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.closing-logo { position: absolute; top: 16px; right: 24px; width: 110px; opacity: 0.95; } </style> <script> // Add copy functionality to code blocks document.addEventListener('DOMContentLoaded', function() { // Add copy buttons to all code blocks const codeBlocks = document.querySelectorAll('pre code'); codeBlocks.forEach(function(codeBlock) { // Create container wrapper const container = document.createElement('div'); container.className = 'remark-code-container'; // Wrap the pre element const pre = codeBlock.parentElement; pre.parentNode.insertBefore(container, pre); container.appendChild(pre); // Add copy button const copyButton = document.createElement('button'); copyButton.className = 'copy-code-button'; copyButton.innerHTML = '📋 Copy'; copyButton.onclick = function() { // Get the original formatted text with preserved newlines let codeText = codeBlock.innerText || codeBlock.textContent; // For better cross-browser compatibility, also try innerHTML approach if (!codeText || codeText.trim() === '') { // Fallback: convert HTML to text while preserving line breaks const tempDiv = document.createElement('div'); tempDiv.innerHTML = codeBlock.innerHTML; codeText = tempDiv.innerText || tempDiv.textContent; } // Ensure we have properly formatted text if (codeText) { navigator.clipboard.writeText(codeText).then(function() { copyButton.innerHTML = '✅ Copied!'; setTimeout(function() { copyButton.innerHTML = '📋 Copy'; }, 2000); }).catch(function(err) { // Fallback for older browsers copyButton.innerHTML = '❌ Failed'; setTimeout(function() { copyButton.innerHTML = '📋 Copy'; }, 2000); console.error('Copy failed:', err); }); } }; container.appendChild(copyButton); }); }); </script> --- class: inverse, center, middle # Session 3: Principal Component Analysis & Hierarchical Clustering --- ## Principal Component Analysis (PCA) - Overview .panelset[ .panel[.panel-name[What is PCA?] .pull-left[ **Principal Component Analysis (PCA)** is a **linear** dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional space by finding **new, uncorrelated axes** (called **principal components**) that **maximize the variance** in the data. - **Dimensionality Reduction**: PCA sets the focus on just a few components and their corresponding genes, which contribute the most to the variation in the data. - **Applications**: PCA is widely used in gene expression data (e.g., RNA-Seq) to identify patterns, reduce noise, and cluster samples based on similar gene expression profiles. ] .pull-right[ <div class="image-with-caption"> <img 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alt="PCA Plot" width="100%"> <p class="caption"><font size="3"><b>source:</b> PMID: 34440590 </font></p> </div> ] ] .panel[.panel-name[Applications of PCA] .pull-left[ - **Data Visualization** - Reduce high-dimensional data to 2D/3D for easy interpretation - Capture main sources of variation - **Batch Effect Detection** - Detect (and remove technical) variability between experiments - Improve biological signal clarity - **Population Genetics** - Visualize genetic diversity and ancestry - Identify population structure and relationships ] .pull-right[ - **Noise Reduction & Preprocessing** - Discard irrelevant data and noise - Clean and compress data for further analysis (e.g., clustering) - **Feature Selection** - Identify the most informative genes or variables - Focus on principal components that capture the most variance - **Gene Expression Analysis** - Uncover patterns in large datasets (RNA-Seq, microarrays) - Cluster samples by expression profiles (e.g., healthy vs. diseased) ] ] .panel[.panel-name[Steps in PCA] .pull-left[ 1. **Standardize the Data**: - Biological data may vary in scale (e.g., gene expression values), so data should be standardized (mean = 0, variance = 1). 2. **Compute Covariance Matrix**: - Determine how the variables (genes) relate to each other. 3. **Eigenvectors and Eigenvalues**: - **Eigenvectors** are the directions of the maximum variance (the PCs). - **Eigenvalues** tell us how much of the variance each PC explains. 4. **Select Principal Components**: - Choose the number of PCs to retain, usually based on the total variance explained (e.g., 90% of variance). ] .pull-right[ <div class="image-with-caption"> <img 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" alt="Scree Plot" width="100%"> <p class="caption"><font size="3"><b>source:</b> Iris dataset </font></p> </div> ] ] .panel[.panel-name[Interpreting PCA] .pull-left[ - **Principal Components**: The first few PCs capture the majority of the variance in the data. - **Loadings**: Each PC is a linear combination of the original variables (genes). The loadings tell us which genes contribute most to each PC. - **Biological Meaning**: - PC1 might represent major biological differences (e.g., healthy vs. diseased). - PC2 could capture subtler differences. - PC1 is sometime not Component of interest (!!). ] .pull-right[ <div class="image-with-caption"> <img 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alt="PCA Loading Plot" width="90%"> <p class="caption"><font size="3"><b>source:</b> https://satijalab.org/seurat/articles/pbmc3k_tutorial.html </font></p> </div> ] ] ] --- ## Principal component analysis in R .panelset[ .panel[.panel-name[Data] ``` r ## Load and display the data data("iris") head(iris) ``` ``` ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## 1 5.1 3.5 1.4 0.2 setosa ## 2 4.9 3.0 1.4 0.2 setosa ## 3 4.7 3.2 1.3 0.2 setosa ## 4 4.6 3.1 1.5 0.2 setosa ## 5 5.0 3.6 1.4 0.2 setosa ## 6 5.4 3.9 1.7 0.4 setosa ``` We will use the **iris** dataset, which contains measurements of four features (Sepal Length, Sepal Width, Petal Length, Petal Width) for three species of flowers. ] .panel[.panel-name[Perform PCA] ``` r ## Perform PCA on the iris dataset pca_result <- prcomp(iris[, -5], scale. = TRUE) summary(pca_result) ``` ``` ## Importance of components: ## PC1 PC2 PC3 PC4 ## Standard deviation 1.7084 0.9560 0.38309 0.14393 ## Proportion of Variance 0.7296 0.2285 0.03669 0.00518 ## Cumulative Proportion 0.7296 0.9581 0.99482 1.00000 ``` We perform **Principal Component Analysis (PCA)** on the **iris** dataset, excluding the species column. PCA helps reduce the dataset's dimensionality while retaining most of its variance. ] .panel[.panel-name[Scatterplot (PC1 vs PC2)] .pull-left[ ``` r pca_scores <- as.data.frame(pca_result$x) ggplot(pca_scores, aes(PC1, PC2, color = iris$Species)) + geom_point(size = 3) + ggtitle("PCA Scatterplot (PC1 vs PC2)") + xlab( paste("PC1 (", round( summary(pca_result)$importance[2,1] * 100, 1), "% variance)")) + ylab( paste("PC2 (", round( summary(pca_result)$importance[2,2] * 100, 1), "% variance)")) + theme_minimal() ``` ] .pull-right[ <img src="data:image/png;base64,#PCA_and_Hierarchical_Clustering_files/figure-html/unnamed-chunk-7-1.png" width="504" /> ] This scatterplot shows how samples (flower species) cluster based on the first two principal components. **PC1** explains the most variance, followed by **PC2**. ] .panel[.panel-name[Variance Explained] .pull-left[ ``` r ## Calculate explained variance eigenvalues <- pca_result$sdev^2 explained_variance <- eigenvalues / sum(eigenvalues) cumulative_variance <- cumsum(explained_variance) # Plot explained variance explained_variance_df <- data.frame( PC = factor(paste0("PC", 1:4), levels = paste0("PC", 1:4)), Proportion = explained_variance ) ggplot(explained_variance_df, aes(x = PC, y = Proportion)) + geom_bar(stat = "identity", fill = "steelblue") + ggtitle("Proportion of Variance Explained") + xlab("Principal Component") + ylab("Proportion of Variance") + theme_minimal() ``` ] .pull-right[ <img src="data:image/png;base64,#PCA_and_Hierarchical_Clustering_files/figure-html/unnamed-chunk-9-1.png" width="432" /> ] The **Proportion of Variance Explained** plot shows how much variance is captured by each principal component. ] .panel[.panel-name[Scree Plot] .pull-left[ ``` r ## Scree plot showing cumulative variance explained cumulative_variance_df <- data.frame( PC = 1:4, CumulativeVariance = cumulative_variance ) ggplot(cumulative_variance_df, aes(x = PC, y = CumulativeVariance)) + geom_line(group = 1, color = "steelblue") + geom_point(size = 3, color = "steelblue") + ggtitle("Cumulative Variance Explained") + xlab("Principal Component") + ylab("Cumulative Variance") + scale_y_continuous(limits = c(0, 1)) + theme_minimal() ``` ] .pull-right[ <img src="data:image/png;base64,#PCA_and_Hierarchical_Clustering_files/figure-html/unnamed-chunk-11-1.png" width="432" /> ] This **Scree Plot** helps determining how many PCs are needed to explain most of the data's variance. ] ] --- ## PCA Loadings Visualization .panelset[ .panel[.panel-name[Concept] .pull-left[ **Loadings** represent the contribution of each original variable to the principal components. #### Key Concepts: - **Loading values** range from -1 to +1 - **High absolute values** = strong contribution - **Positive values** = same direction as PC - **Negative values** = opposite direction to PC .small[Loadings help identify which original variables drive each principal component.] ] .pull-right[ <img src="data:image/png;base64,#PCA_and_Hierarchical_Clustering_files/figure-html/unnamed-chunk-12-1.png" width="432" /> ] ] .panel[.panel-name[Structure] .pull-left[ #### Components: - **Rows**: Original variables - **Columns**: Principal components - **Values**: Contribution weights #### Key Questions: 1. Which variables contribute most to PC1? 2. Are any variables negatively correlated? 3. What biological meaning can we infer? .small[The loading matrix is the key to interpreting PCA results.] ] .pull-right[ Table: Example: Iris PCA Loadings for PC1 & PC2 | | PC1| PC2| |:------------|------:|------:| |Sepal.Length | 0.521| -0.377| |Sepal.Width | -0.269| -0.923| |Petal.Length | 0.580| -0.024| |Petal.Width | 0.565| -0.067| ] ] .panel[.panel-name[Extract Loadings] .pull-left[ ``` r # Perform PCA pca_result <- prcomp(iris[, 1:4], scale. = TRUE) # Extract loadings (rotation matrix) loadings <- pca_result$rotation # View the loadings matrix print(loadings) # Get loadings for specific PCs pc1_loadings <- loadings[, 1] # PC1 loadings pc2_loadings <- loadings[, 2] # PC2 loadings # View loadings for PC1 print(pc1_loadings) ``` **What you get**: - **Matrix**: Variables × Components - **Values**: Contribution weights (-1 to +1) - **Names**: Original variable names ] .pull-right[ <img src="data:image/png;base64,#PCA_and_Hierarchical_Clustering_files/figure-html/unnamed-chunk-15-1.png" width="432" /> ] ] .panel[.panel-name[Prepare Data] .pull-left[ ``` r # Convert loadings to data frame loading_df <- data.frame( Variable = rownames(loadings), PC1 = loadings[, 1],PC2 = loadings[, 2], PC3 = loadings[, 3],PC4 = loadings[, 4] ) # For plotting, reshape to long format library(tidyr) loading_long <- loading_df %>% pivot_longer(cols = starts_with("PC"), names_to = "Component", values_to = "Loading") head(loading_long) ``` - **Variable** : Original feature names - **Component**: PC1, PC2, PC3, PC4 - **Loading** : Weight values ] .pull-right[ Table: Prepared Loading Data (Long Format) |Variable |Component | Loading| |:------------|:---------|-------:| |Sepal.Length |PC1 | 0.521| |Sepal.Length |PC2 | -0.377| |Sepal.Length |PC3 | 0.720| |Sepal.Length |PC4 | 0.261| |Sepal.Width |PC1 | -0.269| |Sepal.Width |PC2 | -0.923| |Sepal.Width |PC3 | -0.244| |Sepal.Width |PC4 | -0.124| ] ] .panel[.panel-name[Bar Plot] .pull-left[ ``` r library(ggplot2) # Bar plot for PC1 loadings ggplot(loading_df, aes(x = Variable, y = PC1)) + geom_col(aes(fill = PC1 > 0), color = "black", width = 0.7) + scale_fill_manual(values = c("FALSE" = "red", "TRUE" = "blue")) + labs(title = "PC1 Loadings", x = "Variables", y = "Loading Value") + theme_minimal() + theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position = "none") + geom_hline(yintercept = 0, linetype = "dashed") # Multiple PCs in one plot ggplot(loading_long, aes(x = Variable, y = Loading, fill = Component)) + geom_col(position = "dodge", color = "black") + facet_wrap(~ Component, ncol = 2) + theme_minimal() + theme(axis.text.x = element_text(angle = 45, hjust = 1)) ``` ] .pull-right[ <img src="data:image/png;base64,#PCA_and_Hierarchical_Clustering_files/figure-html/unnamed-chunk-19-1.png" width="432" /> ] ] .panel[.panel-name[Biplot] .pull-left[ ``` r # Biplot showing PC1 vs PC2 loadings ggplot(loading_df, aes(x = PC1, y = PC2)) + geom_point(size = 3, color = "red") + geom_text(aes(label = Variable), vjust = -0.5, hjust = 0.5, size = 4, fontface = "bold") + geom_hline(yintercept = 0, linetype = "dashed") + geom_vline(xintercept = 0, linetype = "dashed") + labs(title = "PCA Loading Biplot", x = "PC1 Loadings", y = "PC2 Loadings") + theme_minimal() + coord_equal() # Add arrows from origin ggplot(loading_df, aes(x = PC1, y = PC2)) + geom_segment(aes(xend = PC1, yend = PC2), x = 0, y = 0, arrow = arrow(length = unit(0.3, "cm")), color = "blue", size = 1) + geom_text(aes(label = Variable), vjust = -0.5, hjust = 0.5) + theme_minimal() ``` ] .pull-right[ <img src="data:image/png;base64,#PCA_and_Hierarchical_Clustering_files/figure-html/unnamed-chunk-21-1.png" width="432" /> ] ] ] --- ## Hierarchical Clustering - Overview .panelset[ .panel[.panel-name[What?] .pull-left[ **Hierarchical Clustering** is an **unsupervised** machine learning technique used to group data into clusters based on similarity, creating a hierarchy of clusters. It builds a **tree-like structure** (dendrogram) that represents nested clusters at various levels. - **Agglomerative Method**: Each data point starts as its own cluster, and pairs of clusters are merged based on their similarity until one large cluster forms. - **Divisive Method**: The reverse process, where one large cluster is split recursively into smaller clusters. ] .pull-right[ <div class="image-with-caption"> <img 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" alt="Dendrogram Example" width="100%"> <p class="caption"><font size="3"><b>source:</b> https://www.gastonsanchez.com/visually-enforced/how-to/2012/10/03/Dendrograms/ </font></p> </div> ] ] .panel[.panel-name[Applications] .pull-left[ - **Gene Expression Analysis** - Group genes or samples based on expression profiles - Identify clusters of similar expression patterns (e.g., healthy vs. diseased) - **Customer Segmentation** - Group customers into distinct clusters based on purchasing behavior - Find patterns in customer demographics and preferences - **Document Clustering** - Group similar documents based on content similarity - Useful in text mining and information retrieval ] .pull-right[ - **Image Segmentation** - Cluster similar pixels in an image - Separate objects or regions based on color or intensity - **Pattern Recognition** - Group data points with similar characteristics - Useful in applications like facial recognition or biometrics - **Marketing Analysis** - Understand market segments and target specific groups of customers - Create personalized marketing strategies based on clustering ] ] .panel[.panel-name[Algorithm] .pull-left[ 1. **Calculate Distance Matrix**: - Measure the distance (e.g., Euclidean) between every pair of data points. 2. **Choose a Linkage Method**: - **Single Linkage**: Merge clusters based on the closest pair of points. - **Complete Linkage**: Merge based on the farthest pair of points. - **Average Linkage**: Merge based on the average distance between points. 3. **Build the Dendrogram**: - Recursively merge clusters and represent the process in a tree structure. 4. **Cut the Dendrogram**: - Choose a threshold to determine the number of clusters. ] .pull-right[ <div class="image-with-caption"> <img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABAwAAALGCAYAAADBQAOeAAAEDWlDQ1BJQ0MgUHJvZmlsZQAAOI2NVV1oHFUUPrtzZyMkzlNsNIV0qD8NJQ2TVjShtLp/3d02bpZJNtoi6GT27s6Yyc44M7v9oU9FUHwx6psUxL+3gCAo9Q/bPrQvlQol2tQgKD60+INQ6Ium65k7M5lpurHeZe58853vnnvuuWfvBei5qliWkRQBFpquLRcy4nOHj4g9K5CEh6AXBqFXUR0rXalMAjZPC3e1W99Dwntf2dXd/p+tt0YdFSBxH2Kz5qgLiI8B8KdVy3YBevqRHz/qWh72Yui3MUDEL3q44WPXw3M+fo1pZuQs4tOIBVVTaoiXEI/MxfhGDPsxsNZfoE1q66ro5aJim3XdoLFw72H+n23BaIXzbcOnz5mfPoTvYVz7KzUl5+FRxEuqkp9G/Ajia219thzg25abkRE/BpDc3pqvphHvRFys2weqvp+krbWKIX7nhDbzLOItiM8358pTwdirqpPFnMF2xLc1WvLyOwTAibpbmvHHcvttU57y5+XqNZrLe3lE/Pq8eUj2fXKfOe3pfOjzhJYtB/yll5SDFcSDiH+hRkH25+L+sdxKEAMZahrlSX8ukqMOWy/jXW2m6M9LDBc31B9LFuv6gVKg/0Szi3KAr1kGq1GMjU/aLbnq6/lRxc4XfJ98hTargX++DbMJBSiYMIe9Ck1YAxFkKEAG3xbYaKmDDgYyFK0UGYpfoWYXG+fAPPI6tJnNwb7ClP7IyF+D+bjOtCpkhz6CFrIa/I6sFtNl8auFXGMTP34sNwI/JhkgEtmDz14ySfaRcTIBInmKPE32kxyyE2Tv+thKbEVePDfW/byMM1Kmm0XdObS7oGD/MypMXFPXrCwOtoYjyyn7BV29/MZfsVzpLDdRtuIZnbpXzvlf+ev8MvYr/Gqk4H/kV/G3csdazLuyTMPsbFhzd1UabQbjFvDRmcWJxR3zcfHkVw9GfpbJmeev9F08WW8uDkaslwX6avlWGU6NRKz0g/SHtCy9J30o/ca9zX3Kfc19zn3BXQKRO8ud477hLnAfc1/G9mrzGlrfexZ5GLdn6ZZrrEohI2wVHhZywjbhUWEy8icMCGNCUdiBlq3r+xafL549HQ5jH+an+1y+LlYBifuxAvRN/lVVVOlwlCkdVm9NOL5BE4wkQ2SMlDZU97hX86EilU/lUmkQUztTE6mx1EEPh7OmdqBtAvv8HdWpbrJS6tJj3n0CWdM6busNzRV3S9KTYhqvNiqWmuroiKgYhshMjmhTh9ptWhsF7970j/SbMrsPE1suR5z7DMC+P/Hs+y7ijrQAlhyAgccjbhjPygfeBTjzhNqy28EdkUh8C+DU9+z2v/oyeH791OncxHOs5y2AtTc7nb/f73TWPkD/qwBnjX8BoJ98VVBg/m8AAEAASURBVHgB7N0HnCRVnTjwN5vZZWGXqEuQzCJwogIqqEjGQz3wxKx3eAY8zgDiqRjACEZEVNQTCR6gBBUVMBBMpwgCCsiSWZIkgQV22Tz9r1/579rumZ6Znu2e0NXfuk/TFV69eu/7erytX733qqeSLclCgAABAgQIECBAgAABAgQIEKgRmFCzbpUAAQIECBAgQIAAAQIECBAgkAsIGPghECBAgAABAgQIECBAgAABAv0EBAz6kdhBgAABAgQIECBAgAABAgQICBj4DRAgQIAAAQIECBAgQIAAAQL9BAQM+pHYQYAAAQIECBAgQIAAAQIECAgY+A0QIECAAAECBAgQIECAAAEC/QQEDPqR2EGAAAECBAgQIECAAAECBAgIGPgNECBAgAABAgQIECBAgAABAv0EBAz6kdhBgAABAgQIECBAgAABAgQICBj4DRAgQIAAAQIECBAgQIAAAQL9BAQM+pHYQYAAAQIECBAgQIAAAQIECAgY+A0QIECAAAECBAgQIECAAAEC/QQEDPqR2EGAAAECBAgQIECAAAECBAhMQjD2ApVKJV1xxRXp1ltvTXfddVf629/+ltZff/201VZbpS233DI9+9nPTtOnT2+qoJHHk08+mafdfPPN0+zZs5s6T6L2CDzxxBPptttuGzSzCRMmpGnTpqU11lgjbbDBBvn3oCeU9GBvb2/685//XNQufuc9PT3FthUCBAgQIECAAAECBMZWoCe7Wa2MbRG69+r3339/+s53vpNOOeWUdOeddw4IEcGD97///enwww8fMnCw5557pl/96ld5XmeffXZ67WtfO2C+4/HA+eefn/71X/91PBatqTL97Gc/Sy996UubShuJ4gZ5o402Svvuu296/etfn/bZZ5+mz+30hIsWLUprrrlmUY3FixfngZRihxUCBAgQIECAAAECBMZUwJCEMeL/4Q9/mLbbbrv0kY98ZNBgQRTv4YcfTv/93/+dosfA7373uzEq8chedt68eWmvvfZKb3rTm0b2QuMs94jX3XvvvenUU0/NgwYROJg/f/44K6XiECBAgAABAgQIECDQjQKGJIxyq8cN4lFHHZW+9KUv1V15t912S/vtt18eFNh4443Tgw8+mHdtj14CcTMdy0MPPZQOPPDAdNlll6XnPve5ded38kbU9VnPelZavnx56brn77rrrmnixIl1zbNy5cq0cOHCfOhIDD+J7epyySWXpOc973l5YGjrrbeu7vZNgAABAgQIECBAgACBURcQMBhl8hNPPLEuWPCc5zwn395jjz0aluTDH/5wOuuss9Lb3va2tGTJkhRj5A844IB01VVXpc0226zhOZ22MwIF8Snjcumll9Z1u+9bxwgc/N///V/60Ic+lK699tr8cASG9t9///T73/8+Pe1pT+t7im0CBAgQIECAAAECBAiMioAhCaPC/I+LXH/99emDH/xgccXoVXD55ZengYIFkTAmyHvjG9+YBw1iPZa///3v6fjjj8/X/aezBWIMfwQHIgB05JFHFpWJOS1i3goLAQIECBAgQIAAAQIExkpAwGAU5d/xjnekpUuX5lecM2dOigny1lprraZKcPDBB6fDDjusSHvGGWfkcxsUO6x0tEAMW/jCF76QXv7ylxf1iJ4lN954Y7FthQABAgQIECBAgAABAqMpYEjCKGnffPPN6Q9/+ENxtaOPPjrNnDmz2G5mJZ5Af+Mb30jxOrrobfDLX/4yn1m/mXOraeI1dgsWLMg3n/nMZ+av9ase6/sd1/nNb35T7H7Ri17Ubzx+9WA8IY/P7bffnh577LG0zTbbpMg/PvFqyEavy3vggQfSTTfdlM/NUM0nxvNX3/IQ+174whemSZMa/0zj9ZF/+ctf8s91112XZs2alXbaaaf8s+222+ZG1Xz7fkf6Rx99NN9dvUbY/OhHP8rnj4g5FeKtBc0GdPrmvzrbYRRvzYi3Jixbtixv5xNOOCH9z//8z6DZteIQGd9www15r5VYj14vU6ZMidX8FZ8xLCJ+t5MnT04xfCbmzpg7d25+vJn/xHCL3/72t/nvYr311kvxG4prRFs1s7TaTvEmkj/96U954OWWW25JG264Yfqnf/qn/BP1qPbaaaYsYRH1ueOOO9LUqVPTC17wgvyz6aab5qfHbzl+07GEVd/fTit1ueeee/K/lbhG/G9JDE0Kw5gI9SUveUn+6tX8wgP8p7aNd99997w942/t4osvzufLiLk01llnnbxtX/e616W11167LqeYeyXyiB5R8Tf3jGc8I//bfsUrXlH8XupOsEGAAAECBAgQIFAOgewfgpZREPjYxz4Wr6/MPzNmzKhkPQ1W66rnnntuJbsxr2Rj/huen908FNfJJkzsl2ao47UnZOPri7yi7NmNae3hfD27maxkN4B16ar1rH7vsMMOlaw3Rb9zs5vjQc+L87PhF/3Oix0nn3xyZfr06QOen93MVbLgRcNzY2f26sPi3CxwUDnmmGOK7Wq5sxuyShb8GDCPRgeyG7C6fBqZNTqvdl/2Wskij+xmtPZQv/VWHSLDV77ylcX17rvvvko2CWVll112KfZVParfr3nNayrZDWu/stTuiN/o85///IZ5ZL0pKl/72tcqfX9f2WsVa7PI11e3nSLvbP6PyrRp0xqWIeqy8847V/7617/2u2bfHVGX+D1V69/3O+v5U8kCPJU3v/nNRZr4u+i7rE5dsgBWJQtwFPn2vXZ1+1/+5V8q0XYDLbVtHO2bBT0qWVCvYb7ZMJlKbfmzuTUqWRCrYdrtt9++8sc//nGgy9pPgAABAgQIECDQ4QKpw8vfMcXPnrQX/+COG5WRWoYKCAx1vLZcfW/o+t78Zk8cK9mTyKJecfOSPZ2uZBP1VbJeAXX74ybx/PPPr82+sjoBg2xCwMo///M/1+Ud182e5va7Ztz4nH766XXXrG7U3rz97//+b7/8Is/sSWw1edPf7QgYXHDBBXXlyZ6M97t+uxwi49qbyewJeiV7O0Pd9as3pbXfkWagYEoEhyIoVps+6z3RL8Dzzne+sy7NUAGDZtspvBrd4MbvIcpRW66sp0AlGwrSz7e64xe/+EXDuvStX/xdZW8wKfKuveGu5jXc39ynP/3pIr9qmbMeEZV11123Et/VfdXv+FvMeiJUL1f3XdvGWQ+ehj7VfOI7e1NLHji64oorKhE4qz3Wd32DDTaoPP7443XXs0GAAAECBAgQIFAOAXMYZP/6HY0lujFXl+imX4YluwlJ2Y1CXpXsqXPe7Tve5BDdwOM7Xv+YPYHMj0f3549//ON11c6epOfdrGuHIERX7+h2Xf3Mnj277py3vOUt6aKLLsr3Rdf5yHP+/Pl5OaJrfnQbj0kEY8kCHunQQw9N11xzTb490H+OOOKI/FBMQPjqV786fwtFlOPf/u3fBjplRPf3/X1EF/C+y0g4xDXitZ233nprilc6nnnmmSm68Uc3++9///uFa6SLNDF8ou8SXdujXRctWpQfileEZgGQfJhKtE8M+4g2iSXrHZF/N/ufZtopfmfRbtnT9jzbaMeYGyLqEd34Y7hMFgTIh61EgphTJF5z+uMf/7hfMSKPmDukWpeYd+QHP/hBeuSRR/JXYma9E9J73/ve/Lz4DV944YX98hhox1B1ue2229JHP/rR4vQYJhB2MVQlJj2Nv6+4fvwOqsMq4m/xy1/+cnHOQCsve9nLcp+sF0k66aST8te33n333fnQl/gbiOXee+/Nfwv77rtvPoQphufE8KSoexYMSe9617uK7OOtHp/5zGeKbSsECBAgQIAAAQIlEihH3GN81yL7R3bdE7rsDQcjVuChehAMdby2YIP1MIguzdmfQf559rOfXclu1GpPLdbjCWU1XXxnN+/FsepKPBWtplljjTWqu/t91z69jyfD2dj0fmliR5Qlu5Er8szmKOiXrvZpb1x71113rTz11FNFuqh7oyfeRYIBVmrLGPn27ZUxwGl1u+Ocqkd8x7CD2qX2Gq06RL61T5/jevvtt1/DITPZTWolLKtl22qrrWqLla+/6U1vKo5vt912lSzY0C9N7Pjc5z5XpKvm18h7uO103HHHFfnGk+9Gv7e4flwruwkv0mZj8itZYCAOFctrX/va4ngMqxmoLt/73veKdNW6DNXDININ9pt7z3veU+R5yCGHFGVqtPLf//3fRdroDdBo6dvG0eOp0W8zmy+jyKtal89//vONsqy89a1vLdJGXSwECBAgQIAAAQLlEzAkYRTaNHudYvEP6/hHePY0c8SuOlRAYKjjtQUbLGBwzjnnFHXKZvavPa3f+tvf/vbK3nvvXYnvbAK8fsebCRjEnA3ZJHXFNQe6ialmHulru9ZnT8irh/Lv2hvRuOmeN29e3fHV3ai9mY+2bnRT1kzetfMzfPKTnyxOabdDZFx7MxlDShoNgagWIMbzV28k4zubQLN6KB+iEENPqsfPO++84lijlb5j84cKGAzVTjGGP8pfvf5Xv/rVRpct9kXZs4n+ivQf+chHimNZL5m64Qs///nPi2ONVmoN4/pDBQwGq0v83cUQm2o9hppnIXuTRpE2zmk0v0Rt+eK3NVAbRxvUDneIQMmKFSsaVbmS9XgorhvDJCwECBAgQIAAAQLlEzAkIfsX9kgv2ZPrukusv/76dduduBFvEagu0cU73i4w0PLNb34zXXLJJSm+440Eq7NEt/wYphBLvDXh8MMPHzSbSBPdtavLT3/60+pqv+/spmhYM//3y2AEdlS7hkfW0Q29uoykQ1wjCyjlwxGq1+v7Xdvucaz6xo1Yz26qUwwJiCXekhHd+Qdb4k0hw1mGaqdql/3IM2bxf9vb3jZo9vEmgA9+8INFmih/dfnJT34SwdR8M5u8MWW9LqqHGn5nQZ2G+wfaOVhdsvkR8iEDUZ8sAJW/jWCgfGL/ZpttVnc4hisMtsRbEmLISaMlmyQyf5NE9VgWaBjwzSibbLJJNVk+VCELNhTbVggQIECAAAECBMohIGAwCu0Yr3KrXaqv86vd12nrcUNYvVGJceBxc7jHHnvk48WzHhVtr06Mma8u8erEbOhCdXPA73gFYHWpPb+6r/qdda2vro6b7+rcEFGgqnOs19aj3Q59rxXbfZd4vWKtfW0wI16fWF2yiT2LsfXVfX2/s14nfXcNuj1UO2W9RIrz46Y45rgYatlrr72KJPG6wupSO69G1t2+unvA75h3ou+rCAdMnB0Yqi4RMIrgzAEHHDBgNhGcyXp8pBNPPLEuTdYjoG6770Y2hKjvrrrt2nlD4rWNAy0R2KhdhrpubVrrBAgQIECAAAECnSHQ+AX3nVH2jill9taAurLGpGVlWE455ZQUTyCrN7cxKVp83v/+96dslvqUdftP2RsN8u94ctnKUnujnHXBzp8gD5Vf1n2/SFJ7frHz/68M9LS1b7rR2o4ntRGEqS5bbLFFdbUuYNBuh7hIPJkfaokb8erT5N7e3iJ5TI5YXWKyw6GW9dZbL8XvYqgn4tV8hmqn2oBBbZClen6j79ob4pgUMSbsfPrTn55i8sbqMpy8oldAM8tQdanNI34LkW9MchiTIcYEjhHciPVGdtWeEbV51K7XBgRq9zdaH6wdq5MtNjrPPgIECBAgQIAAgXIICBiMQjtm45VTNsY377Ybl4t/6Jdhiaez2Wv40qte9apiuEC1XjHD/Le//e38k43HzocHfPazn23qqW81j9rv2hv+GOIRs7oPZ4nZ3aP7fDYpXL/TmrlJ7nfSCO6IQEDtMlDAoN0Occ3h3EzWljHWY7b86jLYjWY1TXxHumb/HoZqp+qQlci32Zv8bA6DFE/zs3kD4rR0++235wGD2rpEAKGZJerSbMBgqLrE9R5++OGUzcOQvv71r+dvRhioDPGkv/omh4HS1O6fOXNm7eag64ICg/I4SIAAAQIECBAovYCAwSg18Z577pmySeDyq8V4/k984hOrdeUzzjgjXXrppSnyi/Hmzd4Yrc7FhnpSGXnGaxPjye7vfve7lE2EmI9jjyegtUs8uY3XvcXr2M4///y890Ht8WbWa59kZ7PvpxgDPtxloPpkE/UNN6sRTR9O1SWGANTefI+kQ/Waq/sdZa0utT0kqvsafUcwrdllqHaqzavRk/dG14lu/bVpswkB82QR4KsuzfYIilc2NrsMVZdsItD0ghe8oHg9ZG2+0WMphitkk0amCNrttttudcMhenp6apP3WxcE6EdiBwECBAgQIECAwAACAgYDwLR79yte8YoiYHDllVcO+LR7qOt+7WtfS3F+BA7iprmV+QKGGnMcN/rNLjGZYXVCwzvvvDMPHMSEbb/85S+L7ut//OMfU/YKuHTmmWc2m22RLuZMqC4xBnt18qieP96/f/CDHxRFjLrW3uCNZ4faJ/F33XVXUYfBVobbU2SwvKKb/2WXXZYnmT9//mBJi2PRE6b27yB7FWN+LJyrczLEzXszS7vqEr0dYu6CKFssEYh517velQ/tiUBB30lTayeejPQDBcbimIUAAQIECBAgQIDAcAQmDCextKsvcOCBBxazjcdTzey1gMPOLLrlR7CgurzjHe+orjb9Xftks3bCukYZNPtkte+5MS78sMMOSxdccEG69957U/Ye+SJJBBBW54am9kb52muvLfIr20p0ab/88suLar3vfe8r1mNlPDvUdrNvJmAQT+Sz107W1a+Vjdp5AZoNGNSmi8BM9Wa8Nq9mhkzEnA618x60Uo8IelSHpcTbPqJH0he/+MW0zz77FOWrzT+G29QutQGQ2v3WCRAgQIAAAQIECAxXQMBguGKrmT7GSv/Hf/xHcfaXvvSlFE/im10iyPDOd76zSB5dp9/0pjcV282u1E4+OFRA4Iorrhgw2xNOOCEPBEQvh+pT3UaJo97/8z//Uzwlj3HZMXnbcJfaG+WY8C3Gmg+1nH322WmXXXbJy3nssccOlXzMj0c3/re+9a1FOWIm/X/9138ttmNlPDvEBJfVJYIeQ/2+InjUzqX2Jj/yHur6ce3//d//LYoQXfurwxpe9KIXFfvjlZxD9R74xje+UbxSsjhxNVdq39AQvXZe/OIXD5pT7RCWSBj/W2EhQIAAAQIECBAg0A4BAYN2KDaZx6c//eliUrkYN73vvvvmM543c3o8aY65C6rLoYceWjduubp/qO/aNzb8+Mc/HvBpf7z6MYIaAy0xwVzMyRA3/zF3wWBLjLuv7VXQd+LB2tf0DTT2PV4hWH0dXOT3X//1X4NdMu9mHkGCP/3pT3k5ayexG/TEMTp4xx13pIMOOihdffXVRQnibRO1PULiwHh2iBvuOXPm5OWPbvWD9aKJNvz4xz9e1LUdK/H3tOmmm+ZZRc+F448/ftBsI+h06qmnFmlqe8JEXSK/WKInzmBzjsRbQoa6VnGRJlZqeyr0fSVr39MffPDB9MlPfrJutx4GdRw2CBAgQIAAAQIEWhAQMGgBb7inxmvkjjvuuOK0uGGJic1OPvnkAWc5v+GGG/LxzLXvWo93zH/hC18o8hnOSu173eMNB3HtvksEM2LOhdr30vdN8+pXv7rYFT0IYr6CgZa42aoGDGLCwnjlYu1SnWgu9sWN5DXXXFN7OF+P7uIxY3x1Qref/exn6UMf+lDd+PPqSXGtf//3fy+CMXHOu9/97urhcfEdgZG42YsgUASDnvnMZ6aoU3UJ37e85S3VzeJ7PDuE86c+9amirPGbPeuss4rt6krc0L73ve8tut1X97f6HYGn2iDXSSedlL75zW82zDYCNPF2j+rNdfwm+/bYqa1LvEI0flPV10lWM73uuuvSrrvuWveGiOqx1f2OeQqqSwxHiFc9NloiqNco6NjOYR6NrmsfAQIECBAgQIBA9whM6p6qjo+axrwD8Tq8uEmMG9v4R/9//ud/pqOPPjr/x3+8Qi9ebRfjpuMpfnQ3ru1iHN2uY26A2qEFw6lZPMWOp8DVp5hx4/bDH/4wvfzlL08xfCC6cv/iF79IDzzwQIreCGuvvXbDwEG8pSGCD3GTGzf5L3vZy9Ib3/jG9MpXvjLFHAbxVDZuyiKYEDc9scR47HgSW73pr5Y7bvTiWnHNWPbee+8UQZGod9yoVZ9ax1PfN7/5zen000/P00VeUd64sY4n79GdPCaBjDcy/OUvf8nTxH+iZ8fcuXOL7dFcqZ1tv3rd8KreqFb31X6/5jWvySd17Nu7oJpmPDtEz5cIEkSbR1DkDW94Q95G8fuI33bMP3HaaaflEwpG8CParO9NeLWeq/MdQzji9xOBmPgNxlwaEcyKAFj0UImb75h8M4IZ0TMglihHDE3o+0rJCAR85CMfKYIg8buLv72dd945bbLJJnldIqAXbRnnxqsNq/OC9P2ND6cu//Iv/5L3vgi/mJ8g/tbiN/z85z8/fy1pvJHk17/+dYoeQjGsKa4Vr1asvhoyhv1YCBAgQIAAAQIECLRFILtptYyBQDbLf2WttdaqZI3Y9Ce7Ga9kk8kNWtqXvOQlRX7ZGP6GabMx2ZVsWECRrlEZspu7Sha0qGRBgSJd9uSyLr9sdvZK1mOgON4on+q+rHdFJRvXXnd+7UYWMGmYT3azV5uskt1EVbKeBZXsZrph+ur1qt9Zt/6686sbL33pS4vzv/Od71R3t/wd5a1ee7jfWaClknXjr2Q3oEOWo10OcaH4XVXL+vWvf33Ia2dBpCJ9FtTqlz67ca1k8xkUaap5135nM/9Xvv/979f9frLAQb+8Vqedsrd7VLK5IAa9frUs2SSHlZ///Of9rlu7I/5WswDdgPllPQIqWW+hSq1LNmlhbRb5+nDqkgU0BrxetezxHX9X8fecBf6K9EcddVS/aw+njbPeLkVeWeClX17VHVlwpEgXZQl3CwECBAgQIECAQLkEDEnI/qU7FsvrX//6/A0C0c1+sKff8VQ+npjGU9Hzzz+/GKPdSpnjjQ3xpDfyjVe21S7xpDR6IcRTzC233LL2UL/16H0QY+7jiX7fYQbVxNFzIJ6YXnXVVSkLZlR39/uO7t9HHnlk0ZugmiB6WdQuU6ZMSZ/5zGfynhcxnKM6SV1tmliP3g/RO+Nzn/tc30PjYjt6c+y44475q/Le9ra3pZ/85Cd5r5LsZq/fvAWNCjyeHeJpdzyJjyEBtW9OqNYjeslEr5baYS3VY+34njlzZt6z5aKLLsqNG/XUiOFB0csn3kqx3377DXrZ+FuN32/MFRC/5fitR6+Xgw8+OJ+nIX5n0Xti+fLlRT7xt9HKEkNoovwxVKXREj0coldSDBuKv+faOsRkn9GLxUKAAAECBAgQIECgVYGeiH+0monzWxeIGd3jfe/xibHtcUMSM+JH9/4IGozUEt2eYxz2LbfckmLs9Pbbb99vyEAz145u2VH2mE0+PnFD/JznPCc9/elPb+b0ujTRbTw+cVNUfc1dXYKajRi2EK+brHYNjyBH3Lw1GgpQc1rpVserQ/wuom2ijSLIEcMphmrTdjdOzMkxb968fILOCGZEECPe7hHladcSQxFimFD1f05jeEBcq9Ulbvzj1Y8x30kMI4rfd5S978ShrV7H+QQIECBAgAABAgQaCQgYNFKxjwCBrhaI+TRifo4I2m277bZDBqBizoyYRyOWeLNBdT6OrkZUeQIECBAgQIAAgY4XGLlH1x1PowIECHSrQAzViQkcY4meAjGB5wYbbNCQI3oVxOSh1WWfffaprvomQIAAAQIECBAg0NEC5jDo6OZTeAIERkIghrRUh9PE2w/ijRwDLfHqxl/96lfF4ZgDxEKAAAECBAgQIECgDAKGJJShFdWBAIG2C3z2s59NH/zgB/N849WFMeFhTIC42Wab5XMVxFwd5557bj4ZafXVp/H6xpjw0UKAAAECBAgQIECgDAICBmVoRXUgQGBEBF73utel733ve03l/bKXvSx997vfNSFhU1oSESBAgAABAgQIdIKAIQmd0ErKSIDAmAicddZZ6Qc/+EHaZZddBrz+8573vHTKKaekH//4x4IFAyo5QIAAAQIECBAg0IkCehh0YqspMwECoy4Qr/qMVxzGJyY6jFefxis8N91001EviwsSIECAAAECBAgQGA0BAYPRUHYNAgQIECBAgAABAgQIECDQYQKGJHRYgykuAQIECBAgQIAAAQIECBAYDQEBg9FQdg0CBAgQIECAAAECBAgQINBhAgIGHdZgikuAAAECBAgQIECAAAECBEZDQMBgNJRdgwABAgQIECBAgAABAgQIdJiAgEGHNZjiEiBAgAABAgQIECBAgACB0RAQMBgNZdcgQIAAAQIECBAgQIAAAQIdJiBg0GENprgECBAgQIAAAQIECBAgQGA0BAQMRkPZNQgQIECAAAECBAgQIECAQIcJCBh0WIMpLgECBAgQIECAAAECBAgQGA0BAYPRUHYNAgQIECBAgAABAgQIECDQYQICBh3WYIpLgAABAgQIECBAgAABAgRGQ0DAYDSUXYMAAQIECBAgQIAAAQIECHSYgIBBhzWY4hIgQIAAAQIECBAgQIAAgdEQEDAYDWXXIECAAAECBAgQIECAAAECHSYgYNBhDaa4BAgQIECAAAECBAgQIEBgNAQEDEZD2TUIECBAgAABAgQIECBAgECHCQgYdFiDKS4BAgQIECBAgAABAgQIEBgNAQGD0VB2DQIECBAgQIAAAQIECBAg0GECAgYd1mCKS4AAAQIECBAgQIAAAQIERkNAwGA0lF2DAAECBAgQIECAAAECBAh0mICAQYc1mOISIECAAAECBAgQIECAAIHREBAwGA1l1yBAgAABAgQIECBAgAABAh0mIGDQYQ2muAQIECBAgAABAgQIECBAYDQEBAxGQ9k1CBAgQIAAAQIECBAgQIBAhwkIGHRYgykuAQIECBAgQIAAAQIECBAYDQEBg9FQdg0CBAgQIECAAAECBAgQINBhAgIGHdZgikuAAAECBAgQIECAAAECBEZDQMBgNJRdgwABAgQIECBAgAABAgQIdJiAgEGHNZjiEiBAgAABAgQIECBAgACB0RAQMBgNZdcgQIAAAQIECBAgQIAAAQIdJiBg0GENprgECBAgQIAAAQIECBAgQGA0BAQMRkPZNQgQIECAAAECBAgQIECAQIcJCBh0WIMpLgECBAgQIECAAAECBAgQGA0BAYPRUHYNAgQIECBAgAABAgQIECDQYQICBh3WYIpLgAABAgQIECBAgAABAgRGQ0DAYDSUXYMAAQIECBAgQIAAAQIECHSYgIBBhzWY4hIgQIAAAQIECBAgQIAAgdEQEDAYDWXXIECAAAECBAgQIECAAAECHSYwqcPKq7gECIyxwHve85505plnFqWYM2dOio+FQJkEFi1alObNm1dUac0110xz584ttq0QKIvAtddem1auXFlU56677kozZswotq0QIECAQHcL9FSypbsJ1J4AgeEI7L///ukXv/jFcE6RlgABAgQ6ROCxxx5Ls2bN6pDSKiYBAgQIjLSAIQkjLSx/AgQIECBAgAABAgQIECDQgQICBh3YaIpMgAABAgQIECBAgAABAgRGWsAcBiMtLH8CJRN41rOeVTck4Y1vfGM69NBDS1ZL1el2gaeeeirdfPPNBUOM6d5mm22KbSsEyiJw4IEHpiVLlhTVmTJlSrFuhQABAgQICBj4DRAgMCyB2bNn16XffPPN01577VW3zwaBMgi87GUvK0M11IHAoAKTJtX/U3DCBJ1PBwVzkAABAl0m4P8rdFmDqy4BAgQIECBAgAABAgQIEGhGQMCgGSVpCBAgQIAAAQIECBAgQIBAlwkIGHRZg6suAQIECBAgQIAAAQIECBBoRkDAoBklaQgQIECAAAECBAgQIECAQJcJCBh0WYOrLgECBAgQIECAAAECBAgQaEZAwKAZJWkIECBAgAABAgQIECBAgECXCQgYdFmDqy4BAgQIECBAgAABAgQIEGhGQMCgGSVpCBAgQIAAAQIECBAgQIBAlwkIGHRZg6suAQIECBAgQIAAAQIECBBoRkDAoBklaQgQIECAAAECBAgQIECAQJcJCBh0WYOrLgECBAgQIECAAAECBAgQaEZAwKAZJWkIECBAgAABAgQIECBAgECXCQgYdFmDqy4BAgQIECBAgAABAgQIEGhGQMCgGSVpCBAgQIAAAQIECBAgQIBAlwkIGHRZg6suAQIECBAgQIAAAQIECBBoRkDAoBklaQgQIECAAAECBAgQIECAQJcJCBh0WYOrLgECBAgQIECAAAECBAgQaEZAwKAZJWkIECBAgAABAgQIECBAgECXCQgYdFmDqy4BAgQIECBAgAABAgQIEGhGQMCgGSVpCBAgQIAAAQIECBAgQIBAlwkIGHRZg6suAQIECBAgQIAAAQIECBBoRkDAoBklaQgQIECAAAECBAgQIECAQJcJCBh0WYOrLgECBAgQIECAAAECBAgQaEZAwKAZJWkIECBAgAABAgQIECBAgECXCQgYdFmDqy4BAgQIECBAgAABAgQIEGhGQMCgGSVpCBAgQIAAAQIECBAgQIBAlwkIGHRZg6suAQIECBAgQIAAAQIECBBoRkDAoBklaQgQIECAAAECBAgQIECAQJcJCBh0WYOrLgECBAgQIECAAAECBAgQaEZAwKAZJWkIECBAgAABAgQIECBAgECXCQgYdFmDqy4BAgQIECBAgAABAgQIEGhGQMCgGSVpCBAgQIAAAQIECBAgQIBAlwkIGHRZg6suAQIECBAgQIAAAQIECBBoRkDAoBklaQgQIECAAAECBAgQIECAQJcJCBh0WYOrLgECBAgQIECAAAECBAgQaEZAwKAZJWkIECBAgAABAgQIECBAgECXCQgYdFmDqy4BAgQIECBAgAABAgQIEGhGQMCgGSVpCBAgQIAAAQIECBAgQIBAlwkIGHRZg6suAQIECBAgQIAAAQIECBBoRkDAoBklaQgQIECAAAECBAgQIECAQJcJCBh0WYOrLgECBAgQIECAAAECBAgQaEZAwKAZJWkIECBAgAABAgQIECBAgECXCQgYdFmDqy4BAgQIECBAgAABAgQIEGhGQMCgGSVpCBAgQIAAAQIECBAgQIBAlwkIGHRZg6suAQIECBAgQIAAAQIECBBoRkDAoBklaQgQIECAAAECBAgQIECAQJcJCBh0WYOrLgECBAgQIECAAAECBAgQaEZAwKAZJWkIECBAgAABAgQIECBAgECXCQgYdFmDqy4BAgQIECBAgAABAgQIEGhGQMCgGSVpCBAgQIAAAQIECBAgQIBAlwkIGHRZg6suAQIECBAgQIAAAQIECBBoRkDAoBklaQgQIECAAAECBAgQIECAQJcJCBh0WYOrLgECBAgQIECAAAECBAgQaEZAwKAZJWkIECBAgAABAgQIECBAgECXCQgYdFmDqy4BAgQIECBAgAABAgQIEGhGQMCgGSVpCBAgQIAAAQIECBAgQIBAlwkIGHRZg6suAQIECBAgQIAAAQIECBBoRkDAoBklaQgQIECAAAECBAgQIECAQJcJCBh0WYOrLgECBAgQIECAAAECBAgQaEZAwKAZJWkIECBAgAABAgQIECBAgECXCQgYdFmDqy4BAgQIECBAgAABAgQIEGhGQMCgGSVpCBAgQIAAAQIECBAgQIBAlwkIGHRZg6suAQIECBAgQIAAAQIECBBoRkDAoBklaQgQIECAAAECBAgQIECAQJcJCBh0WYOrLgECBAgQIECAAAECBAgQaEZAwKAZJWkIECBAgAABAgQIECBAgECXCQgYdFmDqy4BAgQIECBAgAABAgQIEGhGQMCgGSVpCBAgQIAAAQIECBAgQIBAlwkIGHRZg6suAQIECBAgQIAAAQIECBBoRkDAoBklaQgQIECAAAECBAgQIECAQJcJCBh0WYOrLgECBAgQIECAAAECBAgQaEZAwKAZJWkIECBAgAABAgQIECBAgECXCQgYdFmDqy4BAgQIECBAgAABAgQIEGhGQMCgGSVpCBAgQIAAAQIECBAgQIBAlwkIGHRZg6suAQIECBAgQIAAAQIECBBoRkDAoBklaQgQIECAAAECBAgQIECAQJcJCBh0WYOrLgECBAgQIECAAAECBAgQaEZAwKAZJWkIECBAgAABAgQIECBAgECXCQgYdFmDqy4BAgQIECBAgAABAgQIEGhGQMCgGSVpCBAgQIAAAQIECBAgQIBAlwkIGHRZg6suAQIECBAgQIAAAQIECBBoRkDAoBklaQgQIECAAAECBAgQIECAQJcJCBh0WYOrLgECBAgQIECAAAECBAgQaEZAwKAZJWkIECBAgAABAgQIECBAgECXCQgYdFmDqy4BAgQIECBAgAABAgQIEGhGQMCgGSVpCBAgQIAAAQIECBAgQIBAlwkIGHRZg6suAQIECBAgQIAAAQIECBBoRkDAoBklaQgQIECAAAECBAgQIECAQJcJCBh0WYOrLgECBAgQIECAAAECBAgQaEZAwKAZJWkIECBAgAABAgQIECBAgECXCQgYdFmDqy4BAgQIECBAgAABAgQIEGhGQMCgGSVpCBAgQIAAAQIECBAgQIBAlwkIGHRZg6suAQIECBAgQIAAAQIECBBoRkDAoBklaQgQIECAAAECBAgQIECAQJcJCBh0WYOrLgECBAgQIECAAAECBAgQaEZAwKAZJWkIECBAgAABAgQIECBAgECXCQgYdFmDqy4BAgQIECBAgAABAgQIEGhGQMCgGSVpCBAgQIAAAQIECBAgQIBAlwkIGHRZg6suAQIECBAgQIAAAQIECBBoRkDAoBklaQgQIECAAAECBAgQIECAQJcJCBh0WYOrLgECBAgQIECAAAECBAgQaEZAwKAZJWkIECBAgAABAgQIECBAgECXCQgYdFmDqy4BAgQIECBAgAABAgQIEGhGQMCgGSVpCBAgQIAAAQIECBAgQIBAlwkIGHRZg6suAQIECBAgQIAAAQIECBBoRkDAoBklaQgQIECAAAECBAgQIECAQJcJCBh0WYOrLgECBAgQIECAAAECBAgQaEZAwKAZJWkIECBAgAABAgQIECBAgECXCQgYdFmDqy4BAgQIECBAgAABAgQIEGhGQMCgGSVpCBAgQIAAAQIECBAgQIBAlwkIGHRZg6suAQIECBAgQIAAAQIECBBoRkDAoBklaQgQIECAAAECBAgQIECAQJcJCBh0WYOrLgECBAgQIECAAAECBAgQaEZAwKAZJWkIECBAgAABAgQIECBAgECXCQgYdFmDqy4BAgQIECBAgAABAgQIEGhGQMCgGSVpCBAgQIAAAQIECBAgQIBAlwlMKnt9L7zwwnTGGWekY489Nm233XYDVvemm25K5557brrrrrvSjBkz0o477pj22muvtMUWWwx4jgMECBAgQIAAAQIECBAgQKCsAqUOGFx//fXpC1/4QlqxYkVaunTpgG143nnnpRNPPDE/vuaaa6Zly5ala665Jp1zzjnp+OOPT895znMGPNcBAgQIECBAgAABAgQIECBQRoHSDkm49tpr04c//OE8WDBYw0VQ4Stf+UqaMmVK+vSnP50uuuii9LOf/Sy9+93vTosXL05HHXVUeuCBBwbLwjECBAgQIECAAAECBAgQIFA6gdIFDJ566qm8V0Hc8D/22GNpwoTBq3j66aenSqWS3vjGN6YXv/jFqaenJ02ePDkdcsgh6VWvelVavnx5+tGPflS6hlchAgQIECBAgAABAgQIECAwmMDgd9ODnTlOj731rW9NF1xwQZo+fXr62Mc+ljbffPMBSxrBhSuvvDI/vv/++/dLV93305/+dMieCv1OtoMAAQIECBAgQIAAAQIECHSwQOkCBgsWLEhxo3/aaaelfffdd9CmmTdvXt67YJNNNklz5szpl3bu3Llp5syZ6fHHH0933313v+N2ECBAgAABAgQIECBAgACBsgqUbtLDU089NW244YZNtdd9992Xp5s1a9aA6ePYk08+me65554B35jw5z//OV188cUN84hJFN/1rnc1PGYnAQIECBAgQIAAAQIECBAYrwKlCxg0GyyIBlm0aFHeLoMFDNZaa608TTVtvtHnP3/84x/T0Ucf3WfvPzaf/vSnCxg0lLGTAAECBAgQIECAAAECBMazQOmGJAwHO+YwiCWGHQy0RA+BWJYsWTJQEvsJECBAgAABAgQIECBAgEDpBErXw2A4LTRjxow8+bJlywY8benSpfmxqVOnDphmzz33zOdMaJQgJl+0ECBAgAABAgQIECBAgACBThPo6oDBeuutl7fXE088MWC7xfwFsVSDC40SbrPNNik+FgIECBAgQIAAAQIECBAgUBaBrh6SUA0YVIMCjRq1GkyYPXt2o8P2ESBAgAABAgQIECBAgACBUgp0dcBggw02yBs13oCwfPnyfg0cr1N89NFH04QJE9LWW2/d77gdBAgQIECAAAECBAgQIECgrAJdHTCYM2dOmjt3blq4cGGKNx30XS6//PK0cuXKPI25CPrq2CZAgAABAgQIECBAgACBMgt0dcAgGvZ1r3td3r6nnnpqqh2a8NBDD6Wzzz47P3bIIYfk3/5DgAABAgQIECBAgAABAgS6RaCrJz2MRt5jjz3Sdtttl+bNm5fe+ta3pnjjwYoVK9Ill1ySHnnkkbT77runvfbaq1t+D+pJgAABAgQIECBAgAABAgRyga4PGEycODGddNJJ6YQTTki/+MUv0plnnpnDxP5XvepV6R3veEc+h4HfCwECBAgQIECAAAECBAgQ6CaB0gcMTjvttCHbc+rUqemDH/xgOuqoo9Ltt9+eKpVK2mSTTQZ9leKQmUpAgAABAgQIECBAgAABAgQ6WKD0AYPhtM2kSZPStttuO5xTpCVAgAABAgQIECBAgAABAqUU6PpJD0vZqipFgAABAgQIECBAgAABAgRaFBAwaBHQ6QQIECBAgAABAgQIECBAoIwCAgZlbFV1IkCAAAECBAgQIECAAAECLQoIGLQI6HQCBAgQIECAAAECBAgQIFBGAQGDMraqOhEgQIAAAQIECBAgQIAAgRYFBAxaBHQ6AQIECBAgQIAAAQIECBAoo4CAQRlbVZ0IECBAgAABAgQIECBAgECLAgIGLQI6nQABAgQIECBAgAABAgQIlFFAwKCMrapOBAgQIECAAAECBAgQIECgRQEBgxYBnU6AAAECBAgQIECAAAECBMooIGBQxlZVJwIECBAgQIAAAQIECBAg0KKAgEGLgE4nQIAAAQIECBAgQIAAAQJlFBAwKGOrqhMBAgQIECBAgAABAgQIEGhRQMCgRUCnEyBAgAABAgQIECBAgACBMgoIGJSxVdWJAAECBAgQIECAAAECBAi0KCBg0CKg0wkQIECAAAECBAgQIECAQBkFBAzK2KrqRIAAAQIECBAgQIAAAQIEWhQQMGgR0OkECBAgQIAAAQIECBAgQKCMAgIGZWxVdSJAgAABAgQIECBAgAABAi0KCBi0COh0AgQIECBAgAABAgQIECBQRgEBgzK2qjoRIECAAAECBAgQIECAAIEWBQQMWgR0OgECBAgQIECAAAECBAgQKKOAgEEZW1WdCBAgQIAAAQIECBAgQIBAiwICBi0COp0AAQIECBAgQIAAAQIECJRRQMCgjK2qTgQIECBAgAABAgQIECBAoEUBAYMWAZ1OgAABAgQIECBAgAABAgTKKCBgUMZWVScCBAgQIECAAAECBAgQINCigIBBi4BOJ0CAAAECBAgQIECAAAECZRQQMChjq6oTAQIECBAgQIAAAQIECBBoUUDAoEVApxMgQIAAAQIECBAgQIAAgTIKCBiUsVXViQABAgQIECBAgAABAgQItCggYNAioNMJECBAgAABAgQIECBAgEAZBQQMytiq6kSAAAECBAgQIECAAAECBFoUEDBoEdDpBAgQIECAAAECBAgQIECgjAICBmVsVXUiQIAAAQIECBAgQIAAAQItCggYtAjodAIECBAgQIAAAQIECBAgUEYBAYMytqo6ESBAgAABAgQIECBAgACBFgUEDFoEdDoBAgQIECBAgAABAgQIECijgIBBGVtVnQgQIECAAAECBAgQIECAQIsCAgYtAjqdAAECBAgQIECAAAECBAiUUUDAoIytqk4ECBAgQIAAAQIECBAgQKBFAQGDFgGdToAAAQIECBAgQIAAAQIEyiggYFDGVlUnAgQIECBAgAABAgQIECDQooCAQYuATidAgAABAgQIECBAgAABAmUUEDAoY6uqEwECBAgQIECAAAECBAgQaFFAwKBFQKcTIECAAAECBAgQIECAAIEyCggYlLFV1YkAAQIECBAgQIAAAQIECLQoIGDQIqDTCRAgQIAAAQIECBAgQIBAGQUEDMrYqupEgAABAgQIECBAgAABAgRaFBAwaBHQ6QQIECBAgAABAgQIECBAoIwCAgZlbFV1IkCAAAECBAgQIECAAAECLQoIGLQI6HQCBAgQIECAAAECBAgQIFBGAQGDMraqOhEgQIAAAQIECBAgQIAAgRYFBAxaBHQ6AQIECBAgQIAAAQIECBAoo4CAQRlbVZ0IECBAgAABAgQIECBAgECLAgIGLQI6nQABAgQIECBAgAABAgQIlFFAwKCMrapOBAgQIECAAAECBAgQIECgRQEBgxYBnU6AAAECBAgQIECAAAECBMooIGBQxlZVJwIECBAgQIAAAQIECBAg0KKAgEGLgE4nQIAAAQIECBAgQIAAAQJlFBAwKGOrqhMBAgQIECBAgAABAgQIEGhRQMCgRUCnEyBAgAABAgQIECBAgACBMgoIGJSxVdWJAAECBAgQIECAAAECBAi0KCBg0CKg0wkQIECAAAECBAgQIECAQBkFBAzK2KrqRIAAAQIECBAgQIAAAQIEWhQQMGgR0OkECBAgQIAAAQIECBAgQKCMAgIGZWxVdSJAgAABAgQIECBAgAABAi0KCBi0COh0AgQIECBAgAABAgQIECBQRgEBgzK2qjoRIECAAAECBAgQIECAAIEWBQQMWgR0OgECBAgQIECAAAECBAgQKKOAgEEZW1WdCBAgQIAAAQIECBAgQIBAiwICBi0COp0AAQIECBAgQIAAAQIECJRRQMCgjK2qTgQIECBAgAABAgQIECBAoEUBAYMWAZ1OgAABAgQIECBAgAABAgTKKCBgUMZWVScCBAgQIECAAAECBAgQINCigIBBi4BOJ0CAAAECBAgQIECAAAECZRQQMChjq6oTAQIECBAgQIAAAQIECBBoUUDAoEVApxMgQIAAAQIECBAgQIAAgTIKCBiUsVXViQABAgQIECBAgAABAgQItCggYNAioNMJECBAgAABAgQIECBAgEAZBQQMytiq6kSAAAECBAgQIECAAAECBFoUEDBoEdDpBAgQIECAAAECBAgQIECgjAICBmVsVXUiQIAAAQIECBAgQIAAAQItCggYtAjodAIECBAgQIAAAQIECBAgUEYBAYMytqo6ESBAgAABAgQIECBAgACBFgUEDFoEdDoBAgQIECBAgAABAgQIECijgIBBGVtVnQgQIECAAAECBAgQIECAQIsCAgYtAjqdAAECBAgQIECAAAECBAiUUUDAoIytqk4ECBAgQIAAAQIECBAgQKBFgUktnu90AgQIECBAoEsFHrnmXWnhHd9eVfueyaln4rRV29bGvcADu1RSz7IZq8r50s3T0p6eVdvWxrfAsqUpLV9WlLFnj5enKZ87q9i2QoAAgVYFBAxaFXQ+AQIECBDoUoFK76oblZygsjxVVizvUo3OrPaEZRPThOU1AYLlCzuzIkr9D4FlS0gQIECgrQKGJLSVU2YECBAgQIAAAQIECBAgQKAcAgIG5WhHtSBAgAABAgQIECBAgAABAm0VMCShrZwyI0CAAAEC3SMwdd0XpEXzzygqPGOzN6d1djqh2LYy/gUWv+TR1FupFAWdPnPdYt3K+BdY8e3jUu93v1wUdMJOuxfrVggQINAOAQGDdijKgwABAgQIdKFAT099R8WenklpwqTpXSjRuVWesZ726tzWS9kko5Priz+h/m+y/qAtAgQIDF/A/6oM38wZBAgQIECAAAECBAgQIECg9AICBqVvYhUkQIAAAQIECBAgQIAAAQLDFxAwGL6ZMwgQIECAAAECBAgQIECAQOkFBAxK38QqSIAAAQIECBAgQIAAAQIEhi8gYDB8M2cQIECAAAECBAgQIECAAIHSCwgYlL6JVZAAAQIECBAgQIAAAQIECAxfQMBg+GbOIECAAAECBAgQIECAAAECpRcQMCh9E6sgAQIECBAgQIAAAQIECBAYvoCAwfDNnEGAAAECBAgQIECAAAECBEovIGBQ+iZWQQIECBAgQIAAAQIECBAgMHyBtgcMvvWtb6X3vve96cYbb2y6NJ/85CfTbrvtlo477rimz5GQAAECBAgQIECAAAECBAgQGDmBtgcMLrjggnTiiSemO++8s+lS/+pXv0p/+MMfhhVkaDpzCQkQIECAAAECBAgQIECAAIFhC0wa9hltPGHlypXp1ltvTX/5y1/yXKdPn97G3GVFgAABAgQIECBAgAABAgQIrK5ASwGDAw88MF166aV1116+fHm+ffDBB6cJEwbvwBBpe3t7i/N33nnnYt0KAQIECBAgQIAAAQIECBAgMHYCLQUMvvjFL6Z/+qd/StUgQW01Gu2rPd53fYcddkgHHXRQ3922CRAgQIAAAQIECBAgQIAAgTEQaClgMHfu3HTyySenq666qij6xRdfnO6+++700pe+NG266abF/kYrkydPTjNmzEibb755evWrX51mz57dKJl9BAgQIECAAAECBAgQIECAwCgLtBQwiLL+x3/8R/6pljuGKUTA4PDDD0+xbiFAgAABAgQIECBAgAABAgQ6T6DlgEHfKr/pTW/KX5G47bbb9j1kmwABAgQIECBAgAABAgQIEOgQgbYHDF772td2SNUVkwABAgQIECBAgAABAgQIEBhIoO0Bg9oLPfzww/lrEx999NG0bNmy2kMN17fbbrsUHwsBAgQIECBAgAABAgQIECAwtgIjEjBYsGBBPonhJZdckiqVStM1POaYY9Kxxx7bdHoJCQxX4Ft/fCRddPMTxWlzZk5OG609udi2MrTAL9fYIa330ZOLhD/fYM204sq/FNtWhha4b9FT6a6FTxUJD9x0Tnr/swRLCxArBAgQIECAAAEC40Kg7QGD6E2w7777pmuuuWZcVFAhCNQKXHH3onTTw0uLXbXrxU4rgwo8tUb2NpOtlhdp7sjW7rjp9mLbyvAF1pw8ScBg+GzOIECAAAECBAgQGGGBtgcMvvKVrxTBgu233z69/e1vz1+buOaaaw5ZlXi9ooUAAQIECBAgQIAAAQIECBAYe4G2Bwx+//vf57Xabbfd0m9+85s0ceLEsa+lEhAgQIAAAQIECBAgQIAAAQLDEmhrwKC3tzddccUVeQGOPPJIwYJhNYXEoyGw2ewp6fd3rxo7/uLNZ6SXbDF075fRKFunXONPdzyRrn+opyjuNnNmp+dus0mxbWVogcv/9mD62b0PFAm3mOk3WGBYIUCAAAECBAgQGDcCbQ0Y9PT0pEmT/pHlRhttNG4qqSAEqgIbrFn/k9/xadPS63fKxuRbmhZ4/U67Np1WwsYCTy5fURcwePr0aY0T2kuAAAECBAgQIEBgDAUmtPPaETDYeeed8yz/7//+r51Zy4sAAQIECBAgQIAAAQIECBAYRYG2Bgyi3AceeGBe/B/84AdpxYoVo1gVlyJAgAABAgQIECBAgAABAgTaJdD2gMG73/3udMABB6SY/PCggw5KTz21arx4uwotHwIECBAgQIAAAQIECBAgQGBkBeoHdA/jWpdffnm67LLLGp5RfT3ihRdemDbeeOP0zGc+M2277bZpzpw5DdNXd+61115pzz1unn+nAABAAElEQVT3rG76JkCAAAECBAgQIECAAAECBMZIYLUDBvHKxE996lNDFvuxxx5LMZ9BM3MaxISJAgZDkkpAgAABAgQIECBAgAABAgRGXKDtQxJGvMQuQIAAAQIECBAgQIAAAQIECIy4wGr3MPjABz6Q3vOe97S1gNOmebVYW0FlRoAAAQIECBAgQIAAAQIEVlNgtQMGcXPvBn811Z1GgAABAgQIECBAgAABAgTGuYAhCeO8gRSPAAECBAgQIECAAAECBAiMhcBq9zAYqLC//vWv03333TfQ4Yb7J06cmNZaa6209tprpw033DBtueWWDdPZSYAAAQIECBAgQIAAAQIECIyOQNsDBp/73OfSRRdd1FLp41WMr3rVq/K3MMyYMaOlvJxMgAABAgQIECBAgAABAgQIDF9gXA5JuPfee9OXv/zl9NznPjfFaxktBAgQIECAAAECBAgQIECAwOgKtD1gcMEFF6T4TJjwj6xf+MIXprPPPjtdeeWV6YEHHki33HJL+uUvf5mOPfbYNGvWrLy222yzTbrwwgvTD3/4w/SVr3ylGJJw8803p7e//e2jK+JqBAgQIECAAAECBAgQIECAQGp7wOD2229Pb37zm1Nvb2865ZRT0m9/+9v02te+Nu2yyy75/ARbb7112meffdIxxxyT7rzzznx/BBH+/Oc/p4MOOii9613vStddd106/PDD8+Y5//zz0/z58zUVAQIECBAgQIAAAQIECBAgMIoCbQ8YfPazn02PP/54Ouyww9Jb3vKWQasSPQzOOeecNGXKlPTRj340PfLII3n66dOnp5NOOinNnTs3VSqVvOfBoBk5SIAAAQIECBAgQIAAAQIECLRVoO0Bgz/84Q95AV/zmtc0VdDNNtss7bTTTnmPhBi2UF16enrS3nvvnW/qYVBV8U2AAAECBAgQIECAAAECBEZHoK0BgxUrVqSYdyCWjTbaqOkabLDBBnnaefPm1Z2z7rrr5tt///vf6/bbIECAAAECBAgQIECAAAECBEZWoK0Bg0mTJqX11lsvL/FVV13VdMmvvvrqPG0MQahdrrjiinzzaU97Wu1u6wQIECBAgAABAgQIECBAgMAIC7Q1YBBljVchxvKxj30sLViwIF8f7D+f//zn0/33358nqZ4bG0uWLMknTIz1GLZgIUCAAAECBAgQIECAAAECBEZPoO0BgyOOOCIvfbwtYa+99kqXXHJJw9o8/PDD6QMf+EA6+uij8+P7779//haF2LjxxhvzNyssXrw4TZw4MR144IEN87CTAAECBAgQIECAAAECBAgQGBmBSe3Odr/99kv/9V//lb761a+ma6+9Nu27775p8803T1tssUXaeOON06OPPpruueeefK6DCAjEEq9aPPvss4ui7L777kXvhJg8Mc61ECBAgAABAgQIECBAgAABAqMn0PaAQRT9K1/5Sj6XwfHHH58PLbjzzjtTfBotBxxwQB5cmD17dn44eh5UhzIcfPDB6Zvf/Gaj0+wjQIAAAQIECBAgQIAAAQIERlBgRAIG8UrEY445Jh166KHp4osvTjGp4TXXXJNuuOGGNHny5LxHQUxw+J//+Z/phS98YV31li9fnk4++eS04447pt122y1FXhYCBAgQIECAAAECBAgQIEBgdAVGJGBQrcKmm26a3vGOd1Q308qVK/M5CYodDVbmzJmTDjvssAZH7CJAgAABAgQIECBAgAABAgRGS6Dtkx4OVvCYwNBCgAABAgQIECBAgAABAgQIjH+BUQ0YjH8OJSRAgAABAgQIECBAgAABAgRCYLWHJFx22WXFKxPjlYh77LFHLnr66afnb0BYHd699947xcdCgAABAgQIECBAgAABAgQIjK3AagcMfvvb36bjjjsuL/3aa69dBAzOOeecdNFFF61WraZOnSpgsFpyTiJAgAABAgQIECBAgAABAu0VMCShvZ5yI0CAAAECBAgQIECAAAECpRBY7R4GH/rQh9KRRx6ZI0TPgOpy3nnnpRUrVlQ3h/Vdm8+wTpSYAAECBAgQIECAAAECBAgQaKvAagcMpkyZkuLTd1ljjTX67rJNgAABAgQIECBAgAABAgQIdJjAqA5JWLRoUVq4cGGHESkuAQIECBAgQIAAAQIECBDoPoERDRg8/PDD6aijjsonRJwzZ05ac8010zHHHJMr33nnnelFL3pRiiEMvb293SevxgQIECBAgAABAgQIECBAYBwLrPaQhMHqVKlU0oknnpg+/vGPpwULFjRMOn/+/PS73/0u/7z+9a9Pp512Wpo8eXLDtHYSIECAAAECBAgQIECAAAECoyswIj0MvvzlL6cjjjgiDxZMmjQpPfvZz05bbbVVXc1iYsRqgOCss85K73znO+uO2yBAgAABAgQIECBAgAABAgTGTqDtAYPrr78+xRsUYjnwwAPT7bffnq655pp8vbaa++67b34shiXEEj0Mbr755tok1gkQIECAAAECBAgQIECAAIExEmh7wOCEE05IS5cuzXsVnHvuuWnTTTcdsGqbbLJJ+vnPf57WWWedtHLlynTKKacMmNYBAgQIECBAgAABAgQIECBAYPQE2h4w+POf/5yXPnoZNPOKxUgTPRFiufXWW/Nv/yFAgAABAgQIECBAgAABAgTGVqCtAYPoJfDXv/41r9Fzn/vcpmt2wAEH5Gnvvvvups+RkAABAgQIECBAgAABAgQIEBg5gbYGDCZOnJi/OjGK+/jjjzdd6nj9Yizx6kULAQIECBAgQIAAAQIECBAgMPYCbQ0YRHWe9axn5bW69NJLm65dzGMQyw477ND0ORISIECAAAECBAgQIECAAAECIycwqd1ZP+95z0uXX355+sQnPpEOOuigfq9T7Hu9U089NV188cX57uEMY+ibTyvbUd7rrrtuwCzWW2+99IY3vGHA4w4QIECAAAECBAgQIECAAIGyCbQ9YPCBD3wgnXnmmemee+5JO++8czr++OPTwQcf3M/trrvuSp/85CfTd77znfzYC1/4wvTKV76yX7rR2HHBBRekq6++esBLbbHFFgIGA+o4QIAAAQIECBAgQIAAAQJlFGh7wGDWrFnp9NNPT/vss08+j8E73/nOFJ+pU6fmfmeffXY644wz0t///vfCc/r06Sl6GkyY0PYREsU1Blupvp3h3e9+d1HO2vQzZ86s3bROgAABAgQIECBAgAABAgRKL9D2gEGI7bnnnumKK65IcQMe37EsXbo0/77//vvz7+p/9t5773TSSScNOXShmr7d3w899FB64okn0rrrrpsOOeSQdmcvPwIECBAgQIAAAQIECBAg0JECIxIwCIlddtkl/f73v0/nnXde/h1P8eNTqVTSNttsk7beeus8sPCKV7xiTOGqvQu23XbbMS3HeLj4Y4tXjodijGgZFi/vrcs/tstW79lrTKyrY3XjkSXLqqu+x1jgqRX1f2uLs23tM3aNsu60KQ0v3rt0VU+4hgnsTL0rFtYpVFYuSdzqSEq5MWHqeo3rtcDfTGOYEdy75Kks88qqCyxelJJ2WOVhjcBoCMwa4H8TR+Pao3CNEQsYRNl7enryp/bj+cl934DBihUr8h4H66yzzijwj69L/O2J5eOrQCNQmieW1gcMnsy2y1bvgQIG9y6Kf1RYxoPA48uy4E3Nv++eWLY8aZ+xa5mBAgYrnrpn7ArVIVfuXfpI3W85AgjcOqTxWijmlAECBpUH/M20wLpap1YWPVF3XuXJBUk71JHYIDDiAj0CBiNuPKYXqAYMIlBwxBFHpGuvvTatXLkyxbwF0UsihlXEcAULAQIECBAgQIAAAQIECBDoJoGxmWVwHAnfdttteWm++93v5sGCuXPnphiesGTJknTZZZelN7/5zamaZqBixySOG2ywQcPPjjvuONBp9hMgQIAAAQIECBAgQIAAgXErsNpDEr7xjW+kr3/9622tWPWNCm3NdJDMFi1alP72t7/lKfbff//0vve9L62xxhr5duz/+Mc/nm688cb0mc98Jn3rW99KkyY15lq8eHF6+OGHG15poHMaJraTAAECBAgQIECAAAECBAiME4HGd8BNFO7BBx9M119/fRMpm08SbywYzSWCA2eddVb+iseddtopn3Ohev05c+akT3ziE+kNb3hDPlnjlVdemXbbbbfq4brvadOmpYHmPJg9e3ZdWhsECBAgQIAAAQIECBAgQKATBFY7YDBlypQ0ffr0QesYT97jrQjxlD3SD7VMnjx5qCRtPT5hwoS0ySab5J9GGW+44YYphhT86U9/SnfccceAAYN/+7d/S/GxECBAgAABAgQIECBAgACBsgisdsDgQx/6UIrPYMvmm2+e5s+fnw499NC8S/9gacfrsZibIJZHHslmgrYQIECAAAECBAgQIECAAIEuEVjtgEEZfG6++eZ0xRVX5D0lBnr1Y3WYxMYbb1yGKqsDAQIECBAgQIAAAQIECBBoSqCrAwaPPfZY+va3v53PXbDrrrumZzzjGXVojz76aPrrX/+a79t+++3rjtkgQIAAAQIECBAgQIAAAQJlFujq1yrGRIezZs3K51k47bTT0ooVK4q2jtcqfvazn00xD8Puu++e4nWLFgIECBAgQIAAAQIECBAg0C0CXd3DIN5ucMwxx6QjjzwyXXLJJenqq69O++23X5o4cWL6zW9+k+69994U8zAcccQR3fJ7UE8CBAgQIECAAAECBAgQIJALdHXAIAR23nnndPLJJ6cTTzwxzZs3L33/+9/PYeKVi/vvv38eTBjqbRD5Cf5DgAABAgQIECBAgAABAgRKJND1AYNoy5if4Fvf+lZ6/PHH0z333JNmzpyZv2oxXrtoIUCAAAECBAgQIECAAAEC3SggYFDT6muvvXaKj4UAAQIECBAgQIAAAQIECHS7gEfo3f4LUH8CBAgQIECAAAECBAgQINBAQMCgAYpdBAgQIECAAAECBAgQIECg2wVWe0jCggULUnwGW6qvKXzyySfT/PnzB0uaH4tXHMbHQoAAAQIECBAgQIAAAQIECIytwGoHDOKtAscee2xTpf/e976X4jPUEvnFaw4tBAgQIECAAAECBAgQIECAwNgKGJIwtv6uToAAAQIECBAgQIAAAQIExqXAavcw2HHHHdPrXve6tlZqhx12aGt+MiNAgAABAgQIECBAgAABAgRWT2C1AwavfOUrU3wsBAgQIECAAAECBAgQIECAQPkEDEkoX5uqEQECBAgQIECAAAECBAgQaFlAwKBlQhkQIECAAAECBAgQIECAAIHyCQgYlK9N1YgAAQIECBAgQIAAAQIECLQsIGDQMqEMCBAgQIAAAQIECBAgQIBA+QQEDMrXpmpEgAABAgQIECBAgAABAgRaFhAwaJlQBgQIECBAgAABAgQIECBAoHwCAgbla1M1IkCAAAECBAgQIECAAAECLQsIGLRMKAMCBAgQIECAAAECBAgQIFA+AQGD8rWpGhEgQIAAAQIECBAgQIAAgZYFBAxaJpQBAQIECBAgQIAAAQIECBAon4CAQfnaVI0IECBAgAABAgQIECBAgEDLAgIGLRPKgAABAgQIECBAgAABAgQIlE9AwKB8bapGBAgQIECAAAECBAgQIECgZQEBg5YJZUCAAAECBAgQIECAAAECBMonIGBQvjZVIwIECBAgQIAAAQIECBAg0LKAgEHLhDIgQIAAAQIECBAgQIAAAQLlExAwKF+bqhEBAgQIECBAgAABAgQIEGhZQMCgZUIZECBAgAABAgQIECBAgACB8gkIGJSvTdWIAAECBAgQIECAAAECBAi0LCBg0DKhDAgQIECAAAECBAgQIECAQPkEBAzK16ZqRIAAAQIECBAgQIAAAQIEWhYQMGiZUAYECBAgQIAAAQIECBAgQKB8AgIG5WtTNSJAgAABAgQIECBAgAABAi0LCBi0TCgDAgQIECBAgAABAgQIECBQPgEBg/K1qRoRIECAAAECBAgQIECAAIGWBQQMWiaUAQECBAgQIECAAAECBAgQKJ/ApPJVSY0IECBAgAABAgS6QWDFdz6XKvfdWVS1Z5MtU1pzrWK77Cu9V/+2roq9f7w0rViyqG5fqTeeWFDf/ptvlya98d2lrrLKERhtAQGD0RZ3PQIECBAgQIAAgbYI9F6T3TA/uaDIq3LbDcV6V67cfWvqzT7dulQefTglAYNubX71HiEBQxJGCFa2BAgQIECAAAECBAgQIECgkwUEDDq59ZSdAAECBAgQIECAAAECBAiMkIAhCSMEK1sCBAgQIECAAIGRFehZZ4NUqRmSMOFFB6b0tE1G9qLjKPfKbdenyv33FCXq2WSL1LPZ3GK79Cv33pF6//CLopo96z2tWLdCgEB7BAQM2uMoFwIECBAgQIAAgVEW6Fl7nVSpueaE5+2VJuy4a80eq2UWWHnl5SnVBgxmr1fm6qobgTERMCRhTNhdlAABAgQIECBAgAABAgQIjG8BAYPx3T5KR4AAAQIECBAgQIAAAQIExkRAwGBM2F2UAAECBAgQIECAAAECBAiMbwFzGIzv9lE6Al0pcOFd96UTb7ilqPuUCT1prSmTi+1OX1mwdHldFc654570y/seqNvXyRtPLF+elq1cNar47dttmV61xaadXCVlJ0CAAAECBAh0pYCAQVc2u0oTGN8Ci1euTL2VVTecS7KbzyWLl47vQrdQuuW9vemhEtdv4fIVLeg4lQABAgQIECBAYKwEDEkYK3nXJUCAAAECBAgQIECAAAEC41hAwGAcN46iESBAgAABAgQIECBAgACBsRIwJGGs5F2XAIEBBTabOaPu2A7rrJ0+8pzt6/Z18sZjS5amR5etmsdg1pQpad1pUzq5SnVl/8Jfbkp/evjRYt9Wa69ZrFshQIAAAQIECBDoHAEBg85pKyUl0DUCk3rqOz9NmTAxrTdtWmnqX6a6NGqUyRPr269vezY6xz4CBAgQIECAAIHxJ1D/r7rxVz4lIkCAAAECBAgQIECAAAECBMZAQMBgDNBdkgABAgQIECBAgAABAgQIjHcBAYPx3kLKR4AAAQIECBAgQIAAAQIExkBAwGAM0F2SAAECBAgQIECAAAECBAiMdwGTHo73FlI+AgQIECBAgAABAg0EVpx5UqrceVNxpGejzVOaOavYLvtK7x031FWx95br0orzvl23r+wblZuuXVXFCRPS5KNPWrVtjUAbBAQM2oAoCwIECBAgQIAAAQKjLdD7p1+n9OiDxWUrt15XrHflymMPp96fnN6VVVdpAiMlYEjCSMnKlwABAgQIECBAgAABAgQIdLCAgEEHN56iEyBAgAABAgQIECBAgACBkRIwJGGkZOVLgAABAgQIECBAYAQFetbdMFVqhiRMeME+KW285QhecXxlXXng7lS55fqiUD2z1089O+5abJd+pVJJved9a1U1eyauWrdGoE0CAgZtgpQNAQIECBAgQIAAgdEU6Fln/VSpuWDPc16cJu66Z80eq2UWqPQNGEzoKXN11W2MBAxJGCN4lyVAgAABAgQIECBAgAABAuNZQA+D8dw6ykaAAIExEjjqD9ekmx9/srj6+tOmpjUnN/f/Mm59fGFxXqycdMMt6azb5tftG2jj/qeWpCUrVxaH37fjtuklGz2t2LZCgAABAgQIECAwegLN/etv9MrjSgQIECAwDgQeWLwkLV6x6sb97oVPrXapHszyis/qLAuWr1id0zr2nEev+3Ba8eStRfknzdg8TZg8s9gebyvLn7y5rkhLHv5dWtD7qbp942mjUlmRlj9+Y1GknonT0/rPP63YtkKAAAECBAjUCwgY1HvYIkCAAAECYyaw7PHrUmXpI8X1Vy6+t1jvhJXKskfT4vsv6oSi/v8yGu/bQY2lqAQIECAwBgLmMBgDdJckQIAAAQKNBWqnL2ucwl4CBAgQIECAwGgJ6GEwWtKuQ4AAgQ4S2HKtNdMD2XwC1eXft9087bz+OtXNQb/nPfZkWrB0aZFm21lrpXWmTSm2B1s58fpb0q01cydsNH2NwZKX8Jgn3iVsVFUiQIAAAQIdKyBg0LFNp+AECBAYOYE1JtW/y3lOduM+d9baTV2w2XSNMpve57pTJ9aXo9E5Zdo3aY2N0vKaIQnTN31tmjxz63FbxeUL52dzAvy1KN+kNeakKes8u9gebyu9K59KT970xaJYPZOmF+tWCBAgQIAAgf4CAgb9TewhQIAAAQJjIjBh8qy6605d57lp2nq71+2zsfoCK5ctqAsYpAlTVz8zZxIgQIAAgS4QMIdBFzSyKhIgQIAAAQIECBAgQIAAgeEKCBgMV0x6AgQIECBAgAABAgQIECDQBQICBl3QyKpIgAABAgQIECBAgAABAgSGKyBgMFwx6QkQIECAAAECBAgQIECAQBcImPSwCxp5PFfx5oeXpDOvXVAUcfqUnrT57JGbhOqqexcV14qVq+55KvVk/zdSy12PLU0Ll616r/prnrV22n7DbntN3EjpypcAAQIECBAgQIAAgZEUEDAYSV15Dylw5yNL018fWvWu9zjhqnsXD3leuxLMX7A8zV+wKmDRrnwHyue2R6YJGAyEYz8BAgQIECBAgAABAuNKwJCEcdUcCkOAAAECBAgQIECAAAECBMaHgIDB+GgHpSBAgAABAgQIECBAgAABAuNKwJCEcdUc3VeYWWtMrKv0+jMmpb23WrNuXzs3bvv7knT34yuKLDdea1LaZv1pxXa7V351+8L0wMJV15u9hj+5dhvLjwABAgQIECBAgACBkRFw9zIyrnJtUmDtafU/wQ2ygMEhO85q8uzxn+ymbH6G+oBBfYBk/NdACQkQIECAAAECBAgQ6FYBQxK6teXVmwABAgQIECBAgAABAgQIDCIgYDAIjkMECBAgQIAAAQIECBAgQKBbBQQMurXl1ZsAAQIECBAgQIAAAQIECAwiIGAwCI5DBAgQIECAAAECBAgQIECgWwUEDLq15dWbAAECBAgQIECAAAECBAgMIiBgMAiOQwQIECBAgAABAgQIECBAoFsFBAy6teXVmwABAgQIECBAgAABAgQIDCIgYDAIjkMECBAgQIAAAQIECBAgQKBbBQQMurXl1ZsAAQIECBAgQIAAAQIECAwiIGAwCI5DBAgQIECAAAECBAgQIECgWwUEDLq15dWbAAECBAgQIECAAAECBAgMIjBpkGMOESBAoBD4/QMPp2v+/mixvd60qWmDNaYV2+1cuWXBE3XZ/W3RU+my+x6o29eujd5KSjcteLzIbtKECemwZ25dbFshQIAAAQIECBAg0K0CAgbd2vLqTWCYAj+af28WMHhsmGe1J/kDi5ekz1x7Y3syayIXAYMmkCQhQIAAAQIECBAovYCAQembWAUJECBAgAABAgQIEAiBZe/855SeenIVxtTpKWW9C0uxrFyRlh320s6tSu/KlJYuXlX+mbPSlK/+ZNW2tTEREDAYE3YXJUCAAAECBAgQIEBg1AVWLK+/5NKn6rc7fWvxwk6vwary922rVUesjaKAgMEoYrsUgU4WWGfqlLribzZzRtpyrTXr9rVr4+9Llqaba+YxmDF5Utpp3dntyr4un5WVSvrV3x4q9k3o6SnWrRAgQIAAAQIECBDoZgEBg25ufXUnMAyBTbIAQe3ywqetn/592y1qd3Xk+rKVK+sCBlMmChh0ZEMqNAECBAgQIECAQNsFBAzaTipDAgQIECBAgAABAgTGo0DPnGekyvybi6JNPPqracIzOvftSL2Pr3qDVUo9acLaI9MjswAbwZXe225MKz9/RHGFno07/8FUUZkOXhEw6ODGU3QCBAgQIECAAAECBIYh0FM/wWHP1GmpZ1o28WGHLhM7uOx9yXumTq3f1aet6g/aGi2B+r+Y0bqq6xAgQIAAAQIECBAgQIAAAQLjWkDAYFw3j8IRIECAAAECBAgQIECAAIGxERAwGBt3VyVAgAABAgQIECBAgAABAuNawBwG47p5FI4AAQIECBAgQIAAAQKjK9C7fHnq/c1PV1104sQ0Yc5mq7ZHYK33jnl1uVayCR17b7mubl+7NyqLnkiVRx8uso06Ttju2cW2lZQEDPwKCBAgQIAAAQIECBAgQGCVwMLHU+8ZX1q1na311m2NwsaD96QVnz58FC606hK92ZsZpnz69FU7rCVDEvwICBAgQIAAAQIECBAgQIAAgX4CAgb9SOwgQIAAAQIECBAgQIAAAQIEDEnwGyBAgAABAgQIECBAgACBVQITJq5aj7Vsu2frHev3tXmrsnBBSn+7Z1WuUyanns3mrtoegbXKgr+n9OC9Rc49a6xZrFv5h4CAgV8CAQIECBAgQIAAgREWWHb4y1Zdoacn9Wy27art1Vyr3Hp93Zkrz/1mNlHdhXX7hrtRWbQwpYdqbqA22SpN/uCXh5uN9B0u0DNlSn0Npk1Pk48+qX5fCbZWXvajtPL0LxY16dl0y2Ldyj8EBAz8EggQIECAAAECBAiMoEBvbzZdXDaJXO1Suf6PtZvtWX/ovlTJPu1cKvfPb2d28iJAoMMEBAw6rMEUlwABAgQIdKLAiqfuTU/e/s2i6D0T10iT12r9CWuRYRMrvcuyJ6c1S2XFwrTo3vNr9ozO6rIFf0mpUikuNnvHTxbrVggQIECAwHgSEDAYT62hLAQIECBAoKQCyxfelpY8eGld7Rb/reYd33VHRmmjd1l64qZVXVFH6ar9LyNg0N/EHgIECBAYFwICBuOiGRSCAIHVFVi+ojd955Y7itMn9qS07ay1iu2hVpatrH+r8PKVlfTb+x8a6rTieCV7SjhvwZPF9sTs3TNvnbtVsW2FAAECBAj0ZHMW1C09E9Ok93+hbtfqbPTeekNKj9X8/6wtnpkmrPe01cmqOKf3tr+m3h98u9juefpmxboVAgS6T0DAoPvaXI0JlEpg4Yrl6dw77m5bnVZmAYCPX539A6yFRcCgBTynEvh/7F0HnB1V1T8z773tLbubtuk9IQkJgYQWIBTpCqgUFRQEBUU+UPkQ/EBAUREpElAQkaYUQRCkQ+g1QEJCem+bssn2vvvKfP8zb2fezNuZee/tvpAt5/x+u+/eO3fO3PufeeWee87/CAKCQH9AAPYDdepB3Z5pOnTED0ILttmalOxcW10qgoAg0L8QEINB/7rfMltBQBAQBAQBQWCfIKD47amqlEAhZQ85/ksdixZuo9Y975jXVNQAZQ062qx/WYXmbf/GpWIcBl/WdeU6goAgIAgIAoJAqgiIwSBVxKS/ICAICAKCgCAgCKSMgC9jgO0cX+YgKpz0U1vbl1O5+su5jMdVmplo0UJ66NFVDgkCgoAgIAgIAvsUATEY7FP45eKCgCDQXQR8qj0u1Ic40blDBiatNqxF6JM91WZ/VnfwwFKznqgQwY/+93btMbvFh6maB6QgCAgCgoAgIAgIAoKAICAI9DIExGDQy26YDFcQEATsCARUsAxaJBOsg9cdOM3SsneLIeTWPvGlt82L+BX7eMwDUhAEBAFBQBAQBAQBQUAQEAR6GQJiMOhlN0yGKwgIAoKAICAICAKCgCAgCDghEPrXPRR57anYoewcUvLt4UCxg84lraHOdiCy9EMKXnOerS1RRQu2I3tDzPuOBpRSxq1PJjpNjgsCgkAPREAMBj3wpsiQBAFBQBAQBAQBQUAQEAQEgZQRaKglQvYgU7D4jzcAmMeSLYRDpO3YnGxv536N9c7t0ioICAI9HgHxne3xt0gGKAgIAoKAICAICAKCgCAgCAgCgoAgIAh8+QiIweDLx1yuKAgIAoKAICAICAKCgCAgCAgCgoAgIAj0eAQkJKHH3yIZoCAgCAgCgoAgIAgIAoKAIJAEAqMnEb33ktlRnXMMqd+4yKwnU9AaEcawY4vZVckrJKVslFlPphDZso4if7ne7KoMGWGWpSAICAK9CwExGPSu+yWjFQQEAUGg1yDw4pZyWlqNeNoOKcvJptKsTKPq+Fre1Gxrf3PHLtra2Ghri6+sqKmjsCWn/c+mT6Ysv3y9xeMkdUFAEOj7CCgZcZ+xOXmkprxYx+J+fDezDcHoELHC7c+w1qQsCAgCvQgB+UXVi26WDFUQEAQEgd6EwPNbd9L6uoZuDfmFLTtSPv+HU8aLwSBl1OQEQUAQEAQEAUFAEBAEOiMgBoPOmEiLICAICAKCQFoQ0NKiRZQIAvsagdbKj6l2+bWxYahZ5M8ZHqunWtJse69U+dklqWow+4eatxNFWsx64dQbKHvgXLMuBUFAEBAEBAFBoDsIiMGgO+jJuYKAINBnENhQ30haJPYjvigrOfdJyyk6FqyjsrU1OVywnq5tQ67qDvGpKo0pyDOqfeBV6QNzSH4Kez4+j8KtFeYJvpwRpPiyzHoyhWDdSlu3+jV3UOOWx2xtiSo8Bi0YS2GWO+rblD/m/ESnyXEPBCJBxHSHrOEyzRRsr/Y4I7VDwdovUjvBo3ekrcrjqBwSBAQBQUAQEARSQ0AMBqnhJb0FAUGgjyJw8bufpGVmIWg5Z8GHXda14NRjunxuTzuxFEaX9XWxUU0rLqRxCQwin+yuosYgoxiV6SVFNDAB78GLCH0IWSw3fmXfGCrCrTtti8pQ/SpjGl1+DbfsIP7rjoRbdnbndDlXEBAEBAFBQBAQBPoxAmIw6Mc3X6YuCAgCgsDeRGB4bg7Ux3Y7jxo6iM4Yk36m7De2V1CjxWCQ4fPtzWmJbkFAEBAEBIEegkDonhsosmqJORplUBkp+UVm3amglW+wNYcfvoMiRcW2tvhKZCMMwBZyXf+fniEVXoEigkB/QEAMBv3hLsscBQFBQBAQBAQBQaDLCPiyh9rO9eeOpsIpv7S1pVJprfwA3WMcH1mlXeccqFt9C4Ua15uX9+UMM8tSEAT6OgKRLXj262KGaQ3l2DsrudlrG1ekfA6xkVoMBskBLL16PQJiMOj1t1Am8GUhcPt7u2nR9lgM64AsP+VneVuXN1bH4tN5nH/5uIqKEpyzvS5oSxF359eGUWlO4MuaplynAwF2pydKxrVdA2eB/T4nSh1ogKzhJ0qV5Vx1H7nSG+Ppia/v7KygjytiPwYHZWXR4Bw7L0BrKGwb+ivbdlCWL/b1Vg2eiO2WdI37FRXQV0cPt52TjoovaxgWbutMVQWTryJ/3hiznkwhWLucwpbY+EDBfuTLKk3mVLNP0+Z/Upu+II02+fPGmcek0DUEFJU/D2Ki+HIoo6jraee6c25sFNGS4mdPnpioIGQUEQQEAUFAEBAE0oVA7BdVujSKHkGgjyKwuzFEraHY5HaivtM7PXysc0eJdexO8RxLOHcnfdKQPgQyfCq1h2Okh/885jDy7+Xdg4b2IJ3x2nvmJAoyxDBkgtFReGnLTlpUmRq53F9WxHZb4/VxfU1t/V4xGCj+bNvlAvkTKKNwqq0tUSWzaEaiLgmPt2S8ZOuj+nNtdakIAj0FgUjVbgJ7ZGw42bkgCvU2xMc6R0uR9jZbU6SpgZRGC3mK7ahzRWOi2lAwdhDu6WqW3RATOyglQUAQEAT6FwJiMOhf91tmKwgIAoKAICAICAKCQI9AIHT1d4jak8wqk+SIIxzTnmRft27q0aeTev7P3Q5Lew9CQB0+hiI7t5gjUr/yDVKmzTbrTgVt6UekNcZ2b5Tps0kp8OY9CN/5f0hfGvNmU/byhoLTuKVNENhXCIjBYF8hL9cVBAQBQUAQEAQEAUFAEBAEBIGuI5BXaDtXGT6efDMPt7V1qiQ63ukEorCEDDqgIk39BQExGPSXO70X5vnx1iZ6ennM7a8wU6XhhfY4z0SX3dFgcUVE5001bfTQZ6m5H7eHIrTOwhUwIFulXx49JNGlUz4+tjiD1lXFxnvypHw6aoy3q+/qPW3U2B7b6xhfkgEOA28G91+/UUFNwRhlT3ZGMnH0KU9HThAEegUCJTqXRGyo4wvzaRL+rPLerj0UjsTeM3OHlNrCSbY2NtOy6lrzlOIEaRrNjlIQBAQBQUAQEAQEAUGgnyMgBoN+/gB0Z/rrKtsontTv853dcy1sxkL5+dX13RkWBVILf0z6WtlxiktzfTS+1JtcKtFxp4v7VDYQxBY/vqSI95w0SZsg0PsRGJ5njyM+ZFAJnT9prG1iP91/sq0eX3lx63abwWB4rp1rIL6/1AUBQeBLQiA+BWoOjIHxbYmG0ojfDFrMME+ZeH9nZMbOYjfyNstvE94pjr9GG3gQLDoim9dQ+I3/mDo05jfYsNKsE/gN/N+/Klbvh6X2S04iaom59ZO+A2/Z4LCkIGR4tHVfUPv5R9mRsmDOB9Svfpf83/yBvY/UBIFuItB+8QlwEbGQkA0aRtaQEq0aXCoWibz7EgXXfmFpwfO7Z6dNh/9XfyV15Hhbn75cEYNBX767MjdBQBAQBAQBQUAQEAR6KALKkBGkbVptjs7/iz+ROnqiWU9HIbz4fQrfeY1dFdb/nrJpFYXx5yoKdib6ucEASyg7PLqBIK7N3sNmlIk/FK0nON/5JGkVBLwRaI1lONM7bt8U//Tazw+2kbZtg70tvpZm7pV49T2tLgaDnnZHZDzdRiCIjYab366w6aloDFJDa2wH4rSpSKs2xZvgxqZAKoKAICAICAKCgCDQ+xCI2+nufROQEQsCgoAgsG8REIPBvsW/V199YK798RldFKCjx+WlNKfK5hAt3dlinpOf4aM5I+wuyI8vrbGlMzQ7exQ+LY/pdOpWXmdxTXLqIG2CgCAgCPRABIKNG6lm2S/NkSlqFgUK7CEZrZUfmse50LT1KWqvWxZri4SovX65WWcdAw9+yKxLQRDoUwgIWV2fup0yGUFAEPjyEbCv+L7868sVezECRdl28r6hBQE6dYqdrTYd03tmRR0MBjHvgHTo7O86qlvbaG1tgwlDll+lAZnehJW7my0xoDhzd0srbWmwxC+a2mKFmrZ2270bX5RHpVnevA+xs3tf6TeLllNjMGaM2m9AAQU8coo3W/rybBuDQXps/WbPibP+DXUx3Ecgxv8n09Lrwus5ADm4TxEIt1ZSuGmrbQyhhrW2enwl1LiW+M9dLHHH7p3kiCDQKxFQcuI2MsCqrx5ynG0ukdWfE9VWmW3KqAmkDB1l1jVwIGjvvWjWKaPvfo/FJuldUkaMJ23tUrOT/7p7SB0/zawnUwjNv5Yii94xuypj9zPLUhAE0oYA85q0xTYSfTf8jZRAjOdEq91D2q5t5uWUooHE4VJWCd16JVGNhesgf4D1cJ8vi8Ggz99imaAg0BmBF7fuoIfXbup8IIWW18p3Ef+lImePG0k/mDI+lVN6VV9m649Y3F8XVaaW8SMEpv8HVm9Mac5r6xrEYJASYtJZEBAE+hUC8Wn3BiCLynlXpASBVldDQavBIBsLEBFBQBDoHQiodjZ0tWw0KZkWo9/wMUTT5njPxR+wHVfiiVNtR/teRQwGfe+e9rkZleUHqA474oacOb2QxpfELION7WFatD1mOcxBNoPZw+1hDS8i88IXu2I75CMK7W98Q7e8CgKCgCAgCAgCgoAgIAgIAoKAICAIRBEQg4E8CT0egdwMu2VwYmkmzRpmNwjMG2vPyx4/qYXb7AypeeBKEBEEBAFBoLchoGbYyVrVzBLKH3eJbRpt1Ysp3LrDbAsUTqVALnZQOiQSbqWGNbcZVVL8sltqgiEFQUAQEAQEAUFAELAhIAYDGxxSEQT6BwIZce5Z2X4fDc62uGc5wFDZ2ooYe+Sz7pBcnDMwwTnMc9Acip2T6RHPb+jtza85wMTKYfD1McMpx+/+MdsaDtM7O2IxcX7cl2OHDfaEYFdLCy0oj2UBKcny5p7wVCYHex0Cqs++uFf8eZRTdoptHvF120FUIsEGm8EAwZzxXaQuCAgCgoAg0AsQCD3wB4p8/qE5UqVkMClFxWa9O4UIeJVs0tJIoT9dbWvqTkWr3EUc7mOIeuwZ5D/9fKMqrz0IAfdfsj1okDIUQUAQSC8CowtybQpnlAygm2bvb2tLR+XXIAF8d2dsQTy2II58Kh0X6UE62PBiNRicBc6GRCSPl+w3IaUZfA5eBJvBIDMWnpOSIuksCHQTgcatT9hSsQfyvZ/lYNMW2xXDwVpqq15ka4uvsHEj3LrLbPbnjqSs0sPMuhQEAUFAEOjPCGhb1xPVx/iSNJS1vQUIOJoin3+wt7QTbe8et9beG5hoFoOBPAOCwJeIwIJ1DfT+5ibziqU52NkHR4NVmtrtGSGeWVFLOYFYCEVtS4i218eY+CcNzKRvzexfbK1WvKQsCAgC+waBhrXzu3VhrW0PVS++LCUdvuxhYjBICTHp3NcQaL/wWKJw7DcA5SNMSVVi02yKZdHhxsi7L1L7krhFXkMdDsR+a/ivmU/qpBkxHVISBPYBApH6GopsgQGkQzQmkbZkLyB4qfpmzzMOy+uXiIAYDL5EsHvbpc5+dDOFLGZKH76PrOmMw7HvGn1qH21tprMf22ybJvexqKDLDyulI8f27V1mGwBxlU/Km2hZRYx8Me6wY/W/q2LpD506VDaFxGDgBIy0CQKCgCAgCAgCfQ2BMNzELdl4rLvLjlONICzQkjLSsU+867ljJ2kUBPYyAkhvGvrVBZ4X8T38nudxObh3EBCDwd7BtU9ojbMHUJhX/tbVv8MsQ/EnxfWxppyLOyRVQUAQEAQEAUHAEwEOT6j8zE7yGGreht3SWCadATNvo8wi2S31BFIOCgKCQJ9AQBk+jrRNq825qEedSsqsI816dwpaKETap2+ZKhRwMimzjzbrXgUNngGRx+/y6iLHehECYjDoRTdLhioICAKCgCCwbxBoLP8PNa6/17w4kw36s4eadS4E61bZ6nWrfk9qIJbVQEN2An1x29HLlzWUBh7ykO2c3lWB25nFipxRPNtz+Br4CIKN68w+ippBWjiWEtc84FXQwhSs/cKrB/F1RPY9AuElH1LklSfNgSiFeC8g/7lVtF3l1ipFXv83RQaVxdqaG0mzuCgrQ0eQ/3s/jx2XkiDQ3xHIK7AjgPeYb+ah9rbu1A46oktnRzYNoAR7iF3SKyftGwTEYLBvcJer9lMEBuUyX0EsJGEy+AdmDLWznr+7CSy0lk/ZuaNyKMMfSy25qbqNPimP/cguzZO3cT99nGTaXyICkdYK0kKxhSiX21t3eo4g1LjR83i41fJG9+zZQw9yjJrFNbpk1p0pD3Tngrk4p5fjkPKs+8cJ2srFpK2KkVomcFDUQYm8/7InONpWGJz6u8EgG2GdzbHPIv8fHiclO0ZkHAGTvS0EISeP1MISG67Bm35EtHt7rK1kUKwsJUEgHQj44n6boq6MGGfTrFXAYNhqSXueV0SK5VnUNHw3bIkZmUmJ/Ra2KZLKXkcg7m7u9evJBXoRAgNz/VTRGCPW+fNpw4jbDGEyklAk9hOAOXd8cen6/u/VnbSuqt04hYYX2gn+zAP9pDAwz2ebKRsMzto/tgPJB+PrthNQeXtDo81gYL0n8X2lLggIAoKAINB7EKheeg2118QW2WrWIHipFHpOIFi30na8btXvSMnwJsINNW6AsSdmqBk097+k+r1T69ouIpV9h0DcokkpwCILRgFDfIW490NGGFXHVwWLt9ivN+ankoWYI1DS2GUElLy4z62iEgrceH9K+rRQkIIXHhM7x9+/1xAxIL78Umz19+VfW67YyxBQsZvkszLxkkLIIuct7LEqIggIAoKAICAIOCCgBPIRQgDG9g5h/gHVn29UKQTOgpAljEHNHEgZ+ZPN41yoXXEDhVt2mG3cp7dKuKUcniwxlvtwYyOFU5xMqGkzEf+lIlpscyCV06SvICAICAKCQN9HQAwGff8eywwFAUFAEBAEuomAP3e0TUOgaCblj/uhra29ehGFLfHzmcUzbRwGQezqNqy5zTzHnz3cLPfXgqLadzoDBVPIlxHzusqgaYDmOE94FJ99Z1xVMz37y8EvCYFiu5u7MmwMCNPm2S6uLXqXtJZYqmF1+sFEhcWxPpW7yBqmoMS51sc6SkkQ6DoCoVefIs2SelIZNIzI4hqva+ZsExYJP/+IPXVY9R7SXew7+ijT55D/5G9bzpCiINB7ERCDQe+9dzJyQUAQEAQEgS8JAdWXY7sSL2ozB8y0tcXXbQe5Eu/267Pzl3TqLw2CQC9GQCm2e3oow8eS/4zv22cUX7cfpQjSrFkNBlRkMSbE9ZWqINBVBLRP3yZt3Rfm6drKWFiQ2RhXiDzz97iWuCoIOylFg0EEWQlAjhNTpPpI8Xm78rLbvlW4rgVjocDWY0ZZC+M6kVhIEmXlkBoXUmz0lVdBgBEQg4E8B4KAICAI9CAEHlqzkarbYl/24wvyKMsS+7O6Jua+zcMub2qm18rt5HubG5qoMRhzMf7ayDIaXxTHpNyD5ixDEQQEgSgCgbwJCMHYYMKRM/JblDXoCLPuVGivxqI6WG8eyoD3i5oZFz9sHo0WqhdfgQVD7HOGFIkNjoNIqoLAl45A5LH5FHnjP926rvbUXymIv1RE/dZlpJ54ViqnSN9+hoAYDPrZDZfpCgKCQM9G4KmNW6ktbLH8JxhuRUsr3bJklWevspxsMRh4IiQHBYGegYDij7Hd84j8OcMpEwYAL0l03OlcJrmzk94J4ZATTtImCAgCgoAgIB4G8gwIAoKAICAICAKCQD9HoOL9b1CkbbeJghoA87yaYdYj4RazzIVg/WrajXOsEgnVkRZuM5vyxl5I+WPON+tSEAQEgZ6JgFI61BaSoOx3ICkT97cNNvL+K7bMIurck2wcBtqGlaQtW2ieo5QOMctSEAR6OwLiYdDb76CMXxAQBAQBQUAQEAS6hYCemUCLkZpF2qsS6ItQuNUeChR/QsRCgBl/TOqCgCDQgxCIW9wrUw8i/6nn2geYgG9DJ060GAwoTqddmUstnkdAAX9BIEG4EPMVWFKkgvSgC+eIh5HLHZHmDgTEYCCPgiAgCAgCPQiB3IAfIQmx2OLDhpRSgeUHwx6EIKyqjcUr5/r9dOBAOxHYZ3uqqLI1pqMwQz7qe9AtlqEIAoKAICAICAKdEFDGTSV6/WmzXT3sOPL/8FqznkwhsupzCj/7QKxrbiEpI8bG6ijpPAkNtbG2tUsp1NwQq7c2k7Z5jVlXigeT/+LUxmGeLIU+gYD8iuwTt1EmIQgIAn0FgZLMTKq2LPa/O2EMjS/MT2l6Vy9cAoNBtXnOoGxh4zfBkIIgIAgIAoKAINBHEYhsWEHa6iW22WmL3rHV4yuRz3Cc/1xEy0CqWjEYuKDTP5rFYNA/7rPMUhAQBAQBQUAQEARcEPDnjaFgbSytWtH031CgELt9HaKF2rADt9Wowus3i/y5I8w6F+pX30ZtlR+YbYGCSWZZCoKAICAICAKCQG9FQAwGvfXOybgFgX2MwJLKGrpl6UpzFDlI/TcGKQCtsnhPbJeb2/+xdjO9szNGLBZENoC1dTE3uIKMDLr3iNlWFVIWBAQBQWCvI6Ao9p9DakYx+bPspGWBvFGe42AjglUUSVVohUPKgoAgIAgIAr0UAfs3ZC+dhAxbEBAEvnwEdiOWfndLjBGcR7C5odlzIBvqG4n/3KTK4orv1kfaBQFBQBAQBAQBQUAQEAQ6I6AUldobBw8n9fATbG3a5x+S1lhntqnICkElg8w61VZR5M1nY/WCAbGylPolAmIw6Je3XSYtCAgCgoAgIAgIAoKAICAICAJ9CQGlqMQ2HXXQMPKfdr6tjeLr9qMU2bLOZjBQ8grjeki1vyGg9rcJy3wFAUFAEBAEBAFBQBAQBAQBQUAQEAQEAUEgMQLiYZAYI+khCAgCDgiUZoE11yLDcnPogkljLC1EzGGwpy0WtjC9uIjKcmKM/bXtQbp7+VrznMLMBPmGzZ5SEAQEAUFAEBAEBAFBQBAQBASBvY2AGAz2NsKiXxDoowjkBHy2mRVk+Gle2WBbW3zddhCVXc0tdLelMctn12k5JEVBQBAQBASBJBBo3vkKtVV9bPb0gbyR/6yihdutVWra/hwpasxgG2mrpFBLudknUDSD8oafYdalIAgIAoKAINB/EBCDQf+51zJTQUAQEAQEAUFAEOjjCDTveIGCNYtTmmXD2js8+4caN4jBwBMhOSgICAKCQN9FQAwGHfd29erV9NRTT9GWLVsoNzeXpk+fTscccwyNHTu27959mZkgIAgIAoKAICAICAKCgCAgCAgCgoAg4IKAGAwAzL///W+68847dYjy8vKovb2dFi9eTE8++STdfPPNNGvWLBf4pFkQEAQEAUFAEBAEBAFBQBAQBAQBQUAQ6JsI9HuDwbJly2j+/PmUkZFB119/PR1xxBEUCoXo2Wef1duvvPJKeuyxx2jIEHv8X998HGRWgoAgIAgIAoKAINCbEfBnDqSgZQKBgv0oUDDZ0kLUUvEmkRYx27IGH0OKEkucFWzcSMHaJeZxFTpFBAFBQBAQBPonAv3eYPDwww+Tpml07rnn0pFHHqk/BYFAgM4880zasWOH7n3AxoNLLrmkfz4hMmtBQBAQBAQBQUAQ6DUI+HKG28aaWXoY5Y/9vq2tcPKVtnp8pan8GZvBwJ9t1xnfX+qCgCAgCAgCfReBmDm5787RdWbNzc30ySef6MdPOOGETv2MthdeeEH3OujUQRoEAUFAEBAEBAFBQBAQBAQBQUAQEAQEgT6KQL82GKxatUr3LhgxYgSVlZV1usWTJ0+m/Px8qquro61bt3Y6Lg2CgCAgCAgCgoAgIAgIAoKAICAICAKCQF9FoF+HJGzfvl2/r0VFRa73l481NDTQtm3bXDMmrFu3jhYuXOioIycnh77+9a87HpNGQUAQEAQEAUFAEBAEBAFBQBAQBAQBQaCnItCvDQZNTU36ffEyGBQUFOh9jL5ON/LNN9905TgYOnSoGAycQJM2QUAQEAQEAUFAEBAEBAFBQBAQBASBHo1Avw5JYA4DFg47cBNOs8jS2trq1kXaBQFBQBAQBBwQAJ+siCAgCAgCgoAgIAgIAoJAL0agX3sY5Obm6reuvb3d9Ra2tbXpxzIzM137zJ49m37zm984HvcyRjieII2CgCAgCPQRBBSlj0xEpiEICAKCgCAgCAgCgkA/RUBBSsF+uwe0YMECuvHGG2nOnDl02223OT4C559/Pm3YsEE3CMybN8+xT19pXFFh96JoDUUoYnk8sv0q8jSntgKI15EFHWqKOtowjrBlHJk+lXxqauNoh46QRUeGqpLfl5qOIHQELToC0BFIVUdYo2Aklvs6ACwCwCQVCUFHu0WHHzoyLDqmDs5yVLe0qtZsD+F8qw7cWcry+8zjyRT42WgNh82uPIssf2o2SP74abHo4DuSnaIOHkBzKMQvpuT0EB3ZPl/K75kWzMX6oZwFHam+Z1qhI/aUEWWqvpTfM224L9b3nf6ewTOfirRDR6f3XYo6gmF+38Vmoz/vwCQViX/efXjPZFp0zChx5rFpr/ncvIwWwX2JRA3IeqOC++Jzfq+ZJ8UVNC1MWtj6OQsd/lR1RKCjxaIZn6n+bEs9uWIkFA3Ji/ZWoCMnuRMtvew6CDqiRnhLl4TFSIg9/WJPvOLLSfk9EwkxHrFnRPFlQ0dqz2okxPcl9nmm4N4quMepSITvLe6xIYqaSYqa4mdiuA1kzLHPsy7piATxrMY2QhQ1A+MIGMNK6jX+eVeUACm+DPPcjAEHmGVrQVttec/w53LQ8p7B55CSmeLzzt8P7Zb3TFd08Pdlm+U9g2dDyUrtPaP/VG6NeqXq88VniJKV+ntGa7G+74iU7NTfMxqPw/JbpGs6gIflc5Uwl1R/32ltuC+R2PNOGXjPWD5Xrc+FW7mTjgDeMyl+f2vteMbCsfcMdUVHEO+XUDA2zADeM/4U3zN8PusxBOcr0JOKaPHvGZ+flAz3DUsn3Zq8ZzrBsjfed/HvGWWy82dip8H00obUvsl66STdhl1aWqofYlJDN6mvSxFQHgAAQABJREFUr9cPDRgwwK1Ln23nxX13JR06MtMwDl5Qp/ax3XnmvLBP7evDQQcMDIEUv1DjtbChw99dHWwwSXHhFj8OXsR2ZWFu1cM/ULqrg/X1JR1dMZhYMeVyqoab+PO5bl1QOx1Ppi0Dz2m333cwEAaoe59F/Kx393nnhV+qi794jHgBqnRhUW3Vwwvh7upgfV6Lew0LCQ0L10SGCC8d1jF7lbtiqIjXl2ic8f2d6qkabhx1pGhActKh+LBgotQWCPF62DiQqoGgs440PO+88Etx8ddpHPxd14VFtVWPwt913dWB76ru6uAxdWVxb52LrqMLhorOOlIzmMSfr48jgfGHF2mJ5puqAclxHPqCupvvGV7Yp7i4jx+LbmBI0cjQWYe8Zzph0s33LutL9BzGX9Op3hUDoZOe3trWvV9hvXXWHeMeNGiQXuIMCMGgxbLYcZzTKVZXV5OKL5sJEyb08tnK8AWB3ocA7+rsarbsLvW+KciIBYEehUDrnvdp94dneY6p4t2TqWHjfZ595KAg0F8Q0OprSN/V7wMTTtdcgjf/D4Xf+I8rIuHXn6bgJSeS1tzo2kcOCAKCQO9BoF97GJSVldHkyZNp9erVelrEuXPn2u7cW2+9RWG49uy3337E6RH7mzS0hqmqJURBuMAbDqPs2ReMaLSzIUT5GQodOipKCumGDS/4dtSHqCUYNh1GcTqxnia0LdvVSt8/qMTtdL09HTpY0Zo9rbRwWzPtrA/SgcNz6NhxefTaugY6fkJ+Uq54qY7jkcXV9Mb6BvrxIaV08MhcuuaVHcCis2EqfvIPnzUqvslWTzSO51bW0S+PHmw7J77CLtqbG5pofGGU8HNjfSO9sHU7tYTCdHTZIJozKOp9E39efJ0X81nYAS7KzICHpEbPbi4nDn2YUJRPp4wo09vjzzHqC3dXUi52n6YVO7uDs+5bv1hF03H8exPHGKc5vta3B2lPaxueTbh8dzys7E7P4R/bm1qoICNARw6NGggdFaAxHTo4NGIHrsc4xhykY1ccnJ1FQ3KiLrlhvBG2NNrdU2M93UsDMJcBWYl3Uz7aVUnv7dpN25qaKYRrlWRl0OyBJXTKyLKkdtv5fpbj3GaeSweo/IqPA7x3Q7S4spounTbRfaAdR3rCOBLN5ckNW+i3c2Z4zoV1hJu3ke6Gb7qd47ORwwxCjdRW/RkVTrrCUwcfDAfrKNK6Jxre0IErNKMehP5yUgOFlD34aE89qeoINW3FuKOedKHG9RRuraD2uhWdr8FzbN1BWpA96zhAyFvC7bXUtPVfFGpcp+tU/PkUyBtLOcO+RoH8xM8Ga091Lk4jSoeOdN3fEO5hBLhomvNnfaaLK791Xu21y6l1zzsUwvOWVTqXsstOoebtzwHX05L+rkrHs+o1Dut43cqMqVaxjagFLvTsJq0LPkDwOam1NJK2YhH5z73c7XTSaqtI27be9bh+gEM+cnJJGTiUlLxC175a5S64TcFlPr9I/66KLHiatFVLiEZPJN+8r5JS4O5FGlm9hMJ/+x2ps48ide7JpEw5IKn74DQYdsGPvPsCaVs3kLZnJ/l/cQdFvlhI6tARpAwa5nRKp7Z9MRettYW07ZvMsfAYtB2bKbJhpdlmFuCWry39yKwaBa2mkrTGOqOa9Ks6YpzZN/Sveyjyzgvk+/4vyHfQkRT89SWk7cIzlkAy/vJigh6I0KgoJ60B47OGJljOUifPtNSci93V0d33jDEqxlmr3hMNkej4ntHDR7A5qu0uJ8J7xTd7ntHd8bU7z5mhUGuspfArTxFtXY/xVMBLJ4+UEWNJPeqrpI5KbiM2lblw3+BV3yZlyAgK/OpeCi96j8J/v9kYjuurevK3yX/qd1yP8wE91APvWa16tz2cp+MsNX8AKcNGd9T63ku/Nhjw7fzWt75F119/PT344IM0Y8YMM2PC7t276fHHH9fv+Jlnntn37nyCGf1raS09s7wW8cfuHc+aXgSDgfvxisYg3fRmhW4wcO9FngaDdOjgD+B7F1bRgvUxS/fIogyqbgnTfZ9U07ubmuiyw0ppSL57wEFXxlGS46PRAzIpNyPqyDOsIIMysLjujiQ7Di+DQSUyflz6/meUHwjQ/UcdTLVt7XT1J0uoujUae/d6+S668aDpdPiQgZ5DfWjNRvrnus10/YHT6Agsxh9dv4W4jeX9XXvo44pKuv2QWQjBcJ4zL6pvWryC/njITJpcZP+h98KWcrp31QZqRZ95Q73H8fDaTfT4+s36othtwOfB4HDkULejROnQ8fSmbfTgGh6zk6kgeu3zJoym700aq1ca8cX9w3c/cR+UyxGei5cBhRf1jOu7O/GlBmGjDHvTrqtrwD2pok/3VNP1s6a53hc+ZyeMHtd8upTKG/FD30O8DAY9ZRxJzQX4eBkMQi07qHrJzyiMhbeXJDIYNGy8nxo3PYIfG5Z42ziFeWO/j5aj41pj1a7oCDaspdrlv4opQanq0x/Y6vYKnOJLDrU3xdXaahZTzRe/jBoXECahZgygSNNmCtYuoZadL1LR/n+krJKD4s6yV7syF7sGgidE9zFNx/0Nt1VSzdKrKFi/On6ItvrQ4z601a0V/q6qW/0Hatn+X7M5kDeOWHf96luoZdcrVLTfdeTPcV9YpmMuyYxj4KFPwDg0xhxnfEHbvYNCt19F2s4t8YfsdQ+DQWTtFxT+s/25tZ9sqeFDTj3wSPJd8qtOceOhZ/5OkeceIt9lv8Eicx6Fn3+EIk/fHz150TukLfmQ/L+8yzVmnRc22gGHU+STtynywatEJYNJPfxE8s09kZTBwy2D8C5G1q+g0N2YT030s1nvjXuuLXqXgh++Ruo3f0j+E8/yVLLP5gJXe154WY0GkQXPEP+5iTJxBik5sU2l8Av/8Ozvpifj4ffMQ0rxQFJGjiclN7rZoZThR2iKMf6mso4CG6ZCd/yCtM1r4g/Z6tZx2A6gkhYdaXjP8LhC/3mAIs//A0Y69+8Z9fQLiDwMBt19zngcEfCZhOZfhx0GGGHAPUIw1tH2zaStXQqj2Uvk/+nNpE71/o5IeS7MccLPB96jLEouDBSoJxKlyHvjMvzeSxR+6FZXYxLr9130S/L/4JeJLtVrj/d7g8FRRx1FU6ZMoVWrVtFFF11ERx99NIzfIWJCxKqqKjr88MPpmGOO6bU3uCsD31TdRk8uq8Uupo8OGZVDy3e1EJOEHTAsm8rrgvRpeQsdPz6Pzpha4Kn+oc+qsZsfoulDsmh4gZ9eXttIX52cj91Xjb6AZ0EtvBf+cFLZXtfx8poG3Vhw/IQ8OgZeBbe/X6lfswjzO29mET3+Ra1uULjhuCGuY+nKXE6ZXEj8Z8hPYJToriQzjqfPdf8Rx9f/y4r11IRn/BtjRujDeXXbTt1YcC4Wsydj9/kPS1bSXcvXehoMNsEjgY0F04oLaXR+LrxQIvTUhq00KDuTbjn4AH1x+tvPV9CC7bvoJOh0Ej63BDvlVy9cSrcecoDu7VABr4Lbvlit716X4ti1B0ylQwa747Yei+B/wGBQjN3zI4cMoiVVNfqzOntQMTwomnWjxamjyuiccSOdhqC3pUvHPSvW6R4Vp44cTDx2p5j5yfC8MIQ5Ai6YPNao6q8rq+to4e4qHdfZg0qoFNlZ+F6tqa2nt3bsplmlA+hwDzxYyTMwXLCxgD0Jzps4GmOJejSwgeLZzdthHNlId69YSz/df7Lt2tbKvavW68aCA3C9kXk59BzO++bYEbq3AXsW1MDI9Oe53l/0PWUcycxlwanen/H1a+fDWLCNMooPwmJtFDWXP005I8/BjkMztcOzINJeRSWz/26FsFOZF+2NGx8gNbOUsgbOo/baxejjo8zSQ7FDv5HaKt+n7OGnU96o8zqdazR0VUf2kOPgYYAdXZDyhRrWUUvFa5Q//lJDbexVwQ8sEPUF8idRRuF+sfa4EuuqXfFrEJzlYAF7LYwLh+j8Dsx90F63jBrW3km1S6+k0kMfI3+28/u/q3OxDiUdOlhfOu5v3ZrbdGNBZulhFCjYD4tW7+9H6zyMchOeKzYWZA8/g3KGngwjz/X6IV9mMe7Xj2Ec+RsMCjdTyay7jFM6vaZjLsmMo3rxj2nwkS93ur7REH78z9j53UrKfgcSL+p4YamecDZyVDdTZOUiIizS/Df8zeju+KpOmkna0adR5K3nYAw4ipT95xAVFOOGVZP2xScUwWKf22niNKL1Kyny6Vuk/ekaCvzvbaa+SPlGivz3YdIXr8PHwlYXpMhLT+iLfv9Vd2CRuJbC99ygGwJ8R51qnmctsFHA/+MbdPf6qNHgFV2noVeF4UCdc7RnnDTH84dwHSUjg9QLr0aIQwtFHr1Tv4wKI4G2bQNFHr+LImMmkTpphvXyZnlfzoWJCNkYo987jCjy4qOkjplCtN8sc3xGQSc+xA62OvMwo0l/VWceTlRoWZjBIzDyEjbmEParHn4CKUOx+IcXCO9sR2BAIezkqmf9yKbD95VvEv8Z4r/oGqPY5dfwI/wcrCF1BoykY/He7TBGpKIwLTrS8J6JbFlHkWcfJMICWIVxTFuF7xleRM84BIv1TRT5/ANS8Z7ynfId1+ml4znjUJTQvTeBUCmLfD+4mtTp+I7AM6TB61Nbv5zCj86HkeZqCvzuEXjWOH9HdGUubKAKXB19X/EEVRARqlcf4DrXZA5EyjdR+P7fY/cFz/QczGMcvhuZFyVO1LknxbX0rWq/Nxj48KP9rrvuojvuuINee+01evTRR/U7zO3f/OY36eKLL9Y5DPrWbfeezbqqdr3DVfMG0sTSLHoKxoM3NzTSuQfgixryFsoPLaqis7HYtrLz6wct/9ZVttGMoVl03bFDiJn9F+C80cWZNG9sHnG4w41v7KIPNjfRyJkgm3GRdOh4bV09HTE6ly4+OLrwxG9iXTjTwunTiuC2TvQEjAY8pnwYEZwkHeNw0ptqWzLjeGl1PV0+131XfkVNLZ00vIzOGocvZ8hHCA3wA4uzsahmwj1eaP7u85W65wGHGjjJylpYjCE3HjidCtHncywieWF72uhhNBwLTP57eN0mWlvfQG4fobyQvRXeBVd+vISuWriEvoEF6ePwUmCvgq9Czw8mj0tIZrgaC2ljHFMGFOpGjFe27aCLJo/X29kYcs/KdfS9CWNcSfzSoYMNFSx/OHgmjSvI08uJ/nFWiu+MH212Y88PNrpchHmfMz56b8yDKJyEEI+rP1lK9Q58K9Z+L2/dQYcOKe1kEMiDRwkbhThzwSvlO+mn1pPiyqtwfw8aWEw3Yz4cvvIycBxXkE9fGT5ED93g+/XW9goYPNzn2lPGkcxc/gMjyzUwTrlJsH4FZZTMoZID8MMS2RJadjyPRfUELOpO0l3qaxZfTq27FlBg/A/dVGAhvVI/NmD67yijaBo1bHoQel6kgvGX6O3NKNevvYvyx1xEPhDfOUl3dOTCGMHSXrsM/yMwTJyj17vyr60Ki7XW3VQ650EsjieZKpiQMbNoBvln3kZ7PjyHuJ+/47pmp45Cd+Zi6EqHDtaVlvtbvYgyBx5JxTNuNoaX8mvL9v9Q1pDjqWjy/9rOZbLMvNHn4tlrh9Hpfv2Z8yF0xUnSMZekxrEJ42ivJl9G9HdB/FgiG1aQMm0OBa68FYt0ZG1550V9p4935dltOHTLzyjy8RvYVb8o/tRYHYvsyPsvk++7PyPfsWfE2rk072sUfv3fFH7mAQp89wpSTjyHwjBKhP+B9ygWnEppdANAw64+i//ymxCyUEQRhEEQwiHU475OKtyWCX8R7Miy4YCO0ru6/uPFiG/eqfofhxOEP3qdtMVweX7gD7jun7BAQ8gCjvMiJV4iyz4hwrh8f8BCe8hICrOnQoeoZaNJuWY+BS89FV4Mb7kaDPb1XFTs1PIfSwiLeWaF982a2zGLxC/q9DlYOMLo0yGhJ/6CXedCuI7fR0rhAKNZf9W++t3orj+8L+iEM23HrJXw5++TMmAgqaNjn0PW48mU2QiizjoSz8hvk+nu2CctOtLwntE2Rr9n/Jf9ltTxUyn03EMUee9lCpx5sT7uMN5P4cfuJt8ZF7p6ZqTjOYss/5TYk8Z/499xbyaamDEBqTJxf1J+/kcK/uI7FFn+CfmOiX43mZ06CumYS7zOrtS1NUv00/z/cxPe2zNdVfT1LAmdTSSuUPTdA5nYxbv66qt1g8H9999Pf/vb3+jFF1+kyy+/HMaxrL47cZeZ7WkCP0GmSuNLoj9aRxUFaHdjiBrbwvoZR2OXvgAL67ctLv7xqthAUNMaoVnwSmBhZv9hcPnfUNWm13lhfvrUQnp+FeJ54ZLnJGnRgdjt8rqol4PTNbhtfxg1WKqand230jEO/QId/5bBY+OJpTV089sV+t+TMFYs3dli7eJYTnYcDy2qdscUi78qhB6M6+AuaEQs+sqaep1HwGDnL+l45quwi+wmzC/A3gRsLGBZuLtaf+UYeUNKcMwIczDa4l/ZaHAbvAsKEZf/4OqNNAC7L7cfOosunzYpobGAdVW0tOr8BJOKort5Y+DtwGNrAKcBywkjhuq7/q8izMJN0qGDuRI43Sdfv6vCngX8VmDDjZPMxG7/fgMK9HAPp+Pcxov7bQgnONSDg+IwGBP4vriFG7AOPn4wPBxY2FNiRG6O7uXAdeaDOHvsSHp6M+L53d67PWgcycyFvRDc5sIGgkhbFTwBDuPp6zvpPngZGK7nvHDLHXUuNW17AvcP1kcXCbdiEYO+gY6d+0AuXM0R6hDW+QIQho04dTWzCO78L7logIdpGnRkFE2nomnXu14jmQPsEaFmDbIZC6zn+RCeECiaqocnWNut5XTMJR060nF/mceCw0wyCt2NTta5O5V5HKHGLZQJLxY3ySyOLrbYWOMkaZlLCuNgzg0n0VPD1VVhxxa7mhDeWeSYYm3z6midd58RMxx59V/6jqPe6PCPFxOcqk91WVCoR2OhgXh53QiA89VDjsXFFGLXf0O0yp1ExYN0YwG3RZZ9rB+yLlxpAD7r4PGQijBnAu9IK/tjjuz+jPSRkY9eo9Dv/4eC155PEcRsW0V35S9GKAOMBU7CqfN4IcW76m7SU+bC4/N/+7KUjAVOc4p8/Dqp8BaINxZwX92j4VQYybBYY6JGN+HFb2j+/7kdTtiux6Wz6/64KQn7unVIiw4Y1SgN7xmtqkLnJ1DGRuejDB9HtAe8NI3RzRUfdsGZr4Nd7N0kHc+Z/rzrhpyYscB6PR6Dip16DWFHbpKOubjpTqVda2qA5wtS9bp4/qSiqzf39ffmwad77H58qU2a1HUrZbrHs6/0lWT7qA1x2PjNr4cdlXXE9m+sbsfiOmoAGFucQZtro4syp3GygYCNDi3BmDGgrDBArMOQMYjvZxtEBQgUhxYEjGbzNS06sHPO41iOEIhjx+ebuq2FKhhIWEpynN8O6RgH62fiuYexmH8JIRIsgQ5zHYd4sHCYx4VzSvTdfr0h7l+y4+CQj224N6MGRBfzVjW8+MvHgo/d8AmL6YXgGeCF0hzsJhvy6Z5KvTjIg1iPQwl2t7Tpu815AT993EFgOAULWhYm/1tRU0enjhym173+sa7bYTS48uPPsXseouIOI4TXOcYxdv1vw445z4HTPI6AZwPLOng2zCqNzmkCdvw3NjQap3R6TYeOKeBgaENYBnsaGNftdKEEDY3tIXgzIbc35uEmvJjn58hN+P5mwzuqtj32PovvW9Mafd/mwMPBSVgHG3CaTZIyPCrAdT0wNWR8YZ7O1cAEj+xNEi89aRzJzIWJHZkEdKyDd4ieTjFQhDdwkzlNf+5ICjasMetM8Mfu/mwA8OcMN9utBV/WQKxWWrGwxAcrduL9OdGFQ7B+Lfk6Yv05FIBJCd0kHToM3UzeGG7ZDh6sZoyp8zPlyxqCcIKhRnfbqxLIA3cBSLWwuHRLNam1wYgIHW6SjrmkQ0c67i97APgLplBr5QfwBDjPbcqe7fo4MgoR4rIYxiNn1/hwGxYDEDbWOEla5sLpQ5Mch8/lWefFHscra0x22CHK0JEwGKwzqqSMngB3F7wfELdNQ5zfM1oo+jnm9ZmIhxdfOB2f75xu0I/vPYQ9GKKwCzwWUezVQDn5pH3+YZR4bVzUuMOhAtraZaQcc5pxiudr1LPgNdI+hHcB8zPg81pBHLZ61iWkwngQ+eJjCv/7fgrd9GMK/PZhnYyRFeqkiiB9Y1I8V94DNhYghMNNespceHyRHZthJHH/DWjMwY3YTl9k4/lQfM6/u/TzjRh8Xkw7CLu3ExZz/Gx1VTh8Qhk7GTwWHxHBQNEVSYuONL1nlKJSIhBrRn/AYzcfZJos2pa1+nPKZWXUeKLyDVx0lLQ8ZyA3JBgp2Hiofx44XIkNQYYnkMNh6spc9gbpoYrPikgbk35uJmX4GKeh9os28TDoF7c5tUkOx8K+HQv5D7ZEfyAPwWLejzXMF9gZN2TNnnZiDgAvGQE9nyArgeGZMLIwQ/cwaGLlEHav5weQF/Rukg4dUwZm0kfbmmh9JT5E46S8rp0eW1JLQ/P9ruEIfEo6xvH0sjrdWHDalAL66+nD6fFvjaInvj2a7vv6cDpzWiG9Do+NBz713uVIZhyw1VARjD5uwjHwL8Ft/w5wBfDOKi/u5pUN1hf5dy5fQ//euI04dp0NC25yCHafeV3LLvK/XrRc360+ZvhgXden2Cm/7P3P9IXtPGRcMISzEJz+6ruOfxe8s5Aq4JLPsfE/ABGgtd8TCFNwk1F5ufpC/W3E97OU5WTrBheOszdkFcIWvIwQ6dDBu//Hw13/fnhJsDGGMyCkKsxZwJ4RnGnCSZiXgL1BGHsvmVpcAB6Dchh0Oj/vLfjyfnjdRt07pNjDIDQKnhIfIMuC4anBGK2pbSD2SGFhTNlAw4txN+kp40hmLszR4vWM+HPHgLUerscd3gBcZw8DjuVnaa9fjl9h+HHmEbfuzxkDo0IbtexeoJ+jL7YUP7XXwHWzQ4Jgx1cy3O9vOnTwpRqR2WD3e1+jyoXnU/VnP6bqRZd2+uNwCTfJKJyuz4UJB52kZderMKispYwC9x33dMwlHTp4/Om4v4UTr9C5KBhb47lwwsarLaNof2rd/SbCV1Z16hZq2kING/5KvpwR5BaOwCelYy7JjMOfN941HIHHoZSN1t31jZ1NGobnHx4GRpo9DZwD+o5EXtTIzOfEizrtYCwI64lDDZwk/Mq/dA8DBXH/LNqapfpOvzptttldj6PHezN065U6gSLzKqiHfkVfxESWLaQQWPaZHE6dc4x5TnyBjQrhN5+l4E2XUvDKs3TCRI3P+cZFFLjtKXAm3E6+Q47TCf741f/9q7BoA0cB9BuiTJiuF8P/fQT8BTGDBjdq7W0UAkkdZx1Qxk8zTun02lPmwgML33UdhX71/YR/nSbR0cCLbHXqbIog/aL5jFg6czaJMMgqlZETiIkOnYTd230gzdTgzRECF4JWG/ved+rv1ub7NnSA6yL06lPm8+nW1609HTrS8Z5R8D7TvV0+eVMfqjJoOKhy/PDC+cwcup7ZojC2SWQe6Cik4zlTJ+A5htdN+NkH4tXrdT2ch40YzAfgIl2ai0F62GFEMkgPdSJEJkN0+/MgPVSnHkjqwcdS+O7rKLIJn2EuWTRcptFnmj1Me31mjjKRFBGYOjiLpg7KovkfVtI2LKiZu2DOyBz678p6eAxEaEsNUthhV/4QtHnJWfsX0Q0LKujS58rp9lOH0aHoz1wB17++iyZjEf8OshNMBSFiXqb74jYdOi5A2sYvXthOv3hlF00ozaAGuDWsqGilq1/eQQZfww3HDfaaCqVjHJ+WN9FJE/PouwfaP6jZs+GcmUjHgkXLy2vr6aI50d1ypwElM445I3Ko0MOYc/GU8bQH3gEvItadvQOunjkFC8gsnbPgeZDbHYeF/4WTxjld3mwbgoX51TP3o/nL1tJaLB55V/1ixN6zMCdCC3an+bg1ZSIvyow0jqaiJApMaOgmM0qKaH/83Qyixs2NjTp3wVxkd3hqwzadoI/JGTlEgdvcJB06llTWIH1js47FJe99qi+mGVvYVGxyFlz5z3bgJ+BOvLvNYQB3g3CSs0zMLBlAAzIDuhfH8g4yRB7rgRZvEJvyjsqF4G/4Mcbw/Xc+hhFjKPFin7Hf2dxCHJrBWTF+c9D+TqeabZzN4X/BLXHeWx8hk8YcZJgYqGeSYC+QqfAiWVBeQTwWL6NSTxlHMnM5HGEabnwdDEre2AuoZvH/0J4PzqLSQ/5B2YOO0WPJqxf9BCEG08Fe/yplDJiFhZz74icD6fQC+Ktb/mssLDeBu+BHlDXoKGra8hh+gDRREG7+4dadaDvSvA/xhXToCIL0sAEkjkpGkZ6mz8e71TBcxEsGdszdhF3vswYfS02bH4HBYzFS/x2GcIqBIH+sAUfCEhA4fqgT/2UPPdFNBfDqGXjwANNxfzm9pOLPAbZ36n9cBsid5j/kqJc6tRkNBRMvp0qQaFZ9eiGeK+xqwUDF2SjaPr2IggYHxqz5RnfH13TMJZlxDDrqZcfrG42+084HT8EVFLzqW/pOuzp7HrgC/k6hmy8nBYuJyAevIQZ+JkIF3N8zKnbzeNde5yXAzj3td5Duvq6nW8RiXMMiiPUq2AEMf/o2hf96k06cZ9215NAB38XXUfjh20jDj332BvCd82N9mEwAp2Fh7/vhtaRyOICLMP8An89uyeoRJyO14kmescyc5lFnhOfd3g7R4/9PgLHh1SdB1vgu0q9hYQdh5nXmLWBeBcZDD6voOCf+pafMhcelHv/NaApC6yDZIwDu75zdglMT+r5zufVopzLzSIT+dLX+jKiHHQ/Pi2G6EUljHczxAEON75LrO51nbdBJ/QrATfGvv+h/xF4muE/xkjH/2fgms86hMZSVTZHH5ut/lI37h6wQ8ZJx9/PxTWY9HTrS8Z7R31N4jsJ//Y1uBPGz5wuyh0RefoJCMFRp2zbiHu3U28zBxxXS8ZxxuAEb4ThbQ3DlYlJAgKkMKEV4SS1ynCNLwtIPSQFppnrYCXFXj1W7Mpd0kB7qXgpXnxsbCJfYywXv0dANP8DnOjY5mRgTv6us4vveleQ/7wprU58qK0ifk/pWWJ+CQCZjIMCLaEM4nv9fWNwXY+HJi9lKGAh+DZLC7ch6wDINRoXrscjmXUYveXdTI/1nRR39/sShlOVX6dkVtfSPz/GBAcmAneDKIwbRgcO9DQ/p0MGeBA8ia8OSnbE58hjK4Flw4ewSmlnW+QuGj1ulO+PgHedvPb6FrjxyIM0ZgS8jB2EPCDZq3PnVMhoObww3STSOu742nI4am9fp9KVVUdyNA8yanwM3OOs95AX2GAe3bOOc+FcOBeDwAybUM4Rj45nfIAM7CF7C1+eF8ZFIycjj6KowWeDDazYj40IGnY+UhXuws86kfNs6UgLy7v8tIO+zzjP+Wt3VwQaDR0DymEhOwAKeeRXchPF8EKkpOcMAhzgYwu+d44YNoZ9Mnah7cRjtbq/s5aAbUeBmb5WBuC+X7DeejhrqbSDjc96AceGJDVto/uEH6mSYT6J8H1JdsjBfw3VIzeiVwYL79ZRxJJrLg/MO0QkdecxWaa/53KyyUaBx8z+p5KD7SPVno/woNaz/s35cAUlh0fTf6gtn8wSHQqh1j25o8GFxnT/uIvyAq6Dqz69ABoYtem82OhRjQcjkgW7SXR2NW5/QDQalBz8C4sbxbpdJ2M7hCI0gbmzc/DC2SGPPKp+YDYZ/ZvVndn8v6e5cWHc6dLCe7t7f2hU3YSw7WZWnlB4YfWbcOrEnQd3aP1F71UJbFx9CWAom/hTP2MG2dqdKd+fCOhONo2ACduYdREMqNUN4F5HZ9P3X3kMKFmS8C8wLO10CmeT7CdIczjzU6O74ym7n4afuo8hrT2JQFhf4ALINgNvA940fkAJm/RAW3tq6ZeQDa746ZnInXbr7OhagVgZ8bVc5YhHBbwBdXsI7srz7r84+Wp+HU1/9pzRCH6zGivh+7IYfefM/OlEjNTfEDuP9roLU0ff1C23ji3Wwl7zmwrvJBI8FxttJkpmL03mptOkM+SC1ZPftRFkMmJE/fM+N+uLWeg0FXA++H+BeenhccP/Qvb/WSS6t5zqVA9d2PHcOB0P3/Za0ql0OR+xNgWvusjdYaunQwerS8p6pqdS9MwgLdD+eKa1qN4X++DMzxakyZRZxhhD20PASr+cs0XuG9XI4Qvi5hyjywj8RIhH1LDaup6clPfvHjvwVRh9dR7rmgpAkrXqP7pFkhuDxmPA7VNuNzwHwqvhgfNSvyRke7rxGL6fyz4eMHl7ZJ1LR1RP7isGgJ96VfTQmq8HAaQgcO80pF3knnDkMvBZgTucbbZxOcRvSM44EmWJhFr7cuiBd1cHhEbsagoi/1hCGEKDiHMSvJTB6eA0vlXFc+eJ2GFqy6fyDnH9EL1jfQPcjJOGfZ43SSSK9rht/zDqOw0Z1NhZwf6vBgH/clGNHnGO3eZHKwq/gqqQmuJ2zS/+l0ybq7Xvr3wIsSHlRe+WMKXSixyK6K9fnWH+OuYeTuO7V0JVnNR06ujJ2PofvBXsslDe20NDcLKQ2zE35/RaCwaGyrU2/z+0oM2cBezEUgFiyq1Ld2kZbGptA7ggOA/z4ZU+TRMLPGvNdbGtqQqhThMpys2k4SBQ5FKarYh2Hl2eAl36rjqMsoTPWc6wGA2u7UQ6DDDHYtAn56Mfhxxc+E/3OxkCjv9MrL7x1PgTEwTMXgpexwOl8brPqYFdxbE3rRg2n/o3wCuCF/uB5b3TpWvE6I6EWCjVv0fkbVBgI/Nn4oZ/AUBCvw1q3ziU9eEygSBvYul34GKzXji+n4/7G60y2Hgk2ANdtOjcGh6+wkSmZ76qWXa/Bu2MqODKwW2sR61yYlNJLWve8D4+RUoSUTMaj5DwO9hBxEqvBwPE43MaZEE0ZMTYa1+/UyaGNFx8cQ6zt3IzYZoTEIUWigh/5hvDixmsBxLuGyS4YDJ3Ga3jpxwg3wGdwR1iB0W68claG0P03kzJpf/Kf8X2j2fbKxGnsWcAeEUzkSFigMh8CeyIwpwEbPRJJeMkH2AG/mwK3PO7atf3HyNIw9wSdlNC1Ew6wy3/k3Rfgzr9BH4f/F3eAf2EhqYh5VwbZnx0vPW7HdDb+B/9IgXtehqNNpls3s52NDBxawDu5Clj1vTxPzJP6SYFDLbrynrHCo79/EALA6QCVUcA3ie/f7rxnbNdGClEOBdLAV6IgFIKNQU5El9ZzvMqpziWELCjs6cChR26inn6B63vX7Zz49r6eJaFrq7V4lKTeLxDg1HsTkGYxVeEFej0W6kGLgZEXcJy9gP84BCJVKcr2I04/tcd3zZ5WWghOhZ31Qd2rga/72roGOn5CflI/xHiM3ZnLd2cV0+/erkCGCZVOnlyge1wY8/4EHAtMiHgOwjiY3DCReI3j0/Jmmu3htbETRHXXfLrUlSXfuLbVYMD8A9+Fezov9O6eexDi2/fQH5euMrq6vp6D1I1OKQL5BIOYr9Qj5CBeMXtq8II1GclALBsLk9kNQKz9AI+Yfe7HnhJM4tfCRhRuMEWjZQgJGIywjSE53s8q47QHi+ogLNcddhgYYTTUIzAAtOgZBtijwkus48hBWEMdyBD5+oYkM45Fe6r1dJLXHzgN6RGjMfGPrN2kc058E+krL5g4jjitqJs8DC+Hc5GKMr4P8x7w3/PgWfjr6vX0wonzTBXspVGPsbpJMbLRsDC9w9YO7w+uOxENcruXGONw6pNoHNZzCmE8qUaIxkoQdO6HtJxuUonQg+zBx1Du8K/buvgyS7CQK6HGbf+mhjW3YxH+GhbqzgY724mWChPVJcuuzwu5+nXzadBh2G21iFXHrndOxA7/SVQI93YnCRRO0/kH2kCul9VBtujUL5k2zg4Qaa/SQyoUfwH0wviB0Ar+Y/HnlGGhXuapyot8sb12KXmRL7ZWfoQwgFykcrS7kxt4hFp2Us2SK5DGcgblj73IdRx78/66XtTjQAjZByLttfgMwY46viuZnJL/DMl0Wazz8cZND8E7YDP5YcTKGngU/o7Qs1kYz6qhw+u1HmEVfG8Hz32G1EA+ns/9vLqndEwpwoIBf6mITkzI5IhwqdaJ0HCyhtzoUVM3IEIKRa+d/WQXDNSxw9hpbAhbCN1zg74rq3awzxt9mNsg/K979LGpc+YZzZ1eI0s/ovDfb9bvpw9hDYRFeTIL8wgWWtTUqOvTF/fwYtBj0OOvgO8Z3QDRxN8V7p/tfBpnkQjd/Ss97Z2phs+HQSP4IT7DvvlD8p94lnmoSwV4kLBHiL5ITEAUx0SQWkPHdxy8DZlPwLi3fG2vVHbdJV+0zo1TNGqfvQujFDDHZxk/awqngkQKTzfiPuv5XE6Hjnid3X3PmD9GOhTrmQn29nvGMgn2dlE47WU3Ul9a1On3wov7wNo3smUdRZ59EMReJUh7Oo/0EBbmOeAMLjBccliSevRpSXkGJDKgKDu2kFuKSOuYems5tRVXb52ljDtlBFqRJeF9cAzswOLa6hptVTSrLMcznKAGngS/f2s3bbBkRrCeb5SfPne0UaRHFlfTG9hp//EhpXTwyFy65pUd+hjMDi6Fh7Er7ya8w3nvwipaYEkDObIIC4WWMN33STW9i3ledlgpDenIBuGkpytzidfz5oZGyoIx4FGQLD65rJYG5WKhgDqHezS0R78enwVPBP8ZMqEkg/7vmCFGlZIdx+qfTzbPiS8w0SGHDTCx4Ugw3D8H3gJeRLK3AXsWMPHgn2EUsIrBP2BkTsjFl3oyfARe/AMcX/9hRaVOFMjx/mOxa51MGMMPQYqYqpw3cQx9D39u8jRCAB5cs0Fn/nfrw7Hw30PIg5s8jAX54+s3e2Yx4HEc6R6RQOkYx3+xmJ8PHgQOP7AKp2Rkg88T67cSvi7pgg7OCWsfo/wOCBZ3gPPgF+ChsHpnVIAP4tYvVtHnCMEYEJfN4nGQU/KzlKosOPUY85T78Gy+BG4N9jph3onLPvjMDC0xOzkUnj3hSLO1S+PA7+s9340ZA3gRG6xfib81ut4IFp7MO9Bet8K8jlHQIu3UVvWhUTVfw8gisOeDs0FCN4JKZ/8NZHbvUu3K35nH3Qp5o88F0/655uFQ01aQ6EXdlzmDQhhhDE7j4B+F4Vakz9KJGd0XDJkIe+CQgcYN94C07mosLLFTC++GVKUZpIh1q2/RvRnczs0b83099MLtOBMENm64D4aGFrcu5KVDCzdT7fLrqPiA+Z0WtU3lzyJk5G4YM5rBC3GsTb/uFdEUNWrwga7c3/p1f6bm7c9T0X7X6FwUlZ/+EAt1LDQSyJB5r7j2CLdVUs3Sq8yUnW4dhx7X+Xkz+g6Y/jtka3hPz9jAxoPGTQ8gq8JgGA6O1P/YeMIGFTfh1KDsVeDPc/9OdTo3BEKw8HMPk+97PyffQUdSEGSC2q5tTl1tbRl/edFWj68wEV3k6b/pJILxx4y61+5gOhYM6sTpFCkohlv3z8l/9Z3EzP+6V8EDf9B5FGjAIPL96HqEVxxmDKnTqx67jVYFqeZSEc4sEYaxwiqhX19srdrLMDAx74ObsPGFjR8KjKXqhVeDgBHkjI/eqXdXYSTQtm2gyON3UQREkmoX08hxOrwIE1IiPEJxyYDBF2QuitAdvwAZZvRz1m3MGQ+/53ZIJ1/kcJFE4qWDvVPCf7mBIp++FVXDDP/YgdfHBa8ODek9OXxGceA1MK6bDh2si41BOkFhXTUAihjqba/+7/7MVo+v7Iv3jB73f9W39fSpgV/dS+FFIAlmA1kC4fSq/lO/o/dKh474y2kbV+pN/st+i/CWqRTiEIn3XqbAmdH3kO4JA68d3xkXwvPH/pvJqisZo6Pvol8SuaSAterqrWX3b43eOiMZd7cRYGLDn7+4gyoa3XcL+SL5ICv04h/4GxbpbCyYVZZFE+GZkJuhJhxbCUIERiPdotF3WEEGFpGJz/NS/DLSGLKx4PgJeXTMuDy6/f1KvTtneThvZhE9Dq4GNijccFxsYR6vrytzidfBjgMjizp/II0s8sV3NeuD8+xv0WTG4ZSi0lSIwqraOuw6F9PNiOtnt/uXt+2kcQX5egw375Bz/P9b2yuwoIztlOZiQX8rUh8awrwA/Ncd2dQRX89x7j95f5Guit3L4ze+zxwzks4cN1I/nglehAsmj7VddmUHIeC04kLiTAOl2MlugrfAGpAxvoXsCbMwTs4M4SZ8/XtWrNNJ704dOZg4zaKTu/zkIpDcuAjr+AcMBmwgOXLIID29IhtZZg8qhodDM30Mw8ipo8ronI55OKlJxzhY71PIcsHEhLfgfjFehrCnwf1HldBvFy+nJ9HnOzCAuBloThpRRn/F4j2AH00/33+y7oHzwpbtelaNVhiWOISEuRCscigwtmZeYG+QJzdugWlC0TNIjEBYRRbGUwGOiTe279LDFH4wZZxVBQ0E9myIyuvgtBiZi91jyxxsnV0qXRnHDQdNs2lT1Eyq+uwSk2iODzaXP63/2TpaKoEi8GRYvAt4Ec4cAbxDzsKpCJPhDGC3fqtwtoHa5b+yNoEUD8RLrqJQZsmhrkfZsyDUUo5F6SpkSfgeBgYXVT8/23YjQ96ob9sMF1aF7EFQt/K3elYIXowHiqbicOxZM/oG4NLuJukgXwwUziAVWSWqP/8plRx4lx7SEWrdhbH9HukJQT4KQseiaTci7v9w2zAUNUB1q36vZzUwDqR6f1k3388oduxNATdbhKV0R+rW3KYbCzJBIhko2E/HN1V9/rzRlMd/SO/IRqu2qo9AQvkRtex8hZq3PYXxwrsNeBRNu85RNYfEFEy+gupX3aLzdDBxJXsnJBLeLVewsDY4ApQyGBw8foAn0sfHI1vW64tXji/mXV4qBt+Aw+eBkXPeSWc6Fgy80+z/5XwK/f5yCt3yU1KZvPDFR6NeBceAe+Csi0lhojwP8SFMQFuMRdST9yK9Yx5CMhDGlIA7gdX5DjlWJ1vjEALait3SjxaQevaPHK6ET1oYAXgnN94LwtqZCRwJIRS+PzxKKlzDw0wu2CFq2WhSrplPwUtPJSZi9DIYBG++Qk8RaZxrvjLPRAP4krDYVU8423ORHX7kDn1Rrs7A59VYPO9MJJeipIN8MfwajFIwFug7zaedr5Pz8TA4jCS84GmQdT5I9M87yX/+la6jS4uON8Bv8cjtrtcwD3gYDPbZe8bITlAyWB+mkZ3AHLNLgcOLTEmHDlNZtMDGK/78MD4jlOH4zQFSTc7MwWEvPhCYRl54lMLvvWQaLuJU4HMoSS8Fj/sSr7M31u2rkd44Axlz2hF4b3OTbiw4ZXI+HTc+HzwD+EHpcJUMELF5yTKQKM4Znk2/mBf9APHqaxw7ZXIh8Z8hP8HOf3fltXX1dMToXLr44KguYy7sbn36tCK4i5OevaGhNeyaWrErc4kf92WHp7azEH8+15MZh1eIBxsIqlvb6Vvjoh/SvDAegV1nXlx/BWkBC+C6fzaY/G9btkrfTbfuLjuNpw7eCLwrvrGhkXj3mT0FmDTxZCw4E3kgRPCDIozx8OLWS1inIVmIw//O+NFGldj9/KkNW5EdYZxj6AMvfDn1Y71HvuglVTW6vj/AgDIOY++KrAZ+LDceOJ2mwLX9n+s20ytIXXkRMhawvAqjzD0r19H34ObvtgBOxzj4/nI2hLPgMeJ2HSZdZEMKexCMhleHkxgGGjYasHBWjUXwPuGQjJ8dNF3PihF/HhskjPAHPsbnFuIH8d0gTYwPB/kOMkVc++kXemjLN8aMMFWdgTL/GfK/yOCRqnRlHOzVwNlDDOEd2NI52J3d/E+9qXHLP/V47owBBxldYq/4kcPZETJL58baUGLjQcmBd5ttvLOfeeAss55sIXvIcXqaPi3cSqGGddRS8RoIBS/tfDo+2NjQEcif1Gm3vVNnNmZ4uLVzf9Ujzp0zIbAM2P9myhwwUy+n+q+tZhFO0ajkgDuTMqQ46fdnwUg1689UvfgnVLX4csobeTbxvWKvgmyEj3AmCideCb6/RVNvoLbqz3S1Xbm/eSPOJP4zpGjqtUaxy6/t1YsoE54AxTMS78wlcxFOv5gz5ETco4OoreRgPM+PgGBzKwgeX3Y1GLDedhiVlECRTurJxJ6c8UHxZdsuyUaX4aduMtt8YGPnP4PDIBHZnXmiRyGyajEeES0aCoB0aF2RdCwY+Lq60QCLac7yoHs8DBxG/p/+wdNd3jpeDqHgH1IaFv2hG3+IMirgL2CDnVXUk84hP/6s4oPLNEtk/XL8Ax4nnm09bCvzTjd7DbiRHnIsPBWDsBrGAidhvgGFM0ZU73Y6bLYp+fidFpceUj8IYy8vzhSQWapHnGL2dypEVi4iddaR5L/8t06Hk2ozsHHqbJAvaggHcQ03wYnaOy+QesDcTgYBNmD4YUAI4fdD5F14wngYDNKhgz10CB4ofnh+dNXgtq/eM+nITpAOHfHPAb9viY1teF/o/A3g6GDROKUjMqawKKPw2VK+QS87/UvW6EgwMFESXCRO1+gNbf7eMEgZ45eLQDVc5APYLPou0ikmE0/vNDreXeS/iaWdd9Sd+u+tNiZqZJ6Er07Bl5uL7D80CwYDcBA1hxwNBj1lLukYBxsICmEUaAZhnSEjEJbABIGGjC8EoR1CUjiefziOuQlnBrgRu9UN8Epgw0IRUgBybDrH2/MC+Tez93dcWBr6DkAqRv7rjizcjfhpjehsl5179oJgV3zOxsCpH52E+QWY9X9MvvcOkdO5RhvvmrOxZVJR1PjBujidI2PDqQd5kf44sgxwWkM3Tod0jIPvL3McNMDDwk34PcFSYMls4dSXjQZsVPsLvC9YeGF/waQx4N5I7mvjTXgRnAnjU7yxgHXxOJnfgtM0cprHrhIXsq5EktQ4Fn4O41Ob7l1i6Msomk55o6I/3MNtFVh0zdLjwY3jX+Zr7vDT9cu11y7Da8QcV1fGkFkMwwX+uiMc9sALSHZv77IglIOzS3BIRHeEjQZsmKlafBk1ILzBB86Ewhm3JjRksHeA4fGRrvvLhI0hJsHMn6BPKYgQkmaERrCxJ2vwcdjZP8R1qswZAEsHjD1TXfskeyCM+xPkdIwwQLTDMMOcBixsBOBxZBTP1uuu/3BvON2mnnLTpZOTIcala9ebQ+1EiIVngsOuSjoWDMa1eTfUD6b80M3/o+9Qclx00sLfufhTErD+86LJTfQY6g4Xa7c+wZ98zZP0UCmAZ2B1he4dwGSLjsLGgv0OdDxkNPovvVEvcuYHNn4YJHoaWO05RWEijwv9PCahG5e6UdgYQ6JXxlI97gwKg3xRw+6vE/miTqCH0Bn2hnAT9YDDQZr3CEJsyh1DLNKlg+prSD3jAlLBm9Bl6WHvmS7PI00n6ulLg23wmHmTfJy6cxCeeYTKcNiH2mEw4DAQTj3pJkkbHZENwv/dn7qp6fXtyf3y6/XTlAmkgsB4LPKZoJCJ9YYj1r8rwguN8SVZ9Nn2FjoDu/hdlVXwUuDFq5swa3SWX6E8hDsMKwzortPWvkzUmJ+p0vJdrXQsvCWcpAoGEpaSHOe3Q7rmYlx72a4W4owUm2vwYwgytjiTJg3MpBlD7Ts4Rn/jNV3jGIXF7Ae7KulrI4fpi9lRcBXneiOyI/Bu/irslrMBgA0LbsKZFDjDQTYW2lcdtL/ues+LQGb3ZwK5P2M3nXeQ/37kwWD5956X2zWSaW8EyZ6Ke+zFHs677sYi2UnnlKJCnaeDd/jdjApO51nbOIyhDT+ceP6MHRthWNbBEGPonADvBfbEcJN0jIN1zywZQE8j5IBDI+INPnzfmGuBjQrW8AG3MX0dRgKez93gRMBL0sYCNm41IXSBnwk3CcHDhMV4devH7ZwxgsNl2juMHfF9Z5Q4f8akMg422LiJG4GgW3+vdi+SPz7Pi+SPjRj8t6/FX8jEiSCCw0I0kDemS8NJJ/miD4z+Jexp8PllFG6vAzdDCgs5jD4d9zfUulsPFWGSwIGH/BPjqNFDJSLIpMHSsvMl3SMja5DzD1MOYfEXTNG5BzicoKtSvfQX1AZyTFgfdM+AQOF0yi87FUai2TDOIISC38gJpGjaDQl6INoggYeKocAktLOmRDQO4tWL0I5jjiP4sc9EZcZuoOXUpIpdWTAYsdSuFwjiu7sdHj/Xng8gssxu1nhss7GjoE49EAsU70V4/DlcTzfpoTIh+vkR/u8j5IvLGa+1t1H41SejKSS/9j2n4djaIss/ozD4Dnw/+TUpQ0fpx8LvPE8R6FbhBaGnvHQIIeGOHFqijJ1M2hLs/p96rk1vWisJyBd1MsPMbJAu1rheVquvjh7Lct5ASZcOBdwYtH2z6ziSObCv3jNuY9MNQ8gGorERquM739pXGViGRXyZtalT2Up6Gk/gyJ29SE+VyTOJ/8J//Y1OpumHFxQbByIvP4E0uM3g7NgIF8qdngaDZI2OGnsA9WFxXiH14QnL1BIjMGNIFk3BApaJ+S49tJQyE4QeuGn8/kED6LrXd9ELq+p07oCcjM4xrm7nGu33Lqyk8vrogt5oc3tlw8AVcPufWWZfoPJcPkIWglMq82l8XJaH8rp2egwkhEPz/Y7eBca10jEXXrRyJoSXwKnAEuhYS31a3qLXjx+fRxfOwQ4GFsBuko5xMHnf/368hM5D1oP7j5oDEr6B+iKSd3s5PGBBeQXxAox3xt3ks8oqfUf2nrmzaYIltp8Xl9OKi+j3s2fQ+W9/DDf2Kjo1FxZdD+lOWANzFvxt9QZ6FkR/p4/ufJ13Qd63sqZe3812GwJ7IRyPcIz7V2+kn00P6CkD2TiTirDRhclB34ar/3HQVYZ0g3wfmUTSMBiwIYaJ/NwkHeNg3eeMH0mLq6rponcX0mGDB9IwGGz8uC/sBcGGIeZ3uPlg+86wQTboNjZu/zeMEK/Ac8QqVrJBazvjdxA8Ovi+HFs2uNOz1IoxPLRmE8JW8rCrH/vBbdXB5Srs+l/72Re0rjb6nok/btStxIlGG78mO47p4L8YinuWSDhLQevut5Hubit+ycOzBgvVTOwa55Sdhh22xF+n3SX5M8YXBoN+EwgDQ40gRAMJIsfRB7BTnzPsa9jdnmh083ztjo6s4oP0neqaZdforv3RtJLunxdOA+kK+aJBJOmkT2/T2vXd/D0Lz8P9iD1X8USSbud35/42IHuFFmqk7BFRdvkWkEKysYBJG7OHnUq1K26iurW3gyDR2WDAYyqceAVVLbqU+DnJKTvFxonhNub49jBSMerGAg5HwPOQWTwHXgvgFvB1zfgfrz/ZejoI7dTJB2C3/CQKP/VXxCEj/SDY9p04DLzG1KUFQ0cstZdep2O2eGynDl1oSzfpoYrQDp2DAYYBTvOoG1QwrvBDt+q8BdTSqC+wVOZO8JCwEW+P8AbdmtzRVx03lbQhI6IcD/hM9H/zIlctvm+DE+L3lxGT9PmOOAmpK929K1yVeBxIlnxRnTCNIuAx0HgHuiMG31Crk0I+8wB2lAYjLMXZS5H7pkXH6eeDfPFGhJ6s0An6jDGk8rrP3jMOg2ReAH6uOFuGm3gRlvI53SVwZAOp/5LrKfzM3+HaGP2O8p1zaZTcE88wizJllqfnT7JGR99XYiFquuI+9k8Bg3zUP7WPTUymkzoCvOttCKcevOL57QhNUJA9wO9oNGACQbdde9Zz1wd76Avs7HM2ApZseAI4hTg8dOZI/bjTP96Nv/Xd3VhcK3TCxAIaVoCUO+i4A0aEV9c26N4HZyEVYUtQo9eRIrGhPUzXIavANBg9DNkD8safvrAd6fI0pIXMoO11QRoFzwlewK+riu7y33DcYJo+xH3BkI65/GtpNDvCaVMK6KEeMIUAAEAASURBVORJBVSSCws7Psw4FOJ1zOXfy+t0YsYfdnAtGOO3viYzDl6ofvRjWKrjZGlVrdnyBlzjn4CL/HzEl2fDxfxJlO9btUE/zu75182aRocMRuyXi3DaPSZLfOK4w116EF0D8kQ2OvzyAHcXW6ewhrq2oJ6KkMeRKKyBL/5/4Cjg0ARecPPu+gCERvBu9PIOMkQ2fvxuzgzXmH4eA2dIWAHDAgsbPdjTIt5kcBbc689G7L2T8Mfoz2Fw+QIY84KduQtuQrjGezv30CkgO9xU36iHavzpsFm6QcVJRzrGYehlboc/LllNK0BwySSFhnCoxI+mTjCNGEb7f8BD8UEFXElTFCsRZvypPJ9rP1sKY4VKXxkGIwoMF0wEuQOhGgsQrsDeDv83a6qnEeWGz5bp4SQHwzA0GeEebkYsJ2ORMZ5kxsGGs5NHdt7laK/5XFfDzPFMPNha8aZeV/z8wxYs2qHoM8P8BQP2Bwkg4rrdhEn+KheeD7fYIsoeckLU3VvpbGTIwC6zl1t6G1zNa774ZTQbArBlrgFOwYeVMhZTmVS0/x8TpktMVYe+UP/wW/apwX2eF8hRAc8Ndtbx08vWx4s4kckXGzbeR8HaL6LnYC6JyBcjuF7N0qtt10imkl12MuUgM4SbpOP+Vrx3GowBR1PhpCv0y1R+9iOQZq6gwUe9ioV/NngDXsczdD0NOvJFeEA4E8ayUaGt5jOKwFuBhbkD8MDoZeu/IUe9ZK3aymEYKdpA+Nhe9Sl0faIbLZiMMYDwkUwYejgcgY1KTG5oSFcyPihYVI84bYehwnw1OAxC86/FgvQdSkRo5/vKN8xz4wuRVZ9TGBkStHXLoodwTSYMtC5Q+YB60rfIf8q340836+wmry8YBoC88OsXkla1m0J//BlpO7fofXjB4L/qDrx/Y5iYJ6e5oIcVVO8hYi8F4+c30vAS4uS13eU6QZtv9jzbVcNvPUfpIj1kxbzrG3kTBHu8EG62GGP58+RYkDgCo0QEhO1XnqMvoHXcHMgtORNDBEz5gXtediV3DN33W2IeA6oBHixMHOmQiSDj7uejxx3+J0u+6P/2TxzOjjZFtq6n0PUwbIBzR517IvgDRmMc+K1ZseP/2bsKeDnK63tXnrvE3YknJISgCRA8pUCBQLEGKK7FLUAJlOJFCi0WoIXSFoq7BI0SIQlxz4s+d9md/Z/z7Zt9s/tmZm3SP6S5v9+8sW+++UZ23957zz1HtG/wWUOpgOfqP4gHPAdW5kQf7Nv37H1g8QdfAjPvquQl/DuVbVJue5IzU/upfGY0cHb4bj1HJAuEpSPHi5JBNPl8ufrsI25MZkYCR98d56nPhPvAo21JT939hph1YblNlZGAx0DxGvTC96HJ2PSD+ftOlSGtWCTu488UohR8T94h2nx8vx12gkIpBFYtlpS/fhL8vtMP3MPm7X+t7GEXuPdy4r8DlZBDvP3j7YAKI+qMP3T4oQTdrqNaOOfRrAukCjklapQ8zAEy4Y/HdYFyQjhC4dhBOXL9B9ukuskvZ4FvgSSNN2F95rrasIBBBygN3HdsF3lhfrks2hYMiizf1aSG1BXBkPP3K7INFuhjT/Za5m2pk2MHZss5Y8Kj1CyFOH1UgQoefLCqWi4YF4S16+eNnEcbh64wEXmccf0IZME56XYa6sknwanbWFunMuzRasqpmlCDHziE+1vBzivgtHcCSZ6VOVXWwKDCCwhgvAGnl86hbulAxlCZ4PKhAy3HqLf14J/FCAtYu97G7p4w8HPL6CHy4soNykHmMSTRW4tAwdutUoNBPoU8vTvTebLj0Dtl1v6P40fh92gAcP4GSGb6lMOebcFbEEk2qPeTzJzX+/iBY+WehcvkP0AaGI2lEr+HMsGQAvNSAr0t5RsPAirjLhAtJmqxjMMsWGA8X92mf6pgQUa3X6qMsTc9iBSh/Fzdln9DGvA5qVr5sOQPvtF4WNiyEyR/dJgrlwH+68mEnN9tUEPADzFk8ejwNlctkZpVf5LKxddJ8QGviBe1/GaWSB8KLp9A6YEdcaIaW5zki5FEkmbXl8i2ZJ8vuQu0pl0h7gLe4xY8D8L2GSygEY1CI+rAKmDA/Z6MbmriciJGRYNMKBtwopFHoUkFD+aD9PBFCax5CoGIXDHKOyai+MDSCztzgtBO9U/oOiDFdubKMw/A6Me4GCg4v+2z6SrqKN7pMxTxWSwOg94P53Q0FAS9daOGoIO7FY5vbGe2HIs0GzOukQR9OrFfLKSHZueN3EaUhufIU8QNhQcp2y4BQLJJwEhOA1cMpG28B1K6VdzHIWhgEizg+Uh4qM3+DE53iUKGRI5BX3d16ibCKUFzgnyRqAvvnc+I/6/TRWvNOoeGAwSF57Lf2wYL2NaJPoKSfwgW0GqrgkGi4Fp8f38Cn5nAykVqzN4rp9uWHdldmBMEjgxcUAKV5UBGVRJ+hlUQw24ArftiRikM3z+G3n6+TfYGDH6+z263jXwxnOoKKAacMTJfjhkIiTPIJyZiTqgCzNtSL6cMz2sXLOB4OK4jgHL4fG2tChhwfV+oMizc2tBuuN3zUuX2IzpLLYIL5GZoRCCEjnchZBz5ZRDNkr0W1lJvqmwRoiGsbEy3dFUGshXoDo7XzGIZh51KAvukE2l2zaxp1+vaoxHRUcKQEHzWw58PhYJII4KBMoF2mV+nyhqICOAYpg7qq2rdt9Q2gDchXXqiTID7oplCJsC5TdbopF87si1S3gE8Ac8cOk4RSiJ3qlQj7Mbj1DiM18HnHMljYNy/u5epmPHsBLCzA03A4AkDTAMhm2iFFDCOR5F8guBvsKHkxbg/nuVkxsHzNGx7B+z1h7QLCNBpyukzFRm7ZrR5Fy3bnJJ243OA5K+pDBljZJ+Lx70A2b1BoVMwW5yGDLJ31EOy67vT4SDOFW8rUWKoUetCIn3QUS8eY53VijxHLOtOkC/Gcp5Y2iT7fBm0cUEpgxKYIsdDxvBbfNGCTLVwfOj0TWWz1TKdcytzQmkhsm9vZm9Uz1RjqsK7swtlLGtCyBi9bSKKD3YcBk4R2rkHI+Ay+HF9mI7O43EYeGIlsUcW+9pK8V54mxoLr9N3y7lKe95zyR3KabQaZMzSbMhgWpkbhImcnDJV3kE5TEzxmAqYAKLPe2JlKqiAnS5I2lmZ98JbrXbFvF0nX4z5AIuGbvAHuKbPQAAFhJDbNgHBBVJW3huUVxgDRBaHq83J9qF9/SGQBVDfgGoEiT7NfqfZnZ/7fiqfGfVugBvCNWhktCFb73eAwDGwdYP4n5wmfvBPuOHQu8BfoBAPcZa/OBl0tL7gn/aevQGDn/bz+X8Z3S6QAFIlgdD5lAT5C5wYOAnk6DTYGZpIvQHpkIOgAcsTrIxBhf6Y/tvGWuqe+Sny444mGdcDsDsT24CAAu975+zEERkm3bbbdO+iZXIO5P16wKE2s9mApj/4wwr595HW8Lt9QBQ4sWtHeXXNRiFZIMsXitLShHwESyoqZfaOMqUYQCi6lW2orlOs9EYOBGNbZvQHA4b+A0oLJvfqbtxlukxnnNdkdV2mB+3mjURf8F7tTtP5B64bOVjB+6/4dr5shlpFNLPiH9CPo3O/oaYuJI+5Dg7/u5tY2uOXw/Dsx3W0LlnR+9DnLPGwIibU20TO+ZkZmJcrs1Fucnr/3pG7E1pPZBxB5vuNktnjdMtzpnc4WOqQvfXVbxFvpvm76gTJn692nahssCFYYBwUM9cp+UMB80d2xyJg4EQfxnP+FJZ9DduAukDJC8o9GBAl6qMZpRupkJgkKsQuo+/U802HHGJDCaDjCAw17voav9xTJL3zEZDEBGHnmj9j39soBxirJDh39z3z1W2WxvI5KEvAhPtAgkqWJbAcIbPHKVBrOHC3DoGO6H+F0C6Oq2CWkZJnLmTS+Y5on74OMkV8TnoPFM/EX4hSD7Doj2SALbdNVeoCJDYMGUoJ3IccJ9p3H6EW/0pJuf8V1X9ov2EhVmk2z0nng0gxzXBk+0WWJ2hfvQuJxrUKHeC98RHRfpgDpAOc2xid/2TuB0fkRhmHRoLE/Q5rpxxAOUPtP8+JFHe2rftvf2X/v1uUg84xY0rU7PogeZ+VeoQKslXsFPcvzhF3j36Jnt7R45J5R8hloTU1SKBkgy3CxG7AThA4qpKoK6Yrck2N8przvhA/OUqG7AtJz0OUnKarMIgYtBuL2T5j0FHJmdo8X7Pjf27b9gYMfm5P7L8w3n1AEkiVhHKgDDplJ1fTx3/M5BtoQIda69gZA4AvghpmvywBx8F5Y4tMr4oOIJUDPlxRIxP7ZLdDOlQ3+uTTNTXSr6jtn+sioAtGmagNkK9gS2Wz9IYiAW1DRZPiPGhEcOHg3lkyuhtqRXeznbNvodw7c4fkprvluH1ywTjfdm/ngpSRhIinA4FgxvPg5NDWVdXKJd/MkyuHDQLZX5dQ1ySie3r5Gnl341ZVwx/aYbFw06gh0j0rU15B0GB5a/2/3vRIlDtciKw/nT4rc6KswarvRLYnQ75Icj461HZGbokswOA6g1yPEoxWRv6FXeivBT9GGRCj+bFAFn+WF/DYQ7u0ZSk7ABnSH1n7bPRN65mVZcnXoBrE8IccCJd9M19yUMJAhAARJzfNXYTvhGZ19CdAkLBMgOUCdsbP/xYoHNQjyMAAIE0FArHIkhSSQl42bKBlF5cOGSDXzloor6Pc5Bi8q3xnErFo4yCHxz3guTAzlT1GCUCgpZUp26SRBkZ8WqRWvbFpIiR/xuNV/ynZGAegqoDAW5EsBpowznTrH7wuB/rQx0VuA2atAxpKvFqfL8K8WAcPCYInbhDvZXQ6TG9uOk+2j5q1z0rt+uelYPi94uk0UQVuKK9Ia9r5JVQHvoPs4pO4X+afOaeeb86Ay8XftFMFDQj5zx86Tbx4DiSXbNjyBngrjpHs/heZ3gMnN5aCNLGllXvDhQASeRUYzEgF+aHbmx7XqVhy46vbKBpkISON12X3bHc3oV3keOzWfSA8096aIZ4r7hbP2Inih0Se9vqzwUPAsxBY9J14b3kcmWTzd0SbN1MFCzzX3CeeUQeFTkWIs/e8G0Q75jTx3Xy2kAjQy5ICE4tZmg1Ecd7J1igDEuL5npiGuv8gz4U6FT57ARAYtnz3sbhPuVC8GI+dJXs/2LcbnBHasu+l5ZazleMlnREohVydIDBD7gKSJ3qve8huGGofv5sDOzajPYLclGgMbkUxPzhS0EcA5/CedVXrduuZf+E3Epj/lUIHoJYWgYpicUGe0D3xhKgIAW3lYqV6Yd07fsewVCM7V6lBmGX+WyC16UbwxAMOCDPzf/K6+P/2aJDTwSS7rYJscLKFPBYOWCJcGcbTJvuOKFWQ/Y8Q/xO3oz7zNnEhCGL1+TKe17jsCIEjnptn7AQRTOpdW79CNHzeAwge+F96WE2uPoOh6HGBpZylf9G3or3yBAKCrxqHF7ZMOVPPCWeL9+r7wrbvSSuJ/frak+7A3mtpdweGdEqXA3pmyh9n7pRLxxdJDxAEJqKUsKO2RaZ/vkMFDNqdxLDBKmDAJkejJOIPX+yQK94uAcFitnRFGQEDDoTtf7a2RppQWnD9oUGn5aYPtioSQ3IbGI2yiTd+uE3JKz4yuZtUIdAw/bMdKLsIhjBmgifhxgkdwjL/Ly0ol88QjLh0fLHs3zNLbv5wqzqnsV+z5RdP62W2WW1j6UQ6SCT/DlUGKlB0zPJKKtZLMb6aZlwU7M0fq9WkVvAnE1IKdUBQxDMOOulzLrN2wm4FoeG9qCu/f9Fy+X5XuVw1fJBsQUb6Xsgkck60wNXDrY/Xx8bs+W9QBjClX0+V0d7W0CCFyIx0B7ldAZzYaJZIWQOd6XOg7sBAxRMHjwXr/y55YPHyaKdSKgmnWxAW8mByH9wFksIa9M9AVT6IEzfhXiwBuuEjkDtGI19cUl4JksNlUcehN6BaAjkPUpGJMxpLPF5ds0ERchq3G5fPHtgHAYO2LZH8A9ePGty2M8GlPy9bo9QUfgVZRRrvAYMFZw3orcgB/4h35XFILdoFDLYhuHHzvMXqnbIbhl3AgIGCDK9Hnlq2Wk1cTjUhJ3r9qEMsTxHTOPB70CpgwI5TIGVYu+lf8MOPVk6g8WSarwHkfc8i899ZWD9uZST58zVskZbq5SA/PBfRhegkf5F9ke0+4G9S58vtf3HkbhDrfaRg8Zk9Tm23T9/gRB/si9dcu/4lBAp8etft5tl9z8M264BBsn201K7FGFCewXIMqEQwUFG78RX1LIpG/wn3YqUiq2zY9hEUAya3G5++wYnn60FJQtHoRwH9r8EP4yzAiYMBYaIeise/DBWL9lnDRMgGOWYj/4B+DfrcDcWMrF5nqhKa1LxhoXHo+2OZM4hTseh68DAstW6Oz0yvU9qX/+kHaB+9hsBVBn5oP6amWAjtdDlDwsBTpj0tfjie/uei/wC3kzPUtqyDzN+L4ho4UsG8A2Bs197/h2K9J2FfYMMq8T91p2jffiSeCebvSGA7HNrcQkCZzZEZ7q69xcVSAZzLymKVZpMta626gANdJyQTdKWCnO/8m0Qx+P/9T6q9G0GCwOa1okHmUOszSNwWUHAn7gdPSN4GPiPfs/eKtmSOytzqA6dz6Dnj7pDOvb49ch7YuVV8D98QIp+M3B9atwkYMLPr/zOeHzLHyjKyMTgQ0m5YiX/s30pg6VzxXH63rbNKJn9C12MyIFS8F90ulKcMlKwPHcJsPPvQ1v4Y2hZaQHkDHdRo5jntIvE9eJ343v2beEC+KHlFCZUlJMqVoY8vkXdEfXZvOkvvIjgn1wWCPr47f6v+30kWfpvj95XR7AhLSeCoZFnh5PumTcVzxe+lBEhP9fMx0OPqO1hcPQdIYMT++Mx/LNrMtyWwfrmQH8SNAJNuTsuZ6v3+nOd7AwY/56e3m8ZO2HwFiA43AiJ/44fb1VmyU/FBi/ignzgkV04cal2TPwMkg9uALhgOxYLuUDf4YFWt/ALEhFQroHoCyRX/eGxX26ugcsG9x3SRh7/eJf9ZFmQk1w/oDYj/b8YWKqlEKg2UIIhA4sPxcPCN9gIy90Q4TMY+2hdw3BksOHVYnkzqnyOPzyqVZ+eVhwUMisBt0LsgDdwJwR993XIB5wRrfzKG2ADKEto70j3zw51G4zmYje2AwEI849DbGvsxLvdFXfmTcLafgSoCZe+onlCBDHImsrc3AzVgJEM0Hme1TJWFgSgd4BSPJVLWQJZ9ZtM7tgYkmLHnejQrTDfnhOBxTpAvjoSU5PFg2X9v01ZVFjC2Q6EUoqSiHPd1PoIy3yCwwSDB0II8obwi5R6nQQHgvv1HhYZOzoeXETDgWA/t3FGVevB69+tYiNKAepR5lCoSx9MRoLEzRtEjP6uR7aNxVCxDWcmx3bsKyTBps3aWKplIBof4vHmt9y78USEPrMggiVZhAGo0+CF6guTwLZA/ntK3h0IbEFnAd47vYTSjugKnRC2WcVjJMurnzO13iZTOnSqls86UjC7Hwjntg11eSBqWSP3W9yXQXC4FI+/Xm1vP4yT5i+yI6gnpnY5AFv0lBTUntNyd1gEqCRXSjDKEJmTTU3KHYIz4sWlhTvTBWv3adc8rIr/0DhNx7gU4mwcSkwegRn4dxvGNZKAkIrvX2RajABmfA300Q4WAVjACDOZwzBvL5yN+USuZ3X8l3qweamJQorlmhWSKuTPI4x17vujLjAzQLFjA8yZCNsjj7Kxw5H12u2PaV73iARUsSMkbAmLNA6DE0T4QxvsbzeImtCNUGAR0urSdKytbrUc9j2KTN28VQEae5r1qOurp81VWnE6Me9LJ4kZgQjBp/3leBQ5kgnkf6jqg2a4y4K1IrnYt/XCObOr1Y5Vmo0a8lWlL5qoMvuePf8fYe4ofQQ7dVNDi5sek5bLJSh7RKmDgxP3Qz8m67pTrH1aZWwG5IREBqu6fjmEM5n/1SQls3wR4OAjpuvZCmcgbkHycIoJ7rdQTKsuEZIR25occIoMF7sNQevTL3wjHRFOcEyg70f7zgsjf/iTe31xn2Y3n7GuA2rgdL0mKuA8/UY2FiClyGWhfvInxNIj7JDisQEHQwfQ9epN4fne/aH+HlKohaMDxc7IyFbQyQRfo7bWZ7wSDbJAR1SglCgSLoPY+0uxUI5zgykjoHeFnt1vvyKFGXY9GWCoOEDgqZAGUMLTl34v8uEAUooSfZwbtew8S17Cx4h53eNhYE5IzhSznnmx7AwZ78tNN+NoCSv5saMd02x7y0u1fn9WlTSgpSFdkgz5/QD6Fo86SgIl9s6UG5Q53fbZdvt1QJz1HWTtzVBYozPDKYyd0l0ogA9ZDBrEJfXXJTZGeeSkhx6gwwyMvTwk6N5GDXgFFBMo/6sGN+VsawGQvciICBiwLOHJAtjzyTalCHujXdPw+eQg+tNWeX35g8J9QZN/xrMdCWBitv1jGEY30kOdIw5fwmci4/wjndRUm2qSunWQCatPjMbLvb0UmmXXtQbxG+NGdQPzX2UYpId6yBkLSjVJ+/fOyoe7QSUH0M61+yIUPqd2aE+SLdbj+jwHTvxLw+hN6dw87x+Re3YSyhUQPXD5sgJwKp/stBGqYod8OiUH9/qxofQ53jRkugxFY+NvqDfLh5q1KopEdMsv/1I+r5VzwT/D5WVmyHBXkLigDmqBfayCGhIU/ouSEKhIMFtCKQPBIK4PTbxUwWA5JRwZOGBRhn5Th7JebIyxXIVLkBshuflGyQ6bug4yQhTFwUoQASrzBKGN3sYyDz+dmG/nPlJz+IBp8TikU1ANebjR3eifJGz4dsO+DjZvbLTtF8pc/9A6pzeypGO8jM8AZkA7M6X8pvhet3w8OLNk+mqt+VNfHMoDU/GFSgyx/w9b3REc91GO5etXjIIS8AL/32gdJebATffgbwNSO+89ggeqzbLaapxXvr+b8Q3UCrak0tG624MTz1fsljwVlLgOBFn1T2DwNygm6JUI2qB8bbd646xtp3DkTvBqbUAcE5BTuQ1rxeMns+kvLcha9zyaUNKQW7ItSjif0Te3mdqSHbJwIoZ0LzlTKTcGMOftQkOSb2u4Xt8VrgdJtQTk2BAto2pLgO2LMJkoBAiJwTq2M4/A3N4r/X0+LZwo+XxEoJz9KAQLIgjIIYWVUeuDk/8vdEgASgdJsDA5oH/xDfHBeApuBToBagV3AQDmnYO1nsMDMqFjgGjgC5ROGcoWIhk7cj4gug7/DUJKAn1VxmbZ2GZy1cZJyHTL8+C2hffmeChAxu86Mte/+3ymlBfcpF1j2G/jyXVWHHhkQoCykFwEEH9SctK/eE7EJGGizPkYwKVcYnOA7aLTAkb+SFkouVoPsEs8scNSp4rvrQgngmXsunhYMbOAA7T0EcQBtF9TGR5oil0QwyT3KHKESao//k1SqEE4JmhNcGYm8I+qze/PjCY7a/DAnCBw1BAhUCU9dlTqJC58dSjS6oJpAHg4r+VDP+CMUOiIuOdNxh5lfyB6y1d7j20Mucu9lxHcHhiGrzykZY4CAWfyTugX7YV1+N5QTrC1rUgGDnHQPHPg8+TOy+1OgxkAYuJnNmF+hiA+fPrmH5CNAMbqb+StrlVFV4wBaojfKKmiE969EAGEoUA86h0ABAhI0oir0gIHasAf/YQ36k8uQJYQzyKwv68wpfbekokquB3leP6AQohnh4i+sXAvFCbNQQfDoswf0lnNRtkBrhFNditp8I2t/smUNJFd8cPEK9O6SY3oYcPrqjLH9cYJ8cT4y5mlAoPwCwQEz4/ZnVqxF6UOlTILDfBiCMwwYLMf91gMGOxoaFT/BoFakRp+cLBVQYJkEVQWOxvW9ilr7j/Ds7MorkuWo4DPh+Yh4EJxzDpANRLqMg/Ov27xdpWpRR3ro2/U5AwQsYTijH36Iw9hnD5SRrERQhAED8jBM6dtTHlqyXL0fVp//p5avVsGGV444SO86rnms43hk6Qq5Eegaq3HwpNSuL97/ZdSp70AWfSNSWM3iyegOkkMQjYEln8R73ozE3sF4Lornyun3W8DOz4IzuFGU05xWiHP3RElEocr2RRtLtD6ijcffuB2qAHnCDDQtJauf1MJ594ONn9D8zK7HozTgZShHvC/Zvc8y7c6JPlQwAGMhhJ5QfDrJLig6pOYOU+fUfHUggPwBiIOTTMdg3Bjt+Rrbmi37EZSoWHwDSk74fWRtXSZ9Z70Te5gN89dvBlliHVb8rW2R7cQy0RNNQFHkDbrasg9KbFYunaZkQNmI9wOutwRQnkH1hqbSOUBkgIXdgtMheJ46JdlpeZL/4g5F8DfrE2SiN1vWmbtGHABugQNMR+UCrFsx38MBlcwcCSzE/Qds3cV6cRhh/oFVS8R1+C9Nj+dGkuCx7EF7/xVAy2cjIw6nkKiG2moJrFshgdU/qJIHpRVv0Qt/o3gvvkP84FNgJpvmOf2yYBlBq5Sfiw4MSxssTBEzloPFf8eWoGNp1o7BAmTsrcyJ+2HVdzzblYpCVRnKPILfD4pADmiPwAZ8fhAwoLoC7zlLUgInn9cuSMNzsQ++FwqVYHFy9+iDRANnRWA77hl5FkxMW/ituE+A2kVEsIBN6VC6D4VE5FfviyBgwHXXiPEodZinVDEop0jz4b67EFjy7HuwWk/kTyJBtsjzOMGV4cQ7oqEkhEiFAEo0GEwTJBtcCHa5RgMVh2y+laMeeT1cT4aPgceKHiwYtp86t3vo2JiILZ2WMzW7tp/TNnPv6+d0BXvHutvuAFEAZSgbaIHzH2g9C/wAkK8FZFuND06FSw7oxR8j7Y0Bgpw0d5hiQVcgAtaVN4ca9wHkHyqHsgN9ETEQaXRSauHgsxwgUVPjwDjXVwTP+31Jg8qEjwLyQbeFWwFNghVntn0cXl1UITPX1epNYp7/BYEN3ZzgQUhkHCm4919cGPwnpo/FOP8D+As+Q2a3GLD+O5HNppwf7d2NJSA9XC2Xfj1P1aqzVt7K6EyyrpzZ5ck9O6m+6BRG2j4GSbwmEBpd9PVcuWhw/3ZZ+ETLGiqbg8+12KbkIHJMketOkC+2QGKSZhW44j68zlLLmj5YBhACLHGpJ4y11fg8eI/43tNx7QEYP211dY3sWxx01gcgkLOuxv69dIKj4iDwWLwPdAOJFr9DwIDPdiKCHESUMPDxAUovWGpgJY/I9nkICtSHSKwQe8D1rMG16EZ0CINNRKgYg0j6ft4HIhFYzpCoxToOkjJSEYLlOmZGYr3svlNV5p5EdpyMVrflP1Kz+gnpfNhnoc10Ynd9O0XB4ov3ewbZ3q+k8sd7Q/utFuhgWznZjaWzVH18Wv4IOMgZcIz3AZ4eU6sxUFC1nFn/kZLT9wJ9c7u55muUhh0fwzHdBJ+0sd1+bkhDuYMVm74nvQNSajgOzil+ySNo0lP10VK9SjxFY9VyChQKKOFnZU70kV58kNSsfFQqFv5OPEAa8Hoyup+sAjiNpbOlZs3j9CpQxhEONzUbU1AtYT0CQwPU823B2Ou3gEkf9ye90yTci/Fmh4W2Va18SAULeN9YFkKZxXjNh6BL+aLfib8OqAAbswsY1G36pwoWUB0iu895uBY8Kxi5FageUbv2Oala+XA7iVD9dESnpOQORsnICn1TTHNCuv2QHfScey1Ixg6Vlt8jG0snP4ql/hnZXwsLIPPectt5yLyXWLQIbnajrlwsAgbM6voBHWdtOEsdCIF3H34SPkdeVXvvB5kZSfIiIcmRJ/ROuUT8hR2FEPhAq4Ov2hDGfsyUIBwe33tWpuvBe865JqQH7yrqKN7pMySwcRViOoBH9xpo6hjrfbJunuZ/+yXxnB0eNKKSg5+qBVs3KOdXPyZynuj98L32FBAA74rnvBsdeb5KqpCKFSQ7bDVXl54IGKzWV8XVe0AwSASuA0WqGNoTXFB9QLovUBMknY3YrVYD1eXBzSbQfu4gBwIJFm2NbaD8ELKcPPBHIKBnMIUMAQpF8TkYtrctuvClit+dQDK4wP9g91shGQJHJ7gyEn1HeK0q4IkSEKqQhIx8LvhfQY4NWfyd+N8Bp8sNj1oGcELHYSFZPgY+F9fteHdRzhNYNl/IV+EHoTTlK90oRWA5jHvIaFWupJ+XihUsRwlZOiS6J58l/Azr5gI/i3qWFu+V3m5Pmrd5SHvSVe29lqTvwGuLK+WNpZUCugFLO214PgIGlrulBwIEczfXyzEgLqScYc+8VJm3uVJl+bNSPcKSBf57ZWDBzOg0kRDxL3NK5c1llQqZkN+KBjBrb7VtP8gYfgICQwY+5m2ph/ODADYCHQ0tmrysyA1rFc8Cx6hbRgqI71DmkIw5wYOQyDgyQJRoZ6vh7FMWj2SH2WDB143Q+VFFBfIHENoRPm8XMKCUIu2PgJvHgkZg2xQ4yHTOH0NmnU7odSP3QaChLXDDNvEaVR7Y17Mr1illh745yC7awPXN+k+EfDGyH/IM/AU1++SEODGiJIFt/71us3K+B8JJplF6shlBhrHFyFK1Wi9IQjZh28ytOxUKoSvUFKiswHp/PWBA/gNyIdiZExwVDOrsamhSnAyUIrwJRIodUV5C7oN3wEUwqXsnOX9QP7thSC8gJL7dXion9OymAgu8Pq4T1cI+eS38jDOwYGbcd/nQgfIosv9UMZgEic7CGMg0I/uKZRzkiiDnhJU17vwcmfwSyRsKsqtWMju29SG7XYUgQDOyvq7UNgQG9ynHC6UMntbgAtUJCH2PZm6gBKws4K9H9vh2KRz9mKS2Zvf1tnVb3oSD/AT843ow4x+hb243p8xf6ZxzFDKh3U7DBndKvmXAwJvZB450kzTs/FQywf7voZSkywtehXmS3howaKlcKmkdJxp6DF90pA8gOvKHTZOqFQ8qMknKBub2v1ydiDwKJKTMg1pBGgIoduZr3Cll836r+Ac6jP8b4OcVUr7wGpQyBGHqREoUjLgP9/VQy26ay78H0eChkgyHQPWqxxAs2AxFg7EIwvRSAYvMnqfjXterd0xrLpOi/Z6zHAN3NGx7B+M4pF1AgNwKOX2moq9mtHkXLW+07Cdv0DW4HxeCUHKGZHQF+7fNO6l3omrXBw4PZQ5Zkx5NHlA/1mquAVnAYIH7yFNEsd3nIjCAz2o7S7X+P+Lq0EU8IKnzv/iQynS6kF30nH6p6oKZ5QCk3zwX3iZuQvmjmAfwdE4KlbB1IyQU81CfBRm+GP7n2OnBu/oNiXLm4G5ms91Hn6bkDDWoIiheBOyiE6TN/UJBqFn24Cac2sISvR+UnlP8Eq38BE48X1fX3hJY8LUEyBsAR1q64XtlIdQO4Jwz2x9Yg9InEt1xn4W5BwwTjUEc1I/r3Bd6U0UK+cbziuDSlW/+vcryEjcyzwHKbB4EqHrr9YX6qKlUgRIXiCR1CywBugClFEZLhDgxrCwGnTlB4OgEV0ai7wgRH/5n7hFt9qcKKeMGiair16AgtwGCKUTG8D2lHGfLvZcLSUfd3fsYb2PYshN8DEp9AqgdN5E7J52n3i1FprhsHmRVF4Kj4i3o+uA1+/WV4j361OD5G6AOc+s5YWOxWuE77D71oqSQJVZ9/9S2uxANsnEJf2rD3Tue3XkHlu0IZprWlzfJde9vA9O9R8b3ypSl2/EPFf+kR6O8YEtVC5zuBjmqf7ZMBeFgqkEaMHJsS3DcnZ/uEBImPgx1goZmTa56d6v0KUgRSjd+CXUCSiLeOalz5KGh9SdRskDpxV1QEqBlgHwgnQQEEfbsKT0jtrSt1gLGQNLExeiHY7loXJEc2DtbKMs49d9b5FDIKp45ukCKQS64J5gVhwHJDWlUBNBRBWbXSxg36+epgGBlZPJnm3eOmWAL4448nvD6J1GH/ylg9XQar0bQgpnrRI3X9OKqdfJD67WxH6Ie4GeH2al9eirugLCNhpXpUEigoz64IFepRBSlpQllFunYs+yBZQKPHTjGVibyFsgOzt1ZLvt3LFLZ9wIwWZP0cN6ucuX0H4J6/GljhsrXIECkykAHOOAzJrZlLflVfO3shepaTu/fU3EXcFxfb9slx/fqKush20jVhkcP3FeGgWQxmlWg/ONWECvqHBUnIZBx0ZD+Ci0Q7Vh9fy3qP8kNQeddN46jj0UmXm/D+UIEOq6fvUg952cnjFPkkud/OVeRVA7Fff50yw5wE+TIA+NHGw8LW34QChgL8b6yXIOWDpWETEyR9s9JB0duCq3HMo4xHQvk9SMPCR2jLzS3ytOReZ8IggxA7fMG36KyQ0YHndtzB0BWC07Z7jRfY6mUL7hM1cgXjXlclUkEgxZ/gEOJH7HpHSVvn+vh6B9kOQyOu3rF/ZLZ4zQoB5wAQjsijNqer36gy51uKcPHd7VsweVKvi+r99lw0i+RiiW3q5r5TGS2W0B82AISxsKxT1k66070oY+VMPwAIPzG+08uAU9aRzhz1oEg/fiKJbcBqj9LZeSze58Jfoi/IfjyZ7WeAXWFymXTEWDZIp0OflM/JGxOGP+OmUei/VSgQ84O2xfPyo6vfwFCzf5QWwB7P6Qzd8ycJLmDb5RMEG0q5YIFV6FU4EDwVFxo2i2P2f7FRIBObpSsbr8wbUOiSAZHOhz4TwQluqs21WuekvqSd8LaB3xAA7WWRLi8QPm4wu8jy1p6nLA57BiuBFYsbLct0Q1K2u39VyXl6Q8VIiDRfnicyiRTJ93gEBKmLsjyUx7RzkigpzTp7RphnyuvEHJ+bUFgY3NVWgE1gcCiWaL04Jn5JlFcnHrwzH5qn/8HpQ1whOvxjHRDINMNST/PyeeHXaO+W5/zWlSwYcwEfOpBkJvA/dD7ijZnYMWVEU5CbTyGsoy++68WycqVlHteVM6c75azg0gLBgLAYk9HPeXGR42HhS1rILPzkWMAz9DNUgY4cIS/B3ZsFe2b98E9UCGeq0GKOtr6f4Sqc38EATQgAJSTi9IIElwSIaP4D+Dsem95Qtxg2G+56yKUofwonkvuFFXn3jqaYK387Tg3ECcxECeynMV7/UOKp0O/IN+Hr0HlAuexIXB0T8R3hA0fA79Xffddic/hIkhfnql4F3xP3iHa/C/R7wmKKyOwarF4b30yapAs3s+MQqGgbMd9wJHigYqH1edK27pBfEAg8ZmlPPhPcRF5YWJ+OvMIhnlvfxoO/1DxvTVDtK8/kNQHX1Ot/d98IEQIpfzhb/jsBZGyJt202xQo2xlEHEBBQyFCgGRyI5igS6IS2eR/6ZF2x7VtgNtci+8ElNQENq7GZqBCUV6UsgdLKvLa9wwPqe0p7l1y4A6sBrEg7YaJHWRgcbr8CxKAlAQ8a3QwQkuVgRnfl8mUUfm2AQMqHFx1UDHUDaoUy38RIP9nj86XlxdWokSgBZlggWqCdeSYYyAqgISGnBI1IgemISjBwEEmVA905ycXnAgPT+4qvVr5DWLpnySMczY1QJGhGbXV4FLCuEZ3zVBqCyx/iGY+lHNsqWxW5I9su6GiST5ZXSONLQE5GIGL0d1ig1/bjSPaGPRgwY9whr9BxrcEkPDxcHLJAfDephIw4HezDRaw/8H5eSobTqSBnv2Odl7uJ4SdRIcTu3SUR5esVFKERAiQLNCIdoilL7bR4Cj4EeCgA2pnDE7YWbzki2Z9Td9vpDwHuP7r67fInJ3BzCTbpSCDQXnCqYP6KEeTiIEuQA+QK8JohChSavHFlRtAyhlEiTDTvxYO+tvI6tP47IaAEDGaOcFRwR8eVDLg+8HyABrn+EjKLLw3vA47ScTRKKOg6sY/gA6gmgXRJBcO7id/hToHS1rI+aDLNlpdTzOeLctNkik5iWUcFw8G9NXGsnv9GntdCBo8rlr5m3ZJcxnkujK6Su5wZJ5bs+o2XYD7oBQw7zV2TeBAIDjjzUa/yIqCIyDSvOnFUrjvkwgaXA6H/SrJ7jkFXAF/U6gCQvHpuLsh6WdnJAB0uVMR4LhcQfft2lrt47uaP/ROKCU8i3c6iBDJQX9BGP8b6jCS5lHC0cqc6EPvW4NzqzXuChL8tb6reFMVAsSPwIEb9zKj02F683bz5solCAb9As7+mWpfI2r9iZigPCFLP7IQBKlcivpzIA88KsAS3gXRJF7A+HlcogEDOvtENKT1Pkd1TofcA5SB4kRAwIDvA3krWHKS3e8C3Pf2SDIe4/JkSqAFzqiFUVGD5vIE+YW4TBWEoPIH12IzNxAzduaEtr0LDpq0NEkA2vTKEbQ7YbR9QBIo6TtKH7a+I+qQylJVckmeAk5mphHK/OQ0s11h29wnThUvHA8zo1OUrB48+2XG1EPEBcoqpGy7BECUKID2kyzPyvEyjofBCvICMAnkPuQ4466YIOL6AS1wSt37HSYeBCnMzP/J6+L/26OS8tQHptwAPMYN4jlFHAjCQMpuuqFw4AappPban+GIoUwjJQ2Iiilm3Ye2EXVBskL/X6eLZiwVYYvCTuK57Pe2wQI2cyNo46WMJx3rd//GTSFz9QBK7NdXqGBBoALvCqDqbhAfusdOCLXhQiLEiXR+SaipmxMEjvxeTZYrQx+PxPGZYUkMVR4oqekFGaSduRHU8ZyD0isQgGrzZgYlJE0OcIKPgd0yWBdYuag1SABkAQIWNH5mGERxjzpI9HIftR1lBrHySRA14XvqLhXoCZx5lRCdsaea/a/oPfWq916X7R1gNp9lAv2R/af1gnzhzlqfcrjpfB/WL1veQInAzDW1UBqwz3Qe2idbOOlGpQKqJGwGUqEn+o1GMnj1wfbwa73faHM66qW4rt7IHNPiddTpKBGlMGtTsN4uEyULzGKTk2E+eBEWbWuQaw/pCNi9ddCgDOe/8cNt6t4+AsRFFRAO0z/bocghOaaZQFzcOKFDmLwjtxstlnE8B7RFqs046Ag+Amf9fdSh60aCvbKmJjjxq+RT8BvcMHKIrZQdHdejQF7HUoDfDU+RPigF8ESm9fXOTebjUSP/HLLkz4E0kc4wEQLXjthHBphIJKbDsUyHw2lmdAY5JWusdSeigrKBmyEFuK2hQQrBON0dcn4FgMGzlp6kjcxwWxkDUb+Fgz8V/bAeflMtFD7wvrFEgCR/ul05DBrZaGtmdKqvRamGbkQhPHPoOFX7jwpXlZ23OlY/xgmOim0IEtw8b7GSRdT7NZvbBQwYVCBvwTMT9g8dSplGlhZsxL3hO2OlsKAfcIuNcoHeJtq8HEgLSoUa5UIjxxGLOkh2rzP4a11qVgWzXZlw1nP6XgSH0jw7Ejmu5srFUoksfGwGJFXHCcopj8yQM2hA9vqyBVdIzdq/qqBF3sgHJa1gVExdp+QNRra1GfX+W+Ak9o7pGLNGrI3PH3JzaJcXHAIdQArZUrNS3SeSCJo5taEDsBCtD2bbWfrggtNuZZRNrF3/En4V+qyagH+CTtxhpvuDjvouxV3ABhqIBVuqlkAlYLQKFnAbyRVpdOjNAgbclzfwain7/jKp3fSaIn1k4CceU84+ykCEZIet5s3qGbyfreu8p+RUINGljg7Q2+rzlPzhGMO/JL3z0e24NliiwfvlRpmMJ60tE57d8zQEn07Tu4hpHk0lwQmINsnJtIEjRXtzhriYsbTIREYbsO8jSO+9/ozQAbIyW2cfUHn3r37b/lDA1YVZaCAH3JN+hczqpPZtTLbQqWMwJBY9eJPDg2gH3AuWgUiHrqpenPdIeg8UDzLQihzR7EBsC4Dtn+YqiO+3FeH9RglBFXzZukE0kNq1s5ZmkEPOarfZbIOH2WhMunmPOwPlBUerc7l69LW9Fv0Yd68B4po+I0huCYc+gPOrEhkgBRTPgd7QZu6Go+u+D8HXqgoEK0DYBwfYxePx7Pm8lAE9kvr0B6a9JEqcqHfmFIEj+6O0pPf8G/WuUaoRH1cGD4z3M6P4SoB4ccEBj8UYcPE/cy8IQ5cokkuzY5zgYyB3AWUwIZeBG4PPHchO3adCoWTfg4SBi2SNQQcGP6icoS36DsG8XyXb5U/2ePNf4T/Z4e4d2H/jDhQha96E9DmSe6p8rCvUDWh0jkd0Cf5w61uYKhsq8QFMwMhDEC8XgV1GPVpm3wlH/b0V1SpYcGT/bDl1RL4QLUEjauGDVTVCzofn5pXJxeODPyzNbssL35eDN8Evk/fJUbuJ1KCSxKmQd5wE2cfHUX7x7Lxy24BBLOO45/MdcteR5pkSnvitjSUqWDAZMHeiCqYvWKbGUwAH+YJ9+skM8Bc8vGRFmHyhamD4w7KGEigrEO5+MUgS6cQyi9/6bzXU8jQw4U+BfKOZkc+AzjNlEW+as1hunrvYrJkYlRZMG2Ajyweo2kBCwB2QKuRYCJs/rkdX5WRbHcftzJS/jGu+ZMgAJd9HCb9IGb+zP58lJ0NN4lwbIkj9HAw+9Efgg5OZRXP4I49hf/sA0RGrOcFR8TT4GLYgcEJiQ5IOvoWgDtU0SA7I+0XkwZMHj7UdkpXCAXkI4uUiYPDh6+07ZTPeOQb/KLW4X4cioGG6Ri2xuB5lHgzYHI134dAuHVSJBQeeyDiUU4XMbg1I4/DtGHOwgOdLzR8tJKJrKHkLtf0TJK1wvKoN9zeVg/l+tjTt/FJtT80bgazyMkVcV+6/AfD0R3l4mHngwBYRabDwCmS9q+DEtjl/YQ1NVtIK9pOU/FGQQnxe8obcgu/46AEPQu59detNemu/ydUqo+irW4fMfqFl/TsVDapXPyYdAY03Gh3n1Lwgg/32L4+RDGTX8wZeZWwSWm6pgdLLuueVQ5/eYaI0Vy7APo+kFR8AwsV1KDP4BiSIJ0p2r7NDx0QuBB31XDjmyGjK8UpJABEV9Xz0tk2tco0s+bCyOgQKCN2vWfUnNZnB+Hls5wnvW3UBgsw+UHr4WhE3Um1CX2cQgwGI5uql+NGL0KENoWJuv0ukdO5UKZ11prp3QeSAV/yNJVK/9X04QuVSMPJ+yzHoOxhI4TMnCSQtiB55UwUsSAIZLWCQiLZ9JESbkGYvAgUtt5wt2g+zgw6yCV+B+9DjxAMmezPTNkJ//dXHUQcP1v2JcGZAXGjGOaDQDGYdYJu7W281WewWP2q2/c/fH+zfqhG2M1gfiFMPPrI7Vabx1gzxXHE3EAsTxQ8FAO31Z4PNvv8SJQ/fAT7/ON7F4O+2yOM9B8MZB2+A/59Pi4AngBlhK+h42LHoTykWlLR9D2ifvoFgxRthzYwrLgR7zJQHjG3Mlsk3YMU5YNZeOdslG8Tdq79CiWgg12PZhiDIQT4HN1QNYjFtzVKUa3yNIBAQLSDRZLCANe4sEVBBHj1wENGZE8SJThA4RgwrbJX9x8qVkdBnBvB8Gp95LEYJUCGix0bO1Ak+hoAfkrLD9xcX1CvcIw+Mq3QhlutgG5KV0lnSFn27N2AQ603b227PuAPdQVYIcQL5dmOdTAAaoDMUDEgb8AM4CfSAwcpdzTK+Rzh83glVgMg7GEtGPVpm3wlH/TOQJu7XPaNdQICIi1NB/sjSic/Rxs5WQM7xCAQGiLKgzQcXBO/riQgYUOLxyAHZ8sg3pQp5YIW8iGkca2tsAwbvgJjvsG4dwR8QzGQj5qrGQ4QA5frIjE/SQ2bVjZlx1cjwxwNHdkRR8FoMm8MWo2WQKSv4+NLVyhGlvCCl9yKtm8k2YxsGL+5CrT/5EeiM56elILtfr+r9P9q8Te7eb4Rt2QSvk9wAv5u9ABD6oXJQFFJBnpvHnPPFLCAQMuUJOM7fgpfgAdTbR7PTkWHXJRE53guhGhGvEcZ/CgIxVsYgjF52EtmGagR/AgcC+SfsbHlllYyFjOJ9ILUkp8UHuI/9cnOUJCKv/YY5i+QLIFGm7mOeReXnlu2SUTjg+NgPA1pfbduphsvSBv5mY1CE3BLkh7hj32GKUNPqeg5B+cvbCJKRD+GJZStlQudOSqJyeGFeW+Yo4uCKH25F1voF+I34IjQ1l9RvelUatr6LvcHPD5t1nvihaWtuJHFdw7YPUF9+nWShfMBoWd1/KbWb/wUG+2cld+C1cHDPkLqC18E18JBsn3kUmrqNzduWIe3IjPOuOaj7BeeAbnZKC0Qs5CNQsGvWr6WpDM4FpCHBzKgfGppndp2MTPlktU6G/dLZ54T2xbrAzL5RrcEH9n+WD9CooOBv3CGsqW9neO7+xq3IFFZjV9v9jWzXXBXMbhYMpzLEMARBXsAzeQ+lGRerpvVYrl71OMj+LhBPayAjsg+up4OskIEcIi/osCPSgQz9ERhrvVSDy6Ch5G1FREgn3s48Gd2A+EDWN0GjEkfFgiuhrnGaFI9/WTI6Hq7KPsq/vxwSlsOlYftHcNT3VdKVVqcgsWbxuOfAu/B7kCaGO3NuoEDyhk/H9R5sdbjaHgsJJBEOmRY8CezEEYh2Zbm0/OFy1JP74PyhprxiF3pu/z6wLt/KtOUL6KkHCdZaJfCs2ia63TN+kvj/9RcQun0OErfzTbtJVA/e2JmGcgrt7ReVU+bq3hegmhZIPf5DkfqRQC6wYZX4n7oTtf8fiQeEc2YWINM7bmFgEyRrkRVVX6goaWAgymjuY08XLybd6HCq8oEfv1ebNJQRuPugZIQSkxGmAjIM0EChwmg+BDa0b6y/I41tjcupj/zbuBq2HCjfBUWOC8HFkCvue8CDAM4C3wPXIosQdGC1bz8Uz5X3imfMIWHHGVcYyPG/8IBoX74T2qzI+ODMkixT++4TQNTBXUNEh4k5RZyYCIEjZQNbbvi1QkOksKwCAQ8GdqIZJSu9k880bZbQZ0aH4lcE/1ebdmzYSC4O4ed50CjD1vBFEnhyYulCAO++F7KWVD3QPvgHys3qFR+DoCRHKVSEHxpa86DcQDDp0oyB0m3q+0A14P938DSx5IkBRc9+E0PHxbWA38KK4BXv3p5sexEGe/LTTfDaSJo3tGO6PPZdKUoHmhV3wbiemfL2j9VKWWAj+AdYtjAe24zmhCqAsT8ux5JRj5bZT9ZRZ0azpMonkwfnRQ4vtM5gwutLq2RbdYupRKQPAYWKBr/0buVLqENEZiUCCEM7p6tgATsqaFWAYDuzgEE849gIGcleBamh8ekLdP42IUt7KqD3VjamQ4EKGOwE0ZxVwIAOqZVTatWvcTvl+Z5Fvb9em08HmDX+aTGwTRv7qQPj/n0gEMxA2cINY0cI1QqYkaej+SOCESRYvG3eD/LcoftLF5QXWBkDDT3A4n8HSALJG2B3f9gH6z+JIOjYytpPR9YKUWA8Z6FB/pEZC5YsxGsZNmUR7Et/LolyVPAdKW9sljP6Famh8X4ykLMSaJIjAe3nOzEFz+uhJcvlXJRfmCEmuM0JhYM3gBphsIBIgrMH9g6papCM8U2gHkh4+cSyVXINylmsjKUmZw3oLfNASPlJyXb5fOsO+WjLNsUjcTQQNiytiTRPZjc4oCOUFF3kvkTXG5GlplOf2c285jcL22tXPwmlgfni7XKMkvJjwIDyg+6UgrhOa6e04Aesvuz7S/GjCRrmQIz4wGlgZppy1oN7XAgo5PSDc2Gw5qqlyMZ/B7TCCHA4jEeWvwMc7DrxVS+HZOMnysFO7zDBcAR+myGLX7l0Wtg2EvBZmwskfwdY7vZDpcKF2v6UVsWIlKx+Ugu4vh9jp3OfCTLK2o0vI1DzPrgFzrLsh/wL/qadKmjg8uaiFGSagvP7myulAU53BpQgsvtfZHk8d+QPvc12fyw706GOkD/sDkW6SESBG88+p/9lIGB8Ut07ojeyerY5cVZ9snShGOUh/qYdCMxsxLNuRiCjO8oYALEGgiOa1QD5EQCqIaPHaaopgzAsx6BMo04CSflH24BBgtr2xrFpYDJnBpLlAKyVNxL0GdvZLvuaVS08HezdaS7U28tW3GsLS0YPXu+SuvY071XTlQwcSQOlAeiTSSeLG/B5waT953kVOJDwj57ehSLzI6Gfi6zxNmaGDFC5wJ4BAABAAElEQVQqDa1BF1/5TjhzKNsZic8nvu/pNNNY5y+QnDMlOwRPAYkhnTT/K4+D/BHkikcF31U/eAEYLHD/8jcqaOIDa7//5UdtAwYKKYFggfuwE8HrcKyqSVdjzC1Q8HXtP8+J7/kHJOWmRy2HTqJDH4gTW278tSVxoudXd6vjdeJEPjejkW1fA4Fjy81n2xI4Go9hZlupV0AylObKAmokhsCYFTmn6iSBz4yrY3f1OQv8MBfVX79U3dj9ISmjoHREgHCxMv5GcoKPIVlpRqvx6dsDJRsQ0KwX1+Ax+qY9ch79P8ceedl7L8ruDvBDetXBxfLaD5XIgAej+efuWyh0Qj9cVasOHYagwkAoHRjt+H3yhJNulx9YrC8mPI8po26T2XfCUae0XTo4C6qQ4bCyytZ9lEE0M5ZN5EChYT3uIe178B5omI/qkq7W+Wfh1nq1XNxa7hDa0boQzziyUoP/vNv34YaMXaqQrPAYQLTNbCfk9GiU0dsdNn9XmTz8wwrheZjxvh7O3tAYWP/NxjK/tExKUaP+1MH7yQAw7utGh5VKAn8AEeFvZs6W79Fuchb+oVmYQrKArf+ehcuUPOIWBFWuABEjnWUzYznFgwZ2/0QCKCybiAbrNzt3tG3JclTwmil1WM8MQKuRi2BNdY2+iuBINjgdNNkKrgM+QzPjO5YHSDFJDjklonDwAXg2Duhc3C4gQIJMBgGaMcYP4fxfYzYAwzZeE/vhRFnHmQgaMHAwY+U6NZHD4N9HHhw6Irf/pchSXwrnfWFoW9ILWovqgt+vVhbADsLPaW6Q15GcMLPHFJDunaC2OfGnCWoKJD5kACCzO+quY1B2YNlCdp/fhE7PDHTtxlfhzF5q6oiTQLB80TWqXMIIjM7oPEldH1ERvprVCCx8rBziUMf6Am6Ry50GOPwglCcM0be2mzOYArZDeCn4NkWG1JsZDIS2VK8STysRJfsgmsHOGFxg6QeRFC6QRurcC57UfJXpT8m2/lFr128i+zLAPcBJNxIxZiCA1MLyAIzDikNBb6/P1Y9tcBV4W2U99e2xzGMigVwGEkiQf3oQKIo0JyDa7DMAUj8S33mQ6Y4JNh85EKyTXV0jcSKQBpRU3B3mB9lbYNtGce1/uGX3iejBR3amsqMsqcgGIgCmLZmt5mHyfAUI9NrAvEk0yClZ84IIUFs6X3zTzhPP5b8XV5deqks/HG/t7ZfEfcwU8SDQYyz/ID+BcHLQtDVLxI1yFNU3+mVJhniAhkAG3YXABctQ/CClI/LAittB+/xNlC5MgvrAta0jC35Hc+zMwvuI5EDQgEEfFzLRZuYEcWIiBI4M7KTc9KfQkEii6L5pdGg9kYVEPjMKZYF7qH39nvihyKAIPi1OTtlM3wt/DH62IWVpZ8nyMTghzWg3PpbD6ESZTnyu7M71/71vb8Dg//sJ/ETPzxr9Sw31+JQcpDQiJRf5Q4QcBmaZRScvJ56MulVm3wlHndc0CMERoh0mQI6xODv8Y0PHiRwGHTI9ttwM+/XIkk8Q3GD5wrwt9XBGwfXSK1uhNl5eUC6framV4UAcsMzBymIZR9ccMNLbSEQOA8v+V5DqO6l3tZILNJ5rM8joXoATRSfQiC5wAoLPzPUjP6xU2V2ekzXxvxnY15ZI0Dg2s+UN1XXIOqeFBQuM7VgSMRicBD+g5GByL+uAAY9JRaT+rjEjgEpYpZAP28GFMG3MMGFwwMbHM54uqeVK8AKkAilBGcNkzAmOil4gwfwWvAEnQDGDyha9gL7gOp1tBjqWA23Azz8DC1aWrMIB35fNCEj8CmgGKzsQAYBX1mxUfAtWgYvIYzn+YxEsYznJO6kl8iUQDJx2t6UhC0+VhdrN/5bsHqe0O13dpn/AA8A7kLOP2tdUsVhB5NOKxrVrm8wGjVl5BCKyeuIHdQySg2bnCtb0B5SKgNn+tMJ9JSV3mDTtmtlOQSILnAI0OqW4YJRfRM+YqwNM/ngz+6Ako0kadn4qmUABeCgTCHWD5op5ofO2VC4FN8REk6PbbzILnsQbLCA3Q+POmUHFBgSJSJiYVjweaAfUQceQ3W8/Kvg/ICg0khQa2zTs+AL3crFxU0zLeYOuNm0XDwmkv2G7acDAOYg2UT4IYDNr3dE8wG16EYaNyoE6GLKUKBkQONou6L4bnVhDU8tFP5xQ/4sPt9/PQBUyiwL5QMnMEU+E6oDxAJ6TWf249OCNHWDZlYdgQNkO5bjyfIGFcI4zkFEGkRuNMoaBVUvEdXj0DK86IIk/figS+F/CPYESgfEfpBtjCRDpgJIFEG+I95QLkjiL/aF01vhuuEB6SKMjGgAPgQswdwYLaIo4j/sQRDELGCj+AyofGMov1IGGP+5hIN9kwKBsl2XAgM2TJU5UfThA4GgYekKLiX5mPL++XJSiyBPTJHDEieKZjFI5EDDqppxr8me8+qQICEM9l96J+5mr7243D0B6UqFVIvg44uFjoAQmzXvFPWHSjClAc9B0aUbPSecHywqwTY0TyiiWBgQr1VsYmNPmfoHP3GJR5ROD25fnWPbxM9yR3C/Tn+EF7x1y4neAGe4BkFmMx+j0JyojGE9G3Sqzz7E64aifNbpAbnh/m1z17hal8tAjL1WpAmyvaZEv1tUCfaDJTZChtLNz9y0QEjB+gsBANtAGVwGBweBDNdQSPlpdK4dCVvFMnMfOYhnHn080r7PT+yW534KvIIn3zXyQ6eWqWnNmgy/H+go4grQHxo/Sm6u5ExB8lg8QCt4NpQGUFCQCIFmjM18DeDqdSys0QAVq6TuBHyGagY5K9UEOgC4ZGQppcOW38+XecaOUIx8mxWXRmR/v+7b6BoV6gDJ8u1bs16o0gs/gwR+Wy8GdO8ox3bvISPBD2GWj23XeusEJjgoSTV4/e5GcDZ6GZyeMU2SB5LW4DgSClLD8dMsONT4GE6wsWYUDPs8M/MiubA6icszOU9EYzNpnRinT0I8lSedn4F74YtsOVXbBUhKSf1LWM5qpOtd6MKKTxT6goy/w1mCZ8O2m8vli5YSx75TsPpIKMj4SJjajPCEV8HNmi1ki0FQ2R5qR+U/veBjg9UORef9Cqn6EHBhY8s0yxP6WKiUjGNDwo4U/XmCowkWAoUWpH9jJCKaAVFGpJDSXgr8gMSdMcQuQfM82koaSBxvlglSw+XNKxki8l4KpaunvgSJYr2QlqS5Rt/EVPJM6EPWtAxfCNtzXQ2M6TTOCC427voSzv1nSiw+G1OLxUg9ug0yQVdpfKx+DpsotGnd8rs7lQkkBXAgJQDWiCXKLTaVzpGDEPQgatH1myN/gq98U09iMjTJBBKlbc8UCcBW8rq/GPLd6V+MhgVQBGoszOgHRdg0aqaT7fI/dIh6qJHTtHbdSgrZ8oVD6LLB+BbLhU/FIEJBHVtbo5PIS3MeeId7jf216NUQ3mELp+f6nAX7fGfJsUEmwkmU065SZYXfvQaLVVIEUp1ICgPgHgw+1Zs3VNnIC+P/+mPgevA7M950ksB2OLuQV6UBRqYGa9OR7cI+zRjronVNqTvvqXXAZrBVKM3pvfATEklB76IKyFYtaff1Yzv0fgORzwHDFDaEI7Fp3Eu3gHv4SYP13ivbhqxL45Tm26BDlsJdsUISF7EIRFoJoMBbCQl63ZOWJkmBEsIaSkYK6dNeI/VtHg1WQZdJcQGaYmeoDTmsA74lYEGeSJ4HmKrL/jaf378orwBjG66th82jfI2ys2tjIfBo7JBeALhNo3B5t2ZULYkmM08wS/czwnfZec5/iUAgRYvLzgWuhcgX5BhQaDMEuRXCKz7edaeDLIIcE1UfchxyPdyQYGLI7JnJfQtKMjbXif/j6yK4s191jJojnkmli/BxYNv4Z79gbMPgZP7zdPXTW2X+KjPiG8haw+fuFUPnuuV6ZNCDHNguuj8sJdYJYMurRMvtOOOq9C9Lkj8d2UbwOelmGfp3FQBZce0gxSBGz9E2mcyIHpk3qrJQVMlEyoCM0ctOJ3ugK+cpU0+OMG2MZx+H92qD5xmP1ZTrPhMI/uWy1zAdpHG0pMvA0ZmkvHzoA7PiFal3/4wQEn/8ESdhH2UE7eUL9nLHMh4G4rskfJGk8HwoPkfbZlu2yBgR5J/a2RxdEHkcOg46ZaXLfwuVy5XfzAX1HJimKfQhiwEeXrFTBC6umdooPlJQc37FY1ex/inF3zEhDfX0XxRsQjfhRPx8DJ05wVPD53zxqiPxj7UahU025xwsH91OlBbyfaUBC8FnubhtamCtvrN+ipBgjS2QakF16cfU6dZ/sVBe2I4DzGcoPeE8pmUnbF9d30eDOCM50ULwZDM7YmQ918azZ9oO0z86snDD9mMKRD0AK8Wkhoz7r/0PG0oNWmUZ+TpqQISd5Xt7gW0NN9IVkZQRTIb+Y3ulwqVh8E/q/GdKKkIxDyUE8xox5DcgArdASDHi0AEGQ3eusqN0yAONvKMHvfDyb1uCH8SAPYfUZXYybQsu8V/lD71TEgC5X0BEnH0GQ0f8N1Y4kgakgDLQzBoOqVvxRkRvq7Ygs8KN0o3rF/SAb/BBEkbej5ME6GFu36Z9K2YJKGKz1p2QkjWUOdVv+DULL56QKwaL8wTfqpwC3wocJOfvGgAE5DdJR6uGkxUICyeCWXXmEExBtZu6YGQ6A9d5352+DlwhyO0Ew0Wh2zr5qx+w+yNPszMp54jGUd+RkZyzDoPSgntk2axuvHnxkH9R491x0u3KiGABhiYXn9EtVM8r6BSAb6bnwNnEPHBF5aNi6Bi4EH7LAYiSow2cg8P1X0vLdx+I+5ULxHnNa2DHGFTr5UrpV3MehVIRs9yZGB0+b/RmCNSUK1WHSBEGS5AkL3WMOFm3muygbADScCgfIRntQGhIA8sP/2tNCIkPXkDG2mWw3pTvnzRTtqFNA5LhP2FA1lJpo//6ruDohkGJRjmA8QAWnGASijJ+JuaO8hzyEJJ4BoB6sZEDD3sWGGvHd9huTM9lvspMRVUcm+JlhWYXrjr/imbyN5/EVglqbgwGN9CxxD9tPZOBwFdBSnBv2QwwGCBEk1D55XU2uHugbQR3PgZPwLOz/Z+tdK4QJgmNB2TcEuREQozHIpJcouaCuIVvW6ocgup8u7hPObVuPXNIDhQg0KZlGoJb+F2xvwOB/4SkncI0LS+rl4W92SX1LMHuVgqA8FAGV/efHKlWusH9PewfZCXWCWDLq0TL7TjjqvPA+hWny8PFdpbTOLyUgN6QT2QWSk5yiSTsG71zwr1nJQSzBAr0PJ8ZBgj8y4JM8jvX6LKvonp0hRSDhU9Ft/WQOzlnicAmCEU4a5QYnov78VcDSmaEf3wlyc7gGyiwuqahUTPqDgKI4slvnuE87oUsn1dc0ECESim9nGyDnSBZ+Ztz379gBZRBg3A2WQYYdNjAPP3YtjEGB28D4TyQGIfKfAI1BNQNODIwcjeABa+3tShaC/APOcFQcATJATrqdBoWHSbiPG1G20icnG2oU0QNcPDYZScTz9+kvl0K287wvZ6vgCUsjiHYhiuMjBABYxnE3yC7t7BbIdVI1gyoclMVkECYWxImxz+pVjyFYsFmhAryZvZSTlwlHjcoHzUAWaM1lUrTfc8ZDTJdZG09uhJy+FyrJOl8dMmuAm3tZmw7yPt3yBiGDGMFazn1OyAg2Vy5SjjDr+svmna9OSaI/cgAYLbvXr035CdgmJbs/YPYHKrRE086ZkCAcC8ehEFmkSsDjfwiSIcJRTyscZ+yy3XItAie1a/+K+9jQbp++gc53Tr8L9NV2czrm+UNuDm33QgmgAwj/WpDZB/YcPAgDTe9l6AAs1CFDTyWEjO4nSWaX44AUuEPt9qQVKp6GmnXPIKBwH6QsHzceFrbcsO0dSetwSFhAgA1Y5pDTZyqusRkBgnexpS1gEOoA40wr2h/39GAM2dwBC7WNWGAQwy6QEdE8ptVYSCALx1jfC/0kSUO0+fMDwcmknP3BqOkeHH2s+pjN5n7IpWnI3qfc/6rZbrWt5fITQFR3tLC238yc0oP3MNtKrgSUHxhJID0g/fOccbltNp/jYtkCs/8ucBm5gdpgkEP7+5/UkN0IEjA4QxlKrc8gcVtkgFVWnggHG3UKFVRAr3ZOthOEhZ4plyp0BuUPBcEkBlQU+gKQd0orug88WjwIgNiZgtL/CD4GBKVUeQe4CkjKp5MT8ljvDY/adaECWyQ9DGzA946Npb6IoIaFkSPB9zD6WLvMokVwc1gfdG7BFRFmlIcE2sI1YIRCW7gKO6Bcow7a6MtFm/OZCqC497VGXbmT/MywJMkDEkhONIUuQCAn3t+WVKpw3/qEQsD4Z30sgW8/Vu+q9o8nxT36IHEBVaIkExHcsLJEpBldaeni/ZX1/xyrc+3p2/cGDPb0J5zA9VU0+OTRb3dJDjLilx1QICNQV5+Z6lFO5fIdjfLSwgpk2nfJ/YDld4MEo5Ulq07AfmPJqEfL7OvjS8ZRn7elTgqhYtCvKE06oIyAUyKWTIkGz5fIOLbAWdrV2CjgMW73hU3yODrd0Wx3SQBGO28s+29CJpz16KxlX14RLKnQjyOr/4VAHlAy0spGojSCJRh0RCONZROPHThGBST6AQFgZT+UVapdd40ZHlVq0qoPfTvRHMdBFYCTnh0nf8BDIIqkIgCz4qzB19UQ9OP0eSIcFfqx0ebM5Ntl843HOyGJ2C83W6FhqIShK2ro5+gABMbtY4YqIkN9m9l8AmQVRxYVJFziwT5bqpdJKrgEikZDugz69A1b31H69Mz0sjygYsFV0rj9U0npb/3DlJlmwt2ZmXWDWI/69rrGfeS4zYIFbOOIjCCdMDrSgPLbmRvlEnZWMPJ+oCX+KvWbXwNnwIJQUyoq0PHOG3gNYhDW35MtID2sQSDGBWJBQv496R0xrvbtU3MHh/qOXIi8p/p+njcVpR2xWkPJf5ClP0ry97k+7BAX7hPVFVjCUbvuWfWsjYEdvTHfCV/dRhBUnq5vajenlGHdhhdRgrAFDn53tT8bgQRPVi8gEz5VQRYGc9KLD8FYjlTBFrv71+4EDm6IhQRSvwar0/oXfoP6ZSh8AHafKEQ7WcfFamyxbNcA95e6YHmAgu2DO0Bb+2P7Q5mZJ9S6rgr72v8P0Q9wUg9eKRJkhf8/YllELMbAhZSCe+KPkEXs3FP8kGDUzY1sruvmx6TlssmqNtsqYMD2btRrax/9UwL7HaZKMvQ+OCeXAGv+hbD6/HC0orGdE4SFrIFPuf7hYPAiA4SlregTF6Qivfe8CJnLvsZTmi4TMu+94y8o93hcAijtoBHdQnPhHnnOugoIkzFq3eqP/yXKWq4Mqkb0HRIWzLE6JnK7f8ZDKljg4vEjx5tyLkQeo5zbE84JbVaoDcgOuk+92FQ2UYPcpu/Ba0Vqg79ZQgfuxoVECUv1IRFZ42XGHxM/g9p3H+H9nAlt8i/FD14PzznXWJIsKm6BJKUZ9XH8r8/b/3f+X78je69fFm9tlLrmgPzhmE7SNbctIJAOlr7R3TKlT1GqXPV2iXy1vlbOGGX+o9IJdQL9UTiRUWdfK3c1ypzN9Ur6cEz3TDmiX7Z8vLpGjkKJRbTI54z5FcL69KdP7qEPK+65EyUaiYyDTuZzIDL84JiJ0Ku3/kFjd0G8P7tDAtDunLHuY1ad0nlTUEZAyPm2hgYpBEyyO7gSClplD+36Yhu7dizTuH6UtdPCvmsAh+TnYziQAE5a58wM2b9DEdA9qI9ualLKEqzB59QHxIQ3jR4qdKqNlghHBdUCyC8Rr716xEGWhzghicjOKVf5zKHj1LVvrqtTyJ6ueLYMElnxVhgHRenHZCxIAlcmab2DP8roxHmAMmipXiGCgAEdyCxA76uW3yvZyIRbOfuNZd+Cl+BeDMWl5P4SGZMTMoIkJOSUrAXREhcrtQU/nGDW4rOMwgsH2OoeGM/ZVPE9VgMIwvwJgZP+xl0xLztxT5WzD+nBrJ5nWJ6XSAkGDDSoQ5gFDPhOuKBqEWgpt+xDa65Q+1yejFAbIhhIgMnJBzLKxu2fqeBBxSIgTKDcwOBSRqcjhWUksdzTUMcOLSRDAqnX06c+/G+HRpNcN7oOu5Jy00tfUO9upcMe2LAa7Pp3hp3U9/uLwtbDVvA/0m1Rt852uh582DFxriioOpz9aEa+BSvZvEDJekVUyGCBmbHEwMWSBnIq2JgbfA+UdWy5BRKA+x4CSUcELKBOwGCEKg2A3KP3uocse1AIhCQJC42dG9EW+vZYggWhtlB5cF/3YLAcAJwXAhi7qxOuCaR90X4fsg/W2zNr773qHr3LuOfaioXiQiDGqHoQbyeKswHvt8eCj4NBOKIoWC4QVtpgcqJ4PzMmXTi+yd0PwZQiKIUw2PX2i0pCk6gYGTvB9Fx8dk5IM5p2/j+2cW/A4H/sgcdyudtrWxTLvjFYYDwuHzX3A5BpJyzfypxSJ9D754c+0cw+61OfnlMGPoZgtoB99gRfQHmDX/46txyBjzq5AgSEnVFaYGbMlNaCz6FbbmwQbLM+uC3ZEg2nxmE1Prvtu0sC0O6c8e7LQK39QJQfcPpv2xCck2UdhOv3Blw/WSOy4FMVGGirux+DuvsLgJYYB56DuTtLoWaxXq4Cv8Kzh+4PuH2bE5IIRwVJAxMJCNldZyKSiA8tXqHKe64C8SSNfAn5aSmKQ4HXFW8pgXF8P6JE5RsgNUqgvDC+YxFkRbvIe5tK5HgoQdiZcgZT8lGTCkhnq3lBRqgg763rhL1TKtAPrgOrzKvWHMzouE1k6PR+o82dkBG0yspHO7fVfjqyvB+c4jJk7Qm/92YnHtBx4p6q55uah9KSBQjkTDa9BH/TDrXdTRSEhaWAwLF207+ADgAsPULKUPM1CLkn3NhupXbAYyihyMmH8hcqPxB50ADSRb4z5J1QwQMbmUmLoSW8uRHlNi0VgGbXrFJ9pOQOAinnCChQ7Gfbp5JVBFzd1SXOd8Ki1wCyof4P/yWyaQ3g53gWVAbo0Rd697+IiQgtER12z/gjAPsH6z7rnzetFm3Wp+KeconJCIHcA7zfBSSFu+9gk/3hm5JxwhQD/ZPTwjs0WbOrT1dKAbiHrLVXDrHJ8SpYgLp/O3PDwU6Z9rT4nr1XES7KvC9CzV09gOo7425bh9QJwkL9hMkQJ7KPMDQMkBuuGJ6jfm7OSTxIsknpF/35G48zLqs+wLvgGt5G2GjcH+uyKhMBsadtkINjJQ+FjSXymbHpLuldLKXRgCjQwLFBiVTy3Sj+AJQORCP5TFaaMenB7yEd7A0Y7CEP0snLoGTiv5dUSVm9TyivGGl0XDdVtajMfOQ+47oT6gR6f8mgAz5YWaOCBUcNyJbDgSp4+JtS1W1+ukfOHpUvr/5QqQIKd4KQ0MxITnje2CL5y5xSeXNZpVJJyEd5QryWbImGU+OId9x7WnsnJCIj7wlJAsmlcOf3S+UWlEiwxj8FtbfxWD3+gX9esl0FCnQSSmbSiZ44CqUVRtK/w8ElQAefqgXzdpbJLyJIHePlqCA/ASenLFFJxI21tdKC7xfd7l24THE3TOmf+NgYMHwEZJTvb9qqd6vQGWVAbDy6ZJW63y9MHI9AjzUnizerD8oJvgbU/mRkmHPhHAfXNagjuMGG31y9FMAB1Fpjn5VlAI3QuPNrqQVZII8hD0C8soZOyAg6kZW3usZ4tqfkDcMP7SaoSywISSDGczzbOnFP2U9q/gg8m89R8nEqShnCf/Sz1KBm7V+AKulhii7g8bTcfpdI6dypUjrrTDUukkmi8hkqDSVSv/V9CTSXC8s4YjFvVg/Fe0DuAxI4Nm7/HAGEz6UenA8eKFt0PGj3Zu2JuqiGBGj9ZjjpNJBy0ppKv1HzDEhjkieCwRYzUzXMgHH7X3hAfO+/CpKyo22h6WZ96NuYdfU9dnsQ8k+FA0DNpWSDgoxrX72vWNntMqXJ6LB7DvulGoaGmnABwtB7zBR9WAnNk3XCWI/drl6dI0G9voBcjkoJVGsgq7yVUdmA5n/7JfGcfXVYs0Bzk/hZZrB1gz3pW+tRdMRUOQC/s0FuGECAhQoLZtn+sBO1rjhBWOgEcWKyaBglmdl3HwksmiUy+SyzS426TfXRB989ILNMxtxQiND++ZT4P31DPJNObteVf95MJT3pPv7Mdvv0Dcl8ZvQ+nJgzEKQtnRPkLgCpp5IyhLoDSU7dhxwrLKFJ1BiwcgGtsNdivwPm3/axH7+35R54B8YCrj+qS7r84Ysd4DAoVmR/+mVWQy3hmXmA5wLaTgfczpxQJ0gWHcDxfby6Wg6BZOFF+xer4eqgfNa1nzgsX1o0kX8gaFCDa8tBEMHMloK7gWoGLy+sVFOG1wUIut5T2xHPnmKeUXGqRCOWcRDd8fXFA9oG9RNdIlHg9vpGMNYXQLbQ/L5HDt0JLgUnJCIZdJg6MyjXpI+R/BR1+Ad3KeQpEWOSHPBDRL4hp/XtKVbO71w4/nRgWdpAgsOjkQEfYcPeT1RAHkgWi2zKLmLlqNCvIdZ5RWOTfIAShl/37216SKKSiERn0LFniQRLEep9ftkA1MZ323eZnkffeCB4HazsrY0lqk/KJxJVMH3BMtW0ANBbIjZmQC7yd7MWyBtHAVZrYdl9p4Kn4ErZ9e1pUjz+ZcnoeLiCqJd/fzkyrsPBov8RYOP7qmCCRReQ/VvHagRka1eDbPACNGNmEvJbfFkMRmg8CQfNzAkZwUSy8ipDDqc5XksrGo+a/PGmh6XhfmWAYLB27VNg279JIQ3IGRCPOXFPeb7cgVdJKbLpJIGkrKXWUg2ligXShOfUUhWsWy/Y9zHbobGsonjcc1K57PcgxXwjrK0bRIx5w6cLeQziNZcLEHFvGpQsMiCcCUcPKJbdbbUbZqhgQRbew0yUS3jSOqn31Ne4SyEeatfPkPK0a0EC+SfLoagsYG6+aK/9WU2SnqkkCCMPSH3szchNoXXWwvueng4t9nTx/PYmEJyhrhs/9JUiAZx4JTH4yE2Scu9LcFS7ho4zLiSiw248nsvu/sPUFLk9nnUnnDB3t97Cycr8sz8V//P3i3viCVZNhCz27qNPU/wDhKUrUji09s94UPEWEFXBum83ERYxmvoOQ0lC+DdZ9IOdICxMljjRKTSM59dXie8PV4jvo3+JB84sJQbjNXIl+O6+RHyA2nsmnGApe2jXryIaHXmA+F+GTCYy8q4h+4ILAYS0IFSUVT8EyRBJakjFAgtz4jNj0XVcm7UFX4ufiBoECt2jQHI4ASSHw/ZX3wFWHSmZSSpMxGkufFcp9E2cx/0vNd8bMPhfetoW11oJaP7xM9ZBDq4ts0cHtw4KCde9vw313YArQzqwAZ71jlqfYJd0BOnf3M0NSmLRolslvZisjGCy6ABFMljlk18Mtq4tH4HgyD9+EIWosAoYtOCiCzNwHzAlYk6VaMQyDko2mtnt83GRcRhVFHRzwlHX+9LnG2rqlPNGiUXW6R/YuRgKA8XC8gcr4w+TRKDzGTiHbpESkf3zssH+30kOBTmenQKBfjznDDr0sslIG9sal+2UBXJTvcqhPW9QHxALRpe5Y+nF63ByV1RWQVEhel2rcRxcJiFkvLaotELeBYz/62278D0QsAwYsN9EJBGP69lFyUpSGUI3Xlu06/t08uF683bzdzZskcO6dZSrh++j9sFNV3MGDE8HcqEFcmgvrl4vFVBcKLBQfkiHCkD+sDukdsPfFDrADWb+nP6XQVrwSQXXJrSe8nZ2FgjA3cNE6LqdualYYGF8/5OVEUwkKx/QmuBA/tNiVNab3SnZlgEDIgt8DVvABbFcSuecG0RoeHPQWbjbYafW4MQ95ehZDlC837NStepRaS6boy6opTJIeubJ7ImAwjXCdyCasTSlaNwM0Zp2oawAP1oDzfiyyFRoEg/KHmI1liQ07vxMGnZ8jkDTGnVYCkoRcgZcidKEw2LtJuF2Tbu+kczuv5JcSFQajYoUSrECj4hBkUKQgFpyK0B3naSHwilB05bOU9J/3rueA3niwFAvRDCwzt517QPScuOZyELOFZ2RPdSodSEhHfbITvS+AIsO7ETABtBxMwlQkudxMrP/hhPmGT9J/P/6Cxz/z0H2d77ZMNQ2OuokkvO/8bzKNnMj5QeJkiJCwXPy+e0CmVadEX2hOAu2o8Rh1AHihuwdFQvcQGdEBkMj+3CCsDBZ4kSn0DDaR68hsJUBNY3H1CQgYKTEY6SlPoH73Gq+f0L28av39NXgHOUC2uvgS8GkgmwpQXSPsZGxD+N2fdlz9X0ib6CPD4EWIXxft1QoKhwG9QIEJlRJiL49Yu7kZyai67hWycPhPuMKIJSOjN2Zp8zkrefEdR42tivjibuzPfQA61/me+gF772s9neAyOneBalS36y132nYwixwniEDH5EYM7QMX0xGnSBZdIAXTkFOmluWbm+UI/rzx2h7Ixkhzaz8Qm999cGJ/+jR+3CiRCOWcQztZO5szt9Vrg8l7rkTjnrkSVlHfufYYUr6cA6y65QSpCM+Coz2ByF4wABCcYTjvDu4FGbvKIMk4goMz6Uc9shxmq0z6PDwAfua7ULgTQsj49uMDDlLBKJZeWOLfLh5GyQU82MeB/ukM/3WhpJo3bfbH2vAgJKSlHh8G5l6kkrSKJFJiUU7S0QSkYodL0zYX9YCfVIFFMdfl6+REbgflMtMxPgsNkE29FQQYlrZmA4FKmBQgnZWAQMem4HadE66sdY8o8sx0lK3Hg4hanajqArQ4YzF6dT7t5onKyOYSFaeJRSFY5+yGpLldg8y67aWpFqDU/eUYyRZI1UwyPHgq4d2ODgpPFAz8IA/IJrjo19jY9k8qVn9JykYfg8CJcFa5Jp1z0nFwmtAjHmGIoi0QlFQPUEFCVB+4KtdrbpkACIHMpzpnY4Qb0YX/TS7dc4gTEvtWsnua+1wphUdKLXrn1fBDI7RzLwXIzOYpCmCPqW0YH4OZgRJghZA5lRaJdwiT5mQDntkJ1hn5lh7/RnUZTSY7A1usnM6/ltOmCslTWTrRssxcgfh754jTxH34SchU7I9qPKAUg9yGpB1PxYj+pMlJyrQ0HoAZfCkskz8Lz6EWvNPxHvhLapEIVp/ZiUMsRAWOkWc6AQahtfo6tRNhFOslg9CRRvESKzdRLZjEMQLKcnAyRcgwLVFAsi4uzqgVKQrCGlbVSQijzGuO/WZMfaZyLIbQUFOcZkXcqEHHdPukAB4SEiO6IaqhwBdGGmuHv0jN+1dj7gDewMGETfkf3GV8ol/P72XLAPsPhl7aUG5fLamRi4dj0xxzyy5+cOtstWGGFE/14un9dIXw+ZOoQMGd0iTWZvr5PjSHOlfHP7PcEtVs7yyqFK65HgtyxH0QfEf5NZqSKq1+EUPrRCUAZ8EaAy/LEFQglwHVuZEiQb7jjaOt36sklsOa/9D/R+TDhRvhNa61Vgjt+8ORz0d0NKDO3dUE69pZVV1KHjw2NJVwmkQsuhTkXEfCwQCrQ7O690LloIvoFNcTnXk9RjXK5uRBYQVp7eP5BvbRVuubWkRZsYr4ehS6pFGZY3zv5yrFBtu3XdYO0UDY5/JjoPBlv06FipCvzSyVSdpqyqrVZDgi607pAkqDbSxHQrlWKAADuzYISpHQ6KSiFSsGNtaZjETQaQxOOcRUYITVpfK0og8EJItKqvA+9LVtNnOhia1vRtUF8yMdfbNVcsQGAAKq26DIj9kHTmntOKDHAkCmJ3XbBudSqozcBzMzruRkacyQUruQCHEPxZLJCvPWvW0/JGxdB9zG6fUGnhCcknUl7wtvpo14m/epSD03qzektHtBNsykcjB8v6yZCMQaMGvfxfg/yVq0tul2UhR1iHjXr3iQRAb8rsXKfhWS8kdpggh6zag5l88CABcqO9qVUb4OIgkaCUWJAlkTr8LESSYZEmgGepgNywwoMEAWBPIDtM7mJfpMKDgckOrHFweu9VAbii11ULH0CojGqiusMzqc2yJ6LBHXpO2cY1orz4ukp0nCu5fCJZ2kzI6O7K8/4YT5p/5DpzDjeLaPxxxpQj1Ii9KXy8CKoKTyfXoTczmGmrkGSxgxpr15L6n7go2YxAHkn6UVfQ9/wAY/x8NHa7q51cGkTuhjTEseJERNzP1TmTlSWDjKpFDjlNwe4HyhQt1/Lop1QCsuPDMLM0BNIz3wlstu7fa4T3qFBFOu8FIEqijYVzqc1SlAmv4yarMDg3jxGcmkUsK4LPecvt5cR/KEhvvMacFrwulT2bPgvwhDBh4zroSfCqJJSDiHtgedkDyvyr3sBuy93La7gBr+ssafJB0g85w62Y6xy1wgrbV+CQn1SUH9Gqr0ypC2ULvAkBzWyHxVBVIjZP4re3syPg4hA6YCif+h3dL5MYPt8uA4lSpafKr4MhNH2yV1WVBZ/HOSe0dbONYdkA5YvrnO1TAwLg9ctkuYECkRbIlGrGOwyxgkAdoW7xEfJHXt7vWmcFjdrl/bg5UAArlEygEvLdxq6yE07qsvCoUMGDGmEgJlhEYjc7tcyvWyZkDetvW/RuP0ZePAl/AdztK5Vkcz8BIX9TRx8qpoPfRBJbk3341V3bB+TzNkM32BzQV2CAK4FrUyc8AuZ5VWUKi4zgLPAJEMHwBwkSiJRaVVcpByMgfhqAKHfxYpAf162gEZ8DMbTsQKNgqvKe0biBeTIHjTaSBsUxFP8Zunqwk4t1j48wumAxmWEEeyhx2yUm9q1UAytiE6I8XwJfA4IYZuqAZznnlsrvED+I7M2MpQkbXX0pu/0vwgzs8a+FvqQLnwRQ4VT0Ad38G2eOvpFLJKpr11LYtu/dZYMo/q20DlgJaC1j2nxM6nVgL26evqGw0IOvRJBOdzMrr505m7oeDXgcyP2bV/Y074BjmwGHtK5lw9q2y18bzNZbOlsql0+BU1qrNLhD0NWvfq2WWkOQPuRnyhBOMh7Rb9jeVSsXiG4JSme32tm3oMum7tpWIpbqNr6DcZISSiTS+C0QacKpYipKWTa9Idp/f4F0JBifrNvwd0P7Xcc2ZCr2ikAQIdOjGAIaZWSlxmLVNZFt2/8ulcvF1QM3koTThNASm2gLtJP+sASFi3tDbETRog1z7nrhd/G+h9vrca6GJfqi0/P5iCYCIL5ql/jkCkm04wD1gGBAfIOJ783mVMTXsUov+WZ8oZ9F95K8id4XWndBh11pZ2b03PKI4AEKdx7HghBPmX/QdsvcPtz8r/s+oMgk4iZKJICKcZ92oMNFy2S/0Ves54OpUteA4PVMujupUaZ+/CZ4DBLV+c21rn8EgGQMP3slQ+vC1qKABa+ddCLTQAiCwZKAhbrMIGPwfe9cB3kaVda9Gcq+xnd6L03sjgZCEGgKh99770pa2sNQFdll6h5+y9A4LS00IgUAoIYVUIL13J+7d0ug/58kjS/LMSLLlxAm+3zfR1DdvniRH97xzz2E7sRBOjAUbJupnsriAkyfe7fjeVIDNR/cFFfibD9CMopJeWFlaAShGk41lw8TiO2P0JapX/M6gJWi04UApSEs0/Qi0AAZNP8Z75R3eXVQo/11aKG7z36bqmU4ZlAnAoO7xjuqbIVyM+AusChsbsWAH0I7x/snt5eV5+bJwq49F8Ueeb1axA5gFF47KlkHt7P/gvIJrt4JdMKhdonRKd8mXK0rl6L5pUoEBWgxmQSGAlX9PNp/BDB2DxpRoRNKPD8+K/axPBf6z+g8S6miDM960AbQK/ue4urhUFqA2nrPAi/ILYU+IGQL89uidka4EEek+EC6KMbs/f2e+HNk1etruWmgpMGjh95cffIkGk3rgVUFxcvcultR21vQTLGCCOxalFEYQePjr4L5yUo/OcsHMX9SM/Tm9zd+fhvaDM/LHwSWBy46KSpkJRsC3W3bIbXMXA9SLk/EQBCR4QAFFOm2YBen4H6/dJF8B2KBwIzUfJmNGflLndqpE4smly+VHWBI2JAgGRWqJyBIEih7eMKQfmCet5aof5/nLIOzu/fGk8ZaHL++fK79+ny9XQoyyLxgrFKzkZ+0v2F5WC4r89/Bx9a4v3fA2RPAuxmfRCVBgCpT894e9ne+9pfI968zLNn+kavvd5eska8iDQQmUmqmFEJ6z1mLPgXp+CuOFCy0h+AcTGQ47512K2fMVEAbsqfriSumhkjlPVb5iGlTnz5UqJHL5C6+TVoMf8NPh7e6luyulfOunvln5yi2oR39CqnbNxqw2LBKTo6DV2t0kzDEKCxYsvlW8EBnE4EGMv5XoZeukpnChVGz9XDIHP2jroMBEv3DpXaLBnSK9360Snz1SaUxQpLEaGgQlq55SIoQ5KS+rWX6r7hQtf1iBBQk5+4OtAZ9vG7cLszboKuCp2IqygzPrAUfG+Untj4LbwXT1fhEQCQyvu1wJZ1I8M5KwAy4iuT7cOZVbP4P2QiLG7zmIe/5HNJRDaFqCYkSo9woNFC9/BMuj/qbSSqAB03uQXyGf9Gcz6q//gghWWG5AyzT909el5vdfUSO/P3QRcsRbXCiCmWp90U/igLK8BhcGq+Dfn0b7sLsxsQCqv6NT8PtmdU+z/bFIwhwA/U0TKv5dT0iCN30npUEQpKMAdpCjR3+zLgXvAzDDsgnO1usrF0vcrU9jVt68FFPZGILmrk0+LbiNgC1t4EgfYLArzw8YODETrO13SMBZjV9trHAiAQ19+WKhxoR39W/i3QnQErXzjq65cJs4zFLs0v3us6J/95k4L7g5ZgAZGQHuR25SLBHbkbEBUGLChonFd8b2AcwPUigy7q7n/QfJHuLfEAcFU1tij49AC2Cwx9+C5teBtflV8t6SQiV2OKZrMur/K1Rt+bCOSbIJdopzN1XI4b1S5fgB6bad31RYrdgItifhYPes4Jm5wPNjwQ5ge50y4uX2Q9pJKdgF20pqkJR6UYYQp8QcI6lPXbmzSoZAHJFtUBDy69Wl0g39ntgjVbkr3D1jm/y4rky6DK2jtce6RIPPEUk/vlhWLNfEQHOB9zOiGpT0jyAeF22kImG1AgwooHc3ygsoqMjolJqshPhGwDWBOgZU+N8doWN2xgPmwoBW9p9nOzHGTUi4OUM9pq2vdCK0313AAOiP9tcW+8CJ0OPcjkU/aL1oWCRugt4AKf1kHnyOJDwbJRcT27dVAoBkcwTGf9duVDoITNIPgQDkaOhLJERJUaWzwTRoHUQbbx9ygP+S1gA/yEpIRbkKo0tKStT98DdWu0Kw4ulxI+Xp31Yqdgp3G7aV/Mz9ZUCuHAjBy8Bgkl642EcxzRx8v4lw30B1ekrX06Toj/ulEuJ0FdunSzJU//3hcCGBPEsSskerXSwZSBgx3H840pWyLUzqV0h81gjJGvpIECgRZ8iyQMm+Eir/hZglL17+IO75PoAOc3CI960uXIoZ79tEr9wR0A0vrCO/Q1I7TVJ7XiqpYUQcAy5s0CrLCOgo4IAoYGb/29DnMXg2KODj+1hdtERKVjyuZrlzxr6F+n1zMLYqfw6YBSWSBQYHmRxGaK4k9Z6RoZD38xkAH6YGlQIY5xmv1fnzJaH1eIA+9xu7onplv1mK4HUD+LAKr08rR4ur++7Fo8wDleBWV+y5/fjsEpwKjcB9FLQMit6w2LsYs987fX8DXBfdEnS4oRvOS28XaddZ9M/eUAldYDusVVYCfmH+VoXzYZcuuSL5+C5YCBZqveicgYQaTAPHgJGBXYh4PRbABe0j7SwkzTqjkrA7/8/skOk+z/QPxfPGYxAy/E7pHJidpEoBUtMxHgtEIHJoFrQ7ZDiy60AHZbkI28VYRmOEE3VQ1N0PgiFRtKuuSwAuvXlbxLtyiWJDaEefA3bLxXXHa9cIpjjgOGHoL8QCIPO8/TRYOdAb6D9C6Q2QjaFNOlWxR/TfMZkBfQjXXS/U60vgjoawYbxgg5DNEKhfEfY7AzaKd80f4sB3o6lCX7ZQPC/8E7oDE0Qbd6Q44O5g9/9aU/WjpV3fCLQABi2fhHojYND0b5rYWnqj5v99gAffIEE+a1iWOvdbrL8yf5ecOjRT4mEBZxUPfr9DNmFWPlx8eFY3y1NiwQ4wGidQUAzAAHID6o/ONjg+cDHCSixQWSJW6nI8ABMGHQ86AmxYvatKAQZ0VjhuQIY88/NOOXVI3SxurEs0Iu3Hc7N3yVUH5Phnk5kEklJuNbtsPL/dK0X+Hts/+mSHCaxVsGbfAAtG5GRBl6CNDMNrOyR4uzN4Ty6NifbJSVIBdWM6B7gsEjWydTISrP/kxqIfgc/AZPis3G5qWQMWx3dgHnwHe8IPAQ6wv68fPNZ/emLtD+7FYHjE4fNdDQCFZQ3UmYg0aPPYEAeLwPaP795ZuBhx49B+xmqjXlmywXIK6kwQ3Kl06wCokiQ7AbMXJu9X+eaPQY/fKmm9/2oCFtR1hWKAGX1vxsz8L1Kx5YsgwICieQUL/4pEsp0kdzgKzICjlBp/3dWRrZVvhE1XYnswB+4PAgtCr2apQUqXM5QYHWfXE1rVuZwEnqu7ywAW3IG24iWj/62o/y9Hcu6rM07tfJoSsitZ8QRm2vtCt8C8jcD2GrpetWuOAixyRr+Me/XxN0PVfeoluIY+LHk/nYaxnSOuTsf5jweueMq3KAZHIFgQeNwJtkZcZn/M6m8I3B207tN0cEs87BQbEwSESte/i/IHCBRCLDEwCI6Urn5BfRacCXWgYlK7Q1GKcGjgqc1iPXPAbWH7QYvPwKh56HqpfrivaIMB/Eyc4rNADJPIB15vtm7MMLpOhHgbfOOZTHEWlrPsjnagz2e0MrssaJ9n0WxY3KWIljsoaD+TXvqwe3duU0mjo89gcR1/QdA5xobWFzZ04yYLHQgkFeKAEPeLtuafbYVLwhxde+N7af2byuiP0iTI2ypeghwsRwgJR+sOljaTIafW29QgHsnn9K5aCiudk+odN3ZovYeIPnem6KjB17r3NXarVx06CvoHz0MEsLOfXRB0QhNsGIl7YNN2wok6hDLdj9wM9LRStKPPhbjeQAAAveGHDEAPTAt9A3QrUAqjf/qauCF06Trz6sCmFZhC4UgjYgGQ6WA4OAaOlrgbHlK6Hfp3nytQwjnuCGWL6H7gr6LPniHaSRcZt63/2hA2TGWZuJ+5W1zX/RvsqrpJr9DGje8MgRbPXZcorQyCaU0VGlge3mEHwPUDn7Mfp0GZvK0SNOR4UKCzJXbvCET+a3D39qvlbntwBPLgGkBngV7Zvpn/rplxsgOJNWfnSac/qGeq/Pe3Qpm5qlSOG5hp2dOjYGVYDB2EwKCQIdv6fUeVsnG8cGT4//Abyw4oQLnAv77dIavzQSu0CSvgQlkiYjwqYDNpRIeMOFkT0F53aDdgeGQ7tB3ap/tmxmNdohFpP1gmsbGwRrrC+YIxpWtHtSxCbXtDg3XwVO+PZRwIwcPH90+QeWAa/Jq3Sx5dshyz7GB+IJkdDpbBMC5gGmRg5n5PBssmWO7Az66VWj8ZEUxCX1y2Wi7pB5/rkCT0a1D9qQlwLN6LcEHKfF5lFdg5Hsy2+s4mEEH7v81lFcqhgBaQ0UQ83r8EJPRJtT/gt5ZXBF1+Mfo8FgABXRqoYfDt5h0AC5xgJLSRSdB4qPvkB10WtGEwG4J2NrMNslZC2RVmXaRKPiOx9Tizw0H7NEzzu1J7ISndHLzflQKGwRmg/U8FrfslRe2OzxkjKR2OhljiODWbHnSBxQbr+pPagRqL9sJFHEGCtZgsgiCiWAAGBDf0ym3Seuw7iqbP/hnhSu0m2cOflu3fTwbT4JsmBQzo1qAltgkCC4x+8JWuE3GZA1R5glgABmQQlK59BVT5PIAxdTOZRjtkK/A+KR2PM3bVe2XpiCu9n1Tu/BHaEWfXOx7pDl5bVTBfMRoS24yHGCUU5zFT78FYV+Z9rzQWWg19NNLm9rrznBC/864DnX3eTJEFP4gHpQPauKPEOR4zg23MGSLhHtJshlHrVgcuhbteHUey5372LlH6Az2CAUgP6vA9oJaz/l8bPdGyOR0z6d7tUJtfC8HRO85H4bwTWgFgWIT8ndcmny6uo86wbMfsgJGEmR0z2+eZ9YV4XnkINe2YFbYIO7cGi0v8u715+DtGJwi6LdiE84y/iP77PHHfhbKtnkgaQe33Yka45u5LFb2fl7puesymhYYdikUpgLcE5bZIvkl3V5+Lvvi7GRj4vDqxaMPHied1sC2+el/c2W0grGddgmFc7i0rUa4EVm4aVgwR5fgApoM25CzVlPpcgFnjXbdMhAkyBTePPEM8L90P94MLLIGlhrJhvL/NE8+Tt4vz6vssBUbZMfWdeetJiJlViwO6F00ZBAVcV9wl3vLSWtBgquifvKoWBwArDeOijT5IHLSwbIkmH4EWwKDJh3jvu0F2klOqkPwgP1H/L3bAbDqDCfLg9r5Z9h5Z8bIOSaldHJ6bZnm4rNoj/5ixXeZvrgwSTrS8AAcIVvTCEm288MsuBRYM75CoGBOGKGM07XQGQDBnY7kc0Rt0afShC0oc5m6EWBeeIyXeqUoFOC9AoMUulPMDSjVYzsBYV1Al01eWSCXAiHHdUmRYx2S7yyWSfmCCWDLxHjb3cEIkYABACC7noq6fDgisLWdyznIF0ugZl4MyfmLArPPueq6iqmpV0//phs2yBYn62bndLAED0t4pdvje6g1Ci0iCHZxtJ4OC7g+kwA/MylAlF3b9f3XFWnl7FRTwAU5YxdkYq/HtrY7W7WdJwnfbdoBZsEPIMGCwhv+y/r1kggngQECIy5UYb1pcEjwwFgIgZBBEahFZ1wuRn6F9MAv92IiZfT4XSyNGwfXiqC4d6gkyxqKsoRDvGwUao4k0il1C+NAI1sYzNFcdfdw4ZvbqAh29pmhp0CEKwtHHPg2CiJwlZ01+Zd4PUr3zZ/zQygIbYTKYB0fb1tZz9h+y/ajRj7Qf7VQf9Nr+B3WodsOXqLe1vC8F+yje56nC7GUTBjUdvBCGZP0/Kf1m4YVGg9RqQJgdTwCgE58zGoKFN6Ks4dYgkUSKKRZDm4Bq/kk2gAHbzeh9reyafyVECd9VbBAyR6INZcs48nmUqNyH93s2AAK8d7VBKn9a7j9t9RiMc5vLK51BqI1BnQx8COt1ywmAJmtYXUKojThQ4rFwJtDz0zTx/oAf95/ixz0WUqy1CUdDoO5A2xnM0JvEYoZRg66Cnp6lWASuvz0uqk2yCv7zbwjIzQOw1kacl98pTugj2AbAVuoQ2EUg4yEWiu+h99I3rRXPi/8SSUHyOBpMDjAkIC4Repo4Qmb8651gsoP6BToo5vrUd9VRvmd2QZ0EF0odPG8+Kd4lv6hTvSsW+a4F+8OJOnttgH0bdu1bHYtFKYAHM/eCEhPnZbeLZvOeku1BYMQn1viReFEeYMZIY1+phUDGAnUQ7CL+1VmmhwkQCKwtvRQ7rA2KUHrXrTQ2xdEtVzEiBCwbgV6FWTSIDZOcDr2Gw0X/+SsRgGvOK+6ux6Bh0u7Bd4asEoELk3bK5eK00bAw61tD97GsxgnWEhcvmDVK7PTXWao/BHS0kShZIKsJTCCWV+gLf65/qy3r1T4djCNJSa933NGhi2gdutXb37KjbgTM/5euO96y9iccgU5IjpEHy4/ry2RCj1RphxlzF5LQxdAyMACD5XnVMqazfXJrN3RMso/onS7P/bJTLhmdZVraQHaCjrnNzETfx/SNBfkAMoITqUkAJTpl2s9AL4Fd5OhOSXLzxLZ2XbI9dsrgTLnr6+1y5f82ySNTOsrYLsnyzuJCuXP6NukL28bv1pbJAAgiEkywil1gbtw8FTRngAqPoo2iSrfcC9CkAOUOjJlo4+YJrWV0Z2u0NJJ+jMb7koEyib0tWPbQG/XrRUiyi6pqZAdm2SmASCAhNNaV3vWymQAAQABJREFUlMsMzNobsbbUlxAv2YX/tKExERrdkQwGJoShxwO3fwMtnyDBd1vy1Kw+j1EwkPZ+dkFmAUswqAfwybq62WayMyh6eFZu93rMg8D2KLr4OgCDLCTU48G+IHhiWCXyeWdv3wmmSAc5LcCFIfB6rhMk+L4WJKCYJIOaABf17alKPtqBvREukvDDhRaEXAgQEDSYDh2E/MpqOR/CjbS6PKxTO8U+sHJ84D3IFrn319/gUOBLPFNIAcbfkZV4Tro5zIXbxZ2wmgx07ohFWcPrK9cqPYZwzxl0HP3KO+eEul16lVq3SmTrTqxdg0CcwL/eLDiDnZgzVi16TQm0DmYAPPhCyta/qZa4zGFwBDgaVPaJAGiDZ2y8AAtUYKY6ooAwHcNbWy9vdg1FFXWwFqjAH0qdN86ntkF8q5HGZpO8xmcMwpBVwf3hRbhMXFbvHhQArIF2Q3Lnk/3HlPPEz2f6t9UKHCSoY7Dzl/OUKKUTwpQEWmiJyPeE9pec4U+B64JV0KWBTgXUTeDCdaA69U5vN+GLevsCd5DlkI0kmqwk3p+lCC7YXpKFsjeFYRFp22d8ZwIBA+NcB2dijwZTA4u+6jfRf/hS9F++Ec/vd4mHie64SaKNx4970PrDRSxmGGmh5rr1CXH/6xrMKl+HmvBTRP/8TR+r4ODjxXnKpWFnKDXUTmv9MKsaTeDvvqlAYZg27BTfvcsXqqtdV99rm+iGuYXpYc+PAHg+fFEd06BL4Nz/cNPzAndq7buKRvo8Z9XBwJCqSh9VHOwSq8Q68PqGrLMMoNGlAKt/B9DijEgPQglNjhgvXpQmKK0DC0s+zysPK7CAApOOIQBzYC8ZbTiQsHqRCHtRFkJtBoFjhRdsHSbrTJq9q3z9Fh6ziIawYRQwUmsLqUCD5+8VaocY5TH8HpOlIwDa2EfnZXco4M2iC02629EaAqxDxuJvK/7uUxSRQBeADi6Ozj1FO+taMCX+btkHgh5mQVaOZlGSZHb+n3FfhL9C/oxD8+d9ZtbyD2iTKE/8tFM2FlUr7YLRSJA/+b0YtHxd1heAMo3kdwz2NSaAGWDGETR+lCh0Dkj6Se1+c0GBfL2qRE5Fon7cgEx1G87El1YHJ4MbIcJ416G+WTWzvngwo8mld47vh7TZOZHso4vCNdAF+Oi3ImUbmZ3skrOHZcrrCwplLcaDz3J0X+s/4rzHy/PzMX4emQJ3BQa1IAgWnDwwQw7tlSZPQgPhxbn5toBBJP04b4R9Yqtu3kz+ISCwOL8AYnRcdskGJLwMWvlN6dJR9gdNfkCr+rOrTJ65hMbHEGb8OHQntjkrbwcYlMMZ4BskxbQTNGbjmbiTjs+FegCRhOFWwPY2INlORz1gW4AIZFOEC0Ox/+4Rg6QfnvmNleuQrG9Bst9LXToNifuzv6+UcwE8BAoSbi+vlG+2bFOsgFVFPpCgW1qKnNenh0rqI+27Wf9Y+89yhQv69JQ5YE5Mhaghx512l89CRHDaUQeZXab2ETghWEAmwdm9u0lOoi8Zpo7AxwBUXl2xRp76bYVcBxcJI2JR1jAILIngvxJG68GvuwCA/AhNB0YGxDl3RzBxTAG9nou7bAPKFb6AOv5UKYL4H1Xnk9odDh2C0ywT+Vj0MT5jsGqGVP70Pn8NapIJfNnGd9C3dcr+L+hgjDeoGUAbwbJ1r0k13BIS4VCgJbQGoaIAoowLpWrnT8qxIKn9Ef47q/KB5M7+basVDVRjOi4YQV2EcOFEYs8lFsFkyQqMiUX7Td1G6dqXVblIRt9bUG7TVbE0Qu8ZnzkodFe9bdKjuXhR/60v+BGsA4AHX30g+rT3MDuOYxMwa4glXEQzw2jWlgINbgFocP81SIpfEGndUdVr280um7UTzT4lNhig+B7NtVbnMjFXbgh9hlid0uD92vDxorQPOqOkLgIwJ/BG1A9whJR7BB5vbuteUP/pghAplV0JGuIhvBAd5GfJLHRYRjr6DZc4sFgaGs5jzwOoda3U3HS6xN33KsT+JiqnCX5uHbmwGP0RSTEYEQpMsLtJlGwYNlUHGniRfE9XJSnOC24Sz5fvKD0KQYmkduiJEBm9HFhq435P23Xd6piPWfCVeH+ClSo0Mjj7QAFS7ZTLlG6Kvni2eD54UTwP3yjaEaeCRRAdSEtNjpawH4EWwMB+fP6UR/lj55pxOfIuZtANAbdzh2cBKKiWqbATZAwEqNAbM+sNjZ0ACT79o1gxF9rVljwYbdE6cDr0EVj20CPAQYEpF20Nzx3u+yH48e9F8sO6cvllQ5ns18V8Vp6JWq/sRJm3uUKOt9FbMO5t9zq+e6pwMYJABl0SCFp0yYzDrL7912kZrBwPATBgACDz4DZB5sZxAAwSIR55WG6qPPrDTsU8sGsrXD/271rXR6OvzfGVAMFtc5cANPIxLOgiwJnw/WFLSFcBs2BdPSn10QbbNguCA5+s3yRfb96u2AxkA+wHhwCWFZzXu4cciWS3IZGM2fRIauUD294OS8R0JK6cwWd0R9K/DWAAyxpojzipc3t5e/V6OBFsk9N6dfVf+t6a9WpGnWNDhwM6IXRJqwM4aJloFh1T6s4xOx64j98j2kVyKQDzg+P1JcAMu/gSJSU8PxAQ4PnUEaAYYzVUmQlAXGfXSAOOTYSFJBe7oKYEwQoGnS1eGD/a9PQqqOejLsH0WOBOuxKAwPMC110pXdTMehocCaqLFks5ZrnLN0EVOy5T0npeFHiqrw4eLgjhghT/cEFrx+Qup6r7VebN9KvhFy17QLk9eDErHgdBu8S2h4VrqtHHMwfcKaWwcSxd92q9ko4kOE6k9boCvwvr2FIsFcgZ+Wyj7xvaQCQif6HXmG2zvMJdthalEbnqcE3pKrynH4PoUInxPNRWQNOsvT2xj8+gVxXAKeNC9Hc/yy6QyRFpcJbWiVpjwcJZQX3Ot+L5+GVFKY4EMAi8T7gZRuclt4kG9frQoFWe65YnARpcDfp4Mer2skNPCbvtxQy6/v1n4t2wWlGjXTc/KvriX0RrD3G/GKv/m3VGA8iiQ1/Au3mdEl40O6eh+xRIECVQwHspbYcS/N2x0FSINSijgx3ghaVjtEGhPCMcrduJ/oePrWHss3ulMCbDgZIBs1AilNDBcAyy/r6YXRe6jyUcnL1XDBjU5WtgaminXiH6u88ou0vqSijXhNALA7YbxIapvV6BBhf7Zuf1WZ9jjPD/H589A9+di2+BkGnjni+gmxGteivKFHihEySA1acKfM80iKA64ZDigAiiEc4xh4LVgdKjfwNcgfaBEyyNlojtCIT/JRTb+7W0tpeMAGfQrxhT94MgJ8WlqPi0XCSgwGQ+VNgt9NHu+nqbbC2uCd2t6piLMLPOWcCjMdtOVXYjaOFIsGA/lBDcZFJCkBKn+W0YCWLMha4AgQcrwIDtXjCyldyO0oHP/iiSgyHYmEw6QIwiM8kFvYDwXyPlcFDhkW61TApqHywHgMAyBoIFjFa17RTgPDvAwKzrkfbD7No9ta8GpQOj2mQpNf79UNPeCpZ64YIz6yf16BLuNNvjpMp/g4T3UwAFvxXghyMiNzNNMQkORqJJOvGJ03+wbYPChOd8+7N0QtL9FCz7OFP94KI/bK/hwdN6dg1K9gMvyMHzVyGJZv/43epcy2pYWVwCEcgsdWpueqqsKfGBdoHXcp1sjRl4Li6RxNdTDo7ktHrn8H06GWURXKyCINBG6D6caPNeERh6a9V6VUZhx4Lg+0Fng3I8H8eGwVdWnrBc5VdoXlw5sLdVV4L27wLYQXFNsiRYInHjkH4KiLEq1aDLQVOGG9T/ym3TpXLHt1JTTLppHGa56wtUVG77Cud9FbOuUFuBVP3S1S9CVHCJardi8yd41SS500kALCBkhs9gUwdLPngv2k+6y9eDxr8FLIMs0PhRA43XvSn4Xu6ae7EqP2g95g3xgCmRv+A6JHi71GOwDIVOFxREbM7B9yQuvTfEItfGtJtMqrywhtNnf63YBlJWrJwOIr1JpDOM7nsuR401/o+nMKFZQKyNyvju286D6F2i/wwKyrmmnOnfDl1RtOyn7hAp2FF3CH+HvPO/l5qfvoJy/SUQxTul7pjFWmNAByaU2n6HiOep20UuvU1RsB2u3cOOCn0czra7H0XN/rrloYeCtq1q9gNP0uHIoM+fJbJtkziGjkXJylGif/s/0Q46tt7fIR36GLQbjDYCAQMBi0LgNqCvXwVqPdbDhHfJHKUbIa3qC6vyUjpmOLr3g+AsBAobGc6xhwkXI1xHni7e/ScBJFqL97tHg0odjLYieeWzOA3QgEyDrLYSd8+LYDWYgyWRtNnQc3SMu+fVhxWrRjvwSOVUYgtAwQ2FOhBiwQJpaD9arvONQPhMp2WkWkagdgRcmGXMhc1ipMFa/QoT8T3WZffK1mREp2Q5GDP0gbF0W6XavGyMCfof8gM2C6BGP5ROMPFmUmH1A5eAApPyl+cXqCUJ0/p0HAiNV06unwCxPOIbABiDkdiHaiWwZGIZ3B6O6JPmd5QIbdPYVg4H8Q6UL+DHCmI+GA86Xoe2rxvPBVt8M8E5eK7QiFU/Qtvdk9vGjPXu7gNn7O9f+Dv0BhLkHJQqHAQ7R1LvjaDYYbhQn2FoA7SpBTmYfFIrIFywzMEquqIPVR5dZkKk8FBoBHSA3gC/c0yIDcDgD5QC0CYzMAa2ylR2joH79vQ6mRp0ZKB1plUUVPrAROoWWMVWgA63zF2kQAWrc7g/EsCAmhdPglVAQUSySK4b3MdfJhHadmKbg/EjMBkz+5GBL6HX222zrr1yx0zoGEwFFX8BToUVJ0XxILyX1H6SOAMEDinYl9LVOomxuk9chj29krP2qdAGSOl0ApJ0CMBVbIUrQSZAhE4gVNT9TbJqP9b7NVeSxMPGUbg0IHR3Jco7PhV3ySq8Z1tQW/+EEh50gb3gSu4YcYvUddAhluj1+j6boRcmhFgJBh4vWfkEtBRKJamzL2ms2IIZOoAFqd0vgOjiFCn87V4pWvFIswcM+EzscyGsN6sLl0p85sDAx4x6XUcSpSOhph2cFOcrqrO238EqKdTCUOsbPMMI0T3B7Gw0QQaCVbAfrOF2xGOi5MK/ibcS/3+/+bg6XQNI4IXNnP72k6J37yN2zxQt6EAhvZq/nRXcLZS7SUWpcicAj9xHvQ75bdQQt4bgm4Tf8rz2qAILWE8urNuPkgLOOyitj5cfFP27T/03VEwHgBFMFDmz7LrkVnP2BjUIMONNgCEQ+PE3ZLOijRwPy0QyXO4Xx+3PQrPEGnTxTP9AKOaoHXWmou1bNUuRR4JVbqj4OyccE5Hlp1VbofsdmbARxWIW/Cx6Pnje7JDtPm3wforKb3aSHzTA72oCfJ6pH4jLzsrRrJEY7CODwHkRmA2jDhI7bQ/jVnRQiX+q7rNk7G95jc0I1M9MYtNuSyt70QgUQVzwuNfWCmd8o4mj+6XLFFgnWsX1B7axOmS5fx0cBNqluiQ9DL3faKAnrB8Xbq2UHdBUaJtq/Ue/PcoeuEQT20qgjwChQ+o1nA+WQihgQHBj1roymbmmVC4alQXgwJz2btxzFMQMpwNk4DjP3VSOZBBuOSgfoC7E67/mywwAEyy5CBVOjLYf1KDYm6ISP4C+RH3+muIy2VpRIQ/uN1TmoFyhc0qKdICWQayD9Po4JLQ7KqpU6QFnq+kaQGHESIMCjQ+NGeY/fShcEbg0JoZAWJHiigQz1kHEkdoFBAfeX71Rza6vRfkESxRCAYODUIbApbnFgKx0CEBukkM7tlNikIH9q8B7/urKNQq0ybJhljz3xyoFFtB1ogsYF/+D9gEFJMk2IJBSAHDnaTA87CIfrILHli6Xn+DWQGDnBrAKjkB5h11QhJCLL6G3OzOyY6R5V+36GXoF05RTAp0PHLBJTOp0LNwSjpb4DMxOmQST9/TcK02ORL5LBx2+umChJGSPBqjqYzMZV/s0AVDPHUVSbVwb7avXUy0VO5A0Rhlxqbmg+FvPAjKpLVh6G4QcA2Z+AcJU5n0HVsY0UOsvlVToQtgFXTEKFt0Elof9DGH7Q3+ybKYaTI0kuF6kdvMBPLRpxJusAB8CIikdj0USfqdiHtAysjkH7USpL7Fr3iWKiaKZlB+wPKTdQebvpxcq7h6IkKl6420b1KNyBlaDHZxG6nCENmgNnWHUUGfthCVerIL9IDXb+e83RQMY4aEnfG1QWd0BfYSaK6eoUgsrwKBBoAOSYkfHbsatIn4NdGuI+KIoT9TBFqHugeua+6K8su50MgUIFmiw5NQOnAxQ5m7fQQgGaidf5qvf/8+D0AV4zH+R85jzFDNF/wUMgUX4PkIIUhs2Thz4XGkDR+ErFz6t4Xuo2n8Luha3nisanBBod8jSGSPoGuGBCKQOMUgH2nUec45xSL2633sO5SmfB+0TD8p5eA3FIxOTAY7VtWecGJjQunku3ESijfhHP6i7xF0lOkCNqAMAjzZ4jLpMsX/gwBEaGlwQ+F2m04kbc2zayIlBp1DU0wrICDqxgRsaXUC4tESzGIHw36xm0c2WTjTlCCB/EpYcMHGNJgwqfTTXhDu3GKUKVraH/zisnZ++b7STQBEAhF3frzogeEbWuNbulUm9ARZQzHB/E42Ev4xFTTeEH1+Ys0tegFhha4whWRNWQe0FOiWw5CIVbINr9s+RHIAjxXBLmLayVMbDVvHMYcE/JBvSj5HoA90t9ob4o6BI7p6/VHYiqTOCsNWPSO5mbF4q50O4r7ElCEa7xitr6N879ACZsWW7cgCguCCXXDAEqBMwopGJv3GfaF/JkLl1WH95dfk6aFv4ErtLIThItwPDdYGgRH8TEcho77U7zr8QgMcVs+bKBd/NlsMhHEkGBZkZW8srlA4D7Q/vGTnYtit/FBbJSLhT3A8QiWUOBJZ6pqcplwaWhdz0y0L5FiUY5/c1/7xTyPJJiDOSVTIK7Vw/pK8lq8C2Iw08WAXxvgpVTvANZp+LVStxmUMBRkyRxNaYZd0Ns/msn2dphQZ7wuQORyGpPUpcNlaFDXzUsJfpnnKIO94T9rzQE1J7XGAJGOjuMoAFd2DmL14yYKtId4SSFb7kIrXzaaDVr8L2E6DY95UEjLtVFMF+kWBBAoQX49IxWxpnD/6GtuOr+8/zaxeQRVJTtAROE1DXB1jAMJJusg6aO2BQAtFDMiQYek0h9M4q1XrgP7QNDQwKyTEB9Pz3Jai5L/UdoivC4ScrcUOtU4/A0yNaj8UMI4UCdZQNUEAuUqAitHOkg5OazUTTLCgC5+iNv2X5O8wOq30NAR2UcCJ0F5pbqJp9JMfS0xzojLS/+jcfKwDJdd71tZf4fs9xhpvlIW5a5H2EzxOYFo5U3+QUwRDnYRDfw6KSejhweAkePAqBPn7eRk9UZRsOsFcMlX+z/jgPPwk1bcVKL8CD0goPWRqgslOnQGkWlJeoywhwkaJfD4jguQ0Ac4L6AuCsIU4aQW0kporr708H7YpkI7D+XypKxH3H+baX6WBOcAkMOgu4doOzgHo/EhJ97w1ZD19/KF5qUHTrDavFo5u8TCPwmf/M6y2AwZ/53a999jRYAb5/Zjf5DfaDTRFzN5VBmLBCNhdXK1eELJQpDOuQpJwBQksDspKd8uvmctMSgy4BTgpGP5ft8PXZjMZvnNOQ129WlyhmAZ0QDJHC0HbYd2onUJjx2s82yVsLC2wBAzIH7oCjQ2mVBzoKml8DgmyKR6Z0kK4mz9eQfjz6Q95eARjQSYC2ewlODbO+fdWs8TNI7hicRaYg4XO/r5I+GekyCDPvsQyKCBqOBrQzpHXg10gun1q6wv++0OWAwnzx+PESGtz/3da80N1ht+nU0NOGyUAnASa1RrSGwwIF+VZBxwAmXarsIZx2iHHtnn7lc3L2n4wJA/Aw+tQa5SC3jxigRBGNfaGvBAho5Xh6z2x1iGUOnaEZQYcG2jpSIPJUaCQ8vOQPORfAUuC4UJjxcbyXP0BbgiUP/HzRJnJ3hgfU9vx5V6hbagnZ0AY4G2yCKeJK6bw7u4GENQWz3GeAsj9VSte8hOU/Ep8zRlIwG56Qg5m5CEQdY91hZ2J7SWx3GMaia9imyTCwiqpdv4BZsE1aj30HbXVRz2ic60rtJtnDn5bt308G0+AbW8CgGuKWCa3HS9aQ+43Lo3rlGBJkoA2kyFFweAC7wKtLQpZvBo+NVe2ardrUEqNn3qkLd+M/BAuocdEK4+FK6Wla7kcwJDDcL/xT9PefF/yZUjOyyjpxxIFItIKBhcBrQtc5C+/55HVQzcGG6T1IYjHDqC/6WTwv4X1FQuhEHXRDQlnk5W9XAn8UVDMNggX9R5ge4s5YgA6BjXvx/2dgEqtvXQ/xxfDfJx0zyUzAqIcQOKMe2Ha4dVWz36OveOl3P+WscKebHmf/KV7IWWyr0AaO9AEGu/L8gEHguUx6WeMvWPRtG4WsAy9KX9zQP2BJijb6YAVIaCYODgToXSdcKN4DJonn0zdQVrJKvGjDW4B7wc7QAYFOOhIoRgzBhJBwEXDg0ogw+t6IJtRnQIFVjWnEhVIb6EZEG46uvaO9JOrz3QAg9f+9Is6r7hEnGA4e2Fsa9p8y/zt8Bn+CbeqTUf2diboTLReoEWgBDFo+CFGPAOvOFqEMgHaFdjPqpHo/MitPft5Qru6RHOfAj3qRNfnVyrVg4dYKYdlCoOhhbk68On8pwAtaCNoF6/qpX0CHglAav9l1pPYXI1mHs6FpBFL5f68FIiZCJDFctAZL4LBe6fLpsmIph5hhOFFFs76agQW8b0P7QVDC7D7hnmV3Hp+LsgM6A7w8cT+lIUDleiPokvDw2GEQH5yl7AJjDRgY9+ErtQf+guWSfj3lp+075csNW2U+6O4frNmo1idA4+AQ0OoHZ2X4fzhXgBL/byTC0QbtHe0AA7P2mChH67hg1s6e2MexJeDB8o+NZQRgdFVmQrFIPpdd8DjtDssBzhhBIUiCJ0b0ykiF2KMuW6B1YAgnzoXDxb/w3pCBwH1X9s8VAhTrLMQi2VYiQCvDncJou9GvYIkwIWdpQ0L22D2SmPMZOBNMkcO0XpcjaZ0D7YTPVVlE9c6fUZOdBRBjMpgHR6uEu9HPbNOABm2GrOFPScV2ijzOVHaKLoABSe3gHgAHAZeJ2KNNc+qQu3QNmBNIGgAWmIXDmSBxmYPFU2U98+v14vPldaMsZIBZExHvSwTgULH5f8AJqjG+EHDDuCe2O0SxHopXPYNjn0h81khoVETHXoi4AzE6kePhqcpDacW5EpfaK+JWHW2gTM56437DxJHTLuLrgk70YEb5izfVwrp0JXA2cLSimmsduwWdGumGt7hQneqwEKyLpB1H7iB1mueT18R59rVBl3irq8Qz7T3xblkn2jHnBh0L3IgF6MD2yJjw/O9VkVK4WMERQu3D30hS6x3tOovz8jtNXSLUibwe/fQ8fYd4QJlXGgAjxsPXfoyQzRBNOM9Azf6/rhL3tPcBxEyO+noFdqSmY6YYOi4Wyao33wfKO7LDs0Q1PLt27HkiWHRqSvzyrXjnfov+vadsNOMfesf08QgAuS76m/8YRSkdmM1urkHxTzqFxDIc+Cy4oM3R3ELftEaxGhywPHSAoeQl4+QLvI8Eim6ihsYK8Tx7F8pGpkVkz9rcnm9v608LYLC3vWN7sL/F0Dr4dk2JfAVrxW2wRTx5UIYtYPA5EmiCBYf1SpWTB2cKnRcYTGa/XFEi7y4qlJfm7pLLAtwYaBn41oJCeWb2LnngiPaSllh/dpdtMEn45zc7oJLulVPRvl0UVLjlX9/ukNUAKuziw7O6+Q/nldLRIE4yI9RS6A7XCMa2Erf0yDbvM4+7AbJsgk5Dt1q7yHUFVTJ9ZYlU4jnGoSRhWMdknuaPhvZjQ2GN9G9r3Q//DfbgyjpoFjCRCxQcDOwOHREo6LejsjJwd5Otk0lgWPLtAJAxDayDabD9+wL2gFzO79tDzuzVrd792yUn4ro2oNzbfw55IRkGRjQVS8Fov7m8cianLcaIS7TRFdaSLE85pktHZS3JsgZuU7gwFToSFIEks4DAghGzd+xUYAG3N5WWyy1zFhmHrF8BZOadc4L18QYcYWKYNfSBBlzZNJdQryAxZ6xa9JoSJO4zAB58IWXr31RLXOYwBW4ktgGl1xn9exWu1/wcJGQNV4u3z/VSBZvIyu1fw1LxNSlZ9SwS+0HKyjGpzUFwSPCxSsK1SUcFHcKUFCt0JZvP/FLbIL7VSMumlI5Dej+h5kBqt7Mtzwt3IA2gDIEJggYOV7pkDrhDlX6QaVIBu8ykdkdIaq9LwzWzx49zPOLSB4qnfHNUfXGdc5063ws/+gZHcrq4bn5cdNSle7HoTCbhr66KJVu1QYKLGnUCCJwdr6Woh7uXcxwU5n+dJR7UnAuSYkdnMCZMasvt2qFNozbpFNGRfLK8gTPQDM8rDyndAooQ+majD7FsJhagA8GJmtvOR+nDdqGrgz90DzQAjoRI4DQk8VdL3ANvWVoAKpHCq+5V7ACyLwRJtYdaCf2HQ5PgQJ8eQFb4BF2f9i6Q1iTRoQPARahLYcImCazZ9/e3dkVDIqjPnSk6Zuq17n2DDpMxoUPMz9EWlpURvtf+Bvj+JsB1Cf1TkRf5Z7kxYIFiTWwGcFTrvKCAC7IdIEyojTnErxng72fIivuj/0ArAcCPCauRp3pQwuF55xmJf/6rkCv3zU3vqt/Ug7muuRefgUzRf5uvBD+1Q09AeVBnESw6xozAgUzYN8egOT1VC2DQnN6NZtqXZXlInpaXIPkvE0PmoH+bBCj82zMAZkDgbxTsEQMBAT4iZ75PHpSpxP++wTmBQWvB80ZkyYvz8uWazzbLKQAa+rZOkE4Z8UoxniyBVbuqZCr68wfYBX1yEuSI3vYzNi/8skuBBcM7JEpvuDxYaSQE9iMZGgMbCuGpDS2D0LKJwPOM9e0AUBgsNbAK6hfcPHWr0D3i0SkdpQjaBffO2A5fe/VzSGauLZObJ7SW0RBHNKKh/UjFPZp7tEqIlzzMPG+GZV5HzDibBRN3wyHA7HhT7WuDUgCyAc7K7SYLdxWo2nn8pPLfjnR4ih5+Cx2EWaC9v7Nqg2IO0HFhYod2QhAhXOxOlkK4vsTyOEUsp4ItMlwJFdZ9lnmPLwG8LMkvVOUgvTPtv7c8/2yM/42zF8rZsLB8ccJoGd++tby6Yq3cMHuBDGiVLl9v2i4Ui2SJiRFDs1v5NSCMfeFeCVz9mUKLS4NDwnFqcZdtAJX/C2gtTIXGwD+kePkjSG4PlxQIBVol4Y0dK1L4E1EWwcXrucknCEnwYNXTUrL8MczEj0AfDoPOwwRlU2h1v/gM1I0jSte+Iul9/hp0mtdTJWUb3xF32Tqo/p8XdCx0IwMOFbvmXymlG95VOg8U9Is2CBBlD3sMNf8lAAxQm1yrQ0L3iZwxr2O2vme0Te6x89PBRslfeB3AnNfBkpisABwCPk0drDlXiSuSVwZnFL1r/hB9GQGERZg1/kYEQnOsN3dAEd0xEErvoK07eg3EmJv/nPVSzA1d925YKe67L1GlCcp6rfb9UTfCP6TGu2zo8c5Tr1Azu57/IkGp1WhQ6v5oRzsUNfWgt9uNUSxABybXBAuc190vzqEHGF1XAIjrgptEh2OD+5azxTPjI3GhvtwsmBA7RyK7wkK2qJcuFqB1e1m68dojanFAoNIJr3uWhtiFo21HES4NDCfEBvXf54n7Lti49hyAL3KRePFe19x9Kd53H4PPddNjEbWuShLmgFUwB5oGYBgwHHBv0E6HhSxU9ps6yIao+cclcItIF+2+V8VbXCDuB68XgbYHg+KJzqv/KU6U6ViFF/33bN8szkugmRDAwGMJiRtlNbQllfQsq8v3uf3enVuhHdJGgQV8OH3JbPWMQZ/LVgCX4arREk0/AuZ/YZv+vi13aOYjQBHB79eWyjQwAdZjtprRCrP9B2E2n1aI7dPrfqCbPQpn0jcXuW1dFAgmfLi0SLYW1wS1Nxkig3BsljcWFEBQMF81T0eBeGgGlGMm3ghaEl59QI5Q9d4ulqC8YTTudfPEyJXkWSKwYEulLAMoMRDOBeFi4ZYKCLqBKWViI2lc+/L8fIgzemRK3zS169vVpQosOHlghtJzePLnnfIixBMDAYOG9INjRaeJ5h4Dsny6BG+uXCd/GRhcC1cFiuWHazfKBswQn4mkcU8FfwAOy8lSS2AfuN9wRrgKfadi/7ewQ3xr1Xp5cdkaCBOmy8EoYxgPlwM7FwCjzYayFIzrA19nQIuhX2ZGkzhMBN7HbJ3gz01I8FlqcsWAXDgbBAMGBF9mQKRwOvp41YDeckw381lho22O/S1D+8s7q9crhwNqPLB05Pk/Vgu1J6h/cWJ3zDQExIFwvOASTRB0+LMG6fzpvS6TNLgJVBctlnIkzeWYEdfiAMT0vKjJh8XhjIfV4AS16O4K1P//ADeFb6R42UNStOxBAAE3wGHgaNN+0D0hucupqs+VeTOVPSVPLFr2ANgLSBwgPhiHWvvEtoeZXm/sLMMzO1zJEEh8XC1cR72Gcdj/2m7CF/51qxWCMaGxN4EF7Hv55o8xy4nxAPODi0BUktuBQdCn8zEbA3fFfJ36B6zP1igoiFAzuEj89RVLRFYtEYrmUcHdVnyNJU1YCCrYRSAln0CFuiaAmq6s5g7DTPjBx4vs2iakhhN4IKWdSTip7LRN1Hoh8bWIxoIOrLFnwqgN2d/0Dsqxgc8JKnckwf/HHKjvd3TJFS9s9vQfYX858xOACABpAIoEJWYhDbqQ1DY2WLriuvP/xPPmk+Jd8otqjhaGDAcEJmlVSDaJVXi3bRIPGBJeCh9Cg4DBuno6IDhh3Rlr6r5VP7jf89aT+OKUiePwU9RpnllfKrCAZRLOCVPE/cJ94nn9MVvAwIHSDP0dCBjic++88GYFQHnAUCCrQCrLwSI5SpynX2nXjX3qmCMDYADcKih6Kclp4l3wE5gsYAkRXEJQ88SLvwWOg4/dp567uT5M888qmuvI7aP9IkWeIMEszHZXuDG7juSTM/O/Ink+bWimSmwjeXT6xydCs4CWjVZRWHssCeeFxpS+GbJfpxT5DqDFPFgQ0jaRIAYTYdL/JwK0GNExyRbRZ5vUWeDSG0yEaOIgtP/x78XywdJC6YbaRzs9gC9RerFiZ5Xs3zVZ4jhgFkHw4ZBeaX4RxXmbKjATKnIcAAM6ThyWmyqP/rBTMQ8yakshGtKPyX3SJN6mHxbd2+27WcvPZI/AAGfpu4N+znhsyXL5Dsl3GWaqaTN4cIfIgZ7d/hC4IWvtR7fJUQvLDObk5ctMMA9egCXg07+tkGHZWQAP2ig7RDo0GBELloLRVuArARgCLRzPAwBYHNC2teRm1k9iAq+JxXoN9AkMsICilWQDhMaNsDRkMv8EbA6fgCghmRxj2uaEnha0fQgEDrkYcUrPrsqqcT1EKbunpUommCq7Kzh77C4DVdZENZ59SMy2pr7Hso+qzrxiq3hAx+csYWi4kjtAE6BD6G7LbTdo+5XbqC3wLdwCfkeCiB+sDdAUsLxBhAc4K0/HA00DSEsVfrgg6Db6A2yW+gwU6Ctd/aLUwNqQQb0ANAChyZMAemD2EolRuHAmdUQ7kc+WFq98Gon1p5LZ/xYFduycewm6uyHcbaTdxKlhz2kOJzhR4sHFKrS46FkYVm1Fst8LEVQvaOpkCjAh9m5eB5V7JBKk0yNxtwomnLZJJ2fZf5unwAh/G5Vl4n7mbnFd9+965QuKLt6moziwGEHquefpO8WBJNUOMAgHOhjtWb2qGX0kjgQzxIJRQWs/CUPhV8yCDatQ8jEfQkm/ogQESTrb5fdPMTdGKsFAq34E7vdu3yTeErwPBFlMQulQmOw3dlGoUbvhIaXNwLaEGgIUloRoodn3lrPtntlf+0ACfhYQrG/XwIhwjj4EQIP1Z9a4Z1O86gCwKByohAxxA4rxidMlTpSOOFAaoU08BvX2dyvmgdKzMOkEGS60L9TfBmiA8BbsEO/SudBgwN/zq+/D53j3/P9i0rU9sksbuj/AJFhfPnSDUODSC4tWAnZkE+kAmDxvPYXvgjviz+oeeYh96KYtgME+9GY29FEoTvjZH8XQE8iX5Uh8GT2RlDNZZV09f46e/0H0Mwl9UEpAHYMJ3VKVfaBquPYfahBQw6A1XBEyUYZgFhQTPAmlC1waGmQf9MpOVCKLxw+MvJ1OYBhcPCpL2SVe//kWuWR0tvRFGUZKvFN1pQr931RUI1MBrnwDpgAFHU+x6SdLGwoqqIvgS27KII5IwcYBYC8Y9pQsx2DwPAMwaEg/aPe4t8QlsA3k7Dpp5r8XFKtuUy+Av+/pYnBeH6gVR/Bjv7k8L3UQxiFJ51IBwONniCh+DzcFJsePLVkhV4ONcGQXXyLH54olS8EYgztHDFT3nQ3xvzdX+SwjSbkncMB+DYR4YzjBQaOtaF6nbdqimAVkADCpNwvel33IhTXihd/PlpeXrzEFDCIta9hdYIGnpgjWhDfCKm+p2WP597U/FD8SmzjKoWDPGXRw3y3vlNr9grDsAFr/UXywAu4J1QWsOwdADNp8Guj5Se0nQZwvw7L9WB7weqqVgwBZBWQXeGGN6HClIQk/SIkhJrTy0dOt7sma+9TOJ6O84gTxVIC6CyCFZQDOpE4R21ZmDrjNqnnL/XQ7IMOBfWW4krsosMPygr3oQCTjEeqS0BSP5wXVWF86R7yL4YbBxAk2eCowM03BPiaJGpwJAtkBkfbDW1oonllTFUtBdmxWLAUZOtZ/OUEEz5O3g0Z+n0pQ/AdCVlRdOWeXa6pBSrFmIzIZ1lFrTdFBIVMAgn9MBDVYw0VaN6/1HSaeaghOv/+cKLZCAG2d3fL89JUqMWCNt1XoAAjcT93hA1twEmfytf0niYPASr/hoNRHBi7zvXHDjtC7brnVrdT++Fdn+Y97C3aKmi327wlZofYAFi/fZywGFKpBd8IIz5dvK/tOoWjj2MPFMQYgDUAHIxToYGwEvCoQImA7lqtkvgiezdE1VzXrLS9VZSuOPkMVWMCdDtgwMjhuVoABj7uOAGgA9o7+5uPcVNoZzhMBegawXdSBP8E/ZIg4L71dPK8+rD7XDgAmztOuUE+uL/gRrB4weyH8aTCQ/gRDskcf0TxT26Ndarn57h6BItTQ3/TlVpW8nwrNgAO6pkjHjLqZUIodNiTOGtZKbvpiK7QINilGQGfoEDCBpw7Bt2tKMZOuy98m1p+FbMi97K65YGQruX36NoAiRXIwXA/CuRgYbR3RJ12VQVCA8Z8zd6jdLMuIAy0gD5oFxn9mbQBsXIvSCCb3VkEdhDToIqwtqFanzN9coYScWFZhxIItQPgRoRaR0fajR3Z0bArj/nvilZ+H48EyOKZrJ5VsbquogIBdvHQEiJBoNYOyJzragHtSjI8AAmnzLqxT7X8n7P7MoqEsBbO2umLWnctpvbop8b85AA5+gRAgbSM/XrdJiQVyVv9voPrHMhbtKlTNHYZSjHBBAcQpEDKkE0UZBAxTIGBoRKzLGox2G/NaDGo8wYK4jP7K8UCLB1WyCcILKz7W3msuc32YGrgCFP2OBAb18oltDoFQIKmZPhAzsDtx6X0DN/3rXt3t0wvYNk05JQgU/Vlvn9TpWLglHA2ngH7+c5tyRYEE+b+gbGAGxAYNkCAZmgYoTYCzQEIWbPXwozk0OD6eii2hu4O2XUm+z59evZOPp8KBMoFYAyAEKbgYEUmSbZy7N7xyFtpTDqs6sDzwoaztMmbjsc5Sj1KIZWYNfSimj6JmvqlVQJBg0Wwfm4B3oGMCk1naLWJRgmcNvDOp9voMlDKAys4kn8HELmj2FuKLTEb1n78SefYucV5xdz0hOiaFnv/8G+3MVDPJ2imXi9NEA4HlDR4Is+mfv4lxNH41qNuqf6hgRBq9E7X2dJiwC1L4KXaof/GWGh/qPUgm/haVIrles0y8KxejhGOIAgCs2lEJO5kZCMfAUT7rQSRi0TpbeF6jSv1ylEcAZIFWQCRAg+ez133JvlXnLPYHgg7+U8CIUO8P3iPj0+k/ZrJi2obJeQ3ZpfQzUjLEu36FCMUnKSYJIUoHyjyM0CHeyXCgJj9c0LLRAzDI8/qjuABlI39CsMAYI+fYQ0UDc0dQfhD4GXOi9IPfmWgFTI12W16jH4H6/yNH30bLFXv5CLD2nvoAO8s9Mn8z/giDwj8WzAIrq79IH7dbqwT59+T28sRPOzETXxp0WQ6YBdcfmANRRB8NPehgjDc+BXuCs/gvzy9QSxISfjMhw1dO7lLvzgejhKB9WpxMhzjjkm2VUojZfwrY9oL9Y49W8ZILEcVxYUoRjEZHQcyQ7dSAbTAXZRasGhjXNVWVWrz+a77MWFUKK8lE0/KHWPbD6M/ufiWThfZ3dtEGNeqMnZX8Ief7MUfqPpe9IViSMJclCVu3Y5Z/F9w8YG+JZHg8aPh0UqAgX7iIhqUQri2O26Gg8w+DAOEo6CxQY4HOAbSwjDVgQN0Clpm0SowMsOoFlgGDnwmjZKKpyhrCjVO441WYgY/HTHf2iKfCnWp5vDLvByle+YS02f89y3O2f38kZvcnS0bva0zPqS5cqPa3Gny/JLQaanqO2c4qXFex7SuUHaDW1+2bpY3LHApXhCkQFsQMnasOuDS7Phb7mOxXwYmA2gKVO2f5mATOJEloPU6SAH4kZMPaDXoGdqHXFEveT6fYnWJ6LLUHGBc9LvIfi0U5QQVAF91diX6PbpAlpL8zzXDFDVAmf+FfxWNXYoHfDbEGDFhiQKs+BuvqtSPPVHX0jt6DAGw1/P8AL5NLzL5T88AQxGOiTWcB57gj69HYKThH4TmGSkqfv1fNdBpCdNQqcANIENDjHR26ifOyO6CMn6vOD/yHrgbuey5XwIdyZ0ANukaHhTSwdyCG592xRVHO9QU/gHZ9vbiuvd9WN4Btu069XDxIOj1fvS9eiBv6A+OjHXGqOFEzb/TTfyxgRYONouP2Z0HpBihDFgWcHjxIbqV1R5+AJBgbWn9YY0KV3i50CPBpw8eL65r77E4LOqZRqJF16UbgvvoXb2MaXRPtALAcyBSgHgTGle+X5O8QAjGB4ciFpgVLMppZaCPGQf/hM3GDbaDPB6uCOgRIdPnZ87z7nFAg00E2DJklteF+F+/Dd58Zm/VfARboU9+V6u+/UMCBcUL8M58bq3+OVzAJ+JnwUpsjEHQrBGMFI0CwK1rA688xcLF9yhbAILbjuVe2lo5Z81mX9RK6CcxYXSLvQ4iQS4/asoQhYdwQ7B66O+wDHzmqg+ws88hmiBvSh50JOBezpN2urcYcM+7ZkDb6tU0ULgwmvfx7FU5o0ew+5w5vJXRKmA5gIBVsg2v2z1GlGsVwS5i2slTGA6Q5E6wMq4hVP6zab+r9JdU1ct5MH8oezb3oVnAuluYaBAnmKZBgh/yEEgSCBEkuX2nCRAAFI1tnNbgEIBqWQuD4cKwX5RfIgp2+hZoGDNoPErhoCucJAiNrUcvqRq1xJCUPW8p94FFyXN0MeSzLGgLHozHrvhnVMpXQRtsOa9p1d4m6zF26SmkOVBf9Vr8Z/FHxVCJ5QEKMnz/1j9fu4XEHkuz4zCGW54QeoK1f/jwfjVODXWFyp7PBJpgirpTOoac26TaT/YJFN6t7uFJz4YIAkCBnfyQ3vr+ttCQ0C7Ip6EDAcGgJ6P8JZqfV20fmAi0VGbQ5DIxYlBNUFy6FOOSHqlknyhHIiiB4QGBJo2jiXhzFK54AWLARbhUjUWrRVT1nMlwzvJ5yqYYdpl69S9od8lPTPWFcgjgysiBQgaQVS0PBAmVrRzYBmQKs0WdSjRlxzgA7j0fN+8Qpls9QBxp4cf10UOUTxAknAs+X7yi7P84gK3cEJPCOeHOQlMkgNRcc/UaI64YHQ56jFmBA+QDt4tyP/U3crz4icQ++HbYMz3kYXBmwKNG3Leuh4QAAIhtJk4UdX+BD8hwKQGoURzz+AiFTgvaV3t/mihevOkX2cIF2xtXimlTHoAlsw8uEnVoJPaNjJFFEMVBI0U0xP/Q97o7n8X4H//bxHn2Or+QBNpYS0A8n7AmFSzMLloh4AXBw/AROCaTSq7r7kkIAVR8p1ofzpEuCeu2AfaUDlp0tYT0C7mnvi/7hC9C2sJ5sshU9tW665UiUI9ACGEQ5YPvq6RkADehOwGVdfpV8jbr8WRAcfGlePqSjfLEJbgk1qN23E/YzGx/WalOPgEtgvLEgX35YVy7PHd+0IjVXHdA68LaNWmcCZ/N73rZtCifecWg7Ka3yKPtF1RauSIfA4SNTOkTF6GhMP2w72YQH40HNP6Zbx4ju8NO2nX76floAXT2ii3fDSQSOZgMc+G6rDySgRSJZLGMhfjgRIo2jABKQKdCQaCxL4fZ5i+VnjB8jEcDFwFYZMrlzB8UyIAOgqTQhKEA4Z0e+LM0vUtoM4Z59Xt4uOIvgb0MAIyFWZQ3h7h3NcZ8vfT+pKVkWzWXq3JqSFVK4FPXCAbFr7sUBW6GrDiSddXXUoUddGQORtFVAXG8dbPoiBNEgZJaQMw5sgqNV22ZU/9D7NPW2u3Ql1Pe5PBf2VoHsAJZqZPS9wfYad+VOOCw8oMACB+wR0/tcC4DkyKBrYlFOkNLtbGHpB/UfyPwo3/SBWpAVolRkEAQwAR5k7Qedgz5N9p0LeqgYbtQU/ybx6H/2MNDOUcZSseVTPEcuxnGyKD2PX6+R8o3vo4TlzhjeFU2B1u362xOiQ7PACws1LxTjlWo8BPC0gSxHgG4Bau0dNqJ+FEfUIYqn2AQrl6j+UciPbAJNJZoAAK48OqJ+K9Dg4r+rc/VZnyOxnq9YBZwld118i9JRsGtInw5ACXXYnIW3Az2UMOORp4v+8cvKPtLR1549xLIC2vipkgpOYhQXKms5bw1sKHdsUqKHzlET7brmP0b9Bw3jo1O4EO0w6VXgSkUwK9R/AVYU6NCjL4T9QL2fclbgoajW9dnTwYo4rR5YwEZI83eibff9V9sKBUZ1wyY8mcyBuBsfUeKNkgRb1Vp9CQpyuu6DkweEGUPDCdcNLi1hPgL6eghzvv2k+jxTNFJZLJr8rnLA6aMlmn4EgjO4pr9fyx32ghHoBlbARVjOxWz3HFDnSZVfDDr+pxAwJANhf2gcjO+eKv0hAtiY5KOkSpc8zLjvrlieVym/bCxXNo4jOiXLIdAz+GpliRyem9ao54i2/2aOC40t/4i2D3vi/CT8ALh6YB/bW++srFTigKz1T8H5Vw7MlcM7tbe9Zk8c5Az+HfN8P0aZhHPWfj+ABdQrYORZaBWkwyUhzaS8IpYshc21bAKWIxzZpb2MzMlWNo8NBTAiHd/DUfrw7uoNSmiRY2L2nEZb1FKgyCXHLbBfsShrMO4Ry9eMPtfJLqjgl659RZI6HANfesx+RhBJ7Q4FwwACWHBVcJeslIrtX0laryvrX0kcErPnTC7joZNgFYmY8U1se6gULLlFMgfcBdCgJ66Lszpd7efsfNbQB2zP2R0H+XxJnY6L+lZx6ZH/GCzfNhVgAZJcsDrIXkjve7O4EmMHGAd23gXRQ1eHoyQZC8MNx4rqQoAHBQvVUrLq/7D3/1BjmwH2wSiANfsBQBiF/rRR5zfXfwgQ6FW7JKHbOaqLBJicYBnUFAMwA2BALYiUrmdBePN+yRiAWVQAUrEK/p5woI5f1fKfehlU4qFDATV0JXpImvesL8TDc7rDChD14ZytVuuBSUR5sXj+7x7MuLcVDTPoBAk0CPsZQbHDaEI5GxigAZkGWW0l7p4Xw1L2eQ9v/nbc/zBxIIEMFxq0B3Res2OziA1g4KYewqev+2b4LRrljKvYAAa0gPQuX+grS4CYpBJiRFsUBtQOOkZYOuDIHWTRum+384xrVPkIZ4CdB06OWnhSsRQqygE+2KQhZDEwKCq4F4TS4CgpEIpoqudTfQYlFf33oOzECyaJC1aRLRHZCOh//KpKEFw3PSpaCxMjskFrwrNsvqlNeNeWpveKESCT4AA4HHDZCZE/ChXOAPPgawAIXE4f0jgHg901CPwj/hzKLdhnI7pAoDAfegTPz8mX79eWyVUoD2iXZv/D27g20tfXlC5BiVwxJkf265Iit0zdIltQlhEuXj2la7hT9snjrKt/CjaEpRDB269Ntlw3uI/k1GoaNOcHXl1cKlxeWrYmbDcDyyuaiqXw0Jhh8itKEeZBs+ArjOk7qzYgKdfANMhEKQKXLOmVkSYGwyVspyM8gUKLdIGgI8Qls+bINQCHBmZlKg0HNsFSjQ2lZfLJ+s0ydeNWBQidE1JqEouyhgi7a3tawZLbFTjA5MkIliaUrH5eLQ5Szh316+3bTfjCON3/mlKbJFcryz9dUrue5j8WboWzuXk/nR58mheCqwAhds25APthgwZBP3Qm6JzUrmdIarezgvbt6Q2yAzL73tQk3fBUwY4WwpRVebMwM5kqGf3/7k/kzW7IshB3+QazQ7b7OMNuFa7EtuJqd4QkY2F4kHRXFy6W6qLFsHtcrMQquX93OGnwPg0NAgSOuEwkOWX+JlwpXcCwWe7fjkvrrcoT3GVrFWjlPxDjFQeYBc7xAGSwMAHzrv3Dxz5Akqt/8pqakac/u/PMq6FF4Bt3ZKBCpX3Wv3MGXAfjQEYfZDrDG2l3/aABfkuQveCZ+oG4TrrI9nLWrisGQCq/n+GD9HUGVfStQl+/0vfM1F8YORElBEioaCs5ZIzI5rVC5XjtoGPFedSZVk0okIDlD8oGkeALPO21ky+DHsEBonXoZnld6AF92rugsCWJ/hYYIVg4q86Sj9CIf+rT0F1qm2OqDRglZGFoYwGqBNT28wSCGp7/vqS0LEjdb+5BPQr3IzeJd+t6+65aAAZkjejLF0O88nfxrv4NNfvb4ayQrZwXOD6qhMS+5X3vqBs6VixPMmFn7HsP2/yfqAUwaP7vUbPoYQ7KCU6Gg8JJgzJk6fZKxTqAduBeEV8uL1FgweG5qcol4ZEffHTtTJRhnD00U95eXKgAhbtQLhDLyIawI4UfU+J9MzAd0+NV4hbLe+wLbe3CbPxjS5crGj1ZBTcO6SeTOjc/VkHgWDMBn9K1Q+CuiNb7IFE3ItYsBaPdLFD8KXTIhbEGYMZ8aCws2JUvb0D08EUAG0zMP5403rgkZq/HwAqTjIGHFv0ht81drNrNSsTnHvTMbeWV/vvQSvPWYf2lS2rwzFssyhr8N2nEijOxHRwR+sG5sA5kbERz6tJ40NS50PaPOgS0/iOYWQYqe3XBrxIPdkFSR/zYj6+r5WU5hCvS0oOADmoBbQTs3idXKeZYtAxUYIg5xueMRckCWQVtbJ+VVpKG/oDtiSEH7QCDwFMJ9LAcxV26Gnn3Wj84QQ2JvSFcKd3hojELjJATlH6EsU22jAZAprp4KTAqJHzxkTFtYvHMigaPuntHu87izR2o6u6VYBzp+Xlb/LegknrcYx/5yhK+RxnB/15RiypLGIeZ8EGj/OdarShwYtPaeoc1uCAwMdQ/fVXc+P3DpD0wqLvgyKwdEyY7DLsZdN8Zvn8NJXxjVj3wWO06k0mG66r7kEAOEDefbdaXEnfypWq/54cvhd70zuMvhJK1ua6C11OjSikcw8dBzwEaIiHaAaqhCP9xtO0owqWBQftHghc1N52OGn9YJLItACB8P/UfpyllfOdlMS57aWBfw13meftp8W7b4BM27NBVuUFok05V5R0UiBQAQa67XjBthlob7gevV0KY/hPA3OE4eFFWo3/9X9Gg6eA66WL/4T/DCj/jek2VAsZoqdgSe3YEWgCDPTv+zerurZKAzEcQLEfg0thIqEUcIr1vQ+/3NaObJRQAADK8SURBVMooWH7wt4k+BN/p2CmJcZrkpLjk/FGoRUTy9/L8fFVPnZ4YO3rlWcOy5Kxhdb2+/RDf/ev27N617IBa8d17Z+u7fblxi9y/4HcpRu3luPY5cvvwQdImySeCZn1VcziSIPeMGtKojtCdxJgYXl0ClsJysBSWh2cpXNqvl1zWPzfie7M0ARNJUg2V+gKUUqwsKpFSUCSb6vNwZm436ZeZLv9F2cFc6BSwvCQOHRgEtkHfVulKU+EIAEKBpQjGw5zSs4u8u2aDvLd2g4xqkwV3jPqz+Ma57wL8+L2wWCahZKV9Cmb8YxjpuVcKF9rKRRoEF7Q4+7+LRSseR93+06gNf1Li20+S4lXPSMmKx9QtqvK+k8r8OdJmzBsoM/A9tzMhR9ruj5m8KMJnzVi51wvvhXtkT1WeFCy9C64L05G7wp1jyL8kpdOJ4S5Tx5Vtpfr+OeEScSAsKg9CG+H/7vD9MAuyT6pQikCGQ+XO75UFp+9GcXCzGKnKUBLhBhFvYXdp1uae3JcB3Yedc86VnT+dKm3GfyYpnU8A4+ZFyV9wLZ5nuJRt/h/GbXwQuGX01/CbN7Yb++rdCuFQzKQrYT68ejeu8jWZhNp72AI6IGJI67XA+3Jdo8AcFn3tMvFM/0Cp0XvewHcNCakKlAtQnM7Ulg208po7zrftuv7Jq2A5vBp0jvO0K8UFqzcVtWUSjsTkoL4FXRC4QSYEwvb80hKIBLYCG+BAVSOv9R0mOmbhObNPXQfXlLPhOgBRxnnfietEcwaEa+KxIlwaGXGgiTc2nAQJ4Brhfvh6MA0+CGrO0aG7uP7+jGg25RlBF+zhDR3sFwdKOeIBChCUqQZYxdIaJxgfXggf1txxIRgvPykry8CuUhfD/c+rlaifk0wPWGWyzEbSARwTOIF1peftpwBSvabEKF0X3RJ4+T697kQ5j37w8T6mSYduPrZJYPnRPv30ze/hWgCD5vee7LEedUivTydrys4kI2lnNOV93bCI3AixxktGZ/vv49Ickpag+bcn9U5TgAFIcP59Tfnce6rtTilJe+rW9e67AxZ8N85eKF8AMKCo4eP7D5czenWrd96+vKMVEvmGuD+Mb99awr2XLJH4dst2tfy4LU/KKMoIYIz2jpf376V0Ido2ITDTKaWjX+AyGmcRPtcD+w2Vm35ZKGd885M8uN8wVZ5i2GpSXHJFUbG8DGDlTQAG1IS4c8TAsOPR0M+RM9nnJLDtu0kqGU3riSTEJIpXPSsFsKHrfCxqlmsV/UNPIw2+BABBQs4BktBmgmigsJesfRn14V2k7YGfq/r3nb+cg5ndHyS1+7mhl6vt8i1fSMHim6TjEUtNj3Pnxk86osb8TMkasud1Cyw72cgDZRvfQ/J6HdT68yWx3eGSPfxpqPl3irjVzEH3SRwSXwr3Ve6YKVVQ/k/ucDQS41OgEXFIWF0I3sgDzQJaK6pl+ww4XEA0DuECU4Q6FUltD4Nl5IF7JXCT0oWfe12Klj8MYcd+eIYUyRz0Tylcciu0DH5XDJn0XCQ5ZgEGQEND1YATHFgEwUM4GSg/e4rwMTBTT9FDJ6wDNZQYOPqPVMJ4voPW/2roDyndtDjUkcR5kOR753wrnv+9Kh44KGiHHC/OyaeqZM+vyYRkTTvG/DtofSck+7AsFOP5kwhiOpQgob4xPBDsLc5X5wsFHY02Qm7m6A4NIDyHtAG7jSABwBJ1D2g90PmA4YAopHf7Jss21En4R1lNsqxj5VKlYRCH0gG6QdAekkn87gq+P/HvwaWBTBGKVGLygKKPDibMe0l43TUieA+cF9ysxl1hkV37YFzX+7bxjDzmvvcKcf71AQX28NG8sAWsuRP/p4BdEvfIp6INGxf8xB27iXPo/uqz6H7oBvHQLQACf64zrgo+bx/doj2lslOEjWnNtSfgbwDAPtp9cgYkIJwo83CdfW3AnpbVphiBFsCgKUb1T9rm9pIaKYAuQKRRGMW5kbYZeh7BAZYe/LKhXE4ciD80JrEN/WZEomFQXOmRtQXVUl7tmw0IbW4sBCGNWLy1Qp1rbEf6emx//GAIE9H0I0xTu/3wf9dulL/NWSQFVdVyCBwFHhk7TDrEeIZ4tz9UA26YAqCEegOxjmOnfa/sHdluDlglx3TtBJeE9jIBAoPJKPnY3RGto8f5fXoA3HDKNT/NV6AB+0twg4AH7SG9tQ/QNTVZnjtwlOQGlHnE6tl01G8zMTLCU7Ye238gsZxr7PK/UtCwAmJ74aIK7AFG67HvQjQxWyqQqDLJTOl5GcQOe6ml8Ld7MFP9axBgUAOxRL2mUF1bU7QE9PaNpv2g3yvp70yimUTsi+HVq2XnL+dK+eaP1TOmIWlN7Xausq6sxvtjFWQGOBPq6qCdKFlI73W5WjieZRvhcADwYMePxyuafXLH4xV4kABWgJWoX9Ef90PP4jkARJmS1OZgABeHASQ4FMBFwxNmq/7vif0psFHkYgSFP6mNwXGOzxgQNJ7GOY1+RdJcc/5EfzMOUJK1SaeIgxoEwyDEF4F4oP/ikBVaHzpBgefChNrz2RtqMUoWnJfdIa7zb1RX0Tkg7u9Ph7TQsE39K9jCYYlFqJln2MuRok7gxNGppwIOdAAg2qiJ6hY69B2cVJS3CZ5Tc8vZSpzPfxr1GWZ+Ku6p+Pt0+Z1gSlzpP2S34oEQpf7tJ+KFvoKgDMOR0x4AzaFC4UU7Z4jQNsmQcIQmzKEnNdNt9ZxgtXjLwACpDQeAF335ImNTHH0G+9wnoDUhnfG+ITyfvK6YBa5/vFgfLPBfib/mKOlzXfsvqfl1lnjef16cYLH4wa2A8/bJVfxmYfmMXewNGhd2/d9bju3+X497y8i09DPqEXh+zi55c6Hvh23UFzfhBSM6Jsk0uCGcva1CBrULnmVfAwvJR3/Ig9ZAnGTalGQQ3Ljso42ycGtdHbZZl5dd39e/+3+/FzVoPOwAg4b0w9+hPbxS7dHlUojhfbZhi0pnONNNVkEJRA6Xg1puFaTOM/FtichGIBM0/r8MyFVWiiNh7xhrccPIetG4s07v1VW6p6XI6yvXySwwJMhIiQP4R8HGwdkQbsxuJcd37wRXCmfjbmRxNRX9d827LAg0YHLIxSoSYF1oxS7gNbRCJGOBYAGjErPTjCQkmkY4k9pj5nqbsaleadlH5kFgbPsGM5mW4UCbkyyP7s0H9JqSWrCAT+GVkpVPqCXcM1EEMbP/baanMcFnMsylpmQVNCXeV+BB6dqXxJnYQZI7n+gDD+BQYRYagAguzngI0VEscB8OJ1gxSViaLMAi0I4+G8kvAAIkwE2VCNANwHXh39Ssr3fe9+IB3TtirYFIHz4BOiUNmPXUFGvA/CZMnLi477wIwni/i+vKu0WbeLR43nxCvOUl4l0FkBOz2txnFUxqa247TxyY1Xbe9gyuKxUPxPoYzjP+gjaWige6AqTTa5jdtgraV7rRjj7jI98pZEYgsfUug/sCtBS02TPE9a/XzUs+QhqNFegQ0uxu3XR076vKXrwoBSE7glZ/ZLSQOUEwxLsEYDP/v4LOhRFeADf83PHzHi4IeGkT8F6/8qDILpTT5LQLd8lef1wbcaDEj/hyr3+OfeUBHKCAGRM2+8oztTzHHhqBT/8oktmYyY827pvUPtpLojqfzgTHvLpWSsEKGNI+UdbkV0vvnASp8XiVXSQbe+XkzjIGTgZWce2nUHZfUSKD2yXKhB6pQkFDszhtSB2N7p4Z2xRgALMJGdctRSbiuqTaMgyza419doBBQ/phtLunX/NRy97nvc+j7gZFEG/C0hJ/3hGIpqwhVqNEV4PKHd+q5kjNTsgagbrtifWbp+0cxN+S2h+FhLFO1DL0RKNsodMxm1ViuWXaUCjp75DOR2/CDJILLIISVU6Q1vNi2CA+HHR5yZqXIOpXplT3yza+K61Aqa8XoGk6tESJbzUMfTVPbutds5ftIPOj8Le7o+41ywMCgZlIGqguWgrg4EMACB9CvHClUPiv4+Q61gnFK8ksqYToYsX2b5ToIqZUYZ84RjENyDiIzxy2V8wE8ln06oJIhiXoHDsL0KATWzZiNgLevK3ifv4+cbRuL65L/g62xGapuRp18qh1ZzhGjpe4Jz9Vs9JmN/UgwXffeo7Evf8rrOpyxfPlO+K+62KJ/xmJLRN+uBNUH95VATdxNzxk1oTa54a4oufxW5R1JcEX9ofBen3P+/8nnhf+Kdqx50nc3x5X+83+sQIdpNj3WdQOPDJi0MGs/d25T5/7ndT8ZYrSHoh/C1agAApqToPGRp8hQgtQ/ct3hZahga4R1RceDJqBR+Jf+S6irnrA/iBYFPfqrL1G2yGiB2s5aa8YgRaGwV7xNu0dnTy6X4ZwaW5BjYT3zuwq//x2h/ywrkx1b/7mCvXavVW8/P3gtrZgAU+cs7Fc9uucLK+e0kVdF8k/V4zNke5ZCfL5smKZuaZM5m6skIN7pcqUvumyP0oX4pTqXSQt1Z3TkH7UXb1n1zgbfBkE+6KNkZhVbonoR2AeBAc/B5tjTXEZNAvagc3RVV5buVbOye2+VyQxgU8cbVlD4LUNXTdcDXi9u2KTEnlL7oAfhA2M5PZHQufgBtkx6xhFW2cSmtrjEgUWVGybDm2Cm/Fru0aSTYT70npcqO5atWs2XnVJ7311A3uxd1/GWvrdpc1A8MUBK0i6AjBY7hEYLrBB0rqfrxbab/K9qYAAIwGEwt/uEsGixefUggeHqlfO0DfHKF72gC17xqrPXU/y/T9qdbxlf+xHgIl53N+f8jdMZ4G4N6H5sHwhZvhhscgEFYm/VXjX/IEar04KLDA7h8wDpY2wY7PZYf8+Cj5qsLsMBQQcaZniYi1/VZWPuWEDGHjeeUYxFLTjL1CMDzPQwf3wjfXu4e9EM1rRRk0Q1z9eEs9rj6DOPl00vE/Oq+4Vz5O34b1ZJELGiSGIWdtvB90UUKcfaVAAlEG70T9TkBXjZSkH2DAC8ebQcLTrIo72kf82D72+ZTuyEWgBDCIbp5az9vIR6IHE/cUTOwtr/9cXQoOgRpdumfHSBnaR4WrBPBBOJDvhwO7WDASz4clOdsEloZVatoLl8MXyYiwlculHm6CroMmk3ulyZN80GdUpOSLaeEP7Yda3PbGPNfv3jEIdX0s06QiQNHb97AWKzm/cqB/cCbaD1n8DhCbfX7NRnjpgpHQD5b8lIhuBWCSprpSukjP6P7JrwTWwUZwPZf6DpdXgf6kOVGyF/Rtmz7NHvSSJOdY04Li0vrhuomIj2LEZInuqlrNCR4AlCbRbJKuAehGM+KxReJ/+DSDnhNDT/dtkiNAFgYsMvNsniLj9azAQACDkzYRGwjvq3LjMIdLhUII+zSv4jCmwTAyNsvVvwNazl2JNhB5r2W4+I+BgnXeEtnOqzAMaDt5Na6z97QkWjJxg+YBeuOx4N6yslwAHXqCNP1LR572wDHTU1uwHHud6LECH0Db35LYTehtcjHBBjM85+XQhSOPo1R+JfmvjkHp15A4S+QoOHiuWiNYb62FCR5mHpGMCpTVEL/8k4Qao5HnuH8pi0+qRnXCOcF18q9Xhlv0xGoEWwCBGA9nSTPMZAZYg7Cp3y8C2QMpD1FTTIYAYqmMQrudO1E4PQinC0m32+gV27bQHy+FCWDhyWQfRRAUegHnw7uJCBVpMhlPDlH7ptn2LRT/s+thybN8YAVozsvb/vN7dhVoAl3xfK7aXmCh3DB8o9y/8Xf7686/y38MP3DceeDc8RU3xckxshP/+xyMhtIuULqeiJv5kEAmKMftcV++elnuVSkodTnutjvJtX0Jb4VLcwgGxP4iWtUSjR6CmdLUPJED5QU3RYtVefOZQuALcC2eMk1CK0DXqe5BJkNLldJQkDJXqNgcBMHhXlbfUFGKmsRkGxQy5hAYBA9onZo+IjQBgaPst2w0bATXTDBcG2kcSINZRAsCZaroLOI87r15iGngXx+CxatP9nwfEFVJywHIEWvh51y4T5/k+XYPAa411AhQCW0tvQZ6xq96r13C3SE6rd4w7YgU6mDa+G3Z6pr2nklhtP7iq2MxuO7LbCBezUPoTKN1w33elxL34ta3eg+e952DL+KM4z/mrLYPE7D576z4CKdTTkMxsfK7PV8wYBzQfQoPOIC3R9CNQf+Sb/p4td2gZgSYdgZfm+sQXF1/bB17vsbnVbShbOO3t9fLs7J1yyuBMaBg0/KvTDWUQV4zJUcvyvEqZCtYB9RFe/bVAOmfEyfSLelp2Opb9sLxJy4G9egReAWBwAgQBH6x1YEBlu3oeAk5XDewtVaiZ/PeiP5RLRauE+L36WXdX5/N+Pg2CeMvC3i4SijZV9x0BYAEbjUu1/s4H3lSv2qk2nUl/nhmmwOeP1bq7fIOUbXjXxySoTeLj0gdI5oA7FaAT6fsR2B/dXS7VsGes3PWjVO38GSUKqGN2F6tTaE1Iq0WySlqiZQQaMwLUL/C8dL+47n9DnAcdq2bxPc/d42ty5icQHJwqcc99aZl8arkDwQy4EsDA0/L/7Z0HnBTl+cefmdm9Xrmjo9QTRBEEREH5S1dRbMHYkaixExIssUWTSCyA4t8SjUZD1L8llkRRECxRihSVYgVRQI56B8f1srsz8/8977J3u3e7y97dXtt73s9nbmdn3nnnne8ux80zz/P7udBf6zNQHet5cKZXwJBF+iCsqE+aGnaaOuryzdf+iifoFxGLSPo3u6KMTMyT7SFD3SxHI+jgf87mXre+XoNAzTPqtBq0ILQTx5F+0njSh44mdtmIpLGGhHHjn8mc/3tyXwzLUDghKMtQCBz6Gjt6ePD5WoteVucwDjl5+PbH8qv15TJElmylycHfW2ktS6Dhdz0tO285uxBoUgIPL8+jN74uCjgHlwT878r9akmGeGGcw3sj5t9p1Q05/m8Pux4PRcQEjJUUp6u+uUVei0ffgc01D9/55LVtE/BAuXpLcQndAJeEUG0M7BU5YLCrrJwkYBCKUuD21JybyHez7ttjW27lfFC1fwWyD9yUOWSeb1fYV76xLN2+gNwQVfSUb6dOoxepundH6lEIHPQOe2xyz0vhErCQCr++G8KJ6bC3G0SHy0oIO2A73Vm8eb6q2degT8CZAEnIJHCmDaim4S7dWr3uv+JM6VP91kTwpjJ/OQIDCA4gQMBuFogQqP1GYndK6n42AgRj1cJ6B9KEQGMJWHBGMJ9/iDS4F2h9ByJTyUXmS4+pG3Pn47A2RK28ci6AkKFx9rSQpzNmQDSxWy8lnmjjxpcb20uyy4F+wbXkuPbuOtmZqpPfD+OGP5F1xWhyXTic9DMvIR0uAeRwotRhG5nvvURUkE+Oea/5HVF3tbFBh7ojNt8Wx7SbyRpwPFnrVpC1fiXZnOWBhRmwyCFnHqjsA2R91M509Z+lceH1xCKPrH3gufkCJI/h70roH6jsEdYsgIgkN33SBeS492/1sqr0P0+bXHchqw/aD/xdl9byBCRg0PKfgcygFRLomOygnKyaKG80p8glCe9Dz2Axsgo251epodl94fYxneh0lCb4t6ach/95ZD02CDjwB1+H+HhaAStCtqwM1naVeYXKuicnBdst24IQ8IkOBtkFPYEi2rfsLKrYsxgp7OcF61K9rerAWspffSmZEFGsaTaV734bT7xfpQzUv6ehPCFUcxd9y9UIuDndSHs/5pISTVn6cdaCf0s7aiaEEX/rv0nWgxCwUbdftuMVtQTZXWeTfwZJ0Xd/qRYK1BxpcMk4Q2UQJHYeR04Ef6QJgWgTsL9Zq4Z0zsHT5vQsYmV+QkaAMfUaiBj2I8LCT/YtWBuGCxhoECA2fnkd6b/4NdHeHWTDipFQ3qAd0Ye0hMj+X+Anvk6o+7PDgvXGs5Bi9WtsW3n/C2TA5SBci0bQIdz4TblP69SNjLMuUwufh90qrPWHggcIIphPQcsEC+Fz0keMQfbBBLyOIz7Ov3Ewgd0ujMkXk2fBPLK3fA19iB/JzoP9NKwZtfHnIWuBMz4uCBt48B8zVtb1QSPgJFRBNjINtBPGxMpltdnrkIBBm/3oZOJNSWDa0A7ES31aqcsM2f1nDhIgQLAYgYJNh4IEAzvF0y2jO9Lp/VOpR3rw1PBozyPkBGVHzBA4sVMWLfx5F10zoB8Nyc4MuK4fi0ro/vXfUt+0FMkuCCDT8Df8pD+17zV0YN1NlDX0MTzxTwg6GNsm5q+5XGUEZA3H0yi8P7jxFtU3LQdCiLDxO7jxNtgiDoXw4clBx7Bt/I6BIj9b94VrujPwcw/Xtz3ui4ewpH0oG6Ch1+9MP1aVMHCZQRwsNzUtSvVvDZ2QHBfzBNSNPW7GOVjAzVq1VL3qIyeoV/WjYxei/Xur37N1IbHCfJimoXRANVg22r5+aZk4T/i/gfSjjlMODcQiitt/IBtPhLUeCDogcKHhSfvhWjSCDoc7R3PtZ7cK4/QL1cLntA/kkbXxM7K/Wk3WhlXkuQ+ZBGjxa0rUa+0fzM1591+rN9uVFQjeJFa/b48rXN6hn3kpeZ68lxx3PqEyDTjYJa1lCEjAoGW4y1mbgcAN/9lZr7Owi0KodsW/dqin/xcPCf6H+EvrD9Lsj/fRFzflUEq89xcaiy+++z1nEhTT93neTIKjsuNp5snZNLl/GvWElkF9W0PmUd9zSP+2TWD28ONo2Z48mrjovzQMlpQFVS5aiYyD0/B+3X6vv/VbE6HmLi1qBDQDf9hZLnLDei8O9erBGlvumaid73baV3gCnUOlP79c3c2Z1p+6nLqUct/pTuUQ3wsVMEjsNIZ4kdY4AskQnuSlMS1c1kljxpVjhUAoAloWggF7c8kuKiCCfaG1fDEs/NJJO3aEOkTZz21cTfr5V9UMUXKQXFOH1LyPcC2U8rxdWQ53gxdJ46fmvfp7n3qzVgEW850XyPxkIRkoa9COPj6iMzU26BDRSZq5E38+Fmwu7R+/JQsOCZwxoFpW54hn0t6DBQyKhTzZZcP+fh25Lx9FxMGClAxv2YYfSQNuFI7LJaPOD0mTrErAoEmwyqCtgcCK7WUNngbbLv6433uTz4PshLbAjweq6Ks93nRu/4GrTJs+3Vrqv0mt+8QXWe9gChwQOEjQN6smSLAD9o7B2pGwe/S1aMzDN5a8tg8CPVKSaOnksXTX51/Rx7v3qYtenXdAvXJmwQMjBtPorsFVm9sHoehepac8l4q3oI5Yc4YVL3QXfUdG4hEqWBBsBpyZwE++PQHlCsF6EtI0D+CcjytVfz6/7syAnsExlNL7V1DmD+/UEHxE2dqeCbhYTwPBrsDmfdbsKf8ZuhnvBO469I51GqQ1HwH9lNOJ5t9G7pnnqRt0tjbksgIWELRWf0ieR+8g8rhJn3B+zaTiEkifitKDCJq1bBERWypyQ4ZB7cZWjK6bphChtt6YNUcFDPz7sEid9f5rZC1+RTkwGFzyEElDWYXNWRG4IdQSYfd7YB+e0GM5dKzeytPRbQgJc7mItepDLB+QvWm9d+bOOKU3YVx5m1fTQIT7Ivk2BPZhy1CUZIRryio0XAfZFxUCEjCICkYZpDUS+PTavhQHZfiGtDhDo7uW7KEtB2pu6v9vQyHxEqoN755YnV3g36cMwYeFyDTgJZK26eYB1d2iOY/qQWUl5gn0S0+l1yacTEUuF20tLqVyj0l9ECzokljXajTmYUThAvctOwMuCT/VGclGVoFVxdZiFqXm/AZ2VzXBvtqdjYRO0C7IJRbU8xfP8+9nlu9UInn+22qvV+Yvo/xVFyOhAU8ZkQZvxHckd/EmCO+tgJjiC9Rx1JuU2Hls7cPkvRAISaB02/PVegy1O1Xu+4h4Cdb8NR2C7Zdt0SWgdetJjj/+nTxzfqeeumojxpLjN7PVSczli4jdCRx/fJb0wSOrT8w34M5bH6l+H2yFb9bZJUEFC5Cx4EAwwICQoX9jgcXqYMElM8hAbX3t5vjDU2TBuYHn55kzC2KMR5Jx8mm1u1W/5yfx7lmwmT2kzVC9o9ZKqDT+Wt2a9a0qOfhsiQoQWGs+VloSPAENWRcsZKixZgFudCPVhGjWybeRk+nDRlPcMGTRSGsVBCRg0Co+BplEUxDITHTAVrFhAQMHAg3zzuxGq3aUq6k9u/YADYIw4UlHIvpdq/EpMhINGts30EpnWPckMgOUgGodGMHbaMwjgtNIlzZMYH9lFRW7At01/C8nPS6OWCKjAkGDbSU1WTccQJAWGQE9LpuMhLpZRJruJB3164ldJ1NKr2lhB4vP8v4RX/T9A9Th+PkBfW2zUmUpsHVj+tG3B+zzf2O5i2n/2qvwRDGFOg5/hhK7nIYghQP1+BbU+ldDA+FWyl95PnU9bT05k3v5HyrrQiAkAf7+GgmRp0uHHEh2NDkB47QLSJ/4C6KyYtJQluBrjotuJJr5ACxb6yfWbCIjwDPvFqXGr+Pm3nHH46RBpb92sxa+6M0smDGbHEgBD9ZYt8AYM4V0OAO4LjpBif6FCxh4HvqtChZox8BpYdQk0jq0ncw3zz8e8toq4jNgK0RlqYggQW2LyWCcZFv9CHjLYF4ga8s3EOjcTs4nFqpAjd4Tdpbde9dvMOndYAISMGgwOjkw1gn075hAvHBbm1tGI3ok0/RhHSK+7MkDUIaApbHNfx57oIsw4ogkGt8v0E2hseeQ49sugXmwSHxu89Z6X0D+NL+01Xof3b4O6HgS/lhuZIvLGIQshBlUglKC8l1vqxICHrIAYollO9+CRVoRxXccTUlH/DLkmSr2fagcFrqOXwVxxCHV/dglIQHlDJ1O+Q/tfv84PBH+kJx9rq7eLytCIByBxC4TEXyaGK6L7GtNBCAySIleNwMbPvVs58f13hpu1I1zp5OW2fGws+WUf84qsJa9p3QQHPc8jayCS0Mexw4A3IzJgZkHwQ5gPQPjvCvJfPlxsksR2EgJ/neQtQ5zHv4/FPck5tBGm2LNvFmIEtkZ0qJLwPrmc3LfcXlNqQwPz995aGV4EOwyrocg4sUIlklrcgISMGhyxHKCWCDwI0oTNkG4cPrwyAMGTXHdFx6XQayZ8H1eZdjhj+7kDXSE7SQ7Y4LAyM7Z1bWe4S5ob3klLcrdrbqkxznDdZV9IQiU734PN/v/QXnCFiVyaCR2xY3WJGgHXIkn/YdnmnncA+TAk/+ib+9TGQF8mtJt/8BPHU4L10N1/56w1lleHYTuAcEC/6lyeUJc1giqyl9JqRIw8Ecj60IgJgh4YJtoPvcgOR58iQyk/5sL5pL59H3ea/vkHbJWvE/OpxdDUiV0eZS55HVkFdxMVHxQPdlXWQW17P5qw7L35JKWMwhZAIcPRvCxGhwUuNlwaND6D1br/j+47p/KSvBkvm0GqhzTbyULPJRuAes2vPksoimotx90Yk22AQI4bJsorWEEWMTTfTcCYPEJZMBBwi4vJfOR29RgxiU3QVTyGzIfvZ10CGzqQ0Y17CRyVMQEJGAQMSrp2FYInIiyAU7lb2A1Qp3LtBDNLKpEDXiH+qX61RkoChtmvLOLfiqo0VUINaS/DkKoPrI9Ngic06sH8RKuvb51B92xdqPqMqlHF3r4pMgUrMON2Z72ccr/flgiliMTgJsGK0UNN/muwvVUsWcRVez9gDqOhDd6GA0DdRw0B9L63YDgwLUQmduBZTs0CLLJkdKXdIf3iaE6QYgfely60i6wYa3IpQjBmlWZR46kI4Ptkm1CQAi0YQLWT9+R+fxDSkhP6zsQWUkuMl96DFoBEFN9/B2yN28kD26wrMWvknF23RIprrvnMgDr04VEyWnkwE2YMQVPbyNoWip+92z9jmyIKkZimciBAm5acvBsSLbH0wYOU1oMEZy+1XXRsruQcc50tdgeD9lfr1HCkyx6aD79Z7VQRjbEDschgAA9gxPHk5bVdkouWgNwFvJkgU3H6+tIPzKHTHyvfY0dOpxPLSbXpJ5kfviWBAx8YJrwNfhfHE14QhlaCDQ1gUk5qcRLtJqOCPFdYzvTvR/sJXY+OGdgOmUnt8w/nWkoiThY7gm4NLdlKxeHL3ZWEK/fNVb+UwoA1I7f7KuopJtXraclO/dQmtNJj48aRhf169mOiTTs0ku2PKGCBSm9r6L0gXeSI7GbGshyFVLxT08hY2A2FayfRVnDnqg+geUuoaJND0GAcCLcD0YGBBM0BA6cKb3VUn1ABCvxWSeRbVZQ4Xf3Ueaxf6pzRNmOVxHE2ECpCEpIEwJCILYI+MQBnXMQnEQKvPX5p0psz5h6DW6o+hFhMZGBYG3aUCdgYC59gzxzOauggPSRE71aBZ27RwxI63sM0WdLyf5qNVTrRx/2OGv1R+qJO3UKfQ7HzXPJffV48vwDIovn/Cri7IXDnryZO7BLhXb8yaRjIaTIK0HENR95BRG/+JSsJf9SM+Ksi7gXVzbz7Nru6WxYUlLnHipYEOwqOPNAY4FPn7NHsE6yLWoEWuauJ2rTl4GEQPMQWJNbTh2SHDR3Wb5a2CoxKa5uqtny63KadEJckhCqlVSZdOUbubB4LKPTjgpeMxjqWNkeewTe3JpLt6/dQIUQRJzYvQs9MvJ46pKUGHsX2gxXVLp9ASV2OysgIMCn1eMyKOPoO3ATX0Vl2/+JLTUBA3ZQKN78sFo0I0npEyR2noAAwgRyptU4odRn+vEdTqCkHlOpeNMcqsz7lJK6ngGrxm6wWcxXLgkVexajXGE4Jfc8fJ1xfc4rfYWAEGh5Avbun9UNFAcLuFmrlqpXfeQE9ap+dOxCxBaFhxpnIXj+cCVZ/30bj/uReXnxTaQj+8CGaKK9tdjXrc6rlpkdoIWgn3kJmS/OJ8/zc8jZ71jSgtgu+gYxX39GiRmyvaO/CKPnyXvJfOcFXzfvK57Oc0mFKqtIghAvbCBrt/gl3myF2ttb63vOJNBPv1CVYrAlpLn0dbI//4TsH75qrVNulfNS5S/Q62A7T61Hn+Bz5GDB8FOD75OtUSUgAYOo4pTBYpWAC7oBnVIcammt15gab9AlQzLpHmRC3DuhM8U79NY6VZlXExLIQ1bBLavX0+Jcb1bBY8gquFiyChpMnNP/3SU/KNvEUIMkdTsTN/EPwTLxJ2QN9FXd9LhM6vw/71Nl/nKq3L+cqnCDX7l3CR3EXiOxBwIH4ykBwYOEzuPIiOsQaug627NHPE9FqTnIXphDroI1AfuTe15GmYP+gvsCI2C7vBECQqDtE9CyEAzYm0tsR0hQ57eWw3IOQnvasSPUxXHNt71xNennX1VzseUl3mABb0F5pfnKE2qp6RB8zbj6DnL8+s7qnXrvAeS47RFll+i6bBQ5fv8o7BtPgqChV+jPrqwge/tmMlHLb3FQANt5jICGNH6tT8OCpQHjtMI3rORvf/slWRs/w7IaJQprlZMFT1UDO2W1CDcFaZET0I7zOgtxkMpxy7yAA+2qSvU9trdtIuNXXl2DgA7yJuoEJGAQdaQyYCwSmDvZm4Lc2q8twaGRG8GN3CI39ctqec2F1s4r1ub31jbOKthIB6tcNL5bZ5o/aih1layCRn3MrBXANoZWVV7IcUzoBnDT0c/XlHNBp1MpAQs3zjioKvgCgoQcQFhBZblvUKnKStCQFTBUlS4kIIgQn3ViWAFFFldkccS0/jerQIanbJuyxHOm5OBVypF8/OW14QTKdrxGcR1G1LtkpuFnlCMjIaCfcjrR/NvIPfM8YicCe8cW0n/xa/x+cqj6ec+juEGHxgA/2a9ueGJvNEBFXh/kDUJUj4MVA+ei+ETyzL6BPLOmenchCKCyAvYg+wEBCdW69SLnn58jrjP3b44LryfiJQaaXbifrHUrVImGChBAP4LMQ+WiEJDUTz3La7eIDAPWO5BWfwJ6zrHqu2u+8iS5IOip9RmoBlHuHh/9W5XjaENPIX3Soe9i/U8hR9SDgAZLlkP/wutxlHQVAkKg1RFgy8WZC3cpB4Uvf9Of4qKl+tjqrlQmVJtAPrIKbl2zgd7bsZtSnQ6afcJxdEm/XrW7yfsGEti34lxyHVxPXcevgKDgEQGjWJ4y2vvJeIgRHqQekzcH7Av3hjMXWG+g6sAqlBOsQiYCal1dBdBIuIsyBt4d7lDZJwSalMDuJccjGLWJnOnHUlK3s7FMCenM0aQTkcHrEFAOB3N+571ZwhNr59xXSUtIIvfcWcQOCY7r7iHjjIvqHBfNDdaGz8h8ewFZqM8n2DOSw+l1UOg/hPRjhpE+cWpAKUJjzm3hCTJnN7S25obLhIXSC9UgIKnDHlI/YSxpI8aQ3vOo1jbdNjsfdtNgBwp2B6GSwprr0HUVLHNcezdpyLaR1vQEJGDQ9IzlDDFE4OOfSuiDLaW0raAKAoNEnSB+OLp3Ml0wKIOczXCDPv31HfTzQVcdopxVUFCBX6wI/00flkm3j+lcp49siE0CH+/aR9ev+JwKkFXQNy2F7j9hMHVPTjzsxfbPEJ2Lw0I61MFV+DXt+WgUnvzHU0qvy6BBcDTyTJ1wOdiKLIEXVfZBx1Fv4MbqzIiGZNcFd/F3CBasRdbBGnLhlW/QCKUEHYY8rFwUeCDuxxkE9W1cDlGfMof6ji/9Y5uAu3gTle9+Fw4giw/Zf1ooozmCkrpPUQGE+OyT8W9BElRb6ltgW/jjAxoE/jdKdu5PXn2DuObNLFRzwXNHdj2oT2PHB8rfQ9qAwaTBTaB2Uw4QC+bBNnIexa9ECUYra+a/nyfOMlBBgqOH1vv6W9nltPrpKBvOvTtI6Xjg+6Id0UcFylr9xGNoghIwiKEPUy6l6QiwteKsd3fT+z+UqJOkxiO6qRHsFvEfN9q4vin06BT4ozdx0OB37+5SjgjqpH4/2EYyI8GgMZjH+cekN0vwwu/0stqCBG5HZsFzm7fWewb50/zSVut9dPs7wFW4kfavvQo3+t8GXDzfSGUOfpCSe4TnaVbuVfaLbMFYue9DstysZoA0X1ggspNCYpeJKF8YS7qzJpBjVh2gnQt7BJwvkjeSpRAJJekTCQETWS8V0N6o2AM9jr1L8b0txHc0kxK7TqbsEX+PZAjpIwSqCVjfryf3rReqYIHaiBIHdkswzrmipg+cGDz3zyCuTydnHMWvOFC9T1aEgBBoGQISMGgZ7nLWNkZgwZcF9OAnecQuBTeMzKLOKU51BcWVJr204SA98dl+lWXwp4lSq9bGPto2P913f95Fa/Lq/wfVfShbkFY/AlzBZ1bkImjwAzQJKskBgUNnSr+gmgPct2r/SqrY94G64XIj4MCNHRNY1yCBgwRYnKn9Qk6Cyx0Kv74r5H7/HeW738PcdqpNmYPnUVrOjf67ZV0INIqACngh0FW0aS55IALKrefUikaNKQc3jACLHtp5u4lclTW6AVw/73JBUR6ZBukdyBh3bsMGb8Kj2G7QdfkoVcagHTOcNGgcWMveI4Iwo3PBMtJhO+h57SkyH70dFhAWrApPIcedj4W01WvCqcrQLUjARuaJ+frfyPoGwpHOeGVXaVx0IzIKDp852YLTjvlTS8Ag5j9iucBoEDhrwVY6MiOO/npu8Kd9j67Ipze/KaSmtlWMxrXIGEJACDQ9Af/sAGfGYAQH2FJxIsVnj0SAIS5qE/BU7KGCdTPwBPg9VEmko6RhHqXALUGaEGgMAROaHFX5y2Df+YlaVMkMBtTjslUmDLt7pPae3phTyLENIOB59n6Vps/ihqFabYeDUP2aezvrHnDmgMoo+OV16vTWpg3kvno8GezskJjsvbbkVHLMvB/2j1fA8QWpnNLaDQHWrHDfNCXAGpQvnoNLzn8ul6BBC34TpAitBeHLqdsGAQ+EAbYVuKANENr6jEsSnl5zQOkL9MyM3s1AKEItraUQal6yXQjEGgHLU8oWBwGlAr4ab7Mqn5KQmh2uplszEpWLgRGfTTqWaAYLSne8QgfXQ+wMaeKJXU6nDsOeJEdit1j7COR6mplA3mcXUAUyVvDFVw4h8VkjKaX3FQgUjIMQ4iC5iWvmz8N3OuuHr8j8+wNEUN3Xx55D9pfLUNPkIH3UJLK3fg+bxUWkn3cVGdNm+Q5pVa+q/hwz4kCAr+kDhpA2cBiZH7xBdHA/aUNOJucfnyGt65G+LvLajgiYL8xXwQLjkhn4Ll9JtG8n7BOfJGvl+2S+/Bg5rvx9O6LRui5VMgxa1+chs2mlBAohKMiWhQlOPegMXRAdLKsyKSPRaPI/pkpdJrk9UDdE80XffWYnTswxJa5+4kNBL0g2CoF2ToD/Tdke+JpbsClzplbf6PN7y10MOt5/g4yJxRC5T+3GfXmB/QEEDJEyzA3ChmyNqAIHEE70/Rv27ozsp3dupRi7ik9OupGMYZtX7CyymUqvtkjAchV6v6/4bml6wqHvq7cMry1eT6zMmb3nqQIBzJQMZaVoV6IkBGUJWlqmukTbhd8HFWVEeN+Q3ytNzcnmuWGOGkom/Jtdjmvi8gqkn2vILpDWfglwuQ1BQFNLSQ+AYBdD8wfOCLW3B3SSN01KQAIGTYpXBhcCQkAICAEhIASEgBAQAkJACAgBIdA2CQR/XNo2r0VmLQSEgBAQAkJACAgBISAEhIAQEAJCQAhEiYAEDKIEUoYRAkJACAgBISAEhIAQEAJCQAgIASEQSwQkYBBLn6ZcixAQAkJACAgBISAEhIAQEAJCQAgIgSgRkIBBlEDKMEJACAgBISAEhIAQEAJCQAgIASEgBGKJgAQMYunTlGsRAkJACAgBISAEhIAQEAJCQAgIASEQJQISMIgSSBlGCAgBISAEhIAQEAJCQAgIASEgBIRALBGQgEEsfZpyLUJACAgBISAEhIAQEAJCQAgIASEgBKJEQAIGUQIpwwgBISAEhIAQEAJCQAgIASEgBISAEIglAhIwiKVPU65FCAgBISAEhIAQEAJCQAgIASEgBIRAlAhIwCBKIGUYISAEhIAQEAJCQAgIASEgBISAEBACsURAAgax9GnKtQgBISAEhIAQEAJCQAgIASEgBISAEIgSAQkYRAmkDCMEhIAQEAJCQAgIASEgBISAEBACQiCWCEjAIJY+TbkWISAEhIAQEAJCQAgIASEgBISAEBACUSIgAYMogZRhhIAQEAJCQAgIASEgBISAEBACQkAIxBIBCRjE0qcp1yIEhIAQEAJCQAgIASEgBISAEBACQiBKBCRgECWQMowQEAJCQAgIASEgBISAEBACQkAICIFYIiABg1j6NOVahIAQEAJCQAgIASEgBISAEBACQkAIRImABAyiBFKGEQJCQAgIASEgBISAEBACQkAICAEhEEsEJGAQS5+mXIsQEAJCQAgIASEgBISAEBACQkAICIEoEZCAQZRAyjBCQAgIASEgBISAEBACQkAICAEhIARiicD/A/NCZahL9cfYAAAAAElFTkSuQmCC" alt="Dendrogram Hierarchical Clustering" width="100%"> <p class="caption"><font size="3"><b>source:</b> https://www.datanovia.com/en/lessons/examples-of-dendrograms-visualization/ </font></p> </div> ] ] .panel[.panel-name[Interpretation] .pull-left[ - **Dendrogram**: A tree-like diagram that shows the sequence of cluster merges. The height at which two clusters are joined represents their similarity (shorter links indicate more similar clusters). - **Clusters**: You can choose how many clusters to create by cutting the dendrogram at a certain height. - **Biological Meaning**: - Clusters can group samples or genes with similar biological characteristics. - Patterns may help in identifying disease subtypes, evolutionary relationships, or functional similarities. ] .pull-right[ <div class="image-with-caption" style="height: 350px; overflow: hidden;"> <img 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" alt="Heatmap and Dendrogram" style="width: 90%; height: 100%; object-fit: contain;"> <p class="caption"><font size="3"><b>source:</b> https://bioinformatics.ccr.cancer.gov/docs/data-visualization-with-r/Lesson5_intro_to_ggplot/ </font></p> </div> ] ] ] --- ## Linkage Methods in Hierarchical Clustering .panelset[ .panel[.panel-name[Principle] .pull-left[ <div style="font-size: 0.85em; line-height: 1.3;"> <p><strong>Linkage</strong> defines how the distance between clusters is calculated when merging them during hierarchical clustering.</p> <p><strong>The Process</strong>:</p> <ol> <li><strong>Start</strong>: Each data point is its own cluster</li> <li><strong>Merge</strong>: Find the two closest clusters based on linkage criteria</li> <li><strong>Update</strong>: Calculate new distances using the linkage method</li> <li><strong>Repeat</strong>: Until all points are in one cluster</li> </ol> <p><strong>Key Question</strong>:</p> <p><em>"How do we measure distance between clusters containing multiple points?"</em></p> <p>The linkage method determines the shape and compactness of resulting clusters.</p> </div> ] .pull-right[ <img src="data:image/png;base64,#PCA_and_Hierarchical_Clustering_files/figure-html/unnamed-chunk-22-1.png" width="432" /> ] ] .panel[.panel-name[Complete Link.] .pull-left[ **Distance = Maximum distance between any two points in different clusters** **Characteristics**: - Uses the **farthest** points between clusters - Creates **compact, spherical** clusters - **Sensitive to outliers** - Tends to create clusters of similar size **Formula**: ``` d(A,B) = max(distance(a,b)) where a ∈ A, b ∈ B ``` **Best for**: - When you want well-separated, tight clusters - Avoiding elongated cluster shapes ] .pull-right[ <img src="data:image/png;base64,#PCA_and_Hierarchical_Clustering_files/figure-html/unnamed-chunk-23-1.png" width="432" /> ] ] .panel[.panel-name[Single Link.] .pull-left[ **Distance = Minimum distance between any two points in different clusters** **Characteristics**: - Uses the **closest** points between clusters - Can create **elongated chains** of clusters - **Susceptible to noise** and outliers - May result in unbalanced cluster sizes **Formula**: ``` d(A,B) = min(distance(a,b)) where a ∈ A, b ∈ B ``` **Best for**: - Detecting clusters with irregular shapes - When clusters may have varying densities ] .pull-right[ <img src="data:image/png;base64,#PCA_and_Hierarchical_Clustering_files/figure-html/unnamed-chunk-24-1.png" width="432" /> ] ] .panel[.panel-name[Average Link.] .pull-left[ **Distance** = Average of all pairwise distances between points in different clusters** **Characteristics**: - Uses **all pairwise distances** between clusters - **Balanced approach** between single and complete - Less sensitive to outliers than single linkage - Creates moderately compact clusters **Formula**: ``` d(A,B) = (1/|A||B|) × Σ distance(a,b) where a ∈ A, b ∈ B ``` **Best for**: - General-purpose clustering - When you want robust, moderate-sized clusters ] .pull-right[ <img src="data:image/png;base64,#PCA_and_Hierarchical_Clustering_files/figure-html/unnamed-chunk-25-1.png" width="432" /> ] ] .panel[.panel-name[Ward Link.] .pull-left[ **Distance = Increase in within-cluster sum of squares when merging clusters** **Characteristics**: - Minimizes **within-cluster variance** - Creates **highly compact, spherical** clusters - Tends to create clusters of **similar sizes** - Most commonly used in practice **Formula**: ``` d(A,B) = √(|A||B|/(|A|+|B|)) × ||μₐ - μᵦ|| where μₐ, μᵦ are cluster centroids ``` **Best for**: - When you want well-separated, balanced clusters - Exploratory data analysis - Most biological applications ] .pull-right[ .small[**Note**: Ward is a special form of centroid linkage that incorporates cluster sizes. Simplified visualization] <img src="data:image/png;base64,#PCA_and_Hierarchical_Clustering_files/figure-html/unnamed-chunk-26-1.png" width="432" /> ] ] .panel[.panel-name[Visuals] .pull-left[ ``` r # Compare different linkage methods methods <- c("complete", "single", "average", "ward.D2") # Create dendrograms with different methods par(mfrow = c(2, 2)) for(method in methods) { hc <- hclust(dist(iris[, -5]), method = method) plot(hc, main = paste("Linkage:", method), labels = FALSE, cex.main = 1.2) } ``` .small[Each method produces different dendrogram structures, affecting how clusters are formed and merged.] ] .pull-right[ <img src="data:image/png;base64,#PCA_and_Hierarchical_Clustering_files/figure-html/unnamed-chunk-28-1.png" width="432" /> ] ] .panel[.panel-name[Selection] Detailed selection criteria and recommendations were moved to the next slide ] ] --- ## Choosing Linkage Methods: Guidelines and Applications .panelset[ .panel[.panel-name[General Principles] .pull-left[ <style> .workflow-container { font-size: 0.65em; line-height: 1.2; } .right-panel-small { font-size: 0.65em !important; line-height: 1.2 !important; } .right-panel-small h3, .right-panel-small h4, .right-panel-small strong { font-size: 1em !important; } .right-panel-small ul, .right-panel-small li { font-size: 1em !important; margin: 0.2em 0 !important; } .workflow-step { background: #2c3e50; color: white; padding: 8px; margin: 5px 0; border-radius: 6px; cursor: pointer; border: 1px solid #34495e; font-size: 0.9em; } .workflow-step summary { font-weight: bold; text-align: center; list-style: none; outline: none; color: white; font-size: 1em; } .workflow-step details[open] { background: white; color: #2c3e50; margin-top: 3px; padding: 8px; border-radius: 4px; border: 1px solid #3498db; } .code-block { background: #f8f9fa; color: #2c3e50; padding: 6px; border-radius: 3px; font-family: monospace; font-size: 0.75em; margin: 4px 0; white-space: pre-wrap; border-left: 3px solid #3498db; border: 1px solid #dee2e6; line-height: 1.1; } .arrow-down { text-align: center; font-size: 14px; color: #2c3e50; margin: 2px 0; font-weight: bold; } </style> <div class="workflow-container"> <details class="workflow-step" open> <summary>1. Try Multiple Methods</summary> <div class="code-block">methods <- c("complete", "average", "ward.D2", "single") hc_list <- list() for(method in methods) { hc_list[[method]] <- hclust(dist_matrix, method = method) }</div> </details> <div class="arrow-down">↓</div> <details class="workflow-step"> <summary>2. Evaluate Performance</summary> <div class="code-block">library(cluster) sil_scores <- sapply(names(hc_list), function(method) { clusters <- cutree(hc_list[[method]], k = 3) mean(silhouette(clusters, dist_matrix)[,3]) }) coph_scores <- sapply(names(hc_list), function(method) { cor(dist_matrix, cophenetic(hc_list[[method]])) })</div> </details> <div class="arrow-down">↓</div> <details class="workflow-step"> <summary>3. Select Best Method</summary> <div class="code-block">best_method <- names(sil_scores)[which.max(sil_scores)] final_hc <- hc_list[[best_method]] data.frame( Method = names(sil_scores), Silhouette = round(sil_scores, 3), Cophenetic = round(coph_scores, 3) )</div> </details> <div class="arrow-down">↓</div> <details class="workflow-step"> <summary>4. Validate Results</summary> <div class="code-block">plot(final_hc, main = paste("Best Method:", best_method)) clusters <- cutree(final_hc, k = 3) table(clusters) # Validate biologically</div> </details> </div> ] .pull-right[ <div class="right-panel-small"> <h3>Key Evaluation Metrics</h3> <h4>Silhouette Score</h4> <ul> <li>Measures how well-separated clusters are</li> <li>Range: -1 to 1 (higher is better)</li> <li>Good threshold: > 0.5</li> </ul> <h4>Cophenetic Correlation</h4> <ul> <li>How well dendrogram preserves original distances</li> <li>Range: 0 to 1 (higher is better)</li> <li>Good threshold: > 0.75</li> </ul> <h4>Common Pitfalls:</h4> <ul> <li>Don't rely on one metric alone</li> <li>Always validate biologically</li> <li>Consider data preprocessing effects</li> </ul> </div> ] ] .panel[.panel-name[Selection (General)] .pull-left[ <div style="font-size: 0.65em; line-height: 1.3;"> <p><strong>Choosing the Right Method (Rule of thumb)</strong></p> <table> <tr><th>Method</th><th>Best For</th><th>Cluster Shape</th></tr> <tr><td><strong>Complete</strong></td><td>Well-separated data</td><td>Compact, spherical</td></tr> <tr><td><strong>Single</strong></td><td>Irregular shapes</td><td>Chains, elongated</td></tr> <tr><td><strong>Average</strong></td><td>General purpose</td><td>Moderate size</td></tr> <tr><td><strong>Ward</strong></td><td>Balanced clusters</td><td>Spherical, similar sizes</td></tr> </table> <p><strong>Decision Framework</strong>:</p> <ol> <li><strong>Data exploration</strong> → Start with <strong>Ward</strong></li> <li><strong>Robust approach</strong> → Use <strong>Average</strong></li> <li><strong>Compact clusters</strong> → Use <strong>Complete</strong></li> <li><strong>Irregular shapes</strong> → Try <strong>Single</strong></li> </ol> <p><em>Ward and Average linkage are most commonly used in omics data analysis.</em></p> <p><strong>Key Parameters:</strong></p> <ul> <li><strong>method</strong>: Linkage algorithm</li> <li><strong>k</strong>: Number of clusters (for cutree)</li> <li><strong>h</strong>: Height to cut (alternative to k)</li> </ul> </div> ] .pull-right[ **R Implementation** ``` r # Complete example data <- iris[, -5] # Remove species column dist_matrix <- dist(data) # Try different methods (also single, complete) hc_average <- hclust(dist_matrix, method = "average") hc_ward <- hclust(dist_matrix, method = "ward.D2") # Plot comparison par(mfrow = c(1, 2)) plot(hc_average, main = "Average Linkage") plot(hc_ward, main = "Ward Linkage") # Cut into clusters clusters_average <- cutree(hc_average, k = 3) clusters_ward <- cutree(hc_ward, k = 3) ``` ] ] .panel[.panel-name[Selection (Use cases)] <div style="font-size: 0.8em; line-height: 1.2;"> <table style="font-size: 0.8em; width: 100%; border-collapse: collapse;"> <tr style="background-color: #f0f0f0;"> <th style="padding: 4px; border: 1px solid #ddd;"><strong>Method</strong></th> <th style="padding: 4px; border: 1px solid #ddd;"><strong>Best Distance Metric</strong></th> <th style="padding: 4px; border: 1px solid #ddd;"><strong>RNA-seq Samples</strong></th> <th style="padding: 4px; border: 1px solid #ddd;"><strong>Gene Co-expression</strong></th> <th style="padding: 4px; border: 1px solid #ddd;"><strong>Scalability</strong></th> <th style="padding: 4px; border: 1px solid #ddd;"><strong>Other Use Cases</strong></th> </tr> <tr> <td style="padding: 4px; border: 1px solid #ddd;"><strong>Single linkage</strong></td> <td style="padding: 4px; border: 1px solid #ddd;">Any (but still problematic)</td> <td style="padding: 4px; border: 1px solid #ddd;"><span style="background-color:#ff4d4d; color:white; padding:1px 4px; border-radius:4px; font-size: 0.9em;">⑤</span> Poor; chaining, sensitive to noise</td> <td style="padding: 4px; border: 1px solid #ddd;"><span style="background-color:#ff4d4d; color:white; padding:1px 4px; border-radius:4px; font-size: 0.9em;">⑤</span> Rarely used; elongated chains</td> <td style="padding: 4px; border: 1px solid #ddd;"><span style="background-color:#66cc66; color:black; padding:1px 4px; border-radius:4px; font-size: 0.9em;">②</span> Fast</td> <td style="padding: 4px; border: 1px solid #ddd;">—</td> </tr> <tr> <td style="padding: 4px; border: 1px solid #ddd;"><strong>Complete linkage</strong></td> <td style="padding: 4px; border: 1px solid #ddd;">Euclidean, Manhattan</td> <td style="padding: 4px; border: 1px solid #ddd;"><span style="background-color:#ffcc00; color:black; padding:1px 4px; border-radius:4px; font-size: 0.9em;">③</span> Tight clusters; risk of oversplitting</td> <td style="padding: 4px; border: 1px solid #ddd;"><span style="background-color:#66cc66; color:black; padding:1px 4px; border-radius:4px; font-size: 0.9em;">②</span> Good for compact gene groups</td> <td style="padding: 4px; border: 1px solid #ddd;"><span style="background-color:#ffcc00; color:black; padding:1px 4px; border-radius:4px; font-size: 0.9em;">③</span> Moderate</td> <td style="padding: 4px; border: 1px solid #ddd;"><span style="background-color:#66cc66; color:black; padding:1px 4px; border-radius:4px; font-size: 0.9em;">②</span> Legacy OTU picking (furthest-neighbor, strict % identity); occasionally used for very compact metabolite/protein sets</td> </tr> <tr> <td style="padding: 4px; border: 1px solid #ddd;"><strong>Average linkage (UPGMA)</strong></td> <td style="padding: 4px; border: 1px solid #ddd;">Correlation, Cosine</td> <td style="padding: 4px; border: 1px solid #ddd;"><span style="background-color:#66cc66; color:black; padding:1px 4px; border-radius:4px; font-size: 0.9em;">②</span> Good; works after variance filtering</td> <td style="padding: 4px; border: 1px solid #ddd;"><span style="background-color:#006600; color:white; padding:1px 4px; border-radius:4px; font-size: 0.9em;">①</span> Preferred for gene dendrograms; preserves hierarchy</td> <td style="padding: 4px; border: 1px solid #ddd;"><span style="background-color:#66cc66; color:black; padding:1px 4px; border-radius:4px; font-size: 0.9em;">②</span> Good</td> <td style="padding: 4px; border: 1px solid #ddd;"><span style="background-color:#006600; color:white; padding:1px 4px; border-radius:4px; font-size: 0.9em;">①</span> Phylogenetics (UPGMA guide trees; assumes strict clock), <span style="background-color:#66cc66; color:black; padding:1px 4px; border-radius:4px; font-size: 0.9em;">②</span> Microbiome community clustering, Metabolomics (broader metabolite modules/features)</td> </tr> <tr> <td style="padding: 4px; border: 1px solid #ddd;"><strong>Ward\'s linkage</strong></td> <td style="padding: 4px; border: 1px solid #ddd;">Euclidean only</td> <td style="padding: 4px; border: 1px solid #ddd;"><span style="background-color:#006600; color:white; padding:1px 4px; border-radius:4px; font-size: 0.9em;">①</span> Excellent; balanced sample clusters (with VST/rlog + Euclidean)</td> <td style="padding: 4px; border: 1px solid #ddd;"><span style="background-color:#66cc66; color:black; padding:1px 4px; border-radius:4px; font-size: 0.9em;">②</span> Robust, but not ideal with correlation distances (average preferred)</td> <td style="padding: 4px; border: 1px solid #ddd;"><span style="background-color:#ffcc00; color:black; padding:1px 4px; border-radius:4px; font-size: 0.9em;">③</span> Slower</td> <td style="padding: 4px; border: 1px solid #ddd;"><span style="background-color:#006600; color:white; padding:1px 4px; border-radius:4px; font-size: 0.9em;">①</span> Clinical/patient subtyping, Omics <strong>sample</strong> clustering (spherical/compact groups), <span style="background-color:#66cc66; color:black; padding:1px 4px; border-radius:4px; font-size: 0.9em;">②</span> Neuroimaging (fMRI/voxel patterns), <span style="background-color:#ffcc00; color:black; padding:1px 4px; border-radius:4px; font-size: 0.9em;">③</span> Metabolomics (samples strong; metabolites moderate)</td> </tr> </table> <p style="font-size: 0.9em; margin-top: 10px;"><strong>Rating System:</strong></p> <ul style="font-size: 0.9em; line-height: 1.3;"> <li><span style="background-color:#006600; color:white; padding:1px 4px; border-radius:4px;">①</span> <strong>Best choice</strong> – Excellent performance, well-validated</li> <li><span style="background-color:#66cc66; color:black; padding:1px 4px; border-radius:4px;">②</span> <strong>Good option</strong> – Solid performance, commonly used</li> <li><span style="background-color:#ffcc00; color:black; padding:1px 4px; border-radius:4px;">③</span> <strong>Moderate</strong> – Use with caution, context-dependent</li> <li><span style="background-color:#ff4d4d; color:white; padding:1px 4px; border-radius:4px;">④⑤</span> <strong>Avoid</strong> – Poor performance or major limitations</li> </ul> </div> ] .panel[.panel-name[Troubleshooting] .pull-left[ <div style="font-size: 0.75em; line-height: 1.3;"> <p><strong>🚨 Common Problems & Solutions</strong></p> <p><strong>Problem 1: All samples in one cluster</strong></p> <ul> <li><strong>Cause:</strong> Data not scaled properly</li> <li><strong>Fix:</strong> Use <code>scale()</code> before clustering</li> </ul> <p><strong>Problem 2: Chaining (long, thin clusters)</strong></p> <ul> <li><strong>Cause:</strong> Using single linkage</li> <li><strong>Fix:</strong> Switch to Ward or Complete linkage</li> </ul> <p><strong>Problem 3: Unstable results</strong></p> <ul> <li><strong>Cause:</strong> Batch effects or outliers</li> <li><strong>Fix:</strong> Remove outliers, correct batch effects</li> </ul> <p><strong>Problem 4: Biologically meaningless clusters</strong></p> <ul> <li><strong>Cause:</strong> Wrong distance metric or method</li> <li><strong>Fix:</strong> Try correlation distance for genes</li> </ul> </div> ] .pull-right[ <div style="font-size: 0.65em; line-height: 1.3;"> <p><strong>🔍 Diagnostic Checklist</strong></p> <p><strong>Before Clustering:</strong></p> <ul> <li>✅ Remove low-variance features</li> <li>✅ Scale/normalize data appropriately</li> <li>✅ Check for batch effects</li> <li>✅ Handle missing values</li> </ul> <p><strong>After Clustering:</strong></p> <ul> <li>✅ Plot dendrogram - does it look reasonable?</li> <li>✅ Check cluster sizes - too unbalanced?</li> <li>✅ Validate with known groups/annotations</li> <li>✅ Calculate silhouette scores</li> </ul> <p><strong>⚡ Quick Fixes</strong></p> <ul> <li><strong>Noisy data:</strong> Increase filtering thresholds</li> <li><strong>Too many clusters:</strong> Try Ward instead of Complete</li> <li><strong>Poor separation:</strong> Check if PCA first helps</li> <li><strong>Weird patterns:</strong> Plot sample correlations</li> </ul> </div> ] ] ] --- ## Hierarchical Clustering in R .panelset[ .panel[.panel-name[Data] ``` r ## Load and display the data data("iris") head(iris) ``` ``` ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## 1 5.1 3.5 1.4 0.2 setosa ## 2 4.9 3.0 1.4 0.2 setosa ## 3 4.7 3.2 1.3 0.2 setosa ## 4 4.6 3.1 1.5 0.2 setosa ## 5 5.0 3.6 1.4 0.2 setosa ## 6 5.4 3.9 1.7 0.4 setosa ``` We will use the **iris** dataset, which contains measurements of four features (Sepal Length, Sepal Width, Petal Length, Petal Width) for three species of flowers. ] .panel[.panel-name[Perform Hierarchical Clustering] .pull-left[ ``` r ## Calculate the distance matrix & ## perform hierarchical clustering distance_matrix <- dist(iris[, -5]) hclust_result <- hclust(distance_matrix, method = "complete") # Create color mapping for species species_colors <- c("setosa" = "red", "versicolor" = "blue", "virginica" = "green") leaf_colors <- species_colors[iris$Species] # Load dendextend quietly suppressPackageStartupMessages(library(dendextend)) # Convert to dendrogram and color the leaves dend <- as.dendrogram(hclust_result) dend <- color_labels(dend, col = leaf_colors) # Plot the dendrogram with colored leaves plot(dend, main = "Dendrogram with Species-Colored Leaves", leaflab = "none") # Add colored points at leaf positions leaf_order <- order.dendrogram(dend) points(seq_along(leaf_order), rep(0, length(leaf_order)), col = leaf_colors[leaf_order], pch = 19, cex = 1.5) # Add legend legend("topright", legend = names(species_colors), col = species_colors, pch = 19, title = "Species", cex = 1.2) ``` ] .pull-right[ <span style="font-size: 0.6em;">Q: Why is *versicolor* split across the left and right clusters? See the last slide, try other methods, and interpret.</span> <img src="data:image/png;base64,#PCA_and_Hierarchical_Clustering_files/figure-html/unnamed-chunk-32-1.png" width="864" /> ] ] .panel[.panel-name[Heatmap with Dendrogram] .pull-left[ ``` r ## Create a heatmap with hierarchical ## clustering library(pheatmap) # Define annotation colors for species annotation_colors <- list( Species = c(setosa = "red", versicolor = "blue", virginica = "green") ) # Perform hierarchical clustering pheatmap( as.matrix(iris[, -5]), clustering_distance_rows = "euclidean", annotation_colors = annotation_colors, clustering_method = "complete", annotation_col = iris["Species"], main = "Heatmap with Dendrogram") ``` ] .pull-right[ <span style="font-size: 0.6em;">The heatmap shows the clustering of samples and features along with a dendrogram, which helps identify the groupings.</span> <img src="data:image/png;base64,#PCA_and_Hierarchical_Clustering_files/figure-html/unnamed-chunk-34-1.png" width="576" /> ] ] .panel[.panel-name[Choosing the Number of Clusters] .pull-left[ ``` r library(dplyr) ## Cut the dendrogram to obtain clusters cutree(hclust_result, k = 3) %>% table() ``` ``` ## . ## 1 2 3 ## 50 72 28 ``` <div style="font-size: 0.8em; line-height: 1.2;"> </br> You can choose the number of clusters by cutting the dendrogram at a certain height. Here, we cut the dendrogram into <b>3 clusters</b>. The colored rectangles show the division into 3 clusters, based on the dendrogram structure. This allows you to select the number of clusters that best fits your data.</div> ] .pull-right[ ``` r ## Visualizing clusters cluster_labels <- cutree(hclust_result, k = 3) plot(hclust_result) rect.hclust(hclust_result, k = 3, border = 2:4) ``` <img src="data:image/png;base64,#PCA_and_Hierarchical_Clustering_files/figure-html/unnamed-chunk-36-1.png" width="432" /> ] ] ] --- class: center, middle, closing-slide <img class="closing-logo" src="data:image/png;base64,#images/CECAD-Logo-24-white.svg" alt="CECAD" /> # Thank you! <div class="closing-subtitle">Questions welcome — let’s discuss your data and next steps</div> <div class="chips"> <span class="chip">PCA: variance, loadings, scree</span> <span class="chip">Hierarchical clustering: linkage & distance</span> <span class="chip">Selection strategies</span> <span class="chip">Troubleshooting & diagnostics</span> <span class="chip">Heatmaps & dendrograms</span> <span class="chip">Best practices</span> <span class="chip">Reproducible workflows</span> <span class="chip">Biological validation</span> <span class="chip">Next: integrate PCA + clustering</span> <span class="chip">Try on your dataset</span> <span class="chip">Compare distance metrics</span> </div> <div class="cta-row"> <a class="cta-btn" href="https://github.com/CECADBioinformaticsCoreFacility/Intermediate_R_Course_2025" target="_blank" rel="noopener">⬇️ Get slides & code</a> <a class="cta-btn secondary" href="mailto:cecad-bioinformatics-team@uni-koeln.de">✉️ Contact the team</a> </div> <div class="footer" style="left: 0; right: 0; text-align: center; background: transparent; border-top-color: rgba(255,255,255,0.6);"> <span style="color: rgba(255,255,255,0.9);">CECAD Bioinformatics Core Facility • University of Cologne</span> </div>