From 704bfb3b224153e5bd4364741d97907c4b8747c6 Mon Sep 17 00:00:00 2001
From: Zhiyu <zhiyu.zhao@utsouthwestern.edu>
Date: Wed, 26 Feb 2020 15:34:44 -0600
Subject: [PATCH] V1.7.1: first commit
---
V1.7.1/ODA_1.7.1.R | 1075 +++++++++++++++++++++++++++++++
V1.7.1/ReadMe.txt | 25 +
V1.7.1/data_template_1.7.1.xlsm | Bin 0 -> 96603 bytes
V1.7.1/run_analysis.sh | 17 +
4 files changed, 1117 insertions(+)
create mode 100755 V1.7.1/ODA_1.7.1.R
create mode 100755 V1.7.1/ReadMe.txt
create mode 100755 V1.7.1/data_template_1.7.1.xlsm
create mode 100755 V1.7.1/run_analysis.sh
diff --git a/V1.7.1/ODA_1.7.1.R b/V1.7.1/ODA_1.7.1.R
new file mode 100755
index 0000000..60955fe
--- /dev/null
+++ b/V1.7.1/ODA_1.7.1.R
@@ -0,0 +1,1075 @@
+verNum='V1.7.1'
+#This script was developed by zhiyu.zhao@utsouthwestern.edu .
+#v1.7.1, 12/04/2019: Figures were saved as both .ps and .png files. Euler plot was disabled for >6 samples. Samples / features can be clustered or not on the heatmaps.
+#v1.7.0, 10/22/2019: Figures were saved as .ps files in a Figure folder. Correlation plot was generated with the old method for better speed. Euler plot was disabled for >10 samples. Y axis of volcano plot could be raw or adjusted p-values. DESeq was renamed RLE as I would implement the DESeq differential expression analysis in the future. RLE is the same method used by DESeq but I slightly modified it to allow for zeros, missing values and / or values below the value filter. Due to this reason I couldn't use the DESeq normalization directly.
+#v1.6.9, 10/4/2019: A bug regarding cpglm was fixed. The bug caused an error in cpglm fitting but the class(fit) was still 'cpglm' and fit$converged was still TRUE. Visualization option was added. Version number checkup was added for the data template. Multiple other minor improvements.
+#v1.6.8, 09/19/2019: A bug regarding featureDescription was fixed. The bug was present only when data format was "table with feature description" and samples were excluded which happened to cause some features to be all-empty so that those features needed to be excluded as well.
+#v1.6.7, 08/29/2019: Correlation plot was enchanced. Euler diagram was added. Software version number was reported in the result file.
+#v1.6.6, 08/28/2019: Data table with feature description column was enabled, minor bugs fixed, and volcano plot colors customized.
+#v1.6.5, 08/27/2019: A bug regarding number of features per sample when data imputation = "NA" was fixed.
+#v1.6.4, 08/08/2019: A bug regarding groupMeanLowerBound was fixed. Definition and default value of groupCountFilter were modified. Data imputation was implemented. Missing values were allowed.
+#v1.6.3, 08/05/2019: A bug regarding nSamples and formula string generation (only when technical replicates are present) was fixed.
+#v1.6.2, 07/24/2019: A bug regarding normalization size factors was fixed. The bug caused size factors to be applied to incorrect samples. If you have used versions before this fix, please rerun your analysis using this fixed version.
+#To run this program, first install R (version 3.5.1 was tested by the author), then run the following from the command line of your operating system.
+
+#Rscript path1/this_program.R path2/your_input_file.xlsm path3/your_output_file.xlsx #Depending on your operating system, / can be \.
+
+#Use the data_template_1.6.xlsm file accompanying this program to generate your_input_file.xlsm. All the parameters can be changed inside that file. Please read the instructions there. Excel macros need to be allowed.
+
+#############################Begin Reading Arguments#############################
+if (1) { #Set to 1 for actual files or 0 for debugging a test.xlsm in the R console.
+ args=commandArgs(trailingOnly=T)
+ if (length(args)!=2)
+ stop('Please specify input and output file names.')
+ inputFile=args[1]
+ outputFile=args[2]
+} else {
+ inputFile='test.xlsm'
+ outputFile='Result_of_test.xlsx'
+}
+if (!file.exists(inputFile))
+ stop('Input file is not found.')
+if (file.exists(outputFile))
+ stop('Output file exists.')
+figuresPath=paste0(tools::file_path_sans_ext(outputFile),'_Figures')
+if (file.exists(figuresPath)&&!rlang::is_empty(dir(figuresPath))) {
+ stop('Figures folder is not empty.')
+} else {
+ dir.create(figuresPath)
+}
+#############################End Reading Arguments#############################
+
+#############################Begin Installing/Loading Libraries#############################
+myLibPath='~/R/3.5.1-gccmkl' #Specify your own library path here.
+dir.create(myLibPath,recursive=T,showWarnings=F)
+.libPaths(c(myLibPath,.libPaths()))
+#This program was tested under R version 3.5.1 with the following required libraries.
+if (!require(openxlsx)) {
+ install.packages('openxlsx',lib=myLibPath,repos='https://cran.r-project.org/')
+ library(openxlsx)
+}
+if (!require(data.table)) {
+ install.packages('data.table',lib=myLibPath,repos='https://cran.r-project.org/')
+ library(data.table)
+}
+if (!require(gtools)) {
+ install.packages('gtools',lib=myLibPath,repos='https://cran.r-project.org/')
+ library(gtools)
+}
+if (!require(matrixStats)) {
+ install.packages('matrixStats',lib=myLibPath,repos='https://cran.r-project.org/')
+ library(matrixStats)
+}
+if (!require(cplm)) {
+ install.packages('cplm',lib=myLibPath,repos='https://cran.r-project.org/')
+ library(cplm)
+}
+if (!require(ggplot2)) {
+ install.packages('ggplot2',lib=myLibPath,repos='https://cran.r-project.org/')
+ library(ggplot2)
+}
+if (!require(cowplot)) {
+ install.packages('cowplot',lib=myLibPath,repos='https://cran.r-project.org/')
+ library(cowplot)
+}
+if (!require(pheatmap)) {
+ install.packages('pheatmap',lib=myLibPath,repos='https://cran.r-project.org/')
+ library(pheatmap)
+}
+if (!require(ggcorrplot)) {
+ install.packages('ggcorrplot',lib=myLibPath,repos='https://cran.r-project.org/')
+ library(ggcorrplot)
+}
+if (!require(eulerr)) {
+ install.packages('eulerr',lib=myLibPath,repos='https://cran.r-project.org/')
+ library(eulerr)
+}
+if (!require(GGally)) {
+ install.packages('GGally',lib=myLibPath,repos='https://cran.r-project.org/')
+ library(GGally)
+}
+#############################End Installing/Loading Libraries#############################
+
+#############################Begin Main#############################
+
+#############################Begin Default Settings#############################
+#Below are default settings. DO NOT change them here.
+#If you use the data_template.xlsm file accompanying this program to generate your input, all the parameters can be changed inside your_input_file.xlsm . Please read the instructions there.
+dataFormat='list' #Available options: list; table.
+valueFilter=0 #A cutoff-value used to count the number of features in a sample. If feature value <= valueFilter, the feature is taken as absent from the sample.
+groupCountFilter=3 #A cutoff-value used to count the number of features in a group. If the number of samples < groupCountFilter for a feature, the feature is taken as absent from the group.
+groupMeanLowerBound=0 #A cutoff-value used as group mean when it's lower than the cutoff. This is for the adjustment of fold change when a group has a very low mean value.
+pCutoffs=0.05 #P-value cutoff for the differential expression (DE) analysis. Multiple values are allowed.
+fcCutoffs=2 #Fold change cutoff for the DE analysis. Multiple values are allowed.
+batchEffectReduction=F #Whether to model batch effect. Only available for linear models such as cpglm and glm.
+averagingTechReps=F #Whether to average technical replicates before any analysis steps.
+normalization='RLE' #Available options: No; RLE; BySize; ByMedian; BySum; ByUpperQuantile.
+uqProb=0.75 #If normalization=ByUpperQuantile, this value is used as the quantile cutoff.
+logTransform=T #Whether to log2-transform the data for statistical tests.
+logOffset=1 #An offset-value added to the data before the log-transformation.
+statTest='glm' #Statistical test method for DE. Available options: t-test; cpglm-glm; glm.
+controlGroups=NULL #Control group name(s) for DE. A control group will be tested against every other group.
+dePairs=NULL #Group name pair(s) for DE. Each pair of group names is a DE comparison.
+interestedFeatures=NULL #Interested features to be visualized separately.
+excludedFeatures=NULL #Features to be completed excluded from the analysis.
+dataImputation='half minimum' #Method to impute missing values and those less than or equal to the value filter. It affects statistical tests but not visualizations. See data template for details.
+visual=T #Whether to visualize data using graphs.
+volcanoP='adjusted p-value' #Which p-value as the y-axis of volcano plots.
+hclustSamples=T #Whether to cluster samples on the heatmap.
+hclustFeatures=T #Whether to cluster features on the heatmap.
+colors=c('Red','Yellow','Blue','Orange','Green','Purple','Magenta','Cyan','Gray','Black')
+heatmapColors=colorRampPalette(c('green','black','red'))(256)
+#############################End Default Settings#############################
+message('###################################################################')
+message('Analysis started ...')
+#############################Begin Settings#############################
+message('Reading input settings ...')
+sheetNames=getSheetNames(inputFile)
+if ('Samples' %in% sheetNames) {
+ sampleIDs=read.xlsx(inputFile,sheet='Samples',colNames=T,rowNames=T,skipEmptyRows=T,skipEmptyCols=T,cols=c(1:5))
+ if (!is.null(sampleIDs)&&dim(sampleIDs)[1]>0) {
+ for (i in 1:dim(sampleIDs)[1])
+ if (grepl('\\s',rownames(sampleIDs)[i]))
+ stop(paste0('Sample ',i,': ',rownames(sampleIDs)[i],' has white space(s). Please remove them.'))
+ sampleIDs=sampleIDs[!is.na(sampleIDs[,1]),1:4] #Excluding samples without group ID.
+ }
+ nSamples=dim(sampleIDs)[1]
+ if (nSamples<2)
+ stop('At least 2 samples should have group IDs.')
+ if (sum(!is.na(sampleIDs[,2]))==0) #No biological replicate IDs. Automatic IDs will be generated.
+ sampleIDs[,2]=c(1:nSamples)
+ else if (sum(!is.na(sampleIDs[,2]))!=nSamples)
+ stop('If you provide biological replicate IDs, they should be present for all samples.')
+ if (sum(!is.na(sampleIDs[,3]))==0) #No batch IDs. Automatic IDs will be generated.
+ sampleIDs[,3]='Batch1'
+ else if (sum(!is.na(sampleIDs[,3]))!=nSamples)
+ stop('If you provide batch IDs, they should be present for all samples.')
+ sampleIDs[,1]=as.factor(sampleIDs[,1])
+ sampleIDs[,2]=as.factor(sampleIDs[,2])
+ sampleIDs[,3]=as.factor(sampleIDs[,3])
+ sampleIDs[,4]=as.numeric(sampleIDs[,4])
+} else
+ stop('Sample sheet is missing.')
+if ('Parameters' %in% sheetNames) {
+ if (read.xlsx(inputFile,sheet='Parameters',colNames=F,rows=1,cols=1)!=verNum)
+ stop(paste0('Please use data template ',verNum,' for this analysis.'))
+ parameters=read.xlsx(inputFile,sheet='Parameters',colNames=T,rowNames=T,na.strings=NULL)
+ if (!is.null(parameters)&&dim(parameters)[1]>0) {
+ if (!is.na(parameters[1,1]))
+ dataFormat=tolower(as.character(parameters[1,1]))
+ if (!is.na(parameters[2,1]))
+ valueFilter=as.numeric(parameters[2,1])
+ if (!is.na(parameters[3,1]))
+ groupCountFilter=as.integer(parameters[3,1])
+ if (!is.na(parameters[4,1]))
+ groupMeanLowerBound=as.numeric(parameters[4,1])
+ if (sum(!is.na(parameters[5,])))
+ pCutoffs=as.numeric(parameters[5,!is.na(parameters[5,])])
+ if (sum(!is.na(parameters[6,])))
+ fcCutoffs=as.numeric(parameters[6,!is.na(parameters[6,])])
+ if (!is.na(parameters[7,1]))
+ normalization=parameters[7,1]
+ if (normalization=='BySize'&&sum(!is.na(sampleIDs[,4]))!=nSamples)
+ stop('If you provide size factors, they should be present for all samples.')
+ if (normalization=='ByUpperQuantile'&&!is.na(parameters[7,2]))
+ uqProb=as.numeric(parameters[7,2])
+ if (!is.na(parameters[8,1])&¶meters[8,1]=='No')
+ logTransform=F
+ if (!is.na(parameters[9,1])&&as.numeric(parameters[9,1])>=0)
+ logOffset=as.numeric(parameters[9,1])
+ if (!is.na(parameters[10,1]))
+ statTest=parameters[10,1]
+ if (!is.na(parameters[11,1]))
+ dataImputation=tolower(as.character(parameters[11,1]))
+ if (!is.na(parameters[12,1])&¶meters[12,1]=='No')
+ visual=F
+ if (!is.na(parameters[13,1]))
+ volcanoP=tolower(as.character(parameters[13,1]))
+ if (!is.na(parameters[14,1])&¶meters[14,1]=='No')
+ hclustSamples=F
+ if (!is.na(parameters[15,1])&¶meters[15,1]=='No')
+ hclustFeatures=F
+ }
+} else
+ stop('Parameter sheet is missing.')
+if ('Comparisons' %in% sheetNames) {
+ dePairs=read.xlsx(inputFile,sheet='Comparisons',colNames=T,rowNames=F,cols=c(1:2))
+ tempStr=unlist(dePairs)
+ if (sum(!(tempStr[!is.na(tempStr)] %in% sampleIDs[,1]))>0)
+ stop('Unknown group ID(s) in the Comparisons sheet.')
+ if (!is.null(dePairs)&&dim(dePairs)[1]>0) {
+ controlGroups=dePairs[!is.na(dePairs[,1])&is.na(dePairs[,2]),1]
+ dePairs=dePairs[!is.na(dePairs[,1])&!is.na(dePairs[,2]),1:2]
+ }
+}
+if ('Features' %in% sheetNames) {
+ tempFeatures=read.xlsx(inputFile,sheet='Features',colNames=T,rowNames=F,cols=c(1:2))
+ if (!is.null(tempFeatures)&&dim(tempFeatures)[1]>0) {
+ interestedFeatures=tempFeatures[!is.na(tempFeatures[,1]),1]
+ excludedFeatures=tempFeatures[!is.na(tempFeatures[,2]),2]
+ }
+}
+#############################End Settings#############################
+
+#############################Begin Reading Data#############################
+#Please read instructions in the data_template.xlsm and use the RawData sheet to hold your data.
+#If the data are generated by the metabolomics core facility at CRI, UTSW, usually they are in the list format.
+#If the data are in the list format, in the excel sheet three columns will be read: feature ID in column 1, sample ID in column 2, and value in column 5.
+#If the data are in the table format, in the excel sheet all columns will be read: feature ID in column 1 and sample values in the next columns.
+message('Reading data ...')
+if (dataFormat=='list') {
+ rawData=read.xlsx(inputFile,sheet='RawData',colNames=T)[,c(1,2,5)]
+} else if (dataFormat=='table') {
+ data=read.xlsx(inputFile,sheet='RawData',colNames=T,rowNames=F)
+ featureDescription=NULL
+ rawData=melt(data,id.vars=1)
+ rawData[,2]=as.character(rawData[,2])
+ rawData[,3]=as.numeric(rawData[,3])
+} else if (dataFormat=='table with feature description') {
+ data=read.xlsx(inputFile,sheet='RawData',colNames=T,rowNames=F)
+ featureDescription=data[,1:2]
+ data=data[,-2]
+ rawData=melt(data,id.vars=1)
+ rawData[,2]=as.character(rawData[,2])
+ rawData[,3]=as.numeric(rawData[,3])
+} else {
+ stop('Only list and table are supported data formats. Check your Parameters sheet.')
+}
+colnames(rawData)=c('Feature','Sample','Value')
+if (!is.null(excludedFeatures))
+ rawData=rawData[!(rawData$Feature %in% excludedFeatures),] #If speficied, features are excluded.
+rawData=rawData[!is.na(sampleIDs[rawData$Sample,1]),] #Samples with empty group IDs are excluded.
+if (sum(is.na(rawData$Value)))
+ message('Missing values are detected.')
+#rawData[is.na(rawData$Value),'Value']=0
+if(sum(rawData$Value<0,na.rm=T)&&(normalization!='No'||logTransform))
+ stop('Negative value(s) are not allowed when you normalize or log-transform your data.')
+#The following code is to generate automatic feature IDs in case they are not unique.
+if (length(unique(rawData$Feature))*length(unique(rawData$Sample))!=dim(rawData)[1]) {
+ rowNames=rawData$Feature
+ ind=data.frame()
+ for (i in 1:dim(rawData)[1]) {
+ r=which(rownames(ind)==rawData$Feature[i])
+ c=which(colnames(ind)==rawData$Sample[i])
+ if (is.na(r==0||c==0)||is.na(ind[r,c])) {
+
+ ind[rawData$Feature[i],rawData$Sample[i]]=1
+ } else {
+ #Currently the auto-ID function is disabled so that problems caused by mistakes are not ignored. If preferred, comment the following line to enable this function.
+ stop(paste0('Duplicated feature or sample name(s) found at ',r,' : ',c,',. Please check your data.'))
+ rowNames[i]=paste0(rowNames[i],'.',ind[r,c])
+ ind[r,c]=ind[r,c]+1
+ }
+ }
+ rawData$Feature=rowNames #In case there are duplicated feature names, they are replaced by name.1, name.2, and so on.
+}
+rawData[,colnames(sampleIDs)]=sampleIDs[rawData$Sample,]
+rawData=aggregate(Value~Feature+BatchID+GroupID+BiologicalReplicateID,FUN=mean,data=rawData) #Data are stored in a list. Technical replicates are averaged if present.
+rawData$Sample=paste(rawData$BatchID,rawData$GroupID,rawData$BiologicalReplicateID,sep='_')
+data=dcast(rawData,Feature~BatchID+GroupID+BiologicalReplicateID,value.var='Value') #Data are also stored in a table with features in rows and samples in columns.
+sampleIDs=unique(sampleIDs)
+sampleIDs$OriginalID=rownames(sampleIDs)
+rownames(sampleIDs)=paste(sampleIDs$BatchID,sampleIDs$GroupID,sampleIDs$BiologicalReplicateID,sep='_')
+rownames(data)=data[,1]
+data=data[,-1]
+data=as.matrix(data)
+class(data)='numeric'
+sampleIDs=sampleIDs[colnames(data),]
+#############################End Reading Data#############################
+
+wb = createWorkbook()
+style1=createStyle(textDecoration='Bold')
+style2=createStyle(textDecoration='Bold',wrapText=T)
+addWorksheet(wb,'Visual')
+addWorksheet(wb, 'Data')
+addWorksheet(wb, 'Overall')
+addWorksheet(wb, 'Group_Features')
+addWorksheet(wb, 'Sample_Features')
+
+#############################Begin Sheet "Data"#############################
+message('Writing the data sheet ...')
+if (dataFormat=='table with feature description') {
+ rownames(featureDescription)=featureDescription[,1]
+ featureDescription=featureDescription[rownames(featureDescription)%in%rownames(data),]
+ featureDescription=featureDescription[rownames(data),-1]
+ writeData(wb,'Data',t(rbind(NA,sampleIDs)),rowNames=T,colNames=T,keepNA=F)
+ writeData(wb,'Data','',startRow=1,startCol=2)
+ writeData(wb,'Data',data.frame(Description=featureDescription,data,stringsAsFactors=F,check.names=F),startRow=7,rowNames=T,colNames=F)
+ writeData(wb,'Data','Description',startRow=6,startCol=2)
+ freezePane(wb,'Data',firstActiveRow=7,firstActiveCol=3)
+} else {
+ writeData(wb,'Data',t(sampleIDs),rowNames=T,colNames=T)
+ writeData(wb,'Data',data,startRow=7,rowNames=T,colNames=F)
+ freezePane(wb,'Data',firstActiveRow=7,firstActiveCol=2)
+}
+nSamples=dim(data)[2] #Bug fixed on 8/5/19.
+nFeatures=dim(data)[1]
+groupID=as.factor(sampleIDs[,1])
+names(groupID)=rownames(sampleIDs)
+groups=levels(groupID)
+nGroups=length(groups)
+repID=as.factor(sampleIDs[,2])
+names(repID)=rownames(sampleIDs)
+batchID=as.factor(sampleIDs[,3])
+names(batchID)=rownames(sampleIDs)
+formulaStr='value'
+if (length(levels(groupID))>1) {
+ if (formulaStr=='value')
+ formulaStr=paste(formulaStr,'group',sep='~')
+ else
+ formulaStr=paste(formulaStr,'group',sep='+')
+}
+if (length(levels(repID))>1&&length(levels(repID))<nSamples) {
+ if (formulaStr=='value')
+ formulaStr=paste(formulaStr,'rep',sep='~')
+ else
+ formulaStr=paste(formulaStr,'rep',sep='+')
+}
+if (length(levels(batchID))>1) {
+ if (formulaStr=='value')
+ formulaStr=paste(formulaStr,'batch',sep='~')
+ else
+ formulaStr=paste(formulaStr,'batch',sep='+')
+}
+formulaForFitting=NULL
+if (formulaStr!='value')
+ formulaForFitting=as.formula(formulaStr)
+#############################End Sheet "Data"#############################
+
+#############################Begin Sheet "Normalized_Log2_Data"#############################
+message ('Normalizing and transforming data ...')
+normData=rawData #Long-list
+sizeRatios=data #Wide-table
+sizeRatios=sizeRatios[rowSums(sizeRatios,na.rm=T)!=0,] #All-zero rows are not used for normalization.
+if (normalization!='No'||logTransform) {
+ sheetName='Data'
+ if (normalization!='No') {
+ if (normalization=='RLE') {
+ sizeRatios[sizeRatios==0]=NA #Zero values are ignored.
+# sizeRatios=sizeRatios/exp(rowMeans(log(sizeRatios),na.rm=T)) #Buggy: Each row is divided by its geometric mean.
+ sizeRatios=sweep(sizeRatios,1,exp(rowMeans(log(sizeRatios),na.rm=T)),'/') #Each row is divided by its geometric mean.
+ sizeFactors=colMedians(sizeRatios,na.rm=T) #Median of ratios within each sample is taken as its size factor.
+ sizeRatios=melt(log2(sizeRatios),varnames=c('Feature','Sample'), value.name ='Value')
+ sizeRatios$Sample=as.character(sizeRatios$Sample)
+ sizeRatios[,colnames(sampleIDs)]=sampleIDs[sizeRatios$Sample,]
+ }
+ else {
+ if (normalization=='BySize')
+ sizeFactors=sampleIDs[,4] #If normalize by size, size factors are taken from the input file. Size factors can be e.g. cell counts, spike-in amount etc.
+ if (normalization=='ByMedian') #If normalize by median, the median feature value of each sample is taken as its size factor.
+ sizeFactors=colMedians(sizeRatios)
+ if (normalization=='ByUpperQuantile') #If normalize by upper quantile, the (uqProb*100)-th quantile value of each sample is taken as its size factor. uqProb is taken from the input file or defaults to 0.75 i.e. the upper quartile.
+ sizeFactors=colQuantiles(sizeRatios,probs=uqProb,na.rm=T)
+ if (normalization=='BySum') #If normalize by sum, the sum of feature values of each sample is taken as its size factor.
+ sizeFactors=colSums(sizeRatios,na.rm=T)
+ }
+ if (sum(sizeFactors==0)==0) {
+ sizeFactors=sizeFactors/median(sizeFactors)
+ data=sweep(data,2,sizeFactors,'/') #Raw data are normalized by the size factor per sample. A bug regarding this line is fixed.
+ names(sizeFactors)=colnames(data)
+ normData$Value=normData$Value/sizeFactors[normData$Sample]
+ sheetName=paste0('Norm_',sheetName)
+ }
+ else {
+ message('Size factor(s)=0. No normalization is applied.')
+ normalization='No'
+ }
+ }
+ if (logTransform) {
+ if (sum(data+logOffset<=0,na.rm=T))
+ stop('Offset of log2-transformation is too small to make all values positive.')
+ data=log2(data+logOffset)
+ sheetName=paste0('Log2(x+',logOffset,')_',sheetName)
+ }
+ addWorksheet(wb, sheetName)
+ if (normalization!='No') {
+ sampleIDs=data.frame(sampleIDs[,1:4],SizeFactor=sizeFactors,OriginalID=sampleIDs[,5])
+ if (dataFormat=='table with feature description') {
+ writeData(wb,sheetName,t(rbind(NA,sampleIDs)),rowNames=T,colNames=T,keepNA=F)
+ writeData(wb,sheetName,'',startRow=1,startCol=2)
+ writeData(wb,sheetName,'Description',startRow=7,startCol=2)
+ writeData(wb,sheetName,data.frame(Description=featureDescription,data,stringsAsFactors=F,check.names=F),startRow=8,rowNames=T,colNames=F)
+ freezePane(wb,sheetName,firstActiveRow=8,firstActiveCol=3)
+ } else {
+ writeData(wb,sheetName,t(sampleIDs),rowNames=T,colNames=T)
+ writeData(wb,sheetName,data,startRow=8,rowNames=T,colNames=F)
+ freezePane(wb,sheetName,firstActiveRow=8,firstActiveCol=2)
+ }
+ } else {
+ if (dataFormat=='table with feature description') {
+ writeData(wb,sheetName,t(rbind(NA,sampleIDs)),rowNames=T,colNames=T,keepNA=F)
+ writeData(wb,sheetName,'',startRow=1,startCol=2)
+ writeData(wb,sheetName,data.frame(Description=featureDescription,data,stringsAsFactors=F,check.names=F),startRow=7,rowNames=T,colNames=F)
+ writeData(wb,sheetName,'Description',startRow=6,startCol=2)
+ freezePane(wb,sheetName,firstActiveRow=7,firstActiveCol=3)
+ } else {
+ writeData(wb,sheetName,t(sampleIDs),rowNames=T,colNames=T)
+ writeData(wb,sheetName,data,startRow=7,rowNames=T,colNames=F)
+ freezePane(wb,sheetName,firstActiveRow=7,firstActiveCol=2)
+ }
+ }
+}
+#############################End Sheet "Normalized_Log2_Data"#############################
+
+#############################Begin Sheet "Selected_Features"#############################
+message ('Writing the selected features sheet ...')
+nSelectedFeatures=0
+if (length(interestedFeatures)>0) {
+ selectedFeatures=rownames(data) %in% interestedFeatures
+ nSelectedFeatures=sum(selectedFeatures)
+}
+if (nSelectedFeatures>0) {
+ if (dataFormat=='table with feature description') {
+ report=data.frame(Description=featureDescription,data,stringsAsFactors=F,check.names=F)[selectedFeatures,]
+ } else {
+ report=data.frame(data,stringsAsFactors=F,check.names=F)[selectedFeatures,]
+ }
+ addWorksheet(wb, 'Selected_Features')
+ writeData(wb,'Selected_Features',report,rowNames=T)
+ writeData(wb,'Selected_Features','Feature')
+ addStyle(wb,'Selected_Features',style2,rows=1,cols=1:200)
+}
+#############################End Sheet "Selected_Features"#############################
+
+#############################Begin Sheet "Visual"#############################
+if (visual) {
+message ('Generating figures ...')
+options(warn=-1)
+plotID=0
+plotTypes=c('raw')
+if (logTransform)
+ plotTypes=c(plotTypes,'log2')
+if (normalization!='No') {
+ plotTypes=c(plotTypes,'norm')
+ if (logTransform)
+ plotTypes=c(plotTypes,'log2_norm')
+}
+if (normalization=='RLE')
+ plotTypes=c(plotTypes,'sizes')
+if (nSelectedFeatures>0)
+ plotTypes=c(plotTypes,paste0(plotTypes,'_features'))
+for (plotType in plotTypes) {
+ if (grepl('sizes',plotType)) {
+ plotData=sizeRatios
+ titleText='Log2-Transformed Size Ratios for RLE Normalization'
+ } else if (grepl('norm',plotType)) {
+ plotData=normData
+ titleText='Normalized Data'
+ } else {
+ plotData=rawData
+ titleText='Raw Data'
+ }
+ if (grepl('log2',plotType)) {
+ plotData$Value=log2(plotData$Value+logOffset)
+ titleText=paste0('Log2(x+',logOffset,')-Transformed ',titleText)
+ }
+ if (grepl('features',plotType)) {
+ plotData=plotData[plotData$Feature %in% interestedFeatures,]
+ titleText=paste0(titleText,' (Selected Features Only)')
+ }
+ if (sum(!is.na(plotData$Feature))==0)
+ next
+ tempData=dcast(plotData,Feature~Sample,value.var='Value') #Table format of the plotData.
