diff --git a/001528/HnaskoLab/Lotfi_2025/001528_demo.ipynb b/001528/HnaskoLab/Lotfi_2025/001528_demo.ipynb new file mode 100644 index 0000000..470aa99 --- /dev/null +++ b/001528/HnaskoLab/Lotfi_2025/001528_demo.ipynb @@ -0,0 +1,2374 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# **Fiber photometry of SNc/SNr dopamine and GABA neuron responses to optogenetic STN and PPN stimulation in Parkinson's disease**\n", + "\n", + "This tutorial shows how to access and process data from [DANDI:001528](https://dandiarchive.org/dandiset/001528/draft) for the study detailed in [*\"Parkinson's Disease-vulnerable and -resilient dopamine neurons display opposite responses to excitatory input\"*](https://www.biorxiv.org/content/10.1101/2025.06.03.657460v1)\n", + "\n", + "## Study Overview\n", + "\n", + "This dataset contains fiber photometry recordings examining differential responses of Parkinson's disease (PD)-vulnerable and -resilient dopamine neuron subtypes to excitatory input in freely-moving mice. The study investigates how excitatory inputs from the subthalamic nucleus (STN) and pedunculopontine nucleus (PPN) differentially modulate distinct dopaminergic and GABAergic populations in the substantia nigra (SNc/SNr) and their striatal projections. Using genetically-targeted calcium imaging with GCaMP6f/8f indicators and GRAB_DA sensors, the dataset captures activity across five experimental groups: \n", + "1. SNr GABAergic neurons, \n", + "2. SNc pan-dopaminergic neurons, \n", + "3. Parkinson's-vulnerable Anxa1+ and -resilient Vglut2+ dopamine subtypes, \n", + "4. striatal dopamine release in dorsolateral and tail striatum, and \n", + "5. dopaminergic axon terminals. \n", + "\n", + "ChrimsonR-mediated optogenetic stimulation (635nm, 5mW) was delivered to STN and/or PPN at varying frequencies (5, 10, 20, 40 Hz) and durations (250ms, 1s, 4s) to probe circuit-specific responses. Key findings demonstrate that excitatory inputs evoke frequency-dependent excitation in SNr GABA neurons but heterogeneous, multiphasic responses in dopamine populations. Critically, PD-resilient Vglut2+ dopamine neurons show excitatory responses to STN/PPN input, while vulnerable Anxa1+ neurons are inhibited, revealing opposite functional properties between these subtypes. Striatal recordings show differential regional responses, with increased dopamine release in caudal striatum but inhibition followed by rebound in dorsolateral striatum. Each session includes raw and processed fiber photometry signals (demodulated calcium/isosbestic traces, downsampled signals, and DF/F), precisely-timed optogenetic stimulation epochs, and behavioral videos where available.\n", + "\n", + "## Contents\n", + "\n", + "1. [Setup and Data Access](#setup)\n", + "2. [Session and Subject Metadata](#metadata)\n", + "3. [Fiber Photometry Data and Metadata](#photometry)\n", + "4. [Optogenetic Stimulus and Metadata](#ogen)\n", + "5. [Session Type 1: Varying Duration Optogenetic Stimulation](#session1)\n", + "6. [Session Type 2: Varying Frequency Optogenetic Stimulation](#session2)\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Setup and Data Access \n", + "\n", + "## Import Required Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Core data manipulation and analysis\n", + "from pathlib import Path\n", + "\n", + "import h5py\n", + "\n", + "# Visualization\n", + "import matplotlib.pyplot as plt\n", + "import remfile\n", + "from dandi.dandiapi import DandiAPIClient\n", + "\n", + "# NWB and DANDI access\n", + "from pynwb import NWBHDF5IO\n", + "\n", + "# Configure matplotlib\n", + "plt.rcParams['figure.figsize'] = (12, 6)\n", + "plt.rcParams['font.size'] = 10" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Access Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 188, + "metadata": {}, + "outputs": [], + "source": [ + "def load_nwb_from_dandi(dandiset_id, subject_id, session_id):\n", + " \"\"\"\n", + " Load NWB file from DANDI Archive via streaming.\n", + " \"\"\"\n", + " pattern = f\"sub-{subject_id}/sub-{subject_id}_ses-{session_id}*.nwb\"\n", + " \n", + " with DandiAPIClient() as client:\n", + " client.dandi_authenticate()\n", + " assets = client.get_dandiset(dandiset_id, \"draft\").get_assets_by_glob(\n", + " pattern=pattern, order=\"path\"\n", + " )\n", + " \n", + " s3_urls = []\n", + " for asset in assets:\n", + " s3_url = asset.get_content_url(follow_redirects=1, strip_query=False)\n", + " s3_urls.append(s3_url)\n", + " \n", + " if len(s3_urls) != 1:\n", + " raise ValueError(f\"Expected 1 file, found {len(s3_urls)} for pattern {pattern}\")\n", + " \n", + " s3_url = s3_urls[0]\n", + " \n", + " file = remfile.File(s3_url)\n", + " h5_file = h5py.File(file, \"r\")\n", + " io = NWBHDF5IO(file=h5_file, load_namespaces=True)\n", + " nwbfile = io.read()\n", + " \n", + " return nwbfile, io\n", + "\n", + "\n", + "def load_nwb_local(directory_path, subject_id, session_id):\n", + " \"\"\"\n", + " Load NWB file from local directory.\n", + " \"\"\"\n", + " directory_path = Path(directory_path)\n", + " nwbfile_path = directory_path / f\"sub-{subject_id}_ses-{session_id}.nwb\"\n", + " \n", + " if not nwbfile_path.exists():\n", + " raise FileNotFoundError(f\"NWB file not found: {nwbfile_path}\")\n", + " \n", + " io = NWBHDF5IO(path=nwbfile_path, load_namespaces=True)\n", + " nwbfile = io.read()\n", + " \n", + " return nwbfile, io" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "# 2. Session and Subject Metadata " + ] + }, + { + "cell_type": "code", + "execution_count": 189, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== SESSION INFORMATION ===\n", + "Experiment description:\n", + " SNr GABAergic neurons recordings. Mice were freely moving on a plastic tub. Simultaneous passive optogenetic stimulation and fiber photometry recordings were conducted during the first two days. Excitatory inputs from either the STN or PPN to SN were stimulated. Recordings of GCaMP6f fluorescence signal captured pan-GABA activity in the SNr in VGLUT2-Cre x VGAT-Flp mice.\n", + "Session description:\n", + " The subject is placed in a plastic tub and is recorded for 3.5 minutes. The subject receives a 40 Hz stimulation at various durations (i.e. 250ms, 1s and 4s) 5 times for each duration) with an inter-stimulus interval (ISI) of 10s. \n", + "Session start time:\n", + " 2024-01-18 10:24:56-00:01\n" + ] + } + ], + "source": [ + "# Load session data\n", + "dandiset_id = \"001528\"\n", + "subject_id = \"C4561\" # Example subject\n", + "session_id = \"varying-duration\" # Start with first session type\n", + "\n", + "# Choose data source (DANDI streaming or local)\n", + "USE_DANDI = True # Set to False to use local files\n", + "\n", + "if USE_DANDI:\n", + " nwbfile, io = load_nwb_from_dandi(dandiset_id, subject_id, session_id)\n", + "else:\n", + " # Specify your local directory path\n", + " local_directory = \"YOUR_DIRECTORY_PATH\" # Replace with actual path\n", + " nwbfile, io = load_nwb_local(local_directory, subject_id, session_id)\n", + "\n", + "print(\"=== SESSION INFORMATION ===\")\n", + "print(f\"Experiment description:\\n {nwbfile.experiment_description}\")\n", + "print(f\"Session description:\\n {nwbfile.session_description}\")\n", + "print(f\"Session start time:\\n {nwbfile.session_start_time}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 190, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== SUBJECT INFORMATION ===\n", + "ID: C4561\n", + "Age: P6W\n", + "Strain: Mus musculus\n", + "Genotype: VGlut2-cre x VGAT-flp\n", + "Sex: F\n" + ] + } + ], + "source": [ + "print(\"=== SUBJECT INFORMATION ===\")\n", + "print(f\"ID: {nwbfile.subject.subject_id}\")\n", + "print(f\"Age: {nwbfile.subject.age}\")\n", + "print(f\"Strain: {nwbfile.subject.species}\")\n", + "print(f\"Genotype: {nwbfile.subject.genotype}\")\n", + "print(f\"Sex: {nwbfile.subject.sex}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "# 3. Fiber Photometry Data and Metadata " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fiber Photometry Metadata" + ] + }, + { + "cell_type": "code", + "execution_count": 191, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== FIBER PHOTOMETRY METADATA ===\n", + "All fiber photometry metadata are stored in the fiber_photometry module in lab_meta_data:\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + "

fiber_photometry (FiberPhotometry)

fiber_photometry_table
description: For monitoring calcium dynamics in SNr GABAergic neurons, a fiber was implanted in SNr.
table\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
locationexcitation_wavelength_in_nmemission_wavelength_in_nmindicatoroptical_fiberexcitation_sourcephotodetectordichroic_mirroremission_filterexcitation_filter
id
0SNr465.0515.0GCaMP6f_SNr abc.Indicator at 0x2222092835136\\nFields:\\n description: GCaMP6f calcium sensor in SNr GABAergic neurons\\n label: GCaMP6f\\n manufacturer: Addgene\\n viral_vector_injection: viral_vector_injection_SNr abc.ViralVectorInjection at 0x2222092834800\\nFields:\\n ap_in_mm: -3.3\\n description: Viral injection of GCaMP6f in SNr.\\n dv_in_mm: -4.6\\n hemisphere: left\\n location: SNr\\n ml_in_mm: -1.3\\n reference: bregma at the cortical surface\\n viral_vector: viral_vector_gcamp6f abc.ViralVector at 0x2222092834128\\nFields:\\n construct_name: AAV1-Ef1a-fDIO-GCaMP6f\\n description: AAV1 viral vector expressing GCaMP6f calcium sensor under EF1a promoter for fiber photometry recording in SNr\\n manufacturer: Addgene (catalog number 1283125)\\n titer_in_vg_per_ml: 4000000000000.0\\n\\n volume_in_uL: 0.3\\n\\noptical_fiber_SNr abc.OpticalFiber at 0x2222092842192\\nFields:\\n description: Chronically implanted optic fiber.\\n fiber_insertion: fiber_insertion abc.FiberInsertion at 0x2222092841856\\nFields:\\n hemisphere: left\\n insertion_position_ap_in_mm: -3.3\\n insertion_position_dv_in_mm: -4.4\\n insertion_position_ml_in_mm: -1.4\\n position_reference: bregma\\n\\n model: optical_fiber_model_1 abc.OpticalFiberModel at 0x2222092841520\\nFields:\\n active_length_in_mm: 6.0\\n core_diameter_in_um: 400.0\\n description: Chronically implantable optic fiber.\\n ferrule_diameter_in_mm: 1.25\\n ferrule_name: black ceramic ferrule\\n manufacturer: RWD\\n model_number: <model of the optical fiber>\\n numerical_aperture: 0.39\\n\\n serial_number: <serial number of the optical fiber>\\nexcitation_source_calcium_signal abc.ExcitationSource at 0x2222092841184\\nFields:\\n description: excitation source for the sensor's fluorescence signal (465nm) modulated at 210 Hz.\\n model: excitation_source_model_calcium abc.ExcitationSourceModel at 0x2222092840848\\nFields:\\n description: excitation source for GCaMP6f sensor.\\n excitation_mode: one-photon\\n manufacturer: Tucker-Davis Technologies\\n model_number: RZ5P or RZ10x\\n source_type: LED\\n wavelength_range_in_nm: [460. 490.]\\n\\nphotodetector abc.Photodetector at 0x2222092842864\\nFields:\\n description: <description of the photodetector>\\n model: photodetector_model abc.PhotodetectorModel at 0x2222092842528\\nFields:\\n description: <description of the photodetector model>\\n detector_type: Silicon photodiode\\n gain: 0.38\\n gain_unit: A/W\\n manufacturer: <manufacturer of the photodetector>\\n model_number: <model of the photodetector>\\n wavelength_range_in_nm: [500. 540.]\\n\\n serial_number: <serial number of the photodetector>\\ndichroic_mirror abc.DichroicMirror at 0x2222092839840\\nFields:\\n description: dichroic mirror for GCaMP6f fluorescence signal.\\n model: dichroic_mirror_model abc.DichroicMirrorModel at 0x2222092839504\\nFields:\\n description: dichroic mirror model for GCaMP6f fluorescence signal.\\n manufacturer: <manufacturer of the dichroic mirror>\\n model_number: <model of the dichroic mirror>\\n\\n serial_number: <serial number of the dichroic mirror>\\nemission_filter abc.BandOpticalFilter at 0x2222092840512\\nFields:\\n description: emission filter for GCaMP6f fluorescence signal.\\n model: emission_filter_model abc.BandOpticalFilterModel at 0x2222092840176\\nFields:\\n bandwidth_in_nm: 40.0\\n center_wavelength_in_nm: 520.0\\n description: emission filter model for GCaMP6f fluorescence signal.\\n filter_type: Bandpass\\n manufacturer: <manufacturer of the emission filter>\\n model_number: <model of the emission filter>\\n\\nexcitation_filter abc.BandOpticalFilter at 0x2222061498960\\nFields:\\n description: excitation filter for GCaMP6f fluorescence signal.\\n model: excitation_filter_model abc.BandOpticalFilterModel at 0x2222061489680\\nFields:\\n bandwidth_in_nm: 90.0\\n center_wavelength_in_nm: 445.0\\n description: excitation filter model for GCaMP6f fluorescence signal.\\n filter_type: Bandpass\\n manufacturer: <manufacturer of the excitation filter>\\n model_number: <model of the excitation filter>\\n\\n
1SNr405.0515.0GCaMP6f_SNr abc.Indicator at 0x2222092835136\\nFields:\\n description: GCaMP6f calcium sensor in SNr GABAergic neurons\\n label: GCaMP6f\\n manufacturer: Addgene\\n viral_vector_injection: viral_vector_injection_SNr abc.ViralVectorInjection at 0x2222092834800\\nFields:\\n ap_in_mm: -3.3\\n description: Viral injection of GCaMP6f in SNr.\\n dv_in_mm: -4.6\\n hemisphere: left\\n location: SNr\\n ml_in_mm: -1.3\\n reference: bregma at the cortical surface\\n viral_vector: viral_vector_gcamp6f abc.ViralVector at 0x2222092834128\\nFields:\\n construct_name: AAV1-Ef1a-fDIO-GCaMP6f\\n description: AAV1 viral vector expressing GCaMP6f calcium sensor under EF1a promoter for fiber photometry recording in SNr\\n manufacturer: Addgene (catalog number 1283125)\\n titer_in_vg_per_ml: 4000000000000.0\\n\\n volume_in_uL: 0.3\\n\\noptical_fiber_SNr abc.OpticalFiber at 0x2222092842192\\nFields:\\n description: Chronically implanted optic fiber.\\n fiber_insertion: fiber_insertion abc.FiberInsertion at 0x2222092841856\\nFields:\\n hemisphere: left\\n insertion_position_ap_in_mm: -3.3\\n insertion_position_dv_in_mm: -4.4\\n insertion_position_ml_in_mm: -1.4\\n position_reference: bregma\\n\\n model: optical_fiber_model_1 abc.OpticalFiberModel at 0x2222092841520\\nFields:\\n active_length_in_mm: 6.0\\n core_diameter_in_um: 400.0\\n description: Chronically implantable optic fiber.\\n ferrule_diameter_in_mm: 1.25\\n ferrule_name: black ceramic ferrule\\n manufacturer: RWD\\n model_number: <model of the optical fiber>\\n numerical_aperture: 0.39\\n\\n serial_number: <serial number of the optical fiber>\\nexcitation_source_isosbestic_control abc.ExcitationSource at 0x2222061489360\\nFields:\\n description: excitation source for the sensor's isosbestic control (405nm) modulated at 330 Hz.\\n model: excitation_source_model_isosbestic abc.ExcitationSourceModel at 0x2222061487440\\nFields:\\n description: excitation source for GCaMP6f sensor's isosbestic control.\\n excitation_mode: one-photon\\n manufacturer: Tucker-Davis Technologies\\n model_number: RZ5P or RZ10x\\n source_type: LED\\n wavelength_range_in_nm: [400. 410.]\\n\\nphotodetector abc.Photodetector at 0x2222092842864\\nFields:\\n description: <description of the photodetector>\\n model: photodetector_model abc.PhotodetectorModel at 0x2222092842528\\nFields:\\n description: <description of the photodetector model>\\n detector_type: Silicon photodiode\\n gain: 0.38\\n gain_unit: A/W\\n manufacturer: <manufacturer of the photodetector>\\n model_number: <model of the photodetector>\\n wavelength_range_in_nm: [500. 540.]\\n\\n serial_number: <serial number of the photodetector>\\ndichroic_mirror abc.DichroicMirror at 0x2222092839840\\nFields:\\n description: dichroic mirror for GCaMP6f fluorescence signal.\\n model: dichroic_mirror_model abc.DichroicMirrorModel at 0x2222092839504\\nFields:\\n description: dichroic mirror model for GCaMP6f fluorescence signal.\\n manufacturer: <manufacturer of the dichroic mirror>\\n model_number: <model of the dichroic mirror>\\n\\n serial_number: <serial number of the dichroic mirror>\\nemission_filter abc.BandOpticalFilter at 0x2222092840512\\nFields:\\n description: emission filter for GCaMP6f fluorescence signal.\\n model: emission_filter_model abc.BandOpticalFilterModel at 0x2222092840176\\nFields:\\n bandwidth_in_nm: 40.0\\n center_wavelength_in_nm: 520.0\\n description: emission filter model for GCaMP6f fluorescence signal.\\n filter_type: Bandpass\\n manufacturer: <manufacturer of the emission filter>\\n model_number: <model of the emission filter>\\n\\nexcitation_filter abc.BandOpticalFilter at 0x2222061498960\\nFields:\\n description: excitation filter for GCaMP6f fluorescence signal.\\n model: excitation_filter_model abc.BandOpticalFilterModel at 0x2222061489680\\nFields:\\n bandwidth_in_nm: 90.0\\n center_wavelength_in_nm: 445.0\\n description: excitation filter model for GCaMP6f fluorescence signal.\\n filter_type: Bandpass\\n manufacturer: <manufacturer of the excitation filter>\\n model_number: <model of the excitation filter>\\n\\n
fiber_photometry_indicators
indicators
GCaMP6f_SNr
label: GCaMP6f
description: GCaMP6f calcium sensor in SNr GABAergic neurons
manufacturer: Addgene
viral_vector_injection
description: Viral injection of GCaMP6f in SNr.
location: SNr
hemisphere: left
reference: bregma at the cortical surface
ap_in_mm: -3.3
ml_in_mm: -1.3
dv_in_mm: -4.6
volume_in_uL: 0.3
viral_vector
construct_name: AAV1-Ef1a-fDIO-GCaMP6f
description: AAV1 viral vector expressing GCaMP6f calcium sensor under EF1a promoter for fiber photometry recording in SNr
manufacturer: Addgene (catalog number 1283125)
titer_in_vg_per_ml: 4000000000000.0
fiber_photometry_viruses
viral_vectors
viral_vector_gcamp6f
construct_name: AAV1-Ef1a-fDIO-GCaMP6f
description: AAV1 viral vector expressing GCaMP6f calcium sensor under EF1a promoter for fiber photometry recording in SNr
manufacturer: Addgene (catalog number 1283125)
titer_in_vg_per_ml: 4000000000000.0
fiber_photometry_virus_injections
viral_vector_injections
viral_vector_injection_SNr
description: Viral injection of GCaMP6f in SNr.
location: SNr
hemisphere: left
reference: bregma at the cortical surface
ap_in_mm: -3.3
ml_in_mm: -1.3
dv_in_mm: -4.6
volume_in_uL: 0.3
viral_vector
construct_name: AAV1-Ef1a-fDIO-GCaMP6f
description: AAV1 viral vector expressing GCaMP6f calcium sensor under EF1a promoter for fiber photometry recording in SNr
manufacturer: Addgene (catalog number 1283125)
titer_in_vg_per_ml: 4000000000000.0
" + ], + "text/plain": [ + "fiber_photometry abc.FiberPhotometry at 0x2222092836480\n", + "Fields:\n", + " fiber_photometry_indicators: fiber_photometry_indicators abc.FiberPhotometryIndicators at 0x2222092835472\n", + "Fields:\n", + " indicators: {\n", + " GCaMP6f_SNr \n", + " }\n", + "\n", + " fiber_photometry_table: fiber_photometry_table \n", + " fiber_photometry_virus_injections: fiber_photometry_virus_injections abc.FiberPhotometryVirusInjections at 0x2222092836144\n", + "Fields:\n", + " viral_vector_injections: {\n", + " viral_vector_injection_SNr \n", + " }\n", + "\n", + " fiber_photometry_viruses: fiber_photometry_viruses abc.FiberPhotometryViruses at 0x2222092835808\n", + "Fields:\n", + " viral_vectors: {\n", + " viral_vector_gcamp6f \n", + " }\n" + ] + }, + "execution_count": 191, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(\"=== FIBER PHOTOMETRY METADATA ===\")\n", + "print(\"All fiber photometry metadata are stored in the fiber_photometry module in lab_meta_data:\")\n", + "nwbfile.lab_meta_data[\"fiber_photometry\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 192, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== FP SETUP INFORMATION ===\n", + "Components used in fiber photometry setup:\n", + "dichroic_mirror\n", + "emission_filter\n", + "excitation_filter\n", + "excitation_source_calcium_signal\n", + "excitation_source_isosbestic_control\n", + "optical_fiber_SNr\n", + "photodetector\n" + ] + } + ], + "source": [ + "print(\"=== FP SETUP INFORMATION ===\")\n", + "print(\"Components used in fiber photometry setup:\")\n", + "for key in nwbfile.devices.keys():\n", + " if \"BehavioralCamera\" in key or \"optogenetic\" in key:\n", + " continue\n", + " print(key)" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + "

excitation_source_calcium_signal (ExcitationSource)

description: excitation source for the sensor's fluorescence signal (465nm) modulated at 210 Hz.
model
manufacturer: Tucker-Davis Technologies
model_number: RZ5P or RZ10x
description: excitation source for GCaMP6f sensor.
source_type: LED
excitation_mode: one-photon
wavelength_range_in_nm
NumPy array
Data typefloat64
Shape(2,)
Array size16.00 bytes

[460. 490.]
" + ], + "text/plain": [ + "excitation_source_calcium_signal abc.ExcitationSource at 0x2222092841184\n", + "Fields:\n", + " description: excitation source for the sensor's fluorescence signal (465nm) modulated at 210 Hz.\n", + " model: excitation_source_model_calcium abc.ExcitationSourceModel at 0x2222092840848\n", + "Fields:\n", + " description: excitation source for GCaMP6f sensor.\n", + " excitation_mode: one-photon\n", + " manufacturer: Tucker-Davis Technologies\n", + " model_number: RZ5P or RZ10x\n", + " source_type: LED\n", + " wavelength_range_in_nm: [460. 490.]\n" + ] + }, + "execution_count": 193, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nwbfile.devices[\"excitation_source_calcium_signal\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + "

excitation_source_calcium_signal (ExcitationSource)

description: excitation source for the sensor's fluorescence signal (465nm) modulated at 210 Hz.
model
manufacturer: Tucker-Davis Technologies
model_number: RZ5P or RZ10x
description: excitation source for GCaMP6f sensor.
source_type: LED
excitation_mode: one-photon
wavelength_range_in_nm
NumPy array
Data typefloat64
Shape(2,)
Array size16.00 bytes

[460. 490.]
" + ], + "text/plain": [ + "excitation_source_calcium_signal abc.ExcitationSource at 0x2222092841184\n", + "Fields:\n", + " description: excitation source for the sensor's fluorescence signal (465nm) modulated at 210 Hz.\n", + " model: excitation_source_model_calcium abc.ExcitationSourceModel at 0x2222092840848\n", + "Fields:\n", + " description: excitation source for GCaMP6f sensor.\n", + " excitation_mode: one-photon\n", + " manufacturer: Tucker-Davis Technologies\n", + " model_number: RZ5P or RZ10x\n", + " source_type: LED\n", + " wavelength_range_in_nm: [460. 490.]\n" + ] + }, + "execution_count": 194, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nwbfile.devices[\"excitation_source_calcium_signal\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + "

excitation_filter (BandOpticalFilter)

description: excitation filter for GCaMP6f fluorescence signal.
model
manufacturer:
model_number:
description: excitation filter model for GCaMP6f fluorescence signal.
filter_type: Bandpass
center_wavelength_in_nm: 445.0
bandwidth_in_nm: 90.0
" + ], + "text/plain": [ + "excitation_filter abc.BandOpticalFilter at 0x2222061498960\n", + "Fields:\n", + " description: excitation filter for GCaMP6f fluorescence signal.\n", + " model: excitation_filter_model abc.BandOpticalFilterModel at 0x2222061489680\n", + "Fields:\n", + " bandwidth_in_nm: 90.0\n", + " center_wavelength_in_nm: 445.0\n", + " description: excitation filter model for GCaMP6f fluorescence signal.\n", + " filter_type: Bandpass\n", + " manufacturer: \n", + " model_number: \n" + ] + }, + "execution_count": 195, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nwbfile.devices[\"excitation_filter\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + "

emission_filter (BandOpticalFilter)

description: emission filter for GCaMP6f fluorescence signal.
model
manufacturer:
model_number:
description: emission filter model for GCaMP6f fluorescence signal.
filter_type: Bandpass
center_wavelength_in_nm: 520.0
bandwidth_in_nm: 40.0
" + ], + "text/plain": [ + "emission_filter abc.BandOpticalFilter at 0x2222092840512\n", + "Fields:\n", + " description: emission filter for GCaMP6f fluorescence signal.\n", + " model: emission_filter_model abc.BandOpticalFilterModel at 0x2222092840176\n", + "Fields:\n", + " bandwidth_in_nm: 40.0\n", + " center_wavelength_in_nm: 520.0\n", + " description: emission filter model for GCaMP6f fluorescence signal.\n", + " filter_type: Bandpass\n", + " manufacturer: \n", + " model_number: \n" + ] + }, + "execution_count": 196, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nwbfile.devices[\"emission_filter\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + "

dichroic_mirror (DichroicMirror)

description: dichroic mirror for GCaMP6f fluorescence signal.
serial_number:
model
manufacturer:
model_number:
description: dichroic mirror model for GCaMP6f fluorescence signal.
" + ], + "text/plain": [ + "dichroic_mirror abc.DichroicMirror at 0x2222092839840\n", + "Fields:\n", + " description: dichroic mirror for GCaMP6f fluorescence signal.\n", + " model: dichroic_mirror_model abc.DichroicMirrorModel at 0x2222092839504\n", + "Fields:\n", + " description: dichroic mirror model for GCaMP6f fluorescence signal.\n", + " manufacturer: \n", + " model_number: \n", + "\n", + " serial_number: " + ] + }, + "execution_count": 197, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nwbfile.devices[\"dichroic_mirror\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + "

photodetector (Photodetector)

description:
serial_number:
model
manufacturer:
model_number:
description:
detector_type: Silicon photodiode
wavelength_range_in_nm
NumPy array
Data typefloat64
Shape(2,)
Array size16.00 bytes

[500. 540.]
gain: 0.38
gain_unit: A/W
" + ], + "text/plain": [ + "photodetector abc.Photodetector at 0x2222092842864\n", + "Fields:\n", + " description: \n", + " model: photodetector_model abc.PhotodetectorModel at 0x2222092842528\n", + "Fields:\n", + " description: \n", + " detector_type: Silicon photodiode\n", + " gain: 0.38\n", + " gain_unit: A/W\n", + " manufacturer: \n", + " model_number: \n", + " wavelength_range_in_nm: [500. 540.]\n", + "\n", + " serial_number: " + ] + }, + "execution_count": 198, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nwbfile.devices[\"photodetector\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 199, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + "

optical_fiber_SNr (OpticalFiber)

description: Chronically implanted optic fiber.
serial_number:
model
manufacturer: RWD
model_number:
description: Chronically implantable optic fiber.
numerical_aperture: 0.39
core_diameter_in_um: 400.0
active_length_in_mm: 6.0
ferrule_name: black ceramic ferrule
ferrule_diameter_in_mm: 1.25
fiber_insertion
insertion_position_ap_in_mm: -3.3
insertion_position_ml_in_mm: -1.4
insertion_position_dv_in_mm: -4.4
position_reference: bregma
hemisphere: left
" + ], + "text/plain": [ + "optical_fiber_SNr abc.OpticalFiber at 0x2222092842192\n", + "Fields:\n", + " description: Chronically implanted optic fiber.\n", + " fiber_insertion: fiber_insertion abc.FiberInsertion at 0x2222092841856\n", + "Fields:\n", + " hemisphere: left\n", + " insertion_position_ap_in_mm: -3.3\n", + " insertion_position_dv_in_mm: -4.4\n", + " insertion_position_ml_in_mm: -1.4\n", + " position_reference: bregma\n", + "\n", + " model: optical_fiber_model_1 abc.OpticalFiberModel at 0x2222092841520\n", + "Fields:\n", + " active_length_in_mm: 6.0\n", + " core_diameter_in_um: 400.0\n", + " description: Chronically implantable optic fiber.\n", + " ferrule_diameter_in_mm: 1.25\n", + " ferrule_name: black ceramic ferrule\n", + " manufacturer: RWD\n", + " model_number: \n", + " numerical_aperture: 0.39\n", + "\n", + " serial_number: " + ] + }, + "execution_count": 199, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "for key in nwbfile.devices.keys():\n", + " if \"optical_fiber\" in key:\n", + " optical_fiber = nwbfile.devices[key]\n", + "optical_fiber" + ] + }, + { + "cell_type": "code", + "execution_count": 200, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== FLUORESCENT CALCIUM SENSOR ===\n", + "Fluorescent calcium sensor used in this experiment:\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + "

fiber_photometry_indicators (FiberPhotometryIndicators)

indicators
GCaMP6f_SNr
label: GCaMP6f
description: GCaMP6f calcium sensor in SNr GABAergic neurons
manufacturer: Addgene
viral_vector_injection
description: Viral injection of GCaMP6f in SNr.
location: SNr
hemisphere: left
reference: bregma at the cortical surface
ap_in_mm: -3.3
ml_in_mm: -1.3
dv_in_mm: -4.6
volume_in_uL: 0.3
viral_vector
construct_name: AAV1-Ef1a-fDIO-GCaMP6f
description: AAV1 viral vector expressing GCaMP6f calcium sensor under EF1a promoter for fiber photometry recording in SNr
manufacturer: Addgene (catalog number 1283125)
titer_in_vg_per_ml: 4000000000000.0
" + ], + "text/plain": [ + "fiber_photometry_indicators abc.FiberPhotometryIndicators at 0x2222092835472\n", + "Fields:\n", + " indicators: {\n", + " GCaMP6f_SNr \n", + " }" + ] + }, + "execution_count": 200, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(\"=== FLUORESCENT CALCIUM SENSOR ===\")\n", + "print(\"Fluorescent calcium sensor used in this experiment:\")\n", + "nwbfile.lab_meta_data[\"fiber_photometry\"].fiber_photometry_indicators" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Raw (modulated) FiberPhotometry Series" + ] + }, + { + "cell_type": "code", + "execution_count": 201, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== ACQUISITION MODULE ===\n", + "\n", + "BehavioralVideo - Video recording of the mouse's behavior. Recorded using a camera mounted on the ceiling above the chambers.\n", + "\n", + "raw_signal_SNr - The raw modulated fluorescence signal from GCaMP6f calcium indicator in SNr GABAergic neurons, excited at 465nm and 405nm, respectively.\n" + ] + } + ], + "source": [ + "print(\"=== ACQUISITION MODULE ===\\n\")\n", + "for name, acq in nwbfile.acquisition.items():\n", + " print(f\"{name} - {acq.description}\")\n", + " if \"raw_signal\" in name:\n", + " raw_signal_series_name = name" + ] + }, + { + "cell_type": "code", + "execution_count": 202, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== RAW FIBER PHOTOMETRY SIGNAL ===\n", + "Name: raw_signal_SNr\n", + "Description: The raw modulated fluorescence signal from GCaMP6f calcium indicator in SNr GABAergic neurons, excited at 465nm and 405nm, respectively.\n", + "Data shape: (1580544,)\n", + "Sampling rate: 6103.515625 Hz\n", + "Duration: 258.96 seconds\n" + ] + } + ], + "source": [ + "# Access raw fiber photometry data\n", + "raw_signal = nwbfile.acquisition[raw_signal_series_name]\n", + "\n", + "print(\"=== RAW FIBER PHOTOMETRY SIGNAL ===\")\n", + "print(f\"Name: {raw_signal.name}\")\n", + "print(f\"Description: {raw_signal.description}\")\n", + "print(f\"Data shape: {raw_signal.data.shape}\")\n", + "print(f\"Sampling rate: {raw_signal.rate} Hz\")\n", + "print(f\"Duration: {raw_signal.data.shape[0] / raw_signal.rate:.2f} seconds\")" + ] + }, + { + "cell_type": "code", + "execution_count": 203, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + "

raw_signal_SNr (FiberPhotometryResponseSeries)

starting_time: 0.0
rate: 6103.515625
resolution: -1.0
comments: no comments
description: The raw modulated fluorescence signal from GCaMP6f calcium indicator in SNr GABAergic neurons, excited at 465nm and 405nm, respectively.
conversion: 1.0
offset: 0.0
unit: a.u.
data
HDF5 dataset
Data typefloat32
Shape(1580544,)
Array size6.03 MiB
Chunk shape(1580544,)
Compressiongzip
Compression opts4
Compression ratio2.015033606331657
starting_time_unit: seconds
fiber_photometry_table_region
description: The region of the FiberPhotometryTable corresponding to the raw signal.
table
description: For monitoring calcium dynamics in SNr GABAergic neurons, a fiber was implanted in SNr.
table\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
locationexcitation_wavelength_in_nmemission_wavelength_in_nmindicatoroptical_fiberexcitation_sourcephotodetectordichroic_mirroremission_filterexcitation_filter
id
0SNr465.0515.0GCaMP6f_SNr abc.Indicator at 0x2222092835136\\nFields:\\n description: GCaMP6f calcium sensor in SNr GABAergic neurons\\n label: GCaMP6f\\n manufacturer: Addgene\\n viral_vector_injection: viral_vector_injection_SNr abc.ViralVectorInjection at 0x2222092834800\\nFields:\\n ap_in_mm: -3.3\\n description: Viral injection of GCaMP6f in SNr.\\n dv_in_mm: -4.6\\n hemisphere: left\\n location: SNr\\n ml_in_mm: -1.3\\n reference: bregma at the cortical surface\\n viral_vector: viral_vector_gcamp6f abc.ViralVector at 0x2222092834128\\nFields:\\n construct_name: AAV1-Ef1a-fDIO-GCaMP6f\\n description: AAV1 viral vector expressing GCaMP6f calcium sensor under EF1a promoter for fiber photometry recording in SNr\\n manufacturer: Addgene (catalog number 1283125)\\n titer_in_vg_per_ml: 4000000000000.0\\n\\n volume_in_uL: 0.3\\n\\noptical_fiber_SNr abc.OpticalFiber at 0x2222092842192\\nFields:\\n description: Chronically implanted optic fiber.\\n fiber_insertion: fiber_insertion abc.FiberInsertion at 0x2222092841856\\nFields:\\n hemisphere: left\\n insertion_position_ap_in_mm: -3.3\\n insertion_position_dv_in_mm: -4.4\\n insertion_position_ml_in_mm: -1.4\\n position_reference: bregma\\n\\n model: optical_fiber_model_1 abc.OpticalFiberModel at 0x2222092841520\\nFields:\\n active_length_in_mm: 6.0\\n core_diameter_in_um: 400.0\\n description: Chronically implantable optic fiber.\\n ferrule_diameter_in_mm: 1.25\\n ferrule_name: black ceramic ferrule\\n manufacturer: RWD\\n model_number: <model of the optical fiber>\\n numerical_aperture: 0.39\\n\\n serial_number: <serial number of the optical fiber>\\nexcitation_source_calcium_signal abc.ExcitationSource at 0x2222092841184\\nFields:\\n description: excitation source for the sensor's fluorescence signal (465nm) modulated at 210 Hz.\\n model: excitation_source_model_calcium abc.ExcitationSourceModel at 0x2222092840848\\nFields:\\n description: excitation source for GCaMP6f sensor.\\n excitation_mode: one-photon\\n manufacturer: Tucker-Davis Technologies\\n model_number: RZ5P or RZ10x\\n source_type: LED\\n wavelength_range_in_nm: [460. 490.]\\n\\nphotodetector abc.Photodetector at 0x2222092842864\\nFields:\\n description: <description of the photodetector>\\n model: photodetector_model abc.PhotodetectorModel at 0x2222092842528\\nFields:\\n description: <description of the photodetector model>\\n detector_type: Silicon photodiode\\n gain: 0.38\\n gain_unit: A/W\\n manufacturer: <manufacturer of the photodetector>\\n model_number: <model of the photodetector>\\n wavelength_range_in_nm: [500. 540.]\\n\\n serial_number: <serial number of the photodetector>\\ndichroic_mirror abc.DichroicMirror at 0x2222092839840\\nFields:\\n description: dichroic mirror for GCaMP6f fluorescence signal.\\n model: dichroic_mirror_model abc.DichroicMirrorModel at 0x2222092839504\\nFields:\\n description: dichroic mirror model for GCaMP6f fluorescence signal.\\n manufacturer: <manufacturer of the dichroic mirror>\\n model_number: <model of the dichroic mirror>\\n\\n serial_number: <serial number of the dichroic mirror>\\nemission_filter abc.BandOpticalFilter at 0x2222092840512\\nFields:\\n description: emission filter for GCaMP6f fluorescence signal.\\n model: emission_filter_model abc.BandOpticalFilterModel at 0x2222092840176\\nFields:\\n bandwidth_in_nm: 40.0\\n center_wavelength_in_nm: 520.0\\n description: emission filter model for GCaMP6f fluorescence signal.\\n filter_type: Bandpass\\n manufacturer: <manufacturer of the emission filter>\\n model_number: <model of the emission filter>\\n\\nexcitation_filter abc.BandOpticalFilter at 0x2222061498960\\nFields:\\n description: excitation filter for GCaMP6f fluorescence signal.\\n model: excitation_filter_model abc.BandOpticalFilterModel at 0x2222061489680\\nFields:\\n bandwidth_in_nm: 90.0\\n center_wavelength_in_nm: 445.0\\n description: excitation filter model for GCaMP6f fluorescence signal.\\n filter_type: Bandpass\\n manufacturer: <manufacturer of the excitation filter>\\n model_number: <model of the excitation filter>\\n\\n
1SNr405.0515.0GCaMP6f_SNr abc.Indicator at 0x2222092835136\\nFields:\\n description: GCaMP6f calcium sensor in SNr GABAergic neurons\\n label: GCaMP6f\\n manufacturer: Addgene\\n viral_vector_injection: viral_vector_injection_SNr abc.ViralVectorInjection at 0x2222092834800\\nFields:\\n ap_in_mm: -3.3\\n description: Viral injection of GCaMP6f in SNr.\\n dv_in_mm: -4.6\\n hemisphere: left\\n location: SNr\\n ml_in_mm: -1.3\\n reference: bregma at the cortical surface\\n viral_vector: viral_vector_gcamp6f abc.ViralVector at 0x2222092834128\\nFields:\\n construct_name: AAV1-Ef1a-fDIO-GCaMP6f\\n description: AAV1 viral vector expressing GCaMP6f calcium sensor under EF1a promoter for fiber photometry recording in SNr\\n manufacturer: Addgene (catalog number 1283125)\\n titer_in_vg_per_ml: 4000000000000.0\\n\\n volume_in_uL: 0.3\\n\\noptical_fiber_SNr abc.OpticalFiber at 0x2222092842192\\nFields:\\n description: Chronically implanted optic fiber.\\n fiber_insertion: fiber_insertion abc.FiberInsertion at 0x2222092841856\\nFields:\\n hemisphere: left\\n insertion_position_ap_in_mm: -3.3\\n insertion_position_dv_in_mm: -4.4\\n insertion_position_ml_in_mm: -1.4\\n position_reference: bregma\\n\\n model: optical_fiber_model_1 abc.OpticalFiberModel at 0x2222092841520\\nFields:\\n active_length_in_mm: 6.0\\n core_diameter_in_um: 400.0\\n description: Chronically implantable optic fiber.\\n ferrule_diameter_in_mm: 1.25\\n ferrule_name: black ceramic ferrule\\n manufacturer: RWD\\n model_number: <model of the optical fiber>\\n numerical_aperture: 0.39\\n\\n serial_number: <serial number of the optical fiber>\\nexcitation_source_isosbestic_control abc.ExcitationSource at 0x2222061489360\\nFields:\\n description: excitation source for the sensor's isosbestic control (405nm) modulated at 330 Hz.\\n model: excitation_source_model_isosbestic abc.ExcitationSourceModel at 0x2222061487440\\nFields:\\n description: excitation source for GCaMP6f sensor's isosbestic control.\\n excitation_mode: one-photon\\n manufacturer: Tucker-Davis Technologies\\n model_number: RZ5P or RZ10x\\n source_type: LED\\n wavelength_range_in_nm: [400. 410.]\\n\\nphotodetector abc.Photodetector at 0x2222092842864\\nFields:\\n description: <description of the photodetector>\\n model: photodetector_model abc.PhotodetectorModel at 0x2222092842528\\nFields:\\n description: <description of the photodetector model>\\n detector_type: Silicon photodiode\\n gain: 0.38\\n gain_unit: A/W\\n manufacturer: <manufacturer of the photodetector>\\n model_number: <model of the photodetector>\\n wavelength_range_in_nm: [500. 540.]\\n\\n serial_number: <serial number of the photodetector>\\ndichroic_mirror abc.DichroicMirror at 0x2222092839840\\nFields:\\n description: dichroic mirror for GCaMP6f fluorescence signal.\\n model: dichroic_mirror_model abc.DichroicMirrorModel at 0x2222092839504\\nFields:\\n description: dichroic mirror model for GCaMP6f fluorescence signal.\\n manufacturer: <manufacturer of the dichroic mirror>\\n model_number: <model of the dichroic mirror>\\n\\n serial_number: <serial number of the dichroic mirror>\\nemission_filter abc.BandOpticalFilter at 0x2222092840512\\nFields:\\n description: emission filter for GCaMP6f fluorescence signal.\\n model: emission_filter_model abc.BandOpticalFilterModel at 0x2222092840176\\nFields:\\n bandwidth_in_nm: 40.0\\n center_wavelength_in_nm: 520.0\\n description: emission filter model for GCaMP6f fluorescence signal.\\n filter_type: Bandpass\\n manufacturer: <manufacturer of the emission filter>\\n model_number: <model of the emission filter>\\n\\nexcitation_filter abc.BandOpticalFilter at 0x2222061498960\\nFields:\\n description: excitation filter for GCaMP6f fluorescence signal.\\n model: excitation_filter_model abc.BandOpticalFilterModel at 0x2222061489680\\nFields:\\n bandwidth_in_nm: 90.0\\n center_wavelength_in_nm: 445.0\\n description: excitation filter model for GCaMP6f fluorescence signal.\\n filter_type: Bandpass\\n manufacturer: <manufacturer of the excitation filter>\\n model_number: <model of the excitation filter>\\n\\n
" + ], + "text/plain": [ + "raw_signal_SNr abc.FiberPhotometryResponseSeries at 0x2222092833456\n", + "Fields:\n", + " comments: no comments\n", + " conversion: 1.0\n", + " data: \n", + " description: The raw modulated fluorescence signal from GCaMP6f calcium indicator in SNr GABAergic neurons, excited at 465nm and 405nm, respectively.\n", + " fiber_photometry_table_region: fiber_photometry_table_region \n", + " offset: 0.0\n", + " rate: 6103.515625\n", + " resolution: -1.0\n", + " starting_time: 0.0\n", + " starting_time_unit: seconds\n", + " unit: a.u." + ] + }, + "execution_count": 203, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nwbfile.acquisition[raw_signal_series_name]" + ] + }, + { + "cell_type": "code", + "execution_count": 204, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot raw acquisition signal (simple view)\n", + "data = raw_signal.data[:]\n", + "timestamps = raw_signal.get_timestamps()\n", + "\n", + "# Show first 30 seconds\n", + "end_idx = int(30 * raw_signal.rate)\n", + "time_subset = timestamps[:end_idx]\n", + "data_subset = data[:end_idx]\n", + "\n", + "plt.figure(figsize=(15, 2))\n", + "plt.plot(time_subset, data_subset, linewidth=0.5)\n", + "plt.xlabel('Time (s)')\n", + "plt.ylabel('Fluorescence (a.u.)')\n", + "plt.title('Raw Modulated Fiber Photometry Signal (first 30 seconds)')\n", + "plt.grid(True, alpha=0.3)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Processed FiberPhotometry Series" + ] + }, + { + "cell_type": "code", + "execution_count": 205, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== OPHYS PROCESSING MODULE ===\n", + "Processed fiber photometry signals \n", + "\n", + "--------------------\n", + "Name: deltaF_over_F_SNr\n", + "Description: The deltaF/F signal from GCaMP6f calcium indicator in SNr GABAergic neurons, excited at 465nm and calculated as (Gc - fitted_af) / fitted_af.\n", + "Sampling rate: 100.0 Hz\n", + "Metadata:\n", + " Location: SNr\n", + " Excitation wavelength: 465.0 nm\n", + "--------------------\n", + "Name: demodulated_calcium_signal_SNr\n", + "Description: The demodulated fluorescence signal from GCaMP6f calcium indicator in SNr GABAergic neurons, excited at 465nm and demodulated at 330Hz.\n", + "Sampling rate: 6103.5156 Hz\n", + "Metadata:\n", + " Location: SNr\n", + " Excitation wavelength: 465.0 nm\n", + "--------------------\n", + "Name: demodulated_isosbestic_control_SNr\n", + "Description: The demodulated fluorescence signal from GCaMP6f for isosbestic control in SNr GABAergic neurons, excited at 405nm and demodulated at 210Hz.\n", + "Sampling rate: 6103.5156 Hz\n", + "Metadata:\n", + " Location: SNr\n", + " Excitation wavelength: 405.0 nm\n", + "--------------------\n", + "Name: downsampled_calcium_signal_SNr\n", + "Description: The downsampled fluorescence signal from GCaMP6f calcium indicator in SNr GABAergic neurons, excited at 465nm and downsampled at 100Hz.\n", + "Sampling rate: 100.0 Hz\n", + "Metadata:\n", + " Location: SNr\n", + " Excitation wavelength: 465.0 nm\n", + "--------------------\n", + "Name: downsampled_isosbestic_control_SNr\n", + "Description: The downsampled fluorescence signal from GCaMP6f for isosbestic control in SNr GABAergic neurons, excited at 405nm and downsampled at 100Hz.\n", + "Sampling rate: 100.0 Hz\n", + "Metadata:\n", + " Location: SNr\n", + " Excitation wavelength: 405.0 nm\n" + ] + } + ], + "source": [ + "print(\"=== OPHYS PROCESSING MODULE ===\")\n", + "processed_fp = nwbfile.processing[\"ophys\"]\n", + "print(processed_fp.description, \"\\n\")\n", + "for name, fp in processed_fp.items():\n", + " print(\"-\"*20)\n", + " print(f\"Name: {fp.name}\")\n", + " print(f\"Description: {fp.description}\")\n", + " print(f\"Sampling rate: {fp.rate} Hz\")\n", + " print(f\"Metadata:\")\n", + " fp_metadata = fp.fiber_photometry_table_region[:]\n", + " print(f\" Location: {fp_metadata['location'].values[0]}\")\n", + " print(f\" Excitation wavelength: {fp_metadata['excitation_wavelength_in_nm'].values[0]} nm\")\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Behavioral Video\n", + "NB: not all sessions have behavioral video data" + ] + }, + { + "cell_type": "code", + "execution_count": 206, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== BEHAVIORAL VIDEO ===\n", + "Video file path: sub-C4561_ses-varying-durations_image\\5edc8957-c811-4c13-bc52-491745c3f527_external_file_0.mp4\n", + "Description: Video recording of the mouse's behavior. Recorded using a camera mounted on the ceiling above the chambers.\n", + "\n", + "Video start: 39.0 seconds\n", + "Video end: 249.001 seconds\n" + ] + } + ], + "source": [ + "# Access behavioral video information\n", + "if \"BehavioralVideo\" in nwbfile.acquisition:\n", + " video = nwbfile.acquisition[\"BehavioralVideo\"]\n", + "\n", + " print(\"=== BEHAVIORAL VIDEO ===\")\n", + " print(f\"Video file path: {video.external_file[0]}\")\n", + " print(f\"Description: {video.description}\")\n", + " print(f\"Video start: {video.timestamps[0]} seconds\")\n", + " print(f\"Video end: {video.timestamps[-1]} seconds\")\n", + "else:\n", + " print(\"No behavioral video found in this session.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 207, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + "

BehavioralCamera (Device)

description: Camera mounted on ceiling above chambers for behavioral recording
" + ], + "text/plain": [ + "BehavioralCamera pynwb.device.Device at 0x2222062094096\n", + "Fields:\n", + " description: Camera mounted on ceiling above chambers for behavioral recording" + ] + }, + "execution_count": 207, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "video.device # Display video device information" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "# 4. Optogenetic Stimulus and Metadata \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optogenetic Metadata" + ] + }, + { + "cell_type": "code", + "execution_count": 208, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== OPTOGENETIC STIMULUS METADATA ===\n", + "All optogenetic stimulus metadata are stored in the optogenetic_experiment_metadata module in lab_meta_data:\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + "

optogenetic_experiment_metadata (OptogeneticExperimentMetadata)

stimulation_software: TDT Synapse
optogenetic_sites_table
description: Information about each optogenetic site, including the excitation source, and targeted effector.
table\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
effectorexcitation_source
id
0chrimsonR_PPN abc.Effector at 0x2222092838496\\nFields:\\n description: ChrimsonR opsin used for optogenetic stimulation injected into the PPN.\\n label: ChrimsonR-tdTomato\\n manufacturer: Addgene\\n viral_vector_injection: viral_vector_injection_PPN abc.ViralVectorInjection at 0x2222061494480\\nFields:\\n ap_in_mm: -4.48\\n description: Viral injection into the Pedunculopontine Nucleus (PPN).\\n dv_in_mm: -3.75\\n hemisphere: left\\n location: PPN\\n ml_in_mm: -1.1\\n reference: bregma at the cortical surface\\n viral_vector: viral_vector_ogen abc.ViralVector at 0x2222061495440\\nFields:\\n construct_name: pAAV-Syn-FLEX-rc[ChrimsonR-tdTomato] (AAV5)\\n description: pAAV-Syn-FLEX-rc[ChrimsonR-tdTomato] (AAV5) viral vector was used for optogenetic experiments.\\n manufacturer: Addgene (catalog number 62723)\\n titer_in_vg_per_ml: 4000000000000.0\\n\\n volume_in_uL: 0.15\\n\\noptogenetic_excitation_source abc.ExcitationSource at 0x2222061496400\\nFields:\\n description: Red LED device used for optogenetic stimulation.\\n model: optogenetic_excitation_source_model abc.ExcitationSourceModel at 0x2222061494160\\nFields:\\n description: Red LED device used for optogenetic stimulation.\\n excitation_mode: one-photon\\n manufacturer: Shanghai laser\\n model_number: <model of the LED device>\\n source_type: LED\\n wavelength_range_in_nm: [580. 680.]\\n\\n
optogenetic_viruses
viral_vectors
viral_vector_ogen
construct_name: pAAV-Syn-FLEX-rc[ChrimsonR-tdTomato] (AAV5)
description: pAAV-Syn-FLEX-rc[ChrimsonR-tdTomato] (AAV5) viral vector was used for optogenetic experiments.
manufacturer: Addgene (catalog number 62723)
titer_in_vg_per_ml: 4000000000000.0
optogenetic_virus_injections
viral_vector_injections
viral_vector_injection_PPN
description: Viral injection into the Pedunculopontine Nucleus (PPN).
location: PPN
hemisphere: left
reference: bregma at the cortical surface
ap_in_mm: -4.48
ml_in_mm: -1.1
dv_in_mm: -3.75
volume_in_uL: 0.15
viral_vector
construct_name: pAAV-Syn-FLEX-rc[ChrimsonR-tdTomato] (AAV5)
description: pAAV-Syn-FLEX-rc[ChrimsonR-tdTomato] (AAV5) viral vector was used for optogenetic experiments.
manufacturer: Addgene (catalog number 62723)
titer_in_vg_per_ml: 4000000000000.0
optogenetic_effectors
effectors
chrimsonR_PPN
label: ChrimsonR-tdTomato
description: ChrimsonR opsin used for optogenetic stimulation injected into the PPN.
manufacturer: Addgene
viral_vector_injection
description: Viral injection into the Pedunculopontine Nucleus (PPN).
location: PPN
hemisphere: left
reference: bregma at the cortical surface
ap_in_mm: -4.48
ml_in_mm: -1.1
dv_in_mm: -3.75
volume_in_uL: 0.15
viral_vector
construct_name: pAAV-Syn-FLEX-rc[ChrimsonR-tdTomato] (AAV5)
description: pAAV-Syn-FLEX-rc[ChrimsonR-tdTomato] (AAV5) viral vector was used for optogenetic experiments.
manufacturer: Addgene (catalog number 62723)
titer_in_vg_per_ml: 4000000000000.0
" + ], + "text/plain": [ + "optogenetic_experiment_metadata abc.OptogeneticExperimentMetadata at 0x2222092839168\n", + "Fields:\n", + " optogenetic_effectors: optogenetic_effectors abc.OptogeneticEffectors at 0x2222092838832\n", + "Fields:\n", + " effectors: {\n", + " chrimsonR_PPN \n", + " }\n", + "\n", + " optogenetic_sites_table: optogenetic_sites_table \n", + " optogenetic_virus_injections: optogenetic_virus_injections abc.OptogeneticVirusInjections at 0x2222092838160\n", + "Fields:\n", + " viral_vector_injections: {\n", + " viral_vector_injection_PPN \n", + " }\n", + "\n", + " optogenetic_viruses: optogenetic_viruses abc.OptogeneticViruses at 0x2222092837824\n", + "Fields:\n", + " viral_vectors: {\n", + " viral_vector_ogen \n", + " }\n", + "\n", + " stimulation_software: TDT Synapse" + ] + }, + "execution_count": 208, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(\"=== OPTOGENETIC STIMULUS METADATA ===\")\n", + "print(\"All optogenetic stimulus metadata are stored in the optogenetic_experiment_metadata module in lab_meta_data:\")\n", + "nwbfile.lab_meta_data[\"optogenetic_experiment_metadata\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 209, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== OPTOGENETIC SETUP INFORMATION ===\n", + "Components used in optogenetic setup:\n", + "optogenetic_excitation_source\n" + ] + } + ], + "source": [ + "print(\"=== OPTOGENETIC SETUP INFORMATION ===\")\n", + "print(\"Components used in optogenetic setup:\")\n", + "for key in nwbfile.devices.keys():\n", + " if \"optogenetic\" in key:\n", + " print(key)" + ] + }, + { + "cell_type": "code", + "execution_count": 210, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + "

optogenetic_excitation_source (ExcitationSource)

description: Red LED device used for optogenetic stimulation.
model
manufacturer: Shanghai laser
model_number:
description: Red LED device used for optogenetic stimulation.
source_type: LED
excitation_mode: one-photon
wavelength_range_in_nm
NumPy array
Data typefloat64
Shape(2,)
Array size16.00 bytes

