Z˥&* ݾ~SBJ3òVHj u<1tB\Sz O~ߒۥw8ր.g`e)+Utf;NT)@;؄RAh5M_wNn~Y \'\;.q4!SJR;Dk桹ঞVC1"kKψp@ΘJ͛Vglz F|'րc`\|aeQ$ܘo:7>XoM\u^M%OZ8SY>N|}L[W;J|}ceHo-MD@tO]V\Qw!#k)^šW.zH>]nJJDZW q[Tj^WZN.vĚc=a4٦pc^]j~@z2w@o#i%nӥA?~k_¯O7ï~ f=+1VպHCLaanm$ F RKR+k6R[um3‘_.=5(uM |CwlftE?.=KAYȬ,*o.UpOz8i0:X.%'Qz +7Lo_"ᱻҩ d}b X`+}tv.&9Q,U4wIo#o?Jݑ՟akFّh_ءdI1.Dһt<;(uGbv)[Rk kC|G3Fv >~G zl0og{`~:`2V]KAPWv ٖcy~kqYt!=30&h|_:G?.wykbE<󍘋!o3'#1Ʃ1^bօg]\d:ր߬a_K:+ "%c&$:6!QjG5FxaucčBBqiHLߚ#ÌːLzky_3Fӕ͒Zꑘw%cIa׹SwhFvNYRV_[}}F@7icj  aGYn Q 2րϮ^~349?.5@,ώDdZFTi_u5mJm =jufOHFqF9fȾ͒pz|%nAp;Ix.m dc)FԋhQ#Xjܞ`S񮾥a?G,|J")qPj "q"fz69;'qU4j*^A[QjbC2wzoe5Z!iǫ^"v;ӮKji-"YAeHNH'ր.[ÿ֡+m_no6滸\D8~+ zwsGp娴e]9H'vݐD]ΈJD)L$ܓ.vz.Wbq7/Rs3vSzal%KfkX :{~%V\:l \zhX]w#MBteq W~Tb tnco^MqkHNS[F.5QzF\JA Qge.:|kQ-_FQ$LgJ|xRo zZdZy juJ xKXH`\c7CR8pxgkRC5J=quJ xKXYjDf"=L WVӴ$4vĉgȰg%w6VV*ZWz #0׶7l`oh͟p ?=m>Gj))Q/a pdfӲb^K@Im:qHpEZq)Q/a ]9~#HI'tO'>g߆RA>)hr1:leO}\U)+Jul\H@8nQ/aMv}}0.˾# _ťf½߽tbٻkV<7%ʍ<)$i.D1 [;G5v.9[;ր8 @;Xvg;%wONI;te~b *Rz!2#}کvmzx=c~U*b{\^1*+O3-mF,Oy=b Io i;ȉRAA8.h'ր%O[`TM?w=H:O4Ĵes19wcQFz kw6TٹՋ3FӮقQ]z8OœGqpz* ݽ*4KX|At和jEm\=f-CKj;7Q"}5R q)ր`_kö jcOj §Rq7ܱ|s%: GPz8Әw:rn;3NNIz ko]V/xo7ұ$u|ݤWM.nX#mqĖ6遨(8Ql>^ րmArf_U#VԎJDu%EUJ-C ր?6gίk774VIjn@  .Z5@-g-:P/a|K{}mj)c Rjs9 2YA"/ʽ J @KpX3ZP-^L=-Ԃo/vQ@ 2x){W2F ^d+qe}poPi%UAD2CQVR^w.}^AWR|G*Nhc "CS~wn1H8FEa]mBtV)+K?Zw E@~h^ `j,.m|V 0ۑ{߮R NhaômijPE?1e6,|vyTvQBnY]TlIs/~C3J&0I/Cf\-.-Ai0ӖrKﺤ"528p~ T/àQIz1^L6NQY\$˷wD_0sȟgQ2V]j!G3&* ء|Fց]nG-뒊rؑ~^q9v3J t4dw! =ِc%;AK5qp'Q45IlǨ?@!sσ}}@͵*Ԑ3Bn]T dnzT4`K t4aꄇ8ClV-]uߐ2.+r̿p])G=T7E݌;ڢa0-KGǧ뒊[4 1v UwU?'t3h 6)JR~PT\9פk*BuWM ;rK}-c mYݿr.}~Bݦ>(eWS jl{<]^{yxg803V\ ~I8uݲul#E݌;rPrC^Y9޴[]0xRj %[UPiTvdSݔu3neå~Wi(Ж(o/J$]Q;']SّROuSN\we6RY/9(e 殺=]Q"ŖʎM9qAw尾m9jLmmGJm~+jF8vu;U#ź)'.~V[촥9ʜ5څcߵou9"8ÎJRyّVIuC.\mYv"_߽2#J y ]Q7ı#Y*;o.bF"4& '>]=r KJyA]RO,m6u2 ƒU7Ź"㎶(_ȓc9)O?ɒl:c3ֵ]8vu俗=|1!.~f[k?[SvwaRB[뒊P8k5=㧽+M;ڲݕAޡm|om3uIŭq\ꦜqG[.g[c:ψF,1- 뒊dTXm4}V7E݌[mkvmP-BkE"[H:ĵ뒊H )]Yv= M9qQ7㎶m~͸-ϿkœыS~+Jmyv]QŽjr,[qe8xź%.fj=&j^qc'ك7ׁR[Kdc϶cg2]kR֬rnmk4@>oн=~0tEUO(&IiTvº)'" -L%ޮ\C>m9H@9^ĩ~0N~2.#;jw|W@ ݛ$7\xtqDeG pAbOFıŸeXߩ|;뒊kBumeWߣVݐ t?p-xPa~8K5h۪EM[/iK;fVꒊ8ɲSeݔu3h;F] KGqc)jH |`DRՕ8ζH;*{ZuSN\͸ՖjmM)N|Im(^Cn\ Lu];NeOɪrnƭ4O/l['oE#^>!JŹ./LAN_Q_T7M?p-neφOG?[:vuIE 1qp]F#eO#FWq-j/ĹeMG/w`k(M]R-lE:}nʉڲ,*"m8{RvD ďL~.K*[mq~-9qQ7V["9HkNCtK~ڑՇ%!QǏc/K!>_qt?p-]펜n)u_}c)DwJ#(&R ĖHm枿Ǣ/bԋݎvVp;Sm*>PtGlPNsq-ٗ?&c|5qLJܦa%]ix͎^]Y%݌[mA)Ư̱xm"1أKJTd4dȦ.&.-9pI7V[- Kw ԋ{GCЕr꺤8>3ulP 9qQ7㎶plm[O>GMWsqZ}K*NS<&u\T٠ΐu3hL|۷wE3'(uu]RqR$b.RǢl \͸^u8v{NGE6􇮄O/]Rj88&fZWF69qQ7㎶ 7ezST wHrẤ"y88E%]Sّ?GuSN\͸-5r ڲ@=I ?۳Kjx!痥1Xvº)k[m_ee^ =e#c b +E#~E=d ה і8.K0vyX7]N9f;e7!'.fj]}X8r^4{L;THpքsl&7ӽlPNnp)[mƫr-&Eu@}OKJJn T9qn*r8Oh+K3n=>'t8o tIy9{t]Kz)'.fі8:a)^ϟ%t f?ں^v]wn%uSN\͸^vʶ3]8vsQ%t/!9vSK&;^q][x~릜qG[sJJԖarK㺤oXj#׵l릜q___+~'o*o׿_~v_wۧ]eywS.&v$C,r![g'$Cg.'>Oo~?V?#{;v_[_~.RSg+[IH05-'Y-/I-ݙA&Ӟ_|Â_wE#4s| rW_)oOU~~sS러=,㏾~wifI8~rw]/GډiZ ׿_5&;Pc\FT$~_ӯ~??8DA?55rٖw1/___~o_o_7?wl ¯14K?o^E9Z endstream endobj 394 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xwTSϽ7PkhRH H.*1 J"6DTpDQ2(C"QDqpId߼y͛~kg}ֺLX Xňg` lpBF|،l *?Y"1P\8=W%Oɘ4M0J"Y2Vs,[|e92<se'9`2&ctI@o|N6(.sSdl-c(2-yH_/XZ.$&\SM07#1ؙYrfYym";8980m-m(]v^DW~ emi]P`/u}q|^R,g+\Kk)/C_|Rax8t1C^7nfzDp 柇u$/ED˦L L[B@ٹЖX!@~(* {d+} G͋љς}WL$cGD2QZ4 E@@A(q`1D `'u46ptc48.`R0) @Rt CXCP%CBH@Rf[(t CQhz#0 Zl`O828.p|O×X ?:0FBx$ !i@ڐH[EE1PL ⢖V6QP>U(j MFkt,:.FW8c1L&ӎ9ƌaX: rbl1 {{{;}#tp8_\8"Ey.,X%%Gщ1-9ҀKl.oo/O$&'=JvMޞxǥ{=Vs\x ‰N柜>ucKz=s/ol|ϝ?y ^d]ps~:;/;]7|WpQoH!ɻVsnYs}ҽ~4] =>=:`;cܱ'?e~!ańD#G&}'/?^xI֓?+\wx20;5\ӯ_etWf^Qs-mw3+?~O~ endstream endobj 398 0 obj << /Length 480 /Filter /FlateDecode >> stream x]SMo0 Wh@wڢ١[N Q;GRRƃdU<3`uJ 23޲{JD xetd`s(7v&&X*%7y=PZ x 0_ >VFU:PJ0ɤxi SyU}D*1[r8oY |" 5JD19{ڝOm$<מcMȌlw=m6*J8){f x\~ (G6a!\>23'*0ɟlBbON_r> /ExtGState << >>/ColorSpace << /sRGB 403 0 R >>>> /Length 36300 /Filter /FlateDecode >> stream xˮ%r%68CivAO5 -聠ռK [쓻nfwPY#,cي>?~_(Jhu7>闿_?_}%/.??/oeϏ c?W(?_Q_D|[_i?~|ßoȿ9?zF_oE-N_ԏp/}m?~V1o_/'S-\Yg<ηrgy-[ ߮TgW[o7ۿU?{Ǐ{η {~~ Z>jx+n,'|.WtG9᭼oi_V>۷rOx+^ߺ'glj?n>+_W\η3psjV?o!ҿ;:J\! 5xulS?- |*^O•0n[/[WҔGz0wvg6[3|[w*+ok{1Ż[_v睵ov۷tN_8~>,߿C\'uv{aﴵ,C޽ozMvZk7מI޺nkyv[}T9#8_gl#MMxy͸&UWCGWv'S㚭_u <,X$*z-^3ܨ|>{vzXqkVf.vXž /SCAx=_Tu5MY^W5 "/0kpHg/ փ\-.S~͒BگV(XB]=W.5c%X5p`)%Z9K 5W*j51JX+` Eל▦*3M&,px 5e~Z%%>0"`s' a"Z:焽^\u^sijʽ<'ޝIj!鹗Zm.#!?]U s.Q66 &mkLM<0aLϹ`5LSLi]゗$Mpw"!z/MNx .=7=mYV'-Z&s&7!8&azH9mp'7k򞀶I4x5 `[^현k&朡&A'$ܦmp/XTۺ|s.UsRxNWHzne{Az, 5$6 zNӃHp[p{ [Mm8u1 I½ f2[~ Ϲ f{=]pє{AW# nNثop/H&֥}Ihj&^@½`L­K 71;a99U_3邞3rt"3p+0 7F8rnYv"]J G#-\n!k5Nh33!Mu.ƠOjDW;qV&<'t"vTin̛0 7/!B סM~%@ 4TCS|$&!B 7JpC+lV9|$WN8iosMpC C -Fn[T0='<0='^8$$ 7GCPI aN"&q48NM UP%7IM +[7I n䑳!I8horsB fz7 7a#L m nBTrs83MI a.0 7a<'C>$'sMSC4q,z&Ys-YsX+[!&0E$PdFnhwk;@UGȡȐ\D\qvĴWAd'9.6nw+:09$܄a&IC7y9ܝJ;p&*s:Eסu L.U# 7 ݗS847څ0fE.Wfb"Tko3qg0rwN15Ol(=ě|(qqM͉:܎M3\'Lax N N\l$P0#!4z5~+]j2>/8I\>_߆\ F~5~}Kxup~E:ϸх6iOӿ:č~:H r?p=+ 1Ykěk.ka}#B(qej5fnl_q9ÿJ j* k-L\躑&LW#;",Eu!`m;$l_wd^ך1Uoګ]8XȻ_*Ww.NHJ dă䯋uRbx 1X}ƵT>US@&>_sӿt}ͫߧ}ϫ?Rr[0iOsӿNԋS^쟢b?Ű %7]>Xr{x*z/./OU߃UTJ\پ'Ʋ˓1?!8q8+ӿ??sI[ˤJO\+ Wx\>ؾU/|}_[A mBfJSֲ(qֺ(qA p+:p7]>io|pb}+~<%qցJ*~H% 2|>qN| '޴]iop//} Oÿ?~{$'3bAuӿۻ_CJ:H|8p}^y1Py?OgXqZTV\?ww'bq*A={F'|+~{8WsK)h;^3M 3S *zau(`Mпiؿoh|M8X°$k:X>"Pڈ%4E%$y?_>*W!?>IeZ_(U@S-F?,Tg U#>/^0~jO ߮?߮瓪Ou_Mխ*Dwɺ5ͪ[ۣ IMk;^|Eķ+W߯h^A|b y ן+Wu+jW u (oǻ Cg܏Oۆ~\L:o޻0Y{Oc\{*w𤇺^V*ݯU/U7&8,#[/ Vt/t8Vi{)}QYRxձV|JY 0Xs]o-1L}DwJmSTռJ뫢UƣE@zt;^nBcWiO]tJQ:A4ҕ\=J_U^Z˫.2gTX_E>^U խAS/Ғtur:?|Qto]}]_}[a+R틾Uwu/_$nm_%?~Z[zW~/ UӽU~o/Z"2y7ܿLomzWn__t&Louw׫hOv M_ɻ*[w(u/J*/R!Zo4/j[M~9U^__&57o,uJ(N PlOykݚ:hN Ct/;t\w/ \۾k?xVyUUw}#v9tdZ#EҌi6#R·~[|׍ęP *(I)e[֚CCF7 ^nĄRmg:n*[YӇSV}}&sOʼnW|@-)R7ӰTJ[?rgFXfb 3ۢf[Cp rSMLHk$NHmMٰgrwW_eL)\C/ngj&v@_| u|}xG q._IA!qc栋82C%,t^u?RB9u)5qVNf 7"?rJTayM3-S@SO8>we*ҡ"ԍƔ!LF/_AF4qSA  q24C .6m**`֛ N;czCq4 3RI+ rHU+YQsU@bD쟧QEᶉ!ux:5XkS-Ɣ$SHAJLV%tjHp1d|ϓ[g< 3moPE9J#Q|x(y֕xI;oc t'SCǗCt ԮQ}'AUă3 8BY~QfM %_Կ'Z뿘8~3Ǐs1u,q:0.T~Q]Ler) o۷fwqi^9•)XTanfp{6k=<9G6fF<$ LŁCܒ13nL_[3M_GfKпex)U ok~^> eyjqvwޮ緫} { +}`qLr^Yo&w_׶=/󬪰>$l'?kq{zW}%/_ԧLT^Il/H{|/>kLRw?ju;3N U‡y(:#N%e4*6f+ 6͉3Tx|ěI?&_PLREJ{/xJ߿;)~؞)-ٞ)1WΌMXw~v|kE.yWfW}n#np$,,'s='?o3ZJo|t< u:U^A#4g\'_u2I:__Mio>p'%>{g'J oMXU^s=}l> `Fq}S?ܟxU}h1z3z3_7ǗMcmr";;q("Sl}55Ir%E˕]$r}.V|$UI.oL.oL!.l_sI{S\پr%E+)Zui+LSRw$pyr%Eo+)W_J._L.ߴ7\iH‡8nPR0F%y$lg*KI 4a%E˕uNU&k?kH•˛ØxO$kg#߇H\$)hak/%ka}{>_N2x7&!vWeG4Nj֝۝u'EoGI &Y/mп L{_}W|ߴhZ7m0.iIxǿC:8tzJq|[z^os5+Ћɿ|$J~zq=O/NgQ(W$r?ze7&}ăI:~s'{$PIv0>]$yo9ޜtޜt?|ޛW=W'oo+r˅t|9z%kNםt߯?pcGҽwVNJ_J|4+'ݳ{I}8~z%O_?nIJ^I+`}ҿU"X"}w|x^Wҽ~_EEW}z%s>ݗ.&+^$qﴧJ>{8w{!hB$)!"|$R{M˿7~/-1ă88? U_پ7ۧLߊJ|>`ҽc` h|Ew&TqMH<%0):t~mv.~@O{0Ka w#C~A?u?A|.^q^h0 K"%@~{>$lǃ!x0Kh`h\J'!#B?~?^XM"]ǣZԁ|0H<) EQC|!yۨQ_sVHJ{!0G{%loq>o$^h/ Jo9^2`}#NN^c /D6`}8 cr%xK#%N~͠4'o۟$'Di3K" -߿Y&,#|>?k1&ż1_%~wg Ⓛپ}?|!~wGOC|/QA`A{Vg /+>o~}50Lfn?joom* })"-EU4)}A{Gdž/N|37T5ݷϷkC|{ }o6)-L)•4CՆ_ޯf=ّTg^u}$S]oi";/gw'xPԆ;)}?EvdzXd1J/";a{ ۓɏl'N'^Ƭy7o{}XZU= )W>M{T&>l)q}0͕]LoDm}S|o)fu wh?)?|6/{$%l_UۓS=W=W-ѥjq[}簈sXĉ9"NGX"N"N_L񽾿 |0ϝ&./(Ei/\C{^=+EoXʩ0!eS|_4_{'O}q{y?Ҳ3<8;qmm={OOx}M a}7J0G'/hV~VbW}W?^p;>h-|5E{M}qWGJ|ɿU?p6YHR=2Uտ)/Oo'?o1a6<2'3Lg~<~*Ne__%rϗK]xU c+/K::UOz$_s|a",t}1<]=G)oVq\of}GhXJ*Vq>j/W8'y)]| q>//(47o,~_;?#ڈj#2n$FgF/UnDdn?ݘ^!aLF1 ?,~vcߕ_0~*"^]vŪoWIv韴W|-jڍ]AKkr;CnBʌjxw_h/ڍ"CO  vW߯AF_A|b"y܄++W+vqW2*u/2$VVDO9uG͆Sw\q~Rw{w%!Sf#7Y R WcꏼjC~jCUQ ,ETzJqȧdNg*W M/␷*0$/␟K_U䐢"UWq pxV"y|k!j>ZWqȊEJUEI%9,kX8˫6佹oG ۫T;2xuW%O닒/zU?9_T_*=u*Br7뭶_:rꮯr~Q{E^^e_4!o޽Hoz+|+i/|fnnTO.;Rgtj WMaC*Z j CRџZx>8ĩ3w›jQr7xC}a&u!T nM:?tC.n$T<TzHbC=CTꊞLe6 ]J}>˱R|?dRPiPbCNB6jP Sm2lꐊҲmT\~&j{ڤ:`Cr'!Ϥ:/7pPCꐊ&>TAMEui׉{5G#}MaSIzyݙ4L<6RjާlP EuPbCǧMuH}`>qڤƗMuo[0!NjMueT\ϗ:v^O5ItTM6ۗ:}C?ꐳCRj;ؕTR _/uH:d“jau;9lO]XUEXXmAKaO{_ET|O3Z8K,uHPRjdǍ=aCr|ϛTs(:E7Ar?+uIJuHeh CQ͑3E(TslfIć}CΧ}C*[8ꌉjwnvtS1qz"ǷѨ)uP~ꐊNl4]ߗ|oEm%.'hOw)Z8qa TSL >EBO|_ ?'y/e{'^TO$NnT^Cx#SLPC JDcпcп{п=xPp)C_vv؅pP]PZ]Y?}"jj+-E?7 >a L5@cQ-Jj;; s;ēj䗱o 6M{ )rɑMN TSQ ?z'ssSrl?nq:6!eSr_3pMM>/z_<0?AuHe#%8hև/m[/jy[/q4񚛜Vz_3zX!CRD.$ 8BjY7}gzoC*;  FgGT< Bݣvo:WM&%ojy>9UwVgzϬT7Wn~ގ^ZaV׃ѿJx>;ypk9|vA5'T? 3cҁ18߿SmA9?&բƟ9%u8Ʌnck{!fv[RM ]R‡jmj>G|ouč!6wx.?)[_3<9 RkA5Կ|sӿi5Z}>ԢբBXjQ}}{M%l߲=E-ۓZԲ=Eq~X#>!~V}jQcY-_<ԧioؾԢ3\#*Hբբբբm('B|RVJ,fRj/j/R B|/&o&yjؾK/_ԫpHK xx1fy0 ܅y#>:V'5c5~ԢϓZOG|_pES|բ8:jQ\ObI-jyY~ޖV?jQZ-|xբȇZ-TE^_gG|/,`a aEM?OjQO<Գ:?+?_jQhpRJ\>OZ烧Y-RsxԵ˿8&я^j[7`8f㍲vxsRG|#w|{Εx^5=GU]:sƫ#zW?{~f; gؿ8axvޏ?{{9Gj;~PJ,xw3_δz#gZVxxŹLx|8>f_#~]<|' rRxz9yfq|=jQ_]%<>g9Y$|`Eq=Y8 pxHV{`s@Al,lοv2K-Jvs:Ϸs|9_{=𹩙e\%Z;'O)>R3[J3虲RKOKji/7u7,ؿ?g?JWǢG(p&&ʿp^X/g?Z[b+! lgL{?'=ҽ'Z3n7Ľ1g<х8MXOeFMMnktAV*K_} ˿XJ8[䁕 ܧpkeo˿X ,ލg$:do§=Ua[}R8>c˿؊|V0e![;gglcz[f ;-3ٖ8bo,` <7l)c'4˿Şaw8>ci[Ÿسꊱ -b~/B)DAlu@luHbo؛/K}i|W| 0ȼC"!}!}߿Tq[w:$TLx yqB.n?}$ދB!朿",WK:=!D? u ,Nty =J7"I]*( +\*(AzKY~_KqQ[w?.s=G QCDҍ YztQJ,MEYł棜V>1y}ٔGe^4r#6S~^w֊MmԭfneI6V2?)6Ȁ\`D/TN6$[Xyl esane ҅DVȐxau+$LpFƲ_B}O5ɉU[8q73gowI܇U?bysL<ߡxsfɷ>N17xJ7Z8)~5NI-x~="w܇Oy"޴\X_ T$IT}CVn/=3C4oG`ޖgz阷&~y+B6wE;峈/[fM.y{ߔ!Wkܶ_]f1on15]xxo/^6w<)o7AxoAme#{[!펵ԗx@D͍77}Rw7"͍;w~1k=$-7cj_׻&5o?髬.#`.1]:]Й:v/ޫ]k]јBtpvӯڔ<CGHaQz5_G{!2"}o*P )(_. ٳ`ځ/}(x¯1V{OU L K }zr(Az 9 8#>n(`i|!ٳDQ~41q* 8 ;\ˆp'΃;x N0^(sB⚑-pV((_ ifVm .0  m |[X K[86 ?BO ,0 x[* Xx[L{G*0_`D*%xTvB 9<4Co4[Y HeYl`Zj` ꖈ>64TvІ6/0ޖp1{!,ѬD*Kmm-0L`fX`< ,0e-QɅ~oX}νWuν9汓ũ P64 q:_dV*PE5oK:ՉP30/a5Q*|m dsm /0^c`Y8%aݒHO8fľ@ mmzlALlk-Aiok.0ޖ%'GےY'D82zUXeϬCt HVikx:`<'/xg s=ɾ ےiÞz`0EDLvrƑ;xMOILFMRU +KW.XjS Ufxo2R7O_v1eV:`f>'SaKI[m[NX1n(+xZ1pJT~2Rz9Vn&8nɏSɺ9RJd<%R0ekitxJqjK QF8U}X 8 vKEv&UF ɫZp&T 9 utx 9Au*TN y햐&sG!d~D+0gtz&e8UX/9+7Oi:ir"xJ? `e +88df mti'? ,bqջXH] 0RSֆ.\J@ J2K?\/1V Ki2SyPGk/gTA4a&xbXL>I{$`Uktsb^ 5,EJ9j8_Atַ[Iեf%R]6L=!`e VۂV[h5-/A4`-KB|534DI=@hCڀ"E~TKVK+9oi͠qtrKb}DzB(6w-}2&xK~[Z3 e$- ¶b$4WB@VKli`=-~i0ޒ_zY[OhV0&omҺ?pڀ]QAhJ6T=!` W (c$͓4BKB݃/}'xK Y:Cj]B7#Ē0 ϑ>sj KrrX99d9!I~`KC9dC#v>\$xx GO{O'@~jI$d@yi'Z c,x!)Cb2DfKTKTKSCƓc?O{V9vy3GNmfxAW(MmީM„8Txh-PPASNNa*`VSŵLjņf8Sa- 344෕8S&C?"@{]qn2^j ܩOqT~zj0afj|0d+!"eV$j*a"aG5/h-xET G36xNNgBA2DjhQ- -*T/NAȳp9Ѡknn ܩy$<0Ef8ܞ[^q>W@Tc-강DG‹p/ۿ V2$\05|) Lu_|"uN}/J Z2wI(?vOo% wnAT4PL҆^k60=G#-xoK [AS[wȪi @ a SE+.? lڗKhѰpr?N”~:sW@TPjh$d"x~ 968bt_@ϝb$ cTP Så KLun4Т҃i"shQuiSXI;L$?rsxlG8 0ϔjpn$\FBvFUvT$\$0IQ+0<65L܆y\öZR ZNJႿOUio_ۙ$Z"Yt^%hi݆Gu$$30f +g79wɹe(nN#R!S* 7"3TTAr& [Je78rU->V+Y~[{,?}J TKڿDX]bI%$t1 x򅅯SN*wA\ #es [WIuџV|>/my}/w[#I0Rτ~5/R *\*%pH 1 a~RޯKAf*Ev( z  _//fWqQS*6T8.Qn 3z?s^씯gՔ}! PXIOԄ *H>eA]kFۅ5zA#*a?EQz׸0*VٸF_m.#0n<諰\ mQ"R85I~!iTt \$1/<=.4Ps=PX(ݯE*h:)P^N ԣCS7›xAPS;qn5ts!JM0(RoB6PKK "\&Q A<# (+wyl?lu_c0A5@|4Vr;4\w =h`5}188lCʦpvg@Ahy*$ebouH!) XSuc8p8܏x$=RptCHV:%YKz?|QO/O pTE6$~$PpMHI?EPjNɎPÝs”DТ"AAA#/,aƐ$a<?诱D~АygM*)^%ZT]"uv:⾀E}xyJbAVd$@,\Jt$2u[(ҍ¥H G4̟S]*K h.¥BB4PUq@UťB<.%qwH`?*pCBqHY$.q @:6%åCK uDD`^ҵ|!%)R]q*9 .%(j$*9]y X{,AH 1 4K. h6{Pj\ZHIYP¡8DL?h<%g9 HܤAK8*aCuܡ$ H"(6QLƴk@ʉԻb=~qĽѡ8@ʊuY,fl$TɎz()اdHcjr3UaLd h(m1U}RSvwU}Hf5N~|V;s!NT| i2o8wA!wx*^pxr"ׂgUAFft{>3SH}[ NO" ؿu*}`d?wE]hIzbnô"Ydʧd6Lv:=>eG5B]?J]?JlaMdKnT!j),ea#Y[;));꾸*H 7p{ʁd*!nO"Fsϖ(ń7U{ʎjWT:ܦYn)g椷6 _W95 'mɐLuH&bfv/vSr!IPO9ѢZ|f:/phY]5/hǺJ/JLH&@UaNAWPsS2{LjzJ[ j71e)'ru7] w_4sOyI3O9?i)̻fy\'Bx4+rROxJw-}p92>$*j~+wtJD&~n:\V:~NOI\'U\aKNO9IO-"tHZBOYPu+u+[2nL[ז2Т A-z?hb~6sFȥ6:\w3Tk$ y {)dGUQ~7>2F#cht~}d o a !IOY^%s÷dgn'U-;)' jyORHO;lt^SXa}r8z!cq#}H]YX L,FwUuL,Fwbp#,Uȃ@(Hy3s~e??%;@<4tb@Btdr*@g8H7V 2` IETa&T~*x z:]WY?hotU]@~zDP9*TawA)%Z~PEK%MPUh9tC`* +<'b e_T2tJTJZ~r;h=lvtm :\GDž *et CÅ} jM 4fD@}N{r*{ܠPvPoN>)ˡ+WFx,ᠸp~.ʶ.FGO*!?_ὧXhV?fCTLԘC덪XʄdiBG4AK ˯t`&kl[8HNE v;bʹt(nx/Mq \h[1C޸rH21sDkK);Zm唯rde+ v?WPhX\A"1?WP`?W|ؿZ Kӭ<+oZ.|gM"2maʟo؁=̀_w;n _tS1g[siPQLLWao,qq aQܯWa\(~ p\7~Zp^Sx>@ŋp_Vm"^ugQuY-gMMU5N̟)f\?3Z:}*Nea<\uH ~fԛ]EoPY\(:T6.~z՛ٌG !zܐׯ5n2qz1!2_2n&lxkc^Dw7]/ԫϛ1z?4~"f1~}wwٸ(R΋"~]EeHzz.= a|!e']"ߥ_ }7l׼oﲛ0s^rw_|.ۻ_w7~eK/~#@~,[? //~s?uv~Hxύ_|߳3Dzo= -g jc뿨?ꒋoU:AZy-Coް3zk]6{:Am8 ;J9NP[ j3ևal5R'+;uښ[1_/@T?>Ammp9=u.5R/\_ښ9rݠ>R'*6uڪ"S'|1< ߟvKP[V\ZN jϏU%.j+ G @yU:AmE1Ӿ`ޤ ١ePa:>'@{*gN&.: @o#ȂC|Gn?ٶl~.?W^M]྽(e@^ @1^R8A#ҏt&A7>ӁZva ޑuúu{Nx<a8sHOw^z?`X2ӑuU`]WAJ{sN{{po"rWD^pp^n(bXr?97 (F DF9nʾ-1[)+5$[I(Fcpi QJ|vDq{A$>{w(wЄ;)Yq|CA~Z4ُۑt27K˃o%#R. 9|w !7d;&s7$fkKMWvW57eʉwںTN{c|6rðkLG<0L7 %7:W5t$nmb2݆/*\sáDUP/5_@SpMke#kKr`xs y7j2rX|%7!_| dҎ$NJ^MH`"F4|V_t'% OR jGh6dF\cVntXR>밷E*I2WqU$s?d. :ܪA[ݻ1>NJ:7TJYjy̥ktUH,WRT5\լ8AbeC$V,Db-K"d*n6J,ϾE*~ ϝXMɅɼ2֪qiUF<`$qopcXJ3C8mpUTઔ8u8)*}HUL*%\U&Q_  4YT)k?, UG!$sq& |xjLH7m U_PU;^*HoDzlF3O/#tdΣ(;R8Ł/M_)e7l_@au95 p-?EGDD&ϥ ˿/n~ql^ `#uI-#a6Sh:q>{S=D5Uɂq0pM_DUcRrE +e3_)e!nI95P1x^ZkLnbw3l%h$qKEr3}&#/ kd>$Gyϋ+F&ld$a$AHJd$ #N2|"#uH;6ʇa')Hd$dš PR7Hjc#=J6`#߻Hyv// 9JY<9jXI{`.96QJ"fhWb~=BЉ8Щ?-BѐOՊ!h6)ctQ(,](]rlhxA^X8)!唬F*J 6z$-cƝ=DUoV elN'JYˁA'JI)C>iD}1h^t\ЈRzhDsKH3\diD)+`.2`:ܰzDѼީY]媈Y媈YjJehS~q<BȼCw13\3TnppBpkP=o牉 5kP lhwJ9HږRqxCw :\ ":EZ 5F_w֔娧LYs̛5G?!Gm;n:mMYNqʹ.;%'5o<ޔSd.u+:p w%ˑ_XBlLČzWxyC@p.p+3t[9-ČbF}ۧzr:(pvw-􃈘~3"bfAD̰]Rv:^e?Bp!f# W# ~~k ?h΃JR13e1޽3{" ym̶wM#\Fmn:\шRrK~.-;]ӈR#æ(eN䌄ܮr8P0pwCХRzD)7B~^U  okVQỡc$RAf ]2A堋 Q?Lv\ůA2M* !,㐌s6(%-Se9vpw 6J9>`#l\Rr$C9qp!QNB8P*Ǻy B8P%Jk~+f\mA!G2v=d{r<9/'bHfp&wOɌL<&ݷd7n轔1{ 1d$MIZ'c{rdz:~)Hfi_Bp",#YU/ld܄S8 ioɼh96IˑOٲ-2ב̺Js;^n'Af>Iˁ{<|7y[oG2ה$iJrdf)<'#I} IfFY?}IZNoK2HzdN$r}Idv,HZG% 8)mIvdoɪ$-HZΑ6$X08ɇGֺ$-#ח$Vo)2בK2UI2ݎHvYN+Ke)2\$n$-HZNݒʆ>rI.[\ 1ݎTK%<:%{|%7T$He^Z)HEX/I{ `UpT~T,G/OڿdJKe:[F|ѾRuj}WurM IQ,Ϸ\_XS%wU]U|W5_c/b ex/H/nHz1'nL72nn endstream endobj 405 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xwTSϽ7PkhRH H.*1 J"6DTpDQ2(C"QDqpId߼y͛~kg}ֺLX Xňg` lpBF|،l *?Y"1P\8=W%Oɘ4M0J"Y2Vs,[|e92<se'9`2&ctI@o|N6(.sSdl-c(2-yH_/XZ.$&\SM07#1ؙYrfYym";8980m-m(]v^DW~ emi]P`/u}q|^R,g+\Kk)/C_|Rax8t1C^7nfzDp 柇u$/ED˦L L[B@ٹЖX!@~(* {d+} G͋љς}WL$cGD2QZ4 E@@A(q`1D `'u46ptc48.`R0) @Rt CXCP%CBH@Rf[(t CQhz#0 Zl`O828.p|O×X ?:0FBx$ !i@ڐH[EE1PL ⢖V6QP>U(j MFkt,:.FW8c1L&ӎ9ƌaX: rbl1 {{{;}#tp8_\8"Ey.,X%%Gщ1-9ҀKl.oo/O$&'=JvMޞxǥ{=Vs\x ‰N柜>ucKz=s/ol|ϝ?y ^d]ps~:;/;]7|WpQoH!ɻVsnYs}ҽ~4] =>=:`;cܱ'?e~!ańD#G&}'/?^xI֓?+\wx20;5\ӯ_etWf^Qs-mw3+?~O~ endstream endobj 410 0 obj << /Length 709 /Filter /FlateDecode >> stream xڥVY0~WX}r$b|%v*nPĪ}`!{H@(3>HX±Px2erd,d@$Fpƕ!)A3Tpm EJI:|Iy(*:^;]6Ѥ]ٔ2DLpSʦ2ǘW Xy$jW,[(i@r>')R E{ <8UB'ԩ#֮[<u+ƫ7"9+w6وʷR^pm:;9$=.Fi }݌@nZ?oݩZֻpDy NQ3&Ğ{{-}bu^w)i vg3L(0H$\7fl$ЋYs9> /ExtGState << >>/ColorSpace << /sRGB 415 0 R >>>> /Length 358 /Filter /FlateDecode >> stream xT;O@ +<&پ"@(b@U;TR_8EaϘ`;8d/pPH*T/긁7؏R).wZ^>esIjiTaL27moZ r"$MʪZi+tC=b6Vӭe)?4{ڌ@Vm7#ܯ@Vt!e=r?$G-4SK*1D wCr&]hf1ZD(REυϛc{*>^ͣ,+,M)IjMJĦ)%DoSݩ7> 9F:#$A- %b endstream endobj 417 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xwTSϽ7PkhRH H.*1 J"6DTpDQ2(C"QDqpId߼y͛~kg}ֺLX Xňg` lpBF|،l *?Y"1P\8=W%Oɘ4M0J"Y2Vs,[|e92<se'9`2&ctI@o|N6(.sSdl-c(2-yH_/XZ.$&\SM07#1ؙYrfYym";8980m-m(]v^DW~ emi]P`/u}q|^R,g+\Kk)/C_|Rax8t1C^7nfzDp 柇u$/ED˦L L[B@ٹЖX!@~(* {d+} G͋љς}WL$cGD2QZ4 E@@A(q`1D `'u46ptc48.`R0) @Rt CXCP%CBH@Rf[(t CQhz#0 Zl`O828.p|O×X ?:0FBx$ !i@ڐH[EE1PL ⢖V6QP>U(j MFkt,:.FW8c1L&ӎ9ƌaX: rbl1 {{{;}#tp8_\8"Ey.,X%%Gщ1-9ҀKl.oo/O$&'=JvMޞxǥ{=Vs\x ‰N柜>ucKz=s/ol|ϝ?y ^d]ps~:;/;]7|WpQoH!ɻVsnYs}ҽ~4] =>=:`;cܱ'?e~!ańD#G&}'/?^xI֓?+\wx20;5\ӯ_etWf^Qs-mw3+?~O~ endstream endobj 298 0 obj << /Type /ObjStm /N 100 /First 884 /Length 2093 /Filter /FlateDecode >> stream xZn}W4CR}_ xMfeёٿ)Jcb],$׀e6tU5IV-.NNza-~:g> gEVD)G g)Zd5V0&qj ]]$2נ- 1,yP@سV9qM*QdUQ0(X95q 5POXϳq3&$A`Vq9nG(RbF.^ƥzX~ wCxBۀź<@8-ٖX`ؖh 2 N`1VpgMu;sͨXc㤽GDS'4G:Z1O+MOP3Ÿ Vm]=fX6zxr+u[fFdФ(K,b\k5تĊ}MnŤUM3~QeZNs6~5 b.շ:0ї h#2ʄM$=xDjr;@^oPg-i35?)G1 dH[I.i-hQO0^{:-rt9Ƴ{տD[(Ad6n̆$3{iօ rU{+3| H/5{@(ʺw,@Tx H C.R~Tf2VAivP.*^Z ȯn_0YSL&>?@a.]u3?ǡ3)$ħC)fI2mm۹=/c5._ɾDEݘ(nW?٦/C~lLC6/cENA$L#żקrn%%[d,G9eWNM->^Jd筑"腷I'qژ޺qødFzk FC/Dw#.PǶJ~[Wu3nVVKY'j4nȏ/hob8hIwQz/gvtD#( >Mo9IعȂ vaA s[Ǿ-Sxf~$ݗ>'})gx4H %}˒_!@ SWLAh;e@"$OVc(a9g\`ݗ/g.K}xw8 B@;RЙtwnxsNy~Sq 'i. ^蠏atq:K3G>M_> stream xYnF}WAR}\ Es ɖbTXJZ뻗rH-%9- k3g.;;OΞ\AUVFd1b`9+i1K)p/ý+wCK8NjgOT* >7~E$%t}uzބ%CQ$/qO'??cʊdñS4$s?&rYK?~H?F~^VZёYNrj04ʤ/,OV42j"^ƙ+%o ! @H&_v"P/)&`}Ye\H<Mپino~Db;D{e+VLkH`j֥3A6wAfHۦs8cFk@"|  6qûyY;å]WoL~u)u5&R.@|" T4v,cT]͓XͲ)/S#"T!f0|e (83Pi͡ BgR@/yGt=Se|=|ɍ 횻%ys,F o<'MtgM01#lZb_td]L,:KU+j8+/0%/I/tˊb T|9an/@aa&}O%ZJ=[›ug}mPյ0H"_ 8mOɑb\r(v5zDЏ@32U#uml׎ >#pejtSZ#mX-?ғV)Ҹw?ZtI5FU0g,0*둬 ^pems R#fR;g"`Yt` P6%9p*EgN10j`"5"6#DC^\ho"P'z>stL`"͍ ~n_@+N(V`&<ؑ4i^%;?T&bNyJn{DUDy@ÙgʅՐZʣ+pdEgg\]bO4SvĔ'S8Z\,YbH̞tnT+IjxHr)DMƈs ,7MIDMWWW$N-=,u"K=CR]ZE UhbPt8h"*RkCNZ‰B:'%31 -cDǔn{h5"͉K^5-w;O*}"|<_:@# r]O.CbgFWv;L<U᡿xQWו/D endstream endobj 428 0 obj << /Length 1223 /Filter /FlateDecode >> stream xYmo6_!&+")؀[: Kf˩Dl7ExD٪nـe@7#gyTB M)K&m >FW]HG)^ZvڵkaZOڽ\x#K+jFdbḠ3<(:nG ߬%xLG9][ Oh]uEܪPkqC1ȘWḟrS Q BG7$SFE PPN"ÜVkP C`.aDô)ԁ苫+x ZNɽ `eF~Ikc=woȼKz>:Jam`h1jI vgq_3fEa5x^@_z UJI%1h3m#=lmzzJK}S9k&vkl/)}v?\5x_ &yNgLfMbC@^~?Knń{Ifv욼Ƿvzn+5=KuՑ]4 S"d{GZ8;#3"6r $(x<:weASҾ̟za_/tqM(\/U#":,b:~I|-15qrWTN8C Nh^<tyX ){bg}͔`0n} P JiPn߄t@A&<A~1뭻?{)}⣼ cbM}tIFvbFp*|"]Ԗd 4le E05i6-/7wN62RAv3WL>|,+2d 7=ar'Q endstream endobj 432 0 obj << /Length 331 /Filter /FlateDecode >> stream xUQMk0 WZNv([r[w~AtiJi2=Oh239Ҭ0+Ƭb_ssA,e.8"67$ N?7&.Y94yG\D#>׎:FEV2`@+8ǔB斕7Vk1A:Z)uvHi3pV@w^ɫu4A>&Zs_LJVDK K Y؝܄1u㉊KQpzU&.+;}*[_VjTa}Ei_n枻g6 e endstream endobj 424 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpgqSfj7/Rbuild3bcc0d7ef148ab/EBSeq/vignettes/EBSeq_Vignette-042.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 435 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 436 0 R/F3 437 0 R>> /ExtGState << >>/ColorSpace << /sRGB 438 0 R >>>> /Length 47433 /Filter /FlateDecode >> stream x}K.;n==ȱޏI <0?O?zYWY_,ןu?D5E_kt(7aOқ`c 짨Rލ~?3e7-wq둰R 0%pT߆Kz7V?XA5Tb٥g.GJXUzUT*?uR 4Ի r`lEW?\GJ ]z`S hwcu{:6< i;/I5#a1ܥ`M =]J4лҒz|2uZ;jIjO.hZ#aB; zP*)ꤔhwcu}yg*G"Mß~o.ijOF! r:ٻŸۥ D-sT7FbϘhx?ťDkGEO(6m?*B-bt%ADnBF:S\*)@;,U{r^/{T\{VO_:}֝0ҍAz$T)=[q+Ͼ܊ލ/Ũ?SNrz\AejHX_y BHv)@r ьx*p둰R @z'h*oMbxXb<> 6D[ҭ1(^:S&F n&}1Ԛ{| e ^\7 +;z^skNtFȝd1HOJQDɮ K5BKz7ր{۟p+]WaG/ ~Z?hwc G48ώq5e8DbHC)K5B# X w}1xm&;1N31I/FRڙꄔhwcu*`kvx-p$鉰R 0TOl`G Ь7V_(&W$|naB=hh ( 6o>A넔hwcu8܂:Bd[lQI/M1F` >l)J#7B<12bJaOBg"ig1 => (O@dnC NEc1-=V3*8l!L [8|,U?}f]wy4k)7b<Ep*8#Y*5yp:!3{|jÇN/;&إ'J|4]M*hէhLǹl'c Z.;= ]Z|P+NX_A׳TCh {b`u%cQqSA-` oSBbvz8BMz/W=)կ* ߓK +~T S#rw#|FZcb1%BK 0Q}-=z6crj{auُ"as ]ZΨKy;]T333ۨzcujyn)ƍ8wUpiEJD3.| Tj剭,*^X>GOmoC8~y) |~ jwJ%xIbTcBݍO% ̦J(2Bްʋ>z"T¯*űK ٍ2[]Y]w;).꺻8W6\VTjW36X>SM >1%XH{wF!mjZGj?%lI%TgQH5#ooМ͓z*ZUpiz$RgbX2Zz,Ո{^Tu{auM?‡9[|N҆9&2dS*z/OZј ß O6R!M!#=Iؔ{4g3tMJ^X~\7 _Kr=7B ZTU^X72-IKavi*Ht,8U<߲ԪEi iDk"2l˄9 !1HX9\^T#!4Gc21(S.yabDd44F@[N,nU~c ɇ-:Z}D_F̰HۙK!fPZ}"Vw4 gO_ɉEFA m;1{CnZ7LKmڽ:\0jۘRpXosh#! +UIsqvj٥I.,7V_0dcZ9uE*.mgvbxFf n4/)dS&:|mxbV;,ws)4Gx+ TAO n$=zkuw$B+'Ƴ_P.%XfsZj"4Nv#1+{R ]J4л }TkVSR qs+scIaiw ۈsUC#]71`%b^?RtqxcuXcCviGz$gs0-~B= n3 xЯ:;z\")eBGb"GπRNHfp :w w6X9B#>Xz X* v9iq`ui'h/){'ҁ!zCC7w.1l@ת*Wf}7ր+OGlYmͥ &Ag3MsލOܻ9lj=;/ťzC^8|zTwaU ufʋ` ϩbKhqX"7Gb=q9qXW.&' _@J4]` ÆÇ7+O`gM.gXz$"<"cl z]XU,0c27V1-M$UM@AEåֻ7Jk czkpjЪ;)MN8/pɟ~|Ć=}k+3̶%)7?}fNRzUT*esKؑa~  w.BKu74J|4ݥGwҴ=٥EɌX)Y)h;<(FqW2lI)A'*T'.LHk,tz P*sY5!7ŸͥkEs!ť2|oCDaT*.%^ɉÉRƒލW e ~ Di'm1 Y[@)DPDZؒ32T\ 4Ի:A\f a%jvB XTҬ>O!eWldV2Rk&lm.]zkGo9i KU*{ it7V1QuːҥvvH$o`0)ӠJVr)x#A#Φ&7ZQumL̐. G"rR8=RA(]ڔ[gt?EdW*:p@h.] m]KzT 4.m;bO?zb| o.pGoi9!eX*kGiSu7VR~)N薑Ycqd/" Kz7ր_宿/FDw(TD?fnrg[,w2U*o.%X>rvrW?z_>~@5G4hQǥ‹%lRNnLG Kq6J2]܎z7Vvyhպm xAp$[~',lf9SեD4_*QWo<MdXxO)"t#zV(]4DD 47/5h:!%X ϮGز&82-?5ҍI8SiRLLFz7V9-2R`XHX tH &^jak(slnwcu쇽΁&݇-sd̤艨İ#K60-M/riWz`7VxwtѫYbY(i0&CODY21M"=T#4.%XØ6vǓ!ALe13`Q""eXj}cҡM9:|-ˉNU$ľj艨bMg$ERեC"47Ԟ?}i ϶?O{lQuND ۜ)lgXj-R+{Ƴb؜i ldյ3eu3艨Zw_bƳm 6<]Z4?z"b1lo2{9O+wCh Ө5s M;uӽ&P.-jIåbS.>齰z~_ lݶPs*""Kqz"bvcCY1^X~E"8KK ݴ5,o.҂`D4e?('i/|\J;:!4;,=EL;:q*Ђ27jz"2ZbXLzXRᬳpfX_p b{ jgte^Ԧ+V'鑊QO#=-Hl5_ј +o,5]3!կ,;7hZ  8ټԢՉc.Dh`iy<(Ơ>O>8un6 v+(^90TjqK^z7V·36# 8fAp5m,jHNfG't =]'1rU#Xju:!%v,9quE ;qd6߉7Oܛ@)#ѵ"ȔPU+ҢGoo"v]1&y5>\!=bz(2=U'D+c+FaW]!VAg;yTjtEuB4/<'cL?YӶu sҍ!z$|Y BzwԦCRލoaII}̞Lilw'Lx@THLw'dXAz}ѻ~ 1byD󜀰x`R,Yޥg{TjY H6SSsz2 q葘yNHRI~+.%X~;)Dif$d2&Xu6ztq?.~cuu0=?.BHyfгY* ťD+U3~_m5 65kƳ큳͑څWR 3Nԣ7D$%K.,7VԺ){4UԪå6tp+-eágib$_L-I ^^yöE,.}r[ϷuK ۙ=s惥. çBzk_w7|"u>]=VOGӗA/A'vx@Z$z"vxqj~b܋nmIܐ齰: @L7R/]K_T#\?o[4 4Q\j8G"yЀ#-,mT 9qF`uizHIH:Wţ?.]+௤& j] [xiCxXA=X* åIaxR &yQy46ei^ĔK;`urRd ;f$䎳Kz7VHdQ9<4К6* iQo"kv9vaUE/& !%H5~,:.X]6.Kۑ |=B1*@DYu12ńEF$- nr#v9XW[QI U ; -=K%T UuBlV·_݄O]*l]MvT|VzťWBDOHOzCݥDo)]*r6,"wuGhVz,gԤn )@Gwbİ{!p-xBJ bťHTa.TSuBJ4л: gKİ;|VC`/[c2Ez$R?1N ٵ}*/SӶ]JUR?1,(b)s`|#.]zGoDv92=Z"nFs_p,݌߶Mz j6 CjWJDAF9SzTYS iU;+3:M%m}p|5jۥ<鑨M%m6^-C1M`,V<1̹pӷos.ģ#ѴO s3?R_X'D'>W+ O~n'y7K|cu{1zie*2MbԳT.=c>g/ n0׺}HGWt\@" ~+oߑiJ!gcipb;. p #Db쌸DLGb،]z6cW Zv) Ё&vb؝5ۖo$ALgbAiA*nbSC`7Yј g`bQ7'@eS * BaChB`u~Ǎ'[+A./i]z(KA!4,U~0yEҪ1/azipPcb齰V+E1lxc=(Fm &JrH#tfQ V'KAn7Θ6/ &}g>tNzr5KXJwZKfĊߡ뒑mϓݝG$jgGo5ӝa=TzӥgHmD #)HY%[g 9z7V·F ݑ-j=OX`G9IC=Ƀ0]C㿻J&yhwc%|;qX }1lhvx ʧugoSIGRI6*Q5@Ăoeny.4a;}?fA,~`7):}Ed_%૳ZJ Lf/w,hoX*NNMө9 ɆnU,t)! PZf͹Ck$dìIөcUME eLm`g1Il@SٚB'';LMα{e 6RB2QBc"sMQt0rƳMiء@׵^X1d/nÄ>-VS!N j:UG9L]7ND!%$S| Hay>dOrTu? Su^v'kT14_i6lv=٩Q2W"]R~s쨙ڢԼn@Î 3c= 语CR/j8ǶnŲ}]릜ʋ67psh֊amhgy.ɉH@QߎnbYnʉ7nق]?ᜁ {f{RBsm1S1Qy5r;lhQ-cMD[*";3\>m+؇]1ˮځ}TUj#)]Rcq2= %'.޸e%1NM91f,N-'#BJ»vHߩ;Ol?uSN\E]7^Ó/N=Ygk%[N6]r뒪nFò^7Ewbcag4v)fLctI-jf ]}c M9qQ-[8v/IcCe!ܾ交Zr*-u ;.^Xw{K9toaKAnm),;TzEl=8&]R9L躶4eSr[`gOαb[a=-CbH^@WotV+k5luSN\}-aӷ՝3oujԱT)/v#tE5@S-nn e@nʉY7g8*|ÊV(KiOSmv%]eʖ}-6Zo/΀gN\YR&E芊\8I<> pAα@;[zX _e̥SGSTdű/,儽>nʓF:ӽq;f~H3mToҥ킹`?CtI ms]\vsݴo릜{-־o2^-vJ%%tVuI|j;t웗rM9qQ4`K̀^Eɖ=B>-K$]R~s웻0e78)M-[z*EYx6Ge\2]QS8ǶMnŲv)'f\Jp~?xs/6{ W:!RF]QSc"n;e=9z磹 wزan{=Ek+j[.znv lTs-V[=OJXBrU芚:KKw8U6(_q .p-m1m6 X%tܶTHW-ۢ"b{UvT7n5Aރu,ʟt yo)\sI݂M J, t_Öq}-e}VY3D7= JN+ܾẠP6~Mu\;cSݔ )t_Zܖv9ٝ۲C2Bٲ8KL5 Jr! 7m 9qNAhUgtn8Н\e]ƯɱR}Aɯr"]/jI:;x586Z̵g=xZyE.O0BoЊ e-{<ԎkOS,z;{[*To¾}AɖB K*E`݌([r[ꋚu9E5UUDJN'9A钊LdHu%-!U]c[r;lS-s_;v >=M_o?̳QAQTp-O#][wcCuSN\Խq˖PtsըTS1?|oD!oIo*^9m+J(#Na림˿E([L3jj5JfO'J3K8 u+F#]}'ꦜ{[s9vL̞S >`J}K6ʾ"ꖜ{[{زi h JV{ eL_Vta@a4d"- DyGNPZe\V;0Ay_\!'R޸Ö'C6j!t:O}@ɖ%ZJ~PLe=O:y٠/toܲeX yZFٖ!kUl9~N+6o[]WH-9pI-[pzK-98 F4\[ݹ 'V+*&r[-ݺQv]#QEnقĚeo}uz+o<]E ]!rJ> %tAH.'.pӖwc/=7c>+dO$uI|_٥ʎ<˘eOqqك;Χr[FqMM}IqqlcbJe:ꦜ{[Tr[ = Q8+݉%z7u=p׵Gevnʓ޸e fꘫslq3k߃ĆPJKIWqܤk/)'.޸ vΰ䴊S>^PBwvI;9K&q힑nDeaT7Elqvqͱ8γ@_n}wF)ɱ'_zr찗%^_uSN\Խq--j-ƙQ߰E b꾫Ih.vꦼ[ o]3e''l9{(vtE ot<6<5Oܖ b#NUbFݻnʉ7nbV59pZ>2E^:钊qn}º)'.޸1ݖrv>om5Qn w]Ro}Ǿ@ TMuSN\E_{>W/t>ܨa9!PE]TBNAnF`9K[d#toa@'0㮁ݞ="Ur|(J℉8lEvˎ&r[`Fч|mv:Oʟt Ɠ;iq&pTb릜{-{{ı@_\>q#uEwmtq"KeǍRr/j\{{f}ZEe `9yؑWIf*S..p-r6PY%[988I>@lP |Q_lP'ݖ t-"k5OF JnÿtIlb{Re-+\HM[]Utxz/ Ýv{~f#NعwHTZåUx#;l9mT?"NY=o7G}到K*f .j=)'.޸e Վ);'v,rt%0[>,"]e_ Uݔuoa,ıj ''?Pn 9bG~BAMSݔuoܲd8M؏6QRB'[|sA tc\}Zn{[i.>坹0ϸ>ql9ѭ8W(]R}ql t,{DuKN\Խq}x%9f3qݴc ] ekrp u}{٠~ /DJl] #șXXSXW]g񳮔%5bOQw4Kȉt{'l/5;MX" gX$/SWTB3k}ͲAr l+[h١ 谋Er뒪)6'.Tɽ\M[8쎯YX78~nt$uIMű-7X6(SE|?_~aG}G74e4۰_> 2aKE<~Atݾx_ognvw93?zۿ~ka=]V$^_}>>X#|qV}RΧx Isڧz ɐۗmI^b /yv^rۇ]xgoGUmuL[}5?8ǻg4- .ne Ք<~~_/ꙕ-|0]1Ւ:67__ڡR-h8E_o~x>:Q]GUCퟝ{=rʦ'?x|o9[|b?Sòb%_9yǃǑ-:d3<=|JsB?)'.p#8#:Suidˁ-9L;@9Mqi*lEp%WcOy||:&)e:(˩;%[fq9qQ;l9rSޢ-l_7)+MjSn*S٠d*.'.p_tj؂vrBaYۢdK-!K+ęPʞަ)'.p˖uפs<h>yqRef!%3S񈡋ʞM9qQʖlٿl/[l{);xP6O[ e2^a2<a6֊(v.c9A-EEAjhr[Vk.qsBQݔu_Ö5s;MtݖC^AQTOAN /lix/e?6^#y yӸS~9Hԋ Jr wr}x94Of&mY!oK*G$f/ҝ(l~v9ES3[LWHH>BnKK*k59 e-nʅ+8|/e nܖ&(˖v[^!i\}-KרS sDhKl9 uIeQ9NJwW$U6(uKN\Խq-}gPSNF0x垩ܶ.g:=}QuKN\I^[|?rί$VD޼CAQTOA}QRscl*SdE<-.޸Ö fu[&FoEɖRC>M궰sB٠d!'X_J؂ޜ)m&N޼h嶴)$%wcҝʞ;zꖜҋbٲep9)fL۲B}6ɱSҝvKe-Ee Zx|b2+G*J(}uIwrPW JJw(?%圊TAAѐF6Z4_+ Kw:'cyپ릜{[8Cma) KGiUl#*˘_ƼmDl?u'%pږiȷ-ܶiݖ_O[rmaiKn-wR4,xႾ(ek6e,0ɡ4iMےCN\Խq-GŨJVRøOyǸNݮ(`{a$zڝ>^uS5ҷ;lkGixb]Q3By=1lLcE),gݔg;U snQBG(Z 9Y.|Cbw¼d ?e9qu}.6DF.y.C[KE+ŐKd9|G7ekăoXNFLr%bZOݢ9k9Yk i-%4}S9caceL%uI S2F S~[ x9 bUFP㞑!O1KP-f$PrUeXC].޸/[S؂H=^D-#MI#.:wEwBs1rSn۶IR//KDAZTFS93SEjȋM{.VM) ,!Od9"L}S౱zxprP`-ȉ59V[ g({k%[ SzD 'ݪg4Q>7J!'5p_p4a 3^|%ꈙ !Kk7sDC3ڣ9+$=p㼓$}sBDf;(1%eT6Seii;t+|C>|#J[759QAoBRTM2ijlC͍%pQ}قg[؂V% Vu@.)^sԭHefd9)pQ-[0W30Za-Q/] |n d[qӹb)a mC@_Z/.׍o]Pˮ;릜{[jֈ}s٦{%[ 9⥻4_93(g5)#p- wམ=a1(_1ʖ-Qh} {[:FܖRGonQBJ7sSpRU599Z6P,ž!M9qu `y!ɌŵEɖ\B.q29"u`"NzS WW$Öw529:"jQܖx-Xۂ6V~^v^wr[tdYPh1%gꗺZN\P6N7e o>Z/l-Ảt-Trnhc!n1Vnʉ7FP^K0رr[WRSJPt-x16{);laHm1Owzȹ`M]Qt-Mf<բ[-pQ}قC{{3t->#@N äH>oewmi0;=K޸Ög׺۲ll5>l 8#BLYTۂV^JvmGJMύrȉ7ecdw0[liLSW8HI+.{-Oc}s rb( 2#ךFdkaW5ˊ3KeXd]=7нq-ϑ*gS [[%[FuY^h+jYEWfk{̑<8gu|[kvr[؛3l9u'm>md_pMJ>ՠs5K[@uŵ%Xc+gW')S~߳Z&.{zuBWJ 9qa4ࣿ4x&ܹIufQAʱ%]qwR>^!'.޸Ö^L]hL@-._Y͠Ny:gb+ t1E<-[&/l #/gJ._ΙpB=Xk6#%Ÿs>p_ Z9lO'=޾Jm._vtI=-}lTgfz٠|>BN}[ [J=יM{N,A*ZOc, ֖(1)t_/[`W{^E- %D5x&Z7`sm؁tjÖʙp[*z;m`Й-l窮K e˖9tu54؎)t_{;0KaF|._5BٲE^zz|#/Vȉt)ÖΨ-mx]-x]IQ2(Bn 予CTnɉ/ܗ->ߜlߴ w΍jYХe_YgE+r[Vȁ/a˨z2˖iKη-ܶiK.-9lv(ea0dpY&0-\# 9VD+* /g9ˉ/ܗ-xW5bcVm"JףT9v݃uy٠ܖr e ]ghdwCEԇ-Rٲe36r2 /zf@ۿWp銊m,?TTJ^ZO^Z+WǓnܲeYS̑ϝ޺DɖQBl;-[3TuŲu*}*7}[:G0Zњ7]z\UhF\WTU(ֽ6-\p9qi^u[Rq[ܚѤ>UǪ庢#gmEЏ1D?6n8EٻDvfǚEɖB%Y|is"W;uSN\}[2ZTn 7n`MԐW79p]wk=$Zؓκ9E󥓎pkd C>s.Y"q~6wcZ˅ /ܗ-xvϖ ܒڗ(4`*K*GN}r[zxaJþ1G£f-{;K* 8~$qt)^6( W}3 . pGre~#_t4jY-ZE-<qU31#s|TmP7ꜟUS.&og>(e!.?i{hQZ{(%ΙC钪~jJkMsC4aTb3YjtϝG-}8@I-۷{eˢYLGU-gݞ LY}قު8M&8-箐k.|6FMe_7 1ƙ5Qٲe]hcm7r|IՐ5)Z6j@Ch%qx{-Գ8X7X;VǘEs-B ]fm=|d!'Xl)hfs: g Èc̢|`tJsuGN:]Qvyt wR1x/q0{{ \ p%b6XEcSdK!/yF[,gyĉ#Ċ·У8˲ EܖFWƺ7eͰe%n3ƮC>4wƓ}قAwvrBO}w̮p (;J+jJ:81sU۲CN\/p-{Xp 0hz/3y2Dlt V٤W1<ժPx)վpM]P,9&purō;la÷s-*Dɖ2Cn-joc,-^{f1-"R?/[FW3mX;(RG+ a*[Txn!LLO"m.ҥu[ly5p6%Qe֐G\CK-]úU6)!L zQ,[Tp!'k|S+.,Jr[VmuI%n/m`g.MJrnQlu[:ƺ<ݖ'Knk)J3#ECT۞KSȄ7&Qs1t_ÖC{):b{%役_#.ث Nr3U6))˅nܴ[OkFmO7m2^B@UzQٲ7tEeؕzȉ+i?p-mDgy8'JφZ%[,J!J"eLs"z<ԒBTt[*lጤ–K{ yÛnSk_2j ݤDZWֶ-[܋.Md,!=&&}.M(kȉzvmY@hѨE4EC!m͡;56iKtsGHtнq˖)8yČd`|"zJ*έeĈjfEGϡ~-MB8]_u8R')k3vuI-Db.[2[,7EcƦ'H-;Kc)-Ie˖^-e//[J-[:ܖ'C *#uy9SVv3qO˄3E- ODP(o53mh2:f`eٗ-VETSCUau0D.4r͖eȦnʉ~ݡ;r/[fmeϷ-qlҗu[eۖYp-;ދq|"z. Q86Muk✦Qv ZܣT-x\gj)?U *3Ktq,}9T͈i"ǮrjJz-<S1.{&[N iJ3k4XdK)!+}[Ð3Yxc[y=By,!gQv< |j^_2?lqemXVl%_{˖+[˖ʖaB;p?Fs˾6T'2V)(/g袮S#9˫UQe\[lYjQS{!{z3eϠ|s>4ޥ(;v-7jUwҳZXdn=_!9XPn9JIl^qte-.޸eP|E8X/m{]9RE[5^Ժ_gi'{9!O学6szQj'/;n3 Jx/ 摃3ZD-#vsZʏ9g7llPnq1q-#J\v' 嶬WAp/0({r81EqME =-g~'# &Oc%9~}er[8t}Sb'WE$(L5䶩uI%ylPnGL-)xXUd?@ɖ$ztI^5a,迳y٠dW7QlK^rgF2@,DtK H:n5ie?elW%bs+2Hf0v[VTNv F?^|'meeA S"E5TRFvlꠅKuf _mؐuZսtzp,8YsOIeQ◧rϏ%l{| ,/ELN戴It]]4_Lr◭q 2jIIT#fxHRu%b٠#{>eY,~Ҷ!et{e>^ YRLV $:q6)eR޸] G?EqlrY<MiYR)^$I6@ixy/q_eaxTӴj brYv0 ʊH5-çNSQv%S=X:cRLN6=_'etۖ5[VwyfM!xQirY8|* [^#&[VL3@,+9c^xZ لCN˲'.޸];"&3!NsaWaћOAi,%d"ŴIYG>yҹ qqUG\[Med>vˊJmh&MY+D%)KሱRK$jaOT2R}s>stbp!ٴ8d2Wl.J:7S+ʂn-j*+)GRFFō%\X2 WXqpL]^&N /j5..3eRJu85\|Zhٝ 9 Ոϵ ˊ["F[Rt>[|^neisEYiE@ nW-k98HV[o_KT_+Qu+(KgY:MβMeeI995UcpLU-Ljx\y/\卪__}i;uQlYfCQ5ZctC'|ZdT%]|oOtpv V5i5"-\Tq)_Rn_3<ŷ6Kј04tfxQ۱l02)s*M{ 7*K~ho{3p 񈡕)-+*,9ry-8%)Kf3y&3s$ /)Oˊ nX[)N7_ *zG=e!^oE0-)hxlYQqKJ4lZbiXMs doܧ,5<Å*T^+&5|'bzmԘ9[8o񅫇7Oߨx1 `Owct;r|4~ˊ:>WsdswQʧr?|e/{#f YyhnT8HEYвf9faįTGMe)kؔ9]N5rC☂36y _9n9{{O?nYR9!'f+|ݕB*)KWYV/kgՔ˲fQȒb.ƧTC60| h|1h+˱2gҲ6ۑ:"mRg7j30S)KcQuO/}rYaв˂IK4,>"mR.Kˇ/Z\LJ?%jsn'-~d\ckdY!9.޸OYT޳pq2ل1D;'dMh'eyi?E*o񅋲oOYЛVyT(Yo)½-ɚ:+(!P5Oq4~w 2Jw(2ϼlg/12Ƚ2:0ZYoi׊oY"cbFO2-}e k%9Qe)܏[y|4c:&cmY=%Et3\qLV(QI*I+|#n}bcϞ5ޔ4(GGK,>ˊzZTLJMTeoܧ,^NYY lqFWwj .b,SH]mpIW{ן=?i\%etO?|XVTc/-ي6)ȶ?[@pŲNv 5.EJ\$6\sڲɊyX% C& <]m_8Ca&[MϦ\=6*oT_8$,+[N#:oK7n~ >zmSƝ6L1?u#~\;9e)rqi?qy/\q,gcJo-)Ò5c1βK6)_(eVb1ʁ;l2,/o˲8'(\}}ʢJT k.a9o񅫺2Y7=^jLC3<&YS=cP![0Mq%J7.ʾvYx1l~c2=5XVT4ǰXi7E7*|8q1tuk&z 6ĖuBÃ/<]cy/\q,s>NǜᷧTb~E75N2+)wu:ۑq n?%Хhꐱ41ιs޲ck$N?$ݏ6ب+. OwxI}^1/bѓV;OY';% |ZVõzv+~BJ7* ~ի`Y 6˗b:$۹էI1!{vYx88w轨tgFOaˊ:(FSejFyO\q,@%TXti!}HKSƏeݚAYC:N8oKt>eY2( bxU^WFDV>|R6%bzpțE7. -s |Mޙ{5et{BJwT"aby(Ҏ;#o.pǿoO7Wh͗׿p:/dN'+I:_:~%~a:~ۚE{o|]0}~1;,_|Cm}'?Ƿ`Hӯyo֏o??~ǷOE&pq&^ի'w4ϟ p%NX?6Մړ}4׽z"~!E9 }b_p b?8e+몡?*.7]=pя4mr?T;hȋiϋvhp?ۏzlQ_55JAwA^Owzgw}>~O ׬̙+?;ǿ}~w?l~jgZ{TӮ/IeHpp j_sj}c K h^QpGO =}N^+WEcP2BeY+/\zVћ p8BÑ0|6Ht3r]HeF(/\v5lbRhl7ul7]a#^LݭG4L_XUE^(=7|sV;ZlM!N%%HFDwa$^K ʋ5,JkR^uY'<9Hhiq6c؈莳Qxط^4gu}=Juu\x@=2/렵3D޺k}=<"5}<1g=ç^DcH2]XK7|1h mrr΀zղKN(𰯋hQ;OJEyG ҆K!& `[hB)zhZ ZWLDjհ0o|k;zD4ր3ps &q2 hIbW>ԩ{X-lAybabYטQݽJϰGteQx؟Q4ĠLKL;K!m7csǣݲFEfK&{S4aQz 2oH/qAeAK訓6?Ŷp*^ C6D}zȼ#!&Ve|W @;Sa!yh3x27V3==ol|0bZ3v<*@XCaI>%4v(ǰL `1ެ =/{vF efzBQwc7&&\By ia. .M]a <8P(zJh>Yx"2/4u34MT{'{  ^XMa'5thq&Z@\u 鑬_u1'y{2 3=!C2}H7G0+΍&;I pQ}7ȍMva=Ң'F;~V Շ1A9'<4}CoE_1 I;I8^4_f/\Jkhhȹ95lb=3葄OaX_zR<&%QG^Ĥ*ALM!BMa#սF(SXe%AO}&&Yc@,ߍxAUzUfڶZҊ5T0l>̞fǰhuh5Boh/ Y q7;Xa8y%[4NR^BMюJ1#xL2 ڊG(==Ce.|xbT͈'=m>fK,m.W8źFܳdYz4=b\߹m0ΚD3j{<+Ӽ-Xp-]fQz {v#G+6/'T8%XꉨKlAVOmH%z~!^SCMz3x]8lAy@ _ h9k޷9qI~ލ &!vA]i3U!_:﹛G ≱K7̊SG_C^OUXH4+aU3/Q^e.|AEw&4_\hSXٮ'W|e-%QeNml^~( gv4Ɗ:}KcN B:-[=kis^ǡY+-w+.B< pwc# 0#|cՉBSB:e8==IF9_L_*WhyA#q枷ӻ{*lIH'!ڣg:6δyci`Q L#=4mSF-L]^^ zdF yEm:7ÿnK2cxgd)l&xk?״_nT\u_>vd:c^kYa^D9-ĭ%en|1{Z# J0l Sͫ'6OUz豴r1w棰-!