From 8a6b2d4a1ff26b01e38d7a3e51f57d3f08db88d9 Mon Sep 17 00:00:00 2001 From: Ece Okur Date: Mon, 17 Aug 2026 13:14:40 +0200 Subject: [PATCH 1/2] HADDOCK3 protein-peptide notebook (not finished yet) --- notebooks/HADDOCK3-protein-peptide.ipynb | 13252 +++++++++++++++++++++ 1 file changed, 13252 insertions(+) create mode 100644 notebooks/HADDOCK3-protein-peptide.ipynb diff --git a/notebooks/HADDOCK3-protein-peptide.ipynb b/notebooks/HADDOCK3-protein-peptide.ipynb new file mode 100644 index 000000000..ba98628c0 --- /dev/null +++ b/notebooks/HADDOCK3-protein-peptide.ipynb @@ -0,0 +1,13252 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "db4d0a6e-1024-4975-80de-f491a84b2e6d", + "metadata": {}, + "source": [ + "# Protein-peptide modeling tutorial using a local version of HADDOCK3\n" + ] + }, + { + "cell_type": "markdown", + "id": "43126a32-2c1c-40b6-8a5d-5c72f4b5c4a8", + "metadata": {}, + "source": [ + "---\n", + "## Introduction\n", + "\n", + "In this tutorial, we will focus on docking the N-terminal peptide of p53 to the mouse MDM2 protein. The tumor suppressor p53 is often referred to as the guardian of the genome because of its central role in preventing tumor development. As a transcription factor, it regulates the cell cycle and can induce DNA repair or apoptosis in response to cellular stress. MDM2, an E3 ubiquitin ligase, is a key negative regulator of p53: by binding to its N-terminal transactivation domain (TAD) peptide, it inhibits p53 activity and promotes its degradation. The p53–MDM2 interaction is therefore of major biological and medical importance.\n", + "\n", + "For the mouse MDM2–p53 system, no experimental structure of the complex is available. Modelling such interactions is challenging because protein–peptide docking is generally more difficult than protein–protein docking. This is primarily due to the intrinsic flexibility of peptides, which allows them to adopt multiple conformations, complicating prediction of the bound state.\n", + "\n", + "In this tutorial, we will use HADDOCK3 to model the MDM2–p53 complex. Docking will be guided by pre-defined restraints derived from known interfaces of the human homolog of MDM2. As input structures, we will use an AlphaFold model of mouse MDM2 together with an ensemble of idealized peptide conformations. This combination will allow us to sample and predict plausible binding modes for the p53 peptide. Since no experimentally solved structure is available this complex, the human MDM2–p53 complex will be used as a reference for assessing the docking results (PDB ID: [1YCR](https://www.rcsb.org/structure/1YCR)).\n", + "\n", + "\n", + "
\n", + " \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "0da86c64-b77d-4842-9cae-88a5a9318966", + "metadata": {}, + "source": [ + "---\n", + "# **A brief introduction to HADDOCK3**\n", + "\n", + "HADDOCK3 is the next generation integrative modelling software in the\n", + "long-lasting HADDOCK project. It represents a complete rethinking and rewriting\n", + "of the HADDOCK2.X series, implementing a new way to interact with HADDOCK and\n", + "offering new features to users who can now define custom workflows.\n", + "\n", + "In the previous HADDOCK2.x versions, users had access to a highly\n", + "parameterisable yet rigid simulation pipeline composed of three steps:\n", + "`rigid-body docking (it0)`, `semi-flexible refinement (it1)`, and `final refinement (itw)`.\n", + "\n", + "
\n", + "\n", + "
\n", + "\n", + "In HADDOCK3, users have the freedom to configure docking workflows into\n", + "functional pipelines by combining the different HADDOCK3 modules, thus\n", + "adapting the workflows to their projects. HADDOCK3 has therefore developed to\n", + "truthfully work like a puzzle of many pieces (simulation modules) that users can\n", + "combine freely. To this end, the old HADDOCK machinery has been modularized,\n", + "and several new modules added, including third-party software additions. As a\n", + "result, the modularization achieved in HADDOCK3 allows users to duplicate steps\n", + "within one workflow (e.g., to repeat twice the `it1` stage of the HADDOCK2.x\n", + "rigid workflow).\n", + "Note that, for simplification purposes, at this time, not all functionalities of\n", + "HADDOCK2.x have been ported to HADDOCK3, which does not (yet) support NMR RDC,\n", + "PCS and diffusion anisotropy restraints, cryo-EM restraints and coarse-graining.\n", + "Any type of information that can be converted into ambiguous interaction\n", + "restraints can, however, be used in HADDOCK3, which also supports the\n", + "*ab initio* docking modes of HADDOCK.\n", + "\n", + "
\n", + "\n", + "
\n", + "\n", + "To keep HADDOCK3 modules organized, we catalogued them into several\n", + "categories. However, there are no constraints on piping modules of different\n", + "categories.\n", + "\n", + "The main module categories are \"topology\", \"sampling\", \"refinement\",\n", + "\"scoring\", and \"analysis\". There is no limit to how many modules can belong to a\n", + "category. Modules are added as developed, and new categories will be created\n", + "if/when needed. You can access the HADDOCK3 documentation page for the list of\n", + "all categories and modules. Below is a summary of the available modules:\n", + "\n", + "* **Topology modules**\n", + " * `topoaa`: *generates the all-atom topologies for the CNS engine.*\n", + "* **Sampling modules**\n", + " * `rigidbody`: *Rigid body energy minimization with CNS (`it0` in haddock2.x).*\n", + " * `lightdock`: *Third-party glow-worm swam optimization docking software.*\n", + "* **Model refinement modules**\n", + " * `flexref`: *Semi-flexible refinement using a simulated annealing protocol through molecular dynamics simulations in torsion angle space (`it1` in haddock2.x).*\n", + " * `emref`: *Refinement by energy minimisation (`itw` EM only in haddock2.4).*\n", + " * `mdref`: *Refinement by a short molecular dynamics simulation in explicit solvent (`itw` in haddock2.X).*\n", + "* **Scoring modules**\n", + " * `emscoring`: *scoring of a complex performing a short EM (builds the topology and all missing atoms).*\n", + " * `mdscoring`: *scoring of a complex performing a short MD in explicit solvent + EM (builds the topology and all missing atoms).*\n", + "* **Analysis modules**\n", + " * `alascan`: *Performs a systematic (or user-define) alanine scanning mutagenesis of interface residues.*\n", + " * `caprieval`: *Calculates CAPRI metrics (i-RMSD, l-RMSD, Fnat, DockQ) with respect to the top-scoring model or reference structure if provided.*\n", + " * `clustfcc`: *Clusters models based on the fraction of common contacts (FCC)*\n", + " * `clustrmsd`: *Clusters models based on pairwise RMSD matrix calculated with the `rmsdmatrix` module.*\n", + " * `contactmap`: *Generate contact matrices of both intra- and intermolecular contacts and a chordchart of intermolecular contacts.*\n", + " * `rmsdmatrix`: *Calculates the pairwise RMSD matrix between all the models generated in the previous step.*\n", + " * `ilrmsdmatrix`: *Calculates the pairwise interface-ligand-RMSD (il-RMSD) matrix between all the models generated in the previous step.*\n", + " * `seletop`: *Selects the top N models from the previous step.*\n", + " * `seletopclusts`: *Selects the top N clusters from the previous step.*\n", + "\n", + "The HADDOCK3 workflows are defined in simple configuration text files, similar to the TOML format but with extra features.\n", + "Contrary to HADDOCK2.X which follows a rigid (yet highly parameterisable)\n", + "procedure, in HADDOCK3, you can create your own simulation workflows by\n", + "combining a multitude of independent modules that perform specialized tasks.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "0491aa10-132d-459c-baaf-1671a91f099b", + "metadata": {}, + "source": [ + "---\n", + "# **Software and data setup**\n", + "\n", + "In order to follow this tutorial we will start Jupyter session in Colab or other resources, install haddock3 and download the data for the tutorial.\n", + "\n", + "To run this tutorial on Colab we recommend changing the runtime type to use one of the TPU option, which will give you access to more cores. The haddock3 docking run described in this tutorial should complete in about 15 minutes.\n", + "\n", + "For this follow the following steps (comment out the `pip install haddock3` line if you already have a working installation):" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "7d2f5743-f3f3-4112-b0c1-c9c5d78818ac", + "metadata": {}, + "outputs": [], + "source": [ + "#@title 1. Install haddock3, BioPython and py3Dmol\n", + "#@markdown Execute this cell to download the required packages.\n", + "!pip install haddock3==2026.7.0 --quiet\n", + "!pip install py3Dmol --quiet\n", + "!pip install BioPython --quiet" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "419e06d6-e441-48bc-b812-aaea09b9a1d1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2026-08-17 14:05:47-- https://surfdrive.surf.nl/public.php/dav/files/3GE8k07b8EtuVK8\n", + "surfdrive.surf.nl (surfdrive.surf.nl) çözümleniyor... 2001:610:10a:2:0:a11:da7a:5afe, 2001:610:10b:2:0:a11:da7a:5afe, 145.107.56.140, ...\n", + "surfdrive.surf.nl (surfdrive.surf.nl)[2001:610:10a:2:0:a11:da7a:5afe]:443 bağlanılıyor... bağlantı kuruldu.\n", + "HTTP isteği gönderildi, yanıt bekleniyor... 200 OK\n", + "Uzunluk: 241474807 (230M) [application/zip]\n", + "Kayıt yeri: `HADDOCK3-protein-peptide.zip'\n", + "\n", + "HADDOCK3-protein-pe 100%[===================>] 230,29M 502KB/s içinde 6m 44s\n", + "\n", + "2026-08-17 14:12:31 (584 KB/s) - `HADDOCK3-protein-peptide.zip' kaydedildi [241474807/241474807]\n", + "\n", + "Archive: HADDOCK3-protein-peptide.zip\n", + " creating: HADDOCK3-protein-peptide/\n", + " creating: HADDOCK3-protein-peptide/workflows/\n", + " inflating: HADDOCK3-protein-peptide/workflows/protein_peptide_docking_best_practices.cfg \n", + " inflating: HADDOCK3-protein-peptide/workflows/protein_peptide_docking.cfg \n", + " creating: HADDOCK3-protein-peptide/restraints/\n", + " inflating: HADDOCK3-protein-peptide/restraints/peptide-ens-passive.act-pass \n", + " extracting: HADDOCK3-protein-peptide/restraints/protein-AR3D-active.act-pass \n", + " inflating: HADDOCK3-protein-peptide/restraints/protein-peptide_ambig.tbl \n", + " creating: HADDOCK3-protein-peptide/runs/\n", + " creating: HADDOCK3-protein-peptide/runs/run_bp/\n", + " creating: HADDOCK3-protein-peptide/runs/run_bp/05_emref/\n", + " inflating: 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\n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/05_emref/emref_446.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/05_emref/emref_808.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/05_emref/emref_793.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/05_emref/emref_57.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/05_emref/emref_389.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/05_emref/emref_2502.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/05_emref/emref_2325.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/05_emref/emref_2118.pdb.gz \n", + " creating: HADDOCK3-protein-peptide/runs/run_bp/08_caprieval/\n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/08_caprieval/blosum62_B.aln \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/08_caprieval/capri_clt.tsv \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/08_caprieval/weights_params.json \n", + 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\n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/01_rigidbody/rigidbody_2138.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/01_rigidbody/rigidbody_2884.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/01_rigidbody/rigidbody_2522.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/01_rigidbody/rigidbody_2305.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/01_rigidbody/rigidbody_1940.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/01_rigidbody/rigidbody_1329.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/01_rigidbody/rigidbody_1733.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/01_rigidbody/rigidbody_1114.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/01_rigidbody/rigidbody_630.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/01_rigidbody/rigidbody_843.pdb.gz \n", + " inflating: 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HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_3_model_3.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/seletopclusts.txt \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_4_model_3.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_7_model_4.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_10_model_4.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_6_model_1.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_8_model_3.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_10_model_2.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_1_model_1.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_7_model_2.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_5_model_4.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_6_model_3.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_8_model_1.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_9_model_2.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_1_model_3.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_2_model_4.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_7_model_3.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_4_model_4.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_9_model_1.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_10_model_3.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_8_model_2.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_3_model_4.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_7_model_1.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_10_model_1.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_9_model_3.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_1_model_2.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_6_model_2.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_2_model_3.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_1_model_4.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_6_model_4.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_5_model_3.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_2_model_1.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_4_model_2.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_5_model_1.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_8_model_4.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_3_model_2.pdb.gz \n", + " creating: HADDOCK3-protein-peptide/runs/run_bp/04_caprieval/\n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/04_caprieval/params.cfg \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/04_caprieval/blosum62_A.aln \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/04_caprieval/capri_ss.tsv \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/04_caprieval/weights_params.json \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/04_caprieval/blosum62_B.aln \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/04_caprieval/capri_clt.tsv \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/04_caprieval/io.json \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/04_caprieval/blosum62.izone \n", + " creating: HADDOCK3-protein-peptide/runs/run_bp/06_caprieval/\n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/06_caprieval/capri_ss.tsv \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/06_caprieval/capri_clt.tsv \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/06_caprieval/blosum62_A.aln \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/06_caprieval/weights_params.json \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/06_caprieval/params.cfg \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/06_caprieval/blosum62.izone \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/06_caprieval/blosum62_B.aln \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/06_caprieval/io.json \n", + " creating: HADDOCK3-protein-peptide/runs/run_bp/00_topoaa/\n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/00_topoaa/AF_MDM2_26_109_1_haddock.psf.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/00_topoaa/AF_MDM2_26_109_1.pdb.gz \n", + " extracting: HADDOCK3-protein-peptide/runs/run_bp/00_topoaa/peptide_helix_from_peptide_ens_1_haddock.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/00_topoaa/params.cfg \n", + " extracting: HADDOCK3-protein-peptide/runs/run_bp/00_topoaa/peptide_ens_2.pdb.gz \n", + " extracting: HADDOCK3-protein-peptide/runs/run_bp/00_topoaa/peptide_sheet_from_peptide_ens_3_haddock.psf.gz \n", + " extracting: HADDOCK3-protein-peptide/runs/run_bp/00_topoaa/peptide_ppii_from_peptide_ens_2_haddock.psf.gz \n", + " extracting: HADDOCK3-protein-peptide/runs/run_bp/00_topoaa/peptide_ens_1.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/00_topoaa/io.json \n", + " extracting: HADDOCK3-protein-peptide/runs/run_bp/00_topoaa/peptide_ens_3.pdb.gz \n", + " extracting: HADDOCK3-protein-peptide/runs/run_bp/00_topoaa/peptide_ppii_from_peptide_ens_2_haddock.pdb.gz \n", + " extracting: HADDOCK3-protein-peptide/runs/run_bp/00_topoaa/peptide_sheet_from_peptide_ens_3_haddock.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/00_topoaa/AF_MDM2_26_109_1_haddock.pdb.gz \n", + " extracting: HADDOCK3-protein-peptide/runs/run_bp/00_topoaa/peptide_helix_from_peptide_ens_1_haddock.psf.gz \n", + " creating: HADDOCK3-protein-peptide/runs/run_bp/02_caprieval/\n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/02_caprieval/blosum62_B.aln \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/02_caprieval/blosum62.izone \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/02_caprieval/capri_clt.tsv \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/02_caprieval/capri_ss.tsv \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/02_caprieval/io.json \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/02_caprieval/params.cfg \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/02_caprieval/blosum62_A.aln \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/02_caprieval/weights_params.json \n", + " creating: HADDOCK3-protein-peptide/runs/run_bp/09_rmsdmatrix/\n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/09_rmsdmatrix/params.cfg \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/09_rmsdmatrix/rmsd.matrix \n", + " extracting: HADDOCK3-protein-peptide/runs/run_bp/09_rmsdmatrix/rmsd_0.out.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/09_rmsdmatrix/rmsd_matrix.json \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/09_rmsdmatrix/io.json \n", + " creating: HADDOCK3-protein-peptide/runs/run_bp/11_caprieval/\n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/11_caprieval/blosum62.izone \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/11_caprieval/capri_ss.tsv \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/11_caprieval/blosum62_B.aln \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/11_caprieval/weights_params.json \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/11_caprieval/blosum62_A.aln \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/11_caprieval/capri_clt.tsv \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/11_caprieval/params.cfg \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/11_caprieval/io.json \n", + " creating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/\n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2069.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2254.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2473.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2181.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_958.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_516.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_331.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1238.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1851.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1622.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1005.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2500.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2327.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_55.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_242.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_465.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_197.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_658.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1922.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1484.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1176.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1751.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_425.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_202.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_618.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_983.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_15.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2695.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2367.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2540.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1962.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1711.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1136.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_683.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_918.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_371.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_556.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_299.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2029.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2995.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2433.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2214.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1278.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1811.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1045.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1662.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1390.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1791.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1444.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1263.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1679.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_95.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2032.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2615.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2866.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2428.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_698.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_903.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_770.pdb.gz \n", + " inflating: 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\n", + " inflating: HADDOCK3-protein-peptide/runs/run1/analysis/08_caprieval_analysis/bsa_clt.html \n", + " inflating: HADDOCK3-protein-peptide/runs/run1/analysis/08_caprieval_analysis/lrmsd_score.html \n", + " inflating: HADDOCK3-protein-peptide/runs/run1/analysis/08_caprieval_analysis/fnat_clt.html \n", + " inflating: HADDOCK3-protein-peptide/runs/run1/analysis/08_caprieval_analysis/ilrmsd_air.html \n", + " inflating: HADDOCK3-protein-peptide/runs/run1/analysis/08_caprieval_analysis/dockq_air.html \n", + " inflating: HADDOCK3-protein-peptide/runs/run1/analysis/08_caprieval_analysis/fnat_vdw.html \n", + " inflating: HADDOCK3-protein-peptide/runs/run1/analysis/08_caprieval_analysis/capri_clt.tsv \n", + " inflating: HADDOCK3-protein-peptide/runs/run1/analysis/08_caprieval_analysis/ilrmsd_elec.html \n", + " inflating: HADDOCK3-protein-peptide/runs/run1/analysis/08_caprieval_analysis/elec_clt.html \n", + " inflating: 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" inflating: HADDOCK3-protein-peptide/runs/run1/analysis/06_caprieval_analysis/irmsd_vdw.html \n", + " inflating: HADDOCK3-protein-peptide/runs/run1/analysis/06_caprieval_analysis/ilrmsd_air.html \n", + " inflating: HADDOCK3-protein-peptide/runs/run1/analysis/06_caprieval_analysis/lrmsd_vdw.html \n", + " inflating: HADDOCK3-protein-peptide/runs/run1/analysis/06_caprieval_analysis/lrmsd_desolv.html \n", + " inflating: HADDOCK3-protein-peptide/runs/run1/analysis/06_caprieval_analysis/irmsd_score.html \n", + " inflating: HADDOCK3-protein-peptide/runs/run1/analysis/06_caprieval_analysis/lrmsd_clt.html \n", + " inflating: HADDOCK3-protein-peptide/runs/run1/analysis/06_caprieval_analysis/ilrmsd_clt.html \n", + " inflating: HADDOCK3-protein-peptide/runs/run1/analysis/06_caprieval_analysis/report.html \n", + " inflating: HADDOCK3-protein-peptide/runs/run1/analysis/06_caprieval_analysis/dockq_desolv.html \n", + " inflating: HADDOCK3-protein-peptide/runs/run1/analysis/06_caprieval_analysis/dockq_elec.html \n", + " inflating: HADDOCK3-protein-peptide/runs/run1/analysis/06_caprieval_analysis/fnat_clt.html \n", + " inflating: HADDOCK3-protein-peptide/runs/run1/analysis/06_caprieval_analysis/capri_clt.tsv \n", + " inflating: HADDOCK3-protein-peptide/runs/run1/analysis/06_caprieval_analysis/fnat_vdw.html \n", + " inflating: HADDOCK3-protein-peptide/runs/run1/analysis/06_caprieval_analysis/ilrmsd_vdw.html \n", + " inflating: HADDOCK3-protein-peptide/runs/run1/analysis/06_caprieval_analysis/irmsd_air.html \n", + " inflating: HADDOCK3-protein-peptide/runs/run1/analysis/06_caprieval_analysis/lrmsd_air.html \n", + " inflating: HADDOCK3-protein-peptide/runs/run1/analysis/06_caprieval_analysis/fnat_score.html \n", + " inflating: HADDOCK3-protein-peptide/runs/run1/analysis/06_caprieval_analysis/summary.tgz \n", + " inflating: HADDOCK3-protein-peptide/runs/run1/analysis/06_caprieval_analysis/lrmsd_score.html \n", + " creating: HADDOCK3-protein-peptide/runs/run1/07_seletop/\n", + " inflating: HADDOCK3-protein-peptide/runs/run1/07_seletop/io.json \n", + " inflating: HADDOCK3-protein-peptide/runs/run1/07_seletop/params.cfg \n", + " creating: HADDOCK3-protein-peptide/runs/run1/00_topoaa/\n", + " extracting: HADDOCK3-protein-peptide/runs/run1/00_topoaa/peptide_ppii_from_peptide_ens_2_haddock.psf.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run1/00_topoaa/io.json \n", + " extracting: HADDOCK3-protein-peptide/runs/run1/00_topoaa/peptide_helix_from_peptide_ens_1_haddock.pdb.gz \n", + " inflating: HADDOCK3-protein-peptide/runs/run1/00_topoaa/AF_MDM2_26_109_1_haddock.pdb.gz \n", + " extracting: HADDOCK3-protein-peptide/runs/run1/00_topoaa/peptide_ens_1.pdb.gz \n", + " extracting: HADDOCK3-protein-peptide/runs/run1/00_topoaa/peptide_sheet_from_peptide_ens_3_haddock.psf.gz \n", + " extracting: HADDOCK3-protein-peptide/runs/run1/00_topoaa/peptide_ens_3.pdb.gz \n", + " extracting: 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'/Users/eceokur/Downloads/HADDOCK3-protein-peptide/HADDOCK3-protein-peptide-notebook'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mFileNotFoundError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[15]\u001b[39m\u001b[32m, line 11\u001b[39m\n\u001b[32m 7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m pathlib \u001b[38;5;28;01mimport\u001b[39;00m Path\n\u001b[32m 8\u001b[39m BASE_DIR = Path.cwd()\n\u001b[32m 9\u001b[39m PROJECT_NAME = \u001b[33m'HADDOCK3-protein-peptide-notebook'\u001b[39m\n\u001b[32m 10\u001b[39m PROJECT_DIR = BASE_DIR / PROJECT_NAME\n\u001b[32m---> \u001b[39m\u001b[32m11\u001b[39m os.chdir(PROJECT_DIR)\n", + "\u001b[31mFileNotFoundError\u001b[39m: [Errno 2] No such file or directory: '/Users/eceokur/Downloads/HADDOCK3-protein-peptide/HADDOCK3-protein-peptide-notebook'" + ] + } + ], + "source": [ + "#@title 1. Download tutorial data and setup environment\n", + "#@markdown Execute this cell to download the tutorial data.\n", + "!wget https://surfdrive.surf.nl/public.php/dav/files/3GE8k07b8EtuVK8 -O HADDOCK3-protein-peptide.zip\n", + "!unzip HADDOCK3-protein-peptide.zip\n", + "!\\rm HADDOCK3-protein-peptide.zip\n", + "import os\n", + "from pathlib import Path\n", + "BASE_DIR = Path.cwd()\n", + "PROJECT_NAME = 'HADDOCK3-protein-peptide-notebook'\n", + "PROJECT_DIR = BASE_DIR / PROJECT_NAME\n", + "os.chdir(PROJECT_DIR)" + ] + }, + { + "cell_type": "markdown", + "id": "77ac7552-8805-418c-847a-54ec08369123", + "metadata": {}, + "source": [ + "ecompressing the file will create the `HADDOCK3-protein-peptide` directory with the following subdirectories and items:\n", + "\n", + "* `pdbs`: Contains the pre-processed PDB files, both the docking input, and bound reference.\n", + "* `restraints`: Contains the interface information and the correspond restraint files for HADDOCK.\n", + "* `runs`: : Contains pre-computed docking results for each scenario defined in `workflows` directory, useful for comparison with your own run, or if you prefer to skip the computationally intensive steps.\n", + "* `scripts`: Contains two analysis scripts used in this tutorial.\n", + "* `workflows`: Contains HADDOCK3 workflow used for the docking.\n", + "----\n" + ] + }, + { + "cell_type": "markdown", + "id": "eb32c46b-2c37-44d4-bcd2-f8420a4c1976", + "metadata": {}, + "source": [ + "---\n", + "# Preparing PDB files for docking\n", + "\n", + "The accuracy of docking results in HADDOCK3 depends heavily on the quality of the input structures. In this section, we will prepare the PDB files of the protein and peptide for docking. The protein model is created using AlphaFold, and multiple conformations of the peptide are generated using [PyMOL](https://www.pymol.org/). We will use `pdb-tools`to manipulate the structures to make them HADDOCK-ready, e.g. to renumber residues, assign chain IDs and generate ensemble file. By default, `pdb-tools` are being installed on your machine together with haddock3. `pdb-tools` documentation is available [here](https://www.bonvinlab.org/pdb-tools/).\n", + "\n", + "Note: that pdb-tools is also available as a [web service](https://wenmr.science.uu.nl/pdbtools/).\n", + "\n", + "Note: Before starting to work on the tutorial, make sure to activate an appropriate virtual environment. If haddock3 was installed using `conda`run the following cell:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "cca074f3-a274-418a-8f23-2bd143004d16", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "CondaError: Run 'conda init' before 'conda activate'\n", + "\n" + ] + } + ], + "source": [ + "! conda activate haddock3" + ] + }, + { + "cell_type": "markdown", + "id": "ad173eab-8c81-435b-b993-21c7c33860f9", + "metadata": {}, + "source": [ + "For more information about accepted file formats and preparation steps, refer to the [HADDOCK3 user manual – structure requirements](https://www.bonvinlab.org/haddock3-user-manual/structure_requirements.html)." + ] + }, + { + "cell_type": "markdown", + "id": "2e53546c-fef6-421d-ba9d-2f1570dc2e95", + "metadata": {}, + "source": [ + "# Preparing the protein structure\n", + "\n", + "One of the easiest ways to find information about a protein, including its structure, is to explore the UniProt database. [UniProt](https://www.uniprot.org/) provides detailed protein sequence and functional data, including annotations, sequence features, and links to structural information.\n", + "\n", + "__*Open the UniProt home page (https://www.uniprot.org) and search for “mouse MDM2”.*__\n", + "\n", + "You should see the entry “P23804 · MDM2_MOUSE”. Feel free to take you time and explore the page.\n", + "\n", + "__*Click on the section ‘Structure’ (left side of the page) or scroll down until you reach it.*__\n", + "\n", + "This protein has no experimentally solved 3D structure, only AlphaFold model is available. This model model covers the full-length sequence of MDM2, but for docking we only need its p53-binding domain. This domain corresponds to residues 26 to 109. Check out [Family & Domains section](https://www.uniprot.org/uniprotkb/P23804/entry#family_and_domains) of the UniProt to see all other regions of the protein. The remaining regions, particularly the disordered one, are known not to interact with the peptide, so it’s a good idea remove them, both to make the docking problem easier, and to reduce the computational cost of the docking.\n", + "\n", + "__*Click the download icon at the right end of the yellow ribbon to obtain this model. At the time this tutorial was created, the file name was AF-P23804-F1-model_v6.pdb. With future updates to the AlphaFold Database, the version tag “v6” will change.*__\n", + "\n", + "__*Move downloaded model AF-P23804-F1-model_v4.pdb to your work directory, e.g. HADDOCK3-protein-peptide/*__\n", + "\n", + "To prepare this model for docking, we will:\n", + "\n", + "1 Keep only the coordinates lines, i.e and remove REMARK and other irrelevant lines (`pdb_keepcoord`),\n", + "2. Keep only residues of interest (`pdb_selres`),\n", + "3. Assign chain ID (`pdb_chain`), and\n", + "4. Apply pdb-formatting to the file (`pdb_tidy`).\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "bf5331f7-16c0-40dd-8c63-346f8658125c", + "metadata": {}, + "outputs": [], + "source": [ + "! pdb_keepcoord AF-P23804-F1-model_v6.pdb | pdb_selres -26:109 | pdb_chain -A | pdb_tidy -strict > AF_MDM2_26_109.pdb\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "baacb3f7-a666-4ec1-98e5-448ccae71bb3", + "metadata": {}, + "source": [ + "*Note* when working with the experimentally solved structures, preparation algorithm would be different, e.g. we would use `pdb_delhetatm` and `pdb_selaltloc`.\n", + "\n", + "*Note* `pdb_tidy` attempts to correct formatting only, e.g. ensure each line is long enough (padded with spaces), not the actual content of the PDB file.\n", + "\n", + "It’s a good practice to verify resulting structure visually using PyMOL. Start PyMOL and load the PDB file as followed:\n", + "\n", + "__*File menu -> Open -> select HADDOCK3-protein-peptide/AF_MDM2_26_109.pdb*__\n", + "\n", + "Feel free to compare this structure with initial AlphaFold model:\n", + "__*File menu -> Open -> select HADDOCK3-protein-peptide/AF-P23804-F1-model_v4.pdb*__" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "c665c7dc-a129-4b46-90ad-e539a41b7b9e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AF-P23804-F1-model_v6.pdb \u001b[34mpdbs\u001b[m\u001b[m \u001b[34mscripts\u001b[m\u001b[m\n", + "AF_MDM2_26_109.pdb \u001b[34mrestraints\u001b[m\u001b[m \u001b[34mworkflows\u001b[m\u001b[m\n", + "\u001b[34mHADDOCK3-protein-peptide\u001b[m\u001b[m \u001b[34mruns\u001b[m\u001b[m\n" + ] + } + ], + "source": [ + "! ls" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "b5da13f0-0ab5-4d5e-a7ec-9eb78df8528c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error: File not found at /Users/eceokur/Downloads/HADDOCK3-protein-peptide/HADDOCK3-protein-peptide-notebook/AF_MDM2_26_109.pdb\n", + "Error: File not found at /Users/eceokur/Downloads/HADDOCK3-protein-peptide/HADDOCK3-protein-peptide-notebook/AF-P23804-F1-model_v6.pdb\n", + "Failed to load one or both PDB files\n" + ] + }, + { + "data": { + "text/plain": [ + "(, None, {})" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from haddock.libs.libnotebooks import align_full\n", + "#@title View the top ranking model from the best cluster\n", + "#@markdown It will be shown superimposed onto the reference structure fitted only on the second receptor and the DNA\n", + "# Define paths to PDB files to be aligned\n", + "# Here we are overlaying the reference and 1st model from the 1st cluster\n", + "path1 = PROJECT_DIR / \"AF_MDM2_26_109.pdb\"\n", + "path2 = PROJECT_DIR / \"AF-P23804-F1-model_v6.pdb\"\n", + "\n", + "# Align molecules by chain and display the result.\n", + "# For the full alignement put all chain IDs in the `chains` variable.\n", + "align_full(str(path1), str(path2), chains = ['B','C'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "23b618f5-858c-4dfe-acdf-99954ba56afc", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 47aee41afb323c0ab0cc2a4384f39e1802cd18d5 Mon Sep 17 00:00:00 2001 From: Ece Okur Date: Tue, 25 Aug 2026 10:56:45 +0200 Subject: [PATCH 2/2] HADDOCK3-protein-peptide updated (still needs adjustments but moslty done on my side) --- notebooks/HADDOCK3-protein-peptide.ipynb | 36703 +++++++++++++-------- 1 file changed, 23810 insertions(+), 12893 deletions(-) diff --git a/notebooks/HADDOCK3-protein-peptide.ipynb b/notebooks/HADDOCK3-protein-peptide.ipynb index ba98628c0..676173d16 100644 --- a/notebooks/HADDOCK3-protein-peptide.ipynb +++ b/notebooks/HADDOCK3-protein-peptide.ipynb @@ -141,7 +141,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 2, "id": "419e06d6-e441-48bc-b812-aaea09b9a1d1", "metadata": {}, "outputs": [ @@ -149,12882 +149,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2026-08-17 14:05:47-- https://surfdrive.surf.nl/public.php/dav/files/3GE8k07b8EtuVK8\n", - "surfdrive.surf.nl (surfdrive.surf.nl) çözümleniyor... 2001:610:10a:2:0:a11:da7a:5afe, 2001:610:10b:2:0:a11:da7a:5afe, 145.107.56.140, ...\n", - "surfdrive.surf.nl (surfdrive.surf.nl)[2001:610:10a:2:0:a11:da7a:5afe]:443 bağlanılıyor... bağlantı kuruldu.\n", + "--2026-08-25 09:59:58-- https://surfdrive.surf.nl/public.php/dav/files/3GE8k07b8EtuVK8\n", + "surfdrive.surf.nl (surfdrive.surf.nl) çözümleniyor... 145.107.56.140, 145.107.8.140\n", + "surfdrive.surf.nl (surfdrive.surf.nl)[145.107.56.140]:443 bağlanılıyor... bağlantı kuruldu.\n", "HTTP isteği gönderildi, yanıt bekleniyor... 200 OK\n", "Uzunluk: 241474807 (230M) [application/zip]\n", "Kayıt yeri: `HADDOCK3-protein-peptide.zip'\n", "\n", - "HADDOCK3-protein-pe 100%[===================>] 230,29M 502KB/s içinde 6m 44s\n", + "HADDOCK3-protein-pe 100%[===================>] 230,29M 521KB/s içinde 10m 58s\n", "\n", - "2026-08-17 14:12:31 (584 KB/s) - `HADDOCK3-protein-peptide.zip' kaydedildi [241474807/241474807]\n", + "2026-08-25 10:10:57 (359 KB/s) - `HADDOCK3-protein-peptide.zip' kaydedildi [241474807/241474807]\n", "\n", "Archive: HADDOCK3-protein-peptide.zip\n", - " creating: HADDOCK3-protein-peptide/\n", - " creating: HADDOCK3-protein-peptide/workflows/\n", - " inflating: HADDOCK3-protein-peptide/workflows/protein_peptide_docking_best_practices.cfg \n", - " inflating: HADDOCK3-protein-peptide/workflows/protein_peptide_docking.cfg \n", - " creating: HADDOCK3-protein-peptide/restraints/\n", - " inflating: HADDOCK3-protein-peptide/restraints/peptide-ens-passive.act-pass \n", - " extracting: HADDOCK3-protein-peptide/restraints/protein-AR3D-active.act-pass \n", - " inflating: HADDOCK3-protein-peptide/restraints/protein-peptide_ambig.tbl \n", - " creating: HADDOCK3-protein-peptide/runs/\n", - " creating: HADDOCK3-protein-peptide/runs/run_bp/\n", - " creating: HADDOCK3-protein-peptide/runs/run_bp/05_emref/\n", - 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" inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/params.cfg \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_3_model_1.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/io.json \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_5_model_2.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_4_model_1.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_9_model_4.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_2_model_2.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_3_model_3.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/seletopclusts.txt \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_4_model_3.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_7_model_4.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_10_model_4.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_6_model_1.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_8_model_3.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_10_model_2.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_1_model_1.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_7_model_2.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_5_model_4.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_6_model_3.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_8_model_1.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_9_model_2.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_1_model_3.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_2_model_4.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_7_model_3.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_4_model_4.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_9_model_1.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_10_model_3.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_8_model_2.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_3_model_4.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_7_model_1.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_10_model_1.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_9_model_3.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_1_model_2.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_6_model_2.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_2_model_3.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_1_model_4.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_6_model_4.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_5_model_3.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_2_model_1.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_4_model_2.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_5_model_1.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_8_model_4.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/12_seletopclusts/cluster_3_model_2.pdb.gz \n", - " creating: HADDOCK3-protein-peptide/runs/run_bp/04_caprieval/\n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/04_caprieval/params.cfg \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/04_caprieval/blosum62_A.aln \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/04_caprieval/capri_ss.tsv \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/04_caprieval/weights_params.json \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/04_caprieval/blosum62_B.aln \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/04_caprieval/capri_clt.tsv \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/04_caprieval/io.json \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/04_caprieval/blosum62.izone \n", - " creating: HADDOCK3-protein-peptide/runs/run_bp/06_caprieval/\n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/06_caprieval/capri_ss.tsv \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/06_caprieval/capri_clt.tsv \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/06_caprieval/blosum62_A.aln \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/06_caprieval/weights_params.json \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/06_caprieval/params.cfg \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/06_caprieval/blosum62.izone \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/06_caprieval/blosum62_B.aln \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/06_caprieval/io.json \n", - " creating: HADDOCK3-protein-peptide/runs/run_bp/00_topoaa/\n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/00_topoaa/AF_MDM2_26_109_1_haddock.psf.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/00_topoaa/AF_MDM2_26_109_1.pdb.gz \n", - " extracting: HADDOCK3-protein-peptide/runs/run_bp/00_topoaa/peptide_helix_from_peptide_ens_1_haddock.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/00_topoaa/params.cfg \n", - " extracting: HADDOCK3-protein-peptide/runs/run_bp/00_topoaa/peptide_ens_2.pdb.gz \n", - " extracting: HADDOCK3-protein-peptide/runs/run_bp/00_topoaa/peptide_sheet_from_peptide_ens_3_haddock.psf.gz \n", - " extracting: HADDOCK3-protein-peptide/runs/run_bp/00_topoaa/peptide_ppii_from_peptide_ens_2_haddock.psf.gz \n", - " extracting: HADDOCK3-protein-peptide/runs/run_bp/00_topoaa/peptide_ens_1.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/00_topoaa/io.json \n", - " extracting: HADDOCK3-protein-peptide/runs/run_bp/00_topoaa/peptide_ens_3.pdb.gz \n", - " extracting: HADDOCK3-protein-peptide/runs/run_bp/00_topoaa/peptide_ppii_from_peptide_ens_2_haddock.pdb.gz \n", - " extracting: HADDOCK3-protein-peptide/runs/run_bp/00_topoaa/peptide_sheet_from_peptide_ens_3_haddock.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/00_topoaa/AF_MDM2_26_109_1_haddock.pdb.gz \n", - " extracting: HADDOCK3-protein-peptide/runs/run_bp/00_topoaa/peptide_helix_from_peptide_ens_1_haddock.psf.gz \n", - " creating: HADDOCK3-protein-peptide/runs/run_bp/02_caprieval/\n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/02_caprieval/blosum62_B.aln \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/02_caprieval/blosum62.izone \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/02_caprieval/capri_clt.tsv \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/02_caprieval/capri_ss.tsv \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/02_caprieval/io.json \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/02_caprieval/params.cfg \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/02_caprieval/blosum62_A.aln \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/02_caprieval/weights_params.json \n", - " creating: HADDOCK3-protein-peptide/runs/run_bp/09_rmsdmatrix/\n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/09_rmsdmatrix/params.cfg \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/09_rmsdmatrix/rmsd.matrix \n", - " extracting: HADDOCK3-protein-peptide/runs/run_bp/09_rmsdmatrix/rmsd_0.out.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/09_rmsdmatrix/rmsd_matrix.json \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/09_rmsdmatrix/io.json \n", - " creating: HADDOCK3-protein-peptide/runs/run_bp/11_caprieval/\n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/11_caprieval/blosum62.izone \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/11_caprieval/capri_ss.tsv \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/11_caprieval/blosum62_B.aln \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/11_caprieval/weights_params.json \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/11_caprieval/blosum62_A.aln \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/11_caprieval/capri_clt.tsv \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/11_caprieval/params.cfg \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/11_caprieval/io.json \n", - " creating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/\n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2069.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2254.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2473.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2181.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_958.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_516.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_331.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1238.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1851.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1622.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1005.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2500.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2327.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_55.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_242.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_465.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_197.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_658.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1922.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1484.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1176.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1751.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_425.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_202.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_618.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_983.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_15.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2695.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2367.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2540.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1962.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1711.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1136.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_683.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_918.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_371.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_556.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_299.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2029.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2995.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2433.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2214.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1278.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1811.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1045.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1662.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1390.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1791.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1444.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1263.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1679.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_95.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2032.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2615.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2866.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2428.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_698.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_903.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_770.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_157.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_282.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1310.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1537.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1979.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1891.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2766.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2141.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2294.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2915.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_9.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_870.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_219.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_603.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_998.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1577.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1350.pdb.gz \n", - 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" inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2887.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2306.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2521.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1903.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1698.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1282.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1770.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1157.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1918.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1371.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1556.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1683.pdb.gz \n", - 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" inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2386.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2053.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2674.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2449.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2807.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_962.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_711.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_136.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1197.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1242.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1465.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1658.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_922.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_176.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_751.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_484.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2634.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2013.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2409.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2847.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1958.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1516.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1331.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_238.pdb.gz \n", - 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" inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2484.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2922.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1500.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1327.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1955.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1101.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1726.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2085.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2577.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2350.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2939.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_22.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_235.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_412.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1826.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1468.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1655.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1072.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1580.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2639.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2223.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2404.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_561.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_346.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_489.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1866.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1428.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1032.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1615.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_306.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_521.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_887.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2679.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2444.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2263.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2791.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1915.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1294.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1766.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1141.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_452.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_275.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_787.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_62.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2891.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2310.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2537.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2979.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_941.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_328.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_732.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_115.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2582.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2070.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2657.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2824.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2198.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1406.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1221.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1848.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_832.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_594.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_641.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2724.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2103.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2957.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2519.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1352.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1575.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1087.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1748.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2143.pdb.gz \n", - 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" inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_237.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1957.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1519.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1724.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1103.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1325.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1502.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1118.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_845.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_636.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2753.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2174.pdb.gz \n", - 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" inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2095.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1836.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1478.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1590.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1062.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1645.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_356.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_571.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_499.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2629.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2414.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2233.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1876.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1438.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1605.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1022.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2669.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2781.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2273.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2454.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_531.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_316.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_897.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1905.pdb.gz \n", - 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" inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_717.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2672.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2055.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2380.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2268.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2801.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1204.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1423.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1039.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1985.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2015.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2632.pdb.gz \n", - 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" inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_476.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_251.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_184.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_46.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2334.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2513.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2109.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1497.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1742.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1165.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1358.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1931.pdb.gz \n", - 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" inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1719.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1882.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1524.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1303.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_610.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_863.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2906.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2548.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2152.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2775.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2287.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1398.pdb.gz \n", - 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" inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1992.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1609.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1434.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1213.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1989.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1035.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1612.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1861.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1208.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2443.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2264.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2796.pdb.gz \n", - 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" inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2846.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2012.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2635.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1659.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1464.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1243.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1196.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_623.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_239.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_850.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2935.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2089.pdb.gz \n", - 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" inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_810.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1298.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1682.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1919.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1557.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1370.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2448.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2806.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2675.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2052.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2387.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_137.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_710.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_963.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1619.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1982.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1203.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1424.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1488.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1092.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1347.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1560.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_654.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_581.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_827.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_469.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2942.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2731.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2116.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_59.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1009.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1413.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1234.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_727.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_100.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_954.pdb.gz \n", - 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" inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2370.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2682.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2298.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_5.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1121.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1706.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1975.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_541.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_366.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_694.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_99.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2203.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2424.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2982.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2619.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1387.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1675.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1052.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1448.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1806.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2196.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2464.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2243.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2659.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_326.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_501.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1012.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1635.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1408.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1846.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_42.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2959.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2330.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2517.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_180.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_472.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_255.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1746.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1161.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1493.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1089.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1935.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1137.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1710.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1963.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_982.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_619.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_203.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_424.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2541.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2366.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2694.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_14.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1391.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1663.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1044.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1279.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1810.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_298.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_919.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_557.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_370.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_682.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2215.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2432.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2994.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2028.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1004.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1623.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1239.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1850.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2180.pdb.gz \n", - 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" inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2900.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2369.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2281.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2773.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2154.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_616.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_865.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1884.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1138.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1305.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1522.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_80.pdb.gz \n", - 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" inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_825.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2940.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2329.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2114.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2733.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1178.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1090.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1562.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1345.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1892.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1709.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1313.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1534.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2916.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2558.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2297.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2765.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_2142.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_600.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_873.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1388.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1447.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1260.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run_bp/03_flexref/flexref_1809.pdb.gz \n", - 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" inflating: HADDOCK3-protein-peptide/runs/run1/02_caprieval/capri_clt.tsv \n", - " inflating: HADDOCK3-protein-peptide/runs/run1/02_caprieval/params.cfg \n", - " inflating: HADDOCK3-protein-peptide/runs/run1/02_caprieval/blosum62.izone \n", - " inflating: HADDOCK3-protein-peptide/runs/run1/02_caprieval/blosum62_B.aln \n", - " inflating: HADDOCK3-protein-peptide/runs/run1/02_caprieval/io.json \n", - " inflating: HADDOCK3-protein-peptide/runs/run1/02_caprieval/weights_params.json \n", - " creating: HADDOCK3-protein-peptide/runs/run1/09_rmsdmatrix/\n", - " inflating: HADDOCK3-protein-peptide/runs/run1/09_rmsdmatrix/io.json \n", - " inflating: HADDOCK3-protein-peptide/runs/run1/09_rmsdmatrix/params.cfg \n", - " extracting: HADDOCK3-protein-peptide/runs/run1/09_rmsdmatrix/rmsd_0.out.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run1/09_rmsdmatrix/rmsd.matrix \n", - " inflating: HADDOCK3-protein-peptide/runs/run1/09_rmsdmatrix/rmsd_matrix.json \n", - " creating: HADDOCK3-protein-peptide/runs/run1/08_caprieval/\n", - " inflating: HADDOCK3-protein-peptide/runs/run1/08_caprieval/params.cfg \n", - " inflating: HADDOCK3-protein-peptide/runs/run1/08_caprieval/blosum62_B.aln \n", - " inflating: HADDOCK3-protein-peptide/runs/run1/08_caprieval/io.json \n", - " inflating: HADDOCK3-protein-peptide/runs/run1/08_caprieval/blosum62.izone \n", - " inflating: HADDOCK3-protein-peptide/runs/run1/08_caprieval/capri_ss.tsv \n", - " inflating: HADDOCK3-protein-peptide/runs/run1/08_caprieval/capri_clt.tsv \n", - " inflating: HADDOCK3-protein-peptide/runs/run1/08_caprieval/weights_params.json \n", - " inflating: HADDOCK3-protein-peptide/runs/run1/08_caprieval/blosum62_A.aln \n", - " creating: HADDOCK3-protein-peptide/runs/run1/01_rigidbody/\n", - " inflating: HADDOCK3-protein-peptide/runs/run1/01_rigidbody/rigidbody_985.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run1/01_rigidbody/rigidbody_423.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run1/01_rigidbody/rigidbody_204.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run1/01_rigidbody/rigidbody_73.pdb.gz \n", - 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" inflating: HADDOCK3-protein-peptide/runs/run1/00_topoaa/io.json \n", - " extracting: HADDOCK3-protein-peptide/runs/run1/00_topoaa/peptide_helix_from_peptide_ens_1_haddock.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run1/00_topoaa/AF_MDM2_26_109_1_haddock.pdb.gz \n", - " extracting: HADDOCK3-protein-peptide/runs/run1/00_topoaa/peptide_ens_1.pdb.gz \n", - " extracting: HADDOCK3-protein-peptide/runs/run1/00_topoaa/peptide_sheet_from_peptide_ens_3_haddock.psf.gz \n", - " extracting: HADDOCK3-protein-peptide/runs/run1/00_topoaa/peptide_ens_3.pdb.gz \n", - " extracting: HADDOCK3-protein-peptide/runs/run1/00_topoaa/peptide_sheet_from_peptide_ens_3_haddock.pdb.gz \n", - " inflating: HADDOCK3-protein-peptide/runs/run1/00_topoaa/params.cfg \n", - " extracting: HADDOCK3-protein-peptide/runs/run1/00_topoaa/peptide_ens_2.pdb.gz \n", - " extracting: HADDOCK3-protein-peptide/runs/run1/00_topoaa/peptide_helix_from_peptide_ens_1_haddock.psf.gz \n", - " extracting: HADDOCK3-protein-peptide/runs/run1/00_topoaa/peptide_ppii_from_peptide_ens_2_haddock.pdb.gz \n", - 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"\u001b[31mFileNotFoundError\u001b[39m: [Errno 2] No such file or directory: '/Users/eceokur/Downloads/HADDOCK3-protein-peptide/HADDOCK3-protein-peptide-notebook'" + "replace HADDOCK3-protein-peptide/workflows/protein_peptide_docking_best_practices.cfg? [y]es, [n]o, [A]ll, [N]one, [r]ename: ^C\n" ] } ], @@ -13037,7 +174,7 @@ "import os\n", "from pathlib import Path\n", "BASE_DIR = Path.cwd()\n", - "PROJECT_NAME = 'HADDOCK3-protein-peptide-notebook'\n", + "PROJECT_NAME = 'HADDOCK3-protein-peptide'\n", "PROJECT_DIR = BASE_DIR / PROJECT_NAME\n", "os.chdir(PROJECT_DIR)" ] @@ -13075,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 58, "id": "cca074f3-a274-418a-8f23-2bd143004d16", "metadata": {}, "outputs": [ @@ -13133,7 +270,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 59, "id": "bf5331f7-16c0-40dd-8c63-346f8658125c", "metadata": {}, "outputs": [], @@ -13151,56 +288,23827 @@ "\n", "*Note* `pdb_tidy` attempts to correct formatting only, e.g. ensure each line is long enough (padded with spaces), not the actual content of the PDB file.\n", "\n", - "It’s a good practice to verify resulting structure visually using PyMOL. Start PyMOL and load the PDB file as followed:\n", + "You can compare the structure `AF_MDM2_26_109.pdb` with initial AlphaFold model `AF-P23804-F1-model_v4.pdb` by running the following command: " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b5da13f0-0ab5-4d5e-a7ec-9eb78df8528c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Atoms for alignment - Model 1: {'A': 84}, Total: 84\n", + "Atoms for alignment - Model 2: {'A': 489}, Total: 489\n", + "Model 1: chain A in red\n", + "Model 2: chain A in blue\n", + "Using 84 atom pairs for alignment\n", + "Whole molecule alignment RMSD: 16.470 Å\n", + "Chain A RMSD: 16.470 Å\n" + ] + }, + { + "data": { + "application/3dmoljs_load.v0": "
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This process is computationally expensive and can be challenging in the absence of MD experience. A simpler alternative is to generate several idealized peptide conformations. While less robust, this approach still indirectly accounts for peptide’s flexibility to some degree, and that can be just enough to build a model of acceptable quality.\n", + "\n", + "The sequence of interest (residues 18-32 of the p53_mouse) is:\n", + "\n", + "`SQETFSGLWKLLPPE`\n", + "Note this sequence can be found on the UniProt page for `P02340 · P53_MOUSE`, under the ‘Structure’ section.\n", + "\n", + "We will generate three idealized peptide conformations: α-helix, β-sheet, and polyproline II (ppII). This can be done using PyMOL’s built-in fab command. To see a usage example:\n", + "\n", + "NOT FINISHED!!!!!!\n" + ] + }, + { + "cell_type": "markdown", + "id": "b4408fb5-b259-4525-ae3c-a431bbc4672c", + "metadata": {}, + "source": [ + "---\n", + "# Defining restraints for docking\n", + "\n", + "HADDOCK relies on restraints to guide the sampling process during docking. Several types of restraints can be defined, including unambiguous and ambiguous distance restraints (AIRs). These restraints are specified using CNS ([Crystallography & NMR System](https://cns-online.org/v1.3/) syntax, which defines two atom selections and a distance range that must be satisfied. If a restraint is not fulfilled, a penalty is applied to the HADDOCK scoring function, lowering the rank of the model that violate the restraints.\n", + "\n", + "To simplify, restraints are defined by specifying two types of residue selections, active and passive, together with a distance range that must be satisfied. __Active__ residues are those known to be present in the interface and solvent accessible. If an active residues is not in the interface of a model, then energetic penalty is applied. __Passive__ residues are typically all solvent-accessible surface neighbors of active residues, or other atoms that may plausibly be involved in the interaction. Yet if a passive residue is not part of the interface - no penalty is applied.\n", + "\n", + "A distance restraint is then expressed using CNS syntax as follows:\n", + "\n", + "`assign (selection_1) (selection_2) target_distance, lower_margin, upper_margin`\n", + "\n", + "The allowed range for the distance is then:\n", + "\n", + "`target_distance - lower_margin ≤ distance ≤ target_distance + upper_margin`\n", + "\n", + "Selections can be customized using keywords such as `segid` (chain ID), `resid` (residue number), and `name` (atom name). These can be combined with logical operators AND, OR to construct more complex definitions. Please refer for that to the [online CNS manual](https://cns-online.org/v1.3/) for more info.\n", + "\n", + "AIRs file (`.tbl`) can be generated using the `haddock3-restraints` command line tool (installed with haddock3) or a version [web version](https://wenmr.science.uu.nl/haddock-restraints). We will explain how to use this tool shortly, but first we need to identify which residues participate in the interaction - both active and passive." + ] + }, + { + "cell_type": "markdown", + "id": "6ec66ef7-f1f6-4ccd-9961-9d41e0c29efd", + "metadata": {}, + "source": [ + "# Defining active residues for protein\n", + "\n", + "[ARCTIC-3D](https://wenmr.science.uu.nl/login) is a data-mining tool that clusters all known interfaces of a protein, grouping similar interaction sites into residue sets that are likely to participate in binding. For mouse MDM2, no structural information is currently available, however, such data exists for its human homolog. To define plausible active residues in mouse MDM2, we applied ARCTIC-3D to the human protein and transferred the results onto mouse variant. Resulting residues were filtered based on their solvent accessibility:\n", + "\n", + "`54 55 58 59 62 67 72 73 93 94 100 `\n", + "\n", + "For more details, take a look at the bonus section: How to use [ARCTIC-3D](https://www.bonvinlab.org/education/HADDOCK3/HADDOCK3-protein-peptide/#bonus-how-to-use-arctic-3d-to-predict-interface-residues)?\n", + "\n", + "Let’s visualize predicted active residues on the protein structure we prepared:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "deef8f5f-bb65-4857-bbb6-b24fab4f3ff4", + "metadata": {}, + "outputs": [ + { + "data": { + "application/3dmoljs_load.v0": "
