{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c96cca8d",
   "metadata": {},
   "source": [
    "# Bulk RNA-seq mapping with STAR\n",
    "\n",
    "This notebook demonstrates an end-to-end bulk RNA-seq workflow in **OmicVerse**, starting from raw reads and ending with differential expression (DE) results and visualization.\n",
    "\n",
    "**Workflow**\n",
    "1. Import OmicVerse and set plotting style  \n",
    "2. Download paired-end FASTQ from SRA  \n",
    "3. Quality control and trimming (fastp)  \n",
    "4. Download reference genome and annotation (Ensembl GRCh38 release 115)  \n",
    "5. Align reads with STAR  \n",
    "6. Gene-level quantification (featureCounts)  \n",
    "7. Differential expression and visualization (pyDEG)\n",
    "\n",
    "> **Note**  \n",
    "> The full tutorial series can continue from DE into functional enrichment and PPI network analysis. This notebook focuses on the core preprocessing + DE workflow.\n",
    "\n",
    "**Requirements**\n",
    "- External tools: `fasterq-dump` (SRA Toolkit), `fastp`, `STAR`, and `featureCounts` (Subread).  \n",
    "- Storage: FASTQ + alignment outputs can be large; ensure adequate disk space.  \n",
    "- Memory: STAR genome indexing/alignment can require **high RAM** (see the STAR step below).\n",
    "\n",
    "---\n",
    "\n",
    "## 1) Import OmicVerse\n",
    "\n",
    "We first import OmicVerse and configure a consistent plotting style. `ov.style(font_path='Arial')` will download/install the font if it is not available locally.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "41bb5b31-c768-4e0c-947f-0dee61aa4ec5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔬 Starting plot initialization...\n",
      "Downloading Arial font from GitHub...\n",
      "Arial font downloaded successfully to: /tmp/omicverse_arial.ttf\n",
      "Registered as: Arial\n",
      "🧬 Detecting GPU devices…\n",
      "✅ NVIDIA CUDA GPUs detected: 1\n",
      "    • [CUDA 0] NVIDIA H100 80GB HBM3\n",
      "      Memory: 79.1 GB | Compute: 9.0\n",
      "\n",
      "   ____            _     _    __                  \n",
      "  / __ \\____ ___  (_)___| |  / /__  _____________ \n",
      " / / / / __ `__ \\/ / ___/ | / / _ \\/ ___/ ___/ _ \\ \n",
      "/ /_/ / / / / / / / /__ | |/ /  __/ /  (__  )  __/ \n",
      "\\____/_/ /_/ /_/_/\\___/ |___/\\___/_/  /____/\\___/                                              \n",
      "\n",
      "🔖 Version: 1.7.9rc1   📚 Tutorials: https://omicverse.readthedocs.io/\n",
      "✅ plot_set complete.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import omicverse as ov\n",
    "ov.style(font_path='Arial')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "698a2743",
   "metadata": {},
   "source": [
    "## 2) Download FASTQ data from SRA\n",
    "\n",
    "We download **4 paired-end samples** as a lightweight example:\n",
    "- Disease group: `SRR12544419`, `SRR12544421`  \n",
    "- Healthy group: `SRR12544529`, `SRR12544531`\n",
    "\n",
    "▲ **CRITICAL**  \n",
    "`ov.alignment.fqdump()` will **prioritize using pre-downloaded SRA files** found in `sra_dir`. If not found, it will download the data and convert it into FASTQ according to `library_layout`.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "35203c35-7f38-4876-91a7-b3e08cbca794",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> /home/groups/xiaojie/steorra/env/omicverse/bin/fasterq-dump SRR12544529 -O data/fqdata/SRR12544529 -e 8 --mem 4G --split-files --progress -t data/tmp/SRR12544529\n",
      ">> /home/groups/xiaojie/steorra/env/omicverse/bin/fasterq-dump SRR12544421 -O data/fqdata/SRR12544421 -e 8 --mem 4G --split-files --progress -t data/tmp/SRR12544421\n",
      ">> /home/groups/xiaojie/steorra/env/omicverse/bin/fasterq-dump SRR12544531 -O data/fqdata/SRR12544531 -e 8 --mem 4G --split-files --progress -t data/tmp/SRR12544531\n",
      ">> /home/groups/xiaojie/steorra/env/omicverse/bin/fasterq-dump SRR12544419 -O data/fqdata/SRR12544419 -e 8 --mem 4G --split-files --progress -t data/tmp/SRR12544419\n",
      "join   :|  0.00% 0.01% 0.02% 0.03% 0.04% 0.05% 0.06% 0.07% 0.08% 0.09% 0.10% 0.11% 0.12% 0.13% 0.14% 0.15% 0.16% 0.17% 0.18% 0.19% 0.20% 0.21% 0.22% 0.23% 0.24% 0.25% 0.26% 0.27% 0.28% 0.29% 0.30% 0.31% 0.32% 0.33% 0.34% 0.35% 0.36% 0.37% 0.38% 0.39% 0.40% 0.41% 0.42% 0.43% 0.44% 0.45% 0.46% 0.47% 0.48% 0.49% 0.50% 0.51% 0.52% 0.53% 0.54% 0.55% 0.56% 0.57% 0.58% 0.59% 0.60% 0.61% 0.62% 0.63% 0.64% 0.65% 0.66% 0.67% 0.68% 0.69% 0.70% 0.71% 0.72% 0.73% 0.74% 0.75% 0.76% 0.77% 0.78% 0.79% 0.80% 0.81% 0.82% 0.83% 0.84% 0.85% 0.86% 0.87% 0.88% 0.89% 0.90% 0.91% 0.92% 0.93% 0.94% 0.95% 0.96% 0.97% 0.98% 0.99%-  1.00% 1.01% 1.02% 1.03% 1.04% 1.05% 1.06% 1.07% 1.08% 1.09% 1.10% 1.11% 1.12% 1.13% 1.14% 1.15% 1.16% 1.17% 1.18% 1.19% 1.20% 1.21% 1.22% 1.23% 1.24% 1.25% 1.26% 1.27% 1.28% 1.29% 1.30% 1.31% 1.32% 1.33% 1.34% 1.35% 1.36% 1.37% 1.38% 1.39% 1.40% 1.41% 1.42% 1.43% 1.44% 1.45% 1.46% 1.47% 1.48% 1.49% 1.50% 1.51% 1.52% 1.53% 1.54% 1.55% 1.56% 1.57% 1.58% 1.59% 1.60% 1.61% 1.62% 1.63% 1.64% 1.65% 1.66% 1.67% 1.68% 1.69% 1.70% 1.71% 1.72% 1.73% 1.74% 1.75% 1.76% 1.77% 1.78% 1.79% 1.80% 1.81% 1.82% 1.83% 1.84% 1.85% 1.86% 1.87% 1.88% 1.89% 1.90% 1.91% 1.92% 1.93% 1.94% 1.95% 1.96% 1.97% 1.98% 1.99% 2.00% 2.01% 2.02% 2.03% 2.04% 2.05% 2.06% 2.07% 2.08% 2.09% 2.10% 2.11% 2.12% 2.13% 2.14% 2.15% 2.16% 2.17% 2.18% 2.19% 2.20% 2.21% 2.22% 2.23% 2.24% 2.25% 2.26% 2.27% 2.28% 2.29% 2.30% 2.31% 2.32% 2.33% 2.34% 2.35% 2.36% 2.37% 2.38% 2.39% 2.40% 2.41% 2.42% 2.43% 2.44% 2.45% 2.46% 2.47% 2.48% 2.49% 2.50% 2.51% 2.52% 2.53% 2.54% 2.55% 2.56% 2.57% 2.58% 2.59% 2.60% 2.61% 2.62% 2.63% 2.64% 2.65% 2.66% 2.67% 2.68% 2.69% 2.70% 2.71% 2.72% 2.73% 2.74% 2.75% 2.76% 2.77% 2.78% ------------------------------------------------- 100%\n",
      "concat :|-------------------------------------------------- 100%\n",
      "spots read      : 10,963,094\n",
      "reads read      : 21,926,188\n",
      "reads written   : 21,926,188\n",
      "join   :|-------------------------------------------------- 100%\n",
      "concat :|-------------------------------------------------- 100%\n",
      "spots read      : 16,415,795\n",
      "reads read      : 32,831,590\n",
      "reads written   : 32,831,590\n",
      "join   :|-------------------------------------------------- 100%\n",
      "concat :|-------------------------------------------------- 100%\n",
      "spots read      : 18,850,866\n",
      "reads read      : 37,701,732\n",
      "reads written   : 37,701,732\n",
      "join   :|-------------------------------------------------- 100%\n",
      "concat :|-------------------------------------------------- 100%\n",
      "spots read      : 20,038,226\n",
      "reads read      : 40,076,452\n",
      "reads written   : 40,076,452\n",
      "[{'srr': 'SRR12544419', 'fq1': 'data/fqdata/SRR12544419/SRR12544419_1.fastq', 'fq2': 'data/fqdata/SRR12544419/SRR12544419_2.fastq', 'layout': 'paired'}, {'srr': 'SRR12544421', 'fq1': 'data/fqdata/SRR12544421/SRR12544421_1.fastq', 'fq2': 'data/fqdata/SRR12544421/SRR12544421_2.fastq', 'layout': 'paired'}, {'srr': 'SRR12544529', 'fq1': 'data/fqdata/SRR12544529/SRR12544529_1.fastq', 'fq2': 'data/fqdata/SRR12544529/SRR12544529_2.fastq', 'layout': 'paired'}, {'srr': 'SRR12544531', 'fq1': 'data/fqdata/SRR12544531/SRR12544531_1.fastq', 'fq2': 'data/fqdata/SRR12544531/SRR12544531_2.fastq', 'layout': 'paired'}]\n"
     ]
    }
   ],
   "source": [
    "fq_data = ov.alignment.fqdump(\n",
    "    # Download 4 samples as example\n",
    "    sra_ids=['SRR12544419','SRR12544421',#disease group\n",
    "       'SRR12544529','SRR12544531',# healthy group\n",
    "    ],\n",
    "    sra_dir = \"./data\", \n",
    "    temp_dir = './data/tmp',\n",
    "    output_dir=\"./data/fqdata\",\n",
    "    library_layout=\"paired\", # pair-end data\n",
    ")\n",
    "print(fq_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8bfcaea",
   "metadata": {},
   "source": [
    "## 3) Quality control and trimming (fastp)\n",
    "\n",
    "We run `fastp` on each sample and save the cleaned FASTQ files plus QC reports.\n",
    "\n",
    "**Outputs**\n",
    "- Clean reads: `./result/fqdata/<sample>/*_clean_*.fastq`\n",
    "- Reports: `*.fastp.json` and `*.fastp.html`\n",
    "\n",
    "⏱️ Runtime: typically **~tens of seconds** for this small example, depending on compute and disk.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "30caf73d-df5f-4623-9530-32e820ed5df0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> /home/users/steorra/miniforge3/bin/fastp -i data/fqdata/SRR12544421/SRR12544421_1.fastq -o result/fqdata/SRR12544421/SRR12544421_clean_1.fastq -w 8 -j result/fqdata/SRR12544421/SRR12544421.fastp.json -h result/fqdata/SRR12544421/SRR12544421.fastp.html -I data/fqdata/SRR12544421/SRR12544421_2.fastq -O result/fqdata/SRR12544421/SRR12544421_clean_2.fastq --detect_adapter_for_pe\n",
      ">> /home/users/steorra/miniforge3/bin/fastp -i data/fqdata/SRR12544419/SRR12544419_1.fastq -o result/fqdata/SRR12544419/SRR12544419_clean_1.fastq -w 8 -j result/fqdata/SRR12544419/SRR12544419.fastp.json -h result/fqdata/SRR12544419/SRR12544419.fastp.html -I data/fqdata/SRR12544419/SRR12544419_2.fastq -O result/fqdata/SRR12544419/SRR12544419_clean_2.fastq --detect_adapter_for_pe\n",
      ">> /home/users/steorra/miniforge3/bin/fastp -i data/fqdata/SRR12544531/SRR12544531_1.fastq -o result/fqdata/SRR12544531/SRR12544531_clean_1.fastq -w 8 -j result/fqdata/SRR12544531/SRR12544531.fastp.json -h result/fqdata/SRR12544531/SRR12544531.fastp.html -I data/fqdata/SRR12544531/SRR12544531_2.fastq -O result/fqdata/SRR12544531/SRR12544531_clean_2.fastq --detect_adapter_for_pe\n",
      ">> /home/users/steorra/miniforge3/bin/fastp -i data/fqdata/SRR12544529/SRR12544529_1.fastq -o result/fqdata/SRR12544529/SRR12544529_clean_1.fastq -w 8 -j result/fqdata/SRR12544529/SRR12544529.fastp.json -h result/fqdata/SRR12544529/SRR12544529.fastp.html -I data/fqdata/SRR12544529/SRR12544529_2.fastq -O result/fqdata/SRR12544529/SRR12544529_clean_2.fastq --detect_adapter_for_pe\n",
      "Detecting adapter sequence for read1...\n",
      "Detecting adapter sequence for read1...\n",
      "Detecting adapter sequence for read1...\n",
      "Detecting adapter sequence for read1...\n",
      "CTCCTGGAAGTTAACTGCACCATCAGTGTTGATATCCAACTCTTTGAACCAGACGTCTGC\n",
      "\n",
      "Detecting adapter sequence for read2...\n",
      "CTGGAAGTTAACTGCACCATCAGTGTTGATATCCAACTCTTTGAACCAGACGTCTGCACC\n",
      "\n",
      "Detecting adapter sequence for read2...\n",
      "No adapter detected for read1\n",
      "\n",
      "Detecting adapter sequence for read2...\n",
      "GGCCACTGTGGTCTTAGGGGGTGCCCTCCCCGAGGCCTGGCTTATGGTGGTGGCCAGGGC\n",
      "\n",
      "Detecting adapter sequence for read2...\n",
      "GTGGCTCCTCGGCTTTGACAGAGTGCAAGACGATGACTTGCAAAATGTCGCAGCTGGAAC\n",
      "\n",
      "GTGGCTCCTCGGCTTTGACAGAGTGCAAGACGATGACTTGCAAAATGTCGCAGCTGGAAC\n",
      "\n",
      "GACACAACTGTGTTCACTAGCAACCTCAAACAGACACCATGGTGCACCTGACTCCTGAGG\n",
      "\n",
      "GTGGCTCCTCGGCTTTGACAGAGTGCAAGACGATGACTTGCAAAATGTCGCAGCTGGAAC\n",
      "\n",
      "Read1 before filtering:\n",
      "total reads: 10963094\n",
      "total bases: 559117794\n",
      "Q20 bases: 553775494(99.0445%)\n",
      "Q30 bases: 541507863(96.8504%)\n",
      "Q40 bases: 0(0%)\n",
      "\n",
      "Read2 before filtering:\n",
      "total reads: 10963094\n",
      "total bases: 559117794\n",
      "Q20 bases: 544098436(97.3137%)\n",
      "Q30 bases: 526334946(94.1367%)\n",
      "Q40 bases: 0(0%)\n",
      "\n",
      "Read1 after filtering:\n",
      "total reads: 10687072\n",
      "total bases: 544418300\n",
      "Q20 bases: 539356482(99.0702%)\n",
      "Q30 bases: 527511135(96.8945%)\n",
      "Q40 bases: 0(0%)\n",
      "\n",
      "Read2 after filtering:\n",
      "total reads: 10687072\n",
      "total bases: 544746875\n",
      "Q20 bases: 537103434(98.5969%)\n",
      "Q30 bases: 521266217(95.6896%)\n",
      "Q40 bases: 0(0%)\n",
      "\n",
      "Filtering result:\n",
      "reads passed filter: 21374144\n",
      "reads failed due to low quality: 487504\n",
      "reads failed due to too many N: 1192\n",
      "reads failed due to too short: 63348\n",
      "reads failed due to adapter dimer: 0\n",
      "reads with adapter trimmed: 134829\n",
      "bases trimmed due to adapters: 2669503\n",
      "\n",
      "Duplication rate: 15.5483%\n",
      "\n",
      "Insert size peak (evaluated by paired-end reads): 71\n",
      "\n",
      "JSON report: result/fqdata/SRR12544421/SRR12544421.fastp.json\n",
      "HTML report: result/fqdata/SRR12544421/SRR12544421.fastp.html\n",
      "\n",
      "/home/users/steorra/miniforge3/bin/fastp -i data/fqdata/SRR12544421/SRR12544421_1.fastq -o result/fqdata/SRR12544421/SRR12544421_clean_1.fastq -w 8 -j result/fqdata/SRR12544421/SRR12544421.fastp.json -h result/fqdata/SRR12544421/SRR12544421.fastp.html -I data/fqdata/SRR12544421/SRR12544421_2.fastq -O result/fqdata/SRR12544421/SRR12544421_clean_2.fastq --detect_adapter_for_pe \n",
      "fastp v1.1.0, time used: 84 seconds\n",
      "Read1 before filtering:\n",
      "total reads: 20038226\n",
      "total bases: 1021949526\n",
      "Q20 bases: 1012475638(99.073%)\n",
      "Q30 bases: 990432190(96.916%)\n",
      "Q40 bases: 0(0%)\n",
      "\n",
      "Read2 before filtering:\n",
      "total reads: 20038226\n",
      "total bases: 1021949526\n",
      "Q20 bases: 1003879697(98.2318%)\n",
      "Q30 bases: 975667178(95.4712%)\n",
      "Q40 bases: 0(0%)\n",
      "\n",
      "Read1 after filtering:\n",
      "total reads: 19745691\n",
      "total bases: 1006911636\n",
      "Q20 bases: 997775728(99.0927%)\n",
      "Q30 bases: 976169789(96.9469%)\n",
      "Q40 bases: 0(0%)\n",
      "\n",
      "Read2 after filtering:\n",
      "total reads: 19745691\n",
      "total bases: 1006642443\n",
      "Q20 bases: 994858257(98.8294%)\n",
      "Q30 bases: 968362375(96.1973%)\n",
      "Q40 bases: 0(0%)\n",
      "\n",
      "Filtering result:\n",
      "reads passed filter: 39491382\n",
      "reads failed due to low quality: 415354\n",
      "reads failed due to too many N: 2198\n",
      "reads failed due to too short: 167518\n",
      "reads failed due to adapter dimer: 0\n",
      "reads with adapter trimmed: 146355\n",
      "bases trimmed due to adapters: 5132033\n",
      "\n",
      "Duplication rate: 23.3277%\n",
      "\n",
      "Insert size peak (evaluated by paired-end reads): 70\n",
      "\n",
      "JSON report: result/fqdata/SRR12544529/SRR12544529.fastp.json\n",
      "HTML report: result/fqdata/SRR12544529/SRR12544529.fastp.html\n",
      "\n",
      "/home/users/steorra/miniforge3/bin/fastp -i data/fqdata/SRR12544529/SRR12544529_1.fastq -o result/fqdata/SRR12544529/SRR12544529_clean_1.fastq -w 8 -j result/fqdata/SRR12544529/SRR12544529.fastp.json -h result/fqdata/SRR12544529/SRR12544529.fastp.html -I data/fqdata/SRR12544529/SRR12544529_2.fastq -O result/fqdata/SRR12544529/SRR12544529_clean_2.fastq --detect_adapter_for_pe \n",
      "fastp v1.1.0, time used: 117 seconds\n",
      "Read1 before filtering:\n",
      "total reads: 18850866\n",
      "total bases: 961394166\n",
      "Q20 bases: 952217279(99.0455%)\n",
      "Q30 bases: 931109128(96.8499%)\n",
      "Q40 bases: 0(0%)\n",
      "\n",
      "Read2 before filtering:\n",
      "total reads: 18850866\n",
      "total bases: 961394166\n",
      "Q20 bases: 931425457(96.8828%)\n",
      "Q30 bases: 899828659(93.5962%)\n",
      "Q40 bases: 0(0%)\n",
      "\n",
      "Read1 after filtering:\n",
      "total reads: 18263658\n",
      "total bases: 930838151\n",
      "Q20 bases: 922174625(99.0693%)\n",
      "Q30 bases: 901889848(96.8901%)\n",
      "Q40 bases: 0(0%)\n",
      "\n",
      "Read2 after filtering:\n",
      "total reads: 18263658\n",
      "total bases: 930927790\n",
      "Q20 bases: 917031645(98.5073%)\n",
      "Q30 bases: 889662854(95.5673%)\n",
      "Q40 bases: 0(0%)\n",
      "\n",
      "Filtering result:\n",
      "reads passed filter: 36527316\n",
      "reads failed due to low quality: 1068046\n",
      "reads failed due to too many N: 2060\n",
      "reads failed due to too short: 103592\n",
      "reads failed due to adapter dimer: 718\n",
      "reads with adapter trimmed: 209233\n",
      "bases trimmed due to adapters: 4077359\n",
      "\n",
      "Duplication rate: 17.3068%\n",
      "\n",
      "Insert size peak (evaluated by paired-end reads): 71\n",
      "\n",
      "JSON report: result/fqdata/SRR12544419/SRR12544419.fastp.json\n",
      "HTML report: result/fqdata/SRR12544419/SRR12544419.fastp.html\n",
      "\n",
      "/home/users/steorra/miniforge3/bin/fastp -i data/fqdata/SRR12544419/SRR12544419_1.fastq -o result/fqdata/SRR12544419/SRR12544419_clean_1.fastq -w 8 -j result/fqdata/SRR12544419/SRR12544419.fastp.json -h result/fqdata/SRR12544419/SRR12544419.fastp.html -I data/fqdata/SRR12544419/SRR12544419_2.fastq -O result/fqdata/SRR12544419/SRR12544419_clean_2.fastq --detect_adapter_for_pe \n",
      "fastp v1.1.0, time used: 125 seconds\n",
      "Read1 before filtering:\n",
      "total reads: 16415795\n",
      "total bases: 837205545\n",
      "Q20 bases: 829026406(99.023%)\n",
      "Q30 bases: 810217511(96.7764%)\n",
      "Q40 bases: 0(0%)\n",
      "\n",
      "Read2 before filtering:\n",
      "total reads: 16415795\n",
      "total bases: 837205545\n",
      "Q20 bases: 818343741(97.7471%)\n",
      "Q30 bases: 794204257(94.8637%)\n",
      "Q40 bases: 0(0%)\n",
      "\n",
      "Read1 after filtering:\n",
      "total reads: 15957703\n",
      "total bases: 812314973\n",
      "Q20 bases: 804542876(99.0432%)\n",
      "Q30 bases: 786432142(96.8137%)\n",
      "Q40 bases: 0(0%)\n",
      "\n",
      "Read2 after filtering:\n",
      "total reads: 15957703\n",
      "total bases: 813371311\n",
      "Q20 bases: 803301599(98.762%)\n",
      "Q30 bases: 781597078(96.0935%)\n",
      "Q40 bases: 0(0%)\n",
      "\n",
      "Filtering result:\n",
      "reads passed filter: 31915406\n",
      "reads failed due to low quality: 576316\n",
      "reads failed due to too many N: 1802\n",
      "reads failed due to too short: 338066\n",
      "reads failed due to adapter dimer: 0\n",
      "reads with adapter trimmed: 365804\n",
      "bases trimmed due to adapters: 11104213\n",
      "\n",
      "Duplication rate: 36.3719%\n",
      "\n",
      "Insert size peak (evaluated by paired-end reads): 51\n",
      "\n",
      "JSON report: result/fqdata/SRR12544531/SRR12544531.fastp.json\n",
      "HTML report: result/fqdata/SRR12544531/SRR12544531.fastp.html\n",
      "\n",
      "/home/users/steorra/miniforge3/bin/fastp -i data/fqdata/SRR12544531/SRR12544531_1.fastq -o result/fqdata/SRR12544531/SRR12544531_clean_1.fastq -w 8 -j result/fqdata/SRR12544531/SRR12544531.fastp.json -h result/fqdata/SRR12544531/SRR12544531.fastp.html -I data/fqdata/SRR12544531/SRR12544531_2.fastq -O result/fqdata/SRR12544531/SRR12544531_clean_2.fastq --detect_adapter_for_pe \n",
      "fastp v1.1.0, time used: 141 seconds\n",
      "[{'sample': 'SRR12544419', 'clean1': 'result/fqdata/SRR12544419/SRR12544419_clean_1.fastq', 'clean2': 'result/fqdata/SRR12544419/SRR12544419_clean_2.fastq', 'json': 'result/fqdata/SRR12544419/SRR12544419.fastp.json', 'html': 'result/fqdata/SRR12544419/SRR12544419.fastp.html'}, {'sample': 'SRR12544421', 'clean1': 'result/fqdata/SRR12544421/SRR12544421_clean_1.fastq', 'clean2': 'result/fqdata/SRR12544421/SRR12544421_clean_2.fastq', 'json': 'result/fqdata/SRR12544421/SRR12544421.fastp.json', 'html': 'result/fqdata/SRR12544421/SRR12544421.fastp.html'}, {'sample': 'SRR12544529', 'clean1': 'result/fqdata/SRR12544529/SRR12544529_clean_1.fastq', 'clean2': 'result/fqdata/SRR12544529/SRR12544529_clean_2.fastq', 'json': 'result/fqdata/SRR12544529/SRR12544529.fastp.json', 'html': 'result/fqdata/SRR12544529/SRR12544529.fastp.html'}, {'sample': 'SRR12544531', 'clean1': 'result/fqdata/SRR12544531/SRR12544531_clean_1.fastq', 'clean2': 'result/fqdata/SRR12544531/SRR12544531_clean_2.fastq', 'json': 'result/fqdata/SRR12544531/SRR12544531.fastp.json', 'html': 'result/fqdata/SRR12544531/SRR12544531.fastp.html'}]\n"
     ]
    }
   ],
   "source": [
    "from operator import itemgetter\n",
    "\n",
    "qc_result = ov.alignment.fastp(\n",
    "    samples=list(map(itemgetter(\"srr\", \"fq1\", \"fq2\"), fq_data)),\n",
    "    output_dir = './result/fqdata')\n",
    "print(qc_result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10b4ccaa",
   "metadata": {},
   "source": [
    "## 4) Download reference genome and gene annotation (Ensembl)\n",
    "\n",
    "We download:\n",
    "- GRCh38 primary assembly FASTA\n",
    "- GRCh38 GTF annotation (release 115)\n",
    "\n",
    "Tip: keep these files cached locally to avoid repeated downloads.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e8ddfcba-20d1-4d79-99ac-e6f217730a96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[94m🔍 Downloading data to ./genomes/Homo_sapiens.GRCh38.dna.primary_assembly.fa.gz\u001b[0m\n",
      "\u001b[93m⚠️ File ./genomes/Homo_sapiens.GRCh38.dna.primary_assembly.fa.gz already exists\u001b[0m\n",
      "\u001b[94m🔍 Downloading data to ./genomes/Homo_sapiens.GRCh38.115.gtf.gz\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[92mDownloading\u001b[0m: 100%|\u001b[32m█████████▉\u001b[0m| 104M/104M [00:18<00:00, 5.64MB/s] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92m✅ Download completed\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'./genomes/Homo_sapiens.GRCh38.115.gtf.gz'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ov.datasets.download_data('ftp://ftp.ensembl.org/pub/release-115/fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna.primary_assembly.fa.gz',dir='./genomes')\n",
    "ov.datasets.download_data('ftp://ftp.ensembl.org/pub/release-115/gtf/homo_sapiens/Homo_sapiens.GRCh38.115.gtf.gz',dir='./genomes')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e60d17d",
   "metadata": {},
   "source": [
    "## 5) QC results (cached example structure)\n",
    "\n",
    "The following cell provides an **example `qc_result` structure** (paths to cleaned reads + reports).  \n",
    "This is useful for quickly continuing the tutorial without rerunning QC.\n",
    "\n",
    "If you re-run the QC step above on your machine, you can keep using the `qc_result` produced by `ov.alignment.fastp()`.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4725aacb-d7b5-4b08-9d6a-7f121e03d0ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "qc_result=[\n",
    "{'sample': 'SRR12544419', \n",
    " 'clean1': 'result/fqdata/SRR12544419/SRR12544419_clean_1.fastq', \n",
    " 'clean2': 'result/fqdata/SRR12544419/SRR12544419_clean_2.fastq', \n",
    " 'json': 'result/fqdata/SRR12544419/SRR12544419.fastp.json', \n",
    " 'html': 'result/fqdata/SRR12544419/SRR12544419.fastp.html'}, \n",
    "{'sample': 'SRR12544421', \n",
    " 'clean1': 'result/fqdata/SRR12544421/SRR12544421_clean_1.fastq', \n",
    " 'clean2': 'result/fqdata/SRR12544421/SRR12544421_clean_2.fastq', \n",
    " 'json': 'result/fqdata/SRR12544421/SRR12544421.fastp.json', \n",
    " 'html': 'result/fqdata/SRR12544421/SRR12544421.fastp.html'}, \n",
    "{'sample': 'SRR12544529', \n",
    " 'clean1': 'result/fqdata/SRR12544529/SRR12544529_clean_1.fastq', \n",
    " 'clean2': 'result/fqdata/SRR12544529/SRR12544529_clean_2.fastq', \n",
    " 'json': 'result/fqdata/SRR12544529/SRR12544529.fastp.json', \n",
    " 'html': 'result/fqdata/SRR12544529/SRR12544529.fastp.html'}, \n",
    "{'sample': 'SRR12544531', \n",
    " 'clean1': 'result/fqdata/SRR12544531/SRR12544531_clean_1.fastq', \n",
    " 'clean2': 'result/fqdata/SRR12544531/SRR12544531_clean_2.fastq', \n",
    " 'json': 'result/fqdata/SRR12544531/SRR12544531.fastp.json', \n",
    " 'html': 'result/fqdata/SRR12544531/SRR12544531.fastp.html'}\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6f607f9",
   "metadata": {},
   "source": [
    "## 6) Prepare STAR input list\n",
    "\n",
    "STAR expects a list of `(sample, read1, read2)` tuples.  \n",
    "We construct that list from `qc_result`.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "69ef4870-fba8-4374-9fe8-06e532bcf1ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('SRR12544419',\n",
       "  'result/fqdata/SRR12544419/SRR12544419_clean_1.fastq',\n",
       "  'result/fqdata/SRR12544419/SRR12544419_clean_2.fastq'),\n",
       " ('SRR12544421',\n",
       "  'result/fqdata/SRR12544421/SRR12544421_clean_1.fastq',\n",
       "  'result/fqdata/SRR12544421/SRR12544421_clean_2.fastq'),\n",
       " ('SRR12544529',\n",
       "  'result/fqdata/SRR12544529/SRR12544529_clean_1.fastq',\n",
       "  'result/fqdata/SRR12544529/SRR12544529_clean_2.fastq'),\n",
       " ('SRR12544531',\n",
       "  'result/fqdata/SRR12544531/SRR12544531_clean_1.fastq',\n",
       "  'result/fqdata/SRR12544531/SRR12544531_clean_2.fastq')]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from operator import itemgetter\n",
    "list(map(itemgetter(\"sample\", \"clean1\", \"clean2\"), qc_result))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62a660de",
   "metadata": {},
   "source": [
    "## 7) Align reads to the reference genome with STAR\n",
    "\n",
    "We align cleaned reads to GRCh38 using STAR.\n",
    "\n",
    "▲ **CRITICAL**\n",
    "- The **first run** will create the STAR genome index (genome initialization), which can take **~20 minutes** depending on hardware and I/O.\n",
    "- STAR is memory intensive; the comment in the code notes that **high RAM is required**.\n",
    "\n",
    "**Outputs**\n",
    "- Sorted BAM: `./result/alignment/<sample>/Aligned.sortedByCoord.out.bam` (path may vary by STAR settings)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7c32a7e-1098-4af0-9695-0dd5c8b64a08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> /home/groups/xiaojie/steorra/env/omicverse/bin/STAR --runMode genomeGenerate --genomeDir genomes --genomeFastaFiles genomes/Homo_sapiens.GRCh38.dna.primary_assembly.fa --runThreadN 8 --sjdbGTFfile genomes/Homo_sapiens.GRCh38.115.gtf --sjdbGTFfeatureExon exon --sjdbOverhang 100\n",
      "\t/home/groups/xiaojie/steorra/env/omicverse/bin/STAR-avx2 --runMode genomeGenerate --genomeDir genomes --genomeFastaFiles genomes/Homo_sapiens.GRCh38.dna.primary_assembly.fa --runThreadN 8 --sjdbGTFfile genomes/Homo_sapiens.GRCh38.115.gtf --sjdbGTFfeatureExon exon --sjdbOverhang 100\n",
      "\tSTAR version: 2.7.11b   compiled: 2025-07-24T03:06:21+0000 :/opt/conda/conda-bld/star_1753326220084/work/source\n",
      "Feb 06 17:41:00 ..... started STAR run\n",
      "Feb 06 17:41:00 ... starting to generate Genome files\n",
      "Feb 06 17:41:45 ..... processing annotations GTF\n"
     ]
    }
   ],
   "source": [
    "align_result = ov.alignment.STAR(\n",
    "    samples=list(map(itemgetter(\"sample\", \"clean1\", \"clean2\"), qc_result)),\n",
    "    genome_dir='genomes',\n",
    "    genome_fasta_files=\"genomes/Homo_sapiens.GRCh38.dna.primary_assembly.fa.gz\",\n",
    "    gtf='genomes/Homo_sapiens.GRCh38.115.gtf.gz',\n",
    "    sjdb_overhang=100,\n",
    "    output_dir = './result/alignment',\n",
    "    jobs=8 # 1 job require at least 48GB memory\n",
    ")\n",
    "print(align_result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28a8b792",
   "metadata": {},
   "source": [
    "## 8) Alignment results (cached example structure)\n",
    "\n",
    "The next cell provides an example `align_result` list (sample → BAM path).  \n",
    "This allows you to proceed to quantification even if you skip re-running STAR in this tutorial environment.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7103f918-dbdb-444d-9eb7-fb0f92419bc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "align_result=[\n",
    "    {'sample':'SRR12544419',\n",
    "    'bam':'result/alignment/SRR12544419/Aligned.sortedByCoord.out.bam'},\n",
    "    {'sample':'SRR12544421',\n",
    "    'bam':'result/alignment/SRR12544421/Aligned.sortedByCoord.out.bam'},\n",
    "    {'sample':'SRR12544529',\n",
    "    'bam':'result/alignment/SRR12544529/Aligned.sortedByCoord.out.bam'},\n",
    "    {'sample':'SRR12544531',\n",
    "    'bam':'result/alignment/SRR12544531/Aligned.sortedByCoord.out.bam'},\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fc3b9a5",
   "metadata": {},
   "source": [
    "## 9) Gene-level quantification with featureCounts\n",
    "\n",
    "We quantify reads to genes using `featureCounts` (via OmicVerse wrapper), generating a raw count matrix.\n",
    "\n",
    "**Output**\n",
    "- `count_result`: a pandas DataFrame with **genes × samples** (raw counts), optionally mapped to gene symbols when `gene_mapping=True`.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f41927a9-b006-4082-aa28-bfc774349e7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 SRR12544419  SRR12544421  SRR12544529  SRR12544531\n",
      "gene_id                                                            \n",
      "PRDM16                     2            0            0            0\n",
      "ENSG00000284616            0            0            0            0\n",
      "ENSG00000260972            0            0            0            0\n",
      "EEF1DP6                    0            0            0            0\n",
      "LINC01646                  0            0            0            0\n",
      "...                      ...          ...          ...          ...\n",
      "ENSG00000307722            0            0            0            0\n",
      "ENSG00000310401            0            0            0            0\n",
      "ENSG00000302039            0            2           24            0\n",
      "ENSG00000309831            2            6            0            1\n",
      "ENSG00000309258            0            0            0            0\n",
      "\n",
      "[78899 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "count_result = ov.alignment.featureCount(\n",
    "    bam_items=list(map(itemgetter(\"sample\", \"bam\"), align_result)),\n",
    "    gtf='genomes/Homo_sapiens.GRCh38.115.gtf.gz',\n",
    "    output_dir = './result/count',\n",
    "    gene_mapping=True,\n",
    "    gene_name_field=\"gene_name\",\n",
    "    jobs=8\n",
    ")\n",
    "print(count_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "034a1d8d-2049-4b93-8ff4-0f08d88f1f8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>SRR12544419</th>\n",
       "      <th>SRR12544421</th>\n",
       "      <th>SRR12544529</th>\n",
       "      <th>SRR12544531</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gene_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PRDM16</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000284616</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000260972</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EEF1DP6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LINC01646</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 SRR12544419  SRR12544421  SRR12544529  SRR12544531\n",
       "gene_id                                                            \n",
       "PRDM16                     2            0            0            0\n",
       "ENSG00000284616            0            0            0            0\n",
       "ENSG00000260972            0            0            0            0\n",
       "EEF1DP6                    0            0            0            0\n",
       "LINC01646                  0            0            0            0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_result.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b91c9d79",
   "metadata": {},
   "source": [
    "## 10) Create a DE object (pyDEG)\n",
    "\n",
    "We build an OmicVerse `pyDEG` object from the count matrix for downstream DE analysis and plotting.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8e673c3e-7c86-4fed-b782-7a472e5076f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = count_result\n",
    "dds=ov.bulk.pyDEG(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ea0a5b5",
   "metadata": {},
   "source": [
    "## 11) Basic cleanup\n",
    "\n",
    "We remove duplicated gene entries (if any).  \n",
    "Downstream steps assume the index uniquely identifies genes.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cfdd3af9-cdcd-456f-b4c5-3335d560f357",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... drop_duplicates_index success\n"
     ]
    }
   ],
   "source": [
    "dds.drop_duplicates_index()\n",
    "print('... drop_duplicates_index success')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79cf0c15",
   "metadata": {},
   "source": [
    "## 12) Differential expression (DE)\n",
    "\n",
    "Define treatment/control sample lists and run DE. OmicVerse supports multiple DE methods, including:\n",
    "- `ttest`\n",
    "- `edgepy`\n",
    "- `limma`\n",
    "- `DEseq2`\n",
    "\n",
    "▲ **CRITICAL** (DESeq2)\n",
    "If you use `DEseq2`, ensure the input matrix is **raw integer counts**, and avoid applying external normalization beforehand (DESeq2 estimates size factors internally).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ba57f47e-d311-4a0c-a3c4-57e2aae0933f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚙️ You are using DEseq2 method for differential expression analysis.\n",
      "⏰ Start to create DeseqDataSet...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting size factors...\n",
      "... done in 0.01 seconds.\n",
      "\n",
      "Fitting dispersions...\n",
      "... done in 1.96 seconds.\n",
      "\n",
      "Fitting dispersion trend curve...\n",
      "... done in 0.50 seconds.\n",
      "\n",
      "Fitting MAP dispersions...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logres_prior=1.028669461109852, sigma_prior=0.25\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "... done in 2.49 seconds.\n",
      "\n",
      "Fitting LFCs...\n",
      "... done in 1.89 seconds.\n",
      "\n",
      "Calculating cook's distance...\n",
      "... done in 0.01 seconds.\n",
      "\n",
      "Replacing 0 outlier genes.\n",
      "\n",
      "Running Wald tests...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log2 fold change & Wald test p-value: condition Treatment vs Control\n",
      "                      baseMean  log2FoldChange     lfcSE      stat  \\\n",
      "gene_id                                                              \n",