(s |⎯Ͻ.x6e^=\2 ?1JwŔBܳhb̍iBtPbz wī9(p4鱴|×UJ-Yz8_TDS㍠:fW]N8y]CyݵF#G/SfěO O qDϣ#KJhv'e˪HA čZ2Mg5]dc)Ukꅣ9{+Q롍cX+,s LgDcUtkW3n`XSp[ /]ez q#ҢWv\dN1nz婷s80e.|xb:Z,O %֪k<< s <k=Þ !c#kStjVGab%pk\kK Qض0/SRѬ;M-y] gTaxG4g{3fWGNJ9ɮV6* ]a#^u>.aWt?]agml _ R:E/Q޷/TZC G=0 j1+E1PwWC72su=9ztJ-;t͹5@ _ ^abV*dB\:M􄸢UУ&ByĐأNWn@;&n<+,ī$pܺ#ڈ9##o+it|> /SkiD4(w'Wp3̰;-ADHS۝%(d2>!.~FtG=V44北vlNdHg;>Rs)lG \@?_BL[ߒ#fxSҜp'ixJ^J/Myed _ n;pJ~y"tj"[;%YwK9-ï݅^#+u^-s;kt^t綛0'E bvW|^yG UÁ m?; v"6fޒ~q HO{/#ufXcR۟53=!f; ;ʋx$p 4a}WKw+VS{1{*8(IMWx2E{1Q؈yPqf*F̶`W$BEϩADWؽB?c4g 4\@-M4w;vId87;2Q8nKݴ27ӋҔW~ 1<1f2%zh5A=r}o-S'NPFeJ4d|KRX#fg-mf]Az3RV=AFxDtliMʘyЮ o[=[;BxV!Ʃ#!f & 61[AAܪdn|z.Mzz륯5W\jsʒQxx.-VZ՜Sa!ռLo1u)O=j-^.'꧌#ti<@,¶Bt$>!f硹 0Kf({T&/Ntf 8մtqaˈ s[{.oKsNt(ҢvPӋ}iՄ418d%z^^}ixuyuwU(e&cz3e^AXy n4{ܩN q=je4=% ǰO27@ܧz硗^b=z}s%s(3mk4_ (i3z4g{vxF;jYottzkG NNS؈yպ~is$_u)=m &@'kF)e^𛙸7akǼ!0<]RCkT 2#+/2/4xItH>@]TpG23=H?%ٵ~Uha l'6W+7%Ax޿Ι5Waz { gS<߹v6I/{zU؈y=agJfy sX1M/wd(Wj xkt oN.^ݻAxƭj׭9[Ja!I0Aw>-k7xezWLɼdfzBLn47_ {㉶;ю'ꑫ7V%=O/ͬ}i8Iy?4gӣ!(s Aw+Ɖ ϘU콒W+g%pܭ@z̩֜?Een|DbDs>Ī(BҖaeU2 'w;S߬= Kn0H_硋ww=H':d>:itTr8:yG o5O@?DXwFCQy !e>+#e>e.|Bk>iȐF<]2 hZml!l;1WfгzQSt3_pBܺy gM3]Y̅O^qX;bī|qOVXH^#xZ2 gvNZ{by=2@4b֝NFUö$Vt0 The̫̋9N@ܡF1GEW e{-3 :%p.W&UUh̫O^cFxO4wx^NҊ&k{%p;$ڳ7;xOyG E~U/ ͈٣2kv-\q3qꇙ^~tȖYU?8ݹ4vXYBҴj)p Vt su+Wq̅Ow>S|mT^IcXHj7(ܽ(Vdf9+,ֳ&K'\Isgw.p+BxgC1k(=i:yBO[xk^ܖb]*5b('cE[ZLؓb^U+lz'kia?!S+;{-\X~axxGtRq8U5~dn|Ak٢'s{FjQXHX ;e axkxWK@ CV u;BD{i%-01ax %^h@ dn|DOH4/{5k)qxh%Oa(/F\2bu<9DϛCa!I< Ѽ,*/_a P#z1f{#=&a~Pۢe laP  @H-͞"zde%GjkWO4%.lXkHap;Mڔñ'zHQɲ@jaPx@ĴE*b$]wX%]+0Zf#)l<bn1hT(Ӿ߶N_Hbo9N㐘ɂ]I2,j}s:mlXhݬq+G/;Y`9+sDB k+sx6mWHhlZtn \v#& ˉxɈ7-t4=eH"\GdJ&5ReKXXKpQ8\0"3H$sr(#|@6s>sqr"c@O^ EauvZ9O ( ;w.WwnMzm9݂A.\ t ͣUU;G]E}m8?e2aDOu hdPSy\Jawi<΃6 <lMo#,vD`9%iO Lyš4f+< (vr%&7Bv:h59f,ɒAeO#&QHQB)5N:/X˴kS6ehnɠ͈A Y:,x9T\$2,64\ڮP ;rb:fuΣ6KU16Iߖ3&Ӯ8UL*`y,\J2 T9}_1/\q,S d0'\yL,>^0 YQqVÐ-{i'#oK7SNYT)e,JZ*ceCVT<41!#9x-pI}e2nLeApQ˦,;dEehh6[52eWab1TL uqle9g5N-cXsʛ|D}u7) NbdM#p@=lJ9s#xX+dp44,66ݜ6)e>w,k*#&qV_[ q%˲bp%*kgWV+~eE`=dq@'Ҏ?7E7ܧ,]EYz2U8irJdZIz5S;Xp,c1NK7NWYS.˞wCVTMIriXys doܧ,JG .CB[)F * ,-MɊ 窈)]I;My/\qt|ս^5S&k,M_c*H{S.Kp+eI jE zXW Q7IIVTوَeiw-pIWF*ɱS]=|}Ɋ|ي%]>x}.޸+bg$,Tư,j|Ȋ:[3iV(.:i,eQG3W+z\lף"F#khTIqXyWkpt7*|g"f ۔d7jcNtC8KvŏnRKN/ ;,$RF('V-+j~c?}cYkI/\vY:͸|#b3> ߶l]M=rQqGpAkJ; mpI}ʂhU1z<'WԤn;CŴk ݺhFڤ\t{7.ˠIXɘy,WrYN7?g6NF1g6-r7jchbxaA''w=A%|jYQLe%dSgiτ:oTc׍Er#r,Zot%>%+i׊=I,s7jKPv龿. KbMn5Sa,CG,)#f[|#ovY/Il8i͌)eR˜&(mR.Km.ɾVY2'Wp sJ[MsP|E ڴ$++Air_\5e)|t<Ɏjied[tel($eduf+eͪ/ѵ_bL1.>. YQ)vå0ɦEir=yQJ>3Ɏ)Xh̸rW?r\hmcɦ/َc&qڤe/\q,RB۱ʽ$SF+F%_1M0h1mR< { )ˠѰtĚܫ9{r ~rdMţ',#N6_${vY s#;^(y %ˢ<7_"YSU$H>nH;Ij)9pYUNG~byq !\☄eIJ}1-pI}ʢ!ؾ=VӨ6}{k'* 1 -V3=:/ߞWG$k`YZ N:oY% }RFT͆*2Ok;?B^ݑwcp,%Q]5̋VsgcCfhY2;Ֆuug(B6ciq do. wC3V>zfnah(jOGKˊj1r .,˭ ]bv doܧ,=s  !Ӆ)CL=y,+*:MX׾G5j-pIh&}_L(Rnz|zWF>tyshj|e× DtmaQb&[5岌qF:-H)[jzS\:s1t*t*<[ITVleYQԴb:Givy/\R{|ɨ-dɔсA~"eMslEoNl K7SUVew04Ģ)Ev':BԌ][sI> do.Vap1\F,!X/=xHl#/c1/YJˢY%ˢVG?w*|OCnt{(~M(+*U")ds/R.4SjQ=,i{Pr=9cŴkpKI[JJˢ/}}2eKeQLeNNS.K9U媪dE1mȘzB6&h:zRʾvYcLIL.(-fmCuIjr= %$S@J;SB[|#ցRq=xjcIl'*mn>mx>} YQicɸƲդHlA˲7n%'_@1 -|dV|I| bVd3v>d)dNv A;x0n;LcUMהL1A\F .޸]܃s^cpa&;ZFTaMˈC!Iiy/\qL_S 4=P[jYQ$dri=j?tdo.K->1x _*)wˊ*혌9eyiO8o+Q.a㘊VjY^ay|:[VTnp,;to/eSW:Qon'Cœ\Ok?mBVTyc25ŰMaMMY<+[ ܧ,R,U&vGziiW&eYgjPcYQWF Y\iCp do.KT0v42[E璼eE3)ж,.l%ҭb|QL,uQלuPT:cbY8֯i|e% ;f`)ʶ%v;g2:ėmIYSݏF $COG%7E7*K{L=,:^8QxAc\ݗ/0YXzeQLcY$}CwuY87n% ˈ9c ίHxnY[߭~-08[|rr1\i^ G _1XVԈ1T㴏}.޸]8Ȏ vB_X _ZLDIfh)AMȬIEJ߃_vl2l:|nTZVT<'V3Mfeoܧ,WۖNu[a;:!2:~w,KȚa[:X8.޸]9|GC߼!K)۰j5w1ͳ,u ep,ox_o~o[=Nz"Ʌ\Ͷ#cwP6z(mR%) lν=5>"2R[uq 6ˣ۹Gy/\q,# 1Uu[%&),ZV<-R1xƲ{6)Q_${vY+jߥx&_&V7rY<|L,)4.Zle+ٽhQ#mRn;oT7]//>~͗m*=`/o_˗/:kXD㛯ؤ&-/\RKI/O`[Uͯ_?WE|t +˗?iz}[8 gn I pU-=XG::8q)EW/:\Xi.%="/>cOWǚOlb㫒;۰1#G4rWTN%5){.D4 $wPG,jUVzMk ?~ˏzyVQ;ww?'Te²'tg؏o>?~6?o3ؕuWWYxުB>Wx?pԿЕ endstream endobj 440 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xwTSϽ7PkhRH H.*1 J"6DTpDQ2(C"QDqpId߼y͛~kg}ֺLX Xňg` lpBF|،l *?Y"1P\8=W%Oɘ4M0J"Y2Vs,[|e92<se'9`2&ctI@o|N6(.sSdl-c(2-yH_/XZ.$&\SM07#1ؙYrfYym";8980m-m(]v^DW~ emi]P`/u}q|^R,g+\Kk)/C_|Rax8t1C^7nfzDp 柇u$/ED˦L L[B@ٹЖX!@~(* {d+} G͋љς}WL$cGD2QZ4 E@@A(q`1D `'u46ptc48.`R0) @Rt CXCP%CBH@Rf[(t CQhz#0 Zl`O828.p|O×X ?:0FBx$ !i@ڐH[EE1PL ⢖V6QP>U(j MFkt,:.FW8c1L&ӎ9ƌaX: rbl1 {{{;}#tp8_\8"Ey.,X%%Gщ1-9ҀKl.oo/O$&'=JvMޞxǥ{=Vs\x ‰N柜>ucKz=s/ol|ϝ?y ^d]ps~:;/;]7|WpQoH!ɻVsnYs}ҽ~4] =>=:`;cܱ'?e~!ańD#G&}'/?^xI֓?+\wx20;5\ӯ_etWf^Qs-mw3+?~O~ endstream endobj 443 0 obj << /Length 457 /Filter /FlateDecode >> stream xeSMO0WHxIP$=)7Ҥ6Ф=kjjg Ns#JYmDՉ\ S)L'[a!U :sDshĉ;1x1,%JZAb<$Gk&ɣ*UR@!Zh6{ crds \Ɠ328TTRvux8΢0Y<%f^iT?we<+HY?Nrn"Kj89J)8Mw[2ޯml}]'}gEK[m=ᲃk`N]knikhJO63X3۟|[ D.lzk_X7aF=r4+NO:*^??<)&IR*5dM¿oG endstream endobj 425 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpgqSfj7/Rbuild3bcc0d7ef148ab/EBSeq/vignettes/EBSeq_Vignette-043.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 446 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 447 0 R/F3 448 0 R>> /ExtGState << >>/ColorSpace << /sRGB 449 0 R >>>> /Length 19255 /Filter /FlateDecode >> stream xMI_KyT|\h3c! MZ^5m[ꁥi@W̛Ewթ`7[j|߿\}o7 7?h#on~oOs>g??j4oO[{ۿǟ7zmy?@vo9؏wĜ-$ Jx QS~LJįя?:r{W ;wĻׯykoE=ŭJZ~p9kW3f{?#o>n㳝?%<gt|g#;q 61y=,Ͳj_=B[e̿]s,u^Um_.kKY=}/WWc5lc}]z|o{1̻Y_j;ɋwj^W'wMm3^vx1V^u-=׭u5H݄v{ݕ^]u_l_/w?*]]{{zR xzNeu{`x/3/,C5)/-CR{W/MCٖ?{ơ݋_[r]iKϗ3ni?b㥑;]1^?_pzݟ ε]׳exPKUʓiNW뭜ף9_EWb_<G5=;Зu=z8՛.˓z}rr⯇s'דs{^ON/Gs{spއ}k&W׿[w; 쮀wN?9?~s_ay\0|yw3{'{^s;y$|x0Rg<ͫc< tyf“#q ükYƉ5 ,Z{JrcDv Gδ]S`,_u__ߤQJx0,- ./< &4gt35qkh(.>31 p 1p 1pQ e( dBL7kH` 9Gs c_7!nd.L7BtpO!Ƽc;Lc~!Nal l n am#70219g0Z)Ta&܀ 9Bj;bj a v+!ȅN'97BXx ƼN^Ƽ$ԣRs.t=*(lx Kލ1G0E)<(B05k؈#8< Lc SUqLIUX ^nX1sa 2|䅇£:a6 !Ft]tp1'/\NY+H8݀ fk1|Ps5gqt]$\SDpBs&W7; !`'L7IY0ryA8)<0 pB1S<>4MBstLpSt.4=*݀ p ȧḣoԜ5G '[#L7w:p!;myuSAa> ~n@!!,҅0FB΍66n|7d}x\^y!xBsX Pd,0݀h.1oSј74x G ;Z4t_΅0/':y˹)A7,n@ ق[$Tkt KPNaƘpZn\)|Ԛn@En|WjMct\ "' |W n@ . H hA+պ9VpWSzK@hΆ\z$i̛1OCa iW4W+eEn@&@1o'q.4 cK  a7ـ7)DsxZ'@3'LwQNaثp&n@3P|cpn M:5&n0`64M>i̛qpQ.b5ug&\$?Wy1FW{]xR.<^nCyQ&u0 8z:t/ޔNOMNA;+hF^?qoI%Nē{ /&|(pM]o&43qxFʏ-(0U^;y7>npfz%:q:  S'NW!qNM;l; ʟ%<)f6=eX!TPv^<^qhshp5;0׮0u<@$^[,:̝ xwk}8j}8N#qvdv M8(v&_.D%D.9~Op[X5?AZ5?AZW[P[A'{A<w~) +70| g:\?ӻtcP0:1vI7SޖpPf>|K?wcog s(By1|I_cr{QpkRtJl!Ǥso_]wS^|%]/aC+OL7=oC/c??!l?%~Ĵ܏ƒ]xq<755jݐQw臌?#2jŠ)(?J~Sa|/y{O6_Ϲ_ZNk90;[H̨6~e/{PtM(J~S<K5A˿E|2r]ʯ__%( 2?K9Ik>3`%<(7{o?/y[Op ]`S01KG'{Q(=1 cMp)us}i_Ǥ?ҴG#.y#iM _a0&1e(LxP~*ϒ?,yxfǣ/Е܄'{/_鏴nPZ/Q{+3"tQ`yj)Lc k~ y8p9Kp<0e' I\<J%ow{97%K+ę8q"9J |e>i'.p9'^*MYFYNyw&"po%&&ƒ|_<9^|_|(b|+y<;(8~7i&aZʍ e5ߋJb|&2`gX%K~QFbLƒ? Ռϕ<6C8&c0ʕCEw2KVܿ/ޒo™${ ?qce6wLQIy/*yW;?K>8թ_G}uWŇ;(7}u/|2/]Vp( /|+Zgj(+1| [}MWœ.z_8HoU/KkrZkQt͛S}v)kxZG*޴l֦~Uim#:N<9Y%9Q06k+ޱ6k|ZZ:/t?z_ ]Xgw<2#ZFDY,J돒~ޗQ2WůYȢW鏬ZbzwֻS*xr#ZAdz#{pXYǖWE%(?J(?J>(V|2>[/}7WŻv~Uk.*޵)nw#SGt|+u?tßPԯN_{Q@b#JYKBIxvo?%|Aa{x؞/{OSYPSͪeAG;?J>8~o/D~Q񱽨_ۋUL͚ƨ^GG/ԯJ(<8$?G*^YVC|/ۇ5d~Ul#J".;O}6C6רeeg mGmGFͯZ۸_ZϬ#I;5NȨu#ťk~Eu?A_~UL, G*~HvwpY%){{Fg*i/*~vtWN~)$,3}:e$6y:.L<|ĝ3tG #MyX書_($qPy&:H|(*^v&egrhgIxaq(%:oEi}EEi})8YNyWgS*>v6V;GΦ?j6V_MRNKUE;GrE;^(Z Gס9, sXZ]]+T+u%X!ȣX65_*xA^F_|9+֛.j[T^,OpSʼn VXV<K Whk}kGm2݊_Y#kV5Q;Z*S{ū۵ѵYcavŧ fKuJI<9WS\uWţzյ~L2l$t͇ 6_o?%>g9*dD2rIoڤ~ևMWȐS ܄QeX",6GXhڏlC_` x!41 [,*zߋO:ϱM2'ԯ"bhTվGֵv_cuJUjysvHvqdo찾h`M"0p䯙Q*bSp}"4v{X^Ltxh?̩__cN b^OŽ0'K.,í7UyeAyce 0QT|{pҀ_eTkxc~y~y~Qoԯ(1*HFM,:{'SCx~Q(UtdSu\D'~`={]?88$x:/~Q >_SO׬MRL>ɾR&p~ObOFa`~cf#.~o Bq` b߱ IQAc2`2=.< >K|>k|J@X\JB.?$N" N8: t~,3?_p2=`"Nٞ <ٟ,F&9||2o^<_Z3㈴߾jgG`֋Vd WEv+%W/'e?O4R_R?S~Ϥ}{{=<ڌ}>b=v]ćaUj?>ўi3_ Ç713_=Ϭ3ϼ=c=9~Ԝćq?>63X?~igsغ3||>Һ}y]L}L}ug[؟ƺ/,Kl?>>7yO{=||g<>vFz<>6ڳlgwd|]?>6g̻N}Z3K"Ϥ=z&=|xgg~<|l׌Cxf<|5d:Ë|w8}/q!{,0t $0S@,;JA`#P$+؋bp+A | KHЇ|C6qɄY0A8 FқLIS Չp*6qN]($ݮRuZȍ{]0$9`v^&k/@O`C< ]o]8{0 ymGxZg8 &.` K p#O@Y`8Иx̫ uJӉ,1&/\v &PT,0AT7xxWM0Ca:EڳpOjKIrM֣O~y31 ym$-he.) b…PCy\` %aNƒ+8T/ʏ> 7#Az"ʅOHQ)<'nj@‹\S=HDQlHCƒ>}+7S!'On(?+5 Ihy%UH/_ŋEdŌt i7*,rYOHAdŏTx0w1$y" #IoI#(`:¢Iɛoma%bJSTI~+ R^9 ʔ0+f"^:M؈A$o2Kh^xh]Q_JR2}%"<CQ!ƒ`&!A0CkZ“9!ƒߣpԷ 搹Hy!!A7) ͒ws!!!!A18F>DڃLes~CQf5.,o"| dy"<#2IC{"<XXz" bFL5`* A QLr›Ӆ e6{"=rF"1I>h\xsm  R")Q/H{"2p菈@FMA(y䌒ߔ%D(S7O#"AP2]x  OeJ(%"?|EC2%Hxb%)JPf eJ>HB{Ibh-,.UK2%E(SF.B#2`AL  f8 A`P 2̈P7ۊ GmԯV-ҕnvʄpPAzAzf.Cy+yLɻe8DA_Q/ƒi6K"ȁ@!<"YS4]xPƅ7O}"SLKZӊeE$"ȑ~ZD?$~2Y奟$<(£ĝBܟ b~ f' b~z0I?h/3IEzE0I?_(]Exحs"`ј݊nED[0/&߻E{"`ܽh?w `EԄEL?>p0uGEti ߪ0q<ɄE|"`d"`C> XLX8[1>P1>H$^?U^1>_ueo}Pc|cd|r2O}%>$S|ҤOX(ºSuSX}_Ux'I&0%"A6G|_\u)/NϦ~ҟMR=G_ݷa)9de?/ NC&S~ I_uR凧ŊObԯK?.OB"~GC'Go?%o?X>\F0H\}Sx.J~.J~.JH ; ADpahԯ43JDWN?ڋ/~@~#:kkA%EX_%o,i4 ܄;广 cZ  ;/`CBƉ IDМ T7C?U [1_[S?Bt 7CCxq[;PY}"jN~B`uȏ6jG90e:$(c:od }!lUI(>Y{vQ硍wlC6CB^ې q#  6` ٗuжff2&bOlC݋6d}Lk!iȮz8 YEmdHy3L ,cM~H^xzhHQ`^</!kqSĻG].80:|( W4.a;9i9\wt ( +X߰3 eN EtA!ȺQCOƸsoF]!G΄9쒝RXKܰp6rF\BܵlV YvԜ.ޢ9NEsHLk©oXTb;(KAYBܯE0]r2 )0~J*.&ЃpB\$pWI9(KsY<ۄ#Ķ zWvzљ":}P}ދ{t)]0532{%GIkf/ҮbS:(KD ^=M{hœ>w6#3&e]Bܞ&^Q WC%^7GPܛ{>"u:#]yB\ 9㼀c l.<^Hi؏xsHHxO@cdGW)% Ә7x H{Ӌ [A[/"7`o qx}/%D6A]B jzq9H9LDB\T[ !_3\Pvð64EbpboԜX?z]Z)< p 1oΪu jIlPs%^C][BCyB#Ƽ5+[AQMj>ikTL7{M|l޿J5Բ. CIJɊpPJ4EaD2*Bu0TRPB iE`U m7%TCZԊPƛ_#pV(͐ao獲>Lui43J"3lYDV!,jDIkP`CiX**z7ԠR }0!@CZf(P{V *$g.a濊 5g %I䒤Cϑ]؎J8ʪUfU4`H;*B,DH}Q[JqԕŦqTU#6׃3t : E@ƃٗ!02L!tJ UD+^?* ^Q@UCr7%Y փ;R4,.1xp?"Lڐ埑EO_ fx* V<9z5.j0UKEFߪ]y00PQ+BrQ*\'@*K TQHHvV%(qK?~ܥ8kWq I㪴ܥ;CpB"nr`sG=EʅC@{fB~d*@pڀ0V"U2w}ߙ+Ѓю#.?^MR̴.80~ WNf懭ꤪL;^gyM(j2t|tQnp a߂ 73B8`xUmN8!&r7}*Й&:錹EšN}7;J`z#N}a~<_Ixt'J#%|9tWb6J1Oj {Ċ9BTVO͑+MH wUp@xE~j`}wa“GSU)tWB‰^o/jO#r?5e\M uT;>WŠXiUg1SB0Ω΄0UGl!$z^5*AH&\ VB€0V 'ƻJxa\eMUzX/g ;g `ͯm3vGrz 6kQTm[qu3%܂iW \Fw:L% $Ă{UBv2&kQ4hb>%l%dA) iWM(x U³šP.U.+0^M(,P$7 EْP|Gjp+aZюE!00^ZюE0LtJ.v Uȁ0kVgP83 `Y+U&WL70*%.L7@}jw R:&^% J@;Po:8?TDŽ!p~HR ir".Z.9POx;y\dJ !\n%nupߪ"as]f&LcZ0We!;PInhsRg\yo\9 ;O. LgGM7"<>jQ~䣦G>jaD>ja}t~A-4WӍb 5ݨ QӍ#5ݸQӍK!5|tuW0r#ח295\jM0 1Z>QyJA€0@9aOn(gCvZ\q9b~QJ7a@?`.5>asEB1K aĪw1$tC4}& CڟsOsOPỘZ:MTxZv6 ( EʒpCx0D.LŽrgHtn%\>ZQ+c5M\cnOx)fpBCpC؏O8 .֔g"PIxܢH}Ja {S'3]%[*%Lĝ) AʀCRBA a4|5 A$ Y2 (eM ߾31$2,()pQ djZen 1)DiYTT*,S)pnskKXѬbPɥ:`0ٝV! oIkLw KeX`7' !o 0a:ĔlRͪL CྰpcxM) LVᬅ:pBfy)23\Bfy)Ήf 8r2ʻW] gj`60 Lp!*DZBfY) f Ȭ3$D!#B" &3yL0^)2JYgN“D@TZ&* 2 DFsM4" t2L$" K'a!D!pcth u&wM!(ZM*UTm*qSaI!L'a0'ts_RLRy< ۆ+ede3q(]D@N†SjsȬmxΦ)Ȭ3ޣC)Ȭ3Q$<)`Un#*YgYO1^Ff9'_FfUDn#ΎLFfi~q]H4b Ikq nfuV:Ff}\ۮGf}dٮGf}dٮ]txFUn=X6܅\u\\̊76$tk#Ĕ됰c\*|Yg0MVọUn~r#KVọ:5Ȭcauj۪Ffnx jتFfn8KjYg謢yW~VѼ Oc^Ѽ:hށmcU4h`c U4bIWAfU4 *wpd֙=&ݔ1/yhAfU4 ΔpctyuVѼ:hAfU4 *Dz Set*w!b;HMQL Da0݀)}_g@InLTaS@Lɞ šP)E%a0tivVểUbX. Ly £šP$f Ępa7J85G[LS񺃜;x=A6Blh?6jY g;ȽK8 PL4ft%l{p1gT4& Ւpb2YlԔ!/XK!)3$6nv!pat x&9d3$ ,!J7j(RbL1 9G$#C*?aJIn>9R+s!pYLXW-r90'e)N/ÆUd6U$<8U(7e$<n(x[=*}'1KҘWpbfzjg2d(D є[i̕-Ssav$Y*BeHۣ1]C!u*1]ek޳2џV{= endstream endobj 451 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xwTSϽ7PkhRH H.*1 J"6DTpDQ2(C"QDqpId߼y͛~kg}ֺLX Xňg` lpBF|،l *?Y"1P\8=W%Oɘ4M0J"Y2Vs,[|e92<se'9`2&ctI@o|N6(.sSdl-c(2-yH_/XZ.$&\SM07#1ؙYrfYym";8980m-m(]v^DW~ emi]P`/u}q|^R,g+\Kk)/C_|Rax8t1C^7nfzDp 柇u$/ED˦L L[B@ٹЖX!@~(* {d+} G͋љς}WL$cGD2QZ4 E@@A(q`1D `'u46ptc48.`R0) @Rt CXCP%CBH@Rf[(t CQhz#0 Zl`O828.p|O×X ?:0FBx$ !i@ڐH[EE1PL ⢖V6QP>U(j MFkt,:.FW8c1L&ӎ9ƌaX: rbl1 {{{;}#tp8_\8"Ey.,X%%Gщ1-9ҀKl.oo/O$&'=JvMޞxǥ{=Vs\x ‰N柜>ucKz=s/ol|ϝ?y ^d]ps~:;/;]7|WpQoH!ɻVsnYs}ҽ~4] =>=:`;cܱ'?e~!ańD#G&}'/?^xI֓?+\wx20;5\ӯ_etWf^Qs-mw3+?~O~ endstream endobj 455 0 obj << /Length 683 /Filter /FlateDecode >> stream xڵVMo0 W;@Jv)|vpӤ Av~HIvI$$HXn ` qC#,b96_-n(R+4g#H"?Ѽ)evI/%m6knsf ʮ 'Z=N0Ҽ6#~n gy UÃXdLfέUEQ;M[+U$GiHOhSu)C"M=B3dcX|qљ ٯ'LJMiFƒ*!CX8nM =2UbHºi\]]ަmǥ? Ë3qiMPt%"dg8'{Mx*k^lz )p:Wz850 g?!2 ۴~`gʝO)BUzל  </f_I L/fk ^0h~ёyy(-K."O^UU{rE28-Fi endstream endobj 460 0 obj << /Length 393 /Filter /FlateDecode >> stream xڭTN0+|L$zI D RnUjH/B˦ilnu:g(rY9c%ل ˄g"|SBMXe&C _>'BJ8: ;&E).d M5@8#`*Xgz4*H(C2e9 T~f:Ao:v V=P1B> /ExtGState << >>/ColorSpace << /sRGB 465 0 R >>>> /Length 640 /Filter /FlateDecode >> stream xOk0:2HtMi 9C顄BD^KS.6=ΰy2/b%F=Ő2U)*?ݘ`[1OaXGG'OC<ٓz U(*Ts)#U8Y*pVGHHҪ_^? [%n;E endstream endobj 467 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xwTSϽ7PkhRH H.*1 J"6DTpDQ2(C"QDqpId߼y͛~kg}ֺLX Xňg` lpBF|،l *?Y"1P\8=W%Oɘ4M0J"Y2Vs,[|e92<se'9`2&ctI@o|N6(.sSdl-c(2-yH_/XZ.$&\SM07#1ؙYrfYym";8980m-m(]v^DW~ emi]P`/u}q|^R,g+\Kk)/C_|Rax8t1C^7nfzDp 柇u$/ED˦L L[B@ٹЖX!@~(* {d+} G͋љς}WL$cGD2QZ4 E@@A(q`1D `'u46ptc48.`R0) @Rt CXCP%CBH@Rf[(t CQhz#0 Zl`O828.p|O×X ?:0FBx$ !i@ڐH[EE1PL ⢖V6QP>U(j MFkt,:.FW8c1L&ӎ9ƌaX: rbl1 {{{;}#tp8_\8"Ey.,X%%Gщ1-9ҀKl.oo/O$&'=JvMޞxǥ{=Vs\x ‰N柜>ucKz=s/ol|ϝ?y ^d]ps~:;/;]7|WpQoH!ɻVsnYs}ҽ~4] =>=:`;cܱ'?e~!ańD#G&}'/?^xI֓?+\wx20;5\ӯ_etWf^Qs-mw3+?~O~ endstream endobj 470 0 obj << /Length 1613 /Filter /FlateDecode >> stream xYn6}W A< @]Ф.:K^b@I/Hצ9_ zPȍ5t-pBrSVP} 9@ESji00JaUʻL -k R"n^>؟LƏ9E/`-m̟%wqOl'N= ;ˬȂUzz> VAAH h tq.Jq)D)WƙZM³NմaRC R!QQ'"m|FxpD+3C"Dud %yy"%$Dfb` TrLTu&UzEiZ7j݁mDP7)i1亀|Y JT Sy4²T  o%|ҙB U>/ E:ˤJ,h5X5TnjV a+l[L`(/hlSQ$|ٛjIKÔMac `1>5(%7@Ӱװ L !Àe\J^D"u!9&J9&okGI`-;:' hs炀7 hug8e`qwʆJҀ:sz4HLgN9,^!Ld ol{R$XQUzb!4NӃr|f^U⺋\/÷Hr lxWkkׁ~2OU!bVG}XJd>Tq{8nȏ yqcJ̮ʠ@[(vJ^@NuBHϛN&LV2|>珮Č5Mu3>J/^~]`[ÿV~y~x.& _x*Xó#9%h짯4U!8y|6OeJbzנjOC/X .9`O6.D%#tQY9BseN]GS '7Og_%.Qw E-[=/Tuׇ$GY.IG ]~0VuD}~v}WTxd&'uDQ}y(+ /tmm?#}{9zE endstream endobj 477 0 obj << /Length 503 /Filter /FlateDecode >> stream x}Sۊ0}Wцʫo*]H@޺}&rl4vHH8,%xM늙TbێՊR碁{3Rɴ\Op.Pp| x(R,_ـ d B+{F2M$XL&aZh8kITUMwWof\_E]KU#6TchpE!mƕ3eA18-k]\'//,栆û`w!brNs|'NK/'"mTi}8pw1X܉- `sB8oЊ8fi%c{7=Y_xT҅bit4x!0dQvtAǡ8+d B{W= iAR8n}x hzS-9IB3~'ɅM [nf endstream endobj 473 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpgqSfj7/Rbuild3bcc0d7ef148ab/EBSeq/vignettes/EBSeq_Vignette-048.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 480 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 481 0 R/F3 482 0 R>> /ExtGState << >>/ColorSpace << /sRGB 483 0 R >>>> /Length 42341 /Filter /FlateDecode >> stream xK&Ir%_qw"c;$- 8MA_av1]ΪZd܏Eןǿ~mϏZǷ6?Goן~JRJG/?叿O_~[]OHc룥o!?oyh+r i[T3QR"O0^XO5U7ƓHʖTjAL~5@ U-u=JW C\5+}ƓȬT3"2jUT ۜ{>助 <{a ;{9 - bmhl?VH뷶BJ E{0ժTA @/FfɈo{P!GS%^}*U(hwc ._T{O\AL}k-Hd<#IBR  n:P~'QuW ׸D56oG"+%#}[zVRAkhwc %X TB` Hx|tR=WR X$)@V>_H>IysҘQI"{H۷B/3y{H /%Doqcz$byc^bM3Hh??JE<{G+?vS'i{ey-.B%5HxawL <;;hGk@[W Ɨ}ړyALS}X@=+^)^5AH:Rލ5ᬙۚ/k|:'D,!}b%Hx(4vQ E n_}@xWfo/=1YHz$}2I2t-Ve·#16 RZ^C 4Ի~z(6A,i0ty O.֛&=V /%z7ր?U_iπXFm &R5 O%#٘zJRAmhwc ^9KT]2!nQ"אnTm U]2WA%L5E**?N^ ouO]JD z$r-SJ@l/6AL6Z&=*uFe=P h\5odGՇOzzIqIz"TCb qV>yӗ;>#pdsJ¥d,ZQ *y!x evg d<0F6x H gԼ@ST.HzVRCJxл~ ' )[ _ tHDz$ʾ6{`qkXOy1p˺3 !ᘪϋ(ާPGѭ^T:yBJ4лl{a(Ϗ'e_w_>~U.g.>9ɐPsȐ;%r#"C|?ןX>c}#-D=~#ۏO=鿤km߫;}:_Uް%j;rig. /SɞZA^NrC~漑2oX>_R~>3 _/)f,(?=mB/"wF_>*3?"=Ź€ 4 ۓn>Gޒi\!)d>&Z/ƏE}???[ծufQu$iV[C ےoo9 , ClgvwQ֍g gQ ]QK[#J?ͼ%'.p5ewyq2|nx~uCL dj#]i-D c [tYʅ]un$nmq(-[~?s-yAm/mHeq[oyzzmy~- 5ytI!mPFHDڠdGN}-X3^-`ިEwEgyC!mBNjKװHlYȗ6gt{xzͨy|2(NPWT?bĠ|iǸ/-t绰6|Z.Vhks>,qQID՗EU[Z r e j\Ԑ+D-ȳmJҭ\A-#EcKC|RR@m|D-ȳES"I¥kGڠd j?}lhb{?_-#ǔ&I6 dH;iyC.\}lc \>cI]yA}zjWVo]jG=}ٲK/ /p-yѦ~roIyC.\}>x+mys|]{XK_G^.qdr:dvb)mrܴ68Sm̀@mEy9tIӂ탄nǘi)Or nuWc~qz?:n+z} ,>]RS_i+ﮞ{>LQ– rMRbɱ,]RKy¥k-EAɖޏ{-Q7ǖlޢM#5SԊ8+]6]ȉ7nR[u|ޜR (7H7tIyzz=9:bhAEmG\ҽq_*c \'Bz = QaK; 5%dkuDڠ–uEe (8}D-^1>רdx[#ta:qGio -9qQ}lpǖ Ϗ [&\T2ޝo:#.]R^1tm]GڠdˬGN\Խq_#[>bE|' i-Sk(5#'ҥ6-wQƙ#yn&nmMՏ|K1GsL/'t)%o[:tǻqtg..pu.tJ͖[n2 J߼#}~}lOIa˄ ߖoɶEɖ6y%ur}aTʉ%.޸e :^^D7}~SO"+Q" _KD#DQwO?[GECQy&Jr0joQ𧧼\VKN\Z JA.rCo&/e BNU/[p,Yl;X#(O[mIoAɖU{>ys ZփuhKХdySq_P_-E.-y;}l@7rjҫ"9JAwj8n`N (t_/[tse 䃮R)Gi;@g4[ wך(J-간mnH;kA>򦜸}lp+l(Gcxz_YTزCnXw芪NQRtΚ(GޔWQtpTn 9ûjтS k]Wޔt_-.1#cLœƆBJ /xLVUk@Ƕآ/ܴe$~ޜa޽C+(ȫ,uUqIo}\N\Խq[&G [&K I%tmyH%iir|a8t+Y]VULGŖ8KmQ,۩2:L =u=v.HݣSޔ; nقڈϛs>1/(5u:!Mw2K"D^iRmȉ7nRqdžgR(7IĆ.uIeǧ5iui/eK>]3a >ogQe#ry]ۉ"4#oʉ7n^a7gtxM91:wT=CT}ppnE `]̛r[ o=M3mh=B`2x3a8= uቴ۱ySN\Խq[|m8xWA&JЉ3 ")8L)'.޸e{:OeeOyl=vXvɺG>3ܾ+jƬW?l't}H)Ⱥ-[Y\ Ͻ|[TG Jr{t;)xʛr l#B4qNk0Vv!&8qrɾp= ?;Fzy'ծC8;{ba+}p21*vS]S Cz#o〚2-[e}wk՟%tyyuI ]ϋW,ȋ~_p[6JaF'=:%ts9Z*:%8e˹[p|yS^t_p:صhhm!̉2\&(sBT;9uSJD;):(/i℗18get-w k@WTWXKpܻ!tzvUXKMKYO_pHl!gOr\ S4[Gwnš6Ll)ȫ[;wc' d# )'iRVO6MUFޒALTè49yE5ΣVޒla~B4^SĀ$Jtg+tEy8a Oc餝4)r cƹ7c-;ξд1(-0Uܒ%݄5ySN\Խq!v7!xKN6+l8\3R*&3-c|芪NIw"5-9qܲe=>9hg8ULP{=;EUHGa+mt8}ق籥RCg,v?#/p>.&g[RQ n3--.C-:̝?Ewda֣Kj茆 lÑ)3#$se˄[as/Lu%8o#u]ԌI1DNC7nb&1qБM'R׉..o']ԊHtE鍘7Elɘ=]C<} ;qx8.[zRKL>{-h{vL7N\+:2"3Vv9T:X\EƠPG *j:r[4S֌;\0X4QȏP{W~:1q0nRnidM9qeܲɽ4 Яa /ܖC]R5f+sfl*37M9qQ}l)pw xDb'DɖA5%5U=mڟ[(zO䓶l^-l^@~\8%y8M[ Kek}z^(B|toܲ+B +oQe#GPtIS_N`CW%[x2[&';s%D ]!_ 0ʊ/-I;#oʉ7nٲN7gRK&ñ, x7C588Li-E7cK"HaKx [*.#m-*-ӽ"HyWMÑ7cq)a&qAh&52`[בtVMt% N@!.pӖ[GD*6M@(ЬPu뒊3('51[r[TTp$;z?G!WgHӤK*P;Q8, GⰎrҖϛ&r5K2p@'_?9ktIFpСK]+oʉ7nقGP}^C<0\|g eLWԌ/qptq\rT)7m ϛ]q``WeǮ˛JrE*n ]6L{/[r[,'~lo]psF[r*C78g48~vSeN7..6%8~fpoxV4K0ƱI.KpH;"oʉ7_#AtQN_=>qybG +GƤ_A'^/[~[;{xupusjOX$COF|ވOF|H#>q]q'#>q]r~+=*1givS^Ct\vw٣p͜=|Ts_dGU=j-ΣvK>mN)%F~54z􃙸M"cӏOtQ?+RTz$ZtdyZthwcUGV>,){vMNeڹ %,=M#*1p0pM0S-N)OHz7U~wv{=A[xÎ &= *J/P%ng n~N؝[R@>I}l =M%bTKR-RyBeo>>bt/z>r1$RDӄTjsJJjDfJ DdJW )A3Zù?ov_;zt+ey* 5hJ=ѝB[򊻾I%\-3C c=IRs'r1|%lJ5, qրo&?xbtLwBA iAr=Ms1*)E qVU+ d 7Z mDD [ iAz"R1-{_[c[Vnw wGeEO,f=$iE}Da/Nz|MQkhwc%|쓑k>=+9{kd$<#or$@ U މz/Re)Yen =yjNoௐz@ U7[z=j eI)AJ:me1|=Kv ݃z$H^Q0SrJHz7V&9?J p+ߠJl8MrHDgԋO)PJͧP9:1`;v_c'ВꑈP)1ϤKJ5⤔'D+{TA @½혻ӘcB)e820h j 'Dk!p/(1w1AL֐QHD>=,b#zZ4W n?du>1jjt_!cx6V 璞Gs)ժs'Dk $wqB' ۍ."3cdt'bh/F5گoyv~٤JcrMec,)N<X;ݫ`V ?Qz~RZeS{a ,nOvʲ<|Aєdb +Csڵ?z"J'=wS#j9tи k(qb2`,%BGH|YVzy%B BCh\+n{j\a2E8&o$mG" dd=$>hwc%,("lF%DS iy4Hčb$k4[J5;P0лڜE?71-_5X &bmz$<Z."Q )@J8/Ũ:a#!=Oc1Gh,o,KϧJ5ESл~WYhyĨVFLJl:ϣ@HJQ'=SYGt(OHnoeh}_  I# @J)V˨Gbh}_ ^yRm:T[yBJ4|^ --$yD =C$E61LJydRZiP nU JDWCI+Gb+%DJq )@J~cC`%c{}>k~6.&)s8[ic]UAָyRJ4лrI-1|u4ӣ~,$;pJΟz$Ȩ>_]鵕*雚hwc=H>/H<(Q@ķ~o<(NF̜hwcUAZw\AEvxI>(;Ԓ y`GSKxp '?t+. ~.v"5 6<;#]aMU/(?,e!~]\CL܏zzA˛jw`T;nض ˝{R│pT~`xBFޔt_oŶ[2gNQEɖC#8Cub?2tHțr e t=e(mRe#'.޸-9K?o/?eyTGzn:׻ɻu[קEE7?\Ee.[0rrHZeHEUjB cI.qݭ}liYx*&.x_-%y S6VV!tzGڤdKGNK-}los;߅ ߥӖvh6C)YxMY23ǼC~8ĭrQ˶.&(]ɻgPL[Osu]]JԫH{>6pm~Ԍ.JF=\YAZt 2i-cyQܲ/elU|Kߞ{ɢ.#/p.)B1)vZ!oʅ/ܲe[0+$8{ ;g{ߎR=']R)J 9ɛfZCA/EcKC)-lԀsn93AԈ%"THl)#•RܴZDd$-1,OB!M88}bEl%9.8~i¹(p%.r#Y\M]RYwq&wPڠdGDǖv0,u?H%t)9bK`^U)nyUޒuoǖr q0#岢LWS yWicBP7aF´Aɖ6{>0̄^i$? JDy Դ#I%Uw ]vv9-EQM[xq r!5 wQBgww.)O[OoQ->WE.{7UU|5- 3#_}@*liG&.^q*F<Ӆ#țr21EȂ|#v_M9qQ}lY(ز>ÖjÖQDɖ|aL]R#vjPH~򦜸{lA >e8O>6;P#(]/MYqv"N?%'R޸eKO|cp2>X DmKQ ҩѸ+tiRK֑to_lɧ#--NW=uח:ohdٲuWBygxp[FϛS|ssd`K+Ԥ#.|F$еHi-uoǖNGi(^A-1Dɖю|PT:1ʛr[ӿSG6Ɓ99(obN|J/$Gzbb$kޭX_pD-?kB#l$B%[ltErfqծ}K5/-!}5lӲ 6~9VvW%tAyAx!tE- 2iy7n2""؃/ٙ}i`/~%cB^Q]FySN\Խq?caX_ȩpFQPz τzZ|ZrNPT9-8 ^-BA{GNԽq[&³x,853$T00%dB]PHOܨ[0a匏DJla@&tl'Hy~ JIWT8^@񺆴A-9qA[,C7oҰlPg䞳(>~tE]B7,TiSސ t_e/>{U#( Py(R@Ka!e%UM ݌ȴyC.\}>4[Ҁg`+Iu-DdXp2"}b#oȅ /ܴe|>`+[\p))\ɻ"EQǏ]W#3򦼨4yaKAl{+f !.#,%ii`(]?)Ү )'.޸eF׌5%q۴B6;킒AyE+uIh8ߚlU=|m7nڲxfVp蠕U"B!:ʢ)*\T+NpW j+Үq<)6/&s×ܫ'O>Dz8 'v$zbWc˯[`'[h'k)lSY00O/!OQ􄢞 {{'IO(IOЧ7_kO'il;'ݽ#HGS/1P︧& ݏ. |w_8/@ Rc?X UzCc4.q:ܾ`>.2D^rwF٤VO3&Sb|c6&FmP,^==^b^X}InhhxF;rx`JT08Jo_&7Elq+8 Up4>D m1XUAiVM-k}}lҡgplA (XdO&#tEyriNԭ>SU+7El)CSkxa o2uEu-[Jj4|Ʀ>Tq!+8e9s ]!#tEBVp|1*t}*Ҏțr ӡ(8So|<̞EGF]3tE 9tq1nZ)z3~}ql,p!,YTزC>2Π+jhQ.8~WbmNi_5fs\}- !{C s&7^uQB@ 5 xZKJi,\}l,)la`LF9~N] 0 [t;0L@C&kȅ /ǖλw;,<@%::a,5ȢI^(A cr:C\q1Y˟^ʑWJ 61m`Vڤ^ȁK/ܴe$%9z禍pqFֽ1^ 2E%m}!LIΚ6YJȭ}l)е@,%""@ʫ.qݪVi@)]SiR;нq4 NG3oUqgzFX GQ#,qJ]BYi7gT"nfd]=wwrb(Ҿj󦼩Ŭ c :߅dK0eT"*j@:0uQUWhvq 7Ms//[P;8uoD CZBְe [֑72{>d꺎88o# LS'NfT|яnśulȉ7n27GՏ"˱(Dm`G> iG9YL;)'.޸eԍt Ƭw]|fԋ#n L;SnٲONƀg &S):rlR`K݄6(}^B.U߸iLpGL:\+88\蟁%:U!yե*"X;ui{b˓BA\}l 8LBWrl.]RYnI%H'Q)mPN\Խq˖`'+9~7 d]E!Wfʱ/]R9FoQ7~Nҡ))}-[7@(H-8XM @ \X]XZ<”E\Ҏmțr[t8~_ݳ1@h;˗\E~_JŹ7J+d]A}}lBزbrO&%t}VQ*&.JY7Ee^– ָOnQ8%[f?rHTQ\qWJ;B#oʉ7nٲe un]abevg\QWFH] \Qr+ r lc_)&\Raߓ{.#G&ݢp"}EqPۼRMDސ );>&Cς|x%WB~UWBi1oɉ7nwF``Sڼxn۠_+r1tI'_Z)"ʛrزTû[N q6H%[f:rKwZ&BAEnnDi+oʉDi'7,}qp9 k!k=^!vt?:^!`" J*t*z>llDXGSqОxwwK9Kމ3|X{rc5MySN\Խql֫#ۢpmv&$eU@G1K*ڱx[NEюElǶ!&!zbW@^'v}D>O[~sq[aـpf+OH_">qD D''N@P@8o牿6Vu0`oϧW@w% W3~wE_% ƒa@#H@ĕ |DQ~ӀNc@^v;-Qޒg0r޳E]RMg2LJ;vvySN\Խq6ނ+tͿ#.ț.wR>HRL7RpԴ#SBԔGp|K5t-VySWǖ^aMV!{~(R;\quIӳnVE̛ >(䔱wQ],d qFP uBpF [z8E] &7=^ܲe[|?}C^·(5ȷ۾{ g5*oM*|CAe{}BulyfFsZO}u.Q>ױ7%z?6PRI_p_U߅n.{-V@ ilֶty Ӿna!'ҥ>4'%CRPK%tܜkb8 ި+mRw#'ҥ}l=eƽ ^;,?Givg颒![%]/5Ki- +KB}-ă/Amp?|FJ^#KyUi [UFE[NvިҧmRQ[g*B,]Ę nwG9-7?%vǏbNi9]+*W"]71mR1HG>u._p[S[aP0zݯ.5'YQPIe,ǽlH;țR[,$7oP+M֏.==wqmhIJ;igLGޔt_-&հoBD=EśGԨ+̬xX@oRWżfC7'/-~;F?}%9C]S8+MЕrEz`v%]+7ܲmyys|0ɠ A37|&]?R=n1WMcp~ܴzb8k/T\l8&|Wԭr׌Z>}M9qQ}laGp|Y~rq\HM[4fkn`p#X\yKf[L h.&;׵t͵_~9׍ӛ(~tE}py>tݱ>ț򩰁-/>͙;+sٶ=)(K1ϥ;tҐn}[b_WyC.\}-;9~r<&oZ`V!8u#([r!]R3z q|+tM|z)ܲ[ϛ-`q="JI,E]Rg^?]CvPC-9qQ}l xo tR(+;;u{|%V *Jl%'.޸e/kXWS*xwP=XH! dqgtyKlVŝ1vmDJlJpFl<F%k>i#A 'tWpt'==q;;  'o~[ (ßINOНYzjB4hU~B@fFWos4?e&H@2I70|k@^:7@<ӓN!!1T61&$@N:+ts/M4DzMf:гKI}4?{G|銟!A~PcB6Nf6o"F"+Q'B<$\O>:<t:kr5r}I!Ne^t.|2/3$3殘BpyrV2j~7]`Hg3+1G^t.|8{ qܛKֺ/B#[m@̣ۨXy?iI_lYV;4^quWZjd~Xu;I{!u2Pd^C%ךl3W=ڀ-RHW)=M^J =W;֤Q ^v jT̯⦎~s{Pǽݘ^ʼ \N|?vwopl)OYGїwD=kc^e qay;wZMkHQG;xGE:ZqJ s;!)?;5/U=Κ|:fRq=/ɀ:@\3WGQ=S7wnq]j)s?t3QkQzWcS:@5^֪ejЊ ʼWbf E\J#&0@mD2CzO{~6ը.ٰG7>'LG߈ \rNw%zU{z{m>Q8(D_6=A5Wrkm\ڭzYˆ)͗kHҔcN3jh|LG/ \b'm^9feքMfo뉡3F9JhyA #jmzIxvQ2X[_;I054Fol'7ɀA}m;Qj n ?Pg@(SEzC1N)]s s&!z&⧵O~/{niVHsH йqQߎw]Nxo$:@٩c}ccz>HLйeur8ʧ %9BR6irjtlGyzlVy^5\-{8e0TL[6|˝2_-0F]GGE0-_ 5wڮ*3HE&FL{P{4oK2`΅/?͜Cg_+]g-PS"vQH}ȋ2@ _ x i^p''-&=g_ɶ^ke _ Q.4JZM=IQsAwuY/MĽIt@N/{i7?o]w9+'I͙d kGA#.j~='~uWLH=L/l΅/O?k wEnheVzuT[h{ =)\OMΉOo"~,;WAz~.ulOWA^t.|Sq] k(82Vzt; !1m(+ڢuՙ_z~ w;\Y-r&n@w^ibKFly ahl8ny},ֈy^ wж/1#Qt:K+yA e1uw nWۑ5e"EjHI@jݢ.. j ^j,hNB\. r!n f8Džx|AU }0<ڤ= f`<["<?R_NWo1<ӫ}ߑKI DziK1p+buߖPRK3QM;yh!y8 }kN Py)kOM_0 ~7 9L{VZg4b3ZT/7>3VzoC8 Ɵ~q1u;x{z3.a;Ǵ?B7c|̺h1/?2[ta7ϵ˹2ЀɊwߊla;e󕖘x7#u)F<{N^4ҲJ7p1$xHw4th`bvlO<OM \GZf5+Ű* ǚl&K>Fu1F<3ǓYVA!] |F(!II.^a}w#Kv#b~b=+Ve/;]`53Yb߱-db[u1FiMpYi-Ƨ$Ɗ烲=fy=mr,GƊW?b[bb׌by  +!L cl_vby1H:66<&FH7XT Ds:M[uDOYg?.s|w=swo1g^mdY.ȁ_0ΟVsm=4[?o$iwOvy?dY9b-!{~:T m1(o ݲ1g3SU@ec6t|&֜֝YO # b{Uz'Lǝgyi:F.c\у8ssq?9O C~OߐS[*{#`^~d 'sm/M$eQR1(WOuN$%`:HHc>O Puxpx T$"wڰ>HhcĩNN; 4݆ ~d$>ؐ!lCe7*T Lyi# ?NbrU#FVDc":@ *+ʊ28Go'g:}ArVL4r$qX+; RZ7:u3+s[_~r֍QZ7*?[{)c]ğLZ8F|"?gFvxÊa.I?]牢/(s++ pIL6͜ |՟ǁ| )Ѡ}I|=;q2er4B7TQW%\؁ؚD_E0}fgJa$gѐ<-ܹͥ;S}s5DgQfG>c(i"e֠t k[KQTjTyK~K Z _L#NFr. K5աR[:]N\/hpwBمXk2Ab*l/GcwF\BJm^u*J6½\hOZ##O/|*2~(tty$9U*%^P@cTTxgDkhr$~kP4L '*6Ľ\/]~5JfagyE+ q _U* Q!z -⧨19z'-)A/hd4UjyTTl{vGDSYatkDO\w.vڑ`Xj9יzԑKR0}lG{т6hN|ZU*5Yu2*6Ľ\/wgμܫyk-"EU~@i+\j[NFņ蟿uyXke=2LۋŜ޸ã֤D"5JMt&^>ׯ\G_^Kқq` [䚮GDL8 \̇>'l}Q[C?J?K,o 륿^WO5JM:%K?6붠ߋ\!AO\TVD6577QpfpRNE&e|sSIXxe('kq.cPU1aN,y8wU+[Dj2⒖6dGޙ 4 N15`3&%&/Kb{(K5Tbi |jD뭀"qgZ)O[-47W/؞m`ۤ/ZaN)G{X-:(p)>*:-k~sz"G{TVDd{5 e\R*+M_T-zZw__/}̬h0}6-D4|D[T}ELJK;rgux6G|(Γ6Fw>؈avbMSQj8[@fasoRwe7-h;O_k4Ohz*b:%}kI<,(*J!CyHn\-)pKBM.Ѫ^ ;xyi u'%ˈ#6dPw;jWi뮌Khի2oR@ya"Ypl: v`q)Ӥ`#:gЧJ /v½=EtJd^>Ks ﻜ/}G=\KJ?cE4tU/>1h)Xט5Ǯ%-9^ eءruAB8)}6I=+Iͥ1tmϓ\/}*+^1:$ÜҊ(NE4x;x\xwt )htէ2dSW[w5}Go~THQ>p) gӮ_޹XM%<.>.H<3ݗ!;k{ϸoD(Lq6mԫ 9H06F"oǸ6JeQ1z-Qp9E`1eDw#wh仞HGyQLE^hSX\/Rڮ G0}#$rr{C:to2tQxI㢰NE>8pE{D5n8|xCC`@X囫3'/rt.) ~;ħ,buU~`!:%^/f[t JeZ 'G(XW)KƼ}륯+D/xFlt81Ϥ3a\{dm\g0Zu2*6Ľ\hK 3`gM9Q|NFvx;xЌpJw:Ip}Sg^ƥ-h-8J8#Z66aTRNFņSdhYȎecnYG=dsD;䅓{/(K)TTl&g挿˱Ό[9hDdX{cԐavJ?u**6Ľ\/Nِ;C/MEƸ('(֐ 'Ĕ1 TjuTl{>/}."[5_KD^hs w|CcFU>ɥQ#*fjs|ΦJDoiv@I8J>Qjyv`);meLC:+_#9S ׍;TlxqK$nSԄCE[9QG#clԚ3g#ΙQqW/ZG|@Syj g0Ff',7pJJqtH_(>LN8=2(Wz_C #e҈NFJ=\MB9#GG9'Ghl`ENuR>O2nl>(/JNGݹS8ySɝ-I̙ q Pu*J6=\M'TGdg`h+$/(P7F'r7S-:%yjMҟ4Y%$&+2L2K8-]V+|r"SQzgo3qes,̼JB/*l7;*)}E%_TgqS?$r9[)e^dPF7jKk!q6ɔ"J6=\/ʋ~^Cœ sb&$[^ސbnKK7Td#%8ՑΗPQ2)+h;R\8~*WYd#EK/\ī?{j͜qn)pʡ9+FSY,;םHPtRp8w+qJ;Td9x^"*te8oGt98L. R{l6W0*}L)їccQt"/eq з VSYq]JrೊpzYꕇRpׇ~T6n%Xs*Q94Fh^p]b\aUl.F%' kGƫtb'©]Y+ҶAHǝ5Nv4|Qj(pvLg^bVf8Z!\#^3]jTRzWS6eZE+2)>Q/': g'8u*J6=\/т:EM8ӧ9e%mxQcQB>)W!:M㛫`55$4bROABhGPu*J6=\MJkik\դtVVL֐SrTr:IFzXgR[UUT{~s5}||1ҩR4HQhG<5>,DŽu*jz)}s5!9`:jaZ1]x*E8Amj\j핪SѡǫoULahg:({BF S(C7R@75.3C*ټ1^#7>ǫrdWulK:<ĎS;d>A[HqK)~5WHq)~uWqQŏ=#TK6+$3$2"$Q Tr5&vd^-$$V%>Ԕuf{)ȄOܐFyp2bmZyESѡdoRqy8 ~2LUGGRRjF`(*K:%?"t*Z]Fzp2@ƿqR;aJG~Rz6>3Z)<mځNΥTl{>q$*>RtnuTeq22 p:{C-@::oo~?c,hԻxGF9NF$Mځn\w/J xm/itIJ.ku;1tڣNEɆ?Ɠm2KTzdv<]|u jXc ƽ\/܂Dv.|Csn'zd,t؁N":au*:՟;:-x$wyv`Uخ97g~#c-yv ?^8f׫T?bCگc`0 y@m0E9ъ3ER_p~Ede$hކq/ׇ>3yץƇ0בa2~(wy G#ݎJ.gQ'5Kd#Wjn2bj/(3iJY@/JmV!z33ƣR6R6&?xRkhdȲRi~ q/Kuq2XQJLA_i { l9V; _8^KcTTl{>E F~k; +Bz!$%WD8Hkpk*Ft%d#Nю{ç_]dSKGchdzbA#ԥ&vpׇ>ړ>ہ % AE_vkc}ȨqHlt:5^|Rl8R]F4ѦN^ܶ,i;-JMΙu&=}sC/#2M S2~hCp4N|Z:K PשOz39"# h;#s|Qu5^EV6٣jd:B;$ ٸ Gs]|u=P;+\@; gM ~}&VO^p&wv0}k#(EБ9%.q}X=RpKgYK3>U%dS.:b\i1k*]L\թ(p9!=;Ɏ t iĈLCм]Y%pXjuw5Wu*Z7WnJ6݃d`6eSʜox= h,igSQ0zOA"t TKDZҸF=ϔz >6zzc(UVЧ۳OKiy^hPdnK}3vzOL'D*0*Z2LNlѭbS0v{\d#ҧ€+Gb$fLH:t֝@8Q'7ubBGte*bɍ:!e}s}Ƴ 90x&UxƒGD΍׈ҙpv)/Fw6$G>L2̩RE8ģK#hY4TџpKGȺ˩J3/Z.@B&,,ҪqR4LΈpWEITsGaNE90;Da m餶K!P$^׫lr vu4-5GJ3N]ech U6:U6FNj,Lb8p Z)m#3Dg>!XH񂦃1X}WP`#/4a3?= 'w~_~_~j i2ZnZHѯr@by8"N]M -D,SNGFTKB պA%MiKZ# QfcG%T'bCүY<~^}E㏙ E*q2Z4h9PqcJZu2*6Ľ\M XyhKdVèKZ"Z,OTLK}p4z7\m|m?L6NG|GoP2AϥdTl{^U5c1# n}_@l{>t_t0A~cV(ŒЧC4 :NGFܥw ~bM2XLG8,u3"nrRiQzonhɱkDY:C9rb3K + $rΈsR􊒞pW;i Y,ugvDZp0^tƣҢR_qKte<<, qNJ#wԖIJ{zDƸOe{K2ח- *J1 dtL'nr>Kw>tzmĽ\/}>^e?r?'6rLnN(Ri%^Z^3oA9Z s*9(B0#qJ;U'bC74JJy| H!?W$fD+ZpUʯ98m\իNFņ륿P?EOEs0RSkjv@u8v*vǪS1rE=i5;,YVNXqHY8QayR:jNr=6NEF륿PhR5szOEc0"ʆ)D+Z"JKSQjg?]iׅj<4ib*de};pJp[cYUd#1:֕#cmcnZ64P`Q8 G#}:>[8RV:%^qξo]9T8 Cp\!GYg֥cI{eqW/gO{imOk0"oTȑ!8 Q=]+d#?)ْv]}> ?ٷbh7r` ܂tJ,Gl{>qyL~ʿ Qq7*1XigQjLdT#GC\qy`E;%h(迅CʸKNEFA.NEGc q+Zxgq8ld+XR%Td#10ݘ<~Zn(Ԅ0:vflH2aReF]'fWLJ=}X>J'f9%†X$);81RG,N8/Id{"/9_6C|m{$llp72o|WNfĿ_D9VtSN e;9 [-B#$J9 ů▓PI(n9 ů▓PI|3𿑓(''!I|) |f{$d|D\_$J6DH }9Rߕj#'' GD%gb=]D<>w;c~v'ճtKg n#Vh$OhH^!_IEqW^+vwby#*wl>)ÜxJm:%%>MQΚC_#:u+r}k?ȥC·~Я3p}賣y[rdq_NzDyp4gv$>QE;d#/~RGqى/{0}vN)~MY6.]ju*4,[VK v3DZyF{ :ψB8(:8$toT@~BFjAC!UajIBFaځ,㰎R# u*Dϋ6/}ȧ>FECFo]҅רyC/)htClINE:뺿^'h4u(uI(UF.;8艺8u*J6=\!G >a&#藈. $*ץo!J\&#菈^J\/{ɢ7d aȈ$EBш8vD;6%J-lvpWGENJ W`\yJ zEtoF,96SѮ~)$>+H'Tg9|ܡQfD|5m kjPcJGEk^_Qgu2ARF8ӬFRmSnҧѸ"J'Ab8݋v4v'U#YjbTl{>yK)? )^WKu*J6-_ߪ%ᕟǁ x/{-p4BŎUVu*:T\/=t~yC5ΊfaN8zN7*Ny$sPR;5#d=j Q!Ji[iFgtjSF\Q!09p߅6JvTl{^EmμRkaNeE_Wb$ ҁީJa8*6Ľ\/:tIOwB-0"Z[}@>p(yYr5-ɜ⶿%Sܙ͊NRދC bJ8):s::RlJzSQzW$ǐΜg~8gQv6X/1F+"ټUAC0,"ۍxǽT}eSWthġrt~m$k#Kal{[S8;)Od 9U qv$%. &KA+Io&&ET*31E&q4Tܖq\{R)֭Ud#ߔ W#yOg>JA͈"8%V|L@bZu*J6=\Mb4QQKC[&+u5"o&<&rDf}\/}xSsv3aNyFtk^Ft sJ#:Q1zޫ?l0eSݎ2)T8qg=GIzUcTԫY/}\ TQ :ʄ0dDҘcsZMԱ0u:ޮoi.A< <&eS gdX(i}QFpsDɆ?rOʧ~ D'dS[lakxtN"NJ!j82> bɆk䘃팸vƿ&ɿ^\he^hjg콰cޑ NF$'ځ$CxRӝ7ܜƽ\/ՠƺNTJuZR@ Ɉ3qw! :pr7"0QdL aEp2W;j.5Y=dTl{>Vm'7u?V[:ie9#J dP9l]jXɨr;\'g+Yy0UFiځL:IRs|CU q/Kɖ{g~.xPceD¥q4j Nqŭh/sg6zS{ԙA}Bh9nAAGG=֭F9XQkDt5ZL q W1VN X7WǷRS 6RTDQ p֩hS+?ԵnVxDІ]cKDFOywȄMү}dAg 8Ut'-imf;[nKpݷ}s~sNɞځSOQ'2LTG7NNƌ%7q:bpWӟbXI:e˒Z)'Փé$G7̄qמ p\tR4DQPNf/><ے6AMKx8źq4}t|tzK-Xu*J6=\MN~_60Rd~_QGŪ\5.F«Tlz/9kN!j\߳>-]=jܖ h \z7NEFo1Y ~&}ҏQj=?3&ҏRQ8=FH)pR[LU7Wӧ8G$u h-%yB8-H=אo p_\Ig>Z߷clup3ڐXCi,%B*Op/8b<M(LEuu[7=+8^Eӈ?cFT|I#z`YOKy\ ?ڊWfz`6o%|Sz`F;cNLCi)S)n=0ůS)n=0ů7= {z`ؗ2ϧmWLfeE/)z`}HտVkz`Yw$D>.AVo, endstream endobj 485 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xwTSϽ7PkhRH H.*1 J"6DTpDQ2(C"QDqpId߼y͛~kg}ֺLX Xňg` lpBF|،l *?Y"1P\8=W%Oɘ4M0J"Y2Vs,[|e92<se'9`2&ctI@o|N6(.sSdl-c(2-yH_/XZ.$&\SM07#1ؙYrfYym";8980m-m(]v^DW~ emi]P`/u}q|^R,g+\Kk)/C_|Rax8t1C^7nfzDp 柇u$/ED˦L L[B@ٹЖX!@~(* {d+} G͋љς}WL$cGD2QZ4 E@@A(q`1D `'u46ptc48.`R0) @Rt CXCP%CBH@Rf[(t CQhz#0 Zl`O828.p|O×X ?:0FBx$ !i@ڐH[EE1PL ⢖V6QP>U(j MFkt,:.FW8c1L&ӎ9ƌaX: rbl1 {{{;}#tp8_\8"Ey.,X%%Gщ1-9ҀKl.oo/O$&'=JvMޞxǥ{=Vs\x ‰N柜>ucKz=s/ol|ϝ?y ^d]ps~:;/;]7|WpQoH!ɻVsnYs}ҽ~4] =>=:`;cܱ'?e~!ańD#G&}'/?^xI֓?+\wx20;5\ӯ_etWf^Qs-mw3+?~O~ endstream endobj 488 0 obj << /Length 477 /Filter /FlateDecode >> stream xUSn0+xzhF&nMl9$l8.a@r8ǐ4]YZօ.DӋRB[YZ4;+6D-`\4-4l^B~Z2Spcd)' endstream endobj 474 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpgqSfj7/Rbuild3bcc0d7ef148ab/EBSeq/vignettes/EBSeq_Vignette-049.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 491 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 492 0 R/F3 493 0 R>> /ExtGState << >>/ColorSpace << /sRGB 494 0 R >>>> /Length 60459 /Filter /FlateDecode >> stream xK-;ze׿4]:|3Ցl.\ʲK\ {7WZyWs3x0I÷퟿ן~÷ۿ7ӿo_~||OӿQ?S+tmGV?>˷ӷ>~ҿQ|a||/[9 ?ßߧgQuJUڷWu_˟?HwoOO?oS(JoYo\V}[9r$\GWۣ\\˟ ?_2"9K[?G/ |?ؑ|иG0p懏z9-鼚0ק–QbaO3*Z0__Mo |dQ_)1I^ZL,Mer{:^|y`[ٗh^t*LoT6_V^fnk_ې(o|=7s[{3}ڛlj_f[ٛ, Llv+oFڗyH^fVkoffV({=оLj7sGY}ޗ98orofNiݼ*{99>W7sZ37sGˑi͜Q^^"7sZ2Lie/Z37Sm7SZ2;fNP{37sGYfNo洏27sZ37sGY{ٛ9y_,{=i͜66`ƗIsGx9mƗ9@_[؏[tYt=u׵oZ2/yrgd\~ȢKﳅ֓SG%SF~eޤȏ~K򺢎ΐc=oFZ?&']5+T<&2LƘ&y+ɱ.9C+R#[aV\#VVZB!ihNFhα3Lv=d6:E?w$5GW<^2LVMar,IV%2CdXd$3ǒdHJkH ɘ6I&@ $A $y`$lltqM8B$?l!; IU+#Ψdg䇤dgU%j%!AU=LRUFT5d&;U0٩ 1b{$YG$ٙƺHI1I?L20$c6&0J$Y$+&I`$ 5dfpdf,LI2}LI2΅i1NScdHJ;2L;M2ΔiI20yP3IL1!O<$@5$$Kj<33 IVjFL]r%qSJqd,;#I6sF]2#dtg$XHqd<#B/IC5LjarP zo$ d!4DHI6"FHr]`\׆arqd Idh &$K$Yi$ץu&+ d!