[580. 680.]
" + ], + "text/plain": [ + "optogenetic_excitation_source abc.ExcitationSource at 0x2222061496400\n", + "Fields:\n", + " description: Red LED device used for optogenetic stimulation.\n", + " model: optogenetic_excitation_source_model abc.ExcitationSourceModel at 0x2222061494160\n", + "Fields:\n", + " description: Red LED device used for optogenetic stimulation.\n", + " excitation_mode: one-photon\n", + " manufacturer: Shanghai laser\n", + " model_number: \n", + " source_type: LED\n", + " wavelength_range_in_nm: [580. 680.]\n" + ] + }, + "execution_count": 210, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nwbfile.devices[\"optogenetic_excitation_source\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 211, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== OPSIN ===\n", + "Opsin used in this experiment:\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + "

optogenetic_effectors (OptogeneticEffectors)

effectors
chrimsonR_PPN
label: ChrimsonR-tdTomato
description: ChrimsonR opsin used for optogenetic stimulation injected into the PPN.
manufacturer: Addgene
viral_vector_injection
description: Viral injection into the Pedunculopontine Nucleus (PPN).
location: PPN
hemisphere: left
reference: bregma at the cortical surface
ap_in_mm: -4.48
ml_in_mm: -1.1
dv_in_mm: -3.75
volume_in_uL: 0.15
viral_vector
construct_name: pAAV-Syn-FLEX-rc[ChrimsonR-tdTomato] (AAV5)
description: pAAV-Syn-FLEX-rc[ChrimsonR-tdTomato] (AAV5) viral vector was used for optogenetic experiments.
manufacturer: Addgene (catalog number 62723)
titer_in_vg_per_ml: 4000000000000.0
" + ], + "text/plain": [ + "optogenetic_effectors abc.OptogeneticEffectors at 0x2222092838832\n", + "Fields:\n", + " effectors: {\n", + " chrimsonR_PPN \n", + " }" + ] + }, + "execution_count": 211, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(\"=== OPSIN ===\")\n", + "print(\"Opsin used in this experiment:\")\n", + "nwbfile.lab_meta_data[\"optogenetic_experiment_metadata\"].optogenetic_effectors" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optogenetic Stimulus Data" + ] + }, + { + "cell_type": "code", + "execution_count": 212, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== OPTOGENETIC STIMULATION INTERVALS ===\n", + "Total number of stimuli: 15\n", + "\n", + "First 5 intervals:\n" + ] + }, + { + "data": { + "text/html": [ + "
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start_timestop_timestimulation_onpulse_length_in_msperiod_in_msnumber_pulses_per_pulse_trainnumber_trainsintertrain_interval_in_mspower_in_mWwavelength_in_nmoptogenetic_sitesstimulus_frequency
id
064.58671165.585480True1.025.03910.05.0635.0e...40.0
1100.585964101.584896True1.025.03910.05.0635.0e...40.0
2136.635023137.635922True1.025.04010.05.0635.0e...40.0
3172.720456173.720044True1.025.03910.05.0635.0e...40.0
4208.785244209.785487True1.025.04010.05.0635.0e...40.0
\n", + "
" + ], + "text/plain": [ + " start_time stop_time stimulation_on pulse_length_in_ms period_in_ms \\\n", + "id \n", + "0 64.586711 65.585480 True 1.0 25.0 \n", + "1 100.585964 101.584896 True 1.0 25.0 \n", + "2 136.635023 137.635922 True 1.0 25.0 \n", + "3 172.720456 173.720044 True 1.0 25.0 \n", + "4 208.785244 209.785487 True 1.0 25.0 \n", + "\n", + " number_pulses_per_pulse_train number_trains intertrain_interval_in_ms \\\n", + "id \n", + "0 39 1 0.0 \n", + "1 39 1 0.0 \n", + "2 40 1 0.0 \n", + "3 39 1 0.0 \n", + "4 40 1 0.0 \n", + "\n", + " power_in_mW wavelength_in_nm \\\n", + "id \n", + "0 5.0 635.0 \n", + "1 5.0 635.0 \n", + "2 5.0 635.0 \n", + "3 5.0 635.0 \n", + "4 5.0 635.0 \n", + "\n", + " optogenetic_sites stimulus_frequency \n", + "id \n", + "0 e... 40.0 \n", + "1 e... 40.0 \n", + "2 e... 40.0 \n", + "3 e... 40.0 \n", + "4 e... 40.0 " + ] + }, + "execution_count": 212, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Access optogenetic stimulation data\n", + "ogen_df = nwbfile.intervals[\"optogenetic_epochs_table\"].to_dataframe()\n", + "\n", + "print(\"=== OPTOGENETIC STIMULATION INTERVALS ===\")\n", + "print(f\"Total number of stimuli: {len(ogen_df)}\")\n", + "print(\"\\nFirst 5 intervals:\")\n", + "ogen_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "# 5. Session Type 1: Varying Duration Optogenetic Stimulation \n", + "\n", + "This session contains 40 Hz optogenetic stimulation at three different durations: 250ms, 1s, and 4s." + ] + }, + { + "cell_type": "code", + "execution_count": 213, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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uuUb9zlxW/DFo0CB1XJcvX+5+b926da68vDzXkUceWemc9e/f32v9WB/K9I4dO9zv4TiYr1nrMg4//HBXWVlZyOfW33VsBsvD7x577DH1GtuWnJzsGjp0qKtx48bu71155ZXqGtL7Y1fWL7/88kr1g/m79evXd23bts39/vvvv6/e//DDD/1uY506dVxdunTx+x1sH5b1xx9/eB2/7t27e9UbkyZNUu9j3YHOwdVXX62+++2337rfy8/Pd+2zzz6uNm3auI//Aw88oL733nvvub+3Z88e13777afe//rrr9V7OHYdOnRQdbe5XOC6wDKPPfbYSnXtyJEjvbYJ1xuOo2bVqlWqTN1zzz1e3/vzzz9dqampXu+feOKJtuXB7lyiPKNc4zoP9noCGzZscDVs2FAtD/t/ySWXuF555RWvMl+V7UFZxnsTJkxwv4e6MCsrS9X3r732mvv9xYsXV7qn2d27zOXEXM9Y76O4/oqLi71+h3Xj+jCfn82bN1dar6/1L1iwQL2+8MILvb43duxY9f5XX30V8n3KjmXLlqnfPvLII7afn3766a7evXu7X+O7uI7N4DXKmB0416jf/aGPsb+Hv/vB0qVL1f0D14e5LgwW1Dk4VmeffbbX+zfeeKNa95IlS3y2s+bOnau+8+KLLwasm3UbB+syl3ecK1yLgcojXqNuxznT/P7775XO38knn+zKzs52rV271usYYR3BdO1xLeXk5Pj8XLcLcL8yb5tdubbei/0dm2CPL9oj5nrTjPXanDp1qvrujBkz3O+hvke7MTc3193+rMo9CPfYTp06+TxOhBASTRgqgRBCogzcOwBuimAxO2G0awVuT7gE7Ka0a5AhGG5Du6Q91Z1SZwbTxc2hFbTzEE4R837q97Hd/sDUdThu4WxDOAK4IuBuwtTYQJl+4cyBqwUuKA2mDvuKYYfwDnCWaeASwVQ+8zbCYaqTYcHdgmmjcIfhe3DBVQUkcsK67UJSBCoLiPMGx6B5yrcZOHnhpoXD1ZzEC24xuEX11FbtqIUTyW76tRkcUzisNEgch/AM+C2OCfp2KG+Io4jnKKP6gXOHcqqPFRyAcNoFM00+GLBeTI+0upwClfNQrkXsI1xIgwYN8op5B9cTXMJwSunlaeDyMq8f5QzLgeMzWHDOMPU81HMbLCjvSCClw17AYYT1IVQFHKcIpQHgBoWrtTr1xplnnukVC1Nfd4HqAzikAp0j/bndOTA7qeA8xKwFXH+BwHfggMV+a3DdY5lwi8Elpusc1E3mRDZwCOOcmFmwYIE6nigvqEP09QFHGupQnANrRnckhDKDY4bf6v2EOw+/gdvWfM3B9duhQwdb128g4NjFtsC9juvcTKDzD7cy6mxsN+onuF+xv3BKIiyAOSRHVYBLVYN6BHUwHLdmNzbew2eBylWw4HrQsTVxrBE6B059OFCrU//rUCVm4LwF1us4mPuUHSgrwC4GLcoG6k64zv0BV6Ov2KIo5/g8GBDWAi5O68OfKxnXBup1bD9mYJjrwmDBb5HYEG0H7f5EOXzttdfUOdSuUnM7Cy5gHDuEeUJZsjvP1roZwOmNGSw6FjDADAuUl2BcyXAam2dC4digXtfnGfcP3ANwHzLP9MF2wo0eDrTzHfVuVbE7NqEe32CvI9R1cMNrUN/DtYuZOJgJU9V7EL7nL9QSIYREEwq3hBASZXSm3lAaxRBT0KBHBxWNXHTaMI0M+BNuMbUVnbtIJ8Gwdu61IIhpg3bv+xIczSD2GEQJfBfTURH2AMcM00W1YGIHRDF0BqzYvWe37brBbt5GdNYxnRdCCERciOE4B5hO7O/4+wMdcyR7CRWsH9OkMc0fIomeNowYoBotDOLcm0HnG6Kj/hzTCiEcYBow9gkCKzrXdvuEfbeCDi8EXwg9eGBqIuLH4diYH4jLaU4shyQ46BxCFMNyEWLDGuMzFFDOQw2pEeq1iP3DvlqPqZ7GiTJijilqV7Z0hzGY8q+xTvUO9tyGAjqvOhQC/kPMwAMDHngNkRBinFk4qgpVPR4QZQOdI/25VeC1lluUO4jtwcTaxrH0db715/o/xBarqGmtc7QIjqnu1msE1yAEH+u1F+iYYZkQobCf1mViyr01mWMwaBGjKtcUwPHFFGiEjkB4Ckx71tOmEWKgqkAktE4Hxz0F066txx7vh3KdBQIhDSCiYRswZR3bgTq8qvU/ygwGXqxlBCIU7vHW6ziY+5Q/rII5hESIW5gWj3utPyC4YbDIDoS5CTZGO+p7tGOsD3+JzSAAon5H6AgdKqAqYOAWoq2OWYop+6gDzAO6EKBRRtFuMd/ncV+zO892YTgwCIbjidAIGjxHCCFfbZBQzjOuZ2xnKG2cUIHgGaq5IJhjE+rxDQZcJ6j7zIOYdnV0Ve5BuGbCaXAghJDqwBi3hBASZSAWwSmB2GrBgE4L3FjoEEyZMkU1eiHSwGkAMdHq0AoHvhqrcHvY4csF4+v9UFxX2Fd0hPCAUAgREDHMEB8yHASzjYhDixiScKDBNQZBCx0FuB6rcvxXrlypEv8Em2DOCtYLZyvi7sLxim1DbETEz+zWrVtIy3rggQdUPDh0aOEoRWcey0LCLXMyrEDo4wBXka/YltpZhU4VBB3E0oVbEa4vxMxFpw4x+6IFrikd69Ds0A4X4Sj/oSauqwpwlCLuMAQ7CLUQaFEH4H28Rn2F81td4baqxwPlBXEdIWz6ilWNQRQ4rewGGOIFfY0gyZqv8maN8xromGGZOFcYyLH7bqC4sZEE24U6Gw8MUuHcQMQyu2ZDoTr3mVDvadbEb6gj4XKEEx3uYawT9aQ5yWdVCFYYquq1o8VOqzCFxGaogxGj2zqIgUEQvIf9RNxRCPE4ThANzTFmIebCNVmVGO/B8NBDDymXLY5/detnxLaFmI8Yp3CA4z+OKeKtajAzCfGmcX/FDBN8H+cH37G7z/uqm+G6RWxVzCpBnYV76bRp06J2z6guum0ajBDs6/qxOzahHt9IEMrxxTXjKz4+IYREGwq3hBASA9CJgDMR4QACJatAMhg0/jHNz+wWCGYKLFxgSHiCaWmhJCyCC8EuQ31V3HzhRCdHgZPLX3IcJKqwYvdesGDaIxKeWd1iOEZVadjDqYVOi3kKdqjg3GJaLR5w3aFjCxEWnVwcA4COuXlaPzraEI2tiV86d+6sHrfccotyIvXp00dNcb777rsruQXNIIEYOvbaBQeHDjpy1uXbAfc4pi3ige1CghAkPYKzGq62UJwuOBbBDoSYwdRSdORwzAIlKMM+Yl9xTK1AhIeQb3WYB0Oojp5Qz20waEEW05YRvgKZxQHc3BhcgDCD82UOlRGOfQmlvkRdiQEbu+nGEJkgMGPfrYIByi2uXbObDPUHpk4H2m4ca1/nW3+u/2MWgNWhZa1z9BRoDN5V5TzZgWVivXC4BUokFOz50eWqKteUv2XivmKuu6PpZtPOOtTZcLSGck9D/Y/txwwQ8zZbBw9D2R+UGYhVKJ/mxEsIT4Jt1GWruqDNgGsCdYMZJLtCuwB1vRWIunjA5QqxWoum8+bN87pu8Br7EIlBL1zPY8eOVSKfrzBHoYABH8zWwX7hGKMuQUJAOJzN5xmDjriPmh3Fdm0hf0CIxEwWiM5wmaLthftcOIBwjvtjuNs4ZpBYEGUZIav8tQlxz/HXFrMS7PEN9TrCoB3Kodl1a62jqwKuGYRgIoSQeIChEgghJAYgIz2EEDiP0ImwAhcP3CZmh4DZEYBpZXAuBAIxZhGjy87t4c/BATEA60CDWIMGujXTdaSAKG23fTouoN30ZQ2m+0PkQTxJDWISmqcuhgrOgXV70PFbu3ZtlZaH/TjuuOOqFMIC0/XR2bGeL4imEPgBRCE4lTFF2bzdEJ5xXnWIBkyBx5RZMxBw0QHSy9LgmJrj0CEsAFy62A8cHzxQ3uCetRN8EGrAGndRg21FDEdsq84wjusDBNNpxnoxld+ufPor5xBaMRUXTuNHHnmk0ufoDKKTCecU9g/7in02O9Rw/cK9BRFeh14IBexnKMJAsOc2FCD6IUYrHPxmMQeCLuoidLgx1TdQeQ3lnIUCMoBDsIDb0RqLENcCXPg4Fjo7vBkMkJmz1kOIRpk3x4P0dQ4gUiFMC8q+BtOtsUzEpkaZ1XUO6gJz/G1sF1zMZiB841q9//773dORfV0jwYIBD5RNONWtZR2vzdca9jOYKckYpIBoP336dCXwWZfpDwwU6jiiZnAcsS3mujvY7QkHWjTXsZwBthMhEAJhdw/GfprLBcDATrDlXwug1viymFUDqnId2wHREAOeEFmt4iLqS+tDbxue65j0EDgxy8Q6QwSvsc/h2lZzWwMxi1Gnwp0eLiAAoy5AfYJrzSoI293ncV8IxpVtBoO5qF8wIIh2x/HHHx825ya2EfcAzLZZt26dl2gL1311uffee9X9EEKzefYCrh/ztQNQD4ZybII9vqHcR1BWESYKcYQ1qN+xXMw2QC6IqoB6Cfe+3r17V+n3hBASbui4JYSQGIBGMMQeNI7htsHUOsQThIMBjkeIgpiaCSAWQajB1Hh0ONDhhyAAISOQ2wHLhcME7g90nCHEoLOK5BaXXXaZSi5lBzp1iEOKpCCYOg+xEJ00OLqqmkQiFDClDuvE+jGdXR8XNM4hmOiYqb5EcXSY4BbBctAJQPxIOI8g4FbF5QXH35133qnWi4Y8ptajQ2Z2PAYLHDgQpuForQpwuSJ0Bjq2EI4gpqGTDQFRT/uE8ALnKsQcdBqRNAnOQYQjQMgJ7VpEaIXRo0fL0KFD1blFhwduGy3CmkH5hECF8gD3EpYFzKEN0OnDvqHDD0EU24djjjKDMofnukzD6QSBEHF6EYsTgwsQAHRcPe3uvPnmm9V+QYDANaA7dWYg6EFcxH4gnAV+i3VBSMNx9ueagTCLDhr2C446nGu4iyBY4TqEc0cfVziQ4UqFoIDrB8ceU40hciPOcFXAtuLawrIxNRXXNYQSXwR7bkMFdQOS9UC41+7Egw8+WB1vlDlMLw5mXwCOJcqKdSpyVcF0b5xflA9sEwa8ULbQYX/++eeVaIGBLrtONuoOfb3o44TzZ04k5uscwHkM1xxEGOwTxCsIfXBiYYBCO7xQL6P8IkEOpkhjajnqBzjjgK5z8H3URVjeAQccoOoTCOYQfXHdQPjHDItQ7yXYbpQJDCjAIYlrCNuIegGJ1OBc1PuJOhT3A5QVCBu4puzAwACOE443lgFxH8vHbAHzoJgV1B/Yd9TdWB/uXbi+IQLjeOjY7KFuT3VBnYN7wAUXXKDqC5RNbBOuJ6s4bQV1AuoG7BPKII4t6hWUQbMAD2cr3sM+oT5FeUG9aRcrGHUS3IcQvyBQQWDCPRrlC+fQ7BKvLrjPox7FQJ0eXMJ9VYeKsYJzjW0w7xdCBCEWOepYXNtwxOI+i1kS2M9wgmsNwiru5aiTrOF2dMidfv36qeRTwYYRwDFG+B8MvmGfMOhhPc8ov5gNg/MIYR73rarE1kXbCw5fgGMXTm6//XYlruL+iWSLED5R/6Cc+bs2zeBej/OnB5ngPMf9EoP1KHsol2ZQ5yLhINoFaFthoBRhmkIRpIM9vnBw4/pELH8IqGhvoD42h+nQoG7CPRjt5fnz56v2Ie4ViJmPQZGqxunFdqFc+WojE0JI1HERQgiJGf/8849r1KhRrjZt2rjS09NdeXl5rj59+rgeeeQRV1FRkft7H3zwgeuggw5yZWZmqu/ed999runTp6O34lq5cqX7e3379lUPM4WFha6bb77Ztc8++7jS0tJcTZo0cZ1++umu5cuXu7+D5dx2221ev/v8889dBx54oNqujh07umbMmKG+Y7114PXll1/u9R62Ce9PnjzZ6/2vv/5avf/mm2/6PS6ffvqpa+TIka799tvPlZubq7ahffv2riuuuMK1ceNGr++2bt3aNXz4cK/3fvvtN9cRRxzhysjIcLVo0cI1ceJE18MPP6zWvWHDBq/fnnjiiZXWbz2OOBdjxoxxNW3a1JWVlaXO0dy5cyt9T+/3c88953PfPvroI1dSUlKl/fAFloVl/vLLL+r1li1b1PHGscnJyXHVrl3b1atXL9cbb7xR6bfTpk1T38N5b9y4sevSSy91bd++3f35ihUr1HFu166dKlv16tVzHXXUUa4vv/zS9hyjDHTo0EEd127duqnzaQX7he+2bNnSXd6OOeYY11NPPeX+zpNPPuk68sgjXfXr11fLwvqvu+46186dO72Wddddd7maN2/uSk5O9irrdud869atrtGjR6vvo7zgvOM7OF6BKCsrcz3zzDOqzOB4YruxjhEjRqiyZObXX391DRgwQJXL7Oxsdbx++OEHv+fMWv7Nxw3lEWUQ1z4+0+XJ1zKCPbcA+4/9CIZHH31UrQ/LMdO/f3/1/qxZs7zetyvrOI64Rhs2bKjKuK4rfNUHvuoeX2A5qC9btWql9rtBgwauU045xfXtt99W+q4+frNnz3ZddNFFrrp166pzdu6556qyYsbXOQCoJ1Ff1qlTR10jPXv2VNewFVxLWAbqB+w/6ou3335bLe/HH3/0+i7K1ODBg93lH+fojDPO8DrGuq7dvHmz7X6Z632AdR1++OGqTsADZQPX4ZIlS9zfKSgocJ1zzjlqX7AMXTZ81Vt//fWX67TTTnPvO+4D48eP93uO/vjjD3UtH3zwwao+SU1NVfXm0KFD1bVjJpTtQVnGflnBuTrggAMqvW9Xt8+fP1/VlagfUIamTJliezyt9XpFRYVrwoQJapm67kMZsLu+UBd0795drcNctu3unaWlpa477rjDfW9GnTlu3Dive7+vfbHbTl+gTsZ5eOmllwJ+1+5+rkEdjjKAfUOd/eCDD6pjE4hAdZn1HOI1vm/3MNcVOM64v4QCyiaWg+vNCupP1PmoV1BXoJ5fvHhxpftNoP0BxcXFqs7B/WTPnj2VPg+2LeXrfoe6AuVQnwvcv1Dn4DoNBJZlPqa4j6FdOWTIENdbb73lKi8vr/QbvHfDDTeoY4Pv49gsW7YspGMT7PEFTz/9tKtt27aulJQUr3umXZlH+dbLxfHo3Llzpbos1HvQmWeeqepTQgiJF5LwJ/pyMSGEEBJdECsPzgw4pHwlqIgGcGpi2ircVYSQyAAnLhytiNmrY2NHGzi+rrnmGhVqA85aQmIFnMZwzsMpmwgggRqcvrjG4ASON+BoRWxwOMitsfEjBVzSCxcutI1HT4IHMzngOofbm45bQki8wBi3hBBCEg6EIzCD2IqYooepv7EUbfU0QHN4AUJI4tU5mH6MgSLEiaRoS2INEqlhEANTyBMBxFvFdYWQPPEIYtAi3ANCJkSjvoFYi9j5CB9BqgcGAxAyiKItISSeoOOWEEJIwgFxFB0YxA9G7Fc4XpDIY9asWSrpDiEksYm24xZxaxFDFXUP4jIifiTcb4j3Gkx8YEKI80HSOsSJRVxbxH+NVE4AxNFGXFfE2Ud8WsToRqz13377zSupGCGEkMSAyckIIYQkHMg0jAQVSLCBxEBIsAPxlqItISQSIGETEo9BqEWyICTfwVRbJKAkhNQMIKBi0AYDOBg8ihRITInEiZjWj+Rdhx12mEyYMIGiLSGEJCh03BJCCCGEEEIIIYQQQkicwRi3hBBCCCGEEEIIIYQQEmdQuCWEEEIIIYQQQgghhJA4gzFubaioqFBJbPLy8lRsREIIIYQQQgghhBBCCKkuLpdL8vPzpVmzZpKc7N9TS+HWBoi2LVu2rPaJIIQQQgghhBBCCCGEECv//vuvtGjRQvxB4dYGOG31AaxVq5bUBIfx5s2bpWHDhgGVfkKIQ67LwkKR7783nvfpI5KdLU5F70p6ukhGhkhxsUhJifN2q7C0UL5f872kJ6dLRmpGtZZVXFYsJRUl0qdVH8lOc9JBcHa51NdlTk5DmTs32V0mQU0vlwlRJoHTTqCfurImlEu4VUrySyQ9L912llxClEunnTwLNbFcBiLRy6UT+paJ0rYENb5ckqBwwnVZ09i1a5cyjGr90R8Ubm3QDT+ItjVFuC0qKlL7youYkAS5LlNTRXJyjOeox5zWCrXZFdzTMjNFiopE8vOdt1uppamSk5cjeel5kpmaWa1lFZUVSX5Jviofjur0Obxc6usyN7eW5OQku8skqOnl0vFlMi1NpLTUeSfQT11ZE8olhNui5CLJrJ1pK9w6vlwCp508CzWxXAYi0culE/qWidK2BDW+XJKgcMJ1WVNJCiI8K88YIYQQQgghhBBCCCGExBkUbgkhhBBCCCGEEEIIISTOoHBLCCGEEEIIIYQQQgghcQZj3BJCCCGEEEIIIYSQhIjnWoJsc8TrmJSWlqo4t4xxGx3S0tIkJSUlLMuicEsIIYQQQgghhBBCHA0E25UrVyqhkngn88Qxyc/PDyoZFgkPderUkSZNmlT7mFO4JYQQQgghhBBCCCGOFifXr1+vXI4tW7aks9RybMrKyiQ1NZXCbZSOd2FhoWzatEm9btq0abWWR+GWEEIIIYQQQgghhDgWCJMQy5o1aybZ2dmx3py4gsJt9MnKylL/Id42atSoWmETmJyMEEIIIYQQQgghhDiW8vJy9T89PT3Wm0KIQg8gIL5wdaBwSwghhBBCCCGEEEIcD2O4kkQrixRuCSGEEEIIIYQQQgghJM6gcEsIIYQQQgghhBBCiEN5/vnnpU6dOkF/v02bNjJ16lSJR84//3wZNGhQ1Nd7++23S9euXSXeoHBLCCGEEEIIIYQQQkgM2LBhg1xxxRXStm1bycjIkJYtW8rJJ58ss2bNitg6f/nlF7noooskFjz99NPSpUsXyc3NVWJzt27dZOLEie7PH3roISVEE4PUvf8JIYQQQgghhBBCCCFRYtWqVdKnTx8lYE6ePFk6d+6skll99tlncvnll8vixYsjst6GDRtKLJg+fbpcffXV8vDDD0vfvn2luLhY/vjjD/nrr7/c36ldu3ZMti1eoeOWEEIIIYQQQgghhJAoc9lll6kkVj///LMMGTJE9t13XznggAPk2muvlR9//NH9vSlTpihRNycnRzly8buCggK/y/7www+lR48ekpWVJU2bNpXBgwfbhkqAeIxtWLBggfvzHTt2qPe++eYb9Rr/8RqCMhyyWObRRx8tmzZtkk8//VQ6deoktWrVknPOOUcKCwt9btMHH3wgZ5xxhlxwwQXSvn17ta9nn3223HPPPT5DJeTn58u5556r9h378eCDD0q/fv2UAGzenwkTJsjIkSMlLy9PWrVqJU899ZTXum+44QZ1fLOzs5W7efz48Uokj3co3BJCCCGEEEIIIYQQEkW2bdsmM2fOVM5aiJJWzDFrk5OTlUt14cKF8sILL8hXX30l119/vc9lf/zxx3LaaafJCSecIL/++qtaD0TccMSBnTZtmvzwww/y77//KhEWAvArr7yi1vn555/LI4884vP3TZo0UYL06tWrg17ntddeK99//70Sfb/44gv59ttv1T5ZeeCBB+SQQw6R3377TQnbl156qSxZssT9OQRdhGBYtGiRCseAkA0QgeMdhkoghBBCCCGEEEIIIQmFyyXix/wZMbKzRZKSAn9v2bJl4nK5ZL/99gv4Xau79O6775ZLLrlEHnvsMdvvw8F61llnyR133KHWUVZWJt27d5fqgvUitAOAa3bcuHGyfPly5WAFp59+unz99dfK3WrHbbfdppy/2Ae4Xw877DAlLuN3EKet5OfnK6EawvAxxxyj3nvuueekWbNmlb6L5UCwBVg/RFlsS8eOHdV7t9xyi/u7WP/YsWPltdde8yuAxwMUbgkhhBBCCCGEEEJIQgHRtm/f6K939mwRGwNtJSCoBsuXX36pEngh5u2uXbuUEFtUVKTCEmDqvxWEPRg1apSEm4MOOsj9vHHjxu6wA+b3EPbBFwh1MHfuXBXTds6cOcq5O3z4cHnmmWeUK9gq3q5YsUKFM+jZs6dXDFwtxvraNoR1gLsXoRw0r7/+unItQ2hGmAkcQ4R3iHco3BJCCCGEEEIIIYSQhAJ6JkTUWKw3GDp06KAExkAJyBCD9qSTTlJT/+GkrVevnnz33XfK8VpSUmIr3CIGbbBosdQsJPuK/ZqWluZ+jm03v9bvVVRUBFzngQceqB5wyMI5fMQRR8js2bPlqKOOCnq7rfjbFojFiJMLB/KAAQOU+Au3LcIrxDtxEeP20UcfVTblzMxM6dWrl191HvE8ELAZ38dJ0MGUzTz++ONKaYdyjges1wiWTAghhBBCCCGEEEISH4QrgPM12o9gwiQACLAQEaGJ7d69u9LnSBAG5s+frwRIiIyHHnqoCjGwbt06v8uGJjZr1qygtqNhw4bq//r1693vmROVRZr9999f/bc7Bm3btlWC7C+//OJ+b+fOnfLPP/+EtA44e1u3bi0333yzioML0TyUOLs1WriFVRmBhhHnAsGFu3Tpogqu2c5sBjZwnLh7771X2Z7taNGihfochXvevHkq092pp56qRF9SmV27RLZvD7JmIYQQQgghhBBCCCHVBqJteXm5CgXw9ttvy9KlS+Xvv/9WU/phQgTt27dXDlgk/ULogJdeekmeeOIJv8uFxvbqq6+q/1jen3/+Kffdd5/td+HOhSAMHQ3fhfPVHA82nMA1fNddd6lkYxBOkahs2LBhSjzW+2smLy9PhVK47rrrVLxa6HpwGsMlDDNnsECoXbNmjXLZIlQCju+7774rTiDmwu2UKVNU3I0RI0YolR2FDzbv6dOn234fWfAmT56sgixnZGTYfufkk09WQYlxYjASASt5bm6uKhCkMiNHJslZZ9XjoSGEEEIIIYQQQgiJEjAmwsSIEAFjxoxR4QOOPfZY5ZbFbHIAgyO0Mwiv+Pzll19W8W790a9fP3nzzTflgw8+kG7duimDpNm1agUanE5ghkRoSEIWCfr376+0uaFDhyq9DjPqMfse+1u/fn3b30yZMkWJuggXgd8jOVqnTp3U74LllFNOkWuuuUZGjx4tXbt2VQ7c8ePHixOIaYxbxOKAKxZZ6DRQzXEiEH8iHGDkAoUVlms79R4UFxerhwaBngGs6MHE5nA6a9ZglMJVI/aVEKeA6xExhqp8XeJ3OkYRnjv4+ta7Yn04bbf0OdWP6qCX4bj7lMPLpfm6NJdFUNPLpePLpFNPoJ9dAYleLgN9x/HlUj930rZbqInlMhCJXi6r3YaNAonStgQ1vlxG4FjEAswmh5sWDyt6XyCm4mHmf//7n/s7cKXiYd730047TT0AHLsIOaA/X7lypdfy99tvP+WCNaOvY3ynb9++Xq+B3Trh8MXD1zkYPHiwetihf/Pcc895vc7NzZUZM2a4vwd9D7FqYQL1tT/gt99+83oPwrfVdXzVVVe5Pw+07eGs70OpI2Mq3G7ZskUJq8g6ZwavAwVnDgRs4BBqkWUPJxkWaB03wwpGKnDSrWzevFn9PtEpL6+nzsOmTVsqZfAjhMQGVOSI3YOKvkrXJQaj9M1g61aRggJxKnpXSkqM1/iP107breKyYqkoqJCS1BKRlOotq6S8RCrKKmTr5q1SkOqkg+Dscqmvy6IiNMCS3WUS1PRy6fgyWV7uzBPop66sCeUS98jS3UbyFLvpko4vl8BpJ89CTSyXgUj0clntNmwUSJS2Jajx5dIEhEmUP7hG8SAecD1C8wGhhBeIF3777TdZsmSJmoEPo6V2A5944olxfa6xbSiTW7durZQ4LT8/3xnCbSTp2LGjCqaMm8Zbb72lRgEQp8NOvIXjF3F2NSgILVu2VDE2kNws0UlLQ6Y9kUaNGsXtzZWQmgYqeNxUUQ9V6bosLMQUBuM5ppwEm9o0DtG7kp4uomfDoMHttN0qLC2U5D3Jkp6eLpmpwU/rsaVMpLikWOo3rC/ZaU46CM4ul/q6zMkxrktzmZSaXi6dXiZTUoxeu9NOYIC6MtHLpXbEZNbOtO+IOr1cAqedPAs1sVwGJMHLZbXbsFEgUdqWoMaXSxMw3UEMS01NVQ9SGat46BRSU1Nl6tSpSrxFHYxwDnPmzPGZ9yqethv1IEJAWMM6hBLmIaaluUGDBpKSkiIbN270eh+vq3sCcDIRwBngpCKWx0MPPSRPPvlkpe8iVq5dvFwc4Hi92YSTlBQ0epNqzP4S4hTQ6K3ydYnf6E4snjv42ta7Yn04bbd0AH39qA56GY6rtxOgXOrjnpSEh2d3anq5dHyZdOoJ9LMroCaUS3/fc3y51M+dtO0Wamq59EdNKJfxvn+J0rYENb5cRuBYJCIY6NTHxInH5uCDD1ZhVp2Gv/o+lPoxptWSVsoRhNg8QofXvuLRVhUs1xzHlnhw4HVLCCGEEEIIIYQQQkhCE3P/OEIUIIzBIYccIj179lT2ZwQaHjFihPp82LBh0rx5c3fGPCQ0W7Rokfv52rVrVUgExLHVDluEPhg4cKC0atVKWeVfeeUV+eabb+Szzz6L4Z4SQgghhBBCCCGEEEKIQ4TbM888UyUBu/XWW2XDhg3StWtXmTlzpjth2Zo1a7wsxOvWrZNu3bq5X99///3qgQx3EGfBpk2blOC7fv16qV27thx00EFKtD322GNjsIfxDx23hBBCCCGEEEIIIYTEFzEXbsHo0aPVww4txmratGnjTkTgi2effTas25foOC2ODyGEEEIIIYQQQgghiQ4lO0LHLSGEEEIIIYQQQgghcQaFW0LhlhBCCCGEEEIIIYSQOIPCLaFwSwghhBBCCCGEEJLAIH/Ue++9F/X1JiUlRXS9zz//vNSpU0eiDUK7Yt927NgR0fVQuCUUbgkhhBBCCCGEEEKizPnnny+DBg1KiON+++23S9euXSu9v379ehk4cGCVlzt79mw5+uijpV69epKdnS0dOnSQ4cOHS0lJifr8zDPPlH/++UcSFQq3hMItIYQQQgghhBBCCAk7TZo0kYyMjCr9dtGiRXL88cfLIYccInPmzJE///xTHnnkEUlPT5fy8nL1naysLGnUqJEkKhRuiSSzFBBCCCGEEEIIIYTElLfeeks6d+6sxMj69etL//79Zffu3eqziooKufPOO6VFixZKCIW7debMme7fwoE6evRoadq0qWRmZkrr1q1l4sSJtu5XLL9t27ZqfWb+/fdfOeOMM1ToAThcTz31VFm1apVXeICePXtKTk6O+k6fPn1k9erVKlzBHXfcIb///rsKH4AH3rMLlfDff//J2WefrZaP5UCU/emnn2yPx+eff66E30mTJsmBBx4o7dq1U0Lu008/rfbBV6iEu+++W4m5eXl5cuGFF8qNN97o5QbWTuf7779fHS8c68svv1xKS0vd33nppZfUtmEZ2IZzzjlHNm3aJNGGkh2h45YQQgghhBBCCCEkhkBUhaA5cuRI+fvvv5VIOnjwYHG5XOrzhx56SB544AElNv7xxx8yYMAAOeWUU2Tp0qXq84cfflg++OADeeONN2TJkiXy8ssvS5s2bbzWMX78eBkyZIgSWM8991w566yz1LoAREssE0Llt99+K99//73k5uYqoRSicFlZmRI7+/btq9Y/d+5cueiii5Qwi3AFY8aMkQMOOEDtBx54z0pBQYH6/dq1a9W2Yjuuv/56JUrb0aRJE7UsuG2DBft9zz33yH333Sfz58+XVq1ayeOPP17pe19//bUsX75c/X/hhReUAKzFZn087rrrLrWNEJ4hYEPwjTapUV8jiTuSkmK9BYQQQgghhBBCCCFhBIJncXH0DynCAlRBaIFACXEUYi3csgDuWw0E2xtuuEGJrQDCJETHqVOnyqOPPipr1qxR8V8PP/xwJabqZWjhFwwdOlQ5UAFEyS+++EKFHnjsscfk9ddfVwLqM888o34PnnvuOeVmhYgM9+nOnTvlpJNOUs5X0KlTJ/eyIfKmpqYqsdUXr7zyimzevFl++eUX5bgF7du39/n9oUOHymeffabEXiz30EMPlWOOOUaGDRsmtWrVsv0N9ueCCy6QESNGqNe33nqrcu5CNDZTt25dmTZtmqSkpMh+++0nJ554osyaNUtGjRqlPoeAroE7GcJ4jx491HKwr9GCwi1hqARCCCGEEEIIIYQkFhBtr7oq+ut96CGRzMyQf9alSxclSkKshfP1uOOOk9NPP10JjLt27ZJ169ap0ARm8BqOUAA36LHHHisdO3ZULlkIrFiGmcMOO6zS6wULFqjnWM6yZcuU49ZMUVGRcqZiWVgHtg3rQRgHhFVAqIFgwbq6devmFm0DkZKSosRjhD746quvVEiFCRMmKNH6559/tl033MaXXXaZ13sI74Dfm4E7GMvXYFmIoauBWxcJ13Bctm/f7nYFQyDff//9JVpQuCV03BJCCCGEEEIIISSxgPMVImos1lsFICLCAfvDDz8ohyicozfffLMSKxGDNRAHH3ywrFy5Uj799FP58ssvlagKcfXNN98Mav1wknbv3l2FGrDSsGFD9R8i6pVXXqli68Khe8stt6hthhM2GHRc2lBp3ry5nHfeeeoBp/C+++4rTzzxhIqrW1XS0tK8XsNlrMVZxBWGQI0Hjgf2H4ItXiNsRDRhjFtC4ZYQQgghhBBCCCGJBab7w/ka7Uc14lFCPISLFoLkb7/9Junp6fLuu++qsADNmjVTcWfN4LXZ/YnvIbYskndBWH377bdl27Zt7s9//PFHr9/jtQ53AOEX8XKR1AvhC8yP2rVru38Dx+y4ceOUwIyEYQh/ALCt5eXlfvfvoIMOUq5b8zaFSt26dZU7VidtswLHMUIxmLG+DsTixYtl69atcu+998oRRxyhQinEIjEZoOOWMFQCIYQQQgghhBBCSAyBsxYxVhGSAOIpXiMerBZWr7vuOrnttttUfNmuXbsq9ytEUO2QnTJlihI0IawmJycrpy3iwiJGrXaS4j3EqkUcXPwO4QaeffZZ9RmSlU2ePFlOPfVUufPOO6VFixayevVqeeedd1QCMSTreuqpp1RCNIjICEkAoRfxZgESocHxi23CbxFyIcPiPkbyNYQ6QJKziRMnqu2FQI3lWcM4gCeffFIt77TTTlP7jbANL774oixcuFA5ku244oorVJxa7Gfv3r2VgI1kaohTGyxIaAYhGuu45JJL5K+//lJO31hA4ZbQcUsIIYQQQgghhBASQ+CWnTNnjko2hpi2SC72wAMPyMCBA9XnCFGA5GBjxoxR7k84bT/44AOVkAxAKJ00aZISUxF2AYm0PvnkEyXiauEWTt7XXntNxYCFaPrqq6+6HbvZ2dlq/UiAhgRp+fn5KkQB4u5i2/bs2aOcqC+88IJyo+L3l19+uVx88cXq90OGDFEi71FHHSU7duxQwjJi4pqBGIowENiHE044QSVjw/qRXM2Onj17ynfffafEU8T4RVIwxKZ97733VMIyOyBAr1ixQsaOHauEXoSMwHZApA4WhEZ4/vnn5aabblJJyeBGRnI4iNbRhsItoeOWEEIIIYQQQgghJMpAHNTAWYvYsb6AAAvHLR52wGWKhxWXy6X+Q7xFKAZr4i4zcOhCmLUD4i3CNvgC7tq33nrL5/o1EKTtvmdHt27d5KWXXvL7HYiyVoF4/Pjx6qFBMjWEfLA77hoI5lZ3MB6+9qVfv36V9i0SULglFG4JIYQQQgghhBBCiOMpLCxUicuQSAzOY7iKkawNSdScCIVbwlAJhBBCCCGEEEIIIcTxJCUlqRAR99xzjwqVgGRlSNLWv39/cSIUbgkdt4QQQgghhBBCCCHE8WRlZSmHbaKQHOsNILEnJSXWW0AIIYQQQgghhBBCCDFD4ZbQcUsIIYQQQgghhBBCSJxB4ZbQcUsIIYQQQgghhBBCSJxB4ZbQcUsIIYQQQgghhBBCSJxB4ZbQcUsIIYQQQgghhBBCSJxB4ZbQcUsIIYQQQgghhBBCSJyRGusNILEnea98X1FBEZcQQgghhBBCCCGJQUmJSFlZ9NaXmiqSni6Ool+/ftK1a1eZOnVqRNdz/vnny44dO+S9996L6HoSDQq3RFUsWrglhBBCCCGEEEIISQTR9uefRQoKorfO3FyRnj2DF2/nzJkjkydPlvnz58v69evl3XfflUGDBkVk27755hs5+uijZfv27VKnTh33+++8846kpaVFZJ2k+lC4JZKZaRyE3budNzJECCGEEEIIIYQQYgVOW4i20DkyMiJ/fIqLjfVhvcFqK7t375YuXbrIyJEjZfDgwRIL6tWrF5P1kuBgjFsi2dnGQVi1igeDEEIIIYQQQgghiQNEWxjWIv2oijg8cOBAufvuu+W0007z+Z3HHntMOnToIJmZmdK4cWM5/fTTfX539erVcvLJJ0vdunUlJydHDjjgAPnkk09k1apVym0L8FlSUpIKXaBDJVx99dXuZbRp00Zt07BhwyQ3N1dat24tH3zwgWzevFlOPfVU9d5BBx0k8+bNc//m9ttvV+EWzCD0ApblC3xmDc+AZWBZwOVyqeetWrWSjIwMadasmVx55ZVS06BwS9xs3MiDQQghhBBCCCGEEBIPQByFWHnnnXfKkiVLZObMmXLkkUf6/P7ll18uxcXFKgTDn3/+Kffdd58SWlu2bClvvfWW+g6Wg7AMDz30kM/lPPjgg9KnTx/57bff5MQTT5TzzjtPCbn/+9//5Ndff5V27dqp1xBXI8Xbb7+ttuPJJ5+UpUuXqti4nTt3lpoGQyUQN7t28WAQQgghhBBCCCGExANr1qxRztmTTjpJ8vLylPu1W7dufr8/ZMgQt8DZtm1bJa6WlZW5QyI0atTIK8atHSeccIJcfPHF6vmtt94qjz/+uPTo0UOGDh2q3rvhhhvksMMOk40bN0qTJk3CuMfe+9KkSRPp37+/isEL521PBBCuYdBxS9wUFvJgEEIIIYQQQgghhMQDxx57rBJrIcDC9fryyy9LoR/xBu5chDmAW/a2226TP/74o0rrRSgEDcIzALPbVb+3adMmiRQQiffs2aP2fdSoUSpxGwTomgaFWyLa2V5ayoNBCCGEEEIIIYQQEg/AZYvQBK+++qo0bdpUuV+RzGzHjh2237/wwgtlxYoVSuRFqIRDDjlEHnnkkZDXC4erBvFwfb1XUVGh/icnJ1cKm1AaQGQK9BuEd1iyZImK8ZuVlSWXXXaZChMRaLmJBoVb4qakhAeDEEIIIYQQQgghJF5ITU1V4QImTZqkHLRINPbVV1/5/D4Ez0suuUTeeecdGTNmjDzzzDPq/fT0dPW/vLw87NvYsGFD2bBhg5cQu2DBgoC/Qaxdza5du2TlypVe38nKylLJ1h5++GH55ptvZO7cuUqQrkkwxi1xUwMd54QQQgghhBBCCCExoaCgQJYtW+Z+DeESgifi0SKm60cffaQctHCa1q1bVz755BPlcu3YsaPt8q6++moZOHCg7LvvvrJ9+3b5+uuvpVOnTuozhFyAUxbLRAxbiKJIXBYO+vXrJ5s3b1bi8umnn66SqH366adSq1Ytn785+uij5fnnn1fCLGLuwk2ckpLi/hyflZeXS69evSQ7O1tmzJihthn7UZOg45a4oeOWEEIIIYQQQgghiURxsUhRUeQfWE+ozJs3TyUb0wnHrr32WvUcIiaAoAnnLEROCLBPPPGECptwwAEH2C4PQufll1+uvnv88ccrAffRRx9VnzVv3lzuuOMOufHGG1WM2tGjR0u4wPoQ0gDrQiiHn3/+WcaOHev3N+PGjZO+ffuqxGsnnniiDBo0SNq1a+f+HPv+9NNPq3i9iLn75Zdfyocffij169eXmgQdt0SRkuKicEsIIYQQQgghhJCEIDVVBIbSgoLoGdWwPqw3FKeqNc6rmcMPP1yFCAgWu3i2WL5O6jV+/Hj1MGNdPkIx2C3DTJs2bSq9h/AMeJi56aabvBy0ZuDGfe2117zeGz58uPs5hNxBgwZJTYfCLVHJyRDqpIbFdyaEEEIIIYQQQkiCAp2jZ8/ohoWEaLs3lCwhYYHCLVGkpcFxa2QFJIQQQgghhBBCCHE6EFEppBInwxi3RJGWRsctIYQQQgghhBBCCCHxAoVbYnLc8mAQQgghhBBCCCGEEBIPULglCkwdoHBLCCGEEEIIIYQQQkh8QOGWKOi4JYQQQgghhBBCCCEkfqBwSxTp6QyVQAghhBBCCCGEEEJIvEDhlriTkzFUAiGEEEIIIYQQQggh8QGFW6Kg45YQQgghhBBCCCGEkPghNdYbQOIDOm4JIYQQQgghhBCSSJSUl0hZRVnU1peanCrpKeniJNq0aSNXX321ekSSfv36SdeuXWXq1KkRXU+iQeGWiMtFxy0hhBBCCCGEEEISS7T9+b+fpaC0IGrrzE3LlZ4telZJvL333ntl3LhxctVVV0VE3Hz++eflmmuukR07dni9/8svv0hOTk7Y10fCA4VbokhPFymL3iAUIYQQQgghhBBCSMSA0xaibXpyumSkZkT8SBeXFav1Yb2hCrcQT5988kk56KCDJNo0bNgw6uskDotx++ijjyprdmZmpvTq1Ut+/vlnn99duHChDBkyRH0/KSnJdhRi4sSJ0qNHD8nLy5NGjRrJoEGDZMmSJRHeC2eTluaS4uJYbwUhhBBCCCGEEEJI+IBom5maGfFHVcXhgoICOffcc+Xpp5+WunXren3mcrnk9ttvl1atWklGRoY0a9ZMrrzySp/L+v333+Woo45SelitWrWke/fuMm/ePJk9e7aMHDlSdu7cqbQ0PLBcAH3NrK3hM4jIJ510kmRnZ0unTp1k7ty5smzZMhXuAO7c3r17y/Lly92/Of/885X2ZgahF/B9X2A97733ntd7derUUc5gUFJSIqNHj5amTZsqvbB169ZK76tpxFy4ff311+Xaa6+V2267TX799Vfp0qWLDBgwQDZt2mT7/cLCQmnbtq2ykDdp0sT2OyiQl19+ufz444/yxRdfSGlpqRx33HGye/fuCO+Ns4Xb0lIjbAIhhBBCCCGEEEIIiTzQr0488UTp379/pc/efvttefDBB5WQunTpUiV0du7c2eeyIAC3aNFCOXjnz58vN954o6Slpclhhx2mlgMxd/369eoxduxYn8u56667ZNiwYbJgwQLZb7/95JxzzpGLL75YhXKAEAxBGaJqJHn44Yflgw8+kDfeeEOZMV9++WUlMtc0Yh4qYcqUKTJq1CgZMWKEev3EE0/Ixx9/LNOnT1cFzAqctHgAu8/BzJkzvV5DrYfzFoX2yCOPjMh+JEKoBIi2CJeARGWEEEIIIYQQQgghJHK89tprysQIodWONWvWKNMiRF0IsHDe9uzZ0+fy8P3rrrtOia2gQ4cOSmQtKyuT2rVrK5erLxOkGWh0Z5xxhnp+ww03KOF3/PjxymgJEIdX63iRAvvSoUMHOfzww9V2w3FbE4mpcAvbM8RUKPaa5ORkVSBhww4XsIKDevXq2X5eXFysHppdu3ap/xUVFeqR6ECwTU11qf/FxRWSkhLrLSKEoO7BDbbKdRB+py30eO7gukzvivXhtN3S51Q/qoNehuPuUw4vl+br0lwWQU0vl44vk049gX52BSR6uQz0HceXS/3cSdtuoSaWy0Akermsdhs2CiRK2xLU+HIZ4FiYX1e3/R0Moa7v33//VQLo559/rsIgWLcbnH766SqMAWaeQzQ94YQT5OSTT5bUVHs5D8nHLrzwQnnppZfkmGOOkaFDh0q7du28tsdu26zbDFevfg0jJDjwwAO93isqKlJ6G1y8dss274+v9dgdK/3e8OHD1ez5jh07qn1H6Aa8dgr+6vtQ6siYCrdbtmyR8vJyady4sdf7eL148eKwrAMHA3E1+vTpowqZHYiRcccdd1R6f/PmzaogJjq7d+dIXt5uKSvLkv/+2y516jBeAiGxBnUXboKo6DGgFTIYjNI3g61bEThJnIrelZIS4zX+47XTdgvJCioKKqQktUQkpfoZcivKKmTr5q1SkOqkg+Dscqmvy6IiNMCS3WUS1PRy6fgyWV7uzBPop66sCeUS98jS3aXqOZw4CVcugdNOnoWaWC4Dkejlstpt2CiQKG1LUOPLpQmEyET5g7MUD4D/0JzUI6k84udDr0ttQ1Lg7O/I74QwoYhDa17GnDlzVC4oxL5FfNe//vpLZs2apR4IqzB58mT1HA5cK7fccotyyn766afy2WefqTi2M2bMUKInlg308TGjj50G169+rX+He61+TwuPMGSav2deBj7Tbl+zkKlf6+WZf2M+j0jU9s8//6hZ9V999ZWceeaZcvTRR6uQq04A+4B92bp1a6VzlZ+f75xQCZEGhRqF/LvvvvP5HTh+EWfX7Lht2bKlyqxnHjlIVLKzRWrXLpfU1DSpU6eh7B1MIYTEEFTwuJGhHqpSo7ewEHdb43n9+saF7lD0riCkS2amp8HttN0qLC2U5D3Jkp6erpIXVIsykeKSYqnfsL5kpznpIDi7XOrrMifHuC7NZVJqerl0epnEdCN0QJx2AgPUlYleLrVDJ7N2pq1w6/hyCZx28izUxHIZkAQvl9Vuw0aBRGlbghpfLk3AdAcxDE5U7UZNdaVKSkqK+xFpUlzGeszb4A+4R//44w+v95BADGEOrr/+euXCBUg0hsRfeCCuLJKF/f3333LwwQfbLnf//fdXjzFjxqjYtHDfnnrqqZKVlaXEVbttw/Vqfl/vBzD/18/18dTvwYG7aNEir2Vg3yBY6vd0UjT9GvUEhGv9GjF8kdfKvC316tVT+4AH3MMDBw5Ump2vGfXxBPYB+1K/fn2VXM2M9bXf5UgMadCggTrZGzdu9Hofr4OJuREIFOiPPvpIjVYgOLMvcDHoC8IMDnC83mzCSVKSy33jKivDPsd6iwghADe1KtdD+I3uxOK5gy9svSvWh9N2C+dRN1ZsBYYQ0Mtw3H0qAcqlPu5JSXh4dqeml0vHl0mnnkA/uwJqQrn09z3Hl0v93EnbbqGmlkt/1IRyGe/7lyhtS1Djy2WAY2F+Xd32dzCEuj4YBa2JxnJycpTQp99HziaIrb169ZLs7GyVoAsCLJJ0WdexZ88eFd8W4RX22Wcf+e+//1Ts3MGDB6vv4jdw8cK92qVLF7U8PMzbbt0X/TzQewjLcP/99yuRGPFw4fKFibJbt24+lwv3LJzFvXv3VvuIWLoQevV3kBOradOmahk4v2+99ZbSCuvWrRuV81ld/NX3odSPMa2WMFoJSzgs3uYROrzGia4qOrvdu+++qwokCiwJDJzb5mlMhBBCCCGEEEIIIU4GISWKyooi/sB6wk2dOnXk6aefVuE/ETrgyy+/lA8//FCJu1ZgjMS0/GHDhsm+++6rQibAoapDg0IgveSSS1TIAbhdJ02aFLbtRAxaJC+DU7hHjx7K/Yzt8McDDzygZrsfccQRylE7duxYt5CsncaTJk2SQw45RC1z1apV8sknn8TtoFCkiHmoBIQoQMBhnAhkxkPQ5d27d7uz0+FEN2/eXMWh1TEyYL/Wz9euXSsLFiyQ3Nxcad++vTs8wiuvvCLvv/++OtEbNmxQ7yODHkYmiD1w3FK4JYQQQgghhBBCiNNJTU6V3LRcKSgtUPpRNMD6sN6q8s0333i91iESgjVHvvrqq5XeN8eVffzxx9XDDARR6/fNwKlrfa9fv36V3oNAbJc/yte+NWvWTMXhNbNjxw7381GjRqlHTSfmwi2UfiQBu/XWW5XA2rVrVxV4WCcsW7NmjZeavm7dOmWT1sCKjUffvn3dhUAXQhQkM88995ycf/75Udoz56CvNTpuCSGEEEIIIYQQkgikp6RLzxY9pawicKKwcAHRFuslJGGEW4CwBngEo8jbKf1WAn1OKoPwIHTcEkIIIYQQQgghJFGAiEohlTiZmhUYgviFwi0hhBBCCCGEEEIIIfEBhVviJdwWhz+WNiGEEEIIIYQQQgghJEQo3BI3yNtWVMQDQgghhBBCCCGEEEJIrKFwS9zJySDc7tnDA0IIIYQQQgghhBBCSKyhcEvcZGbScUsIIYQQQgghhBBCSDxA4Za4oeOWEEIIIYQQQgghhJD4gMItcZOV5WKoBEIIIYQQQgghhBBC4oDUWG8AiR/ouCWEEEIIIYQQQkjCUFIiUlYWvfWlpoqkp0dvfQ6nTZs2cvXVV6tHJOnXr5907dpVpk6dKk6Dwi3xinG7axcPCCGEEEIIIYQQQhJAtP35Z5GCguitMzdXpGfPoMXbiRMnyjvvvCOLFy+WrKws6d27t9x3333SsWNHL9Fx9uzZXr+7+OKL5YknnnC/XrNmjVx66aXy9ddfS25urgwfPlwtOxVCchzw/PPPK3F2x44dXu//8ssvkpOTE7PtcgLxcQZJzElKMoTbPXtivSWEEEIIIYQQQggh1QROW4i2EFEzMiJ/OIuLjfVhvUEKtxBkL7/8cunRo4eUlZXJTTfdJMcdd5wsWrTIS9AcNWqU3Hnnne7X2dnZ7ufl5eVy4oknSpMmTeSHH36Q9evXy7BhwyQtLU0mTJgg8UzDhg1jvQlxD2PcEjcMlUAIIYQQQgghhJCEAqItnGqRflRBHJ45c6acf/75csABB0iXLl2UMxXu2fnz53t9D0IthFn9qFWrlvuzzz//XAm9M2bMUOEABg4cKHfddZc8+uijUgLXsYgSfbt16ybTp0+XVq1aKVfuZZddpkTfSZMmqWU2atRI7rnnHvdyXS6X3H777er7GRkZ0qxZM7nyyit97svvv/8uRx11lOTl5ant6969u8ybN0+++eYbGTFihOzcuVOSkpLUA8vVoRLM4Qvw2ZNPPiknnXSS2udOnTrJ3LlzZdmyZcp5DDEbruTly5e7f4PjN2jQIK9tgbsX3/cF1vPee+95vVenTh11/AGO2+jRo6Vp06aSmZkprVu3Vg7mWEDhlrihcEsIIYQQQgghhBASGyBugnr16nm9//LLL0uDBg3kwAMPlHHjxklhYaH7MwibnTt3lsaNG7vfGzBggOzatUsWLlzofg9i56effqrE4ldffVWeffZZ5dT977//lPMXIRpuueUW+emnn9T33377bXnwwQeVkLp06VIldGI9vjj33HOlRYsWKvwBhOcbb7xRuX4htEKchZgLNzAeY8eO9bkciM5wDC9YsED2228/Oeecc1RoCOw3hGAIyhBVI8nDDz8sH3zwgbzxxhuyZMkSdfwhMscChkogbijcEkIIIYQQQgghhESfiooK5RTt06ePEmg1EC7h+ITj9Y8//pAbbrhBiYmIjQs2bNjgJdoC/RqfmZcPxy0csfvvv79yx2I5n3zyiSQnJ6u4uhBvESe3V69eyvkLJ27//v2VAAvnbU/E7/UBvn/dddcpsRV06NDB/Vnt2rWVyxXLCwTcuWeccYZ6fsMNN8hhhx0m48ePV2I0uOqqq9R3Ign2Bdt/+OGHq+3G8Y8VFG6JuFwe4baoiAeEEEIIIYQQQgghJJog1u1ff/0l3333ndf7F110kfs5HK+Yvn/MMccoB227du2CXj4coxBtzeJuSkqKEm3N723atEk9Hzp0qHLKtm3bVo4//ng54YQT5OSTT/aZ8Ozaa6+VCy+8UF566SUl9uL3oWyf5qCDDvLaHmB2+uK9oqIi5Sg2h4wIJwi/cOyxxyoxG/uO0A2IPRwLGCqBKJicjBBCCCGEEEIIIST6YOr/Rx99pNyuCDfgD7hhAeK+ArhYN27c6PUd/drscIVr1gycpHbvwZkLWrZsqRy5jz32mGRlZamYuEceeaSUlpbabhfi1iI0A8IvfPXVV8rV++6774ZwFCpvZxLEKh/v6e2E8IzwCWZ8baN5Gf5+c/DBB8vKlStV2IY9e/YoB/Dpp58usYDCLXHDUAmEEEIIIYQQQggh0UHHa4XACbFzn332CfgbxH4FcN4ChBL4888/3U5Z8MUXXyg3KsTT6gDBFi5bxHxFkjHE08W6fLHvvvvKNddcoxKmDR48WJ577jn1fnp6ukqEFgkaNmyo4ubaHaNgf4MYvua4wQDH78wzz5Snn35aXn/9dRXzd9u2bRJtGCqBuMnOFrGUU0IIIYQQQgghhBASofAIr7zyirz//vsqjIGOSYuYsBBNEQ4BnyNMQf369VWMWwijcL7qkAKYwg+B9rzzzpNJkyapZSDJGJadkZFRyVkaLM8//7wSW+Hwzc7OlhkzZqhtsov3Clcq4tvClQrxGQnPkKRsyJAh7jANBQUFMmvWLOnSpYtaHh7h4Oijj5bJkyfLiy++qERsbCdCTnTr1s3vb6ZNm6a+j31ELF2zq3fKlClKGMcy4Oh98803lXu5Tp06Em3ouCVuatcWKSlhnFtCCCGEEEIIIYQkCMXFhtAR6QfWEyKPP/647Ny5U/r166eEQv2Aw1M7Vb/88kslziLp15gxY5QY+uGHH7qXgTi1CLOA/xAi//e//8mwYcPkzjvvrNZhg0gJtymSpUEkxnZgvRCQrWDdW7duVeuF6xahBQYOHCh33HGH+rx3795yySWXKAcr3K4QmMPFgAEDVPKy66+/Xnr06CH5+flqO/zxwAMPqFAQRxxxhEr+NnbsWC8hGSI6tvGQQw5Ry1y1apU7iVu0oeOWuJOTIaYzQoXs3CmSmckDQwghhBBCCCGEEIeCJFq5uSIFBYZLLRpgfT6Sd9kRyA0LcXH27NkBlwMXLIRFX9x6662VhFw4aq0gHIJm0KBB6hEMEJhfffXVgCI1HmYgiPo7Hm3atKn0HkRu63sQiLVIbId5v0CzZs3ks88+83pvx44d7uejRo1Sj3iAwi1RQLDFwAESDEK43Zu4jxBCCCGEEEIIIcR5pKeL9OwpUlYWvXVCtMV6CQkTFG5JpXAJpkEGQgghhBBCCCGEEGcCEZVCKnEwjHFLvECcZQq3hBBCCCGEEEIIIYTEFgq3pJLjFqESCCGEEEIIIYQQQgghsYPCLXEnJwM6bjchhBBCCCGEEEKIkwiU7IsQp5VFCrfEnZwMULglhBBCCCGEEEKIk0hJSVH/S0pKYr0phCgKCwvV/7S0NKkOTE5GvKBwSwghhBBCCCGEECeRmpoq2dnZsnnzZiWUJSfTp2h2fpaVlaljlKRdeySixxui7aZNm6ROnTruQYWqQuGWeJGTI7JhAw8KIYQQQgghhBBCnAEEyaZNm8rKlStl9erVsd6cuBMSKyoqlJhN4TZ6QLRt0qRJtZdD4ZZUctzu3s2DQgghhBBCCCGEEOeQnp4uHTp0YLgECxBtt27dKvXr16cTOUrA9V1dp62Gwi1hcjJCCCGEEEIIIYQ4HrhKMzMzY70ZcSfcQkjEcWEICefBoB/EC8a4JYQQQgghhBBCCCEk9lC4JZVi3DJUAiGEEEIIIYQQQgghsYXCLfGCjltCCCGEEEIIIYQQQmIPhVviRd26Ivn5IsXFPDCEEEIIIYQQQgghhMQKCrfEiwYNjGRlP/zAA0MIIYQQQgghhBBCSKygcEsUSUmuvf9F9t9fZOlSHhhCCCGEEEIIIYQQQmIFhVtSia5djXAJhBBCCCGEEEIIIYSQ2EDhllSidm2RnTt5YAghhBBCCCGEEEIIiRUUbkkl8vJECgp4YAghhBBCCCGEEEIIiRUUbolKRmYVbhkqgRBCCCGEEEIIIYSQ2EHhlriTkpmF2127eGAIIYQQQgghhBBCCIkVFG5JJWrVouOWEEIIIYQQQgghhJBYQuGWVCI3lzFuCSGEEEIIIYQQQgiJJRRuSSUQKqGwUKSsjAeHEEIIIYQQQgghhJBYQOGWVEpOhlAJYNo0HhxCCCGEEEIIIYQQQmIBhVtSifR04/+MGSLl5TxAhBBCCCGEEEIIIYREGwq3pBJJSZ7n27bxABFCCCGEEEIIIYQQUuOE20cffVTatGkjmZmZ0qtXL/n55599fnfhwoUyZMgQ9f2kpCSZOnVqpe/MmTNHTj75ZGnWrJn6znvvvRfhPUhsdu2K9RYQQgghhBBCCCGEEFLziKlw+/rrr8u1114rt912m/z666/SpUsXGTBggGzatMn2+4WFhdK2bVu59957pUmTJrbf2b17t1oOBGFSfcaNE6mo4JEkhBBCCCGEEEIIIaTGCLdTpkyRUaNGyYgRI2T//feXJ554QrKzs2X69Om23+/Ro4dMnjxZzjrrLMnIyLD9zsCBA+Xuu++W0047LcJbn7jJyUC9esb/FStEfvwx6ptECCGEEEIIIYQQQkiNJmbCbUlJicyfP1/69+/v2ZjkZPV67ty5sdqsGos5ri34/HPP8yuvjPrmEEIIIYQQQgghhBBSo0mN1Yq3bNki5eXl0rhxY6/38Xrx4sVR3Zbi4mL10OzaG9i1oqJCPWqK49a6r2+9JXL66YaiW1TkkvT0WGwdITUTXI8ul6vqdRB+57m4HR3zRO+K9eG03dLnVD+qg16G4+5TDi+X5uvSXBZBTS+Xji+TTj2BfnYFJHq5DPQdx5dL/dxJ226hJpbLQCR6uax2GzYKJErbEtT4cklCKifxfF3WNCpCOBcxE27jiYkTJ8odd9xR6f3NmzdLUVGRJDp79uSq2MCbNu1RrmdNZqbI2LHpMnFinvz66w5p27Y8pttJSE2ryHfu3KlusObrMmgwGKVvBlu3ihQUiFPRu1JSYrzGf7x22m4VlxVLRUGFlKSWiKRUb1kl5SVSUVYhWzdvlYJUJx0EZ5dLfV1iMLOiItldJkFNL5eOL5Pl5c48gX7qyppQLnGPLN1dqp4jKXHClUvgtJNnoSaWy0Akermsdhs2CiRK2xLU+HJJgsIJ12VNIz8/P/6F2wYNGkhKSops3LjR63289pV4LFKMGzdOJUkzO25btmwpDRs2lFq1akmik5UlkpNTKo0a5VW6iM84Q+T995OkqKi+NGoUs00kpEbeXNEJRT1UpZtrYSHizxjP69cXyc4Wp6J3Ba5/DCjpBrfTdquwtFCS9yRLenq6ZKbu3ZGqUiZSXFIs9RvWl+w0Jx0EZ5dLfV3m5BjXpblMSk0vl04vkykpRq/daScwQF2Z6OVSux4za2faCreOL5fAaSfPQk0slwFJ8HJZ7TZsFEiUtiWo8eWSBIUTrsuaRqb5phivwi1ueN27d5dZs2bJoEGD3IUJr0ePHh3VbUGiM7tkZyjQNaNQu/zub926Ips2Jckff4h06VI5Hi4hJDLg5lrlegi/0Rcrnju4LtO7Yn04bbdwHnFO9aM66GU47j6VAOVSH/ekJDw8u1PTy6Xjy6RTT6CfXQE1oVz6+57jy6V+7qRtt1BTy6U/akK5jPf9S5S2Jajx5ZIETbxflzWN5BDOQ0xDJcDlOnz4cDnkkEOkZ8+eMnXqVDVlf8SIEerzYcOGSfPmzVUoA53QbNGiRe7na9eulQULFkhubq60b99evV9QUCDLli1zr2PlypXqO/Xq1ZNWrVrFZD+dDkzH999vPH/uOZHOnWO9RYQQQgghhBBCCCGEJDYxFW7PPPNMFUf21ltvlQ0bNkjXrl1l5syZ7oRla9as8VKh161bJ926dXO/vv/++9Wjb9++8s0336j35s2bJ0cddZT7OzoEAgTi559/Pop7lzjAaatxWswfQgghhBBCCCGEEEKcSMyTkyEsgq/QCFqM1bRp0yZgBs9+/fpVO1s38WbCBBHkaMNpuuIKiOM8QoQQQgghhBBCCCGEJLRwS+Kfrl29XyPxMnJ4EEIIIYQQQgghhBBCIgOjEpOgee894/+vvxqJlwkhhBBCCCGEEEIIIZGBwi1RBJMYtUULhKsQufRSkVde4YEjhBBCCCGEEEIIISRSULglIVG3rvF/7VoeOEIIIYQQQgghhBBCIgWFWxISOrZtXh4PHCGEEEIIIYQQQgghkYLCLQmJ4mLjfyrT2hFCCCGEEEIIIYQQEjEo3BJxuYI/CEVF3v8JIYQQQgghhBBCCCHhh8ItCTo5GTjoIOP/nj08cIQQQgghhBBCCCGERApOeCchcdVVIsnJFG4JIYQQQgghhBBCCIkkdNySkMjOFmnenKESCCGEEEIIIYQQQgiJJBRuSchkZdFxSwghhBBCCCGEEEJIJKFwS0KGwi0hhBBCCCGEEEIIIZGFwi0Rlyu0g5CZyVAJhBBCCCGEEEIIIYTErXBbXFwcvi0hMSUpKfjv0nFLCCGEEEIIIYQQQkgcCbeffvqpDB8+XNq2bStpaWmSnZ0ttWrVkr59+8o999wj69ati9yWkriBjltCCCGEEEIIIYQQQuJAuH333Xdl3333lZEjR0pqaqrccMMN8s4778hnn30mzzzzjBJuv/zySyXoXnLJJbJ58+YIbzaJJXTcEkIIIYQQQgghhBASWVKD+dKkSZPkwQcflIEDB0pycmWt94wzzlD/165dK4888ojMmDFDrrnmmvBvLYkLKNwSQgghhBBCCCGEEBIHwu3cuXODWljz5s3l3nvvre42kThPTgbhFuGNKypEbHR8QgghhBBCCCGEEEJINaHsRkJOToYYt4C56QghhBBCCCGEEEIIiXPh9v3335cXX3wxXIsjcUxOjvE/Pz/WW0IIIYQQQgghhBBCSGISNuEWCctGjBgRrsWROCYlRaROHZFt22K9JYQQQgghhBBCCCGEJCZhE24XL14s5eXl4VociXPq1xfZvDnWW0EIIYQQQgghhBBCSGLCGLck5ORkoHVrkVWrePAIIYQQQgghhBBCCIkEqaH+YM6cOX4/P/LII6uzPcQhNGoksmVLrLeCEEIIIYQQQgghhJDEJGThtl+/fpXeS0pKcj9nuISaQd26IqtXx3orCCGEEEIIIYQQQghJTEIOlbB9+3avx6ZNm2TmzJnSo0cP+fzzzyOzlSQuhdsdO2K9FYQQQgghhBBCCCGEJCYhO25r165d6b1jjz1W0tPT5dprr5X58+eHa9tIHFOnDoVb4s2CBSIvvCDy4IM8MoQQQgghhBBCCCFxk5yscePGsmTJknAtjjhAuF20SKSoKNZbQuKFTz4R+fbbWG8FIYQQQgghhBBCSA113P7xxx9er10ul6xfv17uvfde6dq1azi3jUQRU5jioDjoIOM/ctUdd1xENok4jAYNjP979ohkZcV6awghhBBCCCGEEEJqmHALcRbJyCDYmjn00ENl+vTp4dw2Esekpoo0bSpy002GiNukSay3iMSa9HTj/+7dFG4JIYQQQgghhBBCoi7crly50ut1cnKyNGzYUDIzM6u9McRZpKUZ/x97TOSKK0QaNoz1FpFYsXy5yLRpHuFWu28JIYQQQgghhBBCSJSE29atW1dxVSTROOwwkTVrjNimiHl77bWx3iISK15/3fMcwi0hhBBCCCGEEEIIiZPkZPPmzZM5CHhKHIcl6kXQwGWbnW08f+WVqi+HOJ+SEs9zCrckUpSXi5x3njFgRAghhBBCCCGEJDphE27PO+88Oeqoo8K1OBJlQk1OBhAdo149z+sePUR++imsm0UcQrKpJqFwSyLFlCnG/40beYwJIYQQQgghhCQ+YRNuZ82aJStWrAjX4ojDnJY6punatSJffiny668x3SwSQ+G/sJCHn4SfPXtEFi82nj/xhAgneBBCCCGEEEIISXTCJtw2a9aM8W9rsHCbmuoRV268UeTqq2O6WSTKlJZ6nlO4JZEAg0Jw+LdrZ7x+6y0eZ0IIIYQQQgghiU21hNuioiLZtWuX14PULPLyjP+jRxv/Fy0y/iNZWVlZ7LaLRBcI9hoKtyQS4PZSt65IWpq3y58QQsJSx+xO4YEkhBBCCCHOF24LCwtl9OjR0qhRI8nJyZG6det6PYjzqE5SsUceMRKTHX+8yMUXe6Yyr1sn8uqrYdtEEucUFdmLuISECwwIIBni8OEiJ53knRCPEEKqQ0lpkhx93cGyemMmDyQhhBBCCHG2cHvdddfJV199JY8//rhkZGTIM888I3fccYcKlfDiiy9GZitJXCYnAy1aiOy7r/G8fn2R1atFmjQxXr/7rvcUepK4aLG2Qwc6bklkhVvUOT17ihQX80gTQsJUvxQbbtuNOzJ4SAkhhBBCiLOF2w8//FAee+wxGTJkiKSmpsoRRxwht9xyi0yYMEFefvnlyGwlcQRasG3WTOSee0TWrBE57DCR99+P9ZbVTF56KXoiKhy3998vcuSRFG5J5AYHcnKM5xkZhnBbndkChBCiKS41msOlZVUcxSbEAeCeWV4e660ghBBCSMSF223btknbtm3V81q1aqnX4PDDD5c5TPNdo+nUyfi/bJnIMccY7kvw+usx3awaCUSthx4S+fZb7/e//to7rEE4RbWsLMMRyRi3JBKgXJmFW8BwCYSQcAq32nlLSCLyxRcivXqJbNkS6y0hhBBCSESFW4i2K1euVM/3228/eeONN9xO3DrISEVqLDrEcUWFSGqqyGmnGa/DUSwQRxeiox3ffy+ye3f115FIbNxo/MdYCpy3muuuE3nrrcC/X7FCZNOm4NcHMTgz0xBuGeOWRDJUglm4ZVkjhIRTuC3YQ+E2ntixNV3++WvviB2pNjBWgKef5sEkhBBCnERqqD8YMWKE/P7779K3b1+58cYb5eSTT5Zp06ZJaWmpTJkyJTJbSSJKOKcbI8xxerrx/NRTRTZvFpk1q3rLxDJ00Ro5UqRfPyOj/IwZIiecIHLVVSKXXoqyKZIc8lBEYrJhg/H/s8+Mx3nneT4LRuw64ww46kW++ip0xy1FdBIJ4K7Vgm1amvE/Eu5xQkjNFW5303EbV7wyrZ3883t9eXnmklhviqNnYOH+mZfnuYeifUcIIYSQBBZur7nmGvfz/v37y+LFi2X+/PnSvn17Oeigg8K9fSRKJCWFR73df3/PczQQTz7ZEFjhwq2KqLp1q8g//xiiIMTBxYtFpk83EqLhfThxweOPGy5RxNYlHuHWjI5rVlYW3BHatSt0x60+T4SEGyQ61IItkiniOYVbQkg4KCnTjtuQm8UkghTtdUBv2ZgqDRoH2XghXvTpY/z/8UeRN980nufn8yARQgghTqLa/sTWrVvL4MGDKdoSW5o2NYRCuGZDBWLvgAGGoxbLGTPGCMEAINpa8RVKoSZiJ9zqKXLhTuiE8wQ3B4RbxCBljFsSCVCPaOFWDwxRuCWEhIOikr2O2yKGSognsnMMsfaD1xvEelMcyS+/eJ7PnOmJbWvXTjv9dJEFC6K3bYQQQggJs3D72muvBb3Af//9V75H0FFC9k5pbtxYZO3a0A9HQYHneb16Ii1biqxaVfl7EHeBFnWJyPbtIi1aeB+Jc88N3nELR2OwIq922CJMAhy3FG5JpB23AM/p7iaxBjMZmCQvgUIlULiNO8dtrTqlkp5eEetNcSRvv238h/lh0SLP+9aQVmi3oX2NGW2EEEIIcahw+/jjj0unTp1k0qRJ8vfff1f6fOfOnfLJJ5/IOeecIwcffLBsxfx2QvaCeLRVKRLmqVwQblu1ElmzxvNe165GDNb69Y3XOnYXMYRbX5FLgnEpIhaaVTz3hRZqdYxbCrfxw4r1WXLT8x0kEYVbXO+I3UdILEFc9wsuEBk7luchIZKTUbiNKwoLUqVpy2LZsZ0j81WhXTvPYPzOnUa78JJLRNCVGzLEMziv29Y//CDy/PPhOnuEEEIIiapwO3v2bLnvvvvkiy++kAMPPFBq1aolHTp0kM6dO0uLFi2kfv36MnLkSGnVqpX89ddfcsopp4S0EY8++qi0adNGMjMzpVevXvLzzz/7/O7ChQtlyJAh6vtJSUkyderUai+zphPuqfN2IiBE2D/+CO13aGSahVurg1Qn0NLiIsRKCjmeY9Gzp8hRR1U+x8GIsaF8F65HCGopKR7hNtJligTHzPkN5PNfGySkcIskiAyVQGKNriM3boz1lpDqUFJmTDNhjNv4E25btd0jmzfszXpLQgKzAQ480HDYIm/BSSeJ7LOPyKZNIqtXi/ToIXLbbd5GiWnTeJAJIYQQx8a4hRgL4Xbjxo3y4osvyujRo+Xcc8+V22+/XX766SdZt26d3HvvvdIY8+JD4PXXX5drr71WbrvtNvn111+lS5cuMmDAANmEVoUNhYWF0rZtW7WuJk2ahGWZJLJATJ0wQWTkyODdmBAD77/f46aFSIPkZnA3ffONyE8/iRx3nPEZ/h98sPH8ww8jtBMOFG7r1hW5+WbjtVng0lPkEJsWAuuff1b+vZ6Cbp1OZwfOKdy2AMItlguRjcSPiywRhHS7UAkUbkmswYDV4YcbdR9xLkUlRmxbhkqIL0qKU5Rwu2FtumzbQtdtqOAeCeMDjBBz54rUru1pr2k+/ji4th4hhBBCHJScrEGDBjJo0CC56qqr5MYbb5QLL7xQunfvLslQ1arAlClTZNSoUTJixAjZf//95YknnpDs7GyZ7iPQUo8ePWTy5Mly1llnSYaPufGhLpNEll9/9TzHiH8gEQlCLwzScOjq72rBZv/9RXJzjc6y5tBDRZ580nj+779h33xHsmOHIdxqMcE8ZgGHGI4tHLn4P2KEIfSaBTI8EDM4mMY8nGZ6vEavj52A+CIRBE5rcjIM5tBhT+Khrm3d2hjA4oCVswe58rLKGSohzigrTZJ99i2Uwt3JctV57aVoz94A/HtZ9Hu2vPiYvYmDGPd+hCvTQLi1u28uXWq0r7t04VEjhBBC4pGqqa1hoqSkRObPny/9+/f3bFBysno9F0PDcbJMUj3uu8/zfMUKY2qWv2Qu99wjMmaM9zTU7t39rwPxu3r3Fnn5ZW+RsiYCxyvEhDp1DKELYyobNhifIU4w4g3rzMGff278/+03z++1K7pRo+AE2CVLPHHUdMgEJo2KD9JSjJGPRIg7rAcTzMItyxmJNZhijDA+OoYkcW6ohHp5pXTcxhEYuC8rTZbMLE9istXLM72+8/ZLDeXLj+rGYOucI9zqmWsA4cXQnoYLt1s3471mzYw2IAber75apGHDmG0uIYQQQnwQ03lHW7ZskfLy8krhFfB68eLFUVtmcXGxemh2wRaqBLAK9agJjWM8IrWvRpxVwyVx5ZXGe9u3u3w2DufP9zgqIPD+/LMhPgXavNRU43cvvGB8X4u/NQ0U34qKJMnLc6nzmp2dJGvXuqRu3SSZNMkl55+fJKtW4RgluYXbH35wSb9+HiEiOTlJNex37XIFPO7r1hnChf4e1ldQEPh3xD+4Hl0uHMcqHsiKCinHT10iuwtdUtfBJwRu27KyJElLM8o0HunpSbJnj7PKmT6n+lEd9DIcd5/SMVr0cydtu+W6xG7s3p0kOTkuqVUrSXbuNO5rTtutcJVLJ5fJopJkqVerRDZuyza23Unbb3N56Yd3G89Zu1VcXIHbl6SklsvJZ26RD19vIPff2lIO6Z0vF41Zp76TnKzvCb7Lbk2uK/fsSZLcXJe0aJEk//2HnBMuNWtt5kyRjz6CYJskPXq4ZNasJOW2zcx0qTqtoiK88ZUSqVyyvgyuXFa7DRsFrOXSqWUS1PhySUIqJ/F8XdY0KkI4FwwYJSITJ06UO+64o9LB2bx5sxQlwhzjABQV5UlBQYFs2lRU5ZAXgUhOrifFxR5BdvXqHeJyldt+t7CwrpTujc0JNm3aGtQ6Ro5Mkl9+qSMzZhi/Pe+84H6XaGzalCylpXUlP3+rcsympNSVP/8sltq10yQ5eafk59eXt99GiIkS+fFHI+HH2rUlsmmTkZ3i339TJDW1tqSklMq6dXjfZl7dXiZMyJXZszPkyisL3N9LTq4r//2XL7VqlUVpjxO3It+5c6e6wVbpuiwulu0IeyEuWbthh6TXNmUfcRhG4pT6UlGxTYqKXGpAJy0tR7ZudcmmTc6xExeXFUtFQYWUpJbI7wtqSWqqSz7/sKHs3yVfjh6wRYr2JEtebft60UpJeYlUlFXI1s1bpSA1hIyDsQaDpLqRAvt/KNkS4+i6RDksL0+W3bvrSWrqDsnLy5N16/ZI/foljtstc7kUUxiiUHFymdxcnCSZuSWSX5Ir6zdtlZRsB22/zeVlntWE53jPaeVy644SkfJa4ioqlpMH/Stle0rl43cby5zP82TYhXvb5uWl4iqvkJICI7A+khYnUrmsbl25fXueFBeXSLduKbJyZZYUFW11z0o74IAkGTUqQ8rL8b0cqagoVnlEdu2qKxs3blWzCMK9K4lQLllfBlcuq92GjQLWcunUMglqfLkkQeGE67KmkW/ODhrPwi3i5aakpKiEZ2bw2lfisUgsc9y4cSqZmdlx27JlS2nYsKHUwryiBCczE6PwudKoUa2IXcRPPSVy4YWeVmBGRn01Fd8Kbpr5+XDWiRx0kMj117ukkd0XbcDXkDH3zTeN9dSv38grFm48gAZBpOtJNDZq106SJk2M44bn69alq0zCbdpkSFqacXxOPjnF7W4uK0uVRo2MjBUIq4DfNGiQJqmpWZXOE0aj77wzSY480iU//GCcqw4daru/B2dvZmY92/NLQru5ohOKeqhK12VhoaSXF0maJElOVl1p1Mh7iqmTMHY/SbKzG6r6CiDEOa7vRo1yxSkUlhZK8p5kEVeGPHr/3vgiIvL3wtry9ist1PMxd/wrXXoE0WMoEykuKZb6DetLdpqDMmMhbocuz5jD67CsXvq6zMlpuHeAMUnq1m2g7lfLl2co15rTdkuXy/T0dMlMrUY94eAymV5WJC1qlRv1ZWZ9qdXIQdtvc3khlIyuK7VA4bRyWVReKJJSLjkNUiUzI1PaHVguyR8YdUdmbWPn0rNSJCklWSpSMiSzdrqtcFsT60q00yDQ4ng0bpyl2oVo+7Vu7d0wGzUKM66Mz+rWTZXWrbPV7LV33mmk4uGec054dyURyiXry+DKZbXbsFHArlw6sUyCGl8uSVA44bqsaWSab4rxLNyik4DEZrNmzVIJz3SBwuvRo0dHbZlIcmaX6AwFuiYU6qQkV8T3t2tXIwagnl1TWJhkK2DujVIhEyca8bcaNAhtyL9lS8/z7duT4k48PP54keHDRc49N3zLRLiDzp1Fmjb1NDqQNRjhDkBODsJPGGEq9HtI6HbMMUly++1GowXG8i1bkmTzZiNuKKbS4YFpdtbzhIEhZCH+8UfPuWnWzPM9rM/X+SWhgZtrla/L5GQpK09WUUpKSpxdl8ERhPoDHUr8xwNVNsq6k3YL5wDn9OqzO/v8zktPNJG09PVyQFf/TmIsp1rlI1ZgW7W4gudO2va96OO+Zw/OpxEeBvcexPvGa6ftli6XX7zfULr3LJHmrfwEoU/QMllaniwN6papqfeFe1KkjpO23+by0g+gnzutXJaXpUhyctneej9J2rTzzP7RAm1yirFvC+bVkaNO3mMr3NaEuhIx4NG+fvRRzBY02prwoyB+bU5OkgwYILJokacNaAbJFQH6jvgueO454////hfeXUmEcqnrS/2oKjWhXMb7/lnLpVPLJKjx5ZIETbxflzWN5BDOQ1DCrdmNGogpU6YE/V297OHDh8shhxwiPXv2lKlTp8ru3btlBFLdi8iwYcOkefPmKpyBTj62CK2Pvc/Xrl0rCxYskNzcXGnfvn1QyySx4ZNPRAYONJ77coUjuUtensixx1ZtHXrQAqOncI7Gk3ALkWnbNpEHHxTBmAIEznBw000iRxxhLBcgPIJ52RBxQceOxv/HHxdp29bzPuoL/Obuuw3nBZLJYaQZy7BLTobEZ8AcTk43/AFiPNb0BHHBgOO3bBncypFbR0mZcTNwesQXdEqB2UEPp7dT90sJ6UkiL326WMpKRe67uZUs/jNbThy6VT5+s77cO66V+ozENxjkwj0HdSgEkvXrxdH10evPNJd//tgtY+/8T2oaxUiClV4hOZnlQSXlJNFpM6Wle2K/NWpaKqcP3yyfv1/P/V5JsXGPe3ZaKznq5CU19rRcdpkxY23hQuO1blcgBwEGOdH+e+IJ+9/qlCC4n+K+ioe+59rVd+EMoUAIIYSQMAq3v5lTzvuhKiOPZ555poole+utt8qGDRuka9euMnPmTHdysTVr1ngp0evWrZNuOhWqiNx///3q0bdvX/nmm2+CWiaJDeZkZJi29fPPhpAJZwAiWyCm0JdfGllvq0qdOsZ/LOORR0SeflriBi14gs8+Exk8OHzLNgtYaFxrURZACAcIlQB69PB8huzC7dqJfPWVyIoVHvEcwi0ctxC/7fYjNdU4d2D6dKOhr8EUo71jK8QPcOfBzTJ7dvhEfLtM6cCpAqc5OZlVuEVnNISwQHFFq7Z7ZMj/jAsoNc0IjwAB4o95RkHIymHSACcAcUNP1oFwi/uaU8vkjq1GvPP8nTUz9UFRSYpkpFG4jScgRKameUaI0cXodcQueeuFhjLvh1w5pHeBigtunpmBtklNxNpNg/NWE2gWJowO5jYq2o92wi0MAvDxhCt8AiGEEEKCJ6gmztdffy2RBCEMfIUx0GKspk2bNkFlPfa3TOJNNZObh8TDD4s89pjRwcU0fTgzjztO5MQTPd/Zf/+qL/+oowxRdNIkkVmzqretQ4eKIGdddbbHDARRCMqYsvbvv4a7tVUrkRZGaMsqoQPqa2FLx2wyx2aCMAtxsEGDyr9/8klDsEV2YS1ArF5tNNy141avQzfut2/HdSiycqXRUTKHp9ACPZKf3XUXnRn+wHHUAu7BB0tEKNWOW9/55RwByjdEW/PYIMojHFlOZHd+iuTW8iQhy8xySWZWuXTublj96jcslfFXtJEjj90px56yt6CQuAPihh60gjiCcDWoF53I1o3GDWDFP5lSUpwk6RlRbBjEAcWlSZKx13FbsJuWwnh03IKcXOP1uy83VMJtcZHnXBXsSpE69TjoZcUmEpwt2mmOtp8OW4Z2K2LdahZzIgghhBASExjcgiiiNfWpd29DCDU7k6zT6qsTEB77AcfnFVcY01erKkpDrEQHfMECCRtauN1vP5HffzdiziJkgk7OWhV0FBOItb6E2yFDDIeErxAq+C5+AyER4th//+lYZ0ZDHscSy9DgezjGEG0BnLlmzjvPWI6Tpw1Hgy1bjP+RdCd7HLdJjhfIrE4qJ4dKKIBwm+cRbjW165bLVeP/k/9WZciqZZny4uONZU8hb9NOEG4BJvXoARmnkb8jXVq3M24kLz1R82YnIaxMRppLchkqIW4oLU2qJNxiwCszq0LWrMiQ+25uKYW7U2TMnWvUZ9ddaIRLI94Ek/fk3ntFbrvNeG6esYWBfDNmkwAhhBBCokeVeoTz5s2T66+/Xs466ywZPHiw14OQQEDo+/BDj5MTQqEZPdJf3XVAEPXnyPvzT9+dbL1N5gZsddHOBSQSg8tSgxhkVeXHH43/EJm1AGwVbiEm+AtTbf4u3L8IWWF23P7zjyHCahEcn0O4RZxcYBYutLsDLt9wOTPmzjXi95rFaScAZ/natbEVbt2OW4cKnFbHrRk4bp24X6UlSVJcZC/cmh1l7TvtUf//nJ8jBfkUb+O1XOqZCPq+49T4qPk706RO/TI5pE++KqM1McatO1RCYc3b/3gEbcS0tIpKg/MTH18h2TkV8tevObJ9a6rUqm3UpQibQGGxasJt//7GDDAAYwXADALdFo7mzDxCCCGEVCbk3uBrr70mvXv3lr///lveffddKS0tlYULF8pXX30ltc3zaQjxAQRBZL3Vjs2rr/b+3NwRripajPQn9iFX3Q032H+mxVTdgA2ncAtxVAvKEEitwnWwaKH2qquMDg7CHtjFuA2EWbjFuYEwa05OppOawSF8662GIImwC2+8gUEc+2UiT6COmVsdPv3UcPy+/LLIF1+IY0DnEbF/Z870L9w2b4443pF1kWWllydEqASrY9ypwm3RHkOBhvBgR1a2UTFC2EXIhEcmNJdHJzaP6jaSqjnBUW+i/nUipSXJkp5RIYcfs1O5vWsaRSWGcJubVRbW+z6pOmgnpVqEW9CgcZk8+to/7tdZ2RXSo7cRoHXt6iDjAiQwGDxHyCq02fTrUEAoLJ3gTJsbtNGCEEIIIQ4RbidMmCAPPvigfPjhh5Keni4PPfSQLF68WM444wxppYdrCfGD1QUL8QWO0J49RWbMEJk8ufqHD6IOnKCBOmB2ybfMrt9wuqcg3CJ5mlmYPuigqjtudYP6zDON/4iZq7c5lGRXWng4+mjjdxAUIfzCPYbjp5NUwPn6ySfGczhu/YH9xP5Wl/HjPR0PxMx1ing7Z47x//HHfYfCwHFu3TqyTmKESsjLKk/IUAmOFW4LUyQl1SVp6S6/jtuc3HLJyDS+s+7fDDXQ9f5r9eWv37Jlw1qLzZ3ERagE1FNOFTgg3MLd2HbfIlm7JkM+faee1LxQCRUqVAJj3MZncjIzuB/oEF8NGpXKxVevlo4HFsqq5TVv0MHqhsWAMQa9MbvLnKA2WKZNE/nuO5FGjTztTN2WjlZYNUIIIYRUU7hdvny5nLg3kxSE2927d0tSUpJcc8018tRTT4W6OBIHRHsKlFWAgUsJMVjh5ET8VzgFwgFEyECimK/PdfzdcAq3y5Z5J3kAcFxWNRYskpxpAQtOWB1rNlTHrebss41zATECU+v0tF+7LOlo0EdauDWHsTjmGON/OFy80QAid6DBAQi3GOuKpHBbVp4kedlljhQ4rY5ba70BkcyJ+1VUmCpZWeU+O8BZOYbjFoLF3dNWyomnb1VxHTdvSFPZ1O+7qZVMHt9KJZAisQV1pXkgDs+dKtyWqXiiLqlb3whi+crTjWTr5qDy1yZcqASnO24xWPj++yKrVomjKVMDdhUB265a3G3crEQ2r695g1pmlz+S/mqhtksXkeeeC30WG2a3oA1Yrx76fCJffeVpb1YnJwMhhBBCoijc1q1bV/L3KjnNmzeXv/76Sz3fsWOHFDotCCVxE81R9PPP9465Fal1Q4QMJLzu2GE/VV2LldYOHFyt1hhqs2d7nKi+BEg0dj//vLLbuFmzqjlusSxfQqk1xm0wIORBt26eBr+OcYtl2QmwOnyCL5CErbrCLeLDajcbEp7p7XICGzcaDnJdxuxASIpIC7eIcZuXVeb4UAkQyKyhEpyanAyhEjKzffd+dQgF1IsQ0g7tu0u2bkqVme95HJCb1qfJg7ftDTJN4sYJ7mThVjlu9yaCeuTlpdKoaaks+9shFW41efzpVNm8M10y0ivULIU33rEE1HYYuHd+8IExY+X778WxlJQk+XTc2oHQMps2VFYpMVshkWO0mtup5rAIuGdq121VQFivb78Vuf56z3tODQVDCCGE1Djh9sgjj5Qv9s5XHjp0qFx11VUyatQoOfvss+UYbYsjxA/o3CI5GR6RFG51jFYr1oYnjOLo5ARy3EJ8Rcwwq0g7ZozhePXVoD72WJHRo73j+T70kMjddxvCrb8EVr6WedNN3gIqnLxaIKyKcGsWXPV/HD90diAw6vcxhe6ddzxJLPwtp7pJ5iD8tmxphB1ArLXTTgtvzOFIApftuecaCdzsEuDhuMJxi/3D+YqUi6W0PDFCJfhy3OJ9pyWjQaiEzCz7xGRAC9QpKdpFViq7C1Jk1kd11et6DYwdXrO8ZohqTnLcOj1UQvpe4bZOvXI1ODBtYnNZtSzxY4Y++4Ih1GakueTYblvF6Wza5Hn+xBPi7KSUqb4V1yYtSiTJ1Ivp3L1A5n5TS37/JUcl2Fv/X7p88Hp9mXpbW3lqwn6SqKBdpO+PVW37+Yt1a8aJg6WEEEJIIhDyPLhp06ZJ0d4798033yxpaWnyww8/yJAhQ+SWW26JxDaSBKSuoUEorE66cKEdoxDJ8MB6/vzTSEr2yy/e8cDw6NpV5O+/DTEN/xs39hYKtTB6550ip5wSfHgE8PPPIvvs42lU9+lj/Md6Qk1O9Y8nJ4c884xHuNUO16qGStDLAZgiZ46Te++9RoP90EODW044QiXgeGM5+pj5EuLjBZQVnE84QRHSAWEwUM7thFuUSxxP3THCax3qIuyhErLKZLfDXTK+YtwCHMdIHLtIUVYWnIuseasSd+IdDZ7f8+gKufTMfZVzd9nCWnJk64huLglhQMHpjttUU9zlPkfvlO+/qi3jr9hHbpuyStp3SnzFBqES2jYpqvYAaKxBnQinJWLom8P2ODPGre9RzXET13gNerbfr0j271Io99/aUvbrXCiL/zROYIXLJRUuh57MAGBgG20jtN2QK6JXr/Atu0mTyu/RcUsIIYQ4RLitB0VnL8nJyXLjjTeGe5sICWuohIsvNjrXiKH700/GZ3A7gv33F1m0yHiOpGjmTg6EXAiuOtmX/k0omMMgQMizgvfgTA0loZjZTaMFWjTaEYYBDzSsq9rh1NPs4OSFExodvz/+MMIomJPwBAKCq68QAaEmc9NAnPv3X4lbXn5Z5OmnRS691HgNV7Iv5zHKEkQeDA5gQAHfiYT4iFAJuVnlsrXY+QKZXagEgPLuKOG2FEmg/Au3015ZKrXqlHuJaDu2pcr5ozdIbq0KmfriMrnyvHayfUuIwQtJ2MtlYjluPWXukuvWK+EW3HFtGxU+AU7cRCY7o1zy0spV3aJjkDsRiM5oG7RvbwxKO5WSADFu9ewDcxgEuG4X/Z7tFm01GZkou2kJV/9ArD3hBKP9qOPQRsJgoaFwSwghhMSGKmWeqKiokGXLlsmmTZvUc2soBeIsYh37K1KhEnRyrV9/9ThldUiEKVOM/+ZEaFZnihYNZ80yHLYPP+w7m7g/4bZdO5EhQ0SOPrry54gpiwY3vodwAKEKtxodygBip+60VQV0ABC+QY/PQPS+/PLQRFstJCPcBKqHqjqqIfyak7np8xmvLF1q/H/8cSOkBvYb5xf7gc7zccd5jgVEgQYNjNda3MVxj0SohFzEuM13fqiEFEvYSbx2YpzbcuW49R8bo3Zdb4EMIpqZ+g3LpGuvnVJYUHOSR8UjEGnNGdsd7bjFgEK6d9yRug3KZPsWo4wtXZQtPQ63yVTpcMyhVrIzKySpQqR+PWcLt3rwVs/4sEsw6gTKA4RKsAN1oxnECP/hmzzJznNYTJ0g2LbNE5opEu5wtM1vuEHkvvs87zntfksIIYQkCiFLKj/++KO0b99eOnXqpETafv36uR9HHXVUZLaSJFRyMjOTJhlCVySA0GcWORcs8DzfG6ZZOR7tBFDQu7fx/7//DEH0xx89nfP1640On7lBiwQYViDI4rI44wxDqLM77q1bi6xeHfx+bd4scvjhRmxes4ilRcLqhEqAYGsWmDHtripTLSG4YkCgOuESVq70dtxC4I5GjFsI/VUZzEBCMk3//p7yhLAciCIDtzcc0cjnqIVb/Z3qhpXwHyoBMW4l4WLcAiQ5dJoDSE1JDyHhji9ya5XL7nwKt7Eul+ZBLScLt2VITmYZUHjoxWWSmWW8t3yJKaNoAmGeGZK6N650g/quKs2wiUfhVsfWd2K5DDU5Gding3FDgEP8zBGbZMh5m6VZqyJJTk687GS6jK5aFfyMrVAZOtRjWkBiYafdbwkhhJAaK9xecsklcsghh8hff/0l27Ztk+3bt7sfeE1IKEAk3HffyBwzxA997TXPay36mZ2NWjy74w7jf79+ns8QkxaJxaZPF7noIpGmTQ2xFOENIOYinumbb3q+j6RdVsEPwm0gJyXiiEEIhqgXrEDYvbvIwQd7v4/tw/rCHZsvVLetFtSwDVWtEhYuFPn6a+8MydFw3OLY4Vzr8BnBgvOuYxX/73+ecw4xXb+PMA99+xqdHyR802UPnetIVZ0QbnMzy6TI4aES4HC3Om51mXBKwrpQQiUEQ05uGR23McY68wLCLd5zIiUQbk0xbvXA4hHHGqNKH79VX/7+IzvmM3TCDepe1NM/T/WMUNav54pYnRxN4RaDXZgxA3DPSbQYt3Y0aV4qL326WIX1OOmMber1qDFrZE9h4g1yaSc1ymqkhFttYpg3T+Skk4w2EiGEEEIcINwuXbpUJkyYoBy3derUkdq1a3s9CIkX9rMkEUbj9uabPWIuOjXamXrYYSKffCLSqJHn+/Xrexy4y5cbUw7hSIVLF1PTrI4cuG/NMVjhGEVjt0UL/9sJAe+hhzwJywIBcda8nWYBGA7j6jhuwwnCUNiFdQiGJUuM/2anaDQct3p7Q3HAohx8+qkhKiO+7ciRns8gCOhlff+9Z1ouHDIoX/r8R8LdhTAVFa4kycuG4zYp4ZKTAdxy7JK/xX9ystDECDuyc8sdK9x+8Wt9+X2FKcZAAgm3TnQ2uh23GZXL5bBLNyr3IphwQytZudRw3i7+M0t2brcZTXEYqHsbNnB5hfSpX9/5wq1uAyChKOpJJ866MGZaVH+kICunXPbsTkm4QQfzQHY0EumhPQODgTm8CCGEEELiVLjt1auXim9LSLyDhFoIUWAGjVvdwIWwCFctwJR8iKFw2CIMwZdfGvHtzGMR6PjAHYmO+oQJ3nHjdEzYwYMNwQz89pvxH9md/aEFvEBAELz+epHFiw13rRVsK1w1EA7iRbhFWIeqAHEaxx/u1Gg6brVwG6zg/O67IscfL3LrrZ5zZA63YX4O4dbsztaOW5z/SIiPunOVhxi3Duy0B+O4xXXrOOG2NEyhEvLKpdChoRLGPb+vXPdMR0k04RYzBHDdWUL/OyaEhy8nuDkp2bSJzaUgP1nuub61TH/Y5kbkMJ59ViTPktwQoRKqeu+KB9AGMCfNwwyYYGf0xBMlxaE7bu3IzikXV0WS4++DVjCQrduz0RBudRJQp81yIaQmUbQnWUqKnW3WIITYE3Kv74orrpAxY8bIhg0bpHPnzpJmmUd9ENLQE0eRaC4E8zTP008X+ecfQyRDh7pLF8/nEDfhRpk925M0ConEpk61F96046BjR5FvvxWZM8cQjhC/9PXXPdPdETMPQi6EXWT5DRRqQIu++lzYxRtGh+OPPzzbdOCBlb+DbUHIhWg14gNRHScphFskhNMdhWhNi9dxjIMVbj/7zPu19dyZExeBQw4xXNjAHCoB+xtu9JTtXMS4dWCnPVByMqCT7zgtOVlYQiXkOTtUQo7K8p5YMW51aBc4Hs11lxMoLUmSdBvHrebpd5bIpvXpcvPl+8ikm42sXdpx6ysGdTyBWTKYlWIG99vff8f0hCSRsz3vN2ks8ttCcSyIt28+H2jrxItoiXsmBsiDSVpaUJAkWU2rb+/MzDbqGrTJ6lvadE4GbQncAxG+IBrXHwYDUN/hOJrzDxBC4odbLukoDRtXyG1TQkieQghxBCHf6ocMGaL+jzTNB05KShKXy6X+l9tlaCIkRrRtK/LMM/af6U62v9hgWizCFHgkUUNn4+KLjan833wjMnCgERf3gAOM51bh1irc+XKmavAbq1hsdu+CM8+07/TAcYvYsCAeHLfVEW7XrvWONwwghMBJZHUThVPofPHF4OIBosOP0BaBpkXr8w9BF7954gnDbQvHth5EQFn580+JqOMWbiu4AIPpLMcjuK0kiuNWJScLw/TfnDxnJydLSnL+iKG1LtLP40UkC2fs5cwsl7RqWyw9j8iXn7/Nkzbti1Rc3A1r0+S6C9vJUQN3yKxPastJ562QI6+WuAJ1BOJzYoZEy5aVZ0E8fH+piKkub9rEpZJKYiDUbnaL00LLoK0TDyE8cF0gXBViwAeaiQQQZqhlh+oLt7h3pGeWy+6CCDQcYsh33xlhvtBeivRsJN2OQZvGPNuMEBJfbN+aLru203FLSCIScjd+5cqVlR4rVqxw/yfECWAa/mmnBRcnF48RI4w4plZXpTnsApJSAR3TNFjhFqEZsGx0+n2JUOhAamdXp07234GIBecmlhMPAh1CAFRFuIV7BPuLJHBmtMAeqQ6KFk8vvDCwcIvt0x1hCPi+0CL8qFEi77/vCaeB3yB5HoBj5ocfRAkFkXLcgnjouIc7xi3KvDkrvBNQApkfZ2Ow5CDG7W7nCreIv+x0rE5TPMfDiQl81IBCENPS9z3A2LkBg7bJxnXp8u9KI+bt158aFrwPXmwt8caYMcb/554zBjf17Jir9wrMh/b03u/mzQwBGwNtTh3oMjvB4yVUghb8go2RumJFsjRttScs68YMhS1bnF/naF55RWTXLmNWGYiW4x1tmlByABBCohsmAeTVpomOkEQkZHmndevWfh+EOAG4HiGgBRMnd8YMQwg1O2PHjTP+mwVSdALhCkXohVCEW4Blw3npKyEKpnn26OFfuIXjFt+Llym6cCphe/zx9dci554r8tVXIpddJrJmjciRRxrHwZrUDYI0Oic4rkuXitx2W3i3F2ItnNMIl2En3F53ndFJgnMWYb7xPUxVxPH2FQ5DJ5GDO9csRFtDQAAd5iKcYie6qTkZZY51AQYT49ZpSYRUcrIwOW4RKsFp8VT1pJwKTE9PsFAJup5CqAQnCrfpmYEL0yG98+XQI3ep/4ij9/A9zaXtvvFbuaC+1mGGPvhAZPhwkWuu8f6OdaCzWVOXHHec8+oWXwMKcNzGk3AbzPWBsEg7d4g0aBKei2mf/fJl0ULn1TnrtmbImKcrxwNHuC6AdhJmlVnLdKRwYngiQmoKy/4yErOkhSE2OCEk/gh5jPYDtHxtQJiEzMxMad++veyjMz4RkqBoEdfaAUFnA0Jv48ZGJyUUERXC7dy5hnhoDQUAIRGCLZJg6dioVnTMsWDF4mi4miHE+orbC+bPN8JOfPSRyM8/i/z1l+czc2I4gGXAdQunyVtvGe9h2mV1wibAhfrCC0bYAnTS4RLGwyrcYjIBRGaA7YRryywsP/qovTAKR7aOaesLxL3FubW6WLBOs6CA4whxF9NMg6GsPEnSUiskPQ1hbJwt3PqKcYsyDxHfeVPSq9+ozq1VphLuwIGeuzfsixNwsvPbbkDBWv9AJHOq49YIleBf3KrfsEwuH+cdlDs52SWTnl4h23eVyj1j28bVYIK+R190kchTTxnPcW8OJB5ils0FF/i/f8Ur8RoqQceoDyZWPWYPoS2DAapwULd+iSMdt1//Xk9m/1nPSIRoCQOGWTpor5hNBdEQbs2zXD75RKR7d6PNSwKzuyBZPnuvngz+XxXjiBHih6V/1ZJOXfJlyR+1ZMe2FK/EolUB/TOYcDBDFf20eAjDR0hNJmTH7aBBg+S0005T/62PAQMGKOG2b9++sp1DsiSB0R05awf97LM9UzJDcdxqEWr6dMOBau1kIcYtBDtfoq1Z6IwX4RbCpnLN7PR222HsRyfE06ImEr1pIRciKsRROyCErzbF2w82iZivBsnRR4s8+aTh9sV/iOc6Nq85aR8cWz17GtMEL7/cyERu7qgcfLBI795V2w6Is8jpaA0rcdZZSfLoo7leZQAJ24IVRVTnPcUQbTFV1snCra9QCRgcQPkKt1s50snJUsKQnCwjs0IJZvm7nCVG6LotEZJiKjHF4riFSOa0aw31MhzQoSbNS0oWqVWnXC67YZ00bVEizVsVSUV5UlyFL8GAHAZ9EK4G95uXXjKmmOs647zz7H+HGRK4vzsxnqc1OVm8hUrAPRzPcS+zE9BRN+B+13RvyIpwULueM4VbHVLGGiIKx2jYsOiHxUI7U1/f2AaYCR56KLrb4GT+/j1b3n25gRLVCAk327dkyL4HGpXF7VfvjcdWRVA/oz+K/iza2StXhmkjCSFVJuRb/hdffCE9evRQ/3fu3KkeeN6rVy/56KOPZM6cObJ161YZO3Zs1beKRB2nOUrigXPOqRxuAZ1D3VmEe9Qu0ZgvdAcGN0ezCIjOI5yrRxzh//da1DW7VmMJ3KZwg2DbNcuXi9x5pyG4QvRAyAEziAMLAdtXZwTCKvYPcWgRhqCqyc/0VEOzwILOPDrrWD/EJZwPHRYB24uM5GaxNpyuOjhmtFgMB4suC8iqrdEdNziRgp4um2J0fDMz4nv6NhqI/oQ8X45bJBrCgEawxyQeKA2QBCqUOjs7r8xx8Qa1gOSKQRkLtwhnnZKuhdtgHIXxKKanhxh7+YFnl8vUF5ZJwyZGQO2MTOOsPjTFR+yYGIB7Me4buo2DOhzCE2Y0INzNVVf5vn9hhsfGjeL4AYV4CZWgrwsMkh51lDEYinbNzJneojNCC02eDKdX+GqJWnVLZMtm5zV09X2xcI/3tquZFjEIi4U2LdpKqE/1LKRoxddNBBAqCUy53ZQlkcQc3Mu1KSLYGNzxSP6ONGnY2Lihb92cJndf16rKy/p2TrK73wZw3RNCHCbcXnXVVTJlyhQ55phjJC8vTz3wfPLkyXLddddJnz59ZOrUqUrMJSSRufZakUMPrdyoNU9lD2Uavy/xHB1PNIx1KARfYN0QfEeOlLgBnWTduAfIfqz3yRqOQMfwxVQ8fwInBEgIrNoZ+88/RvgEa8cUYoTZ3esreRfo0EHk3nuN0WVzEjS4fs46y5hii+0yi4cI0xAusC+ffSby66+Gg+X11433IWAPH56kYgBr4dbsOPYF9vmbb5MlY++U/KwsV1wLtxjRP/FEw3Ful+PSl3ALIL6jk+8UytSU9PDMJc/OLZP8/NiKERCOdVzvYCgpNba3pDS6VrGPPzbEonA6fe1CJcCRVp0BpVig685gkpOZgWCblu59QBu32CMNGsaPnRoirfmegvsoRE3U7dYEmHb3r82bxXHEq+N20iT793HfM9cneiAB2x1Ox+3Wrc4TbotKkm0dt3itk+NGE9RvOEeDBolcf73xXqihUXBN/fij1EgKdxsNmT2FcZBBmLi5+24j/vk99xh9O6eyc3u61K5XKg9MN9TWJX9l+xWiF/+ZpcJ32PHpJynu2YmAwi0hsSfkO8fy5cullo2NEO+t2Nvj7tChg2xxWs+FkDABAVCDaf/BcuONIg8/bDgugb7ZQuCEYyiYKXH4Pab9xwurVhlTU7//3niN2LAAkVRQRWC/dKcaTlzgrzPSZu/MH+RB1MItnM9TphgJzsxccomxTLsEM+jYzpplPP/f/0SmTRPp398QYSAQosOIjtF//3l+g86kFn3uuMN3QrKqoN3SF19s/Mf2gM2bk+Xvv43kI7rRZHYw++sYPTU9VTbvNFSlnOzKHb94AZ2+xx83XM2PPeaJXxxMcjJ9XuCKNgvxoYLzqpNmRcNxE45QCVq4jXVjGudrzJjgv68FpOIoC7f6WtazIqoLyoxdqATUaYGSMsYbWiizirBVocuhW6UojINEuGcsWlT130NkMsdLx30U5w2DY4HihWNWB+5hGBx0CiiXVic47muxFm4Rbsg6OwCOWzzMg5Hm+iycoRLguN26JXSRMdboetI6wwf3cz3IHAvh1ly+Qp1h8OKLRl6Amkjh7mRp1bZYtm5Kc1xZNFNYlCz//BeDkYMI1Zn//ms818YBu3ODASbdP4tXCnamSa06ZdKoaak8+ZZx47rjGt8hE+65vrVcMnRfWfS7cS7LSvfeQ0qT5Nd5Rt2jTTZOm0lESCIScs+pe/fuylm72WRDwPPrr79ehVAAS5culZaYw0pIDWT//Y3/COIeSvwxJJuAYxYdfy3QofEAkRBTHZ3Im2+K14jt558b/zE1EkIdOsZIzGUWL/01DrTIu+++xvP77/d8ho4AXFRaGNaZxPVUVzhOMRHg6quNjgM65IjogtcI6WAG4jHEFy0m6/jFZ55pnCO4Q8OJdf1g3DiXLF1q9I4gDukObTCZzq3fQQcvXoVb6xifnTsAooMvoRwuXVCdOLdIKNirl0QxOVl4BImcvDLZtTOp2vGeq4MWzION6xor4VZfEwsWeOqjcOx3Igi3xUVJkpziCku8zKyc8rC6wOGCQizPqoL7iXVKua6/AzlucX/CoCAGB52Crj/jzXH7008iAweKvPaaSL9+xnuYKTF6tDHwpgdFzcLtOf8L35xluNAwOBdP8ZeDQdeTuwuT4ka4RQ4CLeagPRTK4CEG8lEHg1gPOoaboj1JMmrwvvLfat9T7fbsTpFWbYtU8tgdW+MnpEyoPPZRKzlnUgjOlDjG3DZG3gtf/ZDbbzdmxMVz3V9UmKLahSA7x1CfVy3LlAfvaCHvzGjgNePILE6//FRj9dmIU/aTX76tI3/Nq6sGi3R/FDgx6SohiUbIzfRnn31WVq5cKS1atFCJyPDA81WrVskzsIWpCq9AbrnllkhsL4kAiZAkJt7AzR9ZOKuC7nAhBitCC0DAdVpnwzzV9IorPEIJyho6kXDYYL+QXOq++wz3K0JFHHig/1i+6HCjasEyEHPW7GyGWPLII8ZriLLamasFGwgA48aJfPedIfKi037CCfbrwW/QodSCOsJPwJ2F5GBwNYebffYxEpxp3nhD5OST0dEtcovPTzxhfGYNMeFr+8GE4caIe3a2K24bXTruKAQSuM7NcY9RXnAtwbHtS7jF+cHxM7ujQ2XxYs/6opGcLDU1fI5bHD8k9UNZRvmPlnNYo0MFBBtnGO7O5CSXlFUkRdVxZI5VChe73TTupUs9A0u6TASMC2vpo6OjE+9xURGWBXWwFp8N53D4wnfEkzMH22IVuLQQG8hfAOFWUx1HfzSxG1CIdXJKnANkJEdd3b69yKmnegZIUXfDwanD3eiBPNxrwxkKAPGbca06yT0NivYKt9ZQRxA9Y5GIVg+CQOx68EHjWgq2fYqy+c03npAluG8lEuv+zZCiPcky7pK2Pr+zZ0+y1KpdLrl55XLr6I7q+05El8tEQLeNUeegPKMvYmd0iNc2tEYPhKBsac4caYzo//pjrkqKt3Gd58awY5tndG/Nigz5+tM67nK8ZUOm9O1X4c63gv5PvO8/ITWBkGvejh07yqJFi+T999+XK6+8Uj0++OADWbhwoewLG5wg9tEgOc9Xql4SlzA5WXiBCFCdOEkQqyDgaiFBuwqdCBy1EGYPOcRwRZ5xhtGphNAD8RTOZD2V9fnnjTiUvkCUFsRWA5hiqYFAC+CyhTiL9XTtasTE1Z0KPQXq2WeNziOczIGSx6GxAjdmpMNP4FxroRjiJTq4eO/qq3fLq6+6vFypiNurRSNfwHF8cFeXHNfdUHlzcwyXi04yEE9AeIQIf+SRRrIgs3N04kRPY9RfaAq4r80JbqraGcVx8yeGh6PTj+RkqenhEcngrPjy8xR3iI0XXrAXJSOJFumCFc4R47ZWtuEIiaaYhHI1YYJRzn7/3fszdEgwWKJD82NwCCFU5swJIi5sqvNi3CI+95AhHrFMOdrDVCazcmIfd9kMREGr4xb164ABIgcd5P+35rjyTkmAqIVbc7mEAI3yH6up2a++agxCQ6QFhx9uCHhod+L+b74/67ipuA+Gmy5dK5RbNBEct7gf2c3UicaMMj1LCuvHQFUws4AAyiBc/ZjlhDKAtlWsneDhZO3qDC/3rR17didLVna55O9MUe7bZX+Z4rg4iOT4qeKrDe7/uG8PHeqpk+wGH+M9tMXOHUmSkVnuFfLopKHb5Kb7PPHVrruwnWzdnCrnDdxPPn+/rqTsTWAM3n6pofr/4atN5N/ludKqtcvtuG3RIn5n7ZHEAyHNPv001lsRn1RpyCw5OVmOP/54t3A7YMAA9R4hJDwg3hLcDLoTozs8TqRjR+/X2gULh6zZNRsqutOCcAnmBhXEFzh84aZCxxsdQnQaUUUhVAPcuZiuCWE3EOjgh5Jgrjpo8doqXOsQEt26eRzFgeLcQqyoU9vTIMvNdanYXAj1EG+8/76n8w4BVounOKfvvOP5nr9bTHVjj2kB0d9xxYBAOKZMl5UkhS1UQt0GxbJypXcPKtrufO2YDrbzDmEpL8twhESz0w7RADGy4T788EOjfM2bZ3ymy5l1ABODb75cJhg8wfetsZdR56AMhyMcQ6TRIr+KIR2mMglRoiDfOL7mpFOxAkmQzLMZ9CAQktAEGrgbPNjzvDqO/miiB/XMwm3TpsY5jpUTXNcROkY9rhuzmD5+vHG9XHed53uRuO/26FnhTpDq5Bi3cN9apzFHC7igR4zwDAjgAZErGDce2nyYZdW9uzFwFMp9wwmsXZMuRw3coRyPcC1aZ78ghMLughTJyqmQq275T4WVWb4oQCUUpyQnJ85UTZRds7sfbQT0vXS9BWAG0SaCeJ2liuswM7vylKt2Hb3t+r/ONaz6H79VXzKzKuTKm41KcdcOT2Nm0fw60qKFy50gEv0pOm6jS7Rnz8ULuL4Q9lDnxiHeBKW2Pvzww1K0t2eL5/4ehJDwhBhIFMyJYXQDAOJSuIRbGP3hnNICJ3j5ZZHDDjNEFMSzxdRMrCvYjs7eqC9ejqtIgw4NknRZXTRaXMBxxD5hfwOJCEZCHpNwG0QsPDii8YgmEHcgoq1cabzG+YGYBxFWx6zF54FEvj59jP9VbVhqsdNfpz5cQlx5eXLYQiW06ZjvFpjgXoK4H+24gbpzE+x6cR5zs8okKYrCLbYNDwhYmAyEawqzGZDAECED9HnHdYPnmIIMdy5EWV/xaiGQQRyzir24TiEMmkN+xGtnwBMqAYMJ4bETZWaXyb//JqlZFhddVDkhVSTqEKzLV2ca56+qA58QDzHDAekbTGkd4hrEObQOKKCcom6NlfiMwVOEOdLCrd3MFtQjX39tvEZdFgmaNHVVKxZ6LCgqSal0b8PAEAYyAw08RAodYgRlCvUdQpEE40jHfVa301Am0b6KZQiP6oI6Z8lfWVJakiQ/zs5TQliL1sXSok2xfPp2PTn/pP3cpgJ8FyEUfvspV7KyKuSQPgVy/OBNsnObZyoRZn0Ek3w2nhy38e5CDQYMhJjD6eBcob43h0u64QbP83gts2XlIik2bcv0DJdcPHadjL/fyAL54uOeDiYGEnocni8TnzCmPNRtYMyGKi5KkbbtKtzXK0MlRJdffjHybsTrIEE0+hROze0TaSyT/Ox58MEH5dxzz5XMzEz13BdJSUnKgUsIqR6I96lDAZjj7DkVJCnDNCSA8AgQUdD4qY5wC5fmV18ZnRcIV4gpDPcGpsACuDrM4olZ2A2E7mCGM8ZeINDZ3pvf0fZ9HRcQU5Z0BlxfQCypZYp/l5sT/N0fDfFoTaCwhibQMfuQAAJOagguENu0sO2LqVONY4Rp8JgKG+r2ozziOPub4q5FRqs7I9Rjhxi3do3rqrBPxwJ54+0SadsqS60Xxy/a8UXRyILAFaxAp+KpprgkPa1CikuiM98SojvCpuD46PpHhw2BkIO40agfID7o6wyDQaif8Bmc96EkzMMAS7wm3jELQNqdiXOSGibhtnELw93z5ZfGawzKBDO7wQrqOGsYCjtwHtGphsCKOOBIgIXjr88R7jPWwcNQQIcV92CnCLd6QMEKyup774n07Rvd7UHHE65n1M3BzsrRLq9w06SJSwn52B50iq2DLnDlz53rqQPigaKSZMnOKPcKlaDu77Wid5+20qGDISzo44e6E/dOhKDyB+5NZrEZbbh4FcGCYe3qTLn7ur1xuvbSrFWxtN2cJp+8bbgE/l2ZIa3bFXu5GbVbtU69MvlzQY5XiDWEkEB7Jt5BnHqA85dtCUXjNHBPNAu3ekATU7VhZnjySZFFi8Tr+tOzxOKJslLkT7C/jx9+jHeD5KwLNslrzzaSIcOMG1uL1iUy5LzN0rn7bnlsUmNZ91+adNzPJV0PFOnc2Qi1Rsdt9AY6r7nGM6gQzX5oPKAHV52SVyDaBHXbRzKy+nttYHju67FCB6kihFTvwkw2XIfoYKCz5XQgwOnYtJgOrzuY1XUWmzsB6ESgmkI4AXS28dzcgfUXI9WKvlGG8ptIAieuTtoGt4t2TkGoeOqpyt+HaFS7ljlUguczu+RmZhceXDEQP9Fg1WIFBHGzWwtiXThGgh96yPu1uSONsBYHHGA8R0I5CNa+MP+uKo1LNMQRX9eXOAMBSDcifH0Hoh8GWyAg+3OhlIUxORlo1tzl7sBDmDRP74sGWB/OjVm49ReDGTFuIdrmZJRHTWSGqxZigz5GuK51mABcDyjfEGfNZRx1CxxhvgRplAlf07mxDicIt7pM43yFazABUy+7HVzhjiOs3fShgsE4xBkOhHb6wUGNeyaSYWqXtD531RFuAUT/eI9bHEi43W8/kdmzo789eio8wiEEcx8/66zIbQsctzrxqJ0j/uOPRe66K77ONZJA1csr9UpOZsyoieVWed9ztXAbzL3CnFANjionC7e3jbaMOCgBrFhatvHs1Lwf8qSkOEnGXeoZ/WvWyrhB5tUplfwdaV5tMKdMj9ZtDiefP411MF633zAYiGtNz8DTxOu9PZiQR4+8vFROPWuLnDBkm7z06WIZdLanQzDonK3SrmORnHHBejnxnDXqHGPmBtpOELYZ4zY63H6757rC/QrlrSY5bzGDKtHC6ISTao/XlpUhg3AcpRAmIVOTKgQnAZcPOmDRirEaafQ0fPP+WJPGhAO4rdDgAuaqKZQGsd7GeBFuzUC41Y5bNCjthFvEMjR37Dp19CiJY8ZU/r45Liri/+pkbDo4PCZSaOEdcUERhxcDCtVtwOq4tJhSbQXCbaDkQXZURbjF/sMt5CsGJMQhPW1Hx+azAkeCrk/9TUkuQ3KyMMUTtRJu4RbixnHH+ReicY1pFyuOPa5zZCL2dV9B5yI91SV1cktl566kqI3ga+e2HuBZuNB4fd99xrmDcGsOlaGTJvoSbnGOfTlCIyXcYpmffVa9ZZivD+0ih9gXruRkVqETsWTNolMw4Jij/JiPva8yqK81Ha8Y4BihHJ59tlGXW+MQhwrc/jpRKEIRxHObSXXebfb30EONMh3tbUeZxXr1gK0vkIgSsabHjo3ctpi3wU5o1G2EeIpPjRi39XJLKzluoxnGKRCoT4MRbnGvMAu3KBdOTU62Y6vRSKzXoEwenuEZBahdt1x6H71Lpjy3XIZftkHee6WBXDCoo0pGduwp25VY1nZfQ5WpXadMdm43lqOPn1MSRZfvrY/3FDlkg0MQbs1OP+vsNrR1op1HIFjUzBkfjltNnXrlcvrwLX7LWZeeu2TAUO+4YTg+dNxGB/NxhhEJycqDGcTWQPQ1J3l2GjoZO4Xbagq3H374oTyPlO8m7rnnHsnNzZU6derIcccdJ9v9peQmcY1TGgvEuSCEgTlLO1yxkQaxT7XTrirTLgJ1NmOBdtxqV5k1fiOEO7jdWjb3NOA6H+iSV14xjvlff1VeJlyH6FAhZp1ZvIIYAreiOdaXnljx7LNGg6K6HS8IO3bCLYALNlh0bOBQXQEQhHDMMIUYMRZ/+MG7YQ6hA2E+Ag0yfPutkQQJ7jEIELrxYR8qocIRwi06LWg8adHKDqwPnRmICSgrGjiPX3qp8vchEqanVkidnLKodYAgvJvrG5yjP/7wbiSjrJkHd3BPhDhit40Q7yZOTPIpVkTK+YyBjptvth+sMYP6AVOZgw2VUKY6fOFT9Bo22jsVuI73oEaw2CU1s3N24X6C6F0Q0LVLA2CmCkCZ9Of+DhaUZQj9qBsggOL1P/8YMyHiDSXCp9nfyyCgx8KRbxbrfHHssf7D4YS7nWs3c0J3FOMpgRmE27p5pV7XbTw4bs0E67i1Tkl3shj0xdvN1f+pLy6TuvXLlCCLB8oYnIoNm5TKwYcVqHAImmGXeo8M165XKoX5qaptqqcHo45xwjEpLTPkg1AH5eIRa7lEvwH1PNoJ5roAaXxQj8a14zaM93EzOBYU0qKDua2j2zWhDCYi1OIJJ4gj0QPL++9v3FPieZA87oXbKVOmyG5Tj/iHH36QW2+9VcaPHy9vvPGG/Pvvv3IX5hgRQogNaNDqUW0k/oHDJtIgjt2rrxoNaWtm8UBAHDnzTIk70LmFEAXhFFiFCTSu1P52877jIanZtGmGyGG9GUKcQoMUD7Ojb9Uqb4fpn396RAEtHGsBLFS0kI5YxGaQmEaH0AglqdBzzxn/Q50Agu/jeKCsaHexji8FdCP9ggsMFzCwE6shWqKxhLio2IfHHrNfX3k5QiVIRIBAEs5kUPqWP3++/ec4bjpUAo6TNVrSRx/5EG7TKqQ2hNudkR8xhDCP7TfHX0SjENx4o0e0tYvD6itUwkMPGb28Ll18d3IikZRLX7eom3yNk6MTCnH30kvtPzd3tD2O2yRJDavj1thQZOaGwBRqwh10UrTQjnsGXLN2QhvqI5xL7K/ZYeJLtK4qcGtD1DencHjrLaMOjreOBQYV7OoXxI3F4JOvZHuRAvVrJGbWVGfwA/eVRx+t/Jm+puJJuEWMW8Nx63kPdW6sEpP5Em4xkDFypPHf1/R5NdvCNOMK17aThb/jh2zya3qBG/eRl5fJRdeul8tvrFyoatVBJkGjzYbZPrrOc0ICPQwoACeIzKE6bhGmBzkWUG+Z7ysQd3HPRLshHpOyoe6PlHCL/gOOhR4UJUabPxLl31edGOy67Aa+IwVmt37zTfiWpweWEecbbfJECMUSM+F24cKF0hvzH/fy1ltvybHHHis333yzDB48WB544AHlyiWEkEBg+rWOXxoNvv/eiH0YChB6IyWwVXcaMhLwvPuu4fyCW9YsHkBAg3PWDgizaNxZGwYQbiFSWbN4mjsQECQhglt/C4Fo5kxPsid/QNjTgpav+JOIxaiT1YTSOUWnB53CESNCa1Rj3yFGm11h6Hzq/YcgjGNzxhnGf3TU7EQzCEto3B54oOc9q6iD7XJVRM5xi2MQTHbvYNGitY5XagWdGJQnrBfnE3FjMT0dMZKRLBBlwhrjtLTMSE5WOyf8oRLsRDRsFzrG5gECnCfzwMAxx9g72CBI2IXP2L7daDqhrEXTcWsWL325X5AESoeBCCbGLRrGpRFy3CLJ4/HHB18mUT8gxAE6Hv36Ge+dfrpRH5j3CecZA0xYLmJ/6/oC+65jgYcbq4MFrnoQb9NmVbxiH6EhMCDmKxxMpMB1EE/CLUJQYQaFXax31OsYGLX7LNaO25/nJbsFZdTLwbiYowXqSVyzGMQ95xyRa6+1/54atEsQ4XbntnSpXTe4aVxHHLtTDu1b+YaA6zS3Vqls25qk6nPclzBIFK+OTjMl2nGbIKES7OooXGPmey7Kq76vx1MdYZ7NFShUQlXB8UHosnDIPGhvwFnudGBq8TVAXlUwGKz7RkhWaAbGm2CI1mAK2mB33hne8EZw2SKEDvqw2P94TALoGOE2Pz/fnaAMfPfdd3IMejt7OeCAA2RdOHuMhBASJiDMxSoDcyQ47zzjP0QOCC9wg2rQ6PcleMIxAOHR2jHQWaqtgqe+aUK0gJsGjVa7jtYttxjuZF9ON3TqMCED4ucDDxhhCSDoALtpvWgI3HGHhIx2H9u5yrBvdmKXPl44LpgKB/TxQzZhuGe1EwZlCOK3eVoo4sAiDAU6/RBG4DLUWMU7iJwgnCKZGYgOCG8QLhcgth+dFV/xsvT+wXELAQvOSmwDjtO4ccZxs4ZZKC5Okow0I1RCOF2pEIsxkGEF5wodMHOmeggNAOcL08oQP8x8zegBG308rWRlueTSSz1J4axgWSgP4Xbd4jwg/jSOq93ggfW82zldUV+gDCN7uR6EgACfmha+Dl+jvcItygW2NVgH2dy5nvoCdYSuHyDM/vabp36BixMuWzjg0SzVAjwGViZP9iwPXgNfMalDBQKyHdEWQquanEwP+kXbzWeNaxoPoFxANDPf71A3IzQQnPnxNCUYwm1ZuVHR6DBHwYafiBbWkEY68WMg4RaDZfGUCC4U9uxOkZy86mcSa9CkSP5ZkqzqYdTLsUgwWhUS2XGrwbkwt31QXnU4pXg8R5EMlQAwKG/XHgoVODTNs1ecjL9B8qqgZ6mhLYRQdHB/Y7ARvPFGcG37aA38vP++x9QTLnA/MEmNxIagpYzmzZvL33vvxkhG9vvvv3s5cLdu3SrZdjUfiXvibaofIcQ/etq3dneawxX4E24hNNklTsJrNEq1UwtTVOCKhmMOnH++MeKOTri5oa4/15hFEzNffeW5yWPaeqA4tBB7TjxRQkYnULMKjRhT7NnT2Ccr5niB+nhaR3nNDQmIfeblY0oPxD+IJeh44T8aXTjOVgFZh4eIVHIyxJqFsA7hAfV6dTtVODY4x77cJVrYhRCKTjmcyojBrMsaXPVWoUhNnc4qV6ESwuW4xXbqhHpWtxccM1qo1eimChrEcGnitRa7UPZ0XDEItxA/9T0S+9uzZ5Js3pzs1w2Oc48OjmlsuxJwbOJasIvBimN0992V780odyh/KK92jXOr+xONfqsIhfKBuN8ot9hOhENBuQxncrJO+7vUNY/jCgdZsGP65jjk3boZ/1GesM+YYaDdLRgsATg+cGZA3EUmZtRTOI8IU4Ip8RiIwQBCODCH2jBjDeGAbdMDNPEUKgF07ux/WiPKeriTmsSbyKjrcwgw5mtIx2HGPSBe0nVAWIazsVdH48LW9Xm8HVOdR0Djy/FtDZWA+5VThdvSkmRJD0Od2Xa/fJl8X6qKie8k4bakzLh3FzrUMR2scGuu39GO0CHX4DaMN9douGfOWEHbaNEiQ8CtDjpEU7SnweP+Fg7hGei2WbhnZbZubRhXtKEFzyHY6nZtMPcmfVzDHc4DoSHMYeJwzcCA4+88oh0WSugG9DWs7XVSReF26NChcvXVV8tLL70ko0aNkiZNmsihiN69l3nz5klHPV+NOA4mJyPEOUAU++knIy4rXKxAi6H+hFuAz5C8yCySaPESzkO4eJ95xkgaph2DcM6hEQtBCcIPxC6A8BM6Xqj+nhX85rXXPK8j6WaC8xfbYxUazQnZ4KI1i2Vm4RbH5sUXPR0n3SgzC2gQ+yDI6k6/FnUhmGgHJhpdcAHqkAuVhNsINa4R6gL7gP1HDE6cp+qItzgOEG59nTMdv1KLCOiEo+FpPlZWIahgd5LkZpapUAk7wuRINSdFMydAxHmDs8N6DCDiI8alWUQw3wt1ohKUZzRUdedN///77zS/won5M3PCMw2WiTjfF19sCI5WUF7fe6+yqIH1wzXpq3OPjgkavSjD+rq0lkEcCz0wgTKMa3jO7BTJzg2v2qjroFDCd6CcoTOATgquJYiMGMDRjhOAZY0a5Xmtr92TTvIkt8L5Nf8mHGC74PiFA1+L4mgCW4Xbs87yPYBVHVBmgklQYnTefQ9sQaA0Xy9msE8Qv99+25ghEQ7iTWQEuvz37++p2xHW5bDDDGEmXoRb3Unu1LJAjjumwn3Pibdjimv11luNBLT+hFur4xZ1RKgx6eOFkuIUSc+o/n28ZTvPKDZmDOh2VrxT4nbcOj9UAtq0/hy3uJ9gMBflHPVi27bGlPZ4c42quj+MM2es6Huqv2S1vkA9+/HHRj9F1w/RDgmCvpKvmTOhottfvuo6DIjDTBCqeIprXyd1Bagv0cZGCBoMUgdqS5lF0nAL45i9hBmTGh3eBX0ou3YuBs5xX0DyaQj+/kCfFIPsKFt03IZJuEUish49esiVV14pCxYskBkzZkiKqcS++uqrcvLJJwe7OEIIIdVAV7/XXWdMA9cCmQ574At8hun86KTq38CpBwEEmb2feMIjYCEWIGJG6saDDpXQqZPI118b05l1LEqMDNt1OBC8HmBqM1yEaFyic4yGMASmcAPhCsIlXHdawDVv19NPe09vQkPSHN8MjSZ03NEo0aKMWeiFMAmHLaYx+ZuxAOeWVZTTwm0kp7Oh0YNG0H33Ga/Hj6/6snD84KZCA9BOANYCgnngzzxtCg19q7ClHbdGqITwdPp0AxWipgYNQD3AYJ3Ojg6YTkZnxSyIotxjf7RLwyzoBHLcauzcymYx227QVK/n8cc97+H4I8YlxEms29rpQaIulEsIpRBtUc7R4dTuVA2OhU7+p1myOElq1bWx/oYBHbMxmAEECNUIyYJOCsB1ieNjjj98223Gf8QsRv2DmNjRAtui1wenCQZrIMB/+613XYCwDuEG9eW55wZ2xPoLlYDjGEhIR30I4VbPkKgu8SYyWkF4FdzTFi82rht0jnH9xUPyIS3cIrRMrTyXeyAV22cXkzuWnHKKJ9a07sTjetDXBI4nrhdzHH2UC9QLdp3+eKcEjtuM6heSbn22yFnnGAcA59RJoRJSk10BZ0/FOyifqDPNoZQ0OjkZPtPXG9oO8SosIX9Ackpk25aaUK9ZDHri3j17tqc9XR3hFgPbpknfQYH6J9jtRrmAOcZX+14PqqNNb3evQB8D4btCNar4iwmPcvj8875/i/YhRFJNOMKYQHA157hA2wHtYfQ7cQwQJgfHCPtp7f+Z+3foV+Jagph7773Gb83f//JLkS++MH5Dx22YhNusrCx58cUXZfv27SpkwhGWDBBff/213HDDDcEujhBCSJjEBAhXEFlxY8QN0d+Nz/wZGiZW16kZOHAffNB4bg6VAHeC7owjViacNhD4MNprDaCvp29DKNNJ0yCOYn1mt264wLIR1B5TDyF2YHshMsO5p0MpmB1n6LSbQyNo8Q9hFZDUDuKd/h3QU/IBGmy6AWcNrwA3l16+pqw0SZKSfcdGDQc4v0iopuMOVmcqKsoTlgMhyE6AtBNlzEIkpn5iyrx5epp23NbJLZVt26sv3ELoQ6MQosELL3ga6BBu9bHXycgCgUQLEOLMYKo+hHBrKAJfCQCB+ZjYHX8kbtNYO+kom1psN8eMxvu4BiEkQ7i1/g5OcnQWtBsGAgmSiZiFW4i2iAtr54xv0iIyc16xragvgnHd2onKwOyG0qE4cJ7g/ohF8grEQsXgE9y14JprvDtKdqEScG4wkyEUcM71dFxd9iAwVtVxq8utv84kjqcuv+ZZGaGgE1Xp8o96IN4wx1ZHjHIca8ySwPFB5z4eks7pAan0NJc7PAruOXAHhzOuYLjA9YDBIxw7XAMYaNAJdtz7YnLc6pkN4Y4FHg1Ki8Mj3KItsP8BFe66Ug+QxzsI4dG0XrFs3eZsx62u4+xyLaAexPVmdeOGkjQ3mpSXwdAROeHW3G61inSom5A0WbeVrOjwUxjI1/fJ6gi3aBuZDRWoX9AmMIuoMFeYZ9tZ98EfuG9dfrnvmPD4HMYErM9uP3TbE+1mhDoIZl8hgOpZbL7Kqq9QR1j+qad6XqOeDYdwO2yYdw4V3Bs//dToU6FNp+9DmLmlTTx6X8zXC9oxyJUCYRazATG7RzvWX3rJ25EbrwMj8UICpeshhJCaiZ7ChFFoiG3+pgnDaavR7i10FAI1RtGIReMI3zWLJRDFEFZBT5HXsQI1cAdBXIPTS4uikcwybu4EohGELLiIOYv9QygFiGZmEQn7ZHZboAGv3bTgkUe8Y+NCFNGj2nCboAEHNzEaI9Z4kthPs2hpxJ+MbFBxNGgRjh7J4jDl3DztKlT0IIBV7EFjFlPGrcKttVGsBUJ06NHYw/dXr0mSnMxyad+0ULZsNbbxxx+NsmuOi7VihSeWnL8YWRCI8IAIoBt8mJ5ldlY89VRw+4uGpSkClAL7h+mRM2d6O271tHw7zMcBDXd0VnRDVu+XxtpJ18IrGt7oMOrfoazpaxT/rWIHyjocKOakfojLahZuITDimtfnBQMzGHgB9RqZgpeFEQj51rjQ/oRbO0FKCzy49nCuIUrHgxho7qjg+GuxD+fU6sLBgJaezRAsqFfQucE+QxAD1XHcAlwjcOb4cpTiutEiRlUTmcHtjzofZRfl3Zq8Kh7AtF3N668bggOuC9wLcP+0S+xnBscv0vkhikuMRI64hvJyXaqO1Ak07QZfYg3qrD59jGse9R7CtLz5pm/hFs/xcKRwGybHLTj0sAolzCMskVNCJRSXJkmLBkWyabOzhVst/vkSboFVuI03t7umtDQpYvkTzG0zYBUi4S5FOwcmB7t7C9rB6DegDaVFTb0MfD9QfetrxqGugyH8IW+AXiba2niuZ9dpETNY0V239XwNcqJtrO8XdqF19HbgMwxeIaeAPyDKYvYH2kC+ZqggPIe5LsU+6rrCOtCIY62PM46vnhVUVfRgNP6bjwn6BjDt6HYm+lb4DowEAAYau8n4OB8457jvPvSQYfDQOUbouPUPhVvC5GSEOBxMbQVI4IP4q/7cOBAUNVpEQiMj0HRWLbbiRmsnBuJmCxHOvBzEZMRNXossOplZJDtq5m1DI0g3zLUTFMcGjQndqLE6bs3baZ1+r9ExNtFQhVMLYp/VKYj1wR1oFm7R4ApnEig7MJINMCkGDcuqJhtCY0/H28K5xbGEqIjyBQcBGqI4t7pcwHWNmK1m4PiEmw1TfuEiPeooka34TVa5ZGdWyEEHulRM2tGjjTAaGgiv2vkK8Qqfm8E5RSPW7AhE50o35uEqRYwxs4upquh1oGGKxjGO65tvBp7/Bjc7ygUa+HCi6IzAl1zi+Q4GCMwdIOzXjTca1wtcCHDWTZxYOYGKOVQC9hVOC3RcEJ/UPOiAaf1waOpwFdr9q53uV19tCPygXsPICLf6GgpUDlHedPI1K3q/MTiE84pyHUnXeijA3Q7QCdGdJ3TWUGbh4IZrH+iyGcwUfDiJMTCip5VjYEMTyEEfyHELURIDGbpcaXQHGNdqsO5eX2hhA/cKiMTxKNyiwwkHtK7H8NBublx/gRy3OH7oaAeK3VcdcC4g3JrLDRxXqEdj4TQPBpRziBk6kaC+NnTYB2vZxKBMPLibQ6WkODls93LUb3Akovw5yXEL4XbzlsQQbu3qTF1n64FDjbmNGw8hVcx1f0pqZDcI9w/UneYyiusX7bwuXYzXOinviBEiL7/s+Q5mNODeaHXcok5DuzWU8Av6fKGNitio333nCdmCtpeuU3S7Hw5RvR2+ku2a0WKsNdQXnKPI2WE2NfiajQZ0XPqPPvJfVsz3dV/tVYSdQpsA+4uZf2hfIra/HtjHfQttOyR9RJ2C44x1YpASg/YwdFQVvT8IA2Xu0+Beb05Wh+MPV62eXYQ+gHkGEgZ19X0W15F5NiNC8OllEt/ESdOXEEJIVUEnDrFk0ciAwORPuIWYg8YHEuisXBm84xYNBjSWMKLqa5o41gvxBQ0wCHmYKomYVvr7aEQhsYPZqRZuxozxFrThUEKsUx3iQIshehq6nXBrFmvRCLKiG4NIdqCnr9uBEWS9HrjX1q9PkozMyAb0u+kmYyo39gGNbAiuEBcQkyoU9Eg+9g9lBstFAw0NRYiKAHFUdbmB69o8pcqfMxXCLbj9Zu+52IhxpY+X+Rygc2VOsgVXN+K/mmOJanEPzli4ZyCYITwGGszVAY1klB+UazTmW7VySa1agV0t6PShI4XjAuB4gNADgQjxoHGO0LFBuZg/33tAA41tXCcQKvE9q3AL97qekojlaweldYoZBE444uF2xrnCuuHaMzsaMOX/qmvKJCsncuUSYmwg96beB7vZArrjjP0GuoMYD2AgDJ0lCOgYODM7kFAnw82J97VAEEziq1dfNdy5er8htOI5YpBbO5KhCrf63JvjfAO9fRDYsI3oJOrBj1DRIp2e6eAvrEgs6drVEBN0p1F3GHGsMbjnC9Rz+lxjKmmknLe4N2XuFW4HnVyuthXXfTy4zf3RrZvHQYdkp6iLUSbgrrXG9Ebn3WmOWxWvtzRZMsKQnMyKDknlhORkiS7c4h4MrIOOZmEpXhIZeur+CE8D2Ou61G1AHRYMs0LQroEop699xN3XIRK0cAvRT8dp1cdOD46jnQJQn2JA31+9qsVAxMPXsVF1OwkhDjBQrs0SWA76AoiJD4JpC2tXKURZrAt9JtRhEG3vv9/YR7Rr0MaycwtrURoz9nSdocVjvY94T4u5ZuHWl4kGdSe2AW1JtC8ABrCxz3Ctoh+D/zgWWrjFd/UsrPPOkypjLufalKDXj7aCGQjEaKtpQRczvzRw5+q2vXa56zYJygfuF1rAJfZQuCWEkAQAN0p9A7VzrZlBBwqiEMQw3CjROAnkStRTnoGvjji2AcIxxDPzKLS5o4kGVSTzWKLBglineh1oIGEKuQ6HoBOoaFeAnXCLhuiFFxqii6+ssRqINnYJprSog2OMBiO256Yb0iRjr2gZDXC+0ICEuIBGoznLLMIP6MRxdqDziEYZjo0Og2GHOdmMHfq4w6mqRbm8LKPV3aqly8sBDpFUO7XQGDdnbcZrDToIQLssgBa50AiEwxXlEEKmXbboUEAjE4mo4KxG5yOU0BPIwqsxuyaxPN0BRMcRTmU0snWH/dFHjTKFBHvoFOjkXnpfMJigw33g+tXYXZcYPMDghZ66Zh1kwPkbemZkyySEfzTm0QHyFTd1+HDfHWi933rb9XfjBSRKg0MWnVeUc/O0SHTIIK7rcB+BkpXojhzOpVnAgViI9QRy3OIaN09H93W9Wgfq9AwEDLqhkwaXrD9BDeXR2rFGMkYIwtqdgymiKKu+6sd4QNdJ2gUEIFhjsMtXXEJzspbqiDcQNrAODABaQ6joeOB52WXu86VnU1Qn/E00gBtZX8coRxg89FUuUW87Tbh1h30IU6gEM84JlaCF2/hynYaKHuiyq6NQ32LmBAwIZsx1J2bFxAtl2Je9Az2RBG00uGTR/tLJ6TAIDcENA/Vmhz2OK+pHtJ1QF+jY52ZBXNefejAaIc0we8+XMxbtIt2e8jUgrAe8cS3hOdarcx3oeh1uYLSfzLFyURfhXqgT2mIbMAMGbVEMqOprH+YBtP3R59F9CTO4B1rbxhBR9T0T4jGSx+IBMdt8X/c3m8IqOqNvBZEb+4j2FV6jXamFW2vdahd/3wocujhf+hwCHG/cy7UBBn0KHX4B14kO46CPOdoRxxxjvIarFoN3aB9A6EbYKBgJ9D7DRIT7MAw9GLD2F+qPULglhJCEQTsEgonjZHWSBnOz1EKaXfgAgEYMbtg687sm2g6hAw7wdn8idqkGrke81klT7IRbbC+mtPub4quX6S/BGs4HGozmzMuRiiVqh7WDj1itaKii8YjQBZjK7QsdvxYNb39CQTBZpRG/CjE69fSphrU9LWU9lcyMdo7C5YD1Q3jHlHTdQUSDFIMUmJ5n1yDV5y1cAgeWB2EFolsoy0RsZLMwjY4DXJN6GWYRFR0ENHix7xA6gR4oQecRx9ksTqNB/v/27gNOrrL6//iZ7ekhnR6aECCA9Ca9NyOIgP4oEUGQEkCDghT5ozQFRYgiKIqFYhBBEUGkipQIoSq9JLQQ0ssmu5vd+b8+9+bsPnszszuzdWbn+369hiy7s7O3PPfe557n3PNw08nv0PlHpsEXzyh0bWVjdheyLNiXZGlQY9v3IwFrf2SfoE4Y6A75DRDHNYMymbLge5Pf2NM+KFfh5wQySsJMcbQVpCKLJnwagXMHj8WDdsF+5waIzCSfWDKJ81lbgVvKanBuTC5HOIkiN7IM1jBw5Zk9IY4/stC5wSMDiXbIOYXsdoLq3Lj7NSKciK8QedZPODjlZTjaeoSf9eMpBDABjg+M5IrgAcEOAv6cWzLV4Y7OwSsHucK6toX+KKlnLHN8cw7zwG2mOqLFWCqhOwO3xVQqYc0RdavUvCw27dUED8/nycForq3tPQHRk9gXZd04OZnjiRfO+ZRa4vrGvQNZrn4e5frEEzQ+yEWfh+2c7IsQuOW6QQYrGIzm2GJeA2QapOTvcm5JtjnOn1yTnP+cY4ngLucertOcbxmY47rmEy97kNeD0gy+EhwGAUY/PxHI9MArn+GTWWarcesDbWDZuD56uYKwbAHry7rSzyUA2tZAZ5j0wpMN9P/CJBTHPQ3L6AFuShaADOhM9W7pn3tCAfdvrHcYOOfn7FPv63D98/smrpfhU1A8QcY53+8pWR/6cDytx3s9ccgD6PSLqZ2sgG1ucurC/9Sr4efgzDBFRkREegw35NzU5ZLhxIWU4B2ZhDxak0vNPEZQw05OEgHjHXZoCSrQ4SVzqTcuyNzk0jmjs5HMuqRDRUeBnyUnJ8sVlzo+o63ArY/wu732brKxuwQzo3WzZDvwDjGd2UyZj3Rw2Va0Id7jgUAPvFMPlWAOnWA6z7Q3JqNpjw8kENh/8uE6q3w63apN0dEniEkHlE48gVIP+NBRpm4Wgb8wk5GSGGTeUpKD9Qpv/gl+eae8K1DzmA4nZRy8Jmyu6LCTWYcw0Aw+k5pf3/523Emm450MvnJzRBeMgLsfo9ws+ARAdNzZD2QrZzruaevc1LA/PXDe08JH5UCnnsA7HXxeLDvnI27wMmG9qHvMz9vL8O4NfnzQBsnc9MA86xg+Rsl+yxakIgBARorX//OMavY/2fLclHtWKOdt0B6T+5zzWVvbiJ8xIV04GWA4gOU3f16WggwbSh6EQTdu6sDygmM2eVNOFg3r4oMKhYzMKx/09PMVN9JkVXG948bcBwIZdCDQwzbgGsN2JEuIAEayxndb/KbVs3fDjF+3eEnKBgVPaHhwOVlzsxBxXeTFuZnsPC+VkMT5rtgybqNMtBTZjV0fJGOwlHM17YLrsQdTCgnHQENjygb3X2HrjU1HfbwwaNaXAreZ+MAjx2NhlUpIWUW/7g/chgOnHN9hP5enB+jn8G+Sn784vx55ZHxdCZ9qoD8elqXIlHEbZjgzmM/gl5d8oiyb8+xdnzSUAVDONT6o75mjnKN4L/1alsmv1+xjkjPo4/v+5ngMl5frO9f0cAJYx9/0ewMGr3ywh0E+vs+24HrBZ7LetEPuk9orAxXey5DdzBMu3qcIkxd4XziYyLmXcwtzRnDyuvHGsujvsT5sl3/+M77meV171pngN/cCfn6mL0n/gb54W4PDvr3aevIzfDqsEPt0hSyn09WPE3dAn376qdXW1trQlb3TBQsWWP/+/W3UqFEK3IqI9BJGVX1kNRdkXxHA49HyrkLHg8AtI7QEm+gQ5TqTa1cjMJbphpjahgQKWX90ZP3JgCNjlQzCtv4+jwnxmD+vFakGe3wGPb92ZoLrQp4VBp/wiwCpl4BghJ+AGJ1AMh4JYJCVQIZnMnBLmQkyEchaJmBEBy5fyQ4f25AaXAwe0Hn0jEOCsp6JQekKliEMfHpAiPbGcngJDL+xIJOTQYSuwLai5ADtOllHtj1k7BB4zhawI3BNJikdeG4gk4FbshQIWJMp4TfHLA/bg441NyYcX5nauXeKyeAlyNfWIEN3YvnYX569SSA7rItGYCccKMiEbViownIq7BcPpiYfSySAnS1IRc1Av2klIMjNJBnKtGXaEFlA3Oz44Ilnq3t2Nrg5bK9UArhhI+DA8nnQIgzc0k7CZWdSwrDthPUNnddLZ5nJ8uF80lvn/XyF5w5w7eK8HT6RQH3Ar341Pq+wbXwwkustWccgo8oni2mPZ1V6fWuC82R7hZPuRcdEhozbXB53LRRcZwm2EMTvK6US6panrKq6sVtKgPjEk5Mmxed2rrUEdvLp13U3H/Rl4rx11043B8mKEQGzTJngbSFISLCSrMNCyjZu6KFSCRyzZG1yTbrzztYTTPk5CpwjuabRZyII6z/jaTf6JQx6eXCWwVvOrSSSuEwZtwQq6UtQyzW53wgW+2RozgO3PpjK7/z1r3HmKoNv99zT8vQdJc0YsPSyNfRLCdzS92KfU9IgDNSzHTJl3BK45Husrw968qQM/R/+Fscy1+6jj47fzwAhy+RB6PbQP/+//4v7GrRf307ZrgsEbM89N0528OvO229XRPdBTGZLnWCe5mFf8bmOtk3wlfMzf8vnD2mvf+ElHdorMyfdWOP23XffbX794Ac/sK222speffVVmzdvXvTi66233tou5TlMKUqFXINMRLoPnQoeZe4qfkNLkIlRXzojhXh+CR9x68gM3awTI/LtdWIInNLxy6Wz0x0YJfcXE+qwrwkSeqeKSb6YWIlgDJ1AgjQIH8330XFG3+k0d+X+5LMIVvCItU8wAe/UZkIgi7bF5AdkD/LI9sknt34PweiuDBzRvWEb5nussH7cuLRVdoObCrIoqVWWnJiCnxFQT2aO09Gmw86NSXv1qflssm57qw3CHz1EWOcOZGyxHtkm5Sgmfmyw38KBA24K2WfZAvh+DuIGyevY+XFHqQz/mgxzDzSyTT1wQBYzxwI3ne3tZwIO3FyGj2sSsKV9kT3KQEF4A05g3W+wPSPXa9oljzFuTAleFvO+ZN15bDXEYBfZSDwJwHnQtzHlUBwZyOEEdW1JPg7PY62cW8O6wVGN2yDjlvMk2feFPIiRxI0/15IrrsgcICvGjNt4cKR7AmS0LY5Nr3PL8c6AQa51ZAkYtVW7vit47cuqiiYbMzqdscZnsWhvMsdsgxEMInOuLqiM2xVm5T0wOVmyz5x8UoaniEBglycWeKKIDFd/aomEDs4LXLsIhjLo7MFfsmgJbPKZ9Lk8OYDjgd8juEv5qUznEgY5+X0vL8R7OM8m5yZg0JjlotRA2Keihi19YK6r/JxruT9Eznt5qoJlpq9MIBZclwnCep+G9eM8zt/kZ76cPJlGrVwQHOWcxzLRlvyJrGQt5WwYrKW/61nNXpYiDNz6QCqJGNTY51/fBgwgv/NOS1SVwcNkyY+pU+NrEuvAOpF40l5mLBOxEYDm2Mj3yTTpxsnJLrzwQrvuuuts42CImq/Jyr2AitIdMGXKFBs7dqzV1NTYDjvsYNMyFdUKTJ061TbZZJPo/ePHj7f7GBIJfPLJJ3bCCSfYGmusEWUCH3DAAfam342KiEi34aayu28cugLZTR5k7kjgNld0VnujrmgmdJDptPIYZnizRWeVDAZQozgZuPWM2+7cTh7AzSacWM2DWGQAkCFB57ut3+0K/E2C8N0R/PQ6wWT0ZgrCctOQfESaYDCd7VwCt2wjMkZ6GzdGBOgZyPAbC9/3BCbaW49CRqDS95NnX5JF5LhpY/+RMZQJAQBuWMlC8hmbM00KyHvIKiJLhptav0n2xzW5EW7vOPVjJawD7hm33BzT1hl4IyOJm2TKovjTCQRwOZ+xHAw0JM/1HIsEMAtxsC4fZPv7bNfwyWoQBgHIxA9xk5wLAgrJTF+EN9CLl7TOuAUB5d4cgMkXARrKaHCOy5Zx2x01bslaDWs+dqWoXm831Lf14ydTphrBoHCgJRuy8duqXd8VfDKnvhC47UipBMd5spAybqPJyXoocAuCq1x/kk828RQRg+k+iSxZnQw+wK8LnFsZpKIPxzWLNk85JAKgvMfPvX5dC0M4Ye3YJK5Xft2lr5vMuE0KJybjusr5NxyM9CAwT3b5+YTztgcxaQN8BuvH9dCPUfrR4fXDB2/PPrvl/+lXE7j1vm0+8ycQuGUAlsCwZ7yHg36eYctk0D4I6YOpO+2UjjJuPXOeaxHX9eT5mUFCgsq5Xst5+pBtniwRJ10r71udjz/+2FZkyMdubGyMAqb5uuOOO+ycc86xiy++2KZPn25bbrml7b///jY7LHQSePLJJ+2YY46xE0880Z5//nmbMGFC9HqFwiFRw01H///OO+/YPffcE71n3XXXtX322ceW5jKLioiIdBhBAa+PWMjI2uSR2GKpGdhV6CiGma1gwqcQnThGzz0bgQ4d24rZY3tLGLjt6cnuupsHLrgJzBS89AGGMDuEzF8yJbhBKJaAJ8tM5gk3R9STAzcFHqwqlvXIhEyTsKYcdbM5F3Lj58cNxxE3dwSpySYi09hvtjjmyN7kXMRNLOUG2np0kgwmyhEQBGdAgUerXS6P/jJrN+cCbs6mT191kkb2C4Fo3hcef2R5e41l2mO4z8JAcLFjX4V1e7k59nIRPrgF9jE30ZSh8QnZcglkEVhkO55xRutAW3gTPmdO64zbYkQ78UBKT2bcEiwi06871