      "S100A9           551268.969994       -1.996395  0.567251 -3.519423   \n",
      "S100A8           470875.870644       -1.860539  0.580843 -3.203169   \n",
      "MT-RNR2          379360.147119       -1.073334  0.462907 -2.318684   \n",
      "HBB              255919.879002       -3.412494  0.550731 -6.196301   \n",
      "B2M              224562.599264       -0.016376  0.477808 -0.034274   \n",
      "...                        ...             ...       ...       ...   \n",
      "RN7SKP39              0.000000             NaN       NaN       NaN   \n",
      "RNU4ATAC6P            0.000000             NaN       NaN       NaN   \n",
      "TPRKBP1               0.000000             NaN       NaN       NaN   \n",
      "RNU7-107P             0.000000             NaN       NaN       NaN   \n",
      "ENSG00000271220       0.000000             NaN       NaN       NaN   \n",
      "\n",
      "                       pvalue      padj  \n",
      "gene_id                                  \n",
      "S100A9           4.324866e-04  0.036766  \n",
      "S100A8           1.359241e-03  0.074803  \n",
      "MT-RNR2          2.041219e-02  0.334090  \n",
      "HBB              5.780550e-10  0.000002  \n",
      "B2M              9.726589e-01  0.995301  \n",
      "...                       ...       ...  \n",
      "RN7SKP39                  NaN       NaN  \n",
      "RNU4ATAC6P                NaN       NaN  \n",
      "TPRKBP1                   NaN       NaN  \n",
      "RNU7-107P                 NaN       NaN  \n",
      "ENSG00000271220           NaN       NaN  \n",
      "\n",
      "[78899 rows x 6 columns]\n",
      "✅ Differential expression analysis completed.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "... done in 88.55 seconds.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "disease_groups = ['SRR12544419','SRR12544421']\n",
    "healthy_groups = ['SRR12544529','SRR12544531']\n",
    "result=dds.deg_analysis(disease_groups,healthy_groups,method='DEseq2')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c178dde7",
   "metadata": {},
   "source": [
    "## 13) Filter low-expression genes\n",
    "\n",
    "A simple filter can reduce noise and improve interpretability.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8a4448e6-5dd0-40e1-a448-25e59bf2b970",
   "metadata": {},
   "outputs": [],
   "source": [
    "dds.result=dds.result.loc[dds.result['log2(BaseMean)']>1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2b2ad1d",
   "metadata": {},
   "source": [
    "## 14) Set thresholds and inspect top DE genes\n",
    "\n",
    "We set fold-change / p-value thresholds and extract the **Top 10** significant DEGs for visualization.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c59f0a7a-4747-4945-9590-6deb5589bcf2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... Fold change threshold: 1\n",
      "Top10 significant DEGs: ['RPH3A', 'MYOM2', 'HBA2', 'EGR2', 'HBB', 'ASPM', 'HLA-V|ENSG00000290710', 'TOP2A', 'ALAS2', 'ENSG00000287255']\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# fc_treshold = -1 means automatically calculate\n",
    "dds.foldchange_set(fc_threshold=1,pval_threshold=0.05,logp_max=20)\n",
    "res = dds.result.copy()\n",
    "sig = res[(res[\"qvalue\"] < 0.05) & (res[\"log2FC\"].abs() > 0.5)&(res[\"log2(BaseMean)\"]<20)].sort_values([\"qvalue\",\"log2FC\"], ascending=[True, False])\n",
    "\n",
    "gene_list = sig[[\"log2FC\",\"qvalue\"]].head(10).index.to_list() # Top10 significant DEGs\n",
    "print(\"Top10 significant DEGs:\",gene_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4743687e",
   "metadata": {},
   "source": [
    "## 15) Visualize DE results\n",
    "\n",
    "We generate:\n",
    "- Volcano plot summarizing DE results\n",
    "- Boxplots for selected genes (Top hits)\n",
    "\n",
    "Tip: `dds.plot_boxplot()` is helpful for sanity-checking individual gene behavior across groups.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "beb5d7d3-966b-4653-b24b-765d052f149b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[95m\u001b[1m🌋 Volcano Plot Analysis:\u001b[0m\n",
      "   \u001b[96mTotal genes: \u001b[1m25211\u001b[0m\n",
      "   \u001b[92m↗️  Upregulated genes: \u001b[1m88\u001b[0m\n",
      "   \u001b[94m↘️  Downregulated genes: \u001b[1m123\u001b[0m\n",
      "   \u001b[96m➡️  Non-significant genes: \u001b[1m25000\u001b[0m\n",
      "   \u001b[93m🎯 Total significant genes: \u001b[1m211\u001b[0m\n",
      "   \u001b[94mlog2FC range: \u001b[1m-14.16 to 9.33\u001b[0m\n",
      "   \u001b[94mqvalue range: \u001b[1m9.31e-17 to 1.00e+00\u001b[0m\n",
      "\n",
      "\u001b[95m\u001b[1m⚙️  Current Function Parameters:\u001b[0m\n",
      "   \u001b[94mData columns: pval_name='qvalue', fc_name='log2FC'\u001b[0m\n",
      "   \u001b[94mThresholds: pval_threshold=\u001b[1m0.05\u001b[0m\u001b[94m, fc_max=\u001b[1m1\u001b[0m\u001b[94m, fc_min=\u001b[1m-1\u001b[0m\n",
      "   \u001b[94mPlot size: figsize=\u001b[1m(5, 5)\u001b[0m\n",
      "   \u001b[94mGene labels: plot_genes_num=\u001b[1m8\u001b[0m\u001b[94m, plot_genes_fontsize=\u001b[1m12\u001b[0m\n",
      "   \u001b[94mCustom genes: \u001b[1mNone\u001b[0m\u001b[94m (auto-select top genes)\u001b[0m\n",
      "\n",
      "\u001b[95m\u001b[1m💡 Parameter Optimization Suggestions:\u001b[0m\n",
      "   \u001b[93m▶ Wide fold change range detected:\u001b[0m\n",
      "     \u001b[96mCurrent: fc_max=\u001b[1m1\u001b[0m\u001b[96m, fc_min=\u001b[1m-1\u001b[0m\n",
      "     \u001b[92mSuggested: \u001b[1mfc_max=2.6, fc_min=-2.8\u001b[0m\n",
      "\n",
      "   \u001b[1m📋 Copy-paste ready function call:\u001b[0m\n",
      "   \u001b[92mov.pl.volcano(result, fc_max=2.6, fc_min=-2.8)\u001b[0m\n",
      "\u001b[96m────────────────────────────────────────────────────────────\u001b[0m\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxQAAANFCAYAAADiUbrDAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAABibAAAYmwFJdYOUAAEAAElEQVR4nOzdd3hUZdrH8e+U9JBAEkJL6L0KQTosxYaCIAqLDbBhF8uu7FpWXdu7trXiWrBgQ0XEBkgvUqX3ngCBUJKQXiYzc94/hoyJ6UMmBX6f68q1M+c85zn3sEjOPU+5TYZhGIiIiIiIiHjAXN0BiIiIiIhI7aWEQkREREREPKaEQkREREREPKaEQkREREREPKaEQkREREREPKaEQkREREREPKaEQkREREREPKaEQkREREREPKaEQkREREREPKaEQkREREREPKaEQkREREREPKaEQkREREREPKaEQkREREREPKaEQkREREREPKaEQkREREREPKaEQkSkCk2aNAmTyVTij8ViITAwkKioKAYNGsRzzz1HbGxsmf3GxcWV2m95fsrDMAwWLFjA/fffT58+fahfvz6+vr6EhITQqlUrLr/8cl555ZVyxeyJv/3tb+54g4ODSU9P98p9vOnpp592f4Y5c+ZUdzhuy5Ytc8f14IMPVnc4IlKLKKEQEalBnE4n2dnZHDt2jJUrV/Lkk0/Spk0bpk6dSm5ubrXG9uOPP9K+fXsuv/xy3n77bdatW0diYiJ5eXmkp6dz6NAhFixYwN///nfatm3LnXfeSWpqaqXd3+Fw8MUXX7jfZ2ZmFnovIiLVw1rdAYiIXKjuv/9+hg4dWuiYw+EgMzOTY8eOsXr1aubOnYvD4eCll15i9+7dfP/991gsllL7rV+/Pu+//36lxel0OpkyZQpvv/22+1hERARXXnklMTEx1K9fn7y8PI4ePcqCBQv47bffsNvtvP/++6xfv55ff/2VyMjIc47j119/5cSJEwA0aNCAkydP8sEHH3DXXXedc98iIuI5JRQiItWkR48ejB49utQ2mzdv5pprruHw4cP89NNPPPzww7zxxhulXhMYGFhmvxXxyCOPuJMJf39/nn32We6//378/PyKtH388cfZsmULt99+Oxs3bnS//vHHH885jk8++QSARo0aMWnSJF588UU2bdrExo0biYmJOef+L3SDBw/GMIzqDkNEaiFNeRIRqcG6d+/OwoULCQkJAeCtt95ix44dVXb/uXPn8vrrrwMQFBTE4sWL+dvf/lZsMpHvoosuYunSpXTp0gWAn376ifnz559THGfOnHEnJQMHDuTaa691n/vggw/OqW8RETk3SihERGq4Nm3a8NxzzwGuRdHPPPNMldzX6XTyyCOPuN+/88479OvXr1zX1qlTp9AUqenTp59TLF9//bV7DcnIkSOJiYmhVatWAHz55ZdkZmaeU/8iIuI5JRQiIrXA7bffTr169QD44YcfquQBev78+ezZsweAmJgYJk6cWKHrBw0aRLdu3ejXrx8XX3zxOcWSP93Jx8eHESNGAHDDDTcAkJ6ezsyZM0u9vuAuWP/73/8AWLhwIddeey1RUVH4+fnRuHFjrrvuOhYtWlRmPGfOnOHVV19l+PDhREdHExgYSEBAAE2aNGH48OG8++67FV5Ef9FFF7ljLGuXrIMHD7rb3n777YXOpaWl8dJLLzFw4EDCwsLw9fUlMjKSvn378vTTT7vXofxZeXZ5WrlyJRMmTKBVq1YEBAQQHBxMy5YtufHGGytlWpuI1FKGiIhUmYkTJxqAARgff/xxha4dN26c+9p58+YVOhcbG+s+16xZs0qJ9aabbnL3+eabb3rUh91uP+c49uzZ445j5MiR7uN79+51H+/Vq1epfRT883n33XeN+++/3/2+uJ+HHnqoxL6++uorIyQkpNTrAaN9+/bG0aNHi1z/1FNPudt8//337uOvvfaa+/hzzz1X6ud5+umn3W2XL1/uPr59+3ajSZMmpcYVGBhofPvtt0X6XLp0qbvNlClTipyfMmVKmZ956NChRnp6eqmxi8j5RyMUIiK1RI8ePdyvV61a5fX7Ffym/qqrrvKoj7J2pCqPTz/91P365ptvdr9u27YtvXv3BmD9+vVs27atXP298cYbvPXWW4SGhvLwww/z5Zdf8sEHH3D55Ze72/z3v/9l7ty5Ra5dvHgxN9xwA2lpafj5+XHbbbfx4YcfMmvWLKZPn85tt91GQEAAAHv27OG+++4r9+e88cYbsVpde6WUtR1u/vkWLVowcOBAAHJzcxk9ejTHjh3DbDZz/fXX88EHH/Dtt9/y1ltvuT9fVlYWN9xwAwcPHix3bNOnT3dvBtCyZUv+/e9/89VXX/HZZ5/xz3/+k4iICACWLFnCAw88UO5+ReQ8Ud0ZjYjIheRcRihmzpzpvvb2228vdK6yRyhOnTrl7i8sLOyc+/OUw+EwoqKiDMAIDQ01srOzC51/55133HHee++9JfZT8M8HMNq1a2fEx8cXaVdw5GL06NFFzsfExBiAYbFYjBUrVhR7r99//92wWq0GYFitViMlJaXQ+ZJGKAzDMK6++mr3uU2bNhXb/5o1a9xt/vWvf7mPf//99+7jL774YrHXFrz3I488UuhcaSMU3bp1MwCjcePGRmJiYpF+jx07ZjRu3Nj9Z/Pnzywi5zeNUIiI1BL5Oz0BJCUlldju8OHDFa6SHRcXV6iP+Ph49+sGDRpU+mcpr8WLF7tjue666/D39y90fvz48fj6+gKub+2zs7PL1e+7775LkyZNihzPr2INsG7dukLnYmNj2bhxIwDjxo1zjwz8Wc+ePbn00ksBsNvtFRoJmDRpkvt1SaMUn3/+ufv1hAkT3K/37t3rfp2/zuTPpk6dSosWLejbt2+hv09lye+7b9++hIeHFznfuHFj7r//fnfhw4SEhHL3LSK1nxIKEZFawsfHx/06PT3dq/cquOg7LCys1LaJiYnlSlrmzJlT4ThKmu5UMLYrr7wSgJSUFL755psy+wwPD2fIkCHFngsLC3MnGn9O2po3b058fDzLly/n2WefLfUeLVq0cL/OysoqM6Z8I0aMcE8f+uqrr3A6nYXO5+Xl8fXXXwPQv39/905XgPs6gNdeew2bzVak/4CAAA4dOsTq1av517/+Ve648vteuHChO6n6s3/84x/s3r2bX375hfbt25e7bxGp/VTYTkSklkhLS3O/rlOnTontPKmU/edK1gVHAop7MK0K6enpfP/99wA0bdqUQYMGFdtuwoQJ7mTl/fffL3M3qg4dOpR6PjQ0lPj4ePLy8godN5lMNGnSpNiRDYCMjAy2b9/OmjVrCq0/cTgcpd6vIB8fH2644QbefPNNjh8/zrJlywpVU58/fz6JiYkART7n1Vdfjb+/Pzk5OXz88cf8+uuvjB49miuuuIIhQ4YQHBxc7jj+7K9//SuvvvoqaWlp9OrVi6FDh3LVVVdx2WWX0bFjR4/7FZHzgxIKEZFaomBCUbdu3RLbVUal7ILTnMqavhISEuJ+8P+zmTNnur9Rr6hvvvnG/e1+ZGQk//nPf4ptZ7PZMJlMGIbB6tWr2bVrV6kPuaGhoaXeN39htFFK1ej8yuVbt25l//79HDx4kGPHjhV7TWn9FGfSpEm8+eabgGvaU8GE4rPPPgNcCd+4ceMKXVe/fn0+/vhjJkyYQF5eHsePH2fatGlMmzYNHx8f+vfvz8iRIxk3bhxRUVEViumZZ55h1apVrF27FqfTyaJFi9xJU1RUFFdeeSVjxozhkksuqZSF+CJSuyihEBGpJfJrQgC0a9fOq/eKjo4mJCSEtLQ0jh07xpkzZ9x1MP7M19e3xARmy5YtHsdQcLrThg0b2LBhQ7mue//9993VvYuTnzB4IisriwceeICPP/64yHQkcE0pGjRoEGlpaaxZs8aje3Tv3p2uXbuybds2vvvuO6ZNm4afnx+pqan89NNPAIwaNarYxGj8+PF06tSJF198kR9++MGdkOXl5bFs2TKWLVvG3//+d2699VZef/11goKCyhVTUFAQK1eu5J133uGDDz5g586d7nPx8fG8//77vP/++7Rs2ZL//e9/7jUkInJh0BoKEZFaouAD9bkWiiuP/HUGhmGUq9hbZTp06BC//fabR9d+9tln5OTkVHJErsrhI0aMYPr06TidTvz9/bniiit47LHH+OKLL9iyZQtpaWnMnz+fmJiYc7pX/uLs1NRUfvnlFwBmz57t/lwFF2P/WZcuXfjyyy9JTEzk559/ZsqUKXTq1KnQ5/jwww8ZP358hWKyWq1MmTKFHTt2sG/fPt544w1GjhxZaHH3oUOHGDFihMfJlIjUTkooRERqgcTERPcDdkhICH379vX6PQs+cOZXl64qn376qXuq0HPPPYdhGGX+5I/aJCcn891331V6TDNnzmTp0qUA9OrVi7i4OObNm8fzzz/PDTfcQLdu3dyjHykpKed0r4I1KfLXh/zwww8ANGzYsFDNjJIEBARw1VVX8frrr7Njxw4OHz7MU0895Z6S9PPPP5d71OfP2rRpwwMPPMCPP/5IYmIi8+bNcydRNpuN559/3qN+RaR2UkIhIlILfPDBB+Tm5gIwduxYd/E0b7r22mtp3rw54CpYNnv2bK/fE1wjIjNmzHC/v/7668t1XcFFyhVdlF4eP//8s/v1Sy+9VOp2ugWnelV0DQW41ozk7171yy+/kJGRwcKFCwG44YYbil2n8OOPP/LCCy/wxBNPFNtn06ZNefrpp3nwwQfdx7Zu3VpmLHFxcbz//vs8/PDDrF27tsh5Hx8frrjiChYuXOjeiaw8/YrI+UMJhYhIDbd3716ee+45wPXwNnXq1Cq5r4+Pj3txMLim4VRkKsvWrVv55JNPKnzfFStWuOti9O7dm5YtW5bruptvvhmz2ezuo2BdhspQcBvZ0nZMWrBgATt27HC///NuUeWVP+0pOTmZp556yr0eoqRdrJ5//nkef/xxXnjhBY4fP15ivwXrSJRn56fdu3dz55138t///rfU/z/r1KnjrglyLjtKiUjto4RCRKQGW716NZdeeqn7YfLvf/87bdq0qbL7jxw5kn/+85+AaxvXoUOH8uyzz5KcnFziNSdPnuTBBx+kV69eHD582H28vA+ZBR9ayzs6Aa7dhoYNG+Z+/8EHH5T72vIoWFuipClgy5Yt46abbip0zNP1HAVrUuQndt26daNr167Fts/f9ckwDCZNmuQe0Sro9OnT7tGb/J2fyjJ06FB3HB9++KF7pOTPXn/9dXf9ksGDB5fZr4icP7TLk4hINdm0aVOR7V/z8vJIS0sjNjaW5cuXF1qYPGbMmDILqoFrJyJPisi1b9++2IJkL7zwAoGBgTz11FPk5OTwr3/9i+eee46rrrqKmJgYmjZtimEYxMfHs3TpUpYuXVqo9kLz5s157733uOSSS8qMITMzk1mzZgFgNpuLbI1alokTJ7ofeD/99FNeeOEF97fm52rSpEm89957gOvB+tChQ1xzzTXUr1+f48ePM3fuXBYvXlxkilNqaqpH9ytYk8JutwMlj04A3HXXXbzzzjvExsaycOFC2rRpwy233ELr1q2x2+3s3buXDz/80D3Scu+995Zr+1g/Pz+eeeYZ7r33XhwOB5dffjnXXXcdAwcOJDIykoSEBObOnev+c69bt26VjaKJSA1hiIhIlZk4caIBVOgnICDA+Ne//mU4HI4S+42Nja1wv3/+eeqpp0qNfd26dcbAgQPL3V/z5s2NV1991cjIyCj3n8+MGTPc1w8dOrTc1+XLysoyQkJC3H3MnDmzyJ/PqFGjSu2jW7du7rZ/9vzzz5f5uXv27Gl89tln7vd/+9vfCvXx1FNPuc99//33pcayadMmd1ur1WqcOHGi1Pa7du0ymjVrVmaMt912m2G32wtdu3TpUvf5KVOmFOn70UcfNUwmU6n9NmnSxFi1alWpMYrI+UdTnkREahCz2UxISAitWrVi1KhRvPHGG8TGxvLMM8+41wdUl169erFixQo2b97Mk08+yZAhQ2jUqBF+fn74+/sTHR1N3759efTRR1mwYAEHDhzg4YcfLnetAyhce6Ii053yBQQEMHbsWPf7yl6c/dhjj7F06VLGjBlDo0aNsFqtBAYG0qJFC0aPHs0333zDmjVrGDduHGFhYYCrQF/+CENFdenSxf3nd/nll5e6EBxcVcB37tzJG2+8wbBhw2jQoAG+vr4EBwfTtm1bJk+ezKpVq/jwww8rXIDuP//5D2vXruWOO+6gY8eOBAcHY7VaadiwIYMHD+a1115jz5499OvXz6PPKiK1l8kwPNh+QkRERLxu/vz5DB8+HIBZs2Zx7bXXVnNEIiJFaYRCRESkhspfWB4ZGcnVV19dzdGIiBRPCYWIiEgN9PXXX/P9998DcOedd7prPIiI1DSa8iQiIlJDXHrppWRmZpKYmMj+/fsBiIiIYM+ePYXqR4iI1CTaNlZERKSG8Pf3Z9GiRe73FouF9957T8mEiNRomvIkIiJSQwwbNoyIiAiCgoLo168f8+bNY8yYMdUdlohIqTTlSUREREREPKYRChERERER8ZgSChERERER8ZgSChERERER8ZgSChERERER8ZgSChERERER8ZgSilqkZ8+e9OzZs7rDEBERERFxU2G7WuTEiRPVHYKIiIiISCEaoRAREREREY8poRAREREREY8poRAREREREY8poRAREREREY9pUbaIiIhILXL84YdxJCaW3sjHB0twMNbGjQm8+GKCBg/GZC78PfKpF14gd8+e4q83mTBZrZgCA/Fp2BC/Tp0IHjYMS506RZoenTABgJDRowkdM6bM+FNnzyZtzhwAomfMKHLecDrJXLGCzJUryTt6FJxOLOHhBHTvTvBll2ENCyvzHvn9JDz0EI4zZwBo8PTT+LZsWa5rpWKUUIiIiIjUQiYfH0yBgUVPGAZGdjaOM2dwnDlD7s6dZK5aReTUqZh8fYu2t1gwBwcXPW6340xLIzc1ldy9e8lYsICIRx7Br1Wryv8wZzlzc0l8/XVyd+50HTCZMPn6Yk9IID0hgYxly4i47z78O3cus6+crVvdyQRAxpIlhCmh8AolFCIiIiK1UEDv3oRPnlzsOcMwyDt6lNRvvyVn61Zs+/eTMnMm9c6OJhTk16YNkY89Vnw/eXlkrllDyqef4szIIOmtt2j44ouYAwIq9bPkS/n8c1cyYbFQ969/JWjIEMx+ftji4znz0UfYDhwg8e23afTii1jq1Su1r8wVKwDw796dnM2byVq7lrrXX485KMgrsV/ItIZCRERE5DxjMpnwbdqUiClT8ImOBlwP2M7MzIr14+ND8KBB1L3pJgAcyclkrVlT6fEC2E+fdicBoWPGUOeKKzD7+QHgGxVFxEMPYQ4KwsjKImPJklL7cqSlkb1lCwAho0ZhDg3FsNnI/O03r8R+oVNCISIiInKeMlmtBA0ZAoBhs2E7fNijfoIGDgSLBaDkdRfnKGfHDjAMsFgIvuSSIuctderg27YtALbY2FL7yvztN3A4sISH49uiBYF9+gCUmYiIZzTlSUREROQ8Zq1f3/3akZrqUR8mqxVznTo4U1JwZmRUVmiFBA8ZQsDFF+NITCx5SpXD4frfs8lNSTKXLwcgsFcvTCYTQQMHkvHrr9gTEsjZtQv/jh0rM/QLnkYoRERERM5j9pMn3a8tdet61Idhs7kTCbOHfZSHJTgY3+bNiz2Xl5BA7u7dAKUuys7dvx97QgIAgQMHAuDbtCk+zZoBGqXwBiUUIiIiIucpZ24uGYsXA2AODvZ4h6aM5cvBbgfAv0uXSouvLIbdjv3UKdJ++YVTzz2HkZeHb+vWBP/lLyVekz864dOiBb5RUe7jQYMGAZC9cSOOlBSvxn2h0ZQnERERkfOMYbeTu28fqbNmub+tDx07tvhtY0thT04m67ffSD1bN8InKorAXr2KbZs2Z467vkRlOXb33Ri5ue73Ab16EXbbbSV+DmdODlnr1wNn130UENSvHykzZ0JeHhnLlxM6alSlxnohU0IhIiIiUgtlr1vHse3bixw38vIwsrNdC5xx7dQUOnYswWcXZ/9Z7v79HLv//qL95OZi5OS431sbNiRiyhRMJaxfMPn5YfL3LzNuIyenUJJQEkd6OkZeHqbAQIysLACyN2wg2eGg3q23FltkL2vdOlfMPj4E9e1b6Jw5KIiAHj3IXreOzKVLCRk5skixP/GMEgoRERGRWsjIy8MoZZG1X8eO+HfpQmDfvqVXl3Y4cBbTj8nPD0tEBD7R0QRcdBGB/fq5t3EtTp3hwytcKbs05oAAmrz/PmZfXxwZGWQuW0banDlkb9xI3okTNHz2WUzWwo+y+dvOBvToUWy9ieC//IXsdetwJCeTs2ULAT16lBmHlE0JhYiIiEgtFDhgQKHCdk6bjdwdO0idNYu8+HjsJ07gc+WVpScTgF/79iUWtqtOJqsV09nXluBgQkaMwBoZSdLbb2M/dozMFSsIHjrU3T7v+HFs+/cDrtGbo+vWldp/xpIlSigqicZ5RERERM4DZl9fAnr0IPLJJ/Fp1gxHcjKJ//0vWRs3VndolSawVy8sERGAa6pWQfmjE+WVs3079lOnKi22C5kSigKmTZuGyWRi9OjR5WrvcDj46KOPuOKKK2jcuDG+vr7Ur1+fSy+9lBkzZuB0Or0bsIiIiMifmAMCiJgyBXNwMDgcJL/7LrajR6s7rDLZExPJ2bGD3L17S22Xv/WtMz3dfcxwOMhctQqAoKFDafL++yX+NHzxRTCZwDDIWLrUa5/nQqKE4qzdu3czderUcrePjY0lJiaG2267jV9//ZWEhATy8vJITExk0aJFTJw4kcGDB5OibclERESkilkjIqh3222Aq4ZE0jvvYNhs1RxV6VK/+YbTL71E8kcfldjGcDiwnzgB4B6pAMjessW9DiSof3/M/v4l/vg0aYLf2YrbmStWYOTlefFTXRiUUOBKDoYPH05GOSs/JiQkMHjwYLZu3QpA9+7dee2115g5cyb//ve/adSoEQArV67kuuuu81rcIiIicn4x7HbsycnYT53CnpyMcbb2gycCY2LctRfsx4+T8u23lRWmV/hfdBEA9oQEsn7/vdg26fPnuwvsFdy+Nr/2hKV+ffzatCnzXoEDBgCuUY6S7iXld8Evyl65ciXjxo3jxNlstzweeOABjhw5AsB9993Hm2++iclkcp+/5557GDhwILt372bx4sX89NNPjBw5stJjFxERkfODIy0N26FD5B05gjMnB8PpxGQ2u75Rb9oU35YtsYSEVLjfujfeSM6OHTiSk8lYsICAmBj827f3wic4d4G9e5M+fz55cXEkf/ABzvR0Avv3x+znhyMtjfR580ifO9fVtk8f/Dt2BMCRkkLOtm3u4+W6V69epHz2GYbNRsbixQT16+edD3WBuGBHKGw2Gy+++CJDhw6tUDKxbt06Zs2aBcAVV1zBW2+9VSiZAAgPD+f11193v//6668rJWYRERE5/+Tu30/GwoVkb9pE7oED5O7fT15sLLn795N74ADZmzaRsXBhkUXI5WEOCCDs9tvdawaSP/wQZ4HaEjWJyWKh/sMP49O0KUZODmc++YRjkydz7O67OX7//aT/8gsYBoF9+hB2xx3u6zJXroSz61b/XHuiJOaAAAJ69gTAtn9/rVhjUpNdkCMUixYt4q677uLgwYMAWK1Wpk6dyvPPP1/mtZ9//jkAJpOJ//73vyW2GzZsGHfddRd16tSh49kMWkRERKSg3P37yd6yhbyjR8HpxFKvHj7R0ZgsFgyHA2daGo7kZBwpKRgebvbi37kzQUOGkLlkCY5Tp0j56ivCbrmlkj9J5bDUrUuDp58mY9kystasIS8+HmdODubQUPxatyZo8GACunYtdE3+7k4+0dH4REWV+15BAwaQtXo1ABmLFxM2aVKlfY4LjckwzpZRvIBMmjSJTz/9FICOHTvy8ccfExkZSYsWLQAYNWoUc0oouNKkSROOHz9Ov379WHV2N4GqEnX2P5L4+Pgqva+IiIhUPkdaGhkLF2I7fBiTjw/Wxo2LzHoAMJxO7AkJGHl5+DZrRvCll3o0/UnEWy7YKU/h4eG89tprbN68mV4FFvWU5tSpUxw/fhyAvgWG1AzDYNeuXfz666+sWrWK9ALbmImIiIgUx3boEI7UVHA6S0wmAExmM9bGjcHpxJGaiu3QoSqOVKR0F2RCcd9993HkyBEeeughfH19y33djh073K9btmyJ0+nkzTffpEWLFnTq1IkrrriCAQMGUL9+fW677TZOnz7tjfBFRESkljPsdvKOHMGRkoKlXr0Sk4l8JpMJS716OFJSyDty5Jx2fxKpbBdkQtGzZ08CAwMrfF3+zk4AderU4aqrrmLKlCkcPny4ULvc3Fw++ugjevXq5V6nISIiIpLPkZaGMycHZ3Y25nJOXzKHhODMzsaZk4MjLc3LEYqU3wWZUHiq4FSmZ555hvnz59O0aVO++OILkpKSyMzMZOnSpfQ7u/VYXFwco0aNIjs7u9z3iIqKKvEnISGh0j+TiIh415o1azCZTIV+1qxZU91hlVttj7/GstvdW8OaLJZyXWKyWDCZza7F2RqhkBpECUUFFCx8d/DgQVq0aMH69eu54YYbCAsLIzAwkMGDB7N06VKGDRsGwM6dO3nnnXeqK2QRERGpiaxWd3JgOBzlusRwONxJCNYLcqNOqaGUUFTAnzfEevfdd2nQoEGRdr6+vnzwwQdYzn7j8P7775f7HvHx8SX+5FfgFhERkdrNEhKC2d8fc0AAznJOX3KmpWEOCMDs769dnqRGUUJRAcHBwe7X9evX5/LLLy+xbYsWLejduzcA+/fv5+TJk16PT0RERGoHk9WKT9OmWOrWxXHmTJEvLf/McDpxnDmDpW5dfJo2xaQRCqlBlFBUQEiBbwPal6NsfYcOHdyvVTtCRERECvJt2RJLaCiYzdiPHy8xqcivQ4HZjCU0FN+WLas4UpHSKb2tgJYF/gMuTz1APz8/92unh9UtRURE5PxkCQnBv2tXDKeTvKNHyYuNxVKvHuaQkMKVss+cAbMZn+ho/Lt21XQnqXGUUFRAly5d3K/j4uLKbF9wmlOTJk28EZKIiIjUYn5t2gCu4nWO1FRXnYkTJ9wLts0BAVjCwrCEhuLftau7vUhNooSiAurVq0e3bt3YunUr8fHxbNu2ja5duxbb1ul0snbtWgAiIyNp2LBhVYYqIiIitYRfmzZYGzTAdugQeUeO4MzJce/mZPb3x6dpU9f0KI1MSA2lhKKCbrzxRrZu3QrAs88+y7fffltsu1mzZnHs2DEAxo0bh9ms5SoiIiJSPEtICAEXXYR/586uonV2O1itWEJCtABbajw95VbQ5MmTady4MeBKGl566aUibXbs2MG9994LuNZR3HfffVUao4iIiNROJqsVa1gY1shIrGFhSiakVtDf0goKDQ3lo48+4uqrr8ZmszF16lR+/PFHxo8fT3h4OOvXr+e9995zV8d+9tlnadeuXTVHLSIi1aVz586sXr26yLHaorbHLyLeZzLKs13RBSAuLo4WLVoAMGrUKObMmVNq+/nz5zNhwgROnz5d7Hmz2cyLL77Io48+WmkxRkVFAdqCVkRERERqDo1QeOiKK65g3759TJs2jR9++IH9+/eTk5NDs2bNGDhwIFOmTKFTp07VHaaIiIiIiFdphKIW0QiFiIiIiNQ0WpQtIiIiIiIeU0IhIiIiIiIeU0IhIiIiIiIe06JsERERL9q7dy9PPPFEoWPPPfdcrdlSvLbHLyLep4RCRETEi5KTk5k1a1ahYw8//HA1RVNxtT1+EfE+TXkSERERERGPKaEQERERERGPKaEQERERERGPKaEQERERERGPKaEQERERERGPKaEQERERERGPKaEQERERERGPKaEQERERERGPKaEQERERERGPKaEQERERERGPKaEQERERERGPKaEQERERERGPKaEQERERERGPKaEQERERERGPWas7ABERkfNZcHAwffr0KXKstqjt8YuI95kMwzCqOwgpn6ioKADi4+OrORIRERERERdNeRIREREREY8poRAREREREY8poRAREREREY8poRAREREREY8poRAREREREY9p21gREREvys3N5fTp04WO1a9fHz8/v2qKqGJqe/wi4n0aoRAREfGiTZs2ER0dXehn06ZN1R1WudX2+EXE+5RQiIiIiIiIx5RQiIiIiIiIx5RQiIiIiIiIx5RQiIiIiIiIx5RQiIiIiIiIx5RQiIiIiIiIx5RQiIiIiIiIx5RQiIiIiIiIx5RQiIiIiIiIx5RQiIiIiIiIx5RQiIiIiIiIx5RQiIiIiIiIx5RQiIiIiIiIx5RQiIiIiIiIx5RQiIiIiIiIx6zVHYCIiMj5rG3btnzzzTdFjtUWtT1+EfE+k2EYRnUHIeUTFRUFQHx8fDVHIiIiIiLioilPBUybNg2TycTo0aM97uPVV1/FZDJhMpnYsmVLpcUmIiIiIlITKaE4a/fu3UydOvWc+tizZw9PPPFEJUUkIiIiIlLzKaEAYmNjGT58OBkZGR734XA4mDhxIjk5OZUYmYiIiIhIzXbBJxQrV66kX79+HD58+Jz6+c9//sP69esrKSoRERERkdrhgk0obDYbL774IkOHDuXEiRPn1Nf27dt55plnAKhbt24lRCciIiIiUjtckNvGLlq0iLvuuouDBw8CYLVamTp1Ks8//3yF+8rLy2PixInYbDaGDBlCdHQ0M2bMqOyQRUSkloqLi+PNN98sdOyBBx6gefPm1RNQBdX2+EXE+y7IhOLzzz93JxMdO3bk448/JjIy0qOE4rnnnmPz5s0EBQUxffp090iFiIgIQEJCAv/9738LHRs7dmyteSCv7fGLiPddsFOewsPDee2119i8eTO9evXyqI9NmzbxwgsvAPDSSy/RokWLygxRRERERKTGuyBHKO677z6mTZtGYGCgx33k5uYyYcIE7HY7Q4YM4e67767ECEVEREREaocLMqHo2bPnOffx1FNPsXPnTvdUJ5PJVAmRiYiIiIjULhdkQnGu1q5dyyuvvAK4toutzKlOUVFRJZ5LSEigUaNGlXYvEREREZFzdcGuofBUdnY2EydOxOFwMHjwYO65557qDklEREREpNpohKKCHnvsMfbt20dQUBAfffRRpU91io+PL/FcaaMXIiIiIiLVQSMUFbBixQreeOMNoPKnOomIiIiI1EZKKMopMzOTW265BcMwNNVJREREROQsTXkqp99//51Dhw4BsGzZMszmsnOx7t27u1/HxsaqCJCIiIiInHc0QiEiIiIiIh7TCEU5de7cme+//77Mdm+++SZLly4F4LXXXnOvs4iMjPRqfCIiIiIi1UEJRTlFREQwevToMtvNmTPH/XrIkCFcdNFFXotJRERERKS6acqTiIiIiIh4TAmFiIiIiIh4TFOeREREvMjX15cmTZoUOVZb1Pb4RcT7lFCIiIh4UUxMDPHx8dUdhsdqe/wi4n0mwzCM6g5CyicqKgpA/7CLiIiISI2hNRQiIiIiIuIxJRQiIiIiIuIxJRQiIiIiIuIxJRQiIiIiIuIxJRQiIiIiIuIxJRQiIiJetGbNGkwmU6GfNWvWVHdY5Vbb4xcR71NCISIiIiIiHlNCISIiIiIiHlNCISIiIiIiHlNCISIiIiIiHlNCISIiIiIiHlNCISIiIiIiHlNCISIiIiIiHlNCISIiIiIiHlNCISIiIiIiHlNCISIiIiIiHlNCISIiIiIiHlNCISIiIiIiHlNCISIiIiIiHlNCISIiIiIiHlNCISIiIiIiHrNWdwAiIiLns2bNmvHqq68WOVZb1Pb4RcT7TIZhGNUdhJRPVFQUAPHx8dUciYiIiIiIi6Y8iYiIiIiIx5RQiIiIiIiIx5RQiIiIiIiIx5RQiIiIiIiIx5RQiIiIiIiIx7RtrIiIiBcdP36cmTNnFjo2fvx4GjduXE0RVUxtj19EvE8JhYiIiBcdPnyYRx55pNCxvn371poH8toev4h4n6Y8iYiIiIiIx5RQiIiIiIiIx5RQiIiIiIiIx5RQiIiIiIiIx5RQiIiIiIiIx5RQiIiIiIiIx5RQiIiIiIiIx5RQiIiIiIiIx5RQiIiIiIiIx5RQiIiIiIiIx5RQiIiIiIiIx5RQiIiIiIiIx5RQiIiIiIiIx5RQiIiIiIiIx5RQFDBt2jRMJhOjR48uV/uFCxdyww030LJlSwIDAwkMDKR169bccsstrFu3zrvBioiIiIjUACbDMIzqDqIm2L17N7169SIjI4NRo0YxZ86cEttmZGQwYcIEvv/++1L7vO+++/jvf/+L1WqtlBijoqIAiI+Pr5T+RERERETOVeU86dZysbGxDB8+nIyMjDLbGobBuHHjmDdvHgD169fntttuo2vXrtjtdtasWcOnn35KVlYWb7/9NgBvvfWWV+MXEREREakuF/wIxcqVKxk3bhwnTpxwHytthGLmzJlcf/31APTo0YMFCxYQHh5eqM2ePXsYPHgwJ0+eBGD16tX07dv3nGPVCIWIiIiI1DQX7BoKm83Giy++yNChQwslE2X53//+B4DZbObLL78skkwAtG/f3t0O4L333jv3gEVEREREaqALMqFYtGgRHTt25LHHHsNut2O1Wnn88cfLvC47O5uVK1cC0KtXL9q1a1di2xEjRhAYGAjA2rVrKydwEREREZEa5oJMKD7//HMOHjwIQMeOHVm1ahW33357mdedPn2anj170rhxYzp27FhqW6vVSkhICABnzpw596BFRERERGqgC3ZRdnh4OI8//jj33nsvvr6+xMXFlXlN06ZNy70dbGJiIqdOnQIgIiLiXEIVEREREamxLsgRivvuu48jR47w0EMP4evr65V7TJ8+HafTCVApC7JFRKR22rhxI1FRUYV+Nm7cWN1hlVttj19EvO+CHKHo2bOnV/s/evQoL7zwgvv9hAkTvHo/ERGpuWw2G8eOHStyrLao7fGLiPddkCMU3pSdnc11111HWloaAFdeeSWDBg2q5qhERERERLzjghyh8Jbc3FzGjBnD+vXrAYiMjGT69OkV6iO/1kRxEhISaNSo0TnFKCIiIiJSmTRCUUmysrIYOXIk8+fPByAgIIDZs2fTsGHDao5MRERERMR7NEJRCU6fPs3VV1/trjfh7+/PnDlz6N+/f4X7Kq0KdmmjFyIiIiIi1UEJxTnat28fV155pbuuRVBQED/88APDhg2r5shERERERLxPCcU5WLNmDSNHjiQpKQlw1Zv45Zdf6NWrVzVHJiIiIiJSNZRQeGjp0qWMHDmSzMxMAJo3b878+fNp165dNUcmIiIiIlJ1tCjbA7///nuhZKJbt26sWbNGyYSIiIiIXHCUUFRQcnIy11xzjTuZ6NOnD8uXL9duTiIiIiJyQVJCUUEPPPCAu2Jo8+bNmTdvHqGhodUclYiIiIhI9dAaigrYs2cPX331lfv9+PHjWbZsWbmuHT16tHeCEhERERGpRkooKuDTTz/F6XS63//f//1fua81DMMbIYmIiIiIVCtNeaqA7du3V3cIIiIiIiI1ikYozmrevHmZowg///xzFUUjIiIiIlI7KKEQERHxokaNGvHQQw8VOVZb1Pb4RcT7TIYm99caUVFRAMTHx1dzJCIiIiIiLlpDISIiIiIiHlNCISIiIiIiHlNCISIiIiIiHlNCISIiIiIiHlNCISIiIiIiHtO2sSIiIl6UlJTEkiVLCh0bOnQo4eHh1RRRxdT2+EXE+5RQiIiIeNG+ffsYN25coWOrV6+mb9++1RRRxdT2+EXE+zTlSUREREREPKaEQkREREREPKaEQkREREREPFZlayhyc3PZsWMHcXFxJCUlkZ2dTUBAAOHh4bRo0YJOnTrh5+dXVeGIiIiIiEgl8GpCsWPHDr777jvmzp3Lli1bsNvtJQditdK9e3eGDx/OtddeS+fOnb0ZmoiIiIiIVIJKTygcDgdffvklb775Jps2bXIfNwyj1Ovy8vL4/fff+f333/n3v/9Njx49eOCBB7j++uuxWrUZlYiIiIhITVRpT+qGYfDpp5/y9NNPc/ToUfexfNHR0XTs2JHw8HDCwsIICQkhLS2NpKQkkpKS2LVrF/Hx8e72mzZtYtKkSTzxxBM888wzTJw4EZPJVFnhioiIiIhIJaiUhGLt2rXcfffdbNu2zZ1ENGzYkDFjxjBkyBD69u1L48aNy+zn2LFjrF27lqVLlzJ79mxOnDjB0aNHue2223jjjTd499136dOnT2WELCIiIiIilaBSEor+/ftjGAYmk4mrrrqKe+65hyuuuKLCIwpNmjTh2muv5dprr+Wtt95i/vz5TJs2jblz57J161YGDhxIXl5eZYQsIiIiIiKVoFK2jTUMg2uuuYbNmzfz008/MXz48HOenmQymRg+fDg//fQTW7du5ZprrsHhcFRGuCIiIiIiUkkqZYRixYoVDBgwoDK6Klbnzp357rvvWLVqldfuISIiIiIiFVcpIxTeTCYK6t+/f5XcR0REREREykeVskVERERExGNKKERERERExGNKKERERERExGMmo6wS1pVk/vz5/Pjjj8TGxmIYBvXr16dDhw489thjVXH780JUVBRAoQKAIiJSs+Xm5nL69OlCx+rXr4+fn181RVQxtT1+EfE+rycUZ86c4brrrmPZsmWFjufXrdBWsOWnhEJEREREappK2Ta2JA6Hg5EjR7J69Wr3MX9/f/Ly8kpMJN577z0Mw6B37950797dm+GJiIiIiMg58uoais8//5zVq1djMpno168fW7ZsISsri2+//bbEayIjI7nnnnvo168fqamp3gxPRERERETOkVcTii+//BKAsLAw5s6dS9euXQFKraI9YsQIQkJCsNls/PLLL94MT0REREREzpFXE4rNmzdjMpm47bbbCAkJKdc1Pj4+DBs2DICVK1d6MzwRERERETlHXk0o8qcsdejQoULXdevWDcMw2LZtmzfCEhERERGRSuLVRdmhoaEkJSVVeGu56OhoAGJjY70RlohIrfK3/6xwvw4K8OGp+/pgNpc8dTTfuq0JfDt/v/v9o7f3JDI8kPkr41i0+ggAf+kVxcghLcsVx2c/7GbrntOYgDv+2oW2zesV227PoWS270skNj6N9AwbtjwHAf5WGtYPon2LelzcpSFBgT4l3uf37Sf4eu4+9/uBPZswalircsX49udbiDuWBkDTRnV4YELRzT1S0nL5beMx9saeITk1B7vDSUiQLy2bhjKoZxRNGgSX617ltX37diZPnlzo2Pvvv0+XLl0q9T7eUtvjFxHv82pC0axZM5KSkti3b1/ZjQvInx6VkpLihahERGqvzOw8DhxOoW2L4h/mC9q8+3Sxxy/t15Sd+5NIOJ3Jyt/j6dYugqaNS5+Wun1vIlv3uPrrH9O42GTiVFIWM3/Zy5GEdPcxi8WEj9VMRpYr7gOHU1iw6jCX9GvG4N5RmEtZU5dv297TXD20Zanr7wDOpOVw+GwyUZK9scl89sNucnJdOw36+Jgxm0ycSctl445TbN55iquHtWJATJMy4yqvjIwM1q5dW+RYbVHb4xcR7/NqQvGXv/yFjRs38t133/Gvf/2r3NfZbDYA1agQESnABBjA1j2ny0wo0jJsHDqSUuw5i8XM+Kva8eaMzTicBl/P28dDk3pgtRQ/CzYrO4/ZC10jHZHhgVw1uOiIxoEjKXzy3U5ybA78fC0MiGlMj44NaBAR6O5jX1wKy9YfJf5EBnOXx3IkIY2bR3XEUspoiwlITbcRdyyNFlGhpX7mLbtPU1phpTNpOXz6/S5seU4aRwYx5tLWNGsSggGcTMzix8UH2X84hR8WHSSiXgDtW4aVej8REXHx6hqKG264AYAdO3YwY8aMcl+3d+9ewDVlSkREXJo1cY0ibN+fiMNZek3SrXtO4zQgop5/seebNAhmaF/X9NKTiVksWnWkxL7mLDpIemYeFrOJG0a0w8da+FdHcmoOM77fRY7NQWRYAA9N6sHwQS3cyQRAYIAPF3WozwMTujMgpjEAO/Yl8fOSQ+X6zPmjI6XZcnZEpqTPvGxdPLY8J8GBPtw5vivNo0IxmUyYTSYa1Q/itrGdadIgGANY8NvhMu8nIiIuXk0oevTowfDhwzEMg8mTJ5d7G9hZs2ZhMplo27atN8MTEalVOrUJx2oxkZVt50DcmVLb5j9cX9QhssQ2l/Rt6l4vsHTdUY6fKjqNZeeBJDbtOgXApf2bEdWwTpE2Pyw+SFaOnQA/K7eN7UxEvYAS72k2mRh9SWu6tI0A4LdNx4g/kV5i+27t6wOwbW8iTqPkJOp0chbHTmZQL8TPnYT82a6DSQDEdGpAUEDRNRxWi5leXRsCcDQhHVueRslFRMrDqwkFwPTp06lfvz42m40xY8bw0EMPkZSUVGL7Z599ll27dgGuKVMiIuLi52txT8PZUso39smpORw5nkZIsC+tmpY80muxmPnrlW2xmE2uqU9z9+EsMPKRnWPnu19dU52aNwlhaJ/oIn2cSspi137Xv+n9ejQivG7JyURBoy9phcVswjBg+fr4Ett1aRuB2WwiLcNGbHzJxU43F0igTBSdQmUYBh1ahtGpdTgto0v+MwkJ8nW1B/c6CxERKZ3XE4qGDRuycuVKmjdvTl5eHm+++Wah3SJ++OEHVqxYwZdffslVV13F008/DYDVauW2227zdngiIrVK/jf2O/Yn4XA4i22Tv5agW/v6ZS5kbhwZzCX9mgJw7GQGv2085j7387JDpGXY8PVxrbkobmepXQeT3esWYjo1KPfnCK3jR+tmdd192Ev4LEGBPrQ5227rnsQS+8sfkenesX6x500mE2Mua8Mt13aiU5vwEvvJT1qsVnOpO1GJiMgfvJ5QALRt25a1a9dy5ZVXYhgGTqfT/UtuzJgxDBkyhJtvvpn58+djnB3Sfuqpp2jZsnxbGYqIXCg6tg7Hx8dMdo6dfXEpxbbZsts1Ral7h+Ifrv9saN+mRDV0TX1a8Nth92jA+q0nALh6aKsSpzHFHXM9gAf4WakfVr7RiXxtzu4UlWtzcDIxq8R2F539HNtLmPZ0/FQGp5KyiAwPpHGk51u+pqbnsm6b6zO3b1mv1MXiIiLyhypJKAAiIyP5+eefmTdvHoMHD8ZsNmMYRpGfwMBAXnrpJR577LGqCk1EpNbw87XQsZXrG/biFiqfSsri+KlMwuv6l7kVbD6L2cRfr2yHxWIix+bgxyUHmb3gAAbQoVUYfS5qVOK1qemuXflC6/iWORryZ6HBvu7Xyak5Jbbr3CYCi8VEeqaNQ0eLTntyj06UM4EqTp7dyWc/7CbX5sBsgkv7NfO4LxGRC41Xt40tzuWXX87ll19OUlISixcv5ujRo5w4cQI/Pz/atWvHlVdeSXh4ycPRIiIXuova12frntPs2J+I3dGm0Havm92jEyUvxi5Oo/pBXNa/GfNWxLkf0IMCfRg3vPTNMbJz7AD4+1X810nBhdGlLYAO8LfSrkU9dh1IZuvu07RuWrfQefdn7lixz5wvz+7k0+93ugviXTGoeaUXtxMROZ9VeUKRLzw8nHHjxlXX7UVEaq32rcLw87WQk+tgb+wZOrX+40uYrfmLk0tYS1CaIb2j2bE/iaNnC9ONvbwNdYJ8S70mf1pQSWsgSpNr+yOJ8LVaSm17UYdIdh1wVeC+5tLW7vUch4+lcSY1l+hGdUrdXaok2Tl2Pp690z3y0btbQ4b2aVrhfkRELmRenfK0Zs0asrOzvXkLEZELjo/V7F5YXHDa07GTGZxKzqZR/SAaRgRVuF+z2UT7ln8UzOt8dmvX0tQL9QNchfQqKiU9t0g/JenUOtxdcftggYJ95zLdKfFMNm99ttmdTPTr3ojrLm9T4X5ERC50Xk0o+vfvT0hICJ06deKmm27itddeY9myZaSmlrz1n4iIlO2is7s97dqfhN3uGh0416k/nmjexLUFa1qGjaSUin2BlL+jkq+PucwEqOCWuflJlNMw2Lr3NCbTH7tfVeTeb362mVPJrpgvH9CMMZe1qfA6EBERqYIpTw6Hgz179rBnzx6++uor9/HmzZvTo0cPunfvTo8ePejRoweRkVX3S1BEpDZr16IeAf5WsnPs7IlNpnObCLbuOY2JP3ZFqgrd2tfn15VxGMD6bScYPqhFua5Lz7Sx62Ay4Nq5ymot+/utizrUZ/u+RLbvS+Kaywxi41NJy7DRqmkooXVKH+EoaNve03z50x7sDgOLxcS4K9oS07n8W96KiEhhXh2huO++++jTpw/+/v5FdnOKi4tj9uzZPPnkk1x11VU0atSIJk2aMHLkSJ566inmzJnDkSNHvBleEdOmTcNkMjF69OhytT9w4AD33HMPbdq0wd/fn7CwMC6++GJefvll0tNLrvwqInKuLBYzXc5Oe9q2J5HDx11rCZo1CSEs1L/K4qgfFuCeGvXbxuOcSip5+9eCfl56CLvdiQn4y8VR5bqm49m1I5nZrmlPW93Tncr/ZdSO/Yl8/qMrmfD3s3DH2C5KJkREzpFXRyjefPNNwDVKsWPHDjZs2MCGDRv4/fff2bFjBzZb4Tm3CQkJzJ07l7lz5wKuQkR2u92bIbrt3r2bqVOnlrv9zJkzufXWWwutEcnNzXV/xvfee48ffviBTp06eSNcERG6dajP+u0n2XUwicAA1z/nVTk6ke/qYS05eCSFrBw702ft4PaxXUqtSTFvRSwbd7qmZ/WPaUx0ozrluo+Pj4WOrcLYvPs02/acZsf+JCxmE13blb3WA+BkYhZf/LQHp9MgKMCHyX/tot2cREQqQZXs8mSxWOjWrRvdunVzV7/Oy8tj69atrF69mh9//JElS5ZgMpnche2qUmxsLMOHDycjI6Nc7VesWMFNN92Ew+HAx8eH2267jQEDBpCens5XX33FihUrOHjwICNGjGDDhg3aBldEvKJNs3oEBfqQmZXHms0JmD1YS1AZ6oX4c8u1nZg+awdJKTm8/ukmBvVsQo9ODdyJhS3Pwf7DKSxde9S9PWvnNuGMHNqqQve6qEMkm3ef5vftJ3E4DTq2DiMwoHwVrWf9uo+8PCdmE9w8qkOVJRNhYWFcd911RY7VFrU9fhHxvmrbNtbHx4eePXvSs2dPHnjgATZs2MCkSZPYvXs399xzDw0aNGDr1q1ej2PlypWMGzeOEydOlKu93W7njjvuwOFw4Ovry7x58xg6dKj7/J133snUqVN5+eWXiYuL4+mnn+att97yVvgicgEzm010bRvBmi0JOJwGbZvXK3ObV29pERXKlAnd+WbePmLj01i4+ggLVx/BajHhY7WQnfvHaLOvj5lL+jVlcO9ozBVcBN2uRT38/Vxb5kL5pzsdPpZGbLwrkcFk4oufdpd5zZQJ3akbcu7Tx9q1a8e33357zv1Ul9oev4h4X5VVyi5Lz549Wb16Nd26deOjjz7ikksu8eo/YDabjRdffJGhQ4eWO5kAmDNnDvv27QPg7rvvLpRMgGua1n/+8x969uwJwPvvv8/p00Wr2YqIVIZuBaY4dfeg9kRlqh8WyL03XsSdf+1Cry4NiAwPxGQ2kZvnIDjQhzbN6nL10JY8cXdvhvZpWuFkAsBqNdO5jWuKk4+PmY6tyzcCHHt2VATA6TRIz8wr88dZ9QPmIiK1ksmojjlGpdi7dy+dO3emXbt2bNmyBau18gdRFi1axF133cXBgwcBsFqtTJ06leeffx6AUaNGMWfOnGKvve666/juu+8A2LdvH23aFL9n+YwZM5g4cSIA7733HpMnTz7nuKOiXAsX4+Pjz7kvEREREZHKUGNGKPK1a9eOq666it27dzNz5kyv3OPzzz93JxMdO3Zk1apV3H777eW6dtmyZQA0bdq0xGQCYNiwYe7X8+bN8zxYEREREZEarMYlFAADBw7EMIxCdSsqW3h4OK+99hqbN2+mV69e5brm+PHjJCUlAdClS5dS2zZp0oTQUFfBp6pYCyIiIiIiUh2qbVF2aerUcW0huH37dq/0f9999zFt2jQCAwMrdF1cXJz7dfPmzctsHx0dTWpqKkePHsUwDFVgFREREZHzjlcTij59+rh3curZsyedOnUq10P1/v37ATh16pRX4spfMF1RBRdXR0SUve95/rZ6drud1NRU6tat69F9RUSk9kpPT2fHjh2FjnXu3Nn95VlNV9vjFxHv82pCsX79en7//Xf3+4CAALp161YoyWjfvn2hJCM2NpaPPvoIoMY9gGdl/VEB1t+/7K0EC7bJysqqcZ9HRC5wZ87ADz/A8OHQQNWivWXHjh3069ev0LHVq1fTt2/faoqoYmp7/CLifV5NKMxmM06n0/0+KyuLtWvXsnbtWvexwMBAunTpQqtWrUhPT2fp0qWkp6djMpkYNGiQN8OrsLy8PPdrPz+/MtsXbFPeit/5OzkVJyEhgUaNGpWrHxGREhkGzJwJDz4Ip07BTTfBZ59Vd1QiIlJLeTWhyMjIYMuWLWzcuNH9s2vXLhwOh7tNZmYm69atY926dYWu9fX15fHHH/dmeBUWEBDgfm2z2cpsn5ub637t61s9xaZERAo5eBDuuQcWLHC9Dw2FSZOqNSQREandvJpQ+Pv706dPH/r06eM+lpOTUyTJ2L17d6Fv8Dt27Mjbb79Nt27dvBlehQUHB7tf5+TklNm+YJuCyUhpSqsxUdrohYhIqWw2eOUVePZZyP+36frr4bXXoGHD6o1NRERqtSrf5am4JMNut3P48GFSU1OpX78+0dHRVR1WudSrV8/9On/72NIkJycDrqlP+VvIiohUud9+gzvvhF27XO9btoRp0+Dyy6s3LhEROS/UiG1jrVYrrVq1qu4wytSuXTv36yNHjpTZPr9Ns2bNvBaTiEiJkpPhH/+ADz5wvbda4dFH4YknoJyjpiIiImWplMJ2H374IVu2bCn3wuPaql69eu5F0Tt37iy1bXx8PGlpaUDZRfBERCqVYcCXX0KHDn8kE/37w5Yt8PzzSiZERKRSVcoIxeTJkzGZTPj6+tKlSxd69uxJTEwMMTExdOnSBYvFUhm3qREGDx7MV199xYEDBzh69GiJ07MWL17sfj1kyJCqCk9EzkN2h5OMzDwcTicWs5ngIB+slhK+DzpwAO6+GxYtcr2vWxdefhluvRXMlfIdkoiISCGVNuXJMAxyc3PdC63z+fn50aVLF2JiYtyJRufOnWttkjF27Fi++uorAN58801efvnlIm0Mw+Ctt94CXNO5rr322iqNUUTOD+mZNo6eSCfhVCa5NgdOp4HZbMLP10KjyCCiG9ahTtDZHeRsNnjpJXjuOcjfYe7GG+HVV6u0xsTz767jTFouPTs3YPxV7cps//v2E3w9dx8Aj93Vi7BQV/2eaV9u5dDR1GKvMZsgwN+HuiF+tGlelwE9mlA3pOytvAGcToPn3l1HWoZrp74pE7oT3ah8BdpS0nL5beMx9saeITk1B7vDSUiQLy2bhjKoZxRNGgSX3YmIyHmoUhKK9957z51IbN++vdCWqjk5OWzYsIENGzbw3nvvAX8kGQVHMmpLkjFixAjatWvH3r17ef311xk0aBAjR450nzcMg0cffdSdVN1000001A4qIlJBccfS2HMomYxMG6kZNnJtdkwmE4Zh4Odr5UxqDkeOp9MpyEb01lWuUYjdu10Xt2oF774Ll15avR+iEljMJgIDCv+qsjsMsnLyyMzO49jJDNZsTuDmUR1o3zKszP72HEp2JxMAazYfJ7pR2YnP3thkPvthNzm5rm3PfXzMmE0mzqTlsnHHKTbvPMXVw1oxIKZJBT+hiEjtVykJxR133MEdd9wBuIq/bd++nY0bN7JhwwY2btzIjh07Skwy8vn5+dG1a9dCIxmdOnWqcUmGj48P77zzDpdddhl2u53Ro0dz8803c8kll5CVlcUXX3zBihUrAGjUqBH/+c9/qjliEalt4o6lsetAEgmnMzEMg5BgXxpHBmE2m3A6DdIzbaSk55J79BjRk4f+caGPD0ydCo89dt6sk2jWJIR7bii6hbjD4STueBrfLzjAicQsPvthN1Pv6ElIcOkjFeu3nQCgU+twdh5IYvOe04wc2ooA/5J/HZ5Jy+HT73dhy3PSODKIMZe2plmTEAzgZGIWPy4+yP7DKfyw6CAR9QLKldiIiJxPKn2XJx8fH3r06EGPHj0KJRnbtm0rVHuiuCTj999/5/fff3cf8/f3JzMzs7JDPGfDhg3j008/5Y477iAnJ4dPP/2UTz/9tFCb6Oho5s6dS2RkZDVFKSK1UXqmjT2Hkkk4nYmP1UxkeAAmk8l93mw2EVrHjzrBvrT+31Pu4856YZh/WwkdO1ZH2FXOYjHTKrout1zbif97/3dybQ7WbEng8gHNS7wmPdPGroOu7byH9WvKkYQ00jPz2LDjJAN7ljyysGxdPLY8J8GBPtw5vitBAT4AmIBG9YO4bWxn3vpsC8dOZrDgt8NKKETkglMl28b6+Pi4pzblK5hkFBzJyMvLc7cpT/G46nLTTTfRp08fXn/9dX799VeOHTuGyWSibdu2XHPNNUyZMkW1J0Skwo6eSCcj04ZhGEWSiYLMJhOnbriVBjt+x163HmdefoNWF0gyUVB43QAaRgSRcDqT4ydL/wJq446TOJ0GdUP8iG4YzEUdIlm54RhrNh8vNaHYddBVdyimUwN3MlGQ1WKmV9eGfL/wAEcT0rHlOfD1qVmj6yIi3lRtdSgKJhmTJ08GwGazFRnJqCrNmzfHMIwKXdO6dWvefvttL0UkIhcau8NJwqlMUjNshAT7lphM5Mto15n5nywiPTOP5vVDaOZwlrz703nMbHb9OflYS//s+dOdurWvj8lk4uIuDVi54RinkrM5cDiF1s3qFrnGMAw6tAwjNd1Gy+iSvyQKObs43gBycpVQiMiFpUYUtsvn6+tLz5496dmzZ3WHIiJS5TIy88i1Oci12WkcGVSua+oE+ZJ4Jptcm4PMrDxC65Rvt6PzxZm0HE6cdo1MtG9V8lSjuPhUTiVnA9Czs2vXq8aRwTRpEMyxkxms3ny82ITCZDIx5rI2ZcYRG+/akcpqNRMUWHQUQ0TkfHbhfZUlIlJDOZxOnE4Dk8nk/ta9LGazCZPJtVjb7nB6OcKaw+E0OHgkhenf7sDhNGjVNJTuHUtes5Y/OhHVMJhG9f9I1i7u4koudu5PKrT7U0Wkpuey7mz/7VvWw1LO/+9ERM4XNWqEQkTkQmYxmzGbXVvD5tecKIvTaWAYrrbemO5UoaJ6BWzYcZINO06e8/0PH0vjmbfX/Ckmw12XA6BX14aMGtaqxAf5XJuDrXsTAbi4S+FtvHt0iuTnpYewOwzWb0vgkn7NKhRfnt3JZz/sJtfmwGyCS4u5vnPnzqxevbrIsdqitscvIt5XJQnFu+++y4cffsju3btxOp00btyYDh060KNHD/c2sVFRUVURiohIjRUc5IOfrwU/XyvpmbZyTV9Kz7Th52vFz9dSqVNtKlRUrxhWq5kAv7LXEeTlOcmxOUo873AapGfmlXgeYNeBJCLDAxncq/jfI1v3nCbX5sBqMdG9Y/1C5wL9fejcJoIte06zdssJhvZpWu7RoTy7k0+/30ncsTQArhjUvNjidnXq1KFv377l6rMmqu3xi4j3eT2huOOOO/joo48A3Iue4+LiOHz4MPPnz3e3q1+/vnuRdn4diiZNVCBIRC4cVouZRpFBnEnNISU9t8yF2U7DIC3DRt06fjSKDKq0EYryFtVr3zKM5k1Ciu3jovb1K1wpuzgto0OL1KFwOJxk59pJOJ3J6k0JbN+XyM9LD5GWnsvVw1oV6SN/ulPnNhEE+hdNunp1bciWPadJSc9l98FkOrUJLzPu7Bw7H8/e6a7m3btbQ4b2aVrmdSIi5yOvJhQLFixg+vTpmEwmfHx8GDRoEOnp6axbtw6g0K5Kp06dYv78+YWSjAYNGnD8+HFvhigiUqNEN6zDkePppGbYOJWUXeLWsU7D4HRSNiaTieAgX6Ib1qmU+5e3qF5qhs095aikpMJbLBYzwYG+tGnmS5tm9fjip91s3nWalRuO0bNLAxpH/jFKcCopyz2CsGXPabbsOV1q36s3Hy8zoUg8k81Hs3a4F3n3696Iay5tfY6fSkSk9vJqQvHxxx+7bmK1snz5cnr37g2A2ez6Fu3+++/HbrczZ84cEhISgMJJxsmT5z7/VkSkNqkT5Ev7lmE4nQYJpzOJP5FBSLAvdYJ8Cz3Up2XYMJlMNKofRPuWYaVOPyqvihTVO52UTcLpTMxmE+F1/Svl/p4a1qcpm3edxgB2H0wulFDkj06U177YMySlZBNet/hK47HxqXw8eydZ2XYALh/QjEv7V2zdhYjI+carCcX69esxmUxcccUV7mSioGHDhnH11Vfzxhtv8OGHH/KPf/yD9PR0hg8fjtPpZNu2bd4MT0SkRsr/xt9sNrmnHSWeyS407ahuHT+CzyYflTVCUJGiepHhAcSfyCDj7FqLjq3KnibkLRH1/nj4T0nLdb92OA027nR9MdW3eyNGDG5ZYh9n0nJ4dfpGDGDNloRi227be5ovf9qD3WFgsZgYd0VbYs5uQSsiciHzakJx4oTrm6Fhw4aVHoTVyl133cXAgQMZNmwYGzZsYMWKFbRt29ab4YmI1FjNm4QQXtf/nBZGV0RFi+qZTCZCgn1JzbCRcCqTts3rVVtRvdSMP5KI4AIL03cfSHIv6I7p1AA/35IXiTeMCKJ5VAix8Wn8vu0kVwxojrVAobwd+xP5/Mc9OJ0G/n4WJl3Tqdi6FSIiFyKvJhR5ea5/yBs0KP4bHLvdXuh9p06d+OKLL7j00kuZNGlSkW3qREQuJHWCfOnYKpy2zeuRmZWH/Wwl7KDA8m3dWhG1uajeuq1/TGtq1/KP4nb5tSHCQv3LNYpzcZeGxMankZmdx7a9p+nRyfW762RiFl/85EomggJ8mPzXLsXu5lSSvXv38sQTTxQ69txzz9GuXdmL1muC2h6/iHifVxOK0NBQkpOTixwPDAwkOzubrKysIueGDRvGkCFDWLZsGbNnz2bMmDHeDFFEpMazWsxef1ivjUX10jJsrNl8nKXrjgLQtnldd+KQlpHL3kOu3z9/3iq2JF3bRfD9ogPk5TlZvTnBnVDM+nUfeXlOzCa4eVSHCiUTAMnJycyaNavQsYcffrhCfVSn2h6/iHifVxOK5s2bk5ycTGJiYqHjoaGhZGdnuxdi/9nIkSNZunQpM2fOVEIhIlIFamJRPSi+sB0UrV/RpEEwN4xs736/YcdJzm5CRfcOJVfQLsjfz0qXthFs2nmKuGNpJJzOxGZzEBvv2iUKk4kvftpdZj9TJnSnboh/ue4pInI+8GpC0bVrVzZt2sTu3YX/AW7Tpg0nTpxg586dxV6XX39iw4YN3gxPRETOqklF9QoqrrCdCbD6mAkL9adxZBBd2kbQvWNkoSRo/TbXYuxG9YNoWL98U7gAenZuwKadpwBYvek44QUWfDvLUWQPcCcyIiIXCq8mFP369ePjjz8ushaiZ8+erFixgoULF+JwOLBYCi+Uy98uNn9Rt4iIeFdlFdV7/O6iO/qV5uIuDbm4S8Mix/9czK6i/jH5Yo+ua9u8Hq9MHVToWEkVuEVExMWrW3JcddVVAGzbtq1QgbqxY8cCroThvffeK3LdV199BYC/v4aMRUSqSnTDOgQHuRKJU0nZheoCFeStonoiIlI7eXWEomHDhqxevZq1a9dy9OhRGjduDEDv3r3p378/q1at4sEHH+T48eOMHDmS9PR03n33XdasWYPJZKJXr17eDE9ERAqozqJ6IiJSe3k1oQDo06cPffr0KXL83XffpX///mRkZPDiiy/y4osvFmmjXSRERKpWdRXVExGR2svrCUVJOnfuzE8//cQNN9xQaDoUuAomPf/881x22WXVFJ2IyIWrqovqiYhI7VZtCQXAoEGD2LVrFzNnzmT16tWkpKTQvHlzbr75ZmJiYqozNBGRC1pVFtUTEZHazasJxVNPPcU//vEPAgICSmwTEhLC5MmTmTx5sjdDERERD1RFUT0REandvPo107PPPku7du34/PPPvXkbERERERGpJl4ftz527BgTJ06kd+/erFlTtNqpiIiIiIjUXl5NKF577TXq1q2LYRhs2LCBAQMGcP3113PkyBFv3lZERERERKqIVxOKBx98kAMHDnD//fdjtVoxDINvvvmG9u3b88QTT5CZmenN24uIiIiIiJd5fcpTvXr1eOONN9i+fTsjRozAMAxycnJ48cUXadOmDR9//LG3QxARERERES+psm1j27Zty48//sjixYt55JFH2LZtGydOnOD222/nnXfe4b///S8DBw6sqnBERESqRHBwcJECr8HBwdUUTcXV9vhFxPtMhmEYVX1TwzCYPn06//rXvzhx4gQmkwmAa665hpdffpkWLVpUdUi1QlRUFADx8fHVHImIiIiIiEu1VCcymUzcfvvt7N+/n3/+85/4+/tjGAbff/89HTt25B//+Afp6enVEZqIiIiIiFRAtZY7DQoK4vnnn2fPnj2MHz8egNzcXF5++WXatGnDBx98UJ3hiYiIiIhIGaplylNJ1q9fz0MPPeSuV2EymXA4HNUcVc2hKU8iIiIiUtNU6whFPrvdzo4dOzh06BCDBw8mPDy8ukMSEREREZFyqLJdnvIdOHCAHTt2FPrZv38/dru9qkMREREREZFz5NWEYv78+YUSh927d5OTk1OkXXGzrurVq0e3bt28GZ6IiIjX5ebmcvr06ULH6tevj5+fXzVFVDG1PX4R8T6vrqEwm83uLWGh+MTBbDbTpk0bunbtSrdu3dw/+esF5A9aQyEiUvusWbOGfv36FTq2evVq+vbtW00RVUxtj19EvM/rU54KJhGhoaF06dKlUOLQuXNnAgICvB2GiIiIiIh4gVcTijFjxhRKHpo1a+bN24mIiIiISBXzakIxa9Ysb3YvIiIiIiLVrEZsGysiIiIiIrWTEgoREREREfGYVxOK5557jnnz5nHy5Elv3kZERERERKqJV9dQ/Otf/3JvG9uoUSNiYmLo0aOH+38bN27szduLiIiIiIiXVdm2scePHychIYGff/7ZfS4yMrJIkhEdHe3tkEREREREpJJ4NaH48ccf2blzJ4sWLWLx4sVA4boUJ0+eZN68ecybN899LDw8vEiS0bx5c2+GKSIiIiIiHvJqQjFixAhOnTrF2rVrAQgODqZr165ER0eTmZlJXFwcO3fuxOl0uq9JTExkwYIFLFiwwH2sXr169OjRo1CS0apVK2+GLiIiIiIi5eDVhGLVqlXceeedOJ1O7rrrLl544QXq1q1bqM2ZM2f45JNPeOWVV0hISMBkMhUaxQBITk5m8eLF7lEOk8mE3W73ZugiIiIiIlIOXt3l6fXXX8fhcDBs2DCmTZtWJJkA1+jDQw89xM6dOxk9ejSGYXDdddfx448/8swzzzBq1Ciio6MxDKPQT01ht9v55JNPuOyyy6hfvz6+vr5EREQwZMgQpk2bRk5OTnWHKCIiIiLiNV4doVi/fj0mk4nrrruuzLZ169Zl1qxZjB8/nu+++46uXbvy5JNPus8nJiayadMm909NcOrUKUaNGuWe0pUvKSmJZcuWsWzZMqZNm8YPP/ygKVoiIiIicl7yakKRX3+iXr165WpvNpv58MMPWb58Oc899xxjx46lXbt2AERERHDZZZdx2WWXeS3einA4HFx99dWsW7cOgMaNGzN58mRat27NkSNH+Oijjzhw4AA7d+7ksssuY/PmzYSEhFRz1CIiIiIilcurU57yE4kjR46U+5qQkBBuuukmbDYb06dP91Zo52zGjBnuZOIvf/kLu3fv5qmnnuLGG2/kn//8J9u3b2f06NEAHDp0iJdeeqkaoxURERER8Q6T4cUFCcOHD+fXX3+lT58+rF69utzXffbZZ0ycOJHOnTuzbds2b4V3Tq688kr3drd79uxxj6QUdPr0aZo0aUJeXh7t2rVjz54953TPqKgoAOLj48+pHxERqTpJSUksWbKk0LGhQ4cSHh5eTRFVTG2PX0S8z6tTnq677jp+/fVX1q1bx1dffcX1119fvqCsrrAqMrJR1WJjYwEIDAwsNpkAqF+/Pq1atWLPnj3ExcVVYXQiIlJThIeHM3bs2OoOw2O1PX4R8T6vTnmaOHEirVu3xjAMbrvtNmbPnl2u63777TeAGr01bP56iOzsbJKTk4tt43Q6OX36NOCqCi4iIiIicr7xakJhtVr55ptvCAoKIicnh7Fjx3LjjTeye/fuEq9ZsmQJH330ESaTqUbvjNS7d2/AVfn7lVdeKbbNRx99RFJSEgBXXHFFlcUmIiIiIlJVvLqGIt/q1au59tprOXnyJCaTCYCePXsyePBg2rRpQ1BQEEePHmXlypXMnTsXwzAwmUw888wzPPHEE94OzyNxcXF069aNtLQ0TCYTt9xyCw8++CCtWrUiPj6ejz76iFdffRW73U50dDSrV692r4HwlNZQiIiIiEhNUyUJBcCxY8eYOHGie2FXfmLxZ/nhdO7cmbVr1xIYGFgV4Xlk3bp1/PWvf+Xw4cPFnjeZTIwePZr//ve/NGvW7Jzvp4RCRERERGoar055KqhJkyYsWrSI+fPnM3ToUCwWS5Hq1/kjE9dccw1Lliyp0ckEuKY9zZo1i8aNGxd73t/fn/bt2xMcHFzFkYmIiIiIVI0qG6H4s5SUFBYvXkxcXBwnTpzAarXSvHlzhg0bRuvWrasjpAqx2+3cfvvtfPrppwBcfPHFjB8/nqioKE6ePMl3333H8uXLAVcytWzZsnJ9rtKmRSUkJNCoUSONUIiI1CJxcXG8+eabhY498MADNG/evHoCqqDaHr+IeF+1JRS13fXXX8/MmTMBeOKJJ3j22WeLtHnvvfe4++67MQyD6Ohodu7cSZ06dUrtVwmFiMj5Zc2aNfTr16/QsdWrV9O3b99qiqhianv8IuJ9Xq1Dcb5asWKFO5m48sori00mAO6880527drFm2++ydGjR5k2bRpTp04tte/SkoVzXdQtIiIiIlLZqmwNxfmkYD2NKVOmlNr2kUcecb/+6aefvBaTiIiIiEh1qJSEoqoelGvKA/m+ffvcr7t27Vpq26ZNmxIREQHAwYMHvRqXiIiIiEhVq5SEYtSoUQwbNoxVq1ZVRndFrFy5kmHDhnHNNdd4pf9zkZOTU2ab/C1yLRaLt8MREREREalSlZJQWK1Wli1bxqBBgxg0aBBff/01eXl559RnXl4eX331FQMGDGDw4MEsXboUq7VmLPkouJZhw4YNpbY9ceIEiYmJANoRQ0RERETOO5WSUGzatIn+/ftjGAarVq3ihhtuIDIykgkTJvDRRx+xa9eucvWzc+dOpk+fzs0330z9+vW56aabWL16NYZhMHDgQDZt2lQZ4Z6zSy+91P36rbfeorSNst588033+SuuuMLrsYmIiIiIVKVK+cq/c+fOrFixgtmzZ/Pkk0+ye/duUlNT+eKLL/jiiy8ACAkJoW3btoSHhxMeHk6dOnVIS0sjKSmJ5ORk9u7dS3p6urvP/Ifwjh078txzzzF69OjKCLVSjB49mtatW3PgwAFWrFjBww8/zCuvvFJkStPXX3/NSy+9BEBoaCh33313dYQrIiIiIuI1lTqHaMyYMVxzzTX88MMPvPXWWyxbtsydGKSmppY6Pajgt/wmk4mhQ4fywAMPcPXVV1dmiJXCx8eHzz//nMGDB5OTk8Prr7/Or7/+yk033USrVq04c+YMP/74I/PmzQNcn+eTTz4hPDy8miMXEREREalclb4owWQyMXr0aEaPHs3hw4eZPXs28+fPZ926daSlpZV4XUhICL1792b48OFcc801NGvWrLJDq1S9e/dm4cKFjBs3joSEBHbv3s3jjz9epF1ISAiffPJJjRphERERERGpLF5d5dysWTMeeughHnroIQzDIC4ujtjYWJKTk8nJycHf35/w8HBatGhRKxcsDxgwgP379/Phhx/yww8/sGPHDs6cOUOdOnVo27YtV155Jffee69GJkRERETkvFVl2yaZTCZatGhBixYtquqWVSIoKIgpU6aUWeBOREREROR8pErZIiIiIiLiMSUUIiIiIiLiMSUUIiIiIiLisRpRetput7Nv3z7i4uLctSjq1KlD8+bNadu2bY2pkC0iIlJRvr6+NGnSpMix2qK2xy8i3mcySivz7GWbN2/m//7v/5g7dy5ZWVnFtgkMDOSqq67i0UcfpUePHlUcYc0SFRUFQHx8fDVHIiIiIiLiUm0Jxcsvv8w///lPnE4nAGFhYURFRREcHAxARkYG8fHxJCcnA2A2m3nxxRf5+9//Xh3h1ghKKERERESkpqmWhGLu3LmMGDGC4OBgpk6dyg033FDidrKxsbF88cUXvPTSS2RmZvLzzz8zfPjwKo64ZlBCISIiIiI1TbUkFJdccgkrV65k7dq1dO/evVzXbNq0ib59+zJo0CAWLlzo5QhrJiUUIiIiIlLTVMsuT5s2bWLo0KHlTiYAevTowbBhw9i4caMXIxMRERERkYqoloTCZrO510pURFBQEDabzQsRiYiIiIiIJ6oloWjbti1LlizhzJkz5b4mOTmZJUuW0LZtWy9GJiIiIiIiFVEtCcWECRM4c+YMQ4cOZe3atWW2X7NmDcOGDSMlJYVJkyZ5P0AREZFKsmbNGkwmU6GfNWvWVHdY5Vbb4xcR76uWinEPPPAACxYsYP78+fTv35+mTZvSs2dPmjZtSlBQEACZmZkcOXKEDRs2cOTIEQzDYPjw4dx///3VEbKIiIiIiBSjWhIKs9nMTz/9xCuvvMKrr77K4cOHOXz4MAAmkwmAgptPRURE8Le//Y1HHnnEfV5ERERERKpftSQUABaLhalTp/L3v/+d1atXs3nzZuLi4sjIyAAgODiY5s2b0717d/r164fZXC2zs0REREREpBTVllDkM5vNDBgwgAEDBlR3KCIiIiIiUkH62l9ERERERDymhEJERERERDxWqxKKMWPG0KpVq+oOQ0REREREzqpVCUVCQgJxcXHVHYaIiIiIiJxVqxIKERERERGpWapll6cZM2Z4dN3p06crORIRERERETkX1ZJQTJo0yaMCdYZhqLCdiIiIiEgNUm2Vsg3DYOTIkRVKEFasWEFKSor3AhMRERERkQqploSiXbt27Nmzh1dffbVCuzb17duX9evXezEyERERERGpiGpJKHr27MmePXvYtGmTtoEVERGRanH84YdxJCZW6BpTYCBR//tfiedz9+0ja/16cvfvx3HqFM6cHMz+/phDQvBr04aAHj3w7969xBkaqbNnkzZnTulBWCyY/f2xRETg164dIVddhaVevRKb5yUkkP7rr+Tu2oUjOdnVRb16+HXqRJ3hw/Fp0KDMzy1SmmpLKD777DM2btzI2LFjy32dYRhejEpEREQuRCYfH0yBgeVqaw4IKPZ43rFjnPnkE3L37i3QsQlzYCBOmw1nQgL2hAQyV6zAJzqa8HvuwadJk9LvFRpa7HEjLw9nZibOzEzyDh8mc8UK6v/tb/i1bVukbeZvv5H88ceQl+cKyd8fw27HfvIk9pMnyfrtN8LuvJPAiy8u1+cXKU61JBSXX345U6ZMoW0xf/FL8+6775KWlualqERERCpfs2bNePXVV4scqy1qe/zlEdC7N+GTJ3t8fe7evZx+7TWM7Gzw8SF4yBAC+/TBt0ULTBYLAHknT5K9bh1pc+eSd/QoJ599lgZPPYVPo0Yl9tvkrbdKPOdITydz+XJSv/sOIyeHxLfeotErr2D28/sjroMHSf7wQ3A68evYkbo33ohvdDSGw4Ht0CHOfPYZeXFxJP3vf1gbNsQ3OtrjPwO5sJkMfe1fa0RFRQEQHx9fzZGIiIjUfvlTngIHDPA4obAnJ3Py8cdxZmZiCQsj4m9/w/fs7+ti2586xakXXsCRnIxfu3ZEPv54ofMFpzxFl2Ob/bSffiL1228BqDdpEsFDh7rPnX7tNXK2bMHapAkN//1vTD4+ha51ZmZy4vHHcSQnE9C7NxH33lvejy1SiArbiYiIiHgodeZMnJmZYLUS8fDDpSYTANbISOrefDPgGtmwHT58TvcPvuwyOLseI3f/fvdxw24nZ8cOV5uhQ4skEwDmoCACe/d2Xbt79znFIRe2apnyJCIiIlLb2RMTyTq7+2TwX/6Cb9Om5bouoHt3Anr0wCcqqsQ1GeVl9vPDXKcOzrQ0nKmp7uNGbi5BAwbgOHMG35YtS7zeUrcuAM7s7HOKQy5sXk0oXnnlFWJiYujRowehJSwsEhEREamNsjdsAKcTgMA+fcp9nclsJuLBByslBmdmJs70dADMZ5MDcI0+hN16a5nX5+7bB4A1LMyj+xtOJwkPPYTjzBkAGjz9dKkJTEG2o0fJXLnStftUUhLO3FzMwcFYIyLw79yZoIEDsdavX+y1Se+/T9Zvv5XcuY8PZn9/rJGR+LVvT/All2ANDy/S7OiECe7X5jp1aPzWW5jMZU/gyVi2jDMffeR+3/D//g+fxo3LvO585dWE4tFHH3Vvi9ayZUtiYmLcPz169KBugb/4IiIiIrVJ/hQjk78/vm3aVEsM6fPnw9nlsAEXXVSha21xcWRv3gyAfwWvzZezdas7mQDIWLKEsHIkFClffVUodpOvL+aAAJwZGdhSUrAdOEDaL78QcvXVhI4aVXJHJhPmkJCixx0OnFlZ2A4exHbwIBkLFxJ+770EdO9eYlfO9HRyd+3Cv3PnMuPPWru2zDYXEq9Pecpf833w4EEOHTrEt2cXDgG0aNGiUJIRExOjJENE5ALy/LvrOJOWS8/ODRh/Vbsy2/++/QRfz3V9o/rYXb0IC/UHYNqXWzl0NLXYa8wmCPD3oW6IH22a12VAjybUDfErtu3f/rOixHtbLCaCAnyIqBdAx9bh9O/eCB8fS5kxS82XvW4dx7ZvL1fbyKlT8Tm7TsJ+8iTgqulQnm+1K1PeiRNkLl3qeigH/Nq1I6Bnz3Jf78jIIOmdd8DpxOTvT8hVV3kUR+YK138z/t27k7N5M1lr11L3+usxBwWVeE3q99+TPm8eWK2Ejh5NYP/+7tEDw+kkLy6OtJ9+InvjRtK++w6Tjw8hV15ZbF+W8HAav/ZasecMh4OcLVtInj4dZ0YGSe++S8MXXsAaEVG0sckEhkHWunVlJhSOlBRy9+wptc2FxqsJxTfffMPu3btZt24dc+fOBQrXkjh06BCxsbHMmjXLfax58+b07NmTwYMHc9lll6nwnYiIlJvFbCIwoPCvNrvDICsnj8zsPI6dzGDN5gRuHtWB9i1LnuLh62PGz/ePZMEwwG53kpZhIy3DxqGjqazZfJy7r+9K3RD/UmM6fvw4M2fOLHRs/PjxNK4l0yNqe/zlYeTlYaQWn5AWaetw/PE6JwcAc3Bwie1z9+4l8e23Szzv17o1EVOmFHvu2P33Fz3odLrWO9jt7kP+XbsSfvfd5U5qnJmZnH7pJXdCFHbrre61FBXhSEsje8sWAEJGjcJ26BDO1FQyf/uNOpdfXvw1GRmkn30mrHfzzQQPGVLovMlsxrdlS8Lvv5/E114jZ9s20ubMIfgvfyk1SSmOyWIhICaGcF9fTr/8MkZODhmLF1P3r38t0ta3dWts+/eTvXEjxqRJ7u1+i5O1fj04nVgbNHD/GV7ovJpQXHfddaxYsYL33nsPgICAADp27Eh0dDSZmZkcOXKEvXv3FkoyYmNjiYuLcycZgwYN4tFHH2X48OHeDFVERM4DzZqEcM8N3YocdzicxB1P4/sFBziRmMVnP+xm6h09CQkufqTiL72iuHxA8yLHc20Odh5IYvaC/SSl5PDJ97t4cGKPUmM6fPgwjzzySKFjffv2rTUP5LU9/vLwdNtYk68vAM6MjBLbGHl5hRZL/5kzM7Pkc6VcZ4mIIKB7dwJ79cKvXdmje/nsSUmcfuUV7MeOARB63XUVWv9RUOZvv4HDgSU8HN8WLQjs04eMX38lY8mSEhMK26FDGLm5AAT26lVi3yazmZDRo8nZtg0jJ4fc/fsrPKUrn3+XLljCwnAkJ5c4shDQowe2uDicGRnk7NxJQNeuJfaXP90psE8f0n74waOYzjdeTShiY2MZNWoUaWlp3HLLLbz00kuE/2lBTEpKCl9++SWvvfYahw4dcq+5yE8yVqxYwYoVKxgzZgyffvopgeWsZCkiIpLPYjHTKrout1zbif97/3dybQ7WbEkoNmkojZ+vhR4dI3E4nHw9dx/xJzI4cCSF1k3reiVuqdks4eHkxcfjKOXB379z52LrSSS+8w7Z69aV2n/B6wyHA/uJE6TPm0fmihU4zpzBZLVWaO1G7qFDJL7+Os6UFABCx40jZMSIcl//Z5nLlwOuxMBkMhE0cCAZv/6KPSGBnF278O/Yscg1+ckEQF58fKnJkG/LloSMGoU5KAhrgwYexwm4E4qSEjhzQAABXbuSvXEj2evWlZhQ2BMTsR08iLluXfw6dAAlFICX61C89tprpKamMmjQIKZPn14kmQCoW7cu99xzDzt27OCuu+7CMAx69erFkiVLeOKJJ4iMjMQwDGbPns2wYcPIOTu8KCIiUlHhdQNoGOGaNnH8ZMnfDJelU5s/fp8dP1nyt9NyfvM7+zBvZGVhi4vz6r1MFgs+TZoQdvvthF53HTgcpM+bR9K772Kc3WmqNFm//87pF15wJRMWC/Vuv/2ckonc/fuxJyQAEDhwIAC+TZvic7aKesaSJcVe59usmbtuRvKHH5K7d2+J9zCZzYReey11rrii1Iri5eFITgYodWpXfk2O7E2bMApMKSsoa+1aMAxX27OfQ7ycUMyfPx+TycRfi5mr9mf+/v5MmzaNBx54gPXr1/PFF1/w73//m4MHD3LTTTdhGAbr16/nb3/7mzdDFhGR85zZ7HoI8LF6/ivQXOBBwnoO/UjtFnDxxe6HyszVq6vsviFXX03A2elC2evWkTZ7dqnt0xcsIOnttzFsNkwBAdR/5BGCBw06pxjyRyd8WrQoVMwv6Gy/2Rs34jg7ElKQNTKSwL59Adei9lPPP0/C3/7Gmc8+I2v9+mKvOVfZmze7Ewr/Ll1KbOffvTsmX1+cmZnk7NxZbJuC053kD179V/DY2fl5EcWtpi/Byy+/TLNmzfj444/ZunUrgYGBzJgxg7Fjx2IYBu+99x5xXv4WQEREzk9n0nI4cdo1MtG+lWf77gPsPuh6ODGboF2LepUSm9Q+Po0auXdWyli0qGKjFAXWj3oi7NZbsZx9vkr76Sdydu0qtl36okWkfP45GAaWevWIfPzxcm2LWhpnTo67oF/Q2dGJfEH9+oGPDzgcZJxNOoqLPaDA+gn7qVNkLFxI0ttvc/yBB0h49FHOzJhR6uhFeTjS00lftIik//0PAHNoKMFDh5bY3uzn594+N6uY6Wh5CQnkHTniqm2hTYMK8WpCEXJ2X+AjR46U+xofHx9uuukmnE4nH3/8sfv4u+++S2BgYJHjIiIiZXE4DQ4eSWH6tztwOA1aNQ2le8fICveTa3OwaedJvlvgqj8wtE9TwuueW6Vjqd3q3ngj5jp1wG4n8Y03yD1woNT2hsNB2rx5ZG/ceE73NQcGEjZ5snu70+QPPyxS7Tpn1y5SPvsMAEtkJJFPPlnuat6lyVq3zrXDlY8PQWdHG9xxBQUR0MO1UUHm0qXFTscy+foScd991H/0UQJiYtyL2/PZT5wgY9EiTj3/vGsBeVJSibE4kpI4dv/9RX7i77yT4/feS8qMGRjZ2ZhDQ4l46KEyd4oqbdpT1po1rjYanSjCq4uyO3XqxNKlS/nuu+94+OGHy31d+/btAVi4cKH7WFhYGNdccw1ffPEFK1aUvE+4iIjUPht2nGTDjnPffvHwsTSeeXtNoWN2h0GuzYHT6fpGuFfXhowa1gqLueT5z8vXx7N2S0KhY3l2J7m5Dgxc06ZGX9KK/j3On52OxDPWsDAiHn6YxNdew5GUxKnnnyeof38C+/XDt1UrzH5+GIaBIzGR7I0byVi82L3VqMnX95weTv3btyf40kvJWLAAR2IiKV9+SdhttwGuxCV5+nQwDEz+/tR/6KHi6y94IL/2RECPHsU+oAf/5S9kr1uHIzmZnC1b3AlGkfg7d8a/c2cMm43c/fvJ3b2b3L17yT140L0tbs62bZx68UUaPPkkltDQop0YRrG7YZl8fbHUq4dPkyb4d+lC0KBB5dp2NqBbN0z+/hhZWeRs316oEF7+qEXgn5Io8XJCMWbMGJYuXcratWuZNWsW1113Xbmuc57NZuPj4wsd79u3L1988QX7zpaJFxGR84PVaibAr+wicXl5TnJsjhLPO5wG6Zl5pfax60ASkeGBDO4VVWIbW54TW17JC12dToO1WxIIrxtAh3OYOiU1Q0UK2wHUu/HGQomAX6tWNHjmGVI+/5zsTZvIXLHC/dBtCgx07WxUoH4FFguBvXsTeu21WOvXP6fYQ8eNI2fbNuwnTpC5fDkBPXsS0K0b2b//juP0acCVXJz6v/8rs68mb71VZpu848exna0Qnr1uHUfL2KkqY8mSEhOKfCZfX/w7dcK/UycAnLm5ZG/YQNqPP2JPSMBx6hRpc+ZQb+LEItdaIiJKLGznCZOvLwE9epC1ejVZ69a5Ewrb4cPYExLwiY7Gp0mTSrvf+cKrCcXtt9/Of/7zH44dO8Ytt9yCw+Eo1wLtpUuXAuBwFP6lERnpGp5OPruwRkREzg8Xta9f4UrZxWkZHVqkDoXD4SQ7107C6UxWb0pg+75Efl56iLT0XK4eVvw86Ev7Ny2ypWxenoPMbDuHj6exeM0Rjp/K5OPZO7n56g50aVc53/xK9ahIYbv89n9mjYgg4sEHyYuPJ3PNGnL37sWekIAzKwuTjw/m8HB8o6Px69CBwF69PCokVxyzry9hd9zBqeeec019mj6dhi+8QG7BL1/LqIVREZkVnCWSs3079lOnsJ7dtdORmIgjLQ1rRETxIw641jIE9e9PQEwMJ595BvuxY2Rt3FhsQuENgb17k7V6NdmbN2Pk5WHy8fljMbZGJ4rl1YTCz8+PL774gksvvZSsrCxuuOEGvv/+e5588kk6nc1C/+znn3/m888/x2Qy0bp160Ln7GeHv6xWr4YtIiLnEYvFTHCgL22a+dKmWT2++Gk3m3edZuWGY/Ts0oDGkSVXOS7Ix8dCXR8LdUPq075lGK9/uonTydnMWXSA9q3CzmnXKKkelfnNdj6fqCjqjh3r8fWhY8YQOmZMha7xa9OG6E8/LXSs3oQJ1JswweM4imM4HGSuWgVA0NCh1B0/vsS2jqQkTjz2GBgGGUuXuqpTGwYJ//gH5OUROnYsISNHlno/s78/wYMGkfLVVzjT0jAMw12vzJv8u3TBHBTk2u1p2zYCYmJc051MJq2fKIHX//UbOHAgc+bMoW7duhiGwbfffku3bt2IiYlh6tSpvPfee3z11Ve8/PLLXHXVVYwaNYq8s5n/n0czjh8/DlBsPYvqEh8fz+OPP85FF11EvXr18Pf3p2XLltx6661sOVuOXkREao5hfVyLUg3+2K2povx8LQyIcU17SM2wcexEemWFJ1JjZW/Z4h7pCOrfH7O/f4k/Pk2a4Ne2LeAa1TDy8jCZze56Evm7RJUlfzTIGhFRJckEgMlqJSAmBnDV78g9eBBHYiK+rVtX2jqU802VfNV/xRVXsH79eiZOnMjq1asxDIMtW7YU+8CdXyG7a9euPPjgg4XOrT/7ly9/6lN1mzFjBvfeey8ZGYWLGsXGxhIbG8uMGTN44YUXePTRR6spQhER+bOIen/sypSSlltKyzL6qev/Rz/pnvcjUlvk156w1K/vLupXmsABA8jduxdnejpZv/9OUL9+BA8bxpmPPybv8GFSZ88udTTGkZ5Oxtlp8IH9+lXOhyinwN69yVyxguwtWzAHu0YxNTpRsiobn23VqhW//fYbP/74I4MGDcJsNmMYRpEfk8nEddddx6JFiwgI+OMf/fT0dObNm4fJZCLmbNZYnT777DMmTZpERkYGVquVCRMmMH36dGbMmMGECROwWCw4HA6mTp3KZ2e3bBMRkeqXmvHHw39woM859GMr0I9vKS1Faj9HSgo527YB5X+wDuzVy70lbMbixYCrbkV+HYy0OXM49fzzZG3ciDMn5497paeT+dtvnHzmGRxJSVibNKHOFVdU5scpk1+nTpjr1MHIynLFbja7t5SVoqp8McKIESMYMWIEycnJLF68mMOHD3Py5EmsVistWrTgkksuoWXLlkWuO3ToEMOGDWPTpk2MHj26qsMuJCEhgfvuuw/DMAgODuann35i8ODB7vM333wzY8eO5eqrr8YwDP7+978zfvx4fHw8/8UlIiKVY93WE+7X7Vp6tkOTYRis3+bqx9/XQrMmIZUSm0hNlblyJZzdhfPPtSdKYg4IIKBnT7JWr8a2fz+2o0fxjY4mfMoUzsyYQdZvv7m2iT1bwM4UEACG4apxcZZfhw6E3Xkn5sDAyv9QpTCZzQRcfDGZS5aAw4F/585YQvTfeUmqbXVzWFgYYyuwaKlbt27MLqO0fFX597//TVpaGgAffPBBoWQi34gRIxg/fjxfffUVJ0+eZPny5VxyySVVHKmIiORLy7CxZvNxlq47CkDb5nVp7kEicCIxk19XHibumOv3wODe0VqQLee9/N2dfKKj8YkqecvlPwsaMICs1asB1yhF2KRJmP38CL/jDupcdhlZ69eTu3s3jqQknBkZYDZjbdAA31atCOzdu1AdiKoW2Lu3K6FAuzuVRdslVVBeXh7ffPMN4KqLMb6UHQ7uu+8+6tatS3h4eI1aSC4icr4qrrAdFK1f0aRBMDeMbF9iP8UVtjMMV6XsPPsf9Sl6dm7A0D7RlRC5SM3W6OWXPbrOv3NnomfMKPacb7Nm+DZr5lG/4ZMnEz55skfXFlRSbAD+HTqc0/kLSZUlFImJifz888/s2rWL7OxswsPDadWqFf379y92ilNNtWzZMncdjFtuuaXUtv369aNfFS8iEhG5kBVX2M4EWH3MhIX60zgyiC5tI+jeMRJzKZWyiytsZzKBn4+FsIhAmjaqQ0znBrRuWrfMmPr27evecKQ2qu3xV5ucHDh6FMqxeFmktjMZVfCvxCuvvMIzzzxDVlZWsec7dOjA5MmTueuuu/D1rdkL21566SWmTp0KwPbt2+l8dmFReno6u3bt4syZMzRp0oTOnTtX+vZmUWeHGP9cQVxERERqEKcTBg+GlSvht9+gf//qjkjEq7w+6fPxxx9n6tSpZGVlFburk2EY7N69m4ceeoguXbq4t4atqXbs2OF+3bJlS06cOMGECRNo0KABffr0Yfjw4XTt2pXo6Gjef/99fasjIiJyofnkE1cyYTJBJVXEFqnJvDpCsWHDBnr16oXJZMLHx4cJEyYwZMgQwsLCSEtL4+jRo6xatYq5c+eSm+vaxi8oKIjvv/++xi5gHjx4MMuXL8ff3581a9ZwySWXkJSUVGL7iRMn8tFHH2E2n3vuphEKERGRGi4lBdq2hdOn4fbb4YMPqjsiEa/zakIxadIkZsyYgb+/P4sWLSpxPUFSUhLPP/88b775Jk6nEz8/PzZu3EjHjh29FZrHYmJi2LRpE35+fkRERHDs2DFGjRrF448/TpcuXUhNTWX27Nk8/vjjnDlzBnDtCvXkk0+Wq/+oUnZOSEhIoFGjRkooREREaqoHH4Q33oDQUNi3D2pIMd5Kc+IEfPMNdOgAl15a3dFIDeHVhKJVq1bExcVxxx138L///a/M9r/++itjxowhOzubmJgY1q1bVynf7Femdu3asW/fPvf7e+65h3feeadIu+3bt9OnTx+ysrLw9fUlNjaWxo0bl9m/EgoREZFaaudO6NYNHA5XUvHAA9UdUeUwDFi1Ct55B777DvLyIDwcEhOrOzKpIbz6tJ6Q4Npyb8CAAeVqf/nll/P+++8DsGnTJubPn++12DxVMP9q2rQpr7/+erHtunTp4l68bbPZ+OSTT8rVf3x8fIk/jRo1OtfwRURExBsMw5VAOBzQqRPcfXd1R3TuMjLg/ffhootg4ECYOdOVTDRoAC+9VN3RSQ3i1YQiICAAAIvFUu5rbrzxRnr27AnAzJkzvRLXuQgODna/HjduXKnVr6+//nr365UrV3o1LhGR85LD4XpQq8U2btxIVFRUoZ+NGzdWd1jlVtvjrzKzZsHZImi8+SaU8nxQ4+3dC1OmQJMmcOedsG2b6/iAAfDVV3DkCNx6a/XGKDWKV+tQNG7cmJSUFGJjYyt03ZgxY9iwYQObNm3yUmSeCylQdr1Dhw6ltm3Tpg1WqxW73a5pSiIif2J3OMnIzMPhdGIxmwkO8sFqKfA916+/ws03w2WXweefV1+g58hms3Hs2LEix2qL2h6/Nxh2O460NLDbwWrFMn8+phtvdJ285hoYOrR6A/SE3Q4//+ya1rRo0R/Hg4Lgppvgnnuga9fqi09qNK8mFL169WLnzp188803PPbYY+W+rkWLFgBF/gGrCVq2bMny5csByrUlrK+vL3a7HafTWWZbEZELQXqmjaMn0kk4lUmuzYHTaWA2m/DztdAoMojoACd1bh4Py5e7RihOnKjukEUAcKSlYTt0iLwjR3Dm5GA4nZjMZuo89RTuuRjDh1dniBV36hR8+CH873+uQnz52rVzJRETJ7oWmIuUwqsJxfXXX8/HH3/M9u3b+eCDD7jjjjvKdV3+FrL5/1uTdOnSxf06Li6u1LYZGRnuYn5NmjTxZlgiIrVC3LE09hxKJiPTRmqGjVybHZPJhGEY+PlaOZOaQ9j/PUid5WenjrRrB19+Wb1BiwC5+/eTs20bjtRUHCkpOLOzMZnNGE4nAf7+WABHkybY+/bFr7qDLYthwJo1rtGIb791rYsAMJvh6qvh3nth2DBXHQ2RcvBqQjFs2DC6devG1q1bmTJlCqGhoYwbN67M61asWAFQIxchDxkyxP36l19+4dlnny2x7apVq9yvu3Xr5tW4RERqurhjaew6kETC6UwMwyAk2JfGkUGYzSacToP0TBsp6bkE7Nn+x0VvvXX+bbsptU7u/v1kb9lC3tGj4HRiqVcPn+hoTBYLhsNB5pQpZBw4gDMyEp/du8HPD782bao77KKyslwJ+jvvwJYtfxyPjIQ77nCtl4iOrrbwpPby6qJsk8nEp59+SmBgILm5uVx//fXccccdpU5lWrp0KZ9//jkmk4lBgwZ5MzyPXHTRRe76GJs3b2bu3Lkltn3jjTfcr8ePH+/12EREaqr0TBt7DiWTcDoTH6uZqIbBhNbxw2x2fQNqNpsIreNHk4bBLH3iXbaPmMDKGQtJ7/eXao5cLnSOtDRytm0j7+hRTD4++LRogaVePUxnN5wxWSxYwsKw9OyJyceHvKNHXSMZaWnVHHkB+/fDww+7FlnfcccfyUS/fvDFF65F1s89p2RCPObVEQqArl27Mnv2bMaMGUNWVhYfffQRn3zyCYMGDWLAgAG0aNGCoKAgTp8+zdKlS/nhhx+w211D4Pfdd5+3w/PI008/7R5pufXWW1mwYAFd/7RQ6T//+Q/z5s0DXNW1Y2JiqjxOEZGa4uiJdDIybRiGQWR4AKYSplKYTSb8u3ViXYNm1K3jx9ET6XRsFV7F0Yr8wXboEI7UVHA6sTZuXOLfXZPZjLVxY/JiY3GkpmI7dIiAiy6q2mALcjjgl19coxELFvxxPCAAbrzRNa2pOuOT84rXEwqAyy67jOXLl3PzzTezZ88eHA4Hy5YtY9myZUXa5i90fuyxx2rsQ/jYsWOZMGECM2bM4OTJk/Tq1YsJEyYwYMAAbDYb33zzDQsXLgRc28x++OGH1RyxiEj1sTucJJzKJDXDRkiwb4kPZPlMJhMhwb6kZthIOJVJ2+b1Cu/+JFJFDLudvCNHcKSkuEYlyvF311KvHo6UFPKOHMG/c2dM1oo/ahXZRSokpPz9nD4N06e7FlkfPvzH8TZtXIusJ02CunUrHJNIaaokoQCIiYlh69atfPDBB0ybNo1du3YV265evXo888wzNXZ0It/HH39M3bp1eeutt8jNzeWDDz7ggw8+KNQmKiqKn376iVatWlVTlCIi1S8jM49cm4Ncm53GkUHluqZOkC+JZ7LJtTnIzMojtE6NX+Yq5yFHWhrOnByc2dn4lHM6kDkkhLwTJ3Dm5OBIS8MaFlah+xW3i5TZ3x+fpk3xbdkSS4Ht690MA9avd41GfP015G/razbDiBGu0YhLLnG9F/GCKksoAHx8fLjnnnu45557OHjwICtXruTw4cOkpqYSGhpKt27duPTSSwkKKt8vnOpkNpt54403uPnmm3n//fdZsmQJx48fJzAwkFatWjF+/Hhuu+22QnUrREQuRA6nE6fTwGQyuddMlMVsNmEyuRZr2x3adluqid3ufqg3lbNIr8lice/+hN1e7luVtouUOSAAe2IitoMH8e/a9Y8F39nZrkJz77wDBWt3RUT8sci6WbOKfGIRj1RKQvHhhx/Ss2dPOnfujLWcQ3KtWrU6L76579mzp7uyt4iIFGUxmzGbXVvD5tecKIvTaWAYrraa7iTVxmp1P9QbDke5kgrD4XAnIZTzmaisXaScaWk4kpNxpKRg2GxY/v1vrKdOwe+/w5kzf3TUp49rNGLsWPDTqJ5UnUpJKCZPnozJZMLX15cuXbrQs2dPYmJiiImJoUuXLljKmdWLiMj5JzjIBz9fC36+VtIzbeWavpSeacPP14qfr4WgQJ8qiFKkKEtICGZ/f8wBATjT0rDUq1fmNc60NMwBAZj9/YufnvQnf95F6s8Lv00WC5Z69TCHhmJPSCDw8cexxsb+0YG/P9xwgyuR6NHDo88pcq4qbcqTYRjk5uayceNGNm7c6D7u5+dHly5diImJcScanTt3VpIhIgIcf/hhHImJBA4YQPjkyWW2z1y5kuSz67Uavfoq1vr1ATj1wgvk7tmDX/v2RD722DnHdfLpp7EdOgRA+H33Edirl8d9WS1mWn3/Lm1PHCPbvw6HbnrE9e1tCZyGQVqGjbp1/IhO3E/CLU8AEPnkk+6pHvl/bgX/DKDwn09F1L3hhv9n783D5DrLM/37rFXV1VW9Sd3aLUuWLNuyLNsQbywOqzEO22AShhAgiUkmzIQhw2SZmd+VZGZCmGQykEwGAoGsw7BDgIADNgZjLO+bvEmWtXW31K1ea6862/f9/viqTld1Vy9qtVZ/93XpcquqzlY6sr73vO/zPGRuuSX+feP7xLIY+MM/xN20acHtg+PHGf3d3wVg9e/9HsnLLpvzGQO47aKLuGXTJtb83//L8Oc/D4aBmc3iXnQRyd27Sd9445LFt+HYGOU9e/Cee45gZARRLmNYFmYmo/a3axcdN96IuciTauF5FO+8k+rDDxOeOAGOg7NmDR2veAWdr3nNwucwMUHhO9+h9vTTRNPTmOk07ubNdL7+9aQWyV/yXniB4p134r3wAqJaxcpmSVxxBdlbb8VZJAy2fP/9lH70I4LBQfVEv6+P1LXXkn3zmzEXGJte7Fpn35eGbeNs2qTGjY4eRR44QDQ9jfQ8ME3Mzk6cNWtwt2zBcF2kEETT01i9vTibNmHYNlIIyj/9KZX77sMfGoIowu7vJ7l7N5nXvx7/yJEFXaT8wUGCo0fVZ6QkuOoqUt3dZJ99FvOOO+C//leo6zTC8XFG/sN/WPC7m81892vL971nD1N/9Vdk3vxmun/+5xf87KncE5rzlxUpKD7zmc/EhcTTTz+N3xADAbVajUcffZRHH32Uz3zmM8BMkdHcydBFhkaj0Zwb+MPDcTEBULrnnlMqKAA6X/1qql/+f6RqRfz9+3F37GjrmCOkZHyyimEYdKZdMg88QQjY69efdFCYmcksWYRqzLfojiKmPvtZBv7gD5bl1tNgTUcHn7/5Zq7sUxa4sliEjg4MwyCamKA6Pk710UcpfOtb9P3Gb5BYYCRYlMvkvvhFyvfdp8S4jWtIJkFKtb+JCaqPPUb+61+n533vo+PlL2+/r2qVsY99jKDuBmQkEsggwD90CP/QIaoPPwyvfGXbbf2hIcb+6I+QlYratqMDUSxS27uX2t69ZG69le55MpjK993H1Oc+p87fMDBSKaKpKSr33Uf1wQfp/fVfn/ecpz7/ecr33qt+Y9sYtk04MkLxn/+Zyv33s/r3fg9nzZplXeuqj34U03VbtnMuvpj8N75BcORI04sOBAEil8PL5fAHB0lddx2yVALTxOrqwt2yBVGtMvHnf47XMKIxDMyODoLhYYKhIUp3303Hy19OVO9+zP47UX3iifh8MU0wTcJMhuLOnZR276b/d38Xt1n0bZqYXV1tv7eW76JUUraypqn+niyAPzhI7h//cdF9wqndE5rzmxUpKO644w7uuOMOAIIg4Omnn+axxx7j0Ucf5bHHHuOZZ56Zt8hokEgk2LVrV0sn44orrtBFhkaj0ZxhGou15NVXU3viifgJuLN27bL32XvzKxn++lcxwoDVR5/h2e4NZDtdMmm3JSm7UPIxDIO1q9PsWG0TPv8soAqSk2XgD/6gpXuxXILBQQrf/jZd73jH8nYQRXzhda9jW1cXB/N5Pvb44/zXL3yB6+vhraJapfbUU+S+/GWi8XHG//RPGfjDP8QZGJi7q1yOsY9/nPD4cUD9GXW+5jUkduyIOxFRsUjtyScpfPvbhCdOMPmXf4n89V8nfcMNc/Y3+elPExw9itndTd8HP0hy505kFFHZs4fpf/gHvH37aDfkY/g+E//zfyIrFdxLLqH3jjtw1q5FVCoUvvMdit/9LsXvfQ9n40bSN93Usq138CBTf/M3ICXpm2+m6/bbsTIZwvFxpv/+76nt3cvUZz6Ds3HjnMKgcOed6v60LLrf8x46X/1qDMfB27+fqb/+a8KxMSY+8QnWfOxjc/QOS7nW3D/+I72/8ist21X37ImLCWvVKuzVq7H7+zE6OojGx6k9/TSyUqF6//24O3bE3SErm2X8E5+Ii4nMm95E5rbbsDIZomKRwje/Senuuynfdx/2pk1zXKS8AwdUMWEYJK+8EueiizAsi2BsjOojjyCDgIlPfpK1/+N/xNdq9/Wx/n//7zZ/YjPU9u1j/OMfB6D73e/G3bBh3s96Bw8y8b/+F6JcXnCfoO7j5d4TmvOfFVe6OY7DNddcwx133MFnPvMZHn30UYrFIo888gh/9Vd/xR133ME111yD4zhIKeNftVot/syv/uqvcvXVV2uHJI1GoznDyDCksmcPAOlXvALn4osB1aU4Fcx0mvTPqCfOa0cP0NNhUSwHHB7Oc2goz+HhPMVyQHcmwfqBTi6/pI/ew0+rp6iOQ8dZXoAUvvMd/OYn1CdBet8+tnV1kfc8bv/BD7hreBjpzOhCzFSKjuuvZ/Vv/zZGMomsVCh885tz9iOFYOIv/kIVE7ZN34c+xOqPfITUVVe1jDVZmQzpV76Sgf/+30lcfjlISe4f/oGoWGzZn/fii9TqicmrPvQhkjt3AmpmP/3KV9L7q78KQMf+/WyZ9e9x5zPPqHGWTIbVH/1oXGyaHR10//zPk66PSuW/9jXldtRE4etfhygisXMnvb/8y1j1J+T26tWs+vf/HmfTJqTvz/kORK1G8TvfAaDr7W8n87rXYdS/x8Sll7L6d34Hw3UJR0Yo/+Qny7rW8k9+QjAyMvOdhyGFb38bQI2k3XQT9sBAHFwnymWseuEnfR8DSO3eTWLbNmr79lF74gkAsm97G93vfnd8rVYmQ88v/RId11+vukpjYy0FkAxD/AMH1LXt2KFGqurvO/39agTPNInGxuZc60JEhQKTn/oUCEHqZ36GzBvf2PZzUgiK3/8+Y3/0R4hZ9818lO6+e9n3hOb854xYZziOw7XXXssHP/jBtkVGo4CwbXtOkaHRaDSaM0f18ccRxSKG65K88krSN94IQOWnP0U0dZqXQ7r+RN7ya1wZjbB5fZbN67vYuCbD5vVdbF6f5fJtfdx0zTo2r89S+elPAei49tp4IXamcbdtw+joUKNPf/3XyJOwAW3QNTUFwEQmw3v/zb/hIx/5CGvbdHuctWvpqHcRak8/Pef98r334r/4IgA973kPHdddt+BxzUSCvl/7NQzXRZTLVO6/v+X90t13q2u85BISl146Z/uO667DXrsWQ0o+/gu/wEc+8pH4V/b55wFI33wzZkfH3Gt+61vBMIgmJ/HqnwUITpyg9swzAGTf9KY52xm2TebNbwag+uijiKZ1QOXBB9Wojm3T+frXz9nWXr06LjzLy7xWpGz5nrwXX4yfzne9611KB3DNNSQuuYTEtm04F19MateuGbG2bcejedW6ntRMp8nedtucYwJ01hf0slolnJyc+Z6Gh5G+D6aJu2VLyzYyisC2ceqdhdnXuhBTn/88IpfD7Oqi5/3vb/sZ78ABTvx//x+5L3wBwpDk1VcvaYyq9MMfAid/T1wIyDAknJoiHBsjnJpa1v8nznfOaA5FM40io1FoAPi+z969e2M9RrO4W6PRaDSnn8bTzuRVV2Emk3TccAO5L31JLUgffJDOelGwHBKXXYbd3084NkbPwb1sefPPUq4EhJHAtkzSHU5sEesfPqwsNIH0MsadVgqrt5f0q17F9Oc/TzA0ROGf/omud77zpPbRYdvUgEt7e/mzj398QUF6+tWvVo4+nZ1IKVtm6ovf+x6g9CTpRcTS8fn39ND5xjciq1XcerepQWNR13ha347kFVdQGhnhlQMD3P5f/ysAwegoo7/92/H78x3XXreO8Ngxqk89FX8u1hLYNokdO9ofs34+Mgjwnn+e1NVXt5xvYutWzFRq3vMt/+hH+AcOICqVeGF7Mtda3bs3/jM26t2xKJfDWbMGw7ZJ7d5NcufOliTrYHiYaHoagiDeXzg6ChALttvhXnSR0nJ4HuGxY9h1jU04MRF/j4bT6nIWu0j19BAMDs651vmo7t0bd0x6fvEXsTo7236u8tBDynEqmaTrne+k8/WvX1ToHYyOEtUL55O9J85nlh1EeAFy1gqKdriuq3MdNBqN5iwRTk7GT8bTr3gFoGwzU7t3U33sMcr33HNKBYVhGKRf9SryX/satb17MaoVujLtFzXlenfC6u9XYztnkc5Xv5rqo49Se+opCt/9Lqlrrpnz1Hgh3M2bqT35JOHoKFOf+xzd73oXVnd3288mtmwh0Wbf/tGjypUI6Lj++raC9vnovv32Oa+JclktgAFn3bp5t7XrGobg2LG4wAmOHYvfX2hbZ80awmPHCIaH49ca29qrV89ZKDewMhnMdBpRLhMMDcUFRbztEs4XKQmOHSOxbdspXWti69a2AnnDtuMEbBmGsYmB1SyQrgvm5ysmGvuhPsoU5XLxcUWhADBHMN3iIrVxI5UHH2y51vmQYag6DqjCfqHulplOt+g9lsKp3BPnK8sKIryA0WlBGo1GowGIXYPMri6Su3bFrzdGlfxDh/Cb/e+XQfqVr1RuNVFE9aGH2n5GhqFaKAGdr3rVSS2eTxc9H/hAPPo0+dd/jWx6Er0Yna95TbwwrPz0pxz/yEcY++M/pvDtb+Pt369GWxbBe+GF+OeFnrIvlagpDK1lETyLeJQnCNS4UfO2ljVvYdS8bfOxGj8vdMyWbXO5Ods2nuIveL5Nnz+Va10KpXvuiQuARvEDYNXPc6HFc5TPx45IMgwJjx9XY9/1Ua/mTowUgnBkJHaRan7C33yN7Sj/9KdqW1i0w9b19re36D2WwqncE+cjjSBC/+hRoqkprK4uEtu3k7j0UhLbt2N1dRFNTeEfPUr1ySfx6nqYC5lzqkOh0Wg0L1UqP/1prBk4G0gp465A+qabWgSiyauuwuzuRuRylO65Z44Lzslg9fSQvPJKak89RXnPHjpf+9o5n6k+8YQSgpqmKkCWyYk/+IMl2cYmd+2ir+5UOB92by8973mPchI6doz8N76xqB9/A6u7m9Uf/SgTf/mXROPjEEV4zz8/M0du2yQuuYTkVVeRvvHGtuFp4djYzLmsWrWk4y6EqFbjn+e1zKX16bqsViGTUf9dZLvm92XTsRo/L5aN0Thu83k2fl7wiX/TfhufP5VrXQzv4EHyX/kKAM7mzSSbCorUrl2Uf/QjwhMnKD/wQFuXrcYYW+P4MggIDh+eKVibk7Knp8E0cTZuVC5STfdB8zXORkpJ8c47AdWdOB1Py0/lnjjfONkgwmBoCMM0sQcGLujxJ11QaDQazTmA4TjqCfgiSN8/Lf8Ye88/T1RftM5exBumSfqmmyh+97tUHniA7ne/e9F57YVI33wztaeewj9wgHB8fI61a6OwSV511ZKSiedjqe40cgmWmKC+l8qjj1J74gmKd95J6mUvWzAvohn34otZ87GPUb7nHsp79sxkCwCEId6+fXj79lH45jfJvuUtZH7u51oWKbJJnDxfcJsUguMf/vCC5xFbija57CyUr9E8liSjqOW/s21Z52xb32+zo0/88yKZHvFx68dq+Xmh821+r3GsU7jWhfCPHFE2qb6PkUrR92/+TcufWfLqq3G3bME/dIipz30OUSiQfvWrMZNJolJJ2aj+y78oZ69aLQ4kbATYAUTj43hBgJlKYfX2qs5EfYSmxSlpAdek2pNPxt2J7Fvfuuh1LYdTuSfON/xDhxYMImxgmCb2unUEhw/HrmCp3bvP7MmeQc5KQfGaupjsne98J7/xG79xNk5Bo9FozilS11130knZK0kje8LdurVtSnH6Va+i+N3vIn2f8k9/SuYNb1j2sVK7d2N2dSHyecp79ij3lzqNp38AnTffvOxjAHNStFeC3g98gNEXXkCUy0x99rOs+W//bcEn5s2YiYSaTX/Tm4imp6k9/zze/v14zz8fC3il75P/2teIcjl6fumX4m2bF7uiVGo/ViIlIp9f0rm0LJ4XcKRpHu1qLAYb17vYorux3+ZFfHzcRVxw4uM2b+u6KqF6ofNtei8+31O41vmoPfccE3/+58hqFcNxWPWbvzknp8UwTfp+8zcZ/9M/JTx2jNwXvkDui1/ETKUQlYrK4Xj1q5FBQGXPHqyuLjpf/3r8Q4fU+GEUYfb2KhF6G5Fvu2ttR8Py2dm0ieRp0iOdyj0xh0oFzkWXz+9/H1mpEPT0EOVybYMIZ2MYBlb988HgIMmdO08pIPNc5qxc1Y9//GMMw2D3BVypaTQazfmCKJep1oNG/YMHGWpayLajdM89LQWFd+AAE3/xF/N+fnbQlmFZpF/xCtXxmFVQVPbsgShSo1FXXbWcyzmtWN3ddP/iLzL1mc8QjoyQ//rX6X73uxfcZnJykntm5Xi85jWvoe/GG2Nb3nB8nNKPfkTpBz9A+r5KUH7FK2KBdvN4S5TLtS0oDMti4z/8w5zXS/fcw/Tf/V3rZ5tn8xfQcDS/9+0f/IDIdek/coQti2wHqMU/rTqAxs+Lblt/v3lbI5lEet6C+pXGMRufh+VdqzGPixRA6Sc/Yfpv/xaiCCOZZNWHPzyvY5Hd28vAH/wBpe9/n8qDD8bC+uQVVygL2quvZvzP/gxQYvSGCYLZ2YnI50ls3kzn616Hlc3OWYi2u9bZiGqV2rMqHLKjzcjVSmHWj7+ce6KF73wH3vlOOEWL6tOFAXR2d1P96EfnBBHOh5nNEoyOImo1okIhFvNfaFyYZZJGo9Folkz5gQdOSmQcHj9O7fnnSV52GaCeOi71yXiDRscjHBnBP3Qodk2KdRyvfOWC9qpnk/RNN1F99FGqjz1G8V/+hdTLXjbvGJLwPA499BB/8qEP8ej4ePz6nj17uKFpgWevXk33u95FcufOOMW4+thjcUGRuOSS+LPe88/jbt58Stdg9faCYahQtQUEso33amHIv/rFXwTgZ9et4+9f+1oIQ6JicV7xbizAbhpba4iiw0VEue22tfv68PP5JZ1v87FO9loNx2lrqSqlJP/Vr1L8538GwOzqYvVHPrKo45eZSJB9y1vIvuUtbd9vOCRZTd20xrXKIJh3AboUsXn1iSdUR8cwVIjeaaIhQF/OPdFCqXTOFhMxvo9hmouOdzUwLCt2f1qsM3c+owsKjUajeYnTnD3R96EPzfs56fuM/If/gPQ8Sj/8YVxQJC+7rO2T8YVw1q7F3b4d/4UXKN5/P9aq9VSPHCUcHFThV9dcH+dTzCaMBEKoGfNCySfb2/5zp5Oe978fb/9+RKnE1F//NX3zjO9WH3uMNV/6Et+45Rau/epXGV9klCN5+eU4GzcSDA0hmhyO3EsuwertJZqaovzAA2TahMKdDGYigbVqFdH4eDxu1Y7G7P0LTQXj/qbzCkdG5l08BvX9No/QNcLYovFxZBi2Hf+ICoU4TK5l2/Xrled/U5L1nPNtXIthxPalJ3utdpuRP+F5TH7qU3GOg712Las/+tEljdTNd52grJqjeuZEc07Icq91NtX6+bqXXLKgO9ap0vzndLL3RAvvfjfcfDPUXbPOGaSEb36TaGqK4hVXII8dQ0bRkooKGUVxPsVi2qHzmQv3yjQajUazKP7RowRHjgDQceON8ehCW5JJUi97GZX776f62GNE+TzWEhJ058O+/ib8F14gf/9D/LTzajY8+SO2AeO9G/nR3gKrhgI2b8iycU2GTNqlWPYZGi0yMlZmsx/hAo89ewJzNGJtf5qNazJnzAvd6uqi+73vZerTnyYcHSX/ta+1/VzzwunnNm/mb/btW3TfjbERq78/fs0wTTJvfjO5f/xHgiNHKN51F5k2adHtdyjbvpy87DLK4+PUnn123qfnjXGZPU0L8eOVCmE2i10oUHv2WRLbt8/ZLpqeJqw/eU/UC09AhdkZBtL38V58kWSbcLvGMbGsln0nLr+c8n33qSA3z2vrFNXY1tm8uWWs5mSuNdl0vlGhgHfwIIWvfjW2f7V6e+l4+csJjh3DSCTmde6p7t3L5Kc+BWHIuk99CrON1qZhj2wkky1Bf6dyrc00ktXb/RmtJPaqVXFo5cneE3NYu1b9Otf4vd/DDEPM730Pc2oKUSgsyTQiDiJMJi9ol6dzs5+s0Wg0mjNCQ4xtJBKkrrlm0c/HDlBRFG+7HI4cK/CotZ7QdnEqBUr7DzBwbD8Ae7PbOTycZ+/+cR56coT7Hz/OA/X/PndgkiPHCoShcokZHi1y5FiB5w5Mcv/jx5mYPnN2lOkbbiBVD2JtCMln4150Ed7AAAAf2bWLnYvMT1cefljN2Zsm6VkjKp2vfS1u3fIz96UvUap3lhai+tRT5P/pn9q+11HXb3h1cficc3noIcLRUaRh8IWmHAyASv08Sj/8IVGbzIb8t74FgN3f35JpYq9aFS82i9/+NnJWsSPDkOJ3v6vO74YbWkbJUtdco1KlfZ/S978/55jh+DiV++8HIPO61y3rWjFNOuvGMd6BA5TuuovCN78ZFxNmZydmXx/+4CDVxx+ndNdd82YMuJs2IatVpO9TfeSROe9HuVxsG5t5wxtaioZTudZ4//k80eSkOpeTCGJcLg2NxsneE+cThm3jbNqE1d1NND095/6dTRxE2N2Ns2nTBSvIBl1QaDQazQWFqFSUc9Aiv0Rd3Fp54AFALWAWywYA9WSxMS9d+tGPlmX/eORYgedenOSFY2UO9amF6cuOPULWL+A5KXIX7cB1LIJQMDJe5pkXJnj06VEOHJ0mV/TIpB1sW/3ztXFthkzaIVf0OHaixLETSw8kWwl63v/+OWnGs8m96lXkPY+uRIJv3nIL/9+11+KMj7d8d8GxY+S+/GUmP/1pALK33YZdL0QaGKbJqn//73E2b4YgYPpzn2PsT/6EyoMPEjVZ5EbFIuU9exj74z9m4s/+LNa3dMyyA05efjnJK68EYOIv/oLq44+rUDUhKN9/P1Of+xwA5R07ODprgVi86irl1FUoMP4nf4I/OAgoEXDuK1+hXBehZ9/xjjlamK53vQsMg9ozzzD1mc8oC04gnJhg4pOfJBgcxHBdsj/3cy3bmalU3F3If+MbFL73PUS9m+MdOMD4n/wJ0vex162LC4iTvdb0K1+JPTAQB5fV9u+PO3hmJkP65ptJ7tixpOAyq7s7TqTO/d//S/WJJ+I/89rzzzP2sY8hikWsVavI3Hrril1rA7/Jmnih9OqVInPLLcu+J84n3C1bVGfWNOMgwnbMDiI8E0Xd2eTCLZU0Go3mJUgwOMjYH/3Rop8b+G//jXBkJJ5VX6oDjGEYpG+6icK3v000OUntqada0oEXo1j22XdoiqGRItVqyMGBy9h+4lnW5tUT4OF1l+EmXZyEpFwNCUNBoeSDIenscLlobYZEYuafLtM06MokyHS6jE9WyRc9Gt4rSw22AzXGsuYP/3DJ1xFvl83S80u/xOT/+T/zfiZYtYpfuftu/ueNN7Kjp4c7Lr8cvvY1hr/+dcyODkSt1pKxkH3b2+h6xzvaHy+Tof8//2cK3/gGxbvvxnvmGbxnngGarDtniVoTO3aQfcc72o4X9d5xB2P/438QHjvGxCc/qfYhZSzST1x2GcNtwgVlIsGqf/fvGP+zPyM4coQT/+W/YHR0qLyM+qI5+5a3xC5WLeezbRvd73kPuS98gcqePVQeeAAjlYoTo7Es+v7dv5tjwwqQufVWtYh/+GHyX/oS+a9+FcNx4pwOq6eH1R/9aNvZ9qVca88v/VJLcFnjCT+ohXHprrtav4coovbMM5R+8AMM1yWxfTurmrJAut/7XoLhYYLhYSY+8QlwHIz6yBco967+3/u9trkup3KtQItRwnymASuJmU4v+544n7CyWZK7diGFIBgaIjh8WAXZZbMYCwURXsDjTqALCo1Go3nJUqqPLJmdnSR37lzydh2vfCWFb39b7eOHPzypgmJotEip7FOphdi2wWRmDYXOVWRLSpg6uFE9RTYMg3TKZmK6BkiiSGJbJoVywOrE3H+6TMOgvy+FODTz2lKD7aA1q+Bk6bjuOiqPPEL14Yfn/czeqSlu+e53uXXTJm5et463v/zlJMIQUalgdnRg9fWR2rWLjle8AmfNmgWPZyYSdL/73WTe/GYqe/ZQe+YZguFhomIRA1Uc2QMDJHbsIPWyl+EuYG9pdXfP2Jo+9FBsa+ps2kTHTTeRef3rOdhmXAfUXP6aj32Mwne+Q23vXqJcDiOZxK1bnXbUx8HakXnDG3AvvpjinXfi1XM9zK4uVfzcdhvuRRe13c6wLPo+9CEqV19N+d578QcHkZ6H1d9Pavdusm95y7wLt6Vcq2Hb+M8+GweXNQcKEobz5ljIMER6Xlygx8fMZOj//d+neOedVBvjbFLibNpE6mUvI/PGN86rfziVawWl/2hwKkGUJ8Op3BPnE420ccM01WhZLkcwOhq7ObULIrzQMeRiA2CnAdM0MQyDD3/4w/yv//W/zvThz1s21N0xhuuznBqNRnM+EUaCex8e5vBwnlLZp+ZHuI5FMtH+CauUMDFdpeaFOLZFKmnRk01y0fos27/0CdxSjgPv/ghBZkYYmS96FMsBm9dnefXPbDjj7k/teOCBB7hx1lPZ2bax5zLn+/mfDDIMKX7ve3gvvojV1bUk0W00PU2Uz5O45BIyt956Qc/Ja1qJCgXlxjU4iKjVYjendkGEFzr6rtdoNBrNGaFUDvD8iEo1xLJMwiggk54/ZTqKJIYBQkhs2yAIIoxSAd+f/2lrJu0yMV3F8yPKlYCuzOK6EI2mQVQoIGo1RLWqg8s0i9IIIkzu3Kk6QmEItt02iPBC56V1tRqNRqM5a0RC5UcYhvq9gRH/3A6JrH/GYMeTP+btf/cHOKHP2O4bqOy+XO1jViCfaarPCyEJo5MXjGte4oRh/JRZB5dploph2y/5QlIXFBqNRqM5I1imiWkaSFkPLkbGP7fDwKh/RrLjqXtxQiVk7X/yAY5fvIaos5Ob3vsGvFVrKG7dQWnrDgoXb2c0tR5zbeacGHfSnGfYdlwc6OAyjWbp6Dtfo9FoNGeEzrRDwrXoSNmUyj62ZeL50bwaCstSxYdpGvzT7b9Dupxjy3MPkbt8N9JU25hRSObQfjKH9sNdyuf+ZUCQ6cLefRVctQt27YIrr4SdO6Gz80xdruY8xMpmMZNJzFRKB5dpNCeBLig0Go1Gc0awLZO1/Wmm8zWK5QDXUQVFwrXm6VIozxDHNvGEw7c++n/o7+tgda9yxXGmJxm99hY6D+0nc3AfnQefp/PQAWy/hlPMw30/Ub+a2bpVFRe7mgqNrVthieMtmgubRnBZODFBNDWF2d2NscBcXhxc1tt7wQeXaTQLoe98jUaj0ZwxNq7JMHi8SEfSJl/0AEm5GpBOOS1FhZQqh8KxTaJIYlnKJSqbnrF3DXr6mLr2RqauvREhJeOTVUIv4JJokpeLMVIHnoe9e9WvQ3U/2YMH1a/m9OhUCm67Db70pSXnVmguXNwtW/APHiTK5QiPH8det65tUfFSCy7TaBZCFxQajUajOWNk0i47tvQihMQPI4ol5fwUhoJkwsZ1TPxAqNcigWUaZDtdEq4FBoxNVcl2umTSLqapxNfFsk+h5GMYBmvXZNl0ycWk1s8aPSkW4dlnVXHx9NMzhUYuB9UqfOtb6ueXuLBS89IMLpNh+JJ3KdKcGvpu0Wg0Gs2K8keffojpgnfS23UkIyIhQIIX1JN1O12uuWIVXZkE+aJHqeyTL/lMTFdV4rCUJFyb8akqk7ka+aLHra++eO7OMxkmd1zFw/4ALyRfztT2GrXbQlKuSRc+29amuDZKMDebuT2RkDx7YIJnDkwyPFoiX/QIQkFH0qY7m2Drxm6uvnw1G9ZkFt3X4eG8yuc4VqDmhXR2OGy7qIebr9vAmlULJxw/9swJHnxqhONjZYSQdGcTXLl9FTdft4GO5PxhfX4Qce/Dwzy1b5yJXA3bMljd28HLdg5ww+61mOb8Yz6Zrn4ePxBxz5MPUSj5pJI2G9Z0ctM167ls68IF2XKvtVIN+Onjx3nuxUkmpqoEoSDd4bB5fZYbr1nHJZu65922XAm495Fhnn1xkqmcCqrr6UqyY0sPr375hjnWwi8O5virb4wAA/VfwET9V0z99Qg4BP/pDRuZbVD8wpFpHnxyhKPHC5QqAbZlsro3xRWX9PGKa9eTSrZfgn3le/t5+OkT814PgG0ZfPyjcxPM/+pLe3nxaG7BbVf3pvidO16uTl/nKGhWCF1QaDQajea0YNsmqXkE1wBCgoikKiKAgVUdRELi+xGjExUACiWfnmyC665aS7HsMzRaZGSsjOdHCCExTYOEa1HzQiZzNZJtUrQB7n14mDt/cpgwkvG5JRM2tSCiFFocO+xz75HHuOHqdbztdVsxF5ibf+7gJN+860Wm8zNFk2kaJF2LSjWgVAkYHi1x7yPD7Nzex9tfu4uhoaGWfaxevRqAR54e5SvfewGJcrtKuBb5os+jz5zgyX3jvOe2HVx56aq25/GVO1/g4b2jgBKw25bJ+FSVex4c4rFnTvDr774q1ps0U/NCPv3FvRw7UQLAdUzCUDA0UmRopMhT+8a54/adOI76s7vmmmvi85+Y9vnq3cc5PCIAj2TColwN2Hdomn2Hprn5ZzZw28+2H/1Z7rUeO1Hi8197hkJJuXzZloFlGRRKPnv3T7B3/wQ3X7eB226ee9zRiTKf/fLT8baWZWAAY5MVxiYrPPL0CX7lX13B5g1d8Ta2aZCpj9ZJISGKkFEESCXrMQAMKpGJBBzHVB20OlJKvnHXizzwxEj8WiphU/NChkdLDI+WePjpUe64/Ur6++ZmqhwbU2nbyYSFY7cfwZvPwex4/c+0I2ljWe3v4c4OdW3egQMqzbqe9Cyq1Zak53BiAv/gwZdM0rPm1DgrBcVrXvMaDMPg0ksvPRuH12g0Gs0ZYPeO1fzCmxf//3wYCcqVgJof4fsRJybKfOl7L8Tvf+8nR7hiWx+ZtMvlW/vYvrmHciUgjAS2ZZLucBipL8La8cjTo3znR4cwgFe9bD03XL2OVT3JuMNxYrLCfY8e46GnRtnz+HEA3vH6S9ru6yePDPOdew4hUQuzV//MBnZuWxXvT0jJ0EiRh/eO8sjeUZ55QT0V/833Xo09a3F49HiBr/7LASRw3VVruPXVF5NOOUzla3z9+wfYf3ia//fP+/it1dfOKQx+/PAwD+8dxTQN3vrarVy3aw22bXJ4OM+XvrufyVyNv/36M/yHX3kZ1qxuwxe+s49jJ0pkO11+4dZL2X5xD5GQPP7sCb5514scGsrzzbsP8q43bQcgkUiwYcMGal7I33zrUTxfcNG6LD9/63b6+zqoeiE/fGCQHz80zI8fHmZtf5prrxhYkWuteWFcTPR0JXjnG7axbXMPpmkwMV3lX+47wpPPj/Pjh4ZZ1Z3i+t0zPaYoEvz9N5+jUPLp607yzjduY2u9k3FwMMfXvn+AyVyNv/+n5/jtO15Oql6Mbt7Qxe//29YU8NkjQU8d8/ji9w5gAO+5bQfp1Ew36CePHIuLiVdcu47XXL+JbKeLH0Q8e2CSb99zkOm8x+e/9gwf/eVr48Ktcc6jE+pe/sC/uoKtG7vb3oftmMrXqNRUDsZHPnANPdnkvJ/1Dhyg+uSTBENDIARWTw/Oxo2tI11TU0S5nMrXAF1UaBbkrKjP7r77bu666y5+7dd+7WwcXqPRaDTnELZl0pVJMNDXwca1Gbq7WhdC5UrA17//4pzP93Wn6MokFsybiITkX+47AsDPXr+Rt7x2K6t7U7HI1jAM1qxKc/st27n+qjUAPPDEccanKnP29fzBqbiY2Lqpi9+54+X87HUbW/ZnGgYXrcty+y3b+cC/2olpGhwfK3P3A4Nz9vf9+44ihGT75m5uv2V7vCjt7UrygX91Bev60wSh4Af3H2nZzvMj7nlQ7e8NN13ETdesi4uVizd08Wu/sAvHNhmbqvJIvYPR4OixAs8fnALgvW+9jO0XK1tUyzR4+ZVreNebVAH4yNOjjE22fgf3P36cQsknnXL41XftjJ+upxI2t928hRuuVov5O39yBCHkilzrg0+NUij52JbBr/38Li7d0huPY63qSfGLb7ksHrP6wf1HkXLmuPsOTzM+VQXgfW+/PC5ETNNg2+Ye3vd2FY5YLAc880LLPNMcGsFldn8/UzLJ1+86CKAKyu0zXZUoEvyw/mf9M7vW8LbXXUK2U6XBu47F1Zf38/53XIFpwGSuxiPPtI42jU5UVEI8sL7/5CyOGx2ndMpZsJiICgVqe/cSDA1hOA7OxRdj9fTEmRuGZakCY/NmDMchGBpSnYxC4aTOR/PSQttZaDQajeacZccWtVh8+oUJnnhu7KS3n5yuki+qcZerdqxe8LNveMVmAKSEF47kWt4LI8E3fqCesK/qSfKBd1wx7wx8g8u29nLT1esAeODJEaKm5O6J6SovHJkG1KJ0NrZl8rPXbQTg6Rcm8fwofu/J58eoVEMsy+AV166bs21vV5Jrr+gH4LFnWxes99c7MBety3Jx05hPg92XrWZ1bwop4fFnW7/vPU+oba+7ak38NL+Z192wCQPIFTwODuZW5FqfPzgJwGVb+1jVM3d8C4gLmULJZ2yyGr9+oj425zgm69osztf1d8Z/hs3jawshpOQL39mHHwjWrOrglldtbnn/yLFC3CV45cvWt93HReuyrBtQ53NwMN/yXqMo6OtJzTu+Nx+NbdcPLKy78Q8dIsrnQYh5HawADNPEXrcOhCDK5/EbTmkaTRt0QaHRaDSaFaXxjNjzQ3IFj7BpIX2yvOlVm+MRmG/e/SLFsn9S2/vBzOK0MUoyH9lOlzfffDG33Xwxm9a2iqn37huPheZvetXFS17s3XTtOnZs6eG112+M9RtALJy1LGPesZbtm1X3IAwFLzYt0Bs/X7QuO+95bKtve+RYgaoXztl2++b2x2ze9vlDU/Fr41MzhVnjvGbTlUnQv6pjzrancq0Xr+9i5/Y+Lr14/oC5bNqNf641XWsqqZ64B4GIF9vNjE9VqNYX/+20Ju148MkRjp0oYQC3v2n7nO6YY5tcu7OfbRd107/APhtdi+bzhZmiYMPAyQcwzhQU8xsByDAkGBwkyuVUV2IBrRCoDp7V00OUyxEMDiLDcMHPa166aFG2RqPRaFaEhmjaqy+Sxqaq7HniOAnXYm1/mo1rMmSaFn9LwbFNfv7WS/k/X3iSSjXk698/wPvfccWSt1/d2xFb0X7nnkMkEzaXb+2ddyHVeFI+m6f2q5EY1zG54pK+JR9/VU+KX739yjmvN56e93Yl52grGqQ7HFJJm2otZHS8HB+3sW07QW+DxgJZSjgxXmbzhi6qtTAWJy+4bb0TcGKijJQSwzA40VSMLXbcExMVRsdnPn8q1zq7A9COw8Mzozjd2RmvpUsv7sUyDSIh+cdvPcc737idLZu6MFCF1lfvVDqdtavT7JpH+N5MpRbE43PX7hzgonVz3Y82rcuyqc3rzUSRYPB4UZ3vLIepRlEwsKqDB58c4ekXJhibqmCZJgOrOrjm8n52Xbqq7f3bEGSv6kly78PDPHdwkslcDdc2WTfQyc/sWsOWrETUaohqFWdj+3t9NmY2SzA6iqjViAoFbG2trGmDLig0Go1Gc8ocOVZg36EpSmU/fhJfqQYcOZYn4dpM52sMHi+yY0svm2dnRCzC5vVZXv3yDfz44WGeOTDJY8+emCP6nY+Ea/Gql23g7gcGKVUC/vbrz5LtdLlsSy9bN3WxeUMXvV3zz5vH11dftG7Z2DXvovhkyJdUt2P2gnI2XZ0u1VpIvjTTmWl0ChbattkKtbGtChJUNC+8526rir4wklSqIekOJ95HIxdk/vNNtBxT/bz8a10Mz4+495FhANYPdLZcd29Xkne84RK+9v0DTEzX+Ksv7VUuT4ZBGKqMk5+5coDbfnYL1gI6nAb3PjysRs1Mgze84qIln+Ns9jwxQqkSAHB5U3EqpGSkXoj98MEhwrC1szcxXeXZA5Ps2NLDL77lspbuVLFupwzwzbsPztl2bKrKk8+Pc+0lXbw+UtawxhLT4Q3Lit2f0B0KzTzogkKj0Wg0p8SRYwWee3GSkfHGE231erka4o2WkFItlkC5JNmWOcfS8td+fhdrVs8/+/3GV27muYNTjE1W+Ke7D7Ltom6ynQsvUBu84ZUXUfND7n/sOBI1a//Q3lEeqguWu7MJtl/UzZWXrmbHlrljIFUvpFxVC8CFxK7z8fTTT/PBD36w5bXbf/XPAHDdhRd1DQcgr2k0puarn11n/kWw21T0NDQJtSZtguvMf1zHnnmv5occOriPT3zy/7H5ijfheRVuuOEGPvvZz3LllXM7L079nDxv5li1+s/LudaFEFLy5e/tJ1cfRWu3yL/uqrWs6knxt994lpoXEUWSxlCelBIhwQ8EHYtMPHl+FDs3XXNF/5KK0HYMHi/wvXsPA7BhTSeXXzLztH9iqhr/WdmmwZtfu5Vdl64inXKYmK5y32PKiWzfoWm+8J19/Mo7d8bbHm8a6epI2rzpVZu5bGsvSddmZLzMDx8YVMX4i3mMTBc/KwQyipZUVMgoivMp0GF3mnnQd4ZGo9Folk2x7LPv0BQj42Uc26S/L8WRY+ppvhASf5bbTxRJ/GCupiKa9bnZOLbJL9y6nb/8v09SrYV89V8OtCyoFsI0DN72uku45vL+OByt1rTgzRU8Hn76BA8/fYKL1mV45y3bWdtU3DQvjjtS8/+zec+DQ9z36PCc130/YPdrf5tHf/JlHr3vywC87f1qn7MtXWdj1wuvZtekxs8LPVW3mgqKxueb9zFfRgGAbc+8J4SkVCpx7NhxNl8BvlfjwQcfpFSaq0lQ56uOK5rclho/L+da50NIydf+5QB766No1+9e23YU7Z4Hh/jBT4/QnU3y7tu2cMmmbqSUvHBkmu/86BCPPnOCg0M5PvSvr6J7gWLxkadHqdRCTANee8OmRc+vHcdOlPjcV58hCAVJ1+I9P7ejpXgNI8EV2/rIFz3e9abtLULyNauVE1km7XL3nkGePzjFcwcnuXyrumbTNLhsay+VasB733p5Swdq49oM73/HFXzxu/t47JkxHi12sCvZy5pCAatnfm1KA1EoYKZSmMmkDrnTzIsuKDQajUazbIZGi5TKPlJK+vtSLQuktf1pdm6bWeRJKRkeLdGdSXD5tr54MdQgjASlckCh1N5xZ9O6LDdft5F7Hhzi+YNTPPL0KC+/cs2Sz3XTuiz/el1WzbCPFDk4mOfQUJ4jx/JxkXP0eJFPf/Ep/u17dsdaAaepE1Cuzv/03PNDiuWg7XvpTB9OYuYxeKMWWKyQaoyPNRcPjq00IdECYveoaeSlUTw0h6RF0fzHDcPmwkNtEwbqz8S0Fl42NAT4zcVD47jLudb25yf4f/+8Ly4mdmzp5e2v2zrnc3ueOM737j1MV6fLh95zVYt+Z9elq9m8Pssn//4JpvMe3/nRYd771svmPeYDT6ruxGWXzO82tRAvHs3xd994lpofYdsm73v75azubdWirOvv5AOL6INed8Mm9jx+nEot5MnnxuO/Q9s298Ri+vm47eYtPP7sGFIa7HPXsnr6Rczu7gWF2VIIoulprN5enE2bMHSHQjMP+s7QaDQazbIII8HIWJl8ySfb6S7JMSbb6ZIv+YyMldm+uQfbMuckYE/mZqw/XxzMkUra8WLwDTddxLMvTnJiosK3f3iI7Zt7Wubml4JlmVy8oSu2TQ1Dwb7DU/zwgSGGRopUqiHfvucQv3q76oB0JG0SroXnRxQXmO9/06su5k2vunjO6//5f/0Eb1ad0ZgqCtp0a5oJ6i5VyabE8YRr4wc+QTj/tn7Te0nXrm83s4+Ftg3CmY5Msr6N7ylhte0s/F03rqd5vr9x3OVc62xKFZ+/+8ZzcRds57Y+fvGtl80pQiIhuev+owDcfN3GtmYA2c4Er7l+I/9090H27h+nUruEjqQz53Njk5VYWH71Zf0LXkM7Ht47yte/f4BISBKuxfvrmRjLwbZNLlqf5fmDU4u6ls0mk3bp7+vgxESFSSMNpkl4/Pi81rFSCMKRETBNrK4u3C3tE9A1GtC2sRqNRqNZJqVygOdHeH64ZPemTNrF80M8P6JcCThyrMD9jx/nuQOTHDlW4MixfBxGBnDwaI77Hz8eLyBt2+QXbr0U0zSoeiFf/ZcX5jsUoMaZhkeLTOVr837Gtk12blvFv33PVWy7qBuA/Yem4kW3YRixjezh4fyCnYGl0pFQC7j8PN2YBg2hbbNepDHOspB4uVmAna2LrLsyCYw278/dVu3Xtk066gF0xfxY/TWXVMfc/IqZ8/Xq5ztzPzTE2Mu51mZOTFT4i394Mr4Xrr9qDb/0tsvbBhtO52txt+jiDfOP6WzZqK5FSpiYbn+PPF0PvUu4FldcsnSHIykl