\kƯEHI:HHA,KvI2A̴Kk*%2L%6TwK!Ir -Z253dvI2.I&%D$h$%ɃA$y.IKA$yJ4B+ZbdE!KN:BΐGvW5&=NKI-LNmarnv{\{SЇacv'tɁ3Ll䚑Z1u'K!IrH 뚡\$l )%D+&i$ՊIvldI6ldI6%ɘI6ldI6ldI6ldIUMr]\2LlVM2fjZ@q$IVM2&jJU, ɂ , ɂ , ɂ , ɂ ɘָ^W$qE$S_3uQԚILL2ff16些h$cbq2LL2$H2$HI"$y`$l 3%2LVLd$IVLd$IVLd$IVLdI\dI\dLId~dLId~dLId~a2$cn?M2$cn?M2c$3&I2c$3&I2c$3H2$H2$Hr0f~akmvuC?Lr05$\3?//9ar-$\$y`$LI<0̱.f\IX:89օrVLfd g#dkX&z$s,{ar$&[ؘlarbS&'&{$ɁIT3IL$IEg\H&Mr-"l9dBO$ךҳI5ڒ&ךK$֔Mc$;&IRI$L$1I $0I $e\+N/&VK\$ɵbk$׊ӋIn#$I\+N/&V^Lb$+&Ir˰QL$YL$Y1I$$ɂg\Q&֣^MrG}].YCZr$^Mr-OZz5ɵ$GO$3I2$3I2$3&I2c$$L$Lk$j՛In6j՛IjL2VfZ5ժa2fx5ūg<0IJ&LRIkI$ג:I%m7IkI$@IrKL҅+HK%uu.WG{v qXa87y,!WG#GG{#Vؑ#ɇ,arc].9C0V=C0= ȑ$87&'] IHrt$] ɵbk$ k$ׂ;Iw2Lw\(&QLrKJ$QLTd $Iv@Jt$;] NHr䖘\K\+I|&TZg2ɵdk)$ .d $YIn& ] BH $>In&>I}f\+&e\+&VMr3ar3Zg6LHRI.d $I*I2Lt$] ɐ$>I}\+,&YLRZg1ɵbk)$$VYLr쳘g2Lt$@]Yb.If|w>2A\+~ZVY#K~LZg˵IUԌ{2c)"4R>k$Uar>atɉgsT1_]N@.3InՅfk)$=k$ k)$=I{o=I*IrݳZg3Ɂgx&I%Iv@$L$et$;I2]u$cu3$IrȼO 5%k;X6]@߿ {}&w_߷ 縭\KG# ?៾߷t} յ~dZ10^<(Ô 3L|1# 0/ؽ~/x"~b'֏_a|7|6wo~~υF\viڏ}7~aq$RAG ~Acs DѺ* 4`%=;^5v$(O` ƛȠ:3T%QZ:NQfp>ڃ^(xXy:l; 4qh$`> h=xLZxTX<Kr xVeԥ$`ƛ$Rm0S7IGI`bb?3eI1硆4аbڲb{g1!t=fLx1[7<Oq.=n$RPț6@I*t$G7Y&#d}䛬|#>=|c};NrF}U $p$/Oj@nFOCD^EC gXգ_( {ԭ_\OG~=/ɷ[ٓD(4ϟDIBm{6ϧD{_zOAIj=Gm|lO!b{B|#l7۞ #DDfd50&tLu}lO==l|#_֣n jm_9psli{`+N }?؄P@>P/x].T狱A?/Ej0:d>|4ͷ>2?Mm!ߦ@/ɗjoVoV'j[C~|8͗4_/Ne~;͗4_gOe;7(|~7|Pfofo֏x o&o|)tO'l]q=Ij$`O>&C?{|o\Oo7Ié?XaƩ?h7b&e5蠄tj< #ci`}.](oj< fOOc #ߡ?#ߡ?#ߡ?iB' jX='|c>'IxD6a#hHS i#m $55'bkI5J?쏨А+2%P LIx]S^р@b|c`qMh_MM:p$#9ikCCx;<t(mMDo\bq} =54dn7Sm) [5Odfo$Sru5߸~s)ɹpOFJUjJ'|zBMo\OH D^4VҦ?gg- ?Roo\__4 QI]SUFJIo5fGCC0Ϧ=48gOB:Y/#q=[/'Fn- ҭɷr}Y\lCh1W]=eUѢ]eu⮒Ф+'J-Mܫ_E ^̍-*糾 zx=3XKS>wY!XK(X F=OL^'YZrG i*w/7bm kEbu|B*VEFJ5ιXjz4_wNZ:ecm JȲ"2쯐?HOLVQh}h&4uaM)hY]>iY -3/6GK/:o\J8Z|PhZ[Cjh#8wMq}44~|eii_qZ|&UKs&߸^?Ks։5Yo\/M!륹1[, "oOKsY+ˢG7XH5L¸~J :|in.SP;*47oذ470>4~/|&a!E70>4eVY 3ř%5~@h(q}5I hR\3+J6mfliIldZ:ZYSg-謪S糖uf_ig[ﰿ5+,oX$Ş55ܳ|5䳂}Y ?Kj;llib}'-}C]@[z<;m;-a.- mkhqhz4`4+/6K -\mCCHhkP*ZLp |M\mC@KK4+1mkiWfz3PD6qkp_\m.&#r=ш =DO 5>5fKCznMo\>40S}>kqjS Omsu4ع[mioݎO[FoKaƀ4>4'ع AZd]R@  ܉ݸ; 7PNG[ ;/۞Oz֐:'.4TE_n2ߑ8^^#2/7YyQqy"8&e>xY;wo)š]ob2~x?L[2}2owÐ2ت<ލBj(_Fӡ_קLe pQ87 ~υ/_y W4^o~υF̯iy~υ/iU7 nw?.2 npǣz~oy NGa?,}7/#wC|7O~\:ss?*|7.wC|7wC2oxN#5iqB_#d& Wy0I#t\]-8M}.ՋwI; פ]Xk.)5i|7t᰼!}> פ65iCpMf&xqpwFzN4-݃sX+_Ay 05ipMsM\92\vM]p{FMl7Y7Z>O 35=7BRY qs=: 7=MW8n>&<2.Yt|]i\RVrM&KGzRr}s)K=\7Ǿ1L~rMP澹ݹ?Gɥ=QKyi{p)å=q~ͥ 2C\p)å{<\p)åly<\?}s)åo.emj8OPߘR/+o?xC\O&.7euMGa}{X_ ?a}pO냛xZ&7>RNKx\Yp=.%{s)cK}s)>:7񿹔]wgk=6W~~Wo~m_O/|kQΟj?us?:٫'b[|o~O7W]r't7Y?rUMO/C5_ևf|9\̗/R7es)OۃM뵾M!~^z8o.?p)r\Jͥ\JֳͥY~87}s)97v#n{K}s)o=e>\f{zȷ6KȷVK\ e;rMߓk45i|?2 -Oȗdo}G|o}|U}/W7>OeZjU柹 |4}=l_U}/W5>\&?n7}/Wھ\cs)~\Jͥd\JG6씛R2m.}nЏ\UU}/WG~w|U}!WU?rUqkI~z]Iq=n,פUկ\UUկ\UU՟\U?=G'pMjGUmjU Wf5\Մzz7Kyܩ?j___sSp~α5ɮ?K W\լrUue=\J֣ͥ%ߡ_ݚ|~W~W~|~ɷ|Y6v4^m.%R^m.%R^m.%R^m.C)hm%ߦTKMrU+WU-WUrUOU\Y ,niKДW4672ֳͥL ,NiKpJOE)])ss)~ǍSR>t-s)cK\yn.C8yn.e3orM.~ck45ivf&|~7|&ߤM'ߤP ,hkrr?iGlj+|c=|&XrMo27ԿO/\~_~.ک_h~ᢡ72K&pD\8n pC\yn.CSr?:z972˹SpNw_w_~~~_~_g\X?R>4҄&XORz:72ӹ[\XORz:7_p=~湹]w7gKthR:7o<72׹ͥ]oss)Yo7vs)Yo7_p?/Sr?/Sc\JKg.RT8|p) ΥpM*|>IM\Ko?r)\J˥Կ\ʭo|>|c}?\ʭ)j -PgMKޟ.eG˥cC˥j/R-ȥ?r)OIp&CޟKД75NJKYY˥j&\CݟS q7&`?7rk=G.RRG.RkqY15{E_Mqpb?ܣ4DŽKR 7M?rĭ&R?\)R7ͥn≖8pZnp;ZnbSMh[q?;CCpD#7Qr\J6ͥZnbQMh`s)&_6ͥ`s)\ʇh&hMrrϝx;7qĥo M\~7nq}\l.%㉛xiE 71&&u{r5BCErCh&Gn&&џͥ`s)|>\J64r}\l.%KĢh&_n?&C!?r}\l.C!,hY 0=4~\S o u{R8哾q/=n¥_n&~W.ou᢭냥V4\u=4ŒC㇆C|¼wm ?r/OrgMWS;OdP;p&ZPh~[kh~U ׯeu{;OCr/Or/Orr/Or//n.eOor \rrIO\,y_-\?%\sJ^7:+ׯ\_L_c_WJ~/dt|}F/ԏ_^wBO^/HO\x~_wq(~3ׯ۹~q{g>u@oW/zE//B!_<}x8S"籁 ͿBB x)Kx)|,C6LK;fˣ=~cpϰ{5uܫVgFSgkSᳬWYeoBs%cʋ$ /)Y^[5,/#^n /^ؿç OoB2}7_u|Yfš_38nwpn ߌBv08(|:}>8|.|}&/%kwcU!8 ލ< ?w# jr!x/|7}/M~Ɵ\nnƟO\n ڏ ex3ʫ—*<_??~L@ ßB@<>MUn}yۑ}4sn IpФ iH@#A (_o? eadE߃QH#,GZ{Կ{bck<}Zcx}5<K^WՍ Hk}o>@i|Z4>]:;#3A[4j=heX<~xH8҇:wgBk),ZvC룥tHk7=DC'=G?~-zd =2;Gfr葧+_v#+ nw 򥣜G{xOk4vS_:voR|j-WO>sfΙ:g9%ca{ؾtNۇ#v?ÿ)o3't99=tsv{} ?߷/p~H;>؝t H;mL n"} tx<ӡ_㡟v~ {~c>n"}#Ge7D>nKC?z"}E#KҔWud$MOdoxMAlllq=2T񘥟`#yeXzG~Zߝ.[6UoOGt٦A][tn}eA,HeZm>2Y@-G㹘o>GȷZV#b}[|o~oq#^7W#Q̗_̗_͗_7oLjǩa}1O /J?%#ZOO~zu/t٩_K~.; ]mzoU_ΟI~8ڦ[@エެOm{_zKmKɷYމk[|o5Oo|9ߺ͗/_ Ͽn7[?f'l䛭|/c7d䛬|Yzm>u=|'ɗ/0_!kH?eO9_SW^랯C,.;,ې.;,.;N.;O3 [?ta]vtnr=7r>Oe}4߸>=6'_iɗs/4f{G4jȷVCȷڟ~o=-Gr{/i\oϛt|Yz;|pn:o=MG7&ۓl{l|#==lO:jo7yH?Bt"}Ȥ#Ѝ!}~:m=1MG=ȶ'F]6##Ў/-JGҦ#'ud&D"}q>Hv\@;.G_|o=s#Ў}W)Hv\@;.G#ЎhE"}q>79o?3o7G/~c>Hv\@;.G#ЎhǾv\@;tdۗlґ/f'dl|%dґY~c~eӑuY7'#ЎEHs~*l{>tN"}蜧CEGUtdۗlґ/iOg>S櫺U~#gueOS*J?t~ ?#'t ?ɷ>v'nm|KG}_:o}'#o\?fo|ڦ#gtC mDo}Oɷ>V'jґڰo}-Ow|~ȷ>'߸>R2ue_/_7_nۦG2?nz$GȑC:蜧~s}齶n4"}8?S?9C眶stΩS?{}_+}8_B)ΗS:'4_i\Mek{'Zz~|~ȷ>v'_vHՏ^HՏ^ۗky/iq?*}8;{ZzoWCmhU~~|/!ߢޫޫ;w[s#{E"}zo>H2KS~70ߤޫU~~7Gz~:t^=C~Gz~d1J[@u"}n97=2s#c=7=2s#OHՏ^HȩS?#~:[@9=rGz~zd̿GOrnzdG||nzd̿G{nzd̿G1So4ܥovoG#nhEcμ.!ߦYt"}m>ͶH^[?G"}zm>viCU?[CU?׻G#>HF[@-GѺTm>}v5KߘH-G#f tYwJ]rKߤ..7zl>"=ͷc KG=C|ۦ65tي.[c3ZlR@CcHM<}Se']V?e#]V?~M@ߛ7}oG2oz$G2oz$G2oz$G2oz$yGs#~w|~wG~G~ȷ|7=|ȷ|7=}#7=}#7=}#7=}#χ6oӏSH?ՏSH?ՏSH?ՏӢUOGUO'4mz$G2oz$|#N i}Di>"ML\?O#T?O#T?O#T?|?7|7nNe?7fӤ~zɗ4_|Oe?_?='TO'TF]_Ch~!=yz٩e~B4:牖99Z:gW5c#_M\=6=r:t?Ω?Ω?Ω?#ߡ?#6=(S ssv5tΆY9 Z:gVȦmΩvoKKԏtNHԏt΄Ω?#ߪ?o#kWCljh-sftΤyɷjhME[GE[GE9'S9GYGYvK.鑹Y5ˌ&ߜ.S =rG5Ȏ&ߤ?M#ߤ?M!ߤyU=?z䉖9#ZzdWClhU}G^N\F4EI_!???6=a#Y6=I/c}x#ZzdUC,hY =2G|ȷ|ȷ|ȷ|~GGz~G~鑬͗a#Y6=a#Y6=a#Y6=O|ȷ|ȷ|OOMd}HևMd}HևMd}Xw7tåG~G~^4'P 'P '0mz$æG>lz$æG>lz$4_ևMd=HփM<#P? Ƨ~ÝCS?N4wꏯO-p-pnoz@R3Opt8S:)}Zn~M5=Iӏ=Hӏ=H3{M+􍾷y]w1RK 6Y 1C Cz'C.s"/784bq8dkߓ~Y ,+^x*\3^U o5P^͵^x̴~X>+yUi9O`V]rsrw=lnsr&[ݭwî嘷xQw++[2|\2 _'d,{ܺq!wxܗ1w+{3ڗ!T&uڏ=~J@O_ܭ͈oF\2N׃ׅst&ߌfw#q}=yu\^&e2n~sOe2BEٛ7neoȯc_SY{ /vv?p=.~qvxe|3Í7mR/%xemmΧ/-r>S\>1WUn<[osfpX9nșH >Ԝy9.(X/(-ŸOWkC%]>JqSK&<L#ғF]r(vJ2}Oǟk[%]|.֞KvJ$$zd<].dr+=xx#Iy(G|AYs$lJb%Job%'=ZI֝ƌ{NW|W\HzмD|s@K&aؕdyj$$_r$0(`I~SKFiD|T\2SHsK`($c% a2 Fx<\#IvsWJ`$KL-IJEג$gFKRZFݕ$x/& =a2Fl$.a2I4.y` 6ΰѵHҩElT/E[$6\vIZII1>]Vl)O$K6jaQUx"I6GK&d,;d& 0`$V/ɏϰ/uI_dI$c$:s/ɏW~ꑤz$ޥI5G4ꑤ>%Jm@=tO$#IE`\&?a3LIeDnY?//'G$ޔIedG$c&i$3 d!L4DH2I4D AC$$c%iJpoTW;F%ױdB%c_%;҂< hqs@kqsXZ$Եǹ$5$ybJj$J=K6d|RJg\ױſdV-Vqs &j&٩$5Ѩ$5dflLq5LI2VfIR3IVj&B$']3B$']78IIM2Nn u\WPeɆ I2S3I9M2Q3I9M2Q3I&@ruLL] ɃI $yP3It$ B+4Ӓy!WZ\[by% =C809uo&]ar.09hhAf|NH2NiqNvd\Nvd,$cY&h$}J&hD&Yi$&Yi$+] JC$YIV"BHI@H2'O48O48O48O4Dq:&h$t>M2I&"DC$h$ AC$yI4Dqv&yIdEJsOsNsMsLs~D5=$xm{IC% MacJG}} i{'{dZBZ܏H>Z':8! @ % ֹR/$d=`8sȖL!SdL$^MIDQMIv"u߫I6"ɘ(I6"FC$ I6"FC$Yi$+ d$E3uЛIƼLr]3f1o4$ch&Yh$ d!4D1o44DH2A4D1o4L4DH2AL4D1otyd$c&ƺ#Yr$ɘ7I4D zջI4{$ s7z$=4\$ $aauq@_uq _uq2F$^uqxG$ s䪏H{V}D:8y{^vqYuqWI$; d!idd!4Dd!lt$ d!lt$cޘ&$c&$c&4ɸޘ&4JC$Yi$c&Yh$c&Yh$ d$Yh$ d!4Dd!\w"4ɘFN$c9M2$םH?M2Mג4DH2$"ɘFN=QL2fj13Td $cf&3C5ɘI6"FC$DF5FC$SA5FC$SA5JC$YIV"JHI@HЅ3lTL2f14dL$c*h&SA3LC$⫾%i$5X,K&"8I&j&DH2Q3I&@5AH $yIt$&n.K'q!o,c,YCmX"_[X.'{Ml^zd Q3,0TF S_ =L0䠡&]arP .aS6X8Kv$ &&3aqvlLqvlLqv~df~_ILI,LIP3Ij&u 0I ?48I;M2iI2S3I&j&D$$&$5z8 %j&ɃIf<$j^I3dgט$;Ik$pqFl뒱$j_|1HE_d8#IX3Y1Hm`$6Hm`}-y d&;50I+l&1IIr0ɵ$I7\Kj#_K&d\']_Kd\k0<[(vVZϙ-dl${lf %E3[(٘%[(NI+{];[:#B 3=r7Y0#Ș5XCcPM8vZS ^j>Uå1uGG]A럂9$R<+߹R,,Z-+淳NyoR).bC/OS{_`~ 'ط_<k*l*iSiϸs|xߐS/XvEڻ)(;?s㋦M]IWS[ @(E)pCjgRʨO *E"F/,6 I>[ \R_HU) >Ѿe|Ḥlg&I;_`.ͯ2/~ Ǘc [Ipb?롟U_c1_w[cGco//"~X?/ {P3Y(C޸j51Cq|P|mLfJP3Yc 6~I83i(opϬs}[F?kA 1o(á?[g 18Am 0߆6!O_߆6!xo6xk\R~o,J_pai}+Ic 4DQd_L!2o=6|lI!r'?C'cm [}mؾcK@"2ۭ̞'"Ub~ioz /ķݷ'X(lm{?xd a04jx9 F?l B~F$6loؾ0pHy Rxl G$*,ao2 Oē="xzHn;i6Du@x` /5 Dj#xH$ D-߶=JB$KD/I'K;d AQFy| ڴa([g#{Ļ'Z"Q`K' +S [7Z0!Um7Z1^z2(xԎx 0@P.]C/[/[=eMٰPfbRz ZVA¡g Wvp\E,ȅC".dlKǃ?|-|ͦdfC͸~p3sn 8 qnY#a⚿G>⪿W=!"ae=fߣo|7{=^}c>Q.&,7Ta%0B6ܳY|6/lgchw}6Y7>guZ0?hrUUO ʕ7 W빫/cW߸kЗ=^\Ҏxɧї<^\-{y<^M˗G<.xs/cAM2b<^\?ģ?!5lQsE;"{97v<^\92<896ܹ% lsϝ=ϩǝ>\,!ʏ8_tk0R;8?BK.Cg(|[eٿ0_|#?y>ʆl^r!k?DB$z?LB%l/Kojq}#QO"<zoVG |$ER(\S}8\T}H\U'ajGYf58zS̞y>Aߤ?Y{=I|O^XaA-m{S^ǖ״?{7eO^Ŗ?Pg%lO#O"<|x6<gq? H_o}a8L@ #_C5|1ְOO_ȧ?|E;mC O,O0з}d󅿃9q8?8#:10OFOJON_Ҏx,>ӟ> ӟ>& ߁F>~A`qq11t>m}ܧ?}ݧ?}'Owmvӟ ?LSB>/ )y!߇J )E$."ҟ?ӟ@Iԟ@?)?9?IH6tk~`[`׾XiןD@_C߮? ? ?Cߦ?m ??jrX[/H~9EnWE[E'+P'-PYK~q?8Ŭ??%#/H~9 Fˁ0_2^CK'@0cKԟAA[3glOOgLacSp'ܟ26:8-O0i[/K|?Pxu+xap]]']Pˍ|rPMd^d^1 ^yO!6F>XUd }#m,C߸_X􇾱XA߬?͞fo|7΃p11?@#Cq<p#r11}#r􇾑?9^CAKʖlT|`-+[?pec~Y[䏙EY[܏,-~dfp/f7.2EY Cov}o|з?6\o=/1|$_# c#@/`/+䇃~,o|з?f}o|7?f}b$P b $p b$ / /G_A^/!/t<Pga-vSw<qAM6ey79|q 8ey7qq :kIo$=lS{"ިIzo8a90IQY̦ѷ?6Gf}r}w|\}\}їp#oz>b}/GfGl~7?&GߤGHr= x0I$~K{IzRҸ^e[ sn/йiP9M<70^sa >71pIێx:)ˏwz2r=Ne}o?mƋxѷ/X?Gj[}o?-̟9aN2_$גu7&Cd}e{^e0[nep.Li^%.0ݟ.quq.1K`ݰ?u@uY׍h]_%.G%0'+A.xзVAj<[}xf%GQOx_I}cJ=}ǣou87!U^ǣ{yd@hxvz`+d< \PYL`v ?PMvgVX=?q'/зh|=f/50~_ԗR7*ر\_zXN ?a"=A]m$hK0o ]V=4e .N|1SQ~ 2U |P>XУ Y0d]/sO˟=ZSTsT_YO^_]_`~u"<}*/dA/^c,[dkwɂyds ~Ǭ￸ȂsyO_}E}Z6cLר*>MGc_o&Q0G`Q.;%PbҺ~i}Ĵ~oFؗ7ӽ(zi:ĈA/:FAʧ d"C{H?M##v:Ǐx?4}?c0^[?k8g֣0_1 _ZgV?ά |q:Jcw߆6 ʧϣ/1 _Z c{;mՄ'm-Яp~0|m"A mopsX?[g׏AYm c%YU~} m ocp}M ~xL#C/3 ;rit^x:zI:jAC?OӶt*C/I|望MIKMe%݄z4mMhhC7O}e%GnO$vd%|2n DN^"-gn)}˦hC/Ǿc^~^҆vKKjҾV^F~3|]7zt&5B]t@h:;otl%iGCװzIFe;tvzUd%(赤OFՎjqaN'ajC?`]Kxmh :~{7^ۢ-Tmh ~u,:B^hۦSݽ}^^3twB/P; K/Ft}R@^]`og%ob^W>t'%?Y=?@x<Gb=}z^I}ǣoxM)kS]T{<^|=ܪʧz~b߫.e%IFA:>K/iO>lߊxQ}Zz YzIYzIs|}j,Y;G?зVCj[}E}狾17fC_{U|iS>ї_՗_՗_7U?lz{5l%룪eT.x]?V7^cϨOuTճVz Yz Yz YzI57c>yUfBS}F\on-\M}S}SKxѷy>Է?6}/gW_Ϯao-Tgm/leշ}/oWߢ?=C߬?C߬?C߬?I %}o*>ԗq'6r>^CK{QtS?cKߠ=kSI[Tǒ/iu,/bVǒ?iu,:|j,OX=T_奄]E߮ѷ}狾M䛥ܯ.%,u/g/K}GK}GK}ѷ}ѷ}ѷ}=K/)槥EVѷ}KFߢqo/%Tg%%,,,j]nYz 9YzIe\Mz YzI~Fz Yz YzIFz Yz YzI~g\{}#ߍq#,,,,gK2Oz Yz ҮߨV_Q;SnK/:a7PdL^BFnVG|E}Nz fUz _KKKK2Vz }#J/7-ƃxзAb<[}P=_}#㪾㪾㪾㪾^^U}U}U}P=ުy|l~ꛌ}U}o2qd%|]2>}M}TzI67%7%7%7%~KܯJ/If2P}nƇvCn|ۍ}/zI2%%%%%%%k%`o3>mƇзVCj|[}M!K/IC}o܏K/IS}2?Le~0՗a/T_懩S}o6>Ƈ7fCl|}I0>ƇЗK/b/š0_,e8拥ǖC\x?Ex2^}/C&t`|b>^]"K/.\'KMlM|',|"E^"K/O@KSd%d"K/Vwt}Yd%,Ez@K.%.M{Fߘ_fRߘ_@64зv㓮a|5Oo7>mƇз6㓮a|˓^˓^kdlI/I/ffI/ffI/I/ƋзVCj|5}o1>-Ƈз|||||}cE}cyKbE}cyKbE}o6v'$/&Cd|蛌}o2>MƇ7$iC/#ޘoooofUߘon=^m%EJ/1;$K^bKWz$%7e|+?^,m%ϡ0z ϡ0z ^ZJ~KUh,]c_x0>}V}m%?%xw/E_C/as%?^s%?^s%?^՗Ko3>mƇз6c|aO:I1>mƇЗKo2z ͡0z ͡0 e|3a6Eb[}o1^-Ƌxѷ/}/T_曩7S}o2Le|3՗f/͔|37&Cd|蛌}o2>MƇ2>}/CЗf/R_u;[N҆saC/a~9C/a~Y/,㫴k7:O~KAY'}ti|aAfߢ}'m;Ez _{wb>^Y%!P^N!tmnƇзvCn|wb^~7%Ј6!:Fߘ?j!zQ^EYzWRj<ҍ}H72Fƃxзt#nd|ҍ"1?1?H/7%zR^hJYz _CKܿ-#f%ЖMo+o6~1=q1e%|M1K/q@kKb^i۴'75|1K/7I^)K/{%ПK/ =СvxG(efMg7oˬyg~?C߮?%MC_/6/jo6}зy>[A_R_R_tb[hny--7?fGl}or䏥7?&A_R}UK}w~( vM\.ot=ulrz/%]n_庤\tiA]ntiA/tB/tB]G<ߨ*W./툷  iC+<}F[ѷ}oIofo&ߣe{^?Vׯv}ciC`Sz_.ޗkSss}@?C+=1X?X+o}K 5/v>}E} /^}ڷ_<}eO+}+6#wL}Eo>J +woH{mE_}E_Dd_<#KXʁc.k|?6-{8voK x)QyڷGw[ +=/t7 NW;ߡl/g41wݓSS +Q1x?wؚiNo\/2F×w"ֽ~qT2~ g/TƏxo_U1Z?R1_1D2~ [~nN緡8 ב8c$;:gL+87/oM:(mopbe~E#c[׏1xW7~|ۧCmo#p}FO?hmoCp}yߒO~׏x,S~?χ7~^ xo2U> XDz}ovv!xo2_2tO=IxI8o;KFh}3}v _Ԗ׵/*2^_ 6v./yכCE "px1Td*2^` m&҆37O/06&(J2qxTd_/0M^< H02`,ѓ~/\26̟ cgڛ6̟]` 0^a </C)O|7~v}ѷ}'B>9d_r/䗪x!TmGVѷ}ѷ}~xƃU[U[}o? B:x2^_B:WS_WS߸M}g0t}o6G_[Sl<蛍}o2:ƃx7&A__W߸_͇]}ɇ0t6 -CghGǮq-%/^Ƈe<2tG1:a eEd Ca|;}0>Ƈз o/0^ׇB>X>/x! {/0^Ff|2t}o3>mƇз6Cf|2tOo5>:a/% 0^B?azxa>8xa>X[}o1>-Ƈз}o1^:+Cx7/|1/a/0|6!Oçx0.l:KFߘOdy蛌}o2^MƋFd蛌We{/|3|#~- h7OF߸</1=/1Mo0~ʓ񲌷7Ma'eo1ʓwܘ?x #02Ȉj~T&7@xjf:džדדדדד27</ Ta2WFѱѷ?6d6d;_F(iT0:62?O=62_Oe>멾χoLd0_K7vc*'io7~ƏO\*OK|З0^iTZԗ{/Rc/R_量0e@emP (Gj{lV}o5~PdžG "Sre@%m⹰7uo2^6̥-kÀj2_Աa@y>2_ˀ:6f}ˀ2;xUd*2^`@=wTiOJ{lPKƈ*2^`D/06˛-e2}Ao2~PVߧ jjÀ2|77{/0 *c&ބ1?/e@ c3ey>0^ڍӆixY M6cj۴7zcVm0^b_E}烾w-m 0~ƏOfUOUߘWUߘWUߘW=cJ2.mU }6}c_U}c_U}:6-GƖ#cel-m[6Gf󽌗 ckj26-cēel%m/;ckՖ5al-㗱e2ely>2_ƖÀƏO806-c0:2we~2߻>m6㗱e2_ֱal?fGl蛍}A_C}al-l}/P_C}χޘ`e e>ꛌݘ`Et>a?^Əe{/>m#S}20 C|(b2")͆Qh 3af2̌Wٱa?i0(/,evi0 ,a0#^0+k0Z/ ì0+k0^f+0̌}0~f+xe Gi޴aUlq.񩍁VK}[K}6 -lj0ی}o3^mƋxi\3~mƋxѷ/ֈ7oڴ7mk}6̵5kߘkJ[/s^I}Ԇf[Y1^- sxѷ/Eb蛍}ox siGbo.o2 #.aˈa-lMS;MƋx78Ed2OFɈ3>}/CdE|E} #jÈ+ozÈKuaˈ[0& #xe8g|vヱ2>q'#d8ヱ5#>o0Uel͢ΌhO0.lqK;}ЎxGFa<;}0ìvAn<ۍ}zo7>ƷnLz5e>jԗ0ci|ԗ/MS_曦xз6Aj<ԷVAj<[}o5|ƃE|՗/EW_~|E_F_惮]}f||Yoҿ >o mӿ >/|/{Ԇ7e5툗|=da_`đqK0ȷCF??q7#nF?qOo|9e/ >76oLm auG_Tn | m6Gfی}ST47Tߪ>KYKYKYR_R_Rb[}o?Ƈ\K}[<}o2Mǣ{韊e+˗=r*uCav2KY/ʟb`8,CS./ggܰOLXh?5zu %xo*#_ְNkl:}?F郭X剹؅J¶+-inEC}M "h72y+uk3i[z=]ֳue[z[[zr6/yцqd>0_Ƒj'R޿%9̮:ݩ?{cvQ[ 7XwF[[%Uȏms|jwT77nE&ƛdv||SzS~MgzU[{izvjm?=>n]7nݗ=v e?QN}xor4}T}l4u}lˍ߻|ZSכXmXOorB_W^0G=azݚG߇=cQc7}[}~}lݚ>9kucw_>^a5GzijfyXjɿ7}X}VKvQ5G->o? jZaIyTQuq"ۯx<;8oU]{9|"+=<9GiRj.L^wdSm B#q:{ u뮓TAa5ރʷ 7uXuףzPRw|" }|Yg7}ݛ};{ftvJM<LknCgOi%q/ٯXwyfgw_T޸vv ۪&nGgQݚwv]/S7礳[% u3W;3v!fw_tw%M=ljsn|Yǻ]z}S*l|'K.o070q[vnXNު}3t߫mݷbֵNkove^uم2}7vߡl$NȞv;v9e^X7լbRv<ԭº(o6K(o6o c}vvoM|@%#+wdX,ؑeYܑ%YB,/4,mhl?x@MCʻ]OOwXvug0X7Ц<||nM%X`/,޺\W6¦C tX{mڑU^;G:ThoP;f:YChEfh͎fhvq\/" .)⸇fp83KWN}_ݻB:,sW/zhVÉqiG9.:Y\{SW^;?#kvdq~4hY%Ь=4+x QfY{hgYωfqO4l:,ff1&D f^B^B^B^B^B^ArÓnC*Îgˮ0po]>{a]3cpoXCެa]S!]]/֡]3î}iVVw]_ό]Eaս5Cb"a{[jV5z fBJdY%Ьr~uBڅf{JlB=ۅfަ]h`l׬]h34e" 2f>CDBDY,gh343434۹%4fKh#f h{Si:CXu@:Cj{z]ءx0.^tx]moй hb:Vuԭ[->O:zV~vVuxdfi{h2<25CFԡYЬuhVslCJԡYChV:4+x+hXA 4+Y\q+hejڬhעmSuxI-Бa!X@Bj{b(wG-Z]~סX.jC.jK:Q[6;O@7b]:PudwtA-PZC=fq74_kh~afqM74xB5hwd"M46,Dn2Cg2C,=4KxC,/4Kmh%f YdfEv[hlY䳅fq,۾m ": jQ/jTK]xc@t+FtuU}95j{xQc@54Ҩ:>ը{C0b@ ڑ|6b@s(iT;X`lEҙp=[KiT= BƅfY{hufY_h9Ь=4x:C}pFHhHhHh;W v> *BFBЬ=4x*CЬ/4l$4+x Cf`#پ?o5jGFNlglglglg[5vfY_h3X|fW͓f Y_h%64KxC Yپ#c&ƣ`1F(hs(hsƞwdcnTX&>ЌZtVx::lVxw9krYר6Ш5ҘhXPs [8/4{|p:NB+|:ׂgVWu@+|7? -χfP?5v(R*Q⚎O?5+<řW_zj|/x:U<ƷgR0l :}f>CFYl_3YЬghgЬghV34U *}f8CBٞgF}͌f{G #gF}ǛZxf4+nf4wѦnə,gh,Yh,4KD]]D]D]D]DЌsl읜`$5읜`ܻ;{'g_:nTX5S[3|97>;ڑ bi;A,mG6:Y;YYB9f1+hV4wٞefE=̊fsV4+$>hV8.4۳Ŭh9.4e DžfBqY"B ]C1>ؓ8^cOul;`w3&&5Xc0>u_ˌ%5vͽni;s;fإ77|v;f5Fj|p64JGbތu{f.u{2XO]Pk R?yGHcfP?!=׬?cgcO?@ ? @B~;Rw!@)vKy ^0@-i|ۦӎli|ǧN;3(@ Jc}qeo d8ڳ=l{i>}ig|Qw@  $擢JJe+qyRv^ڝZLIQQpOQgLI}O7^k=)/GУo-?u;Իk0>VR{m{>ع>c'Q>^,+XGC־I>׶σ#*#1w496|8}:ܽ}Ƚt>r3xrY>m>e"}dp3jrxoou{om>Yep>f- jx{mI}826KcHIˀ#&/m/}Ko}2ᶮ7 zoqg{5}m}Io}ݢ3РUs*olRԥO*P0~^mdh?,OQ>6ۆ5u>çw) RT(Qx? B<`fu2A6ST?!GĜrynksҭݺΦW'jٟ:8Q>JUʆī@^%ե^]: Օ47iykӥ]~Vwvn3ZfR_S NU/j V f0  U  ?V*8HX +{F8L8LJqEIwEIG%X+5X+Xˤb 3XkcMd,. XèZ*Xݨuxcu (Y$YMGȪ: H_JV-wCI %K %JJf %dždcCIyC䱡屡 dž'جZAZ tW,fͳ*5YdQYc5<6Z ZcE*D/D{lPHbph5 KFǪJVOa 3 dc)dRQQ7$ LPdMs f(I &82 %]ɮBd <^,ERɁgJ^veWZveWb" ښ3H[$GiWۚvaWvaWvĭaWv/]tUN]״tM{B״@tM{;iksp=ko9wquQ]quY]qE~gR*.emH)e2 qKQ0f(9lJƬJ);[RKvKQ/-Ežl%R(BƢ^B[ YgwS(R\Ҭv_[}o).-D.~9W&A&{%c> {f>V\Q/ .EH)=|xpJQ/.gP^1yu^\B%{\E{qZ:*OP\ҽ=R+k3(9O?yq :ObzbS\ꂾ (P_\b)݅}qJi1ɥ${1Qr)*eɥ&_\ P8=y9WrKQFN\d.ŝsO^KqOy Kq/i.E鿨/5ǻyi>is){bys)BR$=r.LüARPzow\ ܹHs)HRf %/J>b,~n֪=LtIf0&&Х.lơK8"5px*.uaSG7]R¦.uAaSG<.#av~uK #3 ;./ 0,ta$ aL2úU'w3iC{7@]Ø:u H'xԥKtQNoե HKUx}cե{7Z] |OեKAXuu)2R@nԺu)82;.I@r6]Rե 42Q2K<\f] @*ΰKh]l6wdumsr] eVY(y] %/1kЬ`Ve4V4EFR"# eq3EF˖~!70\S oH˦~50_Hy<4d k7\C"#1!Ȍ%Ɍ$ʌK0܋{!͌$ά7 %Hͺ~CɦP7l %~{J6Mdo(Ym.J7#A7#A7## 8"%%,nz4,Oh6%P7)0 do(J=ij(Jfq`:J:J=_;&$.-na~CKPo(y7J^@S-j7Tq4-j7TKؐFFFFFFpb]6MB/9‘똀@| C;Х!~!y$LHPHPH"ҸeFFF(IFJ6[V _iKCZZ/9 %~KCfZ 44nĦ䦑q$9$:$;$<-\9_s %HK~'؟p=aJ&'8A% ÌUn`ɰs\"#MjDG"Ֆ~ut.kÐKSQ"MjSA:  y Q"M k)2"M!k9SZM9kP2nP(35MGdCɦP(l`(JV %#Lks9SZ)z-9SZ$)}(3EGdMlEGd$)-7: %'JfYGdA(L:ZҔ )-̔Ye d)-9miZtƭ`q 3: %cr:)@s88ynhnnnq1-kC`{䥣Pk_ۥPk_ۥP 9RSԲ`H-O! RsK^EEAeP=S2#"ڵ?RJptP]FjمpbKyabyˋ)-鍛QoCap1 =S35-9 琸dsd: }z$Yۥ(y9J^z_o_o_o_oLXodsd)W%$3T \3L3=Gɢ^71$ds#w}fz$SR"˱H8L8(ǜ)&Np,Kș L0gf8g&2IHg& LWt$6xgx+ggw*WVuH= msnBt%/]>3]A?UlMWQ @*JV]Eɪ(Yu%de.,qY,OK\%A.zvP+{̱ie%-)riD4aQ0Ъ2 jFxU4Z^bմ AVM jzmUkC^WF3NaĮ+h_^W׆Ř, . +՛ӈn[A`r J2 JV@Q(JVEIUAbdGQ4($1rDͥ0.Ys%8p C_A^ w6XUp`]Weu^ eV!] :_vP,78T\ 0sASCƱa1ۿN6w>Y_* a\"n fq_SHX9_~VjKQHX) +9-#d'$]sCJvvW{vW{BvW0#W)HXο +}mHX+$" s!_ ?h=[0_w+?ԭ]f_t=;(KR|0ny{h %G=ϋ~_ϗzo\7.TɆ/!-[(}pÞ4諷T?{-ʵYj7~7^?]8~9ٴr_}Sz \+sp9Sq???0~l'/ /#n|e[ƞom?e{9ޯn?۾A ;n?_9ޯ_a:w6>9],m_ko~ϑ9/i?۾!e}m4|k:H]ϑCT/C2xEϑh#m2x>ϑ~]KI[ @[ i}e+!ϑh=ρ~l:E_Z_`hoJvd|Z{ 6w%{u@}>3toBڼdLc 5$ΙNb&R6Ȓh]b{Jv*)ђy}Sܷ) T 6WCm]٦w=%&dN6ᱬm^GS 6@lV 6\f5ᱴ96@l;uɮc-bߋK6?ߦc1PDþzbU("4Bv!<`_q\߽,c@l^f ik(""rU("Yذl81ClCD cb0L#ĆazQ6U 秲 χڒc\0&6<^I2nbn6a_͹b K:Q 6@(9)Vlbsv~9`8 S5r:~*]Nǧ SoCĆP^ĆYshsObCy:&Ć7lbCZ$צ.Ml^%FQzm,^%FMl( P6!U &6^嵃0 FTMl`t/͙,*T 6™e=34FPT 6"g@W9ӝBtIl`IlS3@,mc&6t]պe.]07@ĆKWptg@WQ ΀dUUWp.L8@+j& (YfZpF8@wCSQJ(.4I8zaQs<%=$baQ PSL7S$2%$23$23$r(zt/ rt$s+DpX/N3.t3.t=ᰘJw8.z.Dnd7Pdu]Ć{$ NbNb~G1nbC3PdJlh$SRbC5Pv!JVEj(Y %d5P,@Q +Hbn]J2%6dn/@@Q-0P< %1 aa(y/\e/!6x 1bK LO/̜DLN"s3XVIdc%uc%r|Y(&'qsyDIaY1bo3JvFIㅒԍy ZŒq$Uee(y7J^ƍ qd3nlƍ͸Q(ٌ%qd3n8P2%hrW )Aؐ )AؐgMlH JVFj( 4d1(Y%qd &6ӈ4d1?%OFGԫqd&6ndR6!w#ؐ )_ؐ WĆ4au6d&6я}@lD~o7 /8b>g<8/8@l༿_8_ 6p@lAlm Ćɳ((9L#Jӈ8_ 6p@lی4d (¸ )d &6&6NnbC&6NnbC&6NiD4epL#J^g4d3(L#J6ӈMq 3JnbU$UTbCnFCIĆjpL΀iDjQF,x̵9eY0+8fg$5Bnb%BIJJ\ \ \ \ cĆӬ U< ΀YEìaVQ0(yU<*Jf%3`Vp#@lGA0,3@aXClΙnsL7s{UtOʙiV9=ͪwP6aU*r{U Ӭr{UNì P6r-r=Pr-r=P2@(I@@*Jvݬd7(*JvݬeVQ2(yU*J^f%)((%)phY(@1(I1@1@1`V bfl& $d5(YM2JV`LbIBlHP4_ĆیĆ|obМP^ĆK3Ć 6U,f%YAl0+ f%O`VQ4(2dž?Ml8M2JobC*&6 b\Q 6 *)ĸ?53_)`M piȐggGnIi]i87Wio1GA*n3`Ɵi8>h)ߏ_'gO>WGl|^?ޏ ?ޏx+=n>oCo6xms>N?9mρ~aѩsܽ_ ;h|on~!?{Qh`3?i:W6on2ֿ.4s}4~qCt}_,/n}~ WzS]g/} /6ocnʮ1r~^9~}ӭ\;>{/[/xvox۾֎_]n[J ~c|Ņm׷U`j0 p1 Lý:U y5Si}~#yc8-m>/@k0 -6h`[j0 O5L`;c>,̏o >в_¸M|4 D5Q~7x_iX4 h?Z䳯q1 45G`o-iO?×4 0W[I!ҵ͇mx$ЊLÀ4QiG^`LCNcJB@ Lr Ӱy6(A.ei,z as0 Q/gYPt`,iq04p MЧCY:bk0 28&Zi7k0 }@q;ikˋ['L_^C ֍8tWi4P n8邢Z A` +RPtÁjANؠ8uII?8,v!JŬiVQ4(h0 }5LCgk>T:k0 d`:[o3^ildY:;k0 5sȻ|̴d&9B$Mr>IvO׮''T`:ۯy/F( ;FQzm^%FQxm,3JFkd*((yzmd d <6J":{yk0 ݺ5Wd'ك"Hb{Avf 7Y0 ;fd0 ;i\f`O4F7Oi~M#Ӯ^ Uৗi'I TC|QmЛc$cdUUWQ*JV]Eɪ(Yt%$ŭdnil4m(Ɉm(y*Jf%OYEIYil4fa 5^ӥO?] t*өO?^LCgb An,{^LCgKm#;tF$&l6LЫ%7(n3%`zu?`:o3JR;J6]EIBGfQ$M2J6=Gɪb`:{k0 oo3JV=Gbd1(Y@If@If@If@If@s`FSQ2Ps<%=G;^8%==5$=y49٥`d#2`>`nXs5RЄD71.允4fղ5~ } LTLmv6hEn7Ht 6 4Sv`D)n;MMMPB!nBqd3%D(J`nm vHd@ %dQHtunۦnۦnۦnۦntn=C0 i 3Jm()n4h@ %;$ڸQ0P<`< $:r8X!$i~̵Lò ! 3i\ j@_}#`$0 aZV!$@@ h6P6:73ڝ-˪2Q2 S6U%GUE6ⶩ*ⶩ*/EI˸Q2$iXVqۗq$UEv3$Nq6nlm( ](IUBImFmSdmSdmW1@W0 WS6Efd1.hEIb(y(Jv0Jm v0J'%n;ELdmȀi)Ώ97ٹic+i\4r`&EL( ؂\4n~̹L\4Ln]F6EL_x4k`3U :s`0 sns09Dn([ud7n^e_Kv7Pv0J^ƍee",?f߄ ² ,W0 ~^s'r]Sd,q͸Q<(uΝ uۮFj\mlA@%Ss0 ~_Ss4@:Kv4`> 6v'> %@mƽ^mvu> 4ƦW0 ÚSAv.67aiO͵yLð0 +ai ;)2M@(NL9 0Pt `Ed7|QE `E$EBI̅ %)2@ޥ97 "se(y(JRd. @yپLl aXd.lFj }z] 7:JNfۦtΝ w,qT@y T_ݺmE@Q"n4@  i v0Ja(y(JRUmSFms2#RFxDn]xD T_ 8`ed5P@@MRFi& hrhM` `Nn3;4"0 22͹Lۄ4t#k( %/5Y5Y5YEf( %d3PlZƹV0 ݻ܍,P,P,@``j>PʰPҜ쐯y7Mio,< %OEɧ240 Q,mOnՇv4@;m`60 Mx Lymbi96C0Io3Pi@]~j@]oy]341_\}_O$4 6GCsl6hi4m`60 >py ;B%/.!;650 l0k``t60 7D( Ҝy ; SQ,;I%K׼6:ӣ 2mVc Ǽa>拆yQs<+f< hRπ&'J2+JMmcͬM@KsnHm3I=3I=&d4녒|̵ّ hRA =E7ݸ0z4g@流Ylk#o|1p(y9J2J2J^z#.L&%IFɦ^7Z1F+>gf)zxmGxD|#>fIBIfم\xxg݈ yPWQc3 o3J2.< &xCژی䡫(y*J k*دԖP[ @m Pcccfjcj΍M~ꧫ7c~ ԯb;5yt%/]3(L#J6{%=Eɚ4DѱcMEE""L"^rwQQtk$X鵠ׂ7mE絠`(ע-ŨEGEǨE](:(@] @2Pj@5{ZGQf%YP3+j1(:Ɔ(\vQ%$ ſ{b^`(A k*د`v!/n\د`.[jl `j9Ȱ|t@-3<81M.A3WUab; endstream endobj 496 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xwTSϽ7PkhRH H.*1 J"6DTpDQ2(C"QDqpId߼y͛~kg}ֺLX Xňg` lpBF|،l *?Y"1P\8=W%Oɘ4M0J"Y2Vs,[|e92<se'9`2&ctI@o|N6(.sSdl-c(2-yH_/XZ.$&\SM07#1ؙYrfYym";8980m-m(]v^DW~ emi]P`/u}q|^R,g+\Kk)/C_|Rax8t1C^7nfzDp 柇u$/ED˦L L[B@ٹЖX!@~(* {d+} G͋љς}WL$cGD2QZ4 E@@A(q`1D `'u46ptc48.`R0) @Rt CXCP%CBH@Rf[(t CQhz#0 Zl`O828.p|O×X ?:0FBx$ !i@ڐH[EE1PL ⢖V6QP>U(j MFkt,:.FW8c1L&ӎ9ƌaX: rbl1 {{{;}#tp8_\8"Ey.,X%%Gщ1-9ҀKl.oo/O$&'=JvMޞxǥ{=Vs\x ‰N柜>ucKz=s/ol|ϝ?y ^d]ps~:;/;]7|WpQoH!ɻVsnYs}ҽ~4] =>=:`;cܱ'?e~!ańD#G&}'/?^xI֓?+\wx20;5\ӯ_etWf^Qs-mw3+?~O~ endstream endobj 502 0 obj << /Length 2112 /Filter /FlateDecode >> stream xYKoFWERp&PR4nQ !ɁdKD:.iKvЃ}ɓg'Q8P͢L.?HzA2*'ya8F>o r8vkl(Rwišja˳tY(UbolK8TI/'UL3 cɇ~|F:\Kc> !B;ñQni%(d1$}+ E!ErTndU-ؙOElby1b|8"uQ+X@BFhj.1[&!OpPa'r+hj+6V9sd0X\ +29 /LX%Ž4rnwgEFa®O "#j9L)_pw`4ctՏ?ItsK.+*̖LO&+32N$zChwl.6*꘥K+$q0-VNQc<(n89⏂v9Q\"{ҡ Jg {W0u,,E {d#7Pnr :>g7 \<}6xI\n(]I2$ ^h/0s}^;o(yLА=PKZ)8u) W)53lj>7:`Ϸ3-[jc \][]oe*B.< ^t{ j3ԐQm"ÅSb·ܠwLs:GoZ`g婎/1[J=2ɬÞv5ȍfQ`0_U,em`UĞSuŞ0&f9(6U:XLm FuL SEYmu!%aƴi9 MOKN"AdZ8asi, vQz1 Bg`S]H犜$ZWliFtиEui뒝 {ʍmfuXڲj9w*B'. $Gn6?JP+焗 ddg[!-Y'Kfo{csJ"}[nS誗lv'_Im}κʫYYִ7to8ϒ*_KQi$qz$w$4$uPA}9䌶l \EF#]K]מ0{="QQ侢Xk](B:[ey]{٫O=vWğ_huGԺ GVLJd!\H*Z2t(!cAvRGCCk"LrxGTWnAo%Dgpl9ՠ@neT,B-H~cJ`'Q^n= OP1 ġZs+vmcW'WO .un_X ~]Jγ 5DUHkppm k߼,n ~] 䮜vtߵ-<z)oc#k>^m7Ýӷ!8_7j?j˟2G5QwyYxq8(9ˌ"Фr@ j>+Xeq]AO'OL  endstream endobj 507 0 obj << /Length 1273 /Filter /FlateDecode >> stream xXK60$V$Rσ 4n"IfV`yA~}g8C^'&9|3Rjv&G"FE2͖;+pl~=\׵ޤ;DJa-GlY(Ƈl}BϚH`zµ ^B[쏗7MؑGgv,фСxNR۠q`VEtïnOF8dt*_gڋ#| C!X>}V/P#$E t"}inaI2 \܊H7}4a8E=] z+TpQ˲XZQh36Ib&4-eZ|_hU߸%VQKQP!Faa&8Oٓ@p1L9}=207 go,U ל kcAs st$x͈',Fk=jI4ƅ̙/w܈63(5xSs_JkuÙWzާ^V}t>=rK$jZ2Ƀ^_O/z\P:NȦ$M\p=3ݔuAOwM#ٟP72^pOCY }nכ2v b AYcP E0(abPTb-{0i Tnm> stream xY[o~ϯhryo'r8F[ '(hH(Eyo\+k-wgggggY{vu/Rf̌0(MG\y/BVC^Ak5ЦОC{/c# _$%4# +駋OwqE)(&shlm3W$~ERnwch]TJdkod9<|/ C' ̓_ay Ko,w:A |oŞJAA[:7ۊU5Bo&{i2>㉆=RS]]'((N`SdˆcAz1"kb46)\ֲ D^AJgFT2σPd%^ h(ͻCt1؛ X ɼ}cۖ'&~ ˆb%9תkHߖd"? GpO0CI=No!ݿEB\C}ddcܿ\hwљËh;naʯ |x\P[p}9K\sˊ'lem2}ʽ=c~L1%,yω? p9i\<(78U'>iTWO֫z~^ | FhNz9[խr[E@&BSUa\G^sx4I2m'5iF׻짲]Z?wjfmQ'췦̄IJMOgZzޔ{) f.3ݣ*6!jQ\6jl` $?%ą$\hj_vyITy՛,C>;<\}mPb% XA!qTR<*J0%J_>`B0LUh)YhT6id@Af2So"a[`Z~ǖ?€#$3ݦa}U3-!M-AwG -t+tE.; oO] }As B`pŠp%9sqk Ir#LE9WZ6*-} Mqo2G{ nd1؛p Ԕ<1xhb$Z|ƐDo"V}NM.S+)qrr㗝 Aw6j̜ ~FϫỊ (Nk/y89ZҾ[S]DBn nCbN x#|~M9AG+gO7[hDk yHr˯h?OD :U+ȾŒCć3izZqK[xP~~X(Źgob endstream endobj 522 0 obj << /Length 3277 /Filter /FlateDecode >> stream xn=_a(*+ypIYcbL[B(Q!)ٯ C$gΜ9ss/_+Ϫ欈ϊ( ¤8>mVl7᷽/<^a7ppma$fkY175mi& ͨx)hhv]-|qm+icgiWo_ΡQa*a{&nZgִk!Fj_,<[DQPess6y\Fn/;nׄ hD$c )Dy{(A? F~F`ZvE WӦ0[$aXP@(Şk,[ 蛏v )] jM}k,7(oWJR+xmrc;k' s`@&WR:!sW_Ě("Upv9/Y'cliDnCo>"(!0/Zюj*%T6YO2$pA[aAY!3Ry|L"^'T A\"&APHEڑSA^Σ8-Eחz^ e_D"M, -g(Ҫb3,aK.ߋ#P8Hn2I"P6{6Us5Ѵe3## r:=.*H8!,@N殺XJ7l  $bRB8F[ -F72ٍo;]nsvr! AD&C-#^ (6/J*M*xE* Gfl,0f+"r _@i6p40Y,kG찈TA0r}ChUax6C'4 Б`Lwk\y΢ِS"F.||*]Hi. yB>gPL.;7I aIq<"s^"kpG hjPo= /%R -|6aib[Zȱ ח F =JapA KR;spD͕zb%?PL;<^i0E%c $aMC4(63,NfQY1 y`t?e,^:%SVj9g/ I|%ݾ@v`,e<Pl|ϓROI LGIE `YrYah4J Ȃ/^^֢t!H fZh[#: ō'HLYI|it %3a!t ܽ aP lC*(1{Z#}Ou $ Tt;fr=hZ9U*#џ:rDZ uarqID1pQG>EPF c @gO_2{:F{GRUJf_URφEMp ?4s4]*[8 aGTV%z2N9>gz(QkTa!cҡzh˷&ḓCR'<&]s9hw k\3.K[t:Y0E!ԍj9i|Ke.FHMObn)Ozwo>+McouzУMp{h%͑0ie Nx&Dべ#=Mx!{E+T6@u9jj8$pL2@5iYV-eK֕08մ܅gQ$ȹa>(\0ݵBFdNܣƃxNt&z.yG O_"bAE\O{fsp\5V>ʸ0ca'O槀y#[@ )JjɓKv/]`Iz=!g7jƒ'+Rޢ!.ED@:@r] Æ٣ 5ɵ5F븍<؏n-"&Rd-bPm4iPKIG8@m#AǓۓNw h &|FBQp}EcJ*SqyQ)̃891:.X %7Wh|!?絹TU R?aEa74zavFl$K96^k5^!j6aDK/%kd:,ͻ=/Ӏm_!EVaz{Q~VHYtc';V͑&I:j/Ӥr*q"wÌ6}eBZx,14|KUoݧ-)0=1" KL2q!>mQ > stream xڕWMs8 W(ĪH-id6I<űcOm+v@II!!E GWt`إgrĉrh oqM&j_g2.3t2YI!?:e]oXY#+o ytlMqa<&i[D[,WK97kG:D&zނx]9L߮Tޙ@jw TYs+]l\®8QFin` ̪Dl36N(NQqȘ^yC*.M G&N9`|FRl}"!:#>:.aj2t1EFDP"^2n*b+%b+Qd;~zqv!8_dD.aYHkZF0_|'_w“#{Orα^O 4׍"i«Z6PZgbDoI!E#=vO3LyL6M@XcàS /[|dGf-.#xEs ve@bq*v h^ >zblC K@ކ'u%PIF[ÎZ#R qƍ6 xB[~I|B  n$u]mpǞJ̳*1hIqA)s[(M)2y8+}f-.ao1 cTN4Ŧ~Yl< ]sd@zYRHM3=-ի>$&op).Evvuل(bvluHMzut4?O7הVq&U>˳ (U孬-߲> stream xZKoGW4~BYƐH#yG!}߾_ %%E`u zc^'V q$Yɣὰ:'affxŸ'q>g9xFd~@:EŋM =7pMh0Jhd&y `HCWC 铆6"@x>sЦOՄF B3Jpt1Y@zX4;^{;Kۘ)Θ JH21 HA6bu4I1ޠX 1 hrs4VsO-2okZ{vH; q#h242Dd;)E{@ /d@"]4 4AĠOY`-FPA1芙S&&$ƀ$ @`cL[Pb%,%v"NhMh)s݁:8 Il4:|}GbwWװc&'`yR[ ^ԯ?*^vt2nҔGU+zwZէV|Y y5PSؠuXM٤]t~u$5:8 lOL1\La*Va`%o )@acHq#Hz ;2n^(oNovRm݌/`]3|p}ݎ0r(Lu9?YӁj£^Z w,cm^ ? VϪb^fXϻh,ݽ, {!bt瓅nf̹~~{i};|PUޯcyuQgScִը:kqhz5lVJ嬭TYO٨z4EQN:T,jXci=UrܴLM*aQl|^Lfb֪WUY0eQV'ם.Y&D\R#>9KrFp]pn_yB-E!~Y1VKɹ 2_!;_VܣE퍡ͱY_0GXD3V9Q?j H0xlK ]d\STR"^#4uN ^V^_ۯ%@F&{B/b5lyd5Vo֊=K@+T⪑5<ڻY}f}>6,bH"E bƘ,K3'Zp,/;jɟsL_n.K_REuO{XVU0[#},dy.(_*?evmO2qv̚ﭳ =-eZs^@bT|%gC4TP ҹuB:!ml?J<)/k t٥ԑ=z9E7(xcIze+f0ð.o_O%̾?^?vvi!tUYoQ0߿eVژMuB:~k%Il'NR{ Cvw^gcH-^|hVWQq*Of35VObaZӥ%I?o;=Z쫫U3F0>zyT~{ !YP`8R !'& (~jC[>lvgDߗޥ#8x3s֠%.˔C,y3d8tң Q endstream endobj 546 0 obj << /Length1 2313 /Length2 16683 /Length3 0 /Length 18041 /Filter /FlateDecode >> stream xڌPܲ 0KwH]Cp/g'WuoQw^k QVc1w0J:ػ202DYl̬p W[b8 M 1gLN f `app23Xy n2(0d.pb^ K+׷4@mF` btL\voLljf ruueb`4satpx\@;WE;?1Qԭ@.,\=L7- hfot%c@w6F@ MM@ -$)J07dkbfw&I[b rtuat"_aަ,ao.`gwu>q3m^L`7 s7G& {PF_7o%:fVLWr[ց# /puvzoca\@K=ob?v O'2wm2J(HtoNV+3-=l?|e-GFH`ʶQcDG'xf #^BXu>N>69NnHyw=Rս_GBvU~:PrOڇ6ܯj6~fT/f*E{ݲjkp BiTS]OGqf`GDx RZ.z |@lN_;cٶS/7ɰ /|/ |TY.&>0kt|>U'tk&20"ͫ=̊ScP* qHV$hW\u4j;sү} `*({e:DһijG-O.ԣB1Cv Sz;cG{eOF0xA|,xd D1GraSfP44|y0cBC>D99҂BE ]kqd .,Kt*~jTHg~8Ti e/\RTbN\9&c5v^E?`܀L|S ,0瑇RRIˏkBĎcCM:Y9qyaO(F31@a' ~M]=*o _2J$ Dz)DueQR>  mhYտZ7vrzDR3 ?m&iavBlz mY2i"ޝo.|ki=6 #.ۜt#;|s!)~]/-o)w 4 ],>U0^=ʎFTϘ\*keQ;ׅʗxٗt⿲B7AD1ڃ,/BĻ_RZK4]b=GPt)] +_Q9Iqdvna %YE VT[QA^k2yndրKt"*h8uΕ eb*@qSnyGC*$:+Nn {%tL)1^s ]Ɂjy=w?E)sTC!ea~3uYM5ʫ6bg+Æ;Gv9AaLi5-~f3=@q53Ǘľ<\[. N4Fa$pѹA(Qr2~a]-.8_7'M zʠ_$ ӆy bWH>\Ŷ0S%5ɦ6U%F30(3W{TK~]=q7$2+ɅDeQ!'&g1KyhBҋc;Bnfc_OR"䠂ZB{Ru:GΰL\XEEjxtӂVw𨥰?cۙ"ppcOf'U83*Vv^SGfі"wʶ ~] (x0z??eth`4$m*-Ms,`qU[$ZԀßZ\:$5-|&ѓ!5x.%q9>,!`⠋CFԃz|nLg,W-5L16ʣEhc@-ךW-Adsz8ke.Y_}TRlsp"K*|WF]^t0ڽ8(VۼseyŵDVȜAh&d< ^K$,FCZp:r(s?95eK"A-VjNX% ;EYFr.1KA;rw!|ݵʇc0U"9Xx ztLol %Li,-9Ƶ|YRӎP%` TJl%f2[u~b?/(͵r&g?bwz:8Pd㨥Q pw9yxJ!QҐa]/Y9|b-=Nn57u/mYyU#4]na &C;_r $p; [l2s#ncCgVzG !BU8bI` }?t )#?~j h:)Tl5at :~^Bm,F"A}?Bif8ɝv-wpՊ.M8 V'e, 'FA2o@K 6߇B.AFwxȈ |gbEH$:0zMF9*JN=J:0s2T b_._JșBR|Hc6ol o7} =L.;8a6)RcqWXG1©vJRTKE&W*`p-|Ƶ}Ƃͅ}Dx\NY?tNwK}.bpԳlS#:^B#ůh;omO$޻ͷ'qTrEaC 7x%# cTNsvm|%H41vGdRuaΦ8MhG?ri*gTAp-C,K[)'$̟qfD'[gtvR]|I`ʀ"(}1$F9b>̢U_hN//KlJg!QB[_lVq*ߟttVi+4J5$E0,jFeGxba9kj˛=Nɋa8%av8'L*{}}.60Ә/]x'3 G7adLj53Vۏ8|-$7IЯgV.@  W|ʈe((]os-&B z ;$hnl,Nו3}R6/Rsh5&B)|ʝ/CI7#恶'wD()Imz=U{Gz-4Z%(<XLo!8i읒,A!|InHxӘ{f@X4~_kc9〥$&? [u>\BNu ?V\h޳`6/W&Sl J]@lKA m(j:0725.Bv\\#p^$wD(sjN^J^7w̳T'|s=b3_z'G*VNdn־Pn 7FJчśc5My ||sќEd 268Y7>mauSgK&V'N]e=QMFչuew xP= wծ#/2BiɖLjE^dp!|TDc(8/?B ݬE ף@'eȒo^ͧcOwlM˫:B[T&_x'ñsg };׺͋| #E,+pf߼/%5^S FEnwHjϷ24_&.%;ĕgٗÃ}sK#MD!b&tFd^V!;:/8`k9kQ$m+ZCG󚺍ZQ6najte`w)p1pXAF5P Zz~u1SlCGXGe4(bХk8P%- U@Ħۇ”;Lzc;ItbOH)l)S݌fX! N[}3#?_NCN b!\zDԔXf<5] Y"hZ̾'A7>3B&k)+B|/!8dr B y^ŵP;'MU>Q$e,M?q^ s̥&6!J.SOJfXav3=±/K'$pCqJgġ.vP9O=t' w7JaYɈί4xjj_G9\)Ƹ AG\L%Ux20M t#D, W\{(n شa1*.}&Z;MPGwC4Ybj(<%i-Z]jA'G#H2AMdq :Ur[+F=uJ\$}{34SNL Ba7|a!s. BCxz+8/o`{wr4)x*X<0t^fqسwF" rSbFރdGhO-q-Y8AWlb2(rTP̙M1dJVfĎNt0X׍\#rf'<>b0ö@c:!Z!(!|}yyP 6!6 ?Ӄ-𾆒~y!>FfB۬CPc(!y{J4G; ^R|/N 8[rI0T9L;ly̟tzRŠw`R mA?)Y J\=ئӱSE[G}Oo/A]bx\BNfc }Z].oJۓuz e53w>ί(瑭wrw/}#Ug,lg=r[m$=؂j,ۮ@NꪅC.oSx>/YB ˈ4QOR؀ӇX^lLD\̅;h$ `_Y|o68^08Q3*ǘNP >?Ը2F@9]I: !!Ĭ << Q 8hW=ds%p{.Ǎͨޗ ][rٱYs8mmgeA"V(רQ<<Ae: )T2 #L!l]WPO!" C|$-`(75U*NDjadyF !nSdX_Je\Ps'sFn'.q%qGKxK!~ȑM2㔔T4A\ MCdH ꃱ)"B=)mTQ*#7n~YfHtm!c>f0ǹ 3dt>FhgQ_y+IŸKH TRHLe 9-ZWU:̪/CUJ+aR4O5[e~iK Je*V̼x2#L>8pt@@*3EIuPŽ%z`{nuX0JZg" jY^h0^v" W@S\qӾ~Iw?5`#\*+)Ͻy* ^7TѢ1wIjPg>B+)kNE2)j|yʝ]]нg1SV6Y=d&Xl-Oy ,Б#w͛B~[gܷ ָ.uOL[Enzl*mw$t,+;A.Կtڜu'`֤ 02m)5eu 1]51,$Tx(/&uneVzf09g­J'':~if+2Mao1X*Bjn͊B/+7$F|%t:-zHV}AΞlYRBVԉZaT^3sFuuF-;4L~ne`A)vWj(B*zum5%M/ 81GBӣ 6Lgs5JVm RMJjath'xCHEק?$o 5l4KKT#}65?'[kE\ƩKan\5SN_;HL2YTʯ;}akmFl*@D Zqݼi?r=jwE}O3ﯟ+Yjcf;2;wOŶo{!Po*ܨ][oVd-mJv+I5̧u4b/qbR7[ :`pSءO5{7Ic3JI5U!|·)A)7!`rV9Ztw)#75kI7tݕ2.Ws١/W'D+rZ۵c7S;uv—geNvS䦻ݦ+x=¸wVyy>d1: 35_Ƽbs(%W`}]917y̮wn:iFgQzZiQuljtY\`X< ;n\&}5ȵ[EIuWH|<&/S;L\j]m7 Op@q=Zǖ`FQ=GLMi3vQ_!HL89wn}]B2FNLoNF<_AF'rƹy~7pizc<.2NnZң ˵hew{t)ي%zc#ݒxLsNvddz0E8+%՜ b~j]iEqTg2pn{*l#KfPZ>V'X@ܸO졹8dAuqUCEEP,زHh=[pQ\DoԈ.'NUzFrFVToTC̋շKaM7eT#3:#;y7Xځ5[/e9!jt ljOC؊TM7ab@S4[UoܔHp2XneW!ɦiwGZِlt _nY[}f*n\emS&M a@֊CH˵bw]4#Ivtm֠) Vu #9{Pd!C"*)Qnw}]Ҡ.%Ⱥ/K#cZb<"ײXЎ2Q"YG)B&"#JP 7xC=*8.~v$-#`b/e,x5o (69Ԅ3ȧbj%(hL\Ӿ@ou/PqkJ4a,{1f2!ssᶟމ.)dEHr}3 8,!NY:@H23݉V}@r- \w4ʥi4O4q\dw>n~E3 q1@M " %<=vU/BxaCt9|3fex+9.ijCBxHdY>+>xE<{-SsIqG 2TNJzrs9#r|% .R6_ BA/TXe+Q IiKrDd Ux}Ќ6۝^TT1LSmYC f@AӞ%Y^hJ2X2d}@#F1wi_ smjҦv%VXUUFӕ9WZ$S1zHG~02lTGLbB|o>DQ1?1oXHUlԾN$C,|+ >]XMULԕIQIB:E'(C\LTeSrUD fpXOy@_:kihW,t>TY1z ";=C GC=+hI] =݅Hxf${2sVn|cTګ".҇WHZ}v1'La'& m d=dO;~qݰQjyUE%E&1!P'N\Z|&?)l1kp,o8Ľ []S/dJrOO%ES*d *K(+ :`Dh"WJ 1kН)4$ u7 I DVʢ~rR#(Lm=7} %˨i/pN;\JhD[ TqN|"E-"2 t)feO~$Fv4c~@jc;l(1ɵ;2" ZOXU?MȐ$x*oՎ*SsbdF˦}[3}Se)C|]pZkb:< ˹YoZxS|ф{ϻk {#ۑWɠKFYko :ŵwn4R~FTLq_BU< ,du30w@i8 B-~ehz©.5hO1\g)3-̟ ;F߰VLe$98y31:!qx넪%[+ŪY ɰٕ)[I@iG2Ѥ>u+$vWQId%Rfu-n+/V=RutJ9zu.T#vX9)7r9٧}FIa^0" W]Vw X)=qT 6RBL7'4_" p U8L5Ӏ_]_%&x8[;(2*0cMQ<ֳ'0o+㼤kBtK'yPʊ!᜿AU tBORM;rZc2EdH nYV9v:aHwVo"W!Esn n6r"cV*LLy1>"f.AʫvbZ#faA BW/\}>.Elxutx,ZjI$Z ՁGe\GP: `V= 8JgykD<̩.xaeqzGs;oj̖2a0{F菁c MA)|_ CX*lz܇ 2Cqj]9TNRY|"xDd 9ud&[JZrGLV}^NGע^.KV:n)aK7F%#_"d6Wd?:̻\ UȱJYɆh(ZTf bSB;:{=J?8ypd͏2-Y){<Ʃ0++])Ze u jϕE@I lznəiS-ių,Ȝ#PGh"ϻ+c R?;[Fґ 2ym-܊KNjʚ|۶9(r$~ ,ȅzLDÍ#R"f@$ʡ1-6wFmjpAӋ6DN5<sV~bA>P+gg%:ikl]VCbƟXU xvNӗLӧ ,ɱC6\n?@m@)I'[`^9T$õ5 Ьc]B_xRms\A9̎z45s*H,m=D7x6p BJm6'tc~l/4W vjBS? 5m.D/7]B:cVu@;CoG]!sWyXb"8Ej=R)@P.lۆex`.låK+ CWTu=`3" 1 /xU yƩ>*SkpdҌ*'vUf2aA`ΐŒPEI6ZU)Ѿs;ŏYct3As(\襗L2vWٸKbZ43~֪&e&uZ9b_/a!?eR$] ⵺?<[ }K@d/̡9Iч&0 ޟ=T2>v$H7뒻$ i(  [ Z>IZW%NE {|.8^;Ten˓- -2iD dH6Iᒨ~0lXBBEL%۔e1* lƪ>vJr 5\:܊."~4kbm4QhY$RD[ !jLer=KfY%?F.oWaǞpv!!v`a+d`41MboIPDɅA2ʐs!տjcf$D.{UԸS0qQ IeT쨄0+dEVJFQP;am-GMf"o66o$|Ap󰭓[-w!tyv>b xVflNab1;$B*1^=$2QVjb`mZfٖ͹pqHD䧲!afݘ{G;ㄬ6r ؆4OdNY@Obk1 'pyp? lД&C*.pe$E[LK^ep'y.H6te}>Y3K2*8dr!EnBʲ"9rY\VT6yޝ' |/NJl.ğpFg]*[-aQkVdqRVq|Wh 0È{T͈Ed릊'ǰךV 9YYJP4S גpr2Wt#J 5 x|!R `OskUJFQil _!KSX;$S(i!g $Ӯ8{Oܻ'I_HZ_:0[_L#DBc PXܺ7;k6ظW [N =R6Ni~4ϏDR/yiB59) RgzVA=wMH^3OAio̲ TG[ kD뢩JgpZ$9}>k*3{|?x2#R, $9Ro/j.Oh>K4Ōѭ#]W,%,r? ݵ"e|`"ţt?Ӏm< \MZGjlt~?T9_CE+|$$i Ziyׄ.U!?{Rvc HO7]"kYg{LjvyF@xX-3?蝈wԹFyd٩G3s*czb8d eI(Ҳm@0epy;{fs`ӌ=]Md"^NAtOg)$_^"W\!T-R+Aɏ\|D8$ʍt<^+e`!O/> stream xڍtT6ݠ49twJw0330 ]ҍJ"J twHR?zCIpCP%$x~ܠHhPNP W! C ry!< `": 7XFN0ϿCn 00y@Z]w(/_|+ #Ap?stUP(Ez"nA +;u@UAO}`$ sU#0mVCnnP8ʓW~0$|w??u#|`pï2 ^pTCDo#@=P_ ܡA/m Awm   ^Рy":~k:u?  ַ ~1**"|^A QPXD &&g =O"w@$QG6X:[BV oE_z9"søv o)ՆB`^nj@wtW#a0_(D;ƿv!|M(T` t>?[n$Aւ[FCɀ1$59SPW.}ВRO1mG!:Hw&Z'w\IKfen]NhQkV '.|! ԝM%楓oJJX4t}l)C!.?KЧeϹĊͼ}^| Nx5u6_n|G\aPܽ0?Vwaw\/WM*_{KVhw5CmTUコ7̙)[>/˗zǢ) 5ЈwJR*$X"]m8v(G}MtfP?2F;(]rd7g;YߛКn9;/BR\G+ <}1 % ^U U[W;4p?ۜ-(kL"SŋuCQ'/86`Y zjEdl  Fޭ3:+ȒXLhP|] nyP\(.ɷEp{kmw$~dy~]yidTТA_TwR)IP[r%ۦж#7oꣷTWڵ|`82ΪCDTQutrJLRNƉ@Mp׌akQ_c38z)^@{/Y5Hj7VUO1uX 9DO`?Daʋ޺rdcuX4*ѭ$f|."*1]c7m*v8Vti W%(?{rELg7/ꄳȦLEf&E<`Cv̀B] v#RwQ/vqw s+sxў㴍Cs.Ei9awBu0PYsɴ3?ƆH/xe;r ^q^QfL6 &o3:HQy.E4__,mV_SRw7!C \j 9֜M~d{za;A 4kԮi7(`QRO Xm|x^"{3;Y.lq2u];ܖɊG\hixRbyuHS9 -lBi%#sZe6 z&y2 FJP)/bsU'gxTHC~qVْ(5qpF]DRj>Oxߥu@:+I]cwo{d[KlbI[]sA_EEgb[ֱՇAub*} NӪ8璏>m>PxsPrĀKa[>K0.i5!,UUOybbanQ#ʘHX(/?YShfa^2KTN-gX'<#9ּʫ|ߟeQlJr5lKKOV)10\Hl 5%a{M{,щ(ȏi։4vRkۭuk=LYJg^G8W/IP_:l(S {BCbs贈k*gBZb>S |ȥ6'/˒`Àj#C*љ%^ۃeViّѥ]N1'BˈZy.I јi:xv =*o#DY$3*_Xd*)椱;[\\h `3(t Ih뎁zFM,_2hRE%2,⪭ZZcGynL !9{cO.cz׈`vvX7 ߠO|A+Fa||]-6hߌ4k?eOIH&hvNnUKZv+ҕGɴ~h3G[&S RHs#:|<ҤqBCnGǼ+yiщt+*c T5Iɂx SkLg][r hųiM(m;W2Bwӑ 8{G[_8J¸9,ш M̻k-D^'z_K80t5Ou40;$4^ʬɇW3{6\ݷ?4*1Eyuӑo!]]հ@@6 tȕV+f\n*xy} 0Vk;,<=]6EQ~v'ʃ} mCMna-jrog#5 ɱ/5ɶ*^Xqe%M L3_"' m_Zj0"6}@ t""ϭ~<T<&w܅64|>)ܽIVfLI%KJr^ `aGo[V"E²QwhU "(!6>0a oR'D4r[v5T-4zѭ6}eq҇KJReB" ECh傢sK~o09k]CkivyT!+ɽw&2 ;M$Xs%ЭwP5T#9_+\M 5$C.MWgC 8*]oO&U*,Ecܕt[%#,mCo1lc̀ߔî906S._;fB<_P3o()=>4 + u~S6G8w9pIlТALqGx4dws09aCw)#laF5w>lV^CZ^kwyjsn .~` m1vݞބ&%%%װt8o/Z)ZgblHQҜqZ;ޠw?l֬ojh6ҋ]a J=pW# `LUC2(q*y ^X,}6k#"EqRjiȫwBRHb]WAę ʹZ&z}SgD}߁8 +{hOaؼR4Tœ.jٷ ބDWYZMJaUVt~$:n[6>3%fUr_GYլx60.N W0Qq]OR}N $PqoL ^ De]Fఢ3cX ho^m;Fx[ Y/w)򰧂X5/7Q4m՚R=U=/|xa! LW.LJVy*^ع,?]­ F{@h簉%Q3row_&75s|LRiPLQ㐑c%c˱]zeO,B"k~ #FD7`XN`2ǐe?J@㟗yU5IZ*d {*~tDJ2̧@`n^^:38E mQI-WI# mHc&%c~ }%Qd|-F^SwwMeջ2/t}Kk4!&Yb{l~K:KN{;!ݜb('(zvmb̙FwPϞ :s Ĺ{O?&**ut{ 5B$3!:ŧڨ 2E^Js;>4V{r4Yvא+^ ~# ;/mFXr%ԭ56\|!is&ٵ׵8=V193D[ѨiJTYGgsIdQTP:BM6!wOLBF'5D܎0T5n9>CÃFhrjU iǨK&4ȳ#N?.Σ=~ ^]-zλZF|V(6='&ܪu@ UVky~l\g)VV<9*Y(ָ[hFNI$/c߰AW={V >q=N*v9ES]K+]ZfJ娤frӑw󥤪U^a}_ï!z_ ,ř sovQj{"z><|mY[p"U%;0$M~ȃuW_: [|@j3bש2[cEmEeAq{3^I3%6./ʤJ-tVf+.t;`j!Es-맂 P8]ϥ 7]O9OD3|oXq_tX;6t-@uE 5D|H*%AS+2}k\ ki| sԡ nB]Tݠ.UC:oIQ˱۞f˫ OB"k \&z:׉\кI 8u/dt^F60DͩkWIJeABCTUFJjƌ O*VA~]W|RǪ-Γ4}L&C`RKSٙl, 63<\3^'(qS^g[_󨨣\ynXp)*TgR]d>( !9f~s/yxYD6YJ=.>I}^P+xVZ5ïӪ5y)#p^iӻ@(T-}wJ< TZ8-"ʼnG]I՜;[j4Oc<X(<Ppv)!W!xdEUnX@:wxԓcXbCΥaMx07i[,.xGn?eg` QqOsJ+c,~`N4{$wT+,=})uWQ 5~sSfoWzrղɸ:pԺ'k?nm#%.%'ׂKCuxe^ Z,UQ\"ܑ{fßV NEk˯e*%mW8_ڣꂷCmH|z+@7?;d:?+{O`% ֞f- NprJf> stream xڍT\  5@и;[pw'AAȽxo{Vͪ5kBUI$moPRccr0PSkm@Pk `{;1$@@W$dowqظxYY쬬|!;$`33@Awp[X:G)p- l (-A+m`tvvgaqsscB,n`gK:rr( ]3 5@ ˡaot^ 6`S5 x]!PqEV{sllMwvMMmv`; 9PVdvvwf m @W hJS: -Vw}S'3F?Ҽn->Iu=X>\k;{7; se8hف]@rs^M(,@.V^^.r7dcMПN?̯5x9;_A?(^+  l 0YPj_ ~l?>}2x03{;1<%).nb`0sX9y<>G[ǿb$CFGM@7kt>+~3_"i?@[ߌuq~%aT_2_3u,^[/;" vM-jZ jqżF딙Z^#ꐲ37cع@''".똚n kf `HXdAE`QXxY,8++S_:L,@,|&_ՙqsX^&x,пk2viP^ *|d___|b/vGk&ב7U '_^AYUlڹ_! y=yטe/'3uqrzm?ELQM;nkĈݘvRo?3"9KMmkde.*I/IZu+;f_zmד7xpf"޷ 7{ =uCB9,N_JՃ,bfѯAg ^4|HrWOMI"> sɐMۘ(tJ;xje>ү;TRC$9ŮG8^&2GfktɷuG=<ƉQlǞwxNfKa̓ 74MRkřGJB>ԝѮ0mk3}̃lӆMZҸ7X˓9Kfѱ:.'s~;0{iZmoj A["J[yQhpʥOd0fP%ʹWcP(%QGOG0eO27S܃0 /RT9mXG9Z񆼺۶q7B.;J*>LKԆc:֯Zs"kgN:JJ28,.30?v9'.\AlС$?!n8ד'R_E)WN _K\#-e&at4ԲYUY` ttfʗ,M辺~aJ<qkjIO4uh>g>pzj\۩_wEDaA8&bbs L=GZ#!F[8lg И:u] 4~JWeax@?bn{~XwȒоY0kh0/,\gܺw{gC<'9CT5'5m[0q{-Zu\&Dc&Nӡyk๕i*߫FWTXi|[L}A;o#~Dwl^?pr?fK/zhvv#nVz+N|Aŷ5eqMX$wqA3[?JY 9m+I Opn6J Gu{DP^8 Ioq3! VI*AmX=h1ZYm!^3,եk;>yKo,9#p" $Ԯ0``3IuF$40I]x)l vR`JP!= n{BwTiZ$/uY\jzYC2^l/r5ܴFNJ!fA+eG`o31vWjKC W?up 8"\|ˊɡv4`ct}baK}XOkۣFY{|GY/? <ۜ ů~8ܔՍswߖfʠ0mTS7q8*l˲FV&Y9yvVb%Fal1VV[LeO`$F ]4uǯ*Dz&Zn}&=(B#^Y:'߱j\ 4 L}Xǹq nUy?#Og2WTk~,Q{|6(=0qim;,CHilf鈵шgE#?3dy9^!ڟ7 }`˹7LPۏJI4EbDP/fMqy(x{O]n{/69#Zh Uy.p,hv~j|0~r}vs BS)ͼ伟zPSY5+.* EPۙѦoŸ$}7* JԈ.K/r%^eu<&!hυ5(9-ԯ:@1$|}]69p.nq.s ]61YA]ڼgvZE%X[||TٙTy>EU$^" [!Ξgsf5_%y%s=1a[wf$[m\=+UFߝkAbR!H~v9:GE k+p g֥,@K0_;M  95ּ X=bk73]V,0oc_PslHX>Qr8pua fgZE#}ݶ,|oβgwdi,(;ad/0o - B0pWiM-O~H c$-$ z-T_Xb*xih[(k; *Vjwk'4~=qvk_Iѽo#2B}?鬫3Zȼ&q÷2/qHG*7zV[x?gADK5ßl ~Hakp_L"%/{iB>d ̤9u/5} 7vhs2{ r<1SUi>+1tUC̥5B~X.9킁6<6&2XÀYZB$W%\RQTρ#'#1b0NԐOc ֑2R34"oBUIY[| @ugcLD_oB&vB5M6W;ˏ9zֿ9VfԙZ0*+'Zݖ|nT_ğCK*+\_T]98cyQiClb 1͎Zxꅴ%@:X`R?ucK}+O9\ԗ16 $-Lo]2vLo>ۺ,qlzƆI͹_#nb.w  ȲWt։t7ԙl?C\Q >d!b3 MJX~~`ZC8L :.╡w~4Ь:1EY|vkRhEr$;^4Kiay~\CѼ'@AB/ P{-Q#c5;1?杸5# f!K'k drPN?s3T82T(vSQ;&B-n<kaϢP`mN?sh{,Hf+g VhOηbu.>urRBAş =>mr'+UЋҞ?"פƛgW)dZ(Q<*SF!')uqqtY!I4JmU(y-5ø,%; |*C™oMԥAb 푤@UMpڂ;4iA"T&aLYc0t}m 9tegVⳡxF~ ЊSq^c%-Jw28Ka?Xxʁ-atlcU_r!] z'}*vdQ,s]PGZyA0ƹN1-ށ~XzTobQ;5Q(pvj&} ښAy6܆'LqPrz+z‹Ͽ'~Sw?[?sGd^,y&)Kx:c_m+,`CaYZ@P%GȣoEy 6&S3>Îm j+t/d/ARUBSAibWD/u$ w+9Wt/Wpx>SZ"t#gOI2?&C5kR)JdQ"i~#dM4猎;k=LG4KiF!@& 'ɋţ@ZGn}=&%5[[wu +M4T4g0Z֔OӇ]*dtk?vUr` #~1zIzbHR %G0dr7Zӳǭf%+"#  GdB7w&do7:l9u^0 t21gi:Pr zvoN $۳q$BuGMfŧi:0U"`siB/Yr}'~ 2qqP$pGbt(3Szi6sU=De=F[cY 4.RM&#e#oXF#@W0nK4NIe\&r6S Ұ4դ"j(V!? 7z3՘*b_5hazacd+U0GծEt ӌhžKZIKR\|Zz|!o(bFөV~V泚1vT TM9=y4aE SIR=WPryY,_0M/^O[?Vfֆ٭E c 4@Gfz!~Is]Tq(9';{ϷZs+F,xdNY)<t:.R;4J!a]N 4:}iq-FE30 tYS e~޼p_֐DVgM'S#ѷ}RUQK?qPlx[ЂXV˻dP[f*]ۿt<؁fRϥ.G*9bdٱ-XMf.Wi`# gARaqjp Z'R$.zc1ui^Qχ=k0vP]\lfq' }G8Q9ZGz4 z} *,y#c!4Z6ܺmjm@`C'ݻ KP$ T$6T5 "`+C=ihH1^=;qj*o= 3+i6dDsZ(mIw.'N-95R;]2RH:#IeDbpzN!w[OZ )s>(u Zkׁ" ow  sZ{SG.o: AsFl%|C1)[ܟh*&u1HF,یUx<Ī T ]NՁfI9 JڢwX "7Uұv*$%kvZ M(xJKŖ ].TmFl5t!f`g (X0'q)"h=E-'cz'ĘcN9÷$mN:urnđwY-^^=Fc6-m :&& =jdMXV 0maVJt\v?P[p ٦! Mh\:T/,Ru`ڳG9s$J^%*ˉ:w[Vl`V6-5 $u9Roks&95T'v[Q,joSe(P~!kdf~?;C˦ L]TEgUV;A1cE/ٞeNX`#>~hVorsRYNpTUm>)M̯dD{*xz4ͥ,\ZScx?Q;8qE}%Th`@UET(;8~hA%UZwގNJj@py\J*tE# kXOEU6̉}b)3r#>;Ӽr3.ȹ-x0Yap=ad#H C ΅( ")aN|0+3yx%7.T^ vz*V::K!p-[^nh##=T~0m*Hˤ?BP6nc#` fx*@R.zOX`Zl0Ìp8_%K'e:)@t E ѯ̫koJW/IhtE)Ʒ.Ydf X@~; rgQ|WJY+P:)gL\'? ü2Bcmb#iѥCc*87V޶/KTjǚTbRt7dJCfM35$ʕ%1A5yPռgڜN5ءoXߔs7:uحF!laSc1'NgZ( )d; Vcf'^:6 ]z$F6̟cLKpӆ3> cbR5 -l'*}gE 9$ *[K{#*m1ܲ'%ښxq ~ Ü tC[R5m t'AokASyI%GwV] $dn4"Mf-)t}`Ӹ:GOoGqR"ӻ+y͔oR[46ammW7A` ԑ O<3p:FcOϿ\0(3NXlV-Eהڐpo?%N4QDw0~@}^FeW=G ;s_bHn1A|d6حL4ԯے0^.ts{3T'^=JIhwo[߻93Mu!xɏrF78fzCOK`ax.Jt3V{{ Qm+|1!%S?thǕ2kH=1UjmhUeoAc; Èn^# b} nUӮS7,W%L-,btgW{ O#Zͣ.V/0zE>M-"u~"QBnxJ_iCa J= \S|˴Pi;bty-fw>2 /AԬZSՓOyˉ?hc0ߣ/u1+>APMI+,4n Rr</{< 4!)U7Qo`MQcïVTB!;3)UM@o&Ћt(]؆*ƵH AS!Ԩy.iǃ˘ Us6z5iSXVm6~9]#YkQb1o{)Ͷ+b?}rGKނR%xP!;{Y %ePixښ(J] p^mo} ~XcfףЬSj?|kVmF:3vƺ/5Or_eadw__PgO.O\)N5]0;gx"׍D얃+EFЭf'ik'"C(s/~֒]ݫgz *)#2&F,T\b6~b`ZV/}\:qH6'aT%a5"և/l='>9ybV&~n x2;k$X 5+v{A^M6t{mZS&8į'wѦ4"v(1 [pj+"/1lS?J]r׏!;[LY/S ^<[]bc`Q$0 KˢOf vM$LƯT_Cʚ̚)F{{ð®ոW"dQ8NP؏ Z_.բާpK @Tc8UL(#Ne,ͅRF:W6M-%/*V9cY,ˡs_c e?6R[ B7 dfJtC) " e3co!3P()ڗ“БWEiR;|> NfW+j4Dpq8V*Mh._<5qp%T Fx>0Gꞏ{@BWq4U8 vh|ϵ.:bӿ߃ʻslʵy~dHXxi;&d4u{9rh9h?@Rh^=?Jʬ9,;*Om]cOiJ̮>pS}ai4GϦ;5#0nq O5-2! M #Bl(ώK;Mg]\Kae!6=D-ȹMh4PvQ |64T" CV-8Qv){!ve{ }NVO2'O\/H19Xln])4/iп<OP1uJ qE^;3Eb)iAʸ&xOxUGWvk_U\5 |>6vT͎Ȯ]N!̰Peb o)KWWh %$D}5;lR*Owo1z8 t873 pLe&+P:IS.,NPM,={CJq+ۦ/HSW&Sc~xȰb!#A 1UJ zRxBY$$y{LV +ڪa endstream endobj 552 0 obj << /Length1 1433 /Length2 6656 /Length3 0 /Length 7635 /Filter /FlateDecode >> stream xڍtT>) "  ]CwJw#)30 C ]RJwHtwI7{{}kώ9sd"khA ~nIt1B]\a sCP;eu`ԀjsBr~ ֏P8W0ؽx38a[͉svlH HD_:Vv<{9A;y |N@P?  v.nP?tZì@: QP~2Gw;+*s>99'Ї_'BU@(Z6WSU6 /OcW/qYLb-7@ +ԇL)?Rtspf`G_(Һ!Q@dP#j@anUAQBۢ+ \aPkmeC_o * /J_VP+\`Wؐ E}(ֿt'(PW>(ZC=3 G Q)@~@ ޯkFxA@0ƈe%ъ廴iD4m}YZ&=>( k~m,F.ҩ"xDɒmƠ&/ds)ꅪ{P$xCyl|>{2[mslgevˇ[έՔ۹J ӲbdKƛgjpr'LG7TVFw$lL;cHc|!"CioRBŸ{g- "iYl,_ 'Y˻(]QʬacZEp?'<jh; 2rKy8\:nV}2f0-uր:Rp4G$ ˲tm#" %Ts߀%X#'Alx5ܸSm-ͻ9nFܪyL h蝟ޒInry?y1X;&ϨqNXETg_q|(Lg BP2iqf gb95)VB߶NFk^fڴ鱉xf0;!$?zu&M9`mS*:̌gZf `~0]SX bblGRǸ7i@ωS8m׾o(Sηv~Y';vNnVYkg\*] on-멂}>SR߿ny.rIY+ M\g[6z‰fKa5oZ:;qYeTH ْ1ĆaTzS\c%"B5Ŀ{lD@3pk`Eѩ&7({)O D͵Mgobʋǜf!?t(=8qqV-^X独OƶJ;Z?hjPhW([JzH)ڪ&I%W0>Z&+K+_qf3b`yЛqQIU"I'}QqR[Un\i*{ 95@dImWD:s?% .:{6gBEZ킺 ^ɓV]; )],s'CD?A^Y~HU{Ub 0~qg ˔`7!׵$K}N){HxSQv^%rײo8X:XfOێ >~yJꁰPp)X/9/ņiƉS(|;aAQՔ)Yx2VZǒ%3&./#=NZIC^r3Nϸ 'SJB;cD͠ K)A;SNo|c׋`qF~9 0p$SԂBW2W`j) OmO LC\Ƅ0VT =^qX|#N䢵ߟ)Yn]h}Q 016:w RQfEgY5#@꣺\zd}w,Ω (]Y=M? WZtwj¯q-.ɪn  &eM1Co%zMZg3츒UT˩xzev{ B)5ӝ2!ʋ 2=~?mOg=ј}Aچ>f {)vx꘹Q'V9ԕob)PS/Z 9KY?d$9HL;♜ LPƚ/qLFҾl2Ni$rp7eg!~-e29d2HKL*DR&\nͳs݄x7gxmMx,yXZ-h$ ˬM7闼%w|x0}}=գ X"|A/ÉօCM^"֍:&li}ªscc v;S!ϓ|dS/LRDoWܭ'[6hsI_Xc n?Hphi5k]dXJ'C" _z(fFgrO2LxT}I`*7CfuVeeJ۩-㥒º]@TTڽ3ȃ}?,u]R]jYtI[[vHDns.˸cؙ*~Ө M2nGMJ6J\4qc +_p4+"x{ʟ-Vf/G|D31T`YXjըZW?ti|e_XfWo1fHo֕݇΍0+u=п;#M|-9D~t*O vkҮOOo$%BB*aƺģ  ?ްZ($oc緄j8- ѡgRQ7sq fȋf(ݗܱh =03z F <1PSYbĹ+lQ5)Y:{!n=X'̞f˿K[WndJ ;h|g㿈i޻N(l1FUMKR+ΥfylQVrLu0]we*`!QbLFlף޺Dw8x}KR!\fa'guiX,O w{u.g5l-U*ٮ)DFC1:xT#Շ M.0ceS}6?|Y33⎔scPmV9}vtF4?ѭf߳rh;D5<|k#pqσ*[J.s1Z|_$Xcri$eτ JbPmMlXp{;6Ԏ/WjGv!_&"D/L~շvجi~#شm1AyI9 FHګ雞6sC^wMEm3ѨԛOvqzWȼd}EKgk;wMlKc54= (nʼnpĻ_6u1=R~FMneFl2N+xV~iS ,l x ppz<|qJ<@AqbUe=~> y}{ y\#El9LJoyΚzH8򕉕3uN6>׼誘qб|h5Vh܆̓̊Ǹ\pDCt/L(wc3u)7׾Kĺմ\ҧ]yG/>`H}"'#"؍O=N̩Tq1^ S2; :r(1% /7U}Ld>R.7Vr8Ve$.2vy)kG]:i] 9_D:ԧlkI$ j}tx'p*CL.8sjt=hj)ǻ$6fj/j30ų5TŒNlb 9gڭ>~+'YJR{gq@ޒ_e83he6ˡcco8dccθkEMK~ Ĺ,;_ ΩqorO:[_ڶ O9ka*Sn Gd ({*c"5wFWD^z]fXN\{*Q7.4;x921{`CTTTN_96QkCWY6J1?aj@:+D=آ,hSQ{g 'pV~ٮ$@C,74f%?*rRwoH/ endstream endobj 554 0 obj << /Length1 1533 /Length2 8430 /Length3 0 /Length 9460 /Filter /FlateDecode >> stream xڍT-[[pw` 68.K Kp@p =hpy${{kwծ]MO&ano؀B)~''7;''=6f یA vrC 6=٤MaO<{(@B.NN흄Ҧs ;@ vƠwpXZbY$N)b =2hك ``88M,EYn@ vr~7 P5;=@ ]f<l! 0)jvtGN 6L%b *aSoS)rSs9A`-rNt2Ps){;;0>itݬ 7@-~7a8<0Ya^N~n.vYqN6?u`xj?ax90':as0[Bd2-Oq|'yCm=_m5M :ORqr@~ iYM!WOڧcWŮ `w2U'ՂLܐ?KOg ufM D{1/UЪ!.vU> Il@vNgY;\Y%:G;C~-OQ{/˿\Os1i(qLL=0 Ojvl;x`aRM?7C P7xiF' ?S.mm@'_\D_σA3 ֫J r71+}.\#4X̤jVFR|PȺCU*F U%YT% N#\^ ^8u/b施Z8ތsn _* ӕ(u4aMfڛ$Ykv1둆aI4J"qn?z(;#]6d(yP+tr \S{K3d/LG ۊr,g_|.TKɇn1ҷIq5XKM]:;T9DwǷtpOm]Deru^xmsbZ$aw6 E&A#l9**s͹(QM TlO,>W F/_)%1 ~gL֧w&L6wRc wB۽vD8Of |O`L5|6^TX|1S}JJPȉ-Q0k_H1hNיޢN}~*u\!o="+]aq;eNWKyTn@7DC?5 1y ""XVL@ M _bÔ/FgOt_m9LѶYky* nukSr]ƯCfߟt1/ACEulC҆9.(;CO.G_S U؛٫%!g,Sa%:ΰ׍xZXhhumڥ^V `;]Q 757qS2#2EJnuDLIn{Œ==3G^leygQ 5v0Ijs6%YlZ͙k\ }4a"~ Pez U I;k0űd|) xa St̉]PM9UE ^5TCi~Ĕ=ژ:c)s* BL!Fl`o ^0jBk> 8ɛbs +N0(A+7Z(^Qk 5!tO QgT?B%qZ#hy)fe!(g-on/Iкk$V&dA(=y;"F6!`ej  *Zj˘³ 7OWG^kC!V=Nm gOc@h[R"nH-_&`'NXX'dχRG-`aaSFuk@yQPvE⚅ren;O)@߷# %<|0U8Xq:F,__ȚǓӷׄPl رQ؀ryԈs'r`#踈aN7)FiO5 n޻bcM6Ʃ-GSq9:ňwO[Üb NT#vemua|M&z)ٲD`gs3ͱGo)f>M~•t!iޝ i-s'Z\cW&ǓSW_)x.T=>?ԅ䘐_*Ҙh.Lg{)TV_Vk^mгo?^#|1nޗfsPh^ID810@4-}SV9ك>|;Rڲ\u!%LBߦDrA^r}>c!-gTŌ\`u5mQs՞Y qgD-zr$5Fg*{2((By4AN+fm7‰MwSx2?NL~P =CPc[b0آ^@`1tsc$DS>['"?nތoyPg-D03!;r*_m}r8Tf,R#QQe7*62ιxئ;m-'θߨ8JkT,QCu!esb}^PQmk5?K*EUiKqJ#N -X=gYҡ#K֠hNڕcQmZȡhRM`]Xq/J $CKD6 [Fإ]i ;7}K>Kbtҭ)ϸwak(dSW]^t:n S <^ŢkW_."oGN*,I}.AdfuCvͲ`ڶ<ؾxOKZ2wlgu77+,\fD,+c}C2>iQa/5_NJ:B0b{'Fha4"Y<>n$c\,VO|g֐9DXx̜p9s OX_Ē`6*G|yh떗Qԅ~k2-9=?kҼ0S4>fR`pl\[SEeyut>UR[Ž>fᥱͯ1͋0zt]r:d<'j2 mu2v}Y4Q#}b $i1%< LU~8À$%9Qe>'ѰpZo/,h6n<^{c驈yG#*)Nsdh7pD9;~ygjw~cf%yķ>oCO| *Ֆ/:6t" )!u`-ݵӟW46)RS1ni ?ż%MA$Sn=YP9p!m5w.|SW?Zbe: l.3ܥFИ)iֹ5H+ٜC}:BG/<8;|~&<Ie}l]d$#tbOPNWxWgqevF&n[~Sٍeׯ ?Sh!?zqi=d&X@ۏ۲BG вI1\$8_LR 9!N%GxvT:6hn*Zs)quUdbTM' P}9/ od4]q'mk=͎xOűX],םit:bdzVX^j>Ug;ijLy?@-LKIC%>왦#sW&&`-#GׇRf}#[q~}jC $>`$L}.9%8qibWMHcEV^V'3ߴ=kFW گ#fPpEQ$9SN3se=(Dꢁ3e4؀} ?ib`iPa֫z9~{[ͧ}i3 _ /0lTIr6Y˚l)r~"C nB!eK.]ߔKuee՜ʧ Ele,"ww~ ףNTgBDoB=. < ~$"O wgU uc Eww LURi{Xmk+Zr6?o9/Bf[vI•!IlŖ>~3!]A"@e@3OO[iټ0Y N[ (6k$:/ͺ23QOHT:Sk QM@ZxiҀea65T_QGJ'/L#<-m B/oٖdedeSc};,䙙ˋ@L,Q5o,98J̝̲(WclߠNTwYm򻹺!f7VEژe_ ثfKrb?HNy}"m髦͐7 UpEQ)wΖa 1X^n˒)v$OGL Jq]0uuUE_AOzPLl M.|,wtʥ\MbRnðQ~.QuEI䃫Y0O6YVyZ l%U^#2G+{gIWNWi2Mi 3d;0iofNOfaTCpa7zsY/e u,:VpJxwK.MNum Qwa6=ڽ哟,cjB{I&UU.z`!czC֢BF@T{,̆hļE! yE3vajUL EV \XeѫF)|*ZHמ%>JF`J_}^ ; 9! ,JSzhAS ~Zo+oZML%HQm~r`e\1|-|N@/ڹ?IʳXO-X^f*ĘbPі?`$3obA*8$M?+">n\CO#kTs+q$7*BC%70zb:p1rCX:u[XUO?')\T&ӅLL.,AxG/Q&@ms33 Y+sab/k,ldg+[G{9FjfS334)oj0fzو8s|Q ,X.#]) f^I6\Ч){辅L?.㐼m &T%ʘ%IddX)#cw+SGUp#BdZw\հ; R}5^AJ Km阮PTgʹ3c"1Ա'<Cr;Nܜ%c&;`笯K23i.R돫'Kr(POoQN&g; |U<eޘ#h7\U I; jrQ>Yy4~푷KNbzt7̿Wv2:6Cޑ/ 9McٲO=˟S<f[RV&4+ӧhRE{|ͦdd f U0Po`댑(UAhS-!+T<)v)Sp>ݼNS=u\ WCrq|kގ#1j endstream endobj 556 0 obj << /Length1 2688 /Length2 23801 /Length3 0 /Length 25310 /Filter /FlateDecode >> stream xڌP "]!Kp ;'L2{UUk  Pޕ `ffcdffPr[ O tvr@h n6 '/ /33?μqcw+3#@O!lea JG) _;=@hhjl Ps0zOj~KWWG^&&Fc;Fg Az%@tv~ P4S#<@3Z]@nf@g(9@MFXz{`ado{de/gcSS;Gc{/+{ -$)J07mhl7v7561@RD` *幘:[90X.wP%.[9MAmbg6>Vf濋0ssdҰrrʈ$#8x@'Ԓwxu/G࿔,Š |"~V@xcw < `$Aw|` Ae`o_eQԔDE<> Vf%=oecW[P @o,E\߆lmZZ7W(8j9ZʸAmr)[Z-5~_=PkсNpT@oJ {S' 0vv6 8>,[4zkL {0O Rke0,;w=!o:߉JP v4|0Quz~i k{ɩH !f dWY'7n$")Ϧ}zN9X9ByRhWBXZ+OۻYoIJt~gl>[q5.=为8hӔ>Gi؋>kCKk (GVm _X RTC{ mΤJ l6@£/]w+<3Mo9D^_NcC Fv{ʅ,03rC\pmR5h끦©f5גM('0[7p#J2RtU(^E,ñc]Fn4J/BV3Xd,a%hD4$lh箺ZA#Bt4n[+0j`~RH4D|? ٹ;][e- !15=B埧iO&QHۀJ:%$\<(&; &ZFO9?s: =|T9$hA*ɑC-ܷ-Ap2aȿ?6q)gcj t8ӧlQ\#S,_n=Y o\?H #qfv9˹jvm!Zx7%t ]ݝCOMϘCx̛ :5>8) z|,R6\{Ȕ\959Q^zl191]Qnx"BUj(9~V%Y:Nerr_O5 [zpNxr㋔)&eq3jXp7ܒ2 >qfuҖ!!VO|(K2~["ߵRr7_]fzb%+Jw ߅KX=HfvNX4bFD~>0':x??NR ~fwX~Cv;*maA]J3YJ"Vs]{PV2~ϓ~9JڣyT|j^SB e@ʻaBYP* i͇ڭP0/g@>e &o&3vS(KiШ3Wa]pWo|A& e/ D  䐄;Mgua" 렀`0J؍赧pX]`}oG +rZȔ &8͋,sYAaqJjPe]g1phUդ}uwKZ~4 FVp /6/UѰڱJ;%c!sMz>+10=|SQ]=8F`Ԟ#!4ƈr`(lɆ'l>퍫1i)cHjo8>^n10gj.V HSok"b NQ)oˊz(?<"_@O`KTrINAWF͠8[j[uoz/*,؟<;N㥀$"l{$^X8f?8ޗF/|T~ ?) !Qtvs5+3,C""<,JQ󡂆ks}cUVTsLXiߦk[ }$[6W lM6O΍aWK4 ;}#w.7>'d @wypI݄sr0IZ@%#6e m=$:`ƶG2q pQ0#gֻ˦wov3@Z+䰷W{awBs]'7]IqKV?Xx,>pK{ˇȬ}u]8I#Nu'a~n fW9bNq—ǟoj+/ƒ=KwJ\p) ڋfS!(Vf'BN U˦)Rm!% UgsTzF.mGInvֳj}8Y&zvH2.;_1+GqdZ*Q~F+[@ty\F?P @v3iJe~1@AYGr~],il00aij]ḩ$چۄveLդ)ߠ.уr% v5okEHvya䙒{^A|Wx]f2cL>V6K*UZ{He2 3G|֮3k8Ha=1L"2ZI vt"5cu@l{!Rc<2L*PG& aO?kqr᲌k02ELm㡝Vu|W|m! >Sl57?*AKg\ L: -Y*@]d:>|'t)Rf.jVط=}y6rL(~{bFZ HqE}P@Aٸdwsnoj8 * D>jܷlOTo+[}:-~}N ?M9R RH`\AgVtuԥJ!H$̅ E0ծ/ypp]7'{q n͒әO^U5SO]#woBڬ} YEًYSN@Ɯ IIS\g4C%7 y+jEH}UX]_>d^\3 rYy6IT?)RϊQ=,ԥ*"!̲ȑ{ꃈh+hijx:d8uoB`ߩ$4\0k^K +ߍށ[ "<>xW gee), ZkY)tE-o5e}?F.sR9B0y(NJQナ"풻%LT/hC;{7K$ăD˄=Q #Jq LeP媰yVYR&0 *]:knzٹm}ppJ^ ݪ2JAQWZznq:%v:s MtP7yxe*R:4Q =Bb{XU(`w[׌|6QKl+C?F$s!ǗKn7+h$"5Js31|# ?xk87k9v<ډ 9uR}:*)'즩qK֥)ie^zs=QFQ4 Eȝ|r40*;DgV}=C N`y'4|콿q(cETUJU?G+9h)qV1 m .a!ԭQ/u2Aѵk"~hG/ %3e'zD"|>#مsrP| \MM~g~U u3$!.b>bjA4"G39H0-@e~|yv: ?Z}WغV%F[+!Ȱ{L[nxwwKk C1 ϻ\ןZ-mA1]`Hm^_9Py?#=VN쭋:^ v𐽏DLU!+yF G5ذi˺P6B_a1}mN8QO|X: }zZ-a@aqgj-.&8G[jL-s/7ҧwb{g(MMY!g[(x_ b\ k}LOL^X*u-sA6$%ؙV}1D| H,w<#zC4/nsQ+ώz̢j3=$i4T1}:(g@dry|+p9}[!k01.B@EDKzqcڒ϶&g3 agA$>8Z|ݩQOY=5NdmRb|;¬ԞyA8OX7Sj}å04;jѢcP~fC[?`qv?4~(m*bw> ;a5 잛'|u\'ˏ8aEO M^{\hTC >3YdVMV̡j׋$W*6QaN)Sr6<>55D/-o$g0;3'-'jgn-Hft?2t]b fa#%ԧ{3A#Z CY񓊖㛧``9ZWxE\L:tKշM>F%_tH 0mդ "NWٕ7|v_4%O\GQtX,ʼZ8L9{j߲P: N$}9H<|XK>h9N~R3LJw 7$!]VGA?^_JGDю:a OD]j1 #7EwhYd839zjڧ[)c)=|@~9#*[[vM,,T/h׆pb5MiytE[H;ITcdSٺN5ID8kEI|ۊ'QݬPT ,+}3aVp1/E~Lf ! H+bGPz3ƥ!lqn^/# xf"%-e~1 ԹqE3#GM(,?쬝qԲdhu:υ_Œ&,?rNЂه M~:ׄUmrU[+-bX2"r<]Ų#uaQORJ9!;,숞Lغ4jC$;@pVt YUr݉YٶJUCqp|cgoZCAwAM;qVf5Gme6 )|dHQ26>rB kvaBS}Uy9*`dN W9a:͔Lۗ Q\bhs1[&hz;}_Dג4/Hae1S M֞x{󙊃uF2W%sZi9Slg['-^P y'`V9sqM+v>siR_㘭,?;"O+clLZf ->#ħdJ,r"߅,vT)W;c[T~k6]nN"Z/8u>ۥ`1.%'e AޭEʃ0{k!#'>[KqXVqrR_QOl&1]ؗI?mO2ϨK J"8ZYoCuc47\Do}F/z'hreoœ'N LSU:^> nԱԷUL F3BTWE˪jmk:h|8xr\MW^[ Bŀ'qyM y6̈fз/oSg 1t8>ۗ2˽wԺftnٽ\Ax=̐ĖDs'us}oAk$y$AA3ٺfl?m[.Y| ϧuN@[E\ hHPa* ~$g`M ∋)%jv (Jm;8;[0mY0?v O—ҞnW|#|Yj0>IlZJmmԃY9N4E&:S-T2l5a!fmT\A=v(OUNYD^0iky1&bq>A F{nb|T!H)eʗv!!$ dYI[eF3?SUV7xZ.F~Oב[ZFyYVޮjw`dD_x,m(\J8fϨJ$ d0*r Mo*ӀD"sX@M@ʦ(Nw8!85K(\ث㘛&m LlDх{JfbVzY3٫M,rxm`:w'X\hRG͋mG (~S^erVBܗ rEIJ'C:R窻уa -.ۏc"ӥۖE7ZRoӜу{a}ǮmZŒmQts:T]$Լ߿t=ڕFZݧk)ྛO ڄZE}ڂ!vA?!Z1,ŗ+ d>9Vn҄iyeVQߗ3k>i)oئ3 ܍8D4y=i d$x$(9) 穙(v>5h $A"S F=@|~b bvWNJ*:)<QfQ,(r]0Trƽfq98H ~"Wh~(|,MyEzmiۙÜ'{O۠&bǣjp,FM թmH\x|^.&<2#&Be&<,rVv)ũЫcϦS6IdflEbtt>*(OQ^gRw"~9EJ;"$[4og)cKm{ "Rx8?eG)ļ;H m\ط9o Y}>]IKcM-XQrUJ.|a$9ckqD=tK}C梨]Mci/M6WOp3f5E%>͖ObJiR噔uA֐ kՊu5zv>}~HFVc宰6jz+ ;sYe*FZԢf7RӤ\#>0#Y$.(kAxu4 Qr%9.oBUpUE|Kd׉pgjqn$@9W@<]R gN1pw٥oHH_\ªoU›Rʨ)PuoVvQs#Di2b,|S ᚅPy?q5+GWdVA{͜n-S GR bq9 I-jԀҧˊ`wZ{t)25J0u==4v N'D?2Lf1ťCq|a^Yе/\' fva(p~߲Z380p?&J^( B!< ;HH iy{2K+XL&s[:/Sl*I//ߋPYi~!j4>ݺ#yM|H&}4sI &wi<Uw(5?,}`pZ8Ϗ*Xds٠"b40{k ^s*Zvn-|gz}<9l!mrS*[!h ыqkTDڬltiaho?Z&i ~NxQc$1 Lȫ? ڿw/`x΁UϙL|*Wo~i,ȏvIx8Ժ%X[7 OiL\Ln2B *Tp:dς)]ToJi"v `,/b >4NDiCɸcؾoj^2l+;ԶrH_ޱ!@{8v\wl(:hIi 'p"@yUWvuqE'^OvVBZ-&ɧFNࡂ])IᄊF){-hHwr@82r)-בLfv>zm}st )_ox۰)а-2d&gT=^Vo ŭ +p*i7}}#y}J]gIB8ު0]}h ,NCT֨ew0HvKNO@E8`=!|W;SjRG]# Le\!'^]?99mgpxy.0&#;f>=?g$OxT49(g.Nbxi*]4RH}F흛NYJu`;9uȵP&_zLOAQy͖ y+TG03jʺx/$ho%=M)\% g;@>zϔAUs$Jg޳Li ꪃͼqgpzuvF# FΚt++W43J}KPVfոf m"T?p.J)ădا-f"2̑o$[~鲕߂:XA[RJɷGI,A8vgMm[HZ)5PkџEIY,`;R=hXyBCx[wx\9>_\rvkxK+6 \y>qҗSc.g0-0BL*(YOz7SiT 1KYM⯄c]AcR@uFD?ܿ?l.k\dhO=en[IV}Zzx_JKC^@2WVfuq3̂9`J|3U"IC]VDr]oYQ[QZk8I5yLzLf9^t8|~H("\34<*/|/eEX6Cy LɅW 2{٫s7b&ڣo3WU S_O|fz#^a+!v kǺؗz]Wf P-ZmT(3"^WevJhϩ K^OU̟avKqċ.RTP!C=?54FMRwۍ|uɻGVWFSƑe RϹ7eJrt=,)xƱhK3¸omÉOb# p+֎IZ)O˨18zWg+GKX1O3ǎ#)-o&>nQ׻\e6NVK8~|_,^k‡G[[*Ɂaj'O!ϐv 7wE{^*eK8muڢaO=Edŵ nj!"^4w߈Nb p։#9zn?7wo^=.pn=-2"k"5L8FPnV]E'{)|OESAm-=z8G0OI'EڳRzHRyj9*yGap;ӝu:0?;;;cH{K$Ńt2sNXRQODž B&’ XK~H=~Ed B5SA`)$))cY_8%Z3U+kz"Kk.(nb{yJəWU&P_& k j-T(o޻I$߀nVG|GʰCC>B/s )+a{]K &`AKZ(˜.]M0t6 ԪVR|LMLO-Mm*-WcejS%4dx.-"vc)f4m71 Iv_tuFED:d>T)xk#Ĝ> ?ƪE1)hkN%he&5Y"IzGHL~!nX=A*⭄`~LEL+m+Q\}r N9^Y+xh(_Xa6=lxX5_ԟ{Yw9O2=]ڠ½v,I;y~ga`T4.g`j}QI + '$U`NVW5@x_UЩUR/(1ǩ ֆn[-6i֒ٮЕ)rqax[ ?sv'CH2iР3t; =*Oq!GkS]r56v&v"x AY\zrwi+F 'Qĝ!:I.`*BRzU|[H2[#_N[ʉ V:R@]L(l~O'Co)f"qi[K@sh  $@2J(i0lGw+K0E9kQCzʅhNV{CB4AS$6TكozEy'<Bkg(ξP= 'NSti*<cXbQGy#MȒ污]ֳq4Y!=PvBHkl"utL()KX}PZG6$n`7t'٭K>LPL)/Kkr[BQ(ڶ*] $qH~t{| }dqm( &3odWND,3fsOLZ+|ȣ Q.ig`\K˛x]ZrNb ZqoiG~r(A?nпPԙ4 ^Up=Wg|YaJ)GvLS;#!;ϐ2ٓGZ8XC*ϟyۣ%)ePt{\u.sT7U'?'md +,,Apakخd!1T5[69eD1c#YUiJ:~ =ny*5PQZm* QS6Ɍ6qK]@ :kZ#AR}/y#k!f#;yԩ @G yC-mQAF2@`B9>V]i|byC]~g[[#&be{G)U `]2MeϪOL(]\54jTfsX&v޼&5A;V"ExWʰړ:6!"Ds\)3Sֹ9:d6UqB+Ǽ}6L\:e?f?%|b3xM.6H9,{QLߡWI6KX-JʐwTD!CO=M<_uVqgc'Vr \=FɑbLGF3S+ 46a\s GsCEV~pcpSu .ZX(9{4-]^y"p'c6E?s`rRbLV׭=pSSEL~SD f=%>=A!Ҭh{7UL3CfHU3 A }uI GLhSYZd`>;Ƒz"Rȣ]mJTδ=#a`CT9N#rMC&m llm d{yzԵEvڞ#Pi1`cIb?hpobd3@Qt#b מܹBQ tWlqcskb8a\ EA%9S5j1[T >)cŅz=3ϒHpKF!^ؤG6~y@*!l/ŰhW]?k׫ob8[ySzg|[8H)9ƛ0 *WČ-%.cmGŒ\nIM'B\$1K}Cacj$PL *T)ȅNi@N|(VB*)b~k 7yzuUQ  (P{b F ܚcq[f[|]aCrB& 9xU~-ԙ!b &f.#6ɦ⥰..gjf*n9*z`J~؇ʊGe8e"9PzeTgdŏ/' Qk4Uj6{enTq3YY=$pb[ᧁsfF)dYa:= {-=}',ɶ0<[xQ#F:{y6@o(dRKm*ߐ>gBNFHJcL l7@H1,kytˎ>RHU%6$c"2^/kT:jè]Y`WO M'SpZ7VA_R4&6-rjX<( z+\Ys\.wJ*&b>ƣb%B =y{T!Lե;RޕspgO9. kPAکD(eV&2/`Y4|'T+uumd42[d!#񢔈#QN`jݩ{꤁9@pĮ1Sd6sC- - \ɒKt@`lN]3zu!UN3JSo}* :hC=,sN=`_ax aGe\\ >&S0}x5zw '\LP;}F<wZw ʷ*0؅ E:O9RJiJ䉳ACEfQ(x^+s/0ُ]ƑTbXe+!_~e2V2I <1aaAگȅ#r>?(GGjW]D{8P3mY5_C񩉻C(lB*'+>{FF>bsrKM!o^Ͻ(3/JV?P!HADS9U `boc{_lN[}Q?GmΑ˧1Ŏ!Q6#T M{*4z}5 i‡18wLKx~IKf. YӍ=?Ϛ!=7]J?A]F ,>QXZ=VսCAw^4<6{..ak_WokKEjcZ!vW)E~kqY{DHH#Th 3Lj&ǚ?"s@ȴH?T]\'9B&uj*>ڵ9JML}ۃ@({ۘ 0Iv xHUJB7<ޚoK,ϟ٘O*T!21TA31(ԄppXIU5y$(e!(w^$S R7M -XrK Os.{7=NȾvΝTd\ K 4 ͧMXZVp3N/?}?~|r/2T  s_c̊A~,ٌze~(+R."0݁#|1R\U>=X5MuHҿ(C<&mE/i4Wpi4nAa0y(;o~Iuy3g=#șVE=@tcg&aL@%{>v{s,4S yuU:m._:ե[ [Q(L4Dw";0.gB` 3^2|n7K..ո'VPdHOe˸ @7m[3bDx n{OYC\M OedJx c>Kuk dH5?!L@KJI'4i2q< 1^Oa epz9KUQou d$BmTǹ~3[a8TV{XvQS797 D^c~sa[I,L鑀TN;z0I_,sm]E2UXr:4&ʬG[U;틎W }KZ]{l%}!V\|AQn]s;NM o˩CI]|oM@0KvǾ՞۴pQ$Z$k7P%nL{Ee}]2R"N~N%ŏt_ -u^ȶAUΧ 7V4 ԡS w'8fcqfk$L#p*ηRD2t!hY\1Y'ZH6ptz逝{Gm 3]ww;3]q;Su&{Q!/ LD[DEp] {6z <{pWe*76=5ڏsA7yHt"hB?rM5As%0ťr퀿ߐhP/{V7ouw( J7g*J(F(U}fבuy7=bX t\x)M^hb0. W `n\iL2޼ב' VsXՁ)Wb.C -W#{}\b/.=<ԅ`w c2dAW_ Yt;ͱ9LAp q Ө-ɞ,WZ'\*vI]/9Rҝ`?yAՐKh[Q(Ucwy R 3"b-庶4D-V?1:.Jq-װdm:AFqfGdVDbzzldT-{#1cɳ$ pUz.gDJx&`\ M(h!nYf.LVze(hw_t@N{ie{O+dLMIy!CbI^* H9F~~8Y*(DLR9<)oB.g8 tWIhqdmnrPK|0QqkFl`=p&x ?=RTjjt՘=w)t'oO2/h HKgҷS826$Bˣ¬:F׺Ôʡ)?A6Q& &XwS{K P@pfX?]hŠ[]8> M^+~$Bf) 4$QJЈIܙe4CY&gwbVl#sG<$VDg.-Q'- WʦH T_VA ,_k\a>b﷮˄ .OS, ]%ǯG.]--5q苻:I[aC$ >[AZu'_7b!.X@Dܐ̴`,~ج}sWl@rz-}y0̕IQ8#ZԇՔnP]^ ou|wIG_w6(:)jZ /X,1cqէ{ȱg0$Z!4CDW4뤚2(SSθz"; S) )<~E䬢Zq@+|e:"2jz 1BՕ "u$YoϷ. a*oc:. ? nD MBANUx*YR,= P -͋)[Os20CMA# .K[^1mM5r*&T52%@2!Vd?˺[~W{٠nۙA5(B΍+$% 3aG(ˉ}cwDd6ϨJSq|Cc3,}-O.vQ C97-SF-K227O7H2EW8vh3Bcƣ P4@18<ŝb]3`+BdmR*D͵Rh~ֽey*s_y}248_WQ\g NJ\ g oOÂ7yw[T~8ʻNj5=߅YLf* <(*/r,s5{ۂO'5u 1Prӯ  -]O{ș8^ endstream endobj 558 0 obj << /Length1 1766 /Length2 10716 /Length3 0 /Length 11837 /Filter /FlateDecode >> stream xڍTj6 R )9tݍtKI 0 C4]% !Hw}xyZ߷fvoMik%H spsEj< ``A1A0W0"/, dYyjP@ <@0; PB@ Pg/XX‚@0f9=Vp@ `ÝE<<<8-\90[ vn` kN'`ځ]@m0A\# 8@GY MVlܜIOD`_VVP'g b ; pO8;bh }p;ZX> x\``g++\<0n\zHYʣ @Aa>^q?g3qjan ?;apsVp% ?^> 0>j?GyYC!^/"'#p8xBBA~ ￳hZ'Tb)O??\Gт4nZ=~q?+?nH/7_ QnGAwV d vs^eHCls`W'Z [,~/#҄*n 7p}T_.wIy ,`0 / ࣐x>܏h K.Nx`aQAoH%$R\/  ?J?HȴVA| ?'e/  l >r?f >f䱯_԰ԮNe~<c%_ OU[`_ ' cnj%b_qY#MU|a gvR;N!|&5O}--QUV;7O?))8t}\|ZTr]܄p4 .==C^`ޖsDEO2YfMҢ9(>;ĝ<;x3@φ3h'j{Bǵ9s#RJgc>2;*$3>%E+)Α5w܎;[ڗc3FQ\HoVڔiPUV<[-]5ژ[=9;hDuavDIԐ^0JĜzF-ہhe%asy΀u:%]EʓG*VO]#Nwjڒr0gQ E%e6jb2ɒQq޿AXW ]]+_s$lmčXۚ5{ʏ'#NL 8be:2X5T&b̈́z E_R ẏM˳<ϾqxB"i4K\dػ&kPѠy# Cw*@De'@1')P7^#%>@ V z=2"ƫ3FƺJ/4* ۳.7bLܹ'frMQx} :O_ 7Y:oNYB~iNЉq+ir]*lpaVӻOljG |HL;q,gdވe1wÍ C#QUQJ꧗xg~Y;IM\(HϨo%徐,I@X`mVY5BhI.-!h%mozTf7p[r «@}ݘfs9j0q:?.8-8 g5HOK?[¯wiL`76+qSc=Pڱ!w@*Q9,_SÌQ+)"zPIψh]0;Jₘ9V3JҞk4HJ:=z?%iGUh,pL$nMglE*}oI(h]z"Za{.Z}oFd/U^HJ`Z]1:J㷜8*jUwuM_ö,g;,0W,KHC#ʏkJIW C $ ʓBvWoAq[̀*%ȇ)@ǜ@) 0Bm=n\1>bZ'v_q_R$%Ȭ)1o"'y}Bʍ5p:y5E-hf:7",6zXFm K"^$bsjGM*PJ,hKўW"mP9:H4î@;ºDPiAC͘ bw‰ቄnC]ۛRjf dZBE-CPV;D3nT d/uϘOrܔje?X6DFa 91[DD|-1 *8!2Xo?ӗQ"B+k{~",r+/68)v1 XuV&da2˻6E95X}+;\ >q)J_TR>mT 0̸zG{1Z! >LD8 #a,ښR wOҚT^hxј~𐷘5 vzȌ#粬RDvb#V'nm`ݨ@Zz}Y3a8/Vr4&e'xD:AJP; m m^74ژ2YrQ6sIt f{,kB|~ve%pXRrF3jQ|z Pu=y:O ! [4*F W;KhLgf8J#F9bT_收v+]l`ߋvlG5z*=s'~߾^baɾF[K]kZDr@ v tEm'@%);ɖcqصzvϗݻ_ ^M>wHmE-}c^FmsJּm*wWP%_Ri7![%݄DR9&f??Ҥj+9~/^U5ZOKY- (o 9K Р' ߳n I\tJQ<|h*-4ku[`, w[q]kxI[7fS.tӼtLY|n6ǥMf;#ep[ >Ȼ@zU]/(wɩkL>U-n~V)}FYx}r{BLB$"|5| ?EAҩͶ2w =iPH9J9[1/g aQ ryX*X0[^])p=kE<]k˻W OM0|ztD2m<"U4 .ag䧸"߸=4GN:b(dG3}I%2&(Xa>0G\]澂GG[ѵ/+V!M-z483$b=R_zgCl Ix-v0O8.3Jczk9=i+^ՠDwyaK'm2hqkhylN .XYR[ ȸqiW G܍|o /({4d9եKąIl !:~zz++=[!~ %-!ɞ '_aS۶}@CAtԻEj1xї! 5h+5,;-o֓0]ԏVk.Yt;#$` (VQp&H92eyC| #DM4z:B+rhv!)}xNIm񂶄,Eۊߵi[ G~F +h/Ꭼ'hWX=*Tj ͥ`iXJ(E>g_}/!a^Owfy8ӖH#_W56izS9Jr'[EPzMnYgT]¥LBi+T!MQDOP8ѮzmZdJJʟpj lXjQ7"!qw ͇ W׸&`dCZ3F`x۔PgqЉWӀJ#_ :'}3t66mXW B> [}7KqbRÝ :IRy<'h`M%>;L7;0>pL!dAzMYYd~<gsϔq@ĖD|&AX=}gxQY2w5W[3>Lr_ȭ򵬔5zT8/ڕLj{AH(6T~'\ss9OSRjy`]s7v3wmӞ;yc7Βt?= RےPŧn2JL?cH ʈh &A'\~^#Y ~M׹> 7S5^&$*,+Nj ]h&]=߆e+r Tȴ2__8=Tk(Vnli2٘2jc[6Hm:I NYkk 5\Rzb@L@Mz [ `6UZL;9N@XT[H kUcՖ9#"od=*ỈS>F){q jܴES?,f\Wq\]qv5YOgX' 8bRPrD5DrB\s1,5}]6"SQ׾!Mѯqi_7sGVc l]ھ>x p+3u7t$3t&VlQ{ފ`\ =ZwR!:ϒLPsR'p3>"} &&=L,U7͝ӼW "u%"@Ȃ22&w. f,M6vܕ.4c"i=[~v(W;wrp障o'єn&>"T=fUhA \FE$1'iYaWϑK{";C񢂢'cL4hiˮC~ v&!xAIaM o!o ._WLK8ͭ8scWnd|5Y' G.E#IXdj=-[&Ѧ3͹ͶKg~kN; D#!HlV\{L?b7^z)3RFZzl<}7}jiS&UKOՆ )y׮yԃЮ#(˟%p}0RW#ڎ(o9_]MH{MI-d|ez%-~TRĻxb3m}55=}EG,2zӜޝaNz?<{!½ex-V WN5OBI=>zh\8 xZEf=LW(}t,=z|!/# 99ߚ,^C>eHD,C$7,ڬ'24Z){o)%mY1"#/ֵ\+D+#]n$&BC%){^Ci'r#rO)'Uv}̳wA|~AjC6=;OCiJQV<^~\ |GpXxPk= ?;\iQDxl#nUJ$u)DC&,AmXu@ϴ0ZT:;$Z. B[L.l7 k4Or1Ds:9>L2/L)ٔ`)bsD;CS%`ei9PN#3 }<k4xG!N6ܡaGcH.E";'͵A(򺙨m策Q>q&w]2O7D{W|yam%v>^y|~GBNlf;;Ic4\o_<Q,ɔ@ze{(N)!|ظp 4}= mICI AՏ(r!N"`L> gWp! ]–倏J{(]TE`gw'~Ma<_~hX2^y:tmd][m#'GaGl WOWZ~Qz&}j<bIM(/~/o5O3yuj&u-(M\3e)ޘN V4. lm-VwFE 0&IIzuq(ᠢJ!m՘!8{G{M&%7Yb/VS-Ee+ >>CVwl^qٔ/Ug3|ADPSP/9|tnxF(9y!N8V!9Ձ{ky6/$Ocν}-`הz tnmK 8KCkb3M)[L qS1[_XmW,=tSnn!w՘Q2%r9ȫ( J4O8 Sz!?βVW{:ۈE&nb伀mb=bڝ~cѶoUA#9p 9VTJu }"L%#^ZKiīgrM ɚ/A"VӃ|yVL3?\Q*xdߤZ`:Y_np0KB|a@F#@A?3d:,4F3[(*{1~L0/;,! :_S"X|SIo(,f%7uQ8g\6!W[*S>=>-F7#u $ rWbэ[1?];ihH1rP= ;=YԊVDQ-^w!e(A2K{<: 9ĩ6Ywk[$`:j;}gDu S 9 2a Uoqvo6dk ʹt-bu:De*a.>aA VB`=!sb/:Pyq؈]"Cj7B#HtlZrzyxokitqOѦ$]m^8 ,>I9􃄅 ],U]Gt" m/C֌A" N&'~R"ʈ5C=/@;2*z>C+tN6?`m8xX[Ki-rxiب!Is_I{'>l$JXj^6W~l9nlոV〞Wx ɘ~Kғ1 TUv. ?h.5Gxen]T4$dL;IJxJIӀ_ ܄%?T?Bة9@6LȝjMwy9_׌l.h?٨t)iR |w-}fgIͭ1ծ2p)eRhR@Oɗ9jg~`m,nykk6\#AWHy@NX4W[G$ P]Ҩwbul^n>ƵEɡ59,]FT4#|QñN]Q[0"LSɑ8!~l?-:jE!G~tUbUO6ixQG D| o^{~u9lK8J ԇ\[kT|q5q.K`#X.r\ȨV.@oYM؁5~ۭ_2;CKME0=9 Q4Zӊdxu}f/S /.͛']@cʁFWL"V5u)L0@Ft(Xu|X҂/ i~ 5ŽNGFF`B=n\e/7oY#[ʐ:v 2?k*:^c7q~Y8iM'A F6E̳6E/Djko |U-=AJݱe|"6]sh+p)?ռ;M(bLea7.>¹x54hǼN֬ίjv0q@%hrNlB_WӍN5 n~'7l1^KB\+$֑\Y쇌ˏÁIxW!3k_x fƱ":cD4\\ͱ'HOOJܷ-\J4R + Ӯu) n}ˣ+-CyCn.]K[w' V5`{,<(Ioǖ$b,+pN->Ьmκ;},p4l>tϾV4oy%bY/ IU--${¸iUf$ #<wӮ w,􈚮h 8tU@ׂn=gL"ޘGG\sK\| kv)-\Cj@ JDPU9}ꝧT_c(nlrkq endstream endobj 560 0 obj << /Length1 1767 /Length2 11008 /Length3 0 /Length 12126 /Filter /FlateDecode >> stream xڍP-.$@#ݸw!H  !3sUUWѽ9k %( 21$x̬(oh\Qh]@0$f/6I3ח8% f`qYYqH,JyF q})Z :13 P2s:T0h@,@@Wquuga`6spa8[ 1<@6u h =0@d(o6 ?+W3g `]^2@gKq"@3XF_g`cfD fG3l *Ҋ̮3@3{K%bj="oS[J@`WI/ځ!`li{K7G-0 ('Wȋ 5:6,58~_&q8^/_(>.f@ߎF(llK+h bZ_. 0`}_F/򲄀 ~Yd%do8`bbqrx8~͢j RVߟ;vi :s)C^D qCV.V?lJ#OYo߆p@^hEJ-oϝUZ+jb`k" Z\-l˟vKfU!.߯ |/ear(eqb{عff^(/Bb% Xחx~+36, >߈7e8,#>?ob?SX X//NA֗ex9b f/Ҏ_ZWjV_j; rX_ Kn@/uun/"?V h8 rS-F;.4fW'gѹ=]UVm)K%G-jm&ꓻm( '~"0i~}tmƋZs+YWqޫ.]Ԅأo}?~jfa/x6 OAkv\QF[Oׁ~H'_$ꑢxv :@R^1[`_u̺OdGY;Z^;k'1yj,uwXHE:m{q:-^f  ~Y5s5{i ٗ6BX `9iM R%YMI/} jk?k;'lYl_x>,b>77oq?֙= ڿܲ@*}8I0GwDC=c`CjVFƇVSFͺYr$5aM+bWw$r|PtsO$]1ׁ fKGɂ'2t$ӛuD=&bP8}UR RCf)_gSG{Ñ.{5U_<4S. DZ [f -z=Y 7_ǩj>|nG֮-e"?,JȻ̈vu<Ѱ89'Jfz8S}Ix{_1+hHh`$^%tt2O&^rnv)eiuUB=+5e^7eہ<#AC*`}?ŗxGUʇ7:UF'.o_MFxPL`) OQ介\~*6򳜌_#=s9RpdmRb\HĔC5^Aa𦚽0Q6YC2/26+Ӫ(ʇy{S .blJ0%!}Q@A#TU6 h䛫]hNƄ]0fy O Y6QbiaTsM)#KK |s8h[&щ2bWGCmK=Jt4{C\)|gm'0um>Xp)~eqHc˨9tѼԡUM͹V1T?~)z`VHەA{=|Nx'zx%-L7Ё̊@FRjңi;kuR%ي}C߄C-ׁ],VwPBehΆ aԠb)NW5)a%\.곶5 3(,eG:j,LGБʦmA疂zeբdZhCœ)UOjgכѼ,ÚyCYpmWb^$emciH£P򠻻 Q]ɏBgmϫ#Hv=7sym8L i` 7 ;8.i~Ū;x@Teb>W颩`#W@fd#ee|SaU[:|ƴqo-,Q)5w9b#!c$5hy?4=^D'SYHi;pRR]9=jA}=*Ǘ,$${5/G  tϖoz(oU9\Oo͚JpwU0UOXz-P_=oB)N`d!/<=/Ah--Z*j%mgcil}mV: Uܦ.K[͇e6"*ypM;Z@:l['&LR?Q$J ,ҠYd== ^bub xV1wxXfXFbꙜbW|AG坍B~r)'b/QAet*N.sI%>k}DDT-Ps['v攄AUe[XDckC:@dhBV`'s:$jӰX܃cR3oSk]n:`dPn\R/bid=QV:O1 [jo4LmcGPY5WW' [GxNfV"( =B_qh9b)vaM4Mz#]]-9aKjVQEJөݯ,|Fz7ߒ)#KhQ˕ފ8i.%RB}^(P* Gw%&T9n~ip|+|6e|ji9/o$c" :GD͞uӂ1_WZØܢ=ho.p$'iғ\(h%3t!Vv ~n7 ֌v,B[ lPpM\DlVGYj 8~>xnl5bOts[D7JG;p ݙSLĈAM""hd ($K]6Mc$Mq5[te5q|a1*''4]pm"xm"BM isa|Zͱ/DKAU`>zIKKn@gS9 +:jvcc6w]ve)+`ְ˶luRC+Ƨ,WeS܅i'*pc3tuFK&0킵dsQO&ˤWEh,DHM` fo-<4Ƽmt#A2xG!ѭ/'ivZMApؠV֤ø*Ƕ&džDʟBp ۛI`>GŖsƿvե^|TYГĵR @ڒ< \*Z񕵸WlnTs<. _ `So?.FayV6a7FWl;$<se#,Xpl*낐 AwmJ]5{|K򲸩D2*Z_,y$Iv U7AM8SUSyRaح?Ur;} j%l9لLmm=!8E^H vD$]7y6G^< 1btT7BX2PC*c=XhyM눆)pGk},i)f#h۵C70 oVYv #*m7UP?{j[^BR*C֛r?0b~No}fqkߨ$QxI9Ќ|M=B9tABeC-V4RZP_)qs *۰Nivs_PTr/tfU^<F{V%H2WX iw.G,h[]!j- CU$e\-H}Z*TOZeO9sF(E %(̫ ǡws_3=׷wJIZet; }#/3_oT Ɔv=%MEj:r,'{jx43_ @M'HS̡}F2(B垷Lgzūu&~F+f{çۻ  ѵ++}@ īye1JB6oG}ApЩhE,徸AuGKdsիe%ʗN*z)q4XƐeWhE8D|SDՉ7XR~k*ik*d|Z,*R :Cvl>W 3*|)&r+_, iF s>ɣb;J1G uR(TFhaD£ ͤB_kBӕyhs/3]A ÄR}=N^"XJBސooBs3Ob (yǏ}oZDO3tȖvzk;^6*~r㗡;O,>leI\D: 5:fH},\,l4QIlr(UXqTuf;a&2 y#Q w5~y@W9z[}3j'""v,0~]hQX_:D$'&]wՂQ *8r p#-`eQy"K^Ag5JN*N;.eaA\@|_ۨM} E$jΝGS{Jm-pͨt`"",tr!EoĖd }.fu"VB:im(>ӬEu̍gݓ9S#/YDA)@tBv BP4j)`)ltGxVRuq`SD%w=`d4 TGE\@`w-ZeiCxlo[jtw_amغrdA4l e?Y{ <$@As"S|јILmP\){[snLYআdt>*QcvhW@MOVaK\ӨAp0KZW lߥ\يpyDGktV[dҷ'\O|PWaғ=W,mnC;'er-sa|W|tLlw ݀p>f( ʷIMO{&j#e&,U|&[1GcwUY=L1Րhp":!2z493DЌL2oaȯ9qsxWCV7\tʫ%by/)>x]ҙy( JOf75#`Hk KQIBUTw+Gr`DCb'"o c(ïwoN/IK diS.+S+T};` fXUk|emuwuO6NN0{peڪhf}/BKS +,OAHPćZQ\>4ˑ#hdDsy\d|4D$5۸NW{'2\wfbuyPSeGfP~e)gkGTtz ;.BO6 w6hI~{Hgœb; Iz>2KJ 8 ;2f܄ҭ̖9e[(iY-4[fOV){Y<#TQ<ϣ ME0uJ^`rc_\4"AX dZò.wӪfW{y냺xj+#n2h 4D m.M5B^o.GT2wqO8l3p>3 o)vJ'2b& ;z (ZpF?OfJqH"í6Ő uY f)ۡglokl!~ ZKHԿ]PWeMup=W 3PC2l%e*˜cVC4EO:\SY3Rp'r`r0-pz{FI^[Ueqט#~ xy.؀?-J*̜>uXf ϩSȅA}rayjTO/_tGSZ1{W֘/(ϗi|לD0Fk0>Zj,JC,!(ueuX5sY ÉmlGAQ}:*K+f:3hg&mv'R (="u4~ڷI/{"0bQ*B?niyY,xj,@623V]M0GXB'0XB0*:Z5g`; #x" ;߅mͷD^}"rR-T1Y|!zQL kZw77uso㴓DIZ,ymIlGptC{xGx)5P22wWj2t5h2 Ꮓ2K!0O SkoM|[h׾62:КJȮXjzk00\iD1\%)5hV䠢;eanW|6ʣeFƨd)l]`}td|?Kō%T/U_r^9YST,dHZӕ9Q[3%-AU#>ZϊWAWyeKyA,i\a} uku;)eT\gt=v[q3}F15Қ4\Oũ$>ğWQ\O4LmH`l͈[a1@Eځ틦& ;8)v]ZW.mf"YU[ |SE/C…^}u+5&Uv{A|4gCV _!#UQR "}Em'xօYF^[s4(: O6+|Ff&ˎ\nUJ^1ou%Dp+8quh؞KĴ@U lwRb ;jY8&J\z]tB10hEF8Hb!^z1c6nza<`p:o#@fbU"gKU>!?c'c[%˕ ٠70VXZфN$էH76*2{س endstream endobj 562 0 obj << /Length1 1357 /Length2 5950 /Length3 0 /Length 6889 /Filter /FlateDecode >> stream xڍVTZ[@Jj$C:``I%钔nSDiި{kfs>{}g -"xǚ" P$`cӇ l`w8ocw0)(7M J IA P_0wIb `8c;:_K @@BBw8@A u >H)@J{yy\|0wG\</ =_\#`;@z0; @!6`(PgT5Z`g?