+ rownames(tempData)=tempData[,1]
+ tempData=as.matrix(tempData[,-1])
+ nonZeroFeatures=rownames(tempData)[rowSums(tempData,na.rm=T)!=0]
+ if (length(nonZeroFeatures)>0) {
+ plotData=plotData[plotData$Feature %in% nonZeroFeatures,] #Zero features are excluded from visualization.
+ plotFile=file.path(figuresPath,paste0(plotType,'.ps'))
+ plotFile2=file.path(figuresPath,paste0(plotType,'.png'))
+ p1=ggplot(plotData, aes(x=Sample, y=Value, color=GroupID))+geom_violin(color='black')+geom_jitter(position=position_jitter(0.2),size=0.5)+geom_boxplot(color='black',outlier.size=-1,width=0.1)+facet_grid(.~BatchID,scales='free_x',space='free_x')+theme(axis.text.x = element_text(angle = 90, hjust = 1),legend.position = "none")+ggtitle(paste0('Violin-Boxplot of ',titleText))
+ p2=ggplot(plotData,aes(x=Value,color=GroupID))+geom_histogram(bins=20)+facet_grid(.~BatchID+Sample)+ggtitle(paste0('Histogram of ',titleText))+theme(strip.text.x = element_blank(),axis.text.x = element_text(angle = 90, hjust = 1),legend.position = "none")
+ statsData=melt(do.call(data.frame,aggregate(Value~Sample,FUN=function(x) c(Median=median(x),Sum=sum(x)),data=plotData)),id.vars='Sample')
+ colnames(statsData)=c('Sample','Stat','Value')
+ statsData$Sample=as.character(statsData$Sample)
+ statsData$GroupID=sampleIDs[statsData$Sample,1]
+ statsData$BatchID=sampleIDs[statsData$Sample,3]
+ p3=ggplot(statsData,aes(x=Sample,y=Value,color=GroupID))+geom_bar(stat='identity')+facet_grid(Stat~BatchID,scales='free',space='free_x')+theme(axis.text.x = element_text(angle = 90, hjust = 1),legend.position = "none")+ggtitle(paste0('Statistics of ',titleText))
+ p=plot_grid(p1,p2,align='v',axis='tblr',nrow=2,rel_heights = c(3/5, 2/5))
+ p=plot_grid(p,p3,ncol=2,rel_widths=c(3/4,1/4))
+ ggsave(plotFile,plot=p,width=min(50,max(dim(data)[2],12)),height=8,units='in',limitsize=F)
+ ggsave(plotFile2,plot=p,width=min(50,max(dim(data)[2],12)),height=8,units='in',limitsize=F)
+ insertImage(wb,'Visual',plotFile2, width=dim(data)[2], height=8, unit='in',startRow=plotID*45+1,startCol=1)
+ plotID=plotID+1
+ tempData=tempData[rowSums(is.na(tempData))==0&apply(tempData,1,sd)>0,] #Clustering doesn't work with NAs. Scaling doesn't work with zero-sd rows.
+ if (!is.null(dim(tempData)))
+ if (dim(tempData)[1]>1) {
+ plotFile=file.path(figuresPath,paste0(plotType,'_heatmap.ps'))
+ plotFile2=file.path(figuresPath,paste0(plotType,'_heatmap.png'))
+ p=pheatmap(tempData,main=paste0('Heatmap of ',titleText),border_color=NA,show_rownames=grepl('features',plotType),treeheight_row=0,annotation_col=sampleIDs[colnames(tempData),c('BatchID','GroupID')],annotation_legend=T,na_col='gray',scale='none',col=heatmapColors,cluster_rows=hclustFeatures,cluster_cols=hclustSamples)
+ ggsave(plotFile,plot=p$gtable,width=min(50,max(dim(data)[2],12)),height=8,units='in',limitsize=F)
+ ggsave(plotFile2,plot=p$gtable,width=min(50,max(dim(data)[2],12)),height=8,units='in',limitsize=F)
+ insertImage(wb,'Visual',plotFile2, width=min(50,max(dim(data)[2]/3,4)), height=8, unit='in',startRow=plotID*45+1,startCol=1)
+ plotFile=file.path(figuresPath,paste0(plotType,'_scaled_heatmap.ps'))
+ plotFile2=file.path(figuresPath,paste0(plotType,'_scaled_heatmap.png'))
+ p=pheatmap(tempData,main=paste0('Scaled Heatmap of ',titleText),border_color=NA,show_rownames=grepl('features',plotType),treeheight_row=0,annotation_col=sampleIDs[colnames(tempData),c('BatchID','GroupID')],annotation_legend=T,na_col='gray',scale='row',col=heatmapColors,cluster_rows=hclustFeatures,cluster_cols=hclustSamples)
+ ggsave(plotFile,plot=p$gtable,width=min(50,max(dim(data)[2],12)),height=8,units='in',limitsize=F)
+ ggsave(plotFile2,plot=p$gtable,width=min(50,max(dim(data)[2],12)),height=8,units='in',limitsize=F)
+ insertImage(wb,'Visual',plotFile2, width=min(50,max(dim(data)[2]/3,4)), height=8, unit='in',startRow=plotID*45+1,startCol=ceiling(max(dim(data)[2]/3,4)*2))
+ plotFile=file.path(figuresPath,paste0(plotType,'_pca_cor.ps'))
+ plotFile2=file.path(figuresPath,paste0(plotType,'_pca_cor.png'))
+ plotData=data.frame(prcomp(t(tempData))$x,sampleIDs)
+ p1=ggplot(plotData,aes(x=PC1,y=PC2,color=GroupID))+geom_point(aes(shape=BatchID))+ggtitle(paste0('PCA of ',titleText))
+ if (dim(tempData)[1]>2)
+ pMat=cor_pmat(tempData)
+ else
+ pMat=NULL
+ if (sum(is.na(cor(tempData)))>0)
+ hClust=F
+ else
+ hClust=T
+ p2=ggcorrplot(cor(tempData), hc.order = hClust, outline.col = "white",p.mat=pMat)+ggtitle(paste0('Pearson Correlation of ',titleText))
+ p=plot_grid(p1,p2,ncol=2,rel_widths=c(1/3,2/3))
+# p2=ggpairs(as.data.frame(tempData),lower=list(continuous='smooth'),title=paste0('Scatter Plot of ',titleText))
+# p=plot_grid(p1,ggmatrix_gtable(p2),ncol=2,rel_widths=c(1/3,2/3))
+ ggsave(plotFile,plot=p,width=min(50,max(dim(data)[2]*2,16)),height=8,units='in',limitsize=F)
+ ggsave(plotFile2,plot=p,width=min(50,max(dim(data)[2]*2,16)),height=8,units='in',limitsize=F)
+ insertImage(wb,'Visual',plotFile2, width=min(50,max(dim(data)[2]*2,16)), height=8, unit='in',startRow=plotID*45+1,startCol=ceiling(max(dim(data)[2]/3,4)*4))
+ plotID=plotID+1
+ }
+ }
+}
+} #End if (visual)
+options(warn=0)
+#############################End Sheet "Visual"#############################
+
+#############################Begin Sheet "Overall"#############################
+message('Writing the overall summary sheet ...')
+featureExpressed=(data>valueFilter)
+nValueFilteredFeatures=sum(rowSums(featureExpressed,na.rm=T)>0)
+if (sum(data>valueFilter,na.rm=T)>0 && groupMeanLowerBound==0) {
+ if (logTransform)
+ groupMeanLowerBound=min(data[data>valueFilter],na.rm=T)-1 #Bug fix: this was added on 8/8/19.
+ else
+ groupMeanLowerBound=min(data[data>valueFilter],na.rm=T)/2
+}
+report=matrix(nrow=19,ncol=1)
+colnames(report)='Value'
+rownames(report)=c('Samples','Groups','Features','Value-filtered features','Value filter','Count filter','Group mean lower-bound','Adjusted p-value cutoff','Fold change cutoff','Normalization','Log2 transformation','Offset of log2 transformation','Statistical test method','Data imputation method','Formula for linear model','Visualization','Y axis for volcano plot','Clustered samples','Clustered features')
+report[1]=nSamples
+report[2]=nGroups
+report[3]=nFeatures
+report[4]=nValueFilteredFeatures
+report[5]=valueFilter
+report[6]=groupCountFilter
+report[7]=groupMeanLowerBound
+report[8]=paste(pCutoffs,collapse=',')
+report[9]=paste(fcCutoffs,collapse=',')
+report[10]=normalization
+if (normalization=='ByUpperQuantile')
+ report[10]=paste0(report[10],'-',uqProb*100,'%')
+if (logTransform) {
+ report[11]='Yes'
+} else {
+ report[11]='No'
+}
+report[12]=logOffset
+report[13]=statTest
+report[14]=dataImputation
+report[15]=formulaStr
+if (visual) {
+ report[16]='Yes'
+} else {
+ report[16]='No'
+}
+report[17]=volcanoP
+if (hclustSamples) {
+ report[18]='Yes'
+} else {
+ report[18]='No'
+}
+if (hclustFeatures) {
+ report[19]='Yes'
+} else {
+ report[19]='No'
+}
+writeData(wb,'Overall',report,rowNames=T,borders='surrounding')
+writeData(wb,'Overall',verNum)
+writeComment(wb,'Overall',2,2,createComment('Total number of samples',visible=F))
+writeComment(wb,'Overall',2,3,createComment('Total number of groups',visible=F))
+writeComment(wb,'Overall',2,4,createComment('Total number of features e.g. metabolites, proteins, etc.',visible=F))
+writeComment(wb,'Overall',2,5,createComment('Total number of features with at least one sample that\'s greater than the cutoff; these features are considered \"expressed\"',visible=F))
+writeComment(wb,'Overall',2,6,createComment('Feature value cutoff; only feature values greater than this number are considered \"expressed\"',visible=F))
+writeComment(wb,'Overall',2,7,createComment('Within-group count cutoff; only features expressed by more than this number of replicates in a group will be included for comparisons',visible=F))
+writeComment(wb,'Overall',2,8,createComment('Group mean values less than this number will be replaced by it',visible=F))
+writeComment(wb,'Overall',2,9,createComment('Adjusted p-values less than this number will be considered significant',visible=F))
+writeComment(wb,'Overall',2,10,createComment('Fold changes greater than this number will be considered significant',visible=F))
+writeComment(wb,'Overall',2,11,createComment('If Yes, non-zero values are used to calculate a geometric mean per feature, feature values are divided by the geometric mean to obtain ratios, and the median of such ratios per sample is used as an estimation of the size factor per sample. Feature values in a sample are divided by this sample-specific size factor.',visible=F))
+writeComment(wb,'Overall',2,12,createComment('If yes, data are log2-transformed with or without an offset.',visible=F))
+writeComment(wb,'Overall',2,13,createComment('If >0, data=data+offset before the log2-transformation, otherwise only the positive values are log2-transformed but zeros are unchanged.',visible=F))
+writeComment(wb,'Overall',2,14,createComment('Statistical test method.',visible=F))
+writeComment(wb,'Overall',2,15,createComment('Data imputation method.',visible=F))
+writeComment(wb,'Overall',2,16,createComment('Formula for the linear model such as cpglm, glm etc..',visible=F))
+writeComment(wb,'Overall',2,17,createComment('If yes, figures are saved in the result file as well as in a Figure folder.',visible=F))
+writeComment(wb,'Overall',2,18,createComment('Y axis used for the volcano plot.',visible=F))
+writeComment(wb,'Overall',2,19,createComment('If yes, samples on the heatmaps are hierarchical-clustered.',visible=F))
+writeComment(wb,'Overall',2,20,createComment('If yes, features on the heatmaps are hierarchical-clustered.',visible=F))
+
+report=matrix(nrow=nGroups,ncol=2)
+colnames(report)=c('Samples','Value-count-filtered features')
+rownames(report)=groups
+sumGroupCount=matrix(nrow=nFeatures,ncol=nGroups)
+colnames(sumGroupCount)=groups
+for (i in 1:nGroups) {
+ groupCount=featureExpressed[,groupID==groups[i],drop=F]
+ sumGroupCount[,i]=rowSums(groupCount,na.rm=T)>=groupCountFilter
+ report[groups[i],1]=dim(groupCount)[2]
+ report[groups[i],2]=sum(sumGroupCount[,i])
+ groupCount[is.na(groupCount)]=F
+ if (visual&sum(groupID==groups[i])<=6) {
+ ggsave(file.path(figuresPath,paste0('euler_',i,'.ps')),plot=plot(euler(groupCount),quantities=T,col=colors[1:dim(groupCount)[2]],fill=F,fill_opacity=0.7,legend=T))
+ ggsave(file.path(figuresPath,paste0('euler_',i,'.png')),plot=plot(euler(groupCount),quantities=T,col=colors[1:dim(groupCount)[2]],fill=F,fill_opacity=0.7,legend=T))
+ }
+}
+if (visual&nGroups<=6) {
+ ggsave(file.path(figuresPath,'euler_all.ps'),plot=plot(euler(sumGroupCount),quantities=T,col=colors[1:nGroups],fill=F,fill_opacity=0.7,legend=T))
+ ggsave(file.path(figuresPath,'euler_all.png'),plot=plot(euler(sumGroupCount),quantities=T,col=colors[1:nGroups],fill=F,fill_opacity=0.7,legend=T))
+}
+writeData(wb,'Overall',report,rowNames=T,startCol=5,borders='surrounding')
+writeData(wb,'Overall','Group',startCol=5)
+writeComment(wb,'Overall',5,1,createComment('Group name; a group is a set of replicates to be used in a statistical comparison',visible=F))
+writeComment(wb,'Overall',6,1,createComment('Number of replicates in a group',visible=F))
+writeComment(wb,'Overall',7,1,createComment('Number of features that pass both the value and the within-group count filters',visible=F))
+
+report=matrix(nrow=nSamples,ncol=1)
+colnames(report)='Value-filtered features'
+rownames(report)=colnames(data)
+for (i in 1:nSamples)
+ report[i]=sum(featureExpressed[,i],na.rm=T)
+writeData(wb,'Overall',report,rowNames=T,startCol=10,borders='surrounding')
+writeData(wb,'Overall','Sample',startCol=10)
+writeComment(wb,'Overall',10,1,createComment('Sample name',visible=F))
+writeComment(wb,'Overall',11,1,createComment('Number of features that pass the value filter',visible=F))
+addStyle(wb,'Overall',style1,rows=1,cols=1:100)
+setColWidths(wb,'Overall',1:100,widths='auto')
+freezePane(wb,'Overall',firstActiveRow=2)
+
+if (visual) {
+ if (nGroups<=6)
+ insertImage(wb,'Overall',file.path(figuresPath,'euler_all.png'),width=4,height=4,unit='in',startRow=nSamples+4,startCol=7)
+ for (i in 1:nGroups) {
+ if (sum(groupID==groups[i])<=6)
+ insertImage(wb,'Overall',file.path(figuresPath,paste0('euler_',i,'.png')),width=4,height=4,unit='in',startRow=nSamples+4+i*20,startCol=7)
+ }
+}
+
+#############################End Sheet "Overall"#############################
+
+#############################Begin Sheet "Group_Features"#############################
+message('Writing the group features sheet ...')
+report=matrix(nrow=nFeatures,ncol=nGroups)
+colnames(report)=groups
+rownames(report)=rownames(data)
+for (i in groups)
+ report[,i]=rowSums(featureExpressed[,groupID==i,drop=F],na.rm=T)
+groupExpressed=report>=groupCountFilter
+if (dataFormat=='table with feature description') {
+ report=data.frame(Description=featureDescription,Groups=rowSums(groupExpressed),report,stringsAsFactors=F,check.names=F)
+ freezePane(wb,'Group_Features',firstActiveRow=2,firstActiveCol=3)
+} else {
+ report=data.frame(Groups=rowSums(groupExpressed),report,stringsAsFactors=F,check.names=F)
+ freezePane(wb,'Group_Features',firstActiveRow=2,firstActiveCol=2)
+}
+writeData(wb,'Group_Features',report,rowNames=T)
+writeData(wb,'Group_Features','Feature')
+writeComment(wb,'Group_Features',1,1,createComment('Feature name',visible=F))
+writeComment(wb,'Group_Features',2,1,createComment('Each cell in this column tells the number of groups that pass the count filter for a specific feature',visible=F))
+writeComment(wb,'Group_Features',3,1,createComment('Each cell in this or the succeeding columns tells the number of replicates in a group that pass the value filter for a specific feature',visible=F))
+addStyle(wb,'Group_Features',style2,rows=1,cols=1:100)
+if (nSelectedFeatures>0) {
+ report=report[selectedFeatures,]
+ writeData(wb,'Selected_Features',report,rowNames=T,startRow=nSelectedFeatures+4)
+ writeData(wb,'Selected_Features','Feature',startRow=nSelectedFeatures+4)
+ addStyle(wb,'Selected_Features',style2,rows=nSelectedFeatures+4,cols=1:200)
+}
+#############################End Sheet "Group_Features"#############################
+
+#############################Begin Sheet "Sample_Features"#############################
+message('Writing the sample features sheet ...')
+report=featureExpressed
+class(report)='integer'
+if (dataFormat=='table with feature description') {
+ report=data.frame(Description=featureDescription,Samples=rowSums(featureExpressed),report,stringsAsFactors=F,check.names=F)
+ freezePane(wb,'Sample_Features',firstActiveRow=2,firstActiveCol=3)
+} else {
+ report=data.frame(Samples=rowSums(featureExpressed),report,stringsAsFactors=F,check.names=F)
+ freezePane(wb,'Sample_Features',firstActiveRow=2,firstActiveCol=2)
+}
+writeData(wb,'Sample_Features',report,rowNames=T)
+writeData(wb,'Sample_Features','Feature')
+writeComment(wb,'Sample_Features',1,1,createComment('Feature name',visible=F))
+writeComment(wb,'Sample_Features',2,1,createComment('Each cell in this column tells the number of samples that pass the value filter for a specific feature',visible=F))
+writeComment(wb,'Sample_Features',3,1,createComment('Each cell in this or the succeeding columns tells whether the value passes the value filter for a specific feature; 1 means yes and 0 means no',visible=F))
+addStyle(wb,'Sample_Features',style2,rows=1,cols=1:200)
+if (nSelectedFeatures>0) {
+ report=report[selectedFeatures,]
+ writeData(wb,'Selected_Features',report,rowNames=T,startRow=2*nSelectedFeatures+7)
+ writeData(wb,'Selected_Features','Feature',startRow=2*nSelectedFeatures+7)
+ addStyle(wb,'Selected_Features',style2,rows=2*nSelectedFeatures+7,cols=1:200)
+}
+#############################End Sheet "Sample_Features"#############################
+
+comparisonsSummary=matrix(nrow=0,ncol=11)
+colnames(comparisonsSummary)=c('Control','Treatment','Control_Only','Treatment_Only','Absent_in_Both','Present_in_Both','Up_in_Treatment','Down_in_Treatment','Significant','Adjusted_p_Cutoff','FC_Cutoff')
+options(warn=2)
+if (dataImputation!='no')
+ data[data<=valueFilter]=NA
+if (dataImputation=='value filter')
+ data[is.na(data)]=valueFilter
+if (dataImputation=='mean')
+ tempData=rowMeans(data,na.rm=T)
+if (dataImputation=='median')
+ tempData=rowMedians(data,na.rm=T)
+if (dataImputation=='half minimum') {
+ tempData=rowMins(data,na.rm=T)
+ if (logTransform)
+ tempData=tempData-1
+ else
+ tempData=tempData/2
+}
+if (dataImputation=='mean' || dataImputation=='median' || dataImputation=='half minimum') {
+ tempData[tempData==Inf]=groupMeanLowerBound
+ tempData[is.nan(tempData)]=groupMeanLowerBound
+ tempData[is.na(tempData)]=groupMeanLowerBound
+ tempData=replicate(dim(data)[2],tempData)
+ data[is.na(data)]=tempData[is.na(data)]
+}
+#############################Begin Sheet "Control_vs_Others"#############################
+message ('Writing the differential expression result for control vs. other groups ...')
+nControls=length(controlGroups)
+if (nControls>0) {
+ for (k in 1:nControls) {
+ report=matrix(NA,nrow=nFeatures,ncol=(nGroups-1)*6+2)
+ controlGroup=controlGroups[k]
+ treatGroups=groups[groups!=controlGroup]
+ group=C(groupID,base=which(rownames(contrasts(groupID))==controlGroup))
+ colnames(contrasts(group))=rownames(contrasts(group))[rownames(contrasts(group))!=controlGroup]
+ if (logTransform)
+ tempStr=c('_log2_Mean','_log2_Adjusted_Mean')
+ else
+ tempStr=c('_Mean','_Adjusted_Mean')
+ colnames(report)=c(paste0(controlGroup,tempStr),as.vector(t(outer(treatGroups,c(tempStr,'_log2_FC','_FC','_p_Value','_Adjusted_p'),paste0))))
+ rownames(report)=rownames(data)
+ for (i in 1:nFeatures) {
+ group1=data[i,groupID==controlGroup]
+ if (sum(!is.na(group1))==0)
+ group1=rep(groupMeanLowerBound,length(group1))
+ if(sum(!is.na(group1))==1)
+ sd1=0
+ else
+ sd1=sd(group1,na.rm=T)
+ report[i,1]=mean(group1,na.rm=T)
+ report[i,2]=report[i,1]
+ if (report[i,2]<groupMeanLowerBound)
+ report[i,2]=groupMeanLowerBound
+ for (j in 1:(nGroups-1)) {
+ group2=data[i,groupID==treatGroups[j]]
+ if (sum(!is.na(group2))==0)
+ group2=rep(groupMeanLowerBound,length(group2))
+ if(sum(!is.na(group2))==1)
+ sd2=0
+ else
+ sd2=sd(group2,na.rm=T)
+ report[i,(j-1)*6+3]=mean(group2,na.rm=T)
+ report[i,(j-1)*6+4]=report[i,(j-1)*6+3]
+ if (report[i,(j-1)*6+4]<groupMeanLowerBound)
+ report[i,(j-1)*6+4]=groupMeanLowerBound
+ if (logTransform)
+ report[i,(j-1)*6+5]=report[i,(j-1)*6+4]-report[i,2] #Log2 FC
+ else
+ report[i,(j-1)*6+5]=log2(report[i,(j-1)*6+4]/report[i,2]) #Log2 FC
+ report[i,(j-1)*6+6]=2^report[i,(j-1)*6+5]
+ if ((groupExpressed[i,controlGroup] || groupExpressed[i,treatGroups[j]]))
+ if (statTest=='t-test') {
+ if (sd1==0&&sd2!=0)
+ report[i,(j-1)*6+7]=t.test(group2,mu=mean(group1,na.rm=T))$p.value
+ else if (sd1!=0&&sd2==0)
+ report[i,(j-1)*6+7]=t.test(group1,mu=mean(group2,na.rm=T))$p.value
+ else if (sd1!=0&&sd2!=0)
+ report[i,(j-1)*6+7]=t.test(group1,group2)$p.value
+ } else if (statTest=='cpglm-glm') {
+ testData=list(value=data[i,],group=group,rep=repID,batch=batchID)
+ fit=try(cpglm(formulaForFitting,data=testData,link='identity'),silent=T)
+ if (class(fit)=='cpglm'&&fit$converged)
+ coefTable=try(coef(summary(fit),silent=T))
+ if (class(fit)=='cpglm'&&fit$converged&&class(coefTable)!='try-error') {
+ rowName=paste0("group",treatGroups[j])
+ if (rowName %in% rownames(coefTable))
+ report[i,(j-1)*6+7]=coefTable[rowName,4]
+ } else {
+ fit=try(glm(formulaForFitting,data=testData,family=gaussian(link='identity')),silent=T)
+ if (class(fit)=='glm'&&fit$converged) {
+ rowName=paste0("group",treatGroups[j])
+ coefTable=coef(summary(fit))
+ if (rowName %in% rownames(coefTable))
+ report[i,(j-1)*6+7]=coefTable[rowName,4]
+ }
+ }
+ } else if (statTest=='glm') {
+ testData=list(value=data[i,],group=group,rep=repID,batch=batchID)
+ fit=try(glm(formulaForFitting,data=testData,family=gaussian(link='identity')),silent=T)
+ if (class(fit)=='glm'&&fit$converged) {
+ rowName=paste0("group",treatGroups[j])
+ coefTable=coef(summary(fit))
+ if (rowName %in% rownames(coefTable))
+ report[i,(j-1)*6+7]=coefTable[rowName,4]
+ }
+ }
+
+ }
+ }
+ for (j in 1:(nGroups-1)) {
+ report[,(j-1)*6+8]=p.adjust(report[,(j-1)*6+7],method='fdr')
+ }
+ report=data.frame(report,data[,groupID==controlGroup,drop=F],stringsAsFactors=F,check.names=F)
+ for (j in 1:(nGroups-1))
+ report=data.frame(report,data[,groupID==treatGroups[j],drop=F],stringsAsFactors=F,check.names=F)
+ sheet = addWorksheet(wb, paste0(controlGroup,'_vs_Others'))
+ if (dataFormat=='table with feature description') {
+ writeData(wb,sheet,data.frame(Description=featureDescription,report),rowNames=T,keepNA=F,stringsAsFactors=F,check.names=F)
+ writeData(wb,sheet,'Feature')
+ freezePane(wb,sheet,firstActiveRow=2,firstActiveCol=3)
+ } else {
+ writeData(wb,sheet,report,rowNames=T,keepNA=F)
+ writeData(wb,sheet,'Feature')
+ freezePane(wb,sheet,firstActiveRow=2,firstActiveCol=2)
+ }
+ addStyle(wb,sheet,style2,rows=1,cols=1:200)
+ for (j in 1:(nGroups-1)) {
+ expressed1=groupExpressed[,controlGroup]
+ expressed2=groupExpressed[,treatGroups[j]]
+ fc=report[,(j-1)*6+6]
+ rfc=1/fc
+ pRaw=report[,(j-1)*6+7]
+ p=report[,(j-1)*6+8]
+ for (pc in pCutoffs)
+ for (fcc in fcCutoffs)
+ comparisonsSummary=rbind(comparisonsSummary,c(controlGroup,treatGroups[j],sum(expressed1&!expressed2),sum(!expressed1&expressed2),sum(!expressed1&!expressed2),sum(expressed1&expressed2),sum(!is.na(p)&fc>fcc&p<pc),sum(!is.na(p)&rfc>fcc&p<pc),sum(!is.na(p)&(fc>fcc|rfc>fcc)&p<pc),pc,fcc))
+ if (grepl('raw',volcanoP))
+ tempData=data.frame(log2(fc),-log(pRaw,10),F)
+ else
+ tempData=data.frame(log2(fc),-log(p,10),F)
+ rownames(tempData)=rownames(report)
+ tempData=tempData[rowSums(is.na(tempData))==0,]
+ colnames(tempData)=c('x','y','Selected')
+ options(warn=-1)
+ if (visual) {
+ plotType='volcano'
+ plotTitle=paste0('Volcano Plot of ',controlGroup,' vs. ',treatGroups[j])
+ if (nSelectedFeatures>0)
+ tempData[,'Selected']=rownames(tempData) %in% interestedFeatures
+ else
+ tempData$Selected=F
+ p1=ggplot(data.frame(tempData),aes(x=x,y=y,color=Selected))+scale_color_manual(values=c('black','green'))+geom_point()+geom_text(aes(label=ifelse(Selected,rownames(tempData),'')),hjust=0, vjust=0)+xlab('Log2(FC)')+ylab(paste0('-Log10(',volcanoP,')'))+ggtitle(plotTitle)+theme(legend.position="none")
+ for (pc in pCutoffs) {
+ p1=p1+geom_hline(yintercept=-log(pc,10),linetype="dashed",color = "green",size=0.2)
+ for (fcc in fcCutoffs) {
+ p1=p1+geom_vline(xintercept=log2(fcc),linetype="dashed",color = "green",size=0.2)
+ p1=p1+geom_vline(xintercept=-log2(fcc),linetype="dashed",color = "green",size=0.2)
+ }
+ }
+ plotFile=file.path(figuresPath,paste0(controlGroup,'_vs_',treatGroups[j],'_',plotType,'.ps'))
+ plotFile2=file.path(figuresPath,paste0(controlGroup,'_vs_',treatGroups[j],'_',plotType,'.png'))
+ ggsave(plotFile,plot=p1,width=7,height=7,units='in')
+ ggsave(plotFile2,plot=p1,width=7,height=7,units='in')
+ insertImage(wb,'Visual',plotFile2, width=7, height=7, unit='in',startRow=plotID*45+1,startCol=(j-1)*13+1)
+ }
+ }
+ if (visual)
+ plotID=plotID+1
+ options(warn=0)
+ if (nSelectedFeatures>0) {
+ if (dataFormat=='table with feature description') {
+ report=data.frame(Description=featureDescription,report,stringsAsFactors=F,check.names=F)[selectedFeatures,]
+ writeData(wb,'Selected_Features',report,rowNames=T,startRow=(k+2)*(nSelectedFeatures+3)+1,keepNA=F)
+ writeData(wb,'Selected_Features','Feature',startRow=(k+2)*(nSelectedFeatures+3)+1)
+ addStyle(wb,'Selected_Features',style2,rows=(k+2)*(nSelectedFeatures+3)+1,cols=1:200)
+ } else {
+ report=report[selectedFeatures,]
+ writeData(wb,'Selected_Features',report,rowNames=T,startRow=(k+2)*(nSelectedFeatures+3)+1,keepNA=F)
+ writeData(wb,'Selected_Features','Feature',startRow=(k+2)*(nSelectedFeatures+3)+1)
+ addStyle(wb,'Selected_Features',style2,rows=(k+2)*(nSelectedFeatures+3)+1,cols=1:200)
+ }
+ }
+ }
+}
+#############################End Sheet "Control_vs_Others"#############################
+
+#############################Begin Sheet "DE_Comparisons"#############################
+message ('Writing the differential expression results for groups ...')