37v3MF+58wUiIcmkHX79F3Ytu5ho0Ci4FioK56ORHxKlMzgbNyKDgODwYaLpaWSkCjoZRUTT0wRHjiCDAGfjRpK7dulxJ82C6A6FRqPRaJZFJARCKFtRc5H5+AamqVx2hJAcOVbg2IlSLObOdrqs60+TK3ocH1NPXwtlHz8U8Vz95vVZNq7N8LPXbeCHDwyx79A0Dz01Mu/x/upLTzExXeP6q9bwzlu2L3hulmVy49XrOHA0h0Q9DW+IsK/asZoDR3NUaiH7Dk+flHVsO7Jp9X1N5WpEkWg75lOq+HFOwppVM+Mxa1Z1MDRSZHxybpp3g0ZRZgAD9dGthGvR05VkKl9rKdrmbluZc8yJ0cPxzz2r57cbHa8Hy61ZNaNBaYjtl3OtDQ4P5/mbrz1L1QsxgDe9ejOvuX5+LUO5MtMSWmjSyjKbx8DaL9CfPaDC9a64pC8Wji9GJCRf/M4+ntw3DkB/b4r3v2EjvVaVcCrAymbnjA89d1B13hKuxY1Xzw0sbNAolJqL+CeeG2MqX6O3K8nVl8/fRWnkuHT395Da3odhmkT5vMqZGB2N3ZzMVAqrtxerq4vkrl0ktm1b0nVrXrrogkKj0Wg0y8IyTUzTUG45Qi6pqBBCIqUkDAWHh/NM5mqxmLvd2MXAqg4q1ZCR8TKmadDXnSSTdnn9TRfx3ItKDP6dew7N6xA1sCrNxHSNZ1+c4m2hWNTyNWhoACwjtkAFlTtw156j5Is+3/7hQbZu7DrpJONmVncZGKinzEeOFdi6qXvOZw7U07pN02hJtb5kUzePPH2CI8cL+EHU1rHpQD2Zev2azpbzvGRTFw8/XePA0Wled2P7BfmBehBd8zkV82NMTwzTs2oDF11ybdvt8kWPE/UiZ+tFM+e7tZ79sJxrBTh6vMDnvvoMnh9hWQa/cOulCy6aodU29+ixwpygwgbDoyoPwgD6uudqI8JQcGxMCdA3L9DpaEbI1mJic4/F7atGcR86TLHulmQmkzibNuFu2RI/+d+7b4JHnzmB65j8zK41bTsvVS9k8Hjdwrjpe3rgyREODeVZ1TN/QTExXWUyp7owF2/Ikti2FntgAP/QIYLBQUStFrs5tTs/jWYh9MiTRqPRaJZFZ9oh4VokXHvJCdbFsk/Ctal6Ib4ftRVzN2MaBv19KaSUlOpaC1BuQr/wZjX6VPOjeAxmNjdevTY+7td/cKDFfWg2QRDxk3qmwdWX9bcUSI5t8s43bscwYDJX42++/uyCY0OggtC+9N39c/QTAB1JI16g/vCBIeSs8wojwY8eGgLgmsv7SSVnioIrtvXhOiZBIPjJI8fm7HsqX+OxZ9WI0k3XtD7pvvoKtdg8OJjn8HB+zrZPPj/O+FQV04Abdq9teW/fUz8E4Krr34YfzP0e735gEIC+7iQ7Lp4ZDerJJpd9rcWy0kx4foRjm/zKv9q5aDEBaixsfT1t+t5HhuckUoP6876nftyLN2Tp8EuEY2OEU1NxIvSxsVIsYJ+vKJnzPewZjIuJbR0e77L2Yx4+gHfgAMHhw3gHDuC9+CLVxx+ndNddeAcOAMQGBn4g+Omjc/9cAf7lJ0fwA4FlGfzMVTOGBI1tJ6ZrPFU/djNSSr5zzyFAaYJ217UgVjZLavduMrfeSufrXkfmNa9R/731VlK7d+tiQrNkdEGh0Wg0mmVhWyZr+9N0dboUSv6cheJshJQUSj6ZdH0uvxIsS8zdcBJaP9DJa69fOO330ot7efmVKgTvkadP8Mm/e5zHnxujUp1Z5de8kKf3T/C//++TDI+WyHa63PLKuZkGl23t5Z1v3IZpwKGhPH/2t49x508Oc3ysFI/LREIyPFrke/ce5o//6hEefeYEAIXcCQYPPNayvze/+mIM4IUj03zxu/vjomwqX+PvvvEsx8fKOI7Ja29ovcZkwo67Cz/46RF+/NBQLGg+Mpzns1/eSxAK+vs6uGZWAOC2i3q49GI1w/9333iOZw9Mxh2mx545wVfu3A/Ay69cM8fN6LH7vkK5OEk608t9T4ccrz+5r3kh37v3cJzT8MZXXDSnW7Xca/3nHx2KP/vON25j+8VL1x/cdvPFGIbS0Xzq/z3FoaF8fI8eO1HiM19+mhMTFWwTXmMfo3T33RTvuUf993vfo/rkkwwfnYj3N7BAQniD8akKd9fF4P1uwM/5z2JMT2F1dZHYvp3EpZeS2L4dq6uLaGoK/+hRKo8/TuWxx7i0K+KiAXWMO+87wj0PDsWFUL7o8dV/eYH7Hz8ef8fNWRg37F5LX7f6/VfvfIGHnhrBr98TE9NV/u6bz/Hsi2p06+2vv6RFoA9g2DZ2by92fz92b692c9KcNIZc7F8AzUkTBAHXX389jz/+OFdddRVPPvnkiux3w4YNAAwPz/U512g0mrNBsexz/+PHOXaiFI8u/fSx49S8CNM04mwBqI87oboOjd835yFcenEPa1an1RP2Z9QT9huvXku6w0EIyeHhPJvXd3HTNevikZYoEvz5PzwRay62bOziN/71VS3nKIQSx9736LEW61LXUSNbzZkU6/rT/OvbdiwYsnd4OM83fvBinGoMamQmmbSpeSHN/6p2JG02rhZ86ANvIPBndAt79uzhhhtu4L5Hj/HtHx5ENvaRUN0bUNar73/HFVy2da4QOBKS//edffHTaMs0sG0zDkbLdrr821/c3TaArVDy+cyX9sbjSY5jIiVxuvLWTV3ccfuV8XjYAw88wI033qi+n4t28o4P/A8SSfX0P5mw8P0o1im89oaNbZ2ugJO+1nzR448+/RBCqs93puc6MM3mfW+7nM1No0CPPnOCr33/QHxttm1imUb8PTmG5K2ZEbZWjyOq1VYNQXc3Pw3X8pPpNLZl8PGPvnLR43/t+wd4sG4x6xLhIGC+8DgpWRdO8ZbphzAzGexNm6i5ab401stIbWZBn0rU76v693bzdRt5881zv+PxqSqf++rT8ViTYSjdTOP+tkyDt7x265yulUazEugS9DTwR3/0Rzz++ONn+zQ0Go3mtJNJu+zY0osQkpHxMsOjpZYgtdnBdgARM69FbQLb2tEs5g6j5owFNfr053//xLw5B6ZpcNvPbuH63Wt54rkxDhzNMZWvUakGRJGkpyvBhoEMuy5dxVU7Vi+qBbl4Qxe/9YFrOHA0x9MvTHD0WIGpXA3PC0klbDrTLpvWZti+uZsrt6/i0Ucfbikmmnnly9azcW2Gex8e5vBwnkotJJN22Lqxm5+9fmM8tjMbyzT4xbfs4PJLennoqVGOj5UIAkFfd5LLt/bxmhvaW6WCKjY+/L6r+ckjx3hq3zgTdZvedf1prrligFdeu27eLIjjR5/h7z/xfj7+F99guuJQKPm4rsWGNRluumYdV25fNe/3drLXevR4IS5UJMyb8dFMOOseeNnOATavz3Lfo8d44cg0uYKHEJJVnRZbzALXVg6SLdewenpwNm7EsCxkFCEKBaKpKYoyC6RJ2ovrg0B1iBr4WPhYEM3/+aqwiMplomoVUa1ip9O8J9nB050beT7sZixK4AcR2YzLlg1d3HjNujkakware1N85P3XsOeJEZ5+YYKxyQphKOjpSrD9oh5e8bL1LYGNGs1KojsUK8zjjz/OddddR1ifv9QdCo1G81LgyLEC+w5NUSr75Es+nh9iGEqwnXBtujpdOuvFR3cmwZ4njnPkWJ6LN3QtWczdrkNxrtII6YuE4LFHH+aNr7+55f1Gh+J8oLlD0eB8Ov/ZRIUCpbvuwj96FMNxFs1hkEGAe9FFdL7+9YtqCmQYUvze9/BefBGrq6ttEnU4NUU4OkqUzyu/WtMEIbD7+nC2bEGWSkTT02CaOBs3ktq9W7ssac55dIdiBfF9n/e9731xMaHRaDQvFTavz9LXnWwJqGs4PyVci7X9aTauyZBJu4SRaBFzL6U4aIi5E65FumPx0ZezxeyQPiEk+1+YWHxDzRnDP3RILeaFmLeYADBME3vdOpXTkM/jHzpEavfuBfcdFQqIWg1RreJsnKvvEZ5HeOIEUT6PYVmYmYxKo24IwcMQq6cHs6uLcGSEYGhIncfAgBZIa85pdEGxgvz+7/8+zzzzDN3d3eRyubN9OhqNRnNGyaRdLt/ax/bNPZQrAWEksC2TdIfTYoHZEHNP52vkit6iwuyGmLs7k2Btf7qtnea5wHxdmpHx0tk+NU0dGYYEg4NEuRxWT8+SDAGsnh6V0zA4SHLnzoUFy2EYW68abbQT0fQ0slYDKTEzyjXKMM24m4cQ8WsnW8xoNGeTc/P/yuchDz30EH/6p38KwCc/+cmzezIajUZzFrEtk65Mgr7uFF2ZRNsCYOOaDJ1pVUiMTVbndYgSUjI+WcUwDDrTLhvXqEVYGAlyBY/JXJVcwWvRVZwNjhwr8NyLkxw7USJX9MikHS7e0MWWjV2sWz1XB9Es6NacOZo7COYSn/ib2SyiWkXUakSF9vbEMbYdC7sbydMNpBCIfB5Rq2GmUi2vS6kCImkK2ptdzEg9/aA5h9EdihWgVqvxvve9jyiKuPXWW3nf+97H+9///rN9WhqNRnPO0k7Mne10yaRdTFOJr4tln0LJxzAM1q5Os2OLcgB67uDkomNVZ5Ji2WffIRWy1y6kz2ijETl6rECx7J/xc33J06aDIIVAep7qDpgmRiKB0bywt6y4SGCRRb2VzWImk5ipFKJQaNFQSM9DhiEyDDESiZbXDdtWvxKt439mNkswOhoXM3bvXMcvjeZcQBcUK8B/+k//if3799Pd3c1nP/vZs306Go1Gc16web16QmyaRjwmNDFdbRFzd2cSsZgb4P7Hj88r/J7O1xg8XmTHlt5432eCodEipbI/b0hfJtvFK25+U/z7ai3EdDoYGi1y+da+M3aey6W3t5d3vvOdc147L2nqIESVCqJQQOTzaqFf7xIYto1ZF1SbiQQyiuIihEXyGQzbxtm0iXBigmhqCrO7e+Z+aO5E1F+TUiLrHQuzq6ulkIGTK2Y0mrOJLihOkfvuu48///M/B+ATn/gE69evP8tnpNFoNOcPSxVzT+ZqPPfiJCPjZaSUZDtd1vWnW7oZuaJHvuTH9rNnoqgII8HIWJl8yZ9XC7Jx01b+y3/7VPz7fNGjWA4YGSuzfXPPOasJaXDppZfy1a9+9WyfxorQ6CAgBP5+FeInajXVNagXp4ZtY1YqRNPT2AMDGIahFvzJ5ILCaBmGRIUCZiaD4bpIIDx+fEb43ayVkCqTRZZKYBgYyWRbR6iTKWY0mrOJvjtPgXK5zAc+8AGEENx66616zEmj0WiWwWJi7sVGikzToCuTINPpMj5ZjTMZTBOy6QSdaee0LdpL5QDPj/D8kHX9S/P4z6RdJqareH5EuRKc8xa4FxKGbYNtIzyPaHoaM5XCSKUwXXdG++D7iGoVajVkFGFIibNxI86mTW0F2VGhgH/oEMHgoCpOhECUy0jPIywWicplnNWrMTo71faWpbQYQoBhYGWz2AMDmIm594EoFJZUzGg0ZxtdUJwCv/3bv83BgwdXdNSpkTXRjpGREdauXbsix9FoNJpzjYaYezaLjRQ1CAOBYcDEdJVCySdXrLG6t+O06isiIRBCjbEsJU8D5g/p05x+okKBKJdTugXTBMtSHYs6hmliJJPIRAJZLBKNj2Mkk7jJJO6WLXP25x04QG3vXqJ8niiXa0nbpv5LlssEQQCmiSiXwfMQQmD19mImk9gDA221EVIIoulprN7eeYsZjeZcQd+dy+SHP/whn/70pwE96qTRaDSni6WMFAHkCh4T01X8ICIIBeVqQM0LKVV8kgnntOkrLNPENNUYS2NUazGEkEipPnuujztdaPiHDinNQiYD2axKxG50KhIJpW2QUi366wJuM5HA6u6e0yHwDhyg+uSTBENDIETbtO1wfFx1OywLq68PZ8MG1ckolTBdF+fii7GaCpoGjVA9TBOrq6ttMaPRnEvogmIZFAoFfvmXfxkp5YqPOi2Ugr1Q90Kj0WguRJYyUpQreIxPVSlVfKSEjqSNAXWdRSdBKE6bvqIz7VywIX0XGs0ZFO769UgpCU0TWaupUaVSqUVHYXV0IFMpNYrUcGeqdwmiQoHa3r0qeK5N2rZhWS0BdcLzsNJpOm68kWh6Gu/FFwmPHSM6fhx6ejCz2ZZCpDkpO7lrlx530pzz6IJiGfzWb/0Wg4ODdHV1aVcnjUajOY0sNlLk+xET06qYME2DdMrBMKDmRUgJGK36ipHxMqZp0NedXJHxpws1pO9CZHaKtWFZmOk00fS0GoOq1VTnqCGS7u5Wtq1Hj86xbV1O2rb0fUShQMe112Jls9RsOx6VCkZH41EpM5XC6u3F6uoiuWsXiW3bzuTXpNEsC11QnCR33nknn//85wE96qTRaDSnm8VGivIlHz9QxUOjmJBSIpEYBjQ+bhoG/X0phkdLlMr+ilq2blyTYfB4kXzJZ2yyOkfnUamUOHLoBaSU5PIqhO9lL9sdh/Sd6xSLRZ555pmW13bu3Ekmc36cf8wiKdYYBoaUsaUrtLdtXYm07cS2bdgDA3PE3IZpYiaTOJs24W7ZojsTmvMGXVCcJF/+8pfjn3/5l3+ZX/7lX17w80899VT8P5tXv/rV/PjHPz6dp6fRaDQXFAuNFAkhKZV9PD8i4VrxOtDzlUuUbZm4zszC0TAMsp0u+ZK/opati4X0HX5xP//hQ605Dl//1l1k0jtP+dhngmeeeYYbb7yx5bU9e/Zwww03nKUzWiazUqyjfJ7wxImZkadZ1rGyWiWanER4Hm6TbevsTsdSaBdQZ2WzpHbvJrlzp3J9CkOwbaxsVguwNecd+o7VaDQazTlHGAlK5YBIiPri3KEwS5jtB4IwUk5JjfElIQSVWkjCMXHduU+hT5dl60IhfcfHS3M+v3b10ixmNStHc4p1MDSEKJfVQl7Kea1jZbGIYduIcnmmW7BAp2O+1O2FAuoM29YJ2JrzHl1QnCS/+Zu/ydve9rZFP/f2t78dgM2bN/OJT3wCgFWrVp3OU9NoNJrzmjASnJgoMzRaYmK6CnLm9clcDc+POH6izLqBdPwkWUqJgYEQgpoXUaoECCERwqRY8vH9iM60S1eni+tap9Wydb6Qvsp054oeR7M8GinWwfHjeC++qEacLEvlQzSPOTWsY12X6MQJJCCqVUSlooqKWZ0Ow7LiXIv5UrfNbFYH1GkuaPRdfZJcc801XHPNNUv+fFdX15IKEI1Go3mpUqxrGp57cYqR8RK+H+EHAiGlGndyTDAMRCQ4MVmhVA1Ys6oDx7YwDAM/DJnMCYIgQtTtWKWEYsWn6plUvZB8yWNVd4psp3taLVvbhfQloqEVP45mebhbtlC+/35kGEIUYdaTsOcgJbJchmQSA6Wl8A8dIrV7d0unQxQKyi2qeXQqCNQ+hMCwbYxKBWNkBMN1dUCd5oJFFxQajUajOWscOVZQKdhjJUYnKlRqAWBgmQYJ18IyDQzTJJWwqEjwQ0EYqsLCNAwmc1VqXoSBwLLUNpkOl1TSRkqJH6jOhedHSCEpVc6MZWtzSF/nCofpaZaP2dGB2dGhNBKAyOXmZFBIz1PZEYaB3d2NkU4jyuVYUN3odIQTEwRDQ0jTVIVFFKnipLEfQPo+VKsqp6K3V3U3dIdCcwGi72qNRqPRnBUODuZ4at84x0+UyJV8hBB0djh0drhAazGAdFjdlyJdcSiUfWzLpCeTIF/ysbwQCWTTDh0pF8tST5wNQxUYrmNSroYUywHFcsDmdRlt2foSJSoUMNNpjGRS5Uv4ftsMCjOVwqinWFtdXXgvvNAiqHa3bKH23HNEpRKyVgPHUfqIKFIjT0JgoBzHEAKkJJqexj9yhKhQ0F0KzQWHLig0Go1Gc0Ypln1eODLNw3tHyRU8giAiEhIhIZVQegjLMluKgVIlwDANNg504ocRJgbjuapydjIMLAMqtQjTDEkmLKymYsEwDDqSNhO5GrZpEAnOG8tWzQpTX+xb6TTO5s0L6h6snh5VdMAcQbWVzWJ1d2MkEohiUXUipATTBMfBNE0QYmb8qT5WFRw/TvmnPyV7661n5fI1mtOFLig0Go3mJUazg5JlmnSmnTP2tL4x4jQ0UmByukrNj9Q5hWpkqVIL8YKIjqRNMmFjGAbplE2hFOD7ESPjFSrVkHI1IAgFUkhsyyQSAj+IyBUEjmORSTv1sSfqSdsRlmlgWSbJpE0qqf/5e0nSJKg2bBtnzRpkf39bZ6YGMormCKplGEIYKpcn24Z6QSGFwPB9BKoIMR0HbBsznUZWKkTT09SeeoqO66/Xzk6aCwr9f9TThJTybJ+CRqPRtNAQPzc7EJl1rcLa/jQb12RWJD16Po4cK/Dci5McHysxmaupMaVOt34uAsc2CSNBGKmHvUBcVCRci1IloFwJsCwDzwuxLBPLNlnVk6LmhRTLPkEoqHkBfhDiWBaWbeDYJsmEjWM7+IGgI2mvqGWs5vxhtqDa6ulRrk6p1LzbiEIBM5VqEVQ3siik7ysHp2JRFRZSKhtaQ2l/jEQCI5XCsCxkIoE4doyoVKL27LN0vvKVZ+qyNZrTji4oNBqN5iVAozPQyEjw/JkQr4RrM52vMXi8yI4tvXGmwkpSLPtKfD1eBgzSKYd8ycN1LDwvwjRMbNvEkpIglPhBhGGAY5tYloltQc1T55xK2FiWSUP/ahiQStpYpkGxElCphkSRAASmaSEluI7FmlUdjE1VT4tlrOb8oFlQHU1NYXZ3L5h0LYUgmp7G6u3F2bQpFlTLWk3pJ6pVME1lDZtMIqN6NVy3pGW2Ha3jIGs1wqEhFaSnBdqaCwR9J2s0Gs0FTqMzMDJeRkpJttNlXX8a01R5DMWyT67okS/5CKFaA/MVFcsdlxoaLVIq+0gp6etOMjwaYGBgGgYYIJEgld7BscEPJFEk8fyIjpRJ1VMhdgYS34wIgkgt2gwoVQJMw6DmhwghsS0DXyjr2EhIXMPADyJGJir4fnjaLGM15wfuli34Bw8S5XKEx49jr1vXtqiQQhCOjIBpYnV14W7ZQlQo4B86hLdvH/7hw0TlMgCmlBBFqhsxT5EghQDLQkYRIghigffJIMNQp2przkn0XajRaDQXMM2dAcc26e9LtSyeTNOgK5Mg0+kyPlllZLyMaRr0dSdbxp9OZVwqjAQjY2Xy9aRr0zRVdwSJaYJpGJimQRhJbFsFz1mWQRgJvEAAAYWSp45pKE0EqNFS04B80VMheIa6Hss2SJoWQkpcR4m7G6F3tmVSqQWn1TJWc25jZbMkd+1CCkEwNERw+LASYGezajQpihCFAtH0NJgmzsaNJHftIjxxgtrevUT5POHUFKJQgHpHQtZqiDCEWg0zncZsM0IlPQ/DcZRWw3HmJGYvRKOQCQYH1ahVXdNhJpM4mzbhbtminaM0ZxVdUGg0Gs0FTHNnYHYx0YxpGPT3pRgeLVGqFw+Xb+0DTn1cqlQO6sLokHX9aQBsS3UJ/EDgupbSToQC27LAAMs0CAKhQu78SAmwJZiWyqhwHJV67fshnq/Gl8z6Pm3LBBOqdTtZ2zJw0w4T0zUMlBVttRaeVr2I5twmsW0boMaQonyeKJcjGB2NBdtmKoXV24vV1UVy1y4Aqk8+STA0BEJg9/SAaeIfOoSsVJCmCUEAYYioC4CMZFIVDfVMClGtqkIjijBte8mJ2d6BA3EhE+VyiGq15TzDiQn8gwdJ7toVX5dGc6bRBYVGo9FcoMzuDCw0Kw5q3Cjb6ZIv+YyMldm+uYfh0dIpj0tFQiCEEqqapjqHzrRL1QupeRHplI3nGYSGCq5zbbN+roYqJBCxI6dpGhimKio6OxymQ4FhCIQEWddG2JaJYYKhZqkQEmq1kIRrAuocmgsmzUuTxLZt2AMDiz75ByjddRfB0BCG48yMSCWThMPDRLWa6lQkEhAEcVq24agumJRSuUAZBhKwenuXnJjtHTjQUshYPT04Gze2dlKmpohyOTVSBbqoOA3oUbPF0d+GRqPRXKC06wwsRibtMjFdxfMjTkxUVmRcyjJNTFN1NBqjUl2dLvmih+dH1PxIWbwCfqAsXlXBohZIUlAPCVP7cx2LjpSD46j9GoaBiVSaiUgSRALXMJFIIiFiZ6hMOkEqaVOthXHBpLUUL22sbJbU7t0kd+6cd8FYffJJonxedSaa9BZWKoU1MIDwfRVuB0jDAN9HGsZMNkWjY+E4iFoNs1ZbUmJ2VChQ27t3biFTx7AsNarV1UU4MqI+Z5oqjE+PP60IetRs6eiCQqPRaC5Q2nUGFqOxQBdCrsi4FEBn2iHhWiRcm2LZpyuTwHUtVvWkkBJKFZ8okji2WtwLocTYYaSE2gCWZcSuT+mUQzJhEYZipqBQOWJEkaRWC/EtVYiYhkG20yZRP1620+XwcB7Pj7R1rCbGsO22AmkZhgSDg0S5nLKYnfV3wN20CVEsEk1OqmyKKFK/QLXUXBfTddVIlBAgBKJWI5yawjtwYMFugn/oUNtCZs65myb2unUEhw8T5fP4hw6R2r172d+FRqFHzU4OXVBoNBrNBUq7zsBiCCHjHJ3J6eopjUs1nv7blsna/jTT+Rq5ohfvrztbTyE2ZjoTyOZCSL3X2eFi22pcBEl9dKlea0iJZRk4loUfqsRtdR1Kh2HXE7fXrOqgs941aRRMZ8o6dufOnezZs2fOa+cL5/v5nwqNvAlRreJs3DjnfTORwN28GR8IT5yo33hWLLw2k0kM28aQUhUX9c5HNDZGbe/eebsJixUyszEMA6unR2lBBgdJ7typR3JOAT1qdvLou02j0WguUNp1BhajWPZJuHWvfcmyx6VmP/3fuCbD4PEi+ZLP2GQ17nh0ZxN0JG3yJZ9SPZiuXA1wbYGQEse22HZRN45jMnyiRLHkU66GpFM2BoBhEEVCFU31QkgCJqpzYTsmfhAxOllhVSTp7HAIAoHnh5QrIV0ZcdrHnjKZDDfccMNpPcbp5Hw//1MiDOMxF8Oy2n7E7u0lmpwkyufVyBMogbZUxXljRMZIJlUB0dOzaDdhsUKmHWY2SzA6iqjVlmVJq1HoUbPloQsKjUajuUCZrzMwH0JKCiWf7kyC1b0pxqeqyx6Xmv30P5N22bGlFyEkI+NlhkdLZDtdMmkX17Xo607i2AbTeY+uTIKeTIJISPIlj3SHg2karOpOIYWkVAkolAJcx0REqvAQoUSisicsE9IpO96u4epUrgRIIRESRicq7N0/zoGj02ckJVxznmLb8ZiLjKK2RYWsjzKZrguuixQCUS4r4XVPj0rm7upSPydUkb1oN2EJhcxsDMuKz/VkLGk1rehRs+WhCwqNRqO5gJmvMzAbISXjk6qA6Ey7bBjIMJmrLWtcar7guIb7k2kasQXtxHS1xYK2tyuJ61pctD7DsRNlql4Yd1fiESmziu+rIqHmRwjRCCeWWCY4tkW6wyGVtOPzyhd9omqAEBLXtXA8k8GRwhlJCdecv1jZrCoIUilEoYDV0zPnM9LzkPUCwOrtVXkThqHC8LZuxUgmMczWvw+LdhOWUMjMOY8oiouQpVrSalrRo2bL56V51RqNRvMSYaHOQLP1a6HkYxgGa1en2bGll4HVHSQOL29cKuFa8wbHbV6fpa87OSckL4wE1VqoNBASjo2WmcpXKVcCSpWApGuRSNgtI1InJsp4vlp0CUONOzmOSSbtxsVEzYuo1EKEEAShrHcvHC7d3INtmyeVEq556WHYNs6mTYQTE0RTU5jd3XMXmUKN3BlGvUNXD7ezVq/G7Ohov99FuglLKWRmIwoFzFRqyZa0mrnoUbPlowsKjUajucBZSmegO5Ogs158ND6/3HGptf3pBXUJmbTL5Vv72L65h3Il4MixAkeOFbAsg2I5YLpQwzAMwlBQqYVEkWDf4Wk2rcvQk03GI1LFkk/Nj3AdqHkhYSQwUBkVUqrORLkaUPNChATHUuNbjm1h2+aSbW81L23cLVvwDx4kyuUIjx+fOwbTSH4XgqhYBMPAqI87zcdi3YQlFTLN+xOCaHoaq7cXZ9Oml+xT8lNGj5otG33HaTQazUuA+ToDpmmQcK22OoLljkttXJNZ0jnZlsl0wePYiRIT09W2wXkjY2VGJyqUqwGHBvMMrApY19+JH0SEkSAIBK5j4roWLhaubRJGgulCjSBU7zeKJstSjp6ObeAHEcmE+idwMdtbzUsbK5sluWsXUgiCoSGCw4eVHiKbVYtO20YGATIIALBXrcIeGIj1Eu1YSjdh0UKmjhSCcGQETFONWdXD+DTLQI+aLZuX7pVrNBrNS4zZnYFGqnS6w2nbUVjuuNRSn+4Xy/6iwXnr13TSkbIZPF7EDyLGp6pUqgESg3yhhhCSVNLGdax4LKvhFpUveohIYtsqCC/pWhRKvupeyNZzWcj29lTZv38//+W//JeW1/77f//vXHrppSuy/9PN+X7+K0HDEtQwzTiXIBgdnXkybRiYnZ0QRVgDAwuOvSy1m7BYIRPbl05Pg2nibNxIctcuPe50CuhRs+WjCwqNRqN5iWFb5pID3ZY7LrUUlhqc19OVpCNpc3CogGFAVyZJR0r981Us+fR1p+jJqrA8QI1DlQOiSCKkR29XMs7jkKh8i3Ya84Vsb0+Fqakpvva1r7W89lu/9Vsrsu8zwfl+/itFYts27IGBtsnJAOHEBKJYhFot1lTM5mS7CYsVMmYqhdXbi9XVpQPWVgA9arZ8XrpXrtFoNOcBYSQolQMiIbBMk850+27C6WQ541KLEUaCkbHykoPzEgmb9QNpCiWfvu4k11y+msefG2dkvEwyYcXFBKjix3VMLMuIw/0APF91ZGzLxHXmjjIsZHurOfeRYUhUKKg5dtvGqofIrSRWNktq926SO3fOOZZ/+HAchraS3YSFChkzmcTZtAl3y5aX9NPxlUSPmi0PXVBoNBrNOUixPsu/Ugv4U+Vkx6UWo1QO8PxoWcF5UkIQwqqeFLmC11Y03nDckUhkPWLb8yOSCYvO+sjWbBazvdWcm0SFwhlfbBu2PWes6XR2ExYqZF7KT8VPB3rUbHnou1Cj0WjOMY4cK7Dv0FQ8YuT5YcuI0dnMTTiZcamFiIRACHlSwXkN16ejxwt4foRhwHTBo1wN8H3BprWdJOpCa9cxsS1VGNQ8ZUdrGOC6Fl2d7Quxpdjeas4tvAMHqO3dGy/gRbXasoAPJybwDx48Y+NAp7ub0K6Q0aw8etTs5NEFhUaj0ZxDHDlW4LkXJxkZL7d1PbpQchMao0hLDc7LFTzGpyqUKwFBIIgigeNY+EGIEJJcoUax7NPf1xF/Xx0ph3zJo1DySSZsMmmHVd2plvGoBidje6s5N/AOHIhHjBACq6cHZ+PG1qfIU1NEuZwSTsOCC7/FRqaWOlKluwkXBnrU7OTQd7ZGo9GcIyzF9ehCyU3oTDsk3KUF56liosp0wYtdnbZu6sa2TYSQHB8rMTZZJYoEY5MV8kWPVNImDAVCgGMrPcWq3lSctt3Mcm1vNWePqFCgtncvwdAQhuPMmXM3LEuNqXR1EY6MqM+ZJvbAwJwF4GIjU9bq1UTj4ye9qNTdhPMfXRwuHf1taDQazTnCUl2PLoTcBNsylxSc5/sRE9NVimWPSEi6MglW9aSwbdVBME2DDWsyrOpJMTRSolwNcByTgb4OXMeiUPKoVENqQUSx5GMaxorZ3mrOHv6hQ0T5PAgxr2gW1MiKvW4dweHDRPk8/qFDpHbvjt9fbGTKO3gQUS5jptMg5VkfqVoqZ0Kg/lJCF4eLo+8ujUajOQc4Wdej05mbcKZYSnBeQ0Pi+UJ1NBLtNRDJhM0lF3UxNFLEcSwM08AwINuZQEqo+iGVaki5GmKZZWzbOmXbW83ZQYYhweAgUS6H1dOzpL8rVk+PmoMfHCS5cyeGbS86MhUMDREMDyNqNcxUCnv9ehLbty97pOpMcDYE6hoN6IJCo9FozglOxfVopXMT5mOlLWwXC84LQ8HEVIV80ce2zQU1EEBd4G2qwqzgke5w8INILTglyPhzJtm0S09X8qy5ZmmWT1QoIGo1RLWKs3Fjy3tSCKTngRBgmhiJhFpQZ7MEo6OIWo2oUMCw7QVHpmQYIqpVMAwM20YCslJBhiGGZZ3USNWZ4lwTqGteWuiCQqPRaM4BluN6dKZyE06nhe1CwXlBIKh6EaZp0NOVZHVPew1Eg1zBo1zxKVeVrW2m0+WiddlYa9EQtEsByYTFResyXLql97zr7LzkCcP4ybthqeJSeB7R9DQin0eGYRwsZ9g2ZleX6mQ0Uq3DEH9wcMGRqWh6GlmrAWCtXo3I5ZC1GtH0NOaaNfHnFhupOlOstEBdozlZdEGh0Wg05wAn63oEZyY34UxY2M4XnOf5IaMTFapeyEVrM/N2JkB1eAZHihRLvkrIFpJ80cfzQhIJm86UQyJhsX6gk8npGrmiz9BoiU3rsrozcb5h23FxIKOIKJ8nPHECWaupMZ9w5h41bBuzUiGcnER6Hq5pgmEsODIlhUDk84haDSOlxvDMVEp1RfJ5ZH9/nI4N849UnSlWUqCu0SwXXVBoNBrNOcDJuB41ON25CWfSwrZdcF65ErJ3/ziDI4VYhN2OXMFj8HiRYtnHD0KkBM+HqXwVAwPTVB2QjqRDZ4f6FYTReStof6ljZbOYySRmKoU/PIwsl1W3QUrMVAojkQDDACmRnoeoVpHFIoZtI8rqXp5vZApAep7qcoQhpquKTSORQJZK6nXPw0ilWraZPVJl9/aeMWH0SgnUNZpTQRcUGo1Gcw6wVNejBqc7N+FMW9jO1md0ZRJ0ZRIcODq9YJE1lasxOlEmX/QIIzUehVTrSSSEIiLyJYZp4PkRVS+k6qmiYyQs09OVPC8F7S9lDNvGXr8e78gRgsOHkYDpunOfthsGRjKJ6bpEY2NIQFQqiGJxzshUC0LEI1MYBjIIQEoQAiGE0mc0IYVA+j6EIVG5THDiBLVnnyUcGkIEATgOpm2fFmH0SgnUNZpTRd9FGo1Gc46wFNcjODO5CWfKwnYxfUZ3NkFX3iVX9OjscJRwPYgIA0GlGjA2VaXqhUSRJBL1MTCAUGJa6vxmuhuSKBJUa2okplwNGJusnBFBu2ZlaLgY+YcOERw/jiiVVCcik0GUShjJZGsYnZTIchkjmQQpMSyL4PjxlpGpOUWFaUIUITwPOTmpiomGZWwuRzg5iV0/RkO3IYKAYHycYHKS2uOPI00TCRhSqsLFdbF6elZcGL2QQH0+2nVTNJpTRRcUGo1Gc46wmOvRmcpNOFMWtkvRZ7iuRc0LKRR9RicqICV+IAijCCFUcSXrD4wlM05OUH+oLCVSxhMwWKaMC4xqTTAxXSVf8nRBcR7Q7GIUTk0hy2XVLRBCdR1qNYxEArOzU40oeZ4SVhuGGpNKpxGVCtHkJIbrKl1EoYDV09NyHFEuq/0FgepOGAZG4zi1GuHkJOHEBKDu/ahSQRQKEASqSGnsyDBUYeG6GEGgNB7T01h9fSsnjG4jUF8Mw7JaBOoazUqgCwqNRqM5h1jI9ehM5SacCQvbpeozql5IsRRQrQUEoSASssUCdj4aNVB9aqXxkBnPjzDMgK5Oty78jjg8VCCdclbECrcdnZ2dXH/99XNeO184F85/touR2dGBtWoVcnwcLAt8XxUAvq86CY6DmUopTUUyqQTIXV14L7yA9H3stWuxpqaIpqYwu7vjojmcmlIC7zDEQHU4jFQKPE8VK4kEolBAeB4GqGMLAUEwZxQKKSGKwPOQvo+0LCX4DkOk76+MMHqWQH0pRYWMorgIQY87aVYIfSdpNBrNOcZ8rkcrZdW6GKfbwnap+gwhJKMTZcqVACHBBEwDBCxYURgG9cWg+phpqt9HUo1FVWshUSgII8lkrspDe0cYn6rQkXJOy/d75ZVX8sADD6zIvs4GZ/v827kYyUqFcGIC03Uxu7uR1apyeKpnUBh1EbTV16ccjhKqwG0svp3164nGxohyOcLjx9U+fZ/wxAmVU5FMIk0To1ZDVqtgmpiOg5FOIwsFqC/KCUP1q7mYMM2ZCraBZamORa0GYUgoJf7g4CkLo5sF6u26Le0QhYIqtpJJ7fKkWTF0QaHRaDTnIO1cj2zLJN2x8k/QZ3O6LWyXos/w/YixqSq1mtJH2LaB61gEQUQQSmwLQtG6ZmvQvLfGQ2KQ1I2oEAJqvloAGgZM52s870WkO2y6MskVscLVrBztXIykac5Yw5omRmenGmkKQ0Q+j5lKYfX24jRlRjQ/mbe6u0nu2oUUQiViHz6sugfVqnKLymahUCCqVtUNZBhgmqrTEEWqaJhdSDTfxw1bWSFmbkLHiT8jPY/g+HG8F144JWG0Yds4mzYRTkzM6ba0QwpBND2tvptNm7QgW7Ni6DtJo9FozmFsyzzj8/3LtbB1HSvOj8gVaDs+tFR9Rr7kUyx5hPVOSRRJymGoHJxMVHEwT5dCSLBM9b7qUszoKJoxTSXaVgUUOLYFyBWzwtWcOvO5GBmJBIZtqxRrz1Oia8PAdBxIp5VdbKGAHBiIMyNmP5lviJEN0yScnsY/cCBO0Y4mJmIRd8PxSZRKarSpWZTTqGgbXQmYmbNr/Nz4nBBQT9026iLvRkfkVITR7pYt+AcPtnRb2v29kkIQjoyAaWJ1deFu2bLsY2o0s9EFhUaj0WhaOFkLW88LOXaijGHAVL7Gw3tPzDuetRR9hhCS8ckKxXIQuzY1dyLkwhNVAESiPvYEbQsPA0i4JkKo/5qmSbkaYFoGGwY6yRf9U7bC1Zw687kYGaaJ2dWFWakgqlWsZHLmvURC5U00ZUbM92Q+sW0b9sAA1aeeIjxxQmkjUik15lQPxjNsW403+b46gBD1ObpZhYMQ8WjTzMkYMzdvo1shJbiuspnN5+NE7uViZbNzui1WTw9mNtualD09DaaJs3EjyV279LiTZkXRBYVGo9Fo5rBUC9vGeJAfRNi2Sb5Yo1TxCUOB41iMjpc5Mlzg8kv62Lw+uyR9hnJe8mMBtmEsLsKeTfM6rkHz723bqJ8DmJZJJm1TLIf4fkSxHJySFa5mBVnAxcjq6VGL5FoNUSxiZpR9stE0DoUQiz6Zt7JZkpdeSjA0hCiXVQCe54FttwTlCc8jGh+fWwA032yzW2HtOhWgxqeiSC32T7GggBm3KMM0ifJ5lTMxOhprRhojYFZX14pZ1mo0zeiCQqPRaDRzWIqF7fGxMicmKnhBSMKx6Mkm6Eg5lCuBynmoBEznawyfKDExXaVSG2Dd6s4F9Rm+HzGVrxGEEcCc7sRSMQ2I5tmu8WBZCIltmdimQSTAtgxqXkip7NPXnVyWFa5mhVnAxchMJLAHBkBKtYiensZMpcBxVDEBRMUi8sSJxZ/M17sQoloFy1Ji70xrvothmsqOtuHopMQ59TeN1o5Fg/mqWiGUfatlYTZ1V06FRrfFP3SIYHBQidTrxdjpCNXTaJrRBYVGo9Fo2rKQhW0YCkqVACEE6ZRDb1cSL4hi69gwEuopsZAEYcjg8QKFks/LrxwgigSmaZIvevR0tS6m8iWfIFBJxcspJJppPBRuuD5hqHEpyzTqOlklxKj5EX6gFodeILAsn1LFP2kr3PnwPI/x8fGW11avXk0icX5kX5zN81/MxSjWHtQdlESthsjlIAwRuRyiowN71pN5GYZEhYISVTfcoLJZlWshJQQBZju3pHpxQ6OoiaKmG8yYea1Z6Dy7YyElWJaypbVtrK4upf9Ywe8rtXs3yZ0751yjFmBrTif67tJoNBrNvMxnYTuVr8b6hlXdSXJFj1IlQEpIJiw6bQchJEJKal5IsRxQGy/z44eHyKRdarWQXKFGGAq6MglcVwm6S2WfSi2cY+l/sjQ715oGWJZJFKnQsUhIDEMVFkJK/CCKR2SiSFKpBhwfK7NmFUu2wl2Ixx9/nBtvvLHltT179nDDDTcse59nkrN5/ktxMbJ7ezHTaaLpaaJcDiYnMbJZ7P5+nIEB7DVrcC++GDOTUVkWbZ7e2+vXq84DqOKkLsRuORfDUONPvj8TCFd3f2r60IzGojHi1NBbQNyhMEwTM5XCXrPmtHQMDNvWCdiaM4ouKDQajUazILMtbGt+xKNPj+IHglTSjosJ0zRIuhZeIKhUfYSUhKEgjNQvKWA6L/B91YGIpGRwpEhXyWdVT4pkwqJUCah5odJPLJNG7oQQde2FQdzxkKg8CwMwTAPXMbHqGRpSSCq1kDCSlCoBY5MVgjBakhWu5vSxFBcjM5HA6O9H1q1czY4OrJ4eJBCMjlLbvx8xNaW6B0KoDkGTvsAfHiYcH0eGIWYyiSyVoLNzblGRTGJWq4jm9lmjoGg4Oc14FbfS5AJlJBI469aR2LZNdw40FwT6LtZoNBrNHMJIUCoHREK0JEh3ZRLIgodhGPhBRMK18P1ITXKYBoWyTxSpJ/phKOpdivoCX4IhwbIMertSlKshhZLH2GSFmhfSlUlQrYWq+DiFcadGYdPoLFimqTQbEgwhMUxwHQvXMVsWjJEA2zZJOBaOZZIreji2WiymO5xT/EY1y+VkXIxEtao2MgyiiQlEtYr0PKJCARkEGI6DlcngbNiAvXp1vG0wOooolVShmUiovIlcDiOZVBa1DR1HwzbWqd8PjcLBcWYyKWbfvI0iwzShbltr9/Tg1jUNGs2FgC4oNBqNRhNTrLsaLZTQ3XBqAihXlA2sYUClFiotggTTMuria7DqI+Yykkgk5WpAKumwrj9NwjUplHwKZZ980cMPolMed0JKhFBFgxKQQ6bTpeaF1LxQuXbarcUEEqJIqIIiYZNKWJSrAQnHwrJ0h+JssxQXI5pdoKIIq6cHq7+f4OhR5dhkmmCaiFqNcGwMLEvpK3p6wHWVhWsUqYDGzk6lw6jVlPNTYwyqrkcwMxlkraZcpoSAWm1uOnYDw1D6C9fF6uzE6uvD3bJFW7dqLih0QaHRaDQaAI4cK7Dv0FQswPb8MF5IJVw7tojdMKCcmsJQCa+DQIAh8QP1e9cxCcKZLoNlqYW7WS9CwkhpJZw1nVy0Lkuu6HFstETNjzAMiWkITmHiCTDAkET17ohlqpTthGMSBAIhJUEkcRtqbQl+qIIrLMsg4ZhUaiG2bRIJGXdcdFFxdlnIxQggnJjA8H3Mjo54LCoYHUXWahiA1denRt9KJSVYNk3MdBozkVC2qj09iHIZwhBrYADDcRD5vMqzqGsqDNvG7OrC6ulR51CpqDGrxpjTfEWFbWP19uJu2oS9apW2btVccOiCQqPRaDQcOVbguRcnGRkvI6Uk2+myrj8dW8QWy36cIB2Ggpof4jgW5UpAKCRmfWHuOmpxFwllC2vU1+BCKFGrbRmEkSQUgnzJZ8OaTvr7OgAYPF4kjASmYeCHcsaZqcnifylIKQkDQWgIZUtrQK7gKV2sAQnbIohEvYAhDrmwbRMDKJaVHqQ7kyAIVbvkVFyeNCvHfC5G/uHDiFIJWanExYQUApHPI2o11cFA3YN0diJzubjDYK5Zg1HPqIimpxHlMqJcJrF9O/T3Iz0vFlo3xp/CiQnCsTEM18Xs7sZKp4lKJXUOQaCE241Wm21jJpMqIC8Mcbdu1cWE5oJDFxQajUbzEqdY9tl3aIqR8TKObc4JsTNNg65Mgkyny/hklROTFRKuRSqhuhZRKIgMiWNbYICIZOzwZKuyQI2RmGoMyhDqM9VqEI9UpVPKFcqxTIRp4Ichkrrdax3LnEm+ni+fwjSgI+lgmqoTEoSCUEgwQqy6vacwG2PtMh7dkijdiGWZ2JaBY1u4jhXnVZyKy5Nm5Wl2MZJhSHjsGFEuh9XTE9+70vNUdyEIoKND/dcwVP5DMqnGmfJ5ZH+/Kih6erAmJxHForKcrdWwOjow6sVIg6haxT90SL2fyZDYuRNZqSiNheepLkZPDzKZhFIpFnobySTS8/AOHMBMp3VRobmg0P3bU6RUKvHJT36S1772tfT39+O6Lr29vVx33XX84R/+IZOTk2f7FDUazXlAGAlyBY/JXJVcwTujC9ih0SKlso+Uct5EbADTMOjvS7UUBxhGPBZkGBAEgloQxa5OUSTjMSOY6VKoBAj1eVC2roZhYJjQkbKxGoF3cqaoEFLlSIh5pkpAFT9BFFHzo9ge1jQMpIAgFIhI0J1xWd2bIp1ysG1T/bJmkrsb0QHlqk+u4FEo+3rc6RwmKhRUcVCtYjZpEkStRlQqqU5EPfgump4mmppShYbvq/96HlAPylu7Vi38o4hgcJBoehpZF17LKCKansbfvz/uejhbt6pclhMnEIUCputir1qF2dmJVR+PMiwr1l4YjkMwNERt717VYdFoLhB0h+IUePDBB7n99tsZHh5ueX16epqHH36Yhx9+mE9+8pN88Ytf5JZbbjlLZ6nRaM5lliKCzqTd03b8MBKMjJXJl3yyne68xUQDwzDIdroUy2oEaHV3klLZJwgllarSXAgp4mmPRl1kop70B6Eg4VrIemegUWjYloFhgGNZdHa4cZhco26QJ1Ff2XaTFWw9W6LmR4hIHc8PI3q7UiAlZr1b4TomrmNh112dGscPhSr0JqareuTpXCUMYy1FI0k7nJoiGB5GlMvIIFDvG6qQNUwTIwiQvq9coZpcAKyurlhXYfX0EOXzLeJvI5FQXQ7HwV63DqevL9ZpIOXcdG3DmOmGFAo4l1xCePQoUT6Pf+gQqd27z+AXpdGcPnRBsUz279/PLbfcQj6fB+CGG27gXe96F+vXr2dsbIxvfOMb3HPPPeRyOd761rfyox/9aE4wkEajeWmzVBH0ji29cWr1SlMqK5cmzw9Z159e0jaNBOmOZJrVW3oYPlEiCARR3cXJnNE6z6RUUy8e6qNGpqUW/Ga9gClXA9IdDgZqbKmrM4EfKOtZKcE05JKE2oYhQRpxYWQYBpZpYJmCKJREEiana5SrAVEEjm2SSbskXKsl0Djhmni+iSWVPe6Bozn6+zpOa3GnWZh2CdeGbccJ1lII1UXI5wnrNrBEEVgWhutiOA6GlMoqth5OJ8plwkIBN63ufVEoYPf24mzYgLN5M8GhQ8p6FjBTKQwgHB8nnJ7G3bChrU5jNkYioQqbMATfV4VKLkcwOEhy506dQ6G5INB38TL58Ic/HBcTv//7v88f/MEftLz/oQ99iE984hP81m/9Fr7vc8cdd7B3716sZls7jUbzkuVkRNCNOf/TUVQ0LGANY2bkZzHM+tN/ISSb1qoOSrVux2oZBqYBAtkUFKz2K6QqOKJIqm6AZeA4JkJKCiWf/r4ORCTxggiMiO6MS67gq9/XbfwtUxVcYZvcMBVObBCEEZZpY5gQhpIgjOJUb3XNUK6oHUSRwA8iXMeiI+WQdC1ME8rVEMOAbGcCyzIo1TtJl2/tW4mvXXMSRIVCW2cnM5nE2bQJZ9MmzGQSM5UinJwkmpwkyueVlqG7G9kQSjuO6i7YNkgZ50pE09OI3l4Mx1Ei7c5ODNsmGh2N7xnDUPe8tXo1kedhlkoYlqWKjTBUQXmJ9h0swzTjBwUIgZnNqtyLWo2oXsBoNOc7uqBYBkeOHOH73/8+ADfddNOcYqLBRz7yEe69916+9a1v8dxzz3Hfffdx8803n7kT1Wg05yQnK4IeGS9jmgZ93ckVf0JumWY9L0LG41aLIYSMdRQTuRpdGZdixSeqh8hFQmJEkghZD5STWKaBY5kqTFgqbUNn2gUDxierGIZBb1eSTesyHBkuMDJexq8XHX4Agrp+ok2bwgBMC2zLVF0SqUa5DKGKiyhq6Dfmai+CUKk5okgVHmXLxDJNHMeks8NhVU8Kw4B8yWdkrMz2zT1aT3EG8Q4cUHqDevaEqFZbsifCiQn8gwcxUims7m78I0eQ9SA5M5NRHYtaTY1FeZ7qFggBTQWGqNUIRkYgipQgu1pVr5fL8c8yCDBdF2N0VOVV+L7SVgih9tcIvWtkTjT9fZZCxLaz1MeyGtdAGJ7Fb1ejWTl0QbEMfvCDH8Q/v/e9713wsz//8z/Pt771LUBpLnRBodFoTlYEPTxaOm1PyDvTDgnXIuHaFMv+knQCxbJPwrVxbJNc3kMINaJU80IiofIdhJSEoVBC6PpiyrIMoroDlFrkS46NljAMg7Wr0y2jXVO5GkUJYb2z0LCQNagXBsyMvhsGMyJuVNehgajHdEspF7SdDSOVWxGaEteFRMKiK5Mg26kKuInpaqyr0FqKlWPeMSZUMVF98kmCoSFkFGGm0zh9fdAYbyoWCU6cIJyawl6zRnUKGuLsri4ADMvCTKcRUqruRrE4kxUhBBKIjh0jGh0F18WwbZW+HQRYq1djui6yWFQuYMUicnxc6SUsi9qLL2KlUsh6oaMOaKiORCKhUrZtO3Z+MmxbpXBH0Ux+xhkcd1rou9ZoThV9Jy2TnTt3MjQ0xKWXXrrg53qbWpnT09On+7Q0Gs05znJF0KfrCbltmaztTzOdr5EreoueU2M8qTuToDubYHyqSiQEa1enmczVKFXUiFbSsXDSLkEQUvMiglAQBEoPIYEgEpQrIX3dSTrTblxMHDlWYPB4EdMyiCIRj0wpkbXqqCRcE8syKJYCgnrxIKXKvkBKIgFCihnLWaNFdzs/9XMLA0GlGjCVq1GuBHSm3ThXQ9vHrgyLjTFZq1dT27sX/9AhpO+DZRGOjakRI98HULqIZBIKBUSxiNndDcmkKgBqNQRKv2CmUohaTekpGiF0UrZ2B6IIggBpmpBIEBkGUaGA4bpqPCoM42wL6vawwYEDRN3dqsCIImUda5qqUAgCjFoNo6MDPA8zlVKOT6apxqpSKcxk8owkZS/2XbtbtujEbs0powuKZfDBD36QD37wg0v67LPPPhv/vGrVqtN1ShqN5jzhVETQp+sJ+cY1GQaPF8mXfMYmq/N2TYSU8XhSZ9plYFUHJyYqalypO1nXVoAfROoaKz4GylmnYc9qYBAKQW9XknX9abZe1B07Wc3WlfT3dWBbJoWST6bTIQgEYd2e1jSV/sKvi7ZlvRPRGHpvFC7qN0v7HiQqHwML/EAwla+RStpUaiGVWkBR28euCEsZYxLVqrJ4zeWUS1KpNOPI1KgOTVONECWTGNWqsn91HLVYrxcRjeRr0Qins6xGCMncVOv672UQIHM5QBUtZjaL3dcHhoElJeHUFHJyEqRE5HIY6bRylwpDVeDUCxcZBCql23UxenqwenqQQhBNT2P19uJs2nTaOwRLHRnTyd2aU0UXFKcRIQSf//zn49/fcMMNZ/FsNBrNucCpiqBPxxPyTL1DIIRkZLzM8GiJbKdLJu22iMQLJb9lPCmbTrToL7qzCTqSNvmST6nsE0Yynh23LYOODoekYzEyUWbT2gw3XbOO3m7ljNNOV+L5gnzRxzCJg+aklJSrYez+FKdoUw9WMk/OYnY2EnAsMEwDx1ZWsg372OlCjYmcto89FZrHmBACq6cHZ+NGNWYURYhCQaVQj4yoAsK2lcVrFKki17KgIX6uhx8SRUqo73mY9VEje2BA6SFqNVWUmCaG4yAdB1kuKw1FXe9AGM4UGvX9AUo7UddGSCHic0RKpb8IAnVs31ejTYDh+xiuq3QV9XwLo25BazgO4cgI1FO53S1bzvp3HU1NEeVyqvMCuqjQLBtdUJxG/vIv/5LnnnsOgEsuuUQXFBqN5pRF0KfrCXlDu2CaRmxjOzFdbbGx7c4kWsaTwkjM0V+4rsXq3hR93ck40M40VDfBNA3yRY/ODpdsZ4Js08K8na7EdVTgnG2Z1LwQxzaRQMIxCYJ6ZodBbCcrGml5p0gkwERpN2xLaT8sy8IPBAeO5OjvPTn72C1bL+Fv/+4L8XeRTFps37791E/0DLF9+3a+8pWvzHntZIkKBWp79xIMDcU5Ds2dMMOysHp6EIA4fFiNEiWT6ul/FKluhOsS+/vaNtTtX2V9gS/rImpsG3fbNoLhYZU3YVmYmQzRxMRMweC6M12LRpphc5Vqmmrfvo+sVjE6O5G1mhp/sm3l2tS8XaP4qNXUuSUSGEJgOA6iVCKoVMA0cTZuJLlr12kdM1rqd212dRGOjKjPmSb2wIAef9IsC11QnCb27NnDf/yP/zH+/cc+9rElWcZu2LBh3vdGRkZYu3btipyfRqM5O5yKCDrhWqQ7nNN2bpvXZ+nrTi45aG8h/YVpGiQSrf/Pa9ZfrO1Px8XRfLoStQ8bWaiRK/q4tgl1O9qaX0/jPg3fQyP1u9ENMU2Dzg4HcTT4sQAAdwJJREFU2zJPShw/E1pYpX/zDS3f5YkcuEn/vMi16Ovr4/bbbz/l/fiHDhHl8yDEnAVuMyKfV4v5OrLpSX9LWEh9MS+DINZGSMOAahX/xRdVAVC3dTXTafA8pb8QYkYMLUTDb3imkGi8Bmr/vo/0PGRHhzqXMIR6pgWWBYaBmUzO5E3U3Z8AsCxEqURkmrjbtmH39JyR8aKlfteGaWKvW0dw+LAO29OcErqgOA089dRT3Hbbbfh14dgv/dIvrcj/jDUazfnPqYigmxfhp4tM2uXyrX1s39yjRn0igW2ZpOsL6tnMp78QQuIHEUKqoDrbNpmcrsX6i41rZhKF59OV5AoepbKP5wvCMCIII0xDibWFmGlGWPW065UqLoRURYUfqGvPpBOs7j05+9hzIbTwXEKGIcHgIFEup8Z/5rnnpRAqN6IeSIfvIw0DI5VqKSZkEKhCoqGpaCzgbRsZBIhiEe/JJ8F1VUfBcRDFYss4U/xzo6BoCLYb7zc6D2GICAIolxH18SscB6RUAmzAyGZx6w8ERbGIqFSUDa0QiOlpzGyWxKWXkrrqqtPeAVjqd93AMAwdtqc5ZfQds8I8/PDD3HLLLbGj03XXXcenP/3pJW8/PDw873sLdS80Gs35w3JF0M2L8NONbZlL6p7M1l8cOVaoL/plrBcJ6vaxyYTN5vVZdmzpbXky305Xkit4jE9VKFcDLNPAdS2CIFJuS02C68bXttKdikhAwoBMp8vq3hTd2QRCyCWJ48+V0MJziahQUCLpahVn48Z5Pyc9T2kRpETattIh1PMb4s8Egeo0NPQMjZEl08Ts7ER4HlJKolJJLfrrhUns9CSlGpWCuMNggOpuNIu0m4oOWVVuZjQ6FPX9ND4tpqcRmQz22rXYdfG1rAvB/cOHcS++mOSll54xV6elfNfN6LA9zamiC4oV5M477+Rd73oXpVIJgGuuuYY777yTjo6Os3xmGo3mXGK5IuhzdTymsRgulH1GxjzKlQAviJQ4uv6gN+FYCEfi+XMjrmfrSsJQMJGrUqoEmKZBb3eCSi0iV6hB3Q62sZBTQXktBk8rRhAKKtWQSjUkmbBIJuxFxfHnUmjhOUUYxnalxkLjv/VOg+E4SpNQX+AbTe/HI04NkXaDev6DVRduy7rLk6xWCUdHWzsQDRrah1jd3/TfRvHR+LkekNfYpqVjUqvF7lT2wAB2by9GKhV3WgzbPnOZE0v9rpvQYXuaU0UXFCvEZz/7WX7jN36DqP404/rrr+fOO++ku7v77J6YRqM5J1mOCPpcJ+laytHJNkklHRzbwDRNLBMsy0QISakS8NyLk8DMdzBbV+IHAt+PkBLSqZnUYds24/VeUHd5Mhs62tNwPUEomcpVKZV9jo/Z9HYnCUOxoDh+SaGFEhCSvm6XYycqsdZipUMLzylsO16wyiiaf6FrmurvQL1z0EBIiYnqTtAYdWoeW2pYwdZHnuyODqwtW/CeeAIRhq0FQKMQaZxDo3CAVh1Fc8eiblE7h0b6teMgfZ+ocW6A3duLKBTOaOaEOvASv+smzlbYnubCQd81K8B//s//mY997GPx72+55Ra+9rWvkU4vzWNeo9G8NDlZEfS5SuOp/GSuRm9XklU9ScJQznF4aoxwzX4q36wrmS7UCCPVyUgmrHqBBX79u3EdC8tUIXZSSizTIAhPRzmhiIQSgAdhRM0PcWyLSi1sK45fLLRQCEEYhoRhWHcIgoQrmZgqcWTIZMuGDMnEuf1nvVysbBYzmVT5EIUCVk9P288ZiQSGbWO6LsL31eI2isDzVGFQq80UD7ORkiifx3QctY+ODqW9qNXUWFNnJ7JSaY1Yb/zc6DrM2l/8uYbGovFzYwyr4T7V0YGZySBLJZVGbZoYqdSczIkzkVa91O+6mbNS+CyCTvY+v9B/MqfIhz70IT71qU/Fv3/f+97H5z73OWx902s0miVwsiLoc5F2T+XbPRQ1DYP+vhTDo6U5bkkNXclErkah5BOGgkxaLdqjSBUnqqAwCEKBZRoIwWktJhoYgJQGnqeKmkLRY3i0NKdrNJ+4fHRkmG9+5W8Q9bwEKSW3/Ny76R9YT8KSTNUCSuUqx0cmWDvQQyqVOu3XdDIcOXKEv/iLv2h57Td/8zfZvHnzkvdh2DbOpk2EExNEU1OY3d1tuzeGaapE6UoFymWMdFoJs8NwRrvQsoHR2lWoVBCui4giorExlXwN6qk9xKNQRJHSUTRZvs5LczFhmrS9uVHiZjo7kbkcolYjOHQIM53G6urCWr1aZUKcgbTqpX7X8eWd4bC9xdDJ3ucnetV7Cvz2b/92SzHxu7/7u/zxH//xWTwjjUZzvrJUEfS5xmJP5WdjGAbZTneOW1JDV1KuBEzlqniBwPNVzoVE1hfiqlNB3TXK8+bqMU4HSqOhOgpSSnIFj32HpuboHuYLLRw/McK3vva3Lfu86dW3sH7DZiQS0wwIo4hKtUaxWAQ4p4qKkZERPvGJT7S8dvvtt59UQQHgbtmCf/AgUS5HePz4vHamZlcXDA4qO9ZEAjIZorGx1syHWI3f1EWAuJsRDA4SOo7SMNQ/J8NwJpgOWjsVswXZ89F83Kbzkb6vBOAApkk0NQWdnbjbt2P19lJ98MEzmla91O9aCnFGw/YWQyd7n7/ogmKZfOUrX+FP//RP499//OMf53d+53fO4hlpNBrNmWe+p/ILkUm7bd2SNq/PKr3CeIkgrOH5IZVagBTgecrhybbNWKArl7IAXCEaeg0wqNQCCkVvju6hXWihEIIwCObsz6hfRSPV27FtbMvE8zwMw8BxnAuu021lsyR37UIKQTA0RHD4sApXy2Zb05unpzE6OrBcF6RUdq/N+ofmzIjGa41ORMMCtpE50VwANBcOzR2J5gKlHc1FTGO7eqeioZ0Q5TJhI7+iLnA2Ozux0mnCiYkznlZ9Mt/1mQrbWwyd7H1+c2H93+oMMT4+zq//+q/Hv//whz+siwmNRvOSZL6n8gthmsa8bkk7tvYyOFIkiqbVE1/DoOaHeL6BgXKAUseVS3qgvFIYqNTshg3u+FR1Th5Fu9DCMAwRcv5xmnItxHVMEq5FVyZFGPqEYUitVqOzs/MMXd2Zo7EANEyTcHqacHwcMTioFo2miek4sTAbKZUFarmshNjt/sAbxYRpzgipGwv/xgLfdVWnw3GgWlVjUA272eYMi2ZsGyOZVEVJY9zJrXej6scxkknMjg5EPo/humrG33UxbJuoWMRes4ZoaopwYuKspFU3f9f/f3v3HSdVdf+P/3Xv9NleKEtHFJBAKJYoERsaG4lgS4wY1KhRYxKNGjXmp1EjfmKJYtfwDdYYg6ISLBEpQqJGQQRRQUCl77Js3+kz9/z+uHsud2ZnZmfu7s621/Px8OGwc9vcO/fOeZ/2ljX+kcrKuBp/W2kpbEVFXV7jz8zePR8DCgseeOABI89ESUkJpk6ditdee63N9YYNG4YpU6Z08tEREeVOslr5tmgtA6qTzZZkt6kYMaQQ/kAEe/Y1GwO77XYFMU3vYgXReTM7pSIAqDYFWsuYjSZ/qFULS6ukhXlOfQC2lvxIhRBo9kVRkO9A/1IX7HYVgN0IKPLy8trsQtYT2QcMgL2iAtH9+/UuSIqiz0gUDiMWCACqCuH36xmqZYE+WTBhs+mDtmUAkdgdCdD/Ho0CeXlQCwv1HBWKAng8UL1efYpVtGTjNgUPiterT13bcmwQQh8wrqr634JBKNC78CluN2xFRXAMGaJv026H2LoVIhhErGWGqa7KVu065BDYBwzo9mMSmNm752NAYcHTTz9tvK6rq8OPf/zjjNabM2dO3LpERD1ZNKYhpmmIxTSoqoqGphBKitxtrtfkC8PltMPltCWdLWnowAJ8tnk/fIEowpEYHHYV+V4nbLYofP6IHmSo+gxMuRQMaVAUGMcUjWqtWljMSQsr9/tQ4AVEktBHQKCmIQxF0afGHViuj5mw2WwIh8PGjFAOR+vz05PJPvLR+nrE6uqMsQdaSxclI2Gd3Q7F5YLq9SLm8wGBQHxuCNkS4XDog+blQOtkYjG9W1JtLTTASHanOp1QSkr0zNr19dDkts3Tw8puZzJokf9umbFJ8/n043S79azUqqp32XK59GzZNhvsXZyt2lZYCM+kSXCPH98tZ01iZu/egVcgS/v378fevXu7+jCIiLqMzJsgp7qtaQjC7w+jvjGIaFRDUYELTmfymXA0IdDYHEZxgQsV/fNSzmRlLoLL7lQuh4qAqiAaE+iqenshgGhMIBCMIqZprY7fnLRwd1UTKvdH4E8yeLymPozi/hr6lbpx0JA85Hn0n2NzYSqXY0RyIbRlC3z/+x/CW7bo07c6nYDTCaWldUIEg3qeCZnMzuU60HJgGlxt/D8aBZqbW2e4TqQo+oBsmY8C0IOMUAiKuRUoGtW7RQF6YCOTvckAIxqFZrdDaQloREsCOXtBgT4zVUtXolhdHdS8PKBl2lM1w9r/zs5Wrdjt3TIDNjN79w4MKLJUXl7e6x7yRNT3RGMamn0RxDQNNlVFfl5m09R+u7sRm76uNZLxhcJRfbbOYBSRmIYde5tQ1BxGeYkHxYXxs1bJPBSKoiA/z4mhAwuS7mNnZRPsqoJ8rwOq6kQ4EkMoHEM4EjuQAkABYl30KNaDCg0NTeGkLSwHppMVqKltwteh1k0pXo8dA8o9OGhIHgb395q2feBD9abuTrHGRjSvXInQV1/phXlVBfx+KIEAYqGQHkiYM1OHw9BkUjbZ5SnVb2+qv8tWjJZuSoqiGAGLTOSGaFRvofD74wdry/edTqMrldA0wOc7MAYjEgFsNohIBIrXGz9jUl4eNCGgNDczW3VbmNm7V2BAQUTUhyS2LmSTSO/b3Y34YmsN9lb7IIRAYb4Tg/rnQVUV1NYHsWefD43NIeyr8SMY0pO3lRS5oWkCTb4wGpvDUBQFFf3yMPag0qT7MU9DO6DcC4/LjobmMJp9YQRCUWhaCAjrsz1FIhqiXRRVaAKoawwiEIwm/RwjBheirMiFz7/ag53ftp4OeOhAD6YcWmK0TEixWAyqqkJV1V41y5PvP/9BePt2aH6/PubA49G7BcViwL59B7Jfm4MDTdML7263npwu24KjDMhUFUpL/g9DS/CiNTYi1tSkt4LIIME0E5SQ/5YDv83jOVq6R4lYDJFt2xC126EWFMAxdKgxbWuU2arbxszevQKvAhFRH5GsdUHPRC3gctpR1xDEjj1NGHtQaaukbTIb9t5qHxx21UhgJ5UWu6GqCqprVTQ2h9HoCyOwox5FBS7YVAUupx3FBS7kt3QJSty+lDgNraoqKPA6WqaQbekKo0DvdtKFhADqm0L4+LNKnHjUsKTLFOS7MH50P1TtbP1ZB/f3tgomhBCIRqOw2+1wu929poUiWluL4Pr1elcglwu2lu4pWiCAWGOj3q0pWfZrTdML+i3Tx2bNNB2s4nJBEULPtt2SIE9rbDywH3OQkLgv2XLR0tIRR1Eg/H5EQyHYSkpgLyiAZ9IkOEeORHT37h6frToXektm776OAQURUR+QrnVBtiDUN4XQ0ByG1jIrkbnQnywbdqLCfCdsqgKPy46qGj9cThsK85zoV+rNqAUEiJ+GNhrVsGefD/vrA4hEYojG9GljhQAiLf/vSsFgDOs3VWP4oEKMGlacdBm32w2Ho+2fWiEEwuEwABgBRW8R/PxzxJqbgVgMakthUQsEEGtqgggE9NaCVOMgYjG9dcIKGRzIwMLphBKL6WMp5ADvbNhsUDweI5Gd4nRCzc+HmpcHEQjoA73z8mAfMKDHZ6vOJZ6r3oFXgYiol2urdUFVFRQVuFCQ70R1TQB7q31QVcXIBN1WNuxwOGZ0S4rG9K4lTocN4UgMmgDGH1KGQQPyW43RSDaOQ05DGwhG8NW3dWjyhREKx1p6lyhGAJQmtUPOxDSB6toA/rtuD/qXeZMGSna7HV6vt9XfZfcbIQRisRiiLd15XC4X8vPze013JxGNIrpzpz7NqsMBaBpigQC0uroDLRPJ8kDEbaSdkWM0qifHs9sPJL6zQgg9n4XdrgdH+flwDB0K1e0GnE5EW7p0Bdavh3vMGD1vQkFBj8tW3RV6amZvOqB3PLGIiCilTFoXAEBVFPQv82BXZTOaW8ZajBtVljYbdn1jCPvrAsbA6WhMgwIFmtAQjmioqw/gky/2QRMHWjzSjeMYUOZFQ1MQgWAM/mBEf8+mwu1QobTkuwiFNcRisVaTsbY12U9nCEc07NzdiLWfV+H4I5PPUONytR5DEQqF4Pf7AcAYL2G325Gfnw+Px9Opx5xLscZGRH0+aIGAnu14374D4yFydbHkjFDtHbzbMmuTragItuJi2AcOhF22uIRCEJqG8JYtiFZVIbxjB4TNBs3ng7Db9f/3gGzVXaUnZvameAwoiIh6sWhMw56qZtQ3BpGf54DQBBRVQap5VxVFQWG+Ew3NYSMTdKps2PWNIVTXBtDsD0MIwOW0oSDPaRTs99cFEAzHsLfaB6ejxlgv3TiOqho/dlf5WqZlFbCrCtwtU9AKAShQ4LSriERirfJQdFUXqAZfGOu+2IdJY/uhuDCzrkoul8sIHFRVhdvthtvt7jUtE1J461ZE9uw5MIuTbJXoTrKJRGMxvVvTwIHGVKXR2lpEq6r0blwNDYgFAoiEQnpriMul/z8QgK2gAKKuDqKbZqvuaj0psze11rueXEREZIhGo6ja34j6hmY0+0MozlcRCOrTtsoacVVtPVVsQZ4T++sCRiboZNmww+EY9tfpwYSqKsjzOA6MVxV61wS1ZepXp03F3mpfS0I7G2rqgynHceyu8iEQiCAS0wfKCkWf+ehAAKQfh82mIJYiA3WuaRpQWx/Exq9qcMzhgzNap7CwEMUtfcXtdnuvGYBtFtqyBb7PP0e0vl7PiJ0w4LlbfGJVjc9jIf+vKFCSBT6KAlu/flCKixGLxRCrq0OsqgpaY6M+S5HNBhQUABUVEE4n0NwMNDZCUVVEhYDIy4Nz0CDYVLXbZavuDnpKZm9qjQEFEVEvFAgE0NzcjMbGAMLhCCA0aFoMmqa3QmgxDdFIFE6nE/aEQcOqqs/Zr2kC0ZieqM7ltMHltKPJF0ZRgQsNzWEjL4QMJvSAQ4OmaQiFNSgQAGIoKnCiqiaMhqYgVFVFaZE76TiOgjwn3M4A6jU9cZ0m9NwVoUgMtpbxE0pLR6fEWEKBngxPVVq/lwuhcAybvq7FUZMrMsrnYbfb4XSmHpze08UaG1H/0UcI7dqlT/3qcECRU7O2kK9yFVgkTYBn/rei6Mci/ybzHMj3Wv4eCAT0/BLRKLB7N0RTE2CzAV6vPiuVokCx2WBzOoGyMojiYr2rVziMWGkpouPGwV1aCnd+frfJVt2ddPfM3pQcrwwRUS8TCATQ1NSEUCgEIWKw222AosJms0NRAaEdKPiLlmKdOajQNH1gtaoqsNtU2G0qKvrnoa4hiPqmEPK9DjS3DJZ2OW1QFECLtYxraAkqQmENTrsCt0OB0PRgpskXgdNhQ/mI5LO4hCMaItEYhNCgKKJl+n9FT2Kn6d23DAKwqTC6PQnoPVdsNgUiKlqNr+hsmgCqaprR0BRCWXH8GAin04nBgwe3+ltPYeX46z77DMGaGr3WvrwcaiCgv5GkUC/QdlDRarxMBsu0Ir9z5v2bppaFEHHHImRiPDmAvmUbMQCIRqHU1EANBvUmKq9Xz7xts0F1OvWB2nLKWpsN6sCBEDt3Qvj9iFRXIzB4MNwsIKfVXTN7U3KKYNrnHmPIkCEAgF27dnXxkXSMDz74oF3rDx8+HIMGDUr63p49e7B9+/Z2bf/oo49O+d7atWuNKR6tqKiowIgRI5K+V1NTg6+++srytgFgypQpSQeCAsBnn32G5uZmy9suLS3FmDFjkr7X1NSEjRs3Wt42AIwfPx4FBckzKG/evBm1tbWWt52fn48JEyYkfS8UCuGTTz6xvG0AGD16NMrKypK+9+2332Lv3r2Wt+10OnHYYYelfF/eT9FoFE1NTQiHwy1jHuz48ptGVNcG4XHb4HXpBRgBveAPAdhsNgwZPhL9+lcAABqaQmjyRTB8UAG+P2Ugqir3YvOWb/DF1jpU1/ohAIRCUTQHIvrMRppATItBaAICAuGIgCYAr9uG0mIXbArgLT0E++vCKMx34JDhJcjzuoyAZuvmjYjFYvAHQ6is9sPnj0ATGqIxwGlX4bAriGl6a4W5xKe3oAh48spQVKofu92mQBPC6KYf8DWgbv9Oy+cdAPoPHg27PXkBurpyGyIhvbDsdqo47sgKjB7ZP+Nt98b7KdDcjJ0vvYSNH34I4fVCURQou3YBgUDajNeJQcLIwkKUpHiO7Wxuxj4ZpGRCJqcDgFgMTlXFBHmvysBBEgJrq6sPBBPmVhWXCxg1CsJuh1JdDcXnA+x2CIdDz/ztdEIpKcGw0aMxMCGfgmhogGhsxN68POwdNgx5BQVJZwDLRGf/Pg0fPhzRqJ6c0twtj79Pbd9P6a5Nb8aAogfpbQFFe/sM33///fjtb3+b9L2//OUvuO6669q1/XS3xpAhQ7B7927L27722mvxl7/8Jel7CxcuxHnnnWd52wCwc+dO4/uS6Oijj8aHH35oedvnnHMOFi5cmPS9Dz74AFOnTrW8bQB4//33Uz6Qzz33XLz88suWt33UUUelDGR37dqFoUOTz9KTqX/+858499xzk77329/+Fg888IDlbQ8ePDjtvd/e++mSK27CeRf8ApoQ2Lm3CQUeG0YO9uKgIXl4/PHH8cc//rFd2//L3/6H6roQSgrsqOjngdfj0N8QwGUXTkft/irL2z7smHNx/IyrAegtFHoLi/7e5g0rsOTv7Tv2y29eiIKi5EHC3x+7Ent3fGF5273xftq3ZQu2LVqEqTfdZHnbAPDYscfijOHDk753x5o1+H9ffml52wO9Xvzv7LP1f8h7RzEiVQx/7jnL2waA/+/883HF6afH/U1oGsTXX+OpL7/Enf/8Z7u235m/T7/85S/xxz/+UQ/4hZyMQZ84YMmSJTj//PMtbxvo/b9PfbVYzbY2IiJqGV8hUFndjFgkAkeBipICBYFAwMiR0L7tH8hyrcViiIRNQVAH/QCr0DNo21QgmmXOMuoYmqYh6PPpGal7CtntyTQgu720WAyapsVNeqCoKoSqdo/B6GlEIhEEAgG9BbOFqqqIRCLw+XxdeGTUnTGgICIiBMMatu+uh6ZpKC20Y9gAFwrzXbDZbHA4HO3efiSqD7RWAKi2lnEbcnajdhbgVFUfT+F0KMjzqNA0wB+MIRxp92FTlkKhEDRF0ccf9CQJg7PbvzmBWMtgYhlUCJnEL8nMat2Jpmmw2+2w2WzGlM4y+WJ7ulLlQjQaRTQa7XXTL/cEPONERD1cIJu+5CmEwzF4XQrcLgdGDPJixODCDp3KNBLV4LArsNkU2BQVUPRxHIr+ol1URUGBV4XDoaJ/iR1CKAiENNQ1RWC3dczxU2Y0TUPM4wE6IAiNY7PpeSw6ij6C/8AYicSB2h1A0zQosZg+hkRRgKYmPS+FrXt/KW02W1yBXI6hkAFGdxaNRlFXV9frEkT2BBxD0YP0tjEUHJQ9Iul7HPTGQdnJpBqULWd0ev/9942Zm2QNI3BgKldALyjENBX1zVHUNUYQiWoQLQOqBw8egjEHD0X/UidKirxxBQd5P8muT0II+ANhfL0riLrmKGyqgqJ8OxRVgT6xqz54WmspqDX5NfQbdCgEAJfDhgFldqPQoioqtnz1GULBoN4tSgj4QzHUNEShaXq+CbuqbzOmAbGYBgE9B4WqKHA7VQwe1B8DBgxCICxgswHF+XrBxxfQsGPXfuzY+S1kGgQr2hqUHQ0H4HWrGDPMg8H99eUKCgpSfhfMetv9VF9fj3379iHy+ef4csUKKD4ftPJyIBqFun8/FL8fiESgaJo+nawQUCKR1hdHUXBQQcGBQdmyJavlu5zRoGxF0XNB2O36lLVyn243nJqGCf366VO8hkLxmbsVBWv374ew2SAKC6ENGABt+HBo/foBmgYlGISiaVCrqqBUVUHx+/XPiAPd+IaUl2NAcTFUVYVqs0FVFIhdu6AUF6OyvBw7nU64XC4UFRVZKqS39fsUCoWMWdcURUkbDPj9fvj9fsRiMTgcDgwcOBDDhg1LumxNTQ02bdoEm80Gr9draVC51d+nUCgEn8+HSERverTZbFBV1WhF0TQNBQUFGDVqFFwuFwoKCuKCCg7K7lwMKHqQ3hZQEFH7yNq4YDAIRVHgcDgQDAYRjUb1gozsatHSZQHQuxm53W4IocAfjCIUjkJoUXhcNtjtqpHwzryPcDh8YJpZIYztVdVGsKMyhNpGPdt1vtcOj0uFougF/0BQgy+o77fQa0NUA4JhfYxDaZEDDocDChS9O0Usqs8SJQTqmqPwBzSoCpDntSEQ0uAPaojGBERLYj2HXYHLqWJofycOGeZBoy9mHIsQAl63Co9L7/7U0BzF/vooghGRbpIhSxQFcDsVDBngwpHj8uFxqcZ5HjVqVLev0e1I0WgUtbW1qKurg2huhmPtWqiVlRB2O0RZGZSmJqi1tVAaGoBwWM/rIISeuMzc+pAsX4QMKFT1wPualnrWKEXRZ16y2wGXS28Ei8UAmw1KOAwlFtOPy+UCYjH9WDTN+JvIy4MoL4c2fDhiFRVAXp5p0/o1TfYZjaliWwYyA/rYHltdHUQkAnXwYMSOOAL24mIUFBQgPz+/A6+Afg2CwSCCwWDSQdWJ2diFEKipqUEgEGh176fbRzQahcfjQVlZWU6+44nPOqfTmXS/QgiEw2EIIeB2u1FSUsLuTznCs0xE1EPJ4AGA8QNrt9uNwr/saiFrKGMtA0WjUT2hXb7XDoctBlV1GUGCzdQdIxqNGjWdAIzaQLn9AaV6txZVAQJhAZ8/irpGDQr0qVsddgVetwqvS8WQAS4oCrCjMozaxiiqayMoyAfcDsUIQPzBGPxB/bgryp3G9msbo4hEtZYWCr3VorTQjkH9nMj36McrAxlVAfwtAUijL2LUXpYW2WFTgYjerR1Ou4pAKAZ/UCAc1afPbakwRyQqEIm2nSBPVQGXQ8Hgfk6MHe4xggl57urr61FSUpJ0dpl0s8V0N5kev/w+KooCkZeH2EEHAbEY1OpqKHv3QhQUQCsp0b+Tzc36lKuyMA/EzbYknE7A6dQzrodCkE1MwmYDPB49mIhEjJYHYz2b7UDw4fHogYHdDtGyjtrYCKEoEG43tOJiiKIiwG6HVlSkH5ucAtblgvB6U3ZPEkIAKT6jyMsDbDY9O7jPB9HcrE8zW1GB2MEHA/n5RmDfkWQyS1ngTzaoOhAIxHUHkstpmhZ376djs9mMSoZoNNohY6zakuxZl4wMNkKhkBFcdXTQRskxoCAi6oGEEMaPrDG4GXrNuLmQIIMAWUtpLgTIbnuyIBFr6e8N6P2/w+GwEUyYB2jKYAUABpY5UZhnQ3VdpKXgr7cC6K0IKsqK7CgrshmFbSH0Qn8wAjT5IqiJaEaWbYdNQb5HX3bIAJcRUAzu70QwpAcUNhVwu1TY1NYFiv4lDhR4zceiQWvZn8OuorTQjn4lDjgdirE9QOjZuEN6UjOvW9W7SlUFsa8migZfFNFoSwI9RR9ULrvfF+XZMKDMiZGD3MaxmtXV1aXsHtHbyO+j7I4CAJqcGtRm0wOI5mYooZA+ONlmg+b1HmgZUFVjwLLS0hIhnE5AVRELh/XEeDL48Ou5UBQ97bveouBwQPTrpwcIwaC+bChkBByqzwfhckEbOBDC60Vs0CCIfv30HBKmwCGTxitzx45kn1GtrYVoybotnE6IwkKIoiKIQw4Bhg6Fy+VCfktQYfVcJ+aICAaDRjJLACkHVcv1AMDj8RgtjkDmU0+bl8tFJ5dUz7pU5DmRAUVeXl6fainsKgwoiIh6oFQ1i6qqwuFwGIWIWCwWF1Romp7ROhAIQFVVuFwueDweY2C3LKSYazjN/a9lAUQGFaqqwusGhg1UjYK/gAK7XQ8MVAVGUALoAUhRvh376yOoaVBbsl/rx2a3ASUFNpQX2+Nq+22qgjxPZrWnHpeKYQNdbQYhrbZnqsQszAOKC+yoLougqjaCRl8UvqCGWFQACuB1qSgutKOizIl+JY64YzULh8PtGq/Uk8jvSyQSiasZ14YMgVZSAtvevVCrqgDZoqCqrVsGolGodXVQGxuBSOTAck4nom431Pp6fdyCaTyEcDqh9euH6MiRwIABenAQDCbfn9MJbcCAVl2Y2ivtZ2zZpzJyJJylpbDb7ZYHDKfqzgToU73Kez2xBt88qDocDiMUChldJI0B40Dc9tIxBxG56u7UE1pR+joGFEREPVC6mkX54ykLd7LwAcAIBFwul97tKT8fbrcboVAIqqoiFovFtXLIYCQTsuB/oKtV8uU8LhVDB7hQUa4hEtUHWNts+pgHCK1Daj2zCUJSHaM5MAlHBaJRDY6WzN2pWkkSNTQ09IlEV7LWPGnOkrw8xA4+GLGRI/VB2S3TqSZrGYgNHaq3lCVZLgYA4TDUmhq90O50QisrA5zOrPfX4Uz7tLd00dIAaF4vbE4nHHl5KCgoaDWGIVPpujPJexwA3G53Vt2B8vLyjPFW8t5viwxcVFXNyfiEntCKQgwoiIh6pLZqFh0OB2w2W1w3B/mfw+FAXl5eXLcLt9uNSCRyoA98y7KpBj7K/5uDFfP7ibWY5gKB0fKhKrC79OwUMgBpa9xCrsnAxGp9trnvd2+mKErbSc9sNohMuoClW87phFZRkdlBZbq/DqTY7bDn5enf91hMH5Bts8HpdFrueiNnckvVncnn8xmF/EgkYrQ+JD2+JN2BXC4XQqEQwuGwMd4qFRk4yjEguWih6AmtKMSAgoioR7K3JMxKrFk0TxML6IGFw+Ewujqpqgqv19tqukq3221kxZazpACta/rkwG7z39piDijM6wKIyyScLDjp6YQQceMKequmpqa4rm19WSQSiRvfIINoK11votEompubjW5Kid2Z5H7kf7FYDJFIxJhSNRnZHSgajaKxsRGhUMioTIhGo0Yyy8TxV3IGJQCdMqg8lVTPunRy3YpCDCiIiHokRVFatSrIgZeJtXOyRlPTNDidTng8nla1drJvtxwAKeewl92e5HiMxIAgGZvNFteKYa5VTFVzmNiqkbhMTw402qy57+HkjFZ0IOg2f7flPWPlO9zW7EbmwD/ZTG7JyABH3ufmbpGJGbETB0HbbDa43e52DSrPVuKzrq0Ee13RikIMKIiIeizZqmDurpCsu5EMDmRhIFXNohwoaq7plIWZbApFQghjRinzv82zRMm/t1Wr3ZMDCSkYDGa0XLLZe3pCYcjv9/eJVphMmWv25b0nuyJlI5PZjRL/ljiTW7J1zJUQstVDdnuSA77lPSoDC1VVjVYPWSmRK0KIuJaeUCgEl8uVNg8FkNtWFGJAQUTUY9ntdqMLhXl6V/nDLwsGMiiQXRnS1Sx6PB5jm3IQqCQLIOZCfrICf2LQIP8v+2anauUwz0TVm7QVNGWbjKw7EUKgvr6+VwR+HUG2FMjvugwQrYyjyWR2I3PwIr835pnYEteTBXJzBYOqqohGo0bLiuwqJe9DGUzI/YTDYQQCgU4PKhLvCxmcye5aTqfTCJrMrSsA2j01L2WPZ5qIqIeKRqNGzbB5DIXsngQc6PIkydrJdD+0drsdJSUlEEKgubnZ6I8su1TIFgdzsGAm/y37MJu7U6Rj7r7RVwqoVpKRdRfRaBRVVVXw+/1dfSjdjvk7LAv9oVAoqzEUmc5uZE5m2da0qnJ6WdkyIe9PmXPG/LyQ96xslXA6na2mne2sAnuq+8I8FisYDCIUChmfWY6XaM/UvD21lbA7YEBBRD1aX/4BkN0hZE1jW2MoZDCRSfZY+aMsB2qbB3iaC0vpCv7yh14OUjVn7061bl8JJIC2Z+9JlYysO/D7/di1a1fG3bn6Cvk9l4G3ucY/2yRrmc5uZJ7mOfH+Tzw2mZtBTtYAIK7Abh6fIP8vPw+AnGShbuu+iEQixueQQYQcRG61Ra8ntxJ2Fzw7RNQj9fUfgMT+1bKbhdPpbHU+zOtkkz1W1kBGIhGjJjNZK4O54CMLJuZuGIkFHPP4ir4qFAqhoaEB4XA462Rk3eF7XV9fz2AiBXn/ycKu0+k0csJkM9NTprMbye+PLGwnmwghFosZhXBz6wRwIKBIlnMmWReqzsxC3dasVvJvDofD+Dw2m83I8WF1Wt6e2krYnXT9U4mIKEv8AUjfv7qt6SIzLdjIwZCyBlD+yKebqclcAEm8NvIYUs3o1Jc0NDTA7/dDCAGXy5WyBjpZMrLOqBXOlhz4Sq2Zuzq5XC4jKDe/l4lsZjey2+2tuhWaAz4Z3JifGXLZdDlnkv2tM7NQtzWrlfm45H2RbGatTPXkVsLuhgEFEfUo/AHQZdq/2iyx8J/J8ubWB3MNpqyBNY+lMG9Xjrkwt1jI9ZmvAMbAUhkAx2KxuG4oZrK1orNqhanjyftAFvSlbK9bYn6YVIVs2YIgE1om+x7J1glN0xAIBIztmLtnpfocZtk+RzKVyaxWicfRnvsik9aQ7txK2N3wjBBRj8EfgAMy7V9tlm3BRna5kDO7yJpNcwK6dPuWQYVs6TD3LacDQZY5DwCApIXBzqwVps4RjUaNZ5DU1qDpROb8MKFQCKFQKGUlCqAHILL7j6xUkdPBymVl66G5m6J8L/H45P1t7j7ZngApnUxmtUrUnvvCSmtId2ol7G561y8sEfVq/AE4IBfZY2WXC7/fbxRAZM2hLJgkBgeyFtTc51p265GFGoLRRUOeHxlspcpy3Fm1wtSxzF0CZUsAcKDgW1tbm/UYL3N+GNn6au5ylmp2I4fDgUAgYOSqkX+Tx5g4Pax5emngQKuH3L65RaMzslDnotXVvGxHt4b05QlCAAYURNRD5Lo5vLvLpn81YD17rOzfb/6xNueTkO8lLiNrNO12uxHw2O32Ptn3fvDgwbjxxhvj/jZo0KBWg+fTZTnurFrhTAwfPhx33313XDbswYMH5/QYeioZOMpgIBAIWBrjJfPDyIkoEguuyYKUZC26ieOb5GQOsoJABgtAfB4K81SynZWFOhetrlJHtob09QlCpN7/CYmoV8h1c3guWa3ZyqZ/dXuyx8oZVWTLhKydNBeCzT+i8vrIAoe5O0VfnOGpf//+uOiii1r93XzezN2fkmU57qxa4UwMGjQIP/nJTzirUwaS1ZLbbDYjIOiIMV6J3Q1T1cwna9E1BxYyX435eya/f/JvcjyGHCvVmVmoc9HqKnVUawgnCDmAAQUR9Qi5bA7PlfbWbGXbv9pK9lghhDFVZLL+/gCMcSqysOJ0OuH3+43pLGUhpa1WlL7IHDwkm6IT6Nxa4UxUV1czmLDIHGS3Z4xXWwVX2fohC67pWnTlvmRli3mwNgAj+JdT3sqW3s7OQp2rVle5r/a2hnCCkHgMKIioR8hlc3gudFTNltX+1WbpWkjMteculytt8jwZdJjfMxcQZIGEDjBnJ+6M1qX2am5uRm1tbU732VvI+ybZeJhsxnhZKbi2lT1b3qvm9eS/zQOwI5GIkYemvVmoM5GLVtfELpqZtl6bW0MAcIKQBL3zUxFRr5PL5vDO1tE1W4n9qxODk1StHZm2kJinh5UtEKmS5wHx512OwYjFYvD5fJwyNglZiEvsvpKLWuG2jmvPnj3dsnWvO0nV7ShVPhi5TiZjvKzObCdbLuUyycgWRPP9HAgEjJYJ83odMR4gk66dndnqmvi8k9M1ywod2bUr3bHL4EUGgwAnCJG6zy8sEVEauWwO70ydNfWt/CHOy8vLaDxGNi0kyc57pj+8ct/RaJTBRBvMU3oGg0Gj20ln1wqn8s033/S58S5WpAomZJCY6n7JZIyX1ZntzNPVZtKiKysMZCVAcXGxUYBv74xF2Xbt7IhW10TJnneya6GmacZ5llm4zRJbQ1wuFxoaGjhBSAIGFETUY+RqEHJn6uypb2XwkU62LSRer9f4Qcz2vMt9yUzBlFwkEmk1DqUrZ4nZsmULA8B2kIGYLPgmu1/aGuPVnpntzF2XrLToJk4KkOzYOrriwhwYWG11TSbd8y4SiRjnWAZ3siU2VWsIgF47QUh7MKAgoh4jXXM4AGMuf/lj6na7c95NJJ3uMPWt1RYSt9uddTcEQO9nLPNPJCbG6itdafbt24c333wz7m+nn346+vfvH/e3SCSCkpISFBYWpi2IdradO3fGBROZHj/Fk/dFqu95W2O82juzncvlMgryHdWim01rQ3u7dmbb6prqeNM972TgJCtu5DmPRCJG65KcWtfj8Rittb1tgpCO0D1+ZYmIWrRV85XYHC5ruJJNXSqnPOwuusPUt1ZbSBRFQUFBQVbdEGTNpAwmzIVU85iB3voDK+3evRt//vOf4/42ceLEpAXyuro6DBgwIFeH1kpNTQ18Pl/c37I5fjpAFprlPSfvXVlIlUnjUo3xMg8eljMxpRuXIbctOZ1OhMPhDmvRzaa1weFwdFjXzkxaXVPJ5Hknx4nJ1gpzVzXz80kmCLTb7a2m7m1Ld54gpKMwoCCibiGbmi/ZHN7Q0IBwOJy0QKooCsLhMOrq6uB2u+FyudrdF7i9unrq2/a2kJSVlWXcDSFxX/I6mf9LNZi1r9u0aRPGjh2b8/3W1dWhuro65/vtabL93soCeDAYjBtbAeiteclaBKLRKPx+P4LBoFEjLtczz6iWSB6XfIa63W5omoZQKGQMuDZny041wDmxYicSiRgBAtB2a4PNZsvpoOVkFVEAsn7e2Ww2RCIRI9gDENfqLYM/+Tzr6ROEdKTe+amIqEex0s82EAgY4ymStUzIGUs0TUNTUxMcDgecTidsNluX9Us3FyS6omaro1pIMumGYN6XbNVIbI1IVShjoKEHFaNGjeqQQbFtCQaD+Pbbbztl271Re76byQa5Jxai5fNQToQgC8vy+WHOpm5+hsnAQRbkGxoaEIvFjAIyAONZKgu3iQP/ZcuCuWJHdnXUNM0YlNxWa4O879sai2FeP7Frp/w8snDvcrnisniHQiFjWlvZ+mn+LZDbbOt5J7chz6s8V3I9cyAnp9aV5yYUCvXYCUI6GgMKyrlMB3NR32Cln20sFkNtbW1cX1bgQAuA+QfU/EMqH/6yti7XM+d09dS3HdlC0lY3BHNXDXMiPHNAlW5dArZt24a8vDyjINUZgXBlZSXq6+s7bHuUnVgshubmZgBAYWGhEUzILkjm5IaydSPxnpIFZxlMyGegfD4m3reJYwjMM7nV1dW1qtgxZ9RWFCXu2SXHrZl/z0OhkFHAz3RCDHPFhc/nMyqMElur5TNHBlvmMSoyQJLPS1kZJYObZCKRCMLhcKuxLvJ5aw7g5GBtl8sFAMZ6wWAwZaDQnScI6WgMKChnsp06jno/KwOE5Y+IbHKWhXQZgJhrqxLJAq65GwGQu+ylcnBzR019m21wnssWEnNAmLguA4bM+Xw++Hw+eL1ehMNhNDc3w+v1wuPxtKsyxufzYefOnR18tJQp8ziKUCiEcDhstCgktmKY71lZUJYFeVkIl2MlZHcjcyufefySeRyGeRvy91nOxmae7MJcsI/FYsZzWA5eThawyIqQbM4HoLfW+Hy+VoGK3Iff70+5DbmMbHUxnxM5hsTMnItC7l+ee/k5zEGcbO2x2WxGkk/5947Ml9FT9d5PlkOvvfYa/vrXv+Ljjz9GfX09+vfvj8MPPxyXXXYZzjjjjK4+vG6ho7ICU++S7QBhGZDKH0fzjx5wYCq/RObszeZWDr/fn3IgYGfpiKlvrQbnsisYED9NaTpWW0hkpl75Wex2u/EDzC5N2ZMFKbvdjkAgAJfLBafTmXVlTFVVFerq6jrzUCkD5tY7879TLQscaCmQNfEAjIkpzF2MzK0SMpgwtxiahcPhuEH4cjYj+X0y19zLezpx3FqySgfz/jLpXikrg8zHbm5tkK0BicxBizngAfSKIlmoD4fDrcadmMsisiuTeayK/Czy2SqfX7K7mcvlMirD5JiR9ubL6MkYULRDJBLBhRdeiJdeeinu77t378bu3bvx+uuvY/bs2ViwYEGvjkrb0tFZgal3sDJAGEDcrEGJPybm71DielKy2rlcZi9tbyZYK8G5OQCRNXKylaStAZ7t7ftr/oGWgR2TpVknr3soFILX601ZGZPYerVt27YuPGpKxup9YB6bJMnuQIkBRCb5X8zHIYSA3++H0+ls9TtsLlSb/ybvbXNwJP8vv5ttkWMl5LPd/ExKVVEk92GeNlw+X8LhsDFuTgYBkUjE6K4k7w/z4Gt5zuRvS2JrufyMciyZfGa7XC54PJ64rrby3PSl3he9/xN2oquuusoIJkaMGIErrrgCw4cPx+bNm/HEE0+gsrISzz//PIqLi/Hwww938dF2Datz3ueyxpi6RrYDhOXDPlkNEnDgx0RK1Vdfrif/LgOKXGYvtZoJ1kpwDiAuAJEFAvmjK9dxOBxxYyLa2/c3Go3G/Sibf7gBMGlaBzB3/5DXW87CVVNTg0Ag0FWHRjkiC8CytUq2WsoKEyvMtfzmbkKyhcTMfE+bAwDz81W2hqb7DKFQKK4lJPFZYR6Dlap1Uxb4zUGF7GIqx0rIoMs84Fxu09yqk6yCxbyc+XdLVVV4PB4UFhb26fGhLLFZtHr1asyfPx8AMGXKFKxYsQKFhYXG+1deeSVOOOEEfPHFF3jkkUdw0UUX4bDDDuuqw+0ynZ0VmHqubAcIp/oBkK+TDfw17ytZbZP8gTQXqnMl20ywVoJz2TfY3C9a9v2V96Z5xhK5TEf0/TV3wzKfa9ldI5OWCnaNylxtbS1qa2u7+jAoh8wtAbLgLysWrN435uep7CYkK2ESKyrksonP28TxUnK62lRdO83PP9nNyfx+W4GRubLCHCxEIhFjynD5nAsGg63GUpjPmXncRLJzk7ie+b22Jqro7TIfMUNxzEl+5s+fHxdMAED//v3xj3/8w/gC3nXXXTk9vu7ASpcW89RxLEj0bokDhDOR+MA3r5dJ94HEfrFym+bgJpdkC0RZWRlKSkpQXFyMkpISlJWVtSrEZxucyx/PQCBgNMvL+1C2OMhpJ2VrhN/vh9/vj+vmVFBQYKkLojzXMrmgrHVMVnOaqkaQiDJnDjDa8zwztypGIhHjOZTJNuW+zYFBLBaLm+JbLie77pm7byW2VidrdU71frLjiMVixvNH/if3K7t/xmIxY7rxVMFEsn31hdwS2eAT3IKGhga8/fbbAICpU6di8uTJSZebMGECjj/+eADAm2++iaamplwdYrdgdc57uY4sPFHvlDiFaibM3Z3kd8XcUpFOYs1TYi1aVzZNy5otp9OZdKC0leBczrSiaVrSbcqAweVyGd0A5A+jx+NBQUEBSkpKLI9nMl9f2eIiAxjZ/zhxLvy+1D2AqKPIgrOcjag9wYQs3MsKm0gkYtynbW03sdXZ3LohBz0HAgH4/X4jwJDPA/NYjMRtZiuxhUN2C3O73UYXUo/HY+QlstlsRpcxOfA82X4Tu9v2hdwS2WBAYcGqVauMAtBJJ52Udtnp06cD0AccLV++vNOPrTvJtktL4nJsoejdFEUxHuDJBlMnWz7VgL1UEmu3kv3QyL935xomK8G5eeBmuj7HTqcTeXl58Hg8xg9raWlpu6c4TLy+suXE4/HA7XYbXb4SMweb1yeitslujcnuo/Ywt96meu6Yu5+af+9lQV2+loX4xP8KCgpQVFSUshuklc9j3oasWJHP+MLCQpSVlaG0tBRFRUXGjFZyDJm5VcX83JTHJitAzN1Ie3NuiWwwoLDgs88+M15PmDAh7bLf+c53jNfr16/vtGPqjqx2aTGvT72beYxA4lSEZuaaMvnDJscLmPvYJnZjkuR6yQZx22w2eL3ebv19szLeRMr0c5n7/nbUgOlk19fcDUp2w5JdKxIrFFipQJReYmtkR3UdTKx8STYTnHz2Jk5dbe7eKCuB8vLyUnbt9Hq9cfkeEj+feX+ZSmwZMbckyBbh/Px8YzxFOBw2Kj3kb4x5Agtzq4V83RdyS2SDZ8GCb7/91ng9YsSItMsOHTrUeL19+/ZOOqLuqauzAlP3l+0Uqh6Px+h7K4QwuvKYM5kmMg9WluTy5lrz7iwxOG8rSDDXGHZl62Bb11cOADdn4U3W4mTugsEgg+gAh8Nh3DPhcDjjFsy2JFZKyC6K5sRv5gDDPAGDeQyG/LvL5Uo5aFnOkiTHNJjHVMl9p+sCnayiyDwmE0jekpDq+eR0Oo1jMf+umIORvpJbIhsssVlQXV1tvC4vL0+7bGlpqfG6pqam046pO5JdHjoqKzD1TtlOoQrAmDpV1pyZC86JtevmwYGy2Vou43A4UFRU1O2D12yDc/PsK8n6JSfTWa2DmVxf+UNvs9kQCoWSTsqQbWsnUW9nLvyaK2DMBX8rEoOJZNsztxAn62oln7Oyu1Nbz6CCggL4/X6jEG9uEbHZbHG5NRIlDkaXrSLynKRrSUj2fDK3RpjHdshW1b6UWyIbPBsWmOf+bqvvnPn9dCnjpSFDhqR8b+/evaioqMjgCLuPjsgKTL1ftlOoAq1/AIADM4SYf1QAtJpdRP44FBcX94jpibMNzhMDikwChM5sHcz0+sqAYv/+/fD7/XFjQBIDRfPYEPmjn6zrG1FvZB70bB7YLJNWWg0o5OBr870TCoWMVgYAcd2rkgUK5klV7HY7CgoK2tyv2+1GUVER6uvrjdwV5okbkk3eYf574ueV48EyaUlo6/kkp9uWFVus7EyOAYUF5syT8gZLxfx+X5y1qL1ZganvkN+VvLy8NpMDpfsBMPfplc3Wktye0+lEYWFhRj903UW2wbn5x76trk+5aB3M9Pq63W6UlZXBbrfHTTWZODuX7Aohs9QCQH19vZGnI1k3iK4yefJkfPnll122//bq6cffG8kCs2yxzM/PNxLbKYqCpqamrMdCybFmcj1VVeH1eo3WArn95ubmpIX+xIR6TqcTRUVFGVcQFhcXA9Bn0pRJOGXXVPkMSBwobc4fIY/Z5XLB6/Vm1ZKQze8PJcdSmwXmSNfcdJ+MzDYJoFUylWR27dqV8r10rRfdmdWswNQ3pepnmyjdD4BM3CaDDfkDZ55tpKcFrVbGm8jP3p1aBzO5vvIZIGdTCYfDcX23zbktzM8MWbhInKGFLRbUm9hsNrjdbuOekPet+Zlot9tRX1+fVUWmuZVPVu4VFhbGFaxlhV+6Qr+cBrqoqMgIEjJVXFwMt9uNpqYmBAKBVgO+5bNDtsKY33M6ncjPz4/LuZOtTH9/qLWe9YvaTZi7SASDwbTLmt/vywVlK11aiDKR7Aegt9Y2tWe8SU9rHbTyzCgqKkI0GoXf72/Vt1sGV13dWkF9m8yfY55oIJvuSbJVTn6Hk923iqKgvLwcdrsddXV1GXWBkseSSTDQVqFfTgdrtXJC3tuaphmJ72TLg3m68FTvUdfoHr8cPUxJSYnxuqamBsOHD0+5bG1trfG6f//+nXpc3V1vLeRR99Uba5vaO96kJ7UOZvvMkPPMK4piFDbMSfMCgQAikUjcDFiJ+UjkTFOcUYo6iqy1N9eyyy5CshdDW983ua55hrS27ltZ8G9oaIDf7086E555BqdsgoFMCv3tJY8n2/eoazCgsGDMmDHG6x07dmDKlCkpl92xY4fxuq0pZvuK3ljII8qljhpv0lNaB7N5ZiRrxZGFNnPNsNymXM7cTcq8jOwXTpSOw+Ewxh/J75MMaGXNv7mbnrwn/X4/wuGwcR+bC/xykgkZTJhne8v0vjUX/IPBYFyLpN1uN7oTWg0GWLAnqfv+gnRj5mR1n3/+OWbOnJly2c8//9x43VYSPCKibHTEeJPe2DqYyaB92V88cYpMc2uF/Juc+QZAXNBhbuVg0NE9dNR4GfNYHXn9zQnOZAHf6XTC6/UiLy/PSKKWafBuviflts15HMzHYR58bOW+lQOsEzEYoI6iCLbpZq2xsRH9+/dHKBTCiSeeiGXLlqVc9oQTTsDKlSths9lQU1ODoqIiy/uVg7LTDdwmIqIDzIkQUw3aBxDXEmGeIcvcLSwajRozh5m3J4O6QCAAv99vdKuiziMDPdmFqKioCPn5+ca0qTJZojlodDqdcDgcCIfDCAaDcYP95TU2Z3CXBXf5HTIX+uX3IlXBPtn3rjcG70QSAwqLzjzzTCxevBiKomD9+vVJWx/Wr1+PyZMnQwiBM844A0uWLGnXPhlQEBF1nGSFPgBpC4LpCoryvVgshlAoZBRC5TJyfn5Z2A0EAnEzAfYUsh9/qu5gdrsdHo/HqBGXU4zKZWWWe7fbDa/X2yojseyeIwMzp9NpFPJlf3/5//YU1FnoJ+o4DCgsWrVqFY477jgAwPjx47F8+XL069fPeH/fvn04/vjjjbm7V65caSxvFQMKIqKeL1lBVk4HLLtjAfoMXfX19Z3a2qGqqtGn3zzo19zFR7YC2Gy2uAJ9YouNrOGXY1PSfV4W3Il6FwYU7XDRRRfhmWeeAQBUVFTgyiuvxCGHHIItW7bgscceQ2VlJQDg5z//OebPn9/u/TGgICIiIqLuhgFFO4TDYZx//vlYtGhRymXOO+88PPfccxkltWsLAwoiIiIi6m6YBaQdnE4nXnnlFSxatAgzZszAgAED4HA4UF5ejtNOOw2LFi3CSy+91CHBBBERERFRd8QWih6ELRRERERE1N2whYKIiIiIiCxjQEFERERERJYxoCAiIiIiIssYUBARERERkWUMKIiIiIiIyDIGFEREREREZBkDCiIiIiIisowBBRERERERWcaAgoiIiIiILGNAQUREREREljGgICIiIiIiyxhQEBERERGRZQwoiIiIiIjIMgYURERERERkGQMKIiIiIiKyzN7VB0CZGzhwYFcfAhERERFRHEUIIbr6IIiIiIiIqGdilyciIiIiIrKMAQUREREREVnGgIKIiIiIiCxjQEFERERERJYxoCAiIiIiIssYUBARERERkWUMKIiIiIiIyDIGFEREREREZBkDCiIiIiIisowBBRERERERWcaAgoiIiIiILGNAQUREREREljGgICIiIiIiyxhQEBERERGRZQwoiIiIiIjIMgYURERERERkGQMKIiIiIiKyjAEFERERERFZxoCCiIiIiIgss3f1AVDXO/zww1FZWdnVh0FEREREWRg4cCDWrFnT1YfBFgpq2969e7F3796uPgzqRLzGvR+vce/Ha9y78fr2fj35GitCCNHVB0Hd25AhQwAAu3bt6uIjoc7Ca9z78Rr3frzGvRuvb+/Xk68xWyiIiIiIiMgyBhRERERERGQZAwoiIiIiIrKMAQUREREREVnGgIKIiIiIiCxjQEFERERERJZx2lgiIiIiIrKMLRRERERERGQZAwoiIiIiIrKMAQUREREREVnGgIKIiIiIiCxjQEFERERERJYxoCAiIiIiIssYUBARERERkWUMKPqIyspKFBUVQVEU1NfXt7n8vHnzoChKRv+deuqp7Tq2UCiEefPmYerUqSgqKoLH48HBBx+MX/ziF9iwYUO7tt2XZHuNgdyde17jjjd58uSM79F//OMf7drX1q1bcdVVV+GQQw6B2+1GaWkpjjjiCNx7771oamrqoE9Er732Gs444wz0798fTqcTQ4YMwcyZM/HGG2/0qH1Qa+vXr8/4fnW73e3aF69x7jz22GNQFAUzZ87MaPlcPUu75JktqNeLRqPiBz/4gQAgAIi6uro217n44ouN5dv675RTTrF8bLt27RLjx49PuW2HwyEeeughy9vvK6xc41yde17jjhcOh4XT6cz4Hn3xxRct7+vFF18UHo8n5bZHjRolNm7c2IGfru8Jh8Pixz/+cdprOHv2bBGJRLr1Pii1Z555JuP71eVyWdoHr3FuffHFFyI/P18AEGeeeWaby+fqWdpVz2x7ZmEH9VSapuHSSy/FO++8k9V669evBwCMHj0af/7zn9MuO2DAAEvHFgwGcdppp2Hjxo0AgKlTp2L27NkoKirC+++/j/nz5yMUCuHXv/41KioqcM4551jaT29n5Rrn6tzzGneOTZs2IRwOAwCuuOIKnHLKKWmXP+KIIyztZ9WqVZg9ezZisRgcDgd+/vOf45hjjkFTUxNefPFFrFq1Ctu2bcOMGTOwZs0alJWVWdpPX3fVVVfhpZdeAgCMGDECV1xxBYYPH47NmzfjiSeeQGVlJZ5//nkUFxfj4Ycf7rb7oNTkbyoAvPTSS3A6nSmXtdlslvbBa5w733zzDU477TQ0NzdntHyunqVd+szu8BCFuo26ujoxY8aMVtFpW7XXkUhEuFwuAUD87Gc/67Tju+OOO4xjuvzyy4WmaXHvf/jhh0b0P2DAAOHz+TrtWHoqq9c4V+ee17hzPPfcc8Z5Xb58eafsIxKJiNGjRwsAwul0imXLlsW9r2mauOGGG4zjuPrqqzvlOHq7VatWGedwypQpoqGhIe79qqoqMW7cOGOZNWvWdMt9UHrTp08XAMSwYcM6Zfu8xrmzatUqMXDgwLjf3HQtFLl6lnb1M5sBRS/13nvviYMOOihpc1dbhc2NGzcay953332dcnzBYFCUlZUJAGLgwIHC7/cnXe7hhx82jmXevHmdciw9ldVrnKtzz2vcea6//nrjnO3fv79T9rFw4UJjH7/5zW+SLqNpmjj88MONH7B9+/Z1yrH0ZmeccYZxnj/55JOky2zYsEEoiiIAiFmzZnXLfVB65eXlAoCYMWNGp2yf17jzhUIhMXfuXGG321v95qYLKHL1LO3qZzYDil6murpanH322XFf9BkzZhhfoEwCihdeeMFY9p133umU4/zXv/5l7OP3v/99yuUCgYDwer0CgDj66KM75Vh6mvZe41yde17jznPyyScLAGLQoEGdtg/zd+yrr75KuZy5b/iTTz7ZacfTG9XX1wubzSYAiKlTp6Zd9oQTThCA3r++sbGxW+2D0tu9e3dGz0KreI0739KlS8WoUaOM62i328Utt9ySUUCRq2dpVz+zOctTL7Nx40a88sorAACPx4P77rsPr7/+OvLy8jLehrmv53e/+90OP0YAWLFihfH6pJNOSrmc2+3G1KlTAQAffvgh6urqOuV4epL2XuNcnXte484j79HOuj8BYOXKlQCAYcOG4ZBDDkm53PTp043Xb731VqcdT2+0atUqxGIxAOnvEeDAeQ6FQli+fHm32gel19m/qbzGne/555/Htm3bAADjxo3Df//7X1x66aUZrZurZ2lXP7MZUPRCqqri/PPPxxdffIHrrrsOqprdZZYPv379+mHAgAEQQuDLL7/E22+/jZUrV2LPnj3tPsbPPvvMeD1hwoS0y37nO98BAAgh4tbry9pzjXN17nmNO0dlZSX27dsH4MB59fv9+OCDD/Dmm2/io48+QjAYbNc+9uzZg5qamrh9pDJ48GAUFRUBiC84Udus3CNAduc5F/ug9MznUl6D3bt3Y9myZVi6dCk2b97cru3zGudGWVkZ/vKXv2DdunU48sgjM1onV8/S7vDM5ixPvczo0aOxadOmtNFpW+QXbOzYsbjvvvvw0EMPYefOnXHLHHXUUZg7dy5OOOEES/v49ttvAQB5eXkoLy9Pu+zQoUON19u3b7e0v96kvdc4V+ee17hzmH8A8vPzceGFF2LhwoUIhULG371eLy644ALceeedlmZhk9cO0GeLacvQoUPR0NCAnTt3QggBRVGy3mdflM15tnqP5GIflJ68Z10uFz7//HPMmTMHa9asiVtmxIgRuPnmm3HZZZdlff/wGne+q6++Go899hi8Xm9W6+XqWdodntlsoehlBg0a1K5gorq6GpWVlQCA1atX44YbbmgVTAB615Tp06fjnnvusbwfAG0WNAGgtLTUeC0j8L6sI64x0Pnnnte4c5gTAd522214/vnn44IJQG+x+Otf/4opU6ZYqoGS1w7I7vpFo1E0NDRkvb++KpvzbPUeycU+KD15z4ZCIZx33nmtgglALxD+4he/wNlnn93qfm4Lr3HnO/zww7MOJoDcPUu7wzObAQXFSSx8HHHEEXjttddQXV0Nv9+PTz75BL/4xS+gKAqEELjxxhvxwgsvZL0fv98PABllBDUvI9cj63J17nmNO4f5HlVVFVdeeSXWrVsHv9+PmpoavPrqq5g8eTIAvRn8jDPOMLpIZcp8DXj9Ok8259nqOc7FPii1YDAY16Vp0KBBePTRR7Fz506EQiFs3boVd999NwoKCgAAr776Kq6++uqs9sFr3H3l6lnaHZ7ZDCgozrfffgu7Xe8Jd/755+P999/HmWeeifLycng8HkyePBlPPPEE5s+fb6xz7bXXwufzZbWfSCQCQG8Cbot5mWg0mtV+qLVcnXte486xa9cuAHryq1dffRWPPfYYJk2aBI/Hg9LSUsycORMffPABTj75ZAB6X+3bb789q33Iawfw+nWmbM6z1XOci31Qal9//bVxXseNG4dPP/0UV111FYYMGQKn04lRo0bhpptuwurVq5Gfnw8AmD9/Pj7++OOM98Fr3H3l6lnaHZ7ZDCgozqWXXopgMIivv/4aCxYsMIKLRJdccgl++MMfAtCb2hYtWpTVfjweDwAY2X7TMTf/pssuSpnJ1bnnNe4cK1euRFNTE7Zu3Yof/ehHSZdxuVx4+umnjR+Np59+OqtuFPLaAbx+nSmb82z1HOdiH5TauHHj4PP5sGfPHqxevRr9+vVLutzEiRPjAv8nnngi433wGndfuXqWdodnNgdld3OvvfZa2venTJmCYcOGdeg+bTYbRo4c2eZyF154If71r38B0Ketu/DCCzPeR35+Pvx+f0az0ZiXMd80vUWur3Guzj2vceay/Q7k5+cbtZmpDBo0CNOnT8ebb74Jv9+PNWvW4Pvf/35Gx2PeNq9f58nmPFs9x7nYB7WtoqKizWVmz56N6667DoD+m5opXuPuK1fP0u7wzGZA0c3NmjUr7fsLFizARRddlJuDSXDooYcar7OdSrakpAT79u1DbW1tm8ual+nfv39W++kJcn2Nc3XueY0z11nfgUMPPRRvvvkmgOzu0ZKSEuN1JgM35fVzuVzGdITUtsTzPHz48JTLWr1HcrEP6hj9+/dHWVkZampq2nW/8hp3H7l6lnaHZza7PJFlDofD8rpjxowBADQ2NqK+vj7tsjt27DBeZzIdGqWXq3PPa9z1rN6j8toB8dcmFblMuoIMtZbNebZ6j+RiH9RxrNyzvMbdV66epd3hmc2AopsTQqT9r6NbJ+bPn48777wTN954Y5vLysGhgN69Ihvm5Dqff/552mXl+6qqxq3XW+T6Gufq3PMaZy7T78A333yDefPm4ZZbbsHChQvb3K7Ve7SkpMTootHWtdu1axcaGxsBtJ1QieJZuUeA7M5zLvZBqb399tv485//jOuuuy7pFOxmwWDQqF3O5n7lNe6+cvUs7Q7PbAYUFOfee+/FrbfeinvuucdIM5/K0qVLjddTp07Naj/HH3+88Xr58uUplwsEAnj//fcB6P3ICwsLs9oPtZarc89r3PEqKytxzTXXYO7cuXjooYfSLhuLxbBixQoAerP2lClTstqXvH5bt25NWxBatmyZ8dpqosu+6qijjjIGzqe7R4AD59lms2HatGndah+U2quvvoqbbroJf/nLX/DOO++kXXblypXGbD3Z/KbyGndvuXqWdvkzW1CfcNxxxwkAAoCoq6tLudwvf/lLY7nrrrsu5XJ79+4VJSUlAoDIz88X9fX1WR1PJBIRZWVlAoAYMmSICAaDSZebN2+ecTz33ntvVvvoazK9xrk697zGHS8UChn3naIoYv369SmXffLJJ43zOnv27Kz3tWjRImP966+/PukymqaJww47TAAQdrtd7N27N+v99HU/+tGPjOu5YcOGpMt8+umnQlEUAUCcccYZ3XIflNzChQuN++jwww8XmqYlXS4Wi4ljjjnGWPbdd9/Naj+8xrn3zTffGNfrzDPPTLlcrp6lXf3MZkDRR2Ra2Pz888+F3W43vmyvvfZaq2Wqq6vFkUceaWzvzjvvtHRMd9xxh7GNCy64QEQikbj3P/jgA5GXlycAiJKSElFbW2tpP31FptdYiNyde17jjvf73//eOKff+c53RGVlZatl3nrrLeH1egUA4fF4xFdffZX1fsLhsBgzZozxLFi8eHHc+5qmieuvv944losuusjyZ+rL3nvvPeMcjh8/Xuzbty/u/aqqKnHooYcay6xcubJb7oOSCwaDYuTIkca5veGGG1oFFZFIRFx++eXGMtOnT896P7zGuZdpQJGrZ2lXP7MZUPQR2RQ2//SnPxnLKooizj33XPG3v/1N/P3vfxc33nijUesMQPzgBz9oVUgUIv5GS9UQFggEjC8/ADF58mTx8MMPixdeeEFcffXVwuVyGe89++yzHXEaerVsrnFHnHte467R3NwsJk6caJy38vJy8fvf/1689NJLYv78+eLss882aiEVRRELFixIup3bbrvN2MacOXOSLvPuu+8KVVUFAKGqqpgzZ4547rnnxJNPPimOPfZYY/2KigpRVVXVeR+6l5szZ07cubzjjjvEiy++KO644w4xcOBA472f//znSdcfPny4scyKFSs6ZR9k3fLly4XT6TTO8VFHHSUefvhh8c9//lP83//9X1xBf9iwYWLHjh2ttsFr3P1kGlAI0THP0u7+zGZA0UdkU9gUQoi77rpL2Gy2uAJj4n8//vGPhd/vT7p+JoVNIYTYsWOHGDt2bMp92Gw2cf/991v92H1Ktte4veee17jrVFVVxV3vZP8VFBSIp59+OuU2MvlxEkKI5557Trjd7pT7GTp0qPjss8864VP2HaFQSJx11llpr+d5550nQqFQ0vUzKWy2dx/UPm+88YYoLS1Ne/4nT54stm7dmnR9XuPuJ5uAQoj2P0u7+zObAUUfkW1hUwghvvzyS3H11VeLcePGiby8POF2u8WIESPE7Nmz2+zfmWlhUwi9SfjBBx8URx99tCgpKREOh0MMHTpUXHjhhWLt2rWZfsQ+z8o1bs+55zXuWrFYTLz88sti5syZYtCgQcLpdIri4mIxadIk8Yc//EHs2rUr7fqZ/jgJIcSWLVvEL3/5S3HwwQcLj8cjvF6vmDRpkrj99tuzHj9FqS1atEjMmDFDDBgwQDgcDlFeXi5OO+00sWjRorTrZVLYbO8+qP32798v7rzzTnHUUUeJ4uJi4XA4xKBBg8Spp54qFixYIKLRaMp1eY27n2wDCiHa9yzt7s9sRQghQEREREREZAGnjSUiIiIiIssYUBARERERkWUMKIiIiIiIyDIGFEREREREZBkDCiIiIiIisowBBRERERERWcaAgoiIiIiILGNAQUREREREljGgICIiIiIiyxhQEBERERGRZQwoiIiIiIjIMgYURERERERkGQMKIiIiIiKyjAEFERERERFZxoCCiIiIiIgsY0BBRERERESWMaAgIqKMPPXUU1AUBYqi4KmnnurqwyEiom6CAQUREWVk7dq1xuvDDz+8C48kt8aNG2cEUpn+t2nTpja3u2fPHsybNw9nnXUWxo4di8LCQtjtdhQWFuKggw7CWWedhQceeACVlZU5+JRERNYpQgjR1QdBRETd3+GHH461a9fC6XSiqakJTqezqw+p0/l8PhQWFkLTtIzXKS4uRm1tLRRFSfr+zp07cfPNN+Mf//gHYrFYm9uz2Wz42c9+hrvuugsVFRUZHwcRUa7Yu/oAiIio+wuHw/jss88AABMmTOgTwQQAfPLJJ0YwMWHCBNxxxx1trlNSUpIymFi4cCEuvfRSNDY2AgBUVcW0adNw3HHH4eCDD4bb7ca+ffuwefNmvPHGG/j6668Ri8WwYMECVFRU4K677uq4D0dE1EEYUBARUZs+++wzhMNhAH2ru5O5m9exxx6LmTNnWt7Wo48+il/96leQHQNmzJiB++67D2PGjEm6/EMPPYTFixfj5ptvxhdffIEjjjjC8r6JiDoTx1AQEVGbzAXrww47rAuPJLfWrFljvG7P5168eHFcMDF37lz861//ShlMSD/60Y+wdu1aXHjhhQwoiKjbYkBBRERtyrRgrWkali5diksuuQTjx49HaWkpPB4PDjroIMyZMwcff/xxxvsMBoN49NFHceKJJ2LAgAHweDw4+OCDcfXVV+Prr78GoHchkgOh77nnHusfMAVzIDVlyhRL29i9ezcuueQSI5j4wx/+gJtvvjnj9d1uN5599lkMHjzY0v6JiDobB2UTEVGbDjvsMHzyySdwuVxoamqCw+FotcyGDRswZ84cfPrpp2m3ddNNN+Huu+9Ou8zq1atxwQUXYOfOnUnfLyoqwuuvv4733nsPt912GwDg3XffxfTp0zP7QBloampCcXExNE2Dy+VCc3Mz7PbsewrPmDEDb7zxBgDgmGOOwXvvvQdVZX0eEfUeHENBRERphcNhbNy4EYA+MDlZMPHOO+9g1qxZ8Pv9xnLnnXceRo4ciVgshv/85z949tlnEQqF8H//938oKSnB7373u6T7e/vtt3HmmWcaYzaOOuoonH322Rg2bBj279+Pl156CatWrcJPf/pTTJo0yVjPagtCKuvWrYsbkG0lmFizZo0RTCiKgkceeYTBBBH1OgwoiIgorQ0bNhiF+2TdndauXWsEEy6XCw8++CCuuOKKuGV+9rOfYc6cOTjxxBMRDodx++2346KLLkL//v3jltu2bRt++tOfIhwOw+v14m9/+xt+/OMfxy1z1VVX4ZJLLsGCBQuwZ88eAMBBBx2EkpKSjvzYcd28rAYr5paYWbNmYeLEie0+LiKi7obVJERElFa6hHbhcBgXXnih0TKxYMGCVsGE9P3vfx8/+9nPAAB+vx9LlixptcycOXNQV1cHAPjrX//aKpiQ7rnnHni9XuPfnTFQ3Py5zVnCU/03f/78uPUDgQDefPNN498///nPO/wYiYi6AwYURESUVroZnubPn48vv/wSAHDxxRfj/PPPT7utE044wXj9xRdfxL23ZMkS/Pe//wUAnHPOOfjpT3+acjvl5eUYN25cyuPqCObPnYmjjjoq7t8rV65EMBgEAHi9XvzgBz/osGMjIupO2OWJiIjSkgVrl8uF8ePHx7336KOPAtATtN1+++1tbqu0tNR4HQgE4t679957AehjDW699dY2tzV06FCjW1JiQBGJRPDee+/hjTfewHvvvYctW7YgFAph4MCBmDZtGq699tq0+TSamprw1VdfAQDy8/Px3HPPtXk85gAHiO8yNXnyZEtjMIiIegI+3YiIKKV0A7K3bt1qtDIce+yxGDp0aJvbkxmiAaCsrMx4XVNTY7ROTJw4ERMmTGhzWz6fz3idOMbhvffew8knnwwAGDRoEE444QS4XC5s2LABf//73/HSSy/h0UcfxS9+8Yuk2/7kk0+MaV4nTZpkKaHd3r17jdejRo3Ken0iop6CXZ6IiCgl84DsxBr91atXG6+POeaYjLZnngbWXMhetmwZYrEYAOCkk07KaFs7duwAAIwcOTKu5QPQW0zOOecc/Pe//8Xu3buxePFiLFy4EJs2bcKDDz6IWCyGq6++Gps3b0667Y4YkL1//37jdUcPGCci6k4YUBARUUrpxk9s377deD1y5MiMtidbIQB9kHaybR100EFtbqeurs4IBpKNnzjxxBOxcOFCTJ06Ne7viqLgN7/5DU466SREo1H885//TLr9jsgMnp+fb7xO7N5FRNSbMKAgIqKU0mXIbmhoMF7n5eW1ua39+/cbsx4deuihOPjgg433qqqqjNeFhYVtbuvFF180uiRZKfBPnjwZALBr166k73dEC8WQIUOM11u2bLG0DSKinoABBRERpZRuQHZRUZHxurq6us1tzZ07F6FQCABw2WWXxb1nHrCcqpAvBQIB3H///ca/rQQUcsB1RUVFq/caGxuxdetWAIDH48Ghhx6a9faB+Bmt3n///YzOERFRT8SAgoiIkgqFQsaA7O9+97utMmSbk7StWLEi7bb+/e9/Y968eQD0sRNXXnll3PvmLlMrV65Mu63rrrsOX3/9tfHvbAOKzz77DG+88QYURcGsWbNavb927Vqj9WPixImw2WxZbV+aNm2aMU4kFArhlltuyWr9jRs34sUXX7S0byKiXGJAQURESW3YsAGRSARA8kL7ySefbAw2fv3117Fs2bKk23nhhRcwa9YsaJoGr9eL5557Dm63O26Zk046CYqiAADefvttLF68uNV2wuEwrrnmGjz++OPG30aMGNFqQHY6fr8fs2fPRjQaxZw5c5Jmru6I8ROA3upy3333Gf/+61//imuuuabN8RRr167FJZdcgsmTJxvnn4ioO+O0sURElFS6DNmAPuj49ttvx69//WvEYjGcfvrpmD17No499li4XC588803WLRokTEewel0YtGiRTj66KNbbWvUqFE4++yz8fLLLwPQE9vNnj0bJ5xwAlRVxaZNm/DCCy/gm2++wZFHHomPP/4YQoi0uSQSRaNR/PSnP8WGDRswYcIEPPzww0mX64jxE9LMmTMxd+5c/P73vwcAzJs3D88//zzOOussTJkyBWVlZQiFQti/fz8+++wzrF69Om68xZFHHtmu/RMR5YQgIiJK4tJLLxUABACxbt26lMv9/ve/N5ZL9d/kyZPFhg0b0u6vurpafPe73025DUVRxMUXXyyWL19u/O2RRx7J6LPEYjFxwQUXCADikEMOEXv37k257MEHH2xs/9NPP81o+2159dVXRUVFRZvnSf7ndDrFmWeeKTRN65D9ExF1JkWIlo6iREREJlOmTMG6devgcrnQ1NTUagyF2ccff4xHHnkEq1atwt69e+FyuTB48GBMmTIFP/nJT3DqqadmlCna5/PhwQcfxEsvvYStW7fCZrNhyJAhOOmkk3DxxRdjypQpuPXWW3HnnXcC0AdXH3LIIWm3KYTApZdeir/97W8YPnw4Vq1ahWHDhiVdtqGhASUlJRBCwOVyobm5ucMyXIfDYbzyyit4++238dFHH6GqqgqNjY1wu90oKSnBIYccggkTJuDYY4/F9OnTUVxc3CH7JSLqbAwoiIiox/D5fBg1ahSqqqowbdo0rFq1qs11rrrqKjz++OMYPHgwVq1alVGeCyIiyhwHZRMRUY8ghMCVV15p5Kz43e9+1+Y61157LR5//HEMHDgQy5cvZzBBRNQJGFAQEVGX+/TTT9HY2Jjy/ZqaGvzkJz/Bc889BwCYNWsWZsyYkXabN998Mx588EH0798fy5Ytw+jRozv0mImISMcuT0RE1OXOOussvPvuuzjttNNw5JFHYtCgQbDb7di3bx8++OADvPrqq/D7/QCAI444AsuXL0d+fn7K7S1evBhnnnkmAODoo49OGUyMHTsWN910U8d/ICKiPoQBBRERdbnhw4djx44daZdRVRWXX3457r//fni93rTLPv3007j44ovb3O9xxx3XZiI9IiJKjwEFERF1ubfeeguvvfYa1q5di3379qGmpgbRaBSlpaUYPXo0jj32WFx88cUcA0FE1A0xoCAiIiIiIss4KJuIiIiIiCxjQEFERERERJYxoCAiIiIiIssYUBARERERkWUMKIiIiIiIyDIGFEREREREZBkDCiIiIiIisowBBRERERERWcaAgoiIiIiILGNAQUREREREljGgICIiIiIiyxhQEBERERGRZQwoiIiIiIjIMgYURERERERkGQMKIiIiIiKyjAEFERERERFZxoCCiIiIiIgsY0BBRERERESWMaAgIiIiIiLLGFAQEREREZFlDCiIiIiIiMgyBhRERERERGQZAwoiIiIiIrKMAQUREREREVnGgIKIiIiIiCyzd/UBEBF1JyIWQ6yhIef7tRUVQbHZcr5fykxME2hqDud0nwX5TthUJaf7JCKyggEFEZFJrKEBe6+5Juf7rXjwQdhLS3O+X8pMU3MYf3r8fznd5x+u/B6KC1053ScRkRXs8kRERERERJYxoCAiIiIiIssYUBARUad48803ccYZZ6B///5wOBzo168fTjvtNLzyyitp1xNC4IUXXsCJJ56I0tJSOJ1OVFRU4KyzzsLy5ctzdPS93x//+EcoioIRI0Z09aF0uXA4jAkTJkBRFASDwbTL1tbW4o477sARRxyBoqIiOJ1ODB48GOeeey5WrlyZdt0tW7bgsssuw4gRI+ByuTBw4ECcdtppePnllzvw0xDlHgMKIiLqcNdccw3OOOMMvPnmm6iurobH48H+/fvx9ttv45xzzsHs2bMRi8VarReNRnHuuedi9uzZWLFiBerq6uB2u1FZWYlXX30V06dPx0033dQFn4h6KyEEfvnLX2Ljxo1tLrtu3TqMHz8et912G9asWYNQKASn04k9e/bg5ZdfxgknnIAbb7wx6bqvv/46Jk6ciPnz52P79u1wOByorq7G22+/jXPPPRezZs1CJBLp6I9HlBMMKIiIqEM988wzmDdvHgA9sKiurkZjYyP279+P3/zmNwCAF154Affee2+rde+66y688sorUFUVd999NxobG9HY2IgdO3bgJz/5CQDgz3/+M/7xj3/k7gNRrxWJRHD55Zdj/vz5bS7b1NSEGTNmYO/evRgxYgTefvtt+Hw+NDc3Y+vWrcb385577sFTTz0Vt+6WLVtw/vnnIxAI4Oijj8b69evR1NSEpqYmzJs3Dw6HA6+99hpuueWWTvmcRJ2NAQUREXWohx56CAAwa9YsPPDAAygvLwcAlJWV4cEHH8SsWbMAAE888UTcepqm4dFHHwUA/OY3v8FNN92EgoICAMDQoUPx/PPP4/DDD0+6LlG2tm3bhmnTpmUUTADAU089hT179sDlcuHdd9/FKaecAlvLVM+jRo3Ciy++iBkzZgDQu5MJIYx17777bgQCAVRUVGDJkiX47ne/C0VR4PV68etf/xo333wzAOCxxx5DU1NTB39Sos7HgIKIiDrUF198AQA4+uijk75//PHHAwB27twZV+iqrq5GdXV1ynVtNhumTZsGANi+fXtHHjL1IdFoFNdeey3GjRuH//3vfygoKDBaF9JZsmQJAGDGjBkYNWpU0mWuuOIKAMDevXuxadMm4+/vvPMOAGD27NkoTTI99JlnngkA8Pl8xv1D1JMwoCAi6sFWrlwJRVGgKEpcAcbsiSeeMJaRnn76aSiKgkmTJgEAnnzySUyaNAlerxcDBw7ED37wAyxevDjlfkeMGAFFUYzgwKy4uBgA8J///Cfpuv/7n57P4eCDD447pqKiIuPfydYVQuCjjz4CAIwePTrlsdEB+/btwx/+8Ad85zvfQV5eHgYOHIiLLroI3377bdr1qqqqcMstt2DixInIz8+H1+vFmDFj8Ktf/QrffPNN3LJLly41vl9ff/11q2199NFHxvv33Xdf0v0NHjwYiqLg+eefB3BgwPg555wDTdPwxBNP4KijjkJRURHy8/Nx+OGH48EHH0Q4nDzZoNzfRRdd1Oq95uZmY90TTzwR69atwymnnJL2fADAMcccg1mzZuEHP/hBymUqKiqM1w2mBJnbt2/Hl19+id/+9rdJ14tGo8Zrh8PR5rEQdTcMKIiI+rjLLrsMV1xxBdavXw+Xy4Xq6mosXboUZ555Jq666ipompbV9n70ox8BABYvXozrr78eNTU1AID6+nrccsst+Pvf/w5FUfDHP/4xbj23242TTz4ZAPDII4/g3nvvRXNzMwCgsrISl156Kf773//C6XSyr3kG1q9fj8mTJ+Ouu+7CF198AVVV0dDQgGeeeQYTJ07EJ598knS9pUuX4tBDD8XcuXOxYcMGaJoGu92Or776Co888ggOPfRQo+APAMcdd5zRNW3p0qVJtyclmwVp3bp12LNnD+x2O84444y49yKRCH74wx/iyiuvNAJRv9+PtWvX4tprr8Xpp5+e9fdTURQcc8wxWLJkCZYtW5aytSHRnXfeiUWLFuHyyy9PuYw5EB46dKjx2mazYezYsRg4cGDS9WQXvkGDBmH8+PEZHQ9Rd8KAgoioD9u4cSPmz5+PU045Bdu2bUNdXR2qqqpw2WWXAQAef/xxPP74463WGzVqFMaMGYNhw4a1em/u3LmYPHkyAOD+++9HeXk5ioqKUFJSgrlz52LMmDF45ZVXcP7557da9/HHH8eIESOgaRp+97vfobCwEEVFRaioqMDf/vY3HH744Vi6dCmOPfbYDj4TvUsgEMCZZ56JPXv2YNSoUVi5ciWamprQ3NyMRYsWweVy4V//+ler9TZu3IiZM2eirq4O48aNw4oVK+Dz+dDY2IhPP/0Uxx13HEKhEObMmWMECk6n06jhTxZQvPvuu8br1atXt5rd64033gAATJs2DSUlJXHvLVmyBP/+979xww03oKqqCg0NDdi7dy9+/OMfAwCWLVuGhQsXttrnmDFjMGbMmLgWA6moqAirV69uFby0V3NzM+6//34AwOTJkzF48OCUywohUFdXh+XLl2PGjBlYsGABVFXFI488AqfT2aHHRZQLDCiIiPqwWCyGo48+Gv/6179w0EEHAQDKy8vx1FNP4ac//SkA4NZbb23VtWTZsmXYtGkTnn322VbbLCsrwwcffIDzzjvP+FtjY6PxOhqNwufzJT2egw46COvWrcNxxx0HQC94ZbouHfDoo49i+/bt8Hq9WLp0qXE+bTYbZs2ahTfeeMMYUGx28803w+/3Y8iQIVi9ejWOP/54oxvaxIkTsXTpUnzve9+Dpmn49a9/bbQO/PCHPwQALF++PC5g8Pv9+OCDD4zuVo2NjVi7dm3cPmVAIccRmGmahptuugn33HMP+vfvDwAYMGAAnn76aZSVlQHQ850k2rRpEzZt2oS77747uxNnkaZpuOSSS7Bjxw4AwO233552+fvuuw+lpaWYPn063njjDRQVFeHtt982Jiwg6mkYUBAR9XFz585N2m/7tttuA6An8somodz27dvxve99D//85z9xzTXXYOvWrQiFQti0aROuuOIKbNu2DRdeeCH+9Kc/tVr3008/xeTJk/H+++/jT3/6E3bt2oVQKIRPP/0UZ599Nj799FPMmDEjaSBDByxatAgAcN5552HkyJGt3j/iiCNw+umnx/2tsbERb731FgDghhtuSDp42OFw4I477gCgF9rXrFkDADj99NOhqirq6uqMvwHAqlWrEAqFMG3aNHz/+98HAKxYscJ4f//+/ca4mGQBBQBceumlrf7mdrsxZcoUAPp4j66kaRouv/xyo6XkF7/4hRFgpbJ9+3bk5+fD5XIB0MdbXHzxxXFdyYh6EgYURER9mNfrNWZOSjR69Gijz7cs9LUlGAziuOOOw/r163HXXXfhgQcewKhRo+B0OjFmzBg8/vjjRqBy6623xs1oU1lZieOPPx7ffvstnn76adxyyy0YPHgwnE4nJk6ciJdffhkXXXQRNE3DlVdeaYzNoHhCCHz88ccAgKlTp6ZcLnFA/dq1a43WhZNOOinlescdd5wRgMrvRXl5uTEzl7nbk+zudOKJJyYNKN566y1omobvfve7STN2ezyelJm85eD/VAOzcyEUCuEnP/kJ/t//+38A9MDq4YcfbnO9uXPnoqmpCT6fDx999BGmTp2K3bt348ILL8QLL7zQ2YdN1OEYUBAR9WFDhw5N2vVFkn3QKysrM9qezALcv39/3HDDDUmXueWWWzBgwAAIIeJyANx3331oaGjA5MmTje5Wie677z44HA74/X4WvFKoqakxZg0aMGBAyuWGDBkS9285ZS+AtP3/XS6X0d3IvI6slZdTpAIHAorp06fjhBNOAKAPXJYZoWV3JzmQP5Ec7J2MqupFmGwHZXeU6upqnHjiiUbLxMyZM/Hqq69mNEtTYWEhAL0L2hFHHIHly5cbLS433nhj0izyRN0ZAwoiol4uXeEkXTBhXjfTgaIffvghAODII49MWbByOBxGzfnmzZtbrStrspMpKyszZsExr0vJmfN8JEq8PumWTSQL8eZpf2VQ8OGHH6K5uRnV1dXYsGEDSktLMWnSJEycOBHl5eXw+Xz4+OOPEY1G8e9//xtA6u5O5u13J19++SW+973v4f333wcAXH755Xj55ZctD6h2uVz41a9+BQDYvXs386xQj8OAgoioBzMXuFLV1JoHNSfat29f2u3v2bMHgD6dZSb279+f9lgkWZgNhUIdsi4dUFZWZpwjef2SSbz2/fr1M17v2rUr5XqBQMDobiYHSgPAoYceilGjRiESiWDFihVYvnw5hBA4/vjjoaoqFEUxWimWL1+O999/H/X19Rg8eDAOO+yw7D9oF/nPf/6DqVOn4ptvvoGiKLj77rvx5JNPpgzOg8Eg1qxZg4ULF6bNgm2+x8wtP0Q9AQMKIqIeTA7qBFIHDuky7+7fvz9lTf/GjRuNQn6m07TKbjQfffRRXLKuRHKmn0MOOaTVurLWN5lgMIjPP/+81bp0gKIoOOqoowDEj1dIlJg88LDDDjO6EZmnek20YsUKo+Vq4sSJce+Zuz3JnBMnnnii8f706dON7Zu7O3XXlohE//vf/3Daaaehvr4eTqcTL7zwAm666aa06+zYsQNHHHEEzjvvPOMzJyO/14qiYPjw4R163ESdjQEFEVEPZu7rvnr16lbvV1ZWJs03YDZ37tykf5eJ5w466CB873vfy+h4ZLeX/fv345FHHkm6zLPPPott27YBgJFPwLzup59+itdeey3puvfccw98Ph9sNhvOPvvsjI6pL5JjUF577TWsW7eu1ftfffUVXn755bi/FRUVGTM/3XfffaitrW21XjgcNgbVDx061AhcJBlQ/Pvf/zYCChlEAAcGe3/wwQfGNU7V3am72bdvH2bOnInm5mZ4PB4sWbIkaS6VRKNHjzYyu99zzz1JA+2amho88MADAPR8HKkS4BF1VwwoiIh6sKFDh2LChAkA9Ey+S5YsgRACQggsX74cxx13HAKBQNptPPvss/jlL39ptEZUVlZizpw5eOWVVwAADz/8cKsa5OnTp2Ps2LH42c9+Fvf3H/7wh8bsQddffz3uuOMOo2Da3NyMBx54wEiad/bZZ8fNNHT55ZdjzJgxAPQC8SOPPGJkyq6pqcEtt9xiBDnXXHMNDj744GxOVZ9y6aWXYtKkSYhEIjj11FPx+uuvQ9M0CCHw7rvv4uSTT0YwGGy13t133w2Px4Ndu3bh2GOPxXvvvWeMrVi/fj1OPvlkrFmzBoqi4KmnnmrVzWfatGkoKirCli1bsGnTJgwaNAhjx4413h81ahSGDx+OcDiMr776CgUFBUY3qI40duxYjB07FjfffHOHbfP66683Jid48sknjazumfi///s/AHpW8DPOOMNojdA0DcuWLcO0adOwc+dOeL1ePPjggx12zES5woCCiKiHe+ihh+B2u9HU1IQf/vCHyMvLg8fjwfTp01FZWYknnngi7fonnHACHnvsMQwYMAClpaUYNGgQnn32WdhsNjz00EOt8hUAwLZt27B582YjkZekKApeeeUVHHPMMYjFYrjttttQVlaG0tJSFBcX47e//S3C4TBOO+00PPPMM3Hrut1uvPXWWxg3bhwCgQB+9atfobCwEKWlpejXrx/mzp0LIQQuuugi/PnPf27/ievF7HY7Xn/9dYwbN86oWc/Pz0dBQQFOPvlkVFVV4aqrrmq13vjx47Fo0SIUFhbi888/x/HHH4/8/HwUFhZi0qRJWLVqFbxeLxYsWIBTTz211foOhyPu7+buTpK5xeK0007rlMzQmzdvxubNm7F3794O2d7u3bvx97//HYD+Hb/hhhswcODAtP+Zu+7NmjUL8+bNg91uxzvvvIPx48cjPz8feXl5OOmkk/Dll1+ivLwcixcvNrLME/UkDCiIiHq4448/HuvWrcOcOXMwaNAgxGIx9O/fH5dddhk+++wzIz9AKosXL8a9996L0aNHG1mSL7jgAnz88cfGzDPZKC0txcqVK/HMM8/g5JNPRnl5OZqamlBSUoJTTjkFL774IpYsWYK8vLxW644cORJr1qzBQw89hGnTpqG4uBjNzc0YMGAAZs6cibfeegsLFixoc3YqAoYNG4YPP/wQ99xzDyZOnAhVVeFyufCjH/0IH374Ycr8I6eeeio2bdqE3/3ud8aMWkIIfOc738Hvfvc7bNy4EXPmzEm5X3NSt7YCilTTxXY3H374oTFuRAiBqqqqNv9LzI/x61//GmvXrsXFF19stNLY7XZMnjwZt956K7788su4c0PUkygim3niiIh6uWhtLfZec03O91vx4IOwJ8lM3FmefvppXHzxxQD0WXvcbnfO9t0T1TeG8KfH/5fTff7hyu+huNDV9oJERF2MLRRERERERGQZAwoiIiIiIrKMAQUREREREVlm7+oDICLqTmxFRajogmkbbUVFOd8nZa4g34k/XJlZLo6O3CcRUU/AQdlERERERGQZuzwREREREZFlDCiIiIiIiMgyBhRERERERGQZAwoiIiIiIrKMAQUREREREVnGgIKIiIiIiCxjQEFERERERJYxoCAiIiIiIssYUBARERERkWUMKIiIiIiIyDIGFEREREREZBkDCiIiIiIisowBBRERERERWcaAgoiIiIiILGNAQURERERElv3/NT4a9RqtZVEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 418,
       "width": 394
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABgYAAAG8CAYAAAAVXx6sAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAABibAAAYmwFJdYOUAAEAAElEQVR4nOzdd3hUVf7H8fedPslkJpUAgYA0BbHrWhYFe+/r2gvWdcWy7qKirqhrX/fnuta119W1rb2iYq8LqCCitAQICQlJpiTT7/39kc1ITCFAYBL4vJ4nzzPMPfecc+cyycz5nvM9hmVZFiIiIiIiIiIiIiIiskmwZbsDIiIiIiIiIiIiIiKy4SgwICIiIiIiIiIiIiKyCVFgQERERERERERERERkE6LAgIiIiIiIiIiIiIjIJkSBARERERERERERERGRTYgCAyIiIiIiIiIiIiIimxAFBkRERERERERERERENiEKDIiIiIiIiIiIiIiIbEIUGBARERERERERERER2YQoMCAiIiIiIiIiIiIisglRYEBEREREREREREREZBOiwICIiIiIiIiIiIiIyCZEgQERERERERERERERkU2IAgMiIiIiIiIiIiIiIpsQBQZERERERERERERERDYhCgyIiIiIyDqbOXMml112GePGjaOkpASXy0UgEGDkyJH89re/5ZFHHiEajWa7m1k1ffp0DMNYq58jjjgi293f6F199dWZ13vp0qXZ7o6IiIiIyHrlyHYHRERERKTvWrBgAeeddx5vvfVWu2PJZJJQKMT8+fN59tlnufLKK/nb3/7Gsccem4WeioiIiIiISCsFBkRERERkrUyfPp3DDjuMcDgMQHl5Occccwzjxo2jtLSUZDLJggULeOmll3j55ZdZtmwZxx13HDU1NVxwwQVZ7n12nXPOOfzud7/rdvlAILAeeyMiIiIiIpsaw7IsK9udEBEREZG+5YcffmCnnXYiEolgGAZXXXUVU6ZMwe12d1j+q6++4ogjjqCqqgqHw8GMGTPYaqutNnCvs2v69OnsueeeAEydOpWrr746ux0SEREREZFNlvYYEBEREZE1NnHiRCKRCAB33nknV199dadBAYCddtqJN954A4fDQSqVYsqUKRuqqyIiIiIiIvILCgyIiIiIyBp55ZVX+PzzzwE46KCD+P3vf9+t87beemtOPvlkAN566y3q6urWWx9FRERERESkcwoMiIiIiMgauffeezOPr7zyyjU69/TTT2fChAncfPPN2GwdfxRNJpPce++97LXXXpSUlOB2uykrK+Poo4/m9ddf77TuCRMmYBhGJnf/W2+9xWGHHUb//v3xeDxsttlmnHvuuSxatKjLPlqWxVNPPcXBBx9M//79cblclJaWctBBB/Gvf/0L0zTX6Jp72quvvophGBiGwdixY0kmk+3KmKbJbrvthmEY2O12Pvzww8yxoUOHYhgGV199NaZpcuedd7LtttuSm5tLIBBgwoQJPPnkk3SWcbT1/Jtuuol58+ax1157kZOTQ2FhIXvssQcLFy5sU/7777/nd7/7HSNHjiQnJ4dAIMB2223Hn//859UGh1566SWOPPJIBg4ciMvloqioiB133JErr7yS5cuXd3reggULuPDCCxkzZgw5OTnk5uYybNgwTj31VN5///0Oz7n66qszr+vSpUs7LFNdXc0VV1zBdttth9/vx+v1MmLECM466yy+/fbbTvuz6msG8PTTT7P33ntTUlKC1+tl8803Z/LkyaxYsaLL10NEREREpMdYIiIiIiLdFIlELJfLZQFW//79LdM0e7T+RYsWWVtuuaUFdPpz9NFHW01NTe3OHT9+vAVY55xzjjV58uROz/d6vdb06dM7bH/lypXWHnvs0WX7u+++u7VixYo1vrb3338/U8fUqVPX+PxVnXTSSZm6br755nbH//rXv2aO//GPf2xzbMiQIRZg/fnPf7aOOuqoTq/zt7/9rZVIJNrV3Xr++eefb/Xr16/NOSUlJW3OufHGGy273d5pG/n5+dabb77Zrg3TNK1TTz21y/uQk5PT4bmvvfaa5fF4ujz3rLPOavd/d+rUqZnjS5YsaVfv888/b/n9/k7rtNls1p///OcO3xOtr9n1119vHX/88Z3WUVxcbM2ZM6fd+SIiIiIiPU0rBkRERESk277++msSiQQA++yzD4Zh9FjdDQ0NTJgwgTlz5uBwOPjd737H66+/zpdffsmzzz7LIYccAsDzzz/PSSed1Gk9L774In/961/ZbLPNuOuuu/jss8947bXXOOywwwCIRqOcdtpppFKpNuclEgn233//zOz6448/nhdffJEvv/ySl19+mZNOOgnDMPjoo4845JBDMq9DNtx+++2UlpYCcO2111JZWZk59uOPP/LnP/8ZgC233JLrr7++wzruu+8+XnjhBcrKyrjnnnv47LPP+Ne//sU222wDwDPPPMP555/faR/uuusu6urq+POf/8zHH3/Mo48+ytSpU3E6nQDcdtttTJkyhXQ6zZgxYzJtfPDBB1x//fUUFxfT2NjIYYcdxpdfftmm7ocffphHH30UaFkJ8tRTT/Hpp5/y1ltvcckll+B2u2lububEE08kHA5nzquvr+fEE08kFovRv39/br31VqZNm8Ynn3zC/fffz8iRIwG4//77eeqpp7r9er/zzjsce+yxhEIhPB4PkydP5t133+WTTz7htttuY8CAAZimyV/+8heuvfbaTuv5xz/+wVNPPcU222zDI488wueff84LL7zA7rvvDkBdXR1nn312t/slIiIiIrLWsh2ZEBEREZG+45///GePzXr/pbPOOssCLLfbbb333nsdlrnmmmsy7b/wwgttjrWuGACsbbbZxgoGg+3OP+KIIzJl3n///TbHrr/+eguwDMOwnnzyyQ7bf/TRRzPn/9///d8aXd+qKwbOOecca+bMmd36+fbbbzus7/nnn8/Ud/jhh1uWZVnpdNrabbfdLMByOp3WjBkz2p3XOnsdsIYPH24tX768zfHm5mbr17/+dWYW/DfffNPp+VdeeWWHfVu0aFFmZcmhhx5qxWKxdmWWLl1qDR48OHO/VtV6L8eOHWslk8l25z744IOZPjzxxBOZ5x9++OHM85999lm786qrq63i4mILsPbZZ582xzpbMZBIJKyhQ4dagOXz+ayvvvqqXb0rVqywRo8enXnNZs+e3elrtv/++1vxeLzN8WQyae20006ZMosWLWrXhoiIiIhIT9KKARERERHptvr6+szjfv36dVounU4za9asLn+WLFmSKb9y5Uoee+wxAM4991z23HPPDuu94oorGDVqFNAyY70zN954I36/v93zp59+eubxnDlz2vT3jjvuAODwww/nhBNO6LDeU045hb333nu17a/OP//5T7bbbrtu/bTOJv+lo446imOOOQZoycX/5ptvcscdd/Dpp58CcNVVV7Hddtt12Y/777+f/v37t3nO6/Xy0EMPYRgGpmny8MMPd3r+Oeec0+Hzd911F4lEApfLxYMPPojb7W5XpqysjBtuuAGAb775ho8//jhzrLq6GoDy8nIcDke7c0866STOOussrr/+esaMGdPuPIDhw4e3O6+0tJRrrrmGyy67jNNOO63T61rVf/7zHxYvXgzANddcw4477tiuTElJSWaFg2ma/OMf/+i0vttuuw2Xy9XmOYfDkdmYG9r+3xQRERERWR/af8oWEREREenEqhvvWp1sTgstaYFWNyh9xhln8MADDwAwffp04vE4QGbgvSN2u5199tmHH3/8kU8//ZRkMplJXbNqmQkTJnR4/mabbZZ5HIlEMo9nzZqVGVTuqn2A/fffn3fffZcFCxawdOlSBg0a1GX59enOO+/k/fffp66ujkmTJmWuYeedd2bKlCldnjty5MhOAzCjRo1il1124bPPPuP111/ntttua1dmwIABnV77m2++CcBWW21FSUlJp33Yf//9M4+nT5/OuHHjANh8882ZN28eb7zxBtdeey3nn38+BQUFmbIul4v77ruvXX2bb7555vFvf/tbbr/9drbeeus2ZX7/+9932p+OTJs2DQCbzdYmsPRLO+20E9tttx0zZ87MnPNLAwYMYPTo0R0e6+z/poiIiIjI+qAVAyIiIiLSbavOwl+xYkWP1fvNN99kHh966KEYhtHpz9133w207BWw6gzxViUlJXi93g7byc3NzTxedY+BVds///zzu2z/kksuyZRdtGjRWl3v1KlTsSyrWz+NjY2d1tOvX7/MSocFCxbQ1NSE1+vl0UcfxW63d9mHXXfdtcvj2267babeVQNCrcrKyjo8L5VK8f333wPw3//+t8vXctVVJ6u+lhdddBE2mw3Lspg6dSr9+vVj/Pjx3HjjjcyaNavTPh988MGZ4MD06dPZZpttGDFiBJMmTeLVV1+lubm5y2vuSOu1jBgxgvz8/C7L/upXvwJg4cKFxGKxdseHDBnS6bmd/d8UEREREVkfFBgQERERkW4bMWJE5nFVVVWn5YqLizsd6O7IypUr16o/HQ2arzrA+kurbpa8al96sv0N7bjjjmP8+PGZf59xxhltZs53ZsCAAV0eLy4uBlrSLNXV1bU73lGqJmhZLdJRIGF1Vn0t99xzT5566qlMH1KpFB9++CGXX3452223HUOGDGHKlCnt7pvL5eLNN99kjz32yDy3YMEC7rrrLg499FCKi4s55phjMumWuqO1ja5SZ7VatUxDQ0O742vzf1NEREREZH1QKiERERER6baddtoJm82GaZq8++67PVbvqjOkX3rpJcrLy7t13qqBip5q/5577mGXXXbp1nmrpn/JlgULFvDVV19l/v34448zZcoUBg4c2OV5HeXuX9Wqg/u/TNcEbQeyV7Xqa3nggQdm9hFYnUAg0Obfv/3tbznkkEN46aWXeOGFF3jnnXcIBoMAVFZWctNNN/Hggw/ywQcftEnPM3ToUD744AO++OILnnnmGV599VV+/PFHoGWVyXPPPcfzzz/PzTffzOTJk1fbrzUZpE+n05nHnb0+IiIiIiK9gQIDIiIiItJtRUVF7Lbbbnz88ccsWrSImTNnrnYvge5YNX98SUlJJo3NhrJq+4FAYIO3v7Ysy+KMM86gubmZnJwckskkwWCQc845h1deeaXLc1fdSLojtbW1ALjd7javz+qsWjadTq/Ta5mTk8Pxxx/P8ccfTzqd5osvvuDNN9/kySefZOHChdTW1nLOOefw4Ycftjt35513Zuedd+Zvf/sbFRUVvPXWWzz//PO88847WJbFZZddxpFHHrna4FJhYSHw8+vRldYyhmGsNu2QiIiIiEg2KZWQiIiIiKyRVTdvvfzyy3ukzjFjxmQerzr7vSP//ve/ueOOO3j11VdJJpMbvP233nqL2267jRdffDHrm8Tec889fPDBB0DLvgUXXXQRAK+++ipPPPFEl+euuq9CR2bMmAG0bCC8JjweT2YlxYwZM9rMov+luro6rr32Wh5//HHmzZuXeT4YDPL555+zfPnyzHN2u53ddtuNa6+9lu+//54dd9wRgI8++iizd0AqlWLu3Ln897//bdPOkCFDOPvss3nrrbe49dZbgZYVEW+//fZqr2fs2LEA/PTTT6tNHfXll19m2vN4PKutW0REREQkWxQYEBEREZE1cuyxx7LDDjsA8Oabb/L3v/+92+e2DmL/0l577YXN1vLR9KGHHuo0R304HOacc87hggsu4Nxzz11tOpzu+tWvfpXJmf/UU0/R1NTUYTnTNJk0aRIXX3wxxx9/fI+0vbYqKiq49NJLAdh66625+OKLmTp1KoMHDwZaNvCtqanp9PzPP/+cxYsXd3hs7ty5fP311wAcfvjha9y3fffdF2gZ+H/xxRc7LXf33XczdepUTjnlFD766COgZePe/Px8dt11V/75z392eJ7b7WbChAmZf7du9Lv33nszZswYfvOb33Ta5v7779/uvK7ss88+QMu9f+SRRzot99///jcTbNlrr71WW6+IiIiISDYpMCAiIiIia8Rms/Hkk09mcsL/4Q9/4Nxzz83M2u5IfX09f/rTnzKDrNA2B/ugQYM48sgjgZaZ7J2tRDjnnHMyeeZ///vf91ged4/Hw1lnnQVAdXU1Z599docz3S+//HLmz58PwCmnnILP5+uR9tfGWWedRSQSwTAM/vnPf+JwOMjNzeWOO+4AWjbNXXV1xy+ZpsmZZ55JIpFo83xTUxOnn346AD6fj4kTJ65x384777zMvbngggtYuHBhuzIzZszglltuAVpSVB133HEADBs2LLN58l133cWSJUvandvc3Myrr74KtMzOb033c+CBBwKwePFi7r333g779swzz2Qeb7/99qu9liOPPJJBgwYBcNVVVzFz5sx2ZVauXMlpp50GtKxsmDRp0mrrFRERERHJJu0xICIiIiJrbPPNN2fatGkccsgh1NTUcO+99/Kvf/2LY489lj322COzefDixYt58803efnll9vMwj/66KO57rrr2tR5++2388EHH1BXV8fNN9/MjBkzOOussygvL2fx4sXcddddmVnlW265JX/4wx969JquvvpqXn75ZX766Sf+9a9/8dNPP3H++eczatQoqqqqePTRR3nppZcA6N+/f7v+r4nq6mpmzZq1RudsvvnmeL1eAB588EHeeecdoCVYsupmyYcffjiHHXYYL7/8Mi+88ALPPPMMv/3tbzus891332XXXXdl8uTJDBs2jB9++IGbb76Z77//Hmh5TcrKytb4+rbeemsuu+wybrzxRqqqqthhhx24+OKLmTBhAslkkg8++IDbbrst83/itttuaxNk+fOf/8xJJ51EXV0dO+64I3/84x/Zfvvt8Xg8/PTTT/z973/nhx9+AOCyyy7LnHf22Wdz2223sWLFCs477zw+//xzDj/8cAYOHEhNTQ0vvPACjz/+OAC77LJLm1UHnXE6nTz00EMccMABhMNhfv3rX3PBBRew//774/F4+PLLL/nrX//KsmXLMn3viX03RERERETWK0tEREREZC2tWLHCOvXUUy273W4Bq/0ZN26c9dprr3Va3+zZs63NNtusyzq23npra+nSpe3OHT9+vAVYw4cP77T+RYsWZer5y1/+0u74kiVLrO22267L9ocMGWJ99913a/xavf/++916jTr7mTlzpmVZlrV06VIrEAhYgNW/f3+rsbGxXVsVFRVWbm6uBVj9+vWzamtrM8eGDBliAdauu+5qjRs3rtP2Onp9Vj1/77337vJ6TdO0Jk+ebBmG0WkbDofD+r//+78Oz//Tn/7U5ethGIZ18cUXtzvv448/tvLz87s8d6uttrKqqqranDd16tTM8SVLlrSr97nnnsu8ph392O1268Ybb7RM01yr12zV/x+PP/54l6+tiIiIiMi6UiohEREREVlrJSUlPPLIIyxYsICbbrqJ/fbbj4EDB+JyufB6vZSXl7Pnnnty7bXXMmPGDD766CMOOuigTuvbcsst+f7777njjjvYc889KS4uxuFwUFBQwPjx47n77rv56quv1moWe3cMGjSIr776ikceeYQDDzyQ/v3743Q68fv97Lzzztx888189913mQ1ps+F3v/tdJp3S3//+90xKp1WVl5dzzTXXALBixQrOP//8dmVycnJ47733uOmmm9hiiy3weDwMGTKE448/nq+//porr7xynfppGAa33HJLZuXHyJEjycnJwe12M3z4cM4880xmzJjR6cqPv/71r0yfPp0TTjiBzTbbDI/Hg9frZdiwYUycOJFPPvmEv/3tb+3O+/Wvf83cuXO5/PLL2X777fH7/TgcDkpLS9lnn3345z//yX//+18GDBiwRtdz9NFH89NPP3HppZey9dZb4/P5yM3NZcyYMVxwwQXMnj2byy67rMfSW4mIiIiIrE+GZVlWtjshIiIiIiIbxtChQ6moqGDvvfdm2rRp2e6OiIiIiIhkgVYMiIiIiIiIiIiIiIhsQhQYEBERERERERERERHZhCgwICIiIiIiIiIiIiKyCVFgQNarRCLBVltthWEYxGKxTssNHz4cwzC6/DnggAPWqg+ffPIJRx55JP369cPtdjN48GAmTpzI999/v7aXJSIiIiIiIiIiItJnObLdAdl4WZbFeeedx+zZs7ssFwwGWbRoEQAlJSXYbB3HqwoLC9e4D48++igTJ07EsixsNht5eXksXbqURx55hKeffponn3ySo446ao3rFREREREREREREemrDMuyrGx3QjY+yWSS3//+9zzwwAOZ56LRKB6Pp13ZDz74gAkTJuB2uwmHwzidzh7pwxdffMG4ceNIpVKcddZZ3HjjjRQVFbF48WLOPfdc3nzzTbxeL9988w0jR47skTZFREREREREREREejulEpIet2DBAnbfffc2QYGuzJgxA4CxY8f2WFAA4KqrriKVSrHvvvty3333UVRUBMDQoUN56aWX2HbbbYlGo1x99dU91qaIiIiIiIiIiIhIb6fAgPSYVCrFH/7wB8aMGcMXX3xBXl4exx133GrPmzlzJgDbb799j/Vl/vz5vP322wD86U9/anfc5XJxySWXAPDCCy8QiUR6rG0RERERERERERGR3kyBAekxkUiEv//97yQSCfbaay9mzpzJ/vvvv9rz1kdg4P333wdaAgDjx4/vsMy+++4LQCwWy5QXERERERERERER2dgpMCA9xjAMxo0bx6uvvsq7777L8OHDV3tOLBbjhx9+AGDQoEFce+217LbbbgwZMoSxY8dy+umn8/XXX69xX+bMmQPAZptthtvt7rBMcXExBQUFAHz33Xdr3IaIiIiIiIiIiIhIX+TIdgdk4xEIBPjoo4/W6Jxvv/2WVCoFwDHHHEMsFmtzfM6cOTzyyCNMmTKF66+/vtv1Llu2DIDBgwd3Wa6srIyGhgaqqqrWqN8iIiIiIiIiIiIifZVWDEhWtaYRAigvL+f555+nvr6e5uZmpk+fzh577IFlWdxwww3ceuut3a43FAoBkJub22W5nJycNuW7MmjQoE5/XC4XO+64Y7f7JyIiIiIiIiIiIpItCgxIVgUCAfbff3/23ntvvvrqK4466igKCgrwer2MHz+ed999l7322guAqVOnsmLFim7V27oKweVydVmuNc1Qa/m1lU6nqa6uXqc6RERERERERERERDYEpRKSrDruuOM47rjjOj3ucDi46aab+NWvfkVzczMvv/wyZ5555mrr9Xq9ACQSiS7LxeNxYPUBBIClS5d2emzQoEGrPV9ERERERERERESkN9CKAen1dtxxR3w+H/DzpsKrk5eXB0A0Gu2yXHNzMwB+v38deigiIiIiIiIiIiLSd2jFgPR6hmHg9/uJRCKZgfzVad10uHUT4s60Hi8rK1u3ToqsAzOVJhGJYqbT2Ox2XD4vNoc9290SEREREREREZGNlAIDklX3338/K1asYIcdduCAAw7osEwqlWLlypUADBgwoFv1jh07FoCFCxeSTCZxOp3tytTW1tLQ0ADAlltuuTbdF1kniUiUYGUN4aqVpGJxzLSFzW7g8LjJG1hEoLwUl8+b7W6KiIiIiIiIiMhGRoEByarrr7+eiooK9tlnn04DA9OnT8/sBbD77rt3q94JEyZgGAbRaJRPP/2U8ePHtyszbdo0oGUfg3Hjxq3lFYisncbF1dTOrSAejBCtD5OMJjBsBpZp4fS6aK5rpLGihpLRQ8gf2j/b3RURERERERERkY2I9hiQrDriiCMAeP/99/n888/bHY/H40yZMgWALbbYgr322qtb9ZaXl2cG+2+44QYsy2pzPJFIcMsttwBw4oknkp+fv5ZXILLmGhdXs2L2IhoXLae5LoQn30fxFuWUjB5C8RblePJ9NNeFaFy0vKXc4upsd1lERERERERERDYiCgxIVl122WUUFBSQTqc56qij+M9//kMikQBg5syZ7LPPPnz99dc4nU7uu+8+DMNoc/7ee+/NFltswSmnnNKu7ptuugnDMHj77bc55ZRTqKmpAaCiooIjjjiCWbNmkZOTw+WXX77+L1TkfxKRKLVzKwhW1mBzOigYPhBvoR+bveXXsc1uw1vop2DYQGxOB8HKGmrnVpCIdL2RtoiIiIiIiIiISHcpMCBZ1b9/f1577TVKSkpYvnw5Rx11FLm5ufj9frbffns+/vhjcnNzefrppztMI7RgwQLmzZtHZWVlu2O77bYbf//73zEMgyeeeIIBAwZQUFDA0KFDeeONN3A6nTz77LOMGjVqQ1yqCADByhriwQiWaeEfVNIu2NXKsBn4B5VgmRbxYIRgZc0G7qmIiIiIiIiIiGysFBiQrNt1112ZM2cOV1xxBVtvvTUul4tUKsWoUaO44IILmDNnDkcdddRa1X3BBRfwySefcNRRR1FSUkIkEqG0tJRjjz2WL7/8koMOOqiHr0Y2dWY6TXNdsMOfSHU9dT9U0rBwOZZlkgg3k4hEO/3BsvAW5hGtDxOuWomZSmf78kREREREREREZCNgWL9Mvi4ia2zQoEEALF26NMs9kWxrrgvy0uk390hd25yyPw6vm7ofKinefDDl47bCk+/rkbpFRERERERERGTT5ch2B0RE+hIznSbWEOn0eLQ+1GNtJZpiAKTjCWLhJppqG9qsGvAU+LDZ7T3WnoiIiIiIiIiIbBq0YkCkB2jFwKbBTKd5fdLthJfVZbsrAOSVFXPQnRcqOCAiIiIiIiIiImtEewyIiHRTrCHSa4ICAOFldV2uXhAREREREREREemIAgMiIt1kps1sd0FERERERERERGSdKTAgItJNsYZwtrsgIiIiIiIiIiKyzhQYEBHpo/LKivEU+LLdDRERERERERER6WO0+bBID9Dmw5uGdDJFw4IqADwFedjs7WOr0foQb//pnh5pr3Sb4Qzf/1eUbFHe4XFPgU8bD4uIiIiIiIiIyBpzZLsDIiJ9hd3poLiTQfr1oWB4GSWjy8kpCmywNkVEREREREREZOOnwICISA/yFPg4/KFLM/+Oh5pY9uVc6hcup3B4GYYByWgCAKfXhWGzYaZN0okklmlh2AxsDjsNi5aTU5inFQEiIiIiIiIiItLjFBgQEelBNrudnOKfZ/h78n00LFxOtD6MlU7jKfTj9ud2WUe0PoQ334czx4PL513fXRYRERERERERkU2MNh8WEVmPbA47eQOL8BbmtQQHVrOti2VaROvDeAvzyBtYhM2hFQMiIiIiIiIiItKzFBgQEVnPAuWluAM+DJtBaGltp8EBy7QILavFsBm4Az4C5aUbuKciIiIiIiIiIrIpUCohEZH1zOXzUjJ6CFbaJFhZQ8OCKryFebgDPmz2lj0G4sEI0fowhs0gMKSUktFDsp5GKJ0yaYokSKdN7HYbuT4XdofiySIiIiIiIiIifZ0CAyIiG0D+0P4AGHZbJggQXl6PYYBltWxEnFPsxx3wUTJ6SKZ8NjRFEiyvDLKiKkw8nsJMW9jsBm63g34D8xhQHiDX58pa/0REREREREREZN0Y1uoSXovIag0aNAiApUuXZrkn0tslIlGClTWEq1aSiiUwTRObzYbD4yJvYBGB8tKsrhRYtriRBXNrCQXjBBtixKNJbDYD07Rwue14c13k+FwM27yIEaNLtIJARERERERERKQPUmBApAcoMCBrykylSUSimOk0Nrsdl8+b9Y2Gly1u5Mc5K6iqDGKZFvmFXvICbpIpi4a6ZmqXh4mEEoBFoNDLoCH5bL51qVYQiIiIiIiIiIj0MUolJCKSBTaHHU++L9vdyGiKJFgwt5aqyhBOp53+g/IwDIPGlVFql4eJRlNEm5OkU2nisRThYJxwY5x4Ik1VRSPDR5dQNjQ/25chIiIiIiIiIiLdoBwQsl4lEgm22morDMMgFot1Wfbxxx9n9913x+/3k5OTwxZbbMGUKVNoaGhYpz689tpr7L///hQWFuLxeBg2bBiTJk1iyZIl61SvyMZkeWWQUDCOZZptggI1VWHqV0aJNiXweh30G5DH4GEF+ANubLaW85YsDvLjnBUsW9yY7csQEREREREREZFuUCohWW8sy+Lss8/mgQceACAajeLxeDose+aZZ/Lggw8C4HK5cLlcRCIRAMrKynj//fcZOXLkGvfhL3/5C1dddRUAdrud3NxcQqEQAPn5+bz22mvstttua1zvLymVkPRV6bRJQ10zMz9bypJFDeT53fjzPS0rARY10NgQw7CB1+sEDJwuW0ugrzlJLJYiv9CLJ8eJacLAQXnstt9w/IGO3+ciIiIiIiIiItI7KJWQrBfJZJLf//73maBAV/72t7/x4IMP4nA4uP322znjjDNwu918/PHHnHbaaSxYsIDDDjuM7777Doej+/9ln3/++UxQ4KqrrmLy5Mn4fD5mz57NxIkT+frrrzniiCOYN28eBQUFa32tIr1dOm2yZEHHK29CjTHuvv6jHmvLsBlsvlVph8cGDy/AbtdCNRERERERERGRbNOKAelxCxYs4MQTT+SLL75o83xHKwYikQhDhw5l5cqVXHfddVxxxRVtji9evJgxY8YQjUa57777OOuss7rVB9M0GTt2LHPnzuXMM8/k/vvvb3O8oaGBsWPHUlVVxeWXX87111+/Flf6M60YkN4qmUzzp5P+QzyWynZXKC3L46o7DlRwQEREREREREQkyzQ6Iz0mlUrxhz/8gTFjxvDFF1+Ql5fHcccd1+U5Tz/9NCtXrsTlcnHBBRe0Oz506FBOPvlkAB577LFu9+W9995j7ty5AEyePLnd8YKCAiZNmgS07G2g+JhsrJYsbOgVQQGAmmVhQg1d7zUiIiIiIiIiIiLrnwID0mMikQh///vfSSQS7LXXXsycOZP999+/y3Pef/99AHbZZRfy8vI6LLPvvvsC8OmnnxIMBrvVl9Z6y8vLGTVqVJf1LlmyhNmzZ3erXhFZe6VlefgLtP+AiIiIiIiIiEi2aY8B6TGGYTBu3Dguu+wyDj74YAA++qjr3OVz5swBYPTo0Z2WaR3YN02TOXPmdGuz4DWpF+C7775jq622Wm29In3N4GEFXHLLPgD48z3t0vik0yaRYJwlixpYPL+eSDBGQVEO1VVhGuuiFBR7MQyjzTmWZRFqjOHxOikqzaGwKIe0abFscZDNNi9im50Hked3t+uLv6B9+yIiIiIiIiIisuEpMCA9JhAIrDYQ8EvLli0DYPDgwZ2WKSsryzyuqqrqsXr9fj8+n49IJNLtekX6GqfTzrDNi7ssU1zqo2RgHpZpsWRxEMMwCOR7SCVNHA4b3lxXpmxrUCAn10V+kZfyYYW4PQ6C9VGKSn0UleRSVh7A7lAAQERERERERESkt1JgQLIqFAoBkJub22mZnJycduV7ot7WuiORSLfqbd1guCPLly9nwIAB3eqbSG+U63MxfHQJadOiqjJEc1MSw4DmpgTeXCemCfFokuamJDabQaDQS8kAH26PA9O0aKyPkl+UQ7+BeQoKiIiIiIiIiIj0cgoMSFalUi2borpcrk7LuN0/pyRpLd8T9a5ad3frFdmYlQ3NB8BuM8jJdbJgbi2hxjiRcAKv14nTZSfX58LtdVAywEdBUQ6maVGzLIxhs+EPuBlQHsjuRYiIiIiIiIiIyGopMCBZ5fV6aWpqIpFIdFomHo9nHq9uoH/VeoEu61217u7Uu3Tp0k6PdbWaQKQvKRuaT35xDssrg7hcdpZWNBKsjwKQk+ukuL+PguIcHE4bwfoojfVRDJuNsvIAw0eXkOvr3ntURERERERERESyR4EByaq8vDyampqIRqOdlmlubs489vv93a4X6LLeVevubr0im4Jcn4sRY0rYbFQRC+bWsvCHOpqaWtIIhYNxIqE4lgVur5P8ohz8ATfDR5dkVhyIiIiIiIiIiEjvpsCAZNXgwYOprq7ObBbckVWPrboR8erq/fzzz7usNxwOE4lE1qhekU2J3WFj1FallG1WwPLKICuqwsTjKcy0hc1u4HY76DcwjwHlAa0UEBERERERERHpQxQYkKwaO3YsX331FfPmzeu0TOsxwzAYPXp0t+t99tlnu6z3hx9+yDzecsstu9ljkU3PqisImiIJ0mkTu91Grs+ljYZF1rN0ytT7TkRERERERHqcvllKVu25554AfPrpp21SBq1q2rRpAOywww7dTvnTWu/8+fOpqKjost5+/fopMCDSDXaHDX++h4KiHPz5Hg1OiqxHTZEE87+v5Yvpi/nvJ5XM+GQJ//2kki+mL2b+97U0RbreQ0dERERERESkKxrVkaw6/PDDyc3Npbm5mdtuu63d8cWLF/P4448DcN5553W73nHjxlFeXg7Adddd1+54Y2Mjd955JwDnnnsuhmGsTfdFRER63LLFjfz3owq+n1XNvNkrmPdtDQt/qGPB3DoW/VTPD9/W8N+PKli2uDHbXRUREREREZE+SoEBySq/38+VV14JwNSpU7n11lszGwZ/+umn7LvvvkSjUUaPHs1JJ53U7vwtttiCLbbYgilTprR53jAMbr75ZgAeeOABJk+eTDAYBGDOnDnst99+VFVV0a9fPy644IL1eYkiIiLdtmxxI7NnVDF75nJ+mr2CSDCGYYBhgN1mkE6mqKsOs2RxkB/nrFBwQEQ2CDOVJtYYoXllkFhjBDOVznaXRERERGQdGZZlWdnuhGy8HnnkESZOnAhANBrF4/G0K5NKpTjxxBN55plnAHA6nXg8HsLhMAADBw7k008/ZciQIe3ObZ3pf+qpp/LII4+0O/7HP/6R//u//wPAbrfj8/kyAYK8vDymT5/O9ttvv87XOWjQIACWLl26znWJiMimqSmS4N0Xf+DHObX/21PAIJU0MQwDy7Jwuux4c5y4PA5sNgO318ngoQF22H2INgAXkfUiEYkSrKwhXLWSVCyOmbaw2Q0cHjd5A4sIlJfi8nmz3U0RERERWQtaMSBZ53A4ePrpp3n88ccZP348OTk5xGIxhg0bxoUXXsjMmTM7DAp0x9/+9jdee+01DjjgAPLz82lqamLw4MGcccYZzJo1q0eCAiIiIj3h2y+XUbGwgUgoht1u4M1xUtzfR8kAH8X9fXi8DpqbEgTro6TTJpFgjFAwzvLKYLa7LrJGNPu8b2hcXE3FR99S8+0CVv64hLp5S2lYWEXdvKWs/HEJNd8uoOKjb2lcXJ3troqIiIjIWtCKAZEeoBUDIiKyptJpk1BDDIBIKM5r/57NsopGPB4nhf1yAEgmTACcLltm5UA4GMc0LTxuB16/m2Gjithu10EUFOdgt2vOh/Remn3edzQurmbF7EUEK2uwTAtvYR7ugA+b3YaZNokHI0Trwxg2g0B5Kf3Gbkb+0P7Z7rZ0wkylSUSimOk0Nrsdl8+LzWHPdrdEREQkyxQYEOkBCgyIiEhH0mmTJQsaOjwWaoxx9/Uf9Vhbv79id/z57VP2AQweXqCggWRV4+JqaudWZAaUk9EEhs3AMi2cXldm4Llk9BANMGdZIhJtWQmwaDk2pwP/oJJM+s5VWaZFaFktZjJF/mYDGLL71grs9DIKxomIiEhXHNnugIiIiMjGKJlM86eT/kM8ltog7XUVZCgty+OqOw5UcECyoqPZ54EhbWefN9eFiNaHsdItq2QUHMieYGUN8WAEy7Q6DQoAGDYD/6ASGhZUEQ9GCFbWUDJm6IbtrHRqdcG45rpGGitqFIwTERHZhCkwICIiIrIeLFnYsMGCAqtTsyxMqCFGQXFOtrsim5hEJErt3IqWPPQG5PYvwGa30zrWbLPb8Bb68eTnEVpWS7CyBsNuI6c4oJnMG4iZThNriLQ8TqWpm7eEcNVK3P4ckk0xLNMkGU0A4PS6MGxtA4wOj5Nw1UrsbhfeQj85JQFsdqWpySYF40RERKQ7FBgQERER2ciVluXhL+g4zZDI+lT3QyX185cSWb4Sd76PxIIYhg3sTgfugA9voR+H26nZ51kUa4jw0uk3r3tF02cx6+E3OPyhS8kpDqx7fbJWWoNxwcqaDlNBKRgnIiIirRQYEBEREVkPBg8r4JJb9gHAn+9pl8YnnTaJBONEIgl++Kaa+XNqsdkNErHU/wbxO07fAWCaJtVLQxSU5LLdroMZNaYEX8Ddaaogf0H79kV6yqozzlfVWFHNgne+Ily1EjOVJtbYhM1pBwtsLgeu+hDh5XXklhTgLcgDWmafN9eFCFetpGjUYG2QKrKGlApKREREukuBAREREZH1wOm0M2zz4i7LFJf6SKdMQvVRmsIJIo0xbHYb6bRFoMBNR8EBy7KorY7gyXHRr7+PXSYM7XTTYZGe0tngP0C0PsTbf7qnx9rqv91IcooDBCtrcPtz2xzzFPiUpmYtdHX/zLTJfreeC7TMNq+e9RONFSvIH9ofw2Yj2Rxn/hufAzDiwF1w5rjbnG+ZJo2Lq8kfUkr/bUdgpk2a64IdtqX71/OUCkpERETWlmFZlpXtToj0dYMGDQJg6dKlWe6JiIj0RfO/r+WHb2uoqmjEsBkEG2JgWXhznLi9Tmw2A9O0iEeTNDcliIQTlJT62OHX5ey692bZ7v56Z6bSJCJRzHQam92Oy+fVTPINyEyneX3S7YSX1WW7K+SVFXPQnRdq4HIN6P5t3Jrrgj2TCup/lApKRERk06EVAyIiIiJZNqA8QFVFI0G/h0Q0SUGhh1gsTbQ5STgYxzDAssDutGGmIc/vZvCwArbeuSzbXV+vEpEowcoawlUrScXimGkLm93A4XGTN7CIQHmpcmJvAM11oV4xqAwQXlZHrCGigcs1EGuI6P71YV2t9oCWFTs9qav6tOJDZOOWTpk0RRKk0yZ2u41cnwu7Q6koRTZmCgyIiIiIZFmuz8Xw0SWkTYuqyhCJeAqf34E3x4lpWqRTJol4uuX5AjfDtihm7PYDyPW5st319aZxcTW1cyuIByNE68MkowkMm4FlWji9LprrGmmsqKFk9BDyh/bPdnc3arGGcLa7ID1kjz+fgieQ22WZRHOc6lnziVTVYTjs5JYESMVSQEvamdZUNJZp0VTbgJVK4xtYTP9tR+D6RZqhVrFgEx/+5bGevZhNQDZWe3SVFkwrPmRNaaC5b2iKJFheGWRFVZh4PJWZiOF2O+g3MI8B5YGN+jOnyKZMgQERERGRXqBsaD4AdptBKBgn2BAjlUhjpi0syyIv4KaoXyGBAg/DR5dkym+MGhdXs2L2IoKVNVimhbcwj8AQHza7DTNtEg9GaK4LEa0PY6VNAAUHNhF5ZcV4CnzZ7kaftT4H53944cP1Vvemqjet9gCt+JDu00Bz37FscSML5tZmPnvGo8lMCku310l9XTNVFY0b/WdPkU2VAgMiskFotoiIyOqVDc0nvzhnk/4ynYhEqZ1bQbCyBpvTgX9QCYZhYKZNks1xLNPE4XETGJJLpHolwcoaDLuNnOKA0gqtJwXDB7LvLb8DwFOQh83e9u93T28+vMVRe7DZntvhym2/qbZSmYiI9G4aaO47li1u5Mc5K6iqDGKZFv58D8X9vIABWMRjaRpXNhNsiJE2W7Yn1T0T2bgoMCAi65Vmi4iIrJlcn4sRY0rYbFTRJhlQDVbWEA9GWr6gDiohHU8SbQgTa4xgJlNYloVhGNicDtyBXFKRJPFghGBlDSVjhma7+xslu9NB8RblnR73FPg4/KFLAYiHmlj25VzqFy6ncHgZNruN5pVBQstqiQebwGbDlevNpKSxTJNUc5xwTQOeQC6lWw9nyO5bkz+kdENd3iZlv1vPxVvo73b5lT8toWrGTzTXNBCPRDGTaQwDsBl4/LnkDSzCWxSgcEQZgcH9Oq2np4NHmwrzfyuiRPqKXw405xd6yRviz6z4CwfjGmjuJZoiCRbMraWqMgQWeHKc1Nc2k0qmM5+1HE47Pr+beCxFVWUIu80gvzhH399FNiIKDIjIeqPZIiIia8/usOHPbz9jemPUurmmmUpTN28J4aqVuP05hJauoGlFI6lYgkRzjHQ0AYaBYbdhdzlw1oewTIt4uBm724W30I/NYdes8g3MZrdnUot48n00LFz+vzRPaVyBXFw+Ly6fl8jyepLROMnmGLGGOJldtS0LbyAX/6B+DNl9KwqHD8zyFW1cVh1ctv43ENcdwSUrCFasIB1NtKzeKSvGk5+H3eXA5rCTbIoSa2xqWcnzvzY6DQ4Yxjpdw6ZK+3tIX7LqQLPTaaf/oDws0yIRT2OaKWw2g7yAm7x8DzXLwhpozrLllUFCwTiRYAy7w0awPkq0OUkykcYwDCzLwumy0xxJ4PI4SKdMQkEPyyuDjBhTku3ui0gPUWBARNYLzRYREZFVtQ7+d6QnZhMv/3oesx5+A+h6VrSCBuuXzWEnb2ARzXWNNNeF8BTkYRgGOUUBXLnen1d/pNJYpoVhQLQhTN6AIgbvNpbC4WXZvoSNzqqDy+9ccm8WeyJ9nfb4kK60DjRbpklhSS611RHCjfF2M9Dz8t0UlnipXhomFIxroHkDSqdNQg0x0mmTRT+uZPFPdSRiaRLxNGDh8Trx5LtJpyxMy8JKm4Qao2AYuFx2Fv+0ErfHQX6RF7vdhr/Ag92+8a9mFdmYGZZldX/aiIh0aNCgQQAsXbo0yz3JrtZ9BCKhGN99vZza5WFcbgf9B7UMCvySaVrULAuTTJoMHhpgh92HaLaIiMhGyEyneX3S7b1iE828smIOuvNCBQfWo0QkSsVH39K4aHmbfSJamWmTdDyJmUoTWVGPARQML2PI7lv3qn0iNpb9kep+qOw1AQG9/9ZMOpmiYUEV0PH+HtDzaZoUWJWutA4sd/T8zM+WsmRRA5gW4VCcRDxNOpUmlbQyC8QcLhserxO3x47H68TpcjBs8yJ2njC0T/5+7Y06u0cAwfooN01+p8fauuyv+xIo7PjvtoIGIn2DVgyIyDr75T4CK5aFWVEdIRFLMWRkIYmEidvd/kuEzWbQf1AelQsaNFtERGQj1lwX6hVBAYDwsjpiDZFM6hvpeS6fl5LRQ7DSJsHKGhoWVOEtzMMd8GUGNlOxONH6MDabjcCQUkpGD+k1QYHWzzXVS4KEw3HSSRO700Zenpv+gwN9bn+k1W0ebaZN4sFIm39X/XceocoVuPK82BxO5r/xOQAjDtwFZ467XRvxUIREOIq/vB+b7bk9Drezw75oYHnNrG5/j/XBW+jX78dN3IYcWB62RREFxV6WVQbJ87f/3aLB5TWTTKb500n/IR5LbZD2uvq/UFqWx1V3HLhR3r+uVsGuT/obKuuDAgMisk6WLKhnzozlhMMJwsE4seYEjSubiYQTeLxOli5qpLYqRK7fgz/gxu11YrO1XT3g9jhYsTyC2+OgfHgBLrd+NYmsTxvLLFjpO5Qne9OTP7Q/AIbdRjwYIVofJry8PjNr1Ol1kVPsxx3wUTJ6SKZ8ti1b3MjcWcupWhKitrqJeCyJAViA2+OkpH8uAwf7Gb3tgD6TAnF1g8vNdcFuzzhvDRB06vPvKd9tLP6y4jXpoqyD1W3+bZkmyWgCaHnfGbb2gaH6BcsoHDaQsl9toVRBm7h02uTa89+gZtmG+bu98IeVLPxhJa/8a3aHxzfmweX1YcnChg0WFFidmmVhQg0xCopzst2VHhdriPDS6Tdv8HYPf+hSBW6lx2n0TXqFvffem/fee6/LMptvvjk//PDDGtU7e/ZsbrrpJt5//31qa2spLi7m17/+NRdffDG77rrrunR5k9LZrJHqpSHmzFzOtBfndXpuVUWw2+18OR0GlAcoK+/8j51mjYisvV+u7jHTFja7gdvtoN/AvD43C1ZkbShH9oaTP7Q/OcUBgpU1hKtWkoolME0Tm82Gw+Mib2ARgfLSXrNSYNniRr78qIIF39eSiKdaNrrGwGYzME2LeCzJ0kUNrKiKEA4n+BXaH6kjZjqd7S5sUla3+TeA25/b6fnR+hDefB85xQEC5aWajbqJa6hr3mBBge7YmAeXN3alZXn4CzzZ7oaIrIYCA9IrzJo1C4DCwkKczo6XHpeUrFmKmWnTpnHwwQeTSCQwDINAIEB1dTXPPfccL7zwArfffjuTJk1a165v9Db0rJE7r/mgy+OaNSLStc5WAyxb3MiCubWEgnGCDTHi0WRmsMvtdVJf10xVRSPDR5dslANdZipNIhLFTKex2e24fF5sDg1+bCirS2WiHNkbL5fPS8mYoRSNGtyr34NNkQT//biSn+bUEmtOkpPrxJvjzKx0NE2LeDRJc1OSaFOCn+bUYjcM8otz+nxAddUZ59B+1rlh0OWMc2g769xbqNmM2dLZ5t+dsUyLaH2YnGI/eQOLes17UisbsyfU2HEKIekbBg8r4JJb9iHcGMMXcGP/xe/rUGOMu6//qMfa+/0Vu+PP73jwf/DwAn1nF+kDFBiQrKuoqKC+vh6AmTNnUl6+7nk0Fy9ezFFHHUUikeCwww7j7rvvpqysjJqaGi655BIee+wxLrzwQrbeemv22GOPdW5vY6ZZIyJ9Q1erAQzDIBSMUlfThGVa5Bd6yRvix2a3YaZNwsE4jSubCTbESJsWsPHMgk1EoqvMVo5nXheHx93rZitvzFaXyuSXqTAWvP01td8vxnDaMQCn143N7cJKpbEsEyttkoolwDDw5PvIKc4nWh/KpMIIDNGs197G5rDjye+9KzUWzatjyaIGmsNxAoVe/PmeNgOqNpuBN9eFJ8dJqDFGsD7KkkUNLJpXx9gdBmax5+tu1Rnn0H7WuafQ3+WMc2g769wT6LqsrF+B8lIaK2qI1ocJLa1tt/l3K8u0CC2rxbAZuAM+AuWlWehtW1rZKL+kWedrxum0M2zzzlO5pdMmNz54GOFQnBkfVzD7v9XYnQZ2uw1/vhswsCyLZMJsqc/VMrAfDsZJp0zSKYuxOw5g+1+Xk+d3azW/yEZAgQHJuhkzZgBQXFzcI0EBgBtvvJFwOMzo0aN57rnnMqsQSktLeeSRR6irq+P1119nypQpfPLJJz3S5sZKs0ZEer+uVgPYHTYiwRjR5hSFJTmMGFPcdrDLbiNQ6CUv30PNsjBVlSHsto1jFmzj4mpq51Zk8psnowkMm4GVTmNz2GmsrKHuh0pKtxlO4fCybHd3k/bLVBiFI8qIBZtIxRPYbAaJphjJxkgmP73d5cBbkIfD68bXvwjDAExTqTAkI502WbKgofPjpklTqGUWvGmafPF+BcuXhsCySKdMGldGOz3XsiySyTTLl4b55O2FpJJp8vI97WZmtuprsyY3llnnm6rVbf7dutl0tD6MYTN6zebfm/rKxt6idcY5gD+//aBvT28+PGCQn61+NZDtdxtMoKD9/0ENPPcsu91GQXEO/nwPSwf6KV3eRCyWIh5PEQklMivl3J6fV8pFm5PY7Da8OS48Hgf9BuRRVh7QKp4+ZOjQoVRUVHDqqafyyCOPrLb8I488wsSJEwFYtGgRQ4cOXb8d7GZfotEoHk/bQOHMmTOZP38+xxxzTJvnWz+33HPPPfzud7/bMJ3toxQYkKybOXMmANttt12P1NfU1MSjjz4KwEUXXdQuNZFhGFx55ZW8/vrrfPrppyxYsIDhw4f3SNuy/mnWiEhbyxY38uOcFVRVBjtcDbB4fgPBhhiJeIpAoYfG+igFRe1X3NhsBv0H5VG5oIFQMM7yyiAjxqxZCrfepHFxNStmLyJYWYNlWngL8/ANcBNrjBBrCBNrjBBathLDBg2Lqhmw3QjKfjU66wMj8vOgZH5dP8LVDbj9OSRCTaSTaSzLwjAM7E477oAPb6Efu9NBw8IqDUpKRjKZ5k8n/WetN2Csq2nuftnqCDM/X9plmb6YBrEvzzqXvrf596qfZcyUhdfnorDYi91hx+mw0RTZeFc29jarm3HuL/Bw44OHtXs+HIrz3VfL+Gn2CuxOG8H6GFgWuXmuNinZErEUsWgSDAOf3w2WRUFxDoUluZ2mpJGeZ3fY6DcwLxNwy8l1EIuliTYnCQfjmd8VDpe9ZbWcx04qZTFgSIB+A/MUFJBe4Q9/+AP/+Mc/mDx5crvAgHSfAgOSda2Bge23375H6vv000+Jx+MA7Lvvvh2W+dWvfoXf7ycUCvH6669z/vnn90jbG6OOZo0EG6J893UVVRVBSgf6ePP5NdsUuiuX/XVfAoWdD8xp1ojIz0LBGN99tYyqpWEcDoN+A3wYhkEs2jIYZqYtok0tOXoNw2BlTYRUsmVTSLe7/UcAT46T/EIvwYYYK6rCbDaqqE9+8E9EotTOrSBYWYPN6cA/qIRofZiGhVWkonESTTHSiRQYkAjFSESiJJuixEJNDNh2ZNYHSKTtoKSVNikcOYh0IoVlmhg2Gw63E8Nm06CkdGjJwoa1DgqsD30xDWJfnXUuP+srm383RRIsmFtL5YKW963TaaNxZTMNtRaGzcDhsJGX76G0zE9DXfNGtbKxL2qdcf5L/nwPSxc2UDLQj4FFbq6LaDRFtDlJqCHWZqA5L9+L1+vAk+PEoiVVlO7lhjegPEBVRSNBvwewCBQ5iATjpFImlvnz+88XcP/vb6qBP+BmQLn2kZHe4T//+Q+maWa7G32eAgOSda2BgZEjR/J///d/vPLKKyxcuJCcnBy23XZbzjjjDPbZZ59u1zdnzhwAPB5Pp0ue7HY7w4cPZ+bMmXz33XfrfA0bs45mjdgdNnx+N06XnUChlyNP2brdeY0NUVbWNBFqjGGmTewOO9GmBCUDfAwcHCCVMgk1xjBsNvoPzGOzzYvoP8ivgX+RX+gqHca872p48Yk1/R3WwDdfVHV4ZMKBw3F6HKysieD22Jn7bQ2+/31R60upMIKVNcSDESzTygQFwlV1ROtb0oQ4czx4C/0YNhtmOk2kqo5oQ5jaOYuxO1o+Gik4kF2/HJRsXFTdblAyVh/SoKT0CX11tWNfm3Uu7fWFzb+XVwZZVhGktiqMJ9dJqDFFKpFuSf1nWjhcdpoiCYIrmynu7yMRj20UKxs3NqvOQG9c2czgEYWEGmKEG2PtBprz8j348z3ULAuRX+TVDPQsyfW5GD66hLRpUVUZItacpLAkB5fbkfk9n4inMt/Zy8oDDB9doiCOyEZGgQHJqhUrVrBs2TIAJk2aRCzWNp/9Dz/8wNNPP81pp53Gfffd1y4tUEda6xs0aFCX+VDLysqYOXMmVVUdD5BJ53J9LtxuB26vk6ZwosMZ/jk+F94cJ7XLI8SjKRoboqTTFk2hBPW1zXhyXZSW+fEH3MoVKtKJdU2Hsaamv7Eg8/j7mdVMe3Fe5t+9PRWGmU4Ta4hgptLUzVtCuGolbn8O0YYwjRUtm2hiM3B53Zhpk1Qimfkb4fTlkIrGiIeaqfuhgmQ0jqcgTxtoZpkGJWVtrS5HNrQEXSPBlhWm4WCM91/9kaolQQqLc/DkrH7QI9qcoKGumYHl+ex58EgGlAc6/f3Ylyc99JVZ59K1Db35dzptEmrofJ+y1vdf2jT5/P1FLPqxjlTSIhZN4vY48OY6MYyWTVATsRQNtU00GAahUByPx0Ek9L/VkDYIFHi7fH/15fdfX5OZgd4Qo6G2mf6D8ijul0s8ns6s+HO77WC0rKQybDbNQM+y1u/gdpuxyv4eTZnPWm6vk/yiHH1nF9mIKTAgWdW6WgCgoKCAG264gYMPPhi/3893333H9ddfz4svvsgjjzxCbm4ud95552rrDIVCAOTmdj2gk5OT06b86gwaNKjTY8uXL2fAgAHdqmdj8MsZIf4CT4dBmIKiHHJyXTSujBJtTpIX8NBvgI9+A/Nwux30G5jHgPKAZh2IdKI3pcPoDakwzHSahgUdB3NjjRE+vO7xdW6jbi7Atxg2g9KthnVYpmD4QG1uu4FoUFLWxupyZLcqLm0ZKE2nTObPrSMSSZJKmfj8LqCLzXYti0gohr8gh8HDChiz3YCNerZrX5h1nk6ZNEVaBovtdhu5PtdGfU96s3Ta5Nrz36BmWXi9tjPrc3jjme9XW663T2zYmPxyBnrlggbyC734Am7sdgfptEk4GKOxPrpJzUA3U+le+7sTWoID+cU5LK8MsqIqTDyewkxb2OyGvrNLh7744gv+8Y9/8OGHH7JixQpyc3MZO3Ysxx13HGeccQZut7vD8+rq6rj33nt56623mDdvHo2NjXi9XsrLy9lvv/248MILKS8vX237p512WmZfUYCbb76Zm2++mfHjxzN9+vR25efPn88NN9zAO++8w4oVKyguLmb8+PFccsklbLvttplyH374IePHjwdg2rRp7L333h22/9xzz3HMMceQk5NDdXU1eXl5q+1zb6bAgGSVw+Hg4IMPZuXKlTz77LNtBt933HFH/vOf/3Dqqafy2GOPcffdd3POOeew1VZbdVlnKtUyiOZydf2Hq/WXVWt5WTOrzgipXhqm/6C8DoMDTpcd07ToV+andEAuY3csw+d36wubSB+T7VQY6WSKF068jlQssUHa++bRtzo9lldWzEF3XqjgwAbSFwYlpW+zO2yM2LKEqopGaqubaKyPkl/opaPggGVZBBuixONp+vX3MmLLkk3m88yGnnXeHU2RhAazepmGuub1HhRYE71hYsOmpKMZ6CuWRzbJGeiJSHSViQ3xzO8nh8fd6yY25PpcjBhTwmajihRklS5dfvnl3HjjjZl/+/1+mpqa+Oijj/joo4+4//77eeWVV9pNrP3www85/PDDaWxsBMDpdOL1egmFQsyePZvZs2fz0EMP8cEHH7D11u1TVa8qEAhQWlpKbW0tpmmSm5uLz+ejsLCwXdm33nqLiy++mGg0isfT8l22qqqKp556iueff55XXnmF/fbbD4A99tiDkSNH8tNPP/HYY491Ghh4+OGHAfjNb37T54MCoMCAZNnee+/d6Zut1S233MITTzyBaZr8+9//Xm1gwOtt+eOaSHQ9eNS6QfHqAgitli5d2umxrlYTbKy6nhFiyywRbp0RMnhoPiO3LGGgloqKdFtX6TDCoTjffLGURfNWMmhoAJut49mt9Stb9vuIR5P48t001sXIL/bSvywPj6ftxwCn20H10hD+Ai8jRhczdGRR5li2l+I3LKjaYEGB1QkvqyPWECGnWL/PNqTeOCgpG49hmxezcG4dzU3JljQlKZOcXBdurxObzcA0LeLRJM1NCaLNKfLy3AzarKBbKxNk/Vi2uJEFc2tXSX+RzNwrt9dJfV0zVRWNG/3gY28Tauw8hZCsm94+87yVZqBD4+JqaudWZFIhJqOJzL4ZTq+L5rpGGitqel0qRLvDhj+/7+2JIxvGbbfdxo033ojH4+HSSy/l97//Pf369SOVSjFt2jQuuOACZs2axaGHHsqXX36ZSQUeDAY55phjaGxsZMcdd+Tee+9l++23xzAMgsEgDz/8MJMnT6axsZHLL7+cV199tct+3H777dx+++0MHTqUiooKJk2axE033dRh2RdffJERI0Zw9913s88++2CaJq+//jqnnHIKjY2NTJo0iR9//DFT/vTTT2fKlCm88MIL3H333e0ykVRXV/PWW29lym4MFBiQXq+0tJTRo0czZ86czMbCXWmN2EWj0S7LNTc3Ay0RTlk7mhEisn51lQ7Dn+9h6cIGQo1xXG5Hh3t9APjyvWBa1K+MEmtO4fO7yC/wMmCQH9sqA/2maVGzLIwnx0XpAB+jtirdqL+wrYu8smI8BRqgFtmY5Ppc7DiuHMu0mD+3lng8TTIRw2iMZQabLQssICfXyYgxJew4rly/J7Nk2eJGfpyzgqrKIJZpkV/oJW+IP7M5eTgYp3FlM8GGGGnTAtDn0E1Utlc89oS+NPO81aY8A71xcTUrZi8iWFmDZVp4C/MIDPFlfj/FgxGa60JE68NYaROgVwUHZOP16KOPtknBsybq6+v585//nKnnt7/9beaYw+HggAMO4N1332XLLbdk1qxZPPHEE0ycOBGAZ555hhUrVuB0OnnppZcYOHBg5txAIMBFF13E999/z/33388nn3yyDlfYXiAQ4MMPP8yk/rbb7Rx66KFcfvnlXHLJJfz0009UVFQwZMgQAE499VSuvPJKIpEIL7zwAieffHKb+h5//HHS6TTDhw9njz326NG+ZosCA9InBAItszJbB/O7MnjwYODnTYg703q8rKxsHXu3adOMEJHs6O5eH263nZIBeaRNi6WLG3G77dhsLYNbQLvVPb0132vB8IHse8vvAPAU5LUJagCZL1oADYuWUz9/KU0rgphmmtjKMN5iP4bR/ouoZVnEGiM4vW5ySwvwFuTRuLia4s0HM2iXMbj97fer8RT4lEZI1phyoPd+rQPHuX43y5cEqauOEI+lMU0LMPB47RT39zFgcIAx2/bXQHOWNEUSLJhbS1VlCKfT3i6dpc1uI1DoJS/fQ82yMFWVIew2g/zinF73t21j1J3Nv4P1UW6a/E6PtHfZX/ftdHIEZH/F47rqqzPPW21qM9ATkSi1cysIVtZgczrwDypp9/vJW+jHk59HaFktwcoaDLuNnOJArwvuyMbH4/Fkxta6Eo1G2+3F+fzzz9PU1ERBQQHHHHNMh+cNHjyYAw88kGeeeYYXXnghExg49NBDeeedd4jFYm2CAqsaO3YsAOFwz6aiO+KIIzrcD3T33XfPPK6qqsoEBgYMGMBBBx3EK6+8wmOPPdYuMNAaWDnttNM6/O7dFykwIFn11FNPsXjxYjbbbDOOO+64TstVV1cDdGuD39ZfKE1NTSxbtqzDgf90Os2CBQsA2HLLLdem67KKTXlGiEg2dXevD3+Bh7rqCHl+N94cJyYGC39Y2adW99idDoq36HozKl9pAQB5A4uxTAssg6YV9bj8OdgdDpy5bb9wtQYFXLkecooCFA4bSLI5hq+0gNx+BQTKS3vlEn3pW5QDvW9ZdcJD9dIQ4VCcdDKN3Wknz++m/yC/7lmWLa8MEgrGsUyT/oMCnX4xt9kM+g/Ko3JBA6FgnOWVQUaMKdnAvd30dGfzb3+Bh8tv25/vvlrGonl12P63+thmM8gLtOwDl0y0zKR2umwYhoFlWYQa4wQbopQNCbDPEVvQr39enx/470rj4mpqvl1A/YIqrLSJtzAP/6B+2F0OzTzvpYKVNcSDEcxUmtzSQpJNMQybDbvb2WZSi2Ez8A8qoWFBFfFghGBlDSVjhmav47JJOPbYY3nkkUdWW+6RRx7JDOq3+uyzzwAIhUJdjstFIi0Ttb7//ufN4fv370///m1/N4VCIRYuXMjcuXOZMWMGL7/8MtAyVteTRo8e3eHzpaWlbfqyqjPOOINXXnmF9957j6VLl2ZSh3/11VfMmTMHm83Gaaed1qP9zCYFBiSr/vnPf/LBBx8wYsSITgMD8+fPZ+HChUDbqF5ndtppJ3w+H5FIhGnTpnHqqae2K/Pll19m3vx77rnnOlyBrGpTmxEikm1rsteHy+tkq53K8Od7sExrox6gdPm8lIwegpU2iQUjxMNRovEEDq8Lw2bHMk2S0QTJpiiGzcBb6Mc3oBC7y0loaS05xX7yBhYpKCDrbNUc6A0rozSH45mAXE6eWznQeylNeOi90imTFVVhGlc24/Y4iDYl2xw30xbxRBozZZJIpHG57DhddlYsj+D2OMgv8rYbRN6YB5Z7K7u9ZYXi0oUNNJb4MLBweZwE66NEQglycp3k+Fy/2N8jSTptUlCUw9ARRYzYYuPe+DtSXc/C92bQMH9ZyySOQC7NdUFiwSY8+T68BXmaed4LmOk0sYaWgVAzlabm24XUfl8BWMSCTZimiZVKY9jt5BQH8Bb4cLh//qzt8DgJV63E7nZRMHxgm2MivUnrZN10Ok1NTc1qyzc0NLT5dyqV4vHHH+fpp59m1qxZrFixos1xm239/D7vLHX4qpMKLMtqc+zggw+mf//+VFdX88QTT3DZZZcBP286vO+++25U+4wqMCBZdcQRR/DBBx8wf/58nnvuOX7zm9+0OW5ZFn/84x8BKCws5Nhjj11tnV6vlyOPPJLHH3+cW2+9leOOOw63292mzuuuuw6A8ePHa8WAiPRpa7PXx6aQ0qR1xpyZSlHz7UIiNfU0LFyOM9cDFthdDlw+L06vG9+AQrwFfkLLajFsBu6Aj0B56WpaEOlaaw70igX1NIUS2AxwOGxggAFEGqOEGqLk+t3Kgd5LacJD79MUSRCPp4iE4nz89sI1OvfL6fD8w7PaPX/jg4dRUJzTMx2UbvtlSsR+A3wYBsSjKaLNScLBnwOpTpednFwnzU0WZUPz2Xzr0o3uc8uqGhdXs+i9GTQsWEb0f+kOo3VBLMvC7nKQiESJrgzhG1BITlFAM8/Xo1UH/jsSrQ/x9p/uWfeGps8if0hpl58/lcpSssk0W1Yl7bLLLpnVA90VCoU44IADMue5XC622WYbtthiC8aOHctuu+3G7NmzufDCC3u832uT7sfhcHDqqady880389hjj3HZZZcRj8d5+umngY1n0+FWCgxIVp1zzjnceeedLFiwgDPOOINgMMjxxx9PTk4O8+fP509/+lNmSdFdd92Fz/fzZo+nnHIKX375JWVlZbz77rtt6r3mmmt47rnnmD17Nocddhj33nsvm222GbW1tUyePJnXX38du93Otddeu0GvV0RkfVjTvT42lcGu/KH9W2bO5eVSPesnmlY0YpkmLp8HV14u3sI83IFc0rEEDQurMGwGgSGllIweotl2sk5ac6D/NKeWplAcl9tOc3OKZCKdSYnhdNnxeB00rGgiEUspB7rIKtJpk1BDrN3zwYYokVCcZKLnUg0E66NdHteKgvVn1ZSIsWiKISMKCDbECDfGSaVMLNNsScNiN0gmTEoCHgYNCTCgfPU5snuL1Q0sr7pHEkCoaiX1C5ay4rtFJMJRXD4PzlxP5m9HKpakqbYRw4BYKEJOcQBPIA/LMgktrcPuduEt9He46lEDy2vGTKd5fdLthJfVbZD2pl/9SJfH88qKOejOC3UPJSuKi1tSxC1ZsmSNz50yZQqfffYZHo+He++9l2OPPRaPp+130a+//rpH+tlTTj/9dG6++Wbmzp3L7NmzqaiooKGhgcLCQg4//PBsd69HKTAgWeX1ennjjTc44IADWLhwIWeeeSZnn302eXl5BINBAJxOJ7fddlu7VEOVlZXMmzePWKz9l4bNNtuMxx9/nOOPP563336bYcOGkZ+fTzDYMtPCMAzuvvvujWYXcRERpb7omMvnZdje25M/tJTqWQuIB8PEgs2YKZNYQ5hofRin10VOsR93wNdrN++TvmV5ZZClFUEaVzZjsxlEm028OU4Chd42qTGizS1pUBpXNrO0IsjAIcqBLpJOm1x7/hvULOvZDQg7s7pNcEvL8rjqjgMVHFgPfpkSsXppmPxCLwXDc0inTJKpNNFIkqZIApfHSVl5gOGjS/pMALUnBpaba9esfMWH3zDr4Tc6PKaB5TUTa4hssKBAd4SX1RFraAkGiWxoO+20E08++STLli1j9uzZmb09f+mggw5i6dKlHHzwwdx4440APPvsswCcfPLJHab6Bpg9e3bmceuYXTaNGjWK3XffnY8++ogXXniBpUuXAnDCCSe0yUiyMVBgQLJu5MiRzJo1i7vvvpvnn3+euXPnEovFGDp0KPvssw8XXnhhp790unL00Ucza9YsbrrpJt577z1WrFhBYWEhu+66K3/84x+ZMGFCz1+MiEiWbSqrAdZU4fAyfKWFBCtrCFetJBVLYJomNpsNh8dF3sAiAuWlWikg3dbZjOZ02mTetzVU/FRHLJbG7baTX+TFZrORSv48y9nusJGb5yIcjBOLpVi2uJElg/xsNqpokw7miYQaYhssKNAdNcvChBpiSje0nnQnJWJhSW6blIh9RXNdSAPLfZj5vw2dRQR+85vfcMkll5BIJLj00kt55ZVX2u0L8MYbb/DGGy2ByVXTAoXDLX/TW9MR/dI333zDM888k/l3MpnE5Vp9ANj+vyBnT29Y3OqMM87go48+4vnnn8/sibCxpRECBQakl8jLy+PSSy/l0ksv7fY506dPX22ZMWPG8Nhjj61Dz0REZGPh8nkpGTOUolGDSUSimOk0Nrsdl8+rjYalQ+m0yZIFDR0eCzXGuPv6j7pVT+WCxtWWKSzxMv+HWkoG5uH7xWzYwcMLNFtZNhlpDcZtctY0JWJfEWvoPQEuWXO6fyI/Kysr45JLLuG6667j9ddf58gjj+TWW29l5MiRJBIJ/v3vfzNp0iQAttpqK0444YTMubvuuivvv/8+jz/+OBMmTOC4447D4XDQ0NDAk08+yRVXXEE8Hs+Ub25u7lZgoKCgAIAZM2aQSqVwOHp2iPuYY47hggsu4NtvvwVg2223ZbvttuvRNnoDBQZERERkk2Jz2PHk+1ZfUDZpyWSaP530H+Kx1AZpr742Sn3tEv77UfvcrUplIpuSUGP7lTiy8VNKxPUvr6wYT4E+//RVun+Sbddccw319fXcfffdvPzyy7z88ssEAgEikUhm1v7QoUN5/fXX8Xp/XoV96623Mn78eCKRCCeffDKnnXYaPp8vkz7cZrNxyimnZCb1Ll68mG233Xa1/fn1r3/Nf//7X9577z38fj8jR47km2++6bHrzcnJ4bjjjuO+++4DYOLEiT1Wd2+iwICIiIiIyC8sWdiwwYICq6NUJiLZU1qWh79AKfo2lI0pJWLB8IHse8vvAPAU5GHrILgbrQ/x9p/u6ZH2hu+/IwO2H0XRyMEdHtfmw2umO/evYVEVH/7l8R5pb79bz8Vb6O/0uO6fZJvNZuOuu+7i2GOP5Z577uGTTz6hpqYGj8fDqFGjOPLII7nwwgvx+9v+P95+++35+uuvue6663j//fepqakhmUyy+eabM27cOM477zy23npr3nrrLWpqanjhhRe6FRi4/vrraW5u5sUXXyQcDhMOh0kkEt1abdBdhx56KPfddx8ul4sTTzyxx+rtTQzLsqxsd0Kkrxs0aBBAZkMSERER6dsWzqvjlkumZbsbgFYMyKYlmUyzZGFLCi9/vqfD//dLFjVw93XdS+W1Opf9dV8ChZ3vL+Mv6LgPIj3BTKeJNUSIh5pY9uVc6hcup3B4GWY6TePi5TSvDGGZFh5/DjaXs82GnJZp0ryiEZvbSU6hn9KthzF0/LbaL2kDal4ZYuE7X2XuW2vwINoQpqm2gVQ0TjzUjJlMZ9JWGg476VgC34Aihu65HSVblAMa+O9LmuuCvHT6zRu83cMfulR7hGxg559/PnfeeSe//e1v+fe//53t7qwXWjEgIiIiIvILg4cVcMkt+wAdD06m0yaRYLzdeZFIgs/eXUjl/HpcHjuBAi9gtCvXyrIsQo1RUgmT4VuWMOGgUeT53W3KaGBSNiVOp51hmxd3XciAQ08Yy6J5deQXemmKJEmnTSzTxLDZsNkMXB47uXlu/AEPNptBOm1SuaCBwcMK2Gqnssz7TO8vySab3U5OcQBPvo+GhcuJ1oex0mlyCv1ggd3pJFofIh1PYrPbsXvdGDYblmm2PJ9K4/J56bflUPptuZmCAhuYJ5BLTnF+5r65ArlAy75Wuf0KiNaHiAcjpJNpLMvCMAzSiQRg0H+bEQzeZYz2uRLppRoaGnj88ZYVQeeee26We7P+KDAgIiKyGmYq3eFmtZ09LyJ9X3cGJ4tL2+f6TadMapeHCdZHScTTpNMWgQI3HQUHWoICMex2O958F4M3K6CsPKB82iKr4Q94KCjKobHERyDfzeBhbhLxNKZptQQF3PZ2aT+C9VHy8r0UFOXofSa9js1hJ29gEc11jTTXhfAU5JFT1JKOw7AZpKJxEk0xYsEmDANM0yIVS+At8FEwvIzSrYeTP7R/lq9i09PRfWtd1eFwO8kbUISvtIBUPIllmoBBeFktuf3yKRg2QN8bRHqZGTNmsHjxYhKJBDfeeCPBYJCddtqJCRMmZLtr640CAyIiIp1IRKIEK2sIV60kFYtjpi1sdgMwMGwGlmkBVuZ5h8dN3sAiAuWlmrElsomyO2wM3qyA6iUhlixqJJUyqa9txpvjxO11YrMZmKZFPJok2pzEAhxOG2VD8hm8WYEGK0W6we6w0W9gHvV1zTSubMZf4MGT4+y0vGlaNNZHyS/Kod/APL3PpFcKlJfSWFFDtD5MaGkt/kEl5BT5cfm8bWaem6ZJItiE0+uicHgZIw/aBV//wmx3f5PV0X1bNeWTYbPh9LqxTIvQslpsDjvugI9AeWkWey3rwlPg4/CHLs1Ku7J+zZ49m1NPPTXzb4/Hk9l8eGOlwICIiEgHGhdXUzu3gngwQrQ+TDKawLAZxMPNpJpjGHYbVtrE4fXg9udgmRZOr4vmukYaK2ooGT1EM7dENlEDygOUDQkQCcaJhOM43Xai0RThYBzDAMsCh8uOJ8dJMp7Gl++mbEiAAeXKGyvSXQPKA1RVNBJsiFG9NEz/QXltBuNamaZFzbIwhs2GP+DW+0x6LZfPS8noIVhpk2BlDQ0LqvAW5uEO+MgbUEROcYDmukai9eH/zTgfSOnWwxUUyLKu7pvNbsNMm5nvE4bNIDCklJLRQzSJqA9rTQEmG5+xY8cycOBAGhoa2G677fjrX//arY2Q+zIFBkRERH6hcXE1K2YvIlhZg2VaeAvzCAzxEWuMkE6kiDWESccTODxuPAUucvvl48nPIx6M0FwX+l+eURNAwQGRTVCuz8Xw0SWkTYvKBQ1EQnFycp048j0YgAWkkmksCwr75TJkeCHDR5eQ63Nlu+sifcaq77OqyhCVCxrIL/TiC7ix222ZfUAa66MYNhtl5QG9z6TXa/3caNhtmcHk8PL6TFDZ6XXhLyvGHfBpEkov0p37llPs130T6eW23357li1blu1ubFAKDIiIiKwiEYlSO7eCYGUNNqcjsxw4FU8SqV5JrDGMM9dDXlkxscYmYo1hbHYbrlwv3kI/nvw8QstqCVbWYNht5BQHNCNIZBNUNjQfALvNIBSM07iymaZwAut/x/35HvKLcvAH3AwfXZIpLyLd98v3WbAhxorlkcxgnNvr1PtM+pz8of3JKQ6sks4ygWma2Gw2HB6X0lb2UrpvItIXGZZlWasvJiJdGTRoEABLly7Nck9EZF3Vfr+Ymm8X0FwXomD4wExagvDylYSX1ZKIRMkpyQcMLMuiubYRl8+Lf1AJeQOKgJYNRRsWVJFT7Kd06+GUjBmatesRkexqiiRYXhlkRVWYeDyV2ZPE7XbQb2AeA8oDmsEsso70PpONlZlKk4hEMdNpbHY7Lp9XG9b2AbpvItJXKDAg0gMUGBDp/dLJFA0Lqog1Rjo8nkqmaaqpp37+Uprrgji9bpxeNwAWFk21jSTCUexOB3bXzwvuUok0ZjKJy+clp18+NlrzGxuk4gnyBhbRb6th2GxtNzocsP1I7K7ON0oUkY1LOmXSFEmQTpvY7TZyfS5tgCrSw/Q+ExEREek+BQZEeoACAyK9X90Plbxzyb3Z7kYbv33uGgUHRERERERERGSD0/QJEREREREREREREZFNiFYMiPSADb1iQDkLRdZcd1IJhZZU07BoOc0rGskpLcikBUolU0RXhoiHmnH7czCMn8+zLDLPewv9OP6XZsiR66WpeiV5ZSX0GzMEZ46nTXtKJSQiIiIiIiIi2eJYfRER6S0SkSjByhrCVStJxeKZjdUcHjd5A4sIlJfi8nmz3U2RXsnudFC8RXmXZczUGBZPn8XKH5fgyffhLfQDYJkmK39cQqSmoWXvgdyf32eJphiuXA+5/QooGjUYm71lMV60PoTDaado1GCG7LGNgnciIiIiIiIi0msoMCDSRzQurqZ2bgXxYIRofZhkNIFhM7BMC6fXRXNdI40VNZSMHkL+0P7Z7q5In2Rz2MkbWERzXSPNdSE8BXkYhoFhs+EO+EhEoiQiUZy5HsDAsiySTVFcPi+efF8mKGCZFtH6MDnFfvIGFikoICIiIiIiIiK9igIDIn1A4+JqVsxeRLCyBsu08BbmERjSMghppk3iwQjNdSGi9WGstAmg4IDIWgqUl9JYUUO0PkxoaS3+QSUYhoG30E+0PkSiKUa0IYwn30essQnDZuD0uvEW5AEtQYHQsloMm4E74CNQXprlKxIRERERERERaUubD0uv8dprr7H//vtTWFiIx+Nh2LBhTJo0iSVLlqx1nZ988glHHnkk/fr1w+12M3jwYCZOnMj333/fgz1fvxKRKLVzKwhW1mBzOigYPhBvoT8zM9lmt+Et9FMwbCA2p4NgZQ21cytIRKJZ7rlI3+TyeSkZPYRAeSnm//YliNaHsDns+PoX4cn3kWyK0bCgilRzDE9+Hr4BhdicDqL1IRoWVmEmUwSGlFIyeojSe4mIiIiIiIhIr6PNh6VX+Mtf/sJVV10FgN1uJzc3l1AoBEB+fj6vvfYau+222xrV+eijjzJx4kQsy8Jms5GXl0cwGATA4/Hw5JNPctRRR/VI/9fn5sO13y+m5tsFNNeFKBg+EGPVXU9pyX2eiiexTBMMg/DSWnL75VO69XBKxgzt8f6IbCo6TN9lQDwSJdUcw7DbsNImDq8Hd54XywKn14W3MA93wKe0XiIiIiIiIhuZdNok1BDb4O36CzzY7ZrfLT1LgQHJuueff57f/OY3AFx11VVMnjwZn8/H7NmzmThxIl9//TUlJSXMmzePgoKCbtX5xRdfMG7cOFKpFGeddRY33ngjRUVFLF68mHPPPZc333wTr9fLN998w8iRI9f5GnoyMGCm08QaIi2PU2mWfDaHxkXLcftz8OTnYZkmyWiCdDJJKpYkGWkmnUpjmWDYwEwkAYOSMUPZ4shxONyude6TyKaq7YbfCUzTxGZr+TDWuscHkHne4XFpI3AREREREZGNVENdM1POeHmDt3vjg4dRUJyzwduVjZsCA5JVpmkyduxY5s6dy5lnnsn999/f5nhDQwNjx46lqqqKyy+/nOuvv75b9e6///68/fbb7Lvvvrz99tttjiUSCXbeeWdmzZrFCSecwJNPPrnO17EmgYFVB/47Eq0P8faf7lnnPgHscO7hDNppi06Pewp82OzaFFVkdcxUmkQkiplOY7Pbcfm82Bz2Tp8XERERERGRjY8CA7IxUWBAsmratGnsu+++AMybN49Ro0a1K3PjjTdy+eWXM3jwYCoqKtql0vml+fPnZ1YBvPXWW+y3337tyjz11FOccMIJeDweamtr8fl863Qd3Q0MmOk0r0+6nfCyunVqr6fklRVz0J0XKjggIiIiIiIiIrIaCgzIxkTJqSSr3n//fQDKy8s7DAoAmcDBkiVLmD17drfrdLlcjB8/vss6Y7FYpvyGEGuI9JqgAEB4WV2XqxdERERERERERGTj8MUXX3DBBRew7bbb4vf7cTqdFBUVsfvuu3PDDTewYsWKbtWzaNEirrnmGsaNG8eAAQNwuVz4/X623XZbJk2axJdfftnpuUcddRSGYeD1ejP7i3bH119/jWEYGIbBU089lXl+woQJGIbBhAkT2p0zdOjQzDm//HE6nRQUFLDVVltx3nnnddnndXX22WdjGAZvvvlml+XS6TQPP/ww48ePp6CgAJ/Px9Zbb83ll19OdXV1j/dLgQHJqjlz5gAwevToTsusGjD47rvvul3nZptthtvt7rBMcXFxZr+C7tTZU8y0ucHaEhERERERERERWbJkCQcddBC77LILd9xxB9988w2pVIrc3Fzq6+v5+OOPueKKKxg5ciSPPfZYp/UkEgkuuugiNt98c66++mo++eQTampqyM3NpampiW+++Ya77rqLnXfemUMPPZRly5a1q+OMM84AWibrPv/8892+hkceeQSAwsJCjjrqqDW6fo/HQ2lpaZufvLw8QqEQs2fP5u6772bnnXfm7LPPJplMrlHdq/P444+3S53ekVAoxH777cfpp5/Ohx9+SDAYxOVyMWfOHG688Ua22GIL3nvvvR7tmwIDklWtvyAGDx7caRm/359J9VNVVdUjdQKUlZV1u86eEmsIb7C2RERERERERERk0zZnzhx22mkn3njjDXw+H1OnTuWnn36iubmZxsZGGhsbeeqpp9hiiy0IhUKceuqp3HNP+70vw+Ewe+21F7fffjvJZJKDDjqIt99+m2g0SkNDA83NzXz99ddMnDgRh8PBq6++yk477dQu+8cBBxyQGZN74oknunUNiUQis0rg5JNP7nQicGeOPfZYqqur2/zU19cTi8X4+uuvOe644wC4//77Oe200+ipzPsPPPAAp512WrfKnnzyyZmB/z/+8Y+sWLGC+vp6ampqmDRpEsFgkAMOOIBZs2b1SN9AgQHJstYlQ7m5uV2Wy8nJaVN+Q9cJLfsIdPazfPnybtXR2+SVFeMpWLf9FUREREREREREpPeJRCIceuih1NTUUFJSwscff8zVV1/NiBEjMmUCgQDHHXccX3/9NTvvvDMAF154IfPmzWtT12mnncYnn3yCw+Hg4Ycf5rXXXmPffffNDNK73W522GEHHnroId58803y8/NZvnw5hxxyCI2NjZl67HZ7ZrB8+vTpHa4q+KVXXnmF+vp6AM4888x1eUnacDqd7LDDDjz11FNceeWVAPzrX//q1gz/roRCIU4//XTOOussTHP12UM+/PBDXn65Ze+Kq666iltvvZXi4mKgJevJHXfcwTnnnEMymeTcc89dp76tytFjNYmshVQqBbTsB9CV1l8yreU3dJ09pWD4QPa95XcAeArysNnbx+ai9SHe/lP7yOzaGH30eMLLagls1p+yHTfHW+Bvc9xT4NPGwyIiIiIiIiIiG6Hrr7+eRYsWAfDss8+yzTbbdFo2NzeXxx57jC233JJkMskNN9zAo48+CsCLL77ICy+8AMAtt9yy2lnwe++9N4899hiHHXYYFRUVTJ06ldtvvz1z/PTTT+eGG27ANE3+9a9/MXny5C7ra00jtMsuuzB27NjVXfZaueaaa3j77bf58ssv+ctf/sKpp566xisTAB566CEuu+wyamtrMQyDM844gwceeKDLc/7zn/8AUFBQwJQpUzosc8UVV/DPf/6Tzz//nP/+97/ssMMOa9y3X1JgQLLK6/UCLUuCuhKPx4HVD/avrzoBli5d2umxQYMGdasOu9NB8RblXZbxFPg4/KFL2z1f+0Mli9+fSWhZHXaXAyuVxnDYMQCby4HT68bhcZHbLz8TALC7nTTXBfHk5ZJbUoAnX6sDREREREREREQ2drFYLJMSaL/99mP8+PGrPWfUqFFMmjSJRCLBPvvsk3n+lltuAWDIkCFccMEF3Wr/0EMP5eCDD+a1117jgQce4JprriE/Px+AYcOGMWHCBN5//32eeOKJLgMDNTU1mU17e3K1wC/ZbDYuueQSfvOb37B06VLee+89DjzwwDWu5x//+Ae1tbUMGzaM++67j+HDh682MPDTTz8B8Ktf/QqPx9NhmcGDB1NSUkJtbS3Tp09XYED6vry8PACi0WiX5Zqbm4GW/QayUeeGZLPbySkOtHt+8C5jiDdGMGwGWBZ2twvLtDBsBjaHHU++D29BHg7Pz4GOaH0Ip9eFw+PC5fNuyMsQEREREREREZEs+eCDDwgGgwAcffTR3T7vtttua/Pvuro6Pv/8cwCOP/547GuQeeKkk07itddeo7m5mTfeeIPjjz8+c+zMM8/k/fff59tvv2X27NmdrgR48sknSaVS+Hw+jj322G63vTb2339/7HY76XSaadOmrVVgYMSIEZx55pmcffbZuFwuFi9evNpz0uk08HPa887YbC2ZR36Z5mltaY8ByarWDYK7yicWDoeJRCLAzxsGr2udqx7vTp29gc1hp2DYAAqHD8Ttz6Vg2EAKhw+kcHgZRaMGkzegqE1QwDItovVhvIV55A0swuZQyiARERERERERkU3BjBkzMo9//etfr3U9n376aWYz3tY9CLpr3333zTxuDS60OuqoozIrCB5//PFO62hNZ3T88cfj863fTBg+n4/y8pZMH999991a1fHcc88xadKkbmcoATJt/nKj5lXV1NRQU1MDQHV19Vr17Ze0YkCyauzYsTz77LNdRrp++OGHzOMtt9yyW3UCLFy4kGQyidPpbFemtraWhoaGbte5OitWrCCdTnc7pdBasyxS8SRWumXjEqODPQoyRVcp43A7wTDWb99ERERERERERNZQ//79+frrr7PdjY3OqimxBw4cuNb1rDrxtnUybncVFRXh8XiIxWLtZs57PB5OPPFE7rrrLp566iluuukmjF+MXc2YMYNvv/0WWL9phFZVUlLCokWLqKur2yDtARx00EHcd999/PTTTzz11FNtVla0+utf/5p5vLosKd2lFQOSVXvuuScA8+fPp6KiosMy06ZNA6Bfv37dGsSfMGEChmEQjUb59NNPu6zT4XAwbty4tel6G06nc42WUq01w8DudGQCAmY6jWVabYpYpoX5vyVIht2G3enodlBg+fLlLF++vGf7LBuM7l/fpvvXt+n+9W26f32b7l/fpvvXt+n+9V26d32b7p90RzKZzDxel5n2rRNrAQKB9qmvV6ewsBCApqamdsdaB/uXLFnC9OnT2x1vXS2w9dZb86tf/WqN214brTP9w+HwBmkPWvZj2GmnnQCYOHEit99+eyZ7Sn19PZdddhn/93//l7mPrSs41pVWDEhWjRs3jvLyciorK7nuuuu4//772xxvbGzkzjvvBODcc89tFznsSHl5OePGjeOjjz7ihhtuYI899mhzXiKRyGyacuKJJ2aWLa2Ljn659UWtKx662mhZei/dv75N969v0/3r23T/+jbdv75N969v0/3ru3Tv+jbdP+mO0tLSzOP6+vo2/14Tq2biiMVia3x+6wB7R/nzt912W7bffntmzJjBk08+mZlADC2BjX/961/AhlstAGT2ZWjdw3RDsNlsvPDCC+y///58//33XHTRRfzxj38kEAjQ2NiIaZqceeaZxGIxnnjiCbzentlHVCsGJKsMw+Dmm28G4IEHHmDy5MmZN+CcOXPYb7/9qKqqol+/fu12Pd97773ZYostOOWUU9rV27r86O233+aUU07J5OCqqKjgiCOOYNasWeTk5HD55Zev5ysUERERERERERHZsAYMGJB5vC456YcMGZJ5XFVVtUbnNjY2ZgIDQ4cO7bBM66D/c889Rzwezzz/6quvUldXh8fj4aSTTlrDXq+92tpaoG1gZaeddqJ///4d/vz73//ukXYHDRrEl19+yXXXXcfYsWMzAZl99tmHl19+mfvvvz+zeqOkpKRH2lRgQLLuuOOO4+KLLwbg1ltvpaioiPz8fMaOHctXX31FXl4eb7zxRmbpUasFCxYwb948Kisr29W522678fe//x3DMHjiiScYMGAABQUFDB06lDfeeAOn08mzzz7LqFGjNsg1ioiIiIiIiIiIbCi77bZb5vFHH33U7fM+/PBDrrnmGj744AOSyWSbjYs//vjjNerDqim+O9u4+IQTTsDr9RIMBnnllVcyz7emETr66KMpKChYo3bXVlVVVSaIssMOO2Ser62tzWz++8ufnsr3D5Cbm8sVV1zBd999RzQaZeXKlbz11lsceuihQMskaoDNNtusR9pTYEB6hb/97W+89tprHHDAAeTn59PU1MTgwYM544wzmDVrFttvv/0a13nBBRfwySefcNRRR1FSUkIkEqG0tJRjjz2WL7/8koMOOmg9XImIiIiIiIiIiEh2bbvttplZ+i+//HK3z7vvvvu4+uqr2XfffQmFQpSVlWX253zsscdIpVLdruuBBx4AwOv1djoOFwgEOProowF44oknAKirq+P1118HNmwaoTfffDPzeO+99848Xrx4MZZldfhz2mmn9Vj7q+4L8UtLlizJbOC8atBiXSgwIL3GQQcdxBtvvEFdXR3JZJLKykoeeOABhg0b1mH51jdlR5uTtNp11115/vnnqampIZlMUl1dzdNPP8222267fi5CRERERERERESkF2hNv/3OO+/w3nvvrbb8Tz/9xDPPPAPA4YcfTlFREQCXXnop0JKiuzUl+OpMmzaNl156CWgZ3O9q1n/r4P+bb75JOBzmueeeI5lMMnLkSCZMmNCt9tZVOp3mH//4B9AyI39DtQst152fn08gEOh0BcJTTz0FtGwk3VN9U2BAREREREREREREZCNz8cUXU1ZWBrQECb799ttOy9bX13PMMceQTCZxu91ce+21mWOHHHIIv/nNbwCYOnUq999/f5ftfvbZZxx33HGYpkl5eXmbujqyxx57MGLECOLxOK+99hrPPvssAGeccUa3rrMnTJ06lW+++QaA6667DsMwNljb22yzDeFwmGg0ynPPPdfu+PLly7n11lsBuPDCCzvcyHltKDAgIiIiIiIiIiIispEJBAI8++yz5ObmsmzZMn79618zZcoUfvzxR9LpNAANDQ08/PDDbLPNNnzzzTcYhsE999zD6NGj29T1yCOPMH78eNLpNGeffTaHHnoo7733Xib9jWVZfPPNN1xwwQWMHz+elStX0r9/f1577TXy8/O77KdhGJx++ulASyqjDz74AKfT2aNpejrS3NzM9OnTOfroo7n++usBOPHEEznhhBPWa7u/NGDAAI499lgALrroIl599dXM/Zk+fToTJkygtraWIUOGMHny5B5r17Asy+qx2kREREREREREREQ2Qg11zUw5o/v5+nvKjQ8eRkHx2s8S//rrrznhhBP46aefMs85nU58Ph+NjY20Dg8XFhZyzz338Nvf/rbDeuLxOFOnTuW2224jkUgAYLPZyM/PJxwOt8mRf8ghh3DPPfcwaNCgbvVx+fLlDB48ODMgfuSRR/LCCy+s9rwJEybwwQcfMH78+HbpxocOHUpFRQUej4dAINDmWHNzM5FIJHPthmFw3nnncdttt+FwOLrV5+5YvHhxZrPgN954gwMOOKDDcitXrmTChAnMnj0bALfbjc1my6QWGjp0KO+//35m34ie0HNXKSIiIiIiIiIiIiK9yo477sj333/PM888w3/+8x9mzpzJ8uXLCYVCFBYWstVWW3HIIYcwceJECgsLO63H7XZz0003cd555/HQQw/xzjvvMHfuXILBIDk5OYwZM4YJEyZw8sknr/EGuQMGDOCggw7ilVdeAXp20+FYLEYsFmvznNPppKioiOHDh7P77rtz6qmnMnbs2B5rc00VFRXxxRdf8Le//Y1nnnmG+fPnY1kW22yzDUcffTQXXnghfr+/R9vUigERERERERERERGR1eirKwZEOqI9BkRERERERERERERENiEKDIiIiIiIiIiIiIiIbEIUGBARERERERERERER2YRojwERERERERERERGR1UinTUINsdUX7GH+Ag92u+Z3S89SYEBEREREREREREREZBOiUJOIiIiIiIiIiIiIyCZEgQEREREREckKLV4WEREREckOBQZERETW0uTJk5k/f362uyEi0mcZhgGAaZpZ7omIiIiIyKbFke0OiIiI9EX77bcf06ZNY+bMmTzwwAMMHTo0210SEekTfvjhB7777jtWrFhBUVER48aNY9CgQdnulsgmJ51OY7fbs90NERERyRJtPiwikkWmaWKztSze0pezvuPKK6/khhtuwOl0kkwmmTBhAg899JCCAyJZYFlWZta59H4XXXQRb7/9Nj/88EPmucLCQm666SYOPvhgBgwYkMXeSXf89NNPzJo1i5UrV1JWVsagQYPYbrvtst0tWQOXXXYZ559/PmVlZdnuioiIiGSRVgyIbGQ0QNK3VFdX4/f78fl8Cgr0EZZlMWjQIHJyckilUjgcDqZPn85ZZ53F/fffr+CAyAaycuVKXC4XHo8Hp9MJ6G9gb3f44YfzyiuvkJeXxzbbbIPL5aKhoYH58+dz6aWX0tTUxO9//3vsdnsmaC69y4UXXsjbb7/NvHnzMs/l5eUxdepULr74YkDvw97uwAMP5K233iIYDHL33XfrXvURel9tPFauXAlAUVFR5jndXxHJFq0YEOmj0uk033zzDbNnz6a+vp5Ro0YxatQoRowYkSmjDxi911133cUnn3zC888/z8CBAxk+fDgXXHABv/71r9t8SJTeKRKJsMceezBr1ix+9atfMWfOHJqamthnn3247777FBwQWY/uvfdevvzySz799FNsNhvbbbcdBx10ECeeeGK2uyZdOOWUU3jiiScYNWoUTz31FMOHD8fhcLBw4UL+8Ic/8O6771JYWMh7773H1ltvne3uSgdaAzt+v5+ysjKKiopYsGABy5cvB+D6669nypQpWe6ldKU1KLDddtvx4osvMnjw4Gx3SbqhubkZr9eLYRiZ73f6ntf33HHHHXz11VdMnz6ddDrNnnvuyY477shFF10E6Lu7iGSHAgMifVAikeDMM89k2rRpVFdXA2Cz2fB6vVx55ZXsscce7LrrroA+YPRGEydO5NFHH8Vut5NOp/F4PMRiMUaMGME555zDeeedh8fjyXY3pROtKZ9efPFFTjrpJHbffXeOP/54LrzwQoLBoIIDvdySJUsoLCwkNzdXX677oOOPP55///vfHR5bdVBS97R3efzxxzn11FMZOHAgn332GYMHD26TPq+qqooDDzyQ7777jpNPPpkHHnhAqwZ6mdNOO43HHnuM4cOH8+yzzzJs2DD8fj/vvvsu//73v3nggQcAeP755znyyCOz3FvpSEdBAaWx7P122203qqqqOOaYYzjuuOPYYYcdMsf0t67vaP380vq5E1q+v5umyTnnnMM999wD6J6KyIanT9sifUwikWC//fbjiSeewGazcfDBB3PAAQewww470NTUxJQpUzj//PMzHy5W/fAh2XfIIYfw6KOP0q9fPx5++GGeeeYZ7r77bkpLS5k/fz4PPvhgZnmp9E6tX6DHjh3L8OHDeeutt4jH49x5553k5eUxbdo0zj77bBYvXpzdjko7u+yyC9tttx0TJ07k3XffJRwOA+gLWB9x1FFH8e9//5uBAwdyyy238MwzzzB16lQOPvhgAK644gr++c9/Arqnvc1XX30FwOTJkxk8eDCpVCrzu9SyLAoKChg2bBgAy5Ytw+l0KijQizzxxBM88cQTlJWV8e6777Ltttvi9XoB2HPPPTnrrLPYYYcdcDgcfP755wD67NnLtAYFtt1220xQoPV9aFlW5n6ZppnlnsqqJkyYwOeff05lZSV/+9vf2G233fjd737HQw89BPz8Pkun09nspqzGkUcemfn8cscdd/DSSy9x++23c8ABB2D/f/buOi6q7P0D+OcMDI0SIqHYit1iYWG3omt3rt3tqmu7a67da66xduzaYmN3KzZigEpIzMzz+4PfPc7AgLpfBlSe9+vF64tz79ydy/3eO+ec55znMTPD4sWLMXbsWADcfmGMpQJijH03tFotde/enYQQVKpUKXr58qXc9vHjRxo7diy5urqSSqUiGxsb+vXXX+V2nU6XGh+Z6alVqxYJIahgwYIUGBhosO327dvk4eFBQgiaPXt26nxA9tVWrVpFQghq3rw5PX36lBYtWkR2dnYkhKDq1asnuM4s9dSvX5+EEKRWq0kIQUIIqlWrFk2ZMoXevn1LUVFRRBT3nCXiZ+a3Zvjw4SSEoNy5cye4r65fv04tWrQgIQR5eXnRw4cPU+dDsgS0Wi1FRkZSrly5SAhBf/75JxEZv7/mzZtHQgjy8PCgly9fkkajSemPyxLRp08fEkLQlClTiIgoNjY2wT6tWrUiIQTlz5+fPnz4kNIfkSVBaX8WK1aMnjx5QkREMTExqfyp2JdQ+n3lypUjPz8/2X4RQlCNGjVo/Pjx9PTp09T+mCwJI0aMkO2TR48eGWy7dOkStWzZklQqFRUvXjzBdsYYSwk8FYex70hoaChOnz4NJycnbNy4Ea6uroiJiQEAWFlZYdy4cZg1axYqVqyIqKgojBs3DiNHjgTAKwdSm/5Mrb179yJbtmzQaDQA4laBeHl5oUePHgDiivjFx9fu26Jcj3r16qFChQrYvXs3Xrx4ge7du2P69Omws7PjlQPfmCJFigAASpUqBT8/P6hUKuzfvx8jR45EnTp10Lt3b9y4cQMfP34E8GnGFt97qS8oKAgHDhyAnZ0dNm7ciGzZskGr1cprU6BAAfj6+gIAHj9+jODg4NT8uEyPEAJqtVp+r0VERCS6r62tLYC4Gi4ajYZXDHwjoqKicOrUKQCQ+ej1r40yw9zHxwcAEBsbK9umLPXVr18f+/btQ/78+bFnzx54enoiNjZWFmx/8OABdu/ejW7duuHnn39Gz5494e/vjxcvXqTyJ0/blO+3WrVqwcbGBo8ePcL8+fOxbds2tG/fHmZmZjhw4ADGjh2LUqVKYciQIdi5c6fBMXgVQep78+YNDh06BFtbW6xbtw5Zs2Y1aL8ULVoUderUARHh0qVLuH37dip/YsZYWsQtbsa+I3fv3sW1a9dgb28PZ2dnxMbGwsLCAsCnjlnLli0xcuRI1K9fH0IITJ06Fb/++isADg6kllq1asmcrtu2bZM5Xc3NzQFAds6UQMGTJ08we/Zs9OvXD+PGjcPRo0f5uqWSxP7uyqCxk5MTatWqhcjISIwePRphYWFo164dZsyYwcGBb4RyDRs1aoRs2bIhMDAQEydOxIkTJzBq1CjY2dnh7NmzWL58OcqXL48OHTrgzz//REREhMzzyqkVUtfly5dx8eJFZMiQAVmyZJHpL/SX27dp0wY5cuRAdHS0TGXC1y31CSFgbm6OSpUqAQBmz56NK1euGFw75TpZWVlBpVLB3t4e9vb2nE7hG0FEcqD/+vXrAAwDA8rvGTNmhEqlwps3b/D69Wtut3wDXr58ifv37wMAMmfODHd3dwCf2jBHjhxBy5Yt0bJlSyxbtgxLlizBokWLUL9+ffTr1w+HDh1Ktc+e1inXqGHDhihbtiyCgoIwbNgw1KtXD8uXL8fx48fRpk0b5MuXD8HBwZg1axYaNWqE1q1bY86cOYiMjOTaEd+AK1eu4Ny5c8iQIQOyZs2K2NhY2X5RnpHNmjVDwYIFAQD37t0DwJNSGGMpiwMDjH1H0qVLByBuINnW1lYOKANxHTOlEVG9enUMHDgQDRo0gBAC06ZNw4IFCwBw3sKUVqNGDezfvx+5cuXCnj17DBqFQNyAiHJNlNlZU6ZMwaBBgzB37lyMHz8evr6+mDBhAh4+fJhq55HWDB48GLdv35Y56I1R7rdBgwbB29sbFy5cwPXr12FtbY2ffvqJgwPfCOX+KlasGEqVKoWXL1+iV69eKFKkCMaPH4+TJ09i9OjRKFWqFD58+IAtW7agU6dOaNy4MYYMGYIXL14gOjra4JjcYUtZyt/f1dUVzs7OMDc3N7gGGo0GQgh5rUNCQgCAZ5x/A5RB/2rVqiFr1qwwMzNL8CxUrlNERAR0Oh2io6MRFRVlcI155mvKo//PO29tbS1XWZ08eRLnz59PsB8Qt/pRp9PB0tISdnZ23N5MZUQENzc3rF+/HnZ2dti/fz8GDRoEADA3N8fhw4fRtGlTnD9/Hrlz50aDBg3QoEEDFChQAOHh4dixYweGDBmCgwcPpvKZpF1arRZCCPTr1w8ODg4ICAjAjRs3oFKpUKZMGSxcuBD79u1Dz549Ub16dQDAX3/9hQEDBsDb2xvjxo2TgXL9Y7KUo6ySUyb16ffdleCAWq2W35WPHz+W2xhjLKVwj4mx74i7uzsyZcqE+/fvY/Xq1QlmQ+rPPqhYsSJ69eqFypUrIzo6GsuWLcPRo0dT4VOnXfPnz8fBgwehUqmQJ08eBAUFAYgL7BARdDqdHBD5448/sGzZMgBAiRIl0KxZM1SsWBH58+cHAIwfPx7r168HwIOSplanTh3MnDkT1atXx5AhQxLMmFM6VcpMcgsLC7Rs2RKhoaEyAOfg4IAWLVpwcOAbodxrv/zyCzJnzozLly/L61qoUCGMGzcO/v7+mDBhAjp37gwAOHjwIGbOnIny5cujR48e2L9/vxyg5tVXKStjxowA4grYbtu2DYBhp1kIASsrKzg4OAAAIiMjAfCz8lugfMdVq1YN/fv3x8iRI9GwYUOj+yr3l7W1NczMzOSzVqvVymD6nj17+DlqYn379sXZs2cN7rFChQoBAJ4+fYq3b98a7K/sp6RhM0ZZEclShjLpRKfToVixYpg5cyYsLCywatUqbNy4EcHBwZgyZQpCQ0MxdOhQHDp0CNu3b8f27dtx4cIFdO3aFZaWlrh27Rrmz5/PE1NSifLcy58/P5ycnHD37l2sXbtWbreyskLmzJkxb948+Pn5yXvRzs4ON2/exJQpU1CxYkX069cPf/75JwAOmKe0rFmzwt7eHo8fP8b27dsTBGaU4I+lpSUAflYyxlKJ6csYMMaSg1arpY8fP9JPP/0ki52+fftWbtOnX9Rvw4YNlClTJhJC0IgRIxJsZ6bVr18/WSSscePGdPjw4QT7LF68WO4zc+ZMevHiBRERRUdHk7+/P9WuXVtuDwgISOlTSFOKFy9OQgiytrY2KPA2aNAg2r17d6LvCwwMpMyZM5MQgnbu3ClfDwsLoyVLlpC9vT0JIah27dp0//79lDgVZkRwcDBVrlyZhBDUpUsX+Xp0dLT8fcuWLfK6p0uXzuD/B02aNKEpU6ZQVFQURUZGpsYppEnPnj2jYsWKkRCC+vTpQy9fvjS6X9myZUkIQQMGDCAiwwKp/L1neqGhobRnzx5auXIlLVmyhE6dOmXQPvnc70qBxpIlS8pt+gWI27dvT0II+u2334wWv2X/O19fX1no9OLFiwbbVq5cSYsWLUr0vStWrCAhBOXMmZOCgoLkPad/Dbdt25agzcpMQ//vHhgYSC1btiQhBLVu3ZrWr19PVlZW1LVrV4P3KN+FWq1WFpx2cnKivXv3puhnZwkp95enpyedPXuWiD49O48cOSLbKZ06daJ///2XypcvT+7u7vJ1MzMzKly4MC1YsIDvwRT0/Plz8vLyIiEEdevWjd68eWN0v4oVK8r+BpHh/at/vbgtwxgzBQ4MMPad2blzp2zkjRw5Ur6eVHBgwoQJJIQgGxsbunXrVop91rRMf9Cif//+BgOLBw8elNsWLlwot61YsSLBcTQaDR04cIBy5sxJQggaN24cESW83ix5KIPGarWaPDw8qFSpUvL62NvbU7NmzejChQsGQTnlXlu6dCmpVCoaPHiwwTHDwsJo6dKl5OzsTEIIatSoEQ9qpaLt27fLTrIS7FHup0OHDsnr3bNnT7px4wY1bdqUChUqZBAgyJYtGw0fPpyvYwqaOXMm5cyZk/74448E2zQaDWk0GsqfPz8JIWjq1KkJtjPTmjlzJlWrVs3gPkmfPj01bNjws99XyvYOHTqQEILKlClDGo3G4P7q1q0bCSHI0tKSbt++bdJzSav0/8ZqtZpKly5Nly5dMrqvsWs6efJkEkJQ8eLFZeBU/97r3LkzCSFowoQJJvn8jKhMmTJUtGhRo9t2795NGTNmlMEblUpFp06dIiLD66lcM61WKwcr69Wrx993JpTYILC+q1evUv78+UmtVhsE6A4ePCifub169ZKvR0ZG0p49e6hr165ka2sr97l7967pToRJ+tdx8+bNlD9/fvr9998T7Kf0IZSJSZMmTTJ43Vj7hYMDjLHkxoEBxr4xGo2GQkJCktznl19+kQ28GTNmyNfjNyb1/12hQgUSQtD69euJiBsVKUG/MacfHGjatClduHCBli1bJl9bvXq13Df+tdHpdFSuXDkSQlD37t1T7POnJfr3StOmTUkIQVZWVjR69GgaM2YMtWzZkhwcHEgIQTly5CA/Pz8KCAgwuFaXLl2i7Nmzk0qlotOnTxscPywsjObOnUuenp4JZmGylPX69WuqVasWmZub05gxY+TriXWuP378SFevXqVhw4ZRjhw55D48OJky9O/Na9euyd/17z2NRkOhoaGUIUMGEkLQnDlzDLYp2rZtS7179+bvv2TWpk0bEkKQra0t5c6dm2rWrEl2dnZkaWlJlSpV+uJ7RQkM1K9f32AVjzJg7eTkRDdv3jTVaaR58+fPl883KysrEkIYBAc+d9/06NGDhBBUrFgxioyMNLj3lGtob29vcB+z5KOsLs2ePTu9fv1avq5/3aZNm2YQ4A4PDzd6LCUIMGvWLBJCUIUKFSgmJsa0J5CGfa7fp+jbty8JIcjDw4M0Gg0dP37caLtF//lJRLR3714aP348Pz9N6M2bN+Tv708nT56kp0+fJrhf7t27J3/Xvyd1Oh29e/dOBu2mT58ut+k/Q5s1a0Z169Y14RkwxtIyDgww9g2JiYmhihUrUr9+/RJNlUBEdOvWLWrVqpVsDOrPoEwsONCiRQsSQtDEiRNN8+GZUfqNc/3gQIkSJeTva9eulfvEv35K56xGjRokhKChQ4emzAdPg/Qb8UpwwM7OjsaNG0fHjx+nc+fOUaVKlWTjXa1WU69evejvv/+W75s6dapMUxMREWFwPcPDwyk0NDQlTynNS2zm3fjx4+Uqqvfv39Pp06eNdq7jd+zOnz9Py5Yt4851Cksq6K348OEDeXh4kBCCNmzYQESGz9+ff/6ZhBBkbm5Or169Mu0HTkMaNWokBxmPHz8u2y6nT5+mZcuW0ZEjR774WHXq1JHBcwUHBUxPGaSKioqi+vXry3ROBQoU+KrggHKP1apVy+hqD76GplOrVi3Ztnz69CkRJZ66S0kplDt3bgoJCUlyRc/WrVtJpVJRyZIl6f3796Y7gTRq2bJl1L17d8qaNSuVK1eOGjduTMePH5erUhXKNXrw4AEVKFCAHB0dZSAufrslsevOK+dMZ+TIkVS6dGkSQpBKpaL06dNT79696ejRo3If5dlp7H579+6dTPurTBTTb38qz1YhBD1//tzEZ8MYS4s4MMDYN+T69etyRvIvv/xCQUFBie576NAhqlu3rkFuemOUgRFlJp7+TASWfO7cuUNr166lhQsX0qZNm+jhw4dyW1RUlPxdPzgghKDhw4fLbfGXaSv/Dg0NJS8vL3J3d6djx44REa/4SAnNmjWTg8eDBg2ikJAQCgsLo71791Lz5s0NrmP79u3J39+f7t+/TzVr1iQPDw/ZOefOmOldv36dVq1aRXPmzKEVK1bQzZs3jXbClNciIiLkKpzGjRt/VeeaJb/g4GD6+PEjEX26Rl/7jHvw4AHZ29uTSqVKMBitDExmzJiRbty4kSyfmZHMQV6wYEF6/PgxERleN/3vvvjiz5jU6XRUqVIlmQOdiKh79+48oJyCdDqdTAdUuHBh6tOnD2XJkuWLgwPKgLOfn598jYMCpqcEBYoVK0ZPnjwhoqTTj5w/f54GDx5M+/btS/SYSvtzzpw58nuSJS/9CV7KZBMlzdOECROMThB7//69DN4p+/fp00du57ZKylP6CpaWlvJZJ0RcrbKSJUsmWZ9MERgYSBkyZCBzc3M6cOCAwTblGeri4sLPUMaYyXBggLFvyMuXL2Uecg8PDxo9enSC4IB+h2zv3r2ygSiEoLFjxxoUxFRmG2i1WipatCjZ2trKAmI8sJx8hgwZInNbKz/ly5c3CNboz/xQBlOUARD9gsTGckoqqz0aN26cYBYR++9OnDhB06dPp759+9Lw4cPpyJEjCZZzKw1+a2trGjhwIAUGBspt69evN5jFkylTJqpataocaO7QoQPfZymgd+/eBil+hBBUtGhRGjNmjOwkxx+EjIqKkkvyuXOdukqXLk1ZsmSh9u3b09mzZxOktvjSeyggIEBe//3798vXeWDSNP79919ycnKijBkz0oMHD4jI8Hsr/nULCQmhJ0+e0OXLlyk8PDzBPUn0aWC5Tp06ctCMr1vKUK5BZGQkFSlShFxcXGjt2rU0fPhwcnFx+aLggLJ6pFGjRkRE1LVrV76GJmYsKKAM6if2PabRaBK0JZUgXvz31KxZk4QQtHDhQiLivkNyadiwIQkhyNXVlaZMmUKLFy+m+fPnU7Zs2eQKrHXr1hFRwmty5swZWTOgdu3a8nVut6Q8JX2Xh4cHLVu2jLZt20abNm2SRdwtLS2pXr16Bn0HY86fPy/bL/qBBG6/MMZSCgcGGPuGvH37VnbAhBDk7u7+2eDA0aNHqXXr1vI97dq1oy1btpBOp6PIyEh6+/atHKj08fH54jyW7Mv89NNPJIQgBwcHKlq0qEHxxSxZssiGPZHhoEm/fv3kfn5+fgbBAWO5lXPkyGGwCoH9bwYNGiQ7YPr5dhs3bpxgma5+cGDIkCEGM461Wi0dO3aMGjduTJkzZyYhBFlYWJBKpaI8efLQ+fPnU/rU0hSlc+3s7Ew1atSgVq1akZ2dHalUKnJ0dKTZs2cn+t4bN26Qvb09CSGoUqVK8nXuXKccJXWMubm57EQ3btyYFixYQGFhYZ8d4NKnnw7qzp07RMQDk6Y0cuRIEkLQ4sWLiSjxlVFnzpyhefPmkZeXF2XKlIns7e2pYMGCNG3atARFMIcPH24QrOPrlrKUa7h8+XISQlDv3r0pNjaWRo0aRa6urkkGB3Q6nZzEUKlSJerYsSNfQxOrUqWKDAo8e/aMiAwnoURFRdH79+/pypUrdOXKFYqKijJaJ+DevXs0Z84cWRspKiqK3r17J/sWRYoUkcdn/7uhQ4eSEIK8vLzo0aNHBttevXpF+fLlk2mh4q+60ul0Bv26tm3bElHcvctBm5SlBOWKFi2a4DoSfeof2tnZ0cGDB5M8VkBAAKlUKhJCyH4Dt18YYymJAwOMfUPCwsIoU6ZM5OjoSNmzZychBGXOnPmzwYHbt2/TmDFj5KCIlZUVFShQgPLmzUtZs2YlIQTlypVLNux54Ct5KI3CbNmy0aVLl+jDhw9ERLRixQry9PQkIRIWC06sIHGTJk0MggNEn4ICzs7OdOvWLdOfUBrRpEkTEkKQo6MjlS5dmmrUqEEWFhbyWvj6+lJwcLDBe/TTCg0ZMiRBMc03b97QhQsXqEGDBjJAYGZmxrlATUgJruXMmdOgqNvBgwflgIl+Sgt9yjNwwIABZGZmRlWrVqUXL15w5zqF9e7dWwatleep8lOlShXq168fPXr0SKYZUhi7RhcvXiRLS0tydnams2fPyvzL3KlOXlqtlqKiouQqOf20TfHbFnPmzKH8+fPL2a3Kc1FJi9CuXTuZgoiIaO3atbLorZOTE12/fj2lTovpuXPnjkwhdODAAQoPD6dhw4aRu7u70eCAcj9OnTqVzMzMZKCP7z3TUVI+OTs7yxST+s/J06dPU+/evSlfvnxkbW1Ntra2VLJkSRo9erS8JjqdjoKDg2no0KFkY2MjV6ZWrlyZChYsKCcofWnxcPZ59+/fpyJFilD69OnlPaQEwJWgzc6dO8ne3p4sLCzo33//NXqcxYsXy2fqjh07UuSzs0+SWqmjXMc3b95Q3rx5SQhBgwYNIqLEV9ycO3eOrK2tydHRkU6dOiXbRvwMZYylFA4MMPYNOXXqFAkRl6/32LFj5O3t/cXBASKiHTt2UPXq1eXMLmXQrFGjRnKAkvOdJw/9RqEysKH/t126dCkJEZfTOv4gc2IFiRs3bixnbHFuZdNQrluePHnoxo0bcjbWqVOnyNvbm8zMzChdunQ0efJkmXJGET84EH+2q+Kff/6h4cOH09WrV1PknNKi+/fvU4ECBYx2romItm/fLoOkSQVn/v77b3n/rVq1ytQfm/0/ZQD55MmTlClTJsqVKxc9ePCADhw4QAMGDJADi8oS/TZt2tDWrVsN7sf4g9B37tyR7/Hy8uLnp4nodDqKiYmRf2NlJqT+oMiePXuoffv2BoGePHnyULVq1WjEiBFUtGhRmUZDf1ArPDxcFnfnWhCm9bkA6LJly0gIQV27diWiuEGuoUOHJhocICJas2aNvN7Ozs58DU3I399fTvwZOXKkwbYDBw7IQqbK96C1tTUJIcjW1paKFStG586dI6K4ejtdunQhNzc3g/s1Y8aMVLVq1UTbOey/2bJli5xl/v79e6P34dWrV8nR0dFgRZZCP92oUmeuXr16RusRMNP40poe0dHRVKFCBRJC0IQJE5I85tOnT8nBwYGEEPK+5vYLYywlcWCAsW/I1q1b5UxJorjBrxIlShgEBz7X+Hv79i09ffqU9u7dS9u2baPHjx9TWFgYEXFQILkk1ShU/jcsLIxcXV2pXr16FBoaSm/evKE7d+4kKDBMZBgcaNq0qawbwY3C5GUsmKM/2/HKlStytn+NGjXk+/SvWVLBAf39eNa5ae3evZuEEFSgQAEKDQ2VryuDxW/fvqUMGTKQr68v3bt3j/z9/WnNmjUUGBhIb968MThW27ZtSQhB+fLl40GQFPbx40d5X/r5+cnn56lTp2jw4MEypYLy07BhQxo7diyFhobKAKtOpyOtVkuvXr2i3LlzywKAjo6OPDBpQsp9U6NGDbpy5QpFRkbS06dPqX379gY1d1xdXalVq1YGbZHQ0FA5qNWgQQN5DYni0iPyfWgaM2fONKi/QZT4CtL79+9T4cKFycLCgs6cOUNEcc/VxFYOEMXVJyhTpgxZWVnxao8U8Ndff8n7bNmyZUREdOnSJdmO6dixI61fv56OHTtGe/bsIR8fHzn4WLhwYbp27RoRxbVXAwICaOLEiTRq1CgaNmwYHTlyhF6/fp2ap/dDWrduHQkhqEKFChQdHW20rRgVFUVlypQhIQRNnTrV6HE0Gg2NGjWKhIirb6Wkn+EV4ab1NYW+Q0JCqHjx4iSEoA0bNiR6TJ1OR2/evJErzbn9whhLDRwYYCyFabVaev/+fYLXo6Ki5Ay7/v37y9cDAwMTBAfirxwg+jQQmdiAJA9UJo8aNWqQEIJKlSolc0rqNwqV32/evEkuLi6UK1cu8vb2phw5cpCLiwtVrFiRVq5cSS9evDA4rhIcUFLacFAgeX2uMa/8vmjRIhJCUIYMGQxWenxpcIDvs5ShrAgoWLAgvXr1Sr6u/P3//vtvsrS0JFdXV3JxcZE5y7Nnz05du3aVqzl0Oh0tWbKELC0tKUOGDLR161Yi4iBqSlAGMM6cOUMuLi7k6upKBw4ckNs1Gg2FhITQ8OHDZVFa5adAgQLUvXt3On36tMG92atXLxIiruYLd6pNQ7luf/75p1ydqNTYiT/ruHnz5rR69WqD9ytpFgICAsjS0pK8vb0pIiIixc8jrVHSq1lYWNCQIUPI39//s+9RUlQ2b95c1qcKDQ1NNDgQExNDmzdvlsWomWnotzOUuhx58uSh48eP07hx40gIQcOGDUvwvtevX9OAAQPIxcWFLCwsqH///hQZGZmSHz3NW79+PQkhKH369IkGzyIjI6lYsWIkhKABAwYkeqzXr19T7ty55YSy+PUIWPJSZv8XKFBA/q2NtRWV1w4fPkzW1tZUp04do/1+IsO+xa+//kpCCEqXLh23XxhjKY4DA4yloKioKCpXrhyNGTMmwTaNRkPTp08nLy8vubRe6UB/aXCAmZYywzF9+vQyfYJ+ITf9mToTJ06UgyMWFhZkZmYmZ7Jmy5aN+vbtm2D1x4ABA+RMEQ4KJJ8vneFDRLRkyRISIq5YWPzgTWLBgeHDh3MNiBSgf3/t3buXVCoVmZuby/zK+pT8rMoz08vLS6YosbGxoerVq8t77MOHD1S4cGESQlChQoUS5LNnpvX06VMqXbo0CSFo4MCB8nX9lGsbN240SIuh/G5mZkZt2rSRRab//fdfatiwoZwJy0wnPDycevbsaZCyRBnUyJo1Ky1cuFAOJhMlfOaeOXNGBtk/fPjAQVUTqlatmnz26afoatmyJd25c0cOWumnKSEievfuHZUoUYKyZMliEACPHxwoX748BQQEpPyJpSH6qxu1Wq38/cKFC1S5cmVSq9X0888/U7Zs2ahixYqyvaJcS+X78/Xr17LOUv78+RNce2Yayt83PDycSpUqJVdL6a94VERFRcl9Ro0aZfRYyvHGjx8vB6t5hYfprFu3Tk4wcXFxkStP468CV+63hw8fkru7O6lUKhkg//3332nNmjW0bds2evLkCYWHhxu89/Dhw+Tr68urrRhjqYIDA4ylECUoIIQgNzc3WQhY37Nnz2ju3Ln09u1b+ZrSyODgQOp6+/Yt+fr6kp2dHanVaqpZsyZduXKFiOIa6fqDHgsWLJCd7759+9K6deto//79tGDBAnJ2dpbBgY0bNxIRGczyGTZsGF2+fDllT+4HVrlyZTmzXJmVaiwooDTulYBO586djR7PWHBACEFjxowxCBIx09D/+ysd55kzZxpcU2XVhxCC5s2bR7dv36aQkBB68OABNWrUiMzNzcnR0ZHmzJkjB0vWrl0r8yrHDwgx01u9ejUJIcja2loWs1WuzaFDh+T17NGjB505c4Zq1apFefLkISEEqVQqmbN55MiRCVJFseSnXJuwsDBatWoVtW/fnmrWrEn16tWjdevWJfgO0x90VO7h5cuXkxCCOnTokHIfPA16/PgxtWzZUhZ9zpAhA9WpU0eu9siVKxd16tRJpgtSxMTEUExMDP3yyy9GvxOV4IBSpLhatWoUFRXFA8wmEhISIlNxxbdgwQJSq9Vy8km/fv2M7qfct/fv3ycXFxcSggvXmlr8dmFUVBT9/vvvVK1aNZo3b16C/XU6HX348IGKFy9OKpWK5syZY7Bdvw0UHR1Nx44do3Tp0nFNKxMLCgqiSZMmUc6cOWVb8c6dO0SUMJVsYGAgFSpUyCBgHv/Hzc2NsmTJQo0aNaKJEyfSvHnz6PXr1/Thw4dUO0fGWNrGgQHGUoB+vkhzc3PKnDmzTEOj0J8JpP9vIg4OfCuCgoKoWbNmZGlpSebm5kZndixevFg2/OIXDSOKy2Pv5OSUII89py5JfjNmzJDXokiRIrLBHf9vrfz7zp075OzsTFZWVkkWotXvmCmrEXiFh+kULlyYWrVqJf+tBNLWrFlDe/fuTbD/1atXSa1W08qVKxNse/v2rQzQ+vj4yGt/+vRpypUrF880T2HK91xQUBBVrlyZLC0tDXIqHzx4UN7DvXr1kq+/f/+ezp07R927dzfIy8u56VPOl+Syjj9IrP/srVChAtnZ2cl7mAeUTefmzZvUs2dPeZ80aNCATp48SeXLl5eDyWZmZjR8+HDavXu3wXsDAwMpY8aM5OzsLFdnKd+BoaGh1Lt3b/Ly8uJZriYyb948at++PWXMmJGyZMlCP/30Ey1dujRBapJ27drJPsaaNWsSPV5sbCxFRkaSt7c3CSFo8+bNpj6FNGn//v00btw4atasGXXt2pXWrFkjVwkHBwfLmgBECZ99QUFBciWWfmBA//k5cOBA6tWrF0VGRiaapoYlr+DgYJo4cSLlyJFDBgeUNofSLn306BEVKVKEhIgrIlykSBHy9fWlvHnzyvfp/yjpYy0tLen+/fupeXqMsTSOAwOMmVh0dLQciCpZsqTMwfu1nWGlE24sOPC5gsTsvxs9erTBDOKgoCD66aefZHCgSpUqMo2M/kxl/YFlZdmv0qifMWMGWVhYkJubGz19+jRlTygNuX//PnXr1k3OjCtRogQFBgYSUcIZPo8ePZKFTrt37/7ZY+sHB/gams6ECROMDgwTJVzCTfTpeuqvyFKencq2nTt3kqWlJWXLls2gjgTP1EoZiQ0oDx48WC7T12g0dOrUKaPXPv4MzBMnTtCcOXM4J28yih8gSypwrd+Gid+e0Z/ooH/d27RpI4PjnP7CdPSvx40bNwyCA2PHjqXo6GjatGmTTC2jpOrq3LkznT17Vq5enTZtGqlUKoM0mPrphvSfoyz5xK+toj/buEuXLgZpaD58+ECVK1cmT09PmeoyMdHR0bIf8ddff5n4LNKevn37yjRbyo+DgwMVL15c1t8wNglMoR8Y+PPPP4nIMLVe9+7dSQhBuXPnNqixxEzPWHBAmRh0//59GRSoVasW3bhxQ7YrIyMj6d27d3T48GH666+/qFevXlSpUiXKlSsXWVtbc/uFMZbqODDAmAnpBwV8fHzo5cuXsgO2bNmyrz6eseBA9uzZacCAAdw4NAGlpkCDBg0MOr7xgwM1a9aksWPHJhoUiE/Jl61Wq+nOnTs8U9IElL/po0ePqEuXLjKFU8mSJWVwwNgMn8aNG8tjfG4VB6/yML39+/dT8+bNZRqMnj17ym1JpW5Kqhj7P//8I3NsP3/+XAYY+D5MXnfu3KG1a9fSwoULadOmTfTw4UO5TX+QWPm7v337looWLUoqlYpatGhhNCig/74vmbHOvl7t2rVlvQcl3Z3iS//mb968SVCrQ6vV0vv37+mnn36Ssyl5hmTKunHjhkH9laFDh8pty5YtoxYtWpC5ublMMdS4cWO6efMmHTt2jDw8PMjKyopOnz4t38PPTNNp1KgRCSHI1dWV5s+fT5s2baJ169ZR4cKFydLSkhwcHGj8+PEUExMj78srV64YTSmjbFe+M1++fElZs2alHDlyJFi9zP43fn5+JIQgZ2dnqlmzJrVu3ZrSpUsnV+bkypVLTlxI7P65ePEiWVtbk4WFBe3fv99gW7du3WRKMF6lYzrx2yj6dR3iBwfc3Nzo8OHDsl9et25dg2MZm8SieP/+vUH6YMYYSy0cGGDMRPSDAhUqVJB5j7t06WIwwPW1g4v6aYWUgo0lS5akd+/eJe8JpHFBQUHUp08fsrOzkw29xIIDSo5rIQRt2bJF7hN/EEXplCn5tKtUqZIyJ5NGJRUcUAak9Gf46Dfmk2rIs5Tl7+9PP/30k7zP9IMDX/P8VK6pUk+gTp06yf5ZWZwhQ4ZQ/vz5DWZMli9fnmbOnGl0f51ORxEREfL7USny16dPH7kPBwJMr1WrVrJugxKMq1mzJi1cuDDBAGJi1yM2NpbatWtHefPmpWXLltGxY8fo9OnT9Ouvv8q6ILly5eKC7SZiLOim/5y8ceMG9enTx6BuhyIkJIT8/f2pSpUq5OHhQUIIcnJyorFjx1LZsmXJwsKCfvnllwQrQFjyGj58OAkhKG/evAnuu2vXrlH58uVJCEGVKlUySCMTf6D56dOnRovbtm3bloQQ1KxZs0TrFrCvp6SWzJs3L929e1dejytXrlCLFi3I0dGRhBDUrVu3JCc2+Pv7y+ewfmBACQo4OTlx+koTCw4OpufPn1NISIjR1aRKcECpOaCkBPLz85PXPal+BPcxGGPfGg4MMGYCiQUFdDodjR49WhZKjIyM/E/HVzp59+/fp9q1a8sZ0Dx7K3k9fPiQRo8eTdbW1kkGB2xsbEgIQfnz55crN+I3+vQ75tWqVSMhBE2cOJE0Gg1fNxPRarWJBgfKlClDx48fNzrDhxvs3wb9++K/BAeUgavY2FiDQSyl8z59+vQE/x32v1NmhDs4OFDRokXl804IQVmyZKF169Yl+t6AgAA5Y7latWrydR6ENL3Y2Fj65ZdfyNLSkgoVKkTDhw8nBwcHmbfc1dWVJk+eTP/++6/B++Lff6dPn5YB9XTp0hkEh2xtbal69eoynQZLfm/fvqW3b9/Sw4cP6fnz50b3iR8c0H+eEsUNeh0+fFjOWjc3N5eBIldXVy7wbULPnz+nkiVLkr29vcxDr7RJlOegsuotqVRADx8+pN69e1OhQoVo3bp1tGvXLtqyZQvVqVNHpiLl+zD5KO2KYsWK0ePHj4mIDNr3d+/elQEdb29vg9RA8Z09e5asra3Jzs5OBgCUoDkHBUzr33//pSFDhpCLiwvZ2dmRp6cn5c6dm1auXEkREREG+yrBAaUAu5WVlZx0FH/FHGOMfes4MMBYMouKiiIfH58EQQFldohSfDZjxox07tw5Ivpvgx7xc6TzYKZpPHz4kEaNGpVocODFixfUtGlTsrKykgWJlaXcxmbrderUSXYM9POgs/9NTEwMHT58mNavX0+bN282Ohs1fnDA1tY2Qfogvo++LfGDA02bNpXBAf2ZrvEHJ8PCwmjv3r305MkTIop7xn78+FEWaCxUqBDXhjABZXAkW7ZsdOnSJTnTbsWKFbJIcGI1PJRr3b17dzIzM6O6devS69evOXiagpSZqkIIWrt2Ld29e5datmxJhQsXJiHiCtSam5tT+/btac2aNUZnG7969Yq2b99O3t7elDlzZhkkqlu3Li1dupTz0ZvIyZMnacqUKVSiRAnKnj07OTo6Us6cOalOnTo0a9Ysg1ReRETXr183CA707t3b6HE3btwoc5orP8pkFJb8/v33XznRJCQkxGj/4OnTp5QrVy4SQtCSJUsSbNdoNLRhwwbKnj07CSFk+1UJ7vCKneSlHxRQ2hz6bRLlGm7fvp2EiCs0qxStNUZ5Dtva2tK+ffuoX79+HBRIAX379qWMGTPK55ydnZ28d+rUqSOvLRElSCukBAdcXV1lvQBON8oY+55wYICxZNajRw+5xFcJCug3Dp48eSKLoY4bNy61Pib7CvGDA/Xq1Uuy5oCvr6/M/ak/K0hZBuzu7p5kp4B9ndmzZ1O9evVkY14ZOJ4yZYosbBl/5YCTkxMJIcjR0VHO7uIZPt8m/UHh48ePU9u2beU11h9k1n/Obtu2jdKnT0/Ozs7UpUsXatSoERUtWlTmg719+3aKnkNakNiMScXSpUtlsb74g8P613jlypXyXt62bVuKfHb2yYABA0gIQc2bNyeiuBzIjx8/poEDB1Lx4sXltbGwsKD8+fPT3Llz6cyZMwmOExUVRU+ePKGLFy/SvXv3Uvo00pRBgwbJQWBlhr/+QLCVlRW5ubnRtm3bDFLLJBUcUGrwKPbs2UOdOnXiAWUT27RpkwwMJLUyQ1mVPHLkSKPbHz16RBMmTKBKlSqRg4MDWVhYkI+PDw0ePJjrCiSjzwUF9G3dulXel4nVV9HpdHTmzBlSq9WkVqupUKFCsmYBBwVMR6n/5+zsTGPGjKHdu3fTnTt36J9//qHevXvTrFmzEn1v/LRCGTNmlH08Dg4wxr4XHBhgLJkFBgZSr169ZEoZY42CwYMHyxUFb9++5TQJ34F79+4ZpBWqVavWZ4MDV65ckdu7du3KM35MoEOHDiSEIBsbG8qRIwcVKFBAzlJt1aqV0Rk+8YMDpUuXlgNX3IhPPUnNCtfPx6vMXjWWVig2Npaio6Opb9++Mp+v8pMhQwaqUqUKB+VMIKnBEeV/w8LCyNXVlerVq0ehoaH05s0bunPnjtFVOkoKk+LFixvcw8z0lIFJOzs7OnXqlMG227dv08qVK6lixYry/rKysiJ7e3saOnQobd261egxlTYOr/xIfg0bNpSrMlq3bk3Lli2jXbt20dq1a2nmzJly9YByTSdPnmww4z9+WiH94ED8ezOp9Cfsf6PcI0eOHCFra2vKlCkTBQQEEJHhfaPT6Sg6OloGuvv27ZvgWPHvs8DAQLpz5w7pdLokc9uzr1OhQgUSQlCBAgVkIM1YG1K5j/744w8yMzOjli1bJnnciIgIKlasmLwnHR0d5Sx0lvyUoECePHm+KPD55MkT+vvvvw1ei1+QmIMDjLHvDQcGGDOBzxUeWrFihexQKw1/lno+N1ihbP/w4QNVrVpVDkjWqVMnyeBA9erV6fbt27LTzUGB5NWgQQMSQpCnpycdPHiQgoKC6OPHj3T58mWaMmUK7dixI8F7kipIrAyWcCM+ZV2+fFn+buxe1A+c/v7773JGbPny5Y0GB4jiOmnnzp2jIUOGyJ+DBw/KFSQs+dSoUYOEEFSqVCk5E1X/HlJ+v3nzJrm4uFCuXLnI29ubcuTIQS4uLlSxYkVauXIlvXjxQu4/e/ZsUqvV5OHhQfv27UtwTGZaSh7y+vXr04cPH+R9qdyL+qs6lBWQSuqLKlWq0KpVqzjdTApo2bIlCSEod+7cdP78eaOr3gIDA2natGlyINnGxobGjx8vJ68QJR0c4PvOtAYOHGgQ/AwPD5dpgurWrZtoP0IZlB4xYkSCbcp9qlw7nU4n72EOziWPdevWkVqtls9AZXVHYvXF7t69SxkyZCAzMzNavHhxosfV6XQUERFBzZs3lwPMHBQwnVGjRpEQgrJmzZpgpaN+kXXluj58+JAKFixIrq6utGzZMoNjcXCAMfY948AAY8lAv/HwJbP/dTodVapUiYQQ1KxZM3r37p2pPyL7jMSum/7r06dPJyEEqdVqsrS0NFpzIH5wQMk7ycuAk5eSsqtQoUKyMf8l11C/U5xUcIBX8aSMatWqUeXKlenIkSPyNf1rpH8dpk2bJu+/Xbt20bVr1xItSMyDHymjbt26JISg9OnT08GDB4nIcHWH/vWbOHGiQRoaMzMz+RzNli0b9e3bl4KCgoiI6OXLl5QtWzYSQpCPj0/KnlQapgxe/P333+Tg4EB58+aV31vKdT148KC8jt26daNdu3ZRv379yMrKiqysrOSAiLm5Oc2fP5+fpSYyfvx4EiKuoLcysBy/rpHy7/DwcNqzZ48sfmpra5ugCHj84EC/fv1S7mTSqJo1axqk7VKu29atW6lGjRo0ceJEo+/TarVUpkwZEkLQ77//brBNfwBSKZbK92DyCwoKokmTJhmkj7lz5w4RJVwt9+jRI8qfPz8JIahr167yGEm1U06cOEFubm6yZhlLfufOnaM8efKQg4ODnKRnbABf/zoWKVJETk7x9PSk5cuXG+wbPziQVNooxhj7lnBggLFk8Pr1a3r//r3Ba0kNUmo0GjlLoUCBAjKNCTfeU1b37t1pzpw58t/x//76/546daoclFy4cCFNnjw50YLESnAgXbp0vFLABHbv3k22trbk5uZmdJZ//IZ9ZGQkhYWF0ZMnT2RHWWEsOKDMeub70bQWLFggUwI1bdqUDh8+LLfpB1uJDIMCW7Zska8fOXIk0eBA/JQJHCxIXm/fviVfX1+ys7MjtVpNNWvWlOnTdDqdwX2oXGsl9cW6deto//79tGDBAnnvZcuWjTZu3Cjfs2TJElkT4uXLlyl+fmnZo0ePZGBG/57SDwrEX6Vz4MABGjNmDLm5ucl9rl27ltIfPU24d+8eeXt7U7p06cjf35+IPj8jNTY2lg4fPkwlS5aU6UmUgUyFEhxQahQMGTLEZOeQ1lWvXl0OHDZu3Nhg2/v37w3q4MT/7nr9+rVMmfjbb7/J1/X/P9C7d2+qVasWhYSEmOgMWFIzxJXUQvqDyQ0bNpTvTep+Vdo+4eHhpvvwTPbrOnToQOHh4Ubb/MaCAsWKFaOCBQvKFcvGggOTJ08mBwcHEkJwTSvG2HeBAwOM/UcxMTH066+/UqNGjcje3p4yZ85MLVq0oAULFnxRQ/zevXsyx/mXziBhyUfJiV28eHGDRp2xlR/6g5JKMcyHDx/SiBEjkgwO1KhRg9zd3WUhYpY8Bg0aREII+vPPP4ko8Q7WpUuXaMmSJVS8eHHKlSsX2draUrVq1Wj69OkG+8UPDuTKlUuuQmCmpRQ6FUJQkyZNDIIDCv37T8lhrv+c9Pf3TzQ4wMu3TSsoKIiaNWtmtPC6YvHixfIaG0uhcOXKFfldWKNGDfn6gQMHKEuWLPz8TGHKvbVq1SoyNzen7NmzU3BwMB06dEhex169esn94wfgnjx5Qr/88gsXHDYhpZB35cqVvyo9WmRkJC1evJg8PT3lwL8yWUVx8+ZN6tixI6VPn54uXbpkgk/PlPZnpkyZZJsjODjY6MCksT5BcHCwvIZKOhP9+7Bbt24khKDMmTPT8+fPTXcizGhwQJkIdP/+fTmYXLduXfmexNJDsZSh1Wrp/fv3cnB/1apVie5HFNdHUFKx1a9fn4iINm7cKFeBeHp60oIFCwzeGxQURL///jvXtGKMfTc4MMDYfxAdHS3zKis5JpUfMzMzKlSoEJ0+fdpovleiT42NuXPnkpWVFXl4eCQoZMRM68SJEzLtQZEiRQxyReoX2DM2KKl4+PAhjRo1KtHgwMuXL+np06emP5k0QqfT0cuXL2X+3ZMnT8pt8TvU8+fPpyJFishrrPwog8dDhw412P/Ro0eyqK1araYHDx6kyDmlVfod4/79+xsEBw4cOCC36a/U0b//9HMmEyUMDrRr1y5lTiQNGj16tKwHQJQwfVqVKlVkAb9FixbJa6vf+VaunzIgOWPGDLKwsCA3NzeDfNuhoaEpc1IsgYsXL8rBrp9//tloUCD+czd+ChuWvJS/a/v27UkIQRMmTPjqY7x48YLKlStHQggqX768fBbrX7Pbt2/LtF4seelPSjl48CBlz56d3N3dv2oA/8aNG5QhQwaytLSkf/75x2CbEhRwcnLi3PTJSL/NEb+YevzggJubGx0+fJhKlCjBQYFvVHBwsJyQcOrUKSIy/r0VFBREWbNmJSEE1atXz2DbmjVrqEiRIqRWqyl//vwGKyaJeNUxY+z7woEBxr5SbGws1atXj4QQVLRoUdq3bx+dOXOGDh06RJ06dZL5Jj08PGjx4sVJpkA4e/asnLHQrl07g8EWZnpnz56VgZ34wQGiT4OSFhYWcqUAkeEs5PjBgYYNG3KH2oQ+fvxIRYoUIQcHB7p48SIRfboesbGxtH//furUqZNBMCBnzpxUsmRJat68uZy9JYSgv/76i4g+NeIfPHhAffv25WW/KURZak9kGBxo2rQpXb582aCmR/yggLHf/f39ZTFOIQTfhyag1BRo0KBBkrVVatasSWPHjk00KBDfxo0b5bW+c+eO0cFKlvIGDx5s8CxNKijAUkZMTAyVKlWKhBA0Y8YMIvrya6HcT1u3biULCwuys7Oj8+fPJ9jOTEMJChQrVkwGApSZ/zt37iSiL7sGp0+fJgsLCxJC0K5du+Tr+kEBTl9pevrXSgkOKH1A5fr4+fnJ/Tgo8O0ICgoiFxcXsre3p3PnzhGR8efopUuXyN3dnSpWrChf02+7KkFza2vrBKnZGGPse8KBAca+0po1a0gIQV5eXgnqCoSEhND+/fvlLJGMGTPS5MmTk5w1Pn/+fNnpnjlzpqk/PosnICBABgcKFy5MK1asIKJPhYbjBwWMddqU4IBSU6B58+acwsQEdDodhYaGkre3NwkhqEuXLhQVFUWhoaH08uVL6tSpExUuXFjeTxkyZKCmTZvS3bt3ZYHv8+fPU7t27UgIQYMHD5bH5Y5byrh3756s4RCffnBAuY5qtZp27Ngh9zF2/+m/dvDgQerQoQPPlDSBoKAg6tOnD9nZ2SVZW8XS0lKu3BBCGNSEiN/xVtJfrF69moQQVKVKlZQ5GZYk5TrduHGDvLy8DFIo6G9nKS82NpYqVqxIQgiDGklf49SpU2Rra0tqtZpOnz6dzJ+QGaMfFFBWRYWHh1OxYsVICEFjxoz54mOdO3eObGxsSAghr1+XLl04KGACISEhtHXrVhozZgw1a9aM2rRpQwMGDKB//vknwcrSly9f0sSJEylLliwkhCArKytZeDaxFeQsdbx+/ZocHR1JCEG//PJLovvFxsYatCeVPoLSdhkwYACZmZlR06ZNTfuBGWPMxDgwwNhXGjZsmMFMLf2ZA4rnz59T+fLl5eDklClTEqwc0O9Y9+jRQw6iLFy40LQnwBLQDw6UKFGCmjRpIoMC+oNaSc3kCgwMpP79+5O7uztdvnw5JT52mrVu3Tp5v3h7e1PhwoXlUl/lx8/Pz2AFiP79tnz5chIirvD3x48feZArBcyePVveV+nTp6cyZcrQb7/9liBIoB8csLCwoF9//VVuSypoo39vRkZGJv8JMCKKC4KOHj36s4XXlUGr/Pnz06tXr4go4fXTD55Wq1aNhBA0ceJE0mg0PHP5GxEeHk61a9cmIQSVKVMmQQoNZnpKyjz976lWrVqREIJq1apFb9++/epj3rt3j1xdXcnS0lLOlmWmo6y00g8KKM/DiRMnkhCCateuTdHR0V/UHjl69CipVCqys7OjAwcO0MCBAzkoYALTpk0jX19fg7al/k+pUqVo6tSpBu+JHxxwdXWVA8s8YSh11KtXT64QJor7/oqNjZXfbY0aNZLP0aS+2+JfvxcvXsjrPHbsWJN8dsYYSykcGGDsCykNAj8/PxJC0OTJk43upzQqwsPD5ayujBkz0uLFixPMGFE6ANeuXaPGjRuTEIKcnZ0pMjKSO94pLCAggMzNzWWD39zcXM5U1mq1X3Q9Hj16JAfBmOm8evVKBuj0f2xtbcnFxYXmzZtnkJZLuXeV++3ChQtkaWlJ1atXT5XPn9Z06NBB3lPKkmvludigQYMERUr79Okjr2mrVq0MChIndR/yMzNlfK62yosXL6hp06ZkZWUlCxJfvXqViD5dI/0OtpL6y9vbm549e5ayJ/MDGzNmzP9UPFa5VmfPnpWF2eMPgjHTKlq0KJUtW1b+W7kmI0eOJCEEZc2aVd5bXzPoePHiRbK1tSV3d3cuEm1iyuBjqVKlZFBAP/iprJaytrama9eufdEx7969S25ubmRubk7ZsmWTfQcOCiSfFi1ayOuSPXt2at++PXXq1IkqV64sV4UrPx06dKDAwEB5TZXggJJWKGPGjLIILQcHUladOnVkYPvjx48G7cSZM2fKa7hkyRL5+ufakso1/Pvvv8nKyopKlSol6yFxO5Qx9r3iwABjX0kZkBw0aBARGV9SrzQaIiIiyMfHR9YcUGZ+xW8YarVa2rx5M/n5+dHDhw9NfAYsMQEBAbJYbfbs2WnTpk1yG88q/7a8evWK1q5dSxUqVKBKlSpR9erVadmyZQb5kokMG+nKfTd79mwSQlD37t1T9DOnRUo9Fnd3d1q6dCmtWLGCFixYQC4uLiSEoHTp0lGPHj3o3bt3Bs/Ffv36Gaz++NLgAEsZ8YMD9erVS7LmgK+vL12/fp2IDIu7Kzmx3d3d5cAJ+98pKzAaN24s/+7/hVLwXRncbNKkiVwlyfehaf3yyy8khDAawL5//z5lypSJhIgrIKxMOvncoKOyXamf1KRJk+T/4EwKDw+nESNGUPHixenx48dElPAahYWFUdGiRUkIQZMmTTK6T3zv3r2Tg85CCHJ0dOT0eclImQCWOXNm2rVrV4J+WVRUFI0dO1amtVRqI+kH2eIXJObgQMpT0neVLFlS3n9En767Xrx4QVWrViUhBNnY2BjU60js+025djqdjqpUqUJCCBo9erTR7AGMMfY94cAAY19JSUMihKBTp04lup/SeAgKCqKSJUuSEILy5MlDr1+/Nrp/bGwsRUREyN9Z6ggICJBFw4oUKWIwi4SDAynvc4NPStqY+PvF/7d+geLy5cuTg4MD7du374v+G+y/UTplRYoUkTMlFffv36e8efOSEILy5s0rO9T6zz79tEJNmjTh4MA35t69ewZphWrVqvXZ4MCVK1fk9q5du3L6CxP48OEDjRo1iszMzEgIQQ0bNvzimciJWblypbwX//zzz2T6pCwpixcvlgNW9+/fN0jjFBUVRSNGjCB7e3sSQlC7du0+GxxQnq0ajYbKlStHdnZ2cvIDP09N58OHDxQWFkZECa+NTqejsLAwGUCvUaPGZ4+nXCslcMRBgeTVt29fEkJQrly5EqQ6jI2NNbiG+/fvl2m9hBDUtm1bg/05OJB6jNX0MDYpb+rUqeTk5CTTi+7evdtgH/1no/7vrVu3lisRlELijDH2PePAAGNfSD9FkJJzsn79+kkuw1bec+jQIcqdOzcJIWj69OlEZDjIzJ2yb8vZs2dlzYEiRYokmqueJT8/Pz8aP348/fvvvwavJ9aJUq6HsXRP+tsUSmO+fv36/yk3M/syxjplSrE2ZYDqwoULsmD3tGnT5Hv1rzUHB1LH5/62yvYPHz5Q1apVZbHhOnXqJBkcqF69Ot2+fVumi+KggGmEhITQb7/9Ju+dBg0a0LVr1776nlH2j4yMpDp16pBKpSIfHx+D2ZcseSl/8xMnTpCFhQVZW1sbrVt07tw5OenE3t6eunbtKmetxq8FoX/d27RpI1cicOpD0/mae+3QoUNkbW1NarWaNm7c+EXv2bNnD+XNm/d/WhHEDO3du5dcXV0N0jIZa3vqtykvX74s0+EZyzUfPzhgZmYmCxIz0/iSoIByf0ZHR8tnokqlIkdHR1q3bp3R48bExNDLly9lvaxMmTJxKjbG2A+DAwOMfQWNRkMxMTE0efJksrW1JUdHRxo9ejQFBQUl+b7Q0FDq3bu3XG7Kvn36BYk5OJAyqlevTkIIsrS0JCEEde3alZYvX26QeuRzPnz4kGD2jlarpaioKGrZsqXMy/zgwYPk/vjs/31pp+z9+/dUqlQpEkLQb7/9ZrA9qeDAkSNHTH4OLPHnnP7r06dPJyEEqdVqed8mVpBYCQ4oxfo4J7ZphYSE0LRp0/7n4IBi0qRJMs3emzdvkvnTsvg+fvwoJ5QsWLCAiBLek//++y+5u7vLQa3atWvT06dPE6S1iI6OptevX1OzZs1kakse0DKdmJiYBO2WxO47nU5HDx8+pGLFipEQgvr27fvF/50PHz78T5+TGRo6dCgJIWjUqFFElPSsfv3reezYMZm+LWfOnLKgt7JPcHAwTZ48mRwcHEgIQbdv3zbhWaRtn2t/6j9DldejoqKoefPmcvWjEIK6dOlCCxcupCdPntCtW7fo1KlTNGzYMCpcuDAJIShHjhx069atlD05xhgzIQ4MMBaPVqulmzdv0vLly2nmzJn0xx9/0NatWykqKko28kJCQqhu3bokhCBPT0+aPn16oimCFCdPnpQ5tYODg3lw+TvAwYGUc+/ePdkgVzrIyk+pUqVozpw5CeoHxO+0RUdH04ABA6h+/fq0ePFiun37Np09e5YmTJhAZcqUISEEZcuWjRvzJlSjRg15zZRl+Il1rl++fEleXl4khKBVq1Yl2G4sOKBWq6l69ep07Ngx05xAGte9e3eaM2eO/Hf855z+v5U85Wq1mhYuXEiTJ09OtCCxEhxQVojwSoGUkVhw4EvpdDq6dOkS7d27l54/f06+vr508eJFE35iRhT37IuIiJDfWwMGDDDYrj8ouWvXLsqVK5esj5QzZ07q0KEDrV27lrZs2UK7du2i7t27U5EiRfg70MRWrFhBvXv3Jm9vbypdujQtXLhQFof+nMmTJ8v79HPBb2MrQdh/p9RSUYJs+rnmP/c+xbx582SAbu3atQn2DQoKot9//51r6ZjQ1wQFNm7cSCdOnJDbo6Ojafjw4XIVlvLj6upKlpaWclVkxowZqVmzZlwPkDH2w+HAAGN6oqOjqVu3buTp6WnQMFAKvK1atYqePn1KRHGDWsrMgezZs9OMGTPo5cuXiR773r17ZGNjQ2q1mp49e8YN+u+EfnCgRIkSNG/evNT+SD8c5V4YNmwYmZmZ0ahRo2jv3r3UokULcnNzk6sIrKysaPTo0QZFofXfv2fPHoN71t3dnWxsbEgIQVZWVlSlShVeKWBCSrA0ffr0dPDgQSL6lD5In9IRO3jwINna2lLJkiXpzp07Ro+p36kbNGiQPL7yHGbJR+lUFy9enJYvXy5fN5aSSxlsVqvVtG3bNiKKK0g8YsSIJIMDNWrUIHd3d05/kYL+68oBnU5HZ8+eJR8fHxJC0OzZsyk8PDyFPjUjIho7dqxMfUeUeL7r48ePU6dOneT3pf6PMqDl7u5OP/30Ew9omYiSplD/x9LSklq3bp1kcEB5rr57904G1tu2bUsvXrxIqY+epin30cOHD8nGxoYcHBwoMDDwq99P9Knoe4MGDQwmkyl4QpHpfE1QYMmSJfKZ+Pr1a7mfRqOh8+fP0+jRoylfvnxkbW0tA6558uShdu3a0ZEjRygkJCRlT44xxlIABwYY+3/R0dGydkC2bNlo+PDh9Ntvv9HAgQMpY8aMcnVA165dZaGv58+fy6Xenp6eNG7cONnp0ul0skgcEdGlS5fIzs6OSpcuzUWnvjMBAQFygLlChQr07t271P5IP6Rt27bJgQylSOnNmzepdevWVLx4cYMOd7169WjZsmUJOnDDhw+nihUrkqWlJVlaWpKFhQVVq1aNFi1a9NmUX+y/CwkJIV9fX7KzsyO1Wk01a9aU11C/c6w8+wIDA2UA9tdff03y2PrPy5EjR9KlS5eS/wQYnThxQnaC46+Q0k+LoR8U2Lp1q8ExHj58SKNGjUo0OPDy5UsO6phIUgP9XxscUIICFStWJCHiCpxyXuyUN3/+fDlr1djEE/3rFxoaSteuXaMuXbpQzZo1KUuWLOTi4kJVq1alXr160alTpyg0NDQFP33a0ahRI5lzfNy4cTRjxgyZulCtVtPixYs/ewyNRkPjx4+Xx9myZYt8nZmGfpvw8uXLZGlpSZkyZfrq2htK3aTu3buTEIIqV66crJ+TJa1mzZpyUoPS3kgsKKAUdRdC0JIlS4zuQ0T06tUrevjwIV2/fp2uXLnC9VgYYz88DgwwRnENgs6dO5MQgkqWLJkgf+6DBw9kehNHR0eqU6eOHPR68eKF3Obs7EzNmjWT+SX11a9fX+Yt/Jqc6ezbcPLkSXJxcZFBIWYarVq1IiEENW7cWN6HMTEx9OjRIxo7diwVKlRIDjra2tqSm5sbzZgxg/bs2SOP8e7dOzpz5gydPHmSTp06xbO0TEwZnAoKCqJmzZrJXPK+vr5yZrhWq5Wd50ePHsm0Fq1bt5bHSeo68eBIykiq8DrRp/RBFhYWcqUAkeH1iR8caNiwIQflTOBrC7V/Lq2QfnqSs2fPUoUKFUgIQS4uLpz2KQXoXzflWty4cYPc3NzIw8Pjq2f6v3//nme2poDRo0fLlcPxi3IrRWkrVapEGo1GfgcSGQ/kvX//nkqUKEFCCCpcuLBsA+m/jyWPevXqUdGiRenZs2dEFNfPU+rkrFmzhoi+PFWTst/s2bPlAHVYWBivDE8BderUISHiCrDr16oyttJRPyiwevVq+bqxlVjx26Ocvosx9qPjwABjFNf58vLyIkdHRzngrwzeKw3ywMBAmX/SxsaGatSoITvVL1++pPr165OtrS0JIcja2prGjRtH8+bNowULFshZd3ny5OEBku9YZGRkan+EH5bSCN+wYQPZ2NhQjhw5ZIE2/YZ4eHg4DRkyxGD1gIWFBanVaurQoQP99ddfRld0cGPeNPSXYBMlLDSrHxwginuOKinY6tatm+A4LPXpp08rXLgwrVixgog+FRqOHxQwdm8pwQGlpkDz5s35GiejrynUrn99PrdyQKPRGAQFuEC06cydO5c2b94sV2IYuz9evHghi3WvW7eOiJL+LlNWqia2jSWviIgIqlSpEtnY2Mi6N7GxsbLfsHHjRjIzM6MRI0YQESVZkFi5/pcuXaKsWbOSEIJ8fHy43WkCStqZ0qVLy/pwr1+/puzZs5MQgoYMGSL3/ZL7RtlH+Y5s0aKFaT44S+D48eNyRXeJEiUMVufop7LUDwro17SKf30/92/GGPtRcWCAMSJas2YNCSEod+7cRpcLKg32efPmkbm5OanValKpVNSsWTPZqQsNDaWpU6dS5cqVE+QZVVYiKCkUeICEMeNCQ0OpUKFCJISgDh06yNeVBv6hQ4fkPdWqVSvy8/Mje3t7+ZqDgwOVKFGCpk6dSpcvX06t00gzjOUcf/HihUFwoEqVKhQUFETv3r0zGhTg2ZDfnvi1VZo0aSKDAkqKC6KkO82BgYHUv39/cnd353sxGf2XQu3695ix4MCNGzdkfmUOCphe6dKl5d/fxcWFihQpQk2bNqUFCxbQ33//TW/fvpVpfzp06EBCCJo1a1aqfmaW0MWLF0kIQXZ2dnTr1i35PFTaK8uXLye1Wk2VK1emYcOGUenSpalHjx70xx9/JHrMyMhI+u2338jDw4OEEPTTTz+lyLmkFfq1dJRc9Mp1Gzx4sLwvt2/f/sXH1Ol0FBERIWevjxo1yiSfnRl39uxZMjc3T3Sl46JFi+R1VVaDECVsv+j3zZWVJIwxllZwYIAx+lSIqEiRIvTu3btEU1r89ddfJISg2rVrk4eHB6VPn55Gjx5tkPLk7du3NGPGDOrcuTOVKVOG2rVrR3/88YeclcJBAcaMU+6NdevWkY2NDeXLl48uXbokG+8HDx6UjftevXrJ923ZsoWGDRtGKpVKrtrx9PSkR48epcp5pAUrVqyg7t27U86cOalx48Y0ZMgQCgkJkQMi8YMD5cqVIy8vLxJCUJ06deRxOCjw7QoICJCdbSEEmZub044dO4goboXPl8yke/ToEefmTUb/a6F2xZs3bwyCA/Xr16f169dzUCAFzJ07l1xcXMjV1VXmoI8/kSRfvnxUvHhxmjVrFpUvX56EENSxY0ci4mfmt+Tu3btka2tLnp6esliw0n+IiooyCACZmZnJGkpKgWFlVWR8gYGB1KpVK/L09EyycDH7OokVqFWu2dGjR+XElGLFitHx48flexP7vlPuR6XmXO7cuenu3btJvoclv8RWOi5btuyrgwKdOnWihg0bGqx2ZYyxHx0HBhijT7MJnJycjKYvUQa71q5dS87OzrRx40Zq37697MCdPHmSiBIO+iuFhxWc65yxz7ty5QplypSJhBAyZ6h+UKB3795G33fq1CkaMWIElSpVijvTJtSpUye5dFv/p2LFirRr1y65ikBJK6QUtFVyLSv0l3mzb1NAQIC8ftmzZzcYaObvs9STHIXaX716JWtGCCHkDGUOCpjWhw8fiChu8Hf79u00a9Ysql27NpUvX57SpUtHFhYWBmnylN/z5MlD79+/JyIODnwrQkNDZaqnGjVq0OvXr+nJkyd0584dGWTLkycPjRs3jjZs2EC//fYbdezYUV7T5s2b08ePH4ko4UDl9evXuVB7MqpRo4ZcKaDMBo/fZ4uNjaWePXvKlLD16tWj06dPy+1JDSYrq+o6duwo73GWsuKvdGzbtq281/7880+5X1LXUSkgnT59ehnsY4yxtIADAyxNUxoHz549o3z58pEQcYUSX758SURxjUT9wY+aNWuSq6srxcTE0KtXryhnzpwJ0mLoFygyVvyIMfZ5kydPljN/5s2bZzQoYOy+iomJkR1tlvzq1asng6i1a9emVq1aUb58+eTgcenSpenChQtyfyU4oAQSfH19ZYFGnk33fQgICJADlEWKFKElS5bIbfzdlnr+a6H2ffv2yWNERkbSxIkT5fXNmDEjBwVMpFWrVnTw4MEk97l8+TIdPnyYpk2bRi1btqSKFSuSk5OTTJdXuHBhWVCYgwMpz1hNgJkzZ5KTkxMJIShHjhzk4eEhJzbkz5+fnj9/bnCM9+/fG6Q2GTNmTIqeQ1qk1GRxdXWlQ4cOydf1r6f+Sg8liGBnZ0eVKlUyeGYSUYIi0soAdM6cORMEYFnKir/SUQhBa9euJSLjKx31gwJdu3bl70HGWJrFgQHGKG4G16BBg8jOzo4cHByoS5cuFBwcTERxjb7o6Gjq0qWLzGuuzAbZu3cvpUuXjtzc3OSsPcbYf6c02i9cuEA5c+Y0mJmunz6IByRTXs2aNUkIQV5eXnTz5k3ZOb5x4wbNmDFDXis/Pz+D98UvSFylShW5RJuDA9+Hs2fPypl48XP48r2YspKjUHunTp3or7/+orCwMIqJiaEJEyaQg4MDr7QykapVq8qB4xMnThhs02q1id5DMTExdOnSJRoyZIhMFVW4cGFZf4CDA6nv3r17NGzYMEqfPr28z+zt7cnOzk7ONtdoNAlWIQ8fPpxUKhX5+PhQREREan38H56SPkhZYTVs2DC6du2a0X2VQeKPHz/KFF4qlYrMzc1p0qRJ9O+//5JGo6F3795RYGAgrV69WtaVc3Nzo1u3bqXkqbFE6K90zJo1K/39999ym/6z1lhQwNnZma8jYyxN4sAASzM0Gg1dvHiRFi9eTHPnzqX58+fTvXv3KDIykojiluHXrl1bzpzLmTMndevWjfz8/Mjb25uEEOTu7m7QAX/w4AHlzp3bYEYCYyx5NGvWTHboOnfuLF/ngciUZ6xgn34qoODgYPr555/JwsKCPD095aw5peMVPzjg6+vLwYHvjP4yfQ4OpL7/tVB7+vTpqVSpUvTbb7/R4cOH5Ux0lrxmz55NQgiDgar4wYH4lPtJeTY+fPiQBg0aJOsScHAgZV29epVWr15NkyZNotWrVxuklyGKuxcvXLhAs2fPpk2bNtHUqVPJzc2Nnjx5kuj326ZNm0gIQTY2NpwyyESUdotS3Ft59vXt2zfRGeHK/fTx40dq3bq1TBWlBFbz5MlD7u7uZGdnJwMHPj4+sq4A+zacOXMmyZWOHBRgjDFDHBhgaYIy4z979uwGs+eKFStGgwcPlh3iGzduUIcOHShbtmwG+9nY2FChQoXkQJZ+g0KpNaAUOmKM/W+UQZHLly9TpkyZyMbGhubPn09EPAiSGhIr2BffunXr5GzJe/fuydeV68nBge8fBwe+DclZqD1LlixcqN2Edu7cSc7OzmRubi5XVWXPnl3WpvpSDx8+pCFDhhgEB969e0dExp/HLHn06tWLcuTIkaCmzuTJk2Vh9fgpaerXr0+urq5JHvf+/fvk5OREjo6OMuc9Sz767RYlnVObNm2+KjgQExNDW7dupZ49exqsCFFqENStW5cWLVpEQUFBKXZe7MsltdJRwUEBxhiLw4EB9sOLjo6mKlWqyLyB3t7e1LhxY5l7187Ojnr16iXTA71+/ZoCAgKoV69e1L17d+ratSutX79ezuhROmCxsbEUExND1apVIyGEHLhkjCWP4OBgee82atRIvs6DyCnnS4ICyvXYv38/CSHI0dGRHjx4YLBPYsGBGjVqcBq270z8An/z5s1L7Y+UZnGh9u9D8+bNSQhBZcuWpVy5csm0Qv9rcMDT01MWJGbJr379+iSEIAcHBypdujRVrVqVChQoIO+vsWPHyn2V70GtVku+vr5kZmZGBw4cSHBMZdB5//79ZGZmRmXLluU2TTJT0vvot1uI4tLGtmvX7ouCA/ED3Xfv3qVz587R/v376ciRI3JSA/u2xZ/MsHjxYrlNSRHMQQHGGOPAAPvBxcbGkp+fn2wg3rp1SwYATp8+TXXq1CEhBOXJk4f279//xcdUBsbCwsIoX7585OnpyYNbjJnAzp07ZSdu0aJFqf1x0hSlpkDx4sVlzRVjM1P1izDGn6Gsv10ZEFGCA8pS/AYNGlB0dLQpT4Uls4CAADn7uUKFCnLmMkt5XKj926X83QMCAsjDw4OKFClCW7dupRIlSvxPwYHhw4fLwa779++b4qOneR07dpT9g2vXrsm0o1euXKE+ffrI+2zz5s3yPTqdjqKiomTamr59+8o+B5HhikelbzJ06FCjRVHZf7Nq1SoSQlDevHkTTOgi+rrgANGngE/865PY6+zbox8cKFasGK1YsYK6devGQQHGGNPDgQH2Q1u5ciUJIShXrlz0+vVrIjJsxF24cEEOTukPZul0Ormf0pC/e/euQQ7J6OhoatiwIQkhqGHDhgaNf8ZY8nj16hXVrl2bVCoVNWnShN6+fcsdsRSgBE3t7e3lTGSihAOMSof77t275OTkRLly5aIVK1bQmzdv6NWrV/Tx40ej6Z+ePXtGtWrVogwZMtCNGzdMezLMJE6ePEkuLi58/VIJF2r/foSEhMhn6ujRo2n//v2UP3/+/xwcuHfvHv3yyy+y5hVLXhs2bCBLS0vy8PCgx48fE9Gn2h06nY4ePXpEvr6+JISgtm3bUmxsrMF9pgxOKwPPyvWNjo6mN2/eUKtWrUgIQV5eXvL4LHm8ffuW5s+fbzQooPja4AD7/ukHB5RC7hwUYIyxTwQRERj7QXXr1g3Lli3DjBkzMGDAAMTGxkKtVgMAiAhCCAwaNAizZs1CqVKlcOrUKahUKgghDI7z/PlzeHp6Qq1Wo1mzZrCwsMDVq1dx4cIF5MiRA8eOHYOHh4c8JmMs+UycOBFjxoyBWq3Grl27UKNGjdT+SD+8EydOoGbNmvj48SOKFy+Obt26oVu3bgAAnU4HlUoFrVYLMzMzBAYGolatWrh37x5cXV3h5OSEqKgo6HQ6mJmZwcXFBRUqVICbmxt8fX3h7u6OjBkzIiIiAu/fv0emTJlS+WzZf/Xx40dYW1un9sdI85o3b47NmzcDADp16oRly5YB+HSvstSjtAvPnDmDSpUqoXTp0li1ahVu376N/v374969e8iePTvWrFmDcuXKffFxNRoNzM3NTfjJ064hQ4ZgxowZGDduHMaMGZPgb01E6NKlC1auXAkvLy+cPXsW9vb2BscYMWIEpk2bBgDw9PSEp6cnoqKi8P79ezx48AAeHh44dOgQvLy8UvTc0gLlnlPaKMaEhYWhd+/eWLNmDQCgT58++Pnnn5EvX76U/KgsBZ09exZVq1ZFREQEnJyccPz4cb7ejDH2/7i3wH5IOp0OISEhOHXqFADA2dkZAGRQAIAcwHdxcQEA3LlzB6GhoUYH9u/cuYMCBQogNjYW69atw8qVK3Hjxg34+PjA398fHh4e0Gq1HBRgLBkpceuBAweiYsWKiI2NxYwZM6DRaMAxbdPy8fHB0aNHYWZmhosXL2LBggVYvnw5AEClUiEmJgZmZmZ49OgRGjVqhHv37sHc3ByhoaG4desWAgMD8fTpUzx8+BABAQGYPn06Bg8ejOrVq8Pd3R316tWDEIKDAt85DgqkLp1OBwAYOXIkPDw8YG1tjeLFiwOIGzjmoEDqE0KAiFC0aFE0adIEJ06cwOnTp1G7dm3Mnj0bXl5eCAwMRNu2bWWb9UtwUMA0tFotTp48CQBwc3MDAIP7SBl0rlq1KtRqNcLDw/Hu3bsExxk0aBDGjRsHAHj69ClOnTqFixcvIjo6GrVq1YK/vz8HBUxE6YslFhQAAHt7e8ybNw9t27YFAMydOxeLFi3CrVu3UuQzspTn7e2Nf//9F66urjh27BgHBRhjTA+3KtkPSaVSwcnJCR4eHrh58yaePn0KAAazR5TGvdIwt7a2TnSw0dfXF2fPnsXy5csREhKCjx8/okyZMqhYsSIcHR2TnJXCGPtvlAEVtVoNDw8P2NraYs6cOTwgkkJKlSqFkydPwsfHB1evXsXcuXPlTEkLCws8ePAAfn5+uHbtGipWrAg/Pz94e3vj7du3ePfuHZ4+fYrjx48jJCQEFy5cgEajwdu3b6FWqzF9+nTY2tqm9iky9l1TBizd3d2RJ08eHD16FAcOHEDPnj1hbm7OqxhTgbG/uRACVlZWaNiwITZs2IBff/0V5cqVQ82aNTFr1iwMGDAAd+7cQdu2bb965QBLPhSXYhdarRZA3KQgwDAwoFxbe3t7aDQavH79GiEhIcicObPBdc+QIQPGjBmDKlWqIDAwEE+ePIGNjQ2qVauGzJkzw8nJKQXPjBmjBAcAYM2aNZg7dy4A8MqBH1j58uXx8OFDntTAGGPxcCoh9sOKjY1Fp06dsG7dOhQvXhxHjhxJsNQXAHbv3o0GDRrA3d0dV65cQYYMGRLsk9TAPy/VZ8z0AgMDodVqkStXrtT+KGnO2bNn4ePjA41Gg0KFCmHUqFEoW7Ys6tevj6tXr6Ju3brYuXNnkgOQ165dw5s3b3Dy5Em0aNGCryNjyWzXrl1o2LAhAGDhwoXo3r17Kn+itKFNmzZo3rw5SpUqJWeYxw8O6P+7devW+Pvvv7F+/Xo0adIEkZGROHbsmAwO/Je0Qix5zZw5E4MHD0aePHkwf/58VK1aVW5TruXOnTvRtGlTpEuXDufPn0e2bNkMjsFBue8HpxVijDGW1vFoJvth6HQ6uaxep9NBrVZj6NChcHNzw82bN3Hw4EGj73v16hUAIDw8XL5focwaUoICsbGxcpsSU+OgAGOmlz17dh5MTiXe3t44ceIEzM3Nce3aNYwbNw7lypXD1atXUb9+fezatUvm89Wn0+nkc7JgwYKoUqUKRo8ezdeRMRMoU6YMatWqBSEEDhw4gJCQEE65ZmJ169bF+vXr8dNPP+Hnn3/GokWLAHyaVa48E5XVbwDQsGFDxMbG4vfff0d0dDRsbGxQpUoVzJo16z+nFWL/O/17pWDBgrJuWHh4uMF+yrX9+PFjomkNjaUW5Xvx22UsrdDSpUtx/fr1VP5kjDHGWMrgEU32w1CpVHKQXvnfHDlyYP78+di4cSMaN25s9H3KYH/69OkN0gkpqwSICAsXLpQpMBQ8E4gxllZ4e3vj5MmTMDc3x+3bt/H8+XMULlwYO3bsAABER0cnWFWlX8idn5eMmZaLiwvKlSsHIsKuXbtw/vx5vu9MyNvbG//88w9sbW0RExODXbt2oWfPnmjSpAmWL1+ODx8+GDwTlbZlgwYN4OPjg7Nnz2LlypUgIlhaWqJq1aoGwYFOnTrB398/tU4vzVCui/69UqNGDUyZMgUjR46Uq3Di0+l0EELAwsJC/hswXGG8efNmPHv2LMHx2bcnfnBg9uzZWLt2LWJiYlL5kzHGGGOmx4EB9l3TaDRYv349hg8fjnr16qFnz55Yv349goODAQC2traoXr066tevDwAGKwKU30NCQgDE5ei1t7eHEAIajUYGBVq2bIlevXph4sSJPOOHMZZmlSpVCidOnIClpSWAuIGO9evXy4Gt+CuuGGMpgwu1p7wJEyYAACIiIuDm5oYqVapACIFt27ahW7duqFSpEnbv3o3AwEAAcYFSrVYLKysrjB07Fra2tjh48KAcMFar1TI4ULBgQdy9exf9+/fHx48fU+0cf2RbtmzB6NGj0aZNG3Tu3Blr167FgwcP5Pa2bduiffv2AGD0u+3ly5cgIjg7O8PZ2VleXyUo0LlzZzRv3hyLFi1KsJqOfZvs7e0xf/58NGrUCADQrl07GfhhjDHGfmRcwZF9t2JiYtCiRQts377d4PVFixahSpUqMm2FnZ2dzPVprIDYo0ePAHxa8h0TEwMLCwsQEVq3bo1NmzYhU6ZMGDRoEM/4YYylad7e3vD394ePjw+uXLmCadOm4ePHj+jcuTNUKhXXXGEsFXCh9pRXs2ZNHDlyBFWqVMHLly/RuHFj1KtXDw8ePMDhw4dx5coVtG3bFvny5UOPHj3QpEkT2NjYAIhbzVqoUCFs3boV69evR6tWrQB8Cg5MnDgRkydPxqJFi7hIpgm0adMGGzZsMBjwX7NmDdKnT4/ffvsNPj4+yJ07N4CENcaU/oSyEgCIu/90Op3cr3v37li5ciVsbW3RsmXLRGuUsW+PnZ0dVq9ejQ8fPsDDwyO1Pw5jjDGWIrj4MPsuxcbGonbt2jh8+DBy586NNm3awNzcHK9evcKyZcsQFRUFrVaLhg0bYs2aNbCzs0v0WO3atcPatWtRsWJFHDlyRHawW7dujQ0bNiBLliw4fvw4PD09kyxCzBhjacW5c+dQvnx5aDQaFC5cGH369EHnzp0BcEF2xlITF2pPWf7+/qhSpQoA4Oeff0aTJk2QL18+jB07Fv7+/rh//z4AoFGjRqhYsSL69+8PANixYwcaN26MmjVrYu3atXBycpKTT2JjYxETEwNbW9tUOacfWdOmTbF161Z4eHigbdu2cHV1xdmzZ3H//n2cP38elpaWaNasGTp27IjKlSsDMF5IeODAgZg9ezYqVqyIvXv3yqBP9+7dsXTpUjg6OuLEiRNcwJYxxhhj3zyeSsS+O0SECRMm4PDhwyhatCiOHj2KdOnSye316tXD+vXrsXnzZuzYsQONGjXC8uXLkTVrVoPjaDQamJub48WLFwAAR0dHOetHmU3k6enJQQHGGItHSSvk4+ODq1evYu7cuQDAKwcYS2XZs2dP7Y+QplSqVAlHjx5F5cqVsWjRIrx//x6//PILli5dikuXLmHbtm2YPn06du7cie3bt2Pz5s3o0aMHSpQogS5dumD16tW4ffs2ypcvL5+barXaoKYVSx4zZszA1q1bkT17dhw+fFj2C6KjoxEaGoohQ4Zg3bp1WL9+PZ48eYKwsDDUr19fThjSDw58+PABAGBjY8NBAcYYY4x91zgwwL47QghcunQJarUas2fPRrp06RATEwNzc3OoVCpUrVoV2bJlQ968eTF58mQcPnwYHTp0wJ9//omsWbPKjpfSwFcGrxwdHQEALVu2xObNm+Hp6YkTJ05wUIAxxozw9vY2CA4sXLgQUVFR6NWrFwcFGGNpRsWKFWVw4K+//gIRYdCgQShRogSKFSuGatWq4dixY1i0aBFOnz6N69evw9PTE9myZUNMTAxGjx6Nbdu2wcHBIbVP5Yd29uxZqFQqTJ06FVmzZpUThCwsLODm5oY1a9bA09MTc+bMwfHjx2V9B2PBgbCwMABxK+R0Oh169uzJQQHGGGOMfZe4586+K1qtFi9fvsSxY8cQGxsri2BaWFgYDETlzJkTnTt3xrRp0+Dg4AB/f3906dIF4eHhCQqEubm5AQACAgLQqFEjDgowxtgXUoID1tbWuHjxIjZu3Ij379+n9sdijLEUpQQHAGDDhg2YNWsWTp06JbeNHj0aAQEB6N27N3Lnzo2bN29i7969AIDr16/j3LlzqfXR04SwsDDcvHkTKpUKrq6uACDb9kIIWWds8uTJGD16NBwcHHD+/HnMnDkThw8flvspGXjTp08PAAgPD0e7du2wZMkSODk5cVCAMcYYY98dDgyw74qZmRmcnZ2RJUsWWFhYyMJh+gXEFE5OTmjVqhWmTZuG9OnT49ChQ2jTpo08TmxsLIBPKwWePn2KnTt3InPmzBwUYIyxL+Tt7Y0DBw4gQ4YMWLRokRwwYYyxtEQ/OLB+/XosXLgQZ86ckdszZcqE3377Ddu3b8eQIUPg5eUFAHj79q38nZmGubk51Go1NBoNrl+/nmC7mZmZDA6MGDECgwcPhpWVFU6cOIEVK1bg9u3bACBXDBQtWhQAcPr0aaxfvx6Ojo44fvw4BwUYY4wx9t3hwAD77qjVauTIkQMxMTH4/fffERYWlmjaCjs7OzRp0gSjR4+GnZ0ddu7ciTFjxsjjAICfnx/s7e0RHh7OQQHGGPsPypUrh8ePHyN//vyp/VEYYyzV6AcH1q1bh/nz5yMgIEBut7KyQubMmTFt2jSsXbsWCxYswI0bN5AlS5ZU+sQ/PiKCtbU1ypcvDwC4dOkSAMMVAEBccECZaDR8+HAMHDgQWq0WW7Zswf79+wFABg/c3Nzk+52cnDgowBhjjLHvFgcG2HdFabC3bdsWdnZ2uHz5Mnbv3g2NRpPoe5ycnODn54cWLVrA3Nwc//zzDx49egQgrrOQOXNmpE+fHh4eHjhx4gSyZMnCQQHGGPtK1tbWqf0RGGMs1cUPDsybN88gOKC0WUuUKIGff/6ZB5RNTJnl7+PjAwBYvnw5Vq5cKbfpBweUdKMAMH78eLRv3x7R0dEYNmwYbt68KfsGfn5+KF++PMzMzHDs2DEOijPGGGPsu8WBAfbdUIoGA0CxYsWQOXNmPHr0CMuXL5dLfBOTPXt2tGrVCunSpcOFCxdw7NgxAHEdgpw5c2LBggU4c+YMBwUYY4wxxtj/JKnggLm5udEUmMw0lEBM06ZN0aFDBwDAsGHDZI0HYysHlODAnDlzUKlSJURHR2PPnj0Gx5s0aRLu3bvHQQHGGGOMfdc4MMC+STqdDq9fv8ahQ4dw7tw5PHjwQNYEACAH8wHg8OHDGDt2LJ49e5bkMStXroxu3boBAP79918AkMesV68eMmfOzEEBxhhjjDH2P4sfHFi0aBFOnDgBAImmwGT/m2fPnsHf3x/79u3DzZs3ERsbC3NzcwBxA/6NGjVC0aJF8ebNG/z66684cuQIAOPBASUFUYkSJQAAhw4dAgB5vAoVKiBbtmwpeHaMMcYYY8mPW6XsmxMTE4NevXqhRo0aqF69OsqXL4/ChQujQ4cO+PPPP+V+lStXxvLlyyGEwLZt2zB48GA8f/7c6DGVmVlKA/7ly5cAPjXuFRwUYIwxxhhjyUE/OLBq1SqsXbsWUVFRqfuhflADBw5E3bp1UaVKFdSuXRuVKlWCj48Pdu3aJVOI1q9fHy1atEDGjBlx4cIFjB8/Xl6f+MEBIQTUajV8fX2hVqsRGRmZZOpSxhhjjLHvEQcG2DclJiYGtWvXxuLFi/HkyRNkypQJBQoUwMePH7Fx40Z06tQJP//8M+7evQudTod27drh999/hxACmzZtQr9+/XD79m25BFgJCCgrA5TXPT09AXzKO8oYY4wxxlhyq1ixIg4cOAAA6NOnD6ysrFL5E/14GjZsiNmzZ+Px48coWLAgvLy8oFarce7cOXTq1AkDBgzAkSNHIITAkCFD0LFjR9jZ2eH48eMYMWKELC4shJB9ByUI8OrVK8TGxsLS0pInEDHGGGPsh8OBAfbN0Gq16NChA44cOYLixYvj9OnTuHz5Mi5duoT169ejU6dOAIAlS5ZgyJAh8Pf3BwD06NEDM2fOhBACW7duRf/+/bFr1y5ERETIpdqWlpYAgE2bNgEAChUqBACc45UxxhhjjJlU1apVER4ejgIFCqT2R/nh9OrVC7t27YKXlxdOnDghfw4ePIjatWvjw4cP2LFjB7p06YIdO3ZACIFJkyahV69ecHFxQUBAALp164Z169YB+DRpSFlVvH37dgBxxYt5QhFjjDHGfjSC9NdMMpaKAgIC0KBBA1hZWeHs2bNwdXU1yPn/6tUrbNiwAf379wcA+Pr6YuDAgahZsyZUKhVWrVolgwd58uRB2bJl0b9/f5ibmyMmJga//PIL9u7di8KFC+PAgQNwcXFJrVNljDHGGGOM/Q8ePHiAWrVq4e3btzh79ixy5cpl0HeIiorC5MmTsWnTJty9exf29vZYvnw5mjZtCiLC77//jvXr1+Pq1asAgKFDh6J06dIoUqQI3r59i5kzZ2Ljxo3IkSMHDh06hKxZs6bm6TLGGGOMJTsODLBvxvr169GmTRv4+fnh77//hkajgbm5OYjIYIbOxo0b0bJlSwBxM7BGjhyJSpUqQaVSwd/fH71798ajR48QEREBKysraDQaWFpaIiIiAvny5cO+ffuQOXNm6HQ6Lv7GGGOMMcbYd2jPnj2oX78+ihUrhjNnzoCIYGFhAQAyQBAbG4sVK1Zg6dKluHjxIuzt7bFq1So0atQIRIQdO3Zg69atWLt2rTyui4sLIiIiEBkZCU9PT+zbtw958+ZNrdNkjDHGGDMZ88/vwljK+PjxIwAgPDwcGo1GDtrHX7bbvHlzWFlZoXHjxjh06BBsbGzg4eEBLy8vVKpUCdu3b8fhw4exceNG3L59Gy9evIC3tzcKFy6MMWPGwM3NzWA2EWOMMcYYY+z7Ym9vDwBwcHCAWq022GZmZgadTge1Wo3OnTvDxsYGf/zxBy5cuID+/fsjXbp08PX1RaNGjVCrVi1UqlQJW7ZswYULFxAZGYlcuXKhdOnSGD58OLJnz54ap8cYY4wxZnK8YoClOmVFgDLrJ1++fDh16hTSp0+f5Kx+/ZUDXbt2xeLFixPsExQUhHfv3sHLywsajQYWFhYcFGCMMcYYY+w7d/78eXh7e8Pc3Bx79+5FtWrVEuyj9DM0Gg1WrlyJ2bNn49atW6hduzZmzpwJLy8vuW90dDTev3+P0NBQZM6cGRYWFgkCDowxxhhjPxLOo8JSnbIioGzZssiaNStu3bqFoUOHyqBAYrGr5s2bY+7cuQCApUuXYt68eXKbUlTY3d0defPmhUqlkg17DgowxhhjjDH2/SIi5M6dGxUrVoRWq8XRo0cRGxubYD8hBIgI5ubm6NChA1q2bAkbGxucOnUKx44dAwD5PgsLC2TMmBFeXl6wtbXloABjjDHGfngcGGCpShn012g0cHJyQteuXWFlZYW9e/dixYoVAD416I1p0aIFfv75ZwDA/v37ER4eDiIyWGWgBB7ipyRijDHGGGOMffu0Wq38XVkFkD59elSpUgVEhClTpmDPnj0APk0QUgghZFqhIUOGoFatWnj//j3mz5+PqKgoGQDgvgJjjDHG0hoODLBUERUVBY1GIxvg5uZx5S6qVauGvHnz4vnz51i3bh3++ecfAIkHB5ydnVGjRg0AwIEDB/Do0SNu1DPGGGOMMfYDiYqKkr/rt/VHjhyJhg0bgojQrFkznDp1yuiKY5VKBZ1OB0tLS/zxxx/w9PTErVu3cPr06RQ7B8YYY4yxbw0HBliKiY2NxYIFC9CxY0f4+vrC19cXkyZNwpEjR+Q+3t7eGDZsGFQqFfz9/bFw4UK5PX5wQPm9UaNGKFu2LKKjo/Hy5cuUPSnGGGOMMcaYSSxevBg9evRA8eLFUb9+ffTu3RuXL19GaGgogLgUoX379kXZsmWh0WjQoEEDnDlzRq4S0KdSqaDVapEhQwZkz54dsbGxePbsWWqcFmOMMcbYN8E8tT8ASxtiYmLQuHFjuQJAcfLkSRARRo0ahXr16qF06dJo3rw53r17hx49emD37t0A4oqB1apVSzbyVSqVLCQWExODsLAwAMCbN29S/NwYY4wxxhhjyatly5bYuHGj/Pe9e/cAAP/88w+KFSuG8ePHI3/+/ChdujS6deuG8PBwXLt2DQ0aNMD27dtRrlw5mXZIn4WFBezs7ADAaF0CxhhjjLG0ggMDzORiY2PRsGFD7Nu3D/ny5UOfPn3g5uaG8+fP4+bNm9i+fTsmTZqE8+fPo3Xr1mjTpg26d++O2NhY9O3bF7t370ZkZCRevXqFNm3aQKVSQaPRAIhLQRQWFobIyEh4enqiaNGiqXuyjDHGGGOMsf9Js2bN8PfffyNLliwYPXo0rKysQESYPXs27t+/j61bt+Lw4cPYunUrKleujCZNmiAyMhKLFy/G1atXUbVqVWzbtg21atWSx4yNjYVarYZGo8GzZ8+QPn16eHl5peJZMsYYY4ylLkGJVXVlLJnMnj0bAwcORP78+XH06FFkyJABQFwqoJiYGMyYMQOjR48GABQqVAjdunVDr169AABLly5Fr169oNFokD17djRs2BBTpkyBpaUlgLgGfvPmzbF9+3bUrl0bf/31F9KlS5c6J8oYY4wxxhj7nyxduhTdu3dHtmzZcOrUKbi5ucltwcHBWLVqFdavX4+rV6/CxsYGq1atQpMmTRAREYGtW7di4cKFMp3Q1KlT4evrixIlSgCI63906tQJq1atQvny5bF9+3Y4Ozun1qkyxhhjjKUqDgwwk2vdujX++usv/Pnnn2jXrh00Go0sNqxYsWIFRo0aheDgYOTOnRt9+/aVwYHNmzdj2rRpuHnzJqKiolC4cGHkyZMHarUaN27cwNWrV5EjRw4cO3YMHh4eRpcMM8YYY4wxxr59/fv3xx9//IHFixeja9euiImJgYWFBbRaLczMzBAeHo6jR49i2rRpOHnyJKytrbFmzRr4+fkhMjISZ86cwR9//IGdO3dCCAEPDw+UL18e4eHhCA4OxoULF+Dq6opjx44hd+7cqX26jDHGGGOphlMJMZOKiorCkydPAEDOxtEPCij1Ajp16gRra2uMHj0a9+7dw9KlS5E+fXq0adMGP/30E7Jly4Z9+/Zh3rx5uHr1Kq5evQoASJcuHXx9fbFq1Sp4eHjIDgNjjDHGGGPs+6H0C86dOwfgU59BrVYDgGzj29nZoWbNmnBwcMAvv/wCf39/tGvXDg4ODvD19UWlSpVQrlw5TJgwAVu2bMHdu3exadMmAICTkxPKli2LlStXclCAMcYYY2keBwaYSVlYWMDa2hoAcO7cOdSpUwdEBJVKBQBQqVSyE9CyZUsIITBw4EBcvXoVa9euRc6cOVG2bFmUKlUKpUqVQufOnXHw4EE8ffoUZmZmKFGiBEqVKoX06dNzUIAxxhhjjLHvnLu7OwDg1atXAGB0JbBarUa5cuUwadIkDB8+HCdOnICfnx9OnjyJAgUKwMzMDJMmTULbtm3x4MED3L59G9HR0ahYsSLy5MmDjBkzpug5McYYY4x9iziVEDMZJaXPmDFjMHHiRNSpUwe7d+8G8GlGkEL/33/++Sc6deoEABgyZAimTZsGAEZTEBl7P2OMMcYYY+z7NGfOHAwYMADZs2fH5s2bUbx48UT31Wg0OHjwIEaNGoVLly6hWrVqWL16tUFdAsYYY4wxZhyPpDKTUWb31KpVCwCwd+9ejBgxAsCnlQIK/X936NABkyZNAgD8/vvv2L9/P4BPy4n1Y1nK7xwUYIwxxhhj7PultOurVauG3LlzIzg4GNu2bcPbt28TfY+5uTkqVKiAdu3aIUOGDLh27RquXbsGAAZ9DcYYY4wxlhCPpjKT0Wq1ICKUK1cOw4cPhxACs2fPxuLFiwEkHRwYNmwYmjdvDgDw9/cH8KmzoL+cmIsMM8YYY4wx9v1T2vX58uWDj48PIiMjsXTpUhw6dCjJQX5bW1u0bNkS+fLlQ3BwMNasWQOAJw4xxhhjjH0Ot5bY/0yr1eL69evYuHEjli1bhiNHjuDFixcwMzOTDfzatWujUqVKiI6OxowZM7B27VoAxoMDiiJFigCIW2kQHR3NQQDGGGOMMcZ+ANevX8eGDRvw119/4e+//8br16+h0WgAxPUH5s+fj9KlS+PVq1cYNGgQDh06lOixdDodMmbMiBEjRkClUuHatWuIiIhIqVNhjDHGGPtucfFh9j+JiYlBt27dcPjwYTx79gxAXDEwR0dH/PLLLyhbtiyKFy+OChUqoGPHjnj69CkePHiA33//HUIItG7d2qAAsUKlUsHHxwfW1tbQarW8FJgxxhhjjLEfQI8ePfDvv//i8ePH8jVvb2+UKFEC48ePh7OzM6ysrLB8+XK0bt0aV65cQefOnbFixQpUrVo1wWQhpQ9hb28PnU6Hp0+fIiwsDNbW1rxqgDHGGGMsCdxSYv9ZTEwMatSogdWrV0Oj0aBq1aqoVKkSvLy88OrVKwwcOBB9+/bF6tWrAQBt27ZFv3794O7ujuvXr2P8+PFYtmwZgLgGvVarBQA5Wyg4OBgfP36Ei4sLLC0tU+ckGWOMMcYYY8miUaNGWLx4McLCwlC2bFk0btwY9vb2uHr1KhYuXAgfHx/8888/+PDhA/Lnz4/Zs2ejYMGCePbsGTp27IidO3fiw4cP8nhEhJiYGABxKwfUajUKFCgANzc3DgowxhhjjH0Gt5bYf6LT6dCrVy8cO3YMJUuWxPnz57F//34cOnQIx44dQ9++fWFra4tTp06hX79+mDlzJgCgd+/eGDVqFHLlyoV79+5h0KBBGDt2LADAzMwMwKciw0uXLgUAlC5dmtMIMcYYY4wx9h0bNGgQdu7cidy5c+P06dM4evQotmzZguPHj2PChAnIkSMH7ty5g549e2L9+vV48+YNKlWqhPnz56NIkSJ4/vw5evTogUWLFuHWrVsA4uoSWFhYAADmzp2L2NhYFCxYEDExMbI+GWOMMcYYM04Qt5jYf/DixQtUq1YNb968wfnz55ElSxbExsZCrVbLfZYsWYKVK1ciICAAADBlyhQMGzYMALB27VqsXLkSR48eBRGhSZMm8PHxQbFixfD27VssWrQIBw4cQP78+XHo0CG4urqmynkyxhhjjDHG/jdBQUGoV68eHjx4gKNHj6Jo0aLQarVyYlBUVBQuX76Mrl274saNG/Dw8MCAAQPQvn17ZMiQATdv3kTnzp0REBAAOzs7FChQAB07doSXlxciIiKwfPlybNu2DZkzZ4a/vz+yZ8+eymfMGGOMMfbt48AA+08OHz6MatWqIU+ePLh48SLMzc3lbB39egHbt2/HggULcPDgQQDAjBkzMGDAAADAiRMn8M8//2DKlCnyuJaWloiOjgYAFCxYEHv37kXmzJkNOg6MMcYYY4yx78e5c+dQpkwZ5MqVC6dPn4aTk5PR/V68eIE6derg6tWr8PDwwMiRI9G6dWukT58e0dHR6NGjBw4ePChrm+nLmTMndu/eDS8vL1OfDmOMMcbYD4GLD7P/JH369AAAa2tr2NjYGGzTLybcqFEjWFlZQQiBAwcO4Ndff4WzszPatWsHHx8f+Pj4oEaNGli3bh0uXbqE0NBQZM+eHWXLlkXv3r2RMWNGDgowxhhjjDH2HbO0tISlpSViY2MRHh5uNDCg0+ng4eEBf39/VK5cGVeuXMFvv/2GzJkzo0GDBrC0tMTy5ctx5MgRHDlyBDt37sT79++RK1culCxZEj169EDWrFlT4ewYY4wxxr5PvGKA/SdPnjxBkSJF8P79e/z1119o3rx5gn2ISNYG2LFjB6ZNm4YzZ86gTJkymDlzJsqUKSP3ISLExsYiNDQULi4uEEJACMFBAcYYY4wxxr5zd+/eRZUqVfDq1Svs3LkTtWvXNugrKDQaDczNzfHy5UvUqFED169fR+7cueHv7w83NzeDfSMiIqDT6WBvb2+wYpkxxhhjjH0Zbj2xr6bT6eDs7Izy5csDAA4ePIjw8HC5TaEM+ANAw4YN0blzZzg7O+P8+fM4fPhwgv0tLCzg6uoKlUolOwkcFGCMMcYYY+z7lidPHlSrVg1arRZDhw7FvXv3EgQFAMDc3Bw6nQ5ubm74888/kStXLty7dw+9evVCTEwMAMj+hbW1Nezs7ADA6LEYY4wxxljSODDAPkur1crfldk4tra2aNasGQBg+fLlWLx4MYBPaYQU+sGBzp07o127dtBoNJgzZw6ePXsmB/65Mc8YY4wxxtiPR6PRAACaNm2KrFmz4sGDB5g7dy6Cg4ON7q/M/C9UqBAGDRoEBwcH3LhxA48fPwbwqd+gP5mI+xKMMcYYY1+PAwPss16+fAmNRgONRmOwRLddu3bo06cPAGDIkCFYtWoVAOPBAeXfkyZNQrFixfDmzRtcvHgRwKdZP4wxxhhjjLHv2+vXr/HmzRv5b3PzuLJ2Pj4+qFChAqKiorBjxw6sX78eoaGhiR5HrVajSpUqcHR0xN27d7Fv3z6Tf3bGGGOMsbSEAwPMqNjYWEyaNAlNmjRBwYIFUbZsWdSrVw979uzBw4cP5X7t27dHgwYNAAAdO3bEunXrACQMDij/trKyQqZMmUBECAwMBMAzfBhjjDHGGPve/fbbb/jpp59QqFAhFCxYEG3btsW0adMAxK06dnR0xKRJk1C4cGE8ffoUixcvxqZNm5IMDuTJkweNGjUCAHz48CElToMxxhhjLM0wT+0PwL49MTExaNiwIfbt2wczMzNotVrcvHkTHz9+xLlz5+Dl5YVRo0ahbt26KFasGLp06YL379/D398fbdu2hUajQfv27RMUANNqtVCpVLC2tgbwafYQY4wxxhhj7PvVpEkTbNu2Debm5hBCIDY2Fhs2bIBWq8XBgwfRtWtX+Pr6wtPTE3///TeqVKmCu3fvYtasWYiKikLr1q2RIUMGg2MqhYhfv34NAAgJCUmNU2OMMcYY+2HxyCwzoNFo4Ofnh3379qFgwYKYOnUqrK2tYW5ujjlz5uD06dM4c+YM6tevj0WLFqFbt26oW7cuoqOjYWZmhsOHD6Njx4549+4devXqJQf/Y2NjoVarodFocPv2bdjb2yNfvnwA4lIJ8aoBxhhjjDHGvj9t27bFtm3bkCtXLsyYMQOurq54/vw5zp8/j9mzZ+PQoUN4+vQprl69ip9//hm5cuXCoUOHUL16ddy9exdz5sxBUFAQunfvjuzZs0Or1cLMzEz2IwIDA2FtbQ0fH59UPlPGGGOMsR+LIE7wzvSsXLkSnTt3Rr58+XD69GmkS5dObtNoNFi/fj3WrVuHAwcOAIhbMjx48GAQEfbt24clS5Zg+/btAICePXuibt26qF27NmJiYhAVFYWOHTti27ZtqFixIrZv3w4HB4dUOEvGGGOMMcbY/2r37t3w8/NDhgwZcPbsWWTOnNlge0BAAJo3b44nT57AxcUFjRs3xogRI2QR4iZNmuDq1auws7NDiRIlMHnyZBQtWhTm5uaIiopC7969sWbNGhQvXhy7du2Cu7t7Kp0pY4wxxtiPh1cMMAPXr18HAHTp0gXp0qVDTEwMLCwsoNVq7th7uwAANK1JREFUYW5ujtatWyNPnjxwcnLCxo0bMXToUADA4MGDUbNmTbi5uSFr1qyYM2cOFixYgFWrViF//vyIjo5GeHg4Hj58iJw5c2LdunVwcHCATqdLkHKIMcYYY4wx9u27fPkyNBoNWrVqhcyZMyMmJgZqtRpCCOh0OpQuXRr79+9H165dcfz4cWzcuBEAMHr0aOTMmRM7d+7EyJEjcfToUfj7+6NSpUooVaoUhBAICQnB7du34ebmhnXr1nFQgDHGGGMsmfGILAMQl84HAB48eAAAUKvVAD7VATAzMwMRwczMDGXKlMGYMWPQtm1bAMDQoUOxZMkSCCFQtGhRzJo1C3/++SfKly8PlUqFc+fO4erVq9Bqtahbty78/f2RKVMmWXOAMcYYY4wx9v1Q+g5K4WB7e3sAgIWFhUwRqlKpoNPpkCdPHqxYsQK1atXC+/fvsXXrVixYsACvX79GlixZ8Mcff2DGjBlo0KABNBoNTp8+jVOnTuHdu3eoUaMGjh07Bi8vr9Q5UcYYY4yxHxivGGAAPuX5V1L73Lp1CwAMBu716wDky5cPI0aMQExMDDZu3Iiff/4Z7u7uqF+/PgCgXbt2qFmzJkJDQ3Hz5k1ERUXB29sbrq6usLe3l7lDGWOMMcYYY98XpV/g5OQEALhw4QLCwsJgY2Nj0MZXggM5c+bEvHnz0LlzZ/j7+2Pz5s3Ily8fWrRoAScnJzRv3hzNmzfH8ePH8e7dOwQHB6NUqVLIkiULHB0dU+UcGWOMMcZ+dFxjgBnYtm0bmjRpggwZMmDNmjWoWbNmkvufO3cOY8aMwb59+1CkSBGsXbsWBQoUAJB4UWEuNswYY4wxxtj3b/v27fDz84O7uzsOHDiA/PnzG50ApKQPvXfvHurWrYv79++jQoUK2L17N+zt7REbGytXLDPGGGOMsZTBeVyYgaJFi6Jo0aIICQnBtm3b8OTJkyT3L1GiBNq0aYMsWbLg4cOHskaBTqdLdPCfgwKMMcYYY4x9/2rWrIkSJUogKCgI7dq1Q0hICMzMzKDT6Qz2U1YO5M6dGxs2bICNjQ2OHz+OyZMnAwAHBRhjjDHGUgEHBhgAQKvVAgCyZ8+Oxo0bQ6fTYdmyZdi6dSuio6ONvoeIoFKp0KxZM5QrVw5hYWFYuHAhYmJiuHYAY4wxxhhjPwj9gX7l99jYWFhbW2PYsGHInDkzLl68iEGDBuHDhw8yEKBPea148eKYOHEi1Go1rly5Ao1Gk6LnwhhjjDHG4vDobRr18uVLPHnyBE+fPgUAg+W+v/zyC9q0aQOdTochQ4Zg3bp1MJZxSggBrVYLtVqNIUOGIF26dAgODsbHjx9T7DwYY4wxxhhjphUUFITXr18jODgYUVFRAD7N8i9Xrhzq1KkDa2trbN26FWPHjkVYWFiiwQEAKFasGDQaDf7999/PrlBmjDHGGGOmwcWH0xCtVou9e/fi0KFD2LJlC0JDQ+Ho6Ig8efKgZcuWaNWqFaytrSGEwPTp0/Hhwwfs3LkT3bt3hxACrVu3hoWFhcExlYACESE8PByBgYEIDg6Gvb09rxpgjDHGGGPsO3bw4EEcOnQIq1evhk6ng1arRd68eeHn54f+/fsDADw8PNCnTx88evQIBw4cwLp16yCEwLhx45AuXTqjNQcKFiyITJkyISgoyOgEJMYYY4wxZnocGEgjYmNj0adPH2zduhVv3rwBAJibm+PVq1d4/vw5smbNinr16sHGxgYAkDFjRkyePBkajQZ79+5Ft27dEBERgSZNmsDd3R1A3DJijUYDCwsLaLVaWFtbo0iRIsiePTsHBRhjjDHGGPuO9e/fHxs2bMCrV68AAI6OjggNDcWJEyeg0+nQrFkzeHh4AAAKFCiASZMmISIiAqdPn8aKFSsQERGBqVOnwtHRUQYHYmJiYGFhgZCQEERERCBHjhxwcnJKzdNkjDHGGEuzODCQBsTExKBevXo4ePAgPDw8MGrUKFSsWBH29vb48OEDVq5cibp168LNzc3gfXny5MH06dNhYWGB7du3Y8CAAbh//z78/PxQsWJFqFQquYLg119/RUREBAoWLJhgyTBjjDHGGGPs++Hn54ft27fDyckJQ4cORZkyZVCgQAGcOnUK9+7dg5OTkwwKAHGrh0uUKIFZs2ZhzJgxOHr0KJYuXYrbt29jzZo1yJw5MwDIvsP48eMRGhqK6tWrw8rKKlXOkTHGGGMsrRPEazd/aLGxsahbty4OHjyIokWLYtu2bciaNavBPuHh4bCzs5P/vnHjBoQQyJ8/P3Q6Hd68eYMhQ4ZgzZo1UKlUyJYtGxo1aoTixYsjLCwMGzduxNGjR5E7d274+/vDzc0NRAQhREqfLmOMMcYYY+x/8NNPP2HLli3IkycPduzYAS8vryT3f/HiBc6fP49atWrBwsIC165dw7x587B7924EBQUhS5YsqFu3LooVK4bw8HDs378f//77L9zd3XHs2DHkzJkzhc6MMcYYY4zp48DAD0yr1aJfv35YsGABChUqhEOHDiFDhgxyKa/+4L1Op4NKpcKNGzdQoUIFhIWF4fz58yhSpIg83qRJk7B582ZcvXo1wX+rePHi2L59OzJnzmw0jyhjjDHGGGPs2zZy5EhMnToVnp6eOH78OLJkySLb9kp/AYB8LTAwEDVr1oSVlRWGDBmCFi1aQK1W4+nTpzh27BhmzJiBy5cvJ/jv5M2bF1u3bkXevHlT+AwZY4wxxpiCAwM/sKNHj6Jt27bQaDQ4duwYcufObXTQXmnk37x5Ew0aNMDDhw8BAOnSpcOxY8dQuHBhue+1a9dw/vx5bNq0CR8+fECWLFlQqlQptGvXziDowBhjjDHGGPt+nDlzBm3btsXbt2+xb98+lCpVymjbXnnt8ePHaNSoEa5cuQIAKF26NHr27CmDAwAQFhaGpUuX4s6dO7h37x4yZMiAChUqoFGjRvD09Ezxc2SMMcYYY59wjYEf2L59+/D8+XP07NkT2bJlg06nSzIo0LBhQzx8+BBly5ZFVFQULl26hIoVKxoEBwoVKoRChQqhffv2CQoMGzs+Y4wxxhhj7Nt35MgRPHjwAO3bt0fBggVBREkGBRo2bIirV6+iVKlSuH79OgICAgAAQgi0aNEC5ubmsLe3x8CBAwEAkZGRsLGxSfHzYowxxhhjxqk+vwv73hARnj17hr/++gsAUK1aNajVaqMD+SqVCrdu3ULDhg3x4MEDtG7dGseOHcOwYcNQqFAhfPjwAZUqVZJLgI0VFlZei398xhhjjDHG2LeNiBAaGop169YBiOs7WFtbJ6gXpkwCevz4MRo3boyrV6+iTp06CAgIwPbt22FlZYWAgAAsWLAAa9asgVarlccHIIMCvGCdMcYYY+zbwCO5PyAhBGJiYvDhwwekT58e+fLlA5CwEa6sFKhcuTIePHiAFi1aYM2aNTAzM0PVqlUxfPhwFCtWDO/fv0fDhg3x8uVLOfivHwTggABjjDHGGGPfJyEEoqKiEBwcDCGETPFjrO/w8uVL1K5dG5cvX0a9evWwe/duAED16tWxadMmWFtb48yZM1i+fDkCAwPl8eP/9xhjjDHGWOrjEd0fVFRUFKKiohAbG4t3794lut+7d+/w+vVrNGjQAOvXrwcAxMbGwtnZGY0aNUKOHDkAADExMdBoNCnx0RljjDHGGGMp6N27d4iJiYG1tTUsLCwAGB/Av3r1KiIiIlC9enXs3LkTQFw/AQDq1auHrl27AgAuXLjAAQDGGGOMsW8cBwZ+QDqdDlqtFjExMYiMjMSDBw8AJGzcExHKlSuHq1evYvv27QAAjUYDtVoNnU4Ha2tr2NvbQwiB+vXrI3PmzLz0lzHGGGOMsR+Mq6srnJ2dERkZiaNHjwKATAWkr0aNGli7di327dsHIK7vYGFhIScQRUdHQ6VSoW7dusiZM2eKfX7GGGOMMfb1ODDwA1KpVChUqBB8fX0BACtWrMCzZ88S7KcECgoWLAggrmFvbm4uj3H37l3s2rULRCSLD/PMH8YYY4wxxn4slpaWSJcuHQDA398fAGBmZmZQX0z5vUKFCgDiAgdK38Hc3BzPnz/Hzp07odPpUKRIkZT8+Iwxxhhj7D/gwMAPSqvVylk6t27dwtWrVwEYLx6sUBr2yuygI0eOIDQ0FFWqVEGnTp0AcLEwxhhjjDHGvlf6qwCUdj0RwdbWFr1794Zarca+ffswdepUAHGThZT94tcVMzMzMzjm6dOnERISAm9vb/Tu3dvgv8EYY4wxxr49HBj4AcTP/a/RaGBmZoYRI0YgW7ZsCAoKwtChQ/H06VOoVKokgwPKeyMjI7F06VLodDrUrl0blpaWAHjFAGOMMcYYY98r/X5A/HZ9kSJF4OXlBQDYvHkztmzZIvdLrP+g1WphZmYGIsL8+fMRHR2N2rVrw9ra2uh/gzHGGGOMfTsE8TSO745Op0NAQAAOHz6Ms//X3p3G13Tu/R//JtkZ1FBTREISOYipNQShxDyLanuM1Zoppebh5i5Vx9Cq6RzqaFWE1sttqNkhoSqNxBDUWBzDiYpShBJTdvb0f+C/dxNiqBM70nzeT5LsvdaVdT1b6/qu6/dLSNC1a9fUrFkzVatWTW3btnUcY7PZNGfOHH3yySdKTk5W1apVtXHjRpUoUcIxls1mc9ywp/+9bdu22rRpk5o1a6b/+7//U+HChZ0/UQAAAAD/tUOHDmn79u367rvv5OHhIW9vb/Xp00flypVToUKFHMfNnDlTo0aNkiS1atVKffr00VtvvSXp9xDALv2zQ7du3bR06VKFhoZqzZo18vPzc+LsAAAA8CwIBnIYk8mk0aNHa/369Tp37txD3//www+Oup+SdPr0aU2ePFnr169XSkqKKlasqHnz5qlSpUoqWrRohnONRqOuXbumXr16aevWrQoODtaOHTvk6+srq9X60PZhAAAAAC+20aNHa+3atTp79myGz//yl79o1qxZCg8Pl4uLi+Nef8iQIZo7d64kKTQ0VF27dtXAgQMd59kDApPJpBs3bqhfv35at26d/Pz8FBMTozJlyjhvcgAAAHhmhuy+ADy9tLQ0tW3bVlu3bpWvr6/69u2r4OBg7du3TydOnFBgYKC8vLwynFO2bFkNGzZMZrNZ0dHROn78uLp166bXXntNb7/9tsqUKSNPT09dvHhR69at0+bNm3Xq1ClVqlRJW7Zska+v70NvBwEAAAB48XXo0EGrV69WoUKF1LdvX/n5+en69etatmyZ/Pz85Ofn57jPt78INGPGDFksFv3zn/9UQkKCEhISFBMTo86dO6tatWoym81KSUnRqlWrtHXrVh0+fFilSpXS5s2bCQUAAAByEHYM5BAmk0lt2rTRtm3bVL16da1atUqlSpVyfH/w4EF5eXmpQoUKkn6vH2p/8+fkyZNauXKlFi1apPPnzzvO8/b2lsVi0fXr1yVJxYsXV5MmTTRjxgz5+PgQCgAAAAA5UKdOnbRq1SoFBwdr3bp1Kl++vOO7K1eu6D//+Y9q166d4Zz05YEmTpyoefPm6caNGzKbzfL09JSnp6esVqvMZrNSU1Pl7e2tBg0aaNq0aQoKCnLq/AAAAPDfIRjIAaxWq0aPHq1Zs2YpJCREUVFRKlq0qCwWi2Pbb/qb+Ee5deuWkpKSNHHiRCUlJWnPnj3KkyePrFarfH191axZM3Xq1Ek1a9ZU/vz5CQUAAACAHGjChAmaNGmSypQpo++++04BAQEym80yGAyOn08jKipKe/fu1RdffKG0tDT99ttvkqRSpUqpTp066t69u2rUqJGhTwEAAAByBoKBHODIkSNq37697t69q/j4eAUGBmZYtE//u9Vq1YkTJ3To0CFdv35dPj4+qlevnnx9fR3jmc1mpaWl6fDhw/Ly8pKbm5sKFiyogIAAxzFPEzQAAAAAeLFcuHBBbdq00fnz57Vz505VqlTpoecF+67i1NRU/fLLL9q/f7+SkpJUrlw5lS5dWhUrVswwZnJysm7evKnk5GR5eXmpePHi8vHxcfrcAAAAkHXoMZAD7NixQ2fOnNHAgQMfCgUkOX5fs2aNtm3bpkWLFslkMjm+b9Kkid58800NGDBALi4ucnNz00svvaTXXnvNcYw9H7I/KBAKAAAAADlPXFycjhw5ou7du6tSpUqOHQL2F3/socDmzZu1efNmLV++XDdu3JDValWePHnk5uamxYsX669//ausVqtsNpuKFi2qokWLqnTp0hn+Fy8TAQAA5FwEAznA6dOnJUmvvPKKJMnFxcWxgG+xWHT16lXNnDlTX3zxhYxGo+Pm38XFRSaTSdu3b9cvv/wib29vdezYMdObd/tn9gcFAAAAADmHfZH+5MmTku73DpMkg8Egk8kkd3d32Ww2JScna/78+Zo+fbpMJpPS0tJkMBgc5Ulv376t9u3bKzo6Ws2aNXvs/yQUAAAAyLkIBl5g9pv7mzdvSpJiY2PVp08fxw6BW7du6ZtvvtGGDRu0detWx3n169dX48aNVbt2bZ06dUrTpk3TyZMnFR0drY4dO2bLXAAAAAA8P/ZF+vz580u6X/4nNTVVXl5ecnd3l8Vi0aJFi7Rp0yZt3LjRcV6tWrXUuHFjhYWF6fjx4/ruu+8UHR2tESNG6LvvvlOxYsWyZT4AAAB4vugx8AKzBwMbNmxQ165d5enpqc6dO6tdu3a6du2aZsyYoTNnzig5OVmSVKVKFbVq1UqTJ0/O8Ob/mjVr1L59e3l6emr//v2qVKlSdk0JAAAAQBZLX2p05cqV6ty5syRp2rRpCgoKUt68efXZZ5/pyJEjjgbCr776qho3bqzPPvtMbm5ucnV1lclk0t69e/XOO+/IZDJp9+7dCgwMzLZ5AQAA4PkhGHiBpKWladeuXapdu7a8vLwcn586dUoffPCBYmJiZDablS9fPlmtVt29e9dxzIgRI9SqVSs1btxY0v2HA+l+/wGLxaK6devqxIkTSkhIULly5Zw7MQAAAABZbunSpXr33XclyVFOVJK6d++ub775xnFcgQIFlJKS4vh74MCBev3119W8eXNJ958d7H3GrFaratSooUOHDik2NlZhYWFOnBEAAACchVJCLwiTyaSgoCAlJydr+fLlat26tTw9PSVJwcHBGjt2rFxdXbVnzx7HTb2fn58qVqyoIUOGKDw83DGW1WrN0Jz44sWLunjxotLS0mQ0Gp07MQAAAABZrmXLltq6davOnTuncePGyWAwKC0tTR4eHvr4449lMpm0fPlySVJKSopefvll1ahRQ++99546dOjgGOfBZ4e7d+8qNTVVkvTSSy/RYBgAAOBPimDgBXHu3DnZbDaZTCYNGzZMNptN4eHhjnCgUaNGCgwM1MWLF3XgwAF5eXmpSZMmyp8/v3x8fDKMZS8jZH9r6NSpU7p+/bqaNGmiypUrO31uAAAAALLO/v37lZiYKHd3d/3973+XzWbT+PHj5eHhIZvNpqCgIC1btkzt2rXTzZs3defOHTVq1Eg+Pj7y9vbOMJb92cFejujUqVO6ePGi6tevr5CQkOyYHgAAAJyAUkIvkMOHD6tHjx46fPiw/P39NXv27AzhQGbsb/A8+CZP+q3E9erVU3x8vObNm6f333+ft34AAACAHC46OlqTJ0/W7t27VaBAAQ0bNkzjx4+XJMfOgUd53LNDeHi4tmzZoilTpmj06NGOEkMAAAD4c3F98iFwlipVqigyMlKVK1dWUlKShg0bpn/961+PLf9jv0m3/7RarZLkuLFv37694uPj1bBhQ3Xq1CnDsQAAAAByFvt7XS1atNCHH36oWrVqKSUlRbNnz9akSZMkSR4eHjKbzY5z7M8I9t/tvQQefHbo2bOntmzZoho1aqh79+5yc3Pj2QEAAOBPimDgBVO1alUtXrz4qcOBpKQkbdmyxdFs2NXVVTdv3tThw4fVqFEjrVmzRqVLl9bSpUtVuHDhDA8FAAAAAHIW+25h6X6fgfHjx2caDhgMBse9v71c0PXr17Vx40adPn1arq6ucnV11Z07d3T69Gm98cYbWrJkiXx9fbV06VL5+fllzwQBAADgFJQSekEdOnRIPXr00JEjRx5ZVshqtap///5auHCh3njjDfn6+qps2bKKiorSyZMnlZSUpJo1a2rNmjUqUaKEo24oAAAAgJwtfTmgqKgoTZo0SXv37n2orJDVanUEA8OGDdPnn38uPz8/denSRe7u7oqLi9O5c+d07tw5VapUSatXr1ZwcHC2zQsAAADOQTCQTZ6mzv+TwoGkpCSNGDFC33777UPnBgUFqXHjxpo8ebJ8fHwIBQAAAIAcKP3C/oOeNhywWCxKTU1V06ZNdejQoYd2I5cqVUqvvfaapk6dqsDAwOc7IQAAALwQCAay2ZMW7J8UDiQnJ2v9+vWKiorSv//9b/n7+6t8+fJ66623VK1aNeXNm5dQAAAAAMjh7AHBgy8YPW04IEnXrl3T5s2btWPHDh0/flyFChVSmTJl1LVrVwUHB6tgwYLOnhYAAACyCcGAkxiNRnXp0kUBAQGqUKGCSpcurZo1a8rd3V158uTJcKx9Id9+k3/48GF169ZNR48edYQDrVq1eui81NRUeXp6PvJBAQAAAEDO0L9/fxmNRtWrV08VK1aUt7e3SpcunelLP2lpafLw8JAkbdmyRVOmTNGePXsyDQfs7t27Jy8vL54VAAAAcimCAScwmUxq0qSJ4uLi5OrqKqvVKg8PDxUvXlwFChRQixYt5OvrqwYNGsjf31958+ZV3rx5M4xx8OBB9ejRQ0ePHlXJkiUdOwe8vLwkPRwAPG7LMQAAAIAXV506dbRnz54MnxUtWlRVqlRR4cKF1aJFCxUrVkxVqlRRyZIlH1rcj4qK0scff6z9+/crX758GjZsmCZMmCDp/rOJu7u7rFarXFxcHM2MCQgAAAByF0N2X0BucPbsWV25ckV58uRx1PO0WCxKSkqSzWbTsWPHJEn58+eXl5eXgoODFRISouLFi6tJkybKnz+/qlWrpm+//VZdunTRgQMHNGTIEElSmzZtHtolIIlQAAAAAMiBLl26pOLFizv+dnd3l6urq5KTk7V9+3ZJ0rp162QymRQYGKhChQopODhY9evXdzw/tGzZUh4eHpo8ebLi4uI0d+5c2Ww2ffzxx45QIP3zAqEAAABA7sOOASc5cOCAhg4dqoSEBElSrVq1VKdOHaWmpio+Pl7Hjh2Tp6enUlJSMpxXsGBBpaWlqVatWipRooRMJpM2bNige/fuydfXV3PnzlXr1q0dOwcAAAAA5Fw2m02JiYmaMmWKIiMjJUk1atRQ165dlZCQ4Hjp6D//+Y8MBoPMZnOG8/39/ZU/f361atVKe/bs0alTp3T16lUVLVpUgwYNyrSsEAAAAHIfgoHnxGKx6MKFCwoMDHR8tn//fg0dOlS7du1SQECAOnfurE8//VSSdPr0aV25ckXbt2/X1atXFRcXp+TkZF2/fl337t1zjOHl5aXU1FS5u7s7tgGvXbtWrVu3dvocAQAAAGSN119/XW+//ba6dOkiSTp37pz+9re/afHixZKk+fPnq0ePHjIYDLp06ZJOnDihq1evKioqSsnJyUpISND169cdpYHsJUztuwEMBoOsVqsmTZqkMWPGZNc0AQAA8IIgGHgOjEajWrRoIT8/P3366acKCAhwfGcvA7Rr1y4VKlRIvXv31pgxY1S4cOEMY9y4cUOpqak6fvy4jh49qp9//lnff/+9bt26pcTERMdxnp6eOnPmjEqUKOG0+QEAAADIOuHh4dqyZYtq1aql77//Xnny5JF0PxyYMmWKIiIiJEkTJkzQuHHjMjQftvcH+Pnnn3X58mUdPHhQe/fu1a+//uroU3Djxg1JUr58+bR//34FBwc7d4IAAAB44RAMZDGj0aimTZsqPj5ejRs31oYNG/TSSy9lOCZ9OODt7a1u3bpp/PjxKlCgQIZmYA/2Cbh7964kKS4uThcuXFBCQoLGjRunkiVLymKxZHhAAAAAAPDia9WqlaKjo1W9enWtXr1aAQEBGZoBnzt3TlOnTtXChQslSZMmTdLIkSPl6ekpm82WYXfAg88PZ86c0d27dxUbG6uLFy/q7bff1quvvur0OQIAAODFQzCQhYxGoxo3bqzdu3erQYMGWrVqlYoWLZrhxt7uceFA+pt6+7n2bcDpx7EfRygAAAAA5Dz2UKBq1apav369/P39M723zywcGDVqlDw8PB4aM7OAAAAAAHgQwUAWSUtLU6NGjbR7926FhYVp7dq1KlKkyGMX7Z82HHiUzAIHAAAAAC++pw0F7BITE/XJJ588VTgAAAAAPAmvkmSBpwkF0ucvVqtVNptN1atX1z/+8Q/VqVNHV69e1ddff61JkyYpJSXFsR34cQgFAAAAgJznSaFAZs8BQUFB+t///V/16dNHkjR+/HhNnz5daWlpTr12AAAA/DkQDPyXjEbjE0MBi8XiWMS/c+eOY9HfHg7MmTPHEQ4sWbLkD4UDAAAAAHKOpwkF7DuHV65cqfj4eMe5pUqVIhwAAABAliAY+C+kpaU5ego8KhSwWq2O3+fOnauePXvql19+kZubmyMsCAkJcYQDycnJf3jnAAAAAIAX3x8JBb766it17txZHTp0UHJysmMMwgEAAABkBYKBZ/Q0OwXS39jPnDlTQ4YM0YYNG3T58uWHxksfDtjLCk2dOlU3b96keRgAAACQw7Vs2VLR0dGqVq2aoqKiHhsKLFiwQP369ZMkTZw4UUWLFs0wVmbhwMyZMwkHAAAA8NRYcX4GRqNRderU0e7du1WnTh2tX79eRYoUkdlszvTGfsaMGRo1apQkafny5QoJCcl03PThwG+//abPPvtMc+fOdc6kAAAAADwX4eHh2rp1q/Lly6fOnTurWLFikn7vGfZgKNC/f39J0pIlS9S3b19JGXuWSQ+HAx9++KHmzJnjlPkAAAAg5yMYeAbTp0/XwYMHJUkVKlRQoUKFZLPZZDAYJD28U2D06NGSpLVr1+rNN9+U9PCNvf2zkJAQzZ07V2XLlpWfn5969uzphBkBAAAAeF7Gjh2rPHny6Pbt21qxYoUWLFggSXJ1dZXJZMo0FFi8eLG6du0q6f5zgj1EkO73MJPuhwPjx49Xhw4dJEktWrRw2pwAAACQs7nYMluhxmMdOHBAX375pRYuXChJ6t+/v2bNmiUvLy+ZzWZHQDBjxowMocAbb7whKfMbe/tOA7tjx47J29tbPj4+mX4PAAAAIOfYt2+f6tSpI4vFosqVK2vQoEHq3bu34/svv/xS77//viTp66+/1rvvvivp8c8OFy9elJ+fn3755Re5urrK19fXiTMCAABATkYw8Aekvyk/cuSIPv/8c0c4MGDAAH366afKly+fpD8eCthsNm3btk3NmzfP8D/T7z4AAAAAkHMlJCQoLCxMZrNZr776qoYOHaqePXsqIiLCUTLoaUOB3r176+rVq5o2bZoqVKjg/MkAAAAgRyMYeApms1lGo1F58+bNsFD/YDjwwQcfaM6cOZo1a5ZGjhwp6elDgS5dumjFihVatGiRevTo4dwJAgAAAHCK9OFASEiIKlasqKVLl0qSIiMj1b17d0mPDwX69++vBQsWqECBAjpx4gQ7BQAAAPCHEQw8QVpamsqXL6+yZctq1apVKlCgwGPDgbp16yo+Pl6StGbNmgw9BR4VCrzzzjtavny5SpYsqfj4ePn7+zt3kgAAAACcJiEhQXXr1nX0CpCkb775Ru+8846sVqtcXFweGQq89957Wrhwoby9vRUTE8NuAQAAADwTatQ8wdmzZ3XlyhVt27ZN/fr1082bN+Xq6iqr1SpJqly5sj744AP16dNHkhyhwIoVK/Tmm2/KZrM5bu7tMgsFAgICHKFA+gcEAAAAAH8uoaGhio+Pl6enpyQpICBAXl5eku43JE7/7lZmoUDhwoX1ww8/EAoAAADgmREMPIbValWFChUUGxurv/zlL1qxYsVjw4H0JYD279+vu3fvysXFJUOPgMxCAX9/f+3cudMRCtBoGAAAAPhzCw0NVUxMjNzd3XX+/HlNmjRJX331lSQ5njUeFQrExcWpfPny2Xn5AAAAyOEIBjKRmpqqsWPH6tatW5KkkJAQrVixQkFBQVq5cuUjw4GhQ4eqd+/ekqTp06dr1KhRSk1NdYxrNpszDQXi4uIIBQAAAIBcplatWoqLi5PBYNCRI0c0b948RURESLofDhAKAAAA4HkhGHiAxWJRWFiYpk2bprVr10q63x+gevXqWrly5RPDgUGDBjnKCs2fP18jR45UamqqbDabDAYDoQAAAAAAh5o1a2YIB+bOnasFCxY4vu/bty+hAAAAALIcwcADrl27psKFC8tgMGjHjh1KS0uT1WqV1Wp96nAgfc+Bf/7znxo+fLijx0CXLl0IBQAAAAA4hIaGZggHvvjiC0VGRqpfv36KiIggFAAAAECWc7Gl72wFSdK0adM0duxYubu7Kz4+XjVq1HA0AHNxcdGBAwfUsWNHJSYmqmPHjvryyy/18ssvy2q1OvoJHDlyRJ9//rkWLlwoSXr//feVnJysVatWEQoAAAAAeEhCQoLCwsJkNpvl4+Ojy5cvEwoAAADguSAYSMdms8nFxUUmk0nNmjVTbGysunfvrnnz5umll17KcMyzhAOSCAUAAAAAPFJCQoKaNGmiO3fuqHDhwtq5c6cqVKiQ3ZcFAACAPxlKCaXj4uLiWPivV6+eJOngwYNKSUmRJFmtVscxf6SsUN++fSVJ3t7ehAIAAAAAHik0NFRRUVHy8fFRbGwsoQAAAACeC3YMPEJiYqJq166tq1evauTIkfrss88yfP9Hdg4cOHBAy5Yt0+DBgxUYGEgoAAAAAOCx7t27pzx58mT3ZQAAAOBPimAgE/ZF/alTp+rjjz9W7dq1tWrVKvn4+GQ47o+EA2azWQaDwfETAAAAAAAAAIDsQCmhTNgX80NDQ2Wz2RQXF6fvv//+oeOepqyQPXexhwGEAgAAAAAAAACA7JRrg4Gn2SjRtGlTDRgwQJL0xRdf6NKlSw8d86RwwMXFJcuvHQAAAAAAAACAZ5WrggGj0ahPP/1U169fl9FolCRHo+AH2T9v2bKlvL29derUKZ0/fz7Tc54UDgAAAAAAAAAA8KLINT0GjEajGjRooISEBIWEhKhhw4bq0aOHXnnlFccx6XsC2KWmpio8PFw7duzQ66+/rtWrVz+yHNCTeg4AAAAAAAAAAJDdcs2OgejoaF2/fl0FCxbUjz/+qFmzZqlWrVr66KOPtGXLFkm/9xaw7wiwWq3y8vLShAkTVKRIER07dkw//fRThmPSe9TOgYEDB+rGjRvOmSgAAAAAAAAAAI+Ra4KBtm3baufOndq0aZOaNGmi4OBg3bt3T1OnTlWHDh303nvvKSYmRrdv33YEBPbmwYGBgSpVqpQSExO1du1ax3eZeTAcCA4O1rJlyzRixIin6msAAAAAAAAAAMDzlCtKCdlL/Nh/3rx5U2fPnlVERIQ2b96sn3/+WZIUEBCg4OBgTZo0SUFBQSpWrJhjjCVLlqhnz54qWbKkNm7cqCpVqjzV/9y7d6/69++vxYsXP/EcAAAAAAAAAACet1wRDKRnX7C3O3DggA4ePKgpU6boxo0bunnzpgoWLKjatWvr3XffVbt27eTh4aG7d++qTZs22r17txYuXKh33303054E6dm/NxqN8vT0dMb0AAAAAAAAAAB4rFwXDNhZLBa5ubk5/j579qz279+vBQsWKDY2VhaLRZLUrl071a1bV0OHDtXMmTM1atQoBQcHKzY2NsOOgsd5MIwAAAAAAAAAACC75Npg4HG+/fZbxcfH6x//+IejBFHz5s31zjvvaNy4cbp9+7YiIyPVtm3b7L5UAAAAAAAAAAD+EIKBdB7cRRATE6OYmBgtXrxY58+fV758+eTq6qqUlBT17NlTERER2Xi1AAAAAAAAAAD8cbk6GHiwKfGDn9tdvXrVUWJo27Zt8vf3V0xMjIKCgrLjsgEAAAAAAAAAeGa5NhhIv/h/+/Zt5cuX76EdA9LvuwisVqskacmSJWrevLlKlCghs9ksg8Hg9GsHAAAAAAAAAOBZuWb3BWSH9KHApk2bVLZsWe3fv/+hUECS4zMXFxe5urqqZ8+eKlGihCwWC6EAAAAAAAAAACDHyXXBQPpQIDo6Wt26ddPly5cVHR392PPSlxaSlGmIAAAAAAAAAADAi+5P8cp7ZiWAMvNgKNC+fXvduXNHEyZM0Icffvi8LxMAAAAAAAAAgGyXI3cMGI1GffTRR/r666919+7dp3573x4KbN26VR06dNCdO3c0btw4TZgwQdL9gAEAAAAAAAAAgD+zHNd8OC0tTfXq1dO+ffvk6empcuXKqV+/fqpVq5ZCQkIcxz1qF8GuXbsUFhYmSRo/frwmTpz42OMBAAAAAAAAAPgzyXGlhCIjI7Vv3z4VKFBAbm5uOnLkiIYMGSJXV1eNGjVKVatWVbt27RyL/OnLB0mS1WpVsWLF1LVrV0IBAAAAAAAAAECuk+N2DKSmpiosLExJSUkaPny4fv31V+3bt0+7du2SJLm6uqply5bq2LGj6tevr1KlSmU432Qy6eLFiwoMDJREKAAAAAAAAAAAyF1yVDBgtVrl4uKiKVOm6KOPPlKnTp20dOlSpaWl6fPPP9emTZu0b98+paamKm/evMqbN6/GjBmjV155RU2bNs0wjqurK6EAAAAAAAAAACDXyVHBgN2JEycUGhqqO3fuaMaMGRo+fLgk6fz58zp58qTGjx+vK1eu6Oeff5bBYJC7u7s6duyo1q1bq3nz5nr55ZcdYz1YaggAAAAAAAAAgD+zHBcM2BfyJ0yYoMmTJys8PFwLFy5UsWLFHDsBrl69qrS0NHXs2FG7d+92nFuwYEGVKFFCI0aM0CuvvKIaNWo4vrOfCwAAAAAAAADAn1mOWwm3v91fs2ZNSdKmTZt04MABSfdDA4vFIm9vb/3444+OUKBNmzYqUKCAzGazfvrpJw0ePFhNmzbVxIkTtXTpUkkiFAAAAAAAAAAA5Ao5bsdAet26ddPSpUvVsGFDLVu2TMWLF5ckrV27Vu3atZMk/c///I8++eQTHT16VDExMYqMjNShQ4ccY1StWlXbt29XoUKFsmMKAAAAAAAAAAA4VY4MBuxlf9asWaOePXuqQIEC2rhxo6pWrarVq1erQ4cOkqQxY8Zo6tSpGc41Go2aM2eOdu7cqR07dujYsWMKDAyk1wAAAAAAAAAAIFfIkcGAXUpKiho2bKhDhw5pwIABatq0qf76179KyhgKWCwWubm5OX7ap3zr1i1HiSGDwZBt8wAAAAAAAAAAwFlybDBgX+RfuXKlevXqJTc3N926dUvS7+WDpCc3FWanAAAAAAAAAAAgN8mxHXfd3NwkSZUqVZKPj48jFBg/fvxThwKSCAUAAAAAAAAAALlKjg0G7CpVqqS+fftKkgoXLqw2bdpIerpQAAAAAAAAAACA3CZbV85NJpMSExMlSc9S0ch+Tnh4uMqVK6c7d+5o+/btzzweAAAAAAAAAAB/dtkWDBiNRjVo0EBvvfWWTpw4IRcXlz+8mG8vA/Tqq6+qevXqMhqNmj9/vi5fvuwoNQQAAAAAAAAAAH6XbcHAxIkTtWfPHv373//WoEGDdPz48WcKB6xWqyRp7NixCgwMVFJSkmbPni2TyfQ8LhsAAAAAAAAAgBwt24KBgQMHqlOnTpKkH374QYMGDdJPP/30h8MBex+B4sWLq3z58pKkhIQE3b59O+svGgAAAAAAAACAHM6pwYDZbNZvv/0mSSpRooRmz56t119/XW5uboqNjdXgwYOfKRyQpCJFimjo0KFydXVVTEyMFi1a9DymAAAAAAAAAABAjuZic1KX3rS0NJUpU0YlS5bUunXrVKxYMUnSr7/+qsGDB2vDhg2yWCyqX7++5syZo0qVKslmszn6CDyO2WzW7du35ebmpiZNmujSpUuKi4tTYGDg854WAAAAAAAAAAA5itN2DCxatEgXLlzQnj171Lt3b125ckXS/RJAc+bMUdu2bZ9p54DValVsbKy6deumhIQE9enTR7t371ZgYKDMZrMzpgYAAAAAAAAAQI7htB0Dp0+f1jfffKOZM2fq3r17at26tRYtWvRf7RywWq2Kj4/XyJEjtW/fPjVq1EhRUVFyd3eXxWKRm5ubM6YGAAAAAAAAAECO4bRgQJISExMVERGhWbNmKTU19b8KB+yhwLBhw/Tjjz8qKChI27dvV6lSpZw1HQAAAAAAAAAAchynNh8OCgpS7969NXz4cHl5eWnz5s3q1avXU5cVslqtkh4OBQICAhQTE6NSpUpRPggAAAAAAAAAgMdw6o4Bu8TERH311Vf6+9//rtTUVLVq1UqRkZFPtXPAZDJp9+7dGj58uCMU2Llzp/z9/SkfBAAAAAAAAADAE2RLMCBJFy5c0JIlS/S3v/1NJpNJLVq00JIlSx4bDsyePVs2m029evXSwYMHCQUAAAAAAAAAAPiDnkswYLVa5eqaeZUis9ksg8EgSRo4cKDmz58vV1dXWa1WhYeHKyIi4pHhQOXKlZWWlqaffvqJUAAAAAAAAAAAgGeQZT0GTCaTNmzYcH/Q/7/Q/yCLxeIIBcaNG6f58+crb9686tSpk7y8vPSvf/3rkT0HDAaDjh49SigAAAAAAAAAAMB/IUuCAaPRqDp16uj999/XokWL7g/8QDiQfgH/ww8/1NSpU5U/f35t3LhRX375pYYNG/bYhsRvvvmmzGazfH19FRsbSygAAAAAAAAAAMAzyJJg4OzZszpw4IAuXbqkuXPnKiIi4v7g/z8ceDAU+OSTT5Q/f359++23atiwofLly6devXpp+PDhjwwHZsyYob59+2rPnj0KCAggFAAAAAAAAAAA4BlkWY+Bffv2KSwsTCaTSZUrV9agQYPUu3fvDMekDwVWr16tpk2bZuhHkJiYqIiICM2aNUupqalq3bq1Fi1a5Og5YD+WUAAAAAAAAAAAgGeTpc2HExISFBYWJrPZrMqVK2vAgAF67733JEljx47VtGnTMoQCkmSz2eTi4uIY48FwoE6dOlq3bp2KFi2aVZcJAAAAAAAAAECulaXBgJQxHKhSpYrGjBmjo0ePOnoKPC4UsEtMTFRkZKQmT54sSbp8+bK8vb2z8jIBAAAAAAAAAMiVsjwYkDKGAz4+Prp8+bKj0XD9+vUlPToUsDtz5ozWrFmjt99+W/7+/hlKDgEAAAAAAAAAgGfzXIIBKWPPATc3Nw0ePFgzZ86UJJnNZhkMhieOYe8lQE8BAAAAAAAAAACyxnN7Bb9mzZqKjY2Vh4eHLBaLfvjhBy1cuFCSZDAYZLVanziGPQwgFAAAAAAAAAAAIGs8tx0DdgkJCapbt64sFosqV66sQYMGqXfv3pJEeSAAAAAAAAAAAJzsua/Kh4aGKj4+XgaDQUeOHNHcuXMVERFx/5+7uj7VzgEAAAAAAAAAAJA1nPK6fmhoqOLi4ggHAAAAAAAAAADIZk6r45NZOBAZGXn/IignBAAAAAAAAACAUzz3HgMPSkhIUFhYmCwWi/z8/DR9+nR17tzZmZcAAAAAAAAAAECu5fRX9UNDQ7Vr1y7ZbDZZLBY1bNjQ2ZcAAAAAAAAAAECu5fQdA3aHDh1SkSJF5O/vL4vFIjc3t+y4DAAAAAAAAAAAcpVsCwbsCAUAAAAAAAAAAHCebA8GAAAAAAAAAACA8zi9xwAAAAAAAAAAAMg+BAMAAAAAAAAAAOQiBAMAAAAAAAAAAOQiBAMAAAAAAAAAAOQiBAMAAAAAAAAAAOQiBAMAAAAAAAAAAOQiBAMAAAAAAAAAAOQiBAMAAAAAAAAAAOQiBAMAAAAAAAAAAOQiBAMAAAAAAAAAAOQiBAMAAAAAAAAAAOQiBAMAAAAAAAAAAOQiBAMAAAAAAAAAAOQiBAMAAAAAAAAAAOQiBAMAAAAAAAAAAOQiBAMAAAAAAAAAAOQi/w8PvsYHYyLXiwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x240 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 222,
       "width": 771
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "dds.plot_volcano(title='DEG Analysis',figsize=(5,5),\n",
    "                 plot_genes_num=8,plot_genes_fontsize=12,)\n",
    "\n",
    "dds.plot_boxplot(genes= gene_list, \n",
    "   treatment_groups=disease_groups,\n",
    "   treatment_name='COVID-19',control_name='Healthy',\n",
    "                control_groups=healthy_groups,figsize=(10,3),fontsize=12,\n",
    "                 legend_bbox=(1.2,0.55))\n",
    "\n",
    "plt.xticks(rotation=45, ha=\"right\")  \n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec2b8dbb-ffef-400b-b866-02efc50429e4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7202b8ab",
   "metadata": {},
   "source": [
    "## 16) Additional boxplot examples\n",
    "\n",
    "A compact example showing how to manually specify a gene list from another upstream analysis step for plotting.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f13185fe-4363-41bc-b217-1302561726b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 160x240 with 1 Axes>,\n",
       " <Axes: title={'center': 'Gene Expression'}>)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 160x240 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 235,
       "width": 185
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dds.plot_boxplot(genes=['TNNT1','EGR2'], \n",
    "   treatment_groups=disease_groups,\n",
    "   treatment_name='COVID-19',control_name='Healthy',\n",
    "                control_groups=healthy_groups,figsize=(2,3),fontsize=12,\n",
    "                 legend_bbox=(1.2,0.55))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f4115a8-0253-4716-a211-2ef7c1118fb7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c2fcb6ac",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Next steps (optional)\n",
    "\n",
    "From this point, typical downstream bulk RNA-seq analyses include:\n",
    "- Functional enrichment (e.g., GO / KEGG / Reactome) on up/down DEGs  \n",
    "- Protein–protein interaction (PPI) network construction and visualization  \n",
    "- Pathway-level scoring and reporting\n",
    "\n",
    "You can extend this notebook by adding those sections once DEGs are finalized for your study.\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "omicverse",
   "language": "python",
   "name": "omicverse"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.17"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}