< 'tEJ\\AP`q4j A 3p@INBWwpw+8W%+Bm\\PW} w } YAvZp7B< yL`o_}\A_fT~0Wp!<8l!65%;; GM 0'{e- kk>!yy7W ̢ UXU X-`Ͽ0b,7̀"@ԗC7v?Qpvr r8@q_R?] x'4 w%BJo6a* apȯ?0lP%R(!@ dh `?@P`),G=KHlN`o 4acuXEgM4.^9V$1n2WEfȊ\@"kM:W?-u7 f?Q0՗v7 vlDoWcv'ΡV)Y~1U!Nd7 ,p)Z/#$gߎS&؏3Y|u9T/OgBˈyJ><'F3W;(E^RmݗUsm_a _)-Y*-_R mvf| Y{mlq=qǹx#yU"4@_^7 Bwm ߥ3vJ LIžzr+Rjr 5bIzJ!H|1i{U'2p}s/ cZaLMB1pۤ} [ Tܱ f5BQ$#ܫCw --io!r8]n-= x5LbEh{Ÿ͝Y_x:<QMGzl6{]Xϑi40Ԓ@ھfQ?%wֵ Ÿ\RRI*ڢF,xfY_%@(D5PYfi3e12Ey}q(l`+XIK%[tNvMCfK+v^n߀ P.:ɶ&OsD :&mއ'HBmՇvz1"߉<;|}>#0RjږRLLj_bG & 1;( .zSFOe͑=Cnry Y 7v Q(6Hɵw{dnP+vUb''>J% M\u+2iAvܠ'Rl'񵷾y>-31N„F0S0cQLRO !^yu4'p?ΠL ԜtΈ 3&$HWc|jw˛. Ώ; Մ8~;Y S |$5?[ɾjx!8hX Zľi\ƨޓ1:en:<-S(G0rHp|[,ؒP=Ǖ !Y jZ}7xCT{E* 7\DB\sSyr=4ض "XU@`ZZ K~8wu4xLSM 4'Yaҡb< 䅮^Q4R_|3%MR9Vl(Q/ik"o~~:A>,{F#~#kQH4iQ)s;H&b$z4zZ6NZcĤ6O=p`T'SH~,۽R]@ ?@Ftp~iBx]e#hԆhB*5M57u7g,'ld˵PruʰvR"G`t2+Fψ N[o\7L>OQս 6@7+t9D39nRޥ-18\1 !~?mdk!w%Y Jpo"%K;>aw2:+,N-Jw*u R#^͕ox*oIˋHp|kgfUVƾ4×)yt-B`Kx6!uP7]0avTdܬvOhJu!*{5Ђfk:iyńd9ƲA E'J" V (ڒ Mo48Z{|pl3}mumgп (\:&Eኅ6+&5i{IDiRf@iVO~I|j)Pٸ K)1E.(>8?hL#5溊[I6TN~c3*zPT$813[{bGE#K9Dv BÏUv*U;Y~mVrUNY1>x¿`;"D~W%稀ډ+#v΢fIۧSϢzЬ+08e-N2MͫNʂ/-T2\)>w{R6;&#xWpw"̉g3|m44/Ȟ?Zy \1K8`}OHV٠i2ʵ-x ޣ#8*zdn|3Hi@M׽axQ|~7G²inmAƎy_4-Ź]FEH"+PCҹfXi(?:ъ|u+iK&lǑP ݮlA9^s)D&8C_5l"``)G 12&zm3x9`iyc30)7.'WĔW4؍K+S588 bI()9z#{n&ovl?EkOu0EIgpݚ$KJ|`9ЭVD,2,0if6vͶ&}-K9`f)a.evMH~U\"䩑UA [ E\}l;}¥ =|PmV$UK37ڐ]&)ECN遊^* uV(|7d$\pqFp8ʲQo.IK[R{sR]Aex<uI=[Nxa9zbN.5Db9j^D.phr }t'C:d?E#x s2^'Жd#Qlny$'#藢V`.@O \}ԡqB򦧇w6)hhf2A}r1w&~㞤9n(MIo͵X\9_lC:w‡iFOTYT/ËpDɄ%E\o)UAmjꪍ7Q^sA~|K8gBYãWL>H:_1`X9ͣh_tqI} BvCu… Jcϋ/hTB{l/)ݫCOA -)lj|gxkk:7T}fy?-iM\Iܽ{6sEls+ge޴$4.'>*4мMH&rȖ0/=Q ;x\ [W,O4e3:/|%QW⫲铓uסMA6ie/ 'l3S>SI&|Mn/WaKvPw\;] AkGU?M4$n 7L [1`<ZyNp]+lMlJTTBT>>Y-tu?;]hSxE{wT\;F?Pqˤ;̝:6m "鿆ak[5&3)8Id`澩Ld)V5s-Ac9e6eSDQs> QMv{7~xcr[]gq)]o7DAe]wdI{im~ןq*C.vLN~xAYX =fo}dガRG<߃BdLޮIW}᷹-O0jxDHY?Ĺ,,{ X ye~R!W)Qj]yeI%D"qDjCǖvӲa^x+n'mOWSYJ~-v8/ֿeo){:g/e& V'UzvO)8W ew)74ד=:+ QSf_'N!,†(\r:=&XCɟhIR_}P\ѩY/0{ $B+-sU>ds<~j,JurвNb4,xHL)NM$!buHr$<~wEi#3ydv$|DSvy?(-鴐m= ;^ 3i"} xb/qkv|(VnzlaݨPiRnRQ\-UWdZۑkc''l*^ryÛ6~l0cI=?%C|$\e,F)H{&h6aK^qQ?G"^(>R$9[k'7F "Jfiܛk?K)6ܨRey0,R8[ROبTWitQ|44ɥZ#yo endstream endobj 564 0 obj << /Length1 1522 /Length2 7225 /Length3 0 /Length 8247 /Filter /FlateDecode >> stream xڍvT7%1ZVFIFtw`ҊHw#%tK4齿{9yyθ MP0u#U% $2E`a.sB/ `1UB}܁`Q XB,)E@ (2@U/ 'F!a.g# KKK 6*y( ԃ`\`w aGxx @?h }aN_!߉ .?aAÀX!>H'}h 4!TSAWi`!m BQd #a@u]!?FA:R{_U8d`+;o(FPlՐN*( * b nH23tJS i`0 PIJ0/ "˹i' ±)Bp 1hXHN(sF x0wl}x` -[N({?꿛+lhe;E(`$PPDEDCȿBEV G [Wy ^}飰y! H~i_^x}Ky~)W z( [>-@3tvou?I?q_@ Qވ_ (K*vixc[ͿTCBQNKD\A!l7q`;N0  !Q ^B~S y`#@a8K,A I;(] Hw X>a\а P8WP4ovbKEà c׊M'J~Cr\/x>gDɼ2J}T_xY/꫈"σ/?6&G> RM"h|l U+G0įKÿdcĪ; ҋςbmq8g`'&滹O9vt>7IN7\ML|բՒx9_V؎ryQ"5$ 3$3]-LhD%98WwU[쓺*ßϴ=23$l( ۤzKD1)gD|hm#,X(hX*{ dc,I?^/|20=jv[zSkkC-JAInCۊsMc34.a{o\>Pr$'|hAKCʜ܎۠GPwʁALp'p̷YA/mNCd2">ᄯr[I<\vE"muu-L}Yk`70ݸb'bp58=^1%f&n^scI~ܧИxY,giIm3?rMfC,LCw,OWJ)Ykt+m%ҋ(zuya~ _;_(ǔ3٢O} lUod@A>;#Jwf\CD-q_\0X A]& 8>7Oq.SYkȪ~[IA67 .Zd7Ze<"RJ<݁a6Umf .ϟz='YcGԶ޳ ]/ʯ&sXϮOeziƒ1U h2}<1/W^bEIhm8 z|boo1s\{2s/YN%=^PoWn$艆ނQ\;ʡ.gOc2({+yפy3Pifv4ƀ'k q3Wuƞu]Qrch h W^KmG) N f`x8 вvzF9ks*=AK%=oL'r wT%zK*^fR)%G=rUHبHAAaGy'WhоkDs;Gy7@VE EgTAZqx4nWFTf*ȵ)*5GQڝBkz é 1`~2ۮƦku[wtCE",~S m=m]I7:gPXqx-2x4z4P$9{Ӫ2e)_aFP9?tbtc%1GqHGf?`5!m|;Gv}c0tdC@"fy²$4m>1?'J3klIq4W:;Pe4{nP&'B=L-΢+1wlDL1˩idN#;4 uWǹPE)!qeޙbzWMkn޶JR#mk$j]xy^AȞYY% U﫬Y^1x8٣[.=$R9w5X1wwOdОqjt1iT-/5G`\k7##|1֙Lb2|֨3~)Q8U)e%R- m-r,A{Fi㼁0+QwwdnO30]XI8ۉ~ `^; Ik\ϭnR*±t=OJ7?-ѐ5?;w:;k?;~QBRsd2/U(~kP|o!,ocvhO6ʃ;NHSU`7'ɶ5ȍ'krT[;o PzuZ +h0Bwq KɛDV:D*ִOJ{z~9|i@j9^tl Rq@]۱Lm˞Nw%. jxm)QAq'ޫhG%ɧ8"賌h` mhUS{|+kR Vi$n4[5ybڷ+GuR a# {_.u]ZҷIۙ"{9LY L ^F]}='P1tm:f6SKr)2CKNiJ9 d>P#q8ڂuY=ƣ{#w4& s_ܖ.sMg9 j:,^gG^4x# 9=S>{ʚ985շO+{Zǿ(0-jL~ N.y(%GtI M<=h-L$]p] l=)bџw'm dfuĥp0M?{ ENBc=BRa{%l z|X\#>w,4a(r;*6R>7 -t*bMkmW'J޿0Xm;((e!=m 1s :5Ƞ*91}FSY=Nz.UpT)iH(+԰v߼ٱ-8䏶/NRjm,h5}@yRt8oH.N{Py&]Cg &H; #D}>5(F18>cVt w({ ׀N1:1tJpX!jz { *JշH-KaF97!qQD!&V𾉗 /IY7SxnOL+K]AEhe8AѓadEqZബcP)G@T3 Wc3bI roP( OJjYjshcv#OljyBN~tM7AB. ?AE/ Z4ׂe:.$f\I5qkM\Yo41OnR<%SPbI $Y {3vƥ_/A o é=>2.ҞER TfC xߨ^k8nP< ceR^%F";3Z>:Ac/2ܢڵIϠu'fdVRg`0{ e#g4~X`lb~9h~wg^mRJ;!*TK -5|b{9|4/X9K <8*8ob `k׋(r]?(4ebm'F,?Ɠf^>rj TopSY^Oj;124:G1= /Kc] 7eoVi@Sߖ<*qÃYFw*?I+Y! 9"@4z//;)G\׆z `"^z,,fiZQ)V3oSMYL%h@.1%Y6|ϩ*A ,Ʒ^nMM$]|@(ǥ̤YT)ų԰Z`u/#I5WG 9"s+ʎ5&pF 4)B|iDߊwڲbEr@j \֡̉EEI՟>̐%g'coF{eDnee_wxSڴeL`#dw:9 r9~ -yBh@:jpt[5mXwNruǽzFτXrfK}J3 *A@Y*z{x 9(W[peaXGPu:?9/9Y~>|qPTB0ƽtL#gxdU?Dj ${W2lp|1~Sʮ{PJa+|ۉyqγN(C[Oִ죏[Z[^s̎^xFϤ 2~j?]bnMӾ٩ϱw<m*1u۫K Ly[$jW&gN@U7:HpO^-1KouK~&/ƚc*?[9wx(KTÉZ0b\Ls -q&L5' rf[B> en|&脑0ĵ(CfQ-gTim\{OS=ߒS(8q84<E f$.VʔFy^~gc,lzyd,D3`c5ƍ 5FKkQQR߲7drѯ}뫮^ P $D')~ Ik-1Ma, L Ľqhm^.$ѕ=dhn4+$^_'`ocNl]Q RR ]ɌOƸr[BhnVr)|>}KjWv̦cCxQl񥓊`r.FBL,lˋ6[r&z(Vit]OFuJ}^\>%^H= eh.Hf9.xciJ^Q{E6Y#pz0Ν"zw@v|ـcƯ *vPMGhXx0Ѭʇ* 7!mvf!'ion-ȽJ3i6N/T򷬹+0*A=\SS`((b b\ -3iTt|+)t/yOo>:;5k+N4B1@YhNK<{he#Zmr"EH=pDʭm֪Zn=}C^Ŭ }?GQ/M KW5O!=#n(74?ſWQ6ksorc 3 %^Th`RK##{=+FZcpV!ԛo\""Ooy$򐞻-u8nxr:> .ʉbnZ )аIp@YCJh=D^})GyPTETP{NOF+QCّy4O3vJtQ}cyj9*|C,PFݦt:GІ ~-vH{Ix|t6O\|Z{۩  ʧ où{yN {6rc'V:܏4]cWcQw.|y%f٪TM#:4^gk'E.iөc{#F@Ww*G.J~{%"2ŒHp>:y4xQi8.3i:i\ endstream endobj 566 0 obj << /Length1 2488 /Length2 16043 /Length3 0 /Length 17490 /Filter /FlateDecode >> stream xڌeTӸm' ܝufkA]ziT5,̀NLl̬ % EMM6V++3++;=Jf/+$4uQ67??++D'W~@ tCprqvEG9/sdn tE47h8}3? 0#tz-(:) imJ ḿn #G +!Pq:MVO{lloG6;98:8Z,miEfwowFo D+yST*t3wqvwcv]%o7FK9ZH898~'i 4uއ#str-m-,b̢h $B#XYYyA tͭY~qd-   z+!,lf@+G?Ab46VX4gN>4?Ew198l<n>n@:R5O"2st/Q?@=/e'ݐe5쿽_ R_ S{p@#Z%'R8_-VZx8_)h-@Mhjnnט-v6@U'7W @fnN@ Z*hd{ع>b<XA&PK'WG`-qX  `,R V`,ES@ P@1@2APAwV9k_@W+ӟ` . 5:!qGGagm霠=$qs(]<܁f* $W{@=. 1%'-}n !rrF w߭_:?t+vPTAP[}wsC{czmv9zn؃o3CPƳ+',MˑT1TL$e' !Mp(̏ v򂵇 )/=P§_VJXb C# ;^E^`1LE>PfvC6}:|g>bfu;|82Wl {/3BrNGv.ƒ|+ƒEE@ J!W51%Oؿ}P1rGd or\b-{S~<=#K{1i3=jB +Y G^bA'fP6B>iW;|x `My}S #"+dAѩ?$mȁ>*Ka{nioO*\}c@pOyjL۟e K-@ 0>1WjWd}<a6qz=$}-kTHeI(E9k=--Rnom#izFUвa:`lb3hUCM|BKpxnT0)ǷjS C;/6 ra}3Owb(W=_XȱYyC/!eksXfTPK|(]7ђzug.ȦsMU 7HBqrۇ~eRolJ%N:)cf_1z9,Os݃U m fOMrETHE^ʶV9OhC ݠ(7g|&"y} rǧ$^CXpfclW&-'W7iʵ۠'zV7gDQx>ʡ3}=%Q^GmIYFV2ट Bi C\5%ҲaP}~3X$A)q3hvͰ WXut5*]ur܂ MCs64$M8$y̕rrP:%ଗigw#m{ S|@K9:d>->;0/SOIZjﶀT k+jnC$4bu5* ,CE`蘡)MÙ]՜, [1zly6ЏzQ2 g0b'tSr\L͌|鋝V }T 09QLVYXM%Z5f)kg0Ak Oݭ9qDecn?nZОfN)4kE_v[kD\妗#kP:D\~f gNЄN"u52]Ѕ(_ZxRZܱ+auYI3>-.JwD|+ɘmst.ܾ?3|dt^Z _F[IWfsFΌsX\ &wd%u~2sɐ~כ9bZ`ج߉L_r"oy#eDb>jv[NfƐ_#}tB g"'hkIusTjvv7q+G>͛0LxyZ$==tp(D=JmFzaJY)Xiیe,+gd= hL,x.68:olH* 5+\ʸsKL)LpڰJrFnJ̻9&wBP$C3׽szЇuD"0{g ۡ~ܨ[EMԗ^AR [cc˜>T! [GⰟ˯dW &z\B[O"Wbɕ$_?*u vrܧ¿gW+CHΰ 6ڥX5i}#Iraj}FEfT(Cja*Â1AD]39S/aC!`V3Gi xL6T#.N@pdOW$q9UpxWœC+:Ï>5tױm)k@u-.AT {I`7/gBYx ܦOi%17'wCllD3U=ׄl뺏#@9ga^k_ۿfE^ٞ3z)Kۯ_W.}1hsa%P'A*i3RpȔɌyG>{ {ۡe}8Lް/&Tg& K_>wsD8" sK+粒 k_ޯD-2Y(1Y Վ (2c'WW""΂:3g kTIs\—Yf,͖fh JJ<yyT@jz8enj=8aX145P2cڊ[2 \iL&0eGtD3B.)[=DŽ@'B:_%]UfՏå( @ݶ dBy-8Rsu*-n\JvQ7cxsUo欧j/ 4s>cTp[qB4Tj |J_١ݹo48Km;aHN l8e6m2,Z'V?'u"em^M*}=(1>gLZnmlyS[l-nq؝zb;6JZe'3{m;7"# u\*h-Q?nQrmXQ'##[z4j\2'>{sZ]7,Y 5fA.A ;)mkM1(CyHo N6^ik| u]ĐΡ И,"ȑz`5auWlo㈲ sQW ._ T^n-b8AjБlLfx=dKyW#`N3Nȼfdc_Ckvd`F ae":e?@4uTp(W28KT$YJJlS-O}Ty|J95칻c , Vecϵ.*b\%;NĻ&w?yJW~b΂ϠtNB\䚌JcM[`J`+J*/Jއj ~{}S`ӃHŞj75rqՍ O40uUZn~!ai7K#YLXu%s,[<~H솽8ހ2YK'2%$iquL,VD`*Y/`"ӉTłtmM $tbhۛ,@|f05'Y,_C #`НıSKIyYOdz'~ Mį7n^B 4/D?ӣ|L=rFX\ ,mP~uK&$|Bu0tѬ"Bҩcg_OW4V4KnH{vmZyHgfrߊpv9FdϒrmjuM 5v:Y-Ԭ$ m{by^ՁlVvBxY7\GNӷ B&eLz # 4,!Yh|PRq1Q>$5s K_a B@v|#EXlTs!r}:D>̺ tr|s( r#<%J2Sn&-H+Q 4q}TdO[mK5c\R'g+%}ޫ0o a]MSblyiqh;#I8a{0lh]Ty)J.'4d}x; ܒfxGg{a/Q:Ŧp+n|^qr瞐'p_]9T(tM)oc85^ ^XؔC0tcqW-Z6线dv4erl>WgQ*85SVΔt9.GWi=]>lyA֬Dō_?=؊/g^X E_M.4rH|4#1RH١SL)'DZCCo{SA6Nk6֌1ĨAT/y"+zh؎DnݍWhRxkޠ "+t2r#f`SأPFj,3p`w(h?z/,[Q׏';z*hwCli'F8DX4tD"•4̟Y%;ȅ '!53í0>q vmAc}}=J-m3#us^-9*mӾCG_卥7s?ipLD-ýׯX?k( =KdUx*1̼dZr3f6F08wp5D^kqQlEjGl$9Ľ]z' ffZ"ϜJ!"m0ؤiLz&2.ɺ!2Z{ᱟ%S8㶥'x=[^Ok6r, D>MӫF)=?)e'ljH87J,(j0Pys2uVK__(ZeF sYOe3ei sՅ5'|S NlFm^O9PM.Q]~>l;Up߅廙>%6+$o[ v 7{F7Ʒ ΙNL(Sȴphb8ògE͌,,bxXk*Kd͢/-1D 7[łSGdA>z^)$aJM+=:> ;<^ @)Qa[G@ ǾE(dh%3BT0btスR ](u0Jl{øa;L,2slAwN ͑qtk<6(|bQps^ɏ K35Q[YpSMx3RIyPj H_ 6zEzXѠahk#ez]R':QMrn]YSxA]]j#]KV}9,CatsE3|w֯_KMIk?~ha皾 Vk7U$U)TnTSO>t*R G)$|"jH72{8$ $/g߿~TUkIJ0b@I\I13 w;8ȝB7uBuul ssUzÉhC Ic=+Vj&/:^5cg94Wqb!@uP&?; 6\4z {y@ϦGRxo>ckֵ= q]G* !s/`_:mn0 O mٺ2`]:iM"^ f~͵>X2\vV<;۾S9bǶE*2$ݲ_hBymQIJ֣W=f3.ov΅2ec[R x}Q 7YE?)'|u1暛8_̟ľTEj3Ӫ/Xֆ~jT*@I+wKan|ܓک8;#{ѴÕLN+g^6Thğ)_ՖWxt_ @?s'T/hAa:GG]YBDEk/z8HVt4UM 7{}ݭRKfwC, L!WI1.ߩt][l87 XqF TlO37gLF'4 1yi p m[Ȋb@.MFh}qs K蕱]&fn^,84x~eFVxu;'6PDt%)W3m5>v@2f0 ٽv]kd䝿Fw^RG==}o6Ef_gVًVP'Hzl!"u{C%a.q4<#WP~W|źcpJoMlAmt>?R i)f0`>[YbvS^Z^?AH#/P#xgE8HQZ/\/52X{"$^7M=4ڪ9>; V?Y'lYzἚZ"!L2ik>v_1`P {!TJG󔯗<#%jLH$*~LKQf UVO=oF@kKsbm] "oc=B 8vi2>~!?%!b]蓕By|("6n!˙>W,Z+`U1sXdҼ*+RL rp1Gܺ  k=bf&dWZO˻aR]rF[ɺ&WoQ; A~u'- l]f5 M(Ldsx'R#W' ,=jge]QobEvDtNy碮QG-jX~Y2Gb`w;f_rOS*;H+X<w]p2r 5(%WpB.82|[֛WY#!LL+2oJ㘆7kI`%; n0 Qn R2nQKYճ%$躜}TfdYГ=,N '@%" E <9Ce ?qDpe7s@4tyзB*Cٙ*5Kç/LWW_vG N9_N/DS@cƒIh?t3Jߦ9h1eurM܍6FsBo] (GC-.UHp&3TrFci{,ƍa?=z^1#R[Npkei4puO]Wea9\(%4,Wջ\(EmcxU=1;t˰ڎ=[rꙌ7?]/mϋGUoKHz`w(_xq Lt58S|yK.)L2մٱ#?|_$>JZ[1<ŒQM@ IBOMu1t]HoB^eL`qzi0OaBtsiv#"]u9+M crueTȔ^}A˘6Yo!T|duˑtόizכIi# ![88IgwF=tԒHϤD?Ɨ}3$0_TW;˴Ym&C{+̚8/85Zy7/~fVPnnX"Z B nlFRs,P[C剢GDCdg(*""bc_˵{_pa$Q(H58mFpYQ( SF~%64q}:Iz@jטjS-X--h)S@Y%Ɵč"T>c/~ $Ba"{D3TԷK34"P1[tK^G Rjvi,-im-|kaDXjs;frL:N'c/p;AN3]I/66|UoELɓ@+o#9(By`j8 Dq_"ؕLiR"Ӷ+St`1t r<:#9 &)Qݺ:TS1OnO!_~m0Z|y~VDđ,)\pl}Qb!ib|s6[9:Kn0yXo:^{;7^v8d8DDL}zGWZ!1xfԉa/gټޚ :X M>E% FwBԄΔEvyo$3d:Ѕl c&+Q #qoD%Dzir#c/E4TN ,J!joS$R=EzZ.](d] v6— 0%!Z ]„$F iI[R oOǏè/i~IdU}cq~'3"9tErʕ3zhSB?(ܩi_F)L3E娿v]$g\|Ӳ, e={B12E;ٚS]F_Ry~qӈgIM"UV C&m7:#]VptQ"n k7;5Vcky"8/lBݽ|RDZpjYYwHH VS Ҧ:29z6*$4- [/uqj@5]fⳚ)Og Eܤ4] \iݕqKGd^;g&2E@+Rz^--|}:;کɤ/ŨYtg/HF Ci!ŅMA>ͳ!juQy$s5PY |,7T~25uf)R|nMA# ]Ʋ=# g/#lRbӬkg F6\:Yq& !O>Ӫ- 9ɋRj3vX~i? G\v ZuѴۧ4E PwG^p~GdVM.13w49f|4JGKMΒ5/tw-jwjr &=qkHcMf)(}t>kq  <'nRnWED(Xxt4iVyvꝋXq; C#pY3"A zxخ0G/2 :!ʭxK5gH~¬x{ zRJ#Z<04Z A$Y^ӗF1+R$)[,򳥌^HG%g,?ޛ<ʔ0k+ZާPA>^9j.܅2mNtydNeߤ?x!o#tW(~R<k:FYWsLUdA#h]f4Ȩ`5TS~PlC)A3#3l @x2 fIQܘ^_y*MȝΪ6mp A3X bF`I y 9‹`\`:x7pۋd\<_a)͢%O,Jle%1p2nq>R&Izfs[mc9;oe#^p[x}FB v ,d %WR!ǁF͜/x}:M[/FM w+WL)ۘo}NH&MV~mRmrHo* l'o_S{2y Ély4|囖[D"'*(`[Wcvyڮ}ƺw@_.᐀=Z=Wdj4 lt]wEȎZ^(3U}5^=l1K5tXga:E̱tVP#p ׋B?V((ңv"-j4<jY3x E 9MZ;7z/AtJBo*={~;SC')HGƹ_r7r7E}&cc5$ѣn[~qթfk(Oġ嫭_)XEI]ugNUP6]4 $ZusrsnBҭYF]%D/fU4UJ S5aki.tBn;~}J_IQWe,i I GhKHqA oT4NDƙ((pPyoNQ ` 5Xm4y%Zb'Ջm8or,_Tݻˡ+lj["۰@FT㤾۸# LW·/UXd ֐B1iN.(k9m0>,jy wT O 霈Un(L2Μ0چeX lDf8v ـ:!6Y|]m5 /7BH̬r8m~> stream xڍx8m۾v=c{=#Jlj{R[(ګV)J~i>>w8}^#R%# A ،(o≄"RPPhLBup ( B@D@ u4pM uvAs%%y!P0\ ` `C!(|ByH ܑOgY.^0 !G wȟ .P_#  #.^pG'` "kE    p =Um~/;"`H @hAU`O ɏ~(+ zUJww$U2Fﻟu#|pG_m8zy 8hߘ3J  _~0 $A A P0 q  CZ 'z0Go#WԵ򿌊_@$OR GSjɿE%{3.\B7T+*H mAPZ^( гoՁ8B۪AV4?P/TB(_ 75o0(@B0h/ l!o$ZM 3 p5lBb'ȏ}( @=b( c I`b/7 $puA`f]C [X%20| MP숧a3,bZ( #4)-H vEsʌdR(,{3RuB 6{5 Dd zWN6 kMubUdu8>aS1p~ +.,17> 4y>!7EP&C&1񙢘>c -.}^LiַýCdܽ7)-MUPZn;`lW]jҖ ,I¤EٺR !3d$n /?}**!|4y|K`?~Рmdc!<[.Kr]MH2y/zGb:X|nPIE#B_Ϛ b,avF:-_Ourdl7]cS8 I DªDە> PƚX ;Z<{GϾ Z]Hlz4y|z Cʱ))Q.McLU1h)LhΏL zOUg5JE{=?4LU⻷Gs=~ G{6ftpb,DVOZɋ-fGUo6k*Ծ4wv9;h_!/ϯt sUOrڒf0ϾodØTRYkc`T-pRV3 #V.rE{;"y&05]+1,5?TGmGhU M'2kƿy}WCv3/ك^Pܣ3&O7~5 oV2N%|LR*zk֥ߛL VjnhZn/OmHgNϯY(f֜K=expP)c;@مdFގ`j$Ul[AR!g1QcHߍ|!=1n(thD_)@kr IIt8fQQt ݓ4aU}UMH~/G=<*΃}Dg#K),.&:rg {rkis$2KȠ 54TQcX (AYtxWD#şi6H5TCgN'mɕ]l+f]X,@ 9q( y!ofHӊ?tK+ZMOG bAxnqXVq/ZvflY[NOl%`I-6u[\f7! S̵燋 kTF,EI;^F '9b2K ȩJ 1ࠀ!™LP[7`и|hiql@neƑ [Fn Usݠ\Y^"̃T,-ۅL,^w]9d#-~O2EEGtieNy"Uz4 ͕@0R0<5%9@ȇR1RCO-mR?;q u&_)ux޷MLVC%1(z.~a}$M6i"UB ld@L}W㤟aሙ"4[5E~O(Ӓ{39y\R&] "V޼@9j!ZEM,lIH:u|:Ovf800-':+v2X8DFue2꣎h,#bo1~[x4Z]* E?NjixFЪӉuՏYa"u"cB IeNe]>n1^ rU$AY&X ?BLwnK v:t3Mk2uF|Yi&4hu{-IۯcVޠgG.G0pdIxNj<.B֚hS]8ͽBԧS>2h#:N:9LZt@.I CUDg9F)A$f(UɲRwf~hǘO{XX D"_CG>-I\\̓޽z"mGgo^_߅Ɖm'{ax}ޟ8L7O¢{}qFٹVa[uhb<:8ПJؖ=Pg6{/Kh{Q~7]g[/aW.,ãVkJތ8\NXB>$]Ko.k[v#W{lE s,O2*@ 5^!չN{7\sɴtT=>|aָ#/'yI{|Ds䳭2ƪX rP/+&WyqF9M> =+߇̕RΘM[#Z-$J4&u(()k,jP8eB!r&y ٜs89ԌF4կg;U._7ܭhzXR*U్zIZ;RQRC'9;y w:8!eZ}2%҄]7%L^M3-&*~m7򋋏s}Yɚ3uπ|Qd6UR߹|"΍z6|]/bP:niEg?&JM L%YM4%ipQ&ڰY nDk<k%Y]Suw~.VIjB=pG>1*lׅYn&GnZlY׮6w沚Er!2: ,8eY%ww U _".1!I>m6%!Dtɔ{OTR)K.ҬRmТXXmDj)NEWd2OFN ?9`>M3VA)qz)q.) l = w?ݵvlXAG'}঺yhb| ~z@7v"'W)NU02tV{^|ws֞}@Y9scv洭X|j꽳}3+֗2wi?: lh3Ivgg1ظuhs>H+9>[˾=IS'xP;d@eq\+Yg{papȷc%/檟l+m"i'hj ~8\;|%Ѭml1 HBvO]<{RSYd8^ɥ>+(i.83HG(+sB/9f롴,'IPݖE/$荃H\ܭJ?Vˆ]Wvbr+>MiUںwKĦJXT`=6i?wKV90fZ kj5N_1wV KcGg<=zQ6 Zߏb>'b/Ⱥpiz8}lLsFzv ~hK p]/{Y7Ֆqp]y&RgY!ϗfE 9#envl^nfv/>uAp@?EJ3oRmwGx|EK^cX/8)e:ξH5ТV7w%DfXHvg)ƠGy2eUA|,_>[oZevngQ<|/SlXp,6˕U,U > stream xڍtT.(0HHw#C0t7R%R  !Hw7 8޵]7gw}EKG a U@Q< ^>1OMrH.H((4O:@HD_DR vYy*8ԅM䉄آ=y; E `8@:w G q[I tww;"60-@EAh&%b\k;  wAg­Hzs@ CVCx{6/f*NC G'0XM5^! E; `70l& P9\x]`F*>ey, GOB sp;5) UKACDl(#aaAQt~ |NkP_5GvPHWsE`j:ZY/  -/+? 2QTR3c227HH"\e5OcWn@U pG|B|-)77;;0Ͽh]Qh#67ǴP+GQ`6h1y0J /9P- ׷_1 ZCP}<3~!a{/$ id@S}$ѯkapW_(?*C\H~ ֿ z@!Dӓʐ ,X@A/bLu}&,e2 ʭB,UZZ3Kk?`p i~51Wѓ;3 q34;=uq qԱ.Oɘ<|&gl:+LX:[l"xGTB0&% d_gp.d%ݘD*Rj5Ft>$kyy1Dprd}r14+AJ|`݇@+2_`?IH~JrRлUZyqGл9P]D^b^j`7% ƻ|r: (eJ;PC ),?J!~73-gU`绘G8&WD<"2~=*AӠD}pt2ܭG oގm J: Л,qydpfJ_M]'bA[s1M_ M[WL顫~R8`Őw1xe=o 2C]̌קf"gt{,@gGf 8JOCHHC#L;[$ igneT+e'Ȕ_vVvONِAI7uҿ+4&[a1[xDW.g~54!A+/c]|x"Zugyco r >^j{Qv%ga?ݢحy&p5x[8u#vlTP`Q \:[';~u20#+K}=Ϸ;4’5~]P&ƛlRƀ5'_^K AR)3 50ȸåv}fc՘).;EEGgyVPJ޸^suvq8҄IW 4=<8JR>3dF u  .,Rcz*&#fH,0sNhmwtjd'R.vOȦ30Vll0.i i^W5f0p'vJ<^ZmҶ{5* ~HKphK3W/lc+>-2xҰ6q6mQț!H]=1h'(EY~'t[w >|P?[\/Y@8|!Zk7}PBL>SOK_XUIS׶}952#a4{^5S}gWWdʣNB7a2l_gc̲@e1U69Nyj #'.o8[ V$;-_2*<S9,u._8zD/;aߌ+2Ҽ3maW(?ORyfO^[cHgM'PhF iMkɭ=aK {;wG)BC][Dz=vsj~'P,WX[/Oa1k"C#JReGGKI-k}َI73hbއ+V-q ~ 3ëĿSȓvIn(/5'Uܖ۝5Ӳ~Rd?zmUY}5s.1oጁ(JXlP+nvs^سi/BR;@|8 }2VT&;#SdkiOZxz rq7p óFܽ_V7Gsh.bJmedfT6*;sBr(bWQN=HnSGpSSm[2!&f(5T FR 1^2svC8j%KzXSxsy yNu2`cPJ |Mvfig&ik'kQ6E6EpU(B i /;U8Rm+4T`a?bǀt7׬qNޔmğ=ȡYMGyG ৣNpF5}N$XvK @~Txr6 <0Ӱ%Dz^͟?NvǨP:&oer#M@qR#?_˸R;7.p~Y栖bz$J}$^:PE~L2]ZYd~oaG9fcUe98 hߞXTyJn[$I{|r~.E]Rvw>]!d}S!2>ta$K$8-,Kmsf҉9窏}-lKt*V~T<?z/!)9>>h֐}0e_=xji6j?fQ"v`>GO<0lNZh.4(u->;9Y&i{OHO8;E;-s3-J6[mދG?ԛIs]OFP<|wٲܟ&^a:h.260<`}c**m\IWrH߫ËiJ&TGB'2m mL}ʭ<_%zT,fJ{tHVg &P.~bjVSRuQR$μp53e&|*]ȪPm(^RTIYӱ 2]Q'BfKg;/'6y^`E)~՗?a3W~E>L:lߟpYpm{'*@]'Х'L÷[z2n hvZM2mjkFf̶GKv 4.`"oE22kIƳ II׶CՓM߀UUZvk胉[dO-(miONP%r>NQuOͰ#q 4I'qSJ<Ri"Zprt.XPii+hg| zy|kخ S̥ZbP/ap k5)5U ng,&BWX(y)je*=R/_Ոeڦ|%* 9{wF޺P6byy7KJh)${g`x̡D4F0Z|XkGhAq@љyd%Dn|R]]=n۲N/[qQgh7f1G~핳5 VQzKD0fQM^<̘x)=}ל +>z"rVkx b,c X83% pϳHҚ7,NJh1ǻvo }QsiVM|7{U<`#HeqW88*4%hδnpwZI9e[?NAQK9k]q!ƕ8N*ۂ|=V*b&:?3[37 qikh0Jb:'u@^DxcrbX+6Q'F'A7‡$ݪ)Gڶg$E_n L:oC|y]0-Of*JHW\  BOGpNvc^oZi+7^ zξGiNn< ĚssVXjY*{ۉ]z&m:[d9;9ER$諩^HL@Wop; |!+xR6na7j(nT)(MygxE&]N9>e}[uEpn2JNQw亪J%%BM43k!%_{ ~!kF=jYW=u^M0@240d߼W3_\ f\34ik?2I endstream endobj 572 0 obj << /Length1 1688 /Length2 11120 /Length3 0 /Length 12213 /Filter /FlateDecode >> stream xڍT-;šxPb ܥPJq+.ŵhqwŊ?>~Zﭬ=g9{ &)DHh)pyAXLLZP-_v,&3& IOD{@ EwH].= $e?b 3 iqa0 bT l д7B q휹,Y9nP@ qrh @ Cv P3)fqϿG"(`Y,Ye.;A:?Ń]P[ϭSlu;s9CmGcKA`pg?' u=˵ٻ,0s?0wqjà.9O&,!p?77Hq@ͬpԃ  vN.:a̡fp) O'3/tNPw;'{e0s{^1PVAVUZ|sxx@|QC?b`ῶtNڲ`{@XKInm,?CTGwG.Y"`;ߌ'@i`KՅ5*sziY>)/;Y1WͬR_v? ;Cxa4df6O4tAf6z'yJsb`S> {'?.V@|/$J rAO> !PHT7~b@#'1! OEm2>i|*doԧÓ9>Er=eu89=M] B 3i{3`ফrIJ7an3 r-@ק_g%&_ lHmEH窛P-L9G n]@Z[ߚ-I5z^Mq8oNȽd/fu7pD8s!!^fɴ- 'ew@kEd]\]J& v,gM,΃ '' =+]IR+G+/֌DZ KQ^\,G$&9);;+@[o,H9!}"9c VMkg s <)zO#!#zx)p^ԁ'Df!P'[``PNM4x lzl%!TtdOvw}j˨*;5o9 XnՆOW_y3'O)`y/8A-&֩B ^"}xyWh~Zb"#vHcW% ^}%>*jVuV3WA+|IԙSCX! " V~lBX%QuaHE݂˔dx¯WfnF@$J̢dX!@eH=ka XS - Δ+'=59X)ރ(EWVPg]%sKUk/k?Y-j ֔շү pR|j:1sZoUaѭm) f/?/Z}}h2*ms=4x#<Ы*HSAּ{خ7WAɬܻbY*agLlb3gLqx_ye%^EdEӮ 3,O;A=__܆1-FtY]HQ̵-o&]hϑLOv 3Pu!Ռi9/,f7߱ZsZg‹J:h|X#(HXjŐ@521,Hc>]C7gGǤ$1&> Yh5s2vf@LYČv1^gntb^~/?b74`e"bInF$ 70!J35yxd {yGKǾB(8|Awb՝# $d?5ow%- 7AGGj1`/՟KAzv]1j>.|(#%n㤑n< K. 5#Wu YA~g;o4=-RGlDQs1Btsx) ?䝣Y^NN==j  /4d P5#@[?:ʿȍ9؊Q9/f6.T (eAyH9FkV~rPKoZ7Z|ἑt%yNw'ABM]Q҄kw>\EϪzx( QͺO@}E;"VxNrt )+DjpgQ=gW sJ25jeΪu;f= eIf[=){Kɔ՜S]pO]ĥG[;Lj׵bnQ(5@'.tЬ pެq42RFKf>FU /&r!&3f8癭")#HG|jᗟ)MĪo$4g]$KkwHZ1 )E;䜨T7(Ơ$PL\Na8rOQz%pPgʼjc"Iٵ.9sa"¹Cy>[C!Cc/@ۦ@4-ǼnX2_yoSXШz㮂E$T{69{+<`h[>z 1I )_4І_G0mÀE{6-!.b\02 W[ ⇝KNK1 2R%V,JP2H q,%gᢘgtgrNٿm]WBqV"{P4M3P!*v(jv= x1^@r>B0(%TiPEX-wwo˲:NVzJ.ʴY{y]'7Q"; ル V(t n`VX9Zom*ԩ,Ce_;ps;Q6‚gCA܆j)BdY34\^e+D ^_sʮIUzk6IcvC{4fm) N:X Da`ЦRmV(o51M<ɘ!+mw vAWoEy&#Mp ߓ Ț"O~4۪ۀh,)\|pjJ z;B4O!gЏ:;~f^iS[ƇVu6aSWbÏ+ B.:Tт}dD1b?:fV)0P)[XF0AEǂm+?p(1b%ʉW}=J,!DZ !! b~b%cn ]e5  *0LR۩[93RK{5Zr]>6Y!6c7 О.Uްl78IYˍSH 1:6b?G " yWv43yT33{R)?ӽiּjKF|a_Bei:;NY({uYWӍdL.v\c 7(-؝=@]|?vZZ6E%a~7ZzfɾHWZAE݊. '3ʋ<{&WHGv=!SK6fb=Gky)F N<B994 8Xְ ?i2s;յ>j2:t6EiPMT(r@M27Mo -4#JmԄ,ȋ%Olj!5[JIs}gf+5Uqe~u1!Bf&-+ЌUKՑE( Q %Z#Q_2&^*=Z U9 @R;Lv?4+~0sPϾX5R7e#+ Le\Va|%^?b33&ġ8.,3:ND%U}]HQ8XˣW+U enV䖩|Q 1k__zԹoiՅf;;TwW#^,xNe4%B Ll]_KxC0]y@?h+ 58, dN\wd]gFCBO&pwr-DpwĞvuDٝ>T>\~"LWE&Ql~vI%{{k{\CTBpD۷۹T&'H2%.9#揨X{HS&;o۟!z2vDŽWhT||l4+Q| ?Pr{O:Db{ T7o;$J{flj^8<کIEUSkv ,HdK, Y0z}0o{fxc|Oة񽑨3yXT=+#퐫pn$ON巋!u#e>6~]chz ͏܍G=M`%6/#z7i -~L?77evK P7⣖7)څgPl^= +yQ1Q=nIz/aOu IۺoС7?>/2K}0vlhv9m0L7J5ݗ[a+be6}gڳO,6񝤆,-uyM-eϮ4$v_~`nr "p)vt.l+P_+-h^-BW(63$ڱJ>[.= dQo `laA`B4eRYdMuTN%YƗd3[FOS (-ܘqrʪ<%#=߾8~H9`OcYd%=w:j KH}R%˿H\'H.#J8ǠLE)tfگj?Tҽ9I Էj?v{١@4D~H hG tv|#g`bEq*Z"{(e'L^[+XCnluRګs<F3]yUiHP縱ڄF I +ULs˘̺X2mp&[*K [=XQ{hɼ$n8j)ݣx1='sNTƙ"9'yd^2g fM_>DD,<%hSz6&ƕVLrap]XH*JCU2 ^g6tȟ[fIRK!21\n./3,?-"cX6M[e;}$>8x#w#qZdhLnJƠr{:nޮawiGYҮx($Wo#R sa˯\cA)|`BYi<@M$ӥJB(LЊ94 J M~s$#p'pgpТ""\J'T9CTu(1P&g0|ŦqeJ kߥX87iㄽ)ZIYAW;Ќ <GYN Pz!߉pذl -fNDٚL:%M^Zu~:#ĀMIJ^lƁ/Иi#3lXKbjMZQ vO5gslY ޸ydW΄qZ%/1irf#1܏/U~\ɵE1׀Tc<'=Ƕ"Y?4|% F -/7MO JB Ci8Hf5>!+U]Y*<`'nܞSn+sAq}VF=7AoKH,J4/L ~;;_N OUdXi^tFAD(Lp:Ql206w(:;b${ڦ1Ut/t1qU!(=dTI{*W37O4dὔ.yhhIʞY?Q~|GȘFKX5Bnbńأkrka}Z /0]ͺϹ9cE<ÀǤIUN& s…Y!#Vę(B|mZ]2(w~>~~Fl]JQ},7Vܳҟ"cKQ{f/ 4{Gh, kȼBZ|-b)|sho+M_/#2=v]:֨^o?ҋm uTHa3OB2Z )RgDZ//OÇg9"u׬L74v/kQ5Ĭ9_KuDKy#[!IBly4|LAϵUAvjkrqxL\Հ GgޕO}[65 $e2ڄL$ˮqꇷ)x+,zV6({d!9x C>J^>/&7JKyWBzKu mzO#=Y@hb! _ud_!L{NͽTV6ld!u!6T '"68H+lg3Y(/+ `(ؑuYwZBDJI&}llai1XrH T"BX_ε~҅~jxKX:8j=@Pzrn#k> .3R\&V,Dw'L/,.szv䔖.iߏ4t T7w,2AxȢcFʒ)fo3z:@Pm]TSOςpHee,. E~­ޭ7M&)R▥ s޸zENngDD6t_pUz6734a+ qA`]sd-ٚ]:']ֽ%ߞeXn{D<.Hinj)۬D(1Ѣ'ړ$/U,/UcjaQr e :<[xl!_HކkT/2|v~ WJhFV~'F -Ms6վ5M[׺,4q>p b2l}2wݝMHoCG߹c~ HpW!y}GB6r&`4bJ3d~76軍˹҄/YCbhKW ~u@aMHB%u_V=8rRjLFn\}hN}qY]ƻUu$΢Pg Rl9N<4٧ixVz6/iR?y䍃Q덓ZCy=-Rݡ~E5NF i@Z dSɳ] ĈXA$j.y|{3UOb"vju L[oP T E)kglA]8d8+=BPqPѠmd:8&M$Sǫ bJo٫KWVTvi(lt[0 TQĤ@ne7y /`OZ?Z˖n]RUWeO/rjq߄=d0Tw6=f>gജeƋ]DsRd#YGSŽ,h|{q{yȇ?3[Gix S%R{lp%<>%i3v({iNȷHXOMg EHK`e0]`\ $-z6 YvrL}+{[R\щg?sԁ}(6O;3UxOoWlZ}zG80a5/8SP7=bK)sҷSsNM Z.O/:P h(ⵐp e[% SZݡ(uQ]ףĭˍ,rTRtf`US/O U>N7WՋyY ]bL[8P' "3߸ ekXM1&#eIzQjjCRH'EY|N2QwiϮ#bki,5[轕m3vj،WqqlD"06w_Xjj7 N֯h~ q;}Vc陘zV@.A . C1E!{7N׻]vPF$ 1ٖgG~8/Tjv͛EOkMKc99R.{|^P:\a]flP;삟_vQ$uگf\rP:JXJ(RidhmżdʙêmD6ȗZalecoΘ {%fIuI"n/CMVРJwg(qϐLsqIq8Z2ǹ,=.;=h˵/tP!WQzT4= }Wm` Lݵq&_/n+3NN3޽QhK~I>0RDyͮ^y&PxPm0g' x]e=QZT&Dže&fhUӉ;y4hKzD|jq\&C(-N4/ΑUIKNJtQj Uo_. #98,y@a0G4 O7³ u\ϸKZOِ)YEC^p8%nZ##\S6wp1Q/y|>wjiLEnJ[#]o.K^?\kuA>FI>yݞQ3QM'߶JR*1mNW ^ 1Y겠:Ǚ_`2#nȚnu:2nQP g6f@ҢV۬ @ A+PD_5 '8ZGfLgS5T4+3g&7 SoȻh]ᘆUJ;AVWPM%J7&bRk\kLd܏3\Z)ߎOJf nUMl,Jx,+5?K鲖h+V[I~_oԝBh[AtiGaaI6ƞAL?q:̿yrgz7ú TCom=Zt9!&Wժ96YI au@V]1lPRXҫSsߤHA0$WoB:qCro|P0=]b]汃i(.Z^ڼ sA;[|fe~~'K[eX%|N7$fbX,ڀ~VA|L#m"}b>kz𯝙`.ߍ&= ޳nۓPxæIҪޱ${(lIE>,׳y\{g鰈5\<Wt o>8pzH5Vi s]9s.av) KC< H;x/:# 4:d> stream xڌP www[-8w  $8}ޢ i}Wz߁LEQ(W`e3!PRjX+GZ;:a!4q$L@9w;+;_CG>9@ tEwtvrG-ow=hfe43;Yݼ'3'+- tx P2S%@? uG 7O $6:\́.Pv@ c0ipxog33G{'okK,0q0oabmgb 2 @JT`\\\\06K:;\'a4ݛõupt/v0 sw'@Yl@"2K t̬J[TTj}T/B`e[L@`X{X@ `O 3wtm3KHjSJ11G//# oX)`YPK g=hK4@1gd1b<e$ng??z{k;,@sEG&8_S-Whnnn&]u֮R^@sk73g?홝Pt2[ft0s4k8&..&,Ib@.Pu G,? ,q#ЄFf7 Y7b0KFlf߈,)F|(oʧ/S@T#P>O70kFF ."^+Hijf :);srĠav "nșA rّJE`fv7'_{?؁_jhggoڠIaOR 1R;dbk+o'+ r? t@%>wlNP,n (_W@w1oxtp=0$9X9r 'ꐓE^  hnAT!m& jnef7+g P  k˟L?V57gP$}gߏ$e_4CXYt4|%dܟJe]qJ[r':҇+Is+J6=Y(QmfaygpTaQCf-dxe;g~i}Ճ.yėYƸ9 x0npt^wsySorgqž[l|6hSCbP}]-+\ee &;SesYh`nDWtGo)%Y`8>ʅ\#V`{VV~JgTba{ElCW?" O_MV9E739/?Q`R3P02)Go09kx;L{iFWޯ19_r?xz5yۥLrbژ%b@ɈfH+Z{x'"f^-2!u>yxkTQlΒTH4j^7'$%M(Z+ZSyuW@~訨 r PrXol*?Nh9v*EK^U6%uTZ|y9֞7/ł;zDߒW j.{z G kQI%eQa4/u?w%nv1]a25:&Cڴpgx0?22GeߺsS5.t䲏%—EǏK08ojTMH<}ݝ:znƐ/I_R9i݄ # R8"8ܢ*(L7beF#rrlto%+I; 9:d;K 8/N_8Xe28.ΐ=u~֩6=;2i01[{n"|8/<$cX \JL"y|/Yٜ?; ~pRZ`Di0M;G_:OhcMq m,<9[a'ƜԜg0XlX:VKCVnZn5Vc-L[?c$Btۇ+O8lv.,a%x"Ž<DfGz}CX,(_?I?*y._sigc0) 4ϘP!OvKZVx8a /w7CbC'"I|4Z `bH.I 8:APab 9eThYW+:[OB3$`4Us0OĈԨ;sByْ[*J |Glj("\y[Œ 8qAkGR8LVߠuBL*1kxᕔua'HZS nz-͙!!`~nBb9=5!wD ;t+J0Kl, @GmGø(3]-y^eS~YI ӄj)x2HSpfM3b /'C&F+U=X*tGVg`[$-Oh1}ڤI \urPk` ]{(XZ&7Y"+ĥ(:|AuA5BaJCI㷸Qxݚ.9]X 9 .>/nj9K( |~n\,e!m*[,`k+ kginwWII48lBW8Oqz3oͰ`-XJ%Z+_aV4בӕayVQ9cQif^77˂zɫ|FwPRnt=n> ' Ob_^pQb)hԉ\09A*ӤUrodORU{d]  AOɸ/|$1շOZo)aPˑ|#NRb$rj4\ztdq3:`ԊMh jG1+UD Z$i[ŒC ^KԺ%N N"hyƱL,B>%ᶳD5]( @j|eݤndٷ:EQml+3xLh9m\ ^7I6쪏M0%w|dNh,G.ב65ԗvYHaᬜ]S%52H0flQ5^ړ0Xd|[mpޑ|lO Vd'ViOgH6Opaga\>E "1^f.kT.=D(g'0mFBpȬ'7`x:?ingq~t >aLKVkz]fX$1!\Tf1NCQ99wW~2eZ:ڋBtfa2=Q. "-SF~J0X}A$(sAu+v{z=fVhr/0WA4j}ǚn#e6n)fC(oFs mMsf*p`,u}J&|we5B[c*(IBΘv$axz2^=gq-3doYDo toZr {|{?H) ̓0h۴wωzW>Ɨd0mE_h D7b?C<~r1B.V/U7v.cɴA$ƒ0gk*\>W\JX:rEqt^%Y}A8 TF=y^Kej쑗ኪj2\j~pV@nDh 5OA!Qܗ;GI YO䪄)N6Y Vڬ ԷeЍ%ΨS4?z9{ÀTXnZ2gxu*͜νg\ _GV^/q k^4BMeVZ^K|?(y8mR9*Ie0n&(s_F [ƯJ=.vatc嶞1c0|_v΅}p ,M'a S珣Qa>W9a5oW1z]bDZF+YpkTXYB:VoNGzhtoi3Yheب $fMg+jYx%;#6WO3s D1f]>tsQL.ڻ@}v)Hy-,Rڰ!K0H~<>nΓNX2Ck E 7Ij?z[؞Q!K6F8Џ6 W+if40~b g䆎Onmv:'`܋M!>7Cb E%{'`Mӂ]V'M.= xâ.5FWv56$YY(LĿP-#"EZ´xK2Ӵ .<+PAyh( x,enG6 j 'Cnd*m$'a=.9ׯN~mFRwEh]X5«uzQUYߟ8;]~EPqqܕyRj:F\q Mۅ+5kL.I'p ;bo>Ne%w$t͚T>Ez֛7 c,aTH.(􊳐"5]6 xwuˏftbeURZSj:*-!UfFX{Dey }B/MQ yh_w-mG $y0[!M`JQ'wج!˞iEvK6 L\voôZ :cq>}1.;43"*w:A"a"VP2tqeڝR(fk(%{0>̱s=w!/Q˙X\iQvQSvzQr8&NhS&5ZU1^#!z< u$ioIq#_=$c-OSo|QI]e S\K^@z&θMէ 0qxM٦!ngOx}B,跁*EbI-Ztĺ#Yʹ'A, A &!=0auBqV{p[eū}Y"D7u`]i 1vI:?ZrnKR'%TCFg!O=hV4j+z$bXEi ?ǦJ.Wjmx;-AK? dR< -B4yT{ܬ@xX~9FR9IDY"avIMB+6vӞ*FAk\uyW;.%8bgDb39STO M2j-]7Qͭ }V:$RYu T7{V? ^%ԏ #|yix%Zf yf6vVޝ6h'L) 8,c633<E9 *t޴ *ߒߖkYLd01JWLʘ0+AN۬EFIIAN/8'4B1t/3jٞ%]9]_`>D*m6"$a2 1 κn<(ioY,N(T[Py"_:Tp hQ! C)r{9a F1'*C_J*'!` d>n!h4JAʒ\hs@( 0rcog4go*̡x<"۬t^k#;zxVV5&q71$\@S{h'ğWIBhd:q;iMF]V5y-'D̍PVr0v, #)nZ(̟d1EN~ͯux.=A98>6a{=cLIꣽ~fV l ԲZYH:W80TiB *+b/.Pjٖאۆ^Ҍd("Mup(앚^qc + $܅zz]v6꣡*L"u\`o78/;}Wznn^ǕuKx%׭XT{fl"wxPg1ګ`~n0bOZslrJ JXšo RY/?EO ut؅ ϧO."Xv3["Ĝ$ K\_1nvJa37'CZ&V%6QHgZ02<6 |̰3ԡ v`tuvս0ͭZ.^Sw,B)FsU=Nϋ.FJD =فFLcb ڷH,K~40ta2Ey?UrL)~}霑 I`w/|ǫ<_emC?24Ɨ)šVEŲ!Z'ۭ2^js[|Gs)tuq@<8䈥ڂu:wMVjbo?T vҁ>*/Tߧ& I#b$h -6k u溜B63%5 <lCVQN6%E;L=t=ax.o}xW?Ȍ@|hSea;XtP8ARo;{ %rGco*pj5Js],t-A-S +S7q͜*Z϶"+z8Cv١DB ZzϬ-K.Q~1^d';I*4$&8S1SEl&,f*l2L/V1_>J!4wQt:D.oM--uw0^RF4*3rskcg u1jLz$N&H!r}xR8VR7|s$م x;/PG.)uFv6(YjhyU:SEQ)zX Ul\sAO?݌bШ2l~e&A2֪8y}YQV$ m:ZEB]-" ocȣ:[Б{kr2X؍aY{:JR^I%) vSV3>hnC3Ad]|/ =Dx#~}ءgCt1br'N}*#$`,[hO V*tWpfU>$|ZTu|=1}*3L9Rڜ/;iEƫMP=Y!r6 %rHc?,0Arv gOQ'Ld/pyF@ )DF#p9krܶNx8J/ ID/Z~!;n¸&qEWӭw|]^|0PD_5,g#"ܟF܎X;ՈwTg1֒+1NKFSv_s085K61u43؎$>n,06UTȣV\mΎ!eɱָ_Yˡtl z>?!̃ =02V[cvnq{Ӫ}J?f<2mw6S\e\L!e&s1D]$v}T 5J-YfI_'/6uŞk R@}5=qUV.YdݟJzE9R˓WorfR7>/`U_Ik=l 0?U)Wxw}s@L$@5*ԛ= Gja |=C+ҵx}kZAxL5i۰8-{1{v{B 0(x)QPR蛣EqXN7mO@ٷ,C6\\o5@63opYB(Qy[\eymu؋fPbKZUuD$Kl/8.7e wŏ>SBA gẅ0O *:C?{zEǗč] F+cU Hh;޿lrXnU$V/`9y_A^ȷFj w_4G՝-caB<6 _f?׻s_[>]BPY ѱejo7\ yS:Mf(̏05``O+0Rt"<+1ֹ8gi0@PzMyGmq{yM*J kJ`uW!G&Ewj@~4. ny9SmgpNsZt{3/Mb!Td܋L{ۄ`ȶ޹"}u}+c8uNz^^p_d{Q5;˓V[|%?JJ=@7)\B%;R 3T%4;IE˥£5&Eռ]ჇQg}IW嬐ȼd 5WT^ݒg8L#q' 2$⎷\B>aJwkⱼrx'.%I`NE@.jDO:nGQz牸tTgY m}T?Uy̌Uə-DrED23t U~;I򣯣PeSزq`XmN@>7\{uM4lF'y$Xo#2{ 1L *ng"A<rurUJDY&^X:݌DRRԍN=MKAT:U)PM };'7~v4C=y{mHW[Տfe=0*HEYq `7̖7|T6%HZD+2"TՔ=FpۑZV%sG 1h*LIS|o'̛8yRٰєn*c?]D"ȗl3by?gz.-"wY`*ߎSs*WKHxG1hLpX {n9>'+=T|_}#r"2#f+&\B_,lU%+.FY&Hv\M:r1ØPLi~_/n[$Z*.*gLo)Öy#:%P* wDJ~ʀ 3'SQh)+OX$кoȍк:DTUO#/KԹZPK:`(122Sdz(vKvġ+xm1i>2*"ug`:R*pόgdCƝ"pܜ*`yFV%g]AҮnT|=zG^+hCjA-ouŎă~E4J'>gi.W`e[>յ-rXw?ν4 !^p%ea+JԞyuj| 1PX.6ޛ/(˙O%lY|.VvW?:;%vDCӫP\mX\Krߌ)y- `C9oǧ:OX1^}xϑq}4g?H;nFtM r۟ez-!^8.3HRrINF0,s2l_p^/"F 6mrh'؝ a @3 kfDCqXT_"H:e Dz+@18Cv}6z-fg)AmU9#zRҕSy ^w ?Vv8aY4:\n]L*uiR72 {I,1ps>ckMvb bfmݱ-Dj1ӗ{gc#9MOb~{U@ qͿ~ (Bk| o"![z~#W+?OS#lwVqZׄJ2-7/Z?WЛB3-ai. ˾=0FOCu޻Xhmp&GVtLȧn{ixhL{s$9E-5xgl2[s|hG"[wW /؁U6`6)F>8a6c)'_ku)k]"QDY^vF9ĐZ=rQ2r:iK ;*]C,qr~_r+EW}]Q =i9 < }1>ָXRfXqY.Fl*a쾁u {PWm6zu@ggEnޤzVezNj9\Lm,<`ˇ5.C(Jی_&Xy#Lȴ/_ Z"E4ZVm}RH]a_ͽ8 1ޥd{O{bWj؁Gޟ !PeOJӷ񦌶Ϣ'Re$F ΰ[vq1:tGio3gWjWNJoUQ%'j Ȏt圅^Տ7Ȕ_;QY$}'E0h;*m}C6݌#O׫f5qE{ʘЄt| $x NPݰjK/lr 5dTO0JKAC|Ą_9t_">' (YbYe~B`$\)H?jQNmBiG'.`£![\aP70}Ue:5̕HP {~-ɻq4hq'/)I(it~fD 6% ;ٹclC0v`coM$W-8!6' d}p@G{WfڀO#XuQW= +c.g/'F`tn ,V-R`n ʔ0VݩJ>"x(Wc9p%Ʋ9'pGR0l^xq4fhAG0Z,I6v}b\mUEkaf&#lSUskːP-J`gjX̺XpR'n E} 2ȥ\| ;\ua3j*&|*RBL;~\$J: =n>ʚIU˸Ix gͯDRF82>OsqH+iP!bfvwV-/Wii{exJ?eф8L̵]y?4nq0"gdck^Q?cȏnR0=72.X@ȣlYVԩGVóyVmtF"ӹi<2/vʕ^z%аYŰ!$HO{JcRY쮄>L: |=S}D-J\^}#kqa)TI$ET>P=ʪs \YS:ŧ6M p|1^!gtq;fwH5dÉ5=l;cDn6N`I ԛ ^xJCQ֪F! X]y:_fO3_/hz1Lߵj}xFPcqu͞a^YRюxΆV$[4![U}k a_1qC8-OwS t #ϯ7Wn'}_UXjwv>4Kyˎ,učljMHY2͡fx 8Eq RBʃO<φl\IY oi:? ہcrl:Q۱5%bvfBG!ͺ . _P<3m?|4SԎ,%雜Mrf½^zOJ`/G F!?BHنTuL)r w,%k~HwAE"G|8*Mh)4 Ⳮ;vWUZxRn~#v<-nT%+ݷ`4n&WIN7|MyxcEuR"R^"L &_#vH>%;Kʑ\#mc՗>hMi};Quk6*,񯋳ᲽG9[ud0b5|`;:A+hmVSXhcq+ BAzq3Bo?Eg`!}ݶ6&+lPg2aM!2%|1Ӎ:w5_D" I:̜Xן:?qtS #ov^_~B )W-6T7OXjM T:ք"a-s'U6@lqؚ;JOyчd}D̦jw4F:pBҜ]AK lQ$=8 ӓ*DVL-ksD*gWN$udİ["[ԼDTodI[T< FB}߸ψjjk㠁lՔMCN; EA\Rq)HWJ/Pa̧M~'g0l8HE\tfƮ,wⵡw>D=xhA6w^[Ps/Y $Ѡ 5z?S. ]Os% 官 7lH|72IyA ޯMEC>ƧZ/ 1hn"lV\fp"Gaw=n8x* ϻ拮+wD9Zα%tW%hF4[x{0o*J?*d7ﭷefm{ߊ9m6q 8ۏ.ʛd*3a o,*ΏZI:%m$9}12RޗɌe"4<tMm뚍B 1J>Ofs#¹_0#LE] +eN|2 R0T0/'|algBؼ˜{yYa)|KɔމL L#59E[r1XrJ LUT%dA ? -2n4NǶP٫oyɋ51`Zb`/'qA3a ^? %JsrdJ>`rނ MO1v\N3ViC]!#eLKYS%[.7c{7D2u7+V[)1 q040;NDwYُFL|?MӜ5I\=G.TX(-I((ii<3 ſ`sk+iU:ϭQD2nPn-I!nwƷ& MyԄ%k1hDfZTRиF^1VU؍kV`pZq8vUv$1^ Mgl !v^`NSȑ0@>-dzyUc'c#DZ{> 5j[ "&PP~fPvf4 b=#TCɆ'{hij?rN uD솩2ަtC'{G;a]s?W+HBhfzz+ܑiNIS5C %r D/}vG'˗EU` ;մFKXPP7X3h|ϋ[N+.3$AAwJ鈺!g󀟄OElG]$1d16$eWLpՠ<yd)ID#x+ #YgI~Ԥ2,""0S (I!#?oj5"2D} scV*u!{uBƬC:pEH/%Z`KB5$pX|Qa% /$*8q!Q[rGo7wM*nRnk]a9BKX@;Rk^ǧ|# hʳ q& %dʨaɷA>=#` ^". )@uW :C3 x,Q:uzm8 hov:۶睘i]*A[29]IK`>%8tL 7~RsCG? ?w_t=v9:&C0T^{b|NsX؜B[IŸʂiC r4uu>=96-Ug q)e8 Ho_%`dFg+ v##W v /eJ:g8ƒ^E8=>8UV#mդLIvI@܅nd^b҄~6PhJ #y΃g\qv.YڳfJ-&k?0x'u}we<\^}?s!$ ͎`nLUF)^rgm0?pTFҁ&l7GXl:'0J2"lBoS#`{3ƏdЗX@= 8"%#(.9*%J!rR{+3xKg)=+4;zDd:?Zθ&7.pryٴT}gco /,6Kg) XW*%)& #s1ҥOACw^la0=<7M>X"1^=ÌpcivzT,MS8b졛e1 W QRQ-(c^,ED‘"oNgC`T zJl9}kMp+TyWG<&*h$E  :oP_ho ?KSl a|aY1x{JhNRXa tV#9+:Prsq 7ё$َCzpoEҋ~ Ɓ"H oo<6}ȟ;᦯LT]5WDzTLx|kߌwcFOAp\Q7xE-ڋg'1+ޘf#C!Hp86_p] Bg86!דȔ> stream x\YsF~ׯc\[&>R[%ٖؖ[q`TH*q뷏8JU/[% >EY QX e}-\B!%\,{[ȈX(W :ljYhVu὇> ufJgt#&Qhl{ _`DY t., N,w h6hg~ςEARf'+hr5tZc'G!5xHaCCj!R zА('EDY #$E<𓠴U蔐) (|@.Q C.PpR`T!:z{+VZB(ADgXg8ɂ {{l^@ |r@.+ 6RFE1$6()bM2^! &C'B$ "`LA vPH/4" X`yb@Z\4H*[ :LSvmF>&􀽋R^l(?Zx^bv3~?i[ɄI6:H;i72v:ȂosbTձp>[?XlqHJ}h L*ժ\OCmU4_>cPz!P {(m#z+Zȧ6(AؑiaB%%H ڥv\u΁m$mO$[F7 l#,5HU-.MM,P 2 +B1ᾅ=ۻK {)MSjGky둌x}KB_ m knLUzj3%YTyVrdҊ2 hP9R.j rB[$ sd)K*(hc`QhpeͭCfh!n5 ,Xt ijy,'#")8O 3>> #plA[DL&hv@Qp.m4PMy!V4fsHaZ(W)c#"3Fׁ+J$Gk`ZTCf.K KX{V qLPuj{3kAh iOz#Ptqoq`9KxytwB7a͉' *fYSR#G- &1p?]spTY^攏Ipg4& ]vC ~u(:}K v&yDL6cB sT%Ā`qicڒhVRV|dP+ 8׹21[hK<ԅr4z F<@򻲔$eEE=ő ᬡ$]ٮz_uKTV% jreY8/#č`nRDO 5ƴ MA5Eg$O "ոC:߹]bho#!hH!?B '᫑@)tx Ci_$q Ĵx%Qb={sbQx[kC b3h˧j#¶BӑIGɞ񬉉ܪH[F7ʨ`v#Mkqz =$E|6`⑭M@piN.; F¼}6 U,8ނHQc mśͤxbM- ֔0b/ehKa;*Ljyk"^Pnh;ᰏSɨ wif*p_xfMy?XH ?57TS69͠1@p0 ?tЁ6+ꯟ{30˭7\ ӑϰXq 9pшb37P ğZßlsJmV6O-nqlѴS#l;aplAE +Dh?3 )yJS?P+} F*,he ~>œ{y?<"v܄ *yM7.͚2 ƒ~ zGZ-&׫ɯޞǓ7Rt|, ̿;U/_7Qy\|W/??qyZg|VUIʯWUu~(/jVNiyUdVruy=^TiuŽ1y{|UNyxQ.e0[NxyY墪՟VU!M| ĢDm.F}xɿ(2%| 4 :mo˶f~yL|7Fԡ.qM~pqvƀ ,v#m*O(ugYQ0Og7W_շufoӔ13'{ԫzz .&7j1oNOO^.NE anzs~%9}Cd?{)xX zyIDc;ca:BtW3~*R(X/y}9ӏ~'~uj?2NASmNӓoДz{J=H|aSRiJSzoĵ)՟ 0xÇZ].`60(:ZW!sܖ[)|>L_뤞.}'w*vW|Qmn}tt|pt=g_`i/%ڡPpu x'Li\rj2fUj.j#S>tkB`B!ie)<f' ;x4^M&.qnIwRkj]znN!L."e՘wK\~`+oNWtxYxv.9r)Ы z|u?/YzHW|2X=lcH\x/w9wtsfݥw]ҍ]zە~[L]zՕu>s vMG%3M=dN]!EN#evp7*ӃnuB+~#rkkq{HrpGS]ٛ1^wKx{s{rZB2hXW|Y|oW%P|Ot/8uB$'!2"ԂedV:sb mO~Phk\FLjxo&k&mx;) w%].L`Y6ag[y+V*sV%^V\ &ph_kcC.CCKv&YM^5[LK16'p M|dm9o@wzX5+Yk%kV1rJs蛩5",Au"kޏMGBw[$ݦ>-|Q7>46LVGl`ax1=roz|ɡqeʑrt[kvmk$23k֮ۆa66Om69{`oE/i{4@mr>jk k#޴W@Lmz laݧn֢ =pn nҠHt1ےjVmeT[2[-e3{y9gӂIDm%T-m{UO^L8u&T?