+nComparisons=dim(dePairs)[1]
+if (nComparisons>0) {
+ for (k in 1:nComparisons) {
+ report=matrix(NA,nrow=nFeatures,ncol=8)
+ controlGroup=dePairs[k,1]
+ treatGroup=dePairs[k,2]
+ group=C(groupID,base=which(rownames(contrasts(groupID))==controlGroup))
+ colnames(contrasts(group))=rownames(contrasts(group))[rownames(contrasts(group))!=controlGroup]
+ if (logTransform)
+ tempStr=c('_log2_Mean','_log2_Adjusted_Mean')
+ else
+ tempStr=c('_Mean','_Adjusted_Mean')
+ colnames(report)=c(paste0(controlGroup,tempStr),paste0(treatGroup,tempStr),paste0(paste0(controlGroup,'_vs_',treatGroup),c('_log2_FC','_FC','_p_Value','_Adjusted_p')))
+ rownames(report)=rownames(data)
+ for (i in 1:nFeatures) {
+ group1=data[i,groupID==controlGroup]
+ if (sum(!is.na(group1))==0)
+ group1=rep(groupMeanLowerBound,length(group1))
+ if(sum(!is.na(group1))==1)
+ sd1=0
+ else
+ sd1=sd(group1,na.rm=T)
+ report[i,1]=mean(group1,na.rm=T)
+ report[i,2]=report[i,1]
+ if (report[i,2]<groupMeanLowerBound)
+ report[i,2]=groupMeanLowerBound
+ group2=data[i,groupID==treatGroup]
+ if (sum(!is.na(group2))==0)
+ group2=rep(groupMeanLowerBound,length(group2))
+ if(sum(!is.na(group2))==1)
+ sd2=0
+ else
+ sd2=sd(group2,na.rm=T)
+ report[i,3]=mean(group2,na.rm=T)
+ report[i,4]=report[i,3]
+ if (report[i,4]<groupMeanLowerBound)
+ report[i,4]=groupMeanLowerBound
+ if (logTransform)
+ report[i,5]=report[i,4]-report[i,2]
+ else
+ report[i,5]=log2(report[i,4]/report[i,2])
+ report[i,6]=2^report[i,5]
+ if ((groupExpressed[i,controlGroup] || groupExpressed[i,treatGroup]))
+ if (statTest=='t-test') {
+ if (sd1==0&&sd2!=0)
+ report[i,7]=t.test(group2,mu=mean(group1,na.rm=T))$p.value
+ else if (sd1!=0&&sd2==0)
+ report[i,7]=t.test(group1,mu=mean(group2,na.rm=T))$p.value
+ else if (sd1!=0&&sd2!=0)
+ report[i,7]=t.test(group1,group2)$p.value
+ } else if (statTest=='cpglm-glm') {
+ testData=list(value=data[i,],group=group,rep=repID,batch=batchID)
+ fit=try(cpglm(formulaForFitting,data=testData,link='identity'),silent=T)
+ if (class(fit)=='cpglm'&&fit$converged)
+ coefTable=try(coef(summary(fit)),silent=T)
+ if (class(fit)=='cpglm'&&fit$converged&&class(coefTable)!='try-error') {
+ rowName=paste0("group",treatGroup)
+ if (rowName %in% rownames(coefTable))
+ report[i,7]=coefTable[rowName,4]
+ } else {
+ fit=try(glm(formulaForFitting,data=testData,family=gaussian(link='identity')),silent=T)
+ if (class(fit)=='glm'&&fit$converged) {
+ rowName=paste0("group",treatGroup)
+ coefTable=coef(summary(fit))
+ if (exists('coefTable')&&rowName %in% rownames(coefTable))
+ report[i,7]=coefTable[rowName,4]
+ }
+ }
+ } else if (statTest=='glm') {
+ testData=list(value=data[i,],group=group,rep=repID,batch=batchID)
+ fit=try(glm(formulaForFitting,data=testData,family=gaussian(link='identity')),silent=T)
+ if (class(fit)=='glm'&&fit$converged) {
+ rowName=paste0("group",treatGroup)
+ coefTable=coef(summary(fit))
+ if (rowName %in% rownames(coefTable))
+ report[i,7]=coefTable[rowName,4]
+ }
+ }
+ }
+ report[,8]=p.adjust(report[,7],method='fdr')
+ report=data.frame(report,data[,groupID==controlGroup,drop=F],data[,groupID==treatGroup,drop=F],stringsAsFactors=F,check.names=F)
+ sheet = addWorksheet(wb, paste0(controlGroup,'_vs_',treatGroup))
+ if (dataFormat=='table with feature description') {
+ writeData(wb,sheet,data.frame(Description=featureDescription,report,stringsAsFactors=F,check.names=F),rowNames=T,keepNA=F)
+ writeData(wb,sheet,'Feature')
+ freezePane(wb,sheet,firstActiveRow=2,firstActiveCol=3)
+ } else {
+ writeData(wb,sheet,report,rowNames=T,keepNA=F)
+ writeData(wb,sheet,'Feature')
+ freezePane(wb,sheet,firstActiveRow=2,firstActiveCol=2)
+ }
+ addStyle(wb,sheet,style2,rows=1,cols=1:100)
+ expressed1=groupExpressed[,controlGroup]
+ expressed2=groupExpressed[,treatGroup]
+ fc=report[,6]
+ rfc=1/fc
+ pRaw=report[,7]
+ p=report[,8]
+ for (pc in pCutoffs)
+ for (fcc in fcCutoffs)
+ comparisonsSummary=rbind(comparisonsSummary,c(controlGroup,treatGroup,sum(expressed1&!expressed2),sum(!expressed1&expressed2),sum(!expressed1&!expressed2),sum(expressed1&expressed2),sum(!is.na(p)&fc>fcc&p<pc),sum(!is.na(p)&rfc>fcc&p<pc),sum(!is.na(p)&(fc>fcc|rfc>fcc)&p<pc),pc,fcc))
+ if (grepl('raw',volcanoP))
+ tempData=data.frame(log2(fc),-log(pRaw,10),F)
+ else
+ tempData=data.frame(log2(fc),-log(p,10),F)
+ rownames(tempData)=rownames(report)
+ tempData=tempData[rowSums(is.na(tempData))==0,]
+ colnames(tempData)=c('x','y','Selected')
+ options(warn=-1)
+ if (visual) {
+ plotType='volcano'
+ plotTitle=paste0('Volcano Plot of ',controlGroup,' vs. ',treatGroup)
+ if (nSelectedFeatures>0)
+ tempData[,'Selected']=rownames(tempData) %in% interestedFeatures
+ else
+ tempData$Selected=F
+ p1=ggplot(data.frame(tempData),aes(x=x,y=y,color=Selected))+scale_color_manual(values=c('black','green'))+geom_point()+geom_text(aes(label=ifelse(Selected,rownames(tempData),'')),hjust=0, vjust=0)+xlab('Log2(FC)')+ylab(paste0('-Log10(',volcanoP,')'))+ggtitle(plotTitle)+theme(legend.position="none")
+ for (pc in pCutoffs) {
+ p1=p1+geom_hline(yintercept=-log(pc,10),linetype="dashed",color = "green",size=0.2)
+ for (fcc in fcCutoffs) {
+ p1=p1+geom_vline(xintercept=log2(fcc),linetype="dashed",color = "green",size=0.2)
+ p1=p1+geom_vline(xintercept=-log2(fcc),linetype="dashed",color = "green",size=0.2)
+ }
+ }
+ plotFile=file.path(figuresPath,paste0(controlGroup,'_vs_',treatGroup,'_',plotType,'.ps'))
+ plotFile2=file.path(figuresPath,paste0(controlGroup,'_vs_',treatGroup,'_',plotType,'.png'))
+ ggsave(plotFile,plot=p1,width=7,height=7,units='in')
+ ggsave(plotFile2,plot=p1,width=7,height=7,units='in')
+ insertImage(wb,'Visual',plotFile2, width=7, height=7, unit='in',startRow=plotID*45+1,startCol=(k-1)*13+1)
+ }
+ if (nSelectedFeatures>0) {
+ if (dataFormat=='table with feature description') {
+ report=data.frame(Description=featureDescription,report,stringsAsFactors=F,check.names=F)[selectedFeatures,]
+ writeData(wb,'Selected_Features',report,rowNames=T,startRow=(k+2+nControls)*(nSelectedFeatures+3)+1,keepNA=F)
+ writeData(wb,'Selected_Features','Feature',startRow=(k+2+nControls)*(nSelectedFeatures+3)+1)
+ addStyle(wb,'Selected_Features',style2,rows=(k+2+nControls)*(nSelectedFeatures+3)+1,cols=1:200)
+ } else {
+ report=report[selectedFeatures,]
+ writeData(wb,'Selected_Features',report,rowNames=T,startRow=(k+2+nControls)*(nSelectedFeatures+3)+1,keepNA=F)
+ writeData(wb,'Selected_Features','Feature',startRow=(k+2+nControls)*(nSelectedFeatures+3)+1)
+ addStyle(wb,'Selected_Features',style2,rows=(k+2+nControls)*(nSelectedFeatures+3)+1,cols=1:200)
+ }
+ }
+ }
+ if (visual)
+ plotID=plotID-1
+ options(warn=0)
+}
+#############################End Sheet "DE_Comparisons"#############################
+
+#############################Begin Sheet "Overall" (Part 2)#############################
+if (dim(comparisonsSummary)[1]>1) {
+ comparisonsSummary=as.data.frame(comparisonsSummary[order(comparisonsSummary[,10],comparisonsSummary[,11],comparisonsSummary[,1],comparisonsSummary[,2]),])
+ comparisonsSummary[,3:11]=apply(comparisonsSummary[,3:11],2,as.numeric)
+}
+if (dim(comparisonsSummary)[1]>0) {
+ writeData(wb,'Overall',comparisonsSummary,startCol=14,rowNames=F,borders='surrounding')
+ writeComment(wb,'Overall',14,1,createComment('Control group name',visible=F))
+ writeComment(wb,'Overall',15,1,createComment('Treatment group name',visible=F))
+ writeComment(wb,'Overall',16,1,createComment('Number of features that are expressed only by control',visible=F))
+ writeComment(wb,'Overall',17,1,createComment('Number of features that are expressed only by treatment',visible=F))
+ writeComment(wb,'Overall',18,1,createComment('Number of features that are expressed by neither groups',visible=F))
+ writeComment(wb,'Overall',19,1,createComment('Number of features that are expressed by both groups',visible=F))
+ writeComment(wb,'Overall',20,1,createComment('Number of features that are significantly up-regulated by treatment',visible=F))
+ writeComment(wb,'Overall',21,1,createComment('Number of features that are significantly down-regulated by treatment',visible=F))
+ writeComment(wb,'Overall',22,1,createComment('Number of features that are significantly up- or down-regulated by treatment',visible=F))
+}
+#############################End Sheet "Overall" (Part 2)#############################
+
+saveWorkbook(wb,outputFile,overwrite=F)
+unlink('Rplots.pdf')
+
+#############################End Main#############################
+message('Analysis completed.')
+message('###################################################################')
+
diff --git a/V1.7.1/ReadMe.txt b/V1.7.1/ReadMe.txt
new file mode 100755
index 0000000..9aa68e0
--- /dev/null
+++ b/V1.7.1/ReadMe.txt
@@ -0,0 +1,25 @@
+To run this analysis tool, first make a copy of the data template, read the instructions in the template and make necessary changes.
+
+To run the tool on BioHPC @ UTSW: Open a BioHPC web visualization node and run the following from command line:
+
+sh /project/CRI/shared/Metabolomics/run_analysis.sh input_path/input_file_name.xlsm output_path/output_file_name.xlsx optional_BioHPC_queue_name #input_file is the copy of the data template with your data. output_file is the analysis result.
+
+#########################################################################
+Change Log:
+#v1.7.1, 12/04/2019: Figures were saved as both .ps and .png files. Euler plot was disabled for >6 samples. Samples / features can be clustered or not on the heatmaps.
+#v1.7.0, 10/22/2019: Figures were saved as .ps files in a Figure folder. Correlation plot was genrated with the old method for better speed. Euler plot was disabled for >10 samples. Y axis of volcano plot could be raw or adjusted p-values. DESeq was renamed RLE as I would implement the DESeq differential expression analysis in the future. RLE is the same method used by DESeq but I slightly modified it to allow for zeros, missing values and / or values below the value filter. Due to this reason I couldn't use the DESeq normalization directly.
+#v1.6.9, 10/4/2019: A bug regarding cpglm was fixed. The bug caused an error in cpglm fitting but the class(fit) was still 'cpglm' and fit$converged was still TRUE. Visualization option was added. Version number checkup was added for the data template. Multiple other minor improvements.
+#v1.6.8, 09/19/2019: A bug regarding featureDescription was fixed. The bug was present only when data format was "table with feature description" and samples were excluded which happened to cause some features #v1.6.7, 08/29/2019: Correlation plot was enchanced. Euler diagram was added. Software version number was reported in the result file.
+#v1.6.6, 08/28/2019: Data table with feature description column was enabled, minor bugs fixed, and volcano plot colors customized.
+#v1.6.5, 08/27/2019: A bug regarding number of features per sample when data imputation = "NA" was fixed.
+#v1.6.4, 08/08/2019: A bug regarding groupMeanLowerBound was fixed. Definition and default value of groupCountFilter were modified. Data imputation was implemented. Missing values were allowed.
+#v1.6.3, 08/05/2019: a bug regarding nSamples and formula string generation (only when technical replicates are present) was fixed.
+#v1.6.2, 07/24/2019: a bug regarding normalization size factors was fixed. The bug caused size factors to be applied to incorrect samples. If you have used versions before this fix, please rerun your analysis using this fixed version.
+#v1.6.1, 07/03/2019: a bug regarding numerical batch IDs was fixed.
+#v1.6, 06/24/2019: Multiple normalization methods; visualization plots; sample exclusion; feature exclusion; cpglm and glm for statistical tests;
+#v1.5, 03/13/2019: If logTransform enabled, data=log2(data+logOffset) before statistical tests; p-values are FDR-adjusted; Bugs fixed; normalization enabled;
+#v1.4, 01/18/2019: Added functionality. (1) Averaging technical replicates. (2) Auto-IDs.
+#v1.1-1.3: Bugs fixed.
+#v1.0: Basic analysis including counting features and t-tests.
+#########################################################################
+
diff --git a/V1.7.1/data_template_1.7.1.xlsm b/V1.7.1/data_template_1.7.1.xlsm
new file mode 100755
index 0000000000000000000000000000000000000000..67dd2c1d744578a2f2ad102812218d7fbe42a7af
GIT binary patch
literal 96603
zcmeFa2V9fc(mx)gNmr?YfP#S15fG)S6qRnFN>@RpN^c1wO(~)xO`3>^f<O=vsiBB~
zfGCJW5F%Y5bVz`Z<o`Smmc4g(fA8MAdwutRU;LOcKIN1-bLPy<`JRcP4kZ;Ej0UzD
z27~Q_-NWc2Mk!#hR(cqW6}EZP8Fg0|&+{&x)~Eg4&U;u&`Z_!9yic`h&pp^C;Q5U|
z|A#ryn$YLMEwd$TMr?`KxH|uX@I@XYb{)yPi%(<jEK)@K*jTQK#O|8K@y>)l%wIlJ
zbh3;hIC&v$Zg6fprXYW>@s<+-CPiYZcGL$(9H&Ov33H1jYcJ__JGwf2Y1w;WPkWnt
z6^3&3VB(K@Y#Z+0sdLz6C~qU=&R_HHb?Z?vR(Q0>)(7TI2d_=O`=YgHX4{L?51-Tu
z4=E)zXCH4M-mRWa)h#*OkT~tL-AHEp)py#$`$UZkNVF$cKkAA-sxmjfJ<27}#LaW%
z923h4RvFbJ+V8pc6~%3@wNd+`W0ER*<}CWlm6=CNDxvK8sh#Xy7|j)36)eTMMMO<h
z(ad6HRzarkO&NGx!hVw{F}}FVGE0bBmZ;&XQqwx~;DIR`4>6-fyuLou+q>^wDyV6X
zISWPNrY5L{G33Yfu}VH2^!UZg@Lj6?*4JWLI0uJ@!j@mG7^rTGG1@XUxpFi+{-C?G
zv@_%9nm+IQjZHDphHApx=!IIQwa4(65%c>of(I5a^h~5*<M$U>ddZ@4&3`Z=T}@=r
z<kZR*qJiOc{@7if?B4G}PzfU~e%)P1$3sIYPr@SKl0BU}wF)FD=??31BgSGvG_(yI
z_#K+&o`jc6osp373)o*B_mP6rnImltnBQbFCCu<2)B~$k_4!SJdcXj8836TQ?S9_L
zLrM~)8*1VIurByT*Pq<h)1j2v5<YX4ysNcJsaI5>!%0f>i{3dRg-kNhMj`p;gK{EX
z>#%3$b3XsnLZ5WM<R$y3S-x3Y0=%~+XwqFxwePGjuG1mDctN{0&Rr`8d#o;4z$te!
zZ%H;rbR-kKq#F5T@P4SI5cvw1*HPn!t14v{7Y~@myK|W@YO~}DS;UX<hGV!5+vFPF
z9NVnqL3v+wxpJj)zk*)j#TI|p=4?T+n-4YK+%#Lh`SHlag8Y5s%<SE@NW|;$Yp)3c
zXDRta%!wIaW*pz0JgFrVc9$}3bOn2W`dQ@zO%K+fPsv(=W73uTSaM8Vq?+&JWr|eJ
zzA<T6*!HIX=_<g5Ur!m?YVO2OlrY!{Iv9)vc*567%E#5+(azP?5nN3l7(2RV9bh9?
zcaUf&(hAvH#ppzN!?wu9+k5Bg=ENJvi_+>Ay>@iY$;=%^&B%w|c~G3^B65jhC?Ixb
z=u=!O+^OYAzyk|pIsFz@`CD3f5A@Bv`0ve_6sc<7z4<`<_z4raLk6es#g3uK-p}(_
z>dOoG<zSCAuDI{z!ANV|?!0}ivpka>drn^JdZ-XeL8E@}aZUZl;tcis^xF5(<r&6)
zUMRJBZ}w^%cUSYn2l3d$2PCeA&T78lmbn+-eCD%u9smBA^!Mi}w>TP0ue=Wy$k$zq
zBIJ&qvpm!z;a!qYpFvAThepY#Qk^NhA!=6Vc*0?x?Ou%P7n7B<oi)q%sHP^lE18;Q
z4#3alMi3soz`uD^sgi)-6y9d7Revb|hL|ebOxlHM)%^@<8Rwte>sIlS?~n_+ebOuU
zkon<Ii-)=$a)($=Kb-NOz3O_`t)3oTf^?>+51xJI6O?}7RKT`3dVL;(1{ohe9EqSc
zDXo8eQL_GmOwkkmrUMw38X^8N>k-{gSYh?;6j7sL4=M$2OHOESy6B_1Y;@Hd9aR~A
z`K|Hd4AT(X!!1pl!-B<*2R`LU)EMmv=cHI|K^a{>Z^&?&z-}reAkbVX&=7ZT&0E8M
z-rt=>n|Azu$Nrr&s?SL)FKD(1;Avl5X&2NkXfAH=!Nn*aVGR}?cX=gsPX}F{w{3c;
zVyxj)^qgn;d&?c=<!2jAGX}%5kB+{K99(HFNnK$%79M~&85h&)vu5~~!hP1@dG$?i
zbnQcw?YItC`{;C59^u`2^kmb$JF|D+<@*kVnO~Y>%Iomw*~xq6=KIb8_g?3I<mu5v
znjUr`$H~*I=kW(MqGfQOvv*I%cf5d=35(0V$~>uktaj7I-M6r}tZvC)c|;`<uX%;}
ze(dU(v&&y5+_zN8(4#sXj%8Ii8Y!F-dpjj1XPi@48mwsIMLir$nAnN9W!JSt$@$cX
ziYHn9UVz6WdhNE`0XiCLs=y%F?qWPLR)s^m_#o>&f*@zW@ZgS$mv@?&qMMugw|}f?
z#9@woWTPFOZu&HNP4$jg_V&CBxf)cF3g%PVzTF%~e*T=Y*)+NP90eZTei+JTDWa4v
zDB*BXyGubv(RxWpd1cEgf1T{v(3Kfl@iWcA%#AfW=_}6ZlT0Os;sl?`?AKt~C8XW;
z`Gcl>{X_XvA3KZ@98W7ka;5K?e$iL&OgAfM9WwQqZGB(=*5lB=v%P7bFBEy6m5-fJ
zxQY>diB0QJlUJO{47<0pug9c2vxtyr=K4I37#z5gcP#tq0!gZVxqNcgpk3x3+fHZo
z!_foXZ&%cQQxUW^N{=Z6YbTBx2IGcprT`Sd`ttcpb?|K|r2s5!;MxD%e=6_jxr9h>
z3B$+x)3y-ocG3|~=<YYl#^xsQ)yPo3py2uFV(wxc^(gGv`@Ws~kI8b#W@Ie&d*i#_
zf1&ExEVTE=)h&WIEk=$cu%XTyDEM$~Z#nnol2Gs<Gh=^tw@EMOqhv3&WbJ1(WqUge
zEzEjVx7hhoGsy;DI$R!iWIWu=Wa9R-Xnr%g-OTl&U*;H7ty(n*sdiQEeWWLjeWPTD
zy!%^r@UfFG9O}8w{t{i_&3R~hr;ur6yVqwfnPe-}T@PYT$I(+1=-|(#=Z^(Fov;1$
zhVE=1Pc5t?@c~&&HQd+supPhdGwEoP<a4%3FLPS$ZIfc~$rib~cVBF^5X++HaqGHS
z9F!9nponzH?+=bmm0i3Vax-YfrwYmJn8A!Mwl`~#8!j}ic*J+K=LL~cU9v-wNb_5Z
z5R?6yb2}9bma_#0;{t^0>muZF;rw||4=Ly$8E`d9HMR7}+Rp}`(Ir#X2QY35QG12c
z-+zNuq%SSar4%k~`dpsk<>rI>J>x+$@l;Kl-W~rCXR45xmnAs<De6u`PPej$m2}=_
zi3CaRm4c9{JJZK5o0hAQ&=z`@IfKWGboQBl&L)J5;X5a~tvMWCr{@X2`a}_W-@N?j
zo0hUMqw%d{$K~$}Nq%llf2uq%E289U)a>>&E^WS*Ay5rBAIes6B&is0^Ol(Yz*n#H
z)D4D$LtJmRF|Xwb?#-i-Fu+hIieOhxTV=~hQ4C%`7m7M=*xlT@f-^KsP(Cj`V<M-2
zaxA{q0EV)ajb6lSy*~c>f+jwYswsW&QwFIu#qW5Jz|t65l1mQ9h1g|BDSvv_<Bs8b
zJ8Ew6wd2Sa)T}KHqashwl#`d2#^}v=SFeewZL{Xg2<q!PGBM2KaVUD(@pe65Ag6nj
zrE3X#D;h3yHe!I-QKhvoDHS_ls(ibTw0qgY5`DvPk~O=1u$IGgS>XL-gUbVR+qb;^
zB015Rw^*sCLyg%=9z2+t#lYC+fu>!S0u*r_jIXY`iiikA98zq~b863>@sx|Xe_1(A
zLm;s~G<D}56{FNqdF9+-HmWC8p;w-VZ+<8!ha37_wHf(&8$}N{#Y&b9cjgx&*AO>l
z4-I9^tHW~10wEFRn;Ld3H($<lUZu*qFc@K<NKOf~P}eoT_VH-4dfRRBd(Nhz{O2eL
z`QGn1J&2#R{I$28w3-YR$)nD4pq=M=UM0$ULgL+yU0eJ_E4%BeJSp!n+|R8cK8>_z
zn4#W%w@%8_W$U4B`>6&JHXr$rIJXmarqWG)t>Ma9m3zCF%ILQCikbF@p3JJVa9l7>
z;c@Q2xL}oX##!~k!il>y7Y$Qmp1Gww5JXuDX_nJnJl=A5XGVUBUtU|x{+VZfg>Cu!
z`Jb)q(F=KYOs}P%-p3(3hqog)vPu5KsCi0h&!-Cu`YHIHPnQ-Br^I&1eK~UX;}`nU
z;|f~5<$A|VF5N`#*BF@INyGb6_(9a3H+%^~?9<G4CbW9TIJb86gzx!uWwTz&mARMc
zS2pV(`>=z9l-<iIJ6u+!p0<>DHasot7QEqf#lj&<uS2UNz1k+XFLOSV<bb~owhLbE
z5}0tY<>@@Dc(b+Xc3a=e3upcBQ8v>^ofC=?CaYQdGZM3cscw?=<Hwy_4C!|Y(t5&Y
z#tuCwe*NJ51uH>Qxk7xGhO}J4)=M$guO+yB8@aZ*ZrzkU+mfiYgJmbz-R{lW^T-AB
zH1!>f!H!hfeTXF{#;FNkAxo}OUKuX`V(uedo3(Rj#xl)#a^G*(zLU7)h-G1GX@#X)
zu%;U2$nhNlo_-xW9#%PVcD>;^IH?)#t#YhXx$~JL5A!3%XOoA^l{S|nlg79z_>N@L
zo^|$mes{BXSSUO!lrou`ld(7QxQ_#erBPm9xFW*`PM+77MP2gs!(@?&tZ~`?zR%*U
zIqF=;{Dat%$0O>Dg=yD>Y-vxP)(^)cmr2JgB$CSSJW@L#A=Jt`;?{C=bgLwWOW&2W
z=Lm_bVakDK#K|DIy{6v3btWl8+rhxJ0Ywp3n6q{q=f?Ybv&E|s-oWvMJC_r-Oe^Nl
zyOzI`E*1<W>+MoKlStK{q}s$lqO3jcKd_Y@MdYlX`0WMQa1%V6wHSzUc~JkJ65IzV
z!L&23ybj*Z7|LO_&Hk=YMw%yX+bZ4jLSYJBFD1J$IA0InU0Shq80GLpv?JH@tmMO6
zAs&hc)uY7S5gu-?`J%ZfLSYbMdFhkd4OKWIXl;(LP_Ga$*3JtL^jYg9#0C*aYYPqv
zB=?oIvUcl!#M<~&VIzNRwHmoFt9^_-89`o)ogp0{E#lizJ9yy-8y%LHR|pHJz?J#l
zQclT01is~6L}>=<AZhB0V9@-+Tn>vXYL-k~EM>?DB6u$1a#liA*Zgfi^(wuX9hf4-
zjwQCwPUQrRdF&vsEf9Q;cHh7dNdsSo5=Y_!6<3$0IIBtRs7mtG!ngxrb{ezd$={Dy
zCCs3RBYkrbm}RygIC+vFMZT*xzT&~(4-X)?^2e%G5hn`+G2|UMOtlie-CzxKi>#1l
zWj|Wp5rLmk<qc9Lt+Xfvjxw}#V_nIw!^AlIs|gFTD+F?>Y8x_Wp!1zte>Vk!czTFD
zqr)vpS}A)~nBlY<gm@aFre@Z#G~SEBkQa$7<bI_UwV;6@<|Y0BYx2-yJ85i<1=FVD
zw>ZAzyt4l+j?!WF*~I=-)NH+vTGiO%q>?Ir{L9!(J3ljqPqor><sRt*3CTGVOIog6
z#k3opA^Qie;a5X?1K9)Q{SOx_j6QdNmvIAuAP@?z(|3?BPktd{5Gz-?IVaWodC9NG
zM%3EjzSh--ai!!`@)-Y2(CRz`xtF9+PwKDrEqb<MBJc3Ro%E>!qZV|KG&_H8c$ifp
z<7(=dK~ejdB5?;Yj)gBXNM&_x<P~9c+nY5FLQcj-bY5OYprj;X{;@D=ExIryR`CGQ
zh52C%!CI{z$1})k<g3Hx5!&64@FUdwWZWPhI2y4csz$~=M+h)tJ}8ug;@h&LQpYQ}
z4?MX(({5mK)pbI4nP1JKhdf=UHW8QJy5_sDIMj;Y%V_v+lh-XC0R>H$>z_Ev1l{qW
zt+uV0Zk1aE0TvCuNabyQ;`18AkDFv~jR{C;SVdND^OKm@8lGKDLeY5o`sa#y`ac0a
z1;7Uhe4YZIXTaw<@Oc4z%Eg9<R2EnGR<E$Y>mrnn22$veJhL^_dgJe+%iS*zy>4!O
z&3?;Kpj*@B+9$R$A@?)GH=6obpSo)fS2t1KiV%p^;EPh;>L)s{J6zVpa4S)uP{S&!