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Instead, we assign all peptide residues as passive, allowing HADDOCK to explore possible interaction sites during docking:\n", + "\n", + "`1 2 3 4 5 6 7 8 9 10 11 12 13 14 15`\n", + "\n", + "# Using haddock3-restraints to generate restraint file\n", + "\n", + "To generate AIRs with `haddock3-restraints`, you need to use its sub-option `active_passive_to_ambig` and provide an `.act-pass` file that lists the active and/or passive residues for each docking partner. This file has the following syntax:\n", + "\n", + "Active residues are written on the first line, separated by spaces.\n", + "Passive residues are written on the second line, also separated by spaces. If no active residues are defined, the first line should remain empty. The same applies to the second line if no passive residues are used.\n", + "\n", + "In our case, this is the file for MDM2, `protein-AR3D-active.act-pass`:\n", + "\n", + "`54 55 58 59 62 67 72 73 93 94 100`\n", + "\n", + "And the file for p53, `peptide-ens-passive.act-pass`:\n", + "\n", + "\n", + "`1 2 3 4 5 6 7 8 9 10 11 12 13 14 15`\n", + "\n", + "Create these files manually, or access pre-made ones in `restraints/`.\n", + "\n", + "This files can then be used as input to `haddock3-restraints active_passive_to_ambig`to generate the restraint file:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "44285c7a-6a33-48bf-a016-1a70cb423858", + "metadata": {}, + "outputs": [], + "source": [ + "! haddock3-restraints active_passive_to_ambig protein-AR3D-active.act-pass peptide-ens-passive.act-pass > protein-peptide_ambig.tbl" + ] + }, + { + "cell_type": "markdown", + "id": "df6cb55f-074f-43c3-b25c-6f097fe16f3a", + "metadata": {}, + "source": [ + "*Note* that *passive* residues on partner 1 should only be defined if *active* residues have been specified on partner 2, and vice versa.\n", + "\n", + "Typically, active residues are complemented by nearby passive residues on the same molecule to account for uncertainties in the binding site definition. But if there’s no active residues on the partner 2 - passive residues of partner 1 have nothing to interact with. In this tutorial, active residues are defined only for MDM2, while no active residues are defined for the peptide. Thus, we only assign the peptide residues as passive.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "eac56476-c1a2-43d0-b7b5-0267cd386520", + "metadata": {}, + "source": [ + "# Restraints validation\n", + "\n", + "After generating `protein-peptide_ambig.tbl``, one can validate the syntax of this file using:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "228ef3b9-7ead-4eea-b1a4-6b39ca804fef", + "metadata": {}, + "outputs": [], + "source": [ + "! haddock3-restraints validate_tbl protein-peptide_ambig.tbl --silent" + ] + }, + { + "cell_type": "markdown", + "id": "3e4bebe5-641f-4d71-987f-2ba0ffc516ca", + "metadata": {}, + "source": [ + "If the file is valid, there will be no output.\n", + "\n", + "*Note*: This command checks only the file’s formatting, not the biological correctness or content of the restraints." + ] + }, + { + "cell_type": "markdown", + "id": "27d82e1e-e536-4a01-9f4f-799d34961333", + "metadata": {}, + "source": [ + "# Setting up the docking with HADDOCK3\n", + "\n", + "Having now all the required input files and restraints, we can proceed with the docking setup!\n", + "\n", + "## HADDOCK3 workflow definition\n", + "\n", + "The first step is to create a HADDOCK3 configuration file that will define the docking workflow. We will follow a classic HADDOCK workflow that include topology generation, rigid-body minimization, semi-flexible interface refinement, and final refinement, followed by model selection, clustering, and evaluation. We will include multiple evaluation instances (`caprieval` module) to compare models to either the best scoring model (if no reference is given) or a reference structure, which in our case we have at hand. This will allow us to assess the performance of the protocol.\n", + "\n", + "The modules included in this workflow are:\n", + "\n", + "* `topoaa`: Generates the topologies for the CNS engine and builds missing atoms.\n", + "* `rigidbody`: Rigid body energy minimization with CNS (it0 in HADDOCK2.x).\n", + "* `caprieval`: Evaluates the rigid-body models using CAPRI metrics (i-RMSD, l-RMSD, Fnat, DockQ).\n", + "* `flexref`: Semi-flexible refinement of the interface (it1 in haddock2.4).\n", + "* `caprieval`: Evaluates the flexref models using CAPRI metrics.\n", + "* `emref`: Final refinement by energy minimisation (itw EM only in HADDOCK2.X).\n", + "* `caprieval`: Evaluates the emref models using CAPRI metircs.\n", + "* `seletop`: Selects the top models based on scoring.\n", + "* `rmsdmatrix`: Generates the pairwisw RMSD matrix for all models to asses structural similarity.\n", + "* `clustrmsd`: Clustering of models based on the RMSD.\n", + "* `caprieval`: Evaluates the clustered models using CAPRI metrics.\n", + "* `seleoptclusts`: Selects the top models of all clusters.\n", + "* `caprieval`: Evaluates the final selected models using CAPRI metrics.\n", + "\n", + "The configuration file can be found in `workflow/protein_peptide_docking.cfg` and is as follows:\n" + ] + }, + { + "cell_type": "markdown", + "id": "b4e47d25-204b-44cd-9175-7431de28f607", + "metadata": {}, + "source": [ + "```toml\n", + " # ============================================\n", + "# Protein–Peptide Docking in HADDOCK3\n", + "# ============================================\n", + "\n", + "# Output directory\n", + "run_dir = \"runs/run1\"\n", + "\n", + "# Compute mode and resources\n", + "mode = \"local\"\n", + "# HADDOCK will use maximum of 40 cores, or as many, as available on the system\n", + "ncores = 40\n", + "\n", + "# Post-processing\n", + "postprocess = true\n", + "clean = true\n", + "\n", + "# Input files\n", + "molecules = [\n", + " \"pdbs/AF_MDM2_26_109.pdb\", # Protein\n", + " \"pdbs/peptide_ens.pdb\" # Peptide\n", + "]\n", + "\n", + "# ============================================\n", + "# Workflow\n", + "# ============================================\n", "\n", - "__*File menu -> Open -> select HADDOCK3-protein-peptide/AF_MDM2_26_109.pdb*__\n", + "[topoaa]\n", "\n", - "Feel free to compare this structure with initial AlphaFold model:\n", - "__*File menu -> Open -> select HADDOCK3-protein-peptide/AF-P23804-F1-model_v4.pdb*__" + "[rigidbody]\n", + "ambig_fname = \"restraints/protein-peptide_ambig.tbl\"\n", + "sampling = 1000\n", + "\n", + "[caprieval]\n", + "reference_fname = \"pdbs/1YCR.pdb\"\n", + "\n", + "[flexref]\n", + "tolerance = 5\n", + "ambig_fname = \"restraints/protein-peptide_ambig.tbl\"\n", + "\n", + "[caprieval]\n", + "reference_fname = \"pdbs/1YCR.pdb\"\n", + "\n", + "[emref]\n", + "tolerance = 5\n", + "ambig_fname = \"restraints/protein-peptide_ambig.tbl\"\n", + "\n", + "[caprieval]\n", + "reference_fname = \"pdbs/1YCR.pdb\"\n", + "\n", + "[seletop]\n", + "select = 200\n", + "\n", + "[caprieval]\n", + "reference_fname = \"pdbs/1YCR.pdb\"\n", + "\n", + "[rmsdmatrix]\n", + "\n", + "[clustrmsd]\n", + "clust_cutoff = 5\n", + "plot_matrix = true\n", + "\n", + "[caprieval]\n", + "reference_fname = \"pdbs/1YCR.pdb\"\n", + "\n", + "[seletopclusts]\n", + "# Selection of the top 4 best scoring complexes from each cluster\n", + "top_models = 4\n", + "\n", + "[caprieval]\n", + "reference_fname = \"pdbs/1YCR.pdb\"\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "938ddfe3-6cda-424e-8191-4a2370615a5f", + "metadata": {}, + "source": [ + "You might’ve noticed that some of the modules do not have any parameters defined. In this case, HADDOCK will use default values, which can be displayed per module. For example, to see all parameters for `clustrmsd`, execute the following cell:" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "0a98a40a-e037-4f48-b3e9-5afa07628036", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "[clustrmsd]\n", + "criterion = 'distance' # $minchars 0 / $maxchars 100 / $title Criterion for fcluster / $short Criterion to be used to cut the dendrogram / $group analysis / $choices ['distance', 'maxclust']\n", + "linkage = 'average' # $minchars 0 / $maxchars 100 / $title Linkage type / $short How to lump together clusters in hierarchical clustering / $group analysis / $choices ['average', 'centroid', 'complete', 'median', 'single', 'ward', 'weighted']\n", + "min_population = 4 # $min 1 / $max 9999 / $title Clustering population threshold / $short Threshold employed to exclude clusters with less than this number of members. By default 4. / $group analysis\n", + "clust_cutoff = 7.5 # $min 0.1 / $max 100 / $precision 3 / $title Clustering cutoff distance / $short Value of cutoff cophenetic distance. / $group analysis\n", + "plot_matrix = false # $title Plot matrix of members / $short Plot matrix of members. By default is false. / $group analysis\n", + "n_clusters = 4 # $min 1 / $max 9999 / $title Number of clusters / $short Number of clusters to be formed / $group analysis\n" + ] + } + ], + "source": [ + "! haddock3-cfg -m clustrmsd" + ] + }, + { + "cell_type": "markdown", + "id": "8c26ee0a-fa8e-4464-a19d-a39d15ca993b", + "metadata": {}, + "source": [ + "This workflow is ready-to-run, and can be executed as-is, using pre-made PDB and restraint files. To use your own files, make sure you provide correct relative or absolute path for each file used during the run (`molecules`, `ambig_fname` and `reference_fname`). If you are using your own reference, make sure the PDB file is adequately preprocessed (for example, using `pdb_tools`).\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "ec282474-d80e-428a-934f-652e2886bad4", + "metadata": {}, + "source": [ + "# Running HADDOCK3\n", + "\n", + "To run the docking (in `local` mode), open the terminal, activate your haddock3 environment, navigate to `protein-pepitde/` and execute:" ] }, { "cell_type": "code", - "execution_count": 18, - "id": "c665c7dc-a129-4b46-90ad-e539a41b7b9e", + "execution_count": 61, + "id": "2fc0ffd6-9ea8-4f7e-acaf-1bba5d02a83f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "AF-P23804-F1-model_v6.pdb \u001b[34mpdbs\u001b[m\u001b[m \u001b[34mscripts\u001b[m\u001b[m\n", - "AF_MDM2_26_109.pdb \u001b[34mrestraints\u001b[m\u001b[m \u001b[34mworkflows\u001b[m\u001b[m\n", - "\u001b[34mHADDOCK3-protein-peptide\u001b[m\u001b[m \u001b[34mruns\u001b[m\u001b[m\n" + "[2026-08-25 11:32:43,360 cli INFO] \n", + "##############################################\n", + "# #\n", + "# HADDOCK3 #\n", + "# #\n", + "##############################################\n", + "\n", + "!! Some of the HADDOCK3 components use CNS (Crystallographic and NMR System) which is free of use for non-profit applications. !!\n", + "!! For commercial use it is your own responsibility to have a proper license. !!\n", + "!! For details refer to the DISCLAIMER file in the HADDOCK3 repository. !!\n", + "\n", + "Starting HADDOCK3 v2026.7.0 on 2026-08-25 11:32:00\n", + "\n", + "Python 3.11.15 | packaged by conda-forge | (main, Mar 5 2026, 16:59:26) [Clang 19.1.7 ]\n", + "\n", + "[2026-08-25 11:32:47,309 libworkflow INFO] Reading instructions step 0_topoaa\n", + "[2026-08-25 11:32:47,309 libworkflow INFO] Reading instructions step 1_rigidbody\n", + "[2026-08-25 11:32:47,310 libworkflow INFO] Reading instructions step 2_caprieval\n", + "[2026-08-25 11:32:47,310 libworkflow INFO] Reading instructions step 3_flexref\n", + "[2026-08-25 11:32:47,310 libworkflow INFO] Reading instructions step 4_caprieval\n", + "[2026-08-25 11:32:47,310 libworkflow INFO] Reading instructions step 5_emref\n", + "[2026-08-25 11:32:47,310 libworkflow INFO] Reading instructions step 6_caprieval\n", + "[2026-08-25 11:32:47,310 libworkflow INFO] Reading instructions step 7_seletop\n", + "[2026-08-25 11:32:47,310 libworkflow INFO] Reading instructions step 8_caprieval\n", + "[2026-08-25 11:32:47,310 libworkflow INFO] Reading instructions step 9_rmsdmatrix\n", + "[2026-08-25 11:32:47,310 libworkflow INFO] Reading instructions step 10_clustrmsd\n", + "[2026-08-25 11:32:47,310 libworkflow INFO] Reading instructions step 11_caprieval\n", + "[2026-08-25 11:32:47,310 libworkflow INFO] Reading instructions step 12_seletopclusts\n", + "[2026-08-25 11:32:47,310 libworkflow INFO] Reading instructions step 13_caprieval\n", + "[2026-08-25 11:32:47,331 base_cns_module INFO] Running [topoaa] module\n", + "[2026-08-25 11:32:47,331 __init__ INFO] [topoaa] Molecule 1: AF_MDM2_26_109.pdb\n", + "[2026-08-25 11:32:47,333 __init__ INFO] [topoaa] Termini of molecule 1:\n", + " N-ter: uncharged C-ter: uncharged 5'phosphate: no\n", + "[2026-08-25 11:32:47,333 __init__ INFO] [topoaa] Sanitizing molecule AF_MDM2_26_109.pdb\n", + "[2026-08-25 11:32:47,337 __init__ INFO] [topoaa] Topology CNS input created\n", + "[2026-08-25 11:32:47,337 __init__ INFO] [topoaa] Molecule 2: peptide_ens.pdb\n", + "[2026-08-25 11:32:47,339 __init__ INFO] [topoaa] Termini of molecule 2:\n", + " N-ter: uncharged C-ter: uncharged 5'phosphate: no\n", + "[2026-08-25 11:32:47,340 __init__ INFO] [topoaa] Sanitizing molecule peptide_ens_1.pdb\n", + "[2026-08-25 11:32:47,341 __init__ INFO] [topoaa] Topology CNS input created\n", + "[2026-08-25 11:32:47,341 __init__ INFO] [topoaa] Sanitizing molecule peptide_ens_2.pdb\n", + "[2026-08-25 11:32:47,342 __init__ INFO] [topoaa] Topology CNS input created\n", + "[2026-08-25 11:32:47,342 __init__ INFO] [topoaa] Sanitizing molecule peptide_ens_3.pdb\n", + "[2026-08-25 11:32:47,344 __init__ INFO] [topoaa] Topology CNS input created\n", + "[2026-08-25 11:32:47,344 __init__ INFO] [topoaa] Running CNS Jobs n=4\n", + "[2026-08-25 11:32:47,344 libutil INFO] Selected 4 cores to process 4 jobs, with 8 maximum available cores.\n", + "[2026-08-25 11:32:47,409 libparallel INFO] Using 4 cores\n", + "[2026-08-25 11:32:48,582 libparallel INFO] 4 tasks finished\n", + "[2026-08-25 11:32:48,583 __init__ INFO] [topoaa] CNS jobs have finished\n", + "[2026-08-25 11:32:48,586 base_cns_module INFO] Module [topoaa] finished.\n", + "[2026-08-25 11:32:48,586 __init__ INFO] [topoaa] took 1 seconds\n", + "[2026-08-25 11:32:48,918 base_cns_module INFO] Running [rigidbody] module\n", + "[2026-08-25 11:32:48,919 __init__ INFO] [rigidbody] crossdock=true\n", + "[2026-08-25 11:32:48,919 __init__ INFO] [rigidbody] Preparing CNS jobs...\n", + "[2026-08-25 11:32:48,921 libutil INFO] Selected 8 cores to process 999 jobs, with 8 maximum available cores.\n", + "[2026-08-25 11:32:48,921 libparallel INFO] Using 8 cores\n", + "[2026-08-25 11:32:50,315 libparallel INFO] 999 tasks finished\n", + "[2026-08-25 11:32:50,357 __init__ INFO] [rigidbody] Running CNS Jobs n=999\n", + "[2026-08-25 11:32:50,357 libutil INFO] Selected 8 cores to process 999 jobs, with 8 maximum available cores.\n", + "[2026-08-25 11:32:50,382 libparallel INFO] Using 8 cores\n", + "^C\n", + "Traceback (most recent call last):\n", + " File \"\", line 1, in \n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/multiprocessing/spawn.py\", line 122, in spawn_main\n", + " exitcode = _main(fd, parent_sentinel)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/multiprocessing/spawn.py\", line 131, in _main\n", + " prepare(preparation_data)\n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/multiprocessing/spawn.py\", line 246, in prepare\n", + " _fixup_main_from_path(data['init_main_from_path'])\n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/multiprocessing/spawn.py\", line 297, in _fixup_main_from_path\n", + " main_content = runpy.run_path(main_path,\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"\", line 291, in run_path\n", + " File \"\", line 98, in _run_module_code\n", + " File \"\", line 88, in _run_code\n", + " File \"/opt/anaconda3/envs/haddock3-env/bin/haddock3\", line 3, in \n", + " from haddock.clis.cli import maincli\n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/site-packages/haddock/clis/cli.py\", line 29, in \n", + " from haddock.gear.extend_run import EXTEND_RUN_DEFAULT, add_extend_run\n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/site-packages/haddock/gear/extend_run.py\", line 21, in \n", + " from haddock.libs.libworkflow import Workflow, WorkflowManager\n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/site-packages/haddock/libs/libworkflow.py\", line 9, in \n", + " from haddock.clis.cli_analyse import main as cli_analyse\n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/site-packages/haddock/clis/cli_analyse.py\", line 60, in \n", + " from haddock.modules.analysis.caprieval import (\n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/site-packages/haddock/modules/analysis/caprieval/__init__.py\", line 37, in \n", + " from haddock.libs.libaa2cg import martinize\n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/site-packages/haddock/libs/libaa2cg.py\", line 40, in \n", + " from Bio.PDB import Entity, PDBIO, PDBParser\n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/site-packages/Bio/PDB/__init__.py\", line 51, in \n", + " from .FragmentMapper import FragmentMapper\n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/site-packages/Bio/PDB/FragmentMapper.py\", line 48, in \n", + " from Bio.PDB.Polypeptide import PPBuilder\n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/site-packages/Bio/PDB/Polypeptide.py\", line 62, in \n", + " from Bio.Seq import Seq\n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/site-packages/Bio/Seq.py\", line 30, in \n", + "[2026-08-25 11:32:54,036 libutil ERROR] 'NoneType' object has no attribute 'terminate'\n", + "Traceback (most recent call last):\n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/site-packages/haddock/libs/libparallel.py\", line 177, in run\n", + " w.start()\n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/multiprocessing/process.py\", line 121, in start\n", + " self._popen = self._Popen(self)\n", + " ^^^^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/multiprocessing/context.py\", line 224, in _Popen\n", + " return _default_context.get_context().Process._Popen(process_obj)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/multiprocessing/context.py\", line 288, in _Popen\n", + " return Popen(process_obj)\n", + " ^^^^^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/multiprocessing/popen_spawn_posix.py\", line 32, in __init__\n", + " super().__init__(process_obj)\n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/multiprocessing/popen_fork.py\", line 19, in __init__\n", + " self._launch(process_obj)\n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/multiprocessing/popen_spawn_posix.py\", line 62, in _launch\n", + " f.write(fp.getbuffer())\n", + "KeyboardInterrupt\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/site-packages/haddock/libs/libutil.py\", line 383, in log_error_and_exit\n", + " yield\n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/site-packages/haddock/clis/cli.py\", line 193, in main\n", + " workflow.run()\n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/site-packages/haddock/libs/libworkflow.py\", line 44, in run\n", + " step.execute()\n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/site-packages/haddock/libs/libworkflow.py\", line 185, in execute\n", + " self.module.run() # type: ignore\n", + " ^^^^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/site-packages/haddock/modules/base_cns_module.py\", line 63, in run\n", + " self._run()\n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/site-packages/haddock/modules/sampling/rigidbody/__init__.py\", line 262, in _run\n", + " engine.run()\n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/site-packages/haddock/libs/libparallel.py\", line 201, in run\n", + " self.terminate()\n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/site-packages/haddock/libs/libparallel.py\", line 210, in terminate\n", + " worker.terminate()\n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/multiprocessing/process.py\", line 133, in terminate\n", + " self._popen.terminate()\n", + " ^^^^^^^^^^^^^^^^^^^^^\n", + "AttributeError: 'NoneType' object has no attribute 'terminate'\n", + "[2026-08-25 11:32:54,056 libutil ERROR] 'NoneType' object has no attribute 'terminate'\n", + "[2026-08-25 11:32:54,056 libutil ERROR] An error has occurred, see log file. And contact the developers if needed.\n", + "[2026-08-25 11:32:54,056 libutil INFO] Finished at 25/08/2026 11:32:54. For any help contact us at https://github.com/haddocking/haddock3/issues. Adéu-siau! Do pobachennya! Mandi!.\n", + " from Bio.Data import CodonTable\n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/site-packages/Bio/Data/CodonTable.py\", line 812, in \n", + " register_ncbi_table(\n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/site-packages/Bio/Data/CodonTable.py\", line 548, in register_ncbi_table\n", + " ambig_generic = AmbiguousCodonTable(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/site-packages/Bio/Data/CodonTable.py\", line 224, in __init__\n", + " list_ambiguous_codons(\n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/site-packages/Bio/Data/CodonTable.py\", line 314, in list_ambiguous_codons\n", + " for codon in [\n", + " ^\n", + " File \"/opt/anaconda3/envs/haddock3-env/lib/python3.11/site-packages/Bio/Data/CodonTable.py\", line 314, in \n", + " for codon in [\n", + " ^\n", + "KeyboardInterrupt\n" ] } ], "source": [ - "! ls" + "! haddock3 workflows/protein_peptide_docking.cfg" + ] + }, + { + "cell_type": "markdown", + "id": "92171155-ac73-47bb-9bc6-791a8987ff55", + "metadata": {}, + "source": [ + "In this case docking log will appear on the screen. Alternative, you can run the docking in the background and save output log to a file, e.g. `haddock.log`, and any kind of error message to `haddock.err`:" ] }, { "cell_type": "code", - "execution_count": 20, - "id": "b5da13f0-0ab5-4d5e-a7ec-9eb78df8528c", + "execution_count": 62, + "id": "471c2772-4f06-48eb-951b-01f71a0da021", + "metadata": {}, + "outputs": [], + "source": [ + "! haddock3 ./workflows/protein_peptide_docking.cfg > haddock.log 2> haddock.err" + ] + }, + { + "cell_type": "markdown", + "id": "d9f0aa92-88bb-42b7-8b5a-559a469ab854", + "metadata": {}, + "source": [ + "---\n", + "# Analysis of docking results\n", + "\n", + "Once your run has completed (or once you open precomputed `runs/run1/`), inspect the content of the resulting directory. You will find the various steps (modules) of the defined workflow numbered sequentially:\n", + "\n", + "> ls runs/\n", + " 00_topoaa/\n", + " 01_rigidbody/\n", + " 02_caprieval/\n", + " 03_flexref/\n", + " 04_caprieval/\n", + " 05_emref/\n", + " 06_caprieval/\n", + " 07_seletop/\n", + " 08_caprieval/\n", + " 09_rmsdmatrix/\n", + " 10_clustrmsd\n", + " 11_caprieval/\n", + " 12_seletopclusts/\n", + " 13_caprieval/\n", + " analysis/\n", + " data/\n", + " log\n", + " traceback/\n", + "\n", + "In addition to the various modules defined in the workflow file, you will also find a `log` file (text file) and three additional directories:\n", + "\n", + "* `data` directory containing the input data (PDB and restraint files) for the various modules, as well as original workflow configuration file;\n", + "* `analysis`directory containing various plots to visualise the results for each caprieval step;\n", + "* `traceback` directory containing the names of the generated models for each step, allowing to trace back a model and it’s rank throughout the various stages.\n", + "You can find information about the duration of the run at the bottom of the log file. Each sampling/refinement/selection module will contain PDB files - models produced by this module.\n", + "\n", + "For example, the `12_seletopclusts` directory contains the best models from top-ranked clusters. The clusters in that directory are numbered based on their rank, i.e. `cluster_1` refers to the best-ranked cluster. Information about the origin of these files can be found in that directory in the `seletopclusts.txt` file.\n" + ] + }, + { + "cell_type": "markdown", + "id": "b7f8d529-c9a5-4abd-be20-b7fafda8a7cc", + "metadata": {}, + "source": [ + "# Docking run overview via visual statistics\n", + "\n", + "The quickest way (though not the most detailed) to get an overview of a docking run is through the visual statistics provided by the caprieval module. Each caprieval step generates a summary table and multiple plots, bundled into a `report.html` file. These reports can be found in the corresponding analysis directories, e.g. `analysis/XX_caprieval_analysis/`. This is one of the reasons why the caprieval module is included after almost every step of the workflow.\n", + "\n", + "The content of `report.html` depends on where in the workflow the corresponding caprieval module is placed. If it follows an early step, the report describes unclustered models and details 10 top-ranked models (e.g. `analysis/04_caprieval_analysis/report.html`). If it follows clustering, the report summarizes clusters and their top models (e.g. `analysis/13_caprieval_analysis/report.html`).\n" + ] + }, + { + "cell_type": "markdown", + "id": "d7aa839e-7812-4af6-92ee-1f571b1042de", + "metadata": {}, + "source": [ + "# Cluster Statistics" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "8b595440-531e-4463-93e8-b3e32af42858", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cluster_rankcluster_idncaprieval_rankscore.meanscore.stdirmsd.meanirmsd.stdfnat.meanfnat.stdlrmsd.meanlrmsd.stddockq.meandockq.stdbsa.meanbsa.std
01141-100.172.483.111.560.230.157.563.980.370.181326.7877.19
12842-84.1510.097.100.460.120.0617.500.910.120.021257.2446.36
23243-82.311.358.540.080.050.0122.040.260.070.001253.5328.78
34344-76.837.4610.100.230.090.0426.370.760.070.011089.08126.97
45545-76.388.248.510.750.090.0521.572.480.090.021126.44129.49
56446-76.374.886.270.330.070.0216.670.890.110.011150.5862.27
67647-72.666.004.010.630.180.0710.702.210.240.071211.1738.30
78748-67.709.649.540.730.070.0324.802.080.070.021035.5265.98
89949-63.501.766.881.030.160.0717.673.440.130.031001.3275.61
\n", + "
" + ], + "text/plain": [ + " cluster_rank cluster_id n caprieval_rank score.mean score.std \\\n", + "0 1 1 4 1 -100.17 2.48 \n", + "1 2 8 4 2 -84.15 10.09 \n", + "2 3 2 4 3 -82.31 1.35 \n", + "3 4 3 4 4 -76.83 7.46 \n", + "4 5 5 4 5 -76.38 8.24 \n", + "5 6 4 4 6 -76.37 4.88 \n", + "6 7 6 4 7 -72.66 6.00 \n", + "7 8 7 4 8 -67.70 9.64 \n", + "8 9 9 4 9 -63.50 1.76 \n", + "\n", + " irmsd.mean irmsd.std fnat.mean fnat.std lrmsd.mean lrmsd.std \\\n", + "0 3.11 1.56 0.23 0.15 7.56 3.98 \n", + "1 7.10 0.46 0.12 0.06 17.50 0.91 \n", + "2 8.54 0.08 0.05 0.01 22.04 0.26 \n", + "3 10.10 0.23 0.09 0.04 26.37 0.76 \n", + "4 8.51 0.75 0.09 0.05 21.57 2.48 \n", + "5 6.27 0.33 0.07 0.02 16.67 0.89 \n", + "6 4.01 0.63 0.18 0.07 10.70 2.21 \n", + "7 9.54 0.73 0.07 0.03 24.80 2.08 \n", + "8 6.88 1.03 0.16 0.07 17.67 3.44 \n", + "\n", + " dockq.mean dockq.std bsa.mean bsa.std \n", + "0 0.37 0.18 1326.78 77.19 \n", + "1 0.12 0.02 1257.24 46.36 \n", + "2 0.07 0.00 1253.53 28.78 \n", + "3 0.07 0.01 1089.08 126.97 \n", + "4 0.09 0.02 1126.44 129.49 \n", + "5 0.11 0.01 1150.58 62.27 \n", + "6 0.24 0.07 1211.17 38.30 \n", + "7 0.07 0.02 1035.52 65.98 \n", + "8 0.13 0.03 1001.32 75.61 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#@title View the cluster statistics table\n", + "import json\n", + "import re\n", + "from plotly.offline import iplot\n", + "from IPython.display import display\n", + "import pandas as pd\n", + "\n", + "# Specify the path to your HTML file\n", + "html_file_path = PROJECT_DIR / \"runs\" / \"run1\" / \"analysis\" / \"13_caprieval_analysis/report.html\"\n", + "\n", + "# Read the HTML file\n", + "with open(html_file_path, 'r') as f:\n", + " html_content = f.read()\n", + "\n", + "# Use a regular expression to find the script tag with id=\"datatable2\" and extract its content\n", + "match_table = re.search(r'', html_content, re.DOTALL)\n", + "\n", + "if match_table:\n", + " json_data_str_table = match_table.group(1)\n", + " # Load the JSON data\n", + " table_data = json.loads(json_data_str_table)\n", + "\n", + " # Extract the 'clusters' list from the JSON data\n", + " clusters_data = table_data.get('clusters', [])\n", + "\n", + " # Create a pandas DataFrame from the clusters data\n", + " df = pd.json_normalize(clusters_data)\n", + "\n", + " # Drop the columns related to best cluster files\n", + " cols_to_drop = ['under_eval', 'total', 'rmsd', 'best1', 'best2', 'best3', 'best4', 'ilrmsd.mean', 'ilrmsd.std', 'air.mean', 'air.std', 'desolv.mean', 'desolv.std', 'elec.mean', 'elec.std', 'vdw.mean', 'vdw.std'] # Add more if there are more 'best' columns\n", + " df = df.drop(columns=[col for col in cols_to_drop if col in df.columns])\n", + "\n", + " # Display the DataFrame\n", + " display(df)\n", + "else:\n", + " print(\"Could not find the script tag with id='datatable2' in the HTML file.\")" + ] + }, + { + "cell_type": "markdown", + "id": "e2441a99-de9b-4b4e-a9ac-b778de169de4", + "metadata": {}, + "source": [ + "At the top of the page, you will find a summary table of the cluster statistics (taken from the `13_caprieval/capri_clt.tsv` file). By default, the table is sorted by cluster rank, which is based on the HADDOCK score. The table is interactive anf you can re-sort columns (corresponding to the various clusters) by clicking the arrow icon (⇄) in the header rows." + ] + }, + { + "cell_type": "markdown", + "id": "03d02269-2cbc-4fbd-bb22-d7687d0e3d80", + "metadata": {}, + "source": [ + "The table reports averages and standard deviations for the HADDOCK score, its components, and evaluation metrics. Some key statistics are:\n", + "\n", + "* `HADDOCK score`: The HADDOCK score (arbitrary units)\n", + "* `Interface RMSD`: The interface RMSD (irmsd), calculated over the interfaces the molecules.\n", + "* `Fraction of Common Contacts`: The fraction of common contacts (fcc) between given model and top-ranked model. In case reference strucutre is povided, this metric displays the fraction of native contacts (fnat) - between given model and reference strucutre.\n", + "* `Ligand RMSD`: The ligand RMSD (lrmsd), calculated on the ligand after fitting on the receptor (1st component).\n", + "* `Interface-ligand RMSD`: The interface-ligand RMSD (ilrmsd), calculated over the interface of the ligand after fitting on the interface of the receptor (more relevant for small ligands for example).\n", + "* `DockQ`: The DockQ score, which is a combination of irmsd, lrmsd and fnat and provides a continuous scale between 1 (exactly equal to reference) and 0.\n", + " \n", + "The iRMSD, lRMSD and Fnat metrics are the ones used in the blind protein-protein prediction experiment CAPRI (Critical PRediction of Interactions).\n", + "\n", + "In CAPRI the quality of a model is defined as (for protein-protein complexes):\n", + "\n", + "* __*acceptable model*__: i-RMSD < 4Å or l-RMSD<10Å and Fnat > 0.1 (or DockQ > 0.23)\n", + "* __*medium quality model*__: i-RMSD < 2Å or l-RMSD<5Å and Fnat > 0.3 (or DockQ > 0.49)\n", + "* __*high quality model*__: i-RMSD < 1Å or l-RMSD<1Å and Fnat > 0.5 (or DockQ > 0.8)\n" + ] + }, + { + "cell_type": "markdown", + "id": "ef0c1ed0-6c9a-48f4-9692-00d8a9dd7ea8", + "metadata": {}, + "source": [ + "# **Visualisation of the HADDOCK scores and their components**\n", + "\n", + "Below the cluster statistics table, you’ll find a series of plots displaying the HADDOCK score and its components against various metrics (i-RMSD, l-RMSD, FCC, DockQ), with clusters represented using color coding. The last rows show plots of cluster statistics, i.e. distributions of values per cluster, ordered by their HADDOCK score.\n", + "\n", + "These plots are interactive. A menu will appear at the top right, just above the last plot in the first row, when you hover your mouse over it. This menu allows you to zoom in and out of the plots and toggle the visibility of clusters.\n", + "\n", + "To view theses plots in the notebook execute the following cell.\n", + "\n", + "__*Examine the plots (remember here that higher DockQ values and lower i-RMSD values correspond to better models)*__" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "6f9b9f0d-9b6c-4347-864e-3891ac5c54d3", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "linkText": "Export to plot.ly", + "plotlyServerURL": "https://plot.ly", + "showLink": false + }, + "data": [ + { + "hoverlabel": { + "bgcolor": "#2E91E5", + "font": { + "family": "Helvetica", + "size": 16 + } + }, + "legendgroup": "Cluster 1", + "marker": { + "color": "#2E91E5" + }, + "mode": "markers", + "name": "Cluster 1", + "text": [ + "Model: cluster_1_model_1.pdb
Score: -103.403
Caprieval rank: 1", + "Model: cluster_1_model_2.pdb
Score: -101.588
Caprieval rank: 2", + "Model: cluster_1_model_3.pdb
Score: -98.641
Caprieval rank: 3", + "Model: cluster_1_model_4.pdb
Score: -97.05
Caprieval rank: 4" + ], + "type": "scatter", + "x": { + "bdata": "y6FFtvP99j8/NV66SQwCQN9PjZduEglAIbByaJFtFkA=", + "dtype": "f8" + }, + "xaxis": "x", + "y": { + "bdata": "bxKDwMrZWcASg8DKoWVZwBsv3SQGqVjAMzMzMzNDWMA=", + "dtype": "f8" + }, + "yaxis": "y" + }, + { + "error_x": { + "array": [ + 1.5623556733343404 + ], + "type": "data", + "visible": true + }, + "error_y": { + "array": [ + 2.4766536394901904 + ], + "type": "data", + "visible": true + }, + "hoverlabel": { + "bgcolor": "#2E91E5", + "font": { + "family": "Helvetica", + "size": 16 + } + }, + "hovertemplate": "Cluster 1
irmsd: 3.109
score: -100.171", + "legendgroup": "Cluster 1", + "marker": { + "color": "#2E91E5", + "size": 10, + "symbol": "square-dot" + }, + "mode": "markers", + "showlegend": false, + "text": [ + "Cluster 1
irmsd: 3.109
score: -100.171" + ], + "type": "scatter", + "x": [ + 3.1085000000000003 + ], + "xaxis": "x", + "y": [ + -100.1705 + ], + "yaxis": "y" + }, + { + "hoverlabel": { + "bgcolor": "#E15F99", + "font": { + "family": "Helvetica", + "size": 16 + } + }, + "legendgroup": "Cluster 2", + "marker": { + "color": "#E15F99" + }, + "mode": "markers", + "name": "Cluster 2", + "text": [ + "Model: cluster_2_model_1.pdb
Score: -91.671
Caprieval rank: 5", + "Model: cluster_2_model_2.pdb
Score: -91.505
Caprieval rank: 6", + "Model: cluster_2_model_3.pdb
Score: -86.359
Caprieval rank: 9", + "Model: cluster_2_model_4.pdb
Score: -67.07
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irmsd: 7.100
score: -84.151", + "legendgroup": "Cluster 2", + "marker": { + "color": "#E15F99", + "size": 10, + "symbol": "square-dot" + }, + "mode": "markers", + "showlegend": false, + "text": [ + "Cluster 2
irmsd: 7.100
score: -84.151" + ], + "type": "scatter", + "x": [ + 7.100250000000001 + ], + "xaxis": "x", + "y": [ + -84.15124999999999 + ], + "yaxis": "y" + }, + { + "hoverlabel": { + "bgcolor": "#1CA71C", + "font": { + "family": "Helvetica", + "size": 16 + } + }, + "legendgroup": "Cluster 3", + "marker": { + "color": "#1CA71C" + }, + "mode": "markers", + "name": "Cluster 3", + "text": [ + "Model: cluster_3_model_1.pdb
Score: -84.252
Caprieval rank: 11", + "Model: cluster_3_model_2.pdb
Score: -82.872
Caprieval rank: 12", + "Model: cluster_3_model_3.pdb
Score: -81.282
Caprieval rank: 15", + "Model: cluster_3_model_4.pdb
Score: -80.844
Caprieval rank: 16" + ], + "type": "scatter", + "x": { + "bdata": "JzEIrBwaIUCkcD0K1+MgQCPb+X5q/CBAFK5H4XpUIUA=", + "dtype": "f8" + }, + "xaxis": "x", + "y": { + "bdata": "46WbxCAQVcArhxbZzrdUwDVeukkMUlTAvHSTGAQ2VMA=", + "dtype": "f8" + }, + "yaxis": "y" + }, + { + "error_x": { + "array": [ + 0.0821142496768979 + ], + "type": "data", + "visible": true + }, + "error_y": { + "array": [ + 1.350289135703906 + ], + "type": "data", + "visible": true + }, + "hoverlabel": { + "bgcolor": "#1CA71C", + "font": { + "family": "Helvetica", + "size": 16 + } + }, + "hovertemplate": "Cluster 3
irmsd: 8.539
score: -82.312", + "legendgroup": "Cluster 3", + "marker": { + "color": "#1CA71C", + "size": 10, + "symbol": "square-dot" + }, + "mode": "markers", + "showlegend": false, + "text": [ + "Cluster 3
irmsd: 8.539
score: -82.312" + ], + "type": "scatter", + "x": [ + 8.5385 + ], + "xaxis": "x", + "y": [ + -82.3125 + ], + "yaxis": "y" + }, + { + "hoverlabel": { + "bgcolor": "#FB0D0D", + "font": { + "family": "Helvetica", + "size": 16 + } + }, + "legendgroup": "Cluster 4", + "marker": { + "color": "#FB0D0D" + }, + "mode": "markers", + "name": "Cluster 4", + "text": [ + "Model: cluster_4_model_1.pdb
Score: -87.644
Caprieval rank: 8", + "Model: cluster_4_model_2.pdb
Score: -79.451
Caprieval rank: 18", + "Model: cluster_4_model_3.pdb
Score: -72.208
Caprieval rank: 22", + "Model: cluster_4_model_4.pdb
Score: -68.029
Caprieval rank: 27" + ], + "type": "scatter", + "x": { + "bdata": "Di2yne+nI0CWQ4ts53skQH0/NV66ySRAd76fGi/dI0A=", + "dtype": "f8" + }, + "xaxis": "x", + "y": { + "bdata": "8KfGSzfpVcC+nxov3dxTwFpkO99PDVLAYOXQItsBUcA=", + "dtype": "f8" + }, + "yaxis": "y" + }, + { + "error_x": { + "array": [ + 0.22847538160598427 + ], + "type": "data", + "visible": true + }, + "error_y": { + "array": [ + 7.460440436060061 + ], + "type": "data", + "visible": true + }, + "hoverlabel": { + "bgcolor": "#FB0D0D", + "font": { + "family": "Helvetica", + "size": 16 + } + }, + "hovertemplate": "Cluster 4
irmsd: 10.099
score: -76.833", + "legendgroup": "Cluster 4", + "marker": { + "color": "#FB0D0D", + "size": 10, + "symbol": "square-dot" + }, + "mode": "markers", + "showlegend": false, + "text": [ + "Cluster 4
irmsd: 10.099
score: -76.833" + ], + "type": "scatter", + "x": [ + 10.099 + ], + "xaxis": "x", + "y": [ + -76.833 + ], + "yaxis": "y" + }, + { + "hoverlabel": { + "bgcolor": "#DA16FF", + "font": { + "family": "Helvetica", + "size": 16 + } + }, + "legendgroup": "Cluster 5", + "marker": { + "color": "#DA16FF" + }, + "mode": "markers", + "name": "Cluster 5", + "text": [ + "Model: cluster_5_model_1.pdb
Score: -90.572
Caprieval rank: 7", + "Model: cluster_5_model_2.pdb
Score: -72.869
Caprieval rank: 21", + "Model: cluster_5_model_3.pdb
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AAj5AgA4VHziJHELeEmDrJEACJEACJEACJEACJEACJEACJJCRAB0qGYl4f5ojIAESIAESIAESIAESIAESIAESIAESyGMCHuBQyeMRsnkSIAESIAESIAESIAESIAESIAESIIFcE4i5FY8bMbEwm822tnb+ewTHTp63pbOOFEyuwWiEyXTbZndZQYeKu0iyHRIgARIgARIgARIgARIgARIgAd8iwNHAaDRh7pJ1aN51KB7oPATNugxF4w6DMGHmfEnnm3krsO6fXTKe0+CvzXvxxTeLc1o9y3qJSSno/ty7WLFuS5blcpJJh0pOqLEOCZAACZAACZAACZAACZAACXggAZpUOAmcuG7AvH/j8cv+BJy/ZXQrBOEwmTBzAd4c2gfbV87AhiVTMHrEs1i+drPb+jl38Qq27j7ktvasDY2f8TMaPjwAx09fsKrceqRDxa042RgJkAAJkAAJkAAJkAAJkEA2CLAoCZBALgkIJ0r3H6Px8foYfPDHTTz6/WX8eSIpl61akS+L9QAAEABJREFUqsfExmPirIUYMbAnOrRtgqDAAEQWCZXxFT+OsxSyC/cfPom+Qz+y0wADR34GsSxIKLfsPIheL45Go0cGokOfkZj103KcPncZM2Yvxa59R2SeyE9KToGYWTL2izlyZkznp9/CT4vWSJ1oR+jnLF6H6Wo90d+KdVuF2kGe790R6xZMQIliEQ557lDQoeIOimyDBEiABEiABEiABEigkBDgMEmABEjAswh8tT0WZjuTjGri6x2xdpqcR0+kzexo27yBQyMB/n4OuviEJOkYsc84cPgUYuMSIJwk/V/7BE0b18S86e/itRd7IfrqDRSLKoKHWzdG5QqlMWJQLyl6nQ7jVGfK7n1H8el7g/D28L6qQ2Ut1v69QzYtnDBjJv2AoyfOo12LBihZPFLqMwZFwkNQslgkRHvIg390qOQBVDZJAiRAAiRAAiRAAh5DgIaQAAkgNdWA85euIiUlNR0NsUnlpSvXYTAa0+mZIAFvIZCQasalOMfP78nrBrcMQTg8REORRcLEIVdiMFjs9PfzQ6kSUWjbvD7eHtYXQYH+uKtcKYSHhaBR3epSUtTf2QXL16PLw80QHhqMsJAgNG1UE2s2WBwqwpAXnuqECe8PxtM92qN+rapCle9Ch0q+I2eHJOA9BHiR4T3nipaSgC8R4FhIgARIwF0ETp65KJcf1H3weTz0xAgsWrnB1rTYBLNJx0Fo2+NV1GnbH/N/XW/LY4QEvIVAkF5ByRCtg7kVI3UOupwoihe1LJW5ej0mJ9XT1QkJDpQOlMlf/yKX/PR5aQx27D2crow1cSn6mowuWvE3xkz6Ucqho2eg094ea3BQgCxTkAEdKgVJn32TgAcT4EWGB58cmuZpBGgPCZAACZCABxK4fOUGOvV7U+6d8MOUt7Bj1Zdo36qRtFTszTDig+l46blu2Lvua0z6cChGf/YdxMaYsgADEvAiAs83CoViZ69WTfRvGGqnyXm0UvnSsvKatKU2MpEWJCQmp8VuH+wdHre1t2NPdmuLnau/xJxp70I4a14Z9YV8i5CiKDCbzbaCkRFhMv7B68/ixy/etsmE94dIvacEdKh4ypmgHSTgQQR4keFBJyNPTGGjJEACJEACJOD7BL6fv0punjn27QGoX+tuBAb4ISLccpO5bfchJCQmoXeXNvKJd7vmDVChbAn8tXmP74PhCH2OwGM1g7CoT3G82Soc77Upgl+fLoHWldwzeyM8LBiDn+6Cjyb/iMUrN8i9UK5cuwmxCWynfm84sLynanmpW79pD27ExEJsHHv9ZqzUXbx8DTNmL0NiUjJqVa+EhnWqISk5FSaTCdWrlMPh42chZsKIemKJT5N69+DjKXNwMfo6Ug1GiA1vv1+wWrblaiCW84klf6J8qsEgl/+JuLuEDhV3kWQ7XkUgJS4FC59YmKksfX6pV43H3cZ63EWGuwfI9kiABEiABEiABHyewD/b9qF0iaIYMXq6fHPI++O/g9gvRQz88tUb0oHi56cXSSliQ8xL0TdknAEJeBuBSpE6PFE7GMK5Uibs9rIYd4xjQN/OGDGwl+pU+Qn3dRqMVo8Nx6jx36JTu/tl8xpFgfoj4+ItQEOe6Yohb01Esy5DsXH7PqlXFAU6nRbLft8o9bXaPAuxnGf8e4Og1+tQu0ZlNKhdFS27D5P5wtHy8VsDIJYJtev5Kuq26y9/j2Nuxcn2RKAoijhkKf/7cCbqPvi8nH32zrivZVwsBcyyUjYy6VDJBiwW9R0CJoMJ57aey1Qu7LyYq8F6e2VeZHj7GaT9JEACJEACJEACx09fgNhjoW2z+niu9yPy6fZzr4yTT6hvxcZD3PjZU/L395NP34VOr1VQLNzfZyXIX4vgAJ3Pju9O5y4y1B/aXJ5j8TkpLKJXHSHPPvEItq+cgb8XT8afCydi62/T8eqLPSWCKWOGYUCfR2VcBIOf6YptK2Zg07KpmPrRcBxY/x1a3l9Hvs1nxY/jsHn5NPyzdAoWzhot9aKOTqvFjHGvYdOvU+XyPDGjTLzqePrYVyCWCP2x4HO5PO/l/o+J4hD655/sKONZBRPeHyz7FzZYpWL5UnDXPzpU3EXSu9spdNYHFAnAY3Mfl9J5Vmc5fr9gP5kW+i5pOplRCIM7XWQEqX+AS0cFwtsEFxbj5C/NsP/LYvKI8784HUOAnxaRoX5O87xtzDmxNyrMH/56TY7HX1S9AC2EvzYcskpA+/u30H/WD37vd4HumzegnHO+0ZxalD8k4NUElCtnoftxFPw+7A79uCehXTYFSE7w6jH5qvFPdX8Qjz70ANq3aoxP3x0I8arVE2cuIiw0WC75sR93cnIKQkOCpMpgMuNmfKrPSlKqCUkpxhyNL+aXmbj5Xi/EvPowbk58BTf/25ejdgqS762EFJiMuTvH8oNSCIOoiDAUL1oEGk3Ws0OEM1MsF3KGSCzniUhbfpcxX7zRRzhT7PUBqrNTOFeE08Ve7wlxL3WoeAI62uDtBMrdVxZCSjcsLYeiqDeQIi3EqpMZhTC400WGNyKJu3oIexc/hVuXdsGYGi+Pe5f0RdyVA944HNpMAh5HQLNtObTr50C5dhFISYDm2E7o5nzgcXbSIBJwBwHd/I+hObgJSIyFEnMF2i3LoPtrnjuaZhtuJHBP1Qo4c/6yrUWxT4NIiNexligaIZ0r1r0VhP7IiXMoWTxCRCH2xkw1mOCrYlIdRkZVsjs+46ZlwB8/Wb7rVSeicnQn8MNor+NkUJ0pZvVMZ3f89uXV6vzJNwKe2xEdKp57bmgZCRQYgTtdZCQkGXDhWqJXyYkDa53yPP7vCodxiCc212NTHPTeNuac2nvtVjKS1SdXOa1/NSbZKWsqfZuA5vA2hwEqN6OhXDnjoKeCBLyagHCinD/qMATl+G4HHRUFS6BD2yb4Zt4KnL90FTGx8fhh4RpEFglFlbvKoFHd6tK4uUvWQWxauXbDTrnHQsv760o9A+cEMv2uv3DMeYXCquW4Cw0BOlQKzanmQEnAdQK8yHCdFUuSAAmQAAmQQDoCt65Cu252pqLZviJdcSbyjkCf7g+iSf0aeOiJEXjg0SH4e+teuZ+DWE4QFOgPse/DuKlzUadtfwx7dwreGd4XZUsVyzuDPLhlmkYCJJAzAnSo5Iwba5GATxPwxYuMkDLNnJ6z0PJtnOqpJAESyB4BU7XGDhXMRYrDXMzy+kSHTCpIwFsJBIbCXKaqg/XmyvWkTokRDpUfVIdKJrJjpSzHIFcEXKos3uAj3iAiNsBc+/NnWDd/AsSbRKyV2zSth3/XfYPf543HnjVfoXfXttYsHjMhkOl3fekqmdSgmgR8mwAdKr59fjk6EsgxAV+7yAiIqI7yD36LwGL1oNEHy2P5dl8jILJGjhmxoncSEOvGr16PkdO/MxuByE9MSsksm3onBEyNO8HY6kmYo0oBfkEwVWkAw5PvOSlJFQl4PwFDzzdhqvEAIJwr4cVgvK8zDC2fkAMzhxeFsW1fKaZGj0gdwqJkWuobpuksOQzzgYDYALNUiSgoiuLQm1arQZmSRaHX6xzyqHAkwO96RybUFG4CdKgU7vPP0ZNAlgR87SIj4u5euLvnRtQacEUeI6r1znL8zPQ9Apt3HECTjoPQsvswOf37meFj5Ws0rSMVmxd26DNS5jd8eADe/eQbpBqM1mwe70DA+NCzSH1tNlLeXwrDc2NhLlvtDjWY7bEEaFiWBMzFysHQZzRS3l2E1JFzYOw8FPAPstQJEw6VfqoDRZWGHaTOLJwubdW0KqZGFp3MYEACXkiA3/VeeNJocp4RoEMlz9CyYRIgARIgAU8joGgUvPdKP2z6dSr+WPA5xCv9pn67xGbm/038AdWrlMeOVV9i+eyPserPbVj1x1ZbPiOeS4CWkQAJkAAJkEBmBJS4WIS+9waiHqiHYnWrIeT5p4Fj3Eg3M17Uu06ADpUMrK7duAUhGdSIjUvAjZjYjGqmSYAESIAEvIjAffVr4NGHHkB4aDBKFIvAw60a4+8te+UbHsQbIDZu34++jz8EsWFhxfKl0PXhpvj9r+3Ig39skgRIwEcIKBePQ7l20UdGw2GQgG8SCJ4+BYE/fAvtxQtQYm7C7/eVwOCBvjlYjipfCdChouIW6+ln/bQczbsORYtuL+PhJ/+nai0/CYlJGPr2JNzXaTCadRmK3oM/hFhbb8llSAIkQAKFhYBvjnPjjv24p2oF6LRaXL12Uw7S/g0P5cuUwMXo61LPgARIgATsCSiHt0E/5nHopwyE/rN+0E98Abh6zr4I4yRAAh5CwG/TBkdLtm+XzhXHDGpIwHUCdKiorD7/cgFmL1iNgf26YMOSKfh19keq1vIzZ/E6HDlxDn8unIgty6dBq9Fg0le/WDIZkgAJeC4BWlaoCBiNJgjHeGZy4ozj0+Nff98EIa+92FOyuhWXII/irRAyogb+/n64fvOWGgPEVoaRoX7wVQkN0sNPp/HZ8bly3jTqSS4SkvNzrFYH/xUeArqlk6DEx9gGrESfgm7dj7Y0IyRAAiRAAr5PoNA7VK6oTyS/mbcCr6oX1E91b4fIIqEoWSzSdubF+vnHO7VE8aJFEBoShL6PP4hFK/6G2Wy2lWGEBNxBgG2QAAnknIAZZtXxEZuppKYa0jUulva88dGXGPXq07i/4b0yT7wFQkTsyyYnp6h/F8KEWu0BSEg2+qwkpxhhUB1TvjzGO43NpP5pT1I53KlcZvlqdflZYVAICNy6BuVmtMNAlfNHHHRUkAAJFDyBlAeaOxrRqBHM4UUc9dSQQDYIFHqHyr8HT0hc+/87ib5DP8LzIz7Fst83Sp0ITp+7DDHlW8SFlCtdXBxgfZIpE4Uz4KhJgARIwGMIiCU7I4f0RmZSrXI5m62r12/DgNfH4/9G9kfPzq1t+qJRlouqsxdu3ySdOnsJpYpH2sqIm21flRSDCVaHgq+O8U7jEif6TmWyyhf1KYWEQIj6faE4uYwOjSgkADhMEvAuAvGDhiKx77MwliotnSgpDz0CTJvhXYOgtR5JwMlfAo+0M0dG/fr7Jrsp4MvTxcXTSdHopSuWtfFFo8LxbK+H0aD23Xjzo1n4bd0WOQtF7KES4O8nikrx99PLY0JCkjwWDfOHr0qUOjZFHaWvjs86rqhQf3WUgJjqbdVZj/56DcQ0eGs6u0e9VpFtMyABEvAMAktXb8Sr70/DGy89icb17sH5S1eliO96sVGtmK3yw8I1SExKwckzF1UH+yY81LKRZxhPK0igkBNIvLoPV/ZOw/WD3yMl7nzB0tBoYarl+MTbWLt1wdrF3kmABJwSMIeEIvaDsbi2aTeu7DmMuK++B6pUcVqWSl8hkD/j8GmHiphFcv2m8yng4uLZirhyhdIY1K8L2jSrL4/iDRBr/toBRVEQFBiA5JRUa1FbPCgoQOpuJabCVyVWHZuYvuyr47OOS4xTnEyxisuqsx5TDWYkJhtyfI4N4nGvaJxCAiTgEW6H2IcAABAASURBVAT2Hjwu7Rj7xRw89MQIm6xev13q3365Dw4cPomGDw9Ap35vqs6Uhni4dWOZx4AESKDgCFze8QmO/NwEF/4ZgbN/DsKh76si9tz6gjNI7dnQ/XUYOg6CqUZTmOq2g6H3uzA16aTm8IcESIAEckCAVbySgMYrrXbRaLEnSmbTvx9s0VC2It7mcPz0BaQajDItAoMaTzUYRBQVypbAmfOXZVwE1qng1rX2Kakm+LKIMfvy+OTYDCYxTLk/gkzbnVOT6mUxGM05Psdqddk2AxIgAc8g8N4r/XBg/XcO0u0Ry5PmiuVLYfXcT/HHgs+xbcUMjHnjeej1Os8wnlaQQCEmEL3rU4fRX9k90UGXrwo/f5iadoehz/sw9BwJU60W+do9OyOBgibA/kmABACfdqi4coLr1aoqZ6HMnL0M4i0R4unlyj+2ommjWrJ6+1aNsODX9Yi+ehNx8YkQU8G7d2ghZ6/IAgxIgARIgAR8jkCJYhEITpuJ6HOD44BIwMsIpNw6DVNqvIPVSTf+c9BRQQJZEGAWCZAACbidQKF3qIiZJpM/HIrvF6xG7bbP4cnBH+LJbm3Rs3MrCfvJbu1QqUJptH58OJp0HITUVAOGPtdd5jEgARIgARIgARIgARLIWwJ+YRWg0Qc7dBIQUd1B51sKjoYESIAESMDTCRR6h4o4QWITws3Lp+L3eeOxfeUMvD2sL3RarciCeEI5fewr2PTrVPy1aBJ+njkKxYsWkXkMSIAEPJPAid8ew/El7TMVU0qcZxpOq0iABEjAmwnkoe3F67/u0HqxesMddFSQAAmQAAnkLYGYW/G4ERMrX+Bi7Wnnv0dw7GQBbxZuNcbJMSY2Xq44cZKVaxUdKmkIhQOlTMmicvlPmirdQbz9oWhkeDodEyRAAp5JIOHCJsSd35CpmM2WPZI803paRQIkkF8E2I/3ECjR8H+4u9dWlG42HuVaT8c9Tx9FaFnLbGLvGQUtJQESIAHvJCC2xpi7ZB2adx2KBzoPQbMuQ9G4wyBMmDlfDuibeSuw7p9dMp7T4K/Ne/HFN4tzWt1pvavXY9Chz0g88OgQueKk89Nv4dffNzktm1MlHSo5Jcd6PkPg9PpTciym1NsbE0sFA68lULHjL6jcdZUU6zTxih0WyrTQa/05y8xrT27hNpyjJ4FCTSCwaC0UqzMYkTWehl9ImXxncezYEZw/fy7f+2WHJEACJOAKgdQLNxG77gDi1h+C4UqsK1VcLiMcJhNmLsCbQ/vIFR0blkzB6BHPYvnazS63caeC5y5ewdbdh+5ULFv5JpMZXR9uhnULJmDL8mnyzY0ffD4biUkp2Wonq8J0qGRFh3k+T+CH9j9g5fBVcpwpcSn48ZEfZZyBdxMILv0AQsq0kKIoejmY4DJNZVropYJBPhBgFyRAAiTg/QQ2bvwbnR5th2efexJP9O6Kfv164sIFz53a7v3EOQISIIHsEhBOlItvz8eNHzbi+ncbcGHkPCTusjw0zm5bGcuL5TITZy3EiIE90aFtE7miI7JIqIyv+HFcxuLYf/gk+g79KJ1+4MjPIJYFCeWWnQfR68XRaPTIQDl7ZNZPy3H63GXMmL0Uu/YdkXkiPyk5RTo+xn4xR86MEbNLflq0RupEO0I/Z/E6TFfrif5WrNsq1OlEbNUxoM+jKFksEqEhQejcvikSEpNw6Kh72IjO6FARFCiFksCRX4/g2pFr6cZ+9b+rOL7meDodE4WIAIdKAiRAAiRAAhkIjB//EWJibtq0J0+dwLffzbKlGSEBEiCBgiYQs3w3YLazwmRGzG977BQ5j544fUFWbtu8gTzaBwH+fvZJGY9PSJKOEZlICw4cPoXYuAQIJ0n/1z5B08Y1MW/6u3jtxV6IvnoDxaKKyNkjlSuUxohBvaTodTqMU50pu/cdxafvDcLbw/vip0VrsfbvHbJV4YQZM+kHHD1xHu1aNEDJ4pFSn1WwfY/l7XB3lSuVVbFs5dGhki1cLOxLBKIPRDsdzvVj153qPVFJm0iABEiABEiABPKOwMWLF3D12tV0HZjNZqxfvw5DXx7gVN5489V05ZkgARIggbwkYE5KhfFavEMXYgmQgzIHCuHwENUii4SJQ67EYDDK+v5+fihVIgptm9eXL4QJCvSHcHKEh4WgUd3qUlJSDViwfD26PNwMYj/TsJAgNG1UE2s2WBwqoqEXnuqECe8PxtM92qN+rapClakcPXkOH03+CYP6dUFkkdBMy2U3gw6V7BJj+dwQ8Ki6kXdHObWnaPWiTvVUkgAJkAAJkAAJFC4CoWFh0GgcL5eTkhKxZ88up7Jv397CBYmjJQESKFACSoAe2qhgBxv0pd2zZ2DxohGybbHBq4zkIggJDpQOlMlf/yKX/PR5aQx27D3stMVL0ZaVBItW/I0xk36UcujoGdvbeEUl8UZecbyTnL90FS/+7zO0aVYPg57ucqfi2cp3/AuRreq+Xpjjyy0B5cpZaFfMgG72O/Io0rlt0131a3S/B1EZnCrCmVKxdUVbFxf3Xcb2r3fjwPz9iLvs6Pm1FWSEBEiABEiABEjA5wiEBIegVau26calKAp69HgSkybNwNiPJ8i84KBgmRa6jz/6TOoYkAAJkEB+EQjvVA9QcPufRkF4x7q307mIVSpfWtZek7bURibSgoTE5LTY7YN4e+7tlGPsyW5tsXP1l5gz7V0IZ80ro76AeIuQoijpXsUcGREmK3/w+rP48Yu3bTLh/SFS72ogXuf8xMDRaN6kNsa88Ty02qxcIK62eruce1u73S5jJAAl7ib0UwdD+88v0Py3VR71UwbC760HMxXdvDH5Sq7v6r54ZOLDsk+/ED/0WdlHxkWwbeo2zGj9Pda+9wfWjFyLb1t9izObzoosCgl4LYHDSw9j88QtmcqNE9e9dmw0nARIgATygsCbb4zC0KGvoUXzVmj/UAd8MHosXh76KurXa4iaterILrU6nUwLXe3a7rmJkQ0zIAESIIGsCKTlhbS6B6XG9ERE36aIfKY5So97AoH170rLzd0hPCwYg5/ugo8m/4jFKzfIvVCuXLsJsQlsp35vODR+T9XyUrd+0x7ciImF2Dj2+s1Yqbt4+RpmzF6GxKRk1KpeCQ3rVENScipMJhOqVymHw8fPQsyEEfXCQoLQpN49+HjKHFyMvo5Ug1FuePv9gtWyLVcC0V6XZ9/G/Q3uxfNPdsTlKzdw/tJVaZcr9V0pQ4eKK5RYJtsENDtXQTfuSSAlKX1dg6MXM32B/E9VaGX5stHotek63zZte7q0McmAXV/uTKdjggS8jcB/v/6HrZO2ZCo3TtzeeNHbxkZ7SYAESCAvCAQEBKBnj94YM2Y83nnnA7Ru3S4vumGbJFAoCHCQeUdALPEJbXsvhHNFV8x9e4QIiwf07YwRA3upTpWfcF+nwWj12HCMGv8tOrW7X2RDoyhQf2Q8KDAAQ57piiFvTUSzLkOxcfs+qVcUBTqdFst+3yj1tdo8C7GcZ/x7g6DX61C7RmU0qF0VLbsPk/nC0fLxWwMglgm16/kq6rbrL98AFHMrTrYnAkVRxCFTOZG2oe5v67bg4Sf/h4eeGCFl3NS5mdbJbgYdKtklxvJ3JpCcAN2v06EYUpyUVWBs0QspH62B4Ym3Zb6pdiuZttfJjAIMbp29BUNCqoMF145dc9BRQQLeRKD6o9XRZNh9UopUjJCm3/3o3TIt9BGV3LPeVjbMgARIgARIgAS8mwCtJwESUAnoVUfIs088gu0rZ+DvxZPx58KJ2PrbdLz6Yk81F5gyZhgG9HlUxkUw+Jmu2LZiBjYtm4qpHw3HgfXfoeX9deTbfFb8OA6bl0/DP0unYOGs0VIv6ui0WswY9xo2/ToVO1Z9icAAP5QoFoHpY1+BWCL0x4LPsXfd13i5/2OiuNSLWScykUnwSJsmsm/Rv72MVR01mVTJtpoOlWwjY4U7EVAunwZSEjItZrq7YaZ5npIRVi4MuiC9gzlRVaIcdFSQgDcRqNalGu4ffp+UyMoWh0r1ztVlWugjKkV603BoKwmQAAkUCAHx9p/p0yfj/ffflP2bjJY3V8hEgQbsnARIgATylkBURBiKFy0CjSbr2SFiw1ixXMiZNWI5T0R4qLMs+UYf4Uyxzwzw95POFeF0sdd7QpwOFU84C75mQ2iEZURiCpbZbImnhcYmnWCuVDct5dmHxoMbpTNQG6BD/QEN0umYIAESIAESIAESyAWBXFY1pcYj7vzfmUpC9K5c9uBYPS4+DgNefBpz5s7G9h3bZIG4uFhcunRRxhmQAAmQAAkUHgJ0qBSec51vIzVHlISp3D2W/oRTBRaniuHJd2DsMsyi94Kw8ZDGGPjn02j3QRs8OK4dnl3/LMo/UM4LLKeJJEACJEACeUWA7XoWgeRbJ3F8ycOZytk/B7vd4F27duDmzRvp2hVXOmvXrkqnY4IESIAESMD3CWh8f4gcYUEQMDwzBoaHnoWp+n0wNewAw3NjYarZsiBMyVWfpWqVQKP+9XBvz5oIKRGcq7ZYmQRIgAQKgAC7JAGfJqDRBSOkTHMpQcXqy7Fq9Ha64hadzHBTcOniBactnT5z2qmeShIgARIgAd8lQIeK757bgh1ZYChMrZ6Eod+HMHR/FaYqXCpTsCeEvZOAtxCgnSRAAgVFICUlVb5S0pxhuW5B2eNKv/7hFVG562opZdtMl1X8wivLtNCXaz1N6twZ1KlTz2lzTRpb3nbhNJNKEiABEiABnyRAh4pPnlYOyt0EUuJScGnvZSTeSHR302zP2wnQfhIgARLwAgKdn34L97Z6Jp1M+26JtFw4UKZ9vxT1HnoBbXq8ghbdXsbeg8dlHgNHAtWq3YOnnnw6XYafXo927dqn0zFBAiRAAiTg+wToUPH9c+wxI1SO74Z+1ms20a77AVCfgmmc6T3GamDdB+sxrdY0zOs6FzPrz8TKYSs9yLrsm8IazgncPLYYZ9Y+j1Mrn8DZHZNhMtB55pwUtSRAAt5KQLxqUryu0ipPdmsnh7LnwDFM/XYxfpjyFvas+QpdH26OV0Z9AZNJ7AwiizDIQGDgwKFYteovfD5hqswJDOKyYAmCAQmQAAkUMgJ0qBSyE55XwxXOEeEsUU7sybQLJT4Gysl/b8uVM4DYtNaZHrZ/DhHRj+7rkQ76vFBc2H0R22buTNf04WWHcfx3339yl9XTzHRAfCBxdf9XOL36Kdw4PAcxJ5bh2NpXceL3Fzx2ZKYDCkyXsn5VnccaT8NIgAQKjECxqHBUKFvCJkXCQyD+/fHPbtzf8F7Ur3U39Hod+j7+kFz6c/i4+ndaFKCkI7Br9w4IOXz4EJKSktRnQ2YYDKlSJ/RC/v038+uhdI0xQQIkQAIk4NUE6FBx++krnA0qV05DOkviYvIcgOhHc3xXnvcjOji/55I4OEj0wSsOOl9UZPY009fGGnNsgcOQrh1ZpF4kGx30BakwLNIivkEgEnsEILHZOPEfAAAQAElEQVSNKk/5w3y9IC1i3yRAAt5EYMHyv/DOuK8x7bslOHP+ss30i9HXULFcSVu6eNEiMh599aY8MrhN4Oy5Mxg2bKBN3nrrNfXZkIKEhASbTuS//r9h0tlyuyZjJEACJEACvkhAA18cFcdU6AmImTLaDQug2fsnkBSfYx4B4QFO6/qH+jvV+5oys6eZvjbOpBtHHIdkNiLl5jFHfUFp1I9x8kd+gN1KJNNuDVJ/0OeJRTfP3sJvL/2GGfVn4qsms7D2jbXcQyhPSLNREsgdgV9/34RZPy13Khu377c13r5VI7S4rzZKlyyKPzbuxmPPj7I5VW7FxiPAP/3ftaDAAMQlWL5wQgN1KBLi5/ESGmT5PtRqlGzZKibLhge7Nr4zJw7bmGYViY+PQ3LiTQh2opxWq8mWTfnJO8BPi0BVctqnn47PZ8U5ppCA1xCgoW4lwG9At+JkY9o/frDtkSKW5tiLdt1sJ4DMDjpNhj1V7NvQz3rVoXxGhW7OB9B/9Tq0K7+E7uePoP/saShxOXvKVrVdJQRFBqXrQqtedFRpXzmdzlcTCzJ5mulr4w0t/6DDkPQhZeAfUc1BX1AK0xkFSHDs3bQ/b77Gl722Gkd/O4qkG4mIi47H/p/3Y/OEzY4GUEMCJFCgBG7FJeD6zVinkpCYZLNtyLPdMKhfFwx+ugvmTnsXoSGBWLdhl8wPCw1GckqKjFsDUTckKFAmUwwmpKQaPV4Mqp3CYLHJbnbshXopkmJwbXwGo0l04ZKkqsxSc2hTduzPbVkxJoPJnOPza1TrugSEhUgghwRYjQQ8mUDeXIl78ohpW54SUKLTlv7Y75VijUe7uBY7/qZl+ZC1XrrjviztV66chWb/hnRlxN4tmq3L0ulcTQSE+eOZ355EwxcbomLriqj7TF30XtYbYWXDXG3C48q542mm+vAPevWJlDeI9QTo1aeDmdlb9v53EBhVw1oU+sCiqPLgFM8aY4Tzr2tNOHJsp0acSHXUWvVoz0aj6o79eVIN0/+c23zWpb50WtX5k74qUyRAAnlE4Knu7TBySG+n8mCLhk57FfukFIssgsRkixOlVPEonDp7e4mrdamPdelPcqoJCclGj5fEFKMcr7i/z469qj9FZeHa+FIMorTs5o5BfFIqklR2oqDY4Dc7Njkru+Xr3VjQfxl+Gbgcu34+4LbzYTCaIRw/zvp0RUeHijjD6YQJEiCBQkRAXDcXouFyqHlNwNi2L1Kf/9SpGNr2cdK9442XqVJdp/VFu4bnxjpp47ZKueB8iYYiNsC9XSxbsfDy4Wj2RjN0+aYLWo1qhaLVimarvqcVdsfTTD+9FkWC9V4hYiq3OAfhWdhbolRVNH1xHx4YdBT39d+NtiOuoGyNrh41vshqOvjXFyNJL5FPIsd26rWWPwHBAenPZ3CAPn0naSmt6nhx5byHBDqvn9YMDyRAAvlMQOyX8t38Vbh05bp642zE8jWbsf/wSTSpd4+0pE2zehDLg3btOyrzZy9cjRLFIlCtcnmZz8CegKNDRcyIEWJfSsQ1Gq04uEXWvbUOf7z7B46uPAqxOf6qV1Zh+/TtbmkbYDMkQAIkQAI5JaDJaUXWIwFnBMzFKsCsOkScCYrf5ayKoy4kItM2TFUaOJa305hLV7FL3Y6ai/Gi0ErDHU8zk9SngFdikuENYk679r166872xmvKIDGgGpJTjbiVkOpx49NOTYR+WCq0rYzQPWZEwFfJiKt/53Fldp7EOMXn4laCId1YY9WnqlVaVxRZ6aRUk7LpymXW7s24lHT1mCjEBK6eg9jTKjPBrWuFGE7+Dn32gtVo2+NV1G3XHyPHzJQzWhrUvlsaUffeKhjYrzP6Dh0j839e+ic+GzUY1llsspAnBZ5mi+q5VxTHB0TuNPPIcse9vg4uPOjOLtgWCZAACZBADgjQoZIDaKziuQTMxcrBVLN5OgPNweEwNemcTsdE1gTu9DQz69rMzSsCSjjg96IBAdNS4P9hCrQPuL6WP7s2df6sPap2rIqAiECEFA9GzV41cf+r92e3GZYv5AS0GxfJPa3EvlbORHNoc6EgVNCDLF+mBNbNn4C/F0/GqjmfYO+6r9GvR3ubWYqiYOhz3bFz9ZdYM288tv42HfVqVrXlM2JPwNFx4qixL5/7eHx0PJJjkx0aunnqJkxGs4OeChIgARIggfwjQIdK/rH26Z6MbfrIZTrmSnXyfJxi6U/qC59l2o/hyfekLcZHBsDQ6y2kvvY9zCFFMi3PDOcEsnqa6bwGtb5EoEi5MHT8oiMG7noRz299Ae3GtkOg6lzxpTFyLJkScFuGuWhZmCvWloLQKNmuuWgZmRZ6hFl0MoNBnhJQFAVREWEoV7o4dFotnP0L8PeTbwHizBRndApOF6w6tYveU8zBgAqt7oKGe1Y5cKGCBEiABPKTAB0q+Unbh/syF7/LskwnC8eFcLZIZ0jaHivG1k8BZjNMqhMmnb6Nqs+ClVxOpF6gZ1FE2mJs3gOmOq2BgOCsijLPCYE7Pc10UoUqEihAAuzaUwmYmnaHcIALMdawzHAyNn3cpjPdY9F5qv20iwQyEggNdW1Tep1OB39//4zVc5xuNaolgovdfutgkYpF0HTEAzlujxVJgARIgATcQ4AOFfdwZCsuEDCHZNgbpcRdgPrEDBn1qnMG/FfgBBTlzk8zC9xIbzXAQ+xO4V4nHnImaAYJkIC3EKhVuw7q1q1vk5o1a0NsSKvVam06kX/ffU1RvFhxtw2rbJOyeGHbAPRd3RdPr3saz/zxDIo5mbXitg7ZEAmQAAmQgEsE6FBxCRMLkUDhJsDR+xaBE2tPYEb9mTi35Zwc2J/v/oGYMzEyzoAESIAESCBzAsFBwZgy+UubjB37ufpsSEFwcIhNJ/LHfjwh80ZykRN1dxQiKkXkogVWJQESIIHcEYi5FY8bMbHSmWxtaee/R3Ds5Hlr0uOOcfGJOHshGvEJSW63jQ4VtyNlgx5AgCaQAAlkQeCPt9ch6UaircSti7HYMnmrLc0ICZAACZAACZAACWSXgHbLUmjXzc5UlLib2W2S5T2EgNFowtwl69C861A80HkImnUZisYdBmHCzPnSwm/mrcC6f3bJeE6DvzbvxRffLM5pdaf1EhKT0O25d9Ck4yA8/OT/VJsHYsykHyDG47RCDpR0qOQAmvursEUSIAESyB8Ct87eQlx0vENnVw5ecdBRQQIkQAIkQAIkQAKuEtBsXqY6U37IVJBAh4qrLHNS7uito/ju6HeYc3wOzsSfyUkTmdYRDpMJMxfgzaF9sH3lDGxYMgWjRzyL5Ws3Z1onuxnnLl7B1t2Hslsty/ImkxltmzWQb7j7d903mDHuVcxZvA57Dx7Lsl52MnPmUMlODyxLAiRAAiTgMQT8wvyc2mK/2aHTAlSSAAmQAAmQgA8SEDdcl65ch8Fo9MHR5e+QTPd3hrFtXynm4HDZubHJozIt9AjiWzcllDwIhBOl7Yq2eHfnuxi5fSRaLG+B1edXu6WnmNh4TJy1ECMG9kSHtk0QFBiAyCKhMr7ix3EOfew/fBJ9h36UTj9w5GcQy4KEcsvOg+j14mg0emQgOvQZiVk/Lcfpc5cxY/ZS7Np3ROaJ/KTkFCQmpWDsF3PkzJjOT7+FnxatkTrRjtAL58h0tZ7ob8U6x9nWIcGBeOm5bvINd1qtBqVLWN4uGBYSLJpwi9Ch4haMbIQESIAEvINAQHgA7u50t4OxlR6q7KCjwnMJJF7Ziyt7p+H6we+RGn/Rcw2lZSRAAiRQgATWbdiFe1s94yDJKanSKrHEQCwFaNvjVdRp2x/zf10v9QxyRsB4XxfVedJPCoItzhPT/bd15izeBpqzHjOvVdhyphycArP63zpuo9mIqQenWpO5Op44fUHWb9u8gTzaBwH+jg/qxD4lwjFiX+7A4VOIjUuAcJL0f+0TNG1cE/Omv4vXXuyF6Ks3UCyqCB5u3RiVK5TGiEG9pOh1OoxTnSm79x3Fp+8NwtvD+6oOlbVY+/cO2bRwwojlO0dPnEe7Fg1Qsnik1DsLxOyXCTPnY+g7kzGoXxdUqVjGWbEc6ehQyRE2VsopAeX4buhnvSZFu+4H2YxyfI9MC71VJzMYkAAJ5AmBBz95CC3fbYmgYsGy/YYDG6HOU7VlnIHnE7i84xMcmX8/LvwzAmf/HIRDP9ZE7DneBHj+maOFJEAC+U1A3GCKp+niKbq9+Ol18in3iA+my6fXe9d9jUkfDsXoz76DuPHKbzsBsEsSyDGBeEM8LiRYnB72jRy7dcw+meO4cHiIypFFwsQhV2IwGGV9fz8/lCoRhbbN6+PtYX0RFOiPu8qVQnhYCBrVrS4lJdWABcvXo8vDzRAeGoywkCA0bVQTazZYHCqioRee6oQJ7w/G0z3ao36tqkLlVIQz58z5aOnUuXTlOlLVtp0WzIFSk4M6rFIICCRG70ZK3Hm3j1RsRqWc/BdSrpyR7SvxNy1pob9yWuoYkAAJ5B0BfaAO9Z6rh5J1SshOyjQsLY8MvINA9K5P0xlqNiTiyu6J6XRMkAAJkEDeEvCe1gP89ahQtkQ6URQF23YfgtiwsneXNtBptWinPn0X5f7avMd7BkdLSUAlEKwLRukgx2u5KmFV1Nzc/xQvanmz2NXrMbluLCQ4UDpQJn/9i1zy0+elMdix97DTdi9FX5P6RSv+xphJP0o5dPSM/H2VGWoQHBSghnf+uadqBUz84CUs/2EsVq/fjpV/Oi4PunMrzkvQoeKcS6HVXj80G/tmFseRBU1x8Puq2PptMxgSr7qNh7lyXaQ+/2mmYmzTx219sSESIAES8DUCKbdOw5Qa7zCspBv/OeioIAESsCPAaKElcP1mLN76eBZGT/gev63bYtsr5fLVG9LJ4uent7ERyw0uRd+wpRkhAW8hMLTGUCjqf6u9WkWLITWGWJO5OlYqb3HWrElbamPfWEJisn1SxoWDUkYyCZ7s1hY7V3+JOdPehXDWvDLqC/nWHUVRYDabbbUiI8Jk/IPXn8WPX7xtkwnv53xcYqZLsahwXL7ivt9zOlTkaWIgCJhS4nB+w+swGeJEUsqNsxtx5V/3rL8TDZpDImCuVDdzKX6XKEYhARIgARJwQsAvrAI0+mCHnICI6g46KrybAK0nARLIPYESxSLx7BOPoGL5UrKx/304Q+7JIBK3YuMhlgOJuFX8/f3kkgCR1moUhATofFb0Og38VMmrMar4BEYE+Wk9kmGQvw7CxtyMXw7QQ4InKz+JdR3W4cMGH2Jco3H4u9PfaF+mvVusCw8LxuCnu+CjyT9i8coN8nfkyrWbEJvAdur3hkMf91QtL3XrN+3BjZhY+VYd4dgUyouXr2HG7GVITEpGreqV0LBONSQlp8JkMqF6lXI4fPwsxEwYUU8s8WlS7x58PGUOLkZfR6rBCLHh7fcLXN9sd/f+o7L/6Ks3kZKSKu0Xe6/Ur+W4n6CwLyeiyUklZ247NAAAEABJREFU1vFNAonXD6pPPmMdBpcYvctBRwUJkAAJkEDBEChe//V0HSu6QBSrNzydrgAS7JIESIAEPI5AreoVMWJgL4h9Fka9+jQ+/N9z8uZKvNEnLDRYLvmxNzo5OQWhIUE2lUa94/ZZUYC8HBsURXLMyz5y17ZqnmpjbtpQW/Con6phVfFM1WcgnCvlg8u71bYBfTvL36WPJv+E+zoNRqvHhmPU+G/Rqd39sh+NylL9kXHhqBzyTFcMeWsimnUZio3b90m9oijQ6bRY9vtGqa/V5lmI5Tzj3xsEvV6H2jUqo0HtqmjZfZjMF46Wj98aALFMqF3PV1G3XX/5BqCYW7cf/iuK5XMmO3ASiNky079fgtaPD0e9h17AO+O+xsghvdV+6FBxgouq3BLQB1n2U8jYji4TfcZyTJMACZCANxAwmcy4ej0GMerTSW+wN6ONJRr+D3f33IzSzcajXOvpuKfPfoSWbZWxGNMkQAIkQAIZCBSLsuwFYVCfdJcoGiFf1Wq/OeWRE+dQsriljFH9W3ErIRW+KsmpJiSlGPNsfOJvrcAfl2TIsz5yc27iEg0QNuamDTG+wiJ61REiZnttXzkDfy+ejD8XTsTW36bj1Rd7SgRTxgzDgD6PyrgIBj/TFdtWzMCmZVMx9aPhOLD+O7S8v458m8+KH8dh8/Jp+GfpFCycNVrqRR3h/Jgx7jVs+nUqdqz6EoEBfihRLALTx74CsUTojwWfQ2wg/XL/x0RxqX/+yY4ynllQ655K0l7R16o5n2DP2q/Rr4d7Zu5Y++QMFSsJHiGmkgeVaOxAokiVxx10WSoSY6FZPwe62e9Ct2gCNMd2ZlmcmSRAAi4QYBG3ENi84wCadBwkn3488OgQPDN8rJw+am183NS5uLfVM+lEbJhmzfeUY2Ax9aKkzmBE1nga+mDLVHZPsY12kICvE0hKSsI3334pZc5P38vhJiXf1v00x6KTGQwKlMCcxeuw898jSExKgXizx5c//oom9e6BeNVro7qWpZJzl6yT+6qs3bBTvuGn5f11C9Rmdk4Cnk4gKiIMxYsWkTOcsrI1OCgAYrmQszJiOU9EeKizLPlGn0DVmWKfKX5nhXNFOF3s9a7EFUWB6Ktc6eIQjiFX6mSnDB0q2aFVCMpWfHQxSjZ5H2F3dVAv1J9Boz5rEFbhoWyNXPfd29D9/i00/22BZsdK6L55A5oj27LVBgt7PwGOgAQ8kYCiUfDeK/3k0w/xpEP8sZ/67RKbqWIztFYP1IV4emKV8aMG2fIZIQESIIHklGR8qzpUhMyZO1sCSUm26P6Y9xUabPkB+lmvSdEtmQSYzVAun5Zpq167VNXLmgzykoB4S0i/lz9Cw4cHoG2PV6Xj5IP/PSe7FK9pFU/VhSO9Ttv+GPbuFLwzvC/Kliom8xmQAAmQgCsE6FBxhVIhKqPzj4CYTl6x40KUbz0NUZXaZWv0ypUz0Jw95FBH+fcvB52HKWgOCZBAISBwX/0aePShB+TTD/Gk4+FWjfH3lr3yIts6fLF+Xrw60yoli0Vas3gkARIoxAQ025ZLp0jIka149tkBTuWJLt1QO0SBcvJfi1w8BrmXREqiJZ2m11xQ9cj8n27OB7IvJT73rynNvBffzxHLEcRSATHVf+PSLzB32rvpHCZtmtbDv+u+we/zxmPPmq/Qu2tb34fCEZIACbiVAB0qbsWZn415Zl/KlbNODdPcuORUTyUJkAAJFCSBjTv2456qFWA/hXTb7kPyFZufTpsnp4oXpH3smwRIwHMIKNcvSaeIX8INPKc6VJzJo492c4vBmjMHZV9mo8Et7RXmRsRSATHVv0h4iFMMWq0GZUoWhdgU02kBKkmABEggCwL551DJwghm+Q4BU8XaTgdjqlTHqZ5KEiABEnAHAaPRhFk/Lc9UTpy56NDNr79vgpDX0jZUEwXuvfsudHukOe4qVxJnL0ZDTBVfvd6yZFFRgJKRgT4rkaH+CNBr8mx8QQE6gRjhQfo86yO350ejnuPiETk/x+IzIgfJgAQ8hMC0WtMwseLETCUlLsVDLKUZJEACPkegkAyIDhUfPtExJ5dj79SgTOXUil7uH31gKAxdh8PsF2hr21ThXhgfcM8TG1ujjJAACZCAHQEzzLh+MzZTsX+Lg6i2cft+vPHRlxCv0by/4b1CJUUsBxr6XHe5U/3kD1+Wy4MWr9wg88xmIPpGos/KjbhkJKWa8mx8icmWJ+2xCal51kduz49JPcdXbub8HIvPiPywMPBpApptv8nlONb9UOyPOhf3RlGiT2fahmgPt66lMVQ/lGkxHkiABPKWAFsngZwQoEMlJ9RYJ0sCpsYdkfr+MqQOmYqUkXNgeHEioDpasqxkl2nadRLGz1fA8OZceRRpu2xGSYAESMCBgFiyM3JIb2Qm1SqXs9VZvX4bBrw+Hv83sj96dm5t0zuLiP1T4hOSbVnihttXxeoMyKvx2dpXaeZVH7ltVzVN7B+KnLYj6lN8n4By/aJcjmPbJyVtXxSZdrY3ivjwZ/SLJCdk3oba3m2Kyu1oDmKD9w3G8JPDpfiF+MkWnOlkBgNvI0B7SYAEPIAAHSoecBLyyoTwip1QZ0iClLs6zJfdhFd8VKaF/q4OP0tdXgXmMncD4dnbKd18OQamn/6B+Zz6ZCbFII8yfelmXpnJdkmABAoRgaWrN+LV96fhjZeeRON69+D8patSEhKTJIXPv1yA46fOI9VglK9T/mnRWjRrXEvmMSABEiABQcDUSH1w9PynSHUihs5DRZH0ItaCZfCLmItXcFrf2qY5ODytjYyemDS1Vx5oNAmQAAn4HgE6VHzvnHr1iMzHLzu133z4olN9TpVnt5yDVc7vuGB5IplqsulE3gVVn9P2WY8ESMAzCew9eFwaNvaLOXjoiRE2Wb1+u9Rv2XkQnZ95G3Xb9UevF0fjoZYN8XTP9jKPAQmQQCEjkMlwzVGlYK5U17mIh0mZ1Eun9g9yXj+tXUWrSyuewROTpuWBBEiABEjAMwjQoeIZ54FW5BGB2Itx0kly89TtGS5JN5PwS++FNvn1hWXyjYap8Sk2nchfqurtzbr631XZVuKNRHs14yRAAl5E4L1X+uHA+u8cRGxEK4bx88xR2LJ8Glb+NA47Vn2JMW88D/GGCJFHIQFPJ0D7SIAESIAESIAE8pcAHSr5y5u93YGAUrmE0xJKtVJO9XdSHl1xVDpJ9v64905F75i/8ZONsq1Ley7dsSwLkAAJeC+B0JAglC9TAoEBlv0GvHckHm85DSQBryJgXepjqt06U7ujo53PtM20QiYZhiffk0uCEBaVSQmqSYAEckJAo/6O6rdshG7zRuCfDRBxe9GdOJaTZlmnEBOgQ6UQn3xPHLpSIhyap5pBKateQPjp5FGmSxbxRHNpEwmQQKEiwMGSAAkUZgK2pT5FimeKYd6fGzDo31s2GXc0DmJdsSGytHSQWPdIcbrXil2rpvI15JIgOxWjJEACbiDg/9cfiOjdHWG9ugGPdpJxkbZK4MypbuiFTRQmAnSoFKaz7SVj1dSvCO0rHaD7uLc8inRuTY9LW/oj9kYRe6a40p7JYMLJf87i9KazXOrjCjCW8TwCtIgESIAESCBfCRw5cwa7bhlt8l+8CWJdcRK00kFi23vF1b1WwH8kQALuJGAsVhypTe6HQRVUrSqbNqXphN5YqbLUMSABVwnQoeIqKZbzagLWpT9ib5Rfn1/mMBaz2SweIKXTp8Qm47uuczGnx3wu9UlHJu8SbJkESKBwETBtOQrj+OUwvDkXxokrYN5/tnAB4Gh9jsBdd1VyOiZ/f3+neipJgATyl0BKq7a4MW8Jbi1YCgwbLjtPTtMJfcKLL0kdAxJwlQAdKq6SYjmvJhBSMgRlm5SVUrpBaadjEW81tM/QwoSK5jNSKpQywr9IgH22iFNIgARIgARySMB86gpMC7bAfPEGkGKA+ew1GL9dD9yIz2GLrEYCBU+gV8+nEBIS6mCITqdz0FFBAiRAAiTg/QQ03j8EjsB1AoW3ZNWOVfH4vMeldP66swMIRXF8LaE/kvGC8pOU5zVzULpezjbGdeiMChIgARIgAZgPX3RKQThanGZQSQJeQKBs2XKYpz79fuedD/DS4OEY+fo7XmA1TSQBEiABEsgpAc92qOR0VKxXqAgY/tIgvkagU0kZq3cLC+3FC3JTObc0xkZIgARIgARIgARyRCAl7jwSo3fnqG5+VQoPC0f7hzqg1xN9ULmKZY+G/Oqb/ZAACZCAVxPwQuPpUPHCk0aTXScQrISidER5FKkQgdz8S7mvKaKqFZVLhgKKBOamKdYlARIgARJQCSjVnM/6U+4qpubyhwTSEzAkXsWxRW1x6PuqOLKgKfbNLI7rh2anL8QUCZAACeQzAXZHAnSo8DPg9QR0LU0IPpgoxX96shyPNk1X60gF9NzVHXX61pZ6EWh0GukYSbenihnQaC36cjXCUdHvMirgnCgOU9lyiBv5Dpq90UwuGSpVr6TU+0pgMplhNJqcDkfkXbpyHQaj0Wk+lSRAAiSQUwLCcaLpcR+UUqrD208HpVwUtM+2AiKCc9ok6/kwgSv/TkX8xc22EZoMcTi/4XWYUuJsOo+MRMdIs8wxCTCt/tdBuBGzxMMg/wiwJxLINYGYW/G4ERML8VIPa2M7/z2CYyfPW5OF6kiHSqE63RysIOAX4icdI7Y9Vb7qDCiAPtSif+y3Z9Hl8MfotO4VGDZvRfzW3TDUrgtf/Ce+CEdP+A4ffP69w/D+2rwXTToOQtser6JO2/6Y/+t6hzJUkAAJkEBuCGjuqwrtiE7Qfdwb2uEdoNQsl5vmWNeHCSRG73IYnSk1FonXDzroPUlhvnLLYs5N1aHy+16YMohx0TYYf9kqN2W2FGSYngBTJEACnkDAqD58nbtkHZp3HYoHOg9Bsy5D0bjDIEyYOV+a9828FVj3j+P3tMx0MRD3Hl98s9jF0tkvdv7SVTR6ZKDN5uy34LyGxrmaWhJwD4EzZ07hrbdeQ4eObfDY4x0x4fNxiE/wjjc4GCpXhbnq3e4B4YGtrF6/DS26vYyFy/9ysC4xKQUjPpiOl57rhr3rvsakD4di9Gff4dzFKw5lqSABEiABEiCBvCagCyrhtAt9JnqnhfNDmaEPpViYRVMkCMq9ZS3xUkWAAD/ArCZjEmDedATGSSthvnRTVfCHBEiABHJI4PBhmL78EubvvgNOncphI86rCYfJhJkL8ObQPti+cgY2LJmC0SOexfK1t2cOOq/puvacep+xdfch1ytko2RsXAIGjZyAhMSkbNRyrSgdKq5xYqkcEhj9wTvY8M9fiI29hejoy1i8eAF++lH9Jc9he6zmPgLNm9TBglmj0enB+x0a3aZ+mYkvnN5d2kCn1aJd8waoULYE/tq8x6EsFZ5DYNWlZXhv32t489+h+OXcHM8xjJaQAAl4LAFvMaxIlccdTA0q0Rh+YRUc9B6lKOqOc64AABAASURBVB4uzVHCg6CpVd4Wh/rgQsyOlQoRmM0w7zghYhQSIAESyDYB4UQxNG4M04gRML78Mgz16sH822/ZbsdZhZjYeEyctRAjBvZEh7ZNEBQYgMgioTK+4sdxDlX2Hz6JvkM/SqcfOPIziGVBQrll50H0enG0nC3Soc9IzPppOU6fu4wZs5di174jMk/kJyWnQDzkHfvFHDkzpvPTb+GnRWukTrQj9HMWr8N0tZ7ob8W6rULtIAajEa9/OB31a92N9q0aO+TnVkGHSm4Jsn6mBIQT5ciR/xzyd+7c5qCjIv8JBAX6o2SxSAQHOW6ye/nqDQgHip+f3mZY5QqlcSn6hi3NiGcR+P7kTIzaNwKrL/2KPy6vxieH3sfYg+96lpG0hgRyT+COLZgOKzCdVe5YjgW8i0BYhYdQufNyRNZ4BmF3dUDJJu+j4qOLvWsQVmsTU6yxdEdz2n4r6ZRMkAAJkIALBIzjxwOqY9ZWVHUimCZMsCVzEzlx+oKs3lZ9wCojdkGAv59dyhKNT0iSjhFLyhIeOHwKYpaIcJL0f+0TNG1cE/Omv4vXXuyFaPW+o1hUETzcujHE/caIQb0gRK/TYZzqTNm97yg+fW8Q3h7eV3WorMXav3fIRoUTZsykH3D0xHm0a9EAJYtHSn3G4JOp85CSYsDbw/pkzHJLmg4Vt2As+EZS4s7j+sHvcWXvNCRe3VfwBtGCAiMgvL/C0+tMnC3vcWboLdUTLbzP9nn+6hdmbFyCVAX561A6KtArREm7ryoV6bq9AX5aRIb6ecX4rOdh1WXHG4u10SuyHIMYpzihGccaFeYPf70my7rWfp0di4b7i2YpkgCD/CJg/FOL+KaBSOwWgMT2qnQOoGMlv+DnUz8h5dqgXOtpqNhxIUo0/B90/hH51LObuzE63wheqZG2JMjN3bE5EiABHycQFwecs7xMw36k5sOH7ZM5jguHh6gcWSRtCaNI5FAMBqOs6e/nh1IlotC2eX3V0dEX4kHvXeVKITwsBI3qVpeSkmrAguXr0eXhZggPDUZYSBCaNqqJNRssDhXR0AtPdcKE9wfj6R7tUb9WVaFKJ3OXrJMz7D8f/RL0el26PHcl6FBxF8kCbCf23Hr5GsGzfw7ChX9G4MjPTXB5xycFaJGl69DQMNx9d3VLwi5s0MD9U63smi/0UbFU5/rNWDgTsSO3K4DC1C8t0Y592eTkFISqX2RCl5BswIVriV4hVmf9xeuu25uUYsT12BSvGJ84D2evxuFU3ClxatJJbGos/rt8KdNxiHGKCunGqp7Xa7eSkZxqyrSe6DMruRqTLJqlkEC+EkgZrQfsJtGZjilInabq8tUKdkYCdyZgvqB+UMX+KfZFi4VBbNJsr2KcBEiABFwiEBIClHV0yCrVqrlU/U6Fihe1OK+vXo+5U9E75ocEB0oHyuSvf5FLfvq8NAY79jp3/FyKvibbW7Tib4yZ9KOUQ0fPyO0IZIYaBAcFqGHmP9/9vErOup/5wzJ8MnUuDhw+iU07DshlRpnXyl4OHSou8hJP5l29GXWxSbcVu7J7okNb0bs+ddAVhGLUe/+H5s1aQjhXihcvgW7deuCpPs8UhCnZ6jPspRcQ8URX6Dp3QtBjnWVcpK2iuXolW+3lZ+HmTWpj5JDeTkV4cV2xpYT6xSmm0aWqnmFr+SMnzqFkccsXqlXnCcfCbINY0tNkzd14YO09MJkdn3gWCyiBMF14YUbEsRcSAmb1K9kUnTYdzW7Mxr28zLHDwainEBAzVMTHVThV0rz+SqR6Q+Qp9tEOEiABryOgHTECsE7LhvpPq4Xm1VfVSO5/KpUvLRtZk7bURibSgoREx4doYv/FtGynhye7tcXO1V9izrR3IZw1r4z6Akb1e1FRFIg3kForRUaEyegHrz+LH7942yYT3h8i9a4Ezz3xCBrWqYYi4SFStFoNxDIlMdvFlfqulNG4UsiXyyxeuQH3tnrGQV59f5ocdkJiEoa+PQn3dRosXw/Ve/CHcId3TjbupiDphuM+JabUeKTcOu2mHnLeTPnyd+Gjjz7Dit/+wC8Lf8Orr4xEcFBwzhvMp5r63Tuh37oZyqaN0G7ZJOMibRUlxfn6ZxfM85gi4otLOEyMRiPE9DsRN5nE1R3kNDth6Nwl62BQ89du2Cnf8NPy/rpCTfESAg+W6OgllhZOM49fN2DJf8lYfTwZVxMtv3uFk0TuR62IZdNOrmg0xcg193TZQp4REE4V9QZCth/oJw8MSIAESCAnBJRnnoFu2zZoxo+HdvJk6HbvhtLRPdeB4WHBGPx0F3w0+UeIe2cx0eDKtZsQm8B26veGg7n3VC0vdes37YGYkCA2jhUz54Xy4uVrmDF7GRKTklGreiXp7EhKToXJZEL1KuVw+PhZea8t6gmnR5N69+DjKXNwMfo6Ug1GiA1vv1+wWjTlkvTq0gYD+jxqk+pVKsilQULvUgMuFHJy+eFCLR8q8mCLhhC7E9tL7RqVERURKkcpPgDiyfyfCydiy/Jp0Go0mPTVLzLPU4KACMdlNRp9MPzC3LHzvaeMMn/tiJnyJW7MXQTDkl9hjioqO7+VphN6Y+kyUufNwS+//YW6Dz4Psa/KklX/yPiSVRvkkMQ6xiljhmHc1Lmo07Y/hr07Be8M74uypYrJfAbuJ2BOMcD09yEYv10P05yNMO29s0N0+PRx+PXbf/H55AWYMHm+7SjiC7/ejpdLOv6Rc7/lbDEnBObtT8JLK2Mxc2cCJm5JQP+lN7HnUmpOmmIdQUALaNtb1mWLpFW0HRx11jweSaDACIQEOHStaVTJQZdbxdLnlmLhEwttkppgAFQf45JnM+r53ZNb1qxPAh5BoFo1aAYMgHCu4K673GrSgL6dMWJgL9Wp8pOcaNDqseEYNf5bdGp3v+xHozqH1R8ZF/swDnmmK4a8NVFOSNi43bK/p6Io0Om0WPb7Rqmv1eZZiOU8498bBL1eB3EP3qB2VbTsPkzmC0fLx28NgFgm1K7nq6jbrr98A1DMrTjZjwgURRGHAhXfc6hkE6c4QeJtJlYRJ+jfg8fR9/H2sqVVf27D451aonjRIhD7R/R9/EF54u2nI8mCBRgUqzfcoffi9V930FHhOgFD/YZIva8pzM2awRxgufBJTdMJvesteW7Jnp1b48D679JJ9w4tbAa3aVoP/677Br/PG489a75C765tbXmMuJ+A6efNMC3dAfP+szDtPAHT7L9h2nQky45Me7QI3hOBOsfvQ93j99uOIh75b0mA18hZ8ivIzJ8PJKXrPkW97//lUHI6HRPZI+D/fynweyMVunZG6Dob4f95CvS91BvI7DXD0iSQ5wSUu0tB80hdKDXKQGlSBdoX20GpXibH/Z7ffl46TTaO35SuDaE/t/UcrGJWnwBDvfe4sOO8TSfyTEazrd7ub3bLto6vOW7TMUICJEACetUR8uwTj2D7yhn4e/FkiMkGW3+bjldf7CnhiAexYiaITKjB4Ge6YtuKGdi0bCqmfjRc3m+0vL8OxNt8Vvw4DpuXT8M/S6dg4azREHq1CsRSoRnjXsOmX6dix6ovERjghxLFIjB97CsQS4T+WPA59q77Gi/3f0wUl/rnn+wIyJRrwYT3B9tsdq3GnUsVeodKRkQTvlyAJ7q0QfkyxWWW2EeifJkSMi6CcqUt+ltpbzsRuoKW0LKtcM/TR1Gu9XSUbjYed/faKne/L2i72L/3ExDrDMuULCq9xt4/Gg8egckMs5MZKebtWV/QBkxOxolJ2/HqSz0RG2jZKOyd5/rL9PZPV0KJytmYE64nYM/8A9g1axcu7ryYs0ZYK1MCl+JMSDLcvoGxFjwTo3pVrAkes01ACQT0/Qzwn5wC/7Ep0DmZsZLtRlmBBPKCgE4LTbta0PZvA23P+yEcLLnpJulmknSQXD9yLTfNyLo3T92QbcVdipdpBiRAAu4n4O0tRkWEyckGGo3qoc1iMGLDWLFcyFkRsZwnIjzUWZZ8o49wpthnin1PhHNFOF3s9Z4Q13iCEXllw6+/b5I7+M76abnDceP2/Q7dbti6D9v3/CfXWIlMMQtF7KEiTqBIC/H304sDEhKS5DEkQAdPkMiiFVC+fn9UbPIyipWt52BTgF4j7dVpNQ55mdkfrI5NVMos3xP1gX5aYTJ06i+4q/ZpV/rJOkgAtMv80vHRaRVYZ5IF+WvT5bnSvla1A/xHAncicPUWkLYxoX1Rc7Sqt1dkiGvqmVDrwZowNEyEQZsqc/dX2o7o2mfRsH0Dmc5ucP3YNcxs+g1+GbQcf3/0N35+/Gese3Ntdpth+SwIlAzRIECnOJQoH275/nLIoIIESIAESIAECo4AeyYBEsiCgOUuO4sC3pwlZpFcvxkLZyIcJfZjMxpNmDDzZ/Tv3UFOLRJ5iqJArAFLTrHcqAidNR4UZFkGIjxz3iLCfiGu2iucAeKS39XynlBOUYTFkE4QV+yJ+0SL2NFpNzEpwK03tBA6a10FCqz/rLrsHK11eSSBLAkUDwciHDdr1tRwbQr4143nQ6/xk110Kt0dc+9fgRCdc6+/LJRFsPvbPUiJVX8Z7Mrsm7cf8Vf4tNIOSa6jve4NSNeG8AU/do9/Oh0TJEACJOASAbOl1PkdF+RyHeu+KakJt69fLSWc+u4xt/UX+OGhb2Td46stMyPTLqes1bzsSHNJgARIIP8I+LRD5anu7TBySG+nIjajtce8av02HDlxDmJtmL1e7K1y5vxlm+rshWgZF9OUROSW+sfKGyQh2TKV3KA6jrJjr/gbnZ3yBV02IdmyXj7VaIYrtiQsdPwVSFim2OqmqrysEwfiEg02vSttizJGkyAoPikUEsiagPaxJkDw7ZtspWQRKA/VzrqSXa5GsXyWKwdXQ4D2djt2RVyK3jh+w2m565nonRam8o4EnqgZgC8eCcWLDYIw/L4gfN2lCOqW1N+xnjcXEPsDCftNC7fA8NoPDmI+dvtvrShHIYFCQyC3A1UsDSTdSJTLdcS+KELM6rWQJSctVC9JFLErbVrSejh9xoDT/16XdeOiLc5z67WPtQyPJEACJEACzglYrsCd5xUabUpKKj6b8TOGPNsNGddytW/VCAt+XY/oqzcRF5+IHxaugdi4U1HS/noVGkq+N1DzLcB81XFc5kvqubX4nxwzqSGBPCKg3FMGug96QPtaJ2jf7Art649CKRaWR71l3mxE5QinmZGZ6J0WptIlApUjdeha3R/tK/ujaKD6veNSLRYigYInQAs8jIDqKBEWlW5YGo/NfdwmuiCdUN8W8TXj5Pr1afN8PG/+Cf2DlqLyg5VleSfFpJ4BCZAACZBAegJ0qKg8Fq3cgNi4RPR97EE1lf7nyW7tUKlCabR+fDiadByE1FQDhj7XPX0hprySgKLeq2prmxxs1zZQdWmrgBwyqSCBPCaglI6AUjQ0j3vJvPl6z9aFX6hl+ZC1VK0naiK4mOOSJGs+jyTgCgGlZjlBBBdXAAAQAElEQVRZTPP4fVAqWzZ71w56CLrP+kpRqlh0spB7A7ZGAj5JQDn7HzT//IIilzcjyD8ZgRGBKHdfWZtotK5d5ldUzqGScgaVEw4iNNxShzNUfPIjw0GRAAnkAQHLt2YeNOxNTYq3+ohXQInXIme0OzgoAOJVTeL1TX8tmoSfZ46SuxpnLMe0dxLQv5EKpWTaox11CEppM/xeT1Vj/CEBzybwz5U/MOvEFJsYzQZp8Nbr/9h0Iv9QzD6pdzWIrBKFFzc+h8emd0KLt1qg18JeaPtxO1ers5xbCLAREiABEsiagHbFl9BPHwrdihkodeh7DOi0HhG63C2bM4eFwxwYmHXHzCUBEiABEkhHgA6VdDgyT4SHBqNoZHjmBZjjlQS0dU0IXGJ5YxNCgaC1SdDUNHnlWGh04SLwz5U/8dXxKTYxWB0q1/6x6UT+f7EHsg0mKDIIdXvei/ov1EepBqXuXJ8lSCAfCaSM1yO+RmCmkvpNhmUO+WibJ3Ql3lBoMBqdmmIymXHpynVkli+WQF++cgOiDacNUOkRBJTkRGj/WZDOFj8/A+6veyadLruJ+CHDUPeZenLJUOWHLEt/stsGy5MACZBAYSNAh0phO+Mcb+YEuMwnczY+lsPhkAAJkICvEli+ZjMeemKEw/D+2rxXLl1u2+NV1GnbH/N/XW8rIxwo075finoPvYA2PV5Bi24vY+/B47Z8RgqegOm4YnMiJra85tQg/2TLixOsmV2+6SKdI507P4GOfk9Ba9arzjKgvb4nHr23N3p80RpPDoiAZsxoJK37GwkDhiCiUoRcMhRSgss8rRx5JAESIIGsCNChkhUd5pGAZxCgFSRQoASCp0xAZNumKHZvJUQ80RX6bVsK1B52XrgJ+I1IRfDBRCn65yxL3ZzpChsl8UbC9r1fxxsffekw9MSkFIz4YDpeeq4b9q77GpM+HIrRn32HcxevyLJ7DhzD1G8X44cpb2HPmq/Q9eHmeGXUFxAzWmQBBh5FwBRf1rk9xSz7FFkzyzQqI50jFf+vJMr3KQ2NRoHYbLZMm9Ko+EVJlOlYB8XffBrGF16E6d57rdV4JAESIAESyAYBOlSyAYtFXSXAciRAAp5IYNkLyzCx4kSbnFh7QpqZUX/s99tPpgMWzUfwhHHQnTgGJSEe+q2bET7oOSjx8bIuAxIoaALa0BPwvzIF+u/fhnblLCg3LhW0SQXSf+mSRfH95Dfx9rC+Dv1v230ICYlJ6N2lDXRaLdo1b4AKZUvgr817ZNk//tmN+xvei/q17oZer0Pfxx/C5Ss3cPh47paQyMYZuIWAprJZOhGFMzFgiYKko73Tt+sXBGOzx9Pr0lJKKOD/biqUtEknAeNToLnr9v5xacV4IAESIAESyAEBOlQENAoJkAAJ+BABfbwfeg8YjO7DVccHlFyN7Oj83XgLb2KuuYutHc31a9AePmRLM+IZBK7t+xLHl7TPVG4eW+wZhrrRCsV4CaGt+kMXswjK4W3QbpgP3bShQFLhc/gJR0nJYpGICA9xIHz56g3pQPHz09vyKlcojUvRN2T6YvQ1VCxXUsZFULxoEXFA9NWb8sjA8wgkHhiCuKMzYegwEMbHX0fqiO9gLlstW4aKGYfFK5ZAQIlIBJWMgojbi3CgZ6tBFiYBEiABbyHgRjvpUHEjTDYFnDlzCkNfHpCpfPrpmHzFZLqkwPCLFqk/6GA6nLsby3w1nJ2RgJsImMGnkG5C6fHNJN06gbjzGzKV1LjzHj+G7BqoT1kHRZuarpoSfxOaE5aZF+kyvDRx4dJVzPppeaYilvPcaWi3YuMRFBiQrpi/vx9i4xKkTuQH+PvLuDUQ5eMSEmVSr1Xgp9N4vOjTXhOsUZAtW8Ug82p8gp1oX1EUaIVhAFSc2bIvo23WcZqSa0DXqge0jR+GvkjUHdu0+tf9tBpZVlEU1ZrMf4TtGft2ltbcoZ3Me2AOCZCAqwRYznMJaDzXNFrmjQQSEhKwZ8+uTOVwPj7VNm7RILFNAJLf9UPKx3okdgtAyszC/fYHb/xM0eaCJWC8u7qDAabIKBir3eOgp6JgCRStOQCVu66SEl6luzQmqtaLMi304VW6SZ0vBYrprNPhKFfOONV7ozLVYMT1m7GZitlsuuOwwkKD5ZIf+4LJySkIDQmSKpGfnJIi49ZALBEKCbK8QjcoQIewYL3HS0ig5W+82CskO/ZqVL9CSFDejC84zSat2kmgv8U+vV6TK5Y5Hac6THl6Q9POpWbFCsQcOo8FvX7CR7pX8ZF5KOa3/lzqTNdvIqhtS5fs1Os0sl0G+Uvg3PqXMp2ReHxJeyTfPJ6/BqXvjSkSKDQENIVmpBxovhAoV74CJk2aIeW1EW/JPsuWLS/TQj8iTScz8jhI/Ubv0EPqLEedQyEqSMBHCPgnBOB080uw7ptyfI3jxZXZ7DiD5ZfnluLdqHGy3pIfLEsCTMEhMAcFI7XJ/YiZ/g3MwWmL8X2ElS8Mwz+8EkLKtJCiDykjh+QfVlmmhd4vTSczfCQw6u5zOhJTlQZO9d6oFHudjBzSG5lJUIaZJ87GWKJoBE6fu4zUVIMt+8iJcyhZPEKmSxWPwqmzl2RcBNalPtalPzHxqbgak+zxciPO4hQyGM3ZstWkfg1ev5U347sZZ5lBZTCaEJdoiSelmLJlX0b21nEaszlO69f9NbuxLv7f79j9837EGfwRp4Rgz5/RWDJyTbbsS041io+Nl4v3mZ8QvTvTGYlitqIpNc77BkWLScALCdCh4oUnzZNNDlZvuOrXawgh1dOebIeoN14iLaR69Rr5Zr7phPVZjF2XCYD5nBO9XZE7RXVH/oP2xPE7FWM+CXgcAUVx7bPvzMmS0rotrhw4gRvzliC1sfObWI8bMA3yeQIG/1ZIOftgunEaW/SCuczd6XSFISF+b4XDxGCw3NzKuNESb1S3ukQwd8k6GFTd2g075Rt+Wt5fV+rbNKuHjdv3Y9e+oxAzYmYvXI0SxSJQrXJ5mc/AAwm4yaTT6087tHR2k/OZXw4FqShQAmVbTUHltFmJfkWqSFvKtppq0/mHV5Y6BiRAAnlLQJO3zbN1Eig4AppK6iOnjN0HAUpZJ3rc+Z9uxzYUbVwLke1bIqrtA4hscz90Rw/fuSJLkEAeEGhWrDWerzzUJjrFMpW8SVQz9Ks4QPYo3uzQfk9bDD85XEqldpWk3j5QFEcny+PfdsWH10bKOh2+6GhfnHES8DgC8TtHIaHiUqQO/gIpo5bC+PDzHmdjfhh0/NQF1H3wefnaZPGGHhF/Z9zXsuugQH9MGTMM46bORZ22/THs3Sl4Z3hflC1VTObXvbcKBvbrjL5Dx6Buu/74eemf+GzUYPmaXVnADQGb8B4CZjFlx3vM9XlLDUnXcePwXFzZPRnxF7fYxhtUvL5tBqJWHyL19jqNn0UnMxiQAAnkGQE6VPIMbcE2HHNyOfZODbLJqRU9pUExJ3+16UT+qRW9pN4XA/1zlqm19mPTv+Cos8/PKh4y/mNorkTbiuhOnkDwJ/m7ya6tc0YKPYFmxdrghUpDbaJVdJJJk0jVoXLXABn31wTgnvBaMs6ABHyZgFkbZnnDib/qNXfPQL2ulSoVy+DA+u/Sydi3LN8FYjBtmtbDv+u+we/zxmPPmq/Qu2tboZaiKAqGPtcdO1d/iTVq/tbfpqNezaoyz5MDU2o84s7/bZPE6J0Q+3CbUuJsOpGfEL3Lk4dRoLZVaFXBof+Eawn45alfHPRU5D+BpBv/4b8fauLM2v64sOkNHFvUBmf/HJL/hrBHEiCBTAnQoZIpGu/KuLb/a+koObd+aI4MT1AvQoSD5eiCFjmq74mVtPeZEPhHEvw/TIHfm6kIXJwEvxdvrx/Prs3aM6ccqugOHXDQUUEC+U3gYuJ5mF3YmDK/7WJ/+UmAfZHAnQlotRqUKVkUer3FAZuxRoC/H0qr+WJT14x5npA2JN+QjpLEq/ukOckxJ3B8ycM2OfvnYIg32aTccqKH5Z9wsNg/5bdofTuMuxyPTZ9uQmraHi7R+28/HGr5Tkvc82g19W+IOU2gIlQglv3s+no3+K9gCVzdOw3GlJvpjLh+8FsYEi6n0zFBAvlJIOZWPG7ExMrvDGu/O/89gmMnfe9tgtbxZXWkQyUrOszzegKakmboHjNC39cATTVzrsZjDnacOmkKC89Vm6xMArkhcDU5Gk9v6Yau/7RGiilFNnUk9gACwgPkcp2BewZKXW6CuztWlW11dPfSn9wYxbokQAKFkkBi9C7pPLm46e0cjd+QeFXWP7XSd2fnZgRjSDbi58fmYdu0bTAZTDL7lyd/wbWj12Q8uHgw6vWuBUVR0kRVK6qoP9f+2q+G/ClIAsk3nS8tT7rhXF+QtrrSt2nvaZjmbITx2/Uw/X0I5pScP+h0pT+WcR8Bo9GEuUvWoXnXoXig8xA06zIUjTsMwoSZ82Un38xbgXX/5G424F+b9+KLbxbL9twZdH76Ldzb6pl0Mu27JW7rgg4Vt6FkQ/lNwKxeC5j2qx/hhPzpOaXdQw4dJT/a1UFHhW8S8MRRzT71Jf5THSj2tv0Z/bt9Ml2886zOsO6nIo7WPVUy6qs8xI3s0oFjggRIgAS8lMC5zWcRez42nfVij5Qjvx216QLC/W1x+0jY3s32SZ+Nm0xmXLpyHQaj0ePG6F+kmlObAiKc650W9hCladMRmGb/DdPOEzDvPwvT0h0w/Vw4PmP5dQpSjgA3vwFifgBST7u3V+EwmTBzAd4c2gfbV87AhiVTMHrEs1i+1n3n8NzFK9i6+5B7DU9r7eX+j2HFj+Ns8mS3dmk5uT+od6O5b4QteA6BawcsS39O/WbZM+VOlt1M21Pl6ILmdyrqUflJQ/yQ0DwQiT39Ed8wEKlTnU9fdqfRcSPfReyH45D8SCckde6OW59PRcKgl93ZhTvbYluFgMDR2P8cRplkSsKpeL6FygEMFSRAAj5DwJh03bL0J3qnS2MypVr2VIm/ZN3QM20Khku1vbvQ9eM3nA7g1rlbNn3peqVQHOpTKpvGEqkbsxlKTPrlJpYc7ww//3KBfEJ9Ky7BNoC/1CfiTToOQtser8oNm+f/ut6W5wmRonUGQ+tXJJ0pkTWehS6oRDqdNyTMexyXzpv3qnf9SZYZtt4wBk+2UThRTrcArrwJRI8ATt0PxK90j8UxsfGYOGshRgzsiQ5tmyAoMACRRUJlXDgpMvay//BJ9B36UTr1wJGfQSwLEsotOw+i14uj0eiRgejQZyRm/bQcp89dxozZS7Fr3xGZJ/KTklOQqH4+xn4xR86METNNflq0RupEO0I/Z/E6TFfrif5WrNsq1E6lWFQ4KpQtYZMi4Y4rD5DDf3So5BAcq7mbgOvtGRZrYfxTm65CylQ9TGeUdLq8SCT2eQYx077GrUnTkdT18bzogm2SgMsEQnShTstmpndauBAqzWYzrt+MxZnz0UhOSXVK4Or1GNsf1wfovgAAEABJREFUbKcFqCQBEigwAglXdsmlO2fFnikZrTAD6q94Om1K2l4rp9I26FdLpMv3hYTpkgLTAcfroIqtHTedFeMt16SMOEjRaBW8EPAL2pg3oLr5KBqZd2OAeTbKBsfAHOL874ys6EXB4pUb8NWc39JZLG7URnwwHS891w17132NSR8OxejPvsM59Sl5uoIFmAiIqI7qffejfLuvUfqBsajS/Q+Uaz21AC3Kedfm6NtOPFsrZvUX9lqcLclIzglcn6TWVXGqoeXHCFyfYonmNjxx+oJsom3zBvJoH4i9t+zTIh6fkCQdIyJulQOHTyFWdWYKJ0n/1z5B08Y1MW/6u3jtxV6IvnoDxaKK4OHWjVG5QmmMGNRLil6nwzjVmbJ731F8+t4gvD28L35atBZr/94hmxVOmDGTfsDRE+fRrkUDlCweKfXOggXL/4J4892075ao13/u3YOIDhVnxF3ReWiZqHv7o86QBNzV0bKe7U5mFqn4qCxftceGOxX1mHzTf84/tqZ9zvUeYzgNIQE3E3iwZEeHFisEVUJR/+IOelcUwZ9/guIVSyC8bDFo1T9sIm4vwZM/c6UZjy7z78HjaNHtZfmk45Gn/ifXAIsLbavRZ85flk9LWnYfhoYPD8C7n3yDVIN6VWItwCMJkIADgf3RBvxvbSy6/XwTzy+7hbn7Eh3KuFOh9Y9ASJnmCCxe32mzSga/gkYfLMsHlbwvrXyGAmlabzyYrwOJT/kjsU0AEnsEIL5BIAyLbj90iqgUifuHW8dtGeFdre5CjR73WhJpYVDxYLRT/kE/LEA3ZRXuUs4juUt3QHu7LXjpv+17/sNHk3/CePWGzH4I23YfQkJiEnp3aQOdOs526s2ieIL91+Y99sUKPK4LiEREtd4oVu9lBJdKfy4L3LhsGKBUK+1QWgkLBMpkfhPsUIEKpwTM8YDhvGOWWALkqM2+Rjg8RK3IImHikCsxpF1T+fv5oVSJKLRtXh9vD+uLoEB/3FWuFMLDQtCobnUpKakGLFi+Hl0ebobw0GCEhQShaaOaWLNhh82GF57qhAnvD8bTPdqjfq2qNr19pH2rRmhxX22ITdf/2Lgbjz0/KmdOFftG7eK8A7WDwaiXEAhzbqcSZnaeQS0J+CiBh0p2wmd1Z6Bj6e7QKJav845luK9PVqdbfEsMe/5xufZ3z9qv0a/HQ/KJhXWmyv9N/AHVq5THjlVfYvnsj7Hqz21Y9UfmU0iz6ot5JFAYCCSlmvF/G+Kx77IBSQYzzscaMfvfJPx1Ku+m8QcVb4DKXVejXOvpjogVR5VfeGVZvmKHtIdNZhOu7J2KUyufwJm1z+PSwYWOlbxEk/qDHqbdlu9/abLqy0r+yA9Qb7BkWg2aDLsPQ/YPgV+wqlfTHb7ooIbpf8xlykpFStMWcmlz7IfjcOuj8VLnzYF4gj34zYmY+MFLqFrRMkbreC6rT8WFA8XPT29VQTwdvxR9e5mUeOOVN4j1Y69oFLhqr3A8CnG1fHbLIe2fRu1E1NU9XAdKySJpWvUQHABtj/tctle04VaxQlNNUU3MkR1qVY/4UYIBXRlHU/zudtTlRFO8aISsJmbvykgugpDgQOlAmfz1L3LJT5+XxmDH3sNOW7wUbVmKuGjF3xgz6Ucph46ekQ5Qa4XgoABrNNPjkGe7YVC/Lhj8dBfMnfYuQkMCsW7DrkzLZzdDk90KLE8CBU1A194AZPjkKqXN0DQ2FbRp7J8E8pXA2IPv4rU9A/HbhUUwqf9F59OPfo4ma+6WsvjcPKFyWeJf+R+iT15GzLkrML/+P1nPqhP6+JdfkzpvDurUqIzHO7VEZJFQ6HValCgWKeMajQZijfDG7fvR9/GHEBjgh4rlS6Hrw03x+1/bvXnItJ0E8pTAyRgjYpIc//5uv6j+rc7TnnPeuCk1Fhf+eR0xJ5bhxuE52PtLD1zd91XOGyzAmnJz/oz9JwDGo+kvlPTBeigaxVYycMFcBE/8VIru07FQzp6BWCtlKlUahrurQ3P1iszTnjtrq+NtEfFq1wGvj8crA3rIp9oZ7b8VG68+FQ9Ip/b395PLEoTST6dB8XB/rxCd1nJuI0P8XLY3yF+HkAC9y+Wzy8JmU6jFphKVolDmox4oObYXSox+DGWn9EXJplXs+8/XeGRYALRpvxMBem2O+hafE0+RyGGqJZaPgRpRf7RA5FD16IafSuUts4vWpC21sW8yITHZPinjOq3auYw5D57s1hY7V3+JOapzQzhrXhn1BYxGExRFUb+GxKMvS73IiDAZ+eD1Z/HjF2/bZML7Q6Q+J4Fer0OxyCJITE7JSXWndTROtVR6HYGompalPmVb5WyxnHjaI5YKVe3xt8ePXVPFjMB5ydA9YYC2pQl+AwwImJMMxfkm9R4/HhpIAiSQ/wTExmijxn8LsQHaG0Ofks6Vq9duSkPKliomjyIoX6YELkZfF1Ep4gLRGyTtGlG9WIT6JEdxScSFpXot41LZjAy0MQkw//g3jO8tgPGDX2BesAVa9WLFvpxGNK5S1KoXfGlRaNWEfZnsxlU/mNoiINrObl1n5UVjzvSu6kR9St4RsC71CYiqmaNOFK0eIaWbwWRMdqh/49gCB503KJSit28+7O3VZKK3lgn45WcETxovRTf+EyjnzkG9m4G9XuRrzqt6eOe/LbsOQOyHcvZCND6ZOhdfzbXsoSI21zx09DTCQoPlkh/70SWr31uhIUFSlWIw4dKNpCzEc/JSjZbPwbVbyS7bG59kQGxiqsvls8vCkGbT1Qw2XdX74ZrKOLvtubv81ZgkGE0WbokpxhxxkB8UDwnC+wIV1Nu4Yh8DxccDd20Ggh9xj3HhYcFydsdHk3+EWCYt9kK5ol4ziU1gO/V7w6GTe6qWl7r1m/bgRkwsxMax12/GSt3Fy9fUa69lSExKRq3qldCwTjUkJafCZDKhepVyOHz8LMRMGFFPLPFpUu8efDxljrwWSzUYITa8/X7BatmWK4FYyv3d/FUQb/IS9Zev2SzbEO26Ut+VMhpXCuVlGbEhlBhcXvZRGNsOr9hJ7o0inCRC7upgmeYaXvFR1BxwGVG1XoQ+pCziLvyDUyt6IfnmMa/CpKlpgv97qQiYngz98FRoipu9yn4aSwLuIPBGjQ8xpvYkhOrCbM1VDamOZc3/xtYHj6Bb2Sdsel+PGNUnG2KX+MzkxJmL6RCIP+jRV28iNdWAm+ofe5FpffOD/fRv8bTy+s1bIlvcayAy1N8rJMBPJ20WTyBdtTk0SA8/rSZH48OirTDuPg1zfBLMqnPFuOUotL//m64tfz+ttCk4QKc6bSyXH2GB+nRlXLXVWu72OLW5asfankYBioTk/BxbHUVyoIUkqBiuRXiA5XzaD7lRKZ19MlvxVceS8eO+JAdZdLkGNldeCn3d/5PtafQhEHupWCWoeP00fbBTvdYvHJk9eEq6cUTW9bZA18FxJpCmjvqkt6yT6yJvG1wu7a1yVxkMe/4xRISHoIgqYepNvGiyiHpz6Kc+pS5RNAJiSZD4OyD0Qo6cOIeSxS3LG0SaQgLeRMDvbvVv2HOAcK7one9HnePhDOjbGSMG9pL7Ed3XaTBaPTYc4sFUp3b3yzY16h9A9UfGgwIDMOSZrhjy1kS5V93G7fukXlEU6HRaLPt9o9TXavMsxHIesb+RmDlSu0ZlNKhdFS27D5P5wtHy8VsDIJYJtev5Kuq26y/fABRz6/ZGxoqiyLazCmarDhjxJi9Rf+SYmRg5pLfajworq0rZyHP8C5iNyjktajAaMe37pXJTQLHpn3V9unid0svvTs5ps6znIoErOz/DtX0zkRp3DsbkG4g5+atcQ+xidRYjARLwIALjDo1CrMFywy/MOhr3H2afmimihUrMMMs391xXn4A4E/sLZgGm04P3Y/rYVzDxg6Hy4uCs+gTTerFtX1Y8rYxM24TNrN6fRN9MgjdIQrLlJis20eCyvTfjUpCsPpHN9viuJyL5wHmBNZ0k7D2Tru/ENJtuqTalqv2IwjfUPrPdn905SFCfsIp24tRjbtqx1hUPK8VTS2s6u0fxGRH2FCYJ0Ct4p3kwapXQIUCnoEyoFv1qB6DlXX7I6b/Vx1Pw07+JyEyuJJhk0/7hFeXeKJW7rpbHsml7qviFV5Zpq75c62myvAj8I6pBH2p5eirSVgkr19oa9aqjtrkJAV8lQ/eYEdpWRuiHpSJghuMMHK8alJuMraw6VAb0eRRW6fmo5Rw/0+sRiDyx8aXoau6SdTCo9yZrN+yEmNHS8v66Qk3JLYEUg2zBfOYqzMcvOYjMZOA1BMQy6WefeATbV87A34sn48+FE7H1t+l49cWecgxTxgyTv2syoQaDn+mKbStmYNOyqZj60XAcWP8dWt5fB+JtPit+HIfNy6fhn6VTsHDWaKlXq0AsFZox7jVs+nWq3MtOLL8uUSwC4npNLBH6Y8Hn8o1cL/d/TBSX+uefdHxBg8xMC8RM43XzJ0ibV835RNbv16N9Wq57DgXiUPln6z5M/XYxWj1QD/ZTq7t3aCk3iBHr2N0zPLbijEDsuT8d1AmXt8GYbJnu7pBJhU8TMKl3EEb16b5PD9JHB3cq/jhupTr+3h66ZXkS4KPDdjos8Ud4pPrEITOpVrmc03piQ0KRIaaXFo0qIqIQzhUZUYNTZy+hVPFINcYfXydw+u/TmP7QbEyuMRWzH/wBu77end0hF9ryNYvr8Em7UCzuVQRfdQ5D71qBuWLRvrIfnqodKCUqyPL08aHK/jIt9MWCcnf5WrblJGgDitpsDCl2L0o2esuW9raI9gET/D9MQcC0FPi9aIASfnsE26Ztw+aJW6SkqA5MkbN96nb8ee4urDU3E8lCK+KtIuImcNzUuajTtj+GvTsF7wzvm+7epNDCccPAzdfjZCvGuZtgnLbGQWQmA68kEBURhuJFi0CjsXw/ZzaI4KAAiOVCzvLFQ6yI8FBnWQgPDZZ72dlnBvj7QThXxPWevd6VuKIoEDaXK11cOm1cqZOdMrn7i5SdnuzKzlu6DmIzmg//9xysF7Miu3aNSuKAC5euyiMDEiCBvCVgVh+njp7wHT74/HuHjjo//RbubfVMOpn23RKHclQUHIFI/6JOO4/yK+ZUTyXk2t8//tklN6AVa4DFbEkxNbXKXWXkH/D7G96LHxauQWJSCk6euYhlv2/CQy0b+Sg6Nw1LvaBSqpVyaEypXtpB56mKxGsJ+G3Ibzi99TwMCam4fuwa/v6/v3B6w2lPNdmn7Xq4ij/61AqQUjxYK8f6sOpkseqKBefu8jWsQnvU7H8Gd/faiup9DqDpwP3wL1JF9uNrwY5pO7B10hYp1rFtn7Ed68/fhT+U5kgyp80kUq8HIMRayHpUb0SsUW8/VqlYRj4lFzdy1rG0aVoP/677Br/PG489a75C765trVk85pZA2tJTpXyUrSWlcglYxaZkhAS8nEDu/iLlcPBifeLdmTwtFCd5XMcAABAASURBVE36+enFgZJHBELLtnZoOahEY2j9LU9nHTLzSqF6rp1NAbTqoObnVddsF1i9fhtadHsZC5f/lSkOMaVOTMuzypPd2mValhn5TyBMF477opo7dPxgyaynPzpU8BaFG+wUe3YNfWcyHnh0CMQa4PWbdmPK/70M6yaEb7/cBwcOn4RYjtqp35uqM6UhHm7d2A09+3YT2sfvg1K3AhAcACUsEJomVaHpUM9rBn1h50VYn97bG312o/e+4cR+HIw7JxBYtBb8wys6z/QRbcPBDdFk2H0O0qrMKbQxb0CAkmIZqXCcCLGkbofOnCy3c30iptVqUKZkUYg9HHxiQB4yCCUyRFqi7d1UHkWgHfwQrCLSFBLwBQIF4lCpfU9l/LZ2C8RSA3uI85dZlqKULVXMXs24mwkUa/CabVNarX8Ewis+ivLt8v91gabtJxym/9lPCRT5bh46m7Mj0LxJHSyYNRpiLwk7dbposahwOYtMzCQTIjZ1A/+5TCA/Cn5UZxJeqvo69IrlKeMHNcfjoZKd8qNrr+yj56Ot5PpZsQ533YIJWPvzBNzXoIZtLBXLl8LquZ9C5Iu1v2PeeL7ALrLP3TLikZ9uZCrPL7u9d45tAAUViQiGtm8L6D7oAe2ox6HpqTpYgvwLyhr2SwIkkEag8eDGuH/4fQ7SuuwptFP+SSvFAwmQAAmQQE4JFIhDZdDTXbB9z3/o1O8NHDp6Gr//tR2D3vgcM3/4FcNfeBz+nKGS0/PpUj2tPhRlW3yOGk8fQc3nz+OuDj8XyFRXJSLo9rQ/9WJcGq8erVMBRb7UMcgTAmLtcMlikQgOsq11d+hnwfK/8M64ryGW+ojXjjkUoKLACQRrQ9D3rhcQqg+TtjSJaiaPDDInINbfinW44vOf2fpfkR8cFJB5I8zxKQKlG5SCX4jFKWk/sHJNne+9Y1+GcRIgARLwJAKxZ9bi6C9tsO/LYjg8pwGu7JniSebRFhLwOQIF4lCpVrkcFn39IcSTwKTkVPyxcTcuRV/D6BHPon9vTlX3uU9ZJgNSGlexTfvTNKosS4mjdSqgyJfKTANmOCOw898jmPXTcqeS1fKejG21b9UILe6rjdIli8rf0ceeHwWrUyXAT4tiRQK8QqwzmIuGu26vv16L8GC9V4zPeh40afuCRWVznOK8h2UYaxH1xlKb1mBQgC7bHER90S4ldwTKhmmx8qkIKbMeDZONiTeoWHVi80+pZJBrAoFRQeg4tSMqNCkDXZAekVWi0OKdlqjQvEKu22YDJEACJJBfBIwpMTi9qg8SLm2BKTUeSTcO4cLGkbh1akV+mcB+SKDQEdAUxIg37ziAU2cvylcoiVcv7f/zWyz+5v/weKeWd9wtuCDsdVufbIgE8oFAQmISrt+MdSo3YmJdtmDIs90wqF8XDH66C+ZOexehIYHyLVyigeRUI27GJnuFWJd/x2TD3hSDEXGJBq8Yn/U82MYZ5/p5STWYxOlEfGJqurHGJqTCaDLLvKQUY7o8a39ZHUV9WZlB4SZw9RyUE3tuS8xViE0vlStnoUk6A41yEZqLB4Fb1zyCU4UWFTDo9354+eAQ9FvTF/X7e88eMB4BkEbcJpCccPtzf+GYRZ+UAM2Vw/JzryResOgKMEx6rBfih42QYhjxP5jLlpW/n/Z6kW8qo+oL0E52nT0CidcOwZjquBw0zskbPrPXMkuTAAlkRiBLh0pmlXKrX7B8PX757W9bM4qS9mjVpmGEBEggpwSaN6mNkUN6O5UXnsrZ3hpio7ZikUWQmGzZvE7cvKcazfAGsXJMVR0ErtorxiccCq6W94RyFvcHYMjGeTGJgaqAjKpfxX4MYuyqWv6Iva7s81yJ29eXjTDwOQKa0wfkDWNWA9Nu/AX6r163iea/LYD69167aRH8r/6MAL810K/8CJpDm5Hpv8RY2Y9y8XimRZhBAp5GQFGdhtbPvm7pJGmecuUM9P98Lj/3fud/k7qCDBJ79EZK24eQ2OspGF5/A+Zy5SF+P4U+fvjrsIqxLJe9gf9sBL78ciqGvjwgU9m//19bWUa8kwCtzj6BAnGoVKlYFqfPXc6+taxBAiTgNgJG9S46NdUAo9EIg8EIERc3z6IDsbTnu/mrcOnKdYi3oixfsxn7D59Ek3r3iGwKCZBAISeg/Wm0dJQocTfylITm/BHZj3blrFz1I16DvHniFlhl01tbsOn/ttjSR349kqv2WZkE0hHwD4S5Ym1HiaoKk7EETIEl0xXP70Tg/Dkodm8lRHR+CEUfqAe/Rx+BehGQ32awvzwgEBh1D7Rpe6rZNx/i5A2f9vmuxk+cPI49e3ZlKrdibmbZlHHZThjGLILhrXkwfvUHzBdz/Tcky/6YSQL5QaBAHCriLQtiScKGrfRi5sdJ9rQ+zp8/h2PHePFa0Ofll9/+Qt0Hn4fYV2XJqn9kfMmqDTazZi9YjbY9XkXddv0xcsxMOeOlQe27bfmMkAAJkIC3ELh27Aa2Ttpik21zt2Db17fTh5cf9pah0E4vIGAuVh6pL3zmICktXkOSoT1SyzxaYKNQ4uIQ8sG7UBLibTZotm2FcrHglyHZDPLpSN4OTusXjgoP/4igkvdBow9GQMQ9KN10HMLu6gB3/HvhhcGYNGmGlJo1a8smBwwYItNCf2+aTmY4Ccx/HQSuq5+95FSYD52H6Yfb151OilNFAl5BoEAcKmO/mIOExCQMHDkB97Z6xkFiYtVfNK/ARyOzQ+DChfPo168nnujdFc8+9yQ6PdoOGzfeXvqVnbZYNvcEenZujQPrv0sn3Tu0kA2XL1MC6+ZPwN+LJ2PVnE/ka2b79Wgv8/IiiEs148h1I8QxL9pnmyRAAnlDQDlz0LIkx36vFGv81lWXOlWunM28jYsnLG3kcmVwVJUINBl4H+rrmqGSprpsMxxRMt3w/gdQrVM1qWNAAr5OQHf6JJT4OIdhCkeLg1IoKF5HILR8O1R97A/UGnAF1Z7ciWJ1h7ptDJUrVUH9eg2lhIWFy3YrV6ws00IfHl5E6lwNzJdjVAeL4+fR1fosRwKeQKBAHCqPtG6C1wc9kakE+Pt5Ahva4GYC3343CydPpV0cq23HxNzEZ599rMb444kEFEVBVEQYypUuDp1Wi7z6N2VrPHrMv4lhK2/J4xfbEvKqK49o13wrAcZpv2cufFrjEeeJRrhGQPfj+3JJjnW/CPuj5uBmx0bS9u2xz9BuXJRpG9qVX1qKWjcJsqSyHUZWiULj1g+ggb45KutqyPoRmqIQ6YaRzXD3o3dLHQPvJ8ARZE3AlHYTnLGUWafLqGKaBEiABEjABQIF4lBp27w+nun1cKbi76d3wXQW8TYCR50s87ly9QquXXPtKaa3jZf23pnA7kupWHHMstGttfRvR5Ox97LBmvS5o2IwwXz8cuZy+orPjZkD8l0CpvI1HPeJsO4dERbl0sDNRctk3kbJSpY2cjlDRTSiFHXulVFCRW6BCTsmgXwlYCxXHqn1Gjj2Way4o44aEshjAkqJcCAyJI97YfMkkLcENHnbfOatJ6ekYvHKDRDLf0aN/xazflqOsxeiM6/AHK8nEBri+IWp0WgQGBjk9WPjAHJG4Mg1o9OKJ2441zst7GVKc2ggtIMftEif5hbr7XV9LcuuLBkMScDTCKS3x9jnfYd9Iqx7R5hqPJC+sEgpjp4RY7PHM23D2GGAqAU494VY8lwMNZXM0NQxOZTWdfBdB67DYKkgAZXAzW9+QtyIN5Es3vLT80mkzF8Ec0SEmsMfEshbAkrLGqoDJRjw18McdQ9MwQ/D8IsWpmjHvw15awlbJwH3ESgQh8rV6zHo/PRbeGfc1/hh4e9YsW4rJs5aiIef/B9Wr9/uvtGxJY8i0Kat4x4crVq1RVAQHSoedaLy0ZhgP+d/QEP0+WhEPnel6LVQKpe0SIVilt71Gkta6CsUtegYuocAWyEBOwIBM5Kh7WhxoChlzAj4Sk03d3Sy2FVhlAR8joC5SAQShgxHzFc/IHbc5zC1bOVzY+SAPJOAtnMD6N7uDlOFPkj5sTlSpoYj+V0/JD4cAOMWjWcaTatI4A4ECuSTO+mrXyDe8vPdxDew6/dZ2L5yBn6d/TFaPVAXr74/FYlJ6ZcA3GEMzPYSAt26PIYPRo9F+4c6oEXzVhg69DW8+cYoL7GeZuYFgWbl9AjSp3eqiHSjMj7sUUHW/5hLAt5AwGxd6qPN230XzAEhluVA1qU/uYSjhAO69ibZiraGCdoHLHGpYEACXkDgl3NzMOHwGGlpnCFWHhmQgLcRSJ2V4TovCUj9JoPO2wZFewstAU1BjHzj9n3o3bUNGtWtDut+KZXKl8LQ57pLc46fPi+PDLyLwH//HUTzFg1t8sKAfnIA/x0+ZNO9N+oNnD59EmPGjEfPHr0REBAgy+Q0MPylQXyNQJskD/KXTRkz6JPS9DKTgTsJ5KqtIgEaTH44FN3uCYBwooijSAt9rhpmZRIggTwlYEhb6oPAzDcgMRctZ3GGpO2pgtBIwGyGOaoMTH7lYDKWgLlkdSCLvVbMZavJ5UC2pT95Oio2TgKeTWDswXfxyaH3sfXaP9LQ6ORL+P7kTBlnQALeQsB8Tn2Q5uT9A6YTqt5bBkE7ScCOQIE4VMJDg3Er1vE3yZnOzlZGPZzA2nW/u2ShcLy4VDBDIavjJIM6m0lL8eTReumESf05b5+uWnpjmBWBMmFaDKgfiA9ahcijSGdVnnkkQALeQcDUtLt0hlj3VDGKPVUUBWLPlOTiTyDJ0B4pHd6B6Z77vWNAtJIECpjAmssrHCxYfmGRg44KEvBkAkpZM+Bktb/Y58qT7aZtJJAZgQJxqDRvUhsLlq+XcvrcZcTExuOPjbvx2YyfEVkkFNUql8/M3sKn54hJgARIwEUCmqAL0P77G7RblkKJPu1iLRYjAe8hYNykQcpUvU2Muy2XMcZNWptO5Jt2WfTeMzJaSgJZEFCfQV7/5xaqHKqJukfvR/XTdSE2ag5IDkTUv6WQuhUwbtPAuI+fe/CfVxDQv5Ca3s4AQP9cBl36Ekx5EIGYW/G4ERMLs1l1jqXZtfPfIzh20vNXmVy7cQtC0sx2y0HjllbsGnElOujprmjaqCbeH/8dOvQZiQceHYKhb0/ChctXMenDl6HXaV1phmVIIE8IBH41A8ETP5WiHTcWyq0Y9cLFjKCvb+tFvtTniQVslASyT0AfuQHhD/WEbs1EaJd9Af3E56HZ9lu2GtL9uweh745E8NNPQvn772zVZWEScCcB80VFOkjE2x+s7ZrOKjAs1yJ1qs4mJptDRWPTiXzjnvSXN6lzdLI983VrazySgGcSMP6rkc4Rc+Jt+0zq74P/gBKY8MV8KW/M+RxQgJI3yuGzKT8j5dlAJD3jj5S3/GD/z6zWE44W8btjr7fGQz94BxFPdIVf107QHNgv1SFpOqHXHTogdQxIwN0E/F40IPCXJPi9mQr/D1MQuCoJ2vtg/QEdAAAQAElEQVRM7u6G7bmRgNFowtwl69C861A80HkImnUZisYdBmHCzPkQ3XwzbwXW/bNLRHMsf23eiy++WZzj+plVNJnMmPXTcml7i24vyxfhZFY2J/r0Vxw5aSEHdQID/PDlpyPw4xdvY9SrT+P1QU+ojpShWD33U9SvVTUHLbKKZxC47aW02iM8l0KsaXF0LCW0rot16Y84Jg30d6zopAPjeo1c4iPqCDFksdQn6NsvETxpvBTNp+OgxMYCioLAb27rRb4mLg78RwKeQsC/3I8Opmj/muugy0yhO3oYkd0eQeCP30G3bg2wZbOlqNFoOTIkgXwkYL6kWBwki3QQN4OJnQOQ2D4AhsU5W6ZpUB0qwtFivqnehebjONgVCWSXQPJIP+kcMUdn/7NqNKf/vjas0Mq2DPOd/97oDu6HfutmaDZvAm7dkqbq03RCL69/pJYBCbifgOYeM/R9DdA9ZoSmuNn9HWTeos/mHDt+HD/MmYN5Cxbg7Llzbh2ncJhMmLkAbw7tI18os2HJFIwe8SyWr027XnRDb+cuXsHW3Yfc0FL6Jj7/cgFmL1iNgf26QNj96+yP0hfIZapAHCqHjp6W3qyoiHD07Nwaz/R6GO2aN8CPv6zB4pUbcjkkVi9MBLJ/uVGY6HCshYaAyQht8AmH4So3rwBJ8Q56Z4qAX+YDJpNDluai50/fdDCaCp8ikDpND9Mxftv71EnlYPKEgCmDQ+VOncS9+yFuzF2ElEXLkPTLUhkXaasYqt9zpyaYny8E2AkJ3JmAcKI83KUL3h8zBm+//z7aduiANX/8ceeKLpQQ23NMnLUQIwb2RIe2TRAUGCC36RDxFT+Oc2hh/+GT6Ds0vdNi4MjPIJYFicJbdh5ErxdHo9EjA+VqFTF75PS5y5gxeyl27Tsi80R+UnIKEpNSMPaLOXJ2Seen38JPi9ZInWhH6OcsXofpaj3R34p1W4U6nVy5dhPCGfTqiz3xVPd20u6SxSLTlcltokAcKsJxsmHrvyhTsmg6+0OCg/DOuK9tkNJlMuEFBBQHGxVFgaIo6fTpU+myXEoEH0yEVfxnJDvWcdKBtpXJVkfU1fUyONajhgS8lYBGC2N8JQfrzUWKAQHBDnpnCu3xo87U0MTEONVTSQL5QUAsWTD8k/1lwA57qtxw8ochPwbAPkgghwRMaUt/xJIdsQzIlWY0STq5XEjW2aaB+WzWn/vUe2sh9b6mMDVtJkXE7cUcFu5Kt45lqPFpAppjO6FbNAHPK6fxdFl/6FLt1qf59Mg9e3DTvvwy3Z4mRqMRM2bNcovRJ05fkO20bd5AHu2DAP/0Sw1FXnxCknSMiLhVDhw+hdi4BAgnSf/XPkHTxjUxb/q7eO3FXoi+egPFoorg4daNUblCaYwY1EuKXqfDONWZsnvfUXz63iC8Pbyv6lBZi7V/75DNCifMmEk/4OiJ82jXogFKFnd0lPx70PLAcf9/J6WT5/kRn2LZ7xtlfXcFBeJQ2XfoBDq2ux9abfruH1RBiIGdOX9ZHCgkQAIkQAIuEkg+28ehpLFlbwddZoqUZi2dZhnLVXCqp5IE8oOAWPqDa3foyclMceNGjWXJ0FSdPOLGHdpgdqEm4ImDty79SXKyN4q0V3zuhciEJVAuaOUSH1FHSGomS30spTMPA5YsRNiwQQgf3F8uA828JHMKGwHNkW3QffMGNDtW4l4lFoPvCkLtba4vLy5svPJrvPEJCbh46ZJDd8dPWJwJDhnZVAiHh6gSWSRMHHIlBoNR1vf380OpElFo27w+3h7WF0GB/rirXCmEh4WgUd3qUlJSDfIlNl0ebobw0GCEhQTJfVjXbLA4VERDLzzVCRPeH4yne7SHs61DLl2xbJ5WNCocz/Z6GA1q3403P5qF39ZtEdXdIuk9Gm5p8s6NBAb44/ylqw4Fz5yPljq9XiePDEjAIwiY1SsWIR5hDI24E4ETvz2G40va28SUGiernFze3aYT+aYUi15m+kCQer05Yn6fD8ODw2Hs/BJSh38FU+OOLo8s8en+SG7X3qG8OSzMQUcFCeQXAaWkGdqWpqy7c/IQXtvUBP0Qg00QkXUTXpZLc32IgPmkguRP9Ega5C+PSLUMTlvLBG0ji2hqmizKjGGGz36SXyJS6yfcrlfOnLHGHdNB0ycj7JUhCFi2CP4rl8uNysPeGnHHeixQOAhodv3uMNCwm+cxduRgLJnyf7i1ez2UE3ukICURMJuhnP0PGuWiFJmXkuTQBhW5IxAcFIRSJUs6NFK5UiUHXU4UxYta/ohevR6Tk+rp6oQEB0oHyuSvf5FLfvq8NAY79h5OV8aauBRteaKyaMXfGDPpRymHjp6BTnt75mpwUIC1eKZHMetlUL8uaNOsPsTx0YcewJq/bjtlMq3oYkaBOFTqq56h+cv+xN6DxyF2DBa2Rl+9iVk//ap6pwJQrlQxoaJ4GYF2bR9yyeLq1Wu4VC5jIbFUR0hGfU7S/qNS5RIgvStLfxT1ikVITjpinTwncFw4T5Y9ausn4cJmxJ3fYBOz2SDz4i9m0MMo9WfWDZCOlpTYMzKdV4F+VEf4vfVgehnfFUH+sxEUPx1ITcl116aE0jDW7gjjfV1gLl4h2+3FzJqNqzsOIO6332Ee/mq267MCCTgnkHOtUsqMgOnJ8P88BbrORmgqmF1qTPuAEX5DUm2iiXCtnkuNsxAJuImAWb1XSOwZAMN3Ohj/0sij+YJ6zaG27/dJCgK+T5biPzZF1WT4sRRLp7wUdRYxX56UdURdXU9DunxXEgELf3Yo5r98qYOOisJDQFEdINIRojpGcMX5RqcXD+xEw+N/oOiCMdB/9boU5ab6oFy9ftb9Mh4BfmukiDzczHwlgnL9onTGKKJu4UHslpEOHjAAinL7i0GrOh0GvvCCW9quVL60bGdN2lIbmUgLEhIdt1+wd3ikFUt3eLJbW+xc/SXmTHsXwlnzyqgvpE9AURTVB3f773VkhOWh3gevPytfZiNeaCNkwvtD0rWXVaKs6lc4fvoCUtNmxoiyYpZMqiH734+irjMpEIfKwL6dUaJYBJ4c/CHu6zQY3Z57B60fH44NW/fhvVf6gTNUnJ0qz9cJR8kG9RfNKrO+nC2Nrl7tHlh14jhr1g9S745Apz65FE4Wq/irF96iXfFE06oTR3FBLvQU3yMgnSdn1+V4YImXd0rni8mQkOM2fKmiKaoojHXqAX56XxpW9sbC0h5HQNfeCHFTqW1n9DjbaBAJ5JSA8W8tzPEZat++l8iQkQ9JoxG6UyccOlJib0ETnflNsEMFKnyLQMwV6SDRLfwE5qoNHcYWbzDjULx7vps1O1bJvpQ9ax36oSJrAk/06IFVS5fi/bffxpj338e6FSvwYJs2WVdyMTc8LBiDn+6Cjyb/KF8gI/ZCEZu9ik1gO/V7w6GVe6qWl7r1m/bgRkwsxMax12/GSt3Fy9cwY/YyJCYlo1b1SmhYpxqSklNhMplQvUo5HD5+FmImjKgnlvg0qXcPPp4yBxejr0uniNjw9vsFq2VbrgT1alWVEzZmqn2KiRxiQsfKP7aiaaNarlR3qUyBOFTESVk++2OMGNgLzZvURsniUejXoz3mz3wfYgqOS5azEAnkgkBKXAoWPrFQyrIXlsmWkm8myfQ3V9pjtvkxqWNAAu4ikDr6N6R8tEaKWe8nm00ZPh8Jyf2QEDwI0Ft0MiObAYuTgK8REEt99GLJTnf3PEHSPWmQy3+UImZfQ8XxeAgBzdGd0G6YD2XfX0CK5Ymt+b/zME773SamdfsB9SNo/u+C1BkWnnZqvfj8I8BpVraUoh2xbEgpa3KtnvpEO7lVW4eyqffcC1PxEg56KgofAVPzx2GqcK9t4PFGMz47EY9E9/hTbO0ykjMCVSpXRt8nn4RwrpQrWzZnjWRSa0DfzvLe/aPJP8kJEa0eG45R479Fp3b3yxoaRYH6I+PiLUBDnumKIW9NRLMuQ7Fx+z6pVxQFOp1Wbgor9LXaPAuxnGf8e4PkhIraNSqjQe2qaNl9mKwnHC0fvzUAYplQu56vom67/vINQDG3bi/bVxRFtp1ZIJwykz8cCuGEqd32OTmhQ8yQ6dm5VWZVsq3XZLuGmyoI0M8+8YjcRGb62Fcwckhv3FvtLje1zmZIIGsCJoMJ57aek3JhxwVbYaE7mVoCp+HeLyFbB4wIAnkix8XSH1VMqRYP+J06se6pkhzr/IL2TvXvlK9cOAbNpsVy4zbcunan4swnARKwI6CUMsvlOrrH0l+lax8wSseIdLaoDhdNPZOspX3AlE6vrWvRy0w10KsOFb8hqVAi1QR/SMDNBHTfvAHdt29Au3IW9HP/D/pP+sI0ZQlMm47CfPzybblyCxDX/rGJUqfRHoHDPzVfzMbSlDDbshTVufLf3Xuwt/IWKf+V2ysdM0n6RJm26g+rerP5dj1dR6Nc/qPvlf73yNawk0j8iDdhqFjJlmMqVhxxo8bY0owURgJpn6mUJODyKRgffAapT7yNWTcCMeJALC6o3pT6YVoEunhXKfZUkUuITlj2WkkXty0HUn8RCiNqDx6zXnWEiHv37Stn4O/Fk/HnwonY+tt0iNcRC7OnjBmGAX1uL8Ef/ExXbFsxA5uWTcXUj4bjwPrv0PL+OvJtPit+HIfNy6fhn6VTsHDWaKkXbYilQjPGvYZNv07FjlVfIjDAT65qEb4CsUTojwWfY++6r/Fy/8dEcQj980/eeb/A+xveq/Y3Fb/PGw9hv9gEV/QlG3FD4OJH3w092TUhptqMn/EzbsbESe3vf+2QrzEa9Mbn4Bt+JBIGeUwgoEgAHpv7uFN5so8fevT0Q/ywETC9PhLm0FD1wsWMxOcGSJ3QCzGFhLjVym/nrYRYm5iaanBru4WlMbn05/wGmM2uXTjGp+21Ys6DpT6a9XOg/2IQdOofC/FqQf1n/aAc311YTgXHSQJ5RkA4ToRjxCraeibZl3C0WHXiqKlv0cvMXAT6LRsR9OU0BCxbDCVWvRnORVus6psElLOHIF4jaz86JeEGtGc2AbeyXkqqKXYe2nKH7KtC18sAbeP0n1+ljBnjXh+GV17uIWVsn+EQjhmxZ4pVJ47j+rwKRcndjajhnntx/Y/NuLZuE66v/gtXt+1DahPLE2jwXyElYPlMKWlLf8Q+KPp5Y/BCRCKm1w7DjDrh8lgqIONtpeqIsXPwWeHpFn4ql/XIdtL2W7HGNXvWpRVT66bFePA8AlERYShetAg0GstnIzMLxYaxYmWKs3wxcyQiXL3HcpIp3ugjnCn2WeL1zGLLkJw6QkS9MiWLyuU/9u26I57xk++ONu/Yxg8Lf8feA8dRJDwEYv2V2IgmLj4Bh4+fkWuz7tgAC2RNgLkuESh3X1k4k+IfDkbkJyMQP/x1GEe+AXNYONQrFCT0Hyh1Qi9E6uG+f2cvXsHw975Amx6v4ItvFuOcmnZf677fUuWuqyBEo3PN0VWx0y+yvD7Ugv0L5AAAEABJREFUss7TnYS06+ema05JTYF2w4J0OiZIgAQ8m4B4ZWxE7+4I+Xg0woYNRFTLJtCeO+vZRtO6fCegiT7jtE9F45oDTldzA/yfXQa/+xch4M2/4T8q1Wl7+a00VqoMw93V87tb9ueRBCzODbFc+Upoaey6mZpOzuojEF+iCsz6gAzWqzfb6k8GJcxlq8FcsbZzKVI8rbiTimk5PJCApxEoEIfKkeNn0a5FA8li/eY9EJGZ6g3s15/9T25Mm5iUIlQUEihUBMSGzIu/+T90fqgpxDq/9r1fh5i1JTZ0MhiNhYpFTgYbUqYFhCganUvVg0s/IMtrdcEulXe1kHLjEhQxLTZDBSU6b5YWZeiGSRIgATcQ0J04BvHKWPumNDeuI/Cn7+1VjJMATMWdO+XNpjCYL8c4ErLcm6bX37gGpchVKAH8W58eDFOeQSDNuVGkBJ7ffR2D9sdJGbgvVh5fOWaAfth0IMLxtb1AWl3c/mfo8T+kvvCZUzHVbZdW0NkvSloWDz5BwJcGUSAOFQHQT2+56dm97yjurlRWThsqUSxSZOHYqfPyyIAEChsB8bvw+uAnsHHpFHw++iWYzSa5oVPrx4YjITGpsOHwyvGa1QsKs1+Ag+3m4hUcdJ6kCJw/BxFPdJUS8ngXKHPnQP0Awl4v8gN++dmTzKYtJJAnBLTHjjptV3vsiFM9lYWXgLncPTBVsTwktFIwBxaBwVQVSHacbWJ2vL8EkhzLWdvikQQ8hYC4Jo2OvuxgTvTlyzB60YM/3fFjCBkzCqHPPAUs+sVhPJkoqCaBTAkUiENF7OArlv3M/3U9lq7eiDbN6ksDj548J49RRZyvp5KZDEigEBAQfnnxh8tgNMnRil2uTSahlUkGdgQqy6U+v9tpshct1+5LufTHL8T5U8bstWYpbWzV2xJJC8U0WWPzHmkpzzxozp+DfutmKdotm4Bz6vexosBeL/K1ajnwHwn4OAFjFfVm2MkYjVXudqKlqrATMDw3FoZnx8L4yAtI7f0OUjp8BDMCoBQPd0CjOGhURUnHcqo23c/j5Z/CfVHN0+lEwqz2pNfoUSu8Hp6pOAjhuiJCTSn0BNwIQO8vl+eg7D0o7uRtT8VLlIBWq3VLh/KhVMXaQJESbmkvYyOaa1cR0fVhBH01A/o/1gDrLHu2KMl8aJmRFdOuEygQh8qLfR9FUnIKRn/2ndy5t1+P9tJisSmn2GymZPEomWZAAoWNwJET5/DJ1LnyVWGvvj8N8QlJ+PitF+Ru2uKVYYWNhyvjFct8Qso0sxWt2HGhdJBYHC2rIPdUUX1RFTta9kyx6rV+lgvYoOIN5NIfjT7I1kZuI6ZWTyL1pekwdBoMQ/dXkfrabJgr18tts6xPAiSQTwQMlaog+ZFO6XozRUQi8amn0+mYIAErAVPVBjA27wlzrZYw6/ws6gC95XiHUHGhXM9yfWEwp0L1n1jErs1UYyr23dytZplQxM8y29su2zuitNJjCZiLFJfLcww9R6Jvn2cd7GzdyrJMx5RhbxSojhgx09Vc5m6YjCWkiL1T4LDXyu0mTY0ekX2Z6ra9rXRjzG/Vb1DiYh1a1F644KCjggRcJVAgDpVypYtj1U+fQLwyae3PExAeGiztfe6JR/Dt52/cccdgWZgBCfgYgQ8/n41uz70j90/p9kgzLPr6Q8yd9q7cUyXjTtc+NnS3Dic4bW8Ui6OlBeSeKgqQUe/WTp00Zi5dBaYHusHU8BEgjE5iJ4ioIgGPJhAz7WvcmLsIcW+Owq1JM3Dtr60wli3n0TbTuPwjUBA9RSddhtySQv2bhrR/ilAoakKV3dd3qBH+kEDeEejcuTt+mD0frwz/H6qkzeS7t0ZN2aHx8delM8S6P4qYbQJFgdgzJcnQHkJEnjkib2afSCPuEOiPO1/OiYT4O9RkNglkTqBAHCrCHD8/PSqULZHOeSKWAgmdyKeQQGEjEBUZjv8b2R/bV87EWy/3QbXKvHAvbJ+B7Iw3oXkg4mvcFvM1S+3M9JbcbIZOXneYzRZYnAS8mkDqfU2RMGAwkjp3gzk0zJvHQtvzkYCmcgloBz8IpaaLf8eLBMvympY1srTy/ZqfYlrDH9C5jPMlpP5a/yzrM5ME3EHgrrsqoXv3nihRopQ7msvXNpJbtHban6lYcad6KknAFQIF5lBxxTiWyR2B+AubcHxJeymXtrwvG4u7eFt3cetoqWPgGQQGP90F3R5pjqBAXhB5xhnxDCuSR/lJx4lhgTbXBhn/1ci2kp5w8hlTnyK51IGr5VxqDPhz027s3n8UqakGF2uwWOEgkDejPPPPGeydvVc2fv34DSTdzLt181evx+CvzXshjrLDtGDD1n04dJRv/UrD4ZuHkAAolUtCaVxZOkqEc0VKm3vV8ZqhVC+dXv9wHVkexbN22t0bXhsNIprgqQrPqe04/jSJauqopIYESMBGIKVVWyT17mtLWyMmJ3vDWPN4JIE7EaBD5U6EvDjfkHQNcec3SEm6flCOxGivu3ZA6hgUHIGZP/wq3+Iz5K2JdzzyLT8Fd55y2vPl6Ms4fPhQTqvnbz1ns1GcOU+clcuFpUtXbUSfl8ag7oPPy9eEz16wWt5sGtM2ZM5F0/lblb15PIFT609hUd9FOLPxjLT12tFrWPLcEhnPi+D7+avxzriv4O+nT9f8ph37MeD18TB40Vsx0g2ACZcJKGFB0lEinSuqgwXFw9W6ChAa6ESvZmXzJ0gbjGBtCEoFlsGT5Z9Dj3KON4rgPxIggXQEbn00Hlf2HkHMr6uB8Z+ly2OCBHJCgA6VnFDzkjrBpe5Ptzln5a6r0qVLNhnlJSPxXTPF/apGDVwR36XgeyO7efMGBg/pj8cf74jnX+iLh9q3wIoVyzxqoJ5izPhRg7D4m//Dh/97DkUjw/HTorV4/IVRuK/TYIiNmROTUjzFVNrh5QQO/mJ5sGA/jEu7L+HW2Vv2KrfFt+0+hMc6tkRoSFC6Nns+2grXb8bi/MWr6fRMkEB2CZQMKI0/2uzCkmZ/Yli1NxCgDchuEyxPAoWSgDksHMY69YDAwEI5fg7avQQ8zqEi3v5j5JNJt5xlXWBRhJRpkakERompp27pio3kkMCAPo9iyphhLklQoPsvlMTN6oVLV5HZK5mF/tKV657wJDWHhAum2rx5P2LfPsuyAmFBYmICJk0aj4SEBJHMkViX/lj3TTFnuBeTE0fMjk0nNLu9z4rTpT6OVfJVo9NqcXelsujeoQXeHPoURgzqhUZ1q0PMyFq9fhsMBi4FytcT4sOdiSU+zoYXt/sYilcsIcUUHoaid1niVl1Ui0bOqt1Rl5iUDD+9zqGc9ddU5DtkukFhVr8MDDmc/ZKSkorLV25AtOEGU9gECZAACZAACfg8AU1+jXDvweOY/+v6LLu7EROLZ18Zh7iExCzLMdPzCIhlDWJ5g+dZ5j6LQt8egbBhgxCwZKELjea8yOlzl7F2w078+vsmiL0lcnphfCcLhr49CQ0fHoAHnxiBlt1fxmcz5qerItb+N+k4CG17vIo6bfvf8fc3XeVCnjhwcJ8DgQTVqXL+/FkHvbsUirsaulM76s3anYpkJ9/iONkuZ6M0euRFDH/vC5QsHimdjDtWfenwdD87bbMsCdgTKN/UcYNQv2A/lKoRYV/MbfF77q6AuUvWQTwosm90/rI/ZbJc6bzZBHH5ms14SP1el53YBZ2ffgv3tnomnUz7zrLkSThQpn2/FPUeegFteryCFt1ehrhus6vOKAmQAAmQAAmQgBMC+eZQERf7oz/7Dt/NX+XEDODM+Wg8MfAD/Ks6XsQTS6eFqMw7AjlsedmyRXI5g1jWIJY3iGUOsbG3ctiaZ1ZTbtyQhvn9uQ4B6njDXhmCoOmTpc6dgdiU862PZ6FDn5EY9u4UvPHRl3JvCXERfOTEOXd2JduqVrm8XGqxc/WX+PB//fHNvBXYd+iEzBMzV0Z8MB0vPdcNe9d9jUkfDoX4/T138YrMZ5A1gcjIKKcFQkJCnepdUfqPTkHwwUSbKEUz1BJfskIyqIP+uV0nYF5yhtzbyaTHn8CNuYukxM9Xb7Kq3g31MbWDpFa753YlN8TE5/zV96di+55DmPD+EAgnyti3BqBN03oIDPBzQw9sggQsBBoNaojSDUtbEmqo0WrQ8r2WUKpURPTJy1I0/1qcocZy5WVa6K/9vV0tnf2fAU91kkt7GrQfIB2G46bORfver+OHhb/juSc6IDjIvbMOz5y/LNsXv1OZWfty/8ew4sdxNnmyWztZdM+BY5j67WL8MOUt7FnzFbo+3ByvjPoi09mLshIDEshDAu+M+1ruNZSHXbBpEiABEnALgXxzqNSuURnDX3gcn06bB/EUxN76ff+dxGPPvycvPH784m2XLjLs67srfjH6eqYXD7FxCRAzaNzVly+0Ixwnn47/CGI5g3U8YpnDgoXzrEmfOCqJCQ7jCFj4s4Mut4pZc37D0tUbpRND/B78OvtjjB7xrGx2+HtT4O6ZKsJZIpZaBPj7odUDdVGiWAQ27zwg+xNr/8XMgd5d2kA4ONs1b4AKZUvgr817ZD6DrAk88nAnhwI1atREqVK3b+YcChSwQtxApt7XFEIMDzSF+mUIKGkeGnFME62bHab9Hm+v3rw1Q1JyKsQF9CfT5mLzjgNINRjBfyTgTgKBkUHouaAn2o21OBEqP1gJ9/bMu6Wvle8qg4WzRqN5k1rYsPVfiA2XxQa1b738FIa98Jg7hybbKl2yKL6f/CbeHtZXpp0FxaLC5Xe5+D4XUiQ8BOLfH//sxv0N70X9WndDr9eh7+MPyaU/h49bNvAVZSgkkJ8EFEVBCt/+lp/I2RcJeDwBTzUw3xwqAsAL6tOaUa8+LZ+CjJ/xs/rw06zeoO3FEwNHIzQkEPNnjkK9mlVF0XwVcZEjnho9NeRDufRhwsz5tv7FTaVYGnFfp8Fo1mUoeg/+0OEViLbChSxy9NgRpyM+evSwU71XKm/etNxYZjBed+oEkMM16hmasiVX/bEVHdveh0H9usjfg0rlS+HxTi3lvhJiGdDps5dsZd0dEe1fvnIDYtaKaPvy1RvyotvP7u0UlSuUxqVoy2wdUYaSOYH7VMfE5xOmomPHLnjggeZ44YXB+PSTSZlX8MAcc7lyTq1Kbt7KqT6nyoZ1qmHMG89j07IvMPGDl1QfjoKX352CBx4dgvfHf4fklNScNp1pPbG8QWwKKmZG5kX7mXbMDI8gEBgRmG923FO1AmaMew3bV87Avj++xbLvP8JT3R+Ujmp3GyGc3yWLRSIizUnirP0Fy/+Sjkux1EfMaLGWuRh9DRXLlbQmUbxoERmPvqr+DZQxBiSQvwRaN62H7Xv+Q0xsfP52zN5IwH0E2FIhIZCvDhXBtGfn1vj03UH4dt5KuV/K4Dc/h7jg+HnGKFRUbyBFmfyUA4dPQUzDFUse/ljwufp05y18PXeFbe3wnMXrIJZb/LlwIr7kuxsAABAASURBVLYsnwatRoNJX/2SnyZ6bF9Rke5f1uBxgy1SBOrjOgezUpq1BLRaB31uFOLGTjwxzNiGeOoodK5eVOz89whm/bTcqSxUL6ZFW/YSn5AEMQNGPJls1riWzLqlXsBk3ATX398PsXGW2Tp+eg2KhPh5hagPueSYwoNdt1ev0yA4QJer8bVr1RxjP/wQM7+YipcHDUT50sVs7SnSIiAsUC9j4i1PmfEs8RlQ5owRRZ/V2uqLshprI7KFzIMwu3FHPaCTbZVcbpZtiXGKmhnHGhKog6ZxY5F1W/z9Yfjg/xBWpYKsK2zITET92xVdi92IiZMb0QaoTrxSxSNlfMHy9UhKTnGtARdLiWWlYn+I5l2H4pGn/icd5YtXbrDVFn8P7m2Vfp+JPi+NseUzQgLZIbB19yGIJTjiYYyYpSLqigdK4hpIxF0RsXF4Zt/pQi+WaLrSTvtWjdDivtoQf1P+2Lgbjz0/ClanivjOD1B/x+3bCQoMgHVPu8hQP5SICCgQ8VO/j4VdES7YEBXmL4pCr1WyZav4Pi1WJO/HFxasl/YF+mmzZV9G9tZx6rI5zoztWNPB6t+7UPXvkTWd3WOAOh45MDcGGvWPt/gMiqXQ4u1vGUVsoOzG7tiUJMCABEggJwQ0OamU2zod2jZRn9i8Kj3PYpnB95PeRLEo9cY1tw3noL71YqFk8QhZW8wKiCwSijPnLsv0qj+3yVkC4mmNePVh38cfxKIVf8vZNbJAIQ4qVKgIsYwhI4K2bR7MqPLqtLmI5bNhHYShYiXEvfGuNem2Y71aVfHd/NU4fvqC7fMllpnNnL1M9mGdPSITWQRiVtX1m7FyCV3Go2jPvqq4EBfr5I1GE6b838uqj8jylRAWGixvaO3LJqs3tuJ3QOiMJjNSUo1eIWZhsCrZsVe83cigMslOneyUVc2RP6lps5yEjdmpL8pGbk1BseO3RRMlm0TUtts6kW8Mz/w8iXGKWgaDKd25TDWYIWwSeSlDhiJh5VrEHTyGpOcHpisn7HAmqWp7oq4r8t38VXLfh9aPD1cde1/gn2370OL+OvJvhHBih6ufRVfacbWMGNew5x/HhiVTsGft1+jX4yH5xF44NEUbYvaKWAJnv8+EeLWzyKOQQHYIiAc2z70yDmIJ5bGT5yGcFqJ+6RJFIZwqrjoLxfK3jN/l9mmz2SSavaMMebabnAE5+OkumDvtXTkzeN2GXbKe+M5PTknvvBR/S0KCAmX+zbgUXI1JLhCxfp/ExKfesX9hpzDYYDTfsaz9eNQ/abh+K+/HF5tgEOYhSf37ad9/duM3Yi3nKrvjzKyfhGQD4pLuzDez+skpRjkudwZLVv0jr0PWb9qDjyb/6CCJ6jUJ3Nkh28oxgaC4q1CucHlgjgGyotcTsNw95cMwrl6PgXgKaBWRFssbxDIDMePDqhdHsTlnPpgku6h3bxWIJ/PiSY3YKG767KUoGhmOlg/UlfliKUT5MiVkXATWXflvpT2p12oU+LJA/ZfV+CaMn4wXBwxB06Yt0KljF0z6fBqaPtBMMtGobNTqcgp/Vm2IPEWxPG5XFEXWFbqciK1PIFftWPtW1HaQ9tQuds5C3PxzM2LWb4H53poutS+quyrD+j8mi4pNaMUT9G7PvSOfnv+2bgvefaWfy3sLNW9SGyOH9HYqYtmd7EQNxGd40BsTEHMrHrMnvwXrWno1CyWKRkB89u1/F8VMLavj0aherCYkG+ENYvUMJKoXfK7aKxxGyammPBufuKkXnBNTLDdCZrM5133Z2szGeRHjFHYkqw4QezbJ6sW+sEnkpfoHIa56LSRo/Fy2UbATdV0R8fT9/gb3Yvx7g/DXoklyScSIgb0gPsdWB54r7bhapk6NytJJLhznep36lLhYJERco7n951D0K2aLWUUso3C1fZYjASuBeUv/QNvm9bFu/gTUq1nFqsb9DWrI+HkXN/kWn8PMvtOFXjzFlw1mI9DrdSgWWQTWm9JSxaNwym5ZqXWpj3iYJJoVDgfxfVEQYv1uEw5gV/oX9oo6rpS1lhF1rPG8PFq/V9WvfOSmH5NoQBitSm7asdYVzQmxprN7FLxVU9z6I5aAHlj/HTKT7DjbDerDi0tXruPi5Wswqg9LMhoqPlsi36CWy5hXUOlTN4349UgyVh5NxuV4y7VCQdmSWb8VkIAlDcPQ+K/p0H/eH/pPnoJy3vl2AJm1Qb13EhD3DuIhrfU7TYxCzJAXDw9EvLDJ7SvIPB65WKsuNhy0F3GjKLr9adEa+YTQmpeQlCzUuRbx2lkxHdaZbNy+X7Yv9ogQU2BLFY+EeNXgF98sRov76iAo0F/OEhBPaAL8/WRZEYgN5cQxISFJHFA03N9nJUodm3AoZDXGSuWK47Whg/DtjGmYMHYMHmnXwsZDLAcQkHRaxabLrK2gAK0oCnHMrIwr+vAgy3RaP73mjn260p6/n1Z1CEnTEFanBiLq1chWu/q0qcqWFrIOS5WIwtr5n0Fs3tyo7j0oqV7gio0B5898H090aZN15WzmJiQmo8+Q/0P01Rv44H/PIT4xCecvXYXYmFk01ahudXGQr/wUFxhrN+yEeMNPy/stjkaZyYAE3EDgrZf74P0Rz+CRNk2kM9sNTbrUhPjDP2r8t5ihOtHfGPoU9DqtrZ6YUSCmmYtN1EU5W0Y+Rm4lm/DjviSbLFMvrEX3t1LMNp3IX3rYPX8vRdsU9xJYv2k3WqnfmYoi/pLebjs8LEQmUty84aa4sBVOcEPahs4ynnaDeOb8ZYjZYOKmUcx4Edc7+w+fRJN6lrd2tWlWD+K6aNe+o3JD6NkLV6NEsQjbvlrS4EIYJBnM2Hg2FQsPJmH7+dRCSMD7h/yz6tis07Y/2vZ4Fe16vYYHn3gN4rNvHdlfm/eiScdBMl+Um//remtWgR2FI2XQb7cwbXsCJm9LwDNLYrD5nOd9/jorl1Aq7fpdwFJuRkO3YqaIUnyQgFF1Rs5dsg7Nuw7FA52HyIe+jTsMwoS0vUfF20LX/WOZ9ZjT4YvfR3EvntP6zuqJyRr3ZljKLdKvvj/NWfEc6fLNoVK7RiX8vXiySxIWEpSjwWSsJJ7A20+LtY8nqDeQovyGrf9i4qyF+HHqO/h55ih8+ekIiH1Tfln+l3ojrUA8+Um22xTRGg9Ke93h5RtJ8FWJVscmnjrkdHxi+qxgLKbr3qmN+ETLNFhxvFPZrPJvxFmmwYon5FmVczUvKcUI8WROjENMdXW1nrWcGLuo66qIJy5iFsmE9wdj+thXIF5xeXflcq5Wd7mc2Avl+OkLELNQuvd/Fw89MUJKzwGjZBvCoThlzDCI/STEBYZ4jfM7w/uibKliMp8BCeSGwK59R7B6/TaXRNz8udKX+EPvzHlu1Z04czFdM+JJpXgKL246b8bE2vLuvfsudHukOe4qVxJnL0aj38sfSTutBcReA65ITIqC+YdSsOGcUe7H40od+zIGaPDTv4k2+TXNcRKrOlrs9b8dTXHavl5r+fPurzqX7du1jwv7Fh5KttUX+yBoNYotbV/WHXF9moPZXz1q1H4EU9Fnbtq2tumnthkcoHPJ9gCViehbq9U4lFdEhiqKkjMOalXbT7Uq5SH2ULEp0iKb0h7ouPv79PipC6j74PNyzxYx+1fExYOqtG4hNuAXN5V12/XHyDEz5SzGBrXvltl1762Cgf06o+/QMRD5Py/9E5+NGgzreZKFClkQn2qGuKn9v7/j8PXuRLy3Pg6vrL79XVHIcOTLcMXfhl4vjpYPd2b+8CuGvDUxU7Fex9/JMHEdP2Pcq9i+ciY2L5+GKneVwYQZ82W1xKQUjPhguny74t51X2PSh0Mx+rPv5AMkWaCAgrn7kxx6/vlAooOuQBUmI0orjnYqF48XqFmFvfO4q4dwevtUnN01C4k3T7oVh3CYTJi5AG8O7aP+Ps3AhiVTIN5GunztZrf1Ix7eOvu7mZsOHmzREPbLuEW8do3KiIoIzU2z6eparrjSqfImodNqodNpkZScAuEwiYoIUwdyWwID/GWeOCqK4hYjnureTl4wjHSy/EHAFZ2Ip4/i7SXCJpFu2qgmGterDqEXaTHVVjzZEXEhZy9Ei4Mcg4wwIAE3ERAzqh5/YRRuxsTJFsUa+0aPvCgvboXHVirdFIi9iw6s/85hKq34crR20aZpPfy77hv8Pm889qz5Cr27trVm8UgCuSIgNuR8VX0y4Iq4etEsdn25nsneQUIvHCf2Rnd68H7ptJz4wVB8NPknWL/bH33oAQx9rjsG9HkUkz98GSItnm5Y6+q0ClyRywkmfLsrHmuOJWVePou2tGqetc+sjuKvpTN70vwV0Kh/T53lC52w74e9CTb7tBpFLQ9bWpRxp6jNQ/wTN+mqWSIKMc7c9GHfpqvtiP5F56JuxjoQQNVMcciY50parWr76dW5tZz5OmbSD7gUfR1HT57Ddz+vks6Mrg83g1haZivshkiVimUcvtPHvjVAtiyWLq+bP0E+1Fo15xOIm8d+PdrLPBEoiiI/9ztXf4k16nf+1t+mo17NqiKr0Mr6U6m4FGdKN/7/rhrw72VDOh0T7iSgQKPVQPxTP5Lq95GSqcDFf+I7XCwhFQ+KxLW+2C+oSLjlRkrMRhR/Y3p3aaN+72nRrnkD+YbDvzbvcbF19xeLSzHjRmL6z53o5VyMo07oC0w0WqSYxTdlegvMAcHpFUzlGwHhRNkwrSYOrnwJ+5cPwF9T7sblw0vd0r94MYaYgDBiYE+IvVCFo1IslxZx4aDI2ImYBdZ36Efp1ANHfma7v96y8yCE87TRIwPRoc9I+SIN8ZBXzBrepT50E3lChN9AOD7HfjFHzowR2yKIlS1CJxoXejERQmzZIfpbsW6rUKeTkOBA+Xst7umFxNyKg3hBQd/H26crl5uE5VsrNy1ko+6QNyfKVyQLOBmriWmoD6lPyT+dPi9jVp6m765UDuJJvZipIqbLiqVJYgOsGnffJftt36oRFvy6HuJJZlx8In5YuAbdO7SAojh+icgKDDyCgMlsxKGYfbiabHGAeYRRdzDit3WbUfmu0igSHiLfLCVuOsVnTazBn/bdkjvUzptsrXphU6ZkUej1urzpwMVW91xKTbfMQSx1sJf90bzAdRGlrZhpvwamw5Y/AYalWpii7/ydZqucy8i4dwZi069TpbRv1Vgu97GmrUfxuRdOPTFry5XuhNPemfPcqqtW2flML/HHVbQv9vUSx4wi9k+JT0i2qcXGmK5IfJLlM2kwmuFK+YxlrJtX2jrOJCL2OshYV6TFvjiiitg7SKSdicgXYs0TNqfm0F5rG1kdxcxB0Z+wyaj2I+JiVmJWde6UZ21TzCa8U1lrvtgvSPQtZhBaddaj2EdC5Ik9Kqy67BxFXauIBzejXnsGS1ZtlNcZ4g2C4hqnY9v78D/1QY+1XH4dFUWRD7LEXnDi98VZv2KJc2n1O9/qdHJWprDoTlx3vsTizC0SS9o5AAAQAElEQVRjYUGQ7+OsX6uq3DBZLMMXTm0xUzYzETd02TFw2e8b5cbnB4+cUh3mnWTVy1dvyBstsfxfKtRAPGS9FH1DjRXMT4ifgohAy99mewvKhjvq7PMLIr7dnP6lDcIGU+3W4kApAALHNwgHxm3Hm9lswIl/xrrFkhOnL8h22qpORxmxC8TfDbukjMYnJEE4RmQiLRAbtYsZ8sIP0P+1T9C0cU3Mm/4uXnuxl3qffQPiBTUPt24M8Ts4YlAvCNHrdBinOlN27zuKT98bhLeH98VPi9Zi7d87ZKvCCSMeWhw9cR7tWjRAyeKRUp9VMOHLBXIrhfJlimdVLFt5+fbbKQa8e/9RiLXqzp7KiLfriKUO85f9KdfvZmsUuSgsTtxLz3XD/038AWId2DPDP4aw46nHLG+qebJbO1SqUBriDRRijaV4yjlUfXqZiy5ZNR8IbL32D57Z9hg6/t0MQ3Y+nQ895r6Lk2cuoUGtu2VDm3bsl8c3hz6Fd4b1k+t9r924JXWFMdhzyWBb+mC/3MEaP3DFcvOagU2+Jv/77yB27d6RqSQmJOSrPVl1ZjqmIPH/2TsPwKaqLo7/s7pb2lIoey9BpmxkI6AgS0BQQAVEAZkOFPRDRBAcIBtkiKAyFZA9ZchGWYLsvUdL98j63rlpQtKkbZq2aZIe6Lnv3XPPXb/3krx33r339fCG/rbBiaLZqkDia1L8qd8gvexZTqMnheQoITlz/qr0JLwcaN9c3nr1Rezaf1z6kX2S5frMC6DRJrv++gf0tIV+2Gf/tE5M7aRh4GQ3VfqhvXzttvgdoics9MP9fN2qlOSQ3I3VpusMNHcMmu8b10zJqNLUa6oYy6An6ZT377u2nZGLTxqGjydryYpFdewI/ObPAdavz1YY3V9uhv3rZmD9kq/EtOL962bi68/eFed7tlbEhWU7gRLBth8klA1WZHtdXGDOE7hy/S7oOkqr1SE6xvB7TG/eSu2Y8fb2Av02UItUChnCgrydLv1rB1D1FvJ27cB026GkIXdSDlq/0N42+3orxLRHe+1T221TFcX4C7GILF4FqlrN4fP6KIR0H2xqpyKlTSH+XlLLDH+py7An7r3eB8oFaUtwnGPHKCTAS4yAopb5qOSmdtvTJqMN5XUF0SbHIiH6plVTaAqQldIBBa25SNlCg4NokyUxrvPl7eUFWj+yZeNaGDOst3Qt5o1SxQuD1hmrU6MSSGitsVUbdqNj2+fF7yaNMmtU51ls32dwqFBD6L6dlkp4o1sb1Kqa/sjKfYdPi7cMk8OW8maXOM2hYlz1t0m9amm2vWmD6iLt2s27YuuMgJ7CDOzTEVuXfYN1P36JHSumgBYFVSkNP5j+fj5iWDg9NaU3UNA6KwXDgp3RNK7DAQJRasPNl87sNZLHIg5i2fXFDpTm3CzBQQGgdX+o1gNH/5W+FCqILxdvb8NCu3cfPKakPCk1CinxejVfkFQpaLjIrV5IJeJCV8Cgy004kyaPx7Bh79qUGstHId+XHeE1+gUhMnWyaKrX993h570EfnFzhB4pepGYg4FmncTr6UMMUZP+jgy6k077SRB1UkALfe85eJJ2LSQ+weDdye7zntZkGfLpdDR8eTDqtx8EWjh0xpdDTdMvaBhqhzfHoEarfmI4auumtfFGd8eHhdKUAaPjLzPbP85bz03XS4RIpI3pz7imytG/N+P+kS9NEnfPMOw1+sZ2k47S9/7zl3BOrkiZo+9l+KkzlZdTO9rZ26A/cEEUr1t9CPrL98W+ds42aN5fahL9JYNeJDopCPx4JEK6vYyAiZ9DN/oTQ62a7HPSenmpQA+Nnq1YGjQC0VABh65OoFUZLxQKsPxOrBSmxDMu8Hvj6uyyo33JyWrQFAMa9k9TAlKL0elhb110bb90xmh0eakJ3h83S2Sj6T/xKWsqCoUUJCUlm34PNDo9ohPUTpe2ZVVY0DEYQ+r7Y2SDAPzSNRg1CynSbYc2ZXhdbKImXTvz/iSrdaDRfea6zOxrJOfUhgdqnKnVFeqenyKxRmuLunXGNiVpYPyXmfKNtlE/A1FTZGlK9HX7+2wsk7bEiqYLU9uSpb6QLrNCeV1BFF4B8A2yHokbEPZMtjSvYJhhNFJao3kzUwlNwSEHyvSFv4Gm/PR6bwKOnTxvs4h7Kfc+v2/aiwnTfhby38UbUCqeXrzQvbrNzKmU5EydMm8F+vV8SSy6nio5S1HLX4osFZV+5ujYOOnm0Mf0JWXLmrxUpE9IuZCmfWdKesNc6ckpvU7Zme3huuwnsOjKLMy/MgPrbhsWGkud80Lsf6lVOR3PdPmN6j4Lejr+0fi54m0LHdo0FGXQMDfaKVwwP23ypNSQnCe9qvqA5NmUi9mahZQiLnQpThZnw5FfOCIcIcrFY1Cx4jOoUaOWEB9fX9GUSik6uTzrX7XKFZNEXfKTO0XZWQl0Vw0jU1KXYRyxklqfk3GaVklvF6HFY89fvimcirQg2fQFv4lqSxYNF9vsCmjEAK0fsWvVVOxcNUU40es/V9lUPDnND22Yjc2/TMaxLT9gwsf9YWs4qylDBjt0Q0ZOv8xKh4o+ViXTUSOhhGeTN6JV4ncoozgvHIsdQv6S4lNMUlLzD5mhoma3SdcqcQq6FDwt7PuWOCnpv0OJxL3CLqcC7YJdIIeJ3mzaVHp1aX87bLC//ig9s2xLkz98AN8Vv1iVJ4+JsdLZo1jwy4Y0F9FMvcBm6hs5e8pnG+cR8FfJMKddED5tEoB+NX3xRbMATG0T6LwG5PGaFi7fJNZVoHsDGuVer1ZltHi+Fh4+jhL3E0ql9GDAAUalSxRGxJMYaLRahEs3iVS22uyNWxeu3EKhgoabR/IHJEtOh9yQogEyvFTWCy+UUSHUW4aM2kBtJRwajS5DW2NZNGWUxBjP7JZeN51encY20fRKsiPJbB1kr3hFDdVgjRBZAT0VA2VXrYiTXpPf/j5TeUahdhnbqNPBbm7G/LQVjXGRoGzj0VJLnl5vymRKlHn+Y0mX9b8yJYqIQrbvfToyRCikIN7Gfbu5w0Mysfp7rXNL0Jpdv87+DOSsGTF2pniluUwmE2/ZNWYIDQkSu198+BZ+njnGJFM+Hyz0mQm27D4illR4q8eLmclml+1T6naZO25UQroojpe8wFHRcWkWQl9qlEiODdqyOJOAe9c17/I0LLg8A8cjj9rsSH6vMJt6V1L269kONLf+4N9nxFtG6E0j1L6ZP67Bs9KTzfwpXyqkY3E9Ap98/D/MmP6DkKKFi4oGfvzJWBFXeT0d7ioScjlQNJauHGy0Qf6cbb0N02xT0VDNNs3qiieR9MapBu0Hoe+IyWKaGy2omRNP9OmHnhZmLlQgFPKUIcnmHaJpqfSb5euT9eNWOEBhcvyR889e6VDB27xJVvvPqjdLDpEpKIHzovxK0pNzKyMbiucKq4R904DjIn8l7T4bVnlHpTxz2nZn1Wrb+gy00rWgGEIul3YykgyK4mQXIOCjlKFRcRW6VvZBnaKG0aIu0Kw80YTd+0+IJ8kTP3lb9PetV9vig3dfxUeDe+D+wwioVE+fUAuDNILZi9fi5NnLoHUbbt97hB9XbEa9ms9AKT3hpikFlG3Z2p3CwbJj39/iDT9NG9QgNYuLEFB118JrsFqIvJBetErZTSPipJeH64UurwfFa72NxoP+ReUXZ+LZ9j+g6ZALCK/YMVuw5Avyx6A3OmLi9J9BU6djYuMl5+YT0CKw7ftYO22eKV9C1Lv7wAlERsWIN+iSI5OU9JbFuUv+QEJiEqpWKoPa1StKn081dJJXq1K54qCHazQShvLRFB/6vH4141fx9i8aZUzTsX9atZWKsltoxNt3c1dg8FudEZIv+x3jTnOo0Orz1OvvF6ymjZWoJe8wDe2ji1y7RoJYlcAKVyAQFx9nWkPi3IVzokmxcWa6c2eFLruDjkW7WxWpx9Mv2KYFX7BKdzUFDVmjufX0pp0vR/UTP/bUxtXzx4m597TPwgSyg4DqVQ0UzbUWRamGqSEv8fQzY5GYgxGaDjHl80H4bcEXoPP+w0E9QHH6HNDbGXKwai6aCUBboZJtCg46Qfu91h5pLaKZWp967QbbDWEtE8ibBB5GPAE5tv18Dc7lO/cfCxB0c0U3Zhev3BLxjAJyorw2aDyeazMArXt8AIVcji8+6iuyUdn0uZw8axmqt+yHYZ/NwKfDe6NY4QIinQMm4G4EAsKeQck6g0HOFd/g0tna/AG9Owin5sTpv4CmTDd7ZTjGfvsj2rdqIOqhhwjSswSxT79vg9/sJEZsPt9xCPYfNTy8kMlk4q2/tEg06au2eAs0nefb/w2UnKRK0OuMn6tWHk27DAOlJyap8dXoAaBpQq26j0SNlOnYUdGxoh4KZDLj2F2K2ZbfN+9DTGwCeqeskWrbynGt0xwqNGWG5kvRorP09PHPA8dBb9c5d+mGeK1gx7fGiFcYfTaij+O94Zy5TuDmjeumNSS++3aiaM+tWzdMum9TdCIhG4MCPranBdQKqYt5tX9F1Xzu8bSBpjp8PPEH9JR+/PekrCvxreRR/XH55mykxUVlNwHj1B+v0YY1Un4t8hiHnw/BMwsGimk6suQkyyr1krtPEksl4PW/dsLeWI75Njum+pjX5zMrGYrnDU4Vn6nJ8HpHY57stH1aIIzeolapXAkxMuvN7m1BI1ZCg7P/CYLTOiVVVMhfIabWtCiT9VEuUnE2/vRCVy5+A+4dnYD4lDVThDKdIObGdmEfc8MwdaxSAUU61tmYdOeJdWGGLljqH9iws7TI1pi2SFEkvPq6VZm6gAArnSMKesJGF4szFv2OM+eviSI27jyEQ/+cFfscMAEmYJtAaHAQHjw2fB80qF0F2/cYphrcvvtIZFDZOeWHpm0e3zZfrJVIi0LTtAFzh0mLRjVxaucibFv+LU5sX4CenVqK8jlwjMDRo4ewbPlS7Ppzhxhx4FgpnpPLk3qiUipA02WObp6LvWum48/V3+PwxjkY+Y7hoTY5Jwf0etnU5UFvdsKRTXNx4I9ZmDVxOM7sXoymDaqD3uaz6efJOLhhNv5aNwP04Jj0lJFGjs2d/D5o7VKadk0jhWmwxZxJI0BThHatmgqatj203ytkLtY57f9aO7GfXtCjYwtQu2kEcnp2jqY5zaFCDaTOjB35Bk6fu4r3Rk9DhzdG45X+/8OoCfMkr1G8eCrZvGFNMmVxUwJ+fn5iDYlSpcqgcOGiKFOmrIjXSFlbgtaZcFbXZJChXZHOqBFS21lVZqkeutgmZ+OR4/+BFnGm1eepwCLhYSCnSmJSMkVZ3JGALFWjpbgdDvVUmXIgarzXNzwAzIEKMi5y5R+7QG9RG/n5LBw8dka6ALN1l51xOa5mUThQLqbWtCpthJy5FgZ5y4RD5vWUxZhfrmg4SIHecqEvlfIGknIJG3H/yATE3T1iVwXkUCH72Fu7hH3lMJXY5uUgZtIURK5aj9jRn0M+OtFclQAAEABJREFU8SsDCqVj6zMYMhvCuw8ipCfiH+KzrxeBhjdfSXnt5LmLN/DhF3PEFAODJYdMgAmkJkALOe87fEqoO7ZuhOXrdon7hv4ffIMKZYqhXGnD1FphkEFAIyHJiZLWFFKFQo6ihcLEE/IMivL05Cz176NRwzHy/fcwe/Y0jB37MW7dvpml8jiz6xKgZQgKhgXbnDZt3mp/Px/QdCFznXGfpvOkNf2GBmL4ppp2TevZkXOFnC7GMlxl61SHilwuQ/cOzbFr1RSxqMyXo/qJ1wfSIoDbln8nnkq6Chh3aocmKRL3j32Nqxu74uafgxB703ChnBt9KFGiFB4+fIBr167g7t3buHLlMpKTksQ6ErS+xIcfjsmRZtUPfd5mubVC6tnUu6KSLhbo1WE7V05BzWfLmZrYIGXBzNt3H5p0vONaBHQV6iJ54naTvHYnP+r9FYn/+s8ROr3KcDP8tNWSR0Vy+D2NG/aSv9go7M3LMu7rqmfPU7Pz687j4PeHhERcjhQVn/vjnIiTPvJKhNA5K/hyVH/QyMSrN+6CLpRffP0jLF6xRbze0lltcMV6giTHifl6K8Y1VYK8ZMJRUyrY8POdr0xHhNcdA//CdTPVDe/gisJebzY1UihyKigSbF0yfQxSawvasEttkwNxde26iH97IPDyy9lW+ppNe1GyWLh48t2ozrOmcts2rwuaskDzyE1K3mECTMCCwP+kB7AzJwwTOpr++cWHfcXnidZAoCfYIgEcugqB//47g4MH/7JojlZrGAVroeQIE/BAAoYrMid3jIbb1Hy2PJrUr47n61YVC26m9kI5uUluXd3V9Z1x7/DniL62CRFnF+PyH+0RfX1brvRp8+b1uH37lkXdZ6Uv2cOHD1josjPyT+QRJOuT8XrJfgjzLmAqekDZYbibeBt/Rx4WYkpw0Z3dB46jWYMakMks7zLyBRmGnierNS7acm6WOxE4t/4cDk87JOTJ1UjR9AvrL4g46SOvGIZYiwQnBPTkgkYvrln0JVb98LkYDjpr8Vo06TwU9MarpGS1E1rhflXIIBONzle2MwrVGQO/QplzHus0hgXiZSnliMI4yFYCqzfuwSvtmqCo9OTbvGB6Uk7xJ+ks0k/pLO5BQH/lAXSrDoHeaKXbegqITUy74ZxiNwFaM4Geghsz0GeJphTQwpj0lNqo561rELh69bJrNIRbwQRygYDTHSq0ZsrAj6eizovvigvmhi8PFvtjJi0Qq/fmAgO3rjI5+jri71sP9Y48/3Ou9Ov69Ws267148bxNfXYoBx7rhUHHeuOX6wvxKOmhqcgfLk8TekojMSW46E7FciVAa6ikbt6Bo/8KlfEiXEQ4YAIOEqj0ciXUG1Y/TQkpkzsjBKg7lSuUAr3tqk+31hQFrTXBU90EimwPNAmGdQiyvWAu0EQgvEAobt15+ptkTLhwxTAMvnDBUKPKZbe53bADN9W4Gml4yv3NgThsuZSU202yqF9//RG0s7ZCd+gi9P/dhm7bSWhnSw+0dHoLO47YR+D4vxdRpdmbeBwZLTL8c9oQpxFdQiEFR0+cEzZRMQansKTiPxcgULp0WRdoBTeBCeQOAac6VHb99Y9YM+XYyfPo0LohPhzYAyMGdMMLTZ7D2i1/gVbvpbUjwP/sJpAYadtRkRh5we4ystOwZMlSNosrX94wvNxmIisFgVc7NBcLNE+YthT3HkTg4tVbYuoDrTHUqe3zoJFdwpADlyFgnOqjeXNCjrdJ8+rHYjqQLotTfyp2rIgGw+unKSFlnH+TR6/B27X/uFgNvkW3EWK9CXpt+K+zPwPNo81xuG5YQVBZw1Qfn/yVHWp9QJFGYqpQQPHmDuW3N5Oifwsov+sNmV/qaW+2S1C8Us9gXzLMtkH6WpdKbfl8Laxcvxtbdx+FRqOFRqvF6f+uiLci0JsMwkLzuVR7Xa0x92J1GL83Fgkag3PiTowO0w7H49wjg4PFFdqrP37Vqhn6+1HA3UgrPSsyJmBcq12fspN6SyXoUtJon8V1CDzzTBU0aGA5/V6hcNKi566DgVuSRwk4zaESExuPURN+QJ0albBz1RQxZ/7NV9uCVuad+Mnb2LHiO4QXCAG94SSPHguHuh1QuCHkKuu3YQSVetGh8rKa6cUXX0bRosUsiqksfcnWq9fQQscRawIvNKmNse+/KTkX94s3YC1ctgnfzFmOdi3r46PBPa0zsMZtCKjHWa6NolcZFipNHr4S8Ul9EO8/UDhLoDLo3aZj2dDQlX/8iRZdh2PImGm4efsBxgzrLVZ3pzW2qlf29CdejgMMLttJTPXxzW9YmyOoeCvhIAmvO0Zs/QoZ1lQJLPECgiu8CpnSz1SZT2hlFG0yVeQPLG7f2jy7riVj8v44TNgXi40XMz9KQDGoNWQNK4g2yLvWh6xsuNhXDGwtHCjkdCGRlTPoRaKbB3SNQ28uGPn5LDH68NPJC9Fj4BfQanX48qO+bt67nG/+6fu2p/udTEOf8y2yrkH/wDCSInVKWvrUdhxnAp5E4OvJ32PKdzMxaNAwjBs3CcWKFvek7nFfmECaBJzmUKGpPvEJiZj4cX/Qqr6pW1Q4PD/GffAW/rt4HY8iolInczwNAnKvABRt/A3kSsM6G2TmX7gBClQbTLu5IsuXrcWXX36DgQOH4uuvp2HevJ9ypR3uWGn3l5th/7oZWL/kK9BizfSKv68/e5ef0rvjwbTRZn10vGE4uPS0mpK1v/4FsSZodKLQ01BxIUv3UbLrSQ606NA//4n1tH6eOQbrFk/Aa51b8vnuAGdynNBaKkbxS1lTJbD4Cyj5wo+o9s4jVHj1MCr1PoOKPY/BO7ic3bWsOJOIbyRnym7JqfLXDTVmHonHjMM83D4jgPQmgm//N1B8l9P1DY3KpTUg1v44AWVLFc0oO6e7AQFZxSI2W2l0GNpMZCUT8GACderUR88evdGieSvI5U67zfRgotw1dyDgtDP96s178PP1QZFUi7OZQ3qmfEkRvXnngdhyYB+B0Gf6oOo7D1Ch234888ZFlOuyE0rfMPsy55BV0ybN8VrPPmhQv1EO1eC5xXp5qUCvC3y2Ymmk9Yo/z+199vbMVUrTJyQLh4lu2QHoL98HUubX628+hlgTVHKwkN4k1x4Y7H/dD0//9/Vn72CC5GinhcplMpmndzdX++cbVhXe+Upnug3bLydb5dlz3fboASvDPKagB0c0rUejNUxLiYtPhF76vHd68XnQiJUWjWqC3nxhbpPHENnd3arhKpu21dPQ2zTOYaW86TOQlS/0tBaVAvIOtYEg36c63mMCTIAJMAGPJiB3Vu8K5M8HutBISLS+MDO24cEjw5zT/CE8r9jIJDNb34I14RXAT70ywyy3bb+YugRten5olHS3NG0ut9vL9TtGQKbVCUeK/laEfQXo9cJeRw4X+3K4lRWtlTXw46mgRQWVCoVYL4jiMbHxpn6cv3wTpKPfDZOSd5xOQKsH7sYanAPmlcep9YhNlhLNlU7a192UQXtELkR/z+CE098w09016JzUHItq6LztMfALPImKFXoanUvxKLM3+py5cE1M/SFnizDiwCaBQgFyfNYkAL5Kw/EsEijHsHp+qBSmsGmfW0rFuy9AMfYVKIa/BMUX3UFOlpxoi17yvyfPUoFE/YtSVKGLkIm40P1q0IkENw9ouvPn3y7Ggl83ip58PXsZKE7y4/JNQscBE2ACTMBVCDjNoVKpbAnR54UpX44iYhbopCc4sxavFaNY+G0mZmBcepcbl1UCVSuVRuumdYTQorNqjUbsG3W0jXgSg+CgACiVnnOxlFVunN+9CURLjpO9h05CrdaIjkTHxIPiySlxUj6JjhU6tcb6Zp7SWZxDQCHdy9oaKVAuVIEALynROc2wqEWzQonEN72FaDYZbq7VK810mw06Y6ZLWy/h+9Lfm2TDuxtEUlp6kciBSxBoWFyF0iGG4/lhQ3+0LeftEu1K3QhZkB9kxfND5pVzv9P6SBnUs5RCNMtT6pGeQ5p0HuBQ8fZSge4BTvx7CQf/PoMr1+9YxEl39cY9oZPziMbUpyHHmQATyCUCcmfVS2ukvP16e8xZsg7D/zcT+4/+i6s37oonkzv2/Y3X3/sSuw+cEOuoyOU5dJHmrM5yPUzATgL0JpP33+0OEh9vL3Rt30zsU9woHw7qgTv3H0GpcNrH1c7Ws1mmCcQn2pclNslgp8+dEQCGyjlkAgYCA2r5okjQ0++fUB8ZBtTyMyTmQigrroeijs6mbPddjVnjv8WVHVey3LKZlWcKJ4w6weD4y3KBXAATyAIBWYgeqsGaNEX5mvufp1UqlsLWZd/YJfQQKgs43T7rozMLce/oBJOo4++JPj0+s8Cko3R13D2h54AJ5FkCTuj40yskJ1Q2+M1OGNb/FWzfewwDPvwW7ft8gk5vfYphn80ADQGntzq81LKeE1rCVTAB1yNw/dY90Nz61C2rUaUcIp7EiDf/pE7juAcQkJwm0p9lRzQ6Q5yfwBk4cIioJMM5QdNsLkRonUqkTIgCC1/Oh/kvB2FOuyD88kowqoYrndoG88pUr2rg81OSTZHX0pqb8j4T8BgCsvyA12B1mqLyAIeKxxysHOjIw+PThKNEq44RpUdIDpX7RybAKJoUx8nj1Pr4+8I+8sIKkT/pySUR58B1CXDL3I+AUx0qKpUSA3q9jAN/zMKS6aNBDpTJY97Br7M/w57fp4Ge1rsfQm5xbhOoHvwcxJtS9JYtCVAGolZIXZNYprpejJ7MLFm1DfEJKaMTUpq4Q3JA0q5SoaANizsT8Paybr3kNJH+LPUB3oa4lafFoOYwbxE4/1iLj3cYLqLJsTJsczQW/JPgdAjFghQoFez530PX9EWxLqIutEkG50xiRLzTWXOF9hPQqeMQe3uvkIQH/4iMenWsiJM+PkUnEjhgAm5K4OGJ6cJ5oks2rM+U2W5EXlwp8ueAQyWzTWF7JuBxBJzqUDHSyxfkj+eqVUCbZnXRumltVK9cFn6+KTcQRiPeMgE7CfxQZxlKB5SDeFsKnv57o/Q7mFP7Z5M8TXHNvcFvdZGcKYmo8+I7GPn5LEyZtxK9h0wErS1Eb0ApVcLsTQKu2QVuVUYE7J22pUz5arbytGRUgSFds0GBhK4+iKvtK7YUN6S4VkijFTfuPIT9R0+LhhnjpDt47IzQcQCsOZcItWGAignHb/8lIlGdyotsSuWdP97+Q0zXobVT1r9jWDPFnIrehrPy4paL+Ljar/hB1gfH4sqLt/NQnt/7rIWOVueliJ3Sue+naNFtBIb/b4bI0W3AWBEn3QfjZgsdB9lDICn6Ki6vbSvk5p8DRaFJUVdEnPQ3/xwkdBwwAQMBDpkAE2AC2Usg5ao9ewtNrzR6yw/dKHZ9e6y4cazZ+m30HTEZy9buTC8bpzGBdAl8WW0qGoY1hY/86asKuxfvk24eV0ukBWp/XzgeLRvXwtET57Bw2SbQm6/6vyoUM1cAABAASURBVNYOsyYOB49QcbUj5prt0V2RIekjL+jOygDpwTptRfyyFHexJn/5/VJ8NH4u5i1dL1o2fuoSESfd/F+sb4KFUR4MbkQZRkqk7vrtGNv61HYct3Y8yVJ74AUkmQhTBxGXI6Bp1Dq12mY8f0gQundojlZNaqNpgxpiS3HaNwqlkc5LpbJZBiszR0Cu9EdA0cZpil/BWpkr0NWsuT1MQBAwfI89OGGY+mOc4iOS0gkepaypkvTkYjpWnMQEmEBWCDjVoUKvxBz48RRxoxicLwC0SO1bPV4EvcWBLqzpYlqrTfUYLiu947x5hkC5gIqYWnM+vqo+3dRnH4WPad9ddiqWLY7p44di39oZOLN7sViYbcSAbqBRXe7Sh7zYzq8mfYEhQweY5PadW6An4F99Nc6gGz0UI6J34n6z0vbhkcuhGPQCFD0b2mdvZqU9qjCLPd3VHrOtf2rhvL1nK5bCjpVT7JJA/9xb/NR5RNKvqUQ+28euaKBtffql5Y3UDvM7YPjV4UJenveydadt+E6exXmU1t94ams2iuWhOt9TfTp7JYqGY+zIN+wSXx8bUwDTKdtdkpzdTu98pVG209Y0pXjz2c5uEtfHBHKAgOFL61HK1B+bi80afC4WdT/+d6GY6pPMa6dYcOEIE8hOAk51qEydv1o8eZ/91Qgs+PZDDH+7Kz5491UsnTEanw7vjeXrduH3zXuzs39closS0F++B93WUybRX7oPWgeFthZ6yc5Fu5Atzbr/MBKLV2wBjdyiAu89jLCIk+7u/cdCl5SspiiLixFITEjArl3bcOLEPyZJTEyETCbD+fP/mXQnzv+LQxGXhaMESsONsKJnI6k30hVQoI/QCycKOVJ6N4GsbCHISoRJ6Z735+WlQuGCoXaJnN/6hs6VfKBK9Wv9yjM+8FEZLrBd4QyRPbwJxaa5UC75VGwp7grtsqMNFiaVcMkiboyU9pF+o4wR3jIBJsAEnE5AulaQ6gyrMRThdcdA5V9IiqX6s/GTkP/ZfsLeK7hcKmOOMgEmkF0EUl2iZVex1uUkSzeDKySHyeC3OqNpg+pWBj07tRRTHVav32OVxgrPIaC/+sDgRDl8CbptJ02ivyJdrEo/BLQ11+sOXxb2+isP0oVQb3sFkIw4/rbJjuJGMSldbOf2vUf4Zs5yxMUbFpi8fdcQj09INLX0xp0HwiYxKdmk4x3XIaDV60BOFXta9CQmSjhKkOIkMDhMpBNfpRB64UQhR0rJMHuKs2mjqGN7Goiitm29zUJY6VIEKuZXYFKrQNGmfN5yTHsxCP1r+Yq4KwSy2CdQzX4Pir9+g/zcYbGlOOldoX2ZaUMdHEdZ/VWLLO30OxCoePqdbJHIESbABJiAUwhI1wpSPQVrDEOhOmOgtOVQkdJT/4VV6S/svYPLp07Kk3Gv3TsR0qOTkKBuHYHvp0oPdPXw/nOH0BnT/ObNzJN8uNOOEXCaQ+XxE8MbCpo3rJFmS2mR2n/PW17IpGnMCW5JQH/toXCi4HGsfe2PiBX2+mvpO1TsK4ytmIDnE5CXkS4Mvk6GvLL0NMsPYutN8bJSPDe6z3VmC4F83oaf6wAvGSqEKrKlzOwqRH7+MJAUb1mcFBd6S22Ox4xTfcq0KuNQXT6yZPTDMnyim4ZBWIxx+m/wvPwo+B8TYAJMgAm4PwHFwwdQHT4oRCltcekSIJNB/uih0BnTFJclPfhfWgSiouMQGRUjprcbbf4+dQGXrt42Rl1ye/dBBHS67L8eNlyhOaHLkU+iRS0F8geLra0gf0iQUBunP4gIB0zADgKHX7gAkvFVvxPWpf3LYVHd1UJHeqHkgAnYQcARE1tfzbSGSuqylixdhH79e0GnzdnRIsr2WviuToT/sQSxpXjqtnCcCWQbgYc3bReVlt62dY5py7UpJ9ZSMa6p0n5ue1EX6d/f2wUT8ZWQ12W/Cz0FgbI4FJPdg0qmoSjoghv8jwkwASaQSwQKpEz1kXsFONSCkPLdxdQf7yxM/Rn18Qg0blLbJAcO7BNtGfXJSJOO0g/s5+UbBBgPC2id02Vrd6JxpyFo2GEwnu84BHVfGijeSkpdXbR8E3b+9Q/tOix7Dp7EzEVrHM6fVsYlq7aiTc8P8frg8WjaZaipzWnZZ1bvNIdKYpJatM3H20tsbQW+PoZXJyerDba2bFjnGQT0kXF2dUQfmTKSxdYda0oJj849wrq+6zCj6gwsGDkXh5J34nLMBfQ90hXTL0xOsfLYDXfMBQnYcqZQMzXSd9uFC+eglrYUdzVRHfwL/t9/A5/vvobsr79E87wOGHSkVx06IHQcMAFzAroKtc2jpn19uZqmfbfakZ5WWjlQ9On8CLlV57ixTIAJuCOBAjUNU30UKsP0z9Aq/YSDhNZTITFOAcqfWu8XLrobUuHVlKk/jq+lcvjIQVFWRsGWrRszMuH0HCLw4PwjHPjhbxxefAIR155kay3kMJkybxU+GdILRzfPFS/QGPfBW9iww77zwp7G3Lr7EIeP/2ePqd02Z85fw+RZyzD+o37YtWoqfpo+Wrwg5+TZy3aXkZGh0xwqxoZ0eHM0WnQbYVPeG/290Yy3HksgpWNPUg0PJ7Wt69XIFDsZGdiWTUM24+qfV6GN1UIZrcRp7RE8enBPGP9yfSESNCllCI19gfKfY1Ad2i9uKmWJiSKTKkVHeqHIxoAWo6XFZx9FGL787j+MAMVJHkVEZWNNXFROEEh9espkMul+TJYTVeVomV4H98N/2rfwnvoNcNDgPFEdPiB0pPeS9nO0AVy4WxLQl6kBbT3DqA9jB3R12kFX7jljlLdMwG4Cs47G46MdMSa59kQLvZTbll5S8x8T8HgCMTd24OJvLXD6hwI4/+tzeHhiJsIkxwmtpWIUlZ9hkdr8KWummPR2rrXi8RDT66Ath7l0HZdeFldMOyw5Ub6rOx9rP9iG34Zuxtc15+LMxgvZ0tSomDh8P381Pni3O15qWQ9+vj4IDQ4U+5t+tn54TUt49B4y0aLud0d9B5oWRMpDf5/Fq++MQ50X38VLvUZh/i8bcP3Wfcxdsg7/nL4g0ig9MSlZvLhj0sxfxciYDm+Mxi+/bxc6Kof0v67ZiTlSPqpv087DpLaQ2JR1KgsVDBH6MiUKi7bfkOoTimwInOZQCQsNQvcOzdG0QY00pVWT2sLGS6XKhq5lUxFcTM4QCPazLldmrUJIih1dTdlIjr4ZjYhLj61SYqOiTbqb8ddM+/bu5BsyACE9u0DZ6WXIHj8S2YJSdKRX3MneOYL0pdHq1fcx8vPZoq5uAz4HxUk+Gj9X6HIioOl1d+49ypH5hDnRXi4zZwkkN2iEuGEfIGnEh9B/NErsU9woyfUa5mwDuHS3JaDtOAzJ/1sD9eBZYqvpPNxt+8INzxqBCftihTMkOknnUEGXI7U4fV9jkgS1HnR5YEvvSAXLTieI9p24x6OhHeHHeZxLQJPwENe39EL8vUPQqeOQGPkf7uz/CDE3tju3IVybyxPY9e0BmPuGdFo9/pySPaNHrly/I/rfsrH1gxJbs0/i4hOFY0RkSglopEhMbDzISdLv/a/RqO6zWD7nM7z/zqt48CgStCxI2+Z1UbZkEXww8FUhKqUSkyVnyvHTF/HN/wZizPDekkNlB3bsPSZKJSfMhGlLcfHKbbRq8hwKFQwVevOgZpVyqFW1Al7pPxZLV28Tzpew0Hxo2rCGuZnlfiZjTnOolCgajrEj37BLfH3SnhaUyf6xuYsSkIX429UyWUjKXFG6mrIrh7VRcb9S1soMNOqaz0FdrwH0DRtBW7+h2Ke4UfRe2XOOlipeCN98NtAu8UuZEpdB0+1OHjJmGmq3HYAXenwg5hN+N3elRV7yAldp9ibMZfbitRY2HPEsAuoGzyNu+IdIfP8j6EZ9IvYpbhS19FnwrB5zb7KVgE8A9EUrANI2W8vlwtyKwLmHBmdIsi4LP9w52OMbUTrhrHmSqM/BWrhoJpA9BOLuHoJW/fQhobHUmBvbjLu8zSoBW6NRzD0TWS3fCfmTYpPx5Jb1efLgvPVDZ0eaQw4PyhcabFjvlPYdFY1GK7J6S/dShcPzo2XjWhgzrDf8fL1Rqnhh5AsKQJ0alYQkqzVYtWE3OrZ9HvkC/REU4IdGdZ7F9n0GhwoV9Pbr7THl80F4o1sbyXFSnlQW4uWlQpP61VC4YCg2bD8o1mhpUr+6qM/CMAsRpzlUbLUxPiEJycn8hMAWG9bZRyCoeBBCiuW3Mi6rewZTp6/CLz/sg2J6Pqv0jBTRM+cjcvlaaP7YgPjf/hD7FDeKLqxARkXYlW4cLkfD5zISlUppV5n2GlUsWwJrFn2Jv7f+IOYV0tzI0/9dscg+tN8roKF8RnmtcyuLdI4A/n7+KFKkqEDhJ32jflDGF+tqB2F7vXyY/Iw/ivs8vanw8vFBhQqVoOJReIIXB0wgtwloi5fAg6v3TSI/dVo0KbX+8V5+048AwwETYAJMIOsEuIRsJuAd4IXgYkFWpRasaH2PZGVkh6JgmGG6THYsQxDg7yscKNMX/iam/PR6bwKOnTxvsxX3HhgcQr9v2osJ034W8t/FG1AqFCZ7fz8f076tnX2HT4GmK/0861OsmDcWP3zzAX5dsxO/bdhjy9whnXT571A+hzPRFIMp81ai69tjJYjvoGbrt9F3xGQsW7vT4TI5o/sQkJUqAHnr6kD+APsaHRog7GWlCqZp/+K77VBcXhYqeCEQ+VBVURe1dU1Q/XJ9FD5TCrqLTj/N4Q7/3uvbGRXKFAMN1WvWsAbCC4Tg4N9nLJpeIH8+lCwWbpLgfHYeN+Stf+PGTcLkyd/jyxbV0a2IDwr5KBCkkqNZfi/82Loapk2bK2TTHzuwcMHPkCue/hAYSekfx0J38AJ0+89Dfz/KqOYtE2ACTMAtCUzYGyum1nxkth6KPfvXnhieXpp32tbD4pmp1lqxp2yyOXFfY1407zMBOwg4x+TwrWSrz8y3F5+FWhZo1YDAEq2tdKzI2wRafNAQ5oNt5AoZmo9skC1QypQoIsrZnjLVRkRSAhogkbJr2pg7PExKs53XOrcUD3R/nf0ZyFkzYuxM0FuEZDIZzF/sEBpicBJ98eFb+HnmGJNM+XywWWnp79K6LTSNiEa3kCWNcKlbs5JpPRfSZVWceqdJ86YGfjxFrKxLN2Y0ROetHi9CrdHiy++XYvzUJQJmVjvF+V2XgKx0QcjbVIO8XjmQY8UosjLhgB6grVEntvXKguxlZdJ2qBRoF4pOyzri3RXv4c0V/dDs1+fh/5MWPouThHgNTwb/S58AzUG8/zASNGrF3HKV5L39dPJCzF68Fjdu3zdP4n0zApUqVUbDBs+jQYjKTGvYDYy4gVqVKqJWzdrw9fMzKFOHyRpoJ66BbvVh6H4/Au3XfwjHSmozjjMBJsAE3IXAuUeGqT/m66HYsx+fLF0MmHWSYtIltpnGsHs50rHynyQ6traLoVYypeXOAAAQAElEQVQ3CbmZbkkgIkEvpqOZf06OPQrGEr95uKaoDa3cHz4hz6BIo68RWOIFt+wjNzrnCNR7swbeP/I2On3bGq9MfxEfHX8XVdpVyJYK8wX5Y9AbHTFx+s9Ys3kf6J7+4eMnoEVg2/f52KqOZ8qXELrdB04gMipGjAiJeBIjdPTCjblL/kBCYhKqViqD2tUrIjFJDZ1Oh0rliuP85ZugkTCUj5wg9Wo+g69m/Iq7DyKEz4AWvP1p1VZRlj1BhTLFcfn6HdBIFXLW3Lj9ANSuyhVK2ZPdLhunOlSmzl+NoyfOYfZXI7Dg2w8x/O2u+ODdV7F0xmh8Orw3lq/bhd8377Wr4Wzk3gRkZQsJRwk5S0hk5SSHigygLcWNQnbI4J8sGFDU1aUp8or6DErwrGTyxNJq2bZkteQgSd1bWjhq+P9mgBZser5uVVNym2Z10KR+NRQpFIZd+4+LxZyMThWl5PX281EiN0SpNHxtqaStPfVDZuiSr7fS7vYq5DJ4q+R22xvbIWUzVJYq9EtVtywl3dcrZaRKojpF83Sj33E6U/WbykxVl7Ftmdn6SO0iBpnJY25L+Z/2hPeYABPIiwTGNA7ApFaBdom5XcmQlO/FFGjiu016apkSNW0G1fbPdNlUT/VC1o5vU6G8wwRykUDdoirTOd2lkmEaQ90iKvR7sT1Kdd6F8NfvoeJrf6NAjfdysZXuXXVS0xaIXPa7kOgVa4Chw6QHunqY6yk94V33ZFywYhgaDngO5FwJLSXdIGXj4RrQu4O4b584/RfUbz8IzV4ZjrHf/oj2rRqIWuTS97T0J/bpLUCD3+yEwaO/x/Mdh2D/UcN0WplMBqVSgT+27Rf6qi3eAk3n+fZ/A6FSKVGtclk8V608mnYZJtLJ0fLV6AGgaUKtuo9EjVb9xBuAoqJjRT0UyGTiV4J2bQotdEuj8r/8finqvjQQbw7/CjSo4/VXss8pKbdZcw4oaa2UFZLDZPBbndG0QXWrGnp2aikWpVm9fo9VGiuYABOwn0B8QiLIC2xLyNtrXhJNwTMOs5vx5VAoFE+/EuizOrBPR+GRXjb7MwQG+GLnvn9EdvrS9JKcKrkhUrWiDUq5DPbUb/yatcfWaCOXylZKLIxxe7fKyvVE28wDecnK8AoMsmirMV1l7IzW+ompPjoBSsljb2/dxt8TKtPePGnZKaT+U3lppWekVxr7Zewob/MkgZs3b2D06PfxUrsWeKVrO5w8edxTOHA/7CBQqYAS1cMzL/4q47d2+pWUD1U4VH6It33lp187pzKB7CeQ309uOqeLBclFBfn9ZCZd0SCF0OVWUK+u4cY5o/rbtmkH3daTQGyiMNX9dV5yWki70vNN3dZTUtpTkbRO/dMVDIe6fiMhmgaNgAoVAOmCx1xP6Zoy5cD/LAmolArQzJKjm+di75rp+HP19zi8cQ5GvtNdGM6YMAwDer0s9ikY9GYnHNk0Fwf+mIVZE4fjzO7FwgdAb/PZ9PNkHNwwG3+tm4HV88cJPeVRKhSYO/l9HFg/C8e2/ABfHy+xJMGcSSNAaz7uWjUVJ3cuxNB+r5A5SN//tXZiP62Arunpfmbrsm+w7scvsWPFFDGog/qTVp7M6g2f1szmcsD+ccown+YNa6SZu02zuqBhPGkacAITYAIZEmhcrxpGDe5pU8gjaywgOjYeNAUvKjoOS6aPBk3DM6al3pLXuEBoMBKSDNOnkjU6PIlT54okqQ3Oh4RkrV31G+feR8Xb31611L+4RI1d5ZtziG/0KrT1O+CxRo5oqZ0xJasj8ZUPrcqRrikE4ugEjdhCYeOrOMgX0XqZVV7z+sz3dSmFRmein+b5zfep7xqt3u66zfPSfqyxX4becZjrBHKnARO/+hz7/tqDmJhoPHhwH1evXs6dhnCtTIAJMAEmkGUCkydNxb69x0zSsGFjUebkr6aYdHv+9yPqxwZDt+0U9EaHyn7JoUJ+TEl0205KaWZCDpbtp0Q5HLgPgfwhQSgYFgxyVqTXalowlqYL2bKh6Twh+QJtJYk3+pAzxTzRx9tLOFfI6WKuz8w+jbrPqM2ZKc9oa+Mq3piUvdvIJ9GiQPJKiR0bAR0cUtNTc9qyMAEmkDME4hOS0Gvwl6DXoH3xUV/EJSTi9r1HYn4i1UhTexav3IJ7DyPEfEV6zRg5O2keI6WzpEHA2w/aDkMw5EEIXjgchVsvvAuEFRPGsqunIbtywiBqg2NKfusM5LK7kHs9FDbmgbzV0+lX5vo8sc+ddBqByAsrcGFlI5z+oQDO/NoA988sy5a64+Pjcfbsv5kqS3dPBs1vCqiXKqE7L115Zyo3G7sKgdEpU33CfF3zGPas6iOmVdQopHQVZNwOJuAxBHQ7/xUOE3s7JBwsW07aa852TMAlCTjNoUJzoIgAeZdoa0t8fbyFOlmtFlsOmAATyBkCtJgULdBEi9F26fcZWvf4QEj3AWNNFS5ZtRUtu40U8xVHTZgnRrw8V62CKZ13nhKwZ0/50ydQLfhQiNFeuXocfLy2w0e7EYqP2kHetR7kXepK+x0gb1TRaMZbJpAjBBIjzuLG9reQ8PA4dOo4xD04jrN/9ALps1qhTm8YSWZvOdpDciS08EHSZ15I/kqFhM4+SJ7HN7z28nMlu2dSpvo42qZBtX2Fw4PWOyEpkU8BGmk4qLafpT7YsekPVB5NRQr2cdolsKMoOB8TYAJMgAm4AQGn/5p0eHM0WnQbYVPeG/29GyDjJjIBtycghsyd2b1YzGc03+5bO0N0rkTRcOxcOUXMkdzy69divmKfbm1EGgeOEdCXrAp96WqWUqwKdNpw6ORFIAsPhbxBBeFIkYXnc6wSzsUEMkEg5sYum9Zp6W0ap6EM8A9A5crPppFqrVYvUlkp1fOtdVZGOaSQJSbC74fZ8PljDWRRT3KoFi7WFoFyoUrTmhHk+PD3ktESByiXas0Ue9dasVWHu+g0GxRI6OqDOMnJRFvNH445kdylv9xOJsAEmIA7EnCaQyUsNAjdOzRH0wY10pRWTWoLGy9V7l1EueNB9Pw2cw9zg4BMJgNNwytepCCyMl8xN9qeK3UmxkLxxwyoJr+GHwpFYPIz/lBF3Tc1RfPWV1C//Z2l9JiARE0bJPp2NNnxDhPwFAKjP/kcjZ9visDAIBQsGI7Spcum2TXdFZl1Wjygv2VDb22Z7Rr5g/sI+Gocgoa9i/zN6kNx62a218EFMoH0COguy5D0kRd0Z6XPgPRZoG3Sx1L8ohRPLyOnMQEXJECvq6WRZi7YNG4SE8gyAac5VOiJ99iRbyAj+d+IPmJF3yz3LDcL4LqZABPIcwQUu36G4tAf0tPshwiQ69EsvxeK7FmS5zhwh92HQGCJFjYbm5bepnE6yuLFS2DixO+waeMu/LZ6I6pXr5mmtbxMyqrK5hZ+gKyYDT1y8N/t21aFy59EwveXn6z0rGACOUlAe8z2aBTtAdv6nGwLl80EskpAJpOJkWZZLYfzMwFXJCAcKq7QsKs37mL24rVo2X0komLiXKFJ3IZcIKBP0kC36QS0C/+E9rfD0N98nAut4CqZQOYJyC6fsMrk9/AakMjfZ1ZgWOESBHxCK6PECz/Ct0BNyFX+8C9YE5U7/AzSO7uBqr7Wa6ep3rbW5XS79Feu2KxCmckFdm0WwkomwASYABNgAi5CgJuRfQRy1aES8SQGy9ftwqvvjEP7Pp9g1uK1KFOyCHjKT/YdYHcrSX/4InQ7T0N/9hb0By5AO20z9PeeuFs3uL1M4CkB6anM0wjvMQHXIhBS4VVU6L4fVQc8RJXXDiK8Ss9sbaDs3CEodi4RIr91QZQtP38Iquj9UClOQPbI4MBQ1NfBd1civMcnw+sTNXzXJMLrHY2wd2YgK1PGZnWaTKwHY7MAVjKBTBJQ1NbazKFoaFtv05iVTMBzCHBPmIDLEnC6QyUxKRnb9x7DkDHT0LjTEIyfugT/nr+Kt19vj92/fY8F337IU35c9nRxQsMS1JaV6PXQH7tiqeMYE3BBAvqyNaxaFV+gFODtZ6VnBRPwJAKyhzeh2DQXyiWfii3Fjf2Tnz8sOVOWCpHdTnGonDsMZfQBqJSnIH941WgKeSE9lK9ooeqtgbyi3qR36k7RolbV6YJDkPD6G1Z6VjCBnCQgL6uH99fJkFeWPgvSzwhtvSdJ8fJSPCcr5rKziUDeLEbe8lnIW1e3u/NkK29rv73dBbMhE3AiAac5VB5FRGHclJ8kJ8pQDP/fTFy4cguD3+qMn2eOEd1t3qgmCuQPFvscMAFzAvoHUeZR3mcCLklA26IXtPU7QJ+vAGJ1Mux+nIw7Tfu4ZFu5URkToN+shMTkjA3zuMW5YwegnzYAir9+g1xylNCW4svmz8CiH3/AOVk+aFv2thJNUEOoNdWgK1DaIYKJ2kRMOz8Jnf5qjha7amHE8bdxKfa8Q2XZyqQrGI7YT8YietpcPN59CNpixW2ZsY4J5CgBZXstfFcnwv9YgtgqO2hzrj4umQlkAwF5q6qQt6kGeetqkAX4iBLljSoC5AeURC45WyyEbF+oJuw4YALuSsBpDpUbtx9g5R9/onDBUOFEoVexDnqjo5ji467wuN3OISCrXMw5FXEtTCArBHwCoO0wBOpRv2LAvVCM+i8O6nzhWSnRaXnpjSZBI99DWJ1nhfgOGwzcf/qGIqc1xMkV7Tl4ElWavQnaIuXfjdv38VKvUWjaZRhqtx2Az75eJN34801MCh6rTcKhzfDSaSz0FL+xdRl+lBwqW28+kZwpfaxEHdQIam0N6MPKWOS1N7Ly5hL8emMR7ibcRpw2Fgce7cGnp0bYmz1DO72PD+IHDEJih86SkzQ4Q3s2cD4BrpEJMAHXJSBvUx0wOlSelxwqMqmtksjJgWImkpb/mIDbE3CaQyUkXwDCC4Tg8vU7eH/cbMz8cQ0uXbVeTd/tiXIHskRAVr6QRX5ZlWKQ1y9voeMIE/BEAvrDl6Bduk8I7TuzjwFf/g8+a1ZB/uihEK/fVkI+5hNnNsHpdZ2/fBMffDHHqt4vv1+KSuVK4NiWH7BhyVfY8ucRbNl12MqOFQYCpQNVhp1U4Yv16+Cttwagbp16qVKyJ3rw0V6rgq7GXcKdhFtWehdRcDOYABNgAkyACTABDyTgNIdK6RKFsWPFFCydMQbNGtbEklXb0PGtMeg58AuBNTomXmw5yNsEZGUKQjmxJxTDX4JyXDco+jbP20C493mDQHwytCsPQn/imhDa1234x2l99969y6ou2c4dVjpPUTx8/AQDP56CsSPfgJ+vYUgy9Y3eMLf/6L/o3bW1WMuLfrc6tW2EbXuOUnIeE/u6G9qgrU3D6l3eQl9yqNRtYDOdlUyACTABJsAEmAAT8AQCTnOoECy5XIZaVcvjfyP64K91MzBt/BCUK12UkvDuqO/w5vBJ+H3TXmi0WqHjII8S8FZCVjy/aahgHqXA3c5LBJItp0xQ13W8GDNhsF/sZDSeSAAAEABJREFUtKR1Ud4bPQ1dXmyC9i9Y3uw/khwtVEyxwgVoI6RE0XDcfRAh9jmwJqCrUBe6Ou0sEk76l4C+jPUizRZGWYw0CGtiVUJp/3Io4stTRK3AsIIJMAEmwASYABPIMQJOdaiY98LbS4VWjZ/D9PFDhXOFnCwJCUlivnpcfKK5Ke8zASbABFyewNq1q8UinLQQZ0Sk4QZ87ZpVJl1kxGOLPlhEdDqLqIjEJECfkCx2czpIatbCqgp9y1ZWOldWaLU6zP9lQ5py5cZd6HR6jJm0AEUlh8mgNztZdSc61jBS0kv6fTIment7IeJJtIjKpDA00AvpyY1Y4IfjiZiwLx6/nVcDSmW69umVZZ4WHKCSagcU0oMJc31a+z4qhbD381HYXX+gnwpeSrnd9sa6g/p8BP+vN2JXlQ5oeTAS+wvWyLAMpVIm2hfk5xifdyv3R99yb6OofzEEKAPQNLwZZjSYmWG91OYAX6Wo21ZfJbwijR4AkW1mxdArUUSuBBrpgRQ5AJOSpXPPRgvoM3DvYUSaD66SpXz3H0ZCr9fbyM0qRwjoo+Ohv3zPIPejDEVES9/vKTrwwvsGJhwyASbABNyUgNwV2h2SLxCvdmyBFfPGYtPPk+Hv93QItiu0j9vABJgAGEEGBMihQotwkkQaHSrrfhMLc5Iu4klk2iUoDTe/5gay0gUg8/UyV+XYfuynXyCxczfowgoISX6lO3QTvsqx+nKiYD30kuMjJk1RqzWgN/ds3X0EgQG++HbOcnw9axniExKxcv2f2Lr7KIIC/ETTyFbsSEFSUjJCg4OkPUg1APFJ2jTl2K1EvLMuEmvOJuCvG0lYcCwW766LQExi2nnSK+96RDIGrI0Q8tl2w43Y/ViNiJP+021P0myLRnIwUaPVGn2aNqnrTkrWSjfaOrvtzfMnyH1x3zs/YrWwqwyjDzFR7Vh9Oo0Kwyp+jE3N9mB/m5OYXnshinuXt6vtyVKdxEYjOdjM+0D7koqSJIdC+seabG1JbrohyKFYvWU/tOo+ErVav42Rn89CVHSc6A8FtPhyvXYD0bLbSJDdyvW7SS2EHCizf1qHmlK+Ft1GoEnnoTh59rJI4yCLBM7dgXb2diG6P8+IwvTnn+q0Ow06kcABE2ACTIAJuB0BpzlUaG76vsOnkJHQGxbcjiI32AUJONYkr71/IqRrexSoUhqhLzSGfO5sxwriXHmOQKdOXcUinG+9NcDmNjQ4JG0mft5AiP/TdGlf/nLtp/Ec3tMVDEf0lJl4dPRfIQnTZgHh4Tlca/YWr1QoMGpwzzSlYtniCPD3wbD+r6BooTAE5wsQQq0I8PeFn683wvIb3uZy884DUgu5dvMe6O10IiIFiZLTIS3Zfsl6dOX1J1qcupuEtPKkp49K0OLkPbWQc48M08Ikf46Ik/7sQ02a5ZKzQGou1Bpdmjap606WbMmhkFpvb1wj5ac6NVp9hnXSSAmyJeeGveVnlx31k+qmNqQuk/Qk5GBInWZPnPLmltA5vXDKR2JB5TWLvsTRE+ewZvM+0Rya6kaLML/XtzNO7lwoplyP+24xbt19KNJPnLmEWT+uwdIZo3Fi+wJ0atsYI8bOFKO6hAEHjhMI8oWsbHjaUtDgsHW8As6ZEYGsjtzKqHxOZwJMIG8TcJpD5fK1O3h31BS7JE9O+cnb56FL9F7++BHyDX4bqr+PQhYfD+WlC1B8OgaKP3e6RPu4Ea5NoJPkUOkrOVPSkpDQ/Gl3QCmH8tMuUI7qYBBpX1YyDPwvewn4+fpgQK+XLYR0L7Woj8b1qiFfoD8a1K6Cpau3g25Ar964iz+2HUDrpnXsasjNKJ1Nu1tRWpv6jJRhfnJMahWYpnzQyD+jItwmXf8kHvqbj92mva7Y0G7tm6F+rcpiQeUKZYqJFwDsPXRSNPXI8f/EaKyeHVuAnI805bpksXDsOXhCpO/667g492tVrQCVSikWZr7/MBLnL98Q6Rw4TkBWqSgUg1qnKfKWzzpeOOfMkEBWRm5lWDgbMAEmwAQkAg47VKS8mfp7pnxJ/L5wvEm+HNVP5F80dZRJZ0wP8PMVaRwwAWcSEI6U2BirKlXsULFiwoocIlAwH0CSQ8VzsRkTGDO0F86cv4rabQegfZ9PJGdKbbRtXjfjjJLFc0VUUmj9VzsNvbWlpcZHKUP1cGWaUim/9VQxyxJcP6ZPVkM7cwu043+D9vtN0HyyDLqDF1y/4S7eQrVGi/1HT6NKxdKipfcfRYIcKObrA5UtWQT3HhimIt598BilixcSthQUDAumDR48eiK2HDABdyUQnC8Ajo7cctc+c7uZQE4RMJZL00kjo2JAozmNur9PXcClq7eNUZfbqtUa3L3/GDRiLbsb5zSHiq+PFyqWLW6SEkXDRV/KlSpq0hnTFQqnNUu0gQMmwASYABPImwSObp6Lpg2qmzpfukRhbF32DXatmoojm+Ziwsf9xRN7k0E6O50reaN6oadOFS/J3/F2LV+E+PJvWprYTt6A/urDp8nJGuhWHwYSbS+q+tQw7+zdufcozcWW6ek7jaZKTePL75cgJjZBjDShtOiYONBoLNo3Ci24HJOyEDOl+3h7G5PEluxj4xMM+94KBPgqc0UUKSsF++ZgG2RSL3Orf65QLy3Q7K2SO3x8lQq5RNA1/7Iycss1e+T5rdJdkyFxiBfi6/sivoUPksaroLd+3mkPCLbJRgJarQ7L1u5E405D0LDDYDzfcQjqvjQQU+atFLUsWr4JO//6R+w7Guw5eBIzF61xNHua+ahtNV7oj1avvi/WEDNOh00zQyYTXPcbMJMdYXMmkFUC6ufqQB8QaFWMunlLKx0rmMD9B/exceM6rFq9HJcuXWAgHkYgvEAIHFkgfVLLAPzSJR+mtw3Eiq7B6PKMj4eRyd7u6B8Z3qCUulS98W0oqRPyYJxGm0Q8iUlzwWW9XmdBZfbitVi9YQ9oBLBxpElQoL+Y8mNuSAsuB6YsxEzpScmWbxWjBZtNI4ZlMshkuSPGNstkOVc/ZFSLDDJZ3hTIstp/yu/6Qp+lzIzccn6PuEYikDTGC9qdCuilnwf9PRk0y5RInqOiJJYMCGjvXkPi7t+QuO8P6B7dycA6c8nklJgybxU+GdIL9DBq39oZGPfBW9iw42DmCkrHmtb1Onz8v3QsMp9E67d+N3clpo8fKtYQmzR6AD6dvBD09sfMl2Y7BztUbHNhbR4koMsfhqhZ8yEcK35+0JSrAO2XE6Blh0oePBvS7/Kxv4+ga9d2mDR5PKZP/xZv9X0Ny5YvTT9TNqdqEjXYO2EvFjVehNlVZ2PLo5WI0Jk96c/m+rg4+wmE+spRPr8SNGUH/C99Al62L5Jlvrb16Rfmmak0VWdUOgsu+/kanHa0yO43s5fjxxVbsOqHz1G1kmG6D1EJDwvB9Vv3QUOeKU5y4cotFCpoWCy7cMH8oAWYSU/yIGWqj9EhEy9938TEq5EboqWVkqVG5WQb6C3RsQm507/cYJq6zmS1DklqbeaPb8o5QYtfS4fI5f++zOTILblcBn8fZa6It0oheCqVcrvql5oq7H297W+vSirbSxJH+6hMGZnk7aWw2UZTm6R00TgpyKguP60SupM2bk8PGeqgYyIVA1+pzIzKyiidRr0Z26hUOHasqS2uIuREiRzXG7HLpiL2568R8VlPJJ8wLEye1TbSy2W+n78aH7zbHS+1rCdGPIYGB4r9TT9Ptir+3/NX0XvIRAv9u6O+A00LIuWhv8/i1XfGoc6L7+KlXqPEKEz6jZq7ZB3+OX1BpFF6YlKyWNNu0sxfxciYDm+Mxi+/G9a5o3JI/+uanZgj5aP6Nu08TGoLOXDsDGjpkZaNa4k1xF5u3RA05fWvw6cs7LISsXHGZqU4zssE3JtAcpPmiFy9AQ/PXEXE9n3QvTvIvTvErc8RAsuX/2xV7pIli6x0jii0p+VI7O+NuNq+SGjjg+Rptm8sT/x0Av8s+AfRt6KRHJuMG4mXsTNprSNVch4mkGsEZOWfrtthakTJMPBaQiYadu/875tFWLxyC6Z8Phj5ggJw+94jIRqtFnVqVBLlLFu7U8wf37Hvb/GGn6YNagh9i+drYv/Rf6UL2Yugp/hLVm8FjdKqWLaESPeUgPuRdwnMdmDkFt0k0Y12boicKpcOF93w21O/TCaTrAHycdhjTzZUtlwKaN8RSalSqlMm3ahaC1IMFJKzQjROCjKqR5GSRzK1/JP0lFfaCL0iC+2mcoTICbJMlCeXChY6hXU/0tOLzC4SxG9eApB32NgenRbxW6yvV43JmdleuX5HmLds/JzYmgc+3l7mUbFPL5ghx4iIpARnzl8DTTMlJ0m/979Go7rPYvmcz/D+O6/iwaNIFMgfLNasI2fHBwNfBYlKqcRkyZly/PRFfPO/gRgzvLfkUNmBHXuPiVLJCTNh2lJcvHIbrZo8Jz0kCBV688DbSyWdo3Ssn2pLlSiEO/cfP1Vkcc+y9CwWll52gtii2wgYZfj/ZgjzbgPGmnTGNIItEjlgAkyACbgggQsXz1u1KjY2Bo8fP7LSZ1aRNMIL2gPSV3M8oLspg3qeEuqVCqtibh64aaV7gkeI3B4L7RG5ECsDVjABFyMgKxEGxTutIKtXDrLKRSF/sQYU/Vuk28r7ifcw/8qMNGXdLcN87nQLcY/ETLXy6Ilzwp6eArbu8QGMcvvuI+lpojdmTBiGybOWifnjwz6bgU+lC9NihQuIPDWqlMO7fTpITxQnoEarflix7k98N3YQ5HLDzYYw4oAJuCGBrIzc0uj0iIpT54okJGkFbRo9ZE8bjCO5YhM0drc3Sa1DYrLWbvvU7TCOTKLRY6nTKE7sqRPUJtqSkD49iZGrIa9uOY2R8qGeoV9arV5EYxMN8fTKyigtJkENXYoDIlmjc4iDaIwLBPqkeOgiHli1RHvvmpXOEQU5PChfaHAQbbIkGo3h3Pb28kLh8Pxo2bgWxgzrLX6nShUvLB4I0EMAkmS1Bqs27EbHts+D3sQYFOCHRnWexfZ9BocKNeTt19tLDxIG4Y1ubVCranlSWUjzRjVBI2a+mLoE2/Ycw4JfN4IcNBZGWYxIV+1ZLMHO7IEBvqAnIUZp1aQ2undobqEzpiklb5SdxbIZE2ACTMDpBCqUr2hVZ0BAIPLnl56sW6XYr9D9J4P+jvUNjPawtUMlrVKTP/dC4pveQvTZ53xPqzrWMwEzAo7tyioUhqJ7Ayj6tYC8VVXI/LzTLehB0j0suDwjTVl3J286VGgx5TO7FyO10JQhAtpCuqg8tXMRti3/Fie2L0DPTi1JLUQmk2FI3y74e+sP2C6lH944BzWftb4wFcZuFGgSIxB5fhkeHp+OuLuH3Kjl3NTsIpCVkVvZ1QYuJ3MEvCckQ9FSCxnduwfqIX9WB5kSSJ6lgu6+4RpJs0op4qQjMeozV5PnWMu8/SAPLWjVIUWhUlY6RxQFwwzTQx9FRDmS3SJPgNYNjPkAABAASURBVL+vcKBMX/ibmPLT670JOHbS+kElZbr3wHAh+/umvZgw7Wch/128AaXi6XVxRuvdVa9cFgu+/RCPIp7g1zU7pG2UWJOsiOTMoTqyQ5zmUKG3+owd+QbsEXojUHZ0jstgAkyACeQEgR49elkV26dPXytdTiqKNyxuVXyIbxiC6wVCUUcnBLZnC1nly7MK7rhbEijoXQj9yw4R0qFoN9GHgt7hIk76jkW6C529gfzBfYT06CRE3/ctkU1hpgsa+q7QeUJAb1EsWigMKpV0d2KjQzR0u4iU7gkjU5KiruDc0mdxY0c/3DnwMS793gI3/xxso9es8mQCWRm55clcXK1v2mNy4SDRHpRDXkoPnxnJ8DuUAIW0r/tXDvUCJdSzlNA/SHGorFaIOOlIjHrRr3iIstSLbH/PCRsPDPxe7APjNCvQP7kCfm2tr1cpKbNSpkQRkWV7ylQbEUkJ4hOSUvaebswdHk+1T/de69xSOPB/nf0ZyFkzYuxMaLWS40xy7utTRg2RdWgIedWALz58Cz/PHGMSmtpK6fZKg9pVxKK0i7//GK2b1hbZale3fjgqEhwInOZQcaBtnIUJMAEm4JIEaj9XF6tXb8THoz7D0KEf4MdFv6Jnj95P25qkhv7mY+jjrX9knhpZ78mf0UNWRG+VoKinFTrzoMYbNVCrfy0EFQuCV4AXSjcvjXa/t4XPT0kmEU93zDPxPhPwAALhPoXwdpkhQjoWNThPCpjrihl09nZVlpQE1eGDQvTHUoYRm+lUJ/+xtyi2cyECD09Mhzb5iUWLIs7+CE38fQsdRzybwNZl31iN2qJRXPaM3PJsMq7VO+0/ktNEcpjoMjEiN60e6BNgcLb8mLeeKvk07oCQsUsR0HMEAnp9hNDxy+BVo3FamDKlzxfkj0FvdMTE6T+DXjlMy3M8fPwEtAhs+z4fW5X1TPkSQrf7wAlERsXg1zU7xagQUt69/xhzl/yBhMQkVK1UBuTYSJSum3U6HSqVK47zl2+KUSSUj6b41Kv5DL6a8SvuPogQa3zR9J2fVm2louwWWmhdrdGKsifPXAYasVm5QvaM3qFGsEOFKLAwASaQEwQ8uszwguFo164junXtgXLlKpj6qtvwDzSjl0P7/SZoP1sJ7c9/mdLs2fGemgxFQx3gB8iL66F6RwNVd61VVqWPEk3GNEHffX0x6PQgdFzUEWGVsjblyKoSVjCBPEBAW6AgIpf9LkS2YSOiUvaNuqjv5+QBCp7XxaRI26/eTIw873md5R5liYBCIUd6I7eyVDhnZgJOJKAoXAo+zV4BOVfkYYZRJdlV/YDeHfDBu69KTpVfUL/9IDR7ZTjGfvsj2rdqIKqQy2SmATL09rnBb3bC4NHf4/mOQ7D/6GlhI5PJoFQq8Me2/UJftcVboOk83/5voBg5Wa1yWTxXrTyadhkm0snR8tXoAaBpQq26jxRrfNHbf6KiY0V5FMhkMtqkK0PGTBN5aXpR9Spl8e3YQenaZzaRHSqZJcb2HkyAu8YEskaARqXo/jxjUYj++FXoTl630KUXUVTVwWdBEvyPJcB3ayK8hqnTM+c0JsAEskrAxwfq+o2EyBo3hrqBYd+o09R8Lqs1cP5cIOAd8ozNWn1Csm+Yt80KWMkEmEDmCaQMztWkTP2hdVFI7F0bRW22pop6kcpQf0qZhgiHWSWgkhwhb/V4EUc3z8XeNdPx5+rvQettjXzHMCqUFj4f0OtlUzWD3uyEI5vm4sAfszBr4nAxUqxpg+ribT6bfp6Mgxtm4691M7B6/jiQnjLSVKG5k9/HgfWzcGzLD6BlQOiNc3MmjQCt8bVr1VSc3LkQQ/u9QuYgff/X2on99IJ5X7+PHSunSO2Zg9FDe4He/JOefWbT2KGSWWKuZM9tYQJMwKUI6G8+tt2etPS2rVnLBJgAE2ACWSRQoMZQKLyCLUoJrfwWlH7hFjqOMAEm4AIEUgYZ6FKm/tC6KCT6lEVojS0Uy2vYcJRoVpmtqfKj0mCeUqYhwmF2EsgfEoSCYcEZvgnO388HNF3IVt00nSckX6CtJPFGH18fL4s0WuOLnCvkdLFIsCMSnC8AhQuGQibLmZPCqQ4V8D8mwASYgAcTkAX52u5dYBp629asZQJMgAkwgSwS8M5XBpV6/4sSrRaiSMNJKNdlF4o3n5XFUjk7E2ACOUIgxUkir6WDarDGJLLwlISUSsX9sI17YmU3rSmP6i2NwVpv2HCYuwTyQu3sUEk5ymq1BrRIjkZrvVYBmdDiO7Q4Du2zMAEmwARsEZCVk558BvhYJinlkFc1LM5lmcAxJsAEmAATyEkCSp9QhFTsiQI1h8K/cP2crIrLZgJMICsEUpwkyto6eA1Wm0SeyqGSVhWqbhpTHlVftcEspUxDxO6QDZlApgmwQ0VCtmj5JtR4oT9avfo+qrfsJ1YvltTiLz4hEbSQDS2+Q4vq9Bw0Xqw8LBI5YAJMgAmYE/DxgnLYi5A3rwLZM0Uhb1wJiuEvAaH+5la8zwSYABNgAkyACTCBbCDARTABJpDbBPK8Q2Xf4VP4bu5KTB8/VCxyM2n0AHw6eSGu3Lgrjg295unClVti4Z1DG2ZDIZdj2oLfRBoHTIAJMAErAqEBkLevBUX/FpB3qgNZ4RArE1YwASbABJgAE8iTBLjTTMAGAUXKVB95Pa2N1MypZL4wTP95K2WkSuayszUTyDQBeaZzeFiGA8fO4JnyJdGycS3QIjcvt26IsiWL4C/J0UJd3fLnEXRt31QsvBMY4IfeXV8Qr3fSi1WRyIKFCbgvgaRkNW7dfYiIJzE2O6HT6XHvYQTSmgpnMxMrmQATYAJMgAl4CAHuBhNgAjlPQJEy1UfRQGdRmeIVjcE5krKuiqygXqQru2pt6kWiH8T0H1XflLVUhJIDJpBzBPK8Q4Vem6SQW2IoVaIQ7tw3vK3j+q37KFE03HQEihcpKPajY+PFlgMm4K4EaCRWrdZvo03PD9G40xD0HjIRT6JiTd3Zc/Ak6rUbiJbdRoqpcCvX7zalOXPnxD01fj6daJJ/Hxp+IE/c05h0lP7vA4PemW3jupgAE2ACLkaAm8MEmAAT8BgCqu5a4RwxrqsiT1lTRWm2ZgqlGfUe03HuiFsRsPQkuFXTM27s+m0HMP+XDTZl/9F/RQHNG9XEv+ev4oupS7BtzzEs+HUjjp++KNJoFAqtoUKvaRIKKSAHjLRBfHwibVAw2MdjpYDUN1rPyRl99PdRCp60dUZ99tbho1Ign7/K4WOsUrruR4ycgyvnfY4TOxZi8y+Tce3mXaxc/6c4DgmJyfjgizl4r29nMRVu2vghGPfdYjGaRRhkIvjrpsEhcvWJY8M4yXHyy6kEGOVMiuOEHC1GHW3PpDhaMtE0YXohQiscM4dvJYs4B0yACTiTANfFBJgAE2ACTIAJMAH3JeC6d3vZwJRGkdBUBltCjhKqonrlsljw7Yd4FPEEv67ZIW2jxPSHIuH5IZPJ4OfrA5oWQbYkxn0/P8ObPCJikuCpEin1TS912hn9S0g23GzT1hn12VtHkkaLmASNw8dYo9FJBF3z753eL6NKxVJQKRUoXDC/aGRwUIDYHjn+H+gz0rNjCzEVrlXj51CyWDj2HDwh0jMTHLiRLJwh1xx0qGSmLkdsLz7WivYducNzbR3hl+fycIc9ioDvwnkIfaExClQpjZCu7aHfudOj+sedYQJMgAkwASbABHKWgEc7VF7v0gqjBve0KS80qW0i26B2FbEo7eLvP0brpgZ97eoVRTrdRN64fV/sU3DzzgPaICjAT2w1Wj08WaiTzugfrdVBddHWWF/CIgViXvJGVE0fxPTwQtJemdNZ01I55m0yts3eLTmkqF+uKsnJasxd8gf6DPsKNauWx0stDa+VvP8oUjhQvLxUMP6jtYXuPYgUUbkMUClkdgnZUiaF5KC0N4+5nTE/lZGepFW+MY9KKsi8XOO+QuoL2cjN0qWmQmEWN9pmx1YZlwDd7G0G+XkfVQ1ExBnikl4v6bKjHmMZjmyp78TAkbyUR2mEaugdh0zAJQnIHz1E4Jf/g/LSBcji46H6+yj0b/SB/PEjl2wvN4oJMAEm4OoEFKu/gWr++0Lw5J5ornLV1/BRbhUii3x6TyUSOWACHkBA7gF9yHIXHjx6ArVGi/OXb2LyzGVo0agmKlcoJcpt06wOVq3fDbKJlW6Elq7eji4vNRGjV4QBBzlCQPuXHMmTVdBdlu524wHdcTmShnsDhvv5HKnTUwr9+9QFm9PcaPrb6g17LLqp1elBb7GKio5FdEw8YqRznAyiY+LE6CzaN4q3txdiUtYO8lIpEBzgZZeoVIavmcN3NFj5X3JGYpX+32PD6CVjO9LannqotcpL9SWnDBJac8F23X/fM6y94qWQm/rjpVQgwFdpitvbV3vsgrwU0nl93yDXH5q6o7ts0OHGoxyp1562GW0CfFWSs+wpD6Pe3i3lN3WMd/IcgVOnjuOjUcPx3ZRJOHfurMv2X/7Q8IDEooExMVAeO2qh4ggTYAJMIKcJXHxsuS7c4duGUbMXU6Yl01pxJEZ9TrfH0fLlt85DdvWUQZKTRDGy2xcgV9wXArVhyQSRwAET8BAChjsdD+mMo90YMmYaarTqh17vTUD1KmXx7dhBpqJe69wKZUoWQfOuw8UCnWq1BkP6djGl807OENDuU1gVrI8DtP+62ilr1cxcV9BUnYgnMWLqWuptZJTl23x8fbww5fNB2Lh0EpSSE2HWj2tE+4MC/cWUHxFJCZKSkkFvuqJoYrIWD6OS7JKkFI/GriuJ+OFobKblhI2pOLZG/hy5lWSz7GSNwXrxP3E20/+6bvjBT1Q/7VOStB8Vp7arf/ZyMNo9hgKKQS+kKXi9cY7Ua6zfnm1UXDKSNTqH2/EkNplOE5Y8RsD4NrCz/53BwYN/Ye3a1Xjn3Tdx9dqVPEaCu8sEmEBeI0DrupHDw9EF8i9JjhNaD84oRseJld7B9d7uxOjEenE7r+bs77Om64dQ9//GShKTXwAJgp++6COvnSPcX88lIPfcrtnfs3lfv48dK6fgyKY5GD20F4wLz1IJ/n4+mDNpBA6sn4U9v0/DinljxSuUKc0thRvt8QQa16tmc5obTX97+/X2Nvsvk8lQpkRh3HsYIdLDw0JAb7giB6JQSAGNZClUMETac+yvUXEVXq/mm2mpEq60qlBmpQFqFLJdvleKb65bFdt11ymqMpRm8LsY9nMwlKkUkJUtlLaUDMvB2rloJpBzBG7dumFVuE6nw59/7rDSu4JCV8Dw1j6LtgQGQlO7joWKI0yACTCBjAicuK8R67E56lDJqPyspt+N1Yr2/ZnDDhV9sYrQl6lhJTp9YZDovXyy2hXOzwRcjoDLO1ScQSw4XwAKFwxNdxpPPumJfVhoPvA/5xBQNLae5iHzBxTPpszfcE4zPLYWmr42Zd5KXL1xV0x3O3n2MtZs/gt1qlcSfa5pZkHtAAAQAElEQVRTw7BdtnYn6Knzjn1/izf8NG1QQ6Q7EjQq4YVeVX0yLc8WsHao2Kq/ZiGlzbK9UtbzeLWKt830ekW9DMXJDJuMwkS1HvRmoCeJfC5mxIrT8xaBJ09sz8m8e/eOS4LQhRVAzKdfQFOuAvR+flA/Vweyn5ZAl5+dmi55wLhRTIAJMAEmkCcIuFsn2aHibkcsj7RX8bwOXqPUkJfVA36AvKYO3t8nAY4PkMgj5Ozrpkwmw4FjZ9C+zyeg6W6vDRovFmR+89W2ogA/X2/MmDAMk2ctQ/WW/TDssxn4dHhvFCtcQKTn1WD5v4novPIJhm2ORs/fovD57ti8ioL7zQSsCJQsaVh7LHVCzRq1UqtcJp7Q7x1EbN+Hh2euInL1BshatnSZtnFDPJhAkhq6P89Au+hPaJcfgP7MLQ/ubN7qmnHqD03/yYwcuq2xC1TqNVWMdUQkGB7ybLyULKb2GPXG7a4ryXaVz0YOEeBMeZwAO1Ty+Angyt1XvaGB7/pE+B9LgO8vSVA0MvxYuHKb3aVtNJVt9fxxOLJpLjb/MhlHN8/DhI/7W0x3o8WZT+1chG3Lv8WJ7QvQs5NjNxoNS3iJaT6lghUuiad8foVoX90iqnTbR/OPfzqZYGFDc5w3XJQcfRZajjCBvEkgJDjUquONGjXBSy91sNKzggnkZQLan/ZCt+Ef4UjRH70sHCv649fyMhKP6fvJlKk/xrVQ7N0etrE2il56ppgaDDlUbJVpdKhsupAkpvakttl1zdyhkrpUjjMBJpAVAuxQyQo9zpsmAb1aC+0ff0Mz4XdoRi+HdsEu6O/aHg6eZiGckOMEyLFSomg4aESKrcoUCjmKFgqDSmXftBtbZTxfXCWm2pR20KFSo5BSODxeT1l/pUpBQ1tqpFozpYqdU4NSt7FCqEK0r16xlKk/qQ1S4teeaFP2LDfnHtnWW1pxjAnkHQJdX3kV839Ygj/WbcOkr6bknY5zT5mAHQT00fHQn7eeBqc7ftWO3HnUxI26XT3c8prFeO2S0dbWNYhMZt3x8tI1i62yQn0Nt3QvVfC2uGYy2rYolf41jnVNrGECTMBeAoZPn73WbMcE7CSg3/cf9HvOAhFxQJIa+v9uQ7d0X8a5kzXQbT0lHDC6VYegv2LjtZYZl8IWHkSAHCfma68Y11SpITlaLPQpjpac6rq/l40rG6myUB/beimJ/5iAUwnc3j8KJ2f5CXl0Yoao+87+j0Sc9A9PzBS6nA6UShUqVaqMkJDQnK7K7vJvH7mN1T1WCzn43UGR75aZ7sAUg04kcMAEskgg3ewPo20m6x/Y1ts0ZqXLEqghPewxvzaxd79+UcPDoow6Rg4VW2UaHSrtynmJh0SpbVqUYYdKRmw5nQk4SoAdKo6S43zpEtCfs376or8fJTlY0l9zQnf0MnTbThocMIcuQjtrK/TXH6VbFyfmXQKRF1bgwspGOP1DAbGleE7ReCZMiYL+ll+ZcsmX0pIvUnIKOZfLBOwmoNVrcSb6FOK0tn9jEiITcOvwLSGPLz4W5Saa6SIuGHQiIe8F3GMnEqC3vMHH+uZWXqmIE1vBVTEBJsAEmEB2EbC8O8iuUrkcJuAogVjr9Sj0PAzWUZoeme9xysJr9+/8ixvb30LCw+PQqePEluKJkedypN9eCuC7NkHoUcUH9KrlduW9MbVtEErmkxJypEYulAlkjkDRRpNRfXB8mlKgxnuZK9Blra0b9l/0afQ93BUtdtXCzIvfWBkUrVMEryzrmqY0GNnAKg8rmEBOEVC8UhfwfepUkRULhaxFlZyqjst1AoEaKVN9ns3h0bKOdqVwgGG9uOaln553jpbF+ZgAE7AkwA4VSx4cyyYCMhtPWmTh+YDQgEzXwMNgM43MYzMs+CcB268ki/5F39gltqmD2Nt/pVZlWzzMV4Y3avjii2YBeK+uH2j9lWwrnAvyXALcsxwjcPix9ed96bX5OBf9r0WdvqF+KF6/WJqSv0J+C3uOMIGcJCCrVRrKL1+FYsRLUI7pIm3bQRbkl5NVctk5TMA41cdRh0q5VGuj1CtqWCjfSp/Bem9pdbNIoFxMBWrJDpW0ELGeCThMgB0qDqPjjOkRkDV+BrKmlSUHij/grYLsmaKQ926cXpY002QVi6SZxgl5h0CiWo/f/kvMOx3OxZ5y1UzAVQlcibuIgcd6mWTVjaUAvQkjlUy/MNlVu8DtYgImArJikiMvVLpOMml4J68SKJ9fKRwexrVPjA6V1GumGPV5lRP3mwm4IgF2qLjiUfGANslUCig6PCeevCgn9oCifwvICodk2DMxskXKazSUlS8EedNnjFHe5mECt2Ms36ZzUdXESMNiG1D0eYs4R5gAE/AcAvGaePwTecQkkeoIQCb90eswpC3tkzxIug/+xwSYABNgAkyACTCBnCYgz+kKuHwmkBkCspJhUHzRHYrhL0Ex9hUo3n0hM9ndxJab6QiBooEKi2z3FRWxzG8monyrQa7yh2+Bmijxwo/wCalkYccRJsAE8h6BAGXmp5fmPUrcYybABJgAE2ACTCCrBNihklWCeSG/k/so81JCVjw/zyd2MndXr85HJcMrz/hYNPOCXxeEd9yPqgMeokL3/Qip8KpFOkeYgKcSiL62CQ+Pfw/auksftbO3QfP+UiH6y/dFs7VzzHSXDDqRkMVALrN0wGaxOM7OBJgAE2ACTIAJMAGbBDzSoWKzp6xkAkzA7Qn0r+WLF8oYVqhvXEKFRR2DUDE/3zi5/YHlDmSKwIXl9XB1Y1fcOTBabM8vq5Op/O5srBcLplj2wJbOsLCKpR3HmAATYAJMgAkwAc8kkJu9YodKbtLnupkAE8g0gfy+hq+tcqFKBPsY9jNdCGdgAm5K4MnF1Uh4fNqi9YkRZ0B6C2UORbZu24R+/XvhhTaN0bnLi2jcpLZJZs7+XtS6YsXPJh2lz5xl0CsGtYbyu95piqxcuMif2UAGmeRmoVVpM5uT7ZkAE2ACTIAJ5AoBrtSDCPDdiAcdTO4KE2ACTIAJeDaBxCfnbXYwLb1NYweVV65expdf/g8XLpxDYkICHj6wb4rOjp1bHazROhs5T6y1kFwqMlj+Sx23TOUYE2ACTIAJZIYA2zIBJpAWAXaopEWG9UyACTABJsAEXIyAT3BFmy1KS2/T2EHlsaOHHczJ2ZgAE2ACTibA1TEBJsAEnESAHSpOAs3VMAEmwASYABPIKoHg8l3hm7+qRTE+oVVAegslR5gAE3ArAtxYJsAE0idwPPIorsZdFkYfnxyCRVdmiX0OmEBuE2CHSm4fAa6fCTABJsAEmEAmCFTocRil261GkYYTxbZiz6OZyO24ae069TLMrNfzWiYZQvIMA+4FE2ACHkrgcqQG/9sdi1dWPsGba6Mw/+8EJGlzt7Nx2lh8fGoI4jVxoiH3E+9g3uVp2H5vo4hzwARykwA7VHKTPtfNBJgAE2ACTMABAkGlXkKBmsNBWweyO5SlTOmy+PTTL1ChQiX4+PrC29vyNeYOFZrJTKX8y+C98h+achnf8EPbniXfwuzaS4V8VGmsycawwyETYAJMgAnYQ2Divjgcva1GvFqP+3E6/H4uEevPJ9qTNcdszkWfwZPkCKvyDzzea6VjBRNwNgF2qDibONfHBJgAE2ACTCAjAi6a3qb1S1i44Gds37oPxYoVt2qlTJazi8EGKAPRu9TbeDZfDYu6veRe6F/mPTwXUk9IpaBnLdI5wgSYABNgAhkTuBmtxZ0YnZXhmYcaK52zFFvu/YGp5yYA5gMgaV+SQ4/2Yc2t5c5qCtfDBGwSYIeKTSysZAJMgAkwgcwQcEfbPQdPokqzN0FbpPybPGuZ0JHeKL3eky7kUtJ54xoEFtZdiak156Nb8V6iQeUDKoGcLSLCARNgAkyACThEQCW37RRX5+KUn/sJd3Ex9hxg3jTalyQi+RFuJ9wE/2MCuUmAHSq5SZ/rZgJMILcIcL15nMD5yzfxwRdzrCjQGiDNGtbApp8nm+TbsQOt7FiR+wQahjXFi4U7iYbIZHw5I0BwwASYABPIAoFCAXIUCbT+Pq1ZSJmFUrOWtU3hl8VUzpEVx0ApN7TDR+6Dl4u8IvSdir6atQo4NxPIIgHrT0wWC+TsTIAJ5AQBLpMJMIHsIvDw8RMM/HgKxo58A36+1uuABAb4oWSxcJMUKhCaXVV7VDkvvfiyXf1p1bKNXXZsxASYABNgArlPYHRjf9QpqoKfSoZwfzm6VPJB+4rWv5Vw0r9CPkXEVM5XS7yB/F5hotYW4S/i0ypfCX0xvxJCxwETyC0C7FDJLfKeXi/3jwkwASbgggQSEpPx3uhp6PJiE7R/oYHNFh45/h9GfzUf38xejr9PXbBpw0rg1R69sG/vMZO8N2i4wPLqq6n0gw16kcgBE2ACTIAJuDSBsiFKfNEsAL91D8biTvnw9nO+8Fa4dJO5cUwgVwmwQyUFP2+YABNgAkzAfQlotTrM/2VDmnLlxl3odHqMmbQARQsXwKA3DVNFUve4SoVS6PxiY5QqXgg37z5An6ETsXX3EWFG660WDvWFp0pokDd8VHKH+xfopxKcAnyVDpeRWbZhUpupUi+l4+02r5OWDwgP8XW4/XSOUHtYmAATYAJMIHsIJMUk4fwf5/HPD3/jQew9UWiCNg7no8+IfQ6YgKMEsisfO1SyiySXk2kC+sv3oNt6Soj+8n2Rn7ZPdYYvTZHAARPIBIErG1/B5bVthGiTn4icVzd0EXHS65JjhY4DzyGghx4RT2LSFLVag0cRUcI5Ehjgi2/nLMfXs5YhPiERK9f/KemPChgvt26IIX27YECvlzF9/FBQfM3mfSJNrwfuRSZ4rERGJyFRrXO4fzEJasEpLlHjcBmZ5ftYutCmStUax9ttXqfkc8ODJ44fYzpHqD0sTIAJMAEmkHUC0bei8WPTxdg8bDP2frUPey9vxj4shfbSemxd3QeD19XDjvubs16Re5TArXRRAuxQcdEDkxeapb/0ALptJ4WQI4X6TFuTTkonHQsTyCyB+DsHEHt7nxBj3ri7B0Wc9Hp97r3+z9ge3mYvAaVCgVGDe6YpFcsWR4C/D4b1fwVFC4UhOF+AEGpFgL8v/Hy9addKaP2UuPgkk55umD1WUnrpaP8kn5YowdH8juajSiVfFxzNb55PlCUVZq7LzD7lZ2ECTIAJeCqBUR+PQOMmtYUcOGB42DDqk5EiTvoD+/eadT3ru6d/OY1E6UGGsaRCoU/wYvv5GPQoAR9cCsX8w0GIWveVMZm3TCBXCLBDJVewc6VEQFauIOStq6cplE52LEwgswRKt/sNZTttSVMU3sGZLZLtPYCAn6+PGHlCo0+MQrqXWtRH43rVRA+n/rAKl6/dhlqjxb/nr+KX33fg+bpVRRoHTIAJMAEm4MEEHOja/TgdNl9MwvoLSbj2ROtACZwlPQKPLz22SK5T8QpUSp2Fruc1b9yLumSh4wgTcCYByAcbfQAAEABJREFUdqg4kzbXZUFAVrYQ5G2qpSmUbpGBI0zATgL+RRoioGiTNMXOYtgsDxI49PdZdHhzDGq06odX3xmH1k1r443u/JaaPHgqcJeZgMsT4AbmLoGDt9R4c20Uph+Jx+yj8Ri4MVo4VnK3VTlb++RJU2G+GHnq/YaNmmRrA/KXy29RXmi+OIu4MRIYzVO5jSx463wC7FBxPnOukQkwASbABFyEwNHNc9G0QXVTa1bMG4tDG2Zj8y+TcWzLD5jwcX/4eHuZ0nmHCTABhwlwRibgUQRWnEmw6s+yfxOtdKywJKA/fAnapfuE0L5lqmWs6utV4RPia1JGRPmb9s13AgpWNI/yPhNwKgF2qDgVN1fGBJgAE2ACrk4gMMAPJYqGw9eHHSmufqxytn1cOhNgAkwgbQK3oiynnpBlZIIOscl62mWxQUC34R9oVx6E/sQ1IbRPOhumQhVULAhv7XkTL057EU0+aQxNXC/oNZa/zYnPd4Le+6nTRWTkgAk4kQA7VJwIm6tiAkyACTABJpBjBLhgJsAEmAATsElAr9dDo7W9xolOp8e9hxFpptssUFIWy2d9GxXiK0eAl0xKzfm/+Af/IPb2XiFatWHKi7nOFd9oqJMcKanJ6I5dSa2yiHsHeqNih4qoNeA5+MvqIWrbKuwvLzlXXnoX6sGzIH9psIV96oj2TwXUPypB29RpHGcC2UHA+psgO0rlMpgAE3ALAknJaty6+1C8atYtGsyN9CgC3BkmwASYABNgAs4gsGH7QbTu8YFVVXsOnkS9dgPRsttIVG/ZDyvX77aySUvxahXrURE9n/VJyzzb9bd2D8HltW2FJD8xLMp6a/dgESd9UtTlbK8zywU+ibcuIiYB+oRka30aGn1yflwuWA6651+BvmiFNKwM6oSOPkgc7IXkb1Rim9DZecfH0AIO8wIBdqjkhaPMfWQCNgh8OnkharV+G216fojGnYag95CJeBJleMJB5h3eGI0qzd60kNmL11JSXhXuNxNgAkyACTABJuBGBG7cvi+ucz6e+INVqxMSk/HBF3PwXt/OOLlzIaaNH4Jx3y0WD5qsjG0oGhRTYXGnfBha1w+D6vhhTrsgvFzB24Zlzqj8CtZEQNHGaYpcFZAzFWehVFmZgla5ZaULQOZrOY3HysgBhWaTArqLlqOFdOdl0O5UOFAaZ2ECaRNgh0rabDiFCbg5gfSbX7xIQayc9zlO7FgoFuC8dvOu9GTmT4tMQ/u9gk0/TzbJa51bWaRzhAkwASaQFQL6m4+h/e0wtAv/hG7TCeijrRd5zEr5eS0vTWm4+yACNPrQkb4nJ6tx/2EkaHqEI/k5DxNwNQJFCoXhp+mfYMyw3lZNO3L8P8QnJKJnxxZQKhRo1fg5lCwWjj0HT1jZpqUI95fjxfLewpFSKjj9G/VEtR4XIrR4kqhLq7hM6Ys1m4mynbamKd7BZTNVnjOM5S8/B4T4P61K2pe/XPtpPBv3dP/Zvs3VXbF0smRjlVxUHiVg+0zLozC427lMgKt3KoF3er+MKhVLQaVUoHDB/KLu4CDLpxkF8ucTFxd0gUESnM8yHfyPCTABJuAgAf29J9BO2wz9gQvQn70F3c7T0E7fDKhtr3PgYDV5Jtv8XzaIKQutuo8Uow9Hfj4LUdFPXzGa3qhDcqDM/mkdarZ+Gy26jUCTzkNx8qwLThfIM0eTO5pdBMhRUqhAKEJsXL/cfxQprnG8vFSm6sqWLIJ7DyJNcZl0750d8svpBHRe+QTDNkej529R+HxPLDIqVzQim+q3VZcoXwpspWW3TqpG/MlL5Ifqsy5QjepgEGlfXiosTRYnnxzDgiszTBKriRXlXIz9z6Sj9COR+63KkFe27bhSlNEbbEVJhkDCbNBJO5npuyE3h3mdADtUsnAGcFYm4O4E6Gnk3CV/oM+wr1Czanm81LI+zP+t2rAHNDWIpvrQsFnzNN5nAkyACWSFgP7kDUCvtywiMg76y/csdRyziwA5vBdO+QjHtvyANYu+xNET57Bm8z6LvGmNOjxx5hJm/bgGS2eMxontC9CpbWOMGDsTtFinRQEcYQIeRCA6Jg5+vj4WPfL29kJMrGGdDy+lHIVCfLMsarkXfj5l+Trlw7fUOHxXn2bZQX5eol1+3so0bbLatgBfJYL8VDlWvnn7RGekwKSrVAiFSDLgeyXpNOZfnmGSOKNDJeacSUfp/8UdtepH8de94FVJqtTsz6syULSbl7ANy+cDhVzyoEjpvt4KoTO1L4N2Ge2krPyXxwjY6i47VGxRYR0TcGMCf5+6AHpSaUtWSw4S865pdXpcuHJLeooZi+iYeMTEPR1u36ZZHTSpXw00XHbX/uN4pf9YGJ0qfj5KFMnvmytCFwDUhyDpIiCn2uDjpUBooFeu9C+n+pSZcvMHecNbJXe4/2H5nDeHnM4FFvckoH8QZbPh+vvRNvWsTJ9At/bNUL9WZfj6eKFCmWJo1rAm9h46aZEprVGHu/46jga1q6BW1QpQqZTo3bW1mPpz/rLk9LIogSNMwHMIBAX6iyk/5j1KSkpGYICfUCVrdLgbkZBl+euyYVSFKNQsOHw9Ps2yo+IMi7TGJ2rStMlq22ITNIiOV+dY+ebtM3bbXGfPfkyC2pg13S31xVZ5qt8T4DMrGV4fqsVWtfrp8XwYlQi6DqaC45O0DnGgvC4u3DwnEGCHihMgcxVMwJkE4hMSxVt7Ip7EWG0jo2IsmkIX3lM+H4SNSydBqVSIJ5RGg8FvdcbAPh0x6I2OWDb7M+kCwxc79/0jkukH/s7jBOSGxEoXANQIugjIqfoTk7WIiEnOlf7lVJ8yU+7j6CQkqXUO9/9RVBIdIhYmkC4BecXCNtNlVYrZ1LPSfgJqjRb7j55GlYqlLTKlNerw7oPHKF28kMm2YFiw2H/w6InY0kNcuRTkhhieHwOyHKwf0r/c6Jur1CmTIJM42h4pu0TQ/f7Cw0Jw/dZ9qNUaU+PpIVOhgiGmeHbs0KuUbZUT4qewpc7juuzvvqK5Fqq3NKBt9pfOJTIBgB0qfBYwAQ8j0LheNYwa3NOmvP16e5u9lclkKFOiMO49jLCZTk8sC4QGI0F6cmPTIBeUMm0Mbu0dgbM/VcC/C4ri2qZXkfTE8NrAXGgOV8kEmEAmCcjqlkNq54m8TTXIwgLTLenytstY3mkZZlaeiYPdjqDsvmfStXf3xDv3HqU56pBGItKbSlL38cvvlyAmNkGMNDGmpTfqkKY++Hhbjizz8/VBbHyCyB4c4IWC+bydJgtPJGH0rlgh16O0og3z/k4QcdJHaxTZ2hbJV4OwIOf1z5ks7anLz1uJAB+Vw0x9vF3XMUDrA5HDRCM5GelEEvtawzlVp4ZhPsiytTtBCzrv2Pe3eMNP0wY1yNQ+scPqmTAlCvrLLSzpnGtcQmWh4wgTYALuScDy0+2efeBWMwEmkEkCsXEJmDJvJa7euAt6kkmLD67Z/BfqVDdcXNDUnsUrtwgHC6Vv2H4Q/56/ino1XefGJez6NDw+PQ/q2FvQJkUi6up63NjRP5Mk2JwJMIHcJKDo2xzKcd2gGP4SlBN7Qt66errNibkdg/XvrMe9k/ehSdAg7mIc6i5tDr/zgenmc+dE+g6OsDHi0KjT63UW3Zu9eC1oeueiqaNgHGlCBumNOqSpD0nJhikGZEsSn5CIAD9f2hUj9u5FJsJZcvpeEv6+nSwkLlkv2nDuoVrESX/zcfa2RSdV8eBJ9pbpLFaZrceWfVyiBjS1wlaaPbqEJIODQhwoFwsuX7uDGi/0x8cTfxDT2Gif1oajZvr5emPGhGGYPGsZqrfsh2GfzcCnw3ujWOEClJxt4iX5m75rE4QeVXxQp6gK7cp7Y2rbIJTMJyVkWy2eWZA+9TpbaXRTD+lDnEYaq5lAThNgh0pOE+bymYALEpDJZDhw7Aza9/kENVr1w2uDxqN109p489W2ptYuWbUVLbuNFOmjJswTI16eq1bBlJ7bO35P9lo1If7+Ecm5YhiibpXICibABFyTQIAPZMXzA9JT8owaeOfvOzZNAk4F29R7gpLesJbWqEPS00gS6ictIPvN7OX4ccUWrPrhc1StVJrUNiX1qMPCBfPj2s2niwEbp/qYO2RsFpRDykG1fTGpVSCJTSkRrMihmrlYTyNQrnRRnNm92EImjR5g6maLRjVxaucibFv+LWhB5p6dWprSsnMnzFeGN2r44otmAXivrh8qhPI5bA9fmUxmjxlk0n+7DNmICeQAAXao5ABULpIJuDoBfz8frJ4/Dkc2zcXmXybj6OZ5mPBxf3h7GYafligajp0rp2DvmunY8uvXOLlzIfp0a+Pq3eL2MQEmwARymUDuVf+/bxaBRhZO+Xww8gUF4Pa9R0JoKkNGow5bPF8T+4/+i39OXxSjFpes3orwAiGoWLZErnSoXKgS1cPTFn+VLFfaxZV6JgGFQo6ihcJAjkbP7CH3igkwgZwkIM/JwrlsJsAEXJsAOVbIeULDXlO3VCaTIX9IEIoXKQilwvWepMQHN0Hqf37hdaHw9twn1an7y3EmkGUCblZAkeeK2GzxhfL/YuCxXkIm/zfWpo2nK4+eOCe6+O6o79C6xwcmuX33kdCnN+qwRpVyeLdPB/QeMkGMSlyx7k98N3YQaJFSkZkDJsAEmAATYAJMwCYBdqjYxMJKewg8jNfh0uOnK6Pbk4dtmEB2EXhUchjyV30HqoBikhMlBPlKv4wSrRZkV/FcjosS4GblbQKBRQNRbcqzeFzqPjReakQWfYQjvf/E7dLX8E/kESHnY87kSUhbl31jMa3BOM2BpgyR4zy9UYcymQxD+nbB31t/wPbl3+Lwxjmo+Wz5PMmRO80EmID7EPBJ8kXFG9Xg/cTPfRrNLfU4AnKP6xF3KMcJxCbr8f62GPRZE4UhW2LQecUTbL6YlOP1cgVMwJyAXhGIYk2movIbF/Bs/9so9dIKeAeXMzdxhX1uAxNgAtlMoO7L9dDp905ofqgJOqx/Ge+PGI3ZtZea5KNKn2dzjZ5RnEyW8ahDH28vFCkUxiNTPOOQcy+YgNsTqJavFvqXHYJ2RbrAT+EHvfQf0j+lXImvDizEpo8uYM53G9HzjfeRONhLSjH8RV59gpuHbqUpMfdiDYYpofe2zShQuRRCO7WF95aNKVreMAH7CLBDxT5ObGVGYO35JJx9+HRkSqJGj+lH4kGOFjMz3nVLAtxoJsAEmIBrEwhQBuK5kHppSqWgZ127A9w6JsAEmAATsItAjZDaeLvMEPyvyiT82eIE8nsXEPk6K15DvRWtxb4x0P6pgGaNQkRPLT2B33quTlMub7kk7HD1qtjKn0RClpAA5cnjyDewLxQ3bwg9B0zAHgLsULGHEttYELgc8dSZYp5wOTIXXptn3gDeZwJMgAkwASbABJgAE2ACTMAjCahkStGvfDcNjhURMQt0Jw23tvlKhqBYvWJCAsL9hUVwqWARJ71/oUtTD+0AABAASURBVAChw4ULhm2q0OvQ/lQajjKBtAkYzrq00zklmwl4QnH+XrZPm1BfXnXfE44v94EJMAEmwASYABNgAkzAuQQuPtbg59OJQg7dUovKL0boRJz0h28bdCIhjwdJQfG2CaT4WWq8UR1dl3cVUv6l8sK2ep8aIk768m15iriAwkG2ELB9Z/y0aN5jAlYEmpVUWekqhilQPMgwzM4qkRVMgAkwASbABJgAE2ACTIAJpEngYoQWv5xKEHLkjsF5cilCI+KkNzpZ0izAQxPUKxVInqUS4vMoUPQy7GJRwLCLlGVVAOmuVtnG9ih6kclWUKGCLS2S6zeyqc8jSu5mJglIp14mc7B5nidQu4gKE1sGoE1Zb9QrqsIb1X0xvrnxWy3P42EATIAJMAEmwASYABNgAkwgUwTKhyrwejXfNKV+MesHmpmqwE2NNauVUM8yiG+EYfpOhZ01gBhDh+S1dFD20MB3eRLk5fQGpb1h6dLCUhccAr2vLzTVayJqziJoi5cQeg6YgD0E2KFiDyUXsvntrGEoIA39syUJ6kx+kTjYt5qFVBhe3w+fNwtAj2d9EOglc7AkzsYEmAATYAJMgAkwASbABPIIgTS6WT6/Er2q+qQp9BAzjawerVZ21UA12CBnXz+Mn9pOxYkee0067ynJ8P6fGvJndQ5zSGr9Ih6evYaItVuQ1Ladw+VwxrxJgB0qbnbcf/8v0TT0j4b/pZYEjXMcKm6GjZvLBJgAE2ACTIAJMAEm4AABzsIEHCGg23oSuq2nhBjzG+O0Neoy2qq6a+E1WC3kv9cP4acXp+BEzz0iTnp5ON/7ZMSQ03OWADtUcpZvtpfe5Rkf01BAH6VhVEiXSk91vim6bK+YC2QCTIAJMAEm4IIELsdewI24ay7YMm5SLhHgapkAE3ABArptkjNlm+RUkcTYHJ20bxSjjrdMwN0JsEPFzY7gK5WfDgX0M7w5DK9U9jYND/RVGZwsbtYtbi4TYAJMgAkwgUwR+CfyCNrtaYTXDrZHtwOt8cr+F3Ax5lymynANY24FE2ACTMDzCMhbV4O8dfU0xfN6zD3KqwTYoZJXjzz3mwkwASbABJiAIwRcJM9357/Eo+SHptbcir+O2ZenmOK8wwSYABNgArlHQN5Gcqa0kZwqaUjutYxrZgLZS4AdKtnL06ml6VJqS9Ck7PCGCTABJsAErAiwwvMIxGvicCX2glXHzkX9a6VjBRNgAkyACTABJsAEcooAO1RyimwOlzt5fxwiEwyLMPX7IwqLjifkcI1cPBNgAk4iwNUwASaQAQE/pT/8FP5WVqHeYVY6VjABJsAEmAATYAJMIKcIsEMlp8jmYLn7bqix+1qyRQ2rzibi0mMeqmIBhSNOIsDVMAEmwAScT+CF8JesKm0dzq+7tILCCibABJgAE2ACTCDHCLBDJcfQ5lzBF9NwnFx9os25Sj2pZO4LE2ACTIAJuD2BjyuPx0fPfI4W4W3QptDLGFf1W7xR+h237xd3gAkwASbABJ4SuJd4B39HHhZyK+GGSLiXYKaLN+hEAgdMIBcIsEMlF6BntsrU9v5ett/k4+/FhzM1K44zASbABJiA5xJ4pdhr+KraDHxR9Tu0LdTBczvKPWMCTIAJ5FECW++ux6BjvYWsu7VSUNh2f4OIk37t7RVCxwETyC0COXEHnlt9yTP1NivlBZXCsrv5fOSoXkhpqeQYE2ACTIAJMAEmwASYABNgAkzATQmE+xZGrZC6aUpR3+Lgf7lOIE83gB0qbnj4w/3lmN42CD4p/pP2FbzxfZtA+Ktsj1xxwy5yk5kAE2ACTIAJMAEmwASYABPI4wRo9OGc2j8jLelcrIcDhDgLE8g+AuxQyT6WTi2pVLDC5EB57VkfFArgQ+nUA8CVMQEmwASYABNgAkyACTABZxDgOpgAE3BZAnnqLlyn00Or1dk8GJR272EENFrbC7vGxMYjMirGZl5WMgEmwASYABNgAkyACTABJmAgwCETYAJMIK8QyDMOFb1ej3FTFuOLqT9ZHds9B0+iXruBaNltJKq37IeV63ebbOITEjFkzDTUbz8Iz3ccgp6DxuNRRJQpnXeYABNgAkyACTABJsAE3JoAN54JMAEmwASYgEME8oRDZevuI2jSeShWb9hjBSkhMRkffDEH7/XtjJM7F2La+CEY991i3Lr7UNj+umYnLly5hT9Xf49DG2ZDIZdj2oLfRJqzgwl7Y/HRjhiTRCfroZcaMX5vnElH6dFJtkfhSKb8xwSYABNgAkwg2wjs+nMHhgwdIGTNutWi3F27tok46deuNehEAgfZSICLYgJMgAkwASbABFyBQJ5wqDSuVx2r5o9D+xcaWDE/cvw/0CiUnh1bQKlQoFXj51CyWDj2HDwhbLf8eQRd2zdFwbBgBAb4oXfXF/D7pr2gES/CIBMBOTs+2RmbiRyWpmcfanD6/lPRSH4TWob23KOnOkonvWVO+2Izj8QLx8z1KNvTnuwrha2YABNgAkwgrxB4+OA+Tpz4R8jt27dEtx8+fCDipL+VooNI4YAJMAEmwASYABNgAp5FIE84VPx8vVGoQCj8/Xytjt79R5HCgeLlpTKllS1ZBPceRIr49Vv3UaJouNinoHiRgrRBdGy82GYmIGfHiXvqzGRxqu2lSINjJsF1m+hUHlwZE2ACeZcA99w+As2at8K0aXPTlE4dX7GvILZiAkyACTABJpANBPZ8sRvfl/7eJMd/PCFKTa3/e9FxoeeACWSVgFs7VP4+dQHzf9lgU2xN77EFKzomDn6+PhZJ3t5eoEVoaRQKjV7xkeJGA+8Ux0t8fKJQBfmpYK+IDFJgr31qO5mMxqNIBWTwF+hrf5vM61DKDeX7+yhFnwKlvpHG3Cav7asUcvh6KwQPR/quSGGawSHjZCaQVQKcnwnkCoHwguGoVbN2mlKsWPFcaRdXygSYABNgAjlHQH/lAbSzt0HzyTJoJq2DbvupnKtMKvlE5DFMOvsZRh4fgPlXZiAy+bGkBeZUnyMcJ0nRSSLuaHAnwkuUs6zjMkeL4Hx5mIDcnftOzo6IJzGwJfa+kSco0F9M+THnkJSULKb3yGQy4WxJSn46ZMO47+dncMLo9HrYK8Y6Bm+IhCMSZWNtFKl6Y7Gm7Sc7ohwq/2qkYaqPTq8TfSKHEq3RopMqyauih/RfguBo/00HhXdSEeAoE2ACTIAJMAEmwASYgNsRSFJD+9Me6C/fB5I1wMNo6LachP74tRzpyumoE3jn2GtYc3sF9j/ajQWXZ2Dgsd7Q6g33LTlSKRfKBDJBwK0dKo3rVcOowT1tytuvt7cLQ3hYCGhaj1otfSGk5KBFaAsVDBExWk/lxm3pC0PEgJt3Hoi9oAA/sY1N0MBeERmk4MRdNRwRTarvDelWH5BJd/uw/PfvfcfKj1cbykqQHDfGPlHJxv28uNVo9UhM1iI2E8fZnJNWZ2BKHFmYABNwHwL0NjdatNx9WswtZQJMgAkwASaQ8wT0tx4DsYaR+ua16c7dMY9m2/72exusyroadwlno09b6VnBBHKDgFs7VOwFptXqQA4TrVYLjeSVoH1dyo1unRqVRDHL1u6ERkrfse9v8Yafpg1qCH2bZnWwav1uPHj0BLFxCVi6eju6vNQEMhlNhhEmmQ4mtQqEIxLobVmnDIb/qRswpkkAHCm/eJBCFCUcNWKPg7xOgD4n9x5GiM9GbrKgtYd+Pp0Ikn8fGpyfx+9pRFzoHhh0udlGrts9CNCIxirN3kRqOfTPWdEBcqC/1GsUmnYZhtptB+CzrxdBLf1uiEQOmAATYAJMgAkwAacSuB531WZ91+Mum/TGqT+0dspxG2uj6G0839w1djdGv3cLo/EJFu4sYiqLd5hAZgm4ikMls+3OlP1vG/egxgv9QeuqrN3yl9hfu2WfKIMWrJ0xYRgmz1qG6i37YdhnM/Dp8N4oVriASH+tcyuUKVkEzbsOR712A4VjZkjfLiLN0aB6uBKOiMrOo1UlTOFQ+X5ehh6Rm8awx2FeIJCcrEbvIRPR9e2xFt3dc/CkOOdbdhspPhsrJceihYETIyck58kvpxJAcibFeXLynlrEhS7FyeLEJnFVbkqApjJS0+dOfh+bfp5skuqVy5EaX36/FJXKlcCxLT9gw5KvQG9627LrsEjjgAkwASbABJhAXicgK5YfCDAsfWDOQl4pZ5wS9fM3Nq/GtN8gfxPTfkY7Mlh7VGzpMiqH07OVgMcUZuctunv3t3uH5jize7GF0CgTY69aNKqJUzsXYdvyb3Fi+wL07NTSmAR/Px/MmTQCB9bPwp7fp2HFvLEoGBZsSucdJuDOBOjmctyUn/DP6QsW3aCpDh98MQfv9e2MkzsXYtr4IRj33WIxesvC0EmRGoWUeL2ab5pSpYDSSS3hajyFQLHCYeINbzStk8TXxwtRMXHYf/Rf9O7aGhQvXaIwOrVthG17jnpKt7kfTIAJMAEmwASyRsBbBcUbTSErGw54KYECQZC3rQ5ZzVJZKzeN3D1LvonaoQ1Mqd5ybwyr8AnyexseflPCwJMDMfzqcCE1+9YklaXYmFnQfFxzTJxZDBPxFfq1tGe6kmWRHGMCRgJy405e3yoUchQtFAaVSvpisAEjX6A/wkLz2UixX0XTcL5uFWh/BidbvlfbT0xFKp6PTwsno8+16hb8uhFnL1zDyHe6W7ThyPH/xGLNPTu2gFKhQKvGz4mbzz0HDa+eszB2QqRGIRV6VfVJU54taPtz64SmcRVuSmDKvJX4dPJCLFm1VThSqBuPHj+hjWmEIkVKFA3H3QcRtCtEpZDBU0Uhl4GuOT21f/b0iw6yMgvHmPKzMAEmwARcgkAONkJWpiAUg1pD+VVPKD/uCPkL1XKwNmDWcz9hY5O/sLjub9jW7CheK/lWjtbHhTOBzBDgO+fM0MqiLU3zqRru+I3fp00ChMODHDMkgSoZaE7gmMaW+lBfxw5rufxKMVXIXyo3i13l7G5AYNueY+Jmcs7kkQj097Vo8f1HkcKB4uWlMunLliyCew8iTXHeYQKuRECr1WH+LxvSlCs37ormekvn9GudW6Ja5bIIDQ4U9m8O+wo09S06Nl7YmJ/33t5eiHgSLfQyKQwO8IKnSoCvCl4KOTy1f/b0S/IpIV8WjjE5pKTThP+YABNwgABncT0Cx44dxqTJ4zHq4xFYsnQRoqOjcq2RYd4F8Uy+qvBRWE83cqRRiqtXgYsXRVZFpOHBiSw2Fsorl4SOAyZgLwG5vYZsl/sEnilgcHiQY4ZEpYB4mli5gEI4QkhHAv6Xpwn8feqCuEm0dXNJ6wgRnNPnrmLMpAWYPWkkChUIJZWFRMfEwc/X8geLbixjUm44fbwUKBDs47HiLX248vmrPLZ/GR07uvH0Usod7j/ltzihnBChxbQjnsRIzg/bQouRUzMCJOfhmGG9QW+Co5FZS6aPxoUrt3Du0g0Y395mtCX7pKRkyfESRLtiBva8MfRrAAAQAElEQVTDqCR4qkTFJSNJo/PY/tlz3Gi9+sdZOMb0kEOcLBzkBQLcRybg0QQOHdqPESMHY+PGdThwYB/mz5+NDz8a5lJ9Nk718Q7yznS7fBYvAKZ9L/KV+me9mPoz9Mqn8J03S+g4YAL2EpDba8h2TIAJuAeB+ITENG8qI6NiRCfWbt6HAvnzYfPOQ/h61jJs2nUY12/dF/vkNAkK9BdTfoRxSkA3loEBfiKWpNbiSUySx0qyRovYBI3H9i+jYxcTr4Zaq3O4/5RfnChODJQKBUYN7pmmVCxb3GZrCoaFCH2C5DgJy29YH+vmnQdCR8G1m/dQuKC105HSWJiAexHg1jIBJsAE7CewecsGK+OzZ//F3bt3rPSupGj6v2YwrqdC25pv1RDNM+o/+rEqxtXbjbr1VUCjRlDXa2Ah2jJlhT0HTMBeAuxQsZdUDtvFq/WYdTQevddEoduqJ/hiTyxuRWtzuFYu3hMJNK5XLc2bSnoqT31u1rAGOrV9HsH5AoT4+/nAx1sl9mk9oXDpJpMcLOZP6ukpfqGChptPegqr1uqlm27PFOqfVnpU7cl9TK9v1HdikJ5NemmUn84zV5Q9B09i6+4jYt2UeMn5OG3Bb2I0Fr3Zh9bKalC7Cpau3g5amPnqjbv4Y9sBtG5axxW74vlt4h4yASbABJhArhG4fuO6zbrv3nNth4rNRpspk5u1ROTytYhetQ7YsEnsU9wo8e+8Z2bNu0wgYwLsUMmYkVMsVpxJxIYLSXgUr0Nssh4Hb6kx5VC8U+rmSvIeAXK6DOj1MozStH51hBcIFXGa6lOnRiUBZdnandBotdix72/xhp+mDQxefpHIARNwQwLJajU+nbwIDV8ejDovvovNuw5hxpdDQc4U6s6Yob1w5vxV1G47AO37fCI5U2qjbfO6lJShsAETYAJMgAkwAU8hUL9uA6uu+Pn6oVLFylZ6VjCBvEyAHSoucvSP3lFbteT8Iw3i1HorPSuYQE4T8PP1xowJwzB51jJUb9kPwz6bgU+H90axwgVyumou33kE8mRNLzSpjYMbZmHXqqlC9q6ZjvrPPb04LF2iMLYu+0akHdk0FxM+7p/m29/yJEDuNBNgAkyACeQJAj169ELVqtVNffWVnCnDhn0APz/D9G9TAu8wgTxOgB0qLn4C8AFy8QPkIc3r3qE5Vs8fZ9GbFo1q4tTORdi2/Fuc2L4APTu1tEh3foRrZALZQ4DWWwkvEAISmYze3WNdLqX5+/lYJ7CGCTABJsAEmICbEoi9uQs3/xyEqxu74ubBSVAnpv32xuDgEMyetRCrV2/EgvlLsW3rXrz0Ugc37bkdzU5IgPfWTfCbNxNef+6wIwObMAEDAb5fN3DI9bBOEZVVGyqGKeHLrzCG2/7zgIbTeipFC4XxE3oPOJbcBSbABJgAE2ACTCDvEoi+vg2X/2iPiLOLEX1tE6799Rn++y1jB0l4wXBUrPiMW4I7s/JfXP3zmmj7veN3xdZWIIuJRv42TZDv3bcQMGk8gvu+jpBX2tkyZR0TsCLADhUrJLmjeLWKD9pX8EaYnxwBXjI0KKbCyPrOHVKXOz3nWpkAE2ACTIAJMAEmwASYABPISQJPLq22Kj7m7mEkR9tefNbK2M0Uf036C9tH7cCTq4ZROOf+OA/S2eqG9/q1UNy8YZGk+ucYvPbsstBxhAnYIuDODhVb/XFbnZ9KhsF1/LC0cz6s6haM/zUNQLEghdv2hxvOBJgAE2ACTIAJMAEmwASYgGsQSIo8b7MhiWnobRq7kfLsb2etWnt+g20GqksXrGxJobh0kTYsrkPAJVsid8lWcaOYABNgAkyACTABJsAEmAATYAJMIFsIBJZsbbOcgMINberdWZkYlYj4R9ZvS429Gwud1vDCj+SYZBz7+RSWd1yOb5YVwJf6YVim74gn+iBT17Xlypv2HdvhXHmBADtU8sJR5j4yASbABJgAE2ACTIAJMAEmYEVAp9Pj3sMIaLRaqzRPUhSoNhj+hRuYuqRQBaBs6zmQewWYdPCQPZ98PihUo5BVb4rWKQq5QoaEx/FY2Hwxfh+yCfdO3UNioh5x8MVpWWWsQxuRT12rNpKbthD7HDCB9AiwQyU9OpzGBJgAE2ACTIAJMAEmwASYgEsSyGqj9hw8iXrtBqJlt5Go3rIfVq7fndUiXTa/wjsY5brsxDNvXESFbvvRcHgkClXr57LtzWrDmn7WFIFFA03F+OTzRuPRjUX831VnEPcgTuwbA5lMBr0euOZdDhHzf0bkbxuNSbxlAukSkKebyokuR+D1357gxV8ihURI3lRq4Ou/R4k46SMSdKRiYQJMgAkwASbABJgAE3AtAtwaFyKQkJiMD76Yg/f6dsbJnQsxbfwQjPtuMW7dfehCrcz+pngFFIVvwZrZX7CLlVi4VmH0+6sfnulieENR/eENEF4tXLQy4pJhoVoRMQsklwp0ShWSm7cy0/IuE0ifADtU0ufDqUyACTABJsAEmAATyKMEuNtMwHMJHDn+H+ITEtGzYwsoFQq0avwcShYLx56DJzy303mwZz7BPla9Di0XYqUjhR4yFKtXTEwLojgLE7CHADtU7KHkQja/vBKMza+HpCmhvnxIXehwcVOYABNgAkzAmQS4LibABJiAnQTuP4oUDhQvL5UpR9mSRXDvgWH0gkwGeKnkHisKuQwkntxH6ptc6iekf0qFzHQsa75WFQEF/SXt0z+5Uo5KL1dAm69amuwof3ryNDfv5WUC8rzceWf1/ehtNVafTcT+m2okavTOqpbrYQK5SuBGlBbrLyRh88Uk3IvlqWi5ejC4cpcl4AkNi4x4jM2b12PFil9w9r8zntAl7kMOEohK1GHn1WT8/l8i/nuoycGauGgmkD6B6Jg4+Plajl7w9vZCTKzh7TAqhRxhQd4eK77eCvj7KD22f8Zj5+ulECeCeV9LlA7BqOPvotdPndFufAv0X9sDkyJGod+vr6BM5QJ2MxEFc5DnCbBDJYdPgRFbY/C/3bFYeDwBX+6NxcCN0YhTs1Mlh7Fz8blMgBwp72yIxuyj8Zh+JB5vrYvCwVvqXG4VV58NBLgIJmBB4OLF8+jRszMmfjUOM2dNxTvvvIFZs763sOEIEzASIEd73z+i8e2BOMz/JwEjt8Vg+uE4YzJvmYBTCQQF+ospP+aVJiUlIzDAT6j0ej2S1TqPFa1ODxJP7iP1jfpIB1SrtTyecl8lqnaqhAaD6qBU45IOHWcql4UJsEMlB8+BY3fUOPdIY1EDPanffY1vLC2gcMTjCCz7N9GqTyvOJFjpcl7BNTABJpCTBFatXi7dkBie5hrrWb7iZyTEW+qMabzN2wTWnU9EfKqHSpsvJSOSF9TP2ydGLvU+PCwE12/dh1r99Fr9wpVbKFTQsL6GWroBfxSdBE+VhCQt4hI1Hts/43FLTNaKMyw2VV8jY5OFQ8lo58hWFMxBnifADpUcPAVuRtue5nAr+ukXdw5Wz0W7IwEPaHNsst7mxfGtKNufBw/oMneBCeRZAtevXbHZ98tXL9vUszJnCWi0Wtx7GIG79x9Dq7X+ztVJT6QpXSPZ2WpJcrIa9x9Ggp7M20rPqi6t66IbaVwvZbU+zs8E0iNQp0Ylkbxs7U7QZ2LHvr/FG36aNqgh9BwwASbABOwhwA4Veyg5aFM8yDbeYkFKB0t0vWzcIiaQmkCAlwwhNhZHLpbP9uchdX6OMwEm4D4ESpYqY7OxZUuXtalnZc4RWLFuF6q37IeW3Uai1avv44Ue7+Pf81dNFe45eBL12g0U6WS3cv1uUxo5UGb/tA41W7+NFt1GoEnnoTh5NvudYmldF5VI43rJ1EDeYQI5QMDP1xszJgzD5FnLxGdn2Gcz8Onw3ihWuEAO1MZFMgEm4KkE8todjlOPY+0iKlQKs3SeFAqQo1kplVPbwZUxAWcT6Pmsj1WVr1bxtdKxggkwAfcm0K1rD/j5GtYbMPakx6u94OtnqTOm8TbnCNDimnMnj8TRzfNwcMNslCtVFFPmrhQVJiQm44Mv5uC9vp1xcudCTBs/BOO+WyyexpPBiTOXMOvHNVg6YzRObF+ATm0bY8TYmaARLZSeXdKxog/8VDKL4l4s52XTCW9hxBEmkEMEWjSqiVM7F2Hb8m/Fud+zU8scqomLZQJMIJcI5Hi17FDJYcRT2wTii2YB6FfTF582CcCcdkHwT3UxkcNN4OKZgNMJvFzBG/PaB2FQHT8MreuHHzvmQ4NiKqe3gytkAkwgZwmUL18Ry5etwehPxuK9wSMwb95PGDx4eM5WyqXbJPBy64ZoXK+a5ODyRlCAH2jBzeB8gaB/R47/h/iERPTs2AJKhQKtGj8nXhe75+AJSsauv46jQe0qqFW1AlQqJXp3bS2m/py/fEOkZ1dQIp8CizoE4YOG/ni7li+mtA7E0Hr+2VU8l8MEHCKgUMhRtFCYOPcdKoAzuRyBEz+dxOoeq4Vc3HRRtO/kkhMiTvqLWy4JnWsG3Cp3I8AOFSccsTpFVeha2QeNiqvgo5Q5oUauggnkPgG6cCbHyovlvUEjs3K/RdwCJsAEcoJASGh+vPjiy3j11ddR+ZkqOVEFl5kJAn9s24/h/5uJsxeuYUCv9iLn/UeRwoHi5fXUsV22ZBHcexAp0u8+eIzSxQuJfQoKhgXTBg8ePRHb7Azy+cjRsrQXujzjg2cKWI7izc56uCwmwAScRMAFq4m6Holbh28Jib1veJPYk2tPRJz0cfdiXbDV3CR3JcAOFXc9ctxuJsAEmAATYAJMwOMJ3Ln3CPN/2ZCm0HQecwhXrt/F48hosShtdIzhbUvRMXHw87Wciunt7YWY2KfpPt7e5sUI+9h4w9vZ8vmrkD/I22NFLj3rCg303P5ldOx8vRXw81EiI7u00r1VCotzx9Uj3D7PJ1Ctdw28sqxrmlK2bTnPh8A9dBoBdqg4DTVXxASYABNgAkyACTCBzBFQa7SIeBKTpuj1OosCh7/dVayF0uWlJnh/3CyRRtN/aMqPiKQESUnJCAwwrHVD6UnJySkphg3ZB/gZ1r6KT9IiNkHtsaLTA7GJLtu/HOeerNYhKdnxY6y28UYpw1nEIRPIHQIhpYNRvH6xNCWwUEDuNIxr9UgC7FDxyMPKnWICTIAJMAEmwAQ8gUDJYuEYNbhnmpJ65Imxz6VLFBZOGHodbHhYCK7fug+1WmNMxoUrt1CoYIiIFy6YH9du3hP7FBin+hin/qg10g033XTbJe5nS31O9tC+JdnRL63kUSKxx9aWTXYvXkzHg4UJMAEm4C4E2KHiLkeK28kEmAATYAJMgAlkPwEPKXH24rU4efYyEpOScfveI/y4YjPq1XxGLEJbp0Yl0ctla3eCHCw79v0t3vDTtEENoW/xfE3sP/ov/jl9ETQiZsnqrQgvEIKKZUuIdA6YABNgAkyACTAB2wTkttWsZQJMgAkwASbABFyRALeJCdgiQE6U1waNx3NtJoCrXAAAEABJREFUBqB1jw+gkMvxxUd9hamfrzdmTBiGybOWoXrLfhj22Qx8Orw3ihUuINJrVCmHd/t0QO8hE1CjVT+sWPcnvhs7CHJaXERYcMAEmAATYAJMgAnYIsAOFVtUWMcEmAATYALZRYDLYQJMwAkEJnzcH8e3zcfWZd9g/7qZ+HnmGJPDhKpv0agmTu1chG3Lv8WJ7QvQs1NLUguRyWQY0rcL/t76A7ZL6Yc3zkHNZ8uLNA6YABNgAkyACTCBtAmwQyVtNpzCBJhAniTAnWYCTIAJuCcBLy+VcKIE57O94KJCIUfRQmFQqZQ2O+jj7YUiUjqPTLGJh5VMgAkwASbABKwIsEPFCgkrmICbEeDmMgEmwASYABNgAkyACTABJsAEmIDTCbBDxenIuUImwASYABNgAkyACTABJsAEmAATYAJMwN0JsEMl4yPIFkyACTABJsAEmAATYAJMgAkwASbABJiA5xPIVA/ZoZIpXGzMBJgAE2ACTIAJMAEmwASYABNgAkzAVQhwO3KTADtUcpM+180EmAATYAJMgAkwASbABJgAE8hLBLivTMCDCLBDxYMOJneFCTABJsAEmAATYAJMgAkwgewlwKUxASbABNIiwA6VtMiwngnkEQLJyWr0HjIRXd8ea9HjDm+MRpVmb1rI7MVrLWw4wgSYABNgAkyACbgcAW4QE2ACTIAJOIkAO1ScBJqrYQKuSECv12PclJ/wz+kLNps3tN8r2PTzZJO81rmVTTtWMgEmwASYABNwnADnZAJMgAkwASbgngTYoeKex41bzQSyhcCCXzfi7IVrGPlOd5vlFcifDyWLhZskOF8A+B8TYAJMIM8TYABMgAkwASbABJgAE5AIsENFgsB/TCAvEti25xiWrNqKOZNHItDf1yaCVRv24NPJC0FTfW7cvm/ThpVMgAm4PgFuIRNgAkyACTABJsAEmED2E2CHSvYz5RKZQK4S+PvUBcz/ZYNNWS05SKhxp89dxZhJCzB70kgUKhBKKitp06wOmtSvhiKFwrBr/3G80n8s2KlihYkVOUOAS2UCTIAJMAEmwASYABNgAi5PgB0qWTxEAb5KeKr4S31LVms9tn/2HDfoAZVS7jADbylvFk+xTGePT0hExJMYmxIZFSPKW7t5H2g6z+adh/D1rGXYtOswrt+6L/ZjYuOFzeC3OmNgn44Y9EZHLJv9GQIDfLFz3z8izcdL4TATe7jnto1eOu5e0rGzvx1Kj+LhrZJDJ0FwtP/+PkpxnnhaoJDLPOo4pz6+dNz1Or1H9zF1n1PHkzVa+Ennb2q9vXGFQuZpp73oD32m7WXgjnbqLB53d+yzeZulrzZk5fuNrgnEieJhAV3DmXPytH25HFk67u7Ow1e6ltVodVn6zfOwU5674yAB6aPkYE7OJggE+angyeKtUnh0/zI6dr7eBsdBRnZppXtJN6biRHFi0LheNYwa3NOmvP16e9GSZg1roFPb50FropD4+/nAx1sl4gqFXNiYByqVEgVCg5GQlCzUdPGUVp89QZ/V4+7uDAJ8VaALDUf7ESA5Y8WJ4mGBQrrrcJSJO+QLoOMufee5Q1tzqo0+WfzNo3PEw0570Z0A6TOdU8xdody8fq1DTkRymjl6LOiaQJwoHhZ4SddwjjJxh3x+3kpk5bi7Qx/Ta2OgdA9H5256Nhmledgpz91xkID1nZODBXE2JpCTBLjs7CVATpcBvV6GUZrWr47wAqEi7ufrI6b2LF65BfceRoCe3G3YfhD/nr+KejWfyd6GcGlMgAkwASbABJgAE2ACTIAJMAE3JcAOlZw5cFwqE3B7ArRgbctuI1GjVT+MmjBPjHh5rloFt+8Xd4AJMAEmwASYABNgAkyACTABJpAdBFIcKtlRFJfBBNyfgF6vh0artdkRnU4vRmyklW4zk5sou3dojtXzx5laW6JoOHaunIK9a6Zjy69f4+TOhejTrY0pnXc8j0B65zWtq2Ncf8fzes49yssE6Htdq9XZRJCcrMb9h5Gg3wWbBqxkAm5KgM5pTRrXOtSlRxFRSEg0TPGlOAsT8BQCdN7T976t/uS9ax1bFFjnCAF2qDhCLQ/k6fDGaFRp9qaFzF681uN7TlNbWvf4wKqfew6eRL12A0EjNqq37IeV63db2XiaQiaTIX9IEIoXKQilQoG88G/yrGUW5zx9Bnq9N8Hju37j9gPQeX3n3iOLvtICx0PGTEP99oPwfMch6DloPOhC28KII25PgBaxpnM9tRz656zb9y29DtBN5bgpi/HF1J8szEg/+6d1qNn6bbToNgJNOg/FybOXLWw44hkE+FrH8jjSm/xe6jUKTbsMQ+22A/DZ14vEtF9LK465OwF6wUDq73uKJ0lOZJfuWxYbR07CLn0/w6adhyxK4msdCxwccYAAO1QcgJZXsgzt9wo2/TzZJK91buWxXaeLiDY9P8THE3+w6iN9AX/wxRy817ezGKkxbfwQjPtuMW7dfWhlywr3JkA3Us0a1jCd83T+fzt2oHt3KoPWk5Pkxdc/smn165qduHDlFv5c/T0ObZgNhVyOaQt+s2nLSvclQOc9tX7u5Pctzv3qlcuR2iNl6+4jwlGyesMeq/6dOHMJs35cg6UzRuPE9gXo1LYxRoydibSealoVwAq3IsDXOk8P15ffL0WlciVwbMsP2LDkK2z58wi27Dr81ID3MkXAVY310IPWy6NrHHPxUildtclZbte3c1cIJ+Hl63esyuJrHSskrMgkAXaoZBJYXjIvkD8fShYLNwm9DQYe+q9IoTD8NP0TjBnW26qHR47/B/Je9+zYQozUaNX4OcFkz8ETVrascH8CgQF+4vgaz/1CBULdv1Pp9GDaF0OwbPZnNi3oYrpr+6YoGBYM4tK76wv4fdNengJhk5b7K4sVDrM49319vNy/U2n0oHG96lg1fxzav9DAymLXX8fRoHYV1KpaASrpBqN319Zi6s/5yzesbFnh/gT4WsdwDKNi4rD/6L+g850++6VLFEanto2wbc9RgwGHHkXAx1tl8X1P1zwymcyj+mjemf4922HnqikILxBirhb7fK0jMHCQBQLsUMkCPE/Pukp6cvfp5IWgqT40gsOT+0tTWujGOSRfgFU37z+KFD86Xl4qU1rZkkVw70GkKc47nkOAHGijv5qPb2Yvx9+nLnhOx9LoCTlL6A1PtpKv37oPWk/HmEbTv2g/OjaeNiweRmDKvJWg73xakDoqJs7DemfZHT9fb9B3vr+fr2WCFLv74DFKFy8k7Rn+6DNCew8ePaENi8sQyJ6G8LWOgeOjx4bzu1jhAgaFFNL3/90HEdIe/3kagYgnMaBrnXFTfsLGnYfSXD/QU/pND4XpO1+ltB6Fw9c6nnKUc68f7FDJPfYuXXObZnXQpH410MiNXfuP45X+Y8WrdF260TnUuGjpxoKGRpoX7+3tBVq8ylzH++5PoEqFUuj8YmOUkm6mbt59gD5DJ4KmBrh/zzLfA5oGQiOzfKRz3ZjbO8WpGB+faFTx1gMI0HF9rXNLVKtcFqHBgZj/ywa8Oewr0KKsHtC9THeBvvN9vL0t8tFvQGx8goUu0xHO4HIE+Frn6SExOsrNHx7RtU7Ek+inRrznEQToIcpbPV4EjUKiDn00fi4mz/yVdvOc8LVOnjvkOdJhdqjkCFb3L3TwW50xsE9HDHqjo5gOEBjgC1rEyv17lvkeBAX6iyk/5jmTkpLFFAhzHe+7P4GXWzfEkL5dMKDXy5g+figovmbzPvfvmAM9kMlkYo61+SJ1xn0/Px8HSuQsrkogwN9XTHd8+/X2GPlOdyyZPlqsnXPuUt6c4kLf+UnJyRaHi5yLATZGs1gYccTtCPC1ztNDFhTgJyJqtUZsKaBrndDgINpl8SACVSuVxgfvvgr6zh878g2M/6gvaB0RTTpvfvKg7lt0RSbjax0LIBxxiAA7VBzClrcyqVRKFAgNRoLkRMhbPTf0NjwsBDQc0PwigxbqLFTQeh6mIQeHnkKAhofGxSd5Sncy3Q+aU20+3e/mnQeiDOOFt4i4TsAtySYCBaXvPCoqr37nFy6YH9du3iMEQoxTfQqGBYs4B55JIK9f64TlN5zfxu95Osr0OShcMJR2WTyYQIH8hutZjUbrwb1Mu2t8rZM2G06xjwA7VOzjlKes6AZq8cotuPcwQrwuj14l/O/5q6hX8xmP5UBD/shhYvwxEfspnvo6NSqJfi9bu1PMMd2x72/xhp+mDWoIPQeeQ2DqD6tw+dptcd7TOf/L7zvwfN2qOdRB1yhWLV1AJavVojG0TyIiUkDD4Vet3w26oYyNS8DS1dvR5aUmkMk8d+E6qdt57m/PwZNiahutm0IjMehNTjTFhd724akwtFod6HteK33P0/c+7Rvf4tPi+Zpicc5/Tl8U3wVLVm8VCxlWLFvCU3HkyX7xtQ7EZ0AjfQboBMgX6C8WY14qfc/T2w2v3riLP7YdQOumdSiZxYMI0GgUWiOOjjNd6//w83pxje9jNsXXg7orukLnOX3PU0St0Yhzn/ZJ+FqHKLBkhQA7VLJCz4Pz0qKELbuNRI1W/TBqwjyMGtwTz1Wr4LE9vnztDmq80F+8Nvn+w0ixT4szUodp8cIZE4Zh8qxlqN6yH4Z9NgOfDu8N84XbyM7jJQ908NDfZ9HhzTGg8/7Vd8ZJF5K18Ub3Nh7d84YvD0bb1z4SfXyp1yi06Dpc7FPwWudWKFOyCJpLunrtBooLEJoSRWksnkOAHGqfTl4EOhfqvPguNu86hBlfDgXdYHlOLy178tvGPaDv/NUb9mDtlr/E/tot+4RRjSrl8G6fDug9ZALou2DFuj/x3dhBkMvZkSgAeVDA1zr9xULUxkM6ZmgvnJEeoNVuOwDt+3wifgPbNq9rTOathxC49+CxWCOOjjNd65Oz4YuP+npI72x346Px80Df+bfuPhTnPO2T05Cs+VqHKLBkhQA7VLJCz0Pz0qruO1dOwd4107Hl169xcudC9OnmfjeVmTk85UoXxZndiy1k0ugBpiJaNKqJUzsXYdvyb3Fi+wL07NTSlMY7nkNgxbyxOLRhNjb/MhnHtvyACR/3hyc/saEjd3TzXIvzft/aGaQW4u/ngzmTRuDA+lnY8/s0EB+e9iDQeFTwQpPaOLhhFnatmiqEvvvrP1fZo/qYujPdOzS3OO/p+59GX5GdTCYTayn9vfUHbJe+8w9vnIOaz5anJBYPIsDXOovFZ8D8WocWKd267BvxPXBk01zxG0hToTzosHNXJAK0VhZ9v9E1/v51M8VaiZ7+kHDK54PE+U7f9Uah8x3SP77WkSDwX5YIsEMlS/iyNbNLFSaTyZA/JAjFixSEUqEA/wMUCjmKFgoDX1x49tkQGOAHutD29fHy7I5monc0UiEsNF8mcrCpuxGg7/nwAiFiaotMJnO35udIe8mZSm+645EpOYLXJQqVyfhax9aBoO8Cusm0lcY6zyBA3290jU+vE/aMHpUAJsgAABAASURBVGW9F3ytk3WGebWELDhU8ioy7jcTYAJMgAkwASbABJgAE2ACTIAJMIG8RID7aosAO1RsUWEdE2ACTIAJMAEmwASYABNgAkyACbgvAW45E3ACAXaoOAEyV8EEmAATYAJMgAkwASbABJgAE0iPAKcxASbgfgTYoeJ+x8zhFh//9yLWbzuQYf6Y2Hjs+usfk+w/+i9oVezUGU+dvSxs6JVrqdPOXboh0owraBvT6RVt2/cew4/LN+OX33eA9umVrMZ087r/PHAch/45i0tXb0Ov1xtNeMsEMkXgxu0HWLN5HyKjYjLMR6+PNZ77tP/fxetW557xHD147IxVeUnJanHe79p/3CKNXsdKbxCic37xii3YuPMQLl69ZWFD9Ym6pbxU9pnz15AslWdhxBEmkAkCW3cfBZ1LGWWhz4g496TvffrepddpRkXHWWVz5Dufvt/pd2fBrxuxcv1u7Dt8CvRqZmPh5nXTZ+DYyfO4+yDCmMxbJpBpAtl9rWM8R+n3IHVj7j+MFN/5VKd5WkbXOmRL57v43Ln3dz51hcUFCMQnJIlrndTXFraaZvwup/Nv94ET+Of0BZvXG8ZzlM7n1OXQbwvlfxIVa5F0/vJN0NvT6Dufrr3+OX0R9BYho5F53fR7QJ+dqBjr3xujPW+ZgDsQYIeKOxylbGrjui378fHEHzIsjZwnQz6dDqMM+PBbtOn5IUZNmAe1RmvKv2DZRmHz9axlJh3tkM2gT6aKNHKYkI7k8rXbaNJ5KIb/byYWLd+E7+evFvv0Sla6wSQb87rfGz0N/UZ+jY5vjUG73h+LixayYWECmSFw+twV8Yq82/ceZZjNeN7SuU/7Xd8eiy79PgPdFBozG8/R/h98gwtXbhnVYrtxx0Fx3g8ZMw1arU7o6EKk58Av0O/9r8U5v1D63Hw0fi46vfUpxk35SdhQQPVRvZSXyu7+zudo1HEI5i1db+XUIXsWJpARgcmzfsXPv2/PyEw4OcS5J33v0/dun6ET0bDDYCxdvc0ib2a/8zftPCxeuU2/O6vW78a47xbj3VFTQK9mvnH7viibLqiNddNn4I1hX6FV95F4c/gk0G+GMOKACWSCQPrXOk8LMn6XG8+/tK51jOcopZPT/GkJwNT5q8R3/oyFv5vUdN5mdK1DxnS+i7ql3wv+ziciLFkhEBUdK651Dth42JO6XON3OZ1/g0d/j95DJorrDXKmm9saz9F1W/8yV4MeltI5S/mv3LhjSps4/WdxzTT22x9B3/mfTl4olT0B7Xp9bLIxr5t+D3q9NwENXx4s7k9i4xJMdrzDBNyJADtU3OloZbGtHw3uKV5/am8xP3zzgXjFGL0ycvBbnbFh+0Fskp6sp85PT0Gv3LhrUm/fcwz01MakkHboCf3orxZIe8CGJV+BXs1Kr2vdsXIK+nRrA42Zo4aMqO5///wRBzfMxoJvP0T+kHziooW85ZTOwgTsJdC6SW3QawErlSthV5Z3er8szvsT2xdgxpdDhdNk2oLVNvOSc8SYQA4Ucn4Y48bt7MVr8e/5q5g5cZh0Ps8S5z69mnnMsN5QKZVGM7E11n108zysnj8OL7Wsh+kLf8O0Bb+JdA6YQGYIrP1xAr7+9F27s9B5R9+7f67+Hi0a1cSkmb+Cns6nLsCe7/z70pP7D8fPQaM6z4Jew0yvYj21c5E4r5s1rGFyOBrLprpP7lwobCePeUeq975wqty888Bokje33OtME/goh651Ip7EiOsgY4PISU+jr4xx2mb2Woe/84kaS3YQCC8QKq51enRsYVdx1SqXFdc69L38+8LxCA0OlB5yzrCZ94ef10Ot1pjSlqRytlMCXZ/TKNx3+3TAsS0/gL7zj2+bj9lfjRBvyCQboxjrpnSye//d7mIE/XuSczExKdloxlsm4DYE2KHiNocq6w3duPOgeEKY2ZIC/H3x9uvtRTaafiN2UoIW0kV32ZJFsPDXjUJDU3Pm/7LeZC+UUhCfkChuKju/+DxKlygsaQx/hQuGYpR08dOxTSODwiyUyWQICvBDg9pVQA6WWlUrgLzl9MTfzIx3mUC6BGj6Gf1IR0oXw+kapkqk12O3eL4W6LyjIaypktH/tXbi4tr4pH33gRNiahw5CM1tjxz/DyWLhaN5w5qmV5DTq5lf69wSo4e+bm5q2vfz9cYz5Uti3AdvoW+PlzD/lw2gqRAmA95hAnYQmPrDKvy6Zocdlk9NZDIZCoYFo3fXNkJJTyLFTkrQIoPv/BQz4Yik/a7tm0kO8SDahUIhF+f1rInDLX4HRKIUKBUKYdv+hQZY/L3hieZnXy+SUviPCdhPICeudaj217u8ALqx1GgNI3V//m07nq1YWlyjUDqJI9c6lI+/84kCS1YIJKvVoGudzF4r0PdyxbLF0bHt8yCnYerpN292bysekm7adVg0j0bsrvzjT6vrfJq6QwY9O7WEr48X7cLLS4WmDapj0dRRIp46oPRihQuI65wZE4bh6IlzoGnRqe04zgRcnQA7VFz9CGVj++7ef4wTZy45VKLxprF6lbIW+WVyGd7t0xFrt/wFelpDQw1pGsTrXVpZ2JFTpl7NZ7Bm81/4Y9t+RNmYn2+RIVWEvpzpBpbUtuYxk56FCdgiEB0bD/qhTz1U25Ztah2tYXL73kPUrl4xdRJeaFpbOEp+XLFFpNGFNl145A8x3DwKpRS0bFwL12/dx9wlf4A+g5IqU39v92ov7KkPYoeD7CTg0WWRM9HWCBN7On399j1hVqViKbE1BvZ+5z9XrYLIQp8LmmufWUd4iaLh6PxiY3GBTZ9DURgHTMAOAvQ9m93XOlTtG93bCKf5tt3HxJpcS1ZtxbtvdIBcckJSOklWr3WoDP7OJwosmSVAo6PoOiEiMjqzWYX9tRt3QQ9I8wX6i7gxKFIoDN07NMe8pX+IkYXkpK9Qphia1K9uNBHbJvWriS2NbKSHUEbHo1DaEZCznh4kHTt13g5rNmECrkWAHSqudTyc1hoaSULTEMwl9cJSJyXnCy049euanXj7g2/EzWOD56pYtbF1s9oILxAieZU3iyfp9OS9QP5gK7sRA7qJMj6ZOF/Mz2/RbQTGTFqAy9fvWNnaUlQsV1yo/z13RWw5YAKOEKCnK+bnPf3wm5dz5fpd0MKctJjagI++Q0xsAjq0bmhuIvYVcrl4QkNPatZvOyBGYPXu1lqkmQddXmoCcqrMWPQ7Wr36vlg/YuDHU0GLPZvbpbVPo7ToCY7hBiEtK9YzgfQJxMUbRgman/vmQ7gp977DJ7FtzzHMXrwWn3+7GDRyMCw0HyVZiD3f+fTE/avRb0tPNiNAc+1rtx2ADm+MBq2dlfq3xqJws0ilciVF7OLV22LLARNwhABNSTY/7+nhj3k59l7rFJVuLOkzMXfJOixbu0vcfDatX8O8KLGf1Wsd/s4XGDnIIoGMrnXI8UIjazftPAwaCbhx5yG8+Wpbm7XSwyJ6METXRYZpPR1h5kcUeWpUKQ+y27zrsFhHpXrLfqD1UegaKfVvjchgI6j6TBmxkDo5h2wks4oJuCwBdqi47KHJ2YYlqzV49Z1xFkILr5nXOku6qKYFpyZMWyrWevhtwXj4+/mYm4h9Gqb9bu8OIMcLDdd7o7vtL2T6olz1w+dY+N1HGP52V1R7pixoZAtdZNPcS1FYOkFCQpJIpekSYocD1yfggi1cvWG3xXk/8OMpFq2khZRpYU5aTI3O5+Vz/4fKFSyf0hsztGtZX8w7pkU3u7VvhkIFQo1Jpi3dkE4fPxRrf/xSTPFp3bQ2yClICxxOmbfSZJfeDg3DDQ4KSM+E05hAugRocebU3/kPHj+xyDPy89kYMXYm6Lv/ZcmJOPGTty3SjRF7v/M7tG6E7Su+w5TPBwnnY2hIkHC6v9DjA4uFno3lpt4mJBq/831TJ3GcCdhN4H9fL7L4zqfRguaZ6Xy351qH8rzV40XxEGjWj2vwTp8OkMtlpLaQrF7rUGH8nU8UWLJCIKNrHVqUmRakpbWuft+0F1982Bf0AMhWnTRtuU2zuqDFZkODA0EPiVLb0Wfhw0E9sOf3aaIsGqkeHRMnFt8nx0pqe1txmjJH5VNZttJZxwRclQA7VFz1yORwu7xUSmz6ebJYIJYWiSVp3qimRa20bsmZ3Ysx8p3uYpjrr+nMxX9ZunD28/URTzTpabpFQWYRmUyG+s9VFhfX33/xnvjipXypVxA3y2La/ffcVbFfo0o5sc2JgMv0fAKvdW4FOt+NsmT6aItOGxcJXDrDoCenR1pDV2n+74BeL4v8dKEtdtIIypcuhte7vIAJH/cXN5l1alQSTznTKttYDD1NpYsM4xQKo563TCAzBKpXLmdx3tP5TyMLzcswLgzbrGENsUDg36cumCdb7Nv7ne/j7QW6ECcn+uLvP8bcye+Dzudd+/+xKM9WhEZl0cU1Tf+xlc46JmAPgW/+N9Di3B/W/xWLbJm51qHvcZqaQJ+dNs3qWJRjHpHJHL/W4e98c5K87yiBjK51jAvDkgOEzudZi9fgUURUmtX1f+0lkfau5Egkp7qI2AjoIdIr7ZpID5B6Yd3iCeJ6n0aI0XIANsxNKq1WhxP/XkKjulVNOt5hAu5CgB0q7nKkbLfTYa1MJhPTb2iBWKPQ3F9bBfbr+ZKYy043llt3H7VlIhagWic9gR/13ms202mRK5rbmTqR6vTxVokL7NRp5nFaGPGLqUvEgoalihcyT+J9JpApAsH5AsSCmMbzPi0HIC1GS0/oaUjs17OWpVkHzS2mm1N6gmPLiEZf0YWCeRrdZNKIE7qx1OvNUyz3ad2JD76YA3I6NqzzrGUix5hAJgj4+nhZnPd0/tu6KCbd5DHviOkM746aItb/sVUNlZfed/7Fq7fEulqp89KCt6Sjc5+2aQlNPaKnprSOSlo2rGcC9hCgxe/pfDcK3fDZymfPtQ7lG/9RP/w6+zPTIuOkM0pWr3X4O99IkrdZJWDvtQ59HsjRTW9mG/zJ96Bz0FbdNFL3D8lB0v6FhraSxXpXNLXUPFEmk6FgWIhQGUccioiN4Js5y8XD2xeb17ORyiom4NoEnOxQcW0Y3Lq0CfxvRB/xtpORn8/CybOXbRrSwlWpF7MyGtKceRryN/Sz6aD5mmcvXAM5Z0Z/NR80tDX1RTPVsWv/cdB8TXKktO/ziZhaMe/r9yGTWQ+xNdbDWyaQnQRovjy94YrmDP/y+3abRXt7qcSNqs1ESfnp5AXo+vb/sGrDbvHZoQU66e0rNLWIXm+oUiokK8PftZv3QA4cWpOFhpS37vE+TkmftwXffQhb04kMuThkAtlLgBzdcyePFIXS1LTIqBixnzpI7zufRhS27vEBJkvOSFqTiL7zyUFC62ZROa0aP0cbk9CUU/pM0Hz7IWOmialHNAJgaKrRBKYMvMMEcoCAPdc6dKOa1vdxZq91+Ds/Bw4iF5lpArTILH3n00iSTyb+IBaftVVI2VIWKJENAAAQAElEQVRFYX7NYm7z26a9aPvah1i4bBMO/X1WXO/8tGor6HqHyq9c3rAmFuWh9VvoYROtt7J45RbpGmkslq7eBnrzIb0ViGxYPImA5/eFHSqef4xNPZTJMueIkJvZ09SG6V8OAT3N7//+N3a/rUQmM9RJw7b7dGuD46cvguZrdhvwOcg5Q0O6J40eIIaEmxoq7dDNJF1U06gYuqEc0reLeCKUPyRISuU/JmA/AeN5LJPJ7Mokk1na0blH84UnTv8FdNNnLEQms7Qz6lNv+73WTlyc0CKfrw0aLxboXPDrRgzs0xEfDe5pYU5ORprTTE5EWiCuddM6oLVXqle2fLuWRSaOMIFsIiCTPT2nyVlCjjyaZz9kzHRoUl4Vm1FVMpmhjGrPlAGtMURvQqE1ieg7nxY+VKmU4rs89TQe+j0Y/r+ZmLv0D8TEJWD8R31Bvw00YiajOjmdCZgTkMkM56C5Lr19428E2Th6rSNXGC6nM3utw9/5RJ0lOwjIZJk771PX2bheNXz83msgxzYtom9Ml8kyKteQTiNLaEocXbf3e/9r0PXO15JDvXnDGmKqJ333G8uk35VBn0wFjcBdtmYnihTKL2xoWrTRJle3XDkTyCQBwy9AJjOxuXsSoBvDXaumZth4em0ZrZ3SoHYVC9uQfIHYuuwbHN08F4XD84MW2iSxMDKLUBn0dJ9UtJDsKOnmcd/aGTiwfpaYz0xbas/LrRuSiRBj3ZSXhOxXzx8HmrPJzhSBiINMEqDzmM4lekNDRlnJjj4n5nYK6UKZznNKowsO4zlaqVwJczPTfv/X2oFsKR8padX7P36aiGNbfsDmXybjz9Xf498/f8R7fTuDRreQDQnlMQp9xmiNo89G9AFdoFA6CxPILIFlsz8TjomM8r3epZU4Z319vCxMyZFH5+TPM8eI6Q30OSCxMDKLkK3xO7+s9CTz68/exeldP4q1suh8ps8AtYnKNWYz1k15Seg3gdZaocUR6ebWaMdbJmAvgSHSAxg6jzKyN36X02+EuW3qax3jOWpuY75Pa7As+PZDobL3WoeM6Xw3Cn/nExEWRwlQPvr+pvPJ/Jqa9LaEvsfpuzh1Wu+urcVvwfC3u4okKu+1zi3Ffuqg5rPlhW2tquVFEo0sWTR1FE7sWAj6/NH9Au3T7wCt0SKMpIDqpnKNQnaka1yP106R8PCfmxJgh4qbHjh3bjZNC6K5zLR1535w25lAZgjQxQ49lS8YFszT1jIDjm3dmgC9rYHm6NMaQ/QZcOvOcOOZQCYI0DUOX+vYBMZKDyZAU4LIgUIj2mnfg7vKXWMCJgLsUDGh4B0mwASYABNgAkyACTABJmBOgPeZABNgAkyACaRNgB0qabPhFCbABJgAE2ACTIAJuBcBbi0TYAJMgAkwASbgNALsUHEaaq6ICTABJsAEmAATSE2A40yACTABJsAEmAATcFcC7FBx1yPH7WYCTIAJMIHcIMB1MgEmwASYABNgAkyACTABQYAdKgIDB0yACTABTyXA/WICTIAJMAEmwASYABNgAkwgJwiwQyUnqHKZTIAJOE6AczIBJsAEmAATYAJMgAkwASbABNyAADtU3OAgcRNdmwC3jgkwASbABJgAE2ACTIAJMAEmwATyHgF2qOS9Y849ZgJMgAkwASbABJgAE2ACTIAJMAEmwASySMANHCpZ7CFnZwJMgAkwASbABJgAE2ACTIAJMAEmwATcgIB7NZEdKu51vLi1TIAJMAEmwASYABNgAkyACTABJuAqBLgdeZoAO1Ty9OHnzjMBJsAEmAATYAJMgAkwASaQlwhwX5kAE8g+AuxQyT6WXBITYAJMgAkwASbABJgAE2AC2UuAS2MCTIAJuCwBdqi47KHhhjEBJsAEmAATYAJMgAm4HwFuMRNgAkyACeQVAuxQyStHmvvJBJgAE2ACTIAJMAFbBFjHBJgAE2ACTIAJOESAHSoOYeNMTIAJMAEmwASYQG4R4HqZABNgAkyACTABJuAKBNih4gpHgdvABJgAE2ACnkyA+8YEmAATYAJMgAkwASbggQTYoeKBB5W7xASYABPIGgHOzQSYABNgAkyACTABJsAEmEBGBNihkhEhTmcCTMD1CXALmQATYAJMgAkwASbABJgAE2ACTibADhUnA+fqmAARYGECTIAJMAEmwASYABNgAkyACTAB9ybADhX3Pn7Oaj3XwwSYABNgAkyACTABJsAEmAATYAJMgAmYEfBQh4pZD3mXCTABJsAEmAATYAJMgAkwASbABJgAE/BQArnXLXao5B57rpkJMAEmwASYABNgAkyACTABJsAE8hoB7q/HEGCHisccSu7I/9m7D8Amyr8P4N/L6l6Mlj0EUVQ2CqgM2Q5EFBVUHKD4CiJu/bsVF+4FbkVRQUFBQQUZgoiAgGyRvVeB0j0y33ueNKFp0jZtkzTjW3gud88994zPpWnyuxEKUIACFKAABShAAQpQgAIUoAAFAiXAgEqgpN3bYQ4FKEABClCAAhSgAAUoQAEKUIACISpQiYBKiI6Q3aYABShAAQpQgAIUoAAFKEABClCgEgIs6o0AAyreKLEMBShAAQpQgAIUoAAFKEABCgSvAHtGgRoQYEClBtDZJAUoQAEKUIACFKAABSgQ2QIcPQUoEPoCDKiE/j7kCChAAQpQgAIUoAAFKOBvAdZPAQpQgAKlBBhQKQXCRQpQgAIUoAAFKECBcBDgGChAAQpQgAL+FWBAxb++rJ0CFKAABShAAQp4J8BSFKAABShAAQqElAADKiG1u9hZClCAAhSgQPAIsCcUoAAFKEABClAgkgUYUInkvc+xU4ACFIgsAY6WAhSgAAUoQAEKUIACPhNgQMVnlKyIAhSggK8FWB8FKEABClCAAhSgAAUoEKwCDKgE655hvygQigLsMwUoQAEKUIACFKAABShAgQgRYEAlQnY0h+lZgLkUoAAFKEABClCAAhSgAAUoQIGqCDCgUhW1mtuGLVOAAhSgAAUoQAEKUIACFKAABSgQBAJ+DqgEwQjZBQpQgAIUoAAFKEABClCAAhSgAAX8LBB51TOgEnn7nCOmAAUoQAEKUIACFKAABShAAQpQoJoCDKhUE5CbU4ACFKAABShAAQpQgAIUCIQA26AABYJLgAGV4Nof7A0FKEABClCAAhSgAAXCRYDjoAAFKBDWAgyohPXu5eAoQAEKUIACFKAABbwXYEkKUIACFKCA9wIMqHhvxZIUoAAFKEABClAguATYGwpQgAIUoAAFakyAAZUao2fDFKAABShAgcgT4IgpQAEKUIACFKBAuAgwoBIue5LjoAAFKEABfwiwTgpQgAIUoAAFKEABCngUYEDFIwszKUABCoSqAPtNAQpQgAIUoAAFKEABCgRCgAGVQCizDQpQoGwBrqEABShAAQpQgAIUoAAFKBCCAgyohOBOY5drVoCtU4ACFKAABShAAQpQgAIUoAAFGFAJ/+cAR0gBClCAAhSgAAUoQAEKUIACFKCAjwWCMKDi4xGyOgpQgAIUoAAFKEABClCAAhSgAAWCUCC0u8SASmjvP/aeAhSgAAUoQAEKUIACFKAABQIlwHYoUEKAAZUSGJylAAUoQAEKUIACFKAABSgQTgIcCwUo4D8BBlT8Z8uaKUABClCAAhSgAAUoQIHKCbA0BShAgZARYEAlZHYVO0oBClCAAhSgAAUoEHwC7BEFKEABCkSqAAMqkbrnOW4KUIACFKAABSJTgKOmAAUoQAEKUMAnAgyo+ISRlVCAAhSgAAUo4C8B1ksBClCAAhSgAAWCUYABlWDcK+wTBShAAQqEsgD7TgEKUIACFKAABSgQAQIMqETATuYQKUABCpQvwLUUoAAFKEABClCAAhSgQGUFGFCprBjLU4ACNS/AHlCAAhSgAAUoQAEKUIACFKhhAQZUangHsPnIEOAoKUABClCAAhSgAAUoQAEKUCC8BBhQCa/96avRsB4KUIACFKAABShAAQpQgAIUoAAFyhEIk4BKOSPkKgpQgAIUoAAFKEABClCAAhSgAAXCRCB4hsGASvDsC/aEAhSgAAUoQAEKUIACFKAABcJNgOMJWwEGVMJ213JgFKAABShAAQpQgAIUoAAFKi/ALShAAe8EGFDxzomlKEABClCAAhSgAAUoQIHgFGCvKEABCtSIAAMqNcLORilAAQpQgAIUoAAFIleAI6cABShAgXAQYEAlHPYix0ABClCAAhSgAAX8KcC6KUABClCAAhRwE2BAxY2EGRSgAAUoQAEKhLoA+08BClCAAhSgAAX8LcCAir+FWT8FKEABClCgYgGWoAAFKEABClCAAhQIMQEGVEJsh7G7FKAABYJDgL2gAAUoQAEKUIACFKBAZAswoBLZ+5+jp0DkCHCkFKAABShAAQpQgAIUoAAFfCjAgIoPMVkVBXwpwLooQAEKUIACFKAABShAAQpQIHgFGFAJ3n0Taj1jfylAAQpQgAIUoAAFKEABClCAAhEjEMEBlYjZxxwoBShAAQpQgAIUoAAFKEABClAgggX8M3QGVPzjylopQAEKUIACFKAABShAAQpQgAJVE+BWISHAgEpI7CZ2kgIUoAAFKEABClCAAhSgQPAKsGcUiEQBBlQica9zzBSgAAUoQAEKUIACFIhsAY6eAhSgQLUFGFCpNiEroAAFKEABClCAAhSggL8FWD8FKEABCgSbAAMqwbZH2B8KUIACFKAABSgQDgIcAwUoQAEKUCDMBRhQCfMdzOFRgAIUoAAFKOCdAEtRgAIUoAAFKECByggwoFIZLZalAAUoQAEKBI8Ae0IBClCAAhSgAAUoUIMCDKjUID6bpgAFKBBZAhwtBShAAQpQgAIUoAAFwkeAAZXw2ZccCQUo4GsB1kcBClCAAhSgAAUoQAEKUKAMAQZUyoBhNgVCUYB9pgAFKEABClCAAhSgAAUoQIHACDCgEhhntuJZgLkUoAAFKEABClCAAhSgAAUoQIGQFGBApVK7jYUpQAEKUIACFKAABShAAQpQgAIUCH+BikfIgErFRixBAQpQgAIUoAAFKEABClCAAhQIbgH2LuACDKgEnJwNUoACFKAABShAAQpQgAIUoAAFKBDqAgyohPoeZP8pQAEKUIACFKAABShAgUAIsA0KUIACLgIMqLhwcIECFKAABShAAQpQgALhIsBxUIACFKCAPwUYUPGnLuumAAUoQAEKUIACFPBegCUpQAEKUIACISTAgEoI7Sx2lQIUoAAFKECB4BJgbyhAAQpQgAIUiFwBBlQid99z5BSgAAUoEHkCHDEFKEABClCAAhSggI8EGFDxESSroQAFKEABfwiwTgpQgAIUoAAFKEABCgSnAAMqwblf2CsKUCBUBdhvClCAAhSgAAUoQAEKUCAiBBhQiYjdzEFSoGwBrqEABShAAQpQgAIUoAAFKECBygswoFJ5M25RswJsnQIUoAAFKEABClCAAhSgAAUoUOMCDKj4fRewAQpQgAIUO6jkpgAAEABJREFUoAAFKEABClCAAhSgAAXCTcA9oBJuI+R4KEABClCAAhSgAAUoQAEKUIACFHAXYE61BBhQqRYfN6YABShAAQpQgAIUoAAFKECBQAmwHQoEkwADKsG0N9gXClCAAhSgAAUoQAEKUCCcBDgWClAgjAUYUAnjncuhUYACFKAABShAAQpQoHICLE0BClCAAt4KMKDirRTLUYACFKAABShAAQoEnwB7RAEKUIACFKghAQZUagiezVKAAhSgAAUoEJkCHDUFKEABClCAAuEhwIBKeOxHjoICFKAABSjgLwHWSwEKUIACFKAABSjgQYABFQ8ozKIABShAgVAWYN8pQAEKUIACFKAABSjgfwEGVPxvzBYoQAEKlC/AtRSgAAUoQAEKUIACFKBAyAkwoBJyu4wdpkDNC7AHFKAABShAAQpQgAIUoAAFIl2AAZVIfwZExvg5SgpQgAIUoAAFKEABClCAAhSggE8FGFDxKaevKmM9FKAABShAAQpQgAIUoAAFKEABCgSzgG8CKsE8QvaNAhSgAAUoQAEKUIACFKAABShAAd8IsBanAAMqTgrOUIACFKAABShAAQpQgAIUoEC4CXA8FPCXAAMq/pJlvRSgAAUoQAEKUIACFKAABSovwC0oQIEQEWBAJUR2FLtJAQpQgAIUoAAFKECB4BRgryhAAQpEpgADKpG53zlqClCAAhSgAAUoELkCHDkFKEABClDABwIMqPgAkVVQgAIUoAAFKEABfwqwbgpQgAIUoAAFgk+AAZXg2yfsEQUoQAEKUCDUBdh/ClCAAhSgAAUoEPYCDKiE/S7mAClAAQpQoGIBlqAABShAAQpQgAIUoEDlBBhQqZwXS1OAAhQIDgH2ggIUoAAFKEABClCAAhSoUQEGVGqUn41TIHIEOFIKUIACFKAABShAAQpQgALhJMCASjjtTY7FlwKsiwIUoAAFKEABClCAAhSgAAUoUKYAAypl0oTaCvaXAhSgAAUoQAEKUIACFKAABShAgUAJ1FxAJVAjZDsUoAAFKEABClCAAhSgAAUoQAEK1JxAmLbMgEqY7lgOiwIUoAAFKEABClCAAhSgAAWqJsCtKOCNAAMq3iixDAUoQAEKUIACFKAABShAgeAVYM8oQIEaEGBApQbQ2SQFKEABClCAAhSgAAUiW4CjpwAFKBD6AgyohP4+5AgoQAEKUIACFKAABfwtwPopQAEKUIACpQQYUCkFwkUKUIACFKAABSgQDgIcAwUoQAEKUIAC/hVgQMW/vqydAhSgAAUoQAHvBFiKAhSgAAUoQAEKhJQAAyohtbvYWQpQgAIUCB4B9oQCFKAABShAAQpQIJIFGFCJ5L3PsVOAApElwNFSgAIUoAAFKEABClCAAj4TYEDFZ5SsiAIU8LUA66MABShAAQpQgAIUoAAFKBCsAgyoBOueYb9CUYB9pgAFKEABClCAAhSgAAUoQIEIEWBAJUJ2tOdhMpcCFKAABShAAQpQgAIUoAAFKECBqgiEVkClKiPkNhSgAAUoQAEKUIACFKAABShAAQqElkAI9JYBlRDYSewiBShAAQpQgAIUoAAFKEABCgS3AHsXeQIMqETePueIKUABClCAAhSgAAUoQAEKUIACFKimAAMq1QTk5hSgAAUoQAEKUIACFKBAIATYBgUoQIHgEmBAJbj2B3tDAQpQgAIUoAAFKBAuAhwHBShAAQqEtQADKmG9ezk4ClCAAhSgAAUo4L0AS1KAAhSgAAUo4L0AAyreW7EkBShAAQpQgALBJcDeUIACFKAABShAgRoTYEClxujZMAUoQAEKRJ4AR0wBClCAAhSgAAUoEC4CDKiEy57kOChAAQr4Q4B1UoACFKAABShAAQpQgAIeBRhQ8cjCTApQIFQF2G8KUIACFKAABShAAQpQgAKBEGBAJRDKbIMCZQtwDQUoQAEKUIACFKAABShAAQqEoAADKiG402q2y2ydAhSgAAUoQAEKUIACFKAABShAgfAPqHAfU4ACFKAABShAAQpQgAIUoAAFKBD+AgEeIQMqAQZncxSgAAUoQAEKUIACFKAABShAASHAFNoCDKiE9v5j7ylAAQpQgAIUoAAFKEABCgRKgO1QgAIlBBhQKYHBWQpQgAIUoAAFKEABClAgnAQ4FgpQgAL+E2BAxX+2rJkCFKAABShAAQpQgAKVE2BpClCAAhQIGQEGVEJmV7GjFKAABShAAQpQIPgE2CMKUIACFKBApAowoBKpe57jpgAFKEABCkSmAEdNAQpQgAIUoAAFfCLAgIpPGFkJBShAAQpQwF8CrJcCFKAABShAAQpQIBgFGFAJxr3CPlGAAhQIZQH2nQIUoAAFKEABClCAAhEgwIBKBOxkDpECFChfgGspQAEKUIACFKAABShAAQpUVoABlcqKsTwFal6APaAABShAAQpQgAIUoAAFKECBGhZgQKWGd0BkNM9RUoACFKAABShAAQpQgAIUoAAFwkuAARVP+5N5FKAABShAAQpQgAIUoAAFKEABCoS/QDVGyIBKNfC4KQUoQAEKUIACFKAABShAAQpQIJACbCt4BBhQCZ59wZ5QgAIUoAAFKEABClCAAhQINwGOhwJhK8CAStjuWg6MAhSgAAUoQAEKUIACFKi8ALegAAUo4J0AAyreObEUBShAAQpQgAIUoAAFglOAvaIABShAgRoRYEClRtjZKAUoQAEKUIACFIhcAY6cAhSgAAUoEA4CDKiEw17kGChAAQpQgAIU8KcA66YABShAAQpQgAJuAgyouJEwgwIUoAAFKBDqAuw/BShAAQpQgAIUoIC/BRhQ8bcw66cABShAgYoFWIICFKAABShAAQpQgAIhJsCASojtMHaXAhQIDgH2ggIUoAAFKEABClCAAhSIbAEGVCJ7/3P0kSPAkVKAAhSgAAUoQAEKUIACFKCADwUYUPEhJqvypQDrogAFKEABClCAAhSgAAUoQAEKBK8AAyq+2jeshwIUoAAFKEABClCAAhSgAAUoQIHwFygeIQMqxRB8oAAFKEABClCAAhSgAAUoQAEKhKMAx+QfAQZU/OPKWilAAQpQgAIUoAAFKEABClCgagLcigIhIcCASkjsJnaSAhSgAAUoQAEKUIACFAheAfaMAhSIRAEGVCJxr3PMFKAABShAAQpQgAKRLcDRU4ACFKBAtQUYUKk2ISugAAUoQAEKUIACFPC3AOunAAUoQAEKBJsAAyrBtkfYHwpQgAIUoAAFwkGAY6AABShAAQpQIMwFGFAJ8x3M4VGAAhSgAAW8E2ApClCAAhSgAAUoQIHKCDCgUhktlqUABShAgeARYE8oQAEKUIACFKAABShQgwIMqNQgPpumAAUiS4CjpQAFKEABClCAAhSgAAXCR4ABlfDZlxwJBXwtwPooQAEKUIACFKAABShAAQpQoAwBBlTKgGF2KAqwzxSgAAUoQAEKUIACFKAABShAgcAIMKASGGfPrTCXAhSgAAUoQAEKUIACFKAABShAgZAUqFRAJSRHyE5TgAIUoAAFKEABClCAAhSgAAUoUCkBFq5YgAGVio1YggIUoAAFKEABClCAAhSgAAWCW4C9o0DABRhQCTg5G6QABShAAQpQgAIUoAAFKEABClAg1AUYUAn1Pcj+U4ACFKAABShAAQpQIBACbIMCFKAABVwEGFBx4eACBShAAQpQgAIUoEC4CHAcFKAABShAAX8KMKDiT13WTQEKUIACFKAABbwXYEkKUIACFKAABUJIgAGVENpZ7CoFKEABClAguATYGwpQgAIUoAAFKBC5AgyoRO6+58gpQAEKRJ4AR0wBClCAAhSgAAUoQAEfCTCg4iNIVkMBClDAHwKskwIUoAAFKEABClCAAhQITgEGVIJzv7BXFAhVAfabAhSgAAUoQAEKUIACFKBARAgwoBIRu5mDLFuAayhAAQpQgAIUoAAFKEABClCAApUXYECl8mY1uwVbpwAFKEABClCAAhSgAAUoQAEKUKDGBfweUKnxEQagAxk5Rhw7VRiUqdBoQTD370RWEYxma1DaOfZpfpEFmblV28d5heYAPANrpokC1cVhFGyPYn+J/RZs/SrZH6PJCvH8L5kXTPPidUO8flSlT2JcNfOsDEyrVTEJ1DYWqy2oX0+z80wQr4uB8qhKOyaLDcczq/Y3Xfw9C8yzMPCtZKn7riqegdhGPKeCuX/p6ntEs/q8CoRFVdvIKTAjJ99UpdcP8Xsd+GdkYFoUv9NVNfX3diezi1CkvpfwdzvVqV+8VzwVxJ+TjqufQ0zV+BwSmGdh6LUSaT1mQMUHe9yqvoEVb2KDMSmKApvNhmDsm+iTVe2boihB2z/RR0V9jtjUJOYrm9SnhrpleP6vqkllDatSXvRN7LeqbBuobRRNcP9uitcNRan672Z4PuvtowrUc6Qq7WjV51VVtgvUNuJ3UygGqr2qtKMSQv3si6psC8cAxSDDLIm/11UxCcQ2glrQB6KtKrWhvtcJ9t9NYShSVcYnnhti23BM6q6r0mtBVRwru414jylfr9SZym4bqPLq2wj5shio9irbjvgMp9EoVd7H4fic55gqL8CASuXNuAUFKEABClCAAhSgAAUoQIEaEGCTFKBAMAkwoBJMe4N9oQAFKEABClCAAhSgQDgJcCwUoAAFwliAAZUw3rkcGgUoQAEKUIACFKBA5QRYmgIUoAAFKOCtAAMq3kqxHAUoQAEKUIACFAg+AfaIAhSgAAUoQIEaEmBApYbg2SwFKEABClAgMgU4agpQgAIUoAAFKBAeAgyohMd+5CgoQAEKUMBfAqyXAhSgAAUoQAEKUIACHgQYUPGAwiwKUIACoSzAvlOAAhSgAAUoQAEKUIAC/hdgQMX/xmyBAhQoX4BrKUABClCAAhSgAAUoQAEKhJwAAyoht8vY4ZoXYA8oQAEKUIACFKAABShAAQpQINIFGFCJhGcAx0gBClCAAhSgAAUoQAEKUIACFKCATwWCMqDi0xGyMgpQgAIUoAAFKEABClCAAhSgAAWCUiCUO8WASijvPfadAhSgAAUoQAEKUIACFKAABQIpwLYo4BRgQMVJwRkKUIACFKAABShAAQpQIBwFTCYzDh09AaPR5DI8q9WGo8czYLZYXPLDa4GjoQAF/CXAgIq/ZFkvBcJAgG8ywmAncggUoAAFKECBUBPwYX/37D+CEeNeRPt+t6P/sAfxw6/LnLUvXbEBXS6/C32uvR/t+ozCd3OWONdxhgIUoIA3AgyoeKPEMhSIQAG+yYjAnc4hU4ACFKBAlQS4UXAKHDt+Clfc/D+k1U3B1Hcfw5p5H2FAr/NlZwsKjXjwufdx98gh2LDoU7w9YRyefX0KDh45LtdzQgEKUMAbAQZUvFFiGQpEmADfZETYDudwKUCBSBPgeCkQEQJffDcPtZIT8PLjo9GxTSvERBuQkpQgx/73uq3ILyjE8MG9odNq0bd7JzRtlIalK9bL9ZxQgAIU8EaAARVvlKpZ5tDLUXAAABAASURBVOj6o5g5bGaZadlLp089rGZT3JwCPhHgmwyfMLISClDAZwKsiAIUoEDlBf78exMapNXBg8++j+vvfBbPvDZF3i9F1HTsxCkZQDEY9GJRphZNG+Bo+ik5zwkFKEABbwQYUPFGqZplCjMLcXDVwTLTif9OVLMFbk4B3wrwTYZvPVlbBApwyBSgAAUoUOMCu/YdRlxsNPpc3BEjh1+Kzdv2YOR9EyFuUJudk4fYmGiXPkZFGZCTm+/M0+sU1EowBGVKjNVDp9UEZd8cZgadBvExuqDtY3K8ARpN1fex84nCmYgWYEAlALs/rV0arpk2VKaLH71YtpjW1j1PruCEAkEg4M2bjGi9BnUSo2o8mQ/8gN0zL8bmj+rKR7GcEKNHVJD0rywjnfoHXPwhL2t9oPNLt5cYp4deV7V9nKRuGwRP4+DuwomD0H31NAwTroZ+4g3Q/vQuUHT6TXxwd569o0AABbJPQvfdROhfvE4mMQ81L4A9YFMhLnDj1f0wqP+FGNDrArz65P9h38Fj2L3/CBIT4uQlPyWHV1RkREJ8rDPLagXyiywBTXlrlyL3lTuR+0B/5L54G/L++Mlj+0UmCyxqBwPdv8q0Z7baYDRZPfa/MvX4q2xBkRk2tY9Vrd/5ROFMRAswoBKA3R+TEoPGXRvJVOfsOrLFknl1W9eVeZxQIFgEvHmTISL6BjVoUUMJol1j5lZs+ekm5Bz7BxZTnnwUy4UZ/8ojDqJMsCZFgQxYBGv/9OpRLzXmI50r20cRiAmW53Kw9kM34xVo/v0LKMiBknUc2pU/QbtkWrB2l/2iQI0J6H79CJr1C6HknpJJzOvmvFdj/WHDoSXQ+sym2H/omLPTVjUAIRaMJjPS6qTI4IpJnRd5Im3ffRD1UlPErEwW9cN2odGCQKWiY4dgm/IUcOA/wFgEHNkN24zXYdy5ya0PRWqgwmaDW36g+upNO1bVz2i2Bm0fpaG6p70Zi6cy6qb8TwFoaEABCgRCILTa8OZNRqH6h/xEdhFqMu3b8qtH2EP/zYP4I1mTfauobXHUJjPXWKN+5fUxO88Ek/omqLwyZa3LUsflcccw0y6gBlE0B7ba50tMle2rSyxxlgIUEALKrnXiwSVpPOS5FOACBYoFLuvTBZ9N/wWHjp5AVk4eps5cIG9S27JZQ5zf/mxZatrsRTBbLFi4bK38hp+e3drL/JqYaHZv8NissmONx3xmUoACNS/AgErN74Pg7AF7FdEC3rzJkEcd1KCKsQaT2aIemvGwpyxWIBj6V56NOKokAhbllanJdSYVUT2wJE/VrWw/TGXsFw+7ilkUoECECyh7N0O76MsyEyymCBfi8KsjcNPV/dCl4znoP+xBXDhoLP5YtQGTXrxXfttPbEwU3n1hPCZOmoZ2fUZh/JPv4ol7R6BRfZ45Xh1zbkuBSBMIm4BKpO04jpcC/hQIlTcZCU16e2RIbtbHYz4zKRAUAjEJsDZu7dYVW6vz3fKYQYFwF9Ds26QGU6aWmZBQ243A2qKDWx4zKOBJQHyDz2tP3YUVcydj4bevY9F3b6DtOS2cRXtf1AEbF32G36a/hvULPsHwq2r2/YP1jHbOvpWcsZ3ZueQi5ylAAQDBgsCASrDsCfaDAkEmEGxvMjzxRNc6B036fY6Yuh2g0cfJR7lc+xxPxZlHASkgzl46kZElT/+WGR4mYn1BodHDGt9kma99GNZzLgRiEmBLqgtL1yth6TXcN5WzFgqEkIC1aRtY+oyQyREosTY7Ty7L/N43wdq+L2zxKTKJefOgu0NohOxqMAgkxseiflptKIri1h2tVoOG9epAr9e5rQt0hi2lHsw3PQNbo7NgM0TBVu8MmIfcB2sTvq8J9L7wQ3usMkwFGFAJ0x3LYVHAFwLB9CajrPGktLoera5bjjajj8tHsVxWWeZTYMWaLehy+V3oefV4efr3rfe+LL9G0yEjbl542U2PyPWdB47Gk698BpPZ4ljtu8c6jdQ3zc/C+OQPMD3yDSxXjgOiYn1XP2uiQIgI2GTw5GY1gHIzbC3tZ57YZJDFnmdt2wvm6x6B6bHvZBLzSKwdIqNjNylQeQHrORfBNOY9mJ6ZC9M9H8J6/mWVr8QnW7ASClDAGwEGVLxRYhkKUIACFAgLAUWj4Kn7bsZfcyZh8Yw3ERcbjUmfz3aO7fm3puLslk2wZt5HmPvlS5j3+9+Yt3iVcz1nKEABClAgSAWCrFtKbg5iP3wPSXfcjISHxiNq4fwg6yG7QwEK+EKAAZVSiidPZUOkUtnIyc3Hqayc0tlcpgAFKECBEBLo2vEcDOp/IZIS4pBWNwUDe12AP1ZukN/wIL4BYvnqzRgxtL+8YWHzJvVx1cCL8NtSfvsO+EOBGhJQTh2DcmhHDbXu32ZZe3gLJI29A/EvT5CBlJiZ02VgJfqnWeE9aI6OAhEowICKutPF9fQffz0X3a8ahx5D7sHAGx5Wc+3/8wsKMe7xt9H1ijG4ePA4DB8zAeLaevtaTilAAQpQIJQFlq/ZjNZnNoVOq8WJk5lyKCW/4aFJwzQcSc+Q+ZxQIMIFAjv87JPQvzcG+ldvgn7SGBieGwLN+sWB7QNbo0AVBTTpx2D443e3raPmMKDihsIMCoS4AAMq6g5886MZ+HLGfPzfzYOxbPa7mPPli2qu/f83sxZh++6D+H3mW1g5dzK0Gg3e/uR7+0pOKUABClAgKAQsFitEYLystHv/Ebd+zvntL4j0wJ3XyXXZufnyUXwrhJxRJ1FRBmRkZqtzp/8nxekRrEn0Mlj7JvoVbdDCoNcEyK9q+0mjKEiIrdq24I/PBHR/fAvlcIkzUwpzofvpHZ/Vz4oo4E8B7e6dHqvXHDroMZ+ZFKBA6ApoQrfrvun5cfWI5GfTf8H96hvqG6/ui1rJCahXt5azcnH9/NAreiK1TjIS4mMxYmg//PDLH7DZbM4ynKEABShAgZoVsMGmBj5yykwmk9mlg+LSnkdf/AhP338LunU+V64T3wIhZkqWLSoyqn8XEkW2M5ktNvgl+aBe0clg7Zvol/jbabMiaP1kH9XnkgjQifnKJuHP5BsB5cBW94oK86Ac3++ezxwKBJmA+bx2sCW4/u0QXTRd1EM8MFGAAmEkEPEBlY3/7pa7c/N/ezBi3Iu4/cFX8dNvy2WemOw7eAzilG8xL1LjBqniAY4jmXKBEwpQgAIRKBBMQxaX7DwydjjKSme1aOzs7vwlf2P0Q6/h+UdG4borL3Hm16mdLOcPHE6Xj2Ky98BR1E+tJWadKa/QjGBNopPB2jfRryKTFSaLNWj9RB/F8ZL8IkuV+ij8mXwjYItP8VhRWfkeCzOTAjUkYIuPR86EibAlJjl7YDqvLfLvuMu5zBkKUCA8BMI6oDLnt7/KPAVcHJ0Uu/Docfu18XVqJ+G26weiU9tW+N+LH+PnRSvlWSjiHirRUQZRVKYog14+5ucXykcxSYjRISXe4FWKj7Fvr9cqXpX3tt6yyol2RJtlra/p/MRYPbSawFhUdazi9PS4aO/3ccl2dOp+Fs8RpqAQYCcogB/nL8f9z0zGo3ffgAs6tMahoydkEq/14ka14myVqTMXoKDQiD37j6gB9r/Qv+f5lKMABQIsYG17iVuL1jM7AzEJbvm+zCg6tQMnNn8iU1HmLl9WzboiTKBw8NU4vmE7Ts1ZgJPL1shHa2pahClwuBQIf4GwDqiIs0gyMnM8ngIu3jw7dm+Lpg1w182D0fvijvJRfAPEgqVroCgKYmOiUWQ0OYo652Njo515RvVoW6HJAm+S0WyR21ls8Kq8N3WWV0a0Y1LbLK9MTa4zmq0ycFWTfaiobYuKaFL7WVE5T+vFDY/lDq/ShBtRgAK+Ftjwr/0D0svvfYP+wx50pvlL7N/k8/g9N2HLtj3oPHA0rrj5f2owpTMGXnKBr7vB+ihAgQoErO0ugXnEc7B07A/r2d1gufQOmG94soKtqrc6Y9s3+O+bdji09B6Z/vu6DTJ3zKxepdw64gXEmSmWRqfPkox4EAJQIMwEwjqgIu6JUtbp3/16qEc51J0pvs1h177DMKlBB3VR/jer8yazWc43bZSG/YeOyXkxcZwK7rjWXuQVGa0oKLJ4lYwmq9gE4oO2t9tUp5xop0htszp1eL2tlwYl6ys0WmBVg0sl84Jt3qJ20KgGVKrSL3VTub85oQAFgkPgqftuxpYlU9zSkEu7yw42b1If86e9isUz3sTfv3yAFx69HXq9Tq7jhAIUCKyAtbUaSBn6EMw3PwdL9+uAqFi/diB9zatu9R/753W3PGZQgAIUoAAFHAJhHVBxDLKsR5Hfoc2Z8iyUD7/8CRaLFeLo5a+LV+Gi89uI1RjQ63zMmLME6ScykZtXAHEq+NWX9ZBnr8gCnFCAAhSgQNgJpNVNQVyJMxHDboAcEAUo4CJgNeaiKGunS55YMGXtgc1mP7tYLDNRgAIUoEDoCvij5xEfUBFnmrwzYRy+mDEfbfuMxA1jJuCGIX1w3ZW9pPcNQ/rijKYNcMnQe9Hl8rtgMpkxbuTVch0nFKAABShAAQpQgAKhL6AxxCMqqaXbQPRJzdWDaFq3fGZQgAIUCIAAmwgBgYgPqIh9JG5CuGLuJPw2/TWs/vUDPD5+BHRa+x9PcYTy/Zfvw19zJmHpD2/j2w+fRmqdZLEZEwUoECICOfsX4ujqF8pMeUdWhshI2E0KUIACFPCXQGrnh9yqTuv4gFseMyhAgbIEmE+ByBNgQKV4n4sASsN6deTlP8VZLg/i2x/q1EpyyeMCBSgQGgI5Bxbh2N8vlJnyjzKgEhp7kr2kAAUo4D+BWmfdgLNv2ICGPd+R6ewbNyH5zKH+a5A117wAe0ABClCgmgIMqFQTsDKbmwrMOLz2sNwk/2S+fOSEAhTwv0BC4z5Iu+BxmWLrdZENxrvkdZV5nFCAAhSgQGQLRKWciTrn3S5TVHKLoMNghyhAAQpQILgEGFAJ0P4oyinC1AFfYtU7q2SL6ZvSMf2ab+U8JxSggH8FEpr0Rb3zH5cprl432Vhi4xJ59RlQkSicUIACFPCtQJm1ffTxZAwbfhUGDOiB++4fiy3/bi6zLFdQgAIUoAAFglWAAZUA7Zltc7Yj+0C2S2tH/zmCgysPuuRxgQIUoAAFKECBmhJgu4EQ+OmnHzB16mc4dOgg8gvysWbNKjz66H2BaJptUIACFKAABXwqwICKTznLruz45nSPK0/uyvCYz0wKUIACFKBAhQIsQIEQEvjrr2UYd89ofPzJZNhsNpd06lQG/lrxZwiNhl2lAAUoQAEKAAyoBOhZULtVbY8tpZ5d12M+MylAAQqEowDHRAEKRK7AyZMnsH79P8jMzISiKG4pNycncnE4cgpQgAIUCEkBBlQCtNvqdayH+HrxLq3V61gf9TvVd+ZpNv0B3XcTofvqGWiW/wAYi5zrOEMBCtSIABulAAUoQAEfCXTrdjHefvsDXH/djfLsFFGkXgqMAAAQAElEQVSt40yVuLh4dLnAfo8rkc9EAQpQgAIUCAUBBlT8vJeO/HMEn178KaYPno6cI7kwJBhki6ltUjHs++vlvJhoVs2FbtoEaNYvhObf5dD9/D7079wBw2P9ykyaxV9BObZXbM5EgWKB4Ho4vOYwDqw8WGYKrt6yNxSgAAUo4E+BOnXqomOHzrj77vsggiuiLZ1Oh/PP74rXX38PScnJIouJAhSgAAUoEDICmpDpaYh2dOmEpcg5ZD+FVVEAY45RjiS2dqx8dEy0/8x3zDoflYwjznlPM7oFU6B/Ww26vHQ9lD0bPRUJ/jz2MKwF5t41F98Pn1lmykvPC+vxc3AUoAAFKOBZoEf3XnLFZZddiTffmIRzzzlPLnNCAQpQgAIUCCUBBlQqubcqU9xqseHYxmNebaIcP+CxnOm+T2F8cQGs518m15uvuhfWs7vIeYgIjZjLyYB27mQxx0SBoBKo37E+GnVpJJOjY+JSN0eeRs+XIIcLHylAAQpQgAIUoAAFKECB4BKoqDf8NFORUDXWa7QKYmrFeFWD9ayubuVsyamw1W3ilq8c3O6Wpzm6Byjk0X43GGbUqMCgDwdh6PShMsXVjZN9GfTBFXJZ5MekePf7ITfkhAIUoAAFwk5A3Kj2tddexMOP3Is1a/4Ou/FxQBSgAAUCLMDmAizAgIqfwc+55hyvWjD3uQm21GbOsra4JJgHj3cul5yxJdQquWifj1I/mEbbP7DaMzilAAUoQAEKUIACwS2wfPkf+PGnH7BixZ9Yu9YeULHarNXqdEH6OuQe+qPMZDHZL8WuViPcmAIU8JEAq6FAaAswoOLn/Xfxoxej38S+aHVFK5k6jurgucU6jWC692OYHvgSpnEfwPT4TNjOusBjWVvbnm751jb2a5HdVjCDAhSgAAUoQAEKhJBA5qlT1ertwaXjsWv2wDJT0akd1aqfG0e4AIdPAQpQoIQAAyolMPw1e+515+Gydy+TqWmP02eheGrPVrs+bPVbeFrlzLP0HA7zdY/C2q43rOd1h2XwPTAPude5njMUoAAFKEABClAgVAWysrOq1fWYuu0R37C7TBp9gqwrpm4HuSzytYZ4mRcpE46TAhSgAAX8J8CAiv9s/VqztX0fmK//H8w3PAVLl0HgDwUoQAEKUIACFKiOgNFowrHjp2Cz2apTTXW3Rb20etWqo1Gvd9DiqvkyRae0knU16vWuXBb5Ucn2PLmCEwpQgAIUoEA1BBhQqQYeN6UABShAAQpQINIFQmP8V97yGM7tdatLmjxltuy8CKBM/uJHdOh/B3pfex96DLkHG/7dJdf5e1KnbqqzCa1WK+djYmLlIycUoAAFKECBYBdgQMXPe2jXb7sw/appeO+c9/DVpV9h75K9XrWoXTYD+o8fcCbN1pVQDxnBPX+FV/WxEAXCQaDg5BYcWvYg9sy9Gkf+egLG7H3hMCyOgQKBFWBrEStwz6hr8MtXE53phiF9pcX6LTsx6fNZmPruY1i/4BNcNbA77nv6PVit/j9T5cJuF2P2rHn4+KMvceutd8j+cEIBClCAAhQIFQEGVPy4p7IPZGPOnXNwdMMxmAvMOPHfCaz7fF25LSrH9sogimbtfCh7NjoTcjMARYFy8pAzT6zXzvtElgd/KBAiAtbtCmyHlEr31pizHztnXIwTGycje988pK97Aztm9oDFmF3purhBaAmwtxSggG8E6tZOQtNGac6UnGS/l8jiP9ehW+dz0bFNK+j1OowY2l9e+rNt134E4qd27To4++xzoNPpwB8KUIACFKBAKAkwoOLHvXVw5YFK164U5smACfK9+5CopO+zl690S9yAAuULlHd6ePlbel5rK7LnF94ejfx+0SgYGgXrUe8DK1m7foLVUlyJvSo1UHkcuQeXFi8FzQM7QgEKUCAoBWbMXYonJn4KcanP/kPHnH08kn4SzRvXcy6n1kmW8+knMuWjLyZz5sxC9x6dnemVV1+Q1ZbM//DD92QeJxSgAAUoQIFQEWBAJUj3lGIsCNKesVvhJ1D2iMo6PbzsLU6v2fjVRvw85mf8Ov5X/Df7PyDn9DoxZ/1XA9PH3h+NLMrcJjZzS0WnPOe7FWQGBShAgTAUmPPbX/j467ke0/LVm50jHtDrfPTo2hYN6tXB4uXrcM3tT8MRVMnOyUN0VJSzrJiJjYlGbv7p9yIxBi0SYnRVTtHq9qJeb5JBp6lUO3q1fLTe8zZajT1wHxtVvf5XZ+zxqpsYd3Xq8Pe2wtxQhmFFbeu1GjE8JgpQgAIRKcBXQD/u9kZdG1e6du2sN+zbFJ1+E2PPUKc1fNd9tQfB8Z+9CJhAWaeHV9SBRY8twuInF2PHrzuw7adtmHffPJisJrfNbFu8fwmKb9TbbXuRkdC4j3hgogAFKBCRAtm5+cjIzPGY8gsKnSZjbxuCu24ejDG3DMa0yU8iIT4Gi5b9I9cnJsShyGiU846J2DY+NsaxaH9U1OBEVZO9Bu+majPiMmdvkyheZllHi1Xttw+2U6D+U9SO+KAu+KkOWS3UTsqZSj6qQ+N/ClCAApEq4P2nmUgVqsa4ExsnYtCHg1CvXRp06tGJOmfXQYfbOpRZo2b7aijH9pW5HuKPXOm16t+80llcpoCvBGaUcXq4qF889cSBP09p+9ztoohLMsM9oKLUtcHT9p7yap05BLVaXe9SZ1qnBxCX1sGtDseviqd6HOvEo6f1gcoTA6npPpQ3VtE30cfyypS1zrGt2J6JAhTwr8CNV/fFI2OHe0z9enT22Lher0PdWskoKLIHUeqn1sbeA0edZR2X+jgu/RErCowW5OSbqpwK1e3jTAlontNSpjhjgqjWYzKarJVqx2i2QtTvqX+W4hvr5heaK1Wnp7qqnFdgEt8rUHPte7HfilTzIpOlSn00Wawe9yMzKUABCkSCgKeASiSMO2BjbNG/BYbNHo67/70bN/16E5r1alZm28qRnYD4lIpK/NgqUZZFKaAK+OL0cLUaREdpkZoS45ZijFYU5RSJIi7JBvcna+2hnuvwVK/IO//66ej94El0HfU3+jySjfaXvebWviiXFGtATBn9i43WyX7Fx+g9biu2D0TSaxXUSoyu0T6UN86U+ChE6TVV6l9KvEEac0IBCgSHgLi0Z8p383D0eAZMZgvmLliBzdv2oEuH1rKDvS/uAHF50D+bdsj1X86cj7S6KTirRRO53heT3LV5GHh0MDpmdpVp4LHBaJzb3BdVsw4KUIACFKieALeuhoCmGttyUx8L2Oq3hIfPnD5uhdVFuoAvTg8XhgVFFhzNKHBLeeqH8Dqt64oiLklbfP289iILdFdZED2pCLk9C9y291RnybyTBTEoiD4PJ3J1ZW6bmWdUj7yW0b8Cs+yXONJYst5Az5ssNpzMKixzDIHuT+n2MtSgmDhiWTrfm+WMHPtRbwnNCQUoEBQCX86Yjz7X3o/2fUfhkRc+lGe0dGrbSvat/bkt8X83X4kR416Q67/98Xe8/vQYaMRpaLJE9SeZC7PcKjk7+zy3PGZQgAIUqFiAJSgQPAIMqATPvoC11fmwpTUNoh6xK+Eo4IvTwyty6fV0T8TVjXUWS26eDEO8Xi5HvWRE1ItGaC/hKcIShBMKFAvoX78Nhsf6lZlw4mBxST5QoHICTRqmYdF3b+CPWe9g3jevYMOiT3HztQOclSiKgnEjr8ba+R9hwfTXsOrn99HhvDOd66s7Y1UDyMYT9mB2ybrizQnQWXQlszhPgfAU4KgoQIGwFWBAJch2ren/3oG5/yjY6jX3qmeW7tfBdPurXpVlIQp4K1DR6eEV1dOoSyPc8fdojJg/ArcsugW3Lr4VGp22os24ngIUoAAF/CSgKApqpySicYNU6LSeX4+jowwQ3wKk8eGZKVB/NFoFhjrugZNcXQ7MWvdAi7oJ/9ewAJunAAUoQAHvBDTeFWOpgAlEx8HaaxjMNzwlAyUiWCKS9awLAJsNlouuccm3XHw1bGe0D1j32FDkCJR3eri3CrVb1UbKGSneFmc5CkS0gOmBz2F8cYFMttqNpIXx/tN5qGPPkys4oUCICST3TXLr8X+Jp7/W2W1l5TJYmgIUoAAFKFAjAgyo1Ah7xY3a6jaWgRIRLBEJiXUA9eiSx3zwhwK+Fajo9HDftsbaKEABCkSaQOSNN75THObV+xH/JK+UaV7ajzgQvyfyIDhiClCAAhQIKwEGVMJqd3IwFPCdgKJUfHq4N62l/3scP478EfnH82TxFW+thLmQp3hLDE4oECoC7CcFqikwaNAQzFvxO75cN12msY/fI2sU+cv+WAOR7rzzbpnHCQUoQAEKUCBUBBhQCZU9xX5SIEQFfhn7M/b8vkdcsSZHsOnrjVj/xXo5zwkF/CXAeilAAQpQgALBLKDsXo+ykmb/v8HcdfaNAhQoIcCASgkMzlKAAr4VyD6Qjcy9mW6VHvjrgFtehGdw+BSgAAUoQAEKRIpA9knoP3mozKT7+rlIkeA4KRDyAgyohPwu5AAoUBMCbJMCFKAABShAAQqEvoDVasPR4xkwWyyBG4xOD1vztjJZm7R2tuspz7mSMxSgQFAKMKASlLuFnfK5ACusEYHExolIbpbs1nbjCxu75TGDAhQIPoGizF04sfkTmYpO7Qi+DrJHFKAABcoRWLTsH5zb61a3VGQ0ya2WrtiALpffhT7X3o92fUbhuzlLZL7fJ7GJMN3xukzmG562N5dQSy6LfPONxXn2NZxSgAJBLMCASpDuHNEtzX8roF30pUzK4R0iC5qtJfIObpN5nFAgmAUum3Q5ml/SXHxJlexmmxvbov0t7eU8JxSgQPAKZO6Yif++boNDS++R6b9v2iFj2zfB22H2jAIUoEApARtsiI2Jxi9fTXRJBr0OBYVGPPjc+7h75BBsWPQp3p4wDs++PgUHjxwvVQsXKUABCpQt4KuAStktcE2VBTRbV6rBlKkyKYeKAyrbVsll7aKpcARZqtwAN6RAAARSz6mLwZ8NRmzdONlat3u7Qhetk/OcUIACwStw7J/X3TqXvuZVtzxmUIACFAhmgegoPZo2SnNJiqLg73VbkV9QiOGDe0On1aJv906yzNIV64N5OOwbBYJFgP0oFmBApRgiGB+srbvC0meEWzL1HIpTZ9TCrrUPYfOnTbB3/giYcniTz2Dch+wTBShAgVAUsNksMGbucut6UdZOWIzZbvnMoAAFKBCsAhmZOXjspY/x7Btf4OdFK533Sjl24pQMoBgMemfXWzRtgKPpp5zLnAknAY6FAv4RYEDFP64+qdV6djc1mHKzWzoQtQU7i+Yg27oPlsITyNr5PQ4sGeeTNlkJBShAAQpQQFG0MCS3QOmfqKSW0BoSS2dzmQIUoEBQCqTVrYXbhl2K5k3qy/49POEDTHzPfulidk6evBxIriieREUZkJObX7wEaDUKYgxav6ZotX7RoKKUakvNL6/tKL0GmkpuU159/lgn/Aw6jV/9qtNvEjg8xQAAEABJREFUYa8SVrl/Yr8xUYABlRB8DmTv/82t1zkHFkEcUXRbwQwKUIACFKBAFQTSOj7gtlVq54fc8phBAQpQQAgEY2pzdnM8+H/X444br8DT99+CCQ+PxDezFsmzVBIT4uQlPyX7XVRkREJ8rDNLo35Sio7Swp8pSg2cyAYVVKqdKL0WSgD6V52xazQKDGrgpzp1+HPbKLVvCpRKuZfsj9xvnES8gCbiBQhAAQpQgAIRJSC+IvNERhay1KOTETXwSg42+cyhOPvGTWjY8x2Zzr5hA2qddUMla2FxCgStADsWgQJ1a6fIUZvNFqTVScG+g8dgMpllnphs330Q9VLtZcSyyWzDqRyjX1NWrlE0BZu1cm1l55tgsVRuG3+PpXT9JrMVuQVmv/qVbrMyy1l5JlhtVTeUO46TiBdgQCUEnwKJTfo7ex1rjEVqbn00iuoBJTvDmc8ZClCAAhRwF1ixZov8isyeV4/HhYPG4tZ7X8bmbXucBSdOmub29Zo33f2Cc32kzUQlt0Cd826XKSrlzEgbfpCNN/S7s23bVnz2+Ucy/bn8Dzmgknn79p3+XZQrOaFANQXE2ShrN26X3+hz9HgGPvpqDrp0aI3oKAPOb3+2rH3abPsZKwuXrZXf8NOzG7+JUMJwQgEKeCXAgIpXTMFVqF7XZ1DrnNvQsLA1zj3eAU2zzkD9XUYYJt4AZde64Oose0MBClAgiAQUjYKn7rsZf82ZhMUz3kRcbDQmfT7b2UObeqSq14XtXb5e87Wn73Ku50wlBFiUAqUEtm//D5+rARWR/vprGQbU1eM+w0FcsPobmWptWwGov4Pa9Yug//gBZ9Ks/qVUTVykgHcCR9NP4uZ7XkTngaPR59r75aU+zz08Um4cGxOFd18YDxFIb9dnFMY/+S6euHcEGtWvK9dzQgEKUMAbAQZUvFEKsjK66FpofMkk1M9p4NYz7bIZbnnMoAAFKEABu0DXjudgUP8LkZQQh7S6KRjY6wL8sXKDfJON4h9x/XzTRmny2x/EY726tYrX8IECFKiOQKtWZ+O220bjrhG3YFbfVri3TX10TNY7U7KtEFAUICsdyp6NzqRdPksGV5TMdPCHApURuP/O67B2/keY980rWP7je5g2+UmXgEnvizpg46LP8Nv017B+wScYflWfylTPshSgAAXAgEqIPgmUU0ehGNU3HqX6r6TvK5XDRQpQIEwFOCwfCCxfsxmtz2wKnVbrrO3vdVvx2Esf49XJ0yFOFXeu4AwFKFAtgbPOao2RakDlhutvQIPC46ilnL53RbkV55yUwRWYisotxpUU8CQgLu9p3CAVyUnxnlZDq9WgYb060Ot1HtczkwIUoEB5AgyolKcTxOtsKfVgM0S79dCW2tQtjxkUCA4B9oIC/hOwWKz4+Ou5Zabd+4+4NT7nt78g0gPqEUzHynNbNcOQS7ujWeN6OHAkXZ4qPn/J347V8rFucjT8nXRa9Si92lqtxKhKtaVuUqny/h5H6frjY3SIidIFdR+1GgV1KunuGKc4uQIR+pNzJBczh80sM82/f36EynDYFKAABSgQzgKacB5cuI/N0mu42xAt3a91yyuZsfnTJtgwKbbMZCk8WbJ4ZM9z9BSgQMgI2GBDRmZOmanktziIQS1fvRmPvviR/BrNbp3PFVkyicuBxo28GqNvGoR3JtwjLw+a9esyuc4xycwpgr+TxWqTzeXkGSvVltjI332rTv0FRRYUGc2VGlN12qvKtsI+s5LujnZs9t0mdkPEJXOBCQdXHSwzHd1wtIRJMVRRfom84lkPiEpRnmNl8SMfKEABClCAAsEhwIBKDe0HW2YeLK/Nhfl/02B56xfYNh+odE+svW6AadwHMF8xBuar74fxkW9ga9Gh0vVwAwpQgAKhLiAu2Xlk7HCUlc5q0dg5RHHGyeiHXsPzj4zCdVde4sz3NCPun5KXX+SyymSxwd/J8Zmysu2IjlZ2m0CWF8EKESsKZJuVbUsYmqu4j8W2kZri6yfgmmlDZRrwxgDJEF8/Xi6L/AGv2/PkCtjPwLLPezG1WIoLVXK74q34QAEKUIACFPCXQE0GVPw1ptCo92gmbEdOAeqROtuBk7B8vgTIyK103231W8B64RBYO18KJFV8V/LzRu1Hu7H5Mmmj68j2zht1QC6LfG10bZnHCQUoQIFwFPhx/nLc/8xkPHr3DbigQ2scOnpCpvwC+z2p3vxoBnbtPQST2YLN2/bg6x8W4uIL2oQjBcdEAZ8K6GN0aNy1kUz12taTdetj9HJZ5Ndrb8+TK1B8hoqHS5fh4bopW1ScfTPHdsVLfKAABShAgZASCMvOMqDi591acKoAB1YedKbjW0+IbwREodGGg8dNznQi0wzbzqN+7g2rpwAFKBDZAhv+3SUBXn7vG/Qf9qAzzV+yWuavXPsvrrz1cbTvOwrX3/ks+vfsjFuuK3lkXRbjhAIUqJZA8ZkmipdvQ503jS7erlptc2MKUIAC3gqwHAUqFvDyL1nFFbGEZ4FjG4/h++EznWn5xD/lwZdjp8yYvSzbmZZt9HAdsecqmUsBClCAAlUUeOq+m7FlyRS3JG5EK6r89sOnsXLuZPz69USsmfcRXnj0dkRHGcQqJgpQwAcCx9KPYdfOHT6oiVVQgAJuAsygAAUCLsCASsDJPTeYX2iF0rLk6bCeyzGXAhSgAAX8K5AQH4smDdMQE81Ain+lWXskCWRmnsKYsaMwdOjluPXeu3HvNiP2pJzhFYH17G4w3f6qV5c2e1UhCwWNQDh0RHvoIPQrl5eZtPv3hcMwOQYKUKAMAQZUyoAJeHZSLFArPuDNskEKUIACFKAABSjgb4Hp07/Cpk0bnM2sOJ6HRxf8g9ybnpfBEhEwsXYeCHFdtLVtb2eezO9zE2xntIfN0z1XnDUGZIaNUMBNIOrH75Ey/OoyU8y3X7ttwwwKUCB8BBhQCZJ9qUTpgqQn7AYFKEABClCAAuEhEDyj2PLvJrfO7M3Kxz59sj1YIgImtRtAXBdtS0k7nSfya9UHfygQrALWBg1h6tJNJkvjJrKbFg95cgUnFKBA2AkwoOLvXVp8I/uSzYivwxSpdF7JZc5TgAIUoEBkCFgXbIT55R9h/t80WCb/Btvu9MgYuKdRMi9sBWrVqu1xbPHxCR7zmUmBUBEovGooTk2fLVPBsJtkt4s85MkVnFCAAmEnwICKn3epYeVfbi2Ie9S7fSugyHQryQwKUIACFAhmger2zbZuL6zzNgDHswGjGbZdx2D5Yqm87KG6dXN7CgSTwKUDr3DrzjnnnIf69Ru45TODAhSgAAUoECoCDKj4eU8ZlqtvjEu34SF44iGr9FZcpgAFKFBdAW4fZALW/w679yi3ELBY3fOZQ4EQFuja9SK8+cYkXH75YFx4YXfccccYvPrK2yE8InadAhSgAAUoADCg4udngebwIa9asB7RoPCWKHu6PcqrbViIAuEvwBFSgAIUoEC4CHTu3AWPPvIkJr78Jm4eMRKJiUlBOzRzwXHkp/8DqykvaPvIjlGAAhSgQM0LMKDi531gat/Rqxa0hTpYVmvs6S/uFq/QgrEQ+0QBClCgEgKasz1c7hAfDWj5d6ASjCxKAZ8K7Pn5Wmz5rCl2zLgYmz6qi2N/v+jT+lkZBShAAQqEjwDfsfl5XyrDr0Wz2JNobtsvUz3lJGADouOi0bB1YzQ8uzHqK03QUNsM0VOK7OmLIj/36nT1nKOA1WqDpYzLC8S6o8czYLZYCEUBCvhBQOnQDJqB7YC6iYBBB6VFGrS39IT4phPwhwJhKmDbdRSeEk7m1PiIM7ZORfben136cXT18yjK3O6SxwUKUIACFKCAEAi1gIroc0il1L5tcNWWCbj6z/swdO6t6Pr5KEAB6nWuh2t/uQbX/noNroi+EV3je0N7gdWezue18+BPQARsNhuefWMKnnvzC7f2lq7YgC6X34U+196Pdn1G4bs5S9zKMIMCFKi+gKZfW+geHQzdS8OhHdMfyhmp1a+UNVAgGASsVljn/gPLJ4thnb0atiOnZK8skxfAU7Ku3CnX1+Sk8ORGj83nHVnlMZ+ZFKAABSjgV4Ggr5wBlQDtIkujxjCd17bc1hYs+BWjbr8J/QZ0l4/zf/ul3PJcSYHqCMxf8jd6DLkHM+cudaumoNCIB597H3ePHIINiz7F2xPG4dnXp+DgkeNuZZlBAQpQgAIU8CiQkQfr71tg23oI1mX/wfKW+r5GzRNnYslUIngol2vHe6wmkJmaqGSPzeli0zzmM5MCFKCAqwCXIk2AAZUg2eP7lF14bsKT2L79PxQWFMjH559/Crv37AqSHrIb4SbQvUs7zPj4WVzRr5vb0P5etxX5BYUYPrg3dFot+nbvhKaN0rB0xXq3st5kbMnaiAJLviyaacqQj5xQgAIUoECYC1hLnXFrtsK6bo88E0ucjaUdO8AJIJaVrmc6l2tqJrnF1YCiRckffUITxDfqWTKL8xQIHwGOhAIUqJYAAyrV4vPdxpt0azxWtmY1TzH1CMPMagvExkShXt1aiIuNcavr2IlTMoBiMOid61o0bYCj6fbTtZ2ZXsy8s30iRv49FHnmXFl65KrrsD1nq5znhALhLmDdocC6Vwn3YXJ8FPBawJae7XXZmigYXas1zhy6FLXPG42EZpcirdPDOPOa36HRRtVEd9imBwFmUYACFAgmAQZUgmlvhEBfzIUZOLVtGo6vewd5R1aGQI8jr4trN27Hx1/P9Zg8Xd7jSSg7Jw+xMdEuq6KiDMjJtZ9lIlbEGLRIS4kuNyUkWPH1vk9FcWcSZ6r8ePSrcrerqN6K1ifFGRAd5bl/cdE62Zf4WJ1f+1BRH/VaBbWTomq0D+X1MSUhClF6TZX6VyvBII0jeSK+tS2/RzQKBqvpsmjkXxoN6zYGViL5OcGx2wWUcxraZ0pNzf+bBtuf20rl1sxibGpHNOr5Fs64/HvU6/oM9HH1q9MRbksBClCAAmEsoAnjsYXU0NqYO3vsb+fzu3jMr4nMoqzd+G/qedi/cBQO//Uodv7QGwd+H1sTXWGb5QiIS3UyMnPgKZ3K8u4bFBIT4uQlPyWbKSoyIiE+1plVaLTgeFZRuWnloY3O8iVntmZuL3e7iuqtaH12vhFFRZ77l19kll3JK/S8vqK6fbXeZLHhVHb5fr5qqyr1ZOUWochkrdJ+ysw1SuNInhS9aIDtxOkAim2fAuNrDDRF8nMidMbuw57GuZ7VoXRoDk27ps4GbEcznfMwmmHLsgftbXmFp/M5RwEKUIACFAhiAQZUvNw54si8tx9GPVUZ987riHvrVUTPmilXa/fulssiT2Q0tbXAU09OQKtWZyM6JkY+PvHEczijeQuxOijSyU0fwGIs8eZH7VXGv5/DnH9MneP/YBHo3qUtHhk73GO648YrvOpmWp0U7Dt4DCaTPfggNtq++yDqpaaIWZls6lR8rXJ5qY4+DRAFRVLLO/4fyU4nO1cAABAASURBVD+EO/++EQ+vG4vytq/qOpvanvrfY91ineiHzWrzuL6qbVZ2O9EHtQs12ody+ywA1U6WW0YdgMf1xduqm0fmf4v6tN9xOpjiQLBu4J9ch4VPH1lZ8ArERUP34jBo770M2gnXQXvTxS59tW074rLsXDhpv0TUucwZClCAAhSgQJAKRPy7u1m/LsO5vW51S/c/M1nuMnG0f9zjb6PrFWNw8eBxGD5mAk5kZMl1lZnEvfkK4t5+DdGzHQGVPXJZ5Dnq6dfvUnz6yVdYMH+ZfBzQ/zLHqqB4LDzl+VTc/OMbgqJ/7ETlBCwWqwyYWCwWmM0WOS8+HItazm9/tnjAtNmLYFbXL1y2Vn7DT89u7WW+txOtRgvxNeEy4fRPhvEE/jn1NzZnVe0mt6dr4hwFglBAPO1jPfSrjj3S5GFNRGatOmTC91sLIR4jEiBSBh2lh9K4NpTYqEgZMcdJAQpQgAIRJBDxAZV+PTrjl68muqS257RA7ZQE+TT4ZtYiiCPzv898CyvnToZWo8Hbn3wv11Vmknffw8gb/yAKrxrq3Cxv3P0yz5kR5DPRKWd57GFs3XYe85kZ3ALf/7wU7fvdLr82efa8P+X87HnLZKdjY6Lw7gvjMXHSNLTrMwrjn3wXT9w7Ao3q15XrvZ3UiUrFJan91cP1p7d49pOP8fY7MzF30lpMmbwIhbdEOZNlZcS/JJ2GCp459qQKArrLTp/d5dhcP8Q9z7Eu0h7H/JyNZ5bk4pN/CuTjXepypBmE03gLTxVgxVsrnWn9F+shzgYsyCid73oARjmrjHuT1I4PJx6OhQIUoAAFwlgg4j+9xMfFQHwdrCNlZedi47+7MGKo/av85v3+N4Ze0ROpdZLl/SNGDO2HH375Q32jULkjjXn3PIC8ex9C4ZChzqdT3v2PyDxnRpDP1G7zf9Aakl16Weuc26CLTXPJ40JoCFx35SXYsmSKS7r6sh7Ozve+qAM2LvoMv01/DesXfILhV/VxrqvMTJYpEyXPUDlvX2e02dUFsdtTEbMxGeLmnY5kO+l+mQQq/cMNKFDzAoZnTIh6ygRdfwt0l1sQNdEI/R0MqIg988feIuzJtIhZZ9qrLi/da3Qucya0BAoyCrHq7ZXOtGHqBijqy3lhpmv+mg9WuwxMqVfiPYVBByXJfmqXEhftUo4LFKAABShAgWAViPiASukd88ZHMzBscG80aZgqV4n7SDRpeDpg0LiBPT+7xLedyIIRMIlKOgNnj9iMJn0/RYMLX0bLqxej8SWTImDkfh5iEFev1WrQsF4d6PW6KvWy4GQ+UvbUgbbw9PZP3XY77r/7OpjrHJB16i7ajegpRTJpu1hkHicUCAcB3TAzot4yIupVI3SD+Nx27FMRPHHMl3zcdYpGJT3Ccd6UbypzWLqXhkO52POZsGVuxBUUoAAFKECBGhYI64DKnN/+8vjVseIrZZev3uxGv2zVJqxe/x9G3zRIrrPZbPKbTqKjDHJZTKIMevGA/PxC+SgmBr0G0QatV8mgO00utjHkaEUVsKnvMSwToqDdqPOqHrGtN0mjHiESbXoqq66C+Inysu+ijvjEuqjf5iY07nIfaje9qNp9jVLtxFEsUXewJq2KqFf3W1X6p24qiCMy/XTHT/iw80dIfbgZrrtnNHRF9t+dzWesQVRyOqL0xUej9enQXmCVSakTkVQcNAUiSqBZsv3vXulBt0jxnF+6HJcpQAEKUIACFKBAsAic/nQfLD3yYT/EWSQZmTnIyMxxS+JmsyWbsliseOPDbzFq+GVIq2v/JhNFURAbE40ioxrtKC7smI+NPX06arQaFIiN0sKbFKU//YYxVqdF7k0Ge83qgbnCaRpkD9NDu1nrVV3etCfOMIhSAyaeyqrDk23HlLHe0za+zos26KBVO+Lren1Zn1arIEoNqFSlTk2ERlQ2fL0Ruxfuls8vx0RfaJCzsWYDJmy4Rs6LiZJsP8VbzDNRgALhL9CjWRSalwqqiCBLz2b214jwF+AIKUABClCAAhTwo0BAq9YEtLUAN3bj1X3xyNjhHpO4GW3J7sxb8re8+extwy4tmQ1xb5X9h4458w4cTpfzifGnPwRm55uRkWP0KuUUnA7OHP/LDNMOWZ3L5NT3Nq/q8qZNk9mKnHyTx/qsNnuzmbne9d2b9ipbJivPCLPakcpuF8jyRpMVuYXe7+OSfTNbipHt1BEzPbruaJljjbYYEGeOcq5XWjd0znOGApEkYF2xHeYHpnpMOJ4d1hSTL0/EM73icXvHGPn4vroc1gMO88Ep+bluI7SJP38iua1hBgUoQAEKuApwKZQFwjqg4u2OMRpNeP2DbzH2tiFISUpw2WxAr/MxY84SpJ/IRG5eAabOXABx405FcVww41K8Ugu2PZ7rsO7mbqkUJAsHnUBcndMBx9KdU+KioLmyM5Qm9ut7lNquv3Oly3OZAhQIT4EuDfW4pnU0xGN4jjByRhU190e3wcq3SaXf5uQDRY8ZYPmd73PcwJhBgVASYF8pQAGnAP+iqRQ//LoMObkFGHFNP3XJ9f8NQ/rijKYNcMnQe9Hl8rtgMpkxbuTVroWquKTtbvG4pfZiz/keCzOTAkEo0Pqa1lBKXe7kXFbzNT1bA/xazCDcc+xSIAU03VpB9/oImTRDu8imNV3PlMuomyiXOaFAKAhot/3nXTfNgHm2FoVjo2D9eiO0y2ZAq9kD2IrvqeVdLSxFgUoLcAMKUIAC/hJgQEWVFd/qs/rXD5BQ4jIeNVv+j4uNxvsv34e/5kzC0h/exrcfPo3UOiW+5k+WqtpE3IDT8PDpS4BELZquVuhvMYtZJgqErEDtM2tj2KxhqH9tGlK6J6PxyIbQJtlfbsxWE9aeWoUM40k5vv35e+UjJxSgAAUoEKICcfGV6njc+U8gessD0P76EaL0yxATNQtKbmal6gjzwhweBShAAQqEiID9E06IdLYmu5mUEIc6tZJ83gX9rcXBEwMQu7QAMZ8V+bwNVkiBmhDQnqXgjX5PY/KIF/FK18eRY7XfEyLbnI0xa0ZgTcYK2a1v9n0mHzmhQLgKWDZqUHhLVJnJ+Ko+XIfOcUWIQOHAy70aqQ4GaOL2w9BwiUt5RSmCZtVPLnlcoAAFKEABCoSCgCYUOhkJfVTUPaHUjYSRcowUoAAFIkwgC7Cs1pSZrNvVPwARRlLjww3jDthsNpgtFo8jtFptOHo8o8z14p5yx46fgqjDYwVlZBou7owLhzZCj/Oy0bPZYXRqa1HrAKL0MejU5CJ01F+MjtqL0U7XDbqk7R5rUY7v95jPTApQgAIUoEAwC/BdXDDvHfaNAmEg0H+dCbNfzEXdLJsczVUrea28hOAkYgQ0bayInlIkk+EB+2WemvNK5D1orNCCBSjgrcDcBSvQf9iDbsWXrtgg7wXX59r70a7PKHw35/RZIiKAMvmLH9Gh/x3ofe196DHkHmz4d5dbHWVlRKfE4IJXh6LjnKfQ4fdXcN4bI6EoQIwtBh2P90AnXXd0MnTHufpOMGe18liNrW4Tj/nMpAAFKEABCgSzAAMqwbx32DcKhLDAkrv/wKUvDUbnyUPw955hKEAMoMZUms28HFc9NxiHN+3D3MKvoa4I4VGy62UIMLuEgJIMaC+wyqQ5yyrXKI68NlnQFS6GbtdCaJQjch0nFKiKwP5DxzBg+EN49MWP3DYvKDTiwefex90jh2DDok/x9oRxePb1KTh45Lgsu37LTkz6fBamvvsY1i/4BFcN7I77nn4P4owWWaCKEyXNJgOJ0WpAEepzHuqP7sF6sDTooc6d/m+zRcHa5crTGZyjAAUoQAEKhIgAAyohsqPYTQqEmsCxv44ieU9D7FGayGRRtIAC7FcaIeZgQxRk5+GIbT8UWzC8DIE/FAi4gHLqGPSv3QLdty9C9+8sRBsWQL93ps/6YczZj12zB5SZ9i+602dtsaKaF2hQrw6+eOd/eHz8CLfO/L1uK/ILCjF8cG/otFr07d4JTRulYemK9bLs4j/XoVvnc9GxTSvo9TqMGNofx46fwrZd++V6bye1endDavM01OpzodxEd2gn6l9fVyZtxlF7Xm8LLHc/CdPtr8Jy6WgUmbqjoGgIbPHFERfwhwIUoAAFKBA6AvwkE+B9ZTupfqL00OY67Up89vlHZaYNG9Z52IpZESsQAgO3mm0h0Et2kQI1J6D5ey6UghyXDmhPbYByyv7B02VFFRas5nzkHlpWZipIX1uFWrlJsAqIQEm9urWQkhTv1sVjJ07JAIrBoHeua9G0AY6mn5LLR9JPonnjenJeTFKLv80w/cTpb97RKAq0mvKT53c4cPlx1KFp2QHoeR0s1uaAYpB1a9Q2oP6IB0c5bx5FeY3auKeyECvVOsta72kbf+SpXZBj9EfdvqhTMIlUlbpUejE8JgpQgAIRKcCASoB2u+04UDA0CkWPGmSLBZpLYJ6rHrGXS4AIqHz++UcoK23YGNoBleJh8oECbgI2MPDihsKMiBBQDu/0OE5f3ZzTEN8ELa6aJ1OTPvbLQAwJTeWyyG/c50OP7TMzuAQOHz2Bj7+eW2YSl/NU1OPsnDzExkS7FIuKMiAnN1/mifXRUVFy3jER5XPzCxyLSIjVoU5SlMckQjh/TViK14y3Y0Lys/ip50vO7ZS1/0DJzALq2QM2tRJd63AUFHXHRtvfF8VGld2WKFc6xRh0iI/Ro3S+WNZr7R/3k+MNHteLMv5OtdUxi2CFv9upTv1xUTrERXs2rKjeaIN9vzn2JR9rRiBrz9wyz0gUZyse3zCpZjrGVikQ5gKaMB9fVYfn8+1Mn+hh/bcEt6KD8TkDbIX2pjpYuuK220bL1LZte5nZseP5clnkt2urHsmRuZxQIFQE3AMl4saHpXuvuBcrXYTLFAhLAVuDlh7H5aubc2r0sYhv2EOmmNROsi2NPk4ui/zY4jy5gpOgFTCZLcjIzCkz2WzWCvuemBAnL/kpWbCoyIiE+FiZJdYXGV1vjiwuEYqPjZHrxSQrz4Rjpwo9pp/UYMqKT/5B1qEcFGQWYufSvWITmU5kFcltHPdjOa6uL1mPLKRORF5ugVmdA/IKzXIbkedNyi8yIzvfc/9MZrtPRo6xUnV60663ZdLVMdvUv3Xelq+JcrmqeW6BZ8OK+lNgtMj9xknNCpjzjpR5RqI4W7Eoy3MQv2Z7zdYpEDQCVe5IiU/4Va6DG3ohYP3PndqWC9j2KHJrEVAZqQZURHIETzp3vgBiWaR27RhQkVCchKyATX0zqUAJ2f6z4xTwtYD1gitgi0lwqdaS0g62FPuRfJcVXIhYAXGvk0fGDkdZKTYmukKbtDop2HfwGEwme8BCbLB990HUS00Rs6ifWht7DxyV82LiuNTHcemPyCsvHVh+wONqq/q673EFMylAAZ8LJDa/wnkGYt12d8v6k5oPOp3XdqzM4yRcBDiOYBFw/5QfLD0Ls34ohuJTUUqNS6nDdxulSLhgTkOcAAAQAElEQVQYLgLi/OYSY5GLHuIpDWIblyjFWQpEjoAtJQ2mB7+A+frHYD5nCAqN/WBqNjRyADhSnwrY1Ki1CJiYzfazBeS8xT5/fvuzZVvTZi+CWc1buGyt/Iafnt3sZ8T2vrgDlq/ejH827YA4I+bLmfORVjcFZ7VoIrer6sTDS35Vq+J2FKBABQL6uPrOMxCjkuxnQOriG5zOS7bnlVWNueA48tP/gdWUV1aR6uVzawqEqYAmTMcVdMOKs37v1qcYLIGS4DnQAj/87Pn5WpdrK63GLNnKnl9c8y3F+XIlJxSoooDi5dkoVzcaXsUWuBkFwkAgJgHWdpfA3KIvrLb6YTAgDqGmBHbtPYz2/W6XX5ssvqFHzD8x8VPZndiYKLz7wnhMnDQN7fqMwvgn38UT945Ao/p15fr257bE/918JUaMewHt+47Ctz/+jtefHgONRpHrK5pEJ0V5LCID6R7X+C+zIH0dcg/94UxWYw7ErbpK51tMar7/usGaw0AgkoYgPiNs+awpdsy4GJs+qotja16JpOFzrBSolgADKtXi837jhLyvkFZ0HWIsi+RGeusOpBbcAP2uHXK5OpOTmz+VgZJT278tt5q8o6vUNxinv/HBZjXJ8nlHVrrkw3r6lGBZoMQk7/Bfsq0jq54tkctZClCAAhSgAAVqUqBl84bYsmSKS3r5sdHOLvW+qAM2LvoMv01/DesXfILhV/VxrlMUBeNGXo218z/CAnX9qp/fR4fzznSuL28m73geDq44qAYtbBBnycCqli4++TbXFosTe7LVjOr/N8n7Q/yBolPlv286uPQe9X3KQGcqzNwOEd8/uHScM2/X7IHl1mNVj9CLoIw4Wo/Q+WFPKVAlgRObP0H23p9dtj266hkUZe12yeMCBSjgWYABFc8uPs+1JiQi1joPSZb3ZN06215okAtL3VS5XJ2JMWefDIiYctU3NNWpyIttzUUZsq2ik/96UZpFIllAH6uP5OFz7BSgAAWCTkCr1aBhvTrQ63Ue+xYdZUADdb23Z6aISjJ2nYIIWEANykD8iJNaRFLnC5RofHX7YmQf9BRUUQtU4n/2nl9kQOT4hncqsVXViooPkiLocuD3MVWrgFtRoAIBm9EM6x9bYfl8CayzVldQ2r+r84/97bGBwpNbPOYzkwIUcBVgQMXVw29LRYOGlKpbj9yOd8ASl1Yqn4sUCA+BQR8NwjXThjpTUWyRevQSWPp/v2LRAz+iwXnNcLnhRsQlxoXHgDkKClAgvAU4Oo8C0QkG1/ziYIoj02LTYO87cx2LfKRA0AmIb6A6ejwD4v5Cgeqc9dsVsP64BrbNB2DduM/erMl+zyP7QuCm+ljPn0W0UUmB6wRbokAICzCgEqCdV3jlEGR9/CWKuveULRZoe+H41teRf/7prySUK6o0scmtTm6xX/qza/YA9SiOPa37ujf+m9lfLluNmbKcy8S+qUvW7lL3WilZ39GVz8iyNnFBspzjhAKeBRp0boDGXRvJZGlTBKvOLA9gnmhxBOlnHUJMUiwaaKt3w0PPLTOXAhQQAkwUCIRA7bProlbL2sVNlYqmFOfm7jhSPFeNh+IzYByX/ohLchwpa/9SZB9YCrFs8fLeKOJyHlHeUypI/6caHeWmwSrw5kczcG6vW5Gdm+/s4tIVG9Dl8rvQ59r75f2FvpuzxLnObzNW9V30hn1u1dtMZre8QGSknHUDoGhR8kef0ASx9bqUzOI8BShQhgADKmXA+CO7qO8A5HYa67Fqm9VjtpeZ9jcwxuy96puJ0/dIyT20DKfEm4yDf8h8W+l7o3gIpogGS99TRdTjSIUZ9kt9FCiiKBMFvBKoFVXHq3IsFNECARu8uM9DRmYO9h9KR5HRfi+p0o2fyMhCQaGxdDaXKUCBUgIarYJrp1+DLuO7onHzUmerFJdtfKYPDh7Z7G9asvfaL/0Rl+Q40obpfbHlu37qwaOBKMrYXtyq/UFuZt/UnlE8PbTkHlneUUfJxwO/3wX+hJfArF+X4ZNvXO8TIl7jH3zufdw9cgg2LPoUb08Yh2dfnyK/Acuvoz+RDXnKbulGLB6eqKXL+GE5ulZrnDl0KWqfNxoJzS5FWqeHceY1v0OjjfJDa6ySAuEnwIBKgPepbXcZ5NV6DbVvXPvckWhx1TyX1PGGRWh9zW8yT2ModeqeiImIVMqg+eXfyfKl6xLL9S54SpZWY+vykRMKlCeQ8L8HkNo8DS3PbIXULPvzdM2DOdgzOgtX/u35g2x59QXXOvYmFAU2/rsLPYbcg+5XjcOlNz6MiwePg3ij7RjL/kPHcNlNj6Dn1ePReeBoPPnKZzCZa+Y0bEef+EiBYBeIqR2Lbvd2xbU/3YTz9a7vc85T0tHsnkHVH4Jif8Oij62H+IbdXVJy4x5IVJPI1xgSXNoSm9nsm7rkx6S2d6lDbOtIsXU7upSNpIXsIiveXpWPm37IxPUzs/Din3lIz6vWUb8a51u9/j+8+M7XeO0p10DZ3+u2Ir+gEMMH94ZOq0Xf7p3QtFEalq5Y798+pyZBSfQQZNS5niXi30641h6b2hGNer6FMy7/HvW6PgN9XH3XAlyiAAXKFHD9q1dmMa7wmUCi/UOlz+qTFdnfKRgSm6tvDnq4pOSmvZAg32T0gKLRy9IVTeLrd3OpI77h6Tqj65wnN1d4hop0CKkJO0sBCsiLFcffPhTLZr+L9Qs/xc3X9scTEz91nqny/FtTcXbLJlgz7yPM/fIlzPv9b8xbvIpyFKCAFwKm5Slor3sEQwwjZelEpRYuTHoV5pTGcrmyE3N+Ok799xWOr38X4ixcsX1i88vVgz7zXVLb4Ytw7nULZV50SitRzCUpLkv2hUa93pPlW1w13+2xUe/37YUicPrFhgLM21mEkwU2iODKsn1GvLf69CUyoUay7+AxjPnfW3jrubtxZvNGLt0/duIUmqoBFIPh9PvjFk0b4Gj6KZdy/ljQDO0KxEW7VK1Ee75ZtEshLlCAAkEnoAm6HoV5h7QdPUf5Fe4Jtz3PjNAXOPjck7hmRj+Zigz2J/kL18bI5T8vqhf6A+QIQk6g3TktMPSKnqiVnAC9ejQwrW4tOa/RaJCVk4flqzdjxND+iIk2oHmT+rhq4EX4bWnNfgNDyCGzwxEpoFv/D/BTuhy7XtHLR4168MVWpINmxr+AySjzUFhof6xgmp++Flu/aoP9i0bj8PJHkL72tQq24GpfCKw57H4fjy3pZtTQ1SjVGlJWdh5GP/Qa7ht9LS46335AsGSF2eprfmyMa1AjKsqAnBL3WInSa9CgdozXKbH4Gw7jY3TlbtOwx5loMulm1J8wFPWevEp2S6OtXFt1k6LUv2Oet0mK08s649QgTWX67+uy0QYtaiUYyrXwdZuVqS8tJRpajVLl/klkTiJeQBPxAr4D8Kompbb9DBWddT+0PazQjzTbt/N0+MS+psJprXNuU4+uzEPKmddVWLa6BeLqdZVtpXV5qrpVcfsIEDBZjfjn1N8yWXE6mCjyMkwnI0CAQwxWgbUbt+Pp1z7HB1/+iEfH3ai+KdXixEn7jbsb1a/r7HaThmk4kp7hXBYzivp6XZ0k6hCpdB0iT/38CUe+WLbOWAHzY9NhfuEHWOesBcwW53pHuZKPYpuSyyXnxTqRSuYFel60L1Kg261Me9Xpn9g2UlPi/WNhWPibx+EnqMF1zSn7UX9tuj3o4rFgiczj69+BtcQNZu3vngB9QtMSpfwzq9HHQ1z+Iy6D8E8LoVWr1YEfWt3Gyn+2yPuhHDicjlcmTcMn0+z3UHnr45nYumMfEhPi5CU/JYdVVGREQnysM6vIZMXhkwVep+x8k9w2t8Ds1TbHY2NwQqOT21hV6Mq0dTyrCCaz5/5l5dn7kVfoXT8q025lyhYaLcjIMXplUZl6fVX22KlCWCrpXrJtueM4iWQBOXaNnHIScAG9bRuiPyiC4UH7C151OhCV1BwxqR1wbN0b+PeLVtj8SUPs/eV6FGXudKm2+WXfymBIi+L7rGj0SRDnvze/zPWeKdpox936XTaXC7qYOuqbjB6IqX2uXOaEAuUJ1IlKxeTOU2VyHLHUKTqIm9TWi2pQ3qZcR4FKCVgsVnz89dwy0+79rt8ycuTYSaSfyITJZEZmVo5sy/HNDyVP/xZHKzMys+V6x6ReSgyqmlLi7Tf5i1KPepasIzHWfjPP2CidrFurHjET7dl2HQOK1L8TGXmwLvkXcWt2yfUlty05L7YpueyYr5MYLVZBp1XK3d5R3l+PSepRU3HE1F/1+6JeYZ+aFF0lJxG4kdAROomxLHEbucaaDYOt8vekKDy1zaUuh21ik34u+aUXYup2UN+nnL7HilYNjogybvmGeJHtMYn3VS2umo/Gl0z2uN6ReTDbgq82FZaZft9rdBQNmcfODXRufW2TpoP60uGWH+wZLZs1xPjbr0FKUjyS1ZRYHChJToyDQa9DWp0UiEuCxN8Bx1i27z6IeqkpjkU+UiDMBDgcfwjUWEBl1bqtePTFjzB8zAQsXbFBju21D77F59N/lfOcVE4gfc2rOLnpQ5hyD8JSdApZe+Zg/8LbXSqJK3VvFEWrB9QjraXzwR8K+FCgU0oXiGSxWWStZpsZGUUncLTosFzmhAK+ELCp0eGMzByUlUq+YRbtXdGvG95/+T689dw4ebNCcQTT8Wa7ZFlxtLJWcqLYxJmOZBSgqikjp0jWI456lqwjK8/+wStfPZoo8s0m+++LLFxikr3xQLlti6Ji+9LpeJb9MguzxVbu9qW38/VylnrUVBwx9XW9vqxPHK08lllYJSf5jTJiJ0RoirPORopxAnS2vVJAsRUgzXg9NMiVy5WZRKec5bF4VHJLj/mOzEa93lEPHp2+L0pUcT2Ner3rmp/sfq8VRx3ePh7OseLrjQVlpt9DMKByS7sYDGwZhdoxChKjNOje1IC7zz99xoa3NsFQroUaUBl90yA40nWDLpHduvX6SyHWnd/+bLk8bfYimC0WLFy2Vp7R0rNbe5nv64ntWBZsu466p33H7U3Z7A+clhDgLAVCQKBGAipbtu3FyPsmQtxde+eeQxDXMAqrBml1IIIqhUX2N5Yij8k7gez97qfZ5h/7G+ZC+yns3tXCUpEoIE4xtahH9/059sMFB2FV//mzDdYd2QLiGxoeGTscZaWzWni+Kaa4IaGQO5GRhTq1k8UsRHBFzqiTvQeOon5qLXXOu/+F6gfxvyf9jR9v/xELHlmI/X/u925DlqJAiAvkWwxYaLsYa8wrsMf4ghxNETLwp1WR+ZvMW2Wet5O67e+BRp/gUrxu+3vVvDiXvJpcaJCgwY1tY2Tq1cwgu9Ig8XTeJcV5ckWITEQQZXyXWHx1dTK+HZqExy6OQ2pcjXxcgL9/YmOi8O4L4zFx0jS06zMK4598F0/cOwIlL/usbB/KK29dsAmWyQvc05Sl9s3MngPp9pWcUoACwSpQI6+Q039cjD7dmEcmoAAAEABJREFUO2LRd2+gw3ktnTbdOp0j5w8dKY7UyiVOKEABfwnY1MOpz74xBc+9+YVbE1fe8hjO7XWrS5o8ZbZbOWZQIJQExFckL/7zH3kDWnHjwclf/IjYmGiIU8OTEuLQrfO5mDpzAQoKjdiz/wh++u0v9O95vtdDnD1yNv567S/sWbQHW77bjB9G/IC9S+xH672uRBTUeP7zrJzJmzkLHqbgE8i3RGGx0l2mlUpn2cF8xMhlkb/FWrmASmxqJ7S+aROa9PkIDS6aiDOH/qE+vijrDZZJo0QtbmoTLZMjeNIwwT0vWPrroR8RldWyeUNsWTIFjrMRxeB7X9QBGxd9ht+mv4b1Cz7B8Kv6iGy/JCUtEUqLNJkQH21vIzUJStO69vniSz3tC5xSgAKhIuD5HZufe7/kr3Xo1a09FEVxaSkp0X49q9FkdsmPlAWb0Ww/DfBU8amxJ3Pty47TA/efKJMisUl/t3WxaRdAF20/4uq2khkRLzB/yd/oMeQezJxbfGTEg8g9o67BL19NdKYbhvT1UKrirAYxjaBR/1VckiUo4F8Bk3oEcNwT7+DCQWPR9YoxEH+P3n3+HudNCB+/5yZs2bYHnQeOxhU3/08NpnTGwEsu8KpTGbsycHTdUbey2+Zud8vzNkO8+UaUHqgVB6XnOVAutp+i7u32LEeBQAnEao3obVtWZjpX07rSXdHFpiLl7JtQt/04xKZ1BlDpKrgBBSoU0Go1aFivDvR6XYVlq1NA068ttGP6y6ScWV9Wpe3XBtpbe8p5qP2wz3BKAQqEkkCNBFTOatkEq9a5H6n4a/VmaeevU+1k5cE8ycqXpwHa1u6RvbSu2iGXnacHfrNc5nuapHZ+CLXb3Al9fCNoo1KQ1HwQmvT9xFNR5lFACnTv0g4zPn4W4l4SMsPDpG7tJIhLIhwpOcke9EQVfvQa9UOhup1BY0D9mIZoFnuGusT/FAiswHWDemHDok+xeMabWDTjDSz89g10LT47UvSkeZP6mD/tVbn+718+wAuP3g5v32Sf2nVKVOGWcg5mu+V5m6G5tht0Lw6D7vGrob2yExS91ttNA1pO3Efi4YU5KCttPBaZB0oQ0L1Qs43FaovQV/mzzNRGV/mASs2OiK1TgAIUoAAFKhaokYDK9VdegrkLVuCFt6fiaHoGduw5iCnfzsMjL3yIqwZe7DxSWHH3w6yEGhmXRyNT4k4PTH3zLPJE0jSpczq/1JxWn4BGPd7EObdsx3m3H0Kzy75FVHLLUqW4SIHTAuLa4Xp1ayEuNuZ0Zqm5GXOX4omJn0Jc6rP/0LFSayu3qBSfoXJXy/sx++Lf0SrxHPCHAjUhoNNqkVY3BeL5r9G4ninp6I9YHxcb7Vj06rFhl4YeyzXs2shjfjBmVrVP+7Kt2KQGTcpKWUW2qlbN7ShAAQpEvEDO/oXY8X1vbPqoLrZ90wnH178X8SYEoECwCNRIQKVfj854+oFbMXvecuzadxifTvsFr74/HZf36YqHxw4PFpuA90NJjpWnAWo6lThynxwn88QpgpobLgp4n9hg6Ams3bi9zK+OLe/yntIjHdDrfPTo2hYN6tXB4uXrcM3tT6NkUCVKr0GtBIPXyXGFX2yUVm5j0NlffuJjdHK5MnVVVFbUaSijf9EGrRxqbLS9HxXV5a/1OvWDfFKc937+6kdZ9SbE6qHXVW4fO+pKVLeVyP6fBFUL0UnR6PNiH+hLjL9B5wbocFv7oOqnPzpzY5tovNw3Qaa2qTrZhLi3hCOvTar9906u4IQCFKAABbwWsBSexL55NyH/6EpYTXkoPLUVh5c/jOx9C7yugwUpQAH/CWj8V3XZNR87fgpdO7bG8h/fxZwvX8K3Hz6tzr+HV578P4ibApa9JddQgAIVCeQXFJb51bGnsnIq2ty5fuxtQ3DXzYMx5pbBmDb5SSTEx2DRsn+c680WK8RXvHqbHMenTWb7dhaLPafIZKlUPd60V2S0wqzW76msWe23GITRZO+HpzL+yzM7x2qx2VBgPL0ciDYr00ah0QKLalWZbRxlxbbCOBJTm+FtMHbLWNzw03CM+ut2XDfjOohAS0UWmlN7oVGOQJOzG8ru9YCxEFCfI8qBrXJZ5IkEY1FFVdXI+qZJWrRL08mUGG0/66dJsn1Z5CdH18jbjRqxiNRGze06wtSlmzPZoqLkc9jctoPMs+nt34Jji6ncmV+R6slxU8AhkHdsLSwm90tHs/bNdxThIwUoUIMCNfIOZ+Kkb/DUq5/DYNDjjCb1cd5ZzVGdezPUoB+bpkDQCXTv0rbMr46948YrKtff4tJ6vQ51ayWjoMhYnAOon7VRqAYlvE2wx09gUgMdYhsRUBCVmcy2StUjtq0omdTOWa2e6zWr7Yt2xWNF9fhzvfpZGSKo4882qlO3SQ18qYRV2jdGdVthHMkptU0aEupXfM8hJTNdBkz0az9DtGEBDDs/h/6Th6DknAAUBfoZr8hlkSeSsuUPKIe2gz8UCDaB7Dcn4dT02c5kadAQ4jmc9dZkmWdLTob4scXFiQdn0ms3ITbqSxge6wft/M9kvnbpNLlcMk+u4IQCISow7p7RKJnGz3kX926diXvefRpPPf2/EB0Vu00BCgiBGgmoiOvWc/MKRPtMFPCpACvzXkCcfWAymWGxWGA2WyDmRRBC1CAu7Zny3TwcPZ4Bk7pO3PNo87Y96NKhtVjNRAEKlCOQuS8LB1Ye9JgObj2IXOvpI43K+oUyYKKYvfubqJ8xEdqfeO18OfxcFYQCiePvguZ4uuxZ7V5dEf/yBDnPCQUiRWD9+n9QMm04sgsbcg9h/a5/sWHjunIZ4tI6QatPdCuT1HSAWx4zKECBwAvUSEBlUP8LsXXHPuzefyTwIw6uFtkbCtSYwPc/L0X7frdD3Fdl9rw/5fzsecuc/flyxnz0ufZ+tO87St4w+pGxw9GpbSvnes5QgAKeBTZP24Tvh8/0mGa9+B12WrbAslyDwuujYN1ZI3+GPXecuRTwg0DUot8Q/dMPLjXHfvge9Js2yDyTpQ3yi26G8cUFHpNlwEhZjhMKhLLA229/AJFeHzLOOYw3zroab7S6Gs/3utmZ52lGG10bTQd+hdh6XaHRxyE6pTUaXPQKEpv281SceRSgQIAFKvlOzje9+/PvTbKiux55A/c8+Y5bEveAkAUiZJKHHLwX9TyuGXo5Lru8N35fsjBCRs5h1qTAdVdegi1Lprikqy/rIbvUpGEaFn33Bv6Y9Q7mffMKNiz6FDdfyyMhEocTClQgkNQkEY26NJIpvviyn8TGiWh4TmPUV5ogXlGPNFoByyYNzHPsN3CFVc2Adz+K4/o574qzFAVqVEC3dbPH9nX//esxn5kUCEeBjh06Q6a8BOfwOiQ2RofERmid73oZnLNAiZmEJn1x5jWL0Wb0cZx1w1rUbX93ibWcpYA/BVh3RQI1ElAxWyzo0bUdzmjaACaTxS1V1OlwW/9V7PuYY5iG9PRjyMnJxoED+8NtiBxPCAooioLaKYlo3CAVOq0W/vyx/PA3LJN/KzPZjmb6s/kaqzvfaMOk1fkYMSsL187IxHNLc3Ew21Jj/WHDvhFoc0NbDJ0+VKbWg+2XybW5oQ0GXzYMV0TdiJa6c50NKUrxzYVKX/IjbrLjLOU6Y4P9pq+uuVyiQHAK2OJPf4As2UNL3dSSi5ynQOQKlPN6H7ko1Rg5N6VAgAVqJKAivjnk/ZfvQ1kpNsLuAL/esNLjbjdazB7zmUmBsBM4lAHbrmNlJhSF5+/C9M0FmLu9CCfyrchVgysrDpowcXl+2O1eDqhsAZutODiilApaqgFNlHEmilJGftmtcA0Fak6gaOAVgMH+DT+OXlhr1Yap0/mORT5SIGIElLMbuI1VaVbXLY8ZFKBA6AjUSEDFwbPv4DEsXLYWc377C+s274A4c8Wxjo+AohS/0QZ/KBDeApoh50M7pp9MSr1kOVjt1RfIZZGvpCXKvHCbrDvqHijamWGWwZVwGyvHA+gudD/7yHmGij7aA5HnvwE8Q8UDFbOCVsBSrz4yfvoNtthY2ceCm0fi1Ox5sCWE5+u6HGR4Tzi6aghoLuvg3NqWp/4OJJwB7eWn85wrOUMBCoSMgKYmemoymfHYSx/jspsewfgn38WjL36Em+5+AVfe8hi27z5YE12q0TbbG7t6bF+v0XrMZyYFwk1AaVQbSot6MtmidPbhlchDtOvRTXsBTikQWgKaDlbobzbZO63+9dW2sUJ7hXuQxV6AUwqEj4D5rNawFV/6k3f3fbA0bhLAwbEpCgSPgBIb5eyM8Y9hKJrRDwW31nLmcYYCFAg9AfUtXeA7/fE3P+PH+ctx98gh+Oq9xzHny5fw7IO3yY7c+9S7EXemyk35d2GQcThSU9OQoB6xacw3GvK5wAkFwl2gQ73i4FGJgbaspUO8wfOZCSWKcTZEBbTdrbLn2ousiP62CJrOdWFr3hYofckPPP/YGp0FW4MWnlcyNzwEwmQUFqtZjuSFfx/H69smwGyzyGVOKEABVwHbSf7NdxXhEgVCS0BTE92dt3gVLu/TFeJeKh3OOxNnNKmPoVf0xP/G3QhxGdC+A0drolsBbbNI0xmFt0TJ9IdhPvZiB+plNUZzYysUHTHBZrNhbvpm3LvyC4y7Z7RMvy34FfyhAAXCR2DYeTG4olUU6sRqZBClWyM9HrnIflp8+IySIylPwNq+D0x3vA5L6tmwWtJgjWtmD7DoDFD/EEAGUNSAiwi6iGS68RlYBo8vr8qAr2ODFCgtkGvOwdHCwzJ73am/8d3+qcg0ZchlTihAgTIEbGXkM5sCFAhqgRoJqBQZTWjaKM0NpkG9OjIvKydPPobjxLbLTm5VUmBZrZHpqPYgNhnWYGORPR23HJX3TzlqzMaGjP1Yv/4fmY4dOxqOJBwTBSJWINagYOz5sZg6JAkzrk3GUz3j0ShRG7EeARp4UDZj6nArCs0DYGw1SgZYbEmpUP8QwHTdo3JZBF1EQlJd8IcCwS6wJmMlrDarSzfFgSKXDC5QgAKuAjxRxdWDSxQIEQFNTfSzQ5szMeW7+di177B6AM4ejj2VlYMPv/xJduesFk3kYzhObLnhOCqOiQLVF1CO7YV25Y/Q5W+EAv6inBblXCgL2LLsvbes0MI4SQ/zHJ3MsO5X5LLIE8nyV4rM54QC4SCwL293OAyDY6AABShAAQpUKFAjAZXxo66RHRM3oe0x5B4MGfkELh48Dj8vWokn77sZcbGevu1AbhKSE/2qFdCvXC6T5uD+kBwDO00BrwVKFDT8vhCxH76HqPm/lMh1nxWBFP3bd0D703swZC9ETNQPUPavdy/IHAqEmIB1t/3PrOUvDUyTdGpAxX4Gkk0NqIhlZ/qpXoiNjN2lgLtAzJRPEPfWq7juu51Qsv/DnphsfPbiYSwefwB1cuxnJuf3jEHeOaeTrSjGvSLmUCACBDT1d0J7fjYMDxTfrDwCxswhUrB0lyAAABAASURBVCAcBezv9AI8svpptbHwu9dx7x1DcX771qiXWhsjhvbHdx8+g2GDewe4N/5vLmXYVUgZfrVM0TNmetUgT431isknhViJfwRqDbkUySNvRPzLE5D0f7dBKSossyHN79+4rdNtmOOWxwwKhJqA7Zh353Db0g2hNjT2lwJuArop7yPu7ddQ/4PVsMG7IKH1MG+y7AbJjIgQ0LdfjKjxB6EbbL+Bc0QMmoOkQBgK1EhA5URGFtZv3okhl3bHG8+Mwfsv34dH774BGZk52LpjXxgylz8kBUr5BYrXFpcqXuIDBYJXwLB0MXTr/3HtoM1+eZ9rprpUdBJKjvvNCpVTh9SV/E+B0BZwXPIT2qNg7yngncDmq7vj7Sui8FVPBgi9E2MpClCAAhQIdYEABFTcib74bj6emPgJogx6l5V/rdmM0Q+9BrMlfL9aT4F7FNqmHsdxgVAXFMU9fFLGx1G1NP9TILgEtDt3eN+hqNqwJdRyK29LaeiWxwwKhJxAkftreciNgR2mgJcCppF3Ife+h1Bw821ebsFiFKBAmQLGIiiblkK77DtodqwtsxhXUCD4BCKrRzUSUPl73VZcc3lPJMTHumhfN6iXPEvl0JETLvlcoAAFQkvA0vLMSnXYeskNbuXN7Qa55TGDAiEn4OmvLKPjIbcb2eGKBXbmbsPv6b8hKrcQCX/86b6BeN6L5L6GORSgQGmBwjwY3rgF+mnPQ/vrx9B9/ih0nz1auhSXfSXAeihQDQFPb/WqUZ13mxYUFsGgt3/TQcktHH9nxfqS+ZynAAVCS8DYszfM7Tu6dtrDWVeOApaug2Ea/zEsV94NY2JfFBRdDVuT9o7V1XrM2b8QR1e/4Ex5R1fK+nIOLHTmifV5R1bKfE4o4FOBeMdfthK18qSVEhicDReBPbk78cP6tzHyhtcw7IstHs699TxSy/HGMK0ZCOMzeljW1sjbUs8dYy4FfCxw4OABzPrxe69q1a7+Gcg+6VJWs3MtlANbZR4nFKBA8AjUyF+u1q2aYtrsRSgsMrpIfPfT73K5cYNU+RiOE71tWzgOi2OigJtAxqxfkfnZ18h99ElkffA5bFHlf3uXLa0ZRGDFqm+AKN1yGH56DvqPH3BL2p/elW2ZC07IgMiJjR/I5bImOQcW49jfLzhTvjOgssiZJ9bnH11VVhUyP33ta7I9q7lQLnNCAW8ElHhvSrEMBcJDYMQSI2rl2cfiFjcUGSLZVzunthONYT3eBKbvdCgcEQXL+hp5a+rsD2f8IhDxlS5a9BtuuGEI3nj9JdTSK1h1cQpio75E1MzRMLw8zO6TkwHDY/1kEmelwENYUpO+316WUwpQIGgEauSv1ugbr5CX9nQaMBr3PzMZEydNw4DhD2HqzN8wcthliIst/4NX0OhVoSMa5MitoixrUL/wMpkGFg7FK6c+wpv/Z099OlwK8S0/l9U5F290GYG33/5Apv79LpXbckKBUBEwXtIX+XfejaIBl3nfZZsRGu0xKEe2Qtmz0T0d3inrshRlyIDIiU0fymV/T9L/eUO2Z7MW+bsp1k8BClAgJAVaHKn+PfAsv9q/WrxmAdg6BXwr8NU3X8gK3z43Dr9ckCTnvZ+cPtPRmtrE+81YkgIUCIhAjQRUWjRriJkfP4vuXdpg2aqN+HLGfHmD2sfuuRHj77gmIAMPZCMnbClYaLtYpvU4TzZ9Sp3+aQVEyjVlop25Ezrf0FGmBufWg6IoaBCdhA61m6Fjh84ypaXVU7fifwqEt4BVVxuFxn4wXfGEvARIjNaW2gym21+VyTLobpHFRIGQEFBqn34jXF6HlVRjeau5jgLlCwTJ2l31vQuGaKLToU3yfMauZUkGLB8sDJIRsRsUqL6AxWLBoYMHKl+R+lmg5EbWlp1ga9y6ZBbnKUCBIBDQ1FQfWp/ZFB9MfACrf/0AmxZ/jp++eBE3Xt0POq13f4xrqt9VaTcDKVisdJdpvWIPqJxUasllkb/Tsrkq1XIbCoSNgOa/Ffa72KuP0ETBaqsPa8NzYWtgv7mtLToWtjPa21NDex6Kv4bZXHhCXooj7oPiSAdXTMDe5c/J/LziS3wqwsoudU8VR12OR4sx015Fcbv2BU4pUL6A5gw1cq4W0V5ohX6sGbpB9iP4ShObXBZ5Ml15VC0VOf850vATsMGGqb0MyIgDDNgKrc39Oa0YMpHQ/S4kDbwaiZeMQvIV/WBo+rMLhiZ2N2w7jsAydZlLPhcoEKoCWvWzTcNGjSvffUOM3Maqvv8x3/YyzCNflsucUIACwSVQIwGVDf/uwmsffIvMrFypsXDZWowY9yLuevRN7D90TOaF0yShY0v0arjXmXrblkEkR15LrRpk0dTIrggn5pAfy+fTf8WCP9bAZDKH/FgqM4Dof0ZC9+VT9rvYq4/RJ770bvPiIzeWwpPyUhxxHxRHOrjieez98zmZn+9lQCV3/yJZ3lFH6Udnp4rbdS5zJhIEqjxGreWk3FbbzQLDWJMaULH/fmvUgIpYdiTtheK8RVmUEwqEpIACBRkJGvR8IQH33B6DqVe0wWNj6qP3241lOpFwDNFnfAdd7U3O8Sm6AsS2eRvQ5ss8pfYhaBpvlfM4wG98tENwGg4CN91wS/nD8HQyY5H990KclWI9s1P523MtBShQYwI18ile3Ctlw5ZdSE6Kx/GTmbjv6feQm5ePbbv248V3vqoxDH81rP1hKtr/+ZYz9VX+hEiOvDN1akDFYPBX86w3RAQOHDmOe596D72vvQ/vfTYLB9XlEOl6lbupb7gImvy9LttrzCeg1bjmuRRwLBSfKaKNro20Cx53SY26PYFmFz8l82LrdXVsUe5jfJM+snzpuhzLzo2L23UuB+UMO1XTAvpVK1DngjaI/vZr2ZXYjyZDt3WLnOeEAuEskBurwZwLDPhkQDRWnqVzGao25T+XZbGg6PMRc9HnMPSeCsMFP0PRm0Q2EwXCSqBPn/745ptZaNbsjDLGpUZUSr+/sClllGU2BSgQTAKamujM9l0H0LeHPdK6ZMV62YUPX3kQn77+MJat2oSCQqPM44QCkSTw1H03Y9Znz+PK/hfhixnz5Y2axVlbS/5aD7PFEpYUuoTiwEmp0WmU4strSuW7LBafKaKLroN65z/ukhp1exLNLnpK5sV5GVBJbNxXli9dl2NZa0i2N1/crn2BUwp4Foh/9nFojqc7V2oyTiL+5QnOZc5QIBIFrEW1PQ7bFl8IJarA4zpmUiBcBBo3aozU1DTPwxHvLUQquTY6tuQS5ylAgSAVqJGAirAw6O1HLdZt2oFWZzRCap1kpNWtJVZh595D8pETCgSrgL/6JX4XHhozDMt/fBdvPns3bDYrxj72Fi655l7kFxT6q1m/1CuOyKcMuwqOpBiLZDsxUz+TeYbly2DOaSbzSk+stuLgRekVXKZAKAioAVD9tuLLFkr0V79ubYklzlIg8gRMB/u4Ddpqrav+rUtwy2cGBUJFQL9yOcR7nuifZkEpsr/XCZW+s58UoED1BWokoNL2nBbyK5K/m7MEP85fjt4Xd5Qj2bHnoHysncw/rBLCdxPWFGICNrW/IphitljVOaCwyASrVeTKxZCYaPfsgrjswZHUAch+a/fvk/makydgOtQHNqTKfMfEqqsDi9VzoMVRRjxqo2rJS3TqtLlTLPo9pXa8X7anaKL83hYbCHEBrRa2uHi3QVjquj7X3QowgwIhLNA8viVubzEOfetdhnhdgvrabv+bJW5W6xiWKb0r1urawWxpCYulEYzmDigy9Xas5iMFQk4gacwopAy/GvEvPYvE8f8nAyshNwh2mAIUqJZAjQRU7hwxSP2AaMSzr09BWt0U3HztADkIcVNOsVwvtbZc5oQCkSawffdBvDJpGi4ePA73PzMZefmFeOmxO/DHrHcQHxcTlhwWzViYb34OlkvvkI+FdW72apy6GPulPnXa/l+55RMa95aBEMe9UBz3VElo3KdUfpdy60nt9KC8JEijiy63HFdSQAgUXTFYPLikwqHXuyxzgQLhJNAy/iycl9gOC4/+glxzDtSIin14alylZFDl7IyuaiDlQhSZe6uBlTZqMQap7VCchpqAbvdORP0616XbSkG+y3LpBfFtPZf9nVU62/OyIRrGFxfAMmCU5/XMpUBYCYTuYGokoNK4QSrmff0KfvlqIhZ++waSEuKk4Mhhl+LzNx+FRqPIZU4oEEkCE978EkNGPiHvnzLk0ovxw6cTMG3yk/KeKjHRhrCmsJ7dDZbu10E8+nqgCU36ykCI414ojnuqJJS6Z0pcfe9uXuvr/rG+8BTIfvE15EyYCMsZLeUACwddhfy77pHznFAgXAWWHl/gHJqi2N/LKYoC8c+5gjMUCBMB7c4dVRpJhsmGLn+ewvLjl+CL2oOw8KLiA0MJtWQARQRRZPrft1WqnxsFSIDNUKBYoEYCKqJtg0GPpo3SXIIn4lIgkSfWM1Eg0gRq10rC84+MwupfP8Rj99yEs1o0DisCK5LkePbH7MKemGzk6obK5Zwf+iDvnBhnsuxsJPM5oUCoCxTcdCuKBlwmh2E+t4185IQC4SxQNyrN4/BSo+shRlu5syw1N3b3WBczKRAsApaWZ1arKx0SG2PYJYPQo3uvatXj7cYsRwEK+EegxgIq/hkOa61IIP2fN3B09QsyWc15svixknmm8k9VlBtw4heBMbcMxpBLuyM2JjxOf1YUxcXJpnh3qYxlc3OX7bgQ3gL7D6WjyBieX5O6Z+Fu7Fm8R+7Ag6sOwphrlPOcRIbAiYwsLF2xAeKx5IjFtxlu3bGvZFbYzF9a/yqPY3mp7bvyvipipfbWntCO6edM9muDbM5l+7r+UJrWEcWZIlcg6EduPqMlii69wqWftphYl2UuUIAC4S/AgEr472OXEaavexPH/n5BJpu5QK47XiLPamZARaIEaPLh1DnyW3zEN/lUlELtW35sNlulFHdrt+GI5kCltmHh0Bd4dfI0dOx/B0Y/9BqmfDsPW7bthdliCfmBbftpG3684yec2HZCjmXv73vx89if5TwnkSHwxXfz8cTETxBl0LsM+K81m+XzPRye5y4DUxcaxDTCy+3ew7mJbRGljYa4r8pjrZ/HeUnt1LX2/0rTulBa1HMmQATfFeeyfZ3nM10Q1D/sXCQKZE3+FKem/YDc/z2N7Lc/QP7oMZHIwDFTIKIFGFCJsN2f2uE+lxtxOm7U6XjU6BhZD+RTQpzEoVEn3qRA9iuQbf2tX4brUi7CmJSrcVutgRhtuhUHC08FsgtsqwYF7h19LZ6872bUSknElzPn47o7n0G3K8bi3qfew7c/LobJHJrBlX+//9dNdd8f+1CYF1pff+42CGZ4LfD3uq245vKeSIh3/bt63aBeyMjMwaEj9mCb1xX6uqCf6rsktT8+6zITf/TeiK+7zcHgRtf5qSVWS4HgEDB1vQgikFJ45RDYosLjLOPgkGUvKBAaAgykUZF/AAAQAElEQVSohMZ+8lkvxVe/Om7O6elRo3d94+ezhlmRR4HRNw3Cuy+M9yrFxnh3yYzHhsrILCg04vDREyjrK5lF/tHjGX47Y0CcxPJe3LPI1mY6e7hP2YvPDq5wLnMmvAVaNG2AYYN74+XHRmPxjDfx7YdP49yzmmHBH2vw3JtfItTOzBJ7S7f9P2Su/E/MuiXLxFfd8pjhnUColSooLIJBr3PrtuPcPbHebaUPMsTZgVU9+8VoNOHY8VMQdfigK6yCAhSgAAUoEPYCAQuo7Np7CJu32a8lL0vVYrHKr4zNzg3Py05027Yi4dnHncPXHtjvnPc0k2G03+PE0zrmha/AvoPHsHDZWsz57S+s27zDb8GMcY+/jc4DR6PfsAfR8+p78PoH37mgimv/u1x+F/pcez/a9RmF7+YscVnvi4VcZCNdd8Stqn9zj7rlMSN8BdJPZOK7n37Hrfe+jOvvfBbi78Ut1w7AV+89jsRSR/dDQUHJyUGrov/cuhqLAjTct8UtnxnhKdC6VVNMm70IhUVGlwGK57rIEN94KB59neYuWIH+6ut66XqvvOUxnNvrVpc0ecpsWUwEUCZ/8SM69L8Dva+9Dz2G3IMN/+6S6zihAAUoQAEKUKBsgYAFVLJy8uUb5X82bffYG/GG46EJ7+OLGfPD8siI9uAB1LqyP2KmfOIcf8pVA6HkZDuXxczhw4fw008/iFl8n74BVy14DcuX/yGXOQlvAZPJjMde+hiX3fQIxj/5Lh598SPcdPcLEG+Ct+8+6PPBn9WiCWZ99jzWzv8IEx4ehc+m/4JNW3fLdsSZKw8+9z7uHjkEGxZ9ircnjMOzr0/BwSPH5XpvJgW334WsyZ9C/YUuTu5bxSIOGpv7y1AtninljuX3nJppYOKkabhk6L149f1vcc6ZTWUQZekP7+DhscPR4bwzoShKzXSsGq2aW52FTpNuRVrzWGctOo0Ffcefh9z/PeXM40x4C4y+8Qp5aU+nAaNx/zOTIZ7rA4Y/hKkzf8PIYZchLta3Zx3uP3QMov5H1b8dZcneM+oa/PLVRGe6YUhfWXT9lp2Y9PksTH33Maxf8AmuGtgd9z39XplnL8qNOKGAHwWemPipvNeQH5tg1RSgAAV8IuD+ScYn1bpX0lp9o9yjazuMGPcilq/e7FIgKzsPtz/wKuYvWY1H1DfRSQlxLusDtXAkPaPMNw85ufk4lZVT5a5EzZkFGF2PUmkyTsKwYrlLnZ9P+RiZWZnOvCxTAV5//SXnMmfCV+Djb37Gj/OXyyCGODI/58uX8OyDt8kB3/vUuz4/U0UES1qd0QjRUQb0urA90uqmYMXaLbI9ce2/uNRi+ODe0Gm16Nu9E8RXmi9dsV6u92ZibtES1pQUqJ+I7QnuP1pFi4uL+rutGFD3HLc8twxmhIVAk4apqJWcIC/tOXj0OI4cy0Bevv2G2aE6QFtCIqIvvwTDF49G+/72m2te0mAPzrh3EMxn87kdqvu1sv1u0awhZn78LLp3aYNlqzbiS/WAkbhB7WP33Ijxd1xT2eoqLN+gXh188c7/8Pj4EWWWrVs7Sb6WN22UJh+Tk+Ihfhb/uQ7dOp+Ljm1aQa/XYcTQ/vLSn2279ovVTBQIuICiKDCqB5oC3jAbpAAFKFBJgYAFVGKiDXhHPcp9eZ+uMuK8+M9/ZFfFEe/hY56Tlza88cwY3HztAJkfyIl4kyOO6tw4doK89OGND09f+iA+VIpLI7peMQYXDx6H4WMmuH0Fojd91e7a6bGYptRlPzt2up/Bc/zEcZw8ecLj9swMHYGKejpv8SqI34+7bh4sj8yf0aQ+hl7RE/8bdyPEZUD7DvjvMhhR/7HjpyDOWhH9PHbilHyzbSjx7RTiXhdH00+J1V4nU/tOEB8uy9vggdwXcGfuo7iwqA/6FA7CE8ozGJzatrxNuC6MBIZf1Qd/zHpH3julpfoB9O1PZkK83orX3Z8XrfR5IFHQicsbxE1B9x9KR5HRJLL8lqJidX6rmxUHv4A4mPTBxAew+tcPsGnx5/jpixdx49X9ZKDa170Xwe96dWshpThI4qn+GXOXQhz5nzxlNsQZLY4yR9JPonnjeo5FpNZJlvPicjw5wwkFAixwyUUdsHr9f8jKyQtwy2yOAhSgQOUEAhZQEd0SRz1eemw0bhjSB+OeeAfiet3hdz2H4yez8MXb/8OAXheIYgFN4is6xWm44pIHcUPEL955DJ9O+8V57fA3sxZBXG7x+8y3sHLuZGg1Grz9yfeV7qPpou4etzFdeLFLfkK8/WhRyUyN2mZM4L/XvmQXOB8AAfHBThw1LN2UOOoo8rx9U7F243Z8/PVcj2mm+mZa1FUy5eUXQpwBI45MXnxBG7kqW30DU/omuFFRBogztWQBdaLXKkiI0ZWfUuJhmvgakJQEBYXqVu7/oxCNIYUj8FTOO3go92X0bt9UFoqL0iJWTWJBq1HKb6dUP6INWvUDi+dtDDqNqBJRek2l6qxwrKX6UFF5dUiIjdbVaB/K62OMal9Zd0d9jv0mob2YKIqC885qrv5t6CvPUjy//dlYvHwdHp7wAcTz04sqvC6y8d9d8v4Q3a8ah0tvfFgGymf9usy5vfh7UPo+E+LSO2cBzlCgEgLizNYffvkD7372A7bu2Ce3FIHClf+4fwuUXOlhIm4cXtZrusgXl2h62Mwta0Cv89Gja1uIvyni9+ua2592BlXEa350qW8nEX8DckucLZYQo0edxKgqJ4140VN7VSvBtQ41S/6vTt3iNT9efT31VIdOq5H1J8dVr/+e6i6ZlxSrl+0Y1L8xJfPFfG3VTX2Zg5gP1hSrvuaLv0lV6V+UXivH7suJRgUTz0FxKfTXPyxE6SRuoOzL9lgXBShAgaoK2P/KVHXrKmynVf+wPXbPTbhzxCB5va6oYtrkJ9G53Vlithqpaps63izUS02RFYizAmolJ2D/wWNyed7vf8uzBMTRmoT4WIwY2g/izZE4wikLeDkpHHItCq+82qV0/p13w9z6XJe83n0GuCyLhV69+iA2NlbMMoWxQIc2Z2LKd/Oxa99hOJ5f4s34h1/+JEftOHtELpQzEWdVZWTmyGv3Sz+K+kpuKt6Ii+vkxQ2h333+HojfT7E+MSFOXoIh5h2pqMgI8TvgWIb6ZsebZLlmKAq274Emyh5Qqf/oN6i314yYQTZZVdK7Frks8kTSnXlI5jvrti/BuexFuwqgFlenHsvC/uNxXVnb+D5fURQooifqI4IwKVD/KWoHq9I3dTNv/4tLQJ994wuIewf1vHq8DLZnZedi1PDL8NGrDyI+NsbbqrwqJ551428fimWz38X6hZ/i5mv7yyP2IqApKhC/e+ISuF9K3GfitafvEquYKFApAXEZcf9hD+HJVz7DB+rr+G71tV1U8N+O/Xjoufe9PvvKZLZ4fD13vL7bbFZRbYVp7G1DIM6AHHPLYIj3XQnxMVi07B+5nXjNLyp1WbL4W1Ly96/AaEZ2ganKySp++dTWcgpd61Cz5P/q1G0yW1Fosnjsm6W44byi6vW/ov7lGy1yHGaL1a0fOfkm9e863PIrqjOQ64tMVhSpY6hKm8JfDt6Hk9nz/pTvQ5b8tR4vvvOVWypQ35P4sLmAVKUUFUA5tB0pOvHHNSBNshEKhLlAcAwvYAEVcdNZcVmBSOI068EDLsaQS7vLNwl7DhyByHcka/Efv0AQdTi3pbxmWBypETeKe//LH1GnVhJ6XtheNi/61KRhmpwXE8dd+avyTUTZb7+P4/9sFdXIlPvok/Kx5GTI4GvQo8clMqt5dC2Mbd0f/3v0abnMSXgLjB9lv6Ze3IRWfMPCkJFPyKPn4mjmk/fd7PUNDLt3aSuP8j8ydrjb4x03XuFEFM/hux59A1nZefjyncfguJZeFEirkyJ/J8WNcsWySOJMLUfgUSyLN1DiTaK3yabYX26KjFaIbUzqm05RT2GRRS6LPJGcb34LzchXkygj8sQ6b1OB+qawrP4Z1TeNok7xxtHb+vxRTowpTx2fP+r2RZ356ocPs8Xmsm+8rTdf3afC2Js0ffYiiHvzdO10Lt589m4Z6BA3S77/zutw0fnnOYN83tTlTZl257SQQfJaauBcr9MirW4tiHmNxv78FHWIwGHT4ntMiEdxGYXIZ6JAZQRm/fKHvHTyt+mvyeeyY9uBl1yADDXofeTYSUdWuY/iOejp9dyRJ47il1uBh5XijOG6tZLh+FBaP7U29pa4rNRxqY84mOTYXLweiNfPqiYZUVArM6mvwSXrULPk/5J5lZ0Xr6dl9c9ms0dyTGYbKltvZcqb1KCOGIh4C+u2XfE6t/xSFjW5XhiKVJU+WIuNxfh9ld567m5sWTKlzFSZ+y2aLRYcPZ4B8TtnKX7vUbKf4nOHWG9Wy5XM9+W8dtFU6J+9EvpJYzGvazJePTvOl9X7pK79WRbM2V6EX3cU4Wiud4FanzQcSZVwrGEpcPodpJ+H9+/2ffIIpDgK6Uizik+zvvep91zW5eTl+6Q3c377y+NlD+IUWXFUVDQi7hEhToGtn1oL4qsG3/tsFnp0bYfYmCj1b79NRsejowyiqEzihnJiJj+/UDzIVCcpCg1qx3iV6rdsKLcRE8c2Yl4kx/J5Z58pFtG3ztkY1rIrzmiY4lXdju1LPkbpNRCnmpbMC6b51ORo6LVKlccXiLGISx9S4g1V6qNB5/2vWP202lj43eu4946hOL99a9RT3+COGNof3334DIYN7i2fE76a5BcU4aaxzyP9xCk89/BI5BUU4tDRExBHVEUb57c/WzzIr/wUbzAWLlsrv+GnZzd7oFGu5IQCPhB48X93QFxu+ZQaNOzfs7MMbvig2gqrEJfGPf3a5/hADaI/Ou5GiOCKYyNxU2Zxmvmrk6dDlHPk89GzwD71TfiGY2Y4UmahTf37CezLtDjzxLrMwsh6gz7z56W45vIeaFivjgtco/p15XKmGsyWMz6aiMCByWSG2Ww/U0LOF39AFPdLmfLdPPmhUpzxIt7vbN62B106tJat9764g/zCgH827YBY/+XM+WqwMcV5Xy1ZyM+TjAIrdpw0o1ANfPi5KVYfQQLf/rgY7fqMQp9r70ff6x9Av2EPQDz3HQRLV2xAl8vvkutFue/mLHGs8tmjcvIwtIu+dKmvRx0DdBrP91d0KRigBRFEuXNuNiavzsc7f+fjth+zsOKgCQFqns1QIKQFNIHqvbiU5u0J4+BNqsrRFk/jEEfgxVEgTylf/QAptlm2aiPe+ngmvpr0hLwpojjF/JtZi/D93KVQFEUNrESjqMRNCx3zsbHRYnOZTmQV4fDJAq+T3EidOLZRZ+V/x3JugVkui4lZPULsyK/KoziF82R25fpXlXaquk16ZiFM1RxjVdv2drsC9Wj7qVyj1/u3ZL3G4qNSYl96k8QRF3EWyRvPjMH7L9+HR+++Aeee1cybTStVRtwLRVxaJM7AunrUk+g/7EGZT+13fQAAEABJREFUrhttPxtKBBTffWG8/JpP8QZj/JPv4ol7R8DxQaBSjbEwBUoJiLOixBFwkcRlZ+KxrCQ+JJba3OOiRT3qKILlZaXd+4+4bCeOVIo2xYfOzBLf4HZuq2by7MlmjevhwJF03HzPi5i/5G+XbcX9GrxNWjVgLDZWFAViG0eQVaOxL4s8kfTFwVdx2Z1YVleLzRBl0MrtRF5ZaWemFVtPWmU5sZGnclF6+z0O1G7Icp7KVDVv2uZCPLowx5k2p5vVv5/A15sKnHli/bYMG3Sqh1YdXFXbqsp2OWZF+hxXj4N4s704GT9KPRjhTdnSZYS/I4mznw4ePu5YdD5u331AzosDOXLGR5Ndew+jfb/bIb42WdxkXMyLG9A6qhc34BcfKtv3HYVHXvhQnsHYqW0rubr9uS3xfzdfiRHjXoBY/+2Pv+P1p8dAPE9lgQBMbvwhC/fMy8H1MzPx47aiALTIJoJNYNe+wxDvycXlyeI+P+I5W1ZyvB8vNQa3RfGZ4oOJ92P1rx9ixdzJaNmsId744DtZTvz9efC593H3yCHYsOhT+Rnl2denyANIsoCPJsrBbR5r0ignPObXROa3W9QXyFINf7uloFQOFylAAU8CAQuoiMsJ+nbvBG+SXmd/4+epw5XJu/HqvvINwyMeLn3o16OzrEocfRTfXpIYHyuXxSnmF3Q423lUUpxqK47syJXq5MDhdHUKOMrLBU4o4AOBDf/uwmsffIvMrFxZ229L16hvbl/EXY++iZLPQbmympO0uikeT6NdNvtdZ829L+qAjYs+gzhdff2CTzD8qj7OdZyhQHUEnpj4CS4Zeq9XSQTGvWnLBpu8jMJTAF3kicBJyXqu6NdNBi3fem4cXnznazhe2wf1vxDjRl6N0TcNwjsT7oFYdpxN6dg+Nkorb5jszaNODR6I7UQgQ5SPKg5saNW/vmLZkRyBFp0acBB5iiI+1gMxBk2FbT29OAf3z8tCodW+jdi+dIpW6xH90Kj1ll5X3WWtxt6uqL+8ZFCDFGL8el3FY6pun0puv/qQSfr8pH5IL5lf1ryijic2Slehu6ftlRIAfS7uCHG0e/6S1fKsEbPFgk1bd0OcGdX2nBby8uISxas927J5Q7fX9ZcfGy3rFZcuL/ruDfmNWvO+eUV+eLy5xLcqKooin/dr53+EBdNfw6qf30eH886U2wZ6YrQAH6zJx6kCa6CbroH22GRJARGAFEHx7Jx8eda4uEl4WanQy3uoiNdwcSm0OFAk3ruL+wUlJyXIZsXZiPnqAdbhg3tDp9XKzyjiff/SFevlel9NbAkpHquyIdpjfqAzC0w2HM93/307kGWFeswz0N1hexQIOQH1LV3g+ixuzDZg+EPyTW/pVjf9twe9r70P4pTU0uv8udzqjMYQEXFxpoo4Eiru7yJugHVOK/tZAeKu+DPmLIE4kpmbV4CpMxfg6st6qEffSr5t8r6HWaZM7wuzZEQJTJ35GzZs2YXkpHgcP5kJcbPY3Lx8bNu1X/3A91WNWIij5eJ0db1e5/P2bYfsv0OmL3QwT/d9/T7vsJcVfrWpEGWl6eqRfC+rCetio0dcickv3SeTCGKLD5eOZcejCHSLdeLoojcY4s2wp+C5I++sFo09ViPePIsVJzKyxINbEvdPyct3PVqekWOEt0mcJSgqtVrVgI+6XU6BSSzCZLYvO+oR99MRK8S9fUSeuJeBWM7MNVXYllq1KIrMXKN8FNuXTll59nZFvaXXVXfZ2zPxcgvMEOMsVD8xV7fNymzvuKePt+2KfSXOSqxMG46y9rt1yN2AW68fiJ7d2uH+ZyZh1bqt8ubHw+56DhaLFc8/PNJeKIBTRVFQOyUR4l5w4vcFHn6iowxoUK8OyjwzxcM2/sran231V9WsN0gFxDcNijNJmjRMhThTd8uSKW5BQkeeOKO3MsP46bflELcY+Hf7XjVgfoXc9NiJU/I+R+Lyf5mhTsTfnqPpp9Q53/23NTkHtuRUlwrFa7HFav9WQ5cVNbAQo1dQN1bj1nLjJA3UGL9bPjMoQAFXAfffHtf1Pls6eSpbfjvO4IEXe7xGvs3ZzdH+3DPx6Tc/QwQ2fNZwBRWJm8PdPXIInn9rKi647C7ceu9LEJdc3HhNP7nlDUP64oymDeSRVHGNpTjKOU49eilXVnLy1KYH0H/JBc6t3tvxqnOeMxTYvusA+vboJCGWFB8d+fCVB/Hp6w9j2apNEKemypVhMDF9rINlvf3lx7JRg6Ln9DA+ow/5kYmjPF9vLEBZydMptSE/6CoMQLzeiw+aIh1Nz8CAXudDzJdM9915rbyng/h2qSo0UeYm4myTxX/+A/E15OLSt8lf/Cgv7WzZzH5/qzc/moFdew+pAQ+LvM7+6x8WQrzJL7NCDyt02/9DzJefIWbaVCj5eR5K+Cfr3+MWrD5ohLhfSem07aRFNiruT1F6XXWXswpLhhFkMx4n4l4r646Y8M9hk8c+VrcfnrYXeYdy7GP32Ck/ZoqgxWtP3SUvJ372wdvw0F3DIC6lnP35C2hR/HzzY/MhX3V86P9JCPl9EOgBiIM44kwSRbEfcPFl+7v3HYH4LCICmuIMGFF3dk6efP0X844UpQYVxd8Gx7JejSiI++h5m2IMWhQoPVG4sQ+0H0fb0+eJsCZ8CFvSCNhiu2Hm4UKM2pgDqy0FcdE6JMUZZHOKRoG37YhyibF6aMvoX2yUVtYpzgoUZStKt3WKl+VLTkZ1SqhUfzy1Ic5IjFfH6GldMOQlxemhUZ9vVe1LSS/OR66A/RNNAMYvIsKimWuv6CkePCZx87aMzBwcOOx+zbHHDXyQKY7CiK8RnD/tVfz4+fNY+O0b8qagep39hSguNhriXhZ/zZmEpT+8Ld8YpdZJrnTLvx2di/lH57hsN3Xvx/gve7NLHhciW8BQfCbIuk070OqMRhDPNXEdvlDZqX7IE4/hkEyzdHIYhzT7cWndczGwzrnos7gdxlzeBbFH30Bs1JeI+nA4dB+Ml+U0+/+F4bF+zuTIlyuDaCKO8tzYNgYiXX9utLNnYlmkknnOlRE+I766fk+p+5sIkrq17a+zntaJ9VVNJrNFfjXzhYPGousVY7Dkr3UQXxkuvtlH1Lly7b+48tbH0b7vKFx/57MQN8q95Tr3r7MXZT0lEUipNaAnEp7+HxIeexBR83/xVAy2zDyYH5jqTNaZq0Q5WFfukHk4ni2XbfO8P/V8wh+5uP2HE/K+JU8uSMdHv853phnLlgNq3CMjJ9eZJ9a/N2+ZLP9oiXugVHZ+Y7pZ9rXkxNOXfnylBhvH/5KJu+eeqnablenj7P9czzAq2c9AzJ93VnP5zVK3Xj8Q4lLKmGj7h6dAtB2qbTRJ0qJZiv3vRKiOgf2unoDRaJL3UxGvw44vsyj5WDLo4U1L4ob/U999TJ5l/sCzk+Qm4vKf/IJCOe+YiCC+4++ByLPagEKTxetkVjco0PZC1tpeyH5TOZ3erYvML+5E5jev4tXdBdiaaw/0mixWFKl/l0Rb4jW6Mm0Vqf2yWT33T9Qr6hRnwnhT58CWBky5OgXju8XhwYviMe3aFFzQUOf1uMtqQ5zxZ1THWNb6ms4vMllVdluVxymMmSgQsICKuIRBnLrteJPsiV7cuFbki5tRicdAp/JOcxWnFoqvU65qn1ad/NPjpguP/SrzA3lWjmyQk6ATEJc9iMt+xDX3P85fjt4Xd5R93LHnoHysnWy/5lcuhPLEqsC23/3o08xOifiknXdjFHfMFwEWzd9zg07ipjbREGlYcUAlWqfIZZl3XnTQ9bd6Har+1t06nYuZc5dCfLuIOHIoahRnY3353XwxKy9RkDM+mlw3qJe8f8TiGW9i0Yw3ZBC9a6dznLV/++HTWDl3Mn79eiLWzPsILzx6O6LVI5bOAhXMxL33ZgUlilcfySyeKf/Bun4vrAs2uhQqSF+H3EN/wGK0B10cK1vX1aFTQwPapOnQJeUwRudd60zXFtwHqL92tWwHnHli/U3GB2R5sY2n1K3WQfRNWIUutdLLLJcU7d1biabqh+T29fXooCZPbfkrr0Gid/2DD34++Xouxj72llep9Ac5HzQf0lVcpn6gu6CBXgakJ/aN56UGIb03q9/5T6f/Ir+pU3wDoriBfpeO58j3RcdPZsmzSnS6qgXcmjepL289YLZYkFYnBaJucfa5o8fbdx9EvdQUxyJEQEJ8OYG3yWS2IsayBHHtf4d+rBmajmrEQ61N09Uql0Weuuj8b1Q/0BcWWeSy+CzgbTuinAgGWNXotZgvnUxmNRKk1mpWgxml15W1nBYDDDzDgD7N9EhW475llatMvvATY6zMNoEsKy4FVQlR1TZVYv6nAAL2LqNu7RSINw9FRlOZ7I5ASu2UxDLLhOqKY4VHPHZdnKUiVlhgfzEV80yRKXDniEEQN1l79vUpEDeNddww8PPpv8rleqm1wwNGY4O2u1WOpaG1CX49vgXzTmxBmqahzPPrhJUHnYA4aiie7+LbRcQZI0PveBqdB47Gz4tW4vrBvSEC3b7utE6rtf9O1a0FjUaNMpRqQBydbNIwDZU9k0DJyoTmuP3G5aWq9OniwT/uw67ZA1F4yvWbI57qEYfPrqmDV/om4AH1KKM3jdaL18jyYhtPaVTiV+h74GqMrb+gzHJtU7VuTSnurLipbQzeuSwZkwallFmXpz5UN2/IWdFu/fNXhhi3Rp14k/zVh1Ctd1yXODx7SbwMQCd7GaQL1bGy3xULLFm+HqOGX4YX/3eHLHzb9QPx4P9dj4fHDsOx4xnQ691fd2TBUpPJU2ZD3PRfvL86dPQEPv/2V3Tp0Bo69e/A+e3PlqWnzV4EEWBZuGyt/Iafnt3ay/yqTmJsSxHfYTEMY03QdrLKanTdLHJZ5MkMTihAgbARCFhARdx9XqiJ69fFo6f0zaxFMrteai35GE6Ti+v2Vg8Oqu8w7QFjOTSbOMlMhEXVJUVdqz7wfwQLiBsFzvv6Ffzy1UR51FycFSU4Rg67FJ+/+ajHD35ifSgmw71GKHGne67UsUFJtp3O4FzECNStnYyfp07E4+NHyMtrxPIwNZAivubyyXtHhJSDLSkZ1rquNx4MqQGws9UWGHXDFRD3SfEmibN2q90gK6BAmAocz8iECGyLe6qIIR4+dlI8yGCIuD3Ajt32s3dlZjkTEUS5YcwEdBowGv2HPQitRoPnim8KLeoWv6vim4Ta9RmF8U++iyfUvzuN6tctp0auogAFKOAqELCASn01SCKONk5480t89NUcZGXnyZ6I09sOqxHjZ16bAhFsETdtE1FjuTKMJsOa3IKvuv2EdimdnaMSARVFUeTyFMM76N6js0xTv/pc5n1y8C9c8ssElzy5Ingn7Fk1BfIKCrF24zZMmjILW7btlbUdOHIcR9LtbyRkRhhMNGfZoO1lPyvL8JAJsX8UAgZ1YDY1lf5fHHQsma3knSq5yPkQFjiSnoHoKCeVPqYAABAASURBVD1uGNJHXl4j7ln15H03o3uXtlAUJeRGlnf3fd712ePQPPwCeMiSN0NRWyksvvSnR8xKiMtyTEeXIWPvEnk5UP6xNWqJiv9bTXmyvLiEyFMy5R6quJIgL1E7ViMvV2qYoA14T8WZtz/88gfe/ewH52u6OPtq5T//BrwvbJACoSRQKzkR6Sftl0Z263wuFiy1v6YdOnJCDkPv5SU/4rLNdb99DHGvxOU/voev3nscJQMmvS/qgI2LPsNv01/D+gWfYPhVfWT9vp6sObwC06ZPxeLfF/q6atZHAQrUsEDAAipinPePvhYDel2Atz/5HhdeORbnX/p/OO+S29BPjRjPmLsE4qik49t1RHn/p8C20DL+LJwR18LZqALFOV/RzPEAnEZeUR+43r8C4oNl/2EP4clXPsMHX/6E3fsOywb/27EfDz33vjwdVWaE2URTr8QnRk+/EiKvdFClxCZhxhFxw3nx7akYfOvjEKdci2/eCXWAgptHImP+UuQ8+xJyXnwNRQMu8zwkb5/D4vnvVoM98+Af98pLfy7cM0RelnP450ux6stLZN7+xXe6beUpw5izT5bfNXugx8esnT942swlr1mSVgYsHPdASYxSIIYnbizqyBOPSWq+y4YBWujWSC8vMbrq7KgAtWhvJlJf0+2j55QC1RMQ91VctmqjrGRw/4sw/cfFuPKWx3D7g69C3LTfcea7LFDBRHwtsgiiJCe5f5ON2FR8u1DDenWgL/5iAJHny/S/xNvx8KK7MHny23j66Ud9WTXrogAFKi3g+w0CGlCJj4vBG8+MgbjL9n1qcEV8e8K1V/TCw2OHY+bHz0IcldQXf7uO74caHDV2rdPDY0duM96LZX+scaaPXpoEg3L6aNqsWTPw0ceTPW4biEyruQDHN0zC3l+HYf/C25G5c1Ygmo2oNmapRzGbNkqTR0kuOv8859gHXnKBvIHakeLTXZ0rQmDm8JrDOLDyoEwWW/GHwH0muXzo+AEvR6BuV+pMBVt8ipfbsliwC9w27FKIN8bPvzUV4pt3nn7tc2zaujvYu11u/8ytzoYIrBQMHwFbbIlr28rdSqxUn+viocIkwhXuhaKSW6JW056Ib9gdsWmd3At4yNHoYmV5sY2npI+r+N5GN7aNkQELx71O2qbp5OECcc8UR554FPkeuhC2WeH4mh62O4sDCzqBp+6/Be+9MF72a1D/C/HcQyMh3iONvW0IPpj4gMwPhcnWjE1YZ1gRCl1lH4NVgP0KeoGABlQcGh3btMLtN1wuT+9+5sFbccu1A9D6zKaO1WH92Cu1n8fxiRvYlVzx7dyZMNrsl0Q48qdO/cwxG/DHA+rRzsN/PoSs3T/h1LZvsG/+jTix+ZOA9yOcG5z581Jcc3kPiKMkJccpjqqI5cziy+TEfKikuXfNxffDZ8pUYLS/3Mz6Okcu//THNORb7Zf+hcp42E/fC4i/B288MxZ/zHoHj959A1av/w/D7noOQ0Y+AfHtP+JGgb5vNdRrtAdeSodV9HH10eWWJWhx1Xw06f2RV4M0JDaT5cU2nlLSmVd7VQ8LuQuE42u6+yj9k2PLzodt9S5Yl26F7cBJ/zTCWoNaID4uxuVb3sT7I3G/kzG3DIa4kTlC5GdH1n8h0lPfdZM1USDSBDSBHrD4Oswp387DvU+9J0/dE9/o8PCED+T9U8L1jfMXez5ElwWtnMlhrshjePalQkshJu983b6gTrfv2alO3f/v27fHPdPPOVZjrsczUrJ2zvBzy5FVfVrdWjh4+LjboLfvtp/JIe5D5LYyyDPqd6yPRl0aydS0bhGa2/ajSXO9XG5Qpwk06r8gHwK7FyAB8e1uI4b2x9wvX8YD/3cdtu8+CHG2Sl5+YYB6EDrN2MyeTQpPbQ+dQURAT8PxNT0Qu00EUCwvzoZl+l+w/rQGlrd+geXX9YFoWrZxNNeKT9cV4KklufhgTT72Zroe3JKFOPGLwM49h3DXo2/Ccfnnjj0H5XJObr6zvW27Dsg88c2hzszqzfh16zOTzvZr/aycAhSoeYGABlTEC2X/YQ/g1fenY+uOfWhQrzZqJSdgxdoteGLip7j2jqedL6I1T1OzPWjVvKXHDjRt2txjvj8zi7J2AaXOlhHtFWXvEw9MPhLoc3FHfDdnCeYvWQ2z2QIRYBSXPogPlW3PaYE6tZJ81FLgqhn04SAMnT5Uppv7pOMO5WvcODpJLg/uOQzRmhhnZ/bf+BJu35DjXC5vxla7AYwvLoD1givKK8Z1ISRwIiMLX86YjytufhSvf/CdPAJ5/53XIWy/CaV+sld7R9O+GTT92rqUja3fBYo2CkqpU1QSGl3iUs4XC9GJLeQlQfr4ii/98UV74VRHOL6mB2L/WJf+C5hcgxi2hZsC0TTyTDbcOz8HM/8txOpDJvy4rQj3zMvGsTxrQNr3rpHwLZWtBk7+WLkBJpNZDjI7Jx9i2Vi8LDIzs3Nlnkl9nySWgz21rtUGHYzdgr2b7B8FKFANgYAFVMQL30MT3pc3fPryncfk3bbFNZAfvfoglv7wjry3ijgi+cJbU6sxnPDZ9PorhrrcQ0WMbMSIkeIh4CmmbjvoY+u7tRvfsIdbHjOqLnDr9QPRs1s73P/MJKxat1UGGcWlDxaLFc8/XDP7vuqjqdqWm3IsuGadgvyim1F05zSY/+9tWZG1yTkygCKCKCI58uVKTkJa4J9NO+RzvufV4yG+urLVGY0h/i4smP46Rg2/DOF6Xy0lOQ6610c4k2ZoF7kf8zq2xK9qIClPPdggMpSB7cWDS2rU8200u3Q6tDF1nPnRtc5B2vmPOZc1+jgZCHHcFyU2tZNcV/qeKTHF+XKlh0ntNqPlJUHJLYd4WMus8gT4ml6sU8kH28EMz1ukZ3nO92HuhqNmZBW6Bk9EbGfJXqMPW2FVkSbwUvYneKXP+xgzZjyeffblSBs+x0uBsBcIWEBl26798hTu158eg05tW7nAajSK/Pafpx+4FeLrBHmKN9D6zNb4pt1teKTNlbh77H34+KMvMfqOMS5ugVxodMm70EbXcTYp3rzXO/8x5zJnqi+g02rx2lN34dsPn8azD96Gh+4aBnG98OzPX0CLZuF7dNj6905YJv8G69d/2hHVAJL4ihDr96tg+WGVPe9IJmxHM+3znIaVwOfTf8H6LTtx7x1D8fvMt/DWc3dD3JRZqw3Yn6eg8ly234TJq/ORWfyhbr36Ac9TBxObDsB5o/aj1fWrcPZNW3DW8DWISm7pLGpIaCIDIS2umi8fG/ex31Ol9D1TmvT50LkNZ+wCvppG6mt6df2URrU8V5Ga5Dnfh7llnYlyINs1yOLDJllVhAh0btANw4eNQO9L+kbIiDlMCkSOQMDese7YfVCeut3+3NNv+Eozd7+gjczavf+IfIz0SW31COPAxu1w/fU34uyzz6lRjsRml7m9eRdvzGu0U2Ha+HlnNcfQK3pCHN3sfVEHxEQbwnSk9mHZdh+DbZea9p8ozrBB3F7IdigDjiOVtkITrN8sB3I93zsC/AlZAXFZjzgb5Y4br0BqHe8ugwnZwVah4z/vLP85H1OnDaKSmlehZm4SKIFIe02vrqump/p+R691qUbpa39/6JLph4U2dV3bdTTRub7OMctHClCAAhSggItAwAIqUQYDxA2kLNayo/yFRfZTKg36yPzDVfImtS57KYgWYuq04Zt3H+6Pb39cjAlvfulVEjd09mHTQVOVTZxP7UVvZIDFXPbrhxdVsEhQCADiflp3Fd94sHmT+ti9/7C8yWBObujeeLC6tEWut4xwVnc0h895J0YIzKzfsgPn9roVJ09ly96KS9rEckZmjlwWE/FNViLPceNNkReuyfSZDsZJeplseYocpulz+7LIh4cvelMa14b2saugHXYhNFd2hvbey6C91P3SN1mZjycta+tw7TnRLrX2amaASC6ZXPCrwII/1sgz1pevtt87x7EszmJfsWaLX9tm5RSgAAUqKxCwgMqZZzSUfVu0bK189DSZs+Avmd28cT35GGkTm7jOIdIGHeHj3XvwGDb8u0umn377C9PVAItj2fEo8n5buhoWSxmfuCLc0O/DZwM+FwjHGw86kJZPXI63mr/lTKt+PCRXLTx0psx7d8Qb+LjgJaz9fRXyzomRqXB0FKI8HxhHvYSA/ZmW/eSkegI2m317W/FM6Uex1lq8TsyHezJN0cE0yZ5QYB9tyTzb6RiqfWXxVEmMhXJ+C2h6toYIsBRnB+RhZIcYfH9dMt4amIhvhybhkYviAtIuGzkt8PxbUyG+AfTDqXNkpjjwJJZF+vjruTKPEwpQgALBIhCwd2otmzVErwvb4/5nJuObWYvk2SoOBHEk562PZ0K8cN45YhAMBr1jFR8pENYCj4wdjpkfPyvTBR3Oxs3XDpDzjjzxKC6FaNQgVV4y5w1GqJVRxPU9pTpd/JmkVC4XKRD8AtmH7GcmVNTTPNvpMxbKK3t5S9ej5eWV5ToKBJuA/lYz9GPLTkpssPXY3p9YvYKzamuRGBWwt8n2hiN8et5ZzbDwuze8SglxQfrkqaF9mJ++FkdXv+BM2fvmyZ7kp69x5on12XvnyXxOKEAB3wkE7C+FoijyRpvdOp+LF96eivMv/T90v2qcfOwx5B6IiPPgARfhrpsH+250rCkUBNjHYoGtO/YhtXZy8dLph0t7d8HGf3fhwOH005lhNVcqfKIuKjxbK6z2MAfjLqC/yYy4fwtkiv6oyFmgexM9xpwfi+RojcxrX08nH2tiIm6Mu3B3EX7YWoj/TvIMuZrYB6Hepn6kGYaxpjIT4kJ9hOy/LwXEAdX6qbXgTRJfaOHLtoO5LhEIOba6/G8HKji2Fsf+fsGZHAEVt/z988sd6oktn8oAjCnvaLnluJICFDgtYH/HdnrZr3N1aiXhk9cekumeUdege5e2uKxPFzzwf9fJbzZ58X93QB/090/xKxErj2CBBml1MGPuElitNheFf7fvlcu5ecXnS8ul8Jm4jlYdl6ImD2etiFwmCgS7gMVkrVYXEwwKBrWKQpx6hLxaFVVz412nzBj5YzZeX5GPj/8pwH3zsvGJ+ljNark5BQIiUJC+DrmH/pDJYsyVbeYfL5Fn8u4MMbkhJxSoYQERKDn693MB6UWGGlAR7ZnzjwWkPTZCgdAQKL+XmvJX+2etOEtFXNojAijPPngbRg67DOed1dw/jQVBrbc0vxOr+m2X6ZPzv3X2qOQ9U6I0URjT8gHnOs5EnsD1V16CfQeP4f8eeR3TZi/C0hUbMPmLH/HGh9+hVnICzmjaICRRbAcVGF/TI2PFGJzUTYTlWEpIjoOd9r2A40aD4XLjweyD2di9cLc7lFvUUC3iKU/NDpb/s/8zosDs2snvtxai0OSaFyz9DbZ+vPr+dDzz2hR88s3PsmuvTJ4ml0Xe59N/kXmc+E/g4NLx2DV7oExFmdtkQ4eWjJPLIr/o1A6ZxwkFKEDYUa3mAAAQAElEQVSBsBTgoAIqUCMBFfFB8fUPvsNNd7+A2x98Vd47RVzuENCR11BjbZI7OFvWanQ4N7GtXFYURT5yErkCg/pfiKfvvwXrNu+EuCHbmP+9iUmfz4I4c+WjVx9ETLQh5HBs6oHBgmHRkN/0kH4esvV3IfOTIbAd4vM95HamHzosnufiJoPi/lmi+lC/8eCmrzfBYjSLoVSYDKtWVFimJgsczPI8jj1ZvPSnvP0SZdCjUf26WK++jq9YuwW79x12WRZ5e/YflXka/t0vj7Ja62Lqtkd8w+5lJq0hvlr1c2MK1ISAuPSnZDq86nnsXvasvETHcYlPRf3KT18jy5esp+S8qYYu9amo31xPgWAWCGhARdztXhxtFx8UP1OP0JhMZpzMyMI7n36PoXc8jV8WrQpmK5/3bUTT2/FZl5k+r5cVhq7AdVdeghVzJ+HXrydi+gdP4/eZb8nL4Vqf2TQkB2VZqYUtw7XrNosW5l+0OF50DA8l3oIHTk3CvVtn4oVd8yBeI9KNuXJZ5L2193fnxtprLoCSzJvQOUFCeOa8s5p5ddNBcXPCULrx4MmdJ9W94iFY6Ckr44RaNnj/N0rSeexc8yStx3xm2gXOadUM86e96lVKiOfrmV3N99NGvd5Bi6vml5miklv5vlHWGEkCNTJWcSlOyXR4pQioPAeR5zGg4uGEwvxS91oR25ZM5vyjNTI2NkqBUBYIaEBlyrfz8Om0X3D7DZdj/YJP5AfFWZ89jzXzPsKAXhfgoQnvY8WaLaHsWa2+23YdhTPtF2+21VdCNejkzBPrZX61mvl/9u4DwImiDQPwm+R6o3N0VERQpAmIDUFAREVBBBG7YkURxIIdEBsWBBFsqPxYUEBBQZqCIirSpCtFem/Hcb0l+febkJDc5e6SuyS3SV50tszOzs48u8ltvuxuuLKOBA4fPQF5XWTn5KlWHTqagi9m/IQa1aqgedMzUbN6ZRw8fFyVyc3LV2WCaVDclSiWHUbkWnOxIWoV1mdtxbqM/dicdRgGgwF5VrOal7z/so+q7hpqV4bhgtC9LVB1MowGofrgwWpnV/PbXjTs3Qzj79/C9PdCGDJO+G079op7NY1CbIRrJOjGc2MQU8HPdrG3j2MKUIAC4SaQfOFzcE51LnoeZ3V4UeUlNexelMP1LVwtj0tuo8o71+M8HRFXS5XjgAIU8FzA6HnR8pU0my2Qq1J6XnUpHru/r8vDZ2NjovDmCw+q56hMmbGw0IZCY/Z/Oz9E+5/OUcneI3uezBdYCmCe+NPpNPUPLVt7J0zNOp0ny/+3BNb9Kdoy/h8KAvsPHYPca5+Zla26s/+gbT4rO0fNy2DPgSOqTE6uLegiecGSTG3d3x5gusx9frH9iokEtPeJYpdzAQV0IND81uaI8EPAwTT3I0S+PwgRcz+AacabiHzrLhj2bfFrjxtVicCnPZPw+MVxuO+CWLzTPQn3amO/bpSVU4ACFAgygZhZM1Dl5l6OFDv1c8BqRcxMW74sly5Frlsjo3KlWu2eg3Oq014CKsNVntuAiputxdVsq8o71+M8HRnPgIobNmYFXCC4NhiwgMqJk+lISU3Hjdd2dCtkMhm1ZZdj1Tr/niS63bgeMgv9skuxTTqRCcvs1cUu5gIK6EnA2MyKyAGuz2KIPv8/RFxr1lMz2RYK+EQgqV4SWnev7VFdSVG5HpUz5OXA9Pt017J5WVreDNc8P8xVjjGi61nR6H1uDJpWM/lhC6ySAhSgQHALGA/sR+TyZY5k2rcXMBhgPGjLl+XQ/hlPpmpD/h+yAuxYWAsELKAiwRSRrlu7uozcprq1a0C+mZdnq7gtEMSZ9l/6+az9t45e/Hj57+qXfyTDZA3YrpDNMVEgYAJRj+cjbmU2ql4+Gg2zGyKx9y/Fb1v7Vqf4hVxCAf0LdLrnLLyK1xypo3WZavRV1sWOPFneofYelV/qIPWw+yJHtZN290uYSwEKUIACOhKQ58U9fOg9DHr0fpXkeXGS5Flxj44fjueef7LE1sotObUufLHEMt4uLK581WYD1C1BEXHJxRVhPgUoUEggYJ/iMzKz1abjY2PU2N0gIT5WZWfl5KpxqA3Gb30Ddy+/0dGt3n90xaa09SiwFmBj3irMXZaOn1Zm4L99odl/R8c5ETYC0T8vQPzYN5Hw8ZuI2z8bRpxA1Km8g//9XsTBWiSHGRQILgGDxeLbBlcu5qS2Rn3fboe1UYACFNCvQFC3TJ4XtzZ3O9au/Vslg8EAg8EAeVbc2u3/YOOmDSX2T27JSW73dIllYgs9G8V+C1CR/AZXlVhPdS2gItvjrT8lMnEhBVwEAhZQsW/1tfFfYsRbk92mj76YbS8WcmPLn7/gv4Uf4KItp29/aLMxHavnvI4leXOwLH8xdhzMw5a9uZi/IgMbdtgCUAA/YobcweCmQ/IwWnn47LEU2yWhh4+mqIfR2vJOulkjOLKiFi1E/Li3VIrcuF41OvpU3j9H/lDzzgODweA865hOSwteA0cnOEGBMghYo2Jgvqyv65pRcVpeH9c8zlGAAgEV2Jdmxhcbchzpl12255ztT7fgiw1F8wPaOG6sYgUq4GrbuJptXJ6NYg+oFH5mStIZbh5ee0or68hqbP/hOmz4KBmbvzgfB5ePPLWEIwpQoCSBgAVUoqMiUa92DaxevxXLVm9ym/7buV+VMRbzoaqkjuh9Wa1bb8bUtzNVsrf1y3eyMPTZxdhp3mzPcow379b+MKtYivsPmI6CnAgJgX4PjETXfo9j6IiJqj997x+h5iXvqVEfqDx/DLJz8nDg0DFYPH2Gj5eNyOvSDZmDn3CbrFU9/0WUnJzTD+n1sgksToGgFzBfcz/yHxqPgmsehLnPk8h/YjKs9ZoEfb/YAR0JBGFT1hzKV8GMTUdOf1EVyG4c0AInX67Phj39eiqgckALtNjzZGwPtASybfZtScDn6438+2n38Ms4hD6z7Jp/KzL2LoIlPx25J3fgyKrROLb+fb+wsVIKhJJAwAIqzZqcgQVT3/QoJSbEhZKx6kvOJZdieWNTkbT2nDNgNVhVGedBaroZYCwFof7vjPq18OYLD3mU4mKifcox6LlxaNv9flx58xPo2PtRvP3BNJf6r7/zWTTrdJdLmjh5lkuZ0mZyu16FzCFPuk1NG15Z2uqO5UZjwN6qHNvkBAX0JGCt3xSWy26E+YJusCZU0VPTKqQt3CgF1hwsUMGMf45VTEBF73sgp8CqfL7ZxICKX/eVu6tRSgmyyPNT/NqmMlSee2IL8tOLPtsrfd8vZaiNq1AgvAQq9FNKVnYu8vLyw0I87cvvMOe9Ibj5yQSVDkbPw6GYBYir9jsSkFTEoEFyZJE8ZoSeQNXKibimS3uPUmRkhE8BmjRqgJmfvozVCz7CqKcG4NOv52LDvztctvHogBsx94vRjnTLDV1dlpdnpl5sQ49Xr5RUSZU1rpyLyHcfQOSIHoic+AiM/xS9bUgV5IACFSRQ0KQpTkz9zpHyLusoLYGlRk1HnizPGP6KyueAAhSgAAXCT0CPQZXw2wvsMQV8IxDwgIrcYjDmw2noc99wtLv6AbTudh/ueWw0ps5a5Jse6biWQec8BfmVn8caPo+W2y9Ci/8uRPS6RFwa2R3RVtsDeaX5VVAd7c87PS95jhSlfaiuw28nHR6cKLPAI/fcgHPOqoeY6Ch0uqQVkmtUUbfiOVdYo1olNKyX7EiVKyXAV/9qRNbE6NTPMKb/Rxg37gM8//xLkBOMKlFxuKJKY5x7VhO0anWB2lxkVBSMe/5BxMx3YDi0A4a8XBj2bUHEFyNgOHFIleHAlwKsq6wC1sQk5F90qSNZqtdQVVmjox15avn5LVQ+BxSgQPkE/j5YgE9WZ+KzNVnqFiC5zSUQ6Zddnn0huD/NjC/W5+CDFekIRLvs2/iGt/qU78Dy49oGgwEGg8GPW/C+6ugqTRCZ2KDIion1riiSJxkz/skp8XiWK6SkHBMFwkEgoAGV9IwsPPT0GHwydS7kg9l9t/bA3TdfjfwCM14e+zlGvTMFZrOPfyFBZ3vxvKTmuPmcOxAzOdeRzvmiHu7/9B70634BbuvRDrfcci6qnFcLkDuBIk0wNEp2JGOLhjBd31ZnvWJzgl1g977DOHz0BOSqFee+TJ+zBM+P/gRyq8+e/cX8fKvzCl5MR5ti0LLgQrRu2A4XtG6Lc+NrqROMeETixbOvwcRGvfFAn7scNRq2rXJMO08Yd6wDnDM4TQEKUIACYSOw9lC+CqhM/jtT3eLy5frTzzXx5/QvO3OLGLu7+2N/ugRUsvHB8vSAtu9r3upTZP8wo2SBM7p/iYT6XWCMTER0pbNQs+0wVG/xkNuVZvybW+LxnFtgdbseMykQigIBDai88/EMrFy7GRNfewyT3noSQ+7rgyce7IfPxz+L54fcjq+/X4zv5v0Wis5F+mS60AKX1DkStd+/HNXHX4rIV9rAdMulgAFA5XiYBnZzJKPka9n8P7gF/Nl6efDzx1/Ogbs0QwuQFN52ZlYOhrw4Hhc0PweXXdjcsfiqTu1w+UUtUKdWdSz+Yw1uvHc4nIMqJu3dIzrSiLIm06lvZyIiDKoO/FzoZwNTs4C/tqn2yIOqI4wGNV14EKE1pHAbIkwGGLXyhfNl3qQtg/ZPxjLvrxSl2WibgbyO3W1Duh8VYVR9d7e8ovMitbZphGVqn6wL/qMABSgQAIFWtSIxoE087rogHre2iA1YuuLM6CK9k/f1wpl1E024TWvXg+0TA9Y2cbi5WUzhpnDeDwK5PW90uZ0zu9+t2heiVuScypexHzbrlyrll4IaXT8bze8/jKa3bUTt9sOL3U6fc6Mdx3O0dh4nBW88N6ZInuQzUSDUBYyB6qA8K+UbLWDy8N03oOPFLYtstn+vLujS4QLMmL2kyDJmVLgAGxBEAlnZOUhJTXebTpxMd+mJ3IL32PD31JVh419+FCYtOGEvIK/Vh+7oiYF39sTUiS8gMSEWi5b+bV8MCWQkxEairEmCHlJZTFQE4g2A5ahr22QZjtnyTNon+9gWF6mswoP4Zm2KtEHqjNQCJ+7aFqUFCqSO6EhTkfXclS9PnmxH65rb7Zi0M+/YmAi3y8qzTV+tGxtlUsdDWeqLjTZJ15mKEdizZxeeffZxXHNtZ9zY51qMeWc0MnOyiynNbApQoCSBC2pHqIDK3a3jcFvzmIClK86ILKlZjmV1kySgEoMHL0wMWNvEod/5DKg4doIfJ8x167nczmlu0BDQ/r5bTuXLGJ7+k6v004Pjb0Gf806/1mJOBVT6nhftOMbteZ52neUoEMwCxkA1/rj2AU+2dcUlrWTkNl3V6UJs3LLT7TLvMlmaAuEr0KF9Cwx7uL/bJLfZ2WXSTt2CdzItE1PefRaVS3g+SmRkBGpUrYzs3Dz76sjNt+B4Wm6ZU26B7fa+jKx8pOSYAS3A4aj81IRZC7bIZL52kpFarTEKbngM1lpnHdAvfQAAEABJREFUwRoVDWu9Jii4bQRSIqsWaUNGdn6x7cvO1balVZqVU1BkvfL0p/C6KZqNthnIJeCFl8l8gcWKkxl5fm2DbKesKU3bL/naPirL+mmZnj1bQHzCMY186Xks/X0J0tPTcOTIYcycOR1f/fp9OFKwzxSgAAVCXkCeF/d2vREY06S3SvK8OEn2+ZGNrlEG1sxcWP7YDGSfPtdSCzigAAU8EKi4IsZAbfpEapraVI1qldXY3aBalSSVLd+aqwkOKEABvwhkZefitodfxpFjJ/DSU/cgMzsH+w8dw8EjKWp7cmvP5GnzcehoinrG0ZyflqlgZ/vW56rlPh8YDTCeVw+F/xkb13bJsrS7BvmPfoj8EXOQP/A9WM671GU5ZyigdwEJomzdqp0wF2ro6i3/wHygEQr+qA/zCtc/zeb8dBxZ8w52/tgXexY9gLSdcwutzVkKhJ9A69oR6vaC86pHhF/nPeixXCEgt/70460/Hmj5t4g8L65lZkO0TqqvksFggMFgUNOSd35iHUcDLAs3wHow1THPiRAWYNdCRsD1rM2P3crJtX1jKb8oUtxmYmOi1aK8fFtZNcMBBSjgcwF5QPT23QcgD6PtPeAFdLv5CZVuun+4Y1tTpi9Al75D0arrAAx75UN1xUubFuc4lvt6wnC17eq1JokGxEVPUSlq1Ti1GfmFn6hnr4SkiA8GqzwOKBBKApa9sShY1wX5C89Gzl3RsB41OLq3e/5tOPjnc0jb9SNObP4cO+f2QerW6Y7lnKBAOAq0rhWpbi9oVjOiQrpfJ9GoAjoStJDU6Ywo1Y46SSaX/CtO5auFAR7IrT8389afAKuH7ubYMwpQwL2A0X22/3Kvv+tZdO77mNv0yLNj/bdh1kwBCjgEkmtUwaZfJxdJS2eNV2Ua1E3Gomlj8NvMdzH/qzewbtEnuKPvVWqZ3wem0x8k/b4tboACfhYwZGW6bCExMQnnnNPUJU9mWuUUekbQqdUseWlI3/OTFHFJJ/5jQMUFhDO6Eyj42YScftHIbBOL7BtiUDA9tJ6tVE8LnEjAwp7sgZO6WqDFnidje77udlDoN4g9pAAFKBAQgYAFVKpXTcJN11+Bjhe3KjZ1vbytKhMVGRmQznMjFKBA8QIGgwFyG179OjURYQrcifCWTAOycu9A7gNTkffqT0VSwYPjYPxvNSK+G4OIKS/A+OtXQHZ68R3hEgpUgIDx4AFUue5KRC+w3Z5jOrAf0T/+oFoy/MWX0eGyjpDgSs2aybi+2s24Kfs+tazwIDdtV+EsNZ97Yqsac0ABPQpY9xmQ+2gUzBu008xswLJFmx8eBcsabV6PDdZFm9iIcBOwhluH2V8KhKhAwP6yyTfew4feidLSi4/dgdgY22WTIWrOblGAAuUQMG5dgYhPn4Zx1TwYN/+FiIWfIWLyc+WokatSwPcCCePHIHLj+tMVWyxIfGGYmm/Q4Ay8+urbmPvjYnw740cM7vAM4rX/1MJCg9jqLWCKqlQoF0hqeGWRPGZQIKACJWzMvNL96WXBn4ELzpfQPC6iAAUoQAEK+EzA/V88n1XveUU79xzExMmz0OWmoTiZfupaZ89XD92S6dkwf/ILLHPXwpqmfc0Tuj1lzyjgkYBh/ZIi5Yx7/4Xh6J4i+cygQEUJmNb+XWTTxhMpMO0tepxG9isA4m2/PuVYKd4xhbodx2lBldMPdI+t0Ro1Wj9+ugCnPBJgIQpQgAJ6EuANznraG2wLBcouUKEBlZTUdHz9/WL0e2AketzxDCZoAZWzGtYBb/kBrMfSbHs1Ow/Wf/bBsmgDzO/OA/ILnXTbSnFIgbARMBYTODEc3Rs2Buyo/gWsNWq6baQlKalIvqGuFTGv/Yvodv9DTPfZiHtzOQyVs1U5477NqBpxDpp3mYsmNyzBuXdsxjk3/YHI+FpqOQcU0KOAqZ3FbbMiLuE5jFsYZlKAAhSgQNAKBDygkpObh59+W4VBz41Dh16DMOqdKdi4ZSfuu7UHfv12LCa99SRv+dEOJ+tGNx8OT2TCuueotpT/UyB8BSzntHPbeUuj1m7zmRkoAW7HWSDnxn7Os2o67/IrYK10+koTlXlqYIg3I7b2N9rfv9GI/uNxGNKOqSUR00YjctKTKsVE1UbMoaMwLZ0Gw4YlQG6WKsMBBfQmYKhnRfS7eTA11wIrsYCxiTY/Mg/G1tq83hrL9lAgEALRnv0alSG5EhATGYgWcRsUoICPBAIWUDmWchIjx/xPC6I8iiEvvoetO/bh4btvwBfv2Z59cMWlrVGjmvsTTR/1NaiqsR7PcNte6+FTV664XcpMCoS+gPmSG2Bp2MzRUWtULAp6DQGi4xx5Hk2wEAVKEJC/Wdk5eSWUKHlRzvU34OTHU2BueIYqmJUQjUceiMfnuz5GpjkDW7duxt9rVjnSmu3/4GheLNIikpFW/UwUNGgG65ktXFLErHGI+OxpmOZ9jMipLyNqzN0wZKSq+jmggN4EIrqaEfNNLuJXZyN2Zg4i+vLqFL3tI7YncAKGM2rA0ChZJcdWrYChZiUYGtaA+hdhhLH3hTDUqaJmOaAABYJDIGABlT37j2DaD7+gds2qKogy/6s3MPDOnpBbfIKDKrCtNJyd7HaDhnNqu81nZugLhFIPo377BVG/a9+wa51KeGU4Yid9oE15+H9sIgoeGIu8YV8h/+EJyB/xAywXXuvhyixGgdMCS5atQ7NOd0HGOPVvz/7DuOa2YejYezDadr8fL7zxKfILyvZBMLfrVdh9ru09OyUmHz9lLsV7297Es+sGY+y4NzF48IOONOSjl3Hdqs248lctzfob6y+7B/n3ve1IBVffD+OOtadaeWqUngLj6vmnZjiiAAUoQAG9Chga14JpYDeVHG00AMYrm8N0V0eVZYiLhqFxbTXNAQUoEDwCAQuoVKmUgOQaVbB99wE8PnIi3vtsJv7buT94pALcUmNL27eazps1Xt0KhuqJzll6nmbbKOBWwHgiBZUevg/GlBS13HjoIBK1oErUn0vVvH1gmb0K5mnLYN160J7lOq5UA9a657jmcY4CHgps2b4XT7z0fpHSL4/9HE3PboBV8z/CnCmvYf4vKzB/8fIi5TzNOJhT9O/cX8eXomGjM9Gq1QUqxcfFq+rOjq2OlnXOVnlx8bY8tUAbGA/v0oZu/uezg9ygMIsCFKAABShAAQoERiBgAZUzG9TGz9+Mwefjn0OnS1pjyvSF6Hn3c+j/0Euqp2npFX0vuGqG/gY1kmAacg0iXu0PY9fm+msfW0QBLwUi1q2BISO9yFqRf6+05RXY7rG37jwK6/L/YP7wZ1g3F/1QaivMIQW8Fzh6PBUPPT0Gw4feibjYGEcF8gtzf6zciNv7dENsTBTk71av7pdi4ZJTx6ajpOcTGQUZbgtfM6AHxr/7kUpnNTpblRnc8Aq8e+MQW96ZjVSefWBJLhpkV8tq1FcjDihAAQpQgAIUoAAFvBHwTdmABVSkuUajARc0b4wXH7sDv38/HuNGDcLZZ9aVRXhw2Nu4a8jr+G7ubygwl+3yalVRqA0MBhjqVwOiI0KtZ+wPBdwLWK1F8i1rdhfJYwYFyiIgz0V55Nlx6H315ehx5cUuVRzTAi2SUa/2qfvZtZkGdZNx8Ijtaipt1uv/a0TXLLJOQkQimldqVSS/pAxrvSawNGnvWiSxKixturvmcY4CFKAABShAgdAUYK90KRDQgIqzQHRUJLp2aIN3Rz2qgisSZMnOzsULb3yKzKwc56KcpgAFQkigoGVrWBNst67lIgerotKwPCoK65JrqV7mWs1Yk7ZXpX8zDqk8HDlpG3NIgWIEzGYLPv5yTrFpx56DsFiseO71SairBUwG3tWrSE1pGbYrJaO0v0/2hdHRUUhJdX0YeKX4SHiamlZqYq9KjZMikzCi1SiX9SO0LxvUQm0QFWl0Wea8nfiBoxH90FuI7PkgIu8ajpjnv0BScg2X8loVLvP29RNibUF5+WLDnuevcaTJKM1AXLSpSFtiokwoqY/+apM39Rq1LzIS4yJhNpowY3MeXl6ahQ//zsbOdBTpT+F6UcH/CrQvpCQAmJuX77Yl8ho4dDSl2C+u8rT1Dh89AaubwLbbCsuYad1+CCo53dKp5lX+4TLWytUoQAG9CbA9FAgHAdtZTwX3tEqlRPTr2RnffDgcc78Yjfi4mApuETdPAQr4S8BSpSpOTvgYlqpVccx4BA/U2II7kpPx4szpapNH8zMwdMt3Kr26Y4HKMzStq8YcUKA4ASusWuAjvdiUn18A+eWeBb+uQGJCLN56/2u8MWEqsrJzMG32L1jw60okJdh+KUrK2reTm5uHqpWT7LNqXGC2ah9IPUtRRtvfs+SY2ph++Wz82X0tute+3mV952uytJiPy7LC27I0bgN07Ae06ARzZGyRstLAwuvIvFlrsyzTPikXWUeWlzd9vDIDA2efUGnNQduH+UmrTuet3Jentisf1K0WqOnybtNf68uxlFdgwSNafz5ZnYk/9uRi1r85GPjDCaw7aOtHcdtWxhU0kIBiyy4D0PWmobig230YOmICTqZlOlojD19uf+1D6NJ3KKTctNm/OpbJfpn4v+/RWluvc9/HcPkNj2LdP9sdy309YZ74E1T68GdH1Wpe5S905HGCAgEW4OYoQAEKeC0QsICK3Ju+dPl6lJbkFxa87kUQrxD/1muo1qk9apzfCJVvvwkRa1YHcW/YdAp4JpB3+RXIu6wjoq3RaFm/DewP55RxyyoN0DKhrkpN45NhOLMGDJc39axilgpbgQiTCcMe7l9satKoPhLiYzD43htRt1Z1VK6UoJKAJcTHIi42GtWrVZZZ7D1wRI1lsGvvIciv08m0PWXmFMDTVGDWogfaikYY0SC6idv1zBJF0crI/wXaB3lP63ZXTupwl5+dZ7uVVjblbnl58/5LKcBaLZAiKTXH1uddqWZH3uF0m1luvgX5mkl5t+fP9eXijLX7cyDtF0/ntGBbttt9aG+Pc9lAT8sx/cmYp9QDlWd++jJWrt2MmfOWqmbIrW7yEOZH7rkB6xZ9om65Hvn2ZOw7eFQtX7vpP0z4bCY+H/8s1v40Cb26d8Bjw99TV3WpAj4e2H8+1u24MX/lxHNulvREoKCcV255sg2WoQAFwlcgYAGV7bsO4MFhYzxK4XLLT+zUzxE/YSxMu3fBkJmhfka28r23h+/RyJ6HnUB1azLG3joJ9odzynhsl/sw9tw+GD9iDF6c/DFMj3SHITYq7GzYYd8LxMXG4P7brnNJkndN54vQoX0LVEqMx8Vtm+HzGT9BPoDu3HMQPyz8E906tvNNY1IyYd133Dd16ayWW5vH4PWuicWm5jVNOmtxyc3Ze9LitkBx+W4LBzizb49OuOiC89QDlc85qx7kBwB++2udasWKNf+qq7H69+wMCT7KLdcN6yVjybK1avni39eoY/+C5ucgMjJCPZj58NET2LJ9j1rus8GpilNY9IMAABAASURBVEwDu6HY9GDXU6U4okD5Bcpz5Vb5t84aKECBcBAIWEDl3MYN8d0noxzp5WEDlO+n7wxz5NmXJ8TFqmWhPogs9DOx0l9jynFEbtogk0wUCG+B5EowVLbdghHeEOx9IAWee/Q2bNqyE227348edzyjBVPaovsVF5a/CWnZKHjlO5jfmYuC57+B9e+d5a9TRzU0rGRCy+SIYlPlmICdbvhEpV3dSFVP4UGbOu7zC5er6Pn8AjP+WLkBzZqcqZpy+NgJSADF+flAjRrWwaEjJ9Tyg0eO48z6tdS0DGpWrywjHDmWqsYcUCBYBcpz5Vaw9pntpgAFAisQsDOc2JgoyCXX9iS/nCBdPfuMui75stx06oF2spyJAhSgAAUo4C+BlfM+QMeLWzqql59KXjD1TSye/g5WzP0Arzx9r/rG3lGgrBNmpysesvNg/nZFWWsqaT0u85FAlVgj7rsgFlFOF9a0rBWJG5pG+2gLnldz4NCxYh+2LN++y9VUhWt7eewUpGdkqytNZFlaeibkaiyZtqfo6CitTJaaleUx0a59k/IZWdlquQyiI4zqQcNx0SbdjSNMBkRVcPuiI20Hi5zCFjaK1czEsHC+nuYjNcPIMhqajAbpni5Tea7cKmuH0k0DcHLxzch9MgqWbfq1KWv/uB4FKOAqYHSd5VwgBfIv6VBkc5aq1ZDfrHmRfGZQIBwE/vjjN3yz5Xf8ecJ/D0MMB8fg6aN+W5pco4p/H5Cekwf+epV+97+0rPe5MfimT2W82z0RX/auhNe7JEh2wJNcbZKSml7sA5et8pRfp1ZNnDwLM+YsgVwBbL/SJCkxXt3y41QM8sDlxFMPYpbluXnaMelUQB7Y7HzFcITJiKhIky6TyWiECqpUYPskGAHtn9FgKGIUHWGCll0kX0+e8mWmyVi07Z60UdbTuq77/+W15M2VW2XpUMYvV+FY1DvI2d0MBT+aUPCLqSzVcB0KUCCIBIxB1NaQa2p2/9uR+fAQmBueAWt8gnpIZ+qkz0Oun+wQBYoT2G/ehUVfLMT3936PYZ0fxwtPPY0PNizAc//NwV3PPFDcahWXzy1TwIcC1pjguH3Eh10OuqpiIgxoXC0CVWMr7nRJbtUp6YHLciWJwFosVrw58Wt89s18TP9oBJo3td3uI8uSq1fB7n2H4fwLVlt37EOtmlVkMWrXrAZ5ALOa0Qb2W33sARktC5m5BUjNyNNlys03IyvXXKFty8i2/bpVvtlatB2ZefIDW0XzdeSZk2eGpLLs47wCpyvw5GDRafL2yi3phharQ3Sk0eOUu+08Wa1IijAZHHU4L4zQ3mOitPpVnhZ182ZbclWWtoqjXud1ZXtSpwS7nPP9MW2QDWlJ+lG4fqMWpIuMMLptY+GyFTVv0Npe1m1rq/J/CsBIg4oVyHziGRz/dTmObtyO1M+noaB1m4ptELceUgJ67syeQzsxN28q/l22ATsX7UTtnfVx6bErYP+3Y99uzH9gMayp9hyOKRA6AoYmdWBIigudDrEnFS7w4pufYvK0+Rgz4mFUSkrA/kPHVJJfOGnXyvZLaVNnLYLM/7x0tfqFn44Xt1Lt7nxZa/yxciP+3rAN+QVmTJmxAHKVVpNGDdRyDigQ7AITy3DllvTZZDQiPjbSoxSTHglLXjTc/YsymRx1OC+PiYpA3KnguhZ7cJTxZJtyK5nJaHS7TlSk7coYCWZ4Uld5ykhQR/ok/ShcT4TWqRgtYFQ4Xy/zsdERMGhtLGt7pN9MFAhYQGXTll3o3PcxRxry4nil3/f+4Y48+/L0DNs9vaoAB+EkwL6GkcB/+/4t0ttqeTWQkJ/kyN+9ehfyxvFbfAcIJ4JGYFv6Zqw+sRzH846pNufFG7HmknysuSATa6+NQsYtp5/bogpwQIFyCqxcu1nV8OCwt9Ht5iccaf/BY4iLjcb4VwZj9ISpaNllAAa/MB7PD7kd9WrXUOu0anY2Hrzjetw+6BW06joA33z/C94ePhBG7YOGKsABBYJUoDxXbkmX8wssSEnL9SidjMmFqdoRWa1IysorcNThvDAjKx+p6bkqS9rq6bak3MnMfC1A6r59WTkFqk656kjK+jNZrGpTqh+Ft5On+aVnn+574eUVPS9XZHnr7txmW885DHeBgAVUEhNiId+E2FPXy9vipuuvcMmzL4uIiAiS/cJmUoAC3gjkPhGFzPNiVUrbk+Z21SSngEq9grNgXmr7lsVtYWZSQKcC7217EwNX3Y7lx39XLTxuScEjSa/jkZpj8LD5BWzK2aTyOaCArwTkYcqbfp2MwkluGZJtdL60NdYv+hQLv34La3+ahP69uki2SgaDAYPu6Y3VCz7CT9ry5T++j9bnN1bL9DYoyEnBiS1TcXTNu8g8+Jfemsf26EygPFdulaUrMU03wGDNcKxqiLFNmpeZkDchUiVbDocUoEBwChRtdcACKg3qJmP40Ds9SvKLQEWbyhwKUCCUBGqb3F9KfiT6sOrmGflno1Pe1WqaAwoEk0DE1s14bcRazBlrQOfNtj+zNU5a8P0Ys8qb+F0iKkXanl0RTP1iW4NfQB48WrdW9WJ/uSomOgp1tOV6vTIl58RmbP78fOz5eQAO/Pk0/vuuM/b+8nDw7xj2wG8CK8tx5VZZGpW350xYDU4PsLZdfALzX0bkT4hQqSz1ch0KlFmAK/pdwHam5/fNcAMUoAAFgOi38hD/T7ZKF646H3Xa1nGwRMZFot6ZZ+CunMEYcfI9fHDye7XM1MGsxhxQIFgEDOnpOGPDPjT7JxWVT9p+OSU6H2ixOUPldT1WB80qtQiW7rCdFNCNwLF1E2HOc32wVso/nyE/0xaI101D2RDdCCyY+maRq7bkKi5PrtwqSycKjtUsy2pcx0mAkxQINgEGVIJtj7G9FAgRgZhKMbhp+k0Y8Oe9uOWH/nh408O48YsbcFPtK3ERLoahqhkRfcyIGpwPy7KtML81BwXPTIV57FxYN+4NEQV2IxQFCs5pghNTv1Mpp2dv1cWvLo/CHx+MUHkZw19ReRxQgALeCeSmbnG7QtbxzW7zmUkBTwRKu3KrlDq4mAIUCHMBBlTC/ABg9ylQ0QKJtRNQs3myaoahjhWR3VYguusUxH55ANEv5QEpR2CZsRzWgyeAvAJY9x6H+bNfgROZah0OKKA3AWtiEvIvulQlS516qnl7ahhxrE0zlZd/Pq9OUSgcUMBLgejKTdyuEVetqdt8ZroTYJ5/BQz+rZ61U4ACuhNgQEV3u4QNogAFnAWs2w45zzqmrbuOOqY5QQEKUIACISrg1K3qLQfCFFXZKQeoet7diIy3BeVdFnCGAhUgYIC16FbdZBUtxBwKUCBYBRhQCdY9x3ZTgAIUoAAFKKA7ATbIfwIxVZqi6e0b0aDrJ6hzyes4u/di1L9igv82yJop4KWAFW6uUHGT5WW1LE4BCuhYgAGVUzsnP78ABw8fR4HZfCrHdZSekYUTJ9NdMzlHAQr4XcDQuJbbbRjOqOE2n5kUoIBXAixMgaASiIipiipN+qNG60cRX/uioGo7GxsOAtZw6CT7SAEKOAkwoKJhfPr1XLS68l507fc4WnYZgJnzlmq5tv+zsnMw6LlxuKjHQFzWcxD6DxyFYyknbQs5pAAF/C5gOKsmjH3aw1C7ChAVAUP9ajDd3QmoEu/3bXMDehRgmyhAAQpQgAIUoAAFKKAPgbAPqCxdvh5vfzAN7456FOsWfYLXn70fz4/+BDv2HFR76KuZi7B1xz78MmMs/pozESajEeMmfauWcUABCgRGwHjxOTA90QMRr/WHacg1MJxfPzAb9sVWWAcFKEABClCAAmEhEFH9SFj0k52kAAVOC4R9QOXPVZtwbuOG6NLhAkSYTLiu2yVo1LAOftcCLcI0/5cV6NOjI2pWr4zEhDjc3udKfDf3N1itvKRPfJiCWyA3Lx/7Dh5FSurp29mce2SxWHHoaEqxt8I5l+U0BShAAQpQgAIUCGeBuNbLUTn/NcS3+gWRDxfAeIFFcZgusqh5yVMZHFCAAiEjEPYBleioSHXVifMePaNBLRw4fFxl7d53GA3qJqtpGdSvU1NGSMvIUmMOKlyADSijgFyJdUG3+3BV/yfRodcg3D7oVaSezHDUtmTZOrS/9iF06TtU3Qo3bfavjmWccC/wxYYc2NPXm3JUoQILHHmy7OuNtny1kAMKUIACFKAABUJGIKrBblQpeA0JrRcj6uF8mNpoJwFa70wXm9W85Gmz/J8CFAghgZAOqMxe+Cc+/nKO2/THyo1qN15xaWts3LITL70zBQuXrMKkr37Emg3b1DK5CkWeoRITHaXmZSABGBlnZZ3+UJQUH4lqSdEeJs/KVU6wbdNkMpSr3sgII5LifN8+X/VX+hlhLF8ffdWW4uqJjjQiMbZshhHa/pPjRY9JgoPTPhyBtT9/gnlfjsauvQcxbfYvqqnZOXl44qX38cg9N6hb4caNGoSRb09WV7OoAjoc7E+zqMDF4l15FdK6nHwrvlyf7UjfOAIq7vMropFzt+Uqo2PZ1orYPLdJAQpQgAIUoAAFKECBkBIoPaASxN2Vq0jkVgZ3SQIl0rWW5zXCpLeexLGUVHw182dtfFLd/lAnuRoMBgPiYmMgt0VIWUn26bi4GJlVKTu3ABnZ+T5NmVqdUrncclGeus1mC7JzzT5tW3naU3jdrJwCmK1W3bZP2ptvtiI7r2z72GzR7wfXB26/Ds2anIHICBNq16wmhxsqJyWo8Yo1/0JeI/17dla3wnXt0AYN6yVjybK1arkeBwczzCqY8cvOigmo6NGkcJvm/5erjE5kmQsv4nwABKJS05D49FBUb98C1S84F0mP3AfT/n0B2DI3QQEKUIACFKAABTwUYDGvBEI6oHJr764Y9nB/t+nKy9s6oC5u20w9lHby2KfRraMtv23LJmq5fIjcs/+wmpbB3gO2h00lJcTJrEr5BVbk5lt8mrIXRyL/z17I+aIvUntEIfMrQ5nql8/z+VpQxdft81V9eQUWaPGUMvXNV20orR4JahVoQZXSyrlbLn1TB4lOB3l5+fhgyg+4Y/BraN28Ma7pYvsJysPHTqgASlRUJOz/5NlCh46csM/CoE0ZtYEvkuW7FTBPXKgSDtl+Rcvy7XI1L/mGw6nwZDtak9T/WixUjd2tY18mY3fLy5OnNlra4JSZFPNHG0pqv9pp2oY92a6U0Yp65F54m/Z1ZX2m0wLnjf8Ssd98CeORwzCeSEHMjz8g8YVhpwtwigIUoAAFwkIgKcKAmIgFiPr1LUR8NdLW5/QURH78uEoRX57Ksy3hsBQBLqZARQqEdEDFU9gjx1KRX2DGlu17Mfq9qeh8aWucd84ZavWrOrXD9Nm/QspkZGbj8xk/ofc1l8Ng0D4VqRK+H1j3acGTFyrDcrImYDbBssWA3OFRsKzh7vK9dujVuHr9Vre3ucntbzPmLHHpsFxBI79idTItA2npWUjXjnE3bH3uAAAQAElEQVQpkJaeqa7Okml7io6OQrrTs4Niok2oWSXWJylSC5hYtx+GJOTmq01a96eoecmrElX6tiolRKv1DmdaMHVjLiavzcJ32wqKpG0nLKrchmOWIsvclfc07/vtBare0gZaDFFtd9KqDEzbnK+mPd1GecudOHWrT5WkmFL3WxXNMzrSWGo5d8dAlVO3LJZmEW7La/2+2qXLf0ZHY8Sm9di1a6fKzyzIVWMOKEABClAgpAWgxVNgNB2G4dhWGPf86+isYed6SHLOcyzUycSMf04/L06eDZejfbEsTZvxb666rVjyJNnzZRkTBUJZwBjKnfO0b4OeG4dWXQfgtkdeQctmjfDW8IGOVW+5oSvOalgHV/QZoh7QmZ9fgEH39HYs98eEeaX73VLwp8kfm2OdISYgt+qkpKbDXTpx0vXXfGJjojBmxED8+PnriIgwYcJnM5VGUmK8uuVHzZwa5ObmqV+6OjWrbiU7lJINX6SC69rANPDKYlNKbHSp2zmRbvswuvekGR8sT1fpQ21cOK07aLslaNnuXBReVp75T7QAid3GPrZC+6/QXV8F2omHbOcDN22TfH+mY1m2YFLKyZxSPVM0T7nqqiz7NyXdZmx34NgmYNaCkrYp4DctmHJ3zZr4VhunpZ1U2e/v/V2NOaAABSigLwG2xtcCKflW5ORdifzLhyL/3jeLpIJbX3S7yZM5FhW0+GGL7ZzHbSE/Z0rgxPmZcbnaeY1sUgIt7vJlWSDTb7vzlNFu7XwwkNvltsJXwP0n9zDz+PCNx/HztDFYMfd9PPvobbA/eFYY4uNi8P7rj+HP2ROw5Ltx+ObD4ahZvbIsYqKALgU6tG/h9jY3uf3tvlt7uG2zwWDAWQ1qq59IlgLJ1atAfuFKAogyL0muZKlVs4pM+jwZ6lWDoVGtYhO0wI+nG62TaMRdreNwb9sE3Noitkg6t0aEquqCOpFFlrkr72nezc1iVL3OAwO0/wzOOYA8aFrqvP/CRNzRsmj7ZJm/UtUYg2qMVdvfaoKDgAoca9PMsb25cadvG7Vn7so5jt0ph+yzHFOAAmUV4HoUCAIBi7U2LDWbwnpWqyLJ0uA8tz1Iz7OqZ6HN3lpxARW3DdNR5tI9ecpoz0nbl0g6ahqbEqICDKhoO7ZypQTUrlkVBoPtwwbc/KukfWNfvWolN0t8n2Vq5/4NIOISPkjS99rhWaPcvjbmw2nYueegut1t3T/bMXPe72jXsqkCadfKNp46axEKzGb8vHS1+oWfjhe3Usv1PKiTaLIFVNrE47bmMUXSedVtAZU2tSKKLHNX3tM8dwEVd06R2ruu1Hl/uwQVUJHpQKVqcdrGtUYZrIUum9Hy+L//BTYNvh3Z/W7VTqCTsS+maABOWrDnxGEZMYWZALtLAQpQgAIUoEBwCtjOroOz7SHbakM9K6JHpcJY6QhgMsPYxIrokXkwtnYfaAlZCHbMbwIGgwF/rtqEHnc8A7nd7ZaBo9QDme/q111tMy42GuNfGYzRE6aiZZcBGPzCeDw/5HbUq11DLff34M+9+RgyPw29vknFwB/TIL9O4+9tsn4K+Fsgr3IS0l8fg2PL1+P8+07fWuq83VZ1GzvP6nWa7aJA+QUycmBZsB7mSYthmf4XrDu0c57y18oaKBDSAvavQ07mWjFlXTY+Wpmhbm+RZ5Y4pxX785XDluNmt8udy3o77emzUab/m4uPV2WqZ9p5u43ylOeVKWrXcxBAAQZUAojtzaYiOuQi8pJZiL51OmJn5iCir9mb1VmWAiUKxMfFYMbHI7Fi7geY9+VorJz3IV55+l6X293k4czrF32KhV+/hbU/TUL/Xl1KrNNXCw9lWDDqtwzISYDcl7sz1Yxxy7Ow+VjJr4HaCSZ1C88VZ0b5qikhV0/3s6OVUZW4cHsek/52Zd8+N6N585YuDetfqw0SY4reCuRSiDMUCAUBi1X9iptl4TpY/90Py1/bYJ6wANbdx0Khd+wDBfwmYDhVc3quBVPWZqmAivNzS+zT9oDK1uMF6vYXe74vxnJudqoZaiRBHklqxmkwY1MO5AH8/1uT5fM2lNSPPXx2itNe4GQgBBhQCYSyr7Yh3+ZM+wvmkTNQ8OJ0mD//DTiR6avaWU8YCkhgpUHdZMgVKe66bzIZUbdWdURG2m6TcVfG13kbDue7rXJdMfn2wnWTjOoWns5nVExAJSbSoIIV9uef9Dv1TJUIo/t8e7vdjv2UeU3jaGVUPdZ+SuanDbHaUgUSE5MwccInmDHjRzRu3ESVv7RKIzXmgAKhLmDdexzWwyeLdNO6xvaLV0UWMIMCFFAC9sBFYrQRd7SKg9w+bD/vcB5fWDdSlT+nWoTLuYlzmbJOR8tPFKnabQM5o5Bkmzs97KOdB8nz7O5sHefzNpTU9gaV+KXR6b3AqUAIMKASCGVvt5GSAcuvm2xraUEU68ETatr84xpYlm+DNS0byMyBde1umL9drpZxQIGKFOC2bQLOz0KxP1MlQnuXdck/3/2zM2w1cBgKAnt+34NlY/9Saf+q/apLtf+pj30fHbTlrbTlyYLkmsmIjY2VSZ+nnJR/sW5CnEpbprZR9eek/KPmJX/L1LYqjwMKBFzgcKrbTVqPpLnNZyYFKGATsAcuKkUb1HPYJKDifI5hn7YHVJpUM6kvUuz5vhjHFAqo2FpWdNj33Gjc1zYed2mBH19s19M6GlTSTryKNoc5FPCbAI84v9GWseKcPBSMmwfLX//ZKsjKhXnsXCAlE9ZtB215TkN1z7HFHq92WsDJkgS4TMcCzZNt36oUbmLLYvILl/PHfH7mQRz6awR2/Hgj9i0Zgqwjf/tjM6wzRAQkoLJ83F+QdGDVAdWrWpvrY//HB13y1AIOKBCGAoamdd322tCkjtt8ZlKAAhSgAAX0KsCAis72jPVf7eQ7I8e1VQUWWDbscc2zz/HXOuwSHIeIQK0EI164PAHyrYpcVnpmZRMGt49D0+oVcwmnxZyLf6ZfgcOr30D6rnk4vvEjbJvRETnat/8hQs5u+FigwWUN0GZQG+DWAuy8YTM2XrcKG3qsRNJd8Wg/+CLUaRuYD40xVc9Fy4ezik1N+q/ycc9ZHQU8FEiKhfH6tkDk6fd1Q+NaMHY818MKWIwC4SmQGGW7jfi6c6LDE8CDXndoEKVuMeKVKh5gsYhPBIw+qaVwJZwvs0Cxl7seOQlD49pF6jU0SgaMhiL5zKBAMAtcUj8SY7snYVa/yph4bRLkYaoV1Z/vfv8NyCwU0LSacWLLVxXVJG5X5wISUNnaax2mdvwIf129GOuvW46N16/ErCu+wMVDLkLddu6/ndd5t9g8CvhUQIInppdugmnINTANvxGmB6/0af2sjAKhKFApxqhu4bm+ScUFVPqcG60CFvbnmMiXX2Ld57wYt/myLJDp8oZRyqghn6USSHYgjLfGgIrOdr7hPPcn2oZz68J0bWsY2zeGQftmB/ExMLRqCNON7XXWAzaHAqElsGb7DrcdyjmxxW0+Mykw7+D3mH3g2yIQh3IPYNzW1/HxjvEq7c3aXaQMMygQTgKGqAgY6lfTzmv461bhtN/Z1+AWkMCJ8/NM7M9UkUCLu3y99pbtooCvBBhQ8ZWkj+pRJxYdz3OpzdD6TBjOrw8kxMB400XaNzl9EPFSX5huvxyoEu9SljMUoIDvBPammbEr8kK3FSY26OY2n5kUWHBoNg5mn37wrAEGhWLQxl/t/hSTto9XaR8DKsqFAwpQgAIUKFWABShAAZ0KGHXarrBulun6Noh4/FqbQY0kmG67zDbNIQUoEFCB+kkmpBjPwILoYS7bPZzUHdXPv9cljzMUoAAFKEABCtgFOKYABSgQHgLG8OhmEPYyMsLWaIPBNuaQAhSoEIF+58fgl9hH8WKlbRifMBevV1mPhtdMr5C2cKOhJWD9dz8KHv8c1p1HHB2z/LVN5Um+ZfpfjvyKmLDkZ6pftCrIPloRm+c2KRBYAW6NAhSgAAUoUAYBBlTKgMZVKECB8BG4q2Usxl2dhDvbVMPNHS7GhBvOUr9AFD4C7Gk4Chxe8So2fFQD26Zfhk2fNsTOH/uGI4Ou+8zGUYACFKAABShQ8QIMqFT8PmALKEABnQucU9WE3ufGoOtZ0agay7dNne+uCm+etdDP2cu8JHvDIibHIWpUEoY99zauWDkOGzMOqkWDNk9X85InaVPTWJUf6EFu6lYcWvmyy2bTdv2IlH8/d8nzcobFKUABClCAAhSgQMgJ8JNByO1SdogCFKAABcovUPYaDAaDy8oGgwEGg8ElT88zmQeXu21ezvH1bvOZSQEKUIACFKAABcJVgAGVcN3z7DcFKBBaAuwNBXwkEBGX7LYmY3Rlt/nMpAAFKEABClCAAuEqwIBKuO559psCFSzAzVOAAvoUSKjXEZGJDVwbZzChcqPernmcowAFKEABh8CnOybgq92fqfl5B7/HmhMr1TQHFKBAaAswoBLa+5e9850Aa6IABSjgNwHnZ6z4bSMeVmw0RaPxjb8guc1TSDzjalQ7/3407rMEMVXP9bAGFqMABSgQXgI/H56HD7ePQ2p+iur4oZwDeHr9IORZ89R8cYOvNmbjhm9Sce8PaZi6Ibu4YsynAAV0LMCAio53TvmaxrUpQAEKUKAiBK6qdR3ubTRIpYbxZ8Kq/SftsI9l2jkVF0wxGCruuSuR8bVR66IROOvab1Gv41jE1bzAucmcpgAFKEABJ4FVx/90mrNNpual4HjuMdtMMcO9Jy3IKbBif7oZU9bnYMmukgMwxVTDbApQoAIF9BNQqUAEbpoCFKAABSjgK4Gra/fEfWcNUulebeyo12qbSoxIsk2cGhoMBrcPrS0u0HJqNY4oQAEKUEAnApvTN7ltyfb0zW7zi8tcebCguEXMp0DoCYRIjxhQCZEdyW5QgAIUoIB3AkuWrUOzTndBxjj1b/SEqSpP8u3ptkdeObXU+1G3Wj1wbtL5asXzK7XEI42fxBnxZ6l5DihAAQpQIDQE/k3b6LYj/2VsVfkFFgZKFESQD9h8CrgTYEDFnQrzKEABClAgpAW2bN+LJ156v0gf5aqQTpe0wtwvRjvSW8MfKlLOm4wqUdVU8fsaPYrbz7gPRoNJzXNAAQpQgAKhIfBAo8FoU7U9Yoyxjg7J1YqtqrRV8xHGCDUubdCutmflSqvn1HKOKECBAAgwoBIAZG6CAhSgAAX0I3D0eCoeenoMhg+9E3GxMUUalpgQh4b1kh2pVo2qRcowgwIUoAAFfC0QvPXdc9bDmNjmc9xz1kBHJ0ac/yZaVbYFVByZhSbqVzIiJsKAuokm3NEiBh3PiCpUgrMUoIDeBRhQ0fseYvsoQAEKUMBnAtk5eXjk2XHoffXl6HHlxW7rXbHmXzz72sd4c+LXWL3edrm224JlzJw44RMs/W2VIzVv3lLVVFy+WsgBBSigPwG2iALlFLjl/FjM7FcZk65PQv/mp69uKWe1XJ0CqP94kQAAEABJREFUFAigAAMqAcTmpihAAQpQwD8CZrMFH385p9i0Y89BWCxWPPf6JNStXQMD7+rltiHNzjkDN1zdAWfUr4W9B4/gjkdfxYJfV7iUrVE5Bt6kqAijWr9SQpTb9SJPLa9czHJvtiUb8qZ8oMsmxEYgNjrCrUOg21Lc9kxGA6onRZepjQYDdP2PjaMABQIvUGAtwH//bsHqj1Zj99LdgW8At0gBCvhVwHaW59dNsHJPBax5BbBuP6SSZe+pn1nLP51n3XMqz9MKWY4CFFACxzd+jO2zrlIpddt0lXds40dqXvJTt81QeRwEr4D8JHFKajqKS/nae+mxlJMqOJKYEIu33v8ab0yYiqzsHEyb/YuWv1J1/rpul2DQPb1x/23X4d1Rj0LmZ85bqpbZB6npufAm5WvBHlk3Myvf7XoFp5anF7Pcm23JdrwpH+iy2blm5Gp/6zzcrlsvf69r1gJvqZl5Zdq29dQvOcl+YKIABcJH4ED2Pjy7fjA+3THR0emT+anIzc3FNznvY8HM2Vj62lIsfugLPB7XAn1qR8GAVPAfBSgQ/AIMqOhpH57MgnniTypZvvrD1rLU03lme55tCYcUoICHArkndyBj/1KV8jP3q7Xy0naqecnPz9in8jgIXoEIkwnDHu5fbGrSqD4S4mMw+N4bUbdWdVSulKCS9DghPhZxsdEyWSTJ81Mys3Jd8vPNVrhP7vPtH7ILtA/q7tazL5cP8u6We5MnDfWmfKDLSh81Bq/8At1GMSzwch/b2yjrMlGAAuEn8PKmZ7Ho8DzkWLIdnf/wv7HY8s9GZCFD5Z1d5xAeuP5X3HTBXjzZKB6x0T/g7dduRYfLS37OilqZAwpQQLcCDKjoaddERsDQKLnYZGxQXU+tZVsoEDQC1c6/D416zS82VW7cJ2j64tOGhlllcbEx6soTufrEniTvms4XoUP7FkrjnY+mY/uu/cgvMGPjlp348rufcdmFzdUyDihAAQpQgAKFBcxWM9akut4aKmVWpixDSspxmVTpovO2q7Hz4K76Mc6znK5ggbRcC7YcNyMrn5cbVvCuCKrNM6Cio91lqBwH08BuxSbjLZfqqLVsCgUCL1DWLUZXOgsJdS8vNkUm1Ctr1VwvxAT+Wv0Prr/rObTqOgD9HhiJbh3b4s6brgqxXrI7FKAABSgQCIGqVaupzRhgQbXK6WraeVAnJhJLFy5xzuJ0BQmM/iMT/WacxJD5abhxWio+XZNdQS3hZoNNgAGVYNtjbG8wCbCtFKCAzgVWzvsAHS+2/cqONPWbD4fjrzkTMe/L0Vg1/yO88vS9iImOkkVMFKAABShAgSICJoMJrStfWCS/be65OKdyXcQhAVYYcTw1sUgZa1wVICa+SD4zAiuwdE8+ft2V57LR6f/k4L/jBS55nKGAOwEGVNyphG0eO04BClCAAokJcWhQNxmxMQyk8GigAAUoQIHSBZ5v9iq6JF+NGOPpnz6+b+F5iNpxFP1iHsJVV3RB+nnXFanI3PTqInnMCLzAtmICJztTzYFvDLcYdALBHVAJOm42mAIUoAAFKEABClCAAhQIpIDVakWB2f2HY4vFikNHU4pd7kk768TWw6stxuGeswY6iifl24IrEYYInJleFWc+dgfyn5iCgl5DkF/ramTnXg/zWZc7ypd3Ij/zIDL2/6ZS7sn/VHUFGQfUvOTnptry1AIOXATiowwu8/aZ+CijfZJjvQjosB08SnS4U9gkCgRKIDcvH/sOHkVKatH7egPVBm6HAhSgAAUoQAEK+FNgzk/L0O3mJ4psYsmydWh/7UPo0ncoWnYZgGmzfy1SxquM/NNBG2t2PMz7mqjVzRvPRlb7WOSvqAfLhdeioOqFsKIyfPkvbeccbJ/VXaWj695TVZ/cOVvNS/7R9RNUHgdFBTqdEYVIk2t+pRgjWtaKcM0swxxXCX0BBlRCfx+zhxRwK/D86E9wQbf7cFX/J9Gh1yDcPuhVpJ7McJS9/s5n0azTXS5p4uRZjuWcoAAFKEABClCAAnoW2LP/sDrPefrVj4o0MzsnD0+89D4euecGrFv0CcaNGoSRb09WXzQVKexphtOn8oIdrWDNjXOsadW+u8p9yX+3kkbE10ZC3Q7FpuhKZzvaUsJEWC5Kjjfi3e5J6NkkGu3qRqLPeTEYe1Ui4iPdX7kSlkjsdLECDKgUS8MFFAhtgfp1amLahyOw9udP1AM4d+09qH0z84tLpx8dcCPmfjHakW65oavLcs5QgAK+E7D8dwiWr/6A+bNfYfntX1jz+DA83+mGR01yS8PBIymQqw/L0uO8vHwcPnoCcntEWdbnOhQIvEDJW6xTqzr+9+4zeG7w7UUKrljzL7Kyc9C/Z2dEmEzo2qENGtZLxpJla4uULVPGyRpFV0sFLDv88yG90pk90KjXgmJTjZYPF21PAHOOZll0/ZDXMyqb8GDbOLzUKQEDWseiVgI/Jgfw8AjqTfFICerdx8ZToOwCD9x+HZo1OQORESbUrllNVVQ5KUGN7YMa1Sqpkws5wZBUuZLrcvAfBSjgMwHrvLWwrN4B68a9sHy/CpZvlvmsblYU+gIffzlH3bLQ9aah6urDoSMm4GRapqPjJV11KAGUif/7Hq273YfOfR/D5Tc8inX/bHesywkfCrCqgApIoKRWjaqo4ub85fCxE+ocJyoq0tGmRg3r4NCRE455Tyb+PrECq08sV2l/9l7HKifPyHVMO08YqludZ0N+OiPXiscXpuOOmScxaH46bvgmFfO2ubcJeQx2MCQFGFAJyd3KTlHAMwH5NvKDKT/gjsGvoXXzxrimy0Vw/jd9zhLIrUFyq49cNuu8jNMUoIB/BazrdgMWq383wtpDRkAC3p+MeUr93PfMT1/GyrWbMXPeUpf+FXfV4dpN/2HCZzPx+fhnsfanSejVvQMeG/4e5GGdLhVwhgIhJJCWnom42BiXHkVHRyE9I8uRFx1pRO2qsSWmIX8PwMBVt6v0/f5pjnXHtnzdMW2fiOsE1DkjFrHRtgd2VE6IKrHukrZdvVK09qVY6e0rqQ5fLDOeuuAmuUpskb7ERJnw4/Y8/HP09BWXOQVWvLsiCwnxMUXK+6I93tRRs0oMTFoHvFnHuax9v3Ic3gIMqIT3/mfvQ1Bg9fqtkG8q3aUZWoDEuctm7cPa1h37tG8xM5CWnoX0zGzH4qs6tcPlF7WAXC67+I81uPHe4XAOqsRqfySTtT9EekyV4qMQo52sVGTb5I+0YBq0Ew137Yg0GVBNOxlyt0wPeVUSoyEnkmVpS9XEKOk6U3kFrFow5VhaeWsJt/XDtr99e3TCRRech9iYKJxzVj10uqQ1fvtrnYtHcVcdLv59DS5u2wwXND8HkZERuL1PN3Xrz5bte1zW5wwFQkkgKTFe3fLj3Kfc3DwkJsQ5svLyLTiUkl1ichQuNPFLmx+wpNUclbu71jZEPZEPw5u2urJzbQ+vPZmRV2LdJW37+Mlc5BeU3r6S6vDFMu1UUvXxyAlb35zrzMkzY8NB91ej/Lkjo8x9d95GeaaPnsiBnAuXtQ7VcQ7CXsAY9gIEoECICcj9wCmp6eqXewqPT5xMd+mtnHiPGTEQP37+OiIiTOobSnuBh+++AQ/d0RMD7+yJqRNf0E4wYrFo6d/2xZA/kke1P+Z6TGlZecjVTlYqsm3HNBvB0j4Sw1078s1WnEjLdbvMXflA553MyEWudiJZlu2maieI0ncmTwRKKFMlHqhZqYQCXEQB9wL5BWb8sXIDmjU506VAcVcdHjxyHGfWr+UoW7N6ZTV95FiqGnNAgVAUSK5eBbv3HUZ+foGje/IlU62aVRzz8je8tOQo7GZif41dKndhuxmIvEfbTjwg9alMbSDT5UlaFaq+8tRR3nWlDZLc1SP58VHuP25WiTVUeNulzdJGGZclybpMFHB/hNOFAhQIWoEO7Vtg2MP93ab7bu3htl8GgwFnNaiNQ0dT3C6XbyxrVK2MbO2bG3sB+cMjl4PrMckX+75q3/5lI/DPlGZY90FNbJvVAxmHVqnL4D3pt7LSGuKurCyTb3XcLdNFntZu1UatkS7t8WT+1LqyPlNRgYzDmfjzzT8xa8D3WPzCYkSnxtgKVXN6RlF8DEw3trflcxjWAgcOHSv2qkO5ElF+qaQw0MtjpyA9I1tdaWJfVtJVh3LrQ0x0tL2oGsutEBlZp69arJIQhTrVYnWZ4mMiUDk+MuBtO54fged/zVTp8w05ym3LsQI1L/nTt+SrNtWuGgvtz6ya1qthYmwEEuPKZhgXHaH6rseBPB9IAiYFWpBR2qemzWaZRLtWTdV46qxFkAc6/7x0tfqFn44Xt1L5HPhGoMuZUUUqalLdhPpJpiL5zKBAMAoYg7HRbDMFKFA+gYzMbIz5cBp27jkI+SZTHj44c97vaNfSdnIht/ZMnjZfBVhk+ZyflmHjlp1o3/rc8m24AtYuzyaPrX8fR1aNRu7JHbDkpyNj7yLsmn9rearkumEuUJBrxjc3fo0VE1dg1+KdWP/FejT482wk5leC6ZbLYHqmF0yP90DES31hOLdumGux+yIg78GFrzZ0nrdaLVLMkSZOngW5vfPTd4bBfqWJLCzpqkO59SE3L0+KOZJc7ZgQF+uYP5GRhwPHs3WZMnMKkJqZH/C27TiSjdX781T673iBskrLtap5yZdbHcTsYEo2JNAv03pN6dkFSM/KL5NhVq6t7wpAZ4Ptuw6g1ZX34ulXP1K3scm0PBtOmhkXG43xrwzG6AlT0bLLAAx+YTyeH3I76tWuIYs9TrmW3CJlreraC9fsHLMt6OaaG/pzF9aLwqtdEnBVo2i0rxuJO1vGYtQViaHfcfYwbAQYUAmbXc2OBolAQJppMBjw56pN6HHHM2jVdQBuGTgK3Tq2xV39uju2P2X6AnTpO1QtH/bKh+qKlzYtznEsD4eJ9H2/FOlmfvoe5J7YUiSfGRTwRODgmoNI35/uUtQAAxpknKnyDNUTYahz+nJzlclBWAvIL6wVd9Wh5MuVJAIkV5K9OfFrfPbNfEz/aASaN7UdU7KscCp81WHtmtWwa+8hRzH7rT7OARnHQk44BORb9te7JqK4dHer0wEpx0qcCKjA2WfWxaZfJ7uk15+939GGzpe2xvpFn2Lh129BHsjcv1cXx7LyTMj7ennWD7V1W9eKxJCL4jCiUwJuPj8GiVGGUOsi+xPGAgyohPHO903XWUswCsTHxWDGxyOxYu4HmPflaKyc9yFeefpeREdFqu40qJuMRdPG4LeZ72L+V29g3aJPcEffq9QyDihAgbILpO896XblxAI+K8UtDDM9FnjxzU8hVxaOGfEwKiUlYP+hYyrJrQylXXXY+bLW+GPlRvy9YZu6anHKjAVIrlEFTRo18Hj74VgwKdqIlskRxaYzK/OWhmA4LkwmI+rWqg4JNJalvdFG19vliqsjxhRT3CLmU4ACQSxgDOK2l63pXIsCFHAISGBFgmRgeBIAABAASURBVCdy2asj89SEwWBAtSpJqF+nJiJM4XlSmFjvChT+F5nYANFVmhTO5jwFXATGb30DD626TaVNqbZfWnlv25v4IOZtl3L2mcMx++2THFOgTAIr125W6z047G10u/kJR9p/8JjKL+mqw1bNzsaDd1yP2we9oq5K/Ob7X/D28IEwGvktssLjgAIUoAAFglfAzy1nQMXPwL6o/nCmBf+l6Pf+VF/0kXVQQI8C1Vs8hJpthyG60lkwRiYioX4XnNH9Sz02lW3SmcB/GVvw94kVKqUV2K5KkbzlMb9jXa+/XFqbUTMNOxP/c8njDAW8FVgw9U2X2xrstznILUMSOC/pqkODwYBB9/TG6gUf4aev38LyH99H6/Mbe9sElqcABShAAR8IsIrgEmBARcf762iWBYPmpeGuWSe1cTr6TkvFb7tdHxqn4+azaRQICYHa7Yej6W0b0fz+w2h0/WzE1WwTEv1iJ/wr8EjjJzGx7edu0yMvPobb1t6G/t/3xwOr7seBtrv92xjWTgFNwGAo/arDmOgo1KlVnVemaF78nwKeCrRL6YA+f92D91/4DJf++AeWR87HWSuGoubmurAetyLPkou6sfU9rS4Yy7HNFAhrAQZUdLz7p23KwX8ptp92k2Zm5Fvx3srTP2EoeUwUoAAFKKA/gcaJTdGmSvtiU/VK1ZHcIhmx1eL013i2iAIUoEBIC/i2cxdO7YjIT2Pw2+GV+DHvW3yfvwRLjxvQZUxPZP+ZgVTrMfSq18+3G2VtFKCAbgQYUNHNrijaEPtP8DkvSc+14FCGxTmL0xSgAAUoQAEKUIACoSrAfulaoEZyDdQ2NFApElGqrdUMyagXkYA6deoj0uDZQ2vVihxQgAJBJ8CAio53WXyU+92TEGXQcavZNApQgAIUoAAFwlmAfadAOAl07nU+esTcqlIVQw3V9csir0LfyFro1edmVDFWV3mhNPjnaAHWHbYls9mquvbPMbMjT2VwQIEwEXD/iT1MOq/3bnY9yxbldm5nm9qRYEDFWYTTFKAABShAgXIJcGUKUIACZRfoUBMm4wnX9bUYg6GuCdaDti9BLfuMMK+wJesBW57rCsE1N+q3TDz9c7pK8kgCaf1LSzLUvOSfzOHV9GLCFB4CDKjoeD93OiMKIzrGo+tZ0WhfNxIDWsfi2Q7xOm4xm0YBClCAAv4X4BYoQAEKUEA3AnFA7BPbEGNcrjXpVCBBi5mk7++NgjkmLQ8omG5Czl3RKhX8aMtTC4J0cG51E5onRxSbTCYNIEj7xmZTwFsBBlS8FQtw+fb1ovD4xXEY0SkBfc6LQVxkcLxBHcuyOi77s18S6Dzel3b6YbsBJuXmKECBQAtwexSgAAUoQIEQFrDe1RymjS0QWX2P6mVa/HqY2lncJkNtqyoTzIMXOybgja6JxaaEIPm8Esz7gG3XjwADKvrZFyHVkj/35jku+5NL/wqn77fkhlR/2ZnQEmBvKEABClCAAhSggNcCkZFqlY1NnkTM/3JdUtTj2xB1y0oYI9cAB223CFnW74ZlwXqVrKfyVAUcUIACQSPAgErQ7Krgami1OKPjMsA6ibbDrFqswZFXNzH4L3fU0R5hUyhAAQpQgAIUoAAFdCxgXacFTxaug0VL1kOpqqXWDXvVvMo7aMtTCzigAAWCRsD2STdomsuGBovApfUjHZcB9moao5p9Sf2oU3mJ6NWUPyGnUDigAAVCVuDXIz/hnuV9cPniFrh12XX4ft+0kO0rO0YBClCAAiULGFo2hLFby2KToXblkivgUgpQQJcCDKh4sltYhgIUoAAFKOCFwKGcAxi27mFsSluPXHMO/svYglf/fR4bUtd4UQuLUoACFKBAqAgYWzSA8aoWxSZD7Sqh0lX2gwLBL+BFDxhQ8QKLRYG0XAu2HDcjKz/4H6jF/UkBClDAXwLrU/92W/VfKb+7zWcmBShAAQpQgAIUKKsA16s4AQZUKs4+6LY8+o9M9JtxEkPmp+HGaan4eHV20PWBDaYABShAAQpQgAIUoAAFKlSAG6dAyAgwoBIyu9K/Hflzbz5+3ZXnspHvNufg32MFLnmcoQAFKEABoEXlC9wyXFT1Mrf5zKQABShAAT0LsG0UoAAF3AswoOLehbmFBHakmgvl2GY3H3Ofb1vKIQUoQIHwFKgVUwejW05As6QWiDbF4OyEJnj23JfRvHLr8ARhrylAgcAKcGsUoAAFKBAQAQZUAsIc/BuJjzS47USVGPf5bgszkwIUoEAYCXSqeSU+bT8Dv3Vejy8vno2e9W4Ko96zqxTwToClKRDMApcvao72P50DeSC5vR8yb0/2PI4pQIHQE2BAJfT2qV96dEn9SESaXKuuFGNEu7qRrpmcowAFKEABCoS+AHtIAQpQgAIUoAAFwIAKDwKPBJLjjXi3exJ6NolWQZQ+58Vg7FWJKO7KFY8qZSEKUIACFAiQADdDAQpQgAL+EvitywYsv3Ir5HZP2UaP2r3VvORJkjwmClAgNAUYUAnN/eqXXp1R2YQH28bhpU4JGNA6FrUSePj4BZqVUoACAA0oQAEKUIACFKAABSigc4Gw+kRssVhhNlvc7hJZduhoCgrM7h+ymp6RhRMn092uy0wKUIACFKAABShAAQpQgAIUoAAFwksgbAIqVqsVI8dMxkvv/K/IHl6ybB3aX/sQuvQdipZdBmDa7F8dZbKyczDouXG4qMdAXNZzEPoPHIVjKScdyzlBgSAVYLMpQAEKUIACFKAABShAAQpQoBwCYRFQWfDrClx+w6OYMWdJEarsnDw88dL7eOSeG7Bu0ScYN2oQRr49GfsOHlVlv5q5CFt37MMvM8birzkTYTIaMW7St2oZB+4F/tibj6d+TnekWZtzYNWKFs3P1XI9/Z/lKEABCoSOwDtj38CgR+9Xacf2/1THXPJ2bld5HFCAAhSgAAUoQAEK6FcgLAIqHdq3xPSPR6LHlRcX2RMr1vwLuQqlf8/OiDCZ0LVDGzSsl4wly9aqsvN/WYE+PTqiZvXKSEyIw+19rsR3c3+DXPGiChQ3qMD8L9dnq2DGusMFFdKKlGwLNmjbtqcD6RYYtJYUzXd/e5VW1K//H8qwKJ+3l2X5dTusnAIUoEBxAtu2bcHatX+rlJmVqYo552Vl2vLUAg4oQAEKUIACFKAABXQp4Aio6LJ1PmpUXGw0atWoivi42CI1Hj52QgVQoqIiHcsaNayDQ0dOqPnd+w6jQd1kNS2D+nVqyghpGfr9ML4nzRbQOJljUW3lwFUgp8CqAj7/pVRMwMm1NZyjAAXCUWDI4CcxbtwHxaYzzjwrHFnYZwpQgAJBJfDLi4sx4+YZjnRMLnC3AvN+i3bkyfLj244HVb/Y2PAQYC99IxDUAZXV67fi4y/nuE3ubu9xR5aWnom42BiXRdHRUZCH0MpVKHL1Sow2by8QfSrwkpWVY89CTJQJ8TERukkRRoNqW7TWLpM2Hej2RUV6dlhFmoyIjTZBa2JA7WKjI5SPp9uNMBkQrfWpLPtYtqE2xgEFKEABJ4FzzmmKC1q3LTYlxCc4leYkBShAAQroUWDXkt3Yt3yfI+Xma63UTsMPHTc68mR5XnqetqCU/1MyYJnzN8yTFsMyayWsB21f7payVjgtZl8poEsBoy5b5WGjJNiRkpoOd8nTX+RJSoxXt/w4bzI3N0/d3mMwGFSwJTdP3h1tJezTcXGngzAm7QO3fOjWSzIYtHdyrbmT12Th/lnH8fCcVAyeG7g0fWO2tnXX/7VgvWuGNvfb7lwM+vEk7pt5PKDte31pmrZ1wAoDPNlnBoNBPTvHk7KFyxgMtn0B/qMABShAAQpQgAIUCCmBgrySrgb3oqs5eSgYNw+WXzbB+u9+WJZuhnnsXCCFt396ociiFKgQgaAOqHRo3wLDHu7vNt13aw+PQJOrV4Hc1pOfX+AoLw+hrVWzipqX56ns2X9YTctg74EjMkJSQpwayyAzuwAnM/N1k/LNtjf3XScKsHp/HtYc0NLBfKwJUNp3suizUdyFFY5mWSBtU20MUNvEYPNR276WK5A82W/5BRZk5ZZtH5stVjlEmChAgSATkF9zk4eWB1mz2VwKUIACZRfgmhUmYP33AJBx+up31RDt/NOyYY+a5IACFNCvQFAHVDxlNWsBBgmYmM1mFBSYIdOWUx9027VqqqqZOmsRCrTlPy9drX7hp+PFrVT+VZ3aYfrsX3HkWCoyMrPx+Yyf0Puay2EwuAsRqFV0M7i1RSzev74qxlxVCa93TQxY6tk02iODS+pF4c1uSfigV7WAtU0cHrvIFgyzMtZR6n6S18mhoynqtVFqYRZQAl9syIGkrzfZTozkmT0yL+nrjbY8VZCDChGQKxqbdboLhdNff/+j2iMB9GtuG4aOvQejbff78cIbnyJf+7uhFnJAAQroSoCNoUBFCxgMRU8m5Qu7wu2yWIt+2ehcxnrEdvW0c56aPpGhRhxQgAL6FQiLgMq3Py5BqyvvhTxXZdb839X0rPlL1V6RB9aOf2UwRk+YipZdBmDwC+Px/JDbUa92DbX8lhu64qyGdXBFnyFof+1DKhgz6J7eapneBw2SjGhTNwqtakegZXLgUr0kk0c01eMMaFUrAm21NgayfedUi1DtC4KYmGqnvwd5efm4fdCr6HPfcJdNLVm2Th3zXfoOVa+NaVpg0aUAZ4oIZOdbIb+yJembUwEVKSTzkpzzJJ8p8ALWU5HUD0Y/jrlfjHakluedrRrz8tjP0fTsBlg1/yPMmfIa5Jfe5i9erpZxQIFyCnB1ClAgxAQMMBTpUdEcwGgo+dzY0LROkXokw3B2LRkxUYACOhYIi4DKTddfgU2/TnZJcpWJfb90vrQ11i/6FAu/fgtrf5qE/r262BchPi4G77/+GP6cPQFLvhuHbz4cjprVKzuWc4ICwSwgHy5Hjvkf/t6w1aUbcqvDEy+9j0fuuQHrFn2CcaMGYeTbk9XVWy4FOeMiEBtpgFwZVlzq1+z0s5dcVuRMwAXq1a6ufuFNbuuUFBsThZPpmfhj5Ubc3qcbZP7MBrXRq/ulWLhkZcDbp48NshUUoAAFKFCSwKkL3l2LlOEbO0PD6jB0PM+lHkPrM2E4v75LHmcoQAH9CYRFQMUTdpPJiLq1qiMy0nb1QuF1KiXGo3rVSoWzdTl/S/MYdQtNi2T3fdFlowPYqFrxRuUz5KL4AG5Vn5ua9NWP+GfrLgx94CaXBq5Y8696WHP/np0RYTKha4c26sPnkmVrXcpxpqjAbdrrr7h08/kMqBQV82GOF1WN+XAanh/9CaZMX6ACKbLqseOpMnJcoSgzDeom4+CRFJl0JPn1Lr0maaRe2ybtMkgDtSTTek1a8yCfh8rSPlmXiQIUoEBZBEzXt0HEqzfDNOQamEbdBNNtl5WlGq5DAQoEWIABlQCDB2JzDSuZILfQVI6pmN17cb0oFbCQ55VI6tkkBnKVfeH865t49qwVX5vFRBpb+691AAAQAElEQVSUT5NqJV9+6evt6q2+hUtWqQ+T748eisT4WJfmHT52QgVQoqIiHfmNGtbBoSP8CT8HiA8mWIXvBMxmCz7+ck6xaceeg2pj0doxfcsNXdDivEaoWjlRlb9r8GuQW9/SMrJUGefjPjo6Cimprve216wSC70m6YBe2ybtSoyPRHxMhG79pI0mLZJSs1JMmdoogRjwHwUoQIFiBI4eO4qPPpqAp4YNwdtjXsfmzbbndzmKR0fCUL8aDHEVc47saAcnKEABjwWMHpdkQQp4KCDPRpGAjj3VTTKqb/sK53v6rBUPN8tipwRWr9+qPiS6+3ApzxGSYhs278Rzr0/CxNeHolaNqpLlktLSM9VPhjtnRkdHIf3UB07Jj440ompilC5TgvaBLUrH7RO3CO1DW6V4ffpJ+xLjIhEZUbZ9nKStK8dIIJMVVi3wkV5skoeRS3sStODhc4Nvh/wSnFyZNeXdZyG/7Lb5vz2OX2+zl5Xyubl5WuAlSSYd6VBKNvSapJF6bZu0Ky0zH5k5Bbr1kzbKr7MdTs0pUxvlywPZB0wUoEB4CMRVc/1CqqRe5+Xl4aGH7sbnX3yGZct+x6xZM/DAg3dh564dJa3GZRSggM4FjDpvH5tHgXIIhOeqWdk5xX6oPHEyXaHMmrcUNapVwrxFf+GNCVMxd/Fy7N53WE1L0CQpMV7d8qMKnxrIB8vEBNsvJElWgdmKrFyzLlNugQVmHbdP3MzaJ6/svAJd+kn7cvPMmqGlTO3L0daVYySQSW5NG/ZwfxSXmjRyfx96zepVVDOztcBJ9Wq252PtPXBE5clg195DqF2zaNBRljFRgAIUoEB4C1z5elfc2s+Ae61fqlTDelRdlX29dYGal/zbeuaiauOq2LhpPQ4fPuQCZrFYsGD+jy55nKEABYJLgAEVPe2vMG7LluNmPLsoA72/ScU9P5zE5HXZYaxRvq53aN+i2A+V8q281N7pklbo1f0yVK6UoFJ8XAxioiPVtDxPKFn7kCkBFudv6uVb/Fo1bR8+pQ75Flc+OOsx5UtAxWKFHttmb5MWT0FevkW3bczTDDXCMrVP1pVjRI9pybJ1WPDrCvXcFAk+jpv0rboaS37ZR56VdXHbZvh8xk+QBzPv3HMQPyz8E906ttNjV9gmClCAAhSoYIGa59dEjdcHI2HXSpVMbVqoq7Jjv/tUzUt+9bHDEJ0Yjd17drtt7e49u9zmM5MCFAgOgXIFVIKji2xlMAi8ujQDaw7lI7vAioPpFnyzMQffb8kNhqYHZRsl6HL/bdfBnjpe1BLJNaqq+bjYGLRr1VT1a+qsRSgwm/Hz0tXqF346XtxK5XNAgWAVyMvPx/OjP8Ul1z2Mdlc/iHmL/8L4lx+FBFOkT889ehs2bdmJtt3vR487ntGCKW3R/YoLZRETBShAAQpQoMwC7dq4/1tyycV8+GyZUbliwAW4waICDKgUNWFOgAV2nijAkUxLka2u1QIsRTKZERCBuNhojH9lMEZPmIqWXQZg8Avj8fyQ211+/SQgDeFGKOBjgSsvb4tlcyZg8fR3VPpt5ru4qM15jq2c2aA2Fkx9Uy1bMfcDvPL0vYgs5tffHCtxggIUoAAFKFCKQL169XHffQNdSl166eW47robXPI441MBVkYBvwswoOJ3Ym6AAvoXuOn6KzDj45EuDe18aWusX/QpFn79Ftb+NAn9e3VxWc4ZCgSrgDxvJblGFUgyGOw/5OvaG1kmt8K55nKOAhSgAAUo4F4g8q8/YDr1jBRDhu2ZdYVL3nH7PVg4/zd8/NEU/PD9Qrz+2phCRYC8tN04+Ofz2DmnN/YvfQLZxzcVKcMMClBAPwIMqOhnX4RtS86sEoGa8UUPxVa1IsPWRC8dl+ep1K1Vnd/Q62WHsB0UoAAFKEABPQmwLUog6bGHsfXmUUjZl6Xmj9w3EqZ9e9V04UFsXByaNj0PVapULbwI5rw0bJtxOY6sGYO03fNxbP1E/Df9MuSl7ylSlhkUoIA+BIp+itVHu9iKMBN4tkMCWmsBlNgIA2onGtHv/Bj0bBIdZgrsLgUoQAEKUIAC/hRg3RTwtYBp7x7Mm5mB7w3dkYVYVf3i3LZY88RXatqbQca+JSjIPuqyisWci5Pbf3DJ4wwFKKAfAQZU9LMvwrolTaqZ8GqXBHzXrzI+vb4S7mpp+4MU1ijsPAUoQAEKhLsA+08BCuhcwLB1K9bh9LO47M3dtLHAPunxOPfEFrdlc1Pd57stzEwKUCCgAgyoBJSbG6MABShAAQqEsgD7RgEKUCA8BLJPZOPnp3/G+EGbkWcoelX1yfw4ryES67t/Xl1Cvc5e18UVKECBwAgwoBIYZ26FAhSgAAX0KMA2UYACFKBAWAtYLFYcOpqCArPZK4dlY5Zhw9cbkJstV6JYi6xb98I6RfJKy4it2Ro1L3jcpViVxv1Q+Wz+EpALCmcooCMBBlR0tDPYFApQgAKlCXA5BShAAQpQgAK+EViybB3aX/sQuvQdipZdBmDa7F89rnj7wu0wGGy/FGe1GmC1ng6qJCbH45IXu3pcl3PB2hePwvn3H0bjvkvRbMA+NOj2mfNiTlOAAjoTYEBFZzskVJoze2surv7yhEoTV9qeeO4uL1T6y34UK8AFFKAABShAAQpQQHcC2Tl5eOKl9/HIPTdg3aJPMG7UIIx8ezL2HTzqUVsLcuXKFFtRiasYDLbgiuRc8/51qNa4mkyWKZkiExFXsw0iYqqWaX2uRAEKBE6AAZXAWXNLQSHARlKAAhSgAAUoQAEKhLrAijX/Iis7B/17dkaEyYSuHdqgYb1kLFm21qOuJzdP9qgcC1GAAqEtwIBKsO9fnbb/unOiMe/WKsWmge28f1CXTrvKZlGAAhSgAAUoQAEKBJnA4WMnVAAlKirS0fJGDevg0JETjvmSJq5+92pEJ7k+jDYiJqKkVbiMAhQIQYGAB1RC0JBdCjOBXalm7Evz7sFlYUbE7oagQEq2BduOFyCn4PQ94iHYzQrv0o6d27F3754KbwcbQAEK+E9gxwkzDqZb/LcB1uyRQFp6JuJiY1zKRkdHIT3Ddqu6LIiONKJOtVi3qdHZVTFq31Dc9XUfXDuqsxrXbWm7aqVGpWi36xRXV1nyZRuREcW3ryx1+nqdmCgTqiZG+d2irO1OrhIDk9FQ5vbJMcJUukCol2BAJdT3MPvnM4G1h/Jx67epeOjHNNw3Ow0DZp/E7pMMrPgMmBXpUiAz34qnF2Xg1u9O4tH56eg3IxXfb8nVZVuDuVGrVq9Ar15X4c47++GWW3uj/y03YOeuHcHcJbadAhQoJLByf772HnoSD89Nwz0/nMSDc9L4BU0ho0DOJiXGq1t+nLeZm5uHxIQ456xSp8+7pjE6Dm4PGZdamAWCQYBtpIBXAgyoeMXFwuEs8N6KbKTknP52/kCaBZPXZoczCfseBgJztuZinRZMtHc1T4shfrAqCyey+e2q3cQX43fHvYXjKccdVe3btxcffTTBMc8JClAg+AXeW5mFtNzT753ypczn63OCv2NB2oPk6lWwe99h5OcXOHqwdcc+1KpZxTGfm2/BgePZHqe8Atv+PXoy1+N1vKnfuaxsI1/b3oHjnrfPef1ATOdoJw0p6Xl+tyhrXw6fyIHZYi1z+xwHCifCWoABlbDe/ey8pwIZuVbsT9c+SRZaQS7bLZTFWQqElMDmY6dPNJ07tkcLKDrPc7rsAllZWdi9Z1eRCrZt21IkjxkUKEmgwGzGoaMpOHj4OMxm2wc75/IW7YODLC/Qyjnn26fz8vJx+OgJl59/tS/juHwC6dp5xJHMovukuPfY8m1N52vrpHntWjVVLZk6axHkNfHz0tXqF346XtxK5XNAAQpQwBMBBlQ8UWKZsBeIjTIgLvL0z+HZQeK1fPs0xxQIRYGqMUWPe+lnwuln+MksUzkEoqOjERsTW6SGhMSkInnMoEBxAt98vxgtuwxAl75D0bXf47jy5sexcctOR/Ely9ah/bUPqeVSbtrsXx3LrFYrJv7ve7Tudh86930Ml9/wKNb9s92xPNwnfNH/OO18wejm7bS491hfbJN1lCwQFxuN8a8MxugJU9VrZ/AL4/H8kNtRr3aNklfkUgpQgAJOAgyoOGFwkgLFCZi0k6BODYt+guzYMKq4VZhPgZAQ6NrI9RcMpFMNKplwRpUImWTygYBJfq6z61VFaura+coiecygQHEC8nDND0YPxcp5H2LZnIk4+4y6GPPBNFU8OycPT7z0Ph655wasW/QJxo0ahJFvT1bfxkuBtZv+w4TPZuLz8c9i7U+T0Kt7Bzw2/D3IFS2ynKn8AnIecVmDoucMXdy8x5Z/a6zBU4HOl7bG+kWfYuHXb6ljv3+vLp6uynIUoAAFlAADKoqBAwqULjCofTweuTAOlzWIRKczovDkpfHo1yym9BVZggJBLHBu9Qi8eWUirjk7ChfWicStLWIxumsC5MNBEHdLd01/4oln8fjQp9GpUxdc2bU7XnhhFG677W7dtdM/DWKtvhC4rtsl6NC+BeRb96SEOCQlxqNypURV9Yo1/6qHb/bv2RkREsDr0AYN6yVjybK1avni39fg4rbNcEHzcxAZGYHb+3RTt/5s2b5HLefANwKPXRSHB9rE4ZL6kehyZhSe7ZCAHo2jfVM5aymzgMlkRN1a1dWxX5ZKNn6zEcvG/qVSxoF0VcXGbzapeclPP5ih8jigAAVCU4ABldDcr+yVnwSu1U58ntNOgIZpwZTOWlDFT5thtRTQlcD5NSMgAcWRVyTgtuYxqBwT5n864J9/vXr1waiXRuPFF19Gtyuv9s9GWGvIC/yw8A8MefE9/LN1F+6/rYfq7+FjJ1QAJSoqUs3LoFHDOjh05IRM4uCR4zizfi01LYOa1SvLCEeOpaoxB74RiIkwoFfTaLxweQKeuCQeHbQvaHxTM2upSIGN0zZh+bi/VMo4ZAuebJq2Uc1LfsZBW5ClItvIbVOAAv4T4Fmx/2xZMwUoQAFdCLARFKBA8AocOHQMH385p9gkt/M4927H7oM4fiJNPZQ2LT1LLUpLz4TcEqRmTg2io6OQnnF6eUy065USUj4j6/Qv2cVHR6ByQpQuU3SkCXHRJui1fZXio2AwAHptn7QrJsoESTLtbYqKCO+PE+ff1AztB19UbEqobbtS7NRLjyMKUCDEBML7HTDEdia7Q4EQEWA3KEABClDglEB+gRkpqenFJqvVcqqkbTTkvj7qWSi9r7kcj4+coDLl9p+s7Bw1bR/k5uYhMSFOzcry3Lw8NW0fSPmEuNMPSy4wW5CXb9ZlMlssKDBbddk2MZN9aLVCt+2TNpq1/Wu2lM3QLJ2zHzhhOD6/3/m4eMhFxabE2glhqMIuUyB8BBhQCZ99zZ76TYAVU4ACFKAABfwjIM86GfZwfxSX5EoSd1s+s0FtFYQpMJuRXL0Kdu87jPz8AkfRrTv2oVbNKmq+ds1q2LX3kJqWoMJVbQAAEABJREFUgf1WH/utP5KXW2BBVq5Zl0kFU3TcPnETQxnrNeVrAan8MhqatXWlf0wUoAAFwlGAAZVw3OvsMwUoQAEKUIACISUwcfIsrPtnO3Jy87D/0DF89s08tG99rnoIbbtWTVVfp85ahAItwPLz0tXqF346XtxK5Xe+rDX+WLkRf2/YBrmaYsqMBUiuUQVNGjVQyzmgAAUoQAEKUMC9gNF9tr5y2RoKUIACFKAABShAgeIFJIhyy8BRaHPV/eh28xMwGY146al71Aryyz/jXxmM0ROmomWXARj8wng8P+R21KtdQy1v1exsPHjH9bh90Cto1XUAvvn+F7w9fCCMRoNazgEFKEABClAgkALBtC0GVIJpb7GtFKAABShAAQpQwI3AK0/fizULP8aCqW/ij+/fwxfvPecImEjxzpe2xvpFn2Lh129h7U+T0L9XF8lWyWAwYNA9vbF6wUf4SVu+/Mf30fr8xmoZBxSgAAUoUKoAC4SxAAMqYbzz2XUKUIACFKAABUJHQH4WWa46qVzJ/UMwTSYj6taqjsjICLedjomOQh1tOa9MccvDTAqEkAC7QgEK+EqAARVfSbIeClCAAhSgAAUoQAEKUMD3AqyRAhSggE4FGFDR6Y5hsyhAAQpQgAIUoAAFglOAraYABShAgfAQYEAlPPYze0kBClCAAhSgAAWKE2A+BShAAQpQgAJlEGBApQxoXIUCFKAABShAgYoU4LYpQAEKUIACFKBAxQswoFLx+4AtoAAFKECBUBdg/yhAAQpQgAIUoAAFQk6AAZWQ26XsEAUoQIHyC7AGClCAAhSgAAUoQAEKUKBkAQZUSvbhUgqEvEBeXj5uH/Qq+tw33KWv19/5LJp1usslTZw8y6WMjmbYFApQgAIUoAAFKEABClCAAgEVYEAloNzcGAXsAvoYW61WjBzzP/y9YavbBj064EbM/WK0I91yQ1e35ZhJAQpQgAIUoAAFKEABClAg3AQYUAm3PV7W/nK9kBSY9NWP+GfrLgx94Ca3/atRrRIa1kt2pMqVEsB/FKAABShAAQpQgAIUoAAFKACEbECFO5cCFChZYOGSVZgyfQHeHz0UifGxbgtPn7MEz4/+BHKrz579h92WYSYFKEABClCAAhSgAAUoQIGKFKiobTOgUlHy3C4F/CSwev1WfPzlHLdphhYgkc1u2LwTz70+CRNfH4paNapKVpF0Vad2uPyiFqhTqzoW/7EGN947HAyqFGFiBgUoQAEKUIACFKAABbwVYPkQEWBApZw7ssBsRVSkEbHRJl0mi8WKiAj9ti9aszObLbq0s+9TKzRDo6FMbYw0Gcp5hHm/elZ2DlJS092mEyfTVYWz5i2F3M4zb9FfeGPCVMxdvBy79x1W0+kZWarMw3ffgIfu6ImBd/bE1IkvIDEhFouW/q2WyXElPbMb6W1s0vaX7De9tcu5PXLc6/m9Q943ZD87t9nT6ZgokzpOQm1gtQLynu+pQ0WUy83X9/up8dRZR0XYeLrNfO1vUoz2t8nT8s7ljNp7T6gd99IfOe4jtL9nzn3V07S00aQdW3pqk3Nb5D0xr0Dfr02D9kfdYCjbuU6Udp4p+yDUkkV709fzuURUhAEF2nm+87Hm32nvP+uIoZ7fO6KjjMgvx2sz1I559qdsAtqfn7KtyLVsAvImkRQXiSoJUbpM8sZaScftqxQfBTnR0KuftCsuOgKJZTSUvtmOlMANO7RvgWEP93eb7ru1h2pIp0taoVf3yyDPRJEUHxeDmOhINW+Ss1JV6vQgMjICNapWRnZunsqUDw2xWhBRfPSY5DUZp+03PbbN3iY5NuT4t8/rbSzvG7Fl3MeV4iPVcRJqA+2zBuQ9X2/7yrk9EqR2ntfbdGJsJOJjInT599JuFRNpQuUy/k2X4wMh+E/6JfvObqS3sRxTem6feOn9tZmgvS4TYsv22ozT1tXlYV/ORhm1N315P5D9p8ck5xCx2hcYemybvU1x2rmYnJPZ5/U2rlzOzyHlPMS4eogIGEOkH+wGBSjghYAEXe6/7TrYU8eLWiK5RlU1Hxcbo27tmTxtPg4dTdEi92bM+WkZNm7Zifatz/ViKyxKAQpQgAIUoICnAixHAQpQgALBJ8CASvDtM7aYAgERkAfWduk7FK26DsCwVz5UV7y0aXFOQLbNjVCAAhSggO4F2EAKUIACFKBA2AswoFKOQyAvLx+Hj56A1WotRy2+WfVYyklk59huxyitRil34NAxyPMRSivrq+V6siquT94Y2uvIzMrBwSMpAbW0b9uX45uuvwIzPh7pqLJB3WQsmjYGv818F/O/egPrFn2CO/pepZbLcSNXrhSYzWq+IgfyvBf7c2FKa4e0V/ZVrva6La2sL5eX5bjy5fZLq6ssr037MSD+pdUfSsulv54eb/7st91fjmlPtnMyPRNHjqV6UtRnZfRi5dqh03PeGtrXPH4iDZLs8+EwLst7hL9cvHk/rYhznbIeV/7ycldvWV6b+fkF2K+dN8qx4K7OUM3z5njzp4G4e/p5Q45Babe87/uzTYXrlm3Ka65wvl7mvTG0t1ks5XxXXjP2PI4p4E6AARV3KqXkSQBl4v++R+tu96Fz38dw+Q2PYt0/20tZyz+L5VdXrrltGDr2Hoy23e/HC298qm7RKG5rg54bp8pdefMT2jqP4u0PphVX1Cf55bFasmwdmnW6CzKGH/95ayhNkTaJ+4XXPIiuNw3Ff7v2S3ZIJYPBgGpVklC/Tk1EmGwPGZV+t7/2IXTpOxQtuwzAtNm/oiL+yYN35Vi+qMdAXNZzEPoPHAX5Y15cW+RXj6S9sq8u0F63Q0dMwMm0zOKK+yS/LMeVbFj+6N8+6FX0uW+4zPotleW1KScVT7/6EZp3vlsdA8Pf+sxv7dNTxd4ebz5ru5uKvHkNymtC3qcuue5hXNFnCK6/81nMXvinm1p9l1Ueq3c+mq7e89NOPRjbd61yrckbQ1lTTqrlPaRDr0Hq7333W56S7JBPZXmP8BeKt++n8vdBzokCda4j/fb2uJJ1JEmgot3VD2LMh/49HyvLa3PnnoOQv0etrrwX3bTzxu/mLZUmh3zy9njzF4i3r8FlqzZBztHkM4G879815HV1u7a/2if1ltWK5zqixxQqAgyolGFPrt30HyZ8NhOfj38Wa3+ahF7dO+Cx4e9VyFUKL4/9HE3PboBV8z/CnCmvYf4vKzB/8fJie9WkUQPM/PRlrF7wEUY9NQCffj0XG/7dUWz58i4oq9WW7XvxxEvvl3fzHq3vreGvf67FwGfeQbeO7ZT5H9+/p4IOHm0siAvJNw+yTx655wZ1xcq4UYMw8u3J2HfwaMB79dXMRdi6Yx9+mTEWf82ZCJPRiHGTvi22HfLg3U/GPKVeJ3L8r1y7GTP9fGLo7XEljZeTp5Fj/oe/N2yVWb8mb1+b8qHy7sdGY+fugxgzYiBWzvsQTz9yq0sbQ3XG2+PNXw7evgZln/XqfhkWTR+jXifdr7gQL70zxeOrGcvSj7Jayetx0lc/lmWTXq3jraFULoEeuQXywTt6Yums8Zg95VXJDvnk7XuEP0G8fT8N9LlOWY4r8ZIg9UPDxkCCHTLvz+Tta/Pw0RPoccczSK5RRZ3vynnmVZ3a+bOJuqnb2+PNXw339jVoMBrw4mN34M/ZE7B4+juQHxyY8NksfzVP1VsWK57rKDoOQkiAAZUy7MzFv6/BxW2b4YLm50B+/eT2Pt3UrT9btu8pQ21lX0Uu5/tj5UbI9mNjonBmg9ro1f1SLFyysthK5cPwOWfVQ0x0FOSXXuQP5bLVm4otX94FZbE6ejwVDz09BsOH3gl5QGp521DS+t4ayh+BcZNm4Lpul2DIfX2UuXxYF/+SthMKy1as+Ved9PXv2VldsdK1Qxs0rJeMJcvWBrx7Ejjs06MjalavjMSEOO01cCW+m/tbsbff9e3RCRddcB5kP8nx3+mS1vjtr3V+a7e3x5W9IfKB8p+tuzD0gZvsWX4be/va/FXbz/9u2403XngQV3W6UHttRit/vzVQRxV7e7z5q+nevgbl9SEPnq5Vo6p6nVx/1aXqNfzvtl3+aiLKYiUBzlff/RJvvfiQ39plr9hbQ/l7JF88yGvy1t5dUbVyIsTTXl8oj719j/CXRVneTwN9ruPtcSVWcsvek6PeV+eS8p4qef5M3r42/zdtvjreX3/uftXGWO08s0qlRH82URd1l+V481fDvX0NynmOnJ9WSoxXgbDu2t/q3/5aBznW4Id/ZbXiuY4fdgarrFABY4VuPUg3fvDIcZxZv5aj9XLSKjOBvkf9mBZ4kO3Wq11DRirJsy/kORFqppTB7n2HVSBIvskppWiZF3trJd/yPPLsOPS++nL0uPLiMm/X0xW9NTxxMkNdGZGZmY0Hh72tLoWdOHkWck79nLCn2y1buYpd6/CxEyqAEhUV6WhIo4Z1cOjICcd8oCbk2JVj3b49uS1Jpj25VSC/wIw/Vm5AsyZnyip+Sd4eV9KIhUtWQb4Ff3/0UCTGx0qWX5O3r0350CsBTjkRklusHn3h3Qq71dGvMG4qL8/x5qa6MmeV9zUo+1A2fkb92jLyS/LWSsoPfGYsxr70CBqfWc8vbXKu1FvD9f/YruDcuHmner+/94k38cPCP5yrDNlpb98j/AVRlvdT57bIMSZXW/jzXMfb40ra98aEr5GXV4DnBt8ms35P4uDN383fV2xAneTqeGLk++j3wEiMeGsy5HkSfm9oBW+gvMebL5tf3tfgH6s24tzGDdWXYL5sl72usljxXMeux3EoCTCgUoa9mZaeiZjoaJc15YNGRla2S56/Z+wfHp0/4EZHRyElNa3UTcvDVIe8OF5963DZhc1LLV+mAtpK3ljJ5enPvT4JdbUA0cC7emlr+/9/bw0PH01RjapWpRJ6X9MRPbVvfD/7Zj5eH/+Vyg/lgexLOc6d+yjHm1yy7Jzn72m5Skguj47RjnX7tqJPBXmysnLsWcWOXx47BekZ2ZAru4otVM4F3h5XG7QPa3LsT3x9KAL17bfszxgv3sf2Hzqqvq0875yGuO+WaxETFYVbBo6C3GNfTi5dr17e482XnZN9VtbX4Lad+yBXgTx0R0+1H33ZLntd3lrJc4zuf/ItPHZ/X1za7nx7NX4de2to/wBZvVol3N2vO+SXzp559WP8uOgvv7ZTD5WLlTfvEf5qs7fvp87tCOS5jjevzamzFqmrO98Z+Yi60tm5zf6Y9va1KW3YvvuAumWky2UX4J7+V6tncdzz2GjIA2pleaim8hxvvjYpz2tw9sI/1TOzHvfjFa/eWvFcx9dHCOvTiwADKk57wtPJpMR45ObluRSXD3gJcf7/Vtl5o0kJcWrW+Y9bbm6edrKcpPKLG8hVIPLMF7PZgvEvPwqTyX+HgTdW8gDFBb+uQGJCLN56/2u8MWEqxHXa7F+w4Nfib2Mqrp+e5JfV8NF7b0S3jm3Rp0dHPDPoFtVRFPAAABAASURBVHVyLScsCOF/si9lfzh3UY43ueXGOc/f0waDAXLimpuX79iUfTouLsaR525CriaaMWcJPn1nmF9vV/H2uJo1bylqaB/Y5mkf0uS4n7t4OeTbRJlO99MDOmV/5nr5PtZZO7Hu36sLZPzas/dr7zWJWLb6H3fUIZNnMJT9ePM1guyzsrwG5aGXDzz1trbfWuOhO3v6ulmO+gwG76z++nuTegbT3gNH1Pv9pKm2Z6iM/XgG/t2221GvLyfKYihX4kkgSo57Gcsl9T8tWeXLZumyLrHy9j3CHx3x9v3U3oZAn+t489qcrH0RI7fMfvj5D+rY37RlJ/5ctQny8GN7+305Nhi8e23at31r7yvVLc5XdboQb77woPq7tGPPQfvikByX9XjzB0ZZX4PyOAB5gLzcOi+PKPBH26ROb614riNqTHoT8EV7/PdJ2het02kdtWtWw669hxyts9/qY7/1x7HAzxPVq1VWW5CTUTWhDaRdtWtW1abc/y/RZHk+iXwzOOXdZyHP/3Bf0je5tb2wSoiPwWAtUFG3VnXVLnvbEuJjtQ/Q0b5pUKFavDWso7VNqtinfQCQsaSCArMK/FitMhe6Kbl6FXUy5RzAkwfD1qpZJeCdlhNRebK8fcP214D9j7s93z6Wq5/enPg15Gqi6R+NQPOm/rvdR7bp7XHV6ZJWkIeHyjEvSR4kFxMdCZn2V8CzthevTelTw7q1sHPPAZl0pJzcfOTlnw5sORaE2IS3x5u/ul+W1+B/O/fj5gdHokP7Fnjl6Xv9GkCXfntjdfYZddV7fpVKCepYt79+KyfFIyoyQqrzefLWUG6plW/q5VZBe2PkPT+/oMA+G7Jjb98j/AXh7fuptCPQ5zreHlf33Hw12rZsoo57+/t8THQU7K8B6YOvkzevTdm23Cri/HfWYrFItvaeH9rHflmONwXjh0FZXoMLtC8m5cq/l4cNwE3XX+GHVp2u0lsrnuuctivHFFfVoQADKmXYKZ0vaw2J/v69YRvytQ/TU2YsgDzc1Z/357prpjx0SiLPn8/4Sf1qw07tW4MfFv6Jbh1tT2GXb7b73Dcc87Rvu2X9rOxc3Pbwyzhy7AReeuoeZGbnQL659PSZK1KHt6k0q5VrN0PaKN/Gy1UH8gBF5yR513S+SH0Y8HbbnpQvzVDqGDpiIt764BuZhJTv0L453vtspgqi7Nl/BN/++BuuvLwtjEaDKhOqg3atmqquyaXKBWYzfl66Wn273PHiVio/kAP5pYHps3/VjuVUZGRmQ14Dva+5HAaDbR9MnjYf8lOP9ja9+OankLwxIx5GpaQEddzLsS/9sJfx5ViOE29em/Jh1/m473hRS+09pSokT14Dvmybva7SXpvi5WzY9fI2WLp8g3rvk6Dad/N+U6+B9q3PtVcZsuPSjrdAdbxdKa9B5/dTadOW7XvR8+7ncHGbZrj3lmvVM7PkuD9xMl0W+yWVZuV8XDXSAipyjNvTTdfZTv7v6nc1ZJk/GliaYeG/m62bN9YC+jH4cMoPMJst6rlB8jf10nbN/dE8XdVZ2ntEoBpb2vuptGOo09/pijjXKe24Kvza7Nezs3p/tx/7Tc9uiAuaN4bkS3/8kUp7bTobyvav6dJe/RKkvGfIw0fl76w8lPls7XUry0M1eXK8Barvpb0Gnd9PpU3fL/gDsh+ffuQWXKj9bZZ9JylLO9+X5b5OnlgNHXH6HFqP5zqFX5vhfK7j6+MjnOpjQKUMe7tVs7Px4B3Xax/YXkGrrgPwzfe/4O3hAyvkA/Vzj94GuVS0bff71c/bdevYFvLTmNIti8WqLptOTcuQWciJonzTJsGL3gNeQLebn1DppvuHq+X+GJRmJc+dkUu7c3Jdb6HyR1uKq7MkQ1lnp/at/IFDx2RSpeeH3IGU1HS0u/pBXH3rU+oe4+eH3K6WhfIgLjYa418ZjNETpqJllwEY/MJ4SL/lG9xA9/uWG7rirIZ1cEWfIWh/7UPqnu5B9/R2NOPosVRs/m+PY17+YMqMPEjYftzLeP/BY5Ltl1TScVX4temXBpRSaWmvzcKGLc9rhCce7Af55qvVlfdCHlAo34DJt5ilbCroF5d2vAWqg6W9Bgu/n+7YbbuiSJ730f2Wp9T7vRz38hr2V5tLsyp8XPmrHcXVW5ph4demXDHw7qhB+N/0BWjR5R713KBbbuiiffPbqbhNhEx+ae8RgexoSe+n0g7nv9MVca5T2nFV+LUpbQ50Ku216Wwobbut95Vof8F56n3jkusexm/L12HCq0PUr+XJ8pBNWsdKO960IgH5v7TXYOH303X/bFftev29r9R+k/d7SQv8dNu8bKw0q8LHlawTyFSaYeHXZjif6wRyv4TathhQKcMeNRgMkA9vqxd8hJ++fgvLf3wfrc9vXIaayr/KmQ1qY8HUN7F4+jtYMfcDdUl35KlLpSslxWPTr5PRv1cXtaHkGlXUvOQ5p6Wzxqvl/hgYDCVbXXFJa9WmJo3qu938ynkfoOPFLd0u81VmSYayjZmfvgy5skGmJUkAYcbHI/HLjLEQO3keR/WqlWRRyKfOl7bG+kWfYqF23K/9aRLsx1agOy63xLz/+mP4c/YELPluHL75cLjLM1GeHHgz5Nixt0teI87HvH1aLoG2l/H1uKTjqvBrs/C25TJdOcYK5/ty3mAo+bVZ2FC2fffNV0Pe9+Z/9QbW/vwJbri6g2SHfCrteAskQEmvwSsKvZ9e3bm9en+1H+/28evP3u+3Jpdm9WSh16ZzQ84+s65qrwQxnPN9PV2SobvXplxttmzOBPW+J+8rzw2+HREmk6+bpbv6DIaS3yMC2eCS3k+lHc5/pyviXEfaUNJxdUWh16aUd05jRgws88/lO9dT0nRpr01nQ6lHfvDgrRcfwrI5E/HzN29j0bQxaKEF1mVZqKfSjrdA9d9gKPk1+GSh99MXH7tDvYfa3+vtY3/+rS7NqvBx5Wynh3OdK9y8NsP1XMd533DaOwEGVLzzcikt97vKMzX0cKuHnEDIH0uXBupoRk9WxbF4ayjPzKlaObG46kI2X57pIc+5sQfuKrKjcrmp3oNZ3h5Xgfb09rUp5eVnqiMjQv8DZeF9oZfjTU+vwcJG9nm9WNnbU3jsraEEUOR9z1+34BVun57m5TXvw3OdcnVN7++n3h5X5cIo48revjYlwFk7uZrjltoybjYoV9PL8aan12BxO1IvVsW1z1tDKR+u5zrFGTK/eAEGVIq34RIKUIACFKAABcJagJ2nAAUoQAEKUIACxQswoFK8DZdQgAIUoAAFgkuAraUABShAAQpQgAIUCJgAAyoBo+aGKEABClCgsADnKUABClCAAhSgAAUoEKwCDKgE654rQ7vX/7Mdi3//W6Wly9djzcZtkJ/CK6mqDZt3YsacJfj4yzlqPfl1m+LKy7Ily9ZBfsbt6+8XY9mqTSgwmx3FZZtbd+xzzNsnVq3bourOzcu3ZyH1ZAYW/7FGbXf6nF8hbXcs5AQFvBCQX3ywH/e//LkGf/39D/7buR9Wq7XYWko4/oqsI79QtWLNZsgxL78E8vPS1eoXtewFN27ZieVr/rXPOsbbd+2HtOvw0ROOPJnw5jUn5ZkoUJyAvB/LMSbvpfJ+vGnLLuQ5vc+6W8+b42/P/sNY8OsKfDJ1Lr6b+xv+2brLUWVWdo46vo8eT3XkyYT8TVCvw9X/yKwjefOac6zECQq4Ediz/4g69uTYl9eAnGMcPJLipuTpLFlHfg3rgyk/YM5PyyC/hnh6qeuUL8915PX4l/ZamDJ9gfolKXmdyt8U1y1yjgKlC5TlXMeb40/OmeR8Zua8peo9X14nzr+Auf/QMfW6yy84fd4vrT5+Ik3lb9m+V2YdyZvXnGMlTlBApwIMqOh0x/ijWZOm/ohBz7+r0oPDxuC2R16B/BTe069+hIzMbJdNSqBl6IiJuPnBkepncuUNVNa9qv+TWLhklUtZmZGgS4degzDwmXcw7Ydf8PYH03DvE2/iihuHqJ9uljJS3yztjVim7UnqvXPwa9iyYy+ioyJVtpz8X3nzExj03DjI8jcmfI3+A0fh0RfeVYEWVYgDDwVYbN/Bo+qYl+P3kWfHYcDQN9Dz7udw7e1Pqz/yhYW8Of7+3rANPe96Dnc/9jo++mI2JmmBx8EvjMdFPQaqY1fqnjJtAV4f/6VMOtK/23bj5odGYdrsX1ClUoLK9/Y1p1bigAIlCMj7sRz38l4q78c3PTACl/YchA8/n10koOjN8Zedk6f+Llx96zDI+/q3Py7BC298ir73j8D1dz4LCY7LB1jZtgRo7E20WKwY/uZnkNehnJzb8715zdnX4ZgCxQks1b4wkmNPkrwG5Byj601DcdeQ1yGBbOf15JiUIMrVtz6Fp0Z9gLmL/sKwVz7ENbcNw3ufzoTZbHEurr5g8tW5jrSln3aONeDxN9QXUf+bNl+dN92kvY4Kf/h0aQRnKOBGwNtzHW+OP3k/f+Cpt9HvgZF49d0vIef58jqRc/URb01Wrflz1UZ1rpXp9HlC/q7cqx3fL4/7HEmJ8apcSa+5CZ8Vfc2plTiggM4FGFDR+Q7ydfPkJ+/kZ9TWLPxY/dzy4w/ehNkL/8QjWvAiJzfPsbkxH05T3zy+PGwAVsx9H3O/GK1+nrZ50zPx2PD3sGPPQUdZOXkZ/tZnuLbLRern9aSsrDP9oxGoV6cmjqWkOco6T/z02yo8P/oT3HPzNXjojp5q0R7tG085+W96dgP8+u1Ytd2V8z7A6OcewKKlf+P1CV+pchxQwFuBj958Aht/+Uwdo5PeehLVqlRSf/zlG0x7XfKNu6fH35Fjqbh90CtITIjDnCmvYfH0d7B01ng17tX9MhT3DedO7bVzx6OvQY7xd0YOgvw0pWzfm9eclGeigCcCD9x+nfoZzZXzPsSMj0fimi7t8e4n32LcpG9dVvfm+Hv/f7Mg36g/+dDNWLfoE/U+LeNxowbhxMl0FBT6htK+oTff/xqz5v+O8a8MxsVtm6lsb15zagUOKOChgBzzclz+NvNddQ4hx5oEVfYeOOKoQQIo4z/9Drf27go5L/rhf69i7U+TcGffq/D+lO/xw8I/HGV9ea4j3+JLwOdYykl8/f6L6u+G/A2Z9uEIZGRl41Htyy8JTDo2zgkKeCjgybmON8efBBWHaF8Urdn4Hz4YPRQrtXPyBVPfVOPH7u+L7bsPuG1ZVnYuHn5mrPYZ4CQmj30atWtWVeVKes1N/J/ra06twAEFgkCAAZVy7qRgXV0+xNWrXUMFM+TkduXazZj8zXzVHTnpmDFniTqhuOHqDo6fyqtetRLefPEhVWbi5FlqLIOXx36OhvWS8coz90F+Xk/yDAYDzjvnDHwx/jm0a9VEslySXNY65MX30K9nZwx9oK9jmXxzKjNvDx+IGtUqy6RKPa68GPfd2kMFfySqrjI5oICXAgaDQR2j8mFOTjouaH6OuqpKvnGXqrwBWUOeAAAQAElEQVQ5/uRkW9YZM2IgzmxQWyZVkp8OfOXpe3H/bdepeeeBfMsj30aepZV///XHEBsTpRZ7+5pTK3FAAS8E4mKjcW7jhhj5xN3qfV9u45RbIaQKb46/A4eOqcu9e19zOe7q1x0RJpNUocZdO7TBwq/fhmxLZToN5PUiQZg3X3gInS9t7VjizWvOsRInKOChgByf1aokQc4h5EOdrCZXU8lYrpIarX1Jc85Z9fD0I7c6gtuRkRF4cuDNOL/JmZBAo3yglPK+PNeRD5USdB/55D1ofu5ZUr1KzZqcob1G74FcbfD9gtPBHLWQAwp4KGAwGEo81/Hm+Fvw60rIrT4vPnYHOrRv4WhBXGwM7r3lWnz4xuOOPPtEfn4Bho54D1u278Vn7zyNBnWT1SJvX3NqJQ4ooEOBwk1iQKWwSBjOd9ZObuVEe9X6Lar3/27bo8bXX3WpGjsP5MTk6s7t8feGrSpb7iWWP/w3XXcFIiNsJ9ZqwamByWRETHTUqTnbSJ6HIpefX9ftEjz36G2OgI0slee6dOlwAWpWryyzLum6Ky9W85u1N2g1wQEFyiEgwQw5GZAq/t22W0bquUKeHn+r123B5Re1dJwoqAqcBoU/VMprZcDQ0UiIi4UEcxLiYx2lvXnNOVbiBAXKKHDfbT3UmvJ+KxPeHH/2sjdd10lWLZLkdWUwGFzyv5q5SN0+MeKJu9QVMs4LpQ2evuac1+M0BbwVkA918iWRfIEkz46Qq0Pkfbmvdv5iNLoeswaDATf26AhZfuTYCTX25bnOxs07VPOvuKSVGjsPOrRvrgUlY/DPll3O2ZymQJkE5D258LmON8ffhlPH6jVdLnK7/cLnOgVmM4a98hGWLt8ACWKefWZdx3revOYcK3HCFwKsw88CDKj4GThYqpdvSOSqEbm30X45bO2a1dw2v26t6jh89IR6uKG9bIO6Nd2WLZy5YfNODHj8TVStnIhR2jczEnCxl5E3YfnGpn5t93XVTra1R8rY1+GYAuURaHJ2fbW6nFx4c/xJWbnM9Yz6tdT6pQ3kJOL+J99StwG989IjqJQU77KK/XXkyWvOZUXOUKAMAnIlYb3aNbB2039qbW+Ov137Dql16tWpocalDeT5Kq+M+xwSiO/bwzUII68jeT+vz/f80hi53EcCTc9uqGratnO/ugpEZmon225FkGnnVPvULQp7DxzF3lO3CfniXEe2Ice9XBljMLgGcmSZJLmKUf7GyDQTBcor4HyuI3V5c/zJVeGNGtaB8/m61FFceumd/6lHBgx//C7IFVfO5SQoKfOlv+aOSjEmCgSNAAMqQbOr/NvQrOwcFeSQb2nkMj7ZWnH37+blF8hiREREOK4+yczKgSf/5MqWerWrq297Pvj8B5dVTEbb4ej8LBfnAjm5+Wq2RrVKaswBBcorkJ2dq6qQ56CYjJ4df/JtjFxGLq+Twg9zVpW5GaSkpqsApCx6ZeznjmmZlyR1ybik15yUkUvRpRwTBcorIMdk5aQEVY0cWzJR0vEny+U9PzYmWiaRdeq1o2ZKGPz651rIyfi8xcvVN5bORU1Gz15zfM93VuN0eQSyc+zv+bGwH8tytYq7OvPybOc6sbHRPj3XkW3FxESV+BqSdtoDOlKeSecCOm+e87mONNWT489+Fa38fZDn+sh6nqRlq/5RnyfGf/It5BZR53U8fc1Vq5rkvBqnKaB7AdvZjO6byQb6U8BstmDtxv9w6YXN1Wbq17FdISJRaZVRaLDlvz2Qb1aMRoPjdofNWl6hYm5nO13SCt9OGgW5/16erD99zq+OcgaDQd2zLPdcOjKdJuRhnjLbulljGTFRoNwCGzfvVHW0ana2uvVM7pkv7fiT5wXJSk0a1cfGU5fCynxJSa7I+mriC3jt2fvUTyi/8OankKvB7Ot48pqTe5cNBoN9FY4pUGYB+XnLLC2I3qbFOaoOT44/+3v+maeuytq6w/UnMFVFbgZPPdwfX054Xv3NeHDY2y4/rWww8D3fDRmz/CggV2XJ+3GDusmoV9t2ldW2HfvdbvG/Xbb8Bto5kZSXQr4415F6zmpQR10h4+7LKHmml1yd0rZlEynql8RKw0tgo9O5jvTck+NPrpKSsmefUVddlX4yLVNmS02fjR2GSW8/BfkSVG7vl1/6sa/kyWtOAjj2bdvX45gCehdgQEXveygA7ZNfXpDL8K6+or3aWovzzlL378pDC50/9MnCdf9sVx8Ir7y8rcxq3/BEqV9rmDxtfpFItBSQhx1K3TItqaF2EiOBmBeH3qmePyE/t+b8KyudL7tAPcdC7nGW8vYkD7L6+MvZql1nNaxjz+aYAmUWkADdS+9MUQ/qtN+648nxZw/odby4Jbbu2IeFS1YVaUN6RhbkWUH2BfJAZ/m25/pul2LwvTdizk/LMPbj6fbF8OQ1d2Hrpo7ynKBAWQXkw9oTL72v3ksvaXe+qsaT48/+nn9ekzPUuu9O+hbySxGqAqfB8jX/uuRLsEauAPvwjScgD2yWn950/pvgyWuO7/lOwOE56ZNey3v1d3N/gzxHRSqU9+T2rc/F/6YvQOEPi/IePumrH9H6/MbqFs3YmCifnutIgFzaMGXGAhm5pKmzflbzrbRtqwkOKFAOAXfnOp4cfx0usj2A9pJTfycmTJ5ZpBUFZjPkcQHOC+rVqgH5wmnCq0PUbc6PPDsOuXm2K8w9ec3JM4QMBn55BP4LKgEGVIJqd5W/sSkn0iABDLn8WoIgfe4bjs9nLMSzj94K+YAoW6iUGI8nH+qnAicDnxmjxvKtvVxNcu+p55/IrztIWUnyYFkZ97z7eXz742+QB3z+sXKj9oFxBq6+dRh27rHdcy9l7CkywoS3hz+krkgZ+Mw7kGeryLI7+l6lTrrvGvI6vv5+sXpC+Io1m9VPCMoDruQbfgnISFkmCngjIMHAxX+swcx5SyGBlB53PKMuS5Un1BsMtj/epR1/8hOBlSslqM1KWbla5bHh70F+9Urql1vavvzuZ1xz2zDItlTBQgP59Z+be3ZWv5QyddYitbS015z8KtG1xTwQTlXAAQWKEdi19xDktpvZC//EhM9motvNj6tg36S3n0StGrZnR5R2/Mk3+vb3fCkrv/YgwcT+D72Exb//DfnW/qffVuGpUR/gnsdGF7mlTZomDxr/+K0n1beW8jyh1JMZkg15HUmgJbTe81XXOKhggaXL10OOy2k//IJBz42DvFdf1akdHtWC2vamyRVUcrXWTQ+MwM9LV2Pbzn2Q9+5+D46E5D8z6FZ7UfUQfZnxxbmOXB12VacL1cOa3/rgG/Wa3PDvDnXe9PYH09SvpzR2epinbJeJAp4IyLmIHMMlneuUdvx16XABLrrgPLW5C5o3VleVy7mNvMf/9fc/2LRlF35Y+AduHfiyOmZVwUID+RJIftVNzoueefVjx1W5Jb3mEhNi8fDdNxSqibMU0L+AUf9NZAt9KSDfDEoAQ76hnDpzEerUqoYPRj+OW3tf6bKZm66/AmNfekR9Ay8nyL0HvAC5mkQix7M+e0V9Q2lfQX4yds6U19CmRWO8+OankCCNnDDLm+8tN3TBuY0b2Iuq2yrsM3JZ34TXhqjLbmUbcq9lrPYt0LeTXoJ8eBz1zhTIdu9+7HV1gjN14guQn+W0r88xBbwRkA+TclItP4MpV48Muqc35DYc+eUqez3FHX9ymfhbLz4EOZ7tZaOjIvH1B8NV3oTJs3DLwFG4fdCrePXdLyC3EMmJu71s4fEzWgCz86WtIT/DKQFOWV7ca05ObN5/fSjkW34px0QBbwQW/LoSDz87VgURf1z0F7p1bIdZn72Mluc1Ol2NNlXc8efuPf+6bpeoX6qSZ08Mev5d3Hjvixjy4nuQE3m5Ais6OtLxXn8qVqltAepZKhLAlAciPvLcOHUlS3GvOflAwPd8xcZBGQWGjpigjkt5Xlt6ZjZGPXUPXn/2fsgzsOxVNj27Ab77ZBSqVknC4BfGo9fdz6vgi5yfzPh4pMtDNX15riPbH/38A3jknhvwzfe/oL/29+NmLUApVwa/8NgdkOC9wWCQYkwU8ErAk3MdqbC44+/BO67H2y8OlCKONPzxO/Hc4NuxbPUmDBj6BiQAKUESo8mIe/pfo8oZYLCNnY7ba7q0x5MDb1YPqR03aYZaXtxrTs6ppn04Qv2dUAU5oEAQCTCgEkQ7q7xNfXfUo9j062RHWjD1TUienDC7q1su8V48/R388f17mPflaKz9+ROMGfEwnD+A2teTEw0JzKxb9Amk3kXTx2D5j++rN2C53UHKrZz3gXpjlWl7kmVSXpbVqVVdZVeplIg3XngQUpdsV77Nh/bP+afXtFn+TwGPBM5t3NBxzMvxv3TWeMiJspw0uDuWCx9/Ul6S/EpJ4Q3Kr6XIScbGXz7DLzPGYv5Xb2DNwo8x/pXBWiDR9msScizP/PRll1XlhF7KSHvsV4ZJAefXnHyYlDy5Ckau6JJpJpsAh54JyPFlT/IeO/eL0ZAPa43PrOe2AufjT957S3rPv7Td+fjhf69i1fyP1N+H32a+q9775QosOb7lHnjZ9hWXtHbZlnzbKflfvPcc7Md14dcc3/NdyDjjpcCtvbu6vOfLeczksU+rb9mjtEB44erk9gR5v5VjWb4cWjnvQ/U3Qv52FC7ry3MdOf4fuqMnVsx9Hz9PG6MCPrI9eYizjJko4I2AHK/y3mpPct5S0rlO4ePv52/ehpzLDNK+bIqMjHDZtLyn33JDF0idf86eoN7z5RxfXjf2L4/69OioXneFf8Xwrpu6q3wJEtordX7N/TD5FXW+JH8H3J2T2dfhmAJ6FmBARc97Rydtk1sc5IFs8uZbWpPkTVceOiWXkpf31hypS7Zrv9S8Y+8heOjpd9Rl5aW1g8spUF4B+/FXtXJiqVUZDAbUrF4Z8rwINyfspa5fuIC85lqc10h9AHjv05no98BI9Q2mXAFQuCznKeBrATn+5L3Xk/d8ucJEyvriRNj+muN7vq/3KOvzRECOZQmYyC+5lVZejlVfnesYDAbIL/r0vOoyxy3P8jDP6+98FvKMitLawuUUKI+AwWA7/monV4PBYLvKpKT65LZPec+X56GUVM6TZfKaa3RGXdzZ9yr1eIEuNw2FXOEujyLwZH2WoYBeBBhQ0cueYDuKFZAPqbOnvIYB/a9RD4fjk++LpfLRAlajF4FnH70N40YNwjmN6qtvcOSKLr20je2ggL8E+J7vL1nWq2cBk8mI6R+NxPDH74K81192YXMYtP/03Ga2jQK+ELi268X4fPyzkOcK1axeBY3Pcn8VpS+2xToo4A8BBlT8oco6fS4gl4/LLRpy/7Pc6++yAc5QIEQF5NsbeW6Q3PsvDwKte+q2uBDtLrtFAYcA3/MdFJwII4FqVZJw03Wd1LNennq4PyTIEkbdZ1fDVECuaL+g+TkYph3zLw8b4HggbphysNtBKMCASgXsNG6SAhSgAAUo46Tv9AAAEABJREFUQAEKUIACFKAABShAgeAW8CSgEtw9ZOspQAEKUIACFKAABShAAQpQgAIU8ESAZbwQYEDFCywWpQAFKEABClCAAhSgAAUoQAE9CbAtFKg4AQZUKs6eW6YABShAAQpQgAIUoAAFwk2A/aUABUJGgAGVkNmV7AgFKEABClCAAhSgAAV8L8AaKUABClDAvQADKu5dmEsBClCAAhSgAAUoEJwCbDUFKEABClAgIAIMqASEmRuhAAUoQAEKUIACxQkwnwIUoAAFKECBYBRgQCUY9xrbTAEKUIACFKhIAW6bAhSgAAUoQAEKUAAMqPAgoAAFKECBkBdgBylAAQpQgAIUoAAFKOBrAQZUfC3K+ihAAQqUX4A1UIACFKAABShAAQpQgAI6F2BARec7iM2jQHAIsJUUoAAFKEABClCAAhSgAAXCS4ABlfDa3+ytXYBjClCAAhSgAAUoQAEKUIACFKBAOQQYUCkHXiBX5bYoQAEKUIACFKAABShAAQpQgAIU0I+AvwIq+ukhW0IBClCAAhSgAAUoQAEKUIACFKCAvwTCtl4GVMJ217PjFKAABShAAQpQgAIUoAAFwlGAfaaAbwQYUPGNI2uhAAUoQAEKUIACFKAABSjgHwHWSgEK6FKAARVd7hY2igIUoAAFKEABClCAAsErwJZTgAIUCAcBBlTCYS+zjxSgAAUoQAEKUIACJQlwGQUoQAEKUMBrAQZUvCbjChSgAAUoQAEKUKCiBbh9ClCAAhSgAAUqWoABlYreA9w+BShAAQpQIBwE2EcKUIACFKAABSgQYgIMqITYDmV3KEABClDANwKshQIUoAAFKEABClCAAiUJMKBSkg6XUYACFAgeAbaUAhSgAAUoQAEKUIACFAigAAMqAcTmpihAAWcBTlOAAhSgAAUoQAEKUIACFAheAQZUgnffseWBFuD2KEABClCAAhSgAAUoQAEKUIACpwQYUDkFEYoj9okCFKAABShAAQpQgAIUoAAFKEAB/wjoKaDinx6yVgpQgAIUoAAFKEABClCAAhSgAAX0JBASbWFAJSR2IztBAQpQgAIUoAAFKEABClCAAv4TYM0UKCrAgEpRE+ZQgAIUoAAFKEABClCAAhQIbgG2ngIU8LsAAyp+J+YGKEABClCAAhSgAAUoQIHSBLicAhSgQLAJMKASbHuM7aUABShAAQpQgAIU0IMA20ABClCAAmEuwIBKmB8A7D4FKEABClCAAuEiwH5SgAIUoAAFKOBLAQZUfKnJuihAAQpQgAIU8J0Aa6IABShAAQpQgAI6FmBARcc7h02jAAUoQIHgEmBrKUABClCAAhSgAAXCR4ABlfDZ1+wpBShAgcICnKcABShAAQpQgAIUoAAFyijAgEoZ4bgaBShQEQLcJgUoQAEKUIACFKAABShAAX0IMKCij/3AVoSqAPtFAQpQgAIUoAAFKEABClCAAiEpwIBKSO7WsneKa1KAAhSgAAUoQAEKUIACFKAABShQukCwB1RK7yFLUIACFKAABShAAQpQgAIUoAAFKBDsArprPwMqutslbBAFKEABClCAAhSgAAUoQAEKBL8AexDqAgyohPoeZv8oQAEKUIACFKAABShAAQp4IsAyFKCAVwIMqHjFxcIUoAAFKEABClCAAhSggF4E2A4KUIACFSnAgEpF6nPbFKAABShAAQpQgALhJMC+UoACFKBACAkwoBJCO5NdoQAFKEABClCAAr4VYG0UoAAFKEABChQnwIBKcTLMpwAFKEABClAg+ATYYgpQgAIUoAAFKBAgAQZUAgTNzVCAAhSgAAXcCTCPAhSgAAUoQAEKUCA4BRhQCc79xlZTgAIUqCgBbpcCFKAABShAAQpQgAIU0AQYUNEQ+D8FKBDKAuwbBShAAQpQgAIUoAAFKEAB3wswoOJ7U9ZIgfIJcG0KUIACFKAABShAAQpQgAIU0L0AAyq630X6byBbSAEKUIACFKAABShAAQpQgAIUCDeBcAyohNs+Zn8pQAEKUIACFKAABShAAQpQgALhKODXPjOg4ldeVk4BClCAAhSgAAUoQAEKUIACFPBUgOWCSYABlWDaW2wrBShAAQpQgAIUoAAFKEABPQmwLRQIYwEGVMJ457PrFKAABShAAQpQgAIUCDcB9pcCFKCArwQYUPGVJOuhAAUoQAEKUIACFKCA7wVYIwUoQAEK6FSAARWd7hg2iwIUoAAFKEABCgSnAFtNAQpQgAIUCA8BBlTCYz+zlxSgAAUoQAEKFCfAfApQgAIUoAAFKFAGAQZUyoDGVShAAQpQgAIVKcBtU4ACFKAABShAAQpUvAADKhW/D9gCClCAAqEuwP5RgAIUoAAFKEABClAg5AQYUAm5XcoOUYAC5RdgDRSgAAUoQAEKUIACFKAABUoWYEClZB8upUBwCLCVFKAABShAAQpQgAIUoAAFKBBQAQZUAsrNjdkFOKYABShAAQpQgAIUoAAFKEABCgSzAAMqnu09lqIABShAAQpQgAIUoAAFKEABClAg9AU87iEDKh5TsSAFKEABClCAAhSgAAUoQAEKUEBvAmxPRQkwoFJR8twuBShAAQpQgAIUoAAFKECBcBRgnykQIgIMqITIjmQ3KEABClCAAhSgAAUoQAH/CLBWClCAAu4EGFBxp8I8ClCAAhSgAAUoQAEKBK8AW04BClCAAgEQYEAlAMjcBAUoQAEKUIACFKBASQJcRgEKUIACFAg+AQZUgm+fscUUoAAFKEABClS0ALdPAQpQgAIUoEDYCzCgEvaHAAEoQAEKUCAcBNhHClCAAhSgAAUoQAHfCjCg4ltP1kYBClCAAr4RYC0UoAAFKEABClCAAhTQtQADKrrePWwcBSgQPAJsKQUoQAEKUIACFKAABSgQTgIMqITT3mZfKeAswGkKUIACFKAABShAAQpQgAIUKLMAAyplpuOKgRbg9ihAAQpQgAIUoAAFKEABClCAAnoRYEDFf3uCNVOAAhSgAAUoQAEKUIACFKAABSgQogJOAZUQ7SG7RQEKUIACFKAABShAAQpQgAIUoICTACd9IcCAii8UWQcFKEABClCAAhSgAAUoQAEK+E+ANVNAhwIMqOhwp7BJFKAABShAAQpQgAIUoEBwC7D1FKBA6AswoBL6+5g9pAAFKEABClCAAhSgQGkCXE4BClCAAl4KMKDiJRiLU4ACFKAABShAAQroQYBtoAAFKEABClSsAAMqFevPrVOAAhSgAAUoEC4C7CcFKEABClCAAiElwIBKSO1OdoYCFKAABSjgOwHWRAEKUIACFKAABShQvAADKsXbcAkFKEABCgSXAFtLAQpQgAIUoAAFKECBgAkwoBIwam6IAhSgQGEBzlOAAhSgAAUoQAEKUIACwSrAgEqw7jm2mwIVIcBtUoACFKAABShAAQpQgAIUoIASYEBFMXAQqgLsFwUoQAEKUIACFKAABShAAQpQwB8CDKj4Q7XsdXJNClCAAhSgAAUoQAEKUIACFKAABYJAoJwBlSDoIZtIAQpQgAIUoAAFKEABClCAAhSgQDkFuHphAQZUCotwngIUoAAFKEABClCAAhSgAAWCX4A9oICfBRhQ8TMwq6cABShAAQpQgAIUoAAFKOCJAMtQgALBJcCASnDtL7aWAhSgAAUoQAEKUIACehFgOyhAAQqEtQADKmG9+9l5ClCAAhSgAAUoEE4C7CsFKEABClDAdwIMqPjOkjVRgAIUoAAFKEAB3wqwNgpQgAIUoAAFdCvAgIpudw0bRgEKUIACFAg+AbaYAhSgAAUoQAEKhIsAAyrhsqfZTwpQgAIUcCfAPApQgAIUoAAFKEABCpRJgAGVMrFxJQpQgAIVJcDtUoACFKAABShAAQpQgAJ6EGBARQ97gW2gQCgLsG8UoAAFKEABClCAAhSgAAVCUIABlRDcqexS+QS4NgUoQAEKUIACFKAABShAAQpQoDQBBlRKE9L/craQAhSgAAUoQAEKUIACFKAABShAgQALVEBAJcA95OYoQAEKUIACFKAABShAAQpQgAIUqACB0N4kAyqhvX/ZOwpQgAIUoAAFKEABClCAAhTwVIDlKOCFAAMqXmCxKAUoQAEKUIACFKAABShAAT0JsC0UoEDFCTCgUnH23DIFKEABClCAAhSgAAXCTYD9pQAFKBAyAgyohMyuZEcoQAEKUIACFKAABXwvwBopQAEKUIAC7gUYUHHvwlwKUIACFKAABSgQnAJsNQUoQAEKUIACARFgQCUgzNwIBShAAQpQgALFCTCfAhSgAAUoQAEKBKMAAyrBuNfYZgpQgAIUqEgBbpsCFKAABShAAQpQgAJgQIUHAQUoQIGQF2AHKUABClCAAhSgAAUoQAFfCzCg4mtR1kcBCpRfgDVQgAIUoAAFKEABClCAAhTQuQADKjrfQWxecAiwlRSgAAUoQAEKUIACFKAABSgQXgIMqITX/rb3lmMKUIACFKAABShAAQpQgAIUoAAFyiEQJAGVcvSQq1KAAhSgAAUoQAEKUIACFKAABSgQJALB00wGVIJnX7GlFKAABShAAQpQgAIUoAAFKKA3AbYnbAUYUAnbXc+OU4ACFKAABShAAQpQgALhKMA+U4ACvhFgQMU3jqyFAhSgAAUoQAEKUIACFPCPAGulAAUooEsBBlR0uVvYKApQgAIUoAAFKECB4BVgyylAAQpQIBwEGFAJh73MPlKAAhSgAAUoQIGSBLiMAhSgAAUoQAGvBRhQ8ZqMK1CAAhSgAAUoUNEC3D4FKEABClCAAhSoaAEGVCp6D3D7FKAABSgQDgLsIwUoQAEKUIACFKBAiAkwoBJiO5TdoQAFKOAbAdZCAQpQgAIUoAAFKEABCpQkwIBKSTpcRgEKBI8AW0oBClCAAhSgAAUoQAEKUCCAAgyoBBCbm6KAswCnKUABClCAAhSgAAUoQAEKUCB4BRhQCd59F+iWc3sUoAAFKEABClCAAhSgAAUoQAEKnBII4YDKqR5yRAEKUIACFKAABShAAQpQgAIUoEAIC1RM1xhQqRh3bpUCFKAABShAAQpQgAIUoAAFwlWA/Q4JAQZUQmI3shMUoAAFKEABClCAAhSgAAX8J8CaKUCBogIMqBQ1YQ4FKEABClCAAhSgAAUoENwCbD0FKEABvwswoOJ3Ym6AAhSgAAUoQAEKUIACpQlwOQUoQAEKBJsAAyrBtsfYXgpQgAIUoAAFKKAHAbaBAhSgAAUoEOYCDKiE+QHA7lOAAhSgAAXCRYD9pAAFKEABClCAAr4UYEDFl5qsiwIUoAAFKOA7AdZEAQpQgAIUoAAFKKBjAQZUdLxz2DQKUIACwSXA1lKAAhSgAAUoQAEKUCB8BBhQCZ99zZ5SgAKFBThPAQpQgAIUoAAFKEABClCgjAIMqJQRjqtRoCIEuE0KUIACFKAABShAAQpQgAIU0IcAAyr62A+h2gr2iwIUoAAFKEABClCAAhSgAAUoEJICDKi47FbOUIACFKAABT22+hEAABAASURBVChAAQpQgAIUoAAFKBD6AuXvIQMq5TdkDRSgAAUoQAEKUIACFKAABShAAf8KsHbdCTCgortdwgZRgAIUoAAFKEABClCAAhQIfgH2gAKhLsCASqjvYfaPAhSgAAUoQAEKUIACFPBEgGUoQAEKeCXAgIpXXCxMAQpQgAIUoAAFKEABvQiwHRSgAAUoUJECDKhUpD63TQEKUIACFKAABcJJgH2lAAUoQAEKhJAAAyohtDPZFQpQgAIUoAAFfCvA2ihAAQpQgAIUoEBxAgyoFCfDfApQgAIUoEDwCbDFFKAABShAAQpQgAIBEmBAJUDQ3AwFKEABCrgTYB4FKEABClCAAhSgAAWCU4ABleDcb2w1BShQUQLcLgUoQAEKUIACFKAABShAAU2AARUNgf9TIJQF2DcKUIACFKAABShAAQpQgAIU8L0AAyq+N2WN5RPg2hSgAAUoQAEKUIACFKAABShAAd0LMKBS7l3ECihAAQpQgAIUoAAFKEABClCAAhQIfQHXHjKg4urBOQpQgAIUoAAFKEABClCAAhSgQGgIsBd+FWBAxa+8rJwCFKAABShAAQpQgAIUoAAFPBVgOQoEkwADKsG0t9hWClCAAhSgAAUoQAEKUEBPAmwLBSgQxgIMqITxzmfXKUABClCAAhSgAAXCTYD9pQAFKEABXwkwoOIrSdZDAQpQgAIUoAAFKOB7AdZIAQpQgAIU0KkAAyo63TFsFgUoQAEKUIACwSnAVlOAAhSgAAUoEB4CDKiEx35mLylAAQpQgALFCTCfAhSgAAUoQAEKUKAMAgyolAGNq1CAAhSgQEUKcNsUoAAFKEABClCAAhSoeAEGVCp+H7AFFKBAqAuwfxSgAAUoQAEKUIACFKBAyAkwoBJyu5QdokD5BVgDBShAAQpQgAIUoAAFKEABCpQswIBKyT5cGhwCbCUFKEABClCAAhSgAAUoQAEKUCCgAgyoBJTbvjGOKUABClCAAhSgAAUoQAEKUIACFAhmAc8CKsHcQ7adAhSgAAUoQAEKUIACFKAABShAAc8EWMpjAQZUPKZiQQpQgAIUoAAFKEABClCAAhTQmwDbQ4GKEmBApaLkuV0KUIACFKAABShAAQpQIBwF2GcKUCBEBBhQCZEdyW5QgAIUoAAFKEABClDAPwKslQIUoAAF3AkwoOJOhXkUoAAFKEABClCAAsErwJZTgAIUoAAFAiDAgEoAkLkJClCAAhSgAAUoUJIAl1GAAhSgAAUoEHwCDKgE3z5jiylAAQpQgAIVLcDtU4ACFKAABShAgbAXYEAl7A8BAlCAAhQIBwH2kQIUoAAFKEABClCAAr4VYEDFt56sjQIUoIBvBFgLBShAAQpQgAIUoAAFKKBrAQZUdL172DgKBI8AW0oBClCAAhSgAAUoQAEKUCCcBBhQCae9zb46C3CaAhSgAAUoQAEKUIACFKAABShQZgEGVMpMF+gVuT0KUIACFKAABShAAQpQgAIUoAAF9CLgv4CKXnrIdlCAAhSgAAUoQAEKUIACFKAABSjgP4EwrZkBlTDd8ew2BShAAQpQgAIUoAAFKECBcBVgvyngCwEGVHyhyDooQAEKUIACFKAABShAAQr4T4A1U4ACOhRgQEWHO4VNogAFKEABClCAAhSgQHALsPUUoAAFQl+AAZXQ38fsIQUoQAEKUIACFKBAaQJcTgEKUIACFPBSgAEVL8FYnAIUoAAFKEABCuhBgG2gAAUoQAEKUKBiBRhQqVh/bp0CFKAABSgQLgLsJwUoQAEKUIACFAgpAQZUQmp3sjMUoAAFKOA7AdZEAQpQgAIUoAAFKECB4gUYUCnehksoQAEKBJcAW0sBClCAAhSgAAUoQAEKBEyAAZWAUXNDFKBAYQHOU4ACFKAABShAAQpQgAIUCFYBBlSCdc+x3RUhwG1SgAIUoAAFKEABClCAAhSgAAWUAAMqiiFUB+wXBShAAQpQgAIUoAAFKEABClCAAv4Q0FdAxR89ZJ0UoAAFKEABClCAAhSgAAUoQAEK6EsgBFrDgEoI7ER2gQIUoAAFKEABClCAAhSgAAX8K8DaKVBYgAGVwiKcpwAFKEABClCAAhSgAAUoEPwC7AEFKOBnAQZU/AzM6ilAAQpQgAIUoAAFKEABTwRYhgIUoEBwCTCgElz7i62lAAUoQAEKUIACFNCLANtBAQpQgAJhLcCASljvfnaeAhSgAAUoQIFwEmBfKUABClCAAhTwnQADKr6zZE0UoAAFKEABCvhWgLVRgAIUoAAFKEAB3QowoKLbXcOGUYACFKBA8AmwxRSgAAUoQAEKUIAC4SLAgEq47Gn2kwIUoIA7AeZRgAIUoAAFKEABClCAAmUSYEClTGynVzp2MhfBkNIy85GZUxAUbXXnmW+2BmXbs3LNpw+WEJrKyK64Y8nd8VFSXrAeO9Inec3Ka1emgymdSM8LoaP9dFeC6VjK0t7vT2rv+8F03NjbKsdPTp45KN/z5Rg5fcSEzpTsE/v+0fM4NSMP2drfXT23saS25eZbkJIWHOeVzv2Qc4LQOdpP90TO4Zz7qefpPO3YOR6Ex46YpmflQ44hmQ62dPpo4VQ4CzCgUs69n1dgQTAki9UKg8EQFG115xlpCs62my3Wch5h+ly9wBwcx70cS8F67EjbDQYD5LUr08GW9Hnklq9VVu19NFj2g8EQvMdOgcUKkzE43/OtWtvLd5Tpc+1gOe7Nmr8xSI8dMZbjXo5/mQ6mJOcE+jxyy9cqOZ6CZT+YtPNkCegGS3ud22k9tZuc84Jl+lTTOQpzAQZUwvwAKNp95lCAAhSgAAUoQAEKUIACFKAABShQmkDwB1RK6yGXU4ACFKAABShAAQpQgAIUoAAFKBD8AjrrAQMqOtshbA4FKEABClCAAhSgAAUoQAEKhIYAexHaAgyohPb+Ze8oQAEKUIACFKAABShAAQp4KsByFKCAFwIMqHiBxaIUoAAFKEABClCAAhSggJ4E2BYKUIACFSfAgErF2XPLFKAABShAAQpQgALhJsD+UoACFKBAyAgwoBIyu5IdoQAFKEABClCAAr4XYI0UoAAFKEABCrgXYEDFvQtzKUABClCAAhQITgG2mgIUoAAFKEABCgREgAGVgDBzIxSgAAUoQIHiBJhPAQpQgAIUoAAFKBCMAgyoBONeY5spQAEKVKQAt00BClCAAhSgAAUoQAEKgAEVHgQUoEDIC7CDFKAABShAAQpQgAIUoAAFfC3AgIqvRVkfBcovwBooQAEKUIACFKAABShAAQpQQOcCDKjofAcFR/PYSgpQgAIUoAAFKEABCuhXID+/APsPHUNeXr5LIy0WKw4dTUGB2eySzxkKUIACngiEZ0DFExmWoQAFKEABClCAAhSgAAWCWmDnnoO4fdCraHXlveh28xP4bt5SR3+WLFuH9tc+hC59h6JllwGYNvtXxzJOUIACISTgx64woOJHXFZNgWAX4Lc2wb4H2X4KUIACFKBA+AocPnoCPe54Bsk1quDz8c9i1fyPcFWndgokOycPT7z0Ph655wasW/QJxo0ahJFvT8a+g0fVcg4oUJEC3HbwCDCgEjz7ii2lQEAF+K1NQLm5MQpQgAIUoAAFfCzwv2nzUbVyIl5/7n5c0PwcxMZEoUqlRLWVFWv+RVZ2Dvr37IwIkwldO7RBw3rJWLJsrVrOgVcCLEyBsBVgQCVsd334dvyfGf9g2di/ik0n95wMX5xTPee3NqcgOKIABShAAQpQIGgFfl+xAXWSq+OJke+j3wMjMeKtyep5KQBw+NgJFUCJiop09K9Rwzo4dOSEY54TFKAABUoTYEClNCEuDzmBf7/9B8vH/VVsOrkvLeT67G2H+K2Nt2IsTwEKUIACFPCjAKsuk8D23QcQHxeDLpddgHv6X42NW3binsdGQx5Qm5aeibjYGJd6o6OjkJ6RpfJMRgMqxUcGRTIaDEiMC462FjaNiTIhOtIYFM6F2w7+o4AmYNQS/6dAWAmce+N5aD/4IpWS6iWpvp/b+1w1L/mVTuWpBWE6KO1bm2jtj1/1pGgEQ5JdWFI7c/77DAcX3YHDvwxAwd5vddWnGO0EIyE2UldtKsnSvkxOOMSdKXwEDPu3wvT9u4iY8jwiFn4KpB0Pn86zp24FQjHTcHQvTHM/UMe5jGU+FPsZan26tfeVuK7bJbiq04V484UHsXvfYezYcxBJifHqlh/n/ubm5iExIc6RVWC2IhiSFVaYzRb/tTU7B7m/TEfOpBeQO+UV5K/+xWfbkuf1WazwWX2B3F+OA4UTYS3AgEpY7/7/s3cegFEUXRz/77X0Sgol9Ca9V6VLUZEmKKggIqCAFBXLZ8OGiiCKCkgREBUQUFBQmiCI9N6lt9ASSC93ubLfzlzuyOU25NKu5YW82Z03M2/e/O647L2dmS2dg6/bvy7aTGjNJbhiCIeQUxdSyazjBaU0ceSujUb6su8Jwl7CvPw8s3E0Tm8Yg7j/VuLmyaU48ftgxO75DHnVd7ZeoRCgUgpu44+j41erFAw7SSkhIMRdgnrWWCj3rIHivz1QbF0K9bfjpKvjrFJCgIZZGggIaUnS+/xFKP/9hb/P2VE960UwfWkYv6eOsU7Nyrhy7ZbVfZPJxM+z9AZER4Tx4AqbrcKVUnLmQizKRoVJZ4BR+pafrjXAE0SUAhIZOmOJ+apb8glMq2dCPLYdpgObYFj8PjK3rS6W/rIMJugl8QTOuX3kbxRKSj0Bz7rqLfUvFwEgAs4hkN9dG22WEbdTdB4hjFhevl4/vowV28jVY0vdZlxavQlpmXq38Scvjrn1yWlZNkwp490EFMf+AUTzlxTLSIWkOAhXTlqydCQCHk9AOPEvoDMvBbEORsorTu+xZunE/Qg83KUVFiz7E9du3kZyajp+WLmJb1Jbo0oFtGh8H3d46erNMBiN+Gv7Af6Enw5tGnM9JdkETEYopEBKds56UB7cYD2nEyJQYgQ8wDAFVDzgRSIXiYCzCeR318Yk3bXJkr7se4IwdnJ+piddh1FnvwGxLukSdFl6yLVxto5xZlNXnd1vUfvTG6VbZQw8SakgIMRfkR2nEB8rqyclEfBEAoo83udIujv7gY8r5TaEC4fzlvirvBolziHwdL+uaNW0LroNnIi2j47BP3uOYObHE/jTfvz9fPD15PGYMnMpGnV5DuPf+RpvTxiMmHKRznHOQ3oR7lyzC5oz1wV6LzMMdkKK0keAAiql7zWnEROBfAmUhrs26oCy8A2va8ciIKY9BEFppycFESAC8gRM1ZvKFog15PWylUlJBNycgKlWc1kPTVUb2ujZsjf1/FeRl7ClQjYNKFOiBNgTfKa9Owq71s7CXz9/js3Lp6Nh3erWPjvf3wRHNy/AxmXTcHjTfAzq08VaVkpO8h2mGFkJYmiUXT1T7dZ2OlIQgdJIgAIqpfFVpzETgXwIlJa7NuXbTYPav6yVhia0Bsq1ft+apxPvJMBm/txOSOZJajscAAAQAElEQVTTv/MaISvP1NLSpbz45NSbWjwM031tcqpg6PYsxDLlbXSUIQKeTMBUqyVMLR6xGYKxVU+I1XItDwkKhygFWbiUiTHXz6mLyNaZSygtMIHCNQgO9Ee56DIQBMHOgFKpQIWyEVCrVXZlpDATMPQeDzEgxJyRUjGqCgxdnpbO6JcIEAEKqNB7gAgQAVkCpeGuTVBMR9R99gJqD9qP+546ijqS+IbXkeVBSu8gsGv/CbR6ZBQ69BvPp38PnfApf4ymZXRs88KHn36dlzfvMRLvfLYAeoPRUkzHPAgYhnwA/ZsroB8zE/pJv8PU8ck8apKaCHguAUPfCch6dxV/n7OjUfqSmXs0pjptoB/xORdju8cAqQILON7V9Zc09EsEPIuAWLsl9G+thH7st9C/shj6CfMACg561otI3pYYAQqolBhaMkwEPJ9Aablrw5b++ITW8PwXjEaQLwFBIeDdl4Zg55qZ2LLiCwT4+2LmwtXWdh99+QPuq1EJ+9fPxdrFn2D933uxfgttOmkFdI8TMTAUYoVaEH387lGLitydAPmXDwHfQP4+h3TMpyYVEwGvIyCWqw6xTDmPG5fy6hUEfvohQoc9haD334Lq9CmPGwM57L4EKKCS67W5k5gCJrnUSE3LQGJyam415YkAESACRMCDCLRuWhePdmuLkKAAREeGoUfHlvhn9xH+hAf2BIgd+45jcP9ufMPCqpXKoU+P+7Fx2z7Qj9sSIMeIABEgAkSACORJQEhNQVifHvCf8w00f/8Fv0XzEd6rG5SxV/NsQwVEoCAEKKAi0WLr6ef9tBbt+oxF+77j0OPJ1ySt+TcjU4uxb81A656j8UDvsRg0+kOwtfXmUkqJABEgAkTAkwns2H8cdWpWhkqpxO07SXwoOZ/wUKlCNG7EJXB98SRkhQgQAU8moDiyGeqZo6F+71F+ZHlPHg/5TgS8nYBm1w4oEu7YDjMrCz5rVtnqKEcECkmAAioSuC/mrsDiFRvwwpDe2L76a6xZ/LGkNf8uWbUZZy7E4u+VX2L32llQKhSYMf8XcyGlRIAIEAFvJ+Ah4zMaTWCB8bzkwpUbdiNZs3EnmLzy/OO8LCUtgx/ZUyH4iZT4+GiQkJQinZl/A3xV8ARRKQX4qJUe4Wtunn4aJRQKwSN9N79LKPVWAkLcZah+/hTCtbMQsrT8yPO3LnnrkGlcRMDjCSiuXpEdg/L8OVk9KYlAQQkoCtrA2+rH30nCgmV/4mXpgvqpfg8iPDQIZSPDrcNk6+f79+yAqIhQBAX6Y3D/rvj1z38giqK1Dp0QASLgPgTIk9JJQIQoBT5S8xS93mADhi3teePjuZj08jNo07weL2NPgWAnOevqdFnS34VgpubCAhWeIIIgSDcAAE/wNbePSikYJMAzfZfcpl8vJiBcPCY7OuHcQVk9KYkAEXA9AUNL+cc769t3cr1z5IFXEFB4xSiKMIijJy/w1sf/u4jBYz/G8IlT8fvGHVzHksuxt8CmfLNzJhXLR7EDLHcyeYYSIlB4AtSSCBCBYiCgUirx+phBeUrt6hWtvWzYuhcjX52Gj15/Do/3untBFVEmlNe5ej2OH1ly6epNlIsKZ6dcktP18ATRG0zI0Bk9wtfcPNMyDTCaRI/0nb9JKCECRIAIEAG3IaBv0AgZz79o44+2Vz9oe/W10VGGCBSWgFcHVNZs3JnnFHB2d5JBuxlvXhsfUSYEzz7RA80a1sL/Pp6HPzbv5rNQMjK18PXRsKpcfDRqfszI0PJjkL8aniC+GiXUKkUx+Oqa8TLYJcGZ3Rlltv19VCXCRqkQmHkSIkAE3ITAbxt24OX3ZuGNF59EyyZ1cO3mbS7ss55tVMtmq/ywchMytVm4eOWGFGDfiW4dWriJ9+QGESACmXGHkJV2zSUgxKoNZPsVazSV1ZOSCBAB9yCQ9sY7iD92Dgmr1yP+4CmkzJjtHo6RF15BQOEVo8hjEGwWSUKS/BRwdvFsaVa9cnmMGtIbnR9oyo/sCRCbtu2HIAjw9/OFLktvqWo99/f3NevY0h9PEOatJ/iZl48l5T+zyySvfouqZ7ZJiAARcBsCR06e5758+s0SdBs40Sobtpqf5PPWuKdx4vRFNO8xEj2H/E8KpjRHj04teRtKiAARcB2BhFOLcWxOFM6suB+nvq+Jc792gSHztlMdEqMqw/DEGxAr1ISo8eVHno+u4lQ/qDMiQAQKTkAMDIKhUROIYXdnnRbcCrXwCAJOdtKrAypsT5S8poB3bd+co2ZPczh/+Tr0BiPPs8QgnesNBnaKyjHRuHLtFj9niWUquGWtfWqmAZ4g2iwj9EbRI3yV48nYy+mLqjNITJjtDIlPUW3JtWfT1pl9EiJABNyDwLsvDcGJrYvspO9D7biDVSuVw4alU7FlxRfY++e3mPzGcKjVKl5GCREgAq4hYMpKw7Xtr8JkSLM6kH5jF+KPzrTmnXViatQF+jGzoH9vDT+yvLP6pn6IABHwTgI0Ks8m4NUBFUdemiYNavJZKHMW/w72lAh293Ldlj24v4V5Wmf3ji2wYs1WxN1OQlp6JthU8H4Pt+ezVxyxT3WIABEgAkTA8whER4YhwDIT0fPcJ4+JgFcR0CWfh0mfajemzLiDdjpSEAEiUOIEqAMiQARyECj1ARU20+SrD8fi+xUb0LDLMDw5+kM82bcLHu/VkWN6su+DqFa5PDr1n4BWj4wCe/rD2GH9eBklRIAIEAEiQASIABEgAiVLQOkTKtuB0idEVk9KImBLgHJEgAgQgZIjUOoDKgwt24Rw19qZ2LhsGvat+xZvjR8M9sQIVsbuUM7+9CXsXDMT236dgZ/nTEJUhPwfdlafhAgQAfchYMiIw9lt7+Hmvsmycuf4PPdxljwhAkSACBABWQKa4Mrwj7bfyyis9tOy9T1eSQMgAkSACBABjyFAAZXsl4oFUCqUjeDLf7JVNgf29IeIcLoTYgOFMkTAzQnoM+Nxbtv7uLV3sqxQQMXNX0ByjwgQAY8g4Awnqz66CmVbvYfgKg8jvO5QVO+1FsGVuzmja+qDCBABIkAEiECeBCigkicaKvB2AnHHbsGYdXczYm8fb2kcn9ovEjU6TEJ0y7dQpv4IjkDlF8XzOXW8gBIiQARKCwEapwcSUPmEIbr5a6j6yEpU7DQLgRU7e+AoyGUiQASIABHwNgIUUPG2V5TGky+BY0uPYWa9mVjSaymu77/O62elZfEjJd5FQOUfhZod3kPZFiygMpIPTi3pWJ6JJcjCCyghAm5LgBwjAkTAHQjs3bsLo0YPQ9fu7TB4yONYvnyJO7hFPhABIkAEiIALCVBAxYXwqWvnE9Ama7H5zc3QZ+htOj+3/pxNnjJEgAgUgQA1JQJEgAh4GYHExAS8++4bOH78KLSZmbh06QK+/mY69uzZ6WUjpeEQASJABIhAQQhQQKUgtKiuxxOIP3VbdgxJl5Jk9aQsHQRolESACBABIkAE7kXgmBRISc9It6kiiiIWLJybpxw8tN+mPmWIABEgAkTA+whQQMX7XlMa0T0IBET6y5aqAzSyejdVkltEgAgQASJABIiAGxA4efI4FkpBFTk5eJACKm7wEpELRIAIEIESJUABlRLFW7qNK84egHL7cgjHtgFZOreAEV49HGWblLXzpWKbGDtdZmImbh65BdpfxQ4NKYgAESACRIAIlCoCDeo3RIB/gN2Yu3V9CM8+OxL9+j3Oy4KDQ3ie6Zo2bc51lBABIkAEiID3EqCAitxrS7oiE1AteAOqhW9AuW4e1Es/gmb6M1JwZQXU817JUxRHNhe5X0cM9FnQB20ntkXVLlUREGW+OCrb2DbIsm78OsxpOgfL+izFrAaz8O+n/zpimuoQAbclkB6fjqu7Y/OUhPMJbus7OUYEiAARcDWBsLBwfPDBp6gvBVZ8/fxQpUo1jBv7Ct5550MMkwIq/fs9wV0MCQnleaZr2oQCKhwKJUSACBABdydQBP8ooFIEeNTUloBix69Qfzkc6ncfhuLcAdvClDsQ/tsN4eLRvCUpzrZNCeV8Q33RckxL9J7fG+E1wu16Of37aTDJWbB/zn7EHbuVU0XnRMCjCFzefgW/DFqZp+ybtc+jxkPOEgEiQAScTaBlyzaYPWsBNm3Yjh8WL8fjjz/pbBeoPyJABIgAJ0CJ+xCggIr7vBYe7YnizD6o/pgNIe4yBH2W/Fj8gqEfPpWLqWEnXsfU4mGeZ3pToy5c5+rk5uEbsi7c/u+2rJ6URMATCARE+COmVQyXsGrh3GX/nLrqYVxHCREgAkSACBABIkAEipkAmSMCXkuAAipe+9I6d2AsoGLtUbCe2ZyIFWtDrNbYLGHRvEwML2fOM31oFNe5OvGPNC8Dyu2HT4hvbhXliYDHEKjcvjL6L+vPpcWYFtzvnLqWo1tyHSVEgAgQASJQMALs6T+7sh+frNVmFqwx1XZTAuQWESACRMAxAhRQcYwT1SoQASmiIuZqEFwGpmY9cindM1v70dpQapQ2zvmFS3f3ZTautalEGSJABIgAESACRKBUEUhMTMBTT/bDV19N4+OOi7uF116fwM+dmlBnRIAIEAEi4BICFFBxCXbv69RUy3zH2zoyKaYClQ+MnZ+GftDbyHr5e4iBodZidz4JjgnGoN8HofHQxqjaqSqaP98cg1YPhE+Qjzu7Tb4RASJABIgAEfAYAiXh6K3Ds7HlEyWOzPSXlWvbXyn2bv/483fcSbhjY3fXrn9x9uxpGx1liAARIAJEwDsJUEDFO19Xp4+KBVQMj4yCGFUZosYXpkp1YRj8HowPPgOxQQdA41nBiIjaEeg4qSN6L+iNB954AMEVg53OlDokAkSACBABtyFAjhABWQJ5BU4uX74kW5+URIAIEAEi4F0EKKDiXa+nS0djur8f9BPmQ//eGhhemAFTzeYu9Yc6JwJEgAiUXgI0ciJQcAJZWXrcik+EKOZet1twW65oEd14FDr/z4hGYzJQod107kJEw1E8b9Z9znXFmdSsWVvWXOXKVWT1pCQCRIAIEAHvIkABFe96PWk0RIAIEAHPJEBeEwEiUKIEej3zJup1HGojsxat5n2yAMqs739Dk24j0HnAS2jfdxyOnDzPyyi5N4FHHu6FMuFlbCq1afMA8gq02FSkDBEgAkSACHg8AQqoePxL6BkDUOxdC/W8V6yiPLwZ0i0wKPf+YdWxcsURSe9GQ8pKy8Lf727B/Lbz8W3jb7Fm5BokXkh0Iw/JlZImcOf4PFxe/xSubHwWiWeWWbujEyJABIiApxEY99xj+PPHKVZ5su+DfAiHT5zDzIWr8MPXb+Lwpvno06MdXpr0DUwmz5ypwgflpCQsLBw/LfkV48ZN5D1GRUXjsylf8nNKiAARIAJEwPsJUEDF+1/jEhmh6tfpPBCC29ccsi8k3oJw8ahVkBwPCAKQeNOq4+VJkh75/wg3zvP+VWtn5l+5CDX2zdyHIz8cRdqNNGiTtTi/9bHG3wAAEABJREFU6Tw2vraxCBZd1pQ6LgSB2K1jEbttPJLOr0Li2Z9xZdMwxB2YVghL1IQIEAEi4HoCkWVCUDkm2iqhIYFgP1v+PYQ2zeuhaYNaUKtVGNy/G1/6c/r8FVZMkg+BAP8AtGnVltfy9fXjR0qIABEgAkSgdBCggErpeJ2Lf5TXzpgDIXpt8dvmFvNJtBnm/qXASj41i1R8cZv9pnI3D92ELlVXJLue0Phe08M9wf+i+iiKRiSe/tnOTOK5lXa6klCI0n+tlI8UuNNBg4wWftC+4APjf1IQsiQ6I5tEgAiUCgIr1m7D21O+A1vqc+XaLeuYb8TdQdWKZa35qAjzU/nibidZdXRiS+Dqlcto1765VZ58+jFe4erVu/qp0z7mOkqIABEgAkTAewlQQKW4Xluy49YEhNN7ody+HIoT/xaLn4JQOr7Y5jU9vFggurkRfWosTIY0Oy/1KZftdCWhMCxRIXWuAFMsIKYDxn8UyJrgvKdlJZ5PxLbJ/2D1c7/xI8uXxDjJJhEgAkUjsGbjTsz7aa2s7Nh33Gq8e8cWaN+6IcqXjcCWHYfw2PBJsARVUlLT4etj+/ni7+eLtIxM3t5Xo4S/j/uLj1oJpULgvmpU5r/TKqU5X9z+79y5lbNxJDEZDdyn/HxglxaewjrnWJQSY0c4UB0iQASIgFcRyB6MIvtIByJQKAKqXz7nS2/Y/if3EgXbM8WuB9FOo8y114rFZvK0F236URZgqY969liov38LynXzoPrpfag/HwJopW+odr3bK6p2sN+lv2yTstAEauwre6Emr+nhXjhUuyFpgivDJ7SWnT6gQjs7XUkojLuUdmZNVwSIseYvCXaFxajIvJOBpX2W4tD8g7i05SI/sjzTF2M3ZIoIEIFiIJCSloGEpFRZycjUWnsY82xfjBrSG6Of6Y2ls95BUKAfNm8/yMuDgwKgy8ri55aEtQ30Ny9fYUEJjRSscHdRS0EUFpRgfiqV5ktcpUIBli9uUUl2LazyOyqkII8j/Ssk5zWqkvHXkf4LW0cp+Z0fAyonAkTA9QTIg5IhYP5rUzK2yWopICBcP2teepNjfxS+F0rufFKcPQ37eArs9lTJtqM/e9imH4WDS30UZ/ZBuPqfTd/CnRtQHP3bRpdXpsWYFmg0uCECywXCN8QX1btWR7fPuqG0/KzIY3p4aRl/hQ5fQu1/dxq8JrQGyrV+3+uHf2b9ObANmXMOlOUvSsGVnDo6JwJEwPUEnur3IF4fM0hWurZvLuugWq1CZHgoMnXmIEq5qDK4dPWmta5lqY9l6U9apgFJaVluL8xPg1HkfmbqjHw8Or2R54vb/8wsA7fvSKIzOOaD0SQiJUNv5+/5gzewbdY+bJ93AFf+u21XXtxjK6i9LIPJEQxUhwgUlADVJwIeQYACKh7xMrmvk4a+L0E/fGq+Ymzc2X4QMnc0TC0elrUV8vJXNnpDz9H29uQ08VfltFDEObbRHpuJ0umDzhi+czheOPwCHp37KMKqhcna9BRlcUwP9/dRoXwZP48Q9rowX6NCfdkp2J1WlndEajd6CA9OvIF2o46jw4tn0GXcWVSr2dQp4w7rYv/xrK4CVGjkW+T+w7JnWLEp23Ic9NdSOKvcSdb1VIf6jgixXTqQ2w7liQARcC4BtrRn0fL1uBmfAL305X7tpl04fvoiWjWpwx3p/EATsOVBB4+d5eWLV25AdGQYalevxMspcYwAe/w0E8dqO1bryOIj+KH7D/h70t/Y/OZmLGi3ABf/vuhYY6rlZALUHREgAqWRgKI0DprGXHwExJjaEKs1zlcQGu1Qp2J4eVlb6tpNbfXlajhkD5EVZeuZokrvRWJxTA/P0Blw/U6mRwh7AzBf45LMU9/Z3UuWL4ikKKohSYxx6ngz+mQiaKQIRQwgBADK9iYop2uLxYdE6U4zpJ8M6Q6uHIeotvL/P8q0cozB7WSdZJ1+iQARcCcCi1dsQJcBL6Pxg8/h9clz+IyWZg3Nyxob16uBF4b0wuCxk3n5z7/9jc8njYZCIbjTENzKF0GQZyOvLbzre7/Za9f4wJwDdrpCKagRESACRIAIFJkABVSKjJAMuDMBU60WECveZ+OiWKYcTA072ehKU6Y4poeXJl6uGqvgCwS/bUKZbVnw35cJ3291UN4nOsWdSg9UQoNBDWz6avhUQ1RsLUV3bLSUIQJ5E1DNHgfNm13zFOHqqbwbU0mxEqhUIRqbl0/HP6u+wvoln+HI5u8wZEB3ax+CIGDssH44sGEuNi2bhj1/zEaT+jWt5e5y4k5+yM1EEQQpnMKkmBxNj0tHerz9nm8J5xOKqQcyQwSIABEgAkUlQAGVohIspe2N/V7mS3DE8HIuISCWq8r7NzwyOt/+9aO+hv6ZyTA+NAKGpyZB/8piwFe65Z9vy9JbIb/p4aWXTOkZeZePu2DUkVEY9Nsgfuz8kcyyvdKDg0ZKBApDwK3aCIKAMmHBqFg+Ciql/abXkH58fTT8KUA0M0WC4Qa/AVEB8MlerprTncg6kTmzdE4EiAARIAIuJKBwYd/UtQcTECvU4ktw4OPv0ChMzR/mARDLfit8hogowtT8IVt9Iwe/tPkG8v7F8o4t/RFrt4Sx3eMw1XvAIX+pEnCv6eHEp3QQ8An2QXTDaLBj6RhxaR9l8Y7fMOorZH28iYupYh1uXJ9DJ2breAElRIAIyBJo+1IbO33jZxvb6UhBBIgAESACriFAARXXcC91vbJlNjZ7rYRFA9LdMrFMrj1TQqNAP64nkN/0cNd7SB4QAQAEgQgQASLgIgId2jt4A0jyz0etkdLC/TYa0giDNwxGp/c7gc0cHLZ9GKp2qlo4Y9SKCBABIkAEip0ABVSKHSkZJALeQUAQhHynh3vHSJ03Clf2pEvR4e93t2B+2/nY8tYW7kpWqo4fKSECRIAIEIGCEahYqTK2/7PfKkt+/IUbqFjxrv7ViW9yXVGTMrXKgAVW2N5WwTHBRTVH7YkAESACRKAYCVBApRhhkiki4GUEaDheRGD313tw5IejSLuRBn2mno/s+oEb/EgJESACRIAIEAEiQASKQkC4cBh5ysWjRTFNbYmAWxOggIpbvzzkXMEIUG0iQATyInB1x1W7osyETOholoodF1IQASJABIgAESACBSOgnv8q8pR5rxTMGNUmAh5EgAIqrnyxqG8iQASIgIsJCILgYg+oeyJABIgAESACriNgMom4GZ8Ag9HoOie8oGexakOYpYF1NOZ8Q5hqNLPq6IQIeBuBAgVUvG3wNB4iQASIQGkhUPH+inZD9Qv3gyaw8Jsl2hkkhdMJZMYfRlbqFaf3Sx0SASJABDyBwObtB1Gv41A70WWZl75u23UErR4ZhS4DXkajLs9h+ZqtnjAst/RRP+JzmGW61T9z/nMYhn1q1dGJZxEgb/MnQAGV/BlRjWIioDh3AMrNi7kIl45xq8K5gzzP9MKl41xHCREgAsVPoPXYVmg0uCECywVC7afmHZRvVo4fKfE8AknnfsXxeeVxZnlbnFp8n3S8H1lp1zxvIOQxESACRKAECYgQ4e/niz9/nGIjGrUKmdosTPxgNl4c1hdHNn+HGR+OxfufL0LsjfgS9IhMlzABMk8EnE6AAipOR156OxTOHZKCJz9wUVw+wUEozufUmYMsvIASIkAEipWAT7APOn3QGcN3DkfnyebHfWqCfIq1DzLmPAKx28bDmJVk7TAz/hDiDk6z5umECBABIkAEzAR8fdSoHBNtI4IgYO+hU8jI1GJQ785QKZV4sF0zXmfbrsPmhk5JqRMiQAQ8nQAFVDz9FXSS/8nnf8PZFe1xdE4EzixrhTsnFha4Z7FGExi7DM5TTJUbFNgmNSACRIAIlDYCWSmXYdTesRt2ZtxBOx0piAARIALFSsADjSUkpeLNT+bh/enf44/Nu617pdy6ncgDKBqNedYmG1r1yuVxMy6RnZIQASJABBwiQAEVhzCV7krs4v3S+kHIiNsP0ZCBzDvHELt1DNJv7SsQGFONZlIwZUieIlapXyB7VJkIEAEiUBoJKH1CZIet9AmV1ZOSCJRmAjT20k0gOjIczw58CFUrmZe4vvbht5jyzRIOJSU1nS8H4pnsxMdHg9S0DJ4TBECtUniEQPpRKwW38VVyh/86wk+pEKCQxJG67lYH9EMEJAIKSeiXCNyTQNq1f2TL02O3yepJSQSIABEgAiVHgAVOgip1tesgvPbTdjpSeBwBcpgIEIFiJNDgvqqY+MITGPFUT0x6+Rl8+NowLFm1mc9SCQ4K4Et+cnan02UhKNCfq1TSl/zQADU8QVhQIjhA4za+coBS4gg7X40SvmqF2/juiM+WOtIQ6ZcIgAIq9CYgAkSACBCBUkWAPSLzdkIykqW7k5468Mo9fkS5tpMRXOURhN03GFUfXonQWgNcMBzqkggQASLgOQQiy4RxZw0GI6IjwnA59hb0egPXseTMhViUjTLX0RtFxCfrPEKMJhF3UtzHV8aSiSP80rUGZOiMHsE593jYGEmIAAVU6D2QL4HACu1l6wTEdLir12VAOLYNyu3LoTh74K6ezogAESACbkRg1/4TaPXIKHToNx5tHx2DoRM+xfHTF60eTpm5FPU6DrWRp1+cbC13lxOlOghRTV5C1UdWoFKXOQiu+rC7uEZ+EIFSQ0Cr1eLgof1cTv5n3mxfp7uru3HzRqlh4a4DZbNRDhw9w5/oczM+AXN/XINWTerA10eDFo3v424vXW2esfLX9gP8CT8d2jTmekqIABEgAo4QoICKI5RKeR1NcGVU6bEU/lHNIaj84VemAWI6zkRAdAtORkhLgmb6s1Av/QjKdfOgWvgGVN+/zcsoIQJEoGgEqHXxEhAUAt59aQh2rpmJLSu+QIC/L2YuXG3tRBRFdGzbGH/+OMUq0yaNspbTCREgAkTAQiA+Pg7jx7/A5aOP3sX9YSq8H5UOzbyJXJT/rACkzxTFqV1Qz3vFKsrtKy0m6FjCBG7G3cGQcR+jeY+R6DLgZb7U54PXhvFe/f188PXk8WCB9EZdnsP4d77G2xMGI6ZcJC+nhAgQASLgCAEKqDhCieogpHpv1BzwDxo+fxu1Bu5BmXrPWqkoDqwHUhOseXaiOL0Hwo3z7JSkdBGg0RIBtybQumldPNqtLUKCAhAdGYYeHVvin91H+EU2sn/Y+vnKMdGwSNnI8OwSOhABIlCaCCj/+p4HQYRLx2WHrfHxQePGTa3StEYVNA1VW6W8RgTYzqZpCRAuHr0rt2PhyA+7YcUCMSrphpUj9amOPYGXn38cBzbMxfoln2HHb99g6ax3bAImne9vgqObF2Djsmk4vGk+BvXpYm+ENESACBCBexCggMo94FCRYwSEG+dkKwpxV2T17qUkb4gAESjNBHbsP446NStDpVRaMew9dIo/YnPqrGVgU8WtBXRCBIhAqSIg3LxoDoJkJMuOOzoqGl9/NdcqA58o3o2hRaPe3P+Vk1ga9zIAABAASURBVLL9k9IxAmx5T8XyUQgNCZRtoFQqUKFsBNRqlWw5KYkAESAC9yJAAZV70XHHMjf0SSxXQ9YrMaqSrJ6URIAIEIHiJmA0mjDvp7V5yoUr9nsZrNm4E0xeke5gWvypV6sK+j7UDlUqlsXVG3F8qviGrXt5sSCl4UEaeIJo1AoE+qrcxleV9IVFwodg//x9CvFXSwEuhdv4XpDXm71H2DhJiICrCeyfsx8rB67MUy7+fdHVLlL/RIAIEAGvIFDiARWvoOQFg7h5ZA6OLO2C86u7y0rSuV8LPUpTsx5AkO2UeFPtVhDLVS+0TWpIBIgAESgIAREiEpJS85ScT3FgdnfsO443Pp7LH6PZpnk9puLClgONHdYPI59+FF99OI4vD1q1bjsvE6WUPYnAE8RoFKEzmPiTE9zBX/ZkJQkfdPr8fcrUG2E05V/PHcaV2wf2HmHjJPESAmy5jjQU5cZFfOkPW35zL5HdG0W0f1fk3lPFYjPti7FQfPuytS/Vkg+l3qXfbD+kM4d/Ey8kIXZPbJ6SEZ/hsC2qSASIABEoCIHSVpcCKqXkFdclX0DSlW1Iu7ZdVvRp1wpNQgwMRdbLC6Ef9DaMD42A4dlPYXjmo/ztpWlh2nAUxvlbYFqxG+KFuPzbUA0iQASIgAwBlVKJ18cMylNqV69obcVmnIx8dRo+ev05PN6rk1Uvd8L2T0nP0FmLtFlGeIIYTSL0UkDFXXw1ZX+pZAGV/HzKkoIurHp+9dyx3PpGoRPvIMDeiNJIhLhL5qU3OfdBkTu/EyvVzvUrFwxJvSNrz3D2MJDDruLqKbOxbD/MGcfS5s83xWNL+3Op2qkqb9T8+eY8z/RVOlbhOkqIABEAISACRSKgKFJrauwxBKIbjkSjQZtRvc96hNR4jPtdpv5Injfr+nFdoRMff4gNOsDY7nGYajbL34x0sW+ctRGmjUcgnroG0+6zMM7cAPHy7fzbUg0iQASIQCEJ/LZhB15+bxbeePFJtGxSB9du3uaSkanlFr+YuwLnL12TghFG/jjln379Cw+0bMDLKCECRKCUEcgOhhgffAb64VPzFeMD/R0CZKrTRtZW4IQZMI2cdrdMulHFDWb7wc8dTMKqhaNi6xgu/pEBvFVYtVCeZ/qAKLOOF1DiYQTIXSJABNyJAAVU3OnVKEFffEOrI7RSBwRWaA9NYAzvySe4Gs+bdRW4zlmJePUOxFvJdt2Jh2hNrx0UUhABIlBsBI6cND997NNvlqDbwIlW2bB1H+9j94GT6DX0LTR+8Dk88fz76NahOZ55vDsvo4QIEIFSRiB7ZohYtirEao3zlwjz9VW+lILKyNpS1WoKVM/RT6W6ZlPZfpgzHpiSy0SACBABLyZAARUvfnHdemi3kmTdExPSZPXFpVz55C/4suqXVonddZWb/iWX/mq2nhdSQgSIgNcQePelITixdZGdsI1o2SB/njMJu9fOwrqfpmD/+rmY/MZwsCdEsDISIkAESgcBGiURIAJEgAgQAUcJUEDFUVJUr1gJCDXKydoTapWX1RdUufG1TTxocnLFiYI2la2/qPP33F7ihUTZclISASLgPQSCAv1RqUI0/Hw13jMoGok3E6CxlRABY9ehfPmNWLleCfWQj9mQSN6/YdA7+VSkYiJABIgAEXAVAQqouIp8ae83PACKXs0BtdJKQqhZFooHalvzdEIEiAARIALeSIDGRAQ8g4AYXcW8NCcg1CGHTfe15gEQtt/KgZZP4afYTEAU8c/tLIw6msLlfLcXYXzAvJedI0b5UqNKdRypSnWIABHIh0DYE70RVTU6T1Hv3Z2PBSomAvYEFPYq0hAB5xBQdKgD5QePQznhYSgnPQblC12d0zH1QgSIABEoCAGqSwSIABFwhEBwhDkAU60xdtxMxRWtCRAE3NaLOJhi5LI/2QAxsiLohwgQASJABLyDAAVUvON19NhRCBoVhIplIAT7l8gYLEt/LPumxO68Yt+PdPcot3LlINu9VpIu0lKf3Iwo774EyDMi4K4ETH8fh/G7v2H68zDEFOnuvbs6Sn4RgSISCA8vI2uhTB562cqkJAJEoFgJJP78G+Iu3uKib9ma25bT8QJKiICDBCig4iAoquYdBEQIdgOR0wkQ7eqRosQIkGEiQAS8nYDeyEcoHr0K8WQsTJuPwfjVOiBbzwspIQJeRKB9+05QKe8ua2ZDCw0NQ9OmzdkpCREgAkSACHgJAQqoeMkLScOQJ9Dts66YcHGCVSq2tZ9mK9jHWDAcS/AxPsGbryt529CqYTk6oFMiQASIABEoEIF0nX31xHSIV+Lt9aQhAl5AoGzZcnh68DA+kuiosnhy0BDMnfM9AgMCuY4SIkAEiAAR8A4CFFDxjtfx3qPwslIxE9A+45O3vOBTbCNWnjtbbLbIEBEgAkSg1BLIYyaKeCul1CKhgXs/gciISD7INq3bYtSocShXrnieZMiNUkIEiAARIAJuQcAtAypuQYaccFsCggkw7lPkKaYDxfe2Ntao6bYcyDEiQASIgMcQ8FPLuirUKierJyURkCNgMmhxfcf/cGpxHRyfWxYX1/ZD5p3jclVJRwSIABEgAh5EwJNdLb5vnp5MgXz3LAIBgO8inVm+zZ5G7ndX5yPpLEt96g6oV+ixmSKjoB0wiLcfuuUZvvQnrJr3Lf0xmUQYjVKUio/UNmFlN+MTYDAabQsoRwSIABEoCIFAX7vaiocaQ4gIstOTggjkReD2sdmIPzwDWamXYdSnIOXyelzZ8Exe1Z2v1xkgnr9pFcSbZ2CxDZi5/uod5/tEPRIBIlASBMgmEbASoICKFQWdeBIBZUsTuDTLDgQIMOeZ3qKTGVD/JY/xwIhlX5WYNuY9VR7L1r+2uDHe/J8aZWa+gztbdsJUJkLGiveoRFHE+9MX4YMvvrcb1LZdR9DqkVHoMuBlNOryHJav2WpXhxREgAgQgYIQUI7oDOWEh6H6eBAUDzYoSFOqSwSQFmv/d0ibeApZKZfdgo6YkArjrE1WMW07xf0ST8SadQsl/1MzuY4SIuBcAtQbESACJUWAAiolRZbsWglotVosX7EUb701ER999C7+/vsva5m7nWS164iMkWOge6QXxMAgd3OvWP3ZsHUv2vcdh5Vrt9nZzdRmYeIHs/HisL44svk7zPhwLN7/fBFib9AGknawSEEEiIDjBPx9IFQsA/ioHG9DNYmApxDQqCFUjzZLTLit16KUTcqA4ZPfIF5LkDL0e08CVEgEiAAR8BACFFDxkBfKk9385NP38fXXn+Of7VuxYeOfeHfSG1j12y+ePCSv8L1dq0ZYMe999Ozaxm48ew+dQkamFoN6d+aPfXywXTNUjonGtl2H7eqSwvUEtEYt/rq1Dj9eno89d/51vUPkAREgAkSgBAgExnS0s+obVgea4Mp2emcrWH9CmUAoR3fjohjYlqnuiiCdMtHpYdr+n5ShXyJABIgAEfAGAhRQ8YZX0Y3HkJGRga1bN9t5uGXzBjsdKZxLwN/PB2UjwxHg72fX8a3biWABFI10t81SWL1yedyMS7Rk6egmBNIMqRiwoyveOjoeX5/5DOMODsPYA8+6iXfkBhEgAm5KwCPdimgwCpGNx0MTVBlKdTCCK/dApe7fe95YbiV7ns/kMREgAkSACMgSoICKLBbPVeqSzkGbcNJtBnDt2lWYTNn7nOTw6saN6zlydFqcBA4cPYN5P62VFbnlPXJ9p6Smw9/PdhNJHx8NUtMyeHVfjRIRIT4eIcxh5mtYoIadQqkUPMJv5jPjHOSvvqe/WxN/R5zuFh+bJdmbsAOxxpN5tgvKfuKKr0aRZx3Wf2ElNJu1xR86EoGiE8jbQtYMNTK7+yK9uR+0w31gPEaXNnnT8uwShcoX5e//BHWGnEL9kTdRteev8CtT3/MGFR3ieT6Tx0SACBABIiBLgK46ZLF4nlKbcAqnfmqI/yQ5vbQ5Ti6shlSZzducPbKaNWsjQmZj1yZNmjvblVLTH1uqk5CUCjlJTE51iENwUABf8pOzsk6XhaBAf67K0huRnK73CGEOM19TMw3sVArwiR7hN/OZcc7QGu7p78k7p/m4cif7bx3Ns12Gzswiy1AyLNIy9bndKX15GrFTCOiXK6Gfo4LpqgBI8V7jTgV0L5mDp05xgDohAnkQEC/mseeYjxqKdvfl0YrURIAIEAEi4GkEKKDiaa9YHv7e2D0JWUnnrKX6jJu4vn2iNe/Kk4kT30RISKjVhapVquHZoSOseXc6UV65jIAvp+Ypvr/87E7uyvrSrlVDvD5mkKyMeKqnbJvcyuiIMFyOvQW93vzFm5WfuRCLslHmx0abREBvMHmEMN+Zr4bsR0OLHuQ742yUEuZ/Tll37Q8M3/0klz23dwLSmHIKe3pTjYA6eb4+zCbjYsphO6f9op4bjMwh1gMJEShZAsY9SrsOxOsCTKekAItdCSmIgPMIiBdzzhzM/kyUDsoBrSBUCHeeI9QTESACRIAIlCgBRYla9yLjbKmDo3f3XTHsjFv77LrVJp6GMSvFTu9sxf33t8faNX9h4YIlWLZ0NRYvXo7y5Ss42w2H+lPEXkHAjGl5iu+vKxyy4wmVjFKAgQVMjEYjDAYjD56wL9jM9xaNzXfPlq7eDINU/tf2A2BP+OnQpjErdgcp1T7EaW/iYOJeLrGZVwD23TGHlPWrgPohjUA/7k/gQqIRN1Ltl0W6v+fkIREgAo4TYB/QUm128KMZVBIJ+iUCRIAIeA2BUh9QWbVuO+p1HGonL783i7/IbPnE2LdmoHXP0Xig91gMGv0hbie432ZiKv9o7m/ORKkOhFITnFPl0vMaNWqhQoUYl/qQX+emmEpIHz+RS2a/x3l1Y8W7Om2/AVxX8MT9WvzyxzY07jocbF+V1ev/5eer12/njvr7+eDryeMxZeZSNOryHMa/8zXenjAYMeUieTklRSMg3kmDadcZmHachliAzQmN65TImqnGQ6ufxPIj2zFh80cYsu4lPLPuZX6seLM6yvvFYGYzD9yksWhIPa71vmt6PLEyGWP+TMGw35PxwtoUxKYYPW4crnZY2cqemVBehKKO6GrXqP9STkCoan9dBh81hIplSpzMhb8uYNeXu60SdzwObHbm+U32+hJ3hjogAkSACHg5gVIfUOnavjn+/HGKjTSsWx1lwoL4S79k1WawpQ5/r/wSu9fOglKhwIz5v/Ayd0rCavS3cye0pr3OrlJeilKqN1aqjPQJr3LRPvY4p2CsmFP3BNd5Q/J4r044sXWRjfR7uL11aJ3vb4Kjmxdg47JpOLxpPgb16WIto5PCExCPX4Xx41UwrdwD0697Yfzsdx5cccSiYb0S+pkqaOaEImJBFfT6/RkMXf8ynln/Ej9WuVULKfpkVPSv7Ig5quNCAt/sy0CKzmT14HKyEUuOaa15OnGMgPpxI9TPG6CoKAL+gLKtCT5fZDnWmGoRgRIkIFTNcQNCKUCoEgkA8PhjAAAQAElEQVTlM9Lf2ADfYus1dncsD5pc3XXVxiYLnOyZsRsWiT8ZB0EAWKDFomNHps/Z8NSqU9xe0qWknGo6JwJEgAgQgXsQ8JqAyj3GeM+iwAA/VI6JtkpyShqOnjyPwf2783br/96L/j07ICoilG/IObh/V/z65z9SpF+6eOM13COJajYRlbouQFjNJxBavS9iOsxATMev3cM58sJjCSiVClQoGwG1WuWxY3A3x41bjtu5ZJLR2VWSFKoeRqjHGLgoqomSBvi78Rp83+MLLOoxHZejzyJQZQ4G88JCJuxuZkpsSiFbU7P8CCRkmhCXbrKrduaO0U5HivwJaMbr4bdBi4D9mfCdr4OygT3b/K1QDSJQcgQULWtAObYHhNrli7WTa3tjedAkdve1YrH73+r/uL3kK+43E7tYBkhGiAAR8CoC7jKYUh9Qyf1CTJ+7AgN7d0alClG86HLsLen87rTNiuXN+pS0DF6ulu46uItE1RmE6g8tQo1HlqBso5HI6ZdSIUD65Tpl9quukBQ567jzOaSfvPyTigDpzkte5ffSC2lSQ0g/u5RQJgucj6W+SuIjlVi5WfQFOWZbZ2ZIiABgEoEbMnf+EqXPE23+d9WVDxmhGaPnoqhu4kT/bvo7vn9oOpcrZc+ha9lHuL4wyenfTmN2o9lY8ugSLGi3gB/T49ILY4ra3INAiK+Cf67krhLuT58YuZlQnggQASJABIiAlxCgYXgpAYWXjosPa83GnZj301pZ2bHP/i7x9j3HsO/wfxj59KO8PXtSBttDxddHw/Ms8dGo2QEZGVp+DA3UwBPE31cFjVrJffWRjsx5Px9z3hP8Z/7K+Rki8Wdl7GuIXPm9dKZPfGA8zloC2jkqpHX1hf/1u69noJ95VoZKikDdy869ylRKs33mIwkR4N+iy9194pWVSJg/4KuxZgty0ii0Ka9e2b8q3qo7GS/WfJXnC5oYtAZseWcLdCk6a1M2U2X/t/YbXlsr0EmhCLCPhQcq2b/e7Svb6wrVATUiAkSgdBCQYvRsoLG7r/KlOruy901hn91Mn5+cX3PSpl3yZZmAf35GqNyLCdDQiAARcISAVwdU2CyShKRUyAkLlOQEZDSaMH3Oz3hu0MOIjgzjRYIgwN/PF7osPc+zxHLu729eAxufrIMnSGqGHtosI/c1Q2dkQ0F6poHnPcF/5rCcn7cl/qyMbbYmV56XLu6KDimLbIMdpjQg7luTlUlSuvl1zzLc1eVlLy+9nh4fy14ekhwElJ3r58iZTxUyOnNJ/mlFKZDCat0f2Qm9Kgxgp4WSxPMJ0KXeDaZYjMSfvG05pWMxEniptT+eb+aPthXV6FJVgzfbBaJnTZ9i7MG1psSdZ2CctdEquGleQmD6ZQ/XmQ5fdq2D1DsR8AYC2Zcx1/Ze40t12L4oTOJOxNmNjl0n5Vae337dpl3SZfP/09z1PCZPjhIBIkAEXEDAqwMqT/V7EK+PGSQrXds3t8G9futevvnsswMfstGz/VWuXLtl1V29bv4jFRwo3VG2aunE0wiYLsq/9cVT8npPGx/5674EhPoVoXyzLxT9W0HRryWUr/WCok0tlzvsHxkg64MmiGZNyIIpotJXJaDPfT54p30gJrYNQLtK5tmPRTTrNs1Nt1Mhnr9lFejMAWoxNoHrkJzhNr6SI0TAVQSK3G/2DJUKLSug1fjWVomqZ16ejhw/0j3CHDnzaR2cQWdxO9q10PO2oZVDzAWUEgEiQASIgMME6NujhCorS4/Pv/0ZY57ti7CQIElz97d7xxZYsWYr4m4nIS09Ez+s3AT2JBRByL4tcLcqnXkQAUVVk6y3Qh15vWxlUhKBQhIQygTyIIri/toQot3jAjYgKgCV29s/Haj2o7ULOUpqVpoJKNrWgnJ0Vy5C47vvK+VjUhBR0guN7upKMycPGzu5624Esi9FY1pXRJsJrdFmdBO07R+GivUcu+lXVziLB4V/0cXvAG8fUllmSaq7jZn8IQJEgAi4GQEKqEgvyK/rtiM1LRODH+sq5Wx/n+z7IKpVLo9O/Seg1SOjoNcbMHZYP9tKlPM4AoL0HVb9pMHGb0G6Qa9+2lZnU4EyRMANCNzS3sSBxD1IzErg3tzRxfPjLe0NrmdlTK5mFHxJxSMzH8EDbzyAag9WQ93+ddF7Xi/U7lWb26eECBSEgBARBKF6WS4IkT5cLY1jynCdEOrYFz5Ls8IdqRURKD0EFDtXQ/NRP6hnvYgHVTPxRKfd0Kj0DgEwBQc7VI8qEQEiQASIgD0BCqhITNhTffat+5Y/FlnK2vwG+Pti9qcvYeeamdj26wz8PGcSf4SyTSXKeCQBzdt6KOqZZ6SoR+jhtyETiuqiR46FnC49BDbfWofR+wfjUOI+Pujdd7bz45Zb67melTH5JXYJ1xck0QRq0Pz55uglBVK6Te2GqlJgpSDtqW4RCVBzWQKmQwpon/HJU/RfetdyKVkIpCQCMgQqtIzhS3WqNA6Eau1MwJBlrVW57B00qnHFmr/XifaxJ3jxfX3u4/ZCKkl3nbiGEiJABIgAEciPAAVU8iOUXR4SFICIcPoDk43Dew5B5qEo7zdBCDefU0oEiIBjBKgWEXAGATEJMO5T5C1nBWe44TF9sCcUGozmzedzO20yibgZn4C8ytkS6FvxiWA2crelvPMJaMdo8gwksiBjhQYxfKlOhQoZkPup31qFZuEPoAyipdcUqKSoiaaqB9Ai8j50qHod4V2bIumH5cjq2IU3r9O3DrcXWoWW/nAglBABIkAEHCBAARUHIFEVIkAEvIIADaKQBNT79iD4zYkIee5pBHw5FYo7twtpiZoRgYITUDY2wXeRjot6gnkJg6LJXZ1mvFlXcMve2WLtpl3oNnCi3eC27TrCly53GfAyGnV5DsvXbLXWYQGUWd//hibdRqDzgJfQvu84HDl53lpOJ64hYDykzDuQKAUZkb1KWQyUvyNUpllNtJ3ZEhH+ZcG2/quirInmNR5AmyUPocmWzxA4dwqyHujgmsFRr0SACBABLyFAARUveSFpGN5IgMZEBIqHwOV/LmPlwJVW2TdzHyACl7fZ6vfO2mvXoergfoQ93gu+S3+Az5ZNCJgxDaED+wJ53AG3M0AKIlBUAmGAsqXJLDVN3JoQIprzkl5RS+Q6a5KZCsWRv6HcvgLChcNWtbefsCcSdh/0Kt74eK7dUDO1WZj4wWy8OKwvjmz+DjM+HIv3P1+E2BvmPZgOnziHmQtX4Yev38ThTfPRp0c7vDTpG7AZLXbGSOE0An7fmAOJvlJAke3zxjr2nZnFg4tcF8I00sd52aoQoyqbMzlSY8NOULY2QdXTyLUaKSDpv04LRY1c/2d4KSVEgAgQASJQGAIUUCkMNWojT4C0RIAIuISAyKIjUs8hV8PRZWofNP35fil39zf9dgZi98RaJfFCAiAAGXdy6c8nIufPf6v/w4oXNmOf2DCnGqpzZ6D+76SNjjLuReD64XmI3fUhbu6bLCv69Bvu5XAxeSMk3oJ62jNQ/fwxlOvmQj3/VaiWflRM1t3bTPmyEfj+q//hrfGD7Rzde+gUMjK1GNS7M1RKJR5s1wyVY6KxbZc54LTl30No07wemjaoBbVahcH9u+FWfCJOn3dsDw67DklRLAQUTU3WwKGoNptUNDFadWaNlCqUMAyfBmOXwTDVbgVTi0egH/kFxEp1pMK7v0KEyDOKWzcRNrBPnhI87gVejxIiQASIABHIn0CpDqjkj4dqFJbA1dirWLBwbp6yYeOfhTXtUDsxFTAdV4AdHWpAlYiAFxDQZPog6mx5hF2JLJbRpFxPweV4HyQizM6e4lqsnY4U7kPg+pH5UkDlI9zaO1lWDOk33cfZYvREsXcthEzpD0AOm4pj2yDEX82h8c5TFigpGxmOsJBAuwHeup3IAygaTfa3cqlG9crlcTPOHES9EXcHVSuWlbTm36gI8x4acbeTzApK3Z6AGBgqBVSGwPDMRzD0nQCxSv08fRZ0Oqj37MpbjhzMsy0VEAEiQAQ8mUBJ+E4BlZKgSjZxLfYKFi6cm6ds3LiuxCjp3lcjo5UfMh/34ces9+5eQJZYp2SYCLiQgADB6b3rmzRzep/UoeMEyjcajpg2byO65VtQ+5u/KJepO4znmU4VYNY5btEzagpxl2UdFeI9d6bF9Zu3Me+ntXkKW84jO+gcypTUdPj7+ebQAD4+GqSmmTczZeW+Pj425ax+WkYm10WE+KB8GT+3lzLBPvBRK7ifIQHmv/0Bviqed7b/UaF3efs76INKKYC1k/NVkf0xXy7cr8Dj8fdR8tcxNFDD20bXqQLlH38g5btfsPGhz/C9OABr/HohfuZyrtcsXMDryfkhp/P3UXH7lLiGgMmQif0/dMLZX7vh/OrudnJhTR/XOEa9FgcBsuEBBCig4gEvkie6WCGmEp59diSXrl0f4kOoUCGG55m+WzezjhcUY2Lcq4DhZ9s/7PrlKhh30lu9GDGTKXclkH3BHRpbBpjoY7tnSi6f2SaUuVTIvdfK8WXHeRVjTEV+5In0pSt10mSYIqN4lhL3JFC+8QgpoPIOyrZ4C6qActzJMvWe43mmU2freIEXJXL7SLDhiZGV2MEjRW8wIiEpNU8RRVO+4woOCuBLfnJW1OmyEBToz1WsXJeVxc8tCVsiFOjvx7O3k3W4fifT7eVOig46vYn7mZyu576naw0872z/45K0vH+WZDjog8EogrWT89VkXq2DGwkFfx0ydEbmBpLSssws0kVcqtgAM149ia3rEnFaqIFdmfXw1UtHcTq4Fm5Uq2+u5+BrnqEzcPuUuIiAyYiEy9uQdm27rKTf2OFEx6grIlD6CNC3zNL3mjtlxBWlL2DDpIAKk+7ZwZMYKcjC8ky6d3u4RPwwnZJ/S5tOyOtLxAkySgRcRSD7gpst/cExhXXPlITzd2Q8yo6+5ChJj8+wtmF7rqRcTeGlut6P4faeo0j4bQPiDp1G5tDhXE8JEXA3AqaWPSH6Bdm4ZWrQAWJkjqCgTan7ZyrHROP1MYPyFDaTJL9RREeE4XLsLej1d7/4nrkQi7JR5uV85aLK4NLVm1YzlqU+lqU/1gI68RoCl/6+hMwE8wwly6CMWUac21DKnu5kGbwHHxWaQLR4egtq9t2Aqj1X8ZEolL6o3mc9l2o9V3MdJUSACJQMAfqWWTJcyaqLCAjB2d8oc/cfnFtR8Lzy6hUEvzgCEU3rIKJVQwS98TKExISCG6IWRKAIBKJ8y6JpWEuEasxfhMI1EYD0to/yKYtawXW4ZW3lDETMCMVjS/tzaTG6JdfnTNgjNHPm2XmV9pV4fUu7+k/UZ2oupqhoGBo2BvzMd6y5khIi4GYExLBo6Cd+D8MTb8L40Ejoh0+FYdDbbuZlybgjiiIPmBgM5tkILHhiyH4aV4vG9/FOl67eDKb7a/sB/oSfDm2k/9NSSecHmmDHvuM4eOws2IyYxSs3IDoyjXXG3gAAEABJREFUDLWre+7MHmlYXvlbXINKuGDePye3vcTzdF2Tm4k75HXJF5F5+1ieroRV6YjAmPYILP+AuY6gQmAFKS9JQPn7zTpKiQARKBECihKxSkbdisCd4/NwYnlXHFnaha+rTDq3kvt35/hcnj+fvd4y6dyvXO/JibK9EZZHC1rGwfKqBw2WbKGPQa+/BN8/fodCCqIo4m7B7+efEDh9SqHtUUMiUBgCD0Y/hNnNf+RBFda+TUR7sC1UupR9CC/XMn9xrFG2Fp7uNRQVW8dwCa8RDkd+/CMDeH1Lu+BKxRCJdKRjqkMEipOAXxBMjTrB2G4AxGrmgEFxmndXW+cvXUfjrsP5Y5PZE3rY+dtTvuPu+vv54OvJ4zFl5lI06vIcxr/zNd6eMBgx5cwbWDeuVwMvDOmFwWMno/GDz+Hn3/7G55NGQ2HZuINbKVRCjdyUQLXOVWU9O/XrKfw6+FfcPHJLtpyUziWQlXIJp5c2x38/1sOZn1vh+HeVkHLpT+c6Qb0RASJwTwKKe5ZSoccQMBkyeXDk4tp+dj7rki8gJfYfJF0xr6/Up13jdXQpF2zWWupTbZ/acS+b3IAbJkIZwG+5FqqhBig7mPiR5Zm+SO5Kd/k0e3bamVDv/NdORwoiQASIABHwVAKe63eNqhVwYusiG/n0zZHWAXW+vwmObl6Ajcum4fCm+RjUp4u1TBAEjB3WDwc2zMUmqXzPH7PRpH5Na7knnNw++i32fumLLZ8ocWSmP65tf5m7ffvobJ5nOibXtr/C9aUhiT8Rh0MLDyPxgv2sk+iG0Wg6vKkNBjbLyaAz4Mq/V/DHmLU2ZZRxDYGb+z6GNuGktXOj9jZit46z5umECBAB1xOggIrrX4Pi8cBkNAdHru8oHnvMSknYZHZLWISqInxe08N3to4fWb6EuyTzRMDpBC6mm9e5r7vxG+87TnsTkXUi+ZKdDu+057qiJnV61+H26j1RD/TjpgTILSJQAAJKpQIVykZArVbJtvL10aC8VO7OM1OOf1eRB0gMWvsggeygHFCWhE0Hui3RKntn7sVPPZdg2wdbcW3fdd5X4sUkfrQk7d9qj7F/PooqpitgwRRBECxFSL2WChaQsSroxCUEMm8ftetXn34d+vS7ex7ZVXAjhenABYgZOjfyiFwhAsVPgAIqxc+ULHojAaUSWa3a2o1M3/YBOx0piEBJE9h08w9cTDvHuzGKBn7cHr8FJn8jX7ITWS+K6yxJ5XaVeGDEsjdK81EtpItnoFK7yjb6FqNbWJrwY1CFIG4vpFIIzxdHQjaIABEgAkSg5AnsnbXPrpPL/1y20/ldOgU/QQtBuBtMsavk5QqTScTN+AS+v5C7DVXlI/P3V1BCqQ50N1et/piW7757vmQHjB+vhngr2aqjEyLgbQQU3jag0j4ekz6VL/05n70vCjta9kyxYSPa5HjmzgnbPVUurO3D9QIEfnRVYjot4NqTQEYLP2R09UXWZ2op43xvUqd8Ae0jvWAKC4cpKhqZTzyFtJdfd74j1KMzCbhlXzvv/GPnl86kxbn0M3Z6pgjItTcK21OFXTsHRPrzgIllz5Tw6o7ttcJskhABIkAEiIB7Ekg4LwUHMvR2zqXHp9vpNIcPoDbMMx5zFgaHKpA7OJ+z3BPPv5i7AvU6DkVK2t2nG23bdQStHhmFLgNeBttfaPmarW41tJAaA+z8Ca3RF+zJPnYF7qBIyYRpz1lbTzKzYNp+ylZHOSLgRQQooOJFL6ZlKGnXtpuX/2Qf9dl7pljK2VEmngJdku2eKuk3drKqEKV//MRFiW6iDzI2A6J0HSBeE6BfpOLibHeMFSsh5Zt5uH3wFH+EbOqn0yFKwRVn+3Hv/qiUCBCB/Aiwqe0JSam4ci0Ouiz7Lx2s/e2EZGRqs9gpCREgAm5I4MR3MXzpD9sXhcm1f16281LuWif3niqsrVF7x66tJytYcFzlL918yjWIACm4nkvFsy2Fw+ho+hdlxAT4iDrUEC+g79OhvMxbklXrtmP+kj9shsM+4yd+MBsvDuuLI5u/w4wPx+L9zxfxJ2DZVHRhJqL+cFTu/hPCaj+JkGq9UP6BqajYeY4LPbp312J8snyFuBR5PWmJgBcQoICKF7yIOYcgqPxRPfu585ZjaI3Hclbh5+zuND/JkZSpP9KmreVZ9kJJzlDJ0b/cqRgPmM4LdkXGPUo7HSmIQGkh0LaM/R4pPgpf1AioVWgEwS+OQNjAPnmK4s7tQtt2p4ZHT55H+77j0K7PWDz01Gt4oPdYsAtti49Xrt3Cw0+/jg79xqN5j5F457MF0BuMlmI6EgEiIEPgeJwBr/2Vir4/J2H47ylYeixTppZ7qES5KIt7uFYgL4w7FMh8ygfpzf2Q+agv9N+rbNq3zLWEkxVWbl+ZHWwkq3Eznu8m/INXhDmYJEzHMOFnRDzekeu9Idl3+D98/NVPmPbuKJvh7D10ChmZWgzq3RkqpRIPtmuGyjHR2LbrsE09V2fYjJRKD85HlYeWIbLRGChUfq52Kc/+hcgQ+bKoYHk9aYmAFxCggEoBX0R3ry4ISgRWaG8j6sAYh9z2Calm0y6w3P28natnqHAnKCECRMBKoGvZR1A1sAbPKwXzRXS7yM7wkwKqXFmIRH3oANR7duUpyPKO2Rrsu9T44f2xffXXOPzXdxgyoBvenvKddabKR1/+gPtqVML+9XOxdvEnWP/3XqzfsqcQRKkJESgdBLR6ER9tT8exWwZoDSKupRqx+KgW2y6V7GdGvedi0WhMhlUqtJ9uB9z+dgykL6SjrG0s7ZW+Zaxt0679g/hDM5B0ZgWMWW56Vz0R0E3wgemQdBmfYb7xlDVFDeO/Uj57JC3HtMRTa59Eh3c7okKL8lwbVtV+1omxXgNeJmo0EP38YGjUBMmzF4DNyuUFHp5cjr2F0f/7El9+8CJqVrW9Hr51O5EHUDQatXWU1SuXx804CbBVQycFIhDsB0WrXE8I89NA0a5OgcxQZSLgTgTy8+XuJ29+NamcCLiAgBAJKKqzr0C2nStb0R1jWyKUK00E/rq1DolZd/iQwzRh/Hg06SBG7X+ay8qrP3FdQZLkr+cicemvXExlInjT5G/m8TzTm8qZL8h5gQcnjepWR/+eHRAeGgS1SonoyHB+rlAokJyajh37jmNw/27w89WgaqVy6NPjfmzcZr+5owcjINeJQLESuJhsRLLWZGdz3w3zhtl2BW6siN38PM6v7oHrO/+Hy5uewakf6sOQGe92HhuPK/gy6NyOGbfbzt5le6A0ebYxwqrl2B9Lq4V69467IgXT2S7lbAlz0oKfkPrGOzCFhkLF9Lk78LB8cko6Rr46DS+NHID7W9S38z5F+sz39/O10fv4aJCavceKRqVAdJivR4hSISAyxAdRkr9sQIIAl/ld7vmOzAUuYSM6otxnA1H2vug8/QnyUyPAV5VneUm9Buz1hfQTFqQpdN9Sc3f8JZ+cTEDh5P6ouxIioNAE8uU61XquKrYeSsJmYZzzmaaDfxdACJCkggj1UAMX0A8RKKUE4rQ3kZRlvoN2R3ebU4jX3cLBxL1cYjOvcF1BEkPT5tC3vp8Lu1PJ2upz6Fjem+TA0TOYNG0hvl38G94Y+xQPrty+k8SHGFNOiuTyM6BShWjciCu+x7Nmm/Wqg+ngBRh//Bem77dB3Cm/MbJXDZgG47UEki/+YTM2o/Y27hyba6Pz9Iwy7hbCBvWzSvCEUYD07VuRSx/y4gh4+s/ugyf4fihXr8fhs5lLMX+p+fX9ct5KnDp7GcFBAXzJT85x6nRZCAr056osgwm3k3UeIUaTiITULNyR/GXOs1uRrvSd+cAko24lJBhwT4ZpWj0ydIZ71imJseil15f5mJyuL3TfrD0JEaCAihe9B9hSn4Dybe1GVKb+CNQbsBGNBm3mQZfQ6o8B0idtRL2RPF+9z3pU7vo9dMnncXFtP1zbPhGZd05wO3nZ5IVOShS1RVRYAvjvy4T/Ji00r+mljJM6p26IgBsS6BL9ECoHVOOeqQQVKvpXwdCqL2BW8x+4PBbzJC8rTYnRaMK8n9bmKReu3LDBcePWHcTdToJeb0BSciovS7Hclcwx/ZvdrUxISuHl0ncOlAv38wjx81EiNFDDfVVLd1nZACJCfHm+OMcQsPM0TD/tgHjoIkxHr8D4yx74rtlv00+gn4p1zyUi2MemTM4XVof5LFcWFuTD7fhqlPnakWtf0jr2HuEOlqKkaogSIb72l5MtyqkKRYHNdjlyy4C8JPCxS3zJjso3vFD25RrVf+4qvw6CdFc/d7k26XRulcvzyvomfpMptyPKdsbcqkLnhfT0Qrd1l4Y1qlTA+OGPISwkEKGSBGcHSkKDA6BRqxAdEQa2JIj9HbD4fOZCLMpGmWd+Mh0LVNiJFLxwN11OX9k5u853pY/cBylxxAdR+k7CxJG6xVlH6lbyEDAV4fXkBigp9QTs/wI6CcmeQ6fwxsdzMWj0h9i26wjvddq3P2PhsnX8nJLiI+ATUg3BFTsgtFIHvkeKOigG7KJBI+lZwMQvsjGu/TsRd07MQ8rl9bh9dBbOrXgAWalXQD9EgAi4H4EMYxoup1/gjhlEA65mXMKii98iVB2GZmGtpACL/caDvLIXJ6J09ZiQlIq8JOcFM8PQs2sbzP70JXz5wVi+WSG7g2m52M5Zl92tDA81b6bHLvhuJmbCEyRTZ0RyWhb3VS8Fm9iY76Roeb44/U/e/h8zbSNpu8/b9JOulW5PZte4k6qzKZPzhdVhdw7lyhLTdNySTm/M145c+5LWsfcId7AUJb5qAW+3C0CDaBV8VQIqBCkxpKEvOlTRoDA/x+ONeOOv1Dzlp6OZsmYjGr6AlhO06Pw/Iw+4VGg3ndeLaDiK5y37pVRo9znX5078ohrlVvG8b2htfnSrRPq+7/OlDoomJsAffGm05nU9lA9IeRlHS6uquhRQGfn0o7DI44924iiGPvEQWFmLxvfx/NLVm2EwGvHX9gN8RkuHNo25nhIiQASIgCMEFI5UKu46J05fwrCXpoDtrn3u4jWwNYysj/LREWBBFa0ui2VJnEQgLXYbcq8RNhl1SD7/u5M8oG5cSYBH5rO/cLnSD+rbcQIHE+X39Nh7Z6fjRrysJntCw+tjBiEvqV29ouyI2RMdWAF7THJEmVB2ChZc4SdScunqTZSLunsnnH1h9gSRXJdCTGBbI0gJy0kH6XZcsfpulAzGm2f3mHvITrVZMGVk8b4t/WWX2OgsZXJHVl9WL3WZV5lcfUd1FzZdwIaJG/Hb8N+x79v90KXa+u+oHeZbaZT6USp89mAQVj0Rivm9gjGogV+hMYT4CDw4wwI0lUOU3E6wj8KqqxSq4rriTnxCayG0el8bs0rfCJRpMNJG5y4Z5f0m+P2kQ8D+TPit0UL9jMHGtcQLCbi6O5ZLRrx5tknihSRcPXQbF8RKSBUDbOqXxoy/nw++njweU2YuRQEfhFQAABAASURBVKMuz2H8O1/j7QmDEZNj2Wdp5FKYMRu/+AOGV36wisWGRSdevWNR0ZEIeB0BlwRUlv22BV3aNcXm5dPRpH4NK9Q2zery82s34vmREucQ0CWelu1I54bTXGUdJWWhCYjSt4T3py/CB198b2ej1zNvol7HoTYya9Fqu3qkIAKeRIA9InnLvwf5BrRs48FZ3/8Gfz9fsKnhIUEBaNO8Hn5YuQmZUlDg4pUb+H3jTnTr0MKThug8XxUChBrRdv0JMeEQ/Ao3O8HOmBMUp38/jd9G/I6TK0/iwl8X8O+n/+KPMea9FhzrnmoVJwFLcIYFaJ5uZA7MNIxS8oAN0w1u6Fuc3dnYqtzjJ74UunzbT/gSoDqDj0PlF2lTx1My++ccxC+DVnK5+PdF7vb+OfuxfMJ2zBeewmlU5zqeSNcCPNrJM96b1KhaASe2LoJlNiIbaef7m+Do5gXYuGwaDm+aj0F9ujA1CREgAkTAYQIuCahs3XkIHds0hiAINo6GBAfyfJbeNsrOlZSUGIHAip1lbQdV6iarJ6V3ENiwdS/a9x2HlWu35Tmgcc89hj9/nGKVJ/s+mGddKnAegaZh8l/wW5ax30PJeV55Rk96gxFj3/4KbR8dg9Y9R4P9Pfr6o3HWTQjfGvc0Tpy+iOY9RqLnkP9JwZTm6NGppWcMzlEvi7GeoldzIDLIalEI8gPXWTXuf3Lyl5N2Tl7+5zK0yVo7PSm8nwBbCh3ZZDxCaw2AUmNe7ueJow6rFoqYVjF2UrFRBKqKVxCENNth5bomty307pxSqUCFshFQq1XePdASHJ3ypUeg+nwwFxZUt3Rl1VUsY1HRkQh4HQGXBFRq16gEtodKbpo79x3nKppqxzE4LfGPaorIxuNt+gur+QRCqj1qoytqRvzvmnUqoGUKoOVo/G5LUc1T+wISaNeqEVbMex9sL4m8mkaWCQFbEmGR0BBz0BP041ICVQNqoG5IQ+6DWqFB7eB6eK/+VFQPrMV13pYU53gef7Qjjmz+DltWfIHNK6bjr5+no3X27EjWT9VK5bBh6VRevvfPbzH5jeEuu8j+/nAmXvsrNU85Fe/6mw9C+TCo3ugD5Ru9oXz1USjf6w+huv2sFcbWXSXhvPxTnBLPJ7qry+QXEciXQPPnm6P/sv528vhX7TBC+Am1hQt3bZTiYMpdCHRGBIgAESgcAZcEVJ7o1QlrN+3C5Bk/4GZcAs5ejMWin9fj9clz0KfHA9Y7hYUbErUqDIHy93+C+iNvoeaA7aj3XCwqdVtYGDPUxoMIsLXDZSPDEeBvnlIt5/qKtdvw9pTvwJb6XLl2S64K6ewJOEVT1rcc7+fDBtOxuNUqPFSuN89Tkj8Btt9KdGQY2PtfobCdKWlpzcoD/H0tWZccLyYZceyWIU9J0bnPBpRCZDCEsqEu4VTUTqt1rmpnwjfMD+Wamv+P2RWSgggQASLgYQQSzyzDlY3P4vL6p3Dn+DwP857cJQLuTcAlAZWu7Ztj0itDsXr9Dpy/fB3fLf0TU2cvwyNdWuO1MYPcm5gXe6dUB8E/qhmK81GEOXEJ91XgUwGVwzpxtVDXnGfTAZXPyS874hUpKRCBA0fP5PnoWPnlPfLmu3dsgfatG6J82Qhs2XEIjw2fBEtQRaNSICRA7RHCRsd8DfIzT+VlX6BZ3hOEcfbzUclyVkuvARubv49Stvxe42NtWFuNZEOuniL7bmWwxEyuPD9dgK+KmScpIoGhjf3w6YNBXOpEmpk+I6MrYjelvnnzUS0RVT/KysEn2AedP6S/SVYgdFI6CaiUpXPcXjjquAPTcGXTMCSe/RlJ51chdtt4xG4d64UjpSERAdcQULii21vxiWjdtA52/PY11iz+BD/PmSSdf4PP3nkBbFNAV/hEfXoQAXL1ngQyMrVISEqVlcTk1Hu2zVk45tm+GDWkN0Y/0xtLZ72DoEA/bN5+kFcxiSIMRs8Q5jD31XIz34N8Z5xNJpMsa5PIRgaYpBM+vgK8HpaHOrFNieXami0DhkLYZvaMUjuLDToWnkCVUCUaRau4BGvMM2mqhih4nunZk08Kb51aWggElQvEk2uexLDtw/hx1JFRqPVITUsxHYmAVxEQfXygb9XGKoZGTcA3pNXY6rPatveqcZfmwSSeW2k3/KSz9jq7SqQgAkTAIQIuCahMmbkE705dCI1GjWqVyqF+7arw5r0ZHHolqBIRKCYC7Vo1xOtjBsnKiKd6FqoXtlFbZHgoMnVZvD370pyuNcAThDnM/MzUmfebYN/1Wd4ThHHW6U2ynI3ZURFtHuX3Gp9Ob2RYoJeCMHL1WCCHVcjQGWX7lmuTU6fNMttnNkhKJwHV7HHQvNmVi9/uN6BQxHMQ6hx6Vi5cPcX17pAExwTbzFRxB5/IB88joNi7lr/vfb5+HBrVbj4Axb4/uI6951WrvuQ6VyWm6LJIXLbaKslfzQEEAcaytvqUr74F/Xg+AVE0Iispx3452UMyZiVDn34zO1fww9q1q7Fg4VxZWXTq74IbpBZEwIMJyAVUSnw4bN16WnpmifdDHRABIpA3AfaFXK83wGg0wmAwgp2z2Q6sBVvas2j5etyMTwB7Kgrb8+j46Yto1aQOKyYhAkSgNBHQppu/DH7Qx2WjVm75kfugPr+owD7E7o7F1XtIgQ1SAyLgqQS0WgROfg9l2jVHZIMaCB32FJRnT3vqaMhvBwgIghKBFTva1fQNrwt1QFk7vaOKtX/8hoVSQEVOFv231WrG8OEvML79M4wLt0KMT7Hq6cTtCJBDRSDgkoDKo93a4tTZy7hw5UYRXKemRIAIFIXAL39sQ+Ouw8H2VVm9/l9+vnr9dqvJxSs2oMuAl9H4wef4htFs1kuzhrWs5XRCBIgAEfAEAisHrcQv9xBPGAP56DkETC17IuvjTdCNXY4M3RBkho/neaZjYug7wWWD8f9+Pvznz4Yy9iqEtFRo/v4LwZP+5zJ/qGPnECjX+n1oQmtYO1P7l0X5dtOs+cKc9HykN559diSXyEjzHlSPZOuG3pcjgJOUATEzC+LxqzAu3VGYrvJoQ2oi4D4EXBJQ+XfvMU5g1OvTMe6dr+yE7QHBK1DitQR+uLYHg1dNRbfu7TF23EgcOXLIa8fqrgN7vFcnnNi6yEb6Pdyeu1upQjQ2L5+Of1Z9hfVLPuOPmR0yoDsvo4QIEAEi4EkEYtpUREzjGJQTKlndZudMKtSraNXRCRHwdgKanf/aDVEhBVfslKQofgIutOgbXgd1njqK+ySpPWg/6j57AUExOYIehfCtZ88+GCYFVJhERUVzCxbd0Drmh09wZc7k8m0eXMmponMi4A0EXBJQMRiNaN+6EapVLg+9ni01sBVvAEtjyJvA5v3bseD6blxNiUdmZgYOHz6It995DekZ6Xk3ohKnExAEAWXCglGxfBTYY2ZRQj9/X8rC2HWp6PtzEj+yfAl15RKzpr9PwLThaJ4iZpn3dnGJc9QpESgIAcvSnzfNe6Ow/SDyEsXVk7aWRRF840tbLdSz7u61YrElvNYFyWPa8yU+Fp3yr+9ztXQ823/JY+g9cCB6+j5lbcTOmfQZ8IRVRydEgAjcJUBn3kfAJ7QG2FIf7xsZjYgIuJaASwIq7Mkhsz99CXmJv5+va6lQ7yVKYM+Jg3b2k5IScfr0KTs9KbybwKUkIz7bkY5zCQZoDSI/sjzTe8vITVukgMrGIzDlITDQBq7e8lrTONyXgKCRAjpy7mXJKUnngQTIZQcIZLV9wK6WKYZmadlBIUXJEKgcAcFPUzK2ySoRcCEBlwRULOO9HHsLf20/gDUbd+LQ8bPS9wr6YmFhQ0ciUBoIHLopPzsjL70nMlF0rgdFt0ZcoFbyISg65dCpzDpeQAkRcGcCvgE2e0Gw/SDyElPFurYjEQSACWx/9KO/srMpfrYZITP/sdEbH3zGtmEBc4rmJtkWitbyetnKxaokY0TA+QQynhmOjOGjYJSCKGJgELI6PYiU9z9xviPUY+khEOoPmMIgRtaDctD9pWfcNNJSRcAlARW93oA3P5mHh59+HePf+RpvfDwXT784Gb2eeRNnLsSWqhegNA62Vb2mdsMODQ1D7dr0BBk7MKTweAI8eNK9IRSSQK3i4xFYkEXKM52gMet4ASVEwF0JeLhfiooifGbcnY6iqC3l38+CsgEFVDz8pSX3C0LA1xdpb72HO9v3I/7YOSQt+AnGmrULYoHqEoECEdAfeRK6DQOQtfh+ZPaMhmEt3UQqEECq7BEEFK7wct6SP/Dbhh14cVhf/PjNW1iz+BO8P/FZ7sqEd7+mmSqchPcmXZq3w7DyrVExOBJ+fv5o3LgpPvrwMwT4B3jvoGlksgSalJUPJuSllzVCSiIgQ4BURCA3AVXXu7Ng/VZpoRpwN5+7LuWJgLsRMInm9+tv11bgYvo5d3OP/CECsgRMJ+9+1RRTgawPaMmPLChSejSBu+9yJw5j/ZY9eKRLa7C9VJrUr4lqlcqhf88O+N/Yp8CWAV2+etOJ3lBXxU3g5KkT/Mk97Ok9TGbN/gqiKOLkyeNcP276WziQchX3V6yDjRv+wddfzUWjRk0cckPMBLTP+NyV530AtjRem0v/gqR3yCJVciWBKqFKvHZ/AGqEq+CrEviR5ZkepeuHRksE3JeAZanPu6td5qOx89Ngy4v01Ye6zAfqmAi4isC2+L+Qakjh3c88OxUDdz6MFVd/5HlKiIAnERDTANMFwZNcJl+JQL4EXBJQ0WXpUTkm2s658mUjuC45lZ72wkF4aLJl8wb+5J7Dhw/y44UL5yAIAlJTU3j+8NkTOJJ2DevOHZAdoe59NQ+YiBftP3AFE2Dcp7grB6W3MKuWW79f0stYNx5QcNtZM9QypaRynEDx1exURYOvHwrCqidC+ZHli886WSICRMAVBAyj7u6Nktn6U5hMkdwNfQ49C5CIUmCdF1BCBIhAngS+vzjHrmzhxdl2OlIQAU8gIASxO6Ge4Cn5SAQcIyD/rdOxtoWu1aRBTSxavgHnL1/nMxeYocTkVMxZ/Ds7Re3qlfiREs8kkJiY6JDjqVkZsvVMJ8wBEzGdRUpkqxRcaWmRJPBgjOlcCdi29EFHIuBGBIRbl4DbtDeVG70k5EpBCEh/Tox7pb8J2WI6I122SNfiYqL0WZ6tY+WmM/SZXhCsVNezCFxOv2Dn8B1dPFIMyXZ6UhABdyagvN8EwRxfd2c3yTciUCAC0pVJgeoXS+Xxzz3G7bBNaNv3HYe+w97GA73H4o/Nu/HOS0MQ4E+PTeaAKHEqASE5CerdO6yiOnkcUsQPsnqnekadEYGCExDOH4LmkyegnjECmunPQj19KIQ4KbgiY0p58QJ8f/qei+r8OZkapCICJUeABUSMec0qPKyAdqiPVfjsQil2YjqSS/+l/KxD0xUBzL7pptSo5IZAlolAsRNIb+uH9Lpm+X3MSYSkh/E+Vr95DFvGX+WibFgWYhJXFyjoqJcAAAAQAElEQVQRkhIR8OVULv4L5/K2iqQknmd6vwVmHS+ghAgUgYDvTB1UfYxQdjJCM1EPny91RbBGTYmAexIoroBKgUZXLroM/lr+OSaM6I8WjeugbFQZDO7fDcvnvIeBvTsXyBZVdj8CbL8UO69E0U6Vl0LIvu7Vvafhy3PYnimxfWA+Z3um5G4oZ1qbXT/nfivSOb8YZ+1l2qhOHEPYoH5WCZr8HiA5o86tf+9N0A8RcHcCqjWzgNQEq5vC7WtQblhgzVtOfNesRpnObRD89mtcwh+8H0JmhqWYjkSgRAnwfbFYwCTHZ3vWJgVuPKJAejM/6CYXbQND/VIVD8YYNyhLdBxknAgUlID2ZQ0PmBjWF+29eSTpAJL1iTbdm04L3HZGX/kblIrkZATMmMbFb9F83lZIuavzXzSP6yghAkUloOxkgs/HWfCdmQX1MAMEev5EUZG6U3vyJZuASwIqtxOScfj4OfR9qB2mvzcasz99CW+8+CQSklJx6uzlbNfo4E0EWPzCLtDClDKDtMReTCelO4vZ+6Vk7gL43ikH7N+y3AxPchgzZtfPbs/bSufWpT7ZQZscLRw+FaS7OA5XpopEwBUEdBkQ4q/Y9Sxc/c9O5//V53Y6ITXVTkcKIuAMAuJ1ARlj1NAdlnrLBMRrRfiwlkzQLxHwdgKvHh6Fo0mHUJAfU0gI0sdPzFMyho4oiDmqSwQ8hAC5SQRKhoD9t9OS6cfG6vfLN+DtKfPho7Gdortz/3GMfHUaDEbp27BNC8p4FAHB/gJYEAQIgmA7jFxZS6GlmmaSHr6LdFxifgU/+s7RWapZj7x+blvSTRlL25xHzVi9uZ1oPlBKBLySgI8/4ONnNzQxKNxWJ0UvVRfsl/gIBoNtPcoRgZImIAVP2NIc/QqlFEWROmOf0RaRsvn9imx/rBx7qjBbTERa6pMfOir3cAINQ5siRB1aoFGIoWFIn/BqnpI5bGSB7FHlEiLgRmaF6+eg2LkKiv3rgJQ7buQZuUIEXE/AJQGVvYdO4bFHOiAoULroz8Hg8Uc78lkq127czqGlU08jkDu2UVD/pe94vImyngnKlmbxux/m82YmXpZvIl2TW9rmPCpqieamgvlQuDTbRuEaUysi4BQCpgYd7foRG3aw1UnRSEO1GrY6KSeqVFJKv0TAuQS0Q32gn6MG2OdzTkH+P6ZDCr60h9nIKbTUJ392VMO1BHTZS3/YfikXywtIru3Ll+vAdhUP29LNHGzM5e4Ho75H9bb38zbMBpPMPJb65GqaZ1Z55TL8lv4Av8ULoDp3Js96uQso750EFFuXQP3NKKjWzoLq1+lQfz4EwqXj3jlYGhURKAQBlwRUMrU6aNT2F+yWr6msvBBjoSZEoGgEpC+XsgZEyzvTUsqu9C3ndHRXAoaMONzcNxlnt73Hj3eOmzfZ02ea9ayMyZ3j3rlW3NB3Aoy9x8FUvx1MjTrD8PgbMHYYZPdyZYx7xU4nBgXZ6UhBBEqagLKFCcqGUtBclHrKKVLW5peV2SgAIQzg7ZmNHCJEy1QG/RABTsCpiem4AvpFKhhWKyHGF7xroeBNCtXCZ9N6lOnQEkFvTkTQpP8hvGs7HlgplDFq5BUElFuX2oxD0GdB+e8Kq85kkj63pdymTeuwb99u6Yx+iUDpIuCSgEqdWpWxdPVmaHVZNrSX//43z1csH8WPlHgmgebNWzrkeO0yMbL1fCZl8eU9QlWTbHlRlIomRm7buvQnP2MsmCLkvoyhC/T8sDmzPOnsLzxgok203R9EnxmPW3sn49y29/nxTnbghAVamN4iFn1On/OymbOOo+dCUhzU817h4mtcC1/VBmgWv8HzTK9aPsVRUwWuZ2z1KAxPvgvDE/+DqXEXyP1oH+2DO1t2IeWjz7gk/LUDop/t7EG5dqQjAsVKwA/w/V4H32U6+M/Sw6eJZF16GwoxMp+3uT+SpaqKxtJnO2ufS5Q9jFKpt/zSODyVQNZUNTIf90HWZ2ro3tQgo7sfxCTzG9lnehYCTmZyqXpdRMhpLT+HFCS0GS+rzsRGCfT5pAE6z6iIg9v+4O2YLb9V2ly1HM/6z/7KrnLAN1/Y6UhROgiw/diELPv3kxB3lQNITEzA2TP/Yc8DYXg9bhPuX/UO/OK/5GUs0bzZFTmF6UiIgLcRcElAZeRTPfnSnmbdR+Ll92Zhysyl6D7oVfywciOGDXwYAf6+3sa5VI2n7f3tMWPGt1Z54fmxYBvS3ndfPbNuwoeYXrsfJrZ5TJaLop7Il/fI7gQeIF10Z++r4suOs3WAKJnxyaWX2WtFqgUhHNy2dekPU1qEBU8s55ajXTCFFQgsIXETAknnf+EBE13C6WLzqFht6rUQLh7losANKJS3IFw5zvNcH1t8fhcWgLFqNWifeoaLoXqNwpqhdkTAlkAhc5quJpT7w4SAA5nwecv2xkshTVIzIuA6Anol9AtVtv1rpUuXS+55LaE6d9bWVymniI+DkJwkndFvaSCgOLWLB0FUP7wLMbISRI399zIxqiJH8cefv0NfAvuuqb57jfsgnD/E+6GECLgzAZcEVKpXqYCV895Hu1YNsH3PUSxesYFvUPvmuKcwfoT8l2x3hki+2RIICQ5B0ybNrVKjRk0IgoCQELO+Se0GaBJcEdXDy9s2dDCXc08UZXMTIADIvWfKPfZaObnyJHZ9uZtLytVkqTHAdDt+uY6/xAeQIIZwHSVEoFgIhERBP3wqlyzBvIeJGBzJ80xvGPBasXRDRopOgCw4n4DgJ318syU6TaXP8hLoXllJ5EuBhLIs8l4CHZBJIiAREK6dAa5dhWnDUauIOySd9LYz3dBINex/xTTBXukGGoN0zZbbDVNkFMSQgm18m9sG5T2XgLGj7XJhUa2B8YEBfEBnz7r+phB3hBIi4EICLgmosPHWqVkZ3055BfvWfYtjWxbi9+8/xlP9ukKllL4ZswokRKCECJz65ST2zNjNJSU2hfdy6tdT2PnrDWwR2iER+Vw0qNWgH/cjkHhuJV/6w/ZFYXI7e8+U/DzVZ++1wtpYRJvwX37N7lnOp8hmP7aY3dkRqzUGE6MQZW6n8eF5phMr3mfWFTylFkTAKwjwpT7zdLJjEaSP45x7oyhqSN9QpV8hBDxQYi2rJSllLKgGGfhSIlV3o0wpqYhA0Qgo9v4B9Xu9oJ45BpqZw6D++1OYNu6V5AhMu6SAigAIQhzkflTtjXyJjqoElqUpaovctn8hlv5kjBpn5276iy/Z6UhRegiYOj4J/YuzYeg5GoZ+L0P/ymKIVepzADVr1uZHSohAaSbgkoDKzfgEbN15GGnpmZz91etxmPX9b1i0fD0ytVlcRwkRKCkCdR6ri1bjW9tJ20FV0bHCJQQ1rgp9qzYw3FcXEEWIQcE8z3RMsto+UCKubd9zDHG3vXlKbYlgsxpNPvcLX/pj3RvlmMxms9J3LukltbZhJ7n3VGHtdbn2Y2H1HBHhyimoP3sK6i+eM4t0zu9cOtKY6hABImBHQNHExAMivt/r+FHzUhYgAHw/rGwdK1NP0KNEfjIzoTp6GIq4WyVinox6MAFdBlSrv4SQZb6WZSNRKuKhVp5ip3dFZYCyypG7eXbmA6ieMbCzPCVgZyYPikxa8gzfIyU5wPzYnz4fN0CnGTFcx/ZOSQko3usGXdceuLNtL1I/nobU9z9BwqbtyBwyLE8/qcB7CViW/rA9UNQ5nvKj+XQgX47D9MNO/IS97cJlIEgXXGBiW8Ta5JaslzpBO6Gj1SYrV9BSH1twlHNrAgpXePfdkj/wydc/wcdHwzemffrFjzBz4SpMnbUMH3/1oytcoj5LEYG6/euizYTWdtLy495o/O+XwOqfkbhsNVLf+QAQBOgbNOJ5pmOS9tb7KImf5b9vQaf+E/DyezOxa/8JmEz2f4hKol9vsRlS4zFEt3zLKmUajLAfmgD2kiLnj8o/ytrG0t4nrHCzRpTr5kBIirOaZ+eqP+dY83RCBIiA5xDwm/8toprURnjv7oho1RChT/X3HOfJ0xInINy6LNuHQrhtp1fV2QNNm1+hum8XVA22wm/pbSgbmOzqOaoQIDhatVD1jJUqI3PQYB5IMdSoVSgb1IgIyMRTCAoR8EoCLgmoHD15AX0eegBqlRJ/7zjEN6hle6rMnToR6//eC4PR6JWwS3pQZN+zCXz0+nC889IQXLxyA8MnTsVDT72GRT+vx53EFM8emJO8D6vRH2VbvGWViPojHepZLQVUcrZj577h9znU1qaSyQjhxgUbFcsIN86zAwkRIAIeRIBtwhk0eRKgu7sUSbNzO/y+owCpB72MJetqUO7H8Ji7E+FnPsmVCqFSEKXqMShjzkAoU/hgSi6zlCUCJUbAVKcNsj7eJCt3/vcLWv2byKXl9gQZH6Sgn3RTMneBnD3NF3/D98utNv2YqjfJ3ZTyRMBtCbgkoJKUkoaoMmEcytFTFxAdGQa2p0qT+jWRkanF2QuxvIwSIlCaCIQEB2Bg785YteAjrJj7Hjq0aYSZi1ajfd9xeO3Db6HL0pcmHJ43VoUSkNsJ3zfA7cfC7sQHfDkVFlGkSkE8UYS/dIfeomNHgendfjTkIBEoOgHViWOyRlSnTsjqSVn6CIhhZWGqWMdu4EZT5bs66XNUzH2bXrxbTGdEwFMJnD13xuNcV9y6Cb/lS+C3cB7UeXzGe9ygPM9hr/TYJQGVGlUr4K/tB3D+8nWs3bQT97dowOHejDdHOH195HdE55UoIQKlgEDdWlXw3KBHMGRANz7aPzbv5svjeIYSGwKh1R/jS3Z8wmvb6IuSKaxNU1Pz65Wzb1PDTjmzbnkeMPsrBMyYZhUhLQ2Q7iz5L5hj1bFyrgf9EAHvJ2AKDpEfZHCwvJ60pZKAYehkGLo9C9N9rWEs00y68fEgjKaYuyykz1EBwt08O8uVZap7SfXAWghRh8ISlxGlIA07Z8dAVRBi/CqhcWhzc517GaIyIlCMBMqElylGayVvSr1vD8p0bIWg119C0AdvI6zng1Bci3WgY6pCBPIn4JKAytDHe+Cf3UfQ65k3+XKfYQMf4p6uXrcd/n6+qFA2gucpIQKljYDeYMSWHYcw5s0v0XnAS/h28e/o+1A7LJn1DkKC3H+mgyter9Caj/FlPr5htst01H6RPNBSo8MkfixTfwR3L/eeKRY9L8xO8rKZXZznwdhjON8B39SwA5iw3fCZLs8GblIgZGW5iSfkBhFwDwKGRk0gt3dEZs8+7uEgeeEeBPyCYOr4JAxDPkRWlb4wiuWL3a9RNV5GlYAasMZlWEAmW9IMqYjNvAI/lT8ahjYF/RABFAMCy1Ifw+AP8rRWuXJV1K1rftJPnpWKUGB47jO+BEgspqU/7MaRoNXaeKS8fs0mTxkiUFgCLgmotGh8H1Yv/Agfvf4c1v00BVUrleP+s6U/k98YDo1GzfOUEIHSRGD573+jc/8JGPvWDFy9Foe3xg/GzjUz+f+TB8RnBQAAEABJREFURnWrlyYUxTJWFjhh+6HU7PAeD7iUyd5TRe0XxfOsjIlcQKUoDpiaPwTDwLe5sPOi2KK2RIAIuIiAUomkZauQPn4idJ27QjtoMBKX/w5D0+Yucoi6Lc0EDKa7QW8hO7JiOTIuu27/AxZcYeeeJuSv5xKY+tkMjBgxGkFBwXwvlSVNn+NBkMzICdZB5d4zxVrg5BPlmf/sezSZ7HWkIQKFIKAoRJtiaVKzagy/816pQrTV3lP9uqJbh+bWPJ0QgdJEYPfBU2jfuhF+/OYt/LZoMp7s24VmpZSmN0A+Y836Rg3tMz5WMe5T8ing+q9y6fcX5mOdFvXng5+KSyEBU5kIpE94Fcnf/YiUj6dB36JVKaRAQ85FIM+sEOPgEohAXyBAk6cduYInKj0DtcLcRiHYf8azfVquZ1yVa0o6IlBiBIKDQzBk8DBUrlyF93FfnXr86I6JsZbtLGbuo8L+/xLXU0IECkiA3kkFBOap1ZMubcKlHR/g5r7JSL+5mw8j9epfPM90GTf3cB0lriPw2TvPg83QalK/JgRBcJ0j1LNLCBg3K3mwRP+9SrZ/8awA4z6FVcQkqZr0NjGdz6VPlJRSUe5f7ShzMAbpuUtYPlcbvkZfZAUlLleuxYE2XC5xzKW+gwt/XcCBuQecyoE9sTAlLcPuyYWpki5Te3fGQck6RdadRUBRtwIU3RrdlTa1wPY6EQL97uqkcmX3RhD8fQrkVvdyj8JfaV722znavEw+p4Eon2jUCq6bU0XnRIAI5CCQPmocRF/fHBrAWL6CTZ4yRKCwBCigUlhyHtYu+cpfuPzvB7i1dzIswRMWUGF5Juk3KKDiipeU7ZeyeMUGMFny61/8yM7lhL50uuIVcl6fYrzAgyWmy7mCG8XkgumgORgjmuQM5gqesIAeE7mqxaybOmspmnYbgZGvTgN7TPiJ05fsvoAWc5dkzp0JlIBvP3T/Ab+P+B3bP9leAtbzNvnjL5vQpudo3Iwzb7hvqfnaR3MweOxkS5aO3kIgyA+K7g2tItwvBVTYx3mQr1XHyoW2kr4IYx5cZTiqBdS0WghRh+H1OnnvdWGtSCdEoBQTYDMM72zdg9QpXyD13Y+QuPYvmCrk2EC6FLOhoRedAAVUis7QIyyEVHoQlR94F9Et35KVgHI0ldkVL+TaTbswZeZSh0Sry3KFi9RnEQjMnTcLAwf1Qffu7fHSy2NwIS2uCNZKsim76i9J+3nbnjByAN55aQjCw4KxeOUGPP78e9KX0DGY8O43+Pm3LWAbNefd2vUl5IF7Eziz5gzunLlj5+T5TeftdMWt+GfXETzeqxNiykXamH5hSC+cOnsZlicb2hRShgjkQ6CcbwUsbfsHfrn/L/zY+nds7LgH7SI759OKiokAETBFl0Xm408i89kR0NczP2GWqBCB4iBAAZXioOgBNkKrdEWV+9+12YyTbchpEf+yFFBxxcs4/b3ROLF1kUMSUgJP+WHTzq/fvA2TKdcMBZh/mJ5d9LOp62YNpY4SWPnLz/jhhwW4di0WGZkZ2L9/D745tyXf5palP2y/lNuPK5E0yLxHipHtmZK7tczLpv9axZcOsfY5RUzljcGmoGefucWheuXyGNi7Mz59cyS2rPgCP8+ZhHq1q2DTP/vxwReLJXZat/CTnPBMAnEn5IOYCecS+ID8li9B2MA+eYrvr8t5vcIk7LOzcoW7+8RZbJSLMu+1cSchxaIq1qMoioWe5ZWVpcet+EQwG8XqFBkrdgIx/pVQM0hmX4hi74kMEgEiQASIwL0IOC2gcv7SNRw/ffFevsBoNOEz6W49W3N8z4pUSAScSEARHwf/ubOg3r3Dab3eSUwBC3QYjMYS65M9Tah5j5HoOnAiOvQbh8+/tf3isE26u9rqkVHoMuBlNOryHJav2VpivuRt2HNLDhzYa+f87aw0O11uhWXpj3GfArrdgH6vwJcCiYm5a8rnjWfNS3uMUvucYq0tMxlF1Gisxa44ibudBPaUq6ETPsUTz78P9vfimQHd+QbNwYH+rnCJ+vQSAlH1omRHEl4jnOsVUsBTvWcX8hKlVM4rFiKpWqkcftvwr13LP7dI/7Elbdkosw/SabH+spmP3aTP9dxGez3zJup1HGojsxat5tVYAGXW97+hSbcR6DzgJbTvOw5HTpb8LB7eOSVEgAgQASJABDyYgNMCKsmpGfxC+eCxM7K42HKGVz+cje9XbKA7I7KE3Fu5adM6PDf8aXTt3o4fWd69Pc7fO0VKMq+kOvMfAj95H2GD+iFk9HNcl2dSxIJV67ajXZ+x/GKWBTpYIOP96d8jOVV2J9Ei9Va7eiWsWvARDmyYiw9few4Llv2JY6cucJts5srED2bjxWF9cWTzd5jx4Vi8//kixN6I5+WUlBwBZWcjfBfpuET8bELoT3p+rmwus/mJTHBEM9Zc32LDcoQlLiHa+842a0sfPxFMMoeOgPQhbCf6Zi0hBgbaNy6ihi1569R/AqbO/hl1a1bmQZRtv36F18YMAm3QXES41By1Hq2FMrXMM0Jy4qjetTrPavsPROLSX7lk9nuc69iUcItO23cA1xUm6d+zA85ciMULr3/OA4Zb/j2ID79YjJkLV/MnupUJCy6M2TzbXLl2C90HvYo3Pp6bZ51xzz2GP3+cYpUn+z7I6x4+cU7yaxV++PpNHN40H316tMNLk77Jc/Yib0QJEShBAj+s3Igv5q4owR7INBEgAkSgeAg4LaBSR7pQZo+EHTz2Y+zYd9zG++SUdAx/ZSo2bN2H16WL6JJY2mDTYR6ZG3EJeV48sF35E5Mtc+bzMOCB6uJw+cLF8/jgw3dwRgo8aDMz+ZHlmb447LvKhs+m9XZd+6xbC9WFc3b64lCwu4pvT/kO1atUwIQR/fHBq8PwSJfW/EL8jclzpe+4Mt+Ei9AxC5bUqhYDXx8NOrZtjOjIMOw6cIJb3HvoFF9qMah3Z6iUSjzYrhkqx0Rj267DvJyS/Ak0kwIQuWtFaPIPSAhRIpQtTVx82ohQtzbnhXAxtznZvKKmub7FhuUoKLOrywRhMoe/gPQJr3LRdehkrmjZlJYdJREDAiAGFe8XQNZRpQpRCA8N4u+32JvxuHErAekZmayIhAgUC4HBGwaj17xeaPe/dnb2jBUrQd/6fi6WDQpNMRV5numN0rldIwcVndo2waSXn8GBo2fBAuNj3/4Ky37bgtZN60hB7GEOWnG8WvmyEfj+q//hrfGD82wUWSaEf5azz3MmoSHmz6Qt/x5Cm+b10LRBLajVKgzu340v/Tl9/grohwi4ggC7wcP2GnJF39QnESACRKAgBJwWUPHz1eCrD8fyL4gjX52GLdKdGuYou+M9aPQHOHT8LKa/NxpDBnRn6rykRPTsiSrsrs5TYz7kSx+mz7m79CEjUwu2NKJ1z9F4oPdYDBr9IW4nmGculIgzHmh0/z75JwTlpfeUISquX5N1VXX8mKy+qEp2oc3uyC/68g2MeKonHnukPT575wW8N3Eo/tl9RPqiab+xYlH7tLS/HHuLXzzXrl6Jq27dTuQX3RqNmudZwva6uBmXyE5JHCDQ/7EnMHjwMFSoEAN/P380b94KL9Zw/40DjdVryo7OULe+rL6oykF9uuCfVV/xvVNqSMHEGfNXgn3ess/dPzbvLvReEEX1i9p7F4FqD1ZDs5HNnD4otintv799jd8WTsbSWe+AnX89eTwiwkOK3RcW/C4bGY6w7CCJXAcr1m7D21Lgftai1WAzWix1bsTdQdWKZS1ZREWE8nO2HI+fUEIEnEygacNa0neDc/Q3wMncqTsi4IYE3N4lpwVUGAl21+OTN0fiyb5dwO7UsPW6g0Z9gPg7yfh+xv/QvWNLVs2pcuL0Jf6EFbbkgW2I+P1Xb+K7pX9a1w4vWbWZT9n9e+WX2L12FpQKBWbM/8WpPlJnriFgKl9BtmND/Qay+qIqL8feROcHmtiZeaBlQ667dvM2P+aXHDh6BvN+WisrK6WL6dzt0zO0mPDu1/zO5AMtzWNLSU2XggC+NlV9fDRgM7WYUqkQ4O+j9Ahh/jJffTXmKRqCAKf5PeHFsfj91zXYvnUH5sz8FtUDzfs5SPjsfAjoIYIt7wkeLlrLVFJFjUrB84w5G0t+4qM212djzinB8/XcfkCEEj6STUg/KqX96+hToypEf8v6IKmS9CuGhQHPPsv9yGkzr3Pmg9TM4V9BEFC/dlXpb8ODfJZii8b3YcuOQ3jtw2/B3p8OG3KwItsvIiEpVfpCGQddlt7BVlSNCBScAJvZ+sdfu7Du7z1QKpVSsCMILFC4++BJh41dlz778/pMZ3q2RNMRY907tkD71g3BZrKw/1+PDZ8k/R+4xZuyz3xfHx9+bkn8/XyRlj1bLMhPhdBAjdMlwFfF3VFLn1mO9B8o+ck+11hdP+lvFGvso1Y63W/Wf5C/mnUP9tnN8o4IqxsstZOrK/054PZCApz/Osj5k1PH/k5x54oxYX8HmLm5P67lT8Vis1VyitFoYsUkRMANCZBLpY2AUwMqDK5SqcCb457G84Mf5et1mY7dtWneqDY7dbpYLhbKRklfGKTeq1Uqx6efX5Hu2EtZrP97L9g6aHa3JijQH4P7d8Wvf/5T7MsvWF+eKs1byD8hKC+9p4xT17WHnau6h3rCUK2Gnb44FOzJJqvX/Wu37GzNxh3cfOWYu3cPuSKPhM2qYl8W5YRd3Odsxi7E2Tp5dmHy9UfjwP5/svLgoAC+BIOdW0SnywL7P8DySumLuEa6SPUEYf4yP9kFOTtXSF/eWd4VIsVymAuSCMjdv1+MEgHtFPCrpbCWKSTOKumLBKsbMlFExM8mq/i0FMGe2BP88l0dK/dvY2+btQ9so+D22blS+hyWnIBCIV8X4eGsGIaXJ8IwZz70ew9CVa2q1S9m415iYc2N5JOwJaBsOcTDT7+ODv3G82B7ckoanhv0MOZOnYhAf798LBSs+OjJ83yPonZ9xuKhp17jMw/Z3kUWK2xPl3odhyKnPP3iZEsxHYmAwwTYMuJuA1/FO58twLeLf8eFy9d52//OXsGrH8x2+M673mCE3Oe5RSeKjn2xHPNsX4wa0hujn+nNZ8sEBfph8/aD3Cf2ma/LyuLnloT9LbH8/8symJClNzpdDNlfmtkT5xzpX28QpeszcD/Z3zU2FqPJNb7rJWasf5Mocn8c8Z/XldrJ1ZXMMHPQu+B1kPMnp86Yx5MCucOFTL6cu4Jfh8xcuAr9R0yyE8v1eyHNU7OcBOicCBCBIhFQFKl1ARqzTWcvS0EKJleuxaF39wfQ96F2/CLh4tUbYHqLsD+cBTBdpKpN6tXgd+bZnRq2Adbsxb+BTcXt0LYxt8t8qpTjsYcVy5vvMNOTiDgenlSrWh3vvvMhatW6D75+fvzI8kzPK3hoYgo2T8k2SONK+98kvmlh8qzvSmw0I556FOelC+4O/caBbSo47duf0XfY23xGVAs4vt0AABAASURBVL+H21unYOfnQLtWDfH6mEGyMuKpntbm7D086o3pSE5Jx+Kv3oRlLT2rEB0Rxv9P6vUGluXCNle0BB6z9CYkpWV5hDDnma+pGeaZCOzCj+VdIZavPeyi2ZH+GecMrYFzTq+QhcwGOqsYQyRrAqCrbLDqWHmqTxavfy/7GTrz68rsy9WzfAYnDXgaCd0eRZLKP1+bOe2kZZrtM/b5ybLVm/nePK2b1cMX77+I7au/5pslv/z847i/RX1rkC8/O46WS2EojB/en/dz+K/vMGRAN74EQpc9U0WUvrWwPYX+zLFx57RJoxw1T/WIgJXAKunmC9unZOOyafy9bCno0aklEpJSHV7GyWzk9ZnO9P5+vhbTDh/VahUiw0ORKQXKWaNyUWVw6epNdsrFstSH3UxiCp30mZ+hM8LZwvpl/bPPbUf61knBBkvdLCm4wtoajKLT/Wa+arOMrHse4GF5R0T6+AFrJ1eXfXYxgxmSXblyV+oYc+ZbccrEUQOxbPa7ssL0Af6Ov+8NRiNuxifw/3PG7CBdTl/Z3zxWbpDq5dS7w/mFRCNupEp/793BGYsPugwof/8a6ilPQvNhP7wYcBsVfaULEks5HYlAKSOgcNZ4T565DHYHMqdY7gpOePcbm7LU9IxicWvNxp2yyx7YFFl2V5R1wvaIYFNgy0WFg20K+s2CVWjfuhH8/XykP4LSH+FMLd+0k9Vl4qMxT+HMyNCyLMqG+3mEsCmibPprSfk7+Il++OPXX3Bs335+ZHlLX2FBPpyVj1rBWYVm59kSDEud/I7MgFydaIk/KxOkz3G58qLowrP9VJcvi+A3XkGZhx/k/hfEZkGmwTZrWIs/YaF2jUr8ruHCZetglP7wv/Hik3j3pSFsmMUmGZk6PD3mI8TdTsQHrw1DuvQ+Z0uK2B1V1ollqu1S6cuuQbrA+Gv7Af6Enw5tzIFGVoeECBQHgY//NwJsuSV7j3fr0BzhoUHFYTZPG43qVuezDlk/apUS0ZHhvE+F4u6fw6BAf7AvsRZh+1LkabCECi4lGXHklsEqyTpR+psEXEwyWXWsPEXnZhfaJcTDE82u/GMb2F5YFcpG2LgfUy6S55OkYDY/KaaEBQP1UhDcYDB/kefn0uc3M8/2S1m0fD3/UqmXytn1zvHTF9GqSR1WzJebsuuig8fOgpUvXrlB+r8RBsu+WrxSKUriM0w4d8dQXCMmO4UgwD5/G9SphrxEpTQv483P9M+/bQF7YmKXAS/jwSdeQdeBr4C99y3ttu06glaPjAIrZ/WWr9lqKXLpcc81PZ5YmYwxf6Zg2O/JeGFtCm6mucfnvXLrUih3/w4hOR7ITEVTjRaTagW6lBd1TgRcSUDhrM7ZUhr26FVHpDB3W+TGwe7As7tAcpIhfYFkbbbvOYov563EjzPf5psisinmbN+UX9ZugyAIUmDF12aNveUupn92ZDwuMROeICkZWdBKd5dc4WtSmo6hBrvTxPpPtuaNDrNjBljb3BIv8Wdl7K5O7rKi5pNSzX5nSXfmCmsry2Bi7jks7AkL86e9in3rvsXxvxfi9+8/xuD+3fhTFxw24kBFthcKmw3DZmD1e+4ddBs4kcvjIyfx1iygyDZOZMsf2AXG+He+xtsTBsPyRYBXooQIFJIAmxXF7oAzYcvO2DEvYV8SC9nNPZuxvYYmTVuIbxf/hjfGPgUWXLE0YE+5evOTeZg6axlYPYueHdVKAY7KrXQTTsQbwYIhjrax1Ft8RIs3/kq1yn+3DdLfJOD7I5lWHSs/k2CU9Uf68wWlwuyrIEgRZ8l5VbbvF6VgDfOLfTxZ+ivJozLHlYZKOs+vL+Yn81iunlIagzQUKKQxyZXnp2NtmcjVyzbNl8LJlTuiY7YtwoJ1sdelLxwWRfbxzIWr/IzdyOEnxZScv3QdjbsO5zMcb8Un8nO2Aa3FPNuAn31pbPzgc3h98hw+i5EF8ll543o18MKQXhg8djIaP/gcfv7tb3w+aTRnwcpLi6RliXhlYyqGrErG2PWp6PtzEtad1ZWW4bt8nOy6hF2Ts+XJbJ8f9p7NSyzX4/k5zb5TfDvlZem6ag52rZ2FGlUqYPq35odPsL8/Ez+YjReH9cWRzd+BfUd5//NF/AZSfnZLuvybPenIGTC/nGzEj8fMN3NLuu/87Atn9tlVaRCsglJXPDfE7YyTggi4OQHp0sY5HrLlBA+2awZHJOeFbVG8e6rfg/yC4XWZ5Q9d2zfnptnFMnt6SbB0R5Ip2BTzlk3us15Eswj5lWu3WBGXq9fj+NFSny0b9QRhAQdR8twVvrK+pa75L+vfkmdHlndEWOO86rEyJnmVF1rPjEpSFG5Sc4d/j5w8D7bMJyk5jbfZ9M8B6eL2Y4x64wvrxoG8oBgS9ojkE1sXIbew5RYW853vb4KjmxeATVc/vGk+BvXpYimiIxEoEoG3p8xHp/4THBIWGHekM6PRlOeMRDYr8cKVGzZmbty6AxbE0Ut39JOSU61l9WpV4ctRq1Qsi6s34jBk3MfYsHUvL2df8kMLsDHn2rN6TNyYgsPxJhSkHaurZpEH3uu9kwBflaxtjVoJS5lSwTwHgvzUYLZn7M3kfqUYFDzPdCUpPpIvllEE5bHhZs7+mZ9sn5+cOss5GxOzxfhYdKEFeE1YWyZybdmsSVbGjnLljuikOA8zwaXLA03B7nZv2LoPbNaIwWjEsVMXwAJ5DetW58uLecViSmpUrWD3mf7pmyO5dbZ0efPy6WBP1Fq/5DP+5XHIgO68jCWCIGDssH44sGEuNi2bhj1/zAZ76hwrK02y+rQOJ+MN1iFrDSK+2puBTD27ErCq6aSECLAAJPu8TknNAJtFxW7q5CXa7OVq+bnyaLe2YEuh2Y0idu3O9gsKDQnizVjwnN1gHdS7M1RKJf+Owq77t+06zMtdlSRkmnA70/4991+O96arfKN+iQARsCfgtIAK65ptzNZ90Kt87TDL55Rj/11E5wEvgU1Jzakv6fNa1SqCRcTZTBV2J5Tt77J152HUlS6qWd9sV/wVa7byC++09Ez8sHIT2H4WgmC+QGV1SIhAcRD4YeVGHDlxHqEhgYi/kwS2WWxaegZOn7+Cj7/6sTi6KLAN9qWGTVdXq1UFblucDdjShrzkRNzdi9/i7NObbYkp5s8v8YIA0ynzuTPHO3JwL8z65CUuLIjNvlxa8pYjC3SzMnZ30RHfRIj8b4vcjESmY4GTnHZ6dm2D2Z++hC8/GCv9//oJlmA5u/hmXyxHPv0ovvpwHFjesjyVXd7GJ+vgqGRmmZdepGYabNo40l6nN7fN6bPceUq6Xta2Tuo7JcNcZpCCTaxtYloWr2swspEAlrwj/hSlDtvbgfXPJDHN7NO97CVKfhokn+XqsPEyO4yPXHl+OtaWiVy9dJ2ZOdu3SK7cER27UcDsMxn6RA90aNMIL783E3sOneJ79Qwc9QFfyvnRa8NYFaeKIAgoExYMthcc+/IImR9fHw3YU4AU2UE4mSperTqfYJAd36Vk83tDtpCUxUaAPWlw37o5qFQhCtPfG20XIMx5EygkKAAF+fl94w6wLQZOnrmEkU+b95O7dTuRL+9ky/8tttjfnptxiZasS44hvgrI/RcM93f+32s5AGKtFnbqYykGGH387fSkIAKlgYDCWYO8k5jCn47Tu8cDCJdZI9/gvqpoXK8mvlvyB1hgw1l+sc3hXhzWFx99+QNaPjwKQyd8ArZx51OPdeUuPNn3QVSrXJ7fSWVrLNlF+VjpLg4vpMStCNzWxeG946/ioW1tubDzON0tt/LxXs6cOX8VD7Zvxqtszb47Muezifju89ewfc8xsKmpvLCUJalZos0SB7bMIae8/096KSNStOEaDyiQNVXNjRiPKZD5mC+y5qh4Po+k2NXs85590WRyMy4BLHDNznPKS88PwI59x8GeLuWIA+wLotxsRIuudvWKsmbY3UhWcDshmR3shO2fkp5RtCn/11Js90PJKziYU5+iMwc97BzKpbiQa08Vi40D1/U4dEMPls/I/o7IlgexfCZb65PLTmnNKu7churoYQjpxf85wt6T094dxZcTvz/xWbw6aiDYUsrVCyejepUKpRW5W487QCN/WRyocY8vsm4NrxicYzdx2EwSQSh+3hcu3wD7LmKUgrVsBgxzNyU1nS/tZ+cW8ZGCimxZNMuz2X1sVp2zJTRAjU5VfZkLNtKtph/y8kUhMQv0UyHQ3/z3XcrmWTcvG47q/R4aAuUDfSCERkHwD8IhvS/eP5PGZ0UyG4ybxXGWz0/YLEaNWmH11y9eDZ8jmjzFP0ltrZuf7XuVW/z091UV2p5lnHQs3QQUzho+iwizvgb07MAOssI2b2N3Eq9et19zLNugGJTsLgx7jOCGpVPx28KP8NfP0zFhRH+oVUpuPcDfF+wu5s41M7Ht1xn8wigqIpSXUeJeBL46MwXrbvyGhKzbXNj59P8+dC8n8/FGkz0T5NCxs6hVLQbsvcbW4bNm5y5dY4dSJ2xfgwbRKjCpG6myjp/lmdSNMP9ftRbAtWe9+/ZAu/bNuQzo2hLHR3e0ysWkpZAixhCS4qCe94pVVMunOM1p/SKJofmBR9Y+9fPU1nNnn6RlZOJiruU4zIfIMubPWbkyVl5YYbNNtvx7EMnShTS7aJ71/W/8grpG9hfcL+auwHnp/5reYOQbF/70619gd00L2x9rt/o/3T2DgjkDhJbznMsOmA0mbBYOO+aU7w9n2tn+asMOfP7rWnz9+zrMXbcBt5OTpfcd8OuO3Tzve+dfVNPvhKg3Ly/Maa84zsWdZ2CctdEq4uFLvH9m2/TLHqvedPgyU7lMQkYMQUTzegjv3R2R9atBs2dnifhSv3ZVvhHy0Cd6gC2l9PPVlEg/ZLToBDpWtv8srC39jakY7F5/Z4o+Us+wwILqbE+VyTN+QG4p6E0mdm3/w9dv8lnmr7w/kwMIDgoAW/LDM9kJC+Kzjcmzs9JnlxTcZlPPnCz/ax+I8a0D0b6KD7rX8MGHXYLR9z7fPP3hfx8kV9k1Rkn7Lvj4QfXYeGgm/QzN5N8xKyMSV7VS5xZGVgekE4vuXkewtnfrZv6oQMIgZZ6iXSMF3O5lz9EyqUv+62h9uXrcACWlnYDTAipsCYO/ny8sF8ly4NnGtUzPNqNiR2fLvaa5hkgfuhHhIc52ifrLh8B/qSd4DaNoxM7b//DznMneOyVzgZyzjyKfZxtgyx7Ysh+25v436QtR5wea8pKzF2P5sYzMzC5e4OWJv1rAZw8GcZnUIYCPNshHwfNM/17HQK5zRqI49g8Pgih3/eZQd+ymZtNQNSxSN1gKZrDbRoYsCBeP3pVrpx2yxyqpln3MfRCE2yxbYDGdV9i3yQBMF6QLFPuSEte0aVYPK9duA3u6CLtzyDpkF8qLl29gp3yJAj8ppoQFSsa+/RXaPjoGrXuOxtadh/D1R+NguYDefeAkeg19C40ffA5Ld+mnAAAQAElEQVRPPP8+2JOHnnm8e5F6Lx+sAAv+FUSCfWReD/Guzl9M5EGRxgFn7Gw/pX8DI9MHWCXSdAGQmvbTvmbVsXIx9Rxy/hi1d5B27R9k3jF/ruYsK8i58Z9TEM/fsgqSpTeY1D+zIcYmWPXi9lNM5RLxW7EUPn9tsOlbvWeXTb4wGfZ+ymsTzdx6RzfVLIwf1KZwBJqXV+PjLoHoXt0HrSqo8UwjP3zYybzfRuEsUqvCEvhj826MfHUaWFB7yarNfMbi/iOnwc7X/70XRqOxUKarVirHl4capPbREWFgm/Pr9dnT+CSLZy7EomxUmHQGGE0i2JJNV4heb0SP6mr8735/TGjlj+Zllff0RZRiEmlaA9IyzWNheWf5zTgxYGzZJOvTkmc6ls9P2EMrsgwm6/gM5YxQtjBxEaJFZgaKiiLPM31WGaO1bn6271Vu8ZMtS71XvXuVcecoKfUEZK6sS4ZJZJkwZGRqca8LCEsgpUxYcMk4QVaLhYA7GZl66j3ujtaUiVRDMj/PmZhgypl16/PnBz8Ktsna+58vQnRkGCwbBi5cto7ny0aVcWv/S4VzKbfNQZCE63kO97dV67H9n/1cQkPNF2V5Vi5EgRD7n9kHZBWiNaCoLvN/wl/SVzNftBTKaBEasbuG7P0+eOxkHuDoP2ISmvcYiT+ki+knenfm+zkUwbxd08cf7cg35Nyy4gtsXjGdz0ps3ayutd7PcyZh99pZWPfTFOxfPxeT3xgOtq+EtUIhTthdRRb8K4jknI1l6ZLF4iznlfQHeHDkCdNn1uCixX5MUOH+tKff2IPzq3vg5u5Jlm4KdRRTMx1rlyIFWhyrWey1VKeKFjTKy6E/pfdtXpto5tazz/u87JDedQSalFVjQmt/vCcF6wfW90UQi4y7zp1S2zPbv5AtB/1r+eecwfxpr2LVgo/4svyY8lEIDPDj+vySWYtWg236z/6/Xbt5Gwt/XodWTeqALclr0fg+3nzp6s1gAZa/th/gT/jp0KYx11PiGgKqgQb4fq/jonrYyJ1QPWGv4wWUEAE3IFC4q65COM52n2fN2HRrdpQTFnVm+rJR4exQXEJ2vJiAr0r6Jpg9PsveO6J16iDQMKRJdqn7H9hGget/+gx//jiFf8ljs6KY18MGPoSFX7xR6h5fycZOUvwE1EMNQK5Z7eoR+uLvyEGLkWVC8ccPU/DW+MF8NgjLD5QCKd9OeRnvTBjsoJWCVWMX0iyIw/ZHYcs+c7dms1UqVYgGLc3ITcZ78qbgkplx+tk7o+65keaJrYus5ZbPeO+hSiMhAsVHgD2JrW3z+ggKMF/nxWfvc/Vwl9Y4evK87FJRud5ZEOXJ0R+iWfeR6DZwIpQKBT7I3hSa7dfC9jViwc5GXZ7D+He+xtvS352YcpFypkhHBIiA5xBwqqcKZ/VWTgqSsLuNH36xGHN/XIPklHTeNfsSfF2KGL83bRFYsIVt2sYudnkhJR5LYM+enbDsIzHx1XF8HBZd+1F98PqZ1VxX1EQt3P1myAMpzKB0oz1ME46HyvfG2/U/ZRqPkfRMLQ4cPY2Zi1bhxOlL3O+rN+JxI+4OP6fEPQgojm3jy25y7oMid/5qefOdFRuv2TxcGwXs9lSx2NJ9NR7GmS/Z9CWkFO29oGxmguZVcwBF2cAEv1+00DwvBVngmp8bcQnw9VHjyb5d+GyQ2Z++hHdeGsIfcykIgmucKqZeKwQr+XKcCL8S/FOrS+DLdNhSHYuYHNwbRZF0xKZtZoJl1kbRuMu1lj6WZajK1ZSpVgIq3SO9IEWpbSyLQcE2+eLIsGsctg/Qrv0ncCs+kZu8cu0W8toEmVeghAgQAfho1EhNy5D+mwqoU7My2HIfhsVgMP+9SkvPZNl8hc0yPLRxHtheiTt++wY/fvMWYnIETDrf3wRHNy/AxmXTcHjTfAzq0yVfm1TBcQI3b95wvHKprkmD92QCJXiVZ4/l5ZED0L1jS8yY/wva9hqDFg+9gPqdnkVXKWK8Yu1WsLuSlqfr2LcmDRGwJ3A5/aJVKbBNAqScIAj4qukCvFd/KqJ8oiWNZ/yyL5bdBr6Kdz5bgG8X/44Ll83LSv47ewWvfjCbT0f1jJGUAi+loIbNHig590PJcV7LfGPNFoj0/rTZNI6VZunMy3hytGX2jecOQ7xwxKYMhizWokgiBJu/3grVRCjqmM+LZLAIjT+e8QN6D30LS1dvBtsotgim3K5p3/t8+HKc1jF3A7+OOlk1VMmDMZZ9V4J8BDb3DpWz9ZXDzH++FYnmZTrnV/fgy3XYUZt01r4bmZc5bc9YaxvW7u5SH5nK9hbz1Mi1FmQCieAjytNMiRYYatZGwqp1yHx6KHSduyJj9HhkDhhUrH2mZ2gxZNwn6Dnkfxg+cSr2HjrF7U/79mcMe8l5G1HzTikhAh5GoGKFKOyXbjAxtzs/0BTT5ywHm0ny1qfzER4ahDq1KrMih4Q9FpkFUUJD5PdcY08XqlA2AursBwM4ZLS0V7rH+P++ZgnOAwMefxTDhz+NW3G37tGCioiAZxMwX5E5aQyBAX6Y/t5osF22X5KCK2yzvwE9O+K1MYOwct77/K6k5ek6TnLJa7pJvboZ8Ye+RNK5X2HUp7p8XK1atcWa3zehQYNGVl/8/PzxvzfexT+zV2NKrT5WfVFOWpZpa9c8XFMGtYLrwtN+Vv35D9gjXNldkvtb1Le636NTSyQkpYJNf7Uq6cSlBEz1HoB++NR8ZVqsIO8nC6rkKBHDImVt+Yz9EspRn9uUIahwSyLT49NxdXcsl8RzCbz3jPgMnmf6hPNmHS9wYvLswIfAloR+9OUPYBvFTpq2EMdOXXCiB+7Z1TON/Xgw5rPsDZnrRqh4yPjZRr5cP6yRef8ApW8ZBFZoZyNKtfyXhnuNVOUXAd/wetlV8njfZpfmd5Btnes9n58NZ5QbGjZG6odTkPzdj0h79U2IgQXndi8/2T5Al67ewKdvjgR70o+l7tP9uuG8FDCPu51kUdGRCBCBXARefLYvHn+0E9cOH/QwenZtA7axc2CAPz57+wW+BwovdDChas4j8MWhNTadnT7zH376aZGNjjJEwJsIODWgYgHXtEEtDH/yET69+72JQ/HMgO58Op+lnI4FI3BhTW9c+P1RXN/5Ji5veBqnf2wEQ2Z8wYyUQO1ly37EsWNHrJYzMzMwY8Y0ZGgzrbqinjxT9Xk7E89WG22n8wTFyj+24bFH2oPdJcnpL7urwvJJ2cvk2DmJiwmERkGs1jhfOa118CNW7StrS1mzCYQaTWzL1D6FGvzl7Vfwy6CVXPbN3sdtXN5+meeZft8ss44XODFhfw+mvzcG/6z6Cm+8+CT2Hf4PA0d9gL7D3uZP/2EbBTrRHY/rKqBsK1Tvs8FGfEJr2o9DLsqRYyqJIfM2IhpYPk9zFNhbyldTtNb5mveYCktX/YWn+nXFo93aIiTY/IQy5jwLILLjrdvmJUDsnMTzCIhZBojHrsL09wmIp64V9wBKvT22zKdDG/NNOTbDZMpbz+PYloX8pmyb5pbgb6nH5HYALl++iBS9/XX+6f9Oup2v5BARKC4CDl7tF1d3QKY2C4t+Xo8J736DXs+8CfZEh9c+/Jbvn0IXzgXnnH5rH1KvbLJpqM+4iYRTi210rsicOHnMrtsMKahyLf6mnb4wiqd2PYond/W0NhUE8zeGz//7EK021bLKubTT1jrufBIdGY7Y6/aBsDMXrnK3y0UVbmYCb0xJqScQEOGPmFYxeUpY9TCXMmJPdxvcvxvWLv4Ur7zwONijK9lsFbZswqWOlaLOs9JiS9FoS36oRqMJGo3KrqPEpBSuCw/1psfx8iGVWHIhyYh3t6Zh7sFMXEsxllg/DhuWrmWNU9fAuGgrTGsPwjh/CwxfrXO4OVWUJ3Du4jWMeuML6/LPsxdjeT41LYM3UCgEnD5/lesyMrVcR4l7EQgPLyPrUHiZCFk9KYmANxBwakCFfVB2G/gKps5ehlNnL6N82TJ8HeSuAyfw9pTvMGDEJOuHqDfAdcYYdAn/yXajTXR9ECGvD9VAP39Zn0u7sssDTbF8zVZs2LoPBoMRBqORL31gXyob1q2OiPCQ0o7I5eM31W/Hl98YWvVymS+GJ/7HfRDFgl2cVG5fGf2X9c9TWo5u6bIxsY7ZJp1sOnfPIW/g82+Xgz2F5+XnH4e/ny8rJslFQOVXhi/z8Stzd3lgrioFzvpF1CsWm4o6MQ71LdSv6FC9EqnkBKMtGt+Hn3/7Gzfj7y6ny8jUYfrcFfzap6wURHeCGx7dRXqWift/PcWEfdf0WHVKi3HrU5GkNet5oQsS06HLQEKabc+Xb0M8R/tE2EIpWC5FCpz8s/sI9HrzxrMpqRlg+azsPLOWlJLGdXrpOonlSdyLQFBQMFpE1bBzqkvnbnY6UhABbyHgtIAK++B79cPZfMOnxV+9yXfb/nbKK5g7dSK2/foV31uF3ZGc/OUP3sLWKePwCb9Pth/fsNqyemcqH+pxd/aIpd+6deujXITnbBRr8dsZx6FP9ACb3vryezOx59ApHmRkSx+M0l3Oj7If8ecMP6iPexAIieTLbxBR4R6V7hbFS9eEo46mIGHA2zwIohM6gG1IKwZH8LxlHxZD/9fuNsrnTKxYh/sgQpNPTc8oPnjsLNh7vkO/8XzDwVrVKvK/C5uWfY7nBj0M2lfL/DrmTv2zl/qUbf1e7iL4RzZBaKUOCI5pzwMkiuw9VZg+sEI7+Ee3hKBQgW/KAvNPSI3HEFpjAF86ZLF5IdGI1f/psOG8DvEZjn+BVfRoBOXorlYRGlWWOjEvBFI+1vKuvkM9Se+9vy8O6wu9wYAuA17Gjn3H8c3CVWDv8607D+MD6TOdbYTpvaMvnpGdS7CfjZKhF3lwpXh6KKSVWPmnrYlxyYU0SM2IgPcQeK/l49bBPPTQo5jyyXQ8+GB3q45OiIC3EXBaQOX0+St8Cvfnk0ajWcNaNhzZFL7uHVti0itDwTZxoyneNnjumQmIboGgSl1t6qj9yyK8zhAbnSsyrVvfjy+mz8Qjj/RG27btMGLEaEz9bIYrXPGIPtnjwqe9Owo/z5mE9yc+C/YI8a8nj8fqhZNRvYpjX+A9YqClyEmd1oCDKUZoLyhgPKuASR8BCAKgF3ie6ZiYou3v5sCzfxz2fuGyP3H4xDlMGNEff6/8El9+8CLYpsz0ZdNhhHYVYzp9g8aDNqPu45t4gMQne0+VmI7f8HzN/ltRb9gVVO76Pcq3/UTSrUeV7rY3M345pcWYP1Mw50AGvtydgSGrknH4pt6uLzmFEBEEoXpZqyA0QKomve+lFDFlrHoh1LtnK4aFBOGPH6bwjfcf6dIa1SqXx4CeHaTP9I/QqW0TRoMkHwJ3Ms2BuNzVrqY4HuDL3bZY8mVDZc0I5cNk9aQkAqWJQECOvd7e/N8ktL2/fWkaPo21FBJwWkDl7IVYPnW7cb28vzi0a9mAkOQ61QAAEABJREFUvwQXrtzgR0ocI1Dt0d9QrdcalG/7MSp3/xG1nz4ClV+kY41LuFbz5q3wxuvvYMqnX2DI4GEIDg4psR5FUZRu/ttffDF9iXVaAobZ0yD6SxfdQ5/ogc73N4Gfr3fMRCgBVMVosgRMJaRD1Bm4YdPWEzBtPAIYzXlk6Hie6ZiIl2/zeqUxYct62GyUEU/1RFSE/JeU0silpMes9AlFaK0BiGwyHoEV7C92fzpqvz/BL6d0Je2W19lnn99s4/3P3nkBsz99iQdXalZ1bEmU18EoxICqhslfpjYrry6EteJromhRDQjP9VSoyhEQqrjHtVfxjZQsEQEiQASIQH4E5P9S5deqEOU+Gg0yMrUwmvK+q6DVZXHLGrWKHylxnEBQxS7ShfEEhNboB6U6yPGG3lST3QBlkmtMgiCjzFXHVdmff9uCD79Y7JCwDZ2tftKJ2xMQE9n6etExP2+Urqd93IpPBNucnL2nq1Yqh/iEJGveAuzGrTtcp8tybFaEpR0di07gZpoJmQb79+6VZPvlF0XvzbssHD5xFvU6DsWdRPPGs2xJG8snJKVaB8qeZMV0yanpVh2dyBOoGmp/PfhITR80irbXy1soIa10o0P56qNQDu0IRc+mUA7vDNW4h4q9M9NBBYx7zSJkfxSaDimtumLv0E0MbvpnP5+xvmPfMe6RJc9mse/af4LrKCECRIAIuAsBpwVUalarwMe8efsBfpRL1mzaydVVK5blR0q8h4AzRiLA/M8ZfRVXH5dib+HIyfNcft+4E8ukAIslbzky3cZt+2A00peZ4uJOdlxL4NrN23xz8vSMTO7ItRvmPAu6c4WUXLkex+tYAu2Sin6dRKBsoAJ+KsGut0ohSjudMxTGvxVIr+vHRTvGh3dp3KrkeabXjtZwnVyy8slfsHLgSquAxYkkyalj53JtC6MTJdusnWVmZO4jKzNZKrEMiUMEWseoMeOhYKx4PBQvtnSPpWKCRgWhQUUoOtWDUMd8jevQYApQKfNFH2iHmkXMjr9px2ju6rx0y5aPvvwB7Amgc35Yw2mxG08sz2TeT2u5jhIiQASIgLsQcFpApUaVCujYtjFefm8WlqzazGerWCCwOzlfzlsJ9sH5/OBHodG4dionAItrdCQCJUrg9TGDsHLe+1xaNrkPQwZ05+cWHTuypRAx5aP4krkSdYaMEwEiQASyCTzV0P7pSo/V8ckudb/DunHreNAkPS77W2e2i7G7riJ2T6xVpLg7mOTUsXPk+MmU4nwsyPLHKPrilgOLS09V0tVqrXAlAtX2gT6XOlbCnSubGKFsYcpToCphB5xsvn7tKvhr+XSHJCjAPQJrTkYk213atX+QeGkr0mL/Qfr1HeCBY5MRTG8RrpdtTUoiQASyCRT6IP2JKnTbAjUUBAFso802zeth8owf0OKhF9Cuz1h+bN93HFjEuXf3+zFqSO8C2aXKpZeAyP9ieM/4T529jKgyoXYDeqhzKxw9eR5XpTv2doWk8AoColeMggbhDAIJmSLY03dKuq/H6vhi5sPBeL6ZPya09sfiviFoXFZd0t3K2ld2MiHgZGae4jsrCzcO3+BBE6Ou6DP52NNYWZDlxkHaz032BSGl0wj4zsyC7/e6PEUIcJorTumI3VAtFxUOR4Q90MIpTrlJJ7rkCzxAok+LtfHIpE/H+dU9sO/Hzji7qjsurO0DFjQ2mTK5npUxubCmL3L/5GUzdz3KuysB8stdCDgtoMIGHBEegvnTXuUy7rnH0K5VQzzcpRVeeeFx/mSTj/83AmraP4WhInGAgMFkcKCW51QpHx2BFWu3wmSy/Xp98swlPoi0dOm2KT+jxNsIlK57rt726jlnPNrsj7sZezLAnr7zxMrkEn90bLUwJfrc54Pu1X0Q6a9wzkDdoJf47D2i9ZkGpN2ynfHiBu6RC0SACJRCAneOz+cBkqSzvxTb6EvC5j2do0Ii4KUEXHKFxGapsKU9LIDCZq0MG/gw6teu6qWIaVglRWD5/etR1b+61bxlrXqzsFbY0/WMVWoE1rbWceeTJ3p1wuXYW3jh9c+xdPVmbNt1BLO+/w3T5yxHeGgQf+SmO/tPvrmWgCjF2/SLVdCN00D3hgaGDa7Z76IgFG7GJ4BtPns7IYk3u5WdN+u8dHMAPtKCJ7EpRptGKToTvtmXYaMrzRlBMIcl/xz3J1/6szJ73xRZJjJ7mLD6P/6ixlRxFJb8Yr400qXosLDjQlzZeVXWzL2UU2cvw3vTFmH+kj94tc9mLeV5pmOPCudKSoqdwK3Ds7HlEyWOzPTHte0vc/u3j87mebPuFa6jhAg4QoDqEAEiQAQcIWC+anCkZjHWYV8UP/92OZ5+cTKGT5zK905hyx2KsQsyVUoIvFjrdbuRPl55iJ3OExSPdmuLSS8/g0PHz4FtyDb6f19g5sJVYDNX5k6dCPb4TU8YB/noGgK6tzXI+lQNw19KGH5XQveSBvqf3XuB/RPPv48Hn3iF763FqA0Y+R7PMx3bfJDpSMwE0vWi+SRHGpduQkKmKYem9J5aAuo3D9/kS3/Ykh0muYmwWIo9SfA2l68rkCiEIjs2w5satQYc/mAtP3ck8dGoEVMuEoelz/FdB07gwuXrNnmmu3jlJtcpcnbkiHGqQwTuTYBKvZqA+ZNLl3yeL/1Ju/aP+Xhjh2OjFm33VGHts9IswWKzbccMUS0iQARyE3BqQIVd8LC77eyL4oJlf0KvN+BOQjK++u4X9B8xCX9u3pPbP8oTgXsSaBbWkpf7Kf1ROaAaP4/xq8SPnpg83qsTdq2diXU/TcGybyfh75Vf8uVwdWpW9sThlDqfly77AQsWzuWycMMKZBr1nMHPNw9i0bXdWHXrCM/bPeWjXBjXFzoxAkaZGSnGVe45S6VKxbKY+s4oh8Tf1303Qi3061WIhmqZl1IhACG+Tv0zXgjPndNEECQYUlcPzXgIjy3tbxVJZfPLqgmCuW7OguHiT3hC/A0s4GJO7pbeuZZ5N5PPWd1aVbBh6VSHJCiwtG+qmQ/MQhRHNx6Fzv8zotGYDFmp0O7zQlilJkTAHQiYP7funDAv/Tm/ugdfAnTx9z72zknxEf5ZlqPEZLTdU4W1Tz5nWT5ktp2jOp0SASJQAAJOvRJb9PN6fLf0Twx/8hEc3jSff1FcteAj7F8/F907tsSrH87Grv0nCuA+VfUIAulaiOdvWgU3EiW3pU/7dJ1Vx8u5XioqxK8CCmgUeT86sxAmndKEPSb22KkLMGQ/Ejk9Q4tTZy6jfNkINLivKqIiQsH2TslZxymOUScFJpCamiIFUuZhoRRQ4bJsEbQmc0Bl+a2D+P76HqyKMwdUsjTSO7ZbIyiyRagcUeD+cjYwXRYAmYkKpguKnNXc5jw8NIjvn8X20MpPaF8t88tWxs/+tXygkgZK6aU313BBmnIHiv3roNi5CsL1cy5w4G6X7IYNy5VrUg4VW8dYhekckWrCFTQSTkKDLEh/nWyaRIRKEUumISECRIAIuIyA+ZNJE1wVgRXaWSWgfFs7j1jN3HFjQVBY21jaqwMqZLdlLbJP6UAEiECBCdhfoRXYhGMNjEYT2KyU3t3vx0sjB9hsPsuWMkx95wW+j8rilRsdM0i1PIaAeOQKjLM23ZV1hyXfBYiXb9/VSeWmlbslfcF+P/vvPd5Aa8pEbOYVfv7Rif9h1P6nucRmmHW8wA2T/85dxcBRHyApOY1799+5KzyfnJLO8yw5ceYS17FgC8uTuCeB9PR0ZGZmOOScQQUouje0iqCRFA61lK+kqCZCKC/aFSo70hdBOygeqogOMP+5blRWhbYV1fzpOy+1vvcMh5IcqnD+ENSfD4Hq1+lQrZ0F9TejoNi6pCS7dIrtTthp049K1KN1oywbHWWIABEgAs4nIPAuI+qPRPU+G6xS7dHfuD5nkjuYwsoEpb+1jaV9aM0BrEgSs23pxKt//ZYvQcCXU62ijL0KNiPRb8VSq46Vc71Xk6DBFTcB8xVacVuVsZeYnIqEpFQ89kgHmVJAqVRIZe2x/8hp2XJSei4B41HHghrixfgCD/J0ygnexigaoTWap2WfSjmOg4l7ubBAC69ACRHwcgI+70pf+nKsHFLUEKEebZ4h4yFDJzcdINDvPh+80z4Q7Ok7virBgRYlU0W5fQUEvfSey2FeuXVpjpxzTx+a8TBf5uMfWbQgU0cpoDIWC9Ba3I8O2IGJ+BZVy2c/Ysm5Q6LeiAARIAJEoBgJ+C1egIAZ06yiuH4NkKJPviuXWXWsnOtBP0TAcQJOC6iwYApzq0K5CHaQlQrlIsGWQLC9VWQrkJII5CLwah3zDBVfhR/G1nwNfWMG4rlqL+LLJt9hVvMfUMG3Yq4WlCUCxUXA1o5lyYGtVj6XkpqKadM+xtGjbLaWfJ2CapXtTQjYkQm/VVr4bZDkdy0UVcSCmqH6RMAhAkLcZbt6QpYWQuJNO70zFOWalOXLfFS+trO9+ufYT4XtrcL2FWDCzgd3TwbbO4VJTh/LC3HoJWxCd+EfBAtpgOviVqAfIkAEiAAj4BNiXuqjDqzAssUihbU5/YspGDtupFUuXjwPdg00ffqnXDf+nwXF4l9xGxEy7s7+Lm7bZK90E3BaQCUt3Tx7IMDPN0/igQF+vCxDq+NHSojAvQhok7RIWZqODboV+Cf1Tyz/bQlWxS7Ddxe+wccn30S1gBrwUxXtbuW9+ve4MnK4RAkIgvSti31Ty9ELu8CQC2nopTv7v/3+K8a8OBzHTxzL0aLop4raIhQV5Xq9t22/+d+CTXVlopACPqy2/3d3dUJqClOREAFOQIyy3yhb1PhCDCvLy90licmxnwrbW4X9N2XCzqtE68D2TmFi9ZcVWjPZJwX/75TdkA5EgAgQgeIhUKb+CL5kJ7Rmf1uDgoLvjRJeuQM/BpQz76kiCEqet+yXIrfXSp42bXuwy23c8CcOHz5olYyMDAiCgLNnT3PdkTv2AXc7I6QgAl5EwGkBFQuzT77+Ce9NWyQrc39cY6lGR28nkOuLJx+u9GHMj9lJfofVw1Zj51c7ccV0Duf0x9H5y14od9z8hJ843S2suf5LfiaonAgUGwEWPJGuKGzsCYIAwUZjn9myeYO90gUa/wVzwKa6MhHSUrkH/t/l1El36rmWEiIAGNsNgKi23Qjc2HGQd6LJ7z+xd47aJaO6kmzEj8e0VvnnsnlZ2ZUUk1XHyi16lzhJnRIBFxDISrmMGzvfxsW1/XBt+0Rk3jEveVeo/Higpfngv1Gz30ZY9lQRlGZ99ez9Vqo9urrYvE5Lp+uBYoNJhryCgNMCKj4aNWLKReLA0TPYdeCErJy7eI3XUUhfQjyQLrlcAAJiEV/jlKspuHnopl2PVXfWtuoupp+3njt6ot65HVFVo7mEPWW+C6DJoQvN1jlqz9F6fYe9jc4DXsKEd7/mTQaMnMTzTDfx/VlcR4l7ExCEwn3runzFPe7kZAx7HunjJ2UFmU0AABAASURBVOYpYmCge78A5J1TCYjVm0D/ymIY+r0MQ8/R0L84G6aOTzrVB+rMPQnczhB58GP9uYLPNmaBk5+OZsIi2y0BFSnQYtGxo0VfEAL/3TYHa/Zf1xekGdUlAi4nYMxKwdmV7RF3aDpSLq/H7aOzcG7FA8hKdWyPQpcPwF0dYDd3mbirf+SXJxDgPjotoFKvdhVsWDrVIQkKpGUa/NXx4kT2q2cBPtSSr8kvPwi+eXdXzqoB1d2eYER4MB7v1QkPtm+ODm0a8yPLs3OLsDKm06jVxT6eTG0Wrt+8DZOJ5rQXO1wHDbZq2cbBmiVbLXP4C0if8GqeIgYFl6wDZN3zCASXgan5QzC17QuxfA2P898UEOCQz2JgkEP1qJKZwO1MEw+IbChEQMVsoWTS/24buF8HblBApWQIk9WSIpAWuw2GzHgb8yajDklnltvoXJnhs3Rd6UBh+mY3wpgUpq3HtiHHS4KA0wIqJeE82Sy9BMJ2VoBGYzvdnNEo4xeNQZvGIMonGo+Wf4ypCiT6tu0Qd/FWnpL008oC2cuvcqUK0Zj08jMOiZ+v/Xjzs3+v8rFvzUDzHiPRdeBEdOg3Dp9/a/uHudczb6Jex6E2MmtR8U0ZvZdvHlWWmcofF1v2z68wqXYwWoQoHXa/WbOWePxxuqvvMDCqSASKkYB20GAkLv3VKtq+AwApsK8dMMiqY+VcX4z9kikiQASIQEEI6BJPy1bXJp2R1TtbWaLBFGcPhvojAoUg4JKAyrZdR/iXt6dfnIzhE6dizg9rcOqse0x7LwRDapIPASHUwRlHAT75WMouTgeE+f5obeoKtfQP2T9lFTF48uqLeHbzRKy4fxPCNGWyS+ggR6B29UpYteAjHNgwFx++9hwWLPsTx05dsKk67rnH8OePU6zyZN8HbcopA6gWvQXVxoXQnNuPRyKV+KZBMMZ0boWoqGgrniaNm+HZZ0eib/mmXBcdVRar563Al//7hOcpIQJEwPkEjBUrQd/6fqsYYyoC0t1KWT3op6AEbmeY+NIftueJo+Lo3iiXk+Vtf384E/P2p8n2SzNTCvoKUv3CEChKm2UntLLv3e26drJmgys/JKt3tlIQBOmjU3B2t9QfEXAbAgpnesIimNPnLMfo/33Bv7zp9QbcSUjGV9/9gv4jJuHPzXuc6Q715SQCitY1IVSPvitlQ6W7gFLn/j53dVK5ok1tSenAbwCgHmNA/Ql1MWLiBAx4ejCGjHwB/V4ehOAXNfB7XoSv0tcBQ6W7yovD+qJWtRj4+mjQsW1jREeG8b2NclKJLBOCyjHRVgkNCQT93CXAHhGruHrqriL77Kn6VfDLyj8QXiaCa96bNBnDWEAlxhxQ8U03IGTuvzBM+R2Gj36FePUOr0cJESACRMBbCNzOFPkSG7bniaMiF1ARYb8k9WquPVUs9r8/nIF5+9Jk+6W9U2TfWaR0IwLLj2tl37vzL9bCVp8xNp6G1XwCoTX62ugoc28Cor/0BeLeVaiUCBSKgKJQrQrZaNHP6/Hd0j8x/MlHcHjTfPw8ZxK/Q75//Vx079gSr344G7v2m3etLmQX1MwNCQhVIqEc3c0qioebAFIgW6gSYdWxcsXDjeHoj2aMHhap8GEZhP/P15rXjDQ4aobqZRO4HHsLt+ITwWatZKv4YcXabXh7yndgS32uXLvFdSyRbkZApRRcIrx/KXG0f6kq91OpkN50LCOJo23zrXfnqmTN/ldx+yrv01JisWP1wGSyFAGJ6RDXHOD1LfUsR8ZZIfltyec8CoLZmlL6FM+pd4dzpeTz3QHSGREgAqWRQISfgKca+hVI2le2X9oqsAsGK0DzScUQpazdZxr7Y0SLQNmy5uWLfx8yszeUEoHiIfB4fV/re9di8Yl6Zl2ZVh+i/shbqDlgO+o9F4tK3RZaqtDRQQKZQ4bZbLxvKl9BusErQtt/oL3eQZtUjQgwAtKlODuUvBiNJj4rpXf3+/HSyAFQq1XWTtneEFPfeQH1a1fF4pUbrXo6IQJEoOAE2JO05v20FnKyUgqQ5LaYnqHlTxdq2qAWHmjZwFrcvWMLtG/dEOXLRmDLjkN4bPgkWIIqPmolwoN8nC5hgT7cPxZLcLR/1oDVDQkwX6izgAPLF4eENmgGwcefdWEjfnVbcDaWuEKo5DfrT7CpdTcjXk/k9VmdnOIrcQ70VcmWaVTmj+9AP7VseU47zj4P9qcvLndfXTpjBJL0meyAOXO/werVK/l5qUhK8SAj/BV4uoFvgUQuoCKHsHKIvO1nGvthRPNA2T6blaPPJTmWpHMfAgOl4Inl/4yfynzFwAIqFp1SHQT/qGZQ+Ya71OmqVas73L94/iYsAp3e4XYlUTHz8SeRnmPzfcsyz8wBg+z1JeEA2fRaAuYrcicMLzE5FQlJqXjskQ6yvSmVCqmsPfYfkd94SbYRKYkAEbAjkJGp5f/X2P+33ML+H+ZswJ7y89Kkb8ACnl9/NA7s/6GlfMyzfTFqSG+MfqY3ls56B0GBfti8/SAv1mYZEZekdbrEJ2t5/+yhRI72zxqwugmpOnYKg1EsNr/jMxXQ9xwFUePHbbPEVLkeUps9yvtgfjLdbclv5kOOeSlMbRXRV83rszo5JVPinJKhly3T6Y28fXK6fHlOO84+T0zL4r5R4nkESsLjJbvX4ZouiZs+eGAfPp/+KaZN+5jnKSECRIAIEAHPIvDO2x9gxoxvrVI5oAzYtg7jKnXE9Nr9uFhGZJy1CRYxnb0709lSTkci4A0EnBZQYV/sGLAK5SLYQVYqlIsE+zLI9laRrUBKIkAE8iXQrlVDvD5mkKyMeKqntX1KWgZGvTEdySnpWPzVm7jX/ihsRllkeCgydfRF2Qow+8TUrAf07/0O/ZiZyHp9CQzPfwkxIDS71LGDonEVxypSrdwEKF9CBNKv70Ts1rG4sPYx3Nw3GYbM+EL3tPn0fru2mzduhJhupyaFFxCI8FPwZQvda/i41Wjui1Bxv2imilu9LOSMBxKoVes+NG3S3CxV6yAAagiCgFoBUWgSXJGL3LDE4/LLpOXqko4IeBIBhbOcTUs3T/cN8PPNs8vAAD9elqHV8SMlRIAIlAyBjEwdnh7zEeJuJ+KD14YhPVOLazdv40ZcAu+QLe1ZtHw9bsYnQG8wYu2mXTh++iJaNanDyymxJyBWqAWERNoVVPJVoOzng6B5syv8TCt4uSCkwN9nMRe/Mhug6GnerJYXUkIEXEwg/eZenFv1IO6c+A6pl9fh1t7JOL+qh3QH0jwrqiDuGY1GXE+Ot2uSpk1DbMt0ZH2mtisjhWcTiPAX+JKbHoUIqFQKVvCgh2XvlXbZe6pUyrVnikVfEFL3RSi5X7SXSkGoUV0iQASIABHIj4DTAioWRz75+ie8N22RrMz9cY2lGh2JABEoQQKpaRk4f/k62Ga0/Z57B90GTuTy+MhJ1l4Xr9iALgNeRuMHn8Prk+fwGS/NGkpBA2sNOikWAn7mvV2KxRYZIQLFQCDxvx/srGgTT0F7+7idPj+FUqlE4wo17apVNdRGuBgB/SIVTMedfili509OhZCYANWZ/3Kq6NxJBFjgxLJfBDta9lRhgRaWt4hF7yS3nNZN6vdAxjgNdK9qYFijdFq/1BERIAJEgAgUnoDTrmJ8NGrElIsE2zBz14ETkJNzF6/xOgrBvBFT4YdFLUsrARq3YwSiI8NwYusiO9m++mtuoFKFaGxePh3/rPoK65d8hiObv8OQAd15GSU5CGSmQrF1CVSL34Hq1+lQnDuQo9B8ekVrws1XliLr403IUI5Ahm4IdG+v5Xmm07+0wFyRUiLgJgR0SWdkPclKvSKrz085vF0faIS7Xw7DjREYnfamtZnxhNMuRax9yp0o7tzmav8FcxHevQMiWjaAeud2rqOECJQ0gaz31Lj9PwH6DQoY/lBC97oG+nl3H+BQ0v2TfSJQbATEvCzlWZBXA9ITAY8g4LSrmHq1q2DD0qkOSVCg/VMzPIJmwZ2kFkTAbQkIgoAyYcGoWD4KKukuM+jHjoBq0VtQbVwIxX+7odi/DqoFb0BxZq9dPVIQAU8iEFSxq6y7AWVbyurzU1aPqoiaAVG82hupU7EkcRsaGJrzPEuEQPe4yNbs/Je5YxVFfByC3nnDmqcTIlCSBAx/2gdP9KvsdSXpA9kmAsVCgO6LFwtGMuI5BAoYUCm5gRmMRuw+eBJvT/kO6RnakuuILLsPgeQMiFtPQrwU7z4+kSdEwEECQvwVKK6esqstHN1mpyMFEfAkAmUajERgTCerywqlLyq0+xwq/2irrrAn5RQVbZoK4YCyQ8H3ZrExUkwZxW37v0WqC+cgJJufUFRM3ZAZImBHQEwBxDQ7NcQrAuAe/z3snSMNESgwAen9XOA21MD1BMiD/Ai4PKBy5kIsvpy3Ep0em4DnXv4Mq9Zth8lkys9vKvdgAmJSBvdejE2Acc0BGL9eD9OK3VxHCRHwFAJC/FVZVxWJN2X1pCQCnkJAqQlG9d5/oO7QC6g54F/UG34NEQ1HFdh94doZCBcOQ5F8XmprfkKY3yvH4TtwHzSd90M9NBl+y7QQAqViN/gVfXzsvBD9AyAGBtnpSUEEipOAEAwIte1nainbSNfDd1fLgX6IABFwgABVIQJOJuCSgEr8nST89Osm9B32Npd5P61FnZqV8cX7L2L/+rmgJT9Ofhc4uTvTCfsvoqbdZ4GUTCd7Qt0RgcITMFVtKNvYVK2RrJ6URMDTCKgDysI/qikUKvMT+Gz8T47ny9wUO1dBuMECJjalPKP6/Wuo578Kn1PzoFAkm3W7v4af9iUEBE+Az8DzEGLsv0Tyii5IDLVq2/Wq690PoCWPdlxIUfwEfN7MgtK8Mo4bFyqL0Ew0ByK5ghKvJeDRA1NKXyUFwbEhhMj8LXGsJdUiAm5NQPpf4Bz/dFl6/Ll5D154/XN0fGwCPv7qJwQG+GPiC09wB8Y82xfdOjSHny898YID8eYk2TxDJfcQxXjzBXduPeWJgFsS8AuCoc8EiJq7FwimyvVgbNvXLd0lpwpG4HZCMjK19GVGjppw/hA0U54E24hZtXYW1F+/gLS183Dw0H4bidOEICWiKlI0ZaE1REAMqwFRCkRaBL53/+/I9SOn0xq1WHble7x+ZAzeO/4qNt9aL1etUDp981a8neG+utA91BOpH05BysfTuI4SIlDSBJQtTKh0WETgGh38/tTCf50WCplZKyXthwP2qQoRuEsgyA9CoC/PCxXCIVSPBjR39/5heYsom1Xl9SghAt5GwGkBlROnL+HVD2eDLfF55YXHsWnZNPzw9Zvo90h7b2NK48mPQIi/bA0hMkRWT0oi4K4ETC0fgf6936EfMxNZry+B4fkvASnQ4q7+yvmlOn0KQe+/hdBhTyHw0w+hvFq4J7nI2XZ33bZdR1Cv41CwI7J/rly7hYeffh0d+o1H8x4j8c5nC6A3GLNL6cAIKLevYAe7IUFuAAAQAElEQVQbCdi1CuPHv2Ajvb5fj66rD6LrllP48WokslqMhX7E51YRo6rY2HAk8+HJN/DF6cnYGrcJ6278hjePjsMvsUsdaepwHd0jvZA86ztkPj3U4TZU0Z0JeJZvyloiFFXcZ+aWZ9Ejb11CoIx53abisVZQju4GISrY6gbLWwRRdJ1vBUMnXkXAaQEVtUrJwd2KT8T5S9dxOfYW2Ea0XElJqSKgqFfRbryK1jWBYD87PSmIgCcQECvUAkIiHXZV/Pc/mA5fhphlcLhNSVRUxl5FeK9u8Fs0H5q//4L/nG8Q1qcHhNSUkujOrWyePn8VEz+YbefTR1/+gPtqVOLLT9cu/gTr/96L9Vv22NUrzQrh+jm74fuIerRv3BCNGzdFVJR0h1KqUa5ceZ5vFFMTUZqi70NiFI3Ycst+Rsof13+RevOiXxoKESACRIAIEAEi4DEEFM7ytEGdatiwdComjOiP/UdOY/jEqXwj2i/mrHCWC9SPmxAQQs0zVISYcCgfbQbl2B5QDGjtJt6RG0SgBAlkLyExbTgK0w//wPTJaiBNW4Id3tu0ZsOfQFaWTSVFwh34/L3ZRudtGbaP16g3pmPSy8/8n737AGyi+uMA/r2M7sEoe4uCgkxBQGUICKg4UFBRwYH6dyGIAxUHbnGgiOBCRFFRQEHZIAiigAzZKCB7QynQ3Wb9772Q0DRJm7Zpkku+6LvcvXv3xueu6fWXuwviYu2XKosxnsnIwp9rtmBAXzXIFBOFBnVr4MZel2PhsjViNdNZAVvN88/OFXiJScDrH07E2A8/Q4+rrpYrrr/+Jrk85rYn0T3lQplXlsmB7L2w2qxuVezL2uOWxwwKUIACFKAABSgQCIGABVTEYGrXqIL77+iN+d+9je8/fhHXdu+AOYtXiVV4+tVP8MnXv2DP/iNymZMIEEiOg9KlCZT6vn+yHwEqHGKYCtj2pQIW1z8Gbek5sK52/7TfO4F/1xh37fRYoX538PrksUN+zBTPRXn0uTG46epO6H1VB5eaU0+elsvid5WcUSd1a1XDkeNp6hz/dwhYOvZzzDpfLVf2d86X10z9+IaoHlPTrforUq50y2MGBShAAQpQgAIUCIRAQAMqjgEpigJxxcozj96OFb98hPFvPo6mjRtg7MSf0HvgsxCfEjrK8pUCFNCwQAR2ffPmjXA8nNN09uqPzVs2Yf2ff2J9+kE3Edux4N1ek9elm1t/REZe16vEi6aSRQ1WiW+M85Z2q8F6q9WGEW9NQC01uP/w3Te6jS89M1vmRUUZ5auYREdHIe30uX2UEGNAUQk6PVYdtmDWznzsOGUrsmxR9RReZ9DrRHcQE6X3qU6DXkGMUS/L6nSK3DY22iCXC9dd0uX4pm1hfPEH6G99Cvo+j8L45ATEXnW7s26jQSfbi1JfRd3iVWREGXXOMiLfW4qN1kP02dP6kS1eQ4WoiqI6mc5PuACPXTTEp3pFfXIjdSLmCydnP8/2u/B6X5bVqoP6v9likQFA8UUAnjoifgaOnkjzest1fr4J4tZsm43P8PDkxzwKUIACFKBAYQFd4YxALxuNBnTu0AKjRz6MFbPG4bXhgxBd4GQ20P1he5EtwNFToKwCI1542vlgzoyzzyJ5XuR98iqG7fgJJ01ZLk0o1c49vM1lRQAW8npcjdzrb3JpKfu+h2Bu3tIlTwsLNtjUwEeG12QymSG+uWfB0tVITIjFux9/j7fHTUF2Ti6mzvoNC5auQVJCnByqKCtn1EleXj4qVTi3j8Qf+t7SsWwrbpuWhpd/S8f41VkYMvc0XliSLoMD3rYpKn/x7jx8uSFbpgNn7A/GXbw7Xy6L/IMZFq91K4oCRf0NL+pX1HGI/0VcRSz7I+krVYOxw7Uwdu4Lfe3zUbBO5WyDOnVG5KsvonkoiuJSTqzzlPRqOVGFp3Wdq1+JlVf/jZld5mJB96WY1W0hGiSd51O9oj6c/SfmCye1WblWURSf6ytcB4L4TwQTW3QbhO63DEPrHvdj2MhxOJN+7v1GPHy53bUPoVu/YRDlps5a6uytCKCM/+pntFK369rvcXTq8xg2btvlXM8Z3wRsR07B/MRkmSzvzpYbecqTKzihAAUoQIGwEFBPtwIzDov66WFmVg6KSnqdDld1aoMY9RPBwPRK861wAH4UiJk5HUlDHkLyw4MQ+80kP9bMqiJJoFnTZhAP5vSUWiTWglHROzmUpFjoLvXwPApnifKfSR/zMU78/Q/SZs7Hic3/IXPEyPJvtBxaMOj1GP5If6+pccM6SIiPwZD7bkat6imokJwgk+hKQnws4mKjkVK5gljEgcPH5auY7D1wFDWqVhKzMqVnm+AtTd2UhTO5rrd1Ld2Th21Hc71u460ukT9vRw4mrsuSaf8Zs2x/0a5cuSzytx/L81qvyWxFTp5FrrdY7VcbZOWa5bKouzxTvslukGuyt593djkv375cXNuZaj9Fn4sqV8N4PiooNUs8Hpz956luZz/P9ttTmeLyzlYflBdxTH8x+mn5QOUZE1/Dmg3/Ysa85bIvObn58iHMj97bBxsXf4Exrw7Gy+9NwsEjJ+T6DVv/w7gvZ0B8++KGRRNwY6+OePyljyCuaJEFOKEABShAAQpQwKNAwAIq4pOOdtc+BPHpSHGJt/x43FfMLEeBuI8/RNLjjyDml58QPW82El8YjqTnnizHFll1uAq8/vq7EA/m9JQ+aH0Hkgwx0PVsDt2ATtA9eyOQEBN0ClvFSjC3aAVbQmLQ+1KeHYiLjcEDd17nkkTeNV3bo2O75khOjEeHNk0xefoiiD9AxTO9flm4Aj06t/WpW/vPXkVSuPD+M9bCWT4td6kXhTuax3pNtZPOBed8qpCFwlqgX+8uaN+6CWJjotDovNroclkr/L5qoxzz6vX/yKux+t/QFSL42L3jJahXuxqWrdwg1y/5Y7089ls3awRx5bB4MPOxE6ewfdd+uZ4T3wSUGhVheG+A16R/srdvFbGUXwXMZbwVzq+dYWUUiDiB8B9wwAIq4iF/zw8dAEcSv6wFr/jWH0ee4zWWV6gIGqYACkTPmunWmsxTfwm7rWAGBcoooFxxIXQt60GJMpSxJm7ub4ERj92Jrdv3oE2vB+QzvXp0boNeV17qUzN1kz0HOOoml+5XbZcGUbizWYzXVEtjARXrvhMQt0GYn50CywdzYdtywCdXFiq5gMlswZ9rNsvn04mtj6WekgGUgs8HalivJo4ePyVW48jxk2hQp7qcF5OqKRXEC46n2h/ULBc4oYAGBcpyK5wGh8su+0OAdVCghAKlO8srYSOiuPjl3P/GbnCkqzq1Edm46ZpOzjzHuoK/8GUhTihQngJq0MSwb69bC0pmBvSHD7nlM4MCFAgfgTXzPpHP8XKMSHxV8oIp72DJtPexeu4neP2Z++Qn9o71Rb1ec0E0kqJdf61eUdcIXkkC+c+2cR/E8ySQb4btwElYvlwKnDr3jA9ZiBM3gcNHUyH+KPSWxNVUhTd67YOvkZGZA8eHV+kZWRBXYxUsJx64nHH2QcxifUx0dMHVsnxmdo7MM+oViIf2BjqJhyuLDojn8fjStiivKAhKX33pX3Fl1K5DPNS5uHKhtl7sH7GfQjGV5Va4UByPpz5pPS9/jYIzz+mQ+1AU8scZYTup9RGx/5EmoIu0AXO8FHAT0OuRf9kVbtnmCxrDUqeuWz4zKFBY4L//duDQoYOFs7msYYFqVSoiPq5kt2NVi9dh4g1JGH55PO5rFYu3uidiRMcEDSuUf9dte0+UfyMab0FcbZJ2OsPrA5dtNtdbysZPmonps5dh4vvDIT7MEsNPSoyXt/yIeUcSD1xOPPsgZrE+7+y3kjnWiwc2J8TFysW4GAOS4o0BT3HRetm+waDzqe34GCOMet/KBmM8xbWpUwNX4tukiisXautFEEjuKN8mAS1VllvhAtrRCG3MskGHU7cYkP2dDpZlepjGGZB7t/q71xKhIBy2JgV0muw1O00BPwtkPT0C5gbnOWu1VqmKjJGvO5c5QwFPAitX/Yne13XHPffejtv634iBA2/BgQPl/8yBHXN3YvHfc7Aw70ds/PVvmHLMnrrHvCAIxBsVdKkfhZubxKBFNd7SFYRdEHZNimedFPXAZceVJ+IBsu+M/x5f/jAf0z4biWYXNnBaVEupiH0Hj8FkOvdesWP3QVSvav8K6hpVK0M8gNmxgeNWH0dA5kyWCaln8gKe0rPt/c03WXxq+0xWPvLNVp/K+j6ewI3bYrHhVGa+5vqfp+4fx7ETyq+mEt4KJ64YMqhBrmAkiMZVTF/bVotClNWr/RXzYnuxHIgkrgoTbapxT9kHRTnbeTWzuPat891/T1p3KVC26mRdurN/qep0ilwurr6SrFdg/yfMSrJdwbL2GjiNdIGzh2mkM3D8kS4grkZJW7ISJxevQNqCZUhdvRmmyzpGOgvHX4zAe++9iTNnTjtL7dm7G19O+ty5XNSMeZYeeU9FIW9oFMzfu59QeNt247ebMPeROdhxcCv2WXdg+dTfsOjphd6KM58CIS2g1K8S0v3TUudefGciJk2dj9EjH0FyUgIOHU2VyWyxoG3LC+VQpsxcDLH86/J18ht+OndoKfO7XtEKf67Zgr8374T4o/Pr6QsgrtJq3JBXaUogTsJCoKS3wokroyolRiMYyfHHfkUf29erAYcK8VGyr2Jnie0D1W+D3v7nZFKcvX0RoBB9EKm4PhiO6UUxtxRzxF5XzNlnzcVF6+XYiquvJOvF/hUNJ8UaS1232J6JAvafgAA47D90DCPfneRME76bI1t9e/wUZ55jvaf7gWVhTihQzgKW8xrC3Mh+4lnOTbF6jQucPJmKY8eOuo3i33+3ueUVzjBPikbecDWQMkcP80I1sPKKEfkjjYWLeVzeOWuHW/7OuTuRl5Hnls8MCoSSgNKiHsS3oEA9QVbqVIb+ni5AxXiUpI/rTv0Fb2nj6XUlqSrsyq7Z8K8c04PD30OP2550pkNHUhEXG42xrw/BqHFT0KLbIAx5Yaz8kgDxhQFio5ZNz8eDA6/HgMGvo2X3Qfjh59/w3ksPQ3wqLNYzUUDrAuNLcSucyWzF8dO5QUk2m138hI/tW6w2pKbnQZQXW4rtA9V34STaTMvMl1Zm1U0si1RcH8wtTaKYW8pqmSfrys61X6WWmWOWy8XVV5L1jn6Lq8JKsl3Bsm4dZ0ZECgQsoJKRmYOV67Y60+59hyF+kW/Y8p8zz7HebLb/8ETkHuGgKUABTQhUqFBR/WPD/S20cuXKxfbftCDKrYx5jgEo5p7hvCFROP1Xutu2NvVE6libPJgXef6kx20DZpRFgNuWUkBXrwr0T/aG4c3+0A+9BsrFdUpU0xnTaTy8doDX9OSGh0pUX7gVFg9T3rp0EgonccuQGGvXy1th0+KJWPj9u9iwaIL8QgCRL5KiKBh8701Yt+AzLFLX/zXnY7S6+AKxKuyS1ZyD7ON/Vq7dNQAAEABJREFUw5R1NOzGxgG5C5TlVjj32pjjbwHjXWZEXW5zVquop0fG28yw7VNgWa2D7YgCqKtt+xW5LPJEkvnOrThDgeAKuP81UE79adq4PhZMecen5HhIWjl1hdVSgAIUKLOAXq9Hly7d3Orp2q2nW55LhlWB7aB74MOWBfuJg0th94Ua+npumXFIQGV9Nbd8ewanFAgPAYPOgNYVL5WpeYXWzkE585JbOfN8nTGu/APxH7wjU9RfK+RmUSvO5Rn/WinzwmWi1+tQq3oKjEY1gOthUDHRUaiprg/XK1NSN32MrRNqYee0K7Bt0nnY9fO1sOS7B6k90DBLowJluRVOo0MO6W7bTkMGRqzb1UDJ2Z5W/MaMaqvNiJ2aC8OtZpi+NyD37miZzPPV8yW1qGnquTyxzjxPzT+7fcEX6xZF1g/1nKpgPucpUJ4CAQuolOcgWDcFKBBGAhoayrPPvITBg59Ap45d0LPHNXjl5bfQ54abix6Bzgb9pSa3MrrzbVBq29zyC2ZEj8nHFUvaoPIF566CiakYi+6fd0X8thwYrrIULM55CoSVQLw+AR+3+Uamd1t+IseWZKwgl0X+e60+lXklmUSt/BPxY96Vybh6ldzUuOpcXtTqlTKPE+0LmLOP4dDyJ2C15DoHk3nwN6Rtnehc5kz4CZTlVrjw0wj+iKybdMhVgyX570e5dEZXFdBdrJ4DeY71upQtaiHvJXsgxrpfKaoY11HArwIMqPiVk5VFogDHHLkCMTExuKVff7z++rsY0e9BdNpihvnZKbC8/Qusy/7xCmN8OAdKPfXE4WwJJcWGqOfyzy4V/ZJcNxkDFg7A3UvvwR1z78CDf/8P53U/r+iNuJYCFPAokN/hcmQNedJryr+0g8ftmKk9gewTGz12OvvE3x7zmRkeAuLq+MK3wYllX26FCw8BjoICFChvAQZUyls49OpnjyhAAX8LZOXC8tXvsO09AeSbYTt2BtZf1sK29aDHlnQNrYibl4vYuWr6ORdxv+dC397qsay3zAr1klHlIn5Dijcf5lPAFwFThyuQNfQpr8nUjgEVXxy1UMYY5/m2SG/5WhgT++gfgeJuhfNPK6zFReDsrT/ieSj5KxWIJOZtR327ssR6wH5rj9imYLJlu7TCBQoEREADAZWAOLARClCAAqUWsB04CeSZ3La37TzillcwQ1ffBt0F565UKbiO8xSgAAUo4D+BmJSLEVPxIrcKk8/v55bHDApQoHwFLGdv/cm9OxqnbjfgZH+9vBXIPNf92SjiG4sK98b8g+szVUQ9IomH2RYuy2UtCmirzwyoaGt/sbcUoAAFKEABClCAAiUUUBQ9GvaZj2qXjkBivatRuekgnN/nV8RXv7SENbE4BShQZoFkQN/WKlNUOxtEEsu66ja3qhW3HECpY5Pbim0KJsQiOP/YakQLMKBydvebTGYcOXYSZovlbI7rS0ZmNk6dyXDN5BIFKEABVUCpUxmINqpzrv8rF9RwzeASBShAAQoETcAQWwXV247Aeb1/RO0uYxFf87Kg9YUNUyCYAsFuW9/Cipiv8mSq+L0ZlX+wyHn9NRb3rnmIqBhvNcvyjjocr7r67gEZ9wqZQwH/CjCgonpO/H4uWl51H7rf+gRadBuEGfOWq7n2/7NzcjF4xBi07/0wrrhhMPo//CpS087YV3JKAQpQQAjEx0B/Vyco9asAUQYo1ZKhu74NlKa1xVomClCAAhSgAAVKL8AtKUABCoSsQMQHVJb/tQnvfTIVH776GDYu/gJvPfcAnh/1BXbvtz/74LsZi7Fj90H8Nv0DrJo9HnqdDmMm/BiyO5QdowAFgiOgNK4J/eBeMLzZH/qnr4eus/u9+sHpGVulAAUoQIHACrA1ClDAo8DZW310F1g9ri5rpu5iq7wVCHFlrYnbU8B3gYgPqKxYuxUXXVAP3Tq2hkGvx3U9LkPDejXxhxpoEYzzf1uNvr07o2pKBSQmxGFA36vw09zfYfP0hCSxARMFNCSQl2/CwSMnkHba8+1sVqsNR0+keb0VTkNDZVcpQAEKUMCbAPMpQAEKBEDAcatP1BMmj63pCj0bRalmA9T/dbVtMlDieF6K4uFZK6LC6Jfz5a1AunrqRiKDiQIBEIj4gEp0lFFedVLQun7d6jh87KTM2nfwGOrWOvdVe3VqVpX56Zn8Xi4JwYlmBcSVWK173I+e/Z9CxxsHY8DgN3D6TKZzPMtWbkS7ax9Ct37D5K1wU2ctda4L5MzGY2Y40tbjFjWYCZitcOaJdVuPmwPZJbZFAQoEWYDNU4ACFKBA+AkYbnN9NopBPFNFATzmh9/wOSKNCug02m+fuj1r4Qp8/u1sj+nPNVtkHVde3gpbtu/BK+9/jYXL1mLCd3OwfvNOuU5chSKeoRITHSWXxUQEYMRrdnaueEGFhChNpLhoA6IMupDoa3ys/eGdxhL0R2BrxbpgPw069beA6HwIJhEcnPrpSGz49QvM+3YU9h44gqmzfpM9zcnNx5OvfIxH7+0jb4Ub8+pgvPzeJHk1iyxQwskWNeAhAh8l3AzpeVY882uGM73yeyYUlTTH5Jr/8u9ZJa0aOSabDMrsSPPwALQS18YNKFCkAFdSgAIUoAAFKEABCoShQFgHVMRVJGmnM+TtDIVfRaBE7M8WTRpiwrtPITXtNL6b8av6ekaWr1mtsvqHm4K42BiI2yJEWZEc83FxMWIR+SaLJpLZaoXVZguJvprF5QWqXkn6oxYPib6XdH+LMYq+h2L634Dr0LRxfRgNetSoWll2sUJSgnxdvf4fiJ+R/jd0lbfCde94CerVroZlKzfI9SWdjFhiD4rkhVDs4nCmRQZqxqzi1Wbu+5M5FAhvASUzA3GffoTk+wci8akhiP51QXgPmKOjAAUoQAEKUKBcBMI6oHLHTd0x/JH+HtNVndo4QTu0aSofSjvpg2fQo7M9v02LxnK9+CNy/6Fjcl5MDhw+Ll6QlBAnX7PVvxC1kPLVT/TNFhtCoa95ahBK4FnU/mQdtuD0aB3S7tXj9LM6ZKzw3EdRPhT6XtI+WG2i56Gb8vNN+OTrXzBwyJto1ewCXNOtPcS/Y6mnZAAlKsp+NZHIE88WOnr8lJgNvRTCPbL+thXWBZtkgsl+a5Jtybk8W749L4SHwK5RIOwEkh+5HwlvvSoDKbHTv5eBlZhfZoTdODkgClCAAoESqLFpPvSLv4Yxa4WzSbEs05JvnHmcoUC4CYR1QMXXnXU89bT6d44F23cdwKiPpqDr5a3QpFF9uXnPLm0xbdZSiDKZWTmYPH0Rbrqmk7x6RRbgpPQCViD37hiYxhlgWaaHaaoBuQOiYdkQ/odl6dGK33Ldph0eb3MTt79Nn73MpQKLGvER32J1Jj0T6RnZyFCPcVEgPSNLXp0l5h0pOjoKGWefHSRuIatZORa+Jp24T0et6EiuDody9D6nI7l6davi/1eH4bXONQfz5brjZ+tSY4ty+USevW6D+uLrOEpTDiKgsnAjrGqCyX6JjgyyqMsir0ZSlFfHuGi9vE2vNO0Gc5vKSdHF7zSWCHuBDRv/xpLffkWeKT/kxhr1u/32xoIdi/nxh4KLnKcABSgQMAHHrdHi9miRLOo5sk39UK5wfsA6VIqGamxeqAZUJsOQvdq5tX7xZJmn//UrZx5nKBBuAvzLVd2jg0eMQcvug3Dno6+jRdOGePelh9Vc+/+39+mO8+rVxJV9h8oHdJrUT5gH33uTfWXkTMtlpNbUJFh3KW51W2aqf+G65TLDVwFxq06al1vdTp1x/Taf2JgojB75MOZMfgsGNbIw7kv7J7RJifHylp+Cbebl5ctvuhJ52XlmHD6Z43Ny3Pr00MyTuO+nVJ/TkNlpojmXpJ5fiAe+u+Rl5luLrfOlxfaraw6mW2TZlxeflnWY1RhHScZS0rK4sil0PVp4TUfS8706ZudZcDrT+/qS9iVQ5U+m50lbTiJT4NfFC+TAV638Ey+99Azu+OIFZJpD/5jQ77I/P012nhMKUIACJRTYftIin82WaxZnKiXb+MXfMuVtyI7nxuWrnxSJz6JeXOqaL57/VrKa7Q/xF4GZkm5X0vJHmvWApdsAmOPawWRuDkubm+WyyLN0v6uk1bE8BTQjEKYBlZL5f/r2E/h16misnvsxnnvsTjgePCtqiY+LwcdvPY4Vs8Zh2U9j8MOnL6FqSgWxiqmMAraT9ud1FK7GuoeHZWGTkix3bNfc421u4va3++/o7bEqRVFwXt0aOHrCHsCollIR+w4egwggOjYQV7JUr1rRsViiV0WxB84urmpAs2q+pyZVDB7bsdd2bpV4+K+3ei+pFSXbvKCyPVAXo1Ypyp5fyb5scwvPnKvXH3M6EVDp2Rw6L0mJUjvkj4ZYBwVCQGDrti04euSwS09SM09jfuo2l7xgL1grVnLrgqnTlW55zKAABSjgq8B7K7JkUOREttXXTcq9XJbJJvs0Ug3MlHdjR5r3UgMoA2GK7wCTpSXMbfvJZUu3gbB0vbO8m2f9ES8QPAD+5araV0hOQI2qlYq8jSdZ/cQ+pVIy+M9/Arq6qR4r03exeMxnpv8ExO1roz+dij37j8BkVj9R2bYLM+b9gbYtLpSNtG1pf50yczHMFgt+Xb5OfsNP5w4t5fqSTsQ3ZoltXuuaiLe7+55Gdo4Xm7mkwsEUsTLOqHitd+LNKXLdsPb2umok6OXy0Pb25yApUEQVTBSggB8Eduzc7rGW/bn2K8Q8rgxCZsaro2BLOvc73XRxc2QOHhaEnrBJClCAAhSISAEOOmwEGFAJm12pvYEo8fmIes4EJfpc33XtrTD0M5/L4Fy5CCiKghVrt6L3wGfl7W63P/yqfCDz3bf2ku3FxUZj7OtDMGrcFLToNghDXhiL54cOQO0aVeT6SJr8tjcfg+dloM8Pp+Xr4j35kTR8jpUCJRJodIH9ge6FN6obU7qr2wrX46/lvGuvx4mNO3Bq1iKcXL5Wvlpr1PRX9ayHAi4Ctj0nYFu6DdZ1u2HLznNZx4XwEVBPreRgtqea5a0/4lkoviaLj3cJbT7hue61B/Ox8agZm4/bz6Gtan2i7a1nl2XHyjjh5hSggGcBBlQ8uzA3QALGO82IXZGD2Km5iFumvk7Mg+L5TqAA9SgymhG3sk3//GWsnvsJ5n07CmvmfYrXn7nP5XY38XDmTYsnYuH372LDognof2O3UuM0q2qUt91E2++yKXU9/twwRq+Tfbrg7K0/nuref8aCt//Mwn9pZoh7osXruyuysPe0xVNx5lEg4gWaNrkY1QsFJlISKqBXSpOQtDFd3ByW2nVCsm/sVHgIWKeuguWj+bDMWgfrd3/C8sZM2I6dCY/BhfYoAt47mxrEEI2+tzJb3mbzzK8ZPr/meYqoOCoUlZ5NhZ+14mjjgZkn8fSidIj1oqioT6x7KQC3+oj2mCgQyQIMqETy3i+nsdt2HIFl6kpYvlgC66+bi/00RokFdBaUv/cAABAASURBVBfboETexQ/ltAd8r1YEVurWqgZxRYqnrfRq0KFW9RQYjWV7zsdrXRPkbTae2igqr/CzUS6qYpBPPNGp71ziOSiO1CSl5JGaWkk62SfHrT+e+rHJyyc769VPgTyVZx4FKAB079ZTMrTvcDlefvktfDvoVSQYClyKKNdyQoEIEEjPgfWvna4DzcmHdfk/rnlyiROtCziuUGmkflDjOD/x9VWveBq9e2ZTL8+iczwvrmlV+/mQTt1UtC3Ke6qZeRSggP8E1D9L/FcZa6KA7d9DsHz6K2x//QfbtkOwztsAy4QlXmFs2w/DunQrrBv2wZZvv0zRa2GuiDiBws9GGdk5Xj7xJN6ok8EQx/NYRnZJiDgbDpgCRQlsn/8Qtk29Crtm9kTeKfsfdAeWPiqXZd7p/4ra3C/rWrZoja5Xdke0Mcov9ZW2kr/GrMLKD+zJUcfK9+3LIt+Rx9cSCrB4sQK2E16uRDmeXuy2LKA9AccFJU9eHu9yjuI4VynqNdpTREUNihRWeE093/FUz+d9KuPdHkl47cpEuYmoT5R7WS0vMzihAAXKTUBXbjWz4ogUsK7f5z7ufanAcfeTCpuab/lsMayz/oZ18u+wvjkTyMx13545FChCIOfkVhxa/iT2zL4JR1Y8j/x0D8dgEdsXtaq5+kmQp/Wtqpftih1PdTKPAv4UyDj2N9IP/o7MQ8thNWfKqnNOrJfLMs9kz5MrwnwigiYiqCKSY6h/fbgKYlkkRx5fKeBvAaXKuQcfu9RdNcllkQsUoAAFKKBdAQZUtLvvQrPnpzyfpNs8fRpTKHhiE5fGri7/T01DE469KqnAfyfNOHJ8L/6bdgVSN41H+r75OL5+NHZO7wRLvn8+/aubrMfT6idN51cyIMagQLw+eVk86lewX1IL/qNA+QmUqeZGPcejSb+FaHjjfI8pOvn8MtUfKhtnq8GiK5e0ws1/dsfH/73vsVsdhrZHuyHek8eNmEkBfwgkxULX7gLXmmKjoOt4kWsel8JCoFGKQT6bTVwdEioDEhe+iFt/mnj5gChU+sl+UEDLAgyoaHnvhWDflYbVPfeqYTXP+YVybcf884dwoWq5GGYCGXlWDJ6fgS9mTYXVkucyOnPOCWTsXeCSV5aFK+tHYezViZhxawX52q1BcG9fKMtYyndb1h5KAknVL0FSnc5IqNXJY9JFhcdtcmabGdmWLBzM3o9Jez7G1P2T3XZDOzWY0kENqnhLbhswgwJ+FNDd0h76R3tBf90l0N1+OfTP3QilWrIfW2BVoSLwZIc4eatP1fiS/3l1cTV7MEYEP0QSz2OxqQNrWsU9X832+X/xYZC49ecV3vrjsxkLUqCkAiX/iS9pCywfUQJKpwuhNCjwdNkoA3R920FRP5HxBUKpxstgfXGK1DK5JnF6AflgWmFQxbpLvLil3DM73PLcMphBAQpoRiDXkovPd491po92vu2x7z8d/M5jPjMpEEwBcV6kdGkC3SXnQYmLDmZX2HaICoiAhwh8OFKMXpHPjHv1ygQZpHHmG5QQHQG7RYHIFWBAJXL3fbmMXARO5CcxL9wM/dBrYHizP3QdGnluKyHGJV8Rl8ZeGh6XobsMzE8LrAbYn25xYdhh6OKy7FhIqne1Y5avFKBAGAjkWXMxYddYZ/rl0HSPozqQvd9jPjMpQAEKUIACFKBAeQgwoFIeqqwTSoU4KHUqFymh1EuB/oFu0F3XGroBnaB79kagUJClyAq4MuIE4qNcP5nZFtULG4x9XByqtByCuKqtXfK4QAEKRIaAQWeIjIFylBSgAAUoQAEKhISALiR6ERKdYCeCIaA0rgldl6bQtawHJYonwsHYB1pqs1ai+8Ngv4//CKZr9+KCfstx8QPHUPPyN7U0JPaVAqUSEM8Kyj7+N6ymrFJtH4iNrCt3wjJ+IWwb9srmrCt3yGWRZ93gv2/jkpWfnUTpeDvFWQq+UIACFKAABShQpIB/VjKg4h9H1kIBCgRQQDxkrV0tI3o2jMYb3RLQpn5VxFW9BHpjYgB7waYoEByBPXP6YevEetg57Qps/qwKjq1+IzgdKa7Vkxmw7ToGnMmWJW2p9uWCeXIFJxSgAAUoQAEKFC/AEiEpwIBKSO4WdooCFChKIEqvYGSXBAxtH4dW1Y1FFeU6CoSVQNo/k5G+d47LmI6ueQ15Z3a75JX3wtq1f+GtUa9i+DOP49nnnsBDD9+LwY89INPCRfNgs9kwO28XHk9f7ExLWhmgf/gqmZQW9fzSRdGOXypiJRSgAAUo4HcBVkiBSBBgQCUS9jLHSAEKUIACYSGQe3KTx3HkHFvnMb88Mlet+hOPD3sEc+b8jBUrlmP58qXYsmUTNmz4W6bjx49BURQcST2GDdu3ONOs1UuhNKxuTxXi/NI1RXF9rpJfKmUlFKBApApw3BSgAAVKLMCASonJuAEFKEABClAgOAK66AoeG9ZFJ3vML4/MxUsWlaratLSTpdqOG1GAAt4EmE8BClCAAsEWYEAl2HuA7VOAAhSgAAV8FKjQ8CZAcX04szGxLhJqd0ag/u3ftydQTbGdcBPgeChAAQqUQuC34wtx3fJOuOq3tnh6w8PYn2V/2HkpquImFPC7AAMqfidlhRSgAAUoQIHyEYipdBEu6LsMlS9+AIn1r0a1S57GBTf/Bp0+unwa9FBru/aXe8gtlGWzFcoAtHhzDviPAhSgQAQIpObY8NWGHLy4NBMfrc7GjjRLSI160+m/cTz3KNLNZ7DsxK8YufWpkOofOxPZAgyoRPb+5+gpQAEKUEBjAnFVW6N25w9w3rU/onr7kTDG13CMICCv/frehmbNWjjbUhT3UIl7OAWwqf85NyrhTLQuBtfX6ufcqmBdTZNb4L6Gg2UaUP8+ZxnOUIACFKBA8QL5auzkiQXp+H5rLtYcMmHOzjw8Pj8d+86oK4rfPCgltp7ZKIMrQWmcjVKgkAADKoVAuEgBClCAAoESYDtaFEhMTML4cV9g+vQ5mPD5ZFStWs1tGIriHmSBpzy3LT1nxOhj8MxFr6BB/Pn2AgUiNo83HoH7zxss08D6D9jXc0oBClCAAj4J/JNqxvEsq0tZq/oeu3h3vkteoBf2Zu3CpjN/e21W3AbkdSVXUCCAAgyoBBCbTVGAAhoXYPfDSmDZyo1o2uVuiFec/Tdq3BSZJ/Id6c5HXz+7li8FBaqpgZTGjS+Ch9BJwWLn5tUT9HMLJZ/TK3p83GayvBKlXWX7bUfxhgQ0S25Z8sq4BQUoQAEKSIED6Z6vRPGWLzcKwGRv1m6IW328NbXixFJvq5hPgYAKMKASUG42RoHACrA1ClDAs8D2XQfw5Csfu6202WzocllLzP1mlDO9+9JDbuWYERyBilGV5ZUorzX/QHZArxjkKycUoAAFKFA6gdbVjR43bFfLc77HwuWQWT/+PDSv0FrWnBxl/4Y7RQ3h11Pz+9UZgO7VrpHrOKFAsAUYUAn2HmD7BQU4TwEKUKDcBU6cPI2HnhmNl4bdhbjYGLf2EhPiUK92NWeqXqWSWxlmnBO4pM2l5xaKmLu0XYci1nIVBShAAQoEQ6Bmog53tYh1aVoEU3qdH7iHnbs0fnahfnxDNE+2B1SuSOkqcztX7Y6pl83Hkxe+gKuqXyvzOKFAsAUYUCnTHuDGFKAABSigJYGc3Hw8+twY3HR1J/S+yvMf+KvX/4Pn3vwc74z/Hus27dDS8ILS14EDBmHMmE+cqXu3nhBX+vTufaMzT6y/7ZY7gtI/NkoBClCAAkUL3HZxDGbcUgFjrk7ClJuTMbJLQtEbcC0FIlbAfeAMqLibMIcCFKAABTQmYLFY8fm3s72m3fuPwGq1YcRbE1CrRhU8fPeNHkfYtFF99Lm6I+rXqY4DR45j4GNvYMHS1bKsok4rJUZBCynKqENCrCEgfW124Xno3ukyZ6pftzYURcEF59Vz5on1F51f16f+JMcbYdDriixbMSEK4p9O3SmhtD/U7ohuMVGAAhTQnECMUUGjSnpUiAmdPw+zs7JwwnIUFpPn57xoDjkYHWab5S4QOj8x5T5UNhBUgaxc2HYdtaejp+xdyc63L4v8I2fz7Gs4pYBfBMzZx7Fz2UgcXfM6Tm75TNZpUvPEskgnt3wu8zjRvoANNqSdzvCaTCYzUtPOyOBIYkIs3v34e7w9bgqyc3IxddZvav4aiXBdj8sw+N6b8MCd1+HDVx+DWJ4xb7lcJ56pmp1rhhaS2WJDXr41KH01m+3fFpFvKl37OXkWWKxFb5uj7gexU2zqTgml/aF2R3SLiQIUoAAFSikgNsvLyMOPd/yISV+Nx8z8L7Hk43lotLgZ1qatQuclLTDwrz6Yd+RnOP+lHoRybK9zkTMUCKQAAyqB1I7gtmx7U2EZv0gm69wNUsK294RcFvnWefY8uYITCvhJwJRzAv8texnHVouAij14Ys45LpcL5vmpOVYTRAGDXo/hj/T3mho3rIOE+BgMue9m1KqeggrJCTKJLifExyIuNlrMuqXqVSohKzvPmZ+rBgm0kMTVOCaLFcHoq9lqDyuI19K0n68GZESgpMht1TJip4iWiiwX4P0l+sREAQpElEBEDnb0+6Mw+LEHZNq7Z7c0GD36Lbk85PeJ2JtzUuaVdrLpm004sOKAc3Or+vvskh86Iv+kCbmWHGxP34qRW57Cwb1LYRx9N6JG3wPjmPsR9eatUHatd27HGQoEQoABlUAosw0gPhpKw2reU/WKVKKA3wWMsVVwfueXUO3SER5T5Yvv93ubrDB0BeJiY+SVJ+LqE0cSedd0bY+O7ZrLjr//2TTs2nsIJrMFW7bvwbc//YorLm0m13FCAQpQQPsCHIG/BQ6mW7D3tMXf1YZ0fTt3bseGDX/LlJWdJfvqyNt4ch+yLPkyr7STI38f8bhp8tFKLvmGhZOgpB46l5eRBsOs8eeWOUeBAAgwoBIAZDYBKPWrQP9wD69Jd01L8B8F/C1giKuKCzqPRPW2IzwmBlT8La79+lat24br7x6Blt0H4db/vYwendvgrlt6an9gHAEFtCrAflMgRAX2nbFg0KwzuH9WOh6ak447fjyNDUdNIdpb/3br8aFPuzx0XDx43JHev+Ju1ItxDXyUtPXo5BiPm+Qk2YM3jpVVjrlfCaOc2A/kupZzlOcrBcpDgAGV8lBlnRSgAAUooAmBNfM+QecOLZx9/eHTl7Bq9njM+3YU1s7/DK8/cx9iou0PQHUW4gwFihDgKgpQIDIEJm3IweF0+zOjxIjTcm34ZG2OmA371KjRhWjdqo3H1KpKAyQYomETz09cswvWZf/AdsA98FEUUvPbm7mtPl0jDRnVzrjkK0kpLstyIToWiImXs5xQIBACDKgEQpltUIACFKCAZgQSE+JQt1Y1xMZERCBFM/uFHaUABSgQSgK7T7nf5nMg3YIsk3i6Uyj1NDh9sX65FJbvV8D6y1pYPpgLyzzfn5dYo3UN9JvaD02atED27fNwAAAQAElEQVRd3flo2L4xljw5E4lRiYjRx6JxUlOMvPgdGFv2cBuctVkXtzxmUKA8BRhQKU9d1k0BClCAAn4WYHUUoAAFKECBkgnYbDaYLe4BEFGLeIj30RNpXteLMp5SfJTilh2lVxBjcM93KxgJGWcfHu4Yqu3XzY5Zn15rta2FLl16oGd0P5zfoTHyEnPRplJ7LOu6EV+3m4Gra9wAS+f+MN/yDKwtusJ6cUdYbngM5j5DnfXbTgDWLQpskXHhkHPcnAmsAAMqgfVmaxQIKYG8fBMOHjkhv2o2pDoWTp3hWChAAQpQgAIUCKrA7EUr0eO2J936sGzlRrS79iF06zcMLboNwtRZS93KeMvoXM/9Ksau9Y1QYyreNimXfKspCwtf02H92Fhs/qyKbMNqzsTGcXEybf60qswLiclx11t2/NEna8tuMN/6LMy3vwhLu+sg/tkygZx7o5HdORY5t8Qg57JYmL4xiFVMFPC7AAMqfidlhRTQhsDzo75A6x73o2f/p9DxxsEYMPgNnD6TCUfvr7/rOTTtcrdLGj9ppmM1XylAAQpQgAIUoEBIC+w/dEye5zzzxmdu/czJzceTr3yMR+/tg42Lv8CYVwfj5fcmyQ+a3Ap7yLi1aQyeujweXepH4Yq6Rjx6aRwGt4v3UJJZToGqyc7Z8pwxTzHAuurcn7m2PCD/DSNsqeXZKuuOVIFzR1qkCnDcWhdg/0spUKdmVUz9dCQ2/PqFfADn3gNH1E9mfnOp7bFBN2PuN6Oc6fY+3V3Wc4ECFKCATwLH02FdsUMm24kMnzZhoZILiFsajhxPg7j6sORbq39w5Jtw7MQpiNsjSrM9t6FAqAnUrJ6Crz58FiOGDHDr2ur1/yA7Jxf9b+gKg16P7h0vQb3a1bBspe/P+uiqBlOGq0GVER0TcO0F0W5tBCJDZ4xHj+etaDU4By0eyXZLzf53PBDdcG/D4PpnptK9mXuZcsqxbHRt29GMdbfnfMd6vlKgNAI8qkqjVqZtuDEFQkPgfwOuQ9PG9WE06FGjamXZqQpJCfLVMalSOVmeXIgTDJEqJLuuB/9RgAIUKEbAunY3zKN+hvXHv2SyvDUTIq+Yzbi6hAKffztb3rLQ/ZZh8urDYSPH4Uz6ua8OLeqqQxFAGf/Vz2jV43507fc4OvV5DBu37SphD1icAqEnIAIl1atUQkUP5y/HUk/Jc5yoKKOz4w3r1cTR46ecy/6aWXfEhGELM9Dnh9P43+x0zPg3z19Vh2w9unu6QH/bZdBd3wb6oddAf3VLn/t6Oj8N6079hWN5R+Q2J/NPyFdHvlgn0q7MHTK/8ERJLJxjX1ZS+MBguwSn/hQoPqDiz9ZYFwUoEFIC+fkmfPL1Lxg45E20anYBrunWHgX/TZu9DOLWoPGTZkJcNltwHecpQAEK+CJgXbzFrZinPLdCzCiRgAh4fzH6aayd/xlmTHwNazb8ixnzlrvU4e2qww1b/8O4L2dg8tjnsGHRBNzYqyMef+kjiId1ulTABQqEkUB6RhbiYmNcRhQdHYWMzGyZZzToULVCTJlTVEwU3vwjC/+cMCPXbMP+MxZ8ti4b/6Ur8Fa/cva5tlV8bF+vU5CSFO21Pm/tlEe+QXUTgJWqJ6PaVU1R/YZWqNaslte+JcQaEBdjcFm/x7QJD68dgEVH50D8W5Vqfy/bcHqdzBfrRPpq/0cu2znHM8j9T9yoRkD1FtHO8mL/irorJkQ585zb++gutg/LxEGVSMD9aCvR5ixMAQqEmsC6TTsgPqn0lKarAZKC/bVYbdix+6D6KWYm0jOykZF17jHoPbu0Raf2zVGzegqW/LkeN9/3kjOoEhOlR0pydMBTZfVkQfRfPW/wuW1RPhh99Uebwjkh1ujzWP3Rpj/qSI43CnYmCgDqewxOpLtLiDyxzn0Nc0op0K93F7Rv3QSxMVFodF5tdLmsFX5ftdGlNm9XHS75Yz06tGmK1s0awWg0YEDfHvLWn+279rtszwUKhJNAUmK8vOWn4Jjy8vKRmBAns8xmK9Iy8sqc/tqbjax8m6yz4GTJf9le63aUPuVj++J87nRWvtf6/DEOX+uwWOy9T8/2rT/ZuRbk5pld+p6ZYypI5XU+/+w+Kty3rMZ5iP82H1G3WWDsYkX0o2ZET8pFWva5/Sn2r6g4XW2r8Pa+LovtmSjAgAqPAQqEmYC4HzjtdIb85p7Cr6fOuD67QJx4jx75MOZMfgsGg15+QungeOSePnho4A14+K4bMGX8C+oJRiwWL/9brs43WZGeZQp4ysi2/4K12eBz26LDweirP9rMN1mQk2/2eaz+aNMfdWTlmgU7EwUAEf2skuQuIfLEOvc1zPGDgMlswZ9rNqNp4wYutXm76vDI8ZNoUKe6s2zVlApy/njqafnKCQXCUaBaSkXsO3gMJtO531niQ6bqVSvK4aqnGjCrwYGyJhHskBWem8g5caudt7ohGldLeVtfOF8t6pe+Fq63NMtiXKI/IrDiy/ZW9aROxNcLlrVYRQ3FJ9FWwe0KzqOVBcYX1aDK+DwYHjbBWsHmYmQ7W72v/SxYt2P+bBV8iXABBlQi/ADg8MNPoGO75hj+SH+P6f47enscsKIoOK9uDRw9keZxvfjEskqlCshRP7kRBcQvv3z1U4FgJNG++CUo2j666UvsnHunTGJe5BVOonzhPK0sixMM8YteK/119FOcaAh3JgoIAV23i8WLS/KU51LAw0L6gXT88dYf+Pnen7Hqtb+QfKiSh1Lhl3X4aCo8XXHoyBPfVFJ41K998DUyMnPklSaOdUVddShufYiJdn2gZlxsDDKz7VctVkqMQrWKMQFJh3J0eH5plkzfb8mV3d9ywiKXRf6MHWav/aio9jPKqPO6PlBjKG07Br39to3Sbl9+2xW978XVlHJHheBE/MEtAiZmNcgouifnLRYxi7YtL5SvU2YuVv/QtuDX5evkN/x07tBS5vtr0qiyHnFGxa26S2rwak43FGZQQIMCDKhocKexyxQoq0BmVg5GfzoVe/YfgUk9yRAPH5wx7w+0bWE/uRDPS5k0db4MsIj1sxetxJbte9Cu1UVlbdpv2x9Z+QIO/PYQTu+cLpOYF3l+a4AVUYACfhHQtTkPhuE3QHdzO5n0z9wIkVeSyvMy8jDlxu+x9tO12PPbHvzz9T/o8XpfxKTaL80vSV1aKyvegwtfbVhw2WZz/Sh3/KSZELd3Tnx/OBxXmogxF3XVobj1IS8/XxRzJnG1Y0JcrFw+nZmP1DN5AUn7T+Zh3aF8mXal2a8cOJ1rlcsi/59j3vtxJtMEk8mK1AD11d8mIhidFkBrf/U/L98eoJAHS4hNdu09jJZX3Ydn3vhM3sYm5sWz4UQ342KjMfb1IRg1bgpadBuEIS+MxfNDB6B2jSpitd9SUrQOz3WMx0VVDIgxKKibrMcDl8ShTU0GVFCCfzbHZTsFtlGgFFjiLAWCI8CASnDc2SoFgiqgKApWrN2K3gOfRcvug3D7w6+iR+c2uPvWXs5+fT1tAbr1GybXD3/9U3nFyyXNGznXB3vm1M5pbl1I+/cbtzxmUIACISBQNQm6yxrJpFRJLHGH9v62Fzlp9gdFOjY2mA2ovq6OY1HTr0V1XnzD2nAvVx2KfHElidhePED2nfHf48sf5mPaZyPR7MIGIttjMhoNqFLp3FWHNapWxt4DR51lHbf6OAIy8mo5dSJuXSjv1CRFj7e6J3pNtzeLgbc+iKsnxRWM3taHer7YAWI/hno/C/dPmIu+h2I6v0EtbF06ySW99dwDzq52vbwVNi2eiIXfvwvxQOb+N3ZzrvPnzCU1jBjdIxEzbq2AT3snoc+FrleE+bMt1kUBCgRWgAGVwHqzNQqEhEB8XAymf/4yVs/9BPO+HYU18z7F68/ch+go+6cldWtVw+Kpo/H7jA8x/7u3sXHxFxjYr2dI9F10IsqaBVPmITHrkszZx2DJ4z3/LihcoID/BQJeY9ruUx7bjDsZ7zE/EjNffGcixJWFo0c+guSkBBw6miqT2WKRDxQX68RtneKKl8JXHXa9ohX+XLMFf2/eKa9a/Hr6AlSrUhGNG9YNOGVyjA4tqhm8pvoV9AHvExsMbwG9Xoda1VMgAo3hPVJtj07xcDWKp6tWtD1K9l6LAjotdpp9pgAF/CMgAisieCIuey1co6IoqFwxCXVqVoVBH1onsPm6eCTUvAyF/8XX6AB9dIXC2VyOeAECaF2g7uWer0Q50ng/Pt89Vqav936m9WGWqf9rNvwrt39w+HvocduTznToSKrML+qqw5ZNz8eDA6/HgMGvy6sSf/j5N7z30sPQ8cHB0o4TClCAAhSggDcBBlS8yTCfAhQIaYEal70JY+K5T0/FvMgL6U772jmWowAFXARqta2Fpvc2dcnbc+l27Gv+HybsGivT5D2fu6yPtIUFU95xua1h69nbHMQtQyJwXtRVh4qiYPC9N2Hdgs+w6Pt38decj9Hq4gsijZDjpQAFKEABCpRYgAGVEpNF5gbZJhvGrcnGgBln0G/aabyyLBMH00P3IWSRuZeCN+pgtBxXtTWaDPwXF96+XiYxH1/90mB0hW1SgAIBEOj4XEfE/aJHzDgd4n7S4eI3m+C+hoOdaUCD+wPQC+02oSjFX3UYEx2FmtVTeGWKdncze06BsBCoYKyI1hUvRbQuRo5H3tpjAyqnV8WoH77Gzy9ulunOz4bBdkIWKfnk7Hczx7/7BpKGPYroubNKXge3oIAqwICKisD/ixeYtjUXs3fkITXbisx8G1YeNOHdlVnFbxiaJdirMBKIrtgYIoXRkDgUClDAg0CMPgYPNBuMB695DA+0egz3nzfYJQ2sf+5Bkx42ZxYFKEABCmhEoGXFNvi4zTfoV+dO2eMmSc0gHqHywryP0HbFlUg8U0Gm8367GPmjouD4d2jNIXzQ4AOvadot0xxFod/9n5yPWr0KMTOmIfmR+xDz7VcyjxMKlESAAZWSaAWtbPAb3nDU5NaJ7akWGVxxW8EMClCAAhSgAAUoQAEKUIACfhA4L6GRrOW8ra63fopMy+8lf86fkpkJXdpJsblLip01w2WZCxTwRaB8Aiq+tMwyFKAABShAAQpQgAIUoAAFKIAtx83YeMyerGc9CuadzeJLEQLieVtD9wyFSP2m9pMlPeUZ9u2R6wpPdAcPFM7isq8CEVyOAZUI3vklGXrL6ka34o1T9EiIUtzymUEBClCAAhSgAAUoQAEK+C7w0tJMPPNrhkx5Zpvc8MUCeTkme55cEcYT634FltU6maxH7H9nxByOR8udHXC89kGgEIO+U8mf6Whq2gwwGt0U8y/v5JbHDAoUJ8CASnFCXC8F+jWNQe9G0UiJ08kgSofaRjzZIV6u44QCFKAABShAAQpQgAIUKL1A06oGNKvmNUGxxxZK34BGtjR/b0Du3dEyWebbb+ep+kt9jP5oKs7b2gS6plYoKZDJcL0FUcPzUZp/lgYNMEAlqQAAEABJREFUXTYzX9AY2Y8MdcnjAgV8EWBAxRelEClzIsvqvBTQcUlgwdfDGdZy62mcUcEjbeMwuU8ypvWrgBc7J6B2kv1NrtwaZcUUoAAFKEABClCAAhoUYJdLKvBKlwS83T3Ra4oxREZERVfXBn1bq0vKaHESGxuuwr6LtsN4rxlxv+fIFP1WPpQqJZW2l7dWrChn0t8Zg5NL/0Lawt9hqVtP5nFCgZII6EpSmGWDK/D7/nx5GaDjcsDCr3N25AW3g2ydAhSgAAUoQAEKaFGAfaYABUotYDtwErZdR+0p1yzrsR1Msy+r+cgzyTxfJobbzIj5Ks8l/TvmTzz+WD989eIoGK6x+FKNz2Us9RrAUq++z+VZkAKFBRhQKSwSwsspsTrnpYDVE+y7rnKc4syrmWTPC+EhsGsUoAAFKEABCvhBgFVQgAIUCBUB6/RVsIxfJBNS02W3CubZjtvz5ApOKBBmAvwLXEM7tHP9KOdlgL0viJY971Iv2pl37dk8uYITClCAAhSgQOgI+LUnU/ZNwm0rrkHnJS1w35pbserkcr/Wz8ooQAEKUMB3AaV2ZSgNq3lP0QbfK2NJCmhMgAEVje0wdpcCFKAABQIhwDZCVWBl6u/4YMcb2JP1H3ItOdh8ej2e2zgEp/JPhmqX2S8KUIACYS2g69ce+od7eE2omhzW4+fgIluAAZXI3v8cPQUoEC4CHAcFIkRg5cnf3UaaZcnExtN/u+UzgwIUoAAFKEABCpSnAAMq5akbgLoX7s5Hnx9OY9jCDKw74vsDnwLQNTZBgSIFuJICFKAABShAAQpQgAIUoICWBRhQ0ejeO5Buf8J1Rp4VuWYb/jlhxhvLs5CuLmt0SKHebfaPAhSgAAVCQKBD5U5uvYjXJ6BFhdZu+cygAAUoQAEKUIAC5SnAgEp56pZj3QfPBlQKNpFtsmHHSXugBSi4hvMUoAAFKECB8BDokNIJQxs9hwbx5yNGH4tmFVrhjRZjUDGqcngMkKOgAAUoQAEKUEAzAqETUNEMGTtKAQpQgAIUoEAwBfrXuxvfXzYXy7puxIS2P6B95Y7B7A7bpgAFKEABPwssPb4I7RY1kmn4xkdk7Z7y5ApOtCkQJr1mQEWjO7J2kt6t53FGBY0qu+e7FWQGBShAAQpQgAIUoAAFKEABCvgswIIU8CTAgIonFQ3k1TkbUEmM1iHGoOCiKgY81zEeSeqyBrrPLlKAAhSgAAUoQAEKUIAC5Seg6Zq7VL0Kf121w2sa1WKcpsfHzoePAAMqGt+XPc6LwoxbK2B0j0RcUsOo8dGw+xSgAAUoQAEKUIACkSnAUVOAAhTQnkBEBVSsVhssFqvHvSTWHT2RBrPF80NdMzKzcepMhsdtmUkBClCAAhSgAAUoEGECHC4FKEABCkS8QMQEVGw2G14ePQmvvP+V205ftnIj2l37ELr1G4YW3QZh6qylzjLZObkYPGIM2vd+GFfcMBj9H34VqWlnnOs5QwEKUIACFKAABbQgwD5SgAIUoAAFKOBfgYgIqCxYuhqd+jyG6bOXuenl5ObjyVc+xqP39sHGxV9gzKuD8fJ7k3DwyAlZ9rsZi7Fj90H8Nv0DrJo9HnqdDmMm/CjXBXKy7YQZ32zOdaa/j5pl89tSXfPXHTHJfE4oQAEKUIACgRJYu/YvTPzyM5k2blovm127drVcFvmbNm2QeSWcsDgFKEABClCAAhQIaYGICKh0bNcC0z5/Gb2v6uC2M1av/wfiKpT+N3SFQa9H946XoF7tali20n7yN/+31ejbuzOqplRAYkIcBvS9Cj/N/R3iihe3yorJ2HvaIgMiy/eXPOjxjxpQ+XZTDhzp77OBk8L564/YAy3FdMVl9drDJtmvf9TgjMsKLlCAAhSgQAkEIrfoWjV48qUaUBFp8+aNEmLdutUQyyJt2mQPssgVnFCAAhSgAAUoQIEwEYiIgEpcbDSqV6mE+LhYt912LPWUDKBERZ17oGvDejVx9PgpWXbfwWOoW6uanBeTOjWrihekZ2bL15JM9qkBFREQ+fNAfkk2K/ey4qoW0a/tqZ6fH1PuHWADFKBAcATYKgX8JNCmzaW4554HvKbmzVv5qSVWQwEKUIACFHAXSP03FdNvm+5MS19aCtiA1H8K5b+s5rtvzhwKlFpA0wGVdZt24PNvZ3tMnm7v8aSUnpGFuNgYl1XR0VEQD6EVV6GIq1di1GVHgeizgZfs7FyZFW3UwddkMChyG72i+LyNo2693r6trKCIiSjn2Kbgq0Gvg17nuV2RL6o0qG0U3CaU5kX/Qqk/vvZF3dWi60x+EmA1FKBAaAq0adMO96oBFW+pefOWodlx9ooCFKAABXwXyDPB+ttWWCb+Bsv3K2DbetD3bUtRMteSi9+OL8TkvZ9jReoyWUN2arYMmsx5dI5cdkzyMvJw8K+DznTinxOA+udTXmah/K1qPs7927clXda3euxf5zI5R4ESCOhKUDbkiopgR9rpDHhKvn4jT1JivLzlp+Dg8vLy5e09iqLIYEte/rlbdBzzcXH2IEx8rBG+pugoA8Q/cevPd1vzUJK0/phZbFps+ifV4rHerzZm44t1WR7XOa5MMRr1Po/F1zH7q5wYuL/qCmA9Mogl+s5EAQpQgAIUoAAFKEABLQtYvvod1tl/y0CKbc0uGVixrd9bLkPKNGeg/8pr8MzGR/HRznfw+Pr7MWj1LbDkW2TQ5Oj6o35pN/tMvqzv5M40v9THSiJPQNMBlY7tmmP4I/09pvvv6O3T3qyWUhHith6T6VzAQjyEtnrVinJ78TyV/YeOyXkxOXD4uHhBUkKcfE1Lz4OvKSvbHpjZfcqMCWsyS5RWe7hNyCZ74DrZdCTfY72frc7Ep6szPK7bctzer5xcs89j8XXM/ionRumvugJZj9niaS+J0TBRgAKhLCC+zU08tDyU+8i+UYACFKAABQIlYEvPhm37YbfmrOv3uOX5I2Ph0Tk4nON6BcyWMxuwLo1XkvjDl3X4T0DTARX46GCxWCECJhaLBWazRc5brfY/dNu2vFDWMmXmYpjV9b8uXye/4adzB/vlyT27tMW0WUtxPPU0MrNyMHn6Itx0TScoiiK3K8nE3iJQL1mPO5rHlii1qnHuGS+ONj31oElVg8d6724Vh0FtEjyuu7CyXlbp6J9c4IQCqoD4OTl6Ik3+bKiLQft/4zEzRBJXYIlOWNSfX7Es0tbj54KhYh0TBYoSEFc0Nu1yNwqnVX9vk5uJAPo1dw5H55uGoE2vB/DC2xNhUn9vyJWcUIACFKAABSJV4ES6x5HbTmV5zC9r5t6s/zxWcTB7v8zPOJwhb9VxPDdl6UgPz0bx8MeNuBVIbPPltlb43HYHVk4/JOuDpz+s7Gs4DSWBEOxLRARUfpyzDC2vug/iuSoz5/8h52fOXy53R1xsNMa+PgSjxk1Bi26DMOSFsXh+6ADUrlFFrr+9T3ecV68mruw7FO2ufUgGYwbfe5NcV9KJ4+e0fkU97mwWU6J0SXX77ULFtdkkxeCx3nvUgMp9l8R7XHdhFXvdjv4V1wbXh5dAfr4JAwa/gb73v+QysGUrN8pjvlu/YfJnY6oaWHQpEKCFjHwbnvk1Q6aRSzNlq9mmc3kv/14+v8hlQ5yEnYB4NpYY1CejnsDcb0Y5U4sm54tsvPbBZFx4fl2snf8ZZn/9JsQ3vc1fwk/DJA4nFKAABSgQsQJKrcpATJTb+JULarjl+SOjfrz993LhumrH1XVmFXxmyvGt9rsInCvVGRlPkRN14ez/+Rn2W3z2ZlbAHqUuTh7Osa8pVM6eWfYpawh/gYgIqNxy/ZXYunSSSxJXmTh2b9fLW2HT4olY+P272LBoAvrf2M2xCvFxMfj4rcexYtY4LPtpDH749CVUTangXM8ZCmhZQPxx+fLor/D35h0uwxC3Ojz5ysd49N4+2Lj4C4x5dTBefm+SvHrLpWAAFvRqpK9ZNQO8pSYp9iusAtAVNhFGArVrpMhveBO3dYoUq54knsnIwp9rtmBA3x4Qyw3q1sCNvS7HwmVrwmjkHAoFKEABClDAq4D3FTFG6G++FIg9F1RRaleCrstF3rcpw5oe1a9FzdjaLjVcnNwSl1RqJ/NiK8fh5il9nanLyC4yv+BE3lCgnkcWzEu5MEVuc/eF63Gf7Vt0uKmWfXWhcvZMTilQvEBEBFSKZwD0eh1qVU+B0WjwWDw5MR4plZI9rvM1s14F+60+l9c590bk67blWe6SGkZ5K1Bj/mFanswhWfeE7+Zg2469GPa/W1z6t3r9PxAPfe5/Q1cY9Hp073iJ/ONz2coNLuUCsRBnVPB290SvaWSXhEB0g22EmcDoT6fi+VFf4OtpCyACKWJ4qSdPixfnFYpioW6tajhynA+qExZMFKAABUJPgD0KpIDSugEMr90K/ePXwDDiJvX1WihJ9udK+rsfCYZETOkwF2+1+AiPXvAU3m/1Ob64dKqzGUO0HnXa13amqk2rOtcVNROdFC23aZB0Gucp+1G5dqy9OK9QsTtwWmIBBlRKTFb6DeqrARVxq0/Huu7PQymu1ouqGGTQw/HsldZqEERsUzi/VQ3PASFR1ltqU9MobwW6KKXk23qrk/mhL7Bw2Vr5x+THo4YhMf7sL5Oz3T6WekoGUKKizh2rDevVxNHjp86W4AsFQkvAYrHi829ne0279x+RHY5Wj+nb+3RD8yYNUalCoix/95A3IW59S8/MlmUKHvfR0VFIO50u88UnXTUqxUILKVY90ayYEKWJvhb2TEmOhtGg02TfxTEiDxZOKOBNgPkU0KDAgYMHMOPnH2U6sH8flNqVgUrx5T6SGH0MrqzaAwPq34/LUjqXe3tsgAKlEWBApTRqQdimiRpQEcEYR2pd3R78aKIGQRx54lVcbRKE7rHJEBJYt2mH/CPR0x+X4jlCoqub/92DEW9NwPi3hqF6lUoiyyWlZ2QhLjbGJU/8YZlx9g9O8cdOcrwRWkhiEFrop6c+Rql/VIo/jD2tC+W8+Bj7+5OwD1SywaYGPjK8JvFgctGXBDV4OGLIAIhvghNXZn394XMQ3+z273/7nd/e5igryufl5auBlyQxC5v66dXRtBxoIeXkWXAqM18TfS3sefJMHkxmqyb7Lo4RebCE0YRDoQAFIltg/oI5uP32Phj93psy3X7nzRB5wVJJrJkob9m5Zuw1peqCcfUqVG1QDeJVVNB8zGB568/Vvwxz5ol8Jgr4KqDztSDLUYAC2hAQt+qknc7w+IflqTMZchAz5y1HlcrJmLd4Fd4eNwVzl/yFfQePyXkRNElKjJe3/MjCZyfiD8vEhDi5JJ69YrbYoIUkOqyFfnrqo1X968xqhSacC/ZffDuUcA9kEremDX+kP7ylxg3reOxO1ZSKMj9HDZykVLY/H+vA4XMPttt74ChqVD0XdFRjKmroBiGf5KDUiVb6W7ifatdLaxzU7US/mShAAQqEk8DkyV+6DcdTnqyy/O4AABAASURBVFuhcswQt/rUaO36MNyUxvZnozieq9L5pS7yg5CC+eK5Kb2x0KVnCUq2vPWnqsLbe11guOCzAAMqPlOVf8Hl+014d0UWXv09EzP/zUOuWZxiln+7bCG8BDq2a+71j0rxqbwYbZfLWuLGXlegQnKCTOLhyzHRRjkvnidUTf0jUwRYCn5SLz7Fr161othc/oGflWuGFpLosBb66amPIkiRZ7Jowrlg/3PyLYI9JNOylRuxYOlq+dwUEXwcM+FHeTXWhefXhXhWVoc2TeXX44sHM+/ZfwS/LFyBHp3bluNYWDUFKEABClAgNAUsFgsOHtzv1jmRJ9a5rQhihuPZKCLYIlLVplUgbsMsmF9h3lcw7N2M43uOeUymS9sHcQRsWqsCDKiEyJ6bvTMPbyzPxOI9+VhxwIRP12Xj/VX2+/lDpIvsRhgJiKDLA3deB0fq3L4FqlWpJJfFrT5tW14oRztl5mI1eGLBr8vXyW/46dyhpcznJIIFND70fJMJz4+aiMuuewRtr34Q85aswtjXHpPBFDG0EY/dia3b96BNrwfQe+CzajClDXpdealYxUQBClCAAhSIKAG9Xo/ateu6jVnkiXVuK5hBgQgUYEAlRHb67/vy3Xryx/58WHiRipsLM8pfIC42GmNfH4JR46agRbdBGPLCWDw/dIDLt5+Ufy/80wJroUBBgas6tcHK2eOwZNr7Mv0+40O0v6SJs0iDujWwYMo7ct3quZ/g9Wfug9HLt785N+IMBShAAQpQIEwFBgy4x21knvLcCjGDAhEiwIBKiOzoA2esbj2xqsGUwxmhe+m8W4eZ4Q+BoNRxy/VXYvrnL7u03fXyVti0eCIWfv8uNiyagP43dnNZzwUKaFVAPG+lWpWKEElRFI/DEOvErXAeVzKTAhSgAAUoEEYC+el7cXLrRKRu/hS5p/51GVmvntfiu+9mYNgTz8r03Tc/QuS5FAqTBeVUGgwb10PJtD9zMEyGxWGUswADKuUM7Gv1l9Q0uhVNiVVQJ0nvlh9aGexNOAvo9TrUqp7CT+jDeSdzbBSgAAUoQAEKRKzAmT2z8c/kJji49FEc+v1xbP+utQyuFASpU7sO+txws0x16tYruCrk5nPSsvHPjH