<=~/VTUp6YU}z&# |xBu5clt"#/wl91TA'WUG /]b|=S? QHpa5F#&a1lqXwmP=eÌCfCDN.n%]{ 5z LS]jd ׏,a&L1:K!;D^qI_hVWGFZC쐉Jq\Z`8BXm(ЅZ3AG&٢AՆ Հbn@G"&h ׯ"|f endstream endobj 608 0 obj << /Producer (pdfTeX-1.40.20) /Author()/Title()/Subject()/Creator(LaTeX with hyperref)/Keywords() /CreationDate (D:20211026172103-04'00') /ModDate (D:20211026172103-04'00') /Trapped /False /PTEX.Fullbanner (This is pdfTeX, Version 3.14159265-2.6-1.40.20 (TeX Live 2019/Debian) kpathsea version 6.3.1) >> endobj 588 0 obj << /Type /ObjStm /N 20 /First 171 /Length 795 /Filter /FlateDecode >> stream xڝn0z ! ([Wt99Hv}ΐCI#i#R Ơ˜A0ie55a$ k֭ >u*9 ǁ^82PtL9IF.Xm(pV(@p6x)YAQ"EPL..CW^=m+ec$o*(n1geHnq=K,RsmD F!R0 B|Kj )a+Q披(RSۅZj*J;TKA3b "+2w=F`D@`[h4!onHnuv-Mvt)xיF"ڱBQa ]8H0k7K<(7iE V48qI , }}$/XeilL3AÏىMSN- h7 :atBVi>t}{J6M9wlZr%nRaNOoLZ1mc/C{; 2.yse͕}v'XҒᇧ,yo[칅gz1±N>% K  {i헼3wҋg,h>G[XFS-TkuΞQW7?k١ڲWӴ5j7A~q2X_x[l:n[?RպS>YSvSAW~w׭` ˇg|>f%L_@?LiOCha7 endstream endobj 609 0 obj << /Type /XRef /Index [0 610] /Size 610 /W [1 3 1] /Root 607 0 R /Info 608 0 R /ID [<8BAA61CF195DD598DACBCBD686E1740B> <8BAA61CF195DD598DACBCBD686E1740B>] /Length 1501 /Filter /FlateDecode >> stream x%YlU/m/t-tcXVZPRZJh+Ƙ #AAcKLސ5 zcFD1}^z;g _a`A _0g ī;,xxǐ)PWwM!@9*#4mƛDF02 rx&yPGVxG!SxcjP7wY xzЀ7w.xo? 4mK-`!}oo&";@'a= ƛۍ\ Vyw!W5x3x;]`- a:$OڎTm 6> Vt=@{) JĪ^ޖ^ޯ^(W ',d`im~Imdi'Nrl!uXuNߢ/VqyP6آ_T{-Itifwژ:lKYIb戙Ӫ ^>#캝p˾fI# a˾Z H#MS`eߘqBG1s Q˾uN}Re_ •/4* 2  r@[>>T%ڲ>j,QKY4_i`k5j|FK-7Qnרgib77iwMV(Ԩ kfA Зf}Ki)Vx#Uh3%%))T2 LdB%*PɄJ&T2I=Cq endstream endobj startxref 1227082 %%EOF EBSeq/inst/doc/EBSeq_Vignette.Rnw0000644000175000017500000013161214136050172016364 0ustar nileshnilesh%\VignetteIndexEntry{EBSeq Vignette} \documentclass{article} \usepackage{fullpage} \usepackage{graphicx, graphics, epsfig,setspace,amsmath, amsthm} \usepackage{hyperref} \usepackage{natbib} %\usepackage{listings} \usepackage{moreverb} \begin{document} \title{EBSeq: An R package for differential expression analysis using RNA-seq data} \author{Ning Leng, John Dawson, and Christina Kendziorski} \maketitle \tableofcontents \setcounter{tocdepth}{2} \section{Introduction} EBSeq may be used to identify differentially expressed (DE) genes and isoforms in an RNA-Seq experiment. As detailed in Leng {\it et al.}, 2013 \cite{Leng13}, EBSeq is an empirical Bayesian approach that models a number of features observed in RNA-seq data. Importantly, for isoform level inference, EBSeq directly accommodates isoform expression estimation uncertainty by modeling the differential variability observed in distinct groups of isoforms. Consider Figure 1, where we have plotted variance against mean for all isoforms using RNA-Seq expression data from Leng {\it et al.}, 2013 \cite{Leng13}. Also shown is the fit within three sub-groups of isoforms defined by the number of constituent isoforms of the parent gene. An isoform of gene $g$ is assigned to the $I_g=k$ group, where $k=1,2,3$, if the total number of isoforms from gene $g$ is $k$ (the $I_g=3$ group contains all isoforms from genes having 3 or more isoforms). As shown in Figure 1, there is decreased variability in the $I_g=1$ group, but increased variability in the others, due to the relative increase in uncertainty inherent in estimating isoform expression when multiple isoforms of a given gene are present. If this structure is not accommodated, there is reduced power for identifying isoforms in the $I_g=1$ group (since the true variances in that group are lower, on average, than that derived from the full collection of isoforms) as well as increased false discoveries in the $I_g=2$ and $I_g=3$ groups (since the true variances are higher, on average, than those derived from the full collection). EBSeq directly models differential variability as a function of $I_g$ providing a powerful approach for isoform level inference. As shown in Leng {\it et al.}, 2013 \cite{Leng13}, the model is also useful for identifying DE genes. We will briefly detail the model in Section \ref{sec:model} and then describe the flow of analysis in Section \ref{sec:quickstart} for both isoform and gene-level inference. \begin{figure}[t] \centering \includegraphics[width=0.6\textwidth]{PlotExample.png} \label{fig:GouldNg} \caption{Empirical variance vs. mean for each isoform profiled in the ESCs vs iPSCs experiment detailed in the Case Study section of Leng {\it et al.}, 2013 \cite{Leng13}. A spline fit to all isoforms is shown in red with splines fit within the $I_g=1$, $I_g=2$, and $I_g=3$ isoform groups shown in yellow, pink, and green, respectively.} \end{figure} \section{Citing this software} \label{sec:cite} Please cite the following article when reporting results from the software. \noindent Leng, N., J.A. Dawson, J.A. Thomson, V. Ruotti, A.I. Rissman, B.M.G. Smits, J.D. Haag, M.N. Gould, R.M. Stewart, and C. Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments, {\it Bioinformatics}, 2013. \section{The Model} \label{sec:model} \subsection{Two conditions} \label{sec:twocondmodel} We let $X_{g_i}^{C1} = X_{g_i,1} ,X_{g_i,2}, ...,X_{g_i,S_1}$ denote data from condition 1 and $ X_{g_i}^{C2} = X_{g_i,(S_1+1)},X_{g_i,(S_1+2)},...,X_{g_i,S}$ data from condition 2. We assume that counts within condition $C$ are distributed as Negative Binomial: $X_{g_i,s}^C|r_{g_i,s}, q_{g_i}^C \sim NB(r_{g_i,s}, q_{g_i}^C)$ where \begin{equation} P(X_{g_i,s}|r_{g_i,s},q_{g_i}^C) = {X_{g_i,s}+r_{g_i,s}-1\choose X_{g_i,s}}(1-q_{g_i}^C)^{X_{g_i,s}}(q_{g_i}^C)^{r_{g_i,s}}\label{eq:01} \end{equation} \noindent and $\mu_{g_i,s}^C=r_{g_i,s} (1-q_{g_i}^C)/q_{g_i}^C$; $(\sigma_{g_i,s}^C)^2=r_{g_i,s} (1-q_{g_i}^C)/(q_{g_i}^C)^2.$ \medskip We assume a prior distribution on $q_{g_i}^C$: $q_{g_i}^C|\alpha, \beta^{I_g} \sim Beta(\alpha, \beta^{I_g})$. The hyperparameter $\alpha$ is shared by all the isoforms and $\beta^{I_g}$ is $I_g$ specific (note this is an index, not a power). We further assume that $r_{g_i,s}=r_{g_i,0} l_s$, where $r_{g_i,0}$ is an isoform specific parameter common across conditions and $r_{g_i,s}$ depends on it through the sample-specific normalization factor $l_s$. Of interest in this two group comparison is distinguishing between two cases, or what we will refer to subsequently as two patterns of expression, namely equivalent expression (EE) and differential expression (DE): \begin{center} $H_0$ (EE) : $q_{g_i}^{C1}=q_{g_i}^{C2}$ vs $H_1$ (DE) : $q_{g_i}^{C1} \neq q_{g_i}^{C2}$. \end{center} Under the null hypothesis (EE), the data $X_{g_i}^{C1,C2} = X_{g_i}^{C1}, X_{g_i}^{C2}$ arises from the prior predictive distribution $f_0^{I_g}(X_{g_i}^{C1,C2})$: %\tiny \begin{equation} f_0^{I_g}(X_{g_i}^{C1,C2})=\Bigg[\prod_{s=1}^S {X_{g_i,s}+r_{g_i,s}-1\choose X_{g_i,s}}\Bigg] \frac{Beta(\alpha+\sum_{s=1}^S r_{g_i,s}, \beta^{I_g}+\sum_{s=1}^SX_{g_i,s} )}{Beta(\alpha, \beta^{I_g})}\label{eq:05} \end{equation} %\normalsize Alternatively (in a DE scenario), $X_{g_i}^{C1,C2}$ follows the prior predictive distribution $f_1^{I_g}(X_{g_i}^{C1,C2})$: \begin{equation} f_1^{I_g}(X_{g_i}^{C1,C2})=f_0^{I_g}(X_{g_i}^{C1})f_0^{I_g}(X_{g_i}^{C2}) \label{eq:06} \end{equation} Let the latent variable $Z_{g_i}$ be defined so that $Z_{g_i} = 1$ indicates that isoform $g_i$ is DE and $Z_{g_i} = 0$ indicates isoform $g_i$ is EE, and $Z_{g_i} \sim Bernoulli(p)$. Then, the marginal distribution of $X_{g_i}^{C1,C2}$ and $Z_{g_i}$ is: \begin{equation} (1-p)f_0^{I_g}(X_{g_i}^{C1,C2}) + pf_1^{I_g}(X_{g_i}^{C1,C2})\label{eq:07} \end{equation} \noindent The posterior probability of being DE at isoform $g_i$ is obtained by Bayes' rule: \begin{equation} \frac{pf_1^{I_g}(X_{g_i}^{C1,C2})}{(1-p)f_0^{I_g}(X_{g_i}^{C1,C2}) + pf_1^{I_g}(X_{g_i}^{C1,C2})}\label{eq:08} \end{equation} %\newpage \subsection{More than two conditions} \label{sec:multicondmodel} EBSeq naturally accommodates multiple condition comparisons. For example, in a study with 3 conditions, there are K=5 possible expression patterns (P1,...,P5), or ways in which latent levels of expression may vary across conditions: \begin{align} \textrm {P1:}& \hspace{0.05in} q_{g_i}^{C1} = q_{g_i}^{C2}=q_{g_i}^{C3} \nonumber \\ \textrm {P2:}& \hspace{0.05in} q_{g_i}^{C1} = q_{g_i}^{C2} \neq q_{g_i}^{C3} \nonumber \\ \textrm {P3:}& \hspace{0.05in} q_{g_i}^{C1} = q_{g_i}^{C3} \neq q_{g_i}^{C2} \nonumber \\ \textrm {P4:}& \hspace{0.05in} q_{g_i}^{C1} \neq q_{g_i}^{C2} = q_{g_i}^{C3} \nonumber \\ \textrm {P5:}& \hspace{0.05in} q_{g_i}^{C1} \neq q_{g_i}^{C2} \neq q_{g_i}^{C3} \textrm{ and } q_{g_i}^{C1} \neq q_{g_i}^{C3} \nonumber \end{align} \noindent The prior predictive distributions for these are given, respectively, by: \begin{align} g_1^{I_g}(X_{g_i}^{C1,C2,C3}) &= f_0^{I_g}(X_{g_i}^{C1,C2,C3}) \nonumber \\ g_2^{I_g}(X_{g_i}^{C1,C2,C3}) &= f_0^{I_g}(X_{g_i}^{C1,C2})f_0^{I_g}(X_{g_i}^{C3}) \nonumber \\ g_3^{I_g}(X_{g_i}^{C1,C2,C3}) &= f_0^{I_g}(X_{g_i}^{C1,C3})f_0^{I_g}(X_{g_i}^{C2}) \nonumber \\ g_4^{I_g}(X_{g_i}^{C1,C2,C3}) &= f_0^{I_g}(X_{g_i}^{C1})f_0^{I_g}(X_{g_i}^{C2,C3}) \nonumber \\ g_5^{I_g}(X_{g_i}^{C1,C2,C3}) &= f_0^{I_g}(X_{g_i}^{C1})f_0^{I_g}(X_{g_i}^{C2})f_0^{I_g}(X_{g_i}^{C3}) \nonumber \end{align} \noindent where $f_0^{I_g}$ is the same as in equation \ref{eq:05}. Then the marginal distribution in equation \ref{eq:07} becomes: \begin{equation} \sum_{k=1}^5 p_k g_k^{I_g}(X_{g_i}^{C1,C2,C3}) \label{eq:11} \end{equation} \noindent where $\sum_{k=1}^5 p_k = 1$. Thus, the posterior probability of isoform $g_i$ coming from pattern $K$ is readily obtained by: \begin{equation} \frac{p_K g_K^{I_g}(X_{g_i}^{C1,C2,C3})}{\sum_{k=1}^5 p_k g_k^{I_g}(X_{g_i}^{C1,C2,C3})} \label{eq:12} \end{equation} \subsection{Getting a false discovery rate (FDR) controlled list of genes or isoforms} \label{sec:fdrlist} To obtain a list of DE genes with false discovery rate (FDR) controlled at $\alpha$ in an experiment comparing two biological conditions, the genes with posterior probability of being DE (PPDE) greater than 1 - $\alpha$ should be used. For example, the genes with PPDE>=0.95 make up the list of DE genes with target FDR controlled at 5\%. With more than two biological conditions, there are multiple DE patterns (see Section \ref{sec:multicondmodel}). To obtain a list of genes in a specific DE pattern with target FDR $\alpha$, a user should take the genes with posterior probability of being in that pattern greater than 1 - $\alpha$. Isoform-based lists are obtained in the same way. \newpage \section{Quick Start} \label{sec:quickstart} Before analysis can proceed, the EBSeq package must be loaded into the working space: <<>>= library(EBSeq) @ \subsection{Gene level DE analysis (two conditions)} \label{sec:startgenede} \subsubsection{Required input} \label{sec:startgenedeinput} \begin{flushleft} {\bf Data}: The object \verb+Data+ should be a $G-by-S$ matrix containing the expression values for each gene and each sample, where $G$ is the number of genes and $S$ is the number of samples. These values should exhibit raw counts, without normalization across samples. Counts of this nature may be obtained from RSEM \cite{Li11b}, Cufflinks \cite{Trapnell12}, or a similar approach. \vspace{5 mm} {\bf Conditions}: The object \verb+Conditions+ should be a Factor vector of length $S$ that indicates to which condition each sample belongs. For example, if there are two conditions and three samples in each, $S=6$ and \verb+Conditions+ may be given by \verb+as.factor(c("C1","C1","C1","C2","C2","C2"))+ \end{flushleft} \noindent The object \verb+GeneMat+ is a simulated data matrix containing 1,000 rows of genes and 10 columns of samples. The genes are named \verb+Gene_1, Gene_2 ...+ <<>>= data(GeneMat) str(GeneMat) @ \subsubsection{Library size factor} \label{sec:startgenedesize} As detailed in Section \ref{sec:model}, EBSeq requires the library size factor $l_s$ for each sample $s$. Here, $l_s$ may be obtained via the function \verb+MedianNorm+, which reproduces the median normalization approach in DESeq \citep{Anders10}. <<>>= Sizes=MedianNorm(GeneMat) @ \noindent If quantile normalization is preferred, $l_s$ may be obtained via the function \verb+QuantileNorm+. (e.g. \verb+QuantileNorm(GeneMat,.75)+ for Upper-Quantile Normalization in \cite{Bullard10}) \subsubsection{Running EBSeq on gene expression estimates} \label{sec:startgenederun} The function \verb+EBTest+ is used to detect DE genes. For gene-level data, we don't need to specify the parameter \verb+NgVector+ since there are no differences in $I_g$ structure among the different genes. Here, we simulated the first five samples to be in condition 1 and the other five in condition 2, so define: \verb+Conditions=as.factor(rep(c("C1","C2"),each=5))+ \noindent \verb+sizeFactors+ is used to define the library size factor of each sample. It could be obtained by summing up the total number of reads within each sample, Median Normalization \citep{Anders10}, scaling normalization \citep{Robinson10}, Upper-Quantile Normalization \cite{Bullard10}, or some other such approach. These in hand, we run the EM algorithm, setting the number of iterations to five via \verb+maxround=5+ for demonstration purposes. However, we note that in practice, additional iterations are usually required. Convergence should always be checked (see Section \ref{sec:detailedgenedeconverge} for details). Please note this may take several minutes: <<>>= EBOut=EBTest(Data=GeneMat, Conditions=as.factor(rep(c("C1","C2"),each=5)),sizeFactors=Sizes, maxround=5) @ \noindent The list of DE genes and the posterior probabilities of being DE are obtained as follows <<>>= EBDERes=GetDEResults(EBOut, FDR=0.05) str(EBDERes$DEfound) head(EBDERes$PPMat) str(EBDERes$Status) @ \noindent \verb+EBDERes$DEfound+ is a list of genes identified with 5\% FDR. EBSeq found 95 genes. The matrix \verb+EBDERes$PPMat+ contains two columns \verb+PPEE+ and \verb+PPDE+, corresponding to the posterior probabilities of being EE or DE for each gene. \verb+EBDERes$Status+ contains each gene's status called by EBSeq. \noindent Note the \verb+GetDEResults()+ was incorporated in EBSeq since version 1.7.1. By using the default settings, the number of genes identified in any given analysis may differ slightly from the previous version. The updated algorithm is more robust to outliers and transcripts with low variance. To obtain results that are comparable to results from earlier versions of EBSeq ($\le$ 1.7.0), a user may set \verb+Method="classic"+ in \verb+GetDEResults()+ function, or use the \verb+GetPPMat()+ function. \subsection{Isoform level DE analysis (two conditions)} \label{sec:startisode} \subsubsection{Required inputs} \label{sec:startisodeinput} \begin{flushleft} {\bf Data}: The object \verb+Data+ should be a $I-by-S$ matrix containing the expression values for each isoform and each sample, where $I$ is the number of isoforms and $S$ is the number of sample. As in the gene-level analysis, these values should exhibit raw data, without normalization across samples. \vspace{5 mm} {\bf Conditions}: The object \verb+Conditions+ should be a vector with length $S$ to indicate the condition of each sample. \vspace{5 mm} {\bf IsoformNames}: The object \verb+IsoformNames+ should be a vector with length $I$ to indicate the isoform names. \vspace{5 mm} {\bf IsosGeneNames}: The object \verb+IsosGeneNames+ should be a vector with length $I$ to indicate the gene name of each isoform. (in the same order as \verb+IsoformNames+.) \end{flushleft} \noindent \verb+IsoList+ contains 1,200 simulated isoforms. In which \verb+IsoList$IsoMat+ is a data matrix containing 1,200 rows of isoforms and 10 columns of samples; \verb+IsoList$IsoNames+ contains the isoform names; \verb+IsoList$IsosGeneNames+ contains the names of the genes the isoforms belong to. <<>>= data(IsoList) str(IsoList) IsoMat=IsoList$IsoMat str(IsoMat) IsoNames=IsoList$IsoNames IsosGeneNames=IsoList$IsosGeneNames @ \subsubsection{Library size factor} \label{sec:startisodesize} Similar to the gene-level analysis presented above, we may obtain the isoform-level library size factors via \verb+MedianNorm+: <<>>= IsoSizes=MedianNorm(IsoMat) @ \subsubsection{The $I_g$ vector} \label{sec:startisodeNg} While working on isoform level data, EBSeq fits different prior parameters for different uncertainty groups (defined as $I_g$ groups). The default setting to define the uncertainty groups consists of using the number of isoforms the host gene contains ($N_g$) for each isoform. The default settings will provide three uncertainty groups: $I_g=1$ group: Isoforms with $N_g=1$; $I_g=2$ group: Isoforms with $N_g=2$; $I_g=3$ group: Isoforms with $N_g \geq 3$. The $N_g$ and $I_g$ group assignment can be obtained using the function \verb+GetNg+. The required inputs of \verb+GetNg+ are the isoform names (\verb+IsoformNames+) and their corresponding gene names (\verb+IsosGeneNames+). <<>>= NgList=GetNg(IsoNames, IsosGeneNames) IsoNgTrun=NgList$IsoformNgTrun IsoNgTrun[c(1:3,201:203,601:603)] @ More details could be found in Section \ref{sec:detailedisode}. \subsubsection{Running EBSeq on isoform expression estimates} \label{sec:startisoderun} The \verb+EBTest+ function is also used to run EBSeq for two condition comparisons on isoform-level data. Below we use 5 iterations to demonstrate. However, as in the gene level analysis, we advise that additional iterations will likely be required in practice (see Section \ref{sec:detailedisodeconverge} for details). <<>>= IsoEBOut=EBTest(Data=IsoMat, NgVector=IsoNgTrun, Conditions=as.factor(rep(c("C1","C2"),each=5)),sizeFactors=IsoSizes, maxround=5) IsoEBDERes=GetDEResults(IsoEBOut, FDR=0.05) str(IsoEBDERes$DEfound) head(IsoEBDERes$PPMat) str(IsoEBDERes$Status) @ \noindent We see that EBSeq found 104 DE isoforms at the target FDR of 0.05. \noindent Note the \verb+GetDEResults()+ was incorporated in EBSeq since version 1.7.1. By using the default settings, the number of transcripts identified in any given analysis may differ slightly from the previous version. The updated algorithm is more robust to outliers and transcripts with low variance. To obtain results that are comparable to results from earlier versions of EBSeq ($\le$ 1.7.0), a user may set \verb+Method="classic"+ in \verb+GetDEResults()+ function, or use the \verb+GetPPMat()+ function. \subsection{Gene level DE analysis (more than two conditions)} \label{sec:startmulticond} \noindent The object \verb+MultiGeneMat+ is a matrix containing 500 simulated genes with 6 samples: the first two samples are from condition 1; the second and the third sample are from condition 2; the last two samples are from condition 3. <<>>= data(MultiGeneMat) str(MultiGeneMat) @ In analysis where the data are spread over more than two conditions, the set of possible patterns for each gene is more complicated than simply EE and DE. As noted in Section \ref{sec:model}, when we have 3 conditions, there are 5 expression patterns to consider. In the simulated data, we have 6 samples, 2 in each of 3 conditions. The function \verb+GetPatterns+ allows the user to generate all possible patterns given the conditions. For example: <<>>= Conditions=c("C1","C1","C2","C2","C3","C3") PosParti=GetPatterns(Conditions) PosParti @ \noindent where the first row means all three conditions have the same latent mean expression level; the second row means C1 and C2 have the same latent mean expression level but that of C3 is different; and the last row corresponds to the case where the three conditions all have different latent mean expression levels. The user may use all or only some of these possible patterns as an input to \verb+EBMultiTest+. For example, if we were interested in Patterns 1, 2, 4 and 5 only, we'd define: <<>>= Parti=PosParti[-3,] Parti @ Moving on to the analysis, \verb+MedianNorm+ or one of its competitors should be used to determine the normalization factors. Once this is done, the formal test is performed by \verb+EBMultiTest+. <<>>= MultiSize=MedianNorm(MultiGeneMat) MultiOut=EBMultiTest(MultiGeneMat,NgVector=NULL,Conditions=Conditions, AllParti=Parti, sizeFactors=MultiSize, maxround=5) @ \noindent The posterior probability of being in each pattern for every gene is obtained by using the function \verb+GetMultiPP+: <<>>= MultiPP=GetMultiPP(MultiOut) names(MultiPP) MultiPP$PP[1:10,] MultiPP$MAP[1:10] MultiPP$Patterns @ \noindent where \verb+MultiPP$PP+ provides the posterior probability of being in each pattern for every gene. \verb+MultiPP$MAP+ provides the most likely pattern of each gene based on the posterior probabilities. \verb+MultiPP$Patterns+ provides the details of the patterns. \subsection{Isoform level DE analysis (more than two conditions)} \label{sec:startisomulticond} \noindent Similar to \verb+IsoList+, the object \verb+IsoMultiList+ is an object containing the isoform expression estimates matrix, the isoform names, and the gene names of the isoforms' host genes. \verb+IsoMultiList$IsoMultiMat+ contains 300 simulated isoforms with 8 samples. The first two samples are from condition 1; the second and the third sample are from condition 2; the fifth and sixth sample are from condition 3; the last two samples are from condition 4. Similar to Section \ref{sec:startisode}, the function \verb+MedianNorm+ and \verb+GetNg+ could be used for normalization and calculating the $N_g$'s. <<>>= data(IsoMultiList) IsoMultiMat=IsoMultiList[[1]] IsoNames.Multi=IsoMultiList$IsoNames IsosGeneNames.Multi=IsoMultiList$IsosGeneNames IsoMultiSize=MedianNorm(IsoMultiMat) NgList.Multi=GetNg(IsoNames.Multi, IsosGeneNames.Multi) IsoNgTrun.Multi=NgList.Multi$IsoformNgTrun Conditions=c("C1","C1","C2","C2","C3","C3","C4","C4") @ Here we have 4 conditions, there are 15 expression patterns to consider. The function \verb+GetPatterns+ allows the user to generate all possible patterns given the conditions. For example: <<>>= PosParti.4Cond=GetPatterns(Conditions) PosParti.4Cond @ \noindent If we were interested in Patterns 1, 2, 3, 8 and 15 only, we'd define: <<>>= Parti.4Cond=PosParti.4Cond[c(1,2,3,8,15),] Parti.4Cond @ \noindent Moving on to the analysis, \verb+EBMultiTest+ could be used to perform the test: <<>>= IsoMultiOut=EBMultiTest(IsoMultiMat, NgVector=IsoNgTrun.Multi,Conditions=Conditions, AllParti=Parti.4Cond, sizeFactors=IsoMultiSize, maxround=5) @ \noindent The posterior probability of being in each pattern for every gene is obtained by using the function \verb+GetMultiPP+: <<>>= IsoMultiPP=GetMultiPP(IsoMultiOut) names(MultiPP) IsoMultiPP$PP[1:10,] IsoMultiPP$MAP[1:10] IsoMultiPP$Patterns @ \noindent where \verb+MultiPP$PP+ provides the posterior probability of being in each pattern for every gene. \verb+MultiPP$MAP+ provides the most likely pattern of each gene based on the posterior probabilities. \verb+MultiPP$Patterns+ provides the details of the patterns. \newpage \section{More detailed examples} \label{sec:detailed} \subsection{Gene level DE analysis (two conditions)} \label{sec:detailedgenede} \subsubsection{Running EBSeq on simulated gene expression estimates} \label{sec:detailedgenederun} EBSeq is applied as described in Section \ref{sec:startgenederun}. <>= data(GeneMat) Sizes=MedianNorm(GeneMat) EBOut=EBTest(Data=GeneMat, Conditions=as.factor(rep(c("C1","C2"),each=5)),sizeFactors=Sizes, maxround=5) EBDERes=GetDEResults(EBOut, FDR=0.05) @ <<>>= EBDERes=GetDEResults(EBOut, FDR=0.05) str(EBDERes$DEfound) head(EBDERes$PPMat) str(EBDERes$Status) @ \noindent EBSeq found 95 DE genes at a target FDR of 0.05.\\ \subsubsection{Calculating FC} \label{sec:detailedgenedefc} The function \verb+PostFC+ may be used to calculate the Fold Change (FC) of the raw data as well as the posterior FC of the normalized data. \begin{figure}[h!] \centering <>= GeneFC=PostFC(EBOut) str(GeneFC) PlotPostVsRawFC(EBOut,GeneFC) @ \caption{ FC vs. Posterior FC for 1,000 gene expression estimates} \label{fig:GeneFC} \end{figure} Figure \ref{fig:GeneFC} shows the FC vs. Posterior FC on 1,000 gene expression estimates. The genes are ranked by their cross-condition mean (adjusted by the normalization factors). The posterior FC tends to shrink genes with low expressions (small rank); in this case the differences are minor. \newpage \subsubsection{Checking convergence} \label{sec:detailedgenedeconverge} As detailed in Section \ref{sec:model}, we assume the prior distribution of $q_g^C$ is $Beta(\alpha,\beta)$. The EM algorithm is used to estimate the hyper-parameters $\alpha,\beta$ and the mixture parameter $p$. The optimized parameters at each iteration may be obtained as follows (recall we are using 5 iterations for demonstration purposes): <<>>= EBOut$Alpha EBOut$Beta EBOut$P @ In this case the differences between the 4th and 5th iterations are always less than 0.01. \subsubsection{Checking the model fit and other diagnostics} \label{sec:detailedgenedeplot} As noted in Leng {\it et al.}, 2013 \cite{Leng13}, EBSeq relies on parametric assumptions that should be checked following each analysis. The \verb+QQP+ function may be used to assess prior assumptions. In practice, \verb+QQP+ generates the Q-Q plot of the empirical $q$'s vs. the simulated $q$'s from the Beta prior distribution with estimated hyper-parameters. Figure \ref{fig:GeneQQ} shows that the data points lie on the $y=x$ line for both conditions, which indicates that the Beta prior is appropriate. \begin{figure}[h!] \centering <>= par(mfrow=c(1,2)) QQP(EBOut) @ \caption{QQ-plots for checking the assumption of a Beta prior (upper panels) as well as the model fit using data from condition 1 and condition 2 (lower panels)} \label{fig:GeneQQ} \end{figure} \newpage \noindent Likewise, the \verb+DenNHist+ function may be used to check the density plot of empirical $q$'s vs the simulated $q$'s from the fitted Beta prior distribution. Figure \ref{fig:GeneDenNHist} also shows our estimated distribution fits the data very well. \begin{figure}[h!] \centering <>= par(mfrow=c(1,2)) DenNHist(EBOut) @ \caption{Density plots for checking the model fit using data from condition 1 and condition 2} \label{fig:GeneDenNHist} \end{figure} \newpage \subsection{Isoform level DE analysis (two conditions)} \label{sec:detailedisode} \subsubsection{The $I_g$ vector} \label{sec:detailedisodeNg} Since EBSeq fits rely on $I_g$, we need to obtain the $I_g$ for each isoform. This can be done using the function \verb+GetNg+. The required inputs of \verb+GetNg+ are the isoform names (\verb+IsoformNames+) and their corresponding gene names (\verb+IsosGeneNames+), described above. In the simulated data, we assume that the isoforms in the $I_g=1$ group belong to genes \verb+Gene_1, ... , Gene_200+; The isoforms in the $I_g=2$ group belong to genes \verb+Gene_201, ..., Gene_400+; and isoforms in the $I_g=3$ group belong to \verb+Gene_401, ..., Gene_600+. <>= data(IsoList) IsoMat=IsoList$IsoMat IsoNames=IsoList$IsoNames IsosGeneNames=IsoList$IsosGeneNames NgList=GetNg(IsoNames, IsosGeneNames, TrunThre=3) @ <<>>= names(NgList) IsoNgTrun=NgList$IsoformNgTrun IsoNgTrun[c(1:3,201:203,601:603)] @ The output of \verb+GetNg+ contains 4 vectors. \verb+GeneNg+ (\verb+IsoformNg+) provides the number of isoforms $N_g$ within each gene (within each isoform's host gene). \verb+GeneNgTrun+ (\verb+IsoformNgTrun+) provides the $I_g$ group assignments. The default number of groups is 3, which means the isoforms with $N_g$ greater than 3 will be assigned to $I_g=3$ group. We use 3 in the case studies since the number of isoforms with $N_g$ larger than 3 is relatively small and the small sample size may induce poor parameter fitting if we treat them as separate groups. In practice, if there is evidence that the $N_g=4,5,6...