za_d25xz%@Ha^H=Y6<E%_A8&@}M~IGtR7H?~Hq&6)ZS0zl#em97@_@FUljTA53-^7p
zD5IpHOV;dV!tTUSi@VlklU-lJ?Oh^2Z7Y*-KRP^3XI3WQZao~_q<-tDK&)o^HDz``
z;dzVUuqK^bCIW?;{nvKzaT6~9-Ja}bH%Q~du6i|`$~jtr0NXK8U~Qs2GNe*Dw)YqH
z=XdqQcPdzgq14nQT@l@d>Ps$OONyR~*1m%8M+OWsy$hNG8W*?IH;P%kUI;4_2pV5o
z{V;p7+K$*D7uXgy#6FqkmL#=fjFu#ts%kU3A~>OE_~Iq9@*sT~%hK6>@R##LvT%)f
zA^6La_ZPS7Z4ZCWv}Pn?eRf>ET+3rkJ%ChS4y*4*af?$QOEk^EPf+$O6rg;F$?t)0
zlw<eDDeZT(SCHH}3Z#eDC?<zUt3okT7h@+ot>A|O&_k<86aKg9OKY56ovQh&_f#fU
z&)BU#?2Y4FINp9B=Z=BDlYxJ$h{}*}*xroA=mpy0xODTPNvYml+1tm&fdBNw6`)je
z#GZ+$<mZ$8J7nKjpHv-xtTGNg%|3s;JC?jA>-QQeffxwf=f7!C&ar((C$~iBKm=4q
zzpH`28dx~+_)Mw(%vfCd<?17<<3wBz9-%t^^_d`l>-Ba-tvql@Roj`d*Z|yb0E;wf
z5`h;5VG;~h?E*SD!G;GNUwI()Tm&uy6kAreHtjo>HUvI;!@A=zSRxDX##=@BO#}Z|
zEA%T5Sj=-+=7HC&cV#Rd4(Ol-Ybd;FuzF8rJmgyiz~f+_hS()XfcBBD1a>5v=SYFy
z!M5t;cZ$F6WOxmA!g_){9dEFDGN2<7EEQ@@i3t4Q`s-l#px1ljs#Cu<VA5C{G3f?A
z05ZX2D6ax2vt{bI^&5b3YeFM&)t}NA<G~J?ytoa%Lpguk`l7+A_E+{)cdXxrSdpzV
zF7~Yl+9<VV5%@?nwYKVnZK3bl{qU+lG=r<^Zhi&4EFo{0k{`D!^x8^w-gdN&Gl2z;
zp6uQeu!4?ARxr89u{VGye7?{uyS1`RyIA+*q(P8F(DNQt>U8^>zFQLKc`Ge(<se%n
zo@Yx&tDXFJJ2U(|;zt+T(V0`??><<Y2CXhU7l#vw9QkLm+A3G9?soF8>Y0~nr}$L^
z#qq2<_q=`5$<c3-eHn#$#MGwkJkVwY6q4xN79ls?KKXo9of|(Ec4lE$41wt4oK$)t
z^DNQZ`N9WBhIVVyVjG@Sre5ipjylc{Gw5*p=I67c433{>@J{N*7tZ*C4Ix<eO1pM-
zlwv9{PkO@<nEkCOC7$v9MEjBbi%JUVL9bKrh{^q}gp%2?rGU~*vq}l&jF!r?Iws@R
z_>_^}x2~9QzxITJ4)U-ohnqSom}k`TOu06>93wsuj$lw|B|P_xCwZaFUNRAdT`m}3
zW@tZp(6tX}kLyslJ#XpksA!u32_dM^T2Z<fwv>Yr9|=c@0k4$;uPvrDd~z#KUa%a<
zG;5Pk7Wp7P7LHI-XbpHVTeV0i%QSl{QMpogH)D->!{tJPCxdH2$J+1=-c!B!5!5-r
z9O+_ed+GwR48KOKcDcae&CqUg(b!uZRm?LwWwA#WRl;+(q^~YT>IuN#PfTqS(8E4(
zBF-fV?>XK()91?JtB$G#x@8G;D-LBg$VB9Dz3{=8q5XOrPX)l0l*&xA5eZ=QJywbP
zfjfR+J5mv-cRZtgma8~Ir)z@0I;x9jln9g%wAyCp`b8l?y}0L$uf9|NV3^bs%rsM*
zzbiDZGYWy~#e-i*MgsHp$qG31D+B~;oM$x6ioQ$*j!4e&^L^O26w_AVIxrWoeJm{<
zg$z^Rzio|gM06|cuc!`FFljC%oJCFYq*!9=Qu2noiA9+8=i7|tCrRj;)mQf2rL%$Y
z_T36NmKLZaP^xBp;6s(247`&*@Uz8}XM7jJ&f^<N*UO4NE`j}9*kkmy%mt%z+Oapj
z?gas!(@z}6<_lbvhsMjM=gN{5Fgm1WHC#n?Q9x(`P-&mKC$Yd4%a^4zbg&$#E`4sd
zC4P@3S-cO8UFAquckzb*XsFyY9(eI<WpSwQZ3aiUz}3kc{&j@EHV>E}lRN}V3|JDt
zrGrG$!HTbM14B{l4fO$%@v$kW!+@-f<J&(${aq0S_=nA?_{t->9E@6k!W*Tk*>cQR
z_5d@*3jd>czdnFHP?7&sFK{E{70+0j)q&UF_-}1Gg!s<>U-s(z<pra=K*PV)BnnJk
zS$~lCVdLL<FaHZ}{6F6N&X~%vZw*|Z*FX63wOM0%asYdOa1|WyA3XX6AHQ|O<f8t!
zL6{Rg1xduNHYvk4rJ7Oo`EdiH2fVYgb0KyP6u2C<rS>Jg1$`;Y?5cVtc~A<iO%Z(=
ztCpDe(D(?UrVW5myabFQFa^Egr%;nA01wvX^D$;g50qy>J}as(_EYTVzx~TsE7X=M
zsviS_p=_P~aY^rMF~Br?0SwkdP?kqQ7Ot1RKKs5E4h-zqM*`!gIUU-U+dcwfm^N2Y
z+$YtZzSLVBLvUClIpG<e$+}h(UlC;y5zm1t9M(9T@s7_N*7%%@CsW=m>-9{VTm*yx
z>`OYU<1d^4qXtO6)*A{=7f_PEHm+?FkO!!{(7?a{abp}1d9Y{e`eUX?zs&;@^Hc<x
z@ch(*1;`+v2J)LJsOrKF?CL{OS@mN-h+EK{g+}J<n6L91m<zxdLW8hAh7fZhk)QB^
z|K{@~ANL%;98S`0Kq%K6&EVL-^gC>aMD1HKDnZ{zQf_ADE03W0__Ym?-2E8gf2qxL
zq6A1TBIL-7JqQ%G7vqPFMeSHyk@~RITTM)J#=L11N4=@X5GSmi3W5se8qX&MnN5pT
zaHC6V3TV9rktxA_^jL2jpG_4!=&G7&THH=#M6e1y_Ogw^rT}hqaZNfcP5_x0Tt$!d
zvT@y{q^v6~KOk*1Ani3E9X}vlG9cYGAiXpo%|0kCKPYW9DD5>U9X}{tGU#?HsxL^(
zee5`zw;>^D!p1~K#+_ZzSmq?V;AxqC?1Cp{wy_Hu%FMF~>dSPn3F^u`WmA`tVPpMm
zQgpI)O9SI6c5NAhhx_i>rR#d_X;4n4KN0D-&*(ne!Z}gRy`#-~68j_b$u31%Xm_QH
zKdU}W?986$YwpAXJG)^~ttWO5M718-ofFl1V7FUT>z>{4UaeHS2Ya=W?9S~Cx3k+L
z{5vE&X;<X-hoWHD&VyZ>Fa)~B4t6b91L)f6$dj#cDdtXWXIv$H*v=p%F8{3l;Z>9(
zCCI)lKdXP3?Y$?QRvN}E8(uaUMVmGpGm18BXgVEztl`+{=tB)nr=kxu96J>)-OzNB
zvEikm?(Yz78Fe;UT_cc{uR&IJWr9)yvGO{=%ExTkNg@g*Xrm_tSEG+=D-OGS*kmiX
zM+>OERggTveT-ORn^MXOR&-y@G_|)Hk~Ua{5qrWWmNH-)y0a#o8mElp46b6t8rc+5
z2C#BPuDoRl(v>!%XsC<G*>}%0N!RO8k#sVBu$}EbqfHHU=R~O^!yx(e!ejLLO8bqR
zR*ug4Oh@_MFqJ==YZof2_0Ud3RO`MSm8e#_-OIgNckM#=Y9;)>yzCJ+j@*795*kT2
zYjNF&`yr|dQ7wq-L)6&K+R4E^3%z<VY>B5~rJKXsMV1viZ?JVnRQS(`tL>#trRnwO
zCXVbKZBOn2S-b?Y_!7wC_YjNOv(f%w#m&{c4RxKoBB;UQyrj`<M$xAmHXB9jHe5R$
zt<kXgbhL8AwNue@4VzCzi#Po4cpB<HVUyK>2GtTA)c;3~rhU1pa4oN+GJW;JrhiiD
zNXuth0kxHb{VM_cH=z#p?;0pVtbhoe64NeP8rCW*H{xaQRB^h>E@&cS$1Z3jbA(;+
zl#Bqo;0c*c?1Ba|gKUC&GIeZ%I=`#ofE!|fhGPXaoFu3QoWX9cf)&eyYG49lY~)EN
zm9i60T#Sx3c(SC}H2k?KQvW^_><9zc5gxE&Rj}e1uwq3}wU*pxJ7eI&mUpJ>#9zcr
zup@xPG`|lAD`o*J76dES2P@9s)9@jVr(wd_>$EN&NoO2w(eP)aMOP2($lq(|^3ZQk
z%q9JM($^LYl}gjw3Z_#m8L<8~3Y#jnqw{NAXuU;{6~TQBScDDBrT{K<Rt+C5ZZ|R~
zScL)WXCt*KU^_am#)=jvEX}q+vEX_1%42%vzOBNiw)>U~>ONGQ^*oxS8}@nphIF!M
zZ6~kozWA#-N_E2zIRLKS2=r%HS}D{=8|jwLH2>#S>mFmD>@2(TB0c1vTo?gO<H`nT
z8i-z^guX-c5*73vqL-*$*?fc*BD*ZdA0@dsu|DA{i`<&W^*C~CE?08o*2i4eBey={
z@{8P>!*wxo>q9QH$jl_J$m_pNO!wL+-34c!7o^glDIWm}>{d`<ML>ZyzGFAp9AoEY
zqA2>1r<Y1htHAE1sMf!&$sY=X6X1W=Z1FcD$6xKQjl7k|RTOzEmn$>!)?=<)k+&Xk
z1xMb>;kq1o>miqI<gM)A?HX_+-Y)zx9oP|0up|4yis9f);RdI?7pQA4Y3_ZV{^>6q
zKxoQ`fgEH4E9M6))&eU|11mN^5t%QtF?4_7&>1<6p@<iWo{h~x#LhSQ4#jx{`|eE@
zeCU`O7aDIdWN&aE9oEGLzo~*B9a6(bgWH2_3|67TUbJc76u^g$s<EQMi6Xm$tLPXT
z0&4&bW0MQ5_M(*Fnx6|%Q7Y&=L`A8A@9S#D*b7oKI=x1^_>!NZX7JO1nt5!np=SPP
zgK<O6Y^a&PRLukxo^T?Cw4pA#dV688<~H4w6<g75H7?ZNN=T*PJ|?V{4W2S!3%b6B
zj~aIXDH5#0gf+8irwrJNZmF@N#wj8df-{p?l5&(b=^B0h;E|+z9->|l4TESrM6*Hq
z>AhVRVBYH&+9~ULO)nV#oR~sa6#=j*CjJ7OqK2nII=LL8JrG@mD0eM*T@j+@08RV<
zg=GN*hYWQ+ei_jKTvABGR3_Mns#l#@jL1=Z&!cJZ@CZb;A$k&|E|-c<Jek%8oS3u!
zk`n{S&Yp({e;FkD;WNF@11t-k2_w+2SP})DG=Fx`ZK{GC8LM^IqQLXCuEO)5=30mW
zUSW*$)t}@Q{u>Pab@W7A=8t-X6&-+Ai2f^F?El#-tcbM4rcWo4Z9FDFVkfFU{(I3&
zbs2tX10SlS+?xC`>EzcnS~7soggn6*_jrpF;9=xafzwdC7Pt%vsEbjKNl{dJ#9!gT
z6atYTuV51(DAZGv2gHZqpN)W^jn=Fvhwv#8Uv`GL$Ly7nQ@Vh&9e@Ye`d;KOh+O>7
zYz5rHpFAUeZk*)5ct-sEq5JPuX49hHZ&mta$hD*6%zuTeWZ)Cpe3~YGdUM=kW+*(f
z3kuI@L*bchFg#QCxB&<n`eax&FngZL`15f*Dk~t!Jh2JXG9{3fS%tJr45VfBAuS{3
z%if#3;ne@J$oht(wBacIm5vhjB;K=ejW#7msr4s@eEZ@4>=h4ER{w(VB&0$(Awz|!
z64=XnPYY_COH`0ua}~5}%xi(*Hn<h?-@8r#*4fWAdN-E%KeW;Ncl1|&^3WC7C067p
zjjExQ`)vP;Kn%En^tuMzK<WwxHjvJVQb8sS9c0q5fhG;GcfQ9MKi!#!KL3|EE0EDr
z4jC=ZkkO(FX&gaN<B)Z7;AeZioIh^MKN%UpCVZRzp7Fihpu-4V#Mq}k+P)X8`ZgN9
zuM{0w;SH}w%r&ix6cS@!P8<0PzpD4Pv-JzhW=}9OJ!yL|ESoLigy|96mC$U~1U=Jz
zwq>F5Tu)xw2+@eim|m~dW;C`#ZVUN(sp4g*BVA%rq9X%Hm_Wh;lC2<N1qpj1dU8at
zC?G!l`W!ON)YQuMV%T~1gwv+_wtK_Qvn3dss@jf+o@Y(aHI=me%{riJC1nQ8>x4!}
zdO%`c4}JrQ`AhH{NX#3AaQ-XDuvXa)cFIm790?|-=C)>GLhK2rOto#dhY7JI7?>*B
zehd|2P0;yOKYnygNB2d7+jYOP=OFn25*$c4E(2sgNQ^IEud-6zR$(%p%*E1sJ#2(M
z!PwN;_ITI`Tf#|GEnB*<5!M8K)BU#1p(ERV$*UiAfb7F=V=WM}u(K+V0Es+ER6wE$
z5<QR@S=beB4AFN{`@z1y*$}z15->rlUX7R_-?ege+(*>83>I92jWsi_%Z8tZZ~)yM
z0+&UXhQJlj_z<ErIyQtThi(rc?n4)bz`3!dwFrJ}Y%OUAwy;*amHZjeC-eF45!5tS
zq9Y@*{H#U6!NtiNm&<706)L?NxvK%)U;}Tc+Rq)s_D^@4V1xYL8;FY+BA?YgrPmP^
z&q3a)+efdnM?3@RS@)DqM_4@Z$Bg~K#;=@BOmdUREJVMsIP*npzhpP}YGh1HNkp~X
zkh{cv@Nx=tS#&kBsw{A~cVUOD4BvBBh88jLeB_h5+w?7a#j}yob=&A$M8wmOPIb5G
zT7-U2TZqY^g@i41Uc9WdI0F(#kOY7v3M45Y$$RnAN{at_U*4i_bi~xlx_NpXG4VX)
zgF1J5oxS2&$k4iZIvo-56y&)&ce-D7=DPy%zE0P@O;Sdxn*$`=AmIav07!&EA}U$b
z-E!0pFRH>iWUEw!tgP#xZxI!Lj7+UNN#C+ZJQEpM*Fo1JES`+CsQX8)_seQ6?dnQG
zby{0W!ax!Zk_eDo1IcxeMCxFd=5L{^wA)|$qV@mOI1OxDaCZhtK1ilOA~6VF14;hi
zwt%XQA^P=N^>-VhICMo2A;Q{cjjUEl^lG%pV8X4~STo{AY^<4Zw#e8JxC&Y!1g?bk
z34yDjSwe^h&;}tyWwcZXQ4yU}i{QcHYe`(#_F57*mPnu`yVlBw;T;)fy1Dub<twc{
zd{MYr1k)hV{3SvDI^L0Wrh89+p;2W}>mVS7eX)f=^+PAr{+z_)9qAL3$bm7S2)2QQ
z2^2v+ki-CFUMA|VoLYoPa4oh5C-Jlxxaz|jKy^_F+822C4XOWTj%_HW|Hie$*^W#y
zKus?bSYoYxnp6Ws2!Ti`8iG5rkcJ?QZmg~5!=}_$^I|J%tNF1JwTK;9KuPdn6KfH?
z*iIW16OLdL#E2WS31Tv~BhjMBDC%xDe}6t;_GadcT`n7a+qv+<BIL`spGCp(#YJVw
z?mfWW#8@-sZ525aOY8)ne&nB9@F5F=12QbaLBnDSvNxij%fis*$Z9)R$&FS2*De-l
zoq_7r86^22nF5IfsA!!*lE1O)|F&`acU$#;mhFrY#_^K@mjOkdUG|E=7W+JHrzO}S
zrDzG4kSrna9oWg*>h0Lx+G-vwp%%e~ZLCFbW5;R{+p*oXs!X_E8x#w!(I$u)*KH#-
z8srA}8*X};);`fK^5KAMBH6tIaCJa+(ak&Lu6G~05eeSlFZSK-?TVii!|^Lm{5k*o
zt%;LGfy5#^4-irSqZK{s)<u`gINpJm^`Oh_@P>Yi4JZ5`VlmL-zY!<d2xWbXZvC-1
zNwpqUjXd7X8$=$Mn`HjzF!R&H7it^fi+_%CZG<lvHX=-4gHC@pLnZ}{BK@!G3+oO0
zVq+g>V;|<1!Jc34(EZI!K)}A}QzMoS&8R6O`gRepY1oC@YA$R`Z8bNx-iDu+;E!~r
zA$TKaXb1=-uwQZ+nL$JFLt4`iypTZHb3b|}geZf~2qDUwUSHw=RH#H_aSC!iV%EKq
z2*@kh33(-P>o*{;q*0vjdS5RQ*thu8!A>w{;S9wr9HE#69~85=23>~sNb<gj;YK%N
z{J+>3{wkdRK|3Y`2$oEL-<SChZl|gOVY5%)C)mag)_-~jD+LXl$17AKk?M!Kd7rlT
zH>>hvqiU;nVDoBK8F819g|q~Fq$@4K1vx`YutfqI<RUVImT(?vO-qnNyM_=|(flFA
zgJ|oJ_#|>Iuv1zi9|q|NE=WgsKsthHJ#uj!y0Hhm5xBlRQdo!%ntPP-r+^Aaz;MNL
zD2gEug)3Mf%!LMcc|R1zFj`~}@Yp~rYye6&043kI?0<!o`Gbbc{vt9d2iQ#|Ml=>G
zoy(|-gR7!BL*NI|CLwSx?2wH$3$D!u!;I^*!LZ=!ZPb`?oi=JLxE31}Gwvc1LqmWg
z)o2JFNEEQ3kV6PwjaMA$A>f4!1iTo13j!~=*MS$?pc^N^8!Jf-0IaFP+Pa85`e%1j
zHw=sooX59ajEx<i|1cB>*iK#e>$X$zlQ$^3*&xf}EM!@<t-B@HAj@JmbR!GAu?~sw
zMpfI9>$4Dl4si)>ia=Jz2gu4$fvk+J&}GQVFn(u(ZQFod{u;IVAM_Kx`yvqh?f(=q
zc7^=qS)xSlYl$;;g?CK$`P@2vVirNUMC2cSIeVI8VbVCnR_Mz+`}&~#|7K*XpB;G>
zGDK)WoJRGs>&L_pxI7wwWq<&Ueii`R^*e5hGZI4!07Y}Y!MdKjTw|E-=A|sy3z;CV
zAQR-SI)we)0+}Gkp&MEdDD*4`0OX!Rz#B*nKSi+=Z@?!1IsUqVY~4V%{x~7=2cur?
z+X5C`$+MF;$Yk0Ka{1YBpsoOgb;vjYjCV}gV3=@38#PAUkc}D>Zovk{h?`sobpeqN
zxo@G4DtN_bvc!vrv);}FQ{45vCE=xx%&y&h{RM|BEu(xZgy+1S2dB6jdP^cpA!Ju6
zVU8WB{?Bfu%zEnqcqBFJvmkcc10*3Ji33R%K-Qxwe;M?-8aNjr|H%XdS%a8Rj>{k>
zR2ag9HozPH8^M1Y!0~U6(f2^XADmu)B>)-3@&B)5``46<g2=xxFnk3e4Cue3F5;7d
zYF#1VczSwcQ1vSa0_p)lKo3I@(A^*e)R9d^CAJ0HNIA8%8c9@gMgO_;PLP|z00l!K
zNEZ(BUNoW0-_NDBZva8Ri~5h4*hX03KO7eLuT!*Y23WOqphhjYks{UK1nr^pGvStO
zFpM|^hz9-bGbYd;N+XB{{SE}R{`&$!RVy|QJ^lR;J^d{B^Tr{9fA<gpU|x{d{yOvG
z-`RMb{}~&v1ndS<<EJ7u{?VHGyTKVZ6w|McYyZI^T6jsC%s}+b;rStC*I>*#sH<;%
z#}xo)NdL~McL>x4oJ8sT#kn-<e`h0QBi{Kd5a5QNu;C|sozQ<bs9HNK0r**0Fi{3{
zGA&BssT6o94LIcvo_DX}2jHN;1Ji&GrCkQ$px=?8oc{(Al&BE9x)H$GSdKSRjQncJ
zhw!a`v$X>VV6?3x$DzbJ4Lm=4Kec~$!yoylBc^}1HRW#}<X2_>fgS$`M92&Wuv<pX
z`Vv%4bD4bfU5os6f?A|i!HG}g6N`Uw@)T(A0^BXbK^as8z}>PQD1%Dgi^BiL<ZOs^
zG<gj|kcWVXQ0vND5E!(IXZ`3q5zNZ<+viOnFes3Jhx<oDF4S)&<Z=ao+%)e{z|r)(
z8e|UOm^vHrjMFa@a{2y@gj{g%4FviB9}wi}xej)fwtp@q!bVEM|6tZg0Jue1M}+dB
zrf<UMwANE`xvU>G!GkdJ-_Dl-rQ%WrVdOs$<X8V41o_BML6CpkK!k1}LN{`yem~3i
zEm6kz><j;3qWyk4+p+7!|K-5v;D)~V=Pl3Q%XTtH-cymtr``UnvMwmQD<oQ=eAg~5
z`km~m8SRb9tc<3ams^~#-m%VDV!Qs-(Yln@7S@s57-Qxo1wYv@G4jhaTa|&@KPA1e
zij}I+Pf4`OCzbJY@)*Y^8vLA$u8l1@KPRX4(8#HulR9>8KK`W@fmpa!K7=8E?y~k_
z#I4%6GU7&UT$yk<n;Ayjw9O0?j%35nh#RosXTr_dWH92!|1qg31o(iEq=OAtgGxW&
zt&1uN<8)+{`oL}V<Y1*~fNv4aoSnpA!`+urCD%C}*`z**S}C^;?gb9G@mm*lBijEg
zuw3c`o1_eRP96l&$%6@U<-r8G@?e5o`AC4|m?{62G7n}3Wx}(A+44Zhs3rs%y$(9T
z#+R*&{u`wP7^<I<V%>M;&d<qYyrFD=BS!m&PA|nK77F9WP`Jq(i9xfAKuWL4!sdQ1
zY+G$VH+HJFe>=9Xwx0(}tX1X0*4L_XV~1*0w_`hNRe64qnB#knK=33AV$O#FIEk{T
zOakUFfEOLPt8UOf<j!;-%pNNI4$K~!1wuzvA%&4^WnH8;#`GsMYX6}Vv9Eug65g0z
zzs@oIdluI(e=1?amieauI^=WwAq4wRT&41^6B`PF96cla%0%EN3|1YVD$oRYAWLZi
z0+2p50X|4PO+XNmg@)jUjHMy?A`NH=p2&8ZU*sYFp2P<@L;o*CK;)tTdks3ecJ(On
z&n5eZa`J?MdLti7)e``v>Jfl0UxzLOsd~gV4AI~4e*X~OXKsE&2X5%V_1~p|OpFY~
zR5z~_`7@9U@M*VoT$KzuC8SCo-5XLRi>?T%Qa}?z;L_-b5V#z=F$BI3of87zj~)ww
z%b=lSvEAQ(6$MD^^)G-qAV&d8Go0~LHd(6NH!R+N^R(+mc;VMjY$2LI6l45};f0^3
z)i2qRSGobX=sZh0kO(k<DF2Zsr0O90Oh}aqS}CMT2^|nprG};rfgeC04S_48MMB_;
zXorwrWb^s{vnb&4wLgw8zuN%BYM{g$$s0z-KhNd=v*NfB$M~n;^815141S_k2jUos
ze_<Tsk7v~A6i?q)QAz-Q`$UIt=_ma5Nuk9?#Q)zslJXP1<ewJt|7lC|f1M7jzKaf8
zDY5pcTnhnCvNc%+>ET}4xH98D*toLb-q_4A<N9r8Sa7vA{LHv_HvBBOW}6IV+((;?
z@4wh+{XJWt(CEK#=rzNB4q#><m_2mhr|ivc#6ExITlp7H$!}~e{QFxA|9$(odDp9O
zj;Lwh6<PoScC(6f7aLRCzXO|J+s}thtL^8-R@L_NV?%0HcVM$>Rr#=SwW_?>;@Tfc
ziJ|OX(9f@FxBezt^ml%~b$xpk$RFCh5y<#eP;z5$;Xj%Y^-ow$g|y%DuK!;hBfu>&
z`CoQR#5UZLe>}$a2Y*m~1BmqNEsSj;e{lZ?!%FDOAdN@xBm9O4*45-*6ln-gUapV8
zkk?|>NG%zNg?6>Ida0m=UJUu8V^9}a1j|D1YUycLBMqq$doqago&4m{(jbxpirj+w
zl13n}nUI@;2;+&?h}HVlxS-Vm0*X8^X1#)0WKkn7wiFTvW=KQ~et{pc(yc}s<wvZo
z7@z_>P^6>e)d(_fK4W>x6|uU~$WIzcoC#W)%L!UZ!I0*1GJ;lC2>d}SQ?8^X1GUPf
zz&`Ssk7`~d6k>vufmt4ktvaz*eKeZ9Qs0hRTgLDoBP)@Cdh!#=c0q)0H6js%T9_wa
zK@nLn<bhc7(o!OsSZckt9KlaoOo>G-mtq2Oo&440MDp@S7V>f-d5zePg0JKRkvOHu
zjZr94ADIv_qc%ofJ4y;#8~a4ALizcTn_I|8GC^vo!i3Blgm+aVuBlm(B*;@_8W@aB
zri2;lP*SnMXkeRRFxW1bSy*`LW?C4mjtd3@{=$5nq<mc69X&3bKkw-w1^pu{>Fey&
zYG&rqyMGH}Mwd(((SHA>&U=~L`|j^iOn>FVEi%5i_2JIz^2eojUidh^i1ML$tGx5z
ziBwPX7@@;aBZIxH48FLDU3$*~bfc2adGt-Bl!q5za~gC~=vJ#_i76^zXFShy=O$zO
z(ou^qg&b;RIi)TBCRiDJ?qf}nWz`Pi8O$`A&l>lNuI_H!-s(iBw>2cgQ(=#L<U1|p
zJHnK`a#e>T2Xzy-JzU(e{ai(i;EtP*bT7YpuqBf9B`l~Yc46>%PyML4A5SFny-C(|
zj+cktyq_1xZ?@(>dWY>?%j(wSwA*@$65?tfWI1rBWnGB;Vwudd^8oFL%g}E7*dvyf
zI$7f*l7eh~!k=%^wrJkM9U5p4FYVkEeZxeNe_!OK<BbW*I?*G{d#YpoF38`<CYrtD
z{_qs5m9%ig)=DBeB}%`=SGJqHFE5b-!$Fp2>Kx?H@5Zd!2@5PsbnVoemDjGNRZ!mQ
z1V<)0+g;NsDAJnqE|AI}pP%4t89Fss6f2|3mS6Ph?G%%~+e_&~NY9wDE{R<}sdL+o
z30oUVyJhaNn^UnG*quD7daWzcz>ZfnT!?0gdup!JKeI^_R$Y7Mw6K%rZOjl;Z~M}9
zl19K$=EBRBy|OUbVFAJ0H4C$kvt<gmY(acEED+!BUtc|PH(g~oZ1C36-O=}-+U(AM
za3+4oUW!j5yKC@=wUJY<aWK_5F5T)2Lgzm5vqTgq@WH7Y4~gz(WZxOiA*;>It#ROj
z@$fa1eU6HovW}*{7=B+ieI>X?fcoYP=ULbNFK7u`?6FdQhcfOqVBMcQpng&FIq9QU
z`KQTnC8uysy8|$!yq)PBm7PAj>Z5n2^j6KbhaVT3X`Q7m+H7xo_0ZGOu2U2Wt+uk2
zTk4r@+&<COq|$5A*1vaiqAohOJ?g-qvBkk9>6;$Tv}KVhJfq0%R@ZE4(*w5g<%rHI
zH<(usUmWn@33M-TuFkw#Xv>^35XR*_JJ+Oo!%#nLvvZ2;g9A;8$`XP5O3L<jw@HI7
zLYt*=rVMo*WPj3WY@in#*|4?wEKX08Xe8gx&_}GpioN6Zd>G$clFC+MK;`&oo1}T@
z4aKau3Afz)Z;oAWh}6u@qMFFPd#4-?*X+6{n{R(B)oJ`b^q6GjQ6+DtoHt%Pe3!Hn
z@0#9jbWXflbk$MF?}_m-oybna>y-EQ6Nf@7l__FQ)wbT`mX|wP;?d9<{E+RjIU^=V
z!^`=U;sM_hC8Pd>W}5fyOMN+#9+<>$u~2Dyj_us#Wvg7=a=PM3uZduZ(*k{Sk@p1d
zo&G7Al6TLJTgv75Y)NmsG#vfD>2Xr|g><@2lb!>d=j4yDhpk0EF4iBqSD=`~aDb&a
zOT!Si8D%#i+4;cLC#^G#Yc+ew>;0pGq&FwWJR8_8GcJ{6-OSIk7h8#EjBmS-4bDwS
zkiMYdm$6srjl%BodY*pg`4_BH5}vyJC$_l`2Q!#|-ZzwgpSpqPLjJ)RmC?5h!k243
znS|&LF4Mi9K9^A<644?q^^skY{u$+dzWawPPHA6=FfA!%W!gm1U;p@vzrk}Xj?>&5
z!6?CcPGV8b?OlcNs*_=yeTd||gWSi@d-PA-cXWH2eQhgR$;U1Id{Q+g3u{tNIz9h!
z#@I`4#EYcNG~iihy=HhakMjJ`8KXG;LFVQCg|m^Ke1-?G{1jcuwQjZkC#rQ$QueF(
zqH1K+_Pks!nty+McF&X4Q{|Px2WG0qMBG>C8n->~K5{Mgww*+crp8ce&Ij^=`JJ`l
zH_swbsSca*p}Xz{q7D>QD|uD!b;ZA-R9oX-)@BNLxBCuLPId9623vz|YlSBC=<YY2
zU4&cb2P>+m`pz=-D!?0yjni!loO|7>Y%WAsl{h{L$(-yBE_*5xr|;ESczmWW#j8E2
z^19mD<?^}|uJh8%H`Ogk`5N1!cG1~Z2OgJsoVSM0ZSEb_YU^q;3lF|19+7_jg^q)>
zc;XxM=E{K!Q-y18gV9Qt>SXAq@AZ_Oj>!6$QInUhs3aU@m^=)(&QFxLh;8k1zesh1
zpQ`|6v7NO&jVqJorH$NW-k^_O7{@zKC3y#;I`>Uoe5-xOlwpX<`(3(sSnaLZy*qM)
zvq~mMf=c;kZy=4|GkmC3^t2G1<-OMVc0UuwI$R4W*H(k?vzZc&lvD_+<K^nVP<FY!
z1bfjfV{LJ$#eA<kO##OJ!tH`BeGO)rLcR>Vi66%}El>BqZ|094zJIG+h;h3Ar5g)=
zii?}QSnZi^_xwaR)x>bZp$dHK?j4Vo?<yq4mpzZUSleIMKDn=o-Qdv0ESXQJC`q@1
zvCw^yb{7_3Z;B9AkfRH*A?sdIqnqcF6w^hY{hYxpexrv?aikl$=q_m9Mm1x&Xxdlj
zxRhFHluITcmw`3%*Q+Pni=L2RN*Ju3?sr#DIdJt%P3XM=te)XB48y#e`wN9u*?N&X
zcFS;B-ebBde6jFi<n!csjj#tSDfegkx1anFc}qds4%^dVFrPvhAt-@KZMnK5yGYw)
z`}vSN+sx?F@-8L5lggNxvA<*5n3u{~etSFoc0#pT)vB{!$LPE1Y)`7q@oiuF%lvQc
zHpZTBo;uY1^y;nr{X#x&j^5YvV}{GDPjfU&>*XA8HqxZDwRN%mGG$Cx_F>TQ^=s;D
zx2SzSqx$lCtS*eL30t`=)7e!mcV52Ct7Xo|gI^Pim+qO!=h<v2Tw8*;vv7{D!7TF3
zCW$gX<@Y|Yf?Y3SMDGO551c$1*-&555$;Nlz9q}!arg}lDSTFK;y^1Q!Nn&zD&c+m
zupZZb{b*%T@qCB(v)xadge4r88F`2TPCa$fmiCto{j|t>Deb&#`_{B)PChyr<h7j;
zTaZwFRgHU7qXy!$5VlrCd9&c%CkbU&%U@o*eycaS`h1l90wv-dMmyb!vc~ah&V{Tc
z!G7f{EPDsf@1p)7J22~UXXg9~7+u|S%qGuk3AYo_Rn>22h-H)_9%UmDbyeLBYN9@b
z=r`k<1f2{-;-yj_w^@;EY~@#$X7#kg`e52I8M5>V2DVd9(kZ&HIzLX*d54LJqxK37
z+N4SHC_5gapcr4;oWN|`+``7SMn`QNk_@|h!+t1%sz}bommIg{ib3mLrD}6-xPW2R
zmh1Tkz4t!nQjesLbUo4b#Zh}F1;_iLOVzqJZtA^j_3h56?eKlAs2j{yvgK9JUOKdE
zS`8CcDOmJ{{K7@`(*C78UWPl-A6_k&(>1lSvp%|deZ2U3!M2ragdY6XF0Qvm0x)bR
zJ1ke`UY-UFeM1a(`s^W->#*gTiHIwg7{7>ag+=AE9FnK;jNe@Id6$3wC&L?C5u=)s
z+okR}?J1qMdRo9HTVS%ok^adkmRoGHpQ)^)s7Lti#<hdDnx8&!U9a<f!MHQ2KSQ@_
zg`T_P%6I^V-(30i4nn#~rfgTB2zl5(M^uNtEX?C3Gm<X(ZFOBtXGw(9vk=6v6n?gD
z*MMPm2%S!&Qx9U)@X8sc{z_>ydTn=Krtq2bPY9bO0}tHcL0#SE%vO128Gj@CL(hTw
z_^e@@5n^odt^yTZa)B+PsK7|;=-&66D!mR0l}DO=_GnTbuD;IU-q>(zf%`~L(c87&
z?!qoIVAB42+9qM$<4!WbU{-p+J8k8mX`9;XGStMbiJ=_kr>ZJ#$nEGtswDAc-J-Z%
zyECpu_RM|fOrcYmU%Y*;t_4JnHI-`w(7lqg5BzA?r8+p8*)}5JidcSbwN!1~=53~i
z!POhRn=xu%@GlBPR8EnJt{JMjgevDVs#33wh!%Y~lu0j%#V2U3zRj^O@T01cQE!W^
zva2h>z`3G~kMa3J&LvBi!U?^lRBOLSDPA8(T~iusy*p2j)Y0UdgqX4o)s|bR;^E<w
zqbeTnSy)>9ysX=;KXiFa#>sW%I(ZO#b4Kh5`+G|pDn`%9jW{9-U6*#m`_2?%#~A91
zS3k7)opPOwz+JK`q_yf<sYqqA7n$);32NuDOl(U`Y>QBy6GoM_w{$%vy115N&a@W>
zAp#3h<`+ao<|P<g{ASL?k)u|~o~PaXrWZ-KTUHPc7gSaXF4nodJ#C%Xm+6%xN~*5n
z(jt@%+CE)W65*K<ne;aCAU?JDgd?BAlHg4l>iEoM!w1v#?@(mij0<)%sbqX)|6|17
z?t>wiaxE?#t&c*~c~!{@pHGgt5xu0?fmPL*hkauEC-_??yaE=z3LUfBy@=TX17j8%
z@7k>MbNze{;a{(fJ1(6nF&}w6Pv|GLIK=dYh7hA0tx;{HW^O5yvPXQc6O9O`s#eF`
z->LTD9Eb{2g-eqexg$fTQj~(;>m4jK@e3rO&q!5t9#;ME+7-9b)fn!{9^P=^MtN}^
zetMP%AFz<<e5TN+IHFHkRlVS&eNzU;p36$_h>T7gUjG*Byg=j|FNK!HON!h~jh$I$
z^HH4ibegU4s~SUcyweV^1J2_0omdzHInf1GQoZxj4<B0xlu^uAqg`H4Yj74s9DLv>
zLP9?-C9X}+OMbFnn(UMQ#8^FgXtY94HBxVITo^x=^57#P?y#hdwWbeas?l80DG&B$
zZ@5V}QNO5u*f+30*)A}rI!3LirQ*0#|6}(B$&Q5yMI)DV%B?9rxt{_B6lPAB`Qbw*
zE0gr&Q+yh0S4PuB6<Ahiv(f$MR1WbS;;)wsXqi5Tp-n4k?l~O6Bi(mzNZw58%aw()
z5Zvt>2W(yqY;mu=;p&Wgch0h7*JvC?lE(om_&!+Njfe-av`OMRkr59JXly#>ZZ0-o
z8G5d&!m#7E>h>$g8`V2RD3NhfR5Md<z|+%j3Mvo>fa^!oo;CZIPCO74=Jo0j)`@r!
zx8IEA?Qs!Z#qkGvAD9)#LywC9kMs<@?QE<S`f=4^8hlde%L7*2qM?ZMQ%|l5;CV{V
z4t4B!rhd=4se5sJb_Og!nU1t+i(3NxAFV$gf$mfX%ImQGT2U-A=cwYvxRMZk|4<c`
zj^im$cs)A=vE#SCmSJl!=D2fdp%yC8)*u#YWD#&v<&cQVt8~7}^;XK@7EOUxc)vX!
zTa?rIwZLc<l@G^L#Ce5*|1ZJP7O4XK@2|H)1l`FB_Uf>RGiQze6@CA@ZWG0EI^3cL
zP>AGl5tZ8Xbf7;8%%?#10QVzRRJzvN#(VV&gGt&VTYEovuY7mf;+?{5uZ~mL@y9@`
z%sO;|68P@LrP~^`d#-n$F~I-fdbJKoi{NdbmF?2w$?H8#x7;bBIAQ2nzPLGzxz~&9
z{?MBpH!VFsXWCpn`K2jj+%Sn*g-!PaJ#T<CuLbM@TT<X2SauS#B<vKuK6!I$+H9iE
zFk=U*=FN@IA0IR1xTa!v_=Pg8VK?7PEB{=k!X<V}GPU~MKKanS+_ujcc1RpreK@&D
zmz`V?0n=sB?cDTPuDh1nA9IL@F}T0oFil?@)^&CK^QJ1-7fj)YW_a8}cD*IEez<x<
z?sBw@CBv0&i|#NA8wOqU)grkQT(Zw$H`~1SFm^b+;eb6WW0t(BTKpV_Z1ZvpUQ8~f
zhPf1kFr=;|myMu!w5dLqdsARJk}9YLod&Pi&U3)3%9z7y+u;vgDP~h9fhyanBw<%Q
zcr8A+wzEmX!aXc2VAmLQ2d~N~Y!xbg0h4|M)a0MxX93$erugE}jBFeOY{Y_=^9y{`
zouVccD#1w-mI}U?&d@q`_11yQHPV?3SH4(8)ZN1;cTm@O0_|B;jVp&edIL1E-@%*{
z)?HR$2}4s0d)J~<uiZ|zgk2a@WTUBS|0D@}0kj2pua7#*b7E>?X6@DZ`z_Ujlvg;D
zK2x9ddL34{3>&0==u07;KIt#QQ^Xo3)68&X9%|7;zuFgRqS3$;U#NxsYtd=$1B)^J
zk9Qqg+GL~By~Ia}<V?a+pY^>jZ684KIepTQYf*Kf3U;L(YSt@G*gU|@S&xi^*08rg
zXKUKw_He(LLMx8CCsLaxJuEpmV#d?>r3>n}q#@3I;0#^@9#<qbK{t6VJXdd-SyO!g
zUi`8sSX{mNNxRpSovwjK^Vj=3fzq~`W&j);U+^yjZ-e8tq-g^O`17FsiBv~j8H3o^
zh4Iw!a%l_Y#=NoU@#|-!&8#0#d$z6$NMxS<z<F^iZ*ldpmM@Y|vM4`rN<DdymF-!X
zAPF|^DBTmOeUni&s}L)`RaFI)3*3cR`n9S>)vt})^y7tN8QmW>&-xE~)~-KJ-`P$0
z-kZs<T?9sWG74x3QnkBQlN>KH|GMJBkN(kz^gDdz2_U=cPf`14Kr8`d@B`<!t}dz;
zHb%W#d`VINwXR>kSi*p;{>IjOc)u_!xrZY6zj8e6@Qm#DZd&E7kHT9*r4=+pU=u6t
zzjfKK4}6oEYn>b5aIbeAkis$cKyWa6s7<ZE4JA;>Z7J?f))VV&gv9rscm|5^hs2pX
z3Bc%$-@XC}fV$`E_3kC1v;Je_70i-uJnV&&I<FS*Fu0SbfM@pT=42!-34uc#rLQJ4
zsRIm8w`9%gw&H5=w%a=g(#t1O6yN}P&WPB<>DJ{#7J87D^BBOsO&`WU_9cET29gx$
z1yG*;`FV03yx4OH8>ZwCyp+v=f78)coH(5xwkY*UO-JFa)r*i1N$Cd`tY1VVxZJOD
z<II;)8MzuXpyw$#6{d7CW_&N_8UX=TsQT*4lF+Y;{{pWr+`qk(RVf*2mLl83pfQ8i
z)op-4fFt&;C!b4IzrBiDnoE1$oRog;_SZLP91!;KKJ|$AOr-@G<;x+VYZnzN9C*5>
zrs*_rRcgdn>Dr59{-kVkPlMeP>7kt}*7m1Vjf3{4G>$C%e|)`TbSBZ#1{!B#+qN}v
zCf3BZZQGt`V%xTDzp?SgwteS2=l(qR{^;(#YW3>9R`;%|r=DF^-O^e?co*ytRT=BX
z{$)V&T~f`(Iey$R9S7UMlzZ3MSMgDmv1gTJp9-`8*?X07l0Spd_lFm-Q}JZF;H|;A
z@wB2dz{GS8;4Ijw%ry1mOyftrXz4A_Y@tcD?R{>j7Wn*Lew|4u?RxD!@A^FRYwWt*
zIF$Ay*6Pe6qMLSojwm}i4(&cxebkugz0N^i-snAwGhG+NxxDiKpE%s|_PQnP9}kT}
z@&Ad#Isb#hwYHo$#t{7fk<ZYGL~o0d9|;tlDfR^?>_&T5tnJqCvdT%*;;5<#4cX@p
z{!DII<e)~w6~|KKcptmY^&lw+pYqq?-S-*Es(SdxHG+3KRJ<j_oPq*?t=`X$H)iMT
z3c1(~6Mg)~_II+%&0NB-m+<%2f)BlJ6V1zj5MvH^{j)b0E<*KF88^uaMaPL)2UGQ=
z@S-rm$zw-X+D4Pgbi{W@peNV9NCJ^A*=|jjxOn}>p=AsG{5=WpvbbdWWg_lHt=^sT
zlBD;J9)-O&r9IHv7>UKvvZm?oLvq!9Y@wknN|#^y&){krWp@|B4yz#2DMu_}!rlAp
z11LmpUwf%f?uuYjXN|Mu_1>|uBBi{y#$R_qX{F=t5P#4Qq{#lrMVIbzsdeduzg^PN
zL@|>W9O~suk+6RH=$}$_bSEL9y~?Y<>10DIyLgW`+TZPw`~~`2YX!QH^oj(=0REfd
z-r4>7GSDqjEmDYeP`za_%#Nc-&l~u6N}d-<Yj#(D57(S%`nt2S{(#2TsE?~M-3Jx)
zouyE0o13H;o7`gB+-!&*y+-*pKS6gapxYx81G*n%;JzB^XZPQp`FF(VO|#B}u3ABi
zcmIQpH`A-_4unhX+m@NT<5XPE9!>#<r#__I84~p+5G4lBl^y#qu8e3(Wu}MV$n&D(
z3Pwa~mCjYxpz;iqp1*T(;>tUn2&H`*OM)uf+wf5Vu5+ZoP83JpJF;u>@*Zj75LA#j
zRI}_`btw?IW`vuqc9bc~uLCc-5^C@X>WP3C{bB+K$~QeB_?u9q>51J8&tHY}&p~qM
zvP1qJFoEqHK+3FN%%QJZx2p%yGsC7zR7lUJw?{(#0SD!Bkyh|u*+>_(lR*xPrgE8H
z@pt8WFJ&4a2RtCv0dH@}+naIvB;Cp(^yEWG+y{w}sE%XvVCfEH08~^@d{pfsm!nt`
zcw73#Wr}(s^OPv~-SmpBDzcl#d<a7sO*q+TY}Dei`?&JZZe!)jF`eps2*px01EZmn
z2@&7m;$|8}lbRs~gHJsB6)-e1sQH%Pp#JM&+1#ZQ0Td-sQ;)c?u5$nGR%iy*zXV`P
zGTrxR5O+^`Hwk&Ax3VC=*oYucU62Gc7{CRR-XmkfA_Vpgg$XacwRR&*O3rSe;Vp_{
zF`wa^!2K|tFzY6DK1T@EVq&h(ugiPHngu)6j>|}U#A<26{ljotI=1O05{SB8(%m-{
zk?$4E*sigt@?3YKYErYJP^K3ciNc1KxgKsD@H?i&nP0MCl1$Mt*oaY2625nZC&Q`y
zl>bm8Sn4pL3|p$H{^j$c&hYJhBka`P0k5QY5M}UbJ(S@}-+~}x`wt{16fWEvf4rkZ
zt~T>$%vbSn8RzA_@a<0*N8`k87=!UE{S$+X?R&i^=E$hO>>5-yX?poD^F~$Mc*>Ms
zL8qZ+!C6+U*5}?X`NQS);~j9O&Pxhu*(ZCGPf^rIeoW(n8_WZ~Wb0?U#&kl%B^zcw
zhD<K@Kf@l*u@lnF8v@CqJeM~za&579H5YuV;bfTf?k<FtpG?)oQSYM*Jcc^laLPP8
z7@`{V2ZYLxEi&L5j5(>*{xY%i_MW@xwA;AjuFHt7-QX_H_L&YwL`7#OPM~0{1^qh=
zYAVKbp^xh08CFNXq_KoK4Mh>uO$JBiwTtY6>x-^N3dq|OT!L$!*`PVP-E|ZdG;CNG
zWK^Py`U|WF9|&w%Hh=lepP4~heULH-gKZxvlunz96pS*l$W@W=Yq5?<;?h@fMmx-5
z8^E52!lJp5fx4q)`nF=rAY3w}r=hALMAZ}=u3Ab4V_;Gslvw`3u|RSFvBM>DM~N$5
z6!Xkj+N^@8ju%bw-->^@yT?CUXIzOzNVZDN#-8x3eXEHdYMMOSWNrxZF=U<P>Yri>
z$c)JTn?bEzadZVK$y|dXW+y*{u&=PzV)$nI@7t!@;+5#P;7xH1CUr!H4Wc9AwCLcn
zu2pilGnoWA#!zE6(9JB3aW>f!YELQLHUfUYxk@NxBbNVhvJpbZkx)x;+=}n0z#+42
z>-oN3cs8apFJoP$`Wu>2`p!793!ykA*577lqDdRv{misjJ>TQTjYu42i4}b=p0Y@|
zj(0D-@OsBdKEDw!+AK4|VSK=acA4#pV`N@jaZsL>-=^cus~vWCMV)Zc)~}$$cuCUe
zriaMjE_iz)`9>t-Sl`5rT;G5aau-IGAnOY>QvI}r<w2qQ<8C2I{{ww^c)nmIN;jn8
zh=aT%daq?ynzaUpOkHjgMUk;mk-8EkL8Alq|0bQV%qN~O&HiDUErhDfcn<M@lI(wy
z^dF|_|0kJ*FgBSW)1u@#%$TYa6(O~bC9lx=@3Sf)6d6aaC6^J%Sq_67r?K0ew4@`P
zc(9$JtYb7pU8WA;m$!N&PE5vds@}a&<{ba9t-9NYy3?E2T{lG7Va^!r;Gq0=m=S8^
zwxB&FEsz?=rep`_h5BvB8qYRcMFQ_~ixBL2VoS)rXZ%tyQhNAiAAij=ydW4@!o#Yv
z#Li1;st446TYEG9>j!8#Ywsu-$bMD6%lhP?$-Or|yl5oY%(xd{SYz*?5Jb+K=VNHq
ztNepTjqjOgu>J)ysjN6??6i@L<$*&PoLHv1UIWt0s3Yp_KYn&UzIJG;>JNF(bv&4O
z1oAzJuNOOaJv6nYthj8Nuu|h()1XR(fZ_%EVh;&?o>Ufa7ePuv+Wt{-pQJRw`xq;V
zWB*v_XZ$XW_v)Z;(`tP=HJF<)c*>OfQ2I{S1*^!Nc`^N}hM%yX+iMCq_EqY5s~|e5
zjeFjY)g1rz_d6W_u(R-1zqxLtjHT5vm!Iks$)|kOvYVw=sfme={{!oPafoM>fZRz)
z5D-or5D>KgZk)SVnA(~${pZZ`f3W33Q$7xl1HG5*mO$LORLbqXRFgyd@<<-5V?%AL
zsZ7FrB;9czC(g#awLSkSA|!-50uGf6@kiOBw9Z7SCUp?$Bi+qK`qV03xb37GZP_@*
z{%iN~@pFzp0MPF8swdn{HU(*D39#2Gdnc3w$4p>9du<&lL}Gl)iKj+DF{QbBg`@wN
z+_>&_hp`>UrdkDmRk6v>A(BbAU9ut#FeGQJgD<_YoA3xC=esMzbaH|BLettPTiIxa
z_`CS5loYUjzCZ(0*W0baTa?F}EQ|n=L_}Ncfq^%J3rm7oAm8pDVgU%p5q9Gnc`mwE
zBq#4Wd3F7~wcbdzvZzEZaEhrS;hN7Cu0j4`j^`yu_<KSsb6TyjTls7SgjU^gaHfvm
zCiRUS1QN80y>)nTZT<2JQ44Yz<WP{H13J6kY;XYYjZ58T+p@eibR|#6-x*R6_p$#E
zre3g<&*X06jCil*E(i5!tNKm$+f3eC{S7X<a>9(-?4z1@W3P@aOgd}FafrM2w4!;T
zf?hr`-o%BHvaBZU%Ar=!6{`SP0GJZF)Y-ULSSfYD$0eCGWtPV~c~o%*l&zsj7a$z0
zp0&vAL9|bb()&vSCx}4N()acu97~?an<+<tr~Uij;0r*7M3}YtO|xPObCu7|3YqKw
z{Hnj>v-kP>vT=;m>#`pj`F%Mm@BeuS$A$c)Xgb$eOZfS9@b4n{zW*MM_<|^bh9*D>
zgSpM&bFpOtQ}vs7qy=C8rBW)|Sd<<HJ=iY%EI*n7*bG^~xXIX(?Vbr9+7_UG<j$at
zWGs+6#B#s%vpxVidQNvF8?mk8h?S2or}VG4lK__^7YdKl^p-?9#`-QF_!T*0@+Y>o
z*>)avGW7sWN{-j488)w)dpPoBEi^e$wyyl)3fUeDrBr6b#E3$PqDcZZVsgNgIAr~9
zy5H75RdJ;P`ejUfAWUe{@B>?UGDdw?r(meqDH_#^$A@Tq)%0LD2%6H4np~7;AjxwO
z8E!YBea5g`e6|+Mw{6f*2rDw?AYn>}=NZMA(y7nv6PqG^!7O2f@=1gx7+q`|fl~Jd
z-0T|p5#CYgblqWxauxP@P1FG{ZGpX98u`&|hM>MHD7~8q%O$GL42Rh5Pc2$Pj@0(G
zg}3Ik+fPTkoNi^4Zp3Gzq7%@JQZD9;s3p|L*!&%qrte<!6Ibuo56|`jBZJekp=!X+
z`1<~Hl7bgC7Wy@uFBq0WzyJ#T*|N}cxG8VS@J-{-3+=WDGKPIeN<m6ifoMgj(&sQc
zJ~G9!*pO5fP~AepWdsI+V(n*;`-6Z`&y_WcGDJ2iq(Bo6J^SSSS(Y*Utt*vmm2$o3
zlD}6TsZi8w>MR3kQ=CQ%hN9-&KNMf6$Q~wUhNEf*qy9*LvZ_o{r__KfOqBJ(j0Zcb
z0_-{N?FiQ?MIFoblDM8GPbz>})<rAai5WNw;W9C#;1!bY$<@}WrE00A|M0{*>wqNJ
z#t$+CWAr^z-s$tW`)Joz*mBbxr(q{96*Cvco=6QkRPJE}c8>I*l5IzTy?qPNj!sAu
zkSqL*D3}QEBvF>gP=KQ22bpk;_vM;Ku`i4mAK@HsJwLuiOoHkhmr!(Ed6>E;q&!Ws
z-ZBD(JfLl@gJp4O%Z3<a2aO|N`ZL~aH3r3(pb)h0hFksTj@9i7G@TS@xC7D1Zar7N
zvme^8sAe97!@l?*ZF`mn+wd@((kY^9hdbp1Dzi173}w#LL=x<O6crU^SpmBlqB718
z$~_jkhkb!5a2Lk~$a18r2H&nr{GBRv0GuMtCD}i!DoZYjr<rtjVXCSq%~ccS+N}r$
zdmUE^=fsNHs(aBD@2HUd%+(oFtiLt8qz9uK|0r%WIrAZ7=UCL<YHN~Z<#=pOAUTyb
zs$oPNwkz3#e~yN}B7tXaLf&Eb>0V_?|J~W%4%Wv1`Ec0UPho2@ci9X2zifYlb7eQn
zzYSqJCJ+#W|Jr_M7f%~g=l@uJ+>O{h&WCQLZ;}ab&Sx7wFf|dzdFhQMX}07y+@L@U
z=X@!RUzMLh0#AZ((4U!4f?gNMsC|g>pk{=)<Po+r!^cO)@9+Xui5f3q_oNf^`DH!I
zqM)UQoH6sS3Lmd)sWyt#6U5n2jwyTjZT+Dyh7bEl!3CQ`FP4FhG$2t;*inBPbOgqn
zP#7Ih(XSgQ<P1$x4y6=?*>Wd`Sx5Vuh=ru+t)q;_rI3q_LU&1E0KXC-URRcsU>i+v
zlIF;x3Y|n8BZW}~e6mV431y88T&r<^QCF$bt%SJhq2+f=xFk?dHIil~UxU<UO`9o3