+wY9aOEvUtachDqNL6IlS6sReqNDsfCW+9WqLtWThyBSIvoBKi+/rOZjGol3wueJIUrcOj7eJDtLfsFgUoQAEKUIACFKAABSigdYHj6951G8Lxde+45WkhI+2/k/iy8yQsGLYAG77eILt8eu9p+VrUJHruLMT88pNLkbhPP4Jx80aXPC5oVKCcu82ASjkD+1p99QQdPumdhInXJ2PcNUn4oW8y2tVyv2rF1/pYjgIUoAAFKEABClCAAhSggDcBm82C3JPb3FbnZx6EJT/dLT/UM9Z/uQH5ma7Ppcw6noXc07lFdt24yR58KVzI8K+7TeEy5bHMOrUlwIBKiO2vGok6nFfx3JUqIdY9docCFKAABShAAQpQgAIUCAMBRdEjpvK5B7M7hhSVUBv6qCTHYnGvIbP+xLYTHvtyavcpt/yTO07i0JrDWPHOCkxZlIifbT1wyFbdpZw1uYLLMhco4EmAARVPKsyjAAUoQAEKUIACFKAABcJQwHVIVqsNR0+kwWyJzC+CqHrJk64g6lLVS55Sp9r7v0qTKh47XfG8is78/SsO4PNLP8PknpMx7ZapWD3uL/y3C/hLuQTjcReO2SrLstZKlZHf4XI5zwkFihJgQKUoHa6jAAUoQAEKUIACFKBAMAXYdrkJLFu5Ee2ufQjd+g1Di26DMHXW0nJrK1QrTm7QGxcN2IbaXT5CrU7vo/Htf6Ny03tDtbtF9qvVPS0RlRDlUia+ajxiKsQ485aNXIqsE9nOZchv+7PJZZuiw4YG1yL7f4/i1Mz5sCVq7yodORBOAirAgEpAucvW2LK9+Xj61wyZZu/Mk5Ut3Zcnl0X+nLN5cgUnFKAABShAAQpQIAgCbJICWhDIyc3Hk698jEfv7YONi7/AmFcH4+X3JuHgEc+3jWhhTKXtY1RSfRlESWn2P8RUvLC01QR9u0rnV8Y9y+5Gz9E90XJgS9mfCvUryFcxEc9XSduVJma9piMXX4nMZ16ApU5dr2W4ggIFBRhQKagR4vOpOVZsPmaW6WimVfb2ZLZNLov8w+n2PLmCEwpQgAIUoAAFfBFgGQpQIAIFVq//B9k5ueh/Q1cY9Hp073gJ6tWuhmUrPT+gNAKJNDnk2EpxuKjPRWh0XSO3/htijTDGefrSD8VZtn6nes55zlDAFwEGVHxRCpEynepG4a3uiV7TtY2iQ6Sn7AYFKEABCpSfAGumAAUoQIGyChxLPSUDKFFR5/7AblivJo4ed3+AaVnb4vahIaDTK2h8XWO3zjjCKed1Pw9N+jV1W88MChQlwIBKUTp+WpdtsmH7SQvS88p2BUmVeB1aVDN4TTUTuTv9tMtYjZ8E9p624GB6ZD7kzU+E4VENRxFRAvv27YFIETVoDrZEAieyrfjvpLlE27AwBfwtkJ6RhbjYc8/WEPVHR0chI9P+fI0ogw41K8dqIul1CqpXjNFEXwubJsUZkRBr8HvfU5LtHzQX3o8DPu2NPqN7otkNF6Jlvybo99G1eGzZ3Xhp9xA8+NOtJeoH+I8CqgD/AlcRyvP/ietzcPPU0xg6Px23Tj+DUX9mlWdzrJsCISEgbkG748fTeGhOOu6flY5Bs85g9yntBFZCApGdoIDGBLZs3Yy+/XrjzgH9ZBLz//67TWOjYHfLUyAz34YnFmZg4IwzGDw/A31+OI15fP5beZKz7iIEkhLj5S0/BYvk5eUjMSGuYBbnw1Cgw32tMWByH9z+xQ1oO7A5areugfgU7vcw3NUBGRIDKuXILD59mbYt16WFpXvzsXy/ySWPCxQoo0DIbf7xumyk5dqfmC46J57vM2ljjphlogAFwlRg7Ifv4tixo87Riflx4z9wLnOGAjO352HbCbMTItdsw4ers5FjOvf7wrmSMxQoZ4FqKRWx7+AxmEznjskduw+ielX7V+zmm604fDJHE8kivvr5VK4m+lrYND3bhMwcs9/7nnomTx5B5bkfZQOcRLwAAyrleAj8m+b5E/mdEX+Zazmis+qgC1jU8+J9p92P/Z0n3fOC3ll2gAIU8IuAxWLBv9v/cavrv5073PKYUX4CZnU/HD2RhiPHTsJicb/N2Cr+6FLXm9VynnqRn2/CsROnYLOpb+SeCpQxb1fauT9cC1a19wx/PxT04HxgBNq2tH+bzZSZiyF+Jn5dvk5+w0/nDi0D0wG2QgEKhIUAAyq+7MZSlqkU45m3Yqzn/FI2w80oEFICegWINaiTQr2KN7rnFSrCRQpQQKMCer0eyRXsn+oWHEJ8QkLBRc6Xo8APPy9Bi26D0K3fMHS/9QlcddsT2LJ9j7PFZSs3ot21D8n1otzUWUud60QAZfxXP6NVj/vRtd/j6NTnMWzctsu53l8z8VGez38Sovj7wV/GrMd3gbjYaIx9fQhGjZsif3aGvDAWzw8dgNo1qvheCUtSgALhKVCCUXn+zVaCCljUu0CL6gYkFwqqGPXAZXWM3jfiGgqEgUDneu7H+FUNo8JgZBwCBSjgTeCaXr3dVnXv1tMtjxnlIxAXG4NPRg3DmnmfYuXs8Ti/fi2M/mSqbCwnNx9PvvIxHr23DzYu/gJjXh2Ml9+bJD+NFwU2bP0P476cgcljn8OGRRNwY6+OePyljyCuaBHr/ZW6ePjd0DhFjzpJ6smRvxphPRQogUDXy1th0+KJWPj9u/LY739jtxJszaIUCB0B9iR4AgyolKO9+ET+g56J6NskBm1rGXFD42h82CsJ1eLJXo7srDoEBAa3i8ejl8bhirpGdKkfhacuj8etTV2fpB8C3WQXKEABPwo8+OBgPDP8BXTtepVMYl7k+bEJVlWEwHU9LkPHds0hPnVPSoiDeOBmheREucXq9f/Ih2/2v6ErDHo9une8RH5d7LKVG+T6JX+sR4c2TdG6WSMYjQYM6NtD3vqzfdd+ud5fkzY1jXijWwJ6NoxGO/W86K4WsXj1Snsf/dUG66FASQX0eh1qVU+Rx35Jt2X5UguUy4YZhzKw8oNVMm2dZn8oevqhdLks8rdO3Vou7bLSyBbgX/blvP+rJ+gwqFUsXumSgAfbxKF+BX4KU87krD5EBK69IBojOiZguBpM6aoGVUKkW+wGBShQjgLXXnsDXh75pkxivhybYtVeBH5Z+CeGvvgRtu3YiwfutF81dCz1lAygREUZnVs1rFcTR4+fkstHjp9EgzrV5byYVE2pIF5wPPW0fPXnpFV1I4a2j8PILgm47eIYJPJ2H3/ysq5yE2DFWhBIP5yOv8askmnbNHvwJONwhlwW+dum24MsWhgL+6gdAQZUtLOv2FMKUIACFKAABSJM4PDRVHz+7WyvSdzOU5Bk974jOHkqXT6UNj0jW65Kz8iCuCVILpydREdHISPz3PqY6Oiza+wvonxmtv3b2eKi9UiINYR8ilX7qdcpId9Pb5Zq1xEf4yfnAO4vg15nP2g4pUCQBZJqJqHdkPZeU5O+TYLcQzYfjgJ8BwzHvcoxUYACFKAABSgQFgImswVppzO8JpvN6jLOoff3lc9CuemaTnji5XFynbj9JzsnV847Jnl5+UhMiJOLYn1efr6cd0xE+YS4WPuiokBRyjcpih/qh6gDUBTxqr2kdhzin6Jore+i10wUCL5AYq1EdBja3mtqekvT4HeSPQg7AQZUwm6XckAUoAAFKEABCpSzQMCqr1e7GoY/0t9riouN8diXBnVryCCM2WJBtZSK2HfwGEwms7Psjt0HUb1qRblco2pl7D1wVM6LieNWH8etP9m5ZmRkm0I+ZeeZYbbYQr6f3izFQ4CzNGJdcAwms2tQTxxDTBSgAAUiRYABlUjZ0xwnBShAAQpEsACHHu4C4yfNxMZtu5Cbl49DR1Px5Q/z0K7VRfIhtG1bXiiHP2XmYjXgYMGvy9fJb/jp3KGlzO96RSv8uWYL/t68E+KKmK+nL0C1KhXRuGFduZ4TClCAAhSgAAU8C+g8ZzOXAhSgAAUoEEQBNk0BCpRIQARRbn/4VVzS8wH0uO1J6HU6vPL0vbIO8c0/Y18fglHjpqBFt0EY8sJYPD90AGrXqCLXt2x6Ph4ceD0GDH4dLbsPwg8//4b3XnoYOvFQD1mCEwpQgAIUoAAFPAkwoOJJhXkUoAAFSijA4hSgAAWCKfD6M/dh/cLPsWDKO/jz54/wzUcjnAET0a+ul7fCpsUTsfD7d7Fh0QT0v7GbyJZJURQMvvcmrFvwGRap6/+a8zFaXXyBXMcJBShAAQpQgALeBRhQ8W7DNRQIZwGOjQIUoAAFwkwgKsoogygVkhM8jkyv16FW9RQYjQaP62Oio1BTXc8rUzzyMJMCFKAABSjgJsCAihsJM0JTgL2iAAUoQAEKUIACFKAABShAAQrwhut/AAAQAElEQVSEjgADKuW1L1gvBShAAQpQgAIUoAAFKEABClCAAmEr4AyohO0IOTAKUIACFKAABShAAQpQgAIUoAAFnAKc8Y8AAyr+cWQtFKAABShAAQpQgAIUoAAFKFA+AqyVAiEpwIBKSO4WdooCFKAABShAAQpQgAIU0K4Ae04BCkSCAAMqkbCXOUYKUIACFKAABShAAQoUJcB1FKAABShQYgEGVEpMxg0oEF4C+fkmDBj8Bvre/5LLwK6/6zk07XK3Sxo/aaZLGS5QgAIUoAAFgiXAdilAAQpQgALBFmBAJdh7gO1TIIgCNpsNL4/+Cn9v3uGxF48NuhlzvxnlTLf36e6xHDMpQAEKUKBYARagAAUoQAEKUCDMBBhQCbMdyuFQoCQCE76bg2079mLY/27xuFmVysmoV7uaM1VITgD/UYACkSLAcVKAAhSgAAUoQAEKFCXAgEpROlxHgTAWWLhsLb6etgAfjxqGxPhYjyOdNnsZnh/1BcStPvsPHfNYhpkUCBkBdoQCFKAABShAAQpQgAIBFGBAJYDYbIoCgRBYt2kHPv92tsc0XQ2QiD5s/ncPRrw1AePfGobqVSqJLLfUs0tbdGrfHDWrp2DJn+tx830vgUEVN6YyZXBjClCAAhSgAAUoQAEKUEC7AgyolHHfRRl00ELSKQrE8zK00FdPfTSZrZpwLtx3vU4p4xFW8s2zc3KRdjrDYzp1JkNWOHPecojbeeYtXoW3x03B3CV/Yd/BY3I+IzNblnnknj54aOANePiuGzBl/AtITIjF4uV/y3UGvU4z+0Orx444lsTPrE792RXzWkvyQAmziaIomjnutXzsGHQKLFabZqwL/mwqat/D7LCXwyk4xlCe16v+Vo0eO8JVHPfi+BfzWkrinEAeKGE2EceTVvaD2WKDUa+d31EFXZWzx03BPK3Mn+06XyJcQBfh4y/z8FOSo6GFlBRvRHyMQRN99eRpNOhK0ffg75u4aD0C/a9ju+YY/kh/j+n+O3rL7nS5rCVu7HUFxDNRRIqPi0FMtFEu6/XubwtGowFVKlVATl6+3D4h1qCZ/aHVY0f8HIifWfGzK+a1lComRsnjJNwm4mRVK/shTn2/T1bf97XS34L9FMdPTJReM+8xBfsujpFwO+7FeMQ+KTjOUJ2vkBCFWPX3bqj2r7h+RRt1qJQUrbljX5wTiOMk3JI4hytun4XKenHsVNbgsSP8EuOMEMeQmNdaCrdjnuMpnYCudJtxq3IRYKUUCJCACLo8cOd1cKTO7VugWpVKcjkuNkbe2jNp6nwcPZEGk9mC2YtWYsv2PWjX6qIA9ZDNUIACFKAABShAAQpQgAIUCG2BMgVUQnto7B0FKFAWAfHA2m79hqFl90EY/vqn8oqXS5o3KkuV3JYCFKAABShAAQpQgAIU0KgAu+0uwICKu0nY5JgtFq9jSU07g5xc++0bXgsFaYXo1+GjqRD3QXvqQn6+CcdOnJLPhPG0nnklF7jl+isx/fOXnRvWrVUNi6eOxu8zPsT8797GxsVfYGC/ns71oTwjjnstHjui30eOpyFPPb69+Ybyz623PjM/MALimSniGPLUmvh5EFebeVvvaZtA5p3JyMLx1NNemxTPdXI8/8lrIa6IWIGijutQPnaKO9cJ9Z/biD3gQmTg4viwWKweexPK58mi3+JcRrzve+y8minWi58PddYf/7MOCpS7AAMq5U4cnAb2HzqOFt0GQQQmCvZAfEvLNXcOR+ebhqBNrwfwwtsT5S0dBcsEc37wiDGyX1fd9qTax8fw3idTnd0RfzCM/+pntOpxP7r2exyd+jyGjdt2Oddzxr8CiqKgcsUk1KlZFQa9Hlr4J34B33TvC5i7eJVLd0P92BHfyiR+XrvfMgyt1eN72MhxOJOe5RxDqP/cOjvKmaAJiNvyeqjvm4U7sGzlRrS79iF06zdM/k6YOmtp4SJBWxYnzeL30WXXPYIr+w7F9Xc9h1kLVzj7Ix6wLX4ntO/9MK64YTD6P/wqxDbOApyJeAFv5zqhfuyI41qcg3k61xE7NZR/bkX/mMpToPi6xTnNy6Mn4ZX3v3IpLPJD+Tx55dqt8veR+BtEvO/fPfQteTu5YxA813FI8FVrAgyoaG2P+dBfcdJ59R1Peyz52geTceH5dbF2/meY/fWbmP/basxf8pfHssHIbNywLmZMfA3rFnyGV58ehInfz8Xmf3bLrmzY+h/GfTkDk8c+hw2LJuDGXh3x+Esfeb2SRW7EScQIvPvJDzIYt2vfYbcxh/qxIx4M/MXop+XPpTj+12z4FzPmLXeOI9R/bp0d5UzABcQJaM/+T+GZNz5za1sEGJ985WM8em8feZXZmFcH4+X3JuHgkRNuZYORIT6pvLHXFVg8bTRWzR6PXldeqv6B8LXz6snvZizGjt0H8dv0D+R6vU6HMRN+DEZX2WYIChR1rhPqx05R5zqh/nPrdigwI6ACC5aulh8oTp+9zK3dUD/XEd+C9uLjA7Fi1jgsmfY+4uNi1PP6mc5xvBbif6M4O8oZChQSYEClEEg4LI55ZTCmjH/BbSji8ro/12zBgL49EBsThQZ1a6hBicuxcNkat7LByhAn/o3Oq42Y6CiIb6KpVqUiVq7bKruz5I/16NCmKVo3awTxrTNiHMdOnML2Xfvlek4iW+C+/tfKP8zEMVNYItSPnX69u6B96yby51Ic/10ua4XfV22Uw9DCz63sKCdBEahZPQVfffgsRgwZ4Nb+6vX/QHxS3/+GrvIqs+4dL0G92tWwbOUGt7LByKiaUgHiwdjVq1RCYkIcru95uezvPzv3yu6IgH/f3p0hyon1A/pehZ/m/s7bPaUOJ97OdYRMUceOWB/sVNS5Tqj/3AbbLtLb79iuBaZ9/jJ6X9XBjSLUz3XEec51PS5DcmI8xLlary6XynMds8UCnuuA/zQswICKhneet66Lk0/xjS2F16eetN+jXrtGFecq8awM8dwGZ0YIzew7eEw+K0V8kiO6deT4STSoU13MyiTGKWaKuvderGeKDAFxlYf4w8xoMLgNWEvHjvhWpT/XbEbTxg3kOLT2cys7zUnABMTteOK4r5ic4NbmsdRTMoASFWV0rmtYryaOHj/lXA6lmTUb/pXdqV+nhnwVvwPE7yi5oE7E7YfqC9Izs8VLpCSO04uAOAfwdK4jimvp2BF9FR8OOc51tPZzK7yZAicQFxsN8Z4fHxfr1qiWznVE5/9cuwUXXVBPBvx5riNEmLQqwICKVvdcKfrtOAkteHIdHR2FtNPppaitfDfJys7F0BfHyqtRrri0mWwsPSMLMdHRct4xEV/xm5md41jkKwU8Cmjp2Hntg6+RkZkjryQTg9HSz63oL1PoCIjjXrxHFuyReM/PKNeARMHWfJ/fuecg3vjwWzw08AZUqpAor0IRV9fEqL+jHLVEnw0MZau/Hxx5fKVAYQHxHAmtHDveznW08nNb2J7LwRUQ7/kxGjlPFs/LEumJ/90i0XiuIxk40agAAyoa3XGl6XZSQpzczGQyy1cxycvLV09ek8RsyCRx/7B4NorFYsXY1x6DXm8/TJMS45GXn+/ST3HSlOAhSu9SiAsRL6CVY2f8pJmYPnsZJr4/XN7mIHacVn5uRV81mcK40+K4F++RBYco3vPF7TMF84I9f+hoKv739HvoekUrPHTXDbI7iqJA/FGZl2+Sy2LimI+LixGLTBTwKKAo2jh2ijrX0cLPrUd8ZgZVQLzn52ngPFk8fkA89+ulYXfJW/kFGs91hAKTVgXsf6lqtffsd4kEUipXkOUPHD4uX8Vk74GjqFG1kpgNiSQi1A89MxriG06+/vA5iNs4HB2rUbUyRH8dy45bfcRlv448vlLAk0CoHzvi4ZzvjP8eX/4wH9M+G4lmF9pv9xFjKfxzK/LEz0GNEPq5FX1iCj2BaikVIW4nKBhEFw95rV61Ysh09r89h3Dbgy+jY7vmeP2Z+5wBdNFB8bwX8dBdMS+S43eX48Rb5DFRwJNAqB87RZ3raOHn1pM584IvUEMD58nioboPPPUuXhs+CLdcf6UTjec6TgrOaFCAARUN7rTiuiyewZBvsn+qJ+ZFEtuIh0CJh7pOnr5IfovCnv1H8MvCFejRua1YHfSUnZOHOx95DcdTT+GVp+9FVk4uxCeXjme8iE8vRVT778075Vc9fz19gXyoleO+46APgB3wJBCwPLPFAscfjiaz2TkvOhDqx86L70zEpKnzMXrkI0hOSpDHvTj2xZhC/edW+DIFT0Dc3iCOe7PZIjsh59WfBbHQtuWF4gVTZi6GOJZ+Xb5OfsNP5w4tZX6wJ9t3HcAN94xAh0ua4r7br5XPzBLH/akzGbJrPbu0xbRZS9XfCaeRmZWDyervrpuu6QRFUeR6TiJbwKQe857OdYRKKB87xZ3rhPrPrfBlCp6AxWKV5zcW9X1evO+L93zxoYzoUaif6/y84E8MGzkezzx6Oy5tdZHzXCdbPd/nuY7Yg0xaFWBARat7roh+i+9273W7/WuTr7lzOLr2HeosPeKxO7F1+x759bK9Bz6rBlPayK+qdBYI4oy4r1985a34RPWmQS+gx21PynTLAy/JXrVsej4eHHg9Bgx+HS27D8IPP/+G9156GDqdP0+uZVOcaFDg6Vc/Rcur7pN/MD4/6gs5L4KGYiihfuysOfswzgeHvyePecexf+hIqug+QvnnVnaQk6AJ7Np7WB7r4vJp8WBL8TMgjn/RIfHwwrGvD8GocVPQotsgDHlhLJ4fOgAFH0wuygUr7d53WDY9Z/EqiN9ZjuNe9FesuL1Pd5xXryau7DsU7a59SP4RMfjem8QqJgqgqHOdUD52ijvXCfWfWx56wRX4cc4y+Z4vbg+eOf8POT9z/nLZqVA/19m4bZfs51sffedyrrNgqf3bRnmuI3k40aAAAyoa3GkuXfawsGbeJ9i6dJIzLZ851lmqQd0aWDDlHSyZ9j5Wz/1EXmJtNBqc64M5I75CrWC/HfOO/iuKAnEyvW7BZ1j0/bv4a87HaHXxBcHsMtsOIYHRIx92HvOOY0cc71D/KUpoHzviZ9LR54Kv4rJ1tfsQ4xBlQvHnVvSPKXgC5zeo5Xbcv/XcA84Odb28FTYtnoiF6nvmhkUT0P/Gbs51wZ65ums7t76L49/R//i4GHz81uNYMWsclv00Bj98+pLz2ULB7jvbD75AUec6oXzsFHeuI2RD+edW9I8peALiNhnxPlkwiSv3RI8UJbTPdV58fKDH9/w+V3cU3ee5jlTgRIsCAQ+oaBEpHPssfqGLEw4tji0mOgo1q6fwyhQt7rwg91nrx46Wf26DvOsjunnxYO9a6ntmqATPS7ozxKXgKZWSS7oZy1MAWj52tP5zy8MveAI81wmePVv2LBDuuQyohPse5vgoQAEKUIACFKAABShAAQpQwBcBlqFAiQQYUCkRFwtTgAIUoAAFKEABClCAAhQIFQH2gwIUCKYA2g21PAAAEABJREFUAyrB1GfbFKAABShAAQpQgAIUiCQBjpUCFKBAGAkwoBJGO5NDoQAFKEABClCAAhTwrwBrowAFKEABCngTYEDFm0wY5k/95Tc89sKHxY5MfKXfkj/+hiP9uWaL/Crawhtu2rZLljl6Iq3wKvz73365zvG1tY4CObn5WPT7Wnz5/Tx8+9Ovcv546mnHahRs+7cV67Hq7234b88h2Gw2ZxnOUKAkAuI47f/wqzhx8txx5m37ZSs3yuNWHPti/p+d+9yOPccxunLtVrdq8vJN9u3/XO+yzmq1YdW6bfKYn/TDfIivid2556BLGdGeaHeJuq2oe+v2vchX63MpxAUKlEDgpXe/xGffzCp2i/2HjtuPW/V9X7zvrtu0A2fSs9y2Ez9L4hgtyXu+eH+ftXAFJnw3B1NnLcXyvzYhOyfXWXfBtsXPwNqN23HkuPvvFOcGnPFFIKLL+Ptcx3GMit8HhWGPnTglf3bWb9npsqq4cx1RWBzv4ueJ7/lCg6msArl5+RDnOuI9tri6HO/l4vhbumID/t68w+P5huMYFcdz4TrFeYrY/vSZTJdV23cdwPTZy+R7/ox5y9W6d8JssTjLFGxb9FX87JzJcP9949yAMxTQgAADKhrYSf7qojgJ3vLvnmKrO3jkBAY//6EzPfDUu+jZ/ykMf/1TmMzn3hQnTJkjy7w9bopLnaLMw8++L9eJ4Ilj5a69h9Cpz2MY+uJHmPj9XHzw+XQ5f2XfofIPTFGuYNuPPjcGg4a9jRvuGYFrBzwjT1pEGSYKlEQgIysH4he4CHYUt53juBXHv5jve/9LuGnQCxB/FDq2dRyj9z35DnbsPujIlq9zfl0pj/vBI8bAYrHKPHEi0v+hVzDoibflMf+F+nPz9Kuf4MZ7nsfLo7+SZcREtCfaFduKum/530hcfsNgfDp5lltQR5RnokBxAuL4PHD4RHHFZJBDHnvq+7543x342Bu47PpHMHn6QpdtS/qeP3fxXxDv78+88RmmzVqKl9+bhAeHj0bbqx/E/kPHZN3ihHrw8/bfN+Jn4K4hb6L7LcNw99C3IH5nyEKcUKAEAv4+13Eco+JcqPDvkfc/nybf88d+8ZOzh+K4Le5cRxQWx7v8uVN/X/A9X4gwlUVAfHAjznUKBzg81el4LxfH3yPPfYABg9+Q5xsimF6wvOMY/XnBHwWzIT4sFces2H73/sPOdW98+I08ZxLBfPGe//yoL9S6X8e1dz7jLFOwbfH74M5HX8dl1z0C8XsiUz1fcxbkDAU0JMCAioZ2VqC7+tk7T2Lr0kn4a87HeOSePpi9aCXmLl7l1o0FS9dg9/4jzvxFy9ZCfGrjzFBnxBv9c29OUOeA2V+/ieUzx2LNvE/w69TRGNivJ8wFAjWikGh7y29fYuXs8Zjw7lOoXDFZnrSIaLlYz0SB8hL434Dr5HG/YdEEjH3tMRk0GTNhusfmRHDEsUIEUETww7HseB0/aSa2bN+Dj94Yoh7P4+Sxv0o9rkcMGQCjweAoJl8dba+Z9ymmf/4yrunWDh9+8SPGTPhRruckRATCtBviuBPvu79N/wBdL2+Ftz76Tg18HHcbrS/v+cfUT+6fevVjXN72Yvw+40MsmPIONi2eKI/rLpe1dAYcHZWLtjcu/kKWHTXif2q7x2RQ5cBh9/Yd2/CVAv4QEOcbvpzrpJ3OkOdBjjYPHU2FuPrKsSxeS3quw/d8ocYUDIHmTRrKcx3xvvzTF6+iUoVE9UPOsR67Iq50NJnMznVfFwq2ixXi/Fxcef7gwOuxdv5n8j1//cLPMf7Nx1Greooo4kyOtsV68bvhiQdvkT9Lj6rBxdy8fGc5zlBAKwIMqGhlTwWxnwnxsbj/jt6yB+L2GzlzdiJOuhvWq4kvvpsjc8StOZ9/O8tZXmaqE3GJt/ijss/VV6BB3Rpqjv3/GlUrYfgj/XFDz8vtGQWmiqIgKSEOHdo0hTjhad2sEUS0XHziX6AYZylQLgJGowFdr2gNcdyJS1gLN3Lf7dfKk2vHJ+1LV2yQt8aJAGHBsqvX/4N6tavhystawaDXy1WJ6nF9e59ueO6xO+Ry4UlcbDQuuqAeXn7yHtx72zX4/NvZELdCFC7n6zLLUcBXAUVRUDWlAgb07Sk3EZ9Eypmzk65qoMWX93xxdYzYpG/vLmpAPEnMQq/XyeN63BtDXX4PyJXqRPx8VK6YhN5XdcCkD+yfaL7w9kR1Df+nQPkLFHWuI1q/46ar5C105rO3L3zz4yJc3LiBPEcR60UqzbmO2I7v+UKBKRgC4n25ccM6uKHXFRBBw8K339x9Sy/5IencJX/J7okrdsVtdY6/C2SmOhG37qgv6H9jN8TGRIlZREUZ0blDC0x8f7hcLjwR62vXqCLPc8a+PgRrNvwLcVt04XJcpkCoCzCgEup7KET65/ijsUXThi49UnQKHhx4A2bO/wPi05oVa7fKT/TvuKm7SzlxotKu1UWYMe8P/LLwT4/357tsUGhBvDmLP2BF9j8794kXpvAVCJmRiWeYHDp6Am1aNHbr01Wd28hAyZc/zJfrxCc44sRD/EEoM85OunVsjX0Hj+GTr3/BkWMnz+b6/nL/nb1lYcfJilzghALlLLDv0FHZQtPG9eWrY+Lre/4lzRvJTcTPhbjXvqSB8Lq1qqHP1R3lCbb4OZSVcUKBchbwdq4jmr3rlp4yaL5w6VqcOpOBr6ctwIN3XQ+dGoQU60Uq67mOqIPv+UKBKdACe/cfgQiWJyfGuzRds3oKbrn+Snw6+Rd5ZeF3M35Fo/Nqo1P7Fi7lOrVvLpfFlY3iQyhH4FFm+jARwXrxQdLaTdt9KM0iFAgtAQZUQmt/BKw34koSccVIwVT4vsuNW/+Tzy35bsZi3P/kO/KPxw6XNHXrY48ubVCtSkU1qjxPfpIuPnmvUrmCW7nHH+gn63j2jc/l/fld+z2OEW9NwK59h93KespofH4dmb3l393yNXQm7ImWBMSnKwWPe/GLv2D/d+87AvFgTvEwtQeefg8ZmTm4vsdlBYvIeb1OJ6/EEp/UzFq4Qt7WM6BfD7mu4OSmazpBBFXGTvwJ3W99Qj4/4qFn3od42HPBct7mxVVa4hOcDerPo7cyzKdAcQJZ2bnyGC147Be8hFtsv/yvjVi4bC3GT5qJke9OklcOplRKFqtcki/v+eIT9zefu1/9ZDMN4l77Nr0ewPV3PQfx7KzCv2tcKi+wcOH59eTSzj2H5CsnFCiNgLglueBxLz78KViPr+c64rYFcTXtJ1//jCkzl8g/Pju3b1mwKjlf1nMdvudLRk7KKFDcuU7aqXSIK2vnLv4L4krAOYtX4e5be3lsVXxYJD4YEudF9tt6bkCBOKLcpmXTCyDKzVvyl3yOSotugyCejyLOkQr/rpEbeJg0u+g8iAC8uHXOw2pmUSBkBRhQCdldU44dU6vON5lx6/9edkniwWvqKuf/49STavHAqdfHTJbPevhxwquIj4txrnfMiMu0HxxwPUTgRVyud9ctnt+QxRvltM9G4ov3nsbQ+/ui+UUNIa5sESfZ4t5LR33eXnNy8uQqcbuEnOGEAqUQmD57qctx/9Azo11qEQ9SFg/mFA9TE8fz95+8iCaNXD+ld2xwbbf28r5j8TC1fr27oHqVSo5VzlfxB+mHrz6GmV++Jm/x6dG5DURQUDzgcPSnU53lipoRl+FWSEooqgjXUaBIgc1qILrwe/7xk6ddthk2cjwef+kjiPf+69Qg4hvP3u+y3rHg63v+9T0ux6If3sPokQ/L4GOlikky6H7VbU+6POjZUW/h15xcx3t+bOFVXKaAzwIvvj3R5T1fXC1YcGNxvPtyriO2uee2q+WHQOO+nIH/DbweOp0isl1SWc91RGV8zxcKTGURKO5cRzxgXzyQVjzr6qe5v+OVp+6F+ADIU5vituWeXS6FeNhspQqJEB8SFS4nfhaeevg2LPtpjKxLXKmenpElH74vAiuFy3taFrfMifpFXZ7WM48CoSqgiYBKqOJpuV9RRgPmfjNKPiBWPCRWpCsvb+UyJPHcEvGgtmH/u0Ve5vrdjF9d1hdcuE49cY6LjZGfaIpP0wuuKzivKAraX9JEnlx/8Mqj8o1XbFf4CeIFt3HMbzn7DUUtm57vyOIrBUoscHuf7hDHuyN9/eFzLnU4HhI4eaw9XwQ9vF26Ku7/feDO6+T24kRbzniZXNCgNu646Sq8/sx98o/Mti0vlJ9yeqvbUY34NFWcZDhuoXDk85UCJRFo0eR8l+NeHP/iysKCdTgeDNvlspbyAYHrNu0ouNpl3tf3/JjoKIgTcRFEn/TBM/hk1BMQx/OSP/92qc/TgrgqS5xci9t/PK1nHgV8EXjnxYdcjv0h993ssllJznXE+7i4NUH87PTs0talnoILilL6cx2+5xeU5HxpBYo713E8GFYEQMTxPG7SDKSmnfHa3H23XyPXPagGEkVQXS54mIgPkW6+tpP6AdKd+HnS6/J8X1wh5niulodNZJbFYsWGLf/h8kubyWVOKKAlAQZUtLS3/NhXRVHk7TfiAbGOJO799dTEoP7XyHvZxR+WC5au8VREPoDqZ/UT+OGP3u5xvXjIladnQIg2Y6KN8gTb44ZnM8WDEV95/2v5QMP6daqfzeULBUouUCE5QT4Q03HcewsAiofRik/oxSWxb4+b4rUhcW+x+ONUfILjqZC4+kqcKBRcJ/7IFFeciD8sbbaCa1znxXMnnnzlY4ig42VtL3ZdySUKlEAgNibK5bgXx7+nk2KRN2rE/+TtDA8OHy2f/+OpGVFfUe/5O/cclM/VKryteOCtyBPHvnj1lsStR+JTU/EcFW9lmE8BXwTEw+/F8e5I4g8+T9v5cq4jtnv16UH4bvwLzoeMizxHKuu5Dt/zHZJ8LauAr+c64udBBLrFN7M98uwHEMegp7bFlbq/qAGS3ldd5mm1fN6VuLW04EpFUVA1paLMclxxKBc8TN75+Hv54e3VV7bzsFYTWexkBAswoBLBO78kQ3/x8YHy206GjRyHjdt2edxUPLiq8MOsHAXFPfPikr/HXvgQ4n7NbTv2QgRnnnvzc4hLWwufNIs2lvy5HuJ+TRFI6T3wWXlrxadvPwFFcb/E1tEOXyngTwFxv7x4kr24Z/jbnxZ5rDo6yij/UPW4Us18ftQE9L3/RUybvVT+7Ij7g9//bBrErUW33dAVRoNeLWX/f++Bo/KeZvFMFnFJeY/bnsAm9edtwntPwdPtRPatOKWAfwVEoPuTUcNkpeLWNPEATrlQaFLUe764orDHbU9ilBqMFM8kEu/5IkAinpslqune8RLx4kzillPxMyHutx88Yoy89UhcAfBYoasJnBtwhgLlIODLuY74Q9Xb+3FJz3X4nl8OO5FV+ihwrph4yKx4zxdXkjz7xmfy4bPn1p6ba1i/lss5y7k1wI9zf0ev25/CF1PmYtW6bfJ856tpCyDOd0T9TS6wPxNLbAzSjrwAABAASURBVCOe3yI+bBLPW5k0db56jvQSJk9fiOceu0N+K5Aow0QBLQkwoKKlvVXGvipKyQIRugLlxa0NH742GOLT/PueeMfnbytRFHub4rLtgf16Yv3mnRD3a/Z7YCREcEZc0v3Wcw/IS8ILDk/8MSlOqsVVMeIPysH33iQ/EapcMalgMc5ToFgBx3GsKEqxZUUBRXEtJ449cb/wGx9+C/FHnygjkqK4lhN5ntKg26+VJyfiIZ+3P/yqfEDnhO/m4KGBN+DpR/q7bCKCjOKeZhFEFA+I69G5LcSzV1o0cf12LZeNuEABPwkoyrljWgRLRCBP3Gc/eMSHMJ/9qtjimlIUex3NLzoP4hlD4ptQxDOJxHu+ePCh0WiQ7+WFb+MRvw+GvvgRPpn8CzKycvDq0/dC/G4QV8wU1ybXU6CggKLYj8GCeUXNO35HiDKlPdfR6e2n0yU91+F7vlD3MbFYkQKKUrLjvnBlHds1xzOP3i4/7BEP0XesV5Ti6rWvF1eWiFvixHn7oCfehjjfeVsNqF95WUt5q6d473fUKX6vPPzs+xBX4E6ZsRg1q1eWZcRt0Y4yfKWAlgTsvwG01GP2tdQC4g/DJdPeL3Z78bVl4tkpHdo0dSlbMTkRC6a8gzXzPkGNapUhHrQpkkuhAguiDvHpvsgSD5Idrv7xuHzmWKyYNU7ezyxeRX+u63GZKCKTo22xrUii/PTPX4a4Z5PBFEnESQkFxHEsjiXxDQ3FbSrKiZ+TguX06omyOM7FOnHC4ThGLzy/bsFizvn7br8WoqzYTmSKp97/8tUbWDv/M8z7dhR+m/4Btvz2JR69tw/E1S2ijEhiG0cSP2PiGUcvPD4Q4gRFrGeiQEkFpox/QQYmitvujpu6y2M2NibKpagI5Ilj8puPRsjbG8TPgUguhQosiLKO9/yG6ieZb7/wIDYv+VI+K0scz+JnQPRJ1OvYzNG22FYk8TtBPGtFPBxR/HHrKMdXCvgqMFj9AEYcR8WVd7yXi98RBcsWPtdxHKMFyxScF89gmfDuUzKr4LmOOMcRt4OKV9Gfguc6orA43h2J7/lChKksAuL9WxxPhY8zT3WK93HxXlx43YC+PeTvgqH395WrRH239+km5wtPWl18gSzbutkFclXnDi0w8f3h2PDrFxDHu/h7QcyL3wPiGS2ykDoRbYt6HUmUE3kd2/HZKSoP/9eoAAMqGt1xWu62uC1I3MssXrU8DvadAiURECc74lP5qikVeNtaSeBYVtMC4tsaxD364hlD4mdA04MJ785zdH4WEOc4PNfxMyqrC3kBcRuzCKCIK9rFfMh3mB2kgB8EGFDxAyKroAAFKEABClAgkAJsiwIUoAAFKEABCgRfgAGV4O8D9oACFKAABcJdgOOjAAUoQAEKUIACFAg7AQZUwm6XckAUoAAFyi7AGihAAQpQgAIUoAAFKECBogUYUCnah2spQAFtCLCXFKAABShAAQpQgAIUoAAFAirAgEpAudkYBRwCfKUABShAAQpQgAIUoAAFKEABLQswoKLlvRfIvrMtClCAAhSgAAUoQAEKUIACFKAABZwCYRtQcY6QMxSgAAUoQAEKUIACFKAABShAAQqErUCwBsaASrDk2S4FKEABClCAAhSgAAUoQAEKRKIAxxwmAgyohMmO5DAoQAEKUIACFKAABShAAQqUjwBrpQAFPAkwoOJJhXkUoAAFKEABClCAAhSggHYF2HMKUIACARBgQCUAyGyCAhSgAAUoQAEKUIACRQlwHQUoQAEKaE+AARXt7TP2mAIUoAAFKEABCgRbgO1TgAIUoAAFIl6AAZWIPwQIQAEKUIACFIgEAY6RAhSgAAUoQAEK+FeAARX/erI2ClCAAhSggH8EWAsFKEABClCAAhSgQEgLMKAS0ruHnaMABSigHQH2lAIUoAAFKEABClCAApEkwIBKJO1tjpUCFCgowHkKUIACFKAABShAAQpQgAKlFmBApdR03JACgRZgexSgAAUoQAEKUIACFKAABSgQKgIMqITKngjHfnBMFKAABShAAQpQgAIUoAAFKECBMBVgQKXAjuUsBShAAQpQgAIUoAAFKEABClCAAuEv4I8RMqDiD0XWQQEKUIACFKAABShAAQpQgAIUKD8B1hyCAgyohOBOYZcoQAEKUIACFKAABShAAQpoW4C9p0D4CzCgEv77mCOkAAUoQAEKUIACFKAABYoT4HoKUIACJRRgQKWEYCxOAQpQgAIUoAAFKECBUBBgHyhAAQpQILgCDKgE15+tU4ACFKAABShAgUgR4DgpQAEKUIACYSXAgEpY7U4OhgIUoAAFKEAB/wmwJgpQgAIUoAAFKOBdgAEV7zZcQwEKUIACFNCWAHtLAQpQgAIUoAAFKBAwAQZUAkbNhihAAQpQoLAAlylAAQpQgAIUoAAFKKBVAQZUtLrn2G8KUCAYAmyTAhSgAAUoQAEKUIACFKCAFGBARTJwQoFwFeC4KEABClCAAhSgAAUoQAEKUKA8BBhQKQ9V1ll6AW5JAQpQgAIUoAAFKEABClCAAhTQgAADKmXcSdycAhSgAAUoQAEKUIACFKAABShAgfAXKDxCBlQKi3CZAhSgAAUoQAEKUIACFKAABSigfQGOoJwFGFApZ2BWTwEKUIACFKAABShAAQpQgAK+CLAMBbQlwICKtvYXe0sBClCAAhSgAAUoQAEKhIoA+0EBCkS0AAMqEb37OXgKUIACFKAABShAgUgS4FgpQAEKUMB/Agyo+M+SNVGAAhSgAAUoQAEK+FeAtVGAAhSgAAVCVoABlZDdNewYBShAAQpQgALaE2CPKUABClCAAhSIFAEGVCJlT3OcFKAABShAAU8CzKMABShAAQpQgAIUKJUAAyqlYuNGFKAABSgQLAG2SwEKUIACFKAABShAgVAQYEAlFPYC+0ABCoSzAMdGAQpQgAIUoAAFKEABCoShAAMqYbhTOSQKlE2AW1OAAhSgAAUoQAEKUIACFKBAcQIMqBQnxPWhL8AeUoACFKAABShAAQpQgAIUoAAFAizAgEqAwUVzTBSgAAUoQAEKUIACFKAABShAAQpoW8CXgIq2R8jeU4ACFKAABShAAQpQgAIUoAAFKOCLAMuUQIABlRJgsSgFKEABClCAAhSgAAUoQAEKhJIA+0KB4AkwoBI8e7ZMAQpQgAIUoAAFKEABCkSaAMdLAQqEjQADKmGzKzkQClCAAhSgAAUoQAEK+F+ANVKAAhSggGcBBlQ8uzCXAhSgAAUoQAEKUECbAuw1BShAAQpQICACDKgEhJmNUIACFKAABShAAW8CzKcABShAAQpQQIsCDKhoca+xzxSgAAUoQIFgCrBtClCAAhSgAAUoQAEwoMKDgAIUoAAFwl6AA6QABShAAQpQgAIUoIC/BRhQ8bco66MABShQdgHWQAEKUIACFKAABShAAQqEuAADKiG+g9g9CmhDgL2kAAUoQAEKUIACFKAABSgQWQIMqETW/uZoHQJ8pQAFKEABClCAAhSgAAUoQAEKlEGAAZUy4AVyU7ZFAQpQgAIUoAAFKEABClCAAhSgQOgIlFdAJXRGyJ5QgAIUoAAFKEABClCAAhSgAAUoUF4CEVsvAyoRu+s5cApQgAIUoAAFKEABClCAApEowDFTwD8CDKj4x5G1UIACFKAABShAAQpQgAIUKB8B1koBCoSkAAMqIblb2CkKUIACFKAABShAAQpoV4A9pwAFKBAJAgyoRMJe5hgpQAEKUIACFKAABYoS4DoKUIACFKBAiQUYUCkxGTegAAUoQAEKUIACwRZg+xSgAAUoQAEKBFuAAZVg7wG2TwEKUIACFIgEAY6RAhSgAAUoQAEKhJkAAyphtkM5HApQgAIU8I8Aa6EABShAAQpQgAIUoEBRAgyoFKXDdRSgAAW0I8CeUoACFKAABShAAQpQgAIBFGBAJYDYbIoCFCgowHkKUIACFKAABShAAQpQgALaFWBARbv7jj0PtADbowAFKEABClCAAhSgAAUoQAEKnBVgQOUsRDi+cEwUoAAFKEABClCAAhSgAAUoQAEKlI9AKAVUymeErJUCFKAABShAAQpQgAIUoAAFKECBUBIIi74woBIWu5GDoAAFKEABClCAAhSgAAUoQIHyE2DNFHAXYEDF3YQ5FKAABShAAQpQgAIUoAAFtC3A3lOAAuUuwIBKuROzAQpQgAIUoAAFKEABClCgOAGupwAFKKA1AQZUtLbH2F8KUIACFKAABShAgVAQYB8oQAEKUCDCBRhQifADgMOnAAUoQAEKUCBSBDhOClCAAhSgAAX8KcCAij81WRcFKEABClCAAv4TYE0UoAAFKEABClAghAUYUAnhncOuUYACFKCAtgTYWwpQgAIUoAAFKECByBFgQCVy9jVHSgEKUKCwAJcpQAEKUIACFKAABShAgVIKMKBSSjhuRgEKBEOAbVKAAhSgAAUoQAEKUIACFAgNAQZUQmM/sBfhKsBxUYACFKAABShAAQpQgAIUoEBYCjCgEpa7tfSD4pYUoAAFKEABClCAAhSgAAUoQAEKFC+g9YBK8SNkCQpQgAIUoAAFKEABClCAAhSgAAW0LhBy/WdAJeR2CTtEAQpQgAIUoAAFKEABClCAAtoX4AjCXYABlXDfwxwfBShAAQpQgAIUoAAFKEABXwRYhgIUKJEAAyol4mJhClCAAhSgAAUoQAEKUCBUBNgPClCAAsEUYEAlmPpsmwIUoAAFKEABClAgkgQ4VgpQgAIUCCMBBlTCaGdyKBSgAAUoQAEKUMC/AqyNAhSgAAUoQAFvAgyoeJNhPgUoQAEKUIAC2hNgjylAAQpQgAIUoECABBhQCRA0m6EABShAAQp4EmAeBShAAQpQgAIUoIA2BRhQ0eZ+Y68pQAEKBEuA7VKAAhSgAAUoQAEKUIACqgADKioC/6cABcJZgGOjAAUoQAEKUIACFKAABSjgfwEGVPxvyhopUDYBbk0BClCAAhSgAAUoQAEKUIACIS/AgErI76LQ7yB7SAEKUIACFKAABShAAQpQgAIUiDSBSAyoRNo+5ngpQAEKUIACFKAABShAAQpQgAKRKFCuY2ZApVx5WTkFKEABClCAAhSgAAUoQAEKUMBXAZbTkgADKlraW+wrBShAAQpQgAIUoAAFKECBUBJgXygQwQIMqETwzufQKUABClCAAhSgAAUoEGkCHC8FKEABfwkwoOIvSdZDAQpQgAIUoAAFKEAB/wuwRgpQgAIUCFEBBlRCdMewWxSgAAUoQAEKUECbAuw1BShAAQpQIDIEGFCJjP3MUVKAAhSgAAUo4E2A+RSgAAUoQAEKUKAUAgyolAKNm1CAAhSgAAWCKcC2KUABClCAAhSgAAWCL8CASvD3AXtAAQpQINwFOD4KUIACFKAABShAAQqEnQADKmG3SzkgClCg7AKsgQIUoAAFKEABClCAAhSgQNECDKgU7cO1FNCGAHtJAQpQgAIUoAAFKECQXaSUAAAQAElEQVQBClCAAgEVYEAloNxszCHAVwpQgAIUoAAFKEABClCAAhSggJYFGFDxbe+xFAUoQAEKUIACFKAABShAAQpQgALhL+DzCBlQ8ZmKBSlAAQpQgAIUoAAFKEABClCAAqEmwP4ES4ABlWDJs10KUIACFKAABShAAQpQgAKRKMAxUyBMBBhQCZMdyWFQgAIUoAAFKEABClCAAuUjwFopQAEKeBJgQMWTCvMoQAEKUIACFKAABSigXQH2nAIUoAAFAiDAgEoAkNkEBShAAQpQgAIUoEBRAlxHAQpQgAIU0J4AAyra22fsMQUoQAEKUIACwRZg+xSgAAUoQAEKRLwAAyoRfwgQgAIUoAAFIkGAY6QABShAAQpQgAIU8K8AAyr+9WRtFKAABSjgHwHWQgEKUIACFKAABShAgZAWYEAlpHcPO0cBCmhHgD2lAAUoQAEKUIACFKAABSJJgAGVSNrbHCsFCgpwngIUoAAFKEABClCAAhSgAAVKLcCASqnpuGGgBdgeBShAAQpQgAIUoAAFKEABClAgVAQYUCm/PcGaKUABClCAAhSgAAUoQAEKUIACFAhTgQIBlTAdIYdFAQpQgAIUoAAFKEABClCAAhSgQAEBzvpDgAEVfyiyDgpQgAIUoAAFKEABClCAAhQoPwHWTIEQFGBAJQR3CrtEAQpQgAIUoAAFKEABCmhbgL2nAAXCX4ABlfDfxxwhBShAAQpQgAIUoAAFihPgegpQgAIUKKEAAyolBGNxClCAAhSgAAUoQIFQEGAfKEABClCAAsEVYEAluP5snQIUoAAFKECBSBHgOClAAQpQgAIUCCsBBlTCandyMBSgAAUoQAH/CbAmClCAAhSgAAUoQAHvAgyoeLfhGgpQgAIU0JYAe0sBClCAAhSgAAUoQIGACTCgEjBqNkQBClCgsACXKUABClCAAhSgAAUoQAGtCjCgotU9x35TIBgCbJMCFKAABShAAQpQgAIUoAAFpAADKpKBk3AV4LgoQAEKUIACFKAABShAAQpQgALlIcCASnmolr5ObkkBClCAAhSgAAUoQAEKUIACFKCABgTKGFDRwAjZRQpQgAIUoAAFKEABClCAAhSgAAXKKMDNCwswoFJYhMsUoAAFKEABClCAAhSgAAUooH0BjoAC5SzAgEo5A7N6ClCAAhSgAAUoQAEKUIACvgiwDAUooC0BBlS0tb/YWwpQgAIUoAAFKEABCoSKAPtBAQpQIKIFGFCJ6N3PwVOAAhSgAAUoQIFIEuBYKUABClCAAv4TYEDFf5asiQIUoAAFKEABCvhXgLVRgAIUoAAFKBCyAgyohOyuYccoQAEKUIAC2hNgjylAAQpQgAIUoECkCDCgEil7muOkAAUoQAFPAsyjAAUoQAEKUIACFKBAqQQYUCkVGzeiAAUoECwBtksBClCAAhSgAAUoQAEKhIIAAyqhsBfYBwqEswDHRgEKUIACFKAABShAAQpQIAwFGFAJw53KIZVNgFtTgAIUoAAFKEABClCAAhSgAAWKE2BApTih0F/PHlKAAhSgAAUoQAEKUIACFKAABSgQYIEgBFQCPEI2RwEKUIACFKAABShAAQpQgAIUoEAQBMK7SQZUwnv/cnQUoAAFKEABClCAAhSgAAUo4KsAy1GgBAIMqJQAi0UpQAEKUIACFKAABShAAQqEkgD7QgEKBE+AAZXg2bNlClCAAhSgAAUoQAEKRJoAx0sBClAgbAQYUAmbXcmBUIACFKAABShAAQr4X4A1UoACFKAABTwLMKDi2YW5FKAABShAAQpQQJsC7DUFKEABClCAAgERYEAlIMxshAIUoAAFKEABbwLMpwAFKEABClCAAloUYEBFi3uNfaYABShAgWAKsG0KUIACFKAABShAAQqAARUeBBSgAAXCXoADpAAFKEABClCAAhSgAAX8LcCAir9FWR8FKFB2AdZAAQpQgAIUoAAFKEABClAgxAUYUAnxHcTuaUOAvaQABShAAQpQgAIUoAAFKECByBJgQCWy9rdjtHylAAUoQAEKUIACFKAABShAAQpQoAwCGgmolGGE3JQCFKAABShAAQpQgAIUoAAFKEABjQhop5sMqGhnX7GnFKAABShAAQpQgAIUoAAFKBBqAuxPxAowoBKxu54DpwAFKEABClCAAhSgAAUiUYBjpgAF/CPAgIp/HFkLBShAAQpQgAIUoAAFKFA+AqyVAhSgQEgKMKASkruFnaIABShAAQpQgAIU0K4Ae04BClCAApEgwIBKJOxljpECFKAABShAAQoUJcB1FKAABShAAQqUWIABlRKTcQMKUIACFKAABYItwPYpQAEKUIACFKBAsAUYUAn2HmD7FKAABSgQCQIcIwUoQAEKUIACFKBAmAkwoBJmO5TDoQAFKOAfAdZCAQpQgAIUoAAFKEABChQlwIBKUTpcRwEKaEeAPaUABShAAQpQgAIUoAAFKBBAAQZUAojNpihQUIDzFKAABShAAQpQgAIUoAAFKKBdAQZUtLvvAt1ztkcBClCAAhSgAAUoQAEKUIACFKDAWYEwDqicHSFfKEABClCAAhSgAAUoQAEKUIACFAhjgeAMjQGV4LizVQpQgAIUoAAFKEABClCAAhSIVAGOOywEGFAJi93IQVCAAhSgAAUoQAEKUIACFCg/AdZMAQq4CzCg4m7CHApQgAIUoAAFKEABClBA2wLsPQUoQIFyF2BApdyJ2QAFKEABClCAAhSgAAWKE+B6ClCAAhTQmgADKlrbY+wvBShAAQpQgAIUCAUB9oECFKAABSgQ4QIMqET4AcDhU4ACFKAABSJFgOOkAAUoQAEKUIAC/hRgQMWfmqyLAhSgAAUo4D8B1kQBClCAAhSgAAUoEMICDKiE8M5h1yhAAQpoS4C9pQAFKEABClCAAhSgQOQIMKASOfuaI6UABQoLcJkCFKAABShAAQpQgAIUoEApBRhQKSUcN6NAMATYJgUoQAEKUIACFKAABShAAQqEhgADKqGxH8K1FxwXBShAAQpQgAIUoAAFKEABClAgLAUYUHHZrVygAAUoQAEKUIACFKAABShAAQpQIPwFyj5CBlTKbsgaKEABClCAAhSgAAUoQAEKUIAC5SvA2kNOgAGVkNsl7BAFKEABClCAAhSgAAUoQAHtC3AEFAh3AQZUwn0Pc3wUoAAFKEABClCAAhSggC8CLEMBClCgRAIMqJSIi4UpQAEKUIACFKAABSgQKgLsBwUoQAEKBFOAAZVg6rNtClCAAhSgAAUoEEkCHCsFKEABClAgjAQYUAmjncmhUIACFKAABSjgXwHWRgEKUIACFKAABbwJMKDiTYb5FKAABShAAe0JsMcUoAAFKEABClCAAgESYEAlQNBshgIUoAAFPAkwjwIUoAAFKEABClCAAtoUYEBFm/uNvaYABYIlwHYpQAEKUIACFKAABShAAQqoAgyoqAj8nwLhLMCxUYACFKAABShAAQpQgAIUoID/BRhQ8b8payybALemAAUoQAEKUIACFKAABShAAQqEvAADKmXeRayAAhSgAAUoQAEKUIACFKAABShAgfAXcB0hAyquHlyiAAUoQAEKUIACFKAABShAAQqEhwBHUa4CDKiUKy8rpwAFKEABClCAAhSgAAUoQAFfBViOAloSYEBFS3uLfaUABShAAQpQgAIUoAAFQkmAfaEABSJYgAGVCN75HDoFKEABClCAAhSgQKQJcLwUoAAFKOAvAQZUyih5KjMfWkiZOWZk5po10VdPnrkmC05nacO6YP9z8y1lPMJCc/NsjRxLp9Wfzzz12Cm4T7Q0r9Wf2zNZptA8cMvYK7PFppn30Jw8C9KzTZrpb8GfSy3/3IpjpIyHWUhuLn6mC+6jgM+r7+W+tCmO+ew87Z7r5KjnDGIMvow1lMqIc4KQPHDL2ClxDhdKzkX1Jd9s1eT7vRhThvq7Sqs/t2U8xLh5mAgwoFLGHSlOWrWQrDYb1P+hhb566qNRr0NevlVz/Tepf4CV8RALyc3FL25P+ynU8nJNVohjJ9T65Wt/tPpzm6v+URCSB24ZOyX2h6/7LtjldDoFJvUEO9j9KE37Wv65tVpt8igLt4n4mS7Nvgz0Nhb1d66iKJo7V3A46dS+a/HnVpwThNsxL8YjzuEc+ybUX6MMOs0e92b1fVOBNn9uxXHCRAEGVHgMUIACFKAABSJbgKOnAAUoQAEKUIACFCiFAAMqpUDjJhSgAAUoEEwBtk0BClCAAhSgAAUoQIHgCzCgEvx9wB5QgALhLsDxUYACFKAABShAAQpQgAJhJ8CAStjtUg6IAmUXYA0UoAAFKEABClCAAhSgAAUoULQAAypF+3CtNgTYSwpQgAIUoAAFKEABClCAAhSgQEAFGFAJKLejMb5SgAIUoAAFKEABClCAAhSgAAUooGUB3wIqWh4h+04BClCAAhSgAAUoQAEKUIACFKCAbwIs5bMAAyo+U7EgBShAAQpQgAIUoAAFKEABCoSaAPtDgWAJMKASLHm2SwEKUIACFKAABShAAQpEogDHTAEKhIkAAyphsiM5DApQgAIUoAAFKEABCpSPAGulAAUoQAFPAgyoeFJhHgUoQAEKUIACFKCAdgXYcwpQgAIUoEAABBhQCQAym6AABShAAQpQgAJFCXAdBShAAQpQgALaE2BARXv7jD2mAAUoQAEKBFuA7VOAAhTQlIDJZMaho6nIzze59NtqteHoiTSYLRaXfC5QgAIU8EWAARVflFiGAhSgAAU0LsDuU4ACFKBAJArs2X8EAwa/gZZX3Ycetz2Jn+YtdzIsW7kR7a59CN36DUOLboMwddZS5zrOUIACFPBFgAEVX5RYhgIRKsBPbYK449k0BShAAQpQgAJlEjh24hR6D3wW1apUxOSxz2Ht/M/Qs0tbWWdObj6efOVjPHpvH2xc/AXGvDoYL783CQePnJDrOaEABSjgiwADKr4osQwFIlCgpJ/aRCARh0wBClCAAhSgQAgLfDV1PipVSMRbIx5A62aNEBsThYrJibLHq9f/g+ycXPS/oSsMej26d7wE9WpXw7KVG+R6TihAAQr4IsCAii9KLBNWAv/M+AcrP1jlNZ3eezqsxluawfBTm9KocRsKUIACFKAABUJJ4I/Vm1GzWgqefPlj3Pq/lzHy3UnyeSmij8dST8kASlSUUSzK1LBeTRw9fkrOc0IBClDAFwEGVHxRCoky7IS/BP6d+S/+GrPKazqz/4y/mtJsPfzURrO7jh2nAAUoQAEKUOCswK59hxEfF4NuV7TGvf2vxpbte3Dv46MgHlCbnpGFuNiYsyXtL9HRUcjIzJYLep2C+BiDJpLosFb6Wrif0UY9DHrtWBfsv3BnokD5BVRoS4EQFbjwxgvRbkh7mSrUS5a9LJiXXNeeJ1dE6KS4T22MBh0S44whnxJiDVAUxWs/o60nkLbuFeyf3xfH/xwGpG/yWjYY4xUnGdFGXUj1yReHuGh9hP7kRO6wdf+tjWr/rQAAEABJREFUg+Gn0TB8/QJ0S78DcjIiF4MjD1sBHufa3LV33HQVrutxGXp2uRTvvPAg9h08ht37jyApMV7e8lNwVHl5+UhMiHNmiT/0tZBEh8uzn/q8TNiWfAvrxBGwTX0X8mdBDYL4o029+teooigyqOKP+gJZh3BnKiAQobPqIRyhI+ewI1bgoj4XocPQ9jIl16sgHQrmVahvz5MrInRS3Kc2MqCiBisSQzwlqJ8sqb+j4amf8VEWbPquEw6segunds3FkQ2fYOM3l0PJ3OGxvKc6yjtPBFOiRPAqxJ0LO8Sp7hH6oxORw9btWA3DxGegWzsPun9XwbDwSxgmjYhICw46fAV4nGtz3150QT3sP3TM2Xmr1Srn801mVEupKIMr4moVmalOduw+iOpVK6pzgMVqw5kskyaS6HB59jV7/NMwz54Ay5YVMK+ag7zxTyJj/Z9+scnOs8BktvqlrpIalLW8cGeiAAMqPAYoQAE3geI+tRG/+DJyzAj1lJlrhs0Gj/088O+vyD2zz3XsNgv2bfreY/lgjDXPZEW+epIRjLbL0ma26u4Ky6VwFtD9vdBteLoD/0A5ddQtnxkU0KqAsmmZW9flcX5iv1s+M0JH4Jpu7TDx+7k4dDQVZzKyMHn6IvmQ2vPr10LblhfKjk6ZuRhmiwW/Ll8nv+Gnc4eWMj8EJ0HpkngvF8d64cY9/UwULsNlCkSCAAMqkbCXOUYKlFCguE9tZEAl24SMEE+ZatDHpkZUPPXz9NFtHlUyTu4JmXHlmSwQQRVP/Q/lPPFpk0dcZoanwImDnsfFgIpnF+ZqUkDnJXCinDjgMh4l7QiU3Ru8p9PHXcqH90LwR3fnTVehXesm6HHbk7jsukfw+18bMe6NofLbfuJiozH29SEYNW4KWnQbhCEvjMXzQwegdo0qwe94CPVA8XLse/uZCKGusysUCIgAAyoBYWYjFNCWQCR8apNUv4fHnZJQs6PHfGZSgAKeBWwXtHFfERUHW61G7vnMoUAoCxTRN2ujth7XWhu2csnXr50H44SnvCZlw2KX8lwoXwHxDT7vvvgQVs4ej19/eA+Lp45G8yYNnY12vbwVNi2eiIXfv4sNiyag/43dnOs4Yxew1rsYUN/T7UvnptaGrc8tcI4CESzAgEoE73wOnQLeBCLhU5voCo1Qve3zLgRJ9a9FpYsGuORxIfwErFYbUtPOyMu/vY1OrM/Jzfe2mvkFBKwd+8Jar6kzxxYVC/N1DwHRcc48zpSPAGsNnIDlsj7ux/mNQ92Oc2vF6rA1aG5PFarKDtoK5OFsnlzBScAEkhLiUKNaZfmg+sKN6vU61KqeAqPRUHgVl4WA+l4u3tPFe7tYFEm851sv7yNmmSgQ8QIMqET8IUAACngWiIRPbapd+hyaPXACF/T7A03v3YcG107zjMHcsBFYuXYr2l37EDrfNERe/n330LcgvkbTMUDx8MJr7hwu17fp9QBeeHsiTGaLYzVfPQjY4ivA/L8PkD/8O5geGQfTyF9gvaSXh5JgHgW0KxCb6H6cX3qt23isba+B6f73ZLK1tF/tYCmQZz2b57YhMygQwgLiPV28t4v3ePFeL97zxXt/CHeZXaNAwAQYUAkYNRuigPYEIuFTG50xHnFVW8MQy3umtXeElrzHik7Bi48PxIpZ47Bk2vuIj4vBuC9nOit67YPJuPD8ulg7/zPM/vpNzP9tNeYv+cu5njNFCCRX4W0+RfBwVZgI8DgPkx3JYZRGQN7Kqf4MlGbbYG8T/esCxH02DuI12H1h++ElwIBKof158lQ6RCqUjYzMbJw6k1E4m8sUoAAFQkeAPSlWoH3rJriux2VIToxHtSoV0avLpfh91Ub5DQ/iGyD+XLMFA/r2kA8sbFC3Bm7sdTkWLlsD/qMABShAAQpQQJsCla6+Esn3D0TCm6/I10o9O2tzIOx1SAowoKLuFnE//effzkbHGwejU5/H0Ov2p9Vc+//ZObkYPGIM2vd+GFfcMBj9H35V3ntvX8spBShQFgFuS4FgC/y5dgsuuqAeDHo9Uk+elt0p+A0PdWtVw5HjaTKfEwpQgAK6P3+C8YP7YBx5HQyfDIFu51qiUIACISwQvWg+DP+6frOjYce/iJl17urUEO4+u6YBAQZU1J30/mfT8PW0BXhw4A1YPnMsZn39hppr//+7GYuxY/dB/Db9A6yaPR56nQ5jJvxoX8lppAlwvBSgQIgKWCxWiMC4t7R7/xG3ns9auAIiPfG/W+S69Mxs+Sq+FULOqJPo6CiknU5X5+z/x0TpoYWkU4Aog04TfS3sGW20n5oUztfCsv0o4TRcBXQ71sAw52Mox/dByc+Fbv82GL57FUqmPRgbruPmuCigZQH9fzs8dt+wdbPHfGZSoKQC9rOWkm4VRuVPqJ9ITvx+LoapJ9R33NQdlSokonqVSs4Rivvn+/bujKopFZCYEIcBfa/CT3N/h81mc5YJ3Rn2jAIUoEBkCNhgUwMfGV6TyWR2gRC39jzzxmd4adhd6NDG/g014lsgRKGCZfPy8tXfC0kiG4o6jYvWQwtJPP8oWg3+aKGv7n00QKdGhNzzQ99eHCPqYcL/w1RABFTchpaXDWX/VrdsZlCAAqEhYDm/kceOmJs285jPTAqUVCC0Aiol7b0fym/atlvWsuXfPRgw+A3c9+Q7+GXhnzJPTPYdPAZxybeYF6lOzariBY5PMuUCJxSgAAUoEFQBccvO8Ef6w1tq3LCOs38Llq7GA0+9i9eGD8It11/pzE+pXEHOHzh8XL6Kyd4DR1GjaiUxq4ZsgLSMfE0kk9mKjGyTJvpa2PRUZj7ErbiF87WwzI9a5I8KJxSgAAVCRiDvql4wX9jEpT/mRhci97obXfK4ECSBMGg2rAMqsxau8HoJuPh0Uuy/oyfs98anVE7GPbf2wiXNG+HZNz7HnMWr5FUo4hkqMdFRoqhM0VFG+ZqdnStf4zTyaaXRoINRr2jik1VPpor6sV9sOXzaqlc/BYX6L9qoKxebs9WrLfB/ClAgFAR+XvAnho0cj2cevR2XtroIh46myiTe68WDasXVKpOnL0JObj727D+iBthXoEfntqHQdfaBAhEvYDFlIPv4Ophz7edugQaxNvLwXhAdB1td+1Vuge4P26MABXwTSJv3G858/jUyn31RvqYtWObbhh5KMYsChQXCOqAiriJJO+35EnBx8uzAaFivJh4aeAO6XtFavopvgFi0bC0URQ1AxMYgL9/kKOqcj4uLkXlRRj20kAzqX/biEnAt9NVTHxUoEEEhT+vKkqfuYoh/RoO+XPajztEA+I8CFAgFgY3bdsluvPXRd+hx25POtGCp/Zt8Rjx2J7Zu34M2vR5A74HPqsGUNuh15aVyG04oQIHgCRz+81ls+awadk7riK1f1Mb+hfcEvDMioGK+9iHYqtaDLSoG1rpNYL79BdgSKgS8L2yQAj4KsNhZgbzuPZH9wCMQr2ez+EIBvwjo/FJLiFYinoni7fLvqzq1kb0W3+awa99hmMwWuSwmZnXeZDaLWdSrXQ37Dx2T82LiuBTcca/96cx8aCHl5FuQqyYt9NVTH602G9KzTX63NlvsF2hn5vi/bjEOs9Vevzh2mChAgeALvPj4QGxdOskt9bm6o+yc+KrkBVPewZJp72P13E/w+jP3wWg0yHWcUIACwRHIPv43TmwY49L4qZ0/4MzuWS55gViwXn4TTEMnwDRyFswPjoH1gjaBaDaC2uBQKUABCmhLIKwDKr7silbNLkBcbAw+/foXiG+JEJ9ezlvyFy5va39QUc8ubTFt1lIcTz2NzKwciEvBb7qmExRF8aV6lqEABShAAQ0KVKtSEfFnr0TUYPfZZQqElYAIqHgaUM7JTZ6yA5vH1ihAAQpQIKIFIj6gIq40+fDVwfhq2gI073Yvbn/4Vdzepxtuub6LPDBu79Md59WriSv7DkW7ax+C+PaHwffeJNdxQgEKUIACFKAABbQkoMW+GqKSPXZbb/Sc77EwMylAAQpQgALlIBDxARVhKh5CuHL2OCz8/l2smfcJRgwZAINeL1bJTyg/futxrJg1Dst+GoMfPn0JVVN4r6zE4YQCIS5gzj6OtL1LkXnod48p99S/IT4Cdo8CES9AAAogsX5PGGKruEjo9NFIbng9+I8CFKAABSgQTAEGVM7qiwBKreop8vafs1kuL+LbH1Iq8ZMQFxQuUCDEBTIOLsOab7pi18xeHtPxNaNCfATsnvYE2GMKUMDfAvqoJFzQ93dUbTUMSfV6IaX5wzi/3x+ISqzr76ZYHwUoQAEKUKBEAgyolIiLhcNFwJRlwrFNx2AxnXsYcbiMjeM4JyA+0axUrzMSanVEdMUL5QpDbFW5bM9rLPMiesLBU4ACFNCAQFRSPdS47DU06P0TanV8F7GVm2qg1+wiBShAAQqEuwADKuG+hzk+N4HV41Zj3MXjMOWGKTiw4oDbemaEtkBJepdYpwvaDvgNDW9cgOptnpWbJtTuIpdFXrW2z8g8TihAAQpQgALFCeTm5mLqtCkYMeJJvPbai/jtt1+L24TrKUABClAgzAUYUAnzHczhuQqc3nsaK95d4ZqpLu1duledlsv/rJQCFKAABShAgTAQePOtlzF27Hv4fflSLFg4Fy++9Axm/PxjGIyMQ6AABShAgdIKMKBSWrmw3S68B3Zyx0mPAzy157THfGZSgAIUoAAFKEABi8WCpUsXu0FM+fYrTPzyM49pw8b1buWZQQEKUIAC4SWg/YBKeO0PjqacBaKSoj22YIwzesxnJgUoQAEKUIACFDh+/BisVqsbxLHjR/GlGlDxlDZtYkDFDYwZFKAABcoqEGLbM6ASYjsknLqj+3cl9MunQryGyrhqtKqBxFqJbt2pe0UdtzxmUIACFKAABShAASFQo0ZNVKtWXcy6pEaNLsQ99zwgU5UqVeW6a665Xi43b95KLnNCAQpEtgBHH94CDKiE9/4N2uiMY+6H4esXoZ/3uXw1fvg/6Jd8A+PnT3hNyq7y/yTHEK3HrT/ehksfvhT1uzZAYk17cCWpVpKL1a6Fu/D9jVPwUZOP8M3V32DL95td1nOBAhSgAAUoQIHIEnjiiWeRnFzBOegG9c/Diy+8hnvVgIpIjoDLdb1vlHktWzCg4sTijJYE2FcKUKAEAgyolACLRYsW0C/8Esb3BiLqxWuhHHN9yKtydDd0//0NZc8m7ykzMM8xSagWj8ueugw3fnEDKl9Q2W1Q6QfSMet/s3B04zGYc8xI/TcVvz67GEfWHXErywwKaEVg+y/b8UGDD7ymeY/N08pQ2E8KUIACQRHo0P5yzJ71K76c+B2+nzITX389FXXq1A1KX9hoQQHOU4ACFAieAAMqwbMPq5Z1q2dDv/Q7KCfVoIMpz+PYrLXOh+m+d2SyNrR/amPpeqdcFvm2hi09bhfozIOrDnhscu/yfR7zmUkBClCAAhSgQOQInH9+I9SqVbv0A5L0kcAAABAASURBVOaWFKAABSgQNgIMqITNrgzuQHTbV5/rgHJutuCcrUFL2M6zJ8RXkKtsVeo682wJFWUeJxSggP8FGl/fGEP3DJXp6jFXywYaX1cg70N7nlzBCQUoQIECApwtXmDz5o1ITT0hC2ZmZcpXTihAAQpQIPwFGFAJ/30chBGqERWbzaVdW/XzYG1ymUteqC7Ubl/HY9fqd6znMZ+ZFKAABSgQUgLsDAUCKjDq7dfw8CODcOTIYdnuCy8Mx969u+U8JxSgAAUoEN4CDKiE9/4N2OisjS91bUtRYItPguXq+2Ee+ApMj33quj6El5LqJOG6T69D9RbVYIg1IOXCFHR/sxtqXFIjhHvNrlGAAtoVYM8pQAF/COxZPBhL3zJg47g4jyl16xf+aMaljrSTqZg9e6ZLXk5ONqZP/94ljwsUoAAFKBCeAgyohOd+DfiorJf2hqXL7bBVVoMOUXGwnn8JzHe9AUvHW2C9sEPA+1PWBhv2aIjbZvbHo9sexZ3z7sTFtzUra5XcngLhI8CRUIACFKCAFNi73/Uh/DJTnezzkq+u4v8UoAAFKBBGAgyohNHODPZQLD3ugemJr5E/8meY730LttqNg90ltk8BKcAJBShAAQoUL5Cfb8KxE6dgK3TbbvFbhkaJBt3GosszZrR4JBuVm94nO1Wry1i5LPJSmg6Sef6c1K9b32N19bzkeyzMTApQgAIU0KwAAyqa3XXseBgLcGgUoAAFKEABvwpcf9dzaNrlbpc0fpL9VhURQBn/1c9o1eN+dO33ODr1eQwbt+3ya/vhWlmlyino3ftGl+HFxsahb9/bXPK4QAEKUIAC4SnAgEp47tcAj6r45nRblkO/+GtnUo7tUTeyQbfld2eeWK8cFfnqqhD6f/8f+7Fo+K/4+b6fsXrcauSezg2h3rEr5S2Qeeh3nFg/Bqd3TIMl73R5N8f6KUABCpSbwGODbsbcb0Y50+19usu2Nmz9D+O+nIHJY5/DhkUTcGOvjnj8pY9gtdrkek6KFhj+9PMYP+4L1KhRUxZ89dVRqF//PDnPCQUoQAEKhLdAZAZUwnufBmR0Ivghkq+N6WRAZbIaPLEn5Zi451iBbusfzjz94smw56PYf8qJ/ep2X0O38bdiy5alwN6le/HTgJ+wdeoW7Fm8ByveXYGZ99o/0StLvdxWGwJ7FwzArpm9cHjFs9i36C78M/li5Gfs10bn2UsKUIAChQSqVE5GvdrVnKlCcgLEvyV/rEeHNk3RulkjGI0GDOjbQ976s30X3++Ejy+pWbMWSEmpIosmxNtd5QInFKAABSgQfIFy7AEDKuWIG85Vi+CHSMEao3LioBpQmQzd5qXl2oXts3e41X90/VGk7Upzyw+3jKIuDw+3sXoaT97pHTjz348uqyx5aTi5ZYJLXnkuZI3V4URXA7IuiUXuXdGwrOVbdnl6s24KhLvAtNnL8PyoLyBu9dl/6JhzuEeOn0SDOtWdy1VTKsj546m8Kk9CeJmMfn8UBj/2gDPt3r1LPn/Gkb9g4Vy5Zdqp8D9nkAPlhAIU8JsAK9KOAM/OtbOv2NOyCORmQjmkBkfU15JUk3Ew3WPxU7tOecwPt0xvl4eH2zg9jSc3bbunbOSe+tdjvr8zzb/okTlaD8tuteYcwLJGh7yh0UC2uhyg//ct34d1n63Djjk7kZ+ZH6BW2QwFKFASgVkLV+Dzb2d7TH+u2eKsqmeXtujUvjlqVk/Bkj/X4+b7XoIjqJKekYWYaPX9xVkaiIuNQWa2+uaj5hkNOkQbQz8Z9Ap0iiL7qtcpEP+Mal559N1sysXCBXOxYcPfzpSdnQVFUbBz53aZd+zYUdEF7N61U/apuH6ILhv1Op/KFldXINfrRMflSDmJYAEOnQIRK6CL2JFz4H4R0Bd4LkpR8/ZnphTfpK7QM1UcdebOngjdr1/BsazbtMRemQ/fRKD/eQyiXukD47hH5Kthxgf2bX2Y1mpf22OpWu1qecwPt0xvl4eH2zg9jSemkudvqYqpeKGn4n7Ps6zUu9VpUz/ktOwIzNv2z4N+xoyBM7D8zeWY++gcfNV1EnJOBjCa4zZ6ZlCAAp4E0jOzkXY6w2PKzjn3zK9H7umDhwbegIfvugFTxr+AxIRYLF7+t6wyKTEeefmuQVOxbUJcrFwfF61HQqwx5FNstAFGgyL7KYJAovPRxvLpe1yUDplZmaKJYlNuTrbsU3GGejWYEhtTPv0tru2yrBdBoGIRQq4AO0QBClDAPwKBOTP3T19ZSwgK6BdPVoMcxSePARUPwRBdoWetOOrPmfMllF/PtaPbtMyuoX4SZJ/xPFV2b4D+r9kuK3Vr5kC3Y7VLnreFVve0RM029ofMiTLGOCO6vdENMckxYjHs0zQvl4eH/cDVAUZXaITk829W5879r4+uhMoX27+K81xu+M0d33YCe5a4PiA660Q2tkzbGn6D5YgooHGBO27qjuGP9PeYrurUxuPoxHNSqlSqgJw8exClRtXK2HvAfjWF2MBxq4/j1p8zWSacTM8L+ZSRbUKeySr7mZtvEUNBZq5ZLvu7/2kZebJ+XyZmq82nPpjMVqR7sf7nz/3Yu/2kT/X4e6zF1Zdnslv7YsEyFKAABcJNgAGVcNujAR6PpdsA+JJs1Rq498xDMMR6cUeP9cVeew9s3c+1ZW3e2V6fh6CMfYV9qhzZbZ8pNFUO/Fsox/OiCJzcMu0WDFpxH27/pT8e2foImvVv5rmwRnL9cXl4bJQe1SrGhHyqkhwNcSmy6GtyglHuoZgS9L3dbdPQ9s4laNT9HTTv8x06D/4Pdeo2Csi4K3V3f3vWVwZqtIsqc/vFWViPZkirwpOMHWk+tV0xMarwplymAAWCKCBu7Zk0dT6OnkiDyWzB7EUrsWX7HrRrdZHsVdcrWkHcHvT35p1y/dfTF6BalYpo3LCuXF+aSbhvY/Ny/uEtv7QeW6duxbim4/Dd9VPwxWUTMLXfVOSk8WrB0npyOwpQgAL+FtD5u0LWF1kClm4D1QBI8cljQMUDlfXiTh7ri+l9L6zd73Kuszbvat/aQ1DGvsI+VWLi7TOFpraESoVyil5MrJGAqs2qFV1II2v9cXl4rvppVOqZPIR6SsvIl1/7KfqZkW2We0h8kiaWfU2Wih2QcNGjMNTug9N5sQEbc+5VeUgcZoFefPNmHGBoa0X0mHycNJXdvTgLY80kaVV4kty4sk/jF59mF96WyxQIcYGw797X0xagW79haNl9EIa//qm8ouWS5o3kuFs2PR8PDrweAwa/Ltf/8PNveO+lh2VAWhbgxE1AURS3PH8HU8Szq5a9sgymbJOzrcNrD+PviRucy5yhAAUoQIHgCuiC2zxbp0D5ClgbtwOi1b9GCzajLtuaXlEwJ6Lm/XF5uPhgzmK1QQtJ7FzRT6vaXzGvpb7HDbaiyhIz4tfmIPqrPCitLX4xL86i8oUpaNC1geBypvgqcWjSt4lP7Tvqd27MmXIQCP0qDd++DOPnT3hNStaZ0B9EmPSwbq1qWDx1NH6f8SHmf/c2Ni7+AgP79XSOTlEUDL73Jqxb8BkWff8u/przMVpdfIFzPWfcBTwFT/7P3nkANlX8cfz7MrsX0LL3lL1kKHupoAKCggIqIH8VGSKKC1EQFUVlCCqyBBEEFBQE2Uv23rI3tKV0j7QZ7//u0oSmSWjapln9hf7u3f1u/e6TkLz3e3f3BEHgm9Jaly6YJulaIrLSjMuycrYQezImZ5LiRIAIEAEi4EYC5FBxI3xv7tq0zMddYxBLlQezwVC//QNNEIPCoH39O+gffQaG2i34kaWZ/oEVi3lmXtPDizmeYjH8p+c9jV6LeqHNe23wxHfd8eLWl+BfIpdzsliQoEEWlIDs+lkIV07YFVF//657Qfugeo4TEAQBJcJDUKFsJBRyOQBYiZ9axZ8CxJZKWmWSwoKAIAgW6aJIqEP8bDarDlHb1JOSCBABIkAEXE+AHCquZ+4TPZqW+jg6mNx7o4hRlaWqIgx1H+WOEeYcYWLUS1l5/ImlKkr1BsHQsEMeJQGxVAXon3gVukGf8iNL51mJCuBB08MJT/EgUKlNJTQd1hQ1u9eAKkhVPAZNo3QaAd0LH0E79CsuYoBxGZnu+fs6hJTMZ19UnAgULwIhFUJQunFpq0E/9MxDVjpSEAEiQASIgHsIyNzTLfVa3AgYHSqDuBOEOWNEvkmtgNx7poilqxQ3NB453rymh3uk0WQUEfA0AsXcHkPFhyBWbcRFkBs3hTZUrMPTTF/M8dDwvZxAUFAwqlat5tAoKlWs5FA5W4V6zu+J1mNbo0qnKqj7bD30XtwbldtXtlWUdESACBABIuAGAuRQcQN06pIIeAMBQRDynB7uDeMgG+8TOL38FNaNWIejC4/eV+aIUZQIEAEiQAQcJ/DhBxMxffoPZqlcuSrY3iqjR7/DdV27PM4bK1euAj8WJPAL88PDwx/G03OfRpcpnVHxUXryUkE4Uh0iQASIQFERIIdKUZGldokAEShqAtR+Pgj8+8W/2DRuM86vPY87R+7wmnfP3OVHCogAESACRCD/BGrWrI0mjZuZJSgoiG9KWytbX7p0Gd6oSkVLJjkICnyXQFYmhMvH7MuN/3x37DSyYk+AHCrF/iNAAFxHgHoiAu4jcG7tOavOk27QU1asoJCCCBABIkAEiAARyB+B5LtQzn3briiWT8lfe1SaCHgRAXKoeNGb5XJTqUMiQAR8gkBWahZS76RajUWfqYcmSWOlJwURIAJEgAgQASJABBwmoFRDrNLAKOVr8WqiUmVMM30Fo45nUEAEfIyATzlUfOy9oeEQASJABJxCgD2hp1zzclZt+Uf4wy/Uz0pPCu8hkBF3EplJV7zHYLKUCBABIuBhBAwGEdF346HT6z3MMi8yJ7QUtK98bZRn3zUaHhJpTEt6nUlnzKGQCDiNgCc0RA4VT3gXiokNQuw189pKpCXyUQt3r5t1QmoC11FABIiA8wm0eb8NgssFWzRcqm4pizQlvIdA8rUNODWvIs7/1gL//VIX55Y2Q2biRe8ZAFlKBIgAEXABgS27jqBu+5esJDNLy3vfsfc4WnR/DZ36jkHDTkOwfM12rqeACBQRAWrWBwmQQ8UH31RPHZJ826/mtZWyS8anjMi3/mLWCZeOearpZBcR8HoCUQ2iMOTfIRi0eRDaftCWj8c/3J8fKfA+Ajd3jIJeE2c2XBN/BjEHPzOnKUIEiAARIAKAKP0L8PfDul+mWIhKqUCGJgtjJ36PNwb3wvEt8zB90gh88vVC3LxDG7bf/+xQjAgQgbwIkEMlL0KUzwmk3d6Dm9tH4PLaZxB9cDJ0Gfn/sRFLVbi/lpKtp8wtQWG8LwqIABEoOgIR1SIQFBVUdB1Qy0VOQJsWDW3Kdat+0mOPWOlIQQSIABEoVgRsDNZPrUSl8lEWIggCDhw9i/QMDfo/3REKuRyd2zTlZXbspRt8NjCSiggQATuBkySrAAAQAElEQVQEZHb0pCYCZgJp0QdwcVVn3Ds9DynX1iPmwGRcWvUYRDF/a031HQeY11Ka11m+8rVZJ1ZrbO6TIkSACBABImCbgCKgFCDIkfulDIzKraI0ESACHk6AzCt6AvGJKXj/85/wyTc/4+8t+8x7pcTEJXAHikqlNBtRrVJZRMfeX4IuEwBvEDYAT7KT2SMIcIgdK8fKe5L9jtrC7CYhAuRQoc9AngSSLq6wKqNJOAtN3CkrPSmIABEgAkSgaAkIkjMlrHovq05Cq/e10pGCCDiZADVHBLyKQFSpCLzc73FUqViG2/3OpB8w5btfeTw5JQ1sORBPZAdqtQopqek8pVLIEBnu7xXCDPYUW0tkb3Yvl7wSjtgUGqCCv1ruFZxzj4dxJyECMkJABPIioEk4b7NIph29zcKkJAJEgAh4CAH2RIe4+CQkSSfTHmJSvs2o0PFHlH30K4RWfQrhtZ5HpW5LULLe0Hy34/sVaIREgAgUZwL1a1fB2Fefwysv9MCEMS9i0juD8euqLXyWSkhwIF/yk5NPZmYWgoMCuCpLZ0B0fIZXCDPYU2y9m6hh5kiMRYfYJaZlISNT71BZTxmjyQ4+UAqKPQFyqBT7j0DeAIIrdLFZKKi8cWNLnpl8D7JD6yHbswrC7YtcRQERIAJEwNMI7D10mj/RoV3vUWj95HC8NPoLnDp3/7HDU2YttXoaxIA3JrtuGA72JFP4o1TD4aj8+DJU7DwXtmasONgUFSMCRKAQBM6f/w9Hjh7ikpaaylsy6aKj7/A0BZ5DoFSJcG6MTqdHVMlwXLsZA61Wx3UsOH/5JkpHGsuwNAkRIAJEIC8C5FDJixDlo1SjNxBUvoOZhEzuh7KPTIEiwLheX7h0FMqvB0HxxzdQrJ0N5XevQbbdOJ3SXIkiRIAI+CQBbxuUIBPw0ZuDsGfNLGxd8S0CA/wwa8Fq8zBEUUT71o0sngYxdcJr5nyKEAEiQARyEvh22pcYNepVLiUTr+OVin5I/WMGTn09GrH7N0lFRcguHYF8yyKzCFdOSnr6cwUBNhvl8InzYE/0ib4bjzm/rEGLxnXgp1aheaPa3ISlq40zVjbvOsyf8NOuVSOup4AIEAEi4AgBcqg4QonKoNrTf+Ohly6jRt9/UXfoLZRqNMJMRb5rBQRtljnNIvLtS9mBhAh4GgGyp5gTaNnkITzZtTVCgwMRVSocj7V/GDv3HYdOf3+TbTbdO+cTIUqXigC9iAARKH4EZLv/4E4QZKTYHXyNGrXQqFETLt1rlsfQiv5maRSqlOoJkkPlmNTOYrMI1xzbg05IiZfqLIJs/1rQq2AEomPvYdDIz9DssWHo1HcM/66f+M5g3liAvxozJ48Cm5nYsNMQjBo/Ex+OHojyZUrxfAqIABEgAo4QIIeKI5SoDCegDCyNgMgmYFPNuSI7EGKvZcfuH4QsDYSE6PsKihWQAFUjAkSgKAnsPnQKdWpU4o/MNPXDHqXJngjx1exlYHc2TXo6EgEiULwIyPcwh8piCJp0uwMf8+Y4zJwxh0u3rk/YLVegjNQEyaGyGLID5FApED+p0pj/PYvDG+bgn1+/xO4/v8PS2eMtHCYdH2mME1vmY+OyqTi2aS769+wk1aI/IkAEiIDjBMih4jgrKmmHgBhZ6X5OdkxU+UEML52dogMRIAJEoGgJ6PUG/LRkrV25fN16L4M1G/eAyVvSCbfJuro1K6PX421QuUJp3LgTy+9sbth+wJSN0EClV4hCLiDQT+ExtrKlVgxiSEDe/IKlMoIgeIzt+XnP2RhJiIAnELiy+TL2TttnV2JOxHiCmS6xgS3vqVA2EmGhQTb7k8tlKFe6JJRKhc18UhIBIkAEHkRA9qDM4pBXXMZ4dcd7OLmsEy6t7mZT0mOPFBiFvk1fiEqVRX19+/4WaUoQASJABIqSgAgR8YkpdiXnpoPMjt0HT+Hdz+bwpz60alaXqbiw5UAjBvfGsAFPYsakkXx50Kr1u3geC3R6Ed4gogjoDZ5jq/T2MHzQOWATc45BquANnHPbyAdJge8QYP+RpNHId/+OnHug2IsLV09KpfP+k106arO9rHXzgY0/m/MKs9Tn8tYr2D99n12JPll8HCp5vyNUgggQgeJEwNljJYeKs4l6aHtpd08i6cZOpN7aZVP0mQkFtlys1hjatxZB13sMdD1eh/aN72Fo/3ye7YnpmTBsPgn9vK3QL98L8bz1HeQ8G6ECRIAIEAGJgEIux7jh/e1KrWoVpFLGvw3bD2DY21Px6bghePap+xtuG3MtQ7Z/Spr0XWXSpml08AZhzhRNlt5jbGWb/TKG6Q7wS8/Ug13HegPn3DayMZL4EAFB4INhTzCUb1ksOToeLI46VISLR222lbluIcRNi+7nFWKpT9WOVdBiVEsukfUi+Tgq59CVrh/FdRQQASLg8QTIQA8nQA4VD3+DnGVe5bafo/5zm1Gt5z8IiGzKmy3b+jOeZjr/yCZcV+AgpAQMzR6HoXUviGWrO9SMfu5WGNYfg3jmFsT9F6H/cTPE/245VJcKEQEiQAQKQuDPDbsx5uPZePeN5/Fw4zq4FR3HJT1Dw5v7ds4KXLp6C1qdHqfOXcGSPzbj0Yfr8zwKiAARKGYEmGdPGjI7t9F3Goi8RKzs2HeFWL2xzbbUT7wEocug+3kP95B6L9hflc5V0Wp0Sy5RDYzOk6qdrHUFa51qEYEHEaA8IlC8CJBDpZi834GR9RFasR2CyrWF3M/4xAq/EvV4mukU6nDXkohPBa7FWfVpOHjZSkcKIkAEiICzCBw/c4k39cV3v6Jrv7Fm2bD9INfvO3wGT730ARp1HoLn/vcJurZrhhef7cbzKCACRKCYEcieoaJ/5BnJycEcHQ8WRx0qhmrMoWLdluqJwUDXF819GVoU3KFSzN6pwg2XahMBIkAECkGAHCqFgEdVC05ATJAcKjaqi7FJNrTOU616cRWmVZlmlms7jU8osqd3Xs/UEhEgAp5A4KM3B+H09oVWwjaiZfb99uME7Fs7G+uXTMGhf+Zg8rtDwTY0ZHkkRIAIEAFPIEA2EAEiQASIgOcQIIeK57wXxcoSoVwJQK20GrOsVlkrXUEUfwz8gztNrv97vSDVrer8OfhP3t6VbVes8khBBIiAbxEIDgpAxXJR8Pez3Gzbt0ZJoyECLiPgtR3pW/cGW+Yj+gW4ZwxB4bx/QyGW/rjHcOqVCBABIlB8CJBDpfi81541Uj8l5D2bASqF2S6hSinI2j9kTlOECBABIkAEiIDrCVCPRMBIwPAIc6gMAvyDjYo8QkO1RtwBwpwwTM5lKaQaIg4majH3egaXA2G1IFaqJ+nz/hODI6T2BoGW/uTNikoQAUcIhD/3NCKrRNkV5YF9jjRDZYiABQFyqFjgoIQrCQgPV4fi8/6Qj34C8vHPQP7GY0CQnytNoL6IABEgAt5PgEZABIiARxAwVGvCHSD6ToOQ+HBP7I1JkewScCBRh5+ua7h8fykFYpX6kp7+iAARIAJEwBcIkEPFF95FLx+DUKEEhLCimU5rWvpj2jfl6o6rDtFaNchyrxVa6uMQNipEBBwiQIWKLwHD2iPgT3jbcAJINT5ZqfjSoJETASJABIiAKwkk/PYnYq/EcNE+3JJ3bUvHMyggAg4SIIeKg6ComO8SELMfi5hzhCLEnEmKF28CNHoiQAQKS0Bv4C2Ih69APHsLho3HoZ+9ETDQdy0HQ4HPEQgODkFERAmrcTVt+rCVjhREgAgQASLgvQTIoeK97x1Z7gCB3ot7Y/SV0Wap3K6yVS1BEKx0g/EbPsPneH+cnNet0qGKVRnPVZBlRIAIEAHPIiBq9VYGiTFJEG/cs9KTggj4CoE2j7bjQ1Gr1YiMjEKvXn3xwoCXuI4CIkAEiAAR8A0C5FDxjffRu0eRX+vTAM2Lavvymjq/Ldotrzh90m4eZRABIkAEiICDBOzNRIlJdLABKkYEvI9AaGgYN/qlF4fi95V/Y8yb4xAYEMh1FBABIkAEiIBvECCHSgHeR6riXgKidKNTf1AGe2I47LyPta4ubRzn3nebeicCRMAnCCjsfC/XKusTw6NBuI7Anf2f4L9f6uHknChc+utJpMcedl3n1BMRIAJEgAgUSwIPGrSdM5wHVaE8IuBeAkII4Lcw0yizMo3GBNzXqb/PhGmpT8VHKxrzCxAaSkVC07c/r/n0/Kd9dumPQbpzrM/e34APNkfA8qLvxkOn1+fQUpQIEAEikD8CgkphWUEph+ypZhBCpS9vyxxKEQG7BO6dno/YQ1OQmXQZBm0KUm9swdV/XrBbnjKIABEgAsWUAA3bhQRkLuyLuiICTiMgf9gAJrJmxo0OBTl4munkTY06W531+rkXd4yY9lWp1LYSL2bSv7OoEd5/T4nIWe/j3tY9MJQoyfN9NWAb8n7yzUJM/PZnqyHu2HscLbq/hk59x6BhpyFYvma7VRlSEAEiQATyQ0D21hOQj5Zk4rOQtauTn6pUlggg9Zb175A25Toy7h73DDrxadC9tdgshs3GZcOGdUeNuo+WQ0xM9wxbyQoi4FEEyBgi4L0EyKHive+dV1l+6NB+fDFlEsa9+yYWLZ6P5OQkj7Q/q017pA8bjszuT0EMCvZIG51l1IbtB9C210isXLvDqskMTRbGTvwebwzuheNb5mH6pBH45OuFuHnnrlVZUhABIkAEHCUgBPlDqFACVjNWHG2AyhEBbyaQpoF+0u8wXIj25lGQ7YwACREgAkQgmwA5VLJB0KHoCOzbtxtvjhmOv//+E3v27MJPP83G2++MKroOqWWHCLRp0RArfvoEPbq0sip/4OhZpGdo0P/pjlDI5ejcpikqlY/Cjr3HrMqSwnMInEs+jTsZtzzHILKECBABIuBEAkHl2lu1pgyuCP9SDa30blFEBELx9UCjfNDbhgkC14nbT/OjKwPqiwgQASJABIqGADlUioYrtZqDwJatm3KkjNEzZ07h2rUrxgSFbiEQ4K9G6VIRCAzwt+o/Ji6BO1BUKqU5r1qlsoiOTTCnKeI5BDbHrEfnbc0waH8v9Py3A17c1wsxGroD6jnvEFlCBIhAPgnYLF6i7mBENhsHdWhVyJTBCKrQCZUfW2KzrCcrxVv0W+rJ7w/ZRgSIABHIDwFyqOSHlheUzUy6gow445pdTzE3Ovq2TVOuXb9mU0/KwhE4fOI8flqy1qbYWt5jq7fklDQE+PtZZKnVKqSkGtd+q5QyhAerPF7CApUQBIHbGehv3BTTW2xnfP3Vcvir5Nx+lrYnX/03ASm6ZPP79V/KaSy5+YPdekXNIjhAabaFIkSgKAmIGiDrSyXSu/ghvbk/NK+qYThnnAVQlP1at00aVxEo02ICag84hfrDYlDtqTUIiGzqqq6d1o9QLtxpbVFDRIAIEAEi4F4C5FBxL3+n9Z6VfBXnljbDf7/UxfnfWuDUvIpIvrrOae0XpqHGjZvZrN64URObelIWjgBbTr9nXAAAEABJREFUqhOfmAJbkpCU4lDjIcGBfMlPzsKZmVkIDgrgKr1ehCZT7/miZRsUG23V8jigNxjT3mC/TidCqzc8kPOlhKtIzErk70vO4Oi9I3brFTWLLK0+pykU90QCPmKT7hcFtAsVEG8JENMA/U4ZMseqfWR0NAyvJiD9ZtqzX2hf114W6YkAESACRMDLCJBDxcveMHvmRh/8DJr4M+ZsvSYON7ePNKfdGenbpx/q17+/vtnfPwBvj30fwcEh7jTLZt9CUiKU+3bbFcWZUzbreZKyTYsGGDe8v0155YUeDpkaVTIc127GQKvVmcufv3wTpSONd9WYUyIjSw9PF41koyiC25mlY84VgDmDPN1uk33MmaKTnFemdM7jjP+mgcmvV62nu4sQEaEsxceds44pXtQsMrOdV+YPjxMi1AQRsEVAv0tupTZcEiDS/tlWXEjhWgLixdj7HUq/QzwhHWVdGkBWozRPUkAEiAARIALeT4AcKg6+h2ypg6N39x1s0qnF0qMPWLWnTbsNbVq0ld7VCuY4mT1rHlau/Btzf1qMjRt24qmnbG3W5mrLrPtjDpPw/r1hT4Imf2xdyUs1er2BO0z0ej10Oj2PGwzS2Z40nuaNakshsHT1Fuik/M27DvMn/LRr1YjrKciTQJEWSNWlYO6lmVwWX/nJZl/dy/ayqSelZxG4GK9DTJrBs4wia4gAESg0AfFejhmhplVo7KjJKnTb1AARIAJEgAh4DoFi71BZtX4X6rZ/yUrGfDybv0ts+cSID6ajZY/X8ejTI9D/9UmIi0/ieZ4UKAOjrM0R5FAElLLWu0kTFRmFWrXquKl3B7sNDoG2RSuj1DFOyRVDQo1pSa97yKhzsDWPLvb73zvQqMtQ/tjk1f/8y+Or/9nFbQ7wV2Pm5FGYMmspGnYaglHjZ+LD0QNRvoznfJ64oV4ciHEpEG8n5GsEur/lyJqlhOrHCEzfvwyf7Z6LQevfxIvrx3ApH10VovSvQ2RXdC3t2GykfBlAhZ1GYPuVLPRdnogR61Pw0uok6ZiM+AxyrOQXsLyN9fIyWTURAn1V5RcllXcyAaGajfMyqQ+hemkpLNq/hEsJuLHvpllSY9LAZmsmXLbWF60l1DoRIAJEwPcJFHuHSpe2zbDulykW0uChaigRHszf/V9XbQFb6rBt5TTsWzsbcpkM0+f+zvM8KQit3tfKnLDqvSAI1tOhrQp6o6KIbNbWa4CEZau5pH74Ce/FQveBUcczvDx49qkOOL19oYX0fqKteVQdH2mME1vmY+OyqTi2aS769+xkzqNIwQmI91Kh/2oN9J+vhv7rtdB9tALi6ZsONahbL4d2loJL/V8fQcvl3fDSP2Pw4j+SU0WSSjE1IEj/2pTq6FB7VMh9BL47mI5UrWg24GK8HivOaMxpijhGQDFAB+VLOgjlRAiBgLytAeqpmY5VplJEoAgJCOUirFoXGleBUK+Clb6gCpPjJDUm1aKJI/OP4Pf+K81yZctlCAJwdJ6l/rKkz1nR5HBJjUnLqaY4ESACRIAIPIBAsXeoBAX6o1L5KLMkJafixJlLGNinG8f2z7YD6NOjHSJLhoFtyDmwTxf8sW6n5OkXeb6jQVGXK1lvKCp1W4LwWs8jtOpTKPvoV6jQ8cei7pba93ECcrkM5UqXhFKp8PGRum54ho0nIEbn2EQ2TQPDyn0OGaB4XA/lcOkCUhKhssjrbG38F35+7FssfOwbXI+6yHWFDe6dvwd2Yl3Ydqi+bQI3kvVIy+FMMZW6lKA3RenoIAHBD1C9o0XAJg0CDmbA74dMyGqJDtamYkTANQRkj9SCfNKzkA941KkdHp5rdJBc3XbFKe0eW3iUO2EubbzklPaoESJABIhAcSBgz6FSHMZuc4zfzFmBfk93RMVykTz/2s0YKX5/2maFskZ9cmo6z5dJHn9PkYgavVC5y1xUfWIZohoNh0LpD5NtkplgwtLccClgdytY2htEMtc8ltz2sjwmufWOpJHMagKGvTIISYBFHWOWmZtFngDLsgJsprOboAMRMBMQb8eb46aImJwBxFveYTTl5TwquuuhGq7lIqtm4Flbm6zGz49/g58lh8qN0saT4CbhLXhefoOb+2/ip4fnYHG3xfi5089Y2HEh7p69m99mqHweBAKVgs0S9vQ2C5OSCBAB7yEQGgAhgJ4+5T1vGFlKBIolARp0AQnICljPK6qt2bgHPy1Za1N2HzxlNYZd+0/i4LH/MGzAkzxPFEWwPVT81CqeZoFapWQHpKdr+DEy3B/eIEH+SgT6K7itaqUM7BUWpOZpb7BfLnkzSoT6WdlbKsyfDQWCdH2S33HgU3/oTxhZZM1TIr2bP8KT/M19hAUbT35UEq/8tm0qr1LIuH0UEAETAcH//veJScc/wAU82e5Z7jnejAABdUMa4IuG36Gsf3muy2+w/ZMdSLtrdBazuolXErF76h4WJXEigQh/GZqWMf6W5Gy2XSUbn42cBShOBIgAEchBQIBxNlb85UTzfils75Q0B5fssJmIrLxJUqONS33YOVWObihKBDyUAJlFBDyDgE9f7bFZJPGJKbAlzFGS8y3Q6w345sffMKT/E4gqFc6zBEFAgL8fMrO0PM0CUzwgwI8lER2f4RWSkqFFaoaO25qpNd7ZTkjJ5GlvGIPeIOJuosbK3pgE6c6+9E5Ivi+rvAeN6875DKQskSrm+DMkA3dmGxmxuvESH5bNeLF0QSRLZ2TN2iEhAoyA0KgyO1iI0LAS4Gd9gW1RyE6iWYmWPCdQEYT5LVaCbUjLFfkMDHoR987FWdWKPRFjpSNF4Qm83yYQQxr7o0U5JTpXVePjdoFoX9l3HCrigYvQz95oFjEtk0PT/7yD61g+V1BABIhAgQmIkiOdVT7y02G+VMe0b8qlzZeZ2lLYiZKlBkfm5qq3yTjL0UbRXDUpWSACVIkIEAGfJODTDpUXenfGuOH9bQrbjDbnO/rP9gN889mX+z2eUw22v8r1W/cvKG7cjuX5IUEB/EiBdxIwnLX90Tdcsq33zlGS1Z5IQNa6JmSD2kLWtCrfnFD2dDPInmvldlNlcgGqQOsLenWIcaaW2w30MQMClAL6POSHj9sH4a1WAWhR3pq9Nw9ZTEiHeCnGLJBuWvDxXI0z6qR8nqaACBCBAhMwzVAJqxKO8i3KmyUwMtBGm4KVriTiUUW8joqV5LxuUHY96X4iL0sBESACRIAI5E1AlncR3y+RlaXF1z/8huEv90J4aLDFgLu1b44Va7YjNi4RqWkZWLxyE9iTUATB+ofJoiIlPJqArI7Bpn2mfSlsZpKSCDiJgKxhJciefwTyl9tD1rYOBJXCSS0XrpmaPWpaNfBQn4esdKQgAnkRkDWvCvnrXbgIzaqZi8teast1LN+spAgRcA6BYteKaYZKs2FN0GdZH/T9uTv6ft0AdTqXtmZh47S1DQ7gFWEJXq5xkNev1s34f5VmqFjjIw0RIAJEwB4BcqhIZP5YvwspqRkY+EwXKWX593yvzqhaqSw69BmNFt1fg1arw4jBvS0LUcrrCAglAUUfvYXdguRLUzyvs9BRgggUJwKdPuuEjpM6osbjNVDrqVp47NvH0Py15sUJAY3VWQQigiBUK22UiPt3y4VKpbgOUr6zuvLedshyIuA8ArLtv0L5yVNQzhqOTpiB3m0OOa9xaokIEAEiQATsEpDZzSlGGeypPgfX/4BgG8t4AgP88P0Xb2LPmlnY8cd0/PbjBESWDCtGdHx3qOqJWZA1MM5UUQ7Rwn9DBmQVRd8dMI3MJwik6VPRYlNNbI/dxMcz/sQYfjTpWR6TjlubcH1+gwYDGqD77O54fPrjqN2zdn6rU3lfJuCmsYlXBGTNUtoV3d9yN1lG3RIB9xKIqBLGl+qEBaZDsXGBhTHVy8egUbVrFjp7iaxH2vKssMrGpUNBpQN5mgIiQASIABHImwA5VPJmxEuEBgeiZEQoj1PgOwSEEONY5K0MEMhPZoRBIRHwEQI0DN8gYLgig3aWwq7o1pNDJec7zZ5QqNNbzsA05RsMIqLvxsNePlsCHXM3AawNUx06uo+Adq7CriORORkbD2rKl+pUrGZ7dm31Olkoo6iIAASBPRAoFBEoI1REOWUYqshuge21kjZ8NDJeGgr2ajy4MW+vWhfj0h+mIyECRIAIEIEHEyCHyoP5UC4RIAJEwFUEPLYf5cH9CHl/LEKHDEDgtK8guxfnsbaSYb5HQFbFAOVwHRfFE0ZHgVBZ5GmmVzyu971BF2JEazftRdd+Y61a2LH3OF+63KnvGDTsNATL12w3l2EOlNk//4nGXV9Bx75vom2vkTh+5pI5nyLuIaCdq7TrSGRORmQZ7RL9Ao2RXGGl7o3Ra+yzqCSvAfZAoPrKh9Ej7AX0XjoYT1/6CqUO/IG0se+BXkSACBABIlBwAuRQKTg7qkkEijkBGr63EIg7F4e90/aZ5cL6C9IdaMCWPveYFEcOIfzZp+C3dDHUWzchcPpUhPXrBdi5A567PqWJQGEJCFVEqIZruZgcKrKqBp5mekV3vWUX6cmQHdsM+a6VkF07bZnnwyn2RMJu/d/Gu5/NsRplhiYLYyd+jzcG98LxLfMwfdIIfPL1Qty8c5eXPXb6ImYtWIXFM9/HsU1z0fOxNnhzwndgM1p4AQrcQkA5VGt2HArZD1xTDtGZdVAZzRIrPgQxLNKYMIWCDIYmnaH6nw6Krsb/I4qndPD/RwN5U4OpFB2JABEgAkSgkATIoVJIgFTdiwiQqUTABwiwO8m5hyFC+ieKCLtZAvX+ao7yR6oyjbnYvXP3sH/6PrNc/OciBAG4dz6Xfv1Fcx0WYfkHJqzDSdFyLxXFxfNQ/neGFSHxUAJX/p2MW/s+RfTByTbFQ80utFnCvdtQTX0RiuVTIF//IxQ/joZi1TeFbtcbGihbuiR+nvEePhg10MrcA0fPIj1Dg/5Pd4RCLkfnNk1RqXwUduw9xstu/fcoWjWriyb1a0KpVGBgn66IuZuAc5eu83wK3ENAOVQH5jRkApND5RWtWSf4ZdulUEH76gwY2veHoXYL6Fs8Ce3rMyFGVjYWCBf5UdZEhCxSkphohPfraVdCRr7Ky1NABIgAESACeRMgh0rejNxWwts7XrbsF8xfMMeupKene/sQyX4i4HICgiB5Qmz1KqlDb0Wg/trmqHC0KgTpn61i+dHduxCPnadCcAq1rKrJbt200pHCcwhc2fURd6jEHJgMW2LQaTzHWCdaIt/3J6BJtWhRdnA9hJR4C50vJpijpHSpCISHBlkNLyYugTtQVCqlOa9apbKIjk3g6Tux91ClQmkeZ0Fk9ub7sXGJLEniDQRCSkDXdTB0gz6F/umREMvVtGu1kJkJ5f699uX4Ebt1KYMIEAEiQAQsCTjToWLZMqWKPYFlyxZjwYI5diUjo2gcKroVcmT08kNaU39onlNDt5k2LCz2H0YfB8CcJ0xcOUxt46au7EE6rpQAABAASURBVI76yieBKm0molzLDxH18AfmmpHNxvE008kUplvb5mzfiMTanlEh3LGcfeVNg70dHYeflqy1K2w5T17jSU5JQ4C/5XuuVquQkmr8HWb5fursKRDZjbHyqekZPBURrEJUuJ/HS2iQCn4qObczQK3gtocEKOAO20uG3ucZ5O+YDWqlDOHBapv2mnzpkWH5fx8CcrEoWasShDVrkf7LKmx58kssEvtglV8vXJ2yhOvl8+YhP8z81XLOmgL3EMhKvYULOz62ORuRzVK8d9ryCVDusZJ69VECNCyJgEwS+iMCRUKgX7+BePnlYVz8/QN4H889N4Cnmd6k4xlOCgxHZcicoILhnABI54H6k1J6pAribSntpD6oGSLg6QTY0p+afzY075lyfv0Fh0y22lPl7/O8nqFEKX7kgXTRlTJhMgylInmSAs8kUOXRD7hDpXTzDyCT+3Ejy7SYAJZmwhW+GERWtDkqsUx1m3pvUGp1esQnptgVUTTkOYyQ4EC+5CdnwczMLAQHGX+bWX5mVlbObF4+KMCf6xJTs3A3KdPjJTktC5lZem5nhnRkxqdm6Hja1fbHp9znmaYx2pSXDZlaA5JSbXMWjat2EJdsO/9BbVuxyBQQU7c5Zn98CVvWJOA/oQYOampjzrhTOKEtj7iaDfLFTJOpZ6hJ3ERAKzlULu74xOZsRDZDMf7sQjdZ5ondkk1EwPkEyKHifKbUYjaBfv0GYLDkUGES4G88KeufUxdgPJHLLu6Ug26P7bskesnR4pQOqBEi4AUEwm6VQO2/Gt7fM8WWQyX75DzncJhDJedeK2zzWpaf1ao14vafQPyfGxB79Jz5EZssj4QIeBIBfcunAb8gC5MMzR+HGBxhofOmBNvrZNzw/rAnAblmntgaW1TJcFy7GQOtVmfOPn/5JkpHhvN0mcgSuHojmsdZYFrqY1r6Y5C+L9gGtZ4uzOkgmco30xVZQhqMO22Xuud/zBZH2LHC9uxleUwcaSd3GdY/ryvBMeXdPROL+Iv3mNpCTq04w/mZyjlylJq1aIMSTiTgQFPKoHKo3m4C2OzDiIde4jWUgWV5muvqGHU8gwIiQAScToAcKk5HSg36KgH5jesIeeMVlGxSByVbNEDwu2MgJPj+unxffT99eVyJ5e7hv6eOo8WollyqP17Derg2Jm2VrFWSlzfVq2GqJ50tGyKjoGvQCMh2jlo3SBoi4H4CYomyyBr7M3TPjoP+8f9B979p0PUa437DXGABu2hmDhOdzjhbgMezn8bVvFFtbsHS1Vugk3Sbdx3mT/hp10r6Py3ldHy0MXYfPIUjJy+AzYhZtHIDokqFo1Y12zN+pCr05+UE4i8Z98/JPYyEy7b1ucvZS5Pe9QRUkkOlRruP+QzEiIde5gYog8rzNJuRWKKuUcczKCACRMDpBGROb5Ea9DgCCeeW4vqeSZJM5OsrM5MucRsTzi/laba+kklm0mWu9+ZA0dp4Ipl7DPLGeU+Jzl0ndzp43Jvw+/svyCQniiw2Bv6/LUHQN1NyF6M0EShSAoHyIOzvch7tI7vwfiY1MD7BhOkn1v+a61o2ewQ/TF+AVqNbcqlpcozwXPsBc6iY6rBjje7ZmxracL7Yb4VyiICbCQSEwNCoM/Rt+sBQqa6bjXFd95eu3kajLkP5Y5PZE3pY/MMp87gBAf5qzJw8ClNmLUXDTkMwavxMfDh6IMqXMS7na1S3Ol4d9BQGjpiMRp2H4Lc/t+HrCa9DJvP5//ycT3EMyrcqb3PYtw/fwW99fsPV7Vdt5pPStQS0KTdwdcNAnJpXEWcWVMONbcOh08S71gjqjQgQgQcSIIfKA/F4T6Zec487R+JO/GBldML5ZbjBHCq7J/H1lVnZjhPmaGFrK01i0psayEy4wNtMOL/cpPL4o0xynKg/yYKslnRL3R+Q1zdAPSMLQlkpXRjrpTt6qv17rFpQ7vnXSkcKIkAEiAARIAJFQ8B+q9WrlMPp7Qst5Iv3h5krdHykMU5smY+Ny6bi2Ka56N+zkzlPEASMGNwbhzfMwSYpf//f36NxvRrmfG+I3Nw+Evu+VWP7FwocnxWAe6fncrNvbR/B00zHJO70PK4vDsEdyTly5KcjSLySaDXcwFKBaD+hPRSqHJcCogi9VgdWb/XLq5FxL92qHilcS+CG9PlNuvg79Jo4aNPvIP7MAkTvn+haI6g3IkAEHkggx7foA8tRpocT0GXEgzlG4k5aO1QKanpm4gXeZuIF73GosLEq+urhv0qDwMMZ8PstE4rOeqYmIQI+RSA+6x4fz8RT4/hRK2aBzTBhy3WqPVad6woblKgRwZcA1Xg8e6ZKYRuk+sWPAI3YowjI5TKUK10SSqXCpl1+ahXKSvmePDOFOUWY2BxAAZQZ905zh8v5ZQ8XoLbnVtk2YRufabLzs524vsf49Kv02FQLgxu91Aivr+mBxoYTALvvJDnWBEEwl2GzVcwJiricgCjqkXJji1W/qTe3W+k8UpGRBcPe80B8mkeaR0YRAWcRIIeKs0hSO75NQC5HVovWVmPUtn7USkcKIlDUBBIkZ8qZJOkEWOooy5AlhUCmPhOXSv3Hl/jUyOVQKVGrBHeMMGcLk+pSvnQjEiVq5tI/bumIYfmtRrdEze41eB/FIaAxEgEiQAS8nUDa3TQcX3TcahjX99yw0vnFXEewIF3w3vejWJXxdQXbeDf6bjzfX8jXx+qK8YlHrhi7SdXAsHI/dJP/gHj2llFHIRHwQQLkUPGxN1WXvfSH7YliEtOeKXkNNf7cUr7Ex1QvwUNmpog3BcQME5DSyg/p7fyQ+ZEKYmJeo3F+fsqUb6Hp/hQM4RFgG3RmPPcCUseMc35H1CIRyIPA8cQj0In8aR0WJQ/G77VImxJs5koryTFiErbZLLsJaUtvqkNHIkAEiAAR8E4C9jacTb9rvYRHcewIqsH2fillm5aBL72+nbMCddu/hOTU+xx27D2OFt1fQ6e+Y/j+QsvXeM7sD0GQI7jC/aV5pvciqHx7U9Qjj4Y956zsMmw7baUjBRHwFQLkUPGVdzJ7HHrJocKW/uSU3Huj8KIiDy0CtkltznqestRHM16FtDWAmCDJXQG6lXJkzVBa2O6KhL5CRSR/9xPijpzlj5BN+eIbiJJzxRV9+04fNBIi4H4C7Gko8YkpuH4rFplZWpsGxcUnIUNjnP1jswApiQARcCsBtuwnp8Rl75mS0yjRxrnOrW0j+BIfU93zy5rnrOIT8Yhq4TbHEVAqwKa+hnAV3Q2bECnGQSlmoaJ4A8/1U8G/hO3yNhvxcOWq9bsw99e/Laxk3/FjJ36PNwb3wvEt8zB90gh88vVC/gQsi4JuTFRoPxOh1Z+B3K8klAFlwJ7iU7rFR2606MFdi8mSsypVY1VIjE220pGCCPgKAZmvDITGYSQg9ythfu48e/Y8E1VoVWNmzlDImTDGw2v2t6gbVuNZY4Y7Qz1gOGj9MdXvk7vGKuqFCHgggYZhTaAQrPdAaB7RqsDWhrzxCsL79bQrsntxBW7bkyqeOHMJbXuNRJueI/D4C+/g0adHgJ1om2y8fisGTwwYh3a9R6HZY8Mw/sv50OqkLyJTAToSASJgRSBRY8AvJzWYsC0VM/en4VSs9Qw6q0qkKBQB3WY5NM+pkdbUHxm9/KBbcf+8iG0423BQQ6v2K7auYKXTNWrCdY8IBzFa+AmfCF/jVeEXVHi5I3zldfDYf/hsxhJM/eg1iyEdOHoW6Rka9H+6IxRyOTq3aYpK5aOwY+8xi3LuTCiDK6Byt8WoN+Q6Hnr5Eip0mAWFX4Q7TXpg30KI5IQL8rMqI0SGWOlIQQR8hYD1laqvjKyYjkMhOVRKN/8AOUUdWs0hGhG1+lvUC/cEh4pDllMhIlC8CISrSuCh0AZ80CqZih/VcjValHiUxwsSKI8ehnL/XruCLN+YrcFuWI8a2ge7Vs/Esc3zMKhvV3w4ZZ55psqn0xajdvWKOPTPHKxd9Dn+2XYA/2zdXxCkVIcIFAsCeuk/1bjNqVhyIgMHbmux7mIW3t6UgrNxRetUaTg8HTmlZN2hVrzZ0sbcynIdZlrUq9nvoEWRpMt/Ifbot0i+tsFC70kJthQ6c6QK+pPSaXwGYDgnIHOClD4upbMN7fBJBzy38jm0fb8tKrauyLUBkUH8mDPQV6rCk6JKDTEgANp6DZA8bTZ0NWtzvbcH127G4PX3pmHaxDdQo0p5i+HExCVwB4pKpTTrq1Uqi+jYBHOaIvknIGtdy6qSrENdKx0piICvELj/zeueEVGvRODBBKQbLrLmBqsy8pZ0x9gKCimKFYFkbRIf76MlO/BjliELP12eyWXhlR+4Lj9B0sw5SFj6BxdDiZK8atJ3P/E00xvKlOU6bw8aPlQNfXq0Q0RYMJQKOaJKRfC4TCZDUkoadh88hYF9usLfT4UqFcug52OPYOMOywsub2dA9hMBZxK4mqDD9STr3+TNlzKd2U2Rt8WeqPLfL/VwdX0/3NnzAa6s7YULK9sVeb8F6UBvY+Yua8dwwPK0vkzTMmjyShOEVQlDXi9DmTK4e/oKEtZsgubpZ/Iq7hX5SclpGPb2VLw5rC8eaV7PyuZk6Ts/wN/PQq9Wq5CSvceKUi6gZIjaK4QNgtkaFmh0DinlMrfZXaKt0aEiC/FHyMBHUerzfijVoqpde4L9lVArXW8vY8S4hUrMGLuCCKtPkm8CPlfB8pvX54ZXfAak8I/gy3VK1n/VaYNWh9Xgbbp76Y/fpCwEPgkI4ZKUEqHoo4dqpBb0IgLFlUCqLgVX0y7x4W+L3ciPLJh7aSaY/HzlR5bMl+iaNIO25SNcRJWK19Xm0HGFDwWHT5zHhKkL8MOiP/HuiBe4cyXunnG36/JlSplHWrFcFO7ExpvTFLEmIO6/CP3iXVxY3LoEaXyZwI1kg83h3UixrbdZ2AOU+swkZCZdtrAkPeYgUq5vstB5e0J+/Roiq0SZpUS7h/mQ7Ol5ppcG+46c5vuh3Lgdiy9nLcXcpX/zkUz7aSXOXriGkOBAvuSHK7ODzMwsBAcF8JTOICI5Q+sVwgxmtqZl6lgUejfanpptg+inhK55NaQFqh/IMCNLB63e9azZ+8tgpUv2Mnb2xf5ngNUnIQLkUPGRz4A8e6lPyQbWDpXwmv1QofV4VHxkPHLuqRJeqz9PM114redx7+xCXPm7D2IOfQldZgLU4TX4EqDwms+6lZJQXkTUHBHBezUI2KGBemIWhLxvtrjVZuqcCBQlgSBFMCoFGvdGivSLQvXgWugU9TiGVhvB5cUq/yvK7j2ybb3egJ+WrLUrl6/fsbD7Tsw9xMYlQqvVITEpheeZnvyQc/q3WrpbGZ9o3ExPkEqVCvODN4hKIUNYkIrbKpnN/4rCbr/NJ6BfvhfisatcWJzpcvYV4Hd/v58SoXnzY3cJZTKB256zHRYPke4kssGolXKb+ayMO0VgHxJmYDGShlH3399OPvE+AAAQAElEQVScw25RTpkz6XCc7cdyPEYHexI2IIUv2XG4wTwK+peoy9sLq97LZklN/Dmbencq5TZm7jJ7ZA87z4klJCayJr1aqlcuh1FDn0F4aBDCJAnJdpSEhQRCpVQgqmQ42JIg9jtgGuj5yzdROjKcJ9mmxllaA7xBmMHMTq1OBKSEQTKepd0lkgmQTHCInU5yphgkB5CrbWUb1DM7GbOC9s3qkxABmbsQJEgnsH+s24mZ8//A6XNXuRl/b9mHfUfO8DgFziPAHCcVmUOl9UfcQaLO3lMlvKZxz5SAyOZIOPcrki7+geSr6xC9/2NcWWP7xMJ5VlFLRIAIFIZAhj6dV4/VxOBiyjlsiVmPCFVJvFJ1BF6qYu1Y5YV9OBAhIj4xxa5oJcdJzuH36NIK33/xJqZNHME3K2R3ME0n2znLsruVEWEhvKoohYkpmfAG0UoOppR0LbdVMpv/FYXdqf+e523nDNL2XeT9mvrTZOnN2cmpDvBLywI7uTbVz3lMk+4Ws8a0OoNFHznLuDPOLiCYfcVJwv1leLVZAFTy+6NuWFqJx6qr7yvyETsWrcO7m1Psyq8nrZ8gwpov334GWr6Zifbv6riDpET2nirl2s/kadN+KyXrDmHFrUQVXttKxxR+EcblCyzuKcJuNKlnZEFeX3Kg+AOyWiLUn0jphlLaU4zMhx1FVbSa5FAZNuBJmOTZJzvwrl567nGwvOaNjO/50tVboNPrsXnXYT6jpV2rRrwcBUSACBABRwi4xaHCpk937fc2f3rCD4v+wuVrt7mt/124jrcnfs+/1LiCApcQSDj3i1U/6TEHkJV8zUpPCt8jwC5c9NLFl++NzHdHlK5Lw93MGKsBbo42Tme2yigGCoVcjnHD+9uVWtWsn27BsLAnOrBjXHwSSpYIY1Ew5wqPSMHVG9EoE3n/iQpa6U6aNwi7sGdTvpmt0jD4H4s7U7Ik5whSMnjbOQMxMR3atEw+hZv1x75jTPksnZewu5WsvK1ypq8qdvfVVn5BdamJGpxa9R/2f38Il3ffMNue3/aY3cVRnq6lxm99wjDjsWAs6R2KLzoFIVApFAhFmJ+A+lEKLhVCjF6aUD8ZTzN9xZCiOXUNr9EX6lxPRQyIao7gil1yj8Mj0orOevj9lonAwxnwX6WBou99xyUzMOFSAm7su8klNSaVqZBwOR43jsbhslgRyWIQ1xXnIMBfjZmTR2HKrKVo2GkIRo2fiQ9HD0TOZZ/FmU9+xq6fvQm6txZz0X+x2lj1bjJPM714Kdqoo5AI+CCBovlVygPUqnU7+a7aG5dNtdgk6rEOD/O7i2wqdh5NULYTCWgSrO8wsuazUsihwjj4srDpjp98sxATv/3ZaphPvfg+6rZ/yUJmL8z+kbQqTQpXEriZfg3svcvd59W0y7lVlM5FgD0ieeu/R/gGtGzjwdk//4kAfz+wqeGhwYFo1awuFq/chAxNFq5cv4O/Nu5B13bNc7VCSUZA8FdBqFKKRS1EqBoJ+KksdJ6cSL6ZjAXtFmL9qPXY+fku/N5/Jf5+3Zeck66h76cQUKOEAhH+hTu1bFRaiS87B3N5ob4fN75RaQVPM/0LDfy5ztmBXBWC2gNOofLjy1Cm9WRU6bEKNfrscHY3LmvvyLwj/LPMPs9Xtlzh/R6ddxTLR+/CXOEFnEV1ruMB88Ay4QnfDapXKYfT2xfCNBuRjbTjI41xYst8sGuSY5vmon/PTkxNQgSIABFwmEDhfvUc7say4Mq/d+CZ7m1RrnRJiwyTRzgxOc1CT4miJRBi4+6LTBmMgFJNirZjat2tBDZsP4C2vUZi5Vr7J4wjhzyDdb9MMcvzvTq71Wbq3EigZshDUMv9jIkcYcuSbXKkKGqLgFanx4gPZ6D1k8PRssfr2L7nKGZ+OtK8CeEHIwfg9LkraPbYMPQY9J7kTGkG5uy31RbpANmTzYDwwPsopLjsyab30wWJubjOySUnoUmwnGlzYf0FfjffxaZQdx5AILTqU4hs/CZCKnXzAGsKbkJ4lXCUb1HeSio0LIkq4nWECHSubaIrl8v4NYlSqTCp6JhPAvLXu0Dx9UAuQsVsR3tYAE8zvVCtdD5bpOJEwHsIuMWhElUqAjdv37WidP7yDa7LOb2aKygoUgKlGo1CYJlW5j5kiiCUa/MVZConTgfNnqet+2SlefofmwJoEv3s+08qMRtCkSIl0KZFQ6z46ROwvSTsdVSqRCifTcaWRTAJC3XiZwL0KgyBmsHGtd+mNqoG1sDQqm+YknS0Q+DZJ9vj+JZ52LriW2xZ8Q02//YNWjZ9yFy6SsUy2LD0K55/YN0PmPzuUOR1km2u7OTIlitZ+OWkxq7csvN0FSeb8cDmhEolofiwNxTjnjKKFBcqlHhgHU/LvHfxnk2T4i8m2NSTkgh4A4EmrzRBn2V9rOTZGW3wirAEdXDh/jDYbspM7msoRgSIABEgAg4ScItDpdOjTbB8zXZs2H4QOuluoU6vx8mzl/kjLBs8VA0lI0IdNJ+KOYOAwr8kqvfegjovXkDNvrtR/3+xiKgzyBlNUxseTICtHS4tOTcDA+xPn16xdgc+nDIPbKnP9VvWe3Z48PB83rQSKuMdoDdrfYDfH9mMpa3/Rjl/2/uE5IJR7JNsv5WoUuFgn3/2NBlbQFh+YICfrSyX6bZJDpUlJzJgT+6kWu6Z4DLDbHUUKf1uM7GV5+G6EtVtO4Aiqhuf9OHh5pN5RIAIEIEHEmBP7mRP8GRP8ryx7XWk3tj6wPKUSQSIQP4IuMWh8tJzj6Fdq4YY8/Es7D96ll+w9XttIvR6Az59Z3D+RkClnUZAFVQO/pGNndaeRUNy40dNMaEP5K915VlCtSjzVED560Ydz6CgUAQOnzhv99GxD1rek7vTbu2bo23LBihbuiS27j6KZ4ZOgMmpopALCPJTuEgK3k+gZCO76cZs9ct+BIW32M5sVilkYMLiuUWR/X+qakhF1I6omq/3Ii8WMgZN+kAEquX5atdko382a6kJ+isEgQ5VVGD7RTApEyzjLXWonEMXZNywk2dQUGAC9V+oD79wS8dyjcdrILxqRIHbpIpEgAgQAU8hwJ7cyZ7gmXx1HeLPLMSlv3og+RrNDPeU94fs8H4CxjM0F49DIZdj6kev4bcfJ+CTsS/j7df6ge2yvXrBZP4YMxebQ90RAecScHNr6RkavrlzfGKK1TEhKcVh64a/3AuvDXoar7/4NJbOHo/gIH9s2XWE1xcEAezOvseLAOklcFslk8FegiDwtMfbLjPaKZlr015kvwoyDtYmqy4IgtPbNtkDehWaQCfJoTKgvh+YlAs2Ok865tQV0dNOCm24lzUQUj4EL+94CY9Pfxxt32uDZ5b2QffZ3b1sFGQuEXAuATEszLkNUmtuIZCZcA7syZ25O0+8uDK3itJEgAgUkIBbHCpsCcGwt6eiXq0q6NOjHdiMFbbLtr+fqoDDoGqFIUB1fYtAmxYNMG54f5vyygs9CjRYtodEqYgwZGRm8fpanQHJ6VqPl5QMHX8aDrM1I9O4PMJbbGc2a7L0yNTaZq3TG/h7kS6Ni5XNj+TFwpD9tAfGLz/tmsqmaXTcNgqKLwHV5/2ger8LF78dYyEI6RyG6ov+XGfKQ3Ic17s7UAerUeupWmgyrCkqtCzvbnOofy8mIDu4Dsqf3oJy2QeQy41P15GbdJJedmyLW0enr1gJsVdizHJvxwFujz09z6TAawloEs7btF1byCd5/vffGcxfMMem/HF+j80+SUkEfJWAWxwqgiAgS1vgE25ffS9oXETApQTYEjut9P9Qr9fzvYxY3GAQuQ1sac/C5f8g+m482FNR1m7ai1PnrqBF4zo8nwIiQASKDwHh8nHuBFH+NNZtg5b//T23QXH9d7fZQB0TAUcIyBKiIVw5AeHmacmRaHySjmDSSXokxjrSTJGUEVJTEPzRuyjRujFKNaqF0GEvQn79apH0RY16BoGgcm1sGhJYrq1NvaPKc+fOYoHkULElZoeKdFNI/8u/MPy8A+Ie244dR/ujcsWegEcDcItDpcMjjXHw2H9ISknzaDhkHBHwZQK//70DjboMBdtXZfU///L46n92mYe8aMUGdOo7Bo06D8G4yT/yGS9NG9Q051OECBABIuANBL6rPRPTqkyzK94wBrLRewjomz0O7dCvoH12EjRZXaBRP2VMM50kYqNObhtM4Pcz4b94AeR3bkNISoR60z8ImjjebfZQx0VPQK4OQ/n234E9wdPUG3uyZ6kGw03JAh1r1aqDl18exqVVq0d5GzVr1ubp3jVb8zTSMyEevQLDievQ/74fhhX7jPpiEdIgixMBtzhUZIKAAH8/vP/5T1jyx2YrycrSFqf3oFiONUWbgV/ObMe4d9/EF1Mm4dCh/cWSgzsH/exTHXB6+0IL6f2E8Y5FxXJR2LL8G+xcNQP//Poljm+Zh0F9u7nTXOqbCBABIkAEiIDHExAjykCs2ghixfowiGVgUJQzppmOSVik28ag2nP/ponJCMX5/0xROvoogRJ1B/MneLInebInerInezJHi8Vw85moXfshDJYcKkwefcR47li7Vh2uMztUcrVpOHoV0n+KXFpKEgHvJ+AWhwq7G842zty+5xg+m/GLlZj2afB+vDQCewTevfAX5p7YiD3Sj/vff/+JN8cMx759u+0VJ70bCAiCgBLhIahQNhJsI2kU0etqoh4/HErHR9tTMe9oBqJTjXuDFFF3Lm/WsOMsDBtO2BUxw7gvjcsNow6JQD4JCFeO82U3pv1PHnREio29UUTrDlWfW+6pwtpUvNsZ8a+1sehLvvsP68oOat74bwSGfzwWr/i/B7lo3NyXxZkM/2Ksg61QMSJABDyJANmSfwL+kY2hCiqX/4rOqpGpBRJpdYKzcFI7nkPALQ6VaRPfsLgrnvsueWhwoOcQIkucTuDa7Rs4kxZt1e6WrZusdKTwbQIxaQaM/CcZf57LxMFbWqw8o8HoDSlI09q48vJSFIZdkkNl43EY7IhADhUvfWfJ7AcSEPkjtu4X4Rsd2/h/LdjQ3a9FMSLgKwQ8YhxZra3309DVrO0RtpERxYBAqRAgIqgYDJSGWNwIuMWhkhPyvYRk3I6Og05vfAJHzjyK+yaBa9E3bQ4sOvq2TT0pfZfA9qtZ0Ob6r5+kMeB4tM5nBi1rUweyrg25IPtJZrI2tXma6UV/lc+MlQbi2wTEKg2R9dkmhwQhJSxhCJKDhYmlFlnvLrNqT/fFZkR8v8tCr3+kd66a+UvKW+f6osmuLm9mW5+dXYwPNHRfJJD22ghkDHwZ+jJlIYaGIbPLY0j9aJIvDpXG5AkEZNL3vmSHmB4EUVcWsmdaSCn6IwK+R8BtDpVV63ehTc8RaNtrJLr0G4uGnYbgk29+Bm1U63sfstwjalyrfm4VTzdu3IwfKSg+BG4k217ecyPZdy5yZO0kh0q3BpBJggAlf3NlbR/iaaYTyKHCCEDQoQAAEABJREFUmVBABApFII/Kshoi1F9kAdlnPbKHpPSXWZBVoxkyeaCjbB8iIAYFI2XiF7i35yjuHjuHpDk/Q1+xsg+NkIbiUQQCA6C9NBhZO55H1qYe0AyoAt0/xmWXHmUnGUMECkkg+9SikK3kszp7BOuHU+ahWuVyGP1KH0x8ezC6d2qJ5X9tw7uT50DkU4Pz2SgV9xoCwYFBeKtSR/gr7t+Zr1+/Ifr26ec1YyBDnUOgRTmlzYaallbY1JOSCPgKARqH6wkontJDyP7Z8V+pgaKH3vVGUI9EoIAERBidf8tvLML+e/8WsBWqRgRcR0BMkcFw/v75nJgCZE7M/hJ2nRnUExEocgJucags+3MrGtergYXT3sUrL/TAM93b4svxr+LjsS9h577juBNzr8gHTh0ULYEPx7+DESOHmSUpOYk7yj748G2MnDIOm++dQ83wcpg27XusXPk3Zs+ah+DgkDyNEjVA1iylWbQ/GS/Ixczc+vtf4Hk2SgXcRqBNRSXaV7b8ce37kB+ql6D3z21viu2OSVuMCYhVG/LlN9pXprqNgr77a9wGXcVn3GYDdUwE3EUgIeseUnXS1ahkwJyLMzDyyGCMOjJEStEfEfBgAlmCtXGJgHjTht66JGmIgNcQcItD5drNaHR8tLEVpEcfbsB1t6JtPB2A51DgDQRu3LyB3bt34tixI2bR6XQQBAGnT5/EsXOncDz1Fo7fvYJ79+IQFRllMSzdCjl3mBiibXzhagHtLMV9mZt94Z2VW290tFg0LCUM5wTetm4jTTmUcHjE37hHAvFbn1BMeywEvz8bhsGN/Z1gFzVBBIiAOwlkvXd/bxRNu6mSQz2Am5P17lLuGDHtxYKQklxPAREgAvYJrLn9OwyiwaLAvnu7cCrpuIWOEkTAowjYu8oMMc628ihbyRgiUAgC9j7qhWgy76p1a1XG6vX/wmCw/A+1ZqPxsbmVypfOuxEq4bEENBnp0Golz4cDFiYmJFiV0v5hdJiIMTYcKlal86cwXJBxZ4x+k4c5VPI3DJ8rHaKWoVYJOQKUzn/PfQ5WPgck3LkE4d6dfNai4kTAQwhIPxH6AzKYxHBe+o6QTh3EBMGsY3lc7yEmkxlEwNkErqRdstnk1bSLNvWkJAKeQEAItHQCMpvkjxgg5D0hnRUlIQJeQ8AtDpVXXngSl67dRrveI/HuZ3Mw9Yff0Gvwh5g+93f0fqItIkuGeQ3A4mqoL45bSEqEct9usyjOnIJ0WxU29b4IgMbkUwSEcwegnNwHypmvQvn1ICinvQLE2X7CFhu44tIFMGFxEiLgSgLMIaI/aPt0RH9UDs1LarNkzVACkk/FcFRm1rF8rof1S7wicMeLGGudRxoi4MkE0lv6I+0ho4ztOwMhGcZz47/eO42to25w6dB5AMTkAoxCozGf6yiPHuYNCBkZZp0iW8czKCACBSXgb4DfrEwoeuoh76CHaqwW6mmZBW2N6hEBjyVg+wymiM1t2qAmFs98H7WqV8SWXUewYNl66PUGvPvG8/jozUFF0Tu16QEEHN1sWJDuPjJzdSsVfHkO2zMlYaqAzO+kdPaeKSz/QSJK39esXm7Rm5b6ZPeRsw3F6ZMI79/bLMGTPwYEAcrc+gnvg15EwNMJKP6cDiEtyWymEHsVis0LzWlTRHHiGEo82hQRnR/lwuJCVpYpm45EoMgJaCSHSeYwtbkf3SEZbvcWkNbUH5mfSA4Uc07+I9qFCu540W2X578y1SACRUggc6yKO0x0fxfus3ks8TAupp6zsFR/TMbbznj+/v+rnAXksTHmc52Q0a/xLNndWLMuNFvHMyggAoUgIO9ggPqzLMmxkgXlYB2EwEI0RlWJQP4IuKy0WxwqOr0e1auUxw9TxuDg+h9watsC/PXzZ+j52KPQSY4Vl42eOnIdAVGEdFPRof7E7IK63+V8eQ7bMyXha3CHiu6n7D1TcrYkgk0kyamRCsNcl9U3iW5z9olLdh+WlRxLyRLjHStIpYiAuwhkpEBIjLXqXbh+1koX/NnHkN+6P3OFxYXk+44YqwqkIAJFSEBMAzQjVcjYK3WSIX23xxbiy1pqgv6IgK8TGHv0f/jh4rf5GqaoVkPbopV9adgkX+1RYSJABJxBgNrwVgJucaj88vsmtOrxOqJjjRemgmA8YXrn0x8xcMRkb2VJdj+IAHuPmTyoTHaeaYaK4hk9lMN1XMLfAtRv6KB4RZddKsdB+vhYNS3dlDHVzXlUdNYbK4rGA4VEwCcJqAMAwcbXe3AEcr8UZ07nVoFmqFghIUUREWBPbmNNs1mF+gMysJmJInvQH/uONgkrkIeIufZUYW0xMZBDJg9ylO3tBBqGNUG1oJr5GoYhqjQSlq22K8kzfshXe1TY9wkImRkQbp2HkJp4f7AUIwJEgBOwccbN9UUa7Nx7HM8+1QHly5Sy6OfVQU/h7IVriL5rdLRYZFKi2BAwzVBR9NFBNVzLJXysyB0qqle0DnEQJIeKqW7Oo7xrtkNFcsI41BAVIgLeSEAmh6F+GyvL9U26WekMITZ2h5O55afByjZS+D4Bwe/+GDUvqZE1RQk+nZF9R5sEuV5irrSUNByR8aU9rI2cot8hl3Lpjwh4LoHMt1V8eQ7bL+V6eRnia6h5Wsw1UVCUPvdMco9k0quLMeipD3gd1gYTjZ2lPrnrPigtv3wJivP/PaiI1+WRwQUjINv+K5SfPAXlrOFQftYXikUfFawhqkUEfJSAW86amcOkUrkoK6RlIktw3b34ZH6kgAi4lIAg2O7O1hmM7ZKk9SACuvRYpN7cifir25F6ayc08WcB6YSU66U00zHRJPjmCaOu99vQdX8NhocegaFRZ+j6j4ehRQ+rdyizR08rnRgQaKUjBREoCgKmGSqsbXlzA+RNDABb2SlKGpNI0bz+hHCA12dt5BChJGskr9qUTwSKnoD+hAxsTx/dajnEOIf6sygkSCkm0qFI/xRnTyOiYyuU6NQaEd3aoeTD9aHcv7dI+6TGPZeAcO82FBsXWBgo+28vZAfXWehY4tKlC9iwcR2SMzNYkoQIFBsCMneMtErFMvhzw79WXa/buo/rSkdaT0vnGRR4BYFy5SogNCTUIVubNGlmVU7ZW8eX+QhRzj8RltUw8LblXfRW/dpUMGeK4IpTGJu9k9IBAhl3j3GHiT7L0hGbenMHLqzqhoO/dMSl1Y8h5vAXYHe+U29t52mmYxJzUNLD8pV2Zy9v01JbsBQ7GZFvWQQmyqyDUMqPQb5nKU8znezY5oI1nFctlRqGR3pDN+Bj6J4dB0P9tjZrpL47HilTvoWmx9NcWNzmrBWbtUlJBApHwDRDhc0q9Ps5E36/ZMJ/YRb8W0vtspVrtn4HbHwly5roweuzNnKIooNeaoj+XEOAerFHIOsrJTT91Mj6UonM91VIf8zf/HQe9VdZCDyTwaXiTQMiLmTyuJD7NIp97pnk6uSpL+qi4/QKmPDri7wea8vv18xcpRxPBk79HIorl80V2Ga1QZ98YE5TpHgREGKu2Byw7OZ5s/7Q1rX4vl4Q3pBdRbnVXyL++iKwzQ2FtHtQ/vSWWRTLrc+3zI1QhAh4MQGZO2zv06Mdzl++iVfHfY3lf23D1n+PYNK3izBrwWq0bdkQJcJtTEF3h6HUZ4EIBAQEYMqUaZg+/QezBAcHS9+tIiZNnILp73yOb2r1xrcdhqJ69ZpWfSj66vkyH1lp0SoPSnCHiDJ7bxXlUJ2xjCq3XmvU5wpltUTetsK09CdnPnOe5EyzODlTGAWPlps73+QOEk38f06z88q6Z3mbOk3hlx8K8bcl58liLkrtISgVJySHym88Ld+yGLKjW5xmd0Ebynj2eSTPnMOFxQvaDtUjAs4goGhmQNk/RAQezoB6gu3vcmf0A2qECLiCgE4O7QKFZU/p0vXmFZmlzkNSyhPHrCxRnjsL6PVWelL4JgHZkY3cCSI/vAGin+0Zq6J/EB98cnISLpw5iSZhSrPUDpYD7PxZr4Vw5cR9uWH5NCo84KVY9S23AXG3HlCKsoiAZxBwy7d5h9aNMWHMizh84gI++eZnjPhwBpb9uRUtm9TBpHcGewYZsqJQBOrVa4AmjZuZRaVUSd+tAurXb4jGtRugcUgFNI6qmu8+2N1M1XDjvirsqMzeU4Xd3WRpkyhtbV6b3dvds3dxY99NLppEDdfG/ReH62dScFmsiAzRj+soIALOICBGlIW+00AuOkUN3qQYVpqnmd7QuBPXUUAE8iLgq/lsqY6sqaFIhidUEvlSIFkpGw76IumRGi1uBEybdSLuLgwbTtyXnZITQvrYGe4E20QiptpUu11psDHDWAyULp7l0kWy260jA1xBQEiI4U4QJMZArPgQxLBIy24FGQxNOnPd9RvX+dHZAd8AlzljsjKc3TS1RwScTsAtDhU2CrYp7b9/zsSfCyZj6ezxYPGZk0ehZETuOY6sNAkRcB6Bf7/4F7/3X8kl5kQMb3jX57vw2+TzmCu8gJsozXX2AlFyDtnLI707CBjnQGuyl/6wfVGYaBKkk1kHzNFlxPLlPayOSSDdVeFV2R0WHslfINy5BOHeHV5JLMEcKoMkB4okilpGXVgZY7rTIL6/CVf6XkAjIgIOEeBLdebaXqIghBsdIszpwkRWXbpClf6EMHBHCdMxkdWQlDZ6Uw7W8aVA8g4GG7mkIgKFI8BmGZo261R98zyUW6fBsPG4UXZJv0ECIFMbzzNy98Q+k2x5jqK782d+yBsZwNr2L8DSH02f53KbisweT1vpSFFMCChU0L46A4b2/WGo3QL6Fk9C+/pMiJGVOYCKFSryIwVEoDgTcItDJfpuPLbvOQatVofqVcohNCQIS1dvxcLl/yBDk1Wc3w8auwsIlKxdEuVblLeSCvXCUTk4Eap6NaBt0Qq62g8BoggxOISnmY5J1iPWT09xhtm79p9EbBw9ji7/LI0XUjd3jsal1Y+ZxdbeKMaSlj2k3tieXed+Xb022VhIev+NEcdCtlGbcnIfKGe+CuXXg6Cc9gqEeKNjxbEWqBQRIAI5CcgaG7hDhDtdfs6EarQWEABZrj1TVKMkPehFBFxHQLh7A/Itiyw6lMtvQCG7aKGDQgd5leOWOj8RikEP/swG7MvgTpEjO/7me6Qk+xvPD576vC46TC/PdWzvlNSAZMu2C5lKf20kkr+dBc1TvZH5eA+kTJqC5M+mFrJVqu5VBLJvJskOb+DLbpS/fQbh2mmw2Viy2GtQrPvRqP/pLZT4bSK+aljSxvCsz7iEpFhzvZx7qxhmvQnNjFEWebTUxwZSUnksAbc4VOb9+jc+n7kEarUKmswsDHjjU8xasApfzV6Gz2b84rGwyDDfINDmvTbos6yPlTyz5kX0PPEx/Nf+goRlq5EyfiIg/aho6zfkaaZjkvrBJyiK1/K/tqJDn9EY8/Es7D10GgaD9Y+RRb+UyCYg8KN/qUYIKtfGLH4Rtbk+ZyDYQOXxbZYAABAASURBVKoIKGWuY6pvriO9/+a4AxHFqukQ0pLMJYXYq5Btpe80MxCKEAEvIqA8sA/h/XqiVN2qiOj0CAJnfuNF1pOpRU1Adv2MzS5kQryVXlF7P5QtV0FRey8U9bfBf47kjKlt4wfJqqZthQDBdoaTtJqefZA8/XskzZ6HjAEvOalVasZrCGTfTBISY/nSH4t9UNgynFxSRWHDOWjr463NstmeePk49BePWebRUh+v+biQoYBbHConzlxGz8cfhVIhx7bdRxGfmIKVP32COV+NxT/bDkBHG1/55GeTBvVgAp+OG4rxbw7Clet3MHTsV3j8hXew8Ld/cC/BuXefHmyFN+Yaf7XLtZ2Gaj03mCWq2XvWgxGsVUHlO5jrmOrL/UoYC2afVBgTDw6FhGgg5Z5VIbb8x0pJCiJABDyagJCagtDXBvPHxQrpaVBcvojAb6ZA/fdfHm03Gec6AmJwuM3ORKhs6mXhdyGvchLy8uchhNMSNJuQSOkZBLJvJumbdIV26Fd2ZZ6qDl47kYwJ51Ks7c5uwyIjtJTNtmSvfQ2/EdMs8sSS5SyqUoIIeDIBtzhUEpNTEVnC+EN04uxlRJUKR50aldC4Xg2kZ2hw4fJNT2BGNhABlxIIDQlEv6c7YtX8T7Fizsdo16ohZi1cjba9RuKdST8gM8vGHQCXWkidPYiA6B8MCNZfqYKdHfLhQS//uT8gcNpXZpGlSE48yZkUkEsvML0H2U2mEIGiIqA4dQKy+HtWzat377TSkaJ4EjBUbQQx92adogC9oVIOIGKOeHZUKpMdowMR8EwC0u8/Nyw8CiL7nNuR9Rdu4UiyHqdTHXMQikq1zfaE6o0hr9HYIg8qf26CswP5tatQnD3t7GapPd8hUKCRWJ/9F6iZ/FVi+6Zs3nUYl67dxtpNe/BI8/q8Aba3Cov4qW1791keCREoDgQeqlkZQ/p3x6C+Xflw/96yjy+P4wkKLAiYlvrIVZJDwyKn4InA0i35MiBBrnS8EclxYqjfxqq8vkEHK52nKQK/n4HA6VPNIqSmAoKAgPk/mnUsn+tBLyJABIgAEYBCZbFZpy6yBTTax2EQw3PAsTEt0tba0xw1ckdDlWGoGmh8QhzLE9nFruSnYUeVTI1S6ijUCamHakE1WTYJEXAZgeCgIJf1VdiO5NevIaJrW5Ro3wIRT3REySZ1IEtIKGyzHlCfTPAEAm5xqLz07GPYue84nnrxfb7cZ3C/xzmL1et3IcDfD+VK29rciBehgAj4NAGtTo+tu49i+PvT0LHvm/hh0V/o9Xgb/Dp7PEKDA3167AUdXPm23/IlO34RdSyakGfvjRJRqR13jviF1+b5Cv9SPG3aL8Wk55nZQZXuK3ibcmVwtsaxg67329B1fw2Ghx7hT+/R9R8PQ4sejlV2YykhK9ONvVPXRMDzCOjqNYAhooSVYZmPtLXSkaIYEwgpAV3XwdAN+hS6yj0lZ4rzz18bhDXBgCpDLSELUlKSLEMm7mbG4ELKf+hZzvrpPFIp+iMC+SZgyF7qw44PqvzYE089KNt2noNaXc/RfAmQWKKsgzUeXCxgxtdQXDhnLiRLiIf8yiVzmiJEoDAEZIWpXNC6zRvVxuoFn+LTcUOwfskUVKlYhjfFlv5MfncoVColT1NABIoTgeV/bUPHPqMx4oPpuHErFh+MGog9a2bx/ycNH6pWnFA4ZazB5dujRu+NaD5wG3eOmPZUyb1nSlTzd53SH29EpYbhkd7QDfgYumfHwVC/LVdTQASIgHcREIOCkfT9fP6ENzEgELqq1ZE2ZhwyuxfdBYR3ESJrXUlAJkrek+wOBcEYFyBkawCtqMXG6LXmNEWcR6A4tiSalvqEl37g8Hs9/QwmfvIFSlYx7qUyJbkU0gZNRlb4E4AoQgwI504R0z4s7LzogQ3myBTL1+JLgKAOyKEteFRx5pR1ZS0tpbeGQpqCEHCLQ4UZWqNKeX7nvWK5KJbk8kLvLujarhmPU0AEihuBfUfOom3Lhvjluw/w58LJeL5XJ5qVUtw+BA8Yb9Z0JTQvqs1iOCyXTliA3Hr9Ybd9rT/AesoiAt5HQPtwS/6Et7unLyN+y26kjRjjfYMgi11GQKjo4OwUfxWE8PwtlSgXUBFyQfrOl0YjQODf/RClhEmk6NX0y1IIEiLgUgIdOnRGl+49+V4qukp1oaz9MAzqsgBz/CnU3Cli3oelgnGmMNzxCglxR6/UZzEhQGfexeSNTos9iaTrO5B6ayf0GuMj/TT3TvE00+kyE4oJCc8d5pfj/wc2Q6txvRrS75B0wuS5ppJlRUBAv1XOnSXaBQqbrRsuCtAflJlFTJSKSR+T3HokSkpYvzRDjM4Y6xymyVVHurPE7i6xHBIi4O0EtBk6nP/7Ag7POQyDQXTZcNgTC5NT062eXJgi6TI0WS6zw7M78iHr6pSFrGvD+9KmDrjTw091X8fyuzQEIgLzNfAGYU0QpAjmdWY3WwSwr+wcIkiKViXagF5EgAjYJpDxZC+rDFvLOq0KkYIIOECAHCoOQPKFIld3voeTv3XGpdWPIT32MB/S7T3v8zTTZcQe4ToKXEuA7ZeyaMUGMPn1j838yOK2hJ7y49r3xtW9Ge4K3FliuC4USdf6vUZnjO3Gc11ksjtLTGwXdqp2zMez+WPC5y1dh5NnL1tdfDq1M2qs2BHITMnEzx0WYN0bf2PX57tgyNIXjkE+av/y+ya06vE6omONNzFMVd/59EcMHDHZlKSjjxAQ/CXHSbcGkJmkreRQYV/nAcr7OpbXTtIXYsw1gutgYOVXLFroVvpJdC3t+ft1WRhNCSLgQgKaF15E0qy50PTqi8yujyNl/CToq1Z3oQXUlS8TIIeKL7+7OcYWWKo+Qiu0tdiMM6hcG3Nars65K32OihQtUgJrN+3FlFlLHRJNJt3RLNI3owgaP3BgL157fTC6dGuDgYOexc7o/4qgF+9uslv75ggJCsDC39aj32sT0bDTEIwcPwO/rtqCC1dugj3JwrtH6F7ri3vvJ389idSYNCsMd45GW+mcrdi59ziefaoDypcpZdH0q4OewtkL12B6sqFFJiWIgAME3qjxNrZ2PIL5LVZiY/sDmFj/awdqUREiULwJZD7xJJK/+Q5JPy5ExuBhgJwug4v3J8J5o6dPkvNYenRLldt9jvr9tvDNOav13GB1DIhs4tH2+6px33z8Ok5vX+iQFMVTfti089vRcXanwbPp8eykn01dB73yRSApKREfffQuTp06AU1GBq5evYw1N4/m2YZ+i5wv/WH7pST0UyD+OWOa75mSu3auiSUsO3OawlyftWESlueJwhwq33w8HLtWz8TGZVPxjfR/IjklHZOnL0bPlz8EWzLhiXaTTd5B4N4Fy9khJqsTLt7jUeXefxE47Su7oty/l5crSMC+Oyvl2CfO1EaZyBI8ei8+mR+dHTAnZEG/s7OytIi5m0COTGe/KUXQXqA8CHVDGiBUGVYErVOTRIAIEAEi4CgBlzlUYuMSkZiUmqddW3YdQXpGZp7lqAARcBUBWdxdBMyZBdWu7a7qEvcSksEcHQU9KXbEUPY0oWaPDUOXfmPRrvdIfP3DcotqO6S7qy26v4ZOfcfwWQPL17hu/BaGeHTCvnFnz55GWrr1nXH7NYw5YpzAl/6w/VKy9gtgwuKig9scGS7KzPVZPZMYW7cdiiq17QwXatlslDUb92D63N9x8Nh/qFm1PN4c1hcB/n4utIK68jUCJWpE2BxSeHWjU0O1dzcCp0+1K6oDBXeoVKlYBn9u+Neq/3Vb93Fd6UjbtvHMQgRs5mNX6Xs9dxNPvfg+6rZ/yUJmL1zNizEnzOyf/0Tjrq+gY9830bbXSBw/Q48U5XAoIAJEgAgQASLwAAIuc6jcvHMXg0Z+hrv32E6Kti1a8sdmPtVbq9PZLkBajyXALh7fHDMc3bq1Rb/+PTHnp9kea6ujhsmSk3hRxbmzCPp8IsIGPYewgc9yXVEFq9bvQpueI/jJLHN0sOUPn3zzM5JS8n9hbtPGHMpa1Spi1fxPcXjDHEx6ZwjmL1vH97BgRdjMlbETv8cbg3vh+JZ5mD5pBD75eiHY/2OWT1J0BOTt9fBbmMkl/FcdIpYa07KmButOBWuVeqSW1zW1YTpal7yvSXttJNJGjeWSPvQ1SLenrURXrwHEoPw9meJ+D/Zj7DP/xIBxfDbKhu0H0PuJtli76HP+2Rz6fHcoFcYnW9hvgXKIgH0C9Z+vj6CoQKsCZRobHwea1eoR/rlnn3/2VB9WUNvyvi7r4VZMVSDp06Mdzl++iVfHfY3lf23D1n+PYNK3izBrwWr+RLcS4SEFatdepeu3YtCt/9t497M59opg5JBnsO6XKWZ5vldnXvbY6YuSXauweOb7OLZpLno+1gZvTvjO7uxFXokCIlCEBBav3Ihv56wowh6oaSJABIiAcwi4zKFSoWwppKZnYMAbk3En5p6F9WxZAbs7/tmMX9CpTRMEBvhZ5LsqcSc23u7JA9uVPyEpxVWmeF0/4z8ah0OH9iM9Ix23bt3E4sXzsfL33wo9Dnc2oNq+1ap71b87oDhq3NTXKrOQCnZX8cMp81CtcjmMfqUPJr49GN07teQn4u9OniNd44qF7MGyOnOWsFkAfmoV2rduhKhS4dh7+DQvdODoWem91KD/0x2hkMvRuU1TVCofhR17j/F8CvImUKdOXem7zPpCLq+aQpQI+cMGLqpWIpiwtBAu5lWV58tq3q/P6pmEZ9oJMoa+irTRb3PJ7NTFWMq0KS07SiIGBkIMdu4FIOvoxNnLuHYzBhFhwej7ZAd079wK7M4+yyMhAoUloA5W48VtL+OJ77qjzXttIFNZOui0rR7ln3v2+c9q0Zp3l9X6vk7bouAOlQ6tG2PCmBdx+MQFMMf4iA9nYNmfW9GySR3JiT2Y9+XMoGzpkvh5xnv4YNRAu82WKhHKv8vZ9zmTsFCjk3Trv0fRqlldNKlfE0qlAgP7dOVLf85dug56EQF3EGCblLO9htzRN/VJBIgAEcgPAZc5VEqVCMPS2R9BLpfhhTc+BbuTwgxlTy5559Mf+N3x53t1wjcfD+cXcCzPVbJoxQZ+V+eF4ZP40odvfry/9CE9QwO2NKJlj9fx6NMj0P/1SYiLN85csGFfsVRdu3YFMTHRVmM/fPiAlc6bFLLbt2yaq7x43qa+sEp2ot24Xg0snPYuXnmhB57p3hZfjn8VH499CTv3HbdyRBa2v5z12QVtzN0EsFkrTB8Tl8BPulUqJUtyqVapLKJjHVx3wmsU7yA0NAwTJ36BevUawM/fH5UrV8WT5Rt7PBR9tRo2bdTZ0dssnA8lu+Bcv2QKhkqf+TWb9qDzs2P49+ySPzbxZW/5aIqKEgGbBJT+CtTsXgNNhzWFTCbYLFNUSrYp7b9/zsSfCyZL50DjweIzJ49CyYhQp3fJnN+lS0UgPNtJYquDFWt34EPJcT974WpPYIdMAAAQAElEQVTzeRgrdyf2HqpUMM7aYenIkmHsALZcm0coIAIuJtCkQU0cPXWRnvzmYu7UHREgAvkmAJc5VJhp7A74r7PGo1REGF4Y/ilfJ/+/d77G+q37+Vp5dleFnRCwsq6S0+eu8iessCUPW1d8K93deR/zlq4zrx1mT5pgU3a3rZyGfWtnQy6T8TX+rrKP+nEfAUPZcjY711avaVNfWOW1m9Ho+Kj1BfejDzfgTd+KjuPHvILDJ87jpyVrbcpK6WQ6d/20dA1GfzST35l89OH6PDs5Jc1q7wq1WgU2U4sVkAkCVAqZVwizl9mqkAssyi+oWNoV8mjrRzBvzkLs2LIbv/26Eu1K1+Y2SPis2Pl3NiB4sRaBgw3mPLlMkL5zjKxlrBLyfilkxvK5xxf8SxZvn+kfxEJRpjTYbJScPYnBwdAN/Z/ZLtbGg0SZzTpnGw+KVywXhRf7duMXnCt/+gRqyZH32YwlYMveimK5G9svIj4xRbqgjAVz6j/INsojAoUhwD5rt6XvbrbcOYo7O4Klz11Mvm7MsPr2vtOZni3RdMTGbu2bo23LBmAzWbbuPopnhk7gtrC67DvfT225lxLbv4jNLGb5AWo5gv0VLhe/7BlF7DvFkf5Zefb9xsqy7yhmu59S5nK7Wf+BfsbZUDLpu5ulHRH2ne9vh7XUDBsOgvxc/z7kZbtSLuO2OTNo3sj4eznnl7X8qVhstkpO0esNzuyO2iICxYgADdXZBJz/DZiHhWx66fxvx6FGlfJ4afQX3Kky5YP/ga2Vz6NqkWSbThZKR4bz9qtWLMOnnl+/GcPT/2w7ALYOmt2tCQ4KwMA+XfDHup1OX37BO/PSoFKlKoiKun9nyzSMpk0fNkW98pjVvqOV3VmPtoOucVMrvTMUdWtVxur1/1otO1uzcTdvvlJ5a8Y8I1fAZlWxi0VbknvZGjsRZ+vk2YnJzE9Hgs0gY82FBAfyJT8sbpLMTOmCXPo/wNJK6QQ1JFAJT5cg6QJAJhO4nf5qBTMdSoUx7Q7bTSfEzOmRu/+wqkpEdJQjrJ6C28vy2cWBOpt1qXeBqBUGs6ibS8MRgfC37+tYflgbubk+a8MkER0UvH2WzouFEG78PhTffgfi9z9CPHIMgY3q2myXtZdbAqQTfsk6h/7Yk1DWbdnPn+rD9lLp88oEsE1p69SoxGdq+UuOPIcacrDQiTOX+B5FbXqOwOMvvMNnHrJ9XEzVp8xairrtX7IQtlTVlE9HIuAoAeasHjTyc/QY9B6Gjv0KbCklqzv1h98w+M0pLOqQaHV62Po+N+lE0bELy+Ev98Jrg57G6y8+zZ2XwUH+YA8CYEaw7/zMrCwWNQv7LQkK8Denwb7AXC6m7iWHuAN98yLILmuump3mmS6O59MGbqLJfp7IYW8+20Lu+kWZNtnmxOO0OSv4ecisBavAfhdyi+n83YldUlOeSoDsIgIeTkDmDvvYHinfTxnD90uJCAuW7ozbnmLuCtsa160u9V+T36lhG2B9v+hPlIwIRbvWjXj31yTHCrt7yhNSUKFspBQC9ChPjsEcTJo4Bc2atUCAfwDKlSuPgQMHo88zz5nzvTFiCAnlZutq1UHqex8hcdFvSFx8fzkYz3Ri8MoLT+LStdt82RnbVJCddPca/CGfEcU26mROPUe6a9OiAcYN729TXnmhh7kJ9hl+7d1vkJSchkUz3gdzdpoyo0qG830ttFqdScU3VzQ5HjOz9IhLyvR4iU/J4g4qZmtKupaPJTPL4Da7DZIDhBmh1YsO2ZCm0SE908g6qWwmUuveF12IHpDOtTMq6iz0CYq835e8WOizDb3X+3ncfawn4hRBDtnLODNJSjOyhgOvydMW4+1J32PnvhNgM6RmTBqJ3X9+BzZThe0llHPZmQPN5VmEvQWjhvbBrtUzcWzzPAzq25UvgcjM0vK6oijyPYVybtw5dcJrPI8CIpAfAn9v2YerN+7gi/eHoV6tKuaqA3p35d/1ji6nYXud2PtOZ/qAAjwJS6lUgM0WzpAc5cywMpElJFvvL9012Wb63WHfQ+x7w9Wikb7/mH1avQGO9J0hlTeVzdIZHU0a6ffKkbrOLpOWYfz9NEjfKY62rZN+G9IzdTbHKjXDUCA1Q2sz39E+iqIcY86Nc2Iw9rV+WPb9R3aFXUs42p1Orwdz3rM9HPXSZyl3PbaXI8vXSeVy5xUk7cw6N5L1YOLMNgvdVvI9KJZPgfKzZ7k0v7gFEUrphKTQDVMDRMA7CbjMocI2l2J3BE3C1snvPXQG7A4Lm9Zt0rMju9BzBk72CE42HdaW7D54infBTtbZFNgykRFgm4J+N38V2rZsKDkG1HwWCrtD45fjDimbis4qpqdr2AElQ9VeIYHSHeMAtbzIbG3TsgkWz5uLw/v2Y/O69Xh/7JsWfclkxi/aiBA1QoOUYC+lQmZR5kEs5VL9iGCVVfkSUnusLXbj5UH1C5IXGmi0U1E6CgFvj0FIj25W/efVLpumzOxzRJo2qMmfsFCrekV+13DBsvXQSz/8777xPD56c5AjTThcJj0jEwOGf4rYuARMfGcw0jI0YEuK2MbMrJHm2VNtl67ewtcvb951mD/hp10ro6ORlSEhAs4gwPZO2fzb19iw9Cu8P3IAd7TndO45o4+cbTR8qBqfdRghOfOVCjnYMgwWl8nu/xyy2YjsItYkbF+KnG1QnAg4QmDpqs14oXcXPNm1NUJDAs1Vqlcpx+Mx0vcvjzgpYM5A5gTX6SRnq9Qmj2dfILJ96xYu/4dfVGqlfHa+c+rcFbRoXEcqCb7clJ0XHTl5ASx/0coN0v+NcPO+WrxQMQrupBhwOcHIsRgM2yOHyL5/69epCnuikMsdsvu3P7eiYach6NR3DDo/9xa69HsL7LNvqrxj73G06P4az2fllq/Zbspy6/FsnA4vrk7CsDXJXFj8fLxnfCYVa76D7NhmCKkJXCrFncfoKv5u5UWdEwF3EpC5qvPQkCD+9Ab2BAeTsE032U7yTEw6dlQqFE4xizlm4hNTuNMm9zFduoBknezafwLTflqJX2Z9iN9+nIA5X40F2zfl97U7IAiC5Fjxs1hjb7qLGRDgx6qD3Yn1BmF3aDK1BrfZm33Dm99VMd210UnOAkfZsTvm7K5K7vLJ2XfC2Z2b3HmFTadpjD9c+bEzd58608D5pyXvgD1hYe7Ut3Fw/Q84tW0B/vr5M7D/H+xuYt61HS/B9kJhs2HYDKzeQ8aja7+xXJ4dNoE3EuCvBts4kS1/YCcYo8bPxIejB6J8mVI8nwIi4CwCzMFRJqqEs5pzuB2219CEqQvww6I/8e6IF6CUnCumymxpxvuf/4SvZi8DK2fSs6Pk24UrZPy2VDy+JMEsh24bZ9Dk1h++o7VpD7NV+gnjeSzOxBV22+qD2cH6Z2IrP7fOVD63nqWNrnlIv8/gY2O6/AiyX7bqCNl5rH9b+Y7ospvgB+YQV6kUPJ4zSEhM5knmyOMRJwWXrt5Goy5DwWY4sk3GWZxtQGtqnm3Azy4qG3UegnGTf+SzGJkjn+U3qlsdrw56CgNHTAbL/+3Pbfh6wut8zymW71lSdNZEpxrw6tpkDP4rCcPXJeO5lUnYf8v4f6/oeqWWTQTuJSSDOfbYcmTmBGT7HNoT9v/LVO9BRzaD64cpY3Bw/Y/Yu3Y2qlcuh29+MM42Zv2Mnfg92FMPj2+Zh+mTRuCTrxfyG0gPatMVeXMOpSM2zWDuisXnHkk3p90ZkV06atV9s1Dr7zqrQqQgAj5KwGUOlYrlIsHutDsi/n4qp+B+oXdnfsIwzsbyhy5tm/E+2Mkye3pJSPbeEI80r4eHG9c2n0QzDzn7UueFpeDG7VgpBEzltTqDdDfH84U5JJi4y14wj4dEjvWv07NJ9+AqlnZEpKrQSvWsykpOGZbHxCqvkO+NLrtt5hMpaNvZw2bm5Slsuun2PceQmpbBy16/FYvZP/8JdleR/ehzpZMCtkH06e0LkVt2rZ5p7qHjI41xYst8bFw2Fcc2zUX/np3MeRQhAoUhMPHbRfzJat36v53nkTn/HOmLnVzbmo1o0l2+fseiGTb1my1rYHfxE5NSzHl1a1ZGr8fboHKF0rhxJxaDRn6GDdsP8Hx2oR0Z7g9HZf6JLHT7JQHbbhgcrmNqm+2dwzvNIwgLUttsm9UPz84zNWFqe/BfydwurVxls66pnLOOgX7G2X7MjpJhDvAL9eMX8rb6Dw0ynh+olfIC2c5sYGKr7UB/4wUBs9dWviM69hlh7TNp3qg2mGMi+m48S3JJz8jEN3NW8L3anD3zic18yf2dzpYbsY4rlovCluXfYOeqGfjn1y/BLh4H9e3GsrgIgoARg3vj8IY52CR95+//+3uwp87xzGIU/HJSg2tJxpspbNjJmQZ8tz+NRUlcQIDNZh/29lQ+e5Y5tJ/938ewJ47uofJk19ZgS6EDpBtF7Nyd7RcUFhrMR8Oc5+nSDdb+T3eEQi5H5zZNwc77d+w9xvPdFUinu7A1G+Wyh8xQcRcX6pcIeCoBmSsNW7F2O8Z/OZ/PGMnd738Xr/M8NmMkd15RpmtWrQB2p571y6bLsotYdlH7kHRSzfrt1r45VqzZLn25J/IL3cUrN4HtZyEIpntZrBQJESg8gXm//o3PZy4Be5qOJjMLA974FGwzNnZS8dmMXwrfQQFaYJvUlitdEs6eIZNfU47H6GBPzt41rlPPb5vFubwhyfj9ZbgqwHDOGHclj/q1q6Bru+Zc2PIarU7H4yYdO7JZhWEhQVAojBe5edknQuS/LayeLWGOk5xt9OjSCt9/8SamTRyBz2YsgclZzk6+2YXlsAFPgu3nwtKmTWuZgzQ6PgOOSrrG+NlMTtc6XMfUNptRmNNee/GElEybbbP68dl5prqmtplznenuJmps1jWVc9YxNeP+Hf7YhLz5xUh28T0NbLBOTM1ipoPNuiyIfbyyFNiqm5q95wWz11a+Izr2GZGa539vDO4F9tlms0LYXffvFqxCu96jwM4x2FJL9v3KC7ooEAQBJcJDwPaCYxePsPHyU6vAngIkY9NxbOT7uupygvH/bM5xxmVI3y0ZhpwqihcRgQYPVeMzxSNLhuPt1/tjxZyP7YrFhskO2PPXxt0Y/dF3OHP+KoYNMO4nx5bdMQeKSnXf6ctuskbHJjjQYtEVYQ/KC1HLrDoIVLn+99rKCElhqGb9RMpDSdb/d6Si9EcEigUB6/+tRTRsdoH45axlYE8KsTXNld1ZOfXfZb4BZxGZYLPZxzo8zKf6fTptMR5+4jW8NPpzsI07X3imC9jr+V6dUbVSWXToM5qvsWQn5SOkuzgsj8TzCPx5czle2Psk2m5tgMH7+2Bb7EbPM9KORSfOXEbPxx+FUiHHtt1H+cUh25iTLUP7Z9sBvpeJnao+rY6XTmTf3ZwCezJ5V6pPj9/Zg9Pvk0E7yXjyaDgtQ0YvP2gXKJzdzQPbYzNA3nr1WTBha/oNuQAAEABJREFUF3B9erTncZY2yduv98PtmDjprmGeP1O8L3aBaGs2oklXq1oFXi53wE6mmS4uPokdrITNIkhLz7TSk8I3CMjuSZ+xE8cgpDl/FkK4dBf878VT8M7w/ujeqSU/l+jbox1WL/gUHVpbX5D4BlHvHkWQjQtW5lvyV3jGhax3083benZ98EjzemAz1SuWiwS7uWlP8uuQvHztDtiSIr3egOSUdG5MckoaX9rPE9kBu6llmhnJ3vsAtRzukCdqqLMtun/oXFVt1xZWitnpp5SxKOSS8SxdFKJ8ZiSEptJ1UlA4IMmNUjUx7UoG/71m/ckk5y0zQhAEu/ayciZRSTbLJS+SKa2OlkN5VGFX1PFyh9o1tWfvaLLTTyUrcHtsnCREwPi/zgUcjp+5xB9/9uqLT9vsjZ0MvzroaZy9cA32TmxtViykkt2FYY8RZBsi/imd5Gz+7RuwJ0uwi1rWNNtFnN3F3LNmFnb8MZ3vsxJZMoxlkXgYgVNJx/HZ2Q9xMfUcMvUanE4+gXePv4HbGTc9zFLb5iQmpyKyhPTjJGWfOHuZbwjIHh3Lpl2nZ2hw4bJ3jEMy36l/Culbqn6UAkxql1SY22ZpJnVy6MyZboysWbMKI0YO4zJ19Iu4+s6TZrmbsA4QRcjuXIDyp7fMIl//k8ss1s43OlNydqj9wVqXM78o49duRkOfvXFmzn7Yng7xiSl8BmFOfWHjbLbJ1n+PIEk6kWYnzWxZHVtjX72ycaPQb+eswKWrt6DV6cE2Llzyx2b+9KHC9Dv7YLp5L5Sc+6I8KH7Qxr4NOWc/mOwZn2uvFVObD38fjY4L7/F+M/XG0qY8tk+EUVM0oXjgIvSzN5rFcPASIBr70v+8w6xn5Yxa94ShrwxCyWZ1EfF0N5SqVxWq/Xucbgi7MHyxbzd8Of5VPiOKOVdqVCnv9H6oQecQaFtJZdXQoxVV8KcnmFhxcYWCzexi+xxOnr4YuSW/S6HZuf3ime/zWeZvfTKLmx8SHAh2fsUT2QG78RscFMBT7BpBpZTDHTKidQjebx+CztX8uLA409mzBdKL5SnZSZMUFwShyOxWl4yC/4sfIuCz1VxO1u2GeK10biMz9il1LVkACIBDNihkMsilSsx+JllLlUh8wb7oNyocape19SCRSfZKJkIhlxW4PVafhAjIXIUgOjaed1WpXBQ/2gpqV6/I1WxtO4+4OHjQNNdQ6UuXPU7ZxSZRd/kgcDh+n83SRxL229R7mpLN0mJP07l07TbWbtqDR5rX5yayvVVYxE9tfaLH9EUubu6ATXv9snMwmIxvG8itifATeJrpPmgbxHWuCOQ7lnIniOy8cU8NW33euXMbx44d4RJ9+RxqKjRmKe9vAKSTBmSmQrhy4r5EX4ajL8WiD7kNgPEOm6P1TOUM/1l/7Ysp0vXuXVMJ1x7r1qqMRSs2Sie1lrNANu88xA1RyOX86KyAOUpGfDgDrZ8cjpY9Xsf2PUcx89ORMJ1A7zt8Bk+99AEadR6C5/73Cbq2a4YXn72/14Sz7CjKdiYlVsMXieXMooTxqXQ5dSxeVDbod5yFeCnGLEhIAwQYX1fjzHpWzqh0fei/YinUmzdYdKzcv9ciXZAE+zyxzV8dEdMm9wXph+oUDYEeNdR4v00QOlVRoXUFJf7XNABvtgwoms6o1QcS+HvLPgx7eyqYU5s9LII5Vw4dP8cfHMFm7dpyxD+wwezMKhXLID4xhc/6jSoZjms3Y8Bmn2dn47x086p0pPHmlk4vgi0zdJe0KSfHWy39ubD4g+xg9rP8lOylizq9wWW2m5a3Zmn1vE/TslKDdBeA2ZSXpGfqkKW7b6+2tA7y5gYuQpTIhgZZBZGnmV4TruP95NVuXvmMEWs8VWKWV1l7+aw+CRGwPrMuIiZsM6i8mtYbDLyII2V5QQqKPYE/bizjDJJ1SZh98WsezxmwfRVypj05/tKzj2HnvuN46sX3+Y/94H6Pc3NXr9/Fp6SyvUy4ggL3Ebh70+gESU20a0OPHj0xffoPXJo0aW63XEEzZFdPG22A8fsyv+3IalvXE4IBwU0PcBr+cm/JmaJB88f/hzEfz8I3Py7HwBGfYdbC1WCzsypXLA1nvp59sj3fkHPrim+xZcU3YLMSWzZ9yNwFe9rbvrWzsX7JFBz6Zw4mvzsUhXVmvt48AOtfCM+XNC9nPWuI+eJMhjbPXMIdJp+FfWDVrrqAPqjbu9/D8VkBuHt0hqmbgh0T0x2r52g5x1rLVynF2dP5Ku9o4XXSReCUWUvhiLCl0I62S+VcR6BNRSXGtg7EeMlZ37O2Gn603Md18HP0xPYvZPsYbl5uPLdjT0BcNf9TsGX55ctGIijQP0dp+9HZ0m8JmyXP/r/dio7Dgt/Wo0XjOmDO+uaNavOKS1dv4Q4WdlPr5p27aNeqEddT4B4Civ46+P2cyUX+mJ4boXj+vk7RzajjGRQQAQ8g4DKHSlXJI8zGe/DYf+xgU9i+ESyDfVGyYzERGmYRE2gS3qKIe3BO8+yHna2t/3TcEH4xx+6isJbZE3nYRV3OTdOYnsQzCZQtWw5NGjfjEh4e7nFGKgdrrWxSvmqtsypURAq2Qe0f8yahU5smYL8P85auQ2xcAoY+3x2zPhvNT3qd3TU7kWb/r9j+KKYpvzn7YLNVKpaL4uv4c+op7jsEDCGhRTKYL8e/ZvX0tNM2nqjGdGzma5EYQY0SAR8gwGart25WD8GBxhlCd7P3uXqiU0ucOHMJV3I9uc3ekJkT5fnXJ6Fpt2Ho2m8s5DIZ2KbQrDy7gTtz8ijuAG3YaQhGjZ+JD0cPRPkybrrDwIwiIQJEwOsIyPJvccFqVKlYFuyC8e1J3+PIyfPI+WJP12FT+9idSfYEHXWO3bZzlqO4dxHo2bMb2rRtxuVe/D1ufM9ej6Ht4O7ocHA67mWkcF1hgnDV/QtW02wUg2iAQqZE3ZAGmNJwFsr6e8969eqVy4HtHXHrThxi7iZwNOxxf03q1+BxCjyDgGz7Mr7sRpljHxRb8UfunbI2WDROX82ZIdz4z2Z7uu9GQztzlEUeNKnZVa3byc544EHe0gDleKMDRVbXAP9VGihf1j2wTlFnsg1j2RN1dq2eyS9G2Z5Wbw7ri9AQ4xKvou6f2ncugZwzacwt2/i42ixnrlC0kczuTwHShVXOXsTgkJxJp8TZ+Q278Nt76LT5O/36rRiX7hXnlIFQI0TAxQTYtQDb50omE8D2k2PLfZgJOp3x9yo1LYMl8xR2Q+roxp/Afld2//kdfvnuAwuHScdHGuPElvnYuGwqjm2ai/49O+XZJhVwjIBer8e5c2cdK0ylPJwAmfcgAi5zqLAvxIlvvwylUsGnc/ca/CHemfQDRo6fgU7PjuFx9qiyt/737IPspTwiYEHgesZVizRLCIKAl6q8ivktVqJDZFem8gpJS9dg0MjP0WPQexg69iscOHqW2z31h98w+M0pPE6BZxAQ4m4Yl93k3AfFRrykNtnaYEFS5XKqCJJz0WJPley2DJeOg0nOPKl29h9rKDuaz4Ms1Hh1K6ssQlbLGM9nE04r/u5nc/DZjCU4d+mG09r0lIZMS32erKkuMpNkVxfyZTpsqY5JDHqNRX/23uGYhUEWde8em25Rr6CJXB9vu804Ws5uA4XI0NWohfhV65Ex4CVkduyC9NdHIaNv/0K0aF2VvtOtmZCGCDhKoEK5SBw6cY4X7/hoE74clC2l++CLuYgIC0admpV4niMBm+HLZp2Ehdrec409MYgtq2bXKI60R2UeTCA5M4MXuH03GkNfGYiu3dpi3bq/uM5lAXVEBFxIwGUOFTYmNoWaPUJw1NBnUKpEGPYePo1T/13hnuf3Rw7A7/Mmwd6XHatPYpuAQZeBpMt/Ifbot0i+ZrnJnu0artGuXr0Bg18eZtHZI4+0xc75f2Nb81Eo4R9skVeQREX/yjarVQ6oalPvyUo2S+vqjTv44v1hqFeritnUAb274tK124iNSzTrKOJeAvq2z0E79Ks8ZXd4XRuGSo4QyemXM8NQvpbNthSvfwPl8G8t8kSVX3ZVe5fJ2dlecmhctzrYk3d6DxmPAW9MxtpNe5Hfpzd4yVDzZeakDkEWe6M0K2vcU8WkH9UiIF/tubSw9BG36s9RnVXFolPoGjRCyqQpSJr3C1Lffh9ikO2LrYJaQN/pBSVH9YgA8MbLvfDskx04iqH9n0CPLq3ANnsOCgzAlx++CrZ0k2f6SOBLw1h3+YDFcDIy0jF9+lSkp6db6ClBBHyFgEsdKgwae4TgsAFPYs5XY8Gmd7ONAdk6+Rd6d4ZSIQe98kdAn5WMc0ub4ur6friz5wNcWdsLF343/gDlryXnl7558wbmL5hj0fDu3Tux7t9NFrrCJDpEdbOqHqYMR5fS3a30nq5YumozXujdBU92bW2x1KF6lXLc9Ji4BH6kwAMIRFaEWLVRnhKndnCfhoAQm23JajQGk5x9CTJFNgBbV6jZWTYO5/46h2lVpnFZP2o9L3FuTQ7dSKOOZ7gweO7pjtizZha++2wUwqW7h+Mm/4i2vUaCzcy67OAaeRea63FdlXhoMBoOT7cQmdzkdDOaa/eTkiPDr2RDlGo0ylihsKGjvj5HyxXWHjfVp+90N4F3ZbepGog37gGZxmUoruzazX0VefdsmU+7Vg15P2yGyZQP/oeTWxdg8cz30apZXa6nwDMJXEyItjIsXXKq3LrlezNRrQZKimJJwOUOFUb55NnL/LFnX3z3K77+YTlWrt0BtsaY5ZHkj0DChRXISr5qUSk9ej9Sb+2y0Lkj8d9/Z2x2e/H6ZZv6/Cq3xW7EmKPDeDU55AiTHCmQTtBVMjVeOzTALMtvLOZlPD3Q6w1QqUwXy/etTUhM5omIsMLP6OENUUAEPIwAc6Z3aN0YMyePwraV0/DSs92wYNl6PDnoPSSlpHmYtb5pjjbpijQw6QtUCunPOQToO905HFkrCRkG/H5Wg53XsqDRFeRzylpxrujnb4Nuwgrop62D7v2lMGw+6dwOimFrMXcTsPC3f8yzFKPvxluk2fYBbLNaViYzy7gXWDHE5PFDDlXbnkEZFETnsR7/5pGBBSIgK1CtAlZKSk7D6I++Q7/XJmLy9MVYvHIj5i9bhwlTF/B9I9jaSJ1eX8DWi2c1zd1jNgeuSTCuO7WZ6SJlkJ3p08GBzplWHauJxvHEw3w0euiRpE0EBOBuZgyOJBwwy830a/CGV/NGtfHbn9vATiBM9qZnZOKbOSv4emH2RBKTno7uIWBoZ1zqo6/e1D0GSL1qB03iS4BE5O//Ua2namH0ldF25fEZj0utu/fv9Lmr+GnJGsz++U9uyCPN60GlVPI4BUVLQBlaRepA+gKVwsL+CQ0rOtSEo+UcaswDCzUvzHe6B47HnSYdj9Fh7pEMfP5vGl75KwlpWvc6VQz7LkA8fdMCiWH9MYhxhd9s36LRYpa4FR2Hr75fhrT0DONdXRAAABAASURBVD5ytkE/S6dnaHiaBddvx/IymswsliTxQAJty9ezsuqhh+qhTJmyVnpSEAFfIOBSh8on3/yMTTsPYeSQZ/hdyFPbFuD4lnn8EbHP9+rE10bOX7rOF7i6bAx+pRrZ7MsvvJZNvSuVTZo0R1RUaYsuZTIZ2jdvY6GjhJHAG4N7QavToVPfMdh98BS+W7AK7XqPwvY9x/gj/timacaSFLqLgFjKuNQHISUcMiFGHY7XTiRjXaWO3AmiVbYERBGGqOrGdPY+LPrHhjrUHiskVq7HlwexuC8Ic7Qv+3Mr2Eblz/7vY6z+Zzf+N/BJ6XfhS740lC0T9YVxOnsMJeoO5st8ynf4zilNRzV5C2Uf+Zy3WarxSHObF+/pcDfdYE7bi+TUyzvXh/z1LmaRNatqzpa91NasZ+XMGT4Yoe/0wr+pt1KsP3txGSI2XXLzxTRb5mNreNHSjR1betIRgWJEoGFUFT7aQP8AtG7dBq+88jq++nI611FABHyRgMscKmxTzQ3bD+DTcUP4yXJkyTAIgsA3lWKb1X4waiBe7NsN0+f+Dq1W54usi2RM4TX6QhVS2aLtgNItEFTO/U4LlUqF779fgIEDXkarVo+iZ88++PGHhahS1rG7lxaDKgaJ8NBgsE2b3xneH907tUTVSmXRt0c7rF7wKdhyiGKAwOeGqLmZjCPJety5BegvyGDQhkH64oOYIfA00zExhFVAcX19OGUuJn27CCUiQvHtJ29gz1/fYaTkdK9YLrIokfh02/VfjUfH9/R4eLSGO0hMe6rk3Gul9vPHUa7dDC61XziJsBp9LJhsvJSJXr8lYsQ/KRi0KglvbUxBksb64taikikREQShWmmzQEqbsoRKpWzqTfm+dKTv9MK/mzFptmct30hy83miv8r24PxpRp1tMKQtjgQiQsMx5YtvMWjgYISEhBZHBDTmYkLAZQ6Vi1eMUyN7dGltF+0z0sUjy7x2M4YdSBwgIFeFoFb/w6j8+DKUaT0ZVXqsQo1ntjlQ0zVFSpUshWHDhuPLKdPw1ph3Ubv2Q07rWIBg1ZbINlGx0nqPgt2NZ47FL8e/iu+/eBPMuVKjSnnvGQBZep9AfBrEK7E8LV64A8PG44DJWZyQxtNMxwRnb/Ny9gPfzXn6sUexfskUzJ36Nrq2awal0nofId8dvftGpg6vgZL1hnJRh1WzMCRDK+KHQxkWe1WcuavDqnOZFuUokTcB+k7Pm9GDSkQFym1mVwh17/eEwGZdSTcFLYwLD4RQsZSFihJEgAgQASLg+wRc5lBJS9cgwN8PCrn9LgMD/DjxnGsluYKCBxKQKfwRWvUpRDZ+EyGVrJ9688DK3pxp7U/xutEcOXkeG7YfcEi0Or33jI8shZiQCjj4nonX44oVsZwbD3Zu0xQqlRJsk8EMzf1p/LTxoPs+ErdT9MiwsfHnxXv0HZTXu3Lp6i289u635s2UL0g3k1g6JTXdXPXcpRu8DJ3rmJHYjZQLtj5nLOkvoEs1OzNE7Lbk3AyhdBjkox6H0LomhIfKQ9apPuQjHweUth1ABe3dcEQG/QGjiNmTcgyHjWmmL2i7nl6P7SXHfgPi4hO5qTF348HSTOLik7iOAiJABIiApxCQucqQ6pXLgZ08nL1gf4PQvYdOc3PYUgceoYAIPICAKFpvSifYmLXygCbMWe6KsCeZjPl4NhwR9v/HXXZSv0TAmQRo40Fn0nR+W4Eq26cGgSrB+Z050KJ+qxxpD/lz0bxhvJC2pbPV1B8D/8DKfivNos8ygE1kzKljcVt1C6JLlhwnO/cdNy9dTk5JB0tnabOvhqVGE5NTuY6c5BIMB/8aRikwtIk/3ns0ED89FYpApXs+iznNFSqUgPyZFpAP6QDZE40ghPjnzHZKPGO4GpqXjIJsn5wmh05Mdko3HtfIc//7BJ2fe4ufGzHj+g77mKeZ7p1JPzAVCREgAkTAYwjYPmsqAvNqV6+ISuWj8OaEWfjv4nWrHtgmnJ/NWIKOjzRGUKDzf5SsOvRuBVnvIwSmfPgq9qyZxaVb+4fxeMcWPG7SsWOnNk34/4vQ4EAfGTUNw5qAtXPQugxpiIBrCJQOkqFWSes77V2qGp0ZrrEif738PsDoOMld6/bBW7i5/6ZZuCNeuhbPqWPx3PVWvbiKO2Fy6yntHgLh/jI8U8cPbSup4KeQ3kD3mOHyXuWN9ZA3N9gVwfq/qcttdGaHlSuUxlfjX3NIAvzUzuzaa9vSZyUj9dZOxF/dzo8ZsUe509iQrWd5TLjea0dJhhMBzyYgc4154FO6p370GuITU/DM0I/AvM/vfjaHe5+fevF9DHt7KiLCgjHhrZdcZRL1QwTcTiDAXw3mKGFy+twVNK5X3ZxmOiYvP/c4tu4+itg449RX0Mt7CIgOmpp7Lb6D1ahY8SOQlCni4j1dkQ98UodgvNjQHy3KKdGtmhqfdQpCs7Lu2XBT3lGPwDMZdsXvuyzc2H2dO02cBeZWtiNGm1H0rJ1lM7XjewT8ZmfB7+dMuwIfu8/CrgOe6NQCjkhx229Lk/Afd5jo0o17s5k+7ZqEc7i0+jHsX9SBH2/tHA02WVsjlWd6k9zc+SZyv9Jjj/A29Vp63HduNpR2BoHi04bLHCoM6UM1K2Pz8q/x2qCnERjgh90HTuLY6QsoW7oExg3vj79+/gwlI2gXaNCrWBJQq5TYsfe41djTMzK57k7sPX6kwIsIOHoj1cbyNS8aJZnqAgI6g9E799XuNP7kHfYEHvYknqLqOlgloF89P3zcPgijWwagcWn3OFOKanx5tpvNO89yVIAIEAEi4AICsQencIdJys3tTuvt1vaRvM3MhAugFwCCQAQKSMClDhVmI7vj/sbgXpj/7TjsWj0TW1d8ix+mvIVBfbuBXVCyMiREwBECHaK6oXPU4+aipif8yAQ5vm38E2Y3W8ylb4WB5jKeHOnWvjnY0reflqwF27SQrcXff/QsZsz9nZtdqVwUP1LgiwQc9bw8eOz6EzJoFyqgWy2HGPfgsp6QSxsPOv4uRKcaLAprdMYn8bAn8lhkFPPEiudW8KU6bF8UJrpMvRURW/7LFX2X45fflZglvowp02Qw1fu500LcOXLHqo28FJt2HsLfW/ZJ3+kneVFTmulM+8XxDAqcSuDKlhHY/oUCx2cF4N7pubztW9tH8DTTxZ2ex3UUEAFfIEBjIAJEwDMIuNyhwobNlv2wO/HfzV8FdvF4+MR55Hy6AytDQgTyIhCpjsLr1d+yKtY0/GG0LtkOTcNbcKkQUMmqjCcqXnmhB7q1fxjTflqJ3kPGo1WP1zH4zSk4de4Kvnh/GMJCgzzRbLLJQwhkfaWEpp8aWV8qkfm+CumP+UN/0i1f8Q4TeY42HnSYVWqWcYZKzgrsSTzsiTw5dcU9fuvALb70h+2LwiQ3D+ZMseW+ZEt8rt2W4ZZQGrocvquU26nYOe7v3M3kmf502mKwzTN/XLyGl5307SKeZjp23sOVFBCB4kWARusEApoE49Ifti8Kk4y7Rx1qNfeeKqyuLouW+jgEjwoRgTwIuPxse9ueo2jTcwRef+9bfL/oT37xOGjkZ+ja7y1cunY7D3MpmwhYEghVhXNFkCIYAyoN5fGqQTX40dsC9ujYbz5+Hb/PnYhPxw3B26/3A0uzmVxPdm3tbcMplvYePnwA8xfM4bJgwwqcTDPe2T6cfAMLb+3DxriznEuKXsOPpkCoWNIULdgxHdAuUFjWlXS6n3PpLEu4LUUbD+YfvcLOrzXbrDP/rflujWd+6Y1nlvYxi1xpCY5vV2TDozIUv2KouARKMQvM6ZKTUPTFFBj01g6tnGVM8bo1K2Pz8m8ckuDAAFM1OjqJQJVOM9H+XR0aDk+3KSXrDslHT1SUCHgegdhDX/BlOpdWP8aPt7aPdsjIDMkRY6pjOmYl0VIfh+BRISKQBwHLM408Chc2m81KeeP96WjRuA5++e4DHFz/A3+iyfxvxyE4KAD9Xp2Iy9eNFyCF7YvqFy8CMsgQ5VfG6wadnqHBybOXodMbp6WnpWv44zaZA+WlZx/jM1ZUSoVFGa8bZDEyeMnSxVggOVS4LFuIk8m3+OiPpNzAz7f3Y9O9/3g6xpAOWdeGZkGdslxf0MBwzcYVotSY/pJLv+KlHh37iwgLdmjTQbYxYXHbeNAewVIB1u8lexJPhL+13l4bRaEX7l4Hk6JouyBtVnikIiq0LG8WQWb7/0butqsK18EkEOngTpccXpUAPwNkcsfaYY7xMpERcERkDtqW21a7acogAkTA5wmow2sjqFwbs/iXaujQmOWqYHMdU32ZgmY+OwSPChGBPAi49Exs5vw/0LheDcz6/E1+DPD34080YQ6WX2eNR3CQPxYt35CHyZTtbQTEizHQvbXYLPrvN/IhiJdy6Wcb9TzTwWDX3a28ZLo+DStuLObxbTEb8NqhAVyWZ+t4hgcG/128gX6vTURiUiq37r+L13k6KTmNp1lw+vxVrmPOFpYm8VwC5y+cc8i4NH0mZN0amEXwL9zjaGWVRJv9yqvlWLtgswQpvYVAqJ/x57ppWSV/8g57Ag97Eo+77BdunYfyyxeg/HaIUaS4cN04A8tRmzyxXANYj6FBjSxPNJVsIgJEoBgSiGr2Lqr13GCWcu1nOETBL7yOuY6pvl94TYfq+koh/+W/InDaV2aR37wBNiXRf8VSs47lc72vDJrG4RICxjM0F3TFLhDPXrgG9ghYfz/riwe2P0S/pztiy7+HXWANdeFKAoZtpx3qTrwUDSTedyQ4UilOc5cX04k63Ei/xuOxmTE4knCAy81sHc+ggAj4KoEAQPlyrse7SjrFi7l0vjp+14zLI3rpVVvNn7zDnsDDnsTjLqMU636EkBhr7p7F5et/NKddHemzzLjMp7D9PiZsR2/8jWq4hqriNTwj/I1OD2sK2yzVJwJEgAgQATcT8F80H4HTp5pFdvsWIAjwW7nMrGP5XA96EQHHCcgcL1q4kvGJybyBWtUr8KOtoGbVCohPTIFeT3dVbfEhnTWBRyM7cGWAPJA/0efjul/hw7qf8zh7yo+3POGHD4KCYkNAp9Xhgw/GYrl0V0Sjcc7FmuptLfyWZUL1jhbqz7IQ8E8G5PXpu7TYfKhcOVCDHrhlvfZeFn0FYHmutCW7r/ItjMt8spPmQ8+fe5n3U2F7q8iUMnZDkusGdk3g+6awvVPMFUQRzXACQ4SlGCr8ChY351GECBABIuAmAurwWnzJjjKglNMs8C/ViLcpV+Vv6c9//53he8WZ9ozbvWcXt+ncubNc/8f5PTydkanhR08JhLT83bT1FLvJDs8n4DKHSmKycUkD2yvFHpbQkECelZqewY8UEIG8CKQfzsAJ7X5c1ZzHlMMf4+PTb+PT0+/hoxNjYBAN8JYn/OQ1zmKR7wODFKSLsdzDEG3o9NJF585d2zFz5tf4/ItPclcpcFrewADlSzooeuohlMxfM4ojh6AbKxYOAAAQAElEQVTct5uLkJXFKytz6LiCAiLACMjkQEAwi1mI6C+dlLM8C617EyZHi2lfFbZviXRDku+xUjkyE2zfFCZmK1mmOUERIkAEiIBnEIhqblzqE1TeeCPRZJVcGcSdIhGV2vEjc5KwPLkqmKdN+6XY2mulfIfv+DIgdVj+lv78+efv9/eLWzAHe0wOlfP/cb3JoZKcmsJMISECPk/AZQ4V0zXFph2H8PeWfTZl7yHHlob4/LvicwMUrUdkQwVRsC73AM2qF1dh9aursF+3FVtT/kSjtx6BOsWP14jLuotv/vuUx4sqoHaJQG4Coo2LMUF48Od6+/Yt0GdvSpy7PVemQ0cMQ3j/3lxk9+J416FvvMLTTC+7c5vrKCACjIChgeVJvT0d03usPPi/psea7cuGXU3U45eTGrP8e0PLh5tbv+u6Uc8zKSACxYCANu0Oovd9jMt/P4ObO0YjPfYIH7VfRB3uFGnx4nZ+LNduOterc+2ZUj5bzzMLGbAZKo40odXR/1NHOFEZ7yfgMoeKCdWEqQvwzqQfbMqshatNxejofAI+1eKdo9G4tvOaxZgCkoNQZXdts+5q+iWk6vLnHVdKXvbIKlFgEv5CH96WKocuLFvHM5wY9Br8ITr2fROjP5rJW+07bAJPM93YT2ZzHQW+ScBgMCA2Nsbtg9M2bgpti1Z2BSrrva/cbjQZ4DYC+seGQtd7DAwN2nFhcaZzm0HUsccQiMsQuUNk3YXMfNt0TXKoLDmRAZP8e904Wy63fvcNoz4/HZyI0XG7jknH/NSjskTA3QQM+kxc+L0DYg5/iZSr63Hv1BxcWNkOmnjrTbTdbatX9c/u9jPxKqPJWE8k4DKHSr1albF5+TcOSXBgAABPxEU2FYyAjduANlQQbE1bsd1jwsV7NjNC70SY9WqZH/zlnv1ZKhkRgmef6oDObZuhXatG/MjSLG4Slsd0KqXSPDZnRTI0WbgdHQeDwXH2zuqb2jESiIoqjTJlyhoTbgyTv/sJCctW2xVDiZJutI669kQChmaPQ9fvQy4s7ok2PsgmUW2c0figMixPVKvZgcRBAgnpeu4Q+edi/h0qDnZRoGKnYrXcrpPkUCkQP6rkPgLp0fuhTbluaYCoR8K5Xy11Lkr5zBkjm0HMxEXcqBtHCHhnGZmrzFaplCgTGeGQsDXOrrKL+vE+AqIGCD4WZdPw6sl10X/T6zzvsdJPQi7IedzRQNu6DWKvxNiVxCUrHW3KoXIVy0VhwpgXHRJbT8dyqBM7hUZ8MB3NHhuGLv3Gol3vkfj6h+UWJZ968X3Ubf+ShcymWWQWjHgi+R4UG+dDsehDfFg7FHWCHP/MhYaG4a233uPNUEAEiIBrCWR27Iy0UWPNon24JdiOtdqWj5h1LD+rbQfXGka9EQEiQARyENAknM+Ruh/VJJy7n3BhzNY9UVv7xbnQJPd1RT0TAYmAyxwqUl/mvx17j/OLtwFvTMbQsV/hx8VrcPaC5fINc2GKEIHcBLKAiFUVUF5WxSInAIF44dIbGLhlNN6p8zHefWiSRT4lLAnUqlYRq+Z/isMb5mDSO0Mwf9k6nDx72aLQyCHPYN0vU8zyfK/OFvnFPqHLgvKHkZBtXwrZf/vRVp6IBY1C8Xrvp1GtWg3IFQqOKCKiBPo9NwBdytTj6epVqmPB/F+xds1mtJIu3riSAiJABFxKQNvqUaSNftssWS1agz1CM6u1pV7bohXo5TgB015S99INfIlNzj1R8orvyt4zJa/ecu+pYmp34bF0zD2UZrPf49E63qzP3F3no6HAlwiw2VPHY3TILbf829ocZkilx23qH6R0Rp6t/0OCYMvN4ozeqA0i4PkEXOpQYd7Lb35cjtff+5ZfvGm1OtyLT8KMeb+jzysTsG7Lfs8nRhbmm4CsQ12H6gjVSgNhgXmXVQHK4To8NaYPevTpi1Zt26Prk0/h+RFDEfyGCiGvKvBM+efzbqeYl3hjcC/UrFoefmoV2rduhKhS4dh72HJj6FIlQlGpfJRZwkKDQK/7BITrZyAkxt5XSDEBIgbUr4SFC5aif7+Bkgbo26cfhg8fja7l6vN0RJYclRcdh/6TlTAs3wekarieAiJABIiAtxMwPe0sXiPyJTam/VAcOZr2TMnJwNbF29UEvc22Fx5NlxwqqTbzTsQaHSpCzsYp7ukEipV972xOwbu2ZF8pbFCPs2ARUrk7StR92ULnqoS3/h8SAx24xnAVROrHpwjIXDmahb/9g3lL12Ho891xbNNc/PbjBLA75If+mYNu7R/G25O+Bz3px5XviGv6EqpHQfH1QLPIX+vKOxaq5dK/btTzzAcEgh+gGq7lUv2rcmjxcyM8NKMqgsYIXKd8xXjSBHo5TODazRjE3E0Am7WSs9KKtTvw4ZR5YEt9rt+y3DiV/aC6Q0z2Odo3K8/KsqNJWNoZIrt709SkxVF29zpY+yYlizMxpZGaCaRpICZnwLD/AvR/H+XlWZmcYiqfU2eKPyjPVMZdR5NtdCQCRKD4ETDNUInwE/BCA/98yaMVpTsmuZCx77FcKlQOl9ts96XGARjaLMhmXoNI44xBMXdjTk1TY0Sg4AQal1aifpSCi6mVetLnluniKo9B/WF3UaPvv6g7+BqqdF9hKkJHBwlkDBpssZzTULYcX+apkW56seWdJuF6B9ukYkSAEZCxwBWi1xv4rJSnuz2CN4f1hVJp/GFjfbO9Ib4a/yrq1aqCRSs3MhUJESACBSRw+MR5/LRkrU1ZKTlIcjeblq7B6I9mokn9mnj0YeMMClamW/vmaNuyAcqWLomtu4/imaETYHKqBKgVKF3C3+USGeHPTAPbZ8mR/iPD/cxlw4KNJ+r+arnT7C7ZvDW3J3cQ1LAl7yPI3/g9FxSg5GmZrSsDqbLsUjTPzz2mEKlesNRGbj1L+6nkUk2AjYulPUlKhKi5bRQQARMBvWjg0U8nT8DMWd/ixnVa5suBuDsogv5NM1RKBMgwoL5fvqRNBaVDFlUOk9ts96VGAZJDJdBmXsPSxu9jO1/DDvVLhYhAURL4rFMQvuwczCX7Jx5fdTGmmV6mDERAZBMo/EsVpRl5tl279kN5lmEFlArH/j+zsq6QjGefNy/xTBv9NvTlKwCCgIy+/a31oBcRcJyAyxwqCUkpiE9MwTPd29m0Ti6XSXltcei4ezZYsmkUKYmAFxJIz9Dw/2vs/1tuYf8Pcw6JPeXnzQnfgTk8Z346Euz/oSl/+Mu98Nqgp/H6i09j6ezxCA7yx5ZdR3h2eqYOd+5luFxi4jN4/+ypRI70H5Og4U8wYmUTU7J43YxMvdPsjpFFQNfVcsqtoXYrJNTpzPtIzdDxPlPStTxt72FKeimD2ZhbkqV6KVIbufUsrcnS87YTpHGxtCfJveRMbhsFRIARSEhLRqI2nUVx6NB+LP9tCV4ZNggJCfFcl5+AyhIBIkAEiIB7CTz99DN4+eVhZmlVtjY3qGZAJF4s2wK9ohrydAiU0L212CyidPOIZ1BABHyMgMscKvGSM4WxK1fG/qM3y5UphXTpYpDtrcLKkhABIpB/Am1aNMC44f1tyisv9DA3mJyajtfe/QZJyWlYNON9PGh/FKVSgVIRYcjINDolzI1QBIb2z0M74S9oh8+C9v0V0A2amG8qQo0y+a5DFbyCgNcamZVyHfdOz0fcyR+hiT9TqHHs+O8Ici+zSEtPw57t/xaqXarsmQTCA4zLcR6rrvYoA+tFKvlSILZ8wqMMI2OIgJcRYDNUBksOFS69XkBrRVk+gpqBkXipXEv0jmrE0/5yFT+aAsPW06YoHYmATxFwmUMlNc14ZznQ388uwKBAf56XrqG7mxwEBUSgiAikZ2RiwPBPERuXgInvDEaa5Mi8FR2HO7HGO8bXb8Vg4fJ/EH03HlqdHms37cWpc1fQonGdIrLIu5sV1f4Qy9WEGBRmNRD2GOUnrm6G8qe3oMzcy/Nlynj4qTfBz28TVBUuQ969Mde7PyALiACQdGUtzi6qjZvb38CtnW/i3NJm3LFSUDY37t2xWfXm5Dhkfe1ZU8JtGkrKfBEo6S/wJTdP1Mi/Q6VSmNEZY9p7xbSnSm79IxUsL9QcMbBBlILb1Ug6OlKeyhABIkAEiAARcISAyxwqJmM+n7kEH09daFPm/LLGVIyORIAIFCGBlNR0XLp2G2wz2t5DxqNrv7Fcnh02wdzrohUb0KnvGDTqPATjJv/IZ7w0bVDTnO8RES8wIlguoHR6LIQrJyAT73GLBYMGMtyR0pKEaoAg+45mXoECIuBCArGHp1r1FnNoipXOUcXDVevZLFo/qzm08xQwnKZdLWwCKobK3HujPJq9p0pufZuKvumIS/tDQOoYJTJHq6BbZtzzpRh+DGjIRIAIEAGvIuAyh4papUT5MqXANsxkj2a1JRev3OJlZAKdXHnVp4iMdZiApxSMKhWO09sXWsmu1TO5iRXLRWHL8m+wc9UM/PPrlzi+ZR4G9e3G8yiwJCD7by/ku5aDHS1zjKmzaXqsq9QR2qFfQaN+CpqsLtA+O4mnmU7/2FBjQQqJgIcQyEyw3stMlx4NfWZigSxsUb0+/GSWF8A9Mp5DQ93DvD3DeZedivD+7AVCVibP8p8zG6Ua1ULosBchv3yJ6yggAkVNQPuTAvdGCshaI4NuoxyZE5XI+tjy/01R20DtEwEiQASIQP4JuOwspm6tytiw9CuHJDgoIP8joRpFQYDaLMYEBEFAifAQVCgbCYXc+EQZ0MuCgHLG/6BY9BHk63/iR+X0VyzyWSJFJyI6sDTEqo1gUJSDQSwDsWJ9nmY6sUw1VoyECHgMAXV4LStbFAGlIVdbL2mzKmhHEaRQ85xPk37E73H78EbaRzzNg2Aeuj1QHjzAbZClJENISoR60z8IGTuC6yggAkVNQLvKekaKbp21rqjtoPaJQH4J5N4jK7/1qTwR8HYCLnOo5AVKp9dj35Ez+HDKPLDHuOZV3nY+ab2JgJiWCcO20xDP3vIms8lWIsAJCCd3QIi+zOOmQIi5CtmJ7aYkHYmAVxKIbDrWyu6oZuOsdAVRVJPXRiCCzVWFCEDe0vjEKrPSTRH5tStWPSuPHubOFasMUhABZxKQ/guI1wWrFsVUQLxrpSYFEfAoAtafXI8yj4zxeQLuH6DbHSrnL9/EtJ9WosMzozFkzJdYtX4XDAaD+8mQBUVHIEtrbDs6AYa1R6CfuxW6GeuNOgqJgJcQkMVes2mpcPe6TT0piYC3EAit0gN1Bv2H8u2/Q7m236JW/0MoWf9/TjFf/aMGygE6yNsaoBysg/8yDYQgpzRNjRAB7yUgB+RtrM99hVoihFLeOyyynAgQARsESOVzBNziULl7LxFL/tiEXoM/5PLTkrWoU6MSvv3kDRz6Zw5oyY/Pfc4sBiReiLFI88S1OIgXbeh5JgVEwPMIGCIr2TRKLFXRpp6URMCbCKiCK6JE3cHckeIX8ZBN04U7lyDYzRRdmAAAEABJREFUeYIPq6D6vB9U73eB346xEIR0poLf8n4ISm2PkIi2UA27A6G8yPWeEOgrVbEyQ9u4KcTQgi91smqQFETADgHV6CwocnwEhZIi1O9n2SlNaiLgOgLUExEgAg8m4DKHSmaWFuu27Mer475G+2dG47MZSxAUGICxrz7HLRz+ci90bdcM/n4qnqbAhwncTbY5ODE2yaaelETAEwmI9dtBLF3VwjQxqjIMDdpb6CjhnQTi4pOQoaGLGVvvnnDuAJST+0A581Uovx4E5bRXgLibtooWiS4xKx7r7/yJX6/Nx8nEo07rQ9s8e5Pc4BDuRMns8hiSp850WvvUEBF4EAFZLRFldxkQuikL/n9qELBTA3lzw4OqUJ5tAqR1JYGwQCAy1KEeZR3rOlSOChEBbyPgMofK6XNX8fak78GW+Lz16rPYtGwqFs98H727t/U2ZmRvYQlE2J7fLZQNL2zLVJ8IuJSAduSP0A2aCP3jr/CjdtRPLu3fGZ0pThxD8PhxCB0yAEFffQZZTLQzmvWKNnbsPY667V8COyL7df1WDJ4YMA7teo9Cs8eGYfyX86HV6bNz6cAIKP6cDiHtvgNciL2KzDU/ok3bZhbSYv1FtPg3gcszB0Vo2k1F1mebzIKQkqy5fMmVtIvo/W9nfHzqbUw//wWGHnwOn5/5MF9t2Cssqowb52YMex13j51D0pyfoa9Km0bb40V6ZxGwbEdeWYSshufM3LK0jlJEwJqArH0drpS1rAHF1wMhVMxepxYWwNNMx0SoVpqXo4AI+BoBlzlUlAo5ZxdzNwGXrt7GtZsxYBvRciUFxYqAULuM9XgrlYRQOfsL2DqXNETAYwkYareCvs2zYEdHjTTsvwjx4CUgJcPRKkVSTnHhHCJ6PQ7/XxZCvXUTAmZPR/gz3YHMzCLpz5MaPXfpBsZO/N7KpE+nLUbt6hX58tO1iz7HP9sO4J+t+63KFVtFRgqExFir4fvdvWqlKwrFb9cXIU2fatH06lvLcS/zroWOEkVIgJomAkSACBABIkAEzARc5lCpX6cqNiz9CqNf6YNDx89h6NivwDai/fbHFWZjKFJMCKiUxoGWDoesRxPIh3aEYuTjRh2FRMCXCaRn8dEZNp2Aftke6D7/E+KteK5zR6D++y8g1ybg8ls3odr7rzvMcVmfbB+v1979BhPGvIgAfz9zv0kpadh98BQG9unKl59WqVgGPR97BBt3HDSXKfYRdQAgWJ86KILDsGvnIS6/LvkD7FW+fEWe3vHBPCxu8CJTFVqupVk+WcvU4NV023qWT0IEiAARIAJEgAgQgaIiYH1WVFQ9Se2WL1MKr7zQA//8+iWWff8Runduhb+37JNygHcm/YAfFv2FK9fv8DQFvk9ACFRD1qEuhDrlfH+wNEIikKkDcu/JkamFYdd/bmMju3nDZt/ySxdt6n1ByfZFeeP96ej9eFv06NLKYkhx9xJ5mv1W8YgUVCwXhTuxLnV6Sb168J9MDkP9NlYG6pt0s9IVhaJSoOW+RaY+KgfY1pvy6UgEiAARIAJEgAgQgaIg4FKHimkAgiCAzVh5943nseev7zD78zdRt1YVzJz/B3oMeg/sLqGpLB2JABEgAr5AQIw2XqxbjSUmyUrlKoWuRSubXWk7dLKpt630DK1ebwB7Ypw9uSw56w0GER98MRflJOf+6y/1tDI8OdX4JBqVaRadVEKtViE+MVmKGf/8VHI8SPSQYfdNHf48n4Wz9wwPLPugdnLnyWQCN0ClkDnUJituKssrSkHuNguaVvR7F8LTw4F6j0Jo0gWygROgavO02S61xEjqDswG1odCbjzVYEeWzlOUxvK2yg2q9jICFZb7cD1TsR/KBZc292+rnknH7GJiSuc8KuRGxg7bKY0zZ30WZ22TEAEiQASIABEgAsWHgPGsxY3jVSoVaNeqIb75+HXsWTMLn44bAnWOk1k3mkZdEwEi4IsEinhMP86ZZd6Y85dfFvDemK7twMfQ4eAM/Ho71/KRKMd2x+cNOTnI6NsfmZ0tZxakjX4buqrVndxT0TcnQpQcHyl2RavVgT25Z8P2AwgO8sfU75fhy1lLkZ6hwfI127Bh+0GEBAVwQ1lZHpGCzMwsRISFSDGAXW4HqOWwJ0lZwIDfE/DpjhT8cDANY/5JwiQpbq98Xvod17RYclLD5VaKcWPcrVezeJrp72XAri1yyYnBHBusD2S/WNwpEhyAwM7PIuh/kxH48ocIeLijhR3+KuOphSAIXK9UMHIAOzrSv59aAeZAslW2Xola2NptJ6Y2+xbvNfgAy9v9ji+afcH7sVU+ty4bhc3ySokZy3fUztxts7RxpKwV94hOr+czqtiTFW1ZwJyK0Xfj7e5hl5WlBdvrThRpU1Rb/PLUxadB99Zio0w2Ln2DLV2eDVEBIkAEiAAR8BYCMlcZqpfuHqamZeBBIpfJ0KVtM/hJdwRdZRf1QwRMBPyX/4qQEcO4sLhJ7+4j9e/DBNRKyNrUdusAk35ahLhDpxH/5wbcPXUZaaPGutWegnaukMsxbnh/u1KrWgUEBfph1NBnUK50SYSFBnFh/QUF+iPAX42SJcJYEjdu39909eqNaJSJjOB6dokZn5IFe/LrsVQkagy8rCnYejkTJ25l2K1jry2mX3M2HXMPpXK5lWR0qGy4oOFppj8bo7HbrlZnQEq6luebbGFtukISU7W8S71BcnJJvDIyjbazoyP9J6ZmgV342yuLrCC0Ce+OnlEvopKqPh+jvbK59dwwKcitZ+mMrPzZyerkFvYZkZp3yx+bndWw0xB0fnYMmnR9BWM+noWk5DSzLexpVi26v4ZOfceAlVu+Zrs5jzlQZv/8JxpL9Tr2fRNte43E8TOXzPkUIQJEgAgQASJABGwTcJlDhf0wsx9yR4SW/Nh+s7xE65VmBn0xCcHj3oTf2j+5sDjTeeVgyGi3EvjfsOF8I07TBp05j9u6vY/nyzaHrEsDyPu1huK9pyGUi3CrvaxzQ4mS0DVoBDEwkCV9VtgGtMMGPImcwnRPdGyJNi0aIDQ4EK2a1cXilZvA9lphe3r9tXEPurZr7hCT69lOj9yFrydZOlly59tLd6iiwgsN/O1KmSDj0/Ps1Sd98SLAnITzvnmHP6Fq1fxPcfDYf1i1fheHwD7P7KlWbwzuheNb5mH6pBH45OuFuHnnLs8/dvoiZi1YhcUz38exTXPR87E2eHPCd9yxxQtQ4BiBiEAovh5oXz7o7Vg7VMqpBHSFnLnlVGOoMSJABHyOgMscKmyTvw9HD4RJ2FMUGE321B+TznT0d+kMFWYFSXEnoF672gqB3++/WelIQQScQUDWojqE5tWAYH9nNEdtOJHAByMH4PS5K2j22DC+p1fXds3wWIeHHeqhYqhtB0fF0IL91HaSHCoD6vvBnpQLKVi7Dg2mCAqJdxKh/3YddO8t5UfD4ctF0EvxbbJvj/Zo2eQh/oSqmlXLo33rxti57zgHcuDoWb68rf/THcFmc3Vu0xSVykdhx95jPH/rv0e5M7FJ/ZpQKhVg52gxdxNw7tJ1nk8BEfBWAoWZueWtYya7iQARyIuAc/NddjYWWTIM/Xt2MkuXts34SHo/0dasM+Xn3BCQF6KACBQlAenOhfzObaseZHF3ISTZ2UjUqjQpiAAR8EYCB9f/wPfxMtlepWIZbFj6Fbau+BYH1v2Aye8O5ReYpvwHHZ+ooUaI2vJn9dGKSpQPse1oQTF7iSeuQ7x5D8jS8aPh192wu1lzMWPj7OFqdXrsPniSb/jP2o6JS+AOlJznV9UqlUV0bALLxp3Ye6hSoTSPs4Cds7FjbJzxN5BtMCyXAleLTOqT2cH2pnGkb1bc0bKOtOfqMmysbAyu7rew/THmzHZPlMLM3PLE8fiiTWIGkPGPAO18BfQ7LX9DfXG8BRoTVfJoAvSp9ei3h4xzCQG5HNrmLay60jVqAjE0zEpPCiKQm8CtWzdx8eL53GpKezGBqFLhCAzwy9cIogJlmP90CMY9Eoihjf3xRedgfNAmKF9tFLfC4rk7xW3I+R7v7ei4Bz7Bii3nyd3op9MWISU1g880YXnJKWlgy9tY3CTsCVYp2U+2Yvl+arUpix9Z+dR06UpHSoUFqVAyVO1yCQ5QSL0DaqXcob6DA1VgmzG7w1Zn9KlWyhAa5HrOhbXdTyXn75MnBoWZueWJ4/EWmxy1U0wFEh5X4d4wGbKmKqF5VY2M5y2/ixxti8oRAXcRIIeKu8hTvx5FIOX9j6EvV95sE4snj59kTlOECNgicPv2LQwa9Cz69e+Jlwc/jx5Pdsbu3TttFXWqLuFKIk5fPYozuiNIuGO8w+zUDqixAhMIVApoX1mFZx7yQ8Mo48VggRujikRAIsBmm8Qnpth9gpUoGqRS9/9mL1yNlWt3YP6342CaaRISHMiX/NwvBbAnWAVnP9mK5WdmZeXM5uWDAvy5jm2+G5OggaslKXuDY41W71DfSalZ0GQ5VtbVY3Gkv0ytAQkpmQ6N1ZH2XFXGtKEz/7B4cMD+L9mZuWW2OufMLaYUBMAdguyXo32z4qayPC4FprQrjlJ3QDYrdkT2K6++9X8rYLiRXTj7YDgmg2G3DLxuto7Hs9t3Vjy7aWM/BWzb1AYdizcBWfEePo2eCBgJsA057/17GPGb/+XC4romxmVpxhIUEgFrAgsW/oQrV+/vA5GUlIipUz+zLmhDo1sjR+bbKmSOVkG3zPEL7/NrzuPnjgux69Qm7NZuwC/j5+PMH2dt9EAqIuD5BIRaZTzfSDdbyPY6edATrNhMEmYiezLSV7OXYcFv/2DFnI9Rv3YVpuYSVTIc127GIOcjwc9fvonSkeE8v0xkCbAnWvGEFJiW+pgcMpKK/jyKABlTEAL5nbmlUshQOtzfLWIan6P9s/KsbIlg4+wOpQttDwlQse4RoFZwVkq58fJSLhN4mtllT/xvK3nd3EHgbTWvG+hnPD8K9lfytL12CqJXSYxYvxESs4LUZ3VYfRIiYPzEu4DD9Vsx+HjqQrPM/fVv3uuXs5eadaZ8W9NXeWEKiEARE9BVqwEmRdwNNe8jBC7YWOYTdy8Od2zsyZNzyNrF/sgcJzlS/pZDt1FyrExUIutj2ycVOeux+L4Z+9nBQg7OOmiRpgQR8EQCQoOKEMqXAFQKfpQ9/wiE0mGeaKpX2vTRV/OxcPk/+Obj4QgNCcKt6Dgu7AknzRsZH8++dPUWsPTmXYf5E37atWrEx9rx0cbYffAUjpy8AHYXf9HKDWDL3mpVq8jzCx1QA0TAzQRmF2DmVpbOgDvxGW4REy5H+2flWdm45EwWhSttT0rL4n2ma3SclVbixhR6g8jTzC57kl5Wy4paSWqZTF43VWqTZSana3naXjsF0TNGrO17ErOC1Gd1WH0SIuAyh0pKagb2Hj5tlsvXboM9+efYqYtmnSlfp9PRO0MEiAAR8HgCwUFBVjbKZDKE5rH3jna9n1U93ZuBji8AABAASURBVDqFlS63QvOtAgkX43OrkXg5AemDVNAfdtlXupUNpCACeREQyoRB/uYTUHzenx9lTavmVcUq/93jb6DFppp2ZVvsRqs6BVF4Y52Dx/7jZr867mt07TfWLLfuxCHAX42Zk0dhyqylaNhpCEaNn4kPRw8EOw9jlRrVrY5XBz2FgSMmo1HnIfjtz234esLrkEl3mFm+L4ku4y7SY4/AoE3zpWHRWOwQKMzMLTtNktqJBBTddZBVyNGgCMiqGSTHO6A/IIN4RwAknXhN4GmmY2KIlvQ5qlGUCLiTgMvOvuvWqsyfnMCenpCXBGev6XUnGOqbCBABIpAXgY6dulkVad++EwICAqz0ZoVBgHhTbk6aImxjNjHZlLJzvCQ5ayDd4c+VHYIIiIfkEBPoBCMXmuKSpHEWkIBy778InPYVF9X+PbwV1Z77OuX+vVznDQE7tzq9fSFyC1syxOzv+EhjnNgyHxuXTcWxTXP5ExaZnokgCBgxuDcOb5iDTVL+/r+/R+N6NViWT8mVv/vi9PxKuLDiUZycUwoxh770qfHRYKwJFGbmlnVrpCksATEJ3DFiOGc8XxGk+1Lh67NQYo4BqrFayDvrYZDOdTQvqcFEv1E6X5KKapcpeJrpmOg3SHobxuhPyHj7onE/bRslSEUEnE/AZQ4V55tOLRIBIkAE3Eug19PPYOInX6Bb1yfQtk17jBjxFt57d8KDjZKJkDfTWpURaokQQqzUFgrVSC1ajmphoWOJ5q83h9/CTMib6FnSC4RMJAIFI/BFw++wv8t5Lh0iu/JGbOl4hgOBau9uBE6fykV5YB+vodx3X6c6sJfrfCWQy2UoV7oklErbM+L81CqUlfJ9cWZK3Km5SL5qXG5uej+j93+MzKT7+2CZ9HT0HQKFmbnlOxQ8ZySGUzLuGMmaatx3hVkm+AP+j4lQDtZBKC8yVYEl810Vbx80g6XADKli/gmQQyX/zKgGESACxY3AA8bboUNnfPjhRHw6bhL6BNaBcsle6H/fD/HGPbu1VK+nQqh0/6RBKClC/X6W3fKmDFlNEbVH18CLW19Ch087cnlx84uo93ZtyB82QIgwlaQjESACjhDIavUI0kaNtStZD7dypBkq4wUE0mMO2LRSc++0TT0pfYNAYWZu+QYBGgURIAJFTYAcKkVNmNonAm4gQF26mIBWD/2M9TBsOQnxzE2Ie85DP309xOhEm4bIqusRsF4D/3WS/KlBwE4N5M0NNsvaUoZXCUPDFxpwCa8WbqsI6YgAEXCAgLbVo0gb/bZd0bZo5UArVMQbCCgDomyaKVeH2tSTsvgQyGvmVvEh4YKRCsY+TEt/9Adk0O4TkLlXMC7VcXBmiXjdWJ7VzynQZLef3Y8xRSERKFoC5FApWr7UumMEqBQR8GoC4qVoICHXBoeiCPH49QeOS1ZZhKzG/ZkqDyxMmUSACBABIlBgAuG1ngcEy30XlMEVEVDaehkl6EUEiEDREMg+5TEt/dG8pEbSC0rcfc64FEj3j+X/UW5Edh0ezw60v1ruqcLaYSJmO2QEG3Wyq9KBCDidADlUCoSUKhEBIkAE7hMQY2zvJivGJt0vRDEiQASIABFwGwG/iDqo0WcHStQbhuDKjyOq6Tuo8cw2yORqt9lEHROBYkcge+YI2zOOzcxlonxYhLol+ExdWWnHPCGyiiIvz+rnFGQ/RFEUih1ZGnCRE7DfATlUstlotTrcibkHnV6frbE8pKSmIyEpxVJJKSJABIiARECoW14Krf9ktcpYK0lDBIgAESACbiEQENkE5dtNQ9Xuv6N0y4+hDKTvaLe8EdRp8SWQ7S+R1TfA7+dMLqFLtSi1XM/j8sf01mxsOEcUz+t4eVMbpqOQ7ZChGSoArEmSpogIkENFAjt/2To06jIUnZ97Cw07DcGq9bskrfEvPUODER9MR8ser+PRp0eg/+uTEBefZMykkAgQASIgERBKBkPWrYEUu//HnCzCw9XvKyhGBIgAESACRIAIEAEiYJMAKYmAtxIo9g6VXftP4OsflmPGpJE4vmUevnh/GD6cMg+Xr9/h7+mvq7bg/OWb2LZyGvatnQ25TIbpc3/neRQQASJABEwEZF0bQvFZf8hHPwHFJ30hH9zBlEVHIkAEiAARIAJEwLcI0GgKQEAIBV+qI6tpKEDtvKvIGxh4+6J/3mWpBBFwFoFi71DZc+g06tSohE5tmkAhl+PJrq1RrVJZ/Cs5Whjkf7YdQJ8e7RBZMgzBQQEY2KcL/li3E6IosmwSIuDVBDKztLh55y7iE20vZzMYRETfjbe7FM6rB18UxqsVECqUAIKyF/EWRR/UJhEgAkSACBCBfBOgCkTA/QRk9YxLfVRva20aI8+1N4oQJV1vSX+yCiJ3lJj2SzEt7cndiPqLLL4USJa99Cd3PqWJQFEQKPYOFbVKyWed5IRbuWJp3I65x1XXbsagYrn7j9qrUDaS65NT0/mRAiLgrQTYTKwmXV9Bt/5vo03PERg44jMkJqWah7Nj73G06P4aOvUdw5fCLV+z3ZznysjxGB1McuauXnJmAlo9zDqWd/auzpUmUV9EgAgQASJQ1ASofSJABIodAUV/y71R5GxPFQHIvWeKopt0Iljs6NCAPZWAzFMNc4ZdazbuwU9L1tqU3QdP8S46PNIYp85dwcRvF2HjjkOY++vfOHryAs9js1DYHip+ahVPs4A5YNgxPV3DDgjyV3iFqJUyqBQyj7DVX218JJpcLjhsjyAAgX7ewTrnZ0IukwznnxTPC5hzcPmPH+PY5nlYv2QKrt64g+VrtnFDMzRZGDvxe7wxuBdfCjd90gh88vVCPpuFF8hncCrW6BTJZzXEZxjw7uYUs0zelQr2WUjRimYdy/90Z2p+m0aq1AZzxlyKJ2dMvuFRBSJABKwIkIIIEAEiQASIABEofgRkvjxkNouELWWwJcxRwsbe8KFqmDv1bcTFJ+LXVZulYxJf/lA2qoR04SYgwN8PbFkEK8vEFA8IME7pFwSBlxMEzz4CzD4p9BA7kf0SBGaXA+Jh9guCAzZLZbKH6ZGH/w18EnVrVYZSIUeZyBLcxrCQIH48cPQs2P+R/k935EvhOrdpikrlo7Bj7zGen99g/LZU7gDR6MT8Vi2y8lfi9dymH49kFFkf1DAR8GACxdo0ITEBAbOmIXToQASPexOqf3cUax40eCJABIgAESACRKBgBHzaofJC784YN7y/TenStpmZWKtmdfmmtAunvYuu7Yz6Zg1r8Xx2EXn9VgyPs+DG7Vh2QEhQAD+mpGvhDZKp1SNTa/AIW9M1xhkBer2I5Bta3PsKuPuigHvvAIm79DZtNIgiUjN0NvM8mb/e4DkOBP6BzRVkZWnxw6K/MGjU52hcvwae6NQS7BUTl8AdKCqVkiW5sL2FomMTeJwCxwkYdpyFYcMJLpC+L1hNw84zPM30YkYWU5HkSYAKEAHnEQgb/AKCpn4O9ZaN8F/+K8IGPgvV9i3O64BaIgJEgAgUIwKhcqBtxhXItyyCIuWIceSaFJ5mOtme1UYdhUTABwn4tEPF0fcrNi4RWp0e5y7dwJTvlqLjI43xUM3KvHq39s2xYs12sDKpaRlYvHITej/RFoIg8HwKCkHAAGhe8oN2lgL6HXJolyugGaiG/hh9LAtBFYdPnLe5zI0tf1u51vIuLHP4sKdYJSWnIjklHSnSZ5z1nZySxmdnsbhJ1GoVUrL3DvJTyREZ5uewmP633EyX4Xqa43JLKm/q/0FHvSjYbPdqioDDt7U8Ly7TaEWa3lg2VmNMKxUyh8eRnzFHZvMRdv8Hw8bjXKAxOk8Mu+7rSioFu/0H+Sv5UreC9OvOOuFB95dJgl7FlkBqagpW/fk7btyL9jgGyqOHrWzy+/03Kx0piAARIAKuIHAyx35xbDkyux8n3Uu02C+O6V1hS0H6CJHOZdporkkOlMVQpBkdKkJWGk/LtyyGfB85VArClep4BwG6cpXepxEfTEejzkMw4I3JaFi3GqZOeF3SGv+e79UZVSuVRYc+o/kGnVqtDiMG9zZmUlgoAoaYcBguGS9qczakXy/PmaR4PgmkZ2j4srX4xBSrY0KS5dN8/P1U+Obj1/H34i+gUMgxa8Eq3ltIcCBf8sMT2UFmZhZ/0hVLshlP8SmZcFRM83RGrInH/1bfc1je/sd6RgxriwmzwySJmQa7bQ79I47nzT2UyosfvJHJ01P/TeZpnc7g8DgcHW/OcrI2taHo1tCuJEqOxZzlc8bTNVpkZOqK1L6c/Tkrnpw9E4cDpqDYEdi7918+5oSEeHzz9ecY8MMH2Bh3lus8OZBfuujJ5pFtRIAIeDiBc/f03AGi0eY+S8nb8A+33d8vju0NJ52aSDdvwZcms7RJ8m7JugRzxJyN01lnOFGTLI15l18l6DsNhC6ktXSjugH0jXvxNNPpW/Z0Ym/UFBHwLALkUJHejx+/fAubl3+DA+u+x/sjB8C08ayUhcAAP3z/xZvYs2YWdvwxHb/9OIE/QpnlkRSOgJgYbLMBwy1rJ4uNgqSyQ6BNiwY2l7mx5W+vvNDDZi1BEFC1YhlE343n+VElw3HtZgyYA5ErpIDNZCkdGS7FwJ+0o9OLcFR4JSmoF6lA/SjHpU4phVQr1590npL7E6KUvsnstdu0nIr3WSFUKiQ1Feov8HTVcGPbUnMOj8PR8eYsJ7apA3RtYFf0KqXd/tkdKiY52/OGOJv5JKGmv2JK4M81RsdszuEvuXMwZ9LtcTHQuF9UTkO0bTvkTFKcCBABIpAvAtP2pXEHSHSadKckXzWLrvBdyRbmjPn837Si60RqOUkP7PSvIjlQBkEb8ii0+kbQNenN0/pOg2BoTQ4VCRP9+SgB4xWGjw7O0WGFhQahTGSE5AnOfZkG8ytUumNfMiLUnM5/hGrkJiArdze3iqflrTznh4gb5IMBW772zY/LceX6HbDlbsfPXMKq9f+iecPafLTNGxmPS1dvkS729di86zB/wk+7Vo14fkGDSR2C8GXnYIdlfJtAq64k34+VLkQl2GxzatcQzO1Vgue9UM+f12scpeTpV5sa01xJAREgAk4hoNfrERN9x6qtm5pEpGo8ZwPo1I8mQQy4//2ibfYw0ofdn51qNQBSEAEiQASIABEgAkTABgH7DhUbhUlFBJxJQAjJgOodLQT1/VZlLQ1QvqC7r6BYkRAQBAF7Dp1Gj0Hv8eVuz78+iW/I/NJzj/H+AvzVmDl5FKbMWoqGnYZg1PiZ+HD0QJQvU4rnF6fgfLwe3x1Ix0fbU/HzsQzEZbA5LcWJAI2VCDhOQC6XI6p0GasK5f3CEOTnOU7MjGefx93Tl5Hw10bE7TmKhBVrYIgwPu3MynhSEIFCEhAvRcOw/QzEo1dBG5EXEqYHV2d7njDzzt/T8aU/bKmNo8Jmo7K6ecn/2bsPwCbKvw/g30ua7hbKKnuIoojIFkEZAiJOQBEFBRVwoQiif3FvfcV3/nhJAAAQAElEQVSNCA4QEVQQUFCQKQgisjeIguw9C91t1nvPExKSJilpybgkX+W53D333DM+d0mTX+4u3upbfaBQtrlDfc8i6sg1WuVyoC/1EW0xUSDaBRhQifYjIMTjN9xvQsJfeUiYko/EJerjuIIQ9yg6mheXsk0b8xpWzf4cc74bjtVzvsBbzw5wudxN3Jx508JxmD/5fWxYMBa9unUsNY79Up/4GO9ngZW68lJumBRru/Snbpr3e/bsPWPGk3Mz8euOAqw+aMTkrfl4al4mCs2lbJSbUSAKBLre2t1tlPdUaeGWp4UMY8NGsFSpqoWusA8RKmD5bhnMoxfAMnMtzN8uhfntGcCpwF5+oSHKqOqK+l2VHO9HK3LlpT/iUhtfk7hnitz47MTbVzfDfsvyWLe4X5xoa/Qq27G1T33/IpYDfanP2e7ygQJRLcCASlTv/sAM3rr9MMxTlsP81SJYftsMa27xQRJF/dJSd4UVSvSd/BCYHVCCWkVgpWa1dIgzUjxtptfrUK1yBRgMMZ5W+5xnv9TH5w3OFoxRX6Gc741yWYUYWNV1RfMv93SvFbVccf8uUgMp4vKjh5slei22dJ8RRb81OpZjwaajRq/bcAUFol2gVatrJUFaWjkMfeo5fPvIW+hcob7M44QC2hbwc+9OZcOybpdrpep7Isvy7a55XIoIAfsZKpeU08t7tTm/fznffFEA8fWT1V6h00r7F1RF67PfL66u2rYonmiwfWl0aQXvXxqJckwUoMCFC6gfVy68EtZAAbuA9Z+DMH/xG6wr/4P174OwzNkA89hF9tVuj9Ztapnft8K6eT+shbzUxw0oyjNS43Tyfici8CHSS22TIN5kFL1nygtt3W8w6Q+6/eo3PJ7q2Z/J+/x4cmGedgTWT+6Cf6Z1xs4ZN8Bizpcd+2/69XJZ5MmMAE+Sk1PQvesdqFG+coBbKr76AysPYP8KWzIV2E4v2/vHXkde8VtrfC27p2kB67EzHvtnPWi7AbzHlcwMWwH7GSpDWyW5vHcR71/Ol2I9xD0URbzjceV4//oUj3WPu6OCzH/8qiS5Qc0yern8wrWBeX8kG+GEAhSQAgyoSAZO/CVgWV3kmxhR8d4TgPotjZh1TtYDJ2WwxTJrHczjF8P83kwgv9C5COcp4JNA5p7ZOL7+Y4hHnzbwsVDzKp7PzGlZzeBjDSxGgdAIZOxegMwDfyD74FJHB3IOLZPLIs9isgVZHCuDNBOKZqbdPQ0/9rIle/vT75vuyDPlM5hvd+GjfwWUSmU8VqhUK+cxn5kUoAAFKBB+AgyohN8+03SPvX4b4+lbmoIil02oQRfL+r2aHh87px2BXRlmHM6yYPvkltj9aw8c+ut5+fjvJP/dq+H6unEoGjy5t2E8qqbwpVM7R0JAexK2lTe553fUv2M+6nab6zHpYuLDdmxFOz5l30Q8veERTNwzBjnm7KKrUfPamqjesrrXFBPvOXDqVhEzKFBSgXLJ0DW9yHWrxDjoWtVzzeNSRAhcXC5GXuoTr6H7xRn0kH0Sl0xHBDIHQQENCvBTgQZ3Sjh3Sbm8unv34wxQaldyz/eUc/S0p1zmUcBFICPfisdmZ+LjaRORd3Kzy7r8U1txesc0l7wLWXi1fTIm3VEGI25MxfSeZXHPlQkXUl2At2X1FLAJpNVsi5QabZFczXOylQrv6dH8I3IAazNWYOnxRfh0x3t4fuNgmec8uX3i7egxuYfX5FyW8xTwt4DunmugH3g9dLc2g/7eNtA/3w0oZ7ssw99tsb7QCjzVKlFeZlM5ueQfrxpUNMjAh/3eKOJqH3HPOPuy/bGkIywbr5N9ev5aHnMltWN5CvgqUPJnvK81s1xUCujbXAalTsVzY4+Ngb5bcyDecC6vuLn0ssWt5bpIEyjleCziXYa6bSXLDnXq/i//9L/umReQI96Q1CunVw9j9+uZL6BabkoBCpRAYGf2dqzNWOlIB3P3um294uRSiHJuK5hBgRAKKHUrQ9f+cihNakNJiA1hT9i0VgXe7pgsAx/2e60Y1E9o4h2Hfdn+qNX+s18UiGYB9ekazcPn2P0ukBwP/eNdoH/pDuiH3ISY/+sF5aqLPTcTVyTIIk6NbVLLc1mN5LIboRU4lns2knK2G8d0l5ydc32IL3upawaXKECBsBcYs/MTDFzTx5HOmGw3/FTkrarPDe/3Y/PPLXCOAhSgAAUoQAEKBFCAAZUA4mqg6pB1QSmbCKVG+WLbV6qXh35AB+huaQr9/e2h/9+tQDy/uSkWLcpXJhkUF4FNsbfhsO5yl7z4cg1Q9pIeLnlcoAAFokegZmLt6BksR0oBClCAAhSgQEgFNBZQCakFGw+BgFK/GnTXNYDSsAaU2JgQ9IBNhpNAUpGTmkTfR6QuwPEmk1C19duoc/M0XNprtchmokBECxRm7UPe8Q3aHuPpHJhHz4dl9U7ZT/EolkWyrvpP5gViYtAxMB8IV9ZJAQpQgAIU8L9A+NfIgEr470OOgAJRJ5BkUNC6hgEd68Ti+TbJ6NS6Kyo2GYLU2jdFnQUHHF0ChdkHsX3KNdg24TL1sTW2jKmK0//9pE0EoxnWnUeBjBxb/9RHsSxTRq4tj1MKUIACFKBAOAmwrxQoIsCAShEQLlKAAtoXiI8BXmqbjKdbJ6FNTQ+nrWh/COwhBUolcGzd+8g7vt6xrbnwNA4scf9lG0eBAM3s37cXI0d9hGeGDcE7776Jhx7qi0FPPCTTG2+8BKvVimOWXDyZudCR3o7ZKH/tRP7iSYsiPyVbyn5a4XpfpVJWw80oQAEKRKwAB0YBCgRWQBfY6lk7BShAAQpQgAL+Esg7ts6tKnP+SRRmuv/ijVtBP2VkZJzCg2oAZcoP32H58j8xa+Z0bPvnb2zYsE6mbf9shaIoKDQWYsO/WxxpydrlOFU2BuIXT1Au2U+9YTUUoECECXA4FKAABcJKgAGVsNpd7CwFKEABCkSzgD6urMfh6+PKeMwPRKYIouTknr2MR23A9VbRaoaXfwUF+SgsLPSytnTZCpTSbcitKOA3AVZEAQpQgALRLMCASjTvfY6dAhSgAAXCSqDcpfe69Tel5vXwFmhxK+yHjCNHj/ihFlYRMgE2TAEKUCDMBDKNp/Hpjvfw5PoH8cG/b+C/7H/DbATsbiQLMKASyXuXY6MABShAgYgSKFvvTtS5aRrSLuuD1No3o0rrt1Cry7dBHWPjxk3P357Vf/c2OX9jLEEBClCAAhcicDDTjC/X5eHlxdnyUSxfSH3+3vb7fV9j4p4x+OvEEkzZNxEPrLwDh/MO+rsZ1keBUgkwoFIqNm5EAQpQgAIU8CgQ8MzUOjehZscvUOfmqajU5EnoDSkBb9O5gaZNmqNb1zscWZ5CJ57yHBuUcqZO8iXQq//L+9CKBs4mnZrXNO0q2FNZQ1opW+BmFKAABaJP4HS+BU/MzcL0bflYfdAoH8WyyNeKRp7J9ZfhCi2FmH9klla6x35EuYAuysfP4VOAAhSIcgEOnwIlF3jqqecwZ/bvGPPlBPS62/0yJEXx/71NHq47GB0qd4G8bYqo/mxqWLYxPmv+rSM1SWsB/kcBClCAAr4JiCBKrlFEqM+VF8t/7jeeywjR3In8o15bXpux0us6rqBAMAUYUAmmNtuiAAUuXIA1UMBPAkuWb0SD9vdDPOLsf8NHTZJ5It+e7n38rbNr+eAskJycgssuuxwGQ6xzdkDnh9R7DjdW6YpysRVg0Nnavbnq7QFtk5VTgAIUiGSB/ZkWj8PTwmU/S44v9Ng3kbny5J84YzwtZpkoEFIBBlRCys/Go0GAY6QABbQn8O/O/Xj69c/cOma1WtG+dWPM/na4I73/yqNu5ZgRGoEKcZXw6hXvYU67v3BthfayE6mG4P3CkWyQEwpQgAIRJNCsqsHjaK6q5jnfY+EAZVZUX/NF1SkxqeJBJiusSDOUl5d5xuhiZB4nFAilAAMqodTXZtvsFQUoQIGIFjh+8jQeffZDvDL0PiQmxLuNNSU5EbWqpztS5Yrl3Mow45zAVS1bnVsoZq5SpXSUK1e+mBJcRQEKUIACwRZolB6Dmy+Jc2n2potj0aSywSUvFAttK3WSzQ6oOwj24PmP1/yGue2Xy8s8k/TJcj0nFAilQAQEVELJx7YpQAEKUCCcBPLyC/H48yNw+41tccv1ngMBq9Zvw/P/NwbvjZ6MtZu2h9PwQtLXyy69HCNGfO5IL77wOsSZPhUqVHLkifVvvvke4uJc37SHpMNslAIUoAAFXAQevyoRU3uWxYgbU+XjoJZJLuu1sSBunAVHYEUbfWIvQiOgrVYZUNHW/mBvKEABClCgFAJmswVjvpvlNe3adxgWixUvvDMW1apUxMD7u3lspUG92uh+YxvUrlEZ+w8fQ98n3sa8xascZcskGRAOKUavICk+Jih9TS+fguuubeVILZs3hqKo7ScmOPLE+lZqvi92qYkGuf35yhpidHK/JMbpgzLO8/VHrJcd4oQCFKBAGAokGxTUK6eHeNRK98+cOY2TlmNa6U7p+8EtI1rA9m4koofIwVGAAtEqkHdyK/774zUcWf0WTu+aLhny1TyxLNLpnTNkHifhLyCuqT51OgvektFowolTZ2RwJCU5Ae9/NhnvjpqE3Lx8TJn5u5q/WiLc2rk1BvW7HQ/deys+eeMJiOXpc5bKdWJiMlsRDslqBcxqACkUfTWrRsJK7UIprcQNEs/vrA5PNCMDZaEYp6c2ZYc4oQAFKBDmAqHu/pl9ZzCx80R8P2kMfir4Cl8NH4VK66vKbr2+dRhe3vwU5h75RS6LiXJ8H0QS80wUCLYAAyrBFo/S9qz/HYXpqYkymT+bLxWsO53yRtvy5ApOKOAngfyTf2OnGlA5uuotnNlpC57kn9oKsSySPc9PzbGaEArE6PUY9lgvr+nSujWQnBSPwQPuQLXKFVC2TLJMosvJSQlITIgTs25J3D8lJ7fAkZ+Tb0I4JBFMyS80h6SvuQVm6SXOCCqNldheBITOt63ZLAIvQL7REpJxeuqfHDgnFKBAsAXYXpAF/lr2BwY98ZBMkyZ/K1tfdjZv8MIv8cORdTKvtJMVn6zEyR0nHZvn5+WhwYTmcnnpsUWYd2QmXtn8NH5d9X8wvHsPDB/1tyV1Xtm3TZbjhALBEmBAJVjSbIcCFAi6QHz5y1G37StIv+oFj6lM3W5B7xMbDJ2AuAGtOPPEOYm8mzpcjTYtr5Qd++jLqdi55yCMJjO2/Lsb3/30G669qqFcxwkFKEAB/wiwFgqEt8CJkyewYcM6mQ4c2CcHc/LUSbm88fhuHMjPkHmlnRz/+7jbpomnkxF/JtEl/5I/FkE5fe6SIDEf8+tolzJcoECgBRhQCbQw65cCysXpiPmgj9ekH9hZluOEAv4USCjfABerAZXKLV6Ap1SWARV/ckdEXSvW/o3b7n8BjTv1x10Pv4bO7Zrjvp43RMTYOAgKlFqAG1KAAm4C09+NVAAAEABJREFUmQUWjFiZi3t/Oo27pp3B23/m4FiO7cw5t8IRltG6dRuXm46LG4/b00fXDUDP9KYXNOL4VPezRi2KBca4Qpd6a2Xazoh0zlQObAcs7vnOZThPAX8KMKDiT03WRQEKUIACYSWwes7naNeqkaPPP3zxClbMGo053w3Hmrlf4q1nByA+LtaxnjPhIcBeUoACFAi0wISN+Zj7XwFO5lkhgitL9xZi5KrcQDeriforVKiIpk2ae0xN0uuiRkKa7Kf1dC6s+89duiMzfZhccms9t1IHmu2COd7kkl8Y6/732ZpUBtDpXcpxgQKBFGBAJZC6rJsCFKAABcJOICU5ETWrpSMh3v2NWoAGw2opQAEKUCDMBDYccf1wL7q/7rARZ+/LLRajOlmmr4L5jR9h/ng2TM9NgmX5dp89Gt1zJW4adTPqXdIAtXT1cO0N12FT/5Uu21dPrAXrlde55IkFS1Oe9S4cmIInwIBK8KzZEgUoQIEIEeAwKEABClCAAuEjYLWKXw7zfBmIuIH2keOnYDJ7Xh8+o9RWT62HT5/rUKEJlmkrYc1zvWTnXAH3uXo3XYKOHW9C57g70KhVMyjxiiw05qrJmNp6Pn68ZgFSbxsG0+1DYbmynUxi3txlgCzHCQWCJcCASrCk2Q4FNChQUGjEgcPH5U/NarB7/usSa6IABShAAQpQIGoFZi1Yjs53P+02/iXLN6LlzY+i451D0ahjf0yZuditjLeMxpVj3FY1rWKA3va5321doDJyjq7GnNcVbByViP+mtZPN5B5dJZdF3o5p7WWeJiaHTl1wN2on1UXNpNqOeizNb4Tp7hdlEvP2FYUjDMi7IR45zROQPyAO5s06+yo+UsCvAjyy/MrJyijgH4Fg1PLi8K/QtPODuKHX/9Cm2yD0GfQ2Tp/JdjR9233Po0H7+13S6PEzHOs5QwEKUIACFKAABbQssO/gUfk+59m3v3TrZl5+IZ5+/TM83q87Ni78CiPeGITXPhgvv2hyK+who2+jeHS5OA7lExSkxunQplYsBl3l+is0HjaL6iwlJSEo4zd+GwPjFzGw7FejW7mA+S8dCp6MDUrbbCT6BBhQib59HogRs84wFKhRtRKmfPEqNvz2lbwB5579h9VvZn53GckT/e/A7G+HO1Lv7p1c1nOBAhSggC8C1sxcWFfvhGXJtlLdoNCXNlgG8pKFw8dOQZx9WBqPwkIjjh7PgLg8ojTbcxsKaE2gauUK+OaT5/DC4D5uXVu1fhty8/LRq2sHxOj16NSmGWpVT8eS5RvcynrKEEGUwS0T8e3tZfFDjzJ4/tokVEoK/kerpPQWuPFlKxo9lusxXdJjMTTxX60KQKUyQemKeYX7frAeUmDZpQZYgtIDNhJNAu5HW1SMnoOkAAUe7nMrGlxaG4YYPapUKi9ByqYmy0f7pGL5MvLNhXiDIVLZMq7rwf8oQAEKnEdA/MKD+e0ZME/+C5Zf1sgbFJrn+PaB5TxVc7WTwJjvZslLFjr1HCrPPhz66iicycxxlCjurEMRQBn9zc9o0vlBdLjzSbTt/gQ2/r3TsS1nKBCuAiJQUrliOaR5eP9y9ESGfI8TG2twDK9urao4cizDseyvmb/2GzFkbia6/XAaA3/NlL8O5K+6tVqP/tZmUFpeDOXyatDd2Bj6AR202lX2KyoEAjdIBlQCZ8uaKaB5AfFt5OcTfkHfwf+HJg0vwU0dr4bzf1NnLYG4NGj0+BkQp806r+M8BShAAV8ELEv+Boxml6LW3za7LHPhwgVEwPurD5/BmrlfYvq4N7F6wz+YPmepS8XezjrcsPU/jPp6OiaOfB4bFoxFty5t8OQrn0LcrNOlAi5QIIIEMrNykJgQ7zKiuLhYZGXnyrwYvYJyKbEXnPKterzxRzb+PWlGgcmK3afNGLEyF4dyvdcvO6BOfG1fLXrB/fS1rfOVi4mxnQWSWq8yKvZvh4pDuqBC92Yon57isY/JCTGINejc1t2w5CpM2vu1GBo++vctZBpPy/nOi69CywX1HEkXl+u2bUo7Wx/kBmcn+upAhUYGR9kYve1jcGpijCPvfGMruv5s1YF5YK1hI2A7ksKmu+woBShwPoG1m7ZDfFPpKU1TAyTO25stVmzfdUD9FjMbmVm5yMrJc6y+oX0LtL36SojTZRctW487BrziCKqIP3xpybEIdiqTFCv7p+gUn9ouk2SAovhWNthj8aW9hDg94mP1Po3Vl/qCVSZFfXMkdxQnFFAFrAe83ITw2Bl1Lf/5S+DOW9rj6qaXIyE+FvUuqo72rZvgjxUbXar3dtbhoj/Xo1XzBmjasB4Mhhj06dFZXvrz7859LttzgQKRJJCakgRxyY/zmAoKCpGSnCizREAxN9+EC00r9ubL+opOlu/L91q3vayvbYvyvpYNVDl7vcJN9Ce/0Ox1fPay4rGg0AKT2epW1moVtZw/5XnYR/reRiQ8aoa+hhVIAgzXWJA0otCljZL2U/S1aDp/71giGgR00TBIjpEC0SQg3hycOp0lf7mn6GPGmSwXCvHG+8NXB+LXie8gJkYvv6G0F3jsge54tG9XDLyvKyaNfkl9g5GAhUvXydVm9Q9fvvqNc7BTgenst9zq30df2i4wWtT+WpEfgr76o02T+k2W2WwJu/4XmoS7Ss9/FFAFlOrl1KmHf5XKeMhklj8EjOpr5bLVm9Hg0jou1Xk76/DwsZOoU6Oyo2ylCmXl/LETtm+E5QInFIgsAaRXSMPeA0dhNJocIxNfMlWulCaX1e+c1L+/4m/whSWj+ndcVlhkIoII+er7FE/JXtTTOk95oryn/FDkWc6+BShUx+1L+8JHBDeKllXf6olhnTflq+85im4rlnWDChE/Lx9Jq/MQO6YA5vpml/1pORuxKVTfa4nypUnn7RwLRIUAAypRsZs5yGgSaNPySgx7rJfH9OA9t3ikUBQFF9WsgiPHPX+TLL6xrFiuLPLUb25EBeLMlrwCs7oc3JSvtinaF9f7Z2efxt4V7+Pvn27Hv7MH4Og/M936k69+OyL+Xoair/5oU7zJMKrBK3/UFcw6CtQ3iGI/MVFACOjaXQ4Y9GLWkZRODR3zvs4Y80zY/usOrP1yLfYu3evrZmFf7tCRE17POhRnIopfKik6yDc/noCs7Dx5pol9XXFnHYpLH+Lj4uxF5WNiQjyyc21nLZZJMqBCalxQ0hmTHj9tN8q06rBZ9mX/GYtcFvkbj1u99iMl0YA4g87r+mCNobTtGGJ0SPVqHRz/0vQ9rsjzW+40jUzE+wURMDGpQUbRJTlvth1XLRpfJrIwacZCmNS835aulb/w065VY5nvr0nDdIPHqhp5yfdYmJkUoIBmBXSa7Rk7RgEKBEwgOycPH34xBbv3HYZRfZMhbj44fc6faNHI9uZC3C9l/JS5MsAi1s9asBxb/t2Nlk3qB6xPJa1479x7cfivF5C551dk/DMRu2f3wOntU0taDctTgAIBFlBqlIf++W7Q390autuaQz/kJuhvLNkHloKsAnxz3deY/fivWPp/SzG973SkvVIlwD3XRvXiNbjo2YbOy1arxaWjo8fPgLi8c9xHw2A/00QUKO6sQ3HpQ0FhoSjmSOJsx+TEBLksTnPPzDOiRKmU5TcfyceY1dkyLdplu1RiV4ZJLov8udvzvPYjr8Ck/k2zel0fjP5fSBtm9Rv9vHxz2PXfaHI9BuVBo5HJzj2H0Pj6AXj27S/lZWxiXtwbTnQvMSEOI98ajOGjJqFRx/4Y/NJIvDikD6pXqShW+y1VTtbhpbbJuLS8HnExCuqU1UP8OtBlFVwDzeB/rgLiGzGnHKu6LJJTFmcpoAkBnSZ6wU5QgAJBFVAUBX+t2Ypb+j6Hxp36o/fAN9C5XXPcf1cXRz8mTJ2HjncOleuHvfWFPOOl2ZX1HOtDOZNkPoasfQvcupDxHwMqbijMoIAGBJTURCgt6kLXrj5EgKWkXdr8/WZkH81x2SxuYxLK7arkknchC1rdVvzCmrezDkW+OJNE9F2cMv/e6Mn4+oe5mPrlq2h4WR2R7TEVPeuwSqXy2LP/iKOs/VIfe0BGnClXaLQgGKm6+uHznisT4C21rmHw2g9xCYU4jT8Y/QxEG+ISE6MaVAlE3YGsU5g7Dh6NzVxcpxq2Lh7vkt55/iFHLztc0wSbFo7D/MnvQ9yQuVe3jo51/pwRx+3HXVIx466yGH1zKrpc7HpGmD/bipi61PeqLmMR95kVySWTCxQIvYAu9F1gDyhAgWALJCXGY9qY17Bq9ueY891wrJ7zBd56dgDiYm2npdaslo6FUz7EH9M/wdzv38XGhV+h7503BLubXtsrb/rP47rCrH0e85lJgQgUiKohndxxyuN4yxxO85gfjZkvvzcO4szCD199DGVSk3HwyAmZTGazvKG4WCcu6xRnvBQ967DDtU2wbPUWrNu8A2L9hGnzkF4xDZfWrRl0ytrqt/f3NoyHt9Smpu3vVNA7xgYjVkCv16Fa5QoQgcaIHWQEDEyB7f8IGAqHEGECuggbD4dDAQqUQEAEVkTwRJz2WnQzRVFQPi0VNapWQoxeW6elHoptCn1sGRT9L6Vam6JZXNaMADtCgdILlL+knMeNz1TJwIIjv2LMrpEy7c7xHGz1uHGEZa7e8I8c0SPDPkDnu592pIOHT8j84s46bNzgYjzS9zb0GfQWGnfqjx9+/h0fvDIQOp0it+WEAhSgAAUoQAHPAgyoeHZhLgUooGEBkxKPau1GqEGVso5eJlRsgopNnnIsX/AMK6AABTQj0LB3QySnJ7n053D9fTh10TEsPDoHY3eOlGlPzi6XMtG0MG/Sey6XNdgvcxCXDInAeXFnHSqKgkH9bsfaeV9iweT3sfLXz9DkikuiiY9jpQAFKEABCpRKgAGVUrFF30an8ix4/68c9PrxjExiXuRFn4R2RxxtPUur1xNXPHgI9Xr+hfp9/1Efl8GQVDnaGDheCkSFQFxKHO77/QFUf7MKYh9SEDdch4s/rYMBdQe5pNpJF0WFR2kGqSjnP+swPi4WVStX4JkppQHmNhSgQFAEbvuzD14Z9zmen/AJOq3ufsFtxs2eiYQp30N3+NAF18UKolNAF53D5qhLKjBmXR4W7i7E6XyLTGL+SzWvpPU4lecsBfwikFCxMWJTgn+dv186z0ooQAGfBQwJMehxz10Y+NxgPNrzCTx40SC3VCfpYp/rY0EKUIACFNCuwIL2q9Cr1gOygx0r34h4fQKe/OH/MGTq22i38WZ0Wtsdz3/7CRLHV4D9v8Prj+DjOh97TZNvn2wvCt3BA3I+cfwYpAx7EhVaN4Hhr6UyjxMKlESAARWftaK74IYjJjeANQeNbnnMoAAFKEABClCAAhSgAAUo4C+BK8s0RawuDtetu82tSuPsGLc8XzL0hw66FUv6crRbHjOiWcC3sTOg4psTS1GAAhSgAAUoQAEKUIACFKBACATSzpRHcn6qe8sHFBixBRMAABAASURBVMBsy67SpDKG7B4i010/3S0zKzvl3X02T79/H2CxyPXOE/1/250Xw2+ePQ6JAAMqIWEPv0YbV3aP/javZgi/gbDHFKAABShAAQpQgAIU0JhA18kZuPE7Wyo8GyCwL4tHjXXXL90pSSUZZU5id+V/3TbRtVSx9G7ZxWaYa9QEdO4fg80X1yt2O66kgCcB9yPJUynmRb3Ag00T0LFOLMrG62QS8w+peVEPQwAKUIACFKAABShAgWgQ4BiDIFAw3ICcyxNkMn0TI1ssVPNmDNyMOkcuBRKtMk9MlFpWxA4p3S0IzFWriSpcUs5DA12WuUABXwQYUPFFiWVQLkGHp1snYdIdZWQS8yKPNBSgAAUoQAEKUIACWhRgn8JJ4Oe70zDnHu8pnMYSyL5aBmYh4ed8JMzOR+KcfOguPhdgKUm7lmrVZfHc+x9E1vCPcOKv9TC2biPzOKFASQQYUCmJVojL/rA133EqoDj1r2gavyEvxD1k8xSgAAUoQAEKUKCUAtyMAhQISwHrwVOw7jxiS/mFcgzWAxm2ZZGfZ8uTK84ziRtmRNLfeW6p2+iG6DCiBix9cqC7xApd7dIFUoo2X3DTrcjr2RuWKlWLruIyBXwSYEDFJyYWogAFKEABClCAAq4CXKIABShAAcD8y1qYRy+QCWpwRZhYflkjl0U+Dp0SWUwUiEgBBlTCaLfe1SDecSrg/Y0TZM895ckVnFCAAhSgAAVcBSJmKcecjYl7xuDpDY/g9a3P4s/jiyJmbBwIBShAgXATUKqlQamb7j3Fx4bbkNhfCvgswICKz1QsSAEKUIACwRVgaxTwLPD8xsH4dMd7WKoGUn499BOeUgMr84/M8lyYuRSgAAUoEFAB/W3NoR/Y2WtCtXIBbZ+VUyCUAgyohFKfbVOAApElwNFQgAIBF8g0ncGKk0vd2vn10HS3PGZQgAIUoAAFKECBQAowoBJI3SDUPX9nIbr/cBqD5mTh9z2+3/ApCF1jE2EgwC5SgAIUCDeBI7kHPXZ5T85Oj/nMpAAFKEABClCAAoESYEAlULIBrjcjz3Zn61P5FuSbrPjvlAnvLsvBvjPmALcc0urZOAUoQAEKRLlAvdTLkWoo66bQqjx/7tINhRkUoAAFKEABCgRUgAGVgPIGrvL9mZ4DJ/+eMAWuUdZMAQpQgAIU0IDAsPqvISUm1dGTy1Ia4IGLHnMsc4YCFKAABShAAQoEQ8A1oBKMFtmGXwT0XvZcgcUv1bMSClCAAhSggGYFOqXfiN+uW4MJLadjxrW/45urpyM9vrJm+8uOUYACFKBAyQWeWv8wHl1zr0yZxtOyAvHrbvY88YtvMpOT0gtwywsW8PKx/ILrZQUBFqiarPfYQuP0GI/5zKQABShAAQpEmsClqQ1QJaFapA2L46EABShAAVVg05n1WJexSiZ1Uf7bdHqdXBb5JgvPzJconIRUgAGVkPKXvvG0BEVuXC5eh/gYBReXi8Ez1ySheqrnQIsszAkFKEABClCAAhSgAAUoECwBtnMBAu83/gyjm0/0msoY3O+ndQHNcVMKlEqAAZVSsWlno851YzH9rrIYeWMKrqsdq52OsScUoAAFKEABClCAAmEmwO5SQDsCjco2Q7O0ll6TdnrKnkSzAAMq0bz3OXYKUIACFKAABSgQzgLsOwUoQAEKUCCEAlEVULFYrDCbPd+1Vaw7cvwUTGbPv56TlZ2LjDNZIdxVbJoCFKAABShAgXAXYP8pQAEKUIACFIgcgagJqFitVrz24Xi8/tE3bntvyfKNaHnzo+h451A06tgfU2YudpTJzcvHoBdG4OpbBuLaroPQa+AbOHHqjGN9sGa2HDPh2835jrThiO0mTFuOF803BqtLbIcCFKAABSJfwKcRnjlzGuO+/lKmaT9OltucyTwjl0X+tB9/kHmcUIACFKAABShAgUgSiIqAyrzFq9C2+xOYNmuJ277Lyy/E069/hsf7dcfGhV9hxBuD8NoH43Hg8HFZ9vvpC7F91wH8Pu1jrJg1GnqdDiPG/ijXlXSy94xZBkT+2FtY0k2xVQ2cfLcpD/a04YgtcLJVDbTY88SjPdBSkgaWHzDKfu08ZQvSlGRblqUABSigLQH2JhQCmWfO4Gs1oCLSTz9NkV3IUgMqYlmkH3+0BVnkCk4oQAEKUIACFKBAhAhERUClTctGmDrmNdxyfSu33bZq/TaIs1B6de2AGL0endo0Q63q6ViyfIMsO/f3VehxSztUqlAWKcmJ6NPjevw0+w+IM15kgRJM9p2xyIDI0n0lD6iUoJkSFxUBFRGM2ZXh+XKnElfIDShAAd8FWJICESCQWqYMHnjgIa/pjjvujoBRcggUoAAFKKBVgazD2Vj+8QpH2jJ5i/p5DXDP36zVIbBfYSoQFQGVxIQ4VK5YDkmJCW676eiJDBlAiY01ONbVrVUVR45lyOW9B46iZrV0OS8mNapWEg/IzM6VjzF6RQ3E+Jb0Z7V1Cnzexl6/TlE3ki0WP9GpxezbOD/q1O11XtfZ6tSpBZy30dK86KGW+uNrX1Ry0fWIShwMBShAgaICZcqURT81oOIt9bjjrqKbcJkCFKAABcJQwLr7OKyL/4Zl7S5YcwsCPoJNp9fh+73jMOfwzzhjPI2sQ1kyaLLlhy0ubWcfycbKESscaeuULVA//iCnSP6WKVtdtztVKOvbPImBFhcYLvgsoPO5pAYLrt20HWO+m+Uxebq8x9MQMrNykJgQ77IqLi4W4ia04iwUcfZKvLpsLxB3NvCSm5svs8qlxMHXlJxgC9ocyLJi6j/GEqXNx307e2TbSYvHeidsysM3G/M8rttz2naj3qT4GJ/H4uuY/VVOrwZ7yiTFlqZ/Id1GBF7kgcIJBShAAQpQgAIUoAAFwljAMmUFzJ/OhXnmWli+Xwbz2zNgPXomYCN6++8X8eDquzFi+zt4dcv/cMefnbB91z8yaLJ1qmtgpLSdyM4olPWJM1pKWwe3i26BsA6oiGDHqdNZ8JR8/UWe1JQkecmP82FQUFAoL+9RFEUGWwoKjY7V9vnERFsQ5tjpfPiazuTY6tl1yoQvV2V5SZ7zVx0ocPTBPmO1zzg9bjhU6LHeL1ZmQSRP7f57wtavrFyjz2Pxdcz+Kme2WHEys0Cz/fM2TqPZ015y2mGcpQAFNCkgbj4u7rGlyc6xUxSgAAUoQIFgC2TmwbJyh2ureYWwLN3mmuenpZMFx/HzwSkutWWZMvHb0TkueVygQKgFLjygEsIRtGl5JYY91stjevCeW3zqWXqFNIjLeoxGk6O8uAlt5UppclncT2XfwaNyXkz2HzomHpCanCgfSzOpWUaPe65MKFFqXNl2dotze54uJ2mQHuOx3geaJqFfsySP6y5Ki3GulvMUcAhY1EDWkeOnYDL7doaUY0M/z2w8aoJIf5+w9cNoVeSyyNt2/Nxz18/NsroIFBAB+Abt70fRtGLd33K04vX+pnuHod3tg9G8y0N46d1xMJpsx50swAkFKEABClAgCgWsx72ciXIsMyAae3J3eaz3SN5BmZ91MEteqmO/b4qvZ5hkn73Xyu8HauM367XY+Jvts52slBPfBFjKRSCsAyouIylmwWy2QARMzOqHQpP6xljMiw+KYpMWjS8TD5g0Y6H80Pjb0rXyF37atWos829o3wJTZy7GsROnkZ2Th4nTFuD2m9pCUTyFM+Qm553ULKPDvQ3jS5SaVPYt6HFFxRiP9fZrmoh+alDFU7t1y+nP22cWiFyBwkIj+gx6Gz0efMVlkEuWb0TLmx9FxzuHolHH/piiPg9cCgRp4VSeBc/+liXTW39ky1azCpzyltry5ApOKHAeAXEppyjy+fCnMPvb4Y7U6PKLRTbe/HgiLru4JtbM/RKzJvwfxI3J5y5aKddxQgEKUIACFIhWAaViGc9Dr5TqOf8Cc2snXuSxhsoJ1WS+2z1TfvBwDxSr7UxxucHZibhJrbjXyuJDdbBIaYNNixhQOUvDh1IKREVA5cdfl6Dx9QMg7qsyY+6fcn7G3KWSTNywduRbgzF81CT5oXHwSyPx4pA+qF6lolzfu3snXFSrKq7rMUR+uBTBmEH9bpfrOKFAuAuID5evffgN1m3e7jIUcanD069/hsf7dcfGhV9hxBuD8NoH42Ww0aVgEBZi1Fephukx8JbqV/At2BiErrKJMBKoXqWCvCG5OAtRpIT4WJzJysGy1VvQp0dniOU6NaugW5drMH/J6jAaGbtKAQpQgAJhLqDN7qcmQNfyEte+JcRC16a+a56flsrHVUTXaj1dakuJSUXH9C4yL7lyMloOvtqRGtzVUOa7TDx8AZ5SxbZd+6q70cG6FFd2sP3giMt2XKBACQTUjyolKB2mRXvedh22Lh7vksRZJvbhdLimCTYtHIf5k9/HhgVj0atbR/sqJCXG47N3nsRfM0dhyU8j8MMXr6BShbKO9SWZEWemiEt92tSMLclmAS/bqrpBXgp0URrPVAk4tsYaGPv9r/h7+x4Mfdj1D9aq9dvkvYV6de2AGL0endo0kx8+lyzfEPQRpMbp8G6nFK/phbbJQe8TGwx/gQ+/mIIXh3+FCVPnyUCKGNGJk6fFgyOgLhbEr7wdPnZKzDJRgAIUoIBHAWZGi4Cu59XQP94F+lubQdf7Guif7wYl3cuZK35Aef7yNzGmxWQMrvcsXr3iPfx47W+ollBD1pxSLQWthlztSFfcfYXMP98kWQ2oiO2uq74HnZQ/0ahTpfNtwvUUKFYgKgIqxQqcXanX61CtcgUYDJ6/7S6TkoQK5cqcLV26h1pl9PJynLa1Sh5QaVAxRgY9REBGJPs9VRpUKprvuf/F9VgEVMSlQHXLlXzb4urlOm0LzF+yRn6Y/Gz4UKQkJbh09uiJDBlAiY09d++eurWq4sixDJdyXKCAVgTEpZ3efvVN5O/ad1h2VfxSW+/uHXHl5XVRrmyK/JW4+wf/H8Slb5nZubKM83EvfvXt1Gnb9eHii65KZeMRDinWoEPZ5Niw6GtRzwpl4qDTKWHZd3GMyIOIE20LsHcUoECJBM5knsG8+bPxw+RvsSX7EJT2l0PX7CIoiXElqqc0ha8s2xS9a/XDjVW6ooyhdF9ql6ZdbkMBXwUYUPFVKsTlrlADJyLoYU+NK9uCH0XvmdLYw81rQ9x1Nh9kgbWbtssPieJDZNEkLnsT3dn8z2688M5YjH5nKCpXLCeyXFJmVo78hSvnTPHBMivb9oFT/Bx0cnwMtJ6S1D6KDzha76e3/sXG6CCSt/VazU+I1TsfOkGZt8Lq8RffTp39JThxuaboSLIaPHxhcB+IG5eLM7MmfPI8xI3I//lvn+Nm4/ayorz41bdyZW3Xh4tLsU9lFSAcktFogfjltnDoa9E+ZmQVQtzn7FSYWDv3Uxwj4rjxd2J9FKAABUIlcODAftx9dze8+ebL+HT0xxj4WH8Mf/fNUHUH4gwTcalPgzsblKoPMTv+RZpEQzooAAAQAElEQVQ6nphttpvRp3/9EdpX24Orsv5AzLatpaqTG0W3gC66h8/RUyDyBHLz8r1+sMw4kyUHPGPOUlQsXwZzFq7Au6MmYfailfLXrsS8CJqkpiTJS35k4bMT8cEy5eyvWymKIr9BFt8iazopUP9TwqOv6jfynixV6rDsP4L8n7g0bZiXX30T+ZfWtZ0iXLRblSqkyay8gkJUKG/75sv+a25ixZ79R1ClUjkxK5PJbEU4JHEbPvFz8yHq6wUZiX4L7HDsu+g3EwUoQIFIEpg+fQqys23vH+3jmjVrBk6dPGFfDOpjStUUeZnPFXe5XuKTXPSeKj2vgAhyJznli/umtMj6E4aVy6Fk2c4+LbdjDTofnIRWu39S81zHGdSBsbGwFWBARUO7btGeQgxfloO3lmbj1x0FGuoZuxJOAr78nHj71o3Rrcu1KFsmWaakxHjExxnkvLj87Xw/J240WZCZa9R8ysozqX9MrZrvpzfL/EIzCozhYe08hpx8k2afMkuWb8S8xasgbkArgo8jxv6IxIR4+cs+4tLOVs0byF9zEzdm3r3vMH6Z/xc6t2uh2fGwYxSgAAUoQIFACuzZu8dj9dt3/OsxP1SZKWfvjSLujyKSuKeK+FLKOb/FlwNw8eRXkTHpJ4/JdFn9UHWf7YaxAAMqGtl5P2zNx3tqMGWxGlT5c58Rn67KxciVORrpHbsRaQIi6PLQvbfCntpd3QjpFcvJ5cSEeLQ4z8+JR5pH2I2HHS61QKHRiBeHj0PrWx9DixsfwZxFKzDyzScggimi0heeuBdb/92N5l0ewi19n1ODKc3R5bqrxComClCAAhSgQNQJ1K5V2+OY611yqcd8LWcaGzSE8eprvCZr6oXdL1PLY2ffAifAgErgbEtU84KdhW7ll+w1uuUxgwLBEDjfz4mXtA8sTwGtCFzftjmWzxqFRVM/kumP6Z/g6maXO7pXp2YVzJv0nly3avbneOvZAV5vVu7YiDMUoAAFKECBCBXo3r0nkpNTXEZ3yy3dUK58BZc8LlAgWgUYUNHAnjdbgcPZZree5BitOJVncctnRsAFoq6Bnrddh2ljXnMZd4drmmDTwnGYP/l9bFgwFr26dXRZzwUKhKuAuN9KesU0iKQo8kY7bkMR68SlcG4rmEEBClCAAhSIMIHcY+twfMNIZPzzLYw5R1xGV716DUyePAMvvvg6Hh84BKNHfYVhz7zoUiZSFpSMU4jZuB5KkXvGRMr4OI7ACDCgEhjXEtWqV9/PN6tqcNumTpoe5RJ82UVumzKDAn4REPdTqVa5Ar+h94smK6EABShAAQpQgALaEjj01wvYMfVaHFo2DPsWPoRt3zZA7rG1Lp0sk1oGN3S+CXfdfS8aNmzksk5rC9lHc7B1yhZsn7W9RF1LHfwoKjatj3LduqBiw4uR/M4bJdqehaNXIDSf1qPX2+vI72+UgKqp53ZHuXgFjzZL9FqeKyhAAQpQgAIUoAAFKEABCpRWwGLMwfH1H7lsbjXl4fiGT1zywmXh4OqD+Lr911gw7Des+2qd7Hb2kWz5WNwkbvZMxP/yk0uRxC8+hWHzRpc8LlyAQARveu4TfAQPMhyGdlGaHl/dWgZjbk3FZzen4rs7yqJhekw4dJ19pAAFKEABClCAAhSgAAXCTKDg9H8ee5yf8a/HfK1nrvlsDcxFfmkw++j5AyqGTRvchiYyYv75WzwwUaBYAQZUiuUJ/srqqXrULqsPfsNskQIUoAAFKEABClCAAhQIR4FS9Tmu7MUet4tPC79f8BEDObH9hHhwTWYrMvdnuuapSxm7MnBo7WGsGrUKk39LwU/Wm/Cftba65tw/S5my5xY4RwEvAgyoeIFhNgUoQAEKUIACFKAABSgQCAHt1GmxWHHk+CmYzO4/EKGdXgamJzpDEio2edKlciUmARUbP+GSFy4LFep5+OUhvYLUGqmOIZzccRLjO4zHNx2/wZQeP2DZe8uw4z9gjdII45Re2G69SJa1lCuPwlbXyHlOKFCcAAMqxelwHQUoQAEKUIACFKAABSgQkQJLlm9Ey5sfRcc7h6JRx/6YMnNxRI6zuEFVbf0WLrnzT1S9ZjhqdvwS9e/disRKzYrbRLPrmj/aHPp411smJKcnu/R32bvLcHr3aUeeoihQHEtqYKVSJ+Q+/DgyZsyFNeVcIMapCGcp4CLAgIoLh7YXft9TiGd+y5Jp7n8FsrOLPOTJFZxQgAIUoAAFKBC1Ahw4BShQvEBefiGefv0zPN6vOzYu/Aoj3hiE1z4YjwOHjxe/YQSuTazUFBUbD0LaZffCkFQ5bEdYrUU1PLD4AVw/vBOa9m8qx5Fc2TWgcnyb+/61qiWtVjEFjlS4DNnPvgRzjZpqLv9R4PwCDKic30gzJY7lWLD5qEmmI9kW2a/jTnlHz+bJFZxQgAIUoAAFwkeAPaUABSgQVIFV67chNy8fvbp2QIxej05tmqFW9XQsWb4hqP1gY/4VSE5PQoOeV6DeLfU8VhyXGu8hXwRTbOep1G5by8N6ZlHAuwADKt5tNLfmutqxeKdTitd0w8VxmuszO0QBClAgMgU4KgpQgAIUCGeBoycyZAAlNtbgGEbdWlVx5FiGY5kzkSdw6a2eAi0KFAWo2rwqmj1kO7Ml8kbOEQVKgAGVQMkGoN5KSTo0So/xmionc3cGgJ1VUiAyBDgKClCAAhSgAAUcAplZOUhMcD1bIS4uFlnZubKMQa+gQmpcWCTR4XDpa9F+piQYEGfQ+d25bJItUGbQu9Z947Brcefnt+LKO+qjwW2X4db3OuOx3+/D/zYNxMB5fVCjdprPfRHuTBTgJ/AAHwN/7TdiyNxMdPvhNAb+mgn7vU8C3Cyrp0BIBcTlaW//mYO7pp3BvT+dxoiVucgssF2m5mvHWI4CFAg/gSlTvkefvj1x/Q1t8OjAfli1ann4DYI9DphAgRkYszYP9884gzumnMbLi7Ox+7SaGbAWWTEFvAukpiTJS36cSxQUFCIlOVFmKYqCWPWDfjgk0eFw6KenPsaogSudzv/WhhidYJFnnhRtt0XvK3Dv191w37fd0ebhZqjdvCoq1i5T4v0tG+Ak6gV0US8QQABxn5M3/sjGvyfNKDBZ5ZsG8cFy23FTAFtl1SESYLNOAp+uzsXSvYUyiHIyzyoDid9szHMqwVkKUCDSBFau/AsjP/0Qe/bsQn5eHrZs2YSXX34WZ86cBv+jgBD4+Z98/KSmozkW5BqtWH3QiHfU4LtYx0SBYAukV0jD3gNHYTSee1++fdcBVK6UJrtiNFtwIrMgLJLocLj0tWg/s/KMKDD63/p0TqFggclsDdg+lA1wEvUCDKgE8BDYfNTosfa1R869cHssEJRMNkKBwAiof7fkjZOL1r7mEI/7oiZcpkAkCYiAStHx5OTmYNu2rUWzuRwgAZPZjCPHT+Hw0ZMwqx8GizZjsVjlepNarug6sVxYaMTR4xmw/9qFyPNnWnPY/X3RvjNmnMqz+LMZ1kUBnwRaNL5Mlps0Y6H6oduM35aulb/w065VY5kvfvSlUP2gHw5JdDgc+umpjyLgIV6bPK27kDyj+mW2cLGoO/JC6iluW1E/EwXCJ6DCfUUBCoSNgE4Jm66yoxSgAAUiQuCHnxehUcf+6HjnUHS66ylcf/dT2PLvbsfYlizfiJY3PyrXi3JTZi52rBMBlNHf/IwmnR9EhzufRNvuT2Dj3zsd6zlDgUgUSEyIw8i3BmP4qEnyuTP4pZF4cUgfVK9SMRKHyzFRIPwEwqTHDKgEcEc1TLfdDKloE80qxxTN4jIFIkZArwZTGlRyP8abV3XPi5hBcyAUoABatmztppCUmIT69Ru45TPD/wLi5pqfDx+K1XO+wPJZo3Fx7Wr48PMpsqG8/EI8/fpneLxfd2xc+BVGvDEIr30wXn4bLwps2PofRn09HRNHPo8NC8aiW5c2ePKVTyG+NRbr/ZWaVzG4VVWzjB7lEvh21A2GGUER6HBNE2xaOA7zJ78vj/1e3ToGpV02EpkCHFV0CvAvWAD3u/jVnZfaJuPS8nrExSioU1aPwS0TUb8iP1iC/0W0wOMtEtGmVixS43Qon6Cgy8VxuK9RQkSPmYOjQLQLiIDKoMeHonbtixCfkIArrrgSr7/+DsqUKRvtNEEZ/62dW6NNyyuRmBCH1OREiBtuli2TAvHfqvXb5M03e3XtgBi9Hp3aNJM/F7tk+QaxGov+XI9WzRugacN6MBhi0KdHZ3npz78798n1/pp0vSwet6spPUmHRIOCFtUMePbaJH9Vz3ooUCoBvV6HapUryGO/VBWE70YR1/OCrALsX3FApmNbjsnxFWYWymWRf2zzUZnHCQX8KcCAij81PdTVuoYBH3dJxYy7ymL0zanyg6WHYsyiQEQJVFLfLD+vvkn+oUcZfHt7WRlIFMGViBokB0MBCrgJ9OzZGxMnTMGCeUvx2ehxuOqqVm5lmBFYgV/mL8OQlz/F39v34KF7b5GNHT2RIQMosbHnzhCpW6sqjhzLkOsPHzuJOjUqy3kxqVTBFgQ7dsK/NxSO0wMPNkvA+G5l8GPPsni9fbL8skm0yUSB8wuwBAWKFzi14xR+7DVNpt9fXiQLn9p5Lm/Ry7/LPE4o4E8BBlT8qcm6KEABClCAAhSggB8FDh05gTHfzfKaxOU8zs3t2nsYJzMy5U1pM7Ny5arMrByIS4LkwtlJXFwssrLPrY+Pizu7xvYgymfn5smFOIMOiWo0ROspVu2nXqdop68lNNOpfQ8Xa+djQZjLA4UTCoRYIDYlFtVbVveaKjWoFOIesvlIFGBAJRL3KsdEAQpQgAIUoEBECBhNZpw6neU1Wa0Wl3EOebCHvBfK7Te1xVOvjZLrxOU/uXn5ct4+KSgoREpyolwU6wsKCyEXzk5E+eRE26WasTE6xBr0mk8GvQ56NSgRDn311EdxDzJDjPadi/ZdmIP/UUADAuUvKY8ek3t4TR3e7KCBXrILkSbAgEqk7VGOhwIUoAAFKBAdAlExylrV0zHssV5ekziTxBNEnZpVZBDGZDYjvUIa9h44CqPR5Ci6fdcBVK6UJperVCqPPfuPyHkxsV/qY7/0JyvPhNPZhZpPOfkmFJosmu+nN0uj2YrsPGPY9V+Yi+OGiQIUoEA0CjCgEo17nWOmAAUoQIEQCLBJCgROYPT4Gdj4907kFxTi4JET+PqHOWjZpL68CW2LxpfJhifNWAgRYPlt6Vr5Cz/tWjWW+R2ubYJlq7dg3eYdMJrMmDBtHtIrpuHSujXlek4oQAEKUIACFPAsoPOczVwKUIACFIh6AQJQgAJhIyCCKL0HvoFmNzyEznc/Db1Oh9ef6Sf7L375Z+RbgzF81CQ06tgfg18aiReH9EH1KhXl+sYNLsYjfW9Dn0FvoXGn/vjh59/xwSsDodMpcj0nFKAABShAAQp4FmBAxbMLcylAgTAUYJcpQAEKRKvAW88OwPr5YzBv0ntYAO7MogAAEABJREFU9vOn+PbTFxwBE2HS4Zom2LRwHOZPfh8bFoxFr24dRbZMiqJgUL/bsXbel1igrl/562docsUlch0nFKAABShAAQp4F2BAxbsN11Ag0AKsnwIUoAAFKOA3gdhYgwyilC2T7LFOvV6HapUrwGCI8bg+Pi4WVdX1PDPFIw8zKUABClCAAm4CDKi4kTDDuwDXUIACFKAABShAAQpQgAIUoAAFKCAEIjugIkbIRAEKUIACFKAABShAAQpQgAIUoEBkC4RgdAyohACdTVKAAhSgAAUoQAEKUIACFKBAdAtw9OEvwIBK+O9DjoACFKAABShAAQpQgAIUoECgBVg/BShQRIABlSIgXKQABShAAQpQgAIUoAAFIkGAY6AABSgQWAEGVALry9opQAEKUIACFKAABSjgmwBLUYACFKBAWAkwoBJWu4udpYD/BQoLjegz6G30ePAVl8pvu+95NGh/v0saPX6GSxkuUIACFKBAdAtw9BSgAAUoQIFoFmBAJZr3Psce9QJWqxWvffgN1m3e7tHiif53YPa3wx2pd/dOHssxkwIUoECYCLCbFKAABShAAQpQwG8CDKj4jZIVUSD8BMZ+/yv+3r4HQx/u6bHzFcuXQa3q6Y5Utkwy+B8FKBBMAbZFAQpQgAIUoAAFKKBVAQZUtLpn2C8KBFhg/pI1mDB1Hj4bPhQpSQkeW5s6awleHP4VxKU++w4e9ViGmRRwEeACBShAAQpQgAIUoAAFokSAAZUo2dEcZvQIrN20HWO+m+UxTVMDJEJi8z+78cI7YzH6naGoXLGcyHJLN7RvgbZXX4mqlStg0bL1uGPAK4jEoIrbwJlBAQpQgAIUoAAFKEABClDABwEGVHxAKq6IXqcgHJIC23/h0FdPfbRYrFCpw8Lauf+izzZ5v03PW1FuXj5Onc7ymDLOZMntZ8xZCnE5z5yFK/DuqEmYvWgl9h44KuezsnNlmcce6I5H+3bFwPu6YtLol5CSnICFS9fJdTpFCY99oR74ZvXYcd4n4TSvdh/iv3Dqs72vot+RlsT+sI9P64/i/kiKooTH81RXpJ8qdLg+b6H2HRH4n9aPd3v/1EMeVivC87hXnwfh+rzVCfgIPO51CsLmWArX10zx3FXUY0d92oaNteizPald5z8KgAGVCzwI0tPiEQ4pJdGAlISYEPQ13i9tGmJ0qFjWP3UFc38lxcdc4BFW8s3btLwSwx7r5TE9eM8tssL2rRujW5drIe6JIlJSYjzi4wxyWa93f1kwGGJQsVxZ5BUUyu1Tkwx+2a+B3hfimBHHTqDbCVT9KWH6vK1QJk4eJ5E2CadjKT5Wj3IpsWHxPC36/Ann522s+rcq0o57MR7xnC66n7S4XDY5Folx+rA87oVnuD5vxXsCcZxEWhLv4cR+CYekV6M/4dBPT30M5+dtpB3zHE/pBNw/OZWunsjZiiOhQBQIiKDLQ/feCntqd3UjpFcsJ5cTE+LlpT3jp8zFkeOnYDSZMWvBcmz5dzdaNqkfBTocIgUoQAEKUIACFKAABSgQFQIXOEgGVC4QkJtTIFIFxA1rO945FI079cewt76QZ7w0u7JepA6X46IABShAAQpQgAIUoIDmBdhBbQkwoKKt/eHX3pjMZq/1nTh1Bnn5tss3vBYK0QrRr0NHTkDcN8VTF0S+OHOiuPF52o553gV63nYdpo15zVGgZrV0LJzyIf6Y/gnmfv8uNi78Cn3vvMGxXssz4rgQx4inPhYWGnH0eAbEdeqe1ocyT/RZPC/PZOV47YZYL54fXgtwRdQKiGNaHPueAMSxpeXXTHHMHztx2lPXZZ6Wn7eyg5yEVMDbcS86Je4JZr93mFjWUhL9PnzsFArUv0ue+qX1562nPjMveALi+DCbLR4b1OBrpqOfot/ivYx43XdkFpnR8vO2SFe5SAEpwICKZIi8yb6Dx9CoY3+IwITz6MSvtNx07zC0u30wmnd5CC+9O05e0uFcJpTzg14YIft1/d1Pq318Ah98PsWlO0uWb0TLmx+FOHNCjG/KzMUu67ngPwFFUVA+LRU1qlZCjF6PcPhPBBtu7/cSZi9c4dJd8WFz9Dc/o0nnB9HhzifRtvsT2Pj3TpcyoVxYvmarPK7F87L1rY/h/iHvyEus7H3S+vPW3k8+hk5AXJbXWX3dLNoDLb9mijfV4u+ROOav6zEEt933PGbO/8sxBK0/bx0d5UzIBLy91xE3ZxfvJ66+ZSCu7ToIvQa+AXG8hayjRRoWv8Qn3sN06jkUTdW/S0NfHYUzmTmOUlp+3jo6GdEz2h6ceG187cPxeP2jb1w6KvLD+b2O1p+3LthcoICTAAMqThiRMiveONx4zzMeh/PmxxNx2cU1sWbul5g14f8w9/dVmLtopceyoci8tG5NTB/3JtbO+xJvPNMf4ybPxuZtu2RXxIflp1//DI/36y7PmBjxxiC89sF4HDh8XK7nJLoF3v/8BxmM27n3kBvEhq3/YdTX0zFx5PPYsGAsunVpgydf+dTrWVBuFQQ4Q9EpePnJvvhr5igsmvoRkhLj1f7OcLSq9eeto6OcCbqACLbd0Ot/ePbtL93a1vprpvimsluXa7Fw6odYMWs0ulx3lfoBYYLj7EmtP2/dwJkRVIHi3ut8P30htu86gN+nfSyPLb1OhxFjfwxq/4prTNwM/qsPn5HvxcR7ntUb/sH0OUvlJqV63sotOYkGgXmLV8kvhabNWuI2XK2/Zp7vvY7Wn7du4MygwFkBBlTOQkTSw4jXB8mfuS06JnF63bLVW9CnR2ckxMeiTs0q6gfLazB/yeqiRUO2LIIl9S6qjvi4WIhfokmvmIbla7fK/qxavw0iet2rawd5xkSnNs1Qq3o6lizfINdzEt0CA3rdLD+YiWOmqMSiP9ejVfMGaNqwHsQvFonnwNHjGfh3576iRUOyfHXTy3Fr59Yok5IE0f8u7a/CHys2wmQ2Ixyet+B/IROoWrkCvvnkObwwuI9bH7T+mlmpQlmIG2NXrlgOKcmJuO2Ga+Rr/LYde+RYtP68lZ3kpFQC/tjI23sdUbf4sqjHLe0gjjFxbPXpcT1+mv2HZi73vPOW9hCv++K9mHjP0751E/maL/qu9eet6CNT6ATatGyEqWNewy3Xt3LrhNZfM8Ux7+29jhiM1p+3oo9MFPAkwICKJ5UwzxNvIMQvthQdxomTtmvUq1ep6Fgl7pUhruF1ZGhoZu+Bo/J+F+KsFdGtoycyZAAlNtYgFmWqW6sqjhzLkPOcRLeA+MZPfDAzxLj/VPXhYydRp0ZlB5B4joiF4u7bINaHKi1bswX1L6klA4fh9rwNlVm0tisuxxPHfVqZZDeCcHvNXK1+Sy8GUbtGFfEADT1vZX840ZaAeB339F5H9FK8fxDvb8S8SOLSVfGYmZ0rHjSVxC/pLVu9GQ0urSP7FW7PW9lpToImkJgQB/Gan5SY4NZmuL1mOr/XEYMJp+et6C8TBewCDKjYJaLg0f5GwjkgERcXi1OnMzU3+pzcfAx5eaQ8o+DaqxrK/mVm5UD8pK9cODsR/Rc3rzq7yAcKeBQQx058XJzLOnEsZefmueRpYUHcQ0Kkpx7uKbsTTs9b2WFONCBg64I47sVxbluyTbX6mrlj9wG8/cl3eLRvV5QrmyI7K/ofLs9b2WFONCEg7iMhzmaNV9/f2DsUd/aLmFz1vYU9TyuPb348AVnZefLsYdEncdyHy/NW9JdJOwLi2IkP0/c64fa81c5eZ0+0IMCAihb2QpD6kJqcKFsyGk3yUUwKCgrVN6+pYlYzSVw/LO5vYTZbMPLNJ6DX2w7T1JQkeTq4c0dF/8XpvM55nKdAUQFx7BQUFrpkizfcyR6+4XEpFOQFcUmeuBfGK0Pvk5coiebD5Xkr+lrqxA0DIiCOe3GcO1euxdfMg0dO4OFnPkCHa5vg0fu6Oror+h8Oz1tHhzmjCQFFUeSXLwWFRkd/7POJifGOPC3MjB4/A9NmLcG4j4bJy5NEn8RxHw7PW9FXJm0JiGOnIEzf6yhK+DxvtbXX2RstCNg+qWqhJ+xDwAUqlC8r29h/6Jh8FJM9+4+gSqVyYlYTSXwb/+izH0Lc7X7CJ89DXMZh71h6hTSI0wGdA0LipnOVK6XZi/CRAh4FqlQqD3Gs21faL/URp4zb80ryGIiy4kZzD/3vfbw5rD963nado4lweN46OssZTQmEw2vmf7sP4u5HXkObllfirWcHOALoAjIcnrein0zaExD3VxM3bLb3zP6+xx6gtueH6lHckPm90ZPx9Q9zMfXLV9HwMtvlPqI/4fC8Ff1k0p5AOLxmenuvIzS1/rwVfWSigCcBBlQ8qYR5nrget9Bo+2ZGzIskhiRueCluzDlx2gL5Kwq79x3GL/P/Qud2LcTqkKfcvALc+9ibOHYiA68/0w85efkQ31za7/HSovFlso+TZiyUN+v8bela+Qs/7Vo1lvmceBWIihUmsxn2YJvRZHLMi8F3UL/5Fmd/rNu8A0aTGROmzZM3f7Xfn0eUCWX6ed4yDH11NJ59vDeualJfHvfi2M9VnwNaf96G0o1tQ95kUxz3JvW4Fh5yXn0uiHmtv2b+u3M/uj7wAlo1a4ABvW+W98wSx33GmSzRfXnGipaft7KTnIRMQLyWe3qvIzp0Q/sWmDpzsfp+4jSyc/IwUX3fc/tNbaEoilgd8vTye+MwfspcfPjqYyiTmux4zRd/x7T+vA05XpR3wGy2yPc3ZvV1Xrzui9d8EaATLOH8Xkf0X+vPW9FHJgp4EmBAxZNKmOe1vvUxdOlt+9nkm+4dhg49hjhG9MIT92Lrv7vlz8ve0vc5NZjSXP5UpaNACGeysnMhfvJ274GjuL3/S+h899My9XzoFdkrcSOukW8NxvBRk9CoY38MfmkkXhzSB8432ZUFOYlKgWfe+AKNrx8gg2wvDv9KzougocBo3OBiPNL3NvQZ9BYad+qPH37+HR+8MhA6nTbeXG/8e6foJt759Ht5zNuP/XmLbb/ApeXnrew4JyET2LnnEMRxLy4VE79cJebF8S86pPXXzF17D4lu4teFK+TfLPtxL17jxQqtP29FH5lCJ1Dce53e3TvholpVcV2PIWh586PyA+igfreHrrNFWl599gbMjwz7wOU1/+DhE9D687bIULgYZIEff10iX/PFpWIz5v4p52fMXSp7ofXXzPO919H681Yic0IBDwIMqHhAKXGWxjZYPedzbF083pGWzhjp6GGdmlUwb9J7WDT1I6ya/bk8xdpgiHGsD+VMesU0R5+99b/DNU2waeE4zJ/8PjYsGIte3TqGsstsW0MCH7460O34Ecc71P8URYF4M7123pdYoB47K3/9DE2uuERdo41/Lz/Z163v4jnQ/cY2soNiHFp93soOchIygYvrVHM7dt55/iFHf7T8mnljh5ZufRfHvb3/iqLt560DmTMhESjuvU5SYjw+e+dJ/DVzFJb8NAI/fPGK4x4lIZbjfvIAABAASURBVOlskUbF67k41osmccmDKKrl563oH1PoBMQlwUWPG3H2leiRomj7NfN873W0/rwVxkwUcAg4zTCg4oQRTbMieCFeuMJxzOImtdUqV4BWAkHhaBitfY6Pi0VV9djRypkpJd0P4fy8LelYWd5/AuH+mhnuz1v/7UnWVFIBcclkhXJlSrqZJsqH+/NWE4hR2olwf80M5+etlg859i1wAgyoBM6WNVOAAhSgAAUoQAEKUIACFKBAyQRYmgJhI8CAStjsKnaUAhSgAAUoQAEKUIACFNCeAHtEAQpEqwADKtG65zluClCAAhSgAAUoQIHoFOCoKUABClDALwIMqPiFkZVQgAIUoAAFKEABCgRKgPVSgAIUoAAFtCjAgIoW94qf+7R+yw7MnP+Xo9ZX3v8aX34707HsbWbfwWNY9Oc6mX7/az3WbtqOM5k5bsU3/b1Tljly/JTbun/+2yfX2X++1l7g2InTsk9jv/8VU2YuxtKVm5Cbl29fjazsXLmdaF+0vWLd3/hv90FYrVZHGc5QoDiB3LwCTJ+zFDt2H5DFxHHaa+AbOH7ytFwubrJk+UbH8Sfmt+3Y63bs2Y/R5Wu2ulVVUGi0bb9svcs6i8WKFWv/xnc//YbxP8yVPxdr75+9oGhPHPeL1G1F3Vv/3YNCtT77ej5S4HwC8xavhjh2RLn8gkKI4168xorl4pJ4jshjT33dX/zXBqzbvN3jsWc/RvPyC92qE+2KOk6fyXZZ9+/O/RA/8yle88Xzct3mHTCZzY4yzn9vRP1rNv6Lw8fc/6Y4NtD+DHsYZAFxDIljK+NMlmx5yi+/44mXPpHzxU3sr+XiuBVp2eotOHD4uNsmon6xXvw9KLry6PEM+Zov3m85rxPPkQV/rMHXk+fI130xL97/2Ms4t833OnYVPpZEIFzf6zj/vRF/n8Rz50yW+2eMkliwLAVCJcCASqjkg9juz3OX4dm3v3S0uH3XAew/5P5mwVHg7Ix4gRv04icQ6fHnR6DvE2+j9W2PYeK0+WdL2B7GTvpVlnl31CRbxtmp0WTGwOc+kuvEm4iz2Zi9cCWu6zFE9mmqGkx57YPxeGTYh2hx4yPYd/CoLCbezIh2RRJt9x/6Lro+8AJu7vOsfNMiC3FCgWIEzmRm48XhX+GvswGPrJw8iD/gIthRzGZylf24FcefmO/x4Cu4vf9LcH4jbD9GBzz9HsRzSm54dvLrb8vlcT/ohREwmy0yV7yx7vXo6+j/1Lv4eMw0fKU+b55543N0e+BFvPbhN7KMmIj2RLtiW1F3z4dfxTVdB+GLiTPdgjqiPBMFigoMH/U9vv1pgcwWQTxx3BcNcMiVLhPA/loujr/Hnv8YfQa9LY89EUx3Lmo/Rn+e96dzNkTgXByzYvtd+w451r39ybfy+SOC+eI1Xzwv+wx6Czff+6yjjPPfG1H/fYP/D516DsX9Q97Bzj0HHeU4QwFvApv/2SVf8w8eOSGLiC95tvyzW84XN7G/lovjVqSH/vc+buj1Pwx76wuI9zH2be3HqFhf9O/IR2Omytf8kV/9ZC8uj9u23Z/AkJc/xbjJs+XrvpgX739+XbhClnNum+91JAknJRQI1/c6zn9vxGeAex9/C61vfUx+NshW36+VkIHFKRBSAQZUQsofnMafeawX/po5qtSNrZ7zBbb8/jV+n/YxOlzTBO98+r0a+DjmVp/4VnTXvsOO/AVL1kB8a+PIUGfE8v/e+AzXtLgCf0z/BPMmvYdNC8dh2pjX0L51Y8eHT7Wo/Pfle0/LtpfPGo2x7/8P5dPKyDct4htMWYATCngRSK9YDst+/hR3d+3gpUTx2Q/3uRVbF4/HhgVjMfLNJ2TQZMTYaR43EsER+woRQBHBD/uy/XH0+BnY8u9ufPr2YCyfNQpLZ4zECvW4fmFwHxhiYuzF5KO9bfHcE8+Nmzq2xCdf/YgRY3+U6zkBQASvAjO+fgvvvviI1/XFrbjy8rryuN+kvi7/9NUbKFc2Rf1AONLjJuJMR6PR5Fg3oUiwXawQr9XijKxH+t6GNXO/lK/56+ePwej/exLVKlcQRVySOOY3LvxK/n0Y/sLD6t+aozKosv+Q+98clw25EPUCnds2l6/5l11cs1QW4v2GeM1f+etneOyB7pi1YLn6BdAKt7pOnc6S6+wrRADH+SxgkS8Cmc//31gxi1kT/k++3q+e8zl+m/Ih+t55A0zqF05y5dmJaFu8z+J7nbMgfPBZIJzf69j/3oi/CeLzwFOP9JRnrz+ufhmVX+B+BqTPKCxIgSALMKASZPBQNPfrwuV47YPxF9S0oiioVKEs+vS4QdYjvomUM2cnItBSt1ZVfPX9rzJHXJoz5ruZePCeW+SyfWL/Jr/HLe3V4EiqzNbrdah/SS2MensI6tSsIvOcJ4qiIDU5Ea2aN4B409G0YT155ov4xt+5HOcp4CxQaDRC/FEWlw4455d03mCIQYdrm0Icd+KyhaLbD+h9s3xzbT+7avFfG+Tp4uJNs3PZVeu3oVb1dFzXugli9Hq5KkU9rnt374jnn7hHLhedJCbEyefGa08/gH5334Qx383ChY6naBtcjjyBj76ciu+n/3ZBAxOvy5fWrYGuXa6F+ABZ9FTs+3t2kQHz2YtWynbE2VviEouir/niNG5RoFe3jkiIjxWziI01oF2rRhj30TC5XHQinh/l01Jxy/WtMP7jZ+Xql94dJx85oYA3AXGJsXjNz1ADHt7K+JKfnJTgeO8iLjUuus09t18vL5s2nb1k7dsfF+CKS+vI9yj2srl5+TKA3v3Ga13e11SpVA7D1C+5ut5wjb2o41FRFL7XcWhwxleBSHivI/4mVK9SUb7PGfnWYKze8A/EZdG+GrAcBUItwIBKqPdAENo/fPQkNmz9zy8t7T14RNbT4NLa8tE+UXQKHunbFTPm/gnxbY24zEIET+65vZO9iHxsdmU9+Si+2RTX2pc0KCLekIsPsKKSbTv2igemwAiEfa3iG0LxYe5URuYFj0Xcw+TgkeNo3uhSt7qub9dcBkq+/mGuXCeObfFhU3wglBlnJx3bNMXeA0fx+YRfIJ6TZ7N9fnjw3ltkWTEmOcMJBbwIiA+W4n4PXlaXKHvPvsMQwfIyKUku21WtXAE9b7sOX0z8RZ5ZKAI49S6qjrZXN3Ip1/bqK+WyOLNRBCTtH0Jlpg+TmtXS0f3GNvINtnge+rAJi0SpQGZ2LsTrY9HLcUrDYQ+QN2pQ123z+3reIIPm8xevgbhfy4Sp8/DIfbdBpwZE7IVFUKZlk/qYPudP/DJ/mcf7z9nLenrkex1PKszzJBBp73U6XNNEfpG0ZtO/nobLPApoUoABFU3uluB1SpxJIi5DcE5Fr7VfunIj5i9ZA3HJwqvvj4f4ZqVCuTJunezcvjnSK6apUeU58pt08c17xfJlXcqJb9z/7/kH1W82T0Fca9+8y0O47b7nIe4pUbRdlw2dFi69uIZc2vLPLvlom3BKgZIJiMvTnI97EQh0rmHX3sMQNwkUNzl86JkPkJWdh9s6t3YuIuf1Op38NlN8Oz9z/l/yW8k+d3aW65wnt9/UFiKoMnLcT+h011PynkGPPvsRxA0Qnct5mxdnaYlvcPwVHPXWDvMjWyAn1/bNufOx73zZjghAirOsxL2uxFkhvy5cgfvv6uIRRQQORZBQPEdsl/V0hdNnSrlN4waXQJSbs2ilvI9Ko479Ia6VF88X53ZlYS+Tyy6uJdfs2H1QPnJCgZIK+PJeZ6P6xZO46ez30xfiwaffk4HyVs0auDUlLlUT74M+n/AzJs1YJAOO7a5u7FbuyYfulHU89/YYef+5Dnc+iRfeGYudew+5lfWUwfc6nlSYV1IBcfag8+u9CGw716G19zqibw3rXyRvrC6CRWKZiQJaF2BAJVR7SCPtFhpNuOvh11ySuPGac/eGvjoaT77yKUaNn4Fb1Q+Ubz/3oPNqx7w4TfuRPrdBvBkRp+vd19Pzm/DbOl+DBT98gA9fHSg/iJZLS5UBmOvvftrlpp+OiovM5OUVyBxxuYSc4YQCpRB4+d1xLse9OHPEuRpxI2Vxk0BxA01xPE/+/GVcXs/1zCx7+Zs7Xi3vNSFu/nznLe1RuWI5+yrHowhCfvLGE5jx9ZvyEp/O7ZpDBAXFDQ4//GKKo1xxM+LSi7KpycUV4ToKFCsgbtxZ9DX/2Mlzv3wlbpIpbkgr7nX10+w/8Pr/+kEEAz1VKi5hu6H9VRA3my1XNgUiYFi0nE6n4H8D78aSn0bIusRZi5lZOfJGzCKwUrS8p+W8fPtrfoKn1cyjwHkFfHmvI97jiJvSvjVioryv1Y9j30BSYrzHuh+4+0YZGBn19XQ83Pc2iOO8aEHxoXDql6/iqw+ewZAHe+DK+nUhzuIVXyKJewsVLV90me91iopwuTQC02Ytdnmv8+izH7pUo8X3OuKSOfE3xdPzyqXzXKCARgR8DqhopL/shp8FYg0xmP3tcHnTNHHjNJGuu6aJSyur53wBcZPA9q0by5tFrd203WW988KtarAkMSFensUivk13Xuc8Hx8XixvUN+LiTcb4j5/F58OfgngBXbRsnXMxj/Nbzt61v3GDiz2uZyYFfBF47+VHXY77wQPucNnMfmPYiSOfl/ki6OHtcgVx/e9D994qy4k32nLGy+SSOtVxz+3X461nB8jAYovGl8lvOb3Vba9GnEEjniP2y+bs+XykQEkEGl1+sctxL17zxZmF9jrsNwkUARCRP2r8dJw4dca+2u1xQO+bZN4j6odKEVSXCx4mIqB4x81t1WDivfh5/FsymC6+NRWXhnoo7pIlzsoSb67F5T8uK7hAAR8FfHmvI+7RtnXxeAx9uKe8pOf7Yu5DJF7HxaUJ4jlyQ/sWXnuhKAqubna5PN4/fv1xGVgU75GK/kKWpwr4XseTCvNKKtC7eyeI13l7mvCJ7T2NvR6tvdcxmy3YsOU/XHNVQ3sX+RhCATbtmwADKr45RWwpRVHkKaniZrD2JK79LTpg8UZZ/OKCuJb+kWEfyntBFC0jlhPiY/Gz+g38sMd7i0W3tGP3AXmPlaIrxA1vRZ74wCgevSVxM9zXP5ogr6+sXaOyt2LMp8B5BcTNAe3HvHgUH/g8bSRuRivOyhKXQbw7apKnIjJP3E9CvGER39rLjCIT8Y2keKPgnC0Ci+KME3HcW63Oa1znxb2Gnn79M4g34q1bXOG6kksUKIGAeI0Wx7tzEq/vRasQzwcR6D56PAOPPfcxxDFYtIxYFmdt/aIGSG65vrVYdEurN/wDcZmR8wpFUVCpQprMsp99Ihc8TMTlpuJMGXEfFQ+rmUUBnwQUxbf3OqKy/r1ukvftEUH0eYtXiyyP6Y1n+uP70S85bjLuXEjcxFncz8U5T8yL91fxcQb5BZJY9pb4XsebDPNLKlC2TLK8MbL9Nd/bl51aeK8jxvbeZ5NlQPPG61qKxZImlqdASAQYUAkJe3g2Kt63XU6/AAAQAElEQVQIfD58qOy8uExB3IxNLhSZiJsVFr2Bob2I+Mal891PY7j6wVTcn+Lv7Xsg3iyL64pFmU5tmokHR9r4904sWrYe4hp9EUi5pe9z8tKKL959CoqiOMpxhgKBFBDXy4tfLxH3ifjupwUem4qLNcg3LR5XqpkvDh+LHg++jKmzFkMc1+KmzOLXWMTptnd37QBDjF4tZfu3Z/8RiACOuCeLOKW8891PYZP6XBj7wf/g6XIi21acUsC/AuIms+I1X5xJ8tzbX8qbz3pqoW7tai7Hr3OZH2f/gS69/4evJs3GirV/y2P/m6nzII59Uf/ll9juj2LfRlxyKp4T4h4rg14YIS83FWcAPFHkDDJ7eT5SIBACLz/ZF+ID5tBXR8lj1lMb4oOqt9djcU84cUnbEy99AnE/IvFeRwRnnv+/MRCXbhYNEIq/CXyv40k50vO0Nb5gv9cR9+wSXzaJe2yNnzJXfY/0CiZOmw/xy4fil+C0pcPeUMC7AAMq3m0iZo2iXFjgQVHObS+CJeJDnbjOftALn8B09mcDz4elKLY6rqx/EcT9JsRd8cX9Ke586FWIGx8aDDHym56ip3SLD5PiTbX4pkh8oBzU73ZZrnxa6vma5PooF1AUxUVAd3ZZUVzzXQo5LSiKazlx7Il7RLz9yXcQH/rsRRXFtZw9v+hj/943yw+k4sbOvQe+IW/KPPb7X/Fo36545rFeLsXFG29xHwsRRBQ3Be3crgXEvVcaXe7+ixMuG3KBAkUEFMW347PIZo7FNi2vxLOP94YIcogbKttXKMr56rWtF98yissjxGt4/6fehTj231UD6te1biwv9RSv/fY6xaP4ADvk5U/x+cRfkJWThzee6Yd3nn/I41kAojwTBewCRV/jFcV2DNrXn+/Rvr0oJy7j/OTNQRDf5g946j2ff5lNp7e9rRaXqPW98was37wD4n5E4r2OOLbF5WvieBaXPIt27Cls3uvYO8xHzQgoiutxbj+OFcU131uHFcW1XDDf64jPEgOf+wjiDNxJ0xeiauXy8u+CuCzaW3+ZTwEtCthe+bXYM/bJbwLixXHR1I8c9U0a/ZJ8k+rI8DJzz+2dIK4nToiPdSkhPtSJ/G8/fUG+yRU32hTJpZDTgigrvt0XWXXVbzLffekRbF70tbyWWNy/Zc3cLyH6JOoVZUSqr35rKbazp6UzRmLamNcgrtNnMEUIMZ1PQBy34vi5tXNrWbRV8wbyeBa/0CAzipmI7cTzxrmIXn2jLI5zsU58yLQfo5ddXNO5mGN+QO+bZXtiO5Epfunkl2/ehjje53w3HL9P+xhbfv8aj/frDnF2iygjkqjfnlbP+Vze4+gl9dtS8aFUrGeiwPkExOupCESIckWfByLPWxLHt9i26Po+PTrLY3nIgz3kKnF89u7eUc4XnTS54hJZtmnDS+Qq8S3juI+GYcNvX0H8HZo36T05L/4OiPtPyELqxP73RtQtkigr7q8lbogrPtyqRfiPAsUKFH2NH9TvdojjqNiN1JX213Kxvbro+JdWJgXieBWvw1XSy8N+jDoKFJkR92AZ+/7/ZK64af4wNVAu3rv8NXOUvIeFeBT9sf9NEgXtbYtjXiRRnu91hAyTrwJFX+PFcSyOJa2/1xF/b0Q/7Uk810Rem5a8d4qv+57ltCPAgIp29kVU9UTcuVtcoy/uNyH+GETV4DnYqBYQx7s4E6tShbK8bC2qj4ToGry4pE0EUMQ3/mI+ukbv82hZMAIFxCXQ4v4V4jECh8chUcCjAN/reGRhZoQKMKASoTuWw6IABShAAQoEVoC1U4ACFKAABShAgegWYEAluvc/R08BClAgegQ4UgpQgAIUoAAFKEABCvhRgAEVP2KyKgpQgAL+FGBdFKAABShAAQpQgAIUoIB2BRhQ0e6+Yc8oEG4C7C8FKEABClCAAhSgAAUoQIGoEWBAJWp2NQfqLsAcClCAAhSgAAUoQAEKUIACFKBA6QQYUCmdW2i2YqsUoAAFKEABClCAAhSgAAUoQAEKaEIgoAEVTYyQnaAABShAAQpQgAIUoAAFKEABClAgoALRWDkDKtG41zlmClCAAhSgAAUoQAEKUIAC0S3A0VPgggUYULlgQlZAAQpQgAIUoAAFKEABClAg0AKsnwIU0JoAAypa2yPsDwUoQAEKUIACFKAABSJBgGOgAAUoEOECDKhE+A7m8ChAAQpQgAIUoAAFfBNgKQpQgAIUoEBJBBhQKYkWy1KAAhSgAAUoQAHtCLAnFKAABShAAQqEUIABlRDis2kKUIACFKBAdAlwtBSgAAUoQAEKUCByBBhQiZx9yZFQgAIUoIC/BVgfBShAAQpQgAIUoAAFvAgwoOIFhtkUoAAFwlGAfaYABShAAQpQgAIUoAAFgiPAgEpwnNkKBSjgWYC5FKAABShAAQpQgAIUoAAFwlKAAZWw3G3sdOgE2DIFKEABClCAAhSgAAUoQAEKUABgQCXSjwKOjwIUoAAFKEABClCAAhSgAAUoQAG/C2guoOL3EbJCClCAAhSgAAUoQAEKUIACFKAABTQnEO4dYkAl3Pcg+08BClCAAhSgAAUoQAEKUIACwRBgGxRwEWBAxYWDCxSgAAUoQAEKUIACFKAABSJFgOOgAAUCKcCASiB1WTcFKEABClCAAhSgAAUo4LsAS1KAAhQIIwEGVMJoZ7GrFKAABShAAQpQgALaEmBvKEABClAgegUYUInefc+RU4ACFKAABSgQfQIcMQUoQAEKUIACfhJgQMVPkKyGAhSgAAUoQIFACLBOClCAAhSgAAUooE0BBlS0uV/YKwpQgAIUCFcB9psCFKAABShAAQpQICoEGFCJit3MQVKAAhTwLsA1FKAABShAAQpQgAIUoEDJBRhQKbkZt6AABUIrwNYpQAEKUIACFKAABShAAQqEXIABlZDvAnYg8gU4QgpQgAIUoAAFKEABClCAAhSINAEGVCJtj/pjPKyDAhSgAAUoQAEKUIACFKAABShAgWIFIiKgUuwIuZICFKAABShAAQpQgAIUoAAFKECBiBDQ0iAYUNHS3mBfKEABClCAAhSgAAUoQAEKUCCSBDiWCBZgQCWCdy6HRgEKUIACFKAABShAAQpQoGQCLE0BCvgqwICKr1IsRwEKUIACFKAABShAAQpoT4A9ogAFKBAiAQZUQgTPZilAAQpQgAIUoAAFolOAo6YABShAgcgQYEAlMvYjR0EBClCAAhSgAAUCJcB6KUABClCAAhTwIMCAigcUZlGAAhSgAAUoEM4C7DsFKEABClCAAhQIvAADKoE3ZgsUoAAFKECB4gW4lgIUoAAFKEABClAg7AQYUAm7XcYOU4ACFAi9AHtAAQpQgAIUoAAFKECBaBdgQCXajwCOnwLRIcBRUoACFKAABShAAQpQgAIU8KsAAyp+5WRlFPCXAOuhAAUoQAEKUIACFKAABShAAS0LMKCi5b0TTn1jXylAAQpQgAIUoAAFKEABClCAAlEkELUBlSjaxxwqBShAAQpQgAIUoAAFKEABClAgagUCNXAGVAIly3opQAEKUIACFKAABShAAQpQgAIlF+AWYSLAgEqY7Ch2kwIUoAAFKEABClCAAhSggDYF2CsKRKcAAyrRud85agpQgAIUoAAFKEABCkSvAEdOAQpQwA8CDKj4AZFVUIACFKAABShAAQpQIJACrJsCFKAABbQnwICK9vYJe0QBClCAAhSgAAXCXYD9pwAFKEABCkS8AAMqEb+LOUAKUIACFKAABc4vwBIUoAAFKEABClCgZAIMqJTMi6UpQAEKUIAC2hBgLyhAAQpQgAIUoAAFQirAgEpI+dk4BShAgegR4EgpQAEKUIACFKAABSgQSQIMqETS3uRYKEABfwqwLgpQgAIUoAAFKEABClCAAl4FGFDxSsMVFAg3AfaXAhSgAAUoQAEKUIACFKAABYIlwIBKsKTZjrsAcyhAAQpQgAIUoAAFKEABClCAAmEqwIBKCXYci1KAAhSgAAUoQAEKUIACFKAABSgQ+QK+jJABFV+UWIYCFKAABShAAQpQgAIUoAAFKKBdAfYsBAIMqIQAnU1SgAIUoAAFKEABClCAAhSIbgGOngLhL8CASvjvQ46AAhSgAAUoQAEKUIACFAi0AOunAAUoUESAAZUiIFykAAUoQAEKUIACFKBAJAhwDBSgAAUoEFgBBlQC68vaKUABClCAAhSgAAV8E2ApClCAAhSgQFgJMKASVruLnaUABShAAQpQQDsC7AkFKEABClCAAtEswIBKNO99jp0CFKAABaJLgKOlAAUoQAEKUIACFPCbAAMqfqNkRRSgAAUo4G8B1kcBClCAAhSgAAUoQAGtCjCgotU9w35RgALhKMA+U4ACFKAABShAAQpQgAJRIsCASpTsaA6TAp4FmEsBClCAAhSgAAUoQAEKUIACpRFgQKU0atwmdAJsmQIUoAAFKEABClCAAhSgAAUooAEBBlQCvBNYPQUoQAEKUIACFKAABShAAQpQgAKRJ1A0oBJ5I+SIKEABClCAAhSgAAUoQAEKUIACFCgqwOULFGBA5QIBuTkFKEABClCAAhSgAAUoQAEKBEOAbVBAWwIMqGhrf7A3FKAABShAAQpQgAIUoECkCHAcFKBARAswoBLRu5eDowAFKEABClCAAhSggO8CLEkBClCAAr4LMKDiuxVLUoACFKAABShAAQpoS4C9oQAFKEABCoRMgAGVkNGzYQpQgAIUoAAFok+AI6YABShAAQpQIFIEGFCJlD3JcVCAAhSgAAUCIcA6KUABClCAAhSgAAU8CjCg4pGFmRSgAAUoEK4C7DcFKEABClCAAhSgAAWCIcCASjCU2QYFKEAB7wJcQwEKUIACFKAABShAAQqEoQADKmG409hlCoRWgK1TgAIUoAAFKEABClCAAhSgAAMqPAYiX4AjpAAFKEABClCAAhSgAAUoQAEK+FmAARU/g/qjOtZBAQpQgAIUoAAFKEABClCAAhSggLYF/BFQ0fYI2TsKUIACFKAABShAAQpQgAIUoAAF/CHAOpwEGFBxwuAsBShAAQpQgAIUoAAFKEABCkSSAMdCgcAJMKASOFvWTAEKUIACFKAABShAAQpQoGQCLE0BCoSNAAMqYbOr2FEKUIACFKAABShAAQpoT4A9ogAFKBCtAgyoROue57gpQAEKUIACFKBAdApw1BSgAAUoQAG/CDCg4hdGVkIBClCAAhSgAAUCJcB6KUABClCAAhTQogADKlrcK+wTBShAAQpQIJwF2HcKUIACFKAABSgQBQIMqETBTuYQKUABClCgeAGupQAFKEABClCAAhSgQEkFGFApqRjLU4ACFAi9AHtAAQpQgAIUoAAFKEABCoRYgAGVEO8ANk+B6BDgKClAAQpQgAIUoAAFKEABCkSWAAMqkbU/ORp/CbAeClCAAhSgAAUoQAEKUIACFKBAMQIMqBSDE06r2FcKUIACFKAABShAAQpQkEJsyAAAEABJREFUgAIUoAAFgicQqoBK8EbIlihAAQpQgAIUoAAFKEABClCAAhQIlUDEtsuASsTuWg6MAhSgAAUoQAEKUIACFKAABUouwC0o4JsAAyq+ObEUBShAAQpQgAIUoAAFKEABbQqwVxSgQEgEGFAJCTsbpQAFKEABClCAAhSgQPQKcOQUoAAFIkGAAZVI2IscAwUoQAEKUIACFKBAIAVYNwUoQAEKUMBNgAEVNxJmUIACFKAABShAgXAXYP8pQAEKUIACFAi0AAMqgRZm/RSgAAUoQAEKnF+AJShAAQpQgAIUoECYCTCgEmY7jN2lAAUoQAFtCLAXFKAABShAAQpQgALRLcCASnTvf46eAhSIHgGOlAIUoAAFKEABClCAAhTwowADKn7EZFUUoIA/BVgXBShAAQpQgAIUoAAFKEAB7QowoKLdfcOehZsA+0sBClCAAhSgAAUoQAEKUIACUSPAgErU7Gr3gTKHAhSgAAUoQAEKUIACFKAABShAgdIJhFNApXQj5FYUoAAFKEABClCAAhSgAAUoQAEKhJNAWPSVAZWw2E3sJAUoQAEKUIACFKAABShAAQpoV4A9i0YBBlSica9zzBSgAAUoQAEKUIACFKBAdAtw9BSgwAULMKBywYSsgAIUoAAFKEABClCAAhQItADrpwAFKKA1AQZUtLZH2B8KUIACFKAABShAgUgQ4BgoQAEKUCDCBRhQifAdzOFRgAIUoAAFKEAB3wRYigIUoAAFKECBkggwoFISLZalAAUoQAEKUEA7AuwJBShAAQpQgAIUCKEAAyohxGfTFKAABSgQXQIcLQUoQAEKUIACFKBA5AgwoBI5+5IjoQAFKOBvAdZHAQpQgAIUoAAFKEABCngRYEDFCwyzKUCBcBRgnylAAQpQgAIUoAAFKEABCgRHgAGV4DizFQp4FmAuBShAAQpQgAIUoAAFKEABCoSlAAMqYbnbQtdptkwBClCAAhSgAAUoQAEKUIACFKAAEOkBFe5jClCAAhSgAAUoQAEKUIACFKAABSJfIOgjZEAl6ORskAIUoAAFKEABClCAAhSgAAUoQIFwF2BAJdz3IPtPAQpQgAIUoAAFKEABClAgGAJsgwIUcBFgQMWFgwsUoAAFKEABClCAAhSgQKQIcBwUoAAFAinAgEogdVk3BShAAQpQgAIUoAAFfBdgSQpQgAIUCCMBBlTCaGexqxSgAAUoQAEKUEBbAuwNBShAAQpQIHoFGFCJ3n3PkVOAAhSgAAWiT4AjpgAFKEABClCAAn4SYEDFT5CshgIUoAAFKBAIAdZJAQpQgAIUoAAFKKBNAQZUtLlf2CsKUIAC4SrAflOAAhSgAAUoQAEKUCAqBBhQiYrdzEFSgALeBbiGAhSgAAUoQAEKUIACFKBAyQUYUCm5GbegQGgF2DoFKEABClCAAhSgAAUoQAEKhFyAAZWQ74LI7wBHSAEKUIACFKAABShAAQpQgAIUiDQBBlTc9yhzKEABClCAAhSgAAUoQAEKUIACFIh8gQsaIQMqF8THjSlAAQpQgAIUoAAFKEABClCAAsESYDtaEmBARUt7g32hAAUoQAEKUIACFKAABSgQSQIcCwUiWIABlQjeuRwaBShAAQpQgAIUoAAFKFAyAZamAAUo4KsAAyq+SrEcBShAAQpQgAIUoAAFtCfAHlGAAhSgQIgEGFAJETybpQAFKEABClCAAtEpwFFTgAIUoAAFIkOAAZXI2I8cBQUoQAEKUIACgRJgvRSgAAUoQAEKUMCDAAMqHlCYRQEKUIACFAhnAfadAhSgAAUoQAEKUCDwAgyoBN6YLVCAAhSgQPECXEsBClCAAhSgAAUoQIGwE2BAJex2GTtMAQqEXoA9oAAFKEABClCAAhSgAAWiXYABlWg/Ajj+6BDgKClAAQpQgAIUoAAFKEABClDArwIMqPiVk5X5S4D1UIACFKAABShAAQpQgAIUoAAFtCzAgIp/9g5roQAFKEABClCAAhSgAAUoQAEKUCDyBRwjZEDFQcEZClCAAhSgAAUoQAEKUIACFKBApAlwPIESYEAlULKslwIUoAAFKEABClCAAhSgAAVKLsAtKBAmAgyohMmOYjcpQAEKUIACFKAABShAAW0KsFcUoEB0CjCgEp37naOmAAUoQAEKUIACFIheAY6cAhSgAAX8IMCAih8QWQUFKEABClCAAhSgQCAFWDcFKEABClBAewIMqGhvn7BHFKAABShAAQqEuwD7TwEKUIACFKBAxAswoBLxu5gDpAAFKEABCpxfgCUoQAEKUIACFKAABUomwIBKybxYmgIUoAAFtCHAXlCAAhSgAAUoQAEKUCCkAgyohJSfjVOAAtEjwJFSgAIUoAAFKEABClCAApEkwIBKJO1NjoUC/hRgXRSgAAUoQAEKUIACFKAABSjgVYABFa80XBFuAuwvBShAAQpQgAIUoAAFKEABClAgWAIMqARL2r0d5lCAAhSgAAUoQAEKUIACFKAABSgQpgIlCKiE6QjZbQpQgAIUoAAFKEABClCAAhSgAAVKIMCivggwoOKLEstQgAIUoAAFKEABClCAAhSggHYF2DMKhECAAZUQoLNJClCAAhSgAAUoQAEKUCC6BTh6ClAg/AUYUAn/fcgRUIACFKAABShAAQpQINACrJ8CFKAABYoIMKBSBISLFKAABShAAQpQgAKRIMAxUIACFKAABQIrwIBKYH1ZOwUoQAEKUIACFPBNgKUoQAEKUIACFAgrAQZUwmp3sbMUoAAFKEAB7QiwJxSgAAUoQAEKUCCaBRhQiea9z7FTgAIUiC4BjpYCFKAABShAAQpQgAJ+E2BAxW+UrIgCFKCAvwVYHwUoQAEKUIACFKAABSigVQEGVLS6Z9gvCoSjAPtMAQpQgAIUoAAFKEABClAgSgQYUImSHc1hehZgLgUoQAEKUIACFKAABShAAQpQoDQCDKiURi1027BlClCAAhSgAAUoQAEKUIACFKAABTQgEOCAigZGyC5QgAIUoAAFKEABClCAAhSgAAUoEGCB6KueAZXo2+ccMQUoQAEKUIACFKAABShAAQpQgAIXKMCAygUCcnMKUIACFKAABShAAQpQgALBEGAbFKCAtgQYUNHW/mBvKEABClCAAhSgAAUoECkCHAcFKECBiBZgQCWidy8HRwEKUIACFKAABSjguwBLUoACFKAABXwXYEDFdyuWpAAFKEABClCAAtoSYG8oQAEKUIACFAiZAAMqIaNnwxSgAAUoQIHoE+CIKUABClCAAhSgQKQIMKASKXuS46AABShAgUAIsE4KUIACFKAABShAAQp4FGBAxSMLMylAAQqEqwD7TQEKUIACFKAABShAAQoEQ4ABlWAosw0KUMC7ANdQgAIUoAAFKEABClCAAhQIQwEGVMJwp7HLoRVg6xSgAAUoQAEKUIACFKAABShAAQZUIv8Y4AgpQAEKUIACFKAABShAAQpQgAIU8LOABgMqfh4hq6MABShAAQpQgAIUoAAFKEABClBAgwLh3SUGVMJ7/7H3FKAABShAAQpQgAIUoAAFKBAsAbZDAScBBlScMDhLAQpQgAIUoAAFKEABClAgkgQ4FgpQIHACDKgEzpY1U4ACFKAABShAAQpQgAIlE2BpClCAAmEjwIBK2OwqdpQCFKAABShAAQpQQHsC7BEFKEABCkSrAAMq0brnOW4KUIACFKAABaJTgKOmAAUoQAEKUMAvAgyo+IWRlVCAAhSgAAUoECgB1ksBClCAAhSgAAW0KMCAihb3CvtEAQpQgALhLMC+U4ACFKAABShAAQpEgQADKlGwkzlEClCAAsULcC0FKEABClCAAhSgAAUoUFIBBlRKKsbyFKBA6AXYAwpQgAIUoAAFKEABClCAAiEWYEAlxDuAzUeHAEdJAQpQgAIUoAAFKEABClCAApElwIBKZO1Pf42G9VCAAhSgAAUoQAEKUIACFKAABShQjECEBFSKGSFXUYACFKAABShAAQpQgAIUoAAFKBAhAtoZBgMq2tkX7AkFKEABClCAAhSgAAUoQAEKRJoAxxOxAgyoROyu5cAoQAEKUIACFKAABShAAQqUXIBbUIACvgkwoOKbE0tRgAIUoAAFKEABClCAAtoUYK8oQAEKhESAAZWQsLNRClCAAhSgAAUoQIHoFeDIKUABClAgEgQYULnAvZiZa4RzKjCaUWC0uOQ5rw/H+bwCM7LzXMcZjuNw7rPYT87LgZovVI+FCzzENLl5fqHZ5RjPLTDJ4z473+SSHyjXYNQrjnlx7AejrWC1kavunxw1Bbq97DyTJo/bC+2U2WJ1Ob6z1NdF8XqfV+T5EGjfQNcfrNfHQI/DXn+W+ne66GuWfZ2/H8UxcqHHmRa3F8/pTNXR7iU8xbEvbO154f4o/o4F4/UxmE5iP4nXqUC3KdrR4nF7oX0S7+Gc7cTxIY578eicH87z4vgQ+y+cx1C072L/iOdz0fxALF/oMcbtI0OAAZUL3I/iTYZzijPoEWfQqQEIU8QkRQEKTdaIGY/YXzF6HXJloCiw+6nAZEEk/if++ApHe4IV8rg3mSPnOBHHvDj27WOMhEd198CiBgUCPRbxZiYSj3vxYdnZLr/QIo97vU6JqNfH2Bg9xD50Hms4ztv7LF7rDTHB+btsFk+yCDz4ix4P4m+oeK8jgol253B/tKp/xyLpb5jYHzr1talAfZ0S84FM+WpQOQIPe4j3cM5u4m+AOO7FseKcH87z4osjvT44r4/BcjLK997B+bscicc9x1RyAQZUSm7GLShAAQpQgAL+FmB9FKAABShAAQpQgAJhJsCASpjtMHaXAhSggDYE2AsKUIACFKAABShAAQpEtwADKtG9/zl6CkSPAEdKAQpQgAIUoAAFKEABClDAjwIMqPgRk1VRwJ8CrIsCFKAABShAAQpQgAIUoAAFtCvAgIp290249Yz9pQAFKEABClCAAhSgAAUoQAEKRI1AFAdUomYfc6AUoAAFKEABClCAAhSgAAUoQIEoFgjM0BlQCYwra6UABShAAQpQgAIUoAAFKEABCpROgFuFhQADKmGxm9hJClCAAhSgAAUoQAEKUIAC2hVgzygQjQIMqETjXueYKUABClCAAhSgAAUoEN0CHD0FKECBCxZgQOWCCVkBBShAAQpQgAIUoAAFAi3A+ilAAQpQQGsCDKhobY+wPxSgAAUoQAEKUCASBDgGClCAAhSgQIQLMKAS4TuYw6MABShAAQpQwDcBlqIABShAAQpQgAIlEWBApSRaLEsBClCAAhTQjgB7QgEKUIACPgoYjSYcPHIChYVGly0sFiuOHD8Fk9nsks8FClCAAr4IMKDiixLLUIACFKCAHwRYBQUoQAEKUCC4Arv3HUafQW+j8fUD0Pnup/HTnKWODixZvhEtb34UHe8cikYd+2PKzMWOdZyhAAUo4IsAAyq+KLEMBaJUIOq/tYnS/c5hU4ACFKAABSJB4OjxDNzS9zmkV/bdJf0AABAASURBVEzDxJHPY83cL3FD+xZyaHn5hXj69c/weL/u2LjwK4x4YxBe+2A8Dhw+LtdzQgEKUMAXAQZUfFFiGQqEiYA/u8lvbfypybooQAEKUIACFAi2wDdT5qJc2RS888JDaNqwHhLiY5FWJkV2Y9X6bcjNy0evrh0Qo9ejU5tmqFU9HUuWb5DrOaEABSjgiwADKr4oBanM/hUH4C0dWnMoSL0IajNsTKMC/NZGozuG3aIABShAAQpQwGeBP1dtRtX0Cnj6tc9w18Ov4dX3x8v7pYgKjp7IkAGU2FiDWJSpbq2qOHIsQ85zQgEKUMAXAQZUfFFylAncTM6xHPzYa5rXNOvRWYFrnDVToIgAv7UpAsJFClCAAhSgAAXCTmDn3kNISoxHx2ubol+vG7Hl393o9+RwiBvUZmblIDEh3mVMcXGxyMrOlXk6BYiP1TtSjN72sSlGrzjynNeH43ycQQelyDjDcRzOfTbE6KBXd55zXqDm5YHCSYQLnH94uvMXYYlgCOjUF7TqLatDpMpNqziaFMsiVXHKc6zkDAUCJHC+b23EH6uURAPsSSyLrog/WPa8QD0qWVuRte0L5P73LeIsxx198Hd7CXF6iDdP/q43lPWJN05xBn3AzOxjS1TtxPHAdE5AOfQfdH9Nh27NHCDz5LkVnKMABbQjcPIQdKt+hX7Fz1CO7dVOv9iTCxK45/brcWvn1rih/VV476VHsPfAUezadxipKUnykh/nygsKCpGSnCizdOqHcvH3zJ5iYxSZH6PXwZ4Xbo/xeRkwrJuLmBXTEXd8lxoYioFejaiE2ziK62+sGlARQa/iyvhrnTwgtDZhf4IuwIBK0Mk9N5iQloAek3vIdOvnt8hCSRWT5LLIv/WLW2UeJxQIhsD5vrURAZSUhBjYk1gW/YpXA4P2vEA8HlvzLjZMaIFdi57EjnkPYc2Yy2A6vsLRD3+2maB+KyX+IPuzzlDXJd5kxAZ4H4kxJsbHgP+dE9At/h6GTx9FzKzRiPnpQxg+6Atl5/pzBThHAQqEXMC86U8Y37oHMTM+hv6XT2H4eIAMrIS8Y+zABQnUv6QW9h086qjDYrHI+UKjCekV0mRwxajOy0x1sn3XAVSulKbOASazFaeyCh0pt8D2s8r5hWZHnvN6rc9nbF6HnNd7oeD7d1E4bSTyhg/AmVnjYbK4jjPY4/B3ezn5JhQYLUHZR/JA4STqBXRRL0AAClDATeB839oYTRZk5ZkcSSyLSvLVP2DO+f6e3/nn26IZR7KY8rDjz/cd/fBne3nqGybxZsqfdYa6rkJ1vxUGeB+JMeaqb2YcO4kz0C+e5KKgGAuhXzrVJY8LFKBAaAVMv33v1gHd7+55boWYoWmBmzq2xLjJs3HwyAmcycrBxGkL5E1qL65dDS0aXyb7PmnGQjV4YsZvS9fKX/hp16qxzPcyCdts/Z9TIf7+OA9AWcRj3NmD8xQojQADKqVR4zYUiHCB831rIwIoWblG2JNYFiTiWxt7nr8fTxzcArMxRzTjkrJPbHP0w59t5qnfRJnMauDIaZz+rD8UdYlvbAqM5oB4OY/H/i2ey46K0gUl4wiUwny30fNyAjcSZlAgpAJWD5f4KFmngLyskPbrwhuP7hruvf16tGx6OTrf/TRa3/oY/li5EaPeHiJ/7ScxIQ4j3xqM4aMmoVHH/hj80ki8OKQPqlepGJFoyrH9buMSf5/MR3h5mxsMMyhQAgEGVEqAxaIUiBYBLX5rE5d2KXSGJLddEK/mu2UygwIaEbCmVYY11vWmh6Jr1kq1xAMTBShQVOAClvVzxyL2+eu9Jv0fP3itXfHwnLSmlAMSUrxuwxXaFxC/4PP+y49i+azR+O2HD7Bwyoe48vK6jo53uKYJNi0ch/mT38eGBWPRq1tHx7pIm7FWquE2JPH3SV+Zf4/cYJhBgRIIMKBSAiwWpUC0CGj1W5tKTf/nsguUmARUbDzYJY8LFNCagLl9L5cuWQ2xMLe50yWPC+ErwJ5HhkBMp95uA7Fc557nVogZYSGQmpyIKunloSiKW3/1eh2qVa4AgyHGbV0kZZivvRPi74/zmKwdeIw7e3CeAqURYEClNGrchgJRIKDFb23Smz+Dej2Xo+q176PGdZ+h/r1bkFS1dRTsDQ7RnwIWixUnTp2R19N7q1esz8sv9La6RPmW9r1hfPwzmG4ZCNPtQ2F8agKsdZuUqA4/FmZVFIhYAXOXASh8e4FM5rZ3yXF6ypMrikz0V14LwwvfwdRtCMy3PQ7jkLEwX921SCkuUiB8Bay1r5B/f8TfIfH3SPxdAoOG4btD2XPNCDCgopldwY5QQHsCWvzWJqFiI1RsNBDlLr8PhqQq2kNjj/ws4N/qlq/ZipY3P4p2tw+W19PfP+QdbPl3t6MR8WsQN907TK5v3uUhvPTuOBhNtl92cBQqxYy16sWwtO4OS/MbgdTypaiBm1CAAgEXKF8VlqtuloEUXpYXcG024CcB/YH9SJg0EQkTxiFm+z/F16r+/RF/h8TfI/F3qfjCXEsBCvgiwIBKEaWTGZkQqUg2srJzkXGGNyYr6sJlClCgiAAXNS2g6BS8/GRf/DVzFBZN/QhJifEY9fUMR5/f/HgiLru4JtbM/RKzJvwf5v6+CnMXrXSs5wwFKEABClBAKwJxC+aifJvmSHn+aaS88hzK3dBOBla00j/2gwLRIMCAirqXxenfY76bhTbdBqFt9yfQpfczaq7tX25ePga9MAJX3zIQ13YdhF4D35CnitvWckqB8BfgCCgQTQJXN70ct3ZujTIpSUivmIYu7a/CHys2wmQ2y0uAlq3egj49OiMhPhZ1alZBty7XYP6S1eB/FKBABAnkZUG3+HvETHgJMT99CN1/ayNocBxKNAkkfvaJ23CTPv3ILY8ZFKBA4AQYUFFtP/pyKiZMnYdH+nbF0hkjMXPC22qu7d/30xdi+64D+H3ax1gxazT0Oh1GjP3RtpLTUAiwTQpQgAJ+E1i2ZgvqX1ILMXo9Tpw8Let1/snMmtXScfjYKZkvJga9AnuKOfsXVKfAkWdfF86PRccZzmMRfY9R91mwxiTaYdK+QMz4FxAz/2vo/lkB3Zo5iBn3LCzbVmq/4+whBYoIxPy3o0gOoDt+DMoZ298zt5XMoAAF/C6g83uNYVbhcfUN9LjJszH04Z645/ZOKFc2BZUrlnOMQpzu3eOWdqhUoSxSkhPVby6vx0+z/4DVanWUKX6GaylAAQpQINACZrMF4kxDb2nXvsNuXZg5/y+I9JT6+i9WZmbnigeIn9mUM+okLi4Wp05nqnOAAqBscqwjpSQa1BzIQLtzfrjPix/BKOM0znAfT5mkWHUfKY79FsjxCDt5UHCiWQEl4wh0+7e59c+0cq5bHjMooHUB08WXuHXRUrESrGXKuuUzgwIUCIyAe0AlMO1ottZNf++Sfdvyz270GfQ2Bjz9Hn6Zv0zmicneA0chvqEU8yLVqFpJPMD+xlsucEIBClCAAiEVsMKqBj6yvCaj0eTSP3Fpz7Nvf4lXht6HVs0byHXiZzXFjHPZgoJCNdCeKrLVFoDjZwocKSPbKPONajDHOT/c58X3BSecxhnu4zmZWQCzxerYb4Ecj7CTBwUn2hVQAyqeOmc5tt9TNvMooGmB3EefcOtfzuNPuuUxgwLFCnDlBQlEdEBlpvrto7dvK8WbaSF35LjtVO4K5cvggbu6oNmV9fDc22Pw68IV8iwUcQ+VePUbSlFWpLhY2zeSubn5YhFxBp1LkpnqpGh+SZZj1TrVKuS3oSXZLlBldepXbuK06UDVH4p6FRU4Nkbnsu8C0Q+VTm2J/yhAgUALxOj1GPZYL6/p0ro1HF2Yt3gVHvrf+3hzWH/0vO06R36F8rZv9PYfOubI27P/CKpUKudY5gwFKBDeAtZq9YDYRLdB6C9r4ZbHDApoXaDg+i44uXQNst5+H1mv/R9OzVuCvL79tN7tC+4fK6CAlgQiOqAiziI5ddrzN5YiUGLfEXVrVcWjfbuiw7VN5aO4YeGCJWugKAoSE+JRUGj7FlKUt88nJsaLRSQnGFySzFQnRfNLspwUbwvaqM271F2SOvxZNiZGQXxcjCb64q9xiV/6SIoP/Jj0OkU9GviPAhTQisDP85Zh6Kuj8ezjvXFVk/o4eOSETOJvgrhRrThbZeK0BcjLL8TufYfxixqY79yOH7S0sv/YDwpcsEBcIky3PgprbIKjKkutBojpcJdj2Xkmc+987PixAzZ/WRH/ft8Mxzd86rya8xTwRSCgZczVayCvVx8ZSDHVuyygbbFyClDAXUDnnhU5OeKeKN6+sby+bXM5UHHzwZ17D8FoMstlMTGp80aTScyiVvV07Dt4VM6Lif2bS/up4eJUYuckyojknFfS+YysAlEFLFagpNsGonyh0YKsXKMm+uKv8YlfdsrILgz4mExmdSfKvckJBSigBYGNf++U3Xjn0+/R+e6nHWneYtsv+bzwxL3Y+u9uNO/yEG7p+xw6t2uOLtddJbfhhAIUiAwBS7MuML76C4yPjULhsO9hevhjKMll3QZnyjuO3bO6IffICliMOcjP2IZDy55B1v6FbmUjK4OjoQAFKEABXwUiOqDiC0KThpfIs1C+mPALxE0NxZvtOYtW4poWDeXmN7RvgakzF+PYidPIzsmD+Oby9pvayrNXZAFOKEABClAgbARefrIvti4e75a639hGjkH8VPK8Se9h0dSPsGr253jr2QEwGGLkOk4oQAGNCpSyW/LynzIVvW6dc3iFx3XZBxZ7zGcmBShAAQpEn0DUB1TEmSafvDEI30ydhys79kPvgW+gd/eO6Hlbe3k09O7eCRfVqorregxBy5sfhdFowqB+t8t1nFCAAhSgQGQKpFdMQ9LZSzsjc4QcVSgF2DYFKEABClCAApEhEPUBFbEbxTXzy2eNwvzJ72P1nM/xwuA+EDc4FOvEG+rP3nkSf80chSU/jcAPX7yCShXcTwsVZZkoQIHwEMg5sgrZB//wmiwm202nw2M07CUFAi7ABigQlQJJVa72OO7k6rYv3TyuZCYFKEABCkSVAAMqZ3e3CKBUq1xBXv5zNsvlQdyssEK5Mi55XKAABcJTYO/8vtg5o4vXZMo9Gp4DY6/PCvCBAhSgwIULxCRURJ1bZiCx8tXQGZIQn1YfVa95Fyk1Ol545ayBAhSgAAUiQoABlYjYjRwEBShQEoGkylchuVobmezbJaa3kMsiX9HH2bOD88hWKEABClBAkwKptTrjkjsWoeFDx3Fp77Wo2PhxTfaTnaIABShAgdAIMKASGnePra77aj0mXD8R49p+LdebC2y/NCQXOKGAhgTCvSu1Ok9A3W7zZIpNqSWHU+uGiXJZ5BuSKss8TihAAQpQoPQCa9asxDvD38CwZ5/EhInjkJl5pvSVcUsKUIACFKCABgUYUNHITtm7dC/+eHMJTv13EqY8o+xV/pkC5J3MlfOcXJDry7WmAAAQAElEQVQAN6YABShAAQpQIIgCK1Ysw5NDH8Ovv/6Mv/5aijFjRuN/zwwOYg/YFAUoQAEKUCDwAgyoBN7Ypxb2L9vvVO7c7KG1h88tcI4CFKAABShAAQqEgcDCRQvcevn331vw22/zsG79Go9pw4Z1btswgwIUoAAFKKBlAf8EVLQ8QvaNAhSgAAUoQAEKUCCoAvv27vbY3muvv4DBgx/xmAY98ZDHbZhJAQpQgAIaE2B3HAIMqDgogj+jnDwM5fBO2XCNa2rIx6KTqs2qFM3iMgUoQAEKUIACFNC0QLPmLd36l5iQiIYNG6Nx46Zo1KiJY71YFqlZs6sceZyhAAUo4E8B1kWBQAkwoBIo2WLqNe7bjcLne8HwQV8YRj4C67O3ooJlJ9q+2A7lLi6PmASD3Dq+TBwSyifK+Qud6Db/Af3CCV6TcnTPhTbB7SlAAQpQgAIUoIAU6HlnLzV40kjOi0mCGkwZPPhpjB41FiM/+RKfjhwjsmUSyyJ9/NFoucwJBSgAElCAAmEiwIBKCHZU9lefIhknHC3H6fJhmPUJmvZvgr4L+qDfHw/Idfq4GPnoj4luqwioTFQDKp6TcnSvrZnMk4iZPw4xE16E/udPoBzcbsvnlAIBEpjxwAx8XOdjr2nPYgb7AkTPailAAQoETKBs2TQ1ePIVpk37FWPHTMT8eX/gpptuC1h7rDjUAmyfAhSgQHQKMKASgv0en3U2eOHUdrIhB4XHTzrl+HfW0qAtzB37yGStVFtWbrmijVwW+db0WoCpEIbPn4Bu8STo/lkJ/cqZMIweBMsRz9dBy0o4oQAFKEABClCAAl4E0iul49JL63tZG8JsNk0BClCAAhTwgwADKn5ALGkVBWbbJT3O21ksChDnn8t7nOu1z1saioBKXzWA0hfWymrwRF1huaKdXDZ3VPPSa0PZ9zeU08fUNU7/rBYYV853yuAsBfwr0O3rbhiye4hMtdvXlpV7ypMrOKEABSgQpQLROuy83Fz888/fyMg4Fa0EHDcFKEABCmhYgAGVEOycjPJXu7W6L6c2YlMT3PKDmaHLOOqxOav9ciCPa5lJAQpQgAIUcBNgBgUuWODrr79E5y5t8eBDfXFb18549rmhF1wnK6AABShAAQr4U4ABFX9q+lhXtaGPYqOuG/49XAs7D1fFxuw2SHz8dR+3DlwxS+2GHivX13e/U7/HgsykAAUoELYC7DgFKKAlgX379mCcGlBx7tOyZX9g9uxfnLPOO28uzMLOGTd4TXvn9z1vHSxAAQpQgAIU8Cag87aC+YETiE2ORf03H0Odr8eixtffoP4nL6PspZUD16CPNVvLV4Wp8wMupS2XtYKhTVeXPC5QgAIaEGAXKEABCkSwwJYtmzyO7r//SnizfIsJ2QeXek05R1Z5bIeZFKAABShAAV8EGFDxRSmKylja94bxlV9gfGwUjM9Phalv6M+ciSL+iB4qB0cBClCAAhTwVaBcufIeiyanpHrM95api01B3W5zZap1/TeyWExiulwW+bU6T5B5nFCAAhSgAAVKI8CASmnUInwba1wCrNXqwZpcNsJHWuzwuJICFKAABSgQVQKFhUYcPZ4Bq9Ua8nE3bdoC6emuZ+/qdDpcd12nEvVN0cUguVpbmRKr2C5h1unj5bLIT6p8VYnqY2EKUIACFKCAs4DOeYHzwRPYPnsH5g2dh5kPz8T6cethzDNdYOPeN9dt/gP6hRMcSTm6Ry1shW7LEkeeWG/LV1fxHwU0LJB3fCOObxyNU39/A2POYQ33lF2jAAUooB2B2+57Hg3a3++SRo+fITsoAiijv/kZTTo/iA53Pom23Z/Axr93ynWhmsTGxuKzz75Gn3sfQKtW16Jbtx744vPxqFP7olB1ie1SgAIUoAAF3ARCF1Bx60r0ZGz8bhNmP/Yrtk3fhp3zd2LJG0uw4Jn55wVQMo7KAIhu7dzzlnUuoNsqAioT1W1tSZG/2qOoAZWljjz9womw5cOn/5TCfHXbCdAvneJTeRaigD8Ejq55F9untMKhP5/G/t8fxbZvr0DOob/8UTXroAAFKBDxAk/0vwOzvx3uSL27d5Jj3rD1P4z6ejomjnweGxaMRbcubfDkK5/CYgntmSoVK1TEQw89hneHf4ynhj6Lyy67XPaXEwpQgAIUCEOBCO2yLkLHpelhbZv2t1v/dszeAYv5PG9cTh9TgxhqUGTd+YMvbg34O8MoAioToftjqr9rZn0aESju28zSdtG6W0HBuwbkPxonH8VySeo6tu49l+JWUx6ObxjhkscFClCAAhTwLFCxfBnUqp7uSGXLJEP8t+jP9WjVvAGaNqwHgyEGfXp0lpf+/Ltzn1jttzToiYfgnMSZMSI55w15cqDf2mNFFKAABS5EgNtSwBcBBlR8UfJzmYydGW41WtVvgU7vcc93KxiEDKUgD8rB7VCyTwehNTahZQFv32aWps/Wk0Bez3jsHLsb639bLR/Fssj3pb6CjH9hMea4Fc1X890ymUEBClCAAm4CU2ctwYvDv4K41GffwaOO9YePnUSdGpUdy5Uq2O6hduyE7X2ATqdA75QUBfI/nTrjnO9tfvCQR/CEGkzZsGEdnJOiKFAUxSVv7dpVsuywYUNc2vRWt7d8tVq3PopxyEx1UnQ7UV6n4ILaLFpnqJfVYUKMOdD9EHaiLaaQC7ADFKBACAQYUAkBep0OddxaTamWgnJ1y7nlu2ZYbYsZtkt/9E73RSluXjkq7pli27S4qbinSsxX/4PhtVthGDUQhrd7wPDhAyic/TWs88fDuQ2emVKcZOSs8/ZtZmlGaPotBlNPjMF84zSsNC2Sj9NOjoX5D71P1cWlXQqdIcmtbLya75bJDApQgAJRIjBz/l8Y890sj2nZ6i0OhRvat0Dbq69E1coVsGjZetwx4BXYgyqZWTmIj4tzlBUziQnxyM7NE7Mom2RAhTJxjiQ+oIsVaSmxjjzn9UXn161bg/VqMEVs40sSZTdv2ehT3UXbsi8nxsXIphLj9bDnlVP7KzJF/+159seE2BgkJbiO074uXB9jY3Ru+y4QY4kz+PZ3XNi7Ji5RgAIUCH8BBlRCsA9bPtES5S8593OA8WkJ6PBGBx96osgyiv3Sn4UT1SDH+ZPHgIr1bHBG1mibKFv+gG7nBnVBtGNLyokDKJwzXg2oTHBti/dOUZ0i/99UL99mlmbkO+b/iwyccNn0lPU4ts/d7pJX3EKlpv9zWa3EJKBi48EueVygAAUoEE0Cmdm5OHU6y2PKzct3UDz2QHc82rcrBt7XFZNGv4SU5AQsXLpOrk9NSUJBYaGct0/EtsmJCXLxVFYhjmbkO5Lp7CXKJzMLHHnO64vOy0pKOBFvU4rW47y8Y/MxbF66z2v7Ofm2m/1n55kcZU6cKZC9MFusjjx7nbkFJmTlGt3y7evlo5NBOCwXmiwouu8C0e/8QrN05YQCFKBANAowoBKCvZ52URr6zO+D+xc/gHtm34NH1j2MOte5n7Xi3jVbEMRathLMHfv4nKzptd2r8nB+prVmA7WcaMM16arVhdK5r2t7bXqqZfkvHAX88W2m+DaqfGoc7CkuVi8pkuNjHHn2dfbHvIqnZJmik7zypxzbGNRv08T61ESDI8++vXi8vMNLaNFvLS7p9DHq3zQWrR/ZjpqXXeexrCjvS9KJc7zVRssmx7rUk6L2Idagc8nzpT4tl0mM0yOxmH3kr76XUb/NVkn5jwIUCILAPbd3wrDHenlM17dt7rEHBkMMKpYri7wCWxClSqXy2LP/iKOs/VIf+6U/jhV+nhH3T/FapYioeFiZdTgb39/6Pca1GScfP2v0Gbb/usNDSWZRgAIUoAAFAi/AgErgjb22ULZWGVSsX9HrevcV4qwRNTctXQ1uiACHb8ljQEWtpug/y8VN1SzRhmvS12+pBlTud2nT0vZOtSz/haOAP77NNJotyM4zOpJR/RZMWBQYzY485/ViPq2d7Zp8Uc45pbUp69jGrNYr1uWp33aJbTwlJbUB0q54BCn17oUxpqJjW09lfcmzv6HPzT83HrFdvvptpclkveD6RV1aSQVGi/ottPd95K9+5hbw20pxHDOVWIAbBEhAXNozfspcHDl+CkaTGbMWLMeWf3ejZZP6ssUO1zaBuDxo3eYdcv2EafOQXjENl9atKdcHcmJ/DXZrw8MXP6LMqpErcWzLMTErU0FmARa9tEjOc0IBClCAAhQItoAu2A2yPQ0LVKgOcfaLSw8VHQwtO4P/RY6AP77NFD+lKT+ciw/oahLLQshotsI533n+ohsvQfl65y51E+UrXFYBIt9eziJOjlJXiACNPS/Qj/YvQQvV4IlzW2IsFnWlc164z4vT3EUK9DjE/lN3YxT80+4QY759FYYxT3lNyMvSbufZs4AITJg6Dx3vHIrGnfpj2FtfyDNaml1ZT7bVuMHFeKTvbegz6C25/oeff8cHrwyE/Qw+WSgAE0VR5E1pPVatvv56yj+84dyZNPb1+Rl5yNyfaV/kIwUoQAEKUCBoAgyoBI3aDw2VPXupT9MABTh0MTA+8gks7XvBcllLmFveCuPAkdBVruPeeUM8xGVHPFPFnSYScs73bWZpx9hnXh/cNuY2tHmujXy8d869pa2K25VGgNtEjYCy728ouzd5T2aeRRQ1B4M60JrV0rFwyof4Y/onmPv9u9i48Cv0vfMGdY3tn6IoGNTvdqyd9yUWTH4fK3/9DE2uuMS2MlRTtU+emo5Pdb15riij6BTEp8WLWSYKUIACFKBAUAUYUAkq94U1Zk2zXepjadalRBVZGrSVwQ8RABHJml5L3d4KyxVt3PNTy8PUuR9Mfd+EuesTsFarp5Z1/2eNFQGVvjDzXiruOBGSU9y3mRcyxIs6XYRmDzWDeDxfPVxPAQqUTsB8zyswDnhPJiSkyEpM974ql0W+NbmszOMkegQURUH5tFTUqFoJMXrbfa9Q5L/4uFiIXwEK9JkpRZot0WLD3g3dyte8tiZik2Pd8plBAQpQgAIUCLSALtANsP7QC1gaioCKGvzoaEtWeZNaRQ2otFMDKrY8s7rOlh/6/oZxDyKm6+f7NjNiBsqBUCBCBSy1GsB6UWNb0sfIUVprXm5bVvNlBicUCJLAJyO+wIgRn/vcmig7/J2PPJa/9LZL0XXMbbi8x+UyMH/ts9fi5lE3g/9RgAIUoAAFQiHAgEoo1DXTJjtCAe8CinL+bzO9b+37mvzT+Vg1ahV+HvAzTvxzwvcNWZICFKAABcJCoEmTZmjapLkMqohgiT2JG9KKZF8WjyM/+VKWvfLKxl7HVqfTRej8Xmd56Wjzh5vz7BSvUlxBAQpQgAKBFgivgEqgNVg/BSgQdIEZ/Wbgr/f/wu6Fu5F12HajzKObjga9H2yQAhSgAAUCKyCCKs5JURR5U1rnvMaNxS8OBrYfrJ0CFKAABcJEIAy6yYBKGOwkdpECkSogfpXhyPojbsPb9+c+Wx3QgAAAEABJREFUtzxmUIACFKAABShAAQq4CijH9yP2+eu9JsNH/Vw34FJABVh59AkwoBJ9+5wjpoBmBM4c9Pwzl1mHszXTR3aEAhSgAAUoQAEKRKgAh0UBClygAAMqFwjIzSlAgdILpF9RCbFJ7r/MkN6wUukr5ZYUoAAFKEABCkSogP+HZbFYceT4KZjMZv9XHoQarRVroPDtBTIZnxwnW/SUJ1dwQgEK+F2AARW/k2qzQuXYHii7NthS9hnZSeX4XtuyyM8+LfM4oUAwBcTPXLZ7uR0MiQaXZi+58WKXZS5QgALhJXB09Tv457vG2PxFJeyccQNyDi0LrwGwtxTwlwDrCanAwqXr0KD9/W6poNAo+7Vk+Ua0vPlRdLxzKBp17I8pMxfLfE4oQAEK+CrAgIqvUmFeTr/oWxjG/k8mEVgRw9EvnCiXRT52bxRZTBQIukCDng3w2NbH0PuXXqjRqoZsPzYlTj5yQgEKhJ/A6R3TcGTV6yg4vR0WUzayDy7Fnrm9YTbabjodfiOKrh5ztBSIJAErrEhMiMfsb4e7pFhDDPLyC/H065/h8X7dsXHhVxjxxiC89sF4HDh8PJIIOBYKUCDAAgyoBBg4UNUXZOzAwWXPYtevd8hHsVxcW9aKtWCtc6XXhKQyxW3OdRQIuEClhumIiY8JeDtsgAIUCKxA5r75bg2Y8o4j79h6t3w/ZLAKClCAAsUKxMcZUKt6uktSFAWr1m9Dbl4+enXtgBi9Hp3aNJNllizfUGx9XEkBClDAWYABFWeNMJkXb0x3TG2DExs+QdaeOfJRLIt8b0Mwd+wD44MfeE3Wixp725T5FKAABSjgNwFWRAEKUIACwRQ4dToLz//fGLz24Tf4deEKx71Sjp7IkAGU2Nhzlx3XrVUVR45lBLN7bIsCFAhzAQZUwnAHnt75M8xG119HEcuZe+aG4WjYZQpQQNMC7BwFSiiQWrOz2xYxCRWRUKmJWz4zKEABCgRSIL1iOTxw942oU7OKbOaZNz7H8E+/l/OZWTnyciC5cHYSFxeLrOxcuRQbo0PV8gmOlHr2fm/JCTGOPOf1WpivVDZe9j1Gr/jUR1He4GNZLYzPlz6UTY5FYpzep/H7Ul9xZSQ2J1EvwIBKGB4Chaf/9djrwux9HvOZSYFoEuBYKXA+AfGLDidOncEZ9c30+cpyfckFyl7SA5WvehlxZetBF5OM5GptULvL99AbUkpeGbegAAUocAECDS+rg6cfuQsP3nMLXhl6H954ph++n75QnqWSmpIkL/lxrr6goBApyYkyq9BkwaGTeY6UmWuU+dl5Jkee83otzB87nS/7aDJbfeqjKG/0sawWxudLH05nFyK3wOzT+H2pr7gyEpuTqBdgQCUMD4HkGtd77HVy1TYe8/2VqRzdA5w44K/qWI9NgFMKUCCIAsvXbJW/6NDu9sFofetjuH/IO9jy725HD4aPmuT2axD3Pv6WYz1nfBNIb/EsLrtnAxo+fAx1u81DUtVrfNuQpShAAQoEUKBi+TRZu8lkRnqFNOw9cBRGo0nmicn2XQdQuZKtjFhmogAFKHA+AQZUziekwfWptTqjfIP+Lj0rf8WD6reAbV3y/LVg/ncdrK/fCcOIBxH74QMwfHg/lGNqcMVfDbAeClCAAkESUHQKXn6yL/6aOQqLpn6EpMR4jPp6hqN1q9WK9q0bu/waxPuvPOpYzxkKUODCBNq0bQ57stdkXxaP9rySPur+Wwv9wgmOpNu7Ra3CCuf82KU/I6WAZ0qpMFHzT5yNsnbTdvmLPkeOn8KX385Eyyb1ER8XixaNL5MOk2bYzlj5bela+Qs/7VrxvoIShhMKUMAngcgPqPjEEH6FqrcfiSsePIxL7vxTPlZvNyJggyiY9gmsmScd9SsnDkI/b5xjmTMUoAAFwkXg6qaX49bOrVEmJQnpFdPQpf1V+GPFRnn6N87+J073dv5FiMoVy51dwwcKUCDgAvk5EIER3Z8/lqgp5b/16nYTHUnZu1XdXoFzfuyfvyC5MFXN9/2fbvH3ap0TfN+AJTUlcOTYSfR94m007/IQOt45VL7Wv/5MP9nHxIQ4jHxrMMSZiY069sfgl0bixSF9UL1KRbmeEwpQIEwFgtxtBlSCDO7P5vSxZZBYqSnEoz/rdamrIBeWo3tdssSCcnineGCiAAUoENYCy9ZsQf1LasmfzLQPRPyUpvhFiPdGT4b4ZtOez0cKUODCBZb+sQbFJajvO/QLJ0K/7McLb8wPNegXT1YDKhOhFOT5oTZWEWyBoQ/3xNp5X2Lu9+9i2c+fYtLol1wCJh2uaYJNC8dh/uT3sWHBWPTq1jHYXWR7FAAJwluAAZXw3n+B770hDkqc7Y7hzo1ZE3jKrLMH5ylAgdAKmM0WjPlulte0a99htw7OnP8XRHpKfcNtX9mgXm10v7ENateojP2Hj8lvNuctXmVfjeT4GEdKjNXLfL1OceQ5r9fKvCJ7CSQ59b24vonivpYtrh6trEtUx62oCMHoj7BjKl5g5acrsfzjFV5T8VtzLQVKLiAu76lRtRLKlkn2uLFer0O1yhVgMMR4XM9MNwFmUIACTgI6p3nORplAYeZeHFn9ltd0attEQKdHTLNObjLWK9u55TGDAhSgQKgErLDi1Oksr8n5poOij8tWb8Gzb38pf/WhVfMGIksmcTnQoH6346F7b8UnbzwhLw+aPmepXCcmOjV4Yk+KOi/yFHViz9PiI0QHRR/VqIIv/VOLQa+OzZey4VBGr45fURGC0VeVmf/OI7Bq1GqsHLHCazIX2G4Qqpy99EfvdF+U4uYVec+U8zSurk7NL4PKJ1JQtC7TnK9hmTfeLV8ptJ2ZIl5j1M35LywF2GkKUIACgRNgQCVwto6ardsOwvL7Vlg374e10PZGwbEyhDOFWXtxdNVbXlPGv9/K3sXd/RSU2wfDckUbWBp1gKnnszC36yXXcUIBClBACwIxej2GPdbLa7q0bg1HN8UZJw/97328Oaw/et52nSPf04y4f0pOboFjlfjZTHvKybe9npssVtjztPhotdq6n5Vn9KmforwWx1HaPmXlmWBRB1Xa7UuynU2a0+IErnqsBVoOvlome7kWj10ll0W+Pi7Glm2/9Edc/uND0u0RN6G1bWqfqrvdPut4TC1MQ/qJMmrgZKJLMs79BuZ537jk6dV27RuKoJx9PuCPbIACFKAABcJGgAGVAO8q0ydzYB67CJZZ62Aevxjm92YC+YUBbtW36mNTaiH9qhdkSrvsXrmRIaWmXBb5aZfa8sQKpXVXmHq/DNNdz8HSmNeXgv9RgAJhKfDzvGUY+upoPPt4b1zVpD4OHjkhU25evhzPR19Oxc49B2E0meXPKX/302+49qqGch0nFKCAZ4GS5LZ8vCVaDblaJn28LXjS8vGr5LLId9QVlwhzxz4+J0vtKxyb2mfE2Vb2eftjZmwGjlY441avoct90N9wn1u+fTueoWKX4CMFKEABCjgLMKDirOHnees/h4C9J1xrPZUNy3r3m7y6FgrOUmxqLVRu8YJM5S7rIxuNS6ktl0V+ufq2PLmCEwpQgAIRILDxb9sNtd/59Ht0vvtpR5q3eLUc3Yq1f+O2+19A4079cdfDr6Fzu+a4r+cNch0nESPAgYSBgDU+SQ1u9PU5WWtd4dOoMuPP4EiFLLd6Y258ALob7nfLt8YmyHp5hopk4IQCFKAABYoIMKBSBMSfi9ajZzxXd/S0S35eRh6m3T3NkWY9PAuwAvkZ+Y48sX7mwzNdtuMCBShAAQqUTODlJ/ti6+LxbknciFbU9MMXr2DFrNGY891wrJn7Jd56dgDEDQ3FutAltkwBClCAAhSgAAUooEUBBlQCuFeU9DJutRcarfjxi3/w84CfHessJgsOrDzgSIfXHwYUwGwyO/LE+iPrj8D+X/7ZIMyvj6rBF3smHylAAQpoQSDM+5CSnIia1dKREB8b5iNh9ymgPYEFC+ag/4B7cf0NbVBYWOjeQfulPtfc4b4uBDnm9ndDXHpkjbOdqRKCLrBJClCAAhTQsAADKgHcOcplVYFaFVxasJZJwsF/TuLQWjVo4rKmZAumQluw5fC6C6unZK2yNAUiU4CjogAFKECBwAvs2bMLr7/xErZv/wf5eXmwWiyy0d1qvpwRk7OX+liuLVlAxXpxExn4EMEPkay1Gqi1WeGcX3jtbciOzVTzff9nad9brbev7xuwJAV8FNCdPIG0u7t5TWUG9vexJhajAAVCKcCASoD1Y564EfoBHaC7pSn097eHbtANAW6R1UeBAIdIAQpQgAIUCDuB9RvXe+zzurW2exh5XOljpuXiZjB3PHfPFYu8p4oC5/zCNl2RFZflY40sRoEACxQWwrByufe0bk2AO8DqKUABfwgwoOIPxfPUodSvBt11DaA0rAEl1nZH+4LT+fix27f48dZvIO+ZUqQOq4ff+nO+18qsgb8W2ULLi+wbBShAAQoEQsC67wQsS7bBunonkJUXiCZYJwUoQAEKBEDAUqUqMib9JNOZT8fIFixp5eSyyD8zaqzM44QCFNC2AAMqnvZPkPL2bzyB/Vsy4OmyHQUKisZUzEaz454qR3ipT5D2EpuhAAUooE0Byy9rYR4xB5Zf1sA8+S+Y/u9nWA+e0mZn2SsKqAJNGjVRp+7/mjZr4Z7JHApEgYDx6msg09nngDUuzrYs8s/mRQEDh0iB0AtcQA8YULkAvAvZNM5agAHW72Tqbf3RvSoFUNQEp/8Syibgjkk9ZLp59M1OazhLAQpQgAJRJVBggmXJ365DLjDCsvQf1zwuUUBDArVrX4SXX3oD9epdhviEBCg629vQOmq+hrrJrlCAAhTQvAA7qB0B218y7fQnanqigxkXKftkqqUc8GncOqMe6WvryFRhU025jTVHgfF722VEMoMTClCAAhSIeAHrkdOex3j0jOd85lJAIwLXX38jvhr7LRbMW4rYWP6SlkZ2C7tBgUALsH4KRKwAAyrhtGtzAeOoGFv62hZEseYAJgZUwmkvsq8UoAAFLlygYorHOpSqaR7zmUkBCgRewJR3HEdWv4Vds+7AgcWDkHPor8A3yhYCJMBqKUABCvgmwICKb05+KxVfNh69+qWgr+LhMp/ztZIIxPQyyVJJ5ZPRtefd6NKrK2J62/LkCk4oUEIBi8UKs9n205VFNxXrjhw/BZPZXHQVlylAgRAKKIlx0LW73LUHcQYoreu55nGJAhQIioDVasbO6V1wdNVbyNo7Bye3foX/pndCzpFVQWkfbIUCFKAABUIioAtJq1HeaPpL/VF21KvI734nCjrfCLz8HO4Yf6u8N4q4R8rNn90ib0gbXybekSfyb/nmZhjuMUk9XZoVdYZXRs23K8LAgIo04aTkAuLXpF77cDxe/+gbt42XLN+Iljc/io53DkWjjv0xZeZitzLMoAAFQiegu60Z9INvhO625tDf3Roxz3WFUq1c6DrElqNawFpoggrpDzEAABAASURBVHX/SVgzc31y8LWQ+Zc1MD010WuyLNnma1UBLZd/YgvyM9z7cua/qQFtl5VTgAIUoEBoBRhQCZF/wU23IvPDT3Hmi/HI6/cQarSri4qNK2BH3j9YuX+ZvCGtPlaPGldXd6SqzauGqLdsNhIF5i1ehbbdn8C0WUvchpeXX4inX/8Mj/frjo0Lv8KINwbhtQ/G48Dh425lmUEBCoROQKlZAbp29aG0qAukJISuI5HfMkdYjIC4GbL55Skwfzwb5td+hPnzBYD6d6SYTSJuVUHGdo9jyveS77EwMylAAQpQIOwEGFDRyC7LzslG797d8corz+Lr8WNkrzIzeXNBCcFJQATatGyEqWNewy3Xt3Krf9X6bcjNy0evrh0Qo9ejU5tmqFU9HUuWb3ArW9KMLFMm/s7cDPFY0m1ZngIU8FWA5SgQJIHMPFhmrAaM5y4Nte44AsuKHX7pgP625oj5oI9Miho8FJWKs7LseSKgKPJCnZKrt/XYhZTq13nMZyYFKEABCkSGAAMqGtmPs2fPxPETrt/+G41GbP17i0Z6yG5EmkBiQhwqVyyHpET3b7WPnsiQAZTYWINj2HVrVcWRYxmO5dLMvPP3S+j0e3M8sPIO+Th82yulqYbbRKIAx0QBCoSlgPW45y9/rPtPheV4StvpmMR0VGvzAXT6eEcVyWowpVyDfo5lzlCAAhSgQOQJMKCikX26b+9ujz3ZvuNfj/nMpIA3gbWbtmPMd7M8Jk+X93iqJzMrB4kJ594UijJxcbHIyrZdG2+I0SE10eBIYlmUSYjVO/Kc14v5LTkrMf3gD6KYI/10YBL+yV3r2CZGr8h1iXExjjyxbSCTTrG1mZzg2mZinB4xetdxOvcjHOfjDDrEGbzvI3+NKTE+Ru5HTmwC1jz1y/sJMSh4IhYFz8bCNE9vW8EpBSJAQImP9TgKJd7gMT9gmdn5sMzbBPPYRbBMXQHradvfq4C156HiClc+igYDDuKSO//E5ffvQt2uv0Ifm+qhJLMoQAEKUCBSBHSRMpBwH0fNWnU8DqHeJZd6zA+3zDO7ZmLH1LbY9EUFbJ/cEie3fh1uQwib/opLdU6dzkKRJJczzmT5NI7UlCR5yY9z4YKCQqQkJ8osqxWwqBN7UhfP5ltd8u3rxeOWjE2yTNHJ36e3OrZRq5SrrbA68sS2gUyyQXXi3oaaGcR+uLfvfwPhaw3CmMTNjoUek02g4MVYFL5jgOk3PUy/6FHwZCyMPzDoZNPhNOwFqqRBSS/jNgzlqovd8gKWYbHCPHo+LPM3wrrtoLzcyLpuV8CaK65iXUwCEis1hSGpcnHFuI4CFKAABSJEgAEVjezIm266FRUrVHTpjcFgQKP4eBhWLHOkmA3rZBklL1fm6Y4dlctanhRm7sWeOXch99gaWE25yDu5GQcWP4bcIyuL6TZXlVagTcsrMeyxXh7Tg/fc4lO16RXSsPfAURiNJkf57bsOoHKlNLlsMluQnWdyJJPJIvPzja75zmUMSJZlxAd6OXN2svz4cny09SOZ9mTbztTKKzA76nauIxDzIpAhupKb79pmfqEZJrM1aP0IxNiK1lmo7qfCYvZR0fKlXRb7T5gyqQJmwOzhjBTzbL26kv8oEAECOgX6gZ2h69wISv1q0F19CfSP3QClVgUE7b/DGbAe9XzpUdD6wIYoQAEKUCAqBRhQ8XG3i0sdiv1238d6vBVLTkrG999Px2uvvYMH7n9QFktNLYOEL0YhrdftjpT61CC5Tn/wgMyLW7xQLmt5kn3wD4/dy9z/m8d8ZgZHwKwGRYxqwMRsVgMHJrMMnljUb/lE6y0aXyYeMGnGQjWoYMZvS9fKX/hp16qxzC/NpEOlzojTxblt+sexhRi7c6RM+3P3uq1nBgXCWcCyVwFs8UaXYVh288+vCwgXwlsgOR66G66EfkAH6O68GspFlYI6HuuxzKC2x8YoQAEKUIACOEsQ9e/ops9Zigbt73dLQ18dLYly8/Ix6IURuPqWgbi26yD0GvgGTpwKzLcg8fHx6HBdJ3Tv1kO2LSbmi+rC2LKVTJaK596gWBMSZJ7ZKU+UZ6KArwI//roEja8fAHFflRlz/5TzM+YulZsnJsRh5FuDMXzUJDTq2B+DXxqJF4f0QfUqFeV6XyfWI6dhPW57o3uq8CTyLflyUyvO/S8yetbsgwF1B6FGYi2x6EiWrQrMq3RekzXHUZQzFNCkgO4iK3SV7BfFneui/hrzuYUwmRu/MQ/9fjmD2384jecXZuPfk+E3hjChjthuHlx1EPtXHHAkq1n9W6A+PQ4UyS8pgFI3vaSbsDwFKECBqBPggAMjEPUBlevbNsfsb4e7pCsvr4vyaSlS/PvpCyEudfh92sdYMWs09DodRoz9Ua4LxiT34ceRMXmGTAVqsMXeprlaDZlX6JRnX6e1x+Rqnn9KMLVGJ611Nar60/O267B18XiXdPtN5/ZVh2uaYNPCcZg/+f3/Z+88AJsq/jj+fZlNd8tooeytIHuJMmSLKENAUEAFRNnugfoHZSiKOBBUZMmQqYCy95C9996jtED3yM7/3aVJkyaBdKVJ+4P87t397u53d5+kyXu/d3cPxzbNRJ+ubdzmY7ocA8PY5TB8+y8MX6+C/uuVOHB1M4Qs/0W/CrfZu9xreKMSc6hU4GlLoB2ngPo1pUsxXinyX2EWVHT0YgKKMTrAvFqO91JSxQT5UFHHU74RrDqvwZJTakQnG5GuN+HoXR0m7krxjc5TLwuEwO2DZueJbeN/v7oCf/VZbhWjzgC2L/jKLHq9OnO56a39ZgeMrR2HeLAKkq6NALnNUrqwAIdipCACRIAIEAEikNcEJHlt0NfsBQaoUL5MhFUSk1Jw4sxl9OvRgQ9l/bYD6NG5JUoWD+Ubcvbr0Q5/r90J2nSR43ErUASXR4Vnl8C/ZEMIMn+oij2BMq2mwT+yiVv1s1+IauQVAalUgqjI4pDLs7eBpmHlQZiS0zO7cS+ZbxSYqXAvJjxmhLSRWeBvriN53GTVCQEms5JCIuDFBKTPGBCwOx2qFWqoNojyjxqSsr712T0mOlCyIo5NNeJmkiGrmtJEgBNYNWAVd5xoU7Q8ndNgee/l3M6j6kua14D0y16Qvt0J0jEvQlKv4qOqUD4RIAJEgAgQgVwTKPIOlawEp8xYht5dWqNcVEmexTbmLBeVOZW0bGmzPinj8bG8UG4DD9RnSyMMJyUwJXigMSdNhFR6HlV77kTtN++jWu/9KFbzdSelSFUoCLB9WKIdP2gNr5ZC+f1V0WF8D/QY/gY/svTDxqwU7+z7/aEBE2kVIy+q+J+Wp5mOLafgSgqIgA8QkFQ3+ZwjxQewUheJgJWAoJBBKFsMQnCGB96aQxEiQASIABEgAvlDIEcOlfzpSt5b/XfjHvy+cLVT2X3wlEODu/afxMFj5zC47/M8j81CYXuo+CkVPM0CpULODkhLU/Nj6WIq2ApXioGtLrvxyHCVaAGQCLCz7a/MnMoqkwp2eQ9rQzFdhbRGKqhfUiKtmQqSz+z7zOoWDzFvFqqQSxzs+imkCA9SOOhZPV8VqQg3Mswv38ekkBXqPzH+OXUIRLZQyh3UoQ8i0GxWO4TfKAm5Vs6PT81uj+DoUIeypCACRMC7CNSNdPybLhkgQdngzN8l7+ox9cZbCNjum2JiDnc3Oma7p4obxakIESACRIAI5DMBMu+aQKG+2mOzSOISkuFMmKPEFgt74smU35ZgYJ9OiChhXuwuCAL8VX7QaDPXulvi/v5+vPqdB+mwFa4UA1tdduN348xLJdh5h23dNI1BtGx+6Q0mu3Zty9nGb+1UI/5ncx1LmPw3cONPrV39+4kanq3VGe30zJZaa0Bcsn15pvdlMYhw78arHcaa12PS6o2ca1ELJHXtN5dl478pCWAHBylxobSDjhREgAg4EjBuPQXjhhMuxaTVO1bKI02X6kq8VMsPpYIkUMkE1BMdLKObB+aRdTJTmAmsGriKL9lhe6cYNVk+oyZx5EzEg+1rZf+/rXVs9RQnAkSACLhJgIoRAY8RKNQOlVe6t8VHw/o4FbYZrS3l9dsPgG0++3rvZ23VYPur3LgdY9XdvBPL48GB/vzo7YHhlPO7h8ZThfqt9/a3pdD3jz02U/piE0hql4NQryIkrzwFoWqpQj9uGiARyE8Cpq2nYdx43KVAn+l0z49+vFZHhdkvhODvl0IxsU0gqhdz/vuSH22TTR8koNPyTpc33URF0w0ugmUncp4jBoIoWXWiKqpRFMo0KcNFTLr9Yk+F082WQb9GCpO5ebfrUkEiULAEqHUiQAR8lQBdVYvvnFarw3e/LsGw17shLCRI1GS+OrRqhGX/bkfs/QSkpKZj/vJNYE9CEQR+FpBZ0EtjQjEnt37EvgrFnevFLHoRgTwhIDSrBsmrLSHt+zQk9SuhfPNyTu3eq3bHqZ6URIAI2BMQWteEpH0dLpanmUiesdHJyMFhT4xSBUlA0Jtno7yGpXhDWMhFCidOPyfnU93md0ePxT24uDsG9bsK/kQ47WQ5NB8ooF8ic7cqlcsJAapDBIgAESACnAA5VEQMf6/bheSUdPR7sZ2Ysn+93K0tKpUvjWd6vI0mzw2BTqfHiAHd7Qt5cUra1AAh3L6DggKQtTXYKylFBPKZQLFqxdDxh44oWaskZP5yfjw75DCSSjluYJvPXSHzRMAnCUha14KkQ20ukJsvFgXmZMnQsQ05fXJg1GkikEsCpqsCDOuzOBTNK5mtlm0j/ulyyGZ/DMXYLpB/1x/SjXNssylOBIgAESACRMBtAhK3SxbiguypPgfX/YogJ8t4Avz98MvX72DPv9Ow4+8fseS3MShZ3Hc20RQCAdViNeQD9JC2MELeVw+/pWoIUTRDpRB/pL12aKOLDcOPI8di/g8/8uOxevt5X7v/1wZNNlXDnvs7ePqB9h4/UkAEiiIBtiRTO00OV6LfTj/dReBz4fNDLFNZxZf5SGCzl5iT2SiPGmi5p8s9cumP4XL2/ibK3w6H5NJhQJsG4UE0pNv/hH7H34/qCuUTASJABIgAEXAgkL1fIIfqRUcREhSA4uEhPjlgoYwJivd18PtVA8VoHSTVTD45Duo0ESACRKAoEDCclkA3TeZSDDuy3In3CijUCW8hYDKZoDcYnHbHyDZkvxfnMp8tgY65Fw9mw6mBbCif3zAMLw8vAUmFMjAFBEL7dEuUqVeCO0cs+6MIggC2hUpUxp4ppcuUBRPjLJXVodi56Ut4oXkfaH+VuWxdWtnGaeOylDnDT6eCQu/41CrjRdHBYi5CIREgAkSACBABtwmQQ8VtVFSQCBABTxIQkpPg988K+M+YDvm+3Z5smtoqCgS8eIzSmkbIh+m5SFsaeU8ldjrnF8u8IAVFnsDqTXvRvvf7Dhx27D3Oly636fku6rQZiKX/breWYQ6U6X+sQr32b6B1z3fQottIHD9z2Zqf00jq+5/gwfZkv3PnAAAQAElEQVT9uHfqMhLmL0XXv1/l+6JY9keRKEXnoOhT6TavG16c2wPPPejLxalDcYajE8TSL6GiCdKOWf4ulJZcOhIBIkAEiAARyD8C5FDJP7ZkmQgQgUcQqLCvGmr908gqgTEh4p1R4OaS8zjWeCgOjFyAfRO3wNB7IKRXrzzCWuHOptEVHQKSWkYohum4SFsZ+MBtdbJWRq7Lq0BISYBw+wIETXpemSQ7BUCAPZGwQ58P8PHEGQ6tp6u1eP/LXzB8QDcc3zILP44bgS++m4tb0ebllcdOX8K0OSswf+poHNs0E107Nsc7Y34Gm9HiYCyfFIIfuBNRzpyJb+qtrfA00w3WWXXOIn5TtPCbq+EzcpXfaiF7KdOGbXm1PB1amaMtSdUGtsUoTgSIABEgAkTALQISt0pRISJABIhA9gg4lD6/6jzK/V0NQXdDrHnl9lfBE6sbWSX4XijYDPBrW+9jm7ohtgrNucQhDEJCPK93dusZ7P1hHxKuJfA0BUSACLhHQJt8Cym3d9oJZgyDfGJPyKeJxy9egHTLfPeMUSmvI1A6sjj++OkTfDqqn0PfDhw9i7R0Nfp0aQ2ZVIq2zRugfJkI7Nh7jJfd+t9RPNmwJuo/UQ1yuQz9erRHzL14nL98g+fnd+C36i+E9e6KiF3PcSn5X1fepCBoeDqk+Fwo3nLuIOEFMwJpYyPYnnGy5wxgG/BnqB0O16PiYKwiOlAU/jAVKwVDq5cha+k7DxxwGBApiAARIAJEoMAIkEOlwNDbN6xWq3Hk6CEuJ04d55k6nZanmf7M2dNcR0F+ESC7+U3g/OrzqLCiuuhQCc1sSsiMuhu7sOMs9v+4D4k3E92tQuWIABEQCSRcXo7LKztaJXFhfyiuXRBzMl/SLfNguOuZi+jMVimWFwSYoySyRDjCQgIdzMXcj+cOFIVCbs2rXL407saaHdXRsQ9QsWykNa9kcfP3dOx9zziupXduQb5/b6Yc3G/ui1F0kIh6yc28/UymqXTQD/ga2rGroHtP/My3f93cHoVEgAgQASJABLJJgBwq2QSWF8VNqYBmnBxprf2Q1lQF9QgFYo/dw6hRb3H57LMPeTNJSUk8zfTjx/+P66wBRYhAISAgQCgEo6AhEAHfICAPiEJgVHMu8qBy8NcFOO24/sopp3pSFgyBO3fv4/eFq10KW87zqJ4lJafCX+VnV0ypVCA5JY3rWL6f0n7TEVY+Jc28DCwkQI7iIUqrSKXm7+6wQIVVZ5tviav0BtxafwnnF5xA2vn7DmXNVgDVK31g/OdfLqa+fXmfeCD2ielVA1/jdVUKKVcHqmQ8bWnH2VHF9mcRSwf4ZZYNC1KIGkAqERzqM9vu2HXWlrfq5FIJQh7xHuVF35Vy8/vC4VLgNQTizy3A3YMTXIo26brX9JU6QgR8mYDHHCq+DCmv+677TQ79IhlMdwWYkgDDFilM3weibt36XKpVrW5t0qJ7/LGaVh1FiIAvEmCbHrJ+lz9Y1W7PFKZ7lBw11cR+2R0c1u1CQnSCuTg9rMrMgUIi4CaBsKo9UbnrBi7FavSDQaJ3WlMSEu5UT8qCIaDTGxCXkOxSTCbjIzsWHBTAl/zYFtRotAgK9Ocqlq/RanncErAlQoH+Kp5MVeuRlKqzitFo/gJOSbfX25a5euwuvqv/G5YPWY11n2/FjI4LsGz4WqsNVpYbF4Ok8Egk1WvKRVOqrKjJeEkkXJdUIorX0+rMY1VrDTzNbLgSZ2VT0sx7p7D+Z62n0RuRrnm03az1vDmtNxqRmp75vuVXX9lnNOMdo4MXEYg7txAxBya4FG0yOVS86O0qlF0pKoMih0oBvNP6XY6e/OKnS+OniTMw9acZePdt8wwVqUTK00z32WdfFkBPqUkikHcEBEHgxiocrJa5Z0qseVo5z3hIcFyohYNpZ3BE/x+SYhLNJc3mzHEKiQARyDaBONV9hzqm0JKQV6/voCdFwRFge518NKwPXIm/yn7mibOeRhQPw/VbMdDp9NbsC1duIbJkGE+XKlkM127e5XEWWJb6WJb+6A0maEWHg0VMZn8KdAajnd6Sz44HZh6BNtneSXP8z5OIj0621skwY02zevoMZw3rB8tnOosYMhrO2h9Lvu3RkGGHHS16nTgOZ3ZZPnOy2JZlOl8XhssdVrkdZwZqhpbEiwiE13gFEY0/5aIIKs97FlajL08zvUXHMyhgBEiIQI4IkEMlR9jyp1LG9aaDcdWCuQgZOhDyvbsd8khBBHyFgImd2YmdvdboAk52PsglqWTGbBNR/7BX9a410CDqKdSXPY3giBBzUXambY5RSASIQA4IpMvTEdOqAQxNnoexRhMYW/WB7q2fAJl5WUQOTFKVAiTAvmOZw0SvN/Be8LjBHG9UtwbXLVq5BXpRt3nXYf6En5ZP1uX61k/Xw+6Dp3Dk5EXoxPrzlm9ARIkwVK9cjufnJIi/HO+0WpwLvdPCpCQCROAhBB6exZwnkY0+BRNFcAVeOFx0qLA0E0Ww2cnCMyggAkQgxwQkOa5JFXNMQNbcfIJja0BSxwgE2GrM8eDR7yPo84+gXLcashvXzMo8CNm6Sdt1lXHn5nOrbPqfnf6sWc8zKSACuSAgCOYpJSMHv49ZUxdy0ZXSuGXxMdGh0qjM02ggb47QUhmzWszm3KpPhYgAEXBOQBsaBEOXkdD3Hw99+wFAcDHnBUnr9QQuX7uDuu0G8ccmsyf0sPhnk2bxfvurlJg6YRQmTVuEOm0GYtTnU/HZ2/1QplQJnl+3ZhW81f8F9BsxAXXbDsSSVdvw3ZihkEhy/kUbVtk8+4U3YBOEu9DbFKFoYSVA4/IZArrUaNzYPAin51TkwuK61MxZbD4zEOooEfAAAXKoeABy1ibkb+og66OHEGmCEAxI2xignKDNWoynlatX8aOz4N7RH/hGU0ZdqrPsh+q0yTfs1lSyjatYBeZQsV1vGX9uIVNnW5KuruV9S4s9ku26VIEIEAEiQASIABHIHoEqFaNwevtcO/l69GCrkdZP1cOJLbOxcfFkHNs0E326trHmCYKAEQO64/CGGdgk5u9f8wvq1apqzc9JpGTNEmCzZmzrqsJVCCgRAE2SBjEnYgCTbW7+xB+cnonLKztwub6hP29En3aXp636jWY9z7QJKEoEiiqB2zvfRfz5P6FPi+HC4tG7PymqOGjcROChBMih8lA8+ZMpBADKz3Xw36qG/750+E3VQlLB2VmFCUJykotOmBB77EfuFMmJQ8WF0TxTJ11fz/uWdu9ontkkQ75NoHrn6mgyqinCKoXmaiDVWj7G7YSUzVj6kytrVJkIEAEiUGgIPHIgUqkEUZHFIZfLnJb1UypQWszPzcwUi+E7R8x3s5lTha34ZJIel44Nb2/AL3V+waIui6BX6y3F4bdyOYJHDYHfPyusOha5tWMUd35o4s+zZLZFm3gNKbd3cUmLOcDrGw0anrbq7x7k+uwGCReW8b49ODUru1WpPBHIUwKme0kw3U3IM5spt7Y72Eq+7ahzKEQKIlAECZBDxavfdAE6l0/3Eby659Q5IpCVQPUu1fHk28yhkvkEkXvNbvO9VE50PsCPSSUSxDuaQHjHMO40YQ4YJiHlM50wj7V+nNsJrZCpy9oWpYkAEfAFAtTHwkwg/nIcBEHIEIhH8O/3s6vOIuu/u5/8jOB3honOlL8hu3DOLjv93jHu/DBok+303pDQptzkfdMm592SbG8YV1HrA9uQ+O69OL6/kK+NnTlR9F+vhOHrVTB8+y8MUzf42hCov0TA5wmQQ6WA3kL9Bik0HyugGamAbp4MpnTnHUkZMwHGEiWdZ2ZoY4/+wJfX2O598qi4Zc+UDBMuD9rk67j635e4tW9cttpIiz1stsluSZljFBIBBwJ3ml3HqRcOWiUlIpGfdF9tco47TZgDhklYRXKeOMAjhecJUItEgAi4TaDsU44b2soUUqf1Y3decKonJRHISwLfz1iGmq1eQ1JKmtXsjr3H0eS5IWjT812+v9DSf31rFoZx7VHgXrJ1PKZUtTWem0hgmVYO1YOiHHUOhUhBBIogAXKoFMCbrlsig+YdBfT/SKHfLIX2azk0nzl/qoKuyZO4f+Ak4jbsgPrZ55329t6xH/jyGtu9Tx4Vjz+7wKmtrErmULkmOlRu7x2frTbSLUt9xLtTWW1SmghYCBiMBkvU7hijjrZLUyJnBKiWcwJsCUJcQjJu3I6FRqtzWuh+XCLS1VqneaQkAkTg0QTqD6iL0g1LWwvK/eWo0aW6NW0bCUq6bZu0xgWbmzJs6c/V2JG4Vuo7XLk6mC+1uZyxN4qzY/zFJVY7thGTbSIjrku7ixOL2uD00raPtGvb1oPTtNQnA6HXH1as24WZf66x6yf7jn//y18wfEA3HN8yCz+OG4EvvpvLn4BlV9CLE6Y7zp+mxaeD5aLfUS2mIKz6y5D5R3Bh8VJPfZULi1SVCBReAuRQKYD31rDC8Q6NYYOoc35tyXuor1YDpuBgHs8alKj7NiIaf5otCXusb1YzTtOKoPKo8PT/EPXkZ9myrypRz2zP5mTIrKCQCGQSkEvsHInWjAaGigj44Vs7kdw1O1lUi+ZDtfRPa1mKEIHsEDhx5jJadBuJ5l1H4NlXPsTTXUaAnWhbbNy4HYNOfT9Cy+6j0LDjYHz+zWzo9A/5crZUpCMRIAJ2BFTh/njxnd7o120IXmzWH2+MHIWnhj4NRaDj937VJsXt6loSJpubMunijZpUzTGkqi4gNfUwX2pj2QPF2VGXfMtixu4o2KXMCbanSsLNnUgUxZktVzpt0lWzgUIWbr2mxaTdqZiwKwVrLmp8fnQHj53DxJ8WYvL/htiN5cDRs0hLV6NPl9aQSaVo27wBypeJwI69x+zKeXVC5fj3xPtr87fD09kM5AGlUK7tTNR8/SoXFpcHRGbTChUnAkWDADlUCuB9Nl5xgt0IGK87+5l/dAdL1nsb7Hny2ZHwGv0ebVgswRwqFUWHSpmmn2erDf+SDcTa4iuXX+iiBS9+UddyS0CS8fnwk6rsTLWQ1EbAj5PtRBJ9h5dRLVkIv2WLeJwCIpBdAuzu9KhBPbBr5VQc2zwL/Xu2x2eTZllnqoz/YT5qVCmHQ+tnYPW8r7B+2wGs37o/u81QeSJQ5AkYdkmgHqSE3/pQhB+Ngu5HOYTRYeizsg/qDaqPCq0rQiLLOB/6+D3oK1ZyysxkEk+QxJxST05AxRI/osKdd1Gpwm+o3HX9QyW0ai+xlnsvuSoCdXpvRq1emx5qM2ubxWoORGH7t+S0Gt+KzpTtolPlvxs6/HwgDVP3Z/9pkt7C5fqtGAz95Af88OVwVK1Yxq5bMffjuQNFoZBb9ZXLl8bdWBezPqylvCciqVvBezpDPSECRZRAxi9ZER19AQ1b2srxbqdQ2gRJJXaqn4+dItNEwAsILL7xB36/MpVLki6R96hLVE90LfMSSqnMJzvKkmWROup9Luk9TZujUAAAEABJREFU+/AyUJjvwqS/9ArUFp05h0Ii4DaBOo9XRo/OLREeGgS5TIqIEuE8LpFIkJicit0HT6Ffj/ZQ+SlQsVwpdO34FDbuyNkTQNzuFBUkAoWQgH6t45OEjMclCFWGo+WnLdB1VhdI5ObTUH2VaojbuhcPtuxB+mtvWGnE1f0Slj3Zovd8ivvJSxGgro6AwIYIjGrxUFEElbXaeVREIvNDSLmWCC7b8qE2s7apCK74KNM+l7/pstahzzuu6xx0vqBITErF4A8m453BPfFUo1oOXU4Sv/P9VX52eqVSgeSMPVZkUgGhgQqrqDL2AFKKR1t9XsRD/M1OHYnEvs1H2Q7vUh+BA1pC2bgylPUrwL9zPT4eaRY7bCwsI1Alt46H2Q4W281alul9Wfz9ZFCIzlpPjIExJSEC5l8yL+VQWLslH6qDpIqN8yQMUP5Pm+3hlqw7Cmypj0QekO26+V0huHxH3jf/EuYv9vxuj+z7DoElokNl5uWpYJKsT+IdX3pjPlbeWoLodPMUbWOx4kh9+wMu6l4v8zImhYIf0/v0Q3qGjisoIAI5IHD4xAWMmTwHv85bhY9HvMKdK/cfJHBLZUqV4EcWlIuKQHRsHIv6vtxNgHHXOZj2X4JJk/m4Wt8fGI3AGwmYrjqfdWt0oi9ZoxxKVoxAsTbNoPpjJh+ONvQ84hqM4XFLkKzejfjAPZYkHfOYgEE8NY1Ocbzpl6ozIS7dmMet5b+5fUdO8/1Qbt6JxTfTFmHmIvMeKj/8vhxnL15HcFAAX/Jj2xONRougQH+uYk//0eoMsIjeKAIScwwGo1Vnycv10bK0VGwiu7Yk9StC2b85lANaQVKvothDwGQy2fUxo+vQi+3Y2dcbHcra5duM31f0enFMxizjz6++c9gU5CkBXzRGDpUCeNckFUxQ/aOGaoMoK9QI2J0OaYvs/1Apw6pBKgtAyu0d2R6FIqgcmDPGImE1zHuqKILKZ9G/km3brEJwxU58iZB/yfosSUIErAReKvcqBlUeYZV+Fd5Aw/CmKOdfAQGyQGs5ihCB7BBgJ7i/L1wNV3LlhnkPHovN6JgHiL2fAJ1Oj4RE8xMSLE9+sJ3+ze5WxiWYHX/s8jA8SAGLBPub78DLpBKrzpKXV8cApZR32U+8I2prU7z5yPVhgZn9sc23jVvK6n/ZBOPKgzAs3QvTLvOja1WifduyLM5W4rFjYRF2l5Ld9fXEeNhnhL8xFEDypON5jWBMRtisAW7RUUfs5eUE8QITopgy4GqUN7k+ZMgA7oRRrl/D03kdJKiNOB6jdynXEw0Iq9aLLxEKf9y9MeV1H/PanlRk3KC0eaaEre2KYVKEq3zvkqFKhSiMGvQiwkICESpKcKA/H1ZocAAUchkiioeBLQlivwM8QwwuXLmFyJLinU4xzpwQaRoDLKITL9RFNfSi58miy6tjutb898IcAbmxqRYdIKyPWfvOnENMr9YZreNh7ai1BmQty/S+LFrxfcqP98gZEwAMK0kRJ1Bg347x4gns32t3Yursv3H6/DX+NqzZsg/7jpzh8aIQSMqaIKkuniXkYLDXnqqNq2t64M6e0fx4YXGTbFlRBJfnDg/LviuWPVWYQ8WiY8fwx9zbayVbjVPhIk2gt+hQeaPSCFjkSPwBHIrbhxtp15CqT+FsziSd5EcKiIC7BEziFVdcQjJcie0JM7PZud2T+OXrd/DDlyP4ZoXsDqblZNu2LLtbGR4azKqILQBpar1VNOKJKMtgJ6q2+ryMsxND1ga742ZrV7z5xtRIt+mPbb5t3FKWV7AE4gUBi7ILBNuyLM7Kp7phl5X1BUnX6GESrxg80dec/aKzd6LwibyfTrxbbswcmEKPYvqPIEjde6yrNC1jA0zxIh+iiC9uS2I0XxTzhBtBeM2B3OlRuet6lG8/D+wPWa6KsOoqi/py7f9wsHQy1oCPNye7lIUn1ZAHluFLhJQh5lkBDkZ8UPFaHRVKB2deHoT7CRjSIHvMvWXYlUWHyuC+z8MivZ5/hnfttZeeBctrVLcGTy9auUV0khiweddhPqOl5ZN1ub5wBjQqIkAE8ppA5jdmXlt+iD02fbp97w/40xN+nfcPrly/w0ufu3gDH3z5C/9S4woKnBJIrrIE2uBTdnnpD04i8epqOx0liIA7BNjFoMFgc9LrTqU8KnMn/RZOJx53sHYwjqZ0O0AhxUMJyKRSfDSsj0upXtn5fgrsiQ7MMHtMcvFioSwK5lzhETG4dvMuSpUMF2PmF7u7ZxGN3nz5zO4oWnSujjeP3cXeGUdweOEJ3L+VDFflsup1GY4PNtXcNs/cMqAW78TZ6p3FTUbXf9/sLl7WOmykGvEuZla9r6bZWBgvT/SfsSMxExDEPxvVQg1UW9VQLVMjbMpaBBkWmDPdCP3vPANZUrksJaUIScnY9D5Ljqskc3ZY9j7xj2wM7pyR+XFHiEUfwPSw/xeiFPBEhIxLuRDzTLEQPwlPM315G6eDfU3fTlUKk2LW8yH4/flg/PJcMBa+GMrHXGCjyseG/VVKTJ0wCpOmLUKdNgMx6vOp+OztfrBd9pmPzT/UtOl+MgzTN7qWJeYZXA81QplEgAh4hECBOFRWrN0JdhK7cfFku02iOj7TmN9dZFOxPTJ6H21EW+Ko055r4s451ZOSCLgiwNbYfjFlLr783vHu3AuvjkbNVq/ZyfS5K12ZypE+Wn3bab0Ytf3yDKeFSEkEckCAPSJ5639H+Aa0bOPB6X+sgr/KD2xqeEhQAJ5sWBPzl29CulqLqzei8c/GPWjfslEOWsqscmDaASzs/Cd2fLkdmz7ajDmt5uDGHvOyhcxS+RiTFMhPfT4OiEz7EgFJpAmSmsylZe71hC118EPFH7gY1Hqu/J/wIUbjEy56k+i8MJkgGJQou3wbKu5ToPhlKcoclaPJfCUqLpmCyNdaIb+W+vAOiUFt0ZnyTdsgMOlbWyVqgCdKSnma6V7J0PEMm6CwRMsES1EhVHwvCsuAxHFUqRiF09vnIjhj6Y+oQuun6uHEltlg1yTHNs1En65tmLrgRaeH6XKMS8HNBwXfR+oBESACnECBnGUtX7MDLz7XAlGRxXknLIHFI5yQlGpR0dEJAcU95xu9KsPNUxedVCEVEXAgsGH7AbToNhLLV+9wyLMoRg58EWsXTLLKy93aWrLy5PhYUC34SwMcbNUIruWgIwURyAsCOr0BIz77Cc2eH4amnYdi+56jmDp+pHUTwk9H9sXp81fRsONgdO7/iehMaQjm7M9N2wemH7Srzi4ij8w4bKfzeIJtluDxRqlBLyTg1V2SpZVClf+UqLfCH49tViI4pnBd4IP+eQUBqVTCr0nkcplX9Id1QggPgnRoO7P0epKpIBQLNKdFveQls45nUEAEiECBEigQhwp7TOWtO/ccBn7hivmOne30aodCpEDQpZegSLK/4PTTlEHAz/HQvzffKqZTZp6EjAg4I9C8SR0s+/0LsL0knOUzXYliIXw2GZtRxoRt6oY8/OcvC8C71T+FSupvZ7VNyQ52aUoQgbwi0Ov5Vji+ZRa2LvseW5ZNwWbxbnfTBo9bzbNHJW9Y9C3PP7D2V0z4eBByc5KddDMJ+jSd1b4l8uCS5+8uyoa0g6RrI0jFk3Oh+WOWrjzyuPWaFgtOql3KrSTDI23kXQGy5OsEPm1zHG9ffdsqo98zYSK+sopMED9PgmXHlMzRpo54F9E/rsGd7pNxd+52aDo+l5lJMSJQ2AgoZRAqR3JBuWLm0SnkPM31ZTN05hwKiQARKEACBeJQafN0fSz9dzs2bD8I9vguvcGAk2ev8EdY1n68MoqHhxQgEt9ousLuEyivHIPI+y+i3N2hqHz7c9/oOPXSawiwtcORJcIR4G+eyuysY8tW78Bnk2aBLfW5cTvGWZFc656P6oHtrY/hjyZ/o0FYU24vQBbEjxQQgfwgIJNKEVEiDOzzL7E8AidLQyw/wN8vizb7yeCywZD5yx0qFqtSACfDkaGQNK8BoUkVCOLJukOnXCi2XdVi4Yl0l3In2eiiJqmJABEgAkQgJwTizvyB6xv7c4m7tjgnJqgOESACHiJQIA6V117qiJZP1sG7Y6dh/9Gz/IKt95AvYTAYMf7DAR4auu83EzroI0SMmY+wcZMh1CrLByR9vRVk3/XjYtHxDAqKDIHDJy64fHTsw5b3ZAXUoVUjtGhaG6Uji2Pr7qN4cdAYWJwqUvEiVKWQwiJsuiyrr5AKVp0lz91jveJ1oJIpmRkoZRKrHaXc/DUl8BzY5blr+2HlBMFs2U9sx7acQuxD1nHa5vtiXCa+PzJpJltnY8gLnUJkmfF20UEk0Hio/R4sUj8Z6g9uIOb4xuuZigqw/SKYlA4y/z22quCo843RUC+JABEgAt5NIHrv57i5bQgSLi7ncuvIe4gJ/9u7O029IwJFmID5zMjDAGTi3cHJ/xuCJb+NwRfvv44PhvQG22V75ZwJqFwhysO9oeaIQOEikJau5ps7xzl5hCx7XLm7ox32ejcM6d8FQ1/tgkXTP0dQoApbdh3h1aXihbmfUgqLsDTLkItOCIsuJ0ep6KjhduSZtjMvzs2OD6VCYm3XTynNdTzDnwKlIrNN1nfWLpu9wOKFReRSCeQyIdfMHsVDKb5/oH9WAo2HNcYrq19Gy/+1QrtJbfH69tdRrllZa763R1qLzpO+T/iBSVSwlHfXVlcmQ8czKCACLghoW7dD7NUYJM5y/0k/LkxxdeIvs7k9WvrDcVBQiAjEnXP8G0kMOFSIRkhDIQKFi0CBOFTYEoLBH0xGreoV0aNzS7AZK62fqgeVn6Jw0aXREAEHAvmvaN6kNj4a1sepvPFK5xx1QC6XoUR4KNI1Wl5fqzMiPllrFa3WwPWpGoNVZ5vvblxnMHE7Kek6q53kND3XmWDOSxLT7tpzp5zRaLabmJrZJquXkq6HTm8/Tqb3ZUkX36f0XL5H7ow/2cmeIfxNLMJBiZolUe/1uqjZqxYCIwLylYRswVjIf3/PKkJaMmAywVZfctdRSA1m50i+doaME4FHEEgb/g53jDBnC5OU9z621jCpVNa8VBu9tUCWiOTAauvnXnJ8K8+VHFhj1cnO7Oc6CoiApwlcu3YFR44ecinxCQm8S3q5Afq0GB63DXSyePEsiJZX2jKhOBHwFgIF4lARBAFanfkiyVtAUD9cECB1oSXAltjpxL9Dg0H88dYbwOIW5wJb2jN36XrcvRcnOhUMWL1pL06dv4om9R4rtDxoYETA2whIjm6C6tLbUJRbk62uCTfOQLh6wiowir+34u+urV75IBES8b+7hoUrx/lFabtLi9ytQuWIgEcJSB7ch2LjKuvnXki8x9sX4qKtOklyPNdRQAQ8TWDevNkYNeotl3Lw1HHeJZlOCv+IRjxuGwSoq0AQbzoZVx+BYeZWGFcehCmaPs+2jChOBAqKQIE4VJ55qh4OHjuHxOS8fzxyQYGkdomAr3q2jbwAABAASURBVBH4a80O1G03CGxflZXr/+Pxlet3WYcxb9kGtOn5Luq2HYiPJvzGZ7w0qF3Nmk8RIkAEck7gwuoL+KHiDy5l7Yi1EOJjIEk9Dql/dPYacnyoUPbqOyktpCXxi9ISqfT0OCd4SFXQBMQbA6G9uyFw8XoEbr5kFWOLQdAN+jZTajheqBZ016n9okGgfPkKqFu3PpewsHA+6PLlK/I004eHhHIdC0o//S3kQeVYlIvcvwwiHrwI071kGLedhunsbRh3nYPhh7VAHF1LcUgUFGkCBT34AnGoSMQ7Zf4qP4z+6ncs/Huzg2i1+XA2WNCk3Wj/3Lkz+G7K1/h5+g+8tAnmpQg8QQERyGMCvV54Bqe3z7WT7p1a8FbKRUVgy9Ip2LniJ6z/8xsc3zIL/Xt24HkUEAEi4L0EDMckMCV6b/+oZ0QgPwjIxfMn2aULogNSC9m9NKv47xTv4leqC5NFQgrg6Vr5MWCy6XMEXn11EKb+NINLwwaNef9f7T+Qp5m+4RN1uI4FAZGN8Xj/c6jx8lEuj3XcD5W2ApCxRJmV4aI3wnjyBo9S4FMEqLOFjECBOFTY3fC0dDW27zmGiT8tcBDLPg2FjPVDh3P12hW8+dZrWLlyOU6dOsHLGo1GaLVaHqeACHiagCAIKBYWjLKlS0ImlcJr/qVrYLp817Xk8xRYtd6Elec0GLczBZP3pGLXjaLpAPaaz4OPdqRa52p4++rbXDpN7cRH4UzHMhRl18FfO4ovuZFn7I3iZ1gNP9kGKOZ9bKdXLH8XgjyZVbMXJ/75Kg9qoOSuo3b1mf2k74ZDNiNzDxamk27+g9sTTE4M8RwKiEDBEZBevuS0cekV53qnhUlJBLyMgDKsOpg8tFvxKQ/NzptMskIEiMDDCBSIQ+WHL4fb3RXPepc8JCh/N+x7GJCCytu2bTOYAyVr+0eOHMyqojQRKNIETNGJMEzf5FKM647lK5/v96Xht8Np2HNThy1XtZi4KwWrL2rytU0yXrQJSAJiIDUd50tuLHujSBANiTQGwo1TdnqpUSwnyerkcz7fMVAXBLaXisWm5ai7eMzOJtfHXudvgkl0tPIIBUTAiwhon3zKaW80rds51ZOykBMoQsMTqpYqQqOloRIB7yRQIA4VWxQP4pNw5+596A0GW3WRi0dH33E65hs3zCexTjNJSQSKIgE/OYTKEWaJzFhz7K80p5k+MizfqKTrTPjvhuOssZ3XHXX51gkyXOQIaG50RJrs+8x9IAZ9C7XwHNTadtD1nWivLzMFRm1wFkYCnPlBLoWdRexTdezqs/0mgt/5CXqxDRa3iKFNP26TZqhwDBR4GQFjiZJIHjMBUCqtPdM2a470PubPrVXppRHqFhFwi0Bg5ueblRfqVYRQswyLkhABIlCABArMobJi3S407zoCLbqNRLve76NOm4H4YsofRXaj2np16zv9GDRt2sypnpREoKgSEEqHQTq0PRdJp3ocg1CxBE8zvaRTXa7Lj+BOsgFZlzCzdmJS6FGGjANJ/hAwpUXCIK2XuQ9EpbowSkqLn8VSMFaobaeXDH0CkMnc6kiKMgWaEmF29dleE/Lq9WGqXBcsbpWIitwmzVDhGIp64JXjT39tEGKPnkfcqg24v/8EEhYuhykoq3PRK7tOnSICbhEQgv0hm9gb0rc7QTquF6R9n4YpAdD8T4G0ln5IfUoFzVdyt2xRISJABPKOgCTvTLlviT2C9bNJs1C5QhTefqMHvvxgAJ5r0xRL/9mGjyfMgKkIrtHu1OkFPPWUeUNQC0lBEFCuXAVLko5EgAgUMIHK4TIUUwkOvXgigk5gHKCQosAICCEF1rSXNkzd8jYCyfoknEk6CXbM076pVNDXrgtjyYg8NUvGiIDXEFDKIZQtBsHfPFtF+5Mc+uVSmO6J5ybxgGGX1Gu6Sh0hAkWFQIE4VBav2op6tapi7g8f441XOuPF51rgm8/fwtj3X8POfccRHfOgqPDn40xMTMDsOTNQrVoN9OndD08/3ZLrWTB7+gz8PmQml7nHwpAke5OpYYoXoJ0mt4r+SDmup4AIEIH8JTCiSQCClZlfneVDpOj7hF/+NkrWixaBjNEa67WDutIUaG48l6EpuIOpwhN8adCWyr0LrhPUcqEgMOnsGLTd1hCv73+RH78+83mhGBcNgggUBAHDPnKgFAR3apMI2BLIvCqw1eZz/Pqtu2j9tHmqvm1TTzeuzZO3797nx8IexP+Wzh0li+ctwBzRocJk0eL5+G/3Tj50NlNnzuIZmHf6Vy5Lko/ilxAd1vgtFR0qgG6azCrGo+V5HVeB4biEO18MuwrkLXfVLdITAZ8j0CRKjiU9QjCtUzBmvxCCXzsHIzJQ4nPjyMsOk638IWAKi4AhqB6MadnbdNDwyhju/LDsf2LyCwRMJuhf+Z9Vz/dOkerc7rgpMJQvAYoJJOe929CooAOBw/H78fetRXb6FbeXYM/9HXY6ShABIkAEiAAR8BUCkoLoaM3qFbBy3X8wZtmM4N+Nu3l3ypeJ5MfCHiQuUXNHybpNq90aqgEGLPCfjrXKJW6Vty1kPCHhzhfDf1JbNcWJQFEkkCdjrhQmRamgAvkKzZP+k5HCS8BYviZ3flj2P4FMDrYrral8Laue7Z0C+kcEPEzgQtJZpy2eTjrhVE9KIkAEHk5A2rRoP9Tj4XQolwh4hkCBXA288crzuHz9Dlp2H4mPJ87A5F+XoNuAz/DjzL/QvVMLlCwe6pnRUytEwCcIFK5OCkmJkJ04Bnb0lpHJ9HIo/5kJ+cReXGRLJ8GUWDRmynnLe0D9MBPQLZEh9XGVVbRfiM4QMUufRa8/0lTU0osI+BaBIHmQ0w6XfyBA003NJXFxD9xVrgJMYlENuE7zkgnqV5XQzqolKulFBIiAhYBipA6yHgYIJcQ/mDBA2txgyaIjESACHiJQIA6VBrWrYf7U0ahepRy27DqCOYvXwWAw4uPhL+N/7/T30NC9oBnxu4/1Ii05lR0eKSmSZJdlTOkK6C82gG5ZMb60x3Z/FRY3eHKTKpe9pIyiTiB49PsoUacawrt04Megzz70CiTlkipCfnovhJR4LpJjm2FaOdUr+kad8H0CpnRA95PM7CRpqIJ+Q/7PFNSMVPD29Jvzvy3ff4doBPlJQPuVnDtDjOcFPFW8FQKkgXbNsXSHHanQnw/joo2rBrW0GSCAO1W4/qQ/DAclMF4LEZWuX8ajEt6W7gezI9J1ScohAoWDgCDeg1Z+qYX/DjUCdqdD+Yn7SzkLBwEaBREoeAIF4lDRGwyoUrEMfp30Lg6u+xWnts3BP39MRNeOT0MvOlY8jaXA2hPMLafr08yRR4SpQlJmCVNmlMVMaQoYLpkdKrZ7q1jihv8K5K1mXSMhApyA4r8d8Fs0n8ctgWrhH5Dv221JFtgxWOPkJP3SkQLrDzVcuAhoPlNA+4v5As8k+s817yjAZqIUrlHSaIiAI4EY9V0c2nOIO0Pe3NoXHXc8iVRDirWgSuaPuU3+gqJsFasuNxFTgsDbMlzMOMHKjTGqSwSIABEgAoWWQF4OrECushf8tQlPdh6Ku7FxfCyCYP7h+3D8b+g3YgLXFYnAZB6lv6AyRx4RBpiCM0uYkVnTgr8W0iqHIe/5AMFhsxGq+wp+PW5BPkzPRfq00VqWIkSgIAjITh532qzs9CmnelISgUJBwAAYnMxIMaylmSOF4v2lQeSKQJAsGOUCKkLd5cVc2aHKRIAI5JJAWhKEW+cBjXs3eXPZGlXPPgGq4cUECsShsnPvcfR64RmUKVXCDs1b/V/A2YvXcfee2dFil1kYE4J5UP5K906sA43O1x4zK4JKC1nVDIdK+GyE6UWHSs9bUAzTcaE1lYwSSUESMAY7mQXCOhRs4yhk6QKQJGWiY6tV6jvqSEMEsknAeF38orfxZ2fcP+B30S17pajfVXCretHJYtkzhSssQYbz3ZJkR8OV6tCsG4y0hqF8aY/FluVIS30YJZKCJqCUKFAhoBLvxsS1s/H9T8vw88+rsGPBdS7LZu7nS3Q0g5S8jF3g5HMPvQS6fc9DM6Uar8f2VbEVLS31sUNICSLgDoG0OV9CMf5FyKcPh+KLLpCun+lOtUeUoWwiUHQIFIhDhTlMykdFOFAuVbIY1z2IS+JHCtwnILt/G6X/fh8RzzeE/NQJ9ytSSSLgIQLajs/BFGjvFGRpTet2HuqBuZnUuweQcnunVUx6NW4EX4W6clUYVQFcNNWeADoPNlegkAjkgoCkkgmSks6uDHNhlKoSAR8gcChuHzrsaIprqVd4b/0vB6PO5aZ4/GJ97lBke6JY5bDz01FT1j8dMW2MLwXjxUBHG2yPlYuiA5O1pmUBCRHIBgEPFb106QLS0r1nFohwYgd0BzfZjV66cwmE2xfsdJQgAkTANQHnv2Cuy+dJTsVypbBqw38OttZu3cd1kSXD+bGwByEv+aF/zbfwfJXH3BqqFFL0TRuKTpqX3CpvW0hS25ix9Mdgq6Y4EfAYAWOx4ohfuR5pg96CRnSisCNLM312OqFJv8qdIbqUW4+uJjoapVvmwVa0fw6Het4bXBL+7Addegz0Mh1OquficPhGLidSf4VOrn+0/SwlDLpk3rf02KNZcihZlAkoxuiAjAla7AJRUsUE1QY1As6kc/GbYr76k3UygJfNCivjGtFWLa10HspnZ8D/UAK3YbFlOcraGmyLU5wIeJzAv7eX27U5tfv/8O7wXg7y5btvodhj4+3K8oT4ubfM6OJpFkhNkDf+F8q3L8BvrsZO5K+I39kZE34N/0mRPkAJU+Z2Lax2oRIajG8R2LtvNzo/3xavD3gZu//bwTv/IC73TxMUEmIh//09q7CnFEL8oRHi7fWlLxt4mw6BC8eJcOuCQ1FSEAEi4JyAxLk6f7U9OrfEhSu38NZH32HpP9uw9b8jGPf9PEybsxItmtZBsbCCXwKQvwTM1sPeVOGNXwbhlXq1MTwxEW9VqITXXx+Mzp1e4AXEcwkMS9RiUHJtLi+nRGBIohzPqXtBanrA90lhe6X4tzoKYzs97nSfjJh/D0FXqzavbxtI6xgzlv4YbdUUJwIeJaCvXAUpn36BxFkL+JGls9uB+JhZuLyyI+IvLH1kVUncbdGZMt9OIu6HoHRyOS7hafbLDh9p8BEFNHHned9u7Xz7ESUpuygRkD5jgP9yNR+ypJToTPlHDUlZ8VY711BABAongVtpN2wHhstRZ3Cs6l4HOVP9MAKuL7Qr6zIhMUFSLBqS6imQNjZaRVLJCN1CGSD6VCx1jfsk0C8TdRYFHYlAARL47ruvkJiYYNeDbds226XT60YivW4IhNsX7fQPTeg0EK6eyJTb5wHmidTb61WpJjj9FxTuVC3cOsedNJLTjjfAnVYgJREowgQkBTH2Z5rVw5h3X8XhExfxxZQ/MOKzn7B41VY0rf8Yxn04oCC6VKBthvr5YURSEjqqHkO7xzqhafkfBPeHAAAQAElEQVTmvD9SSNE5UYkX40+iV/wxDE5egWD9b+Y8032+T0qY/iv4tzoCWX37ExdeiAIiUMQJGMOjYGjTj4uxThtOQyPX4U7QDS5x/ve4jgIi4DECdH3nMdS5a4hq55ZAGf9ybpswlCjpXlmjeFHIXufuwLjhRKbsSXJa33BK4lRPSiLgSQLJyUmIibnr0OStWzftdIYwFfRhCkDtualVpppPAzK5XT9MAaGARGZ20iQ/sMujBBEgAo4EJI4qz2jYprT/rZqKVXMmYNH0z8HiUyeMQvHwEM90wMtaSTYFYP7GMPzVZzn2f76f905mVGCm8AqXhejOdRQQASJgJqBNuorU+P1I9TuPVMMJvtTGdm8UHtdcRmKNClxSy5lP2NUyLW4H3+Ty4CEOleTo/Ui4vsO5XZs9WHg7Gem0e8fMnaOQCGQhIJQx8aU5/hvVWXLyKJnFjPInLW+Plv5kAUNJjxF4PqoHhl0OQfX670PVrTmGJd1EzSQFTCaTQx80nZ530DlVsLoCYLogOlQ2HocxQ0xJNg8zsDEvCcpMmA5ehnHnWadmSUkE8pOAv38AJBLHS67AwMD8bNY922ERCPpoJozNusJUvTEMzXtBP3QqIJW5V59KEQEiAMe/bg9A0en0SExOhUwqRZWKUXi8egVcvHIbJ89d9UDr3tdEev8BiDt6GmWalOFSok5x3FPEINnvHiqabnAph9tudzz+302IvRoD3RN13K5DBQuewK79JxF7P6HgO5KPPZAc3wL5tKGQj32eH1k6p809OD0Ll4/3w7XSU3BV+wlfanN5ZUeXx+g9n/GmTEYtPz4quLymH44uauPSnrO2bu8Y+SizlJ9BgA7OCchf0nNHiGUvFMueKrIseln9fc4NkJYIeAmBx85exqAboSilA/xER0jjRDm+OVUcgiDY9VCAgPSRwxA8aCOKNfgaxeuMQ/BL/4peE7GY1Mj3TGH7pnBptFZUOr4kpdMhqSw6TwQxzyJiVNrVIIbml+HQFXKomFFQ6GECUvF6p1Ur8yxZ26br1G1gm7TGpWt/48ttLHujKP4aCz/ZBiiTltvpWb5s6dfWetaI+PdmjWdE5Bqgxr1aCF4xz86G5Nd3kb70ewjRVwCtGpJb5yBb/i2klqU+4p9Vhgk6EAEi4IJAgThUfv9zDdr2eg8paen8TsUrQ8fj9Xe+Ru+3vsCsRc5/LF30v9CoVWEq9Fjcg0uLn5pjZ8QmnK1/Dl2uTeHS/tovSO/1snW8+irVuNOEOU5s9dYCFPE5Akv/2YpneryNd8dOw95Dp2FkU5t9bhSuOyzEXINsydd8bbAg/mizNcI8HXvddaWH5CiCKyIwtDEC0qshQPIEAqOaP1RUxWuarQmOX3smc45dGFiqMULLtbTYdOuoKlHPzgYliEB2CcgP7ENY764oUbMSwts8BWWWNfbZtUfliUBBEfC7dMKh6dIaOaolZ1leAPM3sOHd5lDPH4X0Re9D/1FbiH4WsNt+fM8Utm8Kk3DHZRNg/6Si02au2rz5fksD5L308JuvgbSukeWSEIECJ/DJx2MwYsR7aNG8FUqViuL9adSgMT9mDSTRl83LbSx7o9w5A4k0BhL9bXs9y2d7pmQ1wJyWWZwqgvinEKQNgezOdTsbEG3oLx6z07E9WWBZ6iNkNU5pIkAEshJwvLLIWiIf0uxikW1MGxIUgH2Hz+DU+av44v3X8fYbPbDw70350CKZJALeT2D8R4Pw+Tv9cfVGNAa9/y2efeVDzF2yHg/ik3LQee+rIlw64rRTwtWTTvWPUharORCV6i5Ahej3UFH5NSp33fBQKfXkBG5SIlXwo23g7HyhynMLUO/lLQ+1mbXNMi1/tDVLcSKQLQKCXo+QIQMg378XQloqZFcuQbl5Q7ZsUGEi4GsE1OqUzL1QLPuibMr4XXDzxoLp/B2YDp2ArMphKLoeguzJwxDuH7Ozi7hkX0ND/S1EBPz8/NCrZx9MmDAZtZw8PMJ2qP8KpTHkRJKd/H4tBJqg7tAN+tZeenxoWzUzzpwqmSnolCacK3YSSV362dU3Dp6MgLd/tNOxNoyPNzPXNpkPFBIBIuCaQIE4VGLvx6NapTK8V0dPX4K/yg/dnm2Ol7q0Rsy9eFy/FcPzKChCBGioCAkOQG/xb2DF7PFYNmMsWj5ZB9PmrkSLbiPx4bhfodHqigwlv5XLEfDDt1xUS/8ExDstglbLxy8/c8p8PH6MHykgAoWFgJCQAEkcbQBYWN7Poj4OdZXaDgjuKHW4EGT/W5aIZFzas8m6HwrfF2VLhkNF/O53MOLkAo85VHi9jD1VnMURl+pgihREIL8ICKmiU/zEMUge3M92Ezsu3MCRJIOd/HU3FkZ5WZgq1bUTlH3MLfsmiYBkvyToy1Swr1+5LmTV6tvpeBvBJcx2nd1xMudQSASIQAaBAnGolCwehrMXb4jXSCas37qfP91HKpUgLV3Nu6XWmC+ceMJLA+oWEchPAo9Xq4CBfZ5D/57teTNrtuyDr/9dmKrU52PJGij3Hs2qgt/KvxDwo3jXRBS/ZYsAdqclw6ESuPcKwm5KEXjwLGz/yY4dAXPCKApwiYRUEcSXBqlK1LXtGsWJABEgAkWOQEqDNphZLgE3VFqkSo3YG5aOD2s5v7g81EwDSfs6mdLmCTMv9t1vjmWGTi7whOqlM+va2rGJIzwg0wbFiEA+EmDnLyVqVUJ4lw4o3rAmQt7on4+tkWkiQAQ8RcBVOwXiUOnS4Sm+tKdxpyG4fP0OXu7Wlvdv597j/FimVAl+pIAIFDUCOr0BW3cfxbDRP6B1z3fw67x/+OytP6d/DrZEzpd5mCIqQP/SxzBFVYVJ4QdTZCX477mOwN/nZGtYpU6WRsMl/ih5LxK2/+THj4KdxCh3bLVVO8SlilDu9AjM2HNFIlXyMv4Rje30QoaeZ7oZKMOq8yVCtPTHTWBUzI6AKTQUxvBidjpKEAFfJZBmSMHcagZ0aRqNp1vcxJC6MTgdrIVJ/G8ZE4sLEP/XEB0iHWpDYpF2GQ4V8a66pezDjtyhYqnr4ojwoIeZoDwikCcEpNeugt3csTXGlm6q2M0hW6WTuOpoNPyPxMO/Wl2H3PrBZR10+akwNOvKlwEZHn8qP5sh2/lLgKx7iECBOFRefK4F3zOlTfP6+Gr0G3hS9N6y8R4/c1m8K98JAf5+LElCBIoUgaX/bEPrHm9jxKc/4ubtWHw6qh/2/DsN4z8aiDqPVy4ULIx12kA3bDp0Y/+FfuAUKK4nenxcqhJ1uNOjcsaeKzJ/s2OmfId5dnpFgFnv8Q5Sg0WWgEkmQ+Ivs6Fr8iRM/gHQV6oCTdsORZYHDdy3CSy5MQ8p+sx9S0S3CR+Q5cgTGUHjYs0yYnQgAr5NQH7C+XJk+dHDjxyYNEENaaIeb74zGh3ad0KY6GBn0r55Wwwp19x5/ZAS3PHB9j1hou/xAWASXZVZ9HcqZfOSr3gZvgwIwcWdt5svWjJKBHyTQDb/uvJmkIIggG1K+/XowXihfabnc8LHg/Dum73yphGyQgR8jMC+I2fRomkdLPj5U6yaOwEvd2vj87NSHvoWiD/4LF9IS+V3c9gdHenVK0zFJVnSH/GyT6yiF6LMetlrXJd8uSuEz6/C/8RBBO1YBuX2LTyfAiLgywR0jZsifvFK3Dt9BXFbdkPzTFtfHg71vQgTuJ4qfp8LIgC254mNRMojoTL5QWKSIEgWgrG1vkXlwGpiwfx9SRtWgqTFY/nbCFkvWgScjNZQsqQTLWAoGeFU70xZongJfPbZl/hn5QYunw3/CMXkzpes8Rm/tvuqsD1VxOsssJnANvr0wAK55HM2PNIRgUJHgP66Ct1b6v6ADJoEpNzeySX9nnm5lV7rqHPfIpXMDYFvPn8TE0SnYr1aVSEI7Cw0N9a8t65hvwTaaXIYjiisnWRLdZhIr4kn4BnaZNmrSJB/YhWDpDTPsegT1cOReq49VJcvIXDnX1C4cKiwtrQzZLwuBUTA2wgkXE9E0u3C8SQvb2NL/SlYAuUDKkEQ/7OXrcx+8i9sb38Ce9ufw5ZnDuHZUl1cd1T8LbTdW0UoXwJsxZBQrTTs9Y++iy40qlzkHSquQVNOXhHQ1WsIQ1QZe3MSCTTPvWCvoxQRIAKFhoCk0IyEBpJtAun3T+Dyyo5c7uw2P3ZN7USXbcNUwW0CN27H4PT5a1zOX7rJj5Z01qPBYHTbrjcXNByUQjdNBsORTCdH6qj3wcRQoVK2u55avjFSWrwIbas2DnVNqeBt6X+XO+SRwjsIvDt2Oga9/y1mLVqLk2evQG8weEfH8rkXiTcSMb/9fMxtNQe7vtrFW0u9m8KPFBQOAlt2HUGPN8bg6o1ouwGxp7Z9MeUPO11hTLxUrj8CpIF2Q+sa1QvFlKJTxE7rMgFIBFj3VelQG6ggOk4EQKghOlTEtCVPqJANm6B/RCAfCSiViP9rDdKGjoKmdTuk930NcSvWQV+1ej42SqaJABEoSALkUClI+gXctlQRYrcJp2WTTstRVVw8eSngPhb25r+dvhi93hzrlqSkpRcuHOKdRzagBP8A/FKsGIbfvYMLxuxfTKdVbIrklj2hceJQYfZJvJtAh1aNEBzoj7lL1qH3kC9Rp81AjPz8J/y5YgsuXr0FU8bSMO8eRfZ7t++n/Xhw0f4RyTEnY7JviGp4EQH7rqxcvwsRJcJQsVwpu4yOzzQG2zMrMUn0+NrlFK5ExYAq+PvpzWBLekZV+xgzGy3BJ4+PL1yDpNEQAScEjBGRSPlgNBJnLUDyuEnQ13bcZNZJNVIRASLgowQkPtpv6nYeEMi6OWfljE06LcfST3+bB62QiYcR+GBoHyybMdYtCfRXPcyU7+SxtfRibw2H5HwvlIFB1fH779OxZ88uxN6LFXOAg8o2MAhRPP6owHCzBvR7qiLtv8bcXsrpjnxJEVvqo/vNPDOlkF6TPwqNT+Qzh8qUscOwa+VUbFw8GVPGDkVSchom/DgfXV//DEkpaT4xjux28t6Zew5V9GoDtFN+s+4pFPDDt1Bu2cjLsY0OWVqIj+PpPAnISL4SYE8xbFLvMYc26j1Rletu33X8DPCMXAbMCZnTmV5arQ4x9+LzzJEZqgjnS3peLj8AT4TWy+XIqDoRIAJEgAgQAe8j4DGHSuz9BCQkPno6M5sim5au8T5SBdwj2YVzEJJonX0Bvw153ny5qJJ4vFoFt0Qqzfs/13S1Fnfu3ofRmOHlyDJCpr97Ly5vl2EI5kYMh+U4p+yPUwrHi4o9fnLoBfOeKebSEE/wxZiTbhpuPWZ2qOxujAT5J0g53YEv82HLinQzzcuKMibDiAbo5a0E2GyUfzfuwY8z/8LBY+dQrVIZvDO4J/xVmU9989a+56RffsFKx2ri30b4ij/A9hOyiHLrJl5OduIY10sSEniaAu8nULJ4GPYePuPQ0X0ZutDgQIe8vFCs3rQX7Xu/72DqhVdHo2arC2A+tgAAEABJREFU1+xk+tyVvBxzwkz/YxXqtX8DrXu+gxbdRoI9eZFnUkAEiAARIAJEgAi4JCBxmZPHGbei76H/yIm498D1yeDCvzfzqd46vT6PW/c+c9u2bcb48f/Dp5++j6XLFkGtVjvtpHz/XhRv/ATCO7SE37p/nZYhZeEhwO5ofjxxBtiJLzupZXtLrN2y36XDIzcjZ49nbthxMNqJJ94tu4/Ed78utTO3Y+9xNHluCNr0fJcvw1j673a7/BwnMpwi0kZGPOh+w6mZB4rLkJnu2OVxp4h4wWmnFBPSMmcha3YR8mF6Rxlk/i4poBkqYu/o9SgCK9btQqe+H/HZKBu2H0D3Ti2wet5XWDF7PAa9/BzkMumjTPhkftXnqzn0O6hUEAwDXuf7CbE9hUwhodYybMo40xlDQ606ing3gWeeqoed+47z/YEuXb3Nbyqxz/jU2X+jfJkIRJYslqcDYHtydejzAdhviCvDIwe+iLULJlnl5W5tedFjpy9h2pwVmD91NI5tmomuHZvjnTE/58tvD2+QAiLwCAJ7D50G+3t5RDHKJgJEgAgUOAGPOVTKli4BtgdE3+ETEB3zwG7g7C44u5ib+NMCtGleHwH+fnb5nkpEx8a5PHlITklDfGJynnRlxaq/8L8xH2PDxrXYuWs7pk79Dl99/YVT24FffApJxjIIpwVIWWgInDx3lTtS2F36kiXC0LB2dZy/dAMfjPsFP836K8/HWb1yOX7RenjDDIz7cCBmL17LNwVlDbGZK+9/+QuGD+iG41tm4cdxI/DFd3PBHKMsP1eS4RSRNjGg1sfV4K/ydzBXKwSQmm476J0ppGXPcYeKYpgOWUX+po5X4c4YHqPA2wicOHsF12/FIDw0CD2ffwbPtX3SYc8Jb+tzXvSnziu10Wnac3is22OIqBPJTUbWjUT6628g9e0PuBjDwrieBbradbnOFBbOkiQ+QOCV7m3RoVVjTPltKbq8/ime6jIc746dDnY+MemztyCRCHk6itKRxfHHT5/g01H9XNotUSyEO3OYQ4dJaIh5lszW/47iyYY1Uf+JapDLZejXoz1f+nP+snOnN+gfEchnAstWb8dfa3bmcytknggQASKQewIec6iUKBaKRdP/B7Zs4ZXh48HupLDua7Q6fDj+V8xevBYvd2uDKWOHQSb17B3Jecs2gN3VeWXYOLQU79Szkx/WNyZp6WqwO/lNOw/F011GoM/Qcbgfl8iycizr1/7jUHf79i0wZH26hckI+fmzDmVJkUGgkB1+nbcKZUqVwKH1MzBz8gf45vO3sHPFTxjYpxN+X7ia393MyyEzZwlbVuGnVKBVs7p888S9h0/zJg4cPQv22e/TpTX/e2zbvAE/Cd+x9xjPz6vA398fo0a9D1UWp0qbsuXzqgmy4+UExrz7KtYtnIRBr3TGv5v2oG2vd/n37MK/N/HlaF7e/Vx1r1qnqugwpQMavtGA2xGkeXuBzY1SUGAE2LnMlLFDxXOfz8E+5++/9RKmjh8pft6/wRM1KuZ5v1h7kSXCEZbhJHHWwLLVO/DZpFmYPnel9TyMlYuOfYCKZc2OPZYuWdw8E4ot12ZpEiLgaQJVKpYBc7Z7ul1qjwgQASKQXQIec6iwjrHd7v+c9jlKhIfilWHj+Tr5Nz/8Duu27udr5T8V76qwEwJW1lNy+vw1TJq2iN+h37rse/Huzmg+Pdeydpg9aeLClVvYtvwH7Fs9HVKJhK/xz0n/LHVu3LhuiVqPRqMRt25luRMkSGAKMN89shakSKElcOLMZfTo3BIqP4V1jIIg4CXRqcEUV25Es0O+yPVbMfxuJJu1whqIuR/PHSgKhZwluVQuXxp3Y+N5XOyW+LcgWEUQzBeC7IarVAweJorGRiiH6yFrbOL1n+/cBVs27cLsWQvQoH5DsH9+T7dgB7dFEARuK2u7siCBt6UYrIdE7BfEf6yntuVEFX+xfFs9K561rG2+L8ZFTGCS331nLDlUN4NyURF4tWcHfuG5/PcvoBQ/dxN/WsiXoyUmp7ppxf1ibL+IuIRk8YIyFsyp735NKkkEskeAfdaCAv1RtnRJdGrTFK2fro+4hKRs3Zhh+1wxp7orYTMK3elVh1aN0KJpbbCZLFt3H8WLg8aIfwPmJ0sliX9nfkqlnRm2fxGbWcyUfgop/JWZwr4fnelty+Q2zuyz72BbO/IMp6NCJrHrj22Zh8VVCgkzK34PCg71ZaJtV3aVcnM9mTjwh9n3tjyJ+IWf9b3Ljz5KRXYcbB4GvZ5vJf6tJGPX/hN5aJVMEQEiQATynoD5F8K53XzRsumls7//CFVFz/Nrb3/NnSqTPn0TbK18vjT4CKOWk4XIkmG8ZKVypRAeGoQb4gUmU6zfdoBf5LK7NeykqF+Pdvh77U6wkySWnxN5sllzh2oREZEoX97xjpVGvNh0KEyKQkmgfJlI/veQdXBHTlzgKva3wyOPCA6L5V2deC8X705mrZ6apsbb/5vKp3o/3fgJns1OrtnJNE9kBEqlgk9VZ0k/uRTFQ5RWYSdsTB+okll1tvm28YgOckR9KkVkO7ld2acb10VggIqZQWCdmggaLt5prbkeoRHzEar7CjLZfZ4X9DIQ9p5Zgl5JgOCXCoV4smvbhm2ctRX1oRQh/mbnkDxLWeZcYIbDgxR2/QkOUEAhXkTY2vL1eIBShgA/e+75MaaQADlD6pawTY/ZPkHsqT5sL5Ueb4zhfwePVS2PN17pDJVS4ZYddwsxxyXbcLN51xF49pUP+cxDto+LpT5zsNdsZb9xJ1uqasmnIxFwlwD7bu0/8it07v8J2H5YbOYfqzv51yUY8M4kFnVLdHoDv7CME52AzsRkMrplZ9jr3TCkfxcMfbULd14GBarAHgTAKgcHBYjORS2LWoXNUrQ8XY47G8TvfUWGCILAy8llEvH7V5ovgox/ljbZUSoxn7ayC3iWzq7IZeYZ0IJoO2tdqUQCV3ZlUnO7EomQL2PN2pe8SkskgEx0duSVPVd2pBmfBxFrnr2+/vlPsM/gWx9NsdtI2fL9nB/O9jzrPBkiAr5LgHqeAwKSHNTJdZUAfz/8Muldvl8Kc17Uf8L8CMFcG86BgXo1q/ALSXanZv7yjfhl3ioUDw9By2Z1uTV2557dPeUJMWB3mcQDklLS2CFH8vprb6BihUrWuiEhoXjvvU+sadtI0sTJSB43CZpnO0NfroJtFsULGYEuHZ/C7oOn8OG4X8Eu8LbvOYZvpy/GN9MXoVb1inbTsR82dHYC4uykm+my7gPE7myyjQcNBiOfis6W5DHb7OSa2WFxi2g0WjCnIkunaw2IiVdbJV2jZ2okpemtOtt8d+NanfnCID5Fi/QRpaFd1hL4thzC9F9BIr/H29C9oIF2YDoXQ9d7EFRp4oWAfX+ctcdsMgOsDdt8Q8YTju4nauz6niD2QaN9tF1bW94eT1HrkZKusxtnfvQ5Ptn+woxxdyUTfpjP9wnaue8EmEPvp3EjsXvVz2AzVd5+o4fo1HLfOeOqDVu9SUyMGtQDu1ZOxbHNs9C/Z3u+BMIyU8VkMvElcLYbd04eM0SsRS8ikD0Ca7bsw7Wb0fh69GD+HW6p3bd7e7ANyN1dTsP2OvloWB+4En+Vn8W020e5XAY2Wzhd/F5nlUqVLCb29S6LcrH0jd1MYoqUdD3Yd6JFLN+byWk6O70lPy+OrF3292prS60zMDXSNYYctZsk9pcZMIp/57Z2WVwj2nZlN1X87mT1tHpjjtpl9gtC9AaT+J1v/97lRz8YF8YnL+XZZ5rggyG9XYpfNpzteoMBzHnP9nA0iOc7WftpFM8DWL5eLJc1L0fppAeQbZwN2bzPIF31E4Tb5htjObJFlR5BgLKJQMET8JhD5eTZK2B3BC3C1snvPXSG33Vp1/t9u7zcOCtskbLNPV3dqWcXrqwsW9LApsCWKhkO9qjBn2evQIumdeCvUvJZKGnpath+abOp6KxemnhXnx3ZXW1bYTomtrqs8TqPV8HqFSux/p81+HvJMuzZvhPPtXsGlnIhgeYLCKl4J4TpVEMGQzp/PiQtmjPTXCx5LJ+JQrxLxDICVTKrHabPC2G288NuXvQtpzYkItvQQEWes8raH3ZniL0v7kiP51qCXUCyk3C2xn3Y6B8wd+l61BWdfj+NH8mnKLtjp3mT2i5PvNkdf4sN9nc25OMpSExKxbyfRsN2BkxE8TAwZ6JOp7cUB1v6Fpkxk8uqpAgRyCUBtnfK5iXfYcOibzF6ZF/uaLf9LObSvEP1Oo9X5rMOmTOf3a2OKBHOZyVKJJk/h8xxyC5iLcL2pXAwRAoi8AgCi1Zsxivd2+H59s0QEhxgLV2lYhSPx9yP58e8CpgzkH1n6/VmpwOPZ1wgsn3r2O8Ju2jUifnsfOfU+atoUu8x3nzrp+txh/6RkxfB8uct3wC2TNuyDJQXooAIeJBAm+b18dpLHV2K5Xz8UV1asmorf1Ihe2Jh25feQ7ve74F99i31duT1Ew31Wsh/HQnJ9kWQnNsP6f5/IZ8+AkLsNXOTBRyuPKdBvxWJ6LksAV/uSMHNRPP3RQF3i5onAj5NQOKp3ocEB+K5tk/ayYvPteA7ybPd5G3z5DJZnnSLXTCyu/LOhDlKWCNsbeYPvy/HgmmfYclvYzDj2/fB9k35a/UOfgHrL975sdy5ZOUtcX9/8x2hNPEuia2wMkxsda7ixUqWRtnyVZA1P11rZCbMDh0b++xOA88QA3Z3xbaeXvSui2poxDv8tvq8iDPb2nywmxd9y6kNk8hLrdE7sM+pPVf12F0P9r64I4IggDk82Ka0q+ZM4J9Htint1Amj+ImtOzbcLZOWrkHfYeMRK57Qf/nhAKSKjsPbd++DPemK2WhUtwY7YNHKLWB3bDbvOsyf8NPyybpcTwERyCsCzMFRKqJYXplz2w5bGjdm8hz8Om8VPh7xCuQyqbUuW5ox+qvfwWaIsXLWjCIY+d/2FDy7MN4qB2/rOAVXep5JASdgEO+EKxQyHrcN4hOSeDI8NIgf8yq4fO0O6rYbhI8nzuB7YrE4c85b7LMN+NlFZd22A/HRhN+4471B7Wo8u67ouH+r/wvoN2ICWP6SVdvw3Zih1r2neKEcBHOPp2PAP4noviQBo7ek4PwDunjLAcYCreLJxh/EJ3HHXrpaC+YEZPscuhL29+VO39h5/K+T3sXBdb9h7+rpqFIhClN+Xcqrsnby+omGwo0zEBJiuX1rYDJCcnKnNVmQkb03tbifZkSK1oS9t3T4aldqQXaH2iYChYKAxxwq5aJK4uPhL7slKr+8WTP/Sve2/IThIydTZdu1MG9+yU6W2WabwYHmR7c+1agWGterAaZn7zC7Q8m+1Fmcyc075i9JS3m11gBbYWWY2OqyG9eKNpkN8ZrfzrZlii3LM5lgl2e5cNfpjXb67LbtrDyzrc0Hu87a8pROxAe16CTK7/bYe8jeL3eE3Tlky3wM4h1Fdujq72wAABAASURBVAczKMAfS/7ZxmepsB99d2y4W4Y9tpNNOWezULoP/Bzte7/PpdfgMdwEm6HFHDlsP4k6bQZi1OdT8dnb/fhTiHgBCohALgh8+f08/mS1Dn0+eOSRfVbdaYqdXLuakcj0WTd1ZlO/2bIGnU6PhMRkaxM1q1VAt2ebo0LZSNyMjkX/kROxYfsBni/6PFEqXGWV4sFKrlfIJFadbb67cTZbjhlSKaSQKZXcedF/ZSKkEoGpuQT4yezasGRFhmX2x1V7lrIRNmUDVXJulx2z1mPjjMwYp2UzTl74IUF4kNKuf1lteiJ9Jl7g7CbvS7frS8kwP87SE31g7CyYmGOaOSbYd7tFx5zZU2Ys47Oi8nrmE/vdOL19LmyFLTdibZeLisCWpVP4k+PW//kNfxx+/54dWBYXQRAwYkB3HN4wA5sWT8b+Nb+gXq2qPC+nwZqLWiw5pUZ0shHpehOO3tVh4q6UnJpztx6V82ECbDb74A8m85s9zKHd682xcCUpaelujZTNEGMzd/1VSrBzd7akOTTE7MxkzvO0dDXy8omGwr1bcPZPuHfDmbrAdRfj9EjWsLPiAu8KdYAI+CwBjzlUGKFlq7fj829mg80YYWlbOXfpBs9jM0Zs9fkdr1apLNiFJWuXTZe9cTsW7KL28Wrm/UrYrvjL/t0ufrknICU1HfOXb0L3Ti347JX87hvZL1oEZv25Bl9NXQi2+atao0Xf4eMxbc4Kfpd84k8L8hQGm8p9OsuJN0vvWjnV2k7rp+rhxJbZ2CieXB/bNBN9urax5vlSxHhRgPFa5oWpL/W9sPb1iRoV0b5lIy5seY1Or+dxi44d2e9EaHAgZDLHO/zOuJhg4r8trJ4zYY4T23qd2z2JX75+Bz98OQITf1oIi7OcnXyzC8vBfZ8H28+FpdmeRqwuc2TfjUuHRR4kaZgazJFt0eXkyPY0YIaYgzc23nyRwBzZtk70NLXe2i5rg/WF1YkRy7P0w8Ti2GW2LeVS0nWsOtjRorMcme2YjHGyWY+84COC+GSNXf8stjx5tOVo2+69eDUYS1tdfsUZOwuq4QO6iZ8NPdisELbM+Gfx+7xl91H8HIPNDDTvWWUpnf9HQRBQLCyYP3FIJs2ckQWbf2yJM3sKkMTihbPJy2700B2tQ5XYVCMuixdwDhmkIAIigdqPV+YzxUsWD8MHQ/tg2YyxLsWyYbJYza3XPxt34+3//YwzF65hcN/OvA5bdsdunCoUZgczU7KbrJYnGrJ0dsVUpb7TKqaqjZzqSUkEiIDvE5B4agjsAvGbaYvBNrZ0Ns2V3Vk5de4Kfpz5l6e6xNvp+ExjsJOe8T/MR+NOQ/Da21+BLbt45cV2YP9e7tYWlcqXxjM93kaT54aAnZSPEO/isLzcyJaY9Rh76gN8dHwYFt/4A2qDOjfmqG4hIHDizBV0ffZpyGVSbNt9lF8cso052TI09rQpvcHg8VGyE/6oyOJgGxh6vPFcNmg4KEFaCz+kdxGlkx80n+du5luC2sjvftsuf7CN9/krMZc9LjrV2QyQ997qBSbsAq5H51Y8ztIW+WBob9yJuQ+Z1L2fKZl4gehsNqJFV71yWaeA2ck0y7gf5/z9Y7MIUtPMjhNWziQGtiImRVcO3BLp2dOQXL/mUJbZYGKxy+JZxZJne2RlbNOu4qwcE9t8lraIrZ7FmZ4dmbC4nbhIsLLeIJbuZe0L02fV5UeatWORMPEu+Jr5k/DhsD54rk1Tfi7Rs3NLrJwzHs80q2cpRkciQAQyCISHBuGpRrXAZqqXiyoJdnPTlbDzk4xqbh2uXI8GW1JkMBiRlJzG6zzqiYaiDxJKucQqMqnA60lFh6Ot3jauiCwDtH+Vl7MGjzeDrOmzVjsWh6VMJlh1SjHOyguCjS6jbYVMwrLEm7nILJ+RZ9s2i7sqK5hNcDu2QbViMgT7CY+0y2z7irD36WHvUV6Ow5YlxYsuAYmnhn78zGXz489e7eK0SXYy/Fb/Ljh78Tpcndg6rZhLJftSY48RZBsirhJPcjYvmQK2MahcJuWW+ROJxLuYe/6dhh1//8j3tShZPJTn5TT469YijD4xEuuiV2F77CZ8f34Cxp35OKfmqF4hIZCQlIKSxcyP7z5x9grfN4U9OpZNu2ZTUi9ecT6N1NeHP+X7SRgxcjCX02dO8uFM/+Unnmb6K3ejuS67ge7bWPgVnw1VjVlc5IqNogkThAe3IN0yzyphiSpRT6+CInD91l2wZW5Z22d7OsQlJPMZhFnzspPOWpbNNtn63xEkJqeCLSea/scq+Kv8+Lp6Vvb7Gctw+dpt6PQGsI0LF/69mT99iOXlVJSbN6B4/ccQ3qk1irVqgvD2LSC9cf2h5h6km3A7yWgtc/Z4LOZ/u98q2nQ99+IsmXrYqmP5Xebdc3D8xaeb7TCnn8UJuOCk2YnPjhad5Vh36h10XGDeN+XALfNMFmtHxIhJlKyvz7PstWKx5cnj+J3et5xE5afAqz074JvP3+IzophzpWpF8YIrK8BCmG5Y2tGJXTJAgsrhskI4WhpSfhBg1wO79p/Ehu0HHYR9R2enTXZuP3/qaLBZ5u99MY1XZct/2PkVT2QE7MYvmznJkjLRcRKoksMiioxrA4XozLDonB2DXhiIoO82wP/DGQj86h8EDf3aaoOVl2c4ZvwUMq7XvKfA/RYVcVWVhOsJ5xBXVWknwq4A1h1IRGcLq/8w8Vea/77Y9Y1tOalEwm20rOiHiEApgpQCWHxi+1BI3bBra8vb44yrTCpwtvndVw6VgiJPwPzX5QEMd2PjeCvloyL40VlQo0o5rmZr23nEw8HDprmGBAWAPU45L7q05o7jLJytMethMHl+BkJejIds5A0BNkuLbf56+fodrN60R7xL8wQ3bFl/z+7kc0UhCy5ePI9jx45wSUoyb9Z45colnmb6VLUammrFoKj8DwR55l4XD8Ug/ilJ7t+GX405VlGU3SRWEUSHyh3RmTLfKqFJ5v2TxMyHvkL9JFj3Shg21DqCf4r/gyjtHVh0TL/oxZCH1nczs8gVq1m9AuYt2yg63DNngTAIm3ceYgfIpFJ+zKuAnYSP+OwnNHt+GJp2Horte45i6viRsJxA7zt8Bi+89infmPOlN79A+5YN8WqvzL0mctKPoM8+hCQ+zlpVJn7m/X/6DsZDl2HccAKmFLNzw1rASaRSWhL63L1gFQX7vRCAHrGXrTqWLzeZnSdOTOCzxDr4OiGKS1v1d7wIO1p07NhWPYXrrYHYhjVuiTjzqDjTWcoXoSPbc83VJppZ9exOeWFG81xVBV6q5YdSQRKoxLvv9SLlGN08sDAPmcaWhwROiDdiW3Yfhbc++g7vjp3mIFkdIe42XbFcKT4DWG8wIKL4w59oqDOYwJZ2WiRNIzqyxYbSNQY7vSXf7qiRICG0IuJMKoeylqWUKWk6nqfRu/7eFptDqtrcLlu6aNdGkobXt9UlpJqX2hmy9F2f0UanKnLM7RKMpT1C8XEzFULkRrCHT9ja8PU4W8rKGHtiHOz9ISECHnOosM2gHoXbYDR/obhT9lG2vDn/eupVmMT/tn00iifBzTY/hiabqqHHnva2WRQvIgRe69URO/cdxwuvjuY/9gN6P8tHvnLdLn4HPSqyOE97V5D73rzz9of48cdfufirzLNFunfrydNMXzEiUnSoFIe8yr/uO1TEa3BBmfu+ObMgOb4NATsWIEpz11k26bJJYNjr3UVnihqNnn2TnzBP+W0p+o2YiGlzV/JNMSuUi8ymxYcX7/V8K74h59Zl32PLsilgsxKbNnjcWok97W3f6ulYt3AS2BO3Jnw8CLlxZkpiYyCJuWu1b4nIjx6G6dAVGDceh8mJQ6WYSkBUsM1PdPBVnK70Ji6WGWsx4fS4rGcomIPPVsJUNnac1spU9n3CD8dGlMb6vmHcTuMoeWZmRky8mZkRyzx8ZWzNHTUrO8Xyerbteyr+WYvAzA4VUOz7GUtdbqLZ6037DTbd3VSzgIaSJ82+VkeF2S+E4O+XQjGxTSCqFxO/nPPEMhkp7ARmLloDNkv3z+mf86GumD0ebLZ4m+b10fqpemA3OnnGI4Lp4m/JcdE5w7YeYE8znLNkHZrUe4w769nG0az6ogJ+oqFyshZBW6+gYnowAmRrWJfAdAFn0sFE9ox5by2eQQERIAJeR8D9s6xcdr2S6BFmJg4eO8cOToXtG8EyypQuyQ6FVp4u/oxbY4tRR7tVjgplg4AXF2U/7Gxt/fiPBvKLOXYXhXWXbSDLLupsN01j+sIi1arVQP16DblIpTI+rNJRZXma6QMznCw8IxuBtJEhG6WpaEERYBvU/j1rHNhJMvt9mLVoLWLvx2PQy89h2sS3+UlvXveNzXphf1eRJcIhkQgO5tlslXJREXwdv0NmNhXGYqIjVOL4U2sqUSKblqi4LxB4Z/BLeNhGmrZ52d1U0xfGT30kAnlF4NLV23x5DpvFyGyypaHMifL6S89i6+6j4u9EAlM/UpgT5eWh49Cgw2D+NEOpRAK2KTSr6K9Sgp5oyEiQEAEikBsCktxUzk7diuVKg10wfjDuFxw5eQG2/9jTddZs2Qd2Z5KtbVQqHO+I2Zb39fjASsNROaCadRj+skCYEgHpHyrI/vCHbKU/z9PEazBg6Mt8L4nx4//HdRQUbgJVKkSB7R1xO/o+Yu7F88Gyx/3Vf6Iqjxf1wK/yUshP/QHLHiiyEyshlx6DPG6nVWfJU9Ta4BYupVaGqKSyUOxaBUtddjRtnAvt2jl2OqYX7hfOvWzcgpVPhdiGseyJOuwpU6e3zwXb0+qdwT0REhyQTy160KxUCnWn5x0aTH++W6bucow5Hp0Av60n8fKdC+h64wJMqRqznoUZMzhZ9GFi3HqaLyNiS4ksAo3jPigPs0F5OSdQzsVGmuwcqGqlsnabbGZ3U82c94pqEgHfI2AwGHmnZeJ3aJlSJXAhYx+58NBgrr999x4/PipgN6SObvyd/67sXvUzFvz8KZg9S73WT9UrFE80tIyHjkSACHieQF47VFyOQCLeBfzyg9f500LYdO5uAz7Dh+N+xcjPf0KbXu/yOHtU2Xtv9nJpo7BklAuogEXN1uCvpzZjQdN/MKXubxD0AqQ35JDckEFyRwr2j+kunroAto/EmbOnmYqkEBNITVOj/8iv0Ln/Jxj0/rc4cPQsH+3kX5dgwDuTeLyoB8rKyyE7xTaUnS86OkQ5uQpy2QnI4v4zp7eIugyRHNviiMvkuNEDc6iUTi4HxX//2NkwbZwH7bq5djqpaFu4f9vRLmlyTODjiTMw8aeFOH/5Zo5teHvFpG9+RPLn46Bp/yzU3XoicdpMqF951dpt0xWzQ8V0NwHKbSf4nihdb54H0sxr4XnBjM+uVhENtvTHlZy6/jROXmpqJ3pDHDfhTnD34ASsHyfg2M/+OD7NHy+eLsmX8nydsfeKq6Mk+bw75vO1zFNl5Xy5kTdhCkimAAAQAElEQVQs/WED1RsMYJseN+86Ag07Dsb6rfuZmu8Jwc59eIICIkAEnBJgy5zZI45ZZrNGtTB97kqwDcV/m/8PUyE7mzuzGb7MiRIa4nxZIHNusvbkchm3nR/BzZs3MHr0e+j0XGvs3LWdN6HW0FIeDoICXydQ5PvvMYcKI10uKgLsEYKjBr2IEsVCsffwaZw6d5WvkRw9si/+mjUOrr7sWP3CJmX8y6FqUA3zsIJN0PZLga5fqp28PeEDvpfEZ599aS6XEW7W/ouBg/qiXYfm/Lj58pGMHM8eDNpEpMUeATt6tuXC1xqbpXXtZjS+Hj0YtapXtA6wb/f2YBvVxt5PsOqKakRzuQf0tfrD0KafWZ7oAp2+NvThT5vTFr14NNZt44hJEBx0GoUed4JuQPv0C3Y2hPb9oXj2NTsda9dUPMrBBilyTqBezSpgT97pPvBz9B0+Aas37UW62saRkHPT3lNTpUL6gMFI/G0ukqb8DE2WGStCJfNm7UJkKCTt61gF/go+Br3ffUTX/4jHKfAdAv/tP4lpc1agVbN6dnfEu3dqiS27jvAnTfnOaKinRMCzBHp0boXSEcV4o+xpnGwTWrah+KoNu/H+Wy8hMEDF83wlmPjVWOz6bweSk5OgUZsdKTt2bvOV7heyftJwiEDeEvCoQ4V1nT1CcHDf5zHj2/exa+VUbF32PV8n/0r3tuKdZvPMDBTFf3JAXlEKUwWDVRrVb4oXm7/E95J4/LGaVirXpJfwjfoTXLhwDur0dH4cv/NPXEt7YC3jicit7SNw6vdSuLjsaX68tWOUJ5ottG0sWrEZr3Rvh+fbN7Nb6sCe/sMGHXM/nh0Knax8fSV+qPgDl8D75qm8x8Ye42mmv3o005GkvtwLulqvik4O5lTpD33trtAZ6kIX3sKqM7Tpz+NGZw4VJ/SYQ+V28E1om3fh9Sz1hfavQdHpdTsdyzMVL+PECqlySuClLq35RoM/TxyFMPHu4UcTfkOLbiPBZmZduRGdU7O+Va+y2aGCUqGQdKhtFSFAyceRWG8y1CFneNzEQ0Bi9MNjV39EzSu/2Unt1+NRZ1iancj8M+xn1H3YIbLRp+j4uQl1h9vbsNj8q2YsPg69DW3XRLs2lGE1Hma2SOYtXrUFL3drg3EfDkD5MpnvQe3HK3Eed+7e50cK8p+AKSENppuePUfK/1EV7hY6tWmCoa915YMsWTwU2/76ge9PtG/1dLyesWk/z/SBIC0tDWfOnHLo6dUrVxx0ThWkJAJEwKsJSAqidyfPXsGfK7bg65//xHe/LsXy1TtwtaicOIvALyafw5BDfa0y5dx4sIf+KCRKVAyogihVWVQJrI4KgeaTLrGK3euofI9d2pI4nuK5pQhJ1zfiwelZlqb58cGp35FyeyePU5B9AgaDEQqF43TT+IQkbiw8NIgfKSAChY2AXCbFM+JdfLY54LblP+C1Xh0wZ/E6PN//E7qLL77Z6oh9Ymj/MkrU0Mjd20PAvialPEWA7flQrXJZl80pFOKdFJe5lJEnBFLUMPy8HoZxf8Hww1roJ62ymj17T4+/z6qx5aoWiWqjVU+R3BPIqQW2d9zcJeutsxTv3ouDJc32Unm8WgWkpKZznUbrO3tDGU30+crpZ4LqEQFfIOBRh0piUire/t/P6D3kS0z4cT7mL9+I2YvXYszkOXzfiEnTFkFvMPgCt1z1MUWfjCPxB6xyIeUsIACphhRcTb2E2+k3cSnlPJjjBV76Ly32oNOepd874VRPykcTaFS3Bpas2gZ2AmEpnZauwZQZy8CcKZElwi3qQnXsOqcr3r76NpdzHY/jr7IL0HZ1G55m+or1QqG8cB+6S8/DpCt4p5KxzjNIbdkXt5WRhep98IbBnD5/Db8v/Bds3wnWn6ca1YJCXngvOoWGlcCW+AiBfmy4LkWa5vyzJjX61pR3lwMspBm1H6uMNZv3wWi0zCsyD3TpP+Zp/mxPB7OGwvwiYNh1DqarNo5HnfkcM1l0oLy7MRm/H0nH5D2pGPBPEq7G6/OrG2TXTQLsiTzf/rIYqWnmJTFsg36WTktXWy3cuBMLplNrfGdpaGBAIB5/vJZ1DJZIxUrOb5xa8ulIBIiAbxDwqEPliyl/YNPOQxg58EWwu5Cnts3B8S2z+CNi2bTYecs2YPaitb5BrgB7WU/XzGnrdQI9t7eDTBXhtA9SZYhTPSkfTWD4gG7Q6fVo0/Nd7D54Cj/PWYGW3Udh+55j/BF/bNO0R1spnCWUFx5Ae/mFbDlUjOFRYHueWMRYt7UIxwRTsdJ2+oTgNFHv/stYp43oUOmH24rS7leiki4JMEf74lVbwTYq7/XmWKxcvxtv9nte/F34hi8NZctEXVb2sYwr8QZEJ2feqZQ0rAy2xOdRDpWgs6+BOd0h/hN972II+GsqQaErDskzNcGcMhaBrGCWztZ4+QhfAqQMq877RwEw5NUuOHjsnHjD6GOcvXgdG3ccxJCPv8dv8//F22/0gDLHM1SIrtsEXCzz0eqMdibSdCYsP51mp6MEEchLAqM/GYvmT7dEUFAwlH5mZ3jLFs/kZRNkiwgQgQIi4DGHCttUc8P2Axj/0UB+sszWQwqCIJ77ScE2q/10VD+82rMDfpz5F3Q6fQHh8I1mKxiq4EO/r1CtWg34qVT8+FmLl1HBv5jHBhBauQukcvN+F5ZGWTq4QkdLko7ZJBAWEsQ3bf5wWB8816YpKpUvjZ6dW2LlnPF8OUQ2zRXa4qZbD2C6fJcLotm+MiYgVc3TFr2J6Yszh4p5PxW294mR76kiiA6VMqJDJVMfH5JeaFn5wsA+mzQT476fh2LhIfj+i+HY88/P3OleLqqkL3TfrT7uv63DS8sTMWxtkngnPBFvrU7C3RT7C7qHGQq42RHlEtcj/PHXEFyhEyKbjBXT74E5WYTWNcGcMhYRnCwbRMa/mgOucacH2w+F7ZXC1OzI0haJaPwpU+etFFFr1SuXxd+zxqFiuVJQa3TYuvso7sY+wBfvv46BfZ4rolQ8PGyVwmmDRvbHkyXnwn0698yChJJ5SKBs2XKYOPE7rF2zFS2at+KW/ZRmxwpPUEAEiIDPEvCYQ+XS1VscUud2zmdXsMwXxYtHdrx+y/wISRYvKmLKeCSmu+Ntq3ges2YuwKYNu/ixbeX67lbNk3IyVQlU7bkLxeuORFCFZ/mRpZk+TxoookbY3fhXRcfiN5+/hV++fgfMuZKdRwP6KjbuCLl+363uG5fvh2H6JrOsOybWEZ0k1+6b0xl6I9eLWQ97xaVyJwyyTMd/WBXKy3sCXTo+jXULJ2Hm5A/QvmVD5OdjK93tfV6X+3l/KpI0RqvZ64kGLDiZOYXdmvGQiL+uDco+Mx0Vn1uOiIYfQmoMeEhpyvIWAsypMm3i2zi47lewWbkrZo9HD/FcRyKxzDXylp4Wzn5IGlVyOjCDeEMva0a14o57mGUtQ2kiQASIABEgAlkJeMyhkpqmhr/KDzKp6yYD/M3ryG3XSmbtcGFIC07ujLBxZXWquCrHynqDKMOqIuqpr1Hpub/4kaW9oV++1IcjJy+AzdxyR3R6gy8Nzf2+JqSCO0jmeXZDY+OhS7xdiHeO3e+sV5b0uU6x73i2OTnbM6tt8wYoFhYCS9oyGLbxYFadJc+XjnHpRtxPNzl0+dw9uhvuAKUQKC5fu82X9SQmp/LRXBRvJrFlPskpaRAEgevOX77Jy7C/A66gIN8ICDWiIH2zLYQmVSA8HgVJq8d5W/5y83vBE2LA0j1q+osxenkDAbaXXHTMA9yPS+DdibkXB5Zmcj8ukesoIAJEgAh4CwHX3o087mGVClFgJw9sHbEr03sPneZZbKkDjxTSwMQe6ZNlbIIgWE+2LFnOylny6Fg4CLAnmbw7djrcEfb3UzhGTaMo6gTOXbrJNydPSEzhKM5dusHTbD8VrhCD0xeucR1zxotJn32F+EmQdTJCYEwCym0+ir0/7ONyYe1FsEmK988/4Ok9o/ZjV8uD2B0zEYnS4Xzshq1SqF9VctH9QXfSORQvDJJEx8nOfcetS5eTktPA0lqbpcwJSSlcV2id5Dl8X0xqIPVxFZe0+hlLIWx0qRZdNu0L1UpB2utJSAe2huTpGrx2sFLAlPZBeEO0+X6zAMx+IRgVw+jvisPxguClN79A25fe4+dGrDs9B4/laab7cNyvTEVCBIgAEfAaAh5zqNSoUg7ly0TgnTHTwE6esxJgm3BO/GkhWj9VD4EBGT+kWQsVsfSl5PNFbMReOtx87Nakz97Cnn+ncenQqjGebd2Exy06dmzTvD7/uwgJCsjHnniBaTYDR60zd+T2A74chy8FEuNcmZ2lOWmazPoZ+63wfVVEQyZLXnyamKIXEchfAomXH6DVoZOIPHHN2lBAbAKC/z2K/T/u43Jp3UXRoQ7EXXzA0wf+2YvDN3bjgkYHnbQKr/cg5j4O7NmNS3svwHhd4DoKiAARyDmBx0rI0P0xP7SpqABzfObcEtXMKwIVykbi28+HuCX+fsq8apbsEAEiQARyRcBjDhWFQo7J/xuCuIRkvDjof2De548nzuDe5xdeHY3BH0wGezTsmPdey9WAClPlJH0iTiUez/aQqILvEPBXKcEcJUxOn7+KerWqWNNMx+T1l57lmxnG3jdPfUVh/ZeihumueYyGvw7w5ThsKZBxxUHziLUulkiwW/vmEtbQdPWetT6zwcS4+og5//p9nmc6cMmcppAI5COBuMvxkK84gidv3USzsnJ+8dathnl566Oa9VMcQCl1JwR8fQ0pg+/giP4/3Gh8FvL+Lv4WHmWQ8omAFxMQxD+LgDPpcClH0r2499S1vCDArgM6tWkCd6Qw7reVFwwtNnTJN3F8mr9VUm7v4FmXV3a06lh+8i3PLrfmnaCACBQyAh5zqDBuj1ergM1Lv8OQ/l3A9kvZfeAkjp2+iNKRxfDRsD7454+JKB4eAvqXSeB88pnMBMUKNQGl6HTcsdfRgZaWruHjjo59wI+FORD85Hx4QlQ4hMoRXCDGuTLrugmuBEyCkBGzOQQoeV2LDX4sFWYu4K/geQgv5DN+zKOl0EsIlA6S4K0S6RgUpcdjxaVu9UoqiYOf6T9Ia6dBUtnE6wjFTJBUMMe5IpfB2fsGvLk6Cd2WJODdjck4dCdjllgu7VJ1IkAEiAARyDmBuKTfuePj1vaROTeSTzX16fd4307PrpBPLZDZIkCgUA3Row4VRo7dcR8+oBtmf/8Rdq2ciq3Lvsevk95D/54dwC4oWZnCLlWCqmN6w/lWebf6p3zIDnumiOfM1YPMG6jxAhQUagIdWjXC7oOn8PvC1WCbFrK1+PuPnsVPM//i4y4fFcGPvh4YtkmhmyMDO9qNJVR0cESGcpXkxSaQDm1vlm6NuQ4K5+vbnbhTIFQoYa5rsSEeJZ3NT8ISypvzJI0qm+1SSATykYBlAtXljZex4NmFmNNyDjZ9vMmtFmO0IdhsnVZGqwAAEABJREFUehp75p7DxXUXzXXE3wVzJG/Cw9E63Eg0QK034ew9PT7flgK2kW7eWC+6VjbtPIQ1W/aJ3+knOQRLmuks+8XxDArynYAu9S4ur+zA5fqGfrw9bfJ1nmb66xv7cx0FRIAIEAHXBCiHCLgm4HGHCusKW/bD7sT/PHsFv3g8fOIC0tVallUkJEgWjAZhTaxSLcNpohAUduNvUuxp1AqpY6ejROEl8MYrndGhVWP88PtydB/4OZ7sPBQD3pmEU+ev4uvRgxEaEujzg0/v4gf1MAW038r5Mb2bn8+PiQaQcwLdBnyG1j3fwdv/m8qN9Bw8hqeZ7v0vpnOdrweWCVTsKW4szkSX4mQWiBNHSawuFFuF5tj7x3lc2mBeoia7cAZCfFy+Yjl2p+j8HucXyPE/zAfbPPO3+f/yJsZ9P4+nmY45zbmSAo8QMBk0SLm9i0taTMYSUrFliy717gExRS8iUMgI0HCIABHwGAGPO1S27TmK5l1HYOgn3+OXeav4xWP/kRPRvvd7uHz9jscG7o0NPR5SGxNq/4gR1T7ET/VnY2qDOd7YTepTPhFg+wxNGTsUf838EuM/GogPhvYGS+9aORXPt2+WT616zqx+rRTGi/bzSS5cOoeDf5zGscBUHFMkICUlBezC8/z5szhy9BCXw5cuIJldheZ1V8MDzUt/JB7/GszrkficveLhwej1wjNo26IhWj5Zlx9ZmsUtwvKYTiE3LwPzuUFmdFidoOYxNlMlU5x4T+z/NHidkriH1qZdXGqazJuUy86dgyTR/rGhAT98C//pP/I6FBQsgZp8afMUbF76aAkKoMf0euLdkvlHoHLX9S6lfPt5nugGtfEIApTtnMCD0zNx4u8onK70Jk6pevGlNmzvE1dybmFdbsigd7LxvpOfngt/dcDmCZJH2rVt7/Ts8rwNCogAETAT8OiVBJuVMnz0j2hS7zEs+PlTHFz3K3+iCVv+ExToj95vfYkrN6LNPSuCoUSQoG3Es+hbfhDY7JQiiICGLBJgT8Tq9mxzvNarI5+xEh4aJGp9/2W8mvl1c1lyDsdlB/BTwFi8+9dQvLNtJt5ZMx3Xr1+FIAj44YdvMGrUW1yGTf8BG1UqHJOcQjrSIJQJ544Qvi8KWyLEThD8lZk6tvdKZMZ+KQ/BJmlYGWxZEZTOlxI9pCpl5ZJAuagIjHn3VbdE5afIZWsFW10ZrLR2QPxoi59vMSmI4sYrUnSotBX+A5NaOJdRg33gM6LiQUhNQcCPkxEw/ScxlbNXqD4J1dIvQ2UwO3/qlvZt5jmjkDe1mGO8VMlwuCMSiZsfhLzpWpG1IpH5ITCqhUsJiMxYVpo9QlSaCHg1AaffLk6VXj0M6hwR8AkCmVc4Huju1Nl/o16tqpj21Tv86K/y4080YQ6WP6d9jqBAFeYt3eCBnhR8E9Hpt/H7lalc/r1j3iPjTvotnmb6f28vf3gnE1Khf2++VUynbvLyhjnbrTqWb9HzTAq8jsDRUxdRs9VreBCfxPt25KQ5zZbFcYUYHDx2jpdJTE4VU777klQ0Wjs/I3ASPgp9HWoh3ap7WOTX4GC8qxqLaOlNSHo05Y4Q5gyRdKoHiCcIQsUSVp1Zb75DA/pHBAqYgODlF80D4y7ir/OD8MuVT7D6XH/MCfgH4SqPnhoU8DtUVJqncRIBIuBtBKSXzkO+b7dZjhwETKLD3Gjg3ZTExvBjiZJdULv7bdS88htqpS9FnWFpD5Uarxzj9SQy92bAVXtxA9p+anyozaxt1hxwnbdBAREgAmYCHjtrSkxKxdmL18EeAevsjmNoSCB6d2mNLf8dNveskIfR6tuYeXkqlzV3/uajjVFH8zTTr44263hGPgf6CSu4EwZxKfncEpnPSoD9djIdW+bi7Mh0RkshlvBhkXUyQFJVPFmwGYOQefPeRktRIlB4CIRXDkOjoY3x+IuPo3L7ynis22NoWuF+tgcYAfPyn5o4D/+5vyPo+FoEndmAgBk/Z9uWpULx1JIoGf/AkuTHcgcWwHD3Bo8XaECNEwEiQAQKCQH5oQPcaZJ1OAFTv0dYn+5cQoa9AQgCBK15DyvF1k1g/xT/bWcHq0ji47gt2bkzVh1FiAARKFgCHnOoxCWY78BXr1LW5YirVSqLuIRkGAyZd7JdFvbxjFJ+UehVrj+6l+mDQZVHcBmIXhhwqQUGmnqic6nuPj5C6j4RcCSgWqWG3zQtJBnLbzUl0hwLOdFoMnTiqUZGjA5EwLsIuOpNeJVieOqDZujwXXs8/9vz6DClAxqUTeD7omTuj3KW35gsaTI7Tax6nDPfsRSNRwr3zEt/hAtQzZ2JoJPrEHRuEwJmThdzc/by1wU4rai/csqpnpREgAgQASKQfQL7hwzAgjdfw5zpP2D2nBm4fOUSN7LR3x9Tg4Otcl726CXIsiOHuAMm4NuJ3AYFRIAIFDwBjzlUEpJS+GiDAl1PQQsJNp/cpaS5twyAG/TB4EbqNbx7dDCW3piHv28twrIbC1AzuDYGmXph4KWW/Ph8VI8cjSwh2YCTV9K5xCeZpw3myBBVIgL5RED6jAFCpNlpGn33jl0rbKYOEzulmLibcZKhhRp///ktPhsxGOPH/w/bD+8Wc+mVAwJUpQAJlAhM586Rp58Lw5NvN0W1TlXZjUlEyBO4nu2ZwuQJ4Tx4Buz/pb82CMlPPIvkGu2QOmiofWY2UgaJ3mlpSUi4Uz0piQARIAJEwH0Cu/7bwR0oy+Uy/BwSgtmLF2CO6FC5YuNQYXqLLAkIwOzAbbghuYLIk9XRbnIQqh+IcL9BD5WUqUrwJUI1B1zzUIvUDBHwbgIST3XPsmph045DWLNln1PZe+i0p7pToO3MuvIzrqRetPYhURePSWfHWNPGjSf4Ehy2B4qtGPdn1jFpHE+EL97UYsGmBOw4lsZl4eYEnB67waktW7ugpT5W9hTxEAFB4A35+dk/NlkQzHqeaRMoTealQvP8p+KnPYuw4/gRbNi4Fv+b+S1WxZ6wKUlRIuADBDI+55rOXZH69gfQdHrB3GmFe5vBpr32BpLrdELy4x2QOni4uW4OwjjVfYdaptCSkFev76AnBREoSgQS1EYcj9Fb5XqCgc8iS1CbrDqWfz3RUJSw0FizSeA/0aHCHChJGd/5j6p+TPwNmB24HbdkmY4KtgRIcWQ/FPcuQXH7HGTnzz7KDM+XB5XlTg/L/ieBUS25vnLX9Xb6oDItuD63gTb5Bh6cno37J3+DOo6WI+WWJ9X3LQIec6hYsIyZPAcfjvvVqUybu9JSrFAfzySddBjfXfUdJCHFQR94fhuK75jOxe9u5peoLC2O65TRmbojFxyXTxw+X7hn+zgA8xZFNvvx7S+LMXbyXMz8cw2v+c30RTzNdHMWr+W6QhVkeFjDw4o5DEsQHJ0qkTI5L3dEvpcfbYNtDy7YJilOBIiAmwTS5emIadUAhibPw1ijCYyt+kD31k+AzD3HjpvNUDEi4BaBK/EG7qxI1pod6G5VyqdCJ2MN+HhzslUWnkznk8VOxeqtOpa/4ER6PvUge2ZZv5iDJ3u1qLQvEJA8uI+w4a+i+K5fUWzltwj0wqU+iVdX4+y8Gri1fThu73wH5xc15I4VX+BLfSQCeUFAkhdG3LFRq3oFbF46xS0JCvB3x6TPlgmSBzn0nT0yOQAqrpe0rw3Zd/24yGuGQPHgChepJtPhIhh0GbpkXsdoBBJSHO+UJKQZYRjbi9uy2Mx6RHgguBEKPE5AqZCjTKkSOHbqEvYePo0r1+/YpZnu6o27XCdx4mjIbYfT1VrcuXsfRmPBnMDK5WZHyaPGoS9VmhcxCuIHnccyg7uapMwExYiADxBgM1JSR70PQ5Wque6tKSAQzFbq0JE5sqUNDYKhy0jo+4+Hvv0AINjRyZkjw1SJCGSTwOyj6dxZceGBPps1qfiY7SmcXZquYH7L6R14OIG7bp6/xUil3JAJme+jSaGArm4jaItVgrZ0Neir1eBlvCmIPTzZoTsxhyY56EhBBAorAY85VBTihWOpkuFwRyQSxzvUWd4An04+V/pFh/63jugIqfg/a0b6m8MQN2cl7o3ZxiV++DxeRB9YCjF1vkGash2MCSUgkQChgeYvYl4gIwgtFQhlkDIjRQdvI1BTdDRuWPQt3JGH7T+Uk3GN+PRHNOw4GO16v4+W3Ufiu1+X2pl54dXRqNnqNTuZnkezyCpXrYa6detDLn4v2DXqIlGhfAXUVjREqMFxb4e6wWVc1MqeWoiLgWT/ai7CvZvZq0yliUA2CKg7d+FLffRVq9vV0leoZHaOiM4W5iQxhYQCJhPSXx1opzeGhNjVS337A6QNHWWnowQRIAJEgAh4B4EYJw4VZ/vF3cvYL06AYO24sVhxxE2fj/sth+JBt4+Q8uGn1jxviWjizzt0RZ92FwZNgoOeFEQgFwS8tqp4Ge75vsUlJGPH3uP4efYK/L5wNQ6fuAB2p9zzPSmYFl8s0wcTa/+EZ0t1QauS7fBO9U/x+eNfO+2MvlIVaMs1Q8o3DbikzarJyxnTgpF24S1ojr0E/YlWXFe/mj8/2gaNetWwTVKcCFgJVK9cDitmj8fhDTMw7sOBmL14LU6evWLNZ5GRA1/E2gWTrPJyt7ZMnWsZOfxdTP1pBmrXrscdK1XFC8vSpaOgEO/EsJMMlq4rOlws8tZbI/Bd8Tl4J3UcQgLEi8yMHlQsVRb9SzfJSOX8EJ5eHAG/jYZs1Y9c5N8PgOnwxpwbpJpEIAcEDJUqc0cLc5AwMYaFAeKJONszhaUtYgpzdCyC/hGBQkLgWoJ56Q9bwpJV7qWaZyneSTby5UFZ83OTPnJHh6PROm7X3b1RErPsqZKb9nNT15A5oaGQfAoK1zAiRMd41hEJQqbTxJJXwmCeaW47Q8WS58mjZQmZq89k1r4ow+xvDrB8mX8kpMrM8zWmK3pCIy4qBDzuUNm25yiadx2BoZ98j1/mrcIPvy9H/5ET0b73e7h8/U5R4Y42ER0xtta3mFRnGnqXexV+Uj9YlvpIOtSx4yD4AdJGRjuRlImDJCzaLCH3ePmqZRXo2y4ULev6c3mlbSgea12O5z0skH3ajS8JoqU/D6NU+PKGD+iGapXKwE+pQKtmdRFRIgxsiZHtSEsUC0H5MhFWCQ0JRF7+e/edj7hjZfashViyeBWYI0UQBLz77sdcz5wuTCqKd+5Zu011z2DVpK2YM/tPLF60En/8bypK+4UAD1Jg+OYf6D9ZBMPU9TCdu82Kuy2RSVEOZU1bFjroSEEEiAARIAL5S2DmEfPSH7ZHSVbZdUPLG199QY2seblND18dj1FrE7hdZ3ujOLkmxokse6rktg85ra/RmzgXCryTQIR/gFsdi9Cbl7sJEFyWN4WGQdfkSRjyY+lPRqufbzMvIXP1eVRn+byVbPB+Rs3MQ0TDjzITFCMChZyARx0qbFbK8NE/okm9x7Dg509xcN2v2PPvNMz+/oCHeYIAABAASURBVCOw5Qy93/oSV25EF3Lk2R+eUNoEvz80dqLaqIJqdygXv81lIdQqyw0XG9kG9VYM5lLi99esep5JARFwQeD6rRjE3IsHm7ViW2TZ6h34bNIssKU+N27H2GYVaLxKlWqIiipj7YPpbgJMMYmAVg/TtXsw/L4VSFFb8x8aEc+S/QwqxyL3bsOUnuqoJw0RIAJEgAjkG4GKoVI8ESFzKsX9zaetpYJcl3FV91H6eqXkqCsKK1c+xHEJtbMBh/hJnPaT2fCkOOtbUdD5yhjZ7MPs9tUgnucwx4m+XgO7qroGjRC/eCVSPvrMTp+XiVolM//+LHZtdX4ye4dPSMXOeKz/OZRp9TOiWnyP6n0OofgTb1qq0pEIFHoC5l8mDw1z6uy/Ua9WVUz76h1+9Ff5ISQogDtY/pz2uehUUWHe0g0e6g01QwQKJwG2hI4tpXMmy0UHSdZRp6ap8fb/pqL+E9XwdOMnrNkdWjVCi6a1UTqyOLbuPooXB42BxamiUkoRGa6yispPxuuF+MutOtt8d+MKmfkrqViw0sGO3EleaKCCt+ssCLmfaGcjLGMvIaVcYqeXSiVQS9MdTEgjykAVEmxX1jKOkmEqXp5t92TR+cIxUCVDYC7fI3fGGS6+fxwQBQ8lcO/+PaxZswp79+1+aDnKJAJFicDA+ip80zbIqbQob/7Of76a0mm+q3ru6Kc9H4afOoVyu31rm7/jbbkL9teQPKt2SSkv7479b9oG5VtZf7mTzvEeUlCQBJ5+uiVef30wAoOC3epGXa0Wr6vboYy+At83izlOEqfPcqtuXhYa90yg9bPql+E8sdU5a0sRVA7Fag7gjhS/8MedFSEdESi0BCSeGlliUirOXryO1196Fio/8w+ibdtsKUHvLq2x5b/DtmqKEwEikE0CaelqsH2KnEl8ovmpUBaTbO+id8b8DIPBiKnjR4I5Fyx5w17vhiH9u2Doq12waLrZ4bll1xGena4xIDY+3SrpGj3XJ6fprDrbfHfjOr15fXx8ssbBjrO8xBTz9G/eeJaA5dm2m5ii4SU0OqOdbYPRhLvBjkuETM+8DLXWfpwWe/cTzA4YsaqdLUu+tx5T0/VIzeV75M7Y2PvHYVPgksCJE8fQu09XfD1pHNat+5eXu3bNfg8jrqSACBQYAWqYCBCB3BBoLjpUBogOlc7PdeGOFeZcYVKpUhVutmWL1lb9gJ598FJqKgaIDpVyxkowzN8F/XvzuRgmr+blTdHxPJ1VzzMpIAJEoMAIeMyhEpeQxAdZvUpZfnQWVKtUll8Isos7Z/mkIwJE4NEEmjepjY+G9XEqb7zS2WogKSUNQz6eAubsnPfTaDCnpjUzS0Qul6FEeCjSNZkODOZMsIjlCX/MHWLR5eRoymjXWd2MLNjmWcpb8uyOFUrYlWX1LPksbhGmi1PdR+qbE6HvMoqL7p3ZQIP2LMvBhm09VsCS9oUj48Ukv/tqYo0wOCQuCSxePB9ajdnJZyl07dpVS9ThqE+/j/jzi3Dv6E9Iu7vfIZ8UAAiCTxOokLHUJ0jpsVNTn+Zl2/maJWVgS4zYrElbPcW9g0CrVm3AHCv9T6nApLLFodKyNdezvIE9X0b1jD1U8rLXxkNXuBOGL4vOS8NkiwgQASsBj/1qJSSl8EbZXik84iQICQ7g2pQ0891fnqCACBCBPCeQlq5B32HjEXs/Hl9+OACp6Wrcvnsf0bFxvC22tGfu0vW4ey8OOr0BqzftxanzV/nyPF7AywIhMhRCRAigkEEQHSnSN1oDgX4Qbp6D/Pf3uEjXz+S9Fm6e5Wmmt+hYhik8AsYmnbmYSrh2/LKyJEQgtwSuXb+GrDYMBgOuX3d0qqSnXMa5+bVwY/NA3NnzMS7+9QyigxZkrW5N636RIa2TH1IbqKB+VQnorFkUIQJeS2BQxlKfauHSAu9jiFLgDgrmpGBSLkTK7xsEZ9FXEPUF3lmxA1+2CuRLNCzLM0QVvYgAESACRMBDBDzmULHcsdy04xDWbNnnVPYeOu2hYVMzRKBoE0hOSeNP1WKb0XYf+Dna936fS6/BY6xg5i3bgDY930XdtgPx0YTf+IyXBrWrWfO9KlIsENIPX4Dsqz6QjugIoYb5qT1CehKEqyfMctd8oSqkJ5vTol4SY9Z51Vi8tzPUszwkUKF8BQdrUqkU5ctXdNDfv7oQBl2SnT4uYDMMEsdNk/XrpdBOlcN0TQDEexOGgxKY7Kva2aEEEchPAibRR6//RwrdHBmMRz12ypnrIdWOkHEHxTcZe7mwPVXEvyhk1b/iZK+VXDdOBogAESACRMCnCHj8123M5Dn4cNyvTmXa3JU+BY86SwR8lQB7RPLp7XORVXatnMqHVC4qAluWTsHOFT9h/Z/f4PiWWejfswPP850AMJapAd2gb12KvsPAhw7HlJYMyfFtkO5aBuHKsYeWpUwikB0CvXv3g0KptKtSoUJFyA/sQ1jvrihRsxIkt27x/NSks/yYNdDIY7KqYNzn5Gfd5FCMFEQg3wkYLwtI76CC5mMFtN/Kkf6KEtox8nxvlxogAt5OwHjEfDPHsMBmn5Qvlpu7rdObj87CLN/lWfdU4XurfL3KXDM2kS/1MS6iTc/NQCgkAvlHwMmZV/40Vqt6BWwWL9DckaAA//zpBFklAt5MwMv6JggCioUFo2zpkpCJd87hi//8g2GqVNe1RFZyPaq4u0j7og9kSyZCum4G5DM/gGzReNflKYcIZINA7dp1sXjRSnz80ed49tnnec2KZcohZMgAyPfvhZCWCkFn3rMoODmI52cNlLqIrCpKEwGvIaBbIIMp1b47umWi7r69jlJEgAi4QcDEvClMbMpmSdrkUJQIEAEPEvCYQ0WhkKNUyfCHSqC/Hw4eOwutjhZ85/lnQKOD6eYDmNLsN0HMTTtUlwgUZgKmff+Kfy/JdkOUnNwB4d5NOx0liEBOCZQoXgLPPdcFTzZ9ipsQEhIgiXvA47ZB1JlgSOXBtiqEp7aF1Bhgp2MJSVO2NTSL2Qhbq2CTpCgR8AQB0xXnp5hGF3pP9InaIALeQEBS37y0U9q3OWTf9eMiHdPD3DW5zHzMGoo3ucDERi+UDuN1LTb48eMu5hIlQ3iepI/598WspJAIEIH8IOD81y4/WnJhU28wYNf+k3yPhqadh+KTib9Do80Xh4qLHhR+tXH1EehHL4bhh7UwfL4UhgX/Ff5B0wiJQG4JxFx3akG4d8OpnpREIL8I+GuCUaPfKZRrOwulm32Nqi9uQ6nkvk6bk3U0QDFCB6GCeOtSBUgbGSHY+2Kc1iMlEchrAkIlo1OTEhd6p4VJSQSIABEgAkQg5wQ8UrPAHCpnL17Ht9MX48nOw/DWR99h639H8Ur3tljw86cIDqQlP3n17rNZKcZt9pv9mo5ehfG484vFvGqX7BABnycQUd7pEEwlyjnVk5II5JaAKTQUxvBiDmb0TZ6ETFUcYdX7oES9kfCPbOJQxlYhH6JHyNMvIKzD0wiOagFBE8+z5RN7QTG6HRepxryciGc4C7QaCLfOA0mOM2acFScdEchKQN5XDyHLJCp5T1FXPGtJShMBIkAEiICZAIW+SMCjDpXo2DjMWbwOnfp+hB5vjMHcpevRtP5jnNvM7z7A6JF9Ua9WVQiCwHUU5J4Ac6g4tXKTTpKdciElEcggIDR9HoJ/UEbKfDA+0RKmEmXNCQqJQC4JxGpi8M/tZdhzfwe3ZJLJkPjLbOhEB4rJPwAmuYLrNa3b8aOngvTNSyAf1x3y6cOh+Lo3ZLM+9FTT1E4hIiCpbIJqQzqUX2uh+EAH1UINFF/oCtEIaShEIHsE+JKc7/plr1IuS0saVuJLf4SIkFxaclGd1ESACMBjDpWT566iba93MfnXJSgXVRKTPn0TB9b+ivEfD6K3IR8JCMEq59aDXOidlyYtEfBJArJLF6CaNxuqRfMhvZHNWVnhkfAfswj6l0bD8Oxg/qQgfZ/PfJIDdTr7BO7HJSJdrc1+RTdrTFv8PV5s9xy+7TMJ66eu47W2bNmIpu8PR607t/DhawNhLFOG63MS6EYvhXbiJpdiUJqdNVltC8lxSFs+FdBnjl1y+Sgku//OWpTSROCRBIRwQPaCAfLX9ZDUc74E6JFGqAARyEMCvmJK2s9mf5X3O/NuC6XCuHPE4piRZuh5JgVEgAgUGAGJp1rWZTwGrHyZCHRo1RgtnqyDAH8/TzVfZNsRqkQAgVk4yySQPEHLForsh6KIDJw5UsLbNUfQmE8QNPp9FGvZGMpN67M1ejZDxVjnGRia9+RPCspWZSrs9QR27D2Omq1eAzsi49+N2zF8FmXL7qPQsONgfP7NbOj0hozcvDsceOCdj7IUoi85HaQQfdmpnpREgAjkKwEyTgSIABEgAl5OwGMOlceqlsfXowejdGRxfDZpFp7sPBTvjp2Orf8d8XJEPt49PwVko56F5JmaEB6LgqR5DUjf7gSEZ1nY7OPDpO4TgawEAn6YnFUF/x+nOOhIUTQJnL98E+9/+YvD4Mf/MB81qpTDofUzsHreV1i/7QDWb93vUC63irS6ydB+nsTFUN88G8QUZsT8dUuwa+chfPDBp7ltIkf1TX72y9wsRgQ/+s2wsKDjwwhQHhEgAkSACBCBokXAYw4VlXhh/3z7Zpg5+QNsWTYFHwzpjUtXb3HnCkO+8K9NOHT8PAwGmhLKeOSphAdC0rk+pINaQ9K1EdiUwTy1T8aIgJcREBITIIl33CdIdumil/WUulMQBO49SMCQj6dgzLuvwl+VOYMvMTkVuw+eQr8e7cF+syqWK4WuHZ/Cxh0H87ybFQMqO9gUIKCCE71DwXxUmMpUg7RUBYcWDLWfcdAVCgUNgggQASJABIgAESACuSDgMYeKbR8jS4TjtZc64p8/JmL5719gQO9O2Hv4NF4d9RVavTgKqWlq2+IUJwJEgAhki4ApKAQGZZBDHX1whIOOFEWLANsXZfjoH9H92Rbo3O5Ju8HfFx0tTFGmVAl24FIuKgJsQ3WeEAO5VIBFZBm/oBIBVp0l71HH/hUHwk+a6cwRTSNYHmJnh+mY2NqyxMUmWZZdeUueO0cJ67RogR3tystlCHn3Z6BdP6BGU6BJZ5je+gGyio/ztix7xsvE+nb1bLh4m14m9k0cKu9/dvtmMAFXEwxI1prcqs/aISECRIAIEAEiQASKDgFJQQ+VLQV6761e2PbXD5jx7ft4qvETBd0lap8IEAEPEnhryOto3qIhl9OnT/KWh9joTmXoeIa7gXixl1yrg0Pp5PrPOuhIka8EPGaczW78feFquJIrN6JhNJrw6dczESU6TIa+1tWhb0kpaVynUMj5kQVKpQJxCUksCubECA1UwCJB/uZyUonEqrPkPer4TLlm2NpxB75u8A0aFGvI7YcqQuzsSMXPMctg7WS1Z3FshARk9idrmYel/RRSZhrsmLWcJDgUId3eQMiIbxDS/0PcL10blxJORp5kAAAQAElEQVTBJU0L/u+OiMqiE2Qyu35ntVfQacaIscxuP9Zc1qH7kngMXZOEXssS8MnW1EeO0/K+cEgFEOgNBu4A1GidP02H/Q3cvRcHVs5Z97RivZh78TCZRE+SswIFoDPtPg/D9I1cTMfMm4ub9mTqjBm6AugaNUkEiAARIAJEwHNP+XkUa5lUiqca1QLbZyXA3/6u3aPqUv6jCSi2bYb/bz9DuWEtkJ7+6ApUggj4OAH1S/0Q2/YDJNTphsR6PRDT/hNoe774iFFRtq8SMMEkOj6SXQrbGJ09uWfD9gMIClRh8i+L8c20RUhLV2Ppv9uwYftBBAf68+GzsjwiBhqNFuGhwWIMYgvAvUSNVeJTzBetOoPRqrPNf1Rcqg3DM+FdUc7PvPzHIDp8bOuwNGs4LlnrYF8syrJwPymzP7Z1HxVPU+t5fXbMWpZdS9+3Ged3/yXirVVxXE7Fmsf8875knmb63VfSHPqX1WZBph+IjBjL7PThwt10fL87GVoDx8SDg7c0mLE/6aFjZex44QIImDOxTpuB/ImK9du/gXfHTkNiUqq1J2zz5SbPDUGbnu+ClVv673ZrHnOgTP9jFeqJ9Vr3fActuo3E8TOXrfkFGTE+SIbpcgwXJIqePLEzpvuOOlFNLyJABIgAESACHifgsRkq5y7dwEtvfuGWpKTSBX9efhLCXnwOoQNeQeDX4xDy1uso1qEFhGTzHde8bIdsFQCBQtDkr7/M4Ztwso04nUmtmjmbtSbp2RSm11+AulsfpHXvA9ObL0DSplYhIEZDcEaAOeU/GtYHrqR65bIIDPDDqEEvIiqyOEJDArkwW4EBKvirlCheLJQlcfNOLD+y4NrNuyhVMpxFi6xUCJHiiQiZSwlSeuxUwmPvweV4G0+KTauu9DZFCizKPtOzpnzIN1ReMXs8Dh47hxXrdvH+sKVubBPm4QO64fiWWfhx3Ah88d1c3Iq+x/OPnb6EaXNWYP7U0Ti2aSa6dmyOd8b8zGd18QIFGEiaVYd0aDuXItQpX4C9o6Z9gQCbkcWWbuZ05pYvjJH6SASIQMER8NhZEJtCHVWqOCzC0qfOX0V4WLBVZ8mTSDzWrYIj76GWFTu2Qn7kkF1r0ps3oPx3pZ3OUwlqhwh4koDQrBokr7aEtO/TkNSv5MmmqS0vJMA2oB3c93nYCtN1at0UzZvURkhQAJ5sWBPzl28CuwC9eiMa/2zcg/YtG7k9GtOVWBiX7YNh5lYYN5wAUnx/T7AB9VT4pm2QS6lRXOo2H18pGKBgC7wcexukcNR5i6Zn51ZoWv9xvqFytUpl0KpZPezcd5x378DRs3w2Vp8urcGcj22bN0D5MhHYsfcYz9/631H+2a//RDXI5TK+MXPMvXicv3yD5xdkIBQPglA50rWEmmeWFWQfqW3vJZCbmVveOyrqGREgAt5EwGOei0rlSmHK2GFWefuNHpzD+I8GWnWWfHankGcWfODzPZC6eKqJ/NIFnx8bDYAIEAEikNcEPh3ZF6dFZ3/DjoPRuf8nojOlITo+09itZkzX78MwbQOM+y7CdPY2jBuP830fYFmf45YVKuQNBKoVk6FciKOjqEV5L/ao2IDT6Q3YffAkalavyLUx9+O5A4XdzOIKMahcvjTuxsaLMSA69gEqlo3kcRaULB7KDoi9n8CPFBABXyWQm5lbuRmzYY8Eujky6NdLYaKJ97lBSXWJgKcJZLs9jzlUst0zqpAnBAxVqjq1o6tSzamelESACBCBokTg4Lpf0fLJOtYhVxSd/xsWfYuty77HgbW/YsLHg/gde2uBh0RMR6865JpiEmG6+cBBTwrvJsAeDDSpbSBeqa1C49JydKqiwLftgvBYcZnHO37n7n2Xmy2zu+9sNlXWTo3/YR6SU9L5TBOWl5ScCjYbi8UtwjZcTs7YiJnl+ymVlix+ZOVT0sxXgsVDlChdTGUVy5OTSob6WXW2+b4YD/CTISRA7nQ84UFmR5pKIXWa763jVcgkyPre5Udf/ZUy/pnxxiA3M7dyOh71ICWYaL+VQ/OuAupn/WCin4Gc4qR6jyRABQqaADlUCvodyOf2tS1bQ1ff/AQJS1OGsuWgeb6rJUlHIuAxAps2rcPAQX3RrkNzfmTpvGhcrzPiwpqLODzjMK7vup4XJslGEScQUSIM2d0g3RTrYm+qmIQiTtM3hx/qJ0HfJ/zwxTOBGNEkALVKFsxFI5ttEpeQ7HLDZZPJaAd4+tyVWL56B2Z//xEsM02CgwL4kh/bgmzD5aCMjZhZvkab8QinjEJsw+ZAfxVPsU2K7zxIh0X07HnSYk5sgtqqs+T56jFVrUdiqs7peNjG0OJwka41OM331jFr9UZkfe/yo69pGvMm14yRN4sumzO3cjIWw3EJ2OwU27rGWAH6FQXz/WHbD6+JU0eIQCEjQA6VQvaGOhtO/F9rkDB7IVI+/hyJv87Bgw07YQoKdlaUdEQg3whcuXoZX477HBcunIM6PZ0fWfratSu5alOjNWLetLNYO3wNdn21Cyv6r8Df/f7mNo2XBej+lEG3VArjTed7IvCCFBCBPCAgVC/t1IpQI8qpnpREwB0CbK8TV5stMz2bScLssEcifzt9MeYsWY9lM8biiRoVmZpLRPEwXL8VA9snWF24cguRJcN4fqmSxcA2YOYJMbAs9bE4ZEQVvYiAzxPI7swthUyCUuEqWCRIJecMAlUyO70lnx2DY+xnevEKYuB3S26to1JKRQ0QGqiw6iJC/bhOyDhVsc0rEWLOYzPDWBsPE1dlFXLzJV+xIKW1TWaHlZdLBTsd01skozuIDFe5LGMp6y3HEJGrv8jYE/3hbxoFRZ6A+a/LAxjY3ZU1W/bBImxtL2t2085DVp0lz/YHn5UhyT0B7TNtkfbmcGg6dAJUqtwbJAtEIJsEDh3c77TG0eNHnerdVZ67rkFqsv3dsRv/3cDNr+8h/Xk/aMfLoR2rQHoHPxi2mE9i3LVN5YhAdghIWj4GoWrmPhSQSyF5oSEQTN+52eFIZXNG4H/fzsbcpev5vnQhwYG4ffc+F/aEk0Z1a3Cji1ZuAUtv3nWYP+Gn5ZN1ub710/Ww++ApHDl5Eewu/rzlG8BmaVWvXI7nU+BTBKizTghMz8HMLa3eiLvx6VZJSddxyylqvVVnm8/iyZEaXiZroCmjs9ZhM51YfmKq1qqLTVQzFUwmfkCCTd79JHOe3mCylmdtOZN7GXaylmVjYZbjkjV2Npht3UPsZnQHMTYcnLXrTbqkFC3SNAa7ceZX/xhTEiIg8RQCdufjw3G/wiK/zf+XNz3u+3lWnSUvTe38y4hXoIAIEIEiQ0DzhRzqV5VcDJfMX1faLxU8zfRG8Y5PXLL9dHcLnJgl9y1R61E7U2aNU4QI5AcB6VvtIB3zIqRvd4L0y15gThbWzuwr09BkUzWnsuL2ElaEhAjkigB7TDIz8NZH36F97/etcjv6PvxVSkydMAqTpi1CnTYDMerzqfjs7X4oU6oEq4K6Navgrf4voN+ICajbdiCWrNqG78YMhURiuT/Ni+VTQGaJQP4RyM3MLdYr5uCwClMwEb0MVl2WuKS2EdJm9uclkpImSLvpubOE1YOJGYE1zXRMzNqMUCzDdBbJ0DrUseRbjpZy7GjRsaO1TTGDpW1FVLm0y/KY2Jb3+jjrsCie6KfYDL2IAMxXKB4AUat6BWxeOsUtCQrw90CPqAkiQAQ8SaBhoyZOm6tXp55TPVOazkpgOGgWpDENYDwjWHUmtQThQc6/xoql2cwUMFeF6bLzshnZdCACeUJACPaHULYYBEX2HXiyo4ch37ebi5Bm/tDLj2XqkG7eJDRPOkpGckbAS2uxzZRPb5+LrMKWDLEut36qHk5smY2Niyfj2KaZ6NO1DVNzEQQBIwZ0x+ENM7BJzN+/5hfUq+V8U3tewUNB0vWNuPhXa5ycUQLn/2yAe8d+9lDL1ExhIZCbmVs5ZeA3UwMmig90UE7Rwm+dGkIx59Y07yuQ+rgKya0r4aoqCan653hBi57lpbUpDcOdylxPAREgAt5HwGNXF+xRfaVKhsMdoTsi3vdBoR4RgdwSqFSxMv73+ThUq1YDfioVP7J0hQqVXJpWfC6eiMwVT0xciCRKixrllQgIsr9wLfd0OURElHawK1Q2OuhIQQQ8QWBApWHY3+4Clzcrj+JNWnTdol7iaRaEvDMUYX26c5HExjAVgt8ZxtNML70bzXXZCagsEbAQkEoliIos7vLJVX5KBUqL+d5wHqZPv4erq7si7e4+GHWpUMefxZ3dHyL55hbLcOhIBB5JIDcztx5p/CEFTDEC2FN+DLukEDyw6jP1SRV3zJiSH9IpyiICRCBfCEjyxSoZJQJEgAg4IdCu3bOYNXMBNm3YxY8s7aSYVSWpaYK0sdGlCCoTlAoJ+g97DJ1+fg7NP2mObvO6ofv87pC/ZV7rbDUmRhSD9EBsEgz/HIJh1jZ+hMEo5tDLiwgU6a7o6tSHrsmTLsUkOiOLNCAafJEhkBq9z+lYU25td6onJRFwRiA3M7ec2SMdESACRCArAXKoZCVCaSJABHyOgEwuQbXnqqLB4AYo37w877/8ZT1U/6qh+EwHxVgtVBvUkDZJhf6HtTDtOAvTmVv8iKTcLqHgzVFABPKEQNKPvyB+8UqXYowslSftkBEiQASIABEwE3jUzC1zKQqJABEgAs4JkEPFORfSEoHCS6AIjUxS2QTmWJH3MkBS1gTjiRuARmdPgO1aZq+hFBEgAkSACBQwgYBSTZ32ILBMK6d6UhIBbySgXyHlS3HYXigW0a+W8q5qPlBAt8Yc54qMwNlpif5YG6hn93SwZbGJxIzKdCACRMDjBMih4nHk1GB2CVB5IpBnBGLpjCPPWJIhIkAEiEA+EpCpSqBi55Xwj2wKiTwAfmGPofRT3yCobOZmuvnYPJkmAkSACBABIuAWAXKoZGDS6fSIjnkAvcGQobE/JKekIT7RrZ2e7CtSiggQAa8hIDxexmv6Qh0hAkSACBCBhxMILt8eVV/ciicG30P1lw+jRN3hD69AuUTAywjIuhkQcCbdTmSdzdcaym+1kD9njtt2WxBsU+a4rO4W+A1YZmfH1i5CzOUoJAJEwPMEJIDnG/W2FmcvXou67Qah7UvvoU6bgVixbpe1i2npaoz49Ec07TwUT3cZgT5Dx+F+HN3ltgKiCBHwIQJCtVKQNM3yKFCl3IdGQF0lAkSACBABIkAEiAARIAK5IUB185JAkZ+hsmv/CXz361L8NG4kjm+Zha9HD8Znk2bhyo1ozvnPFVtw4cotbFv+A/atng6pRIIfZ/7F8yggAkTA9whIejaFbPxLkL7diR8RoPS9QVCPiQARIAJEgAgQASJQVAjQOImAFxMo8g6VPYdO47Gq5dGmeX3IpFI8374ZKpcvjf9ERwt739ZvO4AenVuiZPFQBAX6o1+Pdvh77U6YnO0YxSqQEAEfIqDR6nAr+h7iyA3FfgAAEABJREFUEpwvZzMaTbh7L87lUjgfGqp9V1UKCGWLAeLRPoNSRIAIEAEiQASIABHIHQHLUh/lBG3uDLlZO2BvOl8OJAS5WYGKEQEikGcEirxDRamQ81kntkQrlIvEnZgHXHX9VgzKRUXwOAvKli7JDkhKSeNHCoiArxJgM7Hqt38DHfp8gOZdR6DfiIlISEyxDmfH3uNo8twQtOn5Ll8Kt/Tf7dY8ihABIkAEiAARIAKFhgANpIAIKCdruSMkaOsVVEwPRoBsDe+JRc/2SfHfcgfS0pe5ngIiQAS8j0Chdqj8u3EPfl+42qnsPniKvxvPPFUPp85fxZffz8PGHYcw8881OHryIs9js1DYHip+SgVPs4A5YNgxLU3NDvBXSu2EK8Ugqz6/01KJeQcrpVxi15+8aJfZzg+7edG3nNpgG36pFPbvXU5tPaxextsifiK878Wcg0t/G4tjm2dh3cJJuHYzGkv/3cY7mq7W4v0vf8HwAd34Urgfx43AF9/N5bNZeIEiFiSojXh2YbxV+vxl3kfJlb6I4aHhEgEiQAQ8TICaIwJEgAgQASLgHQQKtUOFzSJhSxmcCXOUsLegzuOVMXPyB7gfl4A/V2wWj4l8+UPpiGIQBAH+Kj+wZRGsLBNL3N/fjyWhkEvtBBn/surzOy3JuHKXSSV2/cmLdpnt/LCbF33LqQ0BgshJIoo0X4Wxg5f+e7Pf86hZvQLkMilKlSzGexkaHMiPB46eBfsb6dOlNV8K17Z5A5QvE4Ede4/xfE8Ei0+ruQPjj2PpnmiuULUxdnsKZ7f/tq5QjYsG4x4BITkJfv+sgP+M6ZDv2+1eJSqVvwTIOhEgAkSACBABIlAoCUgK5agyBvVK97b4aFgfp9KuRcOMUsCTDWvyTWnn/vAx2rc06xvWqc7z2UXkjdsxPM6Cm3di2QHBgf78mJCiha1wpRjY6jwR1+mNYqtAqloPZ+09WKvHvZ+MuL/SgPh79n12Vt5Wx2y7smtbzpfiRpMJiak6p6zychx6g4m/L94aaLU6/DrvH/Qf9RXqPVEVndo0BfsXcz+eO1AUiswn4LC9he7GxrPsgpVUNUyX75olOqM/qRpzmuktuoLtJbVOBAqEgPTWTRRr2QTBo95C4FdfIKxPd4QMHZjtvlAFIkAEiAAR8A0CglYN4coxs9w8a+60ne6cWUchESAC+UKgUDtU3CUWez8BOr0B5y/fxKSfF6H1U/XweLUKvHqHVo2w7N/tYGVSUtMxf/kmdO/UAoIg8HxfCNJfVkL9lhLayXJoRiqQ3sUPpsytMnxhCNTHbBA4fOKC02VubPnb8tU77CwZjCb+FKvEpBQkJachWfyMswJJyal8dhaLW0SpVCA5Y+8gpVyKYsFKqygVUl4s0E9m1dnm5yTur5RxmyqlfVuBMQkwTN/ExbjuGC9junaPp5letvkkstOeZRZRaKDCrl6QvxwKuYTrwoKUvJ1HBWyiWHbazq+yCvH9YX0NUtm/H3x5Wh6+R676HxKQ6Yhj/fCA+GwTt2/fQmKSeQlZXgxCtfAPSOLj7Ewp162G7MolOx0liAARIAJEoJAQSIyFfOYHXGTLv+WDEhLv8TTTy5Z9w3UUEAEikD8EyKEich3x6Y+o23Yg+g6fgDo1K2PymKGi1vx6uVtbVCpfGs/0eJtv0KnT6TFiQHdzpg+Ehv8kMB6zf5tNtwTo15gvVn1gCNTFbBJgS3XiEpL50rWsx/hE+6f5qPwUmDJ2KNbM/xoymRTT5qzgrQUHBfAlPzyREWg0Wv6kK5bUGYxISddZhc1iYnqNzmDV2ebnJK4VnZzM5pwjqWj8y12rvLEpDScDi7mU+dEKa1nbeq7id5IMrBl0WXDPrl7DadGoNzWa6zrOjeVlbAOTmDCxQDxaXg/Sjby8q7Yy9ZnjyQ/drmtq3iW11v790OiM0GTR5eS9eVSdNI2ZKe8EBc4JxAv499N/0LtPV2zfsZWXUWvM7xtP5DCQXrrgtKb0knlvMKeZpCQCRIAIEAHfJSD3g6libddSxjzr3p0BdluSwJcMW/aNU+vNJzqu9O7YpDJEoLATsL/SLuyjdTG+3755D5uXTsGBtb9g9Mi+sGw8y4oH+Pvhl6/fwZ5/p2HH3z9iyW9j+COUWZ4viPGy87fYdMV3Ztj4Amdv6mPzJrWdLnNjy9/eeKWz064KgoBK5UrxRySzAhHFw8CecMUciCwNMbhw5RYiS4aJMcBoNIFfnLMLdFFYmmXoDPZ62zLZjbtaLnUuMAyjqz3pUuZHuX/iwPrsjphPJ7KUZMosf0ZZklkqeD6Z9f1gM5KYZPe9yG55XcYSRM+P2HdalO5UIvGO/cyUhCwzS3IyGkOVak6rGapUdaonJREgAkSACBQcge3XtNyBMf1AWo47YQotCd0b37kUfa+Pcmzbmyv+djiNs1t5TuPN3aS+FQECzq+2i8DAbYcYGhKIUiXDH7qMJ0S8Y188PAS+9k9S2by3StZ+C5XY1WBWbeFJ00geToAtX5vy21JcvRHNl7sdP3MZK9b9h0Z1avCKjeqaj4tWboHeYMDmXYf5E35aPlmX53sy6F3TD+teCcs3iQw0fw3+0TXEro1trxfH/rciuG7xi45/+6IPClkdKKF+El4+P/vrju0mUbTkxpOf0Zy0JcSYl8nZ1jUYjYiOvmOrynY8/ZVXYQwLt6unebYz9JWq2OkoQQSIABEgAkSACBABIpB7AuYridzbIQu5I5BvtaVPGyGpa+9UEcqYIHtOn29tkmHvJyAIAvYcOo3O/T/hy91eHjqOb8j82ksdeef9VUpMnTAKk6YtQp02AzHq86n47O1+KFOqBM/PaXA7yYAZR9Lxv+0p/MjSObVF9YiATxNQmpx2PyQk1KneXaWhTFk82LEfST/+ipRPxiB+0d9InD7L3epUjgj4DoG4VJhuPfCd/lJPiQARIAJEoFASyKFDpVCyKLSDUv2pgd+vGije10H5kxaqVWoIgYV2uDQwNwiwpWzLf/8CB9b+inULJ+Hgut8w4eNBdsvd2ObMJ7bMxsbFk3Fs00z06drGDcuuiySojRi5Phkrzqpx8LaOH1ma6V3XohwiUDgJGGrpHAamUqng7+/voM+uwhQUDPUL3ZA2eCh0TZ/KbnUqTwS8moApIQ2G79dAP+Fv8bgW+s+WwHjsulf3mTpHBB5FYO8tLS+iMZid7V/9l8qXs7C9TIasSeJ5V+INVh3TZ0eOxZhvpH60KdnORrt5cWB7xllsqXXm9nmDYsD2ijPBXieq0TXLXiuW+p480lIf9k54UqgtVwTIoeKKTCHTS1sYIR+gh6ytAYLKPDh2UmLafwnGXedguhNvVlJYpAgE+PuhXFQE/FVKp+OWSiWIiiwOuVzmND87yv9u6pCW5YeapZlzxZkdy1KfV+tmfGCdFSKdUwJjWwXypUe09McpHq9Qmhro0Hxoc3Ro3wmlI0vzPoWFhvMjBUSACLgmYNp0AqZbcZkF0rUw/rU/M00xIkAE8p8A87Tkfyu5a4FqEwEPESCHiodAe7SZ2ESAyUMaNV68C8O4v2BYuhfGlQdh+G41jJtPPqQGZRGB3BFwtbznZpIxd4Y9UDs0y94oi14M4a260vNMCojAQwgok/1QRl4Br7YdjDrlGvCSglrAzX23uDy4mPdLGVJu78TdgxO4pNzZydtkR6tOzOdKCoiAFxMw3rjv2Ls0DRCX4qgnDRHwEQJPllGAdVUpNe/O9snTAfzGCNs37ZfnglkWKoVJrTqmz47UjTDfGJvULsjOxqb+4Tg0rJRV5yc3t88bFANBTAoOO8YBK3vn39527o6raw3nNwPFbtOLCHiUADlUPIo7/xszLNsL/aR/zDL+b5iuOznxELth2n5aDO1fxi2n7BWUIgJ5SKCxi41SG5SW52Er3mdKHXcG90/+hgenZ0ObfMP7Okg9KhACJc9HQfu+CX/1WY6E9Ym8D5I7Mp5muv0/5f0d95Q7uxBzYAKXlNu7eJvsaNWJ+VxJARHwYgKCSuHYO3bV508XV45g8lVDxokAESACREAkQA4VEUKheqWId2ksA4pP5bNPLEnboykm0TZpjmv1dIfHTILCfCBQL1KOTlXsT4Sfq6pEnYy7JvnQZIGbZI6U84sa4vbOd3Br+3CcnVcDiVdXF3i/qAOeJaA2qLH4xh/46PgwjD31AS6nXIQmSA1JbaBMkzLwq+yHe4oYmEoZeJrpilXJ++U/gaWbI6Lxpy6F5XuWDLVGBLJPQPKk46PBhWqlAL9HOeez3xbVIAJEgAgQASLwKALkUHkUIR/PN918ABgdN5MSIkIcR6aQAeGBjnrSEIE8IjCiSQCW9QrFj88G8+PwxrnfgDOPuoYZh9PQ569ELpP3pOJeWu6XIsUcmuTQvdjDkx10pCjcBMad+Rjfn5+A7bGbsC56FTbFrEFs9dtQThHQY3EPlB5ZEjsjNkHfTc3TTNdkVNM8hxIY1QKRjT51KSw/zxslgzknQDWdEhDqVYB0wDMQGlWGULMMJJ3rQ/pqC6dlSUkEvJ1AqwoKsCUuQ73ofMjbmVn692YDf86Olv5YiNCxoAiQQ6WgyHuq3UA/QCI4tCa0qumgk7Sp5aAjBRHIawKBcgHVwqVgx7y2nRt7u2/qwJ44xGTLVS2m7svdenyDJgH6tLsOXdLEn3fQkaLwEjCYDNhyd53TAUan38bh+P24nnaV56caUvnRVwPqNxHwFAHmSJH2bsYdK5JnxPMZpdxTTVM7RKBQE1jxUih3UjAnDxM/mfkawpW+UMOgwREBNwmQQ8VNUL5aTNKwktOuS6pGQvr5i5D2ehKSro0gfa8zJG2fcFqWlESgKBI4Eu34WNvscJAqQyHzj3Soogyr7qAjRYEQ8EijN9OuwSj+t21MyNjgb130Sgw91A8Lr8/i2ddTzY4VnqCACBABIkAEiAARIAJEwOsJkEPF69+i7HVQ8lR1CHUrcOHOks71XRoQQv0hNKkCSfMaEEqHuSxHGUSACOSMQETDjxwqlmzwvoPOPQWV8kUCFQIqI0BKSyl98b2jPhMBIkAEiAARIAJE4FEEyKHyKEI+ls82ZpP2aw4mzFniY92n7hYmAj4+lvqlcj+FvPgTb6J6n0OIavE9yrT6GY/1P4eQip19nAx1P7sEmpdond0qTssbt56CccMJLtDpeRnT1tM8zfQmtrE411JABIgAESACRIAIEAEi4AkC5FDxBGVqwycIUCeLNoGnysoR6ifh0qaiAiOa5s2sAr/wx8EcK8VqDoAiqFzRhlxER18hwPnSy+zi4M6TjcdhFAU6A69u3CY6VMQ01+nNOp6RD0H0/i9wbkEtnJwRgcv/PI+02MP50AqZJAJEgAgQASJABIiA7xAgh4rvvFdZe0ppIkAE8pDA4Ab+WPRiCJf3mwWghD99PeYhXjJ1VwLhgBySY3LgkhSSHQpIdyi5SC6IOgBx1x5g9pwZVtm9e6eozXwJrWtC0pdDGtsAABAASURBVL6OS4FMmlk4j2P3T/yC2EOToEm8AqMuGSk3t+Da+lfyuBUyRwSIABEgAkSACBAB3yIg8Vx3qSUiQASIABEgAkWPwPF/j0PxeyBk6/0g+1cF2SJRdvpBulN0qDC5mOFQuR6HOaJDxSKr16zizpX9+/dwaJLWtSDpUNulCAoZL5cfQfKtbQ5mdck3QE+tcsBCCiJABIgAESACRIATKBoBOVSKxvtMoyQCRIAIEIECInBi9Qn7lo32SVep+Hizg2X/wX2uipCeCBABIkAEiAARyCsCZIcI5IAAOVRyAI2qEAEiQASIABFwh0B09B3oNeYNZN0p761lgso849A1eVA5KMOqO+hJQQSIABEoygRiNTH45dIUvHNsML45OxZnkk7mGw4yTASIQMETIIdKwb8H1AMiQASIABEopARKlSoNmTJnS3FiY2O8hkrx2kNQsuFHUIZUgkQehMCybVCh40Kv6R91hAgQAZ8gUOg7qTFq8MaBlzD36q/Yc287/rr1JwYe6IkrqRcL/dhpgESgqBIgh0pRfedp3ESACBABIuARAk91e9quHUEQ7NIsYTKZ2MFOvMmhwjpWqskY1Oh7Ck8MjkHlF/6Ff8kGTE1CBAoxARoaEcgegVOJx3BXfceuktFkxJrb6+x0lCACRKDwECCHSuF5L2kkRIAIEAEi4IUExg+djNmzFqJ9+2d571Sh/vxoGwiCo5OlZMkI2yIUJwKPJkAliAARKFAC+2Mu8fZNsHeS77i7H4fjzRKnvc/LUEAEiEDhIEAOlcLxPtIoiAARIAJEIJsEduw9jpqtXgM7IuPfpGmLuI7pLdJ3+ISM3JwfqlatjkaNmnIDQcUD+fFRQVFwqDyKAeUTASJABHyJQDgaO+3uTc1BDD3Uj8uBOPOT25wWJCURIAI+R4AcKj73llGHiQARIAJEILcEzl++ife//MXBDFt606pZXaxdMMkqk8cMsZSjIxEgAkSACBABlwTqFq8Ev5Q3zPlskgoXKU9XCaqB+mGNEa4oztMUEAEiUDgIkEOlcLyPNAoiQASIgBMCpHJG4N6DBAz5eArGvPsq/FV+DkWCAv1RvkyEVSJLhDuUyYmiSuWqeP31wQgv5569sLBwXr5JxsyWnLRJdYgAESACRMBzBGoUl+Kp4CEIj92BoPg5CH2wHipDfd6Bt6t9gl8aLkDj8GY8TQERIAKFgwA5VArH+0ijIAKFgwCNggjkM4F0tRbDR/+I7s+2QOd2Tzpt7cDRsxj91e/4dvpiHD5xwWmZnCjZsp8BokOlaotqMLTQoHaX2txh0qxZc26uerUaPM2cLkw6P9cFrHyTJnTyzQFRQASIABHwAQIT2wRiYusSGFK7AT5tVgaPlcjZk958YKjURSJABEQC5FARIdCLCOSUANUjAkTAOwgYDEb8vnC1S7lyIxpGowmffj0TUaVKYOhrXZ12vGa1Cuj2bHNUKBuJm9Gx6D9yIjZsP2AtG+gng0X8FeZp3FKJYNVZ8h52jKpaEoaWGjTsUQ8jhwxF65atuP2aNWvyNNMx6dCmdbbsPqzN7OSxzgTYjDM7db2xrL84Frbnryf6xtiREAEiQATql5Ljxcf80KK8AuJPBAEhAkSgEBOQFOKx0dAcCZCGCBABkUDq3QNIub2Ti9GgFjVAWvR+nmZ6o96s4xkU+AQB9kSFuIRkuBKdTo/7cYncORIUqMLkXxbjm2mLkJauxtJ/t4n6g3ycz7dvhhEDumNw3+fx07iRYOkV63bxPBZIxDNjiwhinOnY83ksOneOgsBqAOzAy5uTYAeeFu0W5JH1izmJCrIPedm2VAQriHTz0qYrW6B/RIAIFHkCsZoY/HJpCt45NhjfnB2LVH1KnjC5dDkFi7suws+P/4wFzy7AqcUn88QuGSECRCB3BLzcoZK7wRX22oY526F/bz4X06mbfLjOdDyDAiJABKwErm/sj8srO3LRp8Vw/fVNr/I001t0PIMCnyAgk0rx0bA+LqV65bIIDPDDqEEvIiqyOEJDArmwwQUGqOCvUrKog7D9U1LTNFZ9UpoOFklV67lebzRZdZa8hx01OgOvp9EZeb10rTmt05vTD6vriTyTCbxfnmjLE20kp+thFAflibb4G0sBESACRZaAxqjBGwdewtyrv2LPve3469afOJN0AsIZKTYsXovZc2bg8uWLnM+OHVt4muuuX+M6V0FSqgHr1t/F3eMx0IvfaffP3cfmT7Yg+uhdV1Uc9BKjFP4710E+6WUoxnWHbMEYINZ8/eBQmBREoMAI+F7D5FDxvfeMekwEiEAuCQRENkZgVHOXIkiVuWyBqnsjAX+VH595wmafWITpOrVuiuZNavMufz9jGS5fuw2d3oBT569i4d+b8XTjJ3geBUSACBABIkAEHkbgVOIx3FXfcSgiOScXHSrrMEd0qFy5epnn79i5jaeZ7vKNhztUrsdoeZ2sQfSR6Kwql+nSyVHwO3kIQuI9ID0ZkjN7IFk80WV5ynCTABUr8gTIoeLDHwHp660g+66fSxFqlfXh0VHXiUD+ESjffh4qd93gUuQBkfnXOFn2agL7Dp/BC699irptB+KlN79A+5YN8WqvDl7dZ+ocESACRIAIeAeBa6lXnHbEWEOHDr2ftdt4nG0+bpHK5So4rWdRyiSCJWp3NGjNMyXtlC4SQdpQnmMX3L4IU1qynaqoJtZf0mDomiR0XZKAt9cnYc9NXVFFQePOJgFyqGQTGBUnAkSACBCBwkPg4Lpf0fLJOtYBLfltDPatno51Cyfh0PoZmPDxIPgpFdZ8ihABIkAEiIBHCXh9Y6Z7STDdTeD9bBzu/KlspscNokOlE39yG3t6W1apXP7hDpXSxeXcftagbLNyWVWUzgGBs/f0+HF/Gq4mGKDRm3D+gQHjdqYgNtWYA2tUpagRIIdKUXvHabxEgAgQASLwUAJBgf4oFxUBlR85Uh4KijKJABFwQoBURYUAc6Lov14Jw9erYPj2XxjGLkfUbSXeqDTCDkGoPMwunZNESKAUz3aMRGSdCMhUMhSvURxtv2rD0+7aS1aYnT525aOqQvAPslMVxcThu85n+pwRHS1FkQeNOXsEyKGSPV5UmggQASJABIgAESAChYcAjYQIFAECJpMJeoN5A/CswzUaTbh7L85lftbylrRx7VHgXuZyGVNyOkwrD2JQ5RHY/swxzGnyF9a33IuqQY9ZquTqWKVyIHqv7IPhZ4aj77q+qNU7e/t73Qm6DfUTDWEKKQGogmB8vBmMvUfnqk9UmQgQAYAcKvQpIAJFmIBGq8Ot6Hv8UbNFGAMNnQgQAR8iQF0lAkSACGSXwOpNe9G+9/sO1XbsPY4mzw1Bm57vok6bgVj673aHMq4UpjvxDlmmmERArYVK5o/Hg59AmKKYQ5m8VJgSAM3/FEhr6YfUp1RQv6uA6Y7Am7i9+yNcXtmBS/q94zBKDLgo2YZz1R/g7BOpSO7QHShJ+y0yWA0iZezgII+XcK53KEiKIk2AHCpF+u2nwRdlAp9NmoX67d9Ahz4foHnXEeg3YiISElOsSF54dTRqtnrNTqbPXWnNpwgRIAJuEaBCRIAIEAEiUEAEbtyO4ec5H0+c4dCDdNHx8f6Xv2D4gG44vmUWfhw3Al98N5ffaHIo7EyhUjhq5VJAIXfU55NG+5Mc+uVSmO6JThTRv2NYL4VmrLlf6nsnkXJ7FxeDVvS8iH1Q3z/B00xv0IrOH1FHL+Ax0XEyqok/KoZKoZQJqF5Mis9bBKJkAF0qg/49kgB9Sh6JiAoQgcJJoGzpklj621gc2zyLb8B57Wa0eGdmm91gRw58EWsXTLLKy93a2uVTojASoDEVBgKmpDSYbj6AKRtPgCgM4y7qY2BLGqJj48BmH+aEhVarQ8y9eLDlETmpT3WIgLcRKB1ZHH/89Ak+HdXPoWsHjp5FWroafbq0hkwqRdvmDVC+TAR27D3mUNaZQlK3goNaUr8i4OKJPA6F80Bh2Cd1sGLYI17eGYDST3+Nyl3XuxRV8doOdYuyomMVJaY/F4yVL4Xih47BaFbWc46xosz9/+ydB5xTRdfGnyTbKwu7SwcRFBCpgoBKR8QOCioK6ieIiiKKvSA2VCwo8lIFRCwgoKAiHQRRQDrSQap0VhbY3r97ZklIdrO7yZJsbpJnfzu3nJk7c85/JpPJuXfm+oLt2ifOF8ygDSRAAs4SeKzP7WhQ9zIEBphQOb6CurxcVITamzdxFaLV4EIGGBLKRdvGg38kQAK6I5AzbjFy3voBOZ/NQ84bM5C7YqfudKRCrifwxbdz1ZSFzvcMVk8fDn5zNM6dT7EUVNxTh+JAGfPVT2ja5VF07Pks2nZ/Glt27LNcywMS8FYC4iipFFceMXbGLycTEtUYJ8jqiZLaNavgxKlEh8w1droaxgeuh6FpLRgb1YDp7pYw9mzl0LVlkUgcJhFV26KoYAq28xplK8UW7stAluaYEdHiAxmyYwDIgAQKEaBDpRASCkjAfwjI3chxU3/Gg4PeR9OGV+CWTq1g/Tdz7grI1CCZ6iOPzVrH8ZgESEB/BMR5krf3xEXFtNFw7s/rgfNpF2U88kkC4vCeNOJFrF8wAbMnv4t1m3dh9vyVNrYW9dTh5u3/YPSXs/H1qFexefFEdOvaBs8O/R9ksU6bDHhCAj5E4HxSCsJCQ2wsCg4OQlJyqpIFGA2IDg+0hJCg/KdBggONFllMm7oo/1gHxAzojHI3NlDyQZsfRsvFV1rC+jOrVX5PbXjIIpP4nanrVHopIyr0wlodBpUUYSEmS1zkhThjAX3kutA2efkXWG2DNVl0lFnv4veRYYEw2cn3220Z+GxNKnJy8/MfvTYNIpMy9R7CggMQFHCxjtyprxV2HvoxAaMf207TScAnCWz4ew/kTqW9MEtzkFgbLV+Ue/Yf0e5iJuN8UiqSUi7+6LqpfQu0bdUI8rjssj834e5+Q2F2qgRqX1RR2pewOci55BuqDTbMMm/fhwWbEGAywp4dkaGBYi4MBtiNt3eNHmQyCAwONLld57CQAMWHm7InYG+RRNEi79gZ2TH4MIGet7VHq2ZXqdd9X3l5NbS/ril+X7PFxuKinjpc9scmtG7eAM0aXonAwAD06dFFTf3Zve+wzfU8IQG3EyjDAqIiw9WUH+siMzIyERkRpkS52jYrJw/mIGMmTQRNZJGZ46z3efk+CElabMjWnBWW67Rj68TZWuEX4/Jj5Ekyi0xTQo7DnslByL25MMYDhvJA8C25CH83u1j95DpzyNYKspfvr7vTUfBvsXpi5SIPcx5622fn5iJXq4Sy0KsgI577JwE6VPyz3mm1DxOQ+cBnziapN/cU3Ceeu/h6P0EQGhKEEW8OwK9ff4CAAJO6QylyCU/+X3c88eCdGPDQnZg2Zog2wAjF0pUbJQra95T6spIvLAl5SgpNnmcjlzjvDWJU0fZIrARvsk/qLQ95bq8jGZwJG4ayJ2C44OzrtKOWAAAQAElEQVQrVHJocCERBb5LICs7B3+u24oGdWvZGFnUU4fHT/2HWtUrWdLGx5ZTx6cSzqo9N0UTYIz3EqgYG4NDR04iKyvbYoTcZKoUH6PO5Qmt1PRsmEOW5nyQCHFCmGX29mbHi6QtLqRn5ljyTsvIyU96YUCVaRWXnpGvn3yHFywvLTQbpqEZCF2ehrA/0hDwcQYyKlzUuWD6gudpWjniy7GWnzqfhcR0zaOTr5FlezolB0lpjudtnWdZHmdm5SJbcziVRZkWODzwawJ0qPh19dN4XyTQpmUjvPRkL7vh0Qdus2uywWDA5TUq48Rp+3ex5Y5lXPlySNPu3EgG2Tm5SNa+VM1BBhciT9e+xMwyb9/LQCdb+0K2Z0fi0Y24Ln0SGqZMQ2LCvzYs7KXXiyxTGwzKQMPd+qSZB4bSKBjcSuDE5hPYOHEjdszagdSEVDWXv2CBhorRMFTPXyepYBzP9U/g2ImEIp86lCcR5U0lBa1497OpSEpOU0+amOMuPHUIe08dytSHkGBbp5tMhUhOzX9qMTI0AOUigizBZDSobGW6gLXcm4/l6b3QYJPFxjNZRszanaXCmqNZyt6jSXnqXOQbTuZa0urV7gCTAQXrzh26yvQKBUiHG3HwZ2kOk2zNySjqqeOcHDlEiyb11H7anKXaD/AcLFm5Qb3hp13rJkrur5uIIAPqVTAVMr9BfAC0JlVITgEJ+DsBOlT8vQXQfr8kkJyShhHjZ+DA4ePI0gYZsvjg7Pl/oEXj/MGFTO2ZMmOBcrBI/NzFq7Ft9wG0bFrfL3lZG31y/Yc4Puc63JH+Bm45Pxg7v7kaKcdWWSfhMQmUCYHf31uJ6d2n4/dhv2PRC4swpf0UnE7Jg+nJm2BsdQUM9avC2KUxTAO6oCzfOuFa45mb9MFninjqUOR5ebk2kMZMmQOZ3jn505dgftJEEhT31KFMfcjIzJRklpCalo6IsFB1nqmcsTnIzMoPeXKrXIuRO/Zmmbfvc3JzkaM50c12/JOQhUnrk1VYtj9DsxY4kJitzkW+5J80Cw/zNXrbayYh08omd+mXI484KEL62+w7eAxNbuyHl9+boKaxybGsDSeahoUGY9SwQRg+ehoad+qLQUNG4fVn+qBa5TiJdmkwf2ZcmqkbM+vfPAzx4Rd/JsaFGdCvWf5UKDcWy6xJwCsJXPykeKX6VJoESKA0BAwGA1at347bHnwFTTr3xf0D3kGXds3x8L1dLdlNnbkQnXoOVvEvDRuvnni5ptGVlnh/PTi18SMb0/Oy03B680gbGU/8mEAZmZ6VkoWNX2ywKS0zJRObv9wEw+Xx6k0Tpn4dYbypERBhu+iizUU80T0BecNaUU8dilyeJBEjZHrCR2Om48vvF2DmhDfRsF4tEdsNBZ86rBxfAQf/vbiYsXmqj9khk5GVi9SMHEsw/35Oz7wos473xmN5IlEcR2bdK2q/HR9oFIqiQutqgRYe5mv0tpcpqRllUEd6dqjUqVUV25dPsQkfvNrf8rnoeH1T/L10MhZN/xiyIHOvbp0sce4+OJ1+0t1FlDr/+rEB+KpbNIIDDCqPL26PxpXlCz+1oiK5IQE/J0CHip83AJrvnwTCw0Iw64u3sHbeOMz/djjWzR+PYS/3Q3BQoAJSo2pFLJ0xAr/P/hwLvvsQW5ZOwoM9b1Jx/rzJSNyN3KyUQgjSNXkhoZcIqKZ3EkjYnWBX8TP/nLErp9D3Cbzx0WTIk4Uj3nwS0VEROHoiQYXsnBy1oLjEybTOrOwcFHzqsOMNTfHnum3YuHWvempx6qyFqBgXg7q1a/g+uCIsrBltQu+GIUWGtjWDiriSYm8jYDIZUbVSLMTR6C7dDYZ8x4R1/r8cm2V9qstjO2rrUk8qRQKeJGD0ZOEsmwRIwLMExLEizhN57LWgJgaDARViolC9SjwCTLq5KwFP/gXH1IUxMLyQCiGavJCQAhKwIrAxcS2+2D9KhfVn1qiYTRdkKxOWqXNnNjGX5y+aWPCauHqxBUU89xMC6zbvUpY+/tIn6HLf85Zw9Hi+8624pw6bNKiDxx+8A30GDlNPJX7/02/4ZOgAGC+slaIy5oYESMClBA4k73NpfsyMBEjAMwToUPEMd5bqNwRoqK8RiG/2go1JhoBQxDUZZCPjCQkUJLA5cR0m7hulwobEv1T0pguylaedd6iElAtBs0evUfmYN0HhQWjYp7H5lHs/I7Bw2kc20xq2X5jmIFOGxHFe3FOHBoMBAx+5CxsWTsDi6R/jr1/HounVV/gZQZpLAmVLICooqmwLZGkkQAJuIUCHiluwlpzpysNZ+HhVCt75PRlzdmUgPfvCe9JKvtS9KZg7CZBAsQQqNn8Rlbutws8hb2Ne1AjU770N4VWuK/YaRpJAk5gW6Fd7oN3QJq5jqQC1fbUN7pt9H9q+1hZdPuqCh5c/jPirXL+YYqmU40W6I2AwlPzUYUhwkHoLEJ9M0V31USEvIhCZGIO+c1/Ee+On4OkZ76Lu4UZ2tW9Voa2SBy+ch9Cvv1TH3JAACXgfAa93qHgfcmDu3gy8tzIZSw9kYtW/WRi/IRWfrkn1RlOoMwn4JYGgCo2wKqQv/g7rhcDwyn7JgEY7R6BZzLV49PKBdkOb2NI5VESDSk0qoVm/Zriqx1UIiw0TEQMJkAAJkICHCORlAG98MA4PLB6IVjs6odufD2H0iF/Q6odOuPfz/rh75P+h5+d90W/s86jxWh38VO9VzHhsCQ6P/jVf4wwtg/wjbkmABIogoDcxHSoeqJGl+wp3ln8czkQOH1LxQG2wSBIgARIgARIgARIgARK4dAK5W4zIO2awyciYZ0TtPVfBuC0AQdtDEbAtGCmbUnFkw3EcyIjHAUMNnEeEusZQ4DXoSsiNtxOg/j5OgA4VD1Twv+dzC5UqryA8lpRTSE4BCZAACZAACZAACZAACZCA/gnkHrD/06padC1cPb4+7p7WA5d3vhzy1/KqVPTL+1aF+nl7RaSTQDVIgAScIWD/U+9MDkzrNIFrqwYWuiY+3IjqUaZCcgpIgARIgARIgARIgARIgASKIKAjsam1/Zujm69ZjejmUajeqhoiKkZA/qLrxOJyw2EVogzJItJF6P79Wdz8baIK5jUe7cl0oSyVIAEdEKBDxQOVcH/DENSMvug8iQo24qkWnPvugapgkSRAAiRAAiRAAiRQpgRYmO8SMNbIQ9CTWTYGbm+2HgtbzrSRyUl2o8bIqXkZTgdOxoGwJBwIPY/kgB4ShYzng5ByVagK2b+FKhk3JEAC+iRAh4oH6qValAnjbovC5DuiMfqWKHzfIxot7Dy14gHVWCQJkAAJkAAJkAAJWBPgMQmQgBMEAp/MRvj6NITOyEDYyjR89eyHdq/OCwnFf8v/QlaLlnbjPSWcfW85zH8gpsgQEmC7Royn9GS5JKAXAnSoeLAmKkcacXnMxSdVPKiKU0XP2pGuHgM0Pw5YcD9pU5pT+TExCZAACZAACbiOAHMiARIgAQ8TCAOMV+fCUKFkPQK+jUXksv2olRaFGtF1Eb4jzSYEdOC4umSKTEECniNAh4rn2LNkEiABEiABEgDIgARIgARIgASsCOQdT0T2c1+rkPPxXBVjT6YiuCEBEvAoATpUPIrfOwvvcVWI5THAvk3z53Xak3mnddSaBEigJAKMd45Aek46ph/+Ci9teRJvbnsBS08ucC4DpiYBEiABEiABEiABEtAlATpUdFktVIoESMCFBJgVCXiUwDs7Xsanu4dh+anFmH/8J7z699P44cg0j+rEwkmABEiABPRLwFA5BgGf9CkymJ6/Tb/KUzMS8DMCdKj4WYXTXG8gQB1JgAR8hUBOXg6W2XkiZcmJX33FRNpBAiRAAiRAAiRAAn5LgA4Vv616+4YnpOXhq81peGN5Mv63NhV7zth/n77N1TwhARIgARKwS+Df1IPIzcstFHcwZX8hGQUkQAIkQAIkQAIkQALeRcAvHSreVUVlp22m5jt5buF5TN+ejnVHs/Dr3gw8u+A89idml50SLIkESIAEfIjAZeG1ER9csZBFrWLbFJJRQAIkQAIkQAIkQAIk4HoC7syRDhV30vWyvHcmZONUiu2d1Nw8YN7udC+zhOqSAAmQgH4IvFj/LUQHxlgUujz8CvS7/CnLOQ9IgARIgARIgARIwIoAD72IAB0qXlRZ7lb1VHKO3SIOneUTKnbBUEgCJEACDhBoE9cRi9r/hW9a/Ywfrl+Cadf9iqqh1R24kklIgARIgAS8kcBne97HE+t7q7D7/HZlgln2++kl6ty3NrSGBPyXAB0q/lv3hSxvEB9YSCaCVtWDZMdAAiRAAiRwCQSuiKyHamE1LiEHXkoCJEACJOASAm7OZG/SLmxMXKtCUvZ5Vdo/F2SnM06pc25IgAR8gwAdKr5Rjy6xokqkEQ81DrXJq2XVQNzdIMxGxhMSIAESIAESIAESIIGyI8CSvIvAoCtfxpjmX9sNbeM6e5cx1JYESKBYAnSoFIvH/yLvuzoEs+8ph5E3R2Ha3dF4s32E/0GgxSRAAiRAAiRAApdCgNeSgF8TuDKyPq6JaWk3xAXH+zUbGk8CvkaADhVfq1EX2BMSaMCV5U0oF8Lm4QKczIIESIAESED3BKggCZAACZAACZAACThPwK9+Mefm5iEnx/YtNmZkEnfi9Blk59hfmDUpORWJ55LMybknARIgARIgAc8RYMkkQAIkQAIkQAIkQAIeJ+A3DpW8vDy8NWIK3v70q0LQV6zegpa3PoFOPQejcae+mPHLckua1LR0DHxtJFrdNgA33DkQvQa8g4Qz5yzx/nZwOiUXW05mW8KxpFxoaNXrlq3lxzW5v7GhvSRAAkUTYIwtgYSE09i4ab0Kh/89rCJPW8n+PXxIybghARIgARIgARIgARLQLwG/cKgsXL4Wbbs/jVlzVxSqibT0TDz/9lg89Uh3bFk6CSPfGYi3PpmCI8dPq7TfzV6KPfuP4LdZn2HN3DEwGY0YOfEHFVdWm13/5SgHRnp2XlkVWWQ5Kw5l4uUlSZYw/58MGAzA7wXk8zR5kZmUYYQ4ebaf4mufyxC5rxRFO0jArQRWrVqJQYMeV+H7779RZa1e/Yc6F/n0CzIVwQ0JkAAJkAAJkAAJkIAuCfiFQ6VNy8aY+cVbuO3G1oUqYe2mnZCnUHrd2REBJhM6t7kGNatVxIrVm1XaBb+tRY/b2iE+thwiI8LQp8eN+HHe75AnXlSCMth8/GeKcmAkpNqfrlQGKnhlEUmZeYrbmyuSvVJ/55RmahIgAW8iEFshFk2aNCsyVK9e05vMoa4kQAIkQAJFENi/dD9Wf7bGEk78fVL7HQEcWHrAIls1cRuO5lUqIgeKSYAE9EzALxwqYaHBqBRXHuFhtq8Eloo5mZCoHChBQYFyqkLtmlVw4lSiOj505CRqVK2ojmVTvUq87HA+OVXt5ekM66CE2sZa5opjLUv1JIgr8nI2D1W2tpHrW6SqkQAAEABJREFUtJ1j/wYUr28ZxUP704oqpIsmLiQT+1wdpBwGEiABEihI4Lrr22LU5xOKDPfd17vgJTwnARIgARLQOYGDyw8qJ8nxTScsmh787SD+GrnGEk5tPanGoAd/O2CRrZm0HUdw0aFyYEm+E+ak5nyxZOTkQd7uY8hdvh25mw8hL5NPazuJj8lJwGECXu1Q2fD3Hnzx7Vy7wd70HntUzielICw0xCYqODgIsgitPIUiT6+EBAfBnCD4guMlNTVdiSrFhMI6KKG2sZZd6rHJKC4B4ESaCf+mlH3YeioHe89BlX0u07Emk5RlUOk9oa+5zGOp+bqKk6RgHQjT+OgQm7ormMYV50EB+TpoTYL/JEACJEACJEACJEACPkzg0O/5zpMTm09ckpX7l+U7W05tO1mqfHLGL0HOhKXI/WUjcr/+HbnvzwGS83+7lCpDXkQCfkjAUZO9+teeODvOnE2CveDoG3miIsPVlB9rYBkZmWp6j8FgUM6WjMwsS7T5OCws3wlz/EwarIM5obXsUo+zc/LXTnl9cSIenZ1Q5qHfj1qZErSyZ25LMZtY7H7BnrQy17Mgm0FzzygdczV8BesgRxOePJtuU3cF07jiPDOb07RUJXBDAl5GQBYflzW2vExtqksCJEACJODnBPIOJSBvz3EbCnnn05C79h8bGU98jgAN8hABr3aotGnZCC892ctuePSB2xxCWjE2BjKtJysr25JeFqGtFB+jzmU9lcNHL3qH/z12SsmjIsLUviw3dSuY0LBiQJmHplWC0LhSoCq3cmThJiNv+SnIIS7cqNJ7Ql9zmVfFBRRUi+dOEsjVHE8nTp9Bdk6Ok1cyOQnok4A44Bu0fxgFw5qNO5TC0t/f0vsltLtrEJp37Y8hH05GVjbbv4LDDQmQAAmQgEMEDq3In/oja6cc23LMoWt2og6WJTVRU4ZObr3428Ohi60TnTxrfWY5zjt53nKsjwNqQQK+QaDwr2PfsMvGipycXIjDJEf7UZitDYzlWH4oSqIWTerJDtPmLFU/Gpes3KDe8NOudRMlv6l9C8z8ZTlOJZxFckoavp61GHfd0hYGQ/40HJWojDbPXxeODztHlnkYd0cMPrs5WpV76xXBhay1h6J9zSCV3hP6mst8s31EIV0pKEwgMzMLfQa+hx6PDrWJXLF6C1re+gQ69RyMxp36Yob2ObBJ4GcnZ9NzcfO3iSr0+uGcst6eTEVwo1sCMpVTlBs3/DnM+2a4JTS+qo6I8e5nX6NenRpYv2AC5k59H7Iw+YJlf6k4bkiABEiABPyYgBOmH9QcKn9dWDfl9NaEQlfauxm5W3Oo/JbSRK2rcmpb/g3cQhc6IqhYzm4qQ8Uou3IKSYAELo2AXzhUfvh1BZrc2A+yrsqcBX+o4zkLVipysmDtqGGDMHz0NPWjcdCQUXj9mT6oVjlOxd/fvTMur1kFHXo8o35cijNm4CN3qThuSMDbCciPy7dGfIWNW/fYmCJTHZ5/eyyeeqQ7tiydhJHvDMRbn0xRzkabhDwhAS8lUK1yrFqQXJ5ClBAaEoRzSSn4c9029OnRBXJeq0ZldOt6PRatWOelVlJtEiABfyZA2z1H4LJ2l6HloFYqVGpUsZAi9m5G1sU/6BC+WV0Tf3X+SzAKXeiAwFAzFoYrK9ukNESFwnhtHRsZT0iABFxDwC8cKvfc0QHbl0+xCfKUiRlhx+ub4u+lk7Fo+sfYvHgienXrZI5CeFgIxn7wLFb9MhorfhyJ78cPRXysfc+v5SIXH5in+gQHlP1TMS42pUyzM2m4ZPpPA079KZL7xO9+xY49BzH4sXts0qzdtFOtLdTrzo4IMJnQuc016sfnitWbbdL500m5ECPmPxBTZJh2d7Q/4fB6W0eMn4HXh0/C1JkLlSNFDEr4L/8x6WoXHOoik7e8HT+Vvx6TnDOQAAm4jQAzJgGfIVBTc6i0fqYVJFS041CxZ2h9zaHSMXJz/jUNCzth7F1TlMz0WGeY+neC8fZmMPZpC+Mr3YCIkKKSU04CJHAJBPzCoeIIH5PJiKqVYhEYaH/djejIcMSWj3YkK5eneeH6/Kk+cWGer65YTQdxUphDpQgj8jSLC8tNmtSz/2GBBjXtiFN/7NfDohXr1Y/JscMHIzI81CbRyYRE5UAJuvBWK4msXbMKTpxKlEMGEtAdAZnaWdRb30S+/3D+An3yprb7u3dCo6tqo3y5SPWWuIcHvQ+Z+nY+OVXZZd3u5a1vZ87mzzs3aLEVooJhDtHhgZoECNS+P8wyX9jLndPykRft9HabYiKCYDQaLPXmOnsKM5I2ohoFNyRAAn5NICszE7t27UBioucc8oa6VWBs3wDGJjVhCLL/+8avK4nGk4CLCBhdlA+z8RMC7S8LUk4K8zolsqaKDCDbF5IH+QkR/Zm54e896kei/IgsGGTam2i8ddcBvPbBRIz5YDAqxZUXkU04n5Si3nBlLZQflkkXfnCatB8nIUEmmIM4JCVtoMlgkZnjvHUfFGBUP8K8VX97egdo9SPBXpwrZcJO2kNZhjzNtXumiLe+iVyma4o+EZrz8LVBfSALl8uTWVM/fxWyEPmufw7DvNi4Oa2kl7e+lS8XJYdaCUByWpYlpGVkK7m8NcxarttjK92L01Hm9qekX7SzuLTeEJei1VNebp6l3typs9xgUI2CGxIgAb8kULPtZQhuG4h3Jw3Bo/0fxB13dsGaNascZmE6cRwx93VDw/XfoV2dk4i/uqLD1zIhCZCAZwjQoeIZ7iyVBNxGwJHXic+ZvxJxFaIxf+kafDh6GuYt+0u97UqOxWkSFVn068RFcflRHhZsgjnIucjlh7RZ5u37oEAjAjTHkbfbYa1/oMmIwICL9WYd58rjYM3ZBhf8OZNFgMlk941v5jfB1a1d3W528bExSp6WkYnYCvnTOc1vc5OIg/+eQOX4i07HjKxcmENmdv7P51zNA2GW+cJe7PYFO8w2ZGp1JjVlPnfnXtgxkAAJ+C+BgDomfHfgSySG/GeBsOnsWpS/JUatjSLrqsQ3rAjtawO1rquMjnkrLaEaTqhrAv9ajav3zsWNSfPg6HQhdSE3JEACHiFAh4pHsPtOoYfP5b9O9Fhyru8Y5eWWOPI68fbXNUG3rjegXHSECrJWUEhwoDqWp00qaj8yi3uduPwgOZOUCXPIyMxvBykZORaZOc5b98lp2cjMznXGHt2nTdPqSZ6qcHedJKVm6fZTtGL1Fixcvlatm5Kalo6RE39QT2PJm31kamfr5g3U29xkYeYDh4/j50Wr0KVdC93aQ8VIgARIgAT0Q0Cm+RTU5kTYMZyqdVStjSJrqsgitTK18rJb6uOa6YMtIWz6GCRO+9ESzo2eWDArnpMACeiQAB0qOqwUb1HpiV/PY9G+TKXun4czMUA7Vyfc6J6AOF36974d/Xvnh3atGqNiXHl1HhYaghZN6ikbps1ZiuycHCxZuUG94add6yZKzg0JeCuBzKwsvD58Mq67/Um0uPlxzF+2BqPefRriTBGbXnu6N7bvPoDmXfvjtgdf0ZwpzdG1w7USxUACJEACJEACxRIoX6GC3fjyFWLtyrNaXY8iwzV05tuFRiEJ6IwAHSo6qxBvUWfFwUwcPJv/VIJZ5wPa+eojbrwzbS6Ie7cTCAsNxqhhgzB89DQ07tQXg4aMwuvP9IH120/crgQLIAE3ELixbXOsnjsay2Z+qsLvsz9Hq2uuspRUq0ZlLJz2kYpbO28chr3cD4GBAZZ4HpAACZAACZBAUQSubtAIFStWsok2Go3o0KGzjYwnJEACvkOADhU31qUvZ70v0daZYrbVPAXIfM69dxC4544OmPXFWzbKdry+Kf5eOhmLpn+MzYsnole3TjbxPCEBbyUg661UjIuBBINBltUubInEyVS4wjGUkAAJkAAJkIB9AkFBQRg79kv06f1/aN36BnTr1gPjx01Brcsut39BMdKMxN2QUEwSRpEACeiAgLVDRQfqUAVvIVA7xmRX1RrR9uV2E1OoewKynkrVSrG8Q6/7mqKCJEACJEACJEACeiAQFxuH/v2fxIfDP8Nzg19GvXoXn4I8vfM0MpPyp8uLrqHTv0H5mzsg7qrLUL5bVwQvmo+UE2uxY2o97PquqQpynHpqoyRnIAFXE2B+LiBAh4oLIPpjFu0uC8Jl5WydJ7W089bVAv0RB20mARIgARIgARIgARIgAbsEDvx2AOOajce3t3yLXT/tUmky/t6LyFeeQ8CuHTCkpSFgyyZEP/Ywji0bhKykwyqNbOT4+KpX5JABREAC+iNAh4r+6sRrNBp7axS61A5S+l5fIwhjtHN1wg0JkAAJkAAJkAAJkAAJ+DuBC/YvG7IM6YlpF87yd/t/O5R/YLXNM+QhNXGrlST/MO303/kH3JIACeiOAB0quqsS71LIPMWnSgSbknfVHLUlARIgARIgARIgAVsCPHMNgX2L92H5m8uxbfo2JJ9IRtLRpEIZnz1fSARDngGBxohCEcbg6EIyCkiABPRBgL+C9VEP1IIESIAESIAESIAESMA5AkxNApdMIDc3DydOn0F2jv0XLjhbwJftvsQv/X/B5q82Y8krSzBZOzcYCy+AHhQVajfr8rXvLSSPuaJnIRkFJEAC+iBAh4o+6oFakAAJkAAJkAAJ+DwBGkgCJKAnAitWb0HLW59Ap56D0bhTX8z4Zfklqbd33l6cO3zOJo/czByEx4fbyOSkUuuaSHr/E2TXuwp5oaHIbtwU58ZPQaUuI1G9w1iUu6KHCnJcufU7cgkDCZCADgnQoaLDStG7SttPZeObrekqbDierdTdfvqibOPxLCXjhgRIgARIwMsJUH0SIAES8FECaemZeP7tsXjqke7YsnQSRr4zEG99MgVHjp8utcWH/7y4mKx1JqbgALQb0g61u9RGTK0YFVX12mpIu683zsz/Dad3HMSZOQuQ0eVmFVf+qodQs8tUFeRYCbkhARLQJQE6VHRZLfpWakdCNr79O02FzSfynSc7NYeKWbbxgpNF31ZQOxIgAV8kQJtIgARIgARIwBECazftRGpaOnrd2REBJhM6t7kGNatVxIrVmx253G6a2HpxduVR1SLR9JGmuH387ah+fXW7aSgkARLwTgJ0qHhnvXlU66tiA/BAo9AiQ7PKAR7Vj4WTgBcRoKokQAIkQAIkQAIeIHAyIVE5UIKCAi2l165ZBSdOJVrOnT1ocE8DmIJNtpflAa0GtbKV8YwESMBnCNCh4jNVWXaGNIgPQO+GIUWGZpUvfjGVnVYsqWwIsBQSIAESIAESIAES8H4C55NSEBYaYmNIcHAQkpJTlSzQZEBsVLAlhIfk3zAM1Rwm1nLr40pxYXhuw+No2L0+4q6ogDodauHReQ+gcafLLfmEBuU7XCK0/Kyv9cRxTEQQAoy2dnpCD1eWGREaiJBAo4W3K/MumJdqKNz4PQE6VPy+Cfg4ABeadyI5F+/9kYJ7Z51D735hW/kAABAASURBVB/PYuRfqTifkevCEpgVCeiTwLStaej383l0//4sXlyShG2n8tdO0qe2vqnVhC/G4L5e3XDTTW3x7OAnsXPndvCPBEjA/QTWH8vC4EVJqv97bO55zN6V4f5CWUKZEIiKDFdTfqwLy8jIRGREmBIZDAYEaT/MzcGkOR4kQvZmmb19bI0o9PmqG17Y0B/9f7oPV7SpYZOP8UI+AZrDxt71ZSkLDDBCM9NGv7Is3x1lCVdh7I68C+Yp7YGBBIxEoC8C1Ea/BD5bk4KVhzKVE+W/tDws+CcDU7ek61dhakYCLiCw5EAmpv6djqNJOUjPzsPWk9l4d2UK0rPyXJA7s3CEwKwfvsfXX0/G0aNHtMF/Ktav/wtD3njJkUuZhgRI4BIInE3PxZDfkiHrxEn/d/hcDiZsSAUX378EqDq6tGJsDA4dOYmsrGyLVnv2H0Gl+PxFY7NycpFwPsMSUtLz06Vl5Fhk1vGOHqdn5r+eOVnLz9Fr3JUuMTkT2bl5l2SPu3Qrbb7JaVnaGMW27kqbV0nXWRoOD/yawKU6VPwaHo33HwI52m/HrXbuym8+kf/l6j8kaKm/EbDXxs9pPzIOaD8s/I2Fp+zdsGFtoaJPnjyBQ4cOFJJT4N8EsnNycOL0GRw/+R9ytB+DBWnkaj+cJD5bS1cwTs4zM7Nw8nQi8vK0Lz0R+HnYcTr/h29BDPb6xYJpeK5/Ai2a1FNKTpuzFPKZWLJyg3rDT7vWTZRcPgaZWbkwhxzt8yMRsjfLSrOX6yWfbG1wWZrrXXlNVnau9nmHxUZX5u2pvISr9HVlUb7Uox8GmlyAAB0qBYDwlARIgARIgARIgAS8jcD3Py1D40590annYHS+9znceN9z2Lb7otNtxeotaHnrEype0s34ZbnFRHGgjPnqJzTt8ig69nwWbbs/jS079lnieUACvkggLDQYo4YNwvDR09RnZ9CQUXj9mT6oVjnOF831Y5toOgm4lwAdKu7ly9x9hIDJADSMDyhkTZNKhWWFElFAAl5MwF4bjw4xolZ0/qJ6Xmya16h+zTXXFtK1YsVKqFmzViE5Bf5LICw0BOOGD8a6+eOxeu4Y1LmsKkaMm6GApKVn4vm3x+KpR7pjy9JJGPnOQLz1yRR1N14SbN7+D0Z/ORtfj3oVmxdPRLeubfDs0P9B7vJKvL+Gq+Ls93P2+kV/ZeTtdne8vin+XjoZi6Z/rNp+r26dPG8SNSABEvAqAnSoeFV1UVlPEnimVTja1AxCVLARFUIN6FonGA82DvGkSiybBNxOoHOtIDzYKARVI00ICTCgYcUAvN4mHCGBBreXzQLyCfS4+1706fMIqlathrDQMDRv3hLvvD08P5JbErhA4PYu16FNy0ZaGwlGVEQYoiLDUS46EvK3dtNOpKalo9edHRFgMqFzm2vU62JXrN4s0Vj2xya0bt4AzRpeicDAAPTp0UVN/dm977CK99dNOc15/E6HCNSPC4D0fzU0R3L/a8LAtxnC5s/bT0wmI6pWilVt3122JO5PxL9rjqiQfDJZFXP2gLUsRcm4IQES8D4CdKh4X51RYw8RqBRhxKs3hOP7HtH45q5yGNQyTDlXPKQOiyWBMiPQq2EoJt4Rhdn3lsOHnSNxtZ2ntcpMGT8tqP+jAzB92hwsXPg7Ph0xGvXrN/BTEjS7JAI/L/oTz7zxP+zYcxD9e9+mkp9MSFQOlKCgQHUum9o1q+DEqUQ5xPFT/6FW9UrqWDbxseVkh1MJZ9XexzZOmdO8SiBGdIlU/d/426LQvV6wU9czMQkIgU1fbsIPvWapsH/JfhFh06SLsn1LOMVOQeGGBLyQAB0qXlhpVJkESIAESIAESMA/CBw7kYAvvp1bZJDpPNYk9h86jv8Sz6tFac8npaqo80kpkClB6uTCJjg4CEnJF+NDgm0dBZI+OTVNpQ40GRAUYLQEg0GJEajd2beWe/Ox0WiASQvebENB3aWeCtZdwTSuODdKQeBfcQTKXRaDai2rFRkiK0YUdznjSIAEdEzAqGPdqBoJkAAJkAAJkIC3EaC+LiWQlZ2DM2eTigx5ebk25T3zaA+1Fspdt7TFc2+NVnEy/Uem/KiTC5uMjExERoSpM4nPyMxUx+aNpI8IC1WnYSEBiAoPtARxPkhERKit3DqNtx0Haw6j0GCTxUZv09+evgFGo+ZIu1hv9tK4QhaosZP2wFA0gWZ9m6LH9B5Fhss7X170xYwhARLQNQGjrrWjciRAAiRAAiTgZgLMngT0TKBmtYp46cleRYawUPtredWqUVk5YbJzclAxNgaHjpxEVla2xdQ9+4+gUnyMOq8cXwEH/z2hjmVjnupjnvpzLiULCecyLCEnJ0+SITE50yKzjvfG47TMHCSnZfuMPVIHWTm5OFcGdZSRlaPaAzckQAIk4I8E6FDxx1qnzSRAAt5MgLqTAAmQQCECY6bMwZYd+5CekYmjJxLw5ffz0bJpfbUIbYsm9VT6aXOWIltzsCxZuUG94add6yZK3vGGpvhz3TZs3LoX8kTM1FkLUTEuBnVr11Dx3JAACZAACZAACdgnYLQvppQESIAEXEWA+ZAACZAACbibgDhR7h/wDq65qT+63Pc8TEYj3n7xEVVsWGgwRg0bhOGjp6Fxp74YNGQUXn+mD6pVjlPxTRrUweMP3oE+A4ehSee++P6n3/DJ0AEwGi8slqJScUMCJEACJEACJFCQAB0qBYnwnARIgARIgARIgAS8jMCwl/th06IvsHDaR/jzp//hm/+9ZnGYiCkdr2+Kv5dOxqLpH2Pz4ono1a2TiFUwGAwY+Mhd2LBwAhZr8X/9OhZNr75CxXFDAiRAAiRAAiRQNAE6VIpm4zUxVJQESIAESIAESIAEgoIClROlXLT9N4aYTEZUrRSLwMAAu7BCgoNQRYvnkyl28VBIAiRAAiRAAoUIeMKhUkgJCkiABEiABEiABEiABEiABEiABEiABHyOgE8bRIeKT1cvjSMBEiABEiABEiABEiABEiABEnCcAFOSgOME6FBxnBVTkgAJkAAJkAAJkAAJkAAJkIC+CFAbEiABjxGgQ8Vj6FkwCZAACZAACZAACZAACfgfAVpMAiRAAr5CgA4VX6lJ2kECJEACJEACJEACJOAOAsyTBEiABEiABOwSoEPFLhYKSYAESIAESIAESMBbCVBvEiABEiABEiCBsiBAh0pZUGYZJEACJEACJEACRRNgDAmQAAmQAAmQAAl4IQE6VLyw0qgyCbiSQGZmFvoMfA89Hh1qk+0dD72KBu0ftgljpsyxScMTEvBXArSbBEiABEiABEiABEiABOhQYRsgAT8mkJeXh7dGfIWNW/fYpfB037sx75vhlnB/985201GoewJUkARIgARIgARIgARIgARIwMUE6FBxMVBmRwLeRGDid79ix56DGPzYPXbVjqsQjZrVKlpCuegIlM0fSyEBEiABEiABEiABEiABEiABfROgQ0Xf9UPtvIWAF+q5aMV6TJ25EGOHD0ZkeKhdC2bOXYHXh0+CTPU5fPSk3TQUkgAJkAAJkAAJkAAJkAAJkIA/EqBDxR9rHQDN9l0CG/7egy++nWs3zNIcJGL51l0H8NoHEzHmg8GoFFdeRIXCTe1boG2rRqhSKRbL/tyEu/sNBZ0qhTBRQAIkQAIkQAIkQAIkQAIk4KcEvMWhotvqqVIhFNbBrKi1zNuPQ4JMKB8ZZGOnt9tkMhpQKSbE7TZFhgaYm0SZ7VPT0nHmbJLdkHguSekxZ/5KyHSe+UvX4MPR0zBv2V84dOSkOk5KTlVpnvy/7njiwTsx4KE7MW3MEERGhGLpyo0qrlyEbXsIC8m3s1x4oNuZllXbkzYvbb+syiuLcqQ9Roa5v44qap8t1VB8bBMUYLRp33HRwcrCgvKyqEt3lmEwAJXLh9rY6s7y3J23tEfp891djuQfFOibwyphKPaZQ4BJayRa648v5/7vUXOZ7t6Ha99j0T70HSa8pG+K1fopOXZnkDGB1hx87l++M625RWnfn2JkhDa2s5Z787F8hgO1z7M321BQd2mPYcGmMvkOk/bA4DYCXpOxb37zew1+KkoCrifQpmUjvPRkL7vh0QduUwW2v64JunW9AeWiI1QIDwtBSHCgOjaZCncLgYEBiCtfDmkZmep6bkiABEiABEiABEiABEiABMwEuPdXAoV/OfkrCdpNAn5EQJwu/XvfDnNo16oxKsaVV+dhoSFqas+UGQtw4vQZZGXnYO7i1di2+wBaNq3vR5RoKgmQAAmQAAmQAAn4KAGaRQIk4BICdKi4BOPFTGS6hHlaxUWpdxwlnDmHtHTHnkCQdMdOJCA3N0+3xolu4hDIzsnRrY56VkwWrO3UczCadO6Ll4aNV0+8XNPoSrsqZ2Zm4eTpROTl6bc92FVcEzqju7Qp+ZycS0rRrtTvvzf3Q/qlal8zaQ/SH9qP1a9U2rIz/aO0+VMJZ/VrkKaZM59lLTn/L4GAN/cxzugu44fjp84gQ/uOuwRcbr/UW/shZ8F4Or2z/aan9bUu35n+UeyUNiX9vnUeejt25rOsN92pj28RoEPFRfUp61YMfG0kWt02ADfcORC9BrwD6YxclL1bs5GFRm/p/RLa3TUIzbv2x5APJ6unEooqVOyUdDfe97x2zdP4ZNyMopJ6TL5i9Ra0vPUJiEOgcae+mPHLcod0Oao5iVrc/DhGjNefTQ4ZUMpE99zRAbO+eMtydY2qFbF0xgj8PvtzLPjuQ2xZOgkP9rzJEm8+EAfKmK9+QtMuj6Jjz2fRtvvT2LJjnzla13tndV+9frtqU/I5ue72J/HwMx+op3b0ZOSl9EOfTpiJBu0fxvkLa+joyS496uJsv6knG5zpH+V7TL4fpM136PEM7njoVfyyaJWezFGO3NL2Q8JC2r3sdWWUTpW5lD7G0yY5q7ss7i7jh873DEYz7Ttu8Jujce58ijNmuD1tafsh+XHdZ+B76PHoULfr6CsFSB9RmnGlp+3nWMe2BjjWseXBM9cQoEPFNRzx3eyl2LP/CH6b9RnWzB0Dk9GIkRN/cFHu7s3m3c++Rr06NbB+wQTMnfo+Fvy2FguW/VVkoXVr18Dsye9iw8IJeOfFvpg8fR627txfZPqyjpC7xc+/PRZPPdJdOQJGvjMQb30yBUeOny5WFfF0P/HSCMigq9iEfhJpMBhQISYK1avEI8Bkgr2/zdv/wegvZ+PrUa9i8+KJ6Na1DZ4d+j9dP7lktsNZ3Q1GA9549kGs+mU0ls38FLLuzOgv55iz08W+tP3Q7PkrMfG7X3Vhg7co4Wy/qRe7nO0f5U5lt643YOnMEeq7rWuHa/H2p1MdfpqxLOx29rNs1mn3vn8h3xXmc+5LJlDaPqbknN2fwlndZY2xSSNeVGMjGfOs27wL0le6X1PHSyhNPyQ/sN8a8RU2bt3jeEF+ntLZflNPuJztHznW0VPtURdvIUCHiotqSpwQPW5rh/jYcoiyH9FQAAAQAElEQVSMCEOfHjfix3m/qztnLirCLdnI43x/rtum6dsFoSFBqFWjMrp1vR6LVqwrsjxxVFx5eTWEBAdBFjetGBeD1Ru2F5m+rCPWbtqpnCK97uyoHAGd21yDmtUqYsXqzUWqIo/1vvDOWDRreCVuan9tkekYYUtg2R+b0Lp5A8VNFq7t06OLmvqze99h24Q6PHNW91bNrsLtXa5DdGQ4pM131drJ72u2QNoOdPJXmn5IfiS89/m3+PiNJ3Rihf7VKE2/qRernO0f5TtN1lqS16vLd9sdN12v+tedew963qQLGjj7WZbLTv93Fk+8PAJDBz8EWTdKZAwlEyhNH1NyrmWTwlnde97WHtLvy9hIxjztr2sK6fPLRtuSSyltPyTO8x17DmLwY/eUXAhTKALO9pvqIp1snO0fpc1zrKOTyqMaXkOADhUXVZW8clamSZizk7v6cqz3x+cTtEGl6FmtcpzsVBA7ZM6wOilhI3afPJ0IeWqlhKRlFn0yIVE5UIKCAi1l1q5ZBSdOJVrOCx58OHo6MjOz8dqg3gWjeF4MgeOn/kOt6pUsKeTHl5zofa0F0fFSdf9z/TbUv6KmctpJfnoI8nmUz69Zl5L6IUk/4JXP8NnbT+GKWtXMl3FfAoHS9pslZFsm0aXpH60VEwecnF9WvbLsdBGc/SzL3eanXh2Ju25ui9tubK0LG7xFCekznOlj9GTXpegui7P/uW4rGtStpRuTStMPLVqxHrI+2tjhgxEZHqobW/SuyKX2m560z9n+saCuHOsUJMJzEihMgA6VwkyclsjjkzJNRJ7YMF8cfOHHfGpqulmky73Z4WPtfAgODsKZs+dL1DdFs+2ZN0appxNuuLZhienLKsH5pJRCdxzFJpnSY0+HaXOWqqdXPn3rKchTFvbSUGafgLAOCQ62iZS7vcmpaTYyPZ5ciu6yhoSE53R0h8/ZfkjWAuj/wsd4tn9PXN/iantVRFkRBC6l3ywiyzITS7uXz6h1gcX1j9bp9h44Anma6YkH70T5cpHWUR49Fpsc7YdkCtNrH0xEVe0mwoCHu3lUb28r3Nk+Rk/2Xaru7342FUnJaeppXr3Y5Ww/tHXXAUjbH/PBYMgTZ3qxwxv0kD6mtP2mp+0T3R3tHwvqKuMcCRzrFCTDcxKwJUCHii2PUp0ZDAb1Az4jM8tyvfk4LCzEItPjQVREmFIrKytb7WWTkZGpDZaj5LDIIHf4ZK2MnJxcjHr3aZhM+mlKUZHh6pF0a+XFJnlc3VpmPp7y/QL1RMv4r3/Gh6OnYfvuA1i1fjtkQTpzGu7tExDWGZmZNpHiXIwI88SdLxs1Sjwpre4yRe7l9yaoqQIy3anEgsoogcHgXD+0ZuN2ta7Qv8dOqXY/cVr+GiqffTELO/ceKiOtvbOY0vaberBW2r18Rq11Ka5/NKeTBbsfe/ETdLyhKZ546E6zWBd7scnRfkgW2V24fC0iI0Lx8djpqu0Ljxm//IaFy4ue6qoLQz2shMHgXB/jYXVtijcYSq/7mClzMGvuCkz+9CU1rdsmYw+eONsPzZm/EnEVojF/6RrV7uct+wvy1I6Me5K4GHmxNSl9jPQT1okc6Tet03vqWHR3tH+01pFjHWsaPCaB4gno51dw8XrqPlbW6JDV1s2Kyo8UOTZ/4cmxHkNshXJKLbO+cnLw3xOoHF9eDu0GuSsic8/lDvfUz1+FLNxmN6GHhBVjY9QgwdpJJAsGV4qPsavRI/fdjOaN6yo7xBZxDoUEB8HtdWdXG+8SVo6vAGkvZq3NU33MU3/Mcj3uS6P7Qu2HmDzV8e5LfSFvRtKbXc70Q3Uuq4pB/e5GTHSEavvm9l4uKhxBgQF6M01X+pSm39SLAc72j6L3PweO4r7H30Kblo0w7OV+unKgi37OfJYjwkNUu69aKVa1e+nzJY+I8FDtxkiwHDIUQ8CZPqaYbDwS5azu8jTTR2Om40vtpsvMCW+iYT39TPcRgM72Q+2vawJZYFravARZWD0kOBByLOMeyZPBPoHS9Jv2cyp7qTP9o1k7jnXMJLgnAccI0KHiGKcSU93UvgVm/rIc8oMyOSUNX89ajLtuaQuDwVDitZ5MIAtsyl120VeeOjlw+Dh+XrQKXdq1sKg1+M0x+Hjc9xBBaloGej/5rmZnIt5+8RGkpKVD7lw6uuaK5OHu0KJJPVWETOXJzsnBkpUb1J34dq2bKLnciZFXBc7X7s6I4N47O0IWXTSHenVqolnDKyByiWcomkBH7W613MXYuHUvZI751FkLIQu26mlNnaK0L0n3KTMWQF4rab7+p4V/YrD2WXj5qftxbdP6qt1L20/VPgPmNJ7el9QPif7mz3JtzaFibvOyv+f2Dkr9h++9GRKnTrixS8CRftPuhToQOts/7t73L+78v9fQ+poG6Hf/rWrRaWn3ieeSdGBNvgolfZbXbd6lXg8rd+PlsX1p79ZBZLd0bKUcRvk5clsUgZL6mKKu04O8JN2t+0fR942PJkO+B0a8+SSioyIsfb6MKyTe06GkfqjgWEccotbtvl2rxtr3dXk1/pHPgKft0XP5LUoYV+pZd2f6R7HDF8Y68rk1j99kPGPd7jnWkVpmcDUBX3aouJpVsfnd370zLq9ZBR16PIOWtz4BeTpi4CN3FXuNXiJfe7q3mubSvGt/3PbgK5ozpTm6drjWot6Bw8dw7ESCOpcv6H2HjkEGpnf1HYIu9z2vwj39h6p4PWzCQoMxatggDB89DY079cWgIaPw+jN9YF54V+46yZSGs+eT9aCuV+vQpEEdPP7gHZrjYRiadO6L73/6DZ8MHQCjUd+ORIFeku6nE85i1z+HJakKW3bsU/sP/vedavPmtr9QR9MESuqHrD/LyhhuSk2gpH6z1Bm7+UJn+8f9Wn8vKv26dA263v+ipe1L/ypyPYSSPsuyppP0+ekZttMT9aC7t+lQUh+jZ3tK0r1g/yiOOLHn8Zc+sbR76fePHk8QsS5Ccf0Qxzquq6KS+k3XleT6nJztH31hrFNw/OZ6qsxRxwQ8ohodKi7CLo9Ojv3gWaz6ZTRW/DgS348fqqu5tsWZWatGZSyc9hGWzfwUa+eNU490B1o98j978ruQOzSShzx9sH35FBQMK+eMkmjdhI7XN8XfSydj0fSPsXnxRPTq1smiW3RUuNLfWmaJ1A5GvDmArxPUODjybzAYII7DDQsnYLHG+q9fx6Lp1Vc4cqnH0xgMxev+woD7sG7+OIuebzz7oGo3Bdt+95vbWNJ4+qCkfsj6s1xQ1zq1qir7zFN/Csbz3JZASf2mbWp9nTnTP97csaVqFwXb/Qev9teNUQZD8Z/lDtc1VTbUrV3drs7yOW/XurHdOAptCZTUx9im1tdZSboX7B9lXFSw3cu5TB3Si2XF9UMljXVk2uqsL97Siym616O4flPPyhsMzvWPvjDWeaHA+M26fvQ31rHWjsfeSoAOFRfXnDyCGVs+2sW5lk124iyRAUfZlOb+UmROsMyTt3YOub9U/yxB1pypUinWK55MKVhD3qx7QVvM597cD5lt8Ja9t/abvtg/+uJnWa+fA2/uY7xZ96Lag7f2Q0XZo1e5N/ebLusfdVQ5vvhZ1hFequIEATpUnIDFpCRAAiRAAiRAAiRAAiRAAt5BgFqSAAmQgLsJ0KHibsLMnwRIgARIgARIgARIgARKJsAUJEACJEACXkaADhUvqzCqSwIkQAIkQAIkQAL6IEAtSIAESIAESMC/CdCh4t/1T+tJgARIgARIwH8I0FISIAESIAESIAEScCEBOlRcCNPXspJXJC/7YyPshX+PnbKYm5eXh227D2D2/JWYNG0e5i5ebXnNsiWRdiCvrFy7aRem/7QMX81ciCUrN0DK0KL4TwK6IrBi9Ra77X7rzv02eh4+ehILl69V7f7Heb9jx56DNvHmE0fTmdNzTwJmAmW5P3z0lN12L98BySlpFlUc7cvPnE2CfJamzFig+v3V67cjOyfHkg8PSEAPBGQcIm3cXijNWMfRMZEebKcO/k3g7x377Pb50m9bk3G0L+dYx5oaj/2JAB0q/lTbTtp65PhpDHz9c7vhz3XbVG7HT53BYy9+gnsfewvvff4tZvz8G14aNh433vc83vx4ikojm41b9+LOh1/D/z37ASZ88wsmfjsXg4aMQqvbBihHjKRhIAG9EBjwyqd22/2X389XKqalZ2L46Gm4+YGXMPjNMfjh1xUY8uFk9Oz/Ju546FVkZGY5lU4l9o0NrfBiAiv/+ttuu5fvgWMn/1OWOdqXz5q7Am26DcQA7bMk3wufjJuBfs9/hA53P4Odew+pvLghAT0QcOVYx9ExkR7spg4kMHHar3b7/OffHmuB40hf7uiYyJIpD0jAxwjQoeJjFeoOc8a8/yw2L5lkE+65vQNycnLxjOYU2bTtH4wbPhjr5o/Dwmkfqf2z/Xti36FjSp1TCWfRZ+AwREaEYe7U97Fs5qdYOWeU2nfregMOHTmp0nFDAmVPoOgS+91/q02bl8/Ah0MeVxeM/WoOps5ciBeeuA9blk7CvG+Gq/3IdwYi8VwSsrNznEqnEnNDAjohsOqX0YXa/pWXV4Ojfbk4ZoZ+/CVu7dQKq+eOUZ+PtfPGYuaEN1GtSjwSzpzXiaVUgwQuErjUsY6jY6KLJfKIBDxP4Oq6tQr196vnjlaKOdqXOzomUplyQwI+SIAOFR+sVFebFBBgQmCBYDQasHD5OjXV541nH0Sblo0sxYaFhkB+jI7/8DklGzv1J7Uf8eYA1KpRWR3LpmJcDIa93A/9e98upwyOEGCaMiNgMhkLtfsAk0lNZ5s0bR7uuqUtHr63K0QG7U/2ndtcg0XTP0FYaLDD6bRL+U8CuiIQGBBQqO2Lgo725e9+9jVqVquIYa88iijNkS7XGgwGXHXlZfhm1Gto0aSuiBhIQFcELnWs4+iYSFdGUxm/J2AsYqwjYBzpy4+dSFDTnksaE0l+DCTgqwToUPHVmnWhXXv3H4E85m0O+w4eVblv3bVf7W/R7kKqgwIb+VEpog1bdqNtq8aoUbWinBYK5nSFIiggAQ8SkCkO5jYv+y079iltdu49rPb33N5e7QtuQkOCYDAY4Gi6gtfznAQ8TWDL9n9s+nx5MkV0cqQvP3M2CTKF4p7bOyinjFxnHcRRGRIcZC3iMQnogsCljnUcHRPpwlgqQQIXCJw7n2zT38t4JzUtHY725RzrXADJnV8ToEPFtvp5ZofAR2Onqyk7Mm1HwogJM1UqcazUrlkFMkBWAjub7JwcNfXnsuqV7MRSRAL6JfDLolU27f7+Ae8oZQ8eOaH21arEqX1RG0fTFXU95STgKQKy1on09eaw4Le/4Ghfbl7Es0bVeE+pz3JJoFQELmWsIwU6MiaSdAwkoCcCMu3e3Neb9wcOn4CjfTnHOnqqTepSSgKXfBkdKpeM0Pcz+GjIE1jx40hLeP/V/spomdqTnHrxzQ9KWGAjIISTkwAACpJJREFU0yBUOqs3RBRIwlMS0CWBB+660dLmpf3/PvtzpWdoSLDap6ZlqH1RG0fTFXU95STgKQLzv/3Qpu33vL2DmtrmSF9ufvokJTXdU+qzXBIoFYFLGetIgerzUcKYSNIxkICeCNS/oqZNfy/jnbp1qsPRvpxjHU/UJsvUGwE6VPRWIzrUJzoqHLHloy3BPCe+zmVVcfJ0Is6dTylW67q1q2PbhelBxSZkJAnoiEBEeIilzUv7rxATpbSrdeFpqz37/1XnRW0cTVfU9ZSTgKcISHu3DqEh+VN0HOnLa1yY2rnrn/ypcZ6ygeWSgLMELnWs4+iYyFm9mJ4E3EkgMDDAZqwjfb/cDHW0L3dorONOA5g3CeiAgFEHOlAFLyVwXYurleajp8xWe+uNPB6+ev12JWrXujH27D+CRSvWq3PrTVJyKv6+sDaFtZzHJKBXAlfVvQxyJ/LziT8g68LbfKx1/WvTTiV3NJ31tTwmAT0TcKQvF+dL6+YNMGXGArUwc0F7Dh89qdZYKSjnOQnolYCjYx1H0+nVTup1kQCPAEf7co512FpIAKBDha2g1ASaNbxCvenk2x+X4MV3xmHNxh3Yvvsgfl70Jx4Y8C4++2KWyvvBnjepNz48O/R/GDNlDmRxz41b90Cuu6X3S1j25yaVjhsS8AYC0ZHhkDdbiZOw1xNvY9kfGyF34xf/vl59Dh55djgyM7PgaDpvsJk6koAQcLQvf+3p3pIcd/7f6/jh19+xc+8h/Llum/pOuPmBlyDz81UCbkjACwg4OtZxNJ0bTGaWJOAWAo705RzruAU9M/UyAnSoeFmFeUJdo8FQZLFDn3sIrw3qg9UbtqPv4A9xz2Nv4pX3voC8hu2RXreo64KDAjF93FDc370TRmsOFVncs8/A9/De59+gSYM6uKl9C5WOGxLQEwGDoeh2f3uX6zDho+eV42Tg65/j7n5v4Jk3/qechYP63Y3g4EBliqPpVGJuSEAnBAwG+23f0b68Vo3KmDv1fVzT6Aq88dFk9Hh0KPq/8LFyosv3QP0raujEUqrhGQL6LPVSxzpilSNjIknHQALeQMDRvpxjHW+oTeroTgJ0qLiTrpfnXf+Kmti+fArk8e2iTJF5ljJAXjlnFFb9Mhrzvx2Ov34di2ljhtg4SmTdFXG8bPvtS/w26zMs+O5DbFr0BUYNGwQpp6j8KScBTxCQdj/wkbuKLfr6Flfj56/ew/oFE1S7l0VrF077CP17364W8DRf7Gg6c3ruScBTBB64q7Pq8+VR76J0cLQvl4H4uOHPYcvSSZDPxdKZI9R3g3wPyBz9ovLXpZxK+TQBGYNIn++KsY6jYyKfBkrjvIbA5+88rcbrxSnsaF/OsU5xFBnn6wToUPH1Gi5D++SxP1nEKiI8tMhSDQYD4mPLoXqVeAQF5d/FLzIxI0jACwjIj09p9+ZFa4tS2dF0RV1POQk4SqAs0hkMjvXl8gOzWuU4VIorD6PR/pMvZaEvyyABVxFwZKwjZTmaTtIykIDeCTjal4eGBMGRMZHe7aV+JOAMATpUnKHFtCRAAiRAAq4mwPxIgARIgARIgARIgARIwCsJ0KHildVGpUmABDxHgCWTAAmQAAmQAAmQAAmQAAmQAPiWHzYCEvB5AjSQBEiABEiABEiABEiABEiABEjA5QT4hIrLkTLDSyXA60mABEiABEiABEiABEiABEiABEhA7wToULn0GmIOJEACJEACJEACJEACJEACJEACJEACvk/AxkI6VGxw8IQESIAESIAESIAESIAESIAESIAEfIUA7XAnATpU3EmXeZMACZAACZAACZAACZAACZAACThOgClJwIsI0KHiRZVFVUmABEiABEiABEiABEiABPRFgNqQAAn4LwE6VPy37mk5CZAACZAACZAACZCA/xGgxSRAAiRAAi4iQIeKi0AyGxIgARIgARIgARIgAXcQYJ4kQAIkQAIkoE8CdKjos16oFQmQAAmQAAmQgLcSoN4kQAIkQAIkQAJ+QYAOFb+oZhpJAiRAAiRAAkUTYAwJkAAJkAAJkAAJkIDzBOhQcZ4ZryABEiABEvAsAZZOAiRAAiRAAiRAAiRAAh4nQIeKx6uACpAACfg+AVpIAiRAAiRAAiRAAiRAAiTgawToUPG1GqU9JOAKAsyDBEiABEiABEiABEiABEiABEigWAJ0qBSLh5HeQoB6kgAJkAAJkAAJkAAJkAAJkAAJkEBZEqBDpSxpXyyLRyRAAiRAAiRAAiRAAiRAAiRAAiRAAl5MwEGHihdbSNVJgARIgARIgARIgARIgARIgARIgAQcJMBkjhKgQ8VRUkxHAiRAAiRAAiRAAiRAAiRAAiSgPwLUiAQ8RIAOFQ+BZ7EkQAIkQAIkQAIkQAIkQAL+SYBWkwAJ+AYBOlR8ox5pBQmQAAmQAAmQAAmQAAm4iwDzJQESIAESsEOADhU7UCgiARIgARIgARIgARLwZgLUnQRIgARIgATcT4AOFfczZgkkQAIkQAIkQAIkUDwBxpIACZAACZAACXgdATpUvK7KqDAJkAAJkAAJeJ4ANSABEiABEiABEiABfydAh4q/twDaTwIkQAL+QYBWkgAJkAAJkAAJkAAJkIBLCdCh4lKczIwESIAEXEWA+ZAACZAACZAACZAACZAACeiZAB0qeq4d6kYC3kSAupIACZAACZAACZAACZAACZCAHxGgQ8WPKpum2hLgGQmQAAmQAAmQAAmQAAmQAAmQAAmUlgAdKqUlV/bXsUQSIAESIAESIAESIAESIAESIAESIAGdEHCjQ0UnFlINEiABEiABEiABEiABEiABEiABEiABNxLwz6zpUPHPeqfVJEACJEACJEACJEACJEACJOC/BGg5CbiAAB0qLoDILEiABEiABEiABEiABEiABEjAnQSYNwmQgP4I0KGivzqhRiRAAiRAAiRAAiRAAiTg7QSoPwmQAAn4PAE6VHy+imkgCZAACZAACZAACZBAyQSYggRIgARIgAScI0CHinO8mJoESIAESIAESIAE9EGAWpAACZAACZAACXiUAB0qHsXPwkmABEiABEjAfwjQUhIgARIgARIgARLwJQJ0qPhSbdIWEiABEiABVxJgXiRAAiRAAiRAAiRAAiRQJAE6VIpEwwgSIAES8DYC1JcESIAESIAESIAESIAESKCsCNChUlakWQ4JkEBhApSQAAmQAAmQAAmQAAmQAAmQgJcSoEPFSyuOanuGAEslARIgARIgARIgARIgARIgARIgASFAh4pQ8N1Ay0iABEiABEiABEiABEiABEiABEiABNxAQGcOFTdYyCxJgARIgARIgARIgARIgARIgARIgAR0RsD71aFDxfvrkBaQAAmQAAmQAAmQAAmQAAmQAAm4mwDzJ4ECBOhQKQCEpyRAAiRAAiRAAiRAAiRAAiTgCwRoAwmQgHsJ0KHiXr7MnQRIgARIgARIgARIgARIwDECTEUCJEACXkWADhWvqi4qSwIkQAIkQAIkQAIkoB8C1IQESIAESMCfCdCh4s+1T9tJgARIgARIgAT8iwCtJQESIAESIAEScBkBOlRchpIZkQAJkAAJkAAJuJoA8yMBEiABEiABEiABvRL4fwAAAP//BxaQigAAAAZJREFUAwBoK+sfTGo6UQAAAABJRU5ErkJggg==" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#@title View the various statistics plots\n", + "import json\n", + "import re\n", + "from IPython.display import display\n", + "import pandas as pd\n", + "\n", + "# Specify the path to your HTML file\n", + "html_file_path = PROJECT_DIR / \"runs\" / \"run1\" / \"analysis\" / \"13_caprieval_analysis/report.html\"\n", + "\n", + "# Read the HTML file\n", + "with open(html_file_path, 'r') as f:\n", + " html_content = f.read()\n", + "\n", + "# Use a regular expression to find the script tag with id=\"data1\" and extract its content\n", + "match_plot = re.search(r'', html_content, re.DOTALL)\n", + "\n", + "if match_plot:\n", + " json_data_str_plot = match_plot.group(1)\n", + " # Load the JSON data\n", + " plot_data = json.loads(json_data_str_plot)\n", + "\n", + " # Set width and height to None to make the plot responsive\n", + " if 'layout' in plot_data:\n", + " plot_data['layout']['width'] = None\n", + " #plot_data['layout']['height'] = None\n", + "\n", + " # Display the plot using iplot\n", + " iplot(plot_data)\n", + "else:\n", + " print(\"Could not find the script tag with id='data1' in the HTML file.\")" + ] + }, + { + "cell_type": "markdown", + "id": "8d84b6b5-81d7-4bc3-89c4-3f867249c0a4", + "metadata": {}, + "source": [ + "\n", + "Depending on the docking models, there could be a set of unclustered models. It will be explicitly shown in `report.html` as ‘other’. You can see the origins of these models in `traceback/traceback.tsv`.\n", + "\n", + "Finally, the report also shows plots of the cluster statistics (distributions of values per cluster ordered according to their HADDOCK rank).\n", + "\n", + "To view those distribution plots execute the next cell.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "1a434611-61af-4bfb-9e89-73b735912239", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "linkText": "Export to plot.ly", + "plotlyServerURL": "https://plot.ly", + "showLink": false + }, + "data": [ + { + "alignmentgroup": "True", + "boxpoints": "outliers", + "customdata": { + "bdata": "AAAAAAAA8D8AAAAAAAAAQAAAAAAAAAhAAAAAAAAAEEA=", + "dtype": "f8", + "shape": "4, 1" + }, + "hovertemplate": "Cluster Rank=1
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#@title View the various distribution plots\n", + "import json\n", + "import re\n", + "from plotly.offline import iplot\n", + "\n", + "html_file_path = PROJECT_DIR / \"runs\" / \"run1\" / \"analysis\" / \"13_caprieval_analysis\" / \"report.html\"\n", + "\n", + "with open(html_file_path, 'r') as f:\n", + " html_content = f.read()\n", + "\n", + "match_plot = re.search(r'', html_content, re.DOTALL)\n", + "\n", + "if match_plot:\n", + " json_data_str_plot = match_plot.group(1)\n", + " plot_data = json.loads(json_data_str_plot)\n", + "\n", + " if 'layout' in plot_data:\n", + " plot_data['layout']['width'] = None\n", + " # Remove the template entirely — it contains deprecated trace types\n", + " # (e.g. heatmapgl) that newer plotly versions no longer recognize\n", + " plot_data['layout'].pop('template', None)\n", + "\n", + " iplot(plot_data, validate=False)\n", + "else:\n", + " print(\"Could not find the script tag with id='data1' in the HTML file.\")" + ] + }, + { + "cell_type": "markdown", + "id": "7f25d230-10cb-478e-8521-172ddcedbfc4", + "metadata": {}, + "source": [ + "__*Examine report.html for one of the unclustered steps. Are any of the top-10 models as good as cluster_1_model_1 based on their iRMSD?*__" + ] + }, + { + "cell_type": "markdown", + "id": "35de97f5-f14c-4cda-bd25-0a1bf8c21c6c", + "metadata": {}, + "source": [ + "# Detailed information about models and clusters\n", + "\n", + "To extract most of the avalilable information about the model(s), one should look at the `XX_caprieval` directories. This directory will always contain a `capri_ss.tsv` file, which contains the model names, rankings and statistics, e.g. `11_caprieval/capri_ss.tsv`:\n", + "\n", + "```\n", + "model\tmd5\tcaprieval_rank\tscore\tirmsd\tfnat\tlrmsd\tilrmsd\tdockq\trmsd\tcluster_id\tcluster_ranking\tmodel-cluster_ranking\tair\tangles\tbonds\tbsa\tcdih\tcoup\tdani\tdesolv\tdihe\telec\timproper\trdcs\trg\tsym\ttotal\tvdw\tvean\txpcs\n", + "../05_emref/emref_280.pdb\t-\t1\t-103.403\t1.437\t0.458\t3.139\t3.184\t0.620\t1.249\t1\t1\t1\t11.045\t66.508\t10.291\t1266.690\t0.000\t0.000\t0.000\t-42.226519.082\t-110.279\t13.374\t0.000\t0.000\t0.000\t-139.461\t-40.226\t0.000\t0.000\n", + "../05_emref/emref_331.pdb\t-\t2\t-101.588\t2.256\t0.229\t5.365\t5.385\t0.417\t1.941\t1\t1\t2\t9.775\t64.115\t13.897\t1420.890\t0.000\t0.000\t0.000\t-16.012541.892\t-295.776\t16.619\t0.000\t0.000\t0.000\t-313.399\t-27.398\t0.000\t0.000\n", + "../05_emref/emref_195.pdb\t-\t3\t-98.641\t3.134\t0.188\t7.947\t7.992\t0.302\t2.658\t1\t1\t3\t30.399\t59.802\t10.347\t1236.320\t0.000\t0.000\t0.000\t-31.515\t541.989-164.995\t13.680\t0.000\t0.000\t0.000\t-171.762\t-37.167\t0.000\t0.000\n", + "../05_emref/emref_87.pdb\t-\t4\t-97.050\t5.607\t0.042\t13.802\t13.778\t0.128\t4.714\t1\t1\t4\t12.931\t66.559\t10.655\t1383.200\t0.000\t0.000\t0.000\t-30.812\t534.429-154.782\t15.061\t0.000\t0.000\t0.000\t-178.425\t-36.575\t0.000\t0.000\n", + "../05_emref/emref_535.pdb\t-\t5\t-91.671\t7.347\t0.125\t18.121\t18.097\t0.115\t6.173\t8\t2\t1\t37.430\t56.527\t10.334\t1267.570\t0.000\t0.000\t0.000\t-25.055\t530.791-221.283\t12.885\t0.000\t0.000\t0.000\t-209.955\t-26.103\t0.000\t0.000\n", + "../05_emref/emref_516.pdb\t-\t6\t-91.505\t6.299\t0.167\t15.940\t15.926\t0.147\t5.332\t8\t2\t2\t3.678\t59.310\t10.443\t1193.220\t0.000\t0.000\t0.000\t-28.827\t523.618-164.442\t14.663\t0.000\t0.000\t0.000\t-190.921\t-30.157\t0.000\t0.000\n", + "../05_emref/emref_969.pdb\t-\t7\t-90.572\t7.556\t0.146\t18.350\t18.325\t0.120\t6.340\t5\t5\t1\t36.431\t56.119\t9.840\t1306.030\t0.000\t0.000\t0.000\t-28.212\t528.441-135.020\t14.626\t0.000\t0.000\t0.000\t-137.588\t-38.999\t0.000\t0.000\n", + "../05_emref/emref_271.pdb\t-\t8\t-89.669\t3.531\t0.104\t9.525\t9.502\t0.233\t3.053\t1\t1\t5\t3.724\t65.185\t10.717\t1142.830\t0.000\t0.000\t0.000\t-29.924\t520.532-70.314\t14.768\t0.000\t0.000\t0.000\t-112.645\t-46.054\t0.000\t0.000\n", + "../05_emref/emref_852.pdb\t-\t9\t-87.644\t9.828\t0.146\t25.599\t25.541\t0.089\t8.298\t3\t4\t1\t58.163\t55.462\t10.341\t1304.070\t0.000\t0.000\t0.000\t-26.725\t526.028-121.548\t12.783\t0.000\t0.000\t0.000\t-105.811\t-42.426\t0.000\t0.000\n", + "...\n", + "```\n", + "\n", + "In case where the caprieval module was called after a clustering step, an additional `capri_clt.tsv` file will be present in the directory. This file contains the cluster ranking and score statistics, averaged over the minimum number of models defined for clustering (4 by default), with their corresponding standard deviations, e.g. `11_caprieval/capri_clt.tsv`:\n", + "\n", + "```\n", + "cluster_rank\tcluster_id\tn\tunder_eval\tscore\tscore_std\tirmsd\tirmsd_std\tfnat\tfnat_std\tlrmsd\tlrmsd_std\tdockq\tdockq_std\tilrmsd\tilrmsd_std\trmsd\trmsd_std\tair\tair_std\tbsa\tbsa_std\tdesolv\tdesolv_std\telec\telec_std\ttotal\ttotal_std\tvdw\tvdw_std\tcaprieval_rank\n", + "1\t1\t125\t-\t-100.170\t2.477\t3.108\t1.562\t0.229\t0.150\t7.563\t3.983\t0.367\t0.179\t7.585\t3.960\t2.640\t1.297\t16.037\t8.367\t1326.775\t77.191\t-30.141\t9.327\t-181.458\t69.133\t-200.762\t66.680\t-35.341\t4.791\t1\n", + "2\t8\t5\t-\t-84.151\t10.090\t7.100\t0.463\t0.115\t0.056\t17.505\t0.912\t0.117\t0.024\t17.503\t0.917\t5.970\t0.369\t23.103\t16.128\t1257.242\t46.358\t-22.902\t7.088\t-139.000\t57.498\t-151.657\t49.813\t-35.759\t8.016\t2\n", + "3\t2\t16\t-\t-82.312\t1.350\t8.538\t0.082\t0.052\t0.010\t22.035\t0.263\t0.071\t0.004\t22.085\t0.274\t7.229\t0.078\t9.269\t3.596\t1253.533\t28.777\t-12.127\t1.805\t-135.983\t5.649\t-170.630\t2.214\t-43.916\t2.976\t3\n", + "4\t3\t11\t-\t-76.833\t7.460\t10.099\t0.229\t0.089\t0.037\t26.372\t0.764\t0.068\t0.013\t26.323\t0.764\t8.541\t0.203\t25.125\t22.462\t1089.075\t126.968\t-18.911\t5.253\t-114.106\t15.228\t-126.594\t27.678\t-37.614\t3.485\t4\n", + "5\t5\t8\t-\t-76.379\t8.245\t8.514\t0.753\t0.094\t0.052\t21.566\t2.476\t0.088\t0.021\t21.533\t2.461\t7.179\t0.662\t18.685\t11.196\t1126.436\t129.493\t-19.390\t8.808\t-132.176\t35.568\t-145.914\t32.684\t-32.423\t4.286\t5\n", + "6\t4\t9\t-\t-76.368\t4.881\t6.269\t0.328\t0.068\t0.023\t16.667\t0.891\t0.110\t0.009\t16.736\t0.899\t5.301\t0.268\t24.795\t13.112\t1150.585\t62.269\t-24.017\t3.038\t-52.364\t14.345\t-71.926\t11.076\t-44.357\t4.763\t6\n", + "7\t6\t7\t-\t-72.663\t6.002\t4.009\t0.629\t0.177\t0.074\t10.705\t2.214\t0.236\t0.067\t10.682\t2.189\t3.427\t0.548\t26.332\t15.668\t1211.172\t38.298\t-17.933\t15.664\t-107.707\t63.530\t-117.197\t64.538\t-35.821\t7.280\t7\n", + "8\t7\t7\t-\t-67.701\t9.637\t9.540\t0.728\t0.068\t0.034\t24.795\t2.084\t0.066\t0.018\t24.756\t2.102\t8.109\t0.624\t3.507\t4.671\t1035.520\t65.979\t-12.564\t9.268\t-91.881\t22.162\t-125.484\t21.545\t-37.111\t6.027\t8\n", + "9\t9\t4\t-\t-63.502\t1.756\t6.879\t1.034\t0.156\t0.067\t17.669\t3.435\t0.134\t0.028\t17.629\t3.433\t5.860\t0.923\t34.061\t17.099\t1001.322\t75.611\t-21.500\t3.965\t-70.569\t48.643\t-67.802\t52.681\t-31.294\t8.104\t9\n", + "```\n", + "\n", + "In this file you find the cluster rank (which corresponds to the naming of the clusters in the previous `seletop` directory), the cluster ID (which is related to the size of the cluster, 1 being always the largest cluster), the number of models (column `n`) in the cluster and the corresponding statistics (averages + standard deviations).\n", + "\n", + "These simple text files can be somewhat cumbersome to read “as-is”, but they can be easily manipulated using command line. The following commands will extract the best 3 models based on the DockQ score:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "c631af3c-5894-4c46-97ae-ffa36bc1ea6e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/eceokur/Downloads/HADDOCK3-protein-peptide/runs\n" + ] + } + ], + "source": [ + "%cd runs/" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "9124f940-2deb-4600-bd48-b62617dfdb4d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "model\tmd5\tcaprieval_rank\tscore\tirmsd\tfnat\tlrmsd\tilrmsd\tdockq\trmsd\tcluster_id\tcluster_ranking\tmodel-cluster_ranking\tair\tangles\tbonds\tbsa\tcdih\tcoup\tdani\tdesolv\tdihe\telec\timproper\trdcs\trg\tsym\ttotal\tvdw\tvean\txpcs\n", + "../01_rigidbody/rigidbody_51.pdb\t-\t3\t-45.820\t1.820\t0.292\t4.525\t4.543\t0.492\t1.555\t-\t-\t-\t81.581\t751.737\t196.085\t986.491\t0.000\t0.000\t0.000\t-33.082\t479.547\t-3.596\t564.742\t0.000\t0.000\t0.000\t68.631\t-9.354\t0.000\t0.000\n", + "../01_rigidbody/rigidbody_246.pdb\t-\t4\t-45.793\t1.774\t0.250\t4.393\t4.414\t0.485\t1.515\t-\t-\t-\t121.731\t751.736\t196.081\t998.318\t0.000\t0.000\t0.000\t-33.246\t479.547\t-3.714\t564.742\t0.000\t0.000\t0.000\t111.417\t-6.600\t0.000\t0.000\n", + "../01_rigidbody/rigidbody_146.pdb\t-\t2\t-46.105\t1.914\t0.271\t4.795\t4.793\t0.470\t1.640\t-\t-\t-\t41.692\t751.735\t196.094\t936.571\t0.000\t0.000\t0.000\t-33.651\t479.547\t-3.420\t564.742\t0.000\t0.000\t0.000\t29.698\t-8.574\t0.000\t0.000\n", + "sort: Broken pipe\n" + ] + } + ], + "source": [ + "!sort -r -k9 run1/02_caprieval/capri_ss.tsv | head -4" + ] + }, + { + "cell_type": "markdown", + "id": "4b1148ac-8058-4e09-b733-e367bb411d2c", + "metadata": {}, + "source": [ + "# Performance analysis (when reference is available)\n", + "\n", + "In case a reference structure is available, one may want to analyse the performance of the docking protocol, for example to count how many models of different quality were generated at each step of the workflow. This can be done with a simple bash script provided in `HADDOCK3-protein-peptide/scripts/`. This script extracts CAPRI statistics per model and reports the number of models of acceptable or better models from each `caprieval` steps. To use it, run the script with the path to the run directory you want to analyse as its argument:\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "d0e82ecb-a0ae-45e8-b15a-30db0d09fb44", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/eceokur/Downloads/HADDOCK3-protein-peptide\n" + ] + } + ], + "source": [ + "%cd .." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "e9645129-97ee-4118-bc27-1213d56a574c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==============================================\n", + "== ./runs/run1/02_caprieval/capri_ss.tsv\n", + "==============================================\n", + "Total number of acceptable or better models: 391 out of 999\n", + "Total number of medium or better models: 295 out of 999\n", + "Total number of high quality models: 0 out of 999\n", + " \n", + "awk: write error on /dev/stdout\n", + " input record number 391, file \n", + " source line number 1\n", + "grep: stdout: Broken pipe\n", + "awk: write error on /dev/stdout\n", + " input record number 391, file \n", + " source line number 1\n", + "grep: stdout: Broken pipe\n", + "awk: write error on /dev/stdout\n", + " input record number 391, file \n", + " source line number 1\n", + "grep: stdout: Broken pipe\n", + "awk: write error on /dev/stdout\n", + " input record number 391, file \n", + " source line number 1\n", + "grep: stdout: Broken pipe\n", + "awk: write error on /dev/stdout\n", + " input record number 556, file \n", + " source line number 1\n", + "grep: stdout: Broken pipe\n", + "awk: write error on /dev/stdout\n", + " input record number 556, file \n", + " source line number 1\n", + "grep: stdout: Broken pipe\n", + "awk: write error on /dev/stdout\n", + " input record number 556, file \n", + " source line number 1\n", + "grep: stdout: Broken pipe\n", + "awk: write error on /dev/stdout\n", + " input record number 556, file \n", + " source line number 1\n", + "grep: stdout: Broken pipe\n", + "sort: Broken pipe\n", + "sort: Broken pipe\n", + "sort: Broken pipe\n", + "sort: Broken pipe\n", + "First acceptable model - rank: 1 i-RMSD: 2.651 Fnat: 0.229 DockQ: 0.363\n", + "First medium model - rank: 2 i-RMSD: 1.914 Fnat: 0.271 DockQ: 0.470\n", + "Best model - rank: 2 i-RMSD: 1.914 Fnat: 0.271 DockQ: 0.470\n", + "==============================================\n", + "== ./runs/run1/04_caprieval/capri_ss.tsv\n", + "==============================================\n", + "Total number of acceptable or better models: 366 out of 999\n", + "Total number of medium or better models: 267 out of 999\n", + "Total number of high quality models: 0 out of 999\n", + " \n", + "awk: write error on /dev/stdout\n", + " input record number 420, file \n", + " source line number 1\n", + "grep: stdout: Broken pipe\n", + "awk: write error on /dev/stdout\n", + " input record number 420, file \n", + " source line number 1\n", + "grep: stdout: Broken pipe\n", + "awk: write error on /dev/stdout\n", + " input record number 420, file \n", + " source line number 1\n", + "grep: stdout: Broken pipe\n", + "awk: write error on /dev/stdout\n", + " input record number 420, file \n", + " source line number 1\n", + "grep: stdout: Broken pipe\n", + "awk: write error on /dev/stdout\n", + " input record number 688, file \n", + " source line number 1\n", + "grep: stdout: Broken pipe\n", + "awk: write error on /dev/stdout\n", + " input record number 688, file \n", + " source line number 1\n", + "grep: stdout: Broken pipe\n", + "awk: write error on /dev/stdout\n", + " input record number 688, file \n", + " source line number 1\n", + "grep: stdout: Broken pipe\n", + "awk: write error on /dev/stdout\n", + " input record number 688, file \n", + " source line number 1\n", + "grep: stdout: Broken pipe\n", + "sort: Broken pipe\n", + "sort: Broken pipe\n", + "sort: Broken pipe\n", + "sort: Broken pipe\n", + "First acceptable model - rank: 1 i-RMSD: 2.574 Fnat: 0.083 DockQ: 0.318\n", + "First medium model - rank: 12 i-RMSD: 1.517 Fnat: 0.396 DockQ: 0.584\n", + "Best model - rank: 121 i-RMSD: 1.849 Fnat: 0.229 DockQ: 0.467\n", + "==============================================\n", + "== ./runs/run1/06_caprieval/capri_ss.tsv\n", + "==============================================\n", + "Total number of acceptable or better models: 363 out of 999\n", + "Total number of medium or better models: 263 out of 999\n", + "Total number of high quality models: 0 out of 999\n", + " \n", + "awk: write error on /dev/stdout\n", + " input record number 452, file \n", + " source line number 1\n", + "grep: stdout: Broken pipe\n", + "awk: write error on /dev/stdout\n", + " input record number 452, file \n", + " source line number 1\n", + "grep: stdout: Broken pipe\n", + "awk: write error on /dev/stdout\n", + " input record number 452, file \n", + " source line number 1\n", + "grep: stdout: Broken pipe\n", + "awk: write error on /dev/stdout\n", + " input record number 452, file \n", + " source line number 1\n", + "grep: stdout: Broken pipe\n", + "awk: write error on /dev/stdout\n", + " input record number 687, file \n", + " source line number 1\n", + "awk: write error on /dev/stdout\n", + " input record number 687, file \n", + " source line number 1\n", + "awk: write error on /dev/stdout\n", + " input record number 687, file \n", + " source line number 1\n", + "awk: write error on /dev/stdout\n", + " input record number 687, file \n", + " source line number 1\n", + "sort: Broken pipe\n", + "sort: Broken pipe\n", + "sort: Broken pipe\n", + "sort: Broken pipe\n", + "First acceptable model - rank: 1 i-RMSD: 1.437 Fnat: 0.458 DockQ: 0.620\n", + "First medium model - rank: 1 i-RMSD: 1.437 Fnat: 0.458 DockQ: 0.620\n", + "Best model - rank: 11 i-RMSD: 1.810 Fnat: 0.271 DockQ: 0.491\n", + "==============================================\n", + "== ./runs/run1/08_caprieval/capri_ss.tsv\n", + "==============================================\n", + "Total number of acceptable or better models: 68 out of 200\n", + "Total number of medium or better models: 14 out of 200\n", + "Total number of high quality models: 0 out of 200\n", + " \n", + "sort: stdout: Broken pipe\n", + "sort: stdout: Broken pipe\n", + "sort: stdout: Broken pipe\n", + "sort: stdout: Broken pipe\n", + "First acceptable model - rank: 1 i-RMSD: 1.437 Fnat: 0.458 DockQ: 0.620\n", + "First medium model - rank: 1 i-RMSD: 1.437 Fnat: 0.458 DockQ: 0.620\n", + "Best model - rank: 11 i-RMSD: 1.810 Fnat: 0.271 DockQ: 0.491\n", + "==============================================\n", + "== ./runs/run1/11_caprieval/capri_ss.tsv\n", + "==============================================\n", + "Total number of acceptable or better models: 68 out of 192\n", + "Total number of medium or better models: 14 out of 192\n", + "Total number of high quality models: 0 out of 192\n", + " \n", + "sort: stdout: Broken pipe\n", + "sort: stdout: Broken pipe\n", + "sort: stdout: Broken pipe\n", + "sort: stdout: Broken pipe\n", + "First acceptable model - rank: 1 i-RMSD: 1.437 Fnat: 0.458 DockQ: 0.620\n", + "First medium model - rank: 1 i-RMSD: 1.437 Fnat: 0.458 DockQ: 0.620\n", + "Best model - rank: 11 i-RMSD: 1.810 Fnat: 0.271 DockQ: 0.491\n", + "==============================================\n", + "== ./runs/run1/13_caprieval/capri_ss.tsv\n", + "==============================================\n", + "Total number of acceptable or better models: 9 out of 36\n", + "Total number of medium or better models: 5 out of 36\n", + "Total number of high quality models: 0 out of 36\n", + " \n", + "First acceptable model - rank: 1 i-RMSD: 1.437 Fnat: 0.458 DockQ: 0.620\n", + "First medium model - rank: 1 i-RMSD: 1.437 Fnat: 0.458 DockQ: 0.620\n", + "Best model - rank: 1 i-RMSD: 1.437 Fnat: 0.458 DockQ: 0.620\n" + ] + } + ], + "source": [ + "! bash ./scripts/extract-capri-stats.sh ./runs/run1" + ] + }, + { + "cell_type": "markdown", + "id": "6e87c110-8bd3-4a99-9d56-f55e89c5736f", + "metadata": {}, + "source": [ + "__*Look at the single structure statistics. How does the quality of the best model change after flexible refinement? After final energy minimisation? Consider all 3 metrics*__.\n", + "\n", + "*Note*: To extract similar statistics per cluster, use `scripts/extract-capri-stats-clt.sh`.\n" + ] + }, + { + "cell_type": "markdown", + "id": "740d5190-cea9-480a-988c-6e160465e8a9", + "metadata": {}, + "source": [ + "---\n", + "# Visualisation and comparison with the reference structure\n", + "\n", + "It’s time to visualise some of the docking models! This part is not only nice and colorful, but also quite important. Model visualisation allows you to check whether the models look as expected, if the clusters well-defined, zoom in on the interface, etc.\n", + "\n", + "To visualize the models from the top cluster of your favorite run, start PyMOL and load the cluster representatives you want to view, e.g. this could be the top models from cluster 1. These can be found in the `runs/run1/12_seletopclusts/` directory. Each run has a similar directory. Alternatively, in `analysis/XX_caprieval_analysis` you can find `summary.tgz` with either top-models of best clusters (decompress with `tar -xf summary.tgz`), or top-10 models among all unclustered ones.\n", + "\n", + "If you want to get an impression of how well-defined a cluster is, repeat this for the best X models you want to view (cluster_1_model_X.pdb).\n", + "\n", + "Load the reference structure `1YCR.pdb` from `pdbs/`. Alternatively, if reference has been used in `caprieval`, it can be found in corresponding `run1/data/XX_caprieval/`" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "ce2e3fad-822d-4588-a509-23c81ed29037", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Error: File not found at /Users/eceokur/Downloads/HADDOCK3-protein-peptide/HADDOCK3-protein-peptide-notebook/AF_MDM2_26_109.pdb\n", - "Error: File not found at /Users/eceokur/Downloads/HADDOCK3-protein-peptide/HADDOCK3-protein-peptide-notebook/AF-P23804-F1-model_v6.pdb\n", + "Error: File not found at /Users/eceokur/Downloads/HADDOCK3-protein-peptide/runs/run1/12_seletopclusts/cluster_1_model_1.pdb\n", "Failed to load one or both PDB files\n" ] }, { "data": { "text/plain": [ - "(, None, {})" + "(, None, {})" ] }, - "execution_count": 20, + "execution_count": 78, "metadata": {}, "output_type": "execute_result" } @@ -13211,18 +24119,27 @@ "#@markdown It will be shown superimposed onto the reference structure fitted only on the second receptor and the DNA\n", "# Define paths to PDB files to be aligned\n", "# Here we are overlaying the reference and 1st model from the 1st cluster\n", - "path1 = PROJECT_DIR / \"AF_MDM2_26_109.pdb\"\n", - "path2 = PROJECT_DIR / \"AF-P23804-F1-model_v6.pdb\"\n", + "path1 = PROJECT_DIR / \"runs\" / \"run1\"/ \"12_seletopclusts\" / \"cluster_1_model_1.pdb\"\n", + "path2 = PROJECT_DIR / \"pdbs\" / \"1YCR.PDB\"\n", "\n", "# Align molecules by chain and display the result.\n", "# For the full alignement put all chain IDs in the `chains` variable.\n", - "align_full(str(path1), str(path2), chains = ['B','C'])" + "align_full(str(path1), str(path2), chains = ['A'])" + ] + }, + { + "cell_type": "markdown", + "id": "3b27996a-e29a-434d-a2ec-7bd3fa5d9373", + "metadata": {}, + "source": [ + "---\n", + "# BONUS: How to use ARCTIC-3D to predict interface residues?" ] }, { "cell_type": "code", "execution_count": null, - "id": "23b618f5-858c-4dfe-acdf-99954ba56afc", + "id": "f30e6937-e59d-4a67-bd0b-1379b3a0bbd2", "metadata": {}, "outputs": [], "source": []