$ groups should be treated as separate groups, a user can change \verb+TrunThre+ to define a different truncation threshold. \subsubsection{Using mappability ambiguity clusters instead of the $I_g$ vector when the gene-isoform relationship is unknown} \label{sec:detailedisodeNoNg} When working with a de-novo assembled transcriptome, in which case the gene-isoform relationship is unknown, a user can use read mapping ambiguity cluster information instead of Ng, as provided by RSEM \cite{Li11b} in the output file \verb+output_name.ngvec+. The file contains a vector with the same length as the total number of transcripts. Each transcript has been assigned to one of 3 levels (1, 2, or 3) to indicate the mapping uncertainty level of that transcript. The mapping ambiguity clusters are partitioned via a k-means algorithm on the unmapability scores that are provided by RSEM. A user can read in the mapping ambiguity cluster information using: <>= IsoNgTrun = scan(file="output_name.ngvec", what=0, sep="\n") @\\ Where \verb+"output_name.ngvec"+ is the output file obtained from RSEM function rsem-generate-ngvector. More details on using the RSEM-EBSeq pipeline on de novo assembled transcriptomes can be found at \url{http://deweylab.biostat.wisc.edu/rsem/README.html#de}. Other unmappability scores and other cluster methods (e.g. Gaussian Mixed Model) could also be used to form the uncertainty clusters. \subsubsection{Running EBSeq on simulated isoform expression estimates} \label{sec:detailedisoderun} EBSeq can be applied as described in Section \ref{sec:startisoderun}. <>= IsoSizes=MedianNorm(IsoMat) IsoEBOut=EBTest(Data=IsoMat, NgVector=IsoNgTrun, Conditions=as.factor(rep(c("C1","C2"),each=5)),sizeFactors=IsoSizes, maxround=5) IsoEBDERes=GetDEResults(IsoEBOut, FDR=0.05) @ <<>>= str(IsoEBDERes) @ \noindent We see that EBSeq found 104 DE isoforms at a target FDR of 0.05. The function \verb+PostFC+ could also be used here to calculate the Fold Change (FC) as well as the posterior FC on the normalization factor adjusted data. <<>>= IsoFC=PostFC(IsoEBOut) str(IsoFC) @ \subsubsection{Checking convergence} \label{sec:detailedisodeconverge} For isoform level data, we assume the prior distribution of $q_{gi}^C$ is $Beta(\alpha,\beta^{I_g})$. As in Section \ref{sec:detailedgenedeconverge}, the optimized parameters at each iteration may be obtained as follows (recall we are using 5 iterations for demonstration purposes): <<>>= IsoEBOut$Alpha IsoEBOut$Beta IsoEBOut$P @ Here we have 3 $\beta$'s in each iteration corresponding to $\beta^{I_g=1},\beta^{I_g=2},\beta^{I_g=3}$. We see that parameters are changing less than $10^{-2}$ or $10^{-3}$. In practice, we require changes less than $10^{-3}$ to declare convergence. \subsubsection{Checking the model fit and other diagnostics} \label{sec:detailedisodeplot} In Leng {\it et al.}, 2013\citep{Leng13}, we showed the mean-variance differences across different isoform groups on multiple data sets. In practice, if it is of interest to check differences among isoform groups defined by truncated $I_g$ (such as those shown here in Figure 1), the function \verb+PolyFitPlot+ may be used. The following code generates the three panels shown in Figure \ref{fig:IsoSimuNgEach} (if condition 2 is of interest, a user could change each \verb+C1+ to \verb+C2+.): \begin{figure}[h!] \centering <>= par(mfrow=c(2,2)) PolyFitValue=vector("list",3) for(i in 1:3) PolyFitValue[[i]]=PolyFitPlot(IsoEBOut$C1Mean[[i]], IsoEBOut$C1EstVar[[i]],5) @ \caption{ The mean-variance fitting plot for each Ng group} \label{fig:IsoSimuNgEach} \end{figure} \newpage Superimposing all $I_g$ groups using the code below will generate the figure (shown here in Figure \ref{fig:IsoSimuNg}), which is similar in structure to Figure 1: \begin{figure}[h!] \centering <>= PolyAll=PolyFitPlot(unlist(IsoEBOut$C1Mean), unlist(IsoEBOut$C1EstVar),5) lines(log10(IsoEBOut$C1Mean[[1]][PolyFitValue[[1]]$sort]), PolyFitValue[[1]]$fit[PolyFitValue[[1]]$sort],col="yellow",lwd=2) lines(log10(IsoEBOut$C1Mean[[2]][PolyFitValue[[2]]$sort]), PolyFitValue[[2]]$fit[PolyFitValue[[2]]$sort],col="pink",lwd=2) lines(log10(IsoEBOut$C1Mean[[3]][PolyFitValue[[3]]$sort]), PolyFitValue[[3]]$fit[PolyFitValue[[3]]$sort],col="green",lwd=2) legend("topleft",c("All Isoforms","Ng = 1","Ng = 2","Ng = 3"), col=c("red","yellow","pink","green"),lty=1,lwd=3,box.lwd=2) @ \caption{The mean-variance plot for each Ng group} \label{fig:IsoSimuNg} \end{figure} \newpage \noindent To generate a QQ-plot of the fitted Beta prior distribution and the $\hat{q}^C$'s within condition, a user may use the following code to generate 6 panels (as shown in Figure \ref{fig:IsoQQ}). \begin{figure}[h!] \centering <>= par(mfrow=c(2,3)) QQP(IsoEBOut) @ \caption{ QQ-plots of the fitted prior distributions within each condition and each Ig group} \label{fig:IsoQQ} \end{figure} \newpage \noindent And in order to produce the plot of the fitted Beta prior densities and the histograms of $\hat{q}^C$'s within each condition, the following may be used (it generates Figure \ref{fig:IsoDenNHist}): \begin{figure}[h] \centering <>= par(mfrow=c(2,3)) DenNHist(IsoEBOut) @ \caption{ Prior distribution fit within each condition and each Ig group. (Note only a small set of isoforms are considered here for demonstration. Better fitting should be expected while using full set of isoforms.)} \label{fig:IsoDenNHist} \end{figure} \clearpage \subsection{Gene level DE analysis (more than two conditions)} \label{sec:detailedmulticond} As described in Section \ref{sec:startmulticond}, the function \verb+GetPatterns+ allows the user to generate all possible patterns given the conditions. To visualize the patterns, the function \verb+PlotPattern+ may be used. \begin{figure}[h!] \centering <>= Conditions=c("C1","C1","C2","C2","C3","C3") PosParti=GetPatterns(Conditions) PosParti PlotPattern(PosParti) @ \caption{ All possible patterns} \label{fig:Patterns} \end{figure} \newpage \noindent If we were interested in Patterns 1, 2, 4 and 5 only, we'd define: <<>>= Parti=PosParti[-3,] Parti @ \noindent Moving on to the analysis, \verb+MedianNorm+ or one of its competitors should be used to determine the normalization factors. Once this is done, the formal test is performed by \verb+EBMultiTest+. <>= data(MultiGeneMat) MultiSize=MedianNorm(MultiGeneMat) MultiOut=EBMultiTest(MultiGeneMat, NgVector=NULL,Conditions=Conditions, AllParti=Parti, sizeFactors=MultiSize, maxround=5) @ \noindent The posterior probability of being in each pattern for every gene is obtained using the function \verb+GetMultiPP+: <<>>= MultiPP=GetMultiPP(MultiOut) names(MultiPP) MultiPP$PP[1:10,] MultiPP$MAP[1:10] MultiPP$Patterns @ \noindent where \verb+MultiPP$PP+ provides the posterior probability of being in each pattern for every gene. \verb+MultiPP$MAP+ provides the most likely pattern of each gene based on the posterior probabilities. \verb+MultiPP$Patterns+ provides the details of the patterns. The FC and posterior FC for multiple condition data can be obtained by the function \verb+GetMultiFC+: <<>>= MultiFC=GetMultiFC(MultiOut) str(MultiFC) @ \noindent To generate a QQ-plot of the fitted Beta prior distribution and the $\hat{q}^C$'s within condition, a user could also use function \verb+DenNHist+ and \verb+QQP+. \begin{figure}[h!] \centering <>= par(mfrow=c(2,2)) QQP(MultiOut) @ \caption{ QQ-plots of the fitted prior distributions within each condition and each Ig group} \label{fig:GeneMultiQQ} \end{figure} \begin{figure}[h] \centering <>= par(mfrow=c(2,2)) DenNHist(MultiOut) @ \caption{ Prior distributions fit within each condition. (Note only a small set of genes are considered here for demonstration. Better fitting should be expected while using full set of genes.)} \label{fig:GeneMultiDenNHist} \end{figure} \newpage \clearpage \newpage \subsection{Isoform level DE analysis (more than two conditions)} \label{sec:detailedisomulticond} Similar to Section \ref{sec:startmulticond}, the function \verb+GetPatterns+ allows a user to generate all possible patterns given the conditions. To visualize the patterns, the function \verb+PlotPattern+ may be used. <<>>= Conditions=c("C1","C1","C2","C2","C3","C3","C4","C4") PosParti.4Cond=GetPatterns(Conditions) PosParti.4Cond @ \newpage \begin{figure}[h!] \centering <>= PlotPattern(PosParti.4Cond) Parti.4Cond=PosParti.4Cond[c(1,2,3,8,15),] Parti.4Cond @ \caption{All possible patterns for 4 conditions} \label{fig:Patterns4Cond} \end{figure} \newpage <>= data(IsoMultiList) IsoMultiMat=IsoMultiList[[1]] IsoNames.Multi=IsoMultiList$IsoNames IsosGeneNames.Multi=IsoMultiList$IsosGeneNames IsoMultiSize=MedianNorm(IsoMultiMat) NgList.Multi=GetNg(IsoNames.Multi, IsosGeneNames.Multi) IsoNgTrun.Multi=NgList.Multi$IsoformNgTrun IsoMultiOut=EBMultiTest(IsoMultiMat,NgVector=IsoNgTrun.Multi,Conditions=Conditions, AllParti=Parti.4Cond, sizeFactors=IsoMultiSize, maxround=5) IsoMultiPP=GetMultiPP(IsoMultiOut) @ <<>>= names(MultiPP) IsoMultiPP$PP[1:10,] IsoMultiPP$MAP[1:10] IsoMultiPP$Patterns IsoMultiFC=GetMultiFC(IsoMultiOut) @ The FC and posterior FC for multiple condition data can be obtained by the function \verb+GetMultiFC+: \noindent To generate a QQ-plot of the fitted Beta prior distribution and the $\hat{q}^C$'s within condition, a user could also use the functions \verb+DenNHist+ and \verb+QQP+. \newpage \begin{figure}[h!] \centering <>= par(mfrow=c(3,4)) QQP(IsoMultiOut) @ \caption{ QQ-plots of the fitted prior distributions within each condition and Ig group. (Note only a small set of isoforms are considered here for demonstration. Better fitting should be expected while using full set of isoforms.)} \label{fig:IsoMultiQQ} \end{figure} \begin{figure}[h] \centering <>= par(mfrow=c(3,4)) DenNHist(IsoMultiOut) @ \caption{ Prior distributions fit within each condition and Ig group. (Note only a small set of isoforms are considered here for demonstration. Better fitting should be expected while using full set of isoforms.)} \label{fig:IsoMultiDenNHist} \end{figure} \clearpage \newpage \newpage \subsection{Working without replicates} When replicates are not available, it is difficult to estimate the transcript specific variance. In this case, EBSeq estimates the variance by pooling similar genes together. Specifically, we take genes with FC in the 25\% - 75\% quantile of all FC's as candidate genes. By defining \verb+NumBin = 1000+ (default in \verb+EBTest+), EBSeq will group genes with similar means into 1,000 bins. For each candidate gene, we use the across-condition variance estimate as its variance estimate. For each bin, the bin-wise variance estimation is taken to be the median of the across-condition variance estimates of the candidate genes within that bin. For each non-candidate gene, we use the bin-wise variance estimate of the host bin (the bin containing this gene) as its variance estimate. This approach works well when there are no more than 50\% DE genes in the data set. \subsubsection{Gene counts with two conditions} \label{sec:norepgenede} To generate a data set with no replicates, we take the first sample of each condition. For example, using the data from Section \ref{sec:detailedgenede}, we take sample 1 from condition 1 and sample 6 from condition 2. Functions \verb+MedianNorm+, \verb+GetDEResults+ and \verb+PostFC+ may be used on data without replicates. <<>>= data(GeneMat) GeneMat.norep=GeneMat[,c(1,6)] Sizes.norep=MedianNorm(GeneMat.norep) EBOut.norep=EBTest(Data=GeneMat.norep, Conditions=as.factor(rep(c("C1","C2"))), sizeFactors=Sizes.norep, maxround=5) EBDERes.norep=GetDEResults(EBOut.norep) GeneFC.norep=PostFC(EBOut.norep) @ \subsubsection{Isoform counts with two conditions} \label{norepisode} To generate an isoform level data set with no replicates, we also take sample 1 and sample 6 in the data we used in Section \ref{sec:detailedisode}. Example codes are shown below. <<>>= data(IsoList) IsoMat=IsoList$IsoMat IsoNames=IsoList$IsoNames IsosGeneNames=IsoList$IsosGeneNames NgList=GetNg(IsoNames, IsosGeneNames) IsoNgTrun=NgList$IsoformNgTrun IsoMat.norep=IsoMat[,c(1,6)] IsoSizes.norep=MedianNorm(IsoMat.norep) IsoEBOut.norep=EBTest(Data=IsoMat.norep, NgVector=IsoNgTrun, Conditions=as.factor(c("C1","C2")), sizeFactors=IsoSizes.norep, maxround=5) IsoEBDERes.norep=GetDEResults(IsoEBOut.norep) IsoFC.norep=PostFC(IsoEBOut.norep) @ \subsubsection{Gene counts with more than two conditions} \label{norepisode} To generate a data set with multiple conditions and no replicates, we take the first sample from each condition (sample 1, 3 and 5) in the data we used in Section \ref{sec:detailedmulticond}. Example codes are shown below. <<>>= data(MultiGeneMat) MultiGeneMat.norep=MultiGeneMat[,c(1,3,5)] Conditions=c("C1","C2","C3") PosParti=GetPatterns(Conditions) Parti=PosParti[-3,] MultiSize.norep=MedianNorm(MultiGeneMat.norep) MultiOut.norep=EBMultiTest(MultiGeneMat.norep, NgVector=NULL,Conditions=Conditions, AllParti=Parti, sizeFactors=MultiSize.norep, maxround=5) MultiPP.norep=GetMultiPP(MultiOut.norep) MultiFC.norep=GetMultiFC(MultiOut.norep) @ \subsubsection{Isoform counts with more than two conditions} \label{sec:norepmulticond} To generate an isoform level data set with multiple conditions and no replicates, we take the first sample from each condition (sample 1, 3, 5 and 7) in the data we used in Section \ref{sec:detailedisomulticond}. Example codes are shown below. <<>>= data(IsoMultiList) IsoMultiMat=IsoMultiList[[1]] IsoNames.Multi=IsoMultiList$IsoNames IsosGeneNames.Multi=IsoMultiList$IsosGeneNames IsoMultiMat.norep=IsoMultiMat[,c(1,3,5,7)] IsoMultiSize.norep=MedianNorm(IsoMultiMat.norep) NgList.Multi=GetNg(IsoNames.Multi, IsosGeneNames.Multi) IsoNgTrun.Multi=NgList.Multi$IsoformNgTrun Conditions=c("C1","C2","C3","C4") PosParti.4Cond=GetPatterns(Conditions) PosParti.4Cond Parti.4Cond=PosParti.4Cond[c(1,2,3,8,15),] Parti.4Cond IsoMultiOut.norep=EBMultiTest(IsoMultiMat.norep, NgVector=IsoNgTrun.Multi,Conditions=Conditions, AllParti=Parti.4Cond, sizeFactors=IsoMultiSize.norep, maxround=5) IsoMultiPP.norep=GetMultiPP(IsoMultiOut.norep) IsoMultiFC.norep=GetMultiFC(IsoMultiOut.norep) @ \section{EBSeq pipelines and extensions} \subsection{RSEM-EBSeq pipeline: from raw reads to differential expression analysis results} EBSeq is coupled with RSEM \cite{Li11b} as an RSEM-EBSeq pipeline which provides quantification and DE testing on both gene and isoform levels. For more details, see \url{http://deweylab.biostat.wisc.edu/rsem/README.html#de} \subsection{EBSeq interface: A user-friendly graphical interface for differetial expression analysis} EBSeq interface provides a graphical interface implementation for users who are not familiar with the R programming language. It takes .xls, .xlsx and .csv files as input. Additional packages need be downloaded; they may be found at \url{http://www.biostat.wisc.edu/~ningleng/EBSeq_Package/EBSeq_Interface/} \subsection{EBSeq Galaxy tool shed} EBSeq tool shed contains EBSeq wrappers for a local Galaxy implementation. For more details, see \url{http://www.biostat.wisc.edu/~ningleng/EBSeq_Package/EBSeq_Galaxy_toolshed/} \section{Acknowledgment} We would like to thank Haolin Xu for checking the package and proofreading the vignette. \section{News} 2014-1-30: In EBSeq 1.3.3, the default setting of EBTest function will remove low expressed genes (genes whose 75th quantile of normalized counts is less than 10) before identifying DE genes. These two thresholds can be changed in EBTest function. Because low expressed genes are disproportionately noisy, removing these genes prior to downstream analyses can improve model fitting and increase robustness (e.g. by removing outliers). 2014-5-22: In EBSeq 1.5.2, numerical approximations are implemented to deal with underflow. The underflow is likely due to large number of samples. 2015-1-29: In EBSeq 1.7.1, EBSeq incorporates a new function GetDEResults() which may be used to obtain a list of transcripts under a target FDR in a two-condition experiment. The results obtained by applying this function with its default setting will be more robust to transcripts with low variance and potential outliers. By using the default settings in this function, the number of genes identified in any given analysis may differ slightly from the previous version (1.7.0 or order). To obtain results that are comparable to results from earlier versions of EBSeq (1.7.0 or older), a user may set Method="classic" in GetDEResults() function, or use the original GetPPMat() function. The GeneDEResults() function also allows a user to modify thresholds to target genes/isoforms with a pre-specified posterior fold change. Also, in EBSeq 1.7.1, the default settings in EBTest() and EBMultiTest() function will only remove transcripts with all 0's (instead of removing transcripts with 75th quantile less than 10 in version 1.3.3-1.7.0). To obtain a list of transcripts comparable to the results generated by EBSeq version 1.3.3-1.7.0, a user may change Qtrm = 0.75 and QtrmCut = 10 when applying EBTest() or EBMultiTest() function. \section{Common Q and A} \subsection{Read in data} csv file: \verb+In=read.csv("FileName", stringsAsFactors=F, row.names=1, header=T)+ \verb+Data=data.matrix(In)+ \noindent txt file: \verb+In=read.table("FileName", stringsAsFactors=F, row.names=1, header=T)+ \verb+Data=data.matrix(In)+ \noindent Check \verb+str(Data)+ and make sure it is a matrix instead of data frame. You may need to play around with the \verb+row.names+ and \verb+header+ option depends on how the input file was generated. \subsection{GetDEResults() function not found} You may on an earlier version of EBSeq. The GetDEResults function was introduced since version 1.7.1. The latest release version could be found at: \url{http://www.bioconductor.org/packages/release/bioc/html/EBSeq.html} \noindent The latest devel version: \url{http://www.bioconductor.org/packages/devel/bioc/html/EBSeq.html} \noindent And you may check your package version by typing \verb+packageVersion("EBSeq")+. \subsection{Visualizing DE genes/isoforms} To generate a heatmap, you may consider the heatmap.2 function in gplots package. For example, you may run \verb+heatmap.2(NormalizedMatrix[GenesOfInterest,], scale="row", trace="none", Colv=F)+ The normalized matrix may be obtained from \verb+GetNormalizedMat()+ function. \subsection{My favorite gene/isoform has NA in PP (status "NoTest")} \indent The NoTest status comes from two sources: 1) In version 1.3.3-1.7.0, using the default parameter settings of EBMultiTest(), the function will not test on genes with more than 75\% values $\le$ 10 to ensure better model fitting. To disable this filter, you may set Qtrm=1 and QtrmCut=0. 2) numerical over/underflow in R. That happens when the within condition variance is extremely large or small. we did implemented a numerical approximation step to calculate the approximated PP for these genes with over/underflow. Here we use $10^{-10}$ to approximate the parameter p in the NB distribution for these genes (we set it to a small value since we want to cover more over/underflow genes with low within-condition variation). You may try to tune this value (to a larger value) in the approximation by setting \verb+ApproxVal+ in \verb+EBTest()+ or \verb+EBMultiTest()+ function. \pagebreak \bibliographystyle{plain} \bibliography{lengetal} \end{document} EBSeq/inst/doc/EBSeq_Vignette.R0000644000175000017500000003627714136070500016027 0ustar nileshnilesh### R code from vignette source 'EBSeq_Vignette.Rnw' ################################################### ### code chunk number 1: EBSeq_Vignette.Rnw:172-173 ################################################### library(EBSeq) ################################################### ### code chunk number 2: EBSeq_Vignette.Rnw:198-200 ################################################### data(GeneMat) str(GeneMat) ################################################### ### code chunk number 3: EBSeq_Vignette.Rnw:208-209 ################################################### Sizes=MedianNorm(GeneMat) ################################################### ### code chunk number 4: EBSeq_Vignette.Rnw:235-237 ################################################### EBOut=EBTest(Data=GeneMat, Conditions=as.factor(rep(c("C1","C2"),each=5)),sizeFactors=Sizes, maxround=5) ################################################### ### code chunk number 5: EBSeq_Vignette.Rnw:240-244 ################################################### EBDERes=GetDEResults(EBOut, FDR=0.05) str(EBDERes$DEfound) head(EBDERes$PPMat) str(EBDERes$Status) ################################################### ### code chunk number 6: EBSeq_Vignette.Rnw:289-295 ################################################### data(IsoList) str(IsoList) IsoMat=IsoList$IsoMat str(IsoMat) IsoNames=IsoList$IsoNames IsosGeneNames=IsoList$IsosGeneNames ################################################### ### code chunk number 7: EBSeq_Vignette.Rnw:302-303 ################################################### IsoSizes=MedianNorm(IsoMat) ################################################### ### code chunk number 8: EBSeq_Vignette.Rnw:324-327 ################################################### NgList=GetNg(IsoNames, IsosGeneNames) IsoNgTrun=NgList$IsoformNgTrun IsoNgTrun[c(1:3,201:203,601:603)] ################################################### ### code chunk number 9: EBSeq_Vignette.Rnw:339-345 ################################################### IsoEBOut=EBTest(Data=IsoMat, NgVector=IsoNgTrun, Conditions=as.factor(rep(c("C1","C2"),each=5)),sizeFactors=IsoSizes, maxround=5) IsoEBDERes=GetDEResults(IsoEBOut, FDR=0.05) str(IsoEBDERes$DEfound) head(IsoEBDERes$PPMat) str(IsoEBDERes$Status) ################################################### ### code chunk number 10: EBSeq_Vignette.Rnw:368-370 ################################################### data(MultiGeneMat) str(MultiGeneMat) ################################################### ### code chunk number 11: EBSeq_Vignette.Rnw:378-381 ################################################### Conditions=c("C1","C1","C2","C2","C3","C3") PosParti=GetPatterns(Conditions) PosParti ################################################### ### code chunk number 12: EBSeq_Vignette.Rnw:389-391 ################################################### Parti=PosParti[-3,] Parti ################################################### ### code chunk number 13: EBSeq_Vignette.Rnw:396-399 ################################################### MultiSize=MedianNorm(MultiGeneMat) MultiOut=EBMultiTest(MultiGeneMat,NgVector=NULL,Conditions=Conditions, AllParti=Parti, sizeFactors=MultiSize, maxround=5) ################################################### ### code chunk number 14: EBSeq_Vignette.Rnw:403-408 ################################################### MultiPP=GetMultiPP(MultiOut) names(MultiPP) MultiPP$PP[1:10,] MultiPP$MAP[1:10] MultiPP$Patterns ################################################### ### code chunk number 15: EBSeq_Vignette.Rnw:427-435 ################################################### data(IsoMultiList) IsoMultiMat=IsoMultiList[[1]] IsoNames.Multi=IsoMultiList$IsoNames IsosGeneNames.Multi=IsoMultiList$IsosGeneNames IsoMultiSize=MedianNorm(IsoMultiMat) NgList.Multi=GetNg(IsoNames.Multi, IsosGeneNames.Multi) IsoNgTrun.Multi=NgList.Multi$IsoformNgTrun Conditions=c("C1","C1","C2","C2","C3","C3","C4","C4") ################################################### ### code chunk number 16: EBSeq_Vignette.Rnw:441-443 ################################################### PosParti.4Cond=GetPatterns(Conditions) PosParti.4Cond ################################################### ### code chunk number 17: EBSeq_Vignette.Rnw:448-450 ################################################### Parti.4Cond=PosParti.4Cond[c(1,2,3,8,15),] Parti.4Cond ################################################### ### code chunk number 18: EBSeq_Vignette.Rnw:455-459 ################################################### IsoMultiOut=EBMultiTest(IsoMultiMat, NgVector=IsoNgTrun.Multi,Conditions=Conditions, AllParti=Parti.4Cond, sizeFactors=IsoMultiSize, maxround=5) ################################################### ### code chunk number 19: EBSeq_Vignette.Rnw:463-468 ################################################### IsoMultiPP=GetMultiPP(IsoMultiOut) names(MultiPP) IsoMultiPP$PP[1:10,] IsoMultiPP$MAP[1:10] IsoMultiPP$Patterns ################################################### ### code chunk number 20: EBSeq_Vignette.Rnw:485-490 (eval = FALSE) ################################################### ## data(GeneMat) ## Sizes=MedianNorm(GeneMat) ## EBOut=EBTest(Data=GeneMat, ## Conditions=as.factor(rep(c("C1","C2"),each=5)),sizeFactors=Sizes, maxround=5) ## EBDERes=GetDEResults(EBOut, FDR=0.05) ################################################### ### code chunk number 21: EBSeq_Vignette.Rnw:492-496 ################################################### EBDERes=GetDEResults(EBOut, FDR=0.05) str(EBDERes$DEfound) head(EBDERes$PPMat) str(EBDERes$Status) ################################################### ### code chunk number 22: EBSeq_Vignette.Rnw:506-509 ################################################### GeneFC=PostFC(EBOut) str(GeneFC) PlotPostVsRawFC(EBOut,GeneFC) ################################################### ### code chunk number 23: EBSeq_Vignette.Rnw:530-533 ################################################### EBOut$Alpha EBOut$Beta EBOut$P ################################################### ### code chunk number 24: EBSeq_Vignette.Rnw:552-554 ################################################### par(mfrow=c(1,2)) QQP(EBOut) ################################################### ### code chunk number 25: EBSeq_Vignette.Rnw:570-572 ################################################### par(mfrow=c(1,2)) DenNHist(EBOut) ################################################### ### code chunk number 26: EBSeq_Vignette.Rnw:593-598 (eval = FALSE) ################################################### ## data(IsoList) ## IsoMat=IsoList$IsoMat ## IsoNames=IsoList$IsoNames ## IsosGeneNames=IsoList$IsosGeneNames ## NgList=GetNg(IsoNames, IsosGeneNames, TrunThre=3) ################################################### ### code chunk number 27: EBSeq_Vignette.Rnw:600-603 ################################################### names(NgList) IsoNgTrun=NgList$IsoformNgTrun IsoNgTrun[c(1:3,201:203,601:603)] ################################################### ### code chunk number 28: EBSeq_Vignette.Rnw:634-635 (eval = FALSE) ################################################### ## IsoNgTrun = scan(file="output_name.ngvec", what=0, sep="\n") ################################################### ### code chunk number 29: EBSeq_Vignette.Rnw:648-652 (eval = FALSE) ################################################### ## IsoSizes=MedianNorm(IsoMat) ## IsoEBOut=EBTest(Data=IsoMat, NgVector=IsoNgTrun, ## Conditions=as.factor(rep(c("C1","C2"),each=5)),sizeFactors=IsoSizes, maxround=5) ## IsoEBDERes=GetDEResults(IsoEBOut, FDR=0.05) ################################################### ### code chunk number 30: EBSeq_Vignette.Rnw:654-655 ################################################### str(IsoEBDERes) ################################################### ### code chunk number 31: EBSeq_Vignette.Rnw:660-662 ################################################### IsoFC=PostFC(IsoEBOut) str(IsoFC) ################################################### ### code chunk number 32: EBSeq_Vignette.Rnw:673-676 ################################################### IsoEBOut$Alpha IsoEBOut$Beta IsoEBOut$P ################################################### ### code chunk number 33: EBSeq_Vignette.Rnw:695-700 ################################################### par(mfrow=c(2,2)) PolyFitValue=vector("list",3) for(i in 1:3) PolyFitValue[[i]]=PolyFitPlot(IsoEBOut$C1Mean[[i]], IsoEBOut$C1EstVar[[i]],5) ################################################### ### code chunk number 34: EBSeq_Vignette.Rnw:713-722 ################################################### PolyAll=PolyFitPlot(unlist(IsoEBOut$C1Mean), unlist(IsoEBOut$C1EstVar),5) lines(log10(IsoEBOut$C1Mean[[1]][PolyFitValue[[1]]$sort]), PolyFitValue[[1]]$fit[PolyFitValue[[1]]$sort],col="yellow",lwd=2) lines(log10(IsoEBOut$C1Mean[[2]][PolyFitValue[[2]]$sort]), PolyFitValue[[2]]$fit[PolyFitValue[[2]]$sort],col="pink",lwd=2) lines(log10(IsoEBOut$C1Mean[[3]][PolyFitValue[[3]]$sort]), PolyFitValue[[3]]$fit[PolyFitValue[[3]]$sort],col="green",lwd=2) legend("topleft",c("All Isoforms","Ng = 1","Ng = 2","Ng = 3"), col=c("red","yellow","pink","green"),lty=1,lwd=3,box.lwd=2) ################################################### ### code chunk number 35: EBSeq_Vignette.Rnw:735-737 ################################################### par(mfrow=c(2,3)) QQP(IsoEBOut) ################################################### ### code chunk number 36: EBSeq_Vignette.Rnw:749-751 ################################################### par(mfrow=c(2,3)) DenNHist(IsoEBOut) ################################################### ### code chunk number 37: EBSeq_Vignette.Rnw:768-772 ################################################### Conditions=c("C1","C1","C2","C2","C3","C3") PosParti=GetPatterns(Conditions) PosParti PlotPattern(PosParti) ################################################### ### code chunk number 38: EBSeq_Vignette.Rnw:779-781 ################################################### Parti=PosParti[-3,] Parti ################################################### ### code chunk number 39: EBSeq_Vignette.Rnw:787-793 (eval = FALSE) ################################################### ## data(MultiGeneMat) ## MultiSize=MedianNorm(MultiGeneMat) ## MultiOut=EBMultiTest(MultiGeneMat, ## NgVector=NULL,Conditions=Conditions, ## AllParti=Parti, sizeFactors=MultiSize, ## maxround=5) ################################################### ### code chunk number 40: EBSeq_Vignette.Rnw:797-802 ################################################### MultiPP=GetMultiPP(MultiOut) names(MultiPP) MultiPP$PP[1:10,] MultiPP$MAP[1:10] MultiPP$Patterns ################################################### ### code chunk number 41: EBSeq_Vignette.Rnw:809-811 ################################################### MultiFC=GetMultiFC(MultiOut) str(MultiFC) ################################################### ### code chunk number 42: EBSeq_Vignette.Rnw:820-822 ################################################### par(mfrow=c(2,2)) QQP(MultiOut) ################################################### ### code chunk number 43: EBSeq_Vignette.Rnw:830-832 ################################################### par(mfrow=c(2,2)) DenNHist(MultiOut) ################################################### ### code chunk number 44: EBSeq_Vignette.Rnw:847-850 ################################################### Conditions=c("C1","C1","C2","C2","C3","C3","C4","C4") PosParti.4Cond=GetPatterns(Conditions) PosParti.4Cond ################################################### ### code chunk number 45: EBSeq_Vignette.Rnw:855-858 ################################################### PlotPattern(PosParti.4Cond) Parti.4Cond=PosParti.4Cond[c(1,2,3,8,15),] Parti.4Cond ################################################### ### code chunk number 46: EBSeq_Vignette.Rnw:865-876 (eval = FALSE) ################################################### ## data(IsoMultiList) ## IsoMultiMat=IsoMultiList[[1]] ## IsoNames.Multi=IsoMultiList$IsoNames ## IsosGeneNames.Multi=IsoMultiList$IsosGeneNames ## IsoMultiSize=MedianNorm(IsoMultiMat) ## NgList.Multi=GetNg(IsoNames.Multi, IsosGeneNames.Multi) ## IsoNgTrun.Multi=NgList.Multi$IsoformNgTrun ## IsoMultiOut=EBMultiTest(IsoMultiMat,NgVector=IsoNgTrun.Multi,Conditions=Conditions, ## AllParti=Parti.4Cond, ## sizeFactors=IsoMultiSize, maxround=5) ## IsoMultiPP=GetMultiPP(IsoMultiOut) ################################################### ### code chunk number 47: EBSeq_Vignette.Rnw:878-883 ################################################### names(MultiPP) IsoMultiPP$PP[1:10,] IsoMultiPP$MAP[1:10] IsoMultiPP$Patterns IsoMultiFC=GetMultiFC(IsoMultiOut) ################################################### ### code chunk number 48: EBSeq_Vignette.Rnw:894-897 ################################################### par(mfrow=c(3,4)) QQP(IsoMultiOut) ################################################### ### code chunk number 49: EBSeq_Vignette.Rnw:907-909 ################################################### par(mfrow=c(3,4)) DenNHist(IsoMultiOut) ################################################### ### code chunk number 50: EBSeq_Vignette.Rnw:941-949 ################################################### data(GeneMat) GeneMat.norep=GeneMat[,c(1,6)] Sizes.norep=MedianNorm(GeneMat.norep) EBOut.norep=EBTest(Data=GeneMat.norep, Conditions=as.factor(rep(c("C1","C2"))), sizeFactors=Sizes.norep, maxround=5) EBDERes.norep=GetDEResults(EBOut.norep) GeneFC.norep=PostFC(EBOut.norep) ################################################### ### code chunk number 51: EBSeq_Vignette.Rnw:959-972 ################################################### data(IsoList) IsoMat=IsoList$IsoMat IsoNames=IsoList$IsoNames IsosGeneNames=IsoList$IsosGeneNames NgList=GetNg(IsoNames, IsosGeneNames) IsoNgTrun=NgList$IsoformNgTrun IsoMat.norep=IsoMat[,c(1,6)] IsoSizes.norep=MedianNorm(IsoMat.norep) IsoEBOut.norep=EBTest(Data=IsoMat.norep, NgVector=IsoNgTrun, Conditions=as.factor(c("C1","C2")), sizeFactors=IsoSizes.norep, maxround=5) IsoEBDERes.norep=GetDEResults(IsoEBOut.norep) IsoFC.norep=PostFC(IsoEBOut.norep) ################################################### ### code chunk number 52: EBSeq_Vignette.Rnw:981-993 ################################################### data(MultiGeneMat) MultiGeneMat.norep=MultiGeneMat[,c(1,3,5)] Conditions=c("C1","C2","C3") PosParti=GetPatterns(Conditions) Parti=PosParti[-3,] MultiSize.norep=MedianNorm(MultiGeneMat.norep) MultiOut.norep=EBMultiTest(MultiGeneMat.norep, NgVector=NULL,Conditions=Conditions, AllParti=Parti, sizeFactors=MultiSize.norep, maxround=5) MultiPP.norep=GetMultiPP(MultiOut.norep) MultiFC.norep=GetMultiFC(MultiOut.norep) ################################################### ### code chunk number 53: EBSeq_Vignette.Rnw:1005-1024 ################################################### data(IsoMultiList) IsoMultiMat=IsoMultiList[[1]] IsoNames.Multi=IsoMultiList$IsoNames IsosGeneNames.Multi=IsoMultiList$IsosGeneNames IsoMultiMat.norep=IsoMultiMat[,c(1,3,5,7)] IsoMultiSize.norep=MedianNorm(IsoMultiMat.norep) NgList.Multi=GetNg(IsoNames.Multi, IsosGeneNames.Multi) IsoNgTrun.Multi=NgList.Multi$IsoformNgTrun Conditions=c("C1","C2","C3","C4") PosParti.4Cond=GetPatterns(Conditions) PosParti.4Cond Parti.4Cond=PosParti.4Cond[c(1,2,3,8,15),] Parti.4Cond IsoMultiOut.norep=EBMultiTest(IsoMultiMat.norep, NgVector=IsoNgTrun.Multi,Conditions=Conditions, AllParti=Parti.4Cond, sizeFactors=IsoMultiSize.norep, maxround=5) IsoMultiPP.norep=GetMultiPP(IsoMultiOut.norep) IsoMultiFC.norep=GetMultiFC(IsoMultiOut.norep) EBSeq/data/0000755000175000017500000000000014136050172012253 5ustar nileshnileshEBSeq/data/IsoMultiList.rda0000644000175000017500000001707314136050172015354 0ustar nileshnileshyUǧIh#ZDAFUUʛ}Ai_۴M$i,MƎ؉=}{}axi01í?5~p~<ϗ iYÍF~i¼lO{5{a30wôkɰx7 ϯ>RtC}Ni5ݝ3:gy{ᡔScQÆL5v՝mӃ3/cw ?ez9¸lk0lg^o#kP0²y%*?c~K;zO_[󆿀)819^_70\C;O0zZṓߧdqW6LxȒ//r9Т>ξ`0f k0~_P)> CieVoy-ׄ)L'eWhؖ52} 2kk0 y~z?rv_fP,i^ف*rô#=@{џUxl~>Pn So6릃8:h0oiu ŷ_^/;A~ğH5u'/>ϛ#Y/#^ECy߰u.x  7mI<v~7D; ?]+Ղx ag]1=<1`P=I?'(w0oޖ3\v`9B:}rWaȿQӻl(=?dhP~c'O~>sok5KeQ?X?ӳ<aaO.G J?4v{}zpJ\njS 7dgn+:fCz Nwsx >-T6wK,^>l_'6ў؊a7m{De(>1^<=:ۋ,Cqw;89뷍df ^SԔ3bݴ{sq~o@/z!o37e^8Z+íwم<~ =m!Êgits']~uΡei_s ]7sשsXpq~'Nugr?c#s?1K{ opN2Lʿh+uʯz7aga=>DSn2ze˿]4ơq=_=L]JS/K/_3y=G:Jon :<;G'r}vN|տ4|3Cu~=-;Ѯμ:eiOO糼%Z;W Gؿ̫PC@9~Wo(>ٝqG:0;AbbWodWPNJ쥿{ 籧/c-^jW<)P]w0OZjOcY'3.[ǐx gy_G$M{¯Ke?]5ү2rۊ:~P|x\qѼDgANʵYx kxn`WQܞrڥ;?t|e 4;t^MEn?ױg~?.}-< r?;}h\:N"c\y6 uu:O;'O1;⡇wūW( 82gkq 3;=0:?Ǩo'X >8h\}xv~;uz e/ȯFC;1IeOokoR|iC0T~+͸P޼1.Կvh#?4~F;:jK//y^ՁCf"^59N=:;_7'CW S^X~\~\@nPqR|z"?TvxWav9}BoO9]"ξ:#0O16Ӭ!?I'W$7*纇g)/{epu#.G3xЏE5V=֧}}_mCߟZOcwvFNQ<({d_A_ |ŎWh(.|TE~xhaاS56_3tecodZO#*WoU7Wzԧ88?j6~CGzYk}cGPvW3tPE)J<*W|+,pv3h({{Wscw᥏^ :vѾǘ7Nwq} Ӭv0,GGT\-¼+_IFܪpn/[2Jaո[} Qiv~N7 k,(zxqɃsӉ^U=:N7Am׏{y.?f?$z'q}~Pv`8P|Wwv/-^^kB^cz.ףs,e unu=O(G9.䃍1%;@ f{/A (|"nsWpsD9_^4K;r>QQJ?O_F3_yh9Pq+߇ ?S^iku,{qP;hxku:QJ JGsq>6Q4qێ %o7>~(CsrN\)C<\l~]_ +N83`u?sKcEWe=|qBK0z:^o݃uϱv֏t_z_0].Ws PNqF<ݓPNnٍrC!\|cymWpxy_})Pl?feZa8v'uz{g9och D!.5>EWggs)s+U>i~:ƼϓG|딧>9EosNKO)f3u=OOPzb^?bxu)Ã7q os+h(pYzJ ̃P=*3K@zNqVEK)?Zoٷ_fe/u/зlxrʓU< )ߺ/ֽĻ l.7\< ҋzOrdPn˯n^g)WN C̑#vt:9QSQ~W<,+!:W ޗh=R^[N K9A(X_}Vw}؟ ڝ+Ӛ#St3S=L>L~e#]7섿a'f9Pz͕~oő7)~h?R'u_t?zSN}9xΓ4<*OT+įH!>=Qa(?I~kGa e=t ~5N@> >j3++1FÓ?˸ ~uVRKʿ<ΣO⹾B=:o$QέU^ٗK㹑o).3w έʫ衟SyS9QZ{k|=ˆ8eI+HGkR.w*|T^S;j=+Ǒ ~6Grk:*U܃(;Et/-į(<>W<|]yڟ7үY_9Ydz3{ /aA^xJ89F<[ d&R>D~+O}?9E'xA@,!_7 YRߣ>񫓔wC[Ih^}U\UȿBg%6ߋw*_BO@qNSY 7~P<YZ{{\oF3[H<3N_ʿu{6f!}7l}Nɟߣu?-gx47ҋ:(o~ p?1"G*<'7[+F9khg`Ű7O}~&xü?KG~T>(iƙg}[ȯGi%3ǮtK^<"$0д<̮C};S̐G{^SƌgSX|p9Tikʺ})wSWyu/myq+WV~{^~XLb1b:b:kb6D)'W:9qrprUNvrk%_%_%_%_%_/8~ _p/8~i/vҎ_K;~i/U߭"Eo$I-x[$Hm"q"q"q"q"q"q"q"q"q"q"q"q"q"q"q"q"q"q"q"q"q"q"q"q"q"q"q"q"q"q"q"q"q"q"q"q"q"q"q"q"U9~U_WU9~U_WU;~Վ_WU;~Վ_WU;~Վ_W8~5_W8~5_W:~_W:~_W:~_W9~u_W9~u_W'~wI?Gv#v#v#v#v#v#v#v#v#v#8ng7ٍFpv#8ng7ٍFpv#8ng7ٍFpv#8ng7ٍFpv#8ng7ٍFpv#8ng7ٍFpv#8ng7ٍFpv#8ng7ٍFpv#8ng7ٍFpv#8ng7ٍFpv#8ng7ٍFpv#8?a7 *?A??L+=Jϴ3L+=Jϴ3L+=J4L4L4L4L4L4x3 iLg<4x3M{i4홦=ӴgLӞi3M{i)+$by5s5%y5˫X^ tj+W]?)W]N^ tj+W]Z t(oܺDRt$UERu$DRm$T,Vb!ӱXձXJbVI*Y%1$fĬUJbVI*ĬB*ĬB*ĬB*ĬB*ĬB*JǬ1t*JǬ1t*JǬbVUEŤbUQXST-*SEʢbRT EtQX]T)*늉5beQ1)*bXUT.*kuTQCQ1]T**VkEźbb]XYTL.*V5Eڢb-yKa+KP.Q*Q.Q)Q-QreDD9)Q%e?!'D9(KJKkJkKR%ʕ%Ir(QN(W(W(ה(ז({9*Q,QNJCrDDDDD{UJ_v~ަرyG*jGnM7|o%Z/*ԃEBSeq/data/MultiGeneMat.rda0000644000175000017500000002200514136050172015275 0ustar nileshnileshe uNJk+L[ )6hGom(8v2V˲4$EI)j8 of"FhnLn,A1q4(hh hJSw{>gZZZjyg?O}O>?hdOڎ7Ɔ-v~߮YnO~¡G‘Ý Ixξp{-[.|7B› E1lzߢovXMxԯ&O/%t6&| =WSko/%L{~1 _ 9 $|Wƒ\/!wp{~%; oR~Oi3 .w)w^Kxn4)H)s24C=_%\ Gt#aǹK\7x_'N}nolN8v)Ox |g}k'Մsz.<Ʉ_Mxus oKjԳq_I8J?D6W_J8~3wiV.NޟK8ѕ%UO~M"9m|p =շp=m%L9Zz%qܟW_r]w!wqصi~; ~5{hk}ΠG7P~Q~+oEV_CC_H0v 9'H(q}7\菗i .z-|/:SSzB}w&< 55苣 /m zE XoQn=a?3 >ȖB˺{)M~&5F\VK]O8&w9 O%пs[k- [N8巀c۾K \۔F.r*L0~yA2Ma7k*7blM8KƭL^B3v(9ؑ@W/1~ߢo"oE^%+Jye 'g]z `㱓(7:;@FeO~~ ӎӔ?|ko-sgj>ES圏=ԗC΄Ǒ:n񿰒p#冘GWs5I@s>A;ٞp}5O_{k\2 :?Ooi_k<>O >Hzüiɟí wSOp#a"Ol'Ծ9L?=L?>A[K[(_o9X~Ѯlf$ ȝH(?;W>uf5LߧޕC؁_ݗw3~EnnJ8_<+5o%ǢC})x>qО㧐Nx_/ @L߾w$|_~'wr}~ޮ%\I-氋Wj_HE|'G/f_f>%Ў=//O3:C=쥜c[i1[ȿ3Y~G?\7q==9g7A~K@=+/}܁ ~-ׄ xc%vŻ '4^2?֠}3 ,Wg_ߥkO ]GOf>jgj1Ӄ|;Ƶvy3݁<=^m\8mIۑ*Y-f~Ϲ~ 9l_9|>IF¥O%4{y}]0D~9{Q/m\;[k _ANQQjB/}l_?κ`U䏳޾ؠ_;Fm0q}~ABX\%q7^92Mp]'>l|c>VP9;Ƶ7U3}[ԇ$9㐣~?_C.vq!}#T'^C;"߷ v\S\oGv{?vFYՃ _N>q\\I[ai*ԓ[9Ww{q}-[Yx?zbaa?r׵%tFW\Ixy/(jM=+C΄sԧz{z59@{wQ4Zr/W+^K 8q~7jr 8iU_N]gg|RoycU}}|5a/O?üy>sіpCl>o] j`_^uu ;$==א8l{/.bgy9ybF<1y~lAg]ȖW_LQN/Oc|ud:z:pGC14.s{ 7;s(9ve7~O^7Kqx%q|2Nt2)r~ogF-n~??LiѷYh{7Ͽ>&$0w>yM?4)7̸\$rGLu,:~S9?זi'wy!7^ty,[à2v%+:+Of⃄넋ܟnp~KRn#?H>v]cuS~8 |}e\54>Ea~?xk&OV?nXmggޚ vm>aƶl~?P\1c|3?T-G~Q )^:]ΧMn{vhmoM|k=[Գzo/Wsw"~gAzq@3_">jwxCw;C-~(w4VθɋGuprBy>Pw{Msk!vM{{~vQ5q?+Tk}`ux{qzyɵ~~Z5ܖ]Sd9? nCׁ4E?!j:c#s~8c׸vqW|r;I~~Z/x{]=>"^D䚧zvC>n˷zvgDS{EPv&=ړN1OnpT7/|C5/@{L^{@x}FeA7f wqw_8:I;|^mp=͵ꏺ/e4fS)k_n_#ܗ?WWj[=>(5.A gcg}O[5%~?80SL3> u^w"ζe=:ƸZO߶5.?a^_'u5}g/?cQ2&seoGB%:ô=\OO% SN;Ƶzy;|IY,NSO\%}y=З?rj'Lv3?x3/9\KEB"VSc ϡu9~A?\1/3 9~OzR?8[ y7[4I?\g^1swxXw yu}5a?o oOMGw{Z;~]k蝑wM7R8^/d^5KSrYh,@?gu_r'|okBw/ Ӭƕ9 k?_NxyvvN0?7юsث?Xp3j݌j{;NwW>ǏX_\vN~7Rqz/ӮiO35'|/q/{J9nͷCZk|ݴW?E;u w8D~\9~C<%?K4asCL;u3oG*ǩo2?ryKyS=~v =Y.q`?<>C_o6 }ńǨW@ gs5/Y{sK괋ϼ3.3 g<32.^=N?Ҏڑڛmq sy}ȗOkwOo ?ך v }NKj+fЛuk ſq_uaoq W'1}m<39 /hguf;̺6v懟Iڭgyw^=N}a~P^LK譫%-Sϯwߧ飝}}.G5,?wq_5ĸ~ syyҗ]JE4| ([6g/s>}]_Z~x;S>~x:e{ܬģw&4^p~e+7?z~ {YW Փof,Qvw%\f\OO|:pO?~;擘v"~HlƹgǸ] >XbfJxїAo^z^z@cn£<_]$wGӌN띭 'o]w>WCFv"~熝O)9zn%zȫ'ynzfsÌ ׳f.߷y9¸]Uϡ浛쾉y%C~ _Ҿ<q gO ]~d-sWX/͛?P`ǸPna5q>[̃q;εٚ/QΞITOGs_NDc;r#ϳ#_N>/2ng =_l|ԃg}m[ynļi1╞f}|2?o =h !{fZj{:CFy_ԟ} sY_~W۹C/R1>+wji<ҵZyw^_5l?r ~8wm^ߌ_j7Oq<uo?GG|GsKooϧ>9\#v@5ϓw ClϛGf^{<?zot_cRO} syϟOhq];]1ωz@|&4<_2s+{3?c\W-`N~hrkYX5a3ߥ-)ܯ3Π]c-8s-hy8zLyxN`ӯ1/*+9SkWLO?ܞp~igMu|خ΄~@{Q|)`߇3}R-ڍ\WC;d{%l2zȼc؇]E,χx.{n ~y_k7gGz 1 ГWi_stU;NQ$:3޲}~gOVwym?GI#.izW%1m޹9;"g#_}«xQNkqyR1u 9b'oԋ?:Z> |'>zG EȖm(@S֏˦ /#~?|G>#~?|G>#~?|G>#~?|G>#~?|G>#~? BG!Q(~? BG!Q(~? BG!Q(~? BG!Q(~? BG!Q(~? BG!Q(~? bG1Q (~?bG1Q (~?bG1Q (~?bG1Q (~?bG1Q (~?bG1Q (~?bG1Q (~?JRG)Q (~?JRG)Q (~?JRG)Q (~?JRG)Q (~?JRG)Q (~?JRG)Q (~?ʁrG9Q(~?ʁrG9Q(~?ʁrG9Q(~?ʁrG9Q(~?ʁrG9Q(~?ʁrG9Q(~?*JG%Q ~T?*JG%Q ~T?*JG%Q ~T?*JG%Q ~T?*JG%Q ~T?*JG%Q ~T?*jG5Q ~T?jG5Q ~T?jG5Q ~T?jG5Q ~T?jG5Q ~T?jG5Q ~T?jGC~W}EBSeq/data/IsoList.rda0000644000175000017500000007715114136050172014344 0ustar nileshnileshygYv%AaNK= !!4#/3 TP4FTw`z׬zzDab!`Ȓ6MM(Z͠M!~nջ/~̈S޻ o_YY?}|h[W7;7׿~o++ϬsK^:TW ܻTP~/_,wW |u}x ?(p3p' DO;r^*^+)Gvs=8{/O?^;LXF;^/~^>vnzT<}gsmGCOɼi {>~ _7 ˹~|3>\-CiLS7 |1x6wh9ǹ_.XJ*jkQcku*v%'>0}6h}~_gOτyߩ\?~П?i?<+p7`]O˿5#߸ܗ~q?ir}1/|:~ǯk_{n(/|: қ9g="Oe]h?Wy?ތeg槃[G(#~/g?ps9j<<~V}ܾѓyeڿùC zP~>U.})|[/#G ||,{B]o_=_NI;.~} ~ |0t['O=#'oHor'Co |!o߽h< #ݿ^>ܷ߭LO Ncre{c?v^쁺.>9t[wMW\W=o<;#~6Xڹ>vB?[Gp{%,쓁wJ?ӧ(wuSRG?v99_З>1m|lau^8[y|.?n3? ֡t(?'Vy\ğZú~&t5Lw$GBznz_~TΙU?:DG{'_ ?; ķ؝uxx\0]{~q_ǿ?z@OҋW'S R՗̃j}0R{vGziGŏ_l/!2f/vZp~K &N|`ڏ' d|ִz C;@X,*mf]M~c> [_.=^C{{~_7=ü9z?Tqw?;~G.ГEYNs=)vVg@vg?'ȯӼ#/Ÿ|V8/]+w휥gGxw?ܜN?~$)o~/tu8=Ri\_'[Pm{1m>i` }H.;ZO9ڡ{_"ȣg< n\;<oLq |t7uQ/WgΉ?,.<_=Suﺟ?[N(g99Rc?]/4OGm0G9=84 u9|~V+xh :_zʾZ\upY ~w\r?9?kDw KooQɴO><o^_{Օ;xX/G nWōw?oɿhBfo_o^ ɃxP8~osW_z6_R >Rhu+W }.w_=C=??g߿0G=\ɌLJBt{͵< Υ=㳚^/^1>=b->9R85+oƾ8Ep( =32' |%8~q8|-rWոH>S ?+i\'g;EOv/f>? y2.~)L\T_^tTϼKN{y|X^w8t H鋯]퇮SC_SJMd~_g?^OOu%^.=z<ϧ-y~,׏GΛ/:;~8k?b}&ɻyPBwo~lʷ'z[ |>B/.I3 ^8NY^@ފܼ1}o~%PO~$gO{~=Y_oUu=}z }=8~zz(~Xz3H<eZ|^2d{y/N} [b 䧠=k~0/gxQ/]~}X5BOoS3̺ pޛv;^J_^_y("uX/|/.z#{C'>I~\B_yOu]vٽY ?u?'S_>onf=}vE.M'>aĻey^~'ġ~^~OiE<\K<{WA|C<y!<бH_3NU>r4YOy|$6w}-5ϼ_{v#^K\?U#(+e ᷻N^cAJ5ğO勵ݹ~1u(xrXؽ.c|t^]}+;tx ?Эw~dο݌}/rv\$5Ab9q_ow>zxՏ\ě7׽^ȮG~3i< ~s.ߒwR<~\=N]bZ  _ I?1j\p}\;mR)𣄠Ww>xh(}yjϮH[yMۡ/3_z ByyO~1~oe}O>X~gc߂=`݋g},!}7}>kL] 8.[]}y9vs@i5~7?GAW; $u ?W s7;4x7O\yb{ڼuq=~kL[P^N䃿/8BPw? [5ߍ[</rWcyU:\oL9=h^_[W|>kϯ:/_x\;;3Y_u\o̯tjڷͣ xkCǟoJ[_y>O7ߓ~Z6_uH7 ^w2u\OtKj$ϺAob/VS ;Z|\'y β@S{[#~l~r\=~ovqG=u쯎vQI&¿H>Yip6)_˳H|v,k/Q+.N~gҞߝWş<3KþpeպE{i7i@<75/^!+:ߟZ;_Hny*?w@oy&O7Xr?z| _>=/cY?>|::]~-?I~̽iTpNΕeatJ?i<+~ZJ˓}t@{<r~>?R(ӎGnp<1OqF~z慜1ŏx(_3/y>pswϽs_N~tЫ~I;~/#Sο:ku圇~s+/>:mٴSKy|E~R> tT9m/%OzWb_\~%rh.=`gqzo72;gk YSK;g,WW=~Ȼ?G =.|/6~Ku!c >n>Wy=t~|~#?^7}ofy/yKz-XAȼ=T\EK8WPy*ӸZ /w GγUV8;vΛ'ϛ^ X({#qfuJ/}{6𣾘_ڡձg}SLٿ,F<汥='K{Ч+򌌿7B<\s?W`C㷥7]u@G7[1|_;k|OC8;*~OOgouF_<~__/:ȁ^|A#Y|~;:~1y~iy?v/~}?^?)U>+>x>_hyPYjѝi{lz'K;>UK)SOcWo >Y9_tߺ#^/иX-^(>yƍK_y5tB'|x5XJ;#7(~ ?=:WN/{Ÿցuw!vG;\KҎ}+GO#q,@|oɇ[b7姦_?SO /۹wj_ ~iȗOXc_yTX:?GoRs(8ZW'uQ-|?OߢOo% ?ڿN_Ae} g߭?|GCpHuƟ]~|g9߃[s+JnY>OҗIt@J{q)~C_b{_`y3cTcJ8 ?o_|ӿ;>92^qo }{W#=r:嗦W{(j MyZ ;P'\W].gW㢟|%J`ۻYo̟߰BWg7&U;@gKOW6~./jBZq@f5<3SoJq^̺!=xr(ix:#E<\!@-?yG|En??_}/gWYk9gͿZ'NyQ >S]~V9Whs g_>ΟL_qHAYտ<^u@뜞q7|gO{d|[VV&Wsy.H](œ<:kZÇ۟3r< 4?gt_? ̸;9j]i/PcWk|k-W;2}Nz-_Y}ޞ|8tOi]*1y8P~;}wzM'| Zf%P~9.ޕ1֓:|ҡ+_F"k^鷺<#/կv:ι/%~缒qo8IOI&B?NHş7BO'ϽIoUD0=]^CbO٧Cʿ/9OXʔ~/q¯OvRA?{H;_c% ]BEt@~a{6cO87OBeg/mI [_V ֛.7Tu$~#y9}8մh=c$/8gZ۱+_7_ {Q.MA>8oƎ4a}zf+Y<Ѽh~7 T9}3y֟ݕ.I:+װQy!wC^\+1>zzwoLR{88.~5Q߷o~uo5v[<ϯ(]KdG-zVs}%ߊ%o)p98zXa|L };sw}Qt_)n5kFzOk zڱΞߞbuwu}ze6_ צ{_ۜ|4x䅿omx!9]})Nݸ'3r~hS]7 ߓaO}>@o|CY~}=qZ8G\ccK tͼ_>$.ᜅ|gg}G?FŬg6nNؽjO׾nX<ܲ3q^{5sک~'tKG._uz9F#'g}^1ѫ_߹|;w'ZuP |SWF^7q6SW |.)A=M{zۺ;L|G_5m3uX޴8*zI>z%~˵k'nh^@ڔNq'ռC+o]/`1M۷_-[o2%~nz}.z]i<wݯ{SbG_KzpΥ\Z1b =_;'+EVlCqƑJoHx.sKiK;Szٹ?V{1!>'\Dy7lLq[O{|KKկO5^G.|!z6_\}#z\Gm_&_+'?%|ڼﲄ>=A_eSQU(_\h=P?=%=3z7EF ^f?%~=֕~SzzoEW|anHO@u<}X+/L/G??ɴǞ0GoLݫBj|ƾ/VSrU^J7@}'ۺouuo\x;?_+=,|9zr<ϟײN1k] q׾;+>f|_ _\u__+9J<|\:}-ݬGZ_o+7k|?WrGڟݽǤ~o{lJLf_w~|yp~c#'|^\m|:=@=߶WѴ}oQ_b={k' ZuvuφWt9z|rx|0T_J^H y^f ~n@~Ꮴo.:sOY/薇f=#Wm1$~kݷ?_ڷy!y26|d7ӏ/*=}:?V9J;6>t+b}@qKyqѮ_xTo?=IB/$@$su^\'?z_WG{Ub'9}iE/>? C uDRzǯ#K_~zUT/żk#»Ӯ:[SzynՉ!]ںz%Uzmq; }I;nOO.D:~oO gZ 9GQÿ$8=H!'C|5- GܛP\u7w󩗣w'MOںW )Q;WRLNpbwsև_}BGa?7e/&) ZWnȏf?zkGq_=|O7!Wz >| !=I/Ĵs+__SعwrΜ/S~Ƨ<_';")w9_U@y{Hȯھ/`S/r?==;q[7m{7.o/W}`}/=#c=;"<+xн*WCsU0\R/ʝUwn-ӏ[j{?a>̓h?qУuߍ/7sKϤ_n ^?g]yJp7},Z͟/ #l]kֳ*}{w7>,%oEn 4gWP^^y.;\y5w~\q37 Ϳvw(s9/ϾS_H;K>ol&q8y/O/d\:e=N~O]}݇ oN=3E/WY4s'};W°_y5H}GBK^|rK{g?\7 <1;87'?u+`w8 O+eԼVz1:l|:a؟yow%z^X szx28synK3NՍOUuO«YoϦH>w@q)~pz< V}K9[}0x+Jڗşxu*Ϟ|J\Îs~}r瓏f_*7PO~1zzV^ ~>>Zw't'$oHOR`ߴɺb _j͟ݝz_?צ^ys"/D+9Jzo/𵧦ϯ_zʋ{k;[dח7OfNrS]zy/%h>ܺSߊ:V;_u N?7>H,/^HO$?长X&'nꈼ?קe'#|b۷}ܫ;x:d Y/Dݹ_|=C?~"vJwm]3is|syAɝc'Grwn+?Y|^2ɩ7 Tv,r^:p>u`^ŃK)3?~%NFnToߣk {S dO YJfn3~V;~;oCҿR #r>[=_?`s^2v7 4o6®=|$۸T=W;?AKGi汭_hE?;]~{ |xևz桍_zzdJ;E?fow ~K_1~6?|sƗWRS7>gK7φ7/rMCVx]=|@O)=֡|<|y)!W;~Oܘ_=Op4t~1=v=uB? ==/U291Z׽| ϗ>ylG'ԝzOى +j;?L> Ou!{s?|n|CSo'z5=1Dݚb/a\g^cOg/ ]y>~(>,~T_'ߓǣsk>;R^U~0"/wXqJݒ߷\A?1>wY{yNk/k0>uAc|./ uij]qw6}R5~|??k_Mւ ɷxy^)'.cߗXo?| a7< Ad#G N۩[0uQ&GӾy_R< 皬ך'^e3.}Cf:|ǎ|9W1(_䩽S|7r_b =>|\1tOW˳7 *~b眣1w6'[F A<ćQw}+'8ܟa5oO>קӐ>տzCLj?=NocR#OzrysW79Qvs{q}9~vz!yPۣ^/u|-@Psїr+73o( U}*k\ۧ;>c?T?αh;E V3~=N`*P'@o?xl=ϧ}zq"#{/'׽w[ݢ\I>}W;=,@g/0%oծ?qUNnwL^Wٷ|gy"㭾L$Ͽ]EaW'W=ow w?~Eo|/(j}wu%wc^MrкvgǻBxd?]n%őyG^;e=ҋsQo8{z?6 7-gxߍi; ̑_j$پU'L})5Pƥy =D/~y=:zxB6"=Ϟ_CQm9MyXz׸iiwg_qjz3=޼W=|C'?2UsOnz?=@N97ԅe'^+|މ,e~)։q_ӟ{C'qpv'/7Ob֗Y-sѳsѫ]GkѓI{j0ʷŏU~d]OOU P㈟GNtWַ8}7Bw a''J>O }yn=/!J{aqZyz>j?>RzA89 ?2|׾4쵷C<`|N?W/[=yqh\+#δ}ޕozgr/7z0U/eܓg\ú3{`g!v8BͿ\}$- NǸo>WGSG Oݧ;~xV7εޝk3j3W iAe`;-~qU^{䧰{O)j;G\zgz?@nQi=w@r bO :؛٨'97^?|θzy$䯞ërf+_Ohky_17'wïL;|Q ~GV߮?yN?a'_u~A{>v/~???uaY:=Qً(#|'k\.W|_sGe}`Cx@;V5.;NT\cziG]Wsǫ @x)c|G׳WX׵p|22v?#v|?]Gŏ9Iu|O&΢nu\ȗ}{{䃺Ws~b:%u*6]WGJ{!|!fEu΄ydy}>y"g+^qy9lwI9gŏL">-31SPc人_ L/#L\->foNsLu';m\:G#?dϟ_8W 9}]'Wϓ`_xX/HN^z;)P\>|+X''b@?!(qg<_/|Q^98\CAs#Vޕ@u"7]~)s[-RzyԞe ϣBK$NOGw=U?՚kc3zyn3/{Al;` TW?Q͕_|s}6߉1_N{;/;7ޒ+~(|C/Wy]]i)s$o_#wև.1#T]'}).xڭ{uqZ\RŬvى<S:g|bԼ8W̟~|1h QCnE)!>\^Iwǟ)E?x={MMΡ{N_3*?y ]"j]@߹mrЧtO/k{|wJ>}u<궾>_Zz\K^S )~Nz߱;Z%ϭ>u@NxWp}‡O};ɏuv'xOڷ>s~>d~WEύ)=OW۝?FsLrìZn/Atqq|˹WC?m׉)}o9S Ovjx15/x'~qC's)~չ+ݿY `+{wNoէ>~/ď)/>fޮ[ =j-kn;O;qxοzNmyD"N#`\>Bo<^ agyUlu5?͹}mJv9Tvf}0_Ao;z5~pi=\/goKsć\_ t{v$.һI;߯N9/'֥+/vs$w*Oe_^rN[>{psuXOu]y^ޠ~;"_QV>y>_%wu?3|Aqzh}?_}/W 1/=tkοz]s~ޣ4}MmÈy}وg{kmyC_syl/AɫQziCC#Wìs7Z2>]iۼ|Z^qjwݝ/qM|~GyL=:m/0A~79t|ʝUg?8go ?89>~~yBwOw7Qy={L>ћL\ 4.z^V?sW'?{g ϸF~_?J/_}fݘz2Ps{f5NҟcÒuk@K!ԼNŎQK^qbyOǯοU_M#bz«j|y⬾ٽW.DaGwE~3~"(?{OuM|S[?z=^ly=#_+f'RGOى~=Y}}D\o}ruNp=Źz仑G.=Mvs?k@~-_O|r\ 8y7*P]98=;+@\DT}%(oG~o?_W}=q'v3žo\C)5fw0_k~ޭwsIn|': yOC'ڸ[W އ}o5zOWٴWG/~Fop%+w ]}w .}*5u+P{3_/QOF?7#ǝ_ٟ{K響G׃Sѫ zȯu}@~gaשωcq1~K~Ibor"Ww)n/޹{@s_-ڥ:;WO;D;N5Ng='u\!z!Ow󗓻/D[/_CS Afgu{^iw~.R]3Ϗe?˿j}l-.|P}/G-]?GW׍kqT֭mYO?+w1W`ף߳3c-P^.>Co8 #;_~goҷЧ6~u^//B媗H^4_ЏCN R ._n {=Yv~- _m>O_4V}r:{O\ξ}W^^}s_/Pߧ7ׇ\[z}\_OL<[&uCȵv[_.<Ըi{/g E/L^ };nX`|_Z7ЗTںT:|zt +Y\9y+!wc%s]8k9oeWW+οun}H_gx]|L+8: /nc =qspw<;S!K]t5~ }U~3s5]=r~#z8'GyoOޗs?t@\`7;kݢaS_Nl#3N] m]\:3?t:g#ctΤ~:?Kѓ 'PMR븥=_M_u~b>d׷C<7B:=?ćx)B.<=0P~f{o_gjM+oI?y>W@7:k@z0ys$.}OM53V/; ȯO_;V~>h7{}//9WO싃n:wߘ#NO\IM_(?sы/:EƁG:{ü|Osy}V{8ϫpF[n ->w;<0yuw<@ki?4yu?Ɵ\+o'xÙ[8WAqZƧ'yn_#ϭ+1y8v_u;}-_=s67q|_Խx/~uM^zw<͇wQBF"G"ќ7s.ozqy+K4~{S?җ>h\+,uI<"nCsǓ/oO Z/U`ů)OyW{ӑM9Wgob6u*ﰑs޹̇O}#0}!uZj}_˼_x=~9z.B<zX/f=.e>=z;bן“z#~e=~7~2v(~ȏ7 T_v*xN(zկ+h_~.y][[V u7$<@=4 ^Cϲ|_3kzѓ7Fz8<5?~VA:Hgcx08\GgSI7nuݯ55}]T x~q~9z"̷|%rV#xp|ZRZJn ʁ:O'>0~oO{n=v9G.H@k9~:nw(L}MceqyRSǭ|O^}xޕ~[^M|_RIs_%?WH?T>}V'd~OZASߵ~*nί& GLGg׸Y7@ut}:٫٧VpwRn znIcrnv>x|yuyx;zw5k>f +N^m6^Y #n3_oڳyjX]sޜ}S^i 6Ϝr?HWp+N:hcwwz98._^Įk/kw]윈?=yz9zK?P'oPpK¢Ɖ/O(4_~@/7_P7MEWq@sz*^· SGW灝"#z n~ulWʴ=_9U}՟sx2.u#zwK~_3?8&猣b{s'=֋svu՞d 3~|U.ſ+>+z_^=OA?~ON'b_>d!*ߧ'I87vCU~.y@݃SQ4zi<<7Ɲk1P빏\6rK֗VC;-\5v=/BLjOqe[goLkρyi{s,kοioOǪދ7-dz.ou[_X٣j%OA?QW3WyV}iݕoB(h[W7 y[~{s˷}OAܷ?G??1E):NO)5Edh57|o@CF{ah/Y{a^l/l  cKR:-cKR:-cKR:k)ZJg-YK鬥tR:k)[J-yK鼥tR:o)[J-----------[-[-[-[-[-[-[-[-[-[-----------ZJh)=Rz@K遖-ZJ4텍^ ¼^j/lZJ7ZJ7ZJ7ZJ7ZJ7ZJ7ZJ7ZJ7ZJ7ZJ7ZJ[52jheʨQC+VF Z52jheʨQC+VF Z52jheʨQC+VF Z52jheʨQC+VF Z52jheʨQC+VF Z52jheʨQC+VF Z52jheʨQC+VF Z52jheʨQC+VF Z52jheʨQC+VF Z52jheʨQC+VF zd054LMaj* SSiJTИJCc* 44ИJCc* 44ИJCc* M%xC74 }CC74 }CC76 }cC76 }cC76UʜW]uWݕVwer2hw4;yGy|@G:t4h>ҼҼҼҼҼҼҼҼҼҼѼѼѼѼѼѼѼѼѼѼYal6lsmM ?Z޾>E70E):)9ER5LT S)UÔaJ0jR5LTS)U㔪qJ8jR5NTS)U)U)U)U)U)U)U)U)U)U)U)U)U)U)U)U)U)U)U)U)US6TmNڜR9jsJ攪)US6TmMښR5jkJ֔)U[STmMښR=j{J)USTmOڞR=j{JՁ)UTRu`JՁ)UTRu`JՁ)U[zo4c| зззззззззз74 }CC74 }CC74 }cC76 }cC76 }cC7k5f }YC߬o7k5 }yC߼o7o7 } } } } } } } } } } }[ }[ }[ }[ }[ }[ }[ }[ }[ }[ } } } } } } } } } } }4h;w@C߁ }14chG q>[ oYo,- |{ގW77|\|koǫ}7|\|koǫ}7|\|koǫ}7|\|koǫ}7|\|koǫ}7|\|koǫ}7|\|koǫ}7|\|kok a>.g |s^ a>.g |s^ a>.g |s^ a>.g |s^ a>.g |s^ a>.g |s^ a>.g |s^ a>.g |s^ a>.g |s^ a>.g |s^5gKÒ$>[/o.o-o/oƒ$>.ϖKK[KK-l1$>,K%֒x; ~\o,KlI|$$$$oKÒ$>[/o.o-o/oƒ$>.ϖKK[KK-b|cI|XgK%%%%vb1$>,K%֒x; ƿX%ْ|I|sI|kI|{IxYo,KlI|$$$$7>oS%aI|\-ϗ7ķķ[7ƒ$>.ϖKK[KK-b|cI|XgK%%%%vj1$>,K%֒x; ?X%ْ|I|sI|kI|{IxZo,KlI|$$$$O-7ć%qI|$>_\Z^ooKÒ$>[/o.o-o/oS%aI|\-ϗ7ķķ[7ƒ$>.ϖKK[KKw㛍_||C|O}Vp.?tC&U?h?}`*gEBSeq/data/GeneMat.rda0000644000175000017500000006424214136050172014273 0ustar nileshnilesheeu@LVxh)RYP/ii REM'ݙ8&Nwgwv^I \iהP)9ѶD=3wO#|W_*,$zDl%zr__b{d+rcsDo}$g_s}JD87io{2_Kg$ /{؞z~+у݉s N~q $:+/=I|{{s3ћWw<|@ ?h%zqM~l |CNhߏhW'zO r赓y~a}D% vDC:}u}A(W&ZeZz`-=/%|yg_}C/p?x%:5\m_,;O&D縗Os͉L'ډqVHw:#l2};DWD:{?&ڻc?C]QG(%&zmc|uy.'V.Ki7ƼN [J\O8Ƹ>ǘWYW#߯CJ>!lɺ8ig:c8?ı#Dgw9_ʋI7('v-psNto^Jy~9sгuYs5T`Wdx6P>e8øh7DNn%W>Ÿ|s=}\<=p;QѓP׋l?}s,z[ߏrIez(j$zxq(_`V8!/]d]'/p'2zZ<Kv7c{#ɝhOEac/w 9VUv%:{} ]ηopEt_!c}>EuD<~Vc|zOq?n7[̳fzz8y-0Jt}Wc/~}χzlG8tA&=y{ܟyz;I-~; Χ1Dd=(g#{ֻuw~.|k$Kk߄>}޿{r~Ƹԛ_/}@s~.s=DK?o~vZ_zDaU{K e^/sE?u1|zaz㼁N9w^AεU՟0{ZOG}T|{ρG#1I8 ,-%i?\Q?isKd#yӽWXcsqcܷyu~g$z Ϣtqg˼A?Knm}ĭ?דOd#x;W ;xxFC ܖOv}}c/m1E~0>F<>;nFc[OTǟ`u0׍ӉWjF^$*O3o~ډ/F; {}(oWS;Hw~UOz]P{ 3<|0 _`11I%:/a<_K=#{?mzBnؾ^:x&x.'׋Dwq} H{^r7g|".I9is~Oig}#~?7֒M7θF']|o,ot%ʼd[<~y{ <7$F[S\G}Dq}{ ~G寣; _'x%<|xwC|P֟5;0Wuo'rzt}z'8kj?W_#wy<9D^ozzSDDR_z>8ru}p^}kwJ;|m&-yD=xUNjOO|7Q__$.-+ϲ^K?d~90/h"gN%zy7ɼB#7v6gSNIO58}|3y_m?7.)U8h+.o?xjw?k|V|Uou~.N~?,~2q:/M9>ԃfwFڜp_u!NM>Jx79vy?1^:n@n$|avvu_@o9JW~rR{A>9[ϰux?%M"ݵMQO!?sK6ÝE.S7/K/ ʁeV9e5Pq9֡D_$gWu/|>1OLyx}#q2d&GOGZm[Gz58KwzSk\ydCU/ic罥"ljodQZ8< 9¸== _}<x$_G~묏;wB, n}OKT~3|"}(k㆐{- Xw/?LTW>]7/c^"2|5a:}یoswwzϭ4UXc)׻ Ofz»ZN5o}5yAM}CxwL}e$a)ṀS].2.ި>vsDwM9w}Ƕ~Qy̏2/$Jn-^?i> S~l?猳x+Gj|n8^ƵC'xշs Pϐ3u;~j,^}9yhASY_I+9Y"׮6+WuXկTo-߼)oU^<Ǚzp_L!}O=m}E1!Kп)ǝ,8 S}quE= 8_JT?uH}w\ў.`iGCU>1s?&qD.ͫ7Ϧy~ /\%D?ڼ%8ޣ]<ͼ2ujyg>q6oOf~c=#n_f{ڽc~"1G vik~GkeO;&j b;̳I,K~r <ҏf>~ ^1uRWlo3f>Q9s뽟- umN]lOZk1vP<v~;맾=q)݉Wq]^hv?oCC={1K^f}V?PU^O?Ro_߄_g3cuW:qu~}/|Gځ]s:T|s QMy)++Da[/!G ؗKE߯K=K/t558kq?Jl;pOzu~]$('P/;_O;O֐;D~M^Ypz}72>ݾJRqi3%+$rY+}sR{hh7rŇ܏ymct9丼ycO$=Gyܟ>mbQ\ڙM9xKo&[1ڛU< oWfOf5+Y=8(Q+h"bގq;JG54>_e~T:a޷yяxZmÜw,?:{uvZϬ>fc|3>zj摝~ns|\\~ &`ʝvϭ^z#?~w\v|=W iv u>Q?w/$X kZ_xq)]OSݯ}f 90Q3~oo*3별=g>EN4坃>wywP^kO >."]e>96~,:~&+.S/Rϸ}#svTw/>Ɠgw or7Q+w/jZ}5N>:|]oK,}uO$`~Oβ{:o&~+Sg=ռrǻ#Va/;ՏOg+'[Jt͸`3Sm'KD3;w0>Nc7<Ơ_wyP&##!.'4s;ѭD[ϒ~]q539Ǚ!܌8;̧~n@\sym+k~b33=ޓ#wsKCCA/0WǑt?ZmsD]_֭f>#PgDO$NY#_9ia~7|mDo\OrzύO֓o}~jƼ2٘OdEwCWab+Vw])GNk#wԟc^unMO'NfOh9cCi`_?Q<\%| ;:?ur?#2Su =~)m]sXߍ: ߛyƿg+{mGfqoN2D;`ݺq}MԼsI}g=8630-x磉h|f}%:[?u0C_ X]9֫n{c ͣfsú&_qlW[iŗYPuQڽ7M߉ZoUqu)>|OJf/3.5zʺ5TwZS zG?>z~\9{j~KRo0p'QE':\VK=Ͼpg/Oyjl?}6si_8q&?b^F9ۦ~qxU6_y4):8%u/DžϏs =غ\7Y<8 us]+s{̏>p\5n98c:?oU+xn7/߼[{k8W _J~'9zhq}ȏ3n~v4/' ׮3m܆xdng^zSܞ~ú9߼,f,s'y= 2iC}?Q7?G;8* =#73zӯh.~vֻkڻG!R,ټv/g>|JB 7e}t#Ì_O2cnqs+wWo`-}L iyֿ\߼6ںEgn;QΗI]>kGcr:OoU]ud汙".O;Sw2{v\a~o;j]O F2{KyXG8N4)MY"5[_X"ӞV?.t=X?Dg=i8k؇f`+Q'{e:_{x~I>qf]?^rQya>8nS?ۇg?^뾭 ~k&w hp\?y_Binȼ^y׺>hu*>b^GUX{?? K?߃tۻփqK?zu]WW[ƿ?W8?_AOs؟}\o?Gx$[Oڿ]y>riq~\??ozOj߾̸f{W&j<ĸ?~7O@;~fϱueO=$f|?2oeuƙ1 kgͰ61/Ҹ"|w㪎Ǿ4SOQ~7\=v|\_qAqQyf*rlR̻Tn|>l2Vqޚ;sm+G?^:[c֟Rw'QM]_ߍ17yR:/18o?:.0瑧՗/8zU?M"w]n+ЉN-gqcէr"W.!T.c@;'[1쇧{ꇊڑٰ:cqޘG)^AhG}jzuýo<+UXtո-{3]m__DVwOәD{gިb?KkϤ~ʍ\G|8NLZ>Ga^e߼+I;l"ުfySvkۼ |r};@ 8~e[g9N|Ƕy7s >{Oߵ^fv(ǹ.^ѷI)2zZp<:/ /u5V~]}+l+gc*f]8)@U_4 ߍek;%H|p }~_>8Yޒ`<箥#e{6L?/y1o~sD!.kye%9Vqj=ey懼}}7~żxqynJǫGbm+15;o߄Ǫ8x.>oe}>N~'zӕu}f6.3OKyJs_Cߕ~ָΧ8|-8~rnuu.=lO܇> S]lڼv}ώ|Ab~w&c3.>ћhoްqVg_\6)4t.8~Y7ǛhRKǯw|qi]+2y`?aS_>jwџuͷWǹrKк~>/m5~p]O]YƩJhv&_sm_<̿VtA Ϻ3ԛ/a)ㅎgyWd3|1u6͛ϞVϦfy?L_˾/룉~O2ζzz;I]c=z%Ӳ~&O9a~1G|~r'Qg߯tbyo6Ig}Q7w$˷Cc]q\)o2|Zh[<}C\ϧ!=Uփuk[W}m[ؼD]'a=q1~'\r.NycU(O웶 ɬO} =y5> ~`wT0ƇYEs^'MQ(99SO:8Yo]I1er]yc?ysܴ¸G)gŇDlh<_{\bݷ|~NS BqEk랃o$7ߍvq)ߵ⠧pXwd9Pw!z5~02gB9ͷ,9A?C6}xoL3ԛ>ˏ3\3L{iq>d}ocM~5?G|3{֟G߉:4w]Ao{Y߂DYk5/ij2>6n|i ueƉߧ~MpߧǸ~3Jﳐ~x(roi>u+7=_;uw˕c<|k]ؿߍgsH+| kG(ozuޫq^~GȾ,>Ggwv ?ե#!|0~ڵmsG]4r; qW>`#N<-{VҺ y-q]yO|9oNiܿs}d0NoDGY~~RϮ>~߃JD'no$GukhDꅶѿM6s՜wg1.Z@1o^S=5*sy{[a>"nesi_9hiϲh]Y{:6e~>@GSȏgxQKn:qmz+_!0nԈ+ˏަީ_9y9[`||Rr7o— KO}-z~So7x~GDu9sCY;#oϾD]`zn3>}86gd( /;N2r82wg9q͓7~wz]A_ߛ&( Np}~cZF:Ea!Wz{xʹwR]Gw |~-b:kuC SlDw~x,oeۼK &|Mr'k^xmx"zկ(?|׿w}}\weT擌<دk/Ǻ/>{vKT?yh7uCOmaݔgu|>EםE zvgVU[FTĉ;{8N=m繮܇z,[Ir8EMO3.u|q ;ɼY/Dx~OI}}!?Wڧ3aǙw T3Y~qz,.bٿ<;Y|+Q'þ?t;?WkY^\;5s4^ x_y0Uߟ?!'{8F=z{~~n>AoFŝ>ύ[_`vԙDK:\Gܞ002ְwe.!w%Z`[9Wy|.n@鼱 Zg'.7HܼG^${1Y9>{j\o9k 7fߩ3l??~'_r2X=egG+uy7BHo~WNYpj_7_=|u3(._``g]}j+wO=yi8>h>|[} z[\[\V>v~]y n>_Q/ߏY=r-pP9Z%?|C=f{멧By6&^?~;;]/#"VsMmϙ=V;ٵ۬'M G"Bk3>w]]O~'A{|$ sovNTwߵS>&q/v}/`͡\'~a}ާCeSv}i]43P}꯴vjv+ѽ\at~:5c7}C#k~uV}}_,q>:]"zzv~Zs+'6i^ןzrh;cixdz@vqƿg?w&YϳrS{>C{׭}2>t>Q?̟A KcG6/\{8vfGDE&U_5^sۻ/_ȼߧOP>O8+̯ 5ډa>y8?ȯY^_><+]ϪQ}x]oބ*UY9}oq*r6|U=;l~WkWβ8_T_[}&|pdKObm"A9]_>T)3.f~q!1ϳ|[~7m%s=y5,s =w~9/j~7xՕ#^~[}?b9L>7Nw({0@c*Th|Qsߥ.Hʏ}hXOsNu:i.d_q;Я桩vc_Xsj[ϕcR@?%m"-.S_e}ySqe? 5ǗЏ~1QG;O0e(_~G`\G"xq(//1n> IRXfu7I}E}u`_[zWak'<=0˼_g me+냱~o/"v;2ϵSd}yݭ_~#8zOk|oGK̻8^o~Iow(_طS;nlo^rOO'_ԟ>q3߮?x5BܤF?p:;|Rn^gEf)grgBs=lgcg lg3o~Vca~q=5ԕ > 껎S{( c%j=vs~iq۾~|9n?MWV? 8iu+}>wng~ow] p9;7~vP1Q/]Ǫ7^Wjڑ}y;NԸ}A=OU\7~ Վ9k xUrw1'qh3nֳs~KDeC~ƣuR{Zd[mq9:_aMm}av ߽A|OWQ}ߡ:;r~o"hvU3OM`ԛgq|yQd[l_/:OriԹ]w O@2ž,K3x'Q.hBz oo/_{D?о7m[OF DO*$q,Dy} Kڥw[Yþa__}qa ӯcvi[_+NT=/wC׍#~Yaq"5ƾVx~?d=LT;:"3QXpGGqi>zEoGd=\֐C <~0W=V׍qGujX=ꓵs_?;ߎf\Kgu0ޗߏ#uK<1F r$뷈~\y2ysșfyCɭD~\_@jwN8k5=1?{Jxu̯LN;_ PwZbIyy| {NOW!ֈ?[?uW/@?}ANes<9_7l߀_/=5nIsa_?☾_N֍7X>0^$^i76R{<)&TZ߾GInիճߖ/fՎC}|q5hו}Af>Wճ(}+?>3Y¸Q)'9?#? _ 6%_ȕ<7E~8_U+w>_yJ։xty<[M_6H?W߼xwG*.󝼟xuԧf%3Ǿ$Y|ww8?j}YX`ަWj_qԟlgwy,pgT}g_/.ozuKY8,_/tiߘosj;/s_jm; ӯB2DygEęh:Q֭KuGKMs]x㬧׮:9:|SY?wVw?W=UxSY])v311(ѳqWwfK2bM25+s,po% cG=q4y}hv~,+̗4ۿPݯ~|Dq\}}e~?2 ΤQ眸a_Z79}bnkSig.O=>/k֮:ʕy!s.F9<#;zm?wj.w1^vbPӏ&{'Qo]a瘟^}!-̿WKkh)G}ajj\}*W~W:M%`}A='^4_;Qd֟/?Jtyݮ>9쀬9{g\8u]$_zGN_{BkkMrѼ6' G~x >Uo헯?CUU޸]7ׯkE=2;C*6[z?^;@KOw+Q>x=iݿzн_J_<)wǽc9TnsqŸ~'u1|8{; yt'_Ey:~M{w}3E׉mwh~vmA9rT/A}X_5~5Ƀ=(DC?!M׮$Zz4o)7۷.響g(O//Yvgz>ׯ2~}yifv¼~2>ٜ?Nklg-r5]zpAXN̿]x'b^3~~m^G5(>ٟ_ߠL8w=\w| |0>|R@` ~)}zv}z\!/?מ+rasޚ48>*~\$Y?~ZRc?#TǠYn_yqo?y f}I-Ϡݤ_|"ړYoc lԟ;lPoOm%jyZSJcGIa8cm(Wy?%Gkm]~0o-|UW_5oK/2]1>RoW?#Yo?"NyR`{=y0g ~׏ո |'_#vWJٗ\=]{I_7g`] Eƃ}=e|?4z~\G?mjZ=N~bjba 8$+OWDǹqK-M>kwby'3lo٧4}gߕ:%'{O>1k=Mӧ]>!V˿T}ywjڗ5~:1;M{m^B1J>3֩lo^2_|`S6e){zYvY~nʗƃͿOz?_}CgYÙs7(~?H|Ij]GW=qwی[.`A^;_G9Ʊܝ~~󆍷}t0 (k*2},?a j}L[?W//;':߷x>~'ߣ<#j]}=>*$j=Pڤ~68}Y},CI?ίe;}<:jv? ߁!_ܗ~+o,~|.3ey"3e<۟:D?~n,Y>޳AE=`~]嗸c]}]zYـ_~qP+ʼn1s/{w7VmrG/G{gͿu}[gM1;yկǵ/_ec5{E3^q7Kf7"j\ּ?}h]̙D;í.D<K`gdR~|oo<3w|N!-N(t58xPs^ﮓ\]ok&WXЫ]]>qMq^s; V<,/yckо w]}xzϷr͗^%>f>z}35a/;o[iU~75^$<З5%n:[WG.Dӿd@lv߾Ǿ^er碝i>cʹif~Fo߻/hߝ7tLԸ}{O~\Rr,˼}|e3|suoW0s >W;3폳x/1߻u!y6D99{ dKw4{M0o{m|8]<7㬿 \]A6o's~g0#s7Qe}Ps>݃NO?;~|hVc{')YTWڙNȶz5}c ?if7QuRBܺO2/8WVo,vuqwӍu%rTo킪(/xfG|A֏h?(Q>W׎6qcyw35 S/i~G=/førz98ˬCbEډ~1|aWoߚ/a2a51//&n}gٟt̯>d~4?ټ߳˚'}+M|w~%J]_ױm\e\g{vSsoяwb>O_ 1_T\{w8eX&8:9~׷<$߃}ٮ"_ >jV 90絏_ĬNa3ӿ`KVWƘ Ϣ`yOYLIyi?˺/=8m!N#:?gsj߹ϒa\co'%_.P