z#I1?3CIr{@L!`YMD*e`+dphcw@it;T!1w<X@isJPkWy?)60%0i5MEGX?=2p@$F-}C
z9PFp6NQf{%KjTD_D<4*HP4_^uWf>1VvzJ4bNaQQ{)`jo{<*Dg&4SpC8krj)M>41O(
zfmAS(t!gBW-1{A>LPlLaXH!mvQg-9dLvQ;KzY!wY+WHpv<LdMa!ft0}cQ-?g>k2W4
zuJAJQnH(@gq&LQ9)kQB|>Jd{aGAD2o=KuZn4-9<sO%8j|?@Uh5{6TkDJ}$1`TSqy;
zzZ8J}Lk|<%?2W#>-qDS-M|+V=1#RZg8{K#xJi>9S$1)b`{b&2{Ou<$B+cs71y~voQ
z_ck1eOLvzEjNk%GZCI+70_f{6^wHm}!cHhB*a*bdbdEW1v&b#ifJ}-aI2!V_<f_we
zxexCgXgpOmZ4g$9pSZFOKCm#h*-+&|?Ps_MMtpR?JU!X<<9ArK2cF64WF+6jgDugg
z8rdVHqLf8!^bL4OV)v;M9==#NcEtm{`O)T8_oxmfBJSb^5)aD!LL+VT>3md;*#kbT
zawva>cEKK-q9<&^r{yPV0$zS12mwZ5`0lai-a#yV0~WauL%gJdPLdd0NHiGtr;k-x
z``hK8xfj7KnCh=fI7&Fznux5yEjoG*G$RKSt_Cf@NLv|d8hTSsafWJ`TKNt6y~|PD
z#tOytU`C+ji8})-mU4)8yK9lsd|Wq!yB<3Zc6u3K8(sYwY<GfyPREwQS)uw;ZhG80
z>L-_$ZVVE=Lny);cn-}lS4I`3bUkV1dZ;0Z<a+qs;rmZXsu-izTaQVbZnoDHEi3c&
zKH!6$QOx$|c<N@iIkdec<=evVuBZ>f=2j2>^1FJGui6qp`s(pwe&e_FTF=tmUXkcC
zSat;K@3*d>X*=}*ufm}dfsj+DaA!p+J8!4wj*VY+Zh{=ypNqX@%(@~u=JXTYQ{lai
z+Y=z1<1V%CX%|myYF{bu0Xok;6-2j<$6j!I{DY%g%bmPKtuv1)Je0>j_7Lpbce?!a
ze4UrS(GM<$&Fo_d=ANAJ&H-I+^Sg$%8rcPx1SFT!hmZ&I;_K&$r6eYuEml;?>I*x9
zS|*HppTiEfejg0EYt3riV(i&QN>fgEKfCR9dUt6+_#Z-zx0ihfw`~WnWHpFaJVoZT
znf8ehCg^v506cVs9bx%_kcV<=iaO#n+*cf@3?DQapdST6Vag+}Uv*I+5AM@W{Q7h8
zML1CmI8pmjEk^v(CEq6<Io5(5J<r+OF3VWt+RFloXt!<RFq}I^5jm{{cgmpbIsx6+
zZDkWG=wd-wi9U0!XEGa@^)3!176@8xqHFohs`85Qg;JPzBMF9a4C8LH<oOcA%qm;X
ze1J<~I7?*;7SawaBi*jEjefZzgj$EQHjzpzOx*Jty3;6Ysj#E=gzCx)lZ=YPpDp3>
z6>j+y_a!aA(ggmfHygE}Sq{=&5V~3L*WvowWF%a2OR~Z*B*fcWB?$O6E#0op$-2wV
zai-!M+%32|H;XeAktgds%-bGA7)U7d!KpdEcH!YiO~1=~;8rIbv7*h|Su^7+Jwg1Y
zHqWb7g;c4gH|h^36YyOnb4?#(;Z({Q2qHli=*BXeGaN+LvB91P5Z1e6=#F;%Wg)cZ
zbRJUkU@po<5P4f8ixn22NBjYS2h~5ALnGW_r^z?Xjc7gO=;72zPc3<1uzxE~?BVjG
zc+zBgg>yH(w4IR`c$#G2{gHmh@Rv)ur)vC%ww(!Q#BwOU7Lza^4@6KYWX>h>n+?xU
z$!f-eD>Pw>9E@)c^t}Y~UjJgKb2w`qa946v_{MMp4<O@0K+-Zy7llZN0f)C6&A;bC
zOJ8bpdc#f3cXOgL96YLcA#hJJ7s{{fw@EV3%|Y~Pz@K2rf<!MH-{VW0SBL2rs!I6l
z>M&-9Y^;ZUtZgLT`*bxap4*LL!C|E4oJ>qP>$-a42y2mJLD;5@S|KK!H0<oyr}M%N
zYooNpp1K7$SCZy;vL*P2?e<?Ynz)h*$)1|B1VhLc6YUO~*OH3%6qYC19h+Obrz(Fn
z6V+<ka+v!g5gdx2vGY9EWzL~)Ohs_B7il3n*1~obn-bsu)3+YRX@hP2OQ-t$7gwSG
zzrNMN(8<(9*~Q7y&ip?vwo2319*+z4%dhV<JkZG^Gq@}2vS_+E7H-t72I_9!ISUH(
zUl^q;J?W9UsqI8T*GA+IfOn@5Q%S&VL;7_=!JUOO!*@URa**2DQ4ng^?f7(h!tBjV
zYU`|y&FjzO<$eDFyp8ZtXi-RRd^@!${ya%t=>0kzdKda>{kfosBYbbddh1zkeaN<A
z@h3Um+8X1=nm6~QWU1n&csaj@QUZza<7attJ=uh=IrtU70KTI6v_Fq8VK3j;=*h!S
z=0c*wTW1B2FDpzb71|fFpk7%Xwcam5bgz;QTE6^=7YBd$%B<4#@*um`Cjm)LJxof4
zLM>J6?GB(fKZA_z_?eD(?YgQsQ<IhjY+Z6mazm?$S(6h1Loxz7zN?;Exq1aw;-%H&
z{4dy<W))t6AboA6r1J_RnhPe@evxQ^_5wtgJn8-(N4J-+h-di)pWfLzn9*kRWAJP<
zivafqeuQt5d;hfAhTk|LpR~^Xrs}YRywe`_+LzwDt~VeXp}NvdaHnrp!5S|iblxK&
zg5)D06_aX^t!J)1y&9Q^G#)t<9g2qCUUy~av%S)=_jbmM4;Cx~oSRr!wgzZ?xvRT#
zNI@gNE958N_0YlLY^>_)T3@*%_*(FA&$ds?b&OZzs=WK1_iUp?!n6S3+F^Y?1Iysa
zutIDQ&T0q<jUck>XmKRm<<STsw#9JC9h~oKGIHiEI~ZMbTfVa@l&yp-B2x*Hg)~;*
zHUR(N30$dPJ?wIv;FScJ@kpp$ckJKEE&VP&y&1onz|*{Op=NRwl?nb44x{)>m%Lyv
zaIhOuz6WtWPV8+^koMD}H)Zr)Q1Bz6St7DIm?*Zl?T%T~^U%w@Z#t5z20~Yh7UBk8
zC)$NM1r5J_qeYT{_9n#B;Zi!Q4ne$Ul-5btf(NKvuH8mh29<@g&m|LzrpczRY6pXW
z`e^5nn8B(;QfMXDiV9XGauPCc{t#PyQykj311_Z9$_yF1ntWk!imAW}QV?>ZcZs&K
z<Hdhov8$D95muDL{l=7_$7KTha<mP1z(oS%|9P1u&_R=5@FOBKfKQ)Vxtyj&6I$ap
ziKFIhH&?OtmAhR-@7<@{BBlwUH<TU(i2rLKDvsx`?WH&H_k^o#`*=QIZ4*Cg4D~Vv
zKN3qNS<l@g36}-hbK`nZoV5Wjpi9tJtvOpQ_)2_jF2AgtA6l<M{UhqP$_a8PrYk4u
zW0f^GGs-etUPC%~aXw|A#Lgi`p11s|x3K_47I31#MkIi7-X}DR^z-X?3>T!zY|5Y-
zs$;<T0YMS{>=NYRgccU|38~Bpy%EGOVF56ThM&Uit3S3ZmOHda?HRI2JAUCKI&BxI
zftTS-(lG|a;KXKkR#Y<Z7ZUY}Yc)q}?IB`tqwwD22Ku&{ivW|_b`rxp6wd67@Eoi<
ze+<pnrKX8?$Kg~!-J@Fmqp!wsTdc{=APWt|=+`{iYOR(DzbQz!*?be816tRXa4i|x
zVNF~aQ|=WJd12avx1Wt1#RJs*W~CvAradyn#p*D?I>mmd<^=15lG4rgaq+O$>_GLb
zHYSUX4@T=%X!8*YuF`g-6X_S~1rhwPfzMKOBc?xEFYq!}{83OJa1~vXj>-xFe#bv)
zSVOH)h(YUoAT65|{g{90<d=?q5X%Zy6}7u!MZ?aMGt}&)*p<j1pLX}d%=s5ebxKWl
zHjg_Vf(0#ANOCaGfW5fKKmNyXXp_u?9R?$zr$U93qsHcLipiQ8L?Jp-;h5SqNV=hk
zR3Y9~o*3#ggpnN=!4DBkBSQLw56EHYMb{_9UlK_jF!u7?vj>(3m6M~dA9H#+o$22B
z82X+`)uYJ$M##>r*9yna(;e%hWANe-qz2kt@6<g08BXFT_<*45#?To{S+xaydj;J>
z-C60wAT#PnqjvnbPH+j9#$*{Z@P=aQ2j)xF13`$Mn5x|>-*>W%Yx1+tbq}Eb`Q7GO
z+kWS`GFv1=?dnI@zlN5gp#c2_a9J!|*tcUeo8eIsVkR~6;hhIzRI?VcLq$9G-+Mc_
zaSp?c&mj>T1@7H*W3eCXrUm{mF?m7I(Ii1=k5u%`lP}hET`N`ME@;F%3kv@{O!%W|
z%`JEmM`7G>?vZ!}uBuo1J}V^!7!so_TnWk*>2{oVE6~gc;(0>uyb9?*cDt9~Apu3$
zN1F*SOmE)`WuTPed4lDa5r(>A;zA17!&{2YqNc=~n_gSJ!+epdI5SUby+-0H#+&U~
zf^C38^NZ=BE<%R!fncSVfi8e+GX5C~TPD4NSEQ+hvOX@GFYnGXLueX~4Z+;OhH%Oa
zK1<lnJF2rQAnQt?WqELRT;;9&37w#e1DEE8*z(NK$Jj;T^CH1VybV!&l$|qwJ>p)$
znoF7~Y(FMSyUlhWxv!Ph0v*je=^QfWdDsC~T%(IUB`7ZC@)-hWJJTG4!;$J8s(qw}
zHE$3VIXHY1RS;?kp0o#SLqII*mbjvL$ZoNN0Z^s#>1jinuWZKHXEl@A%Nj1-{JH$8
zo`Fd`<gWVidbc!v_~?Frb6+Af+yF;gXBV_wjhMTcQ7bu#mM%c1P*k^xa5<W&E(fcM
zzu@pUH7OVrd5!d%dnPQ*m$jrf?$o|WB&V8TRcAi<kI5<AbTnj|QKuKJU|q(Y86kS8
zH%gsYtr@<Mjvt#>Pch>YB2Er?2x3>uHbmnr4dER#BSAI+)nae1C=MN8=9laC!e)%8
zr0yb_K|v)ZNQ9&OpJ6SWdPhX<5}hAwIYt*0B*Z~dsXc5fBr1pV7}_z2h{Zh461G+j
zm1!iIEJo!OI3)A3Tg9WKgS8wH2)Je@<LGkhJ?E9B{oKajfzW4zE<^n1WkhDIYB0&+
zokfX!yltCj6hgS*W9IVDfU>LED?31N*byvJ7v`g^L)mdp1F<5(b$gOn@N#6Z0Y^05
z=^f(=!Zx8J7RQ|nXNfs0R>9;gGeIZ<N=EqaK@Vu^07+Sy3jyd&2j~P3;Q6u?Dj3V`
zd|fQwc>?j*Ud##4tc&3F#@wIjyhr(phW(RWm=?-b$De)EP|u~-;dV5H;nR^<SSMk#
zP|UxPmzhY|;KjKSXBBUZIbk0u6M(MIqlG!5#G~tWZyyu8U#wy8SL^0tChoTz{qR!+
zU|^hia!#rclr#y1(c$Nk5U2Vt48?{7A5<!VyqN-ICM-S9;^u5MU994$?K&GH=W}30
zTcSghG6ziX-s_POJe|xApL?vJ2tFH+1KvvRpAlZWw6o;0Beba=uT?J*CZVsN2mH4W
zhEK{VQ(OR+?||a@g@<63SX`(DEe;sp?F+)y7!!#G-yp%AtS_YhMYPCX@|$q}BiV1L
zKtS;R3kFS`4Bh_^2Qs<Y+KB#l%)<C@e4(WqSICL(Tf6vez=Cw2AY1n;okp(2yVTR1
zye{oqrbY*q73mHl0W@KKKliRhz>#d6V-In2OOk{D8(GbAt_9SyZJE&oGB6EJPibL!
zYK+L_h#fMcIg-e<D-3Fw?NZ8AU-wo@PZ(1i8j%<gW^7IJZ>Vn$wz9}qbMu+g4m#mf
zd*Jd<Hj`$?+b>P+MJl|p>S0M?R%@wCqLy2jDOwj*VmF_q$#a!RKX&bjlCzyJEQbN8
zhHcFLYD|#Q3}Xp2Vn5n?=k+pxxmE$kHJ~!Y)c$JCRN2I5oQKk6W^ARX#{^TA3kx{i
zX-0La0B9C7V20rqSiyexM~#V~Py`)IyQ>P!(1#*Lo95hW81kB{fl9dg;}WbsmaHWv
zD0v&9IPa@gdY7QA&}qU_m=KCT7t5|h?HH9dlzAY(G9SWWbbK9*XqRuCWhGR`&t-zh
z^GAv@h#bZ2dW;VbDuwn`!ujJw@$GKJpPg|Qpg)shoN?<!k?+3Rs!i-Fqg01=;N%Z@
z*NzJpS<)+OPE}Px`uuE;@U(x_6bB!DbZ1fpg}FM!?58lvh(JIeb@z_J3R9#dyhG-7
z<j;)-RxolA*bW@|F4%gc?(u%Zv<drlf5TrtBYl?m|E(B^6UryL^PqvdryhU`@v1`@
zGZ6{`bu<j`?LIBjMYY$Vdj<94{6;ROSb;oaE7{t(;d*5^_kct?OLPyANCD8~)W+T>
ze6?HG?Ya&J*E$BQA0G$oxBdjiaa?i0ts;m78F*XM)@>lNGmkX~z6*jtZ-SoIi*JY9
zBWNY!)Q)bPqS!}dI*)d(#J={~4f%w+#WXE&ob14^%f)cw3&p$t+PFlq#jcu(Y3aq<
zs)2bT+r8`wzi~VFx%w4hoa7#1&yW6)1jxQ86R;a@6)7JFN+3UPgYPk0WQN2>3@Ty`
zeG(dP@(b=IH3#$MO_OYjDXN>he0Q6;fV27(YJfkAKT`Xwg9x0W*!7ZaA{ta;XoxgJ
z<LVWzm2b^nY!4+>&mkffZp|N)>W>i>zTD==pYxIU$6UsFYw@RYhu=RGwnzbI?h-0x
zDGdxsXM1@O;tGhSFw6Wn8xd72yYYC)nZ+V~SWV5BWj;l>Q*S$fKp?Go)4j6yHq5oB
zqfRnh89%sJf@oehm>|T_uh7+*^Y0;@4|=W-`TP`jzTYwnh7b6?!Jm8%9j0P74n&Bv
zI8W&Rs`T>I9jJM?qAeEY7Jy4j@jN-VxCmDA8JliCoyBVSXBJ0X(_=45_2<ak_46rW
zw)1xbuf|~=;KzXn@KxA6%z1p0&d^Fc!JjWK6VRW<&dY258{TZp3<C?q9xTT1<33Jj
zv%ZG!nx%0a^}g_@T}Hh4?P&*rF9fI`E2}jK@~vx>%S+k#k~c{WVP1~F)SQ5-O{wWu
zv#`=9isAF}S$mab6&k`)-GI|RU+C93w3^aV4}PA9LMsjRV7G_|yTk`VSG@+l`-&f{
zzngOT7QQO@{8fK<4H^xJjS^{+pVU}7AQmLt5teyDRjstYvBybY5~}{5T`Kxg9#gI@
zKhQN(ORQj*|2)cqFzu$zgxl9|rC-jz<=FZ)@SgUlxOsAVheoqg=<e#3=26_wVhJu1
zpvJ>sxvx_+-AZqJHoPxAOm@w(A6$2xaO$li=+;H}C1UVxy`I}N<WgU6KRmMB6h1B`
z`^s_23!6oVrIuX-b_~=v*HTXQ7Jo8m5wy&{6n<|N_m;f=y&hbDnQ#ec=-><LeVzW_
z#lXJt5bEV%ARw9lxb6RMbIbZ4bDOMdy}^tg_U{MWNWz_CF$=453r!USCp=!Q2rd1L
z;xtjkLsq5)#xykx`n_b6mh*G518!^@k{^yUbCvhE5hl9DFkjK8wsKrChAq^l!<TJJ
zV&Ir+b~-}C>(UFY_F<AQy5v=A74h_&Pu3mkReyC!ICOymILz92T2?)wF=Pyh*^}ky
z6OVS+>IHj`Xh<lVw^?$hMX70ga~dYL@h%%$s1#0K3&St%j+R+UN+9e570~Gm?A($2
zWxYy4xExm(#>@;#&gw@^Q7$)JBx>Q>RN)5>R4aYi<gIzpuG%pb$xaP0=_s<paDQC~
zLlZ_GiwAV&vUlo0j7@fZa=2}8EY$(OK;xG1)r?ASeWax{Uaq7(Ng@>y)VLwTZi^wi
z9q%@=RBz3yr!*#$jT>L*eAGJc`;YR>#{Y6{((Ov8<V?M_Dpj~f&Y4S;y9-1oqcj*x
z)vnN1rbyGZniiu9XWUN=y!7lowgG~<dp$(eq|d?F*|}BCApa?3tDS){#H%B&{_`iI
zFMbd_c4W=Lx;qRWoh~Gp_YfB%A@8-AV@6qq0Mo|cu0((?mIINIvln$w({Rb(h_TtX
z(ZEku{9ZFbrSNFRd@_+*-)Z63SHr<k9p0mRd{1_m@enKbKtF=FrFCY(pgGdayw<o$
zN(MZWLGMw<W~w9b1UUgFd&N&}z&MoyxG(wns)WU%`{9I4c{2i}_r3bTJI;M5jkJZZ
z5qYE}>mNz{zwD$`DCyZ^2iUf2MaJQQ)b4?gI)<HC>N?tM&=>UiW3j%0?_wAHUxwyg
zq7Sra7lV!NuXVnzyFZY|dL!O%iS}}aMgxIZ9=Vi+*MU#u0tn&8;ofgV&;A7NAwF@u
z1+g!H+xKV7You9g%Uqz~-QGXM@INYl+I%cyx&IaH*(3k|t#J1Ltnf5N`*mjIfE}t!
zL5*#2rXun{5Dplnm2&%yBHMYwk_y;J5<3E3Y1tp2kGvcC2+G>F!Tc^@k*l}aOzy`_
zqmgyv+(ETvk_5Z1G?K|C>~VBd=2A3t?J9Dl&PvDly}k=p6RGTnPUEQ37dLICuQB{h
zQq-f)9&*%D$-|I`w`w&Q6)?6cA(j(KDAwFE_id|*dF*PAA>e$>lu+KBM~1e)D{OzE
z$dLIgwB58T_M96hNotm0be5t#b=RLiO=FPNDfT5cBesP`X_%tSyl+m(R8C&b&MtF%
zB+wobh_xm}O51X4Os5wVKf(vnts`qCJ5H1-P^x%h85R?zNs?F;H07c&8n*l?Me7q`
z-;%88yXTL-7;*RB)ui$Ytx7XeiuX{I>QQqk37*(@8V(seRYGBMfa9=MPoj@-$Lt#j
zE2bs;qW*dRXe+)(t4#j0we=Yhjm9>@d}^u62yP^OJt4Z72BWbnVWuP+ZnW)J()Ol^
z%!nsxJ3ZOdkWx=vVy7z23aOZ2$yKvd>u=*q@}fNAyBgO7;#!qM)=>0eYYHON+L!^u
zAk}Zn#xMk5u=ocRyDGbo&Jirt21Xj>d9$zGE<bL(?&Dig4C0P>1S{ogW4Uwzfw5LJ
zXR)SS{@;|sh7)Jm1>4Y0EvTZoLiU7+LRJq0zkX{qKJ}2K>VV&a3Q6Q*wHkb8_8%mw
zF!)g0j7exNT+5oKCf!d!Ri*ZVZOhZ^N028LyTZ5o9)N$Eg;)}K+RaLc@P`G5xw!W4
zL58?M;^gWNcNK^mwi@l*jA6B>XI6VPXm>%pI2o>Nv}0O=$APY=wuOFa0(ZCge|YTV
zYDDB33!H!m-aZ9B%lCal_aW&5wS#vg$lBzlm<0r`a&|WgzFXUnkcNDJw8^v20e}MV
ze026QU0m5(2hD?>Jc3!-R(u~N&w7?IE2;!h9Ta|67S9ILf&#ZA2Z4}&F4VVZ-==$8
zpCv~=HcOp-adLg<lusDof~R0eb#Z~;`}YET!l6ir2fhd0I=i3s_F8W(eexxk&$_lq
zatzbf_LeTK+ceig0!<v-f)*Cy{z2~`U#4l_5dTZ#2(85TYl#0Tqtm}U-~U2*HzPv@
zCwnVXV;4pvOS=nQ7z351c7f*^avWhWwCD&3*B{a-l3DyPl*}@e0hFjx|MCi1r6>)_
z({U1DMtnhh=hxqcw9b_)fp<mA*~=aIku8`T>jlFd56v~c!_@=ci^XuBJN{SvZ0?ka
z%rG^+SvS-C&w}sV->=u2U(TzKnL#Tp+k%g-DMm+Z1iZ{YV@<73P%j|tqNB&_P6EJ#
zgM)+M&kgS}DgymUYoXY;{{|3_ft5`=2RI4|#KT2H-vz+*sThzNK)?n_^`QunFyp-7
zI-oN{{=}sQi-j%$9R(Q$8wI5_;8B2|0+9p3?~^sa6!LK;&1nqe?$gPGpoWwKn+kxe
z1LN#N{JG>{dUnsu7OMj;qlxXW*c2KUdUIiRoPWg1mDtZh3b~j6SGK$7Gfi;kHAs|o
z(6waGnfspkxlBp+$tFM)^%M<I!bYllyZB~lQmGmLScg#-YS$9heDkJ}=onkVs#zdl
z3_(ql4)qa13t9qt3Vnlhn8o)Nl@{<&jugc+Mh-P`9<#V+D47GtowNQe#TXAi`{bVh
zO%7i6t^6%}`8@<q4&L;cdjL5e@b^t{Sd2Bu1mw{_frK1f^qb24?E3+~{5$c37)R){
z7rzYT2$TvuSqOufNF#4Vtm_!GONavqZWRz(@ox0!Nt@FwMBsp12T=ws2b}_jAVgLN
zuYp(vq6TJOwhI_&gn8j{;H-nSfcg-En}uxx1<fNk0Z{}0#lh!jLNyB+62OcMx(v1@
zgoF&~73kX6E<_~M{pte*0}2C4fkr@7VkpT(SJ`#ANjzZCmIo>qGFx|ydGbWj5IHYh
zZCj>}q=G#dVTkTtA!z>-ESwU0A(uEr{!kg6A{XWbz0^?1gLo*(dxZF@zpKTNs2O_3
z*hS^P<{BkKiO5pE+wyyM=^Tr+OH}nx#+z<2WlHTz^^u!0Kz_FM`Kj-!7l!c7vOaai
z{Io<Hz2poBjGhxq3Bh(rf*nQL1QmJiC`BYZeD?AsJIV}p7as}N)sStW9p|$~($R+^
zKM8r*%Cwg<mh$Cs2Hom7QT5G)e_a43|JpeBzt6erbV>vCVvbGWQ^0%kz^Idp4keoV
zuR7z}3+Jv3WbBh5WheP7<?h}4l}u^vbYko$qa2<yQXE#>N`4A*J!=#C$C3L_`UI}o
zv}Bx5GGI_lSuEppaaSqikQ3N*Ze(Ls19}|{&D(}|Xf9bfML(+NmpH!UeC3I2r#A?0
zBoMw*X2{|yuMwMcF)S`08ZU-zFZ@J|-R*luSFm6Pjdz<ygw%@?Ukd}zgxnpn<#Jq_
zOIxXMuVBq`YipP1BBX3_9=>hfV6!5xo-r-NAKri0>BDs;E`AdQZsNCpOL!bx(dd48
zFI?}q?>s01@jQF0UjB>=5n+$KC8JAqP~uc<Af@iS081{ve_rOa*Wdc<&OLRYG{GPI
zvZ`AwVoQZS^*p&RG9yLG8it*WAxDy=jn*ZkHl_|;A`&Kx7ANcqNRds3z7rSAQTU@=
zg|hu1-3^-&PcRfVD#V(|w^nDGBI`o&00eSQz!@Ps|DIzFN)-KBz!cTEl0Q!HcF@LN
zmGJ^`Id?r*dW>MC6B);c-cCjkcPfJx_T%zw$A(=Vu7vv%c|*?UYw~t`Mo-mA+L6;*
z3IER{@qpDv-9Uw*Yad30mZbFM?a3#<Q<6-%UuFBI6{8_#w_y;LuvG?W(ZQdP_zcER
zl^--BrN~MnWyne;oFqlCt4N{otRbQCM%fkd6M9rcwXo$~1`YAJ`M4damf8_~QO_zo
zzR6>=5vpk+{NY=v1}SZwL!+ap8?p=fOI1tJ!cgok{Ya3w?%*R`(?6cG!rXc}-Ej3P
z#V6%Yd;gqrU(J?PGNampcrWiu?H|qIl8HmB{6xLI_4A3BwD{33Q*CSe#%@L=@+LBZ
z4*e&18FfaH|6jY(Nc%mDJE41~Hw8rzGc7-@LF9WT+gHm^BlyEq3qNt^R@nvj{@Su{
zKu{@b3|cU}z_GBK1rIA_qUZO&q{ej7Tmaz!_qr6+^aNrKTZW+NPrr`O@~IK|60Yx@
zl?BcP=TB>ZU`DU;V{&?QE}@{Ibm6azlb-t-G2P_K9Y@^1_wXa~<^4*D0(^e+u|=%u
z@&pqvk*k*rc!JYkD+GeCy+iv{=ih)^Wz0$<!w(Z2vyX%CiMZUJvmN=%m*8bN32Tdw
zR6Zwu4|sM#Jf5$H7!p^lKZL$-rNqTZi^VZRFZT3>|8o5FMGCv?Wk>a!9$DBw>leRP
zC*)oAf5E*nhqL<4`%CYa!ms2~`F$Jgm&)Q9P?zHT;_3Q|ek)x16Q)=#NVM*iSfDS2
z!1eZMZZ$G^4v^(pO^Cge;UmWx{#tm{AmQ)&eke!y$*O5TWAc3pck%5Fa3{F>hQ9uO
zV)FbxKBsDAaW45R{jDMOfp-B_MSE48%z&Mtc~PYk#2+C0l)K&jGWxW-wO0B$^mS(Z
z{tjlEUX{B!%W~5{`K-3IrNC3;w3N$$W%iR$5+NnS<>;Jh+R|NE5U@6ksnRetgX<`$
zbs_i{F%8?MxVgN(Pk#WDNL7|&WN<|HaAL{l9@(=@r3Gbt8LO0rL?G9fi40Tu9=6Ko
z6Jl_{%;?t~Dy#^lUkq@XW;F>FS7cy^aEJxQ-$=<Prh+HEBex~eih!*w*(zz>#e(C`
z;QSY_=T8ci4j;;=g(<1?UrF_{&kIi#v?SpZF-^@MxbvUbd2MbL0FlU6oI*F0ZARWh
zY0sRCzt)F`PKmPbsEnV1{mGK<ac4A0J-=zU#9kFo_t_sb^R8w0b0t4%xAJa#A-`IS
zZ@-aGwC9J2@)@4kzoZ_-=4agldz{if+QRZvOQqbA_E?Hv^y4~KL2MuxAsNBH5ZdsG
zw?Ck6egk2F+I_8obb<cRHDH|J>fk$|eft-kea(H(0p|hVAT`i-Aa)SU;l^B`O>v!&
zfa*SmzUn^PzRNy;Fn5sn=L_h*>^`KxPLLDgdhY=5z;}@K-`*grh#x;*SZ;`K(10XB
zLm&!J5y)Zyee?4MVipM=Bnhi(x-qxnsr2@PTgdku2Z+_j6sQjh0PBVF1HS;v0ndT<
zgNuJ#gzLKoiMprY(}&Un^+Wo=x`779ft(h0L3P!<`J&0ceSE^a*aHE5gg{#$0Z<pn
z5A+2x^+EUP84%qF_I-oB2lj#yff0ccf#-twgMT4@fWJ^VfB>ZxPW%@IEsm@u2PKq+
zb}H@qtlwjB9#ZFLu>!Yq!?vN{TF`keTXGQm7I71jlX5G8-xch)HsIY%2;D>vN9+%&
zLi$NzJy<TzxIC($<4&vvT_#*)`bJb5nflEWZ5NnsPVy2|>x^xRxyWG$yyMi1cM)$8
ztk;%gCur<Qnn<b973+bJ0G!#!h6Z#coJ9m>NyL)`sl)^@tvrpGJdwyim4a-#%;J|O
z;c?LMBB<7&w1QjWp2YRH=|wqQBnhGHxjy%N8kJiwaJ9pR<aJ&9S#x8eZArwsV_|=v
zC@VqCj`&8EP8=tPzeZdh#B61*W4-X*&wj~z(XKKHuz7eTzjmB!ty)RxV<3b8ned-;
zVo?N9@@rL^^<+6VEn3ZqxxXv;u`sX{FW2I$wGCFtzFD1mV@_Daq=X!j$@wA9YWTz`
z?zGllLLECVy14iUWT$8VMu&HTB@bPz4M4*S_|EG4O-eNydGq^2FpE7vBE^y8+{(54
zJ&`yM;rZa~ZS}MJdneeOkUQXZ3Y>__!GA%sS=Mn8%DX@_9!RN1cWqW0KuM-YQL}Zy
z{)+Nxr-npp6gD+&DD!!&_Fm{$R5GMGxS&HWvXQjnhvx+|e%yJJGviP3+>7q1;$&?!
