src/data_preprocess/collate_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### import package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "from collect_data_webcrawl import *\n",
    "\n",
    "root = '../../data'\n",
    "\n",
    "os.makedirs(root, exist_ok=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## fast on same EC and same species\n",
    "路徑: data/sequence_data\n",
    "1. same genus: check if filename which ends with sp. contains same data in the other specific species file\n",
    "2. pick out the rest sequence and search their species\n",
    "3. combine fasta data with same EC and same species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# from utils import *\n",
    "# clean_dir(os.path.join(root, 'species_sequence_data'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed file: 3_2_1_1Aspergillus_terreus_13.fasta\n",
      "Removed file: 3_2_1_1Rhizoctonia_solani_170.fasta\n",
      "Removed file: 3_2_1_1Aspergillus_fumigatus_74.fasta\n",
      "Removed file: 3_2_1_1Bacillus_licheniformis_19.fasta\n",
      "Removed file: 3_2_1_1Paenibacillus_lactis_2.fasta\n",
      "Removed file: 3_2_1_1Bacillus_cereus_76.fasta\n",
      "Removed file: 3_2_1_1Pseudomonas_stutzeri_3.fasta\n",
      "Removed file: 3_2_1_1Bacillus_subtilis_118.fasta\n",
      "Removed file: 3_2_1_1Streptomyces_strain_449.fasta\n",
      "Removed file: 3_2_1_1Penicillium_notatum_10.fasta\n",
      "Removed file: 3_4_21_62Bacillus_licheniformis_3.fasta\n",
      "Removed file: 3_2_1_78Lentinula_edodes_3.fasta\n",
      "Removed file: 3_2_1_78Bacillus_subtilis_79.fasta\n",
      "Removed file: 3_2_1_78Trichoderma_longibrachiatum_3.fasta\n",
      "Removed file: 3_1_1_3Bacillus_subtilis_37.fasta\n",
      "Removed file: 3_1_1_3Staphylococcus_arlettae_1.fasta\n",
      "Removed file: 3_1_1_3Aspergillus_niger_23.fasta\n",
      "Removed file: 3_1_1_3Trichosporon_asahii_5.fasta\n",
      "Removed file: 3_1_1_3Candida_albicans_51.fasta\n",
      "Removed file: 3_1_1_3Acinetobacter_calcoaceticus_45.fasta\n",
      "Removed file: 3_2_1_4Bacillus_licheniformis_33.fasta\n",
      "Removed file: 3_2_1_4Bacillus_sphaericus_12.fasta\n",
      "Removed file: 3_2_1_4Bacillus_vallismortis_1.fasta\n",
      "Removed file: 3_2_1_4Fibrobacter_succinogenes_31.fasta\n",
      "Removed file: 3_2_1_4Paenibacillus_barcinonensis_1.fasta\n",
      "Removed file: 3_2_1_4Staphylotrichum_coccosporum_1.fasta\n"
     ]
    }
   ],
   "source": [
    "from Bio import SeqIO\n",
    "import os\n",
    "import re\n",
    "import shutil\n",
    "from utils import *\n",
    "\n",
    "fasta_dir = os.path.join(root, 'uniprot/research/species/fasta')\n",
    "output_dir = os.path.join(root, 'species_sequence_data/fasta')\n",
    "os.makedirs(output_dir, exist_ok = True)\n",
    "\n",
    "clean_dir(output_dir)\n",
    "\n",
    "unk_specieses = [filename for filename in os.listdir(fasta_dir) if filename.endswith('.fasta') and re.search(r'\\(organism_name:([^\\|]+ sp.)\\)', filename)]\n",
    "\n",
    "k_specieses =  [filename for filename in os.listdir(fasta_dir) if filename.endswith('.fasta') and not re.search(r'\\(organism_name:([^\\|]+ sp.)\\)', filename)]\n",
    "\n",
    "\n",
    "for unk_species in unk_specieses:\n",
    "    ec = re.search(r'ec:(\\d+.\\d+.\\d+.\\d+)', unk_species).group(1)\n",
    "    genus = re.search(r'\\(organism_name:([^\\|]+) sp.\\)', unk_species).group(1)\n",
    "           \n",
    "\n",
    "    species_files = [filename for filename in os.listdir(fasta_dir) \n",
    "                                            if filename.endswith('.fasta') \n",
    "                                            and genus in filename\n",
    "                                            and not re.search(r'\\(organism_name:([^\\|] sp.)\\)', filename)]\n",
    "\n",
    "    fasta_file = os.path.join(fasta_dir,  unk_species)\n",
    "    records = list(SeqIO.parse(fasta_file, 'fasta'))\n",
    "\n",
    "    for record in records[:]:  # Iterate over a copy of the list\n",
    "        id = re.search(r'\\|([^\\|]+)\\|', record.id).group(1)\n",
    "\n",
    "        for species_file in species_files:\n",
    "            species_records = list(SeqIO.parse(os.path.join(fasta_dir, species_file), 'fasta'))\n",
    "            \n",
    "            found = [_ for _ in species_records if re.search(r'\\|([^\\|]+)\\|', _.id).group(1) == id]\n",
    "            \n",
    "            if len(found)>0:\n",
    "                records.remove(record)\n",
    "                break\n",
    "    if len(records)>0:\n",
    "        output_match = re.search(r'\\(ec:([^\\|]+)\\)AND\\(organism_name:([^\\|]+)\\)_reviewed' ,k_species)\n",
    "        output_name = (output_match.group(1) + '_' + output_match.group(2)).replace(' ','_').replace('.','_')\n",
    "        output_path = os.path.join(output_dir, f'{output_name}_{len(records)}.fasta')\n",
    "        with open(output_path, 'w') as f:\n",
    "            SeqIO.write(records, f, 'fasta')\n",
    "            \n",
    "\n",
    "\n",
    "for k_species in k_specieses:\n",
    "    src_file = os.path.join(fasta_dir, k_species)\n",
    "    \n",
    "    output_match = re.search(r'\\(ec:([^\\|]+)\\)AND\\(organism_name:([^\\|]+)\\)_reviewed' ,k_species)\n",
    "    output_name = (output_match.group(1) + '_' + output_match.group(2)).replace(' ','_').replace('.','_')\n",
    "    records = list(SeqIO.parse(src_file, 'fasta'))\n",
    "    output_path = os.path.join(output_dir, f'{output_name}_{len(records)}.fasta')\n",
    "    shutil.copy2(src_file, output_path)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MSA on same EC and same species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 直接做 data/species_sequence_data/fasta/中的MSA\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Collect ec number from Brenda\n",
    "1. crawl all ec number from brenda\n",
    "2. find data with `['Recommended Name']` containing any of ENZYME_TYPES\n",
    "3. find reviewed data with `['Biotechnological use']` containing keywords `detergent`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for enzyme_type in ENZYME_TYPES:\n",
    "    directory_path = os.path.join(root,'brenda',enzyme_type,'reviewed') \n",
    "    for filename in tqdm(os.listdir(directory_path)):\n",
    "        if filename.endswith(\".csv\"):\n",
    "            file_path = os.path.join(directory_path, filename)               \n",
    "            df = pd.read_csv(file_path)\n",
    "            if df.empty == False:\n",
    "                df_detergent = df[df['Biotechnological use'].apply(lambda x: isinstance(x, str) and 'detergent' in x.lower())]\n",
    "                end_index = filename.find('reviewed1') + len('reviewed1')\n",
    "                if end_index != -1:                \n",
    "                    save_file_name = f'{filename[:end_index].strip()}_{len(df_detergent)}_detergent.csv'\n",
    "                save_path = os.path.join(root,'brenda',enzyme_type,'reviewed_detergent', save_file_name)\n",
    "                df_detergent.to_csv(save_path, index=False)\n",
    "                print(f'Save file to path: {save_path}')\n",
    "            else:\n",
    "                print(f'No data found in {filename}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Collect data from UniProt\n",
    "1. crawl by using query in url from UniProt\n",
    "2. find reviewed data with `['Biotechnological use']` containing keywords `detergent`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "directory_path = os.path.join(root,'uniprot','reviewed') \n",
    "for filename in tqdm(os.listdir(directory_path)):\n",
    "    if filename.endswith(\".csv\"):\n",
    "        file_path = os.path.join(directory_path, filename)               \n",
    "        df = pd.read_csv(file_path)\n",
    "\n",
    "        df_detergent = df[df['Biotechnological use'].apply(lambda x: isinstance(x, str) and 'detergent' in x.lower())]\n",
    "        end_index = filename.find('reviewed1') + len('reviewed1')\n",
    "        if end_index != -1:                \n",
    "            save_file_name = f'{filename[:end_index].strip()}_{len(df_detergent)}_detergent.csv'\n",
    "        save_path = os.path.join(root,'uniprot','reviewed_detergent', save_file_name)\n",
    "\n",
    "        df_detergent.to_csv(save_path, index=False)\n",
    "\n",
    "        print(f'Save file to path: {save_path}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `detergent` as query to UniProt\n",
    "1. use `detergent` as query in url to crawl data from UniProt\n",
    "2. remove data containing `detergent-resistent`\n",
    "3. analyze which ec number contains largest amount of datas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "crawl_uniprot_by_query('detergent',save_path = 'data/uniprot/reviewed_detergent/', fields = '',return_format = 'json',reviewed='false')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "crawl_uniprot_by_query('detergent',save_path = 'data/uniprot/reviewed_detergent/', fields = '',return_format = 'json')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "json_file = 'detergent_reviewed1_450.json'\n",
    "json_path = os.path.join(root,'uniprot/reviewed_detergent', json_file)\n",
    "\n",
    "detergent_enzymes = []\n",
    "\n",
    "with open(json_path, 'r') as f:\n",
    "    datas = json.load(f)\n",
    "\n",
    "for data in datas:\n",
    "    data_str = str(data).lower()\n",
    "    if 'detergent' in data_str and 'detergent-resistant' not in data_str:\n",
    "        detergent_enzymes.append(data)\n",
    "\n",
    "    # break\n",
    "\n",
    "\n",
    "save_path = f'{root}/uniprot/reviewed_detergent_ec#/detergent-enzymes_reviewed1_{len(detergent_enzymes)}.json'\n",
    "with open(save_path, 'w', encoding='utf-8') as json_file:\n",
    "    json.dump(detergent_enzymes, json_file, ensure_ascii=False, indent=4)\n",
    "\n",
    "\n",
    "print(f'Save file to path: {save_path}')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Collating datas for target EC numbers \n",
    "\n",
    "|Category|EC Number|Protein Name|Reivewed|Unreviewed|\n",
    "|---|---|---|---|---|\n",
    "|Amylase|3.2.1.1|$\\alpha$-amylase|137|31069|\n",
    "|Cellulase|3.2.1.4|carboxymethyl cellulase (CMCase,endo-β-1,4-glucanase)|302|48445|\n",
    "|Lipase|3.1.1.3|triacylglycerol lipase|202|12762|\n",
    "|Mannanase|3.2.1.78|β-mannanase|61|8541|\n",
    "|Pectinase|4.2.2.2|Pectate Lyase|113|15406|\n",
    "|Pectinase|3.2.1.15|Polygalacturonase (PG)|89|5794|\n",
    "|Protease|3.4.21.14|Microbial serine proteases|0|5|\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtering data using EC number and organism\n",
    "\n",
    "|EC Number|Protein Name|Organism|#Reviewed|#Unreviewed|\n",
    "|---|---|---|---|---|\n",
    "|3.1.1.3|triacylglcerol acylhydrolases|Acinetobacter sp.|0|22|\n",
    "|3.1.1.3|triacylglcerol acylhydrolases|Bacillus subtilis strain|2|8|\n",
    "|3.1.1.3|triacylglycrol acylhydrolases|Candida albicans (C. albicans)|11|48|\n",
    "|3.2.1.1|α-amylase|Bacillus licheniformis AS08E|0|1|\n",
    "|3.2.1.1|α-amylase|Streptomyces strain A3|0|2|\n",
    "|3.2.1.4|CMCase|Bacillus sp.KSM-522|1|0|\n",
    "|3.2.1.4|CMCase|Bacillus sp.KSM-635|1|0|\n",
    "|3.2.1.4|CMCase|Fibrobacter succinogenes S85|1|19|\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "directory = os.path.join(root,'uniprot','research','detergent_enzymes_from_paper')\n",
    "pro_bar = tqdm(os.listdir(directory))\n",
    "\n",
    "for filename in pro_bar:\n",
    "    file = os.path.join(directory,filename)\n",
    "    df = pd.read_excel(file)\n",
    "    for index, row in df.iterrows():   \n",
    "        pro_bar.set_postfix(processing = f'(ec:{row['EC Number']})AND(organism_name:{row['Source Organism']})')\n",
    "        if row['EC Number']!='' and row['Source Organism'] != '':\n",
    "            data = crawl_uniprot_by_query(query=f'(ec:{row['EC Number']})AND(organism_name:{row['Source Organism']})',save_path='data/uniprot/research/fasta/', \n",
    "                                        fields=['ec','accession', 'id', 'protein_name','organism_name','ph_dependence', 'temp_dependence', 'ft_motif','ft_act_site', 'ft_binding', 'ft_dna_bind', 'kinetics', 'cc_cofactor','cc_activity_regulation','cc_catalytic_activity','cc_function', 'cc_biotechnology'],\n",
    "                                        headers=['EC number', 'Entry', 'Entry Name','Protein names','Organism', 'pH dependence', 'Temperature dependence', 'Motif', 'Active site', 'Binding site', 'DNA binding', 'Kinetics', 'Cofactor', 'Activty regulation', 'Catalytic activity', 'Function [CC]', 'Biotechnological use'])\n",
    "\n",
    "            if len(data)>0:\n",
    "                download_fasta(query=f'(ec:{row['EC Number']})AND(organism_name:{row['Source Organism']})',save_path='data/uniprot/research/fasta/')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistics of all original .fasta file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1Yoz9Dw9QMcYYY4wxxoTH9dTV1WFgYABHR0csXboUfn5+0NHREdXdsmULRo4ciRUrVoCI0L59exw6dAgWFhaiek2aNMGsWbOwatUqHD58GIWFhUhPT4ednR3++OMPTJ06FePHj4eZmRmGDh0KY2NjuV/VKyksLAw1a9aEk5OTwvX6+vr44osvsG3bNiHL6WOtW7cOtWvXxoYNG7Br1y6YmZkhICBA7hHDQYMGIT09HSEhITh8+DBatWqFY8eOoW3bth/c9/Tp03Hr1i389NNPePXqFdzc3D7ZABUA+Pr6QktLC/Pnz8ekSZMgk8nQvXt3BAcHQ19f/5P1UxoHBwecO3cOQUFBCAkJQWZmJgwMDPDVV18hMDAQ9vb2nz2GijI1NUV0dDQmTZqEn3/+GW/fvkWDBg2wb98+UbaZmpoaNm7ciICAAHz//ffIz89HaGgoD1AxVg4Jfa4Z+BhjjDHGGGOMMcYYqwCeg4oxxhhjjDHGGGOMKRUPUDHGGGOMMcYYY4wxpeIBKsYYY4wxxhhjjDGmVDxAxRhjjDHGGGOMMcaUigeoGGOMMcYYY4wxxphS8QAVY4wxxhhjjDHGGFMqVWUHwBhjjDH2d1FYWIj79+9DR0cHEolE2eEwxhhjjP3jEBFevXoFCwsLqKhUPC+KB6gYY4wxxv7f/fv3UaNGDWWHwRhjjDH2j3fnzh1Ur169wvV5gIoxxhhj7P/p6OgAeP8fKl1dXSVHwxhjjDH2z/Py5UvUqFFD+H9VRfEAFWOMMcbY/yt6rE9XV5cHqBhjjDHGPkJlp0vgSdIZY4wxxhhjjDHGmFLxABVjjDHGGGOMMcYYUyoeoGKMMcYYY4wxxhhjSsUDVIwxxhhjjDHGGGNMqXiAijHGGGOMMcYYY4wpFQ9QMcYYY4wxxhhjjDGl4gEqxhhjjDHGGGOMMaZUPEDFGGOMMcYYY4wxxpSKB6gYY4wxxhhjjDHGmFLxABVjjDHGGGOMMcYYUyoeoGKMMcYYY4wxxhhjSsUDVIwxxhhjjDHGGGNMqXiAijHGGGOMMcYYY4wpFQ9QMcYYY4wxxhhjjDGl4gEqxhhjjDHGGGOMMaZUPEDFGGOMMcYYY4wxxpSKB6gYY4wxxhhjjDHGmFLxABVjjDHGGGOMMcYYUyoeoGKMMcYYY4wxxhhjSsUDVIwxxhhjjDHGGGNMqXiAijHGGGOMMcYYY4wpFQ9QMcYYY4wxxhhjjDGl4gEqxhhjjDHGGGOMMaZUPEDFGGOMMcYYY4wxxpSKB6gYY4wxxhhjjDHGmFLxABVjjDHGGGOMMcYYUyoeoGKMMcYYY4wxxhhjSsUDVIwxxhhjjDHGGGNMqXiAijHGGGOMMcYYY4wplaqyA2CMMcYY+7tZnPgMmtrvKlR3sovRZ46GMcYYY+zfjzOoGGOMMcYYY4wxxphS8QAVY4wxxhhjjDHGGFMqHqBijDHGGGOMMcYYY0rFA1SMMcYYY4wxxhhjTKl4gIoxxhhjjDHGGGOMKRUPUDHGGGOMMcYYY4wxpeIBKsYYY4wxxhhjjDGmVDxAxRhjjDH2CURGRsLT0xMWFhaQSCTYvXu3skNijDHGGPvHUPoA1bNnz2BiYoKMjAxlh/KPlpycjOrVq+P169fKDkVk2rRpGDx4sLLDqLCgoCA4OzsrOwz2CWVkZEAikSAhIUHZoTD2QVatWgVPT09lhwEAsLKywtKlS5Udhoivry+6deum7DAAAK9fv4aTkxNWrFih7FAYY4wxxv5xlD5ANWfOHHTt2hVWVlafrY/Tp0+jZcuWMDQ0hFQqhb29PZYsWVLmNm/fvoWvry8cHR2hqqpaqf/8HjhwAM2aNYNUKkXVqlXL3DYvLw+TJk2Co6MjZDIZLCws4OPjg/v374vqzZkzBy1atICWlhb09fXl2qlbty6aN2+OxYsXK+wnPDwcVapUwfDhw+XWRUREQCKRoF69eigoKBCt09fXx4YNG4RlKysrSCQSSCQSSKVSWFlZoXfv3jhx4oRcuw8fPsSyZcswZcqUUvf/72b8+PE4fvy4ssP4xwoKChLOD1VVVVhZWWHs2LHIzs5WdmjsLzBkyBBUqVIF27dvF8qKzofSXkFBQQCA+Ph49OrVC6amptDU1ESdOnUwaNAgXLt2TdTHjh074O7uDj09PWhra6NBgwaYOXMmMjMzS42rvPsnABw/fhwtWrSAjo4OzMzMMGnSJOTn5wvrK/I3oeheWvL18OFDoc6HZNgMGDAAFy9eRFRUVLl1P7fY2Ni//ZcOysxi6tixI2bPno3u3bv/ZX0yxhhjjP1bKHWAKicnByEhIfD39/+s/chkMowYMQKRkZFISUnB1KlTMXXqVKxZs6bUbQoKCiCVSjFq1Ci0a9euwn3t2LED3t7e8PPzQ2JiIs6cOYPvvvuu1Po5OTm4ePEipk2bhosXL2Lnzp1ITU3F119/Lar37t079OrVC0OHDi21LT8/P6xcuVL0oapISEgIJk6ciPDwcLx9+1bh9jdv3sSmTZvK3ceZM2fiwYMHSE1NxaZNm6Cvr4927dphzpw5onrr1q1DixYtYGlpWW6bpSEihfvzuWhra8PQ0PAv6+/fqF69enjw4AEyMjIQHByMNWvW4IcfflB2WOwzy8nJwdatWzFx4kSsX79eKH/w4IHwWrp0KXR1dUVl48ePx/79+9G8eXPk5uYiLCwMKSkp+O2336Cnp4dp06YJbU2ZMgV9+vRBkyZNcOjQIVy5cgWLFi1CYmIiNm/eXGps5d0/ExMT0alTJ3To0AHx8fHYtm0b9u7di8mTJwt1KvM3ITU1VbSPJiYmwroPybBRV1fHd999h+XLl1d4m5Ly8vI+eNvijI2NoaWl9Una+lw4i4kxxhhj7B+KlGj79u1kbGwsV3758mXq0KEDyWQyMjExoX79+tGTJ0+E9QUFBRQcHEzW1takrq5ONWrUoNmzZ1eq7+7du1O/fv0qVLd///7UtWvXcuvl5eVRtWrVaN26dZWKpaTz588TALp165bcutDQUNLT01O4XW5uLmloaNCff/4pKr958yZJpVJ68eIFNWvWjMLCwkTrT548SQBowoQJVKNGDXr79q2wTk9Pj0JDQ4VlS0tLWrJkiVzf06dPJxUVFbp69apQVq9ePfrll19E9d6+fUsjR44kY2Nj0tDQoJYtW9L58+flYjl48CA1bNiQ1NTU6OTJk/Ty5Uv67rvvSEtLi8zMzGjx4sXk5uZGo0ePFrbdtGkTNWrUiLS1tcnU1JS8vLzo0aNHcm3/+eef1KhRI5JKpeTq6iqKOTAwkJycnETbNGnShLS0tEhPT49atGhBGRkZorohISFUo0YNkslkNHToUMrPz6fg4GAyNTUlY2PjSp2bAGjt2rXUrVs3kkqlZGNjQ3v27BHW5+fn04ABA8jKyoo0NTXJ1taWli5dKmqj6HydM2cOmZiYkJ6eHs2YMYPy8vJo/PjxVLVqVapWrRqtX79etN3t27epV69epKenR1WrVqWvv/6a0tPTKxy7ouNHRDRo0CAyMzMjIqKEhARyd3cnbW1t0tHRoYYNG1JsbKxQNyoqir744gvS1NSk6tWr08iRIyk7O1t0fHbt2iVqv+Q5eu7cOXJ2diYNDQ1q1KgR7dy5kwBQfHy8UCciIoKaNGlC6urqZGZmRpMmTaK8vDxhvZubG40YMYJGjx5N+vr6ZGJiQmvWrKHs7Gzy9fUlbW1tsra2poMHD4piqUi7I0eOpAkTJlDVqlXJ1NSUAgMDRW0sWrSI6tevT1paWlS9enUaOnQovXr1SlifkZFBXbp0IX19fdLS0qK6devSgQMHSn1PVqxYQTY2NqShoUEmJibUs2dPUTzDhw+n4cOHk66uLhkaGtLUqVOpsLCw1PZKs2HDBmrevDm9ePGCtLS06Pbt23J1FN2/Xr9+TUZGRtStWzeF7T5//pyI3r+vAOTO95L1ylLa/TMgIIAaN24sKtu7dy9pamrSy5cv5eqX9jeh6B5TkViIFJ/PpTl16hSpq6tTTk5OuXXT09MJAG3dupVat25NGhoawjWydu1asre3Jw0NDbKzs6MVK1YI27m6utLEiRNFbT1+/JhUVVXp1KlTRCT/N+D58+fk7+9PRkZGpKOjQ19++SUlJCQQEdGLFy9IRUVFuMYLCgqoatWq1KxZM2H7zZs3U/Xq1YXl8u5D+fn5NHbsWNLT0yMDAwOaMGEC+fj4lPo3ujLHuEhWVhYBoMDImzTv4pMKvT5V34wxxhhj/wZF/5/Kysqq1HZKzaCKiopCo0aNRGUvXrxAmzZt4OLigri4OBw+fBiPHj1C7969hToBAQGYP38+pk2bhuTkZGzZsgWmpqbCend3d/j6+pbab3x8PKKjo+Hm5vZJ9+fixYu4d+8eVFRU4OLiAnNzc3Ts2BFXrlypVDtZWVmQSCSlPopSGnV1dTg7O8s9BhIaGorOnTtDT08P/fr1Q0hIiMLtx4wZg/z8fPz888+V6hcARo8eDSLCnj17AACZmZlITk5G48aNRfUmTpyIHTt2YOPGjbh48SJsbGzg4eEh93jO5MmTMX/+fKSkpKBBgwYYN24czpw5g7179+LYsWOIiorCxYsXRdvk5eVh1qxZSExMxO7du5GRkaHwPJgyZQoWLVqEuLg4qKqqYsCAAQr3KT8/H926dYObmxsuXbqEmJgYDB48GBKJRKhz48YNHDp0CIcPH0Z4eDhCQkLQuXNn3L17F6dOnUJwcDCmTp2Kc+fOVfhYzpgxA71798alS5fQqVMn9O3bVzg+hYWFqF69OrZv347k5GRMnz4dP/74I37//XdRGydOnMD9+/cRGRmJxYsXIzAwEF26dEHVqlVx7tw5fP/99xgyZAju3r0rHDsPDw/o6OggKioKZ86cgba2Njp06IB3794B+N/jS5WdL04qlQpt9O3bF9WrV0dsbCwuXLiAyZMnQ01NTTiWHTp0QM+ePXHp0iVs27YNp0+fxogRIyrcV3Z2Nrp06YK6deviwoULCAoKwvjx40V17t27h06dOqFJkyZITEzEypUrERISgtmzZ4vqbdy4EUZGRjh//jxGjhyJoUOHolevXmjRogUuXryI9u3bw9vbGzk5OZVuVyaT4dy5c/jpp58wc+ZMHDt2TFivoqKC5cuXIykpCRs3bsSJEycwceJEYf3w4cORm5uLyMhIXL58GcHBwdDW1lZ4POLi4jBq1CjMnDkTqampOHz4MFq3bi0Xj6qqKs6fP49ly5Zh8eLFWLduXYWPeZGQkBD069cPenp66Nixo+jR4LIcOXIET58+Fe1jcUX3wbCwMGhra2PYsGFl1vsQubm50NTUFJVJpVK8ffsWFy5cqHR7zs7OMDc3x1dffYUzZ858cFzFNW7cGPn5+ZW6l0yePBmjR49GSkoKPDw8EBYWhunTp2POnDlISUnB3LlzMW3aNGzcuBHA++tz69atICKhjW3btsHCwgKtWrVS2EevXr3w+PFjHDp0CBcuXEDDhg3Rtm1bZGZmQk9PD87OzoiIiAAAXL58GRKJBPHx8cJjv6dOnRL+FlfkPrRo0SJs2LAB69evx+nTp5GZmYldu3ZV+ngWl5ubi5cvX4pejDHGGGNMCT7LcFkFde3alQYMGCAqmzVrFrVv315UdufOHQJAqamp9PLlS9LQ0KC1a9eW2q63tzdNnjxZrrxatWqkrq5OKioqNHPmzArHWdEMqvDwcAJANWvWpD/++IPi4uLIy8uLDA0N6dmzZxXq682bN9SwYUP67rvvFK4vK4OK6H1mmK+vr7BcUFBANWrUoN27dxMR0ZMnT0hdXZ1u3rwp1Cn+rf+qVavIwMCAXrx4QUQVz6AiIjI1NaWhQ4cSEVF8fDwBEGVRZGdnk5qamiiD6927d2RhYUE//fSTKJaieImIXr58SWpqarR9+3ahrChLo3gGVUmxsbEEQMg+KZ5BVeTAgQMEgN68eUNE4gygZ8+eEQCKiIhQ2H5gYCBpaWmJMiw8PDzIysqKCgoKhDI7OzuaN29eqXEWB4CmTp0qLGdnZxMAOnToUKnbDB8+XJQV079/f7K0tJSLoVWrVsJyfn4+yWQyCg8PJ6L3WQx2dnaizJnc3FySSqV05MgRInqfwWJnZ0d3794tNZaSGVRxcXFkZGRE33zzDRER6ejo0IYNGxRu6+/vT4MHDxaVRUVFkYqKivD+oJwMqtWrV5OhoaFQn4ho5cqVogyqH3/8UW5fV6xYQdra2sIxc3Nzoy+++ELueHl7ewtlDx48IAAUExPzwe0SETVp0oQmTZqk8JgQvc80NTQ0FJYdHR0pKCio1PrF7dixg3R1dRVmARXF4+DgIIp50qRJ5ODgUKH2i1y7do3U1NSETNddu3ZRrVq15DKxFN2/goODCQBlZmaW2UfHjh2pQYMGlYqrpNLun0eOHCEVFRXasmUL5efn0927d6lVq1YEgLZs2SJXv7S/CVevXqVVq1ZRXFwcnTlzhvz8/EhVVZUuXLigMB5F53NZqlatWur1U1xRBlXJbDNra2u5/Zk1axa5uroS0f+ypSIjI4X1rq6uovOz+N+AqKgo0tXVFWXdFvWzevVqIiIaN24cde7cmYiIli5dSn369CEnJyfhnmZjY0Nr1qwhoordh8zNzYW/F0TvM5erV6/+URlUgYGBBEDuxRlUjDHGGGMf5h+ZQfXmzRu5b60TExNx8uRJaGtrCy97e3sA7zMsUlJSkJubi7Zt25ba7qZNmzBv3jy58qioKMTFxWHVqlVYunQpwsPDP+n+FBYWAnifodOzZ080atQIoaGhkEgkokmDS5OXl4fevXuDiLBy5coPikEqlQoZHQBw7NgxvH79Gp06dQIAGBkZ4auvvhLNEVOcv78/DA0NERwcXOm+iUjILnrz5g0AiN7fGzduIC8vDy1bthTK1NTU0LRpU6SkpIjaKp55dfPmTeTl5aFp06ZCmZ6eHuzs7ETbXLhwAZ6enqhZsyZ0dHSEb+Vv374tqtegQQPh3+bm5gCAx48fy+2PgYEBfH194eHhAU9PTyxbtgwPHjwQ1bGysoKOjo6wbGpqirp160JFRUVUpqj90hSPTyaTQVdXV7T9ihUr0KhRIxgbG0NbWxtr1qyR28d69erJxeDo6CgsV6lSBYaGhkK7iYmJuH79OnR0dITrzsDAAG/fvsWNGzcAAE2bNsXVq1dRrVq1MuO/fPkytLW1IZVK0bRpU7i6uuKXX34BAIwbNw4DBw5Eu3btMH/+fKHtohg2bNgguvY9PDxQWFiI9PT0Ch27ooy74uedq6urXB1XV1dRJlzLli2RnZ0tZJQB4veh6HgVP4ZFWZtFx/BD2gXen4PF398///wTbdu2RbVq1aCjowNvb288e/ZMuK5HjRqF2bNno2XLlggMDMSlS5dKPR5fffUVLC0tUbt2bXh7eyMsLEx0fwCA5s2bi2J2dXVFWlqa3A8mlGX9+vXw8PCAkZERAKBTp07IyspS+OMJJVGxbJ1PUe9DtG/fHgsWLMD3338PDQ0N2NraCvfM4tdReezs7DBkyBA0atQILVq0wPr169GiRYtyf5Sjokre38tT/D76+vVr3LhxA/7+/qJrbPbs2cJ1aGxsjPbt2yMsLAwAkJ6ejpiYGPTt21dh+4mJicjOzoahoaGozfT0dKFNNzc3nD59GgUFBTh16hTc3d3h7u6OiIgI3L9/H9evX4e7u7vQXln3oaysLDx48ADNmjUTYlBVVZXL1K2sgIAAZGVlCa87d+58VHuMMcYYY+zDKHWAysjICM+fPxeVZWdnw9PTEwkJCaJXWloaWrduDalU+sH91apVC46Ojhg0aBDGjh0r/HrUp1I02FG3bl2hTENDA7Vr15YbQCipaHDq1q1bOHbsGHR1dT8ohszMTBgbGwvLISEhyMzMhFQqhaqqKlRVVXHw4EFs3LhRGFArTlVVFXPmzMGyZcvkfkmwLM+ePcOTJ09Qq1YtABA+qJZ8fytKJpNVqv7r16/h4eEBXV1dhIWFITY2Vnjso+jRkCJFj5QBED6YKzoWwPvHI2NiYtCiRQts27YNtra2OHv2rMK2itpTVFZa+4qUtf3WrVsxfvx4+Pv74+jRo0hISICfn1+Z+1iRuLKzs9GoUSO56+7atWtlTvKviJ2dHRISEpCSkoI3b95g7969wmBOUFAQkpKS0LlzZ5w4cQJ169YV3qfs7GwMGTJE1H9iYiLS0tJgbW0txFxyoOJTTf5cUnnHsLxzpzLtFrWRkZGBLl26oEGDBtixYwcuXLggTPRc9B4PHDgQN2/ehLe3Ny5fvozGjRuX+liujo4OLl68iPDwcJibm2P69OlwcnLCixcvKhVzWQoKCrBx40YcOHBAuMdoaWkhMzOz1IHw4mxtbQEAV69eLbde0WD15zBu3Di8ePECt2/fxtOnT9G1a1cAQO3atT+q3aZNm+L69eufIkS5+3t5it9Hix6pW7t2regau3Lliuie1rdvX/zxxx/Iy8vDli1b4OjoKBqYLS47Oxvm5uZy943U1FRMmDABANC6dWu8evUKFy9eRGRkpGiA6tSpU7CwsECdOnWE9j7VfagyNDQ0oKurK3p9qOzsbCFu4P0gX0JCQrn/B2CMMcYYY0oeoHJxcUFycrKorGHDhkhKSoKVlRVsbGxEL5lMhjp16kAqleL48eMf1XdhYSFyc3M/qo2SGjVqBA0NDaSmpgpleXl5yMjIKPOX7IoGp9LS0vDnn39+1K/IXblyBS4uLgDeDxrt2bMHW7duFf1nPz4+Hs+fP8fRo0cVttGrVy/Uq1cPM2bMqHC/y5Ytg4qKivDT69bW1tDV1RW9v9bW1lBXVxfNyZKXl4fY2FjRoF5JtWvXhpqaGmJjY4WyrKws0c/PX716Fc+ePcP8+fPRqlUr2NvbVyprqSwuLi4ICAhAdHQ06tevjy1btnySdj/EmTNn0KJFCwwbNgwuLi6wsbERZSF9qIYNGyItLQ0mJiZy152enl6l2lJXV4eNjQ2srKygrq4ut97W1hZjx47F0aNH0aNHD4SGhgoxJCcny/VvY2MjtGNsbCzKYktLSxNllDg4OODSpUuiX6os/uG7qE5MTIxooOvMmTPQ0dFB9erVK7Wvn7rdCxcuoLCwEIsWLULz5s1ha2urcKC4Ro0a+P7777Fz50788MMPWLt2baltqqqqol27dvjpp59w6dIlZGRkiDKbSs5pdPbsWdSpUwdVqlSpUMwHDx7Eq1evEB8fL7rPhIeHY+fOneUOhrVv3x5GRkb46aefFK4v2v67775DdnY2fv311zLrfQyJRAILCwtIpVKEh4ejRo0aaNiw4Ue1mZCQIHx58TFu3LiBt2/fCvf3yjI1NYWFhQVu3rwpd30VfbEAAF27dsXbt29x+PBhbNmypdTsKeD9Nfvw4UOoqqrKtVn0JYW+vj4aNGiAX375BWpqarC3t0fr1q0RHx+P/fv3i+aCLO8+pKenB3Nzc9E5m5+f/0HzhH0ucXFxcHFxEd6ncePGwcXFBdOnT1dyZIwxxhhjf39KHaDy8PBAUlKSKMtm+PDhyMzMhJeXF2JjY3Hjxg0cOXIEfn5+KCgogKamJiZNmoSJEydi06ZNuHHjBs6ePSua+NvHxwcBAQHC8ooVK7Bv3z6kpaUhLS0NISEhWLhwIfr16yfU+eWXX+QeG0xOTkZCQgIyMzORlZUl+lYUAM6fPw97e3vcu3cPAKCrq4vvv/8egYGBOHr0KFJTU4WfNe/Vq5ewnb29vZA1kpeXh2+++QZxcXEICwtDQUEBHj58iIcPH4qyYm7fvi18C1tQUCDEUvStOPA+++LevXvCT6Bv3rwZhoaG6N27N+rXry+8nJyc0KlTp1InSweA+fPnY/369Xj9+rXculevXuHhw4e4c+cOIiMjMXjwYMyePRtz5syBjY0NgPePxbRr1w6nT58WtpPJZBg6dCgmTJiAw4cPIzk5GYMGDUJOTg78/f1LjUVHRwf9+/fHhAkTcPLkSSQlJcHf3x8qKipCFkvNmjWhrq6On3/+GTdv3sTevXsxa9asUtusiPT0dAQEBCAmJga3bt3C0aNHkZaWBgcHh49q92PUqVMHcXFxOHLkCK5du4Zp06aJBu4+VN++fWFkZISuXbsiKioK6enpiIiIwKhRo4TH00qe75X15s0bjBgxAhEREbh16xbOnDmD2NhY4XhOmjQJ0dHRGDFihJA1uWfPHtEk6W3atMEvv/yC+Ph4xMXF4fvvvxdlJH333XeQSCQYNGgQkpOTcfDgQSxcuFAUx7Bhw3Dnzh2MHDkSV69exZ49exAYGIhx48ZV6nGukj5FuzY2NsjLyxPO482bN2PVqlWiOmPGjMGRI0eQnp6Oixcv4uTJk6Wek/v378fy5cuRkJCAW7duYdOmTSgsLBQ9Hnv79m2MGzcOqampCA8Px88//4zRo0cL6wMCAuDj41NqzEU/DODk5CS6z/Tu3Rv6+vrC42KlkclkWLduHQ4cOICvv/4af/75JzIyMhAXF4eJEyfi+++/BwA0a9YMEydOxA8//ICJEycK1+Xx48fRq1cvYaJvRSpy/1ywYAEuX76MpKQkzJo1C/Pnz8fy5ctFA3Xl/U1YunQp9uzZg+vXr+PKlSsYM2YMTpw4geHDhwt1PjTDJioqCrVr1xayCT/EjBkzMG/ePCxfvhzXrl3D5cuXERoaisWLFwt1ZDIZunXrhmnTpiElJQVeXl6ltteuXTu4urqiW7duOHr0KDIyMhAdHY0pU6YgLi5OqOfu7o6wsDBhMMrAwAAODg7Ytm2baICqIveh0aNHY/78+di9ezeuXr2KYcOGyQ1OKjOLyd3dHUQk96rojwYwxhhjjP2nfcJ5sD5I06ZNadWqVaKya9euUffu3UlfX5+kUinZ29vTmDFjhIlTCwoKaPbs2WRpaUlqampUs2ZNmjt3rrC9m5sb9e/fX1hevnw51atXj7S0tEhXV5dcXFzo119/FU0iHRgYSJaWlqI4LC0tFU6cWqRo0u3iP4H97t07+uGHH8jExIR0dHSoXbt2dOXKFVG7AIRJnYsms1X0OnnypLBN//79y60zd+5c8vDwEJYdHR1p2LBhCo/7tm3bSF1dnZ48eVLqT6O3b99eFGvJY6Kurk41a9ak3r1704kTJ+T6OHjwIFWrVk10nN+8eUMjR44kIyMj0tDQoJYtW9L58+fljmnJWF6+fEnfffcdaWlpkZmZGS1evJiaNm0qmgx/y5YtZGVlRRoaGuTq6kp79+4VTY6tqO2iydyL3sPik3w/fPiQunXrRubm5qSurk6WlpY0ffp0YX9KTghOpHjyZDc3tzIncy8O5UwC/vbtW/L19SU9PT3S19enoUOH0uTJk0VxVDSGkhPeP3jwgHx8fIT3pnbt2jRo0CBhYjtF53tJio5JkdzcXPr222+pRo0apK6uThYWFjRixAjRhObnz5+nr776irS1tUkmk1GDBg1ozpw5wvp79+5R+/btSSaTUZ06dejgwYNyE/nHxMSQk5MTqaurk7OzM+3YsUN0HhARRUREUJMmTUhdXZ3MzMxo0qRJlJeXV6njRST/fn1Iu127dhXdrxYvXkzm5uYklUrJw8ODNm3aJDpvR4wYQdbW1qShoUHGxsbk7e1NT58+VXjMo6KiyM3NjapWrUpSqZQaNGhA27ZtE8UzbNgw+v7770lXV5eqVq1KP/74o2iS6v79+5Obm5vC9h8+fEiqqqr0+++/K1w/dOhQcnFxEZbL+pGH2NhY6tGjBxkbG5OGhgbZ2NjQ4MGDKS0tTVRv27Zt1Lp1a9LR0RHOkZkzZ8rdM4qryP3zyy+/JD09PdLU1KRmzZrRwYMH5dop729CcHAwWVtbk6amJhkYGJC7u7vcvbHoOir5Kn4OKNK+ffsK/9hC0d+V4ud8kbCwMHJ2diZ1dXWqWrUqtW7dmnbu3Cmqc/DgQQJArVu3VngMil8HL1++pJEjR5KFhQWpqalRjRo1qG/fvqIfyNi1axcBoJUrVwplo0ePJgB09epVUfvl3Yfy8vJo9OjRpKurS/r6+jRu3Djy8fER3fM+9BgXKZrU82MnSWeMMcYY+6/60EnSJUSfcebZCjhw4AAmTJiAK1eufFT2wn/du3fvUKdOHWzZskU0CbkyERGaNWuGsWPHlvkt/Id4/fo1qlWrhkWLFpWZfcUYK527uzucnZ2xdOlSZYfCypCUlIQ2bdrg2rVrlX7kllXey5cvoaenh8DIm9DU1il/AwCTXYw+c1SMMcYYY/8cRf+fysrKqtT8nqqfMaYK6dy5M9LS0nDv3j3UqFFD2eH8Y92+fRs//vjj32ZwCng/n8uaNWtw+fLlj24rPj4eV69eRdOmTZGVlYWZM2cCgDCRMWOM/Vs9ePAAmzZt4sEpxhhjjDH2r6b0ASrg/Zwq7OMUTST7d+Ps7AxnZ+dP0tbChQuRmpoKdXV1NGrUCFFRUcJEvP8EYWFhGDJkiMJ1lpaWSEpK+osjYoz9ExTNK1hk7ty5mDt3rsK6rVq1wqFDh/6KsBhjjDHGGPuklP6IH2P/Fa9evcKjR48UrlNTUyvzlx4ZY6xIZmYmMjMzFa6TSqWoVq3aXxzRvws/4scYY4wx9nH+sY/4MfZfoaOjAx2din3YYYyx0hgYGMDAwEDZYTDGGGOMMfZJ8azkjDHGGGOMMcYYY0ypeICKMcYYY4wxxhhjjCkVD1AxxhhjjDHGGGOMMaXiOagYY4wxxkoY52RYqUk9GWOMMcbYx+EMKsYYY4wxxhhjjDGmVDxAxRhjjDHGGGOMMcaUigeoGGOMMcYYY4wxxphS8QAVY4wxxhhjjDHGGFMqHqBijDHGGGOMMcYYY0rFv+LHGGOMMVbC4sRn0NR+V6G6k12MPnM0jDHGGGP/fpxBxRhjjDHGGGOMMcaUigeoGGOMMcYYY4wxxphS8QAVY4wxxhhjjDHGGFMqHqBijDHGGGOMMcYYY0rFA1SMMcYYY4wxxhhjTKl4gIoxxhhjjDHGGGOMKRUPUDHGGGOMMcYYY4wxpeIBKsYYY4yxTyAyMhKenp6wsLCARCLB7t27lR0SY4wxxtg/Bg9QKfDs2TOYmJggIyND2aH8bSUnJ6N69ep4/fq1skMRmTZtGgYPHqzsMCosKCgIzs7Oyg6DVcKGDRugr68vLP8V72F5ffr6+qJbt26frX9lnqdWVlZYunSpUvr+nCrznk2ePBkjR478vAFV0N9x0MXd3R1jxoxRdhgAgNevX8PJyQkrVqxQdiiMMcYYY/84PEClwJw5c9C1a1dYWVl9tj5Onz6Nli1bwtDQEFKpFPb29liyZEmZ20RERKBr164wNzeHTCaDs7MzwsLCyu1r1KhRaNSoETQ0NCr9IZOI0LFjR7kPJXXr1kXz5s2xePFihduFh4ejSpUqGD58uML9kEgkqFevHgoKCkTr9PX1sWHDBmHZysoKEokEEokEUqkUVlZW6N27N06cOCHX7sOHD7Fs2TJMmTKlUvuoTOPHj8fx48eVHcY/RvHzQSaToWHDhti+fftfGkOfPn1w7dq1f32ffxexsbH/qEHnilq2bJnoXleW8ePHY+PGjbh58+bnDaoCHjx4gI4dOyo7jDIREaZPnw5zc3NIpVK0a9cOaWlpf0nfHTt2xOzZs9G9e/e/pD/GGGOMsX8THqAqIScnByEhIfD39/+s/chkMowYMQKRkZFISUnB1KlTMXXqVKxZs6bUbaKjo9GgQQPs2LEDly5dgp+fH3x8fLB///5y+xswYAD69OlT6TiXLl0KiUSicJ2fnx9WrlyJ/Px8uXUhISGYOHEiwsPD8fbtW4Xb37x5E5s2bSo3hpkzZ+LBgwdITU3Fpk2boK+vj3bt2mHOnDmieuvWrUOLFi1gaWlZgT1TjIgU7s/noq2tDUNDw7+sv3+DovMhPj4eTZo0QZ8+fRAdHf2X9S+VSmFiYvKX9aesPv8KeXl55dYxNjaGlpbWXxDNX0tPT0+UFVcWIyMjeHh4YOXKlR/U16e8r5mZmUFDQ+OTtPW5/PTTT1i+fDlWrVqFc+fOQSaTwcPDo9S/RYwxxhhj7O+BB6hKOHjwIDQ0NNC8eXNR+ZUrV9CxY0doa2vD1NQU3t7eePr0qbC+sLAQP/30E2xsbKChoYGaNWvKDaAU5+LiAi8vL9SrVw9WVlbo168fPDw8EBUVVeo2P/74I2bNmoUWLVrA2toao0ePRocOHbBz584y92n58uUYPnw4ateuXcGj8F5CQgIWLVqE9evXK1z/1VdfITMzE6dOnRKVp6enIzo6GpMnT4atrW2p8Y0cORKBgYHIzc0tMw4dHR2YmZmhZs2aaN26NdasWYNp06Zh+vTpSE1NFept3boVnp6eom1zc3MxatQomJiYQFNTE1988QViY2OF9UXZXIcOHRKyzE6fPo1Xr16hb9++kMlkMDc3x5IlS+QeI9m8eTMaN24sxPfdd9/h8ePHcm0fP34cjRs3hpaWFlq0aCGKueSjUxEREWjatClkMhn09fXRsmVL3Lp1S1R3/fr1qFmzJrS1tTFs2DAUFBTgp59+gpmZGUxMTMo870qSSCRYt24dunfvDi0tLdSpUwd79+4V1hcUFMDf3x+1atWCVCqFnZ0dli1bJmqj6FGluXPnwtTUFPr6+pg5cyby8/MxYcIEGBgYoHr16ggNDRVtd+fOHfTu3Rv6+vowMDBA165dK/RYbdHxtrW1xYoVKyCVSrFv374KxQq8H8h0cHCApqYm7O3t8euvvwrrMjIyIJFIsHPnTnz55ZfQ0tKCk5MTYmJihDolH7crsnr1atSoUQNaWlro3bs3srKyhHWxsbH46quvYGRkBD09Pbi5ueHixYui7V+8eIEhQ4bA1NQUmpqaqF+/vjD4XFqfpVH0WJyzszOCgoIAvB+wCAoKQs2aNaGhoQELCwuMGjWq3HY/dh8lEglWrlyJr7/+GjKZTDhX9+3bhyZNmkBTUxNGRkai7JOS+1LeOQsAp06dQtOmTaGhoQFzc3NMnjxZNEDj7u6OUaNGYeLEiTAwMICZmZlwbIq8ePECAwcOhLGxMXR1ddGmTRskJiYK64uux82bN8PKygp6enr49ttv8erVK6HOH3/8AUdHR0ilUhgaGqJdu3bCY9ElH/Erqy4AeHp6YuvWreW9RQBKv68VFhZi3rx5wjXi5OSEP/74A8D7v2HVq1eXGwSLj4+HioqKcB8qmU1b1nV85coVqKio4MmTJwCAzMxMqKio4NtvvxW2nz17Nr744gthuby/ta9fv4aPjw+0tbVhbm6ORYsWieIlIixduhRTp05F165d0aBBA2zatAn379//2z2ayBhjjDHGxHiAqoSoqCg0atRIVPbixQu0adMGLi4uiIuLw+HDh/Ho0SP07t1bqBMQEID58+dj2rRpSE5OxpYtW2Bqaiqsd3d3h6+vb6n9xsfHIzo6Gm5ubpWKNysrCwYGBpXapiJycnLw3XffYcWKFTAzM1NYR11dHc7OznKDaqGhoejcuTP09PTQr18/hISEKNx+zJgxyM/Px88//1zp+EaPHg0iwp49ewC8/+CTnJyMxo0bi+pNnDgRO3bswMaNG3Hx4kXY2NjAw8MDmZmZonqTJ0/G/PnzkZKSggYNGmDcuHE4c+YM9u7di2PHjiEqKkruw3ZeXh5mzZqFxMRE7N69GxkZGQrf4ylTpmDRokWIi4uDqqoqBgwYoHCf8vPz0a1bN7i5ueHSpUuIiYnB4MGDRRlsN27cwKFDh3D48GGEh4cjJCQEnTt3xt27d3Hq1CkEBwdj6tSpOHfuXIWP5YwZM9C7d29cunQJnTp1Qt++fYXjU/Shdfv27UhOTsb06dPx448/4vfffxe1ceLECdy/fx+RkZFYvHgxAgMD0aVLF1StWhXnzp3D999/jyFDhuDu3bvCsfPw8ICOjg6ioqJw5swZaGtro0OHDnj37h2A/33ILmvQSlVVFWpqanj37l2FYg0LC8P06dMxZ84cpKSkYO7cuZg2bRo2btwo956NHz8eCQkJsLW1hZeXV5kZKNevX8fvv/+Offv24fDhw4iPj8ewYcOE9a9evUL//v1x+vRpnD17FnXq1EGnTp2EwYzCwkJ07NgRZ86cwW+//Ybk5GTMnz8fVapUqcA7WHk7duzAkiVLsHr1aqSlpWH37t1wdHQsc5uP3cciQUFB6N69Oy5fvowBAwbgwIED6N69Ozp16oT4+HgcP34cTZs2LTOWss7Ze/fuoVOnTmjSpAkSExOxcuVKhISEYPbs2aI2Nm7cCJlMhnPnzuGnn37CzJkzcezYMWF9r1698PjxYxw6dAgXLlxAw4YN0bZtW9G948aNG9i9ezf279+P/fv349SpU5g/fz6A94/CeXl5YcCAAUhJSUFERAR69OgBIpLbn4rUbdq0Ke7evVupuRFL3tfmzZuHTZs2YdWqVUhKSsLYsWPRr18/nDp1CioqKvDy8sKWLVtEbYSFhaFly5YKM1PLu47r1asHQ0ND4UuMqKgo0TLwfjDR3d0dQMX+1k6YMAGnTp3Cnj17cPToUURERIjuzenp6Xj48CHatWsnlOnp6aFZs2aigebicnNz8fLlS9GLMcYYY4wpATGRrl270oABA0Rls2bNovbt24vK7ty5QwAoNTWVXr58SRoaGrR27dpS2/X29qbJkyfLlVerVo3U1dVJRUWFZs6cWalYt23bRurq6nTlypUK1Q8MDCQnJ6cK1R08eDD5+/sLywBo165dcvW6d+9Ovr6+wnJBQQHVqFGDdu/eTURET548IXV1dbp586ZQ5+TJkwSAnj9/TqtWrSIDAwN68eIFERHp6elRaGioUNfS0pKWLFmiMEZTU1MaOnQoERHFx8cTALp9+7awPjs7m9TU1CgsLEwoe/fuHVlYWNBPP/0kiqUoXiKily9fkpqaGm3fvl0oe/HiBWlpadHo0aNLO2QUGxtLAOjVq1eitv/880+hzoEDBwgAvXnzhojE78mzZ88IAEVERChsPzAwkLS0tOjly5dCmYeHB1lZWVFBQYFQZmdnR/PmzSs1zuIA0NSpU4Xl7OxsAkCHDh0qdZvhw4dTz549heX+/fuTpaWlXAytWrUSlvPz80kmk1F4eDgREW3evJns7OyosLBQqJObm0tSqZSOHDlCRETnzp0jOzs7unv3rlCn+PmQm5tLc+fOJQC0f//+CsVqbW1NW7ZsEdWZNWsWubq6EhFReno6AaB169YJ65OSkggApaSkEBFRaGgo6enpCesDAwOpSpUqojgPHTpEKioq9ODBA4VxFRQUkI6ODu3bt4+IiI4cOUIqKiqUmpqqsL6iPotfy/3796euXbsKy4quGycnJwoMDCQiokWLFpGtrS29e/dOYX8lfYp9JHp/vo0ZM0ZUz9XVlfr27Vtq3yX3pbxz9scff5Q7t1asWEHa2trCOerm5kZffPGFqJ8mTZrQpEmTiIgoKiqKdHV16e3bt6I61tbWtHr1auGYlLweJ0yYQM2aNSMiogsXLhAAysjIULhfxd+z8uoSEWVlZZV5fyhO0X3t7du3pKWlRdHR0aK6/v7+5OXlRUTv76MSiYRu3bpFRO/fw2rVqtHKlSuF+sX/FlTkOu7RowcNHz6ciIjGjBlDEyZMoKpVq1JKSgq9e/eOtLS06OjRo0RU/t/aV69ekbq6Ov3+++/C+mfPnpFUKhXuzWfOnCEAdP/+fVE7vXr1ot69eys8XoGBgQRA7hUYeZPmXXxSoZcipf3dZIwxxhj7tyv6v2tWVlaltuMMqhLevHkDTU1NUVliYiJOnjwJbW1t4WVvbw/g/TfoKSkpyM3NRdu2bUttd9OmTZg3b55ceVRUFOLi4rBq1SosXboU4eHhFYrz5MmT8PPzw9q1a1GvXr1K7GH59u7dixMnTlTol7OkUilycnKE5WPHjuH169fo1KkTgPdzp3z11VelPibo7+8PQ0NDBAcHVzpOIhKyi968eQMAovfuxo0byMvLQ8uWLYUyNTU1NG3aFCkpKaK2imde3bx5E3l5eaIsDj09PdjZ2Ym2uXDhAjw9PVGzZk3o6OgI2W+3b98W1WvQoIHwb3NzcwAQPQpYxMDAAL6+vvDw8ICnpyeWLVuGBw8eiOpYWVlBR0dHWDY1NUXdunWhoqIiKlPUfmmKxyeTyaCrqyvafsWKFWjUqBGMjY2hra2NNWvWyO1jvXr15GIonpFTpUoVGBoaCu0mJibi+vXr0NHREa4pAwMDvH37Fjdu3ADwPmPk6tWrqFatmqivSZMmQVtbG1paWggODsb8+fPRuXPncmN9/fo1bty4AX9/f9G1PHv2bKFPRcekrPesSM2aNUVxurq6orCwUHic89GjRxg0aBDq1KkDPT096OrqIjs7W4gtISEB1atXh62tbal9fEq9evXCmzdvULt2bQwaNAi7du0qd46ij93HIiWzHBMSEsq8dypS1jmbkpICV1dXUeZhy5YtkZ2dLWTwlWwDeP8+Fz8/s7OzYWhoKDpX0tPTRedKyeuxeBtOTk5o27YtHB0d0atXL6xduxbPnz9XuD8VqSuVSgFAdL8tT/Fjff36deTk5OCrr74S7dOmTZuEfXJ2doaDg4OQRXXq1Ck8fvwYvXr1Uth+Ra5jNzc3RERECO21adMGrVu3RkREBGJjY0X36PL+1t64cQPv3r1Ds2bNhBgMDAzk7s2VFRAQgKysLOF1586dj2qPMcYYY4x9GFVlB/B3Y2RkJPfBIDs7G56engoHUczNzT/ql5Vq1aoFAHB0dMSjR48QFBQELy+vMrc5deoUPD09sWTJEvj4+Hxw36U5ceIEbty4ITfnTc+ePdGqVSvhwwbw/tE6a2trYTkkJASZmZnChyng/eNLly5dwowZM0SDGMD7R7TmzJkDX19fjBgxosIxPnv2DE+ePBGOn5GREQDg+fPnMDY2rnA7RWQyWaXqv379Gh4eHvDw8EBYWBiMjY1x+/ZteHh4CI+oFVFTUxP+XfShubCwUGG7oaGhGDVqFA4fPoxt27Zh6tSpOHbsmDAnWvG2itpTVFZa+4qUtf3WrVsxfvx4LFq0CK6urtDR0cGCBQvkHiGsbFzZ2dlo1KiRwl+hLO/9mzBhAnx9fYU5aoqOaXmxZmdnAwDWrl0r+oALQO5Rusq8ZxXRv39/PHv2DMuWLYOlpSU0NDTg6uoqnCvFr5dPQUVFRe5RsuITkteoUQOpqan4888/cezYMQwbNgwLFizAqVOn5N63iipvH4uUvNY+ZN8/9pwvr43s7GyYm5uL7nVFit8Xy2qjSpUqOHbsGKKjo3H06FH8/PPPmDJlCs6dOyfct4pUpG7Ro4WVub8VP9ZF5/+BAwfkBn2LT3ret29fbNmyBZMnT8aWLVvQoUOHUn/IoSLXcdHcfWlpaUhOTsYXX3yBq1evIiIiAs+fPxfm5ytqr6y/tdevXy93n4seSX/06JEwuFy0XNqv2GpoaHyyid+zs7NFcaanpyMhIQEGBgaoWbPmJ+mDMcYYY+zfijOoSnBxcUFycrKorGHDhkhKSoKVlRVsbGxEL5lMhjp16kAqleL48eMf1XdhYWG5E4ZHRESgc+fOCA4O/mw/vT558mRcunQJCQkJwgsAlixZIjfR9ZUrV+Di4gLg/aDRnj17sHXrVtG28fHxeP78OY4ePaqwv169eqFevXqYMWNGhWNctmwZVFRUhEmGra2toaurK3rvrK2toa6ujjNnzghleXl5iI2NRd26dUttu3bt2lBTUxNNpp6VlYVr164Jy1evXsWzZ88wf/58tGrVCvb29pXKWiqLi4sLAgICEB0djfr168vNCfNXOnPmDFq0aIFhw4bBxcUFNjY2ctlGH6Jhw4ZIS0uDiYmJ3DWlp6dX5rZGRkawsbGBmZmZKEumvFhNTU1hYWGBmzdvyvVZcsCgsm7fvo379+8Ly2fPnoWKioqQ2XHmzBmMGjUKnTp1Qr169aChoSGa+LlBgwa4e/eu6Bz7GMbGxqLsu5cvXyI9PV1URyqVwtPTE8uXL0dERARiYmJw+fLlz7aPpWnQoMFH3zuLc3BwQExMjGiA7syZM9DR0UH16tUr1EbDhg3x8OFDqKqqyp0rRYPhFSGRSNCyZUvMmDED8fHxUFdXx65duz6o7pUrV6CmpvbBGbN169aFhoYGbt++LbdPNWrUEOp99913uHLlCi5cuIA//vgDffv2LbXNilzHjo6OqFq1KmbPng1nZ2doa2vD3d0dp06dQkREhDD/VFF7Zf2ttba2hpqammiA/Pnz56LrplatWjAzMxOdUy9fvsS5c+fg6ur6QceuMuLi4uDi4iL8XRw3bhxcXFwwffr0z943Y4wxxtg/HQ9QleDh4YGkpCRRFtXw4cORmZkJLy8vxMbG4saNGzhy5Aj8/PxQUFAATU1NTJo0CRMnThQelzh79qxocnAfHx8EBAQIyytWrMC+ffuQlpaGtLQ0hISEYOHChejXr59Q55dffhE9+nLy5El07twZo0aNQs+ePfHw4UM8fPhQNGnvrl27hEciily/fh0JCQl4+PAh3rx5IwwcFWU23Lt3D/b29jh//jyA999A169fX/QC3j/iU/yDfEZGBu7duydMRrt582YYGhqid+/eom2dnJzQqVOnUidLB4D58+dj/fr1ol+tKvLq1Ss8fPgQd+7cQWRkJAYPHozZs2djzpw5sLGxAfA+Y6Rdu3Y4ffq0sJ1MJsPQoUMxYcIEHD58GMnJyRg0aBBycnLg7+9faiw6Ojro378/JkyYgJMnTyIpKQn+/v5QUVERBkRq1qwJdXV1/Pzzz7h58yb27t2LWbNmldpmRaSnpyMgIAAxMTG4desWjh49irS0NDg4OHxUux+jTp06iIuLw5EjR3Dt2jVMmzZNNHD3ofr27QsjIyN07doVUVFRSE9PR0REBEaNGiU8hnX+/HnY29vj3r17nyzWGTNmYN68eVi+fDmuXbuGy5cvIzQ0FIsXL/6o/dHU1ET//v2RmJiIqKgojBo1Cr179xayOerUqYPNmzcjJSUF586dQ9++fUWZQ25ubmjdujV69uyJY8eOIT09XZgM/0O0adMGmzdvRlRUFC5fvoz+/fuLssQ2bNiAkJAQXLlyBTdv3sRvv/0GqVSqcCLsT7WPpQkMDER4eDgCAwORkpKCy5cvf9Ajv0WGDRuGO3fuYOTIkbh69Sr27NmDwMBAjBs3Ti6DszTt2rWDq6srunXrhqNHjyIjIwPR0dGYMmUK4uLiKtTGuXPnMHfuXMTFxeH27dvYuXMnnjx5ovB6rkjdqKgotGrV6oOz7XR0dDB+/HiMHTsWGzduxI0bN3Dx4kX8/PPPoh8JsLKyQosWLeDv74+CggJ8/fXXpbZZketYIpGgdevWCAsLEwajGjRogNzcXBw/flz0wyDl/a3V1taGv78/JkyYgBMnTuDKlSvw9fUVva8SiQRjxozB7NmzsXfvXly+fBk+Pj6wsLAQ/Wri5+Lu7g4ikntt2LDhs/fNGGOMMfZPxwNUJTg6OqJhw4aiX/6ysLDAmTNnUFBQgPbt28PR0RFjxoyBvr6+8B/jadOm4YcffsD06dPh4OCAPn36iDJqbt++LcpoKCwsREBAAJydndG4cWOsWLECwcHBmDlzplDn6dOnogyQjRs3IicnB/PmzYO5ubnw6tGjh1AnKytLmBOmyMCBA+Hi4oLVq1fj2rVrwre7RdkQeXl5SE1NrdTcJgAQHh6O9u3bCx9q169fj+7du4uyWor07NkTe/fuLTWjok2bNmjTpo3CeXCmT58Oc3Nz2NjYwNvbG1lZWTh+/DgmTZokt59bt24VPeozf/589OzZE97e3mjYsCGuX7+OI0eOoGrVqmXu2+LFi+Hq6oouXbqgXbt2aNmyJRwcHIQ5royNjbFhwwZs374ddevWxfz587Fw4cKyD1g5tLS0cPXqVfTs2RO2trYYPHgwhg8fjiFDhnxUux9jyJAh6NGjB/r06YNmzZrh2bNnol9u+1BaWlqIjIxEzZo10aNHDzg4OMDf3x9v376Frq4ugPdz7aSmpooeTfvYWAcOHIh169YhNDQUjo6OcHNzw4YNGz46g8rGxgY9evRAp06d0L59ezRo0AC//vqrsD4kJATPnz9Hw4YN4e3tjVGjRsHExETUxo4dO9CkSRN4eXmhbt26mDhxIgoKCj4onoCAALi5uaFLly7o3LkzunXrJnoUV19fH2vXrkXLli3RoEED/Pnnn9i3b1+pj3J9qn1UxN3dHdu3b8fevXvh7OyMNm3aCIPlH6JatWo4ePAgzp8/DycnJ3z//ffw9/fH1KlTK9yGRCLBwYMH0bp1a/j5+cHW1hbffvstbt26Jfp11rLo6uoiMjISnTp1gq2tLaZOnYpFixahY8eOH1R369atGDRoUIX3QZFZs2Zh2rRpmDdvHhwcHNChQwccOHBA7vzv27cvEhMT0b179zIHxCpyHQPvB2ALCgqEASoVFRW0bt1ayBorUpG/tQsWLECrVq3g6emJdu3a4YsvvpD75d2JEydi5MiRGDx4MJo0aYLs7GwcPnxYbn5JxhhjjDH29yKhkhOVMBw4cAATJkzAlStXKvyN+3/Nu3fvUKdOHWzZskX0AUOZiAjNmjXD2LFjy53Hq7Jev36NatWqYdGiRWVmXzHG2Kd26NAh/PDDD7h06RJUVXnqyM/t5cuX0NPTQ2DkTWhq65S/AYDJLhV/9JMxxhhj7N+u6P9TWVlZoi8uy8P/01Wgc+fOSEtLw71790Rzc7D/uX37Nn788ce/zeAU8D7rYc2aNWXOo1NR8fHxuHr1Kpo2bYqsrCwhs61r164f3TZjjFXG69evERoayoNTjDHGGGPsX40zqBhTID4+HgMHDkRqairU1dXRqFEjLF68GI6OjsoOrcLCwsJKfTzQ0tISSUlJf3FEjLFP4fvvv8dvv/2mcF2/fv2watWqvziifxfOoGKMMcYY+zgfmkHFA1SM/Uu9evUKjx49UrhOTU2tzAmxGWN/X48fP8bLly8VrtPV1a3Q3F+sdDxAxRhjjDH2cfgRP8aYiI6ODnR0KvbhijH2z2FiYsKDUIwxxhhj7F+HZwBnjDHGGGOMMcYYY0rFA1SMMcYYY4wxxhhjTKl4gIoxxhhjjDHGGGOMKRXPQcUYY4wxVsI4J8NKTerJGGOMMcY+DmdQMcYYY4wxxhhjjDGl4gEqxhhjjDHGGGOMMaZUPEDFGGOMMcYYY4wxxpSKB6gYY4wxxhhjjDHGmFLxABVjjDHGGGOMMcYYUyr+FT/GGGOMsRIWJz6Dpva7CtWd7GL0maNhjDHGGPv34wwqxhhjjDHGGGOMMaZUPEDFGGOMMcYYY4wxxpSKB6gYY4wxxhhjjDHGmFLxABVjjDHGGGOMMcYYUyoeoGKMMcYYY4wxxhhjSsUDVIwxxhhjjDHGGGNMqXiAijHGGGOMMcYYY4wpFQ9QMcYYY4x9ApGRkfD09ISFhQUkEgl2796t7JAYY4wxxv4x/vMDVM+ePYOJiQkyMjKUHcrf1tOnT2FiYoK7d+8qOxSRkJAQtG/fXtlhVNiGDRugr6+v7DBYCcU/RGZkZEAikSAhIQEAEBERAYlEghcvXnyWvj+2P3d3d4wZM+azxFZWPzk5OejZsyd0dXU/6/EpzX/tWjp8+DCcnZ1RWFio7FD+snOuMoKCguDs7KzsMAAAr1+/hpOTE1asWKHsUBhjjDHG/nH+8wNUc+bMQdeuXWFlZfXZ+jh9+jRatmwJQ0NDSKVS2NvbY8mSJWVuExERga5du8Lc3BwymQzOzs4ICwsrt69Ro0ahUaNG0NDQqPB/2NesWQN3d/dSP2waGRnBx8cHgYGBCrePiYlBlSpV0LlzZ7l1RR/ATUxM8OrVK9E6Z2dnBAUFCcvu7u6QSCSQSCTQ0NBAtWrV4OnpiZ07d8q1+/btW0ybNq3UmP6O+vTpg2vXrik7jH+M4ueDRCKBqakpevXqhVu3bn3Sfh48eICOHTt+0jY/VIsWLfDgwQPo6ekpOxSRnTt3YtasWcLyxo0bERUVhejoaKXE+6mupXnz5qFKlSpYsGCB3LqCggLMnz8f9vb2kEqlMDAwQLNmzbBu3TpRvRUrVsDKygqamppo1qwZzp8/L1r/8OFDeHt7w8zMDDKZDA0bNsSOHTtEdYqf58VfW7duBQB06NABampqFfob8LmVPBf+rrZv3w57e3toamrC0dERBw8e/Ev67dixI2bPno3u3bv/Jf0xxhhjjP2b/KcHqHJychASEgJ/f//P2o9MJsOIESMQGRmJlJQUTJ06FVOnTsWaNWtK3SY6OhoNGjTAjh07cOnSJfj5+cHHxwf79+8vt78BAwagT58+FY4vJycHHTp0wI8//lhqHT8/P4SFhSEzM1NuXUhICEaOHInIyEjcv39f4favXr3CwoULy41l0KBBePDgAW7cuIEdO3agbt26+PbbbzF48GBRvT/++AO6urpo2bJluW2WJS8v76O2rwypVAoTE5O/rL9/g6Lz4f79+9izZw/u3LmDfv36fdI+zMzMoKGh8Unb/FDq6uowMzODRCJRdigiBgYG0NHREZZv3LgBBwcH1K9f/4PjLSgo+OCMoE91La1fvx4TJ07E+vXr5dbNmDEDS5YswaxZs5CcnIyTJ09i8ODBogH8bdu2Ydy4cQgMDMTFixfh5OQEDw8PPH78WKjj4+OD1NRU7N27F5cvX0aPHj3Qu3dvxMfHi/oLDQ3FgwcPRK9u3boJ6319fbF8+fIP3tdPda8reS78HUVHR8PLywv+/v6Ij49Ht27d0K1bN1y5ckXZoTHGGGOMsbLQf9j27dvJ2NhYrvzy5cvUoUMHkslkZGJiQv369aMnT54I6wsKCig4OJisra1JXV2datSoQbNnz65U3927d6d+/fpVaptOnTqRn59fheoGBgaSk5NTpdo/efIkAaDnz58rXF+rVi1at26dqOzVq1ekra1NV69epT59+tCcOXNE69PT0wkATZgwgbS1tenRo0fCOicnJwoMDBSW3dzcaPTo0XL9rl+/ngDQsWPHhLLOnTvT+PHjRfUKCgpoxowZVK1aNVJXVycnJyc6dOiQXCxbt26l1q1bk4aGBoWGhlJeXh6NHDmS9PT0yMDAgCZOnEg+Pj7UtWtXYdtDhw5Ry5YthTqdO3em69evy7W9Y8cOcnd3J6lUSg0aNKDo6GihTmhoKOnp6QnLCQkJ5O7uTtra2qSjo0MNGzak2NhYUd19+/aRra0tSaVS6tmzJ71+/Zo2bNhAlpaWpK+vTyNHjqT8/HwF75Y8S0tLmjNnDvn5+ZG2tjbVqFGDVq9eLaozceJEqlOnDkmlUqpVqxZNnTqV3r17J6wvOq9CQkKoRo0aJJPJaOjQoZSfn0/BwcFkampKxsbGctfD8+fPyd/fn4yMjEhHR4e+/PJLSkhIKDNeRefD5s2bSUtLS1jOz8+nAQMGkJWVFWlqapKtrS0tXbpUrq2QkBCqW7cuqaurk5mZGQ0fPlxYB4B27dpFRP97H+Pj44lI/ppQdF0tWbKELC0theWTJ09SkyZNSEtLi/T09KhFixaUkZGhcB/L64+I6PTp0+Tm5kZSqZT09fWpffv2lJmZKRyjkSNH0oQJE6hq1apkamoquqaIyj/2Rfu0adMmsrS0JF1dXerTpw+9fPlSqFP8vXBzcyMAwsvNzY2IiDIzM8nb25v09fVJKpVShw4d6Nq1a0IbRef0nj17yMHBgapUqULp6elkaWlJs2bNIm9vb5LJZFSzZk3as2cPPX78mL7++muSyWTk6OgoXBvF2ypS1rVUmoiICKpWrRq9e/eOLCws6MyZM6L1Tk5OFBQUVGYbTZs2FZ1LBQUFZGFhQfPmzRPKZDIZbdq0SbSdgYEBrV27Vlgufg6W5tatWwRAdN8pCwD69ddfydPTk7S0tITzYvfu3eTi4kIaGhpUq1YtCgoKory8PCIi8vLyot69e4vaeffuHRkaGtLGjRuJSP66fPv2Lf3www9kYWFBWlpa1LRpUzp58iQRERUWFpKRkRFt375dqO/k5ERmZmbCclRUFKmrq9Pr16+JqGL3innz5pGJiQlpa2vTgAEDaNKkSaLrsnfv3tS5c2fRNs2aNaMhQ4ZU6NhlZWURAAqMvEnzLj6p0EuRiryvjDHGGGP/RkX/n8rKyqrUdv/pDKqoqCg0atRIVPbixQu0adMGLi4uiIuLw+HDh/Ho0SP07t1bqBMQEID58+dj2rRpSE5OxpYtW2Bqaiqsd3d3h6+vb6n9xsfHIzo6Gm5ubpWKNysrCwYGBpXa5lNq2rQpoqKiRGW///477O3tYWdnh379+mH9+vUgIrltvby8YGNjg5kzZ1a63/79+6Nq1aqiR/1Onz6Nxo0bi+otW7YMixYtwsKFC3Hp0iV4eHjg66+/Rlpamqje5MmTMXr0aKSkpMDDwwPBwcEICwtDaGgozpw5g5cvX8pNbPv69WuMGzcOcXFxOH78OFRUVNC9e3e5DJApU6Zg/PjxSEhIgK2tLby8vJCfn69wv/r27Yvq1asjNjYWFy5cwOTJk6Gmpiasz8nJwfLly7F161YcPnwYERER6N69Ow4ePIiDBw9i8+bNWL16Nf74448KH8tFixahcePGiI+Px7BhwzB06FCkpqYK63V0dLBhwwYkJydj2bJlWLt2rdzjqDdu3MChQ4dw+PBhhIeHIyQkBJ07d8bdu3dx6tQpBAcHY+rUqTh37pywTa9evfD48WMcOnQIFy5cQMOGDdG2bVshI6/oUdCIiIhSY8/MzMTvv/+OZs2aCWWFhYWoXr06tm/fjuTkZEyfPh0//vgjfv/9d6HOypUrMXz4cAwePBiXL1/G3r17YWNjU+FjVhn5+fno1q0b3NzccOnSJcTExGDw4MEfnBGVkJCAtm3bom7duoiJicHp06fh6emJgoICoc7GjRshk8lw7tw5/PTTT5g5cyaOHTsmrC/v2APv39Pdu3dj//792L9/P06dOoX58+crjGnnzp0YNGgQXF1d8eDBA+G69PX1RVxcHPbu3YuYmBgQETp16iTK3MnJyUFwcDDWrVuHpKQkIQtqyZIlaNmyJeLj49G5c2d4e3vDx8cH/fr1w8WLF2FtbQ0fHx+F9xag/GtJkZCQEHh5eUFNTQ1eXl4ICQkRrTczM8OJEyfw5MkThdu/e/cOFy5cQLt27YQyFRUVtGvXDjExMUJZixYtsG3bNmRmZqKwsBBbt27F27dv4e7uXmZ8JdWsWROmpqZy9+CyBAUFoXv37rh8+TIGDBiAqKgo+Pj4YPTo0UhOTsbq1auxYcMGzJkzB8D747hv3z5kZ2cLbRw5cgQ5OTmlPrI2YsQIxMTEYOvWrbh06RJ69eqFDh06IC0tDRKJBK1btxau6+fPnyMlJQVv3rzB1atXAQCnTp1CkyZNoKWlBaD88/X3339HUFAQ5s6di7i4OJibm+PXX38VxRQTEyN6XwDAw8ND9L4Ul5ubi5cvX4pejDHGGGNMCT7HaNk/RdeuXWnAgAGislmzZlH79u1FZXfu3CEAlJqaSi9fviQNDQ3Rt98leXt70+TJk+XKizJ7VFRUaObMmZWKddu2baSurk5XrlypUP3PkUE1duxYcnd3F5W1aNFCyFjJy8sjIyMj4dtzInGGyOHDh0lNTU3IAKhoBhXR+2+/O3bsSETvv2EHQJGRkaI6FhYWchlcTZo0oWHDholiKZlhY2pqSgsWLBCW8/PzqWbNmqIMqpKePHlCAOjy5cuitotnmCUlJREASklJISL5rA8dHR3asGGDwvZDQ0PlsiWGDBlCWlpa9OrVK6HMw8OjwlkBlpaWoqy9wsJCMjExoZUrV5a6zYIFC6hRo0bCcmBgIGlpaYmyazw8PMjKyooKCgqEMjs7OyGLJCoqinR1dent27eitq2trYUMrrt375KdnR2dO3dOWO/m5kZqamokk8lIS0uLAJCtrS2lp6eXuZ/Dhw+nnj17CssWFhY0ZcqUUuvjE2ZQPXv2jABQREREmTEWKa8/Ly8vatmyZanbu7m50RdffCEqa9KkCU2aNImIKnbsFb2nEyZMoGbNmon6KX5tjh49WsicIiK6du0aARBlIT19+pSkUin9/vvvRPS/c7pkNkzJ8/LBgwcEgKZNmyaUxcTEEAB68OCB0FZFryVFsrKySCqVCrHEx8eTtra26NpKSkoiBwcHUlFRIUdHRxoyZAgdPHhQWH/v3j0CIMqSJHp/7Jo2bSosP3/+nNq3b08ASFVVlXR1denIkSOibQCQpqYmyWQy0evWrVuiei4uLuVmdRVvc8yYMaKytm3b0ty5c0VlmzdvJnNzcyL63z28eMaXl5cX9enTR1gufi7cunWLqlSpQvfu3ZPrJyAggIiIli9fTvXq1SOi99lbzZo1o65duwr3nXbt2tGPP/5IRBU7X11dXYV7epFmzZqJrks1NTXasmWLqM6KFSvIxMRE0aGiwMBAUVZg0YszqBhjjDHGPgxnUH2AN2/eQFNTU1SWmJiIkydPQltbW3jZ29sDeJ9lkJKSgtzcXLRt27bUdjdt2oR58+bJlUdFRSEuLg6rVq3C0qVLER4eXqE4T548CT8/P6xduxb16tWrxB5+WlKpFDk5OcJyamoqzp8/Dy8vLwCAqqoq+vTpI5eJUMTDwwNffPEFpk2bVum+iUjIQnnz5g0AiN67ly9f4v79+3JzUrVs2RIpKSmisuKZV1lZWXj06BGaNm0qlFWpUkUusy4tLQ1eXl6oXbs2dHV1hUn1b9++LarXoEED4d/m5uYAIJqPprhx48Zh4MCBaNeuHebPn48bN26I1mtpacHa2lpYNjU1hZWVFbS1tUVlpbWvSPH4JBIJzMzMRNtv27YNLVu2hJmZGbS1tTF16lS5fbSyshLNQWNqaoq6detCRUVFVFbUbmJiIrKzs2FoaCi6rtLT04V9rlatGq5evSp6H4D3GR0JCQlITEzE6dOnYWNjg/bt24sm3F+xYgUaNWoEY2NjaGtrY82aNULMjx8/xv3798u8Xj8lAwMD+Pr6wsPDA56enli2bBkePHjwwe0VZVCVpfh7Crw/7ypz7AH597R4GxWRkpICVVVVUXaboaEh7OzsRNefurq6XLwl96EoG9XR0VGu7EOvpZLCw8NhbW0NJycnAO9/sMHS0hLbtm0T6tStWxdXrlzB2bNnMWDAADx+/Bienp4YOHBgmW2XNG3aNLx48QJ//vkn4uLiMG7cOPTu3RuXL18W1VuyZAkSEhJELwsLC1Gdkvfg8pTMMk1MTMTMmTNF50LRPG85OTlQVVVF7969hcnYX79+jT179qBv374K2798+TIKCgpga2sravPUqVPCe+Dm5obk5GQ8efIEp06dgru7O9zd3REREYG8vDxER0cL2WQVOV9TUlJE5xkAuLq6VviYKBIQEICsrCzhdefOnY9qjzHGGGOMfRhVZQegTEZGRnj+/LmoLDs7G56enggODparb25ujps3b35wf7Vq1QLw/oPXo0ePEBQUJAzulObUqVPw9PTEkiVL4OPj88F9fwqZmZkwNjYWlkNCQpCfny/6EEVE0NDQwC+//KLwl73mz58PV1dXTJgwocL9FhQUIC0tDU2aNAHw/oOvRCKRe+8qSiaTVXobT09PWFpaYu3atbCwsEBhYSHq16+Pd+/eieoVf6yoaECttImgg4KC8N133+HAgQM4dOgQAgMDsXXrVuFRmpKPKEkkEoVllZlouqztY2Ji0LdvX8yYMQMeHh7Q09PD1q1bsWjRonLbKKvd7OxsmJubK3x8T19fv8x49fT0hMfxbGxsEBISAnNzc2zbtg0DBw7E1q1bMX78eCxatAiurq7Q0dHBggULhMcLpVJp2QekklRUVOQeMys5+XRoaChGjRqFw4cPY9u2bZg6dSqOHTuG5s2bV7q/isT/KY79x55XFSWVShU+7qjouvmU11JJISEhSEpKgqrq//4EFhYWYv369aIfzVBRUUGTJk3QpEkTjBkzBr/99hu8vb0xZcoUVKtWDVWqVMGjR49EbT969AhmZmYA3n+p8csvv+DKlSvClwtOTk6IiorCihUrsGrVKmE7MzOzch89LXkPLk/Je112djZmzJiBHj16yNUtGvDv27cv3Nzc8PjxYxw7dgxSqRQdOnRQ2H52djaqVKmCCxcuoEqVKqJ1RQPpjo6OMDAwwKlTp3Dq1CnMmTMHZmZmCA4ORmxsLPLy8tCiRQuhvQ+9VxRnZmZW5vtSkoaGxif7oYTs7Gxcv35dWE5PT0dCQgIMDAxQs2bNT9IHY4wxxti/1X86g8rFxQXJycmisoYNGyIpKQlWVlawsbERvWQyGerUqQOpVIrjx49/VN+FhYXIzc0ts05ERAQ6d+6M4OBguV+xU4YrV67AxcUFwPu5djZt2oRFixaJvvFPTEyEhYVFqdlhTZs2RY8ePTB58uQK97tx40Y8f/4cPXv2BPA+C6Nu3bqi905XVxcWFhY4c+aMaNszZ86gbt26pbatp6cHU1NTxMbGCmUFBQW4ePGisPzs2TOkpqZi6tSpaNu2LRwcHD54cKwkW1tbjB07FkePHkWPHj0QGhr6Sdr9ENHR0bC0tMSUKVPQuHFj1KlTB7du3frodhs2bIiHDx9CVVVV7poyMjKqVFtFH4KLsujOnDmDFi1aYNiwYXBxcYGNjY0oe0ZHRwdWVlYffb0WMTY2xsOHD0WDVAkJCXL1XFxcEBAQgOjoaNSvXx9btmz5oP4aNGjwUbF/ymNfFgcHB+Tn54vmHSu6bsq6/j6lil5Lly9fRlxcHCIiIkT3roiICMTExAhzIylStC+vX7+Guro6GjVqJHp/CgsLcfz4cSGjpyjbqXh2IfD+PK7sAODbt29x48YN4R78IRo2bIjU1FS5c8HGxkaIsUWLFqhRowa2bduGsLAw9OrVq9T5vFxcXFBQUIDHjx/LtVc0GCSRSNCqVSvs2bMHSUlJ+OKLL9CgQQPk5uZi9erVaNy4sTCQVpHz1cHBQXSeAcDZs2dFy66urnLXzbFjxz4606oi4uLi4OLiIrxP48aNg4uLC6ZPn/7Z+2aMMcYY+6f7Tw9QeXh4ICkpSTTYMHz4cGRmZsLLywuxsbG4ceMGjhw5Aj8/PxQUFEBTUxOTJk3CxIkTsWnTJty4cQNnz54VPdbm4+ODgIAAYXnFihXYt28f0tLSkJaWhpCQECxcuBD9+vUT6vzyyy+iR3lOnjyJzp07Y9SoUejZsycePnyIhw8fiiY23rVrl/D4YZHr168jISEBDx8+xJs3b4QPX0WZPvfu3YO9vT3Onz8vbPPw4UMkJCQI3/pevnwZCQkJor5ycnJw4cIFtG/fHgCwf/9+PH/+HP7+/qhfv77o1bNnz1If8wOAOXPm4MSJE6LJuYv38/DhQ9y9exdnz57FpEmT8P3332Po0KH48ssvRe/d6dOnRdtOmDABwcHB2LZtG1JTUzF58mQkJCRg9OjRpcYCACNHjsS8efOwZ88epKamYvTo0Xj+/LmQtVG1alUYGhpizZo1uH79Ok6cOIFx48aV2WZ53rx5gxEjRiAiIgK3bt3CmTNnEBsbCwcHh49q92PUqVMHt2/fxtatW3Hjxg0sX74cu3bt+uh227VrB1dXV3Tr1g1Hjx5FRkYGoqOjMWXKFMTFxQFQfF4C/zsfHj58iMTERAwdOhSamprCeVinTh3ExcXhyJEjuHbtGqZNmyYabATeZ9csWrQIy5cvR1paGi5evIiff/75g/bF3d0dT548wU8//YQbN25gxYoVOHTokLA+PT0dAQEBiImJwa1bt3D06FGkpaV98PsaEBCA2NhYDBs2DJcuXcLVq1excuVKPH36tELbV+TYfwp16tRB165dMWjQIJw+fRqJiYno168fqlWrhq5du36yfhSp7LUUEhKCpk2bonXr1qL7VuvWrdGkSRPh3vXNN99gyZIlOHfuHG7duoWIiAgMHz4ctra2wn133LhxWLt2LTZu3IiUlBQMHToUr1+/hp+fHwDA3t4eNjY2GDJkCM6fP48bN25g0aJFOHbsGLp16yaK68WLF8K5XvR6/fq1sP7s2bPQ0ND4qEGW6dOnY9OmTZgxYwaSkpKQkpKCrVu3YurUqaJ63333HVatWoVjx46V+ngf8H5QsG/fvvDx8cHOnTuRnp6O8+fPY968eThw4IBQz93dHeHh4XB2doa2tjZUVFTQunVrhIWFiX4spCLn6+jRo7F+/XqEhobi2rVrCAwMRFJSkiiu0aNH4/Dhw1i0aBGuXr2KoKAgxMXFYcSIER987CrK3d0dRCT32rBhw2fvmzHGGGPsn+4/PUDl6OiIhg0bin7xqygLp6CgAO3bt4ejoyPGjBkDfX194RvmadOm4YcffsD06dPh4OCAPn36iOZGuX37tmjemcLCQgQEBMDZ2RmNGzfGihUrEBwcLPpFu6dPn4oyPzZu3IicnBzMmzcP5ubmwqv4oxlZWVlygzwDBw6Ei4sLVq9ejWvXrgnf5N6/fx/A+8eRUlNTRfOYrFq1Ci4uLhg0aBAAoHXr1nBxccHevXuFOnv27EHNmjXRqlUrAO8/5LVr107hY3w9e/ZEXFwcLl26pPC429raYsCAAXj79q3curVr18Lc3BzW1tbo0aMHkpOTsW3bNrlfafL398fBgweRlZUllI0aNQrjxo3DDz/8AEdHRxw+fBh79+5FnTp1FMZRZNKkSfDy8oKPjw9cXV2hra0NDw8P4ZEXFRUVbN26FRcuXED9+vUxduxYLFiwoMw2y1OlShU8e/YMPj4+sLW1Re/evdGxY0fMmDHjo9r9GF9//TXGjh2LESNGwNnZGdHR0R80X1hJEokEBw8eROvWreHn5wdbW1t8++23uHXrljC3kKLzEvjf+WBubo4vv/wST58+xcGDB2FnZwcAGDJkCHr06IE+ffqgWbNmePbsGYYNGyZqo3///li6dCl+/fVX1KtXD126dJH7ZceKcnBwwK+//ooVK1bAyckJ58+fx/jx44X1WlpauHr1Knr27AlbW1sMHjwYw4cPx5AhQz6oP1tbWxw9ehSJiYlo2rQpXF1dsWfPHtGjaWWpyLH/VEJDQ9GoUSN06dIFrq6uICIcPHiw3F/T+1iVuZbevXuH3377TcjGLKlnz57YtGkT8vLy4OHhgX379sHT0xO2trbo378/7O3tcfToUeH49+nTBwsXLsT06dPh7OyMhIQEHD58WDi2ampqOHjwIIyNjeHp6YkGDRpg06ZN2LhxIzp16iTq28/PT3SvNzc3Fw2khoeHo2/fvsKv3X0IDw8P7N+/H0ePHkWTJk3QvHlzLFmyBJaWlqJ6ffv2RXJyMqpVqyY3r19JoaGh8PHxwQ8//AA7Ozt069YNsbGxosfZ3NzcUFBQIPrlQnd3d7myipyvffr0wbRp0zBx4kQ0atQIt27dwtChQ0UxtWjRAlu2bMGaNWvg5OSEP/74A7t370b9+vU/8MgxxhhjjLG/goRKTqjyH3PgwAFMmDABV65ckXsMg/1P8+bNMWrUKHz33XfKDkXQq1cvNGzYUJSt9ikUFhbCwcEBvXv3xqxZsz5p24wxVllPnz6FnZ0d4uLihLkM2efz8uVL6OnpITDyJjS1dcrfAMBkl0/3yCxjjDHG2D9d0f+nsrKyoKurW+Ht/tOTpANA586dkZaWhnv37qFGjRrKDudv6enTp+jRo0e5E7r/1RYsWIB9+/Z9dDtFj2K5ubkhNzcXv/zyC9LT0/9Wg3GMsf+ujIwM/Prrrzw4xRhjjDHG/tX+8xlUjN25cwfffvstrly5AiJC/fr1MX/+fLRu3VrZoVVYVFQUOnbsWOr67OzsvzAaxtjnFBYWVupjo5aWlnJzMrHK4QwqxhhjjLGPwxlUjH2gGjVqyP363z9N48aNFf6aHGPs3+frr79Gs2bNFK773HN+McYYY4wx9rnwABVj/wJSqRQ2NjbKDoMx9hfQ0dGBjk7FMnsYY4wxxhj7p+BZwRljjDHGGGOMMcaYUvEAFWOMMcYYY4wxxhhTKh6gYowxxhhjjDHGGGNKxXNQMcYYY4yVMM7JsFK/OsMYY4wxxj4OZ1AxxhhjjDHGGGOMMaXiASrGGGOMMcYYY4wxplQ8QMUYY4wxxhhjjDHGlIoHqBhjjDHGGGOMMcaYUvEAFWOMMcYYY4wxxhhTKh6gYowxxhhjjDHGGGNKparsABhjjDHG/m4WJz6Dpva7CtWd7GL0maNhjDHGGPv34wwqxhhjjDHGGGOMMaZUPEDFGGOMMcYYY4wxxpSKB6gYY4wxxhhjjDHGmFLxABVjjDHGGGOMMcYYUyoeoGKMMcYYY4wxxhhjSsUDVIwxxhhjjDHGGGNMqXiAijHGGGPsE4iMjISnpycsLCwgkUiwe/duZYfEGGOMMfaPwQNU/wDPnj2DiYkJMjIylB3KP9rhw4fh7OyMwsJCZYci4u3tjblz5yo7jArz9fVFt27dlB0GKyYiIgISiQQvXrwAAGzYsAH6+vrC+qCgIDg7OyslNvbxmjdvjh07dig7DLnz7O/i7zQQ9Pr1azg5OWHFihXKDoUxxhhj7B+HB6j+AebMmYOuXbvCysrqs/Vx+vRptGzZEoaGhpBKpbC3t8eSJUvK3CYiIgJdu3aFubk5ZDIZnJ2dERYWVm5fo0aNQqNGjaChoVGhD80ZGRmQSCQKX9u3bxfqxcbGom3bttDX10fVqlXh4eGBxMREYX2HDh2gpqZWaozz5s1DlSpVsGDBArl1GzZsgEQiQYcOHUTlL168gEQiQUREhFBWPD6ZTIY6derA19cXFy5ckGs3MTERBw8exKhRo8o9Dn8Xy5Ytw4YNG5Qdxj9G8fNBVVUVNWvWxLhx45Cbm/vJ+mjRogUePHgAPT29T9bm39HLly8xZcoU2NvbQ1NTE2ZmZmjXrh127twJIgIAuLu7K7xX5Ofny63X1NSEra0t5s2bJ2xfpCL3KSLCwoULYWtrCw0NDVSrVg1z5swR1YmIiEDDhg2hoaEBGxsbuWunIhk3U6dOxeTJk5U+uP5POc8yMzPRt29f6OrqQl9fH/7+/sjOzv5L+u7YsSNmz56N7t27/yX9McYYY4z9m/AA1d9cTk4OQkJC4O/v/1n7kclkGDFiBCIjI5GSkoKpU6di6tSpWLNmTanbREdHo0GDBtixYwcuXboEPz8/+Pj4YP/+/eX2N2DAAPTp06dCsdWoUQMPHjwQvWbMmAFtbW107NgRAJCdnY0OHTqgZs2aOHfuHE6fPg0dHR14eHggLy9PaMvX1xfLly9X2M/69esxceJErF+/XuF6VVVV/Pnnnzh58mS5MYeGhuLBgwdISkrCihUrkJ2djWbNmmHTpk2iej///DN69eoFbW3tCh0LRQoKCv7SD656enqi7BxWvqLzIT09Hb/++is2b96M2bNnf7L21dXVYWZmBolE8sna/Lt58eIFWrRogU2bNiEgIAAXL15EZGQk+vTpg4kTJyIrK0uoO2jQILl7hqqqqtz61NRUBAQEYPr06Vi1apVcn+Xdp0aPHo1169Zh4cKFuHr1Kvbu3YumTZsK69PT09G5c2d8+eWXSEhIwJgxYzBw4EAcOXJEqFORjJuOHTvi1atXOHToUIWPV3Hv3r37oO1K+qecZ3379kVSUhKOHTuG/fv3IzIyEoMHD1Z2WIwxxhhjrDzE/ta2b99OxsbGcuWXL1+mDh06kEwmIxMTE+rXrx89efJEWF9QUEDBwcFkbW1N6urqVKNGDZo9e3al+u7evTv169evUtt06tSJ/Pz8KlQ3MDCQnJycKtV+EWdnZxowYICwHBsbSwDo9u3bQtmlS5cIAKWlpQllt27dIgB0/fp1UXsRERFUrVo1evfuHVlYWNCZM2dE60NDQ0lPT48GDRpETZs2FcqfP39OAOjkyZNCGQDatWuXXMw+Pj6ko6NDmZmZRESUn59Penp6tH//flG9zMxM8vb2Jn19fZJKpdShQwe6du2aXCx79uwhBwcHqlKlCqWnp9P9+/epU6dOpKmpSVZWVhQWFkaWlpa0ZMkSYdtFixZR/fr1SUtLi6pXr05Dhw6lV69eybV9+PBhsre3J5lMRh4eHnT//n2hTv/+/alr167C8vbt26l+/fqkqalJBgYG1LZtW8rOzhbVnTNnDpmYmJCenh7NmDGD8vLyaPz48VS1alWqVq0arV+/Xu54KZKenk4AaMeOHeTu7k5SqZQaNGhA0dHRQp2nT5/St99+SxYWFiSVSql+/fq0ZcsWUTtubm40YsQIGj16NOnr65OJiQmtWbOGsrOzydfXl7S1tcna2poOHjwo2q68604RReeDv78/derUSVi+fv06ff3112RiYkIymYwaN25Mx44dE23z9u1bmjhxIlWvXp3U1dXJ2tqa1q1bR0REJ0+eJAD0/PlzIvrf+1ik5LXm5uZGo0ePFrXftWtX6t+/v7C8YsUKsrGxIQ0NDTIxMaGePXuWuo8ZGRnUpUsX0tfXJy0tLapbty4dOHBAFNv+/fvJ0dGRNDQ0qFmzZnT58uUyj1tJQ4cOJZlMRvfu3ZNb9+rVK8rLyyt134pTtL5hw4bUvXt3hfVLu08lJyeTqqoqXb16tdS+Jk6cSPXq1ROV9enThzw8PBTWL+3eQUTk5+dX4ftxUcxr164lKysrkkgkRPT+fuXv709GRkako6NDX375JSUkJBARUWpqKgGglJQUUVuLFy+m2rVrE5H8eUZEFBUVRV988QVpampS9erVaeTIkcL1//PPP4v2f9euXQSAVq5cKZS1bduWpkyZIizv3r2bXFxcSENDg2rVqkVBQUHCe0tEdO3aNWrVqhVpaGiQg4MDHT16VHTckpOTCQDFxsYK2xw6dIgkEonCc0eRrKwsAkCBkTdp3sUnFXopUtb7yRhjjDH2b1b0/6msrKxKbccZVH9zUVFRaNSokajsxYsXaNOmDVxcXBAXF4fDhw/j0aNH6N27t1AnICAA8+fPx7Rp05CcnIwtW7bA1NRUWO/u7g5fX99S+42Pj0d0dDTc3NwqFW9WVhYMDAwqtU1lXbhwAQkJCaKsMjs7OxgaGiIkJATv3r3DmzdvEBISAgcHB9GjkTVr1oSpqSmioqJEbYaEhMDLywtqamrw8vJCSEiIwr6DgoJw+fJl/PHHH5WOe+zYsXj16hWOHTsGALh06RKysrLQuHFjUT1fX1/ExcVh7969iImJARGhU6dOokywnJwcBAcHY926dUhKSoKJiQl8fHxw//59REREYMeOHVizZg0eP34saltFRQXLly9HUlISNm7ciBMnTmDixImiOjk5OVi4cCE2b96MyMhI3L59G+PHj1e4Tw8ePICXlxcGDBiAlJQUREREoEePHqLHpU6cOIH79+8jMjISixcvRmBgILp06YKqVavi3Llz+P777zFkyBDcvXu3wsdyypQpGD9+PBISEmBrawsvLy/hEa63b9+iUaNGOHDgAK5cuYLBgwfD29sb58+fF7WxceNGGBkZ4fz58xg5ciSGDh2KXr16oUWLFrh48SLat28Pb29v5OTkAKjYdVf0KGhZrl27hhMnTqBZs2ZCWXZ2Njp16oTjx48jPj4eHTp0gKenJ27fvi3U8fHxQXh4OJYvX46UlBSsXr36ozLvyhIXF4dRo0Zh5syZSE1NxeHDh9G6detS6w8fPhy5ubmIjIzE5cuXERwcLBfbhAkTsGjRIsTGxsLY2Bienp6ic7oshYWF2Lp1K/r27QsLCwu59dra2qIMqYoiIkRFReHq1atQV1ev1Lb79u1D7dq1sX//ftSqVQtWVlYYOHAgMjMzhToxMTFo166daDsPDw/ExMRUOtamTZvK3bfKcv36dezYsQM7d+5EQkICAKBXr154/PgxDh06hAsXLqBhw4Zo27YtMjMzYWtri8aNG8s9Ah0WFobvvvtOYR83btxAhw4d0LNnT1y6dAnbtm3D6dOnMWLECACAm5sbkpOT8eTJEwDAqVOnYGRkJDwSnZeXh5iYGLi7uwN4//fOx8cHo0ePRnJyMlavXo0NGzYIj00WFhaiR48eUFdXx7lz57Bq1SpMmjRJFFNMTAz09fVF99V27dpBRUUF586dU7gfubm5ePnypejFGGOMMcaU4HOMlrFPp2vXrqJMISKiWbNmUfv27UVld+7cIQCUmppKL1++JA0NDVq7dm2p7Xp7e9PkyZPlyqtVq0bq6uqkoqJCM2fOrFSs27ZtI3V1dbpy5UqF6n9oBtXQoUPJwcFBrvzy5ctkbW1NKioqpKKiQnZ2dpSRkSFXz8XFhYKCgoTlrKwskkqlQiZBfHw8aWtrK8wsIiKaPHky2draUl5eXqUyqN68eUMAKDg4mIjeZxNUqVKFCgsLhTrXrl0jAKIMrqdPn5JUKqXff/9diAWAEC8RUUpKilzWQFpaGgEQZVCVtH37djI0NBTtJ0pkmK1YsYJMTU2F5eIZVBcuXCAACo9zUV1LS0sqKCgQyuzs7KhVq1bCcn5+PslkMgoPDy81ziJFGVRFmUNERElJSQozP4rr3Lkz/fDDD8Kym5sbffHFF3IxeHt7C2UPHjwgABQTE0NE5V93REQ7d+4kOzs7UR0ApKmpSTKZjDQ0NAgAdenShd69e1fmvtarV49+/vlnIvpfdkvJrKoinzqDaseOHaSrq0svX74sM8Yijo6OomtKUWxbt24Vyp49e0ZSqZS2bdtWofYfPXpEAGjx4sXl1nVzcyM1NTWSyWTCa9y4cQrXq6mpCe9PyazJIqXdp4YMGSJkg0VGRtLJkyfJ2dmZvvzyS6FOnTp1aO7cuaLtDhw4QAAoJydHrs3S7h1ERHv27CEVFRXRtVSawMBAUlNTo8ePHwtlUVFRpKurS2/fvhXVtba2ptWrVxMR0ZIlS8ja2lpYVzKrquR55u/vT4MHDxa1FxUVRSoqKvTmzRsqLCwkQ0ND2r59OxG9z3ydN28emZmZERHR6dOnSU1NjV6/fk1E77OpSh6vzZs3k7m5ORERHTlyhFRVVUWZUIcOHRIdtzlz5pCtra3cMTE2NqZff/211OMFQO7FGVSMMcYYYx+GM6j+pd68eQNNTU1RWWJiIk6ePAltbW3hZW9vD+D9N9opKSnIzc1F27ZtS21306ZNmDdvnlx5VFQU4uLisGrVKixduhTh4eEVivPkyZPw8/PD2rVrUa9evUrsYeW8efMGW7ZskZuT682bN/D390fLli1x9uxZnDlzBvXr10fnzp3x5s0bUV2pVCpkxQBAeHg4rK2t4eTkBABwdnaGpaUltm3bpjCGSZMm4cmTJ6XOVVUa+v+soqIMmzdv3kBDQ0OUcZOSkgJVVVVRdo2hoSHs7OyQkpIilKmrq6NBgwbCcmpqKlRVVdGwYUOhzMbGBlWrVhXF8Oeff6Jt27aoVq0adHR04O3tjWfPnomOh5aWFqytrYVlc3NzuUysIk5OTmjbti0cHR3Rq1cvrF27Fs+fPxfVqVevHlRU/nerMTU1haOjo7BcpUoVGBoaltqHIsX33dzcHACE7QsKCjBr1iw4OjrCwMAA2traOHLkiCgbqWQbRTEUj6so47Co3fKuOwDo3r07rl69KhfvkiVLkJCQgMTEROzfvx/Xrl2Dt7e3sD47Oxvjx4+Hg4MD9PX1oa2tjZSUFCHmhIQEVKlSpdIZjR/qq6++gqWlJWrXrg1vb2+EhYWJzpGSRo0ahdmzZ6Nly5YIDAzEpUuX5Oq4uroK/zYwMJA7p8tCJSYwL0/fvn2RkJAgvAICAhSuP3PmDDp27IgpU6agRYsWleqjsLAQubm52LRpE1q1agV3d3eEhITg5MmTSE1NrVRbFSGVSoU+K8LS0hLGxsbCcmJiIrKzs2FoaCg6h9PT04Xz99tvv0VGRgbOnj0L4H32VMOGDYXzvKTExERs2LBB1J6HhwcKCwuRnp4OiUSC1q1bIyIiAi9evEBycjKGDRuG3NxcXL16FadOnUKTJk2gpaUltDdz5kxRe0XzheXk5CAlJQU1atQQZdEVP68+VEBAALKysoTXnTt3PrpNxhhjjDFWeZV/JoL9pYyMjOQ+8GdnZ8PT0xPBwcFy9c3NzXHz5s0P7q9WrVoAAEdHRzx69AhBQUHw8vIqc5tTp07B09MTS5YsgY+Pzwf3XRF//PEHcnJy5PrZsmULMjIyEBMTIwyGbNmyBVWrVsWePXvw7bffCnUzMzNFH9xCQkKQlJQkekSosLAQ69evVzg5vb6+PgICAjBjxgx06dKlwrEXfRgvOsZGRkbIycnBu3fvKv14kVQqrfRExRkZGejSpQuGDh2KOXPmwMDAAKdPn4a/vz/evXsnfEhUU1MTbSeRSEodIKhSpQqOHTuG6OhoHD16FD///DOmTJmCc+fOCfupqD1FZZWZ6L349kXHoWj7BQsWYNmyZVi6dCkcHR0hk8kwZswYuYmiy4urZLvlXXdlMTMzg42NDYD3j6O+evUKXl5emD17NmxsbDB+/HgcO3YMCxcuhI2NDaRSKb755hshZqlUWv5BqQQVFRW597T443Y6Ojq4ePEiIiIicPToUUyfPh1BQUGIjY1VOEH+wIED4eHhgQMHDuDo0aOYN28eFi1ahJEjR36SeI2NjaGvr69w8E8RPT094XiXt/7333+HjY0NmjdvLvc4XlnMzc2hqqoKW1tboczBwQEAcPv2bdjZ2cHMzAyPHj0Sbffo0SPo6upW+j3NzMyETCar8HYymUy0nJ2dDXNzc9EvjhYpek/NzMzQpk0bbNmyBc2bN8eWLVswdOjQUvvIzs7GkCFDFP4Kac2aNQG8f5x8zZo1iIqKgouLC3R1dYVBq1OnTokGXbOzszFjxgz06NFDrr2SX9SUxszs/9i787ia8v8P4K9K+6pNWRJakRZkihQiaposX0xCkWXIxCBEaewxEmMaBolMyhhbxj60KVu0TIu0SBiGSaKQls/vjx73/DrurW6WyZj38/G4j4dzzud8zues1333+byPjlCwu6amBk+ePIGOjo7IdWRlZSErKytW/c2pqKhAQUEBN3379m2kp6dDXV2dOyaEEEIIIUQ06kH1kbO0tEROTg5vnpWVFbKzs6Gvrw8DAwPeR1FREYaGhpCXl8f58+ffadvi/LU+Pj4eLi4uWL9+/T/ylqTw8HB88cUXvAATUJ83SVJSkhe0EUw3DHy8evUKhYWFsLS0BAD88ccfSE1NRXx8PK/HRXx8PC5dutToD+Kvv/4akpKS2LJli9ht37x5M1RUVLgfwYJX1zc8v6ampqipqeHlSiktLUVeXh66d+/eaN3GxsaoqalBWloaN6+goIAX3Lx+/Trq6uoQEhKCzz77DEZGRvjzzz/Fbn9jJCQk0L9/f6xYsQJpaWmQkZHBkSNH3rnet5WcnAw3NzdMnDgR5ubm6Nq1K27duvXO9TZ337WElJQUAHC9+5KTk+Hl5YVRo0bBzMwMOjo6KC4u5sqbmZmhrq4OCQkJ77wfQH3A58GDB9x0bW0tsrKyeGXatGkDR0dHbNiwAZmZmSguLsaFCxcarbNTp0746quvcPjwYSxYsAA7d+7kLRf0ygGAsrIy3Lp1iwvoNEdSUhJffvkloqKiRF6zFRUVXA6yllJSUsLcuXOxcOHCFvXU6t+/P2pqarjeRwC466xz584A6nv3vPkcPnfu3Fv1+snKyuKeW2/DysoKDx8+RJs2bYSuX01NTa6ch4cHDhw4gEuXLqGoqIgX3BdVZ05OjlB9BgYGXNBdkIfq4MGDXK4pBwcH/P7770hOTubmCerLy8sTWZ+kpCRMTU1x9+5d3rXb8LoC6o/506dPcf36dW7ehQsXUFdXx+uZ+qGkpqbC0tKSO1fz58+HpaUlli9f/sG3TQghhBDyb0cBqo+ck5MTsrOzeYEGHx8fPHnyBO7u7rh27RoKCwtx5swZTJkyBbW1tZCTk8PixYuxaNEiREZGorCwEJcvX+Yl/p48eTJv2EtYWBiOHz+O/Px85OfnIzw8HBs3bsTEiRO5Mj/88ANv2GBcXBxcXFzg6+uLMWPG4OHDh3j48CEvSfCRI0eEhocUFBQgPT0dDx8+xMuXL7mgkKC3yP3792FiYiKU1LqgoACJiYmYNm2a0HEaOnQoysrK4OPjg9zcXGRnZ2PKlClo06YNBg0axJW7fPkyZGVluR+I4eHhsLa2xsCBA9GzZ0/uM3DgQPTt27fRZOlycnJYsWIFvv/+e5HLnz59iocPH+LOnTs4d+4c/ve//2H//v3Ytm0b11tBS0sLVlZWuHjxIreeoaEh3NzcMH36dFy8eBEZGRmYOHEiOnToADc3N5HbAgATExM4OjpixowZuHr1KtLS0jBjxgxeTysDAwNUV1dj69atKCoqwr59+7B9+/ZG6xTHlStXsHbtWqSmpqKkpASHDx/G48ePxQ48fAiGhoZcr67c3FzMnDlTqBfL22juvgNEX+/A/18Pf/75JxISErBy5UoYGRlxx8nQ0JBLZp2RkYEJEybwAqv6+vrw9PTE1KlTcfToUdy+fRvx8fH45Zdf3mpfBg8ejBMnTuDEiRO4efMmZs2ahadPn3LLf/vtN3z//fdIT0/HnTt3EBkZibq6OhgbG4usb968eThz5gxu376NGzduIC4uTugaWLlyJc6fP4+srCx4eXlBU1MTI0eOBND4Pd/QmjVr0KlTJ/Tr1w+RkZHIyclBfn4+du/eDUtLS1RUVLzVsQCAmTNn4tatWzh06BA3r7nnlKOjI6ysrDB16lSkpaXh+vXrmDlzJoYOHcr1qvrqq69QVFSERYsW4ebNm/jxxx/xyy+/4JtvvuG2U1FRwdUN/H+PmzeHpCYlJWHYsGFvvY+Ojo6wsbHByJEjcfbsWRQXFyMlJQXLli1DamoqV2706NF4/vw5Zs2ahUGDBolMSi+wePFipKSkYM6cOUhPT0d+fj6OHTvGJUkH6ofStm3bFvv37+cFqI4ePYqqqir079+fK7t8+XJERkZixYoVyM7ORm5uLmJiYhAQEMDtg5GRETw9PZGRkYGkpCQsW7aM1yZTU1MMHz4c06dPx9WrV5GcnIw5c+bgyy+/bHJf3hcHBwcwxoQ+e/bs+eDbJoQQQgj5t6MA1UfOzMwMVlZWvB+i7du3R3JyMmprazFs2DCYmZlh3rx5UFNT44a3BQYGYsGCBVi+fDlMTU0xfvx43rCHkpIS3l+h6+rq4O/vDwsLC/Tp0wdhYWFYv349Vq5cyZX5+++/eb0F9u7dixcvXmDdunXQ1dXlPg2HZ5SXlwvlY5k2bRosLS3x008/4datW9xfmwU9I6qrq5GXlyeU82b37t3o2LGjyB9pJiYmOH78ODIzM2FjYwM7Ozv8+eefOH36NG/4VXR0NDw8PKCgoIDXr1/j559/xpgxY0Qe+zFjxiAyMrLRN415enqia9euIpdNmTIFurq6MDExwaxZs6CkpISrV68KvQ1r2rRpQm/NioiIQO/evfH555/DxsYGjDGcPHlSaEjamyIjI9GuXTsMHDgQo0aNwvTp06GsrMwNjTE3N8emTZuwfv169OzZE1FRUSLzkLWEiooKEhMT4ezsDCMjIwQEBCAkJAQjRox4p3rfRUBAAKysrODk5AQHBwfo6OhwgZB3Ic59J+p6B/7/eujYsSPc3d3Ro0cPnDp1ihtWumnTJrRt2xa2trZwdXWFk5MTL58YAGzbtg3/+9//MHv2bJiYmGD69OmorKx8q32ZOnUqPD09MXnyZNjb26Nr1668QK6amhoOHz6MwYMHw9TUFNu3b0d0dHSj+eVqa2vh4+PDBQeMjIzw448/8soEBwdj7ty56N27Nx4+fIjjx49zvWwau+cbUldXx+XLlzFx4kSsXr0alpaWsLOzQ3R0NL777juoqqq+1bEQ1D158mR8++23XGCwueeUpKQkjh8/Dk1NTQwcOBAuLi4wNTVFTEwMV2+XLl1w4sQJnDt3Dubm5ggJCcGuXbvg5OTElRGnx839+/eRkpKCKVOmvPU+SkhI4OTJkxg4cCCmTJkCIyMjfPnll7hz5w7vDa/KyspwdXVFRkYGPDw8mqyzV69eSEhIwK1bt2BnZ8e1u2EgSEJCAnZ2dpCQkMCAAQO49VRUVNCnTx9e70MnJyf89ttvOHv2LPr27YvPPvsMoaGhXI80SUlJHDlyBC9fvoS1tTWmTZvGveGvoaioKJiYmGDIkCFwdnbGgAEDsGPHjrc+doQQQggh5J8hwVqafZb8406cOAE/Pz9kZWXxkk2Tlvn7779hbGyM1NRULj9Sa3v58iWMjY1x4MCB95Lst6F79+6hU6dOXGJ0QlpDfHw8Bg0ahLKyMpH5q0jzFi9ejLKyMgqy/EOePXsGVVVVBCUWQU5JWax1llhqNl+IEEIIIeQ/QvD/qfLycqioqIi9HiVJ/xdwcXFBfn4+7t+/j06dOrV2c/61iouL8eOPP340wSmgPvl1ZGQk/v7773eu68KFC6ioqICZmRkePHiARYsWQV9fHwMHDnwPLSWEtBZtbW3Mnz+/tZtBCCGEEELIB0UBqn+JefPmtXYT/vX69OmDPn36tHYzhDRMEvwuqqursXTpUhQVFUFZWRm2traIiopqdmjgx2Tt2rVYu3atyGV2dnY4derUP9wiQlrfggULeNM9evTAnTt3RJb96aefmh2aRwghhBBCyMeIhvgRQj4aT5484SXZb0heXh4dOnT4h1tEyMfnzp07jebGa9euHZSVxRuWRkSjIX6EEEIIIe+GhvgRQv711NXVoa6u3trNIOSjJkgaTgghhBBCyKeEMm4TQgghhBBCCCGEkFZFASpCCCGEEEIIIYQQ0qooQEUIIYQQQgghhBBCWhXloCKEEEIIecN8c40WJfUkhBBCCCHvhnpQEUIIIYQQQgghhJBWRQEqQgghhBBCCCGEENKqKEBFCCGEEEIIIYQQQloVBagIIYQQQgghhBBCSKuiABUhhBBCCCGEEEIIaVUUoCKEEEIIIYQQQgghrapNazeAEEIIIeRjsymjFHJKr8Uqu8RS8wO3hhBCCCHk00c9qAghhBBCCCGEEEJIq6IAFSGEEEIIIYQQQghpVRSgIoQQQgghhBBCCCGtigJUhBBCCCGEEEIIIaRVUYCKEEIIIYQQQgghhLQqClARQgghhBBCCCGEkFZFASpCCCGEkPcgMTERrq6uaN++PSQkJHD06NHWbhIhhBBCyL8GBahEKC0thba2NoqLi1u7KR+tnJwcdOzYEZWVla3dFJ7AwEDMmDGjtZshtm+//RYWFhat3QzSAnv27IGamho3/U+cw+a26eXlhZEjR36w7bfmdaqvr4/Nmze3yrY/pJacsyVLluDrr7/+sA0S08cYdHFwcMC8efNauxkAgMrKSpibmyMsLKy1m0IIIYQQ8q9DASoR1qxZAzc3N+jr63+wbVy8eBH9+/eHhoYG5OXlYWJigtDQ0CbXiY+Ph5ubG3R1daGoqAgLCwtERUU1uy1fX1/07t0bsrKyLf6RyRjDiBEjhH6UdO/eHZ999hk2bdokcr3o6GhISUnBx8dH5H5ISEigR48eqK2t5S1TU1PDnj17uGl9fX1ISEhAQkIC8vLy0NfXx7hx43DhwgWheh8+fIgtW7Zg2bJlLdrH1rRw4UKcP3++tZvxr9HwelBUVISVlRUOHjz4j7Zh/PjxuHXr1ie/zY/FtWvX/lVBZ3Ft2bKF96xrysKFC7F3714UFRV92EaJ4cGDBxgxYkRrN6NJhw8fxrBhw6ChoQEJCQmkp6f/Y9seMWIEVq9ejVGjRv1j2ySEEEII+VRQgOoNL168QHh4OLy9vT/odhQVFTFnzhwkJiYiNzcXAQEBCAgIwI4dOxpdJyUlBb169cKhQ4eQmZmJKVOmYPLkyfjtt9+a3d7UqVMxfvz4Frdz8+bNkJCQELlsypQp2LZtG2pqaoSWhYeHY9GiRYiOjsarV69Erl9UVITIyMhm27By5Uo8ePAAeXl5iIyMhJqaGhwdHbFmzRpeuV27dsHW1hadO3cWY89EY4yJ3J8PRUlJCRoaGv/Y9j4FgushLS0Nffv2xfjx45GSkvKPbV9eXh7a2tr/2PZaa5v/hOrq6mbLaGlpQUFB4R9ozT9LVVWV1yuuKZqamnBycsK2bdvealvv87mmo6MDWVnZ91LXh1JZWYkBAwZg/fr1rd0UQgghhBDSAhSgesPJkychKyuLzz77jDc/KysLI0aMgJKSEtq1a4dJkybh77//5pbX1dVhw4YNMDAwgKysLPT09IQCKA1ZWlrC3d0dPXr0gL6+PiZOnAgnJyckJSU1us7SpUuxatUq2Nraolu3bpg7dy6GDx+Ow4cPN7lP33//PXx8fNC1a1cxj0K99PR0hISEYPfu3SKXDx06FE+ePEFCQgJv/u3bt5GSkoIlS5bAyMio0fZ9/fXXCAoKQlVVVZPtUFZWho6ODvT09DBw4EDs2LEDgYGBWL58OfLy8rhyMTExcHV15a1bVVUFX19faGtrQ05ODgMGDMC1a9e45YLeXKdOneJ6mV28eBHPnz+Hh4cHFBUVoauri9DQUKFhJPv27UOfPn249k2YMAGPHj0Sqvv8+fPo06cPFBQUYGtry2vzm0On4uPjYW1tDUVFRaipqaF///64c+cOr+zu3buhp6cHJSUlzJ49G7W1tdiwYQN0dHSgra3d5HX3JgkJCezatQujRo2CgoICDA0NERsbyy2vra2Ft7c3unTpAnl5eRgbG2PLli28OgRDldauXYt27dpBTU0NK1euRE1NDfz8/KCuro6OHTsiIiKCt97du3cxbtw4qKmpQV1dHW5ubmINqxUcbyMjI4SFhUFeXh7Hjx8Xq61AfSDT1NQUcnJyMDExwY8//sgtKy4uhoSEBA4fPoxBgwZBQUEB5ubmuHTpElfmzeF2Aj/99BM6deoEBQUFjBs3DuXl5dyya9euYejQodDU1ISqqirs7e1x48YN3vpPnz7FzJkz0a5dO8jJyaFnz55c8LmxbTZG1LA4CwsLfPvttwDqAxbffvst9PT0ICsri/bt28PX17fZet91HyUkJLBt2zZ88cUXUFRU5K7V48ePo2/fvpCTk4Ompiav98mb+9LcNQsACQkJsLa2hqysLHR1dbFkyRJegMbBwQG+vr5YtGgR1NXVoaOjwx0bgadPn2LatGnQ0tKCiooKBg8ejIyMDG654H7ct28f9PX1oaqqii+//BLPnz/nyvz6668wMzODvLw8NDQ04OjoyA2LfnOIX1NlAcDV1RUxMTHNnSIAjT/X6urqsG7dOu4eMTc3x6+//gqg/jusY8eOQkGwtLQ0SEpKcs+hN3vTNnUfZ2VlQVJSEo8fPwYAPHnyBJKSkvjyyy+59VevXo0BAwZw081911ZWVmLy5MlQUlKCrq4uQkJChPZ/0qRJWL58ORwdHcU6XoQQQggh5ONAAao3JCUloXfv3rx5T58+xeDBg2FpaYnU1FScPn0af/31F8aNG8eV8ff3R3BwMAIDA5GTk4P9+/ejXbt23HIHBwd4eXk1ut20tDSkpKTA3t6+Re0tLy+Hurp6i9YRx4sXLzBhwgSEhYVBR0dHZBkZGRlYWFgIBdUiIiLg4uICVVVVTJw4EeHh4SLXnzdvHmpqarB169YWt2/u3LlgjOHYsWMA6n/45OTkoE+fPrxyixYtwqFDh7B3717cuHEDBgYGcHJywpMnT3jllixZguDgYOTm5qJXr16YP38+kpOTERsbi3PnziEpKUnox3Z1dTVWrVqFjIwMHD16FMXFxSLP8bJlyxASEoLU1FS0adMGU6dOFblPNTU1GDlyJOzt7ZGZmYlLly5hxowZvB5shYWFOHXqFE6fPo3o6GiEh4fDxcUF9+7dQ0JCAtavX4+AgABcuXJF7GO5YsUKjBs3DpmZmXB2doaHhwd3fAQ/Wg8ePIicnBwsX74cS5cuxS+//MKr48KFC/jzzz+RmJiITZs2ISgoCJ9//jnatm2LK1eu4KuvvsLMmTNx79497tg5OTlBWVkZSUlJSE5OhpKSEoYPH47Xr18D+P8f2U0Frdq0aQNpaWm8fv1arLZGRUVh+fLlWLNmDXJzc7F27VoEBgZi7969Quds4cKFSE9Ph5GREdzd3ZvsgVJQUIBffvkFx48fx+nTp5GWlobZs2dzy58/fw5PT09cvHgRly9fhqGhIZydnblgRl1dHUaMGIHk5GT8/PPPyMnJQXBwMKSkpMQ4gy136NAhhIaG4qeffkJ+fj6OHj0KMzOzJtd5130U+PbbbzFq1Cj88ccfmDp1Kk6cOIFRo0bB2dkZaWlpOH/+PKytrZtsS1PX7P379+Hs7Iy+ffsiIyMD27ZtQ3h4OFavXs2rY+/evVBUVMSVK1ewYcMGrFy5EufOneOWjx07Fo8ePcKpU6dw/fp1WFlZYciQIbxnR2FhIY4ePYrffvsNv/32GxISEhAcHAygfiicu7s7pk6ditzcXMTHx2P06NFgjAntjzhlra2tce/evRblRnzzubZu3TpERkZi+/btyM7OxjfffIOJEyciISEBkpKScHd3x/79+3l1REVFoX///iJ7pjZ3H/fo0QMaGhrcHzGSkpJ400B9MNHBwQGAeN+1fn5+SEhIwLFjx3D27FnEx8cLPZtbqqqqCs+ePeN9CCGEEEJIK2CEx83NjU2dOpU3b9WqVWzYsGG8eXfv3mUAWF5eHnv27BmTlZVlO3fubLTeSZMmsSVLlgjN79ChA5ORkWGSkpJs5cqVLWrrgQMHmIyMDMvKyhKrfFBQEDM3Nxer7IwZM5i3tzc3DYAdOXJEqNyoUaOYl5cXN11bW8s6derEjh49yhhj7PHjx0xGRoYVFRVxZeLi4hgAVlZWxrZv387U1dXZ06dPGWOMqaqqsoiICK5s586dWWhoqMg2tmvXjs2aNYsxxlhaWhoDwEpKSrjlFRUVTFpamkVFRXHzXr9+zdq3b882bNjAa4ugvYwx9uzZMyYtLc0OHjzIzXv69ClTUFBgc+fObeyQsWvXrjEA7Pnz57y6f//9d67MiRMnGAD28uVLxhj/nJSWljIALD4+XmT9QUFBTEFBgT179oyb5+TkxPT19VltbS03z9jYmK1bt67RdjYEgAUEBHDTFRUVDAA7depUo+v4+PiwMWPGcNOenp6sc+fOQm2ws7PjpmtqapiioiKLjo5mjDG2b98+ZmxszOrq6rgyVVVVTF5enp05c4YxxtiVK1eYsbExu3fvHlem4fVQVVXF1q5dywCw3377Tay2duvWje3fv59XZtWqVczGxoYxxtjt27cZALZr1y5ueXZ2NgPAcnNzGWOMRUREMFVVVW55UFAQk5KS4rXz1KlTTFJSkj148EBku2pra5mysjI7fvw4Y4yxM2fOMElJSZaXlyeyvKhtNryXPT09mZubGzct6r4xNzdnQUFBjDHGQkJCmJGREXv9+rXI7b3pfewjY/XX27x583jlbGxsmIeHR6PbfnNfmrtmly5dKnRthYWFMSUlJe4atbe3ZwMGDOBtp2/fvmzx4sWMMcaSkpKYiooKe/XqFa9Mt27d2E8//cQdkzfvRz8/P9avXz/GGGPXr19nAFhxcbHI/Wp4zporyxhj5eXlTT4fGhL1XHv16hVTUFBgKSkpvLLe3t7M3d2dMVb/HJWQkGB37txhjNWfww4dOrBt27Zx5Rt+F4hzH48ePZr5+PgwxhibN28e8/PzY23btmW5ubns9evXTEFBgZ09e5Yx1vx37fPnz5mMjAz75ZdfuOWlpaVMXl5e5LNZcD+npaU1ebyCgoIYAKFPUGIRW3fjsVgfURr73iSEEEII+dQJ/u9aXl7eovWoB9UbXr58CTk5Od68jIwMxMXFQUlJifuYmJgAqP8Lem5uLqqqqjBkyJBG642MjMS6deuE5iclJSE1NRXbt2/H5s2bER0dLVY74+LiMGXKFOzcuRM9evRowR42LzY2FhcuXBDrzVny8vJ48eIFN33u3DlUVlbC2dkZQH3ulKFDhzY6TNDb2xsaGhpvlSuEMcb1Lnr58iUA8M5dYWEhqqur0b9/f26etLQ0rK2tkZuby6urYc+roqIiVFdX83pxqKqqwtjYmLfO9evX4erqCj09PSgrK3O930pKSnjlevXqxf1bV1cXAHhDAQXU1dXh5eUFJycnuLq6YsuWLXjw4AGvjL6+PpSVlbnpdu3aoXv37pCUlOTNE1V/Yxq2T1FRESoqKrz1w8LC0Lt3b2hpaUFJSQk7duwQ2scePXoItaFhjxwpKSloaGhw9WZkZKCgoADKysrcPaWuro5Xr16hsLAQQH2PkZs3b6JDhw68bS1evBhKSkpQUFDA+vXrERwcDBcXl2bbWllZicLCQnh7e/Pu5dWrV3PbFHVMmjpnAnp6erx22tjYoK6ujhvO+ddff2H69OkwNDSEqqoqVFRUUFFRwbUtPT0dHTt2hJGRUaPbeJ/Gjh2Lly9fomvXrpg+fTqOHDnSbI6id91HgTd7Oaanpzf57BSlqWs2NzcXNjY2vJ6H/fv3R0VFBdeD7806gPrz3PD6rKiogIaGBu9auX37Nu9aefN+bFiHubk5hgwZAjMzM4wdOxY7d+5EWVmZyP0Rp6y8vDwA8J63zWl4rAsKCvDixQsMHTqUt0+RkZHcPllYWMDU1JTrRZWQkIBHjx5h7NixIusX5z62t7dHfHw8V9/gwYMxcOBAxMfH49q1a7xndHPftYWFhXj9+jX69evHtUFdXV3o2dxS/v7+KC8v5z537959p/oIIYQQQsjbadPaDfjYaGpqCv0wqKiogKurq8ggiq6u7ju9WalLly4AADMzM/z111/49ttv4e7u3uQ6CQkJcHV1RWhoKCZPnvzW227MhQsXUFhYKJTzZsyYMbCzs+N+bAD1Q+u6devGTYeHh+PJkyfcjymgfvhSZmYmVqxYwQtiAPVDtNasWQMvLy/MmTNH7DaWlpbi8ePH3PHT1NQEAJSVlUFLS0vsegQUFRVbVL6yshJOTk5wcnJCVFQUtLS0UFJSAicnJ26ImoC0tDT3b8GP5rq6OpH1RkREwNfXF6dPn8aBAwcQEBCAc+fOcTnRGtYlqE/UvMbqF6Wp9WNiYrBw4UKEhITAxsYGysrK+O6774SGELa0XRUVFejdu7fIt1A2d/78/Pzg5eXF5agRHNPm2lpRUQEA2LlzJ+8HLgChoXQtOWfi8PT0RGlpKbZs2YLOnTtDVlYWNjY23LXS8H55HyQlJYWGkjVMSN6pUyfk5eXh999/x7lz5zB79mx89913SEhIEDpv4mpuHwXevNfeZt/f9Zpvro6Kigro6urynnUCDZ+LTdUhJSWFc+fOISUlBWfPnsXWrVuxbNkyXLlyhXtuCYhTVjC0sCXPt4bHWnD9nzhxQijo2zDpuYeHB/bv348lS5Zg//79GD58eKMvchDnPhbk7svPz0dOTg4GDBiAmzdvIj4+HmVlZVx+PkF9TX3XFhQUiL3vLSErK/veEr9XVFTw2nn79m2kp6dDXV0denp672UbhBBCCCGfKupB9QZLS0vk5OTw5llZWSE7Oxv6+vowMDDgfRQVFWFoaAh5eXmcP3/+nbZdV1fXbMLw+Ph4uLi4YP369R/s1etLlixBZmYm0tPTuQ8AhIaGCiW6zsrKgqWlJYD6oNGxY8cQExPDWzctLQ1lZWU4e/asyO2NHTsWPXr0wIoVK8Ru45YtWyApKcklGe7WrRtUVFR4565bt26QkZFBcnIyN6+6uhrXrl1D9+7dG627a9eukJaW5iVTLy8vx61bt7jpmzdvorS0FMHBwbCzs4OJiUmLei01xdLSEv7+/khJSUHPnj2FcsL8k5KTk2Fra4vZs2fD0tISBgYGQr2N3oaVlRXy8/Ohra0tdE+pqqo2ua6mpiYMDAygo6PD6yXTXFvbtWuH9u3bo6ioSGibbwYMWqqkpAR//vknN3358mVISkpyPTuSk5Ph6+sLZ2dn9OjRA7KysrzEz7169cK9e/d419i70NLS4vW+e/bsGW7fvs0rIy8vD1dXV3z//feIj4/HpUuX8Mcff3ywfWxMr1693vnZ2ZCpqSkuXbrEC9AlJydDWVkZHTt2FKsOKysrPHz4EG3atBG6VgTBcHFISEigf//+WLFiBdLS0iAjI4MjR468VdmsrCxIS0u/dY/Z7t27Q1ZWFiUlJUL71KlTJ67chAkTkJWVhevXr+PXX3+Fh4dHo3WKcx+bmZmhbdu2WL16NSwsLKCkpAQHBwckJCQgPj6eyz8lqK+p79pu3bpBWlqaFyAvKyt7b/fN+5CamgpLS0vue3H+/PmwtLTE8uXLW7llhBBCCCEfPwpQvcHJyQnZ2dm8XlQ+Pj548uQJ3N3dce3aNRQWFuLMmTOYMmUKamtrIScnh8WLF2PRokXccInLly/zkoNPnjwZ/v7+3HRYWBiOHz+O/Px85OfnIzw8HBs3bsTEiRO5Mj/88ANv6EtcXBxcXFzg6+uLMWPG4OHDh3j48CEvae+RI0e4IRECBQUFSE9Px8OHD/Hy5UsucCTo2XD//n2YmJjg6tWrAOpfI96zZ0/eB6gf4tPwh3xxcTHu37/PvSlp37590NDQwLhx43jrmpubw9nZudFk6QAQHByM3bt3895aJfD8+XM8fPgQd+/eRWJiImbMmIHVq1djzZo1MDAwAFDfY8TR0REXL17k1lNUVMSsWbPg5+eH06dPIycnB9OnT8eLFy/g7e3daFuUlZXh6ekJPz8/xMXFITs7G97e3pCUlOQCInp6epCRkcHWrVtRVFSE2NhYrFq1qtE6xXH79m34+/vj0qVLuHPnDs6ePYv8/HyYmpq+U73vwtDQEKmpqThz5gxu3bqFwMBAXuDubXl4eEBTUxNubm5ISkrC7du3ER8fD19fX24Y1tWrV2FiYoL79++/t7auWLEC69atw/fff49bt27hjz/+QEREBDZt2vRO+yMnJwdPT09kZGQgKSkJvr6+GDduHPeCAUNDQ+zbtw+5ubm4cuUKPDw8eD2H7O3tMXDgQIwZMwbnzp3D7du3uWT4b2Pw4MHYt28fkpKS8Mcff8DT05PXS2zPnj0IDw9HVlYWioqK8PPPP0NeXl5kIuz3tY+NCQoKQnR0NIKCgpCbm4s//vjjrYb8CsyePRt3797F119/jZs3b+LYsWMICgrC/PnzhXpwNsbR0RE2NjYYOXIkzp49i+LiYqSkpGDZsmVITU0Vq44rV65g7dq1SE1NRUlJCQ4fPozHjx+LvJ/FKZuUlAQ7O7u37m2nrKyMhQsX4ptvvsHevXtRWFiIGzduYOvWrbyXBOjr68PW1hbe3t6ora3FF1980Wid4tzHEhISGDhwIKKiorhgVK9evVBVVYXz58/zXgzS3HetkpISvL294efnhwsXLiArKwteXl5C5/XJkydIT0/n/mCRl5fHfQd+aA4ODmCMCX327NnzwbdNCCGEEPJvRwGqN5iZmcHKyor35q/27dsjOTkZtbW1GDZsGMzMzDBv3jyoqalx/zEODAzEggULsHz5cpiammL8+PG8HjUlJSW8Hg11dXXw9/eHhYUF+vTpg7CwMKxfvx4rV67kyvz999+8HiB79+7FixcvsG7dOujq6nKf0aNHc2XKy8u5nDAC06ZNg6WlJX766SfcunWL++uuoDdEdXU18vLyWpTbBACio6MxbNgw7kft7t27MWrUKF6vFoExY8YgNja20R4VgwcPxuDBg0XmwVm+fDl0dXVhYGCASZMmoby8HOfPn8fixYuF9jMmJoY31Cc4OBhjxozBpEmTYGVlhYKCApw5cwZt27Ztct82bdoEGxsbfP7553B0dET//v1hamrK5bjS0tLCnj17cPDgQXTv3h3BwcHYuHFj0wesGQoKCrh58ybGjBkDIyMjzJgxAz4+Ppg5c+Y71fsuZs6cidGjR2P8+PHo168fSktLeW9ue1sKCgpITEyEnp4eRo8eDVNTU3h7e+PVq1dQUVEBUJ9rJy8vjzc07V3bOm3aNOzatQsREREwMzODvb099uzZ8849qAwMDDB69Gg4Oztj2LBh6NWrF3788UdueXh4OMrKymBlZYVJkybB19cX2travDoOHTqEvn37wt3dHd27d8eiRYtQW1v7Vu3x9/eHvb09Pv/8c7i4uGDkyJG8obhqamrYuXMn+vfvj169euH333/H8ePHGx3K9b72URQHBwccPHgQsbGxsLCwwODBg7lg+dvo0KEDTp48iatXr8Lc3BxfffUVvL29ERAQIHYdEhISOHnyJAYOHIgpU6bAyMgIX375Je7cucN7O2tTVFRUkJiYCGdnZxgZGSEgIAAhISEYMWLEW5WNiYnB9OnTxd4HUVatWoXAwECsW7cOpqamGD58OE6cOCF0/Xt4eCAjIwOjRo1qMiAmzn0M1Adga2truQCVpKQkBg4cyPUaExDnu/a7776DnZ0dXF1d4ejoiAEDBgi9eTc2NhaWlpZcbrovv/wSlpaW2L59+zsdP0IIIYQQ8mFJsDcTlRCcOHECfn5+yMrKEvsv7v81r1+/hqGhIfbv38/7gdGaGGPo168fvvnmm2bzeLVUZWUlOnTogJCQkCZ7XxFCyPt26tQpLFiwAJmZmWjThlJHfmjPnj2DqqoqghKLIKek3PwKAJZYij/0kxBCCCHkUyf4/1R5eTnvD5fNof/piuDi4oL8/Hzcv3+fl5uD/L+SkhIsXbr0owlOAfW9Hnbs2NFkHh1xpaWl4ebNm7C2tkZ5eTnXs83Nze2d6yaEkJaorKxEREQEBacIIYQQQsgnjXpQESJCWloapk2bhry8PMjIyKB3797YtGkTzMzMWrtpYouKimp0eGDnzp2RnZ39D7eIEPI+fPXVV/j5559FLps4cSINZXtH1IOKEEIIIeTdvG0PKgpQEfKJev78Of766y+Ry6SlpZtMiE0I+Xg9evQIz549E7lMRUVFrNxfpHEUoCKEEEIIeTc0xI8QwqOsrAxlZfF+XBFC/j20tbUpCEUIIYQQQj45lAGcEEIIIYQQQgghhLQqClARQgghhBBCCCGEkFZFASpCCCGEEEIIIYQQ0qooBxUhhBBCyBvmm2u0KKknIYQQQgh5N9SDihBCCCGEEEIIIYS0KgpQEUIIIYQQQgghhJBWRQEqQgghhBBCCCGEENKqKEBFCCGEEEIIIYQQQloVBagIIYQQQgghhBBCSKuiABUhhBBCCCGEEEIIaVVtWrsBhBBCCCEfm00ZpZBTei1W2SWWmh+4NYQQQgghnz7qQUUIIYQQQgghhBBCWhUFqAghhBBCCCGEEEJIq6IAFSGEEEIIIYQQQghpVRSgIoQQQgghhBBCCCGtigJUhBBCCCGEEEIIIaRVUYCKEEIIIYQQQgghhLQqClARQgghhLwHiYmJcHV1Rfv27SEhIYGjR4+2dpMIIYQQQv41PqkAVWlpKbS1tVFcXNzaTflo5eTkoGPHjqisrGztpvAEBgZixowZrd0MsX377bewsLBo7WZ8Ehr+iCsuLoaEhATS09Pf+3bi4+MhISGBp0+fvve6/yv09fWxefPm1m7Gf8rH9Mz28vLCyJEjW7sZPHv27IGamlprN4NTWVkJc3NzhIWFtXZTCCGEEEL+dT6pANWaNWvg5uYGfX39D7aNixcvon///tDQ0IC8vDxMTEwQGhra5Drx8fFwc3ODrq4uFBUVYWFhgaioqGa35evri969e0NWVrbFwRDGGEaMGCH0F9zu3bvjs88+w6ZNm0SuFx0dDSkpKfj4+IjcDwkJCfTo0QO1tbW8ZWpqatizZw83ra+vDwkJCUhISEBeXh76+voYN24cLly4IFTvw4cPsWXLFixbtqxF+9iaFi5ciPPnz7d2M/41Gl4Pgk/Hjh0BAA8ePMCIESNauYXvx4cMsH0Mrl279q8KJH9IL168gL+/P7p16wY5OTloaWnB3t4ex44d48pUVFRgzpw56NixI+Tl5dG9e3ds376dV4+Dg4PQvfHVV19xy5t7Zv+TtmzZwnvOf4wePHiACRMmwMjICJKSkpg3b94/uv0RI0Zg9erVGDVq1D+6XUIIIYSQT8EnE6B68eIFwsPD4e3t/UG3o6ioiDlz5iAxMRG5ubkICAhAQEAAduzY0eg6KSkp6NWrFw4dOoTMzExMmTIFkydPxm+//dbs9qZOnYrx48e3uJ2bN2+GhISEyGVTpkzBtm3bUFNTI7QsPDwcixYtQnR0NF69eiVy/aKiIkRGRjbbhpUrV+LBgwfIy8tDZGQk1NTU4OjoiDVr1vDK7dq1C7a2tujcubMYeyYaY0zk/nwoSkpK0NDQ+Me29ykQXA+CT1paGgBAR0cHsrKyb13vP33u/ynV1dWt3QQhWlpaUFBQaO1mfBS++uorHD58GFu3bsXNmzdx+vRp/O9//0NpaSlXZv78+Th9+jR+/vln5ObmYt68eZgzZw5iY2N5dU2fPp13b2zYsIG3vKlntjhev379Vuu9SVVV9aPqrSRKVVUVtLS0EBAQAHNz89ZuDiGEEEIIaYFPJkB18uRJyMrK4rPPPuPNz8rKwogRI6CkpIR27dph0qRJ+Pvvv7nldXV12LBhAwwMDCArKws9PT2hAEpDlpaWcHd3R48ePaCvr4+JEyfCyckJSUlJja6zdOlSrFq1Cra2tujWrRvmzp2L4cOH4/Dhw03u0/fffw8fHx907dpVzKNQLz09HSEhIdi9e7fI5UOHDsWTJ0+QkJDAm3/79m2kpKRgyZIlMDIyarR9X3/9NYKCglBVVdVkO5SVlaGjowM9PT0MHDgQO3bsQGBgIJYvX468vDyuXExMDFxdXXnrVlVVwdfXF9ra2pCTk8OAAQNw7do1brmgN9epU6e4XmYXL17E8+fP4eHhAUVFRejq6iI0NBQODg68v6Lv27cPffr04do3YcIEPHr0SKju8+fPo0+fPlBQUICtrS2vzW8O8YuPj4e1tTUUFRWhpqaG/v37486dO7yyu3fvhp6eHpSUlDB79mzU1tZiw4YN0NHRgba2dpPX3ZskJCSwa9cujBo1CgoKCjA0NOT96K2trYW3tze6dOkCeXl5GBsbY8uWLbw6BMN11q5di3bt2kFNTQ0rV65ETU0N/Pz8oK6ujo4dOyIiIoK33t27dzFu3DioqalBXV0dbm5uYg2rFRxvwUdLS4vblzfztNy8eRO2traQk5NDz549eddqY+e+uWtGIDk5Gb169YKcnBw+++wzZGVlcctKS0vh7u6ODh06QEFBAWZmZoiOjuat39Qzo0uXLgDqnxMSEhJwcHDg1tu1axdMTU0hJycHExMT/Pjjj9wyQc+rAwcOwN7eHnJyciJ7WTLG8O2330JPTw+ysrJo3749fH19ueWijuWbvRvv3bsHd3d3qKurQ1FREX369MGVK1e45cePH0ffvn0hJycHTU1NXk+QN4f4NXcdAkBCQgKsra0hKysLXV1dLFmyhBdoEeeeraqqwsKFC9GhQwcoKiqiX79+iI+P55YLhnmdOXMGpqamUFJSwvDhw/HgwQNeW5o6B69fv8acOXOgq6sLOTk5dO7cGevWrRM6BwKxsbFYunQpnJ2doa+vj969e+Prr7/G1KlTuTIpKSnw9PSEg4MD9PX1MWPGDJibm+Pq1au8uhQUFHj3hoqKCm95Y8/sxujr62PVqlWYPHkyVFRUuF5vFy9ehJ2dHeTl5dGpUyf4+vpyQweXLl2Kfv36CdVlbm6OlStXAhAe4ldXV4d169Zxzxlzc3P8+uuv3PI+ffpg48aN3PTIkSMhLS2NiooKAPXXooSEBAoKCgA0f56B+nOtp6cHBQUFjBo1ihcQFOz7li1bMHnyZKiqqop1vAghhBBCyMfhkwlQJSUloXfv3rx5T58+xeDBg2FpaYnU1FScPn0af/31F8aNG8eV8ff3R3BwMAIDA5GTk4P9+/ejXbt23HIHBwd4eXk1ut20tDSkpKTA3t6+Re0tLy+Hurp6i9YRx4sXLzBhwgSEhYVBR0dHZBkZGRlYWFgIBdUiIiLg4uICVVVVTJw4EeHh4SLXnzdvHmpqarB169YWt2/u3LlgjHHDYJ48eYKcnBz06dOHV27RokU4dOgQ9u7dixs3bsDAwABOTk548uQJr9ySJUsQHByM3Nxc9OrVC/Pnz0dycjJiY2Nx7tw5JCUl4caNG7x1qqursWrVKmRkZODo0aMoLi4WeY6XLVuGkJAQpKamok2bNrwfng3V1NRg5MiRsLe3R2ZmJi5duoQZM2bwerAVFhbi1KlTOH36NKKjoxEeHg4XFxfcu3cPCQkJWL9+PQICAniBguasWLEC48aNQ2ZmJpydneHh4cEdn7q6OnTs2BEHDx5ETk4Oli9fjqVLl+KXX37h1XHhwgX8+eefSExMxKZNmxAUFITPP/8cbdu2xZUrV/DVV19h5syZuHfvHnfsnJycoKysjKSkJCQnJ3PBAEEvDUEA6V1ywfn5+WHBggVIS0uDjY0NXF1dhX6Ivnnuxb1m/Pz8EBISgmvXrkFLSwuurq5cb6VXr16hd+/eOHHiBLKysjBjxgxMmjSJF1Bo6pkhKPf777/jwYMHXJA3KioKy5cvx5o1a5Cbm4u1a9ciMDAQe/fuFdqnuXPnIjc3F05OTkLH5dChQwgNDcVPP/2E/Px8HD16FGZmZmIf14qKCtjb2+P+/fuIjY1FRkYGFi1ahLq6OgDAiRMnMGrUKDg7OyMtLQ3nz5+HtbV1k3U2dR3ev38fzs7O6Nu3LzIyMrBt2zaEh4dj9erV3Pri3LNz5szBpUuXEBMTg8zMTIwdOxbDhw9Hfn4+V+bFixfYuHEj9u3bh8TERJSUlGDhwoXc8ubOwffff4/Y2Fj88ssvyMvLQ1RUVJPDxXV0dHDy5Ek8f/680TK2traIjY3F/fv3wRhDXFwcbt26hWHDhvHKRUVFQVNTEz179oS/vz9evHjBW97YM7spGzduhLm5OdLS0hAYGIjCwkIMHz4cY8aMQWZmJg4cOICLFy9izpw5AAAPDw9cvXoVhYWFXB3Z2dnIzMzEhAkTRG5j3bp1iIyMxPbt25GdnY1vvvkGEydO5AJp9vb2XICJMYakpCSoqanh4sWLAOqDlx06dICBgQGA5s/zlStX4O3tjTlz5iA9PR2DBg3iXUtvq6qqCs+ePeN9CCGEEEJIK2CfCDc3NzZ16lTevFWrVrFhw4bx5t29e5cBYHl5eezZs2dMVlaW7dy5s9F6J02axJYsWSI0v0OHDkxGRoZJSkqylStXtqitBw4cYDIyMiwrK0us8kFBQczc3FyssjNmzGDe3t7cNAB25MgRoXKjRo1iXl5e3HRtbS3r1KkTO3r0KGOMscePHzMZGRlWVFTElYmLi2MAWFlZGdu+fTtTV1dnT58+ZYwxpqqqyiIiIriynTt3ZqGhoSLb2K5dOzZr1izGGGNpaWkMACspKeGWV1RUMGlpaRYVFcXNe/36NWvfvj3bsGEDry2C9jLG2LNnz5i0tDQ7ePAgN+/p06dMQUGBzZ07t7FDxq5du8YAsOfPn/Pq/v3337kyJ06cYADYy5cvGWP8c1JaWsoAsPj4eJH1BwUFMQUFBfbs2TNunpOTE9PX12e1tbXcPGNjY7Zu3bpG29kQABYQEMBNV1RUMADs1KlTja7j4+PDxowZw017enqyzp07C7XBzs6Om66pqWGKioosOjqaMcbYvn37mLGxMaurq+PKVFVVMXl5eXbmzBnGGGNXrlxhxsbG7N69e1yZzp07MxkZGaaoqMh9tmzZwu2L4Bq9ffs2A8CCg4O5daurq1nHjh3Z+vXrGWOiz31LrpmYmBiuTGlpKZOXl2cHDhxo9Li5uLiwBQsWMMZYs88MQfvT0tJ487t168b279/Pm7dq1SpmY2PDW2/z5s2NtoMxxkJCQpiRkRF7/fq1yOWi7veG9+ZPP/3ElJWVWWlpqcj1bWxsmIeHR6Pbf/O+bu46XLp0qdD1EhYWxpSUlFhtba1Y9+ydO3eYlJQUu3//Pq8tQ4YMYf7+/owxxiIiIhgAVlBQwNtOu3btuOnmzsHXX3/NBg8ezGtrUxISEljHjh2ZtLQ069OnD5s3bx67ePEir8yrV6/Y5MmTGQDWpk0bJiMjw/bu3csr89NPP7HTp0+zzMxM9vPPP7MOHTqwUaNGCW3vzWd2Uzp37sxGjhzJm+ft7c1mzJjBm5eUlMQkJSW555q5uTnv+8zf35/169ePm/b09GRubm7cvikoKLCUlBSh7bi7uzPGGIuNjWWqqqqspqaGpaenMx0dHTZ37ly2ePFixhhj06ZNYxMmTGCMiXee3d3dmbOzM2/5+PHjmaqqqsjjYG9v3+SzXyAoKIgBEPoEJRaxdTcei/URpbHvX0IIIYSQT115eTkDwMrLy1u0Xpt/JAr2D3j58iXk5OR48zIyMhAXFwclJSWh8oWFhXj69CmqqqowZMiQRuttLNdSUlISKioqcPnyZSxZsgQGBgZwd3dvtp1xcXGYMmUKdu7ciR49ejRbviViY2Nx4cIFLrdPU+Tl5Xl/pT937hwqKyvh7OwMANDU1MTQoUOxe/durFq1Smh9b29vhISEYP369Vi7dm2L2skY43oXvXz5EgB4566wsBDV1dXo378/N09aWhrW1tbIzc3l1dWw51VRURGqq6t5PT5UVVVhbGzMW+f69ev49ttvkZGRgbKyMq73SElJCbp3786V69WrF/dvXV1dAMCjR4+gp6fHq09dXR1eXl5wcnLC0KFD4ejoiHHjxnHrAPXDTpSVlbnpdu3aQUpKCpKSkrx5DYcaNqdh+xQVFaGiosJbPywsDLt370ZJSQlevnyJ169fCyXb79Gjh1AbevbsyU1LSUlBQ0ODqzcjIwMFBQW8fQHqex4Jel5YW1vj5s2bQu318/Pj9VTT1NRsdN9sbGy4f7dp0wZ9+vRp8ty35JppWLe6ujqMjY25MrW1tVi7di1++eUX3L9/H69fv0ZVVRWXdyk3N7fZZ8abKisrUVhYCG9vb0yfPp2bX1NTIzQE6c2ehG8aO3YsNm/ejK5du2L48OFwdnaGq6sr2rQR71Genp4OS0vLRntvpqen89oojqauw9zcXNjY2PB6E/bv3x8VFRW4d+8eysrKmr1n//jjD9TW1sLIyIi33aqqKl4eOAUFBXTr1o2b1tXV5dohzjnw8vLC0KFDYWxsjOHDh+Pzzz8X6unU0MCBA1FUVITLly8jJSUF58+fx5YtW7BixQoEBgYCALZu3YrLly8jNjYWnTt3RmJiInx8fNC+fXs4OjoCAC/pvJmZGXR1dTFkyBAUFhby9ufNZ3Zz3ryWMjIykJmZyRs6yhhDXV0dbt++DVNTU3h4eGD37t0IDAwEYwzR0dGYP3++yPoLCgrw4sULDB06lDf/9evXsLS0BADY2dnh+fPnvJ7GDg4OCA4OBlDfg8rPzw+AeOc5NzdXKPm4jY0NTp8+LfZxEcXf35+3n8+ePUOnTp3eqU5CCCGEENJyn0yASlNTE2VlZbx5FRUVcHV1xfr164XK6+rqoqio6K23J8g1Y2Zmhr/++gvffvttswGqhIQEuLq6IjQ0FJMnT37rbTfmwoULKCwsFEpiO2bMGNjZ2fFyeTx58oT34yc8PBxPnjyBvLw8N6+urg6ZmZlYsWIFL4gB1AcN1qxZAy8vL26IiDhKS0vx+PFj7vgJghRlZWVcTqKWUFRUbFH5yspKODk5wcnJCVFRUdDS0kJJSQmcnJyEEglLS0tz/xb8wBYEs94UEREBX19fnD59GgcOHEBAQADOnTvH5URrWJegPlHzGqtflKbWj4mJwcKFCxESEgIbGxsoKyvju+++ExpC2NJ2VVRUoHfv3iLzIzV3/jQ1NbmhPO9DS8+9OL777jts2bIFmzdvhpmZGRQVFTFv3jzu2mh4f4hLkG9n586dQjl+pKSkeNPN7VOnTp2Ql5eH33//HefOncPs2bPx3XffISEhAdLS0pCQkABjjLdOw2TrzbX/bfbvXa/j5lRUVEBKSgrXr18XOl4N//ggqh2CYyHOObCyssLt27dx6tQp/P777xg3bhwcHR15OZXeJC0tDTs7O9jZ2WHx4sVYvXo1Vq5cicWLF6O2thZLly7FkSNH4OLiAqA+mJeeno6NGzdyAao3CdpXUFDAe0a/+cxuzpvXUkVFBWbOnMnLWSYgCLq7u7tj8eLFuHHjBl6+fIm7d+82+pIOwTE9ceIEOnTowFsmeOmBmpoazM3NER8fj0uXLmHo0KEYOHAgxo8fj1u3biE/P58bHi/uef4QZGVl3+lFDQ1VVFRwObWA+tyO6enpUFdXF/rjBiGEEEII4ftkclBZWloiJyeHN8/KygrZ2dnQ19eHgYEB76OoqAhDQ0PIy8vj/Pnz77Tturq6ZhOGx8fHw8XFBevXr/9gr2lfsmQJMjMzkZ6ezn0AIDQ0VCjRdVZWFvdX7tLSUhw7dgwxMTG8ddPS0lBWVoazZ8+K3N7YsWPRo0cPrFixQuw2btmyBZKSklyi3W7dukFFRYV37rp16wYZGRkkJydz86qrq3Ht2jVeD6c3de3aFdLS0rzE2OXl5bh16xY3ffPmTZSWliI4OBh2dnYwMTFpUa+lplhaWsLf3x8pKSno2bMn9u/f/17qfRvJycmwtbXF7NmzYWlpCQMDA15umbdlZWWF/Px8aGtrC91T7zMh8eXLl7l/19TU4Pr16zA1NW20fEuumYZ1l5WV4datW1zdycnJcHNzw8SJE2Fubo6uXbvyrp/mnhkyMjIA6ntiCbRr1w7t27dHUVGR0DETBGpbQl5eHq6urvj++++5H/5//PEHgPogYcPE4Pn5+bxeN4IAyZt5uRouf9fnYUOmpqa4dOkSL2iWnJwMZWVldOzYUax71tLSErW1tXj06JHQ8Wssz96bxD0HKioqGD9+PHbu3IkDBw7g0KFDjR4rUbp3746amhq8evUK1dXVqK6uFgruS0lJNRnAEzy3G/bABPjP7LdhZWWFnJwcof03MDDgrtuOHTvC3t4eUVFRiIqKwtChQ6Gtrd3ovsrKyqKkpESovoa9j+zt7REXF4fExEQ4ODhAXV0dpqamWLNmDXR1dbkeU+KcZ1NTU6Ege8P7+WOQmpoKS0tL7lzNnz8flpaWWL58eSu3jBBCCCHk4/fJ9KBycnKCv78/ysrK0LZtWwCAj48Pdu7cCXd3dyxatAjq6uooKChATEwMdu3aBTk5OSxevBiLFi2CjIwM+vfvj8ePHyM7Oxve3t4AgMmTJ6NDhw7c25zCwsKgp6cHExMTAEBiYiI2btzI+6v0Dz/8gCNHjnA/9OLi4vD5559j7ty5GDNmDB4+fAig/sesYKjNkSNH4O/vzxsaVVBQgIqKCjx8+BAvX77kfrh0794dMjIyuH//PoYMGYLIyEhYW1tzb4B6k56eHu9HWHFxMe7fv8/9BX/fvn3Q0NDAuHHjeENxAMDZ2Rnh4eEYPny4yOMeHBwsMpkzUP92rocPH6K6uhq3b9/Gzz//jF27dmHdunVcTxpJSUk4Ojri4sWLXNBKUVERs2bN4t4kp6enhw0bNuDFixfceRFFWVkZnp6e3Hra2toICgqCpKQkt196enqQkZHB1q1b8dVXXyErK0vkEMaWuH37Nnbs2IEvvvgC7du3R15eHvLz8z9ILzlxGRoaIjIyEmfOnEGXLl2wb98+XLt27a0CIg15eHjgu+++g5ubG1auXImOHTvizp07OHz4MBYtWoSOHTvi6tWrmDx5Ms6fPy/Us0JcYWFhMDQ0hKmpKUJDQ1FWVtZoknqgZdfMypUroaGhgXbt2mHZsmXQ1NTkrj1DQ0P8+uuvSElJQdu2bbFp0yb89ddfXJCruWeGtrY25OXlcfr0aXTs2BFycnJQVVXFihUr4OvrC1VVVQwfPhxVVVVITU1FWVlZo0OoRNmzZw9qa2vRr18/KCgo4Oeff4a8vDw6d+4MABg8eDB++OEH2NjYoLa2FosXL+b1LHJ3d8fatWsxcuRIrFu3Drq6ukhLS0P79u1hY2ODoKAgDBkyBN26dcOXX36JmpoanDx5EosXLxa7jQ3Nnj0bmzdvxtdff405c+YgLy8PQUFBmD9/PiQlJcW6Z42MjODh4YHJkycjJCQElpaWePz4Mc6fP49evXpxvZOa09w52LRpE3R1dWFpaQlJSUkcPHgQOjo6Qj1SBRwcHODu7o4+ffpAQ0MDOTk5WLp0KQYNGsS9hc/e3h5+fn7cOUpISEBkZCQ2bdoEoH5o6v79++Hs7AwNDQ1kZmbim2++wcCBA3lDJ998Zr+NxYsX47PPPsOcOXMwbdo0KCoqIicnB+fOncMPP/zAlfPw8EBQUBBev36N0NDQRutTVlbGwoUL8c0336Curg4DBgxAeXk5kpOToaKiAk9PT+44bd26FVpaWtz3poODA3744QeMHTuWq0+c8+zr64v+/ftj48aNcHNzw5kzZ0QO7xN8V1ZUVODx48dIT0+HjIxMk3/geF8cHByEejESQgghhBAxve9kWK3J2tqabd++nTfv1q1bbNSoUUxNTY3Jy8szExMTNm/ePC4Rbm1tLVu9ejXr3Lkzk5aWZnp6emzt2rXc+vb29szT05Ob/v7771mPHj2YgoICU1FRYZaWluzHH3/kJZoOCgpinTt35qY9PT1FJmC1t7fnygiS/DZkb28vcr3bt28zxv4/sXJcXFyjxwQikrSuXbuWOTk5cdNmZmZs9uzZItcXJHR//PgxL0l6Q8OGDWMAhJKkC9orIyPD9PT02Lhx49iFCxeEtnHy5EnWoUMH3jF8+fIl+/rrr5mmpiaTlZVl/fv3Z1evXuWWN9aWZ8+esQkTJjAFBQWmo6PDNm3axKytrXmJ7vfv38/09fWZrKwss7GxYbGxsbzE1qLqFiRzFxz7hknSHz58yEaOHMl0dXWZjIwM69y5M1u+fDm3P6KS3DdMNiwgbkJfxppPhv3q1Svm5eXFVFVVmZqaGps1axZbsmQJrx3ituHNxNgPHjxgkydP5s5N165d2fTp07kEeILjJzhWoupobF8E1/T+/fuZtbU1k5GRYd27d+ddN42de3GvmePHj7MePXowGRkZZm1tzTIyMrgypaWlzM3NjSkpKTFtbW0WEBDAJk+ezDtOzT0zdu7cyTp16sQkJSV593hUVBSzsLBgMjIyrG3btmzgwIHs8OHDvP1+M7n6m44cOcL69evHVFRUmKKiIvvss894yfzv37/Phg0bxhQVFZmhoSE7efKk0AsMiouL2ZgxY5iKigpTUFBgffr0YVeuXOGWHzp0iGunpqYmGz16NLdMVJL0pq5DxhiLj49nffv2ZTIyMkxHR4ctXryYVVdXc8vFuWdfv37Nli9fzvT19Zm0tDTT1dVlo0aNYpmZmYyx+ufnm4myjxw5IvRMbeoc7Nixg1lYWDBFRUWmoqLChgwZwm7cuNHouVi7di2zsbFh6urqTE5OjnXt2pX5+vqyv//+myvz4MED5uXlxdq3b8/k5OSYsbExCwkJ4b5/SkpK2MCBA5m6ujqTlZVlBgYGzM/PTyiZ5JvP7OY0dr9dvXqVDR06lCkpKTFFRUXWq1cvtmbNGl6ZsrIyJisryxQUFLgXRwi8+cyoq6tjmzdvZsbGxkxaWpppaWkxJycnlpCQwJUpLS1lEhISbPz48dw8wbl58/u6ufPMGGPh4eGsY8eOTF5enrm6urKNGzcKnXtR35sNv5ObI0jq+a5J0gkhhBBC/qveNkm6BGOfzp/6Tpw4AT8/P2RlZQkNqyD1Xr9+DUNDQ+zfv5+XULo1McbQr18/fPPNN2Ilmm+JyspKdOjQASEhIU32viKEfBzonuX7GJ/Zn7pnz55BVVUVQYlFkFNSbn4FAEssG3/pAyGEEELIf43g/1Pl5eXc6AJxfDJD/ADAxcUF+fn5uH//Pr2BpxElJSVYunTpR/VDR0JCAjt27ODy6LyLtLQ03Lx5E9bW1igvL8fKlSsBAG5ubu9cNyHk/aN7tmkf4zObEEIIIYSQD+GTClABwLx581q7CR81QdLZj42FhQUsLCzeS10bN25EXl4eZGRk0Lt3byQlJXFvC/w3iIqKwsyZM0Uu69y5M7Kzs//hFhHyYf3b79kP6c1ndlJSEkaMGNFoecHb9QghhBBCCPm3+aSG+BHyKXj+/Dn++usvkcukpaW5hNiEkP+ely9f4v79+40u/xj/APFvQ0P8CCGEEELeDQ3xI+QToaysDGVl8X4UEUL+W+Tl5SkIRQghhBBCPkmUSZwQQgghhBBCCCGEtCoKUBFCCCGEEEIIIYSQVkUBKkIIIYQQQgghhBDSqigHFSGEEELIG+aba7QoqSchhBBCCHk31IOKEEIIIYQQQgghhLQqClARQgghhBBCCCGEkFZFASpCCCGEEEIIIYQQ0qooQEUIIYQQQgghhBBCWhUFqAghhBBCCCGEEEJIq6IAFSGEEEIIIYQQQghpVW1auwGEEEIIIR+bTRmlkFN6LVbZJZaaH7g1hBBCCCGfPupBRQghhBBCCCGEEEJaFQWoCCGEEEIIIYQQQkirogAVIYQQQgghhBBCCGlVFKAihBBCCCGEEEIIIa2KAlSEEEIIIYQQQgghpFVRgIoQQgghhBBCCCGEtCoKUBFCCCGEvAeJiYlwdXVF+/btISEhgaNHj7Z2kwghhBBC/jUoQPWJKS0thba2NoqLi1u7KR+tnJwcdOzYEZWVla3dFJ7AwEDMmDGjtZshtm+//RYWFhat3QzSQHFxMSQkJJCeng4AiI+Ph4SEBJ4+fQoA2LNnD9TU1FqtfeTtbN++Ha6urq3dDACAvr4+Nm/e3NrN4PHy8sLIkSNbuxkAgMrKSpibmyMsLKy1m0IIIYQQ8q9DAapPzJo1a+Dm5gZ9ff0Pto2LFy+if//+0NDQgLy8PExMTBAaGtrkOvHx8XBzc4Ouri4UFRVhYWGBqKioZrfl6+uL3r17Q1ZWtsXBEMYYRowYIfRX7O7du+Ozzz7Dpk2bRK4XHR0NKSkp+Pj4iNwPCQkJ9OjRA7W1tbxlampq2LNnDzetr68PCQkJSEhIQF5eHvr6+hg3bhwuXLggVO/Dhw+xZcsWLFu2rEX72JoWLlyI8+fPt3Yz/jUaXg9SUlJo3749vL29UVZW9t620alTJzx48AA9e/Z8b3V+bEQF2XJzc9GpUyeMHTsWr1+/Rm1tLYKDg2FiYgJ5eXmoq6ujX79+2LVrF7eOl5cXJCQk8NVXXwltw8fHBxISEvDy8mq2Pbm5ufjiiy+gqqoKRUVF9O3bFyUlJdzymTNnolu3bpCXl4eWlhbc3Nxw8+ZNXh2C66LhJyYmhls+depU3LhxA0lJSWIepQ/n2rVrH30gvTV7MY0YMQKrV6/GqFGj/rFtEkIIIYR8KihA9Ql58eIFwsPD4e3t/UG3o6ioiDlz5iAxMRG5ubkICAhAQEAAduzY0eg6KSkp6NWrFw4dOoTMzExMmTIFkydPxm+//dbs9qZOnYrx48e3uJ2bN2+GhISEyGVTpkzBtm3bUFNTI7QsPDwcixYtQnR0NF69eiVy/aKiIkRGRjbbhpUrV+LBgwfIy8tDZGQk1NTU4OjoiDVr1vDK7dq1C7a2tujcubMYeyYaY0zk/nwoSkpK0NDQ+Me29ykQXA8lJSWIiopCYmIifH1931v9UlJS0NHRQZs2bd5bnR+7a9euwc7ODsOHD8eBAwcgIyODFStWIDQ0FKtWrUJOTg7i4uIwY8YMrieZQKdOnRATE4OXL19y8169eoX9+/dDT0+v2W0XFhZiwIABMDExQXx8PDIzMxEYGAg5OTmuTO/evREREYHc3FycOXMGjDEMGzZMKMAdERGBBw8ecJ+GPYJkZGQwYcIEfP/99293kABUV1e/9boNaWlpQUFB4b3U9aFQLyZCCCGEkH8nClB9Qk6ePAlZWVl89tlnvPlZWVkYMWIElJSU0K5dO0yaNAl///03t7yurg4bNmyAgYEBZGVloaenJxRAacjS0hLu7u7o0aMH9PX1MXHiRDg5OTX51/2lS5di1apVsLW1Rbdu3TB37lwMHz4chw8fbnKfvv/+e/j4+KBr165iHoV66enpCAkJwe7du0UuHzp0KJ48eYKEhATe/Nu3byMlJQVLliyBkZFRo+37+uuvERQUhKqqqibboaysDB0dHejp6WHgwIHYsWMHAgMDsXz5cuTl5XHlYmJihIbwVFVVwdfXF9ra2pCTk8OAAQNw7do1brmgN9epU6e4XmYXL17E8+fP4eHhAUVFRejq6iI0NBQODg6YN28et+6+ffvQp08frn0TJkzAo0ePhOo+f/48+vTpAwUFBdja2vLa/OYQv/j4eFhbW0NRURFqamro378/7ty5wyu7e/du6OnpQUlJCbNnz0ZtbS02bNgAHR0daGtrN3ndvUlCQgK7du3CqFGjoKCgAENDQ8TGxnLLa2tr4e3tjS5dukBeXh7GxsbYsmULrw7B0KC1a9eiXbt2UFNTw8qVK1FTUwM/Pz+oq6ujY8eOiIiI4K139+5djBs3DmpqalBXV4ebm5tYw2oFx7tDhw4YNGgQPD09cePGDW55aWkp3N3d0aFDBygoKMDMzAzR0dG8Opq6X98c4tccUUOj5s2bBwcHB276119/hZmZGeTl5aGhoQFHR8dGh8eWlZXBw8MDWlpakJeXh6GhIXfsBG2LiYmBra0t5OTk0LNnT6F7sCUuXLiAwYMHw9vbGzt37oSkZP1XWmxsLGbPno2xY8eiS5cuMDc3h7e3NxYuXMhb38rKCp06deLd54cPH4aenh4sLS2b3f6yZcvg7OyMDRs2wNLSEt26dcMXX3wBbW1trsyMGTMwcOBA6Ovrw8rKCqtXr8bdu3eFrhc1NTXo6Ohwn4ZBLgBwdXVFbGwsL5jWGMGxPnDgAOzt7SEnJ8f1WN21axdMTU0hJycHExMT/Pjjj9x6tra2WLx4Ma+ux48fQ1paGomJiQCEh/g9ffoU06ZNg5aWFlRUVDB48GBkZGQAAMrLyyElJYXU1FQA9deuuro67zvq559/RqdOnbjp5u6t2tpazJ8/H2pqatDQ0MCiRYvAGOO1mXoxEUIIIYT8O1GA6hOSlJSE3r178+Y9ffoUgwcPhqWlJVJTU3H69Gn89ddfGDduHFfG398fwcHBCAwMRE5ODvbv34927dpxyx0cHJoc6pKWloaUlBTY29u3qL3l5eVQV1dv0TriePHiBSZMmICwsDDo6OiILCMjIwMLCwuhoFpERARcXFygqqqKiRMnIjw8XOT68+bNQ01NDbZu3dri9s2dOxeMMRw7dgwA8OTJE+Tk5KBPnz68cosWLcKhQ4ewd+9e3LhxAwYGBnBycsKTJ0945ZYsWYLg4GDk5uaiV69emD9/PpKTkxEbG4tz584hKSmJFwQB6ntTrFq1ChkZGTh69CiKi4tFnuNly5YhJCQEqampaNOmDaZOnSpyn2pqajBy5EjY29sjMzMTly5dwowZM3g92AoLC3Hq1CmcPn0a0dHRCA8Ph4uLC+7du4eEhASsX78eAQEBuHLlitjHcsWKFRg3bhwyMzPh7OwMDw8P7vjU1dWhY8eOOHjwIHJycrB8+XIsXboUv/zyC6+OCxcu4M8//0RiYiI2bdqEoKAgfP7552jbti2uXLmCr776CjNnzsS9e/e4Y+fk5ARlZWUkJSUhOTkZSkpKGD58OF6/fg3g/wN8TQWt7t+/j+PHj6Nfv37cvFevXqF37944ceIEsrKyMGPGDEyaNAlXr17lyjR3v75PDx48gLu7O6ZOnYrc3FzEx8dj9OjRQgEBAUGbTp06hdzcXGzbtg2ampq8Mn5+fliwYAHS0tJgY2MDV1dXlJaWtrhtR44cgYuLCwICArB+/XreMh0dHVy4cAGPHz9utp6pU6fyApC7d+/GlClTml2vrq4OJ06cgJGREZycnKCtrY1+/fo1OZyssrISERER6NKlCy8oA9QPK9TU1IS1tTV2794tdIz79OmDmpqaFt0fS5Yswdy5c5GbmwsnJydERUVh+fLlWLNmDXJzc7F27VoEBgZi7969AAAPDw/ExMTwtn3gwAG0b98ednZ2IrcxduxYPHr0CKdOncL169dhZWWFIUOG4MmTJ1BVVYWFhQXi4+MBAH/88QckJCSQlpaGiooKAEBCQgL33SHOvRUSEoI9e/Zg9+7duHjxIp48eYIjR46IfUxEqaqqwrNnz3gfQgghhBDSChj5ZLi5ubGpU6fy5q1atYoNGzaMN+/u3bsMAMvLy2PPnj1jsrKybOfOnY3WO2nSJLZkyRKh+R06dGAyMjJMUlKSrVy5skVtPXDgAJORkWFZWVlilQ8KCmLm5uZilZ0xYwbz9vbmpgGwI0eOCJUbNWoU8/Ly4qZra2tZp06d2NGjRxljjD1+/JjJyMiwoqIirkxcXBwDwMrKytj27duZuro6e/r0KWOMMVVVVRYREcGV7dy5MwsNDRXZxnbt2rFZs2YxxhhLS0tjAFhJSQm3vKKigklLS7OoqChu3uvXr1n79u3Zhg0beG0RtJcxxp49e8akpaXZwYMHuXlPnz5lCgoKbO7cuY0dMnbt2jUGgD1//pxX9++//86VOXHiBAPAXr58yRjjn5PS0lIGgMXHx4usPygoiCkoKLBnz55x85ycnJi+vj6rra3l5hkbG7N169Y12s6GALCAgABuuqKiggFgp06danQdHx8fNmbMGG7a09OTde7cWagNdnZ23HRNTQ1TVFRk0dHRjDHG9u3bx4yNjVldXR1XpqqqisnLy7MzZ84wxhi7cuUKMzY2Zvfu3ePKdO7cmcnIyDBFRUUmJyfHALB+/fqxsrKyJvfTxcWFLViwgDHGmr1fb9++zQCwtLQ0xhj/emWMsYiICKaqqsrbfzc3N14dc+fOZfb29owxxq5fv84AsOLi4ibbKODq6sqmTJnSZNuCg4O5edXV1axjx45s/fr1YtUv2AcpKSkmJSXFAgMDRZbJzs5mpqamTFJSkpmZmbGZM2eykydP8soI9v3Ro0dMVlaWFRcXs+LiYiYnJ8ceP37M3NzcmKenZ6PtePDgAQPAFBQU2KZNm1haWhpbt24dk5CQELoPwsLCmKKiIgPAjI2NWUFBAW/5ypUr2cWLF9mNGzdYcHAwk5WVZVu2bBHaZtu2bdmePXuaPUaCY71582be/G7durH9+/fz5q1atYrZ2Ngwxhh79OgRa9OmDUtMTOSW29jYsMWLF3PTDZ9rSUlJTEVFhb169UpoOz/99BNjjLH58+czFxcXxhhjmzdvZuPHj2fm5ubcfWpgYMB27NjBGBPv3tLV1eWegYz9/zX05nUs0Njzv6GgoCAGQOgTlFjE1t14LNbnbbdNCCGEEPIpKi8vZwBYeXl5i9ajHlSfkJcvXwoNC8nIyEBcXByUlJS4j4mJCYD6Hi25ubmoqqrCkCFDGq03MjIS69atE5qflJSE1NRUbN++HZs3bxYaitSYuLg4TJkyBTt37kSPHj1asIfNi42NxYULF8R6y5S8vDxevHjBTZ87dw6VlZVwdnYGAGhqamLo0KGNDhP09vaGhoaGUO8NcTDGuN5FgiE7Dc9dYWEhqqur0b9/f26etLQ0rK2tkZuby6urYc+roqIiVFdXw9rampunqqoKY2Nj3jrXr1+Hq6sr9PT0oKyszPVgaJjcGQB69erF/VtXVxcAeEMBBdTV1eHl5QUnJye4urpiy5YtePDgAa+Mvr4+lJWVuel27dqhe/fu3LAswTxR9TemYfsUFRWhoqLCWz8sLAy9e/eGlpYWlJSUsGPHDqF97NGjh1AbzMzMuGkpKSloaGhw9WZkZKCgoADKysrcPaWuro5Xr16hsLAQAGBtbY2bN2+iQ4cOvG35+fkhPT0dmZmZXIJ5FxcXLh9RbW0tVq1aBTMzM6irq0NJSQlnzpzh2izO/fo+mZubY8iQITAzM8PYsWOxc+fOJpO6z5o1CzExMbCwsMCiRYuQkpIiVMbGxob7d5s2bdCnTx+ha7o58vLyGDp0KHbu3Cly3e7duyMrKwuXL1/G1KlT8ejRI7i6umLatGlCZbW0tODi4oI9e/ZwPSjf7PUVFRXFe4YmJSWhrq4OAODm5oZvvvkGFhYWWLJkCT7//HNs376dt76HhwfS0tKQkJAAIyMjjBs3jpffLjAwEP3794elpSUWL16MRYsW4bvvvhO53w2fWc1p+GyorKxEYWEhvL29efuyevVq7rrV0tLCsGHDuOGAt2/fxqVLl+Dh4SGy/oyMDFRUVEBDQ4NX5+3bt7k67e3tcfHiRdTW1iIhIQEODg5wcHBAfHw8/vzzTxQUFHBDSpu7t8rLy/HgwQNer0PBNfQu/P39UV5ezn3u3r37TvURQgghhJC389/JpPsfoKmpKfTjsaKiAq6uriKDKLq6uigqKnrr7XXp0gUAYGZmhr/++gvffvst3N3dm1wnISEBrq6uCA0NxeTJk9962425cOECCgsLhd7yNWbMGNjZ2XFDTYD6oXXdunXjpsPDw/HkyRPIy8tz8+rq6pCZmYkVK1bwghhA/Q+jNWvWwMvLC3PmzBG7jaWlpXj8+DF3/AQ/hsvKyqClpSV2PQKKiootKl9ZWQknJyduyI+WlhZKSkrg5OTEDaMRkJaW5v4tCKgJfpi/KSIiAr6+vjh9+jQOHDiAgIAAnDt3jss307AuQX2i5jVWvyhNrR8TE4OFCxciJCQENjY2UFZWxnfffSc0RKql7aqoqEDv3r1FvoWyufOnqakJAwMDAIChoSE2b94MGxsbxMXFwdHREd999x22bNmCzZs3w8zMDIqKipg3bx53Xhpem++DpKSk0FCyhsm0paSkcO7cOaSkpODs2bPYunUrli1bhitXrnDXb0MjRozAnTt3cPLkSZw7dw5DhgyBj48PNm7c+F7bLSUlhaNHj2L06NEYNGgQ4uLiYGpqKrRvffv2Rd++fTFv3jz8/PPPmDRpEpYtWybU9qlTp3L3sKjE2l988QUvKNKhQwdISUmhTZs26N69O6+sqakpLl68yJunqqoKVVVVGBoa4rPPPkPbtm1x5MiRRp+X/fr1w6pVq1BVVQVZWVlu/pMnT1r0jGj4bBAMqdu5cydvX4D64yng4eEBX19fbN26Ffv374eZmRkvYNtQRUUFdHV1ec9VAcEzeODAgXj+/Dlu3LiBxMRErF27Fjo6OggODoa5uTnat28PQ0NDrr63vbfehaysLO84v4uKigoUFBRw07dv30Z6ejrU1dXFSrxPCCGEEPJfRj2oPiGWlpbIycnhzbOyskJ2djb09fVhYGDA+ygqKsLQ0BDy8vJcb463VVdX12zC8Pj4eLi4uGD9+vUf7DXlS5YsQWZmJtLT07kPAISGhgolus7KyuISIZeWluLYsWOIiYnhrZuWloaysjKcPXtW5PbGjh2LHj16YMWKFWK3ccuWLZCUlOSSU3fr1g0qKiq8c9etWzfIyMggOTmZm1ddXY1r164J/SBuqGvXrpCWluYlUy8vL8etW7e46Zs3b6K0tBTBwcGws7ODiYlJi3otNcXS0hL+/v5ISUlBz549sX///vdS79tITk6Gra0tZs+eDUtLSxgYGHC9Ot6FlZUV8vPzoa2tLXRPqaqqtqguQWBA0IsuOTkZbm5umDhxIszNzdG1a1feuXtf96uAlpaWUE+3NxOsS0hIoH///lixYgXS0tIgIyPTZM4fLS0teHp64ueff8bmzZuF3u55+fJl7t81NTW4fv26UHBJHLKysjh8+DD69u2LQYMGCT373iS4b0QleBfkOBLkQHqTsrIy7zzLy8tDRkYGffv25b04AABu3brV5Ns4GWNgjDX5vExPT0fbtm15QZPCwkK8evVKrOTtorRr1w7t27dHUVGR0HXbMGDn5uaGV69e4fTp09i/f3+jvaeA+nvh4cOHaNOmjVCdgsC7mpoaevXqhR9++AHS0tIwMTHBwIEDkZaWht9++42Xu7C5e0tVVRW6urq8ILPgGvpYpKamwtLSkjtP8+fPh6WlJZYvX97KLSOEEEII+fhRgOoT4uTkhOzsbF4vKh8fHzx58gTu7u64du0aCgsLcebMGUyZMgW1tbWQk5PjhpRERkaisLAQly9f5iUHnzx5Mvz9/bnpsLAwHD9+HPn5+cjPz0d4eDg2btyIiRMncmV++OEH3jCkuLg4uLi4wNfXF2PGjMHDhw/x8OFDXsLvI0eOcMMPBQoKCpCeno6HDx/i5cuXXOBI0KPk/v37MDEx4ZJI6+jooGfPnrwPAOjp6fF+hBUXF+P+/ftwdHQEUP9WOw0NDYwbN463rrm5OZydnRtNlg4AwcHB2L17t8gfvs+fP8fDhw9x9+5dJCYmYsaMGVi9ejXWrFnD9aSRlJSEo6Mjr9eFoqIiZs2aBT8/P5w+fRo5OTmYPn06Xrx4AW9v70bboqysDE9PT/j5+SEuLg7Z2dnw9vaGpKQk1wNKT08PMjIy2Lp1K4qKihAbG4tVq1Y1Wqc4bt++DX9/f1y6dAl37tzB2bNnkZ+f/1aBh/fF0NAQqampOHPmDG7duoXAwEBe4O5teXh4QFNTE25ubkhKSsLt27cRHx8PX19fLpH61atXYWJigvv37/PWFVwPDx48wNWrV+Hn5wctLS3Y2tpybRb0WMrNzcXMmTPx119/ceuLc7+2xODBg5GamorIyEjk5+cjKCgIWVlZ3PIrV65g7dq1SE1NRUlJCQ4fPozHjx83el6XL1+OY8eOoaCgANnZ2fjtt9+EyoaFheHIkSO4efMmfHx8UFZWxku+b2JiInbSa1lZWRw6dAj9+vXDoEGDkJ2dDQD43//+h9DQUFy5cgV37txBfHw8fHx8YGRkJPSMAeoDhbm5ucjJyeH1JmqOn58fDhw4gJ07d6KgoAA//PADjh8/jtmzZwOoH3K7bt06XL9+HSUlJUhJScHYsWMhLy/PDSU+fvw4du3ahaysLBQUFGDbtm1Yu3Ytvv76a962kpKS0LVrV16vz5ZasWIF1q1bh++//x63bt3CH3/8gYiICGzatIkro6ioiJEjRyIwMBC5ublN9op1dHSEjY0NRo4cibNnz6K4uBgpKSlYtmwZ9+Y+oP5FG1FRUVwwSl1dHaamptxbBgXEubfmzp2L4OBgHD16FDdv3sTs2bPx9OlTXrsqKip4f6AQ9GJ6c3jvh+Dg4MAFIRt+9uzZ88G3TQghhBDyb0cBqk+ImZkZrKyseG8pa9++PZKTk1FbW4thw4bBzMwM8+bNg5qaGjdkLTAwEAsWLMDy5cthamqK8ePH83rUlJSU8HpZ1NXVwd/fHxYWFujTpw/CwsKwfv16rFy5kivz999/83qr7N27Fy9evMC6deugq6vLfUaPHs2VKS8vF+qNMG3aNFhaWuKnn37CrVu3uL9M//nnnwDqexXl5eW1KC8LAERHR2PYsGFcT4fdu3dj1KhRvLfOCYwZMwaxsbH4+++/RdY1ePBgDB48GDU1NULLli9fDl1dXRgYGGDSpEkoLy/H+fPnhV7lPm3aNMTExPCGtwUHB2PMmDGYNGkSrKysUFBQgDNnzqBt27ZN7tumTZtgY2ODzz//HI6Ojujfvz/3WnmgvofLnj17cPDgQXTv3h3BwcHvPARLQUEBN2/exJgxY2BkZIQZM2bAx8cHM2fOfKd638XMmTMxevRojB8/Hv369UNpaSkXOHgXCgoKSExMhJ6eHkaPHg1TU1N4e3vj1atXUFFRAVD/Jsm8vDzecDng/6+H9u3b4/PPP4eioiLOnj0LDQ0NAEBAQACsrKzg5OQEBwcH6OjocD3tBJq7X1vCyckJgYGBWLRoEfr27Yvnz5/zht6qqKggMTERzs7OMDIyQkBAAEJCQjBixAiR9cnIyMDf3x+9evXCwIEDISUlhZiYGF6Z4OBgbnjXxYsXERsby8v5lJeXh/LycrH3QUZGBr/++itsbW0xaNAgZGVlwcnJCcePH4erqyuMjIzg6ekJExMTnD17Fm3aiB7ZrqKiwp0/cY0aNQrbt2/Hhg0bYGZmhl27duHQoUMYMGAAgPqAYlJSEpydnWFgYIDx48dDWVkZKSkp0NbWBlA/xDQsLAw2NjawsLDATz/9xL1NsqHo6GhMnz69Re1707Rp07Br1y5ERETAzMwM9vb22LNnj9CQRw8PD2RkZMDOzq7JYWkSEhI4efIkBg4ciClTpsDIyAhffvkl7ty5w3uzpL29PWpra7lcU0B9IOfNeeLcWwsWLMCkSZPg6enJDd0dNWoUr13Ui4kQQggh5N9Jgr2ZgIT8q504cQJ+fn7IysoSyplE6r1+/RqGhobYv38/Lwl5a2KMoV+/fvjmm2+azePVUpWVlejQoQNCQkKa7H1FyIdUXFyMLl26IC0tDRYWFq3dnH+V7OxsDB48GLdu3WrxMFLScs+ePYOqqiqCEosgp6Tc/AoAllhqNl+IEEIIIeQ/QvD/qfLy8hb9EZiSpH9iXFxckJ+fj/v376NTp06t3ZyPUklJCZYuXfrRBKeA+p4IO3bswB9//PHOdaWlpeHmzZuwtrZGeXk517PNzc3tnesmhPzzHjx4gMjISApOEUIIIYSQTxoFqD5B8+bNa+0mfNQESXc/NhYWFu+tZ8nGjRuRl5cHGRkZ9O7dG0lJSbxhVB+7qKioRocHdu7cmcs1RMh/gSBXnsDatWuxdu1akWXt7Oxw6tSpf6JZhBBCCCGEvFc0xI8Q8tF5/vw5Lzl4Q9LS0k2+JY2QT92TJ094L5hoSF5eHh06dPiHW/RpoSF+hBBCCCHvhob4EUI+GcrKylBWFu+HISH/Nerq6lBXV2/tZhBCCCGEEPJeURZtQgghhBBCCCGEENKqKEBFCCGEEEIIIYQQQloVBagIIYQQQgghhBBCSKuiHFSEEEIIIW+Yb67RoqSehBBCCCHk3VAPKkIIIYQQQgghhBDSqihARQghhBBCCCGEEEJaFQWoCCGEEEIIIYQQQkirogAVIYQQQgghhBBCCGlVFKAihBBCCCGEEEIIIa2KAlSEEEIIIYQQQgghpFW1ae0GEEIIIYR8bDZllEJO6bVYZZdYan7g1hBCCCGEfPqoBxUhhBBCCCGEEEIIaVUUoCKEEEIIIYQQQgghrYoCVIQQQgghhBBCCCGkVVGAihBCCCGEEEIIIYS0KgpQEUIIIYQQQgghhJBWRQEqQgghhBBCCCGEENKqKEBFCCGEEPIeJCYmwtXVFe3bt4eEhASOHj3a2k0ihBBCCPnXoADVJ6a0tBTa2tooLi5u7aZ8tHJyctCxY0dUVla2dlN4AgMDMWPGjNZuhti+/fZbWFhYtHYzSAPFxcWQkJBAeno6ACA+Ph4SEhJ4+vQpAGDPnj1QU1NrtfaRt7NkyRJ8/fXXrd0MAPgogy4ODg6YN29eazcDAFBZWQlzc3OEhYW1dlMIIYQQQv51KED1iVmzZg3c3Nygr6//wbZx8eJF9O/fHxoaGpCXl4eJiQlCQ0ObXCc+Ph5ubm7Q1dWFoqIiLCwsEBUV1ey2fH190bt3b8jKyrY4GMIYw4gRI4R+UHXv3h2fffYZNm3aJHK96OhoSElJwcfHR+R+SEhIoEePHqitreUtU1NTw549e7hpfX19SEhIQEJCAvLy8tDX18e4ceNw4cIFoXofPnyILVu2YNmyZS3ax9a0cOFCnD9/vrWb8a/R8HqQkpJC+/bt4e3tjbKysve2jU6dOuHBgwfo2bPne6vzYyMqyJabm4tOnTph7NixeP36NWpraxEcHAwTExPIy8tDXV0d/fr1w65du7h1vLy8ICEhga+++kpoGz4+PpCQkICXl1eTbRHU0fAzfPhwbnlxcTG8vb3RpUsXyMvLo1u3bggKCsLr16+5MuI8GxcuXIi9e/eiqKioBUfqw3jw4AFGjBjR2s1o0uHDhzFs2DBoaGjwArb/hBEjRmD16tUYNWrUP7ZNQgghhJBPBQWoPiEvXrxAeHg4vL29P+h2FBUVMWfOHCQmJiI3NxcBAQEICAjAjh07Gl0nJSUFvXr1wqFDh5CZmYkpU6Zg8uTJ+O2335rd3tSpUzF+/PgWt3Pz5s2QkJAQuWzKlCnYtm0bampqhJaFh4dj0aJFiI6OxqtXr0SuX1RUhMjIyGbbsHLlSjx48AB5eXmIjIyEmpoaHB0dsWbNGl65Xbt2wdbWFp07dxZjz0RjjIncnw9FSUkJGhoa/9j2PgWC66GkpARRUVFITEyEr6/ve6tfSkoKOjo6aNOmzXur82N37do12NnZYfjw4Thw4ABkZGSwYsUKhIaGYtWqVcjJyUFcXBxmzJjB9SQT6NSpE2JiYvDy5Utu3qtXr7B//37o6emJtf3hw4fjwYMH3Cc6OppbdvPmTdTV1eGnn35CdnY2QkNDsX37dixdupQrI86zUVNTE05OTti2bdtbHaP3+WzQ0dGBrKzse6nrQ6msrMSAAQOwfv361m4KIYQQQghpAQpQfUJOnjwJWVlZfPbZZ7z5WVlZGDFiBJSUlNCuXTtMmjQJf//9N7e8rq4OGzZsgIGBAWRlZaGnpycUQGnI0tIS7u7u6NGjB/T19TFx4kQ4OTkhKSmp0XWWLl2KVatWwdbWFt26dcPcuXMxfPhwHD58uMl9+v777+Hj44OuXbuKeRTqpaenIyQkBLt37xa5fOjQoXjy5AkSEhJ482/fvo2UlBQsWbIERkZGjbbv66+/RlBQEKqqqppsh7KyMnR0dKCnp4eBAwdix44dCAwMxPLly5GXl8eVi4mJgaurK2/dqqoq+Pr6QltbG3JychgwYACuXbvGLRf05jp16hTXy+zixYt4/vw5PDw8oKioCF1dXYSGhgoNgdm3bx/69OnDtW/ChAl49OiRUN3nz59Hnz59oKCgAFtbW16b3xziFx8fD2traygqKkJNTQ39+/fHnTt3eGV3794NPT09KCkpYfbs2aitrcWGDRugo6MDbW3tJq+7N0lISGDXrl0YNWoUFBQUYGhoiNjYWG55bW0tr/eKsbExtmzZwqvDy8sLI0eOxNq1a9GuXTuoqalh5cqVqKmpgZ+fH9TV1dGxY0dERETw1rt79y7GjRsHNTU1qKurw83NTaxhtYLj3aFDBwwaNAienp64ceMGt7y0tBTu7u7o0KEDFBQUYGZmxgt4AE3fr28O8WuOYP8bmjdvHhwcHLjpX3/9FWZmZpCXl4eGhgYcHR0bHR5bVlYGDw8PaGlpQV5eHoaGhtyxE7QtJiYGtra2kJOTQ8+ePYXuwZa4cOECBg8eDG9vb+zcuROSkvVfabGxsZg9ezbGjh2LLl26wNzcHN7e3li4cCFvfSsrK3Tq1Il3nx8+fBh6enqwtLQUqw2ysrLQ0dHhPm3btuWWDR8+HBERERg2bBi6du2KL774AgsXLuRtT9xno6urK2JiYsRqU2PPhrq6Oqxbt467J8zNzfHrr78CqL+uOnbsKBQES0tLg6SkJHcvv9kjtal7ISsrC5KSknj8+DEA4MmTJ5CUlMSXX37Jrb969WoMGDCAm27u+6qyshKTJ0+GkpISdHV1ERISIrT/kyZNwvLly+Ho6CjW8SKEEEIIIR8HClB9QpKSktC7d2/evKdPn2Lw4MGwtLREamoqTp8+jb/++gvjxo3jyvj7+yM4OBiBgYHIycnB/v370a5dO265g4NDk0Nd0tLSkJKSAnt7+xa1t7y8HOrq6i1aRxwvXrzAhAkTEBYWBh0dHZFlZGRkYGFhIRRUi4iIgIuLC1RVVTFx4kSEh4eLXH/evHmoqanB1q1bW9y+uXPngjGGY8eOAaj/0ZaTk4M+ffrwyi1atAiHDh3C3r17cePGDRgYGMDJyQlPnjzhlVuyZAmCg4ORm5uLXr16Yf78+UhOTkZsbCzOnTuHpKQkXhAEAKqrq7Fq1SpkZGTg6NGjKC4uFnmOly1bhpCQEKSmpqJNmzaYOnWqyH2qqanByJEjYW9vj8zMTFy6dAkzZszg9WArLCzEqVOncPr0aURHRyM8PBwuLi64d+8eEhISsH79egQEBODKlStiH8sVK1Zg3LhxyMzMhLOzMzw8PLjjI/jBffDgQeTk5GD58uVYunQpfvnlF14dFy5cwJ9//onExERs2rQJQUFB+Pzzz9G2bVtcuXIFX331FWbOnIl79+5xx87JyQnKyspISkpCcnIylJSUMHz4cG7oliBA0FTQ6v79+zh+/Dj69evHzXv16hV69+6NEydOICsrCzNmzMCkSZNw9epVrkxz9+v79ODBA7i7u2Pq1KnIzc1FfHw8Ro8eDcaYyPKCNp06dQq5ubnYtm0bNDU1eWX8/PywYMECpKWlwcbGBq6urigtLW1x244cOQIXFxcEBAQI9ZTR0dHBhQsXuMBIU6ZOncoLQO7evRtTpkwRux3x8fHQ1taGsbExZs2a1ey+iPPcE1XG2toa9+7da1F+wTefDevWrUNkZCS2b9+O7OxsfPPNN5g4cSISEhIgKSkJd3d37N+/n1dHVFQU+vfvL7J3Z3P3Qo8ePaChocEFIZOSknjTAJCQkMAFRMX5vvLz80NCQgKOHTuGs2fPIj4+Xuj51lJVVVV49uwZ70MIIYQQQloBI58MNzc3NnXqVN68VatWsWHDhvHm3b17lwFgeXl57NmzZ0xWVpbt3Lmz0XonTZrElixZIjS/Q4cOTEZGhklKSrKVK1e2qK0HDhxgMjIyLCsrS6zyQUFBzNzcXKyyM2bMYN7e3tw0AHbkyBGhcqNGjWJeXl7cdG1tLevUqRM7evQoY4yxx48fMxkZGVZUVMSViYuLYwBYWVkZ2759O1NXV2dPnz5ljDGmqqrKIiIiuLKdO3dmoaGhItvYrl07NmvWLMYYY2lpaQwAKykp4ZZXVFQwaWlpFhUVxc17/fo1a9++PduwYQOvLYL2MsbYs2fPmLS0NDt48CA37+nTp0xBQYHNnTu3sUPGrl27xgCw58+f8+r+/fffuTInTpxgANjLly8ZY/xzUlpaygCw+Ph4kfUHBQUxBQUF9uzZM26ek5MT09fXZ7W1tdw8Y2Njtm7dukbb2RAAFhAQwE1XVFQwAOzUqVONruPj48PGjBnDTXt6erLOnTsLtcHOzo6brqmpYYqKiiw6Opoxxti+ffuYsbExq6ur48pUVVUxeXl5dubMGcYYY1euXGHGxsbs3r17XJnOnTszGRkZpqioyOTk5BgA1q9fP1ZWVtbkfrq4uLAFCxYwxliz9+vt27cZAJaWlsYY41+vjDEWERHBVFVVefvv5ubGq2Pu3LnM3t6eMcbY9evXGQBWXFzcZBsFXF1d2ZQpU5psW3BwMDevurqadezYka1fv16s+gX7ICUlxaSkpFhgYKDIMtnZ2czU1JRJSkoyMzMzNnPmTHby5EleGcG+P3r0iMnKyrLi4mJWXFzM5OTk2OPHj5mbmxvz9PRssi3R0dHs2LFjLDMzkx05coSZmpqyvn37spqaGpHl8/PzmYqKCtuxY0ejdTb2bCwvL2/yHmtI1LPh1atXTEFBgaWkpPDKent7M3d3d8ZY/bNIQkKC3blzhzFW/0zs0KED27ZtG1e+4fNUnHth9OjRzMfHhzHG2Lx585ifnx9r27Yty83NZa9fv2YKCgrs7NmzjLHmv6+eP3/OZGRk2C+//MItLy0tZfLy8iKfb2/eD40JCgpiAIQ+QYlFbN2Nx2J9RGnsu4cQQggh5FMn+L9reXl5i9ajHlSfkJcvX0JOTo43LyMjA3FxcVBSUuI+JiYmAOp7tOTm5qKqqgpDhgxptN7IyEisW7dOaH5SUhJSU1Oxfft2bN68WWgoUmPi4uIwZcoU7Ny5Ez169GjBHjYvNjYWFy5cwObNm5stKy8vjxcvXnDT586dQ2VlJZydnQHU530ZOnRoo8MEvb29oaGh8VZ5ThhjXO8iQf6bhueusLAQ1dXV6N+/PzdPWloa1tbWyM3N5dXVsOdVUVERqqurYW1tzc1TVVWFsbExb53r16/D1dUVenp6UFZW5nq/lZSU8Mr16tWL+7euri4A8IYCCqirq8PLywtOTk5wdXXFli1b8ODBA14ZfX19KCsrc9Pt2rVD9+7duWFZgnmi6m9Mw/YpKipCRUWFt35YWBh69+4NLS0tKCkpYceOHUL72KNHD6E2mJmZcdNSUlLQ0NDg6s3IyEBBQQGUlZW5e0pdXR2vXr1CYWEhgPreLjdv3kSHDh142/Lz80N6ejoyMzO5BPMuLi5cwv3a2lqsWrUKZmZmUFdXh5KSEs6cOcO1WZz79X0yNzfHkCFDYGZmhrFjx2Lnzp1NJnWfNWsWYmJiYGFhgUWLFiElJUWojI2NDffvNm3aoE+fPkLXdHPk5eUxdOhQ7Ny5U+S63bt3R1ZWFi5fvoypU6fi0aNHcHV1xbRp04TKamlpwcXFBXv27OF6UL7Z6ysqKor3DBX0vPzyyy/xxRdfwMzMDCNHjsRvv/2Ga9euIT4+Xmg79+/fx/DhwzF27FhMnz5d5H419WyUl5cHAN4zqzkNnw0FBQV48eIFhg4dytuXyMhI7rq1sLCAqakp14sqISEBjx49wtixY0XWL869YG9vzx2PhIQEDB48GAMHDkR8fDyuXbvGe841931VWFiI169f83odqqurCz3fWsrf3x/l5eXc5+7du+9UHyGEEEIIeTv/nUy6/wGamppCPx4rKirg6uoqMoiiq6v7Tm+F6tKlCwDAzMwMf/31F779aMzmwgABAABJREFU9lu4u7s3uU5CQgJcXV0RGhqKyZMnv/W2G3PhwgUUFhYKveVrzJgxsLOz4/1wfPLkCbp168ZNh4eH48mTJ9wPQaB+mFhmZiZWrFjBC2IA9T+u16xZAy8vL8yZM0fsNpaWluLx48fc8RP8GC4rK4OWlpbY9QgoKiq2qHxlZSWcnJzg5OSEqKgoaGlpoaSkBE5OTry3iwH1QTEBQUCtrq5OZL0RERHw9fXF6dOnceDAAQQEBODcuXNcTrSGdQnqEzWvsfpFaWr9mJgYLFy4ECEhIbCxsYGysjK+++47oSGELW1XRUUFevfuLfItlM2dP01NTRgYGAAADA0NsXnzZtjY2CAuLg6Ojo747rvvsGXLFmzevBlmZmZQVFTEvHnzuPPS8Np8HyQlJYWG61VXV3P/lpKSwrlz55CSkoKzZ89i69atWLZsGa5cucJdvw2NGDECd+7cwcmTJ3Hu3DkMGTIEPj4+2Lhx43ttt5SUFI4ePYrRo0dj0KBBiIuLg6mpqdC+9e3bF3379sW8efPw888/Y9KkSVi2bJlQ26dOncrdw2FhYULb++KLL3hBkTcDjwJdu3aFpqYmCgoKeEHEP//8E4MGDYKtrW2jL5No7tkoGLrakmdEw2dDRUUFAODEiRNC7W+Y9NzDwwP79+/HkiVLsH//fgwfPrzRlyGIcy8I8t/l5+cjJycHAwYMwM2bNxEfH4+ysjIux52gvqa+rwoKCsTe95aQlZV9b4nfKyoqeO28ffs20tPToa6uLnbifUIIIYSQ/yrqQfUJsbS0RE5ODm+elZUVsrOzoa+vDwMDA95HUVERhoaGkJeX53pzvK26urpmE4bHx8fDxcUF69evx4wZM95pe41ZsmQJMjMzkZ6ezn0AIDQ0VCjRdVZWFpcIubS0FMeOHUNMTAxv3bS0NJSVleHs2bMitzd27Fj06NEDK1asELuNW7ZsgaSkJJeculu3blBRUeGdu27dukFGRgbJycncvOrqaly7dg3du3dvtO6uXbtCWlqal0y9vLwct27d4qZv3ryJ0tJSBAcHw87ODiYmJi3qtdQUS0tL+Pv7IyUlBT179hTKZ/NPSk5Ohq2tLWbPng1LS0sYGBhwvTrehZWVFfLz86GtrS10T6mqqraoLikpKQD/34suOTkZbm5umDhxIszNzdG1a1feuXtf96uAlpaWUE+3NxOsS0hIoH///lixYgXS0tIgIyODI0eONFmnp6cnfv75Z2zevFkoIHP58mXu3zU1Nbh+/bpQcEkcsrKyOHz4MPr27YtBgwYJPfveJLhvRCV4F+RMEuRUepOysjLvPDcWKLx37x5KS0u53oZAfc8pBwcH9O7dGxEREUKBbkC8Z2NWVhakpaXfutdp9+7dISsri5KSEqHrtlOnTly5CRMmICsrC9evX8evv/4KDw+PRusU514wMzND27ZtsXr1alhYWEBJSQkODg5ISEhAfHw8LyF/c99X3bp1g7S0NC/IXFZWxrtHWltqaiosLS2575b58+fD0tISy5cvb+WWEUIIIYR8/ChA9QlxcnJCdnY2rxeVj48Pnjx5And3d1y7dg2FhYU4c+YMpkyZgtraWsjJyWHx4sVYtGgRN9Tj8uXLvOTgkydPhr+/PzcdFhaG48ePIz8/H/n5+QgPD8fGjRsxceJErswPP/zA60EQFxcHFxcX+Pr6YsyYMXj48CEePnzIS/h95MgRbjiHQEFBAdLT0/Hw4UO8fPmSCxwJepTcv38fJiYmXBJpHR0d9OzZk/cBAD09PV6vieLiYty/f597y9O+ffugoaGBcePG8dY1NzeHs7Nzo8nSASA4OBi7d+8W+cP3+fPnePjwIe7evYvExETMmDEDq1evxpo1a7ieNJKSknB0dMTFixe59RQVFTFr1iz4+fnh9OnTyMnJwfTp0/HixQt4e3s32hZlZWV4enrCz88PcXFxyM7Ohre3NyQlJbkeUHp6epCRkcHWrVtRVFSE2NhYrFq1qtE6xXH79m34+/vj0qVLuHPnDs6ePYv8/Py3Cjy8L4aGhkhNTcWZM2dw69YtBAYG8gJ3b8vDwwOamppwc3NDUlISbt++jfj4ePj6+nKJ1K9evQoTExPcv3+ft67genjw4AGuXr0KPz8/aGlpwdbWlmuzoMdSbm4uZs6cib/++otbX5z7tSUGDx6M1NRUREZGIj8/H0FBQcjKyuKWX7lyBWvXrkVqaipKSkpw+PBhPH78uNHzunz5chw7dgwFBQXIzs7Gb7/9JlQ2LCwMR44cwc2bN+Hj44OysjJe8n0TE5MmA2ANycrK4tChQ+jXrx8GDRqE7OxsAMD//vc/hIaG4sqVK7hz5w7i4+Ph4+MDIyMjoWcMUB8ozM3NRU5ODhc0bE5FRQX8/Pxw+fJlFBcX4/z583Bzc+NeZgD8f3BKT08PGzduxOPHj7lnn4A4z0agfki1nZ3dW/eiU1ZWxsKFC/HNN99g7969KCwsxI0bN7B161bs3buXK6evrw9bW1t4e3ujtrYWX3zxRaN1inMvSEhIYODAgYiKiuKCUb169UJVVRXOnz/Pe7lGc99XSkpK8Pb2hp+fHy5cuICsrCx4eXkJBf2ePHmC9PR0LmiZl5fHfY98aA4ODmCMCX327NnzwbdNCCGEEPJvRwGqT4iZmRmsrKx4bylr3749kpOTUVtbi2HDhsHMzAzz5s2Dmpoa95/6wMBALFiwAMuXL4epqSnGjx/P61FTUlLC62VRV1cHf39/WFhYoE+fPggLC8P69euxcuVKrszff//N662yd+9evHjxAuvWrYOuri73GT16NFemvLwceXl5vH2aNm0aLC0t8dNPP+HWrVvcX6b//PNPAPW9ivLy8lqUlwUAoqOjMWzYMO7NVLt378aoUaN4b50TGDNmDGJjY3mvOm9o8ODBGDx4MGpqaoSWLV++HLq6ujAwMMCkSZNQXl6O8+fPY/HixUL7GRMTwxveFhwcjDFjxmDSpEmwsrJCQUEBzpw5w3uNvSibNm2CjY0NPv/8czg6OqJ///4wNTXlclxpaWlhz549OHjwILp3747g4OB3HoKloKCAmzdvYsyYMTAyMsKMGTPg4+ODmTNnvlO972LmzJkYPXo0xo8fj379+qG0tBSzZ89+53oVFBSQmJgIPT09jB49GqampvD29sarV6+goqICoD5PUF5eHm+4HPD/10P79u3x+eefQ1FREWfPnuWGUAUEBMDKygpOTk5wcHCAjo4O19NOoLn7tSWcnJwQGBiIRYsWoW/fvnj+/DlveJmKigoSExPh7OwMIyMjBAQEICQkBCNGjBBZn4yMDPz9/dGrVy8MHDgQUlJSiImJ4ZUJDg5GcHAwzM3NcfHiRcTGxvJyPuXl5aG8vFzsfZCRkcGvv/4KW1tbDBo0CFlZWXBycsLx48fh6uoKIyMjeHp6wsTEBGfPnkWbNqJHtquoqHDnTxxSUlLIzMzEF198ASMjI3h7e6N3795ISkrihoudO3cOBQUFOH/+PDp27Mh79gmI82wE6oesNpa7SlyrVq1CYGAg1q1bB1NTUwwfPhwnTpwQGvLo4eGBjIwMjBo1qsmAmDj3AlCfh6q2tpYLUElKSmLgwIFc7zwBcb6vvvvuO9jZ2cHV1RWOjo4YMGCA0NtrY2NjYWlpCRcXFwD1ucIsLS2xffv2dzp+hBBCCCHkw5JgbyYgIf9qJ06cgJ+fH7KyskQOJSHA69evYWhoiP379/N+HLUmxhj69euHb775ptk8Xi1VWVmJDh06ICQkpMneV4R8SMXFxejSpQvS0tJgYWHR2s35Vzl16hQWLFiAzMzMRgNs5P159uwZVFVVEZRYBDkl5eZXALDEUrP5QoQQQggh/xGC/0+Vl5e36I/A9D/dT4yLiwvy8/Nx//59Xl4R8v9KSkqwdOnSjyY4BdQPg9mxYwf++OOPd64rLS0NN2/ehLW1NcrLy7mebW5ubu9cNyHkn1dZWYmIiAgKThFCCCGEkE8a/W/3EzRv3rzWbsJHTZB092NjYWHx3nqWbNy4EXl5eZCRkeGGHTUcRvWxi4qKanR4YOfOnblcQ4T8F/zvf//jTX/11Vf4+eefRZadOHEiDWUjhBBCCCH/SjTEjxDy0Xn+/DkvOXhD0tLSXO4wQv6LHj16hGfPnolcpqKiAm1t7X+4RZ8WGuJHCCGEEPJuaIgfIeSToaysDGVl8X4YEvJfo62tTUEoQgghhBDyyaEs2oQQQgghhBBCCCGkVVGAihBCCCGEEEIIIYS0KgpQEUIIIYQQQgghhJBWRTmoCCGEEELeMN9co0VJPQkhhBBCyLuhHlSEEEIIIYQQQgghpFVRgIoQQgghhBBCCCGEtCoKUBFCCCGEEEIIIYSQVkUBKkIIIYQQQgghhBDSqihARQghhBBCCCGEEEJaFQWoCCGEEEIIIYQQQkiratPaDSCEEELI/7F353E1bf//wF+n0jzRHCo0olLGdClEpm5mMmTIXDJ8kIhMyZTpcg1JGSLXNU8ZKylDoWiQJMLNVJSUpLN+f/i1v3anWeRe7+fjcR4Pe+2113qfPR17tdba5GezLj4L0vKfvkvZ8yxUv0u5hBBCCCH/ZtSDihBCCCGEEEIIIYTUKWqgIoQQQgghhBBCCCF1ihqoCCGEEEIIIYQQQkidogYqQgghhBBCCCGEEFKnqIGKEEIIIYQQQgghhNQpaqAihBBCCCGEEEIIIXWKGqgIIYQQQn4wX19ftG3bFgoKClBXV0e/fv2QkpJS12ERQgghhNQZaqCqY1lZWVBXV8fjx4/rOpSf1ps3b6Curo5nz57VdSg8AQEB6NGjR12HUWVBQUFQVlau6zAI+en9TNfKz3T/W7x4MVq1alXXYfCEh4dDIBDg3bt3dR1KtUVERMDV1RXXr1/HhQsXUFRUhB49euDDhw91HRohhBBCSJ2gBqo65uPjA0dHR+jp6X23Oq5evQpra2uoqKhARkYGxsbGWL9+fYXbhIeHw9HREVpaWpCTk0OrVq0QHBxcaV3u7u5o3bo1pKSkqvwgs2PHDtja2kJRUbHMBw1VVVU4OzvD29u7zO2vXbsGcXFx9OnTR2Td48ePIRAIoK6ujvfv3/PWtWrVCosXL+aWbW1tIRAIIBAIICUlhYYNG8LBwQFHjhwRKffjx49YuHBhuTH9jIYOHYoHDx7UdRj/Gunp6Rg+fDi0tbUhLS2NRo0awdHREffv3wfwf+dWXFzcD4vJ1tYWM2bM+GH1/VvUdiNFbV8rxsbGkJKSwosXL0TWLV68GMbGxpCTk0P9+vVhZ2eHGzducOsru//9SLNnz8alS5fqOoxK3b17F506dYK0tDQaN26M1atX13VIZQoNDcWYMWPQokULmJubIygoCBkZGbh161Zdh0YIIYQQUieogaoO5efnIyAgAC4uLt+1Hjk5Obi5ueHKlStITk6Gl5cXvLy8sGPHjnK3iY6OhpmZGQ4fPoy7d+9i7NixcHZ2xqlTpyqtb9y4cRg6dGiV48vPz0fPnj0xf/78cvOMHTsWwcHByM7OFlkXEBCAadOm4cqVK/jnn3/K3P79+/dYu3ZtpbFMmDABmZmZSEtLw+HDh9G8eXMMGzYMEydO5OX7+++/oaioCGtr60rLrEhRUdE3bV8dMjIyUFdX/2H1/ZsVFRWhe/fuyMnJwZEjR5CSkoKDBw/C1NS02o0gnz59+j5Bkmqr6rGozWvl6tWrKCgowKBBg7B7926R9YaGhti8eTPu3buHq1evQk9PDz169MDr16+5PBXd/6qits5BeXl5qKio1EpZ30tubi569OgBXV1d3Lp1C2vWrMHixYsr/L37WeTk5AAAGjRoUMeREEIIIYTUDWqgqkNnzpyBlJQUOnTowEtPSEhAr169IC8vDw0NDYwaNQpv3rzh1guFQqxevRr6+vqQkpKCjo4OfHx8yq3HwsICTk5OaNGiBfT09DBy5EjY29sjMjKy3G3mz5+PZcuWoWPHjmjWrBmmT5+Onj17ltmb6GubNm2Cq6srmjZtWsW9AMyYMQPz5s0T2Q9fa9GiBbS1tXH06FFeel5eHg4ePIgpU6agT58+CAoKKnP7adOmYd26dXj16lWFscjKykJTUxONGjVChw4dsGrVKmzfvh3+/v64ePEily8kJAQODg68bYVCIZYuXYpGjRpxPchCQ0O59SU9bg4ePAgbGxtIS0sjODgYnz9/hru7O5SVlaGiogIPDw+MHj0a/fr147YNDQ3Fb7/9xuXp27cv0tLSRMo+cuQIunTpAllZWZibm+PatWtcntLDluLj49GlSxcoKChAUVERrVu3RmxsLC/vqVOnYGRkBFlZWQwaNAj5+fnYvXs39PT0UL9+fbi7u6O4uLjCfVpCT08PK1aswLhx46CgoAAdHR2Rh0YPDw8YGhpCVlYWTZs2xcKFC3mNeCVDjHbt2gUdHR3Iy8tj6tSpKC4uxurVq6GpqQl1dXWR6+Hdu3cYP3481NTUoKioiK5duyI+Pr7cWBMTE5GWloY///wTHTp0gK6uLqytrbF8+XLuPG3SpAmAL9eXQCCAra0tAGDMmDHo168ffHx8oK2tDSMjIwDA06dPMWTIECgrK6NBgwZwdHTkDe0t2W7JkiVcnJMnT+YaF8aMGYOIiAhs3LiR6+lXsn1ERATatWsHKSkpaGlpYd68efj8+TNXtq2tLaZNm4YZM2agfv360NDQgL+/Pz58+ICxY8dCQUEB+vr6OHv2LACAMQZ9fX2RRt24uDgIBAI8fPiQ26+TJk2ChoYGpKWl0bJlS14j9tWrV9GpUyfIyMigcePGcHd35w1f+vPPP2FgYABpaWloaGhg0KBB5R6TJ0+ewMHBAfXr14ecnBxatGiBM2fO4PHjx+jSpQsAoH79+hAIBBgzZgz3vd3c3DBjxgyoqqrC3t4eALBu3TqYmppCTk4OjRs3xtSpU5GXl8fVVfpaKTnv9u7dCz09PSgpKWHYsGEivTLLEhAQgOHDh2PUqFHYtWuXyPrhw4fDzs4OTZs2RYsWLbBu3Trk5ubi7t27XJ7y7n/lKe97V/TbsmPHDmhra0MoFPLKcnR0xLhx43j74Ws7d+6EiYkJpKWlYWxsjD///JNbN2jQILi5uXHLM2bMgEAg4Hohfvr0CXJycty9VSgUwtfXF02aNIGMjAzMzc3x999/8+o7c+YMDA0NISMjgy5duogMjw8ODsanT5+wa9cutGjRAsOGDYO7uzvWrVtXpX1XV4RCIWbMmAFra2u0bNmyrsMhhBBCCKkT1EBVhyIjI9G6dWte2rt379C1a1dYWFggNjYWoaGhePnyJYYMGcLl8fT0xMqVK7Fw4UIkJSVh//790NDQ4Nbb2tpyD2hluXPnDqKjo2FjY1OteHNycur0L7vt2rUTaVT766+/YGxsDCMjI4wcORK7du0CY0xkWycnJ+jr62Pp0qXVrnf06NGoX78+r3Hu6tWraNOmDS/fxo0b4efnh7Vr1+Lu3buwt7fH77//jtTUVF6+efPmYfr06UhOToa9vT1WrVqF4OBgBAYGIioqCrm5uTh27Bhvmw8fPmDWrFmIjY3FpUuXICYmhv79+4s8TC5YsACzZ89GXFwcDA0N4eTkxGuo+NqIESPQqFEjxMTE4NatW5g3bx7q1avHrc/Pz8emTZsQEhKC0NBQhIeHo3///jhz5gzOnDmDvXv3Yvv27SIPkBXx8/NDmzZtcOfOHUydOhVTpkzhTQqsoKCAoKAgJCUlYePGjfD39xcZjpqWloazZ88iNDQUBw4cQEBAAPr06YNnz54hIiICq1atgpeXF2+Y1ODBg/Hq1SucPXsWt27dgqWlJbp168b1SClp4AsPDwcAqKmpQUxMDH///Xe5DXA3b94EAFy8eBGZmZm88+PSpUtISUnBhQsXcOrUKRQVFcHe3h4KCgqIjIxEVFQU5OXl0bNnT17vlkuXLiE5ORnh4eE4cOAAjhw5giVLlgD4cn5ZWVlxvfwyMzPRuHFjPH/+HL1790bbtm0RHx+PrVu3IiAgAMuXL+fFu3v3bqiqquLmzZuYNm0apkyZgsGDB6Njx464ffs2evTogVGjRiE/Px8CgQDjxo1DYGAgr4zAwEB07twZ+vr6EAqF6NWrF6KiorBv3z4kJSVh5cqVEBcX545Tz549MXDgQNy9excHDx7E1atXuQaL2NhYuLu7Y+nSpUhJSUFoaCg6d+5c7rnj6uqKwsJCXLlyBffu3cOqVasgLy+Pxo0b4/DhwwCAlJQUZGZmYuPGjbzvLSkpiaioKGzbtg0AICYmhk2bNiExMRG7d+/G5cuXMXfu3HLrLvk+x44dw6lTp3Dq1ClERERg5cqVFW7z/v17HDp0CCNHjuR65FX0h4FPnz5hx44dUFJSgrm5OW9dWfe/ipT+3pX9tgwePBhZWVkICwvjysjOzkZoaChGjBhRZh3BwcFYtGgRfHx8kJycjBUrVmDhwoVcTzEbGxvumgK+NKSqqqpyaTExMSgqKkLHjh0BfJk0fM+ePdi2bRsSExMxc+ZMjBw5EhEREQC+NPIOGDAADg4OiIuLw/jx4zFv3jxeTNeuXUPnzp0hKSnJpdnb2yMlJQVv374t83sUFhYiNzeX9/nRXF1dkZCQgJCQkB9eNyGEEELIT4OROuPo6MjGjRvHS1u2bBnr0aMHL+3p06cMAEtJSWG5ublMSkqK+fv7l1vuqFGj2Lx580TSGzZsyCQlJZmYmBhbunRptWI9ePAgk5SUZAkJCVXK7+3tzczNzatVR1hYGAPA3r59W+b6mTNnMltbW15ax44d2YYNGxhjjBUVFTFVVVUWFhbGrU9PT2cA2J07d1hoaCirV68ee/jwIWOMMXNzc+bt7c3ltbGxYdOnTy+z7vbt27NevXoxxhh7+/YtA8CuXLnCy6Otrc18fHx4aW3btmVTp07lxVISbwkNDQ22Zs0abvnz589MR0eHOTo6lhkLY4y9fv2aAWD37t3jlb1z504uT2JiIgPAkpOTGWOMBQYGMiUlJW69goICCwoKKrP8wMBABoDbV4wxNmnSJCYrK8vev3/Ppdnb27NJkyaVG+fXdHV12ciRI7lloVDI1NXV2datW8vdZs2aNax169bcsre3N5OVlWW5ubm8GPT09FhxcTGXZmRkxHx9fRljjEVGRjJFRUX28eNHXtnNmjVj27dvZ4wx9uzZM2ZkZMRu3LjBrd+8eTOTlZVlCgoKrEuXLmzp0qUsLS2NW//1ufW10aNHMw0NDVZYWMil7d27lxkZGTGhUMilFRYWMhkZGXbu3DluuwYNGrAPHz5webZu3crk5eW571bWOTp//nyRsrds2SKy3W+//cat//z5M5OTk2OjRo3i0jIzMxkAdu3aNcYYY8+fP2fi4uLcPvn06RNTVVXlzplz584xMTExlpKSwsri4uLCJk6cyEuLjIxkYmJirKCggB0+fJgpKiryjmVFTE1N2eLFi8tcV969w8bGhllYWFRa9qFDh5iKigq3XPpaKeu8mzNnDmvfvn2F5e7YsYO1atWKW54+fTobPXq0SL6TJ08yOTk5JhAImLa2Nrt586ZInrLuf+Up63tX9tvCmOhv0vbt25m2tjZ3HpW+rzdr1ozt379fpB4rKyvGGGN3795lAoGAvXr1imVnZzNJSUm2bNkyNnToUMYYY8uXL2cdO3ZkjDH28eNHJisry6Kjo3nlubi4MCcnJ8YYY56enqx58+a89R4eHrxj3717d5HzruRemJSUVOb+8vb2ZgBEPt5XHjHf26+/y+drrq6urFGjRuzRo0dlxkcIIYQQ8m+Tk5PDALCcnJxqbUc9qOpQQUEBpKWleWnx8fEICwuDvLw89zE2Ngbw5S/4ycnJKCwsRLdu3cotd8+ePfD19RVJj4yMRGxsLLZt24YNGzbgwIEDVYozLCwMY8eOhb+/P1q0aFGNb1i7ZGRkkJ+fzy2npKTg5s2bcHJyAgBISEhg6NChCAgIKHN7e3t7/Pbbb1i4cGG162aMQSAQAPhy3ADwjl1ubi7++ecfkTmprK2tkZyczEv7uudVTk4OXr58iXbt2nFp4uLiIj3rUlNT4eTkhKZNm0JRUZGbVD8jI4OXz8zMjPu3lpYWAJQ7rHHWrFkYP3487OzssHLlSt6QQeDLcMdmzZpxyxoaGtDT04O8vDwvrbJhk+XFJxAIoKmpydv+4MGDsLa2hqamJuTl5eHl5SXyHfX09KCgoMCLoXnz5hATE+OllZQbHx+PvLw8qKio8K6r9PR07js3bNgQ9+/f5x0HV1dXvHjxAsHBwbCyssKhQ4fQokULXLhwodLvaWpqyuvBER8fj4cPH0JBQYGrv0GDBvj48SNvv5ubm0NWVpZbtrKyQl5eHp4+fVpuXcnJybCysuLOT+DLeZeXl8d789vX+15cXBwqKiowNTXl7TPg/84XbW1t9OnThxuWdvLkSRQWFmLw4MEAvgz3a9SoEQwNDcuMKz4+HkFBQbx9bm9vD6FQiPT0dHTv3h26urpo2rQpRo0aheDgYN71XZq7uzuWL18Oa2treHt784bAVaT0tQR86fXWrVs3NGzYEAoKChg1ahSysrIqrL/0eaelpVXpub9r1y6MHDmSWx45ciQOHTokMjSwS5cuiIuLQ3R0NHr27IkhQ4aIlF36/leZ0t+7st8W4EuvysOHD6OwsBDAlx5Sw4YN411bJT58+IC0tDS4uLjwyly+fDlXXsuWLdGgQQNEREQgMjISFhYW6Nu3L9cjKiIighsa+/DhQ+Tn56N79+688vbs2cOVl5ycjPbt2/PisLKyqvI+KY+npydycnK4T0XXW21ijMHNzQ1Hjx7F5cuXuWHDhBBCCCG/Kom6DuBXpqqqKjLkIC8vDw4ODli1apVIfi0tLTx69KjG9ZX859fU1BQvX77E4sWLucad8kRERMDBwQHr16+Hs7NzjeuuDdnZ2VBTU+OWAwIC8PnzZ2hra3NpjDFISUlh8+bNUFJSEilj5cqVsLKywpw5c6pcb3FxMVJTU9G2bVsAgIqKCgQCQbnDRSojJydX7W0cHBygq6sLf39/bp6Yli1bikx+/PUQvZIGi9LDAEssXrwYw4cPx+nTp3H27Fl4e3sjJCQE/fv3FymrpLyy0sorvywVbX/t2jWMGDECS5Ysgb29PZSUlBASEgI/P79Ky6io3Ly8PGhpafGGGpX4ep6hsigoKMDBwQEODg5Yvnw57O3tsXz5cnTv3r3C7Uof47y8PLRu3brMN2F+fU5/T5Xtt7LOl/Hjx2PUqFFYv349AgMDMXToUK4BTUZGpsL68vLyMGnSJLi7u4us09HRgaSkJG7fvo3w8HCcP38eixYtwuLFixETE1PmcRk/fjzs7e1x+vRpnD9/Hr6+vvDz88O0adMqjKP0sXj8+DH69u2LKVOmwMfHBw0aNMDVq1fh4uKCT58+8RoIv1bdcz8pKQnXr1/HzZs34eHhwaUXFxcjJCQEEyZM4MWor68PfX19dOjQAQYGBggICICnpyeXp/T9rzJlnYMV/bYAX+4zjDGcPn0abdu2RWRkZLlvfC2Zs8vf31+k0ahkmKdAIEDnzp0RHh4OKSkp2NrawszMDIWFhUhISEB0dDRmz57NK+/06dNo2LAhrzwpKakqf29NTU28fPmSl1ayrKmpWeY2UlJS1aqjtri6umL//v04fvw4FBQUuLc8KikpVXp9EUIIIYT8F1EDVR2ysLDAvn37eGmWlpY4fPgw9PT0ICEhengMDAwgIyODS5cuYfz48TWuWygUcn8lL094eDj69u2LVatWibzFri4kJCRwf23//Pkz9uzZAz8/P/To0YOXr1+/fjhw4AAmT54sUka7du0wYMAAkXlLKrJ79268ffsWAwcOBABISkqiefPmSEpK4upWVFSEtrY2oqKieHN7RUVF8XrllKakpAQNDQ3ExMRw8+8UFxfj9u3b3GTEWVlZSElJgb+/Pzp16gTgyxxYtcHQ0BCGhoaYOXMmnJycEBgYyDVQ/WjR0dHQ1dXFggULuLQnT558c7mWlpZ48eIFJCQkuJ5nNSEQCGBsbIzo6GgA4HpIVWWSeEtLSxw8eBDq6upQVFQsN198fDwKCgq4h9Pr169z8yyV1Fm6PhMTExw+fJjXyy8qKgoKCgpo1KhR9b/oV3r37g05OTls3boVoaGhuHLlCrfOzMwMz549w4MHD8rsRWVpaYmkpCTo6+uXW76EhATs7OxgZ2cHb29vKCsr4/LlyxgwYECZ+Rs3bozJkydj8uTJ8PT0hL+/P6ZNm1atY3Hr1i0IhUL4+flxPYP++uuvSrerroCAAHTu3BlbtmzhpQcGBiIgIIDXQFVaWffnr+9/NVHZbwvwpVfogAEDEBwcjIcPH8LIyAiWlpZl5tXQ0IC2tjYePXpU7hxVwJd5qPz9/SElJQUfHx+IiYmhc+fOWLNmDQoLC7lep82bN4eUlBQyMjLKnR/RxMQEJ06c4KVdv36dt2xlZYUFCxagqKiIa1S8cOECjIyMUL9+/XLjrAtbt24FAJHjGhgYWOE8koQQQggh/1U0xK8O2dvbIzExkdcTx9XVFdnZ2XByckJMTAzS0tJw7tw5jB07FsXFxZCWloaHhwfmzp3LDX24fv06b1ibs7Mz7y/vW7ZswcmTJ5GamorU1FQEBARg7dq1vKEnmzdv5g0bDAsLQ58+feDu7o6BAwfixYsXePHiBe8150ePHuWGiJR4+PAh4uLi8OLFCxQUFCAuLg5xcXFcT5/nz5/D2NiYm2AaAF68eIG4uDjuzWD37t1DXFwcr678/HzcunWLaxA6deoU3r59CxcXF7Rs2ZL3GThwYLnD/ADAx8cHly9f5k3O/XU9L168wLNnz3D9+nV4eHhg8uTJmDJlCvemsJJjV7qRaM6cOVi1ahUOHjyIlJQUzJs3D3FxcZg+fXq5sQBf3jDo6+uL48ePIyUlBdOnT8fbt2+5xob69etDRUUFO3bswMOHD3H58mXMmjWrwjIrU1BQADc3N4SHh+PJkyeIiopCTEwMTExMvqncb2FgYICMjAyEhIQgLS0NmzZtqvJbyypiZ2cHKysr9OvXD+fPn8fjx48RHR2NBQsWcG8tLH1exsXFwdHREX///TeSkpLw8OFDBAQEYNeuXXB0dAQAqKurQ0ZGhptsuuQV8WUZMWIEVFVV4ejoiMjISKSnpyM8PBzu7u68YXifPn2Ci4sLkpKScObMGXh7e8PNzY1rSNHT08ONGzfw+PFjvHnzBkKhEFOnTsXTp08xbdo03L9/H8ePH4e3tzdmzZpV5tCs6hAXF8eYMWPg6ekJAwMD3nAqGxsbdO7cGQMHDsSFCxeQnp7OTV4PfHkjY3R0NNzc3BAXF4fU1FQcP36cmyT91KlT2LRpE+Li4vDkyRPs2bMHQqGQe+thaTNmzMC5c+eQnp6O27dvIywsjDtfdXV1IRAIcOrUKbx+/Zr3Rr7S9PX1UVRUhD/++AOPHj3C3r17ucnTa0tRURH27t0LJycnkfvT+PHjcePGDSQmJuLDhw+YP38+rl+/jidPnuDWrVsYN24cnj9/zg2lBETvfzVR2W9LiREjRuD06dPYtWtXhQ1PALBkyRL4+vpi06ZNePDgAe7du4fAwEDeG/NsbW2RlJSExMRE/Pbbb1xacHAw2rRpw/X0UlBQwOzZszFz5kzs3r0baWlpuH37Nv744w9u0vXJkycjNTUVc+bMQUpKCvbv3y/y5tbhw4dDUlISLi4uSExMxMGDB7Fx48Zvvmd+D4yxMj/UOEUIIYSQXxU1UNUhU1NTWFpa8v56X9ILp7i4GD169ICpqSlmzJgBZWVl7mFz4cKF+N///odFixbBxMQEQ4cO5c1XkpGRgczMTG5ZKBTC09MTrVq1Qps2bbBlyxasWrWK90a7N2/e8ObC2b17N/Lz8+Hr6wstLS3u83XPhpycHJFGnvHjx8PCwgLbt2/HgwcPYGFhAQsLC/zzzz8Avjy4paSk8OZS2bZtGywsLLgeBZ07d4aFhQXvL+XHjx+Hjo4O14MoICAAdnZ2ZQ7jGzhwIGJjY8udo8bQ0BDjxo3Dx48fRdb5+/tDS0sLzZo1w4ABA5CUlISDBw/yXp0OAC4uLjhz5gyvUcLd3R2zZs3C//73P5iamiI0NBQnTpyAgYFBmXGU8PDwgJOTE5ydnWFlZcXN1VMyx5WYmBhCQkJw69YttGzZEjNnzsSaNWsqLLMy4uLiyMrKgrOzMwwNDTFkyBD06tWLe2NcXfj9998xc+ZMuLm5oVWrVoiOjq7RfGGlCQQCnDlzBp07d8bYsWNhaGiIYcOG4cmTJ9y8S6XPy0aNGkFPTw9LlixB+/btYWlpiY0bN2LJkiVcDy8JCQls2rQJ27dvh7a2NtdwVRZZWVlcuXIFOjo6GDBgAExMTODi4oKPHz/yelR169YNBgYG6Ny5M4YOHYrff/8dixcv5tbPnj0b4uLiaN68OdTU1JCRkYGGDRvizJkzuHnzJszNzTF58mS4uLjAy8vrm/cdAG7o29ixY0XWHT58GG3btoWTkxOaN2+OuXPnco0dZmZmiIiIwIMHD9CpUydYWFhg0aJF3JBcZWVlHDlyBF27doWJiQm2bduGAwcOlDvPXXFxMVxdXWFiYoKePXvC0NCQuy4bNmyIJUuWYN68edDQ0OAawcpibm6OdevWYdWqVWjZsiWCg4PLnLPvW5w4cQJZWVll9kY0MTGBiYkJAgICIC4ujvv372PgwIEwNDSEg4MDsrKyEBkZydsPpe9/NVGV3xYA6Nq1Kxo0aICUlBQMHz68wjLHjx+PnTt3IjAwEKamprCxsUFQUBBvLiVTU1MoKyujVatW3Px1tra2KC4uFuk5tGzZMixcuBC+vr7ccT59+jRXno6ODg4fPoxjx47B3Nwc27Ztw4oVK3hlKCkp4fz580hPT0fr1q2538qfoRcwIYQQQgipmIAxxuo6iF/Z6dOnMWfOHCQkJHxzb4f/sg4dOsDd3b3SB6YfafDgwbC0tOT1VqsNQqEQJiYmGDJkCJYtW1arZZOf15gxY/Du3TscO3asrkPhiYyMRLdu3fD06VOuQY/8WD/j/e+/LDc3F0pKSvC+8gjS8gqVb1AD8yxUv0u5hBBCCCE/g5L/T+Xk5FQ4xUlpNW4R2bt3L6ytraGtrc3NE7NhwwYcP368pkX+kvr06YOJEyfi+fPndR3KT+vNmzcYMGBApRO6/2hr1qzhvdGupp48eQJ/f39uiMyUKVOQnp5OD6OkThUWFuLZs2dYvHgxBg8eTI1TdeRnvf8RQgghhBBS22rUQLV161bMmjULvXv3xrt377ghHcrKytiwYUNtxvdLmDFjBjcJMhGlqqqKuXPncnMy/Sz09PQqfYNYVYiJiSEoKAht27aFtbU17t27h4sXL9bpfFDVFRkZyXs1fOkP+fc5cOAAdHV18e7dO6xevbquw/lllb7/ZWRkVHitZWRk1HHEhBBCCCGE1EyNhvg1b94cK1asQL9+/aCgoID4+Hg0bdqUe8vQmzdvvkeshJCfVEFBQYW9ACt6kxshpOo+f/6Mx48fl7u+orf0kaqhIX6EEEIIId+mpkP8avS/2PT0dFhYWIikS0lJ4cOHDzUpkhDyLyYjI0ONUIT8ABISEnStEUIIIYSQ/6QaDfFr0qQJ4uLiRNJDQ0P/VcOSCCGEEEIIIYQQQkjdq1EPqlmzZsHV1RUfP34EYww3b97EgQMH4Ovri507d9Z2jIQQQgghhBBCCCHkP6xGDVTjx4+HjIwMvLy8kJ+fj+HDh0NbWxsbN27EsGHDajtGQgghhBBCCCGEEPIfVqNJ0r+Wn5+PvLw8qKur11ZMhBBCCCF1oqaTehJCCCGEkC9+6CTpX5OVlYWsrOy3FkMIIYQQQgghhBBCflE1aqDKysrCokWLEBYWhlevXkEoFPLWZ2dn10pwhBBCCCGEEEIIIeS/r0YNVKNGjcLDhw/h4uICDQ0NCASC2o6LEEIIIYQQQgghhPwiatRAFRkZiatXr8Lc3Ly24yGEEEIIIYQQQgghvxixmmxkbGyMgoKC2o6FEEIIIYQQQgghhPyCatRA9eeff2LBggWIiIhAVlYWcnNzeR9CCCGEEEIIIYQQQqqqRkP8lJWVkZubi65du/LSGWMQCAQoLi6uleAIIYQQQurCuvgsSMt/+i5lz7NQ/S7lEkIIIYT8m9WogWrEiBGoV68e9u/fT5OkE0IIIYQQQgghhJBvUqMGqoSEBNy5cwdGRka1HQ8hhBBCCCGEEEII+cXUaA6qNm3a4OnTp7UdCyGEEEIIIYQQQgj5BdWoB9W0adMwffp0zJkzB6ampqhXrx5vvZmZWa0ERwghhBBCCCGEEEL++2rUQDV06FAAwLhx47g0gUBAk6QTQgghhBBCCCGEkGqrUQNVenp6bcdBCCGEEEIIIYQQQn5RNWqg0tXVre04CCGEEEIIIYQQQsgvqkaTpJdISkpCaGgoTpw4wfuQ7ycrKwvq6up4/PhxXYfy03rz5g3U1dXx7Nmzug6FJyAgAD169KjrMKosKCgIysrKdR0GKUUgEODYsWMAgMePH0MgECAuLg4AEB4eDoFAgHfv3tVZfBX5lvj09PSwYcMGbrmi/fAzs7W1xYwZM6qc/2e6ny1evBitWrWq6zB4fvZzviK+vr5o27YtFBQUoK6ujn79+iElJaWuwyKEEEIIqTM1aqB69OgRzM3N0bJlS/Tp0wf9+vVDv3790L9/f/Tv37+2YyRf8fHxgaOjI/T09L5bHVevXoW1tTVUVFQgIyMDY2NjrF+/vsJtPn78iDFjxsDU1BQSEhLo169flery8fFBx44dISsrW+XGkMWLF8PY2BhycnKoX78+7OzscOPGDW69qqoqnJ2d4e3tXeb2165dg7i4OPr06SOyruRBV11dHe/fv+eta9WqFRYvXswt29raQiAQQCAQQEpKCg0bNoSDgwOOHDkiUu7Hjx+xcOHCcmP6GQ0dOhQPHjyo6zD+Nb4+HwQCATQ0NDB48GA8efKkVuvJzMxEr169arXMn0l5DaMxMTGYOHFimds0btwYmZmZaNmyZa3HM2bMmCrfz6riyJEjWLZsWZXzV3Y/+5Fmz56NS5cu1XUYFarpb1FdiIiIgKurK65fv44LFy6gqKgIPXr0wIcPH+o6NEIIIYSQOlGjBqrp06ejSZMmePXqFWRlZZGYmIgrV66gTZs2CA8Pr+UQSYn8/HwEBATAxcXlu9YjJycHNzc3XLlyBcnJyfDy8oKXlxd27NhR7jbFxcWQkZGBu7s77OzsqlzXp0+fMHjwYEyZMqXK2xgaGmLz5s24d+8erl69Cj09PfTo0QOvX7/m8owdOxbBwcHIzs4W2T4gIADTpk3DlStX8M8//5RZx/v377F27dpKY5kwYQIyMzORlpaGw4cPo3nz5hg2bJjIg/Tff/8NRUVFWFtbV/l7lqWoqOibtq8OGRkZqKur/7D6/gtKzod//vkHx48fx9OnTzFy5MharUNTUxNSUlK1Wua/gZqaGmRlZctcJy4uDk1NTUhI1GjUeq2o6rXZoEEDKCgoVKvsiu5nVfHp06cabVeavLw8VFRUaqWs76Wmv0V1ITQ0FGPGjEGLFi1gbm6OoKAgZGRk4NatW3UdGiGEEEJInahRA9W1a9ewdOlSqKqqQkxMDGJiYvjtt9/g6+sLd3f32o6R/H9nzpyBlJQUOnTowEtPSEhAr169IC8vDw0NDYwaNQpv3rzh1guFQqxevRr6+vqQkpKCjo4OfHx8yq3HwsICTk5OaNGiBfT09DBy5EjY29sjMjKy3G3k5OSwdetWTJgwAZqamlX+TkuWLMHMmTNhampa5W2GDx8OOzs7NG3aFC1atMC6deuQm5uLu3fvcnlatGgBbW1tHD16lLdtXl4eDh48iClTpqBPnz4ICgoqs45p06Zh3bp1ePXqVYWxyMrKQlNTE40aNUKHDh2watUqbN++Hf7+/rh48SKXLyQkBA4ODrxthUIhli5dikaNGkFKSgqtWrVCaGgot76kN9fBgwdhY2MDaWlpBAcH4/Pnz3B3d4eysjJUVFTg4eGB0aNH83oKhIaG4rfffuPy9O3bF2lpaSJlHzlyBF26dIGsrCzMzc1x7do1Lk/pnizx8fHo0qULFBQUoKioiNatWyM2NpaX99SpUzAyMoKsrCwGDRqE/Px87N69G3p6eqhfvz7c3d2r/JZPPT09rFixAuPGjYOCggJ0dHREGkk9PDxgaGgIWVlZNG3aFAsXLuQ1FJQMSdq1axd0dHQgLy+PqVOnori4GKtXr4ampibU1dVFrod3795h/PjxUFNTg6KiIrp27Yr4+PhKYy45H7S0tNChQwe4ubnh9u3b3Pri4mK4uLigSZMmkJGRgZGRETZu3ChSzq5du9CiRQtISUlBS0sLbm5u3Lqvh7ZVpqwhWRs2bOD1wAwPD0e7du0gJycHZWVlWFtbl9vr69OnT3Bzc4OWlhakpaWhq6sLX19fAGUPs3v37h0EAoHIHy6ioqJgZmYGaWlpdOjQAQkJCVwsY8eORU5ODtcTraTXYukhfl8rXffbt28xYsQIqKmpQUZGBgYGBggMDCx3P/39998wNTWFjIwMVFRUYGdnhw8fPmDx4sXYvXs3jh8/zsUTHh5e7rWZlZUFJycnNGzYELKysjA1NcWBAwd4dZUe4leV87y8+1l5bG1t4ebmhhkzZkBVVRX29vYAKv6t2LFjB7S1tSEUCnllOTo6cm/sLet82rlzJ0xMTCAtLQ1jY2P8+eef3LpBgwbxzt0ZM2ZAIBDg/v37AL6cT3Jycty9UigUwtfXl7s+zM3N8ffff/PqO3PmDAwNDSEjI4MuXbqIDHev6W/RzyAnJwfAl0ZMQgghhJBfUY0aqIqLi7m/AKuqqnK9UHR1dWn+hO8oMjISrVu35qW9e/cOXbt2hYWFBWJjYxEaGoqXL19iyJAhXB5PT0+sXLkSCxcuRFJSEvbv3w8NDQ1uva2tLcaMGVNuvXfu3EF0dDRsbGxq/Tt9q0+fPmHHjh1QUlKCubk5b127du1EGtX++usvGBsbw8jICCNHjsSuXbvAGBMp18nJCfr6+li6dGm1Yxo9ejTq16/PG+p39epVtGnThpdv48aN8PPzw9q1a3H37l3Y29vj999/R2pqKi/fvHnzMH36dCQnJ8Pe3h6rVq1CcHAwAgMDERUVhdzcXJEGiw8fPmDWrFmIjY3FpUuXICYmhv79+4s8fC5YsACzZ89GXFwcDA0N4eTkhM+fP5f5vUaMGIFGjRohJiYGt27dwrx581CvXj1ufX5+PjZt2oSQkBCEhoYiPDwc/fv3x5kzZ3DmzBns3bsX27dvF3ngrIifnx/atGmDO3fuYOrUqZgyZQrvHqOgoICgoCAkJSVh48aN8Pf3FxmOmpaWhrNnzyI0NBQHDhxAQEAA+vTpg2fPniEiIgKrVq2Cl5cXb5jo4MGD8erVK5w9exa3bt2CpaUlunXrxvVgKWmgqKjHaHZ2Nv766y+0b9+eSxMKhWjUqBEOHTqEpKQkLFq0CPPnz8dff/3F5dm6dStcXV0xceJE3Lt3DydOnIC+vn6V91l1fP78Gf369YONjQ3u3r2La9euYeLEiRAIBGXm37RpE06cOIG//voLKSkpCA4OrtFw4zlz5sDPzw8xMTFQU1ODg4MDioqK0LFjR2zYsAGKiorIzMxEZmYmZs+eXe3yS+51Z8+eRXJyMrZu3QpVVdUy82ZmZsLJyQnjxo1DcnIywsPDMWDAADDGMHv2bAwZMgQ9e/bk4unYsSO3belr8+PHj2jdujVOnz6NhIQETJw4EaNGjcLNmzcrjLey8xwo+35Wkd27d0NSUhJRUVHYtm1bpb8VgwcPRlZWFsLCwrgysrOzERoaihEjRpRZR3BwMBYtWgQfHx8kJydjxYoVWLhwIXbv3g0AsLGx4V0jERERUFVV5dJiYmK44w58mZNpz5492LZtGxITEzFz5kyMHDkSERERAICnT59iwIABcHBwQFxcHMaPH4958+ZVeZ/8zIRCIWbMmAFra+vvMlSVEEIIIeRfgdXAb7/9xo4ePcoYY8zJyYn17NmTXb16lTk7O7MWLVrUpEhSBY6OjmzcuHG8tGXLlrEePXrw0p4+fcoAsJSUFJabm8ukpKSYv79/ueWOGjWKzZs3TyS9YcOGTFJSkomJibGlS5dWOc7Ro0czR0fHKudnjLHAwECmpKRU5fwnT55kcnJyTCAQMG1tbXbz5k2RPDNnzmS2tra8tI4dO7INGzYwxhgrKipiqqqqLCwsjFufnp7OALA7d+6w0NBQVq9ePfbw4UPGGGPm5ubM29uby2tjY8OmT59eZnzt27dnvXr1Yowx9vbtWwaAXblyhZdHW1ub+fj48NLatm3Lpk6dyoulJN4SGhoabM2aNdzy58+fmY6OToX7/PXr1wwAu3fvHq/snTt3cnkSExMZAJacnMwYEz0mCgoKLCgoqMzyAwMDGQBuXzHG2KRJk5isrCx7//49l2Zvb88mTZpUbpxf09XVZSNHjuSWhUIhU1dXZ1u3bi13mzVr1rDWrVtzy97e3kxWVpbl5ubyYtDT02PFxcVcmpGREfP19WWMMRYZGckUFRXZx48feWU3a9aMbd++nTHG2LNnz5iRkRG7ceMGt97GxobVq1ePycnJMVlZWQaAGRoasvT09Aq/p6urKxs4cCC3rK2tzRYsWFBufgDc/ffr85UxxsLCwhgA9vbtW+77m5ub87Zfv34909XVZYwxlpWVxQCw8PDwCmMsMW3aNNa1a1cmFApF1pWOhbH/O/dLrrGS+EJCQrg8WVlZTEZGhh08eJAxVv69QFdXl61fv75K+8HBwYGNHTu2St/p1q1bDAB7/PhxmevLup+Vd22WpU+fPux///sft1z6vlHV87ys+1l5bGxsmIWFBS+tst8KxkR/Y7Zv3860tbW5a6X0+dSsWTO2f/9+kXqsrKwYY4zdvXuXCQQC9urVK5adnc0kJSXZsmXL2NChQxljjC1fvpx17NiRMcbYx48fmaysLIuOjuaV5+LiwpycnBhjjHl6erLmzZvz1nt4ePDO+a9V9bfo48ePLCcnh/uU7BfvK4+Y7+3X3+VT2uTJk5muri57+vRppfESQgghhPzscnJyGACWk5NTre1q1IPKy8uL64mxdOlSpKeno1OnTjhz5gw2bdpUo4YyUrmCggJIS0vz0uLj4xEWFgZ5eXnuY2xsDOBLz5Hk5GQUFhaiW7du5Za7Z88ebpjO1yIjIxEbG4tt27Zhw4YNIkNV6lKXLl0QFxeH6Oho9OzZE0OGDBEZjicjI4P8/HxuOSUlBTdv3oSTkxMAQEJCAkOHDkVAQECZddjb2+O3337DwoULqx0fY4zrhVJQUAAAvGOXm5uLf/75R2ROKmtrayQnJ/PSvu55lZOTg5cvX6Jdu3Zcmri4uEjPutTUVDg5OaFp06ZQVFTkerlkZGTw8pmZmXH/1tLSAoByhzXOmjUL48ePh52dHVauXMkbMgh8Gd7WrFkzbllDQwN6enqQl5fnpVU2bLK8+AQCATQ1NXnbHzx4ENbW1tDU1IS8vDy8vLxEvqOenh5vzh8NDQ00b94cYmJivLSScuPj45GXlwcVFRXedZWens5954YNG+L+/fu84wB86WUWFxeH+Ph4XL16Ffr6+ujRowdvwv0tW7agdevWUFNTg7y8PHbs2MHF/OrVK/zzzz8VXq+1qUGDBhgzZgzs7e3h4OCAjRs3IjMzs9z8Y8aMQVxcHIyMjODu7o7z58/XqF4rKyteDEZGRiLn/beYMmUKQkJC0KpVK8ydOxfR0dHl5jU3N0e3bt1gamqKwYMHw9/fH2/fvq1SPaV7RRYXF2PZsmUwNTVFgwYNIC8vj3Pnzomck6VVdp4DovezypS+J1T2WwF8OX8PHz6MwsJCAF96SA0bNox3rZT48OED0tLS4OLiwitz+fLlXHktW7ZEgwYNEBERgcjISFhYWKBv375cj6iIiAjY2toCAB4+fIj8/Hx0796dV96ePXu48pKTk3k9EgH+uVRTvr6+UFJS4j6NGzf+5jKrw83NDadOnUJYWBgaNWr0Q+smhBBCCPmZ1GhG2ZL5LABAX18f9+/fR3Z2NurXr1/u0BDy7VRVVUUenPLy8uDg4IBVq1aJ5NfS0sKjR49qXF+TJk0AAKampnj58iUWL17MNe7UNTk5Oejr60NfXx8dOnSAgYEBAgIC4OnpyeXJzs6GmpoatxwQEIDPnz9DW1ubS2OMQUpKCps3b4aSkpJIPStXroSVlRXmzJlT5diKi4uRmpqKtm3bAgBUVFQgEAiq/NBbmpycXLW3cXBwgK6uLvz9/bl5ZVq2bCkyWfLXQ/RKrt3SwwBLLF68GMOHD8fp06dx9uxZeHt7IyQkhHtz59dllZRXVlp55Zelou2vXbuGESNGYMmSJbC3t4eSkhJCQkLg5+dXaRkVlZuXlwctLa0yh+9V9qZJJSUlbjievr4+AgICoKWlhYMHD2L8+PEICQnB7Nmz4efnBysrKygoKGDNmjXc8EIZGZmKd0g1iYmJiQxhLT2Zd2BgINzd3REaGoqDBw/Cy8sLFy5cEJnrDgAsLS2Rnp6Os2fP4uLFixgyZAjs7Ozw999/c40YX9f3Iyf1/1qvXr3w5MkTnDlzBhcuXEC3bt3g6upa5osPxMXFceHCBURHR+P8+fP4448/sGDBAty4cYO7B5an9LW5Zs0abNy4ERs2bICpqSnk5OQwY8aMSicpr8p1Uvp+VpnSsVX2WwF8uW8wxnD69Gm0bdsWkZGR5b7BNS8vDwDg7+8v0mgkLi7OfY/OnTsjPDwcUlJSsLW1hZmZGQoLC5GQkIDo6GhuCGdJeadPn0bDhg155X3vlwJ4enpi1qxZ3HJubu4PaaRijGHatGk4evQowsPDKz3fCCGEEEL+62rUg6osDRo0oMap78zCwgJJSUm8NEtLSyQmJkJPT49rsCn5yMnJwcDAADIyMt/8anChUMj9Vf1nVFZ8CQkJsLCwAPBlrp09e/bAz88PcXFx3Cc+Ph7a2trl9g5r164dBgwYUK15Tnbv3o23b99i4MCBAABJSUk0b96cd+wUFRWhra2NqKgo3rZRUVFo3rx5uWUrKSlBQ0MDMTExXFpxcTFvIu6srCykpKTAy8sL3bp1g4mJSY0bx0ozNDTEzJkzcf78eQwYMKDCiae/t+joaOjq6mLBggVo06YNDAwMyp3cuzosLS3x4sULSEhIiFxT5c1jVJ6SB/WSXnRRUVHo2LEjpk6dCgsLC+jr6/N6oikoKEBPT++br9cSampqePHiBa/R6OtJzEtYWFjA09MT0dHRaNmyJfbv319umYqKihg6dCj8/f1x8OBBHD58mNd48nUPrLLqAoDr169z/3779i0ePHgAExMTAF+ul6pOpF8RNTU1jB49Gvv27cOGDRsqfAupQCCAtbU1lixZgjt37kBSUpKbkLw68URFRcHR0REjR46Eubk5mjZtigcPHnzzdwH497OaqOy3AvjSy3PAgAEIDg7GgQMHYGRkBEtLyzLL09DQgLa2Nh49eiRS3tcNLSXzUIWHh8PW1hZiYmLo3Lkz1qxZg8LCQq4XafPmzSElJYWMjAyR8koai0xMTETm8/r6XKopKSkpKCoq8j4/gqurK/bt24f9+/dDQUEBL168wIsXL7j7BSGEEELIr6ZGDVQfPnzAwoUL0bFjR+jr66Np06a8D/k+7O3tkZiYyGtscHV1RXZ2NpycnBATE4O0tDScO3cOY8eORXFxMaSlpeHh4YG5c+dyQyWuX7/OG9bm7OzM63m0ZcsWnDx5EqmpqUhNTUVAQADWrl2LkSNHcnk2b94sMgwpKSkJcXFxyM7ORk5ODtcIVOLmzZswNjbG8+fPubSMjAzExcUhIyMDxcXF3DYlf00HAGNjY+5h8cOHD5g/fz6uX7+OJ0+e4NatWxg3bhyeP3+OwYMHc9vk5+fj1q1b6NGjBwDg1KlTePv2LVxcXNCyZUveZ+DAgeUO8wMAHx8fXL58ucwXAOTn5+PFixd49uwZrl+/Dg8PD0yePBlTpkxBly5deMfu6tWrvG3nzJmDVatW4eDBg0hJScG8efMQFxeH6dOnlxsL8OUNg76+vjh+/DhSUlIwffp0vH37lmsgrl+/PlRUVLBjxw48fPgQly9f5vUOqImCggK4ubkhPDwcT548QVRUFGJiYrhGhbpgYGCAjIwMhISEIC0tDZs2baryW84qYmdnBysrK/Tr1w/nz5/H48ePER0djQULFnBvLXz+/DmMjY1FHpZLzocXL14gPj4eU6ZMgbS0NHceGhgYIDY2FufOncODBw+wcOFCXmMj8KWnmp+fHzZt2oTU1FTcvn0bf/zxR42+i62tLV6/fo3Vq1cjLS0NW7ZswdmzZ7n16enp8PT0xLVr1/DkyROcP38eqamp5R7XdevW4cCBA7h//z4ePHiAQ4cOQVNTE8rKypCRkUGHDh2wcuVKJCcnIyIiAl5eXmWWs3TpUly6dAkJCQkYM2YMVFVVubdQ6unpIS8vD5cuXcKbN2+qNaytxKJFi3D8+HE8fPgQiYmJOHXqVLnf6caNG1ixYgViY2ORkZGBI0eO4PXr11x+PT093L17FykpKXjz5k2FvcIMDAy43ljJycmYNGkSXr58We34Syt9P6uJyn4rSowYMQKnT5/Grl27yp0cvcSSJUvg6+uLTZs24cGDB7h37x4CAwOxbt06Lo+trS2SkpKQmJiI3377jUsLDg5GmzZtuMYxBQUFzJ49GzNnzsTu3buRlpbGnfslk65PnjwZqampmDNnDlJSUrB///4y38Ra2W/Rz2Lr1q3IycmBra0ttLS0uM/BgwfrOjRCCCGEkDpRoyF+48ePR0REBEaNGgUtLS3qOfWDmJqawtLSEn/99RcmTZoEAFwvHA8PD/To0QOFhYXQ1dVFz549uSE3CxcuhISEBBYtWoR//vkHWlpamDx5MlduRkYGb44RoVAIT09PpKenQ0JCAs2aNcOqVau4OgHgzZs3InMQ9e7dm9eDpeSv/SW9N/Lz85GSksJ7wFu0aBH38PH1NmFhYdzcJCkpKdzrt8XFxXH//n3s3r0bb968gYqKCjcUpUWLFlw5x48fh46ODjp16gTgy/A+Ozu7MofxDRw4EKtXr8bdu3fL/Mu5oaEhxo0bV2YPDH9/f/j7+0NSUhIqKipo3bo1Dh48yA17K+Hi4oI2bdogJyeHi8Hd3R05OTn43//+h1evXqF58+Y4ceIEDAwMROr5moeHB168eAFnZ2eIi4tj4sSJsLe353rriImJISQkBO7u7mjZsiWMjIywadMmbn/WhLi4OLKysuDs7IyXL19CVVUVAwYMwJIlS2pc5rf6/fffMXPmTLi5uaGwsBB9+vTBwoULsXjx4m8qVyAQ4MyZM1iwYAHGjh2L169fQ1NTE507d+befllUVISUlBSRxpOS8wH40lBoZmaGM2fOwMjICAAwadIk3LlzB0OHDoVAIICTkxOmTp3KazQaPXo0Pn78iPXr12P27NlQVVXFoEGDavRdTExM8Oeff2LFihVYtmwZBg4ciNmzZ3PnsqysLHc9ZWVlQUtLC66urrxr/WsKCgpYvXo1UlNTIS4ujrZt2+LMmTPc/WPXrl1wcXFB69atYWRkhNWrV5fZqLJy5UpMnz4dqampaNWqFU6ePAlJSUkAQMeOHTF58mQMHToUWVlZ8Pb2rvYxlZSUhKenJx4/fgwZGRl06tQJISEhZeZVVFTElStXsGHDBuTm5kJXVxd+fn7o1asXAGDChAkIDw9HmzZtkJeXh7CwsHLfXOjl5YVHjx7B3t4esrKymDhxIvr168fdv2qq9P2sJqryWwEAXbt2RYMGDZCSkoLhw4dXWOb48eMhKyuLNWvWYM6cOZCTk4OpqSlmzJjB5TE1NYWysjIMDQ25+ehsbW1RXFwsck9atmwZ1NTU4Ovri0ePHkFZWRmWlpaYP38+AEBHRweHDx/GzJkz8ccff6Bdu3ZYsWIFxo0bxyunst+in8XPFg8hhBBCSF0TsBr8D0lZWRmnT58WmeCZfH+nT5/GnDlzkJCQUObEteSLDh06wN3dvdIHrB9p8ODBsLS05PVWqw1CoRAmJiYYMmQIli1bVqtlE0Lq3s94P/svy83NhZKSEryvPIK0vELlG9TAPIvqDRcmhBBCCPk3Kfn/VE5OTrWmT6hRD6r69eujQYMGNdmUfKM+ffogNTUVz58//+FvGvq3ePPmDQYMGPDTTOheYs2aNTh58uQ3l1MyFMvGxgaFhYXYvHkz0tPT6eGVkP+gn/V+RgghhBBCSG2rUQ+qffv24fjx49i9ezdkZWW/R1yEkHI8ffoUw4YNQ0JCAhhjaNmyJVauXInOnTvXdWhVFhkZyQ2hKsvXc5ARQv5PRkZGhS9SSEpKgo6Ozg+M6L+HelARQgghhHybH9qDys/PD2lpadDQ0ICenp7IK7K/fqMYIaR2NW7cWOTtf/82bdq0+SknLSbkZ6etrV3htaOtrf3jgiGEEEIIIaQW1aiBquRtS4QQUhMyMjLQ19ev6zAI+deRkJCga4cQQgghhPwn1aiBytvbu7bjIIQQQgghhBBCCCG/qBq/Bu7du3fYuXMnPD09kZ2dDeDL0L7nz5/XWnCEEEIIIYQQQggh5L+vRj2o7t69Czs7OygpKeHx48eYMGECGjRogCNHjiAjIwN79uyp7TgJIYQQQgghhBBCyH9UjRqoZs2ahTFjxmD16tVQUPi/N9z07t2bXnVPCCGEkH+9WeYq1XrrDCGEEEII+TY1GuIXExODSZMmiaQ3bNgQL168+OagCCGEEEIIIYQQQsivo0YNVFJSUsjNzRVJf/DgAdTU1L45KEIIIYQQQgghhBDy66hRA9Xvv/+OpUuXoqioCAAgEAiQkZEBDw8PDBw4sFYDJIQQQgghhBBCCCH/bTVqoPLz80NeXh7U1dVRUFAAGxsb6OvrQ0FBAT4+PrUdIyGEEEIIIYQQQgj5D6vRJOlKSkq4cOECrl69irt37yIvLw+Wlpaws7Or7fgIIYQQQgghhBBCyH+cgDHG6joIQgghhJCfQW5uLpSUlJCTk0Nv8SOEEEIIqYGa/n+qyj2oNm3aVOVC3d3dq5yXEEIIIeRnsy4+C9Lyn75L2fMsVL9LuYQQQggh/2ZVbqBav359lfIJBAJqoCKEEEIIIYQQQgghVVblBqr09PTvGQchhBBCCCGEEEII+UXV6C1+hBBCCCGEEEIIIYTUlir3oJo1axaWLVsGOTk5zJo1q8K869at++bACCGEEEIIIYQQQsivocoNVHfu3MH9+/dhYWGBO3fulJtPIBDUSmCEEEIIIYQQQggh5NdQ5QaqsLAwiIuLIzMzE2FhYQCAoUOHYtOmTdDQ0PhuARJCCCGEEEIIIYSQ/7ZqzUHFGOMtnz17Fh8+fKjVgAghhBBCCCGEEELIr+WbJkkv3WBFqicrKwvq6up4/PhxXYfynzdv3jxMmzatrsPg+fTpE/T19REdHV3XoVSZnp4eNmzYUNdhkK8sXrwYrVq14pbHjBmDfv36ccu2traYMWPGd6v/W+oLDw+HQCDAu3fvvktsFdVz7Ngx6OvrQ1xc/Lvun/JU51rq0KEDDh8+/H0DqoIfdbyqSyAQ4NixY3UdRrX5+vqibdu2UFBQgLq6Ovr164eUlJS6DosQQgghpM5Uq4FKIBCIzDFFc07VnI+PDxwdHaGnp/fd6rh69Sqsra2hoqICGRkZGBsbY/369VXe/uHDh1BQUICysnKF+eLj4+Hk5ITGjRtDRkYGJiYm2LhxIy9PZmYmhg8fDkNDQ4iJiVXroTAoKAhmZmaQlpaGuro6XF1duXXh4eFwdHSElpYW5OTk0KpVKwQHB/O2nz17Nnbv3o1Hjx6VWb69vT3ExcURExMjsm7MmDEQCARYuXIlL/3YsWO887/k4U0gEEBMTAxKSkqwsLDA3LlzkZmZKVLutm3b0KRJE3Ts2LHK+6GuxcTEYOLEiXUdxr/C1+eDQCCAjIwMWrRogR07dtRqPbNnz8alS5dqtcxvceTIESxbtqyuw+Dp2LEjMjMzoaSkxKVNmjQJgwYNwtOnT+sk3upcS15eXpg3bx6EQuF3jqpiZe3Hn5GPjw86duwIWVnZSn+76lJERARcXV1x/fp1XLhwAUVFRejRowf1TCeEEELIL6vKc1ABX3pMjRkzBlJSUgCAjx8/YvLkyZCTk+PlO3LkSO1F+B+Vn5+PgIAAnDt37rvWIycnBzc3N5iZmUFOTg5Xr17FpEmTICcnV+nDUVFREZycnNCpU6dKe/ncunUL6urq2LdvHxo3bozo6GhMnDgR4uLicHNzAwAUFhZCTU0NXl5e1WokW7duHfz8/LBmzRq0b98eHz584PU6i46OhpmZGTw8PKChoYFTp07B2dkZSkpK6Nu3LwBAVVUV9vb22Lp1K9asWcMrPyMjA9HR0XBzc8OuXbvQtm1bkRikpaWxatUqTJo0CfXr168w3pSUFCgqKiI3Nxe3b9/G6tWrERAQgPDwcJiamgL4ci1t3rwZS5curfJ+KMunT58gKSn5TWVUh5qa2g+r67+i5HwoKCjAyZMnMWXKFDRr1gzdunWrlfLl5eUhLy9fK2XVhgYNGtR1CCIkJSWhqanJLefl5eHVq1ewt7eHtrZ2jcv9luuvOtdSr169MH78eJw9exZ9+vSpdl21dZ8ovR9/Vp8+fcLgwYNhZWWFgICAug6nXKGhobzloKAgqKur49atW+jcuXMdRUUIIYQQUneq1YNq9OjRUFdXh5KSEpSUlDBy5Ehoa2tzyyUfUrkzZ85ASkoKHTp04KUnJCSgV69ekJeXh4aGBkaNGoU3b95w64VCIVavXg19fX1ISUlBR0cHPj4+5dZjYWEBJycntGjRAnp6ehg5ciTs7e0RGRlZaYxeXl4wNjbGkCFDKs07btw4bNy4ETY2NmjatClGjhyJsWPH8hor9fT0sHHjRq7xqCrevn0LLy8v7NmzB8OHD0ezZs1gZmaG33//ncszf/58LFu2DB07dkSzZs0wffp09OzZU6Sh1MHBASEhISJ1BAYGom/fvpgyZQoOHDiAgoICkTx2dnbQ1NSEr69vpTGrq6tDU1MThoaGGDZsGKKioqCmpoYpU6ZweW7duoW0tDSRh8179+6ha9eukJGRgYqKCiZOnIi8vDxufclwKh8fH2hra8PIyAjAl0a6Vq1aQVpaGm3atOF6d8XFxQEAiouL4eLigiZNmkBGRgZGRkYiPdxKyl67di20tLSgoqICV1dXFBUVcXm+HpbEGMPixYuho6MDKSkpaGtrw93dnZd3+fLlcHZ2hry8PHR1dXHixAm8fv0ajo6OkJeXh5mZGWJjYyvdp8CXhzdlZWWcO3cOJiYmkJeXR8+ePXm902JiYtC9e3eoqqpCSUkJNjY2uH37Nq8cgUCA7du3o2/fvpCVlYWJiQmuXbuGhw8fwtbWFnJycujYsSPS0tJ42x0/fhyWlpaQlpZG06ZNsWTJEnz+/LnSuEvOhyZNmsDd3R1NmjThxRQaGorffvsNysrKUFFRQd++fUXqfvbsGZycnNCgQQPIycmhTZs2uHHjBgDRIX6VKWs4lLKyMoKCggB8ebh3c3ODlpYWpKWloaurW6XzvkTpIX6FhYXw8PBA48aNISUlBX19fZFGg1u3bqFNmzaQlZVFx44dRYY5VbbvBQIBdu7cif79+0NWVhYGBgY4ceIEt/7roWnh4eFQUFAAAHTt2hUCgQDh4eEAgMOHD6NFixaQkpKCnp4e/Pz8eHHo6elh2bJlcHZ2hqKiIiZOnMidl6dOnYKRkRFkZWUxaNAg5OfnY/fu3dDT00P9+vXh7u6O4uJiXllVvZbExcXRu3fvMu9fZSk5J3bu3IkmTZpAWloaAPDu3TuMHz8eampqUFRURNeuXREfHw8AePDgAQQCAe7fv88ra/369WjWrJnIfixx9epVdOrUCTIyMmjcuDHc3d25HkCbN29Gy5Ytubwl96Vt27ZxaXZ2dvDy8uKWKzvWqamp6Ny5M6SlpdG8eXNcuHBB5PsvWbIEM2fO5P4g8G+Rk5MD4Ods5CWEEEII+RGq1UAVGBhYpQ+pXGRkJFq3bs1Le/fuHbp27QoLCwvExsYiNDQUL1++5DUQeXp6YuXKlVi4cCGSkpKwf/9+3lsUbW1tMWbMmHLrvXPnDqKjo2FjY1NhfJcvX8ahQ4ewZcuWmn1BfPnP9rf+R/vChQsQCoV4/vw5TExM0KhRIwwZMgRPnz6tdt3t2rXDs2fPeL2vGGMIDAzEyJEjYWxsDH19ffz9998i5YmLi2PFihX4448/8OzZs2p9BxkZGUyePBlRUVF49eoVgC/H39DQkHtQBoAPHz7A3t4e9evXR0xMDA4dOoSLFy9yPdBKXLp0CSkpKbhw4QJOnTqF3NxcODg4wNTUFLdv38ayZcvg4eHB20YoFKJRo0Y4dOgQkpKSsGjRIsyfPx9//fUXL19YWBjS0tIQFhaG3bt3IygoiGu4KO3w4cNYv349tm/fjtTUVBw7dkzkgXD9+vWwtrbGnTt30KdPH4waNQrOzs4YOXIkbt++jWbNmsHZ2bnK89nl5+dj7dq12Lt3L65cuYKMjAzMnj2bW//+/XuMHj0aV69exfXr12FgYIDevXvj/fv3vHJKGhni4uJgbGyM4cOHY9KkSfD09ERsbCwYY7z9HhkZCWdnZ0yfPh1JSUnYvn07goKCeI3DY8aMga2tbbmxM8YQGhqKjIwMtG/fnkv/8OEDZs2ahdjYWFy6dAliYmLo378/N5wrLy8PNjY2eP78OU6cOIH4+HjMnTv3uw332rRpE06cOIG//voLKSkpCA4O/qZhyM7Ozjhw4AA2bdqE5ORkbN++XaTH14IFC+Dn54fY2FhISEhg3Lhx3Lqq7HvgS6PEkCFDcPfuXfTu3RsjRoxAdna2SDxfN4AdPnwYmZmZ6NixI27duoUhQ4Zg2LBhuHfvHhYvXoyFCxeKnP9r166Fubk57ty5g4ULFwL4cl5u2rQJISEhCA0NRXh4OPr3748zZ87gzJkz2Lt3L7Zv317mvaUkjsqupXbt2lXpDwslHj58iMOHD+PIkSNcQ/XgwYPx6tUrnD17Frdu3YKlpSW6deuG7OxsGBoaok2bNiLDo4ODgzF8+PAy60hLS0PPnj0xcOBA3L17FwcPHsTVq1e5a8fGxgZJSUl4/fo1gC9D2lRVVbkGwaKiIly7do27bio71kKhEAMGDICkpCRu3LiBbdu2idzr/q2EQiFmzJgBa2trXqMeIYQQQsgvhZE64ejoyMaNG8dLW7ZsGevRowcv7enTpwwAS0lJYbm5uUxKSor5+/uXW+6oUaPYvHnzRNIbNmzIJCUlmZiYGFu6dGmFsb1584Y1btyYRUREMMYYCwwMZEpKSlX8Zl9ERUUxCQkJdu7cuTLX29jYsOnTp1dajq+vL6tXrx4zMjJioaGh7Nq1a6xbt27MyMiIFRYWlrnNwYMHmaSkJEtISOCl5+TkMAAsPDycSzt//jxTU1NjRUVFjDHG1q9fz2xsbHjbjR49mjk6OjLGGOvQoQN33I4ePcq+voTCwsIYAPb27VuRmM6ePcsAsBs3bjDGGJs+fTrr2rUrL8+OHTtY/fr1WV5eHpd2+vRpJiYmxl68eMHFoqGhwfvuW7duZSoqKqygoIBL8/f3ZwDYnTt3ytxHjDHm6urKBg4cyPueurq67PPnz1za4MGD2dChQ7llXV1dtn79esYYY35+fszQ0JB9+vSpzPJ1dXXZyJEjueXMzEwGgC1cuJBLu3btGgPAMjMzy42zRGBgIAPAHj58yKVt2bKFaWholLtNcXExU1BQYCdPnuTSADAvLy+RGAICAri0AwcOMGlpaW65W7dubMWKFbyy9+7dy7S0tLjlefPmsVGjRnHLJeeDnJwck5OTYxISEkxMTIwtX768wu/5+vVrBoDdu3ePMcbY9u3bmYKCAsvKyiozv7e3NzM3N+eWvz5fGRO91gCwo0eP8spQUlJigYGBjDHGpk2bxrp27cqEQmGFcValvpSUFAaAXbhwocxtS/bRxYsXubTTp08zANz5XJV9X/qY5uXlMQDs7NmzvHpKrs23b98yACwsLIzbZvjw4ax79+68eubMmcOaN2/OLevq6rJ+/frx8pR1Xk6aNInJysqy9+/fc2n29vZs0qRJvLKqei0xxtjx48eZmJgYKy4uLjdPCW9vb1avXj326tUrLi0yMpIpKiqyjx8/8vI2a9aMbd++nTH25f7XrFkzbl3J8UtOTmaMie5HFxcXNnHiRF55kZGRTExMjBUUFDChUMhUVFTYoUOHGGOMtWrVivn6+jJNTU3GGGNXr15l9erVYx8+fGCMVX6sz507xyQkJNjz58+59SX31tLnNGNV/+36+PEjy8nJ4T4lv7veVx4x39uvv8untMmTJzNdXV329OnTSuMlhBBCCPnZlTx75+TkVGu7b3qLH6m5goICbthFifj4eISFhXFzysjLy8PY2BjAl79UJycno7CwsMK5a/bs2VPmcJzIyEjExsZi27Zt2LBhAw4cOFBuGRMmTMDw4cNrPAdGQkICHB0d4e3tjR49etSojBJCoRBFRUXYtGkT7O3t0aFDBxw4cACpqakICwsTyR8WFoaxY8fC398fLVq04K2TkZEB8KW3Q4ldu3Zh6NChkJD4Mh2bk5MToqKiRIZYlVi1ahV2796N5OTkan0P9v97CJVMql7W8U9OToa5uTlvTjdra2sIhULekCdTU1PefDIpKSncBPIl2rVrJxLDli1b0Lp1a6ipqUFeXh47duxARkYGL0+LFi0gLi7OLWtpaXG9vkobPHgwCgoK0LRpU0yYMAFHjx4VGfJmZmbG/bukp9/XPUNK0sqrozRZWVluuFFZ8b18+RITJkyAgYEBlJSUoKioiLy8PJHvWZW4Pn78iNzcXABfrs2lS5fyrs0JEyYgMzOTO598fX2xZ88ekZgjIyMRFxeHuLg47Ny5EytWrMDWrVu59ampqXByckLTpk2hqKjI9VYqiTkuLg4WFhY/bNjPmDFjEBcXByMjI7i7u+P8+fM1LisuLg7i4uKV9tj8+nhoaWkB+L9zoir7vnQZcnJyUFRUrPJ5BXy5/qytrXlp1tbWSE1N5Q3Na9Omjci2pc9LDQ0N6Onp8XqKaWhofNO1JCMjA6FQiMLCwip9H11dXd48V/Hx8cjLy4OKigpvX6anp3P3u2HDhuHx48e4fv06gC+9pywtLbnfodLi4+MRFBTEK8/e3h5CoRDp6ekQCATo3LkzwsPD8e7dOyQlJWHq1KkoLCzE/fv3ERERgbZt20JWVpYrr6JjnZycjMaNG/PmDbOysqrS/qiIr68vb5qCxo0bf3OZ1eHm5oZTp04hLCwMjRo1+qF1E0IIIYT8TKo1STqpPaqqqnj79i0vLS8vDw4ODli1apVIfi0trXLfQFcVTZo0AfDlIfzly5dYvHgxnJycysx7+fJlnDhxAmvXrgXwpXFFKBRCQkICO3bs4A2/KS0pKQndunXDxIkTefOK1FTJw2rz5s25NDU1Naiqqoo0OkRERMDBwQHr16+Hs7OzSFklw31KHtqys7Nx9OhRFBUV8RoMiouLsWvXrjLn9urcuTPs7e3h6elZ4VDK0koatEoaH1RVVXHv3r0qb/+10i8lqIqQkBDMnj0bfn5+sLKygoKCAtasWcPNY1SiXr16vGWBQFDuULLGjRsjJSUFFy9exIULFzB16lSsWbMGERERXDlfl1fSOFdWWlWHq5UVH/tqeODo0aORlZWFjRs3QldXF1JSUrCyssKnT5/KLacqceXl5WHJkiUYMGCASEylGxpLa9KkCfcmsRYtWuDGjRvw8fHh5iRzcHCArq4u/P39oa2tDaFQiJYtW3IxlzSs1pbS+wwAb54xS0tLpKen4+zZs7h48SKGDBkCOzu7coenVaSqsdfGvq/Oufstyrr+yqq7tq+l7OxsyMnJVXmflo4zLy8PWlpa3PC6r5Wcn5qamujatSv279+PDh06YP/+/by580rLy8vDpEmTePNlldDR0QHwZdj5jh07EBkZCQsLCygqKnKNVhEREbzGy2+5zr6Fp6cnZs2axS3n5ub+kEYqxhimTZuGo0ePIjw8nPudJoQQQgj5VVEDVR2xsLDAvn37eGmWlpY4fPgw9PT0uB49XzMwMICMjAwuXbqE8ePH17juyv4Kf+3aNV6PgePHj2PVqlWIjo5Gw4YNy90uMTERXbt2xejRoyucuL06Sno0pKSkcH9Zzs7Oxps3b6Crq8vlCw8PR9++fbFq1apy306YkJCAevXqcT2rgoOD0ahRI5EJo8+fPw8/Pz8sXbqU15uoxMqVK9GqVStugvLKFBQUYMeOHejcuTPXOGZhYYGtW7eCMcY9kJuYmCAoKAgfPnzgHi6joqIgJiZWYV1GRkbYt28fCgsLuTdsxsTE8PJERUWhY8eOmDp1KpdWXi+x6pCRkYGDgwMcHBzg6uoKY2Nj3Lt3D5aWlt9cdk1ERUXhzz//RO/evQEAT58+5b1koKYsLS2RkpICfX39by5LXFycm4g/KysLKSkp8Pf3R6dOnQB8mXT6a2ZmZti5cyeys7NrpReVmpoab2L51NRUXk8kAFBUVMTQoUMxdOhQDBo0CD179qxR/aamphAKhYiIiICdnV2N4q3NfV8RExMTREVF8dKioqJgaGhY5n2gtlV2LSUkJMDCwqLG5VtaWuLFixeQkJCocE6xESNGYO7cuXBycsKjR48wbNiwCstMSkqq8NjY2NhgxowZOHToEDfXlK2tLS5evIioqCj873//45VX0bE2MTHB06dPkZmZyf3xoqS317eQkpLi7p0/kqurK/bv34/jx49DQUEBL168AAAoKSnVesM0IYQQQsi/AQ3xqyP29vZITEzk9aJydXVFdnY2nJycEBMTg7S0NJw7dw5jx45FcXExpKWl4eHhgblz52LPnj1IS0vD9evXeW/EcnZ2hqenJ7e8ZcsWnDx5EqmpqUhNTUVAQADWrl2LkSNHcnk2b97MGzZoYmKCli1bcp+GDRtCTEwMLVu2RP369QEAR48e5Q37SEhIQJcuXdCjRw/MmjULL168wIsXL7jJcUuUDHXKy8vD69evERcXh6SkJG596XINDQ3h6OiI6dOnIzo6GgkJCRg9ejSMjY3RpUsXAF+G9fXp0wfu7u4YOHAgV3fpCZIjIyO5t00BQEBAAAYNGsT7ri1btoSLiwvevHkj8grwEqamphgxYgQ2bdpU5vpXr17hxYsXSE1NRUhICKytrfHmzRteL60uXbogLy8PiYmJXNqIESMgLS2N0aNHIyEhAWFhYZg2bRpGjRrFmwi/tOHDh0MoFGLixIlITk7GuXPnuN5vJY1fBgYGiI2Nxblz5/DgwQMsXLhQpBGruoKCghAQEICEhAQ8evQI+/btg4yMDK/h8EczMDDA3r17kZycjBs3bmDEiBG18qC3aNEi7NmzB0uWLEFiYiKSk5MREhLC6yXo6elZZs+9kvPhyZMnOHToEPbu3QtHR0cAQP369aGiooIdO3bg4cOHuHz5Mq8nB/Bl2Kmmpib69euHqKgoPHr0CIcPH8a1a9dq9F26du2KzZs3486dO4iNjcXkyZN5vX3WrVuHAwcO4P79+3jw4AEOHToETU1NrpdNdejp6WH06NEYN24cjh07hvT0dISHh4tMzl+Rquz72vC///0Ply5dwrJly/DgwQPs3r0bmzdv5k3C/71U5VqKjIz8piHTdnZ2sLKyQr9+/XD+/Hk8fvwY0dHRWLBgAe9NmgMGDMD79+8xZcoUdOnShTecrjQPDw9ER0fDzc0NcXFxSE1NxfHjx3kvGDAzM0P9+vWxf/9+XgPVsWPHUFhYyBtWWdmxtrOzg6GhIUaPHo34+HhERkZiwYIFInFlZGQgLi4OGRkZKC4u5v3u/Ey2bt2KnJwc2NraQktLi/scPHiwrkMjhBBCCKkT1EBVR0xNTWFpacl7UNPW1kZUVBSKi4vRo0cPmJqaYsaMGVBWVoaY2JdDtXDhQvzvf//DokWLYGJigqFDh/LmNcnIyOD1jhAKhfD09ESrVq3Qpk0bbNmyBatWrcLSpUu5PG/evKl2b5qcnBzevEh///03Xr9+jX379vH+o922bVvedhYWFrCwsMCtW7ewf/9+WFhYcL1dyioX+DKvVvv27dGnTx/Y2NigXr16CA0N5R6qd+/ejfz8fPj6+vLqLj1MJCQkBBMmTADw5bX28fHxGDhwoMh3U1JSQrdu3XgNf6UtXbq03OE6RkZG0NbWRuvWrbFy5UrY2dkhISGBN0xRRUUF/fv3570xS1ZWFufOnUN2djbatm2LQYMGoVu3bti8eXO5cQBferucPHkScXFxaNWqFRYsWIBFixYB+L9hMZMmTcKAAQMwdOhQtG/fHllZWbzeVDWhrKwMf39/WFtbw8zMDBcvXsTJkyehoqLyTeV+i4CAALx9+xaWlpYYNWoU3N3doa6u/s3l2tvb49SpUzh//jzatm2LDh06YP369bwGhMzMTJFhp8CX80FLSwv6+vrw8PDApEmT8McffwAAxMTEEBISglu3bqFly5aYOXMm1qxZw9teUlIS58+fh7q6Onr37g1TU1OsXLmyxr16/Pz80LhxY3Tq1AnDhw/H7NmzuTmAAEBBQQGrV69GmzZt0LZtWzx+/Bhnzpzh7kHVtXXrVgwaNAhTp06FsbExJkyYgA8fPlR5+6rs+9pQcj8OCQlBy5YtsWjRIixdurRaQ3lrqrJr6fnz54iOjsbYsWNrXIdAIMCZM2fQuXNnjB07FoaGhhg2bBiePHnCawBXUFCAg4MD4uPjMWLEiArLNDMzQ0REBB48eIBOnTrBwsICixYt4jVqCQQCdOrUCQKBAL/99hu3naKiItq0acMbiljZsRYTE8PRo0dRUFCAdu3aYfz48WX21l20aBEsLCzg7e2NvLw87nfn64a4nwFjrMzPjzjnCCGEEEJ+RgJWejIS8sOcPn0ac+bMQUJCQo0f/kjVnD17Fv/73/9w9+7dModP1oW7d++ie/fuSEtL402mXBuCg4MxduxY5OTk0FARQv7lPDw88PbtW+zYsaOuQ/kl5ObmQklJCd5XHkFaXuG71DHPQvW7lEsIIYQQ8jMo+f9UTk4OFBUVq7zdz/Gk/ovq06cPUlNT8fz58x/+1qBfzYcPHxAYGPjTNE4BX3oRrFq1Cunp6bw3yNXEnj170LRpUzRs2BDx8fHw8PDAkCFDqHGKkP8AdXV1kaGfhBBCCCGE/NdQDypC/gNWr16NP//8Ey9evICWlhb69esHHx8f3tCtn12vXr0QGRlZ5rr58+dj/vz5PzgiQn5OLVq0wJMnT8pct3379kqH5pGKUQ8qQgghhJBvU9MeVNRARQj5KTx//px7u11pDRo0qJU32BHyX/DkyRMUFRWVuU5DQwMKCt+nUeVXQQ1UhBBCCCHfhob4EUL+1Ro2bFjXIRDyr1CXb8okhBBCCCHke6GZuQkhhBBCCCGEEEJInaIGKkIIIYQQQgghhBBSp6iBihBCCCGEEEIIIYTUKZqDihBCCCGklFnmKtWa1JMQQgghhHwb6kFFCCGEEEIIIYQQQuoUNVARQgghhBBCCCGEkDpFDVSEEEIIIYQQQgghpE5RAxUhhBBCCCGEEEIIqVPUQEUIIYQQQgghhBBC6hQ1UBFCCCGEEEIIIYSQOiVR1wEQQgghhPxs1sVnQVr+U12HAQCYZ6Fa1yEQQgghhHx31IOKEEIIIYQQQgghhNQpaqAihBBCCCGEEEIIIXWKGqgIIYQQQgghhBBCSJ2iBipCCCGEEEIIIYQQUqeogYoQQgghhBBCCCGE1ClqoCKEEEIIIYQQQgghdYoaqAghhBBCCCGEEEJInfplG6iysrKgrq6Ox48f13UoP62kpCQ0atQIHz58qOtQeBYuXIiJEyfWdRhVtnjxYrRq1aquwyDVEBQUBGVlZW75RxzDyuocM2YM+vXr911jID+fn+k+/DOeg6Wvm/+yrVu3wszMDIqKilBUVISVlRXOnj1b12ERQgghhNSaX7aBysfHB46OjtDT0/tudVy9ehXW1tZQUVGBjIwMjI2NsX79+gq3CQ8Ph6OjI7S0tCAnJ4dWrVohODi40rrc3d3RunVrSElJVftBmjGGXr16QSAQ4NixY1x68+bN0aFDB6xbt67M7Q4cOABxcXG4urqW+T0EAgFatGiB4uJi3jplZWUEBQVxy3p6ehAIBBAIBJCRkYGenh6GDBmCy5cvi5T74sULbNy4EQsWLKjWd6xLs2fPxqVLl+o6jH+Nr88HOTk5WFpa4tChQz80hqFDh+LBgwf/+Tp/tNINHOU1eJTcP969e8db/vo+0aJFC+zYsaPC+mq6XUpKCrp06QINDQ1IS0ujadOm8PLyQlFRES/foUOHYGxsDGlpaZiamuLMmTO89Xl5eXBzc0OjRo0gIyOD5s2bY9u2bWXWWdP78I+0ceNG3r37ZxUeHg5LS0tISUlBX1//XxFzVTRq1AgrV67ErVu3EBsbi65du8LR0RGJiYl1HRohhBBCSK34JRuo8vPzERAQABcXl+9aj5ycHNzc3HDlyhUkJyfDy8sLXl5eFT4cRUdHw8zMDIcPH8bdu3cxduxYODs749SpU5XWN27cOAwdOrTacW7YsAECgaDMdWPHjsXWrVvx+fNnkXUBAQGYO3cuDhw4gI8fP5a5/aNHj7Bnz55KY1i6dCkyMzORkpKCPXv2QFlZGXZ2dvDx8eHl27lzJzp27AhdXd0qfLOyMcbK/D7fi7y8PFRUVH5Yff8FJefDnTt30LZtWwwdOhTR0dE/rH4ZGRmoq6v/sPrqqs5/m5SUFGRmZiIpKQmTJk3ClClTqtT4W93t6tWrB2dnZ5w/fx4pKSnYsGED/P394e3tzeWJjo6Gk5MTXFxccOfOHfTr1w/9+vVDQkICl2fWrFkIDQ3Fvn37kJycjBkzZsDNzQ0nTpwQqbOm9+Gq+PTpU422K01JSemn762Unp6OPn36oEuXLoiLi8OMGTMwfvx4nDt3rq5D+2YODg7o3bs3DAwMYGhoCB8fH8jLy+P69et1HRohhBBCSK34JRuozpw5AykpKXTo0IGXnpCQgF69ekFeXh4aGhoYNWoU3rx5w60XCoVYvXo19PX1ISUlBR0dHZEGlK9ZWFjAyckJLVq0gJ6eHkaOHAl7e3tERkaWu838+fOxbNkydOzYEc2aNcP06dPRs2dPHDlypMLvtGnTJri6uqJp06ZV3AtfxMXFwc/PD7t27Spzfffu3ZGdnY2IiAheenp6OqKjozFv3jwYGhqWG9+0adPg7e2NwsLCCuNQUFCApqYmdHR00LlzZ+zYsQMLFy7EokWLkJKSwuULCQmBg4MDb9vCwkK4u7tDXV0d0tLS+O233xATE8OtL+lFcfbsWa6X2dWrV/H+/XuMGDECcnJy0NLSwvr162Fra4sZM2Zw2+7duxdt2rTh4hs+fDhevXolUvalS5fQpk0byMrKomPHjryYSw/VCg8PR7t27SAnJwdlZWVYW1vjyZMnvLy7du2Cjo4O5OXlMXXqVBQXF2P16tXQ1NSEurp6heddaQKBADt37kT//v0hKysLAwMD3gNycXExXFxc0KRJE8jIyMDIyAgbN27klVHS02XFihXQ0NCAsrIyli5dis+fP2POnDlo0KABGjVqhMDAQN52T58+xZAhQ6CsrIwGDRrA0dGxSsNqS/a3oaEhtmzZAhkZGZw8ebJKsQJfGjJNTEwgLS0NY2Nj/Pnnn9y6x48fQyAQ4MiRI+jSpQtkZWVhbm6Oa9eucXnKGza0fft2NG7cGLKyshgyZAhycnK4dTExMejevTtUVVWhpKQEGxsb3L59m7f9u3fvMGnSJK5nTsuWLbnG5+oOVdLT08OGDRt4aa1atcLixYsBfGmIXbx4MXR0dCAlJQVtbW24u7uXW158fDy6dOkCBQUFKCoqonXr1oiNjeXFduzYMRgYGEBaWhr29vZ4+vRpleOtDerq6tDU1ESTJk3g7u6OJk2aiOzj2tiuadOmGDt2LMzNzaGrq4vff/8dI0aM4N27N27ciJ49e2LOnDkwMTHBsmXLYGlpic2bN3N5oqOjMXr0aNja2kJPTw8TJ06Eubk5bt68yauvpvfh8ujp6WHZsmVwdnaGoqIiNyT66tWr6NSpE2RkZNC4cWO4u7tzQwfnz5+P9u3bi5Rlbm6OpUuXAhDt8SYUCuHr68tdj+bm5vj777+59W3atMHatWu55X79+qFevXrIy8sDADx79gwCgQAPHz4E8OVePnv2bDRs2BBycnJo3749wsPDefEEBQVBR0cHsrKy6N+/P7Kysnjrt23bhiZNmsDPzw8mJiZwc3PDoEGDKu29/G9TXFyMkJAQfPjwAVZWVnUdDiGEEEJIrfglG6giIyPRunVrXtq7d+/QtWtXWFhYIDY2FqGhoXj58iWGDBnC5fH09MTKlSuxcOFCJCUlYf/+/dDQ0ODW29raYsyYMeXWe+fOHURHR8PGxqZa8ebk5KBBgwbV2qYq8vPzMXz4cGzZsgWamppl5pGUlESrVq1EGtUCAwPRp08fKCkpYeTIkQgICChz+xkzZuDz58/4448/qh3f9OnTwRjD8ePHAQDZ2dlISkpCmzZtePnmzp2Lw4cPY/fu3bh9+zb09fVhb2+P7OxsXr558+Zh5cqVSE5OhpmZGWbNmoWoqCicOHECFy5cQGRkpMhDa1FREZYtW4b4+HgcO3YMjx8/LvMYL1iwAH5+foiNjYWEhATGjRtX5nf6/Pkz+vXrBxsbG9y9exfXrl3DxIkTeT0n0tLScPbsWYSGhuLAgQMICAhAnz598OzZM0RERGDVqlXw8vLCjRs3qrwvlyxZgiFDhuDu3bvo3bs3RowYwe0foVCIRo0a4dChQ0hKSsKiRYswf/58/PXXX7wyLl++jH/++QdXrlzBunXr4O3tjb59+6J+/fq4ceMGJk+ejEmTJuHZs2fcvrO3t4eCggIiIyMRFRUFeXl59OzZk+vRUdLAV1GjlYSEBOrVq4dPnz5VKdbg4GAsWrQIPj4+SE5OxooVK7Bw4ULs3r1b5JjNnj0bcXFxMDQ0hJOTU4U9VB4+fIi//voLJ0+eRGhoKO7cuYOpU6dy69+/f4/Ro0fj6tWruH79OgwMDNC7d2+8f/+e28+9evVCVFQU9u3bh6SkJKxcuRLi4uJVOILVd/jwYaxfvx7bt29Hamoqjh07BlNT03LzjxgxAo0aNUJMTAxu3bqFefPmoV69etz6/Px8+Pj4YM+ePYiKisK7d+8wbNiw7xJ7ZRhjCA0NRUZGRpmNKrW93cOHDxEaGsq7d1+7dg12dna8fPb29ryGzo4dO+LEiRN4/vw5GGMICwvDgwcP0KNHDy7Pt9yHK7J27VqYm5vjzp07WLhwIdLS0tCzZ08MHDgQd+/excGDB3H16lW4ubkB+HL8b968ibS0NK6MxMRE3L17F8OHDy+zDl9fX+zZswfbtm1DYmIiZs6ciZEjR3INaTY2NlwDE2MMkZGRUFZWxtWrVwEAERERaNiwIfT19QEAbm5uuHbtGkJCQnD37l0MHjwYPXv2RGpqKgDgxo0bcHFxgZubG+Li4tClSxcsX76cF1NVjsu/2b179yAvLw8pKSlMnjwZR48eRfPmzes6LEIIIYSQ2sF+QY6OjmzcuHG8tGXLlrEePXrw0p4+fcoAsJSUFJabm8ukpKSYv79/ueWOGjWKzZs3TyS9YcOGTFJSkomJibGlS5dWK9aDBw8ySUlJlpCQUKX83t7ezNzcvEp5J06cyFxcXLhlAOzo0aMi+fr378/GjBnDLRcXF7PGjRuzY8eOMcYYe/36NZOUlGSPHj3i8oSFhTEA7O3bt2zbtm2sQYMG7N27d4wxxpSUlFhgYCCXV1dXl61fv77MGDU0NNiUKVMYY4zduXOHAWAZGRnc+ry8PFavXj0WHBzMpX369Ilpa2uz1atX82IpiZcxxnJzc1m9evXYoUOHuLR3794xWVlZNn369PJ2GYuJiWEA2Pv373llX7x4kctz+vRpBoAVFBQwxvjHJCsriwFg4eHhZZbv7e3NZGVlWW5uLpdmb2/P9PT0WHFxMZdmZGTEfH19y43zawCYl5cXt5yXl8cAsLNnz5a7jaurKxs4cCC3PHr0aKarqysSQ6dOnbjlz58/Mzk5OXbgwAHGGGN79+5lRkZGTCgUcnkKCwuZjIwMO3fuHGOMsRs3bjAjIyP27NkzLs/X50NhYSFbsWIFA8BOnTpVpVibNWvG9u/fz8uzbNkyZmVlxRhjLD09nQFgO3fu5NYnJiYyACw5OZkxxlhgYCBTUlLi1nt7ezNxcXFenGfPnmViYmIsMzOzzLiKi4uZgoICO3nyJGOMsXPnzjExMTGWkpJSZv6y6vz6Wh49ejRzdHTklsu6bszNzZm3tzdjjDE/Pz9maGjIPn36VGZ9pSkoKLCgoKByYwPArl+/zqUlJyczAOzGjRtVKr+s7zB69GgmLi7O5OTkeB9paWnu/sHY/11nJeslJCSYmJgYW758eYX11XS7ElZWVkxKSooBYBMnTuSd//Xq1RM5z7Zs2cLU1dW55Y8fPzJnZ2cGgElISDBJSUm2e/du3jY1vQ9XRFdXl/Xr14+X5uLiwiZOnMhLi4yMZGJiYty9ytzcnPcb5enpydq3b88tf338Pn78yGRlZVl0dLRIPU5OTowxxk6cOMGUlJTY58+fWVxcHNPU1GTTp09nHh4ejDHGxo8fz4YPH84YY+zJkydMXFycPX/+nFdet27dmKenJ2OMMScnJ9a7d2/e+qFDh/KuGwMDA7ZixQpenpJ7cn5+fpn76+PHjywnJ4f7lPz2e195xHxvv/4pPiUKCwtZamoqi42NZfPmzWOqqqosMTGxzO9FCCGEEFJXcnJyGACWk5NTre1+yR5UBQUFkJaW5qXFx8cjLCwM8vLy3MfY2BjAlx4tycnJKCwsRLdu3cotd8+ePfD19RVJj4yMRGxsLLZt24YNGzbgwIEDVYozLCwMY8eOhb+/P1q0aFGNb1i5EydO4PLlyyJDhMoiIyOD/Px8bvnChQv48OEDevfuDQBQVVVF9+7dyx2e4uLiAhUVFaxataracTLGuN5FBQUFAMA7dmlpaSgqKoK1tTWXVq9ePbRr1w7Jycm8sr7uefXo0SMUFRWhXbt2XJqSkhKMjIx429y6dQsODg7Q0dGBgoIC14MiIyODl8/MzIz7t5aWFgDwhgKWaNCgAcaMGQN7e3s4ODhg48aNyMzM5OXR09ODgoICt6yhoYHmzZtDTEyMl1ZW+eX5Oj45OTkoKirytt+yZQtat24NNTU1yMvLY8eOHSLfsUWLFiIxfN0jR1xcHCoqKly58fHxePjwIRQUFLhrqkGDBvj48SPXS6Ndu3a4f/8+GjZsyKvLw8MD8vLykJWVxapVq7By5Ur06dOn0lg/fPiAtLQ0uLi48K7l5cuX83qGlN4nFR2zEjo6Orw4raysIBQKueGcL1++xIQJE2BgYAAlJSUoKioiLy+Piy0uLg6NGjWCoaFhuXXUpsGDB6OgoABNmzbFhAkTcPTo0Qp7iM2aNQvjx4+HnZ0dVq5cKbK/JCQk0LZtW27Z2NgYysrKItdZdZXMFfT1Z+fOnWXmjYyM5OVZsWIFtm7dWmkdNd3u4MGDuH37Nvbv34/Tp0/zhqtVxR9//IHr16/jxIkTuHXrFvz8/ODq6oqLFy8C+Lb7cGVK9zSNj49HUFAQ77qwt7eHUChEeno6gC+9qPbv3w/gy733wIEDGDFiRJnlP3z4EPn5+ejevTuvzD179nDnTqdOnfD+/XvcuXMHERERsLGxga2tLderKiIiAra2tgC+9AwqLi6GoaEhr7yIiAiuvOTkZJGeb7UxvM3X1xdKSkrcp3Hjxt9c5vciKSkJfX19tG7dGr6+vjA3Ny9zmDMhhBBCyL+RRF0HUBdUVVXx9u1bXlpeXh4cHBzKbETR0tLCo0ePalxfkyZNAACmpqZ4+fIlFi9eDCcnpwq3iYiIgIODA9avXw9nZ+ca112ey5cvIy0tTWTOm4EDB6JTp068eT+ys7PRrFkzbjkgIADZ2dmQkZHh0oRCIe7evYslS5bwGjGALw+2Pj4+GDNmDDecpCqysrLw+vVrbv+pqqoCAN6+fQs1NbUql1NCTk6uWvk/fPgAe3t72NvbIzg4GGpqasjIyIC9vb3IpMNfD4UqaVATCoVllhsYGAh3d3eEhobi4MGD8PLywoULF7g50b4uq6S8stLKK78sFW0fEhKC2bNnw8/PD1ZWVlBQUMCaNWtEhhBWN668vDy0bt26zLdQVnb85syZgzFjxnDzwZXs08piLZnbxt/fX+RBtvRQuuocs6oYPXo0srKysHHjRujq6kJKSgpWVlbcufL19VIbxMTEwBjjpX39lrnGjRsjJSUFFy9exIULFzB16lSsWbMGERERIscN+DL/2fDhw3H69GmcPXsW3t7eCAkJQf/+/Ws17tLk5OS4IV4lSoaJltakSRPuntWiRQvcuHEDPj4+mDJlSoV11HS7koaK5s2bo7i4GBMnTsT//vc/iIuLQ1NTEy9fvuTlf/nyJTdMr6CgAPPnz8fRo0e5xlUzMzPExcVh7dq1sLOz+6b7cGVK3+/y8vIwadKkMuch09HRAQA4OTnBw8MDt2/fRkFBAZ4+fVruizdKrrXTp0+LNDBLSUkB+PLGVnNzc4SHh+PatWvo3r07OnfuzL2xMjU1lWv0z8vLg7i4OG7duiVyrcrLy1f5e5d3XBQVFcu9Bj09PTFr1ixuOTc396dupPqaUCisdI5HQgghhJB/i1+ygcrCwgL79u3jpVlaWuLw4cPQ09ODhITobjEwMICMjAwuXbqE8ePH17juqvxnMjw8HH379sWqVau4yW1r27x580S+h6mpKdavXy8yCXlCQgIGDRoE4Euj0fHjxxESEsLr1VVcXIzffvsN58+fR8+ePUXqGzx4MNasWYMlS5ZUOcaNGzdCTEyMm5S3WbNmUFRURFJSEtcLpVmzZpCUlERUVBT3Zr+ioiLExMTwJjsvrWnTpqhXrx5iYmK4h7OcnBw8ePAAnTt3BgDcv38fWVlZWLlyJfewUjJp9LeysLCAhYUFPD09YWVlhf3794tM2v+jREVFoWPHjrz5lEr3nqkJS0tLHDx4EOrq6lBUVKzWtqqqqiKNFlWJVUNDA9ra2nj06FG5PT9qKiMjA//88w+0tbUBANevX4eYmBjX6y4qKgp//vkn17Pw6dOnvJcsmJmZ4dmzZ3jw4EGt9KJSU1Pj9b7Lzc3lesKUkJGRgYODAxwcHODq6gpjY2Pcu3cPlpaWZZZpaGgIQ0NDzJw5E05OTggMDOQaqD5//ozY2Fiu12FKSgrevXsHExOTb/4uNSUuLs71rPze2wmFQhQVFUEoFEJcXBxWVla4dOkS7z5z4cIFrkdPUVERioqKRBrsxcXFuYbQmt6Ha8LS0hJJSUllXlclGjVqBBsbGwQHB6OgoADdu3cv982SzZs3h5SUFDIyMiqcV9HGxgZhYWG4efMmfHx80KBBA5iYmMDHxwdaWlrctWBhYYHi4mK8evUKnTp1KrMsExMTkYbz0m+ws7KywpkzZ3hpXx+XskhJSXGNaj8zT09P9OrVCzo6Onj//j3279+P8PDw/8QbCgkhhBBCgF+0gcre3h6enp54+/Yt6tevDwBwdXWFv78/nJycMHfuXDRo0AAPHz5ESEgIdu7cCWlpaXh4eGDu3LmQlJSEtbU1Xr9+jcTERLi4uAAAnJ2d0bBhQ26Y35YtW6Cjo8MNFbxy5QrWrl3L+wv25s2bcfToUe6V52FhYejbty+mT5+OgQMH4sWLFwC+dOsvmSj96NGj8PT0xP3797lyHj58iLy8PLx48QIFBQWIi4sD8OUhQlJSEs+fP0e3bt2wZ88etGvXDpqammVOyKujo8P1WAK+vPHs+fPn3KSze/fuhYqKCoYMGSLySvTevXsjICCgzAYqAFi5ciXs7e3LXPf+/Xu8ePECRUVFSE9Px759+7Bz5074+vpyD1RiYmKws7PD1atXuUYrOTk5TJkyhXuTnI6ODlavXo38/HzuuJRFQUEBo0eP5rZTV1eHt7c3xMTEuO+lo6MDSUlJ/PHHH5g8eTISEhKwbNmycsusivT0dOzYsQO///47tLW1kZKSgtTU1O/SS66qDAwMsGfPHpw7dw5NmjTB3r17ERMTwzsPamLEiBFYs2YNHB0dsXTpUjRq1AhPnjzBkSNHMHfuXDRq1Ag3b96Es7MzLl26JNILo6axLlmyBO7u7lBSUkLPnj1RWFiI2NhYvH37ltdLorqkpaUxevRorF27Frm5uXB3d8eQIUO468jAwIB762Nubi7mzJnD67FhY2ODzp07Y+DAgVi3bh309fVx//59CASCcq+ZinTt2hVBQUFwcHCAsrIyFi1axOt5EhQUhOLiYrRv3x6ysrLYt28fZGRkuIbcrxUUFGDOnDkYNGgQmjRpgmfPniEmJgYDBw7k8tSrVw/Tpk3Dpk2bICEhATc3N3To0IFrsKrusayJV69e4ePHjygsLMTNmzexd+/eKjXaVHe74OBg1KtXD6amppCSkkJsbCw8PT0xdOhQrvfZ9OnTYWNjAz8/P/Tp0wchISGIjY3Fjh07AACKioqwsbHhzgNdXV1ERERgz549WLduHQDU+D5cEx4eHujQoQPc3Nwwfvx4yMnJISkpCRcuXOC9eXDEiBHw9vbGp0+fKnzznYKCAmbPno2ZM2dCKBTit99+Q05ODqKioqCoqIjRo0cD+PLykD/++ANqamrcb6GtrS02b96MwYMHc+UZGhpixIgRcHZ2hp+fHywsLPD69WtcunQJZmZm6NOnD9zd3WFtbY21a9fC0dER586dQ2hoKC+uyZMnY/PmzZg7dy7GjRuHy5cv46+//sLp06drvO9+Fq9evYKzszMyMzOhpKQEMzMznDt3Dt27d6/r0AghhBBCasUv2UBlamoKS0tL/PXXX5g0aRIAQFtbG1FRUfDw8ECPHj1QWFgIXV1d9OzZk/sL+MKFCyEhIYFFixbhn3/+gZaWFiZPnsyVm5GRwftruVAohKenJ9LT0yEhIYFmzZph1apVXJ0A8ObNG14PkN27dyM/Px++vr68+ay+fhtSTk4ON+9NifHjx/NeQW5hYQHgS4OInp4eioqKkJKSUq05TADgwIED6NGjB/dQu2vXLvTv31+kcQr4Mixl1KhRvF4jX+vatSu6du2K8+fPi6xbtGgRFi1aBElJSWhqaqJDhw64dOkSunTpIvI9J0yYgNWrV3P7euXKlRAKhRg1ahTev3+PNm3a4Ny5c1zjY3nWrVuHyZMno2/fvlBUVMTcuXPx9OlTbo4rNTU1BAUFYf78+di0aRMsLS2xdu1a/P7775XvuHLIysri/v372L17N7KysqClpQVXV1feOfGjTZo0CXfu3MHQoUMhEAjg5OSEqVOn4uzZs99UrqysLK5cuQIPDw8MGDAA79+/R8OGDdGtWzeuR1V+fj5SUlJ4Q9O+Ndbx48dDVlYWa9aswZw5cyAnJwdTU9MKe9RVhb6+PgYMGIDevXsjOzsbffv2xZ9//smtDwgIwMSJE2FpaYnGjRtjxYoVmD17Nq+Mw4cPY/bs2XBycsKHDx+gr6+PlStX1iiekntL3759oaSkhGXLlvF6UCkrK2PlypWYNWsWiouLYWpqipMnT0JFRUWkLHFxcWRlZcHZ2RkvX76EqqoqBgwYwOvxKCsrCw8PDwwfPhzPnz9Hp06deG/vrO6xrImS3moSEhJo3LgxJk2ahMWLF9f6dhISEli1ahUePHgAxhh0dXXh5uaGmTNncnk6duyI/fv3w8vLC/Pnz4eBgQGOHTuGli1bcnlCQkLg6enJvTVTV1cXPj4+vN+Nqih9H64JMzMzREREYMGCBejUqRMYY2jWrJnIEL5BgwbBzc0N4uLi3B8CyrNs2TKoqanB19cXjx49grKyMiwtLTF//nwuT6dOnSAUCnm9rGxtbbFx40Zu/qkSgYGBWL58Of73v//h+fPnUFVVRYcOHdC3b18AQIcOHeDv7w9vb28sWrQIdnZ28PLy4v3hoEmTJjh9+jRmzpyJjRs3olGjRti5c2e5fxz5NynvbbmEEEIIIf8VAlZ6EpNfxOnTpzFnzhwkJCSIDMEgX3z69AkGBgbYv38/bxLyusQYQ/v27bkhSLXpw4cPaNiwIfz8/CrsfUXIryYoKAgzZszAu3fv6jqUX87PeB/+r8vNzYWSkhK8rzyCtLxC5Rv8APMsVOs6BEIIIYSQKiv5/1ROTk61pnv5JXtQAUCfPn2QmpqK58+f/2smQ/3RMjIyMH/+/J/qoUggEGDHjh24d+/eN5d1584d3L9/H+3atUNOTg6WLl0KAHB0dPzmsgkhpDb8jPdhQgghhBBCvodftoEKwDcP+fmv09fXr3BC3brSqlUrtGrVqlbKWrt2LVJSUiApKYnWrVsjMjKSe1vgv0FwcHC5wwN1dXWRmJj4gyMihNSm0vfhyMhI9OrVq9z8JW/XI4QQQggh5N/mlx3iR8h/wfv370VeqV6iXr163zRnDSHk51NQUIDnz5+Xu/5n/KPCvw0N8SOEEEII+TY0xI+QX5CCggIUFH6OByhCyPcnIyNDjVCEEEIIIeQ/iWYHJ4QQQgghhBBCCCF1ihqoCCGEEEIIIYQQQkidogYqQgghhBBCCCGEEFKnaA4qQgghhJBSZpmrVGtST0IIIYQQ8m2oBxUhhBBCCCGEEEIIqVPUQEUIIYQQQgghhBBC6hQ1UBFCCCGEEEIIIYSQOkUNVIQQQgghhBBCCCGkTlEDFSGEEEIIIYQQQgipU9RARQghhBBCCCGEEELqlERdB0AIIYQQ8rNZF58FaflPdR0GAGCehWpdh0AIIYQQ8t1RDypCCCGEEEIIIYQQUqeogYoQQgghhBBCCCGE1ClqoCKEEEIIIYQQQgghdYoaqAghhBBCCCGEEEJInaIGKkIIIYQQQgghhBBSp6iBihBCCCGEEEIIIYTUKWqgIoQQQgghhBBCCCF1ihqo6lBWVhbU1dXx+PHjug7lP2/evHmYNm1aXYfB8+nTJ+jr6yM6OrquQ6kyPT09bNiwoa7DIF9ZvHgxWrVqxS2PGTMG/fr145ZtbW0xY8aM71b/t9QXHh4OgUCAd+/efZfYKqrn2LFj0NfXh7i4+HfdP+WpzrXUoUMHHD58+PsGVAU/6nhVl0AgwLFjx+o6jO9u69atMDMzg6KiIhQVFWFlZYWzZ8/WdViEEEIIIbWGGqjqkI+PDxwdHaGnp/fd6rh69Sqsra2hoqICGRkZGBsbY/369VXe/uHDh1BQUICysnKF+eLj4+Hk5ITGjRtDRkYGJiYm2LhxIy9PZmYmhg8fDkNDQ4iJiVXroTAoKAhmZmaQlpaGuro6XF1duXXh4eFwdHSElpYW5OTk0KpVKwQHB/O2nz17Nnbv3o1Hjx6VWb69vT3ExcURExMjsm7MmDEQCARYuXIlL/3YsWMQCAS8OAQCAQQCAcTExKCkpAQLCwvMnTsXmZmZIuVu27YNTZo0QceOHau8H+paTEwMJk6cWNdh/Ct8fT4IBALIyMigRYsW2LFjR63WM3v2bFy6dKlWy/wWR44cwbJly+o6DJ6OHTsiMzMTSkpKXNqkSZMwaNAgPH36tE7irc615OXlhXnz5kEoFH7nqCpW1n78GWVnZ2PEiBFQVFSEsrIyXFxckJeXV9dhfbNGjRph5cqVuHXrFmJjY9G1a1c4OjoiMTGxrkMjhBBCCKkV1EBVR/Lz8xEQEAAXF5fvWo+cnBzc3Nxw5coVJCcnw8vLC15eXlV6SC4qKoKTkxM6depUad5bt25BXV0d+/btQ2JiIhYsWABPT09s3ryZy1NYWAg1NTV4eXnB3Ny8yt9h3bp1WLBgAebNm4fExERcvHgR9vb23Pro6GiYmZnh8OHDuHv3LsaOHQtnZ2ecOnWKy6Oqqgp7e3ts3bpVpPyMjAxER0fDzc0Nu3btKjMGaWlprFq1Cm/fvq003pSUFPzzzz+IiYmBh4cHLl68iJYtW+LevXtcHsYYNm/e/M3H/9OnT9+0fXWpqalBVlb2h9b5b5eSkoLMzEwkJSVh0qRJmDJlSq02KMnLy0NFRaXWyvtWDRo0gIKCQl2HwSMpKQlNTU2uQTkvLw+vXr2Cvb09tLW1axzvt1x/1bmWevXqhffv39e4t0xt3SdK78ef1YgRI5CYmIgLFy7g1KlTuHLlyn+iYd3B4f+xd+dxOaz//8Bfd6V906JFKrQiKdmXQpSlY18SWbJLtkiIcCohS3bJekr2Y0vWSiUULVpUsoSTNSKlVPP7o98936a7Fefc+Lyfj8f9eJiZa655z9wzk3nf13WNHQYMGAB9fX0YGBjAy8sLsrKyuHXrlrBDI4QQQgj5IShBJSShoaGQkJBA586dOfNTUlLQv39/yMrKQk1NDePHj8fbt2/Z5eXl5Vi3bh309PQgISEBbW1teHl51bgdMzMz2Nvbo3Xr1tDV1cW4ceNgY2ODqKioOmNcvnw5jIyMMGrUqDrLTp48GVu2bIGlpSVatGiBcePGYdKkSTh16hRbRldXF1u2bIGjo2O9f4F///49li9fjkOHDmHs2LFo2bIl2rZtiz/++IMts3TpUqxZswZdu3ZFy5YtMXfuXNja2nK2DVT85z4kJERgG/v378egQYMwc+ZMHDlyBEVFRQJlrK2toa6uDh8fnzpjbtKkCdTV1WFgYIAxY8YgJiYGqqqqmDlzJlvm7t27yM7OxsCBAznr3r9/H71794aUlBSUlZUxbdo0zi///O5UXl5e0NTUhKGhIYCKJF27du0gKSkJCwsLtnVXYmIiAKCsrAxOTk5o3rw5pKSkYGhoKNDCjV/3hg0boKGhAWVlZcyePRtfv35ly1TulsQwDDw9PaGtrQ0JCQloamrCxcWFU/bPP/+Eo6MjZGVloaOjg7Nnz+LNmzcYPHgwZGVl0bZtW8THx9d5TIGKVnSKioq4dOkSjI2NISsrC1tbW07rtLi4OPTt2xcqKipQUFCApaUl7t27x6mHx+Nh9+7dGDRoEKSlpWFsbIzY2Fg8fPgQVlZWkJGRQdeuXZGdnc1Z78yZMzA3N4ekpCRatGiBVatWobS0tM64+edD8+bN4eLigubNm3NiCgsLQ/fu3aGoqAhlZWUMGjRIYNvPnz+Hvb09lJSUICMjAwsLC9y+fRuAYBe/ulTXHUpRUREHDhwAUJHMcHZ2hoaGBiQlJaGjo1Ov856vahe/4uJiuLm5oVmzZpCQkICenh4CAwM569y9excWFhaQlpZG165dkZGRwVle17Hn8XjYu3cvhg4dCmlpaejr6+Ps2bPs8spd0yIiItiEVO/evcHj8RAREQEAOHnyJFq3bg0JCQno6urCz8+PE4euri7WrFkDR0dHyMvLY9q0aex5ef78eRgaGkJaWhojRoxAYWEhDh48CF1dXTRu3BguLi4oKyvj1FXfa0lUVBQDBgyo9v5VHf45sXfvXjRv3hySkpIAgA8fPmDKlClQVVWFvLw8evfujaSkJABAZmYmeDweHjx4wKlr06ZNaNmypcBx5IuOjkaPHj0gJSWFZs2awcXFBZ8/fwYAbNu2DW3atGHL8u9Lu3btYudZW1tj+fLl7HRd33VWVhZ69uwJSUlJtGrVCleuXOHEm56ejrCwMOzduxedOnVC9+7dsXXrVoSEhOCff/6p1/H7FZSVlSEkJASfP39Gly5dhB0OIYQQQsgPQQkqIYmKikL79u058z58+IDevXvDzMwM8fHxCAsLw6tXrzgJInd3d6xduxYeHh5IS0tDcHAw1NTU2OVWVlaYOHFijdtNSEjAzZs3YWlpWWt8169fx/Hjx7F9+/Zv20EA+fn5UFJS+ub1AeDKlSsoLy/HixcvYGxsDC0tLYwaNQrPnj1r8LY7duyI58+fc8b8YhgG+/fvx7hx42BkZAQ9PT2cOHFCoD5RUVF4e3tj69ateP78eYP2QUpKCjNmzEBMTAxev34NoOL7NzAw4LTc+Pz5M2xsbNC4cWPExcXh+PHjuHr1KpydnTn1Xbt2DRkZGWzrgI8fP8LOzg4mJia4d+8e1qxZAzc3N8465eXl0NLSwvHjx5GWloYVK1Zg6dKlOHbsGKdceHg4srOzER4ejoMHD+LAgQNs4qKqkydPYtOmTdi9ezeysrLw999/w8TEhFNm06ZN6NatGxISEjBw4ECMHz8ejo6OGDduHO7du4eWLVvC0dERDMPU61gWFhZiw4YNOHz4MG7cuIGcnBy4urqyyz99+oQJEyYgOjoat27dgr6+PgYMGIBPnz5x6uEnGRITE2FkZISxY8di+vTpcHd3R3x8PBiG4Rz3qKgoODo6Yu7cuUhLS8Pu3btx4MABTnJ44sSJsLKyqjF2hmEQFhaGnJwcdOrUiZ3/+fNnLFiwAPHx8bh27RpEREQwdOhQtjtXQUEBLC0t8eLFC5w9exZJSUlYvHjxv9bdy9/fH2fPnsWxY8eQkZGBoKCg7+qG7OjoiCNHjsDf3x/p6enYvXs3ZGVlOWWWLVsGPz8/xMfHQ0xMDJMnT2aX1efYA8CqVaswatQoJCcnY8CAAXBwcEBeXp5APJUTYCdPnkRubi66du2Ku3fvYtSoURgzZgzu378PT09PeHh4CJz/GzZsgKmpKRISEuDh4QGg4rz09/dHSEgIwsLCEBERgaFDhyI0NBShoaE4fPgwdu/eXe29hR9HXddSx44d6/XDAt/Dhw9x8uRJnDp1ik1Ujxw5Eq9fv8bFixdx9+5dmJubo0+fPsjLy4OBgQEsLCwEukcHBQVh7Nix1W4jOzsbtra2GD58OJKTk3H06FFER0ez146lpSXS0tLw5s0bAEBkZCRUVFTYhODXr18RGxvLXjd1fdfl5eUYNmwYxMXFcfv2bezatUvgXhcbGwtFRUVYWFiw86ytrSEiIsImdX9l9+/fh6ysLCQkJDBjxgycPn0arVq1EnZYhBBCCCE/BkOEYvDgwczkyZM589asWcP069ePM+/Zs2cMACYjI4P5+PEjIyEhwQQEBNRY7/jx45klS5YIzG/atCkjLi7OiIiIMKtXr641trdv3zLNmjVjIiMjGYZhmP379zMKCgr13LMKMTExjJiYGHPp0qVql1taWjJz586tsx4fHx+mUaNGjKGhIRMWFsbExsYyffr0YQwNDZni4uJq1zl69CgjLi7OpKSkcObn5+czAJiIiAh23uXLlxlVVVXm69evDMMwzKZNmxhLS0vOehMmTGAGDx7MMAzDdO7cmf3eTp8+zVS+hMLDwxkAzPv37wViunjxIgOAuX37NsMwDDN37lymd+/enDJ79uxhGjduzBQUFLDzLly4wIiIiDAvX75kY1FTU+Ps+86dOxllZWWmqKiInRcQEMAAYBISEqo9RgzDMLNnz2aGDx/O2U8dHR2mtLSUnTdy5Ehm9OjR7LSOjg6zadMmhmEYxs/PjzEwMGBKSkqqrV9HR4cZN24cO52bm8sAYDw8PNh5sbGxDAAmNze3xjj59u/fzwBgHj58yM7bvn07o6amVuM6ZWVljJycHHPu3Dl2HgBm+fLlAjEEBgay844cOcJISkqy03369GG8vb05dR8+fJjR0NBgp5csWcKMHz+eneafDzIyMoyMjAwjJibGiIiIMH/++Wet+/nmzRsGAHP//n2GYRhm9+7djJycHPPu3btqy69cuZIxNTVlpyufrwwjeK0BYE6fPs2pQ0FBgdm/fz/DMAwzZ84cpnfv3kx5eXmtcdZnexkZGQwA5sqVK9Wuyz9GV69eZedduHCBAcCez/U59lW/04KCAgYAc/HiRc52+Nfm+/fvGQBMeHg4u87YsWOZvn37crazaNEiplWrVuy0jo4OM2TIEE6Z6s7L6dOnM9LS0synT5/YeTY2Nsz06dM5ddX3WmIYhjlz5gwjIiLClJWV1ViGb+XKlUyjRo2Y169fs/OioqIYeXl55suXL5yyLVu2ZHbv3s0wTMX9r2XLluwy/veXnp7OMIzgcXRycmKmTZvGqS8qKooRERFhioqKmPLyckZZWZk5fvw4wzAM065dO8bHx4dRV1dnGIZhoqOjmUaNGjGfP39mGKbu7/rSpUuMmJgY8+LFC3Y5/97KP6e9vLwYAwMDgWOiqqrK7Nixo9rj9eXLFyY/P5/98P/urrzxiPG59+an+PAVFxczWVlZTHx8PLNkyRJGRUWFSU1NrXa/CCGEEEKEhf/snZ+f36D1qAWVkBQVFbHdLviSkpIQHh4OWVlZ9mNkZASg4pfq9PR0FBcXo0+fPjXWe+jQoWq740RFRSE+Ph67du3C5s2bceTIkRrrmDp1KsaOHYuePXt+076lpKRg8ODBWLlyJfr16/dNdfCVl5fj69ev8Pf3h42NDTp37owjR44gKysL4eHhAuXDw8MxadIkBAQEoHXr1pxlUlJSACpaO/Dt27cPo0ePhpiYGADA3t4eMTExAl2s+Hx9fXHw4EGkp6c3aD+Y/99CiD92S3Xff3p6OkxNTSEjI8PO69atG8rLyzldnkxMTCAuLs5OZ2RksAPI83Xs2FEghu3bt6N9+/ZQVVWFrKws9uzZg5ycHE6Z1q1bQ1RUlJ3W0NBgW31VNXLkSBQVFaFFixaYOnUqTp8+LdDlrW3btuy/+S39KrcM4c+raRtVSUtLs92Nqovv1atXmDp1KvT19aGgoAB5eXkUFBQI7Gd94vry5Qs+fvwIoOLaXL16NefanDp1KnJzc9nzycfHB4cOHRKIOSoqComJiUhMTMTevXvh7e3NGQstKysL9vb2aNGiBeTl5dnWSvyYExMTYWZm9t2tEetr4sSJSExMhKGhIVxcXHD58uVvrisxMRGioqJ1ttis/H1oaGgA+L9zoj7HvmodMjIykJeXr/d5BVRcf926dePM69atG7Kysjhd8yq3zOGrel6qqalBV1eX01JMTU3tu64lKSkplJeXo7i4uF77o6OjA1VVVXY6KSkJBQUFUFZW5hzLx48fs/e7MWPG4MmTJ+yYRkFBQTA3N2f/DlWVlJSEAwcOcOqzsbFBeXk5Hj9+DB6Ph549eyIiIgIfPnxAWloaZs2aheLiYjx48ACRkZHo0KEDOxZXXd91eno6mjVrBk1NTTaGH9G9zcfHBwoKCuynWbNm313nv0VcXBx6enpo3749fHx8YGpqKtBdmxBCCCHkVyUm7AD+V6moqAgMuF1QUAA7Ozv4+voKlNfQ0KjxDXT10bx5cwAVD+GvXr2Cp6cn7O3tqy17/fp1nD17Fhs2bABQkVwpLy+HmJgY9uzZw+l+U1VaWhr69OmDadOmccYV+Vb8h9XKXRhUVVWhoqIikHSIjIyEnZ0dNm3aBEdHR4G6+N19+A9teXl5OH36NL5+/cpJGJSVlWHfvn3Vju3Vs2dP2NjYwN3dvdaulFXxE1r85IOKigpn0PSGqJzAqq+QkBC4urrCz88PXbp0gZycHNavXy/Q5aVRo0acaR6PV2NXsmbNmiEjIwNXr17FlStXMGvWLKxfvx6RkZFsPZXr4yfnqptX3+5q1cXHVOoeOGHCBLx79w5btmyBjo4OJCQk0KVLF4FBohsaV0FBAVatWoVhw4YJxFQ10VhV8+bN2bdgtm7dGrdv34aXlxc7JpmdnR10dHQQEBAATU1NlJeXo02bNmzM/MTqj1L1mAHgjDNmbm6Ox48f4+LFi7h69SpGjRoFa2vrGrun1aa+sf+IY9+Qc/d7VHf9VbftH30t5eXlQUZGpt7HtGqcBQUF0NDQYLvXVcY/P9XV1dG7d28EBwejc+fOCA4O5oydV1VBQQGmT5/OGS+LT1tbG0BFt/M9e/YgKioKZmZmkJeXZ5NWkZGRnOTl91xnfOrq6gKJwNLSUuTl5UFdXb3addzd3bFgwQJ2+uPHjz91kqqyhiQtCSGEEEJ+dpSgEhIzMzP89ddfnHnm5uY4efIkdHV12RY9lenr60NKSgrXrl3DlClTvnnbdf2HNjY2ltNi4MyZM/D19cXNmzfRtGnTGtdLTU1F7969MWHChFoHbm8IfouGjIwMaGlpAah4UHv79i10dHTYchERERg0aBB8fX1rfFtTSkoKGjVqxLasCgoKgpaWlsCA0ZcvX4afnx9Wr17NaU3Et3btWrRr144doLwuRUVF2LNnD3r27Mkmx8zMzLBz504wDMM+kBsbG+PAgQP4/Pkz+3AZExMDERGRWrdlaGiIv/76C8XFxZCQkABQMVh4ZTExMejatStmzZrFzquplVhDSElJwc7ODnZ2dpg9ezaMjIxw//59mJubf3fd3yImJgY7duzAgAEDAADPnj3jvGTgW5mbmyMjIwN6enrfXZeoqCg7EP+7d++QkZGBgIAA9m2Z0dHRnPJt27bF3r17kZeX90NaUamqqnIGls/KyuK0RAIAeXl5jB49GqNHj8aIESNga2v7Tds3MTFBeXk5IiMjYW1t/U3x/shjXxtjY2PExMRw5sXExMDAwKDa+8CPVte1lJKSAjMzs2+u39zcHC9fvoSYmFitY4o5ODhg8eLFsLe3x6NHjzBmzJha60xLS6v1u7G0tMS8efNw/PhxdqwpKysrXL16FTExMVi4cCGnvtq+a2NjYzx79gy5ubnsjxdV32DXpUsXfPjwAXfv3mXHebx+/TrKy8s5Y79VJiEhwd47f2bu7u7o378/tLW18enTJwQHByMiIgKXLl0SdmiEEEIIIT8EdfETEhsbG6SmpnJaUc2ePRt5eXmwt7dHXFwcsrOzcenSJUyaNAllZWWQlJSEm5sbFi9ejEOHDiE7Oxu3bt3ivBHL0dER7u7u7PT27dtx7tw5ZGVlISsrC4GBgdiwYQPGjRvHltm2bRun26CxsTHatGnDfpo2bQoRERG0adMGjRs3BgCcPn2a0+0jJSUFvXr1Qr9+/bBgwQK8fPkSL1++ZAfH5eN3dSooKMCbN2+QmJiItLQ0dnnVeg0MDDB48GDMnTsXN2/eREpKCiZMmAAjIyP06tULQEW3voEDB8LFxQXDhw9nt111gOSoqCj2bVMAEBgYiBEjRnD2tU2bNnBycsLbt28RFhZW7XdnYmICBwcH+Pv7V7v89evXePnyJbKyshASEoJu3brh7du3nFZavXr1QkFBAVJTU9l5Dg4OkJSUxIQJE5CSkoLw8HDMmTMH48eP5wyEX9XYsWNRXl6OadOmIT09HZcuXWJbv/GTX/r6+oiPj8elS5eQmZkJDw8PgSRWQx04cACBgYFISUnBo0eP8Ndff0FKSoqTOPyv6evr4/Dhw0hPT8ft27fh4ODwQ1ogrVixAocOHcKqVauQmpqK9PR0hISEcFoJuru7V9tyj38+PH36FMePH8fhw4cxePBgAEDjxo2hrKyMPXv24OHDh7h+/TqnJQdQ0e1UXV0dQ4YMQUxMDB49eoSTJ08iNjb2m/ald+/e2LZtGxISEhAfH48ZM2ZwWvts3LgRR44cwYMHD5CZmYnjx49DXV2dbWXTELq6upgwYQImT56Mv//+G48fP0ZERITA4Py1qc+x/xEWLlyIa9euYc2aNcjMzMTBgwexbds2ziD8/5b6XEtRUVHf1WXa2toaXbp0wZAhQ3D58mU8efIEN2/exLJlyzhv0hw2bBg+ffqEmTNnolevXpzudFW5ubnh5s2bcHZ2RmJiIrKysnDmzBnOCwbatm2Lxo0bIzg4mJOg+vvvv1FcXMzpVlnXd21tbQ0DAwNMmDABSUlJiIqKwrJlyzgxGRsbw9bWFlOnTsWdO3cQExMDZ2dnjBkzptZ9+RW8fv0ajo6OMDQ0RJ8+fRAXF4dLly6hb9++wg6NEEIIIeSHoASVkJiYmMDc3JzzoKapqYmYmBiUlZWhX79+MDExwbx586CoqAgRkYqvysPDAwsXLsSKFStgbGyM0aNHc7oz5OTkcFpHlJeXw93dHe3atYOFhQW2b98OX19frF69mi3z9u3bBremyc/P54yLdOLECbx58wZ//fUXNDQ02E+HDh0465mZmcHMzAx3795FcHAwzMzM2NYu1dULVIyr1alTJwwcOBCWlpZo1KgRwsLC2IfqgwcPorCwED4+PpxtV+0mEhISgqlTpwKoeK19UlIShg8fLrBvCgoK6NOnDyfxV9Xq1atr7K5jaGgITU1NtG/fHmvXroW1tTVSUlI43RSVlZUxdOhQzhuzpKWlcenSJeTl5aFDhw4YMWIE+vTpg23bttUYB1DR2uXcuXNITExEu3btsGzZMqxYsQLA/3WLmT59OoYNG4bRo0ejU6dOePfuHac11bdQVFREQEAAunXrhrZt2+Lq1as4d+4clJWVv6ve7xEYGIj379/D3Nwc48ePh4uLC5o0afLd9drY2OD8+fO4fPkyOnTogM6dO2PTpk2cBEJubq5At1Og4nzQ0NCAnp4e3NzcMH36dGzduhUAICIigpCQENy9exdt2rTB/PnzsX79es764uLiuHz5Mpo0aYIBAwbAxMQEa9eu/eZWPX5+fmjWrBl69OiBsWPHwtXVlR0DCADk5OSwbt06WFhYoEOHDnjy5AlCQ0PZe1BD7dy5EyNGjMCsWbNgZGSEqVOn4vPnz/Vevz7H/kfg349DQkLQpk0brFixAqtXr25QV95vVde19OLFC9y8eROTJk365m3weDyEhoaiZ8+emDRpEgwMDDBmzBg8ffqUkwCXk5ODnZ0dkpKS4ODgUGudbdu2RWRkJDIzM9GjRw+YmZlhxYoVnEQQj8dDjx49wOPx0L17d3Y9eXl5WFhYcLoi1vVdi4iI4PTp0ygqKkLHjh0xZcqUalvrBgUFwcjICH369MGAAQPQvXt37Nmz55uP3c8iMDAQT548QXFxMV6/fo2rV69ScooQQgghvxUeU3UwEvKfuXDhAhYtWoSUlJRvfvgj9XPx4kUsXLgQycnJ1XafFIbk5GT07dsX2dnZnMGUf4SgoCBMmjQJ+fn5P3wMI0LIf8vNzQ3v37//LZIsv4KPHz9CQUEBK288gqSsnLDDAQAsMVMRdgiEEEIIIfXG//9Ufn4+5OXl673ez/Gk/j9q4MCByMrKwosXL36ZAVl/VZ8/f8b+/ft/muQUUNGKwNfXF48fP+a8Qe5bHDp0CC1atEDTpk2RlJQENzc3jBo1ipJThPwGmjRpItD1kxBCCCGEkN8NtaAi5Dewbt067NixAy9fvoSGhgaGDBkCLy8vTtetn13//v0RFRVV7bKlS5di6dKl/3FEhPycWrdujadPn1a7bPfu3XV2zSO1oxZUhBBCCCHf51tbUFGCihDyU3jx4gX7druqlJSUfsgb7Aj5HTx9+hRfv36tdpmamhrk5H6OpMqvihJUhBBCCCHfh7r4EUJ+aU2bNhV2CIT8EoT5pkxCCCGEEEL+LTQyNyGEEEIIIYQQQggRKkpQEUIIIYQQQgghhBChogQVIYQQQgghhBBCCBEqGoOKEEIIIaSKBabKDRrUkxBCCCGEfB9qQUUIIYQQQgghhBBChIoSVIQQQgghhBBCCCFEqChBRQghhBBCCCGEEEKEihJUhBBCCCGEEEIIIUSoKEFFCCGEEEIIIYQQQoSKElSEEEIIIYQQQgghRKjEhB0AIYQQQsjPZmPSO0jKlgg7DADAEjMVYYdACCGEEPKvoxZUhBBCCCGEEEIIIUSoKEFFCCGEEEIIIYQQQoSKElSEEEIIIYQQQgghRKgoQUUIIYQQQgghhBBChIoSVIQQQgghhBBCCCFEqChBRQghhBBCCCGEEEKEihJUhBBCCCGEEEIIIUSoKEFFWO/evUOTJk3w5MkTYYfySwsLC0O7du1QXl4u7FA4xo8fD29vb2GHUW8TJ07EkCFDhB0GqYOuri42b978Q+v09PREu3btfmid9fHkyRPweDwkJiZ+Vz1WVlaYN2/eD4kpLS0NWlpa+Pz58w+p73v8jNfkgQMHoKioKOww/hM7d+5E27ZtIS8vD3l5eXTp0gUXL14UdliEEEIIIT8MJagIy8vLC4MHD4auru6/to3o6Gh069YNysrKkJKSgpGRETZt2lTrOhERERg8eDA0NDQgIyODdu3aISgoqM5tubi4oH379pCQkKjXwy7/4bS6z/Hjx9lycXFx6NOnDxQVFdG4cWPY2NggKSmJXW5ra4tGjRrVGKOPjw9ERUWxfv16gWUHDhwAj8eDra0tZ/6HDx/A4/EQERHBzqscn4yMDPT19TFx4kTcvXtXoN6kpCSEhobCxcWlzuPws9iyZQsOHDgg7DB+GTweD3///fe/Vn9NiYC4uDhMmzbtX9vu/7pWrVqhc+fO2Lhxo7BD+WWuyYiICJibm0NCQgJ6enq/RMz1oaWlhbVr1+Lu3buIj49H7969MXjwYKSmpgo7NEIIIYSQH4ISVAQAUFhYiMDAQDg5Of2r25GRkYGzszNu3LiB9PR0LF++HMuXL8eePXtqXOfmzZto27YtTp48ieTkZEyaNAmOjo44f/58ndubPHkyRo8eXa/YmjVrhtzcXM5n1apVkJWVRf/+/QEABQUFsLW1hba2Nm7fvo3o6GjIycnBxsYGX79+ZeuaOHEi/P39q93Ovn37sHjxYuzbt6/a5WJiYrh69SrCw8PrjHn//v3Izc1Famoqtm/fjoKCAnTq1AmHDh3ilNu6dStGjhwJWVnZeh2L6pSVlf2nrcIUFBT+Z1pG/MpUVVUhLS0t7DB+a5MmTcLOnTtRWlr6TeuXlJT8kDh+hWvy8ePHGDhwIHr16oXExETMmzcPU6ZMwaVLl4Qd2nezs7PDgAEDoK+vDwMDA3h5eUFWVha3bt0SdmiEEEIIIT8EJagIACA0NBQSEhLo3LkzZ35KSgr69+8PWVlZqKmpYfz48Xj79i27vLy8HOvWrYOenh4kJCSgra0NLy+vGrdjZmYGe3t7tG7dGrq6uhg3bhxsbGwQFRVV4zpLly7FmjVr0LVrV7Rs2RJz586Fra0tTp06Ves++fv7Y/bs2WjRokW9joGoqCjU1dU5n9OnT2PUqFFsYufBgwfIy8vD6tWrYWhoiNatW2PlypV49eoVnj59ytZlZ2eH+Ph4ZGdnc7YRGRmJoqIirF69Gh8/fsTNmzcF4pCRkcHkyZOxZMmSOmNWVFSEuro6dHV10a9fP5w4cQIODg5wdnbG+/fvAVQklk6cOAE7OzvOuu/fv4ejoyMaN24MaWlp9O/fH1lZWexyfouZs2fPolWrVpCQkEBOTg5yc3MxcOBASElJoXnz5ggODhbo5rVx40aYmJhARkYGzZo1w6xZs1BQUCBQ96VLl2BsbAxZWVnY2toiNzeXLVO1O9GJEydgYmICKSkpKCsrw9ramu32xC/r7e0NNTU1KCoqYvXq1SgtLcWiRYugpKQELS0t7N+/v85jCvxfa7pTp06hV69ekJaWhqmpKWJjY9ky7969g729PZo2bQppaWmYmJjgyJEjnHqsrKwwZ84czJs3D40bN4aamhoCAgLw+fNnTJo0CXJyctDT0xPoplPXdfct9u7dC2NjY0hKSsLIyAg7duyo9/5GRERg0qRJyM/PZ1vteXp6AhDs4sfj8bB3714MHToU0tLS0NfXx9mzZ9nl1bXE+vvvv8Hj8WqMPS4uDn379oWKigoUFBRgaWmJe/fufdc+A8CdO3dgZmYGSUlJWFhYICEhQaCOyMhIdOzYERISEtDQ0MCSJUs4iaLPnz/D0dERsrKy0NDQgJ+fn0AdxcXFcHV1RdOmTSEjI4NOnTpxWkM+ffoUdnZ2aNy4MWRkZNC6dWuEhoayy/v27Yu8vDxERkbWuc9AxXeyZs0aODo6Ql5enm3hFh0djR49ekBKSgrNmjWDi4sLew0tXboUnTp1EqjL1NQUq1evBiB4TZaXl8PHxwfNmzeHlJQUTE1NceLECXa5hYUFNmzYwE4PGTIEjRo1Yu8Fz58/B4/Hw8OHD+t1nICK80dbWxvS0tIYOnQo3r17x1m+a9cuNG/eHH5+fjA2NoazszNGjBhRZ0vdX01ZWRlCQkLw+fNndOnSRdjhEEIIIYT8EJSgIgCAqKgotG/fnjPvw4cP6N27N8zMzBAfH4+wsDC8evUKo0aNYsu4u7tj7dq18PDwQFpaGoKDg6GmpsYut7KywsSJE2vcbkJCAm7evAlLS8sGxZufnw8lJaUGrdNQd+/eRWJiIqdVmaGhIZSVlREYGIiSkhIUFRUhMDAQxsbGnK6R2traUFNTE0i8BQYGwt7eHo0aNYK9vT0CAwOr3banpyfu37/Pedirr/nz5+PTp0+4cuUKACA5ORn5+fmwsLDglJs4cSLi4+Nx9uxZxMbGgmEYDBgwgNMSrLCwEL6+vti7dy9SU1PRpEkTODo64p9//kFERAROnjyJPXv24PXr15y6RURE4O/vj9TUVBw8eBDXr1/H4sWLOWUKCwuxYcMGHD58GDdu3EBOTg5cXV2r3afc3FzY29tj8uTJSE9PR0REBIYNGwaGYdgy169fxz///IMbN25g48aNWLlyJQYNGoTGjRvj9u3bmDFjBqZPn47nz5/X+1guW7YMrq6uSExMhIGBAezt7dnkxJcvX9C+fXtcuHABKSkpmDZtGsaPH487d+5w6jh48CBUVFRw584dzJkzBzNnzsTIkSPRtWtX3Lt3D/369cP48eNRWFgIoH7XHb8raH0FBQVhxYoV8PLyQnp6Ory9veHh4YGDBw/Wa3+7du2KzZs3Q15enm1dWNN3BQCrVq3CqFGjkJycjAEDBsDBwQF5eXn1jreqT58+YcKECYiOjsatW7egr6+PAQMG4NOnT9+8zwUFBRg0aBBatWqFu3fvwtPTU2CfXrx4gQEDBqBDhw5ISkrCzp07ERgYiD///JMts2jRIkRGRuLMmTO4fPkyIiIiBJJnzs7OiI2NRUhICJKTkzFy5EjY2tqyCeHZs2ejuLgYN27cwP379+Hr68tp7SguLo527drVmsivasOGDTA1NUVCQgI8PDyQnZ0NW1tbDB8+HMnJyTh69Ciio6Ph7OwMAHBwcMCdO3c4SfXU1FQkJydj7Nix1W7Dx8cHhw4dwq5du5Camor58+dj3LhxbCLN0tKSTTAxDIOoqCgoKioiOjoaQEXyr2nTptDT06vXcbp9+zacnJzg7OyMxMRE9OrVi/NdAEBsbCysra0582xsbDjJ5V/Z/fv3ISsrCwkJCcyYMQOnT59Gq1athB0WIYQQQsiPwRDCMMzgwYOZyZMnc+atWbOG6devH2fes2fPGABMRkYG8/HjR0ZCQoIJCAiosd7x48czS5YsEZjftGlTRlxcnBEREWFWr17doFiPHj3KiIuLMykpKfUqv3LlSsbU1LRB22AYhpk5cyZjbGwsMP/+/ftMy5YtGREREUZERIQxNDRknjx5IlDOzMyM8fT0ZKfz8/MZKSkpJjExkWEYhklISGBkZWWZT58+sWX279/PKCgoMAzDMEuWLGEMDAyYr1+/Mu/fv2cAMOHh4WxZAMzp06cFtltUVMQAYHx9fRmGYZjTp08zoqKiTHl5OVsmMzOTAcDExMSw896+fctISUkxx44dY2MBwMbLMAyTnp7OAGDi4uLYeVlZWQwAZtOmTdUdRoZhGOb48eOMsrIyZz8BMA8fPmTnbd++nVFTU2OnJ0yYwAwePJhhGIa5e/cuA6Da48wvq6Ojw5SVlbHzDA0NmR49erDTpaWljIyMDHPkyJEa4+R7/PgxA4DZu3cvOy81NZUBwKSnp9e43sCBA5mFCxey05aWlkz37t0FYhg/fjw7Lzc3lwHAxMbGMgxT93XHMAxz6tQpxtDQkFOmpvOBYRimZcuWTHBwMGfemjVrmC5dutR7fyufm5Xp6OhwvnsAzPLly9npgoICBgBz8eLFGus5ffo0U/nPUV3XbFlZGSMnJ8ecO3euxjJ17fPu3bsZZWVlpqioiF2+c+dOBgCTkJDAMAzDLF26lDE0NORcO9u3b2dkZWWZsrIy5tOnT4y4uDh7zTAMw7x7946RkpJi5s6dyzAMwzx9+pQRFRVlXrx4wYmlT58+jLu7O8MwDGNiYsK5V1Rn6NChzMSJE2stw6ejo8MMGTKEM8/JyYmZNm0aZ15UVBQjIiLCHgNTU1PO/djd3Z3p1KkTO135mvzy5QsjLS3N3Lx5U2A79vb2DMMwzNmzZxkFBQWmtLSUSUxMZNTV1Zm5c+cybm5uDMMwzJQpU5ixY8cyDFO/42Rvb88MGDCAs3z06NGc80lfX5/x9vbmlLlw4QIDgCksLKz2eH358oXJz89nP/zrbeWNR4zPvTc/xYevuLiYycrKYuLj45klS5YwKioqTGpqarX7RQghhBAiLPn5+QwAJj8/v0HrUQsqAgAoKiqCpKQkZ15SUhLCw8MhKyvLfoyMjAAA2dnZSE9PR3FxMfr06VNjvYcOHYKPj4/A/KioKMTHx2PXrl3YvHmzQNeomoSHh2PSpEkICAhA69atG7CHDVNUVITg4GCBMbmKiorg5OSEbt264datW4iJiUGbNm0wcOBAFBUVccpKSUmxrWIA4MiRI2jZsiVMTU0BAO3atYOOjg6OHj1abQxubm548+ZNjWNV1YT5/62K+C1sioqKICEhwWlxk56eDjExMU6XHmVlZRgaGiI9PZ2dJy4ujrZt27LTGRkZEBMTg7m5OTtPT08PjRs35sRw9epV9OnTB02bNoWcnBzGjx+Pd+/ecY6HtLQ0WrZsyU5raGgItMTiMzU1RZ8+fWBiYoKRI0ciICCA7cLI17p1a4iI/N8tTU1NDSYmJuy0qKgolJWVa9xGdSrvu4aGBgCw65eVlWHNmjUwMTGBkpISZGVlcenSJeTk5NRYBz+GynHxWxzy663rugOAoUOH4sGDB/Xah8+fPyM7OxtOTk6cOv/880+BLqi17W9DVK5HRkYG8vLy31QP36tXrzB16lTo6+tDQUEB8vLyKCgoEDjWfPXZ5/T0dLRt25Zz36vaVSo9PR1dunThXDvdunVDQUEBnj9/juzsbJSUlHCuIyUlJRgaGrLT9+/fR1lZGQwMDDixREZGsrG4uLjgzz//RLdu3bBy5UokJycL7FPV+0ldqraYTEpKwoEDBzgx2NjYoLy8HI8fPwZQ0YoqODgYQMV95MiRI3BwcKi2/ocPH6KwsBB9+/bl1Hno0CF2v3r06IFPnz4hISEBkZGRsLS0hJWVFduqKjIyElZWVvU+Tunp6QLdEH9E9zYfHx8oKCiwn2bNmn13nf8WcXFx6OnpoX379vDx8YGpqSm2bNki7LAIIYQQQn4IMWEHQH4OKioqAg/8BQUFsLOzg6+vr0B5DQ0NPHr06Ju317x5cwCAiYkJXr16BU9PT9jb29e6TmRkJOzs7LBp0yY4Ojp+87br48SJEygsLBTYTnBwMJ48eYLY2Fg2GRIcHIzGjRvjzJkzGDNmDFs2Ly8Pqqqq7HRgYCBSU1MhJvZ/l115eTn27dtX7eD0ioqKcHd3x6pVqzBo0KB6x85PMPGPsYqKCgoLC1FSUgJxcfF61wNUPBQ3pCsZUDGe0aBBgzBz5kx4eXlBSUkJ0dHRcHJyQklJCTugdqNGjTjr8Xg8Tpe9ykRFRXHlyhXcvHkTly9fxtatW7Fs2TLcvn2b3c/q6qtuXkMGeq+8Pv848Ndfv349tmzZgs2bN7Pjbc2bN09gQOq64qpab13XXUPxx/sJCAgQeLgXFRWtMdaqcTVEbcddRERE4Huu3K20OhMmTMC7d++wZcsW6OjoQEJCAl26dKlx8O+G7PO/raCgAKKiorh7967Atvnd+KZMmQIbGxtcuHABly9fho+PD/z8/DBnzhy2bF5eHiehWxcZGRmBOKZPn17tmzy1tbUBAPb29nBzc8O9e/dQVFSEZ8+e1fiSCf4xvnDhApo2bcpZJiEhAaDiHmZqaoqIiAjExsaib9++6NmzJ0aPHo3MzExkZWWx3bvrc5zqQ11dHa9eveLMe/XqFeTl5SElJVXtOu7u7liwYAE7/fHjx586SVVZeXk5iouLhR0GIYQQQsgPQQkqAqBi8PK//vqLM8/c3BwnT56Erq4uJ6nCp6+vDykpKVy7dg1Tpkz55m3X5z/YERERGDRoEHx9ff+TV9oHBgbijz/+4CSYgIpxk0RERDhJG/505Qf5L1++IDs7G2ZmZgAqWgfEx8cjIiKCM3ZWXl4erKys8ODBA7aVTGVz5syBv79/g34h548VxB+HpV27dgCAtLQ09t/GxsYoLS3F7du30bVrVwAVg35nZGTUOp6JoaEhSktLkZCQwI5Z9vDhQ05y8+7duygvL4efnx+bxDt27Fi9468Jj8dDt27d0K1bN6xYsQI6Ojo4ffo058HyvxQTE4PBgwdj3LhxACrO48zMzO8eD6au666h1NTUoKmpiUePHtXYGqY+xMXFUVZW9t3xqKqq4tOnT/j8+TObRElMTKx1nZiYGOzYsQMDBgwAADx79qzWQePrs8/GxsY4fPgwvnz5wraiqvo2NGNjY5w8eRIMw7DXfExMDOTk5KClpQUlJSU0atQIt2/fZpM879+/R2ZmJpt4MTMzQ1lZGV6/fo0ePXrUGHOzZs0wY8YMzJgxA+7u7ggICOAkqFJSUjBixIhaj1NtzM3NkZaWxo73VB0tLS1YWloiKCgIRUVF6Nu3L5o0aVJt2covTqhtDEFLS0uEh4fjzp07bMLa2NgYXl5e0NDQgIGBAYD6HSdjY2Pcvn2bM6/qd9alSxfOAPMAcOXKlVpbWklISLBJtZ+Zu7s7+vfvD21tbXz69AnBwcGIiIj4Ld5QSAghhBAC0CDp5P+zsbFBamoqJ9Ewe/Zs5OXlwd7eHnFxccjOzsalS5cwadIklJWVQVJSEm5ubli8eDHbrePWrVucgb8dHR3h7u7OTm/fvh3nzp1DVlYWsrKyEBgYiA0bNrAP+QCwbds2TrfB8PBwDBw4EC4uLhg+fDhevnyJly9fcgZdPn36tECC5+HDh0hMTMTLly9RVFSExMREJCYmsq0uXrx4ASMjI4FBrR8+fIgbN25Um3Tr27cv3r9/j9mzZyM9PR2pqamYNGkSxMTE0KtXL7bcrVu32FYeQEXCq2PHjujZsyfatGnDfnr27IkOHTrUOFi6pKQkVq1aBX9//2qXf/jwAS9fvsTTp09x5coVjBgxAsHBwdi5cyf7pjRVVVWYm5uzAxMDFcnFwYMHY+rUqYiOjkZSUhLGjRuHpk2bYvDgwdVuCwCMjIxgbW2NadOm4c6dO0hISMC0adM4La309PTw9etXbN26FY8ePcLhw4exa9euGuusj9u3b8Pb2xvx8fHIycnBqVOn8ObNGxgbG39Xvd9DX1+fbdWVnp6O6dOnC7Tc+BZ1XXdA9ec7ADx+/Jg9z/mfz58/Y9WqVfDx8YG/vz8yMzNx//597N+/Hxs3bqx3XLq6uigoKMC1a9fw9u3bBnU3q6xTp06QlpbG0qVLkZ2djeDgYBw4cKDWdfT19XH48GGkp6fj9u3bcHBwqLE1DF9d+zx27FjweDxMnToVaWlpCA0N5bxxDgBmzZqFZ8+eYc6cOXjw4AHOnDmDlStXYsGCBRAREYGsrCycnJywaNEiXL9+HSkpKZg4cSKnq6mBgQEcHBzg6OiIU6dO4fHjx7hz5w58fHxw4cIFAMC8efNw6dIlPH78GPfu3UN4eDjn3H7y5AlevHghMPh3Q7i5ueHmzZvsAONZWVk4c+YMO0g6n4ODA0JCQnD8+PFaE5pycnJwdXXF/PnzcfDgQWRnZ+PevXvYunUrZ/B9KysrXLp0CWJiYuw5a2VlhaCgIE5iqz7HycXFBWFhYdiwYQOysrKwbds2hIWFceKaMWMGHj16hMWLF+PBgwfYsWMHjh07hvnz53/zsftZvH79Go6OjjA0NESfPn0QFxeHS5cuoW/fvsIOjRBCCCHkh6AEFQFQ0dXO3Nyc09JFU1MTMTExKCsrQ79+/WBiYoJ58+ZBUVGRfQDz8PDAwoULsWLFChgbG2P06NGcsWZycnKQm5vLTpeXl8Pd3R3t2rWDhYUFtm/fDl9fX/Y15gDw9u1bztg4Bw8eRGFhIXx8fKChocF+hg0bxpbJz89HRkYGZ5+mTJkCMzMz7N69G5mZmTAzM4OZmRn++ecfABXdijIyMgQetPft2wctLS3069dP4DgZGRnh3LlzSE5ORpcuXdCjRw/8888/CAsL43S/4o/dIi0tjZKSEvz1118YPnx4tcd++PDhOHToUI3dnCZMmIAWLVpUu2zSpEnQ0NCAkZERZs6cCVlZWdy5c0fgrVtTpkxBUFAQZ97+/fvRvn17DBo0CF26dAHDMAgNDRXonlXVoUOHoKamhp49e2Lo0KGYOnUq5OTk2FYopqam2LhxI3x9fdGmTRsEBQVVOw5ZQ8jLy+PGjRsYMGAADAwMsHz5cvj5+aF///7fVe/3WL58OczNzWFjYwMrKyuoq6tjyJAh311vfa676s53AFiwYAF7nvM/CQkJmDJlCvbu3Yv9+/fDxMQElpaWOHDgANs9sj66du2KGTNmYPTo0VBVVcW6deu+af+UlJTw119/ITQ0FCYmJjhy5Ag8PT1rXScwMBDv37+Hubk5xo8fDxcXlxpb9vDVtc+ysrI4d+4c7t+/DzMzMyxbtkygW2XTpk0RGhqKO3fuwNTUFDNmzICTkxOWL1/Ollm/fj169OgBOzs7WFtbo3v37gJvRN2/fz8cHR2xcOFCGBoaYsiQIYiLi2NbXZWVlWH27NkwNjaGra0tDAwMsGPHDnb9I0eOoF+/ftDR0anz+Nakbdu2iIyMRGZmJnr06AEzMzOsWLECmpqanHIjRoxgx4ur63xes2YNPDw84OPjw8Z+4cIFznnVo0cPlJeXc5JRVlZWKCsrY8ef4qvrOHXu3BkBAQHYsmULTE1NcfnyZc53AVR0bb5w4QKuXLkCU1NT+Pn5Ye/evbCxsfmGo/ZzCQwMxJMnT1BcXIzXr1/j6tWrlJwihBBCyG+Fx9Q06Av5n3PhwgUsWrQIKSkpnBYApGHevn0LQ0NDxMfHNygB8G8qKiqCoaEhjh49+kMGFa7s+fPnaNasGTswOiHkxykpKYG+vj6Cg4PRrVs3YYfzP+Hjx49QUFDAyhuPICkrJ+xwAABLzFSEHQIhhBBCSL3x/z+Vn58PeXn5eq9HY1AR1sCBA5GVlYUXL178MgPE/oyePHmCHTt2/DTJKaBisPNDhw7VOm5PfV2/fh0FBQUwMTFBbm4uFi9eDF1dXfTs2fMHREoIqSwnJwdLly6l5BQhhBBCCPntUQsqQkiDXLp0CQsXLsSjR48gJyeHrl27YvPmzd/V/ei/5u3tDW9v72qX9ejRAxcvXvyPIyKkfqKiomrt2sp/ux75dtSCihBCCCHk+3xrCypKUBFC/ufk5eVxBtmvTEpKCk2bNv2PIyKkfoqKivDixYsal9f2lj5SP5SgIoQQQgj5PtTFjxBC6klJSQlKSkrCDoOQBpOSkqIkFCGEEEII+S3RSNiEEEIIIYQQQgghRKgoQUUIIYQQQgghhBBChIoSVIQQQgghhBBCCCFEqGgMKkIIIYSQKhaYKjdoUE9CCCGEEPJ9qAUVIYQQQgghhBBCCBEqSlARQgghhBBCCCGEEKGiBBUhhBBCCCGEEEIIESpKUBFCCCGEEEIIIYQQoaIEFSGEEEIIIYQQQggRKkpQEUIIIYQQQgghhBChEhN2AIQQQgghP5uNSe8gKVsi7DD+5ywxUxF2CIQQQggREmpBRQghhBBCCCGEEEKEihJUhBBCCCGEEEIIIUSoKEFFCCGEEEIIIYQQQoSKElSEEEIIIYQQQgghRKgoQUUIIYQQQgghhBBChIoSVIQQQgghhBBCCCFEqChBRQghhBBCCCGEEEKEihJUv5h3796hSZMmePLkibBD+Wm9ffsWTZo0wfPnz4UdCkdgYCD69esn7DDq7cCBA1BUVBR2GKQKHo+Hv//+GwDw5MkT8Hg8JCYmAgAiIiLA4/Hw4cMHocVXm++JT1dXF5s3b2anazsOPzMrKyvMmzev3uV/pvuZp6cn2rVrJ+wwOH72c558m7KyMnh4eKB58+aQkpJCy5YtsWbNGjAMI+zQCCGEEPIvogTVL8bLywuDBw+Grq7uv7aN6OhodOvWDcrKypCSkoKRkRE2bdpU6zpfvnzBxIkTYWJiAjExMQwZMqRe2/Ly8kLXrl0hLS1d72SIp6cnjIyMICMjg8aNG8Pa2hq3b99ml6uoqMDR0RErV66sdv3Y2FiIiopi4MCBAsv4D7pNmjTBp0+fOMvatWsHT09PdtrKygo8Hg88Hg8SEhJo2rQp7OzscOrUKYF6v3z5Ag8Pjxpj+hmNHj0amZmZwg7jl1H5fODxeFBTU8PIkSPx9OnTH7qd3Nxc9O/f/4fW+TOpKTEaFxeHadOmVbtOs2bNkJubizZt2vzweCZOnFjv+1l9nDp1CmvWrKl3+bruZ/8lV1dXXLt2Tdhh1Ck5ORk9evSApKQkmjVrhnXr1gk7JNJAvr6+2LlzJ7Zt24b09HT4+vpi3bp12Lp1q7BDI4QQQsi/iBJUv5DCwkIEBgbCycnpX92OjIwMnJ2dcePGDaSnp2P58uVYvnw59uzZU+M6ZWVlkJKSgouLC6ytreu9rZKSEowcORIzZ86s9zoGBgbYtm0b7t+/j+joaOjq6qJfv3548+YNW2bSpEkICgpCXl6ewPqBgYGYM2cObty4gX/++afabXz69AkbNmyoM5apU6ciNzcX2dnZOHnyJFq1aoUxY8YIPEifOHEC8vLy6NatW733szpfv379rvUbQkpKCk2aNPnPtvc74J8P//zzD86cOYNnz55h3LhxP3Qb6urqkJCQ+KF1/gpUVVUhLS1d7TJRUVGoq6tDTEzsP47q/9T32lRSUoKcnFyD6q7tflYfJSUl37ReVbKyslBWVv4hdf1bPn78iH79+kFHRwd3797F+vXr4enpWevfL/LzuXnzJgYPHoyBAwdCV1cXI0aMQL9+/XDnzh1hh0YIIYSQfxElqH4hoaGhkJCQQOfOnTnzU1JS0L9/f8jKykJNTQ3jx4/H27dv2eXl5eVYt24d9PT0ICEhAW1tbXh5edW4HTMzM9jb26N169bQ1dXFuHHjYGNjg6ioqBrXkZGRwc6dOzF16lSoq6vXe59WrVqF+fPnw8TEpN7rjB07FtbW1mjRogVat26NjRs34uPHj0hOTmbLtG7dGpqamjh9+jRn3YKCAhw9ehQzZ87EwIEDceDAgWq3MWfOHGzcuBGvX7+uNRZpaWmoq6tDS0sLnTt3hq+vL3bv3o2AgABcvXqVLRcSEgI7OzvOuuXl5Vi9ejW0tLQgISGBdu3aISwsjF3Ob8119OhRWFpaQlJSEkFBQSgtLYWLiwsUFRWhrKwMNzc3TJgwgdPKIywsDN27d2fLDBo0CNnZ2QJ1nzp1Cr169YK0tDRMTU0RGxvLlqnakiUpKQm9evWCnJwc5OXl0b59e8THx3PKnj9/HoaGhpCWlsaIESNQWFiIgwcPQldXF40bN4aLiwvKyspqPaZ8urq68Pb2xuTJkyEnJwdtbW2Bh0w3NzcYGBhAWloaLVq0gIeHBydRwO+StG/fPmhra0NWVhazZs1CWVkZ1q1bB3V1dTRp0kTgevjw4QOmTJkCVVVVyMvLo3fv3khKSqozZv75oKGhgc6dO8PZ2Rn37t1jl5eVlcHJyYnttmJoaIgtW7YI1LNv3z60bt0aEhIS0NDQgLOzM7uscte2ulTXJWvz5s2cFpgRERHo2LEjZGRkoKioiG7dutXY6qukpATOzs7Q0NCApKQkdHR04OPjA6D6bnYfPnwAj8dDREQEp56YmBi0bdsWkpKS6Ny5M1JSUthYJk2ahPz8fLYlGr/VYtUufpVV3fb79+/h4OAAVVVVSElJQV9fH/v376/xOJ04cQImJiaQkpKCsrIyrK2t8fnzZ3h6euLgwYM4c+YMG09ERESN1+a7d+9gb2+Ppk2bQlpaGiYmJjhy5AhnW1W7+NXnPK/pflYTKysrODs7Y968eVBRUYGNjQ2A2v9W7NmzB5qamigvL+fUNXjwYEyePBlA9efT3r17YWxsDElJSRgZGWHHjh3sshEjRnDO3Xnz5oHH4+HBgwcAKs4nGRkZ9l5ZXl4OHx8f9vowNTXFiRMnONsLDQ2FgYEBpKSk0KtXL4Hu7kFBQSgpKWGvoTFjxsDFxQUbN26s17EjP4euXbvi2rVrbCvepKQkREdH/9atRwkhhBBCCapfSlRUFNq3b8+Z9+HDB/Tu3RtmZmaIj49HWFgYXr16hVGjRrFl3N3dsXbtWnh4eCAtLQ3BwcFQU1Njl1tZWWHixIk1bjchIQE3b96EpaXlD9+n71VSUoI9e/ZAQUEBpqamnGUdO3YUSKodO3YMRkZGMDQ0xLhx47Bv375qx7Swt7eHnp4eVq9e3eCYJkyYgMaNG3O6+kVHR8PCwoJTbsuWLfDz88OGDRuQnJwMGxsb/PHHH8jKyuKUW7JkCebOnYv09HTY2NjA19cXQUFB2L9/P2JiYvDx40eBhMXnz5+xYMECxMfH49q1axAREcHQoUMFHj6XLVsGV1dXJCYmwsDAAPb29igtLa12vxwcHKClpYW4uDjcvXsXS5YsQaNGjdjlhYWF8Pf3R0hICMLCwhAREYGhQ4ciNDQUoaGhOHz4MHbv3i3wwFkbPz8/WFhYICEhAbNmzcLMmTORkZHBLpeTk8OBAweQlpaGLVu2ICAgQKA7anZ2Ni5evIiwsDAcOXIEgYGBGDhwIJ4/f47IyEj4+vpi+fLlnG6iI0eOxOvXr3Hx4kXcvXsX5ubm6NOnD9uChZ+gqJp4qSwvLw/Hjh1Dp06d2Hnl5eXQ0tLC8ePHkZaWhhUrVmDp0qU4duwYW2bnzp2YPXs2pk2bhvv37+Ps2bPQ09Or9zFriNLSUgwZMgSWlpZITk5GbGwspk2bBh6PV215f39/nD17FseOHUNGRgaCgoK+qbvxokWL4Ofnh7i4OKiqqsLOzg5fv35F165dsXnzZsjLyyM3Nxe5ublwdXVtcP38e93FixeRnp6OnTt3QkVFpdqyubm5sLe3x+TJk5Geno6IiAgMGzYMDMPA1dUVo0aNgq2tLRtP165d2XWrXptfvnxB+/btceHCBaSkpGDatGkYP358na0+6jrPgervZ7U5ePAgxMXFERMTg127dtX5t2LkyJF49+4dwsPD2Try8vIQFhYGBweHarcRFBSEFStWwMvLC+np6fD29oaHhwcOHjwIALC0tORcI5GRkVBRUWHnxcXFsd87APj4+ODQoUPYtWsXUlNTMX/+fIwbNw6RkZEAgGfPnmHYsGGws7NDYmIipkyZgiVLlnBiio2NRc+ePSEuLs7Os7GxQUZGBt6/f1/v40eEa8mSJRgzZgyMjIzQqFEjmJmZYd68eTWei4QQQgj5PQivPwRpsKdPn0JTU5Mzb9u2bTAzM4O3tzc7b9++fWjWrBkyMzOhoaGBLVu2YNu2bZgwYQIAoGXLlujevTtbXltbGxoaGgLb09LSwps3b1BaWgpPT09MmTLlX9qzhjt//jzGjBmDwsJCaGho4MqVKwIPoJqamkhISODMCwwMZLtc2draIj8/H5GRkbCysuKU4/F4WLt2Lezs7DB//ny0bNmy3rGJiIjAwMCA/WX/w4cPyM/PF/juNmzYADc3N4wZMwZAxZgb4eHh2Lx5M7Zv386WmzdvHoYNG8ZOb926Fe7u7hg6dCiAinMgNDSUU/fw4cM50/v27YOqqirS0tI44/S4urqyY3GtWrUKrVu3xsOHD2FkZCSwXzk5OVi0aBG7TF9fn7P869ev2LlzJ3usRowYgcOHD+PVq1eQlZVFq1at0KtXL4SHh2P06NF1HMUKAwYMwKxZswBUtJbatGkTwsPDYWhoCABYvnw5W1ZXVxeurq4ICQnB4sWL2fnl5eXYt28f5OTk2BgyMjIQGhoKERERGBoasse+U6dOiI6Oxp07d/D69Wu2K92GDRvw999/48SJE5g2bRoaNWrEthSrbMeOHdi7dy8YhkFhYSEMDAxw6dIldnmjRo2watUqdrp58+aIjY3FsWPH2ETBn3/+iYULF2Lu3LlsuQ4dOtTreDXUx48fkZ+fj0GDBrHfm7GxcY3lc3JyoK+vj+7du4PH40FHR+ebtrty5Ur07dsXQEUiRUtLC6dPn8aoUaOgoKAAHo/XoJaY1cVpZmbGJoVrS6Ll5uaitLQUw4YNY/encotOKSkpFBcXVxtP1WsTACehNmfOHFy6dAnHjh1Dx44da4yhrvMcqP5+Vht9fX3O2Et//vlnrX8rDAwM0L9/fwQHB6NPnz4AKlqWqaiooFevXtVuY+XKlfDz82OPQfPmzZGWlobdu3djwoQJsLKywty5c/HmzRuIiYkhLS0NHh4eiIiIwIwZMxAREYEOHTpAWloaxcXF8Pb2xtWrV9GlSxcAQIsWLRAdHY3du3fD0tKSvb/4+fkBAAwNDXH//n34+vqyMb18+RLNmzfnxMn/Qebly5do3LixwH4UFxejuLiYnf748WO9jzP5dxw7dgxBQUEIDg5G69atkZiYiHnz5kFTU5P9vwwhhBBCfj+UoPqFFBUVQVJSkjMvKSkJ4eHhkJWVFSifnZ2NDx8+oLi4mH3gqM6hQ4eqnR8VFYWCggLcunULS5YsgZ6eHuzt7b9vJ36QXr16ITExEW/fvkVAQABGjRqF27dvc8ZMkpKSQmFhITudkZGBO3fusN1kxMTEMHr0aAQGBgokqICKX927d+8ODw8PBAcHNyg+hmHYVihFRUUAwPnuPn78iH/++UdgTKpu3boJdCWr3PIqPz8fr1694jzsioqKon379pzWUVlZWVixYgVu376Nt2/fsstycnI4Caq2bduy/+YnKV+/fl1tgmrBggWYMmUKDh8+DGtra4wcOZKTuJOWluZMq6mpQVdXl3Nuqqmp1dltsrLK8fGTFpXXP3r0KPz9/ZGdnY2CggKUlpZCXl6eU4euri5nzB81NTWIiopCRESEM49fb1JSEgoKCgTG2ikqKmK7STZt2pTtplSZg4MDli1bBgB49eoVvL290a9fP9y9e5eNYfv27di3bx9ycnJQVFSEkpISttvU69ev8c8//9R6vf5ISkpKmDhxImxsbNC3b19YW1tj1KhR1SasgYoBw/v27QtDQ0PY2tpi0KBB3/RmSn4Cgh+DoaEh0tPTv3k/qpo5cyaGDx+Oe/fuoV+/fhgyZAin5VNlpqam6NOnD0xMTGBjY4N+/fphxIgR1SYyqqraKrKsrAze3t44duwYXrx4gZKSEhQXF9c4dhZfXec5IHg/q0vV1rZ1/a0wMDCAg4MDpk6dih07dkBCQgJBQUEYM2YM51rh+/z5M7Kzs+Hk5ISpU6ey80tLS6GgoAAAaNOmDZSUlBAZGQlxcXGYmZlh0KBBbAK+8o8DDx8+RGFhIZu45CspKYGZmRkAID09ndMiEeCeS9/Kx8eHkzgmwrdo0SK2FRVQkTR++vQpfHx8KEFFCCGE/Maoi98vREVFRaCLQkFBAdvdofInKysLPXv2hJSU1Ddvr3nz5jAxMcHUqVMxf/58zhvshE1GRgZ6enro3LkzAgMDISYmhsDAQE6ZvLw8qKqqstOBgYEoLS2FpqYmxMTEICYmhp07d+LkyZPIz8+vdjtr167F0aNHG9RyoaysDFlZWeyv+MrKyuDxeN/cvURGRqbB69jZ2SEvLw8BAQG4ffs2232t6mDJlbvo8RNqVbsB8nl6eiI1NRUDBw7E9evX0apVK86YOJXr4tdX3bya6q9ObevHxsbCwcEBAwYMwPnz55GQkIBly5bVuo/1iaugoAAaGhoC11RGRgYWLVpUa7wKCgrQ09ODnp4eunXrhsDAQGRlZeHo0aMAKsYic3V1hZOTEy5fvozExERMmjSJjfl7rtfqiIiICHRhrTqY9/79+xEbG4uuXbvi6NGjMDAwwK1bt6qtz9zcHI8fP8aaNWtQVFSEUaNGYcSIEey2AHC2918O6l9Z//798fTpU8yfP59N+NXUVVBUVBRXrlzBxYsX0apVK2zduhWGhoZ4/Phxndupem2uX78eW7ZsgZubG8LDw5GYmAgbG5s6Bymvz3VS9X7W0Njq+lsBVNw3GIbBhQsX8OzZM0RFRdXYpaqgoAAAEBAQwKkvJSWFPX94PB569uyJiIgINhnVtm1bFBcXIyUlhdN1nF/fhQsXOPWlpaU1qFuwuro6Xr16xZnHn66pVZ67uzvy8/PZz7Nnz+q9PfLvKCwsFEiMioqKNujvByGEEEJ+PZSg+oWYmZkhLS2NM8/c3BypqanQ1dVlH4z5HxkZGejr60NKSuq7Xw1eXl7O6QLxs6kuvpSUFPaX99LSUhw6dAh+fn6ch5+kpCRoamoKDGTM17FjRwwbNkxgnJPaHDx4EO/fv2e72YmLi6NVq1ac705eXh6ampqIiYnhrBsTE4NWrVrVWLeCggLU1NQQFxfHzisrK+MMxP3u3TtkZGRg+fLl6NOnD4yNjX/Y2CsGBgaYP38+Ll++jGHDhtU68PS/7ebNm9DR0cGyZctgYWEBfX39Ggf3bghzc3O8fPkSYmJiAtdUTeMY1URUVBTA/7Wii4mJQdeuXTFr1iyYmZlBT0+PM3i9nJwcdHV1v/t65VNVVcXLly85SaPKg5jzmZmZwd3dHTdv3kSbNm1qbTEoLy+P0aNHIyAgAEePHsXJkyc5yZPc3NxatwWAkwB7//49MjMz2a6F4uLi9R5IvzaqqqqYMGEC/vrrL2zevLnWt7jxeDx069YNq1atQkJCAsTFxdnka0PiiYmJweDBgzFu3DiYmpqiRYsW7CDP36vy/exb1PW3Aqho5Tls2DAEBQXhyJEjMDQ0hLm5ebX1qampQVNTE48ePRKor3IXO/44VBEREbCysoKIiAh69uyJ9evXo7i4mG1F2qpVK0hISCAnJ0egvmbNmgGo6H5adTyvqsnULl264MaNG5zk6JUrV2BoaFhjqzgJCQnIy8tzPkS47Ozs4OXlhQsXLuDJkyc4ffo0Nm7cyHZtJ4QQQsjviRJUvxAbGxukpqZykg2zZ89GXl4e7O3tERcXh+zsbFy6dAmTJk1CWVkZJCUl4ebmhsWLF+PQoUPIzs7GrVu3OK2NHB0d4e7uzk5v374d586dQ1ZWFrKyshAYGIgNGzawYzcBFeMeVe2GlJaWhsTEROTl5SE/P59NAvHduXMHRkZGePHiBTsvJycHiYmJyMnJQVlZGbsO/9d0ADAyMmIfFj9//oylS5fi1q1bePr0Ke7evYvJkyfjxYsXGDlyJLtOYWEh7t69y3Y/On/+PN6/fw8nJye0adOG8xk+fLhA66vKvLy8cP36dYFBi/nbefnyJZ4/f45bt27Bzc0NM2bMwMyZMznjttjY2CA6Opqz7qJFi+Dr64ujR48iIyMDS5YsQWJiImfsoerMmTMHPj4+OHPmDDIyMjB37ly8f/+ebQHVuHFjKCsrY8+ePXj48CGuX7+OBQsW1FpnXYqKiuDs7IyIiAg8ffoUMTExiIuLq3W8on+bvr4+cnJyEBISguzsbPj7+9f7LWe1sba2RpcuXTBkyBBcvnwZT548wc2bN7Fs2TL2rYUvXryAkZGRwMMy/3x4+fIlkpKSMHPmTEhKSrLnob6+PuLj43Hp0iVkZmbCw8ODk2wEKlqq+fn5wd/fH1lZWbh37x62bt36TftiZWWFN2/eYN26dcjOzsb27dtx8eJFdvnjx4/h7u6O2NhYPH36FJcvX0ZWVlaN3+vGjRtx5MgRPHjwAJmZmTh+/DjU1dWhqKgIKSkpdO7cGWvXrkV6ejoiIyM5Y4RVtnr1aly7dg0pKSmYOHEiVFRU2LdQ6urqoqCgANeuXcPbt28b1K2Nb8WKFThz5gwePnyI1NRUnD9/vsZ9un37Nry9vREfH4+cnBycOnUKb968Ycvr6uoiOTkZGRkZePv2ba2twvT19XHlyhXcvHkT6enpmD59ukBrnm9R9X72Ler6W8Hn4OCACxcuYN++fXUOSL1q1Sr4+PjA398fmZmZuH//Pvbv3895Y56VlRXS0tKQmprKjn1oZWWFoKAgWFhYsMkxOTk5uLq6Yv78+Th48CCys7PZc58/6PqMGTOQlZWFRYsWISMjA8HBwQJvYh07dizExcXh5OSE1NRUHD16FFu2bPnueyD5b23duhUjRozArFmzYGxsDFdXV0yfPh1r1qwRdmiEEEII+RfRGFS/EBMTE5ibm+PYsWOYPn06ALCtcNzc3NCvXz8UFxdDR0cHtra2bPN4Dw8PiImJYcWKFfjnn3+goaGBGTNmsPXm5ORwmtKXl5fD3d0djx8/hpiYGFq2bAlfX192mwDw9u1bTssPoGKg38otWPi/9vNbbxQWFiIjI4PzgLdixQr24aPyOuHh4ezYJBkZGWwXPFFRUTx48AAHDx7E27dvoaysjA4dOiAqKgqtW7dm6zlz5gy0tbXRo0cPABXd+6ytrdmxUSobPnw41q1bh+Tk5Gp/OTcwMMDkyZOrbYEREBCAgIAAiIuLQ1lZGe3bt8fRo0cFfuV1cnKChYUF8vPz2RhcXFyQn5+PhQsX4vXr12jVqhXOnj0rMPh4VW5ubnj58iUcHR0hKiqKadOmwcbGhm2tIyIigpCQELi4uKBNmzYwNDSEv79/teNs1ZeoqCjevXsHR0dHvHr1CioqKhg2bJhQx235448/MH/+fDg7O6O4uBgDBw6Eh4fHd3dF5fF4CA0NxbJlyzBp0iS8efMG6urq6NmzJzvY8tevX5GRkSGQPOGfD0BForBt27YIDQ1lB7uePn06EhISMHr0aPB4PNjb22PWrFmcpNGECRPw5csXbNq0Ca6urlBRUWG70TWUsbExduzYAW9vb6xZswbDhw+Hq6srey5LS0uz19O7d++goaGB2bNnc671yuTk5LBu3TpkZWVBVFQUHTp0YAebByoG3XZyckL79u1haGiIdevWVZtUWbt2LebOnYusrCy0a9cO586dY9+61rVrV8yYMQOjR4/Gu3fvsHLlygZ/p+Li4nB3d8eTJ08gJSWFHj16ICQkpNqy8vLyuHHjBjZv3oyPHz9CR0cHfn5+7Ovsp06dioiICFhYWKCgoADh4eE1Drq+fPlyPHr0CDY2NpCWlsa0adMwZMiQGrsQ11fV+9m3qM/fCgDo3bs3lJSUkJGRgbFjx9Za55QpUyAtLY3169dj0aJFkJGRgYmJCebNm8eWMTExgaKiIgwMDNjxr6ysrFBWViZwT1qzZg1UVVXh4+ODR48eQVFREebm5li6dCmAihd6nDx5EvPnz8fWrVvRsWNHeHt7Y/LkyWwdCgoKuHz5MmbPno327dtDRUUFK1aswLRp07752JH/npycHDZv3ozNmzcLOxRCCCGE/Id4TNUBSshP7cKFC1i0aBFSUlKqHbiWVOjcuTNcXFzqfMD6L40cORLm5uac1mo/Qnl5OYyNjTFq1Cj6dZmQ39DPeD/7nX38+BEKCgpYeeMRJGXl6l6B/FBLzBrWlZoQQgghPx/+/6fy8/MbNHwCtaD6xQwcOBBZWVl48eIFOy4H4Xr79i2GDRv207xxkG/9+vU4d+7cd9fD74plaWmJ4uJibNu2DY8fP6aHV0J+Qz/r/YwQQgghhJAfjVpQEfKLefbsGcaMGYOUlBQwDIM2bdpg7dq17Ju4fgVRUVFsF6rqVB6DjBDyf3Jycmp9kUJaWhq0tbX/w4h+P9SCSrioBRUhhBDy66MWVIT8j2jWrJnA2/9+NRYWFjW+4Y0QUjNNTc1arx1NTc3/LhhCCCGEEEJ+IEpQEUL+c1JSUtDT0xN2GIT8csTExOjaIYQQQgghvyUaZZsQQgghhBBCCCGECBUlqAghhBBCCCGEEEKIUFGCihBCCCGEEEIIIYQIFY1BRQghhBBSxQJT5Qa9dYYQQgghhHwfakFFCCGEEEIIIYQQQoSKElSEEEIIIYQQQgghRKgoQUUIIYQQQgghhBBChIoSVIQQQgghhBBCCCFEqChBRQghhBBCCCGEEEKEihJUhBBCCCGEEEIIIUSoxIQdACGEEELIz2Zj0jtIypYIOwwiREvMVIQdAiGEEPI/hVpQEUIIIYQQQgghhBChogQVIYQQQgghhBBCCBEqSlARQgghhBBCCCGEEKGiBBUhhBBCCCGEEEIIESpKUBFCCCGEEEIIIYQQoaIEFSGEEEIIIYQQQggRKkpQEUIIIYQQQgghhBChogQVIYQQQgghNbhx4wbs7OygqakJHo+Hv//+m7P81KlT6NevH5SVlcHj8ZCYmCiUOAkhhJBf3W+RoHr37h2aNGmCJ0+eCDuUn9bbt2/RpEkTPH/+XNihcAQGBqJfv37CDqPeDhw4AEVFRWGHQerJysoK8+bNY6d1dXWxefPmWtep7uHjR/P09ES7du3Y6YkTJ2LIkCG1rlN1X35W48ePh7e3t1C2/bN+3/VVUlICXV1dxMfHc+YvWbIEc+bMEVJUXD/T8eL7Va4N8uv6/PkzTE1NsX379hqXd+/eHb6+vv9xZIQQQsjv5bdIUHl5eWHw4MHQ1dX917YRHR2Nbt26QVlZGVJSUjAyMsKmTZtqXefLly+YOHEiTExMICYmVucDKJ+Xlxe6du0KaWnpeidDPD09YWRkBBkZGTRu3BjW1ta4ffs2u1xFRQWOjo5YuXJltevHxsZCVFQUAwcOFFj25MkT8Hg8NGnSBJ8+feIsa9euHTw9PdlpKysr8Hg88Hg8SEhIoGnTprCzs8OpU6cE6v3y5Qs8PDxqjOlnNHr0aGRmZgo7jF/Kw4cPMWnSJGhpaUFCQgLNmzeHvb29wEP4fyEuLg7Tpk37z7f7I5w6dQpr1qwRdhi1SkpKQmhoKFxcXDjzf6ZzoKrc3Fz0799f2GEAAMTFxeHq6go3NzfOfFdXVxw8eBCPHj0SUmT/52c6XjWh1izkR+vfvz/+/PNPDB06tNrl48ePx4oVK2Btbf0fR0YIIYT8Xn75BFVhYSECAwPh5OT0r25HRkYGzs7OuHHjBtLT07F8+XIsX74ce/bsqXGdsrIySElJwcXFpUH/aSkpKcHIkSMxc+bMeq9jYGCAbdu24f79+4iOjoauri769euHN2/esGUmTZqEoKAg5OXlCawfGBiIOXPm4MaNG/jnn3+q3canT5+wYcOGOmOZOnUqcnNzkZ2djZMnT6JVq1YYM2aMQGLgxIkTkJeXR7du3eq9n9X5+vXrd63fEFJSUmjSpMl/tr1fXXx8PNq3b4/MzEzs3r0baWlpOH36NIyMjLBw4cL/PB5VVVVIS0v/59v9EZSUlCAnJyfsMGq1detWjBw5ErKysuy8n+0cqEpdXR0SEhLCDoPl4OCA6OhopKamsvNUVFRgY2ODnTt3flOdDMOgtLT0h8T3sx2v6lBrFkIIIYSQX9Mvn6AKDQ2FhIQEOnfuzJmfkpKC/v37Q1ZWFmpqahg/fjzevn3LLi8vL8e6deugp6cHCQkJaGtrw8vLq8btmJmZwd7eHq1bt4auri7GjRsHGxsbREVF1biOjIwMdu7cialTp0JdXb3e+7Rq1SrMnz8fJiYm9V5n7NixsLa2RosWLdC6dWts3LgRHz9+RHJyMlumdevW0NTUxOnTpznrFhQU4OjRo5g5cyYGDhyIAwcOVLuNOXPmYOPGjXj9+nWtsUhLS0NdXR1aWlro3LkzfH19sXv3bgQEBODq1atsuZCQENjZ2XHWLS8vx+rVq9mWFu3atUNYWBi7nN+a6+jRo7C0tISkpCSCgoJQWloKFxcXKCoqQllZGW5ubpgwYQKn1VpYWBi6d+/Olhk0aBCys7MF6j516hR69eoFaWlpmJqaIjY2li1TtYtfUlISevXqBTk5OcjLy6N9+/ZsqxB+2fPnz8PQ0BDS0tIYMWIECgsLcfDgQejq6qJx48ZwcXFBWVlZrceUT1dXF97e3pg8eTLk5OSgra0tkCR1c3ODgYEBpKWl0aJFC3h4eHCSePzuZfv27YO2tjZkZWUxa9YslJWVYd26dVBXV0eTJk0ErocPHz5gypQpUFVVhby8PHr37o2kpKQaY2UYBhMnToS+vj6ioqIwcOBAtGzZEu3atcPKlStx5syZBsd8+PBh6OrqQkFBAWPGjOG06Pv8+TMcHR0hKysLDQ0N+Pn5VXv8Knf5ysrKQs+ePSEpKYlWrVrhypUrAuvUFVt1vmUdoOLa5x/fGTNmoKSkhF1WtRtTcXEx3Nzc0KxZM0hISEBPTw+BgYEAKpLjTk5OaN68OaSkpGBoaIgtW7ZwtsXvVrhhwwZoaGhAWVkZs2fP5sS5Y8cO6OvrQ1JSEmpqahgxYkSNsZeVleHEiROca7oh50Bd3r17B3t7ezRt2hTS0tIwMTHBkSNH6lzv06dPsLe3h4yMDJo2bSrQRadql7Xnz5/D3t4eSkpKkJGRgYWFBdsaNTs7G4MHD4aamhpkZWXRoUMHzj0NqPsaLSkpgbOzMzQ0NCApKQkdHR34+Piwyxs3boxu3bohJCSEU6+dnZ3AvJpERESAx+Ph4sWLaN++PSQkJBAdHY3y8nL4+Piw54WpqSlOnDgBoOLeq6WlJZAES0hIgIiICJ4+fVrt8Xr27BlGjRoFRUVFKCkpYfDgwWx3+5SUFIiIiLA/lOTl5UFERARjxoxh1//zzz/RvXt3drquv931uc6pNQshhBBCyK/pl09QRUVFoX379px5Hz58QO/evWFmZob4+HiEhYXh1atXGDVqFFvG3d0da9euhYeHB9LS0hAcHAw1NTV2uZWVFSZOnFjjdhMSEnDz5k1YWlr+8H36XiUlJdizZw8UFBRgamrKWdaxY0eBpNqxY8dgZGQEQ0NDjBs3Dvv27QPDMAL12tvbQ09PD6tXr25wTBMmTEDjxo05Xf2io6NhYWHBKbdlyxb4+flhw4YNSE5Oho2NDf744w9kZWVxyi1ZsgRz585Feno6bGxs4Ovri6CgIOzfvx8xMTH4+PGjwDgpnz9/xoIFCxAfH49r165BREQEQ4cORXl5OafcsmXL4OrqisTERBgYGMDe3r7G1gcODg7Q0tJCXFwc7t69iyVLlqBRo0bs8sLCQvj7+yMkJARhYWGIiIjA0KFDERoaitDQUBw+fBi7d+9mHxLrw8/PDxYWFkhISMCsWbMwc+ZMZGRksMvl5ORw4MABpKWlYcuWLQgICBDojpqdnY2LFy8iLCwMR44cQWBgIAYOHIjnz58jMjISvr6+WL58Oaeb6MiRI/H69WtcvHgRd+/ehbm5Ofr06cO2yOMn+CIiIgAAiYmJSE1NxcKFCyEiInirqZzoq2/Mf//9N86fP4/z588jMjISa9euZZcvWrQIkZGROHPmDC5fvoyIiAjcu3evxuNYXl6OYcOGQVxcHLdv38auXbsEulbVN7Yfsc61a9eQnp6OiIgIHDlyBKdOncKqVatqLO/o6IgjR47A398f6enp2L17N9tyiZ9sOH78ONLS0rBixQosXboUx44d49QRHh6O7OxshIeH4+DBgzhw4ACboI6Pj4eLiwtWr16NjIwMhIWFoWfPnjXGk5ycjPz8fM413ZBzoC5fvnxB+/btceHCBaSkpGDatGkYP3487ty5U+t669evh6mpKRISEtj7RnWJSKAiWW9paYkXL17g7NmzSEpKwuLFi9l7REFBAQYMGIBr164hISEBtra2sLOzQ05ODqee2q5Rf39/nD17FseOHUNGRgaCgoIEuqdXd5/u2LEjnj9/3qCxFpcsWYK1a9ciPT0dbdu2hY+PDw4dOoRdu3YhNTUV8+fPx7hx4xAZGQkRERHY29sjODiYU0dQUBC6desGHR0dgfq/fv0KGxsbyMnJISoqCjExMZCVlYWtrS1KSkrQunVrKCsrIzIyEkDF3+vK0wAQGRkJKysrAPX7293Q67w+iouL8fHjR86HEEIIIYT898SEHcD3evr0KTQ1NTnztm3bBjMzM85Avfv27UOzZs2QmZkJDQ0NbNmyBdu2bcOECRMAAC1btuT8iqutrQ0NDQ2B7WlpaeHNmzcoLS2Fp6cnpkyZ8i/tWcOdP38eY8aMQWFhITQ0NHDlyhWoqKhwymhqaiIhIYEzLzAwEOPGjQMA2NraIj8/n/PQwMfj8bB27VrY2dlh/vz5aNmyZb1jExERgYGBAftw9eHDB+Tn5wt8dxs2bICbmxv7C7uvry/Cw8OxefNmTsuHefPmYdiwYez01q1b4e7uzo4PsW3bNoSGhnLqHj58OGd63759UFVVRVpaGtq0acPOd3V1ZcfiWrVqFVq3bo2HDx/CyMhIYL9ycnKwaNEidpm+vj5n+devX7Fz5072WI0YMQKHDx/Gq1evICsri1atWqFXr14IDw/H6NGj6ziKFQYMGIBZs2YBqGips2nTJoSHh8PQ0BAAsHz5crasrq4uXF1dERISgsWLF7Pzy8vLsW/fPsjJybExZGRkIDQ0FCIiIjA0NGSPfadOnRAdHY07d+7g9evXbPeeDRs24O+//8aJEycwbdo0NGrUiG0pBoBNKlZ33Kqqb8wHDhxgu7mNHz8e165dg5eXFwoKChAYGIi//voLffr0AQAcPHgQWlpaNW7z6tWrePDgAS5dusSeh97e3gLj69Qntm/Zn6rExcWxb98+SEtLo3Xr1li9ejUWLVqENWvWCCR3MjMzcezYMVy5coVtJdKiRQt2eaNGjTjJrebNmyM2NhbHjh3jPOw3btwY27Ztg6ioKIyMjDBw4EBcu3YNU6dORU5ODmRkZDBo0CDIyclBR0cHZmZmNcb/9OlTiIqKcrrANuQcqEvTpk3h6urKTs+ZMweXLl3CsWPH0LFjxxrX69atG5YsWQKgoit0TEwMNm3ahL59+wqUDQ4Oxps3bxAXFwclJSUAgJ6eHrvc1NSUk/Rfs2YNTp8+jbNnz8LZ2ZmdX9s1mpOTA319fXTv3h08Hq/axI+mpibbYqnyPKDiONd3vMXVq1ez+1lcXAxvb29cvXoVXbp0AVBxzkRHR2P37t2wtLSEg4MD/Pz8kJOTA21tbZSXlyMkJIRzPld29OhRlJeXY+/eveDxeACA/fv3Q1FREREREejXrx969uyJiIgIjBgxAhEREZg0aRL27t2LBw8eoGXLlrh58yZ7XdT1t1tTU7PB13l9+Pj41JoMJoQQQggh/41fPkFVVFQESUlJzrykpCSEh4dzxkHhy87OxocPH1BcXMz+B7c6hw4dqnZ+VFQUCgoKcOvWLSxZsgR6enqwt7f/vp34QXr16oXExES8ffsWAQEBGDVqFG7fvs15YJSSkkJhYSE7nZGRgTt37rDd/sTExDB69GgEBgYKJKgAwMbGBt27d4eHh4fAL+11YRiGfYgpKioCAM539/HjR/zzzz8CY1J169ZNoCtZ5VYa+fn5ePXqFechVVRUFO3bt+e0jsrKysKKFStw+/ZtvH37ll2Wk5PDSVC1bduW/Tc/Sfn69etqH7IXLFiAKVOm4PDhw7C2tsbIkSM5iTtpaWnOtJqaGnR1dTnnppqaWp3dJiurHB+Px4O6ujpn/aNHj8Lf3x/Z2dkoKChAaWkp5OXlOXXo6upyxjNSU1ODqKgoJxFSOa6kpCQUFBRAWVmZU09RURHbTbJp06Z48OABu6y6Vng1+ZaYNTQ02Piys7NRUlKCTp06scuVlJTYpF110tPT0axZM06SlP/g3tDYfsQ6pqamnPGxunTpgoKCAjx79kwgiZGYmAhRUdFaW3Bu374d+/btQ05ODoqKilBSUsJ5cyBQ0e1XVFSUndbQ0MD9+/cBAH379oWOjg5atGgBW1tb2NraYujQoTWO4VVUVAQJCQn2Ggcadg7UpaysDN7e3jh27BhevHiBkpISFBcX1zmmWNXvtEuXLjW+2S8xMRFmZmZscqqqgoICeHp64sKFC8jNzUVpaSmKiooEWlDVdo1OnDgRffv2haGhIWxtbTFo0CCBN5lWvU/z5wEQmF+byvfJhw8forCwUCAxV1JSwiYe27VrB2NjYwQHB2PJkiWIjIzE69evMXLkyGrrT0pKwsOHDwXGRvvy5Qt7X7C0tGS7OEZGRsLb2xuZmZmIiIhAXl4evn79yt7z6/rbzT+PG3Kd14e7uzsWLFjATn/8+BHNmjX7rjoJIYQQQkjD/fIJKhUVFbx//54zr6CgAHZ2dtUOkKqhofFdb0Jq3rw5AMDExASvXr2Cp6fnT5OgkpGRgZ6eHvT09NC5c2fo6+sjMDAQ7u7ubJm8vDyoqqqy04GBgSgtLeU8pDMMAwkJCWzbtg0KCgoC21m7di26dOmCRYsW1Tu2srIyZGVloUOHDgDAvl2p6ndXXzIyMg1ex87ODjo6OggICICmpibKy8vRpk0bzjg/ADhd9PgP21W7AfJ5enpi7NixuHDhAi5evIiVK1ciJCSEbclVuS5+fdXNq6n+6tS2fmxsLBwcHLBq1SrY2NhAQUEBISEhAuO0NDSugoICaGhosN33Kqupm5aBgQEA4MGDB7W2vPmemBty3L5FfWP73nUaip+sqElISAhcXV3h5+eHLl26QE5ODuvXr+d02QRqP6ZycnK4d+8eIiIicPnyZaxYsQKenp6Ii4ur9jtXUVFBYWEhSkpKIC4uDqD+50B9rF+/Hlu2bMHmzZthYmICGRkZzJs3T+D6/R51HVdXV1dcuXIFGzZsgJ6eHqSkpDBixIha7yEA97iam5vj8ePHuHjxIq5evYpRo0bB2tqa08236n2aPw+AwPzaVL5PFhQUAAAuXLiApk2bcspVHvTcwcGBTVAFBwfD1tZWIDFduc727dsjKChIYBk/Tv7YaVlZWUhLS0P37t3x4MEDRERE4P3797CwsGCTjHX97X748GG9970hJCQkfvqB34lwFRQUcM6/x48fIzExEUpKStDW1kZeXh5ycnLYl8zwu/Sqq6s3aAxSQggh5H/dLz8GlZmZGdLS0jjzzM3NkZqaCl1dXTZhw//IyMhAX18fUlJSuHbt2ndtu7y8HMXFxd9Vx7+puvhSUlLYB8XS0lIcOnQIfn5+SExMZD9JSUnQ1NSscQDijh07YtiwYWy3mfo4ePAg3r9/z3azExcXR6tWrTjfnby8PDQ1NRETE8NZNyYmBq1ataqxbgUFBaipqSEuLo6dV1ZWxhmX5N27d8jIyMDy5cvRp08fGBsbf3NyrCoDAwPMnz8fly9fxrBhw7B///4fUu+3uHnzJnR0dLBs2TJYWFhAX19foKvQtzA3N8fLly8hJiYmcE1V7UbK165dO7Rq1Qp+fn7VJj6zBGAAAHxRSURBVJI+fPjww2Ju2bIlGjVqxEnAvH//HpmZmTWuY2xsjGfPniE3N5edd+vWLU6Zb4ntW/cnKSmJbVnIj0VWVrbalhwmJiYoLy/njOVTWUxMDLp27YpZs2bBzMwMenp6nBcC1JeYmBisra2xbt06JCcn48mTJ7h+/Xq1Zfmtsypf0/U9B+ojJiYGgwcPxrhx42BqaooWLVrU+v3yVf1Ob926BWNj42rLtm3bFomJidW+6ZQfw8SJEzF06FCYmJhAXV29QWNC8cnLy2P06NEICAjA0aNHcfLkSc42K9+nK89r1KgRWrdu3eDtAUCrVq0gISGBnJwcgWu48jk2duxYpKSk4O7duzhx4gQcHBxqrNPc3BxZWVlo0qSJQJ38HzdMTEzQuHFj/Pnnn2jXrh1kZWVhZWWFyMhIREREcFrq1vW3+1uuc0J+hPj4eJiZmbHX5YIFC2BmZoYVK1YAAM6ePQszMzN2eIAxY8bAzMwMu3btElrMhBBCyK/ol09Q2djYIDU1lZNsmD17NvLy8mBvb4+4uDhkZ2fj0qVLmDRpEsrKyiApKQk3NzcsXrwYhw4dQnZ2Nm7dusW+AQuoGIC4csuj7du349y5c8jKykJWVhYCAwOxYcMGduwmoGL8jKrdBtPS0tgHnvz8fDYJxHfnzh0YGRnhxYsX7LycnBwkJiYiJycHZWVl7Dr8X8CBijFd+N3yPn/+jKVLl+LWrVt4+vQp7t69i8mTJ+PFixecrhmFhYW4e/cu253k/PnzeP/+PZycnNCmTRvOZ/jw4ZzjUZWXlxeuX7/OGZy78nZevnyJ58+f49atW3Bzc8OMGTMwc+ZM9OrVi/PdRUdHc9ZdtGgRfH19cfToUWRkZGDJkiVITEzE3Llza4wFqBiPxsfHB2fOnEFGRgbmzp2L9+/fsy2gGjduDGVlZezZswcPHz7E9evXOV06vkVRURGcnZ0RERGBp0+fIiYmBnFxcTU+/P4X9PX1kZOTg5CQEGRnZ8Pf31/grY3fwtraGl26dMGQIUNw+fJlPHnyBDdv3sSyZcvYtxa+ePECRkZG7KDVPB4P+/fvR2ZmJnr06IHQ0FA8evQIycnJ8PLywuDBg39YzLKysnBycsKiRYtw/fp1pKSkYOLEidUOzF15nwwMDDBhwgQkJSUhKioKy5Yt45T5lti+dX9KSkrg5OSEtLQ0hIaGYuXKlXB2dq52H3R1dTFhwgRMnjwZf//9Nx4/foyIiAh2EHR9fX3Ex8fj0qVLyMzMhIeHByeBWx/nz5+Hv78/EhMT8fTpUxw6dAjl5eU1dqdSVVWFubk555qu7zkAAH369MG2bdtqjEdfXx9XrlzBzZs3kZ6ejunTp+PVq1d17kdMTAzWrVuHzMxMbN++HcePH6/xfmJvbw91dXUMGTIEMTExePToEU6ePMm+yVNfXx+nTp1iE/ljx45tcCu+jRs34siRI3jw4AEyMzNx/PhxqKurc1qlRUVFCXT7i4qKQo8ePeps5VUTOTk5uLq6Yv78+Th48CCys7Nx7949bN26FQcPHmTL6erqomvXrnByckJZWRn++OOPGut0cHCAiooKBg8ejKioKPY8dHFxwfPnzwFUnAM9e/ZEUFAQm4xq27YtiouLce3aNU431br+dtf3Os/Ly0NiYiKbLM3IyEBiYiJevnz5TceOECsrKzAMI/Dhv1Ri4sSJ1S739PQUatyEEELIr+aXT1CZmJjA3Nyc83YqfiucsrIy9OvXDyYmJpg3bx4UFRXZ/8h6eHhg4cKFWLFiBYyNjTF69GjOOD45OTmclhXl5eVwd3dHu3btYGFhge3bt8PX15fzRru3b98KtFIYMGAAzMzMcO7cOURERHB+gQMqkjkZGRmcV7uvWLECZmZmWLlyJQoKCth1+IkAoOI/3Pn5+QAqxlt68OABhg8fDgMDA9jZ2eHdu3eIiori/Np+5swZaGtro0ePHgAquvdZW1tX241v+PDhiI+PR3JycrXH3cDAAJMnT8aXL18ElgUEBEBDQwMtW7bEsGHDkJaWhqNHj2LHjh2cck5OTggNDWX3AwBcXFywYMECLFy4ECYmJggLC8PZs2cFBh+vys3NDfb29nB0dESXLl0gKysLGxsbdowrERERhISE4O7du2jTpg3mz5+P9evX11pnXURFRfHu3Ts4OjrCwMAAo0aNQv/+/YU62O4ff/yB+fPnw9nZGe3atcPNmzfh4eHx3fXyeDyEhoaiZ8+emDRpEgwMDDBmzBg8ffqUffvl169fkZGRwRkjp2PHjoiPj4eenh6mTp0KY2Nj/PHHH0hNTWXHAfpRMa9fvx49evSAnZ0drK2t0b17d4E3fFYmIiKC06dPo6ioCB07dsSUKVPg5eXFKfMtsX3r/vTp0wf6+vro2bMnRo8ejT/++KPWh5udO3dixIgRmDVrFoyMjDB16lR8/vwZADB9+nQMGzYMo0ePRqdOnfDu3Tt20O76UlRUxKlTp9C7d28YGxtj165dOHLkSK0teKZMmSLQ3as+5wBQMcbQ27dva6x7+fLlMDc3h42NDaysrNhEUl0WLlzItn74888/sXHjRtjY2FRbVlxcHJcvX0aTJk0wYMAAmJiYYO3atew4XRs3bkTjxo3RtWtX2NnZwcbGBubm5nXGUJmcnBzWrVsHCwsLdOjQAU+ePGFfTgBUdBHNz8/HiBEjOOuFhIRg6tSpDdpWVWvWrIGHhwd8fHxgbGwMW1tbXLhwge26zufg4ICkpCQMHTq01oSYtLQ0bty4AW1tbQwbNgzGxsZwcnLCly9fOGOuWVpaoqysjE1QiYiIoGfPnuDxeJwxB+vzt7s+1zm1ZiGEEEII+TXxmB85iq2QXLhwAYsWLUJKSkqtLSb+13Xu3BkuLi4YO3assENhjRw5Eubm5pzWaj9CeXk5jI2NMWrUKKxZs+aH1k0IqV5RUREMDQ1x9OjRagecJ3UbPXo0TE1NsXTpUnbexYsXsXDhQiQnJ0NM7JcfOvKn9/HjRygoKGDljUeQlJWrewXy21piVn0XdkIIIYTUjv//qfz8/DpfFlXZb/E/3YEDByIrKwsvXrygN+/U4O3btxg2bNhPM6A73/r163Hu3Lnvrufp06e4fPkyLC0tUVxcjG3btuHx48c/VTKOkN+dlJQUDh06VGtLKFKzkpISmJiYYP78+Zz5nz9/xv79+yk5RQghhBBCfmu/RQsqQp49e4YxY8YgJSUFDMOgTZs2WLt2LXr27Cns0OotKioK/fv3r3F55THICCH/u2bMmIG//vqr2mXjxo2jrmzfiVpQET5qQUUIIYR8m29tQUUJKkJ+EkVFRZzB8qvS09P7D6MhhPysXr9+jY8fP1a7TF5eHk2aNPmPI/q9UIKK8FGCihBCCPk2/9Nd/Aj5HUhJSVESihBSpyZNmlASihBCCCGE/HZoRHFCCCGEEEIIIYQQIlSUoCKEEEIIIYQQQgghQkUJKkIIIYQQQgghhBAiVDQGFSGEEEJIFQtMlRs0qCchhBBCCPk+1IKKEEIIIYQQQgghhAgVJagIIYQQQgghhBBCiFBRgooQQgghhBBCCCGECBUlqAghhBBCCCGEEEKIUFGCihBCCCGEEEIIIYQIFb3FjxBCCCGkio1J7yApWyLsMAj5n7TETEXYIRBCCBECakFFCCGEEEIIIYQQQoSKElSEEEIIIYQQQgghRKgoQUUIIYQQQgghhBBChIoSVIQQQgghhBBCCCFEqChBRQghhBBCCCGEEEKEihJUhBBCCCGEEEIIIUSoKEFFCCGEEEIIIYQQQoSKElSEEEIIIYSQn8qNGzdgZ2cHTU1N8Hg8/P3335zlr169wsSJE6GpqQlpaWnY2toiKytLOMESQgj5IX6rBNW7d+/QpEkTPHnyRNih/LTS0tKgpaWFz58/CzsUDg8PD0ybNk3YYdSbp6cn2rVrJ+wwfguV/9P55MkT8Hg8JCYm/vDtREREgMfj4cOHDz+87v8Vurq62Lx5s7DD+J/yM92zJ06ciCFDhgg7DI4DBw5AUVFR2GEQQv4Fnz9/hqmpKbZv3y6wjGEYDBkyBI8ePcKZM2eQkJAAHR0dWFtb/xT3S0IIId/mt0pQeXl5YfDgwdDV1f3XthEdHY1u3bpBWVkZUlJSMDIywqZNm2pdJyIiAoMHD4aGhgZkZGTQrl07BAUF1bktFxcXtG/fHhISEg1OhjAMg/79+wv84tSqVSt07twZGzdurHa9I0eOQFRUFLNnz652P3g8Hlq3bo2ysjLOMkVFRRw4cICd1tXVBY/HA4/Hg5SUFHR1dTFq1Chcv35doN6XL19iy5YtWLZsWYP2UZhcXV1x7do1YYfxy6h8PvA/WlpaAIDc3Fz0799fyBH+GP9mgu1nEBcX90slkv9NhYWFcHd3R8uWLSEpKQlVVVVYWlrizJkzbJmCggI4OztDS0sLUlJSaNWqFXbt2sWpx8rKSuDamDFjBru8rnv2f2nLli2c+/zPKiIiAubm5pCQkICent4vETMhRFD//v3x559/YujQoQLLsrKycOvWLezcuRMdOnSAoaEhdu7ciaKiIhw5ckQI0RJCCPkRfpsEVWFhIQIDA+Hk5PSvbkdGRgbOzs64ceMG0tPTsXz5cixfvhx79uypcZ2bN2+ibdu2OHnyJJKTkzFp0iQ4Ojri/PnzdW5v8uTJGD16dIPj3Lx5M3g8XrXLJk2ahJ07d6K0tFRgWWBgIBYvXowjR47gy5cv1a7/6NEjHDp0qM4YVq9ejdzcXGRkZODQoUNQVFSEtbU1vLy8OOX27t2Lrl27QkdHpx57Vj2GYardn3+LrKwslJWV/7Pt/Q745wP/k5CQAABQV1eHhITEN9f7X3/3/5WvX78KOwQBqqqqkJaWFnYYP4UZM2bg1KlT2Lp1Kx48eICwsDCMGDEC7969Y8ssWLAAYWFh+Ouvv5Ceno558+bB2dkZZ8+e5dQ1depUzrWxbt06zvLa7tn1UVJS8k3rVaWgoPDTt1Z6/PgxBg4ciF69eiExMRHz5s3DlClTcOnSJWGHRgj5gYqLiwEAkpKS7DwRERFISEggOjpaWGERQgj5Tr9Ngio0NBQSEhLo3LkzZ35KSgr69+8PWVlZqKmpYfz48Xj79i27vLy8HOvWrYOenh4kJCSgra0tkECpzMzMDPb29mjdujV0dXUxbtw42NjYICoqqsZ1li5dijVr1qBr165o2bIl5s6dC1tbW5w6darWffL398fs2bPRokWLeh6FComJifDz88O+ffuqXd63b1/k5eUhMjKSM//x48e4efMmlixZAgMDgxrjmzNnDlauXMn+56AmcnJyUFdXh7a2Nnr27Ik9e/bAw8MDK1asQEZGBlsuJCQEdnZ2nHWLi4vh4uKCJk2aQFJSEt27d0dcXBy7nN+a6+LFi2wrs+joaHz69AkODg6QkZGBhoYGNm3aBCsrK8ybN49d9/Dhw7CwsGDjGzt2LF6/fi1Q97Vr12BhYQFpaWl07dqVE3PVLn4RERHo2LEjZGRkoKioiG7duuHp06ecsvv27YO2tjZkZWUxa9YslJWVYd26dVBXV0eTJk1qPe+q4vF42Lt3L4YOHQppaWno6+tzHnrLysrg5OSE5s2bQ0pKCoaGhtiyZQunDn53HW9vb6ipqUFRURGrV69GaWkpFi1aBCUlJWhpaWH//v2c9Z49e4ZRo0ZBUVERSkpKGDx4cL261fKPN/+jqqrK7kvVcSUePHiArl27QlJSEm3atOGcqzV993WdM3wxMTFo27YtJCUl0blzZ6SkpLDL3r17B3t7ezRt2hTS0tIwMTER+CW2tntG8+bNAVTcJ3g8HqysrNj19u7dC2NjY0hKSsLIyAg7duxgl/FbXh09ehSWlpaQlJSstpUlwzDw9PSEtrY2JCQkoKmpCRcXF3Z5dceyauvG58+fw97eHkpKSpCRkYGFhQVu377NLj937hw6dOgASUlJqKiocH65rtrFr67zEAAiIyPRsWNHSEhIQENDA0uWLOEkWupzzRYXF8PV1RVNmzaFjIwMOnXqhIiICHY5v5vXpUuXYGxsDFlZWdja2iI3N5cTS23fQUlJCZydnaGhoQFJSUno6OjAx8dH4DvgO3v2LJYuXYoBAwZAV1cX7du3x5w5czB58mS2zM2bNzFhwgRYWVlBV1cX06ZNg6mpKe7cucOpS1pamnNtyMvLc5bXdM+uia6uLtasWQNHR0fIy8uzrd6io6PRo0cPSElJoVmzZnBxcWG7wixduhSdOnUSqMvU1BSrV68GINjFr7y8HD4+Pux9xtTUFCdOnGCXW1hYYMOGDez0kCFD0KhRIxQUFACoOBd5PB4ePnwIoO7vGaj4rrW1tSEtLY2hQ4dyEoIAsGvXLjRv3hx+fn4wNjaGs7MzRowYUWdLZ0LIr8XIyAja2tpwd3fH+/fvUVJSAl9fXzx//lzg3k8IIeTX8dskqKKiotC+fXvOvA8fPqB3794wMzNDfHw8wsLC8OrVK4waNYot4+7ujrVr18LDwwNpaWkIDg6Gmpoau9zKygoTJ06scbsJCQm4efMmLC0tGxRvfn4+lJSUGrROfRQWFmLs2LHYvn071NXVqy0jLi6Odu3aCSTV9u/fj4EDB0JBQQHjxo1DYGBgtevPmzcPpaWl2Lp1a4Pjmzt3LhiGYbvB5OXlIS0tDRYWFpxyixcvxsmTJ3Hw4EHcu3cPenp6sLGxQV5eHqfckiVLsHbtWqSnp6Nt27ZYsGABYmJicPbsWVy5cgVRUVG4d+8eZ52vX79izZo1SEpKwt9//40nT55U+x0vW7YMfn5+iI+Ph5iYGOfBs7LS0lIMGTIElpaWSE5ORmxsLKZNm8ZpwZadnY2LFy8iLCwMR44cQWBgIAYOHIjnz58jMjISvr6+WL58OSdRUJdVq1Zh1KhRSE5OxoABA+Dg4MAen/LycmhpaeH48eNIS0vDihUrsHTpUhw7doxTx/Xr1/HPP//gxo0b2LhxI1auXIlBgwahcePGuH37NmbMmIHp06fj+fPn7LGzsbGBnJwcoqKiEBMTwyYD+K00+Amk7xkLbtGiRVi4cCESEhLQpUsX2NnZCTyIVv3u63vOLFq0CH5+foiLi4Oqqirs7OzY1kpfvnxB+/btceHCBaSkpGDatGkYP348J6FQ2z2DX+7q1avIzc1lk7xBQUFYsWIFvLy8kJ6eDm9vb3h4eODgwYMC+zR37lykp6fDxsZG4LicPHkSmzZtwu7du5GVlYW///4bJiYm9T6uBQUFsLS0xIsXL3D27FkkJSVh8eLFKC8vBwBcuHABQ4cOxYABA5CQkIBr166hY8eOtdZZ23n44sULDBgwAB06dEBSUhJ27tyJwMBA/Pnnn+z69blmnZ2dERsbi5CQECQnJ2PkyJECg+EWFhZiw4YNOHz4MG7cuIGcnBy4urqyy+v6Dvz9/XH27FkcO3YMGRkZCAoKqrW7uLq6OkJDQ/Hp06cay3Tt2hVnz57FixcvwDAMwsPDkZmZiX79+nHKBQUFQUVFBW3atIG7uzsKCws5y2u6Z9dmw4YNMDU1RUJCAjw8PJCdnQ1bW1sMHz4cycnJOHr0KKKjo+Hs7AwAcHBwwJ07d5Cdnc3WkZqaiuTkZIwdO7babfj4+ODQoUPYtWsXUlNTMX/+fIwbN45NpFlaWrIJJoZhEBUVBUVFRbZ1Q2RkJJo2bQo9PT0AdX/Pt2/fhpOTE5ydnZGYmIhevXpxziUAiI2NhbW1NWeejY0NYmNjazxWxcXF+PjxI+dDCPm5NWrUCKdOnUJmZiaUlJQgLS2N8PBw9O/fHyIiv83jDSGE/M8RE3YAP8rTp0+hqanJmbdt2zaYmZnB29ubnbdv3z40a9YMmZmZ0NDQwJYtW7Bt2zZMmDABANCyZUt0796dLa+trQ0NDQ2B7WlpaeHNmzcoLS2Fp6cnpkyZUu9Yjx07hri4OOzevbuhu1mn+fPno2vXrhg8eHCt5TQ1NdkWPkBFQuPAgQNs0mnMmDFYuHAhHj9+zLYK4ZOWlsbKlSuxdOlSTJ06FQoKCvWOT0lJiTOQfU5ODhiG4Xx3nz9/xs6dO3HgwAF2bKKAgABcuXIFgYGBWLRoEVt29erV6Nu3L4CKlhgHDx5EcHAw+vTpA6Ai6Vb1vKicaGrRogX8/f3RoUMHFBQUQFZWll3m5eXFJh6XLFmCgQMH4suXL5zm5ADw8eNH5OfnY9CgQWjZsiUAwNjYmFOmvLwc+/btg5ycHFq1aoVevXohIyMDoaGhEBERgaGhIXx9fREeHl5tK4bqTJw4Efb29gAAb29v+Pv7486dO7C1tUWjRo2watUqtmzz5s0RGxuLY8eOcRK0SkpK8Pf3Z2NYt24dCgsLsXTpUgD/l4yJjo7GmDFjcPToUZSXl2Pv3r1sAm7//v1QVFREREQE+vXrB2lpaRgaGqJRo0aceN3c3LB8+XJ22tvbm9P6pzJnZ2cMHz4cALBz506EhYWx3U/5Kn/3DTlnVq5cya538OBBaGlp4fTp0xg1ahSaNm3KSWrMmTMHly5dwrFjx9CxY0d8+vSp1nsGv1WYsrIyJ0G8cuVK+Pn5YdiwYez3kZaWht27d7P1ABXJX36Z6uTk5EBdXR3W1tZo1KgRtLW160wgVRYcHIw3b94gLi6OTZDzkwNAxTk/ZswYzrljampaa521nYc7duxAs2bNsG3bNvB4PBgZGeGff/6Bm5sbVqxYgc+fP9d5zebk5GD//v3Iyclh57u6uiIsLAz79+9n7+9fv37Frl272GvQ2dmZbfkD1P0d5OTkQF9fH927dwePx6uzy/GePXvg4OAAZWVlmJqaonv37hgxYgS6devGltm6dSumTZsGLS0tiImJQUREBAEBAejZsydbZuzYsdDR0YGmpiaSk5Ph5uaGjIwMgRasVe/ZdenduzcWLlzITv+/9u48LKf8/x/4s6K0I7RIQqUiLQwaSyHK0tiXJpLBjLFERMxYshbKWDIYE+ET2fdEllJZiyKRZGnsI5SUSvX7o2/n1+luxcyNeT6uq+vqPuv7Pu9zTt2v+/V+nTFjxsDZ2VnITDM0NMTq1athY2ODdevWoUWLFjA3N8f27dsxZ84cAEWBs3bt2onOkWI5OTlYsmQJTp48CWtrawBF99OoqChs2LABNjY2sLW1RUBAAPLz85GQkAB5eXkMHToU4eHhcHBwQHh4uHCPrUo/r1q1Cg4ODsJ9wMjICOfOnUNoaKjQrqdPn4q+ZAIATU1NZGRkIDs7G4qKihLvxdvbW3TOE9GXoXXr1oiLi0N6ejpyc3NRv359tGvXTuJLTyIi+nJ8NQGq7OxsicBBfHw8zpw5Iwo6FEtJScHr16+Rk5MjfDAqS3m1liIjI5GZmYkLFy5g5syZMDAwED6kVeTMmTMYNWoUNm7ciBYtWlS6fHUcOnQIp0+fFmr7VERRUVH0LX1YWBjevn2LXr16AQDq1auH7t27Y9OmTVi4cKHE+qNHj4afnx+WLl0qCgBWRWFhoRDcyM7OBiCuIZCSkoK8vDzRB72aNWuibdu2uHnzpmhbJf8JuXv3LvLy8kQf2NXV1dG8eXPROrGxsfDy8kJ8fDxevXolZI+kpqbC1NRUWK5Vq1bC78VByufPn0NPT0+0vbp168LV1RX29vbo3r077OzsMGTIEFFgU19fH6qqqsJrTU1NyMnJib7l09TUFA01rEzJ9ikrK0NNTU20/tq1a7Fp0yakpqYiOzsbubm5EsX2W7RoIdGGli1bCq/l5OSgoaEhbDc+Ph537twRvRegKPOoOPOibdu2uHXrlkR7p0+fLspUq1evXrnvrfgDLwDUqFEDbdq0qbDvq3POlNx23bp10bx5c2GZ/Px8LFmyBLt27cKjR4+Qm5uLnJwcoe7SzZs3K71nlPb27VukpKRg9OjRGDt2rDD9/fv3EsHdyv6pHjx4MFauXImmTZvCwcEBvXr1gqOjI2rUqNqtPC4uDpaWluVmb8bFxYnaWBUVnYc3b96EtbW1KJuwQ4cOyMzMxMOHD/Hq1atKr9nr168jPz8fRkZGov3m5OSI6sApKSkJwSmg6JotbkdV+sDV1RXdu3dH8+bN4eDggD59+khkOpXUuXNn3L17FxcuXMC5c+dw6tQprFq1CvPnzxcCPGvWrMGFCxdw6NAhNG7cGGfPnsWECROgo6MjZPmULDpvZmYGbW1tdOvWDSkpKaL3U/qeXZnS51J8fDyuXbsmGjpaWFiIgoIC3Lt3DyYmJnB2dsamTZswZ84cFBYWYseOHZg6dWqZ279z5w6ysrKEYG+x3NxcWFpaAgA6deqEN2/eiDKNbW1t4ePjA6Aog6o4eFyVfr5586ZEsWRra2tRgOpDzJo1S/Q+MzIy0KhRo4/aJhH9e4rv48nJyYiJiSnz/1YiIvoyfDUBqnr16uHVq1eiaZmZmXB0dMTSpUslltfW1sbdu3c/eH/FWUVmZmZ49uwZvLy8Kg1QRUREwNHREb/99htcXFw+eN/lOX36NFJSUiSK2A4cOBCdOnUS1fJ4+fKl6MNPQEAAXr58Kfp2uaCgANeuXcP8+fMl0qVr1KiBxYsXw9XVVRgiUhVpaWn4+++/heNXHKR49eqVkH1SHcrKytVa/u3bt7C3t4e9vT2CgoJQv359pKamwt7eXqKQcMkMoOIP2MXBrNI2b94MNzc3hIaGYufOnZg9ezbCwsKEmmils4lkZGTKnFbe9stS0frBwcHw8PCAn58frK2toaqqiuXLl0sMIaxuuzIzM9G6desy6yNV1n/16tUrMxPjQ1W376ti+fLlWLVqFVauXAkzMzMoKytjypQpwrlRVvZFZYrr7WzcuFEiO05OTk70urL31KhRIyQlJeHkyZMICwvD+PHjsXz5ckRERKBmzZqQkZFBYWGhaJ2SxdYra/+HvL+PPY8rk5mZCTk5OcTGxkocr5JfPpTVjuJjUZU+sLKywr1793Ds2DGcPHkSQ4YMgZ2dnaimUmk1a9ZEp06d0KlTJ3h6emLRokVYsGABPD09kZ+fj19++QX79+9H7969ARQF8+Li4uDr6ysxDK1Ycfvu3LkjukeXvmdXpvS5lJmZiZ9++qnMrMXioLuTkxM8PT1x5coVZGdn46+//ir3IR3Fx/To0aNo2LChaF7xQw9q164Nc3NzhIeH4/z58+jevTs6d+6MoUOH4vbt20hOThYyqKraz5XR0tLCs2fPRNOePXsGNTW1cs9vBQWFj3pQAxH9MzIzM4UadUBRrdS4uDjUrVsXenp62L17N+rXrw89PT1cv34dkydPRr9+/Sr8coGIiD5vX80gbUtLSyQmJoqmWVlZ4caNG9DX14eBgYHoR1lZGYaGhlBUVMSpU6c+at8FBQWVFgwPDw9H7969sXTp0n/sMe0zZ87EtWvXEBcXJ/wAwG+//SZR6DohIUH4ljstLQ0HDx5EcHCwaN2rV6/i1atXOHHiRJn7Gzx4MFq0aFGtoRGrVq2CrKysUGi3WbNmUFNTE/Vds2bNIC8vj+joaGFaXl4eLl++LMpwKq1p06aoWbOmqDB2eno6bt++Lby+desW0tLS4OPjg06dOsHY2LhaWUsVsbS0xKxZs3Du3Dm0bNkS27dv/yTb/RDR0dH49ttvMX78eFhaWsLAwEBUW+ZDWVlZITk5GQ0aNJC4pqoz1LMyFy5cEH5///49YmNjJYZNllSdc6bktl+9eoXbt28L246Ojkbfvn0xfPhwmJubo2nTpqLzp7J7hry8PICiTKximpqa0NHRwd27dyWOWenhs1WhqKgIR0dHrF69Wvjgf/36dQBFQcKSxWGTk5NFWTfFAZLSdblKzv/Y+2FJJiYmOH/+vChoFh0dDVVVVejq6lbpmrW0tER+fj6eP38ucfzKq7NXWlX7QE1NDUOHDsXGjRuxc+dO7N27t9xjVRZTU1O8f/8e7969Q15eHvLy8iSC+3JychUG8Irv26WHlpe8Z38IKysrJCYmSrx/AwMD4bzV1dWFjY0NgoKCEBQUhO7du6NBgwblvlcFBQWkpqZKbK9k9pGNjQ3OnDmDs2fPwtbWFnXr1oWJiQkWL14MbW1tIWOqKv1sYmIiEWQveT0DRRlVpc/hsLAwUeYkEX0ZYmJiYGlpKdz7pk6dCktLS8ydOxcA8OTJE4wYMQLGxsZwc3PDiBEjJB5sQkREX5avJoPK3t5eeJJHnTp1AAATJkzAxo0b4eTkhBkzZqBu3bq4c+cOgoOD8eeff6JWrVrw9PTEjBkzIC8vjw4dOuDvv//GjRs3MHr0aACAi4sLGjZsKDzNae3atdDT04OxsTEA4OzZs/D19RV9K+3v74/9+/cL/ySfOXMGffr0weTJkzFw4EA8ffoUQNGH2eKhNvv378esWbNEQ6Pu3LmDzMxMPH36FNnZ2cIHF1NTU8jLy+PRo0fo1q0btm7dirZt2wpPgCpNT09P9CHs/v37ePTokfAN/rZt26ChoYEhQ4aIhuIAQK9evRAQEAAHB4cyj7uPj0+ZxZyBoppQT58+RV5eHu7du4f//e9/+PPPP+Ht7S1k0sjKysLOzg5RUVFC0EpZWRk///yz8CQ5PT09oTZScb+URVVVFSNHjhTWa9CgAebNmwdZWVnhfenp6UFeXh5r1qzBuHHjkJCQ8NGp4Pfu3cMff/yB7777Djo6OkhKSkJycvI/kiVXVYaGhti6dSuOHz+OJk2aYNu2bbh8+fIHBURKcnZ2xvLly9G3b18sWLAAurq6ePDgAfbt24cZM2ZAV1cXly5dgouLC06dOiWRWVFVa9euhaGhIUxMTPDbb7/h1atX5RapB6p3zixYsAAaGhrQ1NTEr7/+inr16gnnnqGhIfbs2YNz586hTp06WLFiBZ49eyYEuSq7ZzRo0ACKiooIDQ2Frq4uatWqBXV1dcyfPx9ubm5QV1eHg4MDcnJyEBMTg1evXpU7hKosgYGByM/PR7t27aCkpIT//e9/UFRUFOolde3aFf7+/rC2tkZ+fj48PT1FmUVOTk5YsmQJ+vXrB29vb2hra+Pq1avQ0dGBtbU15s2bh27duqFZs2YYNmwY3r9/j5CQEHh6ela5jSWNHz8eK1euxKRJkzBx4kQkJSVh3rx5mDp1KmRlZat0zRoZGcHZ2RkuLi7w8/ODpaUl/v77b5w6dQqtWrUSspMqU1kfrFixAtra2rC0tISsrCx2794NLS0tiYzUYra2tnByckKbNm2goaGBxMRE/PLLL+jSpYvwFD4bGxtMnz5d6KOIiAhs3boVK1asAFA0NHX79u3o1asXNDQ0cO3aNbi7u6Nz586ioZOl79kfwtPTE+3bt8fEiRMxZswYKCsrIzExEWFhYfD39xeWc3Z2xrx585Cbm1vhk+9UVVXh4eEBd3d3FBQUoGPHjkhPT0d0dDTU1NSE2mq2trZYs2YN6tevL/zdtLW1hb+/PwYPHixsryr97Obmhg4dOsDX1xd9+/bF8ePHJYb3jRs3Dv7+/pgxYwZ++OEHnD59Grt27cLRo0c/+NgRkXTY2tpKZAWX5ObmVm4tSyIi+jJ9NRlUZmZmsLKyEj2lTEdHB9HR0cjPz0ePHj1gZmaGKVOmoHbt2sK32nPmzMG0adMwd+5cmJiYYOjQoaKMmtTUVFFGQkFBAWbNmgULCwu0adMGa9euxdKlS0XFeF+8eCHKVtmyZQuysrKED4TFPyWLIaenpyMpKUn0nsaMGQNLS0ts2LABt2/fFr5Fevz4MYCiDJGkpKRq1SUBgB07dqBHjx7Ch9pNmzahf//+EsEpoGh44KFDh/DixYsyt9W1a1d07dpV9Nj4YnPnzoW2tjYMDAwwYsQIpKen49SpUxIfdseMGYPg4GBRVoGPjw8GDhyIESNGwMrKCnfu3MHx48eF4GN5VqxYAWtra/Tp0wd2dnbo0KGD8Fh5oCjDJDAwELt374apqSl8fHxEj0H/EEpKSrh16xYGDhwIIyMj/Pjjj5gwYQJ++umnj9rux/jpp58wYMAADB06FO3atUNaWhrGjx//0dtVUlLC2bNnoaenhwEDBsDExASjR4/Gu3fvhA/lWVlZSEpKEg0tqy4fHx/4+PjA3NwcUVFROHToUIU1q4rXqco54+Pjg8mTJ6N169Z4+vQpDh8+LGSQzJ49G1ZWVrC3t4etrS20tLSE4FWxiu4ZNWrUwOrVq7Fhwwbo6OgIDysYM2YM/vzzT2zevBlmZmawsbFBYGBgtQOGtWvXxsaNG9GhQwe0atUKJ0+exOHDh4UaPX5+fmjUqBE6deqE77//Hh4eHkL9LKAoKH7ixAk0aNAAvXr1gpmZGXx8fIQhVba2tti9ezcOHToECwsLdO3aVfQEw+pq2LAhQkJCcOnSJZibm2PcuHEYPXq0qFh+ZdcsUDSE1sXFBdOmTUPz5s3Rr18/XL58WaIeXEUq6wNVVVUsW7YMbdq0wTfffIP79+8LDzEoi729PbZs2YIePXrAxMQEkyZNgr29vehvUHBwML755hs4OzsL95vFixdj3LhxAIr64+TJk+jRoweMjY0xbdo0DBw4EIcPHxbtq/Q9+0O0atUKERERuH37Njp16iRkIZR+iMSgQYOQlpaGrKwsiXO/tIULF2LOnDnw9vaGiYkJHBwccPToUdF53alTJxQUFIiedGtra4v8/HzY2tqKtldZP7dv3x4bN27EqlWrYG5ujhMnTojOJaBo+P3Ro0cRFhYGc3Nz+Pn54c8//yz3ixQiIiIi+nzIFFb01cQX5ujRo5g+fToSEhL4iNly5ObmwtDQENu3bxcVlJamwsJCtGvXDu7u7lUqNF8db9++RcOGDeHn51dh9hURfR54zYp9jvfsr11GRgbU1dUx7+xd1FJRrXwFIvrkZlpW/KUUERF93or/n0pPTxcSGariqxniBwC9e/dGcnIyHj16xCfwlCM1NRW//PLLZ/VBR0ZGBn/88YdQR+djXL16Fbdu3ULbtm2Rnp4uZLYVZ7IQ0eeF12zFPsd7NhERERHRP+GrClABwJQpU6TdhM9acdHZz42FhQUsLCw+ybZ8fX2RlJQEeXl5tG7dGpGRkZUOD/ucBAUFlTs8sHHjxrhx48a/3CKif9aXfs3+k0rfsyMjI9GzZ89yly9+uh4RERER0ZfmqxriR/Q1ePPmjcRj0ovVrFnzo+rQENGXLTs7G48ePSp3/uf4BcSXhkP8iKSPQ/yIiL5sHOJH9JVQVVWFqio/FBGRJEVFRQahiIiIiOirxEriREREREREREQkVQxQERERERERERGRVDFARUREREREREREUsUaVERERESlTDXXqFZRTyIiIiL6OMygIiIiIiIiIiIiqWKAioiIiIiIiIiIpIoBKiIiIiIiIiIikioGqIiIiIiIiIiISKoYoCIiIiIiIiIiIqniU/yIiIiISlkRn4ZaKrnSbgYRSdlMy3rSbgIR0X8GM6iIiIiIiIiIiEiqGKAiIiIiIiIiIiKpYoCKiIiIiIiIiIikigEqIiIiIiIiIiKSKgaoiIiIiIiIiIhIqhigIiIiIiIiIiIiqWKAioiIiIiIiIiIpIoBKiIiIiIionJ4e3vjm2++gaqqKho0aIB+/fohKSlJmP/y5UtMmjQJzZs3h6KiIvT09ODm5ob09HQptpqI6MvDABVVKi0tDQ0aNMD9+/el3ZTP1osXL9CgQQM8fPhQ2k0RCQgIQI8ePaTdjCoLDAxE7dq1pd2M/xQZGRkcOHBA2s0AAOjr62PlypXSbsZnJTExEbq6unj79q20mwJXV1f069dP2s0Q4T2DiP4NERERmDBhAi5cuICwsDDk5eWhR48ewr358ePHePz4MXx9fZGQkIDAwECEhoZi9OjRUm45EdGXhQEqqtTixYvRt29f6Ovr/2P7iIqKQocOHaChoQFFRUUYGxvjt99+q3Cdd+/ewdXVFWZmZqhRo0aVPzgtXrwY3377LZSUlKr8wcbLywvGxsZQVlZGnTp1YGdnh4sXLwrz69WrBxcXF8ybN6/M9c+fPw85OTn07t1bYt79+/chIyODBg0a4M2bN6J5FhYW8PLyEl7b2tpCRkYGMjIyUFBQQMOGDeHo6Ih9+/ZJbPfdu3eYM2dOuW36HA0dOhS3b9+WdjO+ODt27ICcnBwmTJhQ7XWfPHmCnj17ftL2fM6BJltbW0yZMkXazagyU1NTtG/fHitWrJB2U7Bq1SoEBgZKuxmVCg8Ph5WVFRQUFGBgYPBFtJmIPm+hoaFwdXVFixYtYG5ujsDAQKSmpiI2NhYA0LJlS+zduxeOjo5o1qwZunbtisWLF+Pw4cN4//69lFtPRPTlYICKKpSVlYWAgIB//BsgZWVlTJw4EWfPnsXNmzcxe/ZszJ49G3/88Ue56+Tn50NRURFubm6ws7Or8r5yc3MxePBg/Pzzz1Vex8jICP7+/rh+/TqioqKgr6+PHj164O+//xaWGTVqFIKCgvDy5UuJ9QMCAjBp0iScPXsWjx8/LnMfb968ga+vb6VtGTt2LJ48eYKUlBTs3bsXpqamGDZsGH788UfRcnv27IGamho6dOhQ5fdZlry8vI9avzoUFRXRoEGDf21/X4uAgADMmDEDO3bswLt376q1rpaWFhQUFP6hln29cnNz/7V9jRo1CuvWrfvgDzmfqq3q6uqffbbSvXv30Lt3b3Tp0gVxcXGYMmUKxowZg+PHj0u7aUT0FSkeule3bt0Kl1FTU0ONGjX+rWYREX3xGKCiCoWEhEBBQQHt27cXTU9ISEDPnj2hoqICTU1NjBgxAi9evBDmFxQUYNmyZTAwMICCggL09PSwePHicvdjaWkJJycntGjRAvr6+hg+fDjs7e0RGRlZ7jrKyspYt24dxo4dCy0trSq/p/nz58Pd3R1mZmZVXuf777+HnZ0dmjZtihYtWmDFihXIyMjAtWvXhGVatGgBHR0d7N+/X7RuZmYmdu7ciZ9//hm9e/cu99v8SZMmYcWKFXj+/HmFbVFSUoKWlhZ0dXXRvn17LF26FBs2bMDGjRtx8uRJYbng4GA4OjqK1i0oKMCCBQugq6sLBQUFWFhYIDQ0VJhfnM21c+dO2NjYoFatWggKCsL79+/h5uaG2rVrQ0NDA56enhg5cqQoay00NBQdO3YUlunTpw9SUlIktr1v3z506dIFSkpKMDc3x/nz54VlSg/XiY+PR5cuXaCqqgo1NTW0bt0aMTExomWPHDmC5s2bQ0lJCYMGDUJWVha2bNkCfX191KlTB25ubsjPz6/wmBbT19fHkiVL8MMPP0BVVRV6enoSQVJPT08YGRlBSUkJTZs2xZw5c0RBPC8vL1hYWGDTpk3Q09ODiooKxo8fj/z8fCxbtgxaWlpo0KCBxPXw+vVrjBkzBvXr14eamhq6du2K+Pj4Stt87949nDt3DjNnzoSRkVGZ2XSbNm1CixYtoKCgAG1tbUycOFGYV3KIX1X6CCjKeOzUqRMUFRXRqFEjuLm5CcMcbG1t8eDBA7i7uwvZfsX27t0rtENfXx9+fn4SbX3z5g2cnJygrKyMhg0bYu3ataL5K1asgJmZGZSVldGoUSOMHz8emZmZomWio6Nha2sLJSUl1KlTB/b29nj16hVcXV0RERGBVatWCW0rHrpc2T3N1tYWEydOxJQpU1CvXj3Y29uX2R/h4eFo27YtlJWVUbt2bXTo0AEPHjwA8P/PjQ0bNqBRo0ZQUlLCkCFDKq1R0r17d7x8+RIREREVLldMX18fCxcuhIuLC9TU1ITgdUX99ssvv6Bdu3YS2zI3N8eCBQsASA7xKygogLe3N5o0aQJFRUWYm5tjz549wvw2bdqIgu79+vVDzZo1hf56+PAhZGRkcOfOHQBATk4OPDw80LBhQygrK6Ndu3YIDw8XtScwMBB6enpQUlJC//79kZaWJpq/fv16NGnSBH5+fjAxMcHEiRMxaNCgSjNyiYiqqqCgAFOmTEGHDh3QsmXLMpd58eIFFi5cKPHlIRERVYwBKqpQZGQkWrduLZr2+vVrdO3aFZaWloiJiUFoaCiePXuGIUOGCMvMmjULPj4+mDNnDhITE7F9+3ZoamoK821tbeHq6lrufq9evYpz587Bxsbmk7+nj5Wbm4s//vgD6urqMDc3F81r27atRFBt165dMDY2RvPmzTF8+HBs2rQJhYWFEtt1cnKCgYGB8GGwOkaOHIk6deqIghNRUVFo06aNaLlVq1bBz88Pvr6+uHbtGuzt7fHdd98hOTlZtNzMmTMxefJk3Lx5E/b29li6dCmCgoKwefNmREdHIyMjQ6Ju0du3bzF16lTExMTg1KlTkJWVRf/+/VFQUCBa7tdff4WHhwfi4uJgZGQEJyencjNDnJ2doauri8uXLyM2NhYzZ85EzZo1hflZWVlYvXo1goODERoaivDwcPTv3x8hISEICQnBtm3bsGHDBtGH5sr4+fmhTZs2uHr1KsaPH4+ff/5ZVAhVVVUVgYGBSExMxKpVq7Bx40aJD78pKSk4duwYQkNDsWPHDgQEBKB37954+PAhIiIisHTpUsyePVs0THTw4MF4/vw5jh07htjYWFhZWaFbt25CRl5x8Kj0B/bNmzejd+/eUFdXx/DhwxEQECCav27dOkyYMAE//vgjrl+/jkOHDsHAwKDCY1BRH6WkpMDBwQEDBw7EtWvXsHPnTkRFRQlBr3379kFXVxcLFizAkydP8OTJEwBAbGwshgwZgmHDhuH69evw8vLCnDlzJAK2y5cvh7m5Oa5evSqch2FhYcJ8WVlZrF69Gjdu3MCWLVtw+vRpzJgxQ5gfFxeHbt26wdTUFOfPn0dUVBQcHR2Rn5+PVatWwdraWshCfPLkCRo1alSlexoAbNmyBfLy8oiOjsb69esljtv79+/Rr18/2NjY4Nq1azh//jx+/PFHUZDuzp072LVrFw4fPozQ0FDhPKuIvLw8LCwsKgzYl+br6yscxzlz5lTab87Ozrh06ZIoqHzjxg1cu3YN33//fZn78Pb2xtatW7F+/XrcuHED7u7uGD58uBBIs7GxEc7XwsJCREZGonbt2oiKigJQVNOlYcOGwvk4ceJEnD9/HsHBwbh27RoGDx4MBwcH4f508eJFjB49GhMnTkRcXBy6dOmCRYsWidp0/vx5iYxae3t7iSBrSTk5OcjIyBD9EBGVZ8KECUhISEBwcHCZ8zMyMtC7d2+YmpqKyjQQEVHlmHNKFXrw4AF0dHRE0/z9/WFpaYklS5YI0zZt2oRGjRrh9u3b0NbWxqpVq+Dv74+RI0cCAJo1a4aOHTsKy+vp6UFbW1tif7q6uvj777/x/v17eHl5YcyYMf/QO6u+I0eOYNiwYcjKyoK2tjbCwsJQr1490TI6Ojq4evWqaFpAQACGDx8OAHBwcEB6ejoiIiJga2srWk5GRgY+Pj5wdHSEu7s7mjVrVuW2ycrKwsjISMgGef36NdLT0yX6ztfXF56enhg2bBgAYOnSpThz5gxWrlwpylSZMmUKBgwYILxes2YNZs2ahf79+wMoOgdCQkJE2x44cKDo9aZNm1C/fn0kJiaKvmH08PAQanHNnz8fLVq0wJ07d2BsbCzxvlJTUzF9+nRhnqGhoWh+Xl4e1q1bJxyrQYMGYdu2bXj27BlUVFRgamqKLl264MyZMxg6dGglR7FIr169hICBp6cnfvvtN5w5cwbNmzcHAMyePVtYVl9fHx4eHggODhYFSQoKCrBp0yaoqqoKbUhKSkJISAhkZWXRvHlz4di3a9cOUVFRuHTpEp4/fy4Mt/P19cWBAwewZ88e/Pjjj6hZs6aQKVZyP4GBgVizZg0AYNiwYZg2bRru3buHJk2aAAAWLVqEadOmYfLkycJ633zzTYXHoKI+8vb2hrOzs1DHydDQEKtXr4aNjQ3WrVuHunXrQk5ODqqqqqLMxhUrVqBbt26YM2cOgKJhs4mJiVi+fLkoWN2hQwfMnDlTWCY6Ohq//fYbunfvDgCi+lH6+vpYtGgRxo0bh99//x0AsGzZMrRp00Z4DRRlNxaTl5cXshCLVXZPMzIyEt7rsmXLyj1uGRkZSE9PR58+fYRz0sTERLTMu3fvsHXrVjRs2BBA0bXVu3dv+Pn5VZgJqqOjI2RiVUXXrl0xbdo04fWYMWMq7Lfiuirbt28X+igoKAjt2rUrM6CZk5ODJUuW4OTJk7C2tgYANG3aFFFRUdiwYQNsbGxga2uLgIAA5OfnIyEhAfLy8hg6dCjCw8Ph4OCA8PBw4UuI1NRUbN68GampqcJ9y8PDA6Ghodi8eTOWLFmCVatWwcHBQbjWjIyMcO7cOVEW6NOnT0VfhgCApqYmMjIykJ2dDUVFRYn34u3tjfnz51f52BLRf9fEiRNx5MgRnD17Frq6uhLz37x5AwcHB6iqqmL//v2iL9WIiKhyzKCiCmVnZ6NWrVqiafHx8Thz5gxUVFSEn+IAQkpKCm7evImcnBx069at3O1u3boV3t7eEtMjIyMRExOD9evXY+XKldixY8enfUMfobimyblz5+Dg4IAhQ4ZIDMdTVFREVlaW8DopKQmXLl2Ck5MTAKBGjRoYOnSoRJZLMXt7e3Ts2FH4gFgdhYWFQqZGdnY2AIj6LiMjA48fP5aoSdWhQwfcvHlTNK1k5lV6ejqePXuGtm3bCtPk5OQkMuuSk5Ph5OSEpk2bQk1NTSiqn5qaKlquVatWwu/FQcryhjVOnToVY8aMgZ2dHXx8fETZHUDRcMeSgTxNTU3o6+tDRUVFNK2yYZPltU9GRgZaWlqi9Xfu3IkOHTpAS0sLKioqmD17tsR71NfXh6qqqqgNpqamkJWVFU0r3m58fDwyMzOhoaEhuq7u3bsnvOeGDRvi1q1bon4ICwvD27dv0atXLwBFxfq7d++OTZs2ASg6ro8fP67wWqzsGJTuo/j4eAQGBoraaW9vj4KCAty7d6/cbd68ebPMcy85OVk0BLM42FHydcnz8+TJk+jWrRsaNmwIVVVVjBgxAmlpacJ1V5xBVR2V3dOKlT7nS6tbty5cXV1hb28PR0dHrFq1SsggK6anpycEp4rfX0FBgShLryyl7y2VKZ09WZV+c3Z2xvbt2wEU3U927NgBZ2fnMrd/584dZGVloXv37qJtbt26VThmnTp1wps3b3D16lVEREQIQavirKqSgfrr168jPz8fRkZGou1FREQI27t586bEMMTS58uHmDVrFtLT04Wfv/7666O3SURfl8LCQkycOBH79+/H6dOnhS+BSsrIyECPHj0gLy+PQ4cOSfz/TERElWMGFVWoXr16ePXqlWhaZmYmHB0dsXTpUonltbW1cffu3Q/eX/EffDMzMzx79gxeXl5CcEfalJWVYWBgAAMDA7Rv3x6GhoYICAjArFmzhGVevnyJ+vXrC68DAgLw/v17USZTYWEhFBQU4O/vD3V1dYn9+Pj4wNraGtOnT69y2/Lz85GcnCxkxmhoaEBGRkai76pKWVm52us4OjqicePG2LhxI3R0dFBQUICWLVtKFGgu+W1icUCt9DDAYl5eXvj+++9x9OhRHDt2DPPmzUNwcLCQyVX6m0kZGZkyp5W3/bJUtP758+fh7OyM+fPnw97eHurq6ggODpaopVTddmVmZkJbW1ti+B6ACotSBwQE4OXLl6KskIKCAly7dg3z588vM1ukKirqo8zMTPz0009wc3OTWE9PT++D9ldV9+/fR58+ffDzzz9j8eLFqFu3LqKiojB69Gjk5uZCSUnpg95zZfe0YlW5LjZv3gw3NzeEhoZi586dmD17NsLCwiTq+FXXy5cvq5VVWbqtVek3JycneHp64sqVK8jOzsZff/1VbuZhcR2po0ePigJuAIQswNq1a8Pc3Bzh4eE4f/48unfvjs6dOwtP60xOThYyqDIzMyEnJ4fY2FjIycmJtlcy4FwZLS0tPHv2TDTt2bNnUFNTK/fcUFBQ4IMCiKhCEyZMwPbt23Hw4EGoqqri6dOnAIoeHqGoqCgEp7KysvC///1PNFy4fv36Evc1IiIqGwNUVCFLS0v873//E02zsrLC3r17oa+vX+aTSQwNDaGoqIhTp0591BC9goIC5OTkfPD6/7Sy2peQkCBkBLx//x5bt26Fn58fevToIVquX79+2LFjB8aNGyex3bZt22LAgAHCMKeq2LJlC169eiUMs5OXl4epqSkSExOFfaupqUFHRwfR0dGi2l7R0dGirJzS1NXVoampicuXL6Nz584AigJiV65cgYWFBQAgLS0NSUlJ2LhxIzp16gQAQp2Zj2VkZAQjIyO4u7vDyckJmzdvFgJU/7Zz586hcePG+PXXX4Vp1Rl2VR4rKys8ffoUNWrUEDLPKpOWloaDBw8iODhYNIQtPz8fHTt2xIkTJ+Dg4AB9fX2cOnUKXbp0+eh2Frc1MTGxwjpW8vLyEoXpTUxMEB0dLZoWHR0NIyMj0T/uFy5cEC1z4cIFYZhcbGwsCgoK4OfnJ2Sj7dq1S7R8q1atcOrUqXKHbJXVtsruadVlaWkJS0tLzJo1C9bW1ti+fbsQoEpNTcXjx4+FoPWFCxeEYZ8VSUhIwKBBgz64TVXpN11dXdjY2CAoKAjZ2dno3r17uU/VNDU1hYKCAlJTUyusFWhjY4MzZ87g0qVLQlDRxMQEixcvhra2tjB80tLSEvn5+Xj+/LlwDynNxMREVLcNkDxfrK2tJYYfh4WFfZJMKyL671q3bh0ASJRn2Lx5M1xdXXHlyhXh/lT6Pnvv3r0q/20nIvqv4xA/qpC9vT1u3LghysSZMGECXr58CScnJ1y+fBkpKSk4fvw4Ro0ahfz8fNSqVQuenp6YMWOGMNzjwoULomFtLi4uosyjtWvX4vDhw0hOTkZycjICAgLg6+sr1G4CiurElB66k5iYiLi4OLx8+RLp6emIi4tDXFycMP/SpUswNjbGo0ePhGmpqamIi4tDamoq8vPzhXVKPgnM2NhYeBrf27dv8csvv+DChQt48OABYmNj8cMPP+DRo0cYPHiwsE5WVhZiY2OFgNCRI0fw6tUrjB49Gi1bthT9DBw4sNxhfgCwePFinD59usxhP1lZWXj69CkePnyICxcuwNPTE+PGjcPPP/8sCkLY29tLBImmT5+OpUuXYufOnUhKSsLMmTMRFxcnqk9UlkmTJsHb2xsHDx5EUlISJk+ejFevXgnZNXXq1IGGhgb++OMP3LlzB6dPn8bUqVMr3GZlsrOzMXHiRISHh+PBgweIjo7G5cuXJWr6/JsMDQ2RmpqK4OBgpKSkYPXq1RJPbfwQdnZ2sLa2Rr9+/XDixAncv38f586dw6+//io8tfDRo0cwNjbGpUuXAADbtm2DhoYGhgwZIjq3zM3N0atXL+H88vLygp+fH1avXo3k5GRcuXJFqFn1ITw9PXHu3DmhUHVycjIOHjwoejKgvr4+zp49i0ePHglPwps2bRpOnTqFhQsX4vbt29iyZQv8/f3h4eEh2n50dDSWLVuG27dvY+3atdi9e7dwfhoYGCAvLw9r1qzB3bt3sW3bNoli5bNmzcLly5cxfvx4XLt2Dbdu3cK6deuEdujr6+PixYu4f/8+Xrx4gYKCgkrvaVV17949zJo1C+fPn8eDBw9w4sQJJCcni87ZWrVqYeTIkYiPj0dkZCTc3NwwZMgQof7U/v37Jeqx3b9/H48ePZIo/l0dVek3oGiYX3BwMHbv3l3u8D6g6GEBHh4ecHd3x5YtW5CSkiKcW1u2bBGWs7W1xfHjx1GjRg3hfdna2iIoKEgU2DIyMoKzszNcXFywb98+3Lt3D5cuXYK3tzeOHj0KAEJmmq+vL5KTk+Hv7y+qPwUA48aNw927dzFjxgzcunULv//+O3bt2gV3d/cPPnZERIWFhWX+FNdQtLW1LXcZBqeIiKqOASqqkJmZGaysrERZCsVZOPn5+ejRowfMzMwwZcoU1K5dW8hqmDNnDqZNm4a5c+fCxMQEQ4cOFdXxSU1NFdVmKSgowKxZs2BhYYE2bdpg7dq1WLp0qeiJdi9evJCoQdSrVy9YWlri8OHDCA8PFzIXimVlZSEpKQl5eXnCtLlz58LS0hLz5s1DZmamsE5xIAAoqh1V/Oh3OTk53Lp1CwMHDoSRkREcHR2RlpaGyMhIUebKwYMHoaenJ3z7HxAQADs7uzKH8Q0cOBAxMTG4du1amcfdyMgIP/zwA969eycxb+PGjdDW1kazZs0wYMAAJCYmYufOnaKi0AAwevRohISEiB5h7+bmhqlTp2LatGkwMzNDaGgoDh06JFF8vDRPT084OTnBxcUF1tbWQv2a4voKsrKyCA4ORmxsLFq2bAl3d3csX768wm1WRk5ODmlpaXBxcYGRkRGGDBmCnj17SrWY8XfffQd3d3dMnDgRFhYWOHfu3AfVCytNRkYGISEh6Ny5M0aNGgUjIyMMGzYMDx48EAo+5+XlISkpSahDtGnTJvTv31/0hLhiAwcOxKFDh/DixQuMHDkSK1euxO+//44WLVqgT58+Ek9trI5WrVohIiICt2/fRqdOnWBpaYm5c+eKhrEuWLAA9+/fR7NmzYQhr8X3keDgYLRs2RJz587FggULJJ7mOW3aNMTExMDS0hKLFi3CihUrYG9vDwAwNzfHihUrsHTpUrRs2RJBQUESteyMjIxw4sQJxMfHo23btrC2tsbBgweFzCgPDw/IycnB1NQU9evXF4pyV3ZPqwolJSXRveLHH3/EhAkT8NNPPwnLGBgYYMCAAejVqxd69OiBVq1aia7d9PR0icD0jh070KNHDzRu3LjKbSmtKv0GFD1ooLimV79+/Src5sKFCzFnzhx4e3vDxMQEDg4OOHr0qKg2S6dOnVBQUCAKRtna2iI/P7/MTAQXFxdMmzYNzZs3R79+/XD58mVhCGL79u2xceNGrFq1Cubm5jhx4oTooQVA0TDxo0ePIiwsDObm5vDz88Off/4pnENERERE9PmSKSzrefdEJRw9ehTTp09HQkJCtT6s/de0b98ebm5u5T6SXRoGDx4MKysrUbbap1BQUAATExMMGTIECxcu/KTbJvpaeXl54cCBA6Isz8rk5ubC0NAQ27dvlygyT/+MjIwMqKurY97Zu6ilolr5CkT0VZtpWa/yhYiISKT4/6n09HSoqalVeT3WoKJK9e7dG8nJyXj06BEaNWok7eZ8ll68eIEBAwZ8NgXdiy1fvhyHDx/+6O0UD1eysbFBTk4O/P39ce/evc8qGEf0NUpNTcUvv/zC4BQRERERffUYoKIqmTJlirSb8FmrV68eZsyYIe1mSNDX18ekSZM+ejuysrIIDAyEh4cHCgsL0bJlS5w8eVKq9aCqKzIyEj179ix3fskaZESfi+InhxbjeUxEREREXysO8SOi/4Ts7GxRsfzSKnq6GdHngufxP49D/IioJA7xIyKqPg7xIyKqgKKiIj+80xeP5zERERERfa1Y8ZqIiIiIiIiIiKSKASoiIiIiIiIiIpIqBqiIiIiIiIiIiEiqWIOKiIiIqJSp5hrVKupJRERERB+HGVRERERERERERCRVDFAREREREREREZFUMUBFRERERERERERSxQAVERERERERERFJFQNUREREREREREQkVXyKHxEREVEpK+LTUEslV9rNICIiIvpoMy3rSbsJVcIMKiIiIiIiIiIikioGqIiIiIiIiIiISKoYoCIiIiIiIiIiIqligIqIiIiIiIiIiKSKASoiIiIiIiIiIpIqBqiIiIiIiIiIiEiqGKAiIiIiIiIiIiKpYoCKiIiIiIiIiOg/xMfHBzIyMpgyZYow7enTpxgxYgS0tLSgrKwMKysr7N27919r01cRoEpLS0ODBg1w//59aTfls/XixQs0aNAADx8+lHZTRAICAtCjRw9pN6PKAgMDUbt2bWk3g6rI1tZWdMPV19fHypUrK1xHRkYGBw4c+Efb5eXlBQsLC+G1q6sr+vXrV+E6pd/L52rEiBFYsmSJVPb9ufZ3VeXm5kJfXx8xMTGi6TNnzsSkSZOk1Cqxz+l4FftSrg0iIiKiz8Xly5exYcMGtGrVSjTdxcUFSUlJOHToEK5fv44BAwZgyJAhuHr16r/Srq8iQLV48WL07dsX+vr6/9g+oqKi0KFDB2hoaEBRURHGxsb47bffKlzn3bt3cHV1hZmZGWrUqFHpB9BiixcvxrfffgslJaUqB0O8vLxgbGwMZWVl1KlTB3Z2drh48aIwv169enBxccG8efPKXP/8+fOQk5ND7969Jebdv38fMjIyaNCgAd68eSOaZ2FhAS8vL+G1ra0tZGRkICMjAwUFBTRs2BCOjo7Yt2+fxHbfvXuHOXPmlNumz9HQoUNx+/ZtaTfji3Lnzh2MGjUKurq6UFBQQJMmTeDk5CTxIfzfcPnyZfz444//+n4/hX379mHhwoXSbkaF4uPjERISAjc3N9H0z+kcKO3Jkyfo2bOntJsBAJCXl4eHhwc8PT1F0z08PLBlyxbcvXtXSi37/z6n41WewsJCzJ07F9ra2lBUVISdnR2Sk5Ol3SwiIiKiz0JmZiacnZ2xceNG1KlTRzTv3LlzmDRpEtq2bYumTZti9uzZqF27NmJjY/+Vtn3xAaqsrCwEBARg9OjR/+h+lJWVMXHiRJw9exY3b97E7NmzMXv2bPzxxx/lrpOfnw9FRUW4ubnBzs6uyvvKzc3F4MGD8fPPP1d5HSMjI/j7++P69euIioqCvr4+evTogb///ltYZtSoUQgKCsLLly8l1g8ICMCkSZNw9uxZPH78uMx9vHnzBr6+vpW2ZezYsXjy5AlSUlKwd+9emJqaYtiwYRKBgT179kBNTQ0dOnSo8vssS15e3ketXx2Kiopo0KDBv7a/L11MTAxat26N27dvY8OGDUhMTMT+/fthbGyMadOm/evtqV+/PpSUlP71/X4KdevWhaqqqrSbUaE1a9Zg8ODBUFFREaZ9budAaVpaWlBQUJB2MwTOzs6IiorCjRs3hGn16tWDvb091q1b90HbLCwsxPv37z9J+z6341WWZcuWYfXq1Vi/fj0uXrwIZWVl2Nvb4927d9JuGhEREZHUTZgwAb179y4zRvHtt99i586dePnyJQoKChAcHIx3797B1tb2X2nbFx+gCgkJgYKCAtq3by+anpCQgJ49e0JFRQWampoYMWIEXrx4IcwvKCjAsmXLYGBgAAUFBejp6WHx4sXl7sfS0hJOTk5o0aIF9PX1MXz4cNjb2yMyMrLcdZSVlbFu3TqMHTsWWlpaVX5P8+fPh7u7O8zMzKq8zvfffw87Ozs0bdoULVq0wIoVK5CRkYFr164Jy7Ro0QI6OjrYv3+/aN3MzEzs3LkTP//8M3r37o3AwMAy9zFp0iSsWLECz58/r7AtSkpK0NLSgq6uLtq3b4+lS5diw4YN2LhxI06ePCksFxwcDEdHR9G6BQUFWLBggZBpYWFhgdDQUGF+cTbXzp07YWNjg1q1aiEoKAjv37+Hm5sbateuDQ0NDXh6emLkyJGirLXQ0FB07NhRWKZPnz5ISUmR2Pa+ffvQpUsXKCkpwdzcHOfPnxeWKT3ELz4+Hl26dIGqqirU1NTQunVrISukeNkjR46gefPmUFJSwqBBg5CVlYUtW7ZAX18fderUgZubG/Lz8ys8psX09fWxZMkS/PDDD1BVVYWenp5EkNTT0xNGRkZQUlJC06ZNMWfOHFEQr3h42aZNm6CnpwcVFRWMHz8e+fn5WLZsGbS0tNCgQQOJ6+H169cYM2YM6tevDzU1NXTt2hXx8fHltrWwsBCurq4wNDREZGQkevfujWbNmsHCwgLz5s3DwYMHq93mbdu2QV9fH+rq6hg2bJgoo+/t27dwcXGBiooKtLW14efnV+bxKznkKzk5GZ07d0atWrVgamqKsLAwiXUqa1tZPmQdoOjaLz6+48aNQ25urjCv9DCmnJwceHp6olGjRlBQUICBgQECAgIAFAXHR48ejSZNmkBRURHNmzfHqlWrRPsqHlbo6+sLbW1taGhoYMKECaJ2/v777zA0NEStWrWgqamJQYMGldv2/Px87NmzR3RNV+ccqExaWhqcnJzQsGFDKCkpwczMDDt27Kh0vTdv3sDJyQnKyspo2LAh1q5dK5pfesjaw4cP4eTkhLp160JZWRlt2rQRslFTUlLQt29faGpqQkVFBd98843ongZUfo3m5uZi4sSJ0NbWRq1atdC4cWN4e3sL8+vUqYMOHTogODhYtF1HR0eJaeUJDw+HjIwMjh07htatW0NBQQFRUVEoKCiAt7e3cF6Ym5tjz549AIruvbq6uhJBsKtXr0JWVhYPHjwo83j99ddfGDJkCGrXro26deuib9++wnD7hIQEyMrKCl+UvHz5ErKyshg2bJiw/qJFi9CxY0fhdWV/uyu7zgsLC7Fy5UrMnj0bffv2RatWrbB161Y8fvz4sxuaSERERPRvCw4OxpUrV0T/f5a0a9cu5OXlQUNDAwoKCvjpp5+wf/9+GBgY/Cvt++IDVJGRkWjdurVo2uvXr9G1a1dYWloiJiYGoaGhePbsGYYMGSIsM2vWLPj4+GDOnDlITEzE9u3boampKcy3tbWFq6trufu9evUqzp07Bxsbm0/+nj5Wbm4u/vjjD6irq8Pc3Fw0r23bthJBtV27dsHY2BjNmzfH8OHDsWnTJhQWFkps18nJCQYGBliwYEG12zRy5EjUqVNHNNQvKioKbdq0ES23atUq+Pn5wdfXF9euXYO9vT2+++47ieEZM2fOxOTJk3Hz5k3Y29tj6dKlCAoKwubNmxEdHY2MjAyJDyNv377F1KlTERMTg1OnTkFWVhb9+/dHQUGBaLlff/0VHh4eiIuLg5GREZycnMrNPnB2doauri4uX76M2NhYzJw5EzVr1hTmZ2VlYfXq1QgODkZoaCjCw8PRv39/hISEICQkBNu2bcOGDRuED4lV4efnhzZt2uDq1asYP348fv75ZyQlJQnzVVVVERgYiMTERKxatQobN26UGI6akpKCY8eOITQ0FDt27EBAQAB69+6Nhw8fIiIiAkuXLsXs2bNFw0QHDx6M58+f49ixY4iNjYWVlRW6desmZOQVB/jCw8MBAHFxcbhx4wamTZsGWVnJW03JQF9V23zgwAEcOXIER44cQUREBHx8fIT506dPR0REBA4ePIgTJ04gPDwcV65cKfc4FhQUYMCAAZCXl8fFixexfv16iaFVVW3bp1jn1KlTuHnzJsLDw7Fjxw7s27cP8+fPL3d5FxcX7NixA6tXr8bNmzexYcMGIXOpONiwe/duJCYmYu7cufjll1+wa9cu0TbOnDmDlJQUnDlzBlu2bEFgYKAQoI6JiYGbmxsWLFiApKQkhIaGonPnzuW259q1a0hPTxdd09U5Byrz7t07tG7dGkePHkVCQgJ+/PFHjBgxApcuXapwveXLl8Pc3BxXr14V7htlBSKBomC9jY0NHj16hEOHDiE+Ph4zZswQ7hGZmZno1asXTp06hatXr8LBwQGOjo5ITU0Vbaeia3T16tU4dOgQdu3ahaSkJAQFBUkMTy/rPt22bVs8fPiwWrUWZ86cCR8fH9y8eROtWrWCt7c3tm7divXr1+PGjRtwd3fH8OHDERERAVlZWTg5OWH79u2ibQQFBaFDhw5o3LixxPbz8vJgb28PVVVVREZGIjo6GioqKnBwcEBubi5atGgBDQ0NREREACj6e13yNQBEREQI38hV5W93Zdf5vXv38PTpU9E3gurq6mjXrp3oy4aScnJykJGRIfohIiIi+tr89ddfmDx5MoKCglCrVq0yl5kzZw5ev36NkydPIiYmBlOnTsWQIUNw/fr1f6WNNf6VvfyDHjx4AB0dHdE0f39/WFpaigr1btq0CY0aNcLt27ehra2NVatWwd/fHyNHjgQANGvWTPQtrp6eHrS1tSX2p6uri7///hvv37+Hl5cXxowZ8w+9s+o7cuQIhg0bhqysLGhrayMsLAz16tUTLaOjoyNR4CwgIADDhw8HADg4OCA9PV30oaGYjIwMfHx84OjoCHd3dzRr1qzKbZOVlYWRkZHw4er169dIT0+X6DtfX194enoK37AvXboUZ86cwcqVK0WZD1OmTMGAAQOE12vWrMGsWbPQv39/AEXnQEhIiGjbAwcOFL3etGkT6tevj8TERLRs2VKY7uHhIdTimj9/Plq0aIE7d+7A2NhY4n2lpqZi+vTpwjxDQ0PR/Ly8PKxbt044VoMGDcK2bdvw7NkzqKiowNTUFF26dMGZM2cwdOjQSo5ikV69emH8+PEAijJ1fvvtN5w5cwbNmzcHAMyePVtYVl9fHx4eHggODsaMGTOE6QUFBdi0aRNUVVWFNiQlJSEkJASysrJo3ry5cOzbtWuHqKgoXLp0Cc+fPxeG9/j6+uLAgQPYs2cPfvzxR9SsWVPIFAMgBBXLOm6lVbXNgYGBwjC3ESNG4NSpU1i8eDEyMzMREBCA//3vf+jWrRsAYMuWLdDV1S13nydPnsStW7dw/Phx4TxcsmSJRH2dqrTtQ95PafLy8ti0aROUlJTQokULLFiwANOnT8fChQslgju3b9/Grl27EBYWJnwQb9q0qTC/Zs2aouBWkyZNcP78eezatUv0Yb9OnTrw9/eHnJwcjI2N0bt3b5w6dQpjx45FamoqlJWV0adPH6iqqqJx48awtLQst/0PHjyAnJycaAhsdc6ByjRs2BAeHh7C60mTJuH48ePYtWsX2rZtW+56HTp0wMyZMwEUDYWOjo7Gb7/9hu7du0ssu337dvz999+4fPky6tatCwCib4vMzc1FQf+FCxdi//79OHToECZOnChMr+gaTU1NhaGhITp27AgZGZkyAz86OjpCxlLJaUDRca5qvcUFCxYI7zMnJwdLlizByZMnYW1tDaDonImKisKGDRtgY2MDZ2dn+Pn5ITU1FXp6ekJad8nzuaSdO3eioKAAf/75J2RkZAAAmzdvRu3atREeHo4ePXqgc+fOCA8Px6BBgxAeHo5Ro0bhzz//xK1bt9CsWTOcO3dOuC4q+9uto6NT6XX+9OlTABB94VT8unhead7e3hUGg4mIiIi+BrGxsXj+/DmsrKyEafn5+Th79iz8/f2RlJQEf39/JCQkoEWLFgCK/v+NjIzE2rVrsX79+n+8jV98gCo7O1si+hcfH48zZ86I6qAUS0lJwevXr5GTkyP8g1uWrVu3ljk9MjISmZmZuHDhAmbOnAkDAwM4OTl93Jv4RLp06YK4uDi8ePECGzduxJAhQ3Dx4kXRB0ZFRUVkZWUJr5OSknDp0iVh2F+NGjUwdOhQBAQElDnO1N7eHh07dsScOXMkvmmvTGFhofAhJjs7GwBEfZeRkYHHjx9L1KTq0KGDxFCyklka6enpePbsmehDqpycHFq3bi3KjkpOTsbcuXNx8eJFvHjxQpiXmpoqClCVfJJBcZDy+fPnZX7Injp1KsaMGYNt27bBzs4OgwcPFgXulJSURK81NTWhr68vOjc1NTUrHTZZUsn2ycjIQEtLS7T+zp07sXr1aqSkpCAzMxPv37+HmpqaaBv6+vqiekaampqQk5MTBUJKtis+Ph6ZmZnQ0NAQbSc7O1sYJtmwYUPcunVLmFdWFl55PqTN2traQvtSUlKQm5uLdu3aCfPr1q0rBO3KcvPmTTRq1EgUJC3+4F7dtn2KdczNzUX1saytrZGZmYm//vpLIogRFxcHOTm5CjM4165di02bNiE1NRXZ2dnIzc0VPTkQKBr2KycnJ7zW1tYWvh3p3r07GjdujKZNm8LBwQEODg7o379/uTW8srOzoaCgIFzjQPXOgcrk5+djyZIl2LVrFx49eoTc3Fzk5ORUWlOsdJ9aW1uX+2S/uLg4WFpaCsGp0jIzM+Hl5YWjR4/iyZMneP/+PbKzsyUyqCq6Rl1dXdG9e3c0b94cDg4O6NOnj8STTEvfp4unAZCYXpGS98k7d+4gKytLIjCXm5srBB4tLCxgYmKC7du3Y+bMmYiIiMDz588xePDgMrcfHx+PO3fuSNRGe/funXBfsLGxEYY4RkREYMmSJbh9+zbCw8Px8uVL5OXlCff8yv52F5/H1bnOq2LWrFmYOnWq8DojIwONGjX6qG0SERERfW66desmkQk1atQoGBsbw9PTU/g/s/SX43JychKjjv4pX3yAql69enj16pVoWmZmJhwdHbF06VKJ5bW1tT/qSUhNmjQBAJiZmeHZs2fw8vL6bAJUysrKMDAwgIGBAdq3bw9DQ0MEBARg1qxZwjIvX75E/fr1hdcBAQF4//696EN6YWEhFBQU4O/vD3V1dYn9+Pj4wNraGtOnT69y2/Lz85GcnIxvvvkGAKChoQEZGRmJvqsqZWXlaq/j6OiIxo0bY+PGjdDR0UFBQQFatmwpqvMDQDREr/jDdnkXpJeXF77//nscPXoUx44dw7x58xAcHCxkcpXcVvH2yppWnQu+ovXPnz8PZ2dnzJ8/H/b29lBXV0dwcLBEnZbqtiszMxPa2trC8L2SyhumZWRkBAC4detWhZk3H9Pmf/pGWdW2few61VUcrChPcHAwPDw84OfnB2tra6iqqmL58uWiIZtAxcdUVVUVV65cQXh4OE6cOIG5c+fCy8sLly9fLrPP69Wrh6ysLOTm5kJeXh5A1c+Bqli+fDlWrVqFlStXwszMDMrKypgyZYrE9fsxKjuuHh4eCAsLg6+vLwwMDKCoqIhBgwZVeA8BxMfVysoK9+7dw7Fjx3Dy5EkMGTIEdnZ2omG+pe/TxdMASEyvSMn7ZGZmJgDg6NGjaNiwoWi5kkXPnZ2dhQDV9u3b4eDgIBGYLrnN1q1bIygoSGJecTuLa6clJycjMTERHTt2xK1btxAeHo5Xr16hTZs2QpCxsr/dd+7cqfQ9F9d7fPbsmSgL+tmzZxIB2pLv/3Mv/E5ERET0sVRVVUWJGUDR/4saGhpo2bIl8vLyYGBggJ9++gm+vr7Q0NDAgQMHEBYWhiNHjvwrbfzia1BZWloiMTFRNM3Kygo3btyAvr6+ELAp/lFWVoahoSEUFRVx6tSpj9p3QUEBcnJyPmob/6Sy2peQkCB8UHz//j22bt0KPz8/xMXFCT/x8fHQ0dEptwBx27ZtMWDAAGHYTFVs2bIFr169EobZycvLw9TUVNR3ampq0NHRQXR0tGjd6OhomJqalrttdXV1aGpq4vLly8K0/Px8UV2StLQ0JCUlYfbs2ejWrRtMTEw+ODhWmpGREdzd3XHixAkMGDAAmzdv/iTb/RDnzp1D48aN8euvv6JNmzYwNDSUGCr0IaysrPD06VPUqFFD4poqPYy0mIWFBUxNTeHn51dmIOn169efrM3NmjVDzZo1RQGYV69e4fbt2+WuY2Jigr/++gtPnjwRpl24cEG0zIe07UPfT3x8vJBZWNwWFRWVMjM5zMzMUFBQIKrlU1J0dDS+/fZbjB8/HpaWljAwMBA9EKCqatSoATs7OyxbtgzXrl3D/fv3cfr06TKXLf7wX/Karuo5UBXR0dHo27cvhg8fDnNzczRt2rTC/i1Wuk8vXLgAExOTMpdt1aoV4uLiynzSaXEbXF1d0b9/f5iZmUFLS6taNaGKqampYejQodi4cSN27tyJvXv3ivZZ8j5dclrNmjWFdOvqMjU1hYKCAlJTUyWu4ZLn2Pfff4+EhATExsZiz549cHZ2LnebVlZWSE5ORoMGDSS2WfzlhpmZGerUqYNFixbBwsICKioqsLW1RUREBMLDw0WZupX97a7Kdd6kSRNoaWmJ/r5nZGTg4sWLZWZIEhEREVGRmjVrIiQkBPXr14ejo6PwsJktW7agV69e/0obvvgAlb29PW7cuCEKNkyYMAEvX76Ek5MTLl++jJSUFBw/fhyjRo1Cfn4+atWqBU9PT8yYMQNbt25FSkoKLly4IDwBCygqQFwy82jt2rU4fPgwkpOTkZycjICAAPj6+gq1m4Ci+hmlhw0mJiYKH3jS09OFIFCxS5cuwdjYGI8ePRKmpaamIi4uDqmpqcjPzxfWKf4GHCiq6VI8LO/t27f45ZdfcOHCBTx48ACxsbH44Ycf8OjRI9HQjKysLMTGxgrDSY4cOYJXr15h9OjRaNmypehn4MCBouNR2uLFi3H69GlRce6S+3n69CkePnyICxcuwNPTE+PGjcPPP/+MLl26iPouKipKtO706dOxdOlS7Ny5E0lJSZg5cybi4uIwefLkctsCFNWj8fb2xsGDB5GUlITJkyfj1atXQgZUnTp1oKGhgT/++AN37tzB6dOnRUM6PkR2djYmTpyI8PBwPHjwANHR0bh8+XK5H37/DYaGhkhNTUVwcDBSUlKwevVqiac2fgg7OztYW1ujX79+OHHiBO7fv49z587h119/FZ5a+OjRIxgbGwtFq2VkZLB582bcvn0bnTp1QkhICO7evYtr165h8eLF6Nu37ydrs4qKCkaPHo3p06fj9OnTSEhIgKura5mFuUu+JyMjI4wcORLx8fGIjIzEr7/+KlrmQ9r2oe8nNzcXo0ePRmJiIkJCQjBv3jxMnDixzPegr6+PkSNH4ocffsCBAwdw7949hIeHC0XQDQ0NERMTg+PHj+P27duYM2eOKIBbFUeOHMHq1asRFxeHBw8eYOvWrSgoKCh3OFX9+vVhZWUluqareg4ARSnH/v7+5bbH0NAQYWFhOHfuHG7evImffvoJz549q/R9REdHY9myZbh9+zbWrl2L3bt3l3s/cXJygpaWFvr164fo6GjcvXsXe/fuFYprGxoaYt++fUIg//vvv692Ft+KFSuwY8cO3Lp1C7dv38bu3buhpaUlykqLjIyUGPYXGRmJTp06VZrlVR5VVVV4eHjA3d0dW7ZsQUpKCq5cuYI1a9Zgy5YtwnL6+vr49ttvMXr0aOTn5+O7774rd5vOzs6oV68e+vbti8jISOE8dHNzw8OHDwEUnQOdO3dGUFCQEIxq1aoVcnJycOrUKdEw1cr+dlflOpeRkcGUKVOwaNEiHDp0CNevX4eLiwt0dHRET3UlIiIioqKnP5csf2FoaIi9e/fi2bNnePv2LeLj4zFixIh/rT1ffIDKzMwMVlZWoqdTFWfh5Ofno0ePHjAzM8OUKVNQu3Zt4R/ZOXPmYNq0aZg7dy5MTEwwdOhQUR2f1NRUUWZFQUEBZs2aBQsLC7Rp0wZr167F0qVLRU+0e/HihUSWQq9evWBpaYnDhw8jPDwclpaWom/Gs7KykJSUJHq0+9y5c2FpaYl58+YhMzNTWKc4EAAU1Y5KT08HUDQm9NatWxg4cCCMjIzg6OiItLQ0REZGir5tP3jwIPT09NCpUycARcP77OzsyhzGN3DgQMTExODatWtlHncjIyP88MMPePfuncS8jRs3QltbG82aNcOAAQOQmJiInTt34vfffxctN3r0aISEhAjvAwDc3NwwdepUTJs2DWZmZggNDcWhQ4ckio+X5unpCScnJ7i4uMDa2hoqKiqwt7cXalzJysoiODgYsbGxaNmyJdzd3bF8+fIKt1kZOTk5pKWlwcXFBUZGRhgyZAh69uwp1WK73333Hdzd3TFx4kRYWFjg3LlzmDNnzkdvV0ZGBiEhIejcuTNGjRoFIyMjDBs2DA8ePBCKEefl5SEpKUlUI6dt27aIiYmBgYEBxo4dCxMTE3z33Xe4ceOGcCP8VG1evnw5OnXqBEdHR9jZ2aFjx44ST/gsSVZWFvv370d2djbatm2LMWPGYPHixaJlPqRtH/p+unXrBkNDQ3Tu3BlDhw7Fd999By8vr3KXX7duHQYNGoTx48fD2NgYY8eOxdu3bwEAP/30EwYMGIChQ4eiXbt2SEtLE4p2V1Xt2rWxb98+dO3aFSYmJli/fj127NhRYQbPmDFjJIZ7VeUcAIpqDL148aLcbc+ePRtWVlawt7eHra2tEEiqzLRp0xATEwNLS0ssWrQIK1asgL29fZnLysvL48SJE2jQoAF69eoFMzMz+Pj4CHW6VqxYgTp16uDbb7+Fo6Mj7O3tRUUmq0JVVRXLli1DmzZt8M033+D+/fvCwwmAoiGi6enpGDRokGi94OBgjB07tlr7Km3hwoWYM2cOvL29YWJiAgcHBxw9elQYul7M2dkZ8fHx6N+/f4UBMSUlJZw9exZ6enoYMGAATExMMHr0aLx7905Uc83Gxgb5+flCgEpWVhadO3eGjIyMqOZgVf52V+U6nzFjBiZNmoQff/wR33zzDTIzMxEaGlru02qIiIiI6PMgU/gpq9hKydGjRzF9+nQkJCRUmDHxX9e+fXu4ubnh+++/l3ZTBIMHD4aVlZUoW+1TKCgogImJCYYMGYKFCxd+0m0TUdmys7PRvHlz7Ny5k8OpPtDQoUNhbm6OX375RZh27NgxTJs2DdeuXUONGl986cjPXkZGBtTV1THv7F3UUlGtfAUiIiKiz9xMy7LLsvxTiv+fSk9Pr/RhUSV9Ff/p9u7dG8nJyXj06BGfvFOOFy9eYMCAAZ9NQfdiy5cvx+HDhz96Ow8ePMCJEydgY2ODnJwc+Pv74969e59VMI7oa6eoqIitW7dWmAlF5cvNzYWZmRnc3d1F09++fYvNmzczOEVEREREX7WvIoOK6K+//sKwYcOQkJCAwsJCtGzZEj4+PujcubO0m1ZlkZGR6NmzZ7nzS9YgI6L/rnHjxuF///tfmfOGDx+O9evX/8st+rowg4qIiIi+Nl9KBhUDVESfiezsbFGx/NIMDAz+xdYQ0efq+fPnyMjIKHOempoaGjRo8C+36OvCABURERF9bb6UABXHCxB9JhQVFRmEIqJKNWjQgEEoIiIiIvrqsKI4ERERERERERFJFQNUREREREREREQkVQxQERERERERERGRVLEGFREREVEpU801qlXUk4iIiIg+DjOoiIiIiIiIiIhIqhigIiIiIiIiIiIiqWKAioiIiIiIiIiIpIoBKiIiIiIiIiIikioGqIiIiIiIiIiISKoYoCIiIiIiIiIiIqligIqIiIiIiIiIiKSKASoiIiIiIiIiIpIqBqiIiIiIiIiIiEiqGKAiIiIiIiIiIiKpYoCKiIiIiIiIiIikigEqIiIiIiIiIiKSKgaoiIiIiIiIiIhIqhigIiIiIiIiIiIiqWKAioiIiIiIiIiIpIoBKiIiIiIiIiIikioGqIiIiIiIiIiISKoYoCIiIiIiIiIiIqligIqIiIiIiIiIiKSKASoiIiIiIiIiIpIqBqiIiIiIiIiIiEiqGKAiIiIiIiIiIiKpYoCKiIiIiIiIiIikigEqIiIiIiIiIiKSKgaoiIiIiIiIiIhIqhigIiIiIiIiIiIiqWKAioiIiIiIiIiIpIoBKiIiIiIiIiIikioGqIiIiIiIiIiISKoYoCIiIiIiIiIiIqmqIe0GEBEREX0uCgsLAQAZGRlSbgkRERHRl6n4/6ji/6uqigEqIiIiov+TlpYGAGjUqJGUW0JERET0ZXvz5g3U1dWrvDwDVERERET/p27dugCA1NTUav1DRZ+3jIwMNGrUCH/99RfU1NSk3Rz6RNivXy/27deJ/fp1KqtfCwsL8ebNG+jo6FRrWwxQEREREf0fWdmi8pzq6ur85/krpKamxn79CrFfv17s268T+/XrVLpfP+SLPhZJJyIiIiIiIiIiqWKAioiIiIiIiIiIpIoBKiIiIqL/o6CggHnz5kFBQUHaTaFPiP36dWK/fr3Yt18n9uvX6VP2q0xhdZ/7R0RERERERERE9Akxg4qIiIiIiIiIiKSKASoiIiIiIiIiIpIqBqiIiIiIiIiIiEiqGKAiIiIi+j9r166Fvr4+atWqhXbt2uHSpUvSbhJVw9mzZ+Ho6AgdHR3IyMjgwIEDovmFhYWYO3cutLW1oaioCDs7OyQnJ0unsVRl3t7e+Oabb6CqqooGDRqgX79+SEpKEi3z7t07TJgwARoaGlBRUcHAgQPx7NkzKbWYqmLdunVo1aoV1NTUoKamBmtraxw7dkyYzz79Ovj4+EBGRgZTpkwRprFvvzxeXl6QkZER/RgbGwvzP1WfMkBFREREBGDnzp2YOnUq5s2bhytXrsDc3Bz29vZ4/vy5tJtGVfT27VuYm5tj7dq1Zc5ftmwZVq9ejfXr1+PixYtQVlaGvb093r179y+3lKojIiICEyZMwIULFxAWFoa8vDz06NEDb9++FZZxd3fH4cOHsXv3bkRERODx48cYMGCAFFtNldHV1YWPjw9iY2MRExODrl27om/fvrhx4wYA9unX4PLly9iwYQNatWolms6+/TK1aNECT548EX6ioqKEeZ+sTwuJiIiIqLBt27aFEyZMEF7n5+cX6ujoFHp7e0uxVfShABTu379feF1QUFCopaVVuHz5cmHa69evCxUUFAp37NghhRbSh3r+/HkhgMKIiIjCwsKifqxZs2bh7t27hWVu3rxZCKDw/Pnz0momfYA6deoU/vnnn+zTr8CbN28KDQ0NC8PCwgptbGwKJ0+eXFhYyOv1SzVv3rxCc3PzMud9yj5lBhURERH95+Xm5iI2NhZ2dnbCNFlZWdjZ2eH8+fNSbBl9Kvfu3cPTp09Ffayuro527dqxj78w6enpAIC6desCAGJjY5GXlyfqW2NjY+jp6bFvvxD5+fkIDg7G27dvYW1tzT79CkyYMAG9e/cW9SHA6/VLlpycDB0dHTRt2hTOzs5ITU0F8Gn7tMYnbTERERHRF+jFixfIz8+HpqamaLqmpiZu3bolpVbRp/T06VMAKLOPi+fR56+goABTpkxBhw4d0LJlSwBFfSsvL4/atWuLlmXffv6uX78Oa2trvHv3DioqKti/fz9MTU0RFxfHPv2CBQcH48qVK7h8+bLEPF6vX6Z27dohMDAQzZs3x5MnTzB//nx06tQJCQkJn7RPGaAiIiIiIqIvwoQJE5CQkCCqfUJfrubNmyMuLg7p6enYs2cPRo4ciYiICGk3iz7CX3/9hcmTJyMsLAy1atWSdnPoE+nZs6fwe6tWrdCuXTs0btwYu3btgqKi4ifbD4f4ERER0X9evXr1ICcnJ/HEmWfPnkFLS0tKraJPqbgf2cdfrokTJ+LIkSM4c+YMdHV1helaWlrIzc3F69evRcuzbz9/8vLyMDAwQOvWreHt7Q1zc3OsWrWKffoFi42NxfPnz2FlZYUaNWqgRo0aiIiIwOrVq1GjRg1oamqyb78CtWvXhpGREe7cufNJr1cGqIiIiOg/T15eHq1bt8apU6eEaQUFBTh16hSsra2l2DL6VJo0aQItLS1RH2dkZODixYvs489cYWEhJk6ciP379+P06dNo0qSJaH7r1q1Rs2ZNUd8mJSUhNTWVffuFKSgoQE5ODvv0C9atWzdcv34dcXFxwk+bNm3g7Ows/M6+/fJlZmYiJSUF2tran/R65RA/IiIiIgBTp07FyJEj0aZNG7Rt2xYrV67E27dvMWrUKGk3jaooMzMTd+7cEV7fu3cPcXFxqFu3LvT09DBlyhQsWrQIhoaGaNKkCebMmQMdHR3069dPeo2mSk2YMAHbt2/HwYMHoaqqKtQ0UVdXh6KiItTV1TF69GhMnToVdevWhZqaGiZNmgRra2u0b99eyq2n8syaNQs9e/aEnp4e3rx5g+3btyM8PBzHjx9nn37BVFVVhfpwxZSVlaGhoSFMZ99+eTw8PODo6IjGjRvj8ePHmDdvHuTk5ODk5PRJr1cGqIiIiIgADB06FH///Tfmzp2Lp0+fwsLCAqGhoRJFtenzFRMTgy5dugivp06dCgAYOXIkAgMDMWPGDLx9+xY//vgjXr9+jY4dOyI0NJR1Uj5z69atAwDY2tqKpm/evBmurq4AgN9++w2ysrIYOHAgcnJyYG9vj99///1fbilVx/Pnz+Hi4oInT55AXV0drVq1wvHjx9G9e3cA7NOvGfv2y/Pw4UM4OTkhLS0N9evXR8eOHXHhwgXUr18fwKfrU5nCwsLCT914IiIiIiIiIiKiqmINKiIiIiIiIiIikioGqIiIiIiIiIiISKoYoCIiIiIiIiIiIqligIqIiIiIiIiIiKSKASoiIiIiIiIiIpIqBqiIiIiIiIiIiEiqGKAiIiIiIiIiIiKpYoCKiIiIiIiIiIikigEqIiIiIiIiIiKSKgaoiIiIiIjoP8vV1RUyMjKQkZFBzZo1oampie7du2PTpk0oKCio1rYCAwNRu3btT9q+HTt2QE5ODhMmTPik2/036OvrY+XKldJuBhF9IRigIiIiIiKi/zQHBwc8efIE9+/fx7Fjx9ClSxdMnjwZffr0wfv376XatoCAAMyYMQM7duzAu3fvpNoWIqJ/EgNURERERET0n6agoAAtLS00bNgQVlZW+OWXX3Dw4EEcO3YMgYGBwnIrVqyAmZkZlJWV0ahRI4wfPx6ZmZkAgPDwcIwaNQrp6elCRpaXlxcAYNu2bWjTpg1UVVWhpaWF77//Hs+fP6+0Xffu3cO5c+cwc+ZMGBkZYd++faL5xRlbR44cQfPmzaGkpIRBgwYhKysLW7Zsgb6+PurUqQM3Nzfk5+cL67169QouLi6oU6cOlJSU0LNnTyQnJwvzvby8YGFhIdrXypUroa+vL7x2dXVFv3794OvrC21tbWhoaGDChAnIy8sDANja2uLBgwdwd3cXjgcRUUUYoCIiIiIiIiqla9euMDc3FwWFZGVlsXr1aty4cQNbtmzB6dOnMWPGDADAt99+i5UrV0JNTQ1PnjzBkydP4OHhAQDIy8vDwoULER8fjwMHDuD+/ftwdXWttA2bN29G7969oa6ujuHDhyMgIEBimaysLKxevRrBwcEIDQ1FeHg4+vfvj5CQEISEhGDbtm3YsGED9uzZI6zj6uqKmJgYHDp0COfPn0dhYSF69eolBJeq6syZM0hJScGZM2ewZcsWBAYGCgG9ffv2QVdXFwsWLBCOBxFRRWpIuwFERERERESfI2NjY1y7dk14PWXKFOF3fX19LFq0COPGjcPvv/8OeXl5qKurQ0ZGBlpaWqLt/PDDD8LvTZs2xerVq/HNN98gMzMTKioqZe67oKAAgYGBWLNmDQBg2LBhmDZtGu7du4cmTZoIy+Xl5WHdunVo1qwZAGDQoEHYtm0bnj17BhUVFZiamqJLly44c+YMhg4diuTkZBw6dAjR0dH49ttvAQBBQUFo1KgRDhw4gMGDB1f5+NSpUwf+/v6Qk5ODsbExevfujVOnTmHs2LGoW7cu5OTkhKwxIqLKMIOKiIiIiIioDIWFhaKhaSdPnkS3bt3QsGFDqKqqYsSIEUhLS0NWVlaF24mNjYWjoyP09PSgqqoKGxsbAEBqamq564SFheHt27fo1asXAKBevXpC8faSlJSUhOAUAGhqakJfX18U+NLU1BSGFN68eRM1atRAu3bthPkaGhpo3rw5bt68WdkhEWnRogXk5OSE19ra2lUaukhEVBYGqIiIiIiIiMpw8+ZNIVvp/v376NOnD1q1aoW9e/ciNjYWa9euBQDk5uaWu423b9/C3t4eampqCAoKwuXLl7F///5K1wsICMDLly+hqKiIGjVqoEaNGggJCcGWLVtETxesWbOmaL3ipxGWnladJxLKysqisLBQNK2s4X8fux8iopI4xI+IiIiIiKiU06dP4/r163B3dwdQlAVVUFAAPz8/yMoWfc+/a9cu0Try8vKiYuQAcOvWLaSlpcHHxweNGjUCAMTExFS477S0NBw8eBDBwcFo0aKFMD0/Px8dO3bEiRMn4ODg8EHvy8TEBO/fv8fFixeFIX5paWlISkqCqakpAKB+/fp4+vSpKIMsLi6u2vsq63gQEZWHGVRERERERPSflpOTg6dPn+LRo0e4cuUKlixZgr59+6JPnz5wcXEBABgYGCAvLw9r1qzB3bt3sW3bNqxfv160HX19fWRmZuLUqVN48eIFsrKyoKenB3l5eWG9Q4cOYeHChRW2Z9u2bdDQ0MCQIUPQsmVL4cfc3By9evUqs1h6VRkaGqJv374YO3YsoqKiEB8fj+HDh6Nhw4bo27cvgKIn8P39999YtmwZUlJSsHbtWhw7dqza+9LX18fZs2fx6NEjvHjx4oPbTET/DQxQERERERHRf1poaCi0tbWhr68PBwcHnDlzBqtXr8bBgweFGkvm5uZYsWIFli5dipYtWyIoKAje3t6i7Xz77bcYN24chg4divr162PZsmWoX78+AgMDsXv3bpiamsLHxwe+vr4VtmfTpk3o37+/qP5VsYEDB+LQoUMfFfDZvHkzWrdujT59+sDa2hqFhYUICQkRhuyZmJjg999/x9q1a2Fubo5Lly4JTySsjgULFuD+/fto1qwZ6tev/8HtJaL/BpnC0oOLiYiIiIiIiIiI/kXMoCIiIiIiIiIiIqligIqIiIiIiIiIiKSKASoiIiIiIiIiIpIqBqiIiIiIiIiIiEiqGKAiIiIiIiIiIiKpYoCKiIiIiIiIiIikigEqIiIiIiIiIiKSKgaoiIiIiIiIiIhIqhigIiIiIiIiIiIiqWKAioiIiIiIiIiIpIoBKiIiIiIiIiIikioGqIiIiIiIiIiISKr+H09f/KLlUgwyAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "directory = os.path.join(root, 'uniprot', 'research', 'fasta')\n",
    "all_files = [file for file in os.listdir(directory) if file.endswith('.fasta')]\n",
    "\n",
    "# List to store the data\n",
    "data_counts = []\n",
    "\n",
    "# Loop through the files in the directory\n",
    "for filename in all_files:\n",
    "    \n",
    "    match = re.search(r'_([0-9]+)\\.fasta$', filename)    \n",
    "    data_amount = int(match.group(1))\n",
    "\n",
    "    pattern = r'(\\(ec:\\d+\\.\\d+\\.\\d+\\.\\d+\\)AND\\(organism_name:.*\\)_reviewed\\d+)_\\d+\\.fasta$'\n",
    "    name_match = re.search(pattern, filename)\n",
    "    filename = name_match.group(1)   \n",
    "\n",
    "    \n",
    "    data_counts.append((filename, data_amount))\n",
    "\n",
    "# Sort data counts by data amount in descending order\n",
    "data_counts.sort(key=lambda x: x[1], reverse=True)\n",
    "\n",
    "# Separate filenames and data amounts for plotting\n",
    "filenames, data_amounts = zip(*data_counts)\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(12, 8))\n",
    "bars = plt.barh(filenames, data_amounts, color='skyblue')\n",
    "plt.xlabel('Data Amount')\n",
    "plt.ylabel('Filename')\n",
    "plt.title('Data Amount from UniProt')\n",
    "plt.tight_layout()\n",
    "\n",
    "# Add value labels to the end of each bar\n",
    "for bar in bars:\n",
    "    width = bar.get_width()\n",
    "    plt.text(width, bar.get_y() + bar.get_height()/2, f'{width}', \n",
    "             ha='left', va='center')\n",
    "\n",
    "# Save the plot as an image file\n",
    "plt.savefig('data/uniprot/research/fasta/data_amounts_bar_chart.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combine data with same EC Number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import pandas as pd\n",
    "\n",
    "directory = os.path.join(root, 'uniprot/research/')\n",
    "all_files = [file for file in os.listdir(os.path.join(directory, 'fasta')) if file.endswith('.fasta')]\n",
    "\n",
    "# store ec contents\n",
    "ec_content = {}\n",
    "df_content = {}\n",
    "\n",
    "for filename in all_files:\n",
    "    # regular expression to get ec number\n",
    "    match = re.search(r'\\(ec:(\\d+\\.\\d+\\.\\d+\\.\\d+)\\)', filename)\n",
    "    if match:\n",
    "        ec_number = match.group(1)\n",
    "        \n",
    "        # read csv file\n",
    "        csv_filename = f'{filename[:-6]}.csv'\n",
    "        csv_path = os.path.join(directory, 'fasta', csv_filename)\n",
    "        if os.path.exists(csv_path):\n",
    "            df = pd.read_csv(csv_path)\n",
    "        else:\n",
    "            print(f\"CSV file not found: {csv_path}\")\n",
    "            continue\n",
    "        \n",
    "        # read fasta file\n",
    "        with open(os.path.join(directory, 'fasta', filename), 'r') as file:\n",
    "            content = [line for line in file if line.strip()]\n",
    "        \n",
    "        if ec_number in ec_content:\n",
    "            ec_content[ec_number].extend(content)\n",
    "            df_content[ec_number] = pd.concat([df_content[ec_number], df], ignore_index=True)\n",
    "        else:\n",
    "            ec_content[ec_number] = content\n",
    "            df_content[ec_number] = df\n",
    "\n",
    "# create new file for each EC number\n",
    "output_directory = os.path.join(directory, 'motif/ec_num')\n",
    "os.makedirs(output_directory, exist_ok=True)\n",
    "\n",
    "# 創建一個函數來讀取 FASTA 文件並刪除重複序列\n",
    "def remove_duplicate_sequences(fasta_content):\n",
    "    sequences = {}\n",
    "    unique_content = []\n",
    "    for line in fasta_content:\n",
    "        if line.startswith('>'):\n",
    "            header = line\n",
    "            sequences[header] = \"\"\n",
    "        else:\n",
    "            sequences[header] += line.strip()\n",
    "    \n",
    "    for header, sequence in sequences.items():\n",
    "        if sequence not in unique_content:\n",
    "            unique_content.append(header)\n",
    "            unique_content.append(sequence + '\\n')\n",
    "    \n",
    "    return unique_content\n",
    "\n",
    "# 在寫入 FASTA 文件之前，先刪除重複序列\n",
    "for ec_number, content in ec_content.items():\n",
    "    fasta_filename = f\"ec_{ec_number}.fasta\"\n",
    "    csv_filename = f\"ec_{ec_number}.csv\"\n",
    "    \n",
    "    # 刪除重複序列\n",
    "    unique_content = remove_duplicate_sequences(content)\n",
    "    \n",
    "    with open(os.path.join(output_directory, fasta_filename), 'w') as file:\n",
    "        file.writelines(unique_content)\n",
    "    \n",
    "    if ec_number in df_content:\n",
    "        # Remove duplicate rows in DataFrame\n",
    "        df_unique = df_content[ec_number].drop_duplicates()\n",
    "        df_unique.to_csv(os.path.join(output_directory, csv_filename), index=False)\n",
    "    else:\n",
    "        print(f\"No DataFrame content for EC number: {ec_number}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistics of combined fasta file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from Bio import SeqIO\n",
    "import os\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "\n",
    "directory = os.path.join(root, 'uniprot/research')\n",
    "data_counts = []\n",
    "\n",
    "for filename in os.listdir(os.path.join(directory, 'motif/ec_num')):\n",
    "    if filename.endswith('.fasta'):\n",
    "        input_file = os.path.join(directory, 'motif/ec_num', filename)\n",
    "        records = list(SeqIO.parse(input_file, 'fasta'))\n",
    "    \n",
    "        data_amount = len(records)\n",
    "\n",
    "        ec_pattern = r'(\\d+\\.\\d+\\.\\d+\\.\\d+)'\n",
    "        ec_match = str(re.search(ec_pattern, filename).group(1))\n",
    "        ec_number = 'EC ' + ec_match\n",
    "\n",
    "        data_counts.append((ec_number, data_amount))\n",
    "\n",
    "# Sort data counts by data amount in descending order\n",
    "data_counts.sort(key=lambda x: x[1], reverse=True)\n",
    "\n",
    "# Separate filenames and data amounts for plotting\n",
    "ec_numbers, data_amounts = zip(*data_counts)\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(9,3))\n",
    "bars = plt.barh(ec_numbers, data_amounts, color='orange')\n",
    "plt.xlabel('Data Amount')\n",
    "plt.ylabel('EC Number')\n",
    "plt.title('EC Number + Organism from UniProt')\n",
    "plt.tight_layout()\n",
    "\n",
    "# Add value labels to the end of each bar\n",
    "for bar in bars:\n",
    "    width = bar.get_width()\n",
    "    plt.text(width, bar.get_y() + bar.get_height()/2, f'{width}', \n",
    "             ha='left', va='center')\n",
    "\n",
    "# Save the plot as an image file\n",
    "plt.savefig('data/uniprot/research/motif/ec_num/data_amounts_bar_chart.png', dpi=300, bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cluster, find representative sequence: EC + organism"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "from Bio import SeqIO\n",
    "import subprocess\n",
    "import re\n",
    "\n",
    "\n",
    "output_dir = os.path.join(root, 'ec_organism')\n",
    "fasta_dir = os.path.join(root, 'uniprot/research/motif/ec_num')\n",
    "all_files = [file for file in os.listdir(fasta_dir) if file.endswith('.fasta')]\n",
    "\n",
    "threshold = 0.9\n",
    "\n",
    "data_counts = []\n",
    "\n",
    "for filename in all_files:\n",
    "    input_file = os.path.join(fasta_dir, filename)\n",
    "    cluster_path = os.path.join(output_dir, f'cluster/{filename[:-6]}')\n",
    "    cluster_file = f'output{int(threshold*100)}'\n",
    "\n",
    "\n",
    "    os.makedirs(cluster_path, exist_ok=True)\n",
    "    \n",
    "    # subprocess.run(['cdhit', f'-i {input_file}', f'-o output', f'-c {threshold}', '-sc 0', '-g 1'])\n",
    "    subprocess.run(['cdhit', '-i', input_file, '-o', os.path.join(cluster_path,cluster_file), '-c', f'{threshold}', '-sc', '0', '-g', '1'], capture_output=True)\n",
    "\n",
    "\n",
    "    records = list(SeqIO.parse(os.path.join(cluster_path,cluster_file), 'fasta'))\n",
    "\n",
    "    ec_num = re.search(r'(\\d+\\.\\d+\\.\\d+\\.\\d+)', filename).group(1)\n",
    "    data_counts.append((f'EC {ec_num}', len(records)))\n",
    "\n",
    "    for record in records:\n",
    "        \n",
    "        id = re.search(r'\\|([^\\|]+)\\|', str(record.id)).group(1)\n",
    "        output_file = os.path.join(output_dir,f'scaffolding-pdbs/{id}.fasta')\n",
    "\n",
    "        with open(output_file, 'w') as f:\n",
    "            SeqIO.write(record, f, 'fasta')\n",
    "\n",
    "\n",
    "# Sort data counts by data amount in descending order\n",
    "data_counts.sort(key=lambda x: x[1], reverse=True)\n",
    "\n",
    "# Separate filenames and data amounts for plotting\n",
    "ec_numbers, data_amounts = zip(*data_counts)\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(9,3))\n",
    "bars = plt.barh(ec_numbers, data_amounts, color='orange')\n",
    "plt.xlabel('Data Amount')\n",
    "plt.ylabel('EC Number')\n",
    "plt.title('EC Number + Organism After Clustering using CD-Hit')\n",
    "plt.tight_layout()\n",
    "\n",
    "# Add value labels to the end of each bar\n",
    "for bar in bars:\n",
    "    width = bar.get_width()\n",
    "    plt.text(width, bar.get_y() + bar.get_height()/2, f'{width}', \n",
    "             ha='left', va='center')\n",
    "\n",
    "# Save the plot as an image file\n",
    "plt.savefig(os.path.join(output_dir,'cluster/data_amounts_bar_chart.png'), dpi=300, bbox_inches='tight')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison before and after clustering: EC + organism"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from Bio import SeqIO\n",
    "import os\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "dir_before = os.path.join(root, 'uniprot/research/motif/ec_num')\n",
    "dir_after = os.path.join(root, 'ec_organism/cluster')\n",
    "\n",
    "data_before = []\n",
    "data_after = []\n",
    "all_files = [file for file in os.listdir(dir_before) if file.endswith('.fasta')]\n",
    "threshold = 90\n",
    "\n",
    "for filename in all_files:\n",
    "    # Before clustering\n",
    "    input_file = os.path.join(dir_before, filename)\n",
    "    records = list(SeqIO.parse(input_file, 'fasta'))\n",
    "    ec_num = f'EC {re.search(r\"(\\d+\\.\\d+\\.\\d+\\.\\d+)\", filename).group(1)}'\n",
    "    data_before.append((ec_num, len(records)))\n",
    "\n",
    "    # After clustering\n",
    "    input_file = os.path.join(dir_after, f\"{filename[:-6]}/output{threshold}\")\n",
    "    records = list(SeqIO.parse(input_file, 'fasta'))\n",
    "    data_after.append((ec_num, len(records)))\n",
    "\n",
    "# Sort data counts by data amount in descending order\n",
    "data_before.sort(key=lambda x: x[1], reverse=True)\n",
    "data_after.sort(key=lambda x: x[1], reverse=True)\n",
    "\n",
    "# Create the combined bar plot\n",
    "fig, ax = plt.subplots(figsize=(9, 4))\n",
    "bar_width = 0.4\n",
    "x = [i for i in range(len(data_before))]\n",
    "\n",
    "# Plot the bars\n",
    "ax.barh([i - bar_width/2 for i in x], [count for _, count in data_before], bar_width, label=\"Before Clustering\", color=\"lightblue\")\n",
    "ax.barh([i + bar_width/2 for i in x], [count for _, count in data_after], bar_width, label=\"After Clustering\", color=\"orange\")\n",
    "\n",
    "# Add labels and title\n",
    "ax.set_xlabel(\"Data Amount\")\n",
    "ax.set_ylabel(\"EC Number\")\n",
    "ax.set_title(\"EC Number + Organism Data Comparison Before and After Clustering using CD-Hit\")\n",
    "ax.set_yticks(x)\n",
    "ax.set_yticklabels([ec_num for ec_num, _ in data_before])\n",
    "ax.legend()\n",
    "\n",
    "# Add value labels to each bar\n",
    "for bars, data in zip([ax.containers[0], ax.containers[1]], [data_before, data_after]):\n",
    "    for bar, (_, count) in zip(bars, data):\n",
    "        ax.text(count, bar.get_y() + bar.get_height()/2, str(count), ha=\"left\", va=\"center\")\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.savefig(os.path.join(root, 'ec_organism/data_amounts_bar_chart.png'), dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import time\n",
    "import shutil\n",
    "\n",
    "\n",
    "def clean_directory(directory_path, days_threshold = 30, threshold = False):\n",
    "    current_time = time.time()\n",
    "    \n",
    "    for item in os.listdir(directory_path):\n",
    "        item_path = os.path.join(directory_path, item)\n",
    "\n",
    "        def clean(item, path):        \n",
    "            if os.path.isfile(path):\n",
    "                os.remove(item_path)\n",
    "                print(f\"Removed file: {item}\")\n",
    "            elif os.path.isdir(path):\n",
    "                shutil.rmtree(path)\n",
    "                print(f\"Removed directory: {item}\")\n",
    "        \n",
    "        creation_time = os.path.getctime(item_path)\n",
    "        if threshold == True:\n",
    "            if (current_time - creation_time) // (24 * 3600) >= days_threshold:\n",
    "                clean(item, item_path)\n",
    "        else:\n",
    "            clean(item, item_path)\n",
    "            \n",
    "\n",
    "# Example usage\n",
    "# directory_to_clean = os.path.join('')\n",
    "# days_to_keep = 30\n",
    "\n",
    "# clean_directory(directory_to_clean, days_to_keep)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed file: log.txt\n"
     ]
    }
   ],
   "source": [
    "# clean_directory(os.path.join(root, 'uniprot/research/motif/interproscan'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get motif by using InterProScan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import subprocess\n",
    "import os\n",
    "import shutil\n",
    "from Bio import AlignIO, SeqIO\n",
    "from collect_data_webcrawl import *\n",
    "import numpy as np\n",
    "import re\n",
    "from tqdm import tqdm\n",
    "\n",
    "clean_directory(os.path.join(root, 'uniprot/research/motif/interproscan'))\n",
    "\n",
    "directory = os.path.join(root, 'uniprot/research/')\n",
    "all_files = [file for file in os.listdir(os.path.join(directory,'motif/ec_num')) if file.endswith('.fasta')]\n",
    "path_to_interproscan = '/home/chialun/tools/interproscan/interproscan-5.69-101.0/./interproscan.sh'\n",
    "\n",
    "\n",
    "\n",
    "for filename in all_files:\n",
    "\n",
    "    # if os.path.exists(os.path.join(directory, f'motif/all_data/{filename[:-6]}_details.csv')):\n",
    "    #     continue  \n",
    "\n",
    "    input_file = os.path.join(directory,'motif/ec_num', filename)\n",
    "    \n",
    "    interproscan_file = os.path.join(directory,f'motif/interproscan/{filename[:-6]}_motif')        \n",
    "    \n",
    "    subprocess.run([path_to_interproscan, '-i', input_file, '-b', interproscan_file, '-f tsv', '-appl Pfam', '-dp'],\n",
    "                    stdout=open(os.path.join(directory, 'motif/interproscan/log.txt'), 'w'))\n",
    "    \n",
    "    motif_df = pd.read_csv(os.path.join(directory,f'motif/interproscan/{filename[:-6]}_motif.tsv'), header=None, sep='\\t')\n",
    "\n",
    "    info_filename = f'{filename[:-6]}.csv'\n",
    "    info_df = pd.read_csv(os.path.join(directory, 'motif/ec_num', info_filename))\n",
    "\n",
    "    records = list(SeqIO.parse(input_file, 'fasta'))\n",
    "\n",
    "    ec_match = re.search(r\"ec_(\\d+\\.\\d+\\.\\d+\\.\\d+)\", filename)\n",
    "    ec_num = ec_match.group(1)\n",
    "\n",
    "    data = {\n",
    "        'EC': [],\n",
    "        'Entry': [],\n",
    "        'Protein': [],\n",
    "        'Organism': [],\n",
    "        'Motif_start': [],\n",
    "        'Motif_end': [],\n",
    "        'pH_left': [],\n",
    "        'pH_right': [],\n",
    "        'Temperature_left': [],\n",
    "        'Temperature_right': [],\n",
    "        'Sequence': []\n",
    "    }\n",
    "    \n",
    "\n",
    "    p_bar = tqdm(info_df.iterrows(), total=info_df.shape[0], desc=f'Processing {filename[:-6]}')\n",
    "    for _, row in p_bar:\n",
    "\n",
    "        # information\n",
    "        data['EC'].append(ec_num)\n",
    "        data['Entry'].append(row['Entry'])\n",
    "        data['Protein'].append(row['Protein names'])\n",
    "        data['Organism'].append(row['Organism'])\n",
    "        \n",
    "        # motif\n",
    "        motif_options = motif_df[motif_df.iloc[:, 0].str.contains(row['Entry'], na=False)].iloc[:, 5:9]\n",
    "        if len(motif_options) > 0:\n",
    "            target_motif = motif_options.loc[motif_options.iloc[:, 3].idxmin()]\n",
    "            data['Motif_start'].append(target_motif.iloc[1])\n",
    "            data['Motif_end'].append(target_motif.iloc[2])\n",
    "        else:\n",
    "            data['Motif_start'].append('')\n",
    "            data['Motif_end'].append('')\n",
    "\n",
    "        # temperature, pH value\n",
    "        organism = row['Organism']   \n",
    "        pattern = r'(\\d+) - (\\d+)'\n",
    "        # temperature\n",
    "        temperatures = crawl_brenda_table_by_ec(ec_num = ec_num, organism = organism, table='temperature')\n",
    "        temp_opt = temperatures['Temperature_Optimum'] if 'Temperature_Optimum' in temperatures else ''    \n",
    "        temp_match = re.search(pattern, temp_opt)    \n",
    "        if temp_match:\n",
    "            temp_left = temp_match.group(1)\n",
    "            temp_right = temp_match.group(2)\n",
    "            data['Temperature_left'].append(temp_left)\n",
    "            data['Temperature_right'].append(temp_right)\n",
    "        else:\n",
    "            data['Temperature_left'].append(temp_opt)\n",
    "            data['Temperature_right'].append('')        \n",
    "        # pH\n",
    "        pHs = crawl_brenda_table_by_ec(ec_num = ec_num, organism = organism, table='pH')\n",
    "        pH_opt = pHs['pH_Optimum'] if 'pH_Optimum' in pHs else ''\n",
    "        pH_match = re.search(pattern, pH_opt) \n",
    "        if pH_match:\n",
    "            pH_left = pH_match.group(1)\n",
    "            pH_right = pH_match.group(2)\n",
    "            data['pH_left'].append(pH_left)\n",
    "            data['pH_right'].append(pH_right)\n",
    "        else:\n",
    "            data['pH_left'].append(pH_opt)\n",
    "            data['pH_right'].append('')\n",
    "            \n",
    "        p_bar.set_postfix(entry=row['Entry'], organism=organism, pH_opt = pH_opt, temp_opt = temp_opt)\n",
    "\n",
    "        # sequence\n",
    "        sequenc=''\n",
    "        for record in records:\n",
    "            match = re.search(r'\\|([^\\|]+)\\|', str(record.id))\n",
    "            if match and match.group(1) == row['Entry']:\n",
    "                sequence = str(record.seq)\n",
    "                break\n",
    "        data['Sequence'].append(sequence)\n",
    "\n",
    "    train_df = pd.DataFrame(data)\n",
    "\n",
    "    output_filename = f'{filename[:-6]}_details.csv'\n",
    "    train_df.to_csv(os.path.join(directory, 'motif/all_data', output_filename), index=False)   \n",
    "\n",
    "print(f'Finished collating detailed information for all sequences in {os.path.join(directory, 'motif/all_data')}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collating motif dictionary for `ec_organism/scaffolding-pdbs`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# clean_directory(os.path.join(root, 'ec_organism/scaffolding-msas'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collating msas data fro `ec_organism/scaffolding-msas`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collating different EC and species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished writing EC Number and species in path ../../data/uniprot/research/species/species.xlsx\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import re\n",
    "import pandas as pd\n",
    "from openpyxl import load_workbook\n",
    "\n",
    "directory = os.path.join(root, 'uniprot/research/')\n",
    "enzyme_path = os.path.join(directory, 'detergent_enzymes_from_paper/detergents.xlsx')\n",
    "enzymes_file = pd.ExcelFile(enzyme_path)\n",
    "enzymes_sheets = pd.read_excel(enzymes_file, sheet_name=None)\n",
    "\n",
    "datas = {}\n",
    "for sheet_name, df in enzymes_sheets.items():\n",
    "    \n",
    "    if sheet_name.lower() not in ENZYME_TYPES:\n",
    "        continue\n",
    "    for ec, organism in zip(df['EC Number'], df['Source Organism']):\n",
    "\n",
    "        ec = ec if ec is not None else 'None'\n",
    "        match = re.match(r'^(\\S+\\s+\\S+)', organism)\n",
    "        species = match.group(1) if match else organism\n",
    "\n",
    "            \n",
    "        if ec not in datas:\n",
    "            datas[ec] = {\n",
    "                'EC': [],\n",
    "                'Species': [],\n",
    "                'Organism': []\n",
    "            }\n",
    "        datas[ec]['EC'].append(ec)\n",
    "        datas[ec]['Species'].append(species)\n",
    "        datas[ec]['Organism'].append(organism)\n",
    "\n",
    "\n",
    "\n",
    "output_path = os.path.join(directory, 'species/species.xlsx')\n",
    "\n",
    "# make sure path exist\n",
    "os.makedirs(os.path.dirname(output_path), exist_ok=True)\n",
    "\n",
    "with pd.ExcelWriter(output_path, engine='openpyxl') as writer:\n",
    "    # 寫入數據\n",
    "    for ec in datas:\n",
    "        df = pd.DataFrame(datas[ec])\n",
    "        df.to_excel(writer, sheet_name=str(ec), index=False)\n",
    "\n",
    "\n",
    "print(f'Finished writing EC Number and species in path {output_path}')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistics of species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# read in excel file\n",
    "directory = os.path.join(root, 'uniprot/research/')\n",
    "excel_file = os.path.join(directory, 'species/species.xlsx')\n",
    "xls = pd.ExcelFile(excel_file)\n",
    "\n",
    "# 儲存每個工作表的物種數量\n",
    "species_counts = {}\n",
    "\n",
    "# 遍歷每個工作表\n",
    "for sheet_name in xls.sheet_names:\n",
    "    # 讀取工作表\n",
    "    df = pd.read_excel(excel_file, sheet_name=sheet_name)\n",
    "    \n",
    "    # 統計不同物種的數量\n",
    "    unique_species_count = df['Species'].nunique()\n",
    "    \n",
    "    # 儲存結果\n",
    "    species_counts[sheet_name] = unique_species_count\n",
    "\n",
    "# 排序結果（可選）\n",
    "species_counts = dict(sorted(species_counts.items(), key=lambda item: item[1], reverse=True))\n",
    "\n",
    "# 繪製橫向長條圖\n",
    "fig, ax = plt.subplots(figsize=(7,4))\n",
    "bars = ax.barh(list(species_counts.keys()), list(species_counts.values()), color='khaki')\n",
    "\n",
    "# 設置標題和軸標籤\n",
    "ax.set_title('Number of Unique Species per EC Number')\n",
    "ax.set_xlabel('Number of Unique Species')\n",
    "ax.set_ylabel('EC Number')\n",
    "\n",
    "# 在長條末端添加數字\n",
    "for bar in bars:\n",
    "    width = bar.get_width()\n",
    "    ax.text(width, bar.get_y() + bar.get_height()/2, f'{width}', \n",
    "            ha='left', va='center')# , fontweight='bold'\n",
    "\n",
    "# 調整佈局\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "plt.savefig(os.path.join(directory, 'species/data_amounts_bar_chart.png'), dpi=300, bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EC Number + Species from UniProt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "from collect_data_webcrawl import *\n",
    "\n",
    "root = '../../data'\n",
    "\n",
    "directory = os.path.join(root, 'uniprot/research/')\n",
    "species_path = os.path.join(directory, 'species/species.xlsx')\n",
    "species_file = pd.ExcelFile(species_path)\n",
    "species_sheets = pd.read_excel(species_file, sheet_name=None)\n",
    "\n",
    "\n",
    "for sheet_name, df in species_sheets.items():\n",
    "    if sheet_name == 'nan':\n",
    "        continue\n",
    "    pro_bar = tqdm(df.iterrows(), total=len(df))\n",
    "    for index, row in pro_bar:   \n",
    "        query = f'(ec:{row['EC']})AND(organism_name:{row['Species']})'\n",
    "        pro_bar.set_postfix(processing = query)\n",
    "        \n",
    "        ''' unreviewed '''\n",
    "        data = crawl_uniprot_by_query(query=query,save_path=os.path.join(root, 'uniprot/research/species/fasta'), reviewed='false',\n",
    "                                    fields=['ec','accession', 'id', 'protein_name','organism_name','ph_dependence', 'temp_dependence', 'ft_motif','ft_act_site', 'ft_binding', 'ft_dna_bind', 'kinetics', 'cc_cofactor','cc_activity_regulation','cc_catalytic_activity','cc_function', 'cc_biotechnology'],\n",
    "                                    headers=['EC number', 'Entry', 'Entry Name','Protein names','Organism', 'pH dependence', 'Temperature dependence', 'Motif', 'Active site', 'Binding site', 'DNA binding', 'Kinetics', 'Cofactor', 'Activty regulation', 'Catalytic activity', 'Function [CC]', 'Biotechnological use'])\n",
    "        if len(data)>0:\n",
    "            download_fasta(query=query,save_path=os.path.join(root, 'uniprot/research/species/fasta'),reviewed='false')\n",
    "\n",
    "\n",
    "        ''' reviewed '''\n",
    "        data = crawl_uniprot_by_query(query=query,save_path=os.path.join(root, 'uniprot/research/species/fasta'),\n",
    "                                    fields=['ec','accession', 'id', 'protein_name','organism_name','ph_dependence', 'temp_dependence', 'ft_motif','ft_act_site', 'ft_binding', 'ft_dna_bind', 'kinetics', 'cc_cofactor','cc_activity_regulation','cc_catalytic_activity','cc_function', 'cc_biotechnology'],\n",
    "                                    headers=['EC number', 'Entry', 'Entry Name','Protein names','Organism', 'pH dependence', 'Temperature dependence', 'Motif', 'Active site', 'Binding site', 'DNA binding', 'Kinetics', 'Cofactor', 'Activty regulation', 'Catalytic activity', 'Function [CC]', 'Biotechnological use'])\n",
    "        if len(data)>0:\n",
    "            download_fasta(query=query,save_path=os.path.join(root, 'uniprot/research/species/fasta'))\n",
    "\n",
    "\n",
    "\n",
    "        \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combine data species data with same EC number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import pandas as pd\n",
    "\n",
    "directory = os.path.join(root, 'uniprot/research/')\n",
    "all_files = [file for file in os.listdir(os.path.join(directory, 'species/fasta')) if file.endswith('.fasta')]\n",
    "\n",
    "# store ec contents\n",
    "ec_content = {}\n",
    "df_content = {}\n",
    "\n",
    "for filename in all_files:\n",
    "    # regular expression to get ec number\n",
    "    match = re.search(r'\\(ec:(\\d+\\.\\d+\\.\\d+\\.\\d+)\\)', filename)\n",
    "    if match:\n",
    "        ec_number = match.group(1)\n",
    "        \n",
    "        # read csv file\n",
    "        csv_filename = f'{filename[:-6]}.csv'\n",
    "        csv_path = os.path.join(directory, 'species/fasta', csv_filename)\n",
    "        if os.path.exists(csv_path):\n",
    "            df = pd.read_csv(csv_path)\n",
    "        else:\n",
    "            print(f\"CSV file not found: {csv_path}\")\n",
    "            continue\n",
    "        \n",
    "        # read fasta file\n",
    "        with open(os.path.join(directory, 'species/fasta', filename), 'r') as file:\n",
    "            content = [line for line in file if line.strip()]\n",
    "        \n",
    "        if ec_number in ec_content:\n",
    "            ec_content[ec_number].extend(content)\n",
    "            df_content[ec_number] = pd.concat([df_content[ec_number], df], ignore_index=True)\n",
    "        else:\n",
    "            ec_content[ec_number] = content\n",
    "            df_content[ec_number] = df\n",
    "\n",
    "# create new file for each EC number\n",
    "output_directory = os.path.join(directory, 'species/motif/ec_num')\n",
    "os.makedirs(output_directory, exist_ok=True)\n",
    "\n",
    "def remove_duplicate_sequences(fasta_content):\n",
    "    sequences = {}\n",
    "    unique_content = []\n",
    "    for line in fasta_content:\n",
    "        if line.startswith('>'):\n",
    "            header = line\n",
    "            sequences[header] = \"\"\n",
    "        else:\n",
    "            sequences[header] += line.strip()\n",
    "    \n",
    "    for header, sequence in sequences.items():\n",
    "        if sequence not in unique_content:\n",
    "            unique_content.append(header)\n",
    "            unique_content.append(sequence + '\\n')\n",
    "    \n",
    "    return unique_content\n",
    "\n",
    "# 在寫入 FASTA 文件之前，先刪除重複序列\n",
    "for ec_number, content in ec_content.items():\n",
    "    fasta_filename = f\"ec_{ec_number}.fasta\"\n",
    "    csv_filename = f\"ec_{ec_number}.csv\"\n",
    "    \n",
    "    # 刪除重複序列\n",
    "    unique_content = remove_duplicate_sequences(content)\n",
    "    \n",
    "    with open(os.path.join(output_directory, fasta_filename), 'w') as file:\n",
    "        file.writelines(unique_content)\n",
    "    \n",
    "    if ec_number in df_content:\n",
    "        # Remove duplicate rows in DataFrame\n",
    "        df_unique = df_content[ec_number].drop_duplicates()\n",
    "        df_unique.to_csv(os.path.join(output_directory, csv_filename), index=False)\n",
    "    else:\n",
    "        print(f\"No DataFrame content for EC number: {ec_number}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check if there's duplicate in sp. versus other specific species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Bio import SeqIO\n",
    "import os\n",
    "import re\n",
    "import shutil\n",
    "from utils import *\n",
    "\n",
    "fasta_dir = os.path.join(root, 'uniprot/research/species/fasta')\n",
    "output_dir = os.path.join(root, 'species_sequence_data/fasta')\n",
    "os.makedirs(output_dir, exist_ok = True)\n",
    "\n",
    "clean_dir(output_dir)\n",
    "\n",
    "unk_specieses = [filename for filename in os.listdir(fasta_dir) if filename.endswith('.fasta') and re.search(r'\\(organism_name:([^\\|]+ sp.)\\)', filename)]\n",
    "\n",
    "k_specieses =  [filename for filename in os.listdir(fasta_dir) if filename.endswith('.fasta') and not re.search(r'\\(organism_name:([^\\|]+ sp.)\\)', filename)]\n",
    "\n",
    "\n",
    "for unk_species in unk_specieses:\n",
    "    ec = re.search(r'ec:(\\d+.\\d+.\\d+.\\d+)', unk_species).group(1)\n",
    "    genus = re.search(r'\\(organism_name:([^\\|]+) sp.\\)', unk_species).group(1)\n",
    "           \n",
    "\n",
    "    species_files = [filename for filename in os.listdir(fasta_dir) \n",
    "                                            if filename.endswith('.fasta') \n",
    "                                            and genus in filename\n",
    "                                            and not re.search(r'\\(organism_name:([^\\|] sp.)\\)', filename)]\n",
    "\n",
    "    fasta_file = os.path.join(fasta_dir,  unk_species)\n",
    "    records = list(SeqIO.parse(fasta_file, 'fasta'))\n",
    "\n",
    "    for record in records[:]:  # Iterate over a copy of the list\n",
    "        id = re.search(r'\\|([^\\|]+)\\|', record.id).group(1)\n",
    "\n",
    "        for species_file in species_files:\n",
    "            species_records = list(SeqIO.parse(os.path.join(fasta_dir, species_file), 'fasta'))\n",
    "            \n",
    "            found = [_ for _ in species_records if re.search(r'\\|([^\\|]+)\\|', _.id).group(1) == id]\n",
    "            \n",
    "            if len(found)>0:\n",
    "                records.remove(record)\n",
    "                break\n",
    "    if len(records)>0:\n",
    "        output_match = re.search(r'\\(ec:([^\\|]+)\\)AND\\(organism_name:([^\\|]+)\\)_reviewed' ,k_species)\n",
    "        output_name = (output_match.group(1) + '_' + output_match.group(2)).replace(' ','_').replace('.','_')\n",
    "        output_path = os.path.join(output_dir, f'{output_name}_{len(records)}.fasta')\n",
    "        with open(output_path, 'w') as f:\n",
    "            SeqIO.write(records, f, 'fasta')\n",
    "            \n",
    "\n",
    "\n",
    "for k_species in k_specieses:\n",
    "    src_file = os.path.join(fasta_dir, k_species)\n",
    "    \n",
    "    output_match = re.search(r'\\(ec:([^\\|]+)\\)AND\\(organism_name:([^\\|]+)\\)_reviewed' ,k_species)\n",
    "    output_name = (output_match.group(1) + '_' + output_match.group(2)).replace(' ','_').replace('.','_')\n",
    "    records = list(SeqIO.parse(src_file, 'fasta'))\n",
    "    output_path = os.path.join(output_dir, f'{output_name}_{len(records)}.fasta')\n",
    "    shutil.copy2(src_file, output_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistics of combined EC number + species: fasta file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from Bio import SeqIO\n",
    "import os\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "\n",
    "directory = os.path.join(root, 'uniprot/research')\n",
    "data_counts = []\n",
    "\n",
    "for filename in os.listdir(os.path.join(directory, 'species/motif/ec_num')):\n",
    "    if filename.endswith('.fasta'):\n",
    "        input_file = os.path.join(directory, 'species/motif/ec_num', filename)\n",
    "        records = list(SeqIO.parse(input_file, 'fasta'))\n",
    "    \n",
    "        data_amount = len(records)\n",
    "\n",
    "        ec_pattern = r'(\\d+\\.\\d+\\.\\d+\\.\\d+)'\n",
    "        ec_match = str(re.search(ec_pattern, filename).group(1))\n",
    "        ec_number = 'EC ' + ec_match\n",
    "\n",
    "        data_counts.append((ec_number, data_amount))\n",
    "\n",
    "# Sort data counts by data amount in descending order\n",
    "data_counts.sort(key=lambda x: x[1], reverse=True)\n",
    "\n",
    "# Separate filenames and data amounts for plotting\n",
    "ec_numbers, data_amounts = zip(*data_counts)\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(9,3))\n",
    "bars = plt.barh(ec_numbers, data_amounts, color='salmon')\n",
    "plt.xlabel('Data Amount')\n",
    "plt.ylabel('EC Number')\n",
    "plt.title('EC Number + Species from UniProt')\n",
    "plt.tight_layout()\n",
    "\n",
    "# Add value labels to the end of each bar\n",
    "for bar in bars:\n",
    "    width = bar.get_width()\n",
    "    plt.text(width, bar.get_y() + bar.get_height()/2, f'{width}', \n",
    "             ha='left', va='center')\n",
    "\n",
    "# Save the plot as an image file\n",
    "plt.savefig(os.path.join(directory, 'species/motif/ec_num/data_amounts_bar_chart.png'), dpi=300, bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cluster, find representative sequence: EC + Species"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## InterProScan of EC Number + species"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "projects\n",
    "└──EvoDiff\n",
    "    └──data  \n",
    "        ├── brenda       \n",
    "        └── uniprot  \n",
    "            └── research\n",
    "                ├── detergent_enzymes_from_paper\n",
    "                ├── fasta\n",
    "                ├── msa    \n",
    "                ├── motif  \n",
    "                    ├── all_info\n",
    "                    ├── ec_num\n",
    "                    ├── interproscan\n",
    "                    ├── original\n",
    "                    └── msa                       \n",
    "                └── species\n",
    "                    ├── fasta\n",
    "                    ├── msa\n",
    "                    └── motif  \n",
    "                        ├── all_info\n",
    "                        ├── ec_num\n",
    "                        ├── interproscan\n",
    "                        ├── original\n",
    "                        └── msa    \n",
    "    ├── src\n",
    "        ├── conditional_generation.py\n",
    "        ├── main.py\n",
    "        ├── rmsd_analysis.py\n",
    "        ├── percent_similarity_msa.py\n",
    "        └── utils.py    \n",
    "    ├── collect_data_webcrawl.py\n",
    "    ├── collate_data.ipynb\n",
    "    └── evodiff.ipynb\n",
    "├──TemStaPro\n",
    "    ├── models\n",
    "    ├── makefile\n",
    "    └── tempstapro\n",
    "├──EpHod\n",
    "    ├── ephod\n",
    "        ├── saved_models\n",
    "            ├── AAC-SVR\n",
    "            └── RLAT\n",
    "        ├── predict.py\n",
    "        ├── utils.py\n",
    "        └── models.py\n",
    "    └── README.rst\n",
    "└──ChatBot\n",
    "    ├── static\n",
    "        ├── css\n",
    "        ├── img\n",
    "        └── js\n",
    "    ├── templates\n",
    "        ├── about.html\n",
    "        ├── home.html\n",
    "        ├── results.html\n",
    "        └── template.html\n",
    "    ├── uploads\n",
    "    └── main.py\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistics of data in `../../data/ec_species/scaffolding-pdbs`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "644\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import urllib.request\n",
    "\n",
    "\n",
    "dir = os.path.join(root, 'ec_species/scaffolding-pdbs')\n",
    "files = [file for file in os.listdir(dir) if file.endswith('.pdb') \n",
    "                                            and 'clean' not in file\n",
    "                                            and 'reres' not in file]\n",
    "\n",
    "print(len(files))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Failed to download A0A8H6F1U9.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A8H6F1U9-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
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      "Failed to download A0A8H7I9Z1.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A8H7I9Z1-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A8H7IEA3.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A8H7IEA3-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A8H7LHN2.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A8H7LHN2-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A8H7LR45.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A8H7LR45-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A8H7LW48.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A8H7LW48-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A8H7LWI8.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A8H7LWI8-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A8H7M197.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A8H7M197-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A8H7M4K5.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A8H7M4K5-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A8H8NSK4.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A8H8NSK4-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A8H8STE0.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A8H8STE0-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A8H8SXP8.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A8H8SXP8-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A8H3CZ09.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A8H3CZ09-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A8H4GIS9.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A8H4GIS9-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A8H4GMK5.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A8H4GMK5-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A8H4EDX1.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A8H4EDX1-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A8H4H4C2.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A8H4H4C2-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A8H4HSS1.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A8H4HSS1-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A9P2BE51.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A9P2BE51-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A927FWY2.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A927FWY2-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
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      "Failed to download A0A927IU37.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A927IU37-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
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      "Failed to download A0A940D7H6.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A940D7H6-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A940DA01.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A940DA01-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A940DUZ6.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A940DUZ6-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A940DXE8.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A940DXE8-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A940WU24.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A940WU24-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A941ASU9.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A941ASU9-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
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      "Failed to download A0AA96MZ11.pdb from https://alphafold.ebi.ac.uk/files/AF-A0AA96MZ11-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0AA96S5B1.pdb from https://alphafold.ebi.ac.uk/files/AF-A0AA96S5B1-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A9X1DMT8.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A9X1DMT8-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0AAE2NX20.pdb from https://alphafold.ebi.ac.uk/files/AF-A0AAE2NX20-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0AA40RQB8.pdb from https://alphafold.ebi.ac.uk/files/AF-A0AA40RQB8-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A9Q4HPN1.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A9Q4HPN1-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A0F2TB35.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A0F2TB35-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A0B5I321.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A0B5I321-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A0G3UJL7.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A0G3UJL7-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A0H4BVP7.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A0H4BVP7-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A0K2AR10.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A0K2AR10-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A0L8LM47.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A0L8LM47-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
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      "Failed to download A0A0U3NHW7.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A0U3NHW7-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A101NB91.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A101NB91-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A101QA59.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A101QA59-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A101QEP8.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A101QEP8-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A101QZY0.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A101QZY0-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
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      "Failed to download A0A101T799.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A101T799-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A117Q2E1.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A117Q2E1-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
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      "Failed to download A0A1D3E0Y9.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A1D3E0Y9-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A1D7YHA7.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A1D7YHA7-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A1E5PCD8.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A1E5PCD8-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A1Q4YMN7.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A1Q4YMN7-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A1Q5D2G4.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A1Q5D2G4-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A1Q5DKC0.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A1Q5DKC0-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A1Q5HC54.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A1Q5HC54-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
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      "Failed to download A0A1Q5JWC4.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A1Q5JWC4-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A1Q5KFV1.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A1Q5KFV1-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A1Q5KNU8.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A1Q5KNU8-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
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      "Failed to download A0A1Q5M215.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A1Q5M215-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
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      "Failed to download A0A1Q5MYS6.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A1Q5MYS6-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A1R0LUK3.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A1R0LUK3-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
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      "Failed to download A0A1V6MRK3.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A1V6MRK3-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
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      "Failed to download A0A371Y1X1.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A371Y1X1-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
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      "Failed to download A0A6I6N5W9.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A6I6N5W9-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A7G1P5S2.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A7G1P5S2-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A7G3UI23.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A7G3UI23-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
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      "Failed to download A0A7W2DT52.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A7W2DT52-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
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      "Failed to download A0A7X0HC77.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A7X0HC77-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
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      "Failed to download A0A7Y9BK50.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A7Y9BK50-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
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      "Failed to download A0A918J9F2.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A918J9F2-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A918K7A2.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A918K7A2-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A918K8R6.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A918K8R6-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A918K8Z2.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A918K8Z2-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A918KKL2.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A918KKL2-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A918KLQ3.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A918KLQ3-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A918KRY7.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A918KRY7-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A918MTA7.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A918MTA7-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A918MTJ7.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A918MTJ7-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A918MU15.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A918MU15-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A918PID3.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A918PID3-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A918PJB2.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A918PJB2-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A918PRZ9.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A918PRZ9-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A918QQ88.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A918QQ88-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A918T6A9.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A918T6A9-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A918T8I2.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A918T8I2-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A918TBM1.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A918TBM1-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A918TXX3.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A918TXX3-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A918UFP1.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A918UFP1-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A918UGM9.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A918UGM9-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A918UML5.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A918UML5-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A918V4K9.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A918V4K9-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A918V4L4.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A918V4L4-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A918WBJ6.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A918WBJ6-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A918WLT4.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A918WLT4-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A918XYN6.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A918XYN6-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A918XZG0.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A918XZG0-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A918YCV2.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A918YCV2-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A918YFC0.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A918YFC0-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A918ZVY5.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A918ZVY5-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A919A636.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A919A636-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A919AC11.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A919AC11-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A919AV39.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A919AV39-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A919AVN3.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A919AVN3-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A919AYH4.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A919AYH4-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A919BB32.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A919BB32-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A919BVI6.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A919BVI6-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A919BVU5.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A919BVU5-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A919BX73.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A919BX73-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A919C4E7.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A919C4E7-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A919CCI0.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A919CCI0-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A919CCU2.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A919CCU2-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A919DV35.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A919DV35-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A919DVF6.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A919DVF6-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A919DYE2.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A919DYE2-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A919E1E9.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A919E1E9-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A919F100.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A919F100-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A919FZ02.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A919FZ02-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A919KEU4.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A919KEU4-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0AA44DCH9.pdb from https://alphafold.ebi.ac.uk/files/AF-A0AA44DCH9-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0AA51M929.pdb from https://alphafold.ebi.ac.uk/files/AF-A0AA51M929-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0AA97AMM0.pdb from https://alphafold.ebi.ac.uk/files/AF-A0AA97AMM0-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download B5HHD5.pdb from https://alphafold.ebi.ac.uk/files/AF-B5HHD5-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download D6ASL2.pdb from https://alphafold.ebi.ac.uk/files/AF-D6ASL2-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download D9VT87.pdb from https://alphafold.ebi.ac.uk/files/AF-D9VT87-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download E4NAC3.pdb from https://alphafold.ebi.ac.uk/files/AF-E4NAC3-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download G2NH70.pdb from https://alphafold.ebi.ac.uk/files/AF-G2NH70-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download K4RBG7.pdb from https://alphafold.ebi.ac.uk/files/AF-K4RBG7-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download Q82AS4.pdb from https://alphafold.ebi.ac.uk/files/AF-Q82AS4-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download S5VL58.pdb from https://alphafold.ebi.ac.uk/files/AF-S5VL58-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A918QE34.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A918QE34-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A8H6C1M5.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A8H6C1M5-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A8H6BR35.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A8H6BR35-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A8H6BU12.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A8H6BU12-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A8H6F0K7.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A8H6F0K7-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A8H6F224.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A8H6F224-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A8H6F4P5.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A8H6F4P5-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A8H6BUV4.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A8H6BUV4-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0A8H6F3L6.pdb from https://alphafold.ebi.ac.uk/files/AF-A0A8H6F3L6-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "DOWNLOADING PDB FILE FROM https://alphafold.ebi.ac.uk/files/AF-A0A1V2RC19-F1-model_v4.pdb\n",
      "DOWNLOADING PDB FILE FROM https://alphafold.ebi.ac.uk/files/AF-A0A2P9EXG4-F1-model_v4.pdb\n",
      "DOWNLOADING PDB FILE FROM https://alphafold.ebi.ac.uk/files/AF-A0A6C0Q9Z6-F1-model_v4.pdb\n",
      "DOWNLOADING PDB FILE FROM https://alphafold.ebi.ac.uk/files/AF-A0A117EEY6-F1-model_v4.pdb\n",
      "DOWNLOADING PDB FILE FROM https://alphafold.ebi.ac.uk/files/AF-A0A1L1QK12-F1-model_v4.pdb\n",
      "DOWNLOADING PDB FILE FROM https://alphafold.ebi.ac.uk/files/AF-A0A1V2RAZ3-F1-model_v4.pdb\n",
      "DOWNLOADING PDB FILE FROM https://alphafold.ebi.ac.uk/files/AF-A0A1V2RGA7-F1-model_v4.pdb\n",
      "DOWNLOADING PDB FILE FROM https://alphafold.ebi.ac.uk/files/AF-A0A1V2RSY1-F1-model_v4.pdb\n",
      "DOWNLOADING PDB FILE FROM https://alphafold.ebi.ac.uk/files/AF-A0A2Z5JWC5-F1-model_v4.pdb\n",
      "DOWNLOADING PDB FILE FROM https://alphafold.ebi.ac.uk/files/AF-A0A7R8P0E9-F1-model_v4.pdb\n",
      "DOWNLOADING PDB FILE FROM https://alphafold.ebi.ac.uk/files/AF-A0A7R8P4X8-F1-model_v4.pdb\n",
      "Failed to download A0AAI8L828.pdb from https://alphafold.ebi.ac.uk/files/AF-A0AAI8L828-F1-model_v4.pdb: HTTP Error 404: Not Found\n",
      "Failed to download A0AAI8PPA9.pdb from https://alphafold.ebi.ac.uk/files/AF-A0AAI8PPA9-F1-model_v4.pdb: HTTP Error 404: Not Found\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import urllib.request\n",
    "\n",
    "fastas = [file for file in os.listdir(dir) if file.endswith('.fasta')]\n",
    "\n",
    "for fasta in fastas:\n",
    "    pdb_id = fasta[:-6]\n",
    "    outfile = os.path.join(dir, f'{pdb_id}.pdb')\n",
    "    if not os.path.exists(outfile):\n",
    "        # alphafold pdb\n",
    "        url = f\"https://alphafold.ebi.ac.uk/files/AF-{pdb_id}-F1-model_v4.pdb\"       \n",
    "        \n",
    "        \n",
    "        try:\n",
    "            urllib.request.urlretrieve(url, outfile)\n",
    "            print(f\"DOWNLOADING PDB FILE FROM {url}\")\n",
    "        except Exception as e:\n",
    "            print(f\"Failed to download {pdb_id}.pdb from {url}: {e}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modify sp to sp. in /home/chialun/projects/evodiff/data/ec_species/motif_dict.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acinetobacter sp\n",
      "Bacillus sp\n",
      "Bacillus sp\n",
      "Bacillus sp\n",
      "Bacillus sp\n",
      "Bacillus sp\n",
      "Paenibacillus sp\n",
      "Paenibacillus sp\n",
      "Bacillus sp\n",
      "Bacillus sp\n",
      "Bacillus sp\n",
      "Bacillus sp\n",
      "Bacillus sp\n",
      "Bacillus sp\n",
      "Bacillus sp\n",
      "Bacillus sp\n",
      "Bacillus sp\n",
      "Bacillus sp\n",
      "Streptomyces sp\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import re\n",
    "import pandas as pd\n",
    "\n",
    "path = os.path.join(root, 'ec_species/motif_dict.csv')\n",
    "motif_df = pd.read_csv(path)\n",
    "\n",
    "for idx, row in motif_df.iterrows():\n",
    "    match = re.search(r' sp$', row['Species'])\n",
    "    if match:\n",
    "        print(row['Species'])\n",
    "        motif_df.at[idx, 'Species'] = row['Species'] + '.'\n",
    "\n",
    "motif_df.to_csv(path, index=False)\n",
    "\n",
    "\n"
   ]
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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