NdTKqGp27ZXOPkc2Wsc5/CJhtoSTuDZEbQ7Mu494zeT+oaUlZelo/PU6FHpVoMapVAqwRVaxi2hmfKK7i+V4Mh+JUibbcCmre0aDrT7NYakj5NOigclPVjo7+MpMfzpT23XTg0DoFyf2gvcJp+gIps8DNySxctExZQs8QGUcNnDWq2sRxi6ypShSnmCsF/nE9R6GYJ8sI3oL3gbDMu2EUznibSQ1+dlYJiMW8o4eD19zhteginkg8jdobuTLfqqvDfb3nvvbV//+tejIKt77rnn7NRTT42Co/m65ppr7KSTTrKJEyfapptuajfccEOUJXszQ/wZXHvttVEG7eTJk23cuHFReQbKNFx//fXRz8msffrpp+3nP/+5bbfddlE2MF8vW7bMbiPdQ0REZOWoP4opg6mzvKPonTIeww8DEf4YNDd+4Q01GXg9XbOKTrI/FkzGH51nak2SFdiXkGniN0CZgpfeTsPALY/QUuIi2+8UKjJ/eWw6zJ7hxoGgYTHXRGMfZLoRoXQA5UjAY5cEZQjAUFvvuOPiNk2NOwK3YYYOWWBt3dgQGPZ6qt4O/JimZnJ7CCgTcGEQh0ANpTQyTdJIsJIgmAd1kssJygcQAOYphr4kvPEmE9pv8pP1pAncEnRnkkXkUsqALCcyoMhAp+QOGWoEJHxyHDK2//VkmQ2sKaKCtll4VnKmmpUcNwxY5VoKIBeeGecT/OWK6x3XlnACoqwZt91UKgEeuOWxb7LXfMIiHtF++OHMv0OAl7bjGYTJR5+TeFA1U21aaoxSQ/P557P/LsEt6tuCjNtsTxG0x+dB6MqJRPPF9urodYdgYHdki2fC4/AMprdlxYqUlZX3XMZtZ3HeYxv60xxgwi76Qp4f6H1Q5j/gvJrpKZQkv27Sf6Qf21bglnIDPLlCENqFZX74rK99rSUInZyEL1kqyOudZ+OD8JRj4PrCvQdlGZJB1lx5+yNb2J+ay8a3JaUWPv203L71rVSr4DPXNbY9P/ea2t//vnUbJjAlM1jyk/cDAgRUjz/+eNt2222tcmVLJgOXLNlf5jllcX19fRT0PS94nqysrCwKAD/lQwEJfJ8M3RB/++6VU+TWrSy+QxmF8DOrq6vtiSeesK9xBCbwO/57WLTyqt3U1BS9+jpWkUzlUlhXkWLB8dip4zI+sFu+LuLj21cl+ersatGZ4xEnMlp6YvP4PvVXZ/hn5Hudiju+KbviinQUkKFu48MPp+yAA9J2222pKMuOx6s8UNjU1MU3Anm0S79R4S3hJGv+vd4+LsO2iI62SwIb1KF96KGUNTby2a1/HnesU9bQ0PKzOIgUd7zLylb9nd5ol7m2yTgQmLLjj09HGSNPPpmK/u3yttbRE0w3nC/5ODJkttkmFWXnggxNAizeLR06NL/9SNDvZz9reY7xjDPS0UAME8e01y4JEu6/fyoK4JD5C25yk/vAMyY5R9TWpqPlHT481ep93oXv6mMy13bZ3ns6eq4cOLBl2xKc2m23dHRzzDk028dcfDFBgPg4busRU5/N2z+HtsDEjM88k7LDDktHjw9Hy25pa+qmNtlT4kfRU1H2W7wv4u/zL4MAdXWpKMhyyinp5keqOyMOEKeiAGs+55Q4qy9ljz2WtoMPzv6+ZcvSVlnV2G3ny7XXTkWlZFZbLT4fxBOVpaIJ7Ah0/OY36VaTBfKea65JRYFQfxrltNMoY5J92S66KG6c06a1fk+c1ZuKAku77NLyM1bT2/Py5U1RmQTa5brrMOrLo9fpVhnq7fVhyf269dZUc9Bp0qTeOfdz619ZmWpul/lcAgjaLVjQ+lzYHViOH/4wZU88ER8jlLChT8AxQ7all+ipr09HGbc9dR3vLErquOSfIev2z39ORYMXO+yQtrfeStnJJ2e6PsZtKLkPaMccz7//ffzzwYOzX1s5PxGwXHPNVBQg79cv83v5TAK5yZ9RA36PPVLRIMSuu6btJz/JvEzg+GQAl8BoV2xanrx59dWUrb9+/Lfa+kwGzLl+DxjAm+L2/sADTVEAmRIP/C6lfvj+N74Rr0N5eef6lm2hBnJ7y1wqmvLYCHkHbkeOHBnVlH3jjTfsNe74ouj9JvaZDhS1mDNnTlRiYXQi753/989OmjVrVsb3831flnXWWScKBv/iF7+wAQMGRPV3P/jgg6jMQyaXX365XcL0gQmffvqpLQ+fz+yjli8fbEuXLrHZs+uiILeIFMaJfOHChVEHqkPHJT1SvxiQJtXWs28FzlfFs0n4l//vitUi46+nsibqVtRZ05Imq68gzaNzn1XfWG9NK5ps7qdzbUlF7huBIPXBB/e3z3ymNnqsixsAMnkeeqjKGhoG2eqrL7Np0/rZEUcssy99aZnNnt3FNyVF3i79uFy+nA5tWasaaZ1tlw0Nw+3DD+ts9uwlGX/W2LjIZs9eWZgsyuCstldfrbDZs5cWRLvMp03+8pdlNmJEkz3wQLXNnDnQ1l13hc2e3UuzFHmbJPW8q04sWQwZ0t/++Md+US3CX/96nj31VKV973uetjPfZs/O7y6GdsF2/PWv50dZQSx6eK5sq10efHC53XNPSyrSyJENNnv2qumG/C5/5+ijG23nneutpibV6TbXle2Sa2TD0vi4SGWIlnb0XMlN8fHHp+z++6vtl78cYGuuudA22CBOBctSzS2qsVhbO9xefXV+tF8yOf30IfbmmxV21FGcX1uead9yywq78spBNnv2fPvggwG2+uplts02s212N7fJnrBixXAbMmSF1dcvbNUu2V0HHtjf7r23nz37bL0dckhi1rYOYNuuWDEkyiidMWNuzrXZ33mn0hoaBtvbb3NtzF4g99NPyqyyrNHqF3XP+XLffSvtoYcGWzo9t7mdHXxwhf3jH0OaJzF64IG5QebqAGtoiJOV/P0E/t98c54NGdL6+k1TWriQQcA4ze4//1lg667b8sjNe+/1s4aG/tFgzuzZ8d+YMaPcTj55qN1yy3wbM6bJPp7dZOnyirhd1s21NdYot8MPr7ApUxZE+/OaawbalCkL2+zD/uIXg6yhoeVRp4cfXmibb97z2eXz5lVbWVmV1dcvzvsaTobrwoXD7IMP5nbrU1v/+U+lPfJIfI24++7F0bYLeVtYumSglTV0X7vsSQRTjzyyn91yS//mEjxrrbXq9XG99YbYaqs12ezZq543li5NWV3dsOjyvmJF+9fWzTYbYO+9V2Nz5rQcW7kaMmQ1O/HERbb22o32wx8Oj77nx08ST2d01T0HWcu8fPLQtpcxfrEd1lprgNXUrLAVKxZF2c3cF4TXtP79B1pDQ3XWdZCutTg5W2kbOjxlCpOJcTLeYIMNrKJQZl6JaihV2l133RXVwB02bJiVl5dHGbwHHnhg1hEogrxhFi8Zt2uvvXYUpB7cldNSFyiSkwcOHGijRg1R4FakQNDp5SaU81CHArekT/nvMZNR+Jx1kfFVoXPsD1PQGSu21aptqLWyZWVWVVVlNRUZnkvOxwqzuvo6Gz5yuPWvzG8jkBVm1vr5emaBPfBAsh8H2n33pWzSpIE2aFA3PINf5O3Sj8sBA+LjMmyTnW2X229Ptl25jRq16i/z6PQaa6zWKouP2YljAwqjXebRJv2RwbXWijOehg+vsFGj2pm2uLt4m+SZWe7au7Fdjh0bry9GjRoVPSbKtpg0KWUbbzwi73rbd97Jo520mVFZz5XZ2iW/QubbDjukogzcm24qt4os+59l/vjjCvvTn6rtxBPTNmpU9xcGz7Vdet++ZkhNxsBtZ86VGDcuXv/PfW5YTkHA9dZL2auvjrBDD81c8uK991JRkH2NNeh3D2xVRoSg2vLlo+wf/0jZt86ss7WjQ6KsKM+VyVnKX3yx3KqqRq3SLsnYZt0feqi/jRrV+RkwCTqOG5eKag0vWzYqp8erHfu5rm6QjRrVOjgWqq6pt+qBS6xqcPecLymDwyPZ/fqNanWf5ucN1NePis6deOmluD3h2GPTUWD3kEMYXBnZqoYn2Xbsh9/9Ln4/GbLPPz+8uX4nOJQYzP7rX1M2eHC8r8ij4m9PmzYiejrgvZrlNrh6kY2iYQ8fboMG1USft2zZ8CgDdMaMlA0cWG01Ndn7sM8/Hy8D5yACRpddNtz++c+ez7qNH1dPWVVVv7z7lrynXz+ydUc1X8+6EoFjLkdcE3zfX3nlaqvUjh02bFR8nilvtMpB1m3tsqdRgsazsrH55iNWOZdSlzXOBu+X8Yk6324bbDAiY83ZZMkq+sF+Lc3Hgw/y3zhge9BBKRsxgmtkNzSKLurDXnPNHBs5coANHZp5GXkCjBr7hboOfU1YJaA9eUdca2tr7YwzzrBbVs5mQubt+uuvH31vzTXXtO985zs5f9aIESOiwGpyUjP+f0xYiC/A99t7/zbbbGMvvPBCNNJHOQYuGjvssENU3iETyijwSuJCUwoZqKkUj3SlSmZ9RYpFp45Lfies8F/Ex7avSvJVbKvFfmSf+qsz/DO66rzNR5B9yyOiPMI0ZEg3TQvfB9qlb/dUilfL6nS2XfIIpD/6l5Ssq1mI7bIjbdJrz1FXrqysm9pcvieYbmyXvr7Mxu3ryyOvBK8GDcp//ZOTqyRXBbmsFsEtAhi5GDGCfWzdLp922db7OnuupB4wTyUMGJDb9mERrrwyFe1jylkwAVkmgwe33o4EgvDFL8Z/p19NysqK/FwZnr94/D9bu2QSIAKt+ZwDpk2LJ/nz0iOObDZKMHBb98EHqVZlBXKpAfnhh223b0o7VFanu/V8mRzA4bxBKQTPMTr88FRUGxPEVpjUiUDs1lvHy0PglwECruVkkXpd7cces+ZzDhMY3XhjKgrGev4ViV8bbxz//fffZzCpJTuQ98afV2b9qprithktd/w3KefgQcWZM1NRrcxw/XhUm4GmZ56J38MyU9P0/vvjgPI996Sin2+wQXw+zDVTujPINmTd432R3yWAnzMQ9+67qbwGB0IEiZlwi9IAIAhJoJyBNDIzKaFCALMt7Gceel7R0BSsS89ex7sDpWT+8x8y4eMB3oqK/NYpjIURYM/l/V5PujPierm91JfJEZm3Q4dm37+cP5O17KX75HOc5X1Ekp364osv2qOPPtoqQkxW6x2ZpqRrA6PoBFkfYlrcYCSA/98pnB4vwPfD9+PBBx/M+P4hQ4ZEQVsmLHv22Wft82EFbBEREWlzVmuR7uQTgRTTBGudQS096qSGs3ET7KA2bW9hgjJq27WFG2gyAeF1NEsFidhk3eaKgJRbmePSLKy+ljzPcu4NJ5jZd6/ErJF92BprxIHb9kr9sf0uuywORjJRV7ICHhP9PfdcPAhJ8Iv/bw8Zn9SFJkDJcrz3XjyxGQNpPtFZMnBb1Y2Tk2VD0DSsC+pTs7D8BO7IWg4HdKhb6TU4Cfh60BYEBTmO+QwmCUpOmkfA9o034u8xeZznNrG92Qc1KycnA5nl/j5/4tf3y/TplXbaaXH9T4KyeOWV+F8mGgX7it/lHPT66/HEaQRz+Z1cnyBmfzEwkMsj4yHaUTKDNR+cF156Kf/fI0BLO6N2NgH5KVPi7/sErGQ7epb0W2/Fj8O78CFnwjBMJheXs0lZRbBf+gLOiQTy28uWFSkVeQdumQTs+uuvt1133bXViM5mm21mb7/9dt4LQImCm266KcrgffXVV+3UU0+1pUuX2sSVZ63jjjuu1eRlkyZNsvvvv9+uvvrqqA7u9773vSgoe/rppze/Z+rUqVFg+Z133rF77rnH9t13X5swYYLtx5VKVtHJOXJERERE8ubVqEolcMuN9i67WEEhI7G9YCzdfWaYpjwH2XqSHSVn4gn4YjNntnwdBnkyZdF72yCTr4grI+SNhyYJohHMasubb5rddVccxPQgL6WpHdmjDzwQB23JRGWiTcqH4MUX4+BeEsHys8+OA7dbbRUHkAl2/uIXLRPohAhcVlX3fFCdY/CQQ8zWX78lQ5iMUYKVySead93V7B//iCdqWzkFTDPa5oQJ8XtAxqcjUMo5mf1BQJjPYKKy3XePs3gJyF5+daUtWd5SRPXoo+NgI3+HwCv8b952W79o0CdEsJJAnGfUErhlHcKkMx9IIvhMIJ4g7sp5w1shsM6t/WGHURM1Hlw666yWn7MPs0xvE2loiAdmOooAeXh854IJsFjmL36xJZuWSbjCIDXvceTExROUxq6/3uyCC+KvOR8T6OacXEcQuko39CJ9Wd6BWybsylTzgmBrR1LzjzrqKPvRj35kF110kW211VZRiQMCsz4B2cyZM1tNKrbzzjvbrbfeajfeeKNtueWWduedd0bB5M2Zlnkl3n/sscdGE5WdeeaZ0de33XZb3stWWnSyFxERkZ4P3BbQVAmSBV18giIlMPVDp7GdCMYQFCfQ8q1vxQEoJjzDzjubbbhh9t8vlYEM55M7felLrbNuCU6SbejZo5Sf8GAtWZm44YZVP4/bVEpUEPAiYEvAnEmOqP/K71LPkoAXPEuTICeBOM5FYQCZQCMZoY4s1d4MkJFhSeCTQDPLznHpZTbCTFAeL7/1VrMnnogfxSdA+6tfmXlFQ7ZNXO/emssuEBzl+OYR/Z//3Oz88+PvU/eW2/If/zgOML4zq/WoAp/15z/HQfMwcFtT07KdyMciAEuGKUFbDxnwuwTX2e/XXdc6CE0mqueEhVmnYRmTcN+A9fWJlsiavv327NuSwYLOXHvYTgT5CQDn6vHHLaoF7FhftgWDEiHKrDi2EduN/cc2JPBOUJ1gultWa30u41ZEOhm4pU7s3/72t+b/92DtL3/5y6zlDdpDtuyMGTOsrq7OnnnmmagerSNz9je/+U2r9x955JH2+uuvR+9/5ZVX7KDE2Zxg7fvvvx/Vt+VzL7300qgsg4iIiIgUBg9Q6ckf6Ut45H78+DhAyyP9BHeo54kDDjD76U+z19D85S/jcgClxjOQyVh2ZCYShCMoyK0h9T/9ls8zaQmQk/UZBsMIqHHbR4CTAC6Pm3ug7phj4qAtwVuChZ7JSSkBapUSoCSrl8/wAJoHjLF8mVlVTe+VsWC9CPa9/77Z3/8eB20zBR8JwFIGhQkIye5k3ajhGWZvUtMep5xCdmycPUqphGR+FoMMvj3w5T1ap7GGn8nXfBb77eOPy+3KK9N21VVxVq6XEfF9F9b9BmEEgpLgGCFo7vuVTGemuCFQS6Yx+45/qe2bRBvxaW3+8AezIGzRjN9nIrvOlErwZY8np8oN68ETDv4QMJ9BO/VSFnvsEf9Le9933/hr6o0SpA1rsLKd11mn9ecqcNsa55LPfrYDO1akQOU9znTZZZfZgQceaP/73/9sxYoVdu2110ZfP/nkk/ZYWEBHRERERCQLD161V9tSpBjx6D6B2JA/5pwNj+tHguBWKSA7lNIEZJFSX5WgnZdBIJDqwV3qtRJ4JeDGtuJRfAKznu0JDzL6A5sEwgnkkVnKZEeO7FOv5Qo+j79BUJSsVUqb8LXXk8WyZSmrHtK79YcJ9n372/HXW2yR+T3nnmt2wgnx12GALxQOHpBhCwK3Xj6AIDbBUuoxezD3e99tsEMGvJc1cMvABPvykENS1tBQHn2GPxS7zTYtAfRwGf7f/2vZxizzPvvEAVk+l/rDBC3JVPVAM+v83e/GXxMMJjOYAD7ZyIlpcCKUzCD4TGYsy8L1hixk9n2QK5a3zTaL//VayPxLm8n2ADLBVbKIjzuOJLM4a5Y2yvYgwPzVr8ZZ+SwX2d+0WTLGqWGcS2Z+ZaWeng0xeaFISWfcUtuWcgYEbcePH2//+Mc/otIJTz31VDTRmIiIiIhIrvRQlPRFZEJSh/Ivf2l5tDuc7Vxa8Pj8tGnx1wTVvM4pE2WBR/15+aRuBPYINhLQW331uBar8xnRw0zZMLjoyIYGgUbqoZKlyqzwBAEJsJPpe9RRrTNaly2nVELvjjRRQsJRDzkTApRXXNFSniObL3yhdRYsGby0U4KLPn1MWI5hs3GrBgfDUg3Uxw1RsiFEoDaZ0UuGrC8H1wLq+PI3+Zcsa+ryhgFqAp1M3EUWsX8+X4f7O8QgAGUyPJBN2/Is7DBTO1/UoSbITJshs5uBBy/BEaKWLUFYD7Z7wJxBCKbx8cCsB5H5f9af9/F5bZVV+etfW75Wxq1I39ahyi4bbLBBNKGY9A16RFFERER6A4/R+mOtIn2NT5ZE4DCsSSmrIlDl2ZWOLEoyJZODOx78I3jG+YMA1je/aXb11S2B2733jh/R/93v4sfOCQJSDoDawzwm7/Vbw4zGAQPiMhdu663Nbrml5f+Xk3Fb07uBW5+4jiB2ptqvjqBie8hcZTIsguaUTCCAzouMUO4PfaK9TTaJJ/9ad520WWJCLoLolKsg4Mvj/ARKn36afTfXBg9uHaWl+iFZvblgeagNzTpSp5gXWZQE7AlDUGKAzGjaiNfeJtDJPpwxI64L+8ILrT+TmtP33BNnLRO03WAD6xRKO/zzn2ZPPRX/Pxm/nhnsy0O5Cvz73/G/PukdAxSgpArXwWzZ021h0ILg/PVTereEh4gUSOB2UaapHLMYrFkLilIH5pUTERER6RQPDIj0ZTwWLe0j2EYglixXarNyf5IM2pKpGD7iTs1QEFikhm1o0qT4BX6Hx+oJ6FJqgczNww5re3kISjJBFu9lOXjcvbcDZNSuJVB96KHZayXng/IQBG7DgDXY9gRHfRtHWdAZSniwLZmAz/G+pqZ08yRhIYLmuaJ0hZcOYV0JtBLc/Oij+OXLSFA5GdDn71xySZxJPXlyHFSGT5vDRGhk6YaDBB2x445x8J9yHgSUTz01Hqz54Q/jwYIlS+JA+/e/H7dbMrnDWrVe8oByER1Fu5/yM+v1AQURKYDA7dChQ5snIcsmnU5H72n0gkQiIiIiIiIiOaJ0ARnKnjmb5JmK7ktfirNDM5VDCBFA+8lP8tsNBG4JTF54YTzB3NIlZlXVvRsgI1j7+c933eetuWbmMgeFhKAywVCyr51P+NfWenlAlDIKXqrAM1W7CpneZAd7SY0nnojLIxC0BZMTvvpq/oHrXLGe/3qqzh6f0fWfLSJFFrh95JFHun9JREREREREpGRlm4wpGwKOnlXb1chbIgDHI/C8mtIp69d/BT+xvmKnneLgYrIebSEiiM4kcx19smP77ePMV0oleF3fziIr2TOTCSaT5fvyy/H/UyeYQLtv20IOjotIHwjc7r777t2/JCIiIiIiIiIF4txz4xqkbrWR9YTrrK/YaCOzm2+2Ps0n+9pll7Yn++osaioTuMX3vmd2yCEt9YgpR9EVpS1EpDR1aHKy+fPn269+9St7dWXe/6abbmoTJ060Ye09oyIFSZOTiYiIiIiIrFqKgTqiV1xhdvDnG2zJ8L4VuC0VTz7ZUs6gu9TUxFm21Gemdm1IQVsR6Yy8x30ef/xxGzt2rP30pz+NAri8+Hq99daLfiYiIiIiIiLSF5AxefnlZltvowmgilV3B23dCSesGrQVEemsvE9hp512mh111FH285//3MrLy6PvMSHZN77xjehnL3tRFxERERERERERERHpmYzbt956y775zW82B23B1+ecc070MxERERERERERERHp4cDt1ltv3VzbNsT3ttxyy04ujoiIiIiIiIiIiIjkXSrhzDPPtEmTJkXZtTvuuGP0vaefftqmTJliV1xxhb300kvN791iiy20hUVERERERERERES6O3B7zDHHRP+ee+65GX+WSqUsnU5H/1L7VopDKtXbSyAiIiIiIiIiIiIdDty+++67+f6KiIiIiIiIiIiIiHRn4HbdddfN91dEREREREREREREpDsDt/joo4/siSeesNmzZ1tTU9MqNXBFREREREREREREpAcDt7/5zW/s61//ulVVVdnw4cOjWraOrxW4LT7pdG8vgYiIiIiIiIiIiHQqcHvhhRfaRRddZOedd56VlZXl++tSoDQ5mYiIiIiIiIiISOHIO/JaW1trRx99tIK2IiIiIiIiIiIiIoUSuD3xxBNt6tSp3bM0IiIiIiIiIiIiIpJ/qYTLL7/cDjnkELv//vtt/PjxVllZ2ern11xzjTariIiIiIiIiIiISE8Hbh944AHbeOONo/9PTk4mxUeTk4mIiIiIiIiIiBR54Pbqq6+2m2++2U444YTuWSLpFYq5i4iIiIiIiIiIFHGN2+rqattll126Z2lEREREREREREREJP/A7aRJk+y6667TphMREREREREREREplFIJ06ZNs4cfftjuvfde22yzzVaZnOyuu+7qyuUTERERERERERERKTl5B26HDh1qhx9+ePcsjfQKTU4mIiIiIiIiIiJS5IHbX//6192zJNKrNDmZiIiIiIiIiIhIEde4FREREREREREREZECy7jFnXfeaX/84x9t5syZVl9f3+pn06dP76plExERERERERERESlJeWfc/vSnP7WJEyfa6NGj7fnnn7ftt9/ehg8fbu+8844deOCB3bOUIiIiIiIiIiIiIiUk78Dtz372M7vxxhvtuuuus6qqKjv33HPtwQcftDPPPNMWLlzYPUsp3UqTk4mIiIiIiIiIiBR54JbyCDvvvHP0db9+/Wzx4sXR18cee6zddtttXb+EIiIiIiIiIiIiIiUm78DtmDFjbN68edHX66yzjj399NPR1++++66llbopIiIiIiIiIiIi0vOB27322sv+8pe/RF9T6/bss8+2fffd14466ij7whe+0PklEhERERERERERESlxFfn+AvVtm5qaoq9PO+20aGKyJ5980g477DD7+te/3h3LKCIiIiIiIiIiIlJS8g7clpWVRS939NFHRy8RERERERERERER6aVSCffff7898cQTzf8/ZcoU22qrrezLX/6yzZ8/v4sWS3paKqVtLiIiIiIiIiIiUrSB28mTJ9uiRYuir19++WU755xz7KCDDoomJ+NrEREREREREREREenhUgkEaDfddNPo6z/96U926KGH2mWXXWbTp0+PArgiIiIiIiIiIiIi0sMZt1VVVVZbWxt9/c9//tP222+/6Othw4Y1Z+KKiIiIiIiIiIiISA9m3O66665RSYRddtnFpk2bZnfccUf0/TfeeMPWWmutTiyK9JZ0WtteRERERERERESkqDNur7/+equoqLA777zTfv7zn9uaa64Zff/vf/+7HXDAAd2xjNIDNDmZiIiIiIiIiIhIEWfcrrPOOnbvvfeu8v0f//jHXbVMIiIiIiIiIiIiIiUt78Atmpqa7K233rLZs2dHX4d22223rlo2ERERERERERERkZKUd6mEp59+2jbccEMbN25cFKTdY489ml977rlnhxZiypQpNnbsWKupqbEddtghqp3blqlTp9omm2wSvX/8+PF23333tfr5kiVL7PTTT49q7vbr18823XRTu+GGGzq0bCIiIiIiIiIiIiIFH7g95ZRTbNttt7VXXnnF5s2bZ/Pnz29+8f/5YnIzJju7+OKLbfr06bblllva/vvvH2XzZvLkk0/aMcccYyeeeKI9//zzNmHChOjF8jg+7/7777ff//739uqrr9pZZ50VBXL/8pe/5L18IiIiIiIiIiIiIgUfuH3zzTftsssuizJuhw4dakOGDGn1ytc111xjJ510kk2cOLE5M7Z///528803Z3z/tddeG02CNnny5GgZLr30Utt6662jSdPC4O7xxx8fZQGTyXvyySdHAeH2MnlLVTrd20sgIiIiIiIiIiIinQrcUsqA+rZdob6+3p577jnbZ599WhaorCz6/6eeeirj7/D98P0gQzd8/8477xxl13744YeWTqftkUcesTfeeMP222+/LlnuviiV6u0lEBERERERERERkQ5PTnbGGWfYN7/5TZs1a1ZUX7aysrLVz7fYYoucP2vOnDnW2Nhoo0ePbvV9/v+1117L+Dv83Uzv5/vuuuuui7JsqXFbUVERBYNvuummrBOn1dXVRS+3aNGi6F8mXktOvtZXEeAulXUVKQYcj506Lvk9T6fn6yI+vn1Vkq9iWy3fp/7qDP+MortOFXm7DI/LsC2i1Ntl0bfJYt2BbawK+nq7bO89Rd8u/etiWvaEUmyX7enr7bLTfdge0Ff6lij5dil5tZNCPi5LTVMe+yLvwO0RRxwR/fvVr361+XupVCpqBPxLILa3EbhlEjWybtddd117/PHH7bTTTrM11lhjlWxdXH755XbJJZes8v1PP/3Uli9fbn3d8uVDbPHixTZ7dn0U5BaRwjiRL1y4MDq3dui4ZDDKLwZz5zJroxUrX5X6+vj/+Zf/L7bVqltRZ01Lmqy+ot6svHOfVd9Yb00rmmzup3NtSUUxbYTibpd+XC5fTse3rLlNotTbZdG3SfqvxbgD2zhXlkK75BrZsLQh+pr7kD7XLlFsOy+hFNtle/p6u+x0H7YH9JW+JUq+XUpOiuG4LDWLFy/uvsDtu+++a11lxIgRVl5ebp988kmr7/P/Y8aMyfg7fL+t9y9btszOP/98+/Of/2wHH3xwcxbwCy+8YD/60Y8yBm7PO++8aEKzMON27bXXtpEjR9rgwYOtr6upMRs0aJCNGjVUB7FIAV1cuQnlPNShi2ttLbVn4q+HDzfr39+Kla9KVVV8vvIOd7GtVm1DrZUtK7OqqiqrqVi5Ih21wqyuvs6Gjxxu/SuLaSMUd7v043LAgPi4DNuklXq7LPY2WV4e37UX2w5s51zZ19ulZz3WDKnJGLgt+naJYtt5CaXYLtvVx9tlp/uwPaCv9C1R8u1SclIMx2WpqQkvil0duCWDtatw0dtmm23soYcesgkTJjQ3KP7/9NNPz/g7O+20U/Tzs846q/l7Dz74YPR9NDQ0RK9kYyRAnC0Vubq6Onol8Rml0Kg9W7pU1lekWHTquOR3/CaWr4v42PZVSb6KbbXYj+xTf3WGf0bRnbf7QLv07Z5K8WpZnVJvl0XfJot1B7axKiiFdtnW+4q+XfrXxbTsCaXaLttSCu2y0Nevr/QtUfLtUnJW6MdlqSnLYz/kHbhdZ511bI899rDdd989+neDDTawziDT9fjjj7dtt93Wtt9+e/vJT35iS5cutYkTJ0Y/P+6442zNNdeMyhlg0qRJ0d+++uqro4za22+/3Z599lm78cYbo5+TIcvPJ0+ebP369YsCzY899pj99re/tWuuuaZTy9qXaXIyERERERERERGRwpF34Payyy6LasZeeeWVdtJJJ0VBVQKlHsjdaKON8vq8o446Kqole9FFF0UTjG211VZ2//33N09ANnPmzFaR6J133tluvfVWu+CCC6KSCPy9u+++2zbffPPm9xDMpfzBV77yFZs3b14UvP3BD35gp5xySr6rKyIiIiIiIiIiIlL4gdv/+7//i174+OOPo2zWe++9177xjW9EpQg6MjkZZRGylUZ49NFHV/nekUceGb2yod7tr3/967yXQ0RERERERERERKQoA7eora21J554IgqqPvLII/b8889HGa9k3IqIiIiIiIiIiIhIDwduKVVAoHbcuHFRoPY73/mO7bbbbrbaaqt1clGkt6yckFdEREREREREREQKRN7Tyb322ms2YMAA22STTaIXAVwFbUVERERERERERER6MXA7d+5ce/jhh23HHXe0Bx54wHbZZZdogrIvf/nLdtNNN3XhoomIiIiIiIiIiIiUprwDt6lUyrbYYgs788wz7c4777S///3vtu+++9rUqVPtlFNO6Z6lFBERERERERERESkhede4nT59ejQpGS8mKFu8eLGNHz/ezjjjDNt99927ZylFRERERERERERESkjegdvtt9/ePvvZz0ZB2pNOOimamGzIkCHds3QiIiIiIiIiIiIiJSjvwO28efNs8ODB3bM00mtSqbS2voiIiIiIiIiISLEGbj1o+9xzz9mrr74afb3pppva1ltv3fVLJyIiIiIiIiIiIlKC8g7czp4924466ih77LHHbOjQodH3FixYYHvuuafdfvvtNnLkyO5YThEREREREREREZGSUZbvLzAJ2ZIlS+y///1vVDaB1yuvvGKLFi2yM888s3uWUkRERERERERERKSE5J1xe//999s///lPGzduXPP3KJUwZcoU22+//bp6+aQHpFXeVkREREREREREpLgzbpuamqyysnKV7/M9fiYiIiIiIiIiIiIiPRy43WuvvWzSpEn20UcfNX/vww8/tLPPPtv23nvvTi6OiIiIiIiIiIiIiOQduL3++uujerZjx461DTbYIHqtt9560feuu+46bVERERERERERERGRnq5xu/baa9v06dOjOrevvfZa9D3q3e6zzz6dXRYRERERERERERER6UjgFqlUyvbdd9/oJcVPk5OJiIiIiIiIiIgUceCWycd+85vf2F133WXvvfdeFMClTMIXv/hFO/bYY6P/l+KkXSciIiIiIiIiIlKENW7T6bQddthh9rWvfS2ajGz8+PG22Wab2YwZM+yEE06wL3zhC927pCIiIiIiIiIiIiIlIueMWzJtH3/8cXvooYdszz33bPWzhx9+2CZMmGC//e1v7bjjjuuO5RQREREREREREREpGTln3N522212/vnnrxK0xV577WXf+c537A9/+ENXL5+IiIiIiIiIiIhIyck5cPvSSy/ZAQcckPXnBx54oL344otdtVzSgzQ5mYiIiIiIiIiISJEGbufNm2ejR4/O+nN+Nn/+/K5aLulhmpxMRERERERERESkCAO3jY2NVlGRvSRueXm5rVixoquWS0RERERERERERKRk5Tw5WTqdthNOOMGqq6sz/ryurq4rl0tERERERERERESkZOUcuD3++OPbfc9xxx3X2eURERERERERERERKXk5B25//etfl/zG6qu+8IW0jRzZ1NuLISIiIiIiIiIiIvkGbqXvIpl69mwFbkVERERERERERIpucjIRERERERERERER6RkK3IqIiIiIiIiIiIgUGAVuRURERERERERERAqMArdi9oc/WL/bb9eWEBERERERERERKRCanEws9a9/WeWyZdoSIiIiIiIiIiIiBUIZtyIiIiIiIiIiIiIFRoFbERERERERERERkQKjwK2IiIiIiIiIiIhIgVHgVkRERERERERERKTAKHArIiIiIiIiIiIiUmAUuBWzigptBRERERERERERkQKiwK2YlZdrK4iIiIiIiIiIiBQQBW5FGbciIiIiIiIiIiIFRoFbMaus1FYQEREREREREREpIArcijJuRURERERERERECowCt6IatyIiIiIiIiIiIgVGgVuxtEoliIiIiIiIiIiIFMxtzoIAAGxhSURBVBQFbkUZtyIiIiIiIiIiIgVGgVtpqXGbTmtriIiIiIiIiIiIFICCCNxOmTLFxo4dazU1NbbDDjvYtGnT2nz/1KlTbZNNNoneP378eLvvvvta/TyVSmV8/fCHP+zmNSnywG1jY28viYiIiIiIiIiIiBRC4PaOO+6wc845xy6++GKbPn26bbnllrb//vvb7NmzM77/ySeftGOOOcZOPPFEe/75523ChAnR65VXXml+z8cff9zqdfPNN0eB2yOOOKIH16wIA7f19b29JCIiIiIiIiIiIlIIgdtrrrnGTjrpJJs4caJtuummdsMNN1j//v2jYGsm1157rR1wwAE2efJkGzdunF166aW29dZb2/XXX9/8njFjxrR63XPPPbbnnnva+uuv34NrVkSqquJ/Z8zo7SURERERERERERGR3g7c1tfX23PPPWf77LNP8/fKysqi/3/qqacy/g7fD98PMnSzvf+TTz6xv/3tb1GGrmSRSsX/LlyoTSQiIiIiIiIiIlIAVj4j3zvmzJljjY2NNnr06Fbf5/9fe+21jL8za9asjO/n+5nccsstNmjQIDv88MOzLkddXV30cosWLYr+bWpqil59XbqpyZiWLL1sWUmsr0gx4FhMp9MdPyb5PZ9wkK+L+Nj2VUm+im21fJ/6qzP8M4ruOlXk7TI8LsO2iFJvl0XfJot1B7axKujr7bK99xR9u/Svi2nZE0qxXbanr7fLTvdhe0Bf6Vui5Nul5NVOCvm4LDVNeeyLXg3c9gRKLnzlK1+JJjLL5vLLL7dLLrlkle9/+umntnz5cuvrahYvtnRdnS3/5BNryFJbWER6/kS+cOHC6ALLkwh5YzDKLwZz55otWWLFylfFy3DzL/9fbKtVt6LOmpY0WX1FvVl55z6rvrHemlY02dxP59qSimLaCMXdLv24XL6cjm9Zq9Lwpd4ui75NMkFrMe7ANs6VpdAuuUY2LG2IvmY+iz7XLlFsOy+hFNtle/p6u+x0H7YH9JW+JUq+XUpOiuG4LDWLFy8ujsDtiBEjrLy8PCpnEOL/qU2bCd/P9f3/+te/7PXXX48mQGvLeeedF02QFmbcrr322jZy5EgbPHiw9XXpQYNseXW11fTvb6lRo3p7cURk5cWVm1DOQx26uNbWUnsm/nr4cLP+/Yt2u/qqUI7bx+DocBfbatU21FrZsjKrqqqymorsg4k5WWFWV19nw0cOt/6VxbQRirtd+nE5YEB8XIZt0kq9XRZ7mywvj+/ai20HtnOu7Ovt0rMea4bUZAzcFn27RLHtvIRSbJft6uPtstN92B7QV/qWKPl2KTkphuOy1NS0kVxaUIFbLnrbbLONPfTQQzZhwoTmBsX/n3766Rl/Z6eddop+ftZZZzV/78EHH4y+n/SrX/0q+vwtt9yyzeWorq6OXkk06FJo1E2plNHVTTU0lMT6ihQLLq4dPg/xO34Ty9dFfGz7qiRfxbZa7Ef2qb86wz+j6K5TfaBd+nZPpXi1rE6pt8uib5PFugPbWBWUQrts631F3y7962Ja9oRSbZdtKYV2Wejr11f6lij5dik5K/TjstSU5bEfer1UApmuxx9/vG277ba2/fbb209+8hNbunSpTZw4Mfr5cccdZ2uuuWZUzgCTJk2y3Xff3a6++mo7+OCD7fbbb7dnn33WbrzxxlafS9bs1KlTo/dJO6izVFHR+hkmERERERERERER6TW9Hrg96qijolqyF110UTTB2FZbbWX3339/8wRkM2fObBWJ3nnnne3WW2+1Cy64wM4//3zbaKON7O6777bNN9+81ecS0OXxqWOOOabH16ko8ZyIArciIiIiIiIiIiIFodcDt6AsQrbSCI8++ugq3zvyyCOjV1tOPvnk6CW5SStwKyIiIiIiIiIiUjBU3EKiUgnKuBURERERERERESkcCtxKS8YtU2mKiIiIiIiIiIhIr1PgViLp6mrVuBURERERERERESkQCtxKrLraUsq4FRERERERERERKQgK3EpEpRJEREREREREREQKhwK3ElHgVkREREREREREpHAocCtm6XRUKkGTk4mIiIiIiIiIiBQGBW6lJeN2xQqzxkZtERERERERERERkV6mwK1E0mTcQhOUiYiIiIiIiIiI9DoFbiUulUDGLerrtUVERERERERERER6mQK3EkmnUmaVlcq4FRERERERERERKQAK3EqccUvgVhOUiYiIiIiIiIiIFAQFbqWFArciIiIiIiIiIiIFQYFbaZauqTFbtkxbREREREREREREpJcpcCst+vdX4FZERERERERERKQAKHArLTVuCdzW1mqLiIiIiIiIiIiI9DIFbqWFSiWIiIiIiIiIiIgUBAVupQUZt0uXaouIiIiIiIiIiIj0MgVupXWpBE1OJiIiIiIiIiIi0usUuJUWyrgVEREREREREREpCArcSkvG7cCBZosXa4uIiIiIiIiIiIj0MgVupcXgwQrcioiIiIiIiIiIFAAFbqV14HbRIm0RERERERERERGRXqbArbQYNsysrs5swQJtFRERERERERERkV6kwK201Ljt189srbXM3n5bW0VERERERERERKQXKXArrQ0ZYrZkibaKiIiIiIiIiIhIL1LgVlobONBs6VJtFRERERERERERkV6kwK2sGrhVxq2IiIiIiIiIiEivUuBWWmrcQoFbERERERERERGRXqfArShwKyIiIiIiIiIiUmAUuJXWlHErIiIiIiIiIiLS6xS4ldYGDzZbuFBbRUREREREREREpBcpcCutjRpltmCBWV2dtoyIiIiIiIiIiEgvUeBWVq1xW1lpNn++toyIiIiIiIiIiEgvUeBWWiOAO2yY2dy52jIiIiIiIiIiIiK9RIFbWRWB208+0ZYRERERERERERHpJQrcyqo23NDsnXe0ZURERERERERERHqJArfSusYtRo5UjVsREREREREREZFepMCtxIHbEBOULVmiLSMiIiIiIiIiItJLFLiVSDrMuB0wwKy2VltGRERERERERESklyhwK6vq189s0SKz669XAFdERERERERERKQXKHArq+rfP/735ZfNPvpIW0hERERERERERKSHKXArq9a4JePWvfuutpCIiIiIiIiIiEgPU+BWVlVR0fL1nXdqC4mIiIiIiIiIiPQwBW6lfQ0N2koiIiIiIiIiIiI9SIFbyW6PPeJ6tx9/rK0kIiIiIiIiIiLSg4Jn4qWka9ymUq2/d+GFZqNHm82caTZrltk66/TW0omIiIiIiIiIiJScgsi4nTJlio0dO9Zqampshx12sGnTprX5/qlTp9omm2wSvX/8+PF23333rfKeV1991Q477DAbMmSIDRgwwLbbbjubSRBScrPWWmaVlWbDhpnNn6+tJiIiIiIiIiIiUkqB2zvuuMPOOeccu/jii2369Om25ZZb2v7772+zZ8/O+P4nn3zSjjnmGDvxxBPt+eeftwkTJkSvV155pfk9b7/9tu26665RcPfRRx+1l156yS688MIo0Cs5Zty61VZT4FZERERERERERKTUArfXXHONnXTSSTZx4kTbdNNN7YYbbrD+/fvbzTffnPH91157rR1wwAE2efJkGzdunF166aW29dZb2/XXX9/8nu9+97t20EEH2VVXXWWf/exnbYMNNoiyb0eNGtWDa9ZHELidN6+3l0JERERERERERKSk9GqN2/r6envuuefsvPPOa/5eWVmZ7bPPPvbUU09l/B2+T4ZuiAzdu+++O/q6qanJ/va3v9m5554bfZ+s3PXWWy/6G2TmZlJXVxe93KJFi5o/i1dfl06no1fGdR0+3Izs5xLYDiKFhOMx63GZ2wfE2fT+dREfw74qyVexrZbvU3911Xm7qK5TRd4uw+MybIso9XZZ9G2yWHdgG6uCvt4u23tP0bdL/7qYlj2hFNtle/p6u+x0H7YH9JW+JUq+XUpe7aSQj8tS05THvujVwO2cOXOssbHRRjMJVoD/f+211zL+zqxZszK+n++DEgtLliyxK664wr7//e/blVdeaffff78dfvjh9sgjj9juu+++ymdefvnldskll6zy/U8//dSWL19ufV3/xYujbdYwe3YUOA+VlZfbgBkzbPEnn2QvpyAi3XIiX7hwYXSBTR6XOWEwyi8Gc+eaLVlixcpXpb4+/n/+5f+LbbXqVtRZ05Imq6+oNyvv3GfVN9Zb04omm/vpXFtSUUwbobjbpR+Xy5fT8S1rbpMo9XZZ9G2ysbE4d2Ab58pSaJdcIxuWNkRfpzL0U4u+XaLYdl5CKbbL9vT1dtnpPmwP6Ct9S5R8u5ScFMNxWWoWL15cHIHb7oxaf/7zn7ezzz47+nqrrbaKauNShiFT4JZs3DCLl4zbtdde20aOHGmDBw+2vi49cKDVDRpkq40atepBPGyYpaqqrF91tdnQob21iCIlh3MZN6Gchzp0ca2t5RGGlsz5/v2tWPmqVFWZealyOtzFtlq1DbVWtqzMqqqqrKaikzXXV5jV1dfZ8JHDrX9lMW2E4m6XflwOGBAfl2GbtFJvl8XeJsvL47v2YtuB7Zwr+3q79KzHmiE1GQO3Rd8uUWw7L6EU22W7+ni77HQftgf0lb4lSr5dSk6K4bgsNTV5zMHVq4HbESNGWHl5uX1CNmeA/x8zZkzG3+H7bb2fz6yoqIjq5Yaoh/vEE09k/Mzq6urolUSDLoVG3ZRKRQdxxvXlajZ0qKXmz4+CuCLSc7Iel7ngd/wmlq+L+Fzmq5J8FdtqsR/Zp/7qDP+MortO9YF26ds9leLVsjql3i6Lvk0W6w5sY1VQCu2yrfcVfbv0r4tp2RNKtV22pRTaZaGvX1/pW6Lk26XkrNCPy1JTlsd+6NU9xmjlNttsYw899FCrkQD+f6eddsr4O3w/fD8efPDB5vfzmdttt529/vrrrd7zxhtv2Lrrrtst69HnMfT46ae9vRQiIiIiIiIiIiIlo9dLJVCi4Pjjj7dtt93Wtt9+e/vJT35iS5cutYkTJ0Y/P+6442zNNdeM6tBi0qRJUbmDq6++2g4++GC7/fbb7dlnn7Ubb7yx+TMnT55sRx11lO2222625557RjVu//rXv9qjjz7aa+tZ0HjMrK1R41GjzJ59lqLEZocc0pNLJiIiIiIiIiIiUpJ6PXBLgJVJwC666KJogjHq0RJo9QnIZs6c2SqFeOedd7Zbb73VLrjgAjv//PNto402srvvvts233zz5vd84QtfiOrZEuw988wzbeONN7Y//elPtuuuu/bKOha91VYze+ops5dfVuBWRERERERERESkFAK3OP3006NXJpmyZI888sjo1ZavfvWr0Uu6IOOWesH33Rd/zTSbAwdqs4qIiIiIiIiIiHQjVSWW9m20kdkvfmE2eLDZ7NnaYiIiIiIiIiIiIt1MgVvJHbVuFbgVERERERERERHpdgrcSlwqIRcK3IqIiIiIiIiIiPQIBW4l1laNWzdmjNnf/mY2b562moiIiIiIiIiISDdS4FZy97nPxf++/ba2moiIiIiIiIiISDdS4FZy17+/2bbbKuNWRERERERERESkmylwK3GN21xKJWD4cLM5c7TVREREREREREREupECt5IfArcvv2zW0KAtJyIiIiIiIiIi0k0UuJX8Mm6HDTObP9/ssce05URERERERERERLqJAreSn002MRs40GzpUm05ERERERERERGRbqLAreSnstLsc58zW7xYW05ERERERERERKSbKHArlqJUQj4GDVLgVkREREREREREpBspcCuxXGvcYvBgBW5FRERERERERES6kQK3kj9q3KpUgoiIiIiIiIiISLdR4Fby19lSCQr6ioiIiIiIiIiItEmBWzHrSI3bZcvMVqyI/3/pUrO//73l5/zso48y/+7rr5t961ttf35Dg1ltrfaMiIiIiIiIiIiULAVuJZLOt1QCliyJ/33pJbO77za79944CPyPf5hdcklLYNe9957ZNde0BGez+f3vzc4+u/X3mpriAHE2BIrb+nlH8Hn8XRERERERERERkR6mwK3kn3FbXm42YIDZ+++bvfWW2YIF8ff/+lezF14wW7gw/v/Jk1sHbz/+uHVWbqi+vuXrd9+N/w2DpvfdZ/bNb2ZfJgLFN97YtXvznHPivysiIiIiIiIiItLDFLiVWCqV35ZYbz2z6683++EPzWbNMtt3X7Odd46DrrNnm62+elzugMCuCwOxYeCWmrdnnNGSwVtVFf976qmtSyx4gHn6dLN//nPVZZozJ791ePlls7vuyvwzX9Z58/L7TMkPgf1nnul76/TOO2Zvv932+55/3mzGjJ5aKhEREREREREpMgrcSsfst1/L1wRn1103DubOnGn2ySdmxx1ntuOOcQYuCLoSzNpmG7MhQ+IyBC++aPavf5n97W/xe/773zjY6wHcMIDqgWWCXX/8o9nUqXGm79NPm113XfvlFxxB5dNOi78m8PzAA2bLl6/6vjfeaB1Elu5BEP7mmzOXpGC/vPlm5iB9ofL2deWVZlddZfboo9nfe8MNZpddlnvGO+39Bz8wa2y0Pod1S2bhF5pnnzX7z396eylERERERESkhFT09gJIEZZKwMYbm1VXm9XVxZmuY8bEwdU//CH++ejRZltuafaLX5h94QtxVu4TT8SBW8osEKD92c9afyYBvM03j4M4hx4al14gSEtGrpdjINi19tpm8+ebffvbrX+fZfH1ISD8uc+tmklMYJmMSIJEZAVTvoH6uOuvH3+f5VxrrZZyDZokrXv96lfxvwTymfTOs6uHDzf77ndb3kcA9+tfNysr4LGmRx4xe/DB1t+77ba4HVJeJBvaJAMf7WFAhPfOnWs2alT8PdosbTzT5+dbn5n3cyz7Z/ekiy4y23RTs1NOsV7BOWPRonhQKZs77ojfs912PblkIiIiIiIiUsIKOAoiBV0qAT/+cRy89UDtmmu2/Izg7NZbm1VWxsEsMhFBNi1ZrJ4lm/TKK3EQ5aCDWoJhZLoRtHIEqTbYYNXfpU6uB58IIGcqneC1dAkAEyxk+b32Ltm7l14afwYlElgHgsZtSU7AJrkLJ5Njgrvzz4+/xwR2f/pT6/eSue2Z2YWCwQEytr39MMhAUJXgH1m3ZHSDYOu558YZ5wxO3HRTPFGfaysrl0OTAYqwdnTYJsnY/elPV/0ltuOkSXGGeq6YVPDCC63Hsc0YdCEomgkZxt2dZcx2ZB+1JdvylTr2HftQREREREREupwybqXjCKB6lquXFPja18wee6zlPYccYva//7UEXrbddtVH3wl08VlhPdlMmZXf+178IuhFUIr31NTEQWOyee+8M66V68FUsnRHjmz5fZbVa9q+9ppZRUVc3uG3v42/RxYvyPRkWcaNaztwSzmFq682+/KX4wzeXXeN6+butJN1CzJRCWqSxVysPv3UbMSIeKCAye3IrKVt+D5gH3oJBWyySbyvcO+9cTboDjt07G8//ni8j8nYXmedOJBKmyALvH//lvcRuGf52hrMYHDh97+Pv6ZN8Zme4Uq2+BZbxF+TiU6Al6Dr7be31LRlMKJfP7OvfjXO5AwRxGabEPAlu5PfJQBLaRF4KRFKg3z4Yebl84EOyo989rO5bR+vycu6dWQgpy3UMSZoTftlQMSxnmRT+98nCM3247gE2/iVV6zy0/lWceCVZoOGWrdgW4P24INR2dBWqelNxr6Y3X9/PIljoZ+XOJd7Vn97OAaosc5+PuKI7l4yacs998Tn7RNO0HYSERERkZKkjFvpnPHjzTbcsOX/CTR961st/09QjIAjwdqJE812223VWrQEzQi4fvObcdBm//1bfkYQhQCrB8EcAVuCemPHxgEwbrAJ6oUZsJQ9CBGoCwOxvJeyCB6MITAIDyAT3GsrcOtZZrfeavbnP8eBwd/8Jv9H1HNB1vJPfhJ/3R2f3xMI0l1wgdmTT8b/TxY2+9SzoAnI+s+87Zx9ttlXvmJ24onx9z74oP2/89BDZq++GmedelsjEEMWtgeY7r47LtNAYPC55+Kfe8mQiy+OPyOJ9kIgNFPbIgjthg1r+Zr26ZOvJScio71SooPMcIKaDASwzFOmRBnhqb/9zcp9cj+CuV4DlhIjLAsZ4m0FyOHHTntYdw/cdketWQ86c4z4tia4TLv+9a9bsvXZvldcEQeqaRcEvVcOqKz/xytswBt5ZBB3JPM7W+aol2rBv/8dDyAV63HY1bztJ58+IEO5UOoxs2xcl7gOPfxw5vfwcwbewLFIG/UBpEz8CQ/pPlx/OWc/9ZS2cgkq++dD1v+9LIOTIiIifQ33rX1t0m7pMgrcSsdq3DrqjhJcy4aMV4JH3OQS5MWECa3fQ9B1jTXMPvOZOEP38MPj71PvksfEjzoqDuiGWYAEbpP+3/+LSzOALFy/CU8GaCjh4PbYw2xoliw+yjFw4xhuH772AEUyK9g/38tChLjBJ9hHEIqTci4ByPB3yUT1IEE4eVt3IQvy+9/Pr20QdOOR80xY3298oyVI/sMfxgFLsm+9jjC1YEGtUwL5J50U/z/BfgYEaB++7fldgpksH8E/yhX4slKygCA3ZS+8XEEyAB/+P8Fb2hovtjH7L9l2wPfICGWCKgJ31GPm8729giAzmdqO4D/LvNFGcRDXlwcMOAwcGH/NRZrs7XPOafUnB1BWAb6NvH1TXsGz3dmGyQn2WAcGJfg3l/ZCnWeWkzbdlSUBfH/58hEwpy1QCoO6tj7ZF0Fs2hsZ+V7KJMjAbzzscKtcMt/G3H2DpeoyTCbYFo63MDib5EEhzhk+KWGIgSfOQ9T1hmcMk5EZBu743b5WE5v9xqBCW+vl+zYMbjNINnlyvL8dQdzeCnR6QJ5JLclw98Eix7mDcwLtkesZg3jUnc4UeH7ySUvNm2dVl11im045zSoWZCjJ05ev+T3JB67aQ4klBdH7Ds7XL71k5XfdbavfGzzBJSIi0pdRGo9+d6FP2Cy9QoFbiXX00WgCSe1l9Z11ltmRR7YEPMJH3cl+3GWXzL/HI95k4hG4I6AL6qB60CvTOhDgBUE/HrEPb1AJSDFh2sknt3yPjM8rr4yzfflsDxzxeCyBQm7cw5MnASfqlyJ5UvUSEMlMSB51J3OLTM7vfMfs9NPjoGKuwoAICC6cd1733qj+6EdxJl2mIHQ2ZEmGWaUe5Ob7yYAuN+QeuKX98AizB9ApO0BQNGyTfM33PODKRY3MVALhBLUJpJJlGwbe+BltgO2UDEZmC2YSjIMHbPhMShxQnoNgIn75y/hfArG0fwYeKK/BOtJWw4D+aqvF/zJowT7j/fwLf2yb9rT33i2/w+8fe6ylCV6zGQn6Om+7BKAIVhAkZt0oHeJZgmB5fYCCjNb2ajIzGR+BKpaXzyMbluC9b28C42FWcXsIRDO5IOtGbWKC+hxT7GuCeT6Jmw9gcBwyyOPBeq9vvVLTvvvbf0//efR1/1dXZkjnivaRCIivEnjceed4GZhMjsEo/3z+9ZHvzTYzO+64uA6wnyfYLmxTgsME3gn6cXxmqq9djGgXrL+Xs8jEay+H2cqUx/G6yWRNc8xcckn8RELyiYueqCfsGej+hIfXpXZ+LuecQZCaY4lJNTn3hsfM4sWW+u1vbdDll1tq3lxLpZts2BN/sR7F+awz5362P4NUtPWwdnwh8kGBbJNS0vZom9TM51ztOG6zbaO2fgaC+mT5FktwuytxvvcBwe7S3nbl5zfeGF/faa6Dg9I6kj8f5BYRkcLBtY77HPrO9HW4L+XpRL/HpP+cbz+30OaCkS6nwK10/w0Kgad99mn9PW6es92MtcVnfQ9rkoYIohEUJQjDDQg3pgTvCN74rPEeEAwDzgRpCVxRcxVkeJLVy3s8yMfve8CJR/rDmrwE7Nzf/96SSQgmPfLMyVB480jHmpvpTHi8nMfvCc4R3CSjlL/NOhFUJLutq/mj68mSAG0ZPLj1ZEWsD0FqAp4EQsMApN9QsL0JqhHc9yCnZ5UmEej0QKIHHMKSBp61GmJfsw7sQ7I6CazxOWHGMwFDgnXhupOBynHB5GNcWC+/vPXnUs/4wAPjr2nHZNZ62wl5MDrM6ibgy3J86Uvx/1MP1z/L2wufv9lmtpTtR/CT4+eww+L3/jwOYEaBCoKJnj3IujNhIAFWgrq0abLGfZCD9sZnedv0ms4gkMV6sw9pb2TDErzn8XE6A2SzkxELtguBWAJaYTCXLEUviUBg3UuP/GVlYIsgM8Folg0+kEPdW9YtySdtI1i6cjvXjt3Uhv/tt9Zvxsq6x/kE7bIFa7z2KfWGnQchCTJTGgEEaxlkop1Sy5vzBe2PwHg4EEOny/dRQuqDD61sWZ4Zw12F7cB+y+eY9veGv0Mb88AnbcHbEduC9kDdZa9L7RnttDmOWY5RH7jIhv1Btq7XHWawi+PTg+GcXyhPQzt1/E2yorPxwTQGihgY8s/2ax8DIQgDmWRYU3s9DEhTS32lpnXG2uKx463mw7etcnYXPs5NO2XdMrVXMhE5//sAZjasB+eu5GewHXjawY8vr9NdqLjm+nWFfcxTD6wbbeB3v4vPlZynEAYcqYvLIFx4jqP900YZLE4OnHJ94GkKrqVcr7iGJ4P7ueL8lin4Sbvmmhhmr/P3stUp7w0MSjG40tU4j9JnYruwDcKBlCTOUX7+GBQ/kdLvjXesfH48QDTgPy9a5aw2fl9a8yC4D7CJSMdw7clnwt++gn5SMoFI2sY9L3OZtBVf4T6Je3iSG/iaBDL6bY4BZAbauZdq7/xNX49ShNxvcZ+X6anRYsFAvAYbs1LgVmJdPRlRe8hQ9EBQPgiAcYOVbQIhgmgE6Aj+EdwlG4cAJwFXLjx+E0gAyW/4QgccEJdy4O+wTQjocML0jDp3yy0tN/FkEvKIPD/3zFKCN5ywwxs4Anbh5Dg8Nu8ndjLUku/3G3VuIskIo4RAWGaCm1CCimQMt1WLtyP4u2yrfDIHPeDKTZlvG79okUlJcJYbZoKCHlBlXxFc5LF+guQ77ph98jEP3PKZfvNLlvPRR5vttVdLcIb3UZaBv8cEUmxbgk0sH3WRw6D/wQfH7ZDsVIKp7D8CS6w/N5oER7228plnxtsaBPByqR/rweiw7i1YDm+Lvsz77dfyyP5KjQQHGUAgY51l9TbutZm3337Vx/pZH7YhwWS2J22XbUa2uLdN1ssDYhwjBCn4uyxHcp8TKPIAEO2Q/cYxyCPn/C2yydkPZNhSf5f9QBCbLFZHuyUwzDEAavuyvQmuEMANB3HCDHyWL/j/sob4+CBY1gpB5OTj/Cwz7cXbJR2jTNmeBIdYb7aztw0eVSJg51m/LGcYmKf9EogmqMsxnMxcZGCAzhZ/j8m7+HrGDKu47Aob94Mbrf//2shg7Qj2b3uZrOwr9htB+Fx5wNaDWGxPgipMQMk2Z/35uwwsEAzlb/D5tKlsxzHnuGzZ22w3r5FOYI7McQ++eUeUTi5/n5rVnJt9IIHzu2eL+zZh39AOCBwzyMH5h31MwJKnAwgisY5eWzvsHNMmCfTyd/lb06ZFAzrpww6z2hNOsIZvnGlzt9zLKhfOsdV/1YFrWTYsK3+PAGuy088gF23SS+4ksV1pt6wnkyaGwW0+i0GG8KmItkqI9Cb2GTW32Z+cf/l/2hXXTAKrDKQFQfQIgwK8n3bp+9ED9LRfbmo4l3HjzcCc4+94IJzzpwvPg2Qn53Ijwc0aNx1eR5k2ST+DNs8+ZR+EQUuuJ5mOR+qv085DHAvJgcmuxvk9nKSSbUkGT2cyvPkc+jD0mbiGIlPgllrztG2230qNXzzCKhbX2hq/vtOG3RNnHw2feq8N+1OcVVQ1I4+SU7lgH9NG+hIGn8Agh4h0HH1ef/KOhIlkyaW+ivtnBsa76ilPsku7I7DoCQI+jwbXFF9m+gLcEyYnR+8Mru/ZAqpcR0nYIsmKPlmmAK73g/m59zf4Pb/XA9dE7q3amm/Bn45zXLfDAHBXIQO4JwYuGPzON9u4hChwK72DG+JMGYq5aG/Wd0fAh0CAB0O58faMXR67z1RugZt6sh89kM37uakn8zGZqcZN70EHtQTcqFcaBui4cfYblS9+MQ4IchNH2QeCsAQbOLGzjJ5dyImcoII/IupZvR4k3GmnOJvvy19uvSyc1Dn5d1UAl3Wj7jA3wnRO2rvBJ1Dj9SxZn+RNNVlurAOBWl4epPbMXscEdvw8E4JrLA+vsANB+Qv2JwimEfDge3xNG/NsOg90kJVNhjRZKF5aw4OpvN8DfdTiBTf8BH4J/BDQZ/sTeMwF76dG83rrtf/ez38+zi7Nlk0eoj0RsKZ9EiDz7FVH3V3aMD9nvRm5pcPi5RN8ojvQJmmLBEgIJnsHgM9km4QBCwJH3lHxoBAZbL6NQXCI9zAI4kFoviYQ5sduVVXcJrbaatV1YxuQ8Q6Cz4E5e33Jlm2wmQ363zOtO0KMWhMI5vjh+3TgGBwiCEgQFnzPs395D9uEABhlO3z7/eAH8WSLrA/BHD93sKxJHPvJfR3uBzpbtDEmLuTroFzI6r/7c9eWOmEdOUe1Nbrv5xSCSxzT/H9bj8rTeaIzycAK5z5elHvxziSj+gRXCQwwQBBmN4JzFG2DMiB0+q+9tqXkTRi4JRDM+YNXGDijox1muvP3WV62p2MQguCrB14J7DEw5J1qzq/cINCuOdZpgxxn8HZN2+H7Yca1l8PhHEhboK1zXiY7cswYW8G5oH9/q13rM7Z40+2jfZl37WXQuQ4zkGkTDDB4NibndK9xnqxj7eUr6KQTmGTbEDin3XLMcw4k6EaQm+uCX7/IJPYnYHo7cEs7zBSA5ji96qp40I3Bt2R2LG3YjzWy3ymrxDFApjaBP3+igoGkbMFR8Dn8HT9uwicxaGfcsDIgxPXZg7G8Jyjj0oofT75vaPd8Tckhr9ebqQwIma7+lIf3VfyJBbCNCChSwiNT0JP92NkbNd8PDGBxbmTbcZyyL7JN2piLTBn+nAvYhpxfOE5p22SGciy6ceMsPWKE9fs4Xt8ygsq+fcrLrXzeAhsz5Zbo3y7DcZZpYtIkjisC6cUg2TeQwpVLGSHpHWHJNc5D3BNQoq0U+HUx2cfrKOaVoK/Wmad9MyUr0B+k/0efkHsQril+Leb+lGtIV5YSYCCVAbFMJej83p1+LH0y+o7J/hZ9GfpiXO/DJ2/8qT54wg3Xprawb7gv9fvXtsqbdRTLkhzYDEv05YK+aLIvG/LPYvlJsOvu0k1FKMcpx0WKEDfdZHmADEUuPh64zZUHbh03eQSfuKHjxirZKSZYRrCCi4ePGBGII1joPFjoj+dzY+MnK3+MlceyCZr5hdIn7kpmXLJ8XKB4dJi/w7JdcYV1yc00N8x0VLiZYpTt+OPjYECm7OzwYug1VQl4crPL9guD9H5h4VH+fMplEBjjpE+Q0fenX+QImLPtuOkMly8Mgvoj9+G2bA8ZrSxvWL4hn2VmWcgGzgWB7Wz1npMISvskaCecEAcguAmmI0m2MduKgIMH/Mn65OsTT2wZuWX/ho8De+DWsV0ZxCCQS6CXm20u0rk8cklHhmWgHASdFd8n/i+B27Z4jeFEpnLd6mNt7udPtNV/eI5tdt0pVj5vX7OjJrQEFrJl8ZMRT8YbxyTBZLIOCcbxSDX8OKa98F7qsTqOJ8+cDnGssb0JjBGEYGDBBzkoa8L2IrOZdSVYGAa32d1z5tmKUSOsS/hjbAR1fODDO5Mcu14ziwA6mYmco9j3bDduQDw4xdMD7COC/L68jP5zfPM+1o3BlrCTyfv9bzIgRfujnfgEkl4SxM99dFYJ+HIcEfxIjuAzyMGghAdtOQ8SOOec6wNc/E3OmV7T2ttxmE3q507OE5zHGCz0ds/5wwP27CPaPSVTQFCWkiSeKe7HCx1I1pvA58oOeLq8wmYf/FUbMGeG9XvzRVu2YZYsYwJ3dODpzBPo5tjgpoK/GZbc8f1IB5zALcc052LW3deV8zDnQD6P7c2xzcBAMhBIEJHSJVwbeIF9x+CLn697O3DL9ZR9ySCLbyeOKT/+CWZSNia8pvM99ifrQZkV9iX71NtRWKYDXkqITCkG3bycC3/Tn6YA+yWsfc7f4WbJb5i4BrBcXmaB5WZy0xDtg/MsbSt8AsDPS1yvw5t/x3pzzuGY4bzhy8PncP2lHXomLN/zturtkgEnjuURI6zi3n9YatvJ+XfvvR368U/b4IkPsG3pw3TkySyCv7RnBjC5fvsEnayXB2oZ+GH70tb5l3PGhhtaenDL4HrFvAW25vd/Gn1d/c5MG3XzHfFmql1mK1Zru19XOWe+papzGCjjPMBxw7kj0znfEcymnfFoa6HjOuXny1xwfWfQLlNfhGtKLk8aSf7oOxCoIJDDE1bStTh3c37JNLF1LngKwgecuU53R4m6QhM9rVff8oQK14h8E664DvqTM0lcY3yC5nwR7ORek+Ap/d7wmgiybf3aSp/Nr11h2TqumQSRPaEgXz4HDH1Mf5ow/Gz4dZuBYM6r9EHoJ3ifgHtqzwL2fh/XHpKR6Ld5P4JzL4MHfv/pT8NQZs6TwBjAZl3oD5M4xjmf9WY703cm2aSzT1eHE/byuWx/7n0yPcHMNqB/Qr/N0e/ie/QnSHoL7znDwRG+zzbQ9WYVugKLpS++2Bo7k1FRqMLMTS4anFy50c1HeFNNMNYDv34RIhstk/Axy+Tj7I5OGqOAnrnByB03suBm3AO3nLySNUD5u9zgERRi3xEk5QTPixOrPx7XER5oI8DFhcY/lwsOI4d+MxfyIBE3YF6ygGALARiWNXny5bPyrXHsJ3gCZSwD+5NOhF+IwgtAcrnIxCKTMlfckHFBypb9W0jYjrwIXnADn+xYEYzzwQf2AzcFXEg5FrhJI0ONDikdmjCb/f/+Lw6i8Xi9Z/kSvON3+F2y4Wij3MDuvns8ikrGtNdq5rM8EzrE8ZDtMXq3555x287QRpr6DbD64atbatbHVvbwg2aPr6w7nQmBKo4zOkJkG7NfCRomhdn3yQ5m2NFLYpvTxpnIimWlA8nx5wMrYHsQDKyutqb337MPy2fb2L88boOefNbmTzhglY9c7e77bcmO21jDmERHtC0+uRYdQM/GJxCUnFyQjhKBK68zDDKVHQFC2nz4ewx00EHkZoUAtNdjpnwEHUOOO283dLwIvLC+2XgAlY432y95biC4SyfYz4cE02gPXl+cYBlZvKwz7Zba2fwsfLSc3/HArWdZhzcInMfJ7qXdcq6mnYTBe8c5kL/nAWPwvjDgmUpZU80AG3HPzTbvHM71K89HbBuyrDknZnv82p+Q4PN4SsGf2iBTk3MtWY+ecUBQmnZMCRIG+8iw3H//1oFKBsMI8HqN9yTOBfCBqJVlVMoee9wGL3rd0j7pXk/gpitZMoMsaL8x4NzDoBHrTDCDts3+4LzANZO249eRMOPFB6R8sAbsB64T4fmE/R5ep2mHbA+2IW00uWz8LMxioXwCxxKDNOH3aPt8n+OKz2SZfaCDz6U9su8yBRA4h/rjtywvN2QsR/h3w2OTn4WDVTfeaKkVZps+c5ot2e0gq913ZXZ5Lvx4CTNkfUCATBuOkeRxzUAY7TvTtddxI8Y5NHljG/I+Q7IMVkOt1Y0aZpVzF1v5wtZPE1XOjvddezXDU8vrbJ2rb7L+m3JOScy1kOTtiOOdc3pbwolQexN9M85ZHDecc5LnU84DBP4JHNBe/OkRzplcE5P9BdomWceZArc8icA1nn0uXcuDY5znuAZ2NMCYKw/k93SZvHzRp+DayHWvo0EcPoPEDq4nDFrni8Fuyi65fOYJKFKpFY1WOekcs7KV25xzfHg/nAl9RPrSnMMZ8GZ/0Z7pazFwyrWPiZr9fM+9a0cDt/6Eik887BmqoM/gA7T0BRgk98EoynvRf+B6RMIF50GSE/w44H6I+xb6nJnQFtkO4c/DZBbaGp/v28oD/N7nZrm93Aa4P+bv8XP6srzfz8neX6G/S/+Wn/F+lpX14skYBrb96Ue2Jddo+jAkXtC/ZL/Rr2Lb05en7WYKonOd4BqS7YlP75fRP/G+mF+r+Tvsy+S1hG3JcobHnPdL2e4EfOnDOvaTD8B7EpusQoFbiW/iOjJRWKHzkxPBCm7Qefw628RX2ZAl4kHU8LF+Ald07LPdjBDkoh4SQZRw4rIQJ2ufZIwTIKNtHjQhC4ybeU6GXIQyda68Y8dIIxdI3kcnns5JPkHKJP4uF18CCGGAx0+24QUyecPDDZpfhMB6hRlTriPtzX+HiyKP2PP4a3tlM1gWLpT5bg+fSC1TOY1C5kGxULIcRThRIAEsMiS5sNPG6Nhwg0d7T5Z34IJO+6KTwkWb44kbP4KVYSkByoO0Vb+KrN/20PHglcWHXznXml57w8bMmGq2cGXwhaAiGbRsA/a71/j1QQw6NQStE5mvkbAd0c7oTBDsJgiUy42N/344suzZ0B7s+NznrLFhG1sw43FreOIlG/Tkc7Z0q82tfmxLkLf6rfei76erqmzBQTlmaoN9wj5gUIPzCIHxMMBDGQA6UAQd+ZoSDtkmm/DfI+DNeY7tQZCWIBkDJr49yESmnIjfxHOO9SzwtlDuhfOaZzvz2WS4suxk4Hl5DDqoBNrp3PkADcflMce0fJYPIjHIwN+nU0p2Mb+XPNeH+5HzFOcszp2eEezZlsnOJ4F5OvYEtrI8trVgj8/b6D/82Aa8+YItH7COVX77B2b1S1vXWc3EHxnzmw/4oAZtx2ua+nXBz4EcfxyLPkhCSRDaOTclBJS4Hvggow+ucNx5WwzPv5MnW/nC+bb24o9t5tZbW9V7H7Rqk1mF2R/5IIjNNvabNz6DGzC2kx+brDfHK9desmU4DrlZoP3y5ARthXYRTjTKtZYMFTJ2CXLTjmiPBD99n9L2+DtsN95D+QuyUHgvAwlkW3Je43PZ3uH+5prsTwAQWCGo6bXpfbCUayfbmwAZ13baWHhu4f/D446bP36f/ePBAM8a5zP9usp28AApN2tkq/D3wklQweAQWTd/edqGPnO/1W+9o60Y3lIzvU0El9lOyUF8D6CzjGHglrq1DDxz7mEbZnskn5+HgV2y/tm+/EuglkAgA8+U98lwPX/rjK/Y0OUp67+80Va/9ldWO34TW77hWGsYOdxG3Hq3lS1eahWfzjWrav33KaFQ9dEs6//fOMDa/72grnEmXn+YgcVsgRn2qz/FBc41tIWgLn2rzFT6KuE5M1/cENOOkjXywcAVbZmsc5aZG2oGsxm8DfF9PxcQfKIdc52i38nABX1cjkWvSZ+s28l2oX/gTyGwDfpa4LazyQ5dgesL13DaIMdgst/W1ej30WfiOOwttG3aV7YsTgaswkw+v5ciuEObzlSyjGsqWf0ku3A9oa1yHg9rgILrSWWllb8/29b59yxr2mRTa9hmR7PUgFU/z8sXgXNZWGqsj6pckCjpw3W4vQQvnyCWBA8P1vk9CfuMaxcvx+dlSgTivMY5M1OJMpfse/hTZeDY8dJE3P9xzgPXRvpIBAgZzPUgYZj5y70rL/pcXIsJdoZz03At5hglmYHzKG03LCHB59Ov4FpJG/WMW5ecGJXzDmXOKPXk5fj8esK9Nud0rvesD8FMrpPcn3hmKtcID6p6f5K+L9dj+icEsD0BiessxwL3R8msfo4ZAsPcGyQHrOlrel+E647301gWAvFs40yBW19G7imJlwT165uXnW3HsvKv749cy2GWKAVupe/iRMzIlneaMwW12kOgwIN4IU40bXV4eDw+l0fkyXDyrCCWkxqSBBC4APrNfBhoy8ZPtmSocXIP17ujGRzcgIaZFvwNbhR5VJcbAG6Cnd/gcnI/+eTur0vDiT15s5QJNyPhcuaKbUfHNtwGxYyswWyPdnFT6I/80kEhIJcpYM3xRCebizRBVb/RS3au6Hy0lXHZBZpq+tviDbayhv/bzCpvvj7uIBE84OWPB2XCscToNR1xlpGAPhm4yRFoOiS8OoPOfZasmYYRq1nN/MU25me32Ifnn2GNgwda9YwPbfSN8bkgXZ49IEaJhdG/+L3NOPkoG/j+DLN1d4szxLhR4aaazo8HUD1TkYEODwgSpKIMAduJNu783EPQjGBmmC3N1wRSw4EqOphewxvJutttdbj5XZaXgIM/tUAHkKBj2EkOg/ec19rq0NEOefGIGDeEdDj5fM7DyUEIL9UR3hx7pkHyOkFb4mbNg08ZahPXjd3EFm+zu425+wYbTtm11RO9Kx6H8wwQBpzoUNPxz1STfJttWtadgDRBE4LVZOB6O+XYIwDqHXW/RtFB5+YEbGPWm4ALQbewnnR4fATLsMF3f2TlqXJ7/9LJlq7OXs6kbGmtrXXJj+2jyafYipHDrfKjT2zwv56xD49YWe/dkWnDPiEQxk02wVCvIcqy8tgcE4YQzPQa0ATd/SbKr7Fh/VWvQ5/ETb0HlPxmkMB8WM6A/cDLf8YNFm2E8wdBW84H/C1u1jh2CIoRXGV5OJ65KYM/WknwloAnN2Ncs9muLAe/z82YHxesNzdT7A+ffI/3EfTzgCfXTCaMJCBK0I8bGTJauX7xOCfLwL7ke57BxLHEZzA4zbV57FhrrBpqy5760AYveN/WuOEi++jrl9jAGTNs8dpBNngSn8V5g+Pes8OZUJTHL7neE/TwjHbOMVwfvKY95xdeyUw2stQ4b3DzFl6rw0xWnzCrrSy4VMoahwyyhuE1NvMH3zbj3Ljypr1+rTFRu6v6cJa9MfnMKCu3bMUKGzD9Zav64GPr91p808xtbeXipSszo2rNfvObeP+E52e2N30fzkOe3e+4sSfQyXYPgxdsc16eKewDELRlntDiPEv7zmVAy7ctgw8MmtHO+ZucJzjPcgPP3/C2TbaYD34x6JAN7YrzHe2ZzKywhAftlcACwWi+z7nSM8U4z7FfOF74OTf9fRXbm7afLH3SkzgH0hcjcM4xky1wy2AAbYG+aS73NLQR+gcE08K/xf6lPXOO5RrA+aUns2+5V6C+O38306SL/DwM2nKfxPmP65pnLB57bDy47JNJ+4SkBHz9M5MDpzxCz3VzZfCpbIXZoI/NKj96xRan6q12m+DawnYJy+fA5/7o4/q/P8ts8CCzJSuf5qJtZqpxyzZnAD+c8yEcUPSnWjKd47MFgn3wiXvObFnWyf0S4tgh+MqAL30PjhOuWR7oZ5n5fV82rgt+f+P8SUP6fVy/eRKB5QozUklc4XfDwVjvq9Av4FxN/42AJt9nMJ7PpS/AQLr31Wi7/nUYNwhL4vmy0b/l/f4UGG2RNk67DJPEWG4GuMP7Pv5uXZ2lXn7ZBhHM9rJFBH6zPUFCkJt14BxJ/5P3ep19rvFcL7l2cF5iX4WDKZ48Qb+J674ndDFwTn+D8w/XWs5l4bmno6UrSoQCt9K3FfpjQFwEuMj4o7dhppTLJ7uBkTger/VHJDqCZeFmkpsEbjLpPHFDy40pE734DSkXLQ+keEeGixPf664RM26CuIHOZ9062gb8Br8vIAiZy6RnyFbbj33KDSCdhPZKHfQUOga0iXAft7e/+XmY9RuWCuhKbWRrzzrmMFv2wadRncY1L7vO6tdaPQo0YPFO21j5wuydUgK8BCjWveoX1tBIkGnlyDkBKNaNDhnHLOcRHo2nE5Q8Hj1bgUdo6bDSEaNTxu9zzCcHqzgvdfWs5LSzcOIpOtde9zuTMKDbFgKc3PQR7CJTIdNTFgS7CJCG68k2oWPdwXIBC3c9xPpPe6z1Nz0LnOAdN9sEbf3xea/vSfsjW4RzNsdVWM6HAKDX5CYY5G2K/cS2olMe3tiwLz3gzvcJBJIhwfk8rCvNoAXrzs3FykDOvO02t9HT48nh+r/4P1u2+cbW1D/+e4P+Nc1WDBtqyzb7jFXN+MCG3XV/9P3qmR9Z2dJlNvQfj1nNW+/ZWh98ZK+dNCH+GwQGCHx6FqcP/oXrRuCMAKo/JoxMT7H4uautDF+2ST7XHbYlNy60jzCY5Z+VbPMEoAmsEUwHxxWPTXoGDVmLBKN9f/BzbnJ8QCEcgOBvJ9sly85ykCHsJS9AwJ22zO+wz7jB4+bNtzH7m33rj7bXmr1zzAW27ot/sRHP/M3G/OZyS9cut7L1XzHb+Tiz/70ZX6c9s4ebL8904aaR45KbbB/s4Iad/UTwjicp/GavLbRlBsfI6qFv0JkngEKVrW9Z0tXVVrUyOLv+D35sNmZ4VAs3afm6a1rZuzOs8rwLWh79ZYCS7GyCEWwL9i3nQLZJGFDg+A2zxByDBwTZwftpxwRGPfDtj6/SX+E8wACAH7+cn8i04rj3R7DDuv0MwNMv9PraBAY4lrh5Z9+wzB60BX8706R1IBBI2+L8SvuivZBJxj4i+OsT7RHI4jik/YHtQRaYT+Lo7/OBK/7ls/0JnO7CkyJ+baJvSttluyUD786XL9enAbw8CxltuQZuOzOpUjbeDv2ppiSCYbQfz1pjfyazq1kufpeBOr9v4NjmM9m3tDn2ldc85xzE9ymrRB+RAaBM/SfaFueBzmQlM2DHyyeO8vbEZ3p79GsXgVcv70Xgir44+4k5M8LH9TOVvQpxzDFwwWfRryH4RiAqi4qFwXFPcPe8b7VO/OB6QLvKdR4RjnOuETwNBJaF7c31zJ/64ZrtZU4YPGdAu63+Ne2bfeT7qa1EhU4Y9Q+2/2Czc74Vn8PpR/i5wbHcBCJpb17yz2uzej+D36GPzgAUfdKw5ES2wK0H4Tm/+pwwDGLRLujT8HOWKRxo9D4F52W/nhE4ZVCL44b+kj9xxrWJ6x7nTfYnx1628lJhG2NdaH/0ARj05fdZFgbluV5y3fb5FjhnkyRCu6WckT9ZxMA01/F89xnrynZmbh2fsAxcD9gP/I2w/8O5MpzDhOzdYIKzFMcbP6fP4tcqcE4P+4reD6GvSHCYgVz6OMQZ2B5sb84PvkweoA8nj+PrMOHBB528zBPrxXmJhASO1bbqy4sCtyK9jkBDeFLjhE6Hwzs2yaLrbaGDQ0eAUUY6B7nUhOJmmouOj5hywvcbZbLh6Gjx//4Is+Okz002nQ8f/eyODm2IThyZA7kGISUWdvQ6g4t6+OhlISj0wZkM0jXVtnyTDa2pX01Uo9GDtgv33c1WDB1sA5572WreeMfq1l3L0nT4CFik01FwbPgdK2u2rlT29DMt5wkGNLyjxTHC77ZV7iNT5mKmJwyKiQd4wwyQJDrwmdazI9n5KzUNHGzvT7zIls1bZiOHvmGV0x6Pg27c0HCT5hmljmw8bpw9y52b12w105F8XJoAby4129n/ycE/blK4kWUSs9paa9p3H/t469WsprKfrfbc/2z4nX+zJTM/tHkT9reqWZ/aan+NMxAX77yNDXjx1SjjFsm2WD1rto3+x7/NTt2z5UbOb848aOulG7xGHB34MCsq08SFHuTs6seZ8ymd5NfSsPQPA6XUjuMmn+yWMKvZbziT2hp0pB2QWcaNux/HfoPjtbY5zrkxJcjCzWFYTzswb9fDbOjrz1jFgjlRxunQ16dZ5fkvm61YmUHlx4gHJQkCeeYPwSH2g9cQ5oaQa28YwKTtEZDgPWAQgeOKQL3f4PsjlblO0Jknr3PrkkHb2V89ylINK2zB+mvYsF/9wWxuunX/hRtLAgHsR4KABEvZ3vSB2Kd8L1xnjlXPUGfQi34bwSAysth34Y1/WBebICsvbnZpwwxikD3oQVF40Jbtz/4Nb7rJaAvrgfvEO66t2d79RtyDq9x4s970O5M8ozysMeh9U/+ZJxkwCOXLmC1jmkxsMrI6GthlexJcJ1uLY4KAkG8z+rjJQR7ez7Yi6EwJHvafZ2OCcxLZ1nwe+yLcnwRGCSoRNOezWV/KeGRQedY3bfSmI6z2sAzXzzakuNZ/OMvqNswwOEUwkyAK2zcM3NK3JlgUPgrukzR6ph/7k/MCbc4HBBmAJKPN1y+ccNURcPInQRgM4kVgi/MzJWl8u5FtzcAQASsytDkmOE4InnGMc77I0icvYznpLzL4xLUgGWwmIOjXRo4hzjWcU9gODEb7IDv3NWT5kyVJmyIolq1kAW3GS8jRJngRxOO4Y1080/K737UG629vP7LUxsycZsOf/6fZbkfboLdetsqpN8QRknAiVtoL+4gBcQ9UJQOnHnDkOsEgOnXPOf7CEishzsE+OSz4HQ/csn95eTv34C59F59smfszyjllq8naQctXH2GNnzvMKj3jk3aSrHHLOnmNffazD055ZiXnSNqFZ45yvvDALduRY5ksUa4dnP8ITnK+Yj1pbwQpGQBl+xL051zE+nK+SSYncTxzreK9HjAkAMjv0Jb8MX5KIRG4JeDLQA3LTfslU5QMbtqMlwVgwNFLLtA384A013nOK6wTAWD6L/z9JNaNa58PIrFsbZSAy+npX+7VffCGZaB9cD5IDlrTRplHgOsJ/T3Wb+X5In3IIdb0z39aikFntnWYJcv2CM+rfv6nXVIuwwfLPChPXCBZhgSUimS7cx1hjhnOx47PYDDb50oBTzzRXgjcdnd97yKnjFuR3pYpuMqF2EeK8z2J0aHiMSFukNvLJuTmhIsnJ01Gu+gw0rnyxze54PgjHCyn31T4zRkXWbIyQEci02RUXU1B297jmWDdXX+tRCzZfisb/NjTUUBs4X67RxmOVe9/ZDXvzIheS3bc2gY+Pd3mHXGQDftTSwBhwYF7Wu2AKhvylwes/M67zPqtPF7Djmwyi1C6Xf3INa22xqzpcxuYHboy2zcsR5EtEMu5ta2gbXchu+eVV6zxC583m/G4zZmwn1XXN9mAF/5rA//zog2ctvLcvlJUf7miwj765tetcu58G/mbONtl0e472sBnnjdbVmvDn3zB7JT0qvXRPZhA5kd4o8u1xoO8YeAz5O/PVA+vpxCIpVxAuOzcqPIiQEJGZJhB3hHcaPoTLVybyerheh72AbxeHYG/dgZZZn31fFvjZxdY04q0NaVXmC1f1rrXTwCGG1nWzR+7Zv08eB5mLfJINYOmtGlujNlvYUCOp3FAsCWZudSRMlU5aBi2mlV+PNuaKOtRHwSkV1r+mfWjdUivWG7vH32QjV063Cp/e2tLtqw/0kvwlXXleGC9CNCQIUZZCnCDTzYXQQl+zwMYZCnSX6IPlClbixtRLykBPtMDIWHQNkRgghIi4cR5IYIKfqNMwJmbd26Sfd8RaCIowXuYwJbl4qaethIOenspDIQBTMcNPe3Oy7E4H6QPA8sE8JIDEty0E9Sjv7jrrpZK04ZX9iVz5YE5aiTyCpMH2AceYGDZCThwDIbBO1AigsxvArYED+FBFl9nArQEOVhnggw+TwOBSoLi9Hf9qRTafkODjXhiun3y2a3ic1Iw03v54qXWuNqQVQK2DDKMmRIH75Zst6XNOzLxODB9b/aRB275OwwIECB3BPS4N2Afsm0IitBO+Pv0+T1oC4KUbeGzCDpxDHNse2DR67ly70GmpAeRCWTzYn0J3vr+55xHoJygTlJdnQ2kzdFOuccIsS+5FhCIdAyihMLzHtuGdfWaoGB/E2zmOCTAxrHpJWscbZ/ybY7AMduV9srn15otHz3C5m6wThS43eCa0+zDzx7c8n6Wm/OyZ0QjnFCLoF14XSKQySAN28+z4JNBW/88PweHA4i+3zmGfa4IAnW0Rx8g4eecR7ysEvs9lbLUaoOsatl867d0rqXHZS7zNvTef9qS7bay6pkfWu0W47KWRKpYUmvpMEGDZSa4yjmD6zj3gh7UpP0R4ONayLFCwJJrou9jR1thAJcMUbYTx2E4oMB51o9R5hWgvbENueaFyU3sY4KQnK8JZLLNwkFdgqX8P8vhT0WET+34BKY8keUZuwSP2dcsP8ecJzBxPmXg1Ns9AwKcLxk88O2SxL5hULM7ApCcm2gHnsVK/zJTcob3DQiOsh28Di4OPtjqFy+2AQw2c6yw7pTj4lpFQoE/icSgitc8J3Ablm1k/di/nD/CUmT8DsvndYU5zjyDmsFmrql+fHpSGkkE7CcSzwje51paqEQpcCtSqMiM4JUvLnLcCHNhpLPLxd07vFzgva4eo5l+s8GFzwvLt1VjxifDIZOCAJ7P2I5MsxBL30KtPX+MWDptwcF7x5OQBTca9auvDMxwf/B0/KhsGLT96NxTbcWIYbZ8xXLr96+nzKhS4jcyfvPMMZ9pUkDpGezPfCfC7A0E7Xg1tAT5F+69qzUNHGCDnmipm0n5jkFPxVl+qRUrbMXIYbZi9Aj79IQvWQNfjxweDSbULVtia1/4Qyu/Y6rZ/EXxZxMcI3DCzQHZU8kMeW6CySAk6yPMykgiCNWbmRht1e72AFJHZ8jOxIMByXX2m50cnnpo6jfAPjzjCqtb1mSrX0eG5cpMJIJTBEEIVhEszyUj0ss9cPNFANL7GtzEh4+/er1VglxkLlLHsrMB7SzmHX6ANQ3oZwv33Nnq5i2x9W/6rX167BHRkwy0yTDw3DiwvzVt/FmzYaPiG0MeYfaAimdZ+cAzwR4P2oIJvPyzvFSGYz3pS/G73MyyXxiUIfjNi4AWA51kTLLdfNuBgBa/R9Y028qDDwy4079iu9Lm6G8RaOM44Wab73NMcSNNYJXALfuQwEMY6CNDjOAky8FxFw56s98od8UxFz4pRUCQgB3bg58lJ5Qh4BQ+WutlQmgXyfeB4M5bb1kF8/Tt/T2zQTm2Bf4+54wwqEyAkH4qtaUJvBDAIrgVPi6dxLYjW8/X0ZMP/NgiiM8TCQTgvP62DzrRlsng9QA8QSfPUKW6xc9+Z/OPOdxqP7tZ9OTMwCefi8rGUCO8Yu58a1gjPq5W+/sjzddyMCjGYJcNG9iyrQgQkX1JwIhl4dgMM6t5OiLMruf67oF3AjhhkgbHKgGlcHCAxAranj8BQZCMzGMPPJG4wWcw4Mtnc3xkymj1yRNDWQK3qUmT4n9pL7RPgrSsK8FgAmT87WxP6RF4DkusEDRlGWmzjvsYsiST9cvb44NfrRY2ZY0Dh0TLNmra36zpBJ7YqYvbBNuD488HtPzaTiIDAapk4BbZSpdw3HKeoI15rV7PEAXHNoMSYW1SMiPDyWbB+YT7Oj933XabVTStsI0Wf2yV5ZVW+7mdbP5h+7b+nXTaBj/+jA2c9qKVLV8eJwdss0XGDHACt62uMZzDaX8sHy/mYKGt0ma4/nEe8sA2+zVbiT+Wl1em5A/Wm7ZB0Jb2wWA254/kwJ9nmrMfuZYlcf5Ono/CfgfnNQKZXvvWsT+8HXvyEutMBrjf6/rj/c6vnVwj2Eacjzi2MrWxrsT6cL0gQStTtjXXIL7vT2WFgW3aurdZroW0YZ428/I7DFBzffCJw/wpizATl33owWGuYf4kFedmD9pSF9cHxhhI9HIsjvOdD0D7Z5FJL23S3bdIX0Pnxm+emAGTl4/QUX6BCxGdEE6wydpqLlkWIYmbFZ+shICtj/xK31YMwahikwxktREU/+Tr/xcFbV3diNXibCcelYLfhNEhyvIItUhbCMhyw0fgduk2423ZuI2sdtxG1u/1t6NH0ckC9yDWsk1b1wgme2fZmqOs7MWXzCqr45sZArdcT7JNskmwkzbc3oSGhfz4HDeKPJrYiVIbq2CbZXoE3W8Uc6zznq6qjuI6H+9+tK2+5idmRx4W/4BAOZmmuWZ6cwPHzRz9C26quVHjZpBgfBi49eDYV74SB3Qy1SzuIk2DBtq8Lx5s6XTaGtIVtnSLcbZs3IbZz6F837N5wgxTgp3Uz/RzJudRAtsMFvBkUVv1Ugm+knHLOTfTtvTHUMmU9EAav8O2J3jhj86Gj6sSUOBFUJ2gF9uaYA+BPA9Seh/NH5ElwEm/LAgsNgdYs9V+Zr0Ipnl9aF5hIMODFgQjPEuVzFafUNf7f2RZJVGP0oP3Kw344HVrWIuARoaSJ2HWLoEoMkhZPrJK+X8GJ9kebCf6m9SeDBMHvAYpPycARF+FQDbL6tmhJB4QZKDdJvcpmV4MioRZqwRt4VnTnsU6dKill8TrNeipZ23E7UG9YeKmF8ZlJurWWdOaBva3ftSVTljjR7+wty74hlmq0Sq/e1Fce5lAJUES+u/Bdoskr+dsA5aXfrj3xX2AgTZO4Ij151/2D0F7th8nAwK4fF6yLA2BOgK3BEP5DA8MOwIvZLgSDAozrslE5PzOMtPmyLYMSwyAts7xxzKH5d/I0CeI5kFqlpEgswfOQh1JZMlD44DBVr4ywzg9ku22cvCBdhkGZ70cE8duuJ8IKJI045PChmh3rJcHQzlHktlImQiOP+pgk0jzq1/FQVsGPTmH8tRF8vzqGNzhPJBh4mGu3/MP3Sf6bJ7iYh6FsiVxAI6gLSjJxWvmVd9t/cvpdJxxy+RkjuOJjOgw2xlkW3Le6MiToZxfCe7Tlsnk55xI+/fgKccDbYu2ynmJpx64dyU7k22SLB+Vq3CgkkQmgsw+SJEcmINPOprpaU/vc3N/HU4m1lOyPVXLMnPNSR6DKwcJmvyegWOKbcs+5PzJkwoMBLJ92S9sY8rqsO7Znnbl98n8JbHHg7bwgVD2mZf/COeqINDvwX7JmQK3In0NFw4yAxgtC2cC9klUyDYIZ8HkwktnnA4dj61wAWvvZi7skHPhUzFxkS5F2YQPvneOVb8z00bf8Dv74OKzrWlA647T7L12sI12HG6VnunHTQGdL5WykE764MJJUeaiB8E++s5p8Q12O3VmF3x2nNljb8SBCG4qyZALH6VLCttuMUtm+HQXrrUdyD5euMkO1rhbhqBdPni8HmEGVBgA44aYbCMyfbhxzKUGc1epqLA5X/mCpXKtec4NOoFlni4iMEMmXJiR5TVu2xuUJvhFNmR7CHbzaC4BGdp8PkFzr93IshDgInjuAV9fX5aVgDCBWzIPCbL5/m2rvJTfwCfxt/ymms8jI4r3kvFLVhTbi2AFj3qHZR0I2JG5xXIQoKOExjPPWOOSBhv250dt4L9vs48nXxNlg/d/6yWrqxptqfdqza69It4XBF0JBBCMInM0fHrBS6kQLOZRdII6jm3i29PrWbJfPRDNhJ3tTXBJu+Xv07/1oG3SQQdZww5b23uv/MNG/2+mDZu28nH2lRqHDrbyBfFkvTyOHqpbfx1bvP1nmwO96/7oJmtcsrglC5n9zEAH25BlIDjKumbKJGW7EEzk5dnWyVrSfAY8COr/JgM6jkxrfzSaoAuZj+x3smwZWCOAxiAF7ZhAD9cC9jEzw2eZWLT22GOtprraUtnKujDww72HB275/0x1znvAp1881RrnLrChd/3KmtZdz+yDlZmwyXZDYJGAK9mgYaDKn1rkEX6ytMmqZTsyYENbTl7jaNfco4XXVK6xlPUgs5i2S7CXIBrbns/lvOTlLAjqEiTmsf2V2ZLvnTDBNvpdnNVf+cmcqD59zZvv2qfHHWFDHnyi+c/UjV3Lqt+LE3YqP/qkOTsc5UuXWaopnXl5mYyMAJ3rzETUnLNYd841ZDb78eyBVbYZmcVkGzMgw+Cot99cJxIMzweZ+CAZ+47jJdu5kusu7T6JY5YAOwMQhY57g5XXi3T//pb+2c8sFQ5ykjHsZWPYFwTROefnUurIj1liCZxHujvjuIQpcCvSF/nItuNi7yPgdCZ4+SNjdNR4/IORci5cucyYGl4Ii/2GW6QANQ4e2HyzN/PK8zNOwtYwfKg1bZ0oUdIbtVKlzyGbcRU5TA7WxER64c1Oe1kxHrjNMYNUCjD7mCAo2VHdVBKhW5C9lQ0BrkwZf531xS+2fE0mab5tnqAvAa7kdiZowOPcDJDwNwjmcL0gc5LM1HyDK2QgJ/t11CEkS5nALRnAPuEpx7jXuqQvGZZqIIOQ4O/++1v6tRlW/au/mlWarXXNOTbr+G/byLum2IgGs9QaX43fT8kJR+CZYKyjVqoHYfm7BCEIFpLJReCKbZMJgS+yKNsL2joPDrGeBCwJ5hKsJOuXwDjBm4Zaqx27ps3Z8DNWPmQ16//Cf6OnFEb96vao1FG/lYFbzP3SodY4ZJA1VVVa/dprRIGeT4YOjgZjKa/QmCxDxvZiWQna8jh6ptqxSZkCSh0RZr9xjiFQiDCLkAAXWbkEaEAWPoHbDNLrrWcreBSaIE5bWev8jKcLSDbppaAtGocOt+U1w+3NEy6z1Yl5L1qZnJIMXNH+uAfiePASBmEJFAbOCXL6/CBtDf4kr6n+5Ikf4xy7tAMCt35Oos17oNwz0EeOtKbp/7GlG65ji7fe3IY+/6oNfvTJKGiLYXfdb+UrM26xdJstosBt/RqjowGGVoHbxUussaYq8xN2LI+XxyFLvbM8k59BII431s0Hen1bkFkcluLj+Mj1eOa4yiWxKHziIV9k4xZTv8HrBWc6Jjm3s485d5OcRe3bXIT3J2zLzgT0pU0K3Ir0ZT6jI+USkuhM0RGlw0snhHpXuQpP0m11yEQkb5+ccqytGBrc1OeaRSbSyxr798uv3XrwSoHb4tVWEFQy60h790dPk8EeshQzBfEI6JJBlu/1I1vNYwJHPFYczoxOsI+AGwEszzxmQiJKFATBkPTarctvjbnlyuagZerTRP1OAl5ksIfLnSkTjkd4CXC1Fezj5x0ZUOCzXbYM1bIyW7jv56IX5n7xYFu+4Vir2m5La6qpsfo1x1iapxYSGIxtGDncymbPtsWfWdfGfFzfEuRjnXmKje1XDFlr7BfKWpAEQgYodS3JAqbeZb6TEhbak3scb2TOZiv3QnIMwVueYPSyHQwudGZgjfNCOIkgMgXvqT1NqQUCbdhuO2vcarNoktHZRx5k5QMGtapT3xy0LSuzmVecF31Zu+lGNujfz1r1jA/iEkj+3sVLbcXANrL0TzghfsS+K4PsBIN5hdgGlJAgm5j90BF+XEluOJ+TVX3bbWaPPto6ASwXDHQpaNutFLgV6csYlSSw6jNrwrMwuOhmm4QsF5lq44pIp3FjJ1KMFm+ynjUO28wqK2ryC2DlmkEjUqrIgCO7ORngyIY+XjiZU2fRl+RR2mSwjcAtNW1BkJTlIwM7vIFPpez9g75uVasPt0HzZtiwv7fUyU29ML0lQIOvfjW3YHNYd7QALN0+LuWwbFj7dRvnHXGQlb31tr2/3Wds7LCtSERuQa1sMhCLpf4jT00weBMO4JAQQmZfclKtYntykcnrsmEgg+Cil0igfEJbj+Xnqq3SJuGxTSZuFnVrrW5cUT8+52Qb9O//2MBnnrd5E/a35RuMbfVUTe34TWzMz26xBQfsGbW5NS+7zj45aDdbMWhAYcx1QfkPtqkGdnsWWfYEbvMZTKE+bjEMNhU5BW5F+jI6v3S2/dEIHmMjm4JR4s5OpMKjOtlmhBURkdKTSlnT9tuZVeZw8wm/IQsnRRKR4shu5saex2p5dJpg0nnntZRQSFi04dbR+Exq7LrW/9XnrL6sJhr8T338UTy7OE+G0a/sxcfle3Jwdvk6oyxdv3jVzFQCY0xGJ4WPtkrpCEpsdEXQtovUfnYze3/TjSxdU23zDtvP5h+0V8bs74Y1x1jd2mvagOdfsX7/fT363oi/PWJzNu2+SSXzQvayT2wlPYe62tQFz+eJ2mIqlVTEFLgV6euozQVGjv0RuLZGkXOlkTUREekMArY8dslEJCJSXPyx8P/9Ly69leNjsrO/fFY09cLAv/zBbO6/4uAlj+iKFBMP1vbU5JS5SqWioG2kssLSYe35hMW7bmcjf3tnq+9Rk1lKGINvegqqIClwK9LXMSvkkiUdrxEkIiLSXTcI4SREIlI8wpIGBx2U3++lzNJlFYU54Z5ILiZNKvo5CJZt1lKzevZJX7bhN/7OypfX9+oyiUhmCtyK9HWnn97bSyAiIiIifQ2Td/GYbAeCr/O32N2alvs0ZSJFpi9MxLQy8Lxskw1s+Ubr2Udf/ZLNW63a1uvt5RKRVRTEdPBTpkyxsWPHWk1Nje2www42bVrLTIiZTJ061TbZZJPo/ePHj7f77ruv1c9POOEES6VSrV4HHHBAN6+FSAE/ylNAtZdEREREpA9gJnGf3T5PdcNWt8ajv9LliyQiufto8ik255j4qcxlG421FYMHavOJFKBeD9zecccdds4559jFF19s06dPty233NL2339/m51lJsonn3zSjjnmGDvxxBPt+eeftwkTJkSvV155pdX7CNR+/PHHza/bbruth9ZIRERERERERKRwrRg5POPkZSJSWHo9cHvNNdfYSSedZBMnTrRNN93UbrjhBuvfv7/dfPPNGd9/7bXXRkHZyZMn27hx4+zSSy+1rbfe2q6//vpW76uurrYxY8Y0v1bLMMOpiIiIiIiIiIiISCHq1cBtfX29Pffcc7bPPvu0LFBZWfT/Tz31VMbf4fvh+0GGbvL9jz76qI0aNco23nhjO/XUU23u3LndtBYiIiIiIiIiIiIifWhysjlz5lhjY6ONHj261ff5/9deey3j78yaNSvj+/m+IyP38MMPt/XWW8/efvttO//88+3AAw+Mgrvl5eWrfGZdXV30cosWLYr+bWpqil59HeuYTqdLYl1FSua45PfS6Zavi/j49lVJvopttXyf+qsz/DOK7jpV5O0yPC7DtohSb5dF3yaLdQe2sSro6+2yvfcUfbv0r4tp2RNKsV22p6+3y2K4t+wrfUuUfLuUvNpJIR+XpaYpj33Rq4Hb7nI0M5yuxORlW2yxhW2wwQZRFu7ee++9yvsvv/xyu+SSS1b5/qeffmrLly+3UmgwCxcujA5kMp5FpA8clwxG+cWAJw6WLLFi5atSXx//P//y/8W2WnUr6qxpSZPVV9SbrTqGmJf6xnprWtFkcz+da0sqimkjFHe79ONy+XI6vmXNbRKl3i6Lvk02NhbnDmzjXFkK7ZJrZMPShuhrJiPuc+0SxbbzEkqxXbanr7fLYri37Ct9S5R8u5ScFMNxWWoWL15cHIHbESNGRBmwn3zySavv8//Upc2E7+fzfqy//vrR33rrrbcyBm7PO++8aIK0MON27bXXtpEjR9rgwYOtFA5iOrusrw5ikT5yXNbWUnsm/nr4cLP+/a1Y+apUVZnV1LR0uItttWobaq1sWZlVVVVZTUUnJ4JYYVZXX2fDRw63/pXFtBGKu136cTlgQHxchm3SSr1dFnub5Iks7tqLbQe2c67s6+3Ssx5rhtRkDNwWfbtEse28hFJsl+3q4+2yGO4t+0rfEiXfLiUnxXBclpqa8KJYyIFbLnrbbLONPfTQQzZhwoTmBsX/n3766Rl/Z6eddop+ftZZZzV/78EHH4y+n80HH3wQ1bhdffXVM/6cicx4JdGgS6VRcxCX0vqK9Pnjkt/xm1i+LuJj21cl+Sq21WI/sk/91Rn+GUV33u4D7dK3eyrFq2V1Sr1dFn2bLNYd2MaqoBTaZVvvK/p26V8X07InlGq7bEsptMtCX7++0rdEybdLyVmhH5elpiyP/dDre4xM15tuusluueUWe/XVV6OJxJYuXWoTJ06Mfn7cccdFGbFu0qRJdv/999vVV18d1cH93ve+Z88++2xzoHfJkiU2efJke/rpp+29996Lgryf//znbcMNN4wmMRMREREREREREREpdL1e4/aoo46KasledNFF0QRjW221VRSY9QnIZs6c2SoSvfPOO9utt95qF1xwQTTp2EYbbWR33323bb755tHPKb3w0ksvRYHgBQsW2BprrGH77befXXrppRmzakVEREREREREREQKTa8HbkG2bLbSCEwolnTkkUdGr0z69etnDzzwQJcvo4iIiIiIiIiIiEhP6fVSCSIiIiIiIiIiIiLSmgK3IiIiIiIiIiIiIgVGgVsRERERERERERGRAqPArYiIiIiIiIiIiEiBUeBWREREREREREREpMAocCsiIiIiIiIiIiJSYBS4FRERERERERERESkwCtyKiIiIiIiIiIiIFBgFbkVEREREREREREQKjAK3IiIiIiIiIiIiIgVGgVsRERERERERERGRAlPR2wtQiNLpdPTvokWLrBQ0NTXZ4sWLraamxsrKFMsX6RPHZW2t2dKl8decy1assGLlq9LQYFZdbVZXZ1ZfX3yrVdtQa0sXL7WGsgarrqju1GfVraiz+qb66Dq1orKYNkJxt0s/Lpuaamzp0rLmNolSb5d9ok2i2HZgG+fKUmiX9NnrF9dbVVOVpVKpvtkui23nJZRiu2xPX2+XxXBv2Vf6lij5dik5KYbjstQsWhlv9PhjWxS4zYAGjbXXXrur942IiIiIiIiIiIiUuMWLF9uQIUPafE8qnUt4twRHIz766CMbNGhQxtH7vhjpJ0j9/vvv2+DBg3t7cUREx6VIQdL1UqSw6JgUKTw6LkUKj47LwkMolqDtGmus0W4WtDJuM2CjrbXWWlZqCNoqcCtSWHRcihQeHZcihUXHpEjh0XEpUnh0XBaW9jJtnYpbiIiIiIiIiIiIiBQYBW5FRERERERERERECowCt2LV1dV28cUXR/+KSGHQcSlSeHRcihQWHZMihUfHpUjh0XFZ3DQ5mYiIiIiIiIiIiEiBUcatiIiIiIiIiIiISIFR4FZERERERERERESkwChwKyIiIiIiIiIiIlJgFLgtcVOmTLGxY8daTU2N7bDDDjZt2rTeXiSRkvG9733PUqlUq9cmm2zS/PPly5fbaaedZsOHD7eBAwfaEUccYZ988kmvLrNIX/P444/boYceamussUZ0DN59992tfp5Op+2iiy6y1Vdf3fr162f77LOPvfnmm63eM2/ePPvKV75igwcPtqFDh9qJJ55oS5Ys6eE1ESmd4/KEE05Y5fp5wAEHtHqPjkuRrnP55ZfbdtttZ4MGDbJRo0bZhAkT7PXXX2/1nlz6rTNnzrSDDz7Y+vfvH33O5MmTbcWKFdpVIt10XO6xxx6rXC9POeUUHZdFRoHbEnbHHXfYOeecYxdffLFNnz7dttxyS9t///1t9uzZvb1oIiVjs802s48//rj59cQTTzT/7Oyzz7a//vWvNnXqVHvsscfso48+ssMPP7xXl1ekr1m6dGl0/WMgM5OrrrrKfvrTn9oNN9xgzzzzjA0YMCC6VnKD6gja/ve//7UHH3zQ7r333ijodPLJJ/fgWoiU1nEJArXh9fO2225r9XMdlyJdh34oQdmnn346utY1NDTYfvvtFx2rufZbGxsbo6BtfX29Pfnkk3bLLbfYb37zm2hwVES657jESSed1Op6Sd9Wx2WRSUvJ2n777dOnnXZa8/83Njam11hjjfTll1/eq8slUiouvvji9JZbbpnxZwsWLEhXVlamp06d2vy9V199Nc1p+6mnnurBpRQpHRxff/7zn5v/v6mpKT1mzJj0D3/4w1bHZnV1dfq2226L/v9///tf9Hv/+c9/mt/z97//PZ1KpdIffvhhD6+BSN8/LnH88cenP//5z2f9HR2XIt1r9uzZ0bH52GOP5dxvve+++9JlZWXpWbNmNb/n5z//eXrw4MHpuro67TKRLj4usfvuu6cnTZqU9Xd0XBYHZdyWKEY6n3vuueiRT1dWVhb9/1NPPdWryyZSSnjkmkdB119//Sg7iEfIwPHJqGl4jFJGYZ111tExKtJD3n33XZs1a1ar43DIkCFRaSG/VvIv5RG23Xbb5vfwfq6pZOiKSPd49NFHo0dDN954Yzv11FNt7ty5zT/TcSnSvRYuXBj9O2zYsJz7rfw7fvx4Gz16dPN7eIJl0aJF0VMrItK1x6X7wx/+YCNGjLDNN9/czjvvPKutrW3+mY7L4lDR2wsgvWPOnDnR4yrhhRP8/2uvvabdItIDCP7wiBg3nTy2cskll9jnPvc5e+WVV6JgUVVVVRQQSh6j/ExEup8fa5mulf4z/iV4FKqoqIg6zTpWRboHZRJ4BHu99dazt99+284//3w78MADoxvQ8vJyHZci3aipqcnOOuss22WXXaJAEHLpt/Jvpuup/0xEuva4xJe//GVbd911o0Shl156yb797W9HdXDvuusuHZdFRIFbEZFewk2m22KLLaJALhfWP/7xj9EkSCIiIrKqo48+uvlrMvi4hm6wwQZRFu7ee++tTSbSjaipSZJBOC+DiBTmcRnOucD1ksl2uU4y6Ml1U4qDSiWUKFLlyUhIzvTJ/48ZM6bXlkuklJGl8JnPfMbeeuut6DikpMmCBQtavUfHqEjP8ethW9dK/k1O6skM2cxor+upSM+g3BB9W66fOi5Fus/pp58eTcL5yCOP2FprrdX8/Vz6rfyb6XrqPxORrj0uMyFRCOH1Usdl4VPgtkTxKMs222xjDz30UKv0ev5/p5126tVlEylVS5YsiUY/GQnl+KysrGx1jPJYCzVwdYyK9Awew6ZDGx6H1OKjdq0fh/zLjSr1/dzDDz8cXVO9cywi3euDDz6Iatxy/dRxKdL1mCeQ4NCf//zn6BrH9TGUS7+Vf19++eVWg50PPvigDR482DbddFPtNpEuPi4zeeGFF6J/w+uljsvCp1IJJeycc86x448/PppQZfvtt7ef/OQntnTpUps4cWJvL5pISfjWt75lhx56aFQe4aOPPrKLL744yoQ/5phjogmQTjzxxOg4pVYmndozzjgjurjuuOOOvb3oIn1qwMSzDnxCMjq1HHdMqkK9sO9///u20UYbRR3iCy+8MKoTNmHChOj948aNi+ptnnTSSXbDDTdEk7PQieZRbt4nIl17XPKiJvwRRxwRDaww4HnuuefahhtuGE10pONSpHsew7711lvtnnvusUGDBjXXpKW/SnmvXPqt++23XxSgPfbYY+2qq66KPuOCCy6IPru6ulq7TaSLj0uuj/z8oIMOsuHDh0c1bs8++2zbbbfdohJDOi6LSFpK2nXXXZdeZ5110lVVVentt98+/fTTT/f2IomUjKOOOiq9+uqrR8ffmmuuGf3/W2+91fzzZcuWpb/xjW+kV1tttXT//v3TX/jCF9Iff/xxry6zSF/zyCOPpOkOJV/HH3989POmpqb0hRdemB49enS6uro6vffee6dff/31Vp8xd+7c9DHHHJMeOHBgevDgwemJEyemFy9e3EtrJNK3j8va2tr0fvvtlx45cmS6srIyve6666ZPOumk9KxZs1p9ho5Lka6T6Xjk9etf/zqvfut7772XPvDAA9P9+vVLjxgxIv3Nb34z3dDQoF0l0g3H5cyZM9O77bZbetiwYVEfdsMNN0xPnjw5vXDhQh2XRSbFf3o7eCwiIiIiIiIiIiIiLVTjVkRERERERERERKTAKHArIiIiIiIiIiIiUmAUuBUREREREREREREpMArcioiIiIiIiIiIiBQYBW5FRERERERERERECowCtyIiIiIiIiIiIiIFRoFbERERERERERERkQKjwK2IiIiIiIiIiIhIgVHgVkRERERK0gknnGATJkzotb9/7LHH2mWXXZbTe48++mi7+uqru32ZRERERKRwpNLpdLq3F0JEREREpCulUqk2f37xxRfb2WefbXSFhw4d2uMb/8UXX7S99trLZsyYYQMHDmz3/a+88orttttu9u6779qQIUN6ZBlFREREpHcpcCsiIiIifc6sWbOav77jjjvsoosustdff735ewRLcwmYdpevfe1rVlFRYTfccEPOv7PddttFWcKnnXZaty6biIiIiBQGlUoQERERkT5nzJgxzS8yVMnADb9H0DZZKmGPPfawM844w8466yxbbbXVbPTo0XbTTTfZ0qVLbeLEiTZo0CDbcMMN7e9///sq2bAHHnhg9Jn8DiUQ5syZk3XZGhsb7c4777RDDz201fd/9rOf2UYbbWQ1NTXR53zxi19s9XPef/vtt3fZNhIRERGRwqbArYiIiIjISrfccouNGDHCpk2bFgVxTz31VDvyyCNt5513tunTp9t+++0XBWZra2uj9y9YsCAqefDZz37Wnn32Wbv//vvtk08+sS996UtZt+lLL71kCxcutG233bb5e/zumWeeaf/v//2/KDOYz6E0Qmj77bePlquurk77S0RERKQEKHArIiIiIrLSlltuaRdccEGU+XreeedF2a8Eck866aToe5RcmDt3bhR8xfXXXx8FbZlkbJNNNom+vvnmm+2RRx6xN954I+N2pa5teXm5jRo1qvl7M2fOtAEDBtghhxxi6667bvQ5BHJDa6yxhtXX17cqAyEiIiIifZcCtyIiIiIiK22xxRbN24Lg6vDhw238+PHN36OEAWbPnt08yRhBWq+Zy4sALt5+++2M23XZsmVWXV3dagK1fffdNwrYrr/++lFG7x/+8IfmrF7Xr1+/6N/k90VERESkb1LgVkRERERkpcrKylbbguBq+D0PtjY1NUX/LlmyJKo9+8ILL7R6vfnmm6uUOnBk8BJ8JXvWUT+XUgy33Xabrb766lFmL9m/lGJw8+bNi/4dOXKk9peIiIhICVDgVkRERESkg7beemv773//a2PHjo0mLgtflD7IZKuttor+/d///tfq+xUVFbbPPvvYVVddFZVieO+99+zhhx9uNQnaWmutFQV+RURERKTvU+BWRERERKSDTjvttCgT9phjjrH//Oc/UXmEBx54wCZOnGiNjY0Zf4eMWQK+TzzxRPP37r33XvvpT38aZetSA/e3v/1tlNW78cYbN7/nX//6VzQ5moiIiIiUBgVuRUREREQ6iAnD/v3vf0dBWoKq1MM966yzbOjQoVZWlr2r/bWvfS2qY+t4/1133WV77bWXjRs3zm644YaobMJmm20W/Xz58uV29913R5OkiYiIiEhpSKXT6XRvL4SIiIiISClhgjKyae+44w7baaed2n3/z3/+c/vzn/9s//jHP3pk+URERESk9ynjVkRERESkh/Xr1y8qhzBnzpyc3s8Eadddd123L5eIiIiIFA5l3IqIiIiIiIiIiIgUGGXcioiIiIiIiIiIiBQYBW5FRERERERERERECowCtyIiIiIiIiIiIiIFRoFbERERERERERERkQKjwK2IiIiIiIiIiIhIgVHgVkRERERERERERKTAKHArIiIiIiIiIiIiUmAUuBUREREREREREREpMArcioiIiIiIiIiIiBQYBW5FRERERERERERErLD8f9TpubZtNbo5AAAAAElFTkSuQmCC", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot optogenetic stimulation and demodulated signal together\n", + "fig, ax = plt.subplots(1, 1, figsize=(14, 6))\n", + "\n", + "# Plot demodulated calcium signal\n", + "for name, fp in processed_fp.items():\n", + " if \"downsampled_calcium\" in name:\n", + " calcium_signal = fp\n", + " elif \"downsampled_isosbestic\" in name:\n", + " isosbestic_signal = fp\n", + "\n", + "calcium_data = calcium_signal.data[:]\n", + "isosbestic_data = isosbestic_signal.data[:]\n", + "timestamps = calcium_signal.get_timestamps()\n", + "ax.plot(timestamps, calcium_data, color=\"blue\", linewidth=0.8, alpha=0.8, label=\"Calcium Signal\")\n", + "ax.plot(timestamps, isosbestic_data, color=\"red\", linewidth=0.8, alpha=0.6, label=\"Isosbestic Signal\")\n", + "ax.set_xlabel('Time (s)')\n", + "ax.set_ylabel('Downsampled Signal (a.u.)')\n", + "ax.set_title('Calcium Signal / Isosbestic Control with Optogenetic Stimulation (40 Hz, Varying Duration)')\n", + "ax.grid(True, alpha=0.3)\n", + "ax.legend()\n", + "\n", + "# Add stimulus intervals as faded boxes over the signal\n", + "duration_colors = {0.25: 'red', 1.0: 'blue', 4.0: 'green'}\n", + "duration_labels = {0.25: \"250ms stimulus\", 1.0: \"1s stimulus\", 4.0: \"4s stimulus\"}\n", + "\n", + "for i, row in ogen_df.iterrows():\n", + " duration = row['stop_time'] - row['start_time']\n", + " # Round duration to nearest expected value for color mapping\n", + " rounded_duration = round(duration * 4) / 4 # Round to nearest 0.25\n", + " color = duration_colors.get(rounded_duration, 'gray')\n", + " ax.axvspan(\n", + " row[\"start_time\"],\n", + " row[\"stop_time\"],\n", + " color=color,\n", + " alpha=0.2,\n", + " label=duration_labels.get(rounded_duration, f\"{rounded_duration}s\"),\n", + " )\n", + " # Remove duplicate legend entries\n", + " handles, labels = ax.get_legend_handles_labels()\n", + " by_label = dict(zip(labels, handles))\n", + " ax.legend(by_label.values(), by_label.keys())\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "# 6. Session Type 2: Varying Frequency Optogenetic Stimulation \n", + "\n", + "This session contains optogenetic stimulation at varying frequencies (5Hz, 10Hz, 20Hz, 40Hz)." + ] + }, + { + "cell_type": "code", + "execution_count": 214, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== SESSION INFORMATION ===\n", + "Experiment description:\n", + " SNr GABAergic neurons recordings. Mice were freely moving on a plastic tub. Simultaneous passive optogenetic stimulation and fiber photometry recordings were conducted during the first two days. Excitatory inputs from either the STN or PPN to SN were stimulated. Recordings of GCaMP6f fluorescence signal captured pan-GABA activity in the SNr in VGLUT2-Cre x VGAT-Flp mice.\n", + "Session description:\n", + " The subject is placed in a plastic tub and undergoes 3 recording sessions corresponding to a fixed duration of stimulation (i.e., 250ms, 1s, and 4s). Each session lasted 8 minutes. The subject receives optogenetic stimulation at varying frequencies (5 Hz, 10 Hz , 20 Hz and 40 Hz) 5 times for each duration with an ISI of 10s. \n", + "Session start time:\n", + " 2024-01-17 16:33:47-00:01\n" + ] + } + ], + "source": [ + "# Load varying frequency session\n", + "session_id_freq = \"varying-frequencies\"\n", + "\n", + "if USE_DANDI:\n", + " nwbfile_freq, io_freq = load_nwb_from_dandi(dandiset_id, subject_id, session_id_freq)\n", + "else:\n", + " nwbfile_freq, io_freq = load_nwb_local(local_directory, subject_id, session_id_freq)\n", + "\n", + "print(\"=== SESSION INFORMATION ===\")\n", + "print(f\"Experiment description:\\n {nwbfile_freq.experiment_description}\")\n", + "print(f\"Session description:\\n {nwbfile_freq.session_description}\")\n", + "print(f\"Session start time:\\n {nwbfile_freq.session_start_time}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Access trial table" + ] + }, + { + "cell_type": "code", + "execution_count": 215, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== TRIALS TABLE ===\n", + "Total number of trials: 3\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " start_time stop_time tags\n", + "id \n", + "0 679.0 1134.024995 [1s]\n", + "1 34.0 474.005010 [250ms]\n", + "2 1268.0 1783.031035 [4s]" + ] + }, + "execution_count": 215, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trials_freq_df = nwbfile_freq.trials.to_dataframe()\n", + "print(\"=== TRIALS TABLE ===\")\n", + "print(f\"Total number of trials: {len(trials_freq_df)}\")\n", + "trials_freq_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Access optogenetic stimulus" + ] + }, + { + "cell_type": "code", + "execution_count": 216, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== OPTOGENETIC STIMULATION INTERVALS ===\n", + "Total number of stimuli: 60\n", + "\n", + "First 5 intervals:\n" + ] + }, + { + "data": { + "text/html": [ + "
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start_timestop_timestimulation_onpulse_length_in_msperiod_in_msnumber_pulses_per_pulse_trainnumber_trainsintertrain_interval_in_mspower_in_mWwavelength_in_nmoptogenetic_sitesstimulus_frequency
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" + ], + "text/plain": [ + " start_time stop_time stimulation_on pulse_length_in_ms period_in_ms \\\n", + "id \n", + "0 74.955981 75.206001 True 1.0 100.0 \n", + "1 155.955200 156.204728 True 1.0 100.0 \n", + "2 236.954092 237.203620 True 1.0 100.0 \n", + "3 318.003446 318.254612 True 1.0 100.0 \n", + "4 399.003320 399.253012 True 1.0 100.0 \n", + "\n", + " number_pulses_per_pulse_train number_trains intertrain_interval_in_ms \\\n", + "id \n", + "0 2 1 0.0 \n", + "1 2 1 0.0 \n", + "2 2 1 0.0 \n", + "3 2 1 0.0 \n", + "4 2 1 0.0 \n", + "\n", + " power_in_mW wavelength_in_nm \\\n", + "id \n", + "0 5.0 635.0 \n", + "1 5.0 635.0 \n", + "2 5.0 635.0 \n", + "3 5.0 635.0 \n", + "4 5.0 635.0 \n", + "\n", + " optogenetic_sites stimulus_frequency \n", + "id \n", + "0 e... 10.0 \n", + "1 e... 10.0 \n", + "2 e... 10.0 \n", + "3 e... 10.0 \n", + "4 e... 10.0 " + ] + }, + "execution_count": 216, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Access optogenetic stimulation data\n", + "ogen_df = nwbfile_freq.intervals[\"optogenetic_epochs_table\"].to_dataframe()\n", + "\n", + "print(\"=== OPTOGENETIC STIMULATION INTERVALS ===\")\n", + "print(f\"Total number of stimuli: {len(ogen_df)}\")\n", + "print(\"\\nFirst 5 intervals:\")\n", + "ogen_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Access processed fiber photometry signal" + ] + }, + { + "cell_type": "code", + "execution_count": 217, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Hhbp06VLmO3wm2D3we05OTmjWrFnF791zzz3u/Xbt2oVWrFhR/P7555/v3vc/G4l33nknlJGR4V6DBg0KnXPOOaG33347tG7dujKfJZ2kVxk7dmwoLS0tNG3atOL3Fi9eHGrRokWZe/Nd3ps8eXLxewsXLnTPc+aZZxa/t2bNmtD69etL3Zfr8LnLL7+81Htc78EHHwyVx/vvvx9XXnAtPkdfCkuXLnW/33DDDVG/wzNkZ2eHdtttt1Lpvv322913qRdAHvH7s88+GzMNfIbXV199Vfzen3/+GapXr17ogAMOKH5v9OjRofbt24f+/fffUt8fOXJkqGnTpsX1mLq/xRZbxLwnzxctf4Jlfskll7jPvvDCCzHreaS/0V74btu2bUOHHXZY6I477nDPFq0cND3z588PZWZmhvbff/9Snxs/frz7nJ8+/e4uu+xSKj2nn366q+PLli2L2dZPOOGEUIMGDVw9LK+NBusB973zzjvd79wnPT09NGLECPe8yqmnnurah6YtUj0++eSTy7R9/7MtW7YMLVmypPj9l19+2b3/6quvxkxjs2bNQn379o35GdLHtb7//vtS+dm/f/9SNuH6669373NvhXrm20TltNNOc5/9+OOPi9/Lzc0NdevWLdS1a9fidjNhwgT3uZdeeqn4c6tXrw717NnTvT9p0iT3HnnXo0cPZ5f9MqY8ueauu+5axo6OGjWqVJpoS+SjMnv2bFc/rrrqqlKf++GHH1zd898fPnx4xPoQqSyHDBkSaty4cZl6HqutaJ1v3bq1ux7PP2bMmNATTzxRqv5WJD3UZd67+uqri9/DztWvX9/Z8qeeeqr4/Z9//rlMfxWpX4rUZiP1kQUFBaG1a9eW+h73pn345bNo0aIy9412/2+//db9fuyxx5b63FlnneXe/+CDDxLugyLx22+/ue/edttt5drIeKEv8Ou1z0033eT+Rl5UBNLTsGHDCn2XcqJvYTzgc/fdd7s0MT5IpK/m+fgeY6igzdXxy4wZM4rfw860atUqol2vyJgikXFKJLTOBV/a5jRtgwcPdnnn2zhs7nHHHVembdNH++9vtdVWLs/99s3YzL+Pn5fBOhOpre+8886hPn36lOrLsDvbb7+9s5+J9pn8xJZtu+22zi77+N8L9pfYfa7/+OOPl/rOW2+9Ver9F198sdT4KxafffaZ++zTTz9d7meN6of6MX369DL1ZEOBNkUbjxfaCza/NkJ7TmQOnSywu+WNV2uiTqYnM1A7q4mGYRhG2LMHop2CHAnfuwwPJrwI8RzBiy3SNnYFzwW8DCMd4pPMLUI777xzqa1nehAGXhr+c+r7pDsWbDPD0xavN7wX8bzCKwTvjOA2uiB4V+CF5R+AyTa8aPHu8CLB60zBo4Itn34a8X7ROHd4hOFxoltDK3Kwih7sxL0jhaEory7gGYQ3YXAbn4IHL160eLb68fnwJGProG4BV09aPJUibcv2IU/xJFE4JA6PI75LnqDtUt/wOuX/1FF9UXbUU80rvFXwwotn+3w8cN++ffuW8dIpr57zN9LPtlS8efDsxVscj8JDDz20TCgJH7zX2QKNJ5NPrAOz8Gr000O9I+/wDo3U1vH6I//4HOXDttJEoC7jOaZhLPAewhOL0BN4oBEaA/ACxcuqMjaB/PI9vLRNldfWecbybKH+XW2nn5++Fy8eh3hL07bKg8/g+cpzK7RproknFV55ak+wO9giBc9g2pIP26rJTzy7sA9a9/Fuwz5SBsGDcIInvJNnfFefE28uvoOXrd+e8Pbl0KhI3r7lgacuacEzjTbsU17546WMPSbd2B68XnlevIPxWKzstmC8UxVsBPYVTzzfC5v3+Ft59SpeaA8ai5e8xnORds2W7crYdsBb3gePWwiG4IinD4oEdQX8dleVaLgiQrhU5aFO0cqJ3UKMC/zQAXi9Uy9pYxXpq/E4DYYnor7RxvGsVegnaHvxHOwWT3kmOk6J1fexA0JffpoBO+XHSuQz9Gt4Jvs2hc8wPlObws4CbBr54++4YWzG81UE2ha7Echf7dt4UUaMEbCfeCAn0mfyPFwLL9hgrOxY9oxwXjwXz+PnA2Mc6ovmg9Z5dh9pTNRoaDuMFZLJMCoCIaQYW7JDExvXqVMnN9ZmHFpVMD6n/dUE7MZjTE9bpA3269fPefsrHE4bDLuyIZM00ZYKlazBlWEYRl0H0QwYaMYLYgvb3JnA0oExCdA4crFEW7a8Mlmo6kNSgpN/HeQzsIj0fjSx0YeYZIgWfJZtqmx7I8/YdqiCSiQYzBNDN0ik9yKlXQfffhqZoLLtDaGEARNCOGXANuNY+R8LJu7EkUsU7s+WRsIOMFnV7cQM6hSd0FD2PogTDPr072wfRVhgWybPxMSJOJKRnolnD0KcZMREhCBeTAaJa0re+C+2H4LGIzz33HPdYAzRjOsilFZmiy31vKLbEclPtl+zbZkQAAi3hHgIbsMNonkYrFdMvKOJKMG6pp/z6xpbKxGfaSvYCvJPhYKK1DUmuRr+gJ+IUbxIJ78jECLE+SJDRYjn2aIJsuXZQv17UNwN1knqFPGd44kHSfkF24ceKKN/15+EkQkKAMFyVwEckSNY/2lfbL0Oll95ecY1EUJ5zuA1qa+R4nuWh47HK9peyF+2UiPqEJKC2Mi6/ZqwAhUFwSUY/oQ2wFbsYN7zfjx9SLyw7Z4t/6SBMBmkA/tcUdtOnUE4DNYRxHb6b3+RJt4+KBbVFUOThRm2tiOu0/cgomInq0vAVUFTY5Wy8IcNIx0qTCbaV0cKsUEZIYj4MVERQ1m8ieeQqXjKM9FxSjTo/xkb6isYnz34fGqneI6gTSEcgtoUraOR+v1IdjMeCBtBXSXcS/DehD+AoE0rz0ZqXPpE7Rn5QH1gwSmYFsItaTpwjsD5gFBT1CUWqgl5E4xN7bdDi5lqJBPGMywmsOBBiCxivLPog0NkrPB4lYW2UBOHthFOBocTQpawcMTcgFB7tEt/DBDpzJMNlaTN8CsS688wDCNVQYjBLhKvNR4YlOJFgsfcxIkTnRCK+IY3D5OTqpgwRRt0RjsMKdqpl9HeT2SSybMi4PJCJEQAxEtCB/mVJZ40EneWiQbeaXiUIXgxKWdgUZH8J3YaXpPxHiYXhPsyqSR2Hx5ApI1VaAZ1rEgnwoQJE1y8PbynmLQxUOJaU6ZMSSh2neYDAmO0eJcaAxJhDMEH7xUGn3gLESMX4YfJUU2BIIUAwCSN+HgIEqzmJ2vRo7y6hujNJBEbQcxIxEKEJDzEELorUtfwJMVrAbEOgQNxlvbN+/yOLeK6lRVtK9rWqQvEEGQSHC2eI4ILHrWRBITagpYNkyrfe84nGNe1vDzjmpQVCzSRPluZw6cqC+nCHvNi8YmyQdjyvWUToTJ9SKL9VfCQN+wfMTXxQEfE4Z7YQP+wzooQr3hT0bajByUmU8COBR6peGnjhYioje0mtigCIH1HZU6/jgeEC8ZBLKyxaM1P8sj3Tk20r452CChxkxlncPgYh5qxw4ddFf7OlWgkY9yTLILPp3lAXFsWEYJUpK+Lt/3pvYkfzAJxJIKidVXlJWmhrQc9kxVdQOLZiHHOeIiY34y3qFuMm3jPt8HaDqPF0DeMioDdoR7ivILjjsIY1T8Qj/khCwqM9bB7zBFw6Ig1TqBOM9ZECOZzjAM15jq7ALGbvBCOWQBirKbjG8arLKLQH3D4KLv/EJLpF/B8Z47DbgIOe/z666+dgwie9MRFZyE7miCMrcUbn5jg/rP60Gdzf+ZAurDPDiB+Z/yMyMuchrQSB1ufB89hFo+w7aSdQyh9b2LG2Tw/C4LYR/oW5iWxzmWoDVRqhsIhHGzNjORdZhiGsaFDp4VHIlv96NRiQaeKoEFH5nsdxLM1FtGHA1DY1pVIp0NnFmlreNBDqLrBQxDw8ooGW9vplINEei9eGLQzGAl6kpFHFRmgM+Flpdjfmp0olC3bbXnhNcLghIkEIoQesIUwimetQr+MYIxHjg+TUl4MYJik4q3D1mfCBgQ9dHw4LIyBl05w8IRkoha8fiQYfOK5xYt0cbgPhyDhUY1QmYi3CnkR7yJIPNBWEJh5Zt2OHkTzmHrlezOx1bOiIgqDXr6Ph7l/KBhlVlFUjGUbKdvdGEwD12fRANGWsvBDX0SiqryHsIXYQQbRkbYeM1lAXKZOBQUIyscPv4UnBrZhr732KjfdlB/tI4iGoNDy5See/YgE/rWC9kQPdWSsG0/9jweuyX2pX4ijySgftQfJbC9ckz7Dt8vV6W2m3nfYY9/7Jp7+CttO+mlzfpqDi4KJPA91BlGI+qme20BIEtKYrIObGQ/QJipjHxIF0ZJFZF6IBIik7FRgPJKseh8LJtGIsizk4AnLYgELusnuqzlkk34NUY+wAewoIdxfsqiKcUo8qJ1CsIxVXlpHI/X7Qbvptz+fYPtT20P/mkwbqfYsES9lvkcYKcY60YR7H3bf8GKMQr2jHiJG+YtU2g79Nm8YlYGQIoig1DtfsFX8/g7bzM4XxgsIt4i9ehh0tHkIu7qw34888ogbh8cTWqo8OBiWA/2YGyC+8mJBnnbDGI17cpgqAmkkGG9z8DH2I96+8owzznAeucyT2QWC0IqzQ3ABnTkSi3ks+tFXEFILRwndPcAcBkcNxsUI2YSX4T3yMaXCI9ChsZWQjoCKhRH3X4ZhGEYYOgDsJAM+JnJB8PAhZo/vaeB7FrCtixXV8sBjENGJDjQRTwUGtNyDiZHChDx46nVVwQQwUvp0QBFrex4eHIhAbKvxBz7RPCrigTIIpgeRKRh/LV54Dk4krohXC30tC6PB8mJgoVv2mBDhocwAzk83E1nKVcMysDWe+I0+iLcM/oLb/8hTPybg3Llz3Uo2z0H+8KK+4TUbSRAifEIwFqNCWomTR1o1bpwOUGPFlVW4L1v8I9XPWPWcCemcOXPKvM89eV7GLsEt2wqiBeUX9JaO1NbiJVJbZyAdbdAdDwzg2daLVz55q9tnEXOxMwxcmYyWVxcTKY9E4ARvxo14OQZDaVHP8awnPxiEB2Hhy48zSFlQn/fcc89S6Y6UZoRdPFcoZ4X4s1wTjwyN24g9oZ37sbRJF97LPojetMMbb7yx1Da+SPU/XljIoE7gfR6sx/zutyOeM57t/NRnBHu2IAbrfnneaywA6gn0PuQjafHtcrzpSaZwo7GbgXQS9qAibY7n9OsFqFdQPPVfFw3Uw0dB5ISKhMWJBOIXC5lfffWVVAf0o0F0Uhxpu3hVoF612AP6+GAM2GT11dhD4r7qbgv6Rd0pkgyqYpwS731ZWEJsjxSjVe0Uu04oW9qQ345Z/AuGp0JYId/99gfBfgs7j0fePffcE3HhvSI2kvEHYx8844Pjolj2DCGJBWYEnCD0IdrOWYANXidancebkMX4oFegUXuhaOnSqvsVr6O4hhRhh0F54BHLghXjF3Y/4HSB/YoGQjA7yxhfsNBADFmcJioL92Wcya4/vGURYBmb8TvjTkLcxXI6YsEUMZrnYEyBV22sMDy5ubnOTjH2YlxOqBTmx5F22tA3I2azwINozEKenxYcV7bffnt3bzyV2RUQKw9rCwnPJBlw8+AUDKuRxMWjk8Q4X3vttVWTSsMwjDoIk0xWHfEypLNkKx4dDQINno5MMuiodFCKqEUHgsCBIIBgwAA4lscpcF1WUFmFZGJNh8lkFg8DOi7ic0WCjpwOjRVRtssjFGLb8faq6OEsiUDAfe7J/RmsaL6wFZPOVGOkRhPE8TblgAmug3jAVhy8kpgUVcQDDG9AthBxXzp0VmCZXPlerPGyevVq11fiyVoR8G5lYMKkA2GJySViJeI/5abCDIMvBmN4DHGIEt4xTKLwSlKPRsIpsNg6YsQIV7ZMVtg2qQKsD/WTCR/1gVVznZD54Qzo63k2PJNYoSZ95Dl1hjqnk37qNKvpDOxYFSc+J2InYobGLVXPT7wAeC4ECtpAJG8Dxh+IjzwH28X4LvdCaCOfGYxGAqGXg5QQ+WgbbClj3MIAkPi2iC7RtmeS7nHjxrmVe/KXfOZ6bGVnIFiRekbdQigmvAT5zDUoj8puBeXZ8ApCeNBF9K233trlJfWJPCgPLQ/SRT3Qg4EqC1u8KTvKnjSxkEW9IUYzYgmTFhawyJsg2AVtC1q/8V73Dw0j3dguJhIM1LGbTGjwOGZ7NWXPM1H2lDveUiw86DZobC51EwGH8kbMoO3roTdaznweO8P1mLRjKxDLqU+0CYQSdk0k2k+QbtoyHsds4ad9kEbaPNv6mFToc2IfsfW0cbY60l4iwWIO+UR+cw2Efa6P540vIgWhLvLs2GXuR79E20UAJj80znqi6aks2BPsOxNEbAF1kzRhByMtygRtO162PBN1kLzFZlAHffEdbzze45mwldQXbGKkWJrYG9owCwAa8oT+l/pFGSbzcGb6cGwkC3CV2dGouyqIqa1l/cknnxRPZIE+EGGOfEKoI+4nbY4wOhXdNcI4R9tdPIdyUlexBSwYQlC0TWZfzfiJtkL7JY58MqmKcUo8UEd0nk77x4ZrO6H90yfrwiNCKGVN2dKvki485LBvwfiS9L38jXRjtwh9FCnmNtoA16MvYoxAuTB2QcBmSzJ9aKLPw4Ik/QZ2hr6MPo7rMIaMtnBDm8S284zYPGwIYwwWchl/0+cgLvF96jj2gedCIGL8zX39HR0qaGPjLKZt3YHzd4cOrf77fvQRC5vlfy6RsR9jbOozu4XUIYOFDNpBpFAE1PvggarJwF/cYpzMvX37y3v0h9FgjIU9wPmD/oa5H/0p9hGv42CImj/++MMtQHFGhm+TIjn3+GmjnTIP8e0U/Ts2H4cGbBx5WCciBYQSpFOnTqFJkya5/zdu3Dj066+/uv8/8sgjoT333DPRyxmGYaQ8M2fODB133HGhrl27hrKzs53t3GGHHUK33XZbaM2aNcWfe+WVV0JbbrllqF69eu6z1113XeiBBx6gNw/NmjWr+HNDhw51L5+8vLzQhRdeGOrWrVsoKysr1K5du9DBBx8c+v3334s/w3UuvfTSUt975513Qr1793bp2myzzUKPPfaY+0ywe+D3k08+udR7pIn3b7jhhlLv00fw/rPPPhszX958883QqFGjQj179gw1atTIpWGTTTYJjR07NrRgwYJSn+3SpUvoqKOOKvXetGnTQjvuuGMoJycn1LFjx9A111wTuvXWW92958+fX+q7w4cPL3P/YD5SFmeeeWaoffv2ofr167sy+vzzz8t8Tp/7wQcfjPpsr732WigtLa3Mc0SDa3HNL7/80v3+77//uvwmbxo2bBhq2rRpaNtttw0988wzZb57++23u89R7m3btg2deOKJoaVLlxb//Y8//nD53L17d1e3WrRoEfrPf/4Teu+99yKWMXWgR48eLl/79etX3Of78Fx8ljGB1redd945dO+99xZ/5p577gkNGTIk1LJlS3ct7n/22WeHli9fXupaV1xxRWijjTYKpaenl6rrkcp88eLFoVNOOcV9nvpCufMZ8isapPXaa691ZUjZZmZmhpo3bx7aaaedQs8991zEcvDbW0FBQejiiy92z0i94HszZsxwzzVmzJioZRhsD34+fvrpp6HtttvOXa9Dhw6hc845J/T222+X+RzPRj7Ewx133OG+T/n77LLLLu79999/v9T7keoxz0r7a926tau/ageitfVodiUaXAdb2LlzZ1dvWrVqFdp3331DH3/8cZnPan5+9NFHoeOPP96VGXbiiCOOcPXAh/ZOG8e28h2/vWIDsYXNmjVz9X+bbbZx7TMI7YRrUCY8P7bg+eefd9ebMmVKGdtz4IEHFtdtyuiQQw4plcdqRxctWlRuHQPuNXjwYNfeedGmaWO//PJL8WdWrlwZOvzww92zcA2tG9Fs0o8//hg64IADip8dG09djsX333/v2unWW2/tbAXthXYzYsSI0DfffFPqs4mkh7rMcwWhrLbYYosy70ey219//bWzg7R96tDEiRMj5mfQZhcWFoauvvpqd021a9SBSO3rs88+C/Xv39/dw6/bkfrF/Pz80GWXXVbc72IPzz///FL9erRniZTOWDaMcnj00UfLXDdoI2NB+qO9FOrwfvvt5+wSecDPww47zI1j4iFSOR900EGuXfn9Urz2jPYaJN6+Ot6xCPWP/uevv/4q87dI9SuR8ox3nBKJaDakvD7Hf/7dd9/djR9o//TBRx99dOirr74qY3t69erl0rj55puHXnjhhYhtg3RQlg0aNHD2+IQTTnA2JpLtwe4eeeSRrt+kbdBn77333qX63ET6TB0jb7/99q7MmzRp4urGk08+WW5/ybiENs336CP69Onj+tx58+a5v2PXqOPYFPKgTZs2Lq3BfKLfJ13BsZNRe1i9enVo+vTp7qdSWEhfVf0v7hsPjGcYb9FHxQIbRP087bTTnL1jbHD//fe7Oqm2lTZFe1fow5lHRoP2ctNNN7n///nnn+5afj+/cOHCUm1R26Zvy4P3VNvVt2/fUCIwDuTaH3zwQXF7pi+Cb7/91v2NNPrQl48bNy7i8yikQ/tx+veMjIzQlVde6ewO/drll19eKv0VSXuidbIiJCza0hFrhmGAv/jii+LBbqTBmGEYhmFUF3TeTE4Qn2oShLOBAwfWaBqMqoMBKwNIBn5G1VCeIFEdMPgnDZHEHMOoTlh4Q9T3YYI6cuRIJ6YhoNck3J90kJ7gfBAR7KyzzgrVVrbaaiu3GLehjVNikciC4YYC5YZIxCKQUTtJlkBW3eyxxx5OV4tkx1UgZcGDBZD169eXcnqIJdoOGzbMLXRHwxc5cf7hWq+//nopx57qEm0Rr7n2q6++Wka0XbFihXt2f9Fn2bJlrq9JRLS98cYbQxtvvHGpv48ePbpOiLYJx7TF9VmDcLOdVWNAsB3MD5RsGIZhGFUJIQh8iLfIdk+25VX1CdflQTw0P6SAkTr1zI9jSew+IzXLmS2HhP7iECRCIBhGTUIMQA4Z5CAWH0KisPU92oEv1QXhG0gH6fEhFANtq6bTFw1iBbOFmDAJG9o4xYgfyo2t24QYsdAIRrIhpAjxWdn+T+gmQngQloht/HqQNaGfCBFAiBLCBWBHygvBRr9BiCh+cj1CyUQLA0N4IM4+IAQanyVOrYbNSTYcDkasafozDiObMmWKs8H0IZEO7m7cuLELn6BhWulXCJVEGIVE2iPjOcLE0E8RHoH8ra5zXKo9pi3xg4ghQ5wYYoUR14W4OFQiDb5vGIZhGFUNHTuiGfGCiZfGAVzEeOLU6ZqGGJJGakD8K+KuEtuOmJ3EgGQQTHw8PfDLqPtwIBixJllw4VAeYlESN66qDw0yjHigbgYPYaJuqijYqVOnGs1I4ucTaxb8Aw+JjUq/XNsgliKHShGvnPiKnD2woY1TjMTiskc6fNIwkgFOkZwLwcFhZ555pjvLBAFT4/VrHHW0NkRXYuBz2CjxbWMtOGF7iN+MQIoYS+xWvhcN4sQjhnJf4sVef/31bqybbDhImXvxbCyIcEYEtvL99993bS0SEydOlDFjxrh+hucgZjiHJevZA/HAWQinn366O+eDQwaJ540tHj9+vNR20nC3rcwFUMfp9FD/k3nipmEYhmHEggNxONyIgy1YaeXADVaTGQwYRrJgIM3gEG8sJtscsMABbnjcIOIaVQNCOY4CeBcOGDCgyrMZ72k8qTisC48XDqSi3KtKzDEMo+Zgks5hZggTeKvhjFQV1NVxCofHffjhh84eGkZdgYUtdoRzmGEiYp5R91i1apXbBcXCG0JzqtfJSou2hmEYhmEYhmEYhmEYhlETmGibukybNs3tfiKEBLuhWHRjYem3335znrqpXifjimkbjE8UC9yUg/GWDMMwDMMwDMMwDMMwDMMwEuHGG290YSLYqYCn7ccff1yrBdtkEpdoS7wJYvEQ14LAxEFQu9944w05/PDD3bYPYlMYhmEYhmEYhmEYhmEYhmFUhH79+rmQrMSWXrJkibz77rvSp0+fDSYz4zqIjNPjXnnlFXdaHYGPGzZs6GK64eK7dOlSmT9/vlO5iX9DYHf+VpcpLCyUefPmuZPq7IRIwzAMwzAMwzAMwzCM2sm6deucjkNcel6GUdNQD6mTiM3UTx+i1Obm5kqHDh0kPT09uTFt//33X3dyMgeQcWIpYi3KN6/yblZXIFh8TZ/CahiGYRiGYRiGYRiGYcSmS5cu7mDBDWXLvFE3QD8dM2aM00+jhZft2LFjzGvYQWQRINxDs2bNXAY2adIkOaVlVC95eVL46aeyKD1dWmdkSHp+vsgOO4g0aJASJZGXJ+KHjk6VR8vLz5NP53wq2enZ7vd1hetkh847SIOsFHg4yRMRCi38bCKstu0gIg1Sp0JmFz0bK4m1tFKy2rlo0SJp3bp1XAuNdejREsbaWx0lhStlTT1aonahIlh7q6NYe6uTVLa9VYdNqDgbwHhSSZG+LcUfrbi9KTU1d8OTccGCBdK1a9dKHfpUmkIRWen+h6tjQUE9yczMst3YRtwHkc2ePdtFIsjWwW0RK1ascI6iy5Ytk6ZNm1Y+PMKGhoZEQLA10baOkpkphQ0ayJqcHGmSnS3pK1dSoCnTO2ZmijRsKJKVJYIenSqPlpmfKQ0bN5TG2Y3d77nrcl0bTA3RFnPbUETCzyaSi5VJjUG2VsjGRc+Wm1trKyUTMTpQ6lU8E7E69GgJY+2tjpLClbKmHi1Ru1ARrL3VUay91Ukq296qwyZUnA1gPJlqE5zUfrTi9paVniX5hfk1NnejzbLYkpGR4V7JAxuQ5razh0Lp7toWQtOIB+oKfUijRo2iLiTEU5dqWy9kGIZhGIZhGIZhGIZhGIaxQWOirWEYhmEYhmEYhmEYhmGkKHh1vvTSSyl334ceesiFN61uPvzwQ/dshDioSky0NQzDqCaIhTR2bDcZMKCv+79hGFVHYaHI1KmNZMWKZG6RMwzDMAzDMIzkcPTRR8v++++fEtk5fvx42Wqrrcq8/88//8iee+5Z4et+9NFHstNOO0mLFi2kQYMG0qNHDznqqKNcHGM49NBDZebMmZKqmGhrGIZRTSxfnimffx4+3PC111pbvhtGFfLWW63kpJO6yx57bG75bBiGYRiGYRg1QLt27SQnJ6dC350+fbrsscceMmDAAJk8ebL88MMPctttt7mDvdavX+8+U79+fWnTpo1s0KLtGWecEffLMIzaw4IFIueeK1Jkz4waZs6ckgDk06c3qtG0GEaq89df4fa2bl26TJ0a+1RWwzAqzxtvNJdJk6ytGYZhGEZFee6556RPnz5OiGzZsqXssssusmrVquJDEi+//HLp2LGjE0Hxan3rrbeKv4vn6SmnnCLt27d3B1916dJFrrnmmoher1x/4403dvfzmTt3rhxyyCEu3ACerfvtt5/Mnj27VEiAbbbZRho2bOg+s8MOO8iff/7pQhRcdtll8t1337mQAbx4L1J4hL/++ksOO+wwd32ugyD7xRdfRMyPd955x4m+119/vfTu3Vu6d8chYw+577773DNEC49w5ZVXOiG3cePGcuyxx8p5551XygtYPZxvvPFGl1/k9cknnyz5nBRYxKOPPurSxjVIw+GHHy4LFy6Umjh+slymTZsW18XsFD3DqD18/73IqFHh///4o0jfvjWdIuP33xvIwIG50qdPnixbVv5JkYZR1RTkp8noA/vKiFHzZIe9OYE6dVi2LEv+7/8WymOPtZEff2wk22xT0ykyjNTlscfay803d3b//+qrv2o6OYaR0hx2WF8ZNGi1nHrqPzWdFMMwkgiCKmImAuUBBxwgubm58vHHH0uoKK7eLbfcIhMmTJB77rlH+vXrJw888IDsu+++8tNPP7mQAbfeequ88sor8swzz0jnzp2dAMvL5+KLL5Zrr73WXQtRcuTIkc57tVevXk6w3H333WXQoEHuvpmZmU78RCT9/vvvJT093Qmdxx13nDz55JNOJJ46darTAQlR8OOPPzoR+b333nP3atq07ELuypUrZejQobLRRhu5tCKGfvPNN06QjkS7du1cvuBlO2TIkLjy8fHHH5errrpK7rzzTicqP/XUUy7funXrVupzkyZNcoItP3/77Tf3DAi7PB+QH1dccYVsttlmTqzFSRWx94033pBaJ9ryEIZh1C3+8uZM8+ebaFsbwPOvc+e10rTpepk9Oy7zaxhVys/fhQdTzz7QQTbe8h+RLqmT4UuXZkmvXgVywAGLZe1aiwZlGFXJpEkti/9fUJAmmdbFGUaV8euvDeTXXxvLnnsulR49UmvB1TA2ZBAnCwoK5MADD3ResoDXrYJX6LnnnuuEVrjuuuucVnfzzTfLHXfcIXPmzHHi7eDBg52QqtfwGTFihPM8BQTJd99914UbQOB8+umnnXj6v//9r9gh88EHH3RerHjY4nW6fPly2XvvvZ3HKyD2Ko0aNXJCL0JrNJ544glZtGiRfPnll87TFjbZZJOonx8xYoS8/fbbTujlutttt53svPPOcuSRR0qTJuGwg0F4ntGjR8sxxxzjfr/kkkucxy6CsU/z5s3l9ttvl4yMDOnZs6cMHz5c3n///WLRdpR6wIk4r2RE8YEDB7rr8KzVhc1iDCNF8Rerli6tyZQYSl5ehjRqVCjNmhU4L0DDqGn+ntVQBgwOn3g6Y1r1n7palSxZkiUtWhRI48brJTfXFCTDqErWrUuTUaMWuP//8Ud4u6JhGMnHP8j2vvvaWhYbRnkNZs2a6n9V8MTpvn37OkESoRaxkhAAS4sm8itWrJB58+Y5z1Effp8xY4b7P16g3377rfMMPfXUU51QGQQv2uDv+n1CG+BxSjgAREleCKtr1qyR33//3f2fe+CNu88++zhvXYTmRCB9eAmrYFseGRkZTjgmpAIeyHjoXn311bLFFltEvfcvv/ziQjj4BH8HrsH1Fbxu/fAHX3/9tXtOvJbJE4RjQByvTio0i/nqq6+cyzWJ1RPblBdeeCFZaTMMoxz+/FMkEL6lmKLQN44lSywra4to26rV+iLR1kQko+ZZsTRbWrVdJ8MPWSDz5zaQVCI3N0OaNFnvRNuFC629GTUPc7jc5RkSCqVJWmo1N7dIsv32i+Tdd5vZoqRhVCHoQbDffovl5ZdLPNwNw4jA2rUi48ZVImsQX4tinIZE0kMZImnpxAWN/bVbbhGpV3KWSbwgIOL5+tlnnznBFY/RCy+80MV7JeZqeWy99dYya9YsefPNN12IAmLTEhM3GLc2GniQ9u/f34UXCNK6dfgQbQRUBGHCIOCZe9FFF7k04wEbDxqHNlE22mgj+e9//+teeAhvuummcvfdd7s4uhUlK6u0ExXexRqmgTjCiNO8yA+eH/2T34MaaFWT8CyGeBC4IpNYKtJuu+0mM2fOlAULFri4G4axIfDvvyIsDqXXoK96bi5bIlg1Etljj7J/X7685P+LF1dr0oworF6dLvXr5zvRdvly87Q1ap7lS7KlY8d10rR5gfwwLbUOEFq1KkMaNCh0wu2KFdk1nRzDkG8+aSmP3hTeRnjT85+kVI6sWZMh9esXSqNG62XlyhKvFcOoKeb92UAWzmohC+dly26Hp04IgdWrwz9POmm+E23p6xo2rOlUGUYtJScnLKBWGE7zXikrV2bK3Lk5sskm6yU9M7t80Zb7VhCEQ7xnebGtnxAHL774ooun2qFDB/n000+LPT6B330vUkIGEJuV18EHH+zi0S5ZsqTYs3XKlClOz1P4Hc9XFX0RYjnAK1roAeDzvM4//3znqUvIA0Tb7OxsWV/OCehbbrmlC7/gpylRmjdv7rxi9YC2IHgaE37Bf05+T4Sff/5ZFi9e7OL/durUqdh5tSZIWLTFFfmmm25yJ6vhIoxLNAF9TzjhBJdxhpHqsPiCSNqmDScl10waZs0Sufzy8P9//10kkm1cFt7x7DBP29okIoVj2uJpi9dVeX0+cGDnxIkiN99cswsFRuqxYmmWNGuRJ+07rZF/5rSv6G6uWuvZ3qBB2NN2xQrztK1LsDPtww9F9txTUorFC0q8bv6dnyPSVVKGNWvSpV49E22N2sO1p/WV9LTwAsJuh/8qqcKaNWmSnh5y4X+yswvl33+zTLQ1jGgw0aqAx2sJ62XNmkKZs7CBSE5IpN56kXhE2wqCRy0xVXGMRDjld+K/atzYs88+Wy699FIXT5YDs/B6JdyAesZOnDjRaXIIqhwa9uyzz7o4sMSkVXiP2LTEveV7HCR2//33u78dccQRcsMNN8h+++0nl19+uXTs2FH+/PNPt5v+nHPOcQdz3Xvvve7wMwRkwhD8+uuvxeJo165dnacvaeK76IU5AQGbg9bQFDnQ7JprrnHpnTZtmrteMHQD3HPPPe56OIjy3IRqeOSRR9zha3giR2Ls2LEuLi3Puf322zshmoPUiEsbL4REQITmHmPGjHGHrOHhWxMkPP0nlgUBeoGHQN1mNeD00093BWgYqY6KoflFOyVqgvvuC+/2wH7j9RtpYquetg0amKdtbWH16rDnH56269enlQphEYvvvhP57LPSh8sZRjJYuSJLmjQrkHYbrZW1azLkjddSx0Mu7H2Ep22BrFiROs+1IfDBByJvv1168TEVWLwwR/YascAtkiycVz+lFrM57A/RlkUSPJIMozaxZnV6Snna4tWOZhTeSWLtzTCqEj9Ou39mTFWAd+vkyZNlr732ctv/CT0wYcIE2bNosk9YAjxuzzzzTBf3lhAFr7zyijt8DBBJifuKWMmBWbNnz5Y33njDCbgK4QTYPY/HK+Lnk08+KZtvvrn7W4MGDdz9ESw5DA2xmAO9EEpJG3/HA/Wggw5y6Tv++OOdMycOnMD7ePb+5z//ceEEuHYQNER27CNK85w8B96sfmxZn2222caFbUA4JQYtXsZ4B7/00kulPI59EJ/xAj7rrLOKQ0YQi7deAgI+6X/ooYecyE3+kEYOgqsJMiviipzLvuyiuBIozmT0smXLJC8vryrSaBi1CkRSQHDDcNeE56PGxya0DR6/kZoeoi0HQ7Jb4rzzSn+X70WxizUOefrWC61l572LMlpSz/MPISkjIyTLlqVJPAdPannfdVf45zXXVG06jQ2HvJWZ0qBRgWRlh6Rdpzy55qpGcvABVeZAUG2w+2DdunTX3po0SbdJbR1DD/et5nMeqpyl/+bIZpvluUWShX+njmjLIjIg2upOEsOoadLSQ3LIMfPk1afbyr/z64n0lJSAMT99G9DeTLQ1jKpHHQAKC5M/QEYYVBBJEWKjgfiKpy2vSOBdyisaoaItdSeddFLUz+CZ+/DDD0f8G8ItoRqigVdtpPi5el+FkA/xxtnt16+fPProozE/gyDLy+fiiy92L2XXXXeVTTbZJGK+KzezrTXgFcwr2rMMGzaszLNVBQnLTUOGDHGBhoET7caNG+cqBg/DSXdG3ebVV0sfYGWUhVADHTuG/z93bsVy6JNPRA4/XOSbb+L7/MyZIgMGlIQ5+OOPcIiGgw8WOeOM8Hv8/fvvS6ezZ0+Rbt3C/2fXxNSpInvtJbLtthU+1LLKmT+3vjz7QAd5/7VWUte44YZwHscWbcPeEeHDyOK77vz54Z+YXl7VHPvcKKK2tpnKLJDkrcqUho3Ck78zrvvB/Sw6pLbawJ4l+wxT7cdYIGnenIF2ZpV7RxjJgzjsLIimmmhLDOlmLQukLaJtCnna6sFIiLa0t6VLLWa7UbNg70OFaTJwx2XStv3asGibIqxenSb166toyxkJtkhiGFWFhiBs336dG5eUE67VqCXk5eW5UBGEUMAzGJGbg9mOOuooqYskLNrefvvtMnLkSPd/TrLDPZtDyHCF1lgYRt2Fw/duuqlm7o0RRJxMFivyMmXGHw0TFloKCmL/HaENT1U8JBOJZ83KOF6vEyaInHZa+FmPP77EizIWjz0W/knsazx9Ee0QXrOzw+EPhgwJ/33UqPDPFSvCnyOdhLDhkEbK1V9U+7WWhvda8Ff4SG2E27Vravd2NsrwzTfDZYvo9PTTIkSJoR1RBkHy8vD8CytHTZvmy/LlaXGLtnhUKw8+KNUGoskjj1Tf/WozlG2Uhec6yXffpbmTcBs2Do9A69UvlNZtRP78s3rTwdDh6qtLDldJBOxxJBuv11IRCc8I/3BGo/qgfJ54omTxqTx++SVsW+kvK7owWlvJXUo4knxp3XadLFlY8UNKaqNoy2JkdnbItbclS0y0NWq+TkK9BuulZQq2N/o2sPAIhlG1FBSkuf6NONLskjQHgLpBWlqaCwuBw2n//v3l1Vdfleeff1522WUXqYskrIhwwhtBgt2X09PlvPPOc3E0iLVB6ASj7qIrR1XpZcU9VCzkkBHf05Ct33ifqjcpMWMRwmJ5FSKWRZqIL1ggct+bneS0G3rJtsf1dZPG6TPSIgppPnxuu+1Ezjyz5Pfg3/FG5qBDrkVs2XghLimGPhjaJZ6dASok46GLhy34Bzri+U8oGwRaPD132imcj927hydSvhiy337hn3feKbWShfPqSa++udKh8xqZ8r6nVFYhRHxBcK2ItzR5z+4KRCf49ttwHbnuunB5+6K8hkeArKyQ/P13fKItbfLcc8NetvvsE653eKJVdBtrvAMO6h35cuutJROgRCAG7wUXVFyQq23Qtr7+OrZ9qyveuJMni4w9Mdv9P6do8gdduhS6g+/wGn/nHRF2I/3wQ9geY4sjPR/vYQ/5HnmUaB6ozedQRYVdCNQ/+gvamoK9J2zWP/+E2y1jr4EDw78HPZGY1OIVUa9eyLW7pUvreMyHOsqkSeGDFB94IL7P33FH+OewYWHRNlp9oj7qtvy6AM+xamWmNG66Xlq2WSdLFkUXkdg5wyvGDscah/S9/37JwUj16q134422bfNl0aKwbTGMmg6xwmJkq7brSh0CmCoxbVW0Xb7cFkkMo6pgbJ+ZGXL9G6ItZ5IYtZ/69es7z9rFixe7M7i++eYbF6O3rlIhN7bCwkKZOXOmfPLJJy5Qsf8y6i4aF7UqwyM8/zyxQcKHKp11VnjCrQLSSy+Ff7LlH482Dg9E7HnqqfDETEUBJj4qMBO65IADSt+Dvw0/KEee/qh98Xvf/dFYjjwu2107Fq+/Hv750Uci8+aJ4FSukyeuO2sWpzqK9OkjcsklxHWOfJ2ffy7rQXvLLaV/RzS9/XaR114r/T7PSZ7QnDRvEL/wtHzjjRLhNcsbo+FJi0hI2fnb8/HCBWKX8+I7O+4YFj180beyJHML64K/68umvVfJgMHLZM5vcQR8DUBdIZ90oo+AX55I+fHHYZEqXg9DxCK8pZVIYgSH6OApRjgKFgGeeCLDxdjUgfYvvzSURx4pu6UtUlq5X+PGxBQPi8Sw++7heqnhMeIB4W2HHcKewPEIayxgqJg9eLDI//1f/PdCcCNtCH8svtCW6mpYB/IqUtqxEZ9+WvI73u+Jiv/VAUKzHzoFtN5s1I3DREve79wlJNOnh58DwR27dcwx4TjK228fFkg5JEpje8PLL4cXirCvePMj8scDefrKK+EdC6SB+7HgwfvsQqBd0V/Q1rR/OuWU8PMQTus//ym5VtEGoFJ2ABFJIRzJkiU20E4ExNNkeCf/9FNJH1/eYpOWM/Woc+fw78EDGOkjsX3UxxEjpM5A/8x2bWJIO9F2YU7URRBl2rTavRCELQh6/nXuvFamT28UMda+YVQXeavSJLveerdwR3vjEMBUDI/QocM6+fPP1BGkDaO2wbxMx8lh0bamU2RsiCQs2nJSGwF8CZKMuzHBd/XFKXFG3UU94RC54iXRyQSxC+HUU0ve+/HH8MQMr1sOAGRChmirIAAgNKkogGiAOILHKddT71lECYTOoIiroq16dF14YXRBa/z48D30vr7nF+lUMeK//yW2TfieixaJEC8cgVk9whC3EOv4DulD3Pntt7BnJmy1VVjQ46BGxF28bYknjpjGc/I3RBW8NlkUQkTp3btk4nv22WXTj6jLMyAWHnlkaZH4iivCr88/D3svITarZxrfIV8oS8RF0pwIeNqQxni2vuL5UJ6AunRRjts62rHLGvl7VsPEElMkuCOoI/DD8OHh/CSd0RYk9LMsKgS9/4IgYmg8chYU9BBJPBKfeabk/8E0TZwYFmg1PMI558ySbt0KiwUJ6gcv0nrPPaW/Tx1SkZ0JyK67lvyNOh/v5JjFEqCe0o5ilYV/TW2vLEbw/1ixeKlHXPes87Nc+0T4JQb033+HFxaqCu5JGIfywptEgq3Y5H00rrxS5MQTy77PotG4ceFn1vviUR8JPkO+V5cA498HoZPQKXg7Uq5q4597aa2cO7G0mtu2bcgJopEWNpRzzgnbX2weoiv540P9j0egx05efnn4//36he0kNlBtsN8PaDuZMSP88733wj8POQTv4HDb1v4F5s5Nc4skyrx5OXLvvakV92/N2jS57bn2smxlZpWMBwjDEitGd7zgLbvppuE2Wt7OEjyrsXX9+3OYhQjnRRAWyMePf0ydqSvkrkhzByPVb1DoRKT8deky5fP0Mm1FRW5th7U5rIfaEuqLirYbb7xG2rVbK1OmlLQ/7CPjGMOoLugT6hUJm61SLDwC/bg6AGy11Sr59tskemHUAFOmxB8+py7A3JDdcZF2NDJ+ZkxPGKDKOMDgwMGcrjaxZnW6TPusuawvSK0F8vCh4+FBNYd4W3gEo06ItmPGjJEBAwbIjz/+KEuWLJGlS5cWv/jdqJsgGOpWf8SVeLYyMwBH+EH0U6gCiI7RJuxcOwhiAjFZib+KGAAIlnja+MIjEzgfPL0QeQFPLT7P9/xJ3HXjfpH/7rFQbnu1S/F7iJq+oMGKGUIUHrSAYNCqVYkogNBF7Fi8jlSUZcUNLyDAg/Wii8ITSd/7UkUd8gNxAlH19NPDE9D//S/8dxXirr027OWLlxkhDhS8zbQT5x6EkNBD0ILUqxf+G2IsAghCdzSIcIIHDwItsW532y3sWYe4SJp98TIWeIAyMAmKOkCsV7yhdIsaIBhHiseqghkCNgPrFq3XSY/eK+WfufUTWkQAvPUADyzyUyGdY8eG66svSCL04dHHgW142lI3EBbIk+CWawR6xG8Gl2PGhAUFnglvy169mKyK3H23yGabhcUsrh30TtWBNpPazz9Pl/33D9d/PAXVW5C2iFc3MDhg8uF7Rl91Velr8n317CbNkSbHPBd1jIUCFQEitUdFwwBcf314kUK9iWkPsUJrXPVwR9nmtEHyyefh7gWhUEUWPDIrIgJR94NhRXx4Dto+iyqR6i7PHiu8A4sp5L1fV6nb2DMG15S7D+/hza1dHp7veqhgNEEbQRH7wEIQ9pXrV4WAS5pod9hmbYNqo4krSl1hIQPatSv7/R0Gh+sn9op6jXCKvSNEB3YS20U9B8pVRVcWkrABujCFF2R58dFVZCXsB57cim/DaYfsbMA+63Ow8IZdxLbSZ2iZI+Aq552X5cKRKAceuMB5SKQSJ07oLg+/2UaOv3WLpF9bwyRVZucNdZ3zHug3VYgPLgrSFvyh48knl7bP1DXiwGNXlWCc20R2G0SCBZlIE2gfbCb9dKJtlnau9phrNGxU4NpTTr2QZGYVyieT011b0V0+oLauU6fwT10MrE3opFXHJ+HwCIXFC4tbb71C3n67pP2x44LF5Iosqhl1D+p8POc1VCV5eWnFom3bDuGDyFLFQ47xjHraduu2RhYuzK6zO5l0YZndl6kCYz0cRRg365kkQF+HU8l554XnlDhCaD/BuBoHGPqZeGB8x5ym6Gz4WsHUSW3kvus2kQnn9pFUgn6ffg02xPAIjL2ZH9XmXT8bAgmLtr/++qtcffXVztO2WbNm0rRp01Ivo27ChAGvDmK1MhhnC315jZOVUSCMgXZKCDIIGvyO0MKEjJVAhAy8R/g/nRWw1RsRR0EkaNu2JFYoYi7CowqlN98cFgUQgQBPV7a066RLRRk6veefWCej95grfTfLlQOHluzHVFGV6zB55HfEAoQo/RtCnAoPiBwItogEKsqopxAiMyIRYh0UhXou9oJFBFTIEyav/jZkBQ8kBaERr13EEf9wQyZtiHYIMbHQzr5nz9ifU+EXgVZXavEY1ffZCq1hIYjXGhStEHyZgB10UMl7eFH6nm66jV8nwyoU4ilH3dBQD1wfwYzyOHDfHFn6b440b5UvTZqul4265sk73sQvKNprGlVs0wgtiKaA115w9Zv6inDF4B1hVb2neR+RijzWfMTTWeHzfJZYzNQJX4z1FxRID5079QGBFOHdLzft+LfbrkTd80WKY48N/9Rtv/psHHznX4OBGu0N0RgY8CF2EfM20uQYYYutrEccUfJe8OA/nhHRm8Hks8+GvSkRxqi3W24ZritsqIgVG/qlj1sW/1/zivRqnvqH4cULbYJQIpFA3EY0VTuhXmo+lCvtnLLxY2CyIOAvOpGX1HWEb54TwTCSdzADZQbecP754XzX52IBSW2Fj8byJg9ZfOH61IuKep9RBpHWSXUS4LdBCIZx8v/m07VbyKWPBTPq9dZbl7Qn6jy7BLBHtFvKVt8/9NBwmBZfQHv8cZG99w73B/QrWs/5P2Wq0BdQZ7F53Au7Qn3GRuDNS1gX0qP1VeN6+7aTcCEQbZFn550Xy7x5pQ0wZY9Xu3rvJhv6UH8hIJnc+Von+eH38E6EP+Y3kDlzo08isOd+vUhEtK2M9xN1gvaImIAdYYyBzfJtE2mjnWG7tS34C1QqXLJYQPvFnvI5PyxCZSJzURdps7royEKff0gnwhNjC8ZI9P3YRR/qJAtcvmCCHSWtLIQydqBu0xd98nGGC42gHDH2d3nzjXD/5ou25D19JLDzIuhpXBvQeo0N4tnD4RFKFLGhQ5fIjBlp7nP0oxo2JpJ9rizVNZFkQfLCezoX35My9ndjVTf00xWNbV/VUOdpq0FY6CPfqs3TtugMgTbt10lBfrrcPDHTLfrUdfFWY7YDB/8Rb3PBgrotJPmLtQ+9u5H8Ma9uhnygbvmOT4xd1UYF+2HG+/Rt9D/qGMG8JBEYg+LowQKaOnvUFH/+Gp6oLKpE2ZFXyQy5l3xP25AsX167D8lONjg0USa1tb/ZUEi41m277bbyW6x9pEadpmHDkskvggKiKeEGVGBT+N33tkNQxYOSA2uAvzEppwNB4CB2rYqQeBYygOcUdrbpMxHCW1W32OIx6sPvCFSkB1EAwU3ThxjEFnWFlVpEpy6dQ3LCXnOd2LRR63Vy9VEz5aTjCpwYyzOyHR4PMYQeX7RRcQ3hC+FURYEtPEcmtuIqeMiwFZhJlcbk5dAV9ZZhQqrb5xHCI4EwDBwahjdoZmZ4wEu+Ac+rnm3lgVjL6m7r1rE/hycx4jQwgNVJMhNMhDYfBhyUj+8hxeEoTJxVNFLhE083BuTEwFSIn0v92Hnn8O9cn7qB6MUARsU4PxZosxbhQum8yUq539vSzIAfoZm8VsEeuD4LAwgw1FnqFuIR3l3UK76nYr9CiA31EsS72Z9gaHkjbKlQhDDAyjZ1kPsF62k0iCuMCHbhhQWy++4lgUA5ZRtYqEDop10g/OHBq15lCAaIc2zH4b4+CF3Uf+oX5YYn4r77lvw9GGKBiTN1H2GSsmCRJOhdRseMRxRtmYUMBDsfxDPEF/VCj0WTxqWFZv+wzmibMnjW4KRePacRW4OTc8RRFW/ID+oUtil4OJE/sfafGY9T6rYPv9P+FbzPEfQpEwbGLNb414sU017bPJNGfR69L6Kt//y0dY1hXR4IICp8sqiltpp8wVOD0AwayoJQL9pWmQxQx/iJLcVe6YGEkcAOcrChguiKsKULDkC4Fg23oTsefLFW6x+iH+0F8QlbRjtjsU5FKf+QMWwe32USwuepo4B3O2gfQlugj/EXIAhToWWq/dXhh5e4yvfokecO/vNFSOo7ZYonuS8ok95kTBpoJ7oQUBFYZAmKpvRXTAofeCe8wvb2xHAFu3ZCpvsbfS6LFPR1LK5RB1mwCYZsiQV5oHYvuLATBE9sbH5wV4K2STyh6QuxsSxk0SfrMzGx1bLCXtMWQA8CVVvsL0LS5hFVdbFKdydU1INTxcfvvw8LHldckeZ2xOjCIGGOqHfafzD8pW5jmylX8pk+zhdddeJMH6VtBDH4oQcyJCunJCZNj97Li8VexGytc1ybfgHo7+IVzrGf5YVSwFbQ/1ZW6NQ0sbBD6BLKUkP/wFZb5bpFEsoJYULtYLKPvqBv17FFZaEORcqX775PkwGnDpIXJ7eUt6c2l9y8DHnz7bAx1B1a1Q31hn46uIhQUditEhwjVRQNZcPP4IKYjiM1JFVVsmplmuQUeaPSdzVvtVZefD7DzWnUYYRdhskIAVPdhNtbybO1abPOibb0Y8GdhNgz3eGGIKh9ZW1DnQFoW7e/2lkOubJuemsyHteDtNmphl1Rb1h/UVnHx4wtGbsyn6QfpP+Ntgsumt1mHslYlPEwtqGmtu/P/b2hHHPm77J2TUbcAh955TuCYDN4Fsa0tUu0Df+/YcOCOr/oU9EzPWynTB0TbceOHStnnnmmPPTQQ/L111/L999/X+pl1G2YDDOIURA/EPXoUDCieJQw0VWvOTonPK4QN1n1Dx7Co5MwYLLGZAxREoFSPVO7dg1Pyn0xNNLkzwfRQT3dEB8R/hCbgofRKLv1XyyjjgofSICQh8joe4UiJHBNxDW9n5920sjkE1EtkrcscG0msAgPt90Wfk5ijzJp4dr6vJFAeI50eA+Tdl+cjId4Hd7p5MkvxBFCRjBwRaxhIMGzMrDzYw8zeaTjCoq6pJtwDD4q3iv+9iAf31sVQXT3PQrlmke+lOyc8Mhkh90WOHFUO3Suw6AfIVZPraZMEHERBRHGKF/ynsk0Xt145RHKAsEID0EtYx/CGPDcKlIibqm3NR7lTKbVg5qBGHmWKAccsF6uuspz4XIhGwqcJyceaYhYCHGACIKXLgIuE3gOIYtW71TY8IUh0s4gXcOLMLFjos69mGTzk7KnLXMQng4Q/TYRTZDUhY1IsbTmLgi7XrJI8vB9Zffq6YIGnnW+Zxq2gzrHNmgEEsqWtDFAoF0Hvf+ohwwkNDQHIgqiiQqwwbiylB95S1tERFm6NE3Gj08rFc4gUqxNtTHkFYI1bdyf8FBPKBed7NLusZ8IXXiNqMc8wi9iJIsaCGm6aKSeFYivDNYpd9qB38YoV8Qjnpf0U7/5vx7KiDhLmeC5O3p0Sf7i4YoHB7YNL2nSz8+gLY2XaPUP2xZs/3jLEpMUm6J12hfdCO9Bu6YexrP4gYBFe1ARGmiD/kIGAi9b6/1yGzWq5ASrFkULQXpwEiA0siACCD/YfCb02BnKKx6ox7Sb4CSJstHwEH5IGMQrypnFJX766QmKibQJylHjRGML6HP3OTjs2n/qwfOkZdMCGX/EbzL1q3S3uMCkjTpKHSSvdGu9v4BSnhhH3lJGCPPYv2jbbhHgNO5wcGGX79BeWeTRBQDymn4Qm80kVhcyqZc+2Ltgv6rhmxTyDhukCwbRxGXEUvXgZRKIbdS0kqe6AMNz6m4d8oA+hwk36GcYa2D/SQtirr/go7ZJJ53sBFJPYZ5Zd/L4cdqbNM+XHXYsLG5bTN4RXWjbesgpYyVsiR5MGpw0+QcCkq/lHbbKtehrKuPlTxq1f6TdMTlnK7qKSH5788FGsaCaTOGWRR/KKhnetmzRZqHdh+vO+KX0VOk/520jl1wZPgkW269jkepEF/6SFZWOvgjbFCuMULzo7hQWrBmbscAWvC59YjJhnhK0wb6nLVx857Ti/7ODA/vKuIF2w3i4Lm39pQ367a1Vq3Xy++9pbpzLuE1DU7AAihcnYyzsCv0Ni3q0m+oU9rgXC/DxhMz45de668WIAMliLWM/5h2M2QhDpc4H2Gv6aeas/liS/oKxI3aVcyD88Sh9k7ZzxjgsEvpjdfoXv89kvKr3iwTCsR8XPpl1cv5fDWTT3rnSrOVamTMnPs9vbCjjBEUXgHEUqI2etoS4o88OOoekEojStFVsou+9bbF8a5aELeNBBx0kM2bMkFGjRsnAgQNlq622kn79+hX/NOoe/kCFCRJbXlX8RISlc+nbNyx80FmwdRXjjDCA2MDEzhflGPSymusbYTyZ8EjQA5ySDZ0coR3igUmQwoSWDjHSNq4gCC5BASIIoh8iDZ1yIjAZVM8aHzxgVVRINpQ1nskMqFWQ8p+VMkPEQJRR8Y/JvMadQvxk4MvkGfFE46zqJJrv+F4oKowjHtIZI5QqKpaOPLxAGjYumZW22Wi1Ey+oS4hZQe837oHgqTDoiebRDHgsM1klHYgYCEa+oI6HJMIiec6gi2tT/1W8ZIJanhdzIowYsb74gLkgDNJ4XvK5PMFFPRIBryb1FMO7DtFfJ1HEBlUQExDQEP1UVGISj6cieRJtwISnKagnuLJ8VaYcdFHYJW7YlkukU8eyMyAGlppWJnMIO0y2uD+TCfVWQ7zAzuB5oDDwZSCLEE89pJwQSrBNCITYLeoxYgpiLnYNIRRbxD14XgbRDzyQJiNHtiglMjGAZdGI7/Md8gDxC49bBAZfsMRjkEUW0qpiExMl8ox2r/Fi8XpW8ZjFKgY72FXSiYc5oUjUo9sXfPDu9mO7IQyx8KAeGgyk/HAbiO4+pE09UwllEEvsr2qwKYSuwcbSl/jbvJm0J+IhR9ukncbyEMbD368zzZoVlGlvTBCY+GgYDLXVTLao/yxgIC7hnUoaNQwF5ccEDHuni0gIaHjIYy81TjmTLkRGyoWyoi0yWcYzCpun3sI64I/mte57nNOO6WOp24j+C4sWaP67R/g/u/bzlLsATNaB54m2NZ2FCxVm/XZPO2MBjAWUIAigGgoD0QM75W8JRcij/IP9Mu0Q4RAxAWiT998fFhYQvyIdgsd1GF5Sf/DapU1go2mvtAXqhb/jxoewBLpVm3JF3EXUpxx5Pl0EgP32KwntQhvFbqh9pjx1Z0YkdOcRdYp7YmvVcxwbxALlJZfly39PK71wd90N+S7PWXBhgUp3Eqloq/aWvCa91AGgDvIM2Ab1FibNvi2JhO/pFSs2eSx0sx31nHZDG//rr5LT7AG7M2VKyayWxQY9BJZnoQzUI7MyaH+SjBAn1K+gmMGY4sZbMqOKvEB/U945EJQdbZF2lgwvLY01nYzDm0iPer5WNA4tNoS+j4UOFlMYNzGGYtyGIIwoSl9KXSd0GAuclRFJsZ+a59gT33uWeo295O85XsiOzKyQXDw+v3gxxl8wo18Inknhw5ynNgkWK1akSZMmJf1b794r5YYbwvUUu0Wes7vKf0YWdHSxnWf1F8SrGsRK7hfJM9xvc7TjxUvSZPPOYaM2Z2HZVV2eSUVL2kFtKReEcB1PqEMKfRd1HWcJbCX5gGMN7zPnIz/oW+gj1NYzRqAu83l2BGJnuJ6eCYHzlMbvp0zpZ/zQEswbGFdEsom0Cdopc7fggk8wdFGi/Dk7TRo1zpemLfKldfs18vfcdNfGWSRQB4UgCIJaJ7Bl2CGNWc9Yx4e+LdKOHqUq64HvaUvfVr9+KO4DoOsijCmoq4yTdcGNsRv1h1c8thvnTsKoVjUffvihpKWlybJYJ2RvqKLtrFmzyrz++OOP4p9G3YKBj7+lWuPtIBpg9BE4EUSZVPmeRwyUEelYSQeMGRNLBBQ8HplEaeeFYMSKoi8K1iRM+BCq6LQQ8ZiUGrFBCPG9LqkPTJYR7jWuJQI+kx68JfmJJxkTa34nz3VyTGgGOj085xhY41WIpyQDhh6blu4JcuoVyiEj15eKwYkIiZii92DLP//n5W9rLw8mEcEBJCK5rlhTL7iePxBg0aK6UC9SBi7leUcyCCTtCKF455K/lBGo9zveYeSV7z2uE3y+x0QGUUkXCaKJfbxPuSF4a97Qge98/kApLAx/KTsreo+uYin3o16p5zNiv4YB8Q9h00kYk3xEAhW59NDC4OEVCDQISog7TBIRWrBrDDhYNFKB/tBDQ26ywCQS4YjnwgsX0Q3RhfznvUh2C8E10iIL8HldwAiK7dR99YQjHeQ1acA+BncpsIPBF1jwuAyiC2N8nwkRLxaAqmqhpyKQh7q7QssfgS7RNNLnMLGJdHha0PMfsWD06LLKyO67h99jMYl6h2iEOIaw4MMkiHJEbCePsXOILiqO6SIli3gqcmDHEHwRBamnGk8bO4dtYzFJd1P4CzUIqZEGwCwK6HiX7+kEzBfltI3mZIdk8jtr3YIpnowac1jrjF5HBWMmG0zoEQj5iZ1TwQJ7w64KoC7RNthEhdjEJI/noy9Q71PqtHpdarxmro9oqyFcfIIHaSK2hidBYTtW3qIYXru6KKEwYaVeUf9pMypA+x7CGmfa90hSEH/vvTdcCPRniPMI+IjVlCMe09Q77BOiqe66AMRgjWmOjVLbpWEVdIwEu+1eKAOHRhbYNQ8Vv3348bwpA+qh70mlk9vyxlhMwnQcRz7oLhIfyq68A42YXCNKs9iqouHjj2fIggWlT4ul3WufiahGPdR6Rnn5ca0rii5+JDNMgb9VV/urBjnr5aZTZ8llo+dI++ZrXZnTTjREix8yB9HB9/ymvOjLWJykz1OniMoIuGqLYgnfTLh1ITTWJFsXeenT4wnlQh1hwUC3fNPW2f1E21PPfkKaYOdU2KAPR6xHdKEuaJ8faUFI4TvcJ5KQTL+PTSWfdUyg7QCBiBA3q1aVHESmsJsLm66LnYxDdKEVcUlDwwQPTMRWBr39qxPamb/dPCjaHnRQafWeMZ2ej+BDfdMxOx73VX3IIWMqxmNazrQB376Q38zFgD6Wscyb76RLx1ZhpejaZ0rHhmNxi7EfoiU2nTrE94Jb8anvLFRW1+Fs1HO1bbRv3xbjRYudYk5Me/R3Q/I3+hZ/vM08GvvM59U+0Aa03/P7BtoBNkXL1AfHm6AI63v3+mcv6LkMjBk0ZJW2I+0jKCvsLHOxSAd3Ll6c5naP6ALJ449muDEB96Qt+4eaIkjTtv3Y9Nyb8TH9HX1HUKBlgTzaHI/0UA8Y9+PwEUnApS5QThV5YW9Xrw4LtbzWrw+5hapY30m07k2ePFn22Wcf6dChgxMhX9KYix6hUEguueQSad++vdSvX1922WUXd9aUT7TvHn300bI/mRgHKtQyZqC+sXuOMRr1mDEO7/OM9HW0ta5du8rNVBaPQw89VGaWF18rCjUZ4qM2k7Bc1SXWHnajzsHAUw0j3hK+QQwukNBoGWTrRCwo6nAglwpFvgdEMj0TkwHpjiSAGLHzDCEEgQlxXg9fiwcGVcGYl4ofq9DVpwgTmFNPK5DpP2S7STTiR7LE/1jhKvz0UVd0JTye7yQLJiQ8L5NsnVREg7wLbrNm4IeAiRDL3/zDfQBvFwQFOmIGvDp4j+ckdsQLOnUmSXi53nIHXUm+XHrMHBm+/VKRck6cZ9Ct3i4ILXjj6ZZ5Bnm+Fy91DuEI8DhgQIdgwUAf4d0/jEqFJjwW8JxBKGQyzeCCa3Cfl14KyaxZS6Rbt9Zl6lKyvFLVcxxxj0m/biEnrzWEgXpVkC4G14AnBZMbBv88Z/DwNQQvPBFZMGGijb0mTzR/6gK+V3hVEQ5tU9ZlZKutQq6+kd962Ad1iT4K4ZYJCd7T5CdionqzKNhABFQmMyxOYpNYDKAuI+DgWY2Yr2I716UdBkEMps0hFjKhQRTkoDzaEqFlmExRb1hwoN/F498/IXrL3iFpGPAqoC0TjsdH2zRewNgBQgUwiWOip3EcdWLP74Q9ok3zd8Q20kf+IAYHt43r91R4QwjRhRQmeXi1ah33CU4uI002E4Vno8xVyERkxyNb08MkmvLEJjARxm5gE1jEYXKs4Sd0soDd17ZH+eqwl4mketvSBpncqhcvE1DKjBc2ifJMBH8iH/T2ZOGcRUnqDIujiF7qwcyEikkU/QU2nmpBWihzPO2D9UG3pWNrKDNdjMUbjLxhTofdjBRGSKFPUrGUeRrCIZ7w552H40bp1RjsnR4cCHiM6dwu2nzO92oqDyaPLLwg3Pmx9GOBNxf55/eJOsFmEYL+knzwJ40Tjv1ZBvYNK5+79ZorawaypaeBs8N4vpFftGW8xXUcTJlRt4K7ISgvyoh6Qh7ggYwoSJ/K4o8fqzsI16LsqLu0Hdoa9ia4iMjiDs9AOWnIM/ofnDSwP3hLI0TwvAq2KtpBjgp2U9sA3vLsdvK9fbGZ2ELmD5q/LFxhK8lz6gJppy3SBrF9LIJQbzWMCnXE9wFiAQTnD4RU2jK2Tj1GWeygz8cxgAVY8hu7hSj0xGMZsu3wyKo4Qpnu/CDvKQvS4oe+Iq9o21oPEG35vSp2r5Be8hZbxliIcmUcQTrJO+wMfRN9D3/75ps0Ofrokj6uS5c18tVXNMoGbgzhHxJMv4bHpkJboc+iLvGT/OS+yRZwERrpM3kGv45Qlgh/vPwzRdhNof3SVhvnyjvTWsvUmSWGkbL1Q1/5OyRZNKANcD+eBxGPPos6T1+NsEkfGssBgr4RuxPL9pAGrk1dxfayoMm9aWu0R+y+H5MdGNfyN7Ux5Z1Rwv3pvxA6aU+UP/0UdZ7xE30MC0s4I9An8UIcR+ClbiKOcj/aGSKmji99kZY+kl1rOobWg8Wxp4w/KQfdicHz0r/4Ma8ZL9He/LawdEmaNGoaLtCeWy2Tdx/vWCrcIvUNkZYxuT+eYfGAXQj+gjRtkudjPKQ7BFX0pQyCbVBFZOox+Yc9wSNUw2+R9zyTLnZFukYsyJesrDRp2jTDCacrVoScbY01nsVpA3tCWejhdLF2Ta5atUr69u3rdrIfGCk2nVtkul5uvfVWefjhh6Vbt25y8cUXy+677y7Tp0+XevEetBIDbB1tktj4OPmQ5/zUhQI0I55bvXABZwXfgYcX9TM7u760aRM4jEXi76OZ7/iL3kYFRNtXohxriLJPhdlkk01cRTLqDjpwVDEhFv4Arzy0c4zmkWbULehs6OD9A4qqC7zGWK2vCW9tJgl0HtUp2Co8rw64KgIDWATQaIMTJoj8jQ6SwQ4ToHhiX6rQgrCDqPTLjAxp03Sd7DO4nL25RTAQZfKmnqLcW8cbCAd4wjMRI9/99DAZ5LsMxJkU6mF6QTR+G9vvGUDjcesP1hs2rNrgdYh1usDAxAIhTAe4DEKCoSUUxAdeeN36Hgg8MxMqjaFLO8RTmnIIivFGbBAOEJ30YDzdbk6Z+Yt5TKyI9U29ZPLJhA7bx4vyU89IbBMiAQNZPC0RPvCI4T6UETCh4jMM3IM7O6gfhP3QAyl9dNKuOwBYWGWCc+fN66TeVFxvI7iNeiBiMgEjLXyP4RsCICD+8XwKcwSNo6cxkAFRSmPzKrQ/JiC+xysiLmIXdZIJGa9IExrEXEQuRNFkeYQjCrHYpJNK9ToHBEhEaN8jXz2OfXTSgb1hwkiIFupAtD6Hv/mTb3aLqFdpRX0bsFsIKkFvZAURHO9u7AATXN1uzMRcvbgpO4QEJujURd/204dSFxDnqVOIDXogIHWaXTF48mo4EJ6diTvtgokb7QBbine4jhdVnB45EsEor0yd5BoI3z7Yfeoz4iOTO9JCfeI5EGXweEQY1LYZC5yM6KPjEZwQRWnPutuBn5Q1goWWM+/xjIgwTEp5/5P31kr2lBXFz5aVGZIs7zERCBAYEMr9xVOEWQUBBSGN9GpoEBWtEeloLzhQ4LkUFG2ZIFMetCf6Brzu6VMQM7A7LOrSh9J3qPgfXMAB8pjvI1yQz4jtXBfbxw4dniHgtFUMYi51RMVU7ZN8MY40aOxxIO9Y1KHO8X36bI1DTj6R79go9ezzha1oO48iHaiIAI14TD5QL30hMJ4xIzaZPCdffe/vV15Jk9WrG5bajUjdpM4j+NL+yRPSFozLHcx32hY2MrhTBPGMNoiwqrttdKcDEAaKewLtmr5Ez5ro0QMVq6xQo/YHERTvSewstoC81h0vjLFoh9hNPbSUtki9Jx+DwqPWQ12A0fEM6UGMo58J2nvdHaJ1hPrB52j3tDt/hwuCuNrgQw5cL7v3+lf+03+F7Hl+X9cu2nYr8X5mYYRFe36Sfuwmu3cAhxL6dh030yfrwbAsjtD2gvZIQRinTbKoRPugj6TPps9nUYu6p0IWNgshWOsLfQ/9c7R5tHoc027j2VbPwhztkwUNteHkg/bL3D94tob2p7qbhD6PuoUAzLPrQgdtnfZL/8E4hgUnDUmg6EIT+YX4rWGT6H/QDBB82dWjdZV2i71s1CTc+AbvMV/efby366do19gqbAvP48ezJl/4O2nEHujYGDtM3WUhDlvhn5NCWVN/sH2M82mLOkYhDWgO/A0xEZtD/jF+U8GW9k6dxO7RHwTHZHyWvPT7TsosJyckjRuHnGiLXaGfpD5EsjHcn/upp7OGFOKe1INIwv2ee+7pXtHgvnizXnTRRbJfUZywRx55RNq2bes8a0dGO9QnArNnz46o1Q0aNFT+97/woRqMX8lD5k7c+7LLLpMHHnhAFixYIE2btpTddjtYLrjgVjnwwGEyd+6fcvrpp7sX/PRTSF588SG54YbTisMWjB8/3qXzlFNOlcsvHy9LliyRI488Um677TaZMGGCTJw4UQoLC+Xkk8fJiBEXunqh6Zw2bZoLwwpcr3nz5jJp0iQZpifSeuh9vmUbWRHkGy+upyEVzjnnHPnpp58kKytLtthiC3niiSdqvWNqwqItrtUItBSgj77Hz8GDB7sMI1ON2k+kyWIywChhf2qbp61RcYJejdUFnWK8B6wlG7yufI/gukas1WQdbNB3+yvi8cBAmdigusL96vivyxWRfBh0MlFhMBTcshzLE15XXv2Y2UEQc1XQZdCq4nR14g/kED4SOQmX8sArEo8K8oJBqHoN1mQ7TBUYyDNZwJsglnOCeor6Y9ugLVC7RPgXhDImOSrWBhc5IoXiYZKsntj+NZlw6QTfF4LcpDnOWGo8mz4f9ZG2gFAH/GTi5U86GK/qwYcKYgZ9OEKFtj0mk0zi/PkFE1uEXyZxTMwRdzRmsw/ihh/PPFkwX8HznDLVHYCIFL6XZ3k89dQSad26tbRqleY8czWubDzgJa2elRWN1Yk3nO8RFwnECERbJuyIYYjg/rZtBEA8PbE5TGBV7MJbC7EcwVp3AujCGVuWuYYv1CB0IPBzr0jxCOP1ao0E3n0Ia+rty1QhGIsXsYXQFJSDtj+EafoNFuP04EmEJHbyaLzYaJ7btCFsqO/1yiJF8ABK0oNgz3ZhJtq6OBYLPo/4oe1UPdx8EPRZEOB6tAHEOPKYtCNiqIchIJ6yGIpnNeI15aZihR+7kj4FsQKBB8EJ28Rin384HeIWzha+V6KGTCAttFPqOeN1+mI9LBTw6KRssAfB3T6IU9go5sC6I8EXbIOOHnown0I58uwshusz+YItYbAQHbHV1BWEdK3Lvkc6O09ob/SRpEm9nBGCv5yWLwvrccpqfO5aPA+iGnaLvEe4e+ONevLvv2luAYH2jW0jbX6IFARjXsEFb0QdbL6OVch3hDMEQK5N3vKZSLubyG8+T/my2KbPpoLt1luHpHVrBLLIHZgfPx78XZAKwhfimLYBFnMQbqm7vCg7BFDKlzSr6KV1kd0Gmh4+iyCPHVQx0Q9rQdulfiAUYqd9W8NYTY/EcYvdawpEJheIZIUHbfuMyCkVzJH+ke9TL2hrjJE0j3luXUyk7tMHaLgTrs3CCXWDNo3HO3WIxRVeuoiiYQ78A0QV9fbkmirY0peSP+U5WNCOuE88BzFSN/RAVSXYhsoDUZlyIZ3+wcHYf9ofoij9BEIpnrT0B4wtddEM6NP9XTYIvjpeYOFVRVue/dffMqXnoHDnl5UdktvuWifjTqrvFsl1R4GWN9egH1Dbyvgchw3sKGI++hx1AhuMndQwNfR35Df1iPahoTWAz9N+/TB29Cu6K4WxAXUGb1FelCEvxj0qeFO+1FvEccR9FvrCB3Jx0GZ4PIW9ycjA2zYs/vrfV8EWW8ZCFQsR/q4B9U5lMSCRRevwgWCzZP78+S4kgtK0aVPZdttt5fPPPy8l2iLC672odxrCSenUqZP848Wf0OsOHBgOqk9bpmy0733uueflpptukqeeesoJnNOnz5fff//O7Uq7+eYX5KCD+srBBx8vBx/sHRLgHRrNT575t99+l2effVOeeuotWbz4dzn44IPl99//kE022VTefvsj+eyzz+Skk0bJZpvtIr16lTMYqiAFBQVOyzzuuOPkySeflHXr1snUqVOdflnbSVi0fffdd+XCCy+Uq666SrYpssw8LC7aqP9UoBNOOEHOOussuT9SL2FsMGDcgrHnDMNIDRAXaOMMau6+NV8yvAF9vDBoiRRjMpkwYIuy06jaYCyQqEcsA/R4dj8YFYMBaUUWFJlMsa0Tj0gmapWNd413EhNLJhOIAZS5xqVONoiZeAYzaeP6wbYXbVs23mA+eIIGvUG5noYnwAOPCU11bm1jEsjz8EKQRyxJdGdG8+ah4gleNC/+8iAfqnLsz/Mx6de81QUcFtAQ4bAbTKQJaYcXLgtWfpzS4M4n2gF1l8kyE2Itb8owmvcq4n9ld2IyKdZwI75gixDLi78xyUOERGxBcOVZQENX6OF/TJqZGFK/mcjz7AjC1HVAmFDhWRcEEThpy4gZiG0IUoiCTFIR7dS7UgXuWNAWECYQTzXcD//nmniYBesDZcDfmfAiTuBBiICiEB4gktcpMfyxEyzyIMIinCMsMBUjjxB7/TE3gihxZfnJQglCF4tKiIQIpoghiGy6OER6EHxZoCG0D0IwwpweZsiCAL9TDrqghdhLHvoxfeMFIYGyQjxDLKFe40CGiOy3XeoKQj3PgPMUZaix6P0YneQJ7Z76Q/3sP6BQJv8Zf9BgBGIVtOGbb0IujxGCsI3UCWyzL9gCAizeqtRR8oL04nmPl5qPxvGMhh7+q96BiIu6uZX2iahOvebnrbcmJ1gr7QzRFrtB3aWOqWcvdYFFE0QrFfPJA9JEiAM/lIY6/zDtRwjGLtFOEQODh8qyWEhbYwxJHfTbR9BmX33UTLng+ZJGqAsE9Ju6s4KxFfWBctLFCYRX7BiOeCyK8AwI4IhH1Bm8SxGmEW39BRZsp9qNIAjClAn2QT3Z8fZFRE5kcbA6oU/QUGTkEc+vYiF1mzLGzlO+eINjF1TMB8qG/GJRgvzU8qGu8Dv1metr++/QpWRFuV+/UPHigX+eBrYymmBJufHiPiwkYqtUsEX0Z9GRMsLrHEGX92jveLRiF7BbPIMudCGq0u5YaMYeB8VLIJwIdp+/a1/Jd+iXIp0hAKSPPpi2yPexRSzAkA7mRAi3/MTLnjzHpvn2AGcXxhjxhIfimojes2aFXdbxrPVp3bqtzJ493+WHjlvOPvswOffc0pmcn79Whg8f7p4tKytD2hUZ/jVr1jgRc9CgQXLSSeOd/Ql6zc+ZM8d9HmEXz9TOLiZbWAPcfvsWkpOTIU2bNpbWrdu579Kn6q4K9bgP7+AplCuvfEAaNmwszZptLgMH/kd++ukXmTDhDUlPT5ehQzeTbt2ukx9/nCQjR1aNaLtixQpZvny57L333tK9aOtBr0RiPtYl0XbcuHFy7733yvaeBd55551daITjjz/euRrjgkxMDsMwDCN10W2NklcoEof3gGGkAggLRbvTkoIuKhC7tqphgl4dB9XVZNiOZMTJrc0ExfCgVx0iIpNKJou+YIvgocJsJDFYr4OHpYaXYCLPxFS9qvGsTFYMbQQqBB3dgsnEXp8NERJBiS2zTMKZ/CIC4TGssRYRDhGreVZEGLYnIyoqCAqIUepQhOcak3mEUj0MS2Mb+nF0EZTCcbHD24HjAfHUj7lfXtxKxAOdRiG64qWLgILwSjpVsFXvMWCyzRyT9CNoqHcinnIIL+zOUBA5EdYUxA082BAAEMp0Vymeqrrt3G+z+vwIPWqf+A7pDi6CIlJWdDcS+aVb9mOFGNBnUG9eni8Idk0PvE0G5PP48SvkhRdauDpIPfE9U4GFO/IeIVsXFYJo3FA8snWhQfEXYIJQNgiI3FO9SyOFLagM3FvbPR6ZlAEiP8/E+9RLXrQD2iJ1E31D6xa2lu/h6YpdwHb4+UMbDMb3RhguHjuWw279F8tup6+VUP0GMRfD+BsLLNQPRCIWPjR9CKy0b7QuFjyo05HEc8RKFk84J4D2R71mtzdp1d0w+ixBm1tboT2wgIHoTx75fT+/swihi3NqsxB6CdWhu8vJSzya/Z1CLEzpmRG6C+zEkwukww64zRfFcPBAyONgOETuWOOPoGiPVy31H9EdsV/bv9pl3lOBWW2hHqJFf0K/oYfUYft0gQpbR/myc58dC9RbPfSN5+SzKtjSN/KZ4KGR1Ce+z+eCB3rS7yCi8j7iJSKmCqosUNEnsVgVPDhYw0yAhgvR8CJ6jIF6r2p7QCAmvXxXPcHPPfcm2W67XVz953N8ZuLEcyU3d70LU8HOKA21gV6Xm5srjz76ruTnpxcfFu4zYsQIp+1tvPHGsscee8hee+3lDk3L9CoF3/N3WwTPRYKOHbvKlls2Lhb5W7ZsKxkZGU6wVXhv+fIIp08miRYtWrhD2YgFvOuuuzoh+pBDDnGHu6WcaPv7779Lkwijcd77o8gHvEePHvKvvz/HMAzDMAzDqFEihWgwUg8m3UyQEVzw0sGjMVqs3CCILwgbCKTBEBfB0B+VhYl1pHAvzOEQaRFXmHjjsUe4Bl5MgkmXL/L5Yi2oF5QKtgiNbMv245OCTk59sYCJJx6DCJROjIwzHEllYu4zreKFcEK4AAQ+yoBnQGjGaxqPTgRA7qOCLSCiEd4IQQ1hl3yLdhAe7d8PA8huA180YFs0npT+IXF4qUEkL7VUZ9CgfNlvv3DoPxX0yVsEG0QKFhvwGA3GF8Xjk/qJoKn1G4GMF+IKixUsoMTajUB90PAD1YHvLcoCDy89DAqPd/UG18M18ZjEVtAe2X2C6BmMpVzRHQtB4t29gDe2xhH1oc2wqxxxmR0ttCddlOLaCGP+waO68IcXKQJZXa37iHMa6iBaHWOBDlHaDweEIKuLKeRPpHEDfQxesCz6sMuhY7f1MvnP6PdiR0Si/ny0D+7hh77x4+xHOwgMu4ZAi9irB7NxLQRY3+uXz2k4Aw3Dh8DK/7G9CKRhgTckjRqV9dpHmORzejCnwv38tGFj1c6Sl9wPMVnDyxEOh7+TXg3nQZ1EENbfW7UKK85TpiyQHXZo731ugfTsGY73itcv9O7dTvbcs6Tx0Yfec09jyc1dViwAY7uuvPJKeeONt+XNN6dKfn5j99yRRHXCKfzyyy/y3nvvuR33J510ktxwww3y0UcfOc/bSOihfvyZ/OB+DRpkubak55XUr58ma9dmORuJKM7nsrPTJD09rFirmOuHZM33g5ZHvG96mRCuwe88+OCDcuqpp8pbb70lTz/9tIsUwHNtp4c+1FISHr73799fzj77bBf8mNhfsGjRIhfQd2DRqOnXX391BWwYhmEYhmEYRvXBdk8OPtJt1YkcIqsT9UgxiasbBEi8qXzhAa+vSCBSM0HEs4y5HpN1vo+gFs3DOBoqTNUEhEhhGzBlgMCAGMarPPg8k95IMVLjBRECb1C87JhcIw4j/Pteuxsy5HFQEKeuEQ8WcYmfCCIq2ETSACgj3yu6NhPpTAcElqCnqe48IQ+oQ4hNSAQ1cXhwrIVJ0oPwzgIO3uR4/cb6fKrv2lCvWWxsRSDWsx4YmRdbS6sUfjkQqoRQCLSzaLs+KFPaKkIxwigLC8EFKkV3YyAi+t6hfvioZs0QAXmVXj3gHojdpAVRmHQi9CLKck/6Ig255NdBFn0QUvVQOhVvfRAxddERUbdr127Stm07+eKL96VXr7BIu3LlCvn++y/k5JNPLPZ8hqCOSloQn7mmesQ//vjzctVVl8s997wpTZuGFfpYcfzr16/vvGt5nXzyydKzZ0/54YcfZOutt5bs7GxZH3RFLkIPL/fbmfp+6rkL/E3/Ttp1kUZ1RuLv9itaqfQPGIsE3yFGr56zFe07XI/X+eef70JDcBBZyom2xKnl1LqOHTsWC7Nz5851LtMvE2zHVaKVTrU2DMMwDMMwDKP6YSJZnbGFkw1zqGjbzoMEDytF8AweXlUXYJ4ZxXmp2vBjkOKh6J19Y0TAz6No3n8bAvrswdjZtQ286RGYbedJ3QRbH+/h1CwyII5GEkYBITfeXSjR8MVef6GCxdNo/S+CMsItCxwckrZq1UqZM+e34hAOq1fPkjlzvpUePVpIRkZnadAgTU4//TS59torpXPnHtK/fze55pqLZaONOsjIkfu7RTb1jo6EetHSL/76649ywQVHyujR50r37lvIv//Od8+wdm22NG4cUJmFMC0POVGWQ88aNGggjz32mBNxu/AQbgdOV5k8ebI7DC0nJ0daJbpKHAXugZB67bXXSrdu3WThwoXl6ovDhg1zzqTXX3+9O+gMb9o333yzOEoAB7oR5nXfffeVDh06OA9inE2P1JhAqSTabrbZZjJ9+nR55513ZGbR/hneIy6EujET0NgwDMMwDMMwDMMwDKO2sCGL6xsS/uGgwTAGFYGd9ziVIoBGC9sRz334ri6q4m368cdfycEHl2wJOffc8JaJo446yommwK72VatWyZVXHi/Lli2TwYMHO1GSc6UgnjAe4XAgX8nq1Xlyzz1XupcydOhQ+TDCKmmzZs2ccHrGGWc48bZPnz7y6quvSsuiEykvv/xyOeGEE9zBXmvXri0TnqAyPPDAAzJ69Gi30x+9ETF2t+DpuB4cKnbnnXfK1VdfLVdccYUcdNBBctZZZzmhFhCdf/75Z3n44Ydl8eLFLpYtnsOkv7ZToehmiLMEIuZlGIZhGIZhGIZhGIZhGLUFPKoