Subbalakshmi_ELF3_2023.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"3CNYUOtv6JUc"},"source":["Starting the transcriptomics analysis on 23/09/2024\n","\n","> The ELF3 transcription factor is associated with an epithelial phenotype and represses epithelial-mesenchymal transition (Mar, 2023)\n","\n","https://pubmed.ncbi.nlm.nih.gov/36864480/\n"]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":true,"id":"f1Z5ttrN5-DN"},"outputs":[],"source":["system('add-apt-repository -y ppa:cran/imagemagick')\n","system('apt-get update')\n","system(\"apt-get install libmagick++-dev\")\n","install.packages(\"magick\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Gblp1wLzfcgc"},"outputs":[],"source":["install.packages(\"BiocManager\")\n","BiocManager::install(c(\"GEOquery\", \"GSVA\", \"biomaRt\"))\n","install.packages(\"ggplot2\")\n","install.packages(\"dplyr\")\n","install.packages(\"R.utils\")\n","install.packages(\"ggrepel\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"xVO2niWluLcs"},"outputs":[],"source":["library(R.utils)\n","library(GEOquery)\n","library(GSVA)\n","library(biomaRt)\n","library(ggplot2)\n","library(dplyr)\n","library(readxl)"]},{"cell_type":"markdown","source":["# Figure A\n"," ### using R-libraries"],"metadata":{"id":"ZPHpfi2avwqB"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"rLj5YMCZCqy6"},"outputs":[],"source":["# Unzip the dataset\n","gunzip(\"CCLE_RNAseq_rsem_genes_tpm_20180929.txt.gz\", remove = FALSE)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pMVLS1cGRmZg"},"outputs":[],"source":["# Read the unzipped file\n","ccle_data <- read.delim(\"CCLE_RNAseq_rsem_genes_tpm_20180929.txt\", header = TRUE, sep = \"\\t\")\n","\n","# Remove the version suffix from Ensembl IDs in ccle_data\n","ccle_data$gene_id <- sub(\"\\\\..*\", \"\", ccle_data$gene_id)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Vyre0qa2RHjO"},"outputs":[],"source":["# Set up the ensembl dataset\n","ensembl <- useEnsembl(biomart = \"ensembl\", dataset = \"hsapiens_gene_ensembl\")\n","\n","# Get the mapping from Ensembl IDs to gene symbols\n","mapping <- getBM(\n","  attributes = c(\"ensembl_gene_id\", \"hgnc_symbol\"),\n","  filters = \"ensembl_gene_id\",\n","  values = ccle_data$gene_id,\n","  mart = ensembl\n",")\n","\n","# Merge the mapping with the CCLE data\n","ccle_data <- merge(ccle_data, mapping, by.x = \"gene_id\", by.y = \"ensembl_gene_id\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2SxEoMtqRq8T"},"outputs":[],"source":["# Drop rows where there is no corresponding gene symbol\n","ccle_data <- ccle_data[ccle_data$hgnc_symbol != \"\", ]\n","\n","# Remove rows with duplicated gene symbols\n","ccle_data_unique <- ccle_data[!duplicated(ccle_data$hgnc_symbol), ]\n","\n","# Set the unique gene symbols as row names\n","rownames(ccle_data_unique) <- ccle_data_unique$hgnc_symbol\n","\n","# Now remove the gene_id, transcript_ids and 'hgnc_symbol' column\n","ccle_data_unique <- ccle_data_unique[, -which(colnames(ccle_data_unique) %in% c('gene_id','transcript_ids', 'hgnc_symbol'))]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"i-5u_V8QQrKL"},"outputs":[],"source":["# Ensure your expression data is in matrix format with genes as rows\n","ccle_expr_matrix <- as.matrix(ccle_data_unique)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FsJtwz4vQnFK"},"outputs":[],"source":["# Load the gene signature file\n","gene_signature <- read_excel(\"EM_gene_signature_tumor_KS.xlsx\")\n","\n","# Split genes based on Epi/Mes category\n","epithelial_genes <- gene_signature$Gene[gene_signature$Type == \"Epi\"]\n","mesenchymal_genes <- gene_signature$Gene[gene_signature$Type == \"Mes\"]\n","\n","# Create a gene set list for ssGSEA\n","gene_sets <- list(\n","  epithelial = epithelial_genes,\n","  mesenchymal = mesenchymal_genes\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HgQnu4PaQlHy"},"outputs":[],"source":["# Perform ssGSEA\n","ssgsea_param <- ssgseaParam(\n","  ccle_expr_matrix,\n","  gene_sets\n",")\n","\n","ssgsea_scores <- gsva(ssgsea_param)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FqOPuuspQjkC"},"outputs":[],"source":["# Transpose the expression matrix for correlation calculation\n","ccle_expr_t <- t(ccle_data_unique)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"KvI-W8dFQiIL"},"outputs":[],"source":["# Calculate correlations with epithelial and mesenchymal scores\n","cor_results <- data.frame(\n","  gene = rownames(ccle_data_unique),\n","  cor_epithelial = apply(ccle_expr_t, 2, function(x) cor(x, ssgsea_scores[\"epithelial\", ], method = \"pearson\")),\n","  cor_mesenchymal = apply(ccle_expr_t, 2, function(x) cor(x, ssgsea_scores[\"mesenchymal\", ], method = \"pearson\"))\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"KYcsYaJBQeQf"},"outputs":[],"source":["# List of genes to highlight\n","highlight_genes <- c(\"VIM\", \"ZEB1\", \"SLUG\", \"SNAI2\", \"GRHL2\", \"OVOL2\", \"KLF4\", \"CDH1\", \"ELF3\")\n","\n","# Assign colors for the specific gene categories\n","cor_results <- cor_results %>%\n","  mutate(\n","    category = case_when(\n","      gene %in% mesenchymal_genes ~ \"Mesenchymal\",\n","      gene %in% epithelial_genes ~ \"Epithelial\",\n","      gene == \"ELF3\" ~ \"ELF3\",\n","      TRUE ~ \"Other\"\n","    ),\n","    color = case_when(\n","      category == \"Mesenchymal\" ~ \"blue\",\n","      category == \"Epithelial\" ~ \"orange\",\n","      category == \"ELF3\" ~ \"red\",\n","      TRUE ~ \"grey\"\n","    )\n","  )\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":804},"executionInfo":{"elapsed":277,"status":"error","timestamp":1727256361470,"user":{"displayName":"Anjaney Pandey","userId":"05531075380284782212"},"user_tz":-330},"id":"uHWpaEiVQaA3","outputId":"5ec3fbde-09c4-4452-d860-a332ed64b1a0"},"outputs":[{"name":"stderr","output_type":"stream","text":["Warning message:\n","“\u001b[1m\u001b[22mRemoved 4008 rows containing missing values or values outside the scale range\n","(`geom_point()`).”\n","Warning message:\n","“\u001b[1m\u001b[22mRemoved 4008 rows containing missing values or values outside the scale range\n","(`geom_text()`).”\n"]},{"ename":"ERROR","evalue":"\u001b[1m\u001b[33mError\u001b[39m:\u001b[22m\n\u001b[1m\u001b[22m\u001b[33m!\u001b[39m Can't add `geom_text_repel(aes(label = ifelse(gene %in% c(\"CDH1\",\n  \"GRHL2\", `, ` \"VIM\", \"ZEB1\", \"SNAI1\", \"SNAI2\", \"ELF3\"), gene, \"\")),\n  box.padding = 0.5, `, and ` point.padding = 0.5, max.overlaps = 0)` to a\n  theme object.\n","output_type":"error","traceback":["\u001b[1m\u001b[33mError\u001b[39m:\u001b[22m\n\u001b[1m\u001b[22m\u001b[33m!\u001b[39m Can't add `geom_text_repel(aes(label = ifelse(gene %in% c(\"CDH1\",\n  \"GRHL2\", `, ` \"VIM\", \"ZEB1\", \"SNAI1\", \"SNAI2\", \"ELF3\"), gene, \"\")),\n  box.padding = 0.5, `, and ` point.padding = 0.5, max.overlaps = 0)` to a\n  theme object.\nTraceback:\n","1. add_theme(e1, e2, e2name)","2. cli::cli_abort(\"Can't add {.arg {t2name}} to a theme object.\", \n .     call = call)","3. rlang::abort(message, ..., call = call, use_cli_format = TRUE, \n .     .frame = .frame)","4. signal_abort(cnd, .file)","5. signalCondition(cnd)"]},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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vNLpdLUZTqo4bD4TCbzY18pGYW6BeKovh8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\nYcPll19ucR545JFH2traEomEeXvjM844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EAWvvUhYOioCKdIxd7OREEiBDrokXkminEA7hoongj9BqBI9gVAI9kWPEDmI8KIIjCShvkQO\nIFT0BNJADeEJUj2JksFIg6Y3mf0nyq0VKwCOvsG2X3HgMXwVSJ1ALyrGEz1A/wsZ+e8f6VLB\nzbH7HOKfK8dO6wCRrl0KxI/y+BB6L2D2Xz5Vt44vugmwCiFmzpw5c+bM22+//b333lu1atWj\njz76hz/8IRgMLly48IILLjj77LPttcRcuPhcQg1gZNbeamHCu4jWEzuCt9wh2c5fQ/9FvPVN\nvCV4q1Aa07G2rl9KMvuPVaUzC0520ahwCNKR6pGfhDlynXwJc45BxnwRW0ev7DqfhSxaPDev\nC8HW8ViDpPbrtd8WixTqILOZXpp1wSLZqmUQQzp4IBSEIFBDROFgknIvZQ0oGqqGkCCJN6H4\n8ZQQO0y8hdLBVE1C6qBz6CmEQKp4Q+hxkp20GKxpoTzG6DgTvkzZCGfHak+kZBDB3uz7E4Fe\n+Gvp2EHPOQz+Sv6LceHicw5PKc3rObCa9g/o2EXbFvTop+3TcccxZM6NGzdu3LhxS5cu3b17\nd0rDu/zyy1VVnTdv3tNPP338XHTh4hNC9VR2P4S/J+i0vEuyGS2GOiydbDfuP/HXdhlLg45d\nAIkwWhgMhDeTPIf1We7IjQrTiBTscVs7pbAQRIuZIwt0hMwwLUcnC7MZy3RmepdlRYqNSxUe\n074tbLckrxgWaObNIrNqON9Q9jeuQGxaWo/y+CPTxYrjLaghKiP4FISGYiAVpI4C0kCPosfT\noq/iTQeSWt8BA+lHUTAkCS+Hk7TGeHMbnQY7JjLspPSux1kYGloYbwVCIdibKXcy5Ou0bkLr\nJDSAXnNztldx4eILhvduZfNSqifTax6951M9lbXWPQi+ePgoSyIGDx58ww03nH/++ffee+/d\nd99tr/TtwsXnEj1OYfg32fYrjATxI6BSPoayoQDNGzm4miGm/M7659l+H7UnIeDIS5m1roqV\n1XUb0bMk1X0EFIilFjY29yoQLC6cbGcfJDuObTdYB6Jjntp+YPE5nzaZHbxA/qKjOCdAzdPF\nzLPzcXFHMpdVakUeXp76C5ESmUARBARqHAFSohi5xhIJUtK5n0Rb+veGIRFxdImh0OghFsKf\nZPhMfL1obOePjzFpFqlKbFon+1dy9O8ceZneX6LXafQ5C8VD5Xh361gXXzgYxJvxlKH6u9qS\nYd67hWFXcsL9XY3/BDvsHTOx++CDD1auXLly5cp33nnH6/XOnz//ggsuOB6euXDxSUMIhl1F\nj5N49yd0bEdKtFaOvo6nHG8ZkYNpMyOJHqXpTUoGoHpo3ki0Ph1fkyAFHi9GMh2TtUtNFg1M\n5rIfC8w2dg0p28scW7TMUgyyzBKnnLbClC4retlnLCa5TeY678i9zC0Wbme+arsiaPHT8WU+\n97IGWT3PIvXl9JVWVpqCPW5uJqlSoMfxqxgCYSBFWp/rivlKhIo0MOKEt2BIFC96DBSEB92g\nIo6QHFHw9kAN0SvIc8/x7W8zZAhSsut37H6Y0qH0OJFoPe/+GCPBgIvyXLALF59PSINDT3D4\nWepfoNcpVE5i4JfxVQJE9mEkc8oaNK2jYzdV1s0wjxWRSOSzvMyg2LzyjRs3Ll26dOzYsaNH\nj77tttsGDhy4fPnyhoaGp556KrV7rAsXXxBUjKViDIlWYvVoUfQYkT2Et4FOrIFtv+Stb/L0\nVPb/lWQ70QY6dnXVMVO8qB6k0VWWrYASVliis4cyyWMv03UzHKbLxyrsc2UNZO63gr2LdOIu\njiMbpgJv2YnsF5KZffsRpv2I2x41tTvyy8L8Nd+12z0pzO3MrkrTjRW5tNIMaQsfW+5MzkRK\nemGsNDLDmkoedo2cOYrU07kLQ6J4uhwSgpIk9X3xlAJISTzOW2/x4YeEt7PjASrG4i1DePBV\nUT6a+hdI5t2P0YWLzyUOPcHmpcSOUDuLZCe7l7Pjf9KyXPlISoew7Vds/B67HuStb/LymQR6\n0bSOw4UijUePHl28eHF1dfW0adNWrVolhOjs7ASEEG+++eaCBQtSe8q3t7dfffXVqWq755xz\nzoEDB4Abbrjh1FNPzQ516623jhkz5rjeADsKKXZSynXr1qX0uV27dpWXl5911lk//vGPzzjj\njC/k+lAXLtKQCWJHCPZLl4FQS4kdItnKtl9y4HGMJPGjdB4Ag9KR6X0FhMBIZb6rCIEaRO9A\nKulceMwyjBP5kLmMAYu0Y/PQ0qiYRnAcxDJdvmHtvSyaouM4WTNpuyK7z/ky1STRBBt2M3Ww\nkz8W2bJbt+0vC/yGdYzwFl6Kmy/kbY9HO2uf2X1HMkaGQJiG63oHDYSKvwLhR+tE6gTqSLSi\nx1ChA972khyEHzo62LAB/1Ge/A+ejjNtLAM9OdXpvKU0rk0vBnLh4osBPc7hZygbkZbolBDl\no9nzR3ovoGoywsPcJ3n7BnY8gKeUHiexcANNb7L+Gt67ld4L8o169tlnV1dXP/XUU3v27Lnq\nqqvMp66//vpvfetbc+bMARYtWmQYxvLly4PB4N13371gwYK1a9deeOGFv/zlL48ePVpbWwv8\n6U9/+spXPun1SYWIXf/+/Q8ePFhTU3Puuef+8pe//NKXvpTaedOFiy84PGWUDiVyAMWPAD1O\n2QiijTQ/Q/wI3koCvQ0tSeyw6Ngu0MC0S1jq0W4kMg95JZ07lX7k5z6/LXCMJNppVrbRkV4U\nZoQWswI8KXu2QBDT0R9sxxbHLO51S9fyDevYSzjdAfsgjkqk+VjJf4vyjdbNJWR2IiFV60SC\nklHsDGeXRGr9rErtSSTDaBFaNpJsRyh4QgiFkjhvSWo7KW9g82bqDvEVLzKBoWC8Tqugaize\nzI9wqSGNtLbnwsUXA4lm6l+kp2krc0XFW0a0nlQVr/KRzH0ip0vJQAZcXGDINWvWbNy4MUV+\nZsyYsXfv3u9973vZs+ecc85ll10GvPnmm2vWrGloaEgVBnnooYf69u27cuXKJUuW9OrV6/HH\nH1+yZMmWLVu2bNly6aWX/uMuuCgUInYHDx4EWlpali9fvnz58gKW9g3aXbj4/MFIprd7UvyE\n+lI+kmQbgLcSI4YRRyZQS3Xpb2smkfAmYnW9Kw4oilBVBSlAoKR0OxVFQQck6LlUQwHZtUts\nFvlitfZdYrMooMaZDQpTt24pi+MIZsJXYPDCMMuKjjTLEtPMupSVuiynpOmOCVtg1D51gTtT\nmKtlxywQhjZDAUSa1qc994KWXhtLJpcO3RoelhJpEKim9UOie9JlipNxhILwYBhU1nHTyRyZ\nyPbdBCN8zUe0J7oXQIYI76bhDfp8CQFIOnYx+DLnnWSz0CI0vEL0IGqI6imUj+ru2ly4+FSR\nKlCnx1FN+1Hp8Y/zA2bz5s1Dhw6tqalJvZwxY4b57JQpU1IHW7duTSaTPXr0yJ7SNO3QoUNC\niMWLF69atWrJkiUrVqyYNWvWoEGDPrIzHw2FiN0nrx+6cPHpoG0L+1cROYhQqRxL6WCih6ia\njK8KwNBo3UHvM2h8QyqBlhY6O/H6KA0lFcXYe3T4oB47FdWHlMgkQsHfAz0Mqe1iLfG1good\nubFUaX3Q5wxlSfayL0HNHhePAsFTu5OFiyfLPP5kz2bDxzLzkjx9ze2WFsV2pSKPpWVwi17o\nCGny0w4jABbwTQAAIABJREFUz/2xD2iAqoKR2eBBz2RhGgCKF8PIkDzz34xIr4qNNaC0YyQR\noHjRDaSBTCIE0UZ6H2Zcf8b40AJImWZ1gFBpCFHTRPM6PKVoHfQ5i6FXZPYTc0K8ifd/RsOr\n+CoxkmxpYMIt9HfXxrn4DMNbzvCr2fdnykenP1/RQ/Q8maqJH3nIZDIpTB8TS4n70tI0Zayo\nqKirqzt8+LB9hAsvvPD0008Ph8MrVqy44YYbPrInHxmFiF1hlc6Fi88r4kc5/AyRg3hLqZqC\nr5rXLiE0gEAtUmfPn+h5EkOWsOP/I1ADgngTQ75Ov/PZ+UAyLsNhXyCIIqRP7RRS9qyoR2pI\nFcUHXgwN4UGLgorQcuYVIDUgnZZnh2PU1QIzmSiSReVDMaQkn729yBwmoobJQ0diZInhKraz\nltHMvRSnGYv03PHW2VEM7bNrgfncECB1JEgDxYOnlGBvkp3EG5A63kq0MHocxYvUTKs8Mo4K\ngZFA9WHE0Y0MM1NRfehxjr4JCnqCaYdQTCqFrtMJ/h5M/O/0PhNVk7rZEHbPwzSto3JSeuZg\nHzYvpXI8ZcML9XLh4tPFkMtJtrHvETylGHF6nsKQJR8nkXTMmDE7duxobm6urq4G1q1b52g2\nduzYI0eObN26dfTo0cDBgwcXL178wAMPTJgwYfbs2ZWVlbfccsvevXsvvrhQ2Pc44aPUsXPh\n4nOMyH7ev53WzXgrMZJsv5/yUYT6UdI/bVAxivoXGPcjZi0nvA1pUDqM6qkIweibeOe26lBM\nKIqqaEmjBE9b0BvWdI9PJJGpIKwfLZxOtrMTk7QYo6aTq3BSp2RGzSk+umpRofIFGVON9s0e\nzPtG5JMJ7RTQbmN/KWxj2l3qljkVw3ezs+eTLR11x3yeZFmdozBJntuVVwsTSBAe0JAayXYM\nDanj70XsEHokzbcM888AJRO9zexFZsQyvkkAoae5oLeMRDM9ptNYj9wDVeBD02htZcIgBi6k\nz8KiZFsjSdtWSgZ02XpK8FfT+q5L7Fx8puGtYOwP6Xce0cN4K6gc9zETSefPnz9+/Pivf/3r\nN9988549e+677z5A2KTuESNGLFq06Pzzz7/nnnt8Pt8tt9zS0dExduxYQFGURYsW/eIXvzjz\nzDOzId1PEu7ezy7+ybD7IcIfUj6aYG9KBlA1iSMvIHJ/4fiqiBykaiIDLmbgl6mZhhBoHcRb\n9rZOiCSC8YS3IxYSRjxmlMaSZUIRKCqkqvxHMKIID5JcgmDmDckuxc6RNMj8tCZfFwoypCzh\nc7RRco0d4ZgG5ziX5awoyMZM7QNr+fM1XDUvj4FjfqFF+bPHbc1mMr8BedinOYsudTZbnK9A\ntNc8b2YmQ/Wni9JJAy2CHiO6N12jTk9gaPjKUHzpbgKEhJQ+l/njMTtsyPRQaoj4UVrfpVct\nHj/qAcIHCOiMqaW0BV8N9S+idRb0MuWjbouIA2paY3bh4rMMoVA5gd4LqJ358ZcHCSFWr14N\nzJs37ze/+c1///d/K4riuGXw8uXLTzvttCuvvHLx4sVVVVVPPPFEdsfkCy+8UNf11DKLTx6u\nYufinwlahM59BHp3tagB1CDJZujb1SiTDvss7fsLDa/0Gj3tzuXXtrckR/TbefmUn3XEe8Ti\n3prKqNeXwEiSbEfxZsrKZWNqmQdySq4TqVSq4hw2bGlkhTs6sg27ilbgrCX7zQ4L0RH5KVcB\nl5yGrQhx0QynU44h48IG9hEsB9hIXrfaYVo5c5q6W1lRkYrWYXptZAQ5HZmqTmygRTC0tFor\nM+N2OZY7qMg4FKsHkArCwKsz/lz8/eEo0T0kfRx+iv1/ocdsRl5DyaA8lweAGiDQi+YNhPpl\nfNRItqR7aWEih/GWEqjLZAq6cPHFxNGjRx977LEVK1akShAvW7ZsyJAhKcVOypwPdigUuvfe\ne++99177IJ2dnWVlZeeee+4n47MF7kfUxT8THB//njJijV37zGidRBuomWY1a3ufUN/yktiX\nF77rCZaufn22pivxmF5SogdDAqHiCSHAU5IOyApPQfVLFBMfc6BNxQRbHdsL8zn7jE5JgDkd\nZe5Lu5pYzAWah83noTD5L3O1q8JCncWTAuywwH0z+5DPIJ+cWSiTT6b/iUFKFNPjuTWdM5cq\nnX4GSIEQGEmMOMKDNwCCypEkdzLkNEp64C2jdgZlw6maTNsWdtxP+1aa3iKyP8+lwqBL6dxD\n5x4SbcQaaXuXQV+legp7V/D2d3ntQl6cz3s/oXNv3hFcuPj8o6Sk5Ac/+MHSpUuPHDny7rvv\n3n777d/4xjeK767rent7+5133rl48WJHne8TgKvYufhnghoi1JfGtV3qhRFHKPQ5l/rn8Vdh\n6CSamfATKsZa+0odDKKHR1du+slZ974/bYpGsG/VYUWRxAWKDyOOoZFsBYGhI5T0JlEOKFq0\ns8cQHXWyIgdJX0im0VFnEk5sifyyWbaLfWOx4j00T2GRJy3MqQBXFrkuWdzIF3g1B3MLMFHz\nPTGcgpYWFMM1UzOqkg4oVfBIZGqRrGZSeQEVIdPLZlPZeOk1FiroCC9GklgDwb6UjybZSsOr\n1L9AzQldc/mq2Plb9j+KlCRaKB9Fj9n0OZ0ep+TIb+WjOOWvHPwbkf14Shj8VfqcwaGneP8O\nykdTeyLSoPE19AjjlqbLTLhw8YVDMBhcvXr1DTfccN9999XV1V100UU33nhj8d3Xr18/c+bM\nvn37/va3vz1+ThaGS+xc/JNh0FeJHqF9C74q9ATRQ4y8jmFX0PIOHbtR/FSOcw5a+WvZ/0hq\nk7GAmpwyYB2GhlTR48hMipJagh5BUUEite4T1qQpZ8tyXuTnWIVh53xm4lI4yczeaB7WkQNl\n6ZG98ki3oqDl2DK441UXlirz3a58iYz2SfPBTI6zCmK+EjOFY99ZmzSxgzKJkKZ6xQqSjHQn\nQSI8SB10UEADiRrEW44RQ3gxdAI9qZqK6iOZKoxi+mKXOu3bSHaghIg3oPppeZvwNnb9L33O\nYtg302SxbAjeCsqGM+p6k5cG9c9TNgxvKYBQKBlC/YvUnU7daQXvlwsXn2PMmjXr73//+0fr\nO3369G3btg0dOjSbb/fJo1hi19bW9p3vfOfFF1+MRCL2s46lXFy4+CyidDDjfsThp4kcwFNK\n9VR6zAZB1RSqpuTtJSXJ9nQhWSOOCKB14qsEH3oSDAyJkGgRFDKULhVsFUiZ96mfpQuKSfEy\nZ6c56mTFpNlZWvLpc90SGjPy1fs1zyJzLbv10MKNuuWd+eal4J3JJ9dlIfKfyje7XReU+Yve\nYXtnsxfb9aanRFyJECiZgsYytfmEQkkv4o1IAQrCg7ccPUnJQOINCC9Ay9tIjWQbtSegR9Bj\n6ZKt8SbC25EGsUMofrQIMgkSfw2HnuTg4/h7oQbpMYu+59LrVI7+nehBPCWUjaTlHfb+ibIh\n6DGCdek/aU8piaY8F+nCxT87FEUZMWLEp+tDscTuhhtu+N3vfjdt2rTZs2db6vW5cPE5Q7A3\nQ44hZwIgfoQDj9PzFDr30NyGx49aQaIVPYqSKmMhkLmbTKQoXc5BCrYnvyUGaqFfFrMCMGtI\n0qmvucWwEanCKCZwmS9gKvJTPYu3ZvprESwdVUAzlyogENpbHP23EDXHCyxAai3voOX+W2a3\nHwPCg9SQMv0HIyWKilBRfHhKSHYSGkC8kXgjyTaqJlA6hqbX6dyNHkkX8fLVsPcvDLiIA6sp\nG4waInIALUqghmQYBFJHetIyc7wZfxVqkJrpxBp5+zpqZ9OyAW8Feozwdvw1qAE69mB8SNkI\nKscD6JF04W4XLlx8JlEssfvb3/62cOHCp5566rh648LFZxR6DKHgKaV8HFqMeAOeMhLNKF6M\nBIA0THlRAgOErUxdDjJKXvEoIIYVlt8ceZVdw8s3qTlY7EhZHIXA7LFS0DGT2/Wt3P00Jwzl\ngul55sq+FLlTmL09VjZm9sR8lxxD5JaOdjdE7r92wmo5Nsz7TKhILb3bRJdXOlJDQPuHBHoh\nJCWD8ZbSuZ/wHgydshEkWwn1Ryj4qgj1J96I1sm4pTSuQQvjq0bxoZaSbE+Hd4UESaIVoaCW\noscwNHyVSMGRF6g7DQThbQDRBqrG0rYNbyVtWzDioFAznerpTvfFhQsXnwkUS+wMwzjnnHOO\nqysuXHx24e9JrznEjuKrpGIkLXHijegaigdDonqQ5u2SswKOQJHWUCypB7YsFLbLl6NmD/aZ\niVS+EfIp7Pl0I3Jpip0YFUgms7CxwtzRvK+roKGd21dz1TwumJ6rwxWQBi0Gjpb5tr6wD5gN\niGfT2xwrv2TLR+fTVrNXp5huiKOxkJDhdoYOIBTMC5JT0Xw9mY7VhneCgaccNYBMMPireEpJ\ntuKrzGyXKfFWk2ynbh79z8NI0vgq7VswYqghtM6uAopGtOtShUAa6FF8ZWnn4i14ytEjKCFK\nB9OyCanRugV/Lck2OnZTPdl2wS5cuPhMoFhiN2PGjG3bth1XV1y4+DQR3k54O0DZcIdS+54Q\nfc5i4w8oHYy3nLJh6DECvUm2kmhBT6RT2hHpZ6TIsBLplH1l197sCVtZFIrcFRGcLVKWswxo\nd6bb2CVFTGTvbs42y+eSvT1flDNrY2GWdo3NEZbtX80hcrP6aBnfsN0u+1yF7owAUBQMHdUP\nCkYi/VeEwFOGEUUJoEdItiPU9FLrQC8i+6h/jmQ7TW+CRHgQHkoHUToMT106wU7xUjGOuvk0\nv03HbowkyHTSnvAhdeLNVPZFqBgaRgIl1OWUSLFMHanhr0YahPpRNZlYA3seomwY3rICV+XC\nhYtPC8USu1tvvXX+/Pnjxo1bsmTJp7jWw4WLfzykZPfv+eAuvJVI0FoZdQODv27dLr33QhQ/\n9c+SaMVfzaDLqBjPtrs48ARaYzomK00KT3Y3d2f5LRuDo8vmGHwugrHJPLpXgY6WFDdye+WD\no+BnlrWUXHGO3INiqKd5rmLorMU3M4HOB2kzTsEszlnuj6N9gaBz9qwUadUWgaIiZbqMYqqk\njlDSK2TT9irCg+JFT+AtRYKRROvAE+TQs6ClC90JHTQ6dpNoYewPiB8l0UqwB0Kl/wU0vUXp\nIDp2g8SI4ylB+Ek0YUhC/QGSzRhxPBli5ykn1oiRQKh07Mbfk3gTwV4IhWAdDa/S/gE1bkDW\nhYvPIgoRu+nTcz63Xq/3qquuuuGGGwYNGhQIBMyn3nrrrePinQsXnwCOvsYHd1M1JS1yGHE+\nuIfSofQ8JcdMKNSdRt1pGHEUf7px1I1oUfavAg0UhEzTNUmGF+aytwLRz3ztsjv2YBeNUrDU\nH7GzkwLz2qcrTIkcR7OnmpnXdhToXhiWqyjMiQsEu81KatYreym+fETtmPiomc7m3AGJkUSQ\nXu5q6HhKMJLpNM2UDicjeAIkk4gkWgdINAMtjBD/P3tvHi9HVeb/v09V73dfs+dmI2FJSEwi\n+yYICSoCIjiOwZ/L4Ir6EwFHxRn1i85rhllwFJxxRERE1K84KCCyyBJAZV9kCdm3m9zcfenb\nW1Wd8/2jq/tW19K3ExIIoT4vX1h96pznPKe6c/vTn+c55yklAAi0Ik20sLIYGqOv8sd3IjMY\no2hxZryb2RdQGMYYpWEBkQbMMVuiG32VoefRIkxbxfLzeeUa9CSxFuJtpDcSbUBK+3DHhrkk\np5eWH8HKESJEiIMS1Yhde3u76+XSpUsPsD8hQrzu6H+C1Eyb1QFanNRMBp6YIHbFjYrF474A\naTD8IlaG1CwaF7Hyu+hR8gPkhxl6CiuPKlKGSrYl/BiDlxlMmg3mxaTym+22X4C1FimLShZV\nC+8Jgm+6W1CQ1xUY9c7u4oi+Rqo/HBfZdWXUVWdv1UXTKkFz5fBbeVYi80QaMNMoCz2GZSJ0\nlAVRVNr+aAkBEqtcbtixDKWwCvQ9ih4lswciWHn6nyDaQNtxROpITkdPQidArof2Y1l4KUIn\nOcOO23bfQa6HeDtTVyE0Mtsxx9CiNC6yszWtHOY4dbMCVh4iRIg3GNWIXbgHNsRbAla2VHy9\nBC2KlUEaDD1Hz/1ktqMUdV10XYQxwpab2fMwWhRznM5T0KLsvh9rHD0Fxb2usvStX1bvSgnx\nggmxSDnIgTeTrMwqglK4nHAldXn1IS98SUlQUFU5nHFaFq6lVTUe5L+vh2X7ziFOm859CfsA\n5bezQU1GYb2N3p7OW66XyvlCgCp9HkoflWJY1soiNFIzSM0ksxMDCgNYpv2ZsT8+jg0WzmsJ\nowV6t9E6RjSCJpAw9iqdZ7Dnj8z5G7bfRnImkQTGKJldLP4G9fMnhrcup3U5yqw44rjjRJ77\nCvl+Ig1YWca3cvhlk1SeDREixBuHWk+ku/jii9etW+dtf+SRRy699NL96lKIEK8vktMoDFW0\nFAbJdPP4J3hwNRuuY+g5jBF6H+LFf2L9dYxtpuko4lMQOpt/Qu9aWpYSbyfbg1JEEhAF4aAI\nJeqhRxDFY8kiUBms9EUt4pYvz9AqLfsSIJepoCitS9ITlZadDM/ZP4hQemdxDOxs5MvnsGpp\nZX8R8JSkn7f4PRAC1lWmcbKSp/qiHEp2DncZdz6E6oFaoZUqwJb8sBmeRryNVBcLP8Oyf2Hm\ne9EjaDqy5OJEfL80yvtwJDQOYRqMSdDQo0jJ+Cai9TQvZeX3mHIqDYuY8V5O+V//za2i8gf/\ntNWsuJamo4ikqJ/H4q8z9+LgtYUIcZDh7rs57TSmTKGhgWXLuP76N9qhAw6hVJU/P6TT6Xw+\nD7S3t//2t7898cQTnXellNdee+211147Pj5+YN18Q2EYxsjISDKZrKs7BMsjDg0NtbQcgseN\njo6OFgqF1tbWyc/Tzu3hxW8zus4+Wz/Xg4J8D3qS9DaiDRijNMyjaQn9f8FM07CA/j8jLWQO\npRAakQRmFrAjtpE6CiMTQpaIE4lhjhFtRggKI6AqVJYy9ipty4sg5a+WnntltohJvfUdGKQ4\nqqr9q0Q2J53du29jUsiqZ8R4FccaA7IuO66N0sXOmg4aWAiBgngrsoAx5jg0pzSmulKYj5KR\nNCZJ6phZoknq5rH835jyjgCHaoA07d8kDoyNjeXz+ZaWlkNvU102mwXeqDruBw6WZQ0NDcXj\n8YaGt8C+5ptu4iMfYeVKzj8fIbjjDv78Z266iQ9/+I327ABikl2xn/vc537yk58Ur88991zf\nPu94x2v4SxEixBuOxBQO/wLbfsX4NgS0HUd6A4lOxtajxxERos2MvEpqNnqcfD99f8LKoUVK\neo+FkSVSrC2m7BAtTOgruo45DgpjlEgj0QaMdMVZZUW4wrJVcsuCIBwWqpCYGtnepEwoKGrp\ntIDHiHON3tix15NJpC9Hf1e3Mm3SKjtXhyhRLt/2IOoZ5Gd1z8trd4qFxZqwRUkPjfwA6KAh\nrApW5zLuVX9jJprANJEW8WbMDO3H0uLR53J7yO0h1kJqJmaGfB/xdiL1/j57WF2IEAc7fv5z\nGhr4wx9oawO4/HKmT+f++9/SxO4DH/jA4sWLgcsvv/zTn/70/PnzXR0aGxsvvPDCA+VdiBCv\nD+rnc9RXsPIISG/j0ffTfjwiYutqQkOLYGYRGsYQZgEB0rC1ICEQIEuFPouFKEQ50KZj5ezD\nLLAoDJWiaR4iIDyEpowg2uQVscqxxaCQaBVy43JgUlLim/rm7OOtWiYmG+VyUgRs+JWePb/V\nSVUV6umbDFfFVBBq7OOrVrosCOxD7IRCCJQJOpKJTRJet12iphQYAl2hFRB1WFlalrLgU8Sa\nJ4ZYGTbdyIbr0WKoHPVHEGug91ESU0m0MeV0pq2m8fAaVhUixEGM225DCMrRtpER8nmy2TfU\npwOOSYjd6tWrV69eDdx5552f/OQnw12xIQ5l6HGAaD3SQpokOhhbh560tyUKyPaAQIDQoSiu\nKIQGIDT7m9WyJuiLklCkdKWmYjUn5dmAiYdUTZqn5ctIROVY7y5UX5mnjEkjp8qzayFIypo0\ngLtXemQQA/Me8lxFVAuSJGuZrtzunMtrs3qgNoDS+3uLQlmla8s+K9jZwfkklbMesUDGiZgY\nkmQnySbaVrDkmzQsqJhl881suZnWY9BjjLxIz/0k2ikMY+UYe4XR9Wz8Ecf8Nx0V6TchQrzJ\nUF/PU09xxx2sW8fmzbz00iHP6qh988SDDz4YsroQbwkkpjHvYsY2Eu+g+WjyfeR2o6dIb6Jh\nIQ0L0IrH9FsIHYRdsl0ZNuHTNZA2gaOsohWFFkd21MQtKhqLCCpUGhRC9V5XYTnlluqlxpzx\nQVFJJrwzihp8wzHW12fnS1eNB69AFfRwqkuSVXwjmHgFPcPaBVHl+W+Qh8530L4QKIGw0AIm\nKH/AFFhQAGmyM4Xeyvz3s/K7HPs/FaxufBs7buPlb5Ocih7DyjDyCokOMruI1BNtIt5Jrpe6\n2Wz/Jdah/y0Y4lDG1VdzzDHceSezZ/PJT/LYYyxa9Eb7dMBRTbE788wz6+rqbr/99uJ1lZ73\n3XfffvYrRIg3CkIw/++wcuz4XyJ1JKfSdCRTV9FxEhuuJ5MiP0yuBz2OsrCyoCMUehIrTySG\nmUWLoiwQKOzUKOHYeumiXJPqaqIytDopmXCWOsWjC1JJHXz1Ni+XcmpOQcfLSU+L1zfnvEEo\n3vKNwLom9d4KislOKn9ScyzVFQJ2uR0UEZ6UdJY7exdYVnlVSZwTlDbVumYRNvkbTzH/baT6\nOPKL1M8r9TEZ+is7/5ettxBtIjdI7yM0HkmyE6GjJMoq5QloCB0twZ6HGN8WBmRDvAnQ3U1P\nD01NzJlDpERsxsb41re45BL++78nelqWr4FDCdWI3fDwsGmaxet0Ov26+BMixEGAeDuLv86s\n95HrJd5K41F2lDY5nZGXqJuDzJAfBFAKJEJHFlASMwfC3s9Y5Hb40SAnaiEc1dmMt7+Lebho\nYhUhMMigcJjyCoFBcViXyOe7Ckf7SIZ7X2ReByvmBjNOl1knYdrbuKp3LUFM1zXKazNIHK2O\noDVWLz4rnFFXx6jiXT1CspVYmtRujv72BKvL9bD+Orb9iuxOtDiROnSdWCuj69B0lFU6+jhi\nT6kkIgbYmQYhQhy0yOW44Qauv55EgkKB88/n4x+nqwtg+3YMg87Oic5PPMGWLSxb9hrnzGQy\nqVRq8n5vEKr9o33yyScffPDB4vWfq+J1cTVEiNcRQqf5aKa+k5blNqsD6ucy/DwjL6BEqeb6\nfGZfwPT3kpqNgEgDWgRKFdxFUITSNVfVl05MGiJUDh7mK5tVkQaDpq4SzPXa8UaZfXmSn+i1\nrZ+LvssPH3B08I5ynpDssuarinkncnro5KyaXx8XvLqps2eVZQbZ8XXYlzWWb5cDry6LChJT\nSUylfi6xJt52DQ3z6P8L2d2g2Pg/9D5KvJNoK4kpFAaJNGGOEklhpqmfhzFMrNWewhilfh7k\nmHI6dV0BawgR4uDArbfy059yzDGsXMlxx/HnP/Nf/2Un0i1axLx5fO97fPnL3Hgjn/kM73oX\nU6bwxBPcc08VkwMDAxdffPG0adOmT5++Zs2a/v7+YrsQ4vHHH1+1atUHP/hBYOPGjeedd96U\nKVMaGxtPO+20559/vtgtGo3efvvtixcvTqVSCxYsuO2224rt/f39F1xwQWtr68qVK3/zm98I\nIYpHxY2Ojn7qU5/q6upqamo655xzdu7c+RofSa2/xpYsWXLllVc+8MADhULhNU4ZIsSbErk9\n9D1K6wrq56MnEDp6gng7sRYSbaRmgIYxgpXHKm6MFRWVJ6rARQtUMCFw9a+ivTlfBrG36gzS\nO6qKiOUUvXzd8JUt90HiEpP55iJ8Tq3L5aHmoWV4GJUXKrh/0Oxea5N+JKq9j3KicaJb6TzF\nSD2RBqa+k9338/glPP15Hnwn6/6Dbb+kYQGiuAlDEGnAHCc5A2OYzA7MNK0riKTI7Sazk2g9\nepzRDcxdM1EWOUSIgxDj4zz+OAsXEosBaBrz5nHvvRQ5ViTC73/PiSfywx/yD//A4CBPP81/\n/Af5PFdfHWRSKXX22WevX7/+1ltv/fnPf75hw4Z3vetd5btf/OIXP/zhD1933XXAe9/73tHR\n0VtvvfW3v/2tUuqSSy4pd7v00ku/8Y1vPPnkk29/+9vXrFmTy+WA97znPdls9u67777iiis+\n8YlPlDuff/75r7766s0333z//fc3NjauWrVqeHj4tTyVWs8lSqfT11xzzTXXXFNfX3/66aef\nffbZq1atmjt37muZO0SINxMGn2HwGZqXUDeXsQ0ok0g9xqh9lMnIOqSBnkAaKAslQKJAExCc\n+OV66Zu85WUnXrKyV1CeCy+qaHK+PatHe8usTnpO5a3ipKi8KEPz8KcgBAmQVdiY5mBsTh7p\nPBWvlsfufX+9Pgiwav9xXXZJs0/hsf0UpU05GmYakSdxLFhku2ldCYpcD+u+a9NBPYWVJ1IH\nAqFT10WsgbZjmfd31M8mvYWBpxnfjIiSnBrKdSHeBBgZYe1aTjqpojGZZKhUTGjRIu66q+Ju\nVxcXXVTF5MMPP/zMM89s3rx59uzZwK9+9av58+evXbv2lFNOAc4555wPfehDgFLq4x//+Pve\n974iEdq5c+dll11WNvLZz372/e9/P/DNb37zF7/4RXd3965du5577rnu7u62trZjjz1227Zt\nX/7yl4HHH3/8kUce6e3tbW5uBn7605/OmDHjtttu+/jHP77PT6VWYrdly5bt27evXbv24Ycf\nXrt27e9+9ztg0aJFZ5999urVq1etWrXPHoQI8eaAlUWP2teROqSJppHdjbLI7KAwhNAQUaJx\nZAEza4divelQyqW1AAFhU5fK5dxCofkxHpepGm9V6e+reOFwwyXC+YpYzpbyirwBR69X3gtZ\n2hoS9JSCnokKOPfO5Y/L1fJL19k0Ts7ndKMcJvY64xuilSXV0HeniBe22bJip9mR2WLEX2ig\nc9iBvWqUAAAgAElEQVRnSExh289oWISVY/RlxjajJFaagcdpXESyk/wgkQTKwBgmORNpsu7f\nEBr18+n6AKk1EzPmeul7lPwA8TY6TiLR6XVq/8BMs/texrejJ2lZStuxE4X4QoSoguZmTj2V\n0VHqS6dqK0UmYx9HvE945ZVX5s6dW2R1QFdXV1dX1yuvvFIkdsuXLy+2CyE++9nP/u53v7vh\nhhvWrVv3wAMPOIuvrFixonjRVvLkhRdemD9/fvnlscceW57OMIyOjo7yWNM0d+3atc/+Uzux\nA2bPnr1mzZo1a9YAPT09d9111zXXXFMsKVa9LlmIEG9uKIue+9l5O8MvkR8hNYPkVFIzyewg\nOQOhk+kGRawZIw0R0EFHmChR+p531JmoMY+NqiFUp+jlGhvEElzc0WnBl595vfISGqe+FeQP\nnlvOIUHBYu8qnKQqKMrpe0yMCpYJXSsN4pq+sVfleSM0v6VR2d/XuPdsF1HFG6Ao2kmEXqpI\nAZrOrPPpOJHtv8YqYBmMrmO8m0Qn0kSmGN8GksbFjL1KZhdNR9G8jPweRl8h3gmSXXeT3cXi\nrxNrARh+gY0/ZOg5InWYGfY8wPxLaDkAh17lB3jpO/T9iUQr0mTD9Sy6lPmXTD4wRIhUipNO\n4tprOeIIkkksi02beM97eA2ns3n5jKZp5Y2k9SUGmU6nTz755EgkctFFF33kIx8555xzLr/8\n8vKQeNydw2AYhnD8XCnXumxqapo6deru3bv32WEv9q5EzNDQ0GOPPbZ27dq1a9c+/fTTpmnW\n19e7CsiGCHGoYcdtvPRPpLpIzSKzncx2mpeSmEKuh4b59P2JaCPJaShFtBFjDCuLECiBJpDK\np3qYE5OmW1WHk+WIEksoW3b2Kb8MOq8ED8Nw2dEq7eCgXy5T3thxEPNzERpvH+HoEySAeV11\nwiutVVEoq48twktnhefJULkK74pcSqQvT7VfFjdZK0e7nFDvtDiRGEpgFdj8EzbdYHcp9JPZ\nQ2o6CFSBxFQamxh8ATPL1DOZ+za6p/LSr9BfIDUH2UehQF095uPs/gNdH0Tm2XwT49tpOso2\nmNnBlpto/A56wu/ZvQZs+wUDT9FytP0yMZ1Xv0/rClqW7+eJQhySuPBCCgWuuYZoFNPkQx/i\nIx/Bw6tqxxFHHLF169adO3fOnDkT2LFjx9atW4866ihXtwcffPDll1/u6ekpFlu/5ZZbqps9\n8sgjN27cODg42NraCjzxxBPF9qOOOmrPnj2vvPLKEUccAXR3d19wwQU//OEPjz766GrmqqJW\nYvf5z39+7dq1f/3rX6WUjY2NJ5988ne+851TTz11+fLlkUhYQDDEoYt8Py98g9blROqItxBt\nJNfD0PPMW8NRX6VhPgp238P67zG+FT2JMYoeBYkes+t+wgRBcIoxApQ2cZTxpPDlSThilC5h\nyckqnKO8SV2+FMTJqMq3gqQy/BqVh/FUZbHJOCvmMtsVQvFVv3zhNe4b8/XSLzxky2Wz+vAg\nphj0oFQlmfNyVuWYSQARNA2Z95sDlImMIcdBQ0q0GELHypHejiZQJsrCSNOygkQHhTGW/ANN\nJ/Ov13LHDzlzkOQwfZuIxWhNIHJMrafxcbo+yNhmeu6j7fiJieJT6fkj8zbSvNjfk32DUoy8\nTGrGRIseI97OyMshsQtRE2IxPvpRzjvPPsdu2rTXGMc/7bTTli1bdtFFF11zzTVKqSuvvHLZ\nsmWnnnqqq1tbW1uhUPjNb35z1llnPfnkk1/72tcymUx/f397e7uv2TPPPHPJkiUf+chHvvGN\nb2zduvUHP/gBIIRYuHDh+eeff9555333u9+NxWLf+ta30um0l0fuFWpN3P3e9773/PPPH3nk\nkT//+c8HBwfvvPPOK6644phjjglZXYhDHNlu9ASROgAtRuPhdJ5K3WwaFrLjNv74Tl78P5hp\nOk8iNQsrh5JISayJSD1aEgAxQbbsf3AKiuUEhP03SIpyc3GED1fwDSYWoVWSA/z+ZZd1Qy+T\ndGaPuabTPD2rQPh1EFXvOjocNpWnruZr53m8rY6gwLG3Q1C3YotWeVf5PRM8q6g9sE7lmxsU\nQHdNpyXBRBk+pLXc08raQf9iBRQrCxKZx8qT2UWkjvbjSXQgTawcjUdw+53ccw/Ll5NsQllE\nIjRnmDnMTAO9l003svnHyAJCn5gzP8DwC+T2sOlH9P3JJ3N0nyEA5fkmFshD/xTZEPsTLS0c\ncQTTp7/27EwhxN133z137twLLrjgwgsvnDdv3t133y08Zk844YRvf/vbV1111bJly2699dZ7\n7rmnq6vLuX/Wa/aOO+4ATj/99Ouuu+473/mOpmnJZBK4+eabzzjjjEsuueSCCy5oaWm56667\nnOl6+7KEGtPjrrzyykcfffTpp58uFAqdnZ0nn3zyySeffNJJJy1btuw1enDwwzCMkZGRZDJZ\nVy4kfAhhaGioqCQfYhgdHS0UCq2treVUhn01tI5HL6L9+IrG3feix0h02uTMHGfGOdTN5NXr\nUSbxNhJTGXiSzDaUiYggTfc3tvOrXQMlQE1CYmrUribtNmnw18swquSHBalc1VG9j3MJviHj\nKuFUJ7zdVACv2itXaxzufIyTDyl9AFzdIvWY4xVx2PISikUmRMRRN6JoIQIgTYRAS9J8BFoc\nPY6ZputvWPQFrvwyW7bQ2op6jsOeIRtnehYjTixGJMfYFOY2sezbbPkpuT1EGlAmA0+hxzHH\naTuW3B6W/COz3ld0Z2xsLJ/Pt7S07PsXwSv/yu4/UD+/tECTwWd4+3W0n7CPBvcTstksUPzq\nPZRgWdbQ0FA8Hm9oaHijfXkLob+//7bbbrv44ouLJxvfdNNNV1999YYNGw7EXLXqbf/yL/8C\n5HK5J5544tFHH3300Uf/8R//cWRkpL6+/oQTTrin6ll/IUK8iVE/j+lnM/wiqZl2y/gOInXI\nAqPr0aJIi1gLW37GiT+nZSlj6ygMkR+wfzgqKuQWpyZXhmQSnuKM1gWRFSrNVqE+kzIb31Sz\nKi6Vr33Dmq67RfhWUfOuAsdYp31v+TLver25a/g9fF8EOROEKgqrK9LqtY9jXV5Y48QaMLKo\n0m+Dio+BQpkIiYiChZRopY3bxa2yMkdmJ8YYepK2Y5jzIRAYBrqOgN4UhQQn5BAQtYjmGGtj\n6ygLD2P99YyuI9+HFkUpRAQrT/tx1M0h0cELX6fjRBJTang0NaDrg4xvY+SvxNqQJtlu5q6h\n7bj9YzxEiIMDdXV1X/3qVzds2HDFFVf09vb+8z//80c/+tEDNNfeBVITicQpp5xyyimnvPzy\ny3/84x+vu+66V1999d577z1AzoUI8cZDizHvY2y4noHH0euwxmlazMBfiNYRK6WDWTlkjv4n\nGHqO0XVEG9DiGIPuBDsqXwUl11OVWPhymvKo8ssgVlfs4NolWrskJvxYiJPeee96d6r60kqv\nPlfu7L3wPivl93JSYdKbNeh0xhdOwcw3OO56f6sYL08tBbpnXts9hZmzd0tIDVk8jq48vHji\niUQYlZ8MaRuPNtB+HFocPcnoy+y+nzkfpKuLjU+yZDPL1jGeJRNBKIxGIs2kTWa1IYcZ3sP0\nVeT7GNvM2AbQ6DyRujkAkXr0FOnN+43YpWaw+Cq672R8m33cydQzw1JmIQ4xJJPJO+6447LL\nLvvBD34wderUCy+88Etf+tIBmmsviN26deseeuihBx988KGHHurt7RVCLFu27O///u/DQ+xC\nHOJoXMTR36L/L+T7ibeTaGfLzcQdGbJ6guxuNv2A0Y0gyfU5qEYlcXBSkCrswSvnVL8VxCG8\ncO5s9Y6qksbn7eCrunlX5yubeU8qDtLSvPpiEI1z3fK+dM1VRST1Vftc/rhW6np0VTRRr/po\nKXQNdIS0i9GVM2QUyFKxH7NSv4SJVERVmrVY9VVoaDp6Emkwuh4l0XS0JCPPwwd5/7kUfoa+\nmaF2hkZoHGYmjOgMDpPOML+JzA7quhA6ianoKbLdRBJukVnbr9nViSnM3/fjWEOEeFPghBNO\n+Mtf/vI6TFTrP85p06b19PQAnZ2dZ5555urVq88666zOzgN2XmWIEAcVok1MK/2AGd9OohVj\njGizHW81xtA08gOg0JNoEjMDyi4SIDS7OoULqnTcbhWoYkyt9DXvVftc5KxKBLZssIxaiOCE\nG44Zg1hL7blrVZiT106Qhle+5XwszvYg0dH10ktSqyzE217LLL5DyhQtJkAiJOgo0Iqn06kK\nOwJ0hQZmmfaVxhc/hJFGzDGbwxEhWo+ZQUkKw0TqMAsYuxhoBEW8m0V5BhdS6KZTg0bI0jBA\nIc68emI7McbtRD0gWk9qFtldKMNuKQzSfjz1h1VdYYgQId4w1ErsFi1a9PnPf37VqlVve9vb\nvNtDQoR4CyExhWnn0P8YuT2IKMpCT6AkSiF0hIbQEDqYIKtFlJzSS5XQp/NG+bJcgEH40Rpf\neLmgcvy3evEDUelhsae3HFaQxOX1wWVTABRMdg7SmKC9sepCnJbLC6+i+QWNovIh1NKfyR5y\nLcTax0PluVXJTIv/X3zauqq0UGKm5pj9M0ApVIH8IEKgxSj0oyxizYgoo68y/FfyA6SaiWfI\nGESaEIJsD5bJwk4icaRE6Aw9Q6SO+rmg0biQ7G7yA+g7sTLkeln4eWLNVdcZIkSINwy1EruH\nHnroQLoRIsSbB3qcOR9k4M80H4nSUIrx7aRmIQ0KwyhQBsiJaJrrh9Ck3/21wHtwHVVDh1Qy\nKhzHFDOZQCUrCVA5NW0fkqCqaG+wbhdLv8InTue/XUG5IPmteiqbd+pa6K+zs2/2XhBU5Shn\nuzbZezTR2QKBsvzYt9M3gVClrbISVaT5pSFaAmmgxbAyoFCQ78McJtZOciqZbmJN5HvJD9np\nBMpCFtATRJsRilw3QkOa9P8ZY4SGw8j2MHcNrW8jP0Sslc6T7GS7fYCS9D3G6CtIg/q5TDl9\n/x93HCLEWx7hKXQhQuw9Ok/jmP+h516yPcRaiLWy5WaEQSSFMVaq5ilK37v2K3BkobnonXT0\nccLLEoTj2tXTSyCCVEBfjuJNfaMyUuwkRnul2jtnVH63Jh1L5QMsF2+dtH4aDr7lRZXng8ey\nqOqt8Fw4X8qaeXC8gUIaAbL8w6Ak0TkXogSWsv9+awKr1EcprCyajjJBQxRpogY6skBmFzJP\nbDqpGeQH7YeoLJSF3kyhF2nabE8aROsZfZnCMA3zaTySme+zT3PcdyjWf5/NN5LoBJ1CP32P\ncdRXX7PZECFCVCAkdiFC7D2EoP042ksnMiiLsU1s/wXGKCi7BpQeQ4E07L2x0kPRcNAd31pe\nONiAVTqFmABOpiptunSmWmQq5behwclXnJTUm5dG8ES1S2VBY6mUCaWD2/kO8T5qUcmelYeK\n+R4R7O3mO0UQys9KDzbrgplB05CmP8svwwJTYEFMIa1S51IPaYJE07AsonVIC6Gj6RijPHUp\nsRaye1CS7G6URBn28T3KIpJCFmw6qCcQcfQ6Iik230C2m6O+OnGcyj6g71E230jLcnvjRd0s\n9jxIw0Lm/X/7bjNEiBAehMQuRIjXjPQW+h6ifg6Z3WT3IAQCok0Yozb/8JZnxckYhB1WC4o2\nikq9x7dbkGLk7e8rGXp7lplTsVF6KNG+EbW9Slmjcq7yWC+nrDKFy5T3moC3JoggquAOXn3R\nectbWtcLL6Xz8U0RqWTzPmqiROlEYpgZO7ZrpRGCSAPtJzC+mZ6HQNnqsizYmQPmCAiETqyV\nQppIHdEUsXZirXTfQftxE1uI9gEjL5PorNhOm5rB6Mv7YqowyJ6HyfcSa6H9hIkzJkOECBES\nuxAh9gN67iM+BT2JlsQYI96KmcEYIzWLbDdWxv4y9vnC1gC0FBjIfGCwr0a9TVSyB19RzWnQ\nG68UAcIVwRRnUj9dmWdVWGaQq8qzoqAHUkUJq/IM9yo67LzlZGnK8dL7YPE8Ad2zIdq5OuUJ\n3bqWVlYf++poyRH123YtFGYeodDjRBooDIFOdgf5frK9aBFkDqGBhrLsTT8KonUInfwA8Tas\nHNHiThaNeDvjW4KfS9FJi75HGN0AgsZFdJ5YsQzl2W4TtGG8OsY28Op/MvgM0QbMLHseZu6H\n3vAyFSFCHDwIiV2IEK8Zo6+S2YGZRosgBPlBtAhaBGUiTZvA+X+BKYggM3ZhqIkUeDVxv3pq\nl69oVEQQRxR+FDDIWhXUGFv0DTE7PSl7W0sfr0vVeVgV9S7o4QQ57OzjvPbmzzkte8XacuTX\n65LXPeWQTl18UYAl0CVSQ1huW8UtsVYeESMxFRTGMEoSaSC9kcx2lEIJtDiaQMnSfos8RhpN\nRxnk+mhZQnImSpHPI82JM1B8IQ1euYbtvybRgVLk+5jzIQ7/IqJUaqx+Pvk+UjMntopn99B5\nWjWbftOw6ceMbaJ5sd2Q38O2X9B4ZLhRN0SIImoldqOjo5dddtl9992XyWS8d/v6+varVyFC\nHEwwRhnfCoq6OUSb3Hdlnr5Hye0h2oiIkpxOfg9mGiKMb0ePg0IaHqOlb2n79DLDIUQp+z4e\n4oWHkbiI0aQkxkkyhIdzOG06BT+nBVU50Bl5dMGXRLpmqU6kqoQsnbOUO7u8IsC4txuOFl+K\niedZea+V30p9Y6++Yqfv2+cMiLseuAJd0ZZDiQoj5au6OYxvQxoYI0gTaaCnsCTGmF3jWIAW\nRdOQBVCY4wgdLYrQ0TWsAnoDW7ayaxd7ulkQoedkpmcJqp266/fs+F9aV9i8rW42226leTHT\nVtsdppzOzPfSfRep6aCR66f1bcy+0N9aEMZ3sut3FfpcrI2+PzG6biLnNUSItzZqJXZf+tKX\nbrjhhpUrVx599NGvtap6iBBvIux5gB3/S9+jIOg4genvZvrZE3elwdOXMfxXhKRgoSSxZmJt\nGOPoUSLtxBqQBXI9mFkUCIWmI3SkBaDHJgpGCSpqvXtRJV5ZRI3SlJMfVAnUOi98E+xcOV5U\ndsDRE4ey5aI+juFHz0b9rDYy55rLS+ac8JJX37V7hc8g/dJF/lzFPLxT+3ruguvxej3xDrSH\nKDRV0WJfx8jsxDIQYIygFFoUaSANBGChLLQEoqwNl2fSUQplEqmn7zm2KuqSLNDZPoO+H2M+\nzOKlNB7O7AuIt1cIjyMvkpo+ocYJncQ0hl+cIHZalMMvp+koRl5GFpgxjxnnVFRwqQXKAA2l\nVS62uAs4RIgQUDuxu+OOOy666KJf/vKXB9SbECEOLoy+wlOfp+lI2o4ByPfz3JUkptC6HKAw\nzDOXs+1nKAs9iZIoyPdh5YjEURJVsE+diDajRTHGS6QmYke7zJz91S2ZONWiCKcwpqpuF6ge\ni/QVgap0rqKcOaU+6bjlUgSDUN7Q6isTOodX4UPeeKVXYAuSzaqjile+TuJ4d7x0tuxw9emC\nLDvhPYPGad9Xj5QCK4/Mg7A/lgiEtPMBrDSyZNTMosdsxa44WOZQCiEQEXZlMA5jsJWeJK07\nmFqg+3HaRum5n223kJhGpC6WmG+2raKlBWm6P1ua7taqIylmXcCsC4Kfy2RIzqDzVHJ7iLXa\nLVYGK73vR+uFCHHIoSZiZxjGnj17zjrrrAPtTYgQBxd6HiQ5nViL/TLaRHIWex6kdTlKsulH\n9NxLrANrHGmiJFoES4JACjQwx4k2EYkRb6UwiJlBATrKRIuhDAQoDYqsrlgzwBMnxU9MwtPi\nyxKqEAtfVKE1ZQrlPDTEGyL0HeW1X50/OdUvZ0/NM5fXTpXQcHV4xbBanCz3V45yIM4+QUaq\nvzXONUZKVWJ9u7mFK5ASFAKkhiztn0UhDYSGiNi1JZLTyO4CiZlDj6JrWIZtToCSGMPU6zTt\npLuR5BiNPegWnQXGN6PSjJhEkohoND4lObaB5m9QP5ee+0g46kzmeqmfV3Wdew89wawLePoL\npGYRbcDKMr6do64KN8aGCFFGTcRO1/WOjo7nnnvuQHsTIsTBBWMEvTKjKJLEGAZIb2LLTcRb\nMUYRdeT7QMPMoCdQBm1vJ99HYZBoA20ryfUxup76w4jWM/oq0XrQyPYgIRK3D5uYYCSlb3Vv\nyBKPXOS667xXI6tz0ccquiBVo5O1jKoeLHZxMmdpDeG5DuJPvsKb61m57tZyerDXSddzqzKv\nc+2uRQXBKcVZNfBU+7FYJX8EUiB1hFFanSj5UNoAG2lg5nmY42R2Ur+A3XcRq8fK2qIdOsqi\nECGu0fUsuUYiBsJiXKfBKB2rqJGYIgpD0YHHxI7fcNjHGX6BgSdITAFFtofOU5nxnslc33tM\neQfH/pie+8j3Emtl0ReYctr+nyVEiDctaiJ2mqb953/+50c/+tGlS5d+7GMfC3PsQrxVEGvF\nGq9oMdPE2gDy/ehJ9CTZXmLNoGMMIgVA4yIaDyPbQF83uV4yu8juQGaROcZ2EW8FsLJgoYHM\nV9gv5uE5X0aSWDmbDrgIij85EBOdq1eAddrxbXeRFV9S5fIKP82sig9B8pu3vfxXp5ZF4XlW\nBDhfti/8KJdXidyHCG/5v7K2mLV34KQcXTivittdFUqigXLIqkIhFUICjK1Dj9O0GKEh0yiL\nSANaBCNd8hXaDMwhNJ36PoB0nMYkMo3Q0RRWDoGKNGv5HtIbiDZx5Ffo/h1jG0Aw473MeO+B\nqirRtpK2lQfEcogQb37UmmP3y1/+ctq0aZdccskXv/jFrq6uaLTi/PFnn332APgWIsQbjWnv\nZP33iDQQ7wQo9JPpZtpZANFGrDwNh1MYmiB5xjj182hZBoLkNNpPwRyh43gG44yuR0FiCtIg\ntxtp2hnfApRA07DMUmSt9N2sR1CSaANKTvA/3/iss105KKBb1irWGPUs08uofGOm3gBlma/s\nLd0pT1ROudP82CGeFqdU5jTlbAw6685XtPPlqeX2KpLkpAKndzm+v4gnle5c1z6dtVKAXIPi\n2SU6mokm7SIozg+NncqpsAqMvER2J9LEyqEU+R70FCJid0MQqUfmoYCpE7NIRaivxxhHKVAI\nDaVKuyUkQLyNeR8NXkyIECFeD9RK7HK53KJFixYtWnRAvQkR4uBC/XyOvYHtv6LnXoCp72TR\n/0/TkQANC5l5Hn2P0rKU9DYKw1gZog00HmEf96UU5hCzLmTB3/HU56mbgzkOcWQBaSJN4m0o\ni8JwSWATduKdFgeJNBBRZJb8AHrdRHq7i0koDxHxpSxF6mMpm1sEaW/lzi4+UT1RzFWjtkad\nzDnWy9X2NjvN14EgO75EtooyV7sb3tD5pCg74PVk0hCzjRKzjiQwDQzQFUpHSFRRohP2/1T5\noWtgIfNke0ChxYm1IE1UHj2JmUEINB1NkUxgKqJJZIZEATWKkiiJFieStH+cKEO1vr1GPg+w\nezfbtxONcthhNDTUPi5EiBC1oFZid/fddx9QP0KEOEjRfiytbyNzKUhSM9HidrsWZcEnQLHz\ndvQkQrHw89TP4cVvk2hHRDGGmXIGc9eQ62XgTzQvZexVxnegciiJ0NFimFk0HWmhFEInNRMr\nV0oJ34EqVu2UmGMI3f5Ctb/4BVKVFL4SqhCaIMXIxQvxYyRB2Wy+cA53WfaardTV1u3i3dfw\nN8fz7YsmY3W1077ao6Uux6pAq9wU7HKpFoopAp688HuA5eGu8ri2FCfQNBBIEy1GsgXZV9qX\nUxylEatDKWQOFFoUpZAFFEgLFCKCgHw/iSkYBjKHUKCwpL3fQkuQ7KAwSGEMEUWLIgvIAiKF\nMSIKI0braXrXmqqPrOyz4pZb+Kd/IpVCSk48kQ98gBNPrGlsiBAhasPeVZ5QSm3btm3Tpk2m\naS5cuLCrqyvMtwtx6EOLUT/Xpz01gyX/SNcHMYaId9CwAARtxzD0nF1PrOMk9DjKQimiKVpW\n0HAYo+sY34aVwxzFHJ+IlClJZjt6kmgD5jhaxD61uBhNUyYIRJkClFhdlfQ4J3wZg6iteqlz\nuO+kvoqXdy7vqEoLBZPNvfSPVXXAadP37qS0bNI+kw53Fo1w5cwFRZN9ebZvf1/flOPNUrjH\niBh6DCtPvJN8P7EItGEOoVqJCVJJUBijqATWOGbOMbD40bIwTRDk+2k/HmOEkReRChRKoCxU\nFiONlaX5aFQeK48xSmEQLQ6a0fXRsa7LmrWox2k/3Hcf//ZvrFhhn3Lc3c2ttzJjBnPm1DQ8\nRIgQNWAviN39999/+eWXP//88+WWJUuWXHvttaeffvoBcCxEiDcDhE7TERUt9fOpn1/REm9n\n7kfYdRcNC4m1UDeH9CZQmDkkaKJ0NmzpS7swiCzYTE6LosWROTsbj0jFwWBedUp5LjSoUo3T\nKwL5Mjzn4XNVIqF7Gz91eo4f9XE65lK8nNO5tw8HeOnbXH1FwtNY5UDBKvANB7tEvipM3UWO\n3aHkAlIj1khmGyKCloAcU46jYSHA8Asc/iVijfzl45hphGafbCeYeNuKeXLSYPh5Ep2gEYkh\nC/Yx2soi30fjYbStQAEmIsLYelpXcvgX82aDylduACpifDvdd5DZjp6iZSnTz0aL88gjzJkz\nUbuivZ2XXuKxx0JiFyLEfkStxO7pp59+97vf3d7e/s1vfnPJkiWapr344ovXX3/92Wef/fjj\njy9btuyAehkixJsb8z+GOUb3nUTqsXK0HcvQUxTS9laGYnZdkbEJjbo5jLwMoEUQUWQeadqc\nQk/YtQSCYBMFMbG1NmgPaRBLwI9eiEpSVYV8TBqrpdTNu1vCN7fMZb/cwXnhpl/Kx9ugHL4q\nz8eL8raMWsLTk+qC3vVOuuHXe1cBFlYOJdHqMUfRoiSmkuqCohqXY/ApmpcQSZAzJo5EcYq1\nquRKYQRZ3DNRZHXCfquEwMqjDETU/taINBBvJzmdMT+JdXwrr/wro68Qa0Wa7LqL0fUceTkj\nIyQSFT0TCUZGqq45RIgQe4daid1VV101bdq0p556qr3drgBz7rnnfupTn1qxYsVVV11154x1\n4/oAACAASURBVJ13HjAPQ4R480PoTFtFagZCp/Fwmpey7t949T8xxtBiaFGURCiwUIrGw2l5\nG5t/DAIri9DsCKxUaHkI2NmKk0Y4+Y6fzuZlJ752lB/V8yUfZaJQC6ujkljgcaasXXkjsEHU\nx6vnubx11b3V/EZNyqt8eaQLZZ+9ddi8PcuwQMKk8cxKbdduUhqYWCYyCwJrnLGNmBk6T2D4\nJcbWs3E75hiyGG/V0BQRhVZMACjaUBM288NEYggNpYFC1+1ZM92M75zIScgPkJod6OfWWxld\nT/0C+2W8jW230nECHR1s2UJj40TPsTE6OiZbdogQIfYCtWbIPfvss3/7t39bZnVFtLW1rVmz\n5umnnz4AjoUIcaig7xFe+DpPf5HNN7Huuwy/iJ5i9kXEWuyjJWQBK4s0UBZmmt330PswdXOx\nCnbundCheHJY3l80qoCA4i7IKHrSJoW+ClyQnuTKG8PDrvC8dMVzawlWlqmV12a5PUhm8yIo\n+U9zXDsvZGVntZfSne8DcfnsSzSdLcWeJliQB9NjOcgZ6WotvVagFNJCi5Ptpudh0psAjFGU\nhQIkEYlQmIJcZZkTQGmgUYhj5VFZDEleUZBISawFASMvYYxijDL2Ku3HM+Pd/k4qyfi2ihIU\nQifeRnozZ5/N1q0MDKAUUrJ9OytW8I53VF1ziBAh9g57kWMnhP/fVKVq+SseIsRbEuPb2fZL\ncnvs8rJWgU03kJjKzHPp+hvWXYuVtYUTPQI6yWlY46SmY4wSbcBMI3SEhjJs5qJAaCBLDKBE\nZ4QA3WYoUqFHkSaWUbrr6Ov6OvfCN42srJ9VyW8re+QL4eFSeFhRLaOcrMhlwetJFbNVXjoR\npFBOanNSN4rtOdAg7nhThONYOt+BgXl+yv6PlQVJrgctgp5A5bDURIRaK6q/Hs+EQukM6nQI\nhCKqUAJLkYlR34yVJzmdeAdCo/MUZr2PqC286ZlNou8vGP1Em2k/HjWfuzaws49EkvmtHDuT\nqIay0KKsXMm11/K733H//QDvex9r1lCpF4QIEeI1olZit3z58ltuueWyyy5ra2srNw4MDNxy\nyy3Lly8/ML6FCPHmx8BfGPorTYfbL/UY9XPpfwwtyq67mXoGIy+Q7UdILIu6GWhxUBhjNC8l\nvYWGw+ytFXqS9AasDHoCNKwMSgJ2vXYUUqGVAnUCLBOl0EQgl6oSVVSOuGQQC6xODb09q/z6\n0+yqWYdN5amraXeda+bVGssXgopjR6itKMWkqW/CsXyCDbqYpW/WYFAw3DU2UnnLOdBXpKye\njEgxrloM35vItD19sacOBnYB2fJZ1rYzCmHSLJFgamiKXIRchG6LulHq2pm+iiX/h8pf+PrI\ns6nnPiHqZxFtxMrx7I+5v4sXtxMfQtTz4BY2D/LBReT6aVoMcMYZnHwyu3eTSNDRQXiuQogQ\n+xu1Erurr776+OOPX7p06Wc+85nFixcrpV566aXrr7++r6/v17/+9QF1MUSINzHMNHq8okWL\nUxhl9z00LCTWTK4XJVA62e02V0NgpBldDwJzjM7TiNST28PYehAohTIQmh1ZE0BpV6OyQCA0\nNL104l2VDbHBspNX1Svu3tVjdnS4TFNqkdkmjcxKu1syxoq5VXs609d8DnUL4EDCryWIGwWx\nNK8PvupgECMsD5Eey0nQATBLnLJilpJd3x0hrikmfFCl1Exlszfl6JOBBMTKwx2PQ0EeLEGz\nYFRD19AVUy2sRvQobce6WB3KjOz4pZWap9XNtG891ctTa1nxLkbXk9tNQ5x7XmFmP+d/ASC7\ni8Q0YjG6uvwebogQIfYD9kKx+/3vf3/ZZZd97WtfKzcuXrz4xhtvXLFixYHxLUSINz/inZij\nFezBGCE5le7f03ECQLSR8a1EmwCkgZ6kMIg0yPViZVGSnbcTa0HEiDdjppHF8mIaSISwqwgI\n0GN2BXdNRwmERMrJaY1vXpoXmqO3i/nVAhdz8k5RoymvSCb8mNmk4Wbl6FldkPN9gC4jrrvC\nb41Or1ycMlJq0f18Fsou9uqksEXu6ExSpHKsACGIt5AbKHG70p4bCRF4Fo6AJs+qBUhJUpCJ\nowme0SjEeTHDZRHmfpjpZ7v7Z3dH+h8oNL19oqU7R1sCK0vrCnK7MNJMHSAzm43/zaYfY4zQ\neTIzz6PzZOLhnokQIQ4I9iLH7owzznjuuee2bNmyceNGYMGCBXPmzAkPKA4RohqmnErvwww+\nQ91sRIT8AONbOfLLmOOYafQUdbPJ95PdjRbFHMMax8yg6VhjKIUFehQRIZoiuYDetWixUsky\nDSVAIhRSgkCBkEgHL/CyFq0yf2vy/FgHEymWKyg3B+leAqzgfVmTiny+dM3VTVRO5zVeha5p\njomqELsgclaG5t3EEGzHRROdS/BNFnQ6XEx9kwohJ0ZJDVHSd72OalHQ0OvQx7DyAFqpTxZ6\nBHUKg4rFl800Qw9k83TqzK8jNcqJCzj6b1j0BfuD516e33qFhhaxt81uf4jxjbQcw8g6jDF2\n/o7eh5nxHuZcTPtxkz2+ECFC7DX2jpYJIebNm3fWWWedddZZ8+bNC1ldiBCTINLAws8x9Qz6\nH6fvMeq6WHkdHSfRspzRjSgTEaH5bcTbSc0kNQMzCwoJWgKl24lQWoTW4zDGmXEOkTqUCaDH\nEaAVv1k1ZAEkSqFMpAxkbU5JKWA7lA1b+xE2ZSyqPb4sysXqlEdMqg5XVNdXaXNSMe/s5Wst\nmB4pz0Q42IxvDLe6n0HBaNdErofjuvAuR1Q6I0rcTsiK4br0GanFEOUQtUFhGGkiBEqjAKZA\nQQrmKo6AlpKvzlgtoEO9okmSNBjLII7k8KPZ+Vt2+KXcpKabU1Zr+Z6JltkJRmK2Ag1kh+nZ\nzhFHkN5KrodEJ/EW8gP0PMiLVzO+zcdmiBAhXhuqKXZnnnlmXV3d7bffXryu0vO+++7bz36F\nCHHIoG42R32VhZdiZe1NhcDcD1EYYsvNROvJ9WMMkZqNFgOJHic+BWscmbcPnpA5hp5FROld\nS/NRUsSEkRbWOAqUqqAOAAJNBKpJysHYqolnwi5fWzzoWFTyJ5/+Hv2JvYnV+lpzyWO+Khce\nTuk7uwDDcUqcb4DVS09xPitV0dmXfbqegy/FdEGrnMjb2WaH0m+ZJSeEQBbFvCjKQujIPALM\nUdttrZgiqZyDJnbFelfRJBAKqdHRzu6tHLWYpiPoe4Tp7ybW7FqAMfNDev4HjK0j2oSVZWWa\n9Ik8u4G2NiyLnVs4u53FHex8lHgn5hi5PpAUBunbwTNf4u3XkZgS/IAAyOXYupVsllmzwi20\nIUJMimrEbnh42DSLZyuRTqdfF39ChDhEEW0sHw8BoKc44gpmnsvQ0zz3daa8g1grhQHG1mGM\nY2WxckgDEUEpRIT0eqRFopO6BcqUYuxljIItjglH9pYSRFNYebvd3jkLuIKYWmmjhu450k1D\nKZRC02yhrsxpqmek+bKcWoYEiXMuU3jaXXe9lKi8ZKuUyqZ5DFYP6drXjtdOBhaUfkclySu3\neM9knlzU9HRSjisJEd1+K0UEmQPdrk1igabs97qo+yqBpuw3xYCYozyJDc1mkELZOXmt/TRa\nDDzJtOX0/olcr4fYoVKz8lPeG8u/IAQkZ7DyBN49i/vuY8sWkkkWTCF/lR0Olia5PvQ4Vp5I\nPUIwtoEtP+WIK6o9gBde4OabufdeIhFyOb7yFf72b8O9tCFCVEE1Yvfkk0+Wr//85z8feGdC\nhHgrQQgaFzH6CnUziLUC6CmURrSJwuBEXBWJkgiBlSXbx9CzWnqTKOo0dpBOIJX9UhMgqJuD\nOUy0DWOEfF8FdZsYpaGknShWQR5kiSwKNA3lUOqCtDE8OR1e2kRlFBgf6rOtnyt+zjuX8Il3\nBFjzRdBdzUGeynyu+inEvqa8S3DaV45r70DnjNLD81w2/XmeRyl0dtNAmmgxRLFWmLJzLi2B\nVFiCqEJIlCBaMiUAQcL5tmol7u582xRKw4xiKcxu9owRiRN1HUUDfX+KbbxZ639Ui0TpOJ6m\nI2k8HOC880o+K17dwrZfk5xBoR9NQ5pEG8DCypKcQnoTVg494bZs2+/jRz9iyxaOOw4gk+Hf\n/53WVt71Lv/+IUKEqD3H7uKLL163bp23/ZFHHrn00kv3q0shQryVYGYnfl/pSVqXYeVRJtJE\nYUdUZQEzB4JYA8N/RUtijqMlKN63lP3dXBRmZBZhoSBaT9sxTD2djpNoOpJYK4k2oo1E64nU\nEWsBzc7Gc/OJ0lEpSvn/ifANF/qyFie8iXeV3UYy/N/HeXqzZ2CQllatn2eionalR/wzC11L\nCFIZXdqh8FBVr9mg7D0nOcMv0j2hdwrHEvyegCw47ioAXVUr7KFptjktgtLREggdIewTUiwN\nqdtVKKJZ4qAJ8n0URhl5yeGexcATbP6xltlpNi1XrSvJD/Pit+h7pGKunTt5LMqGJoZ2kxvE\nLKDHkAUyO5Em/U8ytsGukuyLP/2Jp59m+nT7ZSrFvHk88EBg/xAhQky6KzadTufzeeBnP/vZ\nhRde2FFZ1E9K+Yc//OHGG2/8/ve/fwB93BsciDIYZZuHao2NQ3VdgFLqYFydU6JIzRDmCEra\nuXeJ6SBBL32jWzYR0eNYOcwMykRZiOKxJhGkYefD2fxMIC3Gu0l1kelRsVYx8hJmGi2GlUGP\nIyI0H4WWoDCINLEy9lYM11NSlEq/WzWlypXJSpXO7vchmK+5QqVeLuhDcYJL6ApH1qC0KrhO\nUCacy7KzSXNoby6S52VmmvO4wUp/vE76tigqCrmqSnVwIrYuSoHU0kMolpdQ5eOIy/H0Yl6d\nQEmkRNOQ+YkcPqFjSqIW2TgRE10R0exTeOrnseUmld6EmccYFrnd7LwDY0irWyhERMXaRbSe\nVJfa/UfaT2LoOUZeonu7+Pb/UJhDYzPa0cRf5vhxpoGCeCfRBow0Iy+p/r8w9Z2e9QMwNCRS\nqYqWZJKxMWUYRPbiSId9Q/FPx8H4B+S14YB+owVVqArxemKSfxuf+9znfvKTnxSvzz33XN8+\n7ziYKv0VCoVsNrt/bRY//fl83jCCf1m+aSGlHBkZeaO92P+wLAsYHR09iP7QSDPa/8fIwNpo\n3/1G+xlm24lGx1lEj0y0r4723i+TM9Gj+sgrmpWVydnCSov8IEKgBEoqzRJaXFmGUBJzVClL\nSCljHVp+AAruHDRZkIUhlZhCtlfP9YFS8ZSKNmmFAaXXm6IeEUNXNB8T7X+glEjniY1qAmWg\n6UgxsYWiNENFkNFH6HLQHC8zKxJQLQrSrkxPZRZ/EKvD0cFFIlVl5VMfTVH5MCpRuWonPZ0w\n4qG8Tj81jycVAl6xqoaf/y6DvtTQadB3aU56qqQtwZZ76mCBCRlo0lAgpIPhFV9qSiSEKiBi\nMjFFaSnqomJ4QClNi/aqWDMRDRERZlpK9G3/Vw6+pPS4PvK8jE9RsTYNyPfqVt7UE+gJIaMy\n3Wu9+P341v9SsTZtyzbePSyHmq2ts5DNYrwj8u/3coWhprYhNApjwspYjUvNHffmko6T8ByI\nJhKJkRGrUJhY8fCwbGvLjo/79t+/kFICBcfshwaK32iFQmG//+XXNK2xsXHyfiEOMCYhdh/4\nwAcWL14MXH755Z/+9Kfnz5/v6tDY2HjhhRceKO/2HvF4PB6PT95vb2AYxsjISDwer6ur27+W\nDwYMDQ01N7sTog8BjI6OFgqFpqamg+hQnm2/YNO/UD+PjuPiVl98878Ts5j7YZZeyfb5+vDz\nWHlUF9n1WjSFUJijSNPmQUqgSYFFNIW0hIgTa9SiTWCS64ciFynm0UdRBc0aJ9FOZieJDqyc\nKAyIeBuxVmEMRfvutXlVYqp9xHFRAixTnyLxUsVytJY/7QgS5+z2yswwd4dSKNA2qwJ6BjEd\nPzdqDNcqF/EqXcsAklfjjK4ONoSthlZ3qXy37Jv32XoT8srtsszqBJEI44qYZfcsgIQNgqMB\nSTQJOjKPNCfqWCgpVBahIfNaZgt6EjTqU9QrZFQkmhBgpElO0VWaSFJvnEdmB/EOISTmCEqq\nSKNWGNDNXpFcgJknqtj5IzreTl6y8a9Mm6Z37NILrfQuIBajuZWRiJjXhCwQqadudgQiej4R\n9Cdo1Soef1xft45ZsxCC0VF27OALX4i/Ln+yihpBMpl8HeZ6PWFZ1tDQUCwWa2jwJE2GOCQw\nCbFbvXr16tWrgTvvvPOTn/zk0qVLXxevQoQ45JAf4K/fom0FegpAT9Cc4qV/Zuo7SU5n/sfs\nrRJDz9P/GFYOoSF0InFkDqlKFQNMWlaq3ACZzSLfj5Em3owWsc/DK54Ki8LMIwukN5IfRIva\nZ5vJApaBZaHH0BNIk/QWVDlIp8r/QYlSwYMSfElM8RAQl5jkVO9cbGYinljckGtNTOFv3zHe\n15rvQF/G6auKeXVE4Yl1Bs2Lg43tRzgZ5/9j773D5brKe//PWrtNP3P6Ue/FkmXZlouwZbDp\nAcIvoQVI45IEA+HJTUISSsqll1/i3LQbgnPjH8E3QAgJptvEgBu2415Vra4jnaZTps8ua/3+\n2GufmVNkywnOja35PnrOM7P32muvveZI89X3fd/vuyjmndJJHFY46BDlk3YIIxpwBB4TFCyu\nsLAiBIQBUyEWeODEmXba9BpWCqERLlYOK0UwjbBRIfWTCIvsStw+ph4kvQynQFQHRRgQTmMX\nRDijpUPUwJ+idpTuC0kNIj2oA2hBM0vuNGPrAaSDdOlpay9eP4l33hmft7ub97yHv/97broJ\n22bXLv7H/+BlL/v37W4HHZwjONs0hR/96EeLHv/BD37wx3/8xzfffPNPbkkddPBCROMUlmdY\nXQwrhZ2mNkx6KYAQCIfeHax4M4dvxC0gPaIGWuN1mwTz7s0UNql0U3q9QkLfFTg9HLqB8r4k\nCV8RBQDSwc4RNgmnQSIcrCzRBGi0Imqi1ZzoZ4w4Fy12ONOLsRsxS/sWsJBFop9nPiKS8Cjz\n7jVvNYn+t3AtC2PHCw/OeztP91oY1owW1Lc+DW9bVE9slwNnjX+fVS7APOq5aJB3zvj4cw8g\nvmMTS+DBesFQjkEX3UAFIBnRjGs2KKqQE1hgWwiJDkGjBSoiKCFnsATCJ7OSqEZmBW4PjRGi\nBvl1SIeoTmMM6cYPKIJp0KIKvTtY/y5Ke/CnCKpIh/UDjNXockyTjGqVhmDbpVSPk12GlgST\nVI5w/h8+3YZs3MjHPsa111KrsXw5udyz2c0OOjgX8SzyT7/61a/eeuut8zLY7r777o7FXQcd\nPDNkynyDtn9XqwB7bm44gov/hMxSTn2fmT2kBiFCujTG6dpC1xYAYWmvj9wqtn8Kf4qxH1Ha\nB34Su9QIyzSVsj38yHytSouoibRRAbZNFCSZbRIw7URR2DZRiCDpObGY7KaFkevEmTjZnOHz\nD7WHX9tGLOnmM29l+0ogVs4cUK1oJslxs59t6tqZCFz724Vq4kLKtWjQfnbwQs43O/PCqdqf\n7xkxL29PGM+ZRYaZGVsh2ET+nD0OCGyFDV7ZRGNtTagoQs7GBQ22RmgijQUalEZqtKasCKA3\njWejQ7q3k1uLdBm4moOfxymgfPN/DAF2DrdXCVeE5WjHX1vLXkljjEc+yNSjSBBpujXkqJSZ\nyHD0KCdP8rFPcuUWjvwfjt+EEAy+lPXX0nvZM+yPZbFy5VnsYwcddABnT+yuv/76a6+9tlAo\nhGFYq9VWrVoVRdHw8PDg4OB11133nC6xgw5eCMiuZtnrmXyAzGrz/Vs9ztJXk1s/f6Sd4/w/\nYssH8aew81SeYvxu9v05XVuQbjxE1E+y7DUAJ75OME3XeVQOm695HRg1JSgBSAsdIW2a00jX\nWOKZn7PJbcLkaQlJBEIg06gGdpawaoaJWSYVmhAeZ+jHAOaUWMBvdCL4idkBrRn683zgp9vG\n6+CMaXzzXi+sYFh4ycIkvEXFsIVMtP0WC6fVbS0caOdeC0afVZKiMPLk06QXapIusfOajsyL\nMWviAL4UxppYgqsQkNL4AgWeNrxZgJLUNbbGhahOaGPVGbmNzAHCCoXNFLcxdieZ5fgz2Dn8\nGVyH4DROn5KOyK1CKx7875T3m3CtjlAh3TPktqBewetXcPnl7NgBcMHH2PjrKJ/0EuTclGit\nmbyf0R/RnMDtYfBqenc+Q++7DjroYC7Oltj99V//9WWXXXbnnXdOTk6uWbPmG9/4xvbt2++6\n666f/dmfvfrqq5/LFXbQwQsC0mbdrxI1GLsDO0dUpe9K1v4K1hlqfaRrWi0VL6BrG2gO/A2p\nQaQl6+Nq4KVy5ZsBKodJDZFdi52jvA+tiDTZFfTsoHKU+jGURtqklpAapDFC7SiaOU1VtTYG\nK5ZLeoj8JipP0ZhEdqHrCAuhEB6Wg8ygqgQlQxzVmaoFBdJCRaBNTcZs3NaEcWcJ5dMSsoX2\nJbMhVBYcT+7cdolYJNY8e2be4Pbw7qKkSi88K8wDahAOBG0kdTFStlC5NPuT3FhIdPTMoVsZ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L6S8HydLvU5UI4zIriO/nuY4KqQ+QmqI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wOOozeP+PmzTz4IH37mmPG8u7vDTtGUS7dMnlrkzGqkOqoMfncJqXGF/X7a5g6iMcv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\nHQAAAACAmfgPdDzJ8h7Qe+YAAAAASUVORK5CYII=","text/plain":["plot without title"]},"metadata":{"image/png":{"height":420,"width":420}},"output_type":"display_data"}],"source":["library(ggplot2)\n","library(ggrepel)\n","\n","# Create the scatter plot\n","ggplot(cor_results, aes(x = cor_epithelial, y = cor_mesenchymal, color = color, label = gene)) +\n","  geom_point(alpha = 0.6) +\n","  geom_text(aes(label = ifelse(gene %in% highlight_genes, gene, \"\")), hjust = 1.2, vjust = 1.2) +\n","  scale_color_manual(values = c(\"blue\", \"orange\", \"red\", \"grey\")) +\n","  xlim(-1, 1) +  # Set x-axis limits\n","  ylim(-1, 1) +  # Set y-axis limits\n","  geom_hline(yintercept = 0, linetype = \"dashed\", color = \"black\") +  # Horizontal line\n","  geom_vline(xintercept = 0, linetype = \"dashed\", color = \"black\") +  # Vertical line\n","  labs(\n","    title = \"Correlation of Genes with Epithelial and Mesenchymal Scores\",\n","    x = \"Correlation with Epithelial Score\",\n","    y = \"Correlation with Mesenchymal Score\"\n","  ) +\n","  theme_minimal()\n","\n","theme(legend.position = \"none\") +  # Remove the legend\n","  geom_text_repel(aes(label = ifelse(gene %in% c(\"CDH1\", \"GRHL2\", \"VIM\", \"ZEB1\", \"SNAI1\", \"SNAI2\", \"ELF3\"), gene, \"\")),\n","                  box.padding = 0.5,   # Adjust padding\n","                  point.padding = 0.5,  # Padding around points\n","                  max.overlaps = 0)   # Allow for no overlaps\n","\n","# Save the plot with an aspect ratio of 10:6\n","ggsave(\"scatter_plot.png\", plot, width = 10, height = 6)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":645},"executionInfo":{"elapsed":8455,"status":"ok","timestamp":1727256419600,"user":{"displayName":"Anjaney Pandey","userId":"05531075380284782212"},"user_tz":-330},"id":"k6vp7Abjkuqm","outputId":"6f069379-dc6f-4041-d734-2fd213f8cefc"},"outputs":[{"name":"stderr","output_type":"stream","text":["Warning message:\n","“\u001b[1m\u001b[22mRemoved 4008 rows containing missing values or values outside the scale range\n","(`geom_point()`).”\n","Warning message:\n","“\u001b[1m\u001b[22mRemoved 4008 rows containing missing values or values outside the scale range\n","(`geom_text_repel()`).”\n","Warning message:\n","“\u001b[1m\u001b[22mRemoved 4008 rows containing missing values or values outside the scale range\n","(`geom_point()`).”\n","Warning message:\n","“\u001b[1m\u001b[22mRemoved 4008 rows containing missing values or values outside the scale range\n","(`geom_text_repel()`).”\n"]},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94\nAAAgAElEQVR4nOzdZ2BUVdoH8OfeacnMJJNk0iCVhBAghBJCCIgUBQQEQSlhEUXEvqtrXXdX\nXXWxrWVFdNW14K4CSxMQQSn6CoiCoYaEAOmV9J6ZydT7fjh6HWbSC4OT/+/T3DN3zpy5c5k8\nnPIcThAEAgAAAIDfPt7VDQAAAACA3oHADgAAAMBNILADAAAAcBMI7AAAAADcBAI7AAAAADeB\nwA4AAADATSCwAwAAAHATCOwAAAAA3IR7BnZGo5HrSE1NTecrfOSRRziO27NnDzs0m80XL14s\nLS0VT/jHP/7BcdyGDRt62PJnnnmG47jNmze3+r7dqMFVNmzYwK7zsmXL2jpn37597JwZM2Zc\nybb1lt760vtIJ++EpqamF154YfLkySEhIZ6enpGRkbfccsuOHTuuTCMBAKB3SV3dgL41atQo\njuNafUoikXS72qKioqFDhy5YsAB//zq0a9cuvV6vVCqdn9q6deuVbw/YO3v27HXXXVdTU6NS\nqWJjY6OiooqKinbs2LFjx47Fixdv3LhRKnXznwgAADfj5r/aJ0+e7EkAJ1q1atXUqVMTExN7\nXtVv4n17i0Kh0Ol0e/bsWbx4scNTFotlx44dCoXCaDS6pG1gMBhuvvnmmpqaxx577Nlnn/Xy\n8mLlx44du+OOO7Zu3RoXF/fss8+6tpEAANAl7jkU220mk6nVzXNHjBgxf/78oKCgK9weV71v\nb5k5c2Zbo4HffvttbW3tDTfccOVb5cBqtZpMJle3gojIaDRaLJYr9nY//fRTXl7e+PHjX3/9\ndTGqI6Lk5OTPP/+ciN58880+bY9er7darX1XPwBAP4TAjsaMGTNo0KD8/Pzp06d7enoqFIpx\n48b96U9/0uv14jl/+ctfxLlu8+bNGzx4MBHt3LmT47gHH3zQvrajR48uXrw4MjLSz89v2rRp\nhw4dcni7H374YfHixVFRUd7e3omJie+88047UYX9+zIWi+X111+fMmVKUFCQt7d3XFzcn//8\n56qqqk5+2FWrVnEc99ZbbzmUP/HEExzHPf/88+wwLS0tJSUlKirK09MzJibmrrvuKioq6uRb\n2IuIiEhOTt6zZ09TU5PDU1u2bCEi55486sQl6kzz2q+ETY87cuTI66+/HhgYqFAo/Pz8rrvu\nOufv66OPPpo5c6ZWq42NjV2+fPnZs2edG9zWl/7BBx9wHPfEE084nL98+XKO49hINGvJN998\nc9ddd3l7e8vl8hEjRjz88MM6na6xsfGxxx5LSEhQq9UjRoz46KOP7Cvp4Z1ARBkZGUQ0ZMgQ\n56fi4uJmz549bNiw5ubmLl2NjRs3zpkzZ8CAAQMHDpw9e/Znn31m/+yLL77IcdzJkyd//PHH\nMWPGeHl5ifX3ypcOAAAkuKOWlhb26SwWS4cnjx49OiAgIDo6mohCQ0PHjRsnl8uJaOTIkWVl\nZeycP//5z0S0e/duQRA2btz40EMPEdHQoUOfe+65r776ShCEV155hYiWL18uk8mGDRuWkpIy\nfPhwIpLL5adOnRLf65///KdEIpFIJCNHjkxOTvb09CSi66+/XqfTsROefvppItq0aZPz+wqC\nYDQax40bR0Q+Pj6TJ0+ePHmyRqMhotGjRxsMhlZrcLBv3z4imjJlikN5ZGQkEeXk5AiC8P33\n37MrMGLEiOuvvz40NJRdmerq6s5cfGb9+vVE9Ic//GHNmjVEtH79evtnTSaTn5/fyJEjT5w4\nQUTTp0/v/CXqTPM6rIR9XyysZN9XXFwcEclkshMnTrBzrFbr0qVLicjDw2PChAnx8fFEpFQq\nd+zYYV9JO196RUUFz/NRUVH2n91gMHh5eXl5een1erGSuLg4nuenTZt28803s/mIc+fOHTNm\njI+Pz4IFCyZNmsTu5507d7JKen4nCIKwc+dOVoP4kdvR4dUQBOGOO+4gIolEMmrUqNGjR7P5\necuXLxdPeOGFF4ho3bp1KpUqNDR0xowZ7BvplS8dAAAEQUBgJ4wePZqIpFLpf/7zH1ZSXV09\nZcoUIrr33ntZiUOAlZOTQ0QLFiwQK2F/nonohRdeYCU2m+2uu+4iokceeYSVpKenSySSiIiI\n06dPs5KKiopp06YR0V/+8hdW0n5g99///peIJk+e3NzczEqam5snTJhARIcOHWq1Bgdms1mr\n1UokkqqqKrHwp59+IqJJkyaxw8mTJxPRtm3b2KHFYlm0aBERrVmzpsOLKRIDu9LSUp7n582b\nZ//s119/za6VQ2DXmUvUYfM6U4n4fb344ousxGazrVq1iogeeughVvLJJ58QUVJSUmVlJSv5\n4osvJBKJVqtlAUdnvnR2I6WlpYmffdu2bUS0YsUK+5bIZLJvv/2WlaSmpvI8T0RDhgwpLS1l\nhX/5y1+I6NZbb2WHPb8TBEEwmUwjRowgIp7nZ8yYsXbt2vT0dJvN1urJHV4NNnobHR19/vx5\ndsL58+fZ/5fEL4sFdhqN5rnnnhP/bfbKlw4AAIybB3ZtmTp1qngyC+zuvvtu+xoqKyuVSqVM\nJrt06ZLQ6cBu7Nix9pWkpqban3bzzTcT0f79++3PuXTpkoeHh4+Pj9VqFToK7DZs2LBo0aLD\nhw/b1/Diiy8SkRiVdvjn/O677yaijz/+WCx57LHHiOjDDz9kh1qtViqVms1m8YSMjIxXXnnF\noeXtEwM7QRCmTJkil8vr6urEZ++8804iunjxokNg15lL1GHzOlMJ+76SkpLsz2GNmTt3LjuM\niIjgOE4MU+wrZ19BZ770t99+m4ieffZZ8QQWkYjNY5Xcc8899pWw4dHt27eLJZmZmUR07bXX\nssNeuRMEQaitrb377rvtJ9j5+/svWrRo3bp1TU1N9md2eDVGjRpFRAcOHLA/Ye/evUQ0atQo\ndsgCu8TEROdKevilAwAA4+Zz7NiQkDM2Sc4eG0USBQQELFiwwGw2tzqnqi3z5s2zP/Tz87M/\nTE1N1Wg0119/vX3hgAED4uPj6+vrs7KyOqx/2bJlW7duvfbaa8WSvLw81lPSeSkpKURkn6hl\n27ZtHh4e4nS3uLg41iOSmppqs9lYyZNPPtntbHMpKSkmk4kN/BGR2WzeuXPn6NGjnWd3deYS\nddi8zl/n2bNn25/j7+8vPi4rKyssLExMTBw6dKj9Of/5z39KSkrs1ym3/6XfcsstHMeJV7u5\nuXnPnj3BwcHXXXed/WkOC59ZJePHj3coEX5Z2dMrdwIR+fr6fvDBB5WVld99992zzz47derU\n5ubmbdu23XnnneHh4eLkzg6vhtlszsjIGDBgwPTp0+1PuOGGG4KDg8+dO2e/CMPhsvfKlw4A\nAAzSnfyMjRnZY2FHbm5u598uPDy8raeam5tZQuO22lNXV9eZt6isrNy4ceMPP/yQlZWVl5fn\nMLe9M6ZOnRoQEPDNN980Nzer1eqffvqpsLAwJSWFTdIiovfff/+WW2754osvvvjiC41GM2HC\nhBtvvHHJkiWBgYFdfS9m4cKFDz744KZNm1j0zNbDPv744w6ndfIStd+8Ll3ndr4v1ik7aNAg\nh3Jvb29vb2/7knYqIaKBAwcmJycfPXo0Nzc3Ojr6iy++MBgM9957r0Pz2AQyByqVqp2ae34n\niDw8PKZOnTp16lQiamlp2b9//2uvvXbkyJGFCxeePXt2yJAhHV6N7Oxsq9Xq/I+Ivaq8vLyo\nqCgqKoqVhIWFic/2ypcOAAAiNw/seoJN/e5SIoxW/zwzLK1DcHDwfffd1+oJnclpcvDgwZtu\nuqmpqSk2NnbChAlLly4dMWLE6dOnu5RsTCKRLFy48P3339+7d++iRYvY6tQVK1aIJwwbNuzs\n2bMHDhzYvXv3wYMH9+3bt3fv3qeffpoteOz8G4kCAwOnTZv27bffVldX+/v7t7UetpOXqP3m\ndek6txP0s9F8mUzW4adr50tnFi1adPTo0R07djz++OObNm0ioltvvbXDatvXK3fC//73P5PJ\ndPvtt9sn8fbw8LjpppvmzZs3a9as/fv3r1+//u9//3uHV0NoLUkQ4/zvyD5bda986Z3/yAAA\nbg+B3c9yc3MdQis2BhQTE9Mr9Ws0moCAAI7jepLx9Z577mlubv7f//7H1icyFy9e7Go9KSkp\n77///o4dOxYuXLh169agoCCHIS2ZTDZnzhz2J7OiomLt2rUvvfTSqlWrysrKutfylJSUb775\nZvv27StXrty5c2dCQoLzaHjnL1E7zeuV60y/fO8FBQUO5Tk5OSdPnhwzZkyrWUJatXDhwsce\ne2z79u2rVq3at2/fkCFDep5xulfuhBdeeCEzM3PatGnOnY5sn7f9+/eznfc6vBqRkZESiSQ/\nP9/5XXJzcyUSidhd56BXvvROfVoAgP7BzefYdd6nn35qf1hTU7Njxw6e51lah14xZsyYsrKy\n77//3r6wrq4uKioqOTm5w5fr9frs7OywsDD7v+VE1Opf0/ZNnjw5ODh4z54933//fXFx8a23\n3iruHHXx4sVhw4bZdykFBQW98MILAQEB5eXlOp2uq+/F3HLLLTKZbPPmzd98801dXd2SJUta\nPa3DS9SZ5vXwOjPh4eF+fn4//fSTw+V98cUXly5d6hzitCMiImLs2LHHjh175513zGZzz7vr\neutOSEpKIiKWj8bZd999R0Qs00qHV0Mulw8bNqy0tJS9SvTtt99eunRp+PDh7fRr9sqXDgAA\nDAK7n61bt07czb2urm7p0qVNTU233357OzOonJPuto/1SSxZsuT06dNiDStWrMjPz+/MBgxK\npdLf37+srCw7O5uV2Gy2f//73x988AERGQyGzreE5/mFCxc2NDSw7Mr247AsV/OWLVv2798v\nFn799dfV1dWxsbFs1pfJZNq3b9++ffs6fwX8/PymT59+8OBBtkq01bzE1IlL1Jnm9fA6MzzP\nP/300xaLZcWKFazXioi++eab9evXa7XaiRMndrIeZtGiRcIva0KXLVvWpdc666074S9/+YtC\noXjzzTfvvPPO8vJysby6uvrxxx//6quvoqKiWA9ZZ67GM888Q0T33Xef2KqsrCw2wNp+b1yv\nfOkAAPAzF6/K7RtiupOxY8cmtkHM98HSncydO5eIoqKiJk6cyPKjDhs2rKSkhJ3jkHaE5feX\ny+VLlixZt26d8EvSCoc0vM5ZUVg9HMcNHTr0+uuv9/HxIaLJkycbjUZ2QvvpTv7+978TkVQq\nnTx58pIlS6Kjo728vFj6kvDwcJbTqzNJLgRBOHz4MLtEI0eOdHjq1VdfZU8NHz581qxZLNWZ\nVCoVM1mIQcCZM2faqt8+3Qnzn//8h73KPtuFc4LiDi9Rh83rTCXs+xIzgzCsH05Md2I0Gllg\noVKprr322sTERI7jeJ4Xs5B08ksXBEFcijt+/HiHC9VqS1hPVX19vVjCrrmYa7C37oTdu3eL\nuU58fHxGjBgRFhbGum8DAgIyMjLEMzu8GoIgsE41mUw2bty4xMRENidPzNgn/BLaOlwxoZe+\ndAAAENw+3cnJkydPtMFhas6WLVteffXVwMDA9PT02NjYRx55JDU1NSQkpNVq/f39n3vuObVa\nvXv37ry8vM635+WXX96zZ8/cuXN1Ot3x48cHDx68du3aAwcOdDgBn3nqqafefffd4cOHnzx5\n8uzZs5MmTUpPT3///fcXLVpUU1Nz8ODBzrfkmmuuGThwIF3eXcc8/vjjW7dunTx5cl1d3cGD\nB41G4/Lly0+dOuWQyaKrFixYoFAoqO3uOqbDS9SZ5vXwOjNyufzrr79es2bNuHHj0tPTy8vL\nb7zxxtTUVJZ3rUtiYmLYmH7Px2GZ3roTbrzxxqysrL/+9a8JCQk8z2dnZ8tkssmTJ69Zs6ag\noIDtxsF05mqsX7/+v//977Rp04qKikpKSq6//voNGzaIAX07euVLBwAAIuKEtpez9RNjxow5\nc+ZMS0sLCzsAep0gCNHR0UVFRZcuXUKGDgAA6Dtu3mMHcDU4dOhQfn7+jBkzENUBAECfQroT\ngD7U0NCg0+nYHLKVK1e6ujkAAODmENgB9KGpU6eeOXOGiBITE9kusQAAAH0HgR2tXbu2sbGx\nM3sMAHTVvHnzVCpVUlLS008/zfOY+QAAAH0LiycAAAAA3AS6EAAAAADcBAI7AAAAADeBwA4A\nAADATSCwAwAAAHATCOwAAAAA3AQCOwAAAAA3gcAOAAAAwE0gsAMAAABwEwjsuqOhoaG6urqf\n53ZuaGiwWq2uboUrNTY2VldX22w2VzfElXAbLFu2zNfXt7S01NUNcaXGxsZ+fhs0NTVVV1f3\n84vQ2NhosVhc3QpXukpuAwR2AADdp9fr6+vr+3l8DwBXDwR2AAAAAG4CgR0AAACAm0BgBwAA\nAOAmENgBAAAAuAkEdgAAAABuAoEdAED3rV69+ptvvgkMDHR1QwAAiIikrm4AAMBvWGRk5IAB\nA2QymasbAgBAhB47AAAAALeBwA4AAADATSCwAwAAAHATCOwAAAAA3AQCOwAAAAA3gcAOAKD7\n3n777VWrVtXW1rq6IQAARAjsAAB6IjU1ddeuXQaDwdUNAQAgQmAHAAAA4DYQ2AEAAAC4CQR2\nAAAAAG4CgR0AAACAm0BgBwAAAOAmpK5uAADAb1hKSsrIkSO9vb1d3RAAACIEdgAAPTF37twZ\nM2Z4eXm5uiEAAEQYigUAAABwGwjsAAAAANwEAjsAAAAAN4HADgAAAMBNILADAAAAcBNXS2D3\n6aeftrS0tPqUIAgbNmy466677rzzznXr1lmt1ivcNgCAtuzevXvt2rVNTU2ubggAANFVEtid\nP39+27ZtZrO51We3bNny1VdfrVq16v777z906NB///vfK9w8AIC2bN68efXq1Y2Nja5uCAAA\nkcvz2J05c2bv3r3Hjx9v6wSr1frVV1/dfvvtEyZMIKJVq1a9++67y5Yt8/DwuILNBAAAAPgN\ncHGPnUKhGDp06KxZs9o6oaSkpK6ubuzYsewwISFBr9fn5eVdqQYCAAAA/Ga4OLAbNmzYggUL\npk2b1tYJtbW1HMf5+fmxQ7VarVAo6urqrlQDAQAAAH4zrvYtxZqamhQKBc//GoB6enq2M52l\npaVFp9P1dasEQSCi2travn6jq5kgCPX19a5uhSux26Cf/zcDt4HNZiOihoYGpVLp6ra4jCAI\nbU2S7ifEXwOO41zdFpfBbcBugyvwkyiRSHx8fNp69moP7FQqldFoFARB/NdiMBhUKlU7L7GP\nAvsIW5l7Bd7oamaz2XAF2J3Zn3/KcRswPM/35+uA24D9GvA8389/DfB7yG6Dvn6j9i/y1R7Y\n+fr6si4BX19fIjIYDEajkT1ulYeHxxVYV9HQ0GA2m318fPrzHdzQ0KBWqyUSiasb4jKNjY0m\nk8nHx6c//0nDbZCcnMxxXGBgYDu/S26vsbFRpVL159ugqanJaDRqNJr+fBEaGxuVSqVUerXH\nFX2H3Qbe3t6uvQ2u9j9IERERGo3m9OnT7PDMmTOenp4xMTGubRUAAPPggw9+/PHH4jxgAADX\nukoj62+//dZkMs2ePVsikcyZM2f9+vUhISE8z3/yySczZ85ErhMAAAAAZ1dpYHfw4EGdTjd7\n9mwiWrp0qcViee2112w22zXXXLNy5UpXtw4AAADgasSxRRzQJWyOnVarxRy7fj6hxGQy+fn5\nYY5df74N2Kyafn4bYI4duw18fX3780XAHLur5Dbov79EAAAAAG4GgR0AAACAm0BgBwDQfQUF\nBWlpaf08LysAXD0Q2AEAdN8zzzwzffr0yspKVzcEAIAIgR0AAACA20BgBwAAAOAmENgBAAAA\nuAkEdgAAAABuAoEdAAAAgJtAYAcA0H2BgYERERH9Ods+AFxV8GMEANB9b7zxBttSzNUNAQAg\nQo8dAAAAgNtAYAcAAADgJhDYAQAAALgJBHYAAAAAbgKBHQAAAICbQGAHAAAA4CYQ2AEAdN9t\nt90WEBBQWlrq6oYAABAhsAMAAABwGwjsAAAAANwEAjsAAAAAN4HADgAAAMBNILADAAAAcBMI\n7AAAAADcBAI7AIDue+ONN06cOBEUFOTqhgAAEBFJXd0AAIDfsMDAQI1GI5XitxQArgrosQMA\nAABwEwjsAAAAANwEAjsAAAAAN4HADgAAAMBNILADAAAAcBMI7AAAuu+ZZ56ZPn16ZWWlqxsC\nAECEwA4AoCcKCgrS0tLMZrOrGwIAQITADgAAAMBtILADAAAAcBMI7AAAAADcBAI7AAAAADeB\nwA4AAADATWDjagCA7nvwwQdvuukmPz8/VzcEAIAIgR0AQE8kJSWNGjXK09PT1Q0BACDCUCwA\nAACA20BgBwAAAOAmENgBAAAAuAkEdgAAAABuAoEdAAAAgJtAYAcA0H2bN29evXp1Y2OjqxsC\nAECEwA4AoCd27969du3apqYmVzcEAIAIgR0AAACA20BgBwAAAOAmENgBAAAAuAkEdgAAAABu\nAoEdAAAAgJuQuroBAAC/YXPnzo2IiPDy8nJ1QwAAiBDYAQD0REpKyoIFC7y9vV3dEAAAIgzF\nAgAAALgNBHYAAAAAbgKBHQAAAICbQGAHAAAA4CYQ2AEAAAC4CQR2AADdl5qaumvXLoPB4OqG\nAAAQIbADAOiJt99+e9WqVbW1ta5uCAAAEQI7AAAAALeBwA4AAADATSCwAwAAAHATCOwAAAAA\n3AQCO+in9u7dy3HcY4895vxUcXExx3ELFiwgonPnznEc98gjj7CnVq5cyXHcwIEDbTab8wuf\nfPJJjuM4juvTlgMAALQFgR30U9OnT/f399+2bZsgCA5PffHFF0S0ePHitl5bVlb2448/Opfv\n2LGjdxsJV7/IyMhRo0bJZDJXNwQAgAiBHfRbUql04cKFRUVFqampDk/t3LlToVDMmzev1Rfy\nPK/Vaj///HOH8oyMjOzs7MDAwK62pL6+PiMj4/Tp0wUFBa12BMLVbPXq1d988003vncAgL6A\nwA76r6VLlxLR1q1b7Qvr6uoOHTp0ww03eHt7t/oqnufnz5+/fft2h/Lt27d7eXldd911XWpD\nY2NjWlpaVVVVfX19fn7++fPnu/RyAAAAewjsoP+aPHnygAEDtm3bZl+4Z88ei8XSzjgsEbGu\nvlOnTtkXbt++/cYbb/Tw8OhSG8rKyux76SorKy0WS5dqAAAAECGwg/6L5/klS5YUFhbaj8bu\n2LFDoVDcdNNN7bxw+vTpGo2GTcVj8vLy0tLSbrnllq62wTmMQ2AHAADdhsAO+jWH0diWlpZ9\n+/a1Mw7LyOXyuXPn7tq1SyzZvn27h4fH7Nmzu9oArVZrf6hSqbra5wcAACBCYAf9WnJycmRk\npDgae+DAAZ1O1/44LLNw4cK8vLxz586xwx07dsycOVOtVne1AcHBweHh4TzPE5GXl1dcXFxX\nawAAABAhsIP+LiUlpaCg4MSJE/TLetj2x2GZWbNmKZXKL7/8kojKysqOHj26cOHC7jUgOjp6\n8uTJ1157bWJiokql6l4l4Cp6vb6+vh7LmQHgKoHADvo7cTTWZrN9+eWXM2fObH8clvH09Jw5\nc+bu3buJaOfOnRKJZO7cud1uA8dxUqm02y8HF7r33ntjYmLKyspc3RAAACIEdgCjR4+OjY3d\nunXrkSNHqqqqOjMOy8yfP//ixYsXLlzYvn37tGnT/Pz8+rSdAAAAHUJgB0BLly7Nz8//29/+\nJpfLOzMOy9xwww0KheKjjz46ePBgN9bDugdBEPR6fUtLi6sbAgAARAjsAOiX0dhDhw7NnDlT\no9F08lUqlWratGlr1661Wq3z58/vywZepZqbmzMzM0+cOHH06NGzZ89inhkAgMu527Qes9ls\nNBr7+l2sVisR6XS6vn6jq5nVatXr9e6x4X1oaGh8fHx6evq8efOam5vtn9Lr9URkNptZudls\nJiL22GKxzJs3b+/evcnJyV5eXs4nuL1z586J/9xqamqysrJCQ0Nd26Qrj4Wzer2+n3zprXKn\nX4PuYekncRH0ej1b498/XbHbgOd5pVLZ1rOc8w7ov2lWq/UK5HfV6/VWq1WtVvfnf8N6vd7D\nw6M//xs2GAwWi6Xf3gZWq/Wnn36yL/H29h4xYoSr2uMqS5Ys2bVrV25ubkhIiKvb4jIGg0Gh\nUPTnX4OWlhaz2axSqfrzRTAYDHK5XCKRuLohLsP+KFyB24DjOLlc3taz7tZjJ5FIrsBd1dLS\nYrVaFQpF//yLzrS0tPTzf8Oss0oul/fPn3JBEKRSqf3/ozw8PBQKhQub5BLsR0Amk/XDzy4y\nGo39/NfAZDIRUT+/COw26M8L/E0mk8Vicflt0B//IAFAz3EcFxYWJh7yPG9/2H989tlnVVVV\n/bm7DgCuKv03sgb4LbJYLAUFBXV1dTKZLCwszGFHsp6w2Wxd7XqMjIwkIr1eL5VKBw4c2I2N\nNwAAoHchsAP4LcnMzKypqWGP6+rqxowZ4+Pj08M68/LyiouLBUFQq9UjRozo0ma1vr6+YWFh\nbNzBbDbrdDqFQuHp6dnDJgEAQPcgsAP4zTCbzWJUx5SXl/cwsCstLS0sLGSPm5qaTpw4cc01\n13Rj8mhlZeWFCxfYgvHg4OChQ4f25xmoAACugjl2AL8Zzoniep46rqKiwv7QbDZXVVV1tRKL\nxSJGdURUXl5eWVnZw4Z1j9FofPvtt5OTkwMCAtRq9fDhw5988sna2lrxhLvvvpvjuMcee8z5\ntTNmzBg0aJBDYUVFhVQq5Tju1VdfdXjq3LlzHMf9+c9/7vVPAQDQbQjsAH4zFAqFwzw2f3//\nHtbpnPCoG9tI6HQ6MapjGhsbe9SsbrFYLDNnznzooYfUavXvf//7xx9/PCYm5vXXXx8zZoxD\n/Lp27dqzZ892ps5t27axj7Zly5YOT87Jybn11ltjY2O9vb3HjRv36aefulk+KQC4+mEoFuC3\nZMSIERcvXqyvr5dKpeHh4YGBgT2sMDg42CEI8/Ly6molzpk+XJL746OPPjp8+NqxpowAACAA\nSURBVPAbb7zx6KOPioV79+698cYbH3/88c8++0wslMvl999//5EjRzocL968ebNKpZo6deqe\nPXtyc3Ojo6PbOjMtLW3GjBkWiyUlJWXgwIE7d+5csWJFdnb26tWre/7RAAA6CT12AL8lnp6e\no0ePnjJlyqRJk8LDw3teYUhIiK+vr3gYFBRkf9hJHh4ewcHB4qFCoRgwYEDP29ZVP/74IxHd\nc8899oWzZs1KTEw8cOCAfeHTTz/9448/rlu3rv0KS0tLjxw5Mnfu3FtvvZXa6LQ7fPhwYmJi\nRUXFn/70J51O93//93/vvffeM888k5qaOn369Jdffrm0tLSnHwwAoNMQ2AH89vTiuoSSkpKG\nhgb22MvLa+jQod2rZ+jQocOHDw8NDY2Ojh43bpxMJuutFnYeG/c8c+aMQ/mOHTu+//57+5KH\nH344Li7uySefdFiM4mDLli2CICxevHjOnDkymazVwM5gMBQWFloslsOHD99www2jR49m5RKJ\nZNWqVVarNTU1tUefCgCgKxDYAfStI0do2TKaN4/eeYfMZle35nJFRUU5OTniCoympqZLly51\nryqO44KCgmJiYsLDw10S1RHRkiVLiOiGG2744x//eOjQIbYZABENHDgwJibG/ky5XP7uu+/W\n1NQ8+eST7VS4adMmlUo1e/ZsjUYzbdq0M2fOZGVltXqm0Wh8/PHH77rrLvtCtty4S+ljAAB6\nCIEdQB/6/nu69lr63/9o92568EF6+OHWT/vhB9q8mXJyfj7U6XSlpaUVFRU9X/TaDoPBkJub\n6zC7v6mpqZ2XCIJQXV1dXV3NtlO7Gti3ZN68eZ988om/v//atWunTp3q6+s7e/bsNWvWVFdX\nO79w8uTJK1asWLduHRvAdZafn5+amjp37ly22fb8+fOJaOvWra2erFAoVq9ePWfOHLGkqKjo\ngw8+CAoKmjJlSk8+IABAlyCwA+hD//73ZYcffUQOEZHFQnPn0qRJtHQpxcXRmjVUVlZ24sSJ\nrKyszMzM1NRUc5/18rUaw3l6ejY0NBQVFTmHleXl5YcOHUpPT09PTz969KjDOlOXeO+993x8\nfLKzs8WSO+64Iz8///jx46+88so111xz6NChRx55JCIi4vPPP3d++Wuvvebj43PffffZ73gr\n2rx5MxEtXryYHc6fP5/jOFbYoa+++io5OfnSpUsbN25kcSEAwJWBwA6gD9XVXXZoMpFOd1nJ\nhg20Z8+vz/75z0JWVrYYURkMhqKioj5qm/MQoUwms1gsp06dys3NzczMPHnypMlkamxsNBgM\nOp3uwoULYveeIAgXL15k7ezTbsV2mM3ml156ieM4h9UePM8nJiY++eST+/fvr6ysfO+993ie\nv/32251HmQMCAl566aX09PS1a9c6189iuNzc3H/961//+te/du7c6e/vn56efv78+XZaVVBQ\nMHv27BtvvDE8PPzEiRPXXXddjz8oAEAXIN0JQB+aPp2++urXw7Fjyc/vshMckqn5+BhttssS\nwun1+j5qm7e3t1arFVcPKBSKkSNHHj9+XDyhubn52LFjLIubl5eXw6Ct1Wqtq6vLy8vT6XQS\niSQ8PDwiIqKPmtqq//3vfyUlJQ888ABL5tfY2Lhq1aqbbrrptttuE89Rq9X33XefTqd7/PHH\nDx069Lvf/c6hknvuueeTTz559tlnU1JS7MuzsrLYIgznSXhbt27929/+1mqTPvvss/vvvz8g\nIGDz5s2LFy/G3hsAcOWhxw6gDz30EN1zD/E8EVFCAm3Y4HjC4MGXHVZVKXj+sv9uqVSqvmte\nfHz8sGHDQkNDhwwZMn78eIckw0QkljiP23Icl5ub29zcLAiCxWLJy8u7woOzb775Js/zjzzy\nCDv08vLavXv3+vXrnc9kWZ1bTePC8/x7772n1+sfvnz+46ZNm4iIZRgWFRUVOY/GJiYmbtu2\nzd/ff8eOHXfcccf8+fMzMjKWLFmCqA4AXAKBHUAfkkjo3/+mpiaqqKCTJyk21vGEFSto5Mhf\nDx98kIuNjeH5n/9hKpXKXklW1xaO44KDg2NiYkJCQiQSiVKpbCccEVvFhISE6C4fV24/dUjv\n2rt375kzZ26++ebBv4TGHMctXbp0//79b7/9tv2ZjY2Nb7/9tlqtnjhxYqtVJSQkPPDAA9u2\nbTt58qRYuHnzZk9Pz5tvvtn+zLCwsIkTJ2ZmZp47d04s1Gq1U6ZMkcvlTz75ZHR09Geffdan\nsTgAQPswFAvQ55RKamsCvVJJqam0eTMVFdGECXT99UQUrNFo2N4SWq3WIZzqUzKZzN/fv629\nYoODgwVBqK+vl8lk4eHhPj4+JSUl9idcyaa+9tprROSw5evrr79eUlLy0EMPffLJJwkJCQEB\nAWVlZbt3766pqdmyZYu3t3dbtb3wwgvbtm0rLy/XaDRElJ6enpmZuXTpUocN3Iho6dKlP/zw\nw5YtW55//nn78oyMjOzs7ISEhD/+8Y8OL7nvvvvi4uJ68mEBADoPgR3AlVBbW1tWVmaz2fz9\n/YODg+07xhQKuv32y0729PT09PS80k0kIqL6+vpWy6VSaUREhMN6C39/f/tMIj3fbUIQBJPJ\nJJVKJRJJO6elpaV9991311577YQJE+zLtVrtvn371qxZ89133x04cKC6ujo8PHzWrFmPPvpo\nQkJCOxVqNJo33niDbS9BvyybEA/tLV68+OGHH968ebNDYJefn09Ep06dOnXqlMNLZs2ahcAO\nAK4YDntUd0NDQ4PZbNZqtf15Gk1DQ4NarW7/D7B7a2xsNJlMfn5+HfZUVVdXp6eni4eRkZGD\nBg3q49Z1h8VicdihgfHx8Rk+fLjz9q8WiyUrK0un08nl8rCwMD+HhSFd1NDQkJmZ2dLSwnFc\nWFhYO7uy/u53v9u0adOuXbvmzZvXk3fsFU1NTUajsTO3gRtrbGxUqVT9+deA3Qa+vr79+SI0\nNjYqlUqptP92GF0lt0H//QIArhiHRBulpaVXZ2AnlUo9PT0NBoNDeUREhHNUx84PCQnplfje\nZrNlZGSwvSLYMgUvL6/AwEDnMwsKCrZt2xYbG3vjjTf28E0BANwPAjuAPueQZNhqtQqCcFV1\n97a00HvvUWoq+fuPnjbtrK/vr6siwsPDe9gV16G6urrs7GxxBzCxsNXA7s0337RYLE888UR/\n7iEDAGgLAjuANul0dPQo8TxNnEg92fDT19e3sbFRPPTx8en1qE4QyGYjseNMr9fn5OQ0NTV5\neHgMGjSo/chMEGjhQjHfnseWLUkHD9YFBFhZB16rfXVt1CNUVVUZDAa1Wq3Vajv5Kr1en56e\n7pxppdUNZ+vq6tatWxcUFNTqBDiXePvtt3/66aePP/6YpdMDAHAt/JcXoHWZmTRsGM2YQddf\nT3FxlJfX/aoiIyODgoLYY41GE+uc9aS7DAZDba3+3ntJrSa1mpYvp4YGslqtaWlpNTU1bN+I\n9PR0h7wkDi5cuCyLcmUl7d7t6+/v7+Pj06WoLi0t7dy5c3l5eWfPns3MzOzkC2tqapyjOolE\n0upSjHfffbe5ufmPf/yj87YZrpKamrpr1y7n8WsAAJdAYAfQuj/+kYqLf36cl0eXJ9boGp7n\nhw8fPnny5EmTJiUkJPRKUGI2m0+dOnXs2LHf/776gw9Ir6eWFtqwgR55hJqamlpaWsQzbTab\n/drVjAy67jry9qb4ePriCyKiykrHyruRabi6urrObgO1iooK+07KdjhHdd7e3omJic7rgo1G\n47/+9S+VSnXPPfd0uX0AAP0DAjuA1jmkrbBLXttNEomk1eHF7snNzW1oaCCio0cvG/T88svW\nz9fr9XV1dfX15jlz6LvvqKmJMjJo6VJKS6PRo8khxduUKV1uj3OXVSc7seRyuUOJr6+vsrW8\nf59++mlZWdndd9/d+XFeAID+BoEdQOsiIy87vNqWsbKojog8PGz25UoleXl52XcKchxXX1//\n008/nTlz5tNPM8VuSCJqaaEvvySNhtavJzEXr0YjKBQFP/3007Fjx/Ly8my2y+pvi3MuX+eS\nTr6w1R5NQRDefPNNiUTy4IMPdqZaAID+CYEdQOtWryaxL0mhoL//3aWtcSLOfrvhhjL78pUr\nSSKRjBo1SqvVymQyb2/vsLCw2tpa9izHOc9mIyIKDqbm5p9L5s0rksny9Xq9wWAoLCw8deqU\n82ipMz8/v+DgYPEwIiKiMztrFRcXnz9/3n59q4eHR6uLYb/88svz58+npKRERUV1WC0AQL+F\nVbEArZszh06fpu3bSSKhRYsoJsbVDbpcWFgYm9M2f36pVCocOBBstXIzZ9Y/80w4ESmVypG/\n7EGblZUlvmrIkKbQUH1Jyc8DnUolsd1Q/+//fq156tTLZtg1NTXl5OR0ZsHHsGHDQkND9Xq9\nWq3uTFR36dKlnJwc8dDDwyMoKCg0NJTjuNraWo7jNBqNGPO1uocYAAA4QGAH0Kbhw2n4cFc3\nog1arTYhIeH8+fMGg2Hu3Etz514iIqlUKggDiaRlZWWlpaUWi0Wr1dpPYpPLbf/4x9nPP086\ncYIfNIiee46GDiUi8vX9tWbn9HDV1dVtBXY6na6yspLjuICAAI1G4+Xl5eXl1cmPUHn5qg2T\nyTRo0CCdTpeWlsZy2nl6eg4dOtRoNJ47d+7IkSPTp09vf2cwl0hJSRk5cmQ7G9ECAFxJCOwA\nXM9sNldWVtpsNq1W2+q6gVZpNJqRI0empqaKGwNaLJaSkhKlUnnhwgVWUlJS4u/v7+HhIa6T\nHTvWd9kyx9jt5pvp2WepvJyI6PDhgMjIy9KjtJV1r7q6+uLFi+xxcXFxXFxcq6OoXZKVlSVm\nKjYYDKdPnyai5557jq7W7rq5c+fOmDGj8+EsAECfQmAH4GJ6vf7kyZMWi4WI8vLy4uLiHFLd\nNjY2VlVVCYIQGBjo0DMkCILDds96vb6pqcm+pKamZuLEiRUVFUajUaPRBAQEOLchIIB++IFe\nfZVycigoKFKtrm9urhef9ff3z8zMrKmpsdlsPj4+kZGRhYWFdXV1Dm9dWFjYpcAuICDAPkNK\nQEAAx3EOjSeisrKyH374ISoqatSoUZ2vHACgf0JgB+Bi+fn5LKojIpvNlpOTYx/YVVdXp6en\ns8fFxcXx8fH2z3p6evI8b79wVaVSOSeQk0gkYWFh7TcjKoref5895ARhdElJSXl5uSAIGo2m\ntLSMyEZENhtXXV1XU9Nw5ox3QIBHaKjevgaDwdilDx4SEmK1Wi9dusS6KqOjo9knckinvGnT\nJpvNlpKS0n6aZQAAIKyKBXA5vf6y8KilpUUQBIvFotPpbDZbYWGh/bMFBQX2hzzPx8TEiCsM\n1Gp1aGioQ4efl5dXQ0ND+ytb6+ro+efpttvo9ddJryeO48LCwsaNG5eUlFRba2JRHRHxvNDQ\nIOM469q1MbffPv78+cu6D48e1Wze3LXPHh4enpycPHHixNjYWKlUSkSDLs8rU19fv3//fn9/\n/2nTpjmnLHboMgQAAPTYAbiYWq1uFnONECmVysLCwsLCQpvNJpVKHea3GY2OvWIDBw708fFp\naGiQyWRarZbjuAEDBpjN5tLSUrPZLAhCY2NjWlqaQqEYNWpUq4tVm5spOZnY2tn162nHDjp0\niKS//DZUVhrsh3+9vMx6vcRi4Yno2WdHvPPOycBAIxEVFKjWrImpqaGpU+mX7dO6IyAgICkp\nqaqqiud5vV7/n//8x2g0rly50tfX136TsUuXLhUUFJhMJi8vr9jY2E7mzAMAcHsI7ABcLCoq\nqr6+ni1ukEqlAwYMEJOAWCwWh8Cu1dWXSqXSYclFeHh4eHj42bNna2pqWInRaMzOzh49erTz\ny3fuJLuMKPTjj3TkCE2d+vNhZaXa2/vXMdD8fPWlS56XLnkSUXW14qmnRnIccRzl5qpsNo6I\nVq2i3bu7dgUcqFQqFoDq9fo9e/ao1epHH300Ojpa7Jisr68XF22wzXDHjx/PO6/mBQDof/BT\nCH1FJpNxbbj55puJaMOGDW2d4PtL+o0nnnjC4amgoKAZM2YcPXq01Tc9duxYSUnJlfuQPVNb\nW3v8+PFjx47J5fLo6Oi4uLjk5GSHMVNBEMSdGFQqVUxX8unZdwQ6H4oqKkgqFX73u8L33jv5\n1lunZs0qY8tjfxFVXv5zA5qaZB9+GL12bYw4BBoWJsnJUWdnq1lUR0QHD5LVSiaTKS8vLzMz\ns6ioqLa2NisrKycnp60GtOWTTz6pqal54IEH7IebiUiMVpmWlpau1tyLvvvuu08//RTz/wDg\nKoEeO+grf/3rX513o/ryyy/T0tLs9/qcP39+fHy8w2kOm0rdd999bN6YxWIpKirasWPHlClT\nfvzxx8TERPvTjEbjrFmz3nrrrRUrVnS+nTqdLj8/32AwqNXqyMhI54lcfcRgMGRkZLAwrrGx\n0WAwjB8/XiaTOe8nO3z4cBbWqNXqtjKPtMrT09N+6Nbho508SY88QmfO0MCBdNddeUuWFLHy\n+PgGpbLm7FmbTCYLCwu78071vfeOz8iok8ls1dW+ERHSEyd+riEqit580zR1qryhQSJWy/Nk\nsZhPnjzB3rqi4td0x6WlpaNGjfLx8elM461W65o1a2Qy2R/+8AeHp5w75yQSCbnIunXrvvji\ni8WLFyPjCQBcDRDYQV95/vnnHUrOnDnz2muvBQcHv/zyy2Lh4sWLb7311varevjhh+0T5B45\ncuTaa699+eWXP//8c7FQr9c/+uij4g6qnWQymU6fPm02m4moubm5vr4+KSmpe1GCzWZraWlR\nKBSdfHltba1955zZbK6vrw8ICAgMDCwsLBQDMh8fH29v7y7Fc6JBgwalpaWx8JrnefvNuGpr\nhRtv5FjQdfEivfzyZfuS6fVVbEVHVVXV2LFjP/pIVV6uramhIUNIIqGtW+noUYqIoJSUxry8\n9JUrA9esGSK+dskSqq6udJ4LSERsLUgnA7vt27fn5OTccccdzut5AwMDi4qKxP82eHt7dz75\nHwCAe0NgB1eITqdLSUkxm83r169vNZVa502aNCk4OFiciFZdXb148eJTp045p/noUE1NDYvq\nmJaWlvr6evsOxU4qLy/Pzs62WCw8z0dHR4eGhnb4EudYjZXIZLLExMTi4uKWlhYvL6+QkJDu\nRXVE5OPjM378eLbBQ0BAAOuxKy0tLSwsNBpNf/2r5p//HFJYqOI4QS537FtlrFZrSUmJ2Wyu\nra2VSCSXLoVGRESkpFBKChHRsWOZZrN53rxSo5H/6quBNpv85pu5P/yhwmGo1F6rAV+r/vnP\nf3Ic9/DDDzuUGwyGiooKrVbLlg9rNJrIyMhuXyIAADeDwA6ukN///vdZWVlPPfXU9ddf38Oq\njEZjfX397Nmz2aFUKk1OTk5OTi4uLt6wYUOXqnIeLO7MhvcODAbDxYsXWVU2my07O5ttrtX+\nq7RarVQqFTPYKRQKsSuLTbnrajNa5eHhER4eLh6y6W7scXx8/QsvpN9xx3irlTt2THvddZWt\n1lBRUcGuidVqzcvLk8vlbHWq2Ww2GAxExHG0eHHx4sXF3t7eOp2upKS9C9jJ7rrDhw8fO3Zs\nzpw5DkmJdTrdyZMnxe8oMDAwJCTEfs80AIB+Dosn4EpYv379f//734kTJ7K9obrNarUWFhau\nXLlSKpWuWrWKFfr4+Lz88ssvv/zy/fff39UKfX197SdsSaXSTkYe9hobGx0CxPr6+rZOFikU\nitGjR/v5+Xl4eAQEBIwePVoq7fP/aDn0pYWEGCIi9ES0bt0QtTqQ53mJROLQDIdIt7q6mj2Q\nSqUOc930en37YbFGo3FIU9eW1157jYieeOIJh/Li4mL7t6isrExNTc3Ozu5MnQAA/QF67KDP\nZWdn33///T4+Phs3bnSOXZYvX758+XKHwkWLFm3dulU8HMp2qrezbt26a665pudtUyqVcXFx\nOTk5BoOBrTntRveP83KHTlbi5eXVjW2yrFYrz/PdG3x0Xnag1fJRUbR6tWzcuDhBENimXkeP\nZspkeiKOyDEDMHvfysrK6upqlUol7gDG83z7UZ1arU5ISDAYDOfOnWOD5kqlMiYmRqPROJx5\n8eLFr776auzYsVPFnCu/ELeRtVdSUhIYGOhcDwBAP4TADvqWyWRaunRpc3Pz559/HhER4XxC\nq6ti4+Li7A/FVbFEpNPpDh48uGrVqkuXLj311FM9b6G/v7+/vz+Labr62rIy/s03uYoK3/Dw\nyEmTCjlOICJPT0+HvR96i8FgOH/+fENDA8/zoaGh0dHRFoulS/18QUFBJSUlYv8iz/Pr1xdH\nRUWxStgVOHDAa+HC8Vqtcf36owqFYw3BwcEFBQX5+fliibe3t4+Pj1KpvHDhQjtv7eHh0dDQ\ncPr0aXG7iKamplOnTo0ZM8ahl/TVV1+12WzO3XXsvVqdwNfU1OSqwC4pKUkQhCu2mBoAoH0I\n7KBv/elPfzp16tQDDzxwyy23tHpCN1bF2my2m2666ZlnnrnllluGDRvWK+3sRlRXWspPmeJb\nV8deOGjpUv+nnipUKpVhYWF9lH0jIyODJWyz2WxFRUXl5eUmk0kqlUZFRYWEhLT1KpvNlp+f\nz9KOBAUFxcfH5+XlsZ42m81WWlpaWVmp1WrDw8NbWlpKS0tbWqy/+51fSIhBobisu04mk8XE\nxPj7+58/f96+vLm5efjw4R2OPvM8n5OT47wJWEZGxsSJE8WuxIqKio0bN0ZGRi5cuNC5kvDw\n8IaGhtraWody+7jKaDQ2Nzd7eHi0us1Gr3vwwQfvuecePz+/K/BeAAAdQmAHfejLL7986623\n4uPj33jjjV6sluf5FStW7Nmz58cff+ytwK4b1q2T/RLVERFt2uS1du2Inq33bY/JZHJIw8vG\nJS0WS1ZWllqtbqvLqqCgoKjo5xx17IFSqRSHUInIbDaXl5dXVlaynrwBA+juux2jNJlMNm7c\nOIVCYbPZHIZcbTbb8ePHO1x0otPpWl0Sy/K8iIHRmjVrWlpaHn300VZ7InmeHzVqlF6vv3Dh\ngpjaxtfXV3x5SUlJbm7uLx9kgPMgPgCAe0NgB32ltLR05cqVSqVy8+bNDgmHe451sLl2OWRF\nheN8tUuXqO8CO4lEwnFcW9ve19bWajSaVgeUq6qq7A8rKytb3ZfMeYGwPT8/P3a1eZ739PTU\nszR3v7CP6nieb7UqmUwmlUpbTTQoZpzR6XQffvihr69vQkJCamqqr6/voEGDnCM8pVI5ZsyY\nqqoqnU6nVCoDAwPZp66oqLDvFCwrK2Pj7O18LgAAN4PADvqE1WpdtmxZTU3Nxx9/3OudaoIg\nfPbZZ0SUkJDQuzV3SVKS5bPPfl024edHfdo9JJFIgoKCyi/f7UtUXV1dWVmp1+vZEhAfH5/y\n8vKGhgaFQuEcCwYEBLDkdp1XWVlZUVHBljvExMSkpaW1daZSqWx1g6/Q0FC5XH727FkxwwvD\n87zY1/jBBx/U1NQsX77cZDKZTCadTqfX61tdX8JxXGBgoH1JXl5eYWGhw2mNjY0I7ACgX0Fg\nB33iww8/PHz4sL+/f1FRkfMWFIMHDxbn1bENBpxruP3228XUGO+8847451mv13/33XfHjx9f\ntmyZwxqLK2z5cvP33/NbtiiIyN+fPvuMnJca9K7Y2FilUllbWyuVSpuamuxHNsVYSqfTpaen\n+/r6inlJHCb8BQUFBQYG/jKdrqWTb82iQ71en56ernD6nAaDxNPz5047X19fvV4vdtoplUpv\nb+/g4GC2/+/48eNra2vr6+urqqosFotMJouNjWUduhaL5a233pLL5QsWLBBrrq2tNZlMHXbN\nsr3mnMuxpgEA+hsEdtAn2FT96upq56iOiGbNmmUf2G3fvt35nEmTJtkHdmK5TCaLjo5+9tln\ne2VJbOexXSXs04XwPP3rX03/+IesspKPi6MrMFOf5/mIiAi2uLjVDirGarWKUR079PX1NRgM\ngiAEBweHhoampaWx9Qc+Pj5Go9FgMPA8Hx4efulSvcnUwRoIm81mMBgEgeyHfC9d8oyObiYi\ntr3sgAEDLl26ZLFY/Pz8goKCiIith2Ub8kZHRwcHB8fGxrKITRw73rx5c2Fh4ZIlSxwWIrQ1\n+swa09zczCJX59NUKpVDrx4AgNtrc8oOtKOhocFsNmu12v68kVFDQ4NarXbh5utXjJhkhOO4\nkJCQwYMHs++9sbHRZDL5+fk5J4e7AioqKjIzMzt5skql0ul0RKRQKJRKZV1dnfhUSEhIZGSk\nTCbjOG7XLoNMdtzDo+O9NyorPQIDf+3t27EjdM6cMoXCSkRKpVKj0VRVVdlsNq1WO2TIEJvN\nlpqaKs7DUygUSUlJbOacXk9VVRQaShIJJSQknDlz5vvvv7dPVufl5ZWYmNhqGxobGzMyMli3\npY+PT1NTk/1Uv4CAgGHDhl2B+zM9Pb2ysnLSpEnOHZn9R2Njo0ql6g+/Bm1hPei+vr79+SI0\nNjYqlcorkGj9qnWV3AbYeQKgA5mZmWzKvyAIJSUlJSUlffRG7S9fcBAYGNjWJhkKhcLht5VF\ndfTLbmz2T9XW1ordZsOGee7fH9yZdz9wIKi6+uc45tw5jdHIs6iOiPR6fVlZmcVisdlsVVVV\nFy9erK6utg+5jEYju55/+xv5+FBkJEVG0muvHTh9+vScOXNYlMxxHMdxWq12xIgRbbXh/Pnz\n4mB0fX29/Y+pn5/f8OHDr8xv6zPPPDN9+vSuzlkEAOgj/TeyBugMi8XCtkkQ1dXVhYWF9e67\nlJWV5eXlmUwmtVodGxvb6qpVBxzHDR069PTp0w45RFQqVXx8vE6nu3DhAltt6unpyTZ1ZRw6\n6e23zYiJoUmTLlvuqtNJz53zlsuFkSMbef7X4GzkyIbbbksePLjJaJTk5qo//DC1rXbW1ta2\nGoDu2kWrV//8uKSEnnrqNSKaN28em/bHGhkQEGC/ntpisRQWFjY1NXl4blIiKgAAIABJREFU\neISEhDivzE1OTm5qapLL5R1u1AsA4K4Q2AG0h82rs+9L6/V+oIaGBnHPhubm5vT09OTk5M68\nS3Z2tnNmOLlc7unp6enpOWHCBJ1Op1AoiouLi4uL26rEIbNxbKxUzI5y/rz3U0/F19fLiWjQ\nIOOrr57Qan8eJI2Pr3/++YwtW8IMBsmdd+ayDWdbxTre8vPzxU47uVyu0Wi++87+rHSz+Zvh\nwxOHDBliX1pXVzdgwAD2WBCEtLQ0Mch27iFTKBRyuVyr1bbVEgCA/gCBHUB7eJ4fMGBAaWmp\nWDJw4MDefQuHfRRMJlNTU1Nbw6z2HLoSGXGml0QiYT1/AwcOLCkpse+ok8lk3t7eHMcFBwcH\nXJ55b8CAAWLeuzVrYllUR0T5+Yp166KeeOLXTcOSkmqSklrZ3ctBcHCwUqlk213o9XovL6/B\ngwdLpdLLA7BXiYQHHvizw2vtF8M2Nzfbf16HfMg8z/d6N6qopaXFarUqlcr+PKcWAH4rENgB\ndGDw4MGenp41NTVSqTQkJISl7ehFzp1znVyNoVAoxNS+YlXO8Y1SqXRIKezl5TVy5Mjm5uaK\nioqGhoaAgAC1Wp2bm1tZWcnzfGBgoM1mM5tteXlq+3pycy+LNQWB++KLgYcPB3KcMH16xaxZ\nZRKJhEiw79308PAYPHgwEfn6+o4dO9b+5bfdRm+8QfX1RFRCtEWlGnLffTdnZV0UE/VJJBL7\nGNoh+52DhIQEtVrdzgmtEgShtra2paVFo9G0+nKbzZaRkcF2p/Xw8BgxYgQGeQHgKofADqAD\nrDeohx1CBoOhtraW53l/f3/7aW1EFBAQUFBQIHZBeXl5tRo9GI3Gmpoanue1Wi2rITIyMiMj\nQzwhMDAwKiqq1cxtMTEx6enpLOSSSCSDBg2qq6s7e/YsKykuLvb19RWXylZWVg4aNCgyMjI8\nnPLzf60kMvLXPj+O4zZsCP/44yh2ePq0r9HIL1hQ6hCSSqVSscRsNldVVQmCoNVqPTw8IiIo\nNZXefJP27XszL8/08suPSiT80KFDvby86uvr5XJ5aGioUqkUq/Ly8pLJZA6BrKgbK1JtNtuZ\nM2fEnTDYR3Y4p7CwkEV1RNTS0nL+/PmkpKSuvhEAwJWEwA6gz9XU1GRkZLAoSiqVJiQk2O9P\n7+npOWbMmMLCwpaWFm9v78jISOchv7q6uvT0dBb8yWSyhIQEpVIZEBAwduxYNtssKCionc4k\nPz+/pKSkqqoqtmGDQqE4deqUfdeaw1LZqqqqyMjIF1+kZct+LlEqhcWLf82uIgjC118PsH/J\n118PXLDgkpeXl/2mYWJGOp1Od+rUKdbrxvN8fHy8n59fTAy98krjxo0fBwYG3nXX7UTEcVxo\naOiAAQMMBoND+CuVSuPi4i5cuNDS0uIw61Gj0XRjczm2M4d4mJ+fP3DgQId6HDZA0+l0FovF\nYcWxUqn08fFxScobAABnCOwA+lxOTo4YiFgslvz8fIcsHl5eXu3k9WA1iF16ZrM5Ly+Pne/t\n7d2ZJbRE5OnpGR4ezh7n5+c7hCwOS2VZZPm735FUmvP11xKZzHbddRWBgZct1GhpuWwE2WCQ\nCMKvkRCLIGUy2alTp3iet1gs4liqzWbLyclJSkoyGo1PP/10Q0PDypUrc3JyWIKS6upqtp6X\nBXmDBw/W6XQlJSVms9nX1zc5OdlsNstkspKSkuLiYovFwvaTzc3NNZvNwcHBnZmbyIgpYOxL\nHAI7h0OJROI8bv7vf//baDQ6JFUGAHAVBHYAZLVS36U8EwTBPtsIETnk6egMh5c4ByWdYbFY\nJBKJXq8vKChweMrDw8N+ezG2XYROp/P3L77tttZrS0qq2bv310678eOrOe7X6NDHx0cmk+Xm\n5rb6WnZBMjMzN23apFAobrrppurq6ry8vEGDBmVmZrIQVhCE4uJiDw+P3NxcFhZXVVU1NjZG\nRUVxHCcOjut0uuPHj7PAtLy83Nvbe8SIEZ3pwFM5bRViP/LLhIWFsUzL7DA8PBzrJwDgKofA\nDvq1khK691765htSq+mhh+hvf6Ne/8PNcZxKpRL3cqXWQopW1dbWlpaWso25lEplN2oQ1dfX\nX7hwwWAwSKVS5z4tqVQaHx9fWVnJFk+wbceIyDmXCsf9ulfN73+f09IiOXIkgEi4/vrKu+7K\ntz+zvr7efn8LByyE2rJlS1VV1YIFCzQaDRHV1dUFBAQ4LHctKSmxH3UtLy8vLy/39PQcNmwY\ne9X58+fFJgmC0NDQcOzYscTEROcozUFwcLD9aKyHh0dOTk54eLj9iDbb94LlW9ZqtQ4riAEA\nrkII7KBfu/VWOnyYiKi2lp57jkJDadWq3n8XtnaBjUUqFIqoqKgOX1JXV5eWlsYe19fXa7Va\ng8HAgh65XN6ZGkRWqzUjI4MtO7BYLPbbyDL+/v5qtVqtVjtUq1arHWazDR06VBCEnJwci8Wi\nUln+9rdzFgvHcSSROO5M2M5ehVKpdMiQIYIgbN68mef5RYsWsXKZTObc0+bQ2SkWZmRkTJgw\nged5+45G8fMWFhYOGzasrQYwPM+PGTOmpqYmNzdXr9e3tLS0tLTU1NQ4BIUqlYot7AUA+E1A\nYAf9V0MDff/9ZSW7dvVJYOfj4zN+/Pi6ujqe5/38/DqTfLisrMz+sLa2Njk5ub6+nuX7bWc3\nRqPRWFxcrNfr1Wp1WFiYTCZrampqazEp4zA/zGQysUl4CoUiNDRU7DMLCQkJCAg4fvy4feYR\nqbRrm02zrWnlcvmePXvy8/OnTp0q5jRhy2D9/f2dQ09nJpNJp9N5eXk553yhNsJBZ6wz1X6Y\n22q1lpeXdyluBgC4qiCwg37h4EHasYPkcrrtNho58udCuZwkErLPj9ZaqpDeIZfL2cS1TnJI\n2yYIgkQiCQ7uYCNXi8Vy6tQp1olVU1NTU1MzduzY9vfkVqlU9iOMgiCkp6ezVMA6na6+vn7U\nqFFE5OHhYTAYjh492k6M2NAg02jaiyDpl02y5XL5a6+9RkR/+tOfAgICOI4bMGAAiy9HjBhx\n9uxZh6TN9kPAIva5hg0bduLECYdnO59tzjk9nsNYMADAbwsCO3B/H39Md9318+O1a2nfPpo6\nlYjI05OWLKGNG389c8WKK9+61mm1WjGDGhF5e3s7pP9wZjKZzp8/bz802dzcXF9f7+vr67A2\ngohYOj2lUhkSEmKfqkOv19tv8GCz2Wpra6Oiomw224kTJ9rv+TMYJB0Gdo2NjSdPnrTZbIcO\nHZo6ders2bPr6+srKytrampsNlt1dXWrK0sSExMtFsvZs2fFqEur1bKMfWq1esKECVlZWbW1\ntaxnUa1Ws4x0zc3NOp1OpVK1k7tYpVIpFAr72YRY3woAv2kI7MD9vfDCr49NJnrllZ8DOyL6\n4AOKiKC9e8nXl/7wB5o92xXta83AgQMNBkNpaanNZvPx8Rk6dGj751ut1tOnTztHRSxvSFhY\nWHZ2tn25xWKJj49nj00mk9lsZltmOXeM6fX6kpISjuPaj+qIyGK5bOEJz/Oenp7OC3gFQVi9\nejURPfHEExUVFZmZP6fHKykpaatmm83m7e0dHx9fUVFhNBq9vb3tOy8VCkV8fLzFYmloaJBI\nJBqNhuO4rKwscSO40NDQmJiYVmvmeX7EiBEXLlzQ6XQSiSQyMrKru83ee++9X3/9dUZGhsOu\nuwAALoHADtycINDl09XIPn5Qqeill+ill3r6LjqdrqCgwGAwqNXqQYMGdWMjBAccxw0ePDg6\nOtpmszU1NZ07d06n0ymVysGDB7e6p1ldXZ1zVMfz/P+zd+bxcdT33f/Nsfd972p3JVnS6rQl\nn8ImHHF4hSshlKYkEI60pIEclJKjcZ8mbWibp9AQkhQSCq+HBJ48JA8YHgIlTQiEHBhzy7Lu\n+1xpL+19z+4czx8/M4xndleyLduy9Xv/4Zd2dnZmdma989nv8fnC1lG73S4SdjC4xXHc+Pg4\nnOKlVCo7Ozt1Op1KpRLWqK2srPDTY2vzzjuWF19UfuELsxjGQh87j8fz3nvviVYLBoOHDh1q\nbGy88sorpc9WZGlpCdqOKJVKi8Xi9/vn5+eVSmV7ezt/NuCUi6mpqXw+r1AohG9haWnJbrfD\nUyFFr9f39vZCL5iTcDPJ5/PJZFLYYoJAIBBnEeSWjjjPwTBwwQXHLbnwwnXeBUVR/f39kUgk\nk8kEg8GjR4+u120ewzCGYYaGhjKZDMuy2Wx2aGhI2gcKKtWKEQTR0dGhVCoBAHK5XORjDE1P\nAoEAP5u1WCwODg5yHLdt2zaj0Yhh2AmNcxgcND7++Jbt24sYdkwyhkKhsbGVSGRXMnlc6eIz\nzzzDMMwNN9yA43ipVFp1yziOh8NheEqLxeLy8jJMyBaLxZGREf6N5/P5wcHBbDbLsqy0eUJo\nFlMRkiSRRx0CgTgPQMIOcf7zyCOAN6zo7V2H+BykUCik0+lisRiNRoVpynw+n0qloOnG4ODg\n5OTkGps0K5JIJISijWGYiv5woqlWOI7v2rXLbrfzSzo6OngXD7PZDKvQRJPEaJoeGBhQKBQ7\nduz48Ic/vPbm0KEh41NP1X//+7H9+49raPX7gzffrLvxxr2HDh1rHMlkMr/97W8tFstll10G\nAKgWRYNvAQAgl8tr5EbL5XImkwEAZLNZWLpXbc2KtnYw2LZqihmBQCDOIVAqFnH+09EBRkbA\n4CBQKEBXFzj1qZ4cx42MjMAEpd/vl5bb0zQ9NDTEK7BIJLJnz56Ty89Kh5BWDCzBROrk5GSp\nVJLJZD6fT2RirFare3t7C4UCjuMwjAckI7MAAKlU6tChQy6Xq6mpCU6hXQvbtiXzeeLll811\ndUC4W7O59NBDR+66a8e997ZddFEYw8Dzzz9fKBRuvvlmkiSz2azP58tmszAGSRCEsCPV7XZ7\nPB6FQrG4uFgjFwx7SiYmJqQxSx6LxSLKX3McNzo6Ct8gjuPt7e0n1LOMQCAQGxYk7BAbEYZh\n5ufno9EoQRB1dXW81dlJI5eD3bvX5dAAACASifBSg2XZWCwmNPIlSVIulwvjauVyORQKNTQ0\nnOiOGIaBE+75lKVMJqvWtmmz2Ww2GxR2FcUfhmGiwBWOuxlmWWovHAwG0+n0CQ0u+8lPmqan\ntUZj6aabFoTL29rSl10WeeklZyikslrTL7zwglqtvuaaa6CvypYtW/jMsshnJBQKwZMMffsq\n6ja1Wq3RaDiOg3E7ITKZrKWlpVgsarVaacxvaWmJl60sy46Pj9d2B0QgEIhzBfRFhtiITE1N\n8Q69ExMTcM7V2T0kIaKCLY7j6uvrQ6EQRVEajQaOVRC95ETzfZlMZnx8PJvNQmlbKBRg80RT\nU1Pt0rcTKoz7ylfUCkXzl740LX1KpOpEHRUiikW8tTXDceB//+8tTmfxssvCwmfr6vIAgB//\nuOXyyx+Jx+N/8Rd/Af1HisWidGotT7lchidteXnZbDaLnO0gJpMJSli5XC60LJHL5du3b68x\neE3UgQvrF6XD1hAIBOKcAwk7xIaD4zhREjAcDm8oYcenMnlg7pJlWZg5ZRhGFGSq2MpaKBRm\nZ2ez2axKpdqyZQtvq8tx3PDwMAxlMQzj9/u3bdtmtVrXeHg0TS8tLcEW3bq6uhqDLg4dAsmk\n12otfepTi6KnRNYntcsElUr2618fBwA8+WTDk082iITdzIxWoWCWl9XR6Edvu23u2muv5Z9K\np0ur2fMBUL31QaVSpdPp6elp0VhbHMd5VRcKhRYWFsrlslKpbGpqgvFOaTWe9JqukQceeOAf\n//EfUSYXgUBsEJCwQ2xERBGvjeAlkUgkFhYWoIlaQ0ODRqPhY1p1dXXQLJevhyMIoqura3x8\nnKIoHMcbGhqk2UCGYY4ePQrVG6zi7+3thfIil8uJWl/j8fhahB3LsolEYmJighc6KysrO3bs\nqNbv6XCAZBI88kjzpZdGHI7j9ljR025Vbr55gWXFRYHf+EYUw8aVSqZUInD8Fn4K2ZEjJobB\n9uypEIoTIS00BADkcsrf/Ebb1tYv/XgUi8WZmRmv1xsOh6enj8Ujy+XywMDA9u3bTSaTTqcT\n+j+TJHnSwg4aqaA0LgKB2CAQ99xzz9k+hnMPiqJYloWGrmf7WM4acDBUxTvuKYJhWDabFbqy\neb1ekVvHGSaXy/X39xcKhXK5nM1mU6nUrl27AAAKhaKpqam+vl76EpVK5fF4YCSvmvMc76AL\nAOA4Lp/Pw8Aky7KiXKFGowmFQn6/P5vN6vX6ikG4QqHQ19fHu4FAKIpSq9WFQoFhGGn3hkIB\nXnwR/on19n4gsAiCOGkxjWGcw3Fc/Ewmy0ExRxAcjoNkUl4u40eOmP/n/+w6etR08cVxjWaV\nPDXHcRzHwf9uBEHMzpqeesp9771tzc3+lhZxdR0klUr5/X5pApdlWbvdrtPpIpEIfI84jnd3\nd5+0sCuVSgzDqFQq9G1wOr4NzhX4j8FmPgkURclkss18BjbIxwD9ykRsRNra2nAch00JdXV1\nZ93TnxcBkGw2WygUrFZrqVSqMYEKw7AackEaD0ulUvAPpVIprCrDcZw/gEwmk8lkdu7cKZUR\n09PTFS3uxsfH4WstFsu2bduEL/z854HHA554Ajz3nCcSUVx6aaRcxsfGjHffPV7tsFdl27Z0\n7RWMxhIAYN++6GWXhZ5/3vOjHzX98z8P134JlKrwjJXL2z73uWNCWaer2glbe1Nqtbqnp2d6\nepqmaavVenZ/NiAQCMQ6goQdYiMik8k6OzvP9lF8gFSEnXp22GAwCHtpwftxKZZlg8GgWq0m\nCKJUKikUCpIkA4EAvxrsV5XOP5V2hooONRaLhUIhl8vFsh94vlx1FVhZAQcPgkOHbO+8Y77+\nev9HPhKsuB2I6JilYBi3xjTun/3Z8vPPe667bnnVNYW8+eYSAMeE3ZEjposuWtNUDJ5cLpdO\np2UyWX9/PxR52Wx2fn5er9f7fD6k8BAIxLnO5g2ZIhBrR1Qhp1KpasyVXyMymUxUcW8ymRiG\neffdd6empuAQLY1G09XVJa3fEjmDQKTRQWk64Hvfk9tsQKUCV10FFt63JdFq6Vtumf/2t0ce\nfbTvttvmurtT1Y7ZYrEIp9ZWSzfwadPaWCylH/2ob/v2Cn7LNfD5MjLZMWX5wgt1//VfbgAw\nAIBSqVxL+qNYLB49elSUsAYApNPpoaGhiicWgUAgziHOcsSO47hf/OIXf/jDH1iWveiiiz77\n2c9Ki4eee+65J554gn9IEMQvf/nLM3qUiE2PwWDo7Oycm5ujKMpgMPh8vnUpoWhtbQUAhEIh\njuNMJlNbW1swGBT2nwYCAdh4sbj4QdeqQqGoKCu3bNkyODgIw2kYhvl8PoIgxsbG+BVeecX5\n/e8fU6gvvQQ+/Wnw1luAZVm3++hf/VXlaJ+IWCxmMpl2794di8U4jvP7/dXWXEvETqOhOztX\nydtKsdupv/mbqR//2EdROMdhGNZ6ySXNDMPI5fKBgYGKrigiGIYRjdyAlEqldDotKohkGCaX\nyxEEUcM8BYFAIDYOZ1nYHTx48Ne//vWdd95JkuSPfvQjAMBtt90mWiccDu/cufMTn/gEfLiZ\nK5QRZxGHw7HulhZw5gH0vYM/aUS2HQCAYrFoNBrb2trm5uZKpZJOp2tra6vYPGEymfbs2QOd\nYmw2WyqVEqo6AEAopFSr6Xz+2P/6t98GkQggyWQ+vyZVB/H7/V6vV6fTJRKJGi50p5WPfzxw\n0UUrxeKWD33IXV8PACBgt0c1YxdhCzOkmu4U6fV0Oj08PAwvitFo7O7ulu7ivvvuO3z48MGD\nB4UD3BAIBOJscTaFHcMwv/71r2+99dZ9+/YBAD73uc89/PDDn/nMZ0QZpXA43N7evnPnzrN0\nmAjE6UUoJngrO/4pGCiqq6tTKBQrKys1TOkAAGq1Gg6BBQAcPXpU9Owtt8xfe+3yPfdsPXrU\nCADAMKBQgGLxxJyTy+UyzLRKe2xPziGFRzRSrDZmM7Nrl0EYuJydnRVOHnM4HPX19dAjEMOw\nvr4+4ctxHDebzYlEQnjAGo1GdP7HxsZ4qZ1MJhcWFqTzc0dGRv70pz9JFTkCgUCcFc5mjd3S\n0lIikYC2EQCAnTt35vP52dlZ0WrQnLZYLFarDUcgzhvsdjsf+MFxvLW1Fc5CXVpaGhwcDAaD\nS0tL7733nnBeWUVYlq0460KvL//93x8L4113HTAYgF6vP6G0Mu/rwXHc+rb01+jJkMvlFoul\nq6urvr5er9dbrdbt27eL8tFCXzoAQCQSGRkZgb6Der1epNjS6XQ8HscwTJgB0Gq1QqlH07TQ\ncwcI2pYRCARiw3I2I3bwi5V3i9BqtQqFQnTH4jguHA7/6le/+sEPfsBxnNfrvfPOOzs6Oqpt\nEzYVnt7jfj+PwzDMZs4Lcxx3fleav/QS9vOfYywLPvUp7tprKwSi+I/BqYSppLS3t9fX18Mh\np3K5HJ5kYY0dfCjs34zFYnwGlvcx1mg0FQc22O3FD32I2rdP/s1vsgwD5HK5z+ebmZmhaVra\n8apQKLZv397X18dP0cjlckNDQ3q9PhqNilY+xfMACw0ralaDwQD/1wu7WEQfP5HKhL6As7Oz\nDMM0NDRs3bp1fn4+mUxCoym4juj4w+FwOByGTihQT4vGh/CXQ7QjuKnz+79Dbc77b4NV4b8N\nzvaBnE3g/Xczn4Qz9jHAMKzG7+qzKewymYxCoRAeHBwQJFwnHo/jON7R0fGtb32LpunHH3/8\nO9/5zsMPP2wwGCpus1gsntDk8lOhYv31pkJ0sc4nnn1W8cUvHovxPP00dv/92b/8ywoWceC0\nRXEwDMvlcvDDzHGcKPyWz+d5AZRMJvkmhpWVFbfbDX8suVyu+fl5adwOx/Ff/jKNYRhNA7gN\nuVze3t5OUVQwGBRpQb1en8lkhOIGABCNRqPR6Dq+WUihQBw40Pq97w0xTF70VDqdHhgYMJvN\nWq0WmsLgOM5xXCKRKBQKMplMp9NVG5IbCoWgCLZarVardXR0tPZh5PP56elp6BRttVpDoRBc\njuO4wWCQ6k6oDtPp9Kph1PObE52GfF5yHn8lrhH0MQBn5GNAEERF33vI2RR2Go2GoiihM0Kh\nUBC1nlkslmeffZZ/eNddd91yyy19fX0f+chHKm6TIAhp6c+6UyqVOI47AzvayJTLZZIkz9eY\n5WOPqYUP/9f/Ut9xhzgcVS6XWZaVy+Vn4CRoNBphKYJer+c/fqIUZDQadblcAACFQrFt27Z8\nPl8qlRYWFvgfkXV1daIyViiV8vm8NMJXKBTOTIdEqYT/4Adt776rvueenffd1y/6eUZRFEVR\nqVRKo9HA9Ci0huFVdTh8bDqtNOhYKpUWFxcNBoPNZsMwTKVSVZs8K9wdPL0ej0ev1yeTSYIg\nrFZrxf/y8OrLZLLN/IVwfn8brIUz+W2wYUEfgzP2Mai9/bMp7EwmE8dxyWQSCs9CoUBRVA0R\nCgBQKBQ2m61GqEwul1f74b6OpFKpcrms1Wo38ycY3mVr1/Kfu4iCL/E4LirSAgCk0+lSqaTV\nater1CydTs/OzuZyOY1G09zcLNxjZ2fn0NAQ1DSwSZY3tyuVSsKNwM5ZAABMRMK4eH19vd/v\nh4G3YrGo0WhKpVIymSRJMplMLi8vcxxXcdrpqhpIyMk1T9x3X0ephI+MGFZWFACAt98mc7kc\nTeMkWaGmghd81WxNpJUYHMelUqlUKsWyrM/na2trGxwcrB1X0Ol0/MnX6XS1B5/Aqy9tvFgv\n4KwRhUJhNps37BdOOp0+j78N1kImk6EoapOfhHQ6rVarN/Pc5A3yMTibF6ChocFgMPT398Pw\n29GjR1Uqlc/nE65z+PDhX/ziF/feey9MpuTz+UgkUnE0JwKxjuzfD4RtPPv3n/Y9lkolXnCU\nSqWBgYHe3l7+V4pare7t7c3n8ziOq1Qq4QsJghCmSmHeliCIgYEBOGEMzrflV4hGo6Ojo7FY\nTKSBVs2hyGSyGuvI5XKbzeZyufr6+qrJu4rKb2VF0d//wc85h4MBAKRSpMVSAutKIBBoaWnR\n6/UXXHBBIpGIx+PBYIUZG3K5vKGhYe2bve222y655BKj0bh+R/oBc3NzfMTUaDRu3759w2o7\nBAKxQTibwo4giKuvvvrJJ590u904jj/++OOXX345TBK9+uqrpVLpqquu6u7ufuSRR77//e//\n2Z/9mUwme+qpp7xeL7I+QZxuvvtdsLAAfvc7AAC4+GLw4IOnfY+JREIom8rlciKREDrnYRgG\nCxVYlvX7/YlEgiRJj8djMBhg5wQEx3GSJNPpdMW5sZBoNHoSobWenp5isTgyMlLxtaVSaXl5\nOZFIlssKkqy8ayhJi8UijuO8GL3xxkWhsPvIR8IAgEhEue7CDpZ1kyQpk8nsdnu5XBYJO5fL\nBTO2JxRy2L9//4UXXng67IuLxaIwD55MJqFFwLrvCIFAnE+c5ZDpDTfcQNP0/fffz7Lshz70\nob/6q7+Cy//4xz/mcrmrrrpKp9N9//vff+yxxx544AGCIHbu3PmNb3xjfU0WEAgpZjN4+WVu\nYiKYTq8oFADDnACsszuxiLUoLVi6ALs74ZJYLNba2ipsUPV6vasGdVbdl0KhEBmzKZVKmG20\nWCw1Oify+RxJAuEs2uOfPdYVgWEY3wC7e3f8iSfe8fvV09PaSy8NNzbmMYxoacn+27917twZ\nv/zyMI5z/KtOpfFWr9cLFZvFYhE658nl8paWltOaRSqVSplMRiaTrXEirchsBQgy0QgEAlGN\nU/qi3LTAGjuLxbKZ0yKpVEqr1Z7HBSWLi4szMzP8w/b2dtiUwANr7Mxm87r80iiVSm+//TYf\nx5LJZMJULACApun+/n5p0ZvL5bJarWNjYzRNYxhWV1fn8/k4jnvnnXf46WRCSQT7SWv8x4fj\nyCYnJ/klOI5v27YNNtvOz8/Pzc2t+nZmZ7VNTbXq8+Be4vF4RZk5cfxVAAAgAElEQVRIEEQ2\nC4JBVe2N1EYulyuVykwmw3GcXq/v7OyEIcNcLkdRlE6ny+Vys7Oz+Xxeq9W2tLSc3PxfWFWz\n6sdgZWVlbGwM6kiTydTd3b3qx6ZYLL755pvCJdIP4QYB1djBj4HJZNrMJwHV2G2Qj8HmvQAI\nRG0CgYDo4Wm9p8rl8u7u7pmZmXw+D5snRG1ACwsLFVsZaJqGLnQAAI7jlpeXNRqN2+3u6emZ\nnp5Op9MqlaqxsZGm6Xg8ThBEXV1dIpGYmpqCL5fJZBqNJp1O8zG/hoYGt9tNUdTCwgJcwrLs\nxMREb28vQRBer3dlZQUeSY0Q2vKyKhJR7N0bq/gseN+isppZDMuyKhV3KqoOAFAqlVpbWy0W\nC8Mw0JeO47jh4WEoJQmCaGtrO911HcVisVgsKpXK8fFxPjqYSCSWl5e9Xi98yDAMnBGsUCjq\n6ur4+6JSqWxoaOCvgtFoXPehdggE4vwDCTsEojIi8zbRw3XZ/tzcHCyV83q9NpvNYDBU0xkU\nRfGGaiL0er1wlBYAYHZ21mQyqdXqbdu2CZfzMy00Go3RaFxZWSmVSkaj0WazAQAikQhFUUaj\n0WAwFAqFxcVFisJ/8pOmQ4dsBMFdcUWouTlpt1sIgti9e3c8Hi+Xy2q1enFxUbR3iFrNWq2F\n2meghtvTemUS0um0zWaDsbFisTgwMMDnNxmGmZiYsFqtp++39fT0NLQYlJqw8O+dZdm+vj4+\nxzozM1NfX9/c3AwfNjU1Wa3WdDqtVCo3eYoAgUCsESTsEIjKmM1m3h0NPlzf7Y+NjfFZyFQq\n1dPTU20XHMcNDg6KbE0AADiOu91ut9s9OzsrVEI0TY+MjOzZs6fG3vP5/OLiIsuygUBApVLt\n2rVLWJW/tLTEcdyjj/qef/6Y08fjj29xu7P/+q8AAIBhGElavvUt8JvfgK98he3uFm+cYbBd\nu8SxOml47wzUgQgd+8bHx0VVawzD5HK5NVa81SCVSsEzZrVaPR4PlF/xeJw3jpaasPCtzSsr\nK6LKucXFRZ1Ox6twOBLtFI8QgUBsHpCwQyAq4/P5GIaB9r82m006/f1UoGlaVFsWDoerCbtk\nMilNwvp8PrfbDTWE0+kUNXhms9lSqUTTdDAYpGna6XSKhrVMTk7yaqNQKCwsLLS0tPDPQhH5\nxz/ahC/57//WQGEHALj9dnDwIAAAkKQ4kInjKgAKxy/BWZY98+W8arWaV6ssy1b0vxR5NQMA\nOI7z+/2RSATDMIfD4fF4+FkXFffyk5/85J133rnpppvUajUcWQbjbTXikXK53OPxwL8rNi8n\nEgle2CEQCMQJgYQdAlEZmUy2bds2OBF43RuxpSqn2ozjSCQyNjYmWuh0OnllAABobW3NZrPC\n0RQYhiWTydHRUbijQCDgdDr5IculUknkSCcKGlUx78Defzl4/nkAANDryy0tGdFKBEGL3gpN\nc/D8/f73dr9fo1AwKhVz1VVBuVz8lhkGwzAMx09p3LNKpbJarUql0uVy8WlWeBFFMxw9Ho/U\nz3x+fp43GUmn0ysrK+l0muM4o9HY0dEhHS/x4osv/v73v7/uuuvUajUAIBgMQmEnlYw+n69U\nKslkMqfTCWv+AAAVo3H8swgEAnGiIN8QxOYlGgWS9CYAAKTT6dHR0cHBwcXFxTWqOr8ffOYz\noLkZXHLJMfe72shkMlEIzWq1SlfjOG5iYkKq+erq6oQPcRwXOXs7nc6ZmRmhfAyFQnwMSS6X\ni6QDSZJCfxOoNj784eOK5y67DPzf/wteew2wLIAb/spXJqTiTGpiXCoRAIBDh2ytrdnPfnbu\nhhsWr712+dAhG5BAENzcnOUUZbTdbvd6vWq1WviOYL+wcDW1Wh0IBA4dOiQ9UcLVkskkDDcm\nEomKc2ZFGp1hGLjEZrNBqQexWCwej6epqcnr9QpPvslk4rsoIARBbMzWVwQCcU6AInaIzcjw\nMLj5ZjAwAGQycNdd4P77AV+Vns1m+/v7oZaKxWLZbLazs7P21spl8LGPgaEhAACYnQXXXAPe\negv09KxyDF1dXZOTk/F4HDZPVGx4LBaLoqYNkiTb2tpEohAAYDAYPB4PHA4GACiVStIc3/Dw\nsE6n83g8JpOppaVFKBkjkUgkEvF4PFAgQmF3xx0zBMHC5onLLos+9ZS9WFy84orQoUPcgw9a\nDxzw7d1bYa4XRZEKxXHHrFTSAIBSCfd4Pihx278/wnEYhokjl2Zzure3NxaLzc/Pn9xA8cXF\nxcXFRXge3G53a2srXN7c3KxQKGBunaIoWG/Hsuzi4iJFUY2NjWq1muO4aqFTAEAqlWIYRtRs\nIZLI/OAv2GUSCASKxaJWq61hLNzS0lJXV7e0tFQoFFQqlcfjEQ0XQSAQiLWDhB1iM3L99WB8\nHAAAymXwwAOgowN87nPHngoEAsJbezgcbm1tre3MNDx8TNVBikXw//7f6sJOoVCIulYrriNq\nqKyrq5PJ7P/yL2Bigqmri113XcBqVTc0NGAYtrS0xK8Wi8Xkcrmo34KiKIqiotEolDuwnXZ+\nfp7f/tLSEmyShQZ4CgXz5S9Pf/nL0/DZG26YV6uPKba2tvCzz0Zx/LjMJkStxpgKi4HXe1zj\nAo5zDINJG1JNJiqfz6vV6vfn4eLPPRfz+/NdXWmrlRKvXQlhCG15edlisVgsFvB+0C4ej0vn\nzIbDYX56R41h0xiGFQoFtVotjCnClCuO4xiGmc3mtrY2/inoDrOWY1ar1bwARSAQiFMBCTvE\npsPvP6bqeF5++QNhJ7U1KZfLtYWd1AjlpCJNx5FOp6GnnUKh4H2GFQqF2ezdtw9MTAAACADs\nv/mN4oc/7A8EAqI8IwBAr9dXGxGxvLzsdrs1Go3BYBAFqFKplM1mq1jRz6s6CElWkm8ASINw\nEK1WvD5BVF5zZGQEFsPRtOIf/uGC994zAAAUCvYb3xjbvz9S8SU1SCQSUNgBABYWFqSqDsLL\nQdHIDdE67777LkmSra2tfIQVxuf27Nnj8XjQUBwEAnHWQV9DiE2HXg9EdmDCAe4mk0n4lEql\nWjUv1t0N3vcdO8a1157SEZZKpYGBAdhiWSgUSJJsaGhoa2vr7e39zW/kExMfrDkyYhgaMkJf\nYtFGmIpxs/eBiUhpdAo2B5zcDAZINcM/l2sVWzse/sife84ejxdVKgYAQFH4D37QYbOdcPGZ\nUGxV80NeI1D80TQ9Pj7Oq23I6eiwEe16cXHxyJEjR48eFbrwIBAIhAgUsUNsOgwG8JnPgJ//\n/NhDpRL89V9/8KzL5crlctCWTKPRrFpgBwBQKMCLL4K77gJvvgm8XnDPPWDv3lM6wng8LpRH\nNE0rFIp4PD41NfXmm/UAbDl+5WPiTKPR8M2tqyozuIJarbbZbLzDsEwmi0ajgUBAr9c3NDT4\n/f4aBWcnSrVIXg0++cmlT33Kn88TDz7Y+vLLzmwWz2RsAARXf6WAYrE4Pj4OACiXyxUjkScB\ny7JwpAcAYP/+/QaDoUof8boxOzu7uLgI/04kEhzH1SjaQyAQmxkk7BCbkcceA9u2gd/9Dlit\n4K67gMjKt6WlpampiabpGuVWIjo6wCuvrP9x8gQCAWhl19ERFwo7uZzt6PjAL00mk2EYZrVa\nfT7f7OxsIpGouLWGhgaVSgXteRsbGy0WSyqVwnE8GAxCs7d8Pq/T6Xp7e/v7+2ukJk8OisIV\nijXpRZirVauZr351YnDQuHdvTC6PlMtE7WCkiNMU3+J7Jm677babbrrJKIz6ngZEPoXBYBAJ\nOwQCUREk7BCbEaUSHDgADhyougKO42tXdeuO2WwmSZIP2slkMj4U19GR/vznZ594Yku5jCmV\nzJ13TvEpTn6dQCBgsVjq6upCoZC0sbSpqamhoSGVSo2MjEDRZjKZtm3btrCwIIzPZTIZlmWt\nVqs0yXuKrFHVCZHL2R/+8IjdTlU3/T2jKJVK6Fp8cjO+GIYpFAoymUzqilcRaa/uCUlbBAKx\nqUDCDoHYcMjl8p6enpmZmVwup9Fompqajh49ylf333jjwq23FjSaroYGNhIB6bRGLpeLgnOB\nQCCdTle0C1leXmZZNhQK8aG4RCIxNTUlNcVlWXbLli2BQOD0DY1gGAwADMe5VRO1dvtxgUOC\nIHAcx3F83QOKNdDpdBzH5XK5YrE4ODio1+u3b98uXY3jOIqi5HJ5xaq7WCw2NjYGL01dXZ2w\ni7YaGIZZLBa+bxdUcT1EIBAIgIQdArFxEEaA9Hr9jh07+KccDocwGdfWZrfZAAAys7kdAFAu\nl19//XXhprLZbDUTOIqi+MkKPOFweNu2bXwVFwBApVLBoj2SJE/OT24tEAQHQAVJJx0sK3kh\n0dPT09fXd5oOrCIajSYSifAHlk6nl5eXRd020Wh0YmKiVCoRBLFly5a6ujpYEmcymUiSZBhm\ndHSUj8UGAgGDwbCWpGprayvHcdFoFMdxp9PZ0NCw7u8OgUCcHyBhh0CcfdLp9MTERDabVSgU\nTU1N0ju9z+eTy+WxWIwgCLfbbbMdN7ZBJpMZjUbhLNQTjbHBTF9HR8f8/DxFUQaDobW1NZ/P\nDwwM1FB1JEkqlUrpHNtTZ9Xjt1qt4XC4dm8HbwHIMEBqmHcSkCQp2mMulxMKu1KpNDo6CvOk\nDMNMT08vLCzAEyiTyXp6eoCkaziVSq1F2Mlksq1bt8LTcnL5XwQCsUlAwg6BOHOEw2E4UEGp\nVJrNZpPJZDKZGIYZGhqCZsIURY2NjanVatEIUYIgmpqampqaRBtkGCYSiZTL5fr6etg5K5PJ\nvF5vLBYTmtgRxOoNBzAUBEVGqVQaHh5e1RyE4ziHw3E6hF0NcBy32WzNzc2zs7M1VsMwjBdh\nBCHs2MAwrLJwhA3R0MZZ+qxSqfR6vfxsD4jICieTyYjOMy+Ly+XyxMSE1JJ6jWV2/Jta+8oI\nBGJzgoQdAnEC5PP5UCjEcZzFcsIjTZPJJD9stFwuZzKZhYWFuro6u90uGhERj8els+FzuVw2\nm1Wr1Tqdjt/Ie++9x1t4NDc38+YsOp0ukUhAkUEQBBwgVvvwcrkc39o5NTW1Fss3hmFqq6vT\nQUNDQ2NjIwDAYrEIGzug6IHpbJVKxXGc0GouGlU8+mhLoYDH48bnnhuU9gtrNJr29vZ0Oi1N\n72q1WpfL5XQ6SZJsamqamZmBy9VqtcfjeffddwOBwNVXX61SqaR1ikKy2WwymXQ4HHyjrkwm\nQ2NhEQjE+oKEHQKxVtLpND9GdnFxsaGhQSq/asDbxQkJBAJSp4xAIFBfXy8UjjMzM3wBnMvl\nam9vBwAsLS0JjdlmZ2fdbjdBECzLTkxM8KEjjuMmJiZWrVqbmZmpq6uD8kiY1a3N6eurqAZ/\nWiwWC4zb8QnKjo4Og8Egl8sxDHvrrbeEr1Io2NdftwIACAK4XB6RsFMqlR0dHeB932bR7urr\n64PBILxSW7ZsMZlMyWRSLpfbbDYcx//93//9hRdeWFxc9Hq9SqVSNAJOCLwQF154IdyCQqFw\nu90nFLFDIBCIVUHCDoFYKyJDEGjku/aX18ijCb2FAQAURQWDQbfbDR9ms1lhW0MwGDQYDC6X\nSzT8gOO4YrEIC/wzmQy/HB7zqgqMYZhyuQxNXmQymSiIuEGAeVjhQ/59sSw7Pj7ucDji8Tgs\n/hOenzffPDZVrLubdTisAHQKtS/scu3s7JT6DNM0GBkZxzAWAJDL5WKxWGdnp9vtrhivTSQS\ntcv+GIYpFosulwsF6hAIxGkCjRRDINaKaG4BTdMnFK8SdTzw6PX6LVu2iBYKQ0dCzQcZHx/v\n7+8XFXgRBAGXiATfGiEIgrfuW+Po+jOPXq/n33W5XBaZDzMMEwgEisViNptNJBJClUYQAMOA\nxVK6444jk5OToVBIVAxXKpUGBwfVarVoaAeOs1DVQYrF4pEjR954442K5s+rFjJiGIZCdAgE\n4rSCInYIxFrR6/XCRgG1Wr3GYvZwODw3N0dRFJQahUKBj+s0NjaqVCpC0rTJywuGYSoGgZLJ\nJOyxSKfTAAAcx1tbW2EY6eQmvfp8PpZlMQzDMMzlcrEsOzk5eRLbOa3AaW9ut7tQKBw5cqS2\nD4tQHF99daCzM+10FpRKZnk5Iz3hAACWZZeXl61W66rtIOVyeWRkZOvWraFQCMpuKPFNJpMw\nFQv3IlR7LpdLJpPB5gyk8BAIxOkACTsEYq00NTVlMhmY5VQoFGv0Ekun03zPRC6XUyqVF198\ncSaTKRQKOp0OSj25XN7c3MxX5ZtMJtidGo1Gx8fHq8mXdDq9e/fuZDJZKpUMBoNSqYTLbTab\n3W4X+tmuisFgCIVCExMTOI57PJ6mpqa6urpgMChM6W4EyuXy1NRUJBKBiePaK4viqY2NH8g1\naCknfUmxWBTFQWscSX9/PwAA5qyXlpbq6+tVKlVXV9fU1BTcTmtrK9THFEURBOHxeLxeb39/\nPyxhNBgMW7duBQDk83mlUslfPhGJRGJubq5YLOr1+ubm5jUeHgKB2LQgYYdArBWZTLZr165s\nNsuyrE6ny2azaylEE9qOAACKxWImkzEYDAaDQbi8vr7eYrGk02mlUgmt0RYWFmr3nCoUCgzD\nRAa5kI6ODpGwq908kcvloL8awzALCwsEQZAkqdFoNpqwg6ylY7c2DodDWLbIc3LBTgAA7JWG\nIyL0ej1JknwRntVqLZfLsGF2bGyMb0xJpVKDg4O5XA5G+Dwej8/nE202l8sNDg7CFVZWVrLZ\nbG9v74m2YyMQiE0F+oJAnDkoinrooYf27t1rs9m0Wm1nZ+eBAwfi8Ti/wuc//3kMw772ta9J\nX/vRj35UWogWDodJksQw7Lvf/a7oqZGREQzDvvKVr0g39dZbby0tLZ3cW8AwTKfTGQyGtd9c\npWtWe61Go3G5XCaTqVgs9vf3r+okIiza4zhuenr60KFDr7322sjISDabFc26NZlMNXJ/Itfc\nubk5WIhW+wDOUTAMf/JJ88MP7z56tB7HP4iTKZVKaDS4lgw772zicrlaW1uhbd7S0tKhQ4cO\nHz781ltvCT/Y6XR6enp6bm5OVJkHB/LCv5eWlmKxmGgv0WhUmIgvFAobU2ojEIiNAxJ2iDME\nTdOXX375XXfdpdVqv/zlL3/961/3+Xzf+973duzYISqBf/DBBwcHB9eyzWeffRYm1A4ePLjG\nw6Ao6sorr3z11VdP6ODT6fTAwMDbb789OjoqaqFYFWiKwT/UarWrhoWGh4fXYjgibKpYXFz0\n+/00TUPL4r6+PmE0UalUtre3Q0ldUbWIFkpjeyRJSnXh0JCdpqt+h4iUJU86Xcvs7QzAceyF\nFw4MDGBf/Wrzddfttdk6PB5PY2NjuVxOJpMURQlnuwmx2WwmkwlWzvGJ4DvuuOORRx6pr6/P\nZrNTU1PwA0lR1MjICFxnYWFhcHDQ7/fPz8/XDvHG4/HR0dG33357YGAARiWlF+LM+8sgEIhz\nCyTsEGeIxx577LXXXnvggQd+97vf3XPPPffcc88LL7zw3//930tLS1//+teFa8rl8i9+8Ytr\nuYE9/fTTGo3mYx/7WF9fH1+gVoN8Pv+3f/u3J5rIKxQKR48ejcfj+Xw+HA4PDAzUtrQQodFo\nenp6TCaTWq12Op3d3d21A0IwVytdLq33F1aJSYM9PPX19b29vaVS6fDhwysrKxVP7Kpn22Kx\n2O120cLXX6+/+upL/s//qVxrWE3E6PXrNnlWLpevvYVFCElyV10VBAAkEthLLzl9Pp9MJhOe\nT+kJ2bp1a1dXVy6XkxbnkSRpsViEIToAAE3T6XSa47iFhYUamxWysrISDofz+Xw8Hh8YGMjn\n81arVfirQKlU8vbUPAzDiAKuCARiM4OEHeIM8cYbbwAAbr/9duHCK6+8cvfu3a+88opw4be+\n9a033njjpz/9ae0NLi8vv/766x//+MdvuukmsFrQLhqN7t+/3+VyPfrooyd65NFoVHgvz+fz\nsBF17RiNxu3bt19wwQUdHR21eyFhc4B0uUKh6OrqEuVwzWYz/3eN1DCO4wRBDAwMrHr7hwKl\n4hGGw2G/3y+SUDfdxNA0ZrdXGMB1BpDJZE1NTUIruxNCozmmL6GKrr0RtVpts9mqVVXSND03\nNxcIBETLSZKEMVThQp1O53Q6HQ5Hd3e31WrllyuVSuEoM4ZhotGoVqvt6urSarUkSZrN5u7u\nbqG+Z1l2bGzs0KFDhw4d6u/vrzgJDYFAbDaQsEOcIeCN8+jRo6Llv/zlLw8dOiRccvfdd3d1\ndR04cKBGFAoAcPDgQY7jrr/++quvvlomk9UWdiRJ7t2790tf+hJUgSeEND53EkpiLTEViqL6\n+vpEzRYAAJfL1dvbOzU1JTwSp9MprLGThtN41Gp1sVhctYcUvD/hqsahchzHK0i1Wu10Tvz2\nt+9edtkJtN+eHDqdTtQ0iuP4nj17ZmdnT3pS7XvvHZPFV17JAQBEM+JkMhk/h0Oj0cD21do2\ndaVSSbgFvV6v1+tlMpmoj9VisXR0dHR2dloslq1bt3Z0dHg8npaWltbWVtEG4eW2Wq179uy5\n+OKLe3p6RP7JCwsLsGkDAJBMJsfHx0/4LCAQiPMO1BWLOEN86lOfevLJJ6+44oq//uu//vM/\n//N9+/bBGqy6ujrRmnK5/OGHH7700ksPHDjw2GOPVdvgU089pdForrrqKrVavX///pdffnly\nclJ6d4QYjcZ7770XAHD48OGf//znFdfJZDLSPBcAwGKxzM/P86JKLpef6CSxiYkJ2BfZ1tYm\nk8mi0SiO406nU61W86tRFPXuu++K5BdJku3t7TabLZfLiWyHhZEbjuOCwWDFvRsMBpvNVq3i\nXtQqSxDE8PBw7bej0WgsFksikYAZ7ZrDUdcN6fHDYNXJjsfA/vhH6+yszu0u3HjjAsfFM5lt\nOp1u69ats7Oz0IampaVFp9P5fD6apvliQY1GU2NiGDhe8fM54o6OjuHhYXioJpOpvr4ergCH\n/6pUKmhtwzCMQqHgo244jgvjeRURtWIkk0mWZVHPLAKxyUHCDnGGuOaaax5//PFvf/vbDz74\n4IMPPqhWqy+55JIrrrji5ptvlt7ALrnkks9+9rM//elPb7vttgsvvFC6tbm5uXfeeefTn/40\n1EbXXnvtyy+//Mwzz3zzm988ucP7l3/5l29/+9tqtdrtdrtcLo/H43Q6vV6v0+n0eDww6MUw\njF6v9/l8UFRRFFUoFGp3yObz+ZGREXi/L5fLQtnk9/u3b9/Om56EQiFpUE2n08GwnHQXQmEX\niUSk2WGlUtnc3Gyz2ZLJpLQZxWg0WiwWlUrFHx4AYC3RL97J76xTcfZDbd4XstyHP7zy4Q8f\nG91LUWB4eHjfvn1Go9FoNJZKpUKhsLKyotVqcRznVR3HcaOjo7XLK4XCLhQKWa1Wm81mMBj2\n7t2bzWahgwx8dnZ2lq+9s9vtXV1dBEF0d3dPTExkMhmO41iWnZ6e7urqklXXziR53Bc4juNI\n1SEQCCTsEGeOv/zLv7z11luPHDny6quvvvrqq3/6059eeumlb37zmz/72c8++clPila+//77\n/+u//usLX/jCkSNHRDcwAMDTTz8NALj++uvhw2uvvfbOO+98+umnT1rYXXzxxVdeeeXS0lIw\nGKxY5QYAsFgsvOZTKBRKpRJaAV944YXt7e0Vhxn4/f5qeVuWZRcWFrq7u+HDisEnvn9WpVIZ\njUa+VRbHcYfDwa9WUeKQJAnzs3Nzc0I5gmGYXq+HtnmwA7Ti4Z1/qFSqasPWisViLpdbXl5e\nXl6GS2CWU6lU2u12t9uNYVgmkxG1R/CvLZfLUAWKTubk5CTU5QRBCG0Lc7mcsKMiEono9Xo4\nEU6pVPIaPZFITE9Pd3R0VHtHdXV1wnIFOFyYYZh8Pi+Tyao5HiMQiPMbJOwQZxQcx3fv3r17\n9+4DBw5ks9knn3zy7/7u72699dZ9+/aJcrI2m+3f/u3fvvjFLz744INf/epXRduBwm5mZubH\nP/4xXGK1WoeGhsbGxmrcCGuwf//+/fv3w78piorFYsFgMBAI8P/Ozs4GAgG/318tWWkymVwu\nV11dHfy3qanJ5XIVCgUcx81mc8XOTWG1u9FolLrrCdsjtm7dOj8/n0ql5HK51+uNx+NjY2MA\nALvdXvEWbjKZMplMIBAQBdg4joPWuHCQxprOjoTadscbk9pVhlIjG4qiKIpKpVI0TTc2NlZL\n+953332vvfbae++9x7KsKORZKpVompb+LJmYmBAtmZ6ehn+IPie1XW+sVmtPT08gEGAYxmaz\nuVyuRCIxOjoKD9XhcHR0dNRuGU6lUjMzM/l8XqPRwOxzjZURCMQ5ARJ2iDNBOp3+3Oc+94lP\nfOKWW27hF2q12i984Qu5XO7rX//6n/70pxtvvFH0qttvvx1mbz/96U8Ll09OTsImjAMHDohe\n8swzz/zTP/3TKR6tQqGoq6urq6vbtWuX9NlCoXDo0KHx8fF4PB6NRuG/NE0vLCxMTU3x08OE\nyGQyvV5vOR6Xy9XR0QG3EI/HZTKZyWQSxt7UarUwzCOTyfjJBMKhFNlsFpZzCZWWVqs1m83v\nvfee9GAyGVKnowEAwsLBE0Wk6mD7J/+wWCRlMoYgNpbyq92/UiMNHQqFGhsbdTpdxQI7qJzs\ndrtOp4NDxnhkMplU1aVSqRqGO6ITK8zDlkolhmGUSqVQq5nNZl79w2QxL0DD4bDBYIBhvIpQ\nFDU4OAhPSzKZHBgYuOCCC2pkfhEIxDkBEnaIM4FOp/vVr36VTqeFwg4Cs40V52LhOP6f//mf\ne/bsufvuu4XLn3rqKQDAz372M+HW/H5/Q0PD008/ferCrjYqlaqlpUV0/7v44ovhLTyRSMAI\n3/Dw8Pj4+MrKClR+sVhsenp6cnJSukG5XC7SfDabramp6UMf+hDLsjDDm0ql5ubm8vk8nFEr\nGoclnHYPyeVyFQdXhMPKRx9t+qd/GgWVun1PGpqmhcpyaAod6lgAACAASURBVMiwZ0+tjuZz\nCxjqUygUra2tvAWxRqOBNXOwkyaVSs3Pz4te2NLSIt1atXRwRTweDwCAYZjR0VHYLg1bdIVt\nN8Iti8KKqVSqhrCLx+NCsVsulxOJRI32agQCcU6AhB3iTIBh2A033PDEE0889NBDf/M3f8Mv\nT6fTDz30kFarrdghAQDYuXPnl770pR/96Ecmk4kPXz399NMqleq6664Trun1ei+88MLDhw+P\njIx0dXWdvvcCAHC73YFAgE/teTwePjBjMplMJpPD4SAIoqenBy6Eooem6VQqFYvF4LyvaDQa\nCARGRkZisVgsFquR4XU4HBqNxmw2W61W4b8Oh6NasTzHcZIMLHjlFReGsW73sYRj7QbPE0UY\najqnVR1BECJbEwzDGIbBMGxlZQU+pVAo2tvb9Xo9y7JwydTUlNFo5F9iNptbWlpE7iSQigsr\nAh3vAAALCwu8CU4ulxsfH9+5c6d0femoj2rDPxAIxHkMEnaIM8T3vve9paWlu+666/HHH9+5\nc6fNZgsGg7/61a9isdjBgwdrGIh85zvfefbZZ0OhEBR2Q0NDo6OjN9xwg3Qw1w033HD48OGD\nBw/+8z//82l9LwqFore3d25urlQqORwOaZBD5MAHRQ+0/7VYLDKZTCaTORwOl8sFfZsBABRF\nCXO78XicZdlYLBYIBObn56UxObhBg8FgqY5w5VKJuPzyDyxR1Gq1yWTiewVOCKn0OZ+QvrVy\nufz666/rdDo+hUpR1NjY2M6dO48cOQJzuKI8L0EQ1QScTqfzeDx8PaVWq62WBeaHI4sq7dLp\ndEVbE5Ik3W43f01JkoQBv2qYzWZhDl0ul1cMnCMQiHMLJOwQZwiLxfLb3/72hz/84R/+8IdX\nXnklGo3W19dfeeWVX/3qVyuGH3gMBsMDDzzAGwvDtomKPsPXX3/93Xff/fTTT59uYQcAkMvl\nLperVCoJ+xt4pB2yXV1dgUAAltCVy+VyuQx1ocFggHJBoVC4XC6Xy8W/xOv1wlzexMTExMRE\nrAo1MrxwtqnFYtFo7G63kRd8Dodj69atGIbBKRprUWk6nQ4O3crlcht2gBVBEDqdTiiD1isq\nybKsqDAun8/Pzs5WFNwAgIrTO4rF4sjISDabxTDMZDLZbDadTse3TUAwDINjxFwuF5RZLMuK\nTjhJktUitT6fz2AwJBIJuVxeV1dXuzFWoVB0d3fDdwGbJ1CBHQJxHnDutbZtBFKpVLlctlgs\nJzGk8rwhlUpptdqKHh+bhHQ6DYWd9C6bzWb7+vp4SWEymbZv3/7222+LdABBEL29vdPT0/F4\nHMdxUdsmjAXq9fpUKlXDN5ggiGQyGQ6HAQBTU1ORSAQKvng8nkwmI5FIxf/jer3eZDKJcrs1\nMrx6vd5ut4tUyAakvr5eq9UuLy9ns9nTGlbEcRxKqHvuuee11147ePAgb8eI43hPT48wMwsA\nYFn2rbfeEvZBw0/F0NCQcNYIjuOdnZ3CmSKjo6Pw4vJs2bKlsbFRekgMw8zNzcViMYIgoB2j\naAX4wSAIwuVyVazSO2nS6bRGo9nM3waZTIaiKJPJtJlPQjqdVqvV0oahzcMG+Rhs3guAQJw+\ntFptT0/P4uJiuVw2GAzwNiz1y+A4Ti6Xw3FVHMe9/fbbwsr6cDgcDocJgujo6IANE1CiQQs9\nPoAkl8v3798PhyJEIpGxsTEoKFUqlc1mm56ezmQy0L1FmOeNRqMrKytCNzUhOp3O5XJVTO9W\ns27ZIGSz2ebm5mKxWKPzdF3CeBaLBd7A/v7v//5rX/uasDCAZdmjR4/u2rVL6B6Sy+VEs1wT\niQRN0x6PRyjsWJYdHh52Op3QtYemaZGqM5lMFVUdAGBycjIUCsG/x8fHRWaHi4uLMzMz8O+l\npaUdO3ac0AAVBAJxroCEHQJxSpTL5YWFhXQ6LZfLGxoa+A4POMZAuKbBYBDV3gmjfRiG9fT0\nTE9Pp1IpDMN4FcgwzPj4+EUXXdTY2JhOp3EcV6vVfGUeAACOSdDpdOVymVd1cHksFuNbboXD\n1uRyOdy+sLAvEAgIM7wzMzPVMrw6nQ7atYg0n91uX984EAAgFpNbLCcwNwwOZl1ZWamxzlpU\nXTWjPl4UrqyswFpJAIA048lx3ODg4Pbt2/lKu4pbY1nWZDLt2rVrampKODskFAo1NDSo1Wpp\n0LFaJIDjuEjkuKG94XBYJOyE+/X7/ae7xwiBQJwVkLBDIE4ejuNGRkb44FAikdi9e3e1qnmf\nzwcnHMCHVqu1ra1NuIJKpdq2bRsAYHh4WChNaJouFotw+AQAoFAoiO73MMObSCREkgXaI0t1\nTKlUgsJFWtjHw7IsRVE6ne7QoUORSCQej0PrlpWVlVgsNjk5WVH2Qbs+m81mNpvhWI6PfvSj\nJ92bOTOjfecdy403Vg4rVkSpVE5MTKxlNlptaswL4f8ul8tQy0IfY1GevVQqvfPOO1arta6u\nzmw2a7VaXk9DNBoNPDMwLS4aClcsFtVqtUKh0Gg0/GcGAAD7aaQTlqUHLFzCMIwo0S8KHyIQ\niPMGJOwQiJMH3tH5hyzLRiIRvplRhEql2rNnTz6fxzBMpVLVSGjCsBMPjuPCYnylUinqS4VS\nUnprJwgCTh2V7gKW7tV4aziONzU1Qekp7ZYolUqxWCwajcJoXyQSSSQSUP8FAoG5uTl+Ta1W\ne+mll9bYUQ04Dlx0kSj2hgFQqyyYzzae0F5OOrecyWS8Xm+pVKIoqmIjBTxFRqOxp6dnx44d\nQ0NDcDWdTgdT8BDRyAccx/ncbldX19DQEJ+j5zhuamrKarUK5TLLstFoVK1WCxWtsFCPIAhR\n+63Q+xqBQJxPIGGHQKyJQCAAa+ZMJpPP54NKS6qlahfsYxi2Fhszr9cbCoX46M6WLVvgHFIM\nwyKRyOLiIoZhfK5Qo9F4vV4gEQcAAIvFwhddiXC73fl8vvaUrWAwmMlkXC6X3+/nFyoUCoqi\nYFNwxVAfACCbzULZVygU9u7du+r7rUZLy3GBN5bFcHz9m71OpWKwUCgMDAx4vd7atsOwu8Vu\ntzudTjgpxOPxCFtQbTab0+mEFwvH8ZaWFl63aTQam80mSqRms1m+HZum6b6+PqgX4QdDJpO5\n3W5RVK+jo2NoaAiOTTMajdUK9RAIxLkOEnYIxOpEo1F+vufKygpFUXDamFKpVCqVwhmjIve4\nkwN6VSwsLHAcR5JkLpd7/fXXy+WyaHIXhCRJGPyTjqivVmoGy+q1Wu3U1FQ+n4e7qLhmNpsV\nBfzW0mqq1Wq1Wm1DQ8Oqa54Qp0PVnTo0Tc/Pz69qL5DNZpeXl3nX6Egksnv3bmHBXEdHR319\nfbFY1Gq1IrcUaQ2fMKbr9/v5YCFU/3v37pX2NWu12gsuuCCXyxEEse6lkAgEYuNQ2QwJgUAI\nESmkdDoNS5QwDOvq6tLr9RiGyeXytra2dbF4TSQSvFagaToUCsHQWkUDuVQq1dfXt7i4KA0a\n1RBhLMvOzMwkEgmKooTCVEokclxUb8Oa2J1F1mgaJZwFks/nRZ00AACNRmOxWKQeeE6nUyjF\nXC6XUNiJUsAsy1a7oDiO63Q6pOoQiPMbFLFDIFZHWg/HL9Fqtbt27ao4CeCkEU0aWJVcLjcz\nM7N2FxKWZaHjMXxYOwj385833Hjjgl5fK2l7bsGyYP2uFXj00Uf7+/vvu+8+URO0iEAgIFoi\nbV8ol8u5XE6hUIiKLAmC2L17dygUoihKr9fzhnmpVKpUKomEIEEQtX2JEQjE+Q0SdgjE6jgc\njmDwg3lcJpNJLpcL4yKnrurgCHY4k6C2W0c1qsWNTCZTsVgUxfOk4SIehsEI4tim4nH5L3/p\n9vkyH/lIuNr65xzrqOoAAMFgcHJyctVAprSFhY+0RSKRYDBYKBSKxSK8iE6ns729XajUoeew\ncGsDAwPwBwDsrYEyEcdxn89X7dOYy+WWlpYoijIajR6PZx1/iiAQiI0DEnYIxOqYTKauri6/\n3w+bJ5qamtb+2kwmUyqVdDpdDdePVCo1ODgIxYFMJqvd0wBOcFornGMmooYQOXzYStN4V1cq\nGlU++mhTqYSTpBbDKk+wgHAchmEbsQDurLBGA2SorUOh0NjYmOipUChkNpuFLnQilpeX+bAu\ny7Llcrmrq4thmGKxuLy87Pf7rVZrY2OjULoVCoW+vj74sYnFYul0WtiWi0AgzhuQsEMg1oTd\nbrfb7Sf0EjhFAN6/CYJob2+vtoXp6WleaUlVnd1uz+Vy+Xwe9lK0trY6HI5EIhEIBESetCKq\nueyCmmVh3d1pkmS12rLDUfyP/+gfGDB97GONHk/vkSNHqinOTavqCIKAmVCLxSKXyzmOs1gs\nOI5LE69SaJrmOE4YCRaSSCRqCDuRUR/Lsmq1OpPJzM/PwyW5XK5cLguNEoPBoPDHAOwBqjjT\nFoFAnNMgYYdAnC7gLAf4NxwgAee7S9esbairVCq7uro4jiuVSnK5HGboTCaTyWTSarXz8/PV\n4kMnNwnaaPyg9gvDuO3b48vL8XzetGoccRNit9uhIo/FYna7vbW11eVylUqleDwOM/U1tLXJ\nZBKOGBHBx1nhaJNUKiWXy71er1KplMlkoiI8DMOUSqVokm8kEmltbeXzudLLVy6XkbBDIM4/\nkLBDIE4Xwi5IAADDMPF4nK98FyKylhUBDeowDJPehhsaGtxudzAYDIfDpVKJpmk+KuN0OsPh\nsFBV1BAZq1Ixn4sIh8P8CYctKYFAAArxfD5P07Rerw+FQisrKyIBR5JkJpMZGRmptuVisbiy\nsmKz2YaHh/msK5wqi2GYx+MR+uw0NDSQJCnS96JrbTKZhHFEuVyO2mMRiPMSJOwQiNOFtDlx\neHh469atUm3X3Nw8NDQEb8wkSSqVSl7nQX/aGnshSdLr9VosluHhYVhBr1Qqt2zZ4nQ6U6mU\nsGfipFUdAl6yYrEozYEKH3Icl06n0+l0PB7fs2cPvI6hUEhaEEnTNE3TkUikRgeD3+/XarXS\nFmmO4+CkV5qmS6USP5XYZrMJp4lYLBZh+4Xdbs9kMn6/n+M4pVLZ2dmJ43ihUFheXoaVow6H\nY+2N1QgEYsOChB0CcbpwOBx8zROE47iZmRmpsDObzb29vbFYDMMwg8Fw5MgR/qlisQgHxcKH\n1XxVJiYmeJPhYrGYyWScTqdGo6k9EeH0wbIYOD2WwmuPO4ZCCqdzHSaiYhiWz+crTgwDANx6\n663XXHONaEIXTdMrKyter3dhYaF2m0uNNot8Pl/jtalUyufzCZd4PJ5yuRwMBlmWtVqtomcB\nAM3NzY2NjTRNw9BvPp9/77334C5CoVA6nW5tba1xqAgE4pwACTsEYp3J5/PRaBTHcRgyEamQ\nQqEAxwOIXqVSqTweDzg+uwcAYBgmkUioVKp8Pj8+Pp5OpwmC8Hq9wpFQNE2LRsiHw2GKokSV\nWEJwHG9sbCyVSizLptPp2kV+J8EJSbpYTP7b37pyOfIzn1nQaFbxDVmjqqMowm6vXL52onAc\nV03VAQCqtUjDiyg1q1s7KpVKo9HwViYihBPJIBiGNTU11W7ZJgiCH3exvLws/KQFAoHm5mbh\nMAwEAnEugoQdArGeRKPRkZERGIaZnZ01mUyiSV9arbZ2wksakINLhoeHYUyOpum5uTmVSuVw\nODiOm5iYkHZWlstlaIYHOytXVlaEkSGtVrt161Ze9nEcF4lEVlZWTs4/rzYcB4pFQqWqGnkK\nhZR33LE7k5EBAA4e9P7DP4zt378OnnkKxVrtYE6CilFDkQeNxWJhGEYakBPNoOORmqT4fD4M\nw5qbm0dHR6Urx+PxZDLpcrlqNM/WRlT2B7tzavwYQCAQ5wTIoBKBWE+mp6f52zPDMBiG1dXV\n8UpOJpOtmu0ymUzCJgm5XK5UKo8ePSoa5wr7bf1+fzW/DEg0Gm1paRFFcXw+n/D+jWGYw+EQ\nzYxfF2ga/8Y3tn/84xffeuveb397azBYQTQ884wXqjoAAMNg997bUSqd+vfS6a0Vqxg1ZBiG\nJEkAgFwu7+jo0Ol0ExMTogYaAECxWCRJUq1Wi/S9VqttbW2FATOCIDo6OvR6PQDAarWKomhK\npZJl2VQqlUgkRkdHRc2wIjKZTDgcrjgLWJQ+VigUaGQFAnEegCJ2CMT6kMlkRkdHRTVthUKh\nu7vb5/Mlk0mWZY1GI7z314AkyR07dszNzWWzWY1G4/V6h4aGpKYY8GYvbVYVZe4YhnnjjTfg\nQAu5XE6SpMvl0ul08Xh8aWmJpmmTyeR0OpVKpVqt1mq165uTffVVe1+fCQCwtKRaWlIVCsS9\n9w7CsRa//70jlyNzOeKll1zCl9A0FovJXa5a42vXwNlpE6FpeteuXRiGyWQylmXD4cqhR5qm\nnU6n1+vt7++H0Tu5XN7a2qrT6VwuV7FYVCqVfOCWIIi2trbx8XH4g0Gv14vE4tLSUkNDgzQz\nCwAYHx/ndX99fX1zc7PwWbfbnU6n4UEqFIquri7UPIFAnAcgYYdA1IKiqGKxqFarK944eTiO\nGx4elqbYoFMJjuNms7liaV1FVCpVZ2cn/Bv6mEjXcTqdQDIAHsdxt9s9OzsrOjaO4xKJhNPp\nhPHCZDI5MDAAn02lUvPz8xwH4KGpVCp+sNWpk0h8MGxj+/bEd787wD+kKPwHP2g1GMrNzdnh\n4Q9CRyZTaV06Hs4WAwMD0NnOZDLVWA22PlxwwQXJZDKbzcbj8ZGREZ1O19TUpFarC4VCJpPJ\n5XIEQTgcDofDYTKZYHnlyMiI6OpwHJdMJmE1njC2l0gkhNHcxcVFh8Oh1Wr5JRiGdXZ2Njc3\n0zStUqnQhDEE4vwACTsEoiqzs7OLi4scx+E43tLSIhzWKQIO+hQtVCqVTU1NHMfNzc0tLy+z\nLGs2m9va2mrMFpNSUWPZbDaDwRCNRkU7tVqtdrs9m81WnEixsrICJ5BKs7e84CwUCrVnmhmN\nRpEBR7WiMQCATPZB0dgnPnHcMIbLLgs/84z3P/6jX6FgvvnNbhjY0+nK/+N/jG20ORZQaa1R\n7PITRGo7/2UyGTj4QaVSDQ8Pw+K8QqGQSqVkMpkwbrqwsLBr1y61Wm21WqPRaMVLA9UejuNb\ntmypr6+HC6XB17Gxsd27d4t+XSgUCmRTjECcTyBhh0BUJplMLiwswL9Zlp2enrZYLNWKkKQJ\nVqvV2tXVheP48vIyv51oNMqybE9Pz9oPw2QykSQpGu0KA3XQrlZIJBKJRCI6nW7nzp39/f3S\n0A68qdc24Kg9YUKq6nQ6XTVhd8UVoeef9ywvqz75yaVLLjmuM0MmY7/0pSmdrgwAuP/+ozMz\n2kxG1tqaUatX6Yo989RoiQUAvPjii1NTU7fffrswGLYWUqkUSZKBQEB4OSiKEvXA0jQ9Pz8P\nI7jVIr7wQrMsOzMzYzAYYPGctA0im82Gw2EY60UgEOcrSNghEJURGYhAW5Bqwk4ul9tsNr6r\nFMfxpqYmmNsSya9EIsEwDEyZlctl2IcozYIVCoVwOAwNybZt2zYwMCBsmSRJcm5urloJVyaT\nmZubczqdosgcLwuMRuN6NcAWi8WKQhDHcblcDkDxscfeDYUUDQ1ibYRhYPfuD2Jazc3r7Ldy\nxujr63vttdduvfXWExV2S0tLQj/hGvC6Wa/XC2so4QUVyfdkMgmFncVi0el0ooK8E62hjMVi\n09PT0EmxtbUVOiEjEIiNDBJ2CERlpAlTUcZKZE7R2dnp9/uTyaRMJvN6vRqNBi4XiTYMw+D9\nmM/zymSy9vZ26Fqcz+cnJydTqRS/8YWFhc7OzqamJmHzI8zZ1Tj4VCrV3d0tytzBd8QwzFpG\n1K+divE/lmWhHFEoGKmqQygUijWqOgCATqeD88rgjDiZTAb7ah0Oh9QJhQ8eQ7NrkbBLJpPF\nYhF+JlctCcjn88PDw/CjmMvljh49unv37hPVrwgE4gyDhB1iM7KWPgabzbawsMCn4UwmE7Sf\nAACkUik46YEkyebmZmgUguN4Q0NDQ0ODaDsul0sYtHM4HDiOR6NRPj9bLpfHxsb27duH4/jg\n4KB0VsTs7Oy+ffsUCgWcQKVQKBYXF2sfPI7j77zzjkhy2e12AEAikahofiGFYbD5ec25G0vb\nyEg/ftUmami12sbGxsXFxVAoBJeUy2WtVtvd3S3NEWMYBq8yAICiqKWlJdEKmUyG/2CYzebO\nzs4aXUHxeFz46wWaJu7atWtN7xCBQJwlkLBDbC5KpdLExEQsFsNx3OVytbS0VFN4BEHs3r17\neXm5UChotVqXywXXpGl6aGgIRsJomp6YmNBqtbzmkwKL7ZaXl2matlqtUPmJojU0TWcyGZIk\nK04AoyiK4zi73Q7v2X6/f9W3CUeR8g8VCoXT6dyyZQtYrYROSDZLIlV3mpBWJfp8vsnJSf6h\nTqezWq1Go9FgMGAYJrK5hu45arVar9cLawZ8Ph8v1KqVBvJyPx6Pz8zMtLe3VztI6X+NszWh\nDoFArB0k7BCbi4mJCRg/YxhmaWlJLpdLY2w8BEHwPYY8mUxGpI1isVgNYQcA4DUZjzRMUiMv\nptPphLdYka/s/2fvTePbqM6+/zOj0b5Zm2VJ3rfEa5zYcRxIIECBQlgKtPBAKLQshVI+EChr\noS0p0JYlbOVOU1pKgaRA/sDNzR2WPwQoSZoAcbxv8W7JtmzLsvZ9NPO8ODzTYUaWZcdOHPt8\nX0lHR6MjaaRzzbX8LgzDlEols7tjGCYWizl2Q1ZWVlZW1nRPZxxFHKeRQpGqCYiYA+xOFRaL\nxWKxKJXK0dFRmFiZ/IQRCATwlKisrBwcHJyamiIIIi8vT6vVMnNkMtmMa0heuqvX63t6etin\nBOpLgUAsfpBhh1hG0DTN8Xw4nc4khl1C+M0059BeMyMjw2azMQJ1Op1OLpfTNM2XCBaLxStW\nrGCPqFSqgoKCgYEBiqJwHKdpmu2zgY2hOC/H9t7JZLKVK1f29PTAxhjsbZsTCsRx7GQp/S40\n08U9E0IQBEVR/OZgYPoy1VSABpxSqRQKhdAgU6lU010hmM1mdkCfaWciFAqLioqYcZqm+/v7\n7XY7TdN6vT47Ozt51D55mp1YLC4oKGCSO6GcSsrvD4FAnByQYYdYRmAYxunIOStRVqsVvPoq\n8PuV55+vwLBvzS+CIDjOlVQQiUQ1NTXDw8PhcFilUkGFPAzDKisr+/v7PR6PWCxOT0+Xy+Uq\nlYq/yOzsbLPZHAwGm5qa+AYHfwRWZjCYTKa0tLSBgYGEcneQI0d0EglZUZFqgv+pxawUmDla\nM5zjnH766RaLJRX3GP+5k5OTTqcTLsZsNhcWFsKIPL9AQafTrVq1Cmqj6PX66fq/DQ0NMZbc\n2NhYRkZGdXV1d3c3v7MZJDMzM/kis7KyNBrNxMQEzN5jSoIQCMSiZRaXrQgGj8cTi8V0Ot1y\n7sDj8XgUCsUcnFUnl56eHnZGeWlpaYo91Ds6wLp1AHrTNJroX/4ymJPjJgiisLAweRx24QgG\ng19//XXCh9j2K6zDValUxcXFcGOOx+P19fXJ5dmiUfzpp1decYVtxQofTQOXS6TVJmiAgZhH\nmOCsRqOprKxM/arD7/f39fUFAoF4PM42QwmC2LhxYzAYbGhogPkDGIbpdDocx2GDYJ1ONy8r\n93q9crn8lPs3mEeg4rRGo1nOH4LX65XJZDN2TVzCLJLTYPl+AYjlSUFBgUgkmpycxHHcbDan\naNUBAJ58EjAxUpdLdNNNxUNDXpKMzqj+QFHU4OCg0+kUCAQWiyX1V5wR2FE0YYgQ9qWFYsKw\npZjb7W5oaCgvL9doNIODg8mtOgCASEQ9+GBHX19aS4shN9eNrLoTAJNy53K5bDYbJ0mApumh\noSG/36/RaJhQLAAgFos1NTUlrImBc2QymcFggBo30E1YUlKCZIoRiKUKMuwQy4vpRElmhFOK\n6vUCjwdLJTDV3d3NCAV7PB62IMV0RKNRl8uFYZhWq01y+YvjeEFBQU9PT8JHOS0iAAAkSTY1\nNfHT+KYDw0BhIfcg352AEQSRepntdIdZqpl8xwNHHzsejx86dAh64xwOx/DwcG1tLYZhk5OT\n3d3d030FBoMB3uDE3GGUdmEWjkAgTjLIsEMgUqK2Fnz++X/uFhYCjYbmVSlwoWma0x9ibGws\nuWHn8XhaWlrgFi4SiVavXg3ztyKRyOjoKEmSWq2WiaBlZmampaWNjIxwNIfZFZccUu89MDEh\nfumlwrY2lV4fvfbawbo6J//dxWIxGOpN6DhMjVlYdRQFrFa53S5xOsXxOGaxhGpqpmZ+2qkA\np56Do4bd19fHjrEGg8HJyUmpVNre3p7kk5+amiJJUiAQcOZwupYlgaZpm80G5YHMZjNjKSIQ\niEXLLDLHEYjlzK9+BU4//dvb6englVdSehYMg7JHZjSAent7mS08Go329fUBAEKh0DfffDM4\nODg8PNzS0jIwMMDMVygUBQUFHDugqKhoDun8310n9tBDlZ9/nj4xIenoUD3ySHl3d+JUQpqm\nj8Oqmx0ej+iGG2ofeqjyzTezMzODVVWuE/XK80nC5Dl2uhtBEIw8DYRvjns8Hth6OMkLhcPh\nnp4emFfHHg8Gg5x2c9PR19fX19fndrunpqba2tqSVNskxO/3T05OTtdKGIFALATIY4dApIRS\nCQ4cAI2NwOsFa9cCuRx8N1aWGBzHtVqt0/kfX5der4cuEIFAYDab+dnrnLYQ8K7NZmM7bKxW\na05ODmMfsGVjCYIoKyvTarVqtfrYsWNut4ckAUHMOtY5NCTr6/s2fVAspu6881hhYeLKyuRM\nlwU4NwQCGgCAYeB3v2vLzz9VxZP5HwiGYbFYzGg0CoVCoVBoMpk4lrpcLueIWqelpaXSQQSe\neytWrHA6nezX7enpCYfDGRkZyaXpOPaf3W5PvQa8o6MDuqthzsCMFbgIBGJeQB47BCJVMAys\nWQM2bQKz0nxYuXKlwWDAcVwoFObk5IRCod7eXpfL3qhQLgAAIABJREFUNTk52dLSwrb5IBxP\nGxOHZQ9SFMUWq2tra2MmkCQ5ODgIn1haWjo+ro1GBeHwrEu0aPo/Fd8//Wn/978/huNzyYSb\nX2eeQEDn5gaMxvDiseqOHj26d+/e43RK0TTt8XjGx8djsVhubi6GYcFgkPH1jo+PUxTFrsGX\ny+V6vd5gMMxYfAetf6FQyCnhj8fjg4OD33zzjdfrHR8fb2pqamhogM2L2avifH3Txff5OBwO\nJgmBoqi+vr7U478IBOJ4QB47BGJhEYlE5eXlsDstTdMHDhxgPzo6Ospx2hUUFLS2tsIdFPai\nBTwhXLFYLJFI4O1QKMSxKqAcj1AobGtrMxpT8CsmIicnkJMTHBqSKRTk+ef/x21DURiOg5NV\n7iCXkzt31r/++iKSyf3f//3f/fv319XVMd/I8TAxMRGPx6EWsVQqLS8vHxsb4zeRgy3jZDJZ\nZWXlwMBAOBwWi8Uclx6EKZIQi8X8UmiKotgqdx6PJxqNFhYWwrsYhmk0Gva1R+ryKBzlPIqi\n/H4/xw2JQCAWAmTYIRAnAmiZpeIC0Wg0tbW1U1NTGIbp9XqhUDg8POxwOJgJAoGgpKSEuZuw\nifs333xDkuTxeMsEAvrxx1v+678Kr7nGqlZ/GwVua1NnZYWEwrjdLlUoYkbjSfDBiETUjTf2\nnfjXXSA4oWooRwJvh0Kh9vb26doHu91u2Ex29erVoVDIZrOFQiFO0xGxWMz0irBYLAkLqDnW\nnt1uZww7AMDKlSu7urrg2WgymWCHPWilYRjm9XrdbrdQKExLS+NoF/PNXNSODIE4MSDDDoE4\nceA4rlar2UIkkUjkwIEDsGUt41yRSCTs1gKcPCehUDg6OjowMKBUKmFsl/9C/K5ic8BsDj3+\neCtzd2BAXl7+rU+ooMB/4IDBYJicW3wWpNDUi6KwOR/8FCK58Z1EbpB5YiQSqa+vT9geQyqV\nRqNR2OBEoVDodDp+6B/HcfbVBUVR0LsM7xIEodPppFKpQqHIyMjAMMzn87W2tnLiqna7fc2a\nNUqlkhkxGo3Dw8NMFmBGRsZxVvMgEIgUWWqGXSQSSbjPzS/wfzBh4GP5EI/HvV7vcu69wZwG\ns/oQLBYLSZJ+vx/HcRzH4c5NkmRnZ2csFmNvjQwclbJwOAxjrx6PZ2pqit33fUGRSr/jXKyu\ndlmt0tzcGYSOp2PGnjfLwaqbMziOw2IIiUQyMTExXdMzpVIJfbcAgImJCbFYXFRUFAqFYDIf\nAEAqlapUKrYij0qlYv7ZKIrq6elhjMvR0dGCgoKuri5+thxFUVarFXbGYygqKnI6ndFoVC6X\nc65nlh7w3wD9Jfp8vmX+CYATchrgOJ6k49FSM+xEIlHyttbzgtfrjcViKpVqOZ/BqImQz+eL\nRqMJe7kmx2AwxOPxcDh85MgR9ngwGORXDsLeoCMjIwkPFQwG56srFIcZPWrxOCaXp5pKj5hH\noGrg2NjYxMREaWkp3+cnFAplMpnRaKQoim3zRSKRsbGx4uLicDjs8/mkUmlhYaFcLicIwm63\n0zSt0+lWrFjBBPcnJibYLkOv10vTdJIyEbVazRlJS0s73nd7iuD3+yORiFKpXOZ/iVKpdDm3\nFFskp8FS+wJOpKUFdVlP2MstQpbtJwDT2/1+v0QimduHQBAE/5fPudi12WyDg4MkScrlcq1W\nOzWVWIl3uvHpSLHzBIZhmZmZ4XBYJBI5HI5oNEoQ3zEg2trU69dPJj9IPI5NTEhMpgV3oi8f\n2Dl5sNqUH+KENbYej4ef1ubz+Zqbm6G1F4lEWlpaamtri4uL8/PzbTab3+8fGhrKysoKhUIe\nj4d/nkSjUaFQmDDQn5aWtjz/Ddgs279EBvQJgEXwISw1ww6BWGii0ejRo0eh3wLDMIFAkHp3\nplgs1tvby/SN5XjF2CFXp9PZ29sLb8Pm7mKxmB8Cg7oYs1q/SqWCfpeE0hUej9DpFFksIbGY\n0ul0SqWyr68PLkyvj46PSygK4DgYH5dUVbmSv9DBg4bf/rbs7ru7N29emoYd/Pry8/MDgUDC\nEpZ5RyAQiEQidrZJKBTiJ58wXrqEeSlsHx5sXmcwGFpbW5lQqd1un07WRKlU5ubmdnd3swdh\nlQ/qUYZALBJmZ9j5fL6vvvpqcnLyrLPOUiqVMpkM2eaI5cbQ0BATjaJpure3N/mWRpIkY8zh\nOA7TyWOxWF9fn1AoZBtz7CwCjh8uHA4nzD2naTpJ97CEjI2NTZewv2NH4bvvZlIUplbH7ruv\n0+dr+/BDs9crveACSWZmCABgNH77xpkbCaFpcM89VR0d6vPPHyeIpRmulUgk8Xg8Fotdd911\n7HGKAl6vKC1tHopX+MAI/qyeIhKJ2A42foSdpulAIMBOgEt4OuE4npeXJ5PJZDKZ3++fmJig\naVqr1ebk5Eil0tleXSAQiIVjFobdX/7yl1/+8pdwW/rXv/41PDz84IMPbt++/Uc/+tGCLQ+B\nWHRw9rBYLBaNRpNkdnZ1dbHFSthwDDt2JSw/UDtd5spsNU0482UyGU3ToVDowAHD229/28bK\n4xH+/velDz/cftVV1lkdHIJhYP1659at3VlZ335WUMVvDodatExnYOE4SEuL0jQWCgnk8viM\nNSJs8vPzR0dHk5tuszogAECj0QgEAphCh+N4fn7+wMAAY7oRBJGWlpbcLNPr9RaLRS6XQxU6\nu93ONCZ2OBw0TVdUVMxqSQgEYkFJNel77969t95669q1a9944w04snr1aqFQeNVVV3388ccL\ntjwEYtHB0esSCoVJrDqKovgCEwxSqbS4uDgtLU2j0ZSWlppMJuYho9HItu3g9pzwIHDDnrP0\nq8FgWLduXXFxcWfnd4owCgv969bNLnuPzRVXDDNWHQBgiVl1MxKJ4K+9lvP888UNDZrUnyWR\nSNgKhUnQaDR6vT7hQ+woCo7jaWlpY2Nj0BykaXp8fLy4uFihUOA4rlAoKioqxGKxUqlMkvCu\n0Wi0Wi1zgnHaxXI6lbEhSdJms/X19THKfAgE4gSQqsfuiSeeqKqq2rdvn0AguPrqqwEApaWl\nra2tq1ev/sMf/vD9739/IReJQCwisrOzJycnYfYShmFFRUVJJifPVTAajUajkSMSAZHL5VVV\nVVarNRqNqtXqnJycwcFBlytxWhtFUQaDYXh4eDbv41ucTqdcLjeZTBbLdwzQvLyZ+5AmYblZ\nchxiMWzPnmwAwHvvmbdu7b7kksRFzRykUmmKNpDL5crLy+NPViqVpaWlfr/f6XQSBGE2m7u7\nuxnDi6Zpn883ODhYW1vLLuUmCKK0tLSrqysajeI4DuXu4LM0Go3ZbI5EIlarNRQKyeXyVBrU\nAgBisdiRI0eYrNDMzMzkvxQEAjFfpGrYNTc333PPPRyfgUwmu+KKK/785z8vwMIQiEWKSCSq\nra11Op2wtt9gMCSZjGGYwWBg64RpNJpQKCQQCDIzM9PT0/1+P0EQCbtRqVSq8vJy5m5ubq7L\n5YKFipx2BQAAKFY8OjrKEb2bEb/f39HRoVAorr7a8vLLMbf72yKABcoSWybYbLIzznAQBN3Y\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sZ0zjnnNDU1DQwM9Pb2AgAKCwtzc3NR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T7mAikciRV5cDGGYiCCJ30A3H8dwuQ4/H\nAx02CoVCo9Esm5GnVCpramp4g21tbW+//fYSmRdcJBJJhlnlKa3io7FdXV0nTpxYokbG8sx4\nCj9DyUsikeSw2CiKamtrGxkZ6evrO3ny5ODgYI6dq1Sq7du3V1VVORwOYSbWHCpIIn2g/Qef\nPPR9AAaeF7stYlWz2gw7ZllYztdasaBPYDUdBjqdjnseQVGxHCcaTdNzrRzS6/UGgyHry/Fw\nu90bNmzI7dWbmJjw+Xwul0uv1y+b3EkkEmED0MvPUjjb1nL5F0ShUBQUFNjtdoPBIJPJFHJZ\nobTLSjfzpiWTyaampkQiwTtxotGoz+cbGBjgCkb29/en0+ksp1nCC458ARyyS/6yPi/8SkVF\nxZYtW3i3T+Pj47AsfXZCgh64oY4F/QrMheeeew4ToNPpJBIJe5rfe++9GIa1tLSI3wPkpptu\nWrY3srjA72E5Xysrqy0kn0qlFp5gMSvwpmpWl/vqJpPJhMPhtXxhYA+Dc+tDSKfTNE1LpVLu\nsjEMczgcHo+HpmmSJAsLC8fGxnJnwtlstlgsJuYswHG8oKBAr9d3dXVBVx83oIlhmFwuZ09b\ntVqN43gikTCbzbn7ENA03dnZucy6xJFIZDXVfyzKG1lpH4iCGomTuW5LuMTj8cnJyfLycs6N\nRI3f7wcCXyBFUWNjY1KpFLbggwVJsL6V9wvAMIzP54P+aYqiJiYmYrGYVCot9j6infwdAAAk\nPNiJ26OUPO34dF5eHjexIRgMjoyMaLXaWVeO4wW8STFpaWppNMCFwBP2n/7pn7g+bIZh4OkM\npcihsRuJRLIqk2fdA8TtdsNNDh8+/IMf/KCjo0Mikbjd7nvuuWfWEpazC7woLMOVEcfxHAfJ\najPsZDLZMojtQbkTnU53bl3RFxckdwLlTnQ63cqXO4EwDNPR0QGtJblcvm7dOu5Pg16vLysr\nSyaTCoUCw7D+/v4cu8IwzOPxKJVKoWCYEJqm8/LyJiYmWDEIrnuDYRiTyaRQKKCNaLPZDAYD\nRVFiko2Wv9sEdEvMLxJ9tuBWjC4FK8GqU1AjcnoKZ5LW+JsDms/PNI0gCBzHeb7PRCIxOjpK\n03QikZDL5fn5+S6XKxgMCr9lgiB6e3vhUcc7hrnTcBy3Wq0w5eDkyZNs/mWYuKGOeFOWmYQP\nVVN/AjVfjMfjvIxVHMfFtXTSg9rvgKb/+uiRfb+y+ktKbJl+kGGyxA033HDjjTeygzy5E6iU\nqdFosr6drHvg8tJLL12sM1IzAAAgAElEQVR77bVVVVX/8i//QpLkb37zmyuuuOLvf//7nj17\nFvvdLBpQ7kSr1a5cuRMEArGaGB0dZX1giUSitbV1x44d3AkEQbDJbRaLJUcCE7ySxWIxDMPy\n8/Pj8Ti8WEokkvz8/La2Nt61c3x8nHf14hKNRhmGge2YJiYmVCqVy+VaxADrIrqUYI78Mr/o\nAlmi5rxL+gZxHJ813sRFAuLG5PE0punVfiWFG2ealslksn4a7F1EIpEIBALr16/fsGGD1+vt\n7Oxk50skklQqNeu9BI7jFRUV8LoeDoe5VTVpTOOV7y6IvvTRYwwD2aSIsooTZcf9TZB/NZj+\nEKhcwLZnlWkc3nfffTU1NUePHtVoNACA22+/vby8/Lvf/e5KNuxWCMiwQyDWCryASCKRgC6K\nrJPVanVRUdHQ0BC8uM50FWcYRiaTlZeX87blWT8jIyM5roihUIg7PxqNtra2inhDYoGX5EXc\noRhkMhmvUe/ZYinML6lUuqQFtmJ8sRKJRCqVJpNJDMPSitpespw3QS6Xz+8r6O3t1Wq1NptN\npVL19vZGo1GVSlVaWpo7N8But2u1WoPBwGrRCR2lFMbpxll4PQAAtp0YHh6GY06nc24d2PVu\noHfPYf45AhQ//8///E9o1QEAbDZbdXX1WUxyPYdAhh0CsVbgSXVgGJZb6b6kpCQ/Pz8Wi8nl\ncgzDJiYmYAZed3c3dxov6ABl7Xi7yn2dFu9Smp+XaH5WncjX+tSnPlVZWckr44Ux7hVi2C0F\nFRUVLS0tZ3cNMFsUHjxZrUyHSdE3Op+vIBaLHT9+vK6uTq1Wb9jwiaCdyWRiLTAecrncbDbz\nWg4kEgneUWQ02wDtBBI9qPx3UHgNHCwrK7Pb7ZFIRKVSzcFdt6qRy+XDw8PcXJGpqamOjo4r\nr7zyLK7qXAEZdgjEWsHpdHo8HtaKyqq2wIOrGAeVVzOZzPDwMGuykCRpNJ4R+erv7/d6vdwR\nnU7HcxZqtdp51B6RJOlyuTKZjMfjyVEjZTabaZpeuIKxSAty165dwsFQKLS6W3+2trYuPBS7\n8D3kviXoH5nkavaSdJjCNdwJcrncIh0fDSppjJ+ZnU6n+/v7JRIJhmFWq1WlUgEADAZDZWXl\nwMBAKpXCMCz37UpTUxPssAzBMKyiokLnuBCAx4STVSoVfIncwOqEWactG5/73OeEHaquueaa\nF198cd572LVr17vvvksQhNPphCOPP/740NDQq6++Wl1d/e1vf3vhy171IMMOgVgrqFSqrVu3\njo6OptNpg8GQl5c3j50QBLFx48b+/v5IJKJUKi0WC8+C4V7MAAASiaS8vPzEiRPsCEmSTqcz\nEolwr4s5+pKxUBTV19dXV1dntVpPnTo1kx8uEAgsaa2ASJY/+LsUQLeu8L0sSnh3qXMQGY5V\nJ6d9CdzMfdZisaxbtw77205NVNOhu5vG4GH8STdettZnaGgI2qBmsxnqzzEMc/ToUa5Hlmdv\neb1e3okAALDZbPN+L6lUqqOjY3p6Gsdxh8NRWlq6Eiy8q666av369ezDZDIpkUi4I3PdA/j4\nBpLLn//858HBwfHx8erq6tVxWi01yLBDINYQSqWSlw83DxQKBatQIBQy4NUIkySp0Wiqqqp6\ne3vT6bRMJsNxvL29HXzssMFx3OVyWSyWtra2Wfs30DQ9MjJSXl5uNpthNQaO4xqNhl0GQRAr\nwapbHZSVlVksls7OzkVs4Ha2eprxrDoAwOTk5HvvvVedKbLFf2dINQyqPjui+nTW+gO2jGNy\ncnJ6enrz5s1qtTovL49bXWQymbibBINBkolyM+oYhqFpet7Fkh0dHdBShC5zmUxWUFAwv13N\nj+eee+6+++774IMPWEcaAODaa6/NURUrBt4esvLWW28BADo6Ovbu3XvRRRf19vbOlBmMgCDD\nDoFALCZ2u52bhGe32+HfvLw8iqL6+/tHR0fhUwzDKBSKuro6eLXbunVrNBodHByMRqORSGSm\n/Y+OjgYCAbaTOk3TkUiksLAwlUoRBMHuPAcrp151JsT4L+fKXN81LHDmqnUsHJPJJPRjnUUo\nimqRf2U78Q95ZmJaXi9mk0wmc+LECbfbbbfbMQzzer0YhtlsNr1ePzk5CbMUJBKJXAIKw8/1\naW9lN9QoidwprTkQphb4fL4zDDv/KdD2PRAfBeadYN1/AkkuAfD58dxzz7GlVMtAPB4PhUJG\no5H90Kqqqu64447/+I//aGhoWOFqdmedc0N/C4FALCc0TUej0fnZFvn5+ZWVlXq9XqfTlZeX\ns4EVGNTjWQnxeJwbUVKpVDU1NaWlpblfgrXqIJlMZmhoyOPxiLHqAAClpaXLoHYpRKlU1tfX\nO53OWdPvlkKgZK6XZLlcHo1GF7cHLlvhuHJgANar/ZcMpojjdrGbMExzc/ORI0cymUx9fX1d\nXV1hYeHo6OjQ0FAgEEgkEuFweHI6DDCyKPI8wcQxQBuSDW7lyXkvEsMwniP8DM9fuAv87Xww\n+FvgfQe0PQre+WcAFtn8oijqgw8+KC0tzd2QZhH54x//mJeX96c//Yk7CE/bedvHawfksUMg\nEGfg8Xi6u7spisJxvKSkZB4RH4fD4XA42IepVKq7u9vv95MkyQvTSKVSobwzqxbBolarc/jw\n5kRhYWF+fn4OUb2lIxaLNTQ0lJeXFxUVHT9+/GyFjNVqtUwmC4VCuUOiSqVy0fWfl0hRb4F4\n5RdM5u1i5u7mGBoaMhgMRqMxk8lAFUYu44p92yc/XxL+JY1JcCYFir4vfs+wUxkUOpbJZBiG\n5eXlce9boCP8I/r/D1CcW52Jt0CoC2gr5/p2cnD8+PFwOHz99dcv4j5zs3PnToIgnnnmmauu\nugqOUBT1/PPPq9XqOeXwrU2QYYdAID4hkUiwfbpomu7p6dHpdGIaHOWgtbU1EAiAjzUpcBzn\nWgz/+Mc/AABqtdpqtZIkabFYFAqFw+FgbS+1Wu12u48ePbqQNUBsNpvT6RwdHZ21W4Z43nvv\nveHh4auuuoqneJIViqLa29vXr19fW1vb0NCwWGuYE9Fo1G63r1+/PhAInD59eiZP3uTkZEVF\nhVQqXax0dYIgVkK+f1bmYdVBenp6qqurx8fHhR8jTUD3JIMzKUCqQcHVIvc5OTnZ1tYGz5H+\n/n6Y0ldWViaTyXw+H0EQDofDYrF8sgElKDBPL3JjMZjlduGFF/LGDx061NPTwz6ExRM4jn/+\n858vLi6Ggz/5yU+sVit3K4fDccstt+R+RYfD8b3vfe/gwYO7d+++5JJL0un0yy+/3NLS8otf\n/AIl2M0KMuwQCMQnhEIhnp8mGAwuxLBLp9PQqmMxGAw6nY6iKI/HwxoNrEZ/b2/v5s2bTSYT\nbFwLAJBIJDKZTKVS8SKw8CmSJLnSJ2wmmVCctqCgIBwOHzlyZN7vJSuHDx9+55139u7dK8aw\ng7S3t59xYV5eGIbp6emxWq1dXV054rM0TY+Pj69fv76xsXFRPG2ZTEZkrHzZwDDMbrd7vd55\ne0+j0ejJkyezfowmZRLIbSAdAuoSUP8zoC6NRqPJZFKtVueOxbNdywAAmUymv79//fr1OI4X\nFRUVFRWxr5tMJjUajUQiAfZ9oOOJT7ZXOIC+dn5vZyagYbd7927e+KFDhw4dOiScv2vXLq5h\nx3t269atsxp2AIC77rrLbDb/9re/ffLJJwmCcLvdTz755EUXXTSfN7DGWOlJxCsT2CvWZDKt\n2BvQZQD1ioW9Yo1G47nSK1YMfr+/sbGRO1JdXZ1DGGXWw4CiqHfffZc7YrPZampqYrHYsWPH\nsm5isVj8fj/3WltWViaRSGAtLQ+FQpFOp+FkKLWQSqVIktTr9d3d3dCSwHG8rKwsFArl7hwg\nBrlcrlQquZnsDzzwwDvvvPP73//ebObXXS4EqVRKURRN0/Mr9Zh1K5vNJowe8iAIoqamRiqV\nDg0NTU5OznUNZxFFxhMncqn5wH6s5eXlSqXS6/UubqcTAIAci0hSoyQdccZesyTeYzY/0Zb5\nFNR3JAiioqJipnOKYZi3336b+92pVKr6+nruhNbWVvh1EARRVVVltVpB+/dBy7dBOgx0NWD7\n/wKTqEIQkaRSKYPBkJ+f39nZmXvmPKpiVxmwVyxPp3r5WbtfAAKBEKLX67nqwQqFQq/XDw8P\nUxRlMBjm1uwIAPCxgjHXEoJqXjluiiKRCM+DMj4+bjAYzGaz3+/neY/i8fi2bdvC4TCGYUaj\nkXtRqaioKCgoSCQSKpUKWidzXbwQg8GgVqtTqdRi5fxlRS6X19TUwOpglUrl8/lECo5gGCaV\nSvPz86VSaVY7mIUnIp2VTCbT3NwMACgsLCwuLu7v7xe5/vnBi9FnRaSZq0l1xBW5DDtYZ3r8\n+HGoOUyS5OKmPCYYdUJSCQDwy7as939T3/Uzr+YjL1omk+ns7DSbzVkNIAzD1Go1t2aFJ1zs\n8XhYIzuTyXR0dJhMJqL6HlB1EKRDQPrxGTrxJhj7M5BoQcn/A5QLEkY5evRoLBYTxmERKxZk\n2CEQiE/AMGzjxo0jIyNQf9hqtZ48eRIGTAcGBkpLS4XyobNSU1PT19c3PT0tkUgKCgqg4hds\n9Jk1f0sqlfIaS0SjURiHtVgsUGOCfQrHcehFy/rSCoWCLcWQSqU8RdkconczGRCsaC0PmEC2\n8ACIXC6XSqXRaJSbgafRaGQyWTKZnHVzWHgLAJi1mnVOSx0aGuLGjpdIL8ZsNs9qbop8XSU1\npE+dDkg/6gZmMBhwHBcqrTAMMz093dnZWVhYOFMTUmh+qdVqYZaCSMYV+/Wxn3FHoEbPTLdJ\nJSUlp0+fZh9Go1GaptmwAK9lSyaTiUajWq0WYPgnVl3Hf4OGuz/6v+1xsP840FbPY+UQmAWL\nDLtzCGTYIRCIMyAIgk3l6enp4dpe/f39+fn5YkLPDMOMjIz4fD6YxlRZeUaNnt/vb21thbUU\nrOsOXrNlMhlXcJjH5ORkdXU19/LvcDhEhsJVKhX3osgwTO7L+fbt2z0eT09PjxhjgtWwXSCJ\nRCKZTPJ2Bf2RYjaH01hP2yLCDcUuulWHYZhSqVwsaRWcSVsS77giv2kxfies3VNaWhqJROLx\nuNls9vl8wvl+v9/tdlMUNTY2BmWEuW/Q7XYbDAYAwHvvvccadpKMf5P/7iRunXB+LSEr5WWR\n8qBxRcLAt4pyVADwLPhoNBoMBuEasm7IH2Fo0PzgJw+pCGh7HGz/3xwrzM1bb72FYZgwwQ6x\nYkGGHQKBmBFe/QFN08lkUihHIqS/v39wcBD+7/f7GYbhJhW1t7ezWhsMw7hcLqfTGQgEUqnU\nxMRE7hR7kiS3bNkyODhIUZTZbBYprBWNRoUJdjn0PkiSpGl6YGBApBHD8+WIiSqCGVxfWV9R\n5DLghxwKhcS491YODMMIK2PmjS32FzXVBwBI49pkMsm7ORGC4ziO48XFxQzDQNsOjmMYVlxc\nDC0qhmGgc1fKhIpC/2tP/I2gYyowYPQcBJe3fXiqPUdonpTr5fU/lDd2smeTw+HIYdgJvcjc\nA9XhcIyOjrLfLwy7nzk7CNJn1slGB3O8/dwkEomjR4/mTrRFrDRWT9I3AoFYdNRqNfehRCIR\nqTXAC1lyH6ZSKZ7ZEQ6HpVKpxWIZGRkJhUI5jBgcx9Vq9dDQkM/nCwQCUBJWzHq8Xq9wt0aj\ncaaCX7vdPj09Lab5VU1Nze7du3mKx2IjhqILacV4JQmCiMfjq6mjWmVlpcvlmmsyviLjYQA+\noL4pKFkHRDTt1Wq1p0+fhrI13AxOqB7H/g+/LFvsL/mx1wg6BgDI4MpIWjrd89fcCZcR+XpS\nYayrqysrKysoKHC73TwHNg+jkjIlj2nTnVBnmCAInU4HNcPT6bREIqmvr4e7Wr9+fZYOgVID\n0JSdMbKAWoojR44kEok9e/bMew8rh0wm88tf/vLiiy8uLCyEenhf/vKXuW1yAAB//OMfMQE2\nm+3CCy/861//yk57+OGHMQx7//33ha+yefNmt9sN/7/33nsxDFv00pxZQR47BAIxIwUFBdPT\n0zAwCkvwRMYEeSUOXGsDKl1xHVrQKopGo7zUOl4SHo7j5eXlk5OTbFgwlUq1tbXt3Llz1lUJ\nBTtIkhwcHGQvyVKptKCgYGpqimEYq9XqdDqFHr6sDrbrrrtO+HK8aTNlyGk0mlQqJcZ8FOMC\nhHoiyWSypqZGqPZythDpvMxKT0+P2Wyeq/hAv/ZLo+oDSdwAsn1lhYWFJEmmUilY1Y7juN/v\nz7ofmqbD4TBrsldUVHAD3OPKS7u1d2QwOSaotOb1w4VfcW9vL9To5t0s8Rl+RXXk87VUBADg\nl27qyPtBeVVNJBKBTm4MwwoKCkpLS2eRDd/2K/DuAZCcBAAA806w7r5ck3OyahLsxsfH9+3b\n19zc7HK5duzY4XA4urq6nnnmmV//+tfPPPMMr1/tvn376urq4P+wOe9LL720b9++N954Y9++\nfYu4qp6enm9961snTpwYHx+vrKy84447brrppoWrbSDDDoFAzAhBEJs3bw4GgxRFabVa8c18\nTCYTNxOOKwWCYZjL5WKT20iShAUZQqeUSqVyOp3RaBTDMJ1OZ7FYpFJpS0sLd04qlUokErNG\nh00m0/DwMHeEoiiuty+VSlEUtWnTJnbEaDRye7aSJKnT6XK0Os1RUqBSqYSGHZRNEWPVAQAU\nCoXIFDS4wg0bNvT29k5PTy9694i5IlyAeFMva0eHWWEAliKMgGFgsQ4vjdLj8eTn54dCIV4V\nQlagVRcMBhOJhEajqaura/5gKBP+VZowdum+SgMJAED4nRsMBu7Br9fr29ra2Lrm9vZ2kiSz\ni+PQaXDsi4D66GbDkDq1w3SS0p9/5MgReGvEMMzQ0JBOp5tFW8d6PriqD0x9CCQ6YNwEwPwN\nBZhgd8EFF8x7DyuBdDq9f//+tra273znO9/4xjfYn5re3t59+/bdfPPNbrd7w4YN7Pwrrrji\n9ttv5+7hy1/+8p49e7773e8uomF3+vTpvXv3UhR1/fXXOxyOV1999Qtf+EJ3d/d3vvOdBe4Z\nGXYIBGIWdLo59xSvqKigaXpqagrGs9hqDEhRUZFGo5meniZJ0m63w8unQqHQ6XTcsgm/3+/3\n+ysrK7kNynhePQDArK1XIVardXp6mqKomSwwXpqXTCarra3t6emJxWIqlaqsrKyjo0P40jKZ\nLJ1Oa7XagoKCYDAYi8WE/crC4XBVVdX09HQikZDL5VqtVqVSURQlPkZTWlrq8XigExHDMLPZ\nbLPZWltbs76R06dPw/4EUql02ZqniTfXlsHWlEgkW7ZskclkFEX19/dzP6VUKjVTxQwPk8mk\nVqtbWlpYD7HD4YhgjmbDw4Z0E7TqhBgMhoqKilQqBW8bNBpNSUnJ8ePHuXO8Xm92yyzSB1Jn\nphZMn4hGo7zweiAQyGXY9fwc9P0K0CmQ/8+g5usLsepisdjx48fdbjevdcQ5x89+9rOmpqZv\nfOMb3/zmN7njpaWlzz///LZt237+858/9dRTOfawe/fu/Pz83BJCc+XrX/96NBp9//33N27c\nCAD4xje+sX///kceeeS2225zOp0L2TMy7BAIxOIDtYKhvm7WyILRaDQajdwRDMPWr1/f39/v\n9Xq5fqyenh673c7uhHeRw3G8o6ODYf5/9s48wI267v+fmcnkvpPd7G72vu92j3ZbOaSIyOkj\nYkWUQw7r9aD+/Cn4Q1BQBPRREfQRqfgIlBt+VH5ePD5IoVCgtN1tu9377Gazm2zuO5NkZn5/\nfLfT2Zkkm205Sp3XPzSTuZNl3vkc7w9bVFSEHPLEjIyMcHlVgakeH3HYz2g09vb2ci+VSiV/\nFplarUYWehRFyeXyPBMa0un05OQkGn8JABRFoWrCrCsLMJvN1dXVBoPBZDJVVlYmk0k07BUA\ndDqdeCcsyyJx7Pf7xQXvWq02FoutWgKIYZjFYkHJykJOEgBKS0vdbre4wo8f9Vz1oIX325pS\nh+JEKUVkFxzpdFqpVKZSqf37959AD69Wq0UTYA8ePMgP6yKVHFB0BxTdWc+/pKSkrq6OJMmu\nrq5kMsmybNaceNZTCoVCnsVkHRAY8G6XplocJs/3S2by9/DOtuV/+/shFYDuX+S71Ly8+eab\nFEWdBnnYRx55xGw2f+973xO/tXHjxl27dhXSEIZhmKCU9iTZvXv3hRdeiFQdABAEccMNN7z8\n8svvvPPOZZdddjJ7lponJCQk3itwHF9TvQhJko2NjQLTdpqm+fJCUErPMMzS0hIar5nVgjgU\nCvGr5XKpOmREl19sVVVV8fPFtbW13L9nZmb48kWcVs5kMqhMm6KovXv3Ftj2YTKZOjs7uYip\nRqPhbC8AQOwpKLjbLpeLfybIuq9A9xZkVVPgx6fVamtrazdu3Chw0wUAlUpVSMMNhmE6na6Q\nYwEABmwCL86l6gCAIIj5+fnR0dG1dgdrNBrU5TA3N+d0OsWfUf5+F4qiRkdHkbBWKpUqlQrD\nMPFXTvz18Hg8/f39jkX/Ud1Vx5cqSzL1N6lUKn58Ti6X52tQnd2x4uXMY3nOdlXQJLEPe+dE\nOBzu7+/v6enJ9QU755xz+vr68u9k79698/Pzl1566bt1Vslk8jvf+c6NN97IX4icBE5+GK4U\nsZOQkDgloGn66NGjPp9PEOFA0xS4l6WlpYJGNg6n0ynWOuLUba6jz83Nzc3NNTc3l5aWZl3H\naDRu2LDB7XajACH/OSE4Z5ZljUajQBagB/z4+HghEayysjKj0VhcXMyXVjMzM3NzcwzDoNEU\nRUVFpaWlXMcxQRDiLCd/CcuyBYYJEXlUkSC6lkqlUDyjtLSUPxUeAJLJZCGNukqlsvBzYwGS\nsuyfEYKT0WulqKhIq9Xy/YEFxONxuVxuMBji8bggd4/sjgHA6/U2NDQcM+JhU56DACvk4NLS\nUlNTE1/ecb9JZrTXBsn2YnZIV75pJN4Z2zeO45OVlZUmkykcDhMEkcXfhA+9MjrIUMAygJ1g\nBOfVV1/Fcfyss846sc0/QEKhkEqlQjcK/YE0NjbyV/jv//7vt99+m7/k1ltv5YKjf/vb3zjL\nw0wmc/To0eeff/7cc8/92c9+xt/kzDPPzHr05ubmVc9QqVQKaunm5ua2b99us9lO3jJQEnYS\nEhKnBOPj41lnuWo0Gr64KS8vJwgCqat4PM4P5mVtRCjcUgQxNjYWCoUqKirEwSe0N266OWJ2\ndnZpaclsNvOztFklFIo1FjKLDMOw+vp6QeTS4/HMzs6ifyeTySNHjmzevLmiosLtdiP1RtP0\nqqOxTsZbGMfx9vb2aDS6sLAgELKpVArNPygvL5+dneWfQ4EVdQXq72O8V0O6Uctq/gR0KpXy\nej0AbJ7TmJqaKi0tJQgCjvxEN/3fYPoR/12GYQYGBrq6ujhtxz9iQNETJTcRUQLdZOSnWFNT\nEwwGKYpaXFxEjbHZD2z/JPj2rXh5oqouHo/v37+/o6MjFAp5PB6DwcCviDiVef7552+44YZL\nLrnkiSeegGMdMILP9KWXXvrVr37FX/Kd73yHL+z+9re/8d8lCOL8888XdDRfc801gl+SqVTq\nd79bMWWkQP72t7/deOONgUDgr3/961r/lyVGSsVKSEh88KCMata3xJVzpaWl69ev7+rqErzF\nT1Ny6PX6NQ3kZll2cXHxwIEDCwsLhaiN22+//bzzztNqtYL8mljQoEBggZlQ8aEFrhypVGph\nYWFhYYF/oEwmYzQa36OnL8MwGo1Gp9NlNVIZGRlhWRbDsM7OTu4BqdVqBdVL79G5YWxGxpys\nxXFxcXFRUVEmk1k1pMqywLL5LoRhmKhrEABg7FfW5Bs4K/w0w+Hw/OD/g+Bh9FLQn6TVagU3\neXZ2FgVQUWNsztlrbd+D5v8FciPINFD9BejN1xCQn9dffz2VSjU1Nc3NzblcrrGxsXe3deC9\nIJVKfetb39q6dWs6nf70pz+NFlZVValUKkHn03333cce4wc/+IFgP7/5zW9YHg6H41Of+tQt\nt9yyc+dO/mrbtm378Upuu+22tbaazc7OXnjhhRdffHFlZeX+/fvPPffctV+3EEnYSUhIfPBk\nHcmF47jdbs9j2VVTU8OVHxmNRkG2BZHnOY0sXrO2XKBJ7W+//XaBGT2lUpl/BgaO45FIJJVK\nFZKHxXFcXM0tLqKfmJgQT+loaGgopE1YpVKttRIcwzCSJHNF4OLxOBKjBoNh06ZN69at6+7u\n7u3tRaOB+TspcATcmmAxWQbX5FeNGiyowkLcaYhXQKnzycnJNYYPs4Pv/zrEnV687fWSvzFY\nltr8xaXgvnf29r++0+Vy1dfXc17ZWq1WHJAT/HXkLNDEZND9S/hMAD4bgY88fnx67NpBBXad\nnZ3cErfbfSpPNJmfn9+yZcv999/f0NDw1ltvXX755Wg5hmHNzc0DAwO56msPHz6cf8/l5eUP\nPPAAALz88svv7jnv2LGjvb19dHT0mWeeeeutt9ra2t6V3UqpWAkJiQ8egiBMJhP//7z19fXl\n5eX5H9UEQXR0dGQyGZZlc3nsyWQygWEsBypLzz/Man5+3mKxCBp4Ofx+P9rc4XB0dna6XK5c\nWTyGYdxudzKZLETYifOwAFBSUiIYjQCi5z2O41m1F+qZ4C/XaDStra2hUOjo0aMFTu+w2+0E\nQeSa1cFHJpNxd0zwCTIMo1arMQx7F2eIceSPhsZYI0mSkE6jNXXp8YbQA7r0RFJWOqW70as8\nM5VKeTyeXH7FWTFkxpKazqxyh0lHkvO7Bo0/zJWxjcuqAAAyEBoZaW1t7e7uRoIStVxYLGaf\nb/nPAYcMzlIZ7HhtQAGOkicbGd21axf6++IvpCjq3e0Mfbd45ZVXPv/5z7vd7n/7t3975JFH\njMYVivamm266/vrr77jjDqTP+ExOTvJHSuSirKxMoVDkkoYnxs6dO7/4xS9+7nOf2759e9bC\njxNGithJSEicEq2L+a4AACAASURBVLS0tFitViRNqqurKyoqCkzbIemW9S2GYVDBnPgtNMQM\n8gQ/jpHLGXhxcfHQoUNIyTmdzv7+/vb29urq6jy7CoVCucSHSqVqa2trbm7u6+vL6mKlUql6\ne3tzSUwEwzCHDh0SCDuz2XzGGWcISgMrKioIgjCbzc3NzXke1Wh0VUlJSUtLS319PU3Ts7Oz\nWT+XXOV94p2jtoP8cbv3KGPL6XsZG+v032pID+OQVmfm2oJ3adLTABAKhQSSGrUGq9Vq8WQz\nORNcn/ptru+eknZN+AwFCiyXy4WOglQvALTJXq6OPGpKHbQlX+n23tSZOK5IkPtjwRd9IkQi\nkf7+/ra2Nn6H0OpjMz4IaJq+4447Pv7xj/t8vnvvvXfnzp0CVQcAX/ziF3t6eh588MF77rmH\n/9NobGxs69atBc5oYVm2QJPwAvd2yy231NXV7dix491VdSBF7CQkJE4R5HJ5R0cHqtN6V3YY\niUQGBwdRNEXskVZXV4dSlqtmBnO5DyBvAg7UV9vQ0MC1OGRFfCZoFIdGowmFQnK5PJPJLC0t\nqdVq7iHKsqzT6UQDqex2e/6wgSASptfrW1tbkZkLOjQ6HPfwU6lUfX19gUCA9b5DHrll2HQH\nhR/XjolEgp/gnpqa4id/0SeFLieTyRw8eHDDhg2CJLLNZnM4HOLkJsMwVqsVzXATX8XJNHkU\ngj41ImeO30acTVlTe2NkrclkUqlU4+PjgjPhd8YAywKGqTLO9uCP8PSEwjgXBaHatsf+pMBi\ncdYIUNDo3ixBVuefaqJvANdpE52r76gOhWMKhaK8vPzkHTHy8/rrr2cymfPPP7+srAy595Ek\n2dLS8l6k0U8Gj8dz1VVX/eMf/ygvL3/mmWc+8pGPZF0Nw7CdO3deeeWVt95668MPP7xp06bS\n0tKRkZF//vOfPT09v/vd77Zt25Z1Qz56vX5qauoETvK3v/2twOG5rKxs06ZNExMT3d3d3/zm\nNwXrf+UrXznJnKwk7CQkJE4h+KpuaWnJ6XRmMhmLxVJVVbWmHggA4NuYiYUCTdNHjhxhGCZ/\nakmr1aLAnhhx1jUSieRxyoBsqg7H8aamplgsJhiVBgClpaXIN2FsbIzzNFlaWlrT9NVEIkFR\nVDKZ5FxIWJY9evSozWbjFBhBEFarFTzDkDrSErr3oOm4p4PT6SwuLuZUIOcBgVAoFPxoB03T\nS0tLgikjMpmst7d3YWHB7XYLOoKLi4sFO3zfYETPPgZker3eYrH4fD6Uu89lmIxDhgESMCIs\nq2cBKpZ+GTDfzX0iFma0LPS4VR6AM59T+4xxqqALXDFJInAIJrdDdKWGwGUVFVUVGA4AFEWF\nw2GNRrPWv4jCQQV2W7ZsaWpqqqmpSaVSarX6VFN1r7/++uc+97mFhYVzzz33ySefzOVPjqio\nqHj11Vd/+ctfPvXUUy+++CKO462trffdd9+2bdsCgcArr7wijsgKWLdu3T//+c8dO3ZcffXV\nazrP3/72t4Ilvb29SOr19/f39/cL3r3gggskYSchIXEa4vV6uYlb0WiUoqiWlpbCN2cYJk8V\nl0aj4Udl5HI5CmhRFMV/lqvV6p6enlzPM4PB4Pf7lUqlTqdDenTV0nKxUOju7lar1eJhZQCw\nuLhos9n0ej3fBWat87jS6fTY2JjgmYcy1ML+DEMbACQx4ayqaDTKCbtVg6lZKwhlMpnFYkkm\nk3xhR5KkyWRak0jlwHG8oqJCr9ePjY0VPhuDT1jekpCVqzLz6CVDaEwd19VX9SSTyaGhIXQV\nuaKGDEYCQIIoGTN+FwA06ZnOtgaXJ5ROp8vLy83mLcB+GZmMNJiT+ZUrBqyMlJeVlZlMpvHx\n8UgkkqGiZHTQnAzbqdiKFG/1VWifY2NjKIQmk8lQ9ULhVx0KhcLhsEKhKCoqyv9R7tq1SyaT\nIZ82uVxe4NS+9w2WZR944IHvfve7DMP88Ic//MEPflCI6JTJZDfffPPNN98sWG61Wp966in0\n74svvjjX587vnPj+97///e9/P+tq/f39XHv+Pffcc8899+S5ilXP+cSQhJ2EhMSpiMDTzu12\nI2MwrVZbSK4W1erxn/pms1kul6dSKZPJ5PP5+Ctzqwn2XF5enueB0dTU9M4772SdU1Q4KpVK\noCb5RCIRtVqd/wGg0Wg4CZu1TSQcDouvIsujuuwCqNyqco0LFvOzfjabjZ+ALi0tdTgc/NI6\ni8WCZqxptVpuw/n5ea65GIXBlEplS0uLXC6vqanh0ls4jiuVSrVavWoYz263kyQ5NDR0YmNn\n161bFwqFDrP/URP+vT4zSRgayO67LNYNABAIBAocgMYRI2ucLn9VVRVFUctWF8es45RKZU9P\nz8DAADpPFBPi3y6tTq9QKDwez4q0PtkWItsW1Jdu8G4j2SgY28D+SWj7PgC43W5u+G8mkxke\nHj7jjDNSqRQaZ4xuOMMwyWRSLpcLQlDT09PcUXQ6XXd3d67vdigUOnjwYE9PTyGNMu8/4XD4\n+uuv/7//9/8WFRU9/vjj559//gd9RqcckrCTkJA4FRE8s1mWHRgYAACNRrNu3bpCWvOqq6u5\nsByO47W1tVwleC4bMCShCIIgCMJut+cfxa1UKktKSsSGI4VjsVhkMhlBELk6DzKZjEKhUKlU\n/Bo1JE+5f69bty4QCESjUbVabbPZ3njjDbHcCQaD/NiYTqczGo3xeNzpdKbTaZPJVFJSMj8/\nP0/8r3RRUo7RKXo5x2cymfh+JTU1NcgdGsMwm81GkqTgtEdGRtCp4jheX19vt9tpmuZXJrEs\nW1VVxU1jq6ysRCM6aJpWKpVGo1Emk73zzjv543AKhUIw36JA0NcAzSmuqqqKRi8CuRwnyVgy\nqchk0GdxArtFM8Syotfrzz77bHSBBoMhFouNjIxw+etIJJKrHp8iihZVn6iMPQfn7WZl+lQq\nRWKMwPWapunR0VH0ZcZxHFVqjo6OojxyVVUV1zFDURRfO0YikcXFxVxf79dee42m6VNzRGx/\nf//WrVunp6fPOuusp59+uqys7IM+o1MRSdhJSEiciqBqJ/HyWCw2OTlZSA2K3W5Xq9U+nw/H\n8dLSUn7m0Ww252lwa29vR5MkxsbGKIoyGo25QnclJSWLi4ucYOJLLj78ai273R4KhTKZjE6n\nq6+vR++2tLSMjIyItR1a0t7ePjQ0FI/H0Zh5kiT9fj/DMDqdrrq6WqFQ8IeH8mNgfPhqT6lU\nRqPRgwcPouiU2+1eWlriejJoIDQaTVFRkVqtRjPNMplMLBZTKBRKpbKqqoqrotu3b5/gKJwA\nZRhmcnKyuLiYoiiB0FxYWCgpKeHs9XU63czMDHd0q9Xa2dk5NTWFpCqIZBNBEOl0Ws4EKqNP\nqzMOn3LzguoCglQh5+Q8FnT8nwQURR05cgTpJPTp4DheU1ODXC1ypdSbi5PjXrVYN8vlcmFn\nJUtD3AFyM7hexrxvm9TlUHsdkKTRaNzcXuRY9E86V3d7oYgiMHV6Q+mxsTdTqRRBEEZlEmBF\nhpb7icIwzPT0NEEQ6DPVU4OywQeT7lJl8w1g7BDflhXtICt59dVXAeAUFHaPPfbYV77ylWQy\n+Y1vfOPnP/95AZ4v/6JIwk5CQuJUxG63UxTldDrRc5T/NC18qKhOp1MqlUqlUpBjra6uTiaT\nbrc761ZKpTKRSBw4cADpKp/PFwwG+U6tHHq9vr6+fnFxEcMwpP8WFxfn5+cF+dDS0lKPx8Oy\nrMViqa+vdzqd09PTHo/H7/c3NDSUlpZardZNmzb5fD6BuT8KIGm12r6+Poqi0un0wMAAp//4\n8oijsrJSLpePjo7mSeAitzZ+zlFg3haLxbq6utCD0+PxjI6OooNy/RyI/HE1hmGi0aherxcU\n0qXT6X379p1xxhkoV4juA/eu1+u1WCzr16/nlgSDQdRGg17SNO1ZmO72fUOVcQKAhXq7PPYC\ntWWPqcjOTZk7cuSIWCVXVFRwgd6JkYPh8LKyQTeKYZipqSmDwdDV1TU7OxuNRhUZty953IdC\nlx4rKWoLy4q5ZChHNBo9cuSIQqHo6OjQ6XTgeQPe/ALE5gBwgGMXPvYAfGwXvH0NuF/FNJeD\n/uu57puMjZfG/qKiF/SwmNr4h+HhYfRJ0TTti5EEG6ex45pY8PsErWlL/E9r8B4AgCjA7IOw\n5e9qk3CqaR7jElRgl6vD9AMhGo1u27btqaee0uv1O3bs4MyHJbIiCTsJCYlTlNra2traWpZl\n9+/fz6+7L9AidXx8fGFhAVV0tba28kf9oJ64pqYmh8MxNzfHlzhlZWVqtXpmZoavDHw+XyKR\nEE+DAAC9Xi+TyQKBgNfrDYfDlZWVZWVl/LQXQRCcFEAjbrkoC03T4+PjRqNRpVKRJInif5yv\nHo7jNpuNpumjR496vV6CIHAc55/V7OxsZWWluOKwpKREo9EMDAzkKRcT6B6xCkS7zWQyIyMj\n3H4WFxdNJpPBYMBxXC6XG43GnLOtAABApVIRBFFXVycY4MEwjMPhQIlCsY+gx+Php9iMRqOg\n6k4beQOpOoQ6c1SV3A9gpygKjfoV7FCpVFqtVrlcTtM0QRAAbNC/BNhxZYOzFIMp0MlUVVWh\nNp1MzDW9+1cBWTuNKY2pg3WytzHrDSYIiIUdAh29t7sTXt8KSVQhyovtRafgratgaTcAGKmB\nXHeMYJM93q+pM3PopXfojzSsEDGcqjMYDOXl5VyDEZ/qyI7jL5gUDN0tP/fluro6LpSLku9Z\nT8Dv9x8+fHjTpk18B7sPltHR0a1btx45cqS7u/u5557j8vgSuZCEnYSExCkNqhbiP8DymwAj\nFhcXuRgPanXcvHmzQAMRBFFdXV1VVZVMJoPBYDKZRIYXINI9ABAKhaanp1GBfFVVFVeZnslk\npqenUSYuHo8Hg0FU9IbCisg8mZ8IE7jQCRpUOzo6Zmdng8GgQqGorKzUarWjo6Oc14kAhmFS\nqVRWmatSqdbUBCDoukDFfwAQi8UE+5mYmEBrWiyWuro6v9+ftToQAOx2OyrnLy8v9/l8ggtf\nWlqSy+WCFDl3MvnPVsYIQ7ZY2u9yuSYmJnKVKs7Pz8/PzysUiq6uLlXGKc940uRxYUdAigEF\nALjdbpSDdrlco6OjrPITACADqqZEqWh/DnASWbSgWC+O4wqFgv/hRiKR6dF+IC8OadppTF2U\n3F0RfQbnrOzCY+i/2sx0c+g/JvVfz2ArAq5arbbLMCVzzR2/Fb7XwJI9OhUKhWpqavR6PRfA\ntlgsDMMEAgEFs7L7JOYAgMrKSqvVirpis05VRrz22msMw5xzzjm5VnifeeKJJ7785S/HYrGr\nr776oYceyvrjSkKAJOwkJCROdYqLi5VK5dLSEqrZL8T+XhAHoigqkUiIE5cAgGGYSqVCDwyG\nYVwuF0VRgo5CkiTHx8eRxEGeEevXr8cwzO/3HzlyRCB9FhYWuru7w+FwJpPR6/UCZztxbIxr\nUKUoamFhgWGYiooKVNzGD++JIUkyV/AS9X8UqO0wDGttbU0kEvPz8zRNW61WLigi3j+n/3w+\nH03TuVSdxWLhOxuXlZUJhF08Hh8fH/d6ve3t7bOzs9x+MAwTTN1lWdZkMjkcDm5JzPDR/WQj\nRdj0qeH6yIMq1ueiG/KMqOd2TlHU1NRUe43eSr0ZI4+P4khjy9GpWCw2ODjY29vLz2VnQDGY\nOX+j3BSNRpeWlrjST1SZJzjWUXcCtF9A/46QDSnc2BD+z+X31GWQXM7+l8b/XsIcHKx/iV9I\nWlVVJfOsGG+ly0xq5HQslb2lg6Korq4ul8sVj8d1Ol1xcTHDME6nMxlZp4nuPb6eddPy8dXq\nrH8CfE6dAjuKom6++eYHHnhAq9U++eSTV1555Qd9Rh8aJGEnISHxIUCv16/JfEEc9Vk1DkTT\n9IEDBzjrEL1eH4lEUCbXZDLxw2bBYBAZTIyMjNx+++27d+9+9tlnOUexcDiMYRiX+S0qKuIX\nQtlsNr/fz9XaYxiGToyiqH379iHZ5HQ6g8FgU1NTJpPJ4+ghsLOPRqMo1GexWJDTW/4ZGBws\ny/r9/oWFBSQEE4kEp1dQ56/AeoZ/xFz7FERWioqKsu4HDdvt6+sbGRmJRqMKhaKuro7/QTud\nzqmpKZRClWMphmW0akUgZmRIPQB4lR+JkzUb6pg5jxwgyzhgMZFIBDTtS5oLcq0Qj8dnZ2cF\n+juRSHg8HoGJNMuyq068dWo+ZUwNFiV3+7Qft3TdCv88Lpiwtlta61pnZmZ8Ph+aKVJcXAzM\nJm6FJFF8yPwfcaTqWNaYHgzKVxR6ajQaHMf5aWuCICorK8H4CLx6ybLFsWk9dP0MCmbXrl1y\nuXzz5s2Fb/JeMDc399nPfnbv3r3Nzc3PPfdce3v7B3s+Hy4kYSchIXEaImgDRN4cgnVSqVQg\nEMBx3Gw2EwSxuLjIf06Hw+G+vj6UbhP3mWYyGYqiCnHHraysRIFAlmWLiopqa2sPHjzICTuW\nZWdmZjo7O5HzCLfVwsKC0WicmJjI0wPBGZF4vd6ZmRlOZmm12u7u7pqaGo1Gg5TTqu0mXJMK\nACCRx4XNmpubzWZzKBRSKBTz8/P8SxbbnXCIxwC0tLQ0NDRMTU0JatQoitLr9R0dHfF4XCaT\nce53LpcL5b7RS5qmU2xq09JVTs2/+bTXcJvHidKopSezKJzbkQu1Wp3JZBKYJc86c3NzgiUy\nmWxmZibryigymmtMBQvEEdMdStpFGhsstl64cACm/wh0Aso/BWUXyQAaGhoaGhqOb1DyMWj5\nLoz8BwBMGm6Ky46NOcawkLxNRTsTxLJHSXl5ec4yOH0zXDIEgUOAycC0DrBCPVx8Pt/Q0NAZ\nZ5zxrk8vXRN//vOfr7322kAg8IUvfOGhhx76YE/mw4gk7CQkJE43/H6/oNye3zmBCAQCg4OD\nKEalVCq7u7vFlhAURaFqJIvFwk8FyuVyNG0iq+0ZV8BE0zRN08iGl3MUA5HTBFKTYo04Pj6e\nSzYBgFqtNpvNALCwsDA2NsZ/KxqNOp3OysrK4uLi4uLi6enp/MJO7KIXCoU4YYfS30io4TjO\nN5BD1ipic5Cmpqas4VWZTFZcvKKrFMdxnU4XCoWGhobQfoqKilpbW0OhkDi1SmOqsLwVsukn\ns9nMD6nmSUOj8sFcxoEcgqLDmpqaXM55+cdUIJJESWVpKQCAaT303J9nTQCArp9B000QnQ1P\nYJA6fg4sEAnCjmGYXC5HzSvHByvHZuHQbRAYAE0NdN4B5l7AFWDZuMqBROzatYthmA8wD5vJ\nZO66664f//jHJEn+6le/Es9RlSgESdhJSEicbogTZJOTkxaLhT9EgauZA4BkMjkzMyMQfxiG\ncaECk8lUX1+PSsE0Gk1zczNKVvLlGkKr1dbV1bEsOzExgXpyNRpNW1sbP+qgVqv5SguVPen1\nekGThEB5qNVqThHK5fKuri70UM8aSeLfgaKiorm5ObHywHEcwzBkhnfo0CH+CktLS9FotLW1\nVRATQo4hHo8HWQPqdDr+ZDaOPCPqTSZTVVUV6homCKKhoUGpVPb393Pq0OPxCOKCfGRMtIja\nM6e9ksHk3FX4fL66urpMJoPUvNVqzTOvHR2ouro6v8WxVqs1Go0ej4cgiKqqKovF4na7xQ28\nBaLX6zk34EQi4XK5aJq2WCwGgyEejwtmdtE0fdSVDgY1DLviB4BMJtNoNKFQCF3C9PR0Op0u\nKytTkiz+yicgMg4AEBoGz+tw0SHQVJ/AeaIRsR9U54TT6fzc5z73xhtvVFZWPvvss319fR/I\naZwGSMJOQkLidEPcOscwjN/v56qRGIYRhM2i0WhTUxPn04vjeF1dHf9xW1FRUVFRkclk+H0V\nFRUVSPpUVlbW19fL5XI08czpdHI9ubFYbGhoaOPG4+GT+vr6Q4cOIVkpk8nq6uoAoLS0dHFx\nMU9ozWw219bWopRoaWmpTCZD1mtZNRBfR+p0Op1Ox9+zTCZDM2oxDKNp2uVy6fV6gUtIPB4f\nHBwUtxKjKCD6N2oQERwax/H83S21tbXl5eWJREKj0chksmAwKIj5hUKhrDX+uvSEPjWEQ6Y9\ncMdI8b3pzLLB4ezsLI7j7e3t6JYSBIGaQwXtGgiUka+oqNBqtX6/3+12Z7Ujtlqt5eXldrs9\nHo+jr1NjY+OhQ4f4K+exMhZQWlqaSCRkMhlFUf39/eg8HQ4Hl8a12+1cQnZoaCirNXdLS4ug\naHJ+ft7hcMgIaMhUl8AxhZ0Og+NP0Pwt8R5QMWU6nTYajQLxzbJsNBp95ZVXFArFpk2bxNu+\n17z66qtXXnmly+W69NJLH3300Tx9uxKrIgk7CQmJ0w2LxaLVagWl/fwGRjSWlD8tAKkcNEUU\n+Z5kNVYQdMtyS8rKyvijt8R+v6lUipOJBoMB2RFjGGaxWJDUwDBMq9XmEXY+n6+2traoqAi9\nTKfT/f39WecH6HS6/MPQaJpWqVRI1fH7RQQJSoqiUI9Irv2Ie2YxDGtsbFx1Zjw/RiXuqKBp\nuri4eH5+nt84UpTc3RT6JQ6ZMNkybPw/mcyKnhK3211VVcVlxnEcX7duXTgcpihqbm6Ou6sk\nSXLFf+jyuXwrao5OJpMEQZSVldntdqfTydU4VlRU1NfX9/X1hUIhv9+P+mNKSkoWFhb4OxcP\n6kVwuXKSJPk5Yi6NOz8/bzAYiouLY7GYQNXp9Xqj0VhcXKzT6aanp/lvoXPL0DBm+LYhdURF\nH8txMyvHYBw7Vn9/P/qjwHG8qamJs7JLp9OHDh1yOBxjY2Pr169f67Tck4Rl2Z/97Gff//73\nMQz74Q9/+IMf/CDPgGaJQpCEnYSExOkGhmHr16/fu3cv96AlSRJVpHHU19cPDw8j6UCSJJdU\nNRgM4oK8tSLQf+JqPGThJtgqv/FyIpEIBAJc7+3c3FxWVdfS0mKz2VCYDbl7hEIhQWutTqdD\nz86lpSV+0lYcfhMLWcEJ83tdSZLs6upaa6m7OOiFjP06OjqOHj2KRrqpVKrpaQjK18vpQExW\nCSJP5qygOj+z2exwOMLhsFKp5IZPDA8PC+aOsCyLfF7QrUNuLNy7DocDw7BkMhmLxbg7FgwG\nGxoa0Hg65C+Ty3GQI5fyA4BAIOByucSxOo1Gg2K6AGC1WrP24TKYPCRvUyUWAAAIJZRdJF7H\n4XBwP3UYhhkfHy8uLkZfg5mZmUgkMjAwwLLsunXrxsbGenp68l/Iu4XX67366qtfeuklu93+\n9NNPn3mmcEKGxAkgCTsJCYnTEJIku7u7p6enY7GYWq2uqakRhJGKioo2btzo8/kIgrBarYXM\nnUyn0+LVfvGLX9x+++2CJtCysjK3283JqZKSkkKmy5eVlS0sLHBCx2g0Coq6+LIgq6orLi7m\nTxTgxqHyUalU3Fgw8bv88V9Wq3XVIR/Nzc0mk8nj8Wg0mvLycu4mh0IhZBy4qkmNOLzHMEwk\nEjGbzWazmabpmZkZlIJM4/o0nn1vRqNxdHQ0lUqZTCa73c4P+SAbav7KgUAg6zS5aDTK5Z3F\no4TFrbIA4Ha7kQaKRCKCfp21Eo1Gs8ZruUiw3+8nCMJisYRCIZZlBXE1uaYYkgRoa2H9PWDM\nMv5OoAhpmk7GAurga5CJhoP1AHDw4EEAWL9+PXL5EU80QWQyGb/fz7Ks2Ww+yWmt+/bt++xn\nPzs7O3vOOec89dRTuYZhSKwVSdhJSEicnqjV6vz2VyqVSuCFmwuPxzM+Pp5KpUiSbGxs5BvI\nFRcXGwwGQWRLr9d3dXXNz89nMhmz2cx3GsuDXC7fsGHD4uIiClNpNJp9+/ZxMgvHcaPRyL86\nweY2m43vCZxIJARCoaSkpKKiQqPRoGc2y7Iej0ewk/b2drfbnUqljEZjRUUFrEYikQiFQplM\nBsdx7iaMjIxwYTy73c4/KzE2m02cjeXU3vDwcH7BhDptFxcX0Y3y+XyRSKS1tTXPJrnM51Dq\nGd3YAiULP8ap1WoNBoN4oJmArMYoWdt4cTZRnXhBpfwlmnXBWVUbjcaOjo79+/dzfdxardbY\n8xjgOwByxjIFpQVKCKp3bYDoDAAozHdHFJsGBgYUCkVLS4tcLs+l6mKx2MDAAPqBIZPJ1q1b\ntyZ3ST7bt2+/6aab0un0Lbfc8pOf/KSQXz4SBSIJOwkJCYl8JJNJbl5qOp0eGRnRarW5HPxR\nAZZSqSwuLs4vL7JCkmRlZSX3sqmpCY3JkslkjY2N/GdzRUXF0tISKhPEcbyxsVGQ282qHvht\nDfF4XJAZRBPV+MWC+UkkEgcOHEDiJhgMRiKRjo4OlFLk1nE6nSUlJXke/0jC8sWW2WxG+dxU\nKpVf1WEY1t3djcZ1cAvdbndDQ0MeZZYrWRyLxQ4cONDb26tSqUwmUyGjO/j3CsOwzs7OxcXF\no7Mz6UzODaurqxWxI6NLK86hs7NzdnaWfxMUtHfz0udGjLdM7z8g2EMwGPT5fD09PQ6HIx6P\na7Xa8vJyHMdTqdT8/HwikUBLBFIJfWE4LdiBvYhUHQBURXZMRGrm5+e7u7v5ZQlipqamuO9M\nJpOZnJzs7u7Oc3+yEolEbrzxRuTpvWPHjgsuyGkWLXFiSMJOQkJCIh/BYJD/gEcdl1mF3cTE\nxPz8PPq3w+Ho7u4+yThESUmJzWZDjReCIIpcLt+4caPX602n0yaTSSxWVCqVoIOEa7xAiOvn\nDAZDnhycGJfLxQ9Zeb1eVIUmWC0ajeYRdjiOd3V1zc7Oom4Sm83GSdtVdVVNTY1KpRK7kHBJ\n82QyefToUaR+qqqqUCCQpmmBlOTIZDJOp7O+vh6ptMOHD6Nz4GeoAQDdIpvNJtBAGIZFIpF0\nhlZl5hvC/+lTbHRqLuOvoFQq06491dNXxfVfntNcAQA40LV19Uaj0Waz8XtuymPP+5Qfcas+\nnvXCE4kESZLc5Dd05gcOHEBCH8094wxxgGXAt5dMRzas7/UEU+gLo937I25bfXok/PbXAeCc\nc87p7u7OWNE0OgAAIABJREFUU2MquGmrDt4Qc/Dgwa1bt05OTm7YsOHZZ58tZO6zxFqRhJ2E\nhMTpBkVRwWBQJpOZzebCZUoucnXCCojH45yqA4BoNOpyufJ3pxYChmGo0C2ZTIbDYblcbjAY\n0EURBCEe8MDfsKOjY2JiIhgMyuXyqqoqgYWEQqEoKirisrEYhjkcDqfTWVFRwVcMeRC3AqTT\nabHkXXU+KUmSwgEMAACgUqn47n1wzB2GoiiGYYqKiuLx+N69e/ndzWhvi4uLWq1Wq9VyBiXB\nYNDv9/f29o6Pj3MBRYVCUVlZOTExwd/c4XCEQqH29naj0VheXo4s9/iqzmg0onwljuORSCSd\nTvPd/sLhMAZMZ+A2dWbOrxQ6sSWTSXnk7wBQF37IHtuZIOwaek6+7iWarkAHWr5MJlqa+J95\nfc7pqGId7/F4+PchFAqFQiGj0QipILzycfDvBwBCYSk56wWoOBsAQNcE7le59feMJADg8ssv\nNxgMkAqAexcQSrBtAWJFAletVgt6yXOdYVYee+yxr371q/F4fNu2bb/+9a9X7Z6WODEkYSch\nIXFa4fF4uHZXjUbT1dV1kiXeJpNJpVJxOSylUilIVkYikcnJSfHgCvGSE2ZxcXF8fBxdlNFo\nXLdundgSIp1OT01N+f1+kiQrKipKSkqUSmVHR0ee3ba2ts7PzweDQVQnBwAMwxw9elStVhdS\nya7X6zm7PgAgCAJNL7VarVwKtbi4mF8auFba29tHR0fD4TByIamvr+feOnr0qMD+A5FOp7M2\nOsTjcZfLxU8TUxSFRjgIvADD4fD4+HhHR4eg+E+nVWt1hnA4fPjwYQBIJpOccx6KHQKAUqnE\nI6PqzBwAaNNZTJJZODaHl14imbBTc1l0jgL5GP/bksG1vq7nVNpaGJ8V76HIrBEEXyGHyAYA\nGLoLqToAAMoHe2+ASycAANpvA+efATXS4vJdE3q1Guvt7QXvW/DqJZDyAwBoquG810BzvDag\ntraWMy8kCIL/ceQnmUzedNNNDz/8sE6ne/bZZ7du3VrghhIngCTsJCQkTivGxsa4+EosFpub\nm+PcIk4MgiC6urrm5uai0ahGo6msrORH7JCMEPiJIPL79BYOTdOcqgOAYDCI4mqC1YaHh5El\nL0VRIyMjJEmuWi2H4ziaPPbWW2/xl/v9/kKEnaAzl2EYVNjX0dHh8/mQB57AZWataDSanp4e\nmqbRnAz+W/z4aIEIYntoid1uF0/vQOldQS44HvFHolmakVHrbktLCwBUVVVN+IbR8pLEfy8p\nP+pX9AKvZ8KjPKsy9gzOplggDpl/FpK3QyAFIOzS9dOlOkYm8MbD2Mx6/3eMHb8TnwPKnyoz\ni5Xx5xQZd0TRqlP9NB6PKxZ3ragGiExCKghyI6jL4ZJRcLwAmaiDbp86es75558vl8th/78v\nqzpAk8r+D3zkCW5rnU5XVlaGdDPyfynEG2h8fHzr1q2HDx9ev379c889V7gclDgxJGEnISFx\n+pBKpQShC4FN8YmhUCjEiULEXXfdtXv37ttuu03whLNarXnypGsikUgIhKP4otLptGDQwtLS\nUoFtEOLMsrg00Ov1zszMIOvmhoYGlIMThCRZlk0mkyhLuKYmDAGpVCqZTKrVau7ExOcj9vtY\nFQzDxEZ9FEVlTayjLKHZbOZ6UQGAxnKOSqNpGoX9TCZT26ZPJv+5Xhk9iLGZdf5bgsru1Ppf\nu6Ia5FEXJesHTT9qSj1L4aaQPGfXttvtFnuysJhMiUXBkGUrg8HQWKEt3v81kgkBgJV6K/Ly\n4QOGe5tTRSsUOqkH+bEvKqmD2msBYNdjjwHAli1bgGUgOLhiv4FDK14FAlw0NJPJjI6Omkym\n/BnVnTt3XnfddaFQ6Oqrr/7d73631uytxAkg+TtLSEicPpAkKXhOv9cPkrGxsQMHDvDVpN1u\n7+np6ejoOPnyPoRSqRTsKutUDAH8rliGYaLRaC53XOQYwr1Ec2D5K0Sj0aGhoWg0ijzMDh06\nhISmYJIsQRCFnFh+pqam9uzZc+DAgTfffFNsg8KBYdhaM7wsyy4sLCiVSn4W2+VyZY38obrG\nxvoaC/UOAIsBU5Tck2fnGIZxGX+NRqs8/yWouwH0TVB8tnHzXcW1m1taWqxWK4ZhGIbJqz4p\nv2Rfqu8p8U4gm4rlIJmwYsPPQKaC0DDEjgretadfQ6oOoYvv02Rmjmo+R+O8P4Gyi8SWKK++\n+ioAbKnzwcyjwiGzuhXRNYGZC8MweWalZDKZ733ve5/+9Kcpivr973//2GOPSaru/UESdhIS\nEqcPGIbxEz2oOv49PaJAR8rl8pqamhM298p1CH7zYFb7PZIkBUKH02o+n+/NN9/ct2/fnj17\nBI0CHM3NzTU1NUajsaioSGxO5vV6+SHDZDKJ/HvtdjsXp0RTqk5yGJTf7+cCQjRNj42N5RnG\n2tTUxB29qKiowAbkZDKJhnMIlgukcyAQoCiKVKg7M38823XJ2a6L2gO3a9PZ7x4AlJaWrrh2\npQ36HoZLRuG8V6H0AgAgSbKjo+Pss8/+6Ec/2tLSguO4Xq8X3C7Ukkyy2WPMBJtsDv0HNvso\n/LkZ/toGL1bDa5cCzQuaUsenVqRw86L6YhYj47KqvdZHZnXXLL9h2yLe867/+atOBd3Jn8Pb\n10NyCfBjEThSB50/4q8pDs7lCtc5HI6zzjrrpz/9aWNj4969e2+88casq4lJpVJ33333eeed\nZ7PZLBbLxo0b77vvPv7X4NFHH8V4oC/eVVddtXfvXv5+fvKTn2AYtmdPFjleU1OD8uYC3n77\n7RPI75+CSKlYCQmJ04rS0lK9Xu/3+2UyWVFRUf6hWCcPEgRFRUXI3K66uvokezWyUl1dbTKZ\nUH9rcXFxVhHT1tY2MTERCARQ8wQaPpbJZIaHh1EKEs0kRepNsC0az5DLe0Jcmob2FolEkG6O\nx+NWqzVPPIZlWbfbjc7fbrfnGmghCP+gERS5VlYoFN3d3el0GsMwmUw2Pz/PyVaSjRYldy+o\nskzWAgAcxwsRoMlYUJFJQPeviDc+A5kEALQnHxy3PhiMUFzBHHK8QwN/KYrC5p6BobsgsQBF\nH4Ge+0EnTN/zj6tQKJqbm8fGxvg5ZZZlUwyxMqbGdgZuYwHXp4bkTBD4wsP5Fxi6FzrvXH5p\n2wLD9wJAmGw+ZPl5Blv+OCjCuqQ8pzryGJAGKLtweWXKC1MPQ3xhLtM8O7900XogCWAx2ajm\nq2GyyUq9TSq0RT1fUxlX9EcXFxcfPXqU+z6YTCZB1Bbx17/+9ZprrvH7/Zdddtkf//jHwmf0\nTU9Pf/rTnz506FB3d/fll1+eTCbffPPNb3/7288888zu3bv5IvKyyy5ra2sDAIqiBgcHX3zx\nxeeff/7BBx+87rrrCjyWAIqiLrjggvvvv//aa689sT2cOkjCTkJC4nRDo9GsdWLpSVJXV1fI\nnIaTYdUhtnK5HD3q+MRiMUFhWTAYFAu7/AjmaxEEQZLkO++8g5onZDJZTU1N/uFj4+PjCwvL\nI+qdTueGDRuUSmG9WiQSEedeV1VgnIwuLy/X6XRI0JcEn8yMPOVSfozBhGdFEIRerxc3LAv8\nnHFgNP/TAnQAVGXQ93ugUyBTq8ouXCfT8jfhjyAjg3vxd76w/N7C3yE6AxcPApbvIWuz2eRy\nOZrlxcHwNsGAVWYWLcm3RJsew/M6ANA0TVGU0nYe3vljOHLXlG4bp+oQVam/ge0cWHc3qCsA\nAJJL8Pcu1BI7OAAAsKUVAGBOvdWlOh8A5mRVALA07e81rxB2Mpmst7cX2SDrdDq73S6IdNI0\nfdddd/34xz/Gcfzee++95ZZb8ly+mG3bto2NjT355JNXXrls9cIwzI9+9KM777zzzjvv/MlP\nfsKtecUVV1xxxRXcy8nJyUsvvfRLX/rS2WeffQLNUvF4/Nvf/vaqU0M+LEipWAkJCYnTlsJz\nZ3kQyCAcx/ft28e1xGYyGTTLNRfpdJpTdWh9/kvuEAMDA2K9NTQ0xLftzY/BYKipqamoqCA7\nblFt/nWrYq8CSwCATCZDApEkyebmZhQ1zOeZjLFNwZ/K6AAAQGIB9n0dKi6Dyq0gW9HmLLSM\nXvr7ir2ERyF4pJBzlhG8BzGb4T+XWcCsqpxFbAAAypL5+fk9e/bs3bt3z5497qIvwdZAXNvD\nX0Umk6XX3bfY+mTGuGF50eT2ZaMTgPM74emb4OsfBwAIKleMkYhEIuJ2EzSaorW1taKiQiC7\nvV7vJZdccuedd9rt9tdff32tqm7nzp3//Oc/f/jDH3KqDgBwHL/jjjsaGxt37NghHqbCUV9f\n/+ijj9I0fc8996zpoF6vd8uWLaWlpQ899NCaNjyVkSJ2EhISEqctKpWK70Isl8sFjRGFoFar\n+UG7TCYjeMSm02mKonKlYgUucQAgrpxbWlrK2uWayWTGx8f6mtTOoGzBE6Np2mq11tTUrF5R\nV3FZUcVlRQA0TRMEgdo+ZDIZinriON7d3T02Nra4uMjfaP369QzD6I7eL1/4H94FBMDfD7Zz\n0KtkMhmNRhUKBT8LSVEUncoIO0ew1cv+cBxfz+44mqwMky1KelGfHnZoruCvoMSTUHcDTP1h\n+bVMDZljliuEMlL9zYnxCe5ejY6OGvr6NFp9iqeGaZpGSerp6emenh6lUgnx4wldkoArNgHI\nVJBJyOgVISscxwsfnbJ79+4rrrjC5XJdfPHFjz322AnY3Gzfvl2r1X7ta18Tv/Xwww9PT08n\nEok86f6NGzf29fU999xzDz/8cOEHlclkmzZt2rRpk8PheOKJJ1bf4MOAJOwkJCQkTpybbrrp\nk5/8ZP7HGMuyPp+Poii9Xp+1Juk9pa2tbWFhIRQKKZVKu91+AhG7+vp6brhWrgmq8Xg810NX\npVIJ/NjEOeU83iXxeHxx153jxpvRS4fDkUqlCp/DSxBEOp0+ePAg8ohRKBQdHR06nQ7DsOLi\nYr6gPD6cY0n0GSms6L8LCwsTExOolcRqtba3t2MYNjc3NzMzo0mt78bkOHtMxZrWg6Ggk9R5\nX2ynlv2co2SdQNjpMA9s3A5FZ4FnNyhLoeGr4H0TFv4OpA7qvhSMGAAmuZUZhgmFQnV1dQcP\nHuSCbZwKT6VSs7Ozzc3NYO2DSV6ASqaFC/vB9449pVs6bjgN4kxrVliWfeCBB7773e8yDHP7\n7bffcccdJ9ZDMzIy0tbWljWSetZZZ5111lmr7qGzs3Pv3r1+v79wWWk0GlGQb8+ePZKwk5CQ\nkJCAjRs3rlu3Lo/NB8MwAwMDXFtATU3N+zwfE8Mwu91+MsPNjEZjX18fmuWqUCgOHTokXkcc\nluPAcby1tXV4eBhpu5KSEnHU0GKx8Gdq8VFmXEuqc/hLPB7PmmbaTk9Pc85/FEWNjY319vaG\nQqHBwUGu29dsNh8fpFb1eRj+6fEm05LzkERLp9OcqgMAr9frdrtNJtPU1BQARMimI6Y7qyI7\ntESIKPkorL9XELFLpVJOpzOdThsMhhVtuaQBjgk7bXqqOvbUrGY5F4lDekl5jh4wrPZaqL0W\nAgNw+DagfGDbAo1fB1wup4RedyRJ6nS6TZs2+f1+hmFGR0f57y4nu2uuhcV/wNGnAQBIHfT9\nF+gaQNdgBOi2hZxOJ03TFoulkOBuKBS6/vrrX3jhhaKioocffviiiy4qXNUlk0mSJFFQMB6P\nz83Nbdq0qcBts4K6xWdmZjhhd+aZZ2Zds7m5+WQOdIojCTsJCQmJ95DFxUV+s+fMzExZWdmH\nbkqmQqEoKysDgFzldBqNBiUoszYFm83mzZs3x2IxhUKRtc3CYDA0NTVNT0+n02m5XJ7JZJB+\nwiFTH/ndguaTgvXXJOwEzbbRaJRl2cXFRb6HSyAQQElbAAC1HT7xDoz+EmJHwboZmr4JGA4A\nsViMYRiCTVRGnzJRAxlCn3B+NSa/mNuJT9HnU/SVlpaKdQNFUfv27UPS1ul0hkKhxsbG5ffq\nt8HB4+VoNbW1Qfd8EMoBgAFyPgiy2dmamhoIHIJ/nLHsb+L8MwQGYPNjFouFP+9Op9Mh1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H6IDsOwpqam+vp6hmHEHSoIiqKWlpZQCE1T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\nQmCC","text/plain":["plot without title"]},"metadata":{"image/png":{"height":420,"width":420}},"output_type":"display_data"}],"source":["library(ggplot2)\n","library(ggrepel)\n","\n","# Create the plot\n","plot <- ggplot(cor_results, aes(x = cor_epithelial, y = cor_mesenchymal)) +\n","  geom_point(aes(color = ifelse(gene %in% mesenchymal_genes, \"Mesenchymal\",\n","                                 ifelse(gene %in% epithelial_genes, \"Epithelial\",\n","                                        ifelse(gene == \"ELF3\", \"ELF3\", \"Other\")))),\n","                 shape = 19, size = 1) +\n","  scale_color_manual(values = c(\"Mesenchymal\" = \"blue\",\n","                                 \"Epithelial\" = \"orange\",\n","                                 \"ELF3\" = \"red\",\n","                                 \"Other\" = \"gray\")) +\n","  xlim(-1, 1) +  # Set x-axis limits\n","  ylim(-1, 1) +  # Set y-axis limits\n","  geom_hline(yintercept = 0, linetype = \"dashed\", color = \"black\") +  # Horizontal line\n","  geom_vline(xintercept = 0, linetype = \"dashed\", color = \"black\") +  # Vertical line\n","  labs(title = \"Epithelial vs. Mesenchymal Scores\",\n","       x = \"Epithelial Score\",\n","       y = \"Mesenchymal Score\") +\n","  theme_minimal() +\n","  theme(legend.position = \"none\") +  # Remove the legend\n","  geom_text_repel(aes(label = ifelse(gene %in% c(\"CDH1\", \"GRHL2\", \"VIM\", \"ZEB1\", \"SNAI1\", \"SNAI2\", \"ELF3\", \"OVOL2\", \"KLF4\"), gene, \"\")),\n","                  box.padding = 0.5,   # Adjust padding\n","                  point.padding = 0.5,  # Padding around points\n","                  max.overlaps = Inf)   # Allow for all overlaps\n","\n","# Set the aspect ratio\n","ggsave(\"scatter_plot.png\", plot, width = 10, height = 6, dpi = 300)\n","\n","# To display the plot directly in the console, use:\n","print(plot)"]},{"cell_type":"markdown","source":["#Figure B"],"metadata":{"id":"5Yoy8JZsswse"}},{"cell_type":"code","source":["!wget -O Cell_lines_annotations_20181226.txt \"https://data.broadinstitute.org/ccle/Cell_lines_annotations_20181226.txt\""],"metadata":{"id":"xVwjs-yBzHTc"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["cell_line_annotation = pd.read_csv(\"Cell_lines_annotations_20181226.txt\", sep=\"\\t\")\n","\n","cell_line_annotation"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":635},"id":"_Htk-eyys2-2","executionInfo":{"status":"ok","timestamp":1728251188328,"user_tz":-330,"elapsed":470,"user":{"displayName":"Anjaney Pandey","userId":"05531075380284782212"}},"outputId":"613012d9-0b2c-4edd-fc8d-924ce6c64332"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                       CCLE_ID    depMapID       Name  \\\n","0                                   DMS53_LUNG  ACH-000698     DMS 53   \n","1                       SW1116_LARGE_INTESTINE  ACH-000489     SW1116   \n","2                                NCIH1694_LUNG  ACH-000431  NCI-H1694   \n","3     P3HR1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE  ACH-000707     P3HR-1   \n","4     HUT78_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE  ACH-000509     HuT 78   \n","...                                        ...         ...        ...   \n","1456                       GEO_LARGE_INTESTINE  ACH-002394        NaN   \n","1457                             NCIH2330_LUNG         NaN        NaN   \n","1458                           LP6_SOFT_TISSUE  ACH-001116        NaN   \n","1459                           HS578BST_BREAST         NaN        NaN   \n","1460                                LC1SQ_LUNG  ACH-002156        NaN   \n","\n","       Pathology                        Site_Primary Site_Subtype1  \\\n","0        primary                                lung            NS   \n","1        primary                     large_intestine         colon   \n","2     metastasis                                lung            NS   \n","3     metastasis  haematopoietic_and_lymphoid_tissue            NS   \n","4        primary  haematopoietic_and_lymphoid_tissue          skin   \n","...          ...                                 ...           ...   \n","1456         NaN                                 NaN           NaN   \n","1457         NaN                                 NaN           NaN   \n","1458         NaN                                 NaN           NaN   \n","1459         NaN                                 NaN           NaN   \n","1460         NaN                                 NaN           NaN   \n","\n","     Site_Subtype2 Site_Subtype3          Histology  \\\n","0               NS            NS          carcinoma   \n","1               NS            NS          carcinoma   \n","2               NS            NS          carcinoma   \n","3               NS            NS  lymphoid_neoplasm   \n","4               NS            NS  lymphoid_neoplasm   \n","...            ...           ...                ...   \n","1456           NaN           NaN                NaN   \n","1457           NaN           NaN                NaN   \n","1458           NaN           NaN                NaN   \n","1459           NaN           NaN                NaN   \n","1460           NaN           NaN                NaN   \n","\n","                          Hist_Subtype1  ...            Growth.Medium  \\\n","0                  small_cell_carcinoma  ...       Waymouth's +10%FBS   \n","1                        adenocarcinoma  ...               L15+10%FBS   \n","2                  small_cell_carcinoma  ...  DMEM:F12 (1:1) + 5% FBS   \n","3                      Burkitt_lymphoma  ...                      NaN   \n","4     mycosis_fungoides-Sezary_syndrome  ...                      NaN   \n","...                                 ...  ...                      ...   \n","1456                                NaN  ...                      NaN   \n","1457                                NaN  ...                      NaN   \n","1458                                NaN  ...                      NaN   \n","1459                                NaN  ...                      NaN   \n","1460                                NaN  ...                      NaN   \n","\n","                                            Supplements Freezing.Medium  \\\n","0                                                   NaN          5%DMSO   \n","1                                                   NaN          5%DMSO   \n","2     .005 mg/ml insulin, .01 mg/ml transferrin, 30n...         5% DMSO   \n","3                                                   NaN             NaN   \n","4                                                   NaN             NaN   \n","...                                                 ...             ...   \n","1456                                                NaN             NaN   \n","1457                                                NaN             NaN   \n","1458                                                NaN             NaN   \n","1459                                                NaN             NaN   \n","1460                                                NaN             NaN   \n","\n","     Doubling.Time.from.Vendor  Doubling.Time.Calculated.hrs  \\\n","0                          NaN                         450.0   \n","1                          NaN                         123.2   \n","2                          NaN                          81.2   \n","3                          NaN                           NaN   \n","4                          NaN                           NaN   \n","...                        ...                           ...   \n","1456                       NaN                           NaN   \n","1457                       NaN                           NaN   \n","1458                       NaN                           NaN   \n","1459                       NaN                           NaN   \n","1460                       NaN                           NaN   \n","\n","                  type           type_refined PATHOLOGIST_ANNOTATION  \\\n","0      lung_small_cell        lung_small_cell              Lung:SCLC   \n","1           colorectal             colorectal   Colorectal:Carcinoma   \n","2      lung_small_cell        lung_small_cell              Lung:SCLC   \n","3     lymphoma_Burkitt       lymphoma_Burkitt     Lymphoma:NH_B_cell   \n","4       lymphoma_other  T-cell_lymphoma_other     Lymphoma:NH_T_cell   \n","...                ...                    ...                    ...   \n","1456               NaN             colorectal                    NaN   \n","1457               NaN                    NaN                    NaN   \n","1458               NaN                    NaN                    NaN   \n","1459               NaN                    NaN                    NaN   \n","1460               NaN               lung_NSC                    NaN   \n","\n","         mutRate  tcga_code  \n","0     157.241638       SCLC  \n","1      94.373192  COAD/READ  \n","2     109.534987       SCLC  \n","3     166.456406       DLBC  \n","4     118.217569        NaN  \n","...          ...        ...  \n","1456         NaN  COAD/READ  \n","1457         NaN        NaN  \n","1458         NaN        NaN  \n","1459         NaN        NaN  \n","1460         NaN       LUSC  \n","\n","[1461 rows x 33 columns]"],"text/html":["\n","  <div id=\"df-4335d762-5bde-4a67-b09b-f5e0cc17228b\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>CCLE_ID</th>\n","      <th>depMapID</th>\n","      <th>Name</th>\n","      <th>Pathology</th>\n","      <th>Site_Primary</th>\n","      <th>Site_Subtype1</th>\n","      <th>Site_Subtype2</th>\n","      <th>Site_Subtype3</th>\n","      <th>Histology</th>\n","      <th>Hist_Subtype1</th>\n","      <th>...</th>\n","      <th>Growth.Medium</th>\n","      <th>Supplements</th>\n","      <th>Freezing.Medium</th>\n","      <th>Doubling.Time.from.Vendor</th>\n","      <th>Doubling.Time.Calculated.hrs</th>\n","      <th>type</th>\n","      <th>type_refined</th>\n","      <th>PATHOLOGIST_ANNOTATION</th>\n","      <th>mutRate</th>\n","      <th>tcga_code</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>DMS53_LUNG</td>\n","      <td>ACH-000698</td>\n","      <td>DMS 53</td>\n","      <td>primary</td>\n","      <td>lung</td>\n","      <td>NS</td>\n","      <td>NS</td>\n","      <td>NS</td>\n","      <td>carcinoma</td>\n","      <td>small_cell_carcinoma</td>\n","      <td>...</td>\n","      <td>Waymouth's +10%FBS</td>\n","      <td>NaN</td>\n","      <td>5%DMSO</td>\n","      <td>NaN</td>\n","      <td>450.0</td>\n","      <td>lung_small_cell</td>\n","      <td>lung_small_cell</td>\n","      <td>Lung:SCLC</td>\n","      <td>157.241638</td>\n","      <td>SCLC</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>SW1116_LARGE_INTESTINE</td>\n","      <td>ACH-000489</td>\n","      <td>SW1116</td>\n","      <td>primary</td>\n","      <td>large_intestine</td>\n","      <td>colon</td>\n","      <td>NS</td>\n","      <td>NS</td>\n","      <td>carcinoma</td>\n","      <td>adenocarcinoma</td>\n","      <td>...</td>\n","      <td>L15+10%FBS</td>\n","      <td>NaN</td>\n","      <td>5%DMSO</td>\n","      <td>NaN</td>\n","      <td>123.2</td>\n","      <td>colorectal</td>\n","      <td>colorectal</td>\n","      <td>Colorectal:Carcinoma</td>\n","      <td>94.373192</td>\n","      <td>COAD/READ</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>NCIH1694_LUNG</td>\n","      <td>ACH-000431</td>\n","      <td>NCI-H1694</td>\n","      <td>metastasis</td>\n","      <td>lung</td>\n","      <td>NS</td>\n","      <td>NS</td>\n","      <td>NS</td>\n","      <td>carcinoma</td>\n","      <td>small_cell_carcinoma</td>\n","      <td>...</td>\n","      <td>DMEM:F12 (1:1) + 5% FBS</td>\n","      <td>.005 mg/ml insulin, .01 mg/ml transferrin, 30n...</td>\n","      <td>5% DMSO</td>\n","      <td>NaN</td>\n","      <td>81.2</td>\n","      <td>lung_small_cell</td>\n","      <td>lung_small_cell</td>\n","      <td>Lung:SCLC</td>\n","      <td>109.534987</td>\n","      <td>SCLC</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>P3HR1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE</td>\n","      <td>ACH-000707</td>\n","      <td>P3HR-1</td>\n","      <td>metastasis</td>\n","      <td>haematopoietic_and_lymphoid_tissue</td>\n","      <td>NS</td>\n","      <td>NS</td>\n","      <td>NS</td>\n","      <td>lymphoid_neoplasm</td>\n","      <td>Burkitt_lymphoma</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>lymphoma_Burkitt</td>\n","      <td>lymphoma_Burkitt</td>\n","      <td>Lymphoma:NH_B_cell</td>\n","      <td>166.456406</td>\n","      <td>DLBC</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>HUT78_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE</td>\n","      <td>ACH-000509</td>\n","      <td>HuT 78</td>\n","      <td>primary</td>\n","      <td>haematopoietic_and_lymphoid_tissue</td>\n","      <td>skin</td>\n","      <td>NS</td>\n","      <td>NS</td>\n","      <td>lymphoid_neoplasm</td>\n","      <td>mycosis_fungoides-Sezary_syndrome</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>lymphoma_other</td>\n","      <td>T-cell_lymphoma_other</td>\n","      <td>Lymphoma:NH_T_cell</td>\n","      <td>118.217569</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1456</th>\n","      <td>GEO_LARGE_INTESTINE</td>\n","      <td>ACH-002394</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>colorectal</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>COAD/READ</td>\n","    </tr>\n","    <tr>\n","      <th>1457</th>\n","      <td>NCIH2330_LUNG</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>1458</th>\n","      <td>LP6_SOFT_TISSUE</td>\n","      <td>ACH-001116</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>1459</th>\n","      <td>HS578BST_BREAST</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>1460</th>\n","      <td>LC1SQ_LUNG</td>\n","      <td>ACH-002156</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>lung_NSC</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>LUSC</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1461 rows × 33 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4335d762-5bde-4a67-b09b-f5e0cc17228b')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n"," 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cell_line_annotation['CCLE_ID'].values else None\n",")"],"metadata":{"id":"xssQVES60hol"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import pandas as pd\n","\n","# Extract ELF3 expression and Site_Primary from ccle_data_transposed\n","elf3_expression = ccle_data_transposed.loc['ELF3', ccle_data_transposed.columns[1:]]\n","site_primary = ccle_data_transposed.loc['Gene', ccle_data_transposed.columns[1:]]\n","\n","# Create a DataFrame with ELF3 expression and Site_Primary\n","elf3_df = pd.DataFrame({'ELF3_Expression': elf3_expression, 'Site_Primary': site_primary})\n","elf3_df.index.name = 'Name' # Setting the index name for consistency\n","elf3_df = elf3_df.reset_index()\n","\n","# Filter ssgsea_ccle_df for Epithelial and Mesenchymal scores\n","ssgsea_filtered = ssgsea_ccle_df[ssgsea_ccle_df['Term'].isin(['Epithelial', 'Mesenchymal'])]\n","\n","# Merge ELF3 expression with filtered ssgsea data\n","merged_data = pd.merge(elf3_df, ssgsea_filtered, on='Name', how='inner')\n","\n","# Calculate correlation coefficients grouped by 'Name'\n","correlation_df = merged_data.groupby('Name').apply(lambda x: pd.Series({\n","    'Epithelial_Correlation': x['ELF3_Expression'].corr(x['ES']),\n","    'Mesenchymal_Correlation': x['ELF3_Expression'].corr(x['ES'])\n","})).reset_index()\n","\n","# Filter correlation_df for epithelial and mesenchymal correlations separately\n","epithelial_correlation = correlation_df[merged_data['Term'] == 'Epithelial']\n","mesenchymal_correlation = correlation_df[merged_data['Term'] == 'Mesenchymal']"],"metadata":{"id":"OxaHeSVb9PiR"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"gJ_3A5PUhs7-"},"source":["#Figure C and D"]},{"cell_type":"markdown","source":["## With TCGAbiolinks in R\n","\n","Although the library is well-known, somehow for all the projects the memory (RAM) usage is overshooting in my PC as well as the lab's system"],"metadata":{"id":"DUJhL4Z2vLe2"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"H5McysR-nSEM","cellView":"form"},"outputs":[],"source":["# @title Install Libraries\n","if (!requireNamespace(\"BiocManager\", quietly = TRUE))\n","    install.packages(\"BiocManager\")\n","\n","BiocManager::install(\"TCGAbiolinks\")\n","BiocManager::install(\"SummarizedExperiment\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"KST32ODNiOSn","cellView":"form"},"outputs":[],"source":["# @title Import Libraries\n","\n","library(TCGAbiolinks)\n","library(SummarizedExperiment)\n","library(ggplot2)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3-czWXqrjD2v","cellView":"form"},"outputs":[],"source":["# @title Get Projects\n","\n","# List of TCGA cancer projects\n","projects <- TCGAbiolinks:::getGDCprojects()$project_id\n","\n","# Initialize empty list to store ELF3 expression data for each project\n","elf3_expr_list <- list()\n","\n","projects <- projects[grepl('^TCGA',projects,perl=T)]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"7zxfuhaelqn4","cellView":"form"},"outputs":[],"source":["# @title Download and Merge Data\n","\n","# Loop through each project and download ELF3 expression data\n","for (project in projects) {\n","  query <- GDCquery(\n","    project = projects,\n","    data.category = \"Transcriptome Profiling\",\n","    data.type = \"Gene Expression Quantification\",\n","    workflow.type = \"STAR - Counts\"\n","  )\n","\n","  GDCdownload(query)\n","  tcga_data <- GDCprepare(query)\n","\n","  # Extract expression data for ELF3\n","  elf3_expr <- assay(tcga_data)[\"ELF3\", ]\n","\n","  # Store in list with cancer type\n","  elf3_expr_list[[project]] <- data.frame(SampleID = colnames(tcga_data), ELF3_expr = elf3_expr, CancerType = projects)\n","}\n","\n","# Combine expression data from all cancer types\n","elf3_expr_data <- do.call(rbind, elf3_expr_list)"]},{"cell_type":"markdown","source":["## With downloaded data from UCSC Xena Browser\n","\n","Using Python from this step onwards"],"metadata":{"id":"4H4vc8rmv_KA"}},{"cell_type":"code","source":["# @title  Installing Libraries\n","\n","!pip install gseapy pandas matplotlib seaborn mygene openpyxl\n"],"metadata":{"id":"kPpvnjKzwnTG"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#@title Importing libraries\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import gseapy as gp\n","import mygene"],"metadata":{"id":"N7r29s2I5V4y"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!wget -O CCLE_RNAseq_rsem_genes_tpm_20180929.txt.gz \"https://data.broadinstitute.org/ccle/CCLE_RNAseq_rsem_genes_tpm_20180929.txt.gz\""],"metadata":{"id":"UTmn9ko_tn33"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import pandas as pd\n","import gzip\n","\n","# File path to the gzipped CCLE data\n","file_path = 'CCLE_RNAseq_rsem_genes_tpm_20180929.txt.gz'\n","\n","# Open the gzip file and read it into a pandas DataFrame\n","with gzip.open(file_path, 'rt') as f:\n","    ccle_data = pd.read_csv(f, sep='\\t')\n"],"metadata":{"id":"HwIC8rOY6Pag"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["ccle_data.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":256},"id":"ELAviqcwxnN4","executionInfo":{"status":"ok","timestamp":1728253442017,"user_tz":-330,"elapsed":456,"user":{"displayName":"Anjaney Pandey","userId":"05531075380284782212"}},"outputId":"7973d6b5-7e4d-4555-bae4-dbe36ce3198a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     Gene  22RV1_PROSTATE  2313287_STOMACH  253JBV_URINARY_TRACT  \\\n","0  TSPAN6            5.28             7.01                 22.80   \n","1    TNMD            0.00             0.00                  0.00   \n","2    DPM1           73.38           108.99                 56.51   \n","3   SCYL3            9.76            16.76                  2.58   \n","4   FIRRM           24.51            13.32                 10.86   \n","\n","   253J_URINARY_TRACT  42MGBA_CENTRAL_NERVOUS_SYSTEM  5637_URINARY_TRACT  \\\n","0               22.88                          23.09               57.94   \n","1                0.00                           0.00                0.00   \n","2               45.39                          99.28               98.95   \n","3                3.25                           2.73                3.11   \n","4                5.26                           9.27               31.61   \n","\n","   59M_OVARY  639V_URINARY_TRACT  647V_URINARY_TRACT  ...  \\\n","0      16.30               32.02               60.53  ...   \n","1       0.00                0.00                0.00  ...   \n","2      79.68              125.99               89.59  ...   \n","3       2.69                2.69                2.83  ...   \n","4       9.62               10.45               15.90  ...   \n","\n","   WSUDLCL2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE  YAPC_PANCREAS  \\\n","0                                         0.74          50.29   \n","1                                         0.00           0.00   \n","2                                        41.71         103.70   \n","3                                         5.60           7.76   \n","4                                        20.21          12.84   \n","\n","   YD10B_UPPER_AERODIGESTIVE_TRACT  YD15_SALIVARY_GLAND  \\\n","0                            14.18                24.13   \n","1                             0.00                 0.00   \n","2                            97.07               104.97   \n","3                             2.96                 5.84   \n","4                            11.38                14.58   \n","\n","   YD38_UPPER_AERODIGESTIVE_TRACT  YD8_UPPER_AERODIGESTIVE_TRACT  \\\n","0                           20.94                          47.96   \n","1                            0.00                           0.00   \n","2                           96.86                         119.50   \n","3                            4.17                           5.06   \n","4                           22.13                           9.58   \n","\n","   YH13_CENTRAL_NERVOUS_SYSTEM  YKG1_CENTRAL_NERVOUS_SYSTEM  ZR751_BREAST  \\\n","0                        28.92                        61.08          8.12   \n","1                         0.00                         0.00          0.00   \n","2                        64.09                       109.13        139.23   \n","3                         4.94                         5.91         15.96   \n","4                        13.35                        17.40         17.45   \n","\n","   ZR7530_BREAST  \n","0          16.81  \n","1           0.00  \n","2          56.20  \n","3          13.76  \n","4          11.53  \n","\n","[5 rows x 1020 columns]"],"text/html":["\n","  <div id=\"df-701a74d1-b6ce-485f-b7c6-ba6d64d11cc4\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Gene</th>\n","      <th>22RV1_PROSTATE</th>\n","      <th>2313287_STOMACH</th>\n","      <th>253JBV_URINARY_TRACT</th>\n","      <th>253J_URINARY_TRACT</th>\n","      <th>42MGBA_CENTRAL_NERVOUS_SYSTEM</th>\n","      <th>5637_URINARY_TRACT</th>\n","      <th>59M_OVARY</th>\n","      <th>639V_URINARY_TRACT</th>\n","      <th>647V_URINARY_TRACT</th>\n","      <th>...</th>\n","      <th>WSUDLCL2_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE</th>\n","      <th>YAPC_PANCREAS</th>\n","      <th>YD10B_UPPER_AERODIGESTIVE_TRACT</th>\n","      <th>YD15_SALIVARY_GLAND</th>\n","      <th>YD38_UPPER_AERODIGESTIVE_TRACT</th>\n","      <th>YD8_UPPER_AERODIGESTIVE_TRACT</th>\n","      <th>YH13_CENTRAL_NERVOUS_SYSTEM</th>\n","      <th>YKG1_CENTRAL_NERVOUS_SYSTEM</th>\n","      <th>ZR751_BREAST</th>\n","      <th>ZR7530_BREAST</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>TSPAN6</td>\n","      <td>5.28</td>\n","      <td>7.01</td>\n","      <td>22.80</td>\n","      <td>22.88</td>\n","      <td>23.09</td>\n","      <td>57.94</td>\n","      <td>16.30</td>\n","      <td>32.02</td>\n","      <td>60.53</td>\n","      <td>...</td>\n","      <td>0.74</td>\n","      <td>50.29</td>\n","      <td>14.18</td>\n","      <td>24.13</td>\n","      <td>20.94</td>\n","      <td>47.96</td>\n","      <td>28.92</td>\n","      <td>61.08</td>\n","      <td>8.12</td>\n","      <td>16.81</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>TNMD</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>...</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>DPM1</td>\n","      <td>73.38</td>\n","      <td>108.99</td>\n","      <td>56.51</td>\n","      <td>45.39</td>\n","      <td>99.28</td>\n","      <td>98.95</td>\n","      <td>79.68</td>\n","      <td>125.99</td>\n","      <td>89.59</td>\n","      <td>...</td>\n","      <td>41.71</td>\n","      <td>103.70</td>\n","      <td>97.07</td>\n","      <td>104.97</td>\n","      <td>96.86</td>\n","      <td>119.50</td>\n","      <td>64.09</td>\n","      <td>109.13</td>\n","      <td>139.23</td>\n","      <td>56.20</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>SCYL3</td>\n","      <td>9.76</td>\n","      <td>16.76</td>\n","      <td>2.58</td>\n","      <td>3.25</td>\n","      <td>2.73</td>\n","      <td>3.11</td>\n","      <td>2.69</td>\n","      <td>2.69</td>\n","      <td>2.83</td>\n","      <td>...</td>\n","      <td>5.60</td>\n","      <td>7.76</td>\n","      <td>2.96</td>\n","      <td>5.84</td>\n","      <td>4.17</td>\n","      <td>5.06</td>\n","      <td>4.94</td>\n","      <td>5.91</td>\n","      <td>15.96</td>\n","      <td>13.76</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>FIRRM</td>\n","      <td>24.51</td>\n","      <td>13.32</td>\n","      <td>10.86</td>\n","      <td>5.26</td>\n","      <td>9.27</td>\n","      <td>31.61</td>\n","      <td>9.62</td>\n","      <td>10.45</td>\n","      <td>15.90</td>\n","      <td>...</td>\n","      <td>20.21</td>\n","      <td>12.84</td>\n","      <td>11.38</td>\n","      <td>14.58</td>\n","      <td>22.13</td>\n","      <td>9.58</td>\n","      <td>13.35</td>\n","      <td>17.40</td>\n","      <td>17.45</td>\n","      <td>11.53</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 1020 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-701a74d1-b6ce-485f-b7c6-ba6d64d11cc4')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container 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DataFrame\n","ccle_data = ccle_data.merge(gene_info_df[['query', 'symbol']], left_on='ensembl_id', right_on='query', how='left')\n"],"metadata":{"id":"PxWjW-Ce9N-V"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Remove rows where gene symbols couldn't be mapped\n","ccle_data = ccle_data[ccle_data['symbol'].notnull()]\n","\n","# Drop unnecessary columns and rename 'symbol' to 'Gene'\n","ccle_data = ccle_data.rename(columns={'symbol': 'Gene'}).drop(['gene_id', 'ensembl_id', 'transcript_ids', 'query'], axis=1)\n","\n","# Reorder the columns to have 'Gene' as the first column\n","cols = ['Gene'] + [col for col in ccle_data.columns if col != 'Gene']\n","ccle_data = ccle_data[cols]"],"metadata":{"id":"t2IRtRhy_Cpo"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#@title Make the gene sets - Epi & Mes\n","\n","import pandas as pd\n","import gseapy as gp\n","from gseapy import ssgsea\n","\n","# Load gene sets (epithelial and mesenchymal) from your Excel file\n","gene_sets = pd.read_excel('EM_gene_signature_tumor_KS.xlsx')\n","epithelial_genes = gene_sets[gene_sets['Type'] == 'Epi']['Gene'].tolist()\n","mesenchymal_genes = gene_sets[gene_sets['Type'] == 'Mes']['Gene'].tolist()\n","\n","# Prepare gene sets for ssGSEA\n","gene_sets_dict = {\n","    'Epithelial': epithelial_genes,\n","    'Mesenchymal': mesenchymal_genes\n","}\n"],"metadata":{"id":"oRDPtHt85i6B"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# @title Calculate ssGSEA scores for CCLE cohort\n","ssgsea_ccle = gp.ssgsea(data=ccle_data, gene_sets=gene_sets_dict, sample_norm_method='rank', outdir=None)\n","\n","# Convert the scores to a DataFrame\n","ssgsea_ccle_df = ssgsea_ccle.res2d\n","\n","# Print the ssGSEA scores\n","#print(ssgsea_scores_df)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ryhQrsk1GG4M","executionInfo":{"status":"ok","timestamp":1728253524454,"user_tz":-330,"elapsed":23819,"user":{"displayName":"Anjaney Pandey","userId":"05531075380284782212"}},"outputId":"271b5d54-4642-40b0-fd17-457d2ea17589"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["2024-10-06 22:28:04,533 [WARNING] Found duplicated gene names, values averaged by gene names!\n"]}]},{"cell_type":"code","source":["ssgsea_ccle_df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"SjKbaSFS7jym","executionInfo":{"status":"ok","timestamp":1728253530615,"user_tz":-330,"elapsed":503,"user":{"displayName":"Anjaney Pandey","userId":"05531075380284782212"}},"outputId":"775d404d-2c39-4362-f076-2ce621e30a15"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                       Name         Term            ES       NES\n","0           OE19_OESOPHAGUS   Epithelial  17765.327641  1.112957\n","1      CL40_LARGE_INTESTINE   Epithelial  17729.982399  1.110742\n","2              JHOM2B_OVARY   Epithelial  17684.261037  1.107878\n","3              HCC1588_LUNG   Epithelial  17474.305997  1.094725\n","4               GSU_STOMACH   Epithelial  17460.926378  1.093887\n","...                     ...          ...           ...       ...\n","2033            GSU_STOMACH  Mesenchymal   2637.546361  0.165236\n","2034  LS513_LARGE_INTESTINE  Mesenchymal   2539.188355  0.159074\n","2035          SNU16_STOMACH  Mesenchymal   2521.225216  0.157949\n","2036   CL34_LARGE_INTESTINE  Mesenchymal   2494.115381  0.156251\n","2037           IM95_STOMACH  Mesenchymal   1803.047524  0.112957\n","\n","[2038 rows x 4 columns]"],"text/html":["\n","  <div id=\"df-9505d4d6-b550-4dec-a3e9-8bac2f4d2d0e\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" 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<td>Epithelial</td>\n","      <td>17460.926378</td>\n","      <td>1.093887</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>2033</th>\n","      <td>GSU_STOMACH</td>\n","      <td>Mesenchymal</td>\n","      <td>2637.546361</td>\n","      <td>0.165236</td>\n","    </tr>\n","    <tr>\n","      <th>2034</th>\n","      <td>LS513_LARGE_INTESTINE</td>\n","      <td>Mesenchymal</td>\n","      <td>2539.188355</td>\n","      <td>0.159074</td>\n","    </tr>\n","    <tr>\n","      <th>2035</th>\n","      <td>SNU16_STOMACH</td>\n","      <td>Mesenchymal</td>\n","      <td>2521.225216</td>\n","      <td>0.157949</td>\n","    </tr>\n","    <tr>\n","      <th>2036</th>\n","      <td>CL34_LARGE_INTESTINE</td>\n","      <td>Mesenchymal</td>\n","      <td>2494.115381</td>\n","      <td>0.156251</td>\n","    </tr>\n","    <tr>\n","      <th>2037</th>\n","      <td>IM95_STOMACH</td>\n","      <td>Mesenchymal</td>\n","      <td>1803.047524</td>\n","      <td>0.112957</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>2038 rows × 4 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9505d4d6-b550-4dec-a3e9-8bac2f4d2d0e')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    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...\n","YD8_UPPER_AERODIGESTIVE_TRACT   15.05  upper_aerodigestive_tract\n","YH13_CENTRAL_NERVOUS_SYSTEM      2.49     central_nervous_system\n","YKG1_CENTRAL_NERVOUS_SYSTEM      0.25     central_nervous_system\n","ZR751_BREAST                   711.51                     breast\n","ZR7530_BREAST                  201.35                     breast\n","\n","[1020 rows x 2 columns]"],"text/html":["\n","  <div id=\"df-4e397ede-3fb6-4442-b179-40aa5f1a2576\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>11111</th>\n","      <th>Site_Primary</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Gene</th>\n","      <td>ELF3</td>\n","      <td>None</td>\n","    </tr>\n","    <tr>\n","      <th>22RV1_PROSTATE</th>\n","      <td>37.68</td>\n","      <td>prostate</td>\n","    </tr>\n","    <tr>\n","      <th>2313287_STOMACH</th>\n","      <td>162.92</td>\n","      <td>stomach</td>\n","    </tr>\n","    <tr>\n","      <th>253JBV_URINARY_TRACT</th>\n","      <td>101.73</td>\n","      <td>urinary_tract</td>\n","    </tr>\n","    <tr>\n","      <th>253J_URINARY_TRACT</th>\n","      <td>89.0</td>\n","      <td>urinary_tract</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>YD8_UPPER_AERODIGESTIVE_TRACT</th>\n","      <td>15.05</td>\n","      <td>upper_aerodigestive_tract</td>\n","    </tr>\n","    <tr>\n","      <th>YH13_CENTRAL_NERVOUS_SYSTEM</th>\n","      <td>2.49</td>\n","      <td>central_nervous_system</td>\n","    </tr>\n","    <tr>\n","      <th>YKG1_CENTRAL_NERVOUS_SYSTEM</th>\n","      <td>0.25</td>\n","      <td>central_nervous_system</td>\n","    </tr>\n","    <tr>\n","      <th>ZR751_BREAST</th>\n","      <td>711.51</td>\n","      <td>breast</td>\n","    </tr>\n","    <tr>\n","      <th>ZR7530_BREAST</th>\n","      <td>201.35</td>\n","      <td>breast</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1020 rows × 2 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4e397ede-3fb6-4442-b179-40aa5f1a2576')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 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1m 37s with CPU and 27s with TPU\n"],"metadata":{"id":"Yvnp4YKBG37D"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# @title Calculate ssGSEA scores for TCGA PanCan dataset\n","\n","tcga_data = pd.read_csv(\"tcga_dataset.txt\", sep=\"\\t\")"],"metadata":{"id":"3_9xe48UIVJH"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# @title Replace Ensembl IDs with Gene Symbol\n","\n","# Initialize MyGeneInfo client\n","mg = mygene.MyGeneInfo()\n","\n","# Extract just the Ensembl gene ID without the version (split by dot '.')\n","tcga_data['ensembl_id'] = tcga_data['sample'].apply(lambda x: x.split('.')[0])\n","\n","# Query MyGene.info to get gene symbols\n","# List of Ensembl IDs\n","ensembl_ids = tcga_data['ensembl_id'].tolist()\n","\n","# Query the MyGene API to get the corresponding gene symbols\n","gene_info = mg.querymany(ensembl_ids, scopes='ensembl.gene', fields='symbol', species='human')\n","\n","# Convert the result to a DataFrame\n","gene_info_df = pd.DataFrame(gene_info)\n","\n","# Merge gene symbols into the original tcga_data DataFrame\n","tcga_data = tcga_data.merge(gene_info_df[['query', 'symbol']], left_on='ensembl_id', right_on='query', how='left')\n","\n","# Remove rows where gene symbols couldn't be mapped\n","tcga_data = tcga_data[tcga_data['symbol'].notnull()]\n","\n","# Drop unnecessary columns and rename 'symbol' to 'Gene'\n","tcga_data = tcga_data.rename(columns={'symbol': 'Gene'}).drop(['sample', 'ensembl_id', 'query'], axis=1)\n","\n","# Reorder the columns to have 'Gene' as the first column\n","cols = ['Gene'] + [col for col in tcga_data.columns if col != 'Gene']\n","tcga_data = tcga_data[cols]\n","\n","# Print final dataset with gene symbols and expression values for all samples\n","print(tcga_data.head())"],"metadata":{"id":"J5aN0trgVvrB"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tcga_data"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":496},"id":"r1N_OpCsRxeu","executionInfo":{"status":"ok","timestamp":1728243681122,"user_tz":-330,"elapsed":505,"user":{"displayName":"Anjaney Pandey","userId":"05531075380284782212"}},"outputId":"9b4d264f-bf33-499b-e325-9b9b14d2a74f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["               Gene  TCGA-19-1787-01  TCGA-S9-A7J2-01  TCGA-G3-A3CH-11  \\\n","0         LINC02082          -9.9658           0.2998          -9.9658   \n","2        ST8SIA5-DT          -3.8160          -3.0469          -9.9658   \n","3             RAB4B           5.2998           4.8881           3.5572   \n","5             TIGAR           3.5086           2.3019           0.0990   \n","6         LINC01224           0.8647          -1.0262          -9.9658   \n","...             ...              ...              ...              ...   \n","60494     GABARAPL3          -9.9658          -2.9324          -9.9658   \n","60496         BATF3           1.2271           1.1577           0.2642   \n","60499        PPP6R1           5.2661           4.8151           3.4358   \n","60500  LOC101927377          -0.2159          -3.3076          -2.5479   \n","60502         OR8D4          -9.9658          -9.9658          -9.9658   \n","\n","       TCGA-EK-A2RE-01  TCGA-44-6778-01  TCGA-F4-6854-01  TCGA-AB-2863-03  \\\n","0              -9.9658          -9.9658          -9.9658          -9.9658   \n","2              -9.9658          -5.5735          -9.9658          -3.4580   \n","3               4.2563           5.3162           4.5161           3.6242   \n","5               3.6184           3.5633           2.9109           2.1638   \n","6              -9.9658           1.1316           0.9493          -9.9658   \n","...                ...              ...              ...              ...   \n","60494          -9.9658          -9.9658          -9.9658          -9.9658   \n","60496           0.5069           1.5902           0.6880          -1.6394   \n","60499           5.2851           5.6436           5.5958           6.7544   \n","60500          -5.0116          -1.3548          -9.9658          -0.9132   \n","60502          -9.9658          -9.9658          -9.9658          -9.9658   \n","\n","       TCGA-C8-A1HL-01  TCGA-EW-A2FS-01  ...  TCGA-DJ-A2QC-01  \\\n","0              -9.9658          -9.9658  ...          -1.3921   \n","2              -9.9658          -5.0116  ...          -6.5064   \n","3               4.9782           5.7035  ...           5.4591   \n","5               2.6624           1.2696  ...           1.5998   \n","6               2.0535           0.4447  ...          -2.9324   \n","...                ...              ...  ...              ...   \n","60494          -9.9658          -9.9658  ...          -9.9658   \n","60496           1.0293           1.4600  ...           1.1316   \n","60499           4.4095           4.4081  ...           5.1700   \n","60500          -5.0116          -1.8836  ...           1.7141   \n","60502          -9.9658          -9.9658  ...          -9.9658   \n","\n","       TCGA-A8-A09K-01  TCGA-61-1907-01  TCGA-IB-7885-01  TCGA-95-7947-01  \\\n","0              -9.9658          -9.9658          -9.9658          -9.9658   \n","2              -9.9658          -9.9658          -9.9658          -6.5064   \n","3               5.5364           4.7798           5.0514           6.1607   \n","5               2.8219           2.5876           2.6232           2.9525   \n","6               0.4125           1.0222          -1.7322          -4.6082   \n","...                ...              ...              ...              ...   \n","60494          -9.9658          -9.9658          -9.9658          -9.9658   \n","60496          -0.7346           0.3573           2.5707           1.6140   \n","60499           5.3667           4.1161           4.9552           6.0228   \n","60500          -2.6349          -0.2671          -4.2934          -1.7322   \n","60502          -9.9658          -9.9658          -9.9658          -9.9658   \n","\n","       TCGA-VQ-AA6F-01  TCGA-BR-8588-01  TCGA-24-2254-01  TCGA-DD-A115-01  \\\n","0              -9.9658          -9.9658          -1.9379          -9.9658   \n","2              -9.9658          -9.9658          -5.5735          -9.9658   \n","3               5.2814           3.9599           5.0760           4.0260   \n","5               3.0774           3.4384           2.2082           2.1541   \n","6              -0.4521           1.3511           3.1344          -1.3183   \n","...                ...              ...              ...              ...   \n","60494          -9.9658          -9.9658          -2.9324          -9.9658   \n","60496           0.9862           1.9490           0.9642           0.3115   \n","60499           6.1252           5.1473           6.2593           4.4647   \n","60500          -1.6850          -3.6259          -0.5543          -2.3884   \n","60502          -9.9658          -9.9658          -5.0116          -9.9658   \n","\n","       TCGA-FV-A3I0-11  \n","0              -9.9658  \n","2              -9.9658  \n","3               3.0876  \n","5              -0.5756  \n","6              -9.9658  \n","...                ...  \n","60494          -9.9658  \n","60496           0.6145  \n","60499           3.2174  \n","60500          -3.0469  \n","60502          -9.9658  \n","\n","[43308 rows x 10536 columns]"],"text/html":["\n","  <div id=\"df-e7daff05-f53b-4590-b040-5581113134fe\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Gene</th>\n","      <th>TCGA-19-1787-01</th>\n","      <th>TCGA-S9-A7J2-01</th>\n","      <th>TCGA-G3-A3CH-11</th>\n","      <th>TCGA-EK-A2RE-01</th>\n","      <th>TCGA-44-6778-01</th>\n","      <th>TCGA-F4-6854-01</th>\n","      <th>TCGA-AB-2863-03</th>\n","      <th>TCGA-C8-A1HL-01</th>\n","      <th>TCGA-EW-A2FS-01</th>\n","      <th>...</th>\n","      <th>TCGA-DJ-A2QC-01</th>\n","      <th>TCGA-A8-A09K-01</th>\n","      <th>TCGA-61-1907-01</th>\n","      <th>TCGA-IB-7885-01</th>\n","      <th>TCGA-95-7947-01</th>\n","      <th>TCGA-VQ-AA6F-01</th>\n","      <th>TCGA-BR-8588-01</th>\n","      <th>TCGA-24-2254-01</th>\n","      <th>TCGA-DD-A115-01</th>\n","      <th>TCGA-FV-A3I0-11</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>LINC02082</td>\n","      <td>-9.9658</td>\n","      <td>0.2998</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>...</td>\n","      <td>-1.3921</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-1.9379</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>ST8SIA5-DT</td>\n","      <td>-3.8160</td>\n","      <td>-3.0469</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-5.5735</td>\n","      <td>-9.9658</td>\n","      <td>-3.4580</td>\n","      <td>-9.9658</td>\n","      <td>-5.0116</td>\n","      <td>...</td>\n","      <td>-6.5064</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-6.5064</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-5.5735</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>RAB4B</td>\n","      <td>5.2998</td>\n","      <td>4.8881</td>\n","      <td>3.5572</td>\n","      <td>4.2563</td>\n","      <td>5.3162</td>\n","      <td>4.5161</td>\n","      <td>3.6242</td>\n","      <td>4.9782</td>\n","      <td>5.7035</td>\n","      <td>...</td>\n","      <td>5.4591</td>\n","      <td>5.5364</td>\n","      <td>4.7798</td>\n","      <td>5.0514</td>\n","      <td>6.1607</td>\n","      <td>5.2814</td>\n","      <td>3.9599</td>\n","      <td>5.0760</td>\n","      <td>4.0260</td>\n","      <td>3.0876</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>TIGAR</td>\n","      <td>3.5086</td>\n","      <td>2.3019</td>\n","      <td>0.0990</td>\n","      <td>3.6184</td>\n","      <td>3.5633</td>\n","      <td>2.9109</td>\n","      <td>2.1638</td>\n","      <td>2.6624</td>\n","      <td>1.2696</td>\n","      <td>...</td>\n","      <td>1.5998</td>\n","      <td>2.8219</td>\n","      <td>2.5876</td>\n","      <td>2.6232</td>\n","      <td>2.9525</td>\n","      <td>3.0774</td>\n","      <td>3.4384</td>\n","      <td>2.2082</td>\n","      <td>2.1541</td>\n","      <td>-0.5756</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>LINC01224</td>\n","      <td>0.8647</td>\n","      <td>-1.0262</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>1.1316</td>\n","      <td>0.9493</td>\n","      <td>-9.9658</td>\n","      <td>2.0535</td>\n","      <td>0.4447</td>\n","      <td>...</td>\n","      <td>-2.9324</td>\n","      <td>0.4125</td>\n","      <td>1.0222</td>\n","      <td>-1.7322</td>\n","      <td>-4.6082</td>\n","      <td>-0.4521</td>\n","      <td>1.3511</td>\n","      <td>3.1344</td>\n","      <td>-1.3183</td>\n","      <td>-9.9658</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>60494</th>\n","      <td>GABARAPL3</td>\n","      <td>-9.9658</td>\n","      <td>-2.9324</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>...</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-2.9324</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","    </tr>\n","    <tr>\n","      <th>60496</th>\n","      <td>BATF3</td>\n","      <td>1.2271</td>\n","      <td>1.1577</td>\n","      <td>0.2642</td>\n","      <td>0.5069</td>\n","      <td>1.5902</td>\n","      <td>0.6880</td>\n","      <td>-1.6394</td>\n","      <td>1.0293</td>\n","      <td>1.4600</td>\n","      <td>...</td>\n","      <td>1.1316</td>\n","      <td>-0.7346</td>\n","      <td>0.3573</td>\n","      <td>2.5707</td>\n","      <td>1.6140</td>\n","      <td>0.9862</td>\n","      <td>1.9490</td>\n","      <td>0.9642</td>\n","      <td>0.3115</td>\n","      <td>0.6145</td>\n","    </tr>\n","    <tr>\n","      <th>60499</th>\n","      <td>PPP6R1</td>\n","      <td>5.2661</td>\n","      <td>4.8151</td>\n","      <td>3.4358</td>\n","      <td>5.2851</td>\n","      <td>5.6436</td>\n","      <td>5.5958</td>\n","      <td>6.7544</td>\n","      <td>4.4095</td>\n","      <td>4.4081</td>\n","      <td>...</td>\n","      <td>5.1700</td>\n","      <td>5.3667</td>\n","      <td>4.1161</td>\n","      <td>4.9552</td>\n","      <td>6.0228</td>\n","      <td>6.1252</td>\n","      <td>5.1473</td>\n","      <td>6.2593</td>\n","      <td>4.4647</td>\n","      <td>3.2174</td>\n","    </tr>\n","    <tr>\n","      <th>60500</th>\n","      <td>LOC101927377</td>\n","      <td>-0.2159</td>\n","      <td>-3.3076</td>\n","      <td>-2.5479</td>\n","      <td>-5.0116</td>\n","      <td>-1.3548</td>\n","      <td>-9.9658</td>\n","      <td>-0.9132</td>\n","      <td>-5.0116</td>\n","      <td>-1.8836</td>\n","      <td>...</td>\n","      <td>1.7141</td>\n","      <td>-2.6349</td>\n","      <td>-0.2671</td>\n","      <td>-4.2934</td>\n","      <td>-1.7322</td>\n","      <td>-1.6850</td>\n","      <td>-3.6259</td>\n","      <td>-0.5543</td>\n","      <td>-2.3884</td>\n","      <td>-3.0469</td>\n","    </tr>\n","    <tr>\n","      <th>60502</th>\n","      <td>OR8D4</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>...</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-9.9658</td>\n","      <td>-5.0116</td>\n","      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Pandey","userId":"05531075380284782212"}},"outputId":"766ba70e-90d6-4d6d-e055-72808c66e61c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["2024-10-06 19:49:39,647 [WARNING] Found duplicated gene names, values averaged by gene names!\n"]}]},{"cell_type":"code","source":["# Load ELF3 expression data\n","elf3_expr_data = pd.read_csv(\"ELF3_TCGA.txt\", sep=\"\\t\")"],"metadata":{"id":"89tlBkj5DOx-"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["elf3_expr_data"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":364},"id":"RFcYcLoecKuY","executionInfo":{"status":"ok","timestamp":1728245186660,"user_tz":-330,"elapsed":412,"user":{"displayName":"Anjaney Pandey","userId":"05531075380284782212"}},"outputId":"84a8799f-4923-4d5e-d580-6204dd491c5f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                Sample  ELF3_Expression Cancer_Type\n","0      TCGA-EK-A3GK-01           11.470        CESC\n","1      TCGA-L5-A4OH-01           11.200        ESCA\n","2      TCGA-ZF-A9R7-01           11.100        BLCA\n","3      TCGA-S4-A8RM-01           11.050        PAAD\n","4      TCGA-CU-A3QU-01           10.950        BLCA\n","...                ...              ...         ...\n","10530  TCGA-BT-A20N-11           -9.966        BLCA\n","10531  TCGA-ZF-AA54-01           -9.966        BLCA\n","10532  TCGA-OR-A5JI-01           -9.966         ACC\n","10533  TCGA-OR-A5KT-01           -9.966         ACC\n","10534  TCGA-OR-A5L3-01           -9.966         ACC\n","\n","[10535 rows x 3 columns]"],"text/html":["\n","  <div id=\"df-d7b1f6e1-551e-4eab-98ea-9feed998ad71\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    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columns='Term', values='ES').reset_index()\n","\n","# Merge with ELF3 expression data\n","merged_data = pd.merge(elf3_expr_data, ssgsea_pivoted, on='Sample')\n"],"metadata":{"id":"X5gi2A2hcJTA"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["merged_data"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"mGrNU4gCg1m3","executionInfo":{"status":"ok","timestamp":1728246770847,"user_tz":-330,"elapsed":440,"user":{"displayName":"Anjaney Pandey","userId":"05531075380284782212"}},"outputId":"6a67bf00-fedf-4fcc-a36e-f652f6aaa442"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                Sample  ELF3_Expression Cancer_Type    Epithelial  \\\n","0      TCGA-EK-A3GK-01           11.470        CESC   17492.45334   \n","1      TCGA-L5-A4OH-01           11.200        ESCA  17973.328956   \n","2      TCGA-ZF-A9R7-01           11.100        BLCA  16898.971889   \n","3      TCGA-S4-A8RM-01           11.050        PAAD  18888.890951   \n","4      TCGA-CU-A3QU-01           10.950        BLCA  17269.400755   \n","...                ...              ...         ...           ...   \n","10530  TCGA-BT-A20N-11           -9.966        BLCA   6743.964944   \n","10531  TCGA-ZF-AA54-01           -9.966        BLCA   7864.348203   \n","10532  TCGA-OR-A5JI-01           -9.966         ACC   7937.685795   \n","10533  TCGA-OR-A5KT-01           -9.966         ACC   8518.011959   \n","10534  TCGA-OR-A5L3-01           -9.966         ACC   7821.991183   \n","\n","        Mesenchymal  \n","0       9635.686406  \n","1      10379.584759  \n","2      10361.831723  \n","3      12940.898157  \n","4       8866.534556  \n","...             ...  \n","10530  17404.514241  \n","10531  15477.292877  \n","10532  15021.947788  \n","10533  14534.450825  \n","10534  11781.960644  \n","\n","[10535 rows x 5 columns]"],"text/html":["\n","  <div id=\"df-c9b3c22b-a25e-4269-bfe6-0d40f327883c\" class=\"colab-df-container\">\n"," 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\"samples\": [\n          8.334,\n          9.272,\n          8.203\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cancer_Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 33,\n        \"samples\": [\n          \"ACC\",\n          \"BRCA\",\n          \"DLBC\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Epithelial\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": -2269.3054435807244,\n        \"max\": 19420.550979873184,\n        \"num_unique_values\": 10535,\n        \"samples\": [\n          17314.706788842264,\n          17931.197364964057,\n          15698.798232407182\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mesenchymal\",\n      \"properties\": {\n        \"dtype\": 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hue='Score_Type')\n","plt.xticks(rotation=90)\n","plt.axhline(0, color='gray', linestyle='--', linewidth=0.5)\n","plt.title('Correlation of ELF3 Expression with Epithelial and Mesenchymal Scores Across TCGA Cancer Types')\n","plt.ylabel('Correlation Coefficient')\n","plt.xlabel('Cancer Type')\n","plt.legend(title='Score Type')\n","plt.tight_layout()\n","plt.show()"],"metadata":{"id":"w4ai0M0lebqB"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import pandas as pd\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","\n","correlation_df = pd.DataFrame()\n","\n","# Calculate correlation coefficients for each cancer type\n","for cancer_type in merged_data['Cancer_Type'].unique():\n","    cancer_data = merged_data[merged_data['Cancer_Type'] == cancer_type]\n","    correlation_epithelial = cancer_data['ELF3_Expression'].corr(cancer_data['Epithelial'])\n","    correlation_mesenchymal = cancer_data['ELF3_Expression'].corr(cancer_data['Mesenchymal'])\n","\n","    # Append results to correlation_df\n","    correlation_df = pd.concat([correlation_df, pd.DataFrame({\n","        'Cancer_Type': [cancer_type],\n","        'Epithelial_Correlation': [correlation_epithelial],\n","        'Mesenchymal_Correlation': [correlation_mesenchymal]\n","    })], ignore_index=True)\n","\n","# Highlight specific cancer types\n","highlight_cancer_types = ['CHOL', 'PAAD', 'BRCA', 'UCS', 'STAD']\n","correlation_df['Highlight'] = correlation_df['Cancer_Type'].apply(lambda x: x in highlight_cancer_types)\n"],"metadata":{"id":"sWDe3V0ph3yo"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!pip install adjustText"],"metadata":{"id":"9JHPYq1SlhIx"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import pandas as pd\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","from adjustText import adjust_text\n","\n","# Plotting the correlations\n","plt.figure(figsize=(12, 9))\n","sns.scatterplot(data=correlation_df, x='Epithelial_Correlation', y='Mesenchymal_Correlation',\n","                hue='Highlight', palette={True: 'blue', False: 'black'}, legend=False, s=100)\n","plt.axhline(0, color='gray', linestyle='--', linewidth=1)\n","plt.axvline(0, color='gray', linestyle='--', linewidth=1)\n","plt.title('Correlation of ELF3 Expression with Epithelial and Mesenchymal Scores Across TCGA Cancer Types', fontsize=15)\n","plt.ylabel('Correlation with Mesenchymal Score', fontsize=18)\n","plt.xlabel('Correlation with Epithelial Score', fontsize=18)\n","plt.xlim(-1, 1)\n","plt.ylim(-1, 1)\n","plt.xticks([-1, -0.5, 0, 0.5, 1], fontsize=15)\n","plt.yticks([-1, -0.5, 0, 0.5, 1], fontsize=15)\n","\n","# Annotate highlighted points with adjust_text\n","texts = []\n","for idx, row in correlation_df[correlation_df['Highlight']].iterrows():\n","    texts.append(plt.text(row['Epithelial_Correlation'], row['Mesenchymal_Correlation'], row['Cancer_Type'],\n","                         ha='left', va='bottom', fontsize=15))\n","\n","# Adjust text positions to avoid overlapping\n","# Adjust text positions to avoid overlapping with refined parameters\n","adjust_text(texts, arrowprops=dict(arrowstyle='-', color='black', lw=0.5)) # Add connecting lines\n","\n","plt.tight_layout()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":600},"id":"rx8neUn3kRm2","executionInfo":{"status":"ok","timestamp":1728248165262,"user_tz":-330,"elapsed":937,"user":{"displayName":"Anjaney Pandey","userId":"05531075380284782212"}},"outputId":"019ccf41-ac65-4ebd-e5a1-a340a24660a1"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x900 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["import pandas as pd\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","\n","# Calculate median epithelial scores for each cancer type\n","median_epithelial_scores = merged_data.groupby('Cancer_Type')['Epithelial'].median().sort_values()\n","\n","# Order cancer types based on median epithelial scores\n","cancer_type_order = median_epithelial_scores.index\n","\n","# Create the boxplot\n","plt.figure(figsize=(12, 6))\n","sns.boxplot(data=merged_data, x='Cancer_Type', y='ELF3_Expression', order=cancer_type_order)\n","plt.xticks(rotation=90)\n","\n","plt.ylabel('ELF3 Expression')\n","plt.xlabel('Cancer Type')\n","plt.tight_layout()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":459},"id":"qcSTNCIUn6UM","executionInfo":{"status":"ok","timestamp":1728248283936,"user_tz":-330,"elapsed":1350,"user":{"displayName":"Anjaney Pandey","userId":"05531075380284782212"}},"outputId":"c3627f80-9f2a-43aa-d1a3-bb7c7d29b160"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n","  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x600 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAABKUAAAJOCAYAAABm7rQwAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdeVxUZfs/8A8zsigqiyYDFYgLmCih5kIaWpr7XiqGmlZaQmWa9Tw+LCMqLU9pq7hRuYC55NfMMkt7TKwQyyVQCzfSCqVcwAUEnTm/P/id0xxmBmZ0lgPzeb9evmTOuYdzMzPnzDnXue7rdhMEQQAREREREREREZEDqZzdASIiIiIiIiIicj0MShERERERERERkcMxKEVERERERERERA7HoBQRERERERERETkcg1JERERERERERORwDEoREREREREREZHDMShFREREREREREQOx6AUERERERERERE5HINSRERERERERETkcAxKERHVcStXroSbmxt+++03Z3dFUebOnQs3Nzdnd4OIbGjfvn3w8PDA6dOnHbrdb7/9Fm5ubvj2228dut3a9OnTB3369Km1nZubG5599ln7d8hJqr8Ov/32G9zc3LBy5Uqn9cmWLH2fxbYdOnSw6fZbtmyJyZMnS49vZ3+o79/NR48eRYMGDXD48GFnd4WozmBQiogkYnDD3L+9e/dKbS05we3Tp4/Z3/Xrr78CAIqKijBhwgSEh4ejSZMm8PX1Rbdu3bBq1SoIgmDTPpPttWzZEkOHDnV2NxxGp9MhKCgIbm5u+PLLL53dHZtr2bJljfuT+E+80Lt+/TreeustdO/eHT4+PvDy8kJYWBieffZZHDt2zOj35+XlYcqUKQgNDYWXlxcaN26MqKgovPzyyzh16pTZfo0dOxZubm7417/+ZfXfZG0f6wNL3kMlBlgskZiYiPHjxyMkJES2XBAErFmzBjExMfD19UWjRo3QsWNHzJs3D9euXXNSb12LGKhwc3NDZmamyTY9e/aEm5ubzYMmSjJ58mSz+5yXl5dNtlFUVIS5c+fi0KFDNvl9SrV161b07t0bLVq0QKNGjdCqVSuMHTsW27dvd3bXzGrfvj2GDBmClJQUZ3eFqM5o4OwOEJHyzJs3D6GhoUbL27RpY/Xvuuuuu/Dqq68aLQ8KCgIAnD9/Hn/88QceffRRBAcH48aNG9ixYwcmT56MgoICvPLKKw7vc10zceJExMbGwtPT09ldqff+97//4ezZs2jZsiWysrIwaNAgZ3fJpt5++21cvXpVerxt2zZ8/PHHeOutt9C8eXNp+f3334/z589j4MCB2L9/P4YOHYrHHnsMjRs3RkFBAdatW4fly5ejsrJSes6KFSswffp0NG/eHHFxcWjXrh1u3ryJw4cPY/Xq1Xj77bdRXl4OtVot69Ply5exdetWtGzZEh9//DFee+01i++yW9vH+mLNmjWyx6tXr8aOHTuMlt9zzz2O7NZtO3ToEHbu3IkffvhBtlyn0+Gxxx7Dhg0b8MADD2Du3Llo1KgR9uzZg9TUVGzcuBE7d+5EQECAk3ruWry8vLB27VpMmDBBtvy3337DDz/8YLPATE1CQkJQXl4Od3d3u2/LFE9PT2RkZBgtr358s9TXX38te1xUVITU1FS0bNkSUVFRt/Q7b0dMTAzKy8vh4eFht228+eabeOmll9C7d2/MmTMHjRo1wokTJ7Bz506sW7cOAwcOtNu2b9czzzyDwYMH4+TJk2jdurWzu0OkeAxKEZGRQYMG4b777rPJ7/Lx8TE6MTUUGRlpdLf+2WefxbBhw/Duu+9i/vz5Fp3E2bLPt6OsrAyNGjVy6DbVavUtn+iSdTIzM9G5c2c8/vjj+M9//oNr167B29vbJr/bGZ+d6kaOHCl7fO7cOXz88ccYOXIkWrZsKVs3dOhQHDx4EJ988gkeeeQR2br58+cjMTFRevzDDz9g+vTp6NmzJz7//HM0adJE1n7hwoVIS0sz2adNmzZBp9Phww8/xEMPPYTs7Gz07t3bor9n8uTJFvexLjL3+at+zN27dy927NhR47G4Lvjoo48QHByMHj16yJb/97//xYYNGzB79my88cYb0vJp06Zh7NixGDlyJCZPnlxjdqMgCLh+/ToaNmxot/4bsuWxQ2kGDx6Mzz77DOfPn5cFs9euXYuAgAC0bdsWly5dsmsfbJmVdCsaNGhg0/3NnsGfW6FSqez6+t68eRPz58/Hww8/bBSQA4C//vrLbtuuTq/Xo7Ky0qq/t1+/fvDz88OqVaswb948O/aOqH7g8D0iUqSWLVuirKzMZlkMWq0WKpUK33zzjWz5tGnT4OHhgZ9//hnAP8MP1q9fj//85z/QaDTw9vbG8OHD8fvvv8ueK9Zt2L9/P2JiYtCoUSP85z//AQBUVFRAq9WiTZs28PT0xN13342XX34ZFRUVst+xY8cO9OrVC76+vmjcuDHCw8Ol3yF67733EBERgUaNGsHPzw/33Xcf1q5dK603V1MqPT0dERER8PT0RFBQEBISElBSUmLybzh69CgefPBBNGrUCHfeeSf++9//Wv0a1yQzMxNdunRBw4YN4e/vj9jYWNnr+eyzz6Jx48YoKyszeu748eOh0Wig0+mkZV9++SUeeOABeHt7o0mTJhgyZAiOHDlSaz8seb3NKS8vx+bNmxEbG4uxY8eivLwcW7ZsMdn2yy+/RO/evdGkSRM0bdoUXbt2lb1nNX12/vrrLzz55JMICAiAl5cX7r33XqxatcpoG+vWrUOXLl2kbXTs2BHvvPOOtP7GjRtITU1F27Zt4eXlhWbNmqFXr17YsWOHRX9vTXJzc/HFF1/gySefNAr2AFVZAm+++ab0ODU1FW5ubsjKyjIKSAFVmRXmAtBZWVl4+OGH8eCDD+Kee+5BVlaWXfqYl5eHyZMno1WrVvDy8oJGo8ETTzyBCxcuyJ4n1kM5ceIEJk+eDF9fX/j4+GDKlCkmP7+ZmZno1q2btP/GxMQYXWRZ8nmePHkyGjdujJMnT2Lw4MFo0qQJ4uLiLHotqnv88cfRvHlz3Lhxw2hd//79ER4eLj0Wh2pnZWUhPDwcXl5e6NKlC7Kzs42e++eff+KJJ55AQEAAPD09ERERgQ8//NCoXW3HNHM+/fRTPPTQQ7JMufLycrzxxhsICwszmZU7bNgwPP7449i+fbtsOLc49Pirr77Cfffdh4YNG2LZsmUAgD/++AMjR46Et7c3WrRogZkzZxodu0W5ubkYOHAgfHx80KhRI/Tu3Rvff/+9rI34mTl69Cgee+wx+Pn5oVevXtL62o6PouXLl6N169Zo2LAhunXrhj179tT6mlVX0/u4a9cuuLm5YfPmzUbPW7t2Ldzc3JCTk1PrNkaMGAFPT09s3LjR6HeMHTvW7E0UW74OpmpK2Wsfv1Xid3d2djaefvppNGvWDE2bNsWkSZOMgnaGNaW+/fZbdO3aFQAwZcoUo2HVIku+1y09V6nOVE2pPXv2YMyYMQgODpZ+18yZM1FeXm71a3P+/HlcvnwZPXv2NLm+RYsWssfXr1/H3LlzERYWBi8vLwQGBmL06NE4efKk1ObatWt48cUXcffdd8PT0xPh4eF48803jUpFGB7zxHMocbigpcc4d3d39OnTx+w5AhHJMShFREZKS0tx/vx52b/qJ22W0ul0Rr/LcHiQqLy8HOfPn8dvv/2GVatW4aOPPkJ0dLTFd61r63NSUhKioqLw5JNP4sqVKwCAr776CitWrEBKSgruvfde2e9LS0vDF198gX/96194/vnnsWPHDvTr18/o5OrChQsYNGgQoqKi8Pbbb+PBBx+EXq/H8OHD8eabb2LYsGF47733MHLkSLz11lsYN26c9NwjR45g6NChqKiowLx587Bw4UIMHz5cdkGzYsUKPP/882jfvj3efvttpKamIioqCrm5uTW+HnPnzkVCQgKCgoKwcOFCPPLII1i2bBn69+9vdCF66dIlDBw4EPfeey8WLlyIdu3a4V//+pfNaialpaVh0qRJaNu2LRYtWoQXXngB33zzDWJiYqQg2bhx43Dt2jV88cUXsueWlZVh69atePTRR6ULmTVr1mDIkCFo3LgxXn/9dSQnJ+Po0aPo1atXjcXeLXm9a/LZZ5/h6tWriI2NhUajQZ8+fUwGSFauXIkhQ4bg4sWLmDNnDl577TVERUUZ1cAw9dkpLy9Hnz59sGbNGsTFxeGNN96Aj48PJk+eLAs47dixA+PHj4efnx9ef/11vPbaa+jTp4/sb5k7dy5SU1Px4IMP4v3330diYiKCg4Nx4MABi/7e2l4LoGroaG3Kysrwv//9D3369MFdd91l1XaKioqwa9cujB8/HkBVgPKTTz6xKFhtTR+Bqtf01KlTmDJlCt577z3ExsZi3bp1GDx4sMn6dmPHjsWVK1fw6quvYuzYsVi5ciVSU1NlbVJTUzFx4kS4u7tj3rx5SE1Nxd13343//e9/UhtrPs83b97EgAED0KJFC7z55psmg22WmDhxIi5cuICvvvpKtvzcuXP43//+Z5ThsXv3brzwwguYMGEC5s2bhwsXLmDgwIGyQr7FxcXo0aMHdu7ciWeffRbvvPMO2rRpgyeffBJvv/221O5Wj2l//vknzpw5g86dO8uWf/fdd7h06RIee+wxNGhgegDApEmTAACff/65bHlBQQHGjx+Phx9+GO+88w6ioqJQXl6Ovn374quvvsKzzz6LxMRE7NmzBy+//LLR7/3f//6HmJgYXL58GVqtFq+88gpKSkrw0EMPYd++fUbtx4wZg7KyMrzyyiuYOnUqAMuOjwDwwQcf4Omnn4ZGo8F///tf9OzZ0+TNkprU9j726dMHd999t8njWlZWFlq3bo3o6Ohat9OoUSOMGDECH3/8sbTs559/xpEjR/DYY4+ZfI4jXgd77OM1qX5OIgZaqnv22Wfxyy+/YO7cuZg0aRKysrIwcuRIs3U177nnHin7Ztq0aVizZo1UT01kyfe6pecqltq4cSPKysowffp0vPfeexgwYADee+89af+zRosWLdCwYUNs3boVFy9erLGtTqfD0KFDkZqaii5dumDhwoWYMWMGSktLpc+2IAgYPnw43nrrLQwcOBCLFi1CeHg4XnrpJcyaNcvod/7vf//DzJkzMW7cOLzzzjto2bKlxcc4UZcuXXD48GGT7zkRVSMQEf1/H330kQDA5D9PT09ZWwBCQkJCjb+vd+/eJn/X448/btT21VdflbXp27evcObMGZv2OT8/X/Dw8BCeeuop4dKlS8Kdd94p3HfffcKNGzekNrt27RIACHfeeadw+fJlafmGDRsEAMI777xj9PctXbpUtp01a9YIKpVK2LNnj2z50qVLBQDC999/LwiCILz11lsCAOHvv/82+/eNGDFCiIiIsOg1KCwsFARBEP766y/Bw8ND6N+/v6DT6aR277//vgBA+PDDD43+htWrV0vLKioqBI1GIzzyyCM1blcQBCEkJEQYMmSI2fW//faboFarhbS0NNny/Px8oUGDBtJyvV4v3HnnnUbbFF/37OxsQRAE4cqVK4Kvr68wdepUWbtz584JPj4+suVarVYw/Jqz5PWuydChQ4WePXtKj5cvXy40aNBA+Ouvv6RlJSUlQpMmTYTu3bsL5eXlsufr9XrpZ3OfnbffflsAIGRmZkrLKisrhejoaKFx48bSZ3LGjBlC06ZNhZs3b5rt77333lvje2OJN954Q/bZEo0aNUoAIFy6dKnW3/Hzzz8LAIQXXnjBaN2FCxeEv//+W/pXUVEhW//mm28KDRs2lP7uY8eOCQCEzZs317pda/ooCIJQVlZmtOzjjz+Wff4E4Z/P1RNPPGG0vWbNmkmPjx8/LqhUKmHUqFGy/VAQ/vksWPN5fvzxxwUAwr///W+L/h5DCQkJsn1Bp9MJd911lzBu3DhZu0WLFglubm7CqVOnpGXi8fSnn36Slp0+fVrw8vISRo0aJS178sknhcDAQOH8+fOy3xkbGyv4+PhIr68lxzRTdu7cKQAQtm7dKlsu7jM1fSYuXrwoABBGjx4tLQsJCREACNu3bzf5+zZs2CAtu3btmtCmTRsBgLBr1y5BEKrew7Zt2woDBgyQ7dtlZWVCaGio8PDDD0vLxM/M+PHjZduy9PhYWVkptGjRQoiKipLtI8uXLxcACL179zb7t4ssfR/nzJkjeHp6CiUlJdKyv/76S2jQoIGg1Wpr3Ib4/blx40bh888/F9zc3KTv8Zdeeklo1aqVIAhVxz/Dz4A9XofCwkIBgPDRRx9Jy2y9j5sj7qum/g0YMEBqJ353d+nSRaisrJSW//e//xUACFu2bJGW9e7dW/b3/fjjj0Z/n2FbS77XLT1XEYSq/cXw3E18r8X9QRBMv76vvvqq4ObmJpw+fVpaVv272ZyUlBQBgODt7S0MGjRISEtLE/bv32/U7sMPPxQACIsWLTJaJ+6bn376qQBAWLBggWz9o48+Kri5uQknTpyQlgEQVCqVcOTIEVlbS49xorVr1woAhNzc3Fr/ViJXx0wpIjKyePFi7NixQ/bvVrNmWrZsafS7TN1xHj9+PHbs2IG1a9dKd1KtSfm2pM8dOnRAamoqMjIyMGDAAJw/fx6rVq0yeXd90qRJsmFGjz76KAIDA7Ft2zZZO09PT0yZMkW2bOPGjbjnnnvQrl072R3Shx56CEDVEAkA8PX1BQBs2bIFer3e5N/l6+uLP/74Az/++KPFr8XOnTtRWVmJF154ASrVP4f5qVOnomnTpkbZSI0bN5ZlRnh4eKBbt241zoZmqf/7v/+DXq/H2LFjZa+FRqNB27ZtpdfCzc0NY8aMwbZt22SZdOvXr8edd94pDXXZsWMHSkpKMH78eNnvU6vV6N69u/T7TLHk9TZHzCoRM3YA4JFHHoGbmxs2bNggLduxYweuXLmCf//730b1J6oX5zb12dm2bRs0Go1sO+7u7nj++edx9epV7N69W/pbrl27VuNQPF9fXxw5cgTHjx+36m+1hHjn19RQPHNtGzdubLSuVatWuOOOO6R/YnaTKCsrC0OGDJG207ZtW3Tp0sWiIXzW9BGALCvz+vXrOH/+vFS7yFR22TPPPCN7/MADD+DChQvSdj/99FPo9XqkpKTI9kPgn8/CrXyep0+fbtHfUxOVSoW4uDh89tlnUuYoUPV633///UaTRkRHR6NLly7S4+DgYIwYMQJfffUVdDodBEHApk2bMGzYMAiCIPtbBgwYgNLSUuk1vJVjGgAp89XPz0+2XOx/Te+zuK56xkJoaCgGDBggW7Zt2zYEBgbi0UcflZY1atQI06ZNk7U7dOgQjh8/jsceewwXLlyQ/t5r166hb9++yM7ONjrOVP/MWHp8/Omnn/DXX3/hmWeekdUWmjx5Mnx8fMz+3dXV9j4CVd99FRUV+OSTT6R269evx82bN62qkdS/f3/4+/tj3bp1EAQB69atkx3XnPE62Hofr4mXl5fROcmOHTvw2muvGbWdNm2arCD79OnT0aBBA6PzDWtY8r1u6bmKpQxf32vXruH8+fO4//77IQgCDh48aPXfkJqairVr16JTp0746quvkJiYiC5duqBz58745ZdfpHabNm1C8+bN8dxzzxn9DvFYu23bNqjVajz//POy9S+++CIEQTA6X+zduzfat28vPbbmGCcSj1Xnz5+3+m8ncjUsdE5ERrp162azouHe3t7o169fre1CQkKkKb7Hjx+PadOmoV+/figoKLBoCJ+lfX7ppZewbt067Nu3D6+88orspMNQ27ZtZY/d3NzQpk0bo+E0d955p1EB0uPHj+OXX37BHXfcYfJ3iwU6x40bh4yMDDz11FP497//jb59+2L06NF49NFHpYvYf/3rX9i5cye6deuGNm3aoH///njsscfM1lkAgNOnTwOArC4MUHVS2qpVK2m96K677jIKmPj5+SEvL8/sNix1/PhxCIJg9HqKDE/Ex40bh7fffhufffYZHnvsMVy9ehXbtm3D008/LfVPDLCIJ83VNW3a1GxfLHm9zVm/fj1u3LiBTp064cSJE9Ly7t27IysrCwkJCQAg1a+wZLpzU5+d06dPo23btkb9EWdJE9+7+Ph4bNiwAYMGDcKdd96J/v37Y+zYsbLZiObNm4cRI0YgLCwMHTp0wMCBAzFx4kRERkbW2rfaiK/zlStXpGCfOWJAwNSw3S1btuDGjRv4+eefMXv2bNm6X375BQcPHsSkSZNkr3mfPn2wePFiXL58ucb325o+AsDFixeRmpqKdevWGRXRLS0tNWofHBwseyxegFy6dAlNmzbFyZMnoVKpzB5jAOs/zw0aNLB6CKQ5kyZNwuuvv47Nmzdj0qRJKCgowP79+7F06VKjtqb237CwMJSVleHvv/+GSqVCSUkJli9fjuXLl5vcnvia3soxzZBQbUiT+PkyDK5VZy5wZWrG1tOnT6NNmzZGx8Tqx1PxvXv88cfNbre0tFQWRKu+PUuPj+J+X72du7s7WrVqZXb71dX2Pmo0GrRr1w5du3ZFVlYWnnzySQBVwcoePXpYNZutu7s7xowZg7Vr16Jbt274/fffzQ7dc9TrYOt9vCZqtdqicx/A+O9p3LgxAgMDaxyOXhtLvtctPVex1JkzZ5CSkoLPPvvMqCaWqdfXEuPHj8f48eNx+fJl5ObmYuXKlVi7di2GDRuGw4cPw8vLCydPnkR4eLjZ4btA1WcnKCjI6BhQ/btVVH1f/fvvvy0+xonEY5Wls8USuTIGpYhIkR599FGsWLEC2dnZRneyb8epU6eki4n8/Pzb/n2mAmZ6vR4dO3bEokWLTD7n7rvvlp6bnZ2NXbt24YsvvsD27duxfv16PPTQQ/j666+hVqtxzz33oKCgAJ9//jm2b9+OTZs2IT09HSkpKVbVtqiJuaKz1S/+boVer4ebmxu+/PJLk9sxzJ7p0aMHWrZsiQ0bNuCxxx7D1q1bUV5eLqttIWYerFmzBhqNxuj31XRSasnrbY6YmWPuwvnUqVNWXRyK/blVLVq0wKFDh/DVV1/hyy+/xJdffomPPvoIkyZNkoqix8TE4OTJk9iyZQu+/vprZGRk4K233sLSpUvx1FNP3fK2AaBdu3YAqvahBx54oMa2bdq0QYMGDWT1h0TiLHqm3rfMzEwAwMyZMzFz5kyj9Zs2bTLKNLvVPgJV9WN++OEHvPTSS4iKikLjxo2h1+sxcOBAk5l1tthvrP08e3p61hpAtVT79u3RpUsXZGZmYtKkScjMzISHhwfGjh1r9e8S/44JEyaYDdKIwdBbPaY1a9YMAIwudsWLyry8PKPZI0XihXj1AOHt7IPi3/zGG28gKirKZJvq2YHVt2fN8dGRJk2ahBkzZuCPP/5ARUUF9u7di/fff9/q3/PYY49h6dKlmDt3Lu69916zAVpHvQ7O2MedxZK+W3quYgmdToeHH34YFy9exL/+9S+0a9cO3t7e+PPPPzF58mSrs5Ora9q0KR5++GE8/PDDcHd3x6pVq5Cbm2vxTKzWMrWvApYd40TiscpwBkoiMo1BKSJSJHHo3q3eXTNFr9dj8uTJaNq0KV544QW88sorePTRRzF69GijttWHPAmCgBMnTliUZdK6dWv8/PPP6Nu3b613yFQqFfr27Yu+ffti0aJFeOWVV5CYmIhdu3ZJd1m9vb0xbtw4jBs3DpWVlRg9ejTS0tIwZ84ck1MUixlnBQUFskBJZWUlCgsLLb57awutW7eGIAgIDQ1FWFhYre3Hjh2Ld955B5cvX8b69evRsmVL2fTvrVu3BlAVlLmVv8OS17u6wsJC/PDDD3j22WeNToD1ej0mTpyItWvXIikpSerf4cOHrcoqEIWEhCAvLw96vV4WfPj111+l9SIPDw8MGzYMw4YNg16vR3x8PJYtW4bk5GRp2/7+/pgyZQqmTJmCq1evIiYmBnPnzr3toNSwYcPw6quvIjMzs9aAj7e3N/r06YPdu3fjzz//xJ133lnr7xcEAWvXrsWDDz6I+Ph4o/Xz589HVlZWjUEpa/p46dIlfPPNN0hNTUVKSoq0/HaGPrZu3Rp6vR5Hjx41G7S43c/z7Zo0aRJmzZqFs2fPYu3atRgyZIjR8DjA9Otw7NgxNGrUSMqyaNKkCXQ6nUV/h7XHNOCfIGNhYaFsuTib5tq1a5GYmGjyYnz16tUAgKFDh9bat5CQEBw+fBiCIMiO3wUFBbJ24nvXtGnTW37vLD0+ivv98ePHZVl1N27cQGFhodFEHeZY8j4CQGxsLGbNmoWPP/4Y5eXlcHd3v6XC17169UJwcDC+/fZbvP7662bbOeJ1sMc+bivHjx/Hgw8+KD2+evUqzp49i8GDB5t9ji2yb6w5V6lNfn4+jh07hlWrVskKm9tittfq7rvvPqxatQpnz54FUPV35Obm4saNG7Lsa0MhISHYuXMnrly5IsuWMvXdasodd9xh1TEOqDpWqVQqi859iFwda0oRkVP9/fffJpd/8MEHcHNzM5pp6XYsWrQIP/zwA5YvX4758+fj/vvvx/Tp002O91+9erVsOMgnn3yCs2fPYtCgQbVuZ+zYsfjzzz+xYsUKo3Xl5eW4du0aAJicUUa8eBWnY64+66GHhwfat28PQRBMTucOAP369YOHhwfeffdd2V3RDz74AKWlpRgyZEitf4OtjB49Gmq1GqmpqUZ3lwVBMPr7xo0bh4qKCqxatQrbt283ytoYMGAAmjZtildeecXk32/u8wRY9nqbImZJvfzyy3j00Udl/8aOHYvevXtLbfr3748mTZrg1VdfxfXr143+3toMHjwY586dw/r166VlN2/exHvvvYfGjRtLQbHqr5tKpZICpuY+O40bN0abNm1qnerbEtHR0Rg4cCAyMjLw6aefGq2vrKyUDcdLSUmBTqfDhAkTTA7jq/7afP/99/jtt98wZcoUo9f80Ucfxbhx47Br1y4UFRXZpI9iIKN6P0zNqGSpkSNHQqVSYd68eUZZAuJ2bufzbAvjx4+Hm5sbZsyYgVOnTpmtGZSTkyOrl/L7779jy5Yt6N+/P9RqNdRqNR555BFs2rTJZEac4d9xK8c0oGq46913342ffvpJtrxRo0aYPXs2CgoKkJiYaPS8L774AitXrsSAAQNkAW5zBg8ejKKiIllNpbKyMqMhO126dEHr1q3x5ptvmvxMW/LeWXp8vO+++3DHHXdg6dKlspknV65cKZuZrja1vY+i5s2bY9CgQcjMzERWVhYGDhx4S9kebm5uePfdd6HVamucBdMRr4M99nFbWb58ueyzv2TJEty8ebPG8w1vb28AsOr9r87ScxVLmHp9BUGQzRprjbKyMuTk5JhcJ9Z/EofUPvLIIzh//rzJbD6xP4MHD4ZOpzNq89Zbb8HNza3WcztrjnGi/fv3IyIiwqq6b0SuiplSRGTkyy+/lO4eGbr//vtlmTc//fQTFixYYNSuT58+UmHq2qSlpeH777/HwIEDERwcjIsXL2LTpk348ccf8dxzz1mcbVJbn3/55RckJydj8uTJGDZsGICqE9moqCipPo8hf39/9OrVC1OmTEFxcTHefvtttGnTRprGuyYTJ07Ehg0b8Mwzz2DXrl3o2bMndDodfv31V2zYsAFfffUV7rvvPsybNw/Z2dkYMmQIQkJC8NdffyE9PR133XWX9Pr1798fGo0GPXv2REBAAH755Re8//77suLP1d1xxx2YM2cOUlNTMXDgQAwfPhwFBQVIT09H165drSpWa4kTJ06Y/Bx06tQJQ4YMwYIFCzBnzhz89ttvGDlyJJo0aYLCwkJs3rwZ06ZNkwUvOnfujDZt2iAxMREVFRVGd+ebNm2KJUuWYOLEiejcuTNiY2Nxxx134MyZM/jiiy/Qs2dPs8NMLHm9TcnKykJUVJTZoQzDhw/Hc889hwMHDqBz585466238NRTT6Fr16547LHH4Ofnh59//hllZWXS0Dpzpk2bhmXLlmHy5MnYv38/WrZsiU8++QTff/893n77bek9f+qpp3Dx4kU89NBDuOuuu3D69Gm89957iIqKkoYztW/fHn369EGXLl3g7++Pn376CZ988gmeffbZGvtgqdWrV6N///4YPXo0hg0bhr59+8Lb2xvHjx/HunXrcPbsWbz55psAqgoEv//++3juuefQtm1bxMXFoV27dqisrMSxY8eQlZUFDw8PaQhbVlYW1Gq12QDq8OHDkZiYiHXr1pmcztvaPjZt2hQxMTH473//ixs3buDOO+/E119/bZSVYw3xczx//nw88MADGD16NDw9PfHjjz8iKCgIr7766m19nm3hjjvuwMCBA7Fx40b4+vqafb07dOiAAQMG4Pnnn4enpyfS09MBQDbc7rXXXsOuXbvQvXt3TJ06Fe3bt8fFixdx4MAB7Ny5UwoK38oxTTRixAhs3rzZKIvp3//+Nw4ePIjXX38dOTk5eOSRR9CwYUN89913yMzMxD333FPrvieaOnUq3n//fUyaNAn79+9HYGAg1qxZg0aNGsnaqVQqZGRkYNCgQYiIiMCUKVNw55134s8//8SuXbvQtGlTbN26tcZttW7d2qLjo7u7OxYsWICnn34aDz30EMaNG4fCwkJ89NFHVg0btuR9FE2aNEkq9j5//nyLt1HdiBEjMGLEiBrbOOJ1sMc+XpObN29KQ5CrGzVqlBRUAqoC5H379sXYsWOl7+pevXph+PDhZn9/69at4evri6VLl6JJkybw9vZG9+7dTdZJM8fScxVLtGvXDq1bt8bs2bPx559/omnTpti0aZPRcFtLlZWV4f7770ePHj0wcOBA3H333SgpKcGnn36KPXv2YOTIkejUqROAqs/q6tWrMWvWLOzbtw8PPPAArl27hp07dyI+Ph4jRozAsGHD8OCDDyIxMRG//fYb7r33Xnz99dfYsmULXnjhBSnzsSaWHuOAquy93bt3m8z0JSIT7DexHxHVNeL0xOb+GU49XFO7+fPnC4JgPO2zKV9//bUwdOhQISgoSHB3dxeaNGki9OzZU/joo49k02zfTp9v3rwpdO3aVbjrrrtk01wLgiC88847AgBh/fr1giD8M83xxx9/LMyZM0do0aKF0LBhQ2HIkCGyKY1r+/sqKyuF119/XYiIiBA8PT0FPz8/oUuXLkJqaqpQWloqCIIgfPPNN8KIESOEoKAgwcPDQwgKChLGjx8vHDt2TPo9y5YtE2JiYoRmzZoJnp6eQuvWrYWXXnpJ+h2Gr0FhYaGsD++//77Qrl07wd3dXQgICBCmT58uXLp0yaK/4fHHHxdCQkJqfO0F4Z9p1U39e/LJJ6V2mzZtEnr16iV4e3sL3t7eQrt27YSEhAShoKDA6HcmJiYKAIQ2bdqY3e6uXbuEAQMGCD4+PoKXl5fQunVrYfLkybLpzqtPO23J613d/v37BQBCcnKy2Ta//fabAECYOXOmtOyzzz4T7r//fqFhw4ZC06ZNhW7dugkff/yxtL6mz05xcbEwZcoUoXnz5oKHh4fQsWNHo2m/P/nkE6F///5CixYtBA8PDyE4OFh4+umnhbNnz0ptFixYIHTr1k3w9fUVGjZsKLRr105IS0uTTT1emzfeeMPkZ0tUVlYmvPnmm0LXrl2Fxo0bCx4eHkLbtm2F5557TjbFtujgwYPCpEmThODgYMHDw0Pw9vYWIiMjhRdffFFqX1lZKTRr1kx44IEHauxbaGio0KlTp1r/Bkv7+McffwijRo0SfH19BR8fH2HMmDFCUVGRAEDQarVSO/Fz9ffff8u2Y24//PDDD4VOnTpJx4HevXsLO3bskLWx5PP8+OOPC97e3rX+vaYkJCSYnYJ9w4YNAgBh2rRpJtcDEBISEoTMzEyhbdu2gqenp9CpUyfZVPCi4uJiISEhQbj77rsFd3d3QaPRCH379hWWL18utbHkmGbOgQMHBABGU9gLgiDodDrho48+Enr27Ck0bdpU8PLyEiIiIoTU1FTh6tWrRu1DQkKEIUOGmNzO6dOnheHDhwuNGjUSmjdvLsyYMUPYvn27AMDo7z548KAwevRo6e8JCQkRxo4dK3zzzTdSG3OfGZGlx8f09HQhNDRU8PT0FO677z4hOztb6N27t9C7d+9aXjnr3kdBEISKigrBz89P8PHxEcrLy2v9/YLwz/fnxo0ba2xn7vhny9ehsLDQ6LzFXvt4dY8//niN5yXi88Xft3v3bmHatGmCn5+f0LhxYyEuLk64cOGC0WtW/X3esmWL0L59e6FBgwayv9Wa73VLzlUEoWp/efzxx6XH4ntt+Pk5evSo0K9fP6Fx48ZC8+bNhalTpwo///yz0ftQ/bvZlBs3bggrVqwQRo4cKYSEhAienp5Co0aNhE6dOglvvPGGUFFRIWtfVlYmJCYmCqGhodKx59FHHxVOnjwptbly5Yowc+ZM6Xyzbdu2whtvvGF0rinuK6ZYcowTBEH48ssvBQDC8ePHa/w7iaiKmyDUgWp9REQO8u233+LBBx/Exo0bZVOCExHVR1u2bMHIkSORnZ1tsvaWm5sbEhIS7JqxZY2+ffsiKCgIa9ascXZX6rWbN28iKCgIw4YNwwcffODs7tRLK1euxJQpU/Djjz/abMZjUoaRI0fCzc0NmzdvdnZXiOoE1pQiIiIiclErVqxAq1atLB5y7WyvvPIK1q9fbzSFO9nWp59+ir///ltWtJqIavfLL7/g888/v61hr0SuhjWliIiIiFzMunXrkJeXhy+++ALvvPOOTWbzcoTu3bvLilyTbeXm5iIvLw/z589Hp06djGYcJaKa3XPPPbh586azu0FUpzAoRURERORixo8fj8aNG+PJJ59kMV6SLFmyBJmZmYiKisLKlSud3R0iInIBrClFREREREREREQOx5pSRERERERERETkcAxKERERERERERGRw7GmVDV6vR5FRUVo0qRJnSn6SURERERERESkFIIg4MqVKwgKCoJKZT4fikGpaoqKinD33Xc7uxtERERERERERHXa77//jrvuusvsegalqmnSpAmAqheuadOmTu4NEREREREREVHdcvnyZdx9991SjMUcBqWqEYfsNW3alEEpIiIiIiIiIqJbVFtZJBY6JyIiIiIiIiIih2NQioiIiIiIiIiIHI5BKSIiIiIiIiIicjgGpYiIiIiIiIiIyOEYlCIiIiIiIiIiIodjUIqIiIiIiIiIiByOQSkiIiIiIiIiInI4BqWIiIiIiIiIiMjhGJQiIiIiIiIiIiKHY1CKiIiIiIiIiIgcjkEpIiIiIiIiIiJyOAaliIiIiIiIiIjI4RiUIiIiIiIiIiIih2NQioiIiIiIiIiIHI5BKSIiIiIiIiIicjgGpYiIiIiIiIiIyOEYlCIiIiIiIiIiIodjUIqIiIiIiIiIiByugbM7QERERERERPXD9evXcebMmVt6bnBwMLy8vGzcIyJSMgaliIiIiIiIyCbOnDmDadOm3dJzly9fjrCwMBv3iIiUjEEpIiIiIiIisong4GAsX77caPnp06eRlpaGxMREhISEmH0uEbkWBqWIiIiIiIjIJry8vGrMdgoJCWE2FBFJWOiciIiIiIiIiIgcjkEpIiIiIiIiIiJyOA7fIyIiIiIiIqqnOCMiKRmDUkRERERERET1FGdEJCVjUIqIiIiIiIjIxpSSocQZEUnJGJQiIiIiIiIisjGlZCgpYUbE2wnQARxGWJ8xKEVERERERERkY+YylIDas5TqW4bS7QToAA4jrM8YlCIiIiIiIiKysdoylADHZCkpwe0E6MTnU/3EoBQRERERERER2Q0DdGQOg1JERERERERERASdToe8vDxcvHgR/v7+iIyMhFqtttv2GJQiIiIiIiIiInJx2dnZSE9Px7lz56RlGo0G8fHxiImJscs2VXb5rUREREREREREVCdkZ2dDq9WiVatWWLx4MbZt24bFixejVatW0Gq1yM7Otst2mSlFRERERGSB25nSnNOZExGRUul0OqSnpyM6OhoLFiyASlWVvxQREYEFCxYgKSkJS5YsQc+ePW0+lI9BKSIiIiIiC9zOlOaczpyIiJQqLy8P586dQ3JyshSQEqlUKsTFxSEhIQF5eXno1KmTTbfNoBQRERERkQXMTWnO6cyJiKguu3jxIgAgNDTU5HpxudjOlhiUIiIiIiKyQG1TmnM6cyIiqov8/f0BAIWFhYiIiDBaX1hYKGtnSwxKERERERERUb1xO/XfANaAI9cTGRkJjUaDrKwsWU0pANDr9cjKykJgYCAiIyNtvm0GpYiIiIiIiKjeuJ36bwBrwJHrUavViI+Ph1arRVJSEuLi4hAaGorCwkJkZWUhJycHqampNi9yDjAoRURERERERPWIufpvAGvAEYmqZxRqNBo8/fTT2LhxIxISEqTlzZs3x9NPPw2NRoNjx45Jy22VUcigFBERERFRHcFhSUS1q63+G8AacESWZhSeP38eS5cuNVpuq4xCBqWIiIgUSKfTIS8vDxcvXoS/vz8iIyPtkjJNRHULhyUREd06Bvb/oZSMQgaliIiIFCY7Oxvp6ek4d+6ctEyj0SA+Ph4xMTFO7BkROZtSLiKo7uBNDqJ/MLD/D6VkFNapoFR2djbeeOMN7N+/H2fPnsXmzZsxcuRIab0gCNBqtVixYgVKSkrQs2dPLFmyBG3btnVep4mIiKyQnZ0NrVaL6OhoJCcny4pMarVapKamMjBF5MKUchFxO9kG9SnToCZKCAZlZ2dj8eLFKC4ulpYFBAQgISGB3yXkkhjYV546FZS6du0a7r33XjzxxBMYPXq00fr//ve/ePfdd7Fq1SqEhoYiOTkZAwYMwNGjR13ii4+IiOo2nU6H9PR0REdHy6bjjYiIwIIFC5CUlIQlS5agZ8+evMtNRE51O9kGtsw0UGpwTAnBoOzsbKSkpMDDw0O2/NKlS0hJScG8efMYmCKXw8C+8tSpoNSgQYMwaNAgk+sEQcDbb7+NpKQkjBgxAgCwevVqBAQE4NNPP0VsbKwju0pERGS1vLw8nDt3DsnJyVJASqRSqRAXF4eEhATk5eWhU6dOTupl/ccTRaLa3U62gS0zDZQSHDMkBoPc3d1lyy9evOiwYJBOp8OiRYsAAG5ubrJ14uNFixbxJgeRkyjx2OUsdSooVZPCwkKcO3cO/fr1k5b5+Pige/fuyMnJYVCKiIgU7+LFiwCA0NBQk+vF5WI7sg+eKBLVTinZBuaCY84ahmMYDLpx44ZsnfjYEcGgQ4cOoaSkBADQuXNnTJgwQRoOnpmZiZycHJSUlODQoUPo0qWL3fpBRKYp7djlTPUmKCUWgw0ICJAtDwgIkBWKra6iogIVFRXS48uXL9ung0RERLXw9/cHUHWjJSIiwmh9YWGhrB3Zh1IyQIiodrUFxxwRGDNkGAxSqVTQ6/XSOvGxI4JBBw4cAFA1/DstLU02HDwtLQ3PPvssjh49igMHDjAoReQESjt2OVO9CUrdqldffRWpqanO7gYREREiIyOh0WiQlZUlqykFAHq9HllZWQgMDERkZKQTe1n/KSUDhIjqnp9++kn6uXv37iYzlMR29gwG/fXXXwCAvn37mhwO3rdvXxw9elRqR0TkLKram9QNGo0GAGTFBMXH4jpT5syZg9LSUunf77//btd+EhERmaNWqxEfH4+cnBwkJSXhyJEjKCsrw5EjR5CUlIScnBxMnz6d9T+IiBSqoKAAABAYGIi0tDRERESgUaNGUoZSYGCgrJ29tGjRAgCwc+dO3LhxAwcPHsQ333yDgwcP4saNG/jmm29k7YiInKXeZEqFhoZCo9Hgm2++QVRUFICqoXi5ubmYPn262ed5enrC09PTQb0kIiKqWUxMDFJTU5Geno6EhARpeWBgIFJTUzlTEhGRglVWVgKA0Yx3IrH4udjOXjp37oysrCwcPXoUQ4cOlZUr8fT0lB537tzZrv0gIqpNnQpKXb16FSdOnJAeFxYW4tChQ/D390dwcDBeeOEFLFiwAG3btkVoaCiSk5MRFBSEkSNHOq/TREREVoqJiUHPnj2Rl5eHixcvwt/fH5GRkcyQIiJSOI1Gg8OHD+P06dNITEw0Gr4nzuxZ00gOW4iKikKjRo1QVlZmFAATH3t7e0s384mInKVOBaV++uknPPjgg9LjWbNmAQAef/xxrFy5Ei+//DKuXbuGadOmoaSkBL169cL27ds5NTMREdU5arUanTp1cmofdDodA2NERFYYMGAAdu7cCaDq2kWsIQX8kyUltrM3Dw8PlJWVQa1W4+bNm9Jy8bG5bC4iIkeqU0GpPn36QBAEs+vd3Nwwb948zJs3z4G9IiIiqn+ys7ORnp4um8FWo9EgPj6eQwjJ4a5fvy5lmFgrODiYNyjJYTp37gxvb29cu3ZNFggCID329va2+7C5vLw8lJSUIDIyEnl5eUb96NixI/Lz85GXl+f0GyBE5NrqVFCKiIjIVm7nIheo3xe62dnZ0Gq1iI6ORnJysjT0JCsrC1qtlrWtyOHOnDmDadOm3dJzly9fzpkSya6qf59MnDgRS5cuNbqZLj6eOHEiTp48KS23x/fJxYsXAVQFp1QqFfR6vbROpVIhPz9f1o6IyFkYlCIiIpd0Oxe5QP290NXpdEhPT0d0dDQWLFggTSUeERGBBQsWICkpCUuWLEHPnj05lI8cJjg4GMuXLze57vTp00hLS0NiYiJCQkJMPpfInqz9Plm6dKnssT2+T5o2bSr93L17d6PaVuKwQsN2RETOwKAUERG5pNu5yBWfXx/l5eXh3LlzSE5OlgJSIpVKhbi4OCQkJHDIBzmUl5dXrRftISEh9TJQTMpn7vtEr9fju+++Q2ZmJiZMmIBevXoZHVfF59uaODlUo0aNMH/+fDRoUHXZFxERgfnz52P48OEoKyvDiRMn0LVrV5tvn4jIUgxKERGRS+JFrmniUI7Q0FCT68XlHPJBRFSlpu8TlUqFzMxMxMTEOPT75MiRIwCAsrIypKSkIC4uTjYUu6ysTNaOyJaKi4tRWlpqcfvTp0/L/reGj48PAgICrH4eKQeDUkRERCTx9/cHABQWFiIiIsJofWFhoawdEREpT8OGDQEAgwcPxoEDB5CQkCCtCwwMxKBBg/Dll19K7ej2WBuEAW49EKP0IExxcTEmTJyEG5UVVj83LS3N6ue4e3gic81qRb8mVDMGpYiIiFycYZHehg0bolmzZli2bBni4+NlQ030ej2WLVuG5s2bo2HDhjh27BiA+l30nYioLurfvz927NiBPXv24JNPPsHRo0dx8eJF+Pv7o3379nj00UeldnR7bicIA1gfiFF6EKa0tBQ3KitQ3qo39F4+dt2W6nopcGo3SktLFft6UO0YlCIiIlIgnU6HvLw86SIiMjLSboXFTRXpvXDhAp555hmzzzFcV1+LvhMR1SWGNxiaNGkCLy8vXLlyBY888ghGjhyJyMhI7N+/H8nJybh69SoaNmyIJk2a4NixY7y5cBsYhDFN7+UDvXdzZ3eD6gAGpYiIiBQmOzsb6enpOHfunLRMo9EgPj4eMTExNt+eqSK9Bw4cwMaNG3HhwgVpWfPmzfHoo4+ic+fORs8nIiLnMjcL4NWrV5GZmWm0vLy8XLrBwJsLt49BGFIyJdf5YlCKiMjFOTIjh2qXnZ0NrVaL6OhoJCcnywrTarVapKam2jwwZapIb1hYGMaMGYNt27Zh4cKFePHFFzF48GB+NoiIFMrSGwzNmjXDmDFjZDcYbuXmgpIvcomUQgn7ibPqfFlas45BKSIiF+bojByqmU6nQ3p6OqKjo5GamorDhw8jJycH/v7+SE1NhVarxZIlS9CzZ0+HBIfUajXCw8MBAOHh4QxIEREpmCNvMLCYNVHtlLKfOGuIKYNSRERUI2dk5FDN8vLycO7cOQwbNgwTJkxAcXGxtC4gIADDhw/HDz/8gLy8PHTq1MmJPSUiorrCHjcYWEeJqHZK20+UOsSUQSkiIhdkmJGzYMECaYa1iIgILFiwAElJSQ7NyKEqFy9eBACsWLECnp6esnUlJSVYsWKFrB3Vb4ZFi63FosVE5AhKvcglUhLuJzVjUIqIyAWJGTnJyclSQEqkUqkQFxeHhIQEZuQ4mK+vr/Rz586dMWHCBCmDLTMzEzk5OUbtqP4yV7TYEixaTERERHUBg1JERC5IzLQJDQ01uV5czowcx9Lr9QCqpvKeP38+GjSo+pqOiIjA/PnzMWrUKFy5ckVqR/WbqaLFQFXx07S0NCQmJiIkJMTsc23hdrK1xH4wY4uIiIjMYVCKiMgF+fv7AwAKCwsRERFhtL6wsFDWjhwjLy8PAHDlyhWkpKQgLi5OVuvrypUrUruuXbs6s6vkAKaKFhsKCQmxezbU7WRrAczYIiIiUgpVeYkit8GgFBGRC4qMjIRGo0FWVpasphRQla2TlZWFwMBAREZGOrGXrmvy5MnYvn07EhISpGWBgYGYNGkSVq9e7cSekasxl60FODZji4iIqK5SSjCoYWG23ftxKxiUIiJyQWq1GvHx8dBqtUhKSjLKyMnJyUFqaiqLnDtYVFQU1qxZg/3792PlypXYunUrioqKEBQUhGHDhuGll16S2hE5Qm3ZWoBjMraIiKhuUUogRgmUEgwqD42BvqGvXbehKi+x+u9lUIqIyEXFxMQgNTUV6enpRhk5qampiImJcWLvXFNUVBR8fX2Rn5+PESNGoKKiQlqXkZGBiooK+Pn5MShFREREiqaUQIwSKCUYpG/oq8hZABmUIiJyIdWLFms0GsydOxfHjx9HaWkpfHx80LZtW6hUKhw7dkz2XBYstj+1Wo2BAwdi3bp1uHHjhmyd+HjAgAHMYLOR4uJilJaWWvWc06dPy/63lI+PDwICAqx6DhER1c7aY/mtHscBHsut4exAjJK+45UaDFIKBqWIiFwIp5hXNp1Oh2+//Rbh4eEoKSlBcXGxtK5Fixbw8fHB7t27MXXqVAamblNxcTEmTJyEG5UVtTc2IS0tzar27h6eyFyzmhczREQ2dDvHcmuP44Dyj+VKCtA5MxDD7/i6hUEpIiIXooQp5sm8vLw8nDt3DsnJyWjXrh3y8vJw8eJF+Pv7IzIyEr/++isSEhKQl5eHTp06Obu7Nlc9k89a1mTzlZaW4kZlBcpb9Ybey+eWt2kJ1fVS4NRulJaW8oSV6BYp6WKblIPH8n8wQPcPfi7qFgalyG50Op3RBRXv7BM5lxKmmCfzLl68CAAIDQ2FWq02CjyFhobK2tU3t5PJB9xaNp/ey4cp9aRYjgrEKD0Iw4ttqo0SjuXOLuzNQIwxJXwugP//etWDbdgLg1JkF9nZ2UhPT8e5c+ekZRqNBvHx8SyeTERkwDA7qKysDEDVMbRVq1ZGbU+ePCm1O3bsWL2r82Uukw9gNp+rUFINEGdzZCBG6UEYXmxTXaCUwt5KCcRQ1feMu4cncGq3Q7bn7uEJHx/zx0ilBscYlCKby87OhlarRXR0NJKTk2XTzGu1Ws7qRURkwFR20GuvvVbjcxYuXAig/tX5qi2TD2A2X33GGiByjgrE1KUgDC+2ScmcXdiblCcgIACZa1ZbnfFa2004c8zdbFFacKw6BqXIpnQ6HdLT0xEdHY0FCxZApVIBACIiIrBgwQIkJSVhyZIl6NmzJ4fyERHBODvowIEDWLZsGTp27IhOnTph1apVePzxx3Hw4EHk5+fj6aefRufOnaXnEtUXzIYxjYEYUjJnD1lTEs6wRqYEBATc0veMLW/COSs4dvnyZYvaMyhFNmVYpFcMSIlUKhXi4uLqdZFeIqqdI4tZ1wXVs4PCwsIQFBSE9PR0rFq1CgCwatUqBAYGMtOUXAKDMER1B7N2iOoGJQTHzGFQimzKsEivKfW9SC8R1c4ZxazrmpiYGPTs2RPbtm3DwoUL8eKLL2Lw4MHMMCUiIkXhkDUiul0MSpFN+fv7AwAKCwsRERFhtL6wsFDWjohcD4tZW0atViM8PBwAEB4ezoAUEREpDoesEdHtYlCKbCoyMhIajQZZWVmymlIAoNfrkZWVhcDAQERGRjqxl0TkTCxmTUREREREAKCqvQmR5dRqNeLj45GTk4OkpCQcOXIEZWVlOHLkCJKSkpCTk4Pp06fzjj8RERERERGRi2OmFNlcTEwMUlNTkZ6ejoSEBGk5i/QSEZHScOYoopoVFxdbNWMTUDUU2/B/S5mbzpyoNjyWE9VdDEqRXYhFevPy8nDx4kX4+/sjMjKSGVJERKQoLJ5LZF5xcTEmTJyEG5UVt/T8tLQ0q9q7e3gic81qBqbIajyWkykMVtYNDEqR3ajVanTq1MnZ3SAiIjKLM0cRmVdaWooblRUob9Ubei8fu25Ldb0UOLUbpaWlDEqR1XgsVx7VdesyLO2xDb5fdQODUkREROSyOHMUUe30Xj7cT0jReCxXDh8fH7h7eAKndjtke+4envDxMR00Z7CybmBQioiIiIiIyALW1ti61fpaAGtsUd0UEBCAzDWrrd5P0tLSkJiYiJCQEKu2V9N+wmBl3cCgFBERERERKZ6z68PcTo0ta+trAayxRbfG2fsJUBWYupXPbUhICMLCwm6xV1RXMShFRERERIqhhAsqJbF3X+vSa+HsITKssUV1gbP3EyJrMShFRERERIrBCyo5vh7/UEp9GNbYIiVTyn5CZCkGpYiIiIhIMXhBJWfv16MuvRasD0NUO+4nVNcwKEVEREREisELKjm+HkREVJ8xKEVEREREZIAzrBERETkGg1JERERERP8fZ1gjIiJ7u379Os6cOWO03JKbHMHBwfDy8rJb3xyNQSkiIiIiov+PM6wREZG9nTlzBtOmTTO7vqabHMuXL0dYWJg9ugUA0Ol0KCgoAAAUFBSgdevWUKvVdtseg1JERERERNVwhjUiorpNdd3yYdiO3kZwcDCWL19+y8+1l+zsbKSnp+PcuXMAgIULFyIrKwvx8fGIiYmxyzYZlCIiIiIiIiKiesHHxwfuHp7Aqd0O2Z67hyd8fKzLrPXy8rJrtpMlqg8hPHDgAJYtW4YOHTrg3nvvxVdffYUBAwagqKgIWq0WTz/9NDp37iy1t9UwQgaliIiIiIjILFV5Sb3YBtmekjNRHI2vhXIEBAQgc81qqyasAKrqOKWlpSExMREhISEWP6+uTlhhbghhfn4+8vPzAQBfffWVtHzp0qWydrYaRsigFBERERERmdWwMNvZXVAUBumUl4nizICQ0l4LqhIQEHDLgaKQkBCnZzE5guEQwoKCAixcuBAA4O7ujhs3bkjtDB+/+OKLCA8Pl55vCwxKERERERGRWeWhMdA39LXrNlTlJXUm+OXsfhYXF99SBojh/5YylwFyK5kot5qFUlM/lBAQYlYO1VWGQwgNjw333XcfJkyYgNDQUBQWFiIzMxM5OTlGz7EVBqWIiIiIiMgsfUNfFn034MwgXXFxMSZMnIQblRW39HtrmtHLFHcPT2SuWW02MHUrwRFbZqEoJTjGrByq6y5evAgAaN26NebNm4fDhw8jJycH/v7+mDdvHp5++mmcOnVKamdLDEoRERGRy2INEOXhe0JK58wgXWlpKW5UVqC8VW/ovew7hEt1vRQ4tRulpaWKzsxRQnCMqK67cuUKAKCiogITJkxAcXGxtC4gIADu7u6ydrbEoBQRERG5HCUM+SA5vidEltN7+TB7jYhsxs3NDQDwxx9/QKVSydb9/fff0Ov1sna2xKAUERERuRzWAFEevidERETOERkZKf3coEEDVFZWmnxs2M5WGJQiIiIih1JCkV6ANUCUiO8JERGRc3Xq1Ak9evSAp6cnKioqsHfvXuTm5tptewxKERERkcMoqUgvEREREQF5eXnSz4cOHZIFoTw9PWXtunbtatNtMyhFREREDsMivURERETKNHnyZGzfvh3nzp2Tlvn7++Phhx/G6tWr7bJNBqWIiIjI4Vikl4iIiEgZoqKisGbNGuzfvx9r1qzB4cOHcfHiRfj7+6NDhw6YNWuW1M7WVLU3ISIiIiIiIiKi+igqKgq+vr7Iz89HSkoKPDw8EB0dDQ8PD6SkpCA/Px9+fn52CUoxU4qIiIiISKFU162bFEBpv5+IiJRPrVZj1qxZ0Gq1OHDgAHJycqR1np6ecHNzw8yZM6FWq22+bQaliIiIiIgUxsfHB+4ensCp3XbflruHJ3x87FvjjYiIlC0mJgapqalYvHgxiouLpeV+fn6Ij49HTEyMXbbLoBQRERERkcIEBAQgc81qlJZansl0+vRppKWlITExESEhIRY/z8fHh5MBWMER2WXMYCMipRAEwa6/n0EpIiIiF1JcXGz1Ra7h/9bgha5l+J6QOQEBAbf0foWEhCAsLMwOPXJtjsxeA5jBRkSOlZ2djZSUFHh6esqWl5SUICUlBfPmzbNLthSDUmQ3Op0OeXl5UtX+yMhIu4xBJSIiyxQXF2PCxEm4UVlh9XPT0tKsfo67hycy16xmEKQGfE+ILOfsDCVHZq8BDCITkePodDosWrQIANCpUyd0794dnp6eqKioQG5uLvbu3YtFixahZ8+eNr+mZ1CK7CI7Oxvp6ek4d+6ctEyj0dh1LCoREdWstLQUNyorUN6qN/Re9r37rrpeCpzajdLSUl5U1YDvCVHtlJShxOw1IrIXZyZ1HDp0CCUlJQgODsZvv/2GvXv3Sus0Gg2Cg4Nx5swZHDp0CF26dLHpthmUIpvLzs6GVqtFjx49MG7cOCnCum/fPmi1WqSmpjIwRUTkRHovH+i9mzu7G2SA7wkpGTOUlEdVXlIvtkG25+z9lW6Ns5M6Dh06BAD4/fffER0djeTkZISGhqKwsBBZWVnSbHwMSpHi6XQ6pKenIywsDIWFhbKpJDUaDcLCwrBkyRK7pP0RESmZtXWDgFuvHVQXLqiIlI4X/cxQUrKGhdnO7gIpjJL2V7KOmNRhKhjkqKQOvV4PALjnnnuQmpqKw4cPIycnB/7+/khNTcWMGTNw9OhRqZ0tMShFNpWXl4dz586huLjYbIRVEATk5eWhU6dOzu4uEZFD3E7dIMD62kGsG0R0+3jRf2sZSgBnAXSE8tAY6Bv62nUbqvIS7gd1CDMK6yYxqSM6OhoLFiyASqUCAERERGDBggVISkpySFJH06ZNAQAXLlzAhAkTUFxcLK0zfJ/FdrbEoBTZ1Pnz5wEA3bp1M7lTzZkzB7m5uVI7IiJXwLpBRHUPL/qr3GqGElB/s5SUQN/Ql0N+yQgzCuseMakjOTlZunYWqVQqxMXFISEhwe5JHf7+/gCqbqS6ubnJ1v31118QBEHWzpYYlCKbKikpAQA88MADJneqXr16ITc3V2pHRORKWDeIqO7gRT8REdnbxYsXAQChoaEm14vLxXb2YhhsEgNQph7bIyilqr0JkeV8fX0BAHv27DEab6rX6/Hdd9/J2hERERERERG5IjHIU1hYaHK9uNwewSBDhtfuHh4esnWGj1lTihSvefOqO4q5ublISkpCXFycrKZUbm6urB0RERERERGRK4qMjIRGo0FWVpas/A1QFQDKyspCYGAgIiMj7doPcfY9AOjcuTO6d+8OT09PVFRUIDc3F3v37pXade3a1abbZlCKbErcqXx8fHDy5EkkJCRI6zQaDcLDw3H58mW771REdYVOp0NeXh4uXrwIf39/REZGcmZKIiIiIiIXoFarER8fD61WazKpIycnB6mpqXa/Pvjrr78AAIMHD8aBAwekIBQABAYGYtCgQfjyyy+ldrbEoBTZlOFO1aNHD8TGxkoR1n379mHv3r0O2amI6oLs7Gykp6fj3Llz0jKNRoP4+Hi7T/tKRERERETOFxMTg9TUVKSnp8uSOgIDA5GamuqQ64IWLVoAAH777TesWbMGhw8flm6ad+jQATNmzJC1syUGpcjmDHeqnJwcabkjdyoipcvOzoZWq0V0dDSSk5Nld0S0Wi33FSIXoyovqRfbICIiIuvFxMSgZ8+eThtB0blzZ2RlZeHo0aNISUnBhAkTEB0djcLCQqSkpODo0aNSO1tjUIrsIiYmBj169MCWLVtQVFSEoKAgjBgxwqhoGpEr0ul0SE9PR3R0tGzseEREBBYsWICkpCQsWbIEPXv2ZFYhkYtoWJjt7C4QERGRE6nVanTq1Mkp246KioKvry9KSkqwf/9+WXKJeA3v5+eHqKgom2+bQSmyC1PDkjZt2sRhSUQA8vLycO7cOSQnJ8uKGQKASqVCXFwcEhISkJeX57QvpvqkuLgYpaWlVj3n9OnTsv8t5ePjg4CAAKueQwQA5aEx0Df0tes2VOUlZoNf3E+IiMgZdDodCgoKAAAFBQVo3bo1b8o6gVqtxqxZs6DVauHm5iZb5+bmBjc3N8ycOdMu7w2DUmRzHJZEVLOLFy8CAEJDQ02uF5eL7ejWFRcXY8LESbhRWXFLz09LS7OqvbuHJzLXrOYFN1lN39AXem/nzEzL/YSIiOzt+vXrOHPmjGzZgQMHsHHjRly4cAEAsHDhQqxcuRJjxowxGiYWHBwMLy8vh/XXFRmW4TFMLvH398f06dPtdg3PoBTZFIclEdXO398fAFBYWIh27doZjR0vLCyUtaNbV1paihuVFShv1Rt6Lx+7bkt1vRQ4tRulpaVmL7ZZN4iUSGn7CRER1T9nzpzBtGnTam134cIFLF261Gj58uXLERYWZo+ukQFnlOFhUIpsisOSiGoXGRkJjUaDd999F6WlpUaz7/n4+CAwMBCRkZFO7GX9ovfycVoWiiHWDSIlU8p+QkS1U123britUrdBriM4OBjLly8HAOj1eiQmJuLOO+/EM888g5MnT6K0tBQ+Pj5o3bo1li5diqKiIlmSQ3BwsDO77zKcUYaHQSmyKQ5LIqqdWq1Gnz59sG7dOvj5+WH27NmIjo5GTk4OPvjgAxQUFCA2NpbZhPWQs+sGERFR3ebj4wN3D0/g1G6HbM/dwxM+PvbNoCTX4OXlJWU6HTx4EBcuXMDo0aMxb948oxu0w4YNw4oVK1BeXs5EBgcSy/D06NED48aNg6enJyoqKrBv3z67luFhUIpsynBYUkREhNF6Dksiqhrm+u233yI8PBwlJSV48803pXUajQbh4eHYvXs3pk6dysBUPePMukGGlDCMUAl9ICKqawICApC5ZvUtTUyQlpaGxMREhISEWPw8TkxA9iAmKGRkZJisQ5yRkSFr5wiuXnBdLMMTFhaGwsJC2ex7Go0GYWFhdivDw6AU2ZQ4LCkrK0uWbglUpWlmZWVxWBK5PMNhrqZqSv36668c5kp2pYRMKiX0gYioLgoICLjlQFFISAjr8pDT+fr6AgA6dOhgsg7xjBkzkJ+fL7WzJRZcN028PikuLjYZKMzJyYEgCHa5PmFQimxKrVYjPj4eWq0WSUlJiIuLM/owp6amulTUmag6w2GuarXa6MDOYa5kb0oYRqiEPhAREZFrYcF1086fPw8A6Natm8lA4Zw5c5Cbmyu1syUGpcjmDKeSTEhIkJYHBgbabRwqUV3CYa7kbEoYRqiEPhAREZHjlZSUAADy8/NNJjLk5+fL2tmSqYLrjRs3xpUrV2Q3hP39/dGkSRNcu3bNJQqui6/1Aw88YHLCsl69eiE3N9cu7wmDUmQXMTEx6Nmzp9GwJGZIEXGYKxEREZE9mRqiJTp9+rTsf1Pq6xAtpRBvvE6dOhVbt241SmR46qmnkJGRYZcbtKYKrl+4cAGenp6ydteuXZOCVK5QcF0cKrlnzx4MHjzY6Prku+++k7WzJQalyG5MDUsiIg5zJSIiIrInS4ZopaWlmV1XX4doKYV4g/bIkSNYs2YNDh8+LCUydOjQAVqt1iE3aA2HonXq1Andu3eXZpzLzc3F3r17jdrVV82bV2Wv5+bmmrw+yc3NlbWzJQaliOqhmu4OWYJ3h+yPw1yJiIiI7MNwiJaoejFrAGjWrJnZYtZkP4Y3aLVaLeLi4hAdHY3CwkJotVqH3aAVM6ECAgJQWFgoBaHEZQEBASguLnaJOq9ioNDHxwcnT56UXZ+Is4NfvnzZLoFCBqWI6iFLC/iZw7tDjsFhrkRERES2ZzhECwCys7OxbNkyREdHG2WALFu2jDcEnUAJN2ivXLkCACguLoa7u7ts3cWLF3Hjxg1Zu/rMMFDYo0cPxMbGSllj+/btw969e+0WKGRQiqgeMnV3SHT69GmkpaUhMTERISEhZp9PRESkdDqdDgUFBQCAgoICtG7dmoF9IpLR6XRIT09HdHQ0UlNTcfjwYeTk5MDf3x+pqanQarVYsmQJevbsyeOHgzn7Bq0gCNLPYgDK1GPDdvWZYaAwJydHWm7vQCGDUkT1UPW7Q6aEhIQwG8rJsrOzsXjxYhQXF0vLAgICkJCQwLt1REQGTA1Lrz4UZ+HChVi5cqXRUJxbHZKuul56e51WyDaIXF1eXh7OnTuHYcOGYeLEiTh37py0TqPRYNiwYfjhhx+Ql5fHerhO4Mw6xI0bN5Z+dnNzkwWfDB8btqvvnBEoZFCK7Ean03FYEpEZ2dnZSElJMZkqnJKSgnnz5jEwRUT0/1k6LP3ChQtYunSpbJm1Q9J9fHzg7uEJnNptdT9vhbuHJ3x8fByyLSJXJNYDysjIQHR0NJKTk2XD9zIyMmTtyHWUlv5zY6Br166Ijo6Whqzl5ORg3759Ru1cgaMDhQxKkV1kZ2cjPT3d6E5EfHw8L7TJ5el0OixatAiA+VThRYsWMY2ciOj/MxyWrtfrkZiYiDvvvBPx8fFG01anp6ejqKgICxYsgEqlsnpIekBAADLXrLbqIsSSofHm+Pj4ICAgwKrnEJHlxCnsO3ToIB0XACAiIgILFizAjBkzkJ+fb5ep7knZjh8/Lv38888/S0EoAPD09DTZjmyvXgWl5s6di9TUVNmy8PBw/Prrr07qkXM4O0MpOzsbWq3W5J0IrVbrkoUEnf2ekLIcOnQIJSUlAKpOlPr374+goCAUFRXh66+/RklJCUpKSnDo0CF06dLFuZ0lIlIAw2HpBw8exIULFzBv3jy0a9fOqO3TTz+NhIQElJeX3/KdXnHWJWtxaDwRUd0hDu0OCgqCTqeTldTw8/ODSqVCUVERZyW3s3oVlAKqIt47d+6UHjdoUO/+xBo5O0PJsJCgqTsRSUlJLldI0NnvCSnPTz/9BKDqi9DT0xMbNmyQ1gUEBMDLywvXr1/HTz/9xKAUEVE14hCb0NBQVFZWYsuWLSgqKkJQUBBGjBiB0NBQWTsiZ2EhfmUQbwTm5+cjKSnJaPa9/Px8WTtyHZGRkfj+++9RVFSE7t27o1evXqioqICnpyf++OMP5ObmSu3IfupdxKZBgwbQaDTO7oZTKCFDSSwkmJycLEunBwCVSoW4uDgkJCS4TCFBJbwnpDzHjh0DUFW4t/psHiUlJaioqJC1o/qDhZPrtvp+gakqL6kT2/D39wdQNcx5165d0Ol00rqlS5fiwQcflLUjcobqNyUXLlyIrKwsl7gpaWpiAqBqmKvh/6bc6sQENRGPBVOnTsXWrVuRkJAgrQsMDMRTTz2FjIwMHjNc0KhRo7Bs2TLo9Xrk5uZKQShDKpUKo0aNckLvXEe9C0odP34cQUFB8PLyQnR0NF599dUaawlUVFRIF4AAcPnyZUd00+aUkqFkePfSFFe6e6mU94SUx3CMeufOnTFhwgQpYJmZmSlNwWrYjuo2Fk6u+1zhArNhYbazu2CRyMhINGrUCDt37oSfnx+efPJJREdHIycnBx988AF27tyJRo0a8c42OUz1IMyBAwewbNkytG/fHj4+PigoKEB4eDgaNGgArVaLp59+2iYzRCpVbRMTpKWlmV1n7cQEloiMjIRGo8GRI0ewZs0aHD58WCqp0aFDB2i1WgQGBvKY4STOLHPi4eGB6OhofP/992bbREdHw8PDwyH9cVX1KijVvXt3rFy5EuHh4Th79ixSU1PxwAMP4PDhw2jSpInJ57z66qtGdajqIqVkKIl3GAoLCxEREWG0vrCwUNauPlPKe0LK06xZM+nn6plSho8N21HddiuFk4FbL57Mwsm3x9wFZocOHXDvvffiq6++woABA1BUVGR0gVmXLy7LQ2Ogb+hr122oyktuO/il0+lw/fp1AEDbtm1RWFiIX3/9FZ6enmjbti327duH69evQ6fT1cubPuayUADnZaK4OnNBmCNHjkg/i1mWAG57hkilM5yY4Faea2tqtRrx8fHQarXQarWIi4tDdHQ0CgsLodVqkZOTg9TU1Hp5vFA6Z5c50el0sv3UlCNHjtTb7xOlqFdBqUGDBkk/R0ZGonv37ggJCcGGDRvw5JNPmnzOnDlzMGvWLOnx5cuXcffdd9u9r7amlAwl8U5EVlaWLDsIqJoRJysry2XuRCjlPSHlMQyS79+/H3v37pUeu7u7m2xXVxUXF1s9g5Xh/9ZQeiDmVgsnAyye7GjmLjDz8/Ol2iNfffWVtNzwAvNWLy6VMLRT39AXeu/mdu/H7dqyZQv0ej1at26Nffv2yWZLAoDWrVvj5MmT2LJlC8aMGeOkXtpPbVkogOMzUVydYRCmoKAACxcurPU5L774IsLDw6Xn1yeGExMoRUxMDFJTU7F48WLZ8D2NRsNyGk4iljnp0aMHxo0bB09PT1RUVGDfvn0OK3MiTj7UsWNHvPHGG9i6datUo3DYsGF46aWXkJ+fz8mH7KxeBaWq8/X1RVhYGE6cOGG2jaenZ70YIqOUDCXDOxGmCgm60p0IpbwnpDyGn3/DWijVH9f1/aS4uBgTJk7CjcqK2htXU9MFlTnuHp7IXLNa0YEpqhvMXWB27NgRHTp0gIeHByorK3H48GEpSCVeYFp7ccmhndYrKioCAJw8eRJ+fn54+OGHceedd+LPP//Ejh07cPLkSVm7+uZ2slDE55NtGQZhxM8fALi5uckyoA0fN2jQQHGBG1fg5ubm7C4Q/ilzEhYWhsLCQql0BVAVKAwLC3NImZNDhw4BAKZMmQIvLy+jGxmTJ0/Giy++yKCUndXroNTVq1dx8uRJTJw40dldsTslZSiJdyLS09ONCgm60p0IJb0npCxRUVFYs2YNmjdvbjJTrnnz5jh//jyioqIc3zkbKi0txY3KCpS36g29l30vglXXS4FTu1FaWsqglAWUkJWjZIYXmGLWXtu2bfH333/j448/ltppNBq0bdsWx48fv+XMgFsZ2nmrwzoB5WcUWqJFixYAgEaNGmHjxo2ymZanTZuG4cOHo6ysTGpX3ygxC4X+kZ39z/BUd3d3VFZWmnycnZ0tG+VB9qWErBz6h1jmpLi42OR7snfvXgiC4LAyJ9XLaZBj1aug1OzZszFs2DCEhIRIdR7UajXGjx/v7K7ZndIylGJiYtCzZ0+nFa1TAqW9J6QcUVFR8PX1xfnz59G9e3fcddddRtPP+vn51fmglEjv5VMnhgS5AmblWE+cIvz48eO4//77jWZS/eGHH2TtbsWtDu101WGdhpkOOp0O+fn50rlG+/btTbYjchRz9b5utR3dPqVk5dA/zp8/DwBo06aNyfekTZs2OH78uNTOXsQbxStXrkSnTp2MkghWrlwptSP7qVdBqT/++APjx4/HhQsXcMcdd6BXr17Yu3cv7rjjDmd3zSGUlqGkVqtdvoC30t4TUga1Wo1Zs2ZBq9Xi0KFDsulnPT094ebmhpkzZ/LEiGyOWTnWa9q0KYCqkgDz5s2TsnIiIiIwb948PProoygpKZHakf0VFxcDAMrKyjBo0CDo9XppnUqlkh6L7Ygc6ebNm9LPN27ckK0zfGzYjuzLMCsnOjra6OZCTk6OQ7NyyDE3fCwh3ijOz8/HnDlzjG4U5+fn16sbxUpVr4JS69atc3YXnI4ZSsrD94RMMQxYGs444u/vj+nTpzNgSXbDrBzrXL58GUDViXFycjK6desmG2IgnjCL7cj+goKCpJ8NA1LVHxu2I3KUwMBAKSBa0wy7gYGBDu2XKxOzbbp16yYrqREREYEFCxZgzpw5yM3NtXtWDv1DKTd8xBvFKSkpyM3Nld0oFvFGsf3Vq6AUVWGG0j90Op0igkF8T8gUBiwdR1VeUi+2QY7n6+sLoOoCMjc3VzbEQK1WIzAwEGfPnpXakf0NHToUixcvhru7OzZv3owvv/xSmi1p0KBBGDVqFG7cuIGhQ4c6u6vkgqpPYHPXXXehadOmuHz5Mv744w+z7eo7Z56TizcPHnjgAQiCgIMHD8r60atXL+Tm5to9K4f+YXjDJyUlxajMiSNv+Bw9erTW9bxZbF8MSlG9lZ2dbZSFotFoEB8fzwMLkYtpWJhdeyMiE5o3r6qHdvbsWWmmt6CgIBQVFWHHjh04e/asrB3Z36+//gqgaijUxIkT8cQTTyAuLg45OTmYOHGiNETq119/5Q0hcrjqtcwMA1E1tavPnH1OLt402LJlCzIzM4360aRJE1k7sj/xtW7bti1OnjwpK3NiOImIvd+TyspKbNiwAQDQo0cPdO/eHV5eXrh+/Tpyc3Oxd+9ebNiwAU888QQ8PDzs2hdXxqAU1UviDBumxo1zhg1SCmefpLmS8tAY6Bv62nUbqvISBr/qoYiICKjVanh5eaFBgwbSyStQNQuct7c3rl+/joiICCf20rWIs5Y+8sgj+PTTT7Fw4UJpnVqtxiOPPIJNmzaZnN2UyN4sDTa5SlBKCefk4k2D48ePw8/PD7Nnz0Z0dDRycnLwwQcfSOdhvLngOIbvSXR0NGJjY2VD48WsZHu/J5s3b4Zer0fr1q0xd+5cbN26FceOHUNQUBDmzp2L+Ph4nDp1Cps3b8a4cePs2hdXxqAU1TviDBvR0dEmx40nJSVxhg1yOiWcpLkSfUNfzgBIt+TIkSPQ6XS4du2aUWHi0tJSVFRUSO2YleMY4rCnhx56CE8//TS2bNkiDd8bMWIEjh8/jk2bNrnc8ChShmbNmtm0XV2mlHNyw5sL7u7uePPNN6V1AQEBdr25cP36dbMzLZ4+fVr2f3XBwcHw8vKyeZ+UIDIyEhqNBj4+Pjh58qRsaHxAQADCw8Nx+fJlREZG2rUf+fn5AKpqEA4ZMgQ6nU5at3TpUtx///04deoU8vPzGZSyIwalqN4RZ9hITk6WTesJVM3KExcXh4SEBM6wQU5jeJKWmpqKw4cPIycnB/7+/khNTYVWq2XglEghLM22YVaO44gXM1lZWViwYAHGjBkjrdPr9cjKykJgYKDdL2aITCkvL7dpu7pMKefkhjcXIiMjMX78eJNZOfa4uXDmzBlMmzatxjZpaWkmly9fvrzeTjCiVqsRHx+PlJQUeHp6ytaVlJSguLgY8+bNs/t5cMOGDQEAe/bsgZ+fH5588klZFt2ePXtk7cg+GJQiu3FWQUPxwiA0NNTkenE5LyDIWcSTtGHDhmHixIlGw/eGDRuGH374gYFTIgUQ61l07NgRixYtwuHDh6XvtQ4dOmDWrFnIz89nLRIHEi9mtFotEhMTjWZE3Lt3L1JTUxnUJ6fg8L1/KOWcXPz9iYmJyMjIkGXlaDQaJCYmIi0tzS79CA4OxvLly2XLDhw4gI0bN+LChQvSsmbNmmHMmDHo3Lmz7Ln1nbn9wFH7x0MPPYQdO3YAAD7++GMpM23o0KHo168fBg4cKLUj+2FQiuzCmbVyxHT9wsJCtGvXzigwVlhYKGtH5GjiSU9GRobJ4XsZGRmydkREJBcTE4Nx48Zh48aNRjMijhs3jsOfHcDcsCS9Xo/vvvsOQNX5oF6vN8qSqc/DkgRBsGm7uszwnLxt27ZGQ20ddU4u/v6//vrLZLDjr7/+sls/vLy8ZNlO2dnZWLZsGaKjo41mnFu2bJnLlG8wNWrA8IaPo0YNGB7DYmNjjTKlDNtFR0fbrR+ujkEpsjmxVk6PHj0wbtw42d1LR9TKEdP63333XZSWlhoFxnx8fJjWT04lZlR06NDBZI2FGTNmMPOCSCHEaanz8/MxdOhQqYYUAOn7zbAdOUZ2djbWr1+PHj16oFu3btJsSfv27cP69evRvn17l7iwM+ToDHVLhiVlZmYiMzPTaHl9HpZ05coV6efmzZvj/Pnz0uM77rgDf//9t1G7+ko8J09LS0NxcbFRvZ6AgACHnJNHRkbC19cXK1asMLoZmJmZiRUrVsDX19fu/WD5hn8YDu10d3c3GhngqKGdhteJJSUlsokzzLUj22NQimxKPNiGhYWhsLDQKD02LCzM7gdbtVqNPn36YN26dSZn2CgoKEBsbGy9P9gTEdHts/TOObNvHcdc8WQAGDFihEtOaOKMDPXqw5IOHDiApUuXmm3/zDPPSEOT6vOwJDH7B6j6rI4dOxaBgYE4e/asNEyoerv6Sq1Wo3Xr1vj+++/h7u6OcePGYfDgwdi2bRs2btyIoqIixeynjhguZli+YcKECSguLpbWBQQEYPjw4S5TvkEpQzuDgoIAAMOHD0dubq7sPdFoNOjatSu2bt0qtSP7YFCKbEo82BYXF5sclpSTkwNBEOx6sNXpdPj2228RHh6OkpIS2QwbGo0G4eHh2L17N6ZOnaqIL0FyPYaZF0lJSUbp2+JMIMy8IHI+cdampk2bIisrC1988YU0/GTIkCGIi4vD5cuX7TJrE5mmlOLJSuGsDHXDYUk6nQ4vvPACgKqLe8OhaeLj1atXY8yYMfX+3Ktx48YAgCZNmqC0tBQbNmyQ1qlUKjRp0gRXrlyR2tVnlZWV2Lt3L7y9veHt7Y21a9di7dq1AKqCMFevXsXevXtRWVkJDw8Pu/UjLy8PJSUlmDp1KrZu3YqEhARpXWBgIJ566ilkZGTY/ZghBlhWrFhhsrj3ihUrZO3qM8Ohnaa+Px01tHPEiBFYunQp9uzZg3Xr1uHo0aNStmn79u2lRIYRI0bYtR+ujkEpsikxRblbt24mhyXNmTMHubm5slRmWzM8WTVVU+rXX391qZNVUh7xC7a2kyNmXhA5nzhr06VLlzBq1CjZ8L2MjAzpsT1mbSLTDO+wmxqy5koTmighQx2oypIqKysDALi7u6OyslJaJz4uKyvDgQMH0LVrV7v1Qwl69eqFw4cP48qVK+jatSsaNmyIK1euoEmTJigvL8ePP/4otavvtmzZAp1Oh+nTp2PQoEFG++q2bduwcOFCbNmyRTaLpq2Jx4JRo0YhNjbWqB8VFRXIyMiw+zHDsCxD586dMWHCBNkwQnH/dYXyDdVnUTW8weDIWVQ9PDwwZswYrFu3DrGxsXjiiSekETbz5s3DpUuXEBsba9egKTEoRTYmZnY88MADJu9e9urVC7m5uXbNADE8WVWr1UYXCa50skrKJH4RHzlyBGvWrDFZ3JF1z4iUwdLvCn6nOI4YsN+8eTM+++wzk0NgDNvVZ0rIUAeAr776Svq5+jAow8dfffVVvQ9KjR49GsuWLYMgCFIAqjo3NzeMHj3awT1zvKKiIgBAdHS0yXNysXC02M5eqmflVO+Ho7Jy9Ho9gKosuvnz56NBg6pL8YiICMyfPx+jRo3ClStXpHb1meEsqqZGDeTk5DhsFtVnnnkGALBx40ZZTSm1Wo3Y2FhpPdkPg1JkU2Jkf8+ePRg8eLBR1FucjcWedwCUkg5KZI7hF7FWq0VcXByio6NRWFgIrVbr0C9iIqqZ+H3VsWNHLFq0yCiIPGvWLE5M4GCGRYvNDYFxRNFiJVBChjoAnD17Vvq5pgwQw3b1lYeHB+6//358//33Ztvcf//9LpF5IdbhycnJwdChQ43Wi58Le9frUUpWTl5eHoCqIvfJycno1q2bbLitWPw+Ly+v3gdvgapZVFNTU5Genm40asDRsxA+88wzeOKJJ4xmiHSF/VQJGJQim2revDkAIDc312TUOzc3V9bOHgy/eExNMeqoLx6imohfxIsXL5Z9EWs0GpeZDpiorql+p98V7mYrlTg8rFGjRnjuuedkE5pUVFTgxo0bTu6hYyghQx2AdOHm6emJuXPn4ujRo9KsYnPnzsXw4cNRUVHhEhd4Op0OJ0+eRFBQkFEGkJubGwIDA3Hq1CnodLp6f/NJrNfzwQcfYODAgVJmEADcvHkTH374oUPq9SgpKwcAHnzwQWRnZ8uG26rVavTu3Ru7d+92SB+UIiYmBj179nTozKHmiEP5yPEYlLIhR0/Fq0RiQMjHxwcnT540utgODw/H5cuX7RoQEr94UlJSzE7fPW/ePJd7b0iZHDHbCxHdOk5MoDyHDh1CWVkZgoODUVFRYTShSXBwMM6cOYNDhw6hS5cuTuyp/RlmqA8YMMDoRpwjMtSBf7LPKyoqpACUSDz3MmxXn4lDKqdOnWo0vLRFixYYMmQIVqxY4RK1TQ3r9YwZM0ZWr+fDDz90aL0eJWTlREVFYc2aNdi1axd69OiB7t27w8vLC9evX0dubq4UkIqKirJ7X5TE1NBOci0MStmIM6biVSLDOxE9evRAbGysLC117969DrsTYe5in0EAUgJxtiRTNUDsOVsSEVmHExMoz6FDhwAAL7zwAu69916jG4KHDh3Ciy++6BJBKcMMdXM34gzb2YtGo5F+NuxD9ceG7eorsb5cRkYGoqOjkZKSIvuOz8jIkLWr75RUr8fZWTkdO3aESqWSsmzDwsKkz4Y4mkSlUqFjx44O6Y9SMLGDGJSyAV5cyhneiTBMS3XUnQhxJpro6GiTw/e0Wq1DZqIhMsfwM5qSkoKtW7di586dCAoKQkpKCubNm8fPKJFCcGIC5RIEweXvsIv1tUpKSiAIgsk2jqivFRUVhaysLIva2dr169dx5swZo+WnT5+W/W9KcHAwvLy8bNofMSutQ4cOJut8zZgxw+Xq0CmpXo8zjxlHjhyRAlIHDhzA3r17pXXia6HX611qNlcmdhDAoNRtM7y4NPXFk5SU5JIXl868EyGmTScnJ8Pd3d3ooB4XF4eEhASXSJsmZRI/ox06dMCQIUOg0+mkdUuXLsWDDz6Is2fP8jNKpACcmEB5xCEwK1euRKdOnYyKFq9cuVJq50o6d+6M7t27SxlSubm52Lt3r0MyxA3fA3d3d1lNLw8PD6kGWPW6V7Zw5swZTJs2zez6tLQ0s+uWL1+OsLAwm/fJWcwF6IDag3T2CNAZYr2ef7LjHnnkEWzevFm27ubNm3jkkUewadMml8miY2IHiRiUuk2GARBTBSZdOQDirDsR4oE8NDTU5Hpxuasc8El5xM/ezp074efnhyeffFJWpHfnzp2ydkTkXJyYQFmioqLg6+uL/Px8JCYmGs30lp+fDz8/P5cISuXl5aGkpEQaXmqYeWE4vNTe56GGNdWqnw8bBsXsUXstODgYy5cvv+Xn2poz69DVFqADzAfp6luATonEYd6bNm2SDa8FqoK5mzZtkrWrz5jYQYYYlLpNDIAoj3ggLywsREREhNH6wsJCWTsiR2vatCkAoEmTJti4caM0G83QoUMxcOBAjBo1CleuXJHa3Yri4mKUlpZa3N6SYQ7m+Pj4ICAgwOrnEdU1rEmoDGq1GrNmzYJWq8WBAwdkpQI8PT3h5uaGmTNnusSFjHh+OWrUKMTGxhplqFdUVCAjI8Pu56GGtdeqF/f28/PD0KFD7VZ7zcvLS1HBFGfWoTMXoNPr9Th+/DhKS0vh4+ODtm3bGgUP7RGgI7mIiAipplTnzp2NAuo5OTlQqVQmr19ul9KGuTKxgwwxKHWbGABRHrH+R1ZWlizyDlR9KWdlZbH+BznVqVOnAFTNwmPqi/iOO+7AlStXcOrUKXTt2tXq319cXIwJEyfhRmVF7Y2rqWmYgznuHp7IXLOagSmyiuq65UFTZ2+DQwyUx7B+pWEtEn9/f0yfPt1l3g/D89C2bdvixIkTUs2eiIgIh52Hiude2dnZJoO3e/bscZlzL2fWoTMVoGPNHuXIz8+XakqZo9frkZ+fb/NJGpQ2zJWJHWSIQanbxABIzePXa2OPyLth/Q9TadOs/0HOJp4Ynjx50uRnVAxaGZ5AWqO0tBQ3KitQ3qo39F4+Nuu3KarrpcCp3SgtLWVQiizi4+MDdw9P4NRuh2zP3cMTPj63vh9wiIFyOXsmLSUQz0PT0tJQXFxsVKMwICDAIeeharUaffr0wbp16+Dn54fZs2fLhqUXFBQgNjbWYe+NM2fzUlIdOgbUlUWcOXTy5MnYvn27URbd448/jlWrVtll5lClDXNlYgcZYlDqNjEAYtn4dXPsNX7d8A5q9QM+v4DJ2YKCggAAw4cPx759+4w+o8OGDcPWrVuldrdK7+UDvbd9pwEnslZAQAAy16y2angpUDW0IC0tDYmJiQgJCbH4ebc7vNSVhhjUpew1kavPvqdWq9G6dWt8//33cHd3x7hx4zB48GBs27YNGzduRFFRkUMCpjqdDt9++y3Cw8NRUlKCN998U1qn0WgQHh6O3bt3Y+rUqXbvixIyg5RwHsqZfpWrY8eOeOyxx4xmIxTrjdmD0oa5MrGDDDEoZQNK+OJxppoi77VdRNhz/DrvoJKSGGYUijUFdu3ahVdffRWFhYVSnYfQ0FDMmTNHqilw7NgxAPafFYfIkQICAm45UBQSEuLQE2tXGGJQ17LX6B+VlZXYu3cvvL294e3tjbVr12Lt2rUAqvazq1evYu/evaisrJSmnLcHw+Btu3btjM69fv31V4cEb5WUGeTs81DO9Ks84syh77zzDiorK2WB002bNkn7qCtM0sDEDjLEoJSNOPuLx5ksibw7+iJC5Op3UEk5TGUUXrlyBc8++6zZ58THx0s/c1YcIudwhSEGdS17jf6xZcsW6HQ6TJ8+HYMGDTI6D922bRsWLlyILVu2YMyYMXbrh2Hw1tS5lyOCt4aZQampqTh8+DBycnLg7++P1NRUaLVah2cGOfM8lDP9Kk9UVBQaNWqEM2fOwNfXF2PHjkVQUBCKiorw9ddf49y5c/D29naJoBTAxA76h9VBKZ1Oh5UrV+Kbb77BX3/9ZVSs7X//+5/NOlfXMABCROaYyij85JNPsHPnTtlxVKVSoV+/fnj00UeNnl9XqcpL6sU2yDW5yhCDupS9Rv8oKioCAERHR5tcLy4X29mLEoK3YmbQsGHDMHHiRKPhe8OGDcMPP/zg0MwgZ9a2csRMv2Q9Dw8PlJWVoaSkBBs2bDC53pW4cmIH/cPqoNSMGTOwcuVKDBkyBB06dOD0yEREFjCVUfif//wHs2fPRkZGBjZs2ICxY8fiqaeeqncnJA0Ls53dBaJbxiEGpGRi7cFVq1Zh3759RoGYbt26ydrZixKCt2LGT0ZGhsnhexkZGbJ29ubs2lb2nunXWs4M0ClFXl4eSkpKamxz6dIllxtSycQOsjootW7dOmzYsAGDBw+2R3+IiFyKh4cH+vXrhw0bNqBfv371LiAFAOWhMdA39LXrNlTlJQx+kd1wiAEp1YgRI7BkyRJ89tlnRoGYzMxMfPbZZ1CpVBgxYoRd+6GE4K2vry8AoEOHDiZnypwxYwby8/OldvakhNpW9p7p1xrODtApxfnz523ajqi+sDoo5eHhgTZt2tijL0S3xbCQtLVYRJrIfvQNfZ0+C2BdnFWMlIVDDEiJ1Go1GjZsiGvXruGXX37BqVOnEBAQgFOnTuGXX34BADRs2NAhn1MxeLt48WJZ8Faj0bhU8NawtpWp4FhSUpJDals5aqbf2ighQKcUhll6bm5uEATB5GPW+SJXY3VQ6sUXX8Q777yD999/n0P3SFFMFZK2FItIE9VPnFWMbIlDDEhp8vLycO3aNfTr1w+7du3CwoULpXVqtRr9+vXDzp07HTocyFnXB+KwqPz8fJOZQfn5+bJ29mI4E6GpYXNxcXEOmYlwxIgRWLp0Kfbs2YN169bh6NGjUkC9ffv2iI2NhVqttmsWnVICdEpx6dIl6Wd3d3dUVlaafGzYjsgVWB2U+u6777Br1y58+eWXiIiIgLu7u2z9//3f/9msc0TWMFVIGrBsdqC6XESaiMzjrGJEVJ+JGRWzZs3Cyy+/jC1btqCoqAhBQUEYMWIEbt68iZ07dzok80LMiOnRowfGjRsHT09PVFRUYN++fQ7JiBGLqE+dOhVbt241ygx66qmnkJGRYfeZMg1nIjTFETMRAlWjW8aMGYN169YhNjYWTzzxhDT73rx583Dp0iXExsbatWyAUgJ0SnHs2DHp55s3b8rWGT42bEfkCqwOSvn6+mLUqFH26AvRbTFVSNoQZwcick11dVYxnU6HgoICAEBBQQFat27tEneSichy1We9GzNmjGz98ePHZe3sRcyICQsLQ2FhIXJycqR1Go0GYWFhds+IEYutHzlyBGvWrMHhw4elzKAOHTpAq9U6ZKZMJcxEKHrmmWcAABs3bjTKoouNjZXW24tSAnRKUVFRIf3coEEDWaaU4WPDdkSuwOqg1EcffWSPfhCRi+DsK0S1q14UduHChcjKynK5orBEVDMlzHoH/JMRU1xcbLJ2UE5ODgRBsGtGjGGxda1Wi7i4OERHR6OwsBBardZhM2UaviepqalGwTFHvSeiZ555Bk888YRRFp0jJlZRUoBOCQxf806dOqFHjx5SRuHevXuRm5tr1I7IFVgdlBL9/fff0h3c8PBw3HHHHTbrFBHVT5x9hah2hkVhJ0+ejNdeew3//ve/peWuVBSWiGqmhFnvgH9mC+vWrZvJ2kFz5sxBbm6u3WcVU8JMmeJ7kpKSgqFDh8qyXsQAxLx58xx6Q04cyudoSgmaKoVh8G3//v1SEAqoypQy1c6eeKOYlMLqoNS1a9fw3HPPYfXq1dDr9QCqDr6TJk3Ce++9h0aNGtm8k0RU93H2FaLaGRaFTU1Nxfbt2wEAN27cQGpqKrRarUsVhXV15maVPX36tOx/UzirrOtQQiBGLB7+wAMPmKwd1KtXL+Tm5tq9yDignJkyq8+uBgCCILjURFFKCZoqhUajkX6uqaaUYTt74Y1iUhKrg1KzZs3C7t27sXXrVvTs2RNAVfHz559/Hi+++CKWLFli804SUd3G2VeIzDMMPBQUFODcuXOIjo7GuHHjpDobCxcuxEcffYSYmBj88MMP2LZtG8LDwxl4qOdqm1U2LS3N7DrOKutanB2I8fX1BQDs2bMHAwYMMBqy9t1338na2ZszZ8o0rK9VUlKC4uJiaZ2fnx98fX1d6pxHCUFTpejcuTOysrIsamdPzp6UgKg6q4NSmzZtwieffII+ffpIywYPHoyGDRti7NixDEoRkRHOvkJknqnAw+bNm43aXbx4EZ9++ikASAVrGXio38zNKmvpc8m1ODMQ07x5cwBAbm6u2SFrhu3qM/Gc59y5c0a1gS5duiQFqVzpnMfZQVOl6NixI1QqlTTayBSVSoWOHTvarQ9KmJSAqDqrg1JlZWUmZzFq0aIFysrKbNIpIqofxAyQI0eOAKj6IjQ1za1OpwMAHDlyBN7e3gA49IRch2Hg4ZdffsFbb70FoKoWx+DBgxEUFISioiJs27YNeXl5AICZM2finnvuYeChnqttVlkipYiMjISvry9KSkqMhqyJfH19XaJ2kGHdrOpD9Qwf27u+ltI4M2iqFEeOHJECUtWDU+JjvV6PI0eO2O21UsKkBETVWR2Uio6OhlarxerVq6ULxvLycqSmpiI6OtrmHSSiuqt6Bsjzzz9fY/uMjAxkZGQAYAYIuQ7DwENpaSkAoEmTJli0aJFU+DQyMhL9+vXDqFGjcOXKFQQFBXH/ICJF6ty5M7p37y5lSOXm5mLv3r0uU0tJHHYNVL0WEyZMkC76MzMzpcwUw3bkGsRAZNu2bXH58mXZ0M4WLVqgSZMmOH78uF0DlkqZlIDIkNVBqXfeeQcDBgzAXXfdhXvvvRcA8PPPP8PLywtfffWVzTtIRHWXmAGi1+uRmJiIO++8E/Hx8fj999+RlpaGxMRE3H333UhPT0dRUZHsy5EZIOSKxEyoK1euIDk5Gd26dZPVerhy5YrUrmvXrs7sKhGRJC8vDyUlJZg6dSq2bt2KvXv3SusCAwPx1FNPISMjwyWyL8SbC40bN8bcuXNx9OhR5OTkwN/fH3PnzsUjjzyCq1evSu3IdYiF/keMGIFBgwYZDWfctm0bFi5caNcJAZQ0KQGRyOqgVIcOHXD8+HFkZWXh119/BQCMHz8ecXFxaNiwoc07SER1l2EGyIwZM6QsywceeABA1Uwjq1evRn5+PlJTU9GuXTtndpdIMR588EFkZ2fLaj2o1Wr07t0bu3fvdmLPiIiMiVk/o0aNQmxsrNHFdkVFBTIyMlwiO+jvv/8GAFy9ehXDhg1DZWWltM7Dw0N6LLYj12E4IcDgwYNlAVq9Xu+QCQGq98EwMOWoPhBVZ3VQCgAaNWqEqVOn2rovRFSPGc6+8sMPPwAAXn/9dZecfYXInKioKKxZswa7du1Cjx490L17d3h5eeH69evIzc2VAlJRUVHO7SgRkQF/f38AQGFhISIiIoyyoQoLC2Xt6rMWLVpIPxsGpKo/NmxHrsFwQoDExESjbOjc3FxZO3v3ISkpCXFxcbKaUo7og1LpdDqXL8bvLBYFpT777DMMGjQI7u7u+Oyzz2psO3z4cJt0jIjqH3H2FTE9+cUXX8TgwYN5wCf6/6rPzBMWFiadLIonivaemYeIyFqRkZHQaDTIysqSDcUHqrIvsrKyEBgY6BKFzqOiopCVlQWgKtukf//+0oQVX3/9tTQsytVuLvCC/5/9RKVSITc31ygbOigoCIIg2HU/Efvg4+ODkydPIiEhQVqn0WgQHh6Oy5cvu8S+aig7Oxvp6ek4d+6ctEyj0SA+Pp43zh3AoqDUyJEjce7cObRo0QIjR440287NzU2aRYuchwd9UjK1Wo3w8HAAQHh4OD+bRAYMZ+Y5ePCgrC6Lp6cnANhlZh5xpkxTTp8+LfvfFM6WSeTa1Go14uPjodVqTWZf5OTkIDU11eW+869du4YNGzZIj93d3Z3YG+fhBX8VtVqNPn36YN26dfDz88PDDz8sBSx37NiBoqIixMbG2nU/MdxXe/TogdjYWFm21t69e11uX83OzoZWqzU5G6FWq+WIDgewKChlOF2l4c+urKYT+NrY8+Q9Ozsbixcvls3mEBAQgISEBO5MREQKJ9ZbSUxMxAcffCA7gff398cTTzyBtLQ0m9dlqT5TpilpaWlm13G2TCIyHKZvmH3hasP0xQkrAJgsJG3YzhUmrOAF/z90Oh2+/fZbhIeHo6SkRBawFLOUdu/ejalTp9o1KGS4rxpma7navgpUvSfp6emIjo42ORthUlISlixZgp49e7pUoM7RbqmmVHUlJSUuVwzNkhN4c+x18p6dnY2UlBTpbrqopKQEKSkpmDdvnksdZIiI6hqx3kpQUBCysrKMsl7FCUZsXZdFnCnzdp5PRM6lhEz5mJgY9OjRA1u2bEFRURGCgoIwYsQIeHh4OLQfSjB58mRs377d6ObCww8/jNWrVzuxZ47DC365vLw8nDt3DsnJyWjXrp3J7/iEhASHzFIpltRw9jHD2QzfE1NB5Li4OIe9J67M6qDU66+/jpYtW2LcuHEAgDFjxmDTpk0IDAzEtm3bcO+999q8k0pk7gT+9OnT0lT3ISEhZp9razqdDosWLQIAdO7cGRMmTJDuRGRmZiInJweLFi1ymYM+EVFdZFiXJSUlBSdOnJAu7O655x671WUxnCmTiOoepQyPMtWPTZs2udQwLXHCiv3792PNmjU4fPiwdNHfoUMHzJo1S2pX3/GCX07Mcg4NDYVarTb6m0NDQ2Xt7M1UH1yN4XtiiqPfE1dldVBq6dKlUvG+HTt2YOfOndi+fTs2bNiAl156CV9//bXNO6lEtZ3Ah4SEOPQE/9ChQygpKUHHjh2RlpYmuxORlpaGGTNmID8/H4cOHUKXLl0c1i8iIrKcWOshJSUFAwcOlK1bvHgxAGDevHm8uUBEEqUMj1JKP5wtKioKvr6+yM/PR3JyMrp37w5PT0/89ttvWL9+PfLz8+Hn5+cSQSnDC35TmXyudsFvOEulqUwpV5qlUimqzxxaHd8Tx7A6KHXu3DncfffdAIDPP/8cY8eORf/+/dGyZUt0797d5h0kyxw6dAgAMGXKFAiCgIMHD8oOcpMnT8aLL77IoBQRkcIdPXq01vWucGFHRLVTyvAopfRDCdRqNWbNmoWUlBTs3btXNmGFaObMmfX+dQD+uZDfvHkztm7dapTJN2zYMFm7+k7Mhn733XdRWlpq9Hr4+Pi4zCyVSsGZQ5VBVXsTOT8/P/z+++8AgO3bt6Nfv34AAEEQOPOeAvz888+Ii4vDzJkzMX/+fMycORNxcXGyootERKRMlZWV2LhxIzw9PeHm5iZb5+bmBk9PT2zcuBGVlZUO6Y9Op8PBgwfxzTff4ODBg/yeJ1IYcXhUXFyc2eFRZ8+etft5oFL6oSTiMduQqWN7fRYZGQlfX1+sWLECoaGhWLx4MbZt24bFixcjNDQUK1asgK+vr0Mv+J35vSbOvldQUICKigrMnj0bmzZtwuzZs1FRUYGCggL07t3bJQKWSiFmqOfk5CApKQlHjhxBWVkZjhw5gqSkJOTk5GD69Ol8T+zM6kyp0aNH47HHHkPbtm1x4cIFDBo0CEDV1NVt2rSxeQfJMuL49VWrVhmlTWdmZmLVqlVSOyIiUqYtW7ZAp9NBp9NBpVJBEARpnZubGyoqKqR2Y8aMsWtflFKjhojMU0o9FKX0QwkMs8ZSU1ONakpptVqXyRqzhCODdM7+Xqs++96bb74p64ejZt8jOc4c6nxWB6XeeusttGzZEr///jv++9//onHjxgCAs2fPIj4+3uYdJMt07NgRKpUKer1edhEDQHqsUqnQsWNHZ3SPiIgs8Mcff0g/d+vWTapFUlFRgdzcXGkYiGE7e2BtGKK6QSn1UJTSDyUwLO7t7u5uVEjalYp75+XloaSkBFOnTsXWrVuNLvifeuopZGRkOOS1UML3mpJm3yM5zkboXFYHpdzd3TF79myj5TNnzrRJh+jWHDlyBHq9HkBV1prh+HUxdViv1+PIkSM8yBERKdTff/8NAGjatCl+++032bFco9GgSZMmuHLlitTOHlgbhqjuUEo9FKX0QwmYNfYP8W8cNWoUxowZgy1btkgzyo4YMQI3b95ERkaG3V8LpXyvKW32PZLjbITOY3VNqVWrVuGLL76QHr/88svw9fXF/fffj9OnT9u0c2Q58eCVmJgIPz8/2Tp/f38kJibK2hERkXJdvnzZZP2NK1eu2H3brA1DZN7169dx7Ngxo3/iOfDp06dNrj927BiuX79u8/4Y1kNJTEzE5s2bsW3bNmzevBmJiYkOq4fCuiz/MMwaM8WVssYMC51PmjQJixcvxubNm7F48WJMmjQJmzdvlrWzF6V8r/GzQWSa1ZlSr7zyCpYsWQIAyMnJweLFi/HWW2/h888/x8yZM/F///d/Nu8k1U48eAUFBSErK8tkOqhhOyIiUp5GjRpJPx89ehS7d+/G8ePHUVRUJJuVz7CdrfEuP5F5Z86cwbRp08yuT0tLM7tu+fLlCAsLs3mfYmJiMG7cOGzcuBE5OTnScrVajXHjxjlsqC3rslRh1tg/DAudm6p566hC50r5XuNng8g0q4NSv//+u1TQ/NNPP8UjjzyCadOmoWfPnujTp4+t+0cWMjzIpaSk4MSJE1J67D333MODHBFRHdCmTRvs3LkTKpUKpaWl2LBhg2y9WDvQnhOLsDYMkXnBwcFYvnz5LT/XHrKzs7F+/Xr06NED3bp1g5eXF65fv459+/Zh/fr1aN++vUMDU65el0XMGtNqtUhKSkJcXJysflFOTg5SU1Nd6jWpiSMKnSvle42fDSLTrA5KNW7cGBcuXEBwcDC+/vprzJo1CwDg5eWF8vJym3eQLCMe5FJSUjBw4EDZusWLFwMA5s2bx4Mc0f+n0+lc+qSZlKlZs2YAqu6YNmjQAB07dkSzZs1w4cIF5Ofn4+bNm7J29sA7uUTmeXl52SXb6VaZq5UDACNGjHBKDTjWZWHWmEgphc6V9L3GzwaRMauDUg8//DCeeuopdOrUCceOHcPgwYMBVBXabtmypa37R1YwHNphbj0PdETOnxKYyBzDu7Q3b97EwYMHa21na4Z3chMTE9GtWzdpBsB9+/Zh7969vJNLpBCGs3mZq5XD2bycg1lj8kLnsbGxRq9FRUWFQwqdKy1DiZ8NIjmrg1KLFy9GUlISfv/9d2zatEm6W7t//36MHz/e5h0ky1RWVmL9+vUA/hneIRIfr1+/Hk888QQ8PDyc1U0ip1PClMBEtQkODsb169fx119/ScsCAgLg6emJM2fO2H37SqlRQ1QXVFZWGs0q5qhzLaXUyiHTXD1rrPqwueqvhSOHgystQ8nVPxtKxFEUzmN1UMrX1xfvv/++0fLU1FSbdIhuzaZNmyAIAgCge/fumDBhgqyQYE5ODgRBwKZNmxg8JJellCmBicwpKSkBUFVMuUePHnjggQdQUVEBT09P/Pnnn9i7d6+snb0oqUYNkZItXboUGzduhE6nky0bM2YMnnnmGbtvXym1cgzxwo5ESho2BzBDiczjKArnUtXexNiePXswYcIE3H///fjzzz8BAGvWrMF3331n086R5cTXPiQkBGlpaYiIiECjRo0QERGBtLQ0hISEyNoRuSKlTAlMZI544divXz/8+OOP2LRpEz7//HNs2rQJP/74I/r27StrZw+Gwdu0tDSMGjUKgwYNwqhRo5CWlobo6GgsWbJEdhFO5IqWLl2KdevWoWnTppg9ezY2bdqE2bNno2nTpli3bh2WLl1q9z4YXvQbZskDzrnoz87ORlxcHGbOnIn58+dj5syZiIuLQ3Z2tkO2T8oiDpvLyclBUlISjhw5grKyMhw5cgRJSUnIycnB9OnTHRoUEjOU+vbti06dOjktIKXT6XDw4EF88803OHjwIL9TnUgcRdGqVSssXrwY27Ztw+LFi9GqVStotVoevxzA6qDUpk2bMGDAADRs2BAHDhxARUUFAKC0tBSvvPKKzTtIlrl27RoAoHPnziYvtqOiomTtiFwRhzmQ0onTZ+/cuRNdu3bFjBkz8K9//QszZsxA165d8c0339h9+mwGb4lqV1lZiY0bN8LPzw8bN27E0KFD0axZMwwdOlS2vLKy0q79UNJFPy/syBRx2NypU6eQkJCAwYMHIyEhAYWFhS5bMoHBW+WoPorCMLFjwYIFvBHnIFYHpRYsWIClS5dixYoVcHd3l5b37NkTBw4csGnnyHKtW7cGAGzfvl2anUl08+ZNfP3117J2RK7IcJiDKZzqnpTEzc0NYWFh6N27N8LCwqRps+09fTaDt0S127JlC3Q6HZ588kk0aCCvhtGgQQM88cQT0Ol02LJli937ooSLfl7YUU1iYmKQlZWFt956C8nJyXjrrbeQmZnpsgEppQRvma3FG3FKYXVNqYKCApMHEB8fH7vXuCDzBg4ciG+++Qbl5eV45JFH8NRTTyE6Oho5OTnIyMhAeXm51I7qj+LiYpSWllr1nNOnT8v+t5SPjw8CAgKseo7SKK22AVF1Spg+2zB4265dO6PaGwzeEgFFRUUAgOjoaJM1lKKjo2Xt7M3ZtXI4CyDVhoW9lVXblDWUqvBGnDJYHZTSaDQ4ceIEWrZsKVv+3XffoVWrVrbqF1mpc+fO8Pb2xrVr11BaWoqFCxcatfH29kbnzp2d0Duyh+LiYkyYOAk3Kitu6flpaWlWtXf38ETmmtV1OjCltCmBiapTwvTZYvD23XffRWlpqdEJq4+PD4O35PKCgoIAAKtWrUJubi6Ki4uldQEBAejevbusnSM486KfF3ZEtVNK8JYzUf9DiZNFuCKrg1JTp07FjBkz8OGHH8LNzQ1FRUXIycnB7NmzkZycbI8+kgXUajX+9a9/ISUlxWybf/3rX7zYrkdKS0txo7IC5a16Q+/lY9dtqa6XAqd2o7S0tE4HpQDlTQlsT6ryknqxDVeihOmz1Wo1+vTpg3Xr1sHPzw+zZ8+WMm8/+OADFBQUIDY2lt8n5NJGjBiBJUuW4LPPPoOHh4ds3aVLl/DZZ59BpVJhxIgRTuqhY/HCjqh2hsFbUxmWjgjeKilbSwk4ikIZrA5K/fvf/4Zer0ffvn1RVlaGmJgYeHp6Yvbs2Xjuuefs0Ucy4/r16zhz5oz0WKPR4JlnnsHGjRtx4cIFaXmzZs0wZswYaDQaHDt2TFoeHBwMLy8vh/bZVdlzemS9lw/03s1t8rtchbOHOThKw0IWzKxrlHBypNPp8O233yI8PBwlJSV48803pXUajQbh4eHYvXs3pk6dWu/2Gap77Pn9WhO1Wg13d3dUVFQYFTMXH7u7u7vMPqKEYxeR0olB2c2bN2Pr1q1GmcjDhg2TtbMHpWRrKQVHUSiDVUEpnU6H77//HgkJCXjppZdw4sQJXL16Fe3bt0fjxo3t1Ucy48yZM5g2bVqt7S5cuGByWuLly5cjLCzMHl0jAxyzrUyuUNugPDQG+oa+dt2GqryEwS8bUsLJkeEJq6maUr/++qtLnbCScjnz+/XQoUPSDNTmVFRU4NChQ+jSpYtd+6IESjh2ESmdOMPuihUrjIbOZWZmYsWKFXafYZdDbY250igKpbIqKKVWq9G/f3/88ssv8PX1Rfv27e3VL7JAcHAwli9fbnLd6dOnkZaWhsTERISEhJh9PtkXx2yTM+kb+jKLrg5y9smR4QmrqeCtK56wkvI4+/tVnHE6IiICCxcuxNatW1FUVISgoCAMGzYMs2bNwtGjR3HgwAGXCEoBzj92EdUH9p5hl0NtTXOVURRKZfXwvQ4dOuDUqVNmo6vkOF5eXrVmOoWEhDAbykk4ZpuIbpUzT454wkpKp4Tv17/++gsA0LdvX3h5eWHMmDGy9X379sXRo0eldq6CF3ZE5ilhhl0OtTXPFUZRKJWq9iZyCxYswOzZs/H555/j7NmzuHz5suwfEVURh8DExcWZHbN99uxZ5OXlOamHRKRk4slR37590alTJ4dd1BmesOr1etk6Vz9hJWVQwvdrixYtAAA7d+40uZ988803snauxFnHLiKlM5xhNysrC2+99RaSk5Px1ltvITMzE6NHj5a1swdxqG1OTg6SkpJw5MgRlJWV4ciRI0hKSkJOTg6mT5/O/ZYcyupMqcGDBwMAhg8fLksvFAQBbm5u0Ol0tusdUR3GMdtEVBexNgwpnRK+Xzt37oysrCwcPXoUiYmJmDBhgqw2zNGjR6V2juKsou9EZBklzLAL/DPUdvHixbJsLY1G45Shtjx2kdVBqV27dtmjH0T1DofAENHtcOZJGmvDkJIp4fs1KioKvr6+KCkpwf79+5GTkyOt8/DwAAD4+fkhKirKbn0wxElViJRPaUPn7F2/yhI8dhFwC0Gp3r1726MfRPWO0r54iJxFdb20XmzDkZRwksbaMKRUSvh+VavVmDVrFrRardGFnZubG9zc3DBz5kyH7C/OLvpORJZRSiayUo4ZSukHOZ/VQSkAuHTpEj744AP88ssvAID27dtjypQpzPggMqCULx4iZ/Hx8YG7hydwardDtufu4QkfHx+HbMuelHSSxqKfysNhDsr5fjXMKDQMIPv7+2P69OkO2U+VUPSdiCzn7ExkpRwzlNIPUgarg1LZ2dkYNmwYfHx8cN999wEA3n33XcybNw9bt25lNJPIgLO/eIicKSAgAJlrVqO01PIsptOnTyMtLQ2JiYkICQmxans+Pj4ICAiwtpuKwpM0qokSMuiUQinfr87OKBSLvicnJ5st+p6QkGDX2byIyDrOPG4o5ZihlH6QMlgdlEpISMC4ceOwZMkSacfR6XSIj49HQkIC8vPzbd5JorrM2SesRM4UEBBwS4GikJAQhIWF2aFHysaTNDJHSRl0SsHvV2UUfSfzmNlI5jgrE1kpxwyl9IOUweqg1IkTJ/DJJ5/IDqjiuPrVq1fbtHNE9QWHwBCRJXiSRqYwg848Z3+/ZmdnY/HixSguLpaWBQQEICEhwSFBQiUUfSfTnP3ZIDJFKccMpfSDlEFVexO5zp07S7WkDP3yyy+49957bdIpJSkuLsaxY8cs/nf69GkAVUNQrHnesWPHZF9aRETkegxP0kzhSZprEjPo4uLizGbQnT17Fnl5eU7qoWvKzs5GSkoKSkpKZMtLSkqQkpKC7Oxsu/fBsOi7Xq+XreOkKs6jhM8GkSlKOWYopR+kDFZnSj3//POYMWMGTpw4gR49egAA9u7di8WLF+O1116TnRDV9Q9RcXExJkychBuVFVY/Ny0tzernuHt4InPN6jpfE8XeiouLra5RY/i/NepDjRoiqjuUMKsYKQ8z6JRHp9Nh0aJFAKpu2E6YMEEaUpmZmYmcnBwsWrTI7tlrSin6Tv9QymeDyBSlHDOU0g9SBquDUuPHjwcAvPzyyybXubm5QRAEuLm5QafT3X4Pnai0tBQ3KitQ3qo39F72ndFJdb0UOLUbpaWlDILUgIFCstb169dx5syZW35+cHAwvLy8bNgjIvN4kkamcJiD8hw6dAglJSXo2LEj0tLSZEMq09LSMGPGDOTn5+PQoUPo0qWLXfuilKLvVEVJnw0iU5RyzFBKP8j5rA5KmRtSUJ/pvXyg927u7G4QGCgk6505cwbTpk275ecvX77cJQtuk/PwJI2qYwad8hw6dAgAMGXKFJNDKidPnowXX3zRYYEHFn1XDqV9NohMUcoxQyn9IOeyOihV0xTdYoYUkb0xUEiWCg4OxvLly02uO336NNLS0pCYmGj22BYcHGzP7hGZxJM0MsQMOuUSBMHZXZA4u+g7ySnps0FkilKOGUrpBzmP1UGpyZMnY/HixfD29pYt/+233zBx4kTs2bPHZp0jIrpdXl5etWY6hYSEMBuKFIcnaWSIGXTKEhUVhTVr1mDlypXo1KmTUfbaypUrpXbkWvjZICKyjtVBqZ9//hmRkZHIzMxEdHQ0AGDVqlV4/vnn8dBDD9m8g0RERPZQU70xSyZIYL0xcjRm0ClHVFQUfH19kZ+fj8TERKNi1vn5+fDz82PgwQXxs0FUN+l0On6/OonVQal9+/bhP//5D/r06YMXX3wRJ06cwJdffolFixZh6tSp9ugjERGRzVlSb6ymCRLsXW+MJ0dkCjPolEGtVmPWrFnQarU4cOAAcnJypHWenp5wc3PDzJkzuc+6IH42iOqe7OxspKen49y5c9IyjUaD+Ph4ZiI7gNVBKXd3d7zxxhto1KgR5s+fjwYNGmD37t1S1hQREVFdUFO9MUufby88OSKlYEaheYZDKg33VX9/f0yfPp37qgvjZ4Oo7sjOzoZWq0V0dDSSk5NlNRu1Wi2HyDuA1UGpGzdu4N///jcWL16MOXPm4LvvvsPo0aPxwQcfYPDgwfboIxERkc1ZUm/MGXhyREqi9IxCZ+OQSjKHnw0i5dPpdEhPT0d0dLRsdtuIiAgsWLAASUlJWLJkCf5fe3ceF1X1/gH8MzNsKsqixqKCmJqGkksuxDfU3LdcklLRXFMBc8tMBSUMtNXMEpcEVxSlLDU1c0lJxcwFUUzTQFARzBBwYZHh/P7wNxMjg+Jy71zh8369ePWdey6e53tn7nDvc895jpeXF89dCT1yUurll1/GnTt3sG/fPrRt2xZCCHz66afo378/Ro4cifDwcCniJCIiKvd4cURKo+QRhUrBKZVUGn42iJQtISEB6enpmDVrlsGiBACgVqvh6+uLgIAAJCQk8FyW0GMlpRYuXKhffU+lUuGDDz5Aly5dMHTo0KceIBERUUXBiyNSGqWOKCQiInpSmZmZAAA3Nzej7brtuv1IGo+clIqIiDC6vXnz5jh27NgTB/Q0LFq0CJ999hnS09Px0ksv4euvv0br1q1NHRYRkazUednloo+KhBdHRERERPKwt7cHACQnJ8Pd3b1Ee3JyssF+JI0yJ6U2btyIvn37wsLCAgBw+fJlODs765/k3rlzB9988w2mTZsmTaRltGHDBkyZMgVLlixBmzZtsGDBAnTt2hXnzp3Dc88991j/pjo36+kGaaI+iKhisLGxgbmFJZC0X5b+zC0sYWNjI0tf5R0vjoiIiIjk4eHhAUdHR0RFRRmUTQCAoqIiREVFwcnJCR4eHiaMsvwrc1Jq0KBBuHr1qj6x8+KLLyI+Ph716tUDANy8eRMzZswweVJq/vz5eOeddzBixAgAwJIlS7Bt2zZERkZi+vTpj/VvVkqOfZohEhFJysHBAWvXrEZ2dtlHMaWkpCAsLAyBgYFwdXV9pP5sbGzg4ODwqGGSEbw4IiIiKv+0Wi2L4CuARqOBv78/goODERgYiNatW8PS0hL5+fk4cuQIDh8+jJCQEL43EitzUkoI8cDXSlBQUIBjx45hxowZ+m1qtRqdOnVCXFyc0d/Jz89Hfn6+/nVOTk6JfXLdvFFUyfapx1ucOjeLyS8iemocHBweK1Hk6urK+jEmVPziKCgoCL6+vgar78XFxfHiiIiI6BkWGxuL8PBwpKen67c5OjrC39+fq+uagLe3N9566y3ExMQY5Aw0Gg3eeustvicyeOSaUkp2/fp1aLXaEjdiDg4OOHv2rNHfmTdvHkJCQh747xZVskVRlRpPLU4iIqLSeHt7IyQkBOHh4QgICNBvd3JyQkhICC+OiIiInlGxsbEIDg6Gp6cnZs2aZfDgKTg4mH/nTSA2NhYbNmxA27Zt0bp1a1hZWSEvLw9HjhzBhg0b8OKLL/I9kVi5Sko9jhkzZmDKlCn61zk5OahTp44JIyIioorO29sbXl5eHNpPRERUTmi1WoSHh8PT0xOzZ8/G1q1bsXv3bjg7O2P27NmYM2cOFi9eDC8vL/69l0nx9+T+sgl9+vRBUFAQ3xMZPFJSaufOnfpitkVFRdizZw9Onz4NAMjKynrqwT2qGjVqQKPRICMjw2B7RkYGHB0djf6OpaUlLC0t5QiPiIiozDQaDZo3b27qMIjoIVgbhojKIiEhAenp6WjSpAl69uwJrVarb1uyZAk6dOiAq1evIiEhgX//ZaJ7T2bNmmWQkALulQHy9fVFQEAA3xOJPVJSatiwYQavx44da/BapVI9eURPwMLCAi1btsSePXvQt29fAP8lz8aPH2/S2IiIiIiofGFtGCIqq8zMTADA7t27YWdnh1GjRsHT0xNxcXGIiIjA7t27DfaTGhPq/x1rNzc3o+267XK9JxVVmZNSRUVFUsbx1EyZMgXDhg3Dyy+/jNatW2PBggW4ffu2fjU+IiIiUqa8vDykpqYabUtJSTH4rzEuLi6wsrKSJDai+7E2DBE9imrVqgEAqlatipiYGJiZ3bsV79WrF7p164Z+/frh5s2b+v2kFBsbi0WLFhnMMHJwcEBAQECF+t6yt7cHACQnJ8Pd3b1Ee3JyssF+JI1yV1Pqrbfewj///IPZs2cjPT0dzZo1w88//8zlyp+CjIyMR1piHijbTYQxXGKeiKjiSU1NxZgxYx64T1hYWKlty5Yt4+qRJIvS6pC4u7sjNDSUdUiIqISkpCQAwHPPPWd0qljNmjVx8+ZNJCUloVWrVpLFERsbi9mzZ5coYZOVlaWvbVVRElMeHh5wdHREVFRUiZpSRUVFiIqKgpOTEzw8PEwYZflX7pJSADB+/HhO13vKMjIyMGTo27hbkP9Yv/+gmwhjzC0ssXbN6lITU+rcrMeK41HI0QcREf3HxcUFy5Yte6LfJ5JD8TokQgicOHHCYAoM65AQ0f1003z//vtvBAUFwdfX12CEpS5pVXw68NOm1Woxf/58AECLFi0wZMgQfQxr165FXFwc5s+fX2ES6hqNBv7+/ggODjb6nsTFxSEkJKRCHAtTKpdJKXr6srOzcbcgH7n12qHIykbSvtR52UDSfmRnZ5ealKqUHCtpDEREJD8rKyuOdKJngq6+SFpaGj766KMSNaVGjRplsB8RkbOzMwDg9ddfx5EjRxAQEKBvc3JyQu/evbF161b9flKIj49HVlYWmjZtirCwMINRnmFhYZg4cSJOnTqF+Ph4tGzZUrI4lMTb2xshISEIDw8v8Z5wGrY8mJSiR1JkZYOiKjVMHQZy3bxRVMlW0j7UuVlMfhEREVEJuvoic+fONVpTau7cuQb7ERH16dMHS5YswW+//YaoqChs27YNaWlpcHZ2Rs+ePeHr6wuNRoM+ffpIFkN8fDwAYMSIEUanEA4fPhzvvfdehUpKAfcSU15eXhW+8LupMClFz6SiSraKSI4RERFRxePu7g6NRoNq1aphzpw5+oLF7u7umDNnDnx8fJCTk2O0cC4RVUwWFhbw8fFBdHQ0evXqZbCQ2OLFi1FUVISBAwfCwsJC8liEEJL38azRaDScbm0i6ofv8mAZGRmlrpRDREREj0er1eLEiRPYs2cPTpw4Aa1Wa+qQiOj/JSYmQqvV4saNG5g9ezYSExNx584dJCYmYvbs2bhx4wa0Wi0SExNNHSoRKciLL74IoOTK9rrXunapNGvWDACwcuVKozGsXLnSYD8iOZQ5KXXz5k0MGTIErq6uGDZsGAoKChAQEAAnJye4ubmhXbt2yMnJkTJWIiKiCiE2Nha+vr6YPHkyPvroI0yePBm+vr6IjeWUYiIl0NWKCgwMRFJSEgICAtCjRw8EBAQgOTkZgYGBBvsREelW7XzllVfw888/IyAgAP369UNAQAB+/vlnvPLKK1i8eLGkD6GaNWsGW1tbnDp1CoGBgQYJ9cDAQJw6dQp2dnZMSpGsyjx9b+bMmTh27BimTp2KTZs24c0338Tff/+N3377DVqtFn5+fvjkk08eeZU1KpuMjAxkZ2eXef+UlBSD/z4KGxubUguM04Pl5eU90chBFxcXWFlZPdLvcCVCovIlNjYWwcHBRuvUBAcHs+gmkQLoakU5OzsjKiqqRB2Ss2fPGuxHRFR81U4rKyv4+PgYtMuxaqdGo8GUKVMQHByM48ePIy4uTt9maWkJlUqFyZMns5YSyarMSanNmzdj1apV6NChA9544w3Url0bW7ZsgZeXFwDg008/xXvvvceklAQyMjIwZOjbuFuQ/8i/+zjvh7mFJdauWc3E1GNITU3FmDFjHvv3ly1b9sgrT7EYO1H5oXuK6unpidDQUINVcUJDQxEUFITFixdXmKWaiZTKw8MDjo6OiIqKQmhoqMENZFFREaKiouDk5AQPDw8TRklESqIbOenm5ma0Xbdd6hGWxVebK75yqL29Pfz8/GR/8KXVallgvIIrc1Lq2rVrqF+/PoB7T4UqVapkcPPcpEkTXLp06elHqADqvLKPUJKij+zsbNwtyEduvXYosrKRPo6k/cjOzmZS6jG4uLhg2bJlRttSUlIQFhaGwMBAuLq6lvr7j4orERKVH8WfohpbFUeOp6hE9HAajQb+/v4IDg5GUFAQfH19DUY1xsXFISQkhDdWJsKbXFIi3cjJ5ORko4sgJCcnG+wnJaWsNhcbG1siOebo6Ah/f3+OCq9AypyUql69Ov755x/UqVMHwL0lLW1tbfXtt27dgqWl5VMP0JRsbGxgbmEJJO2XpT9zC0vY2JSedCqysuGKcwpnZWX10JFOrq6ujzwa6kG4EqGyPOpUW+Dxp9tyqm35o5SnqET0cMVHGwQEBOi3Ozk5cZqtCfEml5Tq/hGWxR8+VcQRlkorV1BQUIDNmzcjLS0Nzs7O6NOnjywrIdIjJKU8PDzwxx9/oEWLFgCAdevWGbT/8ccfaNy48dONzsQcHBywds3qR67l9LDRMKXhDSbRs+1JptoCjz7dllNtyx8lPUUloodTymgDukdpN7kcsUXFKWmEpamTt0orV7BkyRLExMQYFJlfsmQJfHx8MG7cOMn7r+jKnJSKiooqMZWgOAcHh3JZT8rBweGxbvie9mgYIlI+TrWlJ8WnqEREj0dpN7mmvuknZVLCCEslJG+VVK5gyZIliI6Ohq2tLbp06QJnZ2ekpaXhl19+QXR0NAAwMSWxMielHvZUtnv37k8cDBFRecCptvS4lPQUlYgejokH5VDSTa4SbvpJuUw5wlIpyVullCsoKChATEwMqlSpAisrK2zcuFHf5ujoiCpVqiAmJgYjR47kVD4JlT706T5vv/02bt68qX998uRJ3L17V5KgiIiIKirdU9SkpCQEBASgR48eCAgIQHJyMm9kCFqtFidOnMCePXtw4sQJg6kGJC9d4qFevXpYtGgRtm/fjkWLFqFevXoIDg5GbCwXCJGTUm5yi9/0h4SEoKCgAHFxcSgoKEBISAg8PT2xePFinrsVnEajQfPmzdGxY0c0b95ctodNuuStr69vqcnbq1evIiEhQdI4ipcrMEaucgWbN2+GVqvF7du3jX6X3759G1qtFps3b5Y0jorukabvff7556hatSoA4NVXX0V8fDzq1asnWXBERERyU0INENapIWM4Kkc5lDLagP6jlJp8upv+3r17Y+jQoSXO1969e+PQoUNcRZVMQinJW6WUK7hy5QoA4OWXXzb6XT5t2jQcPXpUvx9Jo8wjpYQQD3xNRET0rIuNjYWvry8mT56Mjz76CJMnT4avr69JRjyY6ikqKRNH5SiLUkYb0H+K3+QWFRUZtMl5k6u7mV++fDnc3NwwceJETJs2DRMnToSbmxuWL19usB+RnJQyQklXriAuLg5BQUFITEzEnTt3kJiYiKCgIMTFxcHPz0+2a5+GDRsa/S5v0KCBLP1XdGVOShEREZVnvOknpbp/VI67uzsqV66sf5LL6UDyU8poA/qPUm5ybW1tAQB16tRBcnIyvvrqK3z66af46quvkJycjDp16hjsRyQnpSRvAWWUK2jcuDEAYPv27cjPzzeYHp+fn48dO3YY7EfSKPP0PQA4c+aMfgiqEAJnz57FrVu3DPbhikAkB3Vedrnog4iUgVNxSMmUVMCZ7lHKVDEypISVzXRSU1NLFDpfu3Yt4uLiZIuB6H5KW1DF1OUKnnvuOQBAVlYWunfvbpCoU6vV+te6/Ugaj5SU6tixo8G0vV69egEAVCoVhBBQqVR8SkeSsrGxgbmFJZC0X5b+zC0sYWNjI0tfRGQ6vOknJeOoHOVRSj0UKsnUN7nFz0MhBP766y+kpKQgPz/f4D6K5yuZipKSt8B/5QpMwcPDA7a2tsjKyjI6cgy4N6qR3+XSKnNSqrR5p0RycnBwwNo1q5GdXfZRTCkpKQgLC0NgYCBcXV0fqT8bGxs4ODg8aphE9IzhTT8pGUflKI/SRhuQIVPe5GZlZQG4Vzj5jz/+wOHDhw3ievnll3H06FH9fkSmYOrkrRK1bt0aVlZWuHnzJqpWrYq8vDwcOXIEKpXK1KGVe2VOSj3sZj4rKwvbt29/5Jt+okfl4ODwWIkiV1dXNGzYUIKIiOhZx5t+UjKOylEmpY02IGXQ1Yo6evQo2rZtizZt2sDKygp5eXn4/fff9UmqilZTSgkr25IhUyZvlSIhIQFZWVno1KkTfv31V4NZXxqNBh07dsSePXs4Ul5ijzR970FSUlIwdOhQDB48+Gn9k3QfdW5WueiDiEhpeNNPSsZROcrF0QZ0v+IPL1QqFRo2bKg/X48cOWJ0v/IuNjYW4eHh+trEAODo6Ah/f38mb8mkdCPg9+zZg7Zt2+pHS+lGSe3du9dgP5LGU0tKkfQqJXPlJyIiKfCmn5SOo3KUi6MNyBgXFxf8/fffBuerg4MDXFxckJqaasLI5KVb2fb+ou9RUVEIDg7m9xeZlG7EYpMmTRAWFmbwULJPnz6YOHEiTp06VeFGNsqNSalnSK6bN4oq2Urahzo3i8kvIqqQlHbTr4SpDkqIgf7DUTlEyqerFZWamgpLS8sSbfn5+Qb7lWdc2ZaIyoJJqWdIUSVbFFWpYeowiIjKLaXc9CthqkNsbCwWLVqEjIwM/TYHBwcEBATwqbYJcVQOkbKVdVpeRZi+x5VtSel0yeFTp04ZHSl/6tQpg/1IGmVOSi1cuPCB7VeuXHniYIiIiEzN1Df9SpjqEBsbi9mzZxt9yj979mzMmTOHiSkiIiPc3d2h0WhQrVo1REdH48yZM/qHHC+++CIGDhyInJwco4tqlDdc2ZaUTpccfuedd7BlyxaDkfKOjo4YPXo0li9fXiGSyKZU5qTUl19++dB9XFxcnigYIiKiikwJUx20Wi3mz58PAGjRogWGDBmiT4ytXbsWcXFxmD9/PqdbECkMp9sqQ2JiIrRaLW7cuIGQkBD4+vrC09MTycnJCAkJwY0bN/T7lffRQVzZlpROt9BNbGwsVCpVifbffvuNC93IoMxJKd2XBlVsXAGQiEg6SpjqEB8fj6ysLDRt2tSg6Ke7uzvCwsL0RT/j4+PRsmVLSWIg5WMCRFmUMOWX7tGN+gkMDERERESJGoWBgYEICwurEKODuLItKZ1Go0H79u0RHR0NOzs7TJ06FZ6enoiLi0NERATOnTuHgQMH8u+bxFhTih4Ji6ATEUlHCVMd4uPjAQAjRowwmhgbPnw43nvvPSalKjAmQJRFCVN+6T+6UT/Ozs6Iiooqkbw9e/aswX7lGVe2JaXTarXYt28fXnjhBWRlZeHzzz/Xtzk6OuKFF17A/v378c477/BzKqEyJ6V69OiB9evXw8bGBgDw8ccfY9y4cfrlEf/991+8+uqrOHPmjCSBkjJwBUAiIukoaaqDEELyPujZwwSIshSf8hsSEoLTp08jLi4O9vb2CAkJQXBwMFc3k9n9o4OKj2qtiKODlLayLVFxxUeoN2rUyGgSmcX4pVfmpNTOnTv1S5gCwNy5c/Hmm2/qk1KFhYU4d+7cUw+QlIUrABIRSUcJUx2aNWuGNWvWYOXKlWjevHmJGFauXKnfjyoWJdQ8I0O6G6revXtj6NChJUav9e7dG4cOHeINlYw4OqgkpaxsS3S/4iPUjS10w2L88ihzUur+J6Z8gkpERPR0KeFmplmzZrC1tcWpU6cwc+ZMtGnTBpaWlsjPz8fvv/+OU6dOwc7OjkmpCkgJNc/IkO5Gafny5UZHry1fvtxgP5KHbnTQokWLSqzmVVFHB5l6ZVsiY5Q0Qr0iY00pInpiGRkZyM7OLvP+KSkpBv99FDY2NnBwcHjk3yN6Vph6qoNGo8GUKVMwe/ZsHD58GIcPHy6xz+TJk/mEuwJSQs0zMqSbsdCkSROjo9d0CxPo9iN5GVvNi4iUQwkj1OkRklIqlarEFyu/aIkoIyMDQ4a+jbsF+Q/f+T5hYWGP/DvmFpZYu2Y1E1NUrilhqoNKpYKFhYXB1H1LS0sUFBTIFgMpC58oE5UNa68RPRuUMEKdHnH63vDhw2FpaQkAyMvLw7hx41ClShUAMLhoJaKKIzs7G3cL8pFbrx2KrGwk7Uudlw0k7Ud2drbRpBRHbFF5YqqpDsYKJ+sSY02aNGHh5AqMT5SVJysrCwBw6tQpozdUp06dMtiPpMfaa0TPFlOPUKdHSEoNGzbM4PWQIUNK7PP2228/eURE9EwqsrIxaRF8jtgiejpYN4hKwyfKyqMblfbOO+9g69atJW6oRo8ejeXLl3P0moz4HUr07FHCCPWKrMxJqRUrVkgZB5WBOq/sI0CU3AeRFJQ0YovoWaarB5SWloaPPvqoxGpeo0aNMthPDlqtlheKCsEnysqiG72WmJiINWvWGB3ZyNFr8ipee83YdxdrrxEpE4vxmw4LnT8DbGxsYG5hCSTtl6U/cwtL2NhIe1NPJBVTj9gietbpRlTMnTvXaD2UuXPnGuwntdjYWISHh5dIjvn7+zMBYiJ8oqwcxUevBQcHw9fXF56enkhOTkZwcDBHr5mA7rvxhx9+wNatW0t8d/Xu3dtgPyKiio5JqWeAg4MD1q5Z/ci1csLCwhAYGAhXV9dH6o+1coiIKi53d3doNBpUq1YNc+bMgZmZmX77nDlz4OPjg5ycHKOFrp82FgtWLj5RVg6OXlMWDw8P2Nra4ttvvy3x3bV27Vp8++23sLW15ei1Co4jgIn+w6TUM8LBweGxEkWurq5o2LChBBEREVF5lJiYCK1Wixs3bmD27Nkl6gbduHFDv5+USQkWCyYqO45ee7ZwBfOKLTY2FosWLUJGRoZ+m4ODAwICAphEpgpJ/fBdiIiIqKLQ1TkJDAxEUlISAgIC0KNHDwQEBCA5ORmBgYEG+0lFVyzY19e31GLBV69eRUJCgqRxED0rdKPXOnbsiObNmzMhZSIJCQnIysrCO++8g+TkZIPv0IsXL2L06NG4ceMGv7sqqNjYWMyePbvEiphZWVmYPXs2YmNjTRMYkQlxpBQRERHp6eqcODs7IyoqqsTIi7NnzxrsJ5XixYKNYbFgIlIi3XdSv3794OPjg82bNyMtLQ3Ozs7o06cPCgsLsXz5cn53VUBarRbz588HALRo0QJDhgwxmNoZFxeH+fPncwQwVThMShE9poyMjEeu81X8v4+Cdb6eLercrHLRB1VMutW8oqKiEBoaajBFr6ioCFFRUbKs5qVLeiUnJxutX5WcnGywHxGREjyo0Pn333/PQucVWHx8PLKystC0aVOEhYUZTEsPCwvDxIkTcerUKcTHx6Nly5YmjpZIPkxKET2GjIwMDBn6Nu4W5D/y74aFhT3y75hbWGLtmtVMTD0jKiUrd+h1Xl4eUlNTS2wvS9LUxcUFVlZWksVGylB8Na+goKASNaXkWs3r/uRY8Sl8cibHiJ4VLJysDCx0TqWJj48HAIwYMQJCCJw4ccLgfB0+fDjee+89JqWowmFSiugxZGdn425BPnLrtUORlY2kfanzsoGk/cjOzmZS6hmR6+aNokq2kvahzs16rORXamoqxowZU2r7g5Kmy5Yt48IJFYQSVvNSSnKM6FkQGxuL8PBwg1E5jo6O8Pf3Z+FkBWKh84rt5MmT+PTTT0ucr127djVhVESmw6QU0RMosrJBUZUapg6DFKaokq1iPxcuLi5YtmzZY/8uVRxKWM1LCckxIqWLjY1FcHBwiVE5UVFRCA4O5rkis+KFzrdu3Vriu2v06NFYvnw5EhISJF3BlJSnWbNmWLNmDVatWmV0FN2qVav0+xFVJExKERFVIFZWVhztRGWmW83LlJSQHCNSKq1Wi/DwcHh6ehpMc3V3d0doaCiCgoKwePFiFk6WUfFC5wMHDizx3ZWfn89C5xVU06ZNoVarUVRUBCGEQZvutVqtRtOmTU0RHpHJMClFREREiqaE5BiREiUkJCA9PR2zZs0yqLsG3Lu59fX1RUBAAEflyOj+RRruP+5cpKHiSkxMRFFREQDgxIkTOHz4sL7N0tISwL2aiYmJiTxfqUJRP3wXIiIiIiJSGt1oGzc3N6Ptuu0clSOf4os06BIQOlykoWLTnYeBgYGws7MzaLO3t0dgYKDBfkQVBUdKPSauYEVEREREpnT/qJz7cVSO/LhIA5VGdx46OzsjKiqqxNTOs2fPGuxHVFEwKfWYuIIVEREREZlS8VE5xWtKARyVY0pcpIGMuf98LT5Fj+crVWRMSj0mrmBFRERERKbEUTnKxUUa6H48X4mMY1LqMXEFKyIiIiIyNY7KUS4u0kD34/lKVBKTUvRI1HnZ5aIPKr/UuVnlog8iIqKy4qgcomcHz1ciQ0xKPUVarbbcfrnY2NjA3MISSNovS3/mFpawsbGRpS8qXyolx5o6BCIiItlxVA7Rwynlfo3nK9F/mJR6SmJjYxEeHo709HT9NkdHR/j7+5eLYZgODg5Yu2Y1srMfbRRTSkoKwsLCEBgYCFdX1zL/no2NDRwcHB41zAqJo9cM5bp5o6iSraR9qHOzmPwiIiIieoaU9/s1omcVk1JPQWxsLIKDg+Hp6YlZs2YZFKwLDg4uN/ODHRwcHjtR5OrqyhpcTxlHrxlXVMkWRVVqmDoMIiIiIlKIinK/RvQsYlLqCWm1WoSHh8PT09NgKV53d3eEhoYiKCgIixcvhpeXV7mZykfKwNFrREREREQPxvs1ImVjUuoJJSQkID09HbNmzdJ/wemo1Wr4+voiICAACQkJnDdcDpm6qDZHrxERERERlY73a0TKxqTUE8rMzAQAuLm5GW3XbdftR+UL6woRERERESkX79eIlI1JqSdkb28PAEhOToa7u3uJ9uTkZIP9qHxhUe3/mHrUGBEREZGS5ObmYunSpbh8+TJq166NsWPHolKlSqYOq8Lh/RqRsjEp9YQ8PDzg6OiIqKgogznKAFBUVISoqCg4OTnBw8PDhFGSVFhU+z/PQuKMiIiISA6BgYE4ePCg/vXRo0fx448/wsvLC2FhYSaMrOLh/RqRsjEp9YQ0Gg38/f0RHByMoKAg+Pr6GqzmEBcXh5CQEBbNo3KPo8aIiIiI/ktImZubw8fHBz169MD27dsRExODgwcPIjAwkIkpGfF+jUjZmJR6Cry9vRESEoLw8HAEBATotzs5OXF5UaowOGqMiIiIKrrc3Fx9Qmrbtm2wsLAAAIwZMwbDhw9Hz549cfDgQeTm5nIqn4x4v0akXExKPSXe3t7w8vJCQkICMjMzYW9vDw8PD2bcZZSXl4fU1NQS21NSUgz+a4yLiwusrKwki42IiIiIyr+lS5cCAHx8fPQJKR0LCwsMGDAA69evx9KlSzFp0iQTRFhx8X6NSJmYlHqKNBoNlxE1odTUVIwZM6bU9gcNk162bBkaNmwoRVhEREREVEFcvnwZANCjRw+j7T169MD69ev1+5G8eL9GpDxMSlG54eLigmXLlj327xIRERERPYnatWvj6NGj2L59u9GHpdu3b9fvR0RETEpROWJlZcXRTqQI6rzsctEHERERPZqxY8fixx9/RExMDIYPH24wha+goADfffedfj8iImJSiojoqbGxsYG5hSWQtF+W/swtLGFjYyNLX0RERPRwlSpVgpeXFw4ePIiePXtiwIAB+tX3vvvuO9y9exdeXl4sck5E9P+YlCIiekocHBywds1qZGc/2iimlJQUhIWFITAwEK6urmX+PRsbGzg4ODxqmERERCQh3d/0gwcPYv369Vi/fr2+zcvL64F1TomIKhompYiIniIHB4fHThS5urpyCioREVE5EBYWhtzcXCxduhSXL19G7dq1MXbsWI6QIiK6D5NSRFSusJ6T8uXl5SE1NdVoW0pKisF/jXFxcYGVlZUksSmJVqvlstVERM+wSpUqYdKkSaYOg4hI0ZiUIqJygfWcnh2pqalGVyQq7kFTG5YtW1buR5TFxsYiPDwc6enp+m2Ojo7w9/eHt7e3CSMjIiIiInp6mJQionLhceo5PW4tJ4D1nJ6Ei4sLli1b9kS/X57FxsYiODgYnp6emDVrFtzc3JCcnIyoqCgEBwcjJCSEiSkiIiIiKheYlCKicuNx6zmxlpO8rKyseLxLodVqER4eDk9PT4SGhkKtVgMA3N3dERoaiqCgICxevBheXl6cykdEREREzzwmpZ5hrMtCRFS+JCQkID09HbNmzdInpHTUajV8fX0REBCAhIQENG/e3ERREhERERE9HUxKPcNYl4WIqHzJzMwEALi5uRlt123X7UdERETPnoKCAmzevBlpaWlwdnZGnz59YGFhYeqwiEyCSalnGOuyEBGVL/b29gCA5ORkuLu7l2hPTk422I+IiIieLUuWLEFMTAy0Wq3BNh8fH4wbN86EkRGZBpNSzzDWZSEiKl88PDzg6OiIqKgog5pSAFBUVISoqCg4OTnBw8PDhFESERHR41iyZAmio6NhZ2eHUaNGwdPTE3FxcYiIiEB0dDQAMDFFFY764bsQERGRHDQaDfz9/REXF4egoCAkJibizp07SExMRFBQEOLi4uDn58ci50RERM+YgoICxMTEwM7ODjExMejVqxeqV6+OXr16GWwvKCgwdahEsuJIKaInoM7LLhd9EJFyeHt7IyQkBOHh4QgICNBvd3JyQkhICLy9vU0YHRERET2OzZs3Q6vVYtSoUTAzM7wNNzMzw8iRI/HFF19g8+bN8PHxMVGURPJjUoroMdjY2MDcwhJI2i9Lf+YWlrCxsZGlLyIyPW9vb3h5eSEhIQGZmZmwt7eHh4cHR0gRERE9o9LS0gAAnp6eRtt123X7EVUUTEoRPQYHBwesXbMa2dllH8WUkpKCsLAwBAYGwtXV9ZH6s7GxgYODw6OGSUTPMI1Gg+bNm5s6DCIiInoKnJ2dAQBxcXHo1atXifa4uDiD/YgqCialyiGtVsun6/9PymPh4ODwWIkiV1dXFqgnIiIiIqpA+vTpgyVLliAiIgLdunUzmMJXWFiIyMhIaDQa9OnTx4RREsmPSalyJjY2FuHh4UhPT9dvc3R0hL+/f4WrQ8JjQURERERESmBhYQEfHx9ER0fDx8cHI0eO1K++FxkZiRs3bmDgwIGwsLAwdahEsmJSqhyJjY1FcHAwPD09MWvWLLi5uSE5ORlRUVEIDg6uUAVyeSyIiIiIiEhJxo0bBwCIiYnBF198od+u0WgwcOBAfTtRRcKkVDmh1WoRHh4OT09PhIaGQq1WAwDc3d0RGhqKoKAgLF68GF5eXuV+Kh+PBRERERERKdG4ceMwcuRIbN68GWlpaXB2dkafPn04QooqLLWpA6CnIyEhAenp6fD19dUnYXTUajV8fX1x9epVJCQkmChC+fBYEBERERGRUumm8k2cOBE+Pj5MSFGFxqRUOZGZmQkAcHNzM9qu267brzzjsSAiIiIiIiJSPialygl7e3sAQHJystF23XbdfuUZjwURERERERGR8jEpVU54eHjA0dERUVFRKCoqMmgrKipCVFQUnJyc4OHhYaII5cNjQURERERERKR85arQed26dZGSkmKwbd68eZg+fbqJIpKPRqOBv78/goODMXPmTNSqVQv5+fmwtLTElStX8PvvvyMkJKRCFPYufiyCgoLg6+trsPpeXFxchTkWREREJC2tVouEhARkZmbC3t4eHh4evMYgIiIqo3KVlAKAOXPm4J133tG/rlq1qgmjkZe3tzdeeeUVHDx4sESbl5cXvL29TRCVaXh7eyMkJATh4eEICAjQb3dyckJISEiFOhZEREQkjdjYWISHhyM9PV2/zdHREf7+/rzWICIiKoNyl5SqWrUqHB0dTR2GSSxZsgQHDx6EnZ0dOnfujFq1auHKlSvYtWsXDh48iCVLlmDcuHGmDlM23t7e8PLy4tNLIiIieupiY2MRHBwMT09PzJo1y2BUdnBwMB+CERERlUG5qyn18ccfo3r16mjevDk+++wzFBYWmjokWRQUFCAmJgZ2dnaIiYmBv78/+vTpA39/f4PtBQUFpg5VVhqNBs2bN0fHjh3RvHlzJqSIiIjoiWm1WoSHh8PT0xOhoaFwd3dH5cqV4e7ujtDQUHh6emLx4sXQarWmDpWIiEjRytVIqQkTJqBFixawt7fHoUOHMGPGDFy9ehXz588v9Xfy8/ORn5+vf52TkyNHqE/d5s2bodVqMWrUKJiZGb6tZmZmGDlyJL744gts3rwZPj4+JoqSyjN1XrYi+8jLy0NqaqrRNl0Nuvtr0RXn4uICKyurR+6XiIjKr4SEBKSnp2PWrFlQqw2f8arVavj6+iIgIAAJCQlo3ry5iaIkIiJSPsUnpaZPn45PPvnkgfv8+eefaNSoEaZMmaLf5uHhAQsLC4wdOxbz5s2DpaWl0d+dN28eQkJCnmrMppCWlgYA8PT0NNqu267bj+hpsbGxgbmFJZC0X5b+zC0sYWNjU+b9U1NTMWbMmAfuExYWVmrbsmXL0LBhwzL3R0RE5V9mZiYAwM3NzWi7brtuPyIiIjJO8Ump9957D8OHD3/gPvXq1TO6vU2bNigsLMTFixfxwgsvGN1nxowZBsmsnJwc1KlT57HjNRVnZ2cAQFxcHHr16lWiPS4uzmA/oqfFwcEBa9esRnZ22UcxpaSkICwsDIGBgXB1dX2k/mxsbODg4FDm/V1cXLBs2bJH6uP+3yciIirO3t4eAJCcnAx3d/cS7cnJyQb7ERERkXGKT0rVrFkTNWvWfKzfjY+Ph1qtxnPPPVfqPpaWlqWOonqW9OnTB0uWLEFERAS6detmMIWvsLAQkZGR0Gg06NOnjwmjpPLKwcHhkRJFOq6urpKPQrKysuJIJyIieqo8PDzg6OiIqKgohIaGGkzhKyoqQlRUFJycnODh4WHCKImIiJSv3BQ6j4uLw4IFC3Dy5EkkJSUhKioKkydPxpAhQ2BnZ2fq8CRnYWEBHx8f3LhxAz4+Pti6dSuuX7+OrVu3Gmy3sLAwdahEREREzzSNRgN/f3/ExcUhKCgIiYmJuHPnDhITExEUFIS4uDj4+flxgRUiIqKHUPxIqbKytLREdHQ0PvzwQ+Tn58PNzQ2TJ082mJpX3o0bNw4AEBMTgy+++EK/XaPRYODAgfp2IiIiInoy3t7eCAkJQXh4OAICAvTbnZycEBISAm9vbxNGR0RE9GwoN0mpFi1a4PDhw6YOw+TGjRuHkSNHYvPmzUhLS4OzszP69OnDEVJERERET5m3tze8vLyQkJCAzMxM2Nvbw8PDgyOkiIiIyqjcJKXoPxqNBvXr14e9vT3s7e15YUREREQkEY1Gg+bNm5s6DCIiomcSk1LlTGxsLMLDw5Genq7f5ujoCH9/fw4jJyIiIiIiIiLFKDeFzuleQio4OBj16tXDokWLsH37dixatAj16tVDcHAwYmNjTR0iEREREREREREAJqXKDa1Wi/DwcHh6eiI0NBTu7u6oXLky3N3dERoaCk9PTyxevBhardbUoRIRERERERERcfpeeZGQkID09HTMmjULarVhrlGtVsPX1xcBAQFISEhg3YMKIC8vD6mpqUbbUlJSDP5rjIuLC6ysrCSJjYiIiIiIiAhgUqrcyMzMBAC4ubkZbddt1+1H5VtqairGjBnzwH3CwsJKbVu2bBkaNmz4tMMiIiIiIiIi0mNSqpywt7cHACQnJ8Pd3b1Ee3JyssF+VL65uLhg2bJlT/T7RERERERERFJiUqqc8PDwgKOjI6KiohAaGmowha+oqAhRUVFwcnKCh4eHCaMkuVhZWXGkExERERERESkaC52XExqNBv7+/oiLi0NQUBASExNx584dJCYmIigoCHFxcfDz84NGozF1qEREREREREREHClVnnh7eyMkJATh4eEICAjQb3dyckJISAi8vb1NGB0RERERERER0X+YlCpnvL294eXlhYSEBGRmZsLe3h4eHh4cIUVEREREREREisKkVDmk0WjQvHlzU4dBRERERERERFQq1pQiIiIiIiIiIiLZMSlFRERERERERESyY1KKiIiIiIiIiIhkx6QUERERERERERHJjkkpIiIiIiIiIiKSHZNSREREREREREQkOyaliIiIiIiIiIhIdmamDoCIyBRu3bqFefPmIS0tDc7OzpgxYwasra1NHRYRERGVE1qtFgkJCcjMzIS9vT08PDyg0WhMHRYRkaIwKUVEFc64ceNw9uxZ/evk5GT06tULjRo1wpIlS0wYGREREZUHsbGxCA8PR3p6un6bo6Mj/P394e3tbcLIiIiUhdP3iKhC0SWkVCoVunTpguXLl6NLly5QqVQ4e/Ysxo0bZ+oQiYiI6BkWGxuL4OBg1KtXD4sWLcL27duxaNEi1KtXD8HBwYiNjTV1iEREisGkFBFVGLdu3dInpHbs2IGZM2eifv36mDlzJnbs2KFPTN26dcvUoRIREdEzSKvVIjw8HJ6enggNDYW7uzsqV64Md3d3hIaGwtPTE4sXL4ZWqzV1qEREisDpe/TE8vLykJqaarQtJSXF4L/3c3FxgZWVlWSxye1JjgVQcY6HqY7FvHnzAACdO3cu8W9bWVmhU6dO2LVrF+bNm4ewsLCn2jc/G0REROVfQkIC0tPTMWvWLKjVhs//1Wo1fH19ERAQgISEBDRv3txEURIRKQeTUvTEUlNTMWbMmAfuU9oN/rJly9CwYUMpwjKJJzkWQMU7HnIfi7S0NADAm2++abTdx8cHu3bt0u/3NPGzQUREVP5lZmYCANzc3Iy267br9iMiquiYlKIn5uLigmXLlj3275YnT3IsdL9fnijts+Hs7Izk5GRs3LgRM2fOLNEeExOj3+9p42eDiIio/LO3twdwbxEVd3f3Eu3JyckG+xERVXQqIYQwdRBKkpOTAxsbG2RnZ6NatWqmDoeInqJbt26hV69e+ppSxafD5eXloXv37hBC4KeffoK1tbUJIyUiIqJnkVarha+vL+rVq4fQ0FCDKXxFRUUICgpCcnIy1q5dC41GY8JIiYikVdbcCgudE1GFYW1tjUaNGkEIge7duyMsLAx//fUXwsLC9AmpRo0aMSFFREREj0Wj0cDf3x9xcXEICgpCYmIi7ty5g8TERAQFBSEuLg5+fn5MSBER/T+OlLoPR0oRlX/jxo3D2bNnS2xv1KgRlixZYoKIiIiIqDyJjY1FeHg40tPT9ducnJzg5+cHb29vE0ZGRCSPsuZWmJS6D5NSRBXDrVu3MG/ePKSlpcHZ2RkzZszgCCkiIiJ6arRaLRISEpCZmQl7e3t4eHhwhBQRVRhMSj0mJqWIiIiIiIiIiB4fa0oREREREREREZFiMSlFRERERERERESyY1KKiIiIiIiIiIhkx6QUERERERERERHJjkkpIiIiIiIiIiKSHZNSREREREREREQkOyaliIiIiIiIiIhIdkxKERERERERERGR7JiUIiIiIiIiIiIi2TEpRUREREREREREsmNSioiIiIiIiIiIZMekFBERERERERERyY5JKSIiIiIiIiIikh2TUkREREREREREJDsmpYiIiIiIiIiISHZMShERERERERERkeyYlCIiIiIiIiIiItkxKUVERERERERERLJjUoqIiIiIiIiIiGTHpBQREREREREREcmOSSkiIiIiIiIiIpIdk1JERERERERERCQ7JqWIiIiIiIiIiEh2TEoREREREREREZHsmJQiIiIiIiIiIiLZMSlFRERERERERESyY1KKiIiIiIiIiIhkx6QUERERERERERHJjkkpIiIiIiIiIiKSHZNSREREREREREQkOyaliIiIiIiIiIhIdkxKERERERERERGR7JiUIiIiIiIiIiIi2TEpRUREREREREREsmNSioiIiIiIiIiIZMekFBERERERERERyY5JKSIiIiIiIiIikh2TUkREREREREREJDsmpYiIiIiIiIiISHZMShERERERERERkeyYlCIiIiIiIiIiItkxKUVERERERERERLJjUoqIiIiIiIiIiGTHpBQREREREREREcmOSSkiIiIiIiIiIpIdk1JERERERERERCQ7JqWIiIiIiIiIiEh2TEoREREREREREZHsmJQiIiIiIiIiIiLZMSlFRERERERERESyY1KKiIiIiIiIiIhkx6QUERERERERERHJjkkpIiIiIiIiIiKSHZNSREREREREREQkOyaliIiIiIiIiIhIdkxKERERERERERGR7JiUIiIiIiIiIiIi2TEpRUREREREREREsjMzdQBUfl28eBGjRo2CVquFRqNBREQE6tata+qwTEIpx6KgoACbN29GWloanJ2d0adPH1hYWMgaQ25uLpYuXYrLly+jdu3aGDt2LCpVqiRrDIAyjgUAJCUlYfTo0SgqKoJarcby5ctRr1492eMgInoWabVaJCQkIDMzE/b29vDw8IBGozF1WERED6SU61AiJVAJIYSpg1CSnJwc2NjYIDs7G9WqVTN1OM+sDh06wNhHS6VS4ddffzVBRKajlGOxZMkSxMTEQKvV6rdpNBr4+Phg3LhxssQQGBiIgwcPltju5eWFsLAwWWIAlHEsAKB9+/altu3bt0+2OIiInkWxsbEIDw9Henq6fpujoyP8/f3h7e1twsiIiEqnlOtQIqmVNbfyzEzfCwsLwyuvvILKlSvD1tbW6D6pqano2bMnKleujOeeew7vv/8+CgsL5Q2UDJIwlpaWGDt2LCwtLQEAQgh06NDBlOHJSinHYsmSJYiOjka1atUwdepUfP/995g6dSqqVauG6OhoLFmyRPIYdAkpc3NzDB48GGvXrsXgwYNhbm6OgwcPIjAwUPIYAGUcC8AwIWVubo6RI0fC3NzcaDsRERmKjY1FcHAw6tWrh0WLFmH79u1YtGgR6tWrh+DgYMTGxpo6RCKiEpRyHUqkJM/MSKng4GDY2tri8uXLiIiIQFZWlkG7VqtFs2bN4OjoiM8++wxXr17F22+/jXfeeQdz584tcz8cKfVkLl68iOHDhwMAoqOj4ejoqG9LT0/HwIEDAQArV64s91P5lHIsCgoK0L17d1SrVg0xMTEwM/tv1m5hYSF8fHyQk5ODHTt2SDZsODc3F927d4e5uTm2bdtm0E9BQQF69uyJu3fvYseOHZJO5VPCsQDuTdkbOXIkAGDdunVwdnbWt6WlpWHw4MEAgMjISE7lIyK6j1arha+vL+rVq4fQ0FCo1f89Yy0qKkJQUBCSk5Oxdu1aTuUjIsVQynUokVzK3UipkJAQTJ48GU2bNjXa/ssvv+DMmTNYu3YtmjVrhu7du+Ojjz7CokWLUFBQIHO0FdeoUaMA3BsVVDwJA9wbUq8bJaTbrzxTyrHYvHkztFotRo0aZfDHDwDMzMwwcuRIaLVabN68WbIYli5dCgDw8fEp8UfWwsICAwYMMNhPKko4FgAwevRoAPdGSBVPSAGAs7OzfsSUbj8iIvpPQkIC0tPT4evra5CQAgC1Wg1fX19cvXoVCQkJJoqQiKgkpVyHEinNM5OUepi4uDg0bdoUDg4O+m1du3ZFTk4OEhMTS/29/Px85OTkGPzQ49PNjdaNELqfr6+vwX7lmVKORVpaGgDA09PTaLtuu24/KVy+fBkA0KNHD6Ptuu26/aSihGMB3HuSDwBDhw412q4bRafbj4iI/pOZmQkAcHNzM9qu267bj4hICZRyHUqkNOUmKZWenm6QkAKgf128AOb95s2bBxsbG/1PnTp1JI2zvNMNk1+5cqXR9qioKIP9yjOlHAvdSJy4uDij7brt94/YeZpq164NANi+fbvRdt123X5SUcKxAKB/sr9mzRqj7dHR0Qb7ERHRf+zt7QEAycnJRtt123X7EREpgVKuQ4mUxqR3PNOnT4dKpXrgz9mzZyWNYcaMGcjOztb/XLp0SdL+yruIiAgA90ag3Z8MTE9PR35+vsF+5ZlSjkWfPn2g0WgQERFRovB/YWEhIiMjodFo0KdPH8liGDt2LAAgJiamxHTagoICfPfddwb7SUUJxwIAli9fDgC4e/duiadhaWlpuHv3rsF+RET0Hw8PDzg6OiIqKqrEiNKioiJERUXByckJHh4eJoqQiKgkpVyHEimN2cN3kc57771X6tQmnbIW+XV0dMSRI0cMtmVkZOjbSmNpaamv7UNPrm7dulCpVBBCYODAgbC0tISvry+ioqL0SRiVSlXui5wDyjkWFhYW8PHxQXR0NHx8fDBy5Eh4enoiLi4OkZGRuHHjBgYOHChpQcVKlSrBy8sLBw8eRM+ePTFgwAD06NED27dvx3fffYe7d+/Cy8tL0iLngDKOBWD4vaZbgXDgwIGIjo7WJ6Tu34+IiO7RaDTw9/dHcHAwgoKC4OvrCzc3NyQnJyMqKgpxcXEICQmpEKOyiejZoZTrUCKleWZW39NZuXIlJk2aVGL1vR07dqBXr164evUqnnvuOQDAsmXL8P777+PatWtlTjxx9b2no0OHDjD20VKpVPj1119NEJHpKOVYLFmyBDExMQY1rDQaDXx8fDBu3DhZYggMDMTBgwdLbPfy8kJYWJgsMQDKOBYA0L59+1Lb9u3bJ1scRETPotjYWISHhxuMRnZycoKfnx+8vb1NGBkRUemUch1KJLWy5laemaRUamoqMjMzsWXLFnz22Wf47bffAAD169eHtbU1tFotmjVrBmdnZ3z66adIT0/H0KFDMXr0aMydO7fM/TAp9fRcvHgRo0aNglar1Q9VrQgjpIxRyrEoKCjA5s2bkZaWBmdnZ/Tp00f2pzG5ublYunQpLl++jNq1a2Ps2LGSj5AyRgnHAgCSkpIwevRoFBUVQa1WY/ny5RwhRURURlqtFgkJCcjMzIS9vT08PDw4QoqIFE8p16FEUip3Sanhw4dj1apVJbb/+uuv+tEGKSkp8PPzw759+1ClShUMGzYMH3/8cYklNx+ESSkiIiIiIiIiosdX7pJScmFSioiIiIiIiIjo8ZU1t8L1xomIiIiIiIiISHZMShERERERERERkeyYlCIiIiIiIiIiItkxKUVERERERERERLJjUoqIiIiIiIiIiGTHpBQREREREREREcmOSSkiIiIiIiIiIpIdk1JERERERERERCQ7JqWIiIiIiIiIiEh2TEoREREREREREZHsmJQiIiIiIiIiIiLZMSlFRERERERERESyY1KKiIiIiIiIiIhkZ2bqAJRGCAEAyMnJMXEkRERERERERETPHl1ORZdjKQ2TUve5efMmAKBOnTomjoSIiIiIiIiI6Nl18+ZN2NjYlNquEg9LW1UwRUVFSEtLQ9WqVaFSqR7593NyclCnTh1cunQJ1apVkyBCxvEsxsA4lBcD41BeDIxDmXEoIQbGobwYGIfyYmAcyouBcSgvBsahvBgYh/JieFpxCCFw8+ZNODs7Q60uvXIUR0rdR61Wo3bt2k/871SrVs2kHyLGocwYGIfyYmAcyouBcSgzDiXEwDiUFwPjUF4MjEN5MTAO5cXAOJQXA+NQXgxPI44HjZDSYaFzIiIiIiIiIiKSHZNSREREREREREQkOyalnjJLS0sEBwfD0tKScSgkDiXEwDiUFwPjUF4MjEOZcSghBsahvBgYh/JiYBzKi4FxKC8GxqG8GBiH8mKQOw4WOiciIiIiIiIiItlxpBQREREREREREcmOSSkiIiIiIiIiIpIdk1JERERERERERCQ7JqWIiKjCmjNnDu7cuWPqMIiIiIiIKiQWOiciogpLo9Hg6tWreO6550wdCtEzKScnB1FRUYiIiMDRo0dNHQ4RERE9Y8xMHUB5d+fOHcTHx+OVV14xdSiSy83Nxa5du9ChQwdUrVrVoC0nJwf79u1D165dTb68JVFFVq9evTLtl5SUJHEkysDnMkSP59dff0VkZCQ2bdoEGxsb9OvXT/I+X375ZYwePRqDBw9GtWrVJO+Pyo/Lly9jzpw5WLZsmalDqTC0Wi0SExPRoEEDVKpUyaDtzp07uHDhApo0aQK1mhN3AKCoqAjbt29Hr169TB1KhZOdnY1du3bh4sWLUKlUcHNzQ6dOnSrk35m9e/di06ZNBsdiwIAB8Pb2lrRfjpSS2MmTJ9GiRQtotVrZ+rx586bBjZZarYa1tbXk/X711VfYsmUL9uzZY7S9U6dO6NevHwICAiSLQaPRlGk/Od8PwHTvybFjxzB16lRs3ry5xBdrdnY2+vbtiwULFuCll16SLIa9e/di/PjxOHz4sNEYXnnlFSxZsgSvvvqqZDEAQGxsbJn2k/pLd/Xq1WXa7+2335akf7VaDVdXVwwePPiBo4MmTpwoSf+luXLlCr7//nv89ddfAIAXXngB/fv3R61atSTtV61WIyMjAzVr1pS0n7JQwvmqk5OTA2tr6xI3C1qtFrdv35b8Qu2vv/5CVlYWWrdurd+2Z88ehIaG4vbt2+jbty9mzpwpaQw6Fy9exK5du1BQUIB27dqhSZMmsvSrRFeuXMHKlSuxYsUKZGVl4caNG1i3bh3efPNNqFQqyfsfNWoUYmJioNVq0b9/f4waNQrt27eXvF9jbty4gbVr12LYsGFGz9fVq1cbbXua0tLSMH/+fMyePdtoDKGhoZg6dSocHBwkiwFQ1ndXaUxxPW5MVlYW1q5di/Hjx5s0DjmsXLkS33zzDX7//fcS1+eFhYVo27YtJk2ahCFDhsgSj1ITDxcuXEBkZCRWrlyJf/75B3fv3jVpPHJ+Rk2VAClO9/81JyfHYLuNjQ2WLFmCt956S7ZYTH08xo0bh2XLlsHOzg4NGzaEEALnz59HVlYW/P398fXXX0vXuSBJxcfHC7VaLWkfJ06cEN27d9e/tra2Fmq1Wv+j0WjEkSNHJI1BCCFatWoltmzZUmr71q1bRatWrSSNQaVSibp164rg4GDx448/lvojNaW8J4MGDRJz5swptT0sLEz4+vpKGkPv3r3F/PnzS23/6quvRN++fSWNQYh7nw3d8VepVEZ/pD5XdXFUrVpV2NnZCVtbW6M/dnZ2kvW/ceNG0a1bN2FlZSX69esntm7dKrRarWT9lcWiRYuEpaWlUKlUwsbGRtjY2AiVSiUsLS3FokWLJO1bpVLpj/mDfuSghPNVCCE2bdokGjRoIG7fvl2i7datW6Jhw4YP/K5/Gvr27StmzZqlf52UlCQqVaokunTpIiZMmCCsra3Fl19+KWkMQgixd+9eUblyZf13hLm5uVizZo3k/d6vWbNmonnz5g/9kcp3330nunfvLqpUqSIGDBggfvzxR5Gfny/MzMxEYmKiZP0ac/v2bbFixQrRrl07oVarxfPPPy/CwsLE5cuXZY1jzpw5YsCAAaW2+/j4iNDQUEljeO+998Q777xTavvYsWPFtGnTJI1BCOV8dz2IHNfjD7J7924xaNAgYWVlJezt7WXpU6vVioiICNGzZ0/h7u4umjRpInr37i1WrVolioqKJO//f//7n1i/fn2p7Rs2bBCvvvqq5HEIIcSaNWv01xbFf2xtbUV0dLQsMRR3584dsWrVKvHqq68KtVot2rVrJxYvXizS09Nlj0VH7s/o2LFjhUqlEvb29qJt27aiTZs2wt7eXqjVajF+/HjJ+xdCiGPHjgkzMzMxbNgwER8fL/Ly8kRubq44duyYGDp0qDA3Nxfx8fGyxGLq47Fp0yZhYWEhVqxYYfD9oPsesbCwEJs3b5asfyalJCbHH8GRI0eKsLAw/Wtra2sRFRUl9u3bJ3799VcxdOhQMWTIEEljEEIIW1tbkZKSUmp7SkqKsLW1lTSGP/74Q4wbN07Y2tqK5s2bi6+//lpkZmZK2qcxSnlP6tWrJ06ePFlqe0JCgnBzc5M0BhcXF3HmzJlS2//8809Rp04dSWMQQgh7e3vh6uoqgoODxYULF0RWVpbRH6m9+OKLonr16mLixIkPfG+kdvnyZREaGirq168vnJ2dxQcffCD++usv2eP46aefhEajEe+9955IS0vTb09LSxOTJ08WZmZmYtu2bZL1r1KpxFdffSVWrlz5wB85KOF8FUKIzp07i2+//bbU9oiICNGlSxdJY6hdu7Y4dOiQ/vVHH30kXnrpJf3r5cuXG7yWipeXl+jTp49IS0sTmZmZwt/fXzg5OUne7/0+/PBD/U9wcLCwsLAQEyZMMNj+4YcfSta/RqMRM2fOFDk5OQbbTZGUKu7ChQsiMDBQuLi4CDMzM9GjRw/x/fffy9L3Sy+9JHbv3l1q++7du0WzZs0kjcHd3V389ttvpbYfPHhQvPjii5LGIIRyvrsexBRJqdTUVBESEiLq1q0r1Gq1GDx4sNixY4coKCiQvO+ioiLRs2dPoVKpRLNmzcTAgQPFW2+9JTw8PIRKpRJ9+vSRPIaaNWuK5OTkUtuTkpJEjRo1JI9DSYmHI0eOiDFjxohq1aqJ5s2bi88//1xoNBqTfY+a6jNq6gSIzvDhwx/4cOGNN94QI0aMkDwOJRyP3r17i+nTp5faPm3aNPH6669L1j+TUhKT449go0aNxPHjx/Wvra2txd9//61/ffjwYeHi4iJpDLp+jx49Wmr70aNHhbW1teRxCCFEbm6uWLNmjXjttddE5cqVxVtvvSV++eUXWfoWQjnviaWlpUhKSiq1PSkpSVhZWUkew/nz50ttP3/+vOQxCCFEfn6+iI6OFl26dBGVKlUSb7zxhti+fbssTwvvd/jwYTFmzBhhY2MjWrZsKcLDw0V2drbscejs27dPtG/fXqjVatmTuO3atROBgYGltgcGBop27dpJ1r9KpRIZGRmS/fuPQgnnqxBCODk5PfSclToxY2VlJVJTU/WvX3vtNREUFKR/feHCBWFjYyNpDEIIYWNjY3CzcPv2baHRaMT169cl7/tB7v+bIjXd99Urr7wiFi9erP+eMHVSSqeoqEjExMTonyrLwdra+qEP4qpWrSppDJUrV35oDJUrV5Y0BiGU8931IHIlpQoKCsTGjRv11xr9+vUTMTExsp8rkZGRomrVqmLv3r0l2vbs2SOqVq0qVq1aJWkMlStXfmCy8uTJk7J8PpWSeGjatKlwdXUVM2bMEKdPn9Zvl/uzoYTPqKkTIDoNGjQQu3btKrV9165dokGDBpLHoYTjUatWLfH777+X2n748GFRq1YtyfpnofMntGXLlge2JycnSx5DSkqKQT2UOXPmoEaNGvrXTk5OyMjIkDwOd3d37N69Gy1btjTa/ssvv8Dd3V3yOADAysoKQ4YMwZAhQ5CcnIxRo0ahW7du+Oeff2Bvby95/0p5T2rWrIlz587Bzc3NaPvZs2cN4pJCrVq1cPr0adSvX99oe0JCApycnCSNAQAsLCzw1ltv4a233kJqaipWrlyJ8ePHIz8/H8OGDUNISAjMzOT5SmzTpg3atGmDBQsWICYmBitWrMDUqVPRt29fREZGyrYYQF5eHr777jtERkbi999/h4+PDypXrixL3zrHjx/H0qVLS20fOnQoFi5cKFn/ctTBKSslnK/AvVo5hYWFpbbfvXsXN27ckDQGe3t7XL16FXXq1EFRURGOHj2KKVOm6NsLCgpkKVKfk5NjcMwrV66MSpUqITs7G9WrV5e8f6VYunQpFixYgI0bNyIyMhKTJk1C165dIYRAUVGRSWPbt28fVqxYge+//x5mZmZ45513ZOlXo9EgLS0NLi4uRtvT0tIkL+BcqVIlXLx4sdQYLl68WKLAtBSU8N3Vv3//B7ZnZWVJ2r9OrVq10KhRIwwZMgTR0dGws7MDAAwaNEiW/nXWr1+PmTNnokOHDiXaXnvtNUyfPh1RUVGS1a8EgAYNGuDQoUPw8PAw2n7gwAE0aNBAsv51Dh48iPDw8FLbx40bB39/f8njOHfuHN566y106NABL774ouT9lUYJn9Hjx48jKCio1Pb+/fvjjTfekDyOtLQ0NGzYsNT2hg0b4sqVK5LHoYTjcf36ddSuXbvU9tq1a+Pff/+VrH8ud/CE+vbt+8CfyZMnSx6DlZUVUlJS9K8nT55sULTv0qVLstxojhw5Eh999BF++umnEm1bt25FWFgYRo4cKXkcOpcvX0ZoaCg6d+6Ms2fP4v3335etmKFS3pNOnTohLCzMaJsQAmFhYejUqZOkMfTo0QOzZs1CXl5eibbc3FwEBwfLvtKIi4sLZs+ejd27d6Nhw4b4+OOPSxQ4lEOlSpXw9ttvIyQkBK1bt0Z0dDTu3Lkjeb+///47xowZA0dHR8yfPx/9+/fHlStXEB0dLfvqmFqtFubm5qW2m5ubS1qYVo7ERlkp4XwFgLp16+Lo0aOlth89ehSurq6SxtC+fXt89NFHuHTpEhYsWICioiKDgtZnzpxB3bp1JY1BZ+fOndiyZYv+p6ioCHv27DHYVhFUqlQJw4YNw/79+3Hq1Cm4u7vDwcEBXl5eGDx4MDZt2iRbLLq/7/Xr18drr72GixcvIjw8HFevXsWSJUtkiaF58+b48ccfS23/4Ycf0Lx5c0ljaNOmDdasWVNq++rVqw0WC5CKEr67bGxsHvjj6uoqaQJGp7CwECqVCiqVqsyL70ghISEB3bp1K7W9e/fuOHnypKQxDB48GEFBQUhISCjRdvLkScyePRuDBw+WNAZAOYmHpKQkvPDCC/Dz80Pt2rUxdepUnDhxQvaHY0r4jJo6AaJz584dWFlZldpuaWlp9P7laVPC8SgoKHjg9biZmRkKCgqkC0CyMVgkm9dee01MnTq11PYpU6aI1157TZZYfH19hUqlEo0bNxZ9+/YVffv2FY0aNRJqtVoMHDhQ8v51U7Q6d+5sUMi5sLBQ8r6LU8p7opvi0rp1a7FhwwYRHx8v4uPjRXR0tGjVqpWwsbF54DSdpyE9PV04OzuLOnXqiE8++URfbP7jjz8WderUEc7OzrIWdszLyxNRUVGiY8eOonLlysLHx0fs2LFDtv51Ll++LMLCwkT9+vWFk5OTeP/998Wff/4peb8vvviiqFGjhpgwYYJsNRQepFWrVg8shP/FF19IukDC3bt3xcmTJ8WdO3dKtN2+fVucPHlStkLwSjhfhRBi5syZwsXFxeh5efXqVeHi4iJmzpwpaQzJycni+eefFyqVSpiZmYnw8HCD9j59+ohJkyZJGoMQotRFEeReIOF+ck/fK41WqxVbtmwRffr0ERYWFpL3t2HDBtG1a1eh0WiEs7OzmDFjhiznhDHfffedMDMzE19//bXBNUZhYaFYuHChMDc3FzExMZLGsHfvXn1NvuLna3p6upgyZYrQaDRiz549ksYghHK+u5QgNzdXrF27VnTo0EFUqlRJ9O/fX2zatEmYm5vLOkXL3NzcoE7j/a5cuSL5OVtQUCDat28vzMzMRLdu3cSkSZPEpEmTRLdu3YSZmZlo166dLPW1HjZNPz09Xfbv8T179ghfX19RqVIloVKpxPvvvy/OnTsnS99K+IyqVCpx7dq1Utvlek9UKpVYvXq12Lx5s9GfVatWyRaHqY+HSqUSY8eOFZMnTzb6M3bsWEljUAmhoMfE9Fi+//57DBw4EAsWLICfn59+uLhWq0V4eDjee+89rFu3DgMGDJAlno0bN2LdunU4f/48hBBo2LAhBg8ejDfffFPyvqtXr46qVati2LBhGDp0aKlL3ks9YkpJ78nRo0cxfPhwnDlzRv80RgiBF198EStWrECrVq0kjyElJQV+fn7YuXOnfmSKSqVC165dsWjRolKH/D9NR44cwYoVKxAdHY26detixIgRGDJkiCzTOYvbuHEjVqxYgf3796Nr164YMWIEevbsKdvTKrVajSpVqsDMzOyBT+cyMzNliWfVqlXw8/PD559/jjFjxuinUBYWFmLp0qV4//33ER4ejuHDh0vSv9KWrFbC+Xrz5k14enoiNTUVQ4YMwQsvvADg3hScqKgo1KlTB4cPH0bVqlUljaOwsBCJiYmoWbMmnJ2dDdpOnjyJ2rVrV5gpdPdPYf3ggw/w/vvvl5gSNWHCBDnDMnDt2rVS/+Y+LRYWFujZsydGjRqFnj176p8cyzGt1ZjAwEDMmzcPVatWRb169QDcGw1x69YtvP/++/j4448lj2Hp0qWYOHEi7t69i2rVqkGlUiE7Oxvm5ub48ssv4efnJ3kMgDK+u3SysrJw4cIFAED9+vVha2srW9/F/f3331ixYgVWrVqFK1euYNCgQRg+fDhee+01yf/mazQapKenG5SSKC4jIwPOzs6SjkQG7k33/vLLL43eF0yaNAkWFhaS9g/cu+5ZtWoVbGxsjLZnZWVhxIgRkh8LY7KzsxEVFYXIyEgcP34cTZo0MTqyTCqm+oyq1WqMGTOm1Fkjd+7cwbfffiv5e1LWKdZST1NXwvFo3759mUbt/frrr5L0z6TUE4qNjS3Tft7e3pLG8cEHH+Czzz4zemE0ZcoUfPbZZ5L2XxZ5eXn45ptvMHXqVMn6KP7lYuzEEkJApVLJ8odHae9JfHy8wQVBs2bNZO0fuFer5sKFCxBCoEGDBvp57HJQq9VwcXHBsGHDSq17BgCvv/66LHH4+vrCwcGh1P2kurlctWpVmfYbNmyYJP0bM3XqVMyfPx9Vq1bF888/DyGE/lyZMGECvvzyS8n6/t///ofx48dj4MCBRts3btyIb775pszf9U/LiRMn9OdK8fM1NzdXlhox2dnZmDFjBjZs2KCvH2Vra4uBAwciLCxMlnM3JycH1tbWJS4atVotbt++Ldt07IeR4z0pS+JepVIhKSlJkv6PHTuGqVOnYvPmzSWOe3Z2Nvr27Yuvvvqq1NoxT8u1a9dgbm6OoKAgg8+mnZ0dBg4ciNDQUNkTEEeOHEFUVJTB+Tp48GBZps3pXLlyBRs3bjSIYcCAAQ+cDiIVU15rXLx4EQEBASUegHXr1g3ffPONbFN+71dUVISdO3ciIiICW7duhbW1teRTcdRqNbp3717qlPz8/Hz8/PPPJknEyE0piYeHiY+PR2RkpKR1NEsj92fU1AkQpeHxYFLqianVaoMnQsbIlQQ5fPgw1q9fj/PnzwO4V2Bw0KBBaNu2reR96/zzzz/4/fffYWFhgY4dO0Kj0eDu3bsIDw/HvHnzUFhYiOvXr0vW//79+8u0X7t27SSLoTglvCfGFBQUoKCgANbW1pL3pdVqkZiYiAYNGpS4cbtz5w4uXLiAJk2aSF4Qtiz/vhznat26dR/6h0fKm8vbt2+jSpUqkvzbT+L+c6Vhw4YYOHCg5OfKc889hyNHjpR6s5KcnIzWrVvjn3/+kTSOh8nPz8c333yDzz77DOnp6bL1K4TA9evXIYRAzZo1Zat98cMPP+CDDz5AfHx8iSeHt2/fRosWLfD555+jd+/essRjjKneE1MYPHgwGjdujFmzZhltDwsLw59//om1a9dKGkdmZiZeeeUVXL58Gb6+vmjcuDGAezXG1q1bhzp16uDQoUOyPvCg0hUWFiIvL0+Wa41Lly6hVatWMDc3h7+/v8FnY/HixSgsLMQff/xhkkRdcdevX8fq1asNFm6QwogRI8q034oVKySN4355eXnYsGEDbt++jc6dO8tS6FwpcnNzsWvXLnTo0KHESOOcnBzs27cPXbt2lb225/3k+ow+C4qKirB9+3bZa98q0Z9//omIiAh8/vnn0nQg2cTACsLe3l64urqK4OBgceHCBZGVlWX0pyL47bffhI2Njb7GRuvWrUViYqJo0KCBaNy4sVi8eLHRui0krcjISDF+/Hixdu1aIYQQM2bMEBYWFkKtVotOnTpJvrT5ihUrRMuWLY3W9bp7965o2bKlWLNmjaQx0H/q1asnfvvtN1OHoRhKWbJaiHv1zqZPny5atmwpPD09xQ8//CCEuHcOOzk5idq1a4uPP/5YllhMrXPnzuLbb78ttT0iIkJ06dJF8jj4ntxTr169B54nCQkJws3NTfI4Jk6cKJo0aVJqvbOmTZvKUmtMCCH++ecfcfHiRYNtp0+fFsOHDxc+Pj4iKipK8hj8/PzEzZs39a/XrVsnbt26pX9948YN0b17d8nj2LJli1ixYoXBttDQUGFpaSk0Go3o3LmzyMzMlDSGkSNHCm9vb5Gbm1ui7c6dO8Lb21uMGjVK0hiEECIzM1MsXLhQZGdnl2jLysoqta08mjx5shg/frz+dX5+vnjppZeEubm5sLGxEVWqVBGHDh0yYYT3aLVasXXrVsn7WbBgwQPryXbs2FF88803ksfxLHxGz5w5I9577z2T9X/+/HkxY8YM4eTkJMzMzEwWh46pjsetW7fE8uXLhaenp1CpVMLd3V2yvpiUekK6wtpdunQRlSpVEm+88YbYvn27KCoqkjWO4l8e27ZtMyjS9tNPP8kSQ7t27cSgQYPEqVOnxNSpU4VKpRINGzaUvNDnwxQVFYk9e/aIn376SfKLIp1bt26JcePGCWdnZ1GjRg3x1ltvPbCAnVRCQ0NFpUqVRKdOnYS9vb0YN26ccHR0FB9//LH49NNPRe3atcW4ceMkjeF///ufWL9+fantGzZsEK+++qqkMQghxNatW2U/L5Xo/fffF+bm5mLq1KkiPz/f1OGY/MbupZdeEosXLy61fdGiReKll16SNAadadOmCRsbG/HGG2/oL4Teeecd0bRpU7F+/XrZFmxo1qyZaN68+UN/pOTk5PTAwsjnz58XTk5OksYghHLekz179ojGjRuXehPx4osviv3790vWv6WlpUhKSiq1PSkpSVhZWUnWv46rq6v4+eefS23fsWOHcHV1lTwOIYQYOHCgmDJliv51RkaGsLOzE+7u7uL1118X5ubmYvXq1ZLGoFarDQo4V61a1aAAvlzFgtu3b29wM33w4EGhVqtFaGio+P7770WjRo3E5MmTJY3B2dn5gQ9c9u/fL8t3xpw5c8SAAQNKbffx8RFhYWGSx/EgRUVFYvv27eKNN96QtB93d3exefNm/evIyEhhZ2cnLl68KIqKisTw4cNFjx49JI3hQeROPLRq1Ups2bKl1PatW7dKurCLjlI/o3ImQIy5c+eOWLVqlXj11VeFWq0W7dq1E4sXL5Z1MabiTHk8Dhw4IEaMGCGqVKki1Gq1eO+99yRfjIlJqacoJSVFhISEiHr16olatWqJmTNnirt370re79atW0WzZs30r62trUusDCRHYsje3l6/asOdO3eEWq0WP/74o+T9Fnfjxg3x9ttviyZNmojRo0eL7Oxs4eXlpT8WDg4OD3za+7RMnjxZVKlSRYwZM0ZMmDBB1KxZU/Tt21fyfu9Xv359sW7dOiGEEH/88YdQq9Xiu+++07dv375duLi4SBpDzZo1RXJycqntSUlJokaNGpLGIITQr9Y0c+ZMk64CZOqbSyGEiIuLE40bNxbu7u7i+PHjkvb1MKa+sfvkk09E9erVjX4vxMfHi+rVq4tPPvlEsv6Lc3Nz01/Anzp1SqhUKjFixAjZk6kffvih/ic4OFhYWFiICRMmGGz/8MMPJY3BysrqgRdAZ86ckSUJopT3pHfv3g9cpfKrr76S9G9M7dq1H7hK6fbt20Xt2rUl61/HwsJCXLp0qdT2S5cuCUtLS8njEEKIunXrin379ulff/bZZ+L555/XX/d99tlnok2bNpLGcP+qYvevyihXUqpmzZoGf0smT54sunbtqn+9bds2Ub9+fUljUMpn46WXXhK7d+8utX337t0G1+xySkpKEkFBQaJ27drC0tJS9OzZU9L+qlatanC9NXDgQPHOO+/oX584cUKWRGFxpkw82NraipSUlFLbU1JShK2treRxKO0zaooESHFHjhwRY8aMEdWqVRPNmzcXn3/+udBoNLKullmcqY5HRkaG+OSTT8QLL7wgHB0dxeTJk8Uff/whzMzMZDkWTEpJICkpSXTo0EGo1Wrx77//St5f7969RUREhP71/Rcln3zyiSzDt41dHF24cEHyfosbNWqUaNCggQgNDRVt2rQRnp6eom3btuLw4cPiyJEjon379qJXr16Sx1G3bl2xceNG/eujR48KMzMzWZKUxVlYWIjU1FSD12fPntW/vnz5sjA3N5c0BqVMj0pNTdUnjdVqtfD29harV6+WfUqpqW8udfLy8sTUqVOFlZWV6N27t+jXr5/Bj1xMfWOnlCWrhbi3hPfly5f1r62srERCQoIsfT/I/X9T5NCoUaMHTutdvXq1eOGFFySPQynviYuLizhz5kyp7X/++aeoU6eOZP0PHz5c/O9//zPaVlRUJLy8vMTw4cMl61/nYaNhYmNjZbvJtbKyMhjl2b17d/H+++/rX587d07Y29tLGoNSklJWVlYGN9utWrUSn376qf71xYsXJf877+rqKnbu3Flqu1yj6KytrR+aeKhatarkcejk5eWJtWvXig4dOghzc3OhVqvF/PnzZZmeZWNjI/766y/967p16xrcryQnJ8vycEEIZSQerK2txdGjR0ttP3r0qLC2tpYlDlN/Rk2dANFp2rSpcHV1FTNmzBCnT5/Wb5c7DiUcDysrKzFkyBDx888/C61Wq98uVwzSVhauQPLz87Fu3Tp06tQJTZo0QY0aNbBt2zZZlps/deoUvLy8Sm3v3r07jh49KnkcwL2CkgkJCUhISIAQAufOndO/1v1IaceOHfj2228RGBiI77//HocPH8a8efPQpk0btGrVCp988gn++OMPSWMAgMuXLxu8Jy1btoS5uTnS0tIk77u4u3fvGhRMtLCwgLm5uf61mZmZ5IW9GzRogEOHDpXafuDAAVkKXdapUwezZ8/G33//jd27d6Nu3brw8/ODk5MTxo0bJ8vnAri3lH23bt1Kbe/SpQuOHTsmeRz5+fm4du0aVCoVbGxsSvzIJT093aDI+N69e9G/f3+YmZkBuLcaoq74uRTMzc3xyy+/ICwsDFevXsWyZcuwdOlSXL16FWFhYfjll18MzhkpabVag+WxzczMZCkQrET9+/dHYGAgMjIySrSlp6cjKCgIb7zxhuRxKOU9ycjIeODn0MzMTNJi/EFBQTh16hTatGmDjRs34uTJkzh58iQ2bNiANm3a4PTp0wgMDJSsf52uXbsiMDAQBQUFJdry8/Mxa9asB36/Pk3VqlVDVlaW/vWRI0fQpk0b/WuVSoX8/HxZYjG1WrVq4c8//wQA3Lp1CydPnsQrr7yib//3339LXer8aenbty+mTp1q9Dy4du0aPvjgA/Tt21fSGABAo9E88FovLS1N8oVdgHsrZvr7+8PR0RELFixA3759cenSJajVanTt2lWW1UsbN26MrVu3AgASExORmpqKDh066NtTUlIeuArx0+Lh4QEfHx9Ur14dhw4dwvHjx/Hee+/JtnCHjru7O3bv3l1q+y+//AJ3d3fJ41DCZ9TV1RWnTp3CV199hStXrmD+/Pl4+eWXJe3TmHPnzsHb2xsdOnTAiy++KHv/Oko4Hq6urjhw4ABiY2Px119/ydo3AJjJ3mM5c+TIEaxYsQLR0dGoW7cuRowYgY0bN8qSjNK5evWqQeLh119/RZ06dfSvra2tkZ2dLUssHTt2NFiFULdagUqlghBC8tXNMjIy0LBhQwD3LpKsrKwMjoWLi4ssq2gVFRWVuIGQIwFkzJkzZ/QrQwkhcPbsWdy6dQsAJF0JUWfw4MEICgrCK6+8UmK58JMnT2L27NmYNm2a5HEU16FDB3To0AHffPMNoqOjsXLlSrRt2xZNmjTByZMnJe3b1DeXALBr1y6MHDkSTk5OOHbsmH6VIlPQ3di5uroCuPedOmrUKH27HDd25ubmmDZtmuyfw/sJITB8+HD993leXh7GjRtXYrXETZs2mSI8WU2fPh2bN29GgwYNMGTIELzwwgsAgLNnzyIqKgp16tTB9OnTJY9DKe9JrVq1cPr0adSvX99oe0JCApycnCTr//nnn8fu3bsxfPhwDBw40GDV4RdffBG7du0qNbanac6cOXj55ZfRoEEDBAQEoFGjRhBC4M8//0R4eDjy8/OxZs0ayeMAgLZt22LhwoX49ttvsWnTJty8eROvvfaavv2vv/4yuP6QyuzZs/UJn4KCAoSFhekfLNy5c0fy/gHAx8cHkyZNwsyZM7F9+3Y4OjoarJx69OhR/TksleDgYGzfvh3PP/88hgwZYvDZWLduHRwdHTF79mxJYwCA5s2b48cffyx15dgffvgBzZs3lzyONm3a4N1338Xhw4clP/almTZtGgYOHIht27YhMTERPXr0gJubm759+/btaN26teRxnDt3Dm+99ZbJEw8jR47ElClT4O7uXmI1t61btyIsLAzz58+XPA4lfEZ1CRAXFxe4urqiUaNGkvZXmqSkJKxcuRJ+fn7Izc3FoEGD4OvrK3vCUgnH4+zZszh48CAiIiLQqlUrNGzYEEOGDAEAWY4Hk1JPqG3btnBxccGECRPQsmVLAPdGftzv9ddflywGe3t7XLhwQT/a4P7M6vnz52VJkiUnJ0vex8MUFRVBo9HoX2s0GoMTSa4vGSEEOnbsqB/tAdy7OOzdu7fBU/fjx49LHktZEoVSmjx5Mnbs2IGWLVuiU6dO+i/as2fPYvfu3XjllVcwefJkSWMoTdWqVdGxY0ekpKTg7NmzOHPmjOR9mvrmcuzYsVi1ahVmzpyJwMBAg/PFFJRyY6dz8+ZNg/NFrVbLNjJm2LBhBq91FwMVUdWqVXHw4EHMmDEDGzZswI0bNwAAtra2GDJkCMLCwkosqS0FpbwnPXr00I8CsrKyMmjLzc1FcHCw5EtWv/zyyzh9+jROnDiBCxcuQAiBhg0bolmzZpL2W1zt2rURFxcHf39/zJgxQ3+uqlQqdO7cGd98841s3xcfffQROnbsiLVr16KwsBAzZ86EnZ2dvj06Ohrt2rWTNAZvb2+cO3dO//qVV15BUlJSiX2kNnv2bFy5cgUTJkyAo6Mj1q5da/C3Zf369ejdu7ekMdjZ2eH333/HzJkzER0drR/FZmtri8GDB2Pu3LmyXAuPHz8eAwcORO3ateHn56c/DlqtFuHh4fjyyy+xbt06yePo2LEjIiIicO3aNQwdOhRdu3aV/Ua7X79+2L59O3766Sd06dIF7777rkF75cqV4e/vL3kcSkk8jBkzBrGxsXj99dfRqFEjg4ctf/31F958802MGTNG8jiU8Bk1dQJEp1atWggMDERgYCD27t2LyMhIeHl5obCwECtXrsTo0aP1gx2kpJTj4eXlBS8vLyxcuBDr16/HihUroNVq4e/vj8GDB6Nv376oWbOmJH2rRPGrb3pkZRneKPXooIEDB+LOnTvYsmWL0fZevXqhSpUq2LBhg2QxlNXp06fRpEkTyf59tVqN0NBQ/U3kBx98gPfffx81atQAcO+Gc/bs2ZKPWAoJCSnTfsHBwZLGkZKSUqb9dKNUpHL37l39H7nz58/rb2YGDx6MyZMn49y5c5J+Lu6Xm5uLmJgYREZG4rfffoObmxtGjBiB4cOHo1atWpL2/e6772Lfvn34448/jN5ctm7dGh06dMDChQsl6b9JkyZYvXo1WrRoIcm//6gSEhLQsWNH5OTk6G/sPvroI3370KFDUaVKFSxZskSS/uPj4/VP94F7yZDiowtUKhXi4uLQqlUrSfpXovs/e/d/j+pMmDBBlniEELh+/TqEEHjuuedk6VNpMjIy0KJFC2g0GowfP97gZmbRokXQarU4fvy4LFNhlOLGjRv6qb3169eXdYS6zvXr13Hw4EE4OjoaTN0DgG3btuHFF180GBlC8hBC6Ecc16xZU/bkQ2BgIObNm4eqVauiXr16AO4lRm7duoX3338fH3/8sSxxXLp0CStWrMCKFSuQm5uLt956C+Hh4UhISDDpCGlT0iUeNm3ahLy8PEydOlW2xIPOxo0bjV4Pv/nmm7LFoJTPKHBvyq8uAXL48GG0a9dO8gSITmxsLF555RWDQQTZ2dmIiopCZGQkjh8/jiZNmkhefqY4Ux4PY86cOYOIiAisXbsWmZmZuHv3riT9MClVDpw4cQKenp7o3bs3pk2bpv9iPXfuHD755BNs27YNhw4dMtlN6M2bN7F+/XosX74cx44dkzQhVLdu3TJdfChhVJcSZGVlYfv27Rg8eLDsfefk5CA6OhoRERE4evSoLFMbDx8+jMjISGzcuBEFBQXo378/Ro0aZVDjQGqmvrksKCgwGK2nBKa8sRs1ahSef/55zJw5E8C9pNTSpUtRq1YtCCEQGRkJIYRsU4KUoCzHWqVSlRiR8TTZ2dkZ/S63sbFBw4YNMXXqVHTu3Fmy/pUoJSUFfn5+2Llzp8EIoa5du2LRokWSJj+mTJlSpv3kmHpChnJycmBtbV3iIWlRURFu3bolS+0gKunIkSOIiooyGFU4ePBgWaarGbNr1y6sWLECP/zwA+rUqYMBAwbgjTfe0M/ykMKnn36Kd999F5UqVQIAHDx4EC+//LJ+OvTNmzfxwQcfIDw8XLIYSmPqxIMxubm5OHHihEE9Nikp7TMKAH/++SciIiKwZs0aSRMgOhqNBlevXi31gVd8fDwiIyMle1D8MHIfjwe5e/cutm7div79+0vy7zMpJbGioiJs375d8mH1mzdvxujRo5GZmWmw3c7ODsuXL5eluOP9YmNjERERge+//x7Ozs7o378/3njjjQox4iAvLw+//PILOnToUGKKSU5ODvbt24euiY5fBAAAIeJJREFUXbsa1AIzhZMnT6JFixay1roy1efixRdfxNmzZ9GiRQuMGjUKgwcPlrWgd3G8uVSOxo0bY926dfr6CVWrVsXJkyf1Tw5///13vPnmm2Uedfgk+vXrV6akekWoKbVq1Sqj27OysnDs2DFs2LAB3333neRTgpT4nty4cUN/E9GgQQODKWNSuT9xf+DAAbRs2VJ/swnc+w7bu3ev5LEoxerVq8u039tvvy1ZDD/88AM++OADxMfHlygkfvv2bbRo0QKff/65LFPnTJ1Ebt68eZnOVTnKJjyIbhGNb775xiT937hxA2vXrkVkZCQSEhIkvf67/4a/WrVqiI+P1/99zcjIgLOzs0nqrRZn6sSDjimuyZWqsLAQW7ZskSwBoqNWq5Genq74UdhyHQ8AiImJwfr16/XFznXJygEDBkjaL2tKSeTChQuIjIzEypUr8c8//0ie2ezTpw86d+6MnTt36oezN2jQAF26dMGNGzcwZswYLFu2TNIYgHurIq1cuRIRERHIycnBm2++ifz8fPz444+yFBfMy8vD7t279UnAGTNmGBRJNjMzw5w5c0pMm3rali5dii1bthitJVatWjUsXLgQqampGD9+vKRxKIWpPxcA0KlTJ0RHR5coti632NhYeHt7Y/v27aXeXL777rv4+uuvJen/xIkTBq9Lu7mUS48ePbB+/Xp9gvDjjz/GuHHjYGtrC+Deqk2vvvqqZPW+UlJSDIZDz5kzx2CampOTk9EV4KSg+//8IDdv3pQ+EAW4v5bT/Zo1a4Z58+ZJfrOtxPfEzs5O9oc7v/76q8HrqlWrYt26dfqby4po4sSJpbapVCrcvn0bhYWFkialFi9ejGnTphld2a5KlSr44IMP8M0330h+nixYsMDodl0SuVevXpInkYs/fBVCYN68eRg3bpxJpnUmJibi119/haWlJXx8fGBra4vr168jLCwMS5YsMel5Y2dnh3fffRfvvvsuPv30U0n7un/cg1LHQTRr1szkCSk5Xb9+Hbdv3zYo3ZGYmIjPP/8ct2/fRt++fU0yg0JHCIFdu3Zh3bp1siRh5J7ea8zGjRvRt29f/UyGy5cvw9nZWT8CtqCgABcuXJA0hqKiIgwaNAgxMTFo2LChvgZwYmIi3nzzTbz55ptYv369dMdL0FNz584dsWrVKvHqq68KtVot2rVrJxYvXizS09NNGld8fLxQq9WS99OrVy9RrVo1MWjQIPHTTz+JwsJCIYQQZmZmIjExUfL+hRBi8eLFolevXvrX1tbWok2bNqJ9+/aiffv2wtHRUXzxxReSx9GqVSuxZcuWUtu3bt0qWrVqJXkcDyPHZ0MJnwshhLC1tRV2dnYlfurWrSu6dOkifvnlF1nisLGxESdOnCi1ffz48aJq1aqyxCLEvXPk77//lq2/+6nVapGRkaF/XbVqVYN40tPTJf2M2tnZiQMHDpTafuDAAWFnZydZ/8XNnz//ge05OTnilVdekTyOr776qkw/pnTu3DlZ3helvCf9+vUr049cTP29oWRpaWli7NixwtzcXHTt2lXSvpycnMT58+dLbT9//rxwcnKSNIay+OKLL4Snp6esfZrqM7p582Zhbm4uVCqVUKlU4vnnnxd79+4VNWrUEF27dhU7duyQLZa7d++KU6dOiXPnzhls//HHH8VLL70kLCwsJO1fpVIZ/H2//z2R+u+7jlKu/x5Grvu1gQMHiilTpuhfZ2RkCDs7O+Hu7i5ef/11YW5uLlavXi15HPdLSkoSQUFBonbt2sLS0lL07NlT8j5VKpXo0aOHyf+2mvpaWIh71zv29vZi69atJdo2b94s7O3txZdffilZ/xwp9RT88ccfWL58OaKjo/H888/D19cXhw4dQnh4uEmXHpXbjh07MGHCBPj5+aFBgwYmiSEqKqrEsu7Fn+auXbsWixYtKvMUpsd1/vx5vPTSS6W2e3h46Ee0lXdK+FwAyniSCwCjR49Gt27dcODAgRIr8E2cOBErVqzAtm3bJI1BSYSJn6Tqlkb28vIy2r5p0yZZlu8GgJkzZ6J69epGR1bcvn0b3bt3x7///it5HF9++eVD91GpVLIVOjcmPz9fltpoSnlPyjLVuKKMolOqmzdv4pNPPsFXX30Fd3d37Ny5U/J6hTdu3EBhYWGp7Xfv3tWvXGlKvXr1QmhoqKnDkEVoaCgCAgLw0UcfYfny5ZgyZQomTJiA7du3yzrC8fTp0+jVqxcuXboE4N6MisWLF+PNN9/E6dOn8c477+Cnn36SLR5TUsr1n1IcPnwYK1eu1L9evXo17O3tER8fDzMzM3z++edYtGgRhg4dKnks+fn5+O677xAREYEDBw5Aq9Xi888/x6hRo2Srh1e1alWD2QKmYOprYQBYsWIFPvvsM6Mlh15//XV8+umn+OqrrzBp0iRJ+mdS6gl5eHggJycHgwcPxqFDh+Du7g4AmD59uokjk9+BAwcQERGBli1bonHjxhg6dCgGDhwoawwXLlxA06ZN9a+trKwMin+2bt0aAQEBksdRWFiIf/75By4uLkbb//nnnwdeSD4tDxuOfOXKFcljUMLnAlDOdKDPP/8cmZmZ6NSpEw4dOgRnZ2cAwKRJk7B8+XL89NNPki8jTv/x9/fHwIEDUbduXfj5+em/L3RLI3/99deyLN8NAGvWrMHQoUNha2trMPX31q1b6NatG65du4Z9+/ZJHsfDFoK4fPky5syZI3kcDxIREYFmzZpJ3o9S3hMPDw9Mnjy51PabN2+iW7duksdBJd29exdff/015s6di+rVq2PFihWS197QqVu3Lo4ePaqfZnG/o0ePSr66blnIlURWgnPnzmHdunWwtrbGu+++i6lTp+LLL7+UfcrtBx98gPr16+Obb77B+vXrsX79evz5558YNWoUfv75Z9luwpcvX65fEbuwsBArV640WBFbDkq5/ittlXQduRZhSk9PR926dfWv9+7di/79++tXoHv99dcxb948SWM4duwYIiIisH79etSvXx9Dhw7F+vXrUbt2bXTt2lXWBRoWLlyo+JpScjh//jw6depUanunTp2kLTsj2RisCsLCwkIMHTpU/PLLL6KoqEi/Xe6pSQ8i13BQnVu3bomIiAjh5eUlzM3NhVqtFgsWLBA5OTmS921lZSXOnj1bavuff/4pLC0tJY+jTZs24uOPPy61fe7cuaJNmzaSx1G3bt0y/cjBlJ+LspBrOpAQQmi1WtGvXz/RuHFjcf36dTF58mRRqVIlsXv3bln6L87U03DUarW4du2aQTxJSUn613IMWZ42bZpQqVSiWrVqolmzZqJZs2aiWrVqQq1Wi6lTp0ra9/2+/fZbUblyZfHrr78KIe6dN//73/9E/fr1xZUrV2SNpTRy/E2ZPHmy0Z+RI0eKZs2aicqVK4ujR49KGoOOEt4TKysrsWrVKqNtt27dEl5eXuKFF16QrP+TJ08a/FSpUkVs27atxPaKpKioSKxcuVK4uLgIZ2dnsXTpUv30dLnMnDlTuLi4GC0TcfXqVeHi4iJmzpwpa0zGTJgwQXTp0kXWPk31t+1hU9bkUrNmTX2pgKysLKFSqWSfkuXq6qqYa9AHkev6Tzel80E/ctyvPffccyI+Pl7/unr16uK7777Tv/7rr79ElSpVJI1Bo9GISZMmlbhnk/v++f5pc6aihKmudnZ2D/w7npCQIGxtbSXrnyOlnlBSUhJWrlwJPz8/5ObmYtCgQfD19ZW1aNrDisBlZWXJE8j/q1KlCkaOHImRI0fi3LlziIiIwMcff4zp06ejc+fOD31S8CRq166N06dP44UXXjDanpCQgNq1a0vWv87IkSMxZcoUuLu7lxgGuXXrVoSFhcmyuplcT13KwpSfi7KQ80muWq1GdHQ0evbsicaNG+P27dvYsmULOnbsKHnf9y95LITA2bNncevWLYPtchWEF0Jg+PDh+pUo8/LyMG7cOFSpUgUADBYqkMonn3yCfv36Yf369fpptd7e3hg0aBDatm0ref/F6VZR7dOnDzZv3ozZs2cjLS0N+/fv14+qqwjuL8ivU61aNXTu3BmbNm2SdJXK4pTwnph6xFazZs2gUqkMphTo/rbptqtUqgq1apSHhweSkpLw7rvvYtKkSahcuTJu375dYj8pn/hPnz4dmzdvRoMGDTBkyBD9tc/Zs2cRFRWFOnXqyDJyv7SSCNnZ2Th+/Dj++usvxMbGShrD/SPD7x+VoyPHtOOdO3fqp9wWFRVhz549OH36tME+xhbCeZquX7+u/36ysbFBlSpVZP97dvHiRVn7e1xyXf8VFRVJ3kdZtG3bFgsXLsS3336LTZs24ebNm3jttdf07X/99Rfq1KkjaQwdO3ZEREQErl27hqFDh6Jr164mKTguFFR8/0HfG3Lcy3t6emLx4sVYvHix0fZFixbB09NTsv5VQknvxjNu7969iIyMxKZNm5CXl4epU6di9OjRaNiwoaT9jhgxokz7rVixQtI4HkSr1eKnn35CRESEpMmHiRMnYvfu3Th27FiJFfZyc3Px8ssvo1OnTvjqq68ki0FnyJAhWLduHRo1amRwofjXX3/pVzCQQ1FREVauXIlNmzbh4sWLUKlUqFevHt544w0MHTrUpKtOaLVabN26FZGRkSZPSk2aNAlnz57Fzz//LGk/xS+cb968iY8++ghdu3YtkZCS6sJZrVaXuLnUMcXN5fDhw8v0GZTq+2vOnDmYOnWq0dWrTGn69On47LPPULduXezbt0/yC8RHUVGXrTb1e7J8+XJMnDgR27ZtQ/v27XH79m1069YN6enpkifIUlJSyrSfEqaKyaV4aQBj32FyfZdmZ2djxowZ2LBhg75+lK2tLQYOHIiwsDD9qq5SKq12VrVq1fDCCy+gV69eWLt2raSrQJclSa1SqZCUlCRZDIDh5+JBcUj9udBoNPjrr79Qs2ZNCCFQp04dHDhwwGDaFiBt0nTv3r0YP348Dh8+XKKf7OxsvPLKK1iyZAleffVVyWIoi4kTJ+Ls2bPYuXOnLP39+++/qF69OgDg0qVL+Pbbb5GXl4fevXvLcixOnjyJTp06IScnB4WFhZgxY4ZBzbehQ4eicuXKWLp0qaRxXLp0Sb9SfW5uLt566y2Eh4cjISEBjRs3lrRvnblz58LDw8NgAMHq1asRHBysX4nw66+/1j84lUpZvjcAaRObhw4dQvv27dG3b19MnToVjRo1ghACf/75J7744gts3rwZv/76a6k1WJ8Uk1ISyM7ORlRUFCIjI3H8+HE0adKkxOiE8iguLg7//vuvSU/sjIwMNGvWDBYWFhg/frw+IXju3Dl88803KCwsxIkTJ+Dg4CBZDMXFxMQgKioK58+fhxACDRs2xODBg/Hmm2/K0r8QAr169cKOHTvw0ksvGXzBnDp1Cq+//jp+/PFHWWIxtbI+yW3ZsqWkcZj6wpk3l4Y0Gg2uXr2qiHoC94963b59O1566SXUqlXLYPumTZvkDKuEipSUUtp78umnnyIsLEw/YuvKlSvYv3+/5COAlZq8NaX9+/eXaT+5agQKIXD9+nUIIRTxfVZcRfrOUArdAygdXZL0/tdSvievv/46OnToUGo9vIULF+LXX3/FDz/8IFkMgHKu/06dOoXevXvj0qVLaNCgAaKjo9GtWzfcvn0barUat2/fxnfffYe+fftKGgdwbyTdwYMH4ejoiDZt2hi0bdu2De7u7iUSmFLavXs3IiMj8cMPP6BOnToYMGAABgwYgBYtWkjab7du3dChQwd88MEHAO69Ry1atMDw4cPRuHFjfPbZZxg7diw+/PBDSeMoizt37kj+N/iHH37AmDFjkJmZabDdzs4OS5cuxRtvvCFZ30xKSey3337DypUrERERYepQJNe9e3e0b9/e5Cd2cnIy/Pz8sGvXLv1oEJVKhc6dOyM8PFy/Ep+UdKtHbNmyBQUFBXjttdfw4Ycfyr66w4oVKzBx4kRs3ry5xJPMvXv3om/fvvjmm2+MrixV3jzsSa6fn59s04FMSWk3lyNHjnzoPiqVSrLvULVajfT0dEXcxCll1GtZpoTv37+/QtxgKuU9Kc4UI7aUlLxVipycnDLtJ+VIFDs7O6OjtGxsbNCwYUNMnToVnTt3lqz/spIjKaW0UTmmHg2jhKSpq6srfv7551JHvZw9exZdunRBamqqZDEAyhjJB9y7TzIzM8P06dOxZs0a/PTTT+jatSu+/fZbAMC7776LY8eO4fDhw5LG0aNHD6xfv14/Vezjjz/GuHHjYGtrC+DeZ/fVV1/FmTNnJIvh/vukjh07Ijg4GHl5eVi7di0iIyORkJAg+XWGk5MTtm7dipdffhkAEBgYiP379+PAgQMA7g0uCA4OlvRYPEx+fj4WLVqETz/9FOnp6ZL3d+fOHezcuVNfzqJhw4bo0qWL5PcNTEpJrCI9HVLaiZ2ZmYkLFy4AAOrXrw97e3tZ+gWAjz76CB9++CE6deqESpUqYefOnRg0aBAiIyNliwEAunTpgtdee63UmhJz587F/v37ZRuyTKYfUai0m0u1Wg1XV1c0b978gXP7pXqSqlarkZGRgZo1a0ry7z+LlJiIqehMPWJLSclbpbh/JEpppLz+W7VqldHtuqXuN2zYoIil7uW4FlbKqByljIYp7aZfzoejVlZWOH36NOrXr2+0Xbdidm5urmwxGSPXvVqNGjWwd+9eeHh44NatW6hWrRr++OMP/Qits2fPom3btpLXD7r/OrBatWqIj4/XP7TPyMiAs7OzpMejLPdJx48fl3yklJWVFc6fP69/uPO///0P3bt3R2BgIIB7ddGaNm0q+UqR+fn5+PDDD7Fr1y5YWFhg2rRp6Nu3LyIjIxEUFASNRoPx48frB35IwdSJfRY6p6fmxo0bBtPi9u/fj+7du+tft2rVCpcuXZItHnt7e7Ru3Vq2/opbvXo1wsPDMXbsWAD3hqX27NkTy5cvL/O84achISEBn376aant3bt3L1EclKQVEhKCDh066JNSp06dwqhRowxGFDo7O0s2olBpzyH8/Pywfv16JCcnY8SIERgyZIisCWTg3lOgh91c3j+UuTxjskl5dE+0dQYNGiR7DKasP6hEv/76q/5/CyHQo0cPLF++vESiUEpKWepeCU6ePIlPPvmk1PYuXbrg888/lzyOadOmoWnTpoiKisKaNWvQq1cv9OzZ02A0zMcffyx5Umru3LkGN/1fffUVrl27JuvD0Vq1aj0wKZWQkAAnJyfZ4jG1zMxMODo6AgCsra1RpUoVg5pvdnZ2kic/gJLXgaa4LizLfZLUCSkAcHBwQHJyMurUqYOCggIcP34cISEh+vabN2/C3Nxc8jhmz56NpUuXolOnTjh06BB8fHwwYsQIHD58GPPnz4ePjw80Go2kMSxYsADvvPOO0dG9NjY2GDt2LObPn8+kFCmfUk5sJUhNTUWPHj30rzt16gSVSoW0tDRZVv/TyczMfGD9LAcHB31hVJLHyZMnDQpKRkdHo02bNvoL1jp16iA4OFjSaa5KurlctGgR5s+fj02bNiEyMhIzZsxAz549MWrUKHTp0kWWWENCQkrc9BMpiRIShUzeGrp/2pNGo0Hbtm1lKRFQVr169TL4eyMVJawCnZGR8cBrTDMzM/zzzz+Sx/HHH3/oR8O89NJLWLZsGfz9/fU32u+++64sq+Ap4eFojx49MGvWLHTr1s3o4kPBwcElVqgu7+7/DlXS9ZiclHKf1KNHD0yfPh2ffPIJfvzxR1SuXNkg6ZKQkIDnn39e8jhiYmKwevVqvP766zh9+jQ8PDxQWFiIkydPyvYZMXVin0kpemqUcmIrQWFhYYk/wObm5rh7966scWi1WpiZlX6aazQaFBYWyhgRKWFEodJuLi0tLTFo0CAMGjQIKSkpWLlyJfz9/VFYWIjExERYW1tL2v/AgQM5LYnoIZi8ffbItdT9wz4XNjY2kteuVMqoHKWMhlHCTX9QUBA2bdqEhg0bYvz48QYrUS9atAharVY/TaqiGD58uL48Q15eHsaNG4cqVaoAuHe+ykGlUpk8OaaU+6SPPvoI/fv3R7t27WBtbY1Vq1YZfGdGRkaiS5cuksdx+fJl/TTOJk2awNLSEpMnT5b1fTF1Yp9JqSekhKdDSqGUE1sJhBAGf3iAkn98AOlXbDIWR3Fy/QGk/yhhRKGSby51dVqEELLU4quoTymJHhWTt8+eiIgINGvWTPJ+lDCST0mjckx9ww8o46bfwcEBhw4dgp+fH2bMmGGw+FDXrl2xaNEiWVbDVsq92v3TbYcMGVJiHzkWHrr/3sAUyTGl3CfVqFEDsbGxyM7OhrW1dYkpcjExMZI/GAXuDSIofs9sZmYmS7/FmTqxz0LnT4gFYUsq7cTOzMyEtbW1LE/tTE0pnwulxEH/8fPz0w+R/fHHH7Fq1SqkpaXpz4uoqCgsWLAAf/zxhyT9K7FgcX5+vn763oEDB9CrVy+MGDEC3bp1k3yagRKPB5HSKG2BBCWqWrUqEhISZF3FVSlL3StBRkYGWrRooS8IbGxUzvHjxyVPgqjVanTv3l1/s71161a89tprBjf8P//8s+QPXe6Pw1gsgPQ3/To3btzAhQsXIIRAgwYNDEaPSY3XwoaUcDyUEIOSPOx7Q0fK8/Xdd9/Fvn378McffxhN7Ldu3RodOnSQrBYxk1JERDK6fv06+vfvjwMHDuhHFPbr10/f3rFjR7Rt2xZhYWGS9K+0m0t/f39ER0ejTp06GDlyJHx9fVGjRg1Th0VExTB5W9L9oy9McRPxsKXu/fz8ZE2SmVpKSgr8/Pywc+dOo6Ny5DgWSrnZVkocRPRwSjhfTZ3YZ1KKiMgETDWiUGk3l2q1Gi4uLmjevPkDpzjI9TSXiKgslHATQcaZclQOEdGzypSJfSaliIjIZIYPH16mehu8sSMiIiIikpYpEvtMShERERERERERkeykrSBLRERERERERERkBJNSREREREREREQkOyaliIiIiIiIiIhIdkxKERERERERERGR7JiUIiIiIiIiIiIi2TEpRURERHSf9PR0vPvuu6hXrx4sLS1Rp04d9O7dG3v27DF1aI9s+PDhUKlUpf7UrVvX1CESERFRBaUSQghTB0FERESkFBcvXoSXlxdsbW0xZ84cNG3aFHfv3sXOnTuxbNkynD171tQhlqqgoAAWFhYG27Kzs5Gbm6t/7eTkhBUrVqBbt24AAI1Gg5o1a8oaJxERERHAkVJEREREBvz9/aFSqXDkyBG88cYbaNiwIdzd3TFlyhQcPnxYv9/8+fPRtGlTVKlSBXXq1IG/vz9u3bqlb1+5ciVsbW2xc+dONG7cGNbW1ujWrRuuXr1q0F9kZCTc3d1haWkJJycnjB8/Xt+WlZWF0aNHo2bNmqhWrRpee+01nDx5Ut/+4YcfolmzZli+fDnc3NxgZWVV4v+PjY0NHB0d9T8AYGtrC0dHR8ycORMjRoww2P/u3bt47rnnEBERAQBo3749xo8fj/Hjx8PGxgY1atTArFmzUPy5Zn5+PqZOnYpatWqhSpUqaNOmDfbt2/cYR5+IiIgqEialiIiIiP5fZmYmfv75ZwQEBKBKlSol2m1tbfX/W61WY+HChUhMTMSqVauwd+9eTJs2zWD/O3fu4PPPP8eaNWsQGxuL1NRUTJ06Vd++ePFiBAQEYMyYMTh16hS2bNmC+vXr69t9fHxw7do17NixA8eOHUOLFi3QsWNHZGZm6ve5cOECvv/+e2zatAnx8fGP9P939OjR+Pnnnw0SZT/99BPu3LmDt956S79t1apVMDMzw5EjR/DVV19h/vz5WL58ub59/PjxiIuLQ3R0NBISEuDj44Nu3brh/PnzjxQPERERVSycvkdERET0/44cOYI2bdpg06ZN6Nev3yP97nfffYdx48bh+vXrAO6NlBoxYgQuXLiA559/HgAQHh6OOXPmID09HQBQq1YtjBgxAqGhoSX+vQMHDqBnz564du0aLC0t9dvr16+PadOmYcyYMfjwww8xd+5cXLlypcxT8FQqFX744Qf07dsXAODu7o5hw4bpE2qvv/46qlevjhUrVgC4N1Lq2rVrSExMhEqlAgBMnz4dW7ZswZkzZ5Camop69eohNTUVzs7O+n46deqE1q1bY+7cuY9yGImIiKgCMTN1AERERERK8SjP6nbv3o158+bh7NmzyMnJQWFhIfLy8nDnzh1UrlwZAFC5cmV9Qgq4V8/p2rVrAIBr164hLS0NHTt2NPrvnzx5Erdu3UL16tUNtufm5uLvv//Wv3Z1dX2imlCjR4/GsmXLMG3aNGRkZGDHjh3Yu3evwT5t27bVJ6QAwNPTE1988QW0Wi1OnToFrVaLhg0bGvxOfn5+idiJiIiIimNSioiIiOj/NWjQACqV6qHFzC9evIhevXrBz88PYWFhsLe3x4EDBzBq1CgUFBTok1Lm5uYGv6dSqfSJr0qVKj2wj1u3bsHJyclobabi0wiNTTN8FG+//TamT5+OuLg4HDp0CG5ubnj11VfL/Pu3bt2CRqPBsWPHoNFoDNqsra2fKDYiIiIq35iUIiIiIvp/9vb26Nq1KxYtWoQJEyaUSPhkZWXB1tYWx44dQ1FREb744guo1fdKdG7cuPGR+qpatSrq1q2LPXv2oEOHDiXaW7RogfT0dJiZmaFu3bqP/f/pYapXr46+fftixYoViIuLK1H4HAB+//13g9eHDx9GgwYNoNFo0Lx5c2i1Wly7du2RkllERERELHROREREVMyiRYug1WrRunVrfP/99zh//jz+/PNPLFy4EJ6engDu1XW6e/cuvv76ayQlJWHNmjVYsmTJI/f14Ycf4osvvsDChQtx/vx5HD9+HF9//TWAezWZPD090bdvX/zyyy+4ePEiDh06hMDAQBw9evSp/n8ePXo0Vq1ahT///BPDhg0r0Z6amoopU6bg3LlzWL9+Pb7++mtMnDgRANCwYUP4+vri7bffxqZNm5CcnIwjR45g3rx52LZt21ONk4iIiMoXjpQiIiIiKqZevXo4fvw4wsLC8N577+Hq1auoWbMmWrZsicWLFwMAXnrpJcyfPx+ffPIJZsyYAW9vb8ybNw9vv/32I/U1bNgw5OXl4csvv8TUqVNRo0YNDBgwAMC9qX7bt29HYGAgRowYgX/++QeOjo7w9vaGg4PDU/3/3KlTJzg5OcHd3d2gWLnO22+/jdzcXLRu3RoajQYTJ07EmDFj9O0rVqxAaGgo3nvvPVy5cgU1atRA27Zt0atXr6caJxEREZUvXH2PiIiIqIK7desWatWqhRUrVqB///4Gbe3bt0ezZs2wYMEC0wRHRERE5RZHShERERFVUEVFRbh+/Tq++OIL2Nra4vXXXzd1SERERFSBMClFREREVEGlpqbCzc0NtWvXxsqVK2FmxktDIiIikg+n7xERERERERERkey4+h4REREREREREcmOSSkiIiIiIiIiIpIdk1JERERERERERCQ7JqWIiIiIiIiIiEh2TEoREREREREREZHsmJQiIiIiIiIiIiLZMSlFRERERERERESyY1KKiIiIiIiIiIhkx6QUERERERERERHJ7v8A/UT4rcKl7DcAAAAASUVORK5CYII=\n"},"metadata":{}}]},{"cell_type":"code","source":["merged_data['Epithelial'] = pd.to_numeric(merged_data['Epithelial'], errors='coerce')"],"metadata":{"id":"SnfiKgZ8o-kB"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import pandas as pd\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import matplotlib.cm as cm\n","\n","# Calculate median epithelial scores for each cancer type\n","median_epithelial_scores = merged_data.groupby('Cancer_Type')['Epithelial'].median().sort_values()\n","\n","# Order cancer types based on median epithelial scores\n","cancer_type_order = median_epithelial_scores.index\n","\n","# Create a colormap for the color bar\n","cmap = cm.get_cmap('coolwarm')  # Choose a colormap (blue to red)\n","norm = plt.Normalize(median_epithelial_scores.min(), median_epithelial_scores.max())  # Normalize scores for colormap\n","\n","# Create the boxplot\n","plt.figure(figsize=(6, 12))  # Adjust figure size for portrait orientation\n","sns.boxplot(data=merged_data, y='Cancer_Type', x='ELF3_Expression', order=cancer_type_order,\n","            palette=cmap(norm(median_epithelial_scores.values)))  # Apply colormap to boxes\n","#plt.title('ELF3 Expression Levels Across TCGA Cancer Types (Ordered by Median Epithelial Score)')\n","plt.xlabel('ELF3 Expression')\n","plt.ylabel('Cancer Type')\n","\n","# Add color bar\n","sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)\n","sm.set_array([])  # Empty array for the colorbar\n","cbar = plt.colorbar(sm, orientation='horizontal', pad=0.1)  # Create colorbar\n","cbar.set_label('Median Epithelial Score')\n","\n","plt.tight_layout()\n","plt.savefig('elf3_expression_boxplot.png', dpi=300)  # Save with 300 DPI\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"2BBVoKp_oqHk","executionInfo":{"status":"ok","timestamp":1728249159611,"user_tz":-330,"elapsed":2694,"user":{"displayName":"Anjaney Pandey","userId":"05531075380284782212"}},"outputId":"dd482ac1-220d-4482-e9db-8f9b3e9644d4","collapsed":true},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-79-2de33db31d4f>:13: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n","  cmap = cm.get_cmap('coolwarm')  # Choose a colormap (blue to red)\n","<ipython-input-79-2de33db31d4f>:18: FutureWarning: \n","\n","Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n","\n","  sns.boxplot(data=merged_data, y='Cancer_Type', x='ELF3_Expression', order=cancer_type_order,\n","<ipython-input-79-2de33db31d4f>:18: UserWarning: Numpy array is not a supported type for `palette`. Please convert your palette to a list. This will become an error in v0.14\n","  sns.boxplot(data=merged_data, y='Cancer_Type', x='ELF3_Expression', order=cancer_type_order,\n","/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n","  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n","/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n","  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n","/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n","  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n","/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n","  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n","/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n","  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n","/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n","  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n","/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n","  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n","/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n","  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n","/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n","  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n","/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n","  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n","/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n","  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n","/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n","  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n","/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n","  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n","/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n","  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n","/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n","  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n","/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n","  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n","/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n","  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n","/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n","  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n","/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n","  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n","/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n","  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n","/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n","  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n","/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n","  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n","/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n","  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n","/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n","  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n","/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n","  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n","/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n","  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n","/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n","  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n","/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n","  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n","/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n","  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n","/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n","  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n","/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n","  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n","/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n","  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n","/usr/local/lib/python3.10/dist-packages/seaborn/_base.py:949: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n","/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n","  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n","<ipython-input-79-2de33db31d4f>:27: MatplotlibDeprecationWarning: Unable to determine Axes to steal space for Colorbar. Using gca(), but will raise in the future. Either provide the *cax* argument to use as the Axes for the Colorbar, provide the *ax* argument to steal space from it, or add *mappable* to an Axes.\n","  cbar = plt.colorbar(sm, orientation='horizontal', pad=0.1)  # Create colorbar\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 600x1200 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["# Figure E"],"metadata":{"id":"Z6Ek-Ewae3o-"}},{"cell_type":"markdown","source":["## Plot-I"],"metadata":{"id":"Q6D6mSHMtIAq"}},{"cell_type":"code","source":["!wget \"https://ftp.ncbi.nlm.nih.gov/geo/series/GSE118nnn/GSE118407/suppl/GSE118407%5FOVCA429shGRHL2%5Ffpkm.xlsx\""],"metadata":{"id":"GsSoF2rAfIIb"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import pandas as pd\n","\n","GSE118407 = pd.read_excel(\"/content/GSE118407_OVCA429shGRHL2_fpkm.xlsx\")\n","\n","GSE118407"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":461},"id":"pn-buKXggMpB","executionInfo":{"status":"ok","timestamp":1728263091191,"user_tz":-330,"elapsed":18751,"user":{"displayName":"Anjaney Pandey","userId":"05531075380284782212"}},"outputId":"2747fb85-8b98-4ca7-bc52-3c09d174d354"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                   GeneID                                      TranscriptIDs  \\\n","0      ENSG00000000003.10  ENST00000373020.4,ENST00000494424.1,ENST000004...   \n","1       ENSG00000000005.5                ENST00000373031.4,ENST00000485971.1   \n","2       ENSG00000000419.8  ENST00000371582.4,ENST00000371583.5,ENST000003...   \n","3       ENSG00000000457.9  ENST00000367770.1,ENST00000367771.6,ENST000003...   \n","4      ENSG00000000460.12  ENST00000286031.6,ENST00000359326.4,ENST000004...   \n","...                   ...                                                ...   \n","57815   ENSGR0000264819.1                                  ENSTR0000580687.1   \n","57816   ENSGR0000265350.1                                  ENSTR0000577896.1   \n","57817   ENSGR0000265658.1                                  ENSTR0000580266.1   \n","57818   ENSGR0000266731.1                                  ENSTR0000577553.1   \n","57819   ENSGR0000270726.1                ENSTR0000285718.7,ENSTR0000483543.2   \n","\n","        GeneSymbol   Length  Effective Length  OVCA429sh12_L001.genes.results  \\\n","0           TSPAN6  2138.28           1987.22                           13.59   \n","1             TNMD   940.50            789.45                            0.00   \n","2             DPM1  1084.10            933.03                           37.12   \n","3            SCYL3  4253.77           4102.70                            3.69   \n","4         C1orf112  2135.91           1984.85                            5.76   \n","...            ...      ...               ...                             ...   \n","57815   BX649553.4    74.00              0.00                            0.00   \n","57816    RN7SL355P   314.00            163.23                            0.00   \n","57817      MIR3690    75.00              0.00                            0.00   \n","57818   AL732314.1   101.00              0.00                            0.00   \n","57819  AJ271736.10  1604.00           1452.93                            0.00   \n","\n","       OVCA429sh12_L002.genes.results  OVCA429sh12_L003.genes.results  \\\n","0                               14.32                           15.18   \n","1                                0.00                            0.00   \n","2                               37.22                           36.58   \n","3                                3.02                            2.57   \n","4                                7.95                            8.02   \n","...                               ...                             ...   \n","57815                            0.00                            0.00   \n","57816                            0.00                            0.00   \n","57817                            0.00                            0.00   \n","57818                            0.00                            0.00   \n","57819                            0.00                            0.00   \n","\n","       OVCA429sh12_L004.genes.results  OVCA429shLuc_L001.genes.results  \\\n","0                               13.21                            18.15   \n","1                                0.00                             0.00   \n","2                               37.19                            40.41   \n","3                                3.71                             2.35   \n","4                                6.04                            12.12   \n","...                               ...                              ...   \n","57815                            0.00                             0.00   \n","57816                            0.00                             0.00   \n","57817                            0.00                             0.00   \n","57818                            0.00                             0.00   \n","57819                            0.00                             0.00   \n","\n","       OVCA429shLuc_L002.genes.results  OVCA429shLuc_L003.genes.results  \\\n","0                                16.09                            15.40   \n","1                                 0.00                             0.00   \n","2                                40.81                            42.20   \n","3                                 2.41                             3.06   \n","4                                10.44                            12.01   \n","...                                ...                              ...   \n","57815                             0.00                             0.00   \n","57816                             0.00                             0.00   \n","57817                             0.00                             0.00   \n","57818                             0.00                             0.00   \n","57819                             0.00                             0.00   \n","\n","       OVCA429shLuc_L004.genes.results  \n","0                                16.14  \n","1                                 0.00  \n","2                                39.02  \n","3                                 2.23  \n","4                                11.69  \n","...                                ...  \n","57815                             0.00  \n","57816                             0.00  \n","57817                             0.00  \n","57818                             0.00  \n","57819                             0.00  \n","\n","[57820 rows x 13 columns]"],"text/html":["\n","  <div id=\"df-0c24cad2-eb94-4d45-843b-451591c1acee\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>GeneID</th>\n","      <th>TranscriptIDs</th>\n","      <th>GeneSymbol</th>\n","      <th>Length</th>\n","      <th>Effective Length</th>\n","      <th>OVCA429sh12_L001.genes.results</th>\n","      <th>OVCA429sh12_L002.genes.results</th>\n","      <th>OVCA429sh12_L003.genes.results</th>\n","      <th>OVCA429sh12_L004.genes.results</th>\n","      <th>OVCA429shLuc_L001.genes.results</th>\n","      <th>OVCA429shLuc_L002.genes.results</th>\n","      <th>OVCA429shLuc_L003.genes.results</th>\n","      <th>OVCA429shLuc_L004.genes.results</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>ENSG00000000003.10</td>\n","      <td>ENST00000373020.4,ENST00000494424.1,ENST000004...</td>\n","      <td>TSPAN6</td>\n","      <td>2138.28</td>\n","      <td>1987.22</td>\n","      <td>13.59</td>\n","      <td>14.32</td>\n","      <td>15.18</td>\n","      <td>13.21</td>\n","      <td>18.15</td>\n","      <td>16.09</td>\n","      <td>15.40</td>\n","      <td>16.14</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>ENSG00000000005.5</td>\n","      <td>ENST00000373031.4,ENST00000485971.1</td>\n","      <td>TNMD</td>\n","      <td>940.50</td>\n","      <td>789.45</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>ENSG00000000419.8</td>\n","      <td>ENST00000371582.4,ENST00000371583.5,ENST000003...</td>\n","      <td>DPM1</td>\n","      <td>1084.10</td>\n","      <td>933.03</td>\n","      <td>37.12</td>\n","      <td>37.22</td>\n","      <td>36.58</td>\n","      <td>37.19</td>\n","      <td>40.41</td>\n","      <td>40.81</td>\n","      <td>42.20</td>\n","      <td>39.02</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>ENSG00000000457.9</td>\n","      <td>ENST00000367770.1,ENST00000367771.6,ENST000003...</td>\n","      <td>SCYL3</td>\n","      <td>4253.77</td>\n","      <td>4102.70</td>\n","      <td>3.69</td>\n","      <td>3.02</td>\n","      <td>2.57</td>\n","      <td>3.71</td>\n","      <td>2.35</td>\n","      <td>2.41</td>\n","      <td>3.06</td>\n","      <td>2.23</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>ENSG00000000460.12</td>\n","      <td>ENST00000286031.6,ENST00000359326.4,ENST000004...</td>\n","      <td>C1orf112</td>\n","      <td>2135.91</td>\n","      <td>1984.85</td>\n","      <td>5.76</td>\n","      <td>7.95</td>\n","      <td>8.02</td>\n","      <td>6.04</td>\n","      <td>12.12</td>\n","      <td>10.44</td>\n","      <td>12.01</td>\n","      <td>11.69</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>57815</th>\n","      <td>ENSGR0000264819.1</td>\n","      <td>ENSTR0000580687.1</td>\n","      <td>BX649553.4</td>\n","      <td>74.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","    </tr>\n","    <tr>\n","      <th>57816</th>\n","      <td>ENSGR0000265350.1</td>\n","      <td>ENSTR0000577896.1</td>\n","      <td>RN7SL355P</td>\n","      <td>314.00</td>\n","      <td>163.23</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","    </tr>\n","    <tr>\n","      <th>57817</th>\n","      <td>ENSGR0000265658.1</td>\n","      <td>ENSTR0000580266.1</td>\n","      <td>MIR3690</td>\n","      <td>75.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","    </tr>\n","    <tr>\n","      <th>57818</th>\n","      <td>ENSGR0000266731.1</td>\n","      <td>ENSTR0000577553.1</td>\n","      <td>AL732314.1</td>\n","      <td>101.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","    </tr>\n","    <tr>\n","      <th>57819</th>\n","      <td>ENSGR0000270726.1</td>\n","      <td>ENSTR0000285718.7,ENSTR0000483543.2</td>\n","      <td>AJ271736.10</td>\n","      <td>1604.00</td>\n","      <td>1452.93</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","    </tr>\n","  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127.6   \n","\n","       OVCA429shLuc_L004.genes.results  \n","11111                           122.07  "],"text/html":["\n","  <div id=\"df-73850b2c-6a7b-4b19-8742-71b6d1351512\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>GeneID</th>\n","      <th>TranscriptIDs</th>\n","      <th>GeneSymbol</th>\n","      <th>Length</th>\n","      <th>Effective Length</th>\n","      <th>OVCA429sh12_L001.genes.results</th>\n","      <th>OVCA429sh12_L002.genes.results</th>\n","      <th>OVCA429sh12_L003.genes.results</th>\n","      <th>OVCA429sh12_L004.genes.results</th>\n","      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122.07,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          122.07\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":34}]},{"cell_type":"code","source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from scipy import stats\n","\n","# Extract data for shGRHL2 (columns 5 to 8) and control (columns 9 to 12)\n","shGRHL2_data = pd.to_numeric(elf3_i.iloc[0, 5:9].values)\n","control_data = pd.to_numeric(elf3_i.iloc[0, 9:13].values)\n","\n","# Calculate the t-test\n","t_statistic, p_value = stats.ttest_ind(shGRHL2_data, control_data)\n","\n","# Prepare data for bar plot\n","data = [np.mean(shGRHL2_data), np.mean(control_data)]\n","labels = ['shGRHL2', 'Control']\n","\n","# Create the bar plot\n","plt.bar(labels, data)\n","#plt.xlabel('Groups')\n","plt.ylabel('Mean Expression')\n","plt.title('GSE118407')\n","\n","\n","# Add t-test results\n","plt.text(0.4, max(data), f't = {t_statistic:.2f}, p < 0.05', ha='right')\n","\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":452},"id":"HKro6akJnng1","executionInfo":{"status":"ok","timestamp":1728266225955,"user_tz":-330,"elapsed":807,"user":{"displayName":"Anjaney Pandey","userId":"05531075380284782212"}},"outputId":"ece9999b-275e-4f83-f911-2393bacfbdd7"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["## II"],"metadata":{"id":"L2jY3SKEtNWn"}},{"cell_type":"code","source":["!wget \"https://ftp.ncbi.nlm.nih.gov/geo/series/GSE61nnn/GSE61220/suppl/GSE61220_EMTrawcnts2.txt.gz\""],"metadata":{"id":"L_hUkS7Cs0ZM"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["GSE61220 = pd.read_csv(\"/content/GSE61220_EMTrawcnts2.txt.gz\", sep=\"\\t\")\n","\n","GSE61220"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":478},"id":"TyxA8n2tuDp7","executionInfo":{"status":"ok","timestamp":1728266681925,"user_tz":-330,"elapsed":435,"user":{"displayName":"Anjaney Pandey","userId":"05531075380284782212"}},"outputId":"e478bcd2-be5c-4fa1-c6d4-ce79e1165b46"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["      Unnamed: 0  Epi_Control_rep1  Epi_Control_rep2  Epi_Control_rep3  \\\n","0           A1BG                11                 7                 5   \n","1       A1BG-AS1                 1                 2                 4   \n","2 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dtype='object')"]},"metadata":{},"execution_count":66}]},{"cell_type":"code","source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from scipy import stats\n","\n","# Extract data\n","tgfb_data = pd.to_numeric(elf3_ii.iloc[0, 1:6].values)\n","control_ii_data = pd.to_numeric(elf3_ii.iloc[0, 7:12].values)\n","\n","# Calculate the t-test\n","#t_statistic, p_value = stats.ttest_ind(tgfb_data, control_ii_data)\n","\n","# Prepare data for bar plot\n","data_ii = [np.mean(tgfb_data), np.mean(control_ii_data)]\n","labels = ['Control', 'TGF-B']\n","\n","# Create the bar plot\n","plt.bar(labels, data)\n","#plt.xlabel('Groups')\n","plt.ylabel('Mean Expression')\n","plt.title('GSE61220')\n","\n","\n","# Add t-test results\n","plt.text(0.4, max(data_ii), f't = {t_statistic:.2f}, p < 0.05', ha='right')\n","\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":479},"id":"AkW772bXvaMv","executionInfo":{"status":"ok","timestamp":1728267406904,"user_tz":-330,"elapsed":494,"user":{"displayName":"Anjaney Pandey","userId":"05531075380284782212"}},"outputId":"3519bca5-0832-42f6-f1a1-3f7fb7d10815"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 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\n"},"metadata":{}}]}],"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyNoXTAANpGhdQfcTSHqVHNt"},"kernelspec":{"display_name":"Python 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