z>4{61sPxo^Yl_1nD}kS$MD~Y6n1Lc7rA?7QGebjJI@+An6zaFb7}%rb&^=0bnR>o_
z#p1Wa?iQn1M<@!CBS`bM^dpRA12Aw`1qy=!e>By6qJ<n4VPt?Lk!uqpaJ^|94gR$g
zFbSBfme`+R<#=dmlP%g?CEIG6IIxR>TB*Nnlw&_Q&d5U`h;qpsm=m&AN!^c4BhBtt
z>t(NO3T$?R2Fo}7+X9<{b`}B2q)U#$@YtvzVW7s+mfZg5H`{NS!Q+M;CHpombpAQj
zvrG7wPOcdp`c{3tHMUCZP1^d_2<<=e^aSqZ<Hj+j4A1UQ((}5*LzTlN-7%HmB-xgq
zi={HU;%v9ZQ$&;ZVduQwox&{L;isEbw%LM<xE7T(@NONktO!ZrBKPpBzS%t8T=?7o
zwpM?`gRYUAFyo^`HC3WxHv+DeCi+DpoejJ+_2zm*gLR)=eCJ8<s(y9_?n&1AZ<7^8
zxMOU})a7??4fa`Nm?o&tBROULW2mh#Pk!h3-!WTX3V^;5d;CU4TCdg?CjoTPEj*^$
zN^QZ@^u05Xm%G-g+D1dyI{psXSo*AUxaM@s*50?ID;&?Ip>p*D_C<5uV*zQWt&H5v
z+L`EjKO$uap^uf~^&#b-NtG#JG2dVopX3kzRWvN7chc{IXu!WMXc41qFwSgzTLnHZ
z3G=NnEWRW-Y`?#yibRYRStCDl>n#Ln-UXW?xh>HTzi~doRNHl-e}8dWT)GS2@36Mq
zjgI1_r+kYQIr~qn%8W`hl7h3ZllZ2;bG%tXHVqeNJf2;lbYo(pJp>8I<dSi?eD$kt
zLr3VUG|CS~FO<Wo-fxP==!&GqO3yOjcIq4NRMjI{Tr9ANB9GM8UoRY(H--u1)%Vs!
zj#iIvXvO$ejxdV<u}hw$(XHMFsv+n)T$ko^QM0i5cBR#Dm))hZPOdz~`!jTgtWgrE
zDn?J4nbWiol^<TmTZWg5<b>^XYwineQO#)y{pHKsU{bVo9V>SDV?tNMHQ7WeWOsN1
z&UM@RIiG@`&R;{8ikL13*rZ?R(i?hER70dD&C;YA=*|+W{BaKa)+!e}n|<7`*3+si
zYzCw7O_U2~r>;qD7~tFIZL585c<?a|(%BBo-pf}YxPyA(i5IJchEgM^d;J+NMu9uJ
zLxWX&=Cqdnc5Bn?l3Ns)YDk5+LT$+pxK9qhvz;l?xJMzYW|B1!Oq4erEdF#(U$*Bl
z$@uM`Aa*ZS;WhzY+%8|hhcPa#M;RpNDtQQZ<8{8Qh!b_tE6FZG?w?ffH@vht3S1NA
z19NCHNeikX@-smR>h9Yo1+KTL=c(u_PO}~R``qE{;a})n_>pLTM{ySWA;7Q`<e3YE
zUI+h9TPaLZijyW}t(2(fF7zODGyNN}u)Jm5mCbt~uBWfkK|n;!VX(*HSm&keagC5<
zi<UBI+QZM_anpDZRM-#so6Mm<rVG=0uh=Uqm9}xdD{1MEgHjh0jj!_Sr;8Cg+1GO;
z6g1oAbW=*Re1WF_-!AzARZy?_3Q&Ga)xeylGa-4c<UFcsvsvZcLtoVDuvy;fl-Wct
zX#V7sZ7P)71ztx`{^Xv5WKonUMcy|2!X?FF>{Y_Z1MHYhb|Uzzve#MI>kpm6JFqZC
zgmL`HNmPPs3?A`M;ZcgHDRl-@@otf$(pHoo`rT$Nnq-P_?E0vQsOJ!r+2ba`Jj89}
z>FY%=;|hh(3J>xYpFD=tni-9F3i`F9=kc%w)+1UOJ$&O_p=hoySyq-fsxue>pKo0J
zX{}iX3(&X_as~s)Kmrh)iRqaMMinW__m$eioI9KTxGoNN`Z4`p%R>>2^b7sV2R%n|
zIkB-$4wG%Wv~H)EzflOYEAeT+R2Fvcle)G9JAKI$Or>3Almu<Uz5JqZW8eJGxM_QM
zia9GK6(<)WeRGeBd~@Ha8AA$9XZ{W->P&ahKcHR9iFjqTK*M%&>J<zNX`si+JSdTx
z_{ByoPmbCk-byCjSLO+_;W>qK5?g5grQHg8o>XC}8OxQggBC8(XVD*ey-eaho_+eD
z&E`^Q)8PhVk`~5bGF0WS$1J!^T)!(h=SrVPEr;o^IiT?MIO}Hjdbt4n!b0+Hs$-1i
zh@An+W{|af(0?V$V_G7fp88>b3K^`_^BAv68HuRCD9iI`H_DH|5$-UN>?SH*U(W*m
zj>hKjJjhq15a*3P(B__x7tY$526GGjS|N+H+2wFLppWg&MBcgBuTF=w2EO9vmk3);
z^ZE<Uz_b*m_17!{)t+XU3&U9^QmabR9)t?RmsuatmdN~{_-x6+U~{Cn%=yAjDcJ~$
zpk6t-N3vpKfV{j?jNET!Z5N3E+q8A9;v;*EiC*+pt*oGo$Gc2=*2I>!Z<8^uT?3Mx
zfN&?*rDBBQlNtK*rXGu2(aGm@QB?@f7hP3M%N4<C<1y%J|M>wV226yN73IS0$cI`L
zc>#-s)@X)L#Z-Ts`BHup*7PjP4A3u{L;m3`MSH0gs+603z@j?X$Im9Ga|N`Yv54Yy
zE^u;roPpnJr0N0>kxF*YB-1V|Z_MkZt7WYUV@el=>B#zUPbXjw`Mfb!D9&)#bLG#t
z{3EU0%EeKa@iQ}k0MUG%DWF2p1ndDZ1UK?X3_ZCNt^*mI8+802V^8z?(9T%4S^$j?
zdJE=IJw*HSt=<kC?s%?f?h5Nfl}B8waLS^=lk&9v^G(4hFR_FbF_*N0h*=+aCiIm+
z8#UF&rh}>-czvY{ogJj2v{H^r0zg`T@fV`?s?qilEc9Z2N{R*fe*cRl{iVE<$UBvx
zyaTF<Fp1kYCAhLdZqzY5P(vU{ZVD@ziRRIbUqE+)W<HCgQAPk$2siS*9-W(_XDyP&
zwSdE24Y!V$XER$R)Z*8rnm<nOyQ#y?zqidC9PDC?#6^DF_0o~xB;vCXJi^qp8!0AJ
z0^JZ`Lk!IfvLB#hz!wjF0FD+A8Gu3!LJ2AX6598-&jd(q0PDccjFI;`!p;{58VeZ<
zF5Bmlhn)tBkCc`7_KBO*7=RYI)`xGvr~sA(Mg`jBWer*tfKUgH*awpYdME_RjPw(y
z45A|7wGXThvJDgo2w4G2qySD2VS(k(;sEPFs{pqMS_&!?@Gs@ffMyni9ZY5B(4z*#
zqBnILFmzA9^ySJ2fKmgU4j{4s{eRl~%AmNmZd)`E9D)W2?(S|ug1fuByA#~qf(3UA
z?jAh2ySux?>o51#%{iRh`@UQC{=Mujx~i*LW3L{2?(VtfoNG)~Z)RMeYEWz-6X=3{
z(m;B3EILZ%0@|Z+<a$@B>C?B5uyEtBwE6{hwBuPKx<tY>A*Q=)KRP<tL}XMDBjEUB
z>GC570tzS*f`Z-X!KxAo1r7M+?Wu!)WL3jNM1K&R`bu0B8<SN@K+dkqo5$uM52x9l
zInT_vFg_g*Did8xpTr%q&ky4kdoa*%SHmt*_9!C5h8Tew(Wfpb!_{5v0m2<TAI?v%
zODmyIWm`1T!IumXGbc2r;f>?ZM`&WS$Q@_k93Hm%oJ6;qk4`tWViYa4z9znrTgLAh
zOa+G;fPOzQOv7|9Y;$dC)P{Kznp?KubU@v_72Tvo(1yTIX}^%N;I#9Yaol@6jaf8&
ze$J3CFrsBM0)2j{q#l{nq-3DwdFI>s(h0l6^|X)#oxpkhoyZBx?a>X6kLP;s{@__(
zZFhdT=CN^^VYp3Y^kk_e|2EsraoW7W{30U7v`r#KkaH=&7W=F{V#2B{RO4bX*Q;gF
z%R6J`{&D%}u1$4qS-$o3ghgVBUS@PpC4#<(HIySK_LQk<DC|V_Yk4`-C`O8&eq;02
zhnr7U=qd@Gg0%WJaOx$h7SpL>^2HO&5XF*OJ6222pQ8Z^e(TL{0j3m95Vh(dd957o
zaMgeiT4!G+EZ11Awb3fakiORV@&|KRtT{7%7ngc)adnW|?M{|(b1Zt6AoyeQ6?Ws2
z)%z4O8Uo=Dj9%7H!(R(+ny!xgmaZi`ExlNDoSGpX2)UHnGf#`_-@vV>R(*JD1CCYZ
zC0E}b#G$@~SmyH%Hh{lwgn9kc+*kVh*(cx3h`kLN_X0~k_?>vpxOhJBe4_(;d|M{0
z3wBE)iBy(!LD+B3m0Xb?;*vSu@0<_h38qt9bjGXpg5{;@Ei;YZ9r_JO4&IUCXNLvw
zdnt#!_n8)hb;7Gj5tB*WKundla#o~XkyuXj7e3qWS06xS<A$|zkybrGOlD4tBv^76
z=r-Xb;i5s6BeJeZfwaVOtw`6!mq;;Q&E&R4+Gmb_PKz^PBq5_{nf+h|y7N%5lFCV`
zJ$f$)9fu9mTxpZJkjx~9Aegle0oN{Lp*jY&(j|h#r>1~yd~@R{G~$93E2K(-j~$ok
zVFMDNH;XY;e?pNh_wI7_4Mb?B)~5}=aWNTPZo~=+ZM#)deIjcrZIXUh<6`Aa${#9E
zv)DBHrM7U6l7ZX$^fSQG_1A^x{lbs3D^ko_FoN@sykXP9#)$o#Y6<1<d-46}Mg^8}
zt7_{mU1|3iiP`t_6nAOo=}u#zNBR@&iZ=lfNi}jteFLfYBvR{L_h!mp-9>X#Ig&({
z=yLf-5+fs|#&sFR27B*^-=5Ejvox9D9iN4+;qx@Xvgl((j2*O4Dt*P3=w$Nz;W7qe
zR_O9(+jrSTB8u_LX4=BUq_w8hB=T;hN{T%&g`ry-6+^=0{kQsw(1p8E=zM>E^i45L
zk0{)!j%hKEFL=Gm)+}aAt4(Bz))L~XCwkNS$>*k3*m9hK`8^uCbiIJ)VNx|Ch!;nW
zO?sMcl!J}^XiX6gy!jJ;t=%A864I*&P=k0LjvC);IKsAyt_uovYH$wZEhNNDO;Pp6
zEo_*lm?+7ZlxC)f>lf=QhV^aO#D?;Ss16{+E9SMuSQuYs3%Q9U7QZdDN6cm{6qa+k
zb}>`0ez6@|OZN9S7d;H>nT4-N%{8LXUAU`h9K6r9ZezTf*(fa!OLugAA%vOXjOm`(
zq0&i-^bawfUL?uavbj0bjns+w#6EAQ)3r}U2#dz8GP#;ufw!tEg0?BC5ocF=Rh_zm
ze6*x++4s04SXDZLs*+BYrFHGg5^CvX>J)u6hrhreI5VNAG-e0;uC1+USMs4s@Y*P3
z_og(f=fLP_#Hl&`#F=A-FS6whr?K^})vCYN;Nc?eWQSvAyRGhI$jh|6{2<2Z!cNo7
zo-8e$Nz&P_WpX7XUVLQgSje|^Vt{!6q_n}uqRDNZq>hVy$)6}qO)ZQud1TRjXL1gH
zcXGj(EbjYE6>>_YNB$^s4QfcTkOh>)?DuCFe+8sO+|byU-JBN)(_wYfYuWUHWt1l6
zV5i&s#sJJ4lI3o>G@|cLpF^iEq1LDH$>wMGo75ztG>zEPO<wM3+|-qpPcJbdBOmIu
z){8p_2EMJQ5vJ<W2JUR^XU2q*Zl)LpcA7ut@$4Pv*O(k@61bH7=pPRpuUg|Toyl?d
z=5B%Qzv8Vkh?_qY{+&{Us0$-jCWqK$-lrCJeC9}+d}_vCLYeD=1oiw~c*gwK&;w)`
z8{L?J0Dg@O{zPeW^z{;<;JH}c>TGHzJ$1A&_4rIh)gmUUsf2{0g2imH5XuW@Y?CK;
za0O&OY}p|5(D3N*8L|#z9-p&8_3z_tr+6J>qPa}@&Kx@G1%+MlcKHdf&$1xh@DA|y
zqk%F@CVc|SOOPuy^(Cs&Q^Q3!8_bw2$W_xE4T8FBO&1rFr^}QW5}bp;<hM9`de^hu
zKdpZ8t93UWx9zsx5<?cjFkqpA4a}AZ!PcT(5iKvTova%t8HgIy7CB|ugo<1)UHk5l
zOfL`+$EQD&I*wIj7(rPrJQ>+?JAEP5LLc=lVcjf!04pT6uSjbWAqH)V%G9N*=g1-a
zsYs_vwdr6ZrY%iDfANeYHM97cAYgv64?3V&g<AkU!R{89FAR~<oS<II1UR0{j>YWW
zCW}<m0@ip@a^`aHA%JQ?j7%I~mEh`oO-4TX5TxsVumvAYfu!NbF)Dx3a6lDv>Yypz
zJGqS8A7WS0T73#d$b#Vl4Z?HChCY%`u<`|!2Bq^>oEKy)Kv1sx4jw*VU5_d<@--t%
zY{!Wh1V3z2Z+1jcL(}9;I&lV3hv1&qPcXe-HMQL+v{+IG|9%IXSxy|iOofqJ)82B)
zy54bPK0mpnFb}b}o$chjb`un%b<aa)Tqh9DBAKr-3wbRi;7Ul3iD(49!XZ=4<yLBp
zQ9lk<L3<&Pc@-(!ZIn-O?Hn@ii(ioD>W;U3aA!zu0X5+@!HW@LB*9{WUhB^0U)Gc8
zFI7wIrpDwrCOD}Zmj5)CD}1MJJ~-MF$)kz=Q7f$|`yFPFM!}?jLGVa=ij_B++IQ3a
zas3)<0YPT`H=+c77AHV2R%oj_K9Xxt7TaL_Enj3ykPVqr%f{}|N=dlQgZ*SR5(uE>
zqj;MQ$hxYXK5-ai)$EBZ=F*=zySlE&<A=1s_`hBE63m$0ZKk4;_LloZ_0_yuJ$*H1
zY#!-8l$yzSUvB86dxzrqOs2y4;d=q=lX0le+il_7p|LOnIj1jiFY){$)O4znI^S&O
z>AqZh>gqK^5kB+CtvY~dNFI@z&4yFZt%}~QNZuOv7vZT!qms-s3Z&r9GYK@|vNH+<
z;<C#{Tov$_#xdDx4tGx}bfd+&4N^vg(2I#UsDzh)x2#AJBR@o0MoO7Fc3fy?F78sd
zL$p^a>MCu~@RqI?Rojm>r0&#sUNm+mCHR53SVB-gCuCg}xw?(sF_g5KHz};3!K!kw
zq5k|58kPr-e12!D%N->$%YYl7F3lAoaX=UiwWk#oMZ!mA=m9av{|>}>GE7KrvD&ky
zDw99L90n0f^xj>Cfd}nHPcWm1mF@<piU!JFsCiAAaTG0AQumDsTZEeKP=ch7ZY~sr
ziN$o0uA2XlX^qdzT8CQQx1!mD(}Z}JfkTuj$<T!?aEDA+DTRvOy^55<L5t7XdCiN3
zKxNP^3f;%_bzQPDI;CfEDjrh?%s&2>3PU4YW-{oZMy;*JxRa>6%v5kSfo)&YF;Bb_
zilEK6n9XY5J<X`e=}=cFH!~4!aW|EIH+M{FK|u|jJG9oar{CrxF>|BR-QM7MRKf%n
zC;<E<Y&Q@;78h6#GtHGIG~_+1=b9<S$B!@}Qf||8ERvJ2TTYXQVi0aYhzv8al|<AR
zmk#KHiriHwBbIkX2hC_2*nG};RK`-QPl)gCVrMK=jmj7bjxs7zu~k$VSkt<zke*I8
z!~|F9PG{Dr!p5jf#H)4+5?w~?=nR~A^f6uiy^wHy3JQ57pkU7Oe=L||_*qV*AOk3v
zL-52o<bgcGqY=?_lSnCY%sALxV4r>ChMvosfK(js(9>8i>GdR;Fj1Y2X`l!13r>=7
z!tK#`a?BvoG(dZ=N-`g2^`1gqrik1rRum1ERe3GvvuJhvTDcai@GT0GB83JiqU8hE
z67+RJnREcnZYr`L#f55;J2a+#sWi5sx=6Uk{7y~D`MpR$Kh{MU&bf&cokwSd@B(k}
zf+`&qgXfaqWkV04C@Tff`@PGS0cRu}!Lj<6tuR(o_Bp)*O2r*z!{+yZ3TKdMKehaT
z^<6^qRmBR-(do5m1hZTjrc8RFTxa}ROWVTuZcXw<L#!unaOGmCO9#W|%qhtc0$_O4
z4-__mWBqhSIvXh@=N}QX-=_+}DsGBF82au{h8CS0mL7dCKnZcNkNRQCiLDD+69n(R
zDk(-Oh&5!QLEb_w^FtEk+B2(EJ}t<tR*|D+QfNalPF2+4lX-49Svzm6jfK)FHI3yA
zta*w4%JvwFQKlMbr;h@1Wchu-L~5y=vUiT*CZ=B?s+;V4%nIU>iEHZ9mb`1xdg$4(
zlRFGbjS3TF7oFX+E`850J}Xt}*fAK1qcU@zmDvLw0;W8q`-g-qr0!`1Cwx7!g$vlD
zC)v%KYHMy>syT84*Y)=EA>PcKPV0-#X3ZpGRgr_3x-a9H>qyq$FvK{{fK{&)^jylq
zZ1}#xdl3`xyyLDCd@Dg3)~$y+(nI;aDyPlxlb)k+h5dYRFK-qH0T<fjakrpl>OOFj
zFv480lyzN@dk>9chuyid0l&NQLtNm|l)sb1>9v#x*9*T^Z`H<fPus@9W?T6So>pwi
zO)D{f8&T{Fw$@GgfS3nYF3()I6QBg=pMuy^tQ=gYfOj+iwoLFJMLK`!0;6SXVE)%$
zFx3BgT@uqF3Q7l%wD&nd*tV=J#Kj-Fmi5!D;pY7@d>mj&j2ArB+9C($Al5Brm2}t7
zg40E*grkGx_M`g~eTD%jNPqY6g!ZbzgR>ocmb_S68h)lXD5`SJjM5-Us`-|b!DS(2
zo-`fpbn=Hda?9`9p<!=>W4X7rDH2_z`y11+rKys)CAU^!jE%%A_Fy(8oS=2xggNiL
z91q=2tg)BWc#DjLNYT3J$bVG3w43q{oa7}OLhU#AtjD~6pe?QP*ufqVXe`&1;b};C
zd>$0h8YeT5Lb+g=9?-miP1E1cnLeMIkdYmV$a?y1Rg-iYAHu&y*iZTY|0HbWUxWqz
zCt=~Q2z!pgDE4ssFT$d&xC2zmxH}22mHYCIwec_!&^7kmv0$3bajcH{5Ef?V$OIX!
zd3_YzJZjuK`)!{DvPTHXiDJB?*?33=0fdR<g;fxHkayTp4Q5n2aByUdOW5TPSiPxc
z$eXo)GmzH=Uct4=4j^nFKv>$MT6-UWu*6Qftbu9+US`}aC%M6W0AT^GIX&Fs2f8Z<
z5k~k+N|BRNWd<HETh;YBa@5@P2}b&5zX<Ei&Pm1=d;o0T{ilUZ02@R9TZH`-UjGeY
zh6~_`Cbfz9mqk~=y7Wz$i;sBk{y|tN&m-Y=++26YzX(GXucn59`sxJF;)Gkjt9^wG
zO2&%N)!5Xk$~B%N_VhhljFOz_n@>fYePo{@VYy0=Kqj%r56wi?ZQxkua#vh+<{_%>
z6_{N^@roL#^XXb#$3S_KmyrmM6BvsumkZ>9Bt+vZ9wGH4c6fPY;kzYs0|#)<@`~o1
zo@J?&lAnZK7x&)A)>XE?*s7`nEDQ~5i-bJab%@12W=&*i01Yi7hLt1a7$6_@Pr_8E
zt3}WLom=v!Lgw!XQ%l4jH>VNYRX70set2lPyA9v3e6)~)Z(Jwee3_7hbGNPcWov&T
zTvv*0VHM?vKDbZw+tRBRpP8eHCD5QB^L4H;Uy-p$yNViGRJbP!8r%nb!&Q(&Mxfqo
z-v)gd;*~VQz`u*J$737H73UJNaBL(=Rhj~sAOlT)D`&0>ZZfg#JNrR5>5+%SK8>jw
zZhI+u2by3M%qh%PXMge?Mbvq|%HTSbvx=UlM0gA4jEGkpwv@p9iDu7AbHdhB;>2OH
zB%R7goMupivjmHM!kUW4Aj?5y3@cm6HbA@opNtL8$C1(i)I`G=fq?#1{R?PiWewOe
zZf{^KXJBvtbI;JL*K|MY4^^bXFsCqHm@~yPAZ1iDkr7lgi?Sf2oT@a&Nk3N1ENjp_
zGhBs<BrDpIT&uawo`o?nXf~5&KxUFd$SZs%hr_a6mA45R{UH0c1}y@zMh2eCI!gh5
ztMl!Zbj_28l_BSd%EFmXrx2}P_|DVv&5p;)$rE<d$M+~EZ`1Slz_xaKt9c{G-k72l
z2oT>9_GL@BLlltlf6YX>N9>+i#QwCylUGcl_`>L<AF;iw<<!)x>b%V6B)M+)DFP%u
z#{<^`RsvAKMP0eMgSfaNxaL(q9eQ86&!|;0iIY2c@xpq&wgElD%>8A072mI7<;gWW
zrr%YIyKb^;jm|pCvxQSCCox<i;+p&YkC%`HI$uv8w(5`IKeA!Cf*WE4@4!R{Z9Pk~
z?2&Rgsj}j_jWD)QcF)p$nY`M-A_enj;B6E59kh(UZh>`92qGv;rkH|rX_3n;RdBA^
z6>c(1s7ZI2EF`R$@FD~$^27h&z0HC6X8BO;xDpr%gL2_YkWxGiOORqFU%Ax@`#v;S
zNRQvFM6o7)D&zpO2fv@zbDqV5Vm24Hcxi36Ir&th2}ik%U&LnU(ziQTT&OTBUOaEA
zij>B&Bv0AFVj)fluZ%-Ng+LW7`XGKOA*1r3ekfwc5!}QO9!wbxJRT}Qra)$@%Fh?3
zL8DZa!E`Q*&tR-lT(6FVH)7?TgxR1^S5PayaRtI-cfpcrF7KV^2r6iib%Qy3A63y?
zZ%c<<_lcHohH6|x=Ao{Uwc-nCaZV{u)E^5n45Lre>{=2|-HR+OGDX&U*S5wahN5t+
zQ8yQHt(#JbAQ9wCaqDUe8Wz<;Nf_j943aKBR#6sLYF)<cRg0H1nb(hkamC~V8mJw3
zA9Q?%&Blj_#f>8;o(=mt=ebHQdn`c~0bT4d!jBpPRj|mH+Gu>uWgimKQ!9_I+*)na
zhIDIP#yUBKotf170@6o-@~!F1dX{e=NM0ud7kLPiZS9`<0XCjbX9k(;k8Av`?+FK)
zQw8|d>ADq+OCRB=yPv{YuQ^rQkl_Y0v9~=LtpgMs(jUYld(F^Eya<Z7Yw_LyS2}`~
z&V-KXC&*Qop}kjCq?ml~dd3-GyXiQ!gt(l%leI!#29}+L<$fHC?BY=Y&Q74u%7@jf
zV&5>s)M63S5hiaEBy-A{q2R8Q8X+vUUADRvSOhu1?DQ#DXI*7MQg&KhGnY0q;7Om2
z$`Lo8^k7f<AOHmhEFgr&r^KQLn<>g_fct)JLtunz2T*BuwRe7%|3T>7YFX?O=BrW~
z%k+!qbN3>J*PVw-5YMCg=ld5dqDIh4F(?u95eJ09dvqKKPZXyxub^`S@EsXXi4Lkb
zuTV^h(ZT3L3BlID^P5%5>l>-n4WNo=Us9fuRjdIkiux{Ftb5&um2c`DUSO>tSPu^{
zx7gr3pU>fO!?yB2uK))<)hNT^>pr5M>a|xqe`=Q^y<KTPA$JB2k#eV~VEMeFaC*?L
z<gMp5BQq;^%IQwEVU9Q`1#%+z#dlukE-m;GYC})N&C9?8d<A}G=Chs`)9MY%quJ_&
z6VbUDWh8Cg4cp)e+*TMG!r-12$x)h)yOd8l1b6o83h)lf<A>8Xw7p${H$0$;$agzo
zbdY+_Qg})d9f_Y|S7$xpZ&UGK{LVarpFsPr(ArD9c#b(wX$XFVJGXmhdHJ6?Aw3+l
zgMz=H-3o&9fR|ANSG~aKW3MWq+^My@ttdSLYk?X*iPfKg*PT$@BCbxr2iS6wcAZ1o
zHIsP1psq1QS<!ovPHF*J0^{_8SOe#a!Q;d_%D8=1cW)#$alUs>+^A#Ef|@D}WXOk}
zjBzmP|45cGtW4I{%QpFybYzgFC&GP2>~eZ_$sgPCEvJ({hk<+DXo3iPD-8(ZP+q<e
zYfAT<X0@P3Ur5X85a3qVciCk4p&zc+NFXBNnzy3(Vr#8p<|tK7HLG?b4Vi}U`%T)W
zpTp{jv2d83X$`#YoG(^Zw5m*tl7>tfl9z@`VqkjBB1Y&REeP5<RMA@AXY4R-E(qnt
zUT}RG3QxlM;mQM1H5XY)H?+OljBNb*jG9m@A$7{(8%kxsjbd1u$x%G5ne!FK)Z{nr
zQ*$V|_7`*J*xU3WSNfNUquvXiVMF$p4NAiuuT+Q5HgE2WO=b9=;iEm<_?f+AmNMDJ
zk>=xz<8RbT<IXhe9vR`T?=~_bXHNszQp20A{Phk?;x3M-;xSe&-i%OJvuqwSi--}Z
zl}&!!KUXduN9E>J1(m%>S{%2>$xf2I*6q_s+7Xd5Z09hQJ071f2iw-*eUr7dbE&RZ
zT4OvT;W(R87ZuOdMe)Koyn*V>Q@6FJgL=gJbuO!XU`%JQf?kc$J)(lH(J(j{5>>)j
zZPgR?Y5o*UCj*5W!lK{7oIYES?V}5eiZ2xdJJ*ST)+ceSlrJ{w5mi{&;VD6Poz{5>
zk{)a1*dy468IUf0e(-zHV@U{Yw(E=Ece;RN&iY9z`ay5>Q$b7c$~gHtyEC;oL2jS;
zTAW)AvPKF?U+PbK(kv_$khs2ANaVM(lz&>oI4>rowB(_97#BQ&n)8)lOSbYLG~w~X
z%73CA;0N?sR7UA^xTBrP?NUXvEWgVbTmec}POr1RNGW$kZNa{-FIR)9TWF0M2Gxul
zcZnLlit~>1?lRJTi+B340QHP5S#CuP`uOlx%WRCqGkvGg-aIVg?Y16U8pWP$G=0>6
zJ!t)bpGW*oE=4iw6=34dA`^XI(r(*^Y-WD1Rx?N%Zte>3M(zlKfDrz}=g_mVurRQ+
zxBDppx~X~)vCxC^jDveZix4&aCLu;x2D#>peYA!NfkR^2Wb7F;n!u)!FmNrZI6jn{
z&+D4&{*6Zp;5iLLHgTzjpjRlSGgL#PZ!Iaw?rzv=_^_({xH4`jL{=<b%VE*7txMoT
zq4IW0@$gM~qU5Yj<GRZax%klHVaeTLr4gQ&LzSFL@y&Qcgs6jJNm+$%gYx`r0ey8R
z1w7kDm16o0l-7iluhgO)dTEd{1|}m1H@=!Z2Fn#l)K$A!co;NR`<)$!;N_d5d6n}R
zD%c5vi8Ra&<agxVA5R7pPc`m%t%<W`O=`*-5hmphYQnzzUNv}@BiF?+PPjaW%_wAv
zE_ph8wuo5~8F`7+yG~cJEV#8iWw2ODTC!~5=8P@R$SOV1lpr4z4^JM@QXC7qPkFQ1
zcS4W3w^GoU1rK^bw`lKQ<!4r1-e+z!eLWC=nwwP#cy?#ohhMVZd&Vv<`;JXadiDU(
zT=I?nDPZaRvWVe$RV+tVfj+5sU6R*)cf8xS@O%Kyb?9BZ4FmmJF6xj0Ly3`qVsA*j
z`*fE%S8`d>ncj#{t8F3lhmKBb5~=~UF>ek{=#C<RZzs2o!xV729fRo2k=<GA^ixYu
z<F1M%Ej?g2Q#`DSZ4andHz_y}C%`S2X{}-arE>Vo3+bnm+_0DD1Duz;8y=h@&((<<
z#k+T?Qz(x!-V$aVrc}C$WT=Jji3$BT9cqz?<jiVOn~O5(zohW-aZ<>R8PHs_^sOVD
zrBvUa4|r-0R3mNqM^A7L<rt#0AtGdAXqn&wsU?y#=)F(cF`a+WmH_4Vd`e!9E20j|
zk~-3<$qnyJ!eAsQR>{Sj*(x0xX7NrtRELJ)nFaCHngu!iSY?p#_G^^|NN?Pf6_eN$
zs-_lEy|8K>-?%~fyIUI1x9Gd<kuRzTA?II$5#oC&I~g!_1kZ$=KMqT(*naXkQYTg&
z7K&f>WjcBZOMX^8RU?(iDKwn;%y#Gmtd`5!reizgSY)Usd71@j74|`|V%x#A5)?OQ
z<p{p%5R<fiQ8X-r1YbF;fI<=NQoS82RQXM~Br&^MZZ^E)-f5!jUXVPyD^9#CPu|E?
z^ogn!k78-ssri)8#S<$yhHKZ|V!HG2B=%hd4j3qjdVn6u<vkyQb5#c}x|cju<#v*|
zQ^FVLUXjzF5D&&T=e7OILcH6dW@lYQ6RUXN{g?_)g*M6GQ2>Pf<{Zwx2D5@p#&I%L
z$0o*y$!5A)wjDI;j4qU5!5?<mXh4+bF2E}%wji&j(Rn9GZV?w|c*9(}Q$_F9;0G1Y
zV#*XTe{~MdDJR#zBQ4c~`PfKO@UAf?V;#2&%t5sk(R?TyDz5h7&M4iBc}ZhqoahV~
zY9>p*jvf_O;#(K&y2-WQwJ=gStbcoBkQo;fn`v(?y~JFR4*q9}_qToeG=h{ao^MHW
zZvtGkHGDpaaO`==Aa(ad7Z}d1p!t)wl>!UXci#g`tLESr3Ah9Qc+PAX)Bo%?ILMbk
z%29ySTp-P+z;$$(0Q=PENSY1Fyfk;D%~D#*`wa?F<SWSS=g+fq_D+?uqO{+TCEBzI
z=IT{Ad=&zpXi6SYHEuxh%Cf{Azm%_F!(znD8wVL_cd0|pK6jSswqzd~%r1$_t9cX7
z8IkUNfd<N_!XyGZnxiyN%N(Yjb4VK-z-)Q}J7|`T*FGlNK(H7;62&W@RUpVV)iBBd
zIjkY#XP04$p~ZqEKqMujj40R<o*7^d%&OMC7WORS<&y#VCQ+oAV;<Yg^tEom{JUmU
zJ7g5GLg^Z)!pA{x4QVPv&Uh<xbw+3g(Ljp~?E;DYd)m$f6bXUC#;mZ~fR(X*q-a!P
zM_u#}nV_H(vO`4ly)FI1FI@B(qaDD+oAc8HkkYKwmbEIe`D(2bB_$SE7peIi;WbGX
zK#<d?1o|7F>OLN1SuYVI+4zGz2YU|G7Bx3yB-yFsg6btDH9%#af^2~F*e`#8N5ThN
z8HRH#!W;3c^LZaES?X-hFAqa8f@0u-tO?0qm3GH+IK@(~CR};JB+99AE!r*`AQz?V
zL+^_NJBluq89F`B+u3z3fd~?>gZIYT0L;nwhnMAYN?QC4>P0lBctR$F;{!u)Iy{Kc
zx>P^}PpWv&nl5MhFn4Bio!{OGALg2+oa1s}3Q0kDo3m*jF8(-gj)?vnXy7INBh(|9
z94_Dtu3X`qI8<mVF~oEUy`G)XFYVY8Eo=72w_<49SR)C;_H@>1?Km_E%eIHG#5i@a
z=f!z+!q{G@_deZ|0w%F5Z$AR}DS`mQ^A|EZW$-Cy#v16BfsC=XEv#pCIAkOndvMJk
zRdOy}D}AU|^GT2F=8=3BmkIs=Q`4&&fA*ySZM06pgJ?xHmwA9dDo~Tl5$Z|U9A!{C
zR<;L(dq)>^YI$A=cB~m%9oL{f($a_%In)Ilhe?h7^XTA!UyCXe;w+&DxHPQ;l5
zqt@`wg*KKe4|JQC`ay3_IwU3V7&kV;8Isc^Zq6|k1GkI!W&moj#Y4BiH`$KNU2?K$
zyvRzx7IfsYu)rV=OriNp>t}%{aM68cmO?DDAlrg1#k_JuAs;92LtiU?Fk(Ud&wacl
z<P_R^a`%-eeZF`$xt~*OiDV`X`1;{p=D_fTajxq5ECl?9of4V)<HmFRSn<gM*m6!5
zx$|ZUMk8A`LEWlP)bJGGlGet_t5?SviNYI$24{=#h%Ial1K-D^U$G2*;up`-jWvXO
zmJBV0geg*EFHn1in${V{tks?3&%X_@lGYOxOioI)M9kiDF^1u%!_<7gZ6&}Wfj_B?
zPjvAmfjpc=FyY4x8K-Yik?3YJ48P)j<v~jaY*L<}Y}y-)3LiA-`w)3J$tFat`Ry1T
zhTWr$46x5)QrKTjcH$e#tPORUEo;pKf>R6V#0;o;!-gm|m_=mVCR6;jumn;18-a-Y
z^E-k2l_EZ=9S3nC27-lq`1#_`>FNkkLE`e4e6~VCc;SWbI2551vIwY57J)UfLSJ}i
zgBIM$yiWHn!TXE7^;le<6l_<MR)K0MB4|M8=LlgE!^I*Yj(KtCRoHFxf;iQ!`_aXw
zPf(={9lc@9;y)DC3^?2r&ppB0!B}xRul+zy)D$55Vdx0QSb6Lxf+Odd@eO*?=r(Pi
zHRQHZhZGj8B0RP)f;m$CzA`A^>d_|jw<k$<jV48Q?P+skG=^rr>|Lr8n_)?D$|`cH
z$}kZXSm@115Zs|(*`<J>m7?z}Wncs6qxv<)wUez=wq}oJ-}SAUso>kS^DvQfNy|=G
z7~x^dact*z=m}GuktJnna#Ay;d==@57DDKACP>Xtotms*KIZFZFPHBPvk>KVJ03R&
z8EnWz-<Z$bc0^NO(~p%Gs3Bo4Cq8vPYz*ghbDT(s)B0fMTdT4&^zjG38=-k`3Tfun
z)7T~IVShIVn&~%S#yx@ZS`aRobIo(H>MOhoJxoe$S-G7XPK~NW`b3qq7CUBq{ULFC
zPU|uI6!D`$m5;i~4Ga{ePo}Og5-%wT$9zci%t@(D3dT?FSgdSb?h9uptjf-V;2>V;
z5kl?W6$fEEUF{fQ_te8aOa67vn~!Ip5neBVXiu$;?<!+jjGAWh@krjL;J6_)dwj+!
zP+4|5U*F31cEW}M=Sqtej?Vs9tdf?Yph0gQ===+Rh7VV05i2(yMu*{Jyy<>AyDpvx
zt`kxaKNbe6j=+$f36TWy!$zCe3qgm0=moUJ!afwFuJUqhXnzlk&D#i6Fv*<T)*qE{
zlfKn`{?OrUYktGbA-UKwp56crwlQk=Xie|bd1reVme}=NZ~~h+cy~Baweh?H>luS5
z^)(`wTDFxV)!?*RnAKH`{^Q5b@hjeCM{O>%FV!Y(dpm#*_y12~gY<u5gMVU!e`14w
zVuOETgMVU!e`14wVuMli|I64QU7oS-B|zi43{XMvDmKXQvr&Y+L@1yls0HmF$L}}-
zt5?Ka&rO7xj`C7^c1g@i)deebDlmF;(sIwMyje1#650Ts3pFgs<^3||2qr}vMh>qL
zZGxG?>K6WYNaP77rF;+`Zh1bRXb1Tzd1G+!4;|}(=G^(ICS-hUp`6~8;Ff6z^dBkB
z_+*<#u}QtgjR^`b3fT}BFfwW^fK!nPp}JtB<0v>8^kY2y1Y|E3oxGJ!3O1zFtyx;F
zbqG})on+@nN!Bp2f5bb}o&*sY38^jB`;*`|ny~shJyGfDuKvjHU!~7)F{`PV$!Nyu
zPLjJU&1EOK&~brvpEBUos&^=VOFxS2T=(6$tgRT(;KM>~zJut4__Q7Ti~5n3l8y81
z8xbrkA7`g9Uq&v%4rbN-1%sM-g{CK(l?2}oX!IBNt8x<A?P~~jIhj@D2S1Zc*!7|S
zU`~t5R9-ogt?ev`c~cRRZ-7!+8=>sCxHMtMBk)7E>)m}hj);%Fx)bF3wb?%~ck8-+
z*g&rr99MuhhJxN_l-;n}QaKn;a^07?$q;m;1G9y{A94+?R&8b$-3E>h#j$Fh_38`N
ztK=%lwtk0|?Sb-L!4+nFfgHBB*BP5=jhy?i=tZ;n3giv({d;M>>Pt`WHYF^U&jne!
z)r-&F*~MxXD|SQXcK|uH{}&l+#y?)^AFuR}SNg{*{o|GX@k;-ArGLEA|Fu_|Y>U4@
z2$0myh5LtB`m<JbQ%S;VffczKZPyFeevhc~(PA!0?v`H@jMNdj+HJOF49Ng7Q`kEP
zsj2(<23s~}qk6A1umv0vIT#fO$7p*f+mh#l7~h8^X7zpp+TAPAPW>W!YWTs`k@6;5
zxS$GoLwVBvgpS~()s3YGr@BING2&7@NZcWM(vnhC8U1me_{mH8ZbHxgc<RFLY;J+P
z@i4<Y?H>vF@2?*w3UbunNcagRX}-(Jxz8;7SfVL2c%yFZc3uG@n-}`vE1!G}j>NT3
zeJb;UT)$bUD6~}@$QwOi_q8(p95aky=tBcPaeY&64OJZT($G?3tn+5vqlUBtCKJ!H
z1B-L9|IRy>>qlP3!Nd+zgsAPO2XhP{E4k5-nww881#AIr@DIh$){N-^P5svB5&8YZ
z>ZRM+-*~>3eOl1)s!A(;=2K0;uK3)uVx)dOwp4m21jx+4xd4JML=t^R&ETjHi3z`$
zu}o%?lGr-_F@6$uyLxkdG7N5scava#Fr3<wfD*M~Y99P$r#lrHH7|<{(oYp;B9ij+
zJU6rutS>!w9U)tucvK8KT(Hb{)JY`%L8!JRD_D_2Lbq++o@Gj@!uFoev3Z3C82vTm
z{UTGz#`gmBrY0xN<pmuKrh5<GjaLej;XMmF^caVoQ_hS~z5VV8!!!MyixGhbGV~vY
z?tPCIGLKnG_lvxnGIxFGp&i3Wf|LzciKy%_O7d==0oSXIVi0wco1}W{#upC)=hRcI
z9?y+$s|$85fzt*LYwz$&2>XF@t^d35t@ox;@~JQ^Gqx=aAqOs;DVKF3JgHI;#R%U=
zI`2%815?h}=Sq4&)u*ij&6M1+5owA~+6gBn6>vt<&>^Ajqcd&!@Hva9Whx>DKQtsB
z70@^*qiPsDv_lwX`iJeXP2eURvcVj$iehc6zFkz_0X8~|>c~>7u!io0AI+dgiVpdR
zl0@Ux=^FTf%uJ`DDvD-VWKomUeq*U~4|2cTT>;Z^!$})TAvA3DuA8~U)^UMwJ?{=<
zIKkCI8zP#DHXqjIt!m=3Tt|*s6Hi>=VDHo$R6#FdA2A^&#lA1R3RCSNAjA8(qJ3Nh
zw=Xc?Pl*r`a{b+dQ%tN+bGPm6v_GdKEvs>q3`44D(r5S*VZ}=!3urI2D^2+F`^K{z
zldD<Jhd=vJv^%`jj(qh0LRBx{zKrSDgd4%zKKDacpmSyfN|(%mhT?5o+b#sn*|WlX
zOAaI1>m_=)b9s?221q!XW)QVVcksk=kb76UYF@l#d<`AmsTi&rm76{sOK9qpoenz#
zoltJM)I8h4%bI}NmVr@4kr?y5SUy$^LSs#vX6TuXBiz)Z^()gB)o|ummdR%BmSfqp
zT<1&|EgQi(<2Svf;)sRPNZ^U^Lj}dC%8I5bLc7CQBe4DbpBmu;ldnn8tNHNSZN52@
zgIOffLyaizn_;$$O=;^{rup89&qH~fQxn~S5*BI4sHEK^SxgvkmtxwkT7TL;texx0
z?eCN-=fovaGBIbEHzzqWM(>$Gm$36|x}*IXcxG8EY53&8xooj4IkCt`*E+tGAG5lm
zeJ{E$z7hNs)v<-SpIRm3BWLra0&6&(k9K7=-MT=~Z}`b0r*V-%LSE8Z5_S8{#HpZk
zwiE5G;NH&hS`-R?ptY9a#PX0?_>W12sk^Aixi76cog#BoHQXLHBCEv*Y)*_Y?&O&h
z99l-33{%zL8+WCTCr)^BCes>IBJ+;>7oUDxcdUqJGN21s=lcORPapz80_$7p0pv}s
z?P&C@Yz_Xk(8tXJ118M?0{qMW--mmQmQ^P%a`#H&AqQh~9j}9sh+3@0nMf>Aqxo5Q
zheE%q@#njpg-FFC)7nGC;Rc@J1`B2`MT2UHgSauathaoK#yUCU3s@~>hG*!4!**0I
zCXAcAU~m$l4^85e;^@#c)05gQAZXw*j^dMS^DbHpVQI5`-5N+(>_M1%6vtg-mNrA(
zBr712Z!y-OI1?A`7J)Vz39_JNL*P`r<|@I3y)*rws-3<NBfmIv%?>=VjgoU2<vtd|
zrBT~Fn3E0b?r^hDVZaCS+oD&~ee?`jLAx+eV^VCKiwBa|m!;M5cGC;>uTW&m-gzYG
zYI!gWBy#dZoNkf9#gXbhmp?{{oaV5ke^j$1raV-*s0p#3Rc*9$uC#FWMAJv6ud?eZ
zK4*4z)f|$JMq-a33+^`-#F^FZgH&M|v)oRB+X>xAI`vv7%rg-WD2}72*V(K8)C>~B
ze0Jm|=l<KISLhR|NGZVT5&`cb{M9gZtgZjp<*$1&H5TCV(82_qfL`OkEMYb*A;5TN
z=?bG=e$(ZPbu^O9)61+S6b?%~oQbR*n^f_b4__B+_Po_`a5LX8>HbpUBxb`kC>St&
z+u8Ue%Xu`dq$Q95sSaG@RF6y|zOLx?n87>-5vEUPV7@jkD=(mfXF!j_9A!w!p(u)j
ziR;<Za%o@!yX<0TJR05Hf6?%rAd})-J(LkY9V@l!z1Bo$N(2qpiKMr8&Hek27O)6T
zg*YX_+;=gRR-b%pV)SI_mQaWARN42``_JSoU?=N!^)x=FN%z<JHGbL+s0GH1RivU)
zW!%(DIK=s|5Z>p>*9m7trW%BbMW?95%z0lA7o7FHw;@)9Kt|RN&R+@NTeC6iIepRk
z*>|vWSQ|)Z4avr+u;(&+kvHe<`rVg$BXnno)KACPz%z>!;dmqUk*5!Eki<;SxQxQ?
zcCqIJEZ#;j%_-9}koG~WRF;`<7pIy!u9{X6A15xI4m8K*W?cfhXL2=(r)qtIz*jn4
z_ICUlcRYF;8B`Qjua%ppo6#EIxqo<O@Zd_{O1Et-lacJN;}LxU_^khopusAF^nn6Q
z+zasc?yn}U=U``VW%0+#|6=8%Qc}LO=x<J&f>$#4uvKyUaQy|fo190#H*lB?PWwHJ
zs7dQS9y`j^HTANLq-cG;AzFZE^Y&LGsV>q%(u1teN`C85w6>swV}Wb!DQ*6xdYwtV
z!FOT%IigwFqW;Q<0|9c&)cJZ@9+J&<+t%79NI_W0Vr#X_SV}lDJEM8VK0)0jPWSQi
z?qPF6bc5|_Z!zkNR*Z?O$xcMzlwTw4UAGdgD2*~K&qMRU1J;gABSjNwhX`i8ii&`<
zug!ekQmgx-2cI_eAQ_lx)Y@hK%uah{(!C?<I}TgS-EO5=7pG<Bx5JgZ-D`n1nyWq;
zYC@x0{s9>aJIPi67XE2Fu^AwdHh|yk5rAdAiqF&k%WyX37fdnf5Io>Fao%v%jMhv?
z9I^H|o)W3S<RZtkUP??4y9(N?5YAPo-Jhgg&6@z!hK|&9RC2QC7_!-njO<$&-q-H>
z+B*K2FO36M-Lo~A(YwB)7JCEp5t$inUInX)>y0B98xN+45zIXLXGnAOD*7JNK9?w>
z_lT-xAH_)gFN6f<@0sA^KSc*3(Y+tyFqz*GA{~4mkQr@-N7uPxRpeKxgkKatPJRrp
z4ikn&Ju~f6F)qJ5oF3%#j8Y*p-n1Gri;sUC@=?THL91zy%Ti$pgBc@Of-((0zkAcM
zyiT;ePPQJ=3XUWnTo8NVLd5^J9yqf5i^Kv<@~r{4;pB;Q2vp=j8jdA$2aoGsGfN8G
zI3Yj+I)`JZQs0rGN%+muV5rOVo_^@_BS}1p&8N4VTw1#|>#;JoaB7*ohaqD^>_?oW
zDI0LXj-zFK>1>!O85}a~1g75*g!Vjq9Zf&upl?is!hK4OIb&a-TjP;bRMSBx#}nbT
z72|Ca6tZlhKqN#tN(-oQN`X@619E9EUOj9&Z3V}HzqJK6e22d(*(ZF1chis5*La1N
zqs<G6BC0_8qmTv+ZxKq)1cneyrRvK0)AD5kPOdxN#-Rkgq*2;n%H0-YgXqIVj@!fm
zk#C24G)$$75nY=~&QkG<c#kkY?sV)%UDSGT9F;O^&Bx16F&}Xv_`J&Z@8<3>=*p|V
z-QcX62r28h^O~2OF6uL01DUWMcSK7)R(yfTW%2-HYba8<@N`5Blx9uyrS#VzNd1^I
zYMU)cSRM@Y#-D5UR1o@hqrd9X%X+;(cF39SsB;tTx90i3-_FMHCpqvZIq)Yr@FzL&
zCpqvZIq)Yr@FzL&ZzTtSL8t(Y*53dDcFqIg0cEU+du{_3Q$L{owV--^sPS6?_S0M1
zYstG<8`x?5%S)CP`#Zop%~k^t0Kf{svj&9A{&L^|>R|puO5~?n!Rvtxt?v^T0g(zB
zfPMs^8R%aQS%A6@U^D-}6iIZgtjvCQ)}db79ZtYmuK~XN`@akY0#XA^0x<YLPW)Gh
z-%bM7@X=xfFwcCzf&l$*I5V*SH3>Uo0|Wd26h!&C%kT9^kp}U|eOCpb(+>O<$|BNl
zQGTvBUZbe^2c$v*K6)o$B7dI?QO|EtepVH|MnSHh_w@$Q+6AEX?<jghzeV|(1$vG0
z<<K@(7Qm#Y#A}rIW4}fDDdX`P#pLXyHUMB}h(xbZvKD`f@>AR5HOfBmNcte)Ix}Ou
zHc`#3pD6am1{MZ1Kc9blc`EISTj&79QqTYa0j}y_4%LI70Cx5+<_31Zv$L&8HIM}W
zlTUziBmZR}5K!&;e+)I&u{F?_vj+%I82uLEbxYe{AYG3?5&pXdx36h<-K_B!5Ix}j
z1DO18fPS`ad=2!vC*Ch0WbpqE=x2w#*FdjZef<Iofcy#c_r_nZhrbrn`ZZjS?7y4G
zPid{!K(Ez|egRQX{sj8JX&b!;e69BL3s9Zve*pZY{qq{|wHnSZz;NpS0q~bL&TGKe
zYB9e6TWS6Wz+c)iuK{1LK7Rq8(*6W|C8hFOYUQW2%0EC~?Uw!ps?YWl^ly8o|1tWN
zzRR!CFoOTilz#5$cn$ixNb?uyXNkW}>9=35|2<1tQvc4BewOpSp3>_K{jVvZD*tUt
zzx}5Edp7FVSRf!{4Pc;u<fdMq{O>t~zYYi1{>$+H%pklT{r7O}Uq^2OGGxC_{$Ih_
z*8s0$a=##O4gU7ofBTF1-(QY4qkrde{0yK-i-EuS*J<$p4<x`>J%csCuK@Z#Sa7g(
literal 0
HcmV?d00001
diff --git a/V1.7.1/run_analysis.sh b/V1.7.1/run_analysis.sh
new file mode 100755
index 0000000..bb285c4
--- /dev/null
+++ b/V1.7.1/run_analysis.sh
@@ -0,0 +1,17 @@
+#!/bin/bash
+if [[ $# < 2 ]]
+then
+ echo "Usage:"
+ echo "sh this_file.sh input_path/input_file_name.xlsx output_path/output_file_name.xlsx optional_BioHPC_queue_name"
+ exit
+fi
+module load R/3.5.1-gccmkl
+DIR=`dirname $0`
+INPUT_FILE=$1
+OUTPUT_FILE=$2
+QUEUE=$3
+if [ "$QUEUE" == "" ]
+then
+ QUEUE=256GB
+fi
+srun -p $QUEUE Rscript $DIR/ODA_1.7.1.R $INPUT_FILE $OUTPUT_FILE
--
GitLab