{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e872038e",
   "metadata": {},
   "source": [
    "# Pre-processing (Re-annotation)\n",
    "\n",
    "## Foreword\n",
    "\n",
    "`dandelion` is written in `Python 3` and is a single-cell BCR/TCR V(D)J-seq analysis package. For the pre-processing section, it integrates a standard pipeline for V(D)J gene reannotation with `IgBLAST`, similar to the fantastic [immcantation suite](https://immcantation.readthedocs.io/) [[Gupta2015]](https://academic.oup.com/bioinformatics/article/31/20/3356/195677). This work streamlines the pre-processing and exploratory stages for analyzing single-cell BCR/TCR V(D)J-seq data from 10X Genomics (and other technologies as long as they can be formatted or read into `dandelion` accordingly). Post-processed data from `dandelion` can be smoothly transferred to [scirpy](https://scirpy.scverse.org/)[Sturm2020](https://academic.oup.com/bioinformatics/article/36/18/4817/5866543) and [Scanpy](https://scanpy.readthedocs.io/)/[AnnData](https://anndata.readthedocs.io/) [[Wolf2018]](https://doi.org/10.1186/s13059-017-1382-0) object for integration and exploration of scV(D)J-seq data and scRNA-seq data. \n",
    "\n",
    "For some of the more advanced scV(D)J-seq analysis, specifically V(D)J trajectory analysis, there's also an `R` package `dandelionR` [Yu2024](https://doi.org/10.1016/j.csbj.2025.06.047) available on [GitHub](https://github.com/tuonglab/dandelionR) and [BioConductor](https://bioconductor.org/packages/release/bioc/html/dandelionR.html). If you are an `R` user and you want to perform pre-processing and re-annotation of your single-cell V(D)J-seq data, please refer to the `immcantation` suite and its documentation. Alternatively, there is a [singularity container](https://sc-dandelion.readthedocs.io/en/latest/notebooks/Q1-singularity-preprocessing.html) that can automate the steps outlined below.\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "\n",
    "Note\n",
    "    \n",
    "This section will cover the initial pre-processing of files after 10X's `CellRanger vdj` immune profiling data analysis (BCR) pipeline manually.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0a34653c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dandelion==1.0.0a1.dev36 pandas==2.3.3 numpy==2.3.5 matplotlib==3.10.6 networkx==3.6.1 scipy==1.15.2\n"
     ]
    }
   ],
   "source": [
    "# Import modules\n",
    "import dandelion as ddl\n",
    "\n",
    "# Set backend to \"base\" for the purpose of this tutorial as my environment already has polars installed so the default backend is polars. You can set the backend to \"base\" to use pandas instead.\n",
    "ddl.set_backend(\"base\")\n",
    "\n",
    "ddl.logging.print_versions()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b161e253",
   "metadata": {},
   "source": [
    "We will download the 10X data sets to process for this tutorial. The following function will download the tutorial files and place them in a folder called `dandelion_tutorial` in your current working directory.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0171992c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading filtered_feature_bc_matrix.h5 → dandelion_tutorial/vdj_v1_hs_pbmc3_b/filtered_feature_bc_matrix.h5\n",
      "Downloading filtered_contig_annotations.csv → dandelion_tutorial/vdj_v1_hs_pbmc3_b/filtered_contig_annotations.csv\n",
      "Downloading filtered_contig.fasta → dandelion_tutorial/vdj_v1_hs_pbmc3_b/filtered_contig.fasta\n",
      "Downloading filtered_feature_bc_matrix.h5 → dandelion_tutorial/vdj_nextgem_hs_pbmc3_b/filtered_feature_bc_matrix.h5\n",
      "Downloading filtered_contig_annotations.csv → dandelion_tutorial/vdj_nextgem_hs_pbmc3_b/filtered_contig_annotations.csv\n",
      "Downloading filtered_contig.fasta → dandelion_tutorial/vdj_nextgem_hs_pbmc3_b/filtered_contig.fasta\n",
      "Downloading filtered_feature_bc_matrix.h5 → dandelion_tutorial/sc5p_v2_hs_PBMC_10k_b/filtered_feature_bc_matrix.h5\n",
      "Downloading filtered_contig_annotations.csv → dandelion_tutorial/sc5p_v2_hs_PBMC_10k_b/filtered_contig_annotations.csv\n",
      "Downloading filtered_contig.fasta → dandelion_tutorial/sc5p_v2_hs_PBMC_10k_b/filtered_contig.fasta\n",
      "Downloading airr_rearrangement.tsv → dandelion_tutorial/sc5p_v2_hs_PBMC_10k_b/airr_rearrangement.tsv\n",
      "Downloading filtered_feature_bc_matrix.h5 → dandelion_tutorial/sc5p_v2_hs_PBMC_1k_b/filtered_feature_bc_matrix.h5\n",
      "Downloading filtered_contig_annotations.csv → dandelion_tutorial/sc5p_v2_hs_PBMC_1k_b/filtered_contig_annotations.csv\n",
      "Downloading filtered_contig.fasta → dandelion_tutorial/sc5p_v2_hs_PBMC_1k_b/filtered_contig.fasta\n"
     ]
    }
   ],
   "source": [
    "# new function to setup tutorial data\n",
    "from dandelion.tutorial import setup_dandelion_tutorial_bcr\n",
    "\n",
    "setup_dandelion_tutorial_bcr()\n",
    "\n",
    "# change to the tutorial data directory\n",
    "import os\n",
    "\n",
    "os.chdir(\"dandelion_tutorial\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "180c606e",
   "metadata": {},
   "source": [
    "`Dandelion`'s reannotation workflow requires `CellRanger` formatted-files to start, particularly either `all_contig.fasta` or `filtered_contig.fasta` and corresponding `all_contig_annotations.csv` and `filtered_contig_annotations.csv`. If you only have an `AIRR` rearrangement file, you can still load it into `dandelion` and write the files out with `.write_10x` function later.\n",
    "\n",
    "I'm running everything with the `filtered` files to speed up the tutorial but I would recommend starting with the `all` files as they contain more information, and it is easier to filter the contigs than to try and add them back in later.\n",
    "\n",
    "If you followed the installation instructions, you should have the requisite auxillary softwares installed already. Otherwise, you can download them manually: [blast+](https://ftp.ncbi.nih.gov/blast/executables/igblast/release/LATEST/) and [igblast](https://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/).\n",
    "\n",
    "For convenience, in `shell`, export the path to the database folders like as follows:\n",
    "```bash\n",
    "# ~/.bash_profile or ~/.bashrc if using bash or ~/.zshrc if you are using zsh\n",
    "echo \"export GERMLINE=$HOME/Documents/Github/dandelion/container/database/germlines/\" >> ~/.bash_profile\n",
    "echo \"export IGDATA=$HOME/Documents/Github/dandelion/container/database/igblast/\" >> ~/.bash_profile\n",
    "echo \"export BLASTDB=$HOME/Documents/Github/dandelion/container/database/blast/\" >> ~/.bash_profile\n",
    "source ~/.bash_profile\n",
    "```\n",
    "\n",
    "The databases for igblast are basically setup using [changeo's instructions](https://changeo.readthedocs.io/en/stable/examples/igblast.html). \n",
    "\n",
    "If you are using a jupyter notebook initialized via jupyterhub instance, you might want to try the fix to a known issue where pathing requires some adjustments https://github.com/zktuong/dandelion/discussions/146.\n",
    "\n",
    "For reannotation of constant genes, reference fasta files were downloaded from IMGT and only sequences corresponding to *CH1* region for each constant gene/allele were retained. The headers were trimmed to only keep the gene and allele information. Links to find the sequences can be found here : [human](http://www.imgt.org/genedb/GENElect?query=7.2+IGHC&species=Homo+sapiens) and [mouse](http://www.imgt.org/genedb/GENElect?query=7.2+IGHC&species=Mus).\n",
    "\n",
    "The utility function `ddl.utl.makeblastdb` is a wrapper for:\n",
    "\n",
    "```bash\n",
    "# bash/shell\n",
    "makeblastdb -dbtype nucl -parse_seqids -in $BLASTDB/human/human_BCR_C.fasta\n",
    "```\n",
    "\n",
    "This does the same thing:\n",
    "```python\n",
    "# python\n",
    "ddl.utl.makeblastdb(os.path.expanduser(\"~/Documents/Github/dandelion/container/database/blast/human/human_BCR_C.fasta\")\n",
    "```\n",
    "\n",
    "This section will now demonstrate how I batch process multiple samples/files from the same donor, as it will become important later on.\n",
    "\n",
    "Finally, because you are running the preprocessing manually, you will need to install some R packages so that they work later:\n",
    "\n",
    "```R\n",
    "install.packages(c(\"optparse\", \"airr\", \"shazam\", \"alakazam\", \"tigger\"))\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5bffc3c",
   "metadata": {},
   "source": [
    "## Step 1: Formatting the headers of the Cell Ranger fasta file\n",
    "Here, I'm adding a prefix to the headers of each contig in the fasta files, via the function `pp.format_fastas`. The prefix is basically just the folder name, so in this case it's:\n",
    "`sc5p_v2_hs_PBMC_1k`, `sc5p_v2_hs_PBMC_10k`, `vdj_v1_hs_pbmc3` and `vdj_nextgem_hs_pbmc3`.\n",
    "\n",
    "The function will also create sub-folders where a new fasta file and all subsequent files will be located. The function will also add the prefix to the corresponding annotation file automatically and create a copy in the same folder as the formatted fasta file. \n",
    "\n",
    "This is to ensure that the barcodes are consistent throughout so as not to interfere with subsequent integration with the gene expression data, which will be covered in subsequent sections. \n",
    "\n",
    "The file structure should look something like this later on if the settings are left as default. The tmp directory can be deleted once the initial preprocessing has completed.\n",
    "```console\n",
    "sc5p_v2_hs_PBMC_1k\n",
    "├── dandelion\n",
    "│   ├── filtered_contig.fasta\n",
    "│   ├── filtered_contig_annotations.csv\n",
    "│   ├── filtered_contig_igblast_db-pass_genotyped.tsv\n",
    "│   └── tmp\n",
    "│       ├── filtered_contig_igblast.fmt7\n",
    "│       ├── filtered_contig_igblast.tsv\n",
    "│       ├── filtered_contig_igblast_db-pass.blastsummary.txt\n",
    "│       ├── filtered_contig_igblast_db-pass.tsv\n",
    "│       ├── filtered_contig_igblast_db-pass.xml\n",
    "│       ├── filtered_contig_igblast_db-pass_genotyped.tsv\n",
    "│       ├── filtered_contig_igblast_db-pass_heavy_parse-select.tsv\n",
    "│       └── filtered_contig_igblast_db-pass_light_parse-select.tsv\n",
    "├── filtered_contig.fasta\n",
    "├── filtered_contig_annotations.csv\n",
    "└── filtered_feature_bc_matrix.h5\n",
    "```\n",
    "\n",
    "The first option of `pp.format_fastas` accepts a list of the fasta file paths to reformat, or list of names of folders containing the fasta files; each folder should only contain 1 fasta file, and 1 contig_annotation.csv. Make sure there's no hidden files and delete those if present.\n",
    "\n",
    "You can provide `prefixes` and/or `suffixes` to add the the cell/contig barcodes as a list and they will be formatted accordingly. The prefixes/suffixes will be separated by an underscore (`_`) if left as default but that can be adjusted with the `sep` option. \n",
    "\n",
    "If you choose not to provide a prefix/suffix, then the function will simply make a copy of the original files and place it in the `dandelion` sub-folders.\n",
    "\n",
    "For more complex experimental setups, such as with data from multiplexed experiments, please contact me (z.tuong@uq.edu.au) and I can walk you through a slightly more advanced set up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6a6c9dbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Formatting fasta(s) : 100%|██████████| 4/4 [00:00<00:00,  7.11it/s]\n"
     ]
    }
   ],
   "source": [
    "# the first option is a list of fasta files to format and the second option is the list of prefix to add to each file.\n",
    "samples = [\n",
    "    \"sc5p_v2_hs_PBMC_1k_b\",\n",
    "    \"sc5p_v2_hs_PBMC_10k_b\",\n",
    "    \"vdj_v1_hs_pbmc3_b\",\n",
    "    \"vdj_nextgem_hs_pbmc3_b\",\n",
    "]\n",
    "# because I'm using the 'filtered' files, i need to specify the filename_prefix\n",
    "ddl.pp.format_fastas(samples, prefix=samples, filename_prefix=\"filtered\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ad6b44f",
   "metadata": {},
   "source": [
    "Please specify the `filename_prefix` option for all preprocessing functions below (except for `quantify_mutations`).\n",
    "\n",
    "For example, use `filename_prefix=\"all\"` for `all_contig.fasta`, or `filename_prefix=<insertprefix>` for any files that are named `<insertprefix>_contig.fasta`. \n",
    "\n",
    "If you are running more than 1 sample and if each filename prefix needs to be specified, `filename_prefix` in `ddl.pp.format_fastas` will accept a list of prefixes i.e. `filename_prefix=['all', 'filtered', ...]`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cf28d7e",
   "metadata": {},
   "source": [
    "## Step 2: Reannotate the V/D/J genes with *igblastn*\n",
    "\n",
    "Like immcantation, we will reannotate the V(D)J genes with igblastn using the latest IMGT reference databases. Dandelion's `pp.reannotate_genes` will use `flavour=\"strict\"` to run `igblastn`, imposing lower e-value and higher D-penalty cut offs. The original behaviour i.e. with [changeo](https://changeo.readthedocs.io/en/stable/examples/10x.html)'s `AssignGenes.py`, is toggled with `flavour=\"original\"`. Additionally, there is now an additional `assign_dj` option (default is `True`), which will use blastn to assign a stricter call for the D and J genes because [igblastn can return random assignments if it cannot detect a V gene](https://www.ncbi.nlm.nih.gov/igblast/faq.html). In the `tmp` folder, there will also be a table where all alignments generated in this step will be shown. All the column headers are now adhering to the [AIRR](http://docs.airr-community.org/) standard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d54d87ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Assigning genes :   0%|          | 0/4 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         START> MakeDB\n",
      "       COMMAND> igblast\n",
      "  ALIGNER_FILE> filtered_contig_igblast.fmt7\n",
      "      SEQ_FILE> filtered_contig.fasta\n",
      "         NPROC> 8\n",
      "       ASIS_ID> False\n",
      "    ASIS_CALLS> False\n",
      "      VALIDATE> strict\n",
      "      EXTENDED> True\n",
      "INFER_JUNCTION> False\n",
      "\n",
      "PROGRESS> 14:54:26 |Done                | 0.0 min\n",
      "\n",
      "PROGRESS> 14:54:27 |####################| 100% (202) 0.0 min\n",
      "\n",
      "OUTPUT> filtered_contig_igblast_db-pass.tsv\n",
      "  PASS> 185\n",
      "  FAIL> 17\n",
      "   END> MakeDb\n",
      "\n",
      "         START> MakeDB\n",
      "       COMMAND> igblast\n",
      "  ALIGNER_FILE> filtered_contig_igblast.fmt7\n",
      "      SEQ_FILE> filtered_contig.fasta\n",
      "         NPROC> 8\n",
      "       ASIS_ID> False\n",
      "    ASIS_CALLS> False\n",
      "      VALIDATE> strict\n",
      "      EXTENDED> True\n",
      "INFER_JUNCTION> False\n",
      "\n",
      "PROGRESS> 14:54:27 |Done                | 0.0 min\n",
      "\n",
      "PROGRESS> 14:54:28 |####################| 100% (202) 0.0 min\n",
      "\n",
      "OUTPUT> filtered_contig_igblast_db-pass.tsv\n",
      "  PASS> 185\n",
      "  FAIL> 17\n",
      "   END> MakeDb\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Assigning genes :  25%|██▌       | 1/4 [00:13<00:39, 13.13s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         START> MakeDB\n",
      "       COMMAND> igblast\n",
      "  ALIGNER_FILE> filtered_contig_igblast.fmt7\n",
      "      SEQ_FILE> filtered_contig.fasta\n",
      "         NPROC> 8\n",
      "       ASIS_ID> False\n",
      "    ASIS_CALLS> False\n",
      "      VALIDATE> strict\n",
      "      EXTENDED> True\n",
      "INFER_JUNCTION> False\n",
      "\n",
      "PROGRESS> 14:55:37 |Done                | 0.0 min\n",
      "\n",
      "PROGRESS> 14:55:39 |####################| 100% (2,601) 0.0 min\n",
      "\n",
      "OUTPUT> filtered_contig_igblast_db-pass.tsv\n",
      "  PASS> 2409\n",
      "  FAIL> 192\n",
      "   END> MakeDb\n",
      "\n",
      "         START> MakeDB\n",
      "       COMMAND> igblast\n",
      "  ALIGNER_FILE> filtered_contig_igblast.fmt7\n",
      "      SEQ_FILE> filtered_contig.fasta\n",
      "         NPROC> 8\n",
      "       ASIS_ID> False\n",
      "    ASIS_CALLS> False\n",
      "      VALIDATE> strict\n",
      "      EXTENDED> True\n",
      "INFER_JUNCTION> False\n",
      "\n",
      "PROGRESS> 14:55:40 |Done                | 0.0 min\n",
      "\n",
      "PROGRESS> 14:55:41 |####################| 100% (2,601) 0.0 min\n",
      "\n",
      "OUTPUT> filtered_contig_igblast_db-pass.tsv\n",
      "  PASS> 2409\n",
      "  FAIL> 192\n",
      "   END> MakeDb\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Assigning genes :  50%|█████     | 2/4 [01:30<01:42, 51.17s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         START> MakeDB\n",
      "       COMMAND> igblast\n",
      "  ALIGNER_FILE> filtered_contig_igblast.fmt7\n",
      "      SEQ_FILE> filtered_contig.fasta\n",
      "         NPROC> 8\n",
      "       ASIS_ID> False\n",
      "    ASIS_CALLS> False\n",
      "      VALIDATE> strict\n",
      "      EXTENDED> True\n",
      "INFER_JUNCTION> False\n",
      "\n",
      "PROGRESS> 14:56:41 |Done                | 0.0 min\n",
      "\n",
      "PROGRESS> 14:56:42 |####################| 100% (2,059) 0.0 min\n",
      "\n",
      "OUTPUT> filtered_contig_igblast_db-pass.tsv\n",
      "  PASS> 1841\n",
      "  FAIL> 218\n",
      "   END> MakeDb\n",
      "\n",
      "         START> MakeDB\n",
      "       COMMAND> igblast\n",
      "  ALIGNER_FILE> filtered_contig_igblast.fmt7\n",
      "      SEQ_FILE> filtered_contig.fasta\n",
      "         NPROC> 8\n",
      "       ASIS_ID> False\n",
      "    ASIS_CALLS> False\n",
      "      VALIDATE> strict\n",
      "      EXTENDED> True\n",
      "INFER_JUNCTION> False\n",
      "\n",
      "PROGRESS> 14:56:42 |Done                | 0.0 min\n",
      "\n",
      "PROGRESS> 14:56:43 |####################| 100% (2,059) 0.0 min\n",
      "\n",
      "OUTPUT> filtered_contig_igblast_db-pass.tsv\n",
      "  PASS> 1841\n",
      "  FAIL> 218\n",
      "   END> MakeDb\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Assigning genes :  75%|███████▌  | 3/4 [02:32<00:55, 55.74s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         START> MakeDB\n",
      "       COMMAND> igblast\n",
      "  ALIGNER_FILE> filtered_contig_igblast.fmt7\n",
      "      SEQ_FILE> filtered_contig.fasta\n",
      "         NPROC> 8\n",
      "       ASIS_ID> False\n",
      "    ASIS_CALLS> False\n",
      "      VALIDATE> strict\n",
      "      EXTENDED> True\n",
      "INFER_JUNCTION> False\n",
      "\n",
      "PROGRESS> 14:58:14 |Done                | 0.0 min\n",
      "\n",
      "PROGRESS> 14:58:16 |####################| 100% (3,222) 0.0 min\n",
      "\n",
      "OUTPUT> filtered_contig_igblast_db-pass.tsv\n",
      "  PASS> 2976\n",
      "  FAIL> 246\n",
      "   END> MakeDb\n",
      "\n",
      "         START> MakeDB\n",
      "       COMMAND> igblast\n",
      "  ALIGNER_FILE> filtered_contig_igblast.fmt7\n",
      "      SEQ_FILE> filtered_contig.fasta\n",
      "         NPROC> 8\n",
      "       ASIS_ID> False\n",
      "    ASIS_CALLS> False\n",
      "      VALIDATE> strict\n",
      "      EXTENDED> True\n",
      "INFER_JUNCTION> False\n",
      "\n",
      "PROGRESS> 14:58:17 |Done                | 0.0 min\n",
      "\n",
      "PROGRESS> 14:58:19 |####################| 100% (3,222) 0.0 min\n",
      "\n",
      "OUTPUT> filtered_contig_igblast_db-pass.tsv\n",
      "  PASS> 2976\n",
      "  FAIL> 246\n",
      "   END> MakeDb\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Assigning genes : 100%|██████████| 4/4 [04:09<00:00, 62.38s/it]\n"
     ]
    }
   ],
   "source": [
    "ddl.pp.reannotate_genes(samples, filename_prefix=\"filtered\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e0b82a7",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "Note\n",
    "\n",
    "If you did not set a path to the igblast or germline paths in the environment above, you need to specify the path to the folders containing the fasta files directly.\n",
    "\n",
    "```python\n",
    "ddl.pp.reannotate_genes(samples, \n",
    "    igblast_db=\"database/igblast/\",\n",
    "    germline=\"database/germlines/imgt/human/vdj/\")\n",
    "```\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a06ec016",
   "metadata": {},
   "source": [
    "## Step 3 : Reassigning heavy chain V gene alleles *(optional but recommended)*\n",
    "\n",
    "Next, we use `immcantation`'s `TIgGER` [[Gadala-Maria2015]](https://www.pnas.org/content/112/8/E862) method to reassign allelic calls for heavy chain V genes with `pp.reassign_alleles`. As stated in TIgGER's [website](https://tigger.readthedocs.io/en/stable/) and [manuscript](https://www.pnas.org/content/112/8/E862), *'TIgGER is a computational method that significantly improves V(D)J allele assignments by first determining the complete set of gene segments carried by an individual (including novel alleles) from V(D)J-rearrange sequences. TIgGER can then infer a subject’s genotype from these sequences, and use this genotype to correct the initial V(D)J allele assignments.'*\n",
    "\n",
    "This may impact on how contigs are chosen for finding clones later. It is also important when considering to do mutational analysis. For convenience, germline sequences are reconstructed at this step using the corrected V-gene alleles. Therefore, it is highly recommended to run it. \n",
    "\n",
    "However, this will only work properly if there is sufficient contigs. An ideal scenario would be to run it on multiple samples from the same subject to allow for more information to be used to confidently assign a genotyped *v_call*. In this tutorial, I'm assuming the four samples can be split into two sets where sets of two corresponds to a different/single individual. So while important, this step can be skipped if you don't have enough data to do this. \n",
    "\n",
    "`pp.reassign_alleles` requires the `combined_folder` option to be specified so that a merged/concatenated file can be produced for running TIgGER."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5e0432c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing data file(s) :   0%|          | 0/2 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  START> ParseDb\n",
      "COMMAND> select\n",
      "   FILE> filtered_contig_igblast_db-pass.tsv\n",
      " FIELDS> locus\n",
      " VALUES> IGH\n",
      "  REGEX> True\n",
      "\n",
      "PROGRESS> 14:58:28 |####################| 100% (185) 0.0 min\n",
      "\n",
      "   OUTPUT> filtered_contig_igblast_db-pass_heavy_parse-select.tsv\n",
      "  RECORDS> 185\n",
      " SELECTED> 89\n",
      "DISCARDED> 96\n",
      "      END> ParseDb\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing data file(s) :  50%|█████     | 1/2 [00:00<00:00,  2.15it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  START> ParseDb\n",
      "COMMAND> select\n",
      "   FILE> filtered_contig_igblast_db-pass.tsv\n",
      " FIELDS> locus\n",
      " VALUES> IG[LK]\n",
      "  REGEX> True\n",
      "\n",
      "PROGRESS> 14:58:28 |####################| 100% (185) 0.0 min\n",
      "\n",
      "   OUTPUT> filtered_contig_igblast_db-pass_light_parse-select.tsv\n",
      "  RECORDS> 185\n",
      " SELECTED> 96\n",
      "DISCARDED> 89\n",
      "      END> ParseDb\n",
      "\n",
      "  START> ParseDb\n",
      "COMMAND> select\n",
      "   FILE> filtered_contig_igblast_db-pass.tsv\n",
      " FIELDS> locus\n",
      " VALUES> IGH\n",
      "  REGEX> True\n",
      "\n",
      "PROGRESS> 14:58:29 |####################| 100% (2,409) 0.0 min\n",
      "\n",
      "   OUTPUT> filtered_contig_igblast_db-pass_heavy_parse-select.tsv\n",
      "  RECORDS> 2409\n",
      " SELECTED> 1135\n",
      "DISCARDED> 1274\n",
      "      END> ParseDb\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing data file(s) : 100%|██████████| 2/2 [00:01<00:00,  1.73it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  START> ParseDb\n",
      "COMMAND> select\n",
      "   FILE> filtered_contig_igblast_db-pass.tsv\n",
      " FIELDS> locus\n",
      " VALUES> IG[LK]\n",
      "  REGEX> True\n",
      "\n",
      "PROGRESS> 14:58:29 |####################| 100% (2,409) 0.0 min\n",
      "\n",
      "   OUTPUT> filtered_contig_igblast_db-pass_light_parse-select.tsv\n",
      "  RECORDS> 2409\n",
      " SELECTED> 1274\n",
      "DISCARDED> 1135\n",
      "      END> ParseDb\n",
      "\n",
      "      Reassigning alleles\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Error in findNovelAlleles(db, germline_db = igv, v_call = v_call, j_call = j_call,  : \n",
      "  Not enough sample sequences were assigned to any germline:\n",
      "  (1) germline_min is too large or\n",
      "  (2) sequences names don't match germlines.\n",
      "Execution halted\n",
      "ERROR> Database file tutorial_scgp1/tutorial_scgp1_heavy_igblast_db-pass_genotyped.tsv does not exist.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Reassigning alleles\n",
      "null device \n",
      "          1 \n",
      "     START> CreateGermlines\n",
      "      FILE> tutorial_scgp1_heavy_igblast_db-pass_genotyped.tsv\n",
      "GERM_TYPES> dmask\n",
      " SEQ_FIELD> sequence_alignment\n",
      "   V_FIELD> v_call_genotyped\n",
      "   D_FIELD> d_call\n",
      "   J_FIELD> j_call\n",
      "    CLONED> False\n",
      "\n",
      "PROGRESS> 14:58:47 |####################| 100% (1,224) 0.0 min\n",
      "\n",
      " OUTPUT> tutorial_scgp1_heavy_igblast_db-pass_genotyped_germ-pass.tsv\n",
      "RECORDS> 1224\n",
      "   PASS> 1205\n",
      "   FAIL> 19\n",
      "    END> CreateGermlines\n",
      "\n",
      "     START> CreateGermlines\n",
      "      FILE> tutorial_scgp1_light_igblast_db-pass.tsv\n",
      "GERM_TYPES> dmask\n",
      " SEQ_FIELD> sequence_alignment\n",
      "   V_FIELD> v_call\n",
      "   D_FIELD> d_call\n",
      "   J_FIELD> j_call\n",
      "    CLONED> False\n",
      "\n",
      "PROGRESS> 14:58:49 |####################| 100% (1,370) 0.0 min\n",
      "\n",
      " OUTPUT> tutorial_scgp1_light_igblast_db-pass_germ-pass.tsv\n",
      "RECORDS> 1370\n",
      "   PASS> 1370\n",
      "   FAIL> 0\n",
      "    END> CreateGermlines\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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"
     },
     "metadata": {
      "image/png": {
       "height": 300,
       "width": 400
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Writing out to individual folders : 100%|██████████| 2/2 [00:00<00:00,  3.80it/s]\n"
     ]
    }
   ],
   "source": [
    "# reassigning alleles on the first set of samples\n",
    "ddl.pp.reassign_alleles(\n",
    "    samples[:2], combined_folder=\"tutorial_scgp1\", filename_prefix=\"filtered\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "760adb6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing data file(s) :   0%|          | 0/2 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  START> ParseDb\n",
      "COMMAND> select\n",
      "   FILE> filtered_contig_igblast_db-pass.tsv\n",
      " FIELDS> locus\n",
      " VALUES> IGH\n",
      "  REGEX> True\n",
      "\n",
      "PROGRESS> 14:58:50 |####################| 100% (1,841) 0.0 min\n",
      "\n",
      "   OUTPUT> filtered_contig_igblast_db-pass_heavy_parse-select.tsv\n",
      "  RECORDS> 1841\n",
      " SELECTED> 833\n",
      "DISCARDED> 1008\n",
      "      END> ParseDb\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing data file(s) :  50%|█████     | 1/2 [00:00<00:00,  1.18it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  START> ParseDb\n",
      "COMMAND> select\n",
      "   FILE> filtered_contig_igblast_db-pass.tsv\n",
      " FIELDS> locus\n",
      " VALUES> IG[LK]\n",
      "  REGEX> True\n",
      "\n",
      "PROGRESS> 14:58:51 |####################| 100% (1,841) 0.0 min\n",
      "\n",
      "   OUTPUT> filtered_contig_igblast_db-pass_light_parse-select.tsv\n",
      "  RECORDS> 1841\n",
      " SELECTED> 1008\n",
      "DISCARDED> 833\n",
      "      END> ParseDb\n",
      "\n",
      "  START> ParseDb\n",
      "COMMAND> select\n",
      "   FILE> filtered_contig_igblast_db-pass.tsv\n",
      " FIELDS> locus\n",
      " VALUES> IGH\n",
      "  REGEX> True\n",
      "\n",
      "PROGRESS> 14:58:51 |####################| 100% (2,976) 0.0 min\n",
      "\n",
      "   OUTPUT> filtered_contig_igblast_db-pass_heavy_parse-select.tsv\n",
      "  RECORDS> 2976\n",
      " SELECTED> 1330\n",
      "DISCARDED> 1646\n",
      "      END> ParseDb\n",
      "\n",
      "  START> ParseDb\n",
      "COMMAND> select\n",
      "   FILE> filtered_contig_igblast_db-pass.tsv\n",
      " FIELDS> locus\n",
      " VALUES> IG[LK]\n",
      "  REGEX> True\n",
      "\n",
      "PROGRESS> 14:58:52 |####################| 100% (2,976) 0.0 min\n",
      "\n",
      "   OUTPUT> filtered_contig_igblast_db-pass_light_parse-select.tsv\n",
      "  RECORDS> 2976\n",
      " SELECTED> 1646\n",
      "DISCARDED> 1330\n",
      "      END> ParseDb\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing data file(s) : 100%|██████████| 2/2 [00:01<00:00,  1.08it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Reassigning alleles\n",
      "null device \n",
      "          1 \n",
      "     START> CreateGermlines\n",
      "      FILE> tutorial_scgp2_heavy_igblast_db-pass_genotyped.tsv\n",
      "GERM_TYPES> dmask\n",
      " SEQ_FIELD> sequence_alignment\n",
      "   V_FIELD> v_call_genotyped\n",
      "   D_FIELD> d_call\n",
      "   J_FIELD> j_call\n",
      "    CLONED> False\n",
      "\n",
      "PROGRESS> 14:59:20 |####################| 100% (2,163) 0.0 min\n",
      "\n",
      " OUTPUT> tutorial_scgp2_heavy_igblast_db-pass_genotyped_germ-pass.tsv\n",
      "RECORDS> 2163\n",
      "   PASS> 2126\n",
      "   FAIL> 37\n",
      "    END> CreateGermlines\n",
      "\n",
      "     START> CreateGermlines\n",
      "      FILE> tutorial_scgp2_light_igblast_db-pass.tsv\n",
      "GERM_TYPES> dmask\n",
      " SEQ_FIELD> sequence_alignment\n",
      "   V_FIELD> v_call\n",
      "   D_FIELD> d_call\n",
      "   J_FIELD> j_call\n",
      "    CLONED> False\n",
      "\n",
      "PROGRESS> 14:59:22 |####################| 100% (2,654) 0.0 min\n",
      "\n",
      " OUTPUT> tutorial_scgp2_light_igblast_db-pass_germ-pass.tsv\n",
      "RECORDS> 2654\n",
      "   PASS> 2654\n",
      "   FAIL> 0\n",
      "    END> CreateGermlines\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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"
     },
     "metadata": {
      "image/png": {
       "height": 300,
       "width": 400
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Writing out to individual folders : 100%|██████████| 2/2 [00:00<00:00,  2.18it/s]\n"
     ]
    }
   ],
   "source": [
    "# reassigning alleles on the second set of samples\n",
    "ddl.pp.reassign_alleles(\n",
    "    samples[2:], combined_folder=\"tutorial_scgp2\", filename_prefix=\"filtered\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7587731",
   "metadata": {},
   "source": [
    "We can see that most of the original ambiguous V calls have now been corrected and only a few remain.\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "    \n",
    "Note\n",
    "    \n",
    "Similar to above, if you you can specify the path to the folder containing the fasta files accordingly:\n",
    "\n",
    "```python\n",
    "ddl.pp.reassign_alleles(samples[2:], combined_folder='tutorial_scgp2', germline=\"database/germlines/imgt/human/vdj\", filename_prefix=\"filtered\")\n",
    "```\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c546938",
   "metadata": {},
   "source": [
    "## Step 4: Assigning constant region calls\n",
    "\n",
    "Cell Ranger's annotation files provides a *c_gene* column, but rather than simply relying on Cell Ranger's annotation, it is common to use *[immcantation-presto's MaskPrimers.py](https://presto.readthedocs.io/en/version-0.5.3---license-change/tools/MaskPrimers.html)* with a custom primer list. \n",
    "\n",
    "As an alternative, `dandelion` includes a pre-processing function, `pp.assign_isotypes`, to use *blastn* to annotate constant region calls for all contigs and retrieves the call, merging it with the tsv files. This function will overwrite the output from previous steps and add a *c_call* column at the end, or replace the existing column if it already exists. The Cell Ranger calls are returned as `c_call_10x`.\n",
    "\n",
    "Further, to deal with incorrect constant gene calls due to insufficient length, a pairwise alignment will be run against [curated sequences](https://immunology.sciencemag.org/content/6/56/eabe6291) that were deemed to be highly specific in distinguishing `IGHA1` vs `IGHA2`, and `IGHG1` to `IGHG4`. I have also curated sets of sequences that should help deal with `IGLC3/6/7` as these are problematic too. If there is insufficient info, the `c_call` will be returned as a combination of the most aligned sets of sequences. Because of how similar the lambda light chains are, extremely ambiguous calls (only able to map to a common sequence across the light chains) will be returned as `IGLC`. This typically occurs when the constant sequence is very short. Those that have equal alignment scores between `IGLC3/6/7` sequences and the common sequence will be returned as a concatenated call; for example, a contig initially annotated as `IGLC3` will be returned as `IGLC,IGLC3`. \n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "\n",
    "Note\n",
    "\n",
    "The curated sequences can be updated/replaced with a dict-of-dict-of-dict style dictionary via the option `correction_dict`. The provided dictionary should be a nested dictionary like the following:\n",
    "```python\n",
    "primer_dict = {\n",
    "    'IGHG':{\n",
    "        'IGHG1':'GCCTCCACCAAGGGCCCATCGGTCTTCCCCCTGGCACCCTCCTCCAAGAGCACCTCTGGGGGCACAGCGGCCCTGGGC',\n",
    "        'IGHG2':'GCCTCCACCAAGGGCCCATCGGTCTTCCCCCTGGCGCCCTGCTCCAGGAGCACCTCCGAGAGCACAGCGGCCCTGGGC',\n",
    "        'IGHG3':'GCTTCCACCAAGGGCCCATCGGTCTTCCCCCTGGCGCCCTGCTCCAGGAGCACCTCTGGGGGCACAGCGGCCCTGGGC',\n",
    "        'IGHG4':'GCTTCCACCAAGGGCCCATCCGTCTTCCCCCTGGCGCCCTGCTCCAGGAGCACCTCCGAGAGCACAGCCGCCCTGGGC'}}\n",
    "```\n",
    "\n",
    "The key for the first level of the dictionary is used for searching whether the string pattern exists in the `c_call`, and the second level holds the dictionary for the the reference sequences to align to. The keys in the second level are used for replacing the existing `c_call` annotation if it is returned with the highest alignment score. The function currently only accepts 2-4 reference sequences for the pairwise alignment.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "acfceb09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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"
     },
     "metadata": {
      "image/png": {
       "height": 400,
       "width": 400
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/uqztuong/Documents/GitHub/dandelion/src/dandelion/base/preprocessing/_preprocessing.py:4234: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "/Users/uqztuong/Documents/GitHub/dandelion/src/dandelion/base/preprocessing/_preprocessing.py:4449: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAyAAAAMgCAYAAADbcAZoAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjYsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvq6yFwwAAAAlwSFlzAAAewgAAHsIBbtB1PgAAwoNJREFUeJzs3XlY1WX+//HnYREREBSlENJUChHNJTU118ytNE1nJkZsM6dmGqfNbbTFmiGy0vSY6TT1bTUtG7O0zIVFylE0TQshc0EUURLliCwKh8P5/cGPMxLnALIcDF+P6+q68Nz35/N5n9N2XtybwWq1WhEREREREXECl4YuQERERERErh4KICIiIiIi4jQKICIiIiIi4jQKICIiIiIi4jQKICIiIiIi4jQKICIiIiIi4jQKICJ1pFevXgQHB9OrV6+GLkVERETkiuXW0AWINBaZmZlkZGQ0dBkiIiIiVzSNgIiIiIiIiNMogIiIiIiIiNMogIiIiIiIiNMogIiIiIiIiNMogIiIiIiIiNNoFyyRK1Tuoi6XfY3Pk/vroRIRERGRuqMREBERERERcRoFEBERERERcRoFEBERERERcRoFEBERERERcRoFEBERERERcRoFEBERERERcRoFEBERERERcRoFEBERERERcRodRHiVW7x4MXFxcXTp0oXo6Gi7fZKTk4mPjyc5OZns7GwsFgu+vr4EBATQs2dPBgwYQGBgoN1rY2NjMRqNAKxbt67KeqZOncrp06eJiIhg0qRJDvtZrVYeeeQRMjMzAXjttdcICQmp8v6Xeuutt1i/fj2vvPIKnTp1uqxrRURERKRmFEDEoby8PIxGIzt37gTAxcUFPz8/XFxcMJlMZGVlkZyczKpVq5gwYQKRkZEYDAan1JaUlGQLH1AadC4ngFy4cIFvvvmmPkoTERERkUoogIhd+fn5zJ49m/T0dHx8fIiIiGDw4ME0b94cKB2BOHDgABs2bCAhIYHVq1djsVi4//77nVLfli1bAPD19SUnJ4dvvvmGKVOm4O7uXul1VquVw4cP88EHH5CTk+OMUkVERETkEgogYpfRaCQ9PZ3WrVsTFRVVYYqVwWAgLCyMsLAwgoKCWLlyJWvXrmX48OG0adOmXmvLy8tjx44dADz22GO8+OKL5Obm8t1339G/f3+H1z311FNkZGRw4cKFeq2vriy96dXLvmZOPdQhIiIiUpe0CF0qSElJITExEYAZM2Y4XN9RZuLEiXTu3Jng4GBSUlLqvb6EhASKiopo164dvXv3plu3bgDExcVVet2ZM2fw8PDAz88PPz+/eq9TRERERCrSCIhUsGnTJgC6du1KWFhYlf3d3d2ZP39+fZdlUzb9atiwYQAMGTKEvXv3smfPHs6dO+cwXHzwwQfl/nzXXXfVa50iIiIiUpFGQKSCpKQkAHr06NHAlVR05MgRUlNTcXV1ZciQIQD07dsXDw8PLBYLW7dubdD6RERERKRyGgGRcsxmM2fOnAEgKCjIbp+oqCgyMjLstvn7+xMVFWW3rS5GHMpGP26++WbbSIenpyd9+vTh22+/JS4ujvHjx9f6OY6sWLGClStX2m0r+9xERERExDEFECnn/Pnztp+bNWtmt09mZqbDAGI2mx3e21Gg+fW9LRaL3baioiISEhIAuP3228u1DRkyhG+//Za0tDSOHDlCx44dq3xWTeTn53P69Gm7bSUlJfXyTBEREZHGRAFEyvHw8LD9fPHiRbt9li5dWuG1mJgYlixZUum9ly9fXuXzyw4itGf79u3k5+fj6+tLr169yrX16NEDHx8fcnNziYuLq7cA4uXlRUBAgN02FxfNaBQRERGpigKIlOPl5YW7uztms7ncQX9VuXTkpL6UTb/KyclhwoQJDvslJCTw4IMP4uZW9/94T548mcmTJ9ttCw4OdjgyJCIiIiKl9CtbKcdgMBAaGgr8bzF6dRw8eLC+SgLg1KlT7N+/Hyg9fLBsK91f/wWlYei7776r13pEREREpGY0AiIV9OvXj/3797Nnzx5OnTpV5TkgJpOJ3bt312tNMTExWK1WgoODWbZsmcN+Tz31FIcPHyYuLo5+/frVa00iIiIicvk0AiIVjBgxgpYtW2KxWDAajRQWFjrsW1RUxKJFiygqKqq3eiwWC7GxsQDcdtttlfYdNGgQAHv27CEnJ6feahIRERGRmtEIiFTg4eHBzJkzefbZZ0lJSWHmzJlMmjSJHj162Bapm81m9u7dy4oVK0hLSyMkJITDhw/XSz3ff/892dnZuLi42M7+cGTgwIG89957FBcXs3XrVsaNG1cvNTlD7pcbL/+iYaPrvhARERGROqQAInaFh4czb948jEYjaWlpREdH4+bmhq+vL66urphMJtuWu6NHj+ahhx4iMjKyXmopW3x+00030apVq0r7+vv7Ex4eTlJSEnFxcb/pACIiIiLSGCmAiEPdunVj+fLlxMfHs2PHDlJTU8nJycHT05PAwEDCw8MZOnQonTp1AiAkJISsrKw6rcFkMtkWlFc1/arMoEGDSEpK4ujRo6SmptKhQ4c6rUlEREREas5gtVqtDV2ESGNQtg1vUFAQJ06cqPX95j75+GVfE73IWOvnioiIiNQnLUIXERERERGnUQARERERERGnUQARERERERGnUQARERERERGnUQARERERERGnUQARERERERGnUQARERERERGnUQARERERERGn0UGEInWkrg8iFBEREWmMNAIiIiIiIiJOowAiIiIiIiJOowAiIiIiIiJOowAiIiIiIiJOowAiIiIiIiJOowAiIiIiIiJOowAiIiIiIiJOowAiIiIiIiJOowAiIiIiIiJOowAiIiIiIiJOowAiIiIiIiJOowAiIiIiIiJOowAiIiIiIiJO49bQBdTW4sWLiYuLo0uXLkRHR9vtk5ycTHx8PMnJyWRnZ2OxWPD19SUgIICePXsyYMAAAgMD7V4bGxuL0WgEYN26dVXWM3XqVE6fPk1ERASTJk1y2M9qtfLII4+QmZkJwGuvvUZISEiV97/UW2+9xfr163nllVfo1KnTZV0L1fvsoHF9frt372bTpk0cPnyYnJwcmjZtStu2bRk0aBAjRozAze03/6+EiIiIyBWtUX/bysvLw2g0snPnTgBcXFzw8/PDxcUFk8lEVlYWycnJrFq1igkTJhAZGYnBYHBKbUlJSbYvz1D6Rf1yAsiFCxf45ptv6qM0m8b2+b377rusXbsWgCZNmtCyZUuys7NJSUkhJSWFxMREnnvuOYUQERERkXrUaL9p5efnM3v2bNLT0/Hx8SEiIoLBgwfTvHlzoPQ36AcOHGDDhg0kJCSwevVqLBYL999/v1Pq27JlCwC+vr7k5OTwzTffMGXKFNzd3Su9zmq1cvjwYT744ANycnLqrb7G9vnt3LmTtWvXYjAYuP/++7nzzjvx8PDAYrGwadMm3n77bfbt28cnn3xCZGSkU96DiIiIyNWo0a4BMRqNpKen07p1axYsWMDYsWNtX54BDAYDYWFhTJ8+3TbVZ+3atZw8ebLea8vLy2PHjh0APPbYY7i4uJCbm8t3331X6XVPPfUUERERTJ8+nR9++KFea2xsn9+GDRsAGD58OBMmTMDDwwMAV1dX7rjjDsaPHw/A+vXrKS4urt83ICIiInIVa5QBpGw6DcCMGTMcrk8oM3HiRDp37kxwcDApKSn1Xl9CQgJFRUW0a9eO3r17061bNwDi4uIqve7MmTN4eHjg5+eHn59fvdXXGD+/5ORkAAYOHGi3vU+fPgAUFBRw/PjxOq5YRERERMo0yilYmzZtAqBr166EhYVV2d/d3Z358+fXd1k2ZdOHhg0bBsCQIUPYu3cve/bs4dy5cw7DxQcffFDuz3fddVe91NfYPr/c3FyKiooAuOaaa+ze08Xlf1lcIyAiIiIi9adRBpCkpCQAevTo0cCVVHTkyBFSU1NxdXVlyJAhAPTt2xcPDw8KCwvZunWrbTpQQ2lsn5+3tzeffPIJAE2bNrV73+3btwOlU7LatGnj8PkrVqxg5cqVdtvOnDlzme9GRERE5OrT6AKI2Wy2fREMCgqy2ycqKoqMjAy7bf7+/kRFRdltq4sRh7Lf3t98882239R7enrSp08fvv32W+Li4ho0gDTGz89gMODp6enwntu2bePzzz8HYMCAAXh7ezvsm5+fz+nTp+22lZSUVP+NiIiIiFylGl0AOX/+vO3nZs2a2e2TmZnp8Au02Wx2eG9HX8h/fW+LxWK3raioiISEBABuv/32cm1Dhgzh22+/JS0tjSNHjtCxY8cqn1UfrqbPLzc3l3fffZeYmBgArrvuOh5++OFKr/Hy8iIgIMBu26XTuERERETEvkYXQMp2NwK4ePGi3T5Lly6t8FpMTAxLliyp9N7Lly+v8vllB+nZs337dvLz8/H19aVXr17l2nr06IGPjw+5ubnExcU1WAC5Gj4/i8XCxo0bWblyJbm5uQD069ePadOm4ePjU2l9kydPZvLkyXbbgoODHQYzERERESnV6AKIl5cX7u7umM3mcgfVVeXS3/zXl7LpQzk5OUyYMMFhv4SEBB588MEGORCvsX9+P//8M0uXLuXYsWMAXHvttUyZMoW+ffvWT9EiIiIiUk6jmzNiMBgIDQ0F/reYujoOHjxYXyUBcOrUKfbv3w+UHp5XtpXur/+C0i/zVZ0JUl8a8+e3ZcsW/v73v3Ps2DG8vLyYMmUKb7zxhsKHiIiIiBM1uhEQKJ1Os3//fvbs2cOpU6eqPMfCZDKxe/fueq0pJiYGq9VKcHAwy5Ytc9jvqaee4vDhw8TFxdGvX796rcmRxvj5ff/997zxxhuUlJTQrVs3nnrqKVq0aFGvNYuIiIhIRY1uBARgxIgRtGzZEovFgtFopLCw0GHfoqIiFi1aZDsnoj5YLBZiY2MBuO222yrtO2jQIAD27NlDTk5OvdVUmcb4+b3//vuUlJQQHh7Oc889p/AhIiIi0kAaZQDx8PBg5syZuLm5kZKSwsyZM0lMTCz3RdpsNrNr1y5mzJjBvn37CAkJqbd6vv/+e7Kzs3FxcbGdXeHIwIEDcXFxobi4mK1bt9b62YsWLeIvf/kLixYtqvY1je3zy8jI4OjRoxgMBqZNm4a7u3u91SoiIiIilWuUU7AAwsPDmTdvHkajkbS0NKKjo3Fzc8PX1xdXV1dMJpNty9jRo0fz0EMPERkZWS+1lC2evummm2jVqlWlff39/QkPDycpKYm4uDjGjRtXq2dnZWWRkZFx2b/xb0yf36FDh4DSbXIdnVFyqaioKPz9/WtfuIiIiIhU0GgDCEC3bt1Yvnw58fHx7Nixg9TUVHJycvD09CQwMJDw8HCGDh1Kp06dAAgJCSErK6tOazCZTLYF0VVNHyozaNAgkpKSOHr0KKmpqXTo0KHWdRgMhsu+prF8fiaTCSidylWdbXKLi4trXrCIiIiIVMpgtVqtDV2E1K9PPvmEY8eOMWvWrIYupVErOwckKCiIEydONHQ5IiIiIlekRrkGRMrbv3+/bWtdEREREZGGpADSyL377rv88ssvDBs2rKFLERERERFp3GtABEJDQ7n77rvx9vZu6FJERERERBRAGrv+/fs3dAkiIiIiIjaagiUiIiIiIk6jACIiIiIiIk6jACIiIiIiIk6jACIiIiIiIk6jACIiIiIiIk6jACIiIiIiIk6jACIiIiIiIk6jc0BErlC5i7rYfvZ5cn8DViIiIiJSdzQCIiIiIiIiTqMAIiIiIiIiTqMAIiIiIiIiTqMAIiIiIiIiTqMAIiIiIiIiTqMAIiIiIiIiTqMAIiIiIiIiTqMAIiIiIiIiTqODCK9yixcvJi4uji5duhAdHW23T3JyMvHx8SQnJ5OdnY3FYsHX15eAgAB69uzJgAEDCAwMtHttbGwsRqMRgHXr1lVZz9SpUzl9+jQRERFMmjTJYT+r1cojjzxCZmYmAK+99hohISFV3n/37t1s2rSJw4cPk5OTQ9OmTWnbti2DBg1ixIgRuLnpXwkRERGR+qRvW+JQXl4eRqORnTt3AuDi4oKfnx8uLi6YTCaysrJITk5m1apVTJgwgcjISAwGg1NqS0pKsoUPKA06VQWQd999l7Vr1wLQpEkTWrZsSXZ2NikpKaSkpJCYmMhzzz2nECIiIiJSj/RNS+zKz89n9uzZpKen4+PjQ0REBIMHD6Z58+ZA6QjEgQMH2LBhAwkJCaxevRqLxcL999/vlPq2bNkCgK+vLzk5OXzzzTdMmTIFd3d3u/137tzJ2rVrMRgM3H///dx55514eHhgsVjYtGkTb7/9Nvv27eOTTz4hMjLSKe9BRERE5GqkNSBil9FoJD09ndatW7NgwQLGjh1rCx8ABoOBsLAwpk+fbpsqtXbtWk6ePFnvteXl5bFjxw4AHnvsMVxcXMjNzeW7775zeM2GDRsAGD58OBMmTMDDwwMAV1dX7rjjDsaPHw/A+vXrKS4urt83UE1Lb3rV9peIiIhIY6EAIhWUTUcCmDFjhsP1HWUmTpxI586dCQ4OJiUlpd7rS0hIoKioiHbt2tG7d2+6desGQFxcnMNrkpOTARg4cKDd9j59+gBQUFDA8ePH67hiERERESmjKVhSwaZNmwDo2rUrYWFhVfZ3d3dn/vz59V2WTdn0q2HDhgEwZMgQ9u7dy549ezh37hx+fn7l+ufm5lJUVATANddcY/eeLi7/y+JXygiIiIiISGOkACIVJCUlAdCjR48GrqSiI0eOkJqaiqurK0OGDAGgb9++eHh4UFhYyNatW23Tqcp4e3vzySefANC0aVO7992+fTtQOiWrTZs29Va/iIiIyNVOAUTKMZvNnDlzBoCgoCC7faKiosjIyLDb5u/vT1RUlN22u+66q9b1lY1+3HzzzbaRDk9PT/r06cO3335LXFxchQBiMBjw9PR0eM9t27bx+eefAzBgwAC8vb1rXaeIiIiI2KcAIuWcP3/e9nOzZs3s9snMzHQYQMxms8N7Owo0v763xWKx21ZUVERCQgIAt99+e7m2IUOG8O2335KWlsaRI0fo2LFjlc/Kzc3l3XffJSYmBoDrrruOhx9+uNJrVqxYwcqVK+22lQU3EREREXFMAUTKKdsdCuDixYt2+yxdurTCazExMSxZsqTSey9fvrzK55cdRGjP9u3byc/Px9fXl169epVr69GjBz4+PuTm5hIXF1dpALFYLGzcuJGVK1eSm5sLQL9+/Zg2bRo+Pj6V1pefn++wvpKSkkqvFREREREFEPkVLy8v3N3dMZvN5Q76q8qlIyf1pWz6VU5ODhMmTHDYLyEhgQcffNDugYI///wzS5cu5dixYwBce+21TJkyhb59+1arBi8vLwICAuy2XbqQXURERETsUwCRcgwGA6Ghoezfv5+kpCTGjRtXresOHjxYr3WdOnWK/fv3A6WHDzo6cf3cuXOcP3+e7777jn79+pVr27JlC8uWLcNiseDl5cU999zDnXfe6fDwQnsmT57M5MmT7bYFBwc7nJomIiIiIqUUQKSCfv36sX//fvbs2cOpU6eqPAfEZDKxe/fueq0pJiYGq9VKcHAwy5Ytc9jvqaee4vDhw8TFxZULIN9//z1vvPEGJSUldOvWjaeeeooWLVrUa80iIiIiUpHmjEgFI0aMoGXLllgsFoxGI4WFhQ77FhUVsWjRIts5G/XBYrEQGxsLwG233VZp30GDBgGwZ88ecnJybK+///77lJSUEB4eznPPPafwISIiItJAFECkAg8PD2bOnImbmxspKSnMnDmTxMTEckHEbDaza9cuZsyYwb59+wgJCam3er7//nuys7NxcXGxnf3hyMCBA3FxcaG4uJitW7cCkJGRwdGjRzEYDEybNu2yplw1pNwvN9r+EhEREWksNAVL7AoPD2fevHkYjUbS0tKIjo7Gzc0NX19fXF1dMZlMti13R48ezUMPPURkZGS91FK2+Pymm26iVatWlfb19/cnPDycpKQk4uLiGDduHIcOHQJKF4k7OqPkUlFRUfj7+9e+cBERERGpQAFEHOrWrRvLly8nPj6eHTt2kJqaSk5ODp6engQGBhIeHs7QoUPp1KkTACEhIWRlZdVpDSaTie+++w6oevpVmUGDBpGUlMTRo0dJTU3FZDIBpVO5qrNIvLi4uOYFi4iIiEilDFar1drQRYg0BmW7YAUFBXHixIla32/uk4/bfo5eZKz1/URERESuBFoDIiIiIiIiTqMAIiIiIiIiTqMAIiIiIiIiTqMAIiIiIiIiTqMAIiIiIiIiTqMAIiIiIiIiTqMAIiIiIiIiTqMAIiIiIiIiTqODCEXqSF0fRCgiIiLSGGkEREREREREnEYBREREREREnEYBREREREREnMatoQsQEftyF3Wp0/v5PLm/Tu8nIiIiUhMaAREREREREadRABEREREREadRABEREREREadRABEREREREadRABEREREREadRABEREREREadRABEREREREadRABEREREREafRQYRyWRYvXkxcXBxdunQhOjrabp/k5GTi4+NJTk4mOzsbi8WCr68vAQEB9OzZkwEDBhAYGGj32tjYWIxGIwDr1q2rsp6pU6dy+vRpIiIimDRpUoU67XFxccHX15fg4GD69evHyJEjcXd3r/JZIiIiIlJ7CiBSZ/Ly8jAajezcuRMo/aLv5+eHi4sLJpOJrKwskpOTWbVqFRMmTCAyMhKDwVCvNTVp0oTWrVuXe62wsJBz586RlJREUlIS69at4+mnn6Zdu3b1WouIiIiIKIBIHcnPz2f27Nmkp6fj4+NDREQEgwcPpnnz5gBYrVYOHDjAhg0bSEhIYPXq1VgsFu6///56revGG2+0O1JTWFjI7t27ef/998nMzOSZZ57h1Vdf5dprr63XekRERESudgogUieMRiPp6em0bt2aqKioClOsDAYDYWFhhIWFERQUxMqVK1m7di3Dhw+nTZs2Tq/Xw8ODW2+9lc6dOzNr1ix++eUXli1bxj/+8Q+n1+LI0pterdP7zanTu4mIiIjUjBahS62lpKSQmJgIwIwZMxyu7ygzceJEOnfuTHBwMCkpKc4o0aEWLVowZcoUAPbt28fhw4cbtB4RERGRxk4jIFJrmzZtAqBr166EhYVV2d/d3Z358+fXd1nV1qdPH3x9fcnJyWH79u2EhIQ0dEkiIiIijZZGQKTWkpKSAOjRo0cDV1Izrq6uhIaGAnDo0KEGrkZERESkcdMIiNSK2WzmzJkzAAQFBdntExUVRUZGht02f39/oqKi7LbddddddVNkNZQtPj937pzTnikiIiJyNVIAkVo5f/687edmzZrZ7ZOZmekwgJjNZof3dhRofn1vi8VSZb+qNG3aFICioqJK+61YsYKVK1fabSsLYiIiIiLimAKI1IqHh4ft54sXL9rts3Tp0gqvxcTEsGTJkkrvvXz58iqfX3YQYW2VBSlHIapMfn6+w+eVlJTUug4RERGRxk4BRGrFy8sLd3d3zGYzmZmZ1b7u0pGTK0FZqKhqS2AvLy8CAgLstrm4aEmViIiISFUUQKRWDAYDoaGh7N+/n6SkJMaNG1et6w4ePFjPlVVfcXExP/30E0CVu3hNnjyZyZMn220LDg52ONVMRERERErpV7ZSa/369QNgz549nDp1qsr+JpOJ3bt313dZ1ZaQkMCFCxdwdXW1vRcRERERqR8KIFJrI0aMoGXLllgsFoxGI4WFhQ77FhUVsWjRoioXezvLqVOn+OCDDwAYPnw4/v7+DVyRiIiISOOmKVhSax4eHsycOZNnn32WlJQUZs6cyaRJk+jRo4dtkbrZbGbv3r2sWLGCtLQ0QkJCGvTUcYvFwrZt23j//fcxmUy0bt2aBx54oMHqsSf3y411er+5Nbxf9CJjndYhIiIiVzcFEKkT4eHhzJs3D6PRSFpaGtHR0bi5ueHr64urqysmk8m25e7o0aN56KGHiIyMrPe6Dh48yF/+8pdyrxUXF2MymWyjMNdffz3PPvtslTtgiYiIiEjtKYBInenWrRvLly8nPj6eHTt2kJqaSk5ODp6engQGBhIeHs7QoUPp1KkTACEhIWRlZdVrTUVFRRUWhru6utKiRQuCgoIYOnQoAwcOxN3dvV7rEBEREZFSBqvVam3oIkQag7JdsIKCgjhx4kSt7zf3ycfroKra0xQsERERqUtahC4iIiIiIk6jACIiIiIiIk6jACIiIiIiIk6jACIiIiIiIk6jACIiIiIiIk6jACIiIiIiIk6jACIiIiIiIk6jACIiIiIiIk6jgwhF6khdH0QoIiIi0hhpBERERERERJxGAURERERERJxGAURERERERJzGraELEBH7chd1qdF1Pk/ur+NKREREROqORkBERERERMRpFEBERERERMRpFEBERERERMRpFEBERERERMRpFEBERERERMRpFEBERERERMRpFEBERERERMRpFEBERERERMRpdBDhVWrx4sXExcXRpUsXoqOj7fZJTk4mPj6e5ORksrOzsVgs+Pr6EhAQQM+ePRkwYACBgYF2r42NjcVoNAKwbt26KuuZOnUqp0+fJiIigkmTJtntYzabiY2NZdeuXRw9epTz58/TpEkT/Pz8aN++Pb1796Z///54eHhU81OAxx57jIyMDNasWVPta0RERESk5hRApIK8vDyMRiM7d+4EwMXFBT8/P1xcXDCZTGRlZZGcnMyqVauYMGECkZGRGAyGeq0pOTmZhQsXcubMGQA8PDxo2bIlBQUFnDx5koyMDLZt28aKFSt48skn6dKl6lPEDx48SFpaGu7u7vVau4iIiIj8jwKIlJOfn8/s2bNJT0/Hx8eHiIgIBg8eTPPmzQGwWq0cOHCADRs2kJCQwOrVq7FYLNx///31VtPu3buJjo6muLiY0NBQIiMj6dq1K66urgBcuHCBxMREVq9eTUZGBs8//zwvv/wyHTt2tHs/s9nMvn37ePPNN+utZhERERGxTwFEyjEajaSnp9O6dWuioqIqTLEyGAyEhYURFhZGUFAQK1euZO3atQwfPpw2bdrUeT1ZWVksXLiQ4uJihg4dymOPPWYLHmU8PT0ZOnQoffr0Ye7cuRw9epS33nqL+fPnl+t39OhRXnvtNTIyMiguLq7zWuva0pterdF1c+q4DhEREZG6pEXoYpOSkkJiYiIAM2bMcLi+o8zEiRPp3LkzwcHBpKSk1EtNn3zyCfn5+bRr145p06ZVCB+X8vLy4oEHHqBt27bk5+dz/vz5cu0XL14kJycHb29v/Pz88Pb2rpeaRURERMQxjYCIzaZNmwDo2rUrYWFhVfZ3d3evMMpQlwoLC4mPjwdg/Pjx1Vqr0aNHD5YuXWq3LSwsjA8++MD256SkJJ5++um6KVZEREREqkUjIGKTlJQElH6JvxIcOHAAs9kMQM+ePRu4GhERERGpCxoBEaB0YXbZDlNBQUF2+0RFRZGRkWG3zd/fn6ioKLttd911V41qOnnyJADNmjWjRYsWFdqPHz/OSy+95PD6O++8kzFjxtTo2SIiIiJSPxRABKDceolmzZrZ7ZOZmekwgJSNVNjjKND8+t4Wi6Xca7m5uZXWU1hY6LAeoMIakLqwYsUKVq5cabetLMCJiIiIiGMKIAJQ7vC+ixcv2u1jb21FTEwMS5YsqfTey5cvr/L5ZQcR2qvJUT033HCD3UMO58yZQ3JycpXPrIn8/PwKdZYpKSmpl2eKiIiINCYKIAKU7iDl7u6O2WwmMzOz2tfVxyhDGT8/P6D0YMS8vLxq71pVnzV5eXkREBBgt83FRUuqRERERKqiACJA6fkeoaGh7N+/n6SkJMaNG1et6w4ePFhvNXXq1Mn2848//kj//v2rvKagoKDSaVm1NXnyZCZPnmy3LTg4uF6fLSIiItIY6Fe2YtOvXz8A9uzZw6lTp6rsbzKZ2L17d73Vc80119ChQwcAvvrqq2pdExMTo6lQIiIiIlcwBRCxGTFiBC1btsRisWA0GiksLHTYt6ioiEWLFlFUVFSvNUVERAClWwSvX7++0r6pqal89NFH9VqPiIiIiNSOAojYeHh4MHPmTNzc3EhJSWHmzJkkJiaWCyJms5ldu3YxY8YM9u3bR0hISL3W1LdvX0aNGgXA22+/zZIlS0hLSyvXx2Qy8emnnzJ79myKiopo3759vdYkIiIiIjWnNSBSTnh4OPPmzcNoNJKWlkZ0dDRubm74+vri6uqKyWSybbk7evRoHnroISIjI+u1pkceeQRvb28+++wzYmJiiImJwdPTk+bNm2M2m8nOzgbA29ubWbNmkZ+fz2uvvVavNTlD7pcba3bhsNF1W4iIiIhIHVIAkQq6devG8uXLiY+PZ8eOHaSmppKTk4OnpyeBgYGEh4czdOhQ2yLxkJAQsrKy6q0eV1dX7rvvPoYPH87GjRvZt28fv/zyC2fPnsXX15ewsDBuueUWhg4dSosWLS5rFy8RERERcS6D1Wq1NnQRIo1B2S5YQUFBnDhxotb3m/vk4zW6LnqRsdbPFhEREakvWgMiIiIiIiJOowAiIiIiIiJOowAiIiIiIiJOowAiIiIiIiJOowAiIiIiIiJOowAiIiIiIiJOowAiIiIiIiJOowAiIiIiIiJOo4MIRepIXR9EKCIiItIYaQREREREREScRgFEREREREScRgFEREREREScxq2hCxAR+3IXdanwms+T+xugEhEREZG6oxEQERERERFxGgUQERERERFxGgUQERERERFxGgUQERERERFxGgUQERERERFxGgUQERERERFxGgUQERERERFxGgUQERERERFxGh1EeJVavHgxcXFxdOnShejoaLt9kpOTiY+PJzk5mezsbCwWC76+vgQEBNCzZ08GDBhAYGCg3WtjY2MxGo0ArFu3rsp6pk6dyunTp4mIiGDSpEl2+5jNZmJjY9m1axdHjx7l/PnzNGnSBD8/P9q3b0/v3r3p378/Hh4eDp9jsVjYtGkT33zzDSdOnKCgoAAfHx9uuOEGRo8ezc0331xlrSIiIiJScwogUkFeXh5Go5GdO3cC4OLigp+fHy4uLphMJrKyskhOTmbVqlVMmDCByMhIDAZDvdaUnJzMwoULOXPmDAAeHh60bNmSgoICTp48SUZGBtu2bWPFihU8+eSTdOlS8RTxoqIinnvuOVJSUgDw8vLCz8+Ps2fPsmvXLnbt2lVpABIRERGR2lMAkXLy8/OZPXs26enp+Pj4EBERweDBg2nevDkAVquVAwcOsGHDBhISEli9ejUWi4X777+/3mravXs30dHRFBcXExoaSmRkJF27dsXV1RWACxcukJiYyOrVq8nIyOD555/n5ZdfpmPHjuXu8+GHH5KSkoKPjw+PPfYYffr0wWAwkJ+fz3vvvcemTZv4+OOPCQ8Pp1u3bvX2fkRERESuZgogUo7RaCQ9PZ3WrVsTFRVVYYqVwWAgLCyMsLAwgoKCWLlyJWvXrmX48OG0adOmzuvJyspi4cKFFBcXM3ToUB577DFb8Cjj6enJ0KFD6dOnD3PnzuXo0aO89dZbzJ8/39anqKiImJgYAP70pz9xyy232Nq8vLx49NFHOX78OD/99BNr1669IgLI0pterfDanAaoQ0RERKQuaRG62KSkpJCYmAjAjBkzHK7vKDNx4kQ6d+5McHCwbVpTXfvkk0/Iz8+nXbt2TJs2rUL4uJSXlxcPPPAAbdu2JT8/n/Pnz9vaUlNTyc/Px83NjX79+lW41mAw0KtXLwB+/vnnun8jIiIiIgJoBEQusWnTJgC6du1KWFhYlf3d3d3LjTLUtcLCQuLj4wEYP3487u7uVV7To0cPli5dWuH17OxsAPz8/BwuUndxKc3jZrO5piWLiIiISBUUQMQmKSkJKP0SfyU4cOCALQz07NmzVve65ZZb+OSTT2wh49dKSkpsoz9t27at1bNERERExDEFEAFKf+tftsNUUFCQ3T5RUVFkZGTYbfP39ycqKspu21133VWjmk6ePAlAs2bNaNGiRYX248eP89JLLzm8/s4772TMmDEAuLq64unpabdfSUkJ77zzjm3q1ahRo2pUr4iIiIhUTQFEAMqtl2jWrJndPpmZmQ4DSGXTlhwFml/f22KxlHstNze30noKCwsd1gPl35Mjx48fZ9myZbY1LAMHDmT48OEO+69YsYKVK1fabSsLcCIiIiLimAKIAJRbF3Hx4kW7feytrYiJiWHJkiWV3nv58uVVPr/sIEJ7NTmq54YbbrB7yOGcOXNITk6u9Hl5eXmsWrWKDRs2YLFYcHV15fe//z333HNPpWea5OfnV6izTElJSaXPFBEREREFEPn/vLy8cHd3x2w2k5mZWe3rqjPKUFN+fn5AaVjIy8vD29u7TmqKj4/n//7v/2z9unbtyp/+9Ceuv/76Ku/t5eVFQECA3TZH60tERERE5H8UQAQo3YY2NDSU/fv3k5SUxLhx46p13cGDB+utpk6dOtl+/vHHH+nfv3+V1xQUFDicllVSUsIbb7zBli1bAGjTpg0PPvhguTNBqjJ58mQmT55sty04OLjSKWEiIiIionNA5BJl52Ps2bOHU6dOVdnfZDKxe/fueqvnmmuuoUOHDgB89dVX1bomJibG4VSojz76yBY+xo8fz+uvv35Z4UNEREREak8BRGxGjBhBy5YtsVgsGI1GCgsLHfYtKipi0aJFFBUV1WtNERERQOkWwevXr6+0b2pqKh999JHdtvPnz7N27Vqg9ADFKVOmVOtcERERERGpWwogYuPh4cHMmTNxc3MjJSWFmTNnkpiYWC6ImM1mdu3axYwZM9i3bx8hISH1WlPfvn1t2+K+/fbbLFmyhLS0tHJ9TCYTn376KbNnz6aoqIj27dtXuM/27dspLi6mdevWDqdQiYiIiEj90xoQKSc8PJx58+ZhNBpJS0sjOjoaNzc3fH19cXV1xWQy2bbcHT16NA899BCRkZH1WtMjjzyCt7c3n332GTExMcTExODp6Unz5s0xm822U869vb2ZNWsW+fn5vPbaa+XucfjwYaB0F6tp06ZV+rzKzjRxptwvN1Z4be6XG4leZGyAakRERETqhgKIVNCtWzeWL19OfHw8O3bsIDU1lZycHDw9PQkMDCQ8PJyhQ4faFomHhISQlZVVb/W4urpy3333MXz4cDZu3Mi+ffv45ZdfOHv2LL6+voSFhXHLLbcwdOhQWrRoYXcXr7KQUlBQQEFBQaXPq+xMExERERGpHYPVarU2dBEijUHZLlhBQUGcOHGi1veb++Tjdl/XCIiIiIj8lmkNiIiIiIiIOI0CiIiIiIiIOI0CiIiIiIiIOI0CiIiIiIiIOI0CiIiIiIiIOI0CiIiIiIiIOI0CiIiIiIiIOI0CiIiIiIiIOI0OIhSpI3V9EKGIiIhIY6QREBERERERcRoFEBERERERcRoFEBERERERcRoFEJErVO6iLuQu6tLQZYiIiIjUKQUQERERERFxGgUQERERERFxGgUQERERERFxGgUQERERERFxGgUQERERERFxGgUQERERERFxGgUQERERERFxGgUQERERERFxGreGLkAaxuLFi4mLi6NLly5ER0fb7ZOcnEx8fDzJyclkZ2djsVjw9fUlICCAnj17MmDAAAIDA+1eGxsbi9FoBGDdunVV1jN16lROnz5NREQEkyZNstvHbDYTGxvLrl27OHr0KOfPn6dJkyb4+fnRvn17evfuTf/+/fHw8HD4nBMnTvDZZ5/x448/YjKZcHV1JSAggO7duzN+/HhatWpVZa0iIiIiUnMKIFJBXl4eRqORnTt3AuDi4oKfnx8uLi6YTCaysrJITk5m1apVTJgwgcjISAwGQ73WlJyczMKFCzlz5gwAHh4etGzZkoKCAk6ePElGRgbbtm1jxYoVPPnkk3TpUvEE8X379hEVFUVRUREGgwF/f3+Kioo4fvw4x48fJyYmhhdeeIHQ0NB6fS8iIiIiVzMFECknPz+f2bNnk56ejo+PDxEREQwePJjmzZsDYLVaOXDgABs2bCAhIYHVq1djsVi4//77662m3bt3Ex0dTXFxMaGhoURGRtK1a1dcXV0BuHDhAomJiaxevZqMjAyef/55Xn75ZTp27Gi7R1FREQsXLqSoqIi+ffvy5z//mZYtWwJw+PBhFi5cSEZGBkuWLOGNN96ot/ciIiIicrXTGhApx2g0kp6eTuvWrVmwYAFjx461hQ8Ag8FAWFgY06dPt02VWrt2LSdPnqyXerKysli4cCHFxcUMHTqU+fPn0717d1v4APD09GTo0KEsWLCA9u3bU1RUxFtvvVXuPjt27CAnJwdfX19mzpxpCx8AISEhPPnkkwCkp6eTmZlZL+/lci296VV8ntzf0GWIiIiI1CkFELFJSUkhMTERgBkzZjhc31Fm4sSJdO7cmeDgYFJSUuqlpk8++YT8/HzatWvHtGnTygWPX/Py8uKBBx6gbdu25Ofnc/78eVtbWloaAOHh4bi7u1e4tl27drafL168WHdvQERERETK0RQssdm0aRMAXbt2JSwsrMr+7u7uzJ8/v97qKSwsJD4+HoDx48fbDQ6/1qNHD5YuXVrh9f79+9O+fXuHoerQoUMAuLm5VRm8RERERKTmFEDEJikpCSj9En8lOHDgAGazGYCePXvW6l433HADN9xwg+3PJSUlGAwG8vLy+PHHH/m///s/ACZMmFDpLloiIiIiUjsKIAKUbnFbtsNUUFCQ3T5RUVFkZGTYbfP39ycqKspu21133VWjmsrWlTRr1owWLVpUaD9+/DgvvfSSw+vvvPNOxowZY7dt27ZtLFiwoNxrd999N5GRkTWqVURERESqRwFEAMqtl2jWrJndPpmZmQ4DSNlIhT2OAs2v722xWMq9lpubW2k9hYWFDuuB8u/p15o1a0ZgYCDnzp3jwoULAHzxxRc0adKk0hCyYsUKVq5cabetLMCJiIiIiGMKIAJQbtqRo0XY9tZWxMTEsGTJkkrvvXz58iqfX3YQob2aHNVzww032D3kcM6cOSQnJ1f6vF69etGrVy+g9HDCTz75hISEBD755BOuv/56br31VrvX5efnV6izTElJSaXPFBEREREFEPn/vLy8cHd3x2w2X9Y2tJWNMtSWn58fUHowYl5eHt7e3vVSU3BwME899RQZGRkcPnyYLVu2OAwgXl5eBAQE2G1zcdGmciIiIiJVUQARoPR8j9DQUPbv309SUhLjxo2r1nUHDx6st5o6depk+/nHH3+kf//+VV5TUFBQYVqW2Wzmww8/BGD48OFcd911Fa4zGAx07dqVw4cPOxzhAJg8eTKTJ0+22xYcHFzplDARERER0Tkgcol+/foBsGfPHk6dOlVlf5PJxO7du+utnmuuuYYOHToA8NVXX1XrmpiYmApTodzd3dmwYQOff/65bacve4qLiwHHa05EREREpPYUQMRmxIgRtGzZEovFgtFopLCw0GHfoqIiFi1aRFFRUb3WFBERAZRuEbx+/fpK+6ampvLRRx/ZbSs712T79u122wsLC22HMIaHh9e0XBERERGpggKI2Hh4eDBz5kzc3NxISUlh5syZJCYmlgsiZrOZXbt2MWPGDPbt20dISEi91tS3b19GjRoFwNtvv82SJUtsp5qXMZlMfPrpp8yePZuioiLat29f4T5lU8p+/PFH3n//fQoKCmxtqampPPvss2RlZeHp6cnYsWPr7w2JiIiIXOW0BkTKCQ8PZ968eRiNRtLS0oiOjsbNzQ1fX19cXV0xmUy2LXdHjx7NQw89VO9nZzzyyCN4e3vz2WefERMTQ0xMDJ6enjRv3hyz2Ux2djYA3t7ezJo1i/z8fF577bVy9+jVqxfjx4/n888/Z82aNXzxxRe0bNmSvLw8Wxjx8vJi9uzZtGrVql7fT3XlfrmRuV9uJHqRsaFLEREREakzCiBSQbdu3Vi+fDnx8fHs2LGD1NRUcnJy8PT0JDAwkPDwcIYOHWpbJB4SEkJWVla91ePq6sp9993H8OHD2bhxI/v27eOXX37h7Nmz+Pr6EhYWxi233MLQoUNp0aKFw128pkyZQnh4OJs3b+bgwYOcPXuWJk2a0L59e3r27MmYMWPw9/evt/chIiIiImCwWq3Whi5CpDEo2wUrKCiIEydO1Pp+c598HEAjICIiItKoaA2IiIiIiIg4jQKIiIiIiIg4jQKIiIiIiIg4jQKIiIiIiIg4jQKIiIiIiIg4jQKIiIiIiIg4jQKIiIiIiIg4jQKIiIiIiIg4jQ4iFKkjdX0QoYiIiEhjpBEQERERERFxGgUQERERERFxGgUQERERERFxGgUQkStU7qIuDV2CiIiISJ1TABEREREREadRABEREREREadRABEREREREadRABEREREREadxa+gCRERERMQ55j75eEOXcFmiFxkbugSpBxoBERERERERp1EAERERERERp1EAERERERERp9EakN+gxYsXExcXR5cuXYiOjrbbJzk5mfj4eJKTk8nOzsZiseDr60tAQAA9e/ZkwIABBAYG2r02NjYWo7F0zuW6deuqrGfq1KmcPn2aiIgIJk2aZLeP2WwmNjaWXbt2cfToUc6fP0+TJk3w8/Ojffv29O7dm/79++Ph4VHNT6HUwoULSUhIYNSoUTz66KNV9t+/fz9z584FoF27drz++uuX9TwRERERqR0FkEYmLy8Po9HIzp07AXBxccHPzw8XFxdMJhNZWVkkJyezatUqJkyYQGRkJAaDoV5rSk5OZuHChZw5cwYADw8PWrZsSUFBASdPniQjI4Nt27axYsUKnnzySbp0qd4J4PHx8SQkJFxWLVu2bLH9fOzYMQ4fPkxISMhl3UNEREREak4BpBHJz89n9uzZpKen4+PjQ0REBIMHD6Z58+YAWK1WDhw4wIYNG0hISGD16tVYLBbuv//+eqtp9+7dREdHU1xcTGhoKJGRkXTt2hVXV1cALly4QGJiIqtXryYjI4Pnn3+el19+mY4dO1Z631OnTvGvf/3rsmopKChg+/btAPj6+pKTk0NsbKwCiIiIiIgTaQ1II2I0GklPT6d169YsWLCAsWPH2sIHgMFgICwsjOnTp9umSq1du5aTJ0/WSz1ZWVksXLiQ4uJihg4dyvz58+nevbstfAB4enoydOhQFixYQPv27SkqKuKtt96q9L7FxcUsXLiQwsJCgoODq11PQkIChYWFtG3blgkTJgDwzTffYDaba/YGRUREROSyKYA0EikpKSQmJgIwY8YMh+s7ykycOJHOnTsTHBxMSkpKvdT0ySefkJ+fT7t27Zg2bVq54PFrXl5ePPDAA7Rt25b8/HzOnz/vsO9HH33EwYMHufvuu7nxxhurXU/Z9Kthw4YxaNAgXFxcyM3N5bvvvqv+mxIRERGRWtEUrEZi06ZNAHTt2pWwsLAq+7u7uzN//vx6q6ewsJD4+HgAxo8fj7u7e5XX9OjRg6VLl1ba54cffmDt2rWEhIQQGRlZZf8yR48e5fDhw7i6ujJkyBBatGhBly5d+PHHH4mLi6N///7Vuo+IiIiI1I5GQBqJpKQkoPRL/JXgwIEDtqlNPXv2rJN7nj9/nkWLFtGkSROmT5+Om1v183PZ6MfNN99MixYtABg8eDAAe/bs4dy5c3VSo4iIiIhUTiMgjYDZbLbtMBUUFGS3T1RUFBkZGXbb/P39iYqKstt211131aimsnUlzZo1s33hv9Tx48d56aWXHF5/5513MmbMmHKvLVmyhOzsbP761786fJ/2mM1m225Zw4YNs73ev39//vWvf2E2m9m6dSvjx4+v9j1FRETkyrJ371569uzJI488UulGNd9++y2vvPIKe/fuJTs7mzZt2jB69GimT5/O9ddfX67vvHnz+Mc//gHAggULmD59usP7fvHFF7bvEvPmzeP555+v7VtqtBRAGoFL10s0a9bMbp/MzEyHAaSyRdjV+aKfmZmJxWIp91pubm6l9RQWFjqsB6iwBuSrr75i165d9O3bl5EjR1ZZ06V27NhBbm4uzZs3p3fv3rbXvby86N27N9u3bycuLq5aAWTFihWsXLnSbltZCBQRERHnq2oTG4Bly5bx17/+FSj9jhIQEMDx48dZunQpH330EevXr+fWW2+19X/22WfZvHkziYmJPP3004wcOdLucQFnzpzh4YcfBqBfv348++yzdfSuGicFkEbg0sP7Ll68aLePvbUSMTExLFmypNJ7L1++vMrnlx1EaK8mR/XccMMNdg85nDNnDsnJyeVeO3bsGO+++y4tW7Zk2rRpVdbza2XTrwYPHlxh2tbgwYPZvn07aWlpHDlypMrtf/Pz8yu81zIlJSWXXVtllt70KnPq9I4iIiKNT2ZmJm+//TZvvvlmpf2+++472/eIZ555hqeffpqmTZuSnZ3Nn//8Zz799FP+8Ic/kJycjJ+fHwBubm589NFHdO/endzcXO6991527dpVYW3rn//8Z06fPo2Pjw8rVqyodOMdUQBpFLy8vHB3d8dsNpOZmVnt6yrbaaq2yv7FzcvLIy8vD29v7xrVZLFYWLBgAWazmSeeeKLctsLV8csvv/Djjz8CsH79etavX++wb1xcXJUBxMvLi4CAALttLi5aUiUiIuIsL774Im+++Sbp6enV6v/cc89htVoZM2YM//znP22vt2zZkvfee4+EhAROnjzJv/71L/7+97/b2jt06MDrr7/OAw88wL59+3j++ed58cUXbe0rVqxgzZo1QOkvfDt06FBH77DxUgBpBAwGA6Ghoezfv5+kpCTGjRtXresOHjxYbzV16tTJ9vOPP/5YrV2mCgoKKkzLunDhAseOHQNK/8PhyMaNG9m4cSMAK1eutAWemJgYrFYr7u7ueHl52b3WbDaTn59PQkICDz74YKWL2ydPnszkyZPttgUHB1c6rUxERETqzunTpykqKuKaa64BICcnx+HMi5ycHNuMCHuzKZo1a8aoUaP44IMP+M9//lMugADcf//9bNiwgdWrV/Pyyy8zZswY+vXrR0ZGBn/7298AuOeee7jvvvvq8i02WgogjUS/fv3Yv38/e/bs4dSpU1WeA2Iymdi9e3e91XPNNdfQoUMHUlNT+eqrr6oVQGJiYipMY3Jxcal0HYrJZKKgoAAvLy/bqEvZSERJSQmxsbEATJo0iYkTJ9q9x/Hjx5k2bRrnz59n9+7d9O3btzpvUURERBqQ0WjEaDTa/vzAAw/w/vvv2+2bkJCAxWLB1dWVgQMH2u0zdOhQPvjgA/bu3UthYWG5Ke4Ab775Jjt27CA9PZ377ruPvXv38tBDD3Hu3Dnatm1b6cJ3KU8BpJEYMWIEa9asITs7G6PRyAsvvFDhX5wyRUVFLFq0iKKionqtKSIigujoaJKSkli/fj1jx4512Dc1NZWPPvqowuvNmjWrdB3K4sWLiYuLY+DAgTz66KPl2vbu3cuZM2dwcXFhyJAhDu/Rtm1b2rVrx7Fjx4iNjVUAERERaWTK1pcGBwc73CCnXbt2QOkvMNPS0ggNDS3X7ufnx4cffshtt93G4cOH6du3L8nJybi4uPDhhx/afhEqVdOk9UbCw8ODmTNn4ubmRkpKCjNnziQxMZHCwkJbH7PZzK5du5gxYwb79u0jJCSkXmvq27cvo0aNAuDtt99myZIlpKWlletjMpn49NNPmT17NkVFRbRv377Onl821NqtWzf8/f0r7XvpmSA5OTl1VoOIiIg0vOzsbACH6zihdC1IGUfngw0ePJhZs2YB/ws1s2fPZtCgQXVU6dVBIyCNSHh4OPPmzcNoNJKWlkZ0dDRubm74+vri6uqKyWSybbk7evRoHnroISIjI+u1pkceeQRvb28+++wzYmJiiImJwdPTk+bNm2M2m23/QfD29mbWrFnk5+fz2muv1fq5OTk57Nq1C4Dbbrutyv6DBg3iww8/pLi4mK1bt1Z7HY2IiIhc+coCRdOmTR32uXTDnMqOKIiIiGD+/Pnl/iyXRwGkkenWrRvLly8nPj6eHTt2kJqaSk5ODp6engQGBhIeHs7QoUNti8RDQkLIysqqt3pcXV257777GD58OBs3bmTfvn388ssvnD17Fl9fX8LCwrjlllsYOnQoLVq0uKxdvCoTHx9PcXExzZo1q9aUqoCAAEJDQzlw4ABxcXEKICIiIo1I2ba5vz637FKXzhqp7ByzXy80f+CBB0hMTKRJkyZ1UOnVwWC1Wq0NXYRIY1C2C1ZQUBAnTpyo9f1eiv2aOcNG10FlIiIipeY++XhDl3BZohcZq+70/5UtQrd3EvozzzzDiy++SKdOnfjpp5/sXv/f//6XAQMGAHDy5Em7G/o8/vjjLFmyhKZNm7Js2TL+9Kc/YbFYmDVrFi+//PJlvLOrm9aAiIiIiEijduONNwKQlpbmcBTk6NGjAPj6+nLttddWaN+wYYPtAOf58+fz4IMPMnPmTAAWLFjA1q1b66HyxkkBREREREQatbKtdy9evEhiYqLdPtu2bQNKF5obDIZybZmZmTz44IMADBs2jMceewyAF154ga5du1JSUsJ9992HyWSqr7fQqCiAiIiIiEij1r59e/r06QNQ7uyQMmfPnuXjjz8G4N577y3XZrVauf/++zl9+jR+fn689957toDSpEkT3n//fdzd3UlPT+fPf/5zPb+TxkEBREREREQavaioKAwGA59++ilRUVEUFxcDpWeRjRkzhpycHPr168eECRPKXffaa6+xefNmAJYtW0ZwcHC59h49evDMM88AsHr1aj744AMnvJvfNgUQEREREWn0hg8fzj//+U8Ann32Wfz8/GjXrh0hISEkJibSoUMHVqxYgYvL/74e7927l7lz5wKl2+3+8Y9/tHvvuXPncvPNNwMwbdo023oSsU8BRERERESuCk8//TQbN25k1KhReHp6kpWVxY033sjcuXPZuXMnHTp0sPXNz8/nj3/8I0VFRQQFBbFs2TKH93Vzc+ODDz7Aw8OD3NxcJk+eXOmWv1c7nQMiIiIiIr957733Hu+9916V/UaOHMnIkSOr7Ofl5cWBAweq/fzOnTtz8eLFave/mmkEREREREREnEYBROQKpUMIRUREpDFSABEREREREadRABEREREREadRABEREREREafRLlgiV6jcRV0augQREamEz5P7G7oEkd8kjYCIiIiIiIjTVDkCctttt9XLgw0GA7GxsfVybxERERERuTJVGUC2bt2KwWCo04dardY6v6eIiIiIiFz5qgwgbdu2VVgQEREREZE6UWUASUtLc0IZIiIiIiJyNdAidBERERERcRoFEBERERERcZoqp2AdP3683h7etm3beru3iIiIiIhceaoMINdff329LEI3GAwUFxfX+X2lfixevJi4uDi6dOlCdHS03T7JycnEx8eTnJxMdnY2FosFX19fAgIC6NmzJwMGDCAwMNDutbGxsRiNRgDWrVtXZT1Tp07l9OnTREREMGnSpAqvA9xyyy08/fTTVd5r4cKFJCQkABAQEMDbb79d5TUiIiIiUjPVOgndarXWdx3yG5aXl4fRaGTnzp0AuLi44Ofnh4uLCyaTiaysLJKTk1m1ahUTJkwgMjLSKTur7dmzh7y8PLy9vR32KSwsZNeuXfVei4iIyJUgepGxoUsQqTqAzJs3zxl1yG9Ufn4+s2fPJj09HR8fHyIiIhg8eDDNmzcHSsPrgQMH2LBhAwkJCaxevRqLxcL9999fr3W5ublRXFzMjh07GD58uMN+u3bt4sKFC7b+IiIiIlK/FECkVoxGI+np6bRu3ZqoqKgKU6wMBgNhYWGEhYURFBTEypUrWbt2LcOHD6dNmzb1Vlf37t3ZvXs333zzTaUB5JtvvinX/0qy9KZXG7oEERGpxJyGLkDkN0q7YEmNpaSkkJiYCMCMGTMcru8oM3HiRDp37kxwcDApKSn1Wtstt9xCkyZNSEpKwmQy2e2Tl5fH999/T5MmTbjlllvqtR4RERERKeX0AGK1WgkPD6djx47ExMQ4+/FShzZt2gRA165dCQsLq7K/u7s78+fPZ+nSpdx+++31Wpunpye9e/empKSEbdu22e2zY8cOzGYzvXr1wtPTs17rEREREZFS1VqEXpcyMzNJS0vj4sWL7Nmzp96/iEr9SUpKAqBHjx4NXIl9gwYN4r///S8JCQmMHTu2QnvZ9KtBgwZp/YeIiFwVXor9uqFLuCxzho1u6BKkHtQqgBQXF/PSSy/x4YcfcuzYscv+Eufl5VWbx0sDMpvNnDlzBoCgoCC7faKiosjIyLDb5u/vT1RUlN22u+66q05q7NWrF15eXhw8eJDMzEyuvfZaW5vJZCIpKQlPT0969eplm0omIiIiIvWrVgEkMjKS//znP8Dlb9Xbvn17/vjHP9bm8dKAzp8/b/u5WbNmdvtkZmY6DCBms9nhvR0Fml/f22KxVNrH3d2dvn37EhsbyzfffMMf/vAHW9t///tfSkpK6Nu3L02aNKnyeWVWrFjBypUr7baVBTIRERERcazGASQuLo5PP/0Ug8GAt7c3I0eOpFWrVnz22WdkZWXRsWNHbr31VqxWK6dPn+a///0vubm5+Pj48OabbzJ+/HiaNm1al+9FnMjDw8P288WLF+32Wbp0aYXXYmJiWLJkSaX3Xr58eZXPv/TAwcoMGjTIbgC5dPrV5cjPz3f43JKSksu6l4iIiMjVqMYB5MMPPwSgSZMmbNu2ja5duwJw33330b9/f3Jzc3nnnXdsB87l5eXx8MMP8/HHH/PRRx8RERFRB+VLQ/Hy8sLd3R2z2UxmZma1r7t05MQZbrrpJvz8/Dh+/DhpaWlcf/31/PLLL/z88880b96c7t27X9b9vLy8CAgIsNvm4qJN5URERESqUuNvTImJiRgMBsaNG2cLHwB9+/bFw8OD06dP2xYpA3h7e/Phhx/So0cPNmzYwOuvv167yqVBGQwGQkNDAcr9fa7KwYMH66sku1xdXbn11luB/416fPvtt1itVvr374+rq+tl3W/y5Mls2LDB7l+tWrWq8/pFREREGpsaB5ATJ04AlAsfZa6//noA9u7dW+51V1dXZs+ejdVq5aWXXtKUld+4fv36AbBnzx5OnTpVZX+TydQgh/2VTbMqCyA1nX4lIiIiIrVX4wBSWFgIgK+vb4W2Dh06AHDo0KEKbXfeeScAv/zyC/Hx8TV9vFwBRowYQcuWLbFYLBiNRts/E/YUFRWxaNEiioqKnFhhqbCwMAICAjh9+jSbN28mLS0Nf39/wsPDnV6LiIiIyNWuxgGkbLpJ2UjIpTp06IDVauWnn36q0Obl5WULLc6ejiN1y8PDg5kzZ+Lm5kZKSgozZ84kMTGxXBAxm83s2rWLGTNmsG/fPkJCQhqk1oEDBwLwf//3fwAMGDDAtj5JRERERJynxovQe/XqxZdffsnq1at54YUXyu2KVPab5e3bt1NSUlJuce65c+fIycnBYDA4fUGy1L3w8HDmzZuH0WgkLS2N6Oho3Nzc8PX1xdXVFZPJZNtyd/To0Tz00ENERkY6vc5BgwaxZs0aLly4YPvzlS73y40NXYKIyFUtepGxoUsQaZRqPAIyadIkAI4dO8btt99ObGwsBQUFANx2220AnD59mmXLlpW7bt68ebaf27dvX9PHyxWkW7duLF++nEcffZQePXrg5eVFTk4OFy5cIDAwkNGjR/PKK6/wl7/8hSZNmjTIKEj79u1p27YtANdeey033HCD02sQERERETBYL/cEwf/ParXSr18/du3aZZvK8s9//pO5c+cC0Lt3b77//nsARo4cSWhoKLt372b79u1YrVaaN2/O8ePHad68eR29FZGGFRwcTEZGBkFBQXanJl6uuU8+XgdViYhITTXGEZCXYr9u6BIuy5xho+2+/sADD/D+++8zb948nn/++Wrd6/rrr+fYsWOsWrXK6cdBnDt3jsWLFwMwY8YMvL29nfr8K02NR0AMBgPr169n5MiRWK3WCiehv/XWW7i5lc7w2rRpE0uWLLGFD4DFixcrfIiIiIhIo3fu3DleeOEFXnjhBfLy8hq6nAZXq5PTWrduzddff83Ro0f59NNPbTtcAXTv3p0NGzZwww032AKK1Wqlbdu2rF69mgceeKC2tYuIiIiIyG9MjRehX6pdu3a0a9euwuvDhg3jwIEDHD16lMzMTFq0aEGnTp3q4pEiIiIiIvIbVKsRkOpq3749/fr1U/gQERERkTp1+PBh7r33Xq699lqaNm3KDTfcwIwZMzh79my177Ft2zYmTZpESEgInp6eXHvttfTu3Zvo6Gh++eUXu9fs2LGDiRMncu211+Lu7o6/vz9Dhw7lww8/LLc04frrry+38VJgYCAGg4G0tLQav+ffujoJIDt27GD27NmMGzfObvsDDzzA3Llz2bNnT108TkRERESExMREunfvzooVKzAYDPj7+3P48GEWLlxIz549SU1NrfIe0dHRDBw4kFWrVnHs2DGuueYaCgsL2b17N08//TRdu3atcJ+PPvqIAQMG8Nlnn5Gfn0/btm2xWCxs3bqV++67j0ceecTWt2PHjrZDugFCQkIIDQ3F3d297j6I35haBZDs7GxGjBjBgAEDWLBgAVu2bLHbLyEhgZdffpk+ffowcuRI0tPTa/NYERERERE2bdrE9ddfz65duzh16hQZGRns3LmT4OBgjh8/zuTJkyu9/syZM7YjIqKjoykoKCAtLQ2TycS3337LNddcQ1ZWFosWLbJdU1BQwKOPPkpJSQkvv/wyJpOJI0eOcO7cOTZt2oSPjw9vvfUW3377LQCxsbHExsbarv/22285cOAAQUFB9fCJ/DbUOIBYLBZGjhxJbGysbYG5v7+/3b5+fn62PjExMQwcONDhcJaIiIiISHX4+PiwZcsWevfubXutT58+vPnmm0DpLJ3ExESH13///fcUFxfTu3dv5syZU25UYsCAAUydOhWg3AjI/v37bYdpT5s2zbbrK8CIESOYPn067dq108yfStQ4gHzwwQe2DzY8PJzY2FiOHz9ut+/3339PfHw8N998M1arlfT0dB577LGaPlpEREREhN/97ncEBgZWeH306NF07NgRKJ2J48jAgQNJT09n48aNdtszMzOB0l+8l2nVqpXt57lz59oO4i4zb9480tLSeOKJJ6r9Pq42Nd4F6+OPPwZKD1/bsWNHpQeqGAwGBg8ezM6dO/n973/P2rVr+eyzzzh58iRt2rSpaQkijVpjPABLRESkLnXt2tXu6waDgc6dO3PkyJFKDwf29PQkODiYCxcuEBcXx759+zh27BjHjx/n559/5qeffqpwTYcOHZg2bRpLly7FaDTy9ttvM2jQIG699VaGDBnCLbfcUm5URCqq8QjIvn37MBgM/PGPf6z2aY4uLi689tprAJSUlFSaSEVEREREKuPp6emwrWXLlgAUFhY67GO1Wnnttde45pprGDZsGNOnT7cdnt2hQwdGjhxp97rXX3+dr7/+mokTJ+Lq6srXX3/NM888w4ABAwgICGDmzJlcvHixdm+uEatxADl37hxQOgJyOdq1a4efnx9ApYlURERERKQyJpPJYdvJkycBHK5RBnjppZeYPn06RUVFREVFsWvXLnJycvjll1/48ssv6du3r8NrR40axX/+8x+ys7PZs2cPixcvZvjw4eTk5LBgwQL+9re/1fyNNXI1DiAtWrQAICMj47Kus1qttrlyrq6uNX28iIiIiFzlHC30tlgsJCcnA9C9e3eH1y9fvhyAl19+maeffprevXvTvHlzW7u9s0S+//57VqxYwQ8//ACUfp/t2bMnjz/+OJs3b7YtgP/0009r9J6uBjWeoNa9e3c2b97MqlWreOGFF2jSpEm1rtu8eTNFRUUYDIZyeyKLSHm5i7o0dAki0sj4PLm/oUsQqVMbN260u6b4iy++4OTJkzRr1ozRo0c7vL5sV9bQ0NAKbTk5OXz22WcVXt+8eTNz5sxhzJgxrF+/vkJ7ly6l//8uKSm5rPdyNanxCMi9994LQHp6OhMnTrRNyarMoUOH+POf/wxAs2bNGDFiRE0fLyIiIiJXufz8fO666y4OHDhge2379u1MmzYNgKeeeqrciMavhYeHA6UjIGfOnAFKR0+2bNnCoEGDbLtgXbhwwXbNmDFjMBgMfPXVV7z22mvl1nr89NNPPPnkkwDceeedttcvXauiJQi1CCCRkZEMGDAAq9XKhg0bCAkJ4YknnmDjxo0cO3aM/Px8SkpKyMrKIiYmhr/85S90796d48ePYzAYmDt3Ls2aNavL9yIiIiIiV5EpU6bw008/0blzZ9q0aUNgYCC33norp06dYvjw4Tz77LOVXv/iiy/i4uLC1q1badOmDe3bt8fX15cRI0ZgNpt59913gdLDA6+//npycnLo0qULM2bMwGq1Mn36dPz8/OjYsSPXXHMNnTt3JjExkeuvv54FCxbYnhMQEGDbLnjw4MGEhoZe9jKGxqRWJ6F/8cUXdO/eHavVislk4vXXX+fOO++kQ4cONG/eHHd3d6699lpGjhzJv//9by5cuIDVaiUyMpK///3vdfUeREREROQq1K9fP7Zv387YsWMpKioiJyeHbt268dprr7Fhw4YqlwjccccdbNmyhcGDB+Pu7s7Zs2fp1KkTCxcuZO/evdx7771MnToVT09PLl68aFu//Morr/Dxxx9z++234+Pjw/Hjx8nPz6dr167MmTOH77//vtxJ5waDgXfffZdOnTpRXFyMyWTCxaVWX8N/0wxWq9VamxsUFhby3HPP8cYbb1Q4iOXXfH19iYqK4q9//WttHilyRQoODiYjI4OgoKA6GV7VGhARqWtaAyIvxX7d0CVcljnDHK/fkN+uWp+S4uHhwcsvv8zcuXNZs2YN27dv58CBA5w7dw6z2Yyfnx9hYWHcdttt/O53v9O0KxERERGRq1idHdPo6+vLlClTmDJlymVfu3v3bgoKChg0aFBdlSMiIiIiIlegK+Kc+HvvvZdDhw5RXFzc0KWIiIiIiEg9umJWv9RyKYqIiIiIiPwGXBEjIPLbs3jxYuLi4ujSpQvR0dF2+yQnJxMfH09ycjLZ2dlYLBZ8fX0JCAigZ8+eDBgwwLYl3a/FxsZiNBoBWLduncM6srKyeOaZZzh16hRubm7MmDGD/v37V+h34sQJtmzZwg8//EBWVhYXLlzA09OTNm3a0K1bN0aOHEnr1q1r8EmIiIiIyOVQAJE6l5eXh9FoZOfOnQC4uLjg5+eHi4sLJpOJrKwskpOTWbVqFRMmTCAyMhKDwXDZz8nMzOSZZ57h9OnTNG3alDlz5tCjR49yfSwWC++99x7r16+3nUjq4+NDy5YtMZlM/Pzzz/z88898/vnnPPzwwzocU0RERKSeKYBIncrPz2f27Nmkp6fj4+NDREQEgwcPtp1CarVaOXDgABs2bCAhIYHVq1djsVi4//77L+s5J0+e5JlnnuHMmTN4eXnx3HPPERYWVq6P1WplwYIF/Pe//8XFxYU777yTsWPH0qZNG1t7amoqH3/8MTt37mTp0qW4uroybNiwuvkwRERERKQCBRCpU0ajkfT0dFq3bk1UVFSFKVYGg4GwsDDCwsIICgpi5cqVrF27luHDh9uCQVXS09N59tlnyc7OxtfXl3/84x+0b9++Qr/169fbwsesWbMqTM0yGAx07NiRuXPnsnjxYuLj43nzzTfp3bu3LTA1pKU3vdrQJYhIIzOnoQsQEeEKWoQuv30pKSkkJiYCMGPGDIfrO8pMnDiRzp07ExwcTEpKSrWecfToUebOnUt2djatW7dm/vz5dsNHYWEhn3zyCQBjxoyxuy6kjMFg4KGHHsLd3Z2LFy+ybdu2atUiIiIiIpdPIyBSZzZt2gRA165dK0yHssfd3Z358+dX+/6HDx9m3rx55ObmEhQUxD/+8Q+HC8e/++47cnNzMRgMjBs3rsp7N2/enFdeeYWCggJatWpV7ZpERERE5PIogEidSUpKAqiwELwuHDhwgBdeeIH8/Hyuu+46XnzxRfz8/Bz2379/PwBt2rSp9u5WHTt2rItSRURERKQSCiBSJ8xmM2fOnAEgKCjIbp+oqCgyMjLstvn7+xMVFWW3bf/+/fzzn//kwoULAJw/f962o5UjZ8+erbQWEREREWkYCiBSJ86fP2/7uVmzZnb7ZGZmOgwgZrPZ4b1feOEFCgsL6dSpE4cPHyYnJ4fXXnuNf/zjH7i42F/GdPHiRQA8PT2r+xaqZcWKFaxcudJuW1kAExERERHHFECkTnh4eNh+Lvvy/2tLly6t8FpMTAxLliyp9N6FhYX079+f6dOn8+WXX/Luu+/y448/snr1aiIiIuxe4+3tDVQebGoiPz+f06dP222ralRGRERERBRApI54eXnh7u6O2WwmMzOz2tddOnLiyPDhw3n00UdxdXVl/Pjx7Nu3j7179/Lxxx/TpUsXunTpUuGali1bAlxWLampqZw+fRo/Pz86depkt4+XlxcBAQF22xyNxoiIiIjI/yiASJ0wGAyEhoayf/9+kpKSqrXzFMDBgwer7PO3v/2t3HOeeOIJHnvsMXJycli4cCFGo7HCuR3h4eGsX7+eY8eOkZOTg6+vb5XPWbx4MWlpadxxxx0OA8jkyZOZPHmy3bbg4GCHU8xERESuBHOGjW7oEkR0DojUnX79+gGwZ88eTp06VWV/k8nE7t27L/s5LVq04LHHHgNKF5sbjcYKfbp164anpycWi4Uvv/yyynseOHCAtLQ0AG655ZbLrklEREREqkcBROrMiBEjaNmyJRaLBaPRSGFhocO+RUVFLFq0iKKioho9q3fv3txxxx1A6ZkfX3zxRbl2Ly8v2yjMmjVrbNvy2lNQUMCyZcsAuPHGG+nevXuNahIRERGRqtVJACkqKuLrr79mwYIFdtv/9a9/sXnzZoqLi+229+7dm0GDBtVFKdKAPDw8mDlzJm5ubqSkpDBz5kwSExPLBRGz2cyuXbuYMWMG+/btIyQkpMbPmzJlCu3atQPg/fff59ChQ+Xaf/e739G1a1eKi4t5/vnn+fjjj23b8wJYLBb27t3L7NmzSUtLw8fHh6eeegqDwVDjmkRERESkcgar1WqtzQ2io6N55ZVXyM3NxcPDg4KCggp92rdvz/Hjx/Hz82PGjBm2L6ny27V48WLi4uLo0qUL0dHR5dp++OEHjEajbVtaNzc3fH19cXV1xWQy2XamGj16NA899BCRkZH4+vry9ttv2+4RGxtrm1q1bt06h3UcO3aM6dOnU1RUxLXXXsvixYvLbQNcUFDA0qVL2bZtm+01X19fmjZtyrlz52zhqF27dsyZM4c2bdrU+DMpWwMSFBTEiRMnanyfMnOffLzW9xCRxid6UcVppyIivyW1SgFTp07l3XffBaCyHGO1WrFarZhMJp555hm2bt3Kl19+ibu7e20eL1eobt26sXz5cuLj49mxYwepqank5OTg6elJYGAg4eHhDB061LbQOyQkhKysrBo9q127djzwwAP8+9//JjMzkzfeeIOZM2fa2ps1a8asWbMYM2YMW7Zs4aeffuLs2bMUFBTg6+tLjx496N+/PwMHDsTV1bVO3r+IiIiIOFbjEZDY2FiGDx8OlB72NmPGDH7/+9/b3RL1p59+YvXq1SxatIjz589jMBiYM2eOw5OvRX6LNAIiIs6gERCpjdxFFb+nXcl8nnS8hlN+u2q8BuStt94CShf77tq1ixdeeMFu+AAICwtj3rx5JCUl0aVLF6xWK0uWLCE3N7emjxcRERERkd+gGgeQxMREDAYDkZGRhIeHV+ua6667jjfffBMoPVE6ISGhpo8XEREREZHfoBoHkLITpqsbPsr069cPLy8vgAq7FomIiIiISONW4wBSttPQuXPnavzwst2QRERERETk6lDjAHLDDTdgtVrZsGHDZV2XnJxMfn4+QK22PBURERERkd+eGgeQCRMmALBr1y5efPHFal1TWFjIY489BoDBYGDYsGE1fbyIiIiIiPwG1TiA/O1vfyM4OBiA5557jmHDhvHFF1+Ql5dXoe+FCxf45JNP6NWrF1u3brUtXg8MDKx55SIiIiIi8ptT44MImzVrxhdffMHtt9+OyWRi69atbN26FTc3N9q0aUOrVq3w9PTk7NmzHD58mOLiYqD0UMKbbrqJxYsX19V7EBERERGR34gaj4AA9OjRg+3bt9OnTx/baedms5njx4/z/fff89///pcDBw5gNptt7RMnTiQuLo4WLVrU1XsQEREREZHfiBqPgJQJDQ0lMTGRLVu28OGHH7J9+3ZSU1PL9Wnbti233XYbU6dOpX///rV9pMhVQacdi4iISGNU6wBSZvjw4QwfPhyA4uJizp07h9lsxs/PD09Pz7p6jIiIiIiI/Mrzzz8PwNSpU23rtOvTAw88wPvvv8+8efNsz66uOgsg5W7q5karVq3q49YiIiIiIvIrL7zwAgCjRo1ySgCpjXoJICJSe7mLujR0CVJHfJ7c39AliIiIXDFqtQhdRERERETkciiAiIiIiMhv1sGDB3nggQcIDg6madOmtG/fnvHjx/PNN99U6Jubm8s///lPunfvjo+PD15eXvTo0YOoqCjOnz9fof/zzz+PwWBg6tSpnD17locffphrrrmGu+++G4D33nsPg8HA7bffTkFBAbNnzyY4OJgePXqUu8+FCxd49dVX6dWrF82bN6d58+bccsstLFq0iIsXLzp8b5s3b+aOO+7A398fLy8vOnfuzCOPPFJuw6chQ4ZgMBhsf+7Xrx8Gg4GtW7eWu9fJkyd54oknuPHGG/H09KR169aMHj2aNWvWOHz+mTNneOKJJ+jQoQNNmzYlKCiIKVOmcPz4cYfXVIemYImIiIjIb9K6deu45557uHjxIk2bNuWaa64hIyODtLQ0vvjiC6Kjo5kzZw4AR44cYcSIEbYv78HBwVgsFvbt28e+fft455132LBhA506darwnLNnz9K/f38OHjxIYGAg11xzTbn2wsJChg8fzvbt22ndunW5NRgZGRmMHDmS5ORkAFq3bo27uzu7du1i165dLF++nNjYWK677rpy95w7dy4vvfQSAL6+vrRo0YIDBw7w008/8f777/P1118zdOhQ2rZtS2hoKD///DMA7dq1o2nTpjRr1sx2r4SEBMaNG0dOTg4Gg4GgoCDy8/PZuHEjGzdu5K677uLTTz+lSZMmtmuOHz/OrbfeyokTJwAIDAykpKSEd999lw0bNnDjjTfW7G8aGgERERERkd+gn3/+mT/+8Y9cvHiRGTNmcObMGY4dO0ZeXh4zZ84E4LnnniMtLY3CwkLGjx9PamoqAwYM4PDhw6Snp3Py5EkOHTrEoEGDOHr0KGPGjLE7IvH5559jNpvZs2cPJ0+e5F//+le59m3btvHzzz8TExPD6dOnWb9+PVC6M+zdd99NcnIyAwcO5KeffuL06dNkZGRw+PBhBg8ezKFDh5gwYQJWq9V2vw8//JCXXnqJpk2b8uGHH5Kdnc2JEyc4deoUw4cPp7CwkEceeQSADz74gAMHDtiu/fjjjzlw4AB9+vQB4NixY4wfP56cnBwefvhhzpw5Q3p6OtnZ2cTHxxMcHMy6detsQa3M5MmTOXHiBF26dGHv3r2cPHmSU6dO8e233+Lu7s63335b4793CiAiIiIi8pvzz3/+k4KCAkaNGsWrr76Kl5cXAE2aNOHll1+md+/eFBcX8/nnn7NixQr279+Pt7c3X3zxBR07drTdJyQkhHXr1tG6dWuOHDnCe++9Z/d57777Lj179nRYz8KFCxk2bFi511auXMl3331H27Zt+fLLL8uNrnTs2JEvvvgCPz8/du/eTVxcHAAWi4Vnn30WKJ0CNnnyZFxcSr+yX3PNNaxcuZKmTZty6NAh9u3bV+Xn9MILL3Du3DnGjx/Pm2++ScuWLW1tQ4YMYcWKFQAsW7aM/Px8ALZs2cK3336Lt7c3GzdupHv37rZrBgwYwNq1a6t8bmUUQERERETkN8VisfDFF18A2EYCLmUwGFiwYAGvvvoqXbt25dNPPwXgvvvuK/cFvIyvry9/+MMfANi4cWOF9g4dOjB48GCH9TRp0oTIyMgKr69cuRKACRMm0Lx5c7vPveOOOwBsAWT37t0cO3YMV1dXHnrooQrXtGrVirfffptXX3213DQre4qLi23v/YEHHrDbZ/DgwbRp04aLFy+yfft2AP7zn/8A8Lvf/Y6goKAK1/Tq1cs2wlITWgMiIiIiIr8phw8fJi8vD4Bbb73Vbp9BgwYxaNAgAFs46N+/v8N7hoWFAZRb4F2mQ4cOldbTtm1b3Nwqfq3+/vvvAfjoo4/4+uuv7V6blZUFYFvYXTaqERoa6vBcPXthx56DBw/aPqfHH3+c2bNn2+2XnZ1droa9e/cC0Lt3b4f3vummm9i1a1e16vg1BRARERER+U0p+8Ls4uKCv79/tfvb+21+mbLRBHtrQFxdXSu9v6N2k8kElIaMsqDhSNn0p7JaAwICKu1fHWXPh9K1IFUpq6HsOh8fH4d9vb29a1yXAojU2uLFi4mLi6NLly5ER0fb7ZOcnEx8fDzJyclkZ2djsVjw9fUlICCAnj17MmDAAAIDA+1eGxsbi9FoBEp3u6iJ3NxcNm/ezJ49e8jIyCA3N5cmTZrQqlUrwsLCGD58eK12cxARERHncXd3B6CkpISCgoIqvwx7eHhgNpttX+7tycjIAHA46lATzZo14/z58yxevJjHH3+8WteUvbfc3Nw6eX6Zffv20a1bt8u6LjMz02Gfyj7LqmgNiNSrvLw8XnzxRebMmcPmzZs5deoUnp6eNG/eHJPJRHJyMh9++CF//etfWbFiRbkdIOpKfHw8Dz/8MO+//z779+/nwoUL+Pv7Y7VaOX78OJs2bWLGjBksW7YMi8VS588XERGRutWhQwfb2Rc//fST3T4LFy5kyJAh/Otf/yIkJASAH3/80eE9t23bBlDhDI/aCA0NBeDQoUMO+3z77besWLHCtk1vWa1HjhzBbDbbvebee+9lyJAhJCYmVvr8kJAQ2+hMZTX85z//YcWKFZw5c6Zc3WVTsezZs2dPpc+ujAKI1Jv8/Hxmz57Nzp078fHx4U9/+hMffPAB7733Hu+88w5r1qzh5ZdfZvDgwRQXF7N69Wo++OCDOq3hyy+/ZNGiReTn59OjRw9efvllPv74Y9566y0++eQT3n33Xe6++25cXFzYuHEjS5curdPni4iISN1r2bIl/fr1A+Ctt96q0J6Xl8fChQtJSEigdevWtoXeb775pt0pVseOHSMmJgYoXXhdV8aOHQuULka3N6Jx5swZ7r77bu69917bFK2hQ4fSrFkzzp07Z1tAfqk9e/awYsUKtm3bRvv27St9vo+Pj23x/Jtvvmm3z9atW/n973/Po48+ioeHR7m616xZw8mTJytcUzarpaYUQKTeGI1G0tPTad26NQsWLGDs2LHldoAwGAyEhYUxffp0Jk2aBMDatWvt/oNeE4cOHeKdd94BSv9Fev755wkLC7NtZQfg7+/Pgw8+yF//+legdLpXbRK9iIiIOMdzzz0HwDvvvMPbb79tm8Vw+vRpIiIiOHXqFNdddx1jxoxh2rRptGjRgszMTH7/+9/bpltB6Xkiv/vd77BYLNx2220MHTq0zmr861//SmBgICaTiT/+8Y/lvuPs37+fO+64g7NnzzJs2DCGDBkClO6MVTZd66mnnrKNzEDpNKo//vGPAEycOLHcgYhNmzYFsB0cWOaFF17A1dWVmJgYnnnmGQoKCgCwWq1s2LCBe+65B4BZs2bZ1nxERETQuXNnioqKuPvuuzl48KDtft9++y2TJk2qcl1MZRRApF6kpKTYhgVnzJjhcH1HmYkTJ9K5c2eCg4NJSUmpkxo++ugjiouLCQkJYcqUKbahWnuGDx9uG/Is+w2IiIiIXLlGjhzJCy+8gMVi4U9/+hMtW7akbdu2BAYG8tVXX+Ht7c3KlSvx8PAgMDCQ1atX4+3tzZdffsl1111Hu3btCAoKolOnTuzevZtOnTrZzsSoKy1btmTdunUEBATw1Vdfcd1113H99dfTpk0bunbtynfffcfNN9/MqlWryl33/PPPM2rUKH755RcGDhxoO329R48eHDp0iNDQUJYsWVLumrKzOiIjI7nxxhvZuXMnUHpux7///W/c3Nx48cUXadmyJR07dqRly5bceeednD59mgceeIC5c+fa7uXu7s5nn31GmzZt2LVrF6GhoQQHB9OmTRsGDRqEr68vjz76aI0/FwUQqRebNm0CoGvXrrZt7Srj7u7O/PnzWbp0Kbfffnutn28ymWxb340dO7ZaKX3WrFm8+OKLTJw4sdbPFxERkfr33HPPsWHDBm6//XZcXFzIzMykbdu2PPLII/z4448MGDDA1vf222/nhx9+4OGHH6Zt27ZkZmaSn59P7969eemll9izZ0+VvzCtiV69epGUlMT06dPp0KEDp06dwmw2069fP5YsWcKOHTto3bp1uWuaNGnC+vXref311+nZsyfnz5/n/PnzdO3alRdeeIHdu3eXG/0AWLp0KT169MBgMHDmzJly332mTJnC7t27mTRpEi1atODEiRN4eXlx55138sUXX/Duu++WmyECpetA9u7dy6OPPkqbNm3IysqiadOmTJ8+nV27dtk9T6W6DNb6WPUrVxV7u2BNmTKFM2fOcN9999V6LmVNdsH673//y8svvwzAe++9V6t/SaorODiYjIwMgoKCKgx/1kTuoi51UJVcCXye3N/QJYiIAL+9/7fov5+Nk7bhlTpnNpttuyg42m87Kiqq3PzLS/n7+xMVFVWrGsqe7+np6ZTwISIiIiLVowAide78+fO2ny/df/pSmZmZDgOIoy3nLkdhYWGlz6+pFStWsHLlSrttZaFHRERERBxTAJE6V7aFG9g/TRSwu91tTExMhQVVNVV2IFFRUVGd3K9Mfn4+p0+ftttWUlJSp88SERERaYwUQKTOeXl54e7ujtlsrvQEzV+7dOSktsqmXeXl5ZGfn4+Xl1eV15w5c4bDhw/j4uJCnz597Pbx8vIiICDAbtuvF2+JiIiISEUKIFLnDAYDoaGh7N+/n6SkJMaNG1et6y7dY7q2OnfujMFgwGq18uOPP9oOK6rM2rVrWb9+Pdddd53DADJ58mQmT55st61sEbqIiIiIOKZf2Uq9KPvCv2fPHk6dOlVlf5PJxO7du+vs+c2bN7dt/1udnbMKCgr45ptvALjlllvqrA4RERERKU8BROrFiBEjaNmyJRaLBaPRaFsUbk9RURGLFi2q8/UakZGRACQnJ7NmzZpK+7711lvk5OTQrFkzxo4dW6d1iIiIiMj/KIBIvfDw8GDmzJm4ubmRkpLCzJkzSUxMLBdEzGYzu3btYsaMGezbt892Enll8vLyKv2roKDA1rdr1662M0jef/99Fi1axLFjx8rdLy0tjRdffJHY2FhcXFx47LHHaNGiRR19CiIiIiLya1oDIvUmPDycefPmYTQaSUtLIzo6Gjc3N3x9fXF1dcVkMtm23B09ejQPPfSQbdTCkUmTJlXaHhAQwNtvv23783333UfTpk35+OOPiY+PJz4+Hi8vL3x8fGyhBcDHx4cnnniC3r171/Jdi4iIiEhlFECkXnXr1o3ly5cTHx/Pjh07SE1NJScnB09PTwIDAwkPD2fo0KF06tQJgJCQELKysuq0hj/84Q8MGjSIjRs38sMPP3Dq1CmysrLw9vama9eu3HzzzYwaNarOzwwRERERkYoMVqvV2tBFiDQGZbtgBQUFceLEiVrfL3dRlzqoSq4EPk/ub+gSRESA397/W/Tfz8ZJa0BERERERMRpFEBERERERMRpFEBERERERMRpFEBERERERMRptAuWiIiIyFVCi7rlSqAREBERERERcRoFEBERERERcRoFEBERERERcRoFEBERERERcRotQhe5QmmhoIiIiDRGGgERERERERGnUQARERERERGnUQARERERERGnUQARERERERGnUQARERERERGnUQARERERERGnUQARERERERGnUQARERERERGnUQARERERERGnUQARERERERGnUQARERERERGnUQARERERERGncWvoAq4kixcvJi4uji5duhAdHW23T3JyMvHx8SQnJ5OdnY3FYsHX15eAgAB69uzJgAEDCAwMtHttbGwsRqMRgHXr1tWoxtzcXDZv3syePXvIyMggNzeXJk2a0KpVK8LCwhg+fDg33njjZd936tSpnD59moiICCZNmmS3j8ViYdu2bezYsYNDhw6Rk5ODq6srfn5+XHfddfTu3Ztbb70Vb29vu9dX5/OtyokTJ9iyZQs//PADWVlZXLhwAU9PT9q0aUO3bt0YOXIkrVu3dnh9fn4+a9asYceOHZw+fRpXV1euueYa+vTpw4QJE/Dy8qpRXSIiIiJSPQog1ZSXl4fRaGTnzp0AuLi44Ofnh4uLCyaTiaysLJKTk1m1ahUTJkwgMjISg8FQpzXEx8fz73//m/z8fACaNm2Kv78/58+f5/jx4xw/fpxNmzYxatQoHnnkEVxdXevs2cePH+fll18mPT0dAHd3d1q0aEFhYSG//PILp06dYteuXXzwwQc8+uij3HrrrXX2bCgNP++99x7r16+npKQEAB8fH1q2bInJZOLnn3/m559/5vPPP+fhhx9mxIgRFe5x+vRpnn76aX755RcA/P39KS4u5tixYxw7dozdu3fzyiuv4OHhUae1i4iIiMj/KIBUQ35+PrNnzyY9PR0fHx8iIiIYPHgwzZs3B8BqtXLgwAE2bNhAQkICq1evxmKxcP/999dZDV9++SX//ve/AejRowcRERGEhobi4lI6i+7s2bOsW7eOL774go0bN2I2m3n88cfr5NlHjhxh7ty5XLhwgeuuu47JkyfTq1cv3N3dATCbzezevZs1a9Zw8OBBXn31VZo0aULv3r3r5PlWq5UFCxbw3//+FxcXF+68807Gjh1LmzZtbO2pqal8/PHH7Ny5k6VLl+Lq6sqwYcNs9ygpKeHll1/ml19+ISQkhKeeeorg4GAA9u/fz/z58zl69Chr1qxxOAIkIiIiIrWnNSDVYDQaSU9Pp3Xr1ixYsICxY8fawgeAwWAgLCyM6dOn2768rl27lpMnT9bJ8w8dOsQ777wDwNixY3n++ecJCwuzhQ8o/W3+gw8+yF//+legdLrXnj17av3sgoICXnrpJS5cuED37t1ZuHAh/fr1s4UPKB0N6devHy+99BI9e/akpKSEN9980zZSUVvr16+3hY9Zs2bxyCOP2MIHlH7+HTt2ZO7cuQwdOhSAN998k/Pnz9v6bN++nUOHDtGiRQteeOEFW/gA6NKlC5MnTwYgISGhTmoWEREREfsUQKqQkpJCYmIiADNmzHC4vqPMxIkT6dy5M8HBwaSkpNRJDR999BHFxcWEhIQwZcqUSqd2DR8+nJCQEABiYmJq/ewvv/yS06dP4+fnx6xZs2jatKnDvu7u7jz44IO0bduWpk2b2qZr1UZhYSGffPIJAGPGjKF///4O+xoMBh566CHc3d25ePEi27Zts7WVBYu77roLHx+fCtcOGDCAadOm8Yc//KHWNYuIiIiIY5qCVYVNmzYB0LVrV8LCwqrs7+7uzvz58+vs+SaTie+//x4oHf2ozrqOWbNmkZWVhaenZ62fX/b+R48e7XBx+aXatWvH0qVLa/3cMt999x25ubkYDAbGjRtXZf/mzZvzyiuvUFBQQKtWrYDSKVrJyckADqeFeXt72103IiIiIiJ1SwGkCklJSUDpuouGcOkoSrdu3ap1zbXXXsu1115b62efOnWKrKwsoOHe//79+wFo06ZNpbtbXapjx47l/nzixAny8vJwdXWlTZs2xMbGsmnTJo4dO0ZJSQnBwcEMHjyYO++8s9zUMhERERGpewoglTCbzZw5cwaAoKAgu32ioqLIyMiw2+bv709UVFStaih7vqenJy1btqzVvS7XpWtYLl0zUSYvL4+ZM2c6vL5v3761Xoh/9uxZwPHnXx3nzp0DSncNe/3114mPj8dgMODv78/Fixc5cuQIR44cYevWrfzjH/8ot77n11asWMHKlSvttpX9vRIRERERxxRAKnHpIuZmzZrZ7ZOZmekwgJjN5lrXUFhYWOnz61NeXp7tZ3vTuUpKShy+dyidPlZbFy9edPj86srNzQVKdzOLj49n3Lhx3HPPPXh7e2O1Wvnhhx949dVXSU1N5Y033mDOnDkO75Wfn8/p06ftttXVonsRERGRxkwBpBKXngdR9kX41+ytd4iJiWHJkiV1UkPZuouioqI6ud/laNKkie3nwsLCCiGoefPmdg9UXLRoEfHx8XVSQ9n7r02YKwtxULqW5aGHHrL92WAw0L17d+69916WLVtGYmIiJpOJFi1a2L2Xl5cXAQEBdtsu3ZVMREREROxTAKmEl5cX7u7umM1mMjMzq33dpSMntVU27SovL4/8/PxqndR95swZDh8+jIuLC3369Knxs/38/Gw/Z2Zm0qFDh2pdVzbiUBfK3v/lfP6pqam2nbs6depUbvRkyJAhdq8ZOnQo//rXvygpKeHo0aMOA8jkyZNtW/b+WnBwcKUjQiIiIiKibXgrZTAYCA0NBf63GL06Dh48WGc1dO7cGYPBgNVq5ccff6zWNWvXriU6Opr333+/Vs/u2LGjbRSkus+2Wq0cOnSoVs+9VHh4OADHjh0jJyenWtcsXryY6Ohotm7dClAuTNjbghdKR7vK2i4dMRERERGRuqUAUoV+/foBsGfPHk6dOlVlf5PJxO7du+vs+c2bN7dt/2tvutOvFRQU8M033wBwyy231OrZTZo0oWfPnkDpdrzFxcVVXrNz585qB4Xq6NatG56enlgsFr788ssq+x84cIC0tDTgf+//+uuvt21f7Ojv4cWLF20jV46mWImIiIhI7SmAVGHEiBG0bNkSi8WC0Wis9LfjRUVFLFq0qM7Xa0RGRgKQnJzMmjVrKu371ltvkZOTQ7NmzRg7dmytn33PPfdgMBjIyMiockQlKyuLN998s9bPvJSXl5ft/I81a9bYtuW1p6CggGXLlgFw44030r17d6B0dOPmm28GcBhiNm/ejNVqpVWrVrRv374O34GIiIiIXEoBpAoeHh7MnDkTNzc3UlJSmDlzJomJieWCiNlsZteuXcyYMYN9+/bZTiKvTF5eXqV/FRQU2Pp27dqV3/3udwC8//77LFq0iGPHjpW7X1paGi+++CKxsbG4uLjw2GOPVVjH8Je//IW//OUv1RpJKNOxY0fuu+8+AL744gtefPFFfv7553I7PuXl5bFhwwaefPJJzp49W+X7t1gsVb7/Sz/f3/3ud3Tt2pXi4mKef/55Pv74Y9v2vGX327t3L7NnzyYtLQ0fHx+eeuqpcifG33PPPbi5ubF3717efvtt2+drtVr57rvv+Oijj4DSsKfF5CIiIiL1R4vQqyE8PJx58+ZhNBpJS0sjOjoaNzc3fH19cXV1xWQy2XZpKttlqWzUwpFJkyZV2h4QEMDbb79t+/N9991H06ZN+fjjj4mPjyc+Ph4vLy98fHxsX9qhdI3DE088YffE77IF0pe7SH7ixIm4u7vzwQcfsHPnTnbu3ImHhwe+vr5YLBZMJhMlJSU0adKEadOmERwczN///neH9/vpp5+qfP+33XYbTzzxBFA6Fezpp59m6dKlbNu2jZUrV7Jy5Up8fX1p2rQp586dswWWdu3aMWfOHNq0aVPufjfccAOPPfYYS5YsYd26dWzYsAF/f38KCgpsi+bHjRvHsGHDLuuzEREREZHLowBSTd26dWP58uXEx8ezY8cOUlNTycnJwdPTk8DAQMLDwxk6dCidOnUCICQkxHaKeF35wx/+wKBBg9i4cSM//PCD7aRyb29vunbtys0338yoUaOqPDOkJr/hv+uuuxg4cCBff/01e/fuJSMjg+zsbHx8fOjYsSM333wzt912G9deey1msxk3t7r9R6tZs2bMmjWLMWPGsGXLFn766SfOnj1LQUEBvr6+9OjRg/79+zNw4EDbeo9fGzJkCCEhIaxZs4Z9+/Zx9uxZmjVrRq9evbjjjjvo1atXndYsIiIiIhUZrFartaGLEOd59tln6devH3fccUdDl9LolG3DGxQUxIkTJxq6HBEREZErkia7X0UKCgpITU21jdKIiIiIiDibAshV5JlnniE8PLzaBwqKiIiIiNQ1rQG5itxxxx0MHjy4ocsQERERkauYAshV5Pbbb2/oEkRERETkKqcpWCIiIiIi4jQKICIiIiIi4jQKICIiIiIi4jQKICIiIiIi4jQKICIiIiIi4jQKICIiIiIi4jQKICIiIiIi4jQKICIiIiIi4jQ6iFDkCpW7qEtDl1BnfJ7c39AliIiIyBVCIyAiIiIiIuI0CiAiIiIiIuI0CiAiIiIiIuI0CiAiIiIiIuI0CiAiIiIiIuI0CiAiIiIiIuI0CiAiIiIiIuI0CiAiIiIiIuI0OohQbBYvXkxcXBxdunQhOjrabp/k5GTi4+NJTk4mOzsbi8WCr68vAQEB9OzZkwEDBhAYGGj32tjYWIxGIwDr1q2rUY25ubls3ryZPXv2kJGRQW5uLk2aNKFVq1aEhYUxfPhwbrzxxmrd68iRIzz55JOMGjWKRx99tEb1iIiIiMjlUQCRasnLy8NoNLJz504AXFxc8PPzw8XFBZPJRFZWFsnJyaxatYoJEyYQGRmJwWCo0xri4+P597//TX5+PgBNmzbF39+f8+fPc/z4cY4fP86mTZsYNWoUjzzyCK6urpXeb/PmzXVan4iIiIhUTQFEqpSfn8/s2bNJT0/Hx8eHiIgIBg8eTPPmzQGwWq0cOHCADRs2kJCQwOrVq7FYLNx///11VsOXX37Jv//9bwB69OhBREQEoaGhuLiUziI8e/Ys69at44svvmDjxo2YzWYef/xxu/cymUxs3ryZTZs21Vl99WHpTa82dAl1Zk5DFyAiIiJXDAUQqZLRaCQ9PZ3WrVsTFRVVYYqVwWAgLCyMsLAwgoKCWLlyJWvXrmX48OG0adOm1s8/dOgQ77zzDgBjx45l6tSpFUZX/P9fe/ceF2WZ/3/8PcOZ4aCcwiAPRSoqmZqnvh5WSzMLv5uaueo3v1rmr91WrTxUW+mamV9rM9SyNTu4q+aa5iEzNZDVzcQSyxQycQ1RBDmGMCoMML8/eDBJzIwk42Dt6/l4+HjMzH1d9/3hssO8ua7rvkNDNWHCBEVHR2vJkiVKSkpSnz591K1bN1ubdevWafv27SooKGh0TQAAALgybEKHU+np6UpJSZEkTZ8+3eH+jlojRoxQhw4dFB0drfT0dJfUsHr1alVWViomJkYTJ050urRr0KBBiomJkSQlJibWOVZSUqLKyko1a9ZMzZo1k7e3t0vqAwAAQMMxAwKnapcpxcXFKTY29rLtvby8tGDBApddv7i4WAcPHpRUM/txuX0dkjRz5kzl5+fLz8+vzueTJk3SpEmTbO9rN90DAADAfQggcOrw4cOSavZdNIVLZ1E6d+7coD6RkZGKjIy8WiUBAACgEQggcMhisdj2S0RFRdltM2/ePGVnZ9s9Fhoaqnnz5jWqhtrr+/n5KSQkpFHncoVVq1ZpzZo1do+xtwQAAODyCCBw6Ny5c7bX/v7+dtvk5uY6DCAWi6XRNZSXlzu9vruZzWbl5eXZPVZdXe3magAAAH55CCBwyMfHx/b64sWLdtssXbq03meJiYlavHixS2oICAiQJFVUVLjkfI1lMpkUERFh91jtLYEBAADgGAEEDplMJnl5eclisSg3N7fB/S6dOWms2mVXZWVlMpvNMplMl+1TUFCg48ePy2g0qkePHi6rRZLGjRuncePG2T0WHR3tcDYIAAAANfiVLRwyGAxq166dpB83ozfEsWPHXFZDhw4dZDAYZLVa9c033zSoz8aNGzV//nytXLnSZXUAAADANQggcKp3796SpNTUVOXk5Fy2fXFxsQ4cOOCy6wcFBdlu/7tly5bLtj9//rz27NkjSerZs6fL6gAAAIBrEEDg1ODBgxUSEqKqqiolJCTYNoXbU1FRoUWLFrl8v8bYsWMlSWlpadqwYYPTtm+99ZZKSkrk7++v+Ph4l9YBAACAxmMPCJzy8fHRjBkz9Nxzzyk9PV0zZszQmDFj1KVLF9smdYvFoq+++kqrVq1SZmamYmJidPz4cafnLSsrc3rcaDTa7nwVFxenkSNHav369Vq5cqWysrI0fPhwtWrVytY+MzNTq1ev1v79+2U0GjVlyhQ1b968kT990yrdur2pS3CZZ+z8LPMXJTRBJQAAoKkRQHBZHTt21OzZs5WQkKDMzEzNnz9fnp6eCg4OloeHh4qLi2233L377rv10EMP2WYtHBkzZozT4xEREVqxYoXt/YMPPihfX1+tXbtWycnJSk5OlslkUmBgoMrKymyBJjAwUNOmTVP37t0b+VMDAADgaiCAoEE6d+6sZcuWKTk5Wfv27dOJEydUUlIiPz8/tWjRQh07dtSAAQPUvn17SVJMTIzy8/NdWsOoUaPUr18/bd++XYcOHVJOTo7y8/MVEBCguLg4devWTUOGDLlmnhkCAACA+gxWq9Xa1EUAvwa1t+GNiorS6dOnG32+Zx6f6oKqrl0swQIA4D8Tm9ABAAAAuA0BBAAAAIDbEEAAAAAAuA0BBAAAAIDbEEAAAAAAuA0BBAAAAIDbEEAAAAAAuA0BBAAAAIDb8CBCwEVc/SBCAACAXyNmQAAAAAC4DQEEAAAAgNsQQAAAAAC4jWdTFwDAvmcen9rUJeAqmL8ooalLAACgSTEDAgAAAMBtCCAAAAAA3IYAAgAAAMBtCCAAAAAA3IYAAgAAAMBtCCAAAAAA3IYAAgAAAMBtCCAAAAAA3IYHEcLmtdde065du9SpUyfNnz/fbpu0tDQlJycrLS1NRUVFqqqqUnBwsCIiItS1a1f16dNHLVq0sNs3KSlJCQk1D2HbsmXLFdVYWlqqnTt3KjU1VdnZ2SotLZW3t7fCwsIUGxurQYMGqW3btg775+fna9OmTTp48KAKCwtltVoVFhamLl266L777lN4ePgV1QUAAICGIYCgQcrKypSQkKD9+/dLkoxGo5o1ayaj0aji4mLl5+crLS1N77//voYPH66xY8fKYDC4tIbk5GQtX75cZrNZkuTr66vQ0FCdO3dOWVlZysrK0o4dOzRkyBBNnjxZHh4edfqnp6frhRdekNlslsFgUEhIiMrLy5Wdna3s7Gzt3r1bc+fO1U033eTSugEAAPAjAgguy2w2a9asWTp16pQCAwM1evRo9e/fX0FBQZIkq9Wqo0ePatu2bdq9e7fWrVunqqoqjR8/3mU1bN26VcuXL5ckdenSRaNHj1a7du1kNNasIiwsLNSWLVu0efNmbd++XRaLRVOnTrX1r6io0MKFC2U2m3XLLbfoj3/8o6677jpJ0vHjx7Vo0SKdOnVK//d//6fXX39dXl5eLqsdAAAAP2IPCC4rISFBp06dUnh4uF555RXFx8fbwockGQwGxcbG6sknn9SYMWMkSRs3btSZM2dccv2MjAy98847kqT4+HjNmTNHsbGxtvAhSaGhoZowYYL+8Ic/SKpZ7pWammo7vm/fPhUVFSkwMFBPP/20LXxIUkxMjJ566ikZjUbl5uYqJSXFJXUDAACgPgIInEpPT7d9IZ8+fbrD/R21RowYoQ4dOig6Olrp6ekuqWH16tWqrKxUTEyMJk6c6HRp16BBgxQTEyNJSkxMtH2elpYmSbrttttkMpnq9bvhhhtsP9vRo0ddUjcAAADqYwkWnNqxY4ckKS4uTrGxsZdt7+XlpQULFrjs+sXFxTp48KCkmtmPn+7rsGfmzJnKz8+Xn5+f7bOioiJJUkREhMN+tTMqlZWVjSkZAAAAThBA4NThw4cl1ey7aAqXzqJ07ty5QX0iIyMVGRlZ57OZM2eqqqrK4d6OrKwsZWdnS5Jatmx5hdUCAADgcgggcMhisaigoECSFBUVZbfNvHnzbF/cfyo0NFTz5s1rVA211/fz81NISMgVn8fb29vpNRYuXKjq6mqZTCb17dvXYdtVq1ZpzZo1TmsFAACAYwQQOHTu3Dnba39/f7ttcnNzHQYQi8XS6BrKy8udXr8xrFarkpKS9O6776q0tFSenp6aNm1anQ32P2U2m5WXl2f3WHV1tctrBAAA+LUhgMAhHx8f2+uLFy/abbN06dJ6nyUmJmrx4sUuqSEgIEBSzW10XSkjI0PLly/Xd999J0kKDw/X448/rk6dOjntZzKZHO4jufSuXAAAALCPAAKHTCaTvLy8ZLFYlJub2+B+l86cNFbtsquysjKZzWa7d7D6qYKCAh0/flxGo1E9evSoc8xsNuvdd9/Vp59+KqvVKi8vLw0bNkyjRo2qs2ndkXHjxmncuHF2j0VHRzucDQIAAEANAggcMhgMateunY4cOaLDhw/rv//7vxvU79ixYy6roUOHDjIYDLJarfrmm2/Uu3fvy/bZuHGjPvroI91www11Akhubq5mz56tnJwcSVKfPn00fvz4Os8EAQAAwNXFmhE4VfuFPzU11fbF3Zni4mIdOHDAZdcPCgqy3f53y5Ytl21//vx57dmzR5LUs2dP2+fl5eWaM2eOcnJyFBQUpNmzZ2vmzJmEDwAAADcjgMCpwYMHKyQkRFVVVUpISLBtCrenoqJCixYtcvl+jbFjx0qqeZjghg0bnLZ96623VFJSIn9/f8XHx9s+37Fjh86cOSNfX1/NmTNH3bp1c2mNAAAAaBgCCJzy8fHRjBkz5OnpqfT0dM2YMUMpKSl1gojFYtEXX3yh6dOn6+uvv7Y9idyZsrIyp3/Onz9vaxsXF6eRI0dKklauXKlFixbp5MmTdc6XmZmpF198UUlJSTIajZoyZYqaN29uO/7ZZ59JqnmYYUPqAwAAwNXBHhBcVseOHTV79mwlJCQoMzNT8+fPl6enp4KDg+Xh4aHi4mLbLXfvvvtuPfTQQ7ZZC0fGjBnj9HhERIRWrFhhe//ggw/K19dXa9euVXJyspKTk2UymRQYGGgLLZIUGBioadOmqXv37ra+VqtVx48flyQlJyfr888/d3rte+65R/fee6/TNgAAALgyBBA0SOfOnbVs2TIlJydr3759OnHihEpKSuTn56cWLVqoY8eOGjBggNq3by9JiomJUX5+vktrGDVqlPr166ft27fr0KFDysnJUX5+vgICAhQXF6du3bppyJAh9Z4ZUlpaqsrKSkkNe1igK+/iBQAAgLoMVqvV2tRFAL8GtbfhjYqK0unTpxt9vmcen+qCqnCtmb8ooalLAACgSbEHBAAAAIDbEEAAAAAAuA0BBAAAAIDbEEAAAAAAuA0BBAAAAIDbEEAAAAAAuA0BBAAAAIDbEEAAAAAAuA0PIgRcxNUPIgQAAPg1YgYEAAAAgNsQQAAAAAC4DQEEAAAAgNt4NnUBAOwrXdSpqUtwKPDxI01dAgAA+IViBgQAAACA2xBAAAAAALgNAQQAAACA2xBAAAAAALgNAQQAAACA2xBAAAAAALgNAQQAAACA2xBAAAAAALgNDyKEzWuvvaZdu3apU6dOmj9/vt02aWlpSk5OVlpamoqKilRVVaXg4GBFRESoa9eu6tOnj1q0aGG3b1JSkhISEiRJW7ZsuaIaS0tLtXPnTqWmpio7O1ulpaXy9vZWWFiYYmNjNWjQILVt29Zh/927dysxMVGZmZkqKyuTv7+/Wrdurb59+2rQoEHy8PC4oroAAADQMAQQNEhZWZkSEhK0f/9+SZLRaFSzZs1kNBpVXFys/Px8paWl6f3339fw4cM1duxYGQwGl9aQnJys5cuXy2w2S5J8fX0VGhqqc+fOKSsrS1lZWdqxY4eGDBmiyZMn1wsTS5cu1c6dOyVJPj4+Cg0N1Q8//KDDhw/r8OHD2rNnj+bMmSNvb2+X1g0AAIAfEUBwWWazWbNmzdKpU6cUGBio0aNHq3///goKCpIkWa1WHT16VNu2bdPu3bu1bt06VVVVafz48S6rYevWrVq+fLkkqUuXLho9erTatWsno7FmFWFhYaG2bNmizZs3a/v27bJYLJo6daqt/759+7Rz5055eHho8uTJuuOOO+Tl5aWqqirt2LFDy5cv15EjR/TRRx9pxIgRLqu7MZbe8nJTl+BY0idNXcEvytN33N3UJQAAcM1gDwguKyEhQadOnVJ4eLheeeUVxcfH28KHJBkMBsXGxurJJ5/UmDFjJEkbN27UmTNnXHL9jIwMvfPOO5Kk+Ph4zZkzR7GxsbbwIUmhoaGaMGGC/vCHP0iqWe6VmppqO759+3ZJ0tChQzVkyBB5eXlJkjw8PDR06FDdddddkqQDBw64pGYAAADYRwCBU+np6UpJSZEkTZ8+3eH+jlojRoxQhw4dFB0drfT0dJfUsHr1alVWViomJkYTJ050urRr0KBBiomJkSQlJibaPs/MzJQk3XrrrXb7tWrVSpJ08eJFl9QMAAAA+1iCBad27NghSYqLi1NsbOxl23t5eWnBggUuu35xcbEOHjwoqWb2oyGbxGfOnKn8/Hz5+fnZPps0aZKqq6vVrl07u30yMjIkSTfccIMLqgYAAIAjBBA4dfjwYUk1+y6awqWzKJ07d25Qn8jISEVGRtb5rE+fPnXeV1VVSZLy8/OVmJioXbt2ycvL65rZ/wEAAPBrRQCBQxaLRQUFBZKkqKgou23mzZun7Oxsu8dCQ0M1b968RtVQe30/Pz+FhIQ06lyXmjt3rr766ivbe5PJpOeee862FMuRVatWac2aNU5rBQAAgGMEEDh07tw522t/f3+7bXJzcx0GEIvF0ugaysvLnV7/SoWHhyssLEzFxcWqqqqS2WzWggUL9Oyzzzp9jojZbFZeXp7dY9XV1S6tEQAA4NeIAAKHfHx8bK8dbc5eunRpvc8SExO1ePFil9QQEBAgSaqoqHDJ+Wo99thjkmqWYn355Zd67733dObMGb3wwgtasWJFnZ/9UiaTSREREXaPXXpXLgAAANhHAIFDJpNJXl5eslgsys3NbXC/S2dOGqt22VVZWZnMZrNMJtNl+xQUFOj48eMyGo3q0aOH07YeHh7q1auXIiMjNWXKFJWUlOiLL75Q37597bYfN26cxo0bZ/dYdHS0w9kgAAAA1OBXtnDIYDDY7hpVuxm9IY4dO+ayGjp06CCDwSCr1apvvvmmQX02btyo+fPna+XKlZKkb7/9Vu+8847DvRuS1Lp1awUHB0uSwyVWAAAAaDwCCJzq3bu3JCk1NVU5OTmXbV9cXOzSh/kFBQXZbv+7ZcuWy7Y/f/689uzZI0nq2bOnpJo7XW3atEn/+Mc/dOHCBbv9rFarKisrJanO7XsBAADgWgQQODV48GCFhISoqqpKCQkJtk3h9lRUVGjRokUu368xduxYSVJaWpo2bNjgtO1bb72lkpIS+fv7Kz4+XpIUGxtrm0WpfajiTx04cEBms1mS1KlTJxdWDwAAgEuxBwRO+fj4aMaMGXruueeUnp6uGTNmaMyYMerSpYtto7bFYtFXX32lVatWKTMzUzExMTp+/LjT85aVlTk9bjQabXe+iouL08iRI7V+/XqtXLlSWVlZGj58eJ1b5mZmZmr16tXav3+/jEajpkyZoubNm0uquePV7bffrr179+rtt99WUFCQunbtKoPBoKqqKu3atUvvvPOOJKlXr15q2bLlFY+XK5Vu3d7UJdQzf1FCU5cAAAB+4QgguKyOHTtq9uzZSkhIUGZmpubPny9PT08FBwfLw8NDxcXFtlvu3n333XrooYdssxaOjBkzxunxiIgIrVixwvb+wQcflK+vr9auXavk5GQlJyfLZDIpMDBQZWVltkATGBioadOmqXv37nXON3nyZGVlZenUqVP685//LJPJJJPJpMLCQttDCWNjYzV16tSfPT4AAABoOAIIGqRz585atmyZkpOTtW/fPp04cUIlJSXy8/NTixYt1LFjRw0YMEDt27eXJMXExCg/P9+lNYwaNUr9+vXT9u3bdejQIeXk5Cg/P18BAQGKi4tTt27dNGTIELvPDGnWrJlefvllbd26VSkpKcrOzlZhYaECAgLUpk0b9evXTwMGDJCHh4dLawYAAEBdBqvVam3qIoBfg9rb8EZFRen06dONPt8zj197szEswQIAAI3FJnQAAAAAbkMAAQAAAOA2BBAAAAAAbkMAAQAAAOA2BBAAAAAAbkMAAQAAAOA2BBAAAAAAbkMAAQAAAOA2PIgQcBFXP4gQAADg14gZEAAAAABuQwABAAAA4DYEEAAAAABuQwABAAAA4DYEEAAAAABuQwABAAAA4DYEEAAAAABuQwABAAAA4DYEEAAAAABuQwABAAAA4DYEEAAAAABuQwABAAAA4DaeTV0Arh2vvfaadu3apU6dOmn+/Pl226SlpSk5OVlpaWkqKipSVVWVgoODFRERoa5du6pPnz5q0aKF3b5JSUlKSEiQJG3ZsuWKaiwtLdXOnTuVmpqq7OxslZaWytvbW2FhYYqNjdWgQYPUtm1bh/2rqqr06aefKjExUVlZWaqqqlLz5s0VFxen+++/X9dff/0V1QUAAICGIYCgQcrKypSQkKD9+/dLkoxGo5o1ayaj0aji4mLl5+crLS1N77//voYPH66xY8fKYDC4tIbk5GQtX75cZrNZkuTr66vQ0FCdO3dOWVlZysrK0o4dOzRkyBBNnjxZHh4edfpbLBa9+OKLOnjwoCQpMDBQgYGBys/PV1JSklJSUvR///d/atmypUvrBgAAwI8IILgss9msWbNm6dSpUwoMDNTo0aPVv39/BQUFSZKsVquOHj2qbdu2affu3Vq3bp2qqqo0fvx4l9WwdetWLV++XJLUpUsXjR49Wu3atZPRWLOKsLCwUFu2bNHmzZu1fft2WSwWTZ06tc45/v73v+vgwYMKDg7WtGnT1K1bN0nS2bNn9fLLL+vYsWN68803Hc7+AAAAoPHYA4LLSkhI0KlTpxQeHq5XXnlF8fHxtvAhSQaDQbGxsXryySc1ZswYSdLGjRt15swZl1w/IyND77zzjiQpPj5ec+bMUWxsrC18SFJoaKgmTJigP/zhD5JqlnulpqbajhcWFmrr1q0yGAx6+umnbeFDkq677jpNmzZNknTkyBEVFha6pG4AAADURwCBU+np6UpJSZEkTZ8+3eH+jlojRoxQhw4dFB0drfT0dJfUsHr1alVWViomJkYTJ050urRr0KBBiomJkSQlJibaPv/ss89UWVmprl27qkOHDvX6RUdH64knntBjjz0mT08mBgEAAK4WvmnBqR07dkiS4uLiFBsbe9n2Xl5eWrBggcuuX1xcbNuzER8fX29fhz0zZ85Ufn6+/Pz8bJ8dOXJEktS9e3eH/X7zm980rlgAAABcFgEETh0+fFhSzb6LpnDpLErnzp0b1CcyMlKRkZF1Pjt69KgkqWXLlkpPT9eHH36ob7/9VhcuXFB4eLh69Oih4cOHq3nz5q4rHgAAAPUQQOCQxWJRQUGBJCkqKspum3nz5ik7O9vusdDQUM2bN69RNdRe38/PTyEhIVd0jurqapWWlkqS9u7dq23btslqtSo4OFhBQUHKycnR5s2btWvXLs2ZM0c333yzw3OtWrVKa9ascVorAAAAHCOAwKFz587ZXvv7+9ttk5ub6zCAWCyWRtdQXl7u9PoNYTabVV1dLUn6+OOP1bVrVz3yyCO2Z36cPn1ar776qo4fP66XXnpJy5Ytk4+Pj8Nz5eXl2T1Wew0AAAA4RgCBQ5d+Cb948aLdNkuXLq33WWJiohYvXuySGgICAiRJFRUVV3yO2hAjSa1bt9Zzzz1XZy/JpRvQCwoK9OWXX6pPnz52z2UymRQREWH32KV35QIAAIB9BBA4ZDKZ5OXlJYvFotzc3Ab3u3TmpLFql12VlZXJbDbLZDJdtk9BQYGOHz8uo9GoHj16yNfX13asT58+djeyR0dHq23btjp69Kj+/e9/Owwg48aN07hx4+wei46OdjgbBAAAgBr8yhYOGQwGtWvXTtKPm9Eb4tixYy6roUOHDjIYDLJarfrmm28a1Gfjxo2aP3++Vq5cKakmSNXO5gQGBjrsFxoaKqlxsy0AAABwjgACp3r37i1JSk1NVU5OzmXbFxcX68CBAy67flBQkO32v1u2bLls+/Pnz2vPnj2SpJ49e0qqCVI33nijJDn9GWr3doSHhzeqZgAAADhGAIFTgwcPVkhIiKqqqpSQkFBnP8VPVVRUaNGiRS6fQRg7dqwkKS0tTRs2bHDa9q233lJJSYn8/f0VHx9v+/z222+XJCUnJ8tsNtfrd+LECR0/flwGg0G33XabC6sHAADApQggcMrHx0czZsyQp6en0tPTNWPGDKWkpNQJIhaLRV988YWmT5+ur7/+2vYkcmfKysqc/jl//rytbVxcnEaOHClJWrlypRYtWqSTJ0/WOV9mZqZefPFFJSUlyWg0asqUKXWe6XHnnXcqIiJCJSUlWrBgQZ09LVlZWXr11VdltVo1cOBARUdHX/F4AQAAwDk2oeOyOnbsqNmzZyshIUGZmZmaP3++PD09FRwcLA8PDxUXF9tuuXv33XfroYcess1aODJmzBinxyMiIrRixQrb+wcffFC+vr5au3atkpOTlZycLJPJpMDAQFtokWr2eEybNq3eE89NJpP+9Kc/ae7cuTp06JAeeeQRhYWFyWq1qrCw0PZzPvLIIz97fAAAANBwBBA0SOfOnbVs2TIlJydr3759OnHihEpKSuTn56cWLVqoY8eOGjBggNq3by9JiomJUX5+vktrGDVqlPr166ft27fr0KFDysnJUX5+vgICAhQXF6du3bppyJAhDp8Z0qZNGy1ZskQbN27U559/rry8PHl5eal9+/bq37+/7rrrLnl68q8EAADA1WSwWq3Wpi4C+DWovQ1vVFSUTp8+3dTlAAAAXJPYAwIAAADAbQggAAAAANyGAAIAAADAbQggAAAAANyGAAIAAADAbQggAAAAANyGAAIAAADAbQggAAAAANyGAAIAAADAbQggAAAAANyGAAIAAADAbQggAAAAANyGAAIAAADAbQggAAAAANyGAAIAAADAbQggAAAAANyGAAIAAADAbQggAAAAANyGAAIAAADAbQggAAAAANyGAAIAAADAbTybugBcO1577TXt2rVLnTp10vz58+22SUtLU3JystLS0lRUVKSqqioFBwcrIiJCXbt2VZ8+fdSiRQu7fZOSkpSQkCBJ2rJlyxXVWFpaqp07dyo1NVXZ2dkqLS2Vt7e3wsLCFBsbq0GDBqlt27Z1+pw9e1aTJk1q8DWc/fwAAABoHAIIGqSsrEwJCQnav3+/JMloNKpZs2YyGo0qLi5Wfn6+0tLS9P7772v48OEaO3asDAaDS2tITk7W8uXLZTabJUm+vr4KDQ3VuXPnlJWVpaysLO3YsUNDhgzR5MmT5eHhIUny9PRUVFTUZc9fUFCg8vJyBQUFubRuAAAA/IgAgssym82aNWuWTp06pcDAQI0ePVr9+/e3fVG3Wq06evSotm3bpt27d2vdunWqqqrS+PHjXVbD1q1btXz5cklSly5dNHr0aLVr105GY80qwsLCQm3ZskWbN2/W9u3bZbFYNHXqVElSaGioli1b5vT8GRkZmjVrlsLCwvT//t//c1ndAAAAqIsAgstKSEjQqVOnFB4ernnz5tVbYmUwGBQbG6vY2FhFRUVpzZo12rhxowYNGqTrr7++0dfPyMjQO++8I0mKj4/Xww8/XG92JTQ0VBMmTFB0dLSWLFmipKQk9enTR926dbvs+c1ms15++WVVV1frySefVLNmzRpdMwAAAOxjEzqcSk9PV0pKiiRp+vTpDvd31BoxYoQ6dOig6Ohopaenu6SG1atXq7KyUjExMZo4caLTpV2DBg1STEyMJCkxMbFB51++fLlyc3M1bNgwdezY0SU1AwAAwD5mQODUjh07JElxcXGKjY29bHsvLy8tWLDAZdcvLi7WwYMHJdXMftTu63Bm5syZys/Pl5+f32XbHjlyRMnJyQoLC9Pvfve7RtcLAAAA5wggcOrw4cOSavZdNIVLZ1E6d+7coD6RkZGKjIy8bLvKykrb3pAJEyY0KLAAAACgcQggcMhisaigoECSHN5Fat68ecrOzrZ7LDQ0VPPmzWtUDbXX9/PzU0hISKPO9VOJiYk6deqUWrVqpT59+jSoz6pVq7RmzRq7x2prBQAAgGMEEDh07tw522t/f3+7bXJzcx0GEIvF0ugaysvLnV7/SlksFn3wwQeSpFGjRjX4lsFms1l5eXl2j1VXV7usPgAAgF8rAggc8vHxsb2+ePGi3TZLly6t91liYqIWL17skhoCAgIkSRUVFS45X63k5GTl5+crPDxct99+e4P7mUwmRURE2D1We0tgAAAAOEYAgUMmk0leXl6yWCzKzc1tcL9LZ04aq3bZVVlZmcxms0wm02X7FBQU6Pjx4zIajerRo4fdNp988okkaciQIQ3a2F5r3LhxGjdunN1j0dHRDmeDAAAAUINf2cIhg8Ggdu3aSfpxM3pDHDt2zGU1dOjQQQaDQVarVd98802D+mzcuFHz58/XypUr7R7PyMjQv//9bxkMBg0cONBltQIAAODyCCBwqnfv3pKk1NRU5eTkXLZ9cXGxDhw44LLrBwUF2W7/u2XLlsu2P3/+vPbs2SNJ6tmzp902tcdjY2MVGhrqokoBAADQEAQQODV48GCFhISoqqpKCQkJtk3h9lRUVGjRokUu368xduxYSVJaWpo2bNjgtO1bb72lkpIS+fv7Kz4+3m6b/fv3S5K6d+/u0joBAABweQQQOOXj46MZM2bI09NT6enpmjFjhlJSUuoEEYvFoi+++ELTp0/X119/bXsSuTNlZWVO/5w/f97WNi4uTiNHjpQkrVy5UosWLdLJkyfrnC8zM1MvvviikpKSZDQaNWXKFDVv3rzedU+fPm3bz8JTzwEAANyPTei4rI4dO2r27NlKSEhQZmam5s+fL09PTwUHB8vDw0PFxcW2W+7efffdeuihh2yzFo6MGTPG6fGIiAitWLHC9v7BBx+Ur6+v1q5dq+TkZCUnJ8tkMikwMNAWWiQpMDBQ06ZNczi78d1330mqeWL7TTfd1OAxAAAAgGsQQNAgnTt31rJly5ScnKx9+/bpxIkTKikpkZ+fn1q0aKGOHTtqwIABat++vSQpJiZG+fn5Lq1h1KhR6tevn7Zv365Dhw4pJydH+fn5CggIUFxcnLp166YhQ4Y4fWZIRkaGJKl169by8vJyaX0AAAC4PIPVarU2dRHAr0HtbXijoqJ0+vTppi4HAADgmsQeEAAAAABuQwABAAAA4DYEEAAAAABuQwABAAAA4DYEEAAAAABuQwABAAAA4DYEEAAAAABuQwABAAAA4DYEEAAAAABuQwABAAAA4DYEEAAAAABuQwABAAAA4DaeTV0AAPtKF3Wq8z7w8SNNVAkAAIDrMAMCAAAAwG0IIAAAAADchgACAAAAwG0IIAAAAADchgACAAAAwG0IIAAAAADchgACAAAAwG0IIAAAAADchgCCK3L27FkNGzZMw4YN0//+7/+qrKzMafv169dr2LBheuaZZ9xUIQAAAK5FBBA0WlFRkd57772mLgMAAAC/AAQQuMTOnTt16NChpi4DAAAA1zjPpi4Av3xt2rTR999/r9dff11LliyRj49PU5f0q7D0lpfrvH+6ieoAAABwJQIIGu3RRx/V7NmzlZubqzVr1mjChAk/q39KSop27NihjIwMnT9/Xs2aNVNcXJx++9vfqk2bNnXanj17VpMmTZLRaNSmTZv01VdfadOmTTp27JgsFotatGihO++8U/Hx8TIa60/wlZWVadOmTdq/f79yc3Pl5eWlVq1aaeDAgRo4cKA8PDwaNRYAAABwjiVYaLTrrrtO//M//yNJ2rx5szIyMhrUr7q6WgkJCZo/f75SU1NVVVWlkJAQlZSUKDk5WU888YQ+/vhjh/3Xrl2r2bNn68iRIwoMDJSHh4dOnjypt99+WytXrqzXPisrS3/84x+1bt06nTx5UiaTSR4eHkpLS9OSJUs0a9Ysmc3mKxsEAAAANAgBBC5xzz33qEOHDqqurtaSJUtUWVl52T7r169XUlKS/Pz89NRTT2n16tVasWKFVq9erXHjxqm6ulrLly/XwYMH6/Wtrq7WmjVrFB8fr7///e9avny5Vq1apYEDB0qSPv74Y124cMHW3mw2a+7cuSosLFSvXr30zjvv6L333tPf//53LVq0SK1atdKxY8e0ePFi1w0KAAAA6iGAwCUMBoOmTJkib29vZWZm6sMPP3Ta3mw2a8OGDZKkiRMn6vbbb7ctmfL19dWoUaM0aNAgWa1WrVmzxu45+vbtq0mTJsnf31+S5OXlpQkTJshgMKiiokInTpywtf3oo4+Ul5enmJgYzZo1S2FhYbZjN910k5555hkZjUbt27dPOTk5DutetWqVhg4davdPQUFBwwYLAADgPxgBBC5z/fXXa+zYsZKkf/zjHzp9+rTDtqmpqbpw4YICAwM1YMAAu23uuusuSVJGRoZKS0vrHb/77rvrfRYcHKygoCBJqtNn9+7dkqTf/OY3dvd5tGjRQm3btpUkffPNNw7rNpvNysvLs/unurraYT8AAADUYBM6XGrYsGHau3evjh07piVLlmjBggUyGAz12n3//feSpBtvvFHe3t52z9WyZUtJktVqVV5engIDA+scj4iIsNuv9i5cFRUVkqTy8nJlZ2dLkj788EN98skndvsVFRVJkvLz8x3+fCaTyeF17W16BwAAQF0EELiUh4eHpkyZomnTpunbb7/Vtm3bdM8999RrVzs7ERoa6vBcnp6eMhqNqq6utoWJS3l5eTWopkuf0l4bMpwpLy93eGzcuHEaN26c3WPR0dG2oAMAAAD7CCBwuZYtW+qBBx7Q6tWr9be//U09evSo16Y2PFwaDn6quLjYtqypdlnVlbj0uSTPPPOMevXqdcXnAgAAQOOwZgRXxciRI9WmTRtduHBBb7zxRr3jLVq0kCRlZmbKarXaPUd6erokyc/Pz9b+SgQEBCg4OFiSnG4wT01NVXJysvLy8q74WgAAAHCOAIKrwsPDQ1OnTpWHh4dSU1P1r3/9q87xbt26yWAwKC8vT/v377d7jk8//VSS1KtXr0bvr+jevbskaefOnXY3i2dnZ+vFF19UQkICm8kBAACuIpZg4aq58cYbNWLECK1bt8626bxWVFSU+vTpo3/9619644035Ovrq1tvvVWSdOHCBa1bt06HDh2St7e3Ro0a1ehaRo4cqc8++0zZ2dlaunSpJk6cqICAAEnSt99+q9dee02VlZUaNGiQIiMjG309Vyjdur3O+2cueT9/UYK7ywEAAHAJAgiuqgceeED79u3TqVOn6h179NFHdfbsWR07dkzPP/+8AgMD5e/vr6KiIlksFnl4eGjatGmKiopqdB3XX3+9Zs6cqYULFyoxMVHJyckKCwvThQsXdO7cOUlSly5d9MgjjzT6WgAAAHCMJVi4qry8vDRlyhS7S6gCAgL00ksv6aGHHlJMTIwqKytVXFyskJAQ3XnnnVq8eLH69Onjslpuu+02LV68WHfddZeaN2+uwsJCeXh46JZbbtHUqVM1Z86cOhvWAQAA4HoGq6MdwAB+ltrb8EZFRTl9CGNDPfP4VIfHWIIFAAB+qZgBAQAAAOA2BBAAAAAAbkMAAQAAAOA2BBAAAAAAbkMAAQAAAOA2BBAAAAAAbkMAAQAAAOA2BBAAAAAAbsODCAEXcfWDCAEAAH6NmAEBAAAA4DYEEAAAAABuQwABAAAA4DYEEAAAAABuwyZ0wEW8vb1lsVhkNBrVokWLpi4HAPALEhkZqQMHDjR1GYBbeDZ1AcCvRVVVlSSpurpa2dnZTVwNAADAtYkAAriIr6+vLl68KA8PD0VERDTqXAUFBaqurpbRaFRYWJiLKoQ9jLX7MNbuxXi7jyvGOjIy0sVVAdculmAB16ChQ4cqLy9PERER2rZtW1OX86vGWLsPY+1ejLf7MNbAz8MmdAAAAABuQwABAAAA4DYEEAAAAABuQwABAAAA4DYEEAAAAABuw214gWvQmDFjZDabZTKZmrqUXz3G2n0Ya/divN2HsQZ+Hm7DCwAAAMBtWIIFAAAAwG0IIAAAAADchgACAAAAwG3YhA5cI4qLi7Vx40YdOHBAeXl58vf310033aShQ4eqe/fuTV3eL96///1vPf744xoyZIh+//vfO2yXlpamDz/8UCdOnFBpaalCQkLUrVs3/fa3v9V1113nxop/efLz87Vp0yYdPHhQhYWFslqtCgsLU5cuXXTfffcpPDy8Xh/G+8rs3r1biYmJyszMVFlZmfz9/dW6dWv17dtXgwYNkoeHR70+jLVrFBUVacqUKTp37pwWLlyo9u3b12vDWAPOsQkduAacOXNGzzzzjIqKimQ0GhUSEqILFy7IbDZLku677z5NmDChiav8ZVu2bJk++eQTpwFk27ZtevPNNyVJPj4+Cg4OVlFRkSorKxUQEKBnn31WHTp0cGfZvxjp6el64YUXZDabZTAYFBISovLycpWVlUmSAgMDNXfuXN100022Poz3lVm6dKl27twp6cdx++GHH1RRUSFJ6tSpk+bMmSNvb29bH8baNaxWq55//nkdOnRIkuwGEMYauDwCCNDErFarpkyZopMnT+rmm2/WjBkzFBkZqerqaiUnJ+v1119XZWWlZs6cqT59+jR1ub84xcXF2rlzp95//31VV1c7DCAZGRmaPn26rFarRo0apVGjRsnb21ulpaV64403tHfvXoWEhGjp0qUKCAhogp/k2lVRUaFHHnlERUVFuuWWW/THP/7R9lve48ePa9GiRTp16pQiIyP1+uuvy8vLi/G+Qvv27dNLL70kDw8PTZ48WXfccYe8vLxUVVWlHTt2aPny5aqurtb48eM1YsQISfyz7UobNmzQypUrbe9/GkAYa6Bh2AMCNLHPPvtMJ0+elLe3t55++mlFRkZKkoxGo+644w4NHjxYkrRmzZqmLPMXZ926dZo4caLGjx+v1atXq7q62mn71atXy2q1qnv37ho3bpztt8eBgYGaNm2a7beY27dvd0f5vyj79u1TUVGRAgMD9fTTT9dZYhITE6OnnnpKRqNRubm5SklJkcR4X6na8Rg6dKiGDBkiLy8vSZKHh4eGDh2qu+66S5J04MABWx/G2jUyMjK0evVqtWzZ0mEbxhpoGAII0MQ+++wzSVLPnj0VFhZW7/jtt98uSTp9+rROnjzp1tp+yUpKSlRZWalmzZqpWbNmdZaj/JTZbNbXX38tSbrnnnvqHffx8VHXrl0lSZ9//vlVqfeXLC0tTZJ022232X0Q2w033KAWLVpIko4ePcp4N0JmZqYk6dZbb7V7vFWrVpKkixcvSuKfbVe5cOGCXnnlFRkMBj355JN22zDWQMMRQIAmZLVadeTIEUlSXFyc3TaxsbG233J+9913bqvtl27SpEn629/+ZvvjbPnakSNHVF1dLaPRqI4dO9ptc8stt0iSTpw4IYvFclVq/qUqKiqSJEVERDhsYzTW/O+msrKS8W6ESZMmafr06WrXrp3d4xkZGZJqQp/EP9uu8te//lU5OTl68MEH1aZNG7ttGGug4bgLFtCEiouLVVpaKkmKioqy28bLy0vNmjVTfn6+cnJy3Fnef4ysrCxJUlhYmHx8fOy2qb2DU3V1tc6ePavo6Gi31XetmzlzpqqqqmxB+aeysrKUnZ0tSWrZsiXj3Qg/DdJVVVWSau5AlpiYqF27dsnLy8u2/4Oxbrzdu3dr165duvXWWzVs2DCH7RhroOEIIEATqr1DkCQFBwc7bBcQEKD8/HzbXbHgWrV/D87+DgIDA22v+Xuoy9nytoKCAi1cuFDV1dUymUzq27evNmzYIInxdoW5c+fqq6++sr03mUx67rnnbEux+Ge7cc6ePas333xTQUFBmjZtmgwGg8O2jDXQcCzBAprQpQHE2Zc4Pz8/ST/+thOuVftFwNFv8CXJ19fX9pq/h8uzWq1KTEzU1KlTlZWVJU9PT02bNk1BQUGMtwuFh4crLCzM9twPs9msBQsW6NixY7b3EmN9JaqqqvSXv/xFZrNZjz32mEJCQpy2Z6yBhmMGBGhCnp4//ivo7C5NtWuFHU3ro3Fqv7w15O9A4u/hcjIyMrR8+XLbnqXw8HA9/vjj6tSpkyTG25Uee+wxSTVfZr/88ku99957OnPmjF544QWtWLGCsW6E999/X0ePHtVdd92lXr16XbY9Yw00HAEEaEKX3ge+di+IPbUzJc2bN7/qNf0nql0WcemM1E9deoy/B/vMZrPeffddffrpp7JarfLy8tKwYcM0atQo2yyexHhfDR4eHurVq5ciIyM1ZcoUlZSU6IsvvmCsr9C3336r9evXKyoqSg8//HCD+jDWQMMRQIAmdN1118nT01OVlZXKycmxe2ebqqoqFRQUSBIbFq+S66+/XpKUl5enqqoq228yL3X27FlJNWvs+eJQX25urmbPnm27UUKfPn00fvz4Os8EqcV4X5lvv/1W+/btk6+vr8aMGWO3TevWrRUcHKySkhLl5eUx1lfo2LFjqq6uVnZ2tu6//36H7WbOnCmp5jbqvXv3lsRYAw3BHhCgCXl4eNhCR+3teH8qIyNDFovF6a0d0TgdOnSQVPNEb0e3Ov72228lSR07dnS6EfU/UXl5uebMmaOcnBwFBQVp9uzZmjlzpt3wITHeVyo/P1+bNm3SP/7xD124cMFuG6vVqsrKSkk1e8cY6ysTGBioqKgoh39qRUREKCoqSqGhoYw18DMwAwI0sb59+yotLU179uzRuHHj1KxZszrHP/74Y0lS165dFRQU1AQV/vpFRkaqbdu2OnbsmD766CPbF4la586d07/+9S9J0oABA5qixGvajh07dObMGfn6+mrOnDmKiYlx2p7xvjKxsbEyGAyyWq1KSUmxOzYHDhywbYbu1KkTY32FBg4cqIEDBzo8Xns73unTp6t9+/a2zxlroGGYAQGa2J133qkWLVro4sWLeumll2zLrSoqKrRmzRrt3r1bnp6eevDBB5u40l+3sWPHymAwaO/evfrHP/5hu0NNbm6uXnjhBZnNZrVv3962zAI/+uyzzyRJ8fHxlw0ftRjvny88PFy33367JOntt99WamqqrFarpJqlmp9++qleffVVSVKvXr3UsmVLSYy1OzHWQMMYrLX/9QLQZE6cOKHZs2erpKRERqNRoaGhOnfunMrLy2U0GvX73/9egwcPbuoyf9Fee+017dq1S0OGDNHvf/97u23WrVunVatWSaq5XWZgYKAKCgpktVoVGRmpuXPnKjIy0p1lX/OsVqtGjBihyspKpw9gq3XPPffo3nvvlcR4X4kffvhBf/rTn3Tq1ClJNXsJTCaTCgsLbV92Y2Nj9fzzz8tkMtn6MdauVTsDsnDhwjozIBJjDTQES7CAa8CNN96o1157TR988IEOHDig4uJiBQYGqmvXrrrvvvvq/Q8OV8eoUaMUExOjLVu26Pjx4yopKdH111+v22+/Xf/93//NEjg7SktLbXsOamfvnDl37pztNeP98zVr1kwvv/yytm7dqpSUFGVnZ6uwsFABAQFq06aN+vXrpwEDBtTbAM1Yuw9jDVweMyAAAAAA3IY9IAAAAADchgACAAAAwG0IIAAAAADchgACAAAAwG0IIAAAAADchgACAAAAwG0IIAAAAADchgACAAAAwG0IIAAAAADchgACAAAAwG0IIAAAAADchgACAAAAwG0IIAAAAADchgACAPiP8d5778lgMMhgMOif//xnnWO/+c1vZDAY1Lp16yapDQD+UxBAAOAakZ2drXXr1mnlypX64osvZLVaG9Rv8uTJCggI0NmzZ69yhQAANJ5nUxcAAP/pLl68qKlTp2rFihWqrq62fd65c2f9/e9/V1xcnMO+GRkZeuedd/TUU0/puuuuc0e5AAA0CjMgANCEqqurdd9992n58uV1wockHTp0SH379tXRo0cd9n/22WcVHBysGTNmXO1SAQBwCQIIADSht99+W9u3b5ckPf744/r+++9VVFSkNWvWKCwsTCUlJXr44Yft9v3qq6/0wQcf6Omnn1ZQUJA7ywYA4IqxBAsAmtDbb78tSXr00Uf16quv2j7/3e9+p+joaPXv31979+7Vd999p3bt2tXp+9RTTykqKkp/+MMf3FozAACNwQwIADShw4cPS5IefPDBesf69u2rm2++WZJ05MiROsf++c9/aufOnZozZ458fX2vWm2TJ0/WTTfdJD8/PwUFBalLly6aNWuWTp8+3ejzf/zxxxo+fLiuv/56eXt7KzQ0VP369VNCQoLOnz/vtO8nn3yiBx54QC1btpS3t7f8/f1144036oEHHtCmTZsavIH/58jPz9fs2bN12223KSgoSD4+PoqKilJ8fLxWr159Va4JAL9KVgBAk/H397dKsu7bt8/u8ZtvvtkqyfrBBx/U+bxXr17Wdu3aWSsrK69KXS+99JLV09PTKsnun4CAAOvmzZuv6NwXLlywjhgxwuG5JVlvvPFGa0ZGRr2+ZWVl1nvvvddpX0nW/v37W4uLi+v1f/fdd21tkpOT6xzr37+/VZK1VatW9frt27fPGhER4fSaAwcOtJrN5isaEwD4T8ISLABoQh07dtSXX36pv/3tb+rVq1edY3v37tXx48dt7Wpt3LhRKSkpWr9+vTw8PFxe08KFC/X0009Lkry9vTVq1Ch16dJF5eXlSkxM1K5du1RWVqZRo0bpyJEjiomJafC5rVarRowYoW3btkmSQkNDNW7cOLVp00YFBQVav369jh49qhMnTig+Pl7ffPONvLy8bP2nT5+urVu3SpLCw8P1u9/9TjfddJPKysr03XffacOGDTKbzdq9e7cef/xxvfvuu40ej6KiIsXHx6ugoEBGo1H33nuvevbsKZPJpJMnT2rjxo3KzMzUrl279Oijj2rlypWNviYA/Ko1dQICgP9ky5Yts0qyGgwG6xNPPGH9/vvvrcXFxda1a9fafuPeq1cvW/uqqiprhw4drN27d78q9Rw6dMjq5eVllWQNCQmxHjx4sF6bZ5991vZb/4cffvhnnf+NN96w9e3WrZu1qKiozvHy8nLrHXfcYWuzatUq27Hz589bfX19rZKsMTEx1vz8/Hrnz8rKsl5//fVWSVZfX19rRUVFneNXMgOyYMECW5/XX3+93jUvXrxoHTx4sFWS1Wg0Wr/77rufNSYA8J+GPSAA0IQmTZqkQYMGyWq16tVXX1WbNm3UvHlzjR49Wnl5eQoMDNSKFSts7VeuXKn09HS99NJLV6WeBQsWyGKxSJL+8pe/qEuXLvXaPP/887rxxhslSZs2bWrwuauqqmx1e3l5ac2aNWrevHmdNt7e3nrzzTdlMBjqnT8tLU0XL16UJM2YMUNhYWH1rnHDDTdozJgxkmqer5KXl9fg+hxJSUmxvX7ggQfqHffx8dG8efMk1dxWed++fY2+JgD8mhFAAKAJeXh4aPPmzXrooYdkNNb9T3JcXJz27NljW35VXl6uOXPm6M4779Qdd9zh8lrOnz+vDz/8UJLUrFkz/c///I/ddl5eXhoxYoR8fHxUWlqqnJycBp3/n//8p06dOiVJGjJkiNq2bWu3XUxMjHr06CEfH586z0AJCwvTCy+8oBdeeEH33HOPw+tcGkxqw1RjlJeX215/9tlndtt069ZNhw8f1uHDh53WBgAggABAk/Pz89OKFSt08uRJrV27Vu+9955SUlJ06NAh3XrrrbZ2b7zxhrKysurMflRVVWnZsmXq1q2b/P39FRISot/+9rc6cODAz64jNTXV9mV70KBBTveXLFy4UBcvXtTFixfVokWLBp3/888/t70eMmSI07YpKSm6ePGi7S5hktS6dWs9++yzevbZZxUVFWW3X3p6ukv2fVyqZ8+ettdjxozRn//8Z2VmZtZpYzQa1alTJ3Xq1MnuzAwA4EdsQgeAa0R0dLTdJT6SVFpaqpdeekkjR47UbbfdJkmqrKzUiBEjtGXLFlu7CxcuaPPmzfr444/1t7/9Tb/73e8afP1LZxtiY2Ov8Kdwz/lPnjypXbt26dChQzpx4oS+//57ff/99zKbzY0ts57HH39c69atU3p6us6fP685c+Zozpw5uvnmmzVw4EDdeeedGjx4MA+DBIAGYgYEAH4BXnnlFRUVFdn2GkjSnDlztGXLFhmNRj3xxBNKSkrShg0b1Lt3b1VWVmrChAlKT09v8DV++OEH2+ur8Vt8V5z/xIkTuuuuu9S6dWtNnDhRCQkJ+uijj3TkyBGZzWYFBwfXuWOYKwQFBenzzz/XlClTFBAQYPs8IyNDf/3rX3X//fcrPDxco0aN0rfffuvSawPArxEBBACucfn5+Vq0aJEmTJhgexp6aWmp7cnpy5Yt01/+8hcNHDhQw4cP1549e9S7d2+Vl5drwYIFDb7Opfsl/Pz8XPtDuOD8R44cUZcuXbRz505JNftU7r77bs2cOVNvvvmmUlJSlJeXp5EjR7qs5lrBwcFKSEhQXl6eNm/erMcee0ydOnWybZavqKjQBx98oJ49e+rrr792+fUB4NeEJVgAcI2bN2+eLBaLZs+ebfts3759unDhgiIiIjRp0qQ67T09PfWnP/1J9957r5KSkhp8nWbNmtleFxYWNrruy53/5zw/RKp5Bsi5c+ck1dwFa+7cuXafAm+9ik8k9/Pz07BhwzRs2DBJUl5enj766CMlJCTo8OHDKi0t1Z/+9Cd9/PHHV60GAPilYwYEAK5hJ0+e1JtvvqnHHntM0dHRts8LCgokSS1btrT9Fv5SrVq1klQze9JQl+7LOHTokNO2c+fOVevWrdW6desG3+r255x/6NChat26tQYOHCip5k5Uu3btkiS1b99eCxcutBs+JLnk1ru1Lly4oPXr12v9+vXKyMiodzwiIkIPPfSQPv/8c7Vu3VpSzQMkAQCOEUAA4Br2/PPPy8/Pz/Zk8lrh4eGSpO+++04XLlyo1+/gwYOSar4gN1TPnj3l7+8vSdq+fbvOnz9vt111dbVWrlypkydP6uLFiw2+Rm2YkKQNGzY4bJeZmalPPvlEJ0+etJ27sLDQtoTL0e17a2urXaLlChUVFbr//vt1//33a/HixQ7bBQQEqHv37rY+AADHCCAAcI1KS0vTqlWrNGPGDIWEhNQ51rt3b/n7+6u0tFQzZsyos+woNzdXzz33nCT9rOeF+Pv72x7iV1xcrFdeecVuuyVLlujEiROSpBEjRjT4/P369bOFh507d2r37t122z3xxBO217XnDw4Otj0n5dChQw6f7/H000/banOF4OBgtWnTRpK0bt062xKwn7p48aK++OILSXL5JngA+LUhgADANeqZZ55ReHi4pk2bVu9YQECA7Yv666+/rn79+umll17SzJkzdcsttygrK0s+Pj6aNWvWz7rmnDlzFBwcLKlmmdULL7xgu3vV+fPn9frrr9vO6ePjUycsXI7BYNBf/vIX2/sRI0Zo7dq1tjBx9uxZPfroo9q4caOkmqVWv/3tbyVJJpNJ//Vf/yWpZlnauHHjdObMGUk1y7O2bdumO+64QwsXLqxzp6rs7GxVV1f/rDH4qfHjx0uqWdr1m9/8RklJSbaaLRaLkpKSNHjwYJ08eVKS6u3JAQD8hBUAcM3Zt2+fVZJ16dKlDttYLBbrsGHDrJLq/fH09LSuXr36iq69detWq5eXl+1cBoPB2rx5c6unp2eda7z11ltXdP5nnnmmXq3Nmze3GgwG22dBQUHWgwcP1um3d+/eOnVJsgYHB9fp161bN+unn35qe+/t7W01mUy2c7z77ru2Y8nJyXXO379/f6ska6tWrep8bjabrR06dKhXc0hIiNVoNNb5fOjQodbKysorGhcA+E/BDAgAXIOeeuop3XjjjXrkkUcctvH09NSHH36oN954Q7feeqt8fX3VrFkzDRs2TJ9//rltOdXPdc899ygpKcm2adxqtaq4uFiVlZWSavZgbNu2TQ8//PAVnf/FF1/UX//6VzVv3lxSzQMVi4uLbcvIBg4cqC+//FJdunSp0+/222/Xpk2b6uw5KSkpkdVqVXBwsJ599ll9/vnnuvPOO9WrVy9JNfsxGvtwQn9/f+3evVsjRoywLQOrrKxUUVGRbXbFZDJp5syZ2rhxo9MnyAMAJIPVehXvVwgA+NkKCwu1ZMkSDRw4UP369WuyOqxWq1JSUvTVV1/phx9+UPPmzdW1a1f16NHD7p23fq7y8nIlJSXp2LFjunDhgiIjI9WnTx/dfPPNTvudP39eO3bs0LFjx+Tr66ubbrpJd9xxR51ni5SVlWn16tUqKChQ27Ztdf/99ze6Xkk6c+aM9u7dq1OnTun8+fMKCgrSzTffrP/6r//iSegA0EAEEAAAAABuwxIsAAAAAG5DAAEAAADgNgQQAAAAAG5DAAEAAADgNgQQAAAAAG5DAAEAAADgNgQQAAAAAG5DAAEAAADgNgQQAAAAAG5DAAEAAADgNgQQAAAAAG5DAAEAAADgNv8f59WXUqLoTTkAAAAASUVORK5CYII="
     },
     "metadata": {
      "image/png": {
       "height": 400,
       "width": 400
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/uqztuong/Documents/GitHub/dandelion/src/dandelion/base/preprocessing/_preprocessing.py:4234: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAyAAAAMgCAYAAADbcAZoAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjYsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvq6yFwwAAAAlwSFlzAAAewgAAHsIBbtB1PgAAy69JREFUeJzs3XtY1HXe//HncBAREBQlEdJUShHNNC0xj5mWmWXabiSWpd6523p39HBrmblLZKXpmOVWbkezsjVTyzQ5SLmKpmkhZGqIIkqgjMhBYRjm9wc/ZiVmADmMiq/HdXld43xO7++0W/Oez8lgtVqtiIiIiIiIOIHLxQ5ARERERESuHEpARERERETEaZSAiIiIiIiI0ygBERERERERp1ECIiIiIiIiTqMEREREREREnEYJiEg96d27N8HBwfTu3ftihyIiIiJyyXK72AGINBaZmZlkZGRc7DBERERELmmaAREREREREadRAiIiIiIiIk6jBERERERERJxGCYiIiIiIiDiNEhAREREREXEanYIlconKW9StXvvzeWpfvfYnIiIiUhuaAREREREREadRAiIiIiIiIk6jBERERERERJxGCYiIiIiIiDiNEhAREREREXEaJSAiIiIiIuI0SkBERERERMRpdA/IFW7x4sXExcXRrVs3oqOj7dZJTk4mPj6e5ORkcnJysFgs+Pr6EhAQQK9evejfvz+BgYF228bGxmI0GgFYt25dtfFMnjyZrKwsIiIiGDdunMN6VquVKVOmkJmZCcBrr71GSEhItf2f75133mH9+vW88sordOnS5YLaioiIiEjtKAERh/Lz8zEajezYsQMAFxcX/Pz8cHFxwWQykZ2dTXJyMp988gljxowhMjISg8HglNiSkpJsyQeUJToXkoCcPXuW7777riFCExEREZEqKAERuwoKCpg5cybp6en4+PgQERHBoEGDaN68OVA2A7F//342bNhAQkICq1atwmKxMGHCBKfEt3nzZgB8fX3Jzc3lu+++Y+LEibi7u1fZzmq1cujQIT788ENyc3OdEaqIiIiInEcJiNhlNBpJT0+ndevWREVFVVpiZTAYCA0NJTQ0lKCgIFauXMmaNWsYNmwYbdu2bdDY8vPz2b59OwCPP/44L774Inl5efzwww/069fPYbunn36ajIwMzp4926Dx1Zel179ar/3NqtfeRERERGpHm9ClkpSUFBITEwGYNm2aw/0d5caOHUvXrl0JDg4mJSWlweNLSEiguLiY9u3b06dPH3r06AFAXFxcle1OnjyJh4cHfn5++Pn5NXicIiIiIlKZZkCkkk2bNgHQvXt3QkNDq63v7u7O/PnzGzosm/LlV0OHDgVg8ODB7Nmzh927d3P69GmHycWHH35Y4e933313g8YpIiIiIpVpBkQqSUpKAqBnz54XOZLKfvvtN1JTU3F1dWXw4MEA9O3bFw8PDywWC1u2bLmo8YmIiIhI1TQDIhWYzWZOnjwJQFBQkN06UVFRZGRk2C3z9/cnKirKbll9zDiUz37ceOONtpkOT09PbrrpJr7//nvi4uIYPXp0ncdxZMWKFaxcudJuWfnnJiIiIiKOKQGRCs6cOWN73axZM7t1MjMzHSYgZrPZYd+OEpo/9m2xWOyWFRcXk5CQAMBtt91WoWzw4MF8//33pKWl8dtvv9GpU6dqx6qNgoICsrKy7JaVlpY2yJgiIiIijYkSEKnAw8PD9vrcuXN26yxdurTSezExMSxZsqTKvpctW1bt+OUXEdqzbds2CgoK8PX1pXfv3hXKevbsiY+PD3l5ecTFxTVYAuLl5UVAQIDdMhcXrWgUERERqY4SEKnAy8sLd3d3zGZzhYv+qnP+zElDKV9+lZuby5gxYxzWS0hI4JFHHsHNrf7/5z1+/HjGjx9vtyw4ONjhzJCIiIiIlNFPtlKBwWCgc+fOwH83o9fEgQMHGiokAE6cOMG+ffuAsssHy4/S/eMfKEuGfvjhhwaNR0RERERqRzMgUkl4eDj79u1j9+7dnDhxotp7QEwmE7t27WrQmGJiYrBarQQHB/Pmm286rPf0009z6NAh4uLiCA8Pb9CYREREROTCaQZEKhk+fDgtW7bEYrFgNBopKipyWLe4uJhFixZRXFzcYPFYLBZiY2MBuPXWW6usO3DgQAB2795Nbm5ug8UkIiIiIrWjGRCpxMPDg+nTpzNnzhxSUlKYPn0648aNo2fPnrZN6mazmT179rBixQrS0tIICQnh0KFDDRLPjz/+SE5ODi4uLra7PxwZMGAA77//PiUlJWzZsoV77rmnQWJyhryvNtZrf7O/2kj0ImO99ikiIiJyoZSAiF1hYWHMnTsXo9FIWloa0dHRuLm54evri6urKyaTyXbk7ogRI5g0aRKRkZENEkv55vPrr7+eVq1aVVnX39+fsLAwkpKSiIuLu6wTEBEREZHGSAmIONSjRw+WLVtGfHw827dvJzU1ldzcXDw9PQkMDCQsLIwhQ4bQpUsXAEJCQsjOzq7XGEwmk21DeXXLr8oNHDiQpKQkDh8+TGpqKh07dqzXmERERESk9gxWq9V6sYMQaQzKj+ENCgri2LFjde5v9lNP1ENUFWkJloiIiFxs2oQuIiIiIiJOowREREREREScRgmIiIiIiIg4jRIQERERERFxGiUgIiIiIiLiNEpARERERETEaZSAiIiIiIiI0+geEJF6Ut/3gIiIiIg0RpoBERERERERp1ECIiIiIiIiTqMEREREREREnEYJiIiIiIiIOI0SEBERERERcRolICIiIiIi4jRKQERERERExGmUgIiIiIiIiNMoAREREREREadRAiIiIiIiIk6jBERERERERJzG7WIHUFeLFy8mLi6Obt26ER0dbbdOcnIy8fHxJCcnk5OTg8ViwdfXl4CAAHr16kX//v0JDAy02zY2Nhaj0QjAunXrqo1n8uTJZGVlERERwbhx4xzWs1qtTJkyhczMTABee+01QkJCqu3/fO+88w7r16/nlVdeoUuXLhfUFmr22UHj+vx27drFpk2bOHToELm5uTRt2pR27doxcOBAhg8fjpvbZf9/CREREZFLWqP+tpWfn4/RaGTHjh0AuLi44Ofnh4uLCyaTiezsbJKTk/nkk08YM2YMkZGRGAwGp8SWlJRk+/IMZV/ULyQBOXv2LN99911DhGbT2D6/9957jzVr1gDQpEkTWrZsSU5ODikpKaSkpJCYmMjzzz+vJERERESkATXab1oFBQXMnDmT9PR0fHx8iIiIYNCgQTRv3hwo+wV9//79bNiwgYSEBFatWoXFYmHChAlOiW/z5s0A+Pr6kpuby3fffcfEiRNxd3evsp3VauXQoUN8+OGH5ObmNlh8je3z27FjB2vWrMFgMDBhwgRGjhyJh4cHFouFTZs2sXz5cvbu3ctnn31GZGSkU55BRERE5ErUaPeAGI1G0tPTad26NQsWLGDUqFG2L88ABoOB0NBQnnnmGdtSnzVr1nD8+PEGjy0/P5/t27cD8Pjjj+Pi4kJeXh4//PBDle2efvppIiIieOaZZ/jpp58aNMbG9vlt2LABgGHDhjFmzBg8PDwAcHV15c4772T06NEArF+/npKSkoZ9ABEREZErWKNMQMqX0wBMmzbN4f6EcmPHjqVr164EBweTkpLS4PElJCRQXFxM+/bt6dOnDz169AAgLi6uynYnT57Ew8MDPz8//Pz8Giy+xvj5JScnAzBgwAC75TfddBMAhYWFHD16tJ4jFhEREZFyjXIJ1qZNmwDo3r07oaGh1dZ3d3dn/vz5DR2WTfnyoaFDhwIwePBg9uzZw+7duzl9+rTD5OLDDz+s8Pe77767QeJrbJ9fXl4excXFAFx11VV2+3Rx+W8urhkQERERkYbTKGdAkpKSAOjZs+dFjqSy3377jdTUVFxdXRk8eDAAffv2te1H2LJly0WNDxrf5+ft7c1nn33GZ5995jAB2bZtG1C2JKtt27YNFb6IiIjIFa/RzYCYzWZOnjwJQFBQkN06UVFRZGRk2C3z9/cnKirKbll9zDiU/3p/44032n6p9/T05KabbuL7778nLi7Oth/hYmiMn5/BYMDT09Nhn1u3buXLL78EoH///nh7ezusu2LFClauXGm3rPxzExERERHHGl0CcubMGdvrZs2a2a2TmZnp8Au02Wx22LejL+R/7NtisdgtKy4uJiEhAYDbbrutQtngwYP5/vvvSUtL47fffqNTp07VjtUQrqTPLy8vj/fee4+YmBgArr76ah599NEq2xQUFJCVlWW3rLS0tNoxRURERK50jS4BKT/dCODcuXN26yxdurTSezExMSxZsqTKvpctW1bt+OUX6dmzbds2CgoK8PX1pXfv3hXKevbsiY+PD3l5ecTFxV20BORK+PwsFgsbN25k5cqV5OXlARAeHs7UqVPx8fGpMj4vLy8CAgLslp2/j0RERERE7Gt0CYiXlxfu7u6YzeYKF9VV5/xf/htK+fKh3NxcxowZ47BeQkICjzzyyEW5EK+xf36//vorS5cu5ciRIwC0adOGiRMn0rdv3xrFMH78eMaPH2+3LDg42OHMkIiIiIiUaXQ/2RoMBjp37gz8dzN1TRw4cKChQgLgxIkT7Nu3Dyi7PK/8KN0//oGyL/PV3QnSUBrz57d582b+7//+jyNHjuDl5cXEiRN54403apx8iIiIiEjdNboZEChbTrNv3z52797NiRMnqr3HwmQysWvXrgaNKSYmBqvVSnBwMG+++abDek8//TSHDh0iLi6O8PDwBo3Jkcb4+f3444+88cYblJaW0qNHD55++mlatGjRoDGLiIiISGWNbgYEYPjw4bRs2RKLxYLRaKSoqMhh3eLiYhYtWmS7J6IhWCwWYmNjAbj11lurrDtw4EAAdu/eTW5uboPFVJXG+Pl98MEHlJaWEhYWxvPPP6/kQ0REROQiaZQJiIeHB9OnT8fNzY2UlBSmT59OYmJihS/SZrOZnTt3Mm3aNPbu3UtISEiDxfPjjz+Sk5ODi4uL7e4KRwYMGICLiwslJSX1cifIokWL+Otf/8qiRYtq3KaxfX4ZGRkcPnwYg8HA1KlTcXd3b7BYRURERKRqjXIJFkBYWBhz587FaDSSlpZGdHQ0bm5u+Pr64urqislksh0ZO2LECCZNmkRkZGSDxFK+efr666+nVatWVdb19/cnLCyMpKQk4uLiuOeee+o0dnZ2NhkZGRf8i39j+vwOHjwIlJ1S5eiOkvNFRUXh7+9f98BFREREpJJGm4AA9OjRg2XLlhEfH8/27dtJTU0lNzcXT09PAgMDCQsLY8iQIXTp0gWAkJAQsrOz6zUGk8lk2xBd3fKhcgMHDiQpKYnDhw+TmppKx44d6xyHwWC44DaN5fMzmUxA2VKumpxSVVJSUvuARURERKRKBqvVar3YQUjD+uyzzzhy5AgzZsy42KE0auXH8AYFBXHs2LGLHY6IiIjIJalR7gGRivbt22c7WldERERE5GJSAtLIvffee/z+++8MHTr0YociIiIiItK494AIdO7cmXvvvRdvb++LHYqIiIiIiBKQxq5fv34XOwQRERERERstwRIREREREadRAiIiIiIiIk6jBERERERERJxGCYiIiIiIiDiNEhAREREREXEaJSAiIiIiIuI0SkBERERERMRpdA+IyCUqb1E3h2U+T+1zYiQiIiIi9UczICIiIiIi4jRKQERERERExGmUgIiIiIiIiNMoAREREREREadRAiIiIiIiIk6jBERERERERJxGCYiIiIiIiDiN7gG5wi1evJi4uDi6detGdHS03TrJycnEx8eTnJxMTk4OFosFX19fAgIC6NWrF/379ycwMNBu29jYWIxGIwDr1q2rNp7JkyeTlZVFREQE48aNc1jParUyZcoUMjMzAXjttdcICQmptv9du3axadMmDh06RG5uLk2bNqVdu3YMHDiQ4cOH4+am/0uIiIiINCR92xKH8vPzMRqN7NixAwAXFxf8/PxwcXHBZDKRnZ1NcnIyn3zyCWPGjCEyMhKDweCU2JKSkmzJB5QlOtUlIO+99x5r1qwBoEmTJrRs2ZKcnBxSUlJISUkhMTGR559/XkmIiIiISAPSNy2xq6CggJkzZ5Keno6Pjw8REREMGjSI5s2bA2UzEPv372fDhg0kJCSwatUqLBYLEyZMcEp8mzdvBsDX15fc3Fy+++47Jk6ciLu7u936O3bsYM2aNRgMBiZMmMDIkSPx8PDAYrGwadMmli9fzt69e/nss8+IjIx0yjOIiIiIXImUgIhdRqOR9PR0WrduTVRUVKUlVgaDgdDQUEJDQwkKCmLlypWsWbOGYcOG0bZt2waNLT8/n+3btwPw+OOP8+KLL5KXl8cPP/xAv3797LbZsGEDAMOGDWPMmDG2911dXbnzzjs5efIk//73v1m/fj3333//JTELsvT6Vx2WzXJiHCIiIiL1SZvQpZLy5UgA06ZNc7i/o9zYsWPp2rUrwcHBpKSkNHh8CQkJFBcX0759e/r06UOPHj0AiIuLc9gmOTkZgAEDBtgtv+mmmwAoLCzk6NGj9RyxiIiIiJS7+D/zyiVn06ZNAHTv3p3Q0NBq67u7uzN//vyGDsumfPnV0KFDARg8eDB79uxh9+7dnD59Gj8/vwr18/LyKC4uBuCqq66y26eLy39z8ZKSkgaIWkRERERACYjYkZSUBEDPnj0vciSV/fbbb6SmpuLq6srgwYMB6Nu3Lx4eHhQVFbFlyxZGjx5doY23tzefffYZAE2bNrXb77Zt24CyJVkNvYRMRERE5EqmBEQqMJvNnDx5EoCgoCC7daKiosjIyLBb5u/vT1RUlN2yu+++u87xlc9+3HjjjbaZDk9PT2666Sa+//574uLiKiUgBoMBT09Ph31u3bqVL7/8EoD+/fvj7e3tsO6KFStYuXKl3bLyz01EREREHFMCIhWcOXPG9rpZs2Z262RmZjpMQMxms8O+HSU0f+zbYrHYLSsuLiYhIQGA2267rULZ4MGD+f7770lLS+O3336jU6dO1Y6Vl5fHe++9R0xMDABXX301jz76aJVtCgoKyMrKsltWWlpa7ZgiIiIiVzolIFKBh4eH7fW5c+fs1lm6dGml92JiYliyZEmVfS9btqza8csvIrRn27ZtFBQU4OvrS+/evSuU9ezZEx8fH/Ly8oiLi6syAbFYLGzcuJGVK1eSl5cHQHh4OFOnTsXHx6fK+Ly8vAgICLBbdv4+EhERERGxTwmIVODl5YW7uztms7nCRX/VOX/mpKGUL7/Kzc2tcJTuHyUkJPDII4/YPUr3119/ZenSpRw5cgSANm3aMHHiRPr27VujGMaPH8/48ePtlgUHBzucGRIRERGRMkpApAKDwUDnzp3Zt28fSUlJ3HPPPTVqd+DAgQaN68SJE+zbtw8ou3zQ0Y3rp0+f5syZM/zwww+Eh4dXKNu8eTNvvvkmFosFLy8v7r//fkaOHOnw8kIRERERqX9KQKSS8PBw9u3bx+7duzlx4kS194CYTCZ27drVoDHFxMRgtVoJDg7mzTffdFjv6aef5tChQ8TFxVVIQH788UfeeOMNSktL6dGjB08//TQtWrRo0JhFREREpDItWpdKhg8fTsuWLbFYLBiNRoqKihzWLS4uZtGiRbZ7NhqCxWIhNjYWgFtvvbXKugMHDgRg9+7d5Obm2t7/4IMPKC0tJSwsjOeff17Jh4iIiMhFogREKvHw8GD69Om4ubmRkpLC9OnTSUxMrJCImM1mdu7cybRp09i7dy8hISENFs+PP/5ITk4OLi4utrs/HBkwYAAuLi6UlJSwZcsWADIyMjh8+DAGg4GpU6deNkuu8r7a6PDP7KeeuNjhiYiIiNSKlmCJXWFhYcydOxej0UhaWhrR0dG4ubnh6+uLq6srJpPJduTuiBEjmDRpEpGRkQ0SS/nm8+uvv55WrVpVWdff35+wsDCSkpKIi4vjnnvu4eDBg0DZKVWO7ig5X1RUFP7+/nUPXEREREQqUQIiDvXo0YNly5YRHx/P9u3bSU1NJTc3F09PTwIDAwkLC2PIkCF06dIFgJCQELKzs+s1BpPJxA8//ABUv/yq3MCBA0lKSuLw4cOkpqZiMpmAsqVcNTmlqqSkpPYBi4iIiEiVDFar1XqxgxBpDMqP4Q0KCuLYsWN17q+6ZVbRi4x1HkNERETE2bQHREREREREnEYJiIiIiIiIOI0SEBERERERcRolICIiIiIi4jRKQERERERExGmUgIiIiIiIiNMoAREREREREafRPSAi9aS+7wERERERaYw0AyIiIiIiIk6jBERERERERJxGCYiIiIiIiDiN28UOQETsy1vUrV7783lqX732JyIiIlIbmgERERERERGnUQIiIiIiIiJOowREREREREScRgmIiIiIiIg4jRIQERERERFxGiUgIiIiIiLiNEpARERERETEaXQPiFyQxYsXExcXR7du3YiOjrZbJzk5mfj4eJKTk8nJycFiseDr60tAQAC9evWif//+BAYG2m0bGxuL0WgEYN26ddXGM3nyZLKysoiIiGDcuHGV4rTHxcUFX19fgoODCQ8P5/bbb8fd3b3asURERESk7pSASL3Jz8/HaDSyY8cOoOyLvp+fHy4uLphMJrKzs0lOTuaTTz5hzJgxREZGYjAYGjSmJk2a0Lp16wrvFRUVcfr0aZKSkkhKSmLdunU8++yztG/fvkFjERERERElIFJPCgoKmDlzJunp6fj4+BAREcGgQYNo3rw5AFarlf3797NhwwYSEhJYtWoVFouFCRMmNGhc1113nd2ZmqKiInbt2sUHH3xAZmYmzz33HK+++ipt2rRp0HhERERErnRKQKReGI1G0tPTad26NVFRUZWWWBkMBkJDQwkNDSUoKIiVK1eyZs0ahg0bRtu2bZ0er4eHB7fccgtdu3ZlxowZ/P7777z55pv8/e9/d3osjiy9/tV67W9WvfYmIiIiUjvahC51lpKSQmJiIgDTpk1zuL+j3NixY+natSvBwcGkpKQ4I0SHWrRowcSJEwHYu3cvhw4duqjxiIiIiDR2mgGROtu0aRMA3bt3JzQ0tNr67u7uzJ8/v6HDqrGbbroJX19fcnNz2bZtGyEhIRc7JBEREZFGSzMgUmdJSUkA9OzZ8yJHUjuurq507twZgIMHD17kaEREREQaN82ASJ2YzWZOnjwJQFBQkN06UVFRZGRk2C3z9/cnKirKbtndd99dP0HWQPnm89OnT1dZb8WKFaxcudJuWfnnICIiIiKOKQGROjlz5oztdbNmzezWyczMdJiAmM1mh307Smj+2LfFYqm2XnWaNm0KQHFxcZX1CgoKyMrKsltWWlpa5zhEREREGjslIFInHh4ettfnzp2zW2fp0qWV3ouJiWHJkiVV9r1s2bJqxy+/iLCuyhMpR0lUOS8vLwICAuyWubhoRaOIiIhIdZSASJ14eXnh7u6O2WwmMzOzxu3Onzm5FJQnMdUdCTx+/HjGjx9vtyw4ONjhTI+IiIiIlNFPtlInBoPBtoG7fDN6TRw4cKChQrpgJSUl/PLLLwA1OsVLRERERGpPCYjUWXh4OAC7d+/mxIkT1dY3mUzs2rWrocOqsYSEBM6ePYurq6vtWURERESkYSgBkTobPnw4LVu2xGKxYDQaKSoqcli3uLiYRYsWVbvZ21lOnDjBhx9+CMCwYcPw9/e/yBGJiIiING7aAyJ15uHhwfTp05kzZw4pKSlMnz6dcePG0bNnT9smdbPZzJ49e1ixYgVpaWmEhIRc1FvHLRYLW7du5YMPPsBkMtG6dWsefvjhixaPPXlfbazX/mbXsr/oRcZ6jUNERESubEpApF6EhYUxd+5cjEYjaWlpREdH4+bmhq+vL66urphMJtuRuyNGjGDSpElERkY2eFwHDhzgr3/9a4X3SkpKMJlMtlmYa665hjlz5lR7ApaIiIiI1J0SEKk3PXr0YNmyZcTHx7N9+3ZSU1PJzc3F09OTwMBAwsLCGDJkCF26dAEgJCSE7OzsBo2puLi40slUrq6utGjRgqCgIIYMGcKAAQNwd3dv0DhEREREpIzBarVaL3YQIo1B+TG8QUFBHDt2rM79zX7qiXqIqu60BEtERETqkzahi4iIiIiI0ygBERERERERp1ECIiIiIiIiTqMEREREREREnEYJiIiIiIiIOI0SEBERERERcRolICIiIiIi4jS6B0SkntT3PSAiIiIijZFmQERERERExGmUgIiIiIiIiNMoAREREREREadxu9gBiIh9eYu6OX1Mn6f2OX1MERERubJoBkRERERERJxGCYiIiIiIiDiNEhAREREREXEaJSAiIiIiIuI0SkBERERERMRplICIiIiIiIjTKAERERERERGn0T0gV6jFixcTFxdHt27diI6OtlsnOTmZ+Ph4kpOTycnJwWKx4OvrS0BAAL169aJ///4EBgbabRsbG4vRaARg3bp11cYzefJksrKyiIiIYNy4cXbrmM1mYmNj2blzJ4cPH+bMmTM0adIEPz8/OnToQJ8+fejXrx8eHh41/BTg8ccfJyMjg9WrV9e4jYiIiIjUnhIQqSQ/Px+j0ciOHTsAcHFxwc/PDxcXF0wmE9nZ2SQnJ/PJJ58wZswYIiMjMRgMDRpTcnIyCxcu5OTJkwB4eHjQsmVLCgsLOX78OBkZGWzdupUVK1bw1FNP0a1b9Zf4HThwgLS0NNzd3Rs0dhERERH5LyUgUkFBQQEzZ84kPT0dHx8fIiIiGDRoEM2bNwfAarWyf/9+NmzYQEJCAqtWrcJisTBhwoQGi2nXrl1ER0dTUlJC586diYyMpHv37ri6ugJw9uxZEhMTWbVqFRkZGbzwwgu8/PLLdOrUyW5/ZrOZvXv38tZbbzVYzCIiIiJinxIQqcBoNJKenk7r1q2JioqqtMTKYDAQGhpKaGgoQUFBrFy5kjVr1jBs2DDatm1b7/FkZ2ezcOFCSkpKGDJkCI8//rgt8Sjn6enJkCFDuOmmm5g9ezaHDx/mnXfeYf78+RXqHT58mNdee42MjAxKSkrqPdb6tvT6V50+5iynjygiIiJXGm1CF5uUlBQSExMBmDZtmsP9HeXGjh1L165dCQ4OJiUlpUFi+uyzzygoKKB9+/ZMnTq1UvJxPi8vLx5++GHatWtHQUEBZ86cqVB+7tw5cnNz8fb2xs/PD29v7waJWUREREQc0wyI2GzatAmA7t27ExoaWm19d3f3SrMM9amoqIj4+HgARo8eXaO9Gj179mTp0qV2y0JDQ/nwww9tf09KSuLZZ5+tn2BFREREpEY0AyI2SUlJQNmX+EvB/v37MZvNAPTq1esiRyMiIiIi9UEzIAKUbcwuP2EqKCjIbp2oqCgyMjLslvn7+xMVFWW37O67765VTMePHwegWbNmtGjRolL50aNHeemllxy2HzlyJHfddVetxnZkxYoVrFy50m5Z+ecnIiIiIo4pARGACvslmjVrZrdOZmamwwSkfKbCHkcJzR/7tlgsFd7Ly8urMp6ioiKH8QCV9oDUh4KCArKysuyWlZaW1vt4IiIiIo2NEhABqHB537lz5+zWsbe3IiYmhiVLllTZ97Jly6odv/wiQnsxOYrn2muvtXvJ4axZs0hOTq52zNrw8vIiICDAbpmLi1Y0ioiIiFRHCYgAZV+s3d3dMZvNZGZm1rhdQ8wylPPz8wPKLkbMz8+v8alVDRnT+PHjGT9+vN2y4ODgKmdkRERERESb0OX/MxgMdO7cGfjvZvSaOHDgQEOFRJcuXWyvf/755xq1KSwsVBIgIiIicglTAiI24eHhAOzevZsTJ05UW99kMrFr164Gi+eqq66iY8eOAHz99dc1ahMTE6O9GCIiIiKXMCUgYjN8+HBatmyJxWLBaDRSVFTksG5xcTGLFi2iuLi4QWOKiIgAymZl1q9fX2Xd1NRUPv744waNR0RERETqRntAxMbDw4Pp06czZ84cUlJSmD59OuPGjaNnz562DeFms5k9e/awYsUK0tLSCAkJ4dChQw0WU9++fbnjjjvYuHEjy5cv5/Dhw9x9991cc801tjomk4mYmBhWrVpFSUkJHTp04PDhww0Wk7PkfbXR+YMOHeH8MUVEROSKogREKggLC2Pu3LkYjUbS0tKIjo7Gzc0NX19fXF1dMZlMtiN3R4wYwaRJk4iMjGzQmKZMmYK3tzdffPEFMTExxMTE4OnpSfPmzTGbzeTk5ADg7e3NjBkzKCgo4LXXXmvQmERERESkdpSASCU9evRg2bJlxMfHs337dlJTU8nNzcXT05PAwEDCwsIYMmSIbZN4SEgI2dnZDRaPq6srDz30EMOGDWPjxo3s3buX33//nVOnTuHr60toaCg333wzQ4YMoUWLFhd0ipeIiIiIOJfBarVaL3YQIo1B+TG8QUFBHDt2rM79zX7qiXqI6sJELzI6fUwRERG5smgTuoiIiIiIOI0SEBERERERcRolICIiIiIi4jRKQERERERExGmUgIiIiIiIiNMoAREREREREadRAiIiIiIiIk6je0BE6kl93wMiIiIi0hhpBkRERERERJxGCYiIiIiIiDiNEhAREREREXEat4sdgIjYl7eoW5XlPk/tc1IkIiIiIvVHMyAiIiIiIuI0SkBERERERMRplICIiIiIiIjTKAERERERERGnUQIiIiIiIiJOowREREREREScRgmIiIiIiIg4je4BuUItXryYuLg4unXrRnR0tN06ycnJxMfHk5ycTE5ODhaLBV9fXwICAujVqxf9+/cnMDDQbtvY2FiMRiMA69atqzaeyZMnk5WVRUREBOPGjbNbx2w2Exsby86dOzl8+DBnzpyhSZMm+Pn50aFDB/r06UO/fv3w8PBwOI7FYmHTpk189913HDt2jMLCQnx8fLj22msZMWIEN954Y7WxioiIiEjtKQGRSvLz8zEajezYsQMAFxcX/Pz8cHFxwWQykZ2dTXJyMp988gljxowhMjISg8HQoDElJyezcOFCTp48CYCHhwctW7aksLCQ48ePk5GRwdatW1mxYgVPPfUU3bpVvsSvuLiY559/npSUFAC8vLzw8/Pj1KlT7Ny5k507d1aZAImIiIhI3SkBkQoKCgqYOXMm6enp+Pj4EBERwaBBg2jevDkAVquV/fv3s2HDBhISEli1ahUWi4UJEyY0WEy7du0iOjqakpISOnfuTGRkJN27d8fV1RWAs2fPkpiYyKpVq8jIyOCFF17g5ZdfplOnThX6+eijj0hJScHHx4fHH3+cm266CYPBQEFBAe+//z6bNm3i008/JSwsjB49ejTY84iIiIhcyZSASAVGo5H09HRat25NVFRUpSVWBoOB0NBQQkNDCQoKYuXKlaxZs4Zhw4bRtm3beo8nOzubhQsXUlJSwpAhQ3j88cdtiUc5T09PhgwZwk033cTs2bM5fPgw77zzDvPnz7fVKS4uJiYmBoD/+Z//4eabb7aVeXl58dhjj3H06FF++eUX1qxZc0kkIEuvf7XK8llOikNERESkPmkTutikpKSQmJgIwLRp0xzu7yg3duxYunbtSnBwsG1ZU3377LPPKCgooH379kydOrVS8nE+Ly8vHn74Ydq1a0dBQQFnzpyxlaWmplJQUICbmxvh4eGV2hoMBnr37g3Ar7/+Wv8PIiIiIiKAZkDkPJs2bQKge/fuhIaGVlvf3d29wixDfSsqKiI+Ph6A0aNH4+7uXm2bnj17snTp0krv5+TkAODn5+dwk7qLS1k+bjabaxuyiIiIiFRDCYjYJCUlAWVf4i8F+/fvtyUDvXr1qlNfN998M5999pktyfij0tJS2+xPu3bt6jSWiIiIiDimBESAsl/9y0+YCgoKslsnKiqKjIwMu2X+/v5ERUXZLbv77rtrFdPx48cBaNasGS1atKhUfvToUV566SWH7UeOHMldd90FgKurK56ennbrlZaW8u6779qWXt1xxx0O+1yxYgUrV660W1b++YmIiIiIY0pABKDCfolmzZrZrZOZmekwAalq2ZKjhOaPfVsslgrv5eXlVRlPUVGRw3ig4jM5cvToUd58803bHpYBAwYwbNgwh/ULCgrIysqyW1ZaWlrteCIiIiJXOiUgAlBhX8S5c+fs1rG3tyImJoYlS5ZU2feyZcuqHb/8IkJ7MTmK59prr7V7yeGsWbNITk6ucrz8/Hw++eQTNmzYgMViwdXVlT/96U/cf//9Vd5p4uXlRUBAgN0yR8u7REREROS/lIAIUPbF2t3dHbPZTGZmZo3b1WSWobb8/PyAsmQhPz8fb2/veokpPj6ef/3rX7Z63bt353/+53+45pprqu17/PjxjB8/3m5ZcHBwlTMyIiIiIqIERP4/g8FA586d2bdvH0lJSdxzzz01anfgwIEGi6lLly621z///DP9+vWrtk1hYaHDJKC0tJQ33niDzZs3A9C2bVseeeSRCneCiIiIiEjD0poRsSm/H2P37t2cOHGi2vomk4ldu3Y1WDxXXXUVHTt2BODrr7+uUZuYmBiHezE+/vhjW/IxevRoXn/9dSUfIiIiIk6mBERshg8fTsuWLbFYLBiNRoqKihzWLS4uZtGiRRQXFzdoTBEREUDZEcHr16+vsm5qaioff/yx3bIzZ86wZs0aoOwCxYkTJ9boXhERERERqV9agiU2Hh4eTJ8+nTlz5pCSksL06dMZN24cPXv2tG0IN5vN7NmzhxUrVpCWlkZISAiHDh1qsJj69u3LHXfcwcaNG1m+fDmHDx/m7rvvrrBfw2QyERMTw6pVqygpKaFDhw4cPny4Qj/btm2jpKSE1q1bO9zDcanJ+2pj1RWGjnBOICIiIiL1SAmIVBAWFsbcuXMxGo2kpaURHR2Nm5sbvr6+uLq6YjKZbEfujhgxgkmTJhEZGdmgMU2ZMgVvb2+++OILYmJiiImJwdPTk+bNm2M2m223nHt7ezNjxgwKCgp47bXXKvRRniQVFBQwderUKser6k4TEREREakbJSBSSY8ePVi2bBnx8fFs376d1NRUcnNz8fT0JDAwkLCwMIYMGWLbJB4SEkJ2dnaDxePq6spDDz3EsGHD2LhxI3v37uX333/n1KlT+Pr6Ehoays0338yQIUNo0aKF3VO8ypOUwsJCCgsLqxyvqjtNRERERKRuDFar1XqxgxBpDMqP4Q0KCuLYsWN17m/2U09UWR69yFjnMUREREScTZvQRURERETEaZSAiIiIiIiI0ygBERERERERp1ECIiIiIiIiTqMEREREREREnEYJiIiIiIiIOI0SEBERERERcRrdAyJST+r7HhARERGRxkgzICIiIiIi4jRKQERERERExGmUgIiIiIiIiNO4XewARMS+vEXdbK99ntp3ESMRERERqT+aAREREREREadRAiIiIiIiIk6jBERERERERJxGCYiIiIiIiDiNEhAREREREXEaJSAiIiIiIuI0SkBERERERMRpdA/IFWrx4sXExcXRrVs3oqOj7dZJTk4mPj6e5ORkcnJysFgs+Pr6EhAQQK9evejfvz+BgYF228bGxmI0GgFYt25dtfFMnjyZrKwsIiIiGDdunN06ZrOZ2NhYdu7cyeHDhzlz5gxNmjTBz8+PDh060KdPH/r164eHh4fDcY4dO8YXX3zBzz//jMlkwtXVlYCAAG644QZGjx5Nq1atqo1VRERERGpPCYhUkp+fj9FoZMeOHQC4uLjg5+eHi4sLJpOJ7OxskpOT+eSTTxgzZgyRkZEYDIYGjSk5OZmFCxdy8uRJADw8PGjZsiWFhYUcP36cjIwMtm7dyooVK3jqqafo1q1bpT727t1LVFQUxcXFGAwG/P39KS4u5ujRoxw9epSYmBjmzZtH586dG/RZRERERK5kSkCkgoKCAmbOnEl6ejo+Pj5EREQwaNAgmjdvDoDVamX//v1s2LCBhIQEVq1ahcViYcKECQ0W065du4iOjqakpITOnTsTGRlJ9+7dcXV1BeDs2bMkJiayatUqMjIyeOGFF3j55Zfp1KmTrY/i4mIWLlxIcXExffv25S9/+QstW7YE4NChQyxcuJCMjAyWLFnCG2+80WDPIiIiInKl0x4QqcBoNJKenk7r1q1ZsGABo0aNsiUfAAaDgdDQUJ555hnbUqk1a9Zw/PjxBoknOzubhQsXUlJSwpAhQ5g/fz433HCDLfkA8PT0ZMiQISxYsIAOHTpQXFzMO++8U6Gf7du3k5ubi6+vL9OnT7clHwAhISE89dRTAKSnp5OZmdkgz3Khll7/qu2PiIiISGOhBERsUlJSSExMBGDatGkO93eUGzt2LF27diU4OJiUlJQGiemzzz6joKCA9u3bM3Xq1AqJxx95eXnx8MMP065dOwoKCjhz5oytLC0tDYCwsDDc3d0rtW3fvr3t9blz5+rvAURERESkAi3BEptNmzYB0L17d0JDQ6ut7+7uzvz58xssnqKiIuLj4wEYPXq03cThj3r27MnSpUsrvd+vXz86dOjgMKk6ePAgAG5ubtUmXiIiIiJSe0pAxCYpKQko+xJ/Kdi/fz9msxmAXr161amva6+9lmuvvdb299LSUgwGA/n5+fz888/861//AmDMmDFVnqIlIiIiInWjBESAsiNuy0+YCgoKslsnKiqKjIwMu2X+/v5ERUXZLbv77rtrFVP5vpJmzZrRokWLSuVHjx7lpZdecth+5MiR3HXXXXbLtm7dyoIFCyq8d++99xIZGVllTCtWrGDlypV2y8o/PxERERFxTAmIAFTYL9GsWTO7dTIzMx0mIOUzFfY4Smj+2LfFYqnwXl5eXpXxFBUVOYwHKj7THzVr1ozAwEBOnz7N2bNnAVi7di1NmjSpMgkpKCggKyvLbllpaanDdiIiIiJSRgmIAFRYduRoE7a9vRUxMTEsWbKkyr6XLVtW7fjlFxHai8lRPNdee63dSw5nzZpFcnJyleP17t2b3r17A2WXE3722WckJCTw2Wefcc0113DLLbfYbefl5UVAQIDdMhcXnekgIiIiUh0lIAKUfbF2d3fHbDZf0DG0Vc0y1JWfnx9QdjFifn4+3t7eDRJTcHAwTz/9NBkZGRw6dIjNmzc7TEDGjx/P+PHjHfZT1YyMiIiIiOgYXvn/DAaD7Qbw8s3oNXHgwIGGCokuXbrYXv/88881alNYWFgpCTCbzbz77ru8++67pKen221nMBjo3r07gMMlViIiIiJSd0pAxCY8PByA3bt3c+LEiWrrm0wmdu3a1WDxXHXVVXTs2BGAr7/+ukZtYmJiKu3FcHd3Z8OGDXz55ZdVJlclJSWA4z0nIiIiIlJ3SkDEZvjw4bRs2RKLxYLRaKSoqMhh3eLiYhYtWkRxcXGDxhQREQGUzcqsX7++yrqpqal8/PHHdsvK7zXZtm2b3fKioiLbJYxhYWG1DVdEREREqqEERGw8PDyYPn06bm5upKSkMH36dBITEyskImazmZ07dzJt2jT27t1LSEhIg8bUt29f7rjjDgCWL1/OkiVLbLealzOZTHz++efMnDmT4uJiOnToUKmfe+65ByhbyvXBBx9QWFhoK0tNTWXOnDlkZ2fj6enJqFGjGu6BLkDeVxttf0REREQaC21ClwrCwsKYO3cuRqORtLQ0oqOjcXNzw9fXF1dXV0wmk+3I3REjRjBp0qRq786oqylTpuDt7c0XX3xBTEwMMTExeHp60rx5c8xmMzk5OQB4e3szY8YMCgoKeO211yr00bt3b0aPHs2XX37J6tWrWbt2LS1btiQ/P9+WjHh5eTFz5kxatWrVoM8jIiIiciVTAiKV9OjRg2XLlhEfH8/27dtJTU0lNzcXT09PAgMDCQsLY8iQIbZN4iEhIWRnZzdYPK6urjz00EMMGzaMjRs3snfvXn7//XdOnTqFr68voaGh3HzzzQwZMoQWLVo4PMVr4sSJhIWF8e2333LgwAFOnTpFkyZN6NChA7169eKuu+7C39+/wZ5DRERERMBgtVqtFzsIkcag/BjeoKAgjh07Vuf+Zj/1hO119CJjnfsTERERuRRoD4iIiIiIiDiNEhAREREREXEaJSAiIiIiIuI0SkBERERERMRplICIiIiIiIjTKAERERERERGnUQIiIiIiIiJOo3tAROpJfd8DIiIiItIYaQZEREREREScRgmIiIiIiIg4jRIQERERERFxGiUgIiIiIiLiNEpARERERETEaZSAiIiIiIiI0ygBERERERERp1ECIiIiIiIiTqMEREREREREnEYJiIiIiIiIOI0SEBERERERcRq3ix2AXLjFixcTFxdHt27diI6OtlsnOTmZ+Ph4kpOTycnJwWKx4OvrS0BAAL169aJ///4EBgbabRsbG4vRaARg3bp11cYzefJksrKyiIiIYNy4cXbrmM1mYmNj2blzJ4cPH+bMmTM0adIEPz8/OnToQJ8+fejXrx8eHh41/BTKLFy4kISEBO644w4ee+yxauvv27eP2bNnA9C+fXtef/31CxpPREREROpGCUgjk5+fj9FoZMeOHQC4uLjg5+eHi4sLJpOJ7OxskpOT+eSTTxgzZgyRkZEYDIYGjSk5OZmFCxdy8uRJADw8PGjZsiWFhYUcP36cjIwMtm7dyooVK3jqqafo1q1bjfqNj48nISHhgmLZvHmz7fWRI0c4dOgQISEhF9SHiIiIiNSeEpBGpKCggJkzZ5Keno6Pjw8REREMGjSI5s2bA2C1Wtm/fz8bNmwgISGBVatWYbFYmDBhQoPFtGvXLqKjoykpKaFz585ERkbSvXt3XF1dATh79iyJiYmsWrWKjIwMXnjhBV5++WU6depUZb8nTpzgn//85wXFUlhYyLZt2wDw9fUlNzeX2NhYJSAiIiIiTqQ9II2I0WgkPT2d1q1bs2DBAkaNGmVLPgAMBgOhoaE888wztqVSa9as4fjx4w0ST3Z2NgsXLqSkpIQhQ4Ywf/58brjhBlvyAeDp6cmQIUNYsGABHTp0oLi4mHfeeafKfktKSli4cCFFRUUEBwfXOJ6EhASKiopo164dY8aMAeC7777DbDbX7gFFRERE5IIpAWkkUlJSSExMBGDatGkO93eUGzt2LF27diU4OJiUlJQGiemzzz6joKCA9u3bM3Xq1AqJxx95eXnx8MMP065dOwoKCjhz5ozDuh9//DEHDhzg3nvv5brrrqtxPOXLr4YOHcrAgQNxcXEhLy+PH374oeYPJSIiIiJ1oiVYjcSmTZsA6N69O6GhodXWd3d3Z/78+Q0WT1FREfHx8QCMHj0ad3f3atv07NmTpUuXVlnnp59+Ys2aNYSEhBAZGVlt/XKHDx/m0KFDuLq6MnjwYFq0aEG3bt34+eefiYuLo1+/fjXqR0RERETqRjMgjURSUhJQ9iX+UrB//37b0qZevXrVS59nzpxh0aJFNGnShGeeeQY3t5rnz+WzHzfeeCMtWrQAYNCgQQDs3r2b06dP10uMIiIiIlI1zYA0Amaz2XbCVFBQkN06UVFRZGRk2C3z9/cnKirKbtndd99dq5jK95U0a9bM9oX/fEePHuWll15y2H7kyJHcddddFd5bsmQJOTk5/O1vf3P4nPaYzWbbaVlDhw61vd+vXz/++c9/Yjab2bJlC6NHj662rxUrVrBy5Uq7ZeX/DERERETEMSUgjcD5+yWaNWtmt05mZqbDBKSqTdg1+aKfmZmJxWKp8F5eXl6V8RQVFTmMB6i0B+Trr79m586d9O3bl9tvv73amM63fft28vLyaN68OX369LG97+XlRZ8+fdi2bRtxcXE1SkAKCgrIysqyW1ZaWnpBcYmIiIhciZSANALnX9537tw5u3Xs7ZWIiYlhyZIlVfa9bNmyascvv4jQXkyO4rn22mvtXnI4a9YskpOTK7x35MgR3nvvPVq2bMnUqVOrjeePypdfDRo0qNKyrUGDBrFt2zbS0tL47bffqj3+18vLi4CAALtlLi5a0SgiIiJSHSUgjYCXlxfu7u6YzWYyMzNr3K6qk6bqys/PDyi7GDE/Px9vb+9axWSxWFiwYAFms5knn3yywrHCNfH777/z888/A7B+/XrWr1/vsG5cXFy1Ccj48eMZP3683bLg4OAqZ3VERERERAlIo2AwGOjcuTP79u0jKSmJe+65p0btDhw40GAxdenSxfb6559/rtEpU4WFhZW+wJ89e5YjR44A8Pzzzztsu3HjRjZu3AjAypUrbQlPTEwMVqsVd3d3vLy87LY1m80UFBSQkJDAI488ckGb20VERC4ns5964mKHcEGiFxkvdgjSAPRNq5EIDw9n37597N69mxMnTlR7D4jJZGLXrl0NFs9VV11Fx44dSU1N5euvv65RAhITE1NpH4WLi0uV+1BMJhOFhYV4eXnZZl3Kl0KVlpYSGxsLwLhx4xg7dqzdPo4ePcrUqVM5c+YMu3btom/fvjV5RBERERGpBSUgjcTw4cNZvXo1OTk5GI1G5s2bV2FvyPmKi4tZtGgRxcXFDRpTREQE0dHRJCUlsX79ekaNGuWwbmpqKh9//HGl95s1a1blPpTFixcTFxfHgAEDeOyxxyqU7dmzh5MnT+Li4sLgwYMd9tGuXTvat2/PkSNHiI2NVQIiIiIi0oC0a7aR8PDwYPr06bi5uZGSksL06dNJTEykqKjIVsdsNrNz506mTZvG3r17CQkJadCY+vbtyx133AHA8uXLWbJkCWlpaRXqmEwmPv/8c2bOnElxcTEdOnSot/HLN5/36NEDf3//KuuefydIbm5uvcUgIiIiIhVpBqQRCQsLY+7cuRiNRtLS0oiOjsbNzQ1fX19cXV0xmUy2I3dHjBjBpEmTiIyMbNCYpkyZgre3N1988QUxMTHExMTg6elJ8+bNMZvN5OTkAODt7c2MGTMoKCjgtddeq/O4ubm57Ny5E4Bbb7212voDBw7ko48+oqSkhC1bttR4H42IiIiIXBglII1Mjx49WLZsGfHx8Wzfvp3U1FRyc3Px9PQkMDCQsLAwhgwZYtskHhISQnZ2doPF4+rqykMPPcSwYcPYuHEje/fu5ffff+fUqVP4+voSGhrKzTffzJAhQ2jRosUFneJVlfj4eEpKSmjWrFmNllQFBATQuXNn9u/fT1xcnBIQERERkQZisFqt1osdhEhjUH4Mb1BQEMeOHbvY4YiIiFSiU7DkUqA9ICIiIiIi4jRKQERERERExGmUgIiIiIiIiNMoAREREREREadRAiIiIiIil709e/ZgMBj4y1/+UmW977//nlGjRhEcHEyzZs0ICQnhf//3fyvdVQYwd+5cDAYDBoOBhQsXVtnv2rVrbXVfeOGFOjxJ46cEREREREQue++88061dd58800GDhzIV199hclkIiAggKNHj7J06VJ69erFf/7znwr158yZYzvO/9lnn2Xfvn12+z158iSPPvooAOHh4cyZM6eOT9O4KQERERERkctWZmYmUVFRvPXWW1XW++GHH5g6dSoAzz33HKdOnSItLY3MzEz+9Kc/YTKZ+POf/8zp06dtbdzc3Pj444/x8fGhqKiIBx980Hap8/n+8pe/kJWVhY+PDytWrMDV1bVen7GxUQIiIiIiIpedF198kXbt2hEYGMicOXMoLS2tsv7zzz+P1Wrlrrvu4h//+AdNmzYFoGXLlrz//vsEBARw/Phx/vnPf1Zo17FjR15//XUA9u7dW2l51YoVK1i9ejUAS5cupWPHjvX0hI2XEhARERERuexkZWVRXFzMVVddxVVXXWVLKOzJzc1l8+bNALZZkPM1a9aMO+64A4B///vflconTJjAn//8ZwBefvlltm/fDkBGRgb/+7//C8D999/PQw89VLeHukIoARERERGRy47RaCQzM9P25/7773dYNyEhAYvFgqurKwMGDLBbZ8iQIUDZZvaioqJK5W+99RZXX301FouFhx56iPz8fCZNmsTp06dp165dpZkTcUwJiIiIiIg0asnJyQC2k6/sad++PQClpaV2T8Ty8/Pjo48+wsXFhUOHDtG3b182bdqEi4sLH330EX5+fg0VfqPjdrEDEBH78hZ1u9ghiIhIFXyesn8iklx6cnJyAAgICHBYp2XLlrbX529EP9+gQYOYMWMG8+fPtyU1M2fOZODAgfUX7BVAMyAiIiIi0qiVJxRV7RPx9va2vbZ30lW5iIiIKv8u1VMCIiIiIiKNmru7OwAWi8VhnfP3fThaplVUVFRpo/nDDz9McXFxPUR55VACIiIiIiKNWvnyqvKlWPaYTCbb68DAQLt1ZsyYwc8//0zTpk159913cXV1Zc+ePbp48AIpARERERGRRu26664DIC0tzeEsyOHDhwHw9fWlTZs2lco3bNjAkiVLAJg/fz6PPPII06dPB2DBggVs2bKlASJvnJSAiIiIiEijVn707rlz50hMTLRbZ+vWrUDZRnODwVChLDMzk0ceeQSAoUOH8vjjjwMwb948unfvTmlpKQ899FCFWRRxTAmIiIiIiDRqHTp04KabbgLK7g/5o1OnTvHpp58C8OCDD1Yos1qtTJgwgaysLPz8/Hj//fdtCUqTJk344IMPcHd3Jz09nb/85S8N/CSNgxIQEREREWn0oqKiMBgMfP7550RFRVFSUgJAamoqd911F7m5uYSHhzNmzJgK7V577TW+/fZbAN58802Cg4MrlPfs2ZPnnnsOgFWrVvHhhx864Wkub7oHRGpk8eLFxMXF0a1bN6Kjo+3WSU5OJj4+nuTkZHJycrBYLPj6+hIQEECvXr3o37+/w01dsbGxtl8k1q1bV208kydPJisri4iICMaNG1fpfYCbb76ZZ599ttq+Fi5cSEJCAlB2Pvjy5curbSMiIiKXl2HDhvGPf/yD5557jjlz5jB//nz8/f1JT0/HarXSsWNHVqxYgYvLf3+f37NnD7NnzwbKjtt94IEH7PY9e/Zs1q1bx+7du5k6dSoDBgygQ4cOTnmuy5FmQKTO8vPzefHFF5k1axbffvstJ06cwNPTk+bNm2MymUhOTuajjz7ib3/7GytWrMBqtTolrt27d5Ofn19lnaKiInbu3OmUeEREROTievbZZ9m4cSN33HEHnp6eZGdnc9111zF79mx27NhBx44dbXULCgp44IEHKC4uJigoiDfffNNhv25ubnz44Yd4eHiQl5fH+PHjqzzy90qnGRCpk4KCAmbOnEl6ejo+Pj5EREQwaNAgmjdvDpStm9y/fz8bNmwgISGBVatWYbFYmDBhQoPG5ebmRklJCdu3b2fYsGEO6+3cuZOzZ8/a6ouIiMjl6f333+f999+vtt7tt9/O7bffXm09Ly8v9u/fX+Pxu3btyrlz52pc/0qmBETqxGg0kp6eTuvWrYmKiqq0xMpgMBAaGkpoaChBQUGsXLmSNWvWMGzYMNq2bdtgcd1www3s2rWL7777rsoE5LvvvqtQ/1Ky9PpXL3YIIiJShVkXOwCRy5SWYEmtpaSk2I6ymzZtmsP9HeXGjh1L165dCQ4OJiUlpUFju/nmm2nSpAlJSUkOj8TLz8/nxx9/pEmTJtx8880NGo+IiIiIlNEMiNTapk2bAOjevTuhoaHV1nd3d2f+/PkNHRYAnp6e9OnTh//85z9s3bqVUaNGVaqzfft2zGYz/fr1w9PT0ylxiYiIiFzpNAMitZaUlASUHT93KRo4cCCA7YSrPypfflVeT0REREQanmZApFbMZjMnT54EICgoyG6dqKgoMjIy7Jb5+/sTFRVlt+zuu++ulxh79+6Nl5cXBw4cIDMzkzZt2tjKTCYTSUlJeHp60rt3b4e3ov7RihUrWLlypd2y8s9DRERERBxTAiK1cubMGdvrZs2a2a2TmZnpMAExm80O+3aU0Pyx7+qOt3N3d6dv377Exsby3Xff8ec//9lW9p///IfS0lL69u1LkyZNqh2vXEFBge2ekT8qLS2tcT8iIiIiVyolIFIrHh4etteOjpxbunRppfdiYmJYsmRJlX0vW7as2vHPv3CwKgMHDrSbgNR2+ZWXlxcBAQF2y86/uEhERERE7Ks2Abn11lsbZGCDwUBsbGyD9C0Nz8vLC3d3d8xmM5mZmTVud/7MiTNcf/31+Pn5cfToUdLS0rjmmmv4/fff+fXXX2nevDk33HDDBfU3fvx4xo8fb7csODjY4YyPiIiIiJSpNgHZsmULBoOhXge1Wq313qc4l8FgoHPnzuzbt4+kpCTuueeeGrU7cOBAA0dWkaurK7fccgtff/013333Hddccw3ff/89VquVfv364erq6tR4RERERK501SYg7dq1U7IgdoWHh7Nv3z52797NiRMnqr0HxGQyXZTL/gYOHGhLQB566CGdfiUiIiJyEVWbgKSlpTkhDLkcDR8+nNWrV5OTk4PRaGTevHkV9oacr7i4mEWLFlFcXOzkKCE0NJSAgACysrL49ttvSUtLw9/fn7CwMKfHIiIiInKl0yZ0qTUPDw+mT5/OnDlzSElJYfr06YwbN46ePXvaEhGz2cyePXtYsWIFaWlphISEcOjQIafHOmDAAFavXs2//vUvAPr373/Jz+zlfbXxYocgItLoRS8yXuwQRK44SkCkTsLCwpg7dy5Go5G0tDSio6Nxc3PD19cXV1dXTCaT7cjdESNGMGnSJCIjI50e58CBA1m9ejVnz561/V1EREREnK/aBOTo0aMNNni7du0arG9xnh49erBs2TLi4+PZvn07qamp5Obm4unpSWBgIGFhYQwZMoQuXboAEBISQnZ2tlNj7NChA+3atePo0aO0adOGa6+91qnji4iIiEgZg9VqtVZVwcXFpUGWqhgMBkpKSuq9X5GLpfwY3qCgII4dO1bn/mY/9UQ9RCUiIlW50pZgXW7/bbnS/vlcKWq0BKuaHEVERERELgP6Qi+XgmoTkLlz5zojDhERERERuQIoAREREREREadxudgBiIiIiIjIlcPpCYjVaiUsLIxOnToRExPj7OFFREREROQicvo9IJmZmaSlpXHu3Dl2797Nbbfd5uwQRERERK5IL8V+c7FDuCCzho642CFIA6jTDEhJSQn/+Mc/uO666/Dw8MDV1bXaP8HBwZw7dw4ALy+venkIERERERG5PNRpBiQyMpJ///vfwIUf1duhQwceeOCBugwv0qjpqEQRERFpjGqdgMTFxfH5559jMBjw9vbm9ttvp1WrVnzxxRdkZ2fTqVMnbrnlFqxWK1lZWfznP/8hLy8PHx8f3nrrLUaPHk3Tpk3r81lEREREROQSV+sE5KOPPgKgSZMmbN26le7duwPw0EMP0a9fP/Ly8nj33Xdtt6jn5+fz6KOP8umnn/Lxxx8TERFRD+GLiIiIiMjlpNZ7QBITEzEYDNxzzz225AOgb9++eHh4kJWVRVJSku19b29vPvroI3r27MmGDRt4/fXX6xa5iIiIiIhcdmo9A3Ls2DGACslHuWuuuYYDBw6wZ88err/+etv7rq6uzJw5k4iICF566SX+9re/4eKiq0hE7Mlb1O1ihyAiUq98ntp3sUMQkUtArb/9FxUVAeDr61uprGPHjgAcPHiwUtnIkSMB+P3334mPj6/t8CIiIiIichmqdQLSqlUr4L8zIefr2LEjVquVX375pVKZl5eXLWk5cOBAbYcXEREREZHLUK0TkN69e2O1Wlm1apVtNqRcWFgYANu2baO0tLRC2enTp8nNzQXgzJkztR1eREREREQuQ7VOQMaNGwfAkSNHuO2224iNjaWwsBCAW2+9FYCsrCzefPPNCu3mzp1re92hQ4faDi8iIiIiIpehWicg999/PzfddBNWq5Vt27YxfPhwFi9eDMB1113HjTfeCMATTzzBnXfeyVNPPcWAAQNYunQpAD4+Ptxxxx11fwIRERERuaI8/PDDGAwGXnjhhRq3ueaaazAYDHz66acNF5gDp0+f5oUXXuCFF14gPz/f6eNfamqdgBgMBtavX8/tt9+O1WqtdBP6O++8g5tb2SFbmzZtYsmSJWzbts1Wb/HixTRv3rwOoYuIiIiIXPpOnz7NvHnzmDdvnhIQ6pCAALRu3ZpvvvmGw4cP8/nnn9tOuAK44YYb2LBhA9dee60tQbFarbRr145Vq1bx8MMP1zV2ERERERG5zNT6HpDztW/fnvbt21d6f+jQoezfv5/Dhw+TmZlJixYt6NKlS30MKRfZ4sWLiYuLo1u3bkRHR9utk5ycTHx8PMnJyeTk5GCxWPD19SUgIIBevXrRv39/AgMD7baNjY3FaDQCsG7dOodxZGdn89xzz3HixAnc3NyYNm0a/fr1q1Tv2LFjbN68mZ9++ons7GzOnj2Lp6cnbdu2pUePHtx+++20bt26Fp+EiIiIiFwIp9wC2KFDB8LDw5V8XCHy8/N58cUXmTVrFt9++y0nTpzA09OT5s2bYzKZSE5O5qOPPuJvf/sbK1asqLR8r6YyMzOZNWsWJ06coGnTpsyZM6dS8mGxWPjXv/7F1KlTWbNmDampqQC0bNmSc+fO8euvv7Jq1Sr++te/8u2339b52UVERMS5Dh06xIMPPkibNm1o2rQp1157LdOmTePUqVM17mPr1q2MGzeOkJAQPD09adOmDX369CE6Oprff//dbpvt27czduxY2rRpg7u7O/7+/gwZMoSPPvqowneba665psLBS4GBgRgMBtLS0mr9zJe7epkB2b59O19++SX79+9n7dq1lcoffvhh2rZty9ixY22b06VxKigoYObMmaSnp+Pj40NERASDBg2y7fexWq3s37+fDRs2kJCQwKpVq7BYLEyYMOGCxjl+/DjPPfccJ0+exMvLi+eff57Q0NAKdaxWKwsWLOA///kPLi4ujBw5klGjRtG2bVtbeWpqKp9++ik7duxg6dKluLq6MnTo0Pr5MERERKRBJSYmsmDBAgoKCmjTpg3+/v4cOnSIhQsX8vnnnxMfH2+7INuR6Ohonn32WQDc3NwICgoiNzeXXbt2sWvXLhYvXkxiYmKFfj7++GMeeughSktL8fb2pl27dpw6dYotW7awZcsWvv/+e95++20AOnXqhKurq+1H0JCQEFxdXXF3d2+gT+XSV6cEJCcnh4iICGJjYwHw8PCwWy8hIYGjR4/y8ssvc9ttt7F8+XKuvvrqugwtlyij0Uh6ejqtW7cmKiqq0hIrg8FAaGgooaGhBAUFsXLlStasWcOwYcNsiUF10tPTmTNnDjk5Ofj6+vL3v//d7pHO69evtyUfM2bMqDQ7YjAY6NSpE7Nnz2bx4sXEx8fz1ltv0adPn0vigISl1796sUMQEalXsy52ANLobNq0ibCwMN577z369OkDwM6dOxk7dixHjx5l/PjxbNu2zWH7kydP2q6IiI6OZtq0abbEYOvWrdx33338/vvvLFq0iNdffx2AwsJCHnvsMUpLS3n55Zd5+umnbQcvffvtt9x333288847PPjggwwYMIDY2FjS0tJs31W+//572rRp02CfyeWg1kuwLBYLt99+O7GxsbYN5v7+/nbr+vn52erExMQwYMAAh9NZcvlKSUkhMTERgGnTpjnc31Fu7NixdO3aleDgYFJSUmo0xuHDh5k9ezY5OTm0bt2a+fPn200+ioqK+OyzzwC466677O4LKWcwGJg0aRLu7u6cO3eOrVu31igWERERubh8fHzYvHmzLfkAuOmmm3jrrbeAslU65d9N7Pnxxx8pKSmhT58+zJo1q8KsRP/+/Zk8eTKAbfYCYN++fbbLtKdOnWpLPgCGDx/OM888Q/v27dm9e3f9PGQjVOsE5MMPP7R9sGFhYcTGxnL06FG7dX/88Ufi4+O58cYbsVqtpKen8/jjj9d2aLlEbdq0CYDu3btXWg5lj7u7O/Pnz2fp0qXcdttt1dY/dOgQzz33HLm5uQQFBTF//nyCgoLs1v3hhx/Iy8vDYDBwzz33VNt38+bNeeWVV3jxxRfp2bNntfVFRETk4rvvvvvs/uA5YsQIOnXqBJStxHFkwIABpKens3HjRrvlmZmZQNkP7+VatWplez179mzbRdzl5s6dS1paGk8++WSNn+NKU+slWOWXuAQHB7N9+3a8vb0d1jUYDAwaNIgdO3bwpz/9iTVr1vDFF19w/PjxGi+7kUtfUlISQIN8gd+/fz/z5s2joKCAq6++mhdffBE/Pz+H9fft2wdA27Zta3y6Vfm/qEREROTy0L17d7vvGwwGunbtym+//caxY8cctvf09CQ4OJizZ88SFxfH3r17OXLkCEePHuXXX3/ll19+qdSmY8eOTJ06laVLl2I0Glm+fDkDBw7klltuYfDgwdx8880VZkWkslp/Onv37sVgMPDAAw9UmXycz8XFhddee401a9ZQWlpKQkICDzzwQG1DkEuI2Wzm5MmTAA5nJaKiosjIyLBb5u/vT1RUlN2yffv28Y9//IOzZ88CcObMGUpLS6uMp/zkC0ex1NaKFStYuXKl3bLy5xcRERHn8PT0dFjWsmVLoGxZtiNWq5VFixbxwgsvkJeXZ3s/ICCAPn360K5dO9sKj/O9/vrrjBw5kuXLl7N582a++eYbvvnmGwBatGjBpEmT+Mc//kHTpk1r+2iNWq0TkNOnTwNlMyAXon379vj5+ZGbm1tlRiqXl/K1kADNmjWzWyczM9NhAmI2mx32PW/ePIqKiujSpQuHDh0iNzeX1157jb///e+4uNhfRXju3Dmg6n8x1UZBQQFZWVl2y6pLikRERKR+mUwmh2XHjx8HcLhHGeCll17i2WefxcPDg6ioKIYPH07nzp1th9G88MILdhMQgDvuuIM77rgDi8XCTz/9xPfff8/XX39NbGwsCxYs4PTp07zzzjt1eLrGq9YJSIsWLcjOznb4hdIRq9VqWyvn6upa2+HlEnP+CWjlX/7/aOnSpZXei4mJYcmSJVX2XVRURL9+/XjmmWf46quveO+99/j5559ZtWoVERERdtuUz8pVldjUhpeXFwEBAXbLHCVDIiIi0jAcbfS2WCwkJycDcMMNNzhsv2zZMgBefvllnnjiiUrl9u4S+fHHH0lJSaF79+706NEDV1dXevXqRa9evXjiiSdYvnw5//M//8Pnn3+uBMSBWn9juuGGG7BarXzyyScUFxfXuN23335rq1/ducxy+fDy8rKdHFG+Yasmzp85cWTYsGFMnz4dd3d3Ro8ebdtj8umnn9r2evxR+bTrhcSSmppKYmIi+/fvd1hn/PjxbNiwwe6f8zeliYiISMPbuHGjbabjfGvXruX48eM0a9aMESNGOGxffipr586dK5Xl5ubyxRdfVHr/22+/5cEHH+S5556z22e3bt0ArYyoSq0TkAcffBAou5Nh7NixtiVZVTl48CB/+ctfgLJlOsOHD6/t8HKJMRgMtv/zlm9Gr4kDBw5UW+d///d/bbNlBoOBJ598El9fX0pLS1m4cKHdJCYsLAyAI0eOkJubW6NYFi9eTHR0NFu2bKlx/CIiInLxFBQUcPfdd1f48XDbtm1MnToVgKeffrrKu73Kvy+8/PLLtr2cFouFzZs3M3DgQNsPmeX7UKHseH+DwcDXX3/Na6+9VmHlxy+//MJTTz0FwMiRI23vn78kXFsQ6pCAREZG0r9/f6xWKxs2bCAkJIQnn3ySjRs3cuTIEQoKCigtLSU7O5uYmBj++te/csMNN3D06FEMBgOzZ892uFdALk/h4eFA2XToiRMnqq1vMpnYtWvXBY/TokUL2zHOp06dwmg0VqrTo0cPPD09sVgsfPXVV9X2uX//ftLS0gC4+eabLzgmERERcb6JEyfyyy+/0LVrV9q2bUtgYCC33HILJ06cYNiwYcyZM6fK9i+++CIuLi5s2bKFtm3b0qFDB3x9fRk+fDhms5n33nsPKLs88JprriE3N5du3boxbdo0rFYrzzzzDH5+fnTq1ImrrrqKrl27kpiYyDXXXMOCBQts4wQEBNiOCx40aBCdO3e+4G0MjUmdFq2vXbvWthTLZDLZTgTo2LEjzZs3x93dnTZt2nD77bfz9ttvc/bsWaxWK5GRkfzf//1ffT2DXCKGDx9Oy5YtsVgsGI3GKk+dKC4uZtGiRRe0fO98ffr04c477wTK7vxYu3ZthXIvLy/b/R+rV692uFQLym40ffPNNwG47rrrqlwrKiIiIpeO8PBwtm3bxqhRoyguLiY3N5cePXrw2muvsWHDBpo0aVJl+zvvvJPNmzczaNAg3N3dOXXqFF26dGHhwoXs2bOHBx98kMmTJ+Pp6cm5c+dsKzJeeeUVPv30U2677TZ8fHw4evQoBQUFdO/enVmzZvHjjz9WOInTYDDw3nvv0aVLF0pKSjCZTFf03lGD1Wq11qWDoqIinn/+ed54441KF7H8ka+vL1FRUfztb3+ry5ByCVi8eDFxcXF069aN6Oho2/vJycnMmTOHkpISrrnmGsaNG0fPnj1tm9TNZjN79uxhxYoVpKWlERISwqFDhwgICGD58uW2fmJjY20zG+vWrbMbQ3FxMc888wxHjhzBzc2Nl19+mWuvvbZC+bx580hKSqJJkybcd999DBs2zHYahsVi4eeff+bdd9/lyJEj+Pj48Oqrr9b6bprg4GAyMjIICgqql+nV2U9V3gwnIuIM0YsqzyxL4/BS7DcXO4QLMmuo4/0bcvmq8y0pHh4evPzyy8yePZvVq1ezbds29u/fz+nTpzGbzfj5+REaGsqtt97Kfffdp2VXjVxYWBhz587FaDSSlpZGdHQ0bm5u+Pr64urqislksp1MNWLECCZNmkRkZGStxmrSpAnTpk3jmWeeobi4mFdffZXFixfb/jfWpEkTnn32WZYuXcrWrVtZuXIlK1euxNfXl6ZNm3L69GnbLE379u2ZNWuWLsYUERERaWD1dk2jr68vEydOZOLEiRfcdteuXRQWFjJw4MD6Ckcuoh49erBs2TLi4+PZvn07qamp5Obm4unpSWBgIGFhYQwZMoQuXboAEBISQnZ2dq3Gat++PQ8//DBvv/02mZmZvPHGG0yfPt1W3qxZM2bMmMFdd93F5s2b+eWXXzh16hSFhYX4+vrSs2dP+vXrx4ABA3QstIiIiIgT1HkJVn0IDQ3l4MGDlJSUXOxQRGpNS7BEpLHQEqzGS0uw5FJwyex+uQTyIBERERERaWCXTAIiIiIiIiKNnxIQERERERFxGiUgIiIiIiLiNEpARERERETEaZSAiIiIiIiI0ygBERERERERp6m3iwhFpH7pHH4RERFpjDQDIiIiIiIiTqMEREREREREnEYJiIiIiIiIOI32gIhcovIWdbvYIchlzuepfRc7BBERkUqUgIiIiIhcIWYNHXGxQxDREiwREREREXEeJSAiIiIiIuI09ZKAFBcX880337BgwQK75f/85z/59ttvKSkpsVvep08fBg4cWB+hiIiIiIjIJcxgtVqtdekgOjqaV155hby8PDw8PCgsLKxUp0OHDhw9ehQ/Pz+mTZvG9OnTcXPT9hNpXIKDg8nIyCAoKIhjx47VuT9tQpe60iZ0ERG5FNVpBmTy5MnMmTOHvLw8qspjrFYrVqsVk8nEc889x8iRIzGbzXUZWkRERERELkO1noaIjY3l3XffBcDT05Np06bxpz/9yW7db775hlWrVrFo0SLOnDlDTEwM8+bNIyoqqrbDi4iIiMgFutxm1zWT2zjVeglWREQEq1atwsvLi8TERMLCwqptk56ezsiRI9m3bx/e3t5kZGTg4+NTm+HlErJ48WLi4uLo1q0b0dHRduskJycTHx9PcnIyOTk5WCwWfH19CQgIoFevXvTv35/AwEC7bWNjYzEajQCsW7euVjHm5eXx7bffsnv3bjIyMsjLy6NJkya0atWK0NBQhg0bxnXXXVervstpCZZcavQfbhH5o8vtvy3691jjVOsZkMTERAwGA5GRkTVKPgCuvvpq3nrrLW655RYKCgpISEjgrrvuqm0IchnIz8/HaDSyY8cOAFxcXPDz88PFxQWTyUR2djbJycl88sknjBkzhsjISAwGQ73GEB8fz9tvv01BQQEATZs2xd/fnzNnznD06FGOHj3Kpk2buOOOO5gyZQqurq71Or6IiIiI/FetE5DMzEyAGicf5cLDw/Hy8qKwsJCDBw/Wdni5DBQUFDBz5kzS09Px8fEhIiKCQYMG0bx5c6Bsb9D+/fvZsGEDCQkJrFq1CovFwoQJE+othq+++oq3334bgJ49exIREUHnzp1xcSnb/nTq1CnWrVvH2rVr2bhxI2azmSeeeKLexhcRERGRimqdgDRr1ozc3FxOnz5d68G1Eb1xMxqNpKen07p1a6KioiotsTIYDISGhhIaGkpQUBArV65kzZo1DBs2jLZt29Z5/IMHD9r2KY0aNYrJkydXml3x9/fnkUceITg4mNdff53Y2Fj69+/PjTfeWOfxRURERKSyWp+Cde2112K1WtmwYcMFtUtOTrYthamPL5lyaUpJSSExMRGAadOmOdzfUW7s2LF07dqV4OBgUlJS6iWGjz/+mJKSEkJCQpg4cWKVS7uGDRtGSEgIADExMfUyvoiIiIhUVusZkDFjxvDDDz+wc+dOXnzxRZ599tlq2xQVFfH4448DZb9+Dx06tLbDyyVu06ZNAHTv3p3Q0NBq67u7uzN//vx6G99kMvHjjz8CZbMfNdnXMWPGDLKzs/H09Ky3OERERESkolrPgPzv//4vwcHBADz//PMMHTqUtWvXkp+fX6nu2bNn+eyzz+jduzdbtmyxbV6v7ldxuXwlJSUBZfsuLobzZ1F69OhRozZt2rShe/futpkQEREREal/ddoDsnbtWm677TZMJhNbtmxhy5YtuLm50bZtW1q1aoWnpyenTp3i0KFDlJSUAGUbj6+//noWL15cX88glxiz2czJkycBCAoKslsnKiqKjIwMu2X+/v51viOmfHxPT09atmxZp77Ot2LFClauXFnlmCIiIiLiWK0TECj7dXvbtm1MmDCBnTt3AmVfPsuPNi13/lUjY8eO5a233qJFixZ1GVouYWfOnLG9btasmd06mZmZDhOQ+jicoKioqMrxa6ugoICsrCy7ZaWlpfU6loiIiEhjVKcEBKBz584kJiayefNmPvroI7Zt20ZqamqFOu3atePWW29l8uTJ9OvXr65DyiXOw8PD9vrcuXN26yxdurTSezExMSxZsqReYvD29gaguLi4Xvor5+XlRUBAgN2y8qN9RURERMSxOicg5YYNG8awYcMAKCkp4fTp05jNZvz8/LSp9wrj5eWFu7s7ZrPZdl9MTZw/c1JX5cuu8vPzKSgowMvLq9o2J0+e5NChQ7i4uHDTTTfZrTN+/HjGjx9vt6z8JnQRERERZ3vhhRcAmDx5sm2fdkN6+OGH+eCDD5g7d65t7JpqkJ9s3dzcaNWqFYGBgUo+rkAGg4HOnTsD/92MXhMHDhyotxi6du2KwWDAarXy888/16jNmjVriI6O5oMPPqi3OEREREScYd68ecybN49jx45d7FCqpTUj0iDCw8MB2L17NydOnKi2vslkYteuXfU2fvPmzW3H/65bt67a+oWFhXz33XcA3HzzzfUWh4iIiIhUpAREGsTw4cNp2bIlFosFo9Fo2xRuT3FxMYsWLar3/RqRkZFA2eWXq1evrrLuO++8Q25uLs2aNWPUqFH1GoeIiIiI/JcSEGkQHh4eTJ8+HTc3N1JSUpg+fTqJiYkVEhGz2czOnTuZNm0ae/furdH9G/n5+VX+KSwstNXt3r079913HwAffPABixYt4siRIxX6S0tL48UXXyQ2NhYXFxcef/xxndAmIiJyGTlw4AAPP/wwwcHBNG3alA4dOjB69Gjbyobz5eXl8Y9//IMbbrgBHx8fvLy86NmzJ1FRUXb3or7wwgsYDAYmT57MqVOnePTRR7nqqqu49957AXj//fcxGAzcdtttFBYWMnPmTIKDgyvdg3b27FleffVVevfuTfPmzWnevDk333wzixYtcnhgD8C3337LnXfeib+/P15eXnTt2pUpU6ZUOPBp8ODBGAwG29/Dw8MxGAxs2bKlQl/Hjx/nySef5LrrrsPT05PWrVszYsSIKn+kPXnyJE8++SQdO3akadOmBAUFMXHixAqn3dZGvW1CF/mjsLAw5s6di9FoJC0tjejoaNzc3PD19cXV1RWTyWQ7cnfEiBFMmjTJNmvhyLhx46osDwgIYPny5ba/P/TQQzRt2pRPP/2U+Ph44uPj8fLywsfHx5a0APj4+PDkk0/Sp0+fOj61iIiIOMu6deu4//77OXfuHE2bNuWqq64iIyODtLQ01q5dS3R0NLNmzQLgt99+Y/jw4bYv78HBwVgsFvbu3cvevXt599132bBhA126dKk0zqlTp+jXrx8HDhwgMDCQq666qkJ5UVERw4YNY9u2bbRu3brCJvCMjAxuv/12kpOTAWjdujXu7u7s3LmTnTt3smzZMmJjY7n66qsr9Dl79mxeeuklAHx9fWnRogX79+/nl19+4YMPPuCbb75hyJAhtGvXjs6dO/Prr78C0L59e5o2bVrhKoKEhATuuececnNzMRgMBAUFUVBQwMaNG9m4cSN33303n3/+OU2aNLG1OXr0KLfccottT0lgYCClpaW89957bNiwgeuuu652/9DQDIg0sB49erBs2TIee+wxevbsiZeXF7m5uZw9e5bAwEBGjBjBK6+8wl//+leaNGnSILeQ//nPf+bNN99kzJgxdOrUCavVSnZ2Nq6urnTv3p2HH36Yd955R8mHiIjIZeTXX3/lgQce4Ny5c0ybNo2TJ09y5MgR8vPzmT59OgDPP/88aWlpFBUVMXr0aFJTU+nfvz+HDh0iPT2d48ePc/DgQQYOHMjhw4e566677M5IfPnll5jNZnbv3s3x48f55z//WaF869at/Prrr8TExJCVlcX69euBspNh7733XpKTkxkwYAC//PILWVlZZGRkcOjQIQYNGsTBgwcZM2ZMhXvzPvroI1566SWaNm3KRx99RE5ODseOHePEiRMMGzaMoqIipkyZAsCHH37I/v37bW0//fRT9u/fbzvR88iRI4wePZrc3FweffRRTp48SXp6Ojk5OcTHxxMcHMy6detsiVq58ePHc+zYMbp168aePXs4fvw4J06c4Pvvv8fd3Z3vv/++1v/sDNbzn1ZEaq38GN6goKB6OYEib1G3eohKrmQ+T+272CGIyCXmcvtvS1X/Hhs/fjwff/wxd9xxB998802FMqvVys0338wPP/zAokWL8PHxYfLkyXh7e3PkyBHbcf3lcnNzufbaa8nOzmbZsmX85S9/AcqWYM2bNw+ALVu2MGjQoArt3n//fR555BHb6wkTJlQo//DDD5kwYQLt2rUjKSmJ5s2bVxr3mmuu4fTp08TExDB06FAsFgudOnXiyJEjzJ8/n5kzZ1Zoc/LkSa6++mrOnTvHnj17uOGGGwBsy7C2b99O3759bfUnTpzIe++9x+jRo1mzZk2lzzEhIYHBgwfTtGlTTp48iZeXF5s3b2b48OF4e3uzf/9+goKCKrTZtWuX7YfbS+YYXhERERGRhmKxWFi7di2AbSbgfAaDgQULFvDqq6/SvXt3Pv/8c6BsafYfkw8oW+L05z//GYCNGzdWKu/YsWOl5ON8TZo0sbuMfOXKlQCMGTOmUvJRPu6dd94JQFxcHFD25f7IkSO4uroyadKkSm1atWrF8uXLefXVVysss7KnpKTE9uwPP/yw3TqDBg2ibdu2nDt3jm3btgHw73//G4D77ruvUvIB0Lt3b4d3ptWE9oCIiIiIyGXl0KFDtn2ct9xyi906AwcOZODAgcB/T8bs16+fwz7Lj+8/f4N3uY4dO1YZT7t27XBzq/y1+scffwTg448/rjRLUy47OxvAtrF77969AHTu3JlWrVrZbVPdntlyBw4csH1OTzzxRKXZlHI5OTkVYtizZw9AlcvTr7/+enbu3FmjOP5ICYiIiIiIXFbKvzC7uLjg7+9f4/r2fs0vVz6bYG8PiKura5X9Oyo3mUxAWZJRnmg4UlBQUCHWgICAKuvXRPn4QKWTQKuKobydj4+Pw7re3t61jksJiIiIiIhcVtzd3QEoLS2lsLCw2i/DHh4emM1m25d7ezIyMgAczjrURrNmzThz5gyLFy/miSeeqFGb8mfLy8url/HL7d27lx49elxQu8zMTId1qvosq6M9ICIiIiJyWenYsaNt0/Uvv/xit87ChQsZPHgw//znP22nbP78888O+9y6dStApTs86qJz584AHDx40GGd77//nhUrVtiO6S2P9bfffrNdV/BHDz74IIMHDyYxMbHK8UNCQmyzM1XF8O9//5sVK1Zw8uTJCnGXL8WyZ/fu3VWOXRUlICIiIiJyWWnZsiXh4eEAvPPOO5XK8/PzWbhwIQkJCbRu3dq20futt96yu8TqyJEjxMTEANguMa4Po0aNAso2o9ub0Th58iT33nsvDz74oG2J1pAhQ2jWrBmnT5+2bSA/3+7du1mxYgVbt26lQ4cOVY7v4+Nj2zz/1ltv2a2zZcsW/vSnP/HYY4/h4eFRIe7Vq1dz/PjxSm3i4+NtCVNtKAERERERkcvO888/D8C7777L8uXLsVgsAGRlZREREcGJEye4+uqrueuuu5g6dSotWrQgMzOTP/3pT7blVlB2n8h9992HxWLh1ltvZciQIfUW49/+9jcCAwMxmUw88MADFb7M79u3jzvvvJNTp04xdOhQBg8eDJSdjFW+XOvpp5+2zcxA2TKqBx54AICxY8dWuBCxadOmAJWuApg3bx6urq7ExMTw3HPPUVhYCJQdVbxhwwbuv/9+AGbMmGHb8xEREUHXrl0pLi7m3nvv5cCBA7b+vv/+e8aNG1ftvpiq6B4QkXpS3/eAiIiI1LfGdA8IwN///nfmzp0LQPPmzfH19SUjI4PS0lK8vb355ptv6N+/PwAxMTHce++95OfnYzAYuPrqqykpKbElBV26dCEuLo7AwEBb/+X3gNx+++12j+ctvwekc+fOFS4DPN+uXbsYOXIkWVlZuLi4cPXVV1NcXMyJEycAuPHGG/nmm29o3bq1rU1xcTH33HOPbcw2bdpQWlpKVlYWULZEKiEhoUICEh4eTmJiIk2aNKF9+/Z89NFH3HzzzUBZkjZlyhRKSkrw8PAgKCiInJwcTp8+DZQd0fuvf/0LF5f/zk38+uuv3HrrrbbPJygoiNLSUk6cOEHnzp0ZPnw4r7/+uu4BEREREZErx/PPP8+GDRu47bbbcHFxITMzk3bt2jFlyhR+/vlnW/IBcNttt/HTTz/x6KOP0q5dOzIzMykoKKBPnz689NJL7N69u0LyUV969+5NUlISzzzzDB07duTEiROYzWbCw8NZsmQJ27dvr5B8QNm9IuvXr+f111+nV69enDlzhjNnztC9e3fmzZvHrl27KiQfAEuXLqVnz54YDAZOnjxZYYZi4sSJ7Nq1i3HjxtGiRQuOHTuGl5cXI0eOZO3atbz33nsVkg8oS3L27NnDY489Rtu2bcnOzqZp06Y888wz7Ny50+59KjWlGRCReqIZEBERudQ1thkQuTxpBkRERERERJxGCYjIJSpvUbfL7pcqERERkeooAREREREREadRAiIiIiIiIk6jBERERERERJxGCYiIiIiIiDiNEhAREREREXEaJSAiIiIiIuI0SkBERERERMRplICIiIiIiIjTuF3sAC4lixcvJi4ujm7duhEdHW23TnJyMvHx8SQnJ5OTk4PFYsHX15eAgAB69epF//79CQwMtNs2NjYWo9EIwLp162oVY15eHt9++y27d+8mIyODvLw8mjRpQqtWrQgNDWXYsGFcd911F9zv5MmTycrKIiIignHjxtmtY7FY2Lp1K9u3b+fgwYPk5ubi6uqKn58fV199NX369OGWW27B29vbbvuafL7VOXbsGJs3b+ann34iOzubs2fP4unpSdu2benRowe33347rVu3dti+oKCA1atXs337drKysnB1deWqq67ipptuYsyYMXh5edUqLhERERGpGSUgNZSfn4/RaGTHjh0AuLi44Ofnh4uLCyaTiezsbJKTk/nkk08YM2YMkZGRGAyGeo0hPj6et99+m4KCAgCaNm2Kv78/Z86c4ejRoxw9epRNmzZxxx13MGXKFFxdXett7KNHj/Lyyy+Tnp4OgLu7Oy1atKCoqIjff/+dEydOsHPnTj788EMee+wxbrnllnobG8qSn/fff5/169dTWloKgI+PDy1btsRkMvHrr7/y66+/8uWXX/Loo48yfPjwSn1kZWXx7LPP8vvvvwPg7+9PSUkJR44c4ciRI+zatYtXXnkFDw+Peo1dRERERP5LCUgNFBQUMHPmTNLT0/Hx8SEiIoJBgwbRvHlzAKxWK/v372fDhg0kJCSwatUqLBYLEyZMqLcYvvrqK95++20AevbsSUREBJ07d8bFpWwV3alTp1i3bh1r165l48aNmM1mnnjiiXoZ+7fffmP27NmcPXuWq6++mvHjx9O7d2/c3d0BMJvN7Nq1i9WrV3PgwAFeffVVmjRpQp8+feplfKvVyoIFC/jPf/6Di4sLI0eOZNSoUbRt29ZWnpqayqeffsqOHTtYunQprq6uDB061NZHaWkpL7/8Mr///jshISE8/fTTBAcHA7Bv3z7mz5/P4cOHWb16tcMZIBERERGpO+0BqQGj0Uh6ejqtW7dmwYIFjBo1ypZ8ABgMBkJDQ3nmmWdsX17XrFnD8ePH62X8gwcP8u677wIwatQoXnjhBUJDQ23JB5T9mv/II4/wt7/9DShb7rV79+46j11YWMhLL73E2bNnueGGG1i4cCHh4eG25APKZkPCw8N56aWX6NWrF6Wlpbz11lu2mYq6Wr9+vS35mDFjBlOmTLElH1D2+Xfq1InZs2czZMgQAN566y3OnDljq7Nt2zYOHjxIixYtmDdvni35AOjWrRvjx48HICEhoV5iFhERERH7lIBUIyUlhcTERACmTZvmcH9HubFjx9K1a1eCg4NJSUmplxg+/vhjSkpKCAkJYeLEiVUu7Ro2bBghISEAxMTE1Hnsr776iqysLPz8/JgxYwZNmzZ1WNfd3Z1HHnmEdu3a0bRpU9tyrbooKiris88+A+Cuu+6iX79+DusaDAYmTZqEu7s7586dY+vWrbay8sTi7rvvxsfHp1Lb/v37M3XqVP785z/XOWYRERERcUxLsKqxadMmALp3705oaGi19d3d3Zk/f369jW8ymfjxxx+BstmPmuzrmDFjBtnZ2Xh6etZ5/PLnHzFihMPN5edr3749S5curfO45X744Qfy8vIwGAzcc8891dZv3rw5r7zyCoWFhbRq1QooW6KVnJwM4HBZmLe3t919IyIiIo2Jz1P7LnYIIkpAqpOUlASU7bu4GM6fRenRo0eN2rRp04Y2bdrUeewTJ06QnZ0NXLzn37ev7F+Ubdu2rfJ0q/N16tSpwt+PHTtGfn4+rq6utG3bltjYWDZt2sSRI0coLS0lODiYQYMGMXLkyApLy+xZsWIFK1eutFt28uTJGsUnIiIiciVTAlIFs9ls+1IZFBRkt05UVBQZGRl2y/z9/YmKiqpTDOXje3p60rJlyzr1daHO38Ny/p6Jcvn5+UyfPt1h+759+9Z5I/6pU6cAx59/TZw+fRooOzXs9ddfJz4+HoPBgL+/P+fOneO3337jt99+Y8uWLfz973+vsL/njwoKCsjKyrJbVl97XkREREQaMyUgVTh/E3OzZs3s1snMzHSYgJjN5jrHUFRUVOX4DSk/P9/22t5yrtLSUofPDmXLx+rq3LlzDsevqby8PKAseYiPj+eee+7h/vvvx9vbG6vVyk8//cSrr75Kamoqb7zxBrNmzXLYl5eXFwEBAXbLzj8UQERERETsUwJShfPvgyj/IvxH9vY7xMTEsGTJknqJoXzfRXFxcb30dyGaNGlie11UVFQpCWrevLndCxUXLVpEfHx8vcRQ/vx1SebKkzgo28syadIk298NBgM33HADDz74IG+++SaJiYmYTCZatGhht6/x48fbTsz6o+Dg4CoTMhERERHRKVhV8vLysu0JyMzMrHG782dO6qp82VV+fr7tAsLqnDx5ksTERHbu3Fmnsf38/GyvL+T5y2cc6kP581/I+KmpqSQmJrJ//36g4uzJ4MGD7bYZMmQILi4uWK1WDh8+XPuARURERKRKSkCqYDAY6Ny5M/Dfzeg1ceDAgXqLoWvXrhgMBqxWKz///HON2qxZs4bo6Gg++OCDOo3dqVMn2yxITce2Wq0cPHiwTuOeLywsDIAjR46Qm5tbozaLFy8mOjqaLVu2AFSYzbB3BC+UzXaVl50/YyIiIiIi9UsJSDXCw8MB2L17NydOnKi2vslkYteuXfU2fvPmzW3H/9pb7vRHhYWFfPfddwDcfPPNdRq7SZMm9OrVCyg7jrekpKTaNjt27KhxolATPXr0wNPTE4vFwldffVVt/f3795OWlgb89/mvueYa2/HFjv4Znjt3zjZz5WiPh4iIiIjUnRKQagwfPpyWLVtisVgwGo1V/jpeXFzMokWL6n2/RmRkJADJycmsXr26yrrvvPMOubm5NGvWjFGjRtV57Pvvvx+DwUBGRka1MyrZ2dm89dZbdR7zfF5eXrb7P1avXm07lteewsJC3nzzTQCuu+46brjhBqBsduPGG28EcJjEfPvtt1itVlq1akWHDh3q8QlERERE5HxKQKrh4eHB9OnTcXNzIyUlhenTp5OYmFghETGbzezcuZNp06axd+9e203kVcnPz6/yT2Fhoa1u9+7due+++wD44IMPWLRoEUeOHKnQX1paGi+++CKxsbG4uLjw+OOPV9pI/de//pW//vWvNZpJKNepUyceeughANauXcuLL77Ir7/+WuHI2fz8fDZs2MBTTz3FqVOnqn1+i8VS7fOf//ned999dO/enZKSEl544QU+/fRT2/G85f3t2bOHmTNnkpaWho+PD08//XSFG+Pvv/9+3Nzc2LNnD8uXL7d9vlarlR9++IGPP/4YKEv2dJqViIiISMPRKVg1EBYWxty5czEajaSlpREdHY2bmxu+vr64urpiMplspzSVn7JUPmvhyLhx46osDwgIYPny5ba/P/TQQzRt2pRPP/2U+Ph44uPj8fLywsfHx/alHcr2ODz55JN2b/wuP6HpQjfJjx07Fnd3dz788EN27NjBjh078PDwwNfXF4vFgslkorS0lCZNmjB16lSCg4P5v//7P4f9/fLLL9U+/6233sqTTz4JlC0Fe/bZZ1m6dClbt25l5cqVrFy5El9fX5o2bcrp06dtCUv79u2ZNWsWbdu2rdDftddey+OPP86SJUtYt24dGzZswN/fn8LCQtum+XvuuYehQ4de0GcjIiIiIhdGCUgN9ejRg2XLlhEfH8/27dtJTU0lNzcXT09PAgMDCQsLY8iQIXTp0gWAkJAQ2y3i9eXPf/4zAwcOZOPGjfz000+2m8q9vb3p3r07N954I3fccUe1d4bU5hf+u+++mwEDBvDNN9+wZ88eMjIyyMnJwcfHh06dOnHjjTdy66230qZNG8xmM25u9fs/rWbNmjFjxgzuuusuNm/ezC+//MKpU6coLCzE19eXnj170q9fPwYMGGDb7/FHgwcPJiQkhNWrV7N3715OnTpFs2bN6N27N3feeSe9e/eu15hFREREpDKD1Wq1XuwgxHnmzJlDeHg4d95558UOpdEpvwckKCiIY8eO1bm/vEXdAPB5yvG+FxEREZHLjRa7X0EKCwtJTU21zdKIiIiIiDibEpAryHPPPUdYWBgdO3a82KGIiIiIyBVKe0CuIHfeeSeDBg262GGIiIiIyBVMCcgV5LbbbrvYIYiIiIjIFU5LsERERERExGmUgIiIiIiIiNNoCZbIJUrH74qIiEhjpBkQERERERFxGiUgIiIiIiLiNEpARERERETEaZSAiIiIiIiI0ygBERERERERp1ECIiIiIiIiTqMEREREREREnEYJiIiIiIiIOI0SEBERERERcRolICIiIiIi4jRKQERERERExGmUgIiIiIiIiNO4XewALiWLFy8mLi6Obt26ER0dbbdOcnIy8fHxJCcnk5OTg8ViwdfXl4CAAHr16kX//v0JDAy02zY2Nhaj0QjAunXrahVjXl4e3377Lbt37yYjI4O8vDyaNGlCq1atCA0NZdiwYVx33XUX3O/kyZPJysoiIiKCcePG2a1jsVjYunUr27dv5+DBg+Tm5uLq6oqfnx9XX301ffr04ZZbbsHb29tu+5p8vtU5duwYmzdv5qeffiI7O5uzZ8/i6elJ27Zt6dGjB7fffjutW7eu0Ob8z70mqvoMRERERKRulIDUUH5+PkajkR07dgDg4uKCn58fLi4umEwmsrOzSU5O5pNPPmHMmDFERkZiMBjqNYb4+HjefvttCgoKAGjatCn+/v6cOXOGo0ePcvToUTZt2sQdd9zBlClTcHV1rbexjx49yssvv0x6ejoA7u7utGjRgqKiIn7//XdOnDjBzp07+fDDD3nssce45ZZb6m1sKEt+3n//fdavX09paSkAPj4+tGzZEpPJxK+//sqvv/7Kl19+yaOPPsrw4cNtbZs1a0ZQUFC1Y2RkZADQvHnzeo1dRERERP5LCUgNFBQUMHPmTNLT0/Hx8SEiIoJBgwbZvqharVb279/Phg0bSEhIYNWqVVgsFiZMmFBvMXz11Ve8/fbbAPTs2ZOIiAg6d+6Mi0vZKrpTp06xbt061q5dy8aNGzGbzTzxxBP1MvZvv/3G7NmzOXv2LFdffTXjx4+nd+/euLu7A2A2m9m1axerV6/mwIEDvPrqqzRp0oQ+ffrUy/hWq5UFCxbwn//8BxcXF0aOHMmoUaNo27atrTw1NZVPP/2UHTt2sHTpUlxdXRk6dCgA4eHhhIeHVznG+vXreeedd7jxxhu588476yVuEREREalMe0BqwGg0kp6eTuvWrVmwYAGjRo2q8Cu5wWAgNDSUZ555xrZ0Z82aNRw/frxexj948CDvvvsuAKNGjeKFF14gNDTUlnwA+Pv788gjj/C3v/0NKFt2tHv37jqPXVhYyEsvvcTZs2e54YYbWLhwIeHh4bbkA8pmQ8LDw3nppZfo1asXpaWlvPXWW7aZirpav369LfmYMWMGU6ZMsSUfUPb5d+rUidmzZzNkyBAA3nrrLc6cOVOj/g8ePMh7771Hy5Yteeqppyp8riIiIiJSv/RNqxopKSkkJiYCMG3aNIf7O8qNHTuWrl27EhwcTEpKSr3E8PHHH1NSUkJISAgTJ06scmnXsGHDCAkJASAmJqbOY3/11Vf/r717j4uyzvs//prhzHBQQEIhtSQVlUzNQ66H1dLMwjY1c9U7bzPzrm09tB6qrdW7zLxrN0MtW7ODpWamechMTWR1M7FEM4VMXEMUJY4hDAoDzO8PfsxKzAwk4+Da+/l49LiHub7f6/oM1+19z5vreyA7O5smTZowa9YsfH19Hbb18vJiwoQJtGzZEl9fX9twrYYoLS3lww8/BOCee+6hd+/eDtsaDAYmTpyIl5cXFy9e5Isvvqjz/BaLhb/97W+Ul5fz2GOPafiViIiIyBWmIVh12L59OwCxsbHExMTU2d7Ly4sFCxa47PoFBQUcPHgQqHr6UZ95HbNmzSInJwc/P78GX7/68991110OJ5dfqlWrVixZsqTB16329ddfU1RUhMFg4N57762zfVBQEC+99BIlJSWEhYXV2X7dunWcPXuWXr160aNHD1eULCIiIiJOKIDU4ciRI0DVvIvGcOlTlM6dO9erT0REBBEREQ2+9rlz58jJyQEa7/MfPXoUgBYtWtRa3cqRNm3a1KtdVlYW69evx8vLi4cffrhefVauXMnq1avtHsvNza3XOURERER+zRRAnLBYLLYvlY5WUZo3b55t9aSfCw0NZd68eQ2qofr6fn5+hISENOhcv9Slc1iioqJqHS8uLmbmzJkO+/fq1avBE/Hz8vIAx7//hnj//fcpKytj6NChhIeH16uP2WwmOzvb7jFXzXkRERERuZYpgDhx6SRmf39/u22ysrIcBhCLxdLgGkpLS51e/0oqLi62vbY3nKuystLhZ4eq4WMNdfHiRYfXb4jTp0+zd+9ePD09GTFiRL37mUwmh2FFk9dFRERE6qYA4oSPj4/tdfUX4Z+zN99h586dLFq0yCU1VM+7KCsrc8n5fglvb2/b69LS0lohKCgoyO6GigsXLiQxMdElNVR/fleEuUt99NFHVFZW0rdv33oP7QIYN24c48aNs3ssKirKaSATEREREa2C5ZTJZLItN5uVlVXvfvVd/rU+qoddFRcX2zYgrEtubi5JSUl89dVXDbp2kyZNbK9/yecvKipq0HUvVf35f8n1T548SVJSEseOHbN7/Pz58+zduxeAu+++u+FFioiIiEi9KYA4YTAYaNeuHfDvyej1cfz4cZfV0KFDBwwGA1arlW+//bZefTZs2MD8+fNZsWJFg67dpk0b21OQ+l7barWSlpbWoOteqmPHjgCcOnWKwsLCevV59dVXmT9/Pv/4xz/sHk9ISMBisRAZGUn79u1dVaqIiIiI1IMCSB2qd9BOTk7m3LlzdbYvKCjgwIEDLrt+UFCQbflfe8Odfq6kpIQ9e/YA0LNnzwZd29vbm65duwJVy/GWl5fX2Wf//v31Dgr10blzZ/z8/KioqGDLli11tj927Bjp6emA48+/e/duAH7zm9+4rE4RERERqR8FkDoMHjyYkJAQKioqiI+Pt00Kt6esrIyFCxe6fL7G2LFjAUhJSWH9+vVO27755psUFhbi7+9PXFxcg6/9wAMPYDAYyMzMrPOJSk5ODn//+98bfM1LmUwm2/4f69evty3La09JSQmvv/46AG3btuWWW26xW+PJkycBtO+HiIiISCNQAKmDj48PM2fOxNPTk9TUVGbOnElSUlKNIGKxWPjqq6+YMWMG33zzjW0ncmeKi4ud/ldSUmJrGxsby8iRIwFYsWIFCxcu5NSpUzXOl56ezgsvvEBCQgJGo5EpU6bQtGnTGm0effRRHn300Xo9SajWpk0bHnzwQQA2bdrECy+8wPfff19jydni4mK2bt3K9OnTycvLq/PzV1RU1Pn5L/39jhw5ktjYWMrLy5k7dy5r1qyxLc9bfb5Dhw4xe/Zs0tPTCQwM5IknnrC7Y3xycjIAvr6+9d4vRERERERcR6tg1UPHjh2ZM2cO8fHxpKenM3/+fDw9PQkODsbDw4OCggLbKk133XUXEydOtD21cGTMmDFOj4eHh7N8+XLbzw8++CC+vr6sWbOGxMREEhMTMZlMBAYG2r60AwQGBjJt2jS6d+9e65zVKzT90knyI0aMwMvLi/fee4/9+/ezf/9+fHx8CA4OpqKigoKCAiorK/H29ubxxx8nKiqKJ5980uH5vvvuuzo//8CBA5k2bRpQNRTsz3/+M0uWLOGLL75g9erVrF69muDgYHx9ffnpp59sgaVVq1Y89dRTtGjRwu55qyem33TTTfXaVV5EREREXEsBpJ46d+7M0qVLSUxMZN++fZw8eZLCwkL8/Pxo3rw5HTt2ZMCAAbZJzdHR0bZdxF1l1KhR9OvXj23btnH48GHbTuUBAQHExsbSrVs3hgwZUueeIZezX8WwYcPo27cvn332GYcOHSIzM5P8/HwCAwNp06YN3bp1Y+DAgURERGCxWPD0dO3/avn7+zNr1izuuecePv/8c7777jvy8vIoKSkhODiYLl260Lt3b/r27es0WFRPkK/PUyoRERERcT2D1Wq1NnYR4j7PPvsst912G0OHDm3sUq451fuAREZGcubMmcYuR0REROSqpDkgvyIlJSWcPHlSS8+KiIiISKNRAPkVeeaZZ+jYsSM33nhjY5ciIiIiIr9SmgPyKzJ06FD69+/f2GWIiIiIyK+YAsivyB133NHYJYiIiIjIr5yGYImIiIiIiNsogIiIiIiIiNsogIiIiIiIiNsogIiIiIiIiNsogIiIiIiIiNsogIiIiIiIiNtoGV6Rq1TRwk6NXQKB0482dgkiIiJyjdETEBERERERcRsFEBERERERcRsFEBERERERcRsFEBERERERcRsFEBERERERcRsFEBERERERcRsFEBERERERcRvtAyI2r776Krt27aJTp07Mnz/fbpuUlBQSExNJSUkhPz+fiooKgoODCQ8Pp2vXrvTp04fmzZvb7ZuQkEB8fDwAmzdvvqwai4qK2LFjB8nJyWRmZlJUVIS3tzdhYWHExMQwaNAg2rZtW69z/etf/2L69OkMGTKExx577LLqEREREZFfRgFE6qW4uJj4+Hj2798PgNFopEmTJhiNRgoKCsjJySElJYUPPviA4cOHM3bsWAwGg0trSExMZNmyZZjNZgB8fX0JDQ3l/PnzZGRkkJGRwfbt2xkyZAiTJ0/Gw8PD6fl27Njh0vpEREREpG4KIFIns9nM7NmzOX36NIGBgYwePZr+/fsTFBQEgNVq5dixY2zdupXdu3ezdu1aKioqGD9+vMtq2LJlC8uWLQOgS5cujB49mnbt2mE0Vo0izMvLY/PmzWzatIlt27ZhsViYOnWq3XMVFBSwY8cOtm/f7rL6RERERKR+FECkTvHx8Zw+fZpmzZoxb968WkOsDAYDMTExxMTEEBkZyerVq9mwYQODBg2iRYsWDb5+Wloab7/9NgBxcXE8/PDDtZ6uhIaGMmHCBKKioli8eDEJCQn06dOHbt262dqsXbuWbdu2kZub2+Ca3GHJzS83dgk81dgFiIiIyDVHk9DFqdTUVJKSkgCYMWOGw/kd1UaMGEGHDh2IiooiNTXVJTWsWrWK8vJyoqOjeeihh5wO7Ro0aBDR0dEA7Ny5s8axwsJCysvLadKkCU2aNMHb29sl9YmIiIhI/ekJiDhVPUwpNjaWmJiYOtt7eXmxYMECl12/oKCAgwcPAlVPP+qa1wEwa9YscnJy8PPzq/H+pEmTmDRpku3n6kn3IiIiIuI+CiDi1JEjR4CqeReN4dKnKJ07d65Xn4iICCIiIq5USSIiIiLSAAog4pDFYrHNl4iMjLTbZt68eWRmZto9Fhoayrx58xpUQ/X1/fz8CAkJadC5XGHlypWsXr3a7rH/lLklIiIiIo1JAUQcOn/+vO21v7+/3TZZWVkOA4jFYmlwDaWlpU6v725ms5ns7Gy7xyorK91cjYiIiMh/HgUQccjHx8f2+uLFi3bbLFmypNZ7O3fuZNGiRS6pISAgAICysjKXnK+hTCYT4eHhdo9VLwksIiIiIo4pgIhDJpMJLy8vLBYLWVlZ9e536ZOThqoedlVcXIzZbMZkMtXZJzc3lxMnTmA0GunRo4fLagEYN24c48aNs3ssKirK4dMgEREREamiP9mKQwaDgXbt2gH/noxeH8ePH3dZDR06dMBgMGC1Wvn222/r1WfDhg3Mnz+fFStWuKwOEREREXENBRBx6rbbbgMgOTmZc+fO1dm+oKCAAwcOuOz6QUFBtuV/N2/eXGf7kpIS9uzZA0DPnj1dVoeIiIiIuIYCiDg1ePBgQkJCqKioID4+3jYp3J6ysjIWLlzo8vkaY8eOBSAlJYX169c7bfvmm29SWFiIv78/cXFxLq1DRERERBpOc0DEKR8fH2bOnMmzzz5LamoqM2fOZMyYMXTp0sU2Sd1isXDo0CFWrlxJeno60dHRnDhxwul5i4uLnR43Go22la9iY2MZOXIk69atY8WKFWRkZDB8+HBatWpla5+ens6qVavYv38/RqORKVOm0LRp0wZ++sZVtGVbY5fA07+whvkL469QJSIiInKtUACROnXs2JE5c+YQHx9Peno68+fPx9PTk+DgYDw8PCgoKLAtuXvXXXcxceJE21MLR8aMGeP0eHh4OMuXL7f9/OCDD+Lr68uaNWtITEwkMTERk8lEYGAgxcXFtkATGBjItGnT6N69ewM/tYiIiIhcCQogUi+dO3dm6dKlJCYmsm/fPk6ePElhYSF+fn40b96cjh07MmDAANq3bw9AdHQ0OTk5Lq1h1KhR9OvXj23btnH48GHOnTtHTk4OAQEBxMbG0q1bN4YMGXLV7BkiIiIiIrUZrFartbGLELkWVC/DGxkZyZkzZxp8vqenT3VBVe6lIVgiIiJSF01CFxERERERt1EAERERERERt1EAERERERERt1EAERERERERt1EAERERERERt1EAERERERERt1EAERERERERt9E+ICIu4up9QERERESuRXoCIiIiIiIibqMAIiIiIiIibqMAIiIiIiIibuPZ2AWIiH1PT5/a2CXIf4D5C+MbuwQREZFfRE9ARERERETEbRRARERERETEbRRARERERETEbRRARERERETEbRRARERERETEbRRARERERETEbRRARERERETEbbQPiNi8+uqr7Nq1i06dOjF//ny7bVJSUkhMTCQlJYX8/HwqKioIDg4mPDycrl270qdPH5o3b263b0JCAvHxVXsWbN68+bJqLCoqYseOHSQnJ5OZmUlRURHe3t6EhYURExPDoEGDaNu2rcP+OTk5bNy4kYMHD5KXl4fVaiUsLIwuXbpw33330axZs8uqS0RERETqRwFE6qW4uJj4+Hj2798PgNFopEmTJhiNRgoKCsjJySElJYUPPviA4cOHM3bsWAwGg0trSExMZNmyZZjNZgB8fX0JDQ3l/PnzZGRkkJGRwfbt2xkyZAiTJ0/Gw8OjRv/U1FSef/55zGYzBoOBkJAQSktLyczMJDMzk927d/Pcc8/Rpk0bl9YtIiIiIv+mACJ1MpvNzJ49m9OnTxMYGMjo0aPp378/QUFBAFitVo4dO8bWrVvZvXs3a9eupaKigvHjx7ushi1btrBs2TIAunTpwujRo2nXrh1GY9Uowry8PDZv3symTZvYtm0bFouFqVP/vZN4WVkZL730EmazmZtvvpk//vGPXHfddQCcOHGChQsXcvr0af7v//6P1157DS8vL5fVLiIiIiL/pjkgUqf4+HhOnz5Ns2bN+Otf/0pcXJwtfAAYDAZiYmL405/+xJgxYwDYsGEDZ8+edcn109LSePvttwGIi4tj7ty5xMTE2MIHQGhoKBMmTOAPf/gDUDXcKzk52XZ837595OfnExgYyFNPPWULHwDR0dE8+eSTGI1GsrKySEpKckndIiIiIlKbAog4lZqaavtCPmPGDIfzO6qNGDGCDh06EBUVRWpqqktqWLVqFeXl5URHR/PQQw85Hdo1aNAgoqOjAdi5c6ft/ZSUFABuvfVWTCZTrX7XX3+97bMdO3bMJXWLiIiISG0agiVObd++HYDY2FhiYmLqbO/l5cWCBQtcdv2CggIOHjwIVD39+Pm8DntmzZpFTk4Ofn5+tvfy8/MBCA8Pd9iv+olKeXl5Q0oWEREREScUQMSpI0eOAFXzLhrDpU9ROnfuXK8+ERERRERE1Hhv1qxZVFRUOJzbkZGRQWZmJgAtW7a8zGpFREREpC4KIOKQxWIhNzcXgMjISLtt5s2bZ/vi/nOhoaHMmzevQTVUX9/Pz4+QkJDLPo+3t7fTa7z00ktUVlZiMpno27fvZV9HRERERJxTABGHzp8/b3vt7+9vt01WVpbDAGKxWBpcQ2lpqdPrN4TVaiUhIYF33nmHoqIiPD09mTZtWo0J9j+3cuVKVq9ebfdYdVgSEREREccUQMQhHx8f2+uLFy/abbNkyZJa7+3cuZNFixa5pIaAgACgahldV0pLS2PZsmV8//33ADRr1ozp06fTqVMnp/3MZjPZ2dl2j1VWVrq0RhEREZFrkQKIOGQymfDy8sJisZCVlVXvfpc+OWmo6mFXxcXFmM1muytY/Vxubi4nTpzAaDTSo0ePGsfMZjPvvPMOn3/+OVarFS8vL4YNG8aoUaNqTFp3xGQyOZzIfumywCIiIiJinwKIOGQwGGjXrh1Hjx7lyJEj3HvvvfXqd/z4cZfV0KFDBwwGA1arlW+//Zbbbrutzj4bNmzgk08+4frrr68RQLKyspgzZw7nzp0DoE+fPowfP77GniB1GTduHOPGjbN7LCoqyuFwNBERERGpoj/ZilPVX/iTk5NtX9ydKSgo4MCBAy67flBQkG35382bN9fZvqSkhD179gDQs2dP2/ulpaXMnTuXc+fOERQUxJw5c5g1a9YvCh8iIiIi0nAKIOLU4MGDCQkJoaKigvj4eNukcHvKyspYuHChy+drjB07FqjaTHD9+vVO27755psUFhbi7+9PXFyc7f3t27dz9uxZfH19mTt3Lt26dXNpjSIiIiJSPwog4pSPjw8zZ87E09OT1NRUZs6cSVJSUo0gYrFY+Oqrr5gxYwbffPONbSdyZ4qLi53+V1JSYmsbGxvLyJEjAVixYgULFy7k1KlTNc6Xnp7OCy+8QEJCAkajkSlTptC0aVPb8S+++AKo2sywPvWJiIiIyJWhOSBSp44dOzJnzhzi4+NJT09n/vz5eHp6EhwcjIeHBwUFBbYld++66y4mTpxoe2rhyJgxY5weDw8PZ/ny5bafH3zwQXx9fVmzZg2JiYkkJiZiMpkIDAy0hRaAwMBApk2bRvfu3W19rVYrJ06cACAxMZEvv/zS6bXvvvtu7rnnHqdtREREROTyKIBIvXTu3JmlS5eSmJjIvn37OHnyJIWFhfj5+dG8eXM6duzIgAEDaN++PQDR0dHk5OS4tIZRo0bRr18/tm3bxuHDhzl37hw5OTkEBAQQGxtLt27dGDJkSK09Q4qKiigvLwfqt1eHK1fxEhEREZGaDFar1drYRYhcC6pXwYqMjOTMmTMNPt/T06e6oCq51s1fGN/YJYiIiPwimgMiIiIiIiJuowAiIiIiIiJuowAiIiIiIiJuowAiIiIiIiJuowAiIiIiIiJuowAiIiIiIiJuowAiIiIiIiJuo31ARFzE1fuAiIiIiFyL9ARERERERETcRgFERERERETcRgFERERERETcxrOxCxAR+4oWdmrsEtwucPrRxi5BRERErjA9AREREREREbdRABEREREREbdRABEREREREbdRABEREREREbdRABEREREREbdRABEREREREbdRABEREREREbfRPiBi8+qrr7Jr1y46derE/Pnz7bZJSUkhMTGRlJQU8vPzqaioIDg4mPDwcLp27UqfPn1o3ry53b4JCQnEx8cDsHnz5suqsaioiB07dpCcnExmZiZFRUV4e3sTFhZGTEwMgwYNom3btg777969m507d5Kenk5xcTH+/v60bt2avn37MmjQIDw8PC6rLhERERGpHwUQqZfi4mLi4+PZv38/AEajkSZNmmA0GikoKCAnJ4eUlBQ++OADhg8fztixYzEYDC6tITExkWXLlmE2mwHw9fUlNDSU8+fPk5GRQUZGBtu3b2fIkCFMnjy5VphYsmQJO3bsAMDHx4fQ0FB++uknjhw5wpEjR9izZw9z587F29vbpXWLiIiIyL8pgEidzGYzs2fP5vTp0wQGBjJ69Gj69+9PUFAQAFarlWPHjrF161Z2797N2rVrqaioYPz48S6rYcuWLSxbtgyALl26MHr0aNq1a4fRWDWKMC8vj82bN7Np0ya2bduGxWJh6tSptv779u1jx44deHh4MHnyZG6//Xa8vLyoqKhg+/btLFu2jKNHj/LJJ58wYsQIl9UtIiIiIjUpgEid4uPjOX36NM2aNWPevHm1hlgZDAZiYmKIiYkhMjKS1atXs2HDBgYNGkSLFi0afP20tDTefvttAOLi4nj44YdrPV0JDQ1lwoQJREVFsXjxYhISEujTpw/dunUDYNu2bQAMHTqUIUOG2Pp5eHgwdOhQTp06xWeffcaBAweumgCy5OaXG7sE90v4rLEruKY8dftdjV2CiIhILZqELk6lpqaSlJQEwIwZMxzO76g2YsQIOnToQFRUFKmpqS6pYdWqVZSXlxMdHc1DDz3kdGjXoEGDiI6OBmDnzp2299PT0wG45ZZb7PZr1aoVABcvXnRJzSIiIiJin56AiFPbt28HIDY2lpiYmDrbe3l5sWDBApddv6CggIMHDwJVTz/qM0l81qxZ5OTk4OfnZ3tv0qRJVFZW0q5dO7t90tLSALj++utdULWIiIiIOKIAIk4dOXIEqJp30RgufYrSuXPnevWJiIggIiKixnt9+vSp8XNFRQUAOTk57Ny5k127duHl5XXVDL8SERERuVYpgIhDFouF3NxcACIjI+22mTdvHpmZmXaPhYaGMm/evAbVUH19Pz8/QkJCGnSuSz333HMcOnTI9rPJZOLZZ5+1DcUSERERkStDAUQcOn/+vO21v7+/3TZZWVkOA4jFYmlwDaWlpU6vf7maNWtGWFgYBQUFVFRUYDabWbBgAc8884zTfURWrlzJ6tWr7R6rDksiIiIi4pgCiDjk4+Nje+1ocvaSJUtqvbdz504WLVrkkhoCAgIAKCsrc8n5qj3++ONA1VCsr7/+mnfffZezZ8/y/PPPs3z58hqf/VJms5ns7Gy7xyorK11ao4iIiMi1SAFEHDKZTHh5eWGxWMjKyqp3v0ufnDRU9bCr4uJizGYzJpOpzj65ubmcOHECo9FIjx49nLb18PCgV69eREREMGXKFAoLC/nqq6/o27ev3fYmk4nw8HC7x6r3JBERERERxxRAxCGDwUC7du04evQoR44c4d57761Xv+PHj7ushg4dOmAwGLBarXz77bfcdtttdfbZsGEDn3zyCddffz09evTgu+++Y9++ffj6+jJmzBi7fVq3bk1wcDCFhYUOn3AAjBs3jnHjxtk9FhUV5XA4moiIiIhU0Z9sxanqL/zJycmcO3euzvYFBQUcOHDAZdcPCgqyLf+7efPmOtuXlJSwZ88eAHr27AlUrXS1ceNGPvzwQy5cuGC3n9Vqpby8HKDG8r0iIiIi4loKIOLU4MGDCQkJoaKigvj4eNukcHvKyspYuHChy+drjB07FoCUlBTWr1/vtO2bb75JYWEh/v7+xMXFARATE2N7ilK9qeLPHThwALPZDECnTp1cWL2IiIiIXEpDsMQpHx8fZs6cybPPPktqaiozZ85kzJgxdOnSxTZR22KxcOjQIVauXEl6ejrR0dGcOHHC6XmLi4udHjcajbaVr2JjYxk5ciTr1q1jxYoVZGRkMHz48BpL5qanp7Nq1Sr279+P0WhkypQpNG3aFKha8ap3797s3buXt956i6CgILp27YrBYKCiooJdu3bx9ttvA9CrVy9atmx52b8vVyrasq2xS3CL+QvjG7sEERERcSMFEKlTx44dmTNnDvHx8aSnpzN//nw8PT0JDg7Gw8ODgoIC25K7d911FxMnTrQ9tXDE0VyMauHh4Sxfvtz284MPPoivry9r1qwhMTGRxMRETCYTgYGBFBcX2wJNYGAg06ZNo3v37jXON3nyZDIyMjh9+jT/+7//i8lkwmQykZeXZ9uUMCYmhqlTp/7i34+IiIiI1J8CiNRL586dWbp0KYmJiezbt4+TJ09SWFiIn58fzZs3p2PHjgwYMID27dsDEB0dTU5OjktrGDVqFP369WPbtm0cPnyYc+fOkZOTQ0BAALGxsXTr1o0hQ4bY3TOkSZMmvPzyy2zZsoWkpCQyMzPJy8sjICCAG264gX79+jFgwAA8PDxcWrOIiIiI1GSwWq3Wxi5C5FpQvQpWZGQkZ86cafD5np7+63gaoyFYIiIivy6ahC4iIiIiIm6jACIiIiIiIm6jACIiIiIiIm6jACIiIiIiIm6jACIiIiIiIm6jACIiIiIiIm6jACIiIiIiIm6jfUBEXMTV+4CIiIiIXIv0BERERERERNxGAURERERERNxGAURERERERNxGAUTkKvViwme8mPBZY5chIiIi4lIKICIiIiIi4jYKICIiIiIi4jYKICIiIiIi4jYKICIiIiIi4jYKICIiIiIi4jYKICIiIiIi4jYKICIiIiIi4jaejV2AXD1effVVdu3aRadOnZg/f77dNikpKSQmJpKSkkJ+fj4VFRUEBwcTHh5O165d6dOnD82bN7fbNyEhgfj4eAA2b958WTUWFRWxY8cOkpOTyczMpKioCG9vb8LCwoiJiWHQoEG0bdvWYf+Kigo+//xzdu7cSUZGBhUVFTRt2pTY2Fjuv/9+WrRocVl1iYiIiEj9KIBIvRQXFxMfH8/+/fsBMBqNNGnSBKPRSEFBATk5OaSkpPDBBx8wfPhwxo4di8FgcGkNiYmJLFu2DLPZDICvry+hoaGcP3+ejIwMMjIy2L59O0OGDGHy5Ml4eHjU6G+xWHjhhRc4ePAgAIGBgQQGBpKTk0NCQgJJSUn83//9Hy1btnRp3SIiIiLybwogUiez2czs2bM5ffo0gYGBjB49mv79+xMUFASA1Wrl2LFjbN26ld27d7N27VoqKioYP368y2rYsmULy5YtA6BLly6MHj2adu3aYTRWjSLMy8tj8+bNbNq0iW3btmGxWJg6dWqNc7z//vscPHiQ4OBgpk2bRrdu3QD48ccfefnllzl+/DhvvPGGw6c/IiIiItJwmgMidYqPj+f06dM0a9aMv/71r8TFxdnCB4DBYCAmJoY//elPjBkzBoANGzZw9uxZl1w/LS2Nt99+G4C4uDjmzp1LTEyMLXwAhIaGMmHCBP7whz8AVcO9kpOTbcfz8vLYsmULBoOBp556yhY+AK677jqmTZsGwNGjR8nLy3NJ3SIiIiJSmwKIOJWamkpSUhIAM2bMcDi/o9qIESPo0KEDUVFRpKamuqSGVatWUV5eTnR0NA899JDToV2DBg0iOjoagJ07d9re/+KLLygvL6dr16506NChVr+oqCieeOIJHn/8cTw99WBQRERE5ErRNy1xavv27QDExsYSExNTZ3svLy8WLFjgsusXFBTY5mzExcXVmtdhz6xZs8jJycHPz8/23tGjRwHo3r27w36//e1vG1asiIiIiNRJAUScOnLkCFA176IxXPoUpXPnzvXqExERQURERI33jh07BkDLli1JTU3l448/5rvvvuPChQs0a9aMHj16MHz4cJo2beq64kVERESkFgUQcchisZCbmwtAZGSk3Tbz5s0jMzPT7rHQ0FDmzZvXoBqqr+/n50dISMhlnaOyspKioiIA9u7dy9atW7FarQQHBxMUFMS5c+fYtGkTu3btYu7cudx0000NqllEREREHFMAEYfOnz9ve+3v72+3TVZWlsMAYrFYGlxDaWmp0+vXh9lsprKyEoBPP/2Url278sgjj9j2/Dhz5gyvvPIKJ06c4MUXX2Tp0qX4+PjYPdfKlStZvXq13WPVYUlEREREHFMAEYcu/RJ+8eJFu22WLFlS672dO3eyaNEil9QQEBAAQFlZ2WWfozrEALRu3Zpnn322xlySSyeg5+bm8vXXX9OnTx+75zKbzWRnZ9s9Vh1yRERERMQxBRBxyGQy4eXlhcViISsrq979Ln1y0lDVw66Ki4sxm82YTKY6++Tm5nLixAmMRiM9evTA19fXdqxPnz52J7JHRUXRtm1bjh07xr/+9S+HAcRkMhEeHm732KXLAouIiIiIfQog4pDBYKBdu3YcPXqUI0eOcO+999ar3/Hjx11WQ4cOHTAYDFitVr799ltuu+22Ovts2LCBTz75hOuvv54ePXpgMpnw8fGhtLSUwMBAh/1CQ0MB509bxo0bx7hx4+wei4qKcjgcTURERESq6E+24lT1F/7k5GTOnTtXZ/uCggIOHDjgsusHBQXZlv/dvHlzne1LSkrYs2cPAD179gSqgtSNN94I4PQzVA+tatasWYNqFhERERHHFEDEqcGDBxMSEkJFRQXx8fE15lP8XFlZGQsXLmzQfA17xo4dC0BKSgrr16932vbNN9+ksLAQf39/4uLibO/37t0bgMTERMxmc61+J0+e5MSJExgMBm699VYXVi8iIiIil1IAEad8fHyYOXMmnp6epKamMnPmTJKSkmoEEYvFwldffcWMGTP45ptvbDuRO1NcXOz0v5KSElvb2NhYRo4cCcCKFStYuHAhp06dqnG+9PR0XnjhBRISEjAajUyZMqXGnh533HEH4eHhFBYWsmDBghpzWjIyMnjllVewWq0MHDiQqKioy/59iYiIiIhzmgMiderYsSNz5swhPj6e9PR05s+fj6enJ8HBwXh4eFBQUGBbcveuu+5i4sSJtqcWjowZM8bp8fDwcJYvX277+cEHH8TX15c1a9aQmJhIYmIiJpOJwMBAW2gBCAwMZNq0abV2PDeZTPz5z3/mueee4/DhwzzyyCOEhYVhtVrJy8uzfc5HHnnkF/9+RERERKT+FECkXjp37szSpUtJTExk3759nDx5ksLCQvz8/GjevDkdO3ZkwIABtG/fHoDo6GhycnJcWsOoUaPo168f27Zt4/Dhw5w7d46cnBwCAgKIjY2lW7duDBkyxOGeITfccAOLFy9mw4YNfPnll2RnZ+Pl5UX79u3p378/d955J56e+ichIiIiciUZrFartbGLELkWVK+CFRkZyZkzZxp8vhcTPgPgqdvvavC5RERERK4WmgMiIiIiIiJuowAiIiIiIiJuowAiIiIiIiJuowAiIiIiIiJuowAiIiIiIiJuowAiIiIiIiJuowAiIiIiIiJuo13XRK5S2v9DRERErkV6AiIiIiIiIm6jACIiIiIiIm6jACIiIiIiIm6jACJylSpa2ImihZ0auwwRERERl1IAERERERERt1EAERERERERt1EAERERERERt1EAERERERERt1EAERERERERt1EAERERERERt1EAERERERERt/Fs7ALk6vHqq6+ya9cuOnXqxPz58+22SUlJITExkZSUFPLz86moqCA4OJjw8HC6du1Knz59aN68ud2+CQkJxMfHA7B58+bLqrGoqIgdO3aQnJxMZmYmRUVFeHt7ExYWRkxMDIMGDaJt27Y1+vz4449MmjSp3tdw9vlFREREpGEUQKReiouLiY+PZ//+/QAYjUaaNGmC0WikoKCAnJwcUlJS+OCDDxg+fDhjx47FYDC4tIbExESWLVuG2WwGwNfXl9DQUM6fP09GRgYZGRls376dIUOGMHnyZDw8PADw9PQkMjKyzvPn5uZSWlpKUFCQS+sWERERkX9TAJE6mc1mZs+ezenTpwkMDGT06NH079/f9kXdarVy7Ngxtm7dyu7du1m7di0VFRWMHz/eZTVs2bKFZcuWAdClSxdGjx5Nu3btMBqrRhHm5eWxefNmNm3axLZt27BYLEydOhWA0NBQli5d6vT8aWlpzJ49m7CwMP7nf/7HZXWLiIiISE0KIFKn+Ph4Tp8+TbNmzZg3b16tIVYGg4GYmBhiYmKIjIxk9erVbNiwgUGDBtGiRYsGXz8tLY23334bgLi4OB5++OFaT1dCQ0OZMGECUVFRLF68mISEBPr06UO3bt3qPL/ZbObll1+msrKSP/3pTzRp0qTBNbvCkptfBuCpRq5DRERExJU0CV2cSk1NJSkpCYAZM2Y4nN9RbcSIEXTo0IGoqChSU1NdUsOqVasoLy8nOjqahx56yOnQrkGDBhEdHQ3Azp0763X+ZcuWkZWVxbBhw+jYsaNLahYRERER+/QERJzavn07ALGxscTExNTZ3svLiwULFrjs+gUFBRw8eBCoevpRPa/DmVmzZpGTk4Ofn1+dbY8ePUpiYiJhYWH8/ve/b3C9IiIiIuKcAog4deTIEaBq3kVjuPQpSufOnevVJyIigoiIiDrblZeX2+aGTJgwoV6BRUREREQaRgFEHLJYLOTm5gI4XEVq3rx5ZGZm2j0WGhrKvHnzGlRD9fX9/PwICQlp0Ll+bufOnZw+fZpWrVrRp08fl55bREREROxTABGHzp8/b3vt7+9vt01WVpbDAGKxWBpcQ2lpqdPrXy6LxcJHH30EwKhRo+q9ZPDKlStZvXq13WPVYUlEREREHFMAEYd8fHxsry9evGi3zZIlS2q9t3PnThYtWuSSGgICAgAoKytzyfmqJSYmkpOTQ7Nmzejdu3e9+5nNZrKzs+0eq6ysdFV5IiIiItcsBRBxyGQy4eXlhcViISsrq979Ln1y0lDVw66Ki4sxm82YTKY6++Tm5nLixAmMRiM9evSw2+azzz4DYMiQIfWa2F7NZDIRHh5u91j1niQiIiIi4pgCiDhkMBho164dR48e5ciRI9x777316nf8+HGX1dChQwcMBgNWq5Vvv/2W2267rc4+GzZs4JNPPuH666+3G0DS0tL417/+hcFgYODAgb+onnHjxjFu3Di7x6KiohwORxMRERGRKvqTrThV/YU/OTmZc+fO1dm+oKCAAwcOuOz6QUFBtuV/N2/eXGf7kpIS9uzZA0DPnj3ttqk+HhMTQ2hoqIsqFREREZH6UAARpwYPHkxISAgVFRXEx8fbJoXbU1ZWxsKFC10+X2Ps2LEApKSksH79eqdt33zzTQoLC/H39ycuLs5um/379wPQvXt3l9YpIiIiInXTECxxysfHh5kzZ/Lss8+SmprKzJkzGTNmDF26dLFNUrdYLBw6dIiVK1eSnp5OdHQ0J06ccHre4uJip8eNRqNt5avY2FhGjhzJunXrWLFiBRkZGQwfPpxWrVrZ2qenp7Nq1Sr279+P0WhkypQpNG3atNZ5z5w5Y5vPcrXvel60ZRsAT////wkwf2F8Y5UjIiIi4hIKIFKnjh07MmfOHOLj40lPT2f+/Pl4enoSHByMh4cHBQUFtiV377rrLiZOnGh7auHImDFjnB4PDw9n+fLltp8ffPBBfH19WbNmDYmJiSQmJmIymQgMDKS4uNgWaAIDA5k2bZrDpxvff/89ULVje5s2ber9OxARERER11AAkXrp3LkzS5cuJTExkX379nHy5EkKCwvx8/OjefPmdOzYkQEDBtC+fXsAoqOjycnJcWkNo0aNol+/fmzbto3Dhw9z7tw5cnJyCAgIIDY2lm7dujFkyBCne4akpaUB0Lp1a7y8vFxan4iIiIjUzWC1Wq2NXYTItaB6FazIyEjOnDnT4PM9PX1qrfc0BEtERET+02kSuoiIiIiIuI0CiIiIiIiIuI0CiIiIiIiIuI0CiIiIiIiIuI0CiIiIiIiIuI0CiIiIiIiIuI0CiIiIiIiIuI32ARFxEVfvAyIiIiJyLdITEBERERERcRsFEBERERERcRsFEBERERERcRvPxi5AROwrWtipxs+B0482UiUiIiIirqMnICIiIiIi4jYKICIiIiIi4jYKICIiIiIi4jYKICIiIiIi4jYKICIiIiIi4jYKICIiIiIi4jYKICIiIiIi4jYKIHJZfvzxR4YNG8awYcP47//+b4qLi522X7duHcOGDePpp592U4UiIiIicjVSAJEGy8/P5913323sMkRERETkP4ACiLjEjh07OHz4cGOXISIiIiJXOc/GLkD+891www388MMPvPbaayxevBgfH5/GLumasOTml2v8/FQj1SEiIiLiSgog0mCPPvooc+bMISsri9WrVzNhwoRf1D8pKYnt27eTlpZGSUkJTZo0ITY2lt/97nfccMMNNdr++OOPTJo0CaPRyMaNGzl06BAbN27k+PHjWCwWmjdvzh133EFcXBxGY+0HfMXFxWzcuJH9+/eTlZWFl5cXrVq1YuDAgQwcOBAPD48G/S5ERERExDkNwZIGu+666/iv//ovADZt2kRaWlq9+lVWVhIfH8/8+fNJTk6moqKCkJAQCgsLSUxM5IknnuDTTz912H/NmjXMmTOHo0ePEhgYiIeHB6dOneKtt95ixYoVtdpnZGTwxz/+kbVr13Lq1ClMJhMeHh6kpKSwePFiZs+ejdlsvrxfgoiIiIjUiwKIuMTdd99Nhw4dqKysZPHixZSXl9fZZ926dSQkJODn58eTTz7JqlWrWL58OatWrWLcuHFUVlaybNkyDh48WKtvZWUlq1evJi4ujvfff59ly5axcuVKBg4cCMCnn37KhQsXbO3NZjPPPfcceXl59OrVi7fffpt3332X999/n4ULF9KqVSuOHz/OokWLXPdLEREREZFaFEDEJQwGA1OmTMHb25v09HQ+/vhjp+3NZjPr168H4KGHHqJ37962IVO+vr6MGjWKQYMGYbVaWb16td1z9O3bl0mTJuHv7w+Al5cXEyZMwGAwUFZWxsmTJ21tP/nkE7Kzs4mOjmb27NmEhYXZjrVp04ann34ao9HIvn37OHfuXIN+FyIiIiLimOaAiMu0aNGCsWPH8s477/Dhhx/Su3dvoqKi7LZNTk7mwoULBAYGMmDAALtt7rzzTnbs2EFaWhpFRUUEBgbWOH7XXXfV6hMcHExQUBCFhYUUFRXZ3t+9ezcAv/3tb+3O82jevDlt27bl2LFjfPvttzRv3txuTStXrnQYiHJzc+2+LyIiIiL/pgAiLjVs2DD27t3L8ePHWbx4MQsWLMBgMNRq98MPPwBw44034u3tbfdcLVu2BMBqtZKdnV0rgISHh9vtV70KV1lZGQClpaVkZmYC8PHHH/PZZ5/Z7Zefnw9ATk6Ow89nNpvJzs62e6yystJhPxERERGpogAiLuXh4cGUKVOYNm0a3333HVu3buXuu++u1a766URoaKjDc3l6emI0GqmsrLSFiUt5eXnVq6ZLd2mvDhnOlJaWOjxmMpkcBh97q26JiIiISE0KIOJyLVu25IEHHmDVqlW899579OjRo1ab6vBwaTj4uYKCAttThaCgoMuu59J9SZ5++ml69ep12ecaN24c48aNs3ssKirK9qRFREREROzTn2zlihg5ciQ33HADFy5c4PXXX691vHqORXp6Olar1e45UlNTAfDz83M4J6M+AgICCA4OBnA6wTw5OZnExESHQ6xEREREpOEUQOSK8PDwYOrUqXh4eJCcnMw///nPGse7deuGwWAgOzub/fv32z3H559/DkCvXr0aPLype/fuAOzYscPuXI3MzExeeOEF4uPjNZdDRERE5ArSECy5Ym688UZGjBjB2rVrbZPOq0VGRtKnTx/++c9/8vrrr+Pr68stt9wCwIULF1i7di2HDx/G29ubUaNGNbiWkSNH8sUXX5CZmcmSJUt46KGHCAgIAOC7777j1Vdfpby8nEGDBhEREdHg67lC0ZZtNX5++mc/u8L8hfEuP6eIiIiIMwogckU98MAD7Nu3j9OnT9c69uijj/Ljjz9y/Phx/vKXvxAYGIi/vz/5+flYLBY8PDyYNm0akZGRDa6jRYsWzJo1i5deeomdO3eSmJhIWFgYFy5c4Pz58wB06dKFRx55pMHXEhERERHHNARLrigvLy+mTJlidwhVQEAAL774IhMnTiQ6Opry8nIKCgoICQnhjjvuYNGiRfTp08dltdx6660sWrSIO++8k6ZNm5KXl4eHhwc333wzU6dOZe7cuTUmrIuIiIiI6xmsjmYAi8gvUr0KVmRkJGfOnGnw+Z6ePtUFVTmnIVgiIiLibnoCIiIiIiIibqMAIiIiIiIibqMAIiIiIiIibqMAIiIiIiIibqMAIiIiIiIibqMAIiIiIiIibqMAIiIiIiIibqN9QERcxNX7gIiIiIhci/QERERERERE3EYBRERERERE3EYBRERERERE3EYBRERERERE3EaT0EVcxNvbG4vFgtFopHnz5o1djoiI/AeJiIjgwIEDjV2GiFt4NnYBIteKiooKACorK8nMzGzkakRERESuTgogIi7i6+vLxYsX8fDwIDw8vEHnys3NpbKyEqPRSFhYmIsqlIbQPbn66J5cnXRfLk9ERERjlyDiNhqCJXIVGjp0KNnZ2YSHh7N169bGLkfQPbka6Z5cnXRfRKQumoQuIiIiIiJuowAiIiIiIiJuowAiIiIiIiJuowAiIiIiIiJuowAiIiIiIiJuo2V4Ra5CY8aMwWw2YzKZGrsU+f90T64+uidXJ90XEamLluEVERERERG30RAsERERERFxGwUQERERERFxGwUQERERERFxG01CF7lKFBQUsGHDBg4cOEB2djb+/v60adOGoUOH0r1798Yu71fjX//6F9OnT2fIkCE89thjDtulpKTw8ccfc/LkSYqKiggJCaFbt2787ne/47rrrnNjxdeunJwcNm7cyMGDB8nLy8NqtRIWFkaXLl247777aNasWa0+ui9X1u7du9m5cyfp6ekUFxfj7+9P69at6du3L4MGDcLDw6NWH90TEfk5TUIXuQqcPXuWp59+mvz8fIxGIyEhIVy4cAGz2QzAfffdx4QJExq5yl+HpUuX8tlnnzkNIFu3buWNN94AwMfHh+DgYPLz8ykvLycgIIBnnnmGDh06uLPsa05qairPP/88ZrMZg8FASEgIpaWlFBcXAxAYGMhzzz1HmzZtbH10X66sJUuWsGPHDuDfv9+ffvqJsrIyADp16sTcuXPx9va29dE9ERF7FEBEGpnVamXKlCmcOnWKm266iZkzZxIREUFlZSWJiYm89tprlJeXM2vWLPr06dPY5V6zCgoK2LFjBx988AGVlZUOA0haWhozZszAarUyatQoRo0ahbe3N0VFRbz++uvs3buXkJAQlixZQkBAQCN8kv98ZWVlPPLII+Tn53PzzTfzxz/+0faX8hMnTrBw4UJOnz5NREQEr732Gl5eXrovV9i+fft48cUX8fDwYPLkydx+++14eXlRUVHB9u3bWbZsGZWVlYwfP54RI0YA+rciIo5pDohII/viiy84deoU3t7ePPXUU0RERABgNBq5/fbbGTx4MACrV69uzDKvWWvXruWhhx5i/PjxrFq1isrKSqftV61ahdVqpXv37owbN872197AwECmTZtm+wvvtm3b3FH+NWnfvn3k5+cTGBjIU089VWOYTnR0NE8++SRGo5GsrCySkpIA3Zcrrfr3NnToUIYMGYKXlxcAHh4eDB06lDvvvBOAAwcO2PronoiIIwogIo3siy++AKBnz56EhYXVOt67d28Azpw5w6lTp9xa269BYWEh5eXlNGnShCZNmtQYPvJzZrOZb775BoC777671nEfHx+6du0KwJdffnlF6v01SElJAeDWW2+1u5nd9ddfT/PmzQE4duyY7osbpKenA3DLLbfYPd6qVSsALl68COjfiog4pwAi0oisVitHjx4FIDY21m6bmJgY218bv//+e7fV9msxadIk3nvvPdt/zoa5HT16lMrKSoxGIx07drTb5uabbwbg5MmTWCyWK1LztS4/Px+A8PBwh22Mxqr/91VeXq774gaTJk1ixowZtGvXzu7xtLQ0oCocgv6tiIhzWgVLpBEVFBRQVFQEQGRkpN02Xl5eNGnShJycHM6dO+fO8uRnMjIyAAgLC8PHx8dum+qVmSorK/nxxx+JiopyW33XilmzZlFRUWEL3j+XkZFBZmYmAC1bttR9cYOfB/OKigqgaqWynTt3smvXLry8vGzzP3RPRMQZBRCRRlS9og9AcHCww3YBAQHk5OTYVsWSxlF9v5zdq8DAQNtr3a/L42wYXG5uLi+99BKVlZWYTCb69u3L+vXrAd0Xd3ruuec4dOiQ7WeTycSzzz5rG4qlfysi4oyGYIk0oksDiLMvXX5+fsC//+oojaP6S5Kjv8wD+Pr62l7rfrmO1Wpl586dTJ06lYyMDDw9PZk2bRpBQUG6L42gWbNmhIWF2fb9MJvNLFiwgOPHj9t+Bt0TEbFPT0BEGpGn57//CTpbfal6fLSjoQziHtVftupzr0D3y1XS0tJYtmyZbQ5Us2bNmD59Op06dQJ0XxrD448/DlQFh6+//pp3332Xs2fP8vzzz7N8+XLdExFxSgFEpBFduvZ99VwQe6qflDRt2vSK1ySOVQ8ZufTJ1c9dekz3q2HMZjPvvPMOn3/+OVarFS8vL4YNG8aoUaNsTwVB96UxeXh40KtXLyIiIpgyZQqFhYV89dVXuici4pQCiEgjuu666/D09KS8vJxz587ZXWGmoqKC3NxcAE3SbGQtWrQAIDs7m4qKCttfeS/1448/AlVj4vWl6vJlZWUxZ84c28ILffr0Yfz48TX2BKmm+3Jlfffdd+zbtw9fX1/GjBljt03r1q0JDg6msLCQ7Oxs3RMRcUpzQEQakYeHhy10VC/H+3NpaWlYLBany1mKe3To0AGo2qnb0ZLI3333HQAdO3bEYDC4rbZrSWlpKXPnzuXcuXMEBQUxZ84cZs2aZTd8gO7LlZaTk8PGjRv58MMPuXDhgt02VquV8vJyoGrOmu6JiDijACLSyPr27QvAnj17+Omnn2od//TTTwHo2rUrQUFB7ixNfiYiIoK2bdsC8Mknn9Q6fv78ef75z38CMGDAALfWdi3Zvn07Z8+exdfXl7lz59KtWzen7XVfrqyYmBgMBgNWq9W28/zPHThwwDbxvFOnTronIuKUAohII7vjjjto3rw5Fy9e5MUXX7QNtyorK2P16tXs3r0bT09PHnzwwUauVADGjh2LwWBg7969fPjhh7bVe7Kysnj++ecxm820b9+e2267rZEr/c/1xRdfABAXF0d0dHS9+ui+XDnNmjWjd+/eALz11lskJydjtVqBqiGin3/+Oa+88goAvXr1omXLloDuiYg4ZrBW/18REWk0J0+eZM6cORQWFmI0GgkNDeX8+fOUlpZiNBp57LHHGDx4cGOX+avw6quvsmvXLoYMGcJjjz1mt83atWtZuXIlULWUaGBgILm5uVitViIiInjuueeIiIhwZ9nXDKvVyogRIygvL3e6iV21u+++m3vuuQfQfbmSfvrpJ/785z9z+vRpoGrehslkIi8vzxYsYmJi+Mtf/oLJZLL10z0REXs0CV3kKnDjjTfy6quv8tFHH3HgwAEKCgoIDAyka9eu3HfffbRv376xS5RLjBo1iujoaDZv3syJEycoLCykRYsW9O7dm3vvvVdD5RqgqKjINpeg+mmgM+fPn7e91n25cpo0acLLL7/Mli1bSEpKIjMzk7y8PAICArjhhhvo168fAwYMqDXZXPdEROzRExAREREREXEbzQERERERERG3UQARERERERG3UQARERERERG3UQARERERERG3UQARERERERG3UQARERERERG3UQARERERERG3UQARERERERG3UQARERERERG3UQARERERERG3UQARERERERG3UQARERERERG3UQARERERERG3UQAREZFfjXfffReDwYDBYOAf//hHjWO//e1vMRgMtG7dulFqExH5tVAAERG5SmRmZrJ27VpWrFjBV199hdVqrVe/yZMnExAQwI8//niFKxQREWk4z8YuQETk1+7ixYtMnTqV5cuXU1lZaXu/c+fOvP/++8TGxjrsm5aWxttvv82TTz7Jdddd545yRUREGkRPQEREGlFlZSX33Xcfy5YtqxE+AA4fPkzfvn05duyYw/7PPPMMwcHBzJw580qXKiIi4hIKICIijeitt95i27ZtAEyfPp0ffviB/Px8Vq9eTVhYGIWFhTz88MN2+x46dIiPPvqIp556iqCgIHeWLSIictk0BEtEpBG99dZbADz66KO88sortvd///vfExUVRf/+/dm7dy/ff/897dq1q9H3ySefJDIykj/84Q9urVlERKQh9ARERKQRHTlyBIAHH3yw1rG+ffty0003AXD06NEax/7xj3+wY8cO5s6di6+v7xWrbfLkybRp0wY/Pz+CgoLo0qULs2fP5syZMw0+/6effsrw4cNp0aIF3t7ehIaG0q9fP+Lj4ykpKXHa97PPPuOBBx6gZcuWeHt74+/vz4033sgDDzzAxo0b6z2B/5fIyclhzpw53HrrrQQFBeHj40NkZCRxcXGsWrXqilxTROSaZBURkUbj7+9vBaz79u2ze/ymm26yAtaPPvqoxvu9evWytmvXzlpeXn5F6nrxxRetnp6eVsDufwEBAdZNmzZd1rkvXLhgHTFihMNzA9Ybb7zRmpaWVqtvcXGx9Z577nHaF7D279/fWlBQUKv/O++8Y2uTmJhY41j//v2tgLVVq1a1+u3bt88aHh7u9JoDBw60ms3my/qdiIj8mmgIlohII+rYsSNff/017733Hr169apxbO/evZw4ccLWrtqGDRtISkpi3bp1eHh4uLyml156iaeeegoAb29vRo0aRZcuXSgtLWXnzp3s2rWL4uJiRo0axdGjR4mOjq73ua1WKyNGjGDr1q0AhIaGMm7cOG644QZyc3NZt24dx44d4+TJk8TFxfHtt9/i5eVl6z9jxgy2bNkCQLNmzfj9739PmzZtKC4u5vvvv2f9+vWYzWZ2797N9OnTeeeddxr8+8jPzycuLo7c3FyMRiP33HMPPXv2xGQycerUKTZs2EB6ejq7du3i0UcfZcWKFQ2+pojINa2xE5CIyK/Z0qVLrYDVYDBYn3jiCesPP/xgLSgosK5Zs8b2F/devXrZ2ldUVFg7dOhg7d69+xWp5/Dhw1YvLy8rYA0JCbEePHiwVptnnnnG9lf/hx9++Bed//XXX7f17datmzU/P7/G8dLSUuvtt99ua7Ny5UrbsZKSEquvr68VsEZHR1tzcnJqnT8jI8PaokULK2D19fW1lpWV1Th+OU9AFixYYOvz2muv1brmxYsXrYMHD7YCVqPRaP3+++9/0e9EROTXRnNAREQa0aRJkxg0aBBWq5VXXnmFG264gaZNmzJ69Giys7MJDAxk+fLltvYrVqwgNTWVF1988YrUs2DBAiwWCwB/+9vf6NKlS602f/nLX7jxxhsB2LhxY73PXVFRYavby8uL1atX07Rp0xptvL29eeONNzAYDLXOn5KSwsWLFwGYOXMmYWFhta5x/fXXM2bMGKBqf5Xs7Ox61+dIUlKS7fUDDzxQ67iPjw/z5s0DqpZV3rdvX4OvKSJyLVMAERFpRB4eHmzatImJEydiNNb8P8mxsbHs2bPHNvyqtLSUuXPncscdd3D77be7vJaSkhI+/vhjAJo0acJ//dd/2W3n5eXFiBEj8PHxoaioiHPnztXr/P/4xz84ffo0AEOGDKFt27Z220VHR9OjRw98fHxq7IESFhbG888/z/PPP8/dd9/t8DqXBpPqMNUQpaWlttdffPGF3TbdunXjyJEjHDlyxGltIiKiACIi0uj8/PxYvnw5p06dYs2aNbz77rskJSVx+PBhbrnlFlu7119/nYyMjBpPPyoqKli6dCndunXD39+fkJAQfve733HgwIFfXEdycrLty/agQYOczi956aWXuHjxIhcvXqR58+b1Ov+XX35pez1kyBCnbZOSkrh48aJtlTCA1q1b88wzz/DMM88QGRlpt19qaqpL5n1cqmfPnrbXY8aM4X//939JT0+v0cZoNNKpUyc6depk98mMiIj8myahi4hcJaKiouwO8QEoKirixRdfZOTIkdx6660AlJeXM2LECDZv3mxrd+HCBTZt2sSnn37Ke++9x+9///t6X//Spw0xMTGX+Sncc/5Tp06xa9cuDh8+zMmTJ/nhhx/44YcfMJvNDS2zlunTp7N27VpSU1MpKSlh7ty5zJ07l5tuuomBAwdyxx13MHjwYG0GKSJST3oCIiLyH+Cvf/0r+fn5trkGAHPnzmXz5s0YjUaeeOIJEhISWL9+Pbfddhvl5eVMmDCB1NTUel/jp59+sr2+En/Fd8X5T548yZ133knr1q156KGHiI+P55NPPuHo0aOYzWaCg4NrrBjmCkFBQXz55ZdMmTKFgIAA2/tpaWn8/e9/5/7776dZs2aMGjWK7777zqXXFhG5FimAiIhc5XJycli4cCETJkyw7YZeVFRk2zl96dKl/O1vf2PgwIEMHz6cPXv2cNttt1FaWsqCBQvqfZ1L50v4+fm59kO44PxHjx6lS5cu7NixA6iap3LXXXcxa9Ys3njjDZKSksjOzmbkyJEuq7lacHAw8fHxZGdns2nTJh5//HE6depkmyxfVlbGRx99RM+ePfnmm29cfn0RkWuJhmCJiFzl5s2bh8ViYc6cObb39u3bx4ULFwgPD2fSpEk12nt6evLnP/+Ze+65h4SEhHpfp0mTJrbXeXl5Da67rvP/kv1DoGoPkPPnzwNVq2A999xzdneBt17BHcn9/PwYNmwYw4YNAyA7O5tPPvmE+Ph4jhw5QlFREX/+85/59NNPr1gNIiL/6fQERETkKnbq1CneeOMNHn/8caKiomzv5+bmAtCyZUvbX+Ev1apVK6Dq6Ul9XTov4/Dhw07bPvfcc7Ru3ZrWrVvXe6nbX3L+oUOH0rp1awYOHAhUrUS1a9cuANq3b89LL71kN3wALll6t9qFCxdYt24d69atIy0trdbx8PBwJk6cyJdffknr1q2Bqg0kRUTEMQUQEZGr2F/+8hf8/PxsO5NXa9asGQDff/89Fy5cqNXv4MGDQNUX5Prq2bMn/v7+AGzbto2SkhK77SorK1mxYgWnTp3i4sWL9b5GdZgAWL9+vcN26enpfPbZZ5w6dcp27ry8PNsQLkfL91bXVj1EyxXKysq4//77uf/++1m0aJHDdgEBAXTv3t3WR0REHFMAERG5SqWkpLBy5UpmzpxJSEhIjWO33XYb/v7+FBUVMXPmzBrDjrKysnj22WcBftF+If7+/rZN/AoKCvjrX/9qt93ixYs5efIkACNGjKj3+fv162cLDzt27GD37t122z3xxBO219XnDw4Otu2TcvjwYYf7ezz11FO22lwhODiYG264AYC1a9fahoD93MWLF/nqq68AXD4JXkTkWqMAIiJylXr66adp1qwZ06ZNq3UsICDA9kX9tddeo1+/frz44ovMmjWLm2++mYyMDHx8fJg9e/YvuubcuXMJDg4GqoZZPf/887bVq0pKSnjttdds5/Tx8akRFupiMBj429/+Zvt5xIgRrFmzxhYmfvzxRx599FE2bNgAVA21+t3vfgeAyWTiN7/5DVA1LG3cuHGcPXsWqBqetXXrVm6//XZeeumlGitVZWZmUllZ+Yt+Bz83fvx4oGpo129/+1sSEhJsNVssFhISEhg8eDCnTp0CqDUnR0REfsYqIiJXnX379lkB65IlSxy2sVgs1mHDhlmBWv95enpaV61adVnX3rJli9XLy8t2LoPBYG3atKnV09OzxjXefPPNyzr/008/XavWpk2bWg0Gg+29oKAg68GDB2v027t3b426AGtwcHCNft26dbN+/vnntp+9vb2tJpPJdo533nnHdiwxMbHG+fv3728FrK1atarxvtlstnbo0KFWzSEhIVaj0Vjj/aFDh1rLy8sv6/ciIvJroScgIiJXoSeffJIbb7yRRx55xGEbT09PPv74Y15//XVuueUWfH19adKkCcOGDePLL7+0Daf6pe6++24SEhJsk8atVisFBQWUl5cDVXMwtm7dysMPP3xZ53/hhRf4+9//TtOmTYGqDRULCgpsw8gGDhzI119/TZcuXWr06927Nxs3bqwx56SwsBCr1UpwcDDPPPMMX375JXfccQe9evUCquZjNHRzQn9/f3bv3s2IESNsw8DKy8vJz8+3PV0xmUzMmjWLDRs2ON1BXkREwGC1XsH1CkVE5BfLy8tj8eLFDBw4kH79+jVaHVarlaSkJA4dOsRPP/1E06ZN6dq1Kz169LC78tYvVVpaSkJCAsePH+fChQtERETQp08fbrrpJqf9SkpK2L59O8ePH8fX15c2bdpw++2319hbpLi4mFWrVpGbm0vbtm25//77G1wvwNmzZ9m7dy+nT5+mpKSEoKAgbrrpJn7zm99oJ3QRkXpSABEREREREbfRECwREREREXEbBRAREREREXEbBRAREREREXEbBRAREREREXEbBRAREREREXEbBRAREREREXEbBRAREREREXEbBRAREREREXEbBRAREREREXEbBRAREREREXEbBRAREREREXEbBRAREREREXGb/weOlc04CU5YGQAAAABJRU5ErkJggg=="
     },
     "metadata": {
      "image/png": {
       "height": 400,
       "width": 400
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/uqztuong/Documents/GitHub/dandelion/src/dandelion/base/preprocessing/_preprocessing.py:4234: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n"
     ]
    },
    {
     "data": {
      "image/png": "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"
     },
     "metadata": {
      "image/png": {
       "height": 400,
       "width": 400
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/uqztuong/Documents/GitHub/dandelion/src/dandelion/base/preprocessing/_preprocessing.py:4234: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n"
     ]
    }
   ],
   "source": [
    "ddl.pp.assign_isotypes(samples, filename_prefix=\"filtered\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c570ec3a",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "Note\n",
    "    \n",
    "Should you want to use a different reference fasta file for this step, run with the following option:\n",
    "\n",
    "```python\n",
    "ddl.pp.assign_isotypes(samples, blastdb=\"path/to/custom_BCR_constant.fasta\")\n",
    "```\n",
    "\n",
    "The default option will return a summary plot that can be disabled with `plot=False`.\n",
    "</div>\n",
    "    \n",
    "Finally, it's worthwhile to manually check the the sequences for constant calls returned as IGHA1-2, IGHG1-4 and the light chains and manually correct them if necessary.\n",
    "\n",
    "## Step 5: Quantify mutations *(optional)*.\n",
    "\n",
    "At this stage, with all the necessary columns in the files, you can quantify the basic mutational load with `pp.quantify_mutations`, a wrapper of `SHaZaM`'s basic mutational analysis in R [[Gupta2015]](https://academic.oup.com/bioinformatics/article/31/20/3356/195677), before subsequent analyses. This will be covered again later in the `Calculating diversity and mutation` section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e95fe10d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Basic mutational load analysis : 100%|██████████| 4/4 [00:19<00:00,  4.82s/it]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "# quantify mutations\n",
    "for s in tqdm(samples, desc=\"Basic mutational load analysis \"):\n",
    "    filePath = s + \"/dandelion/filtered_contig_dandelion.tsv\"\n",
    "    ddl.pp.quantify_mutations(filePath)\n",
    "    # or dandelion.external.immcantation.shazam.quantify_mutations(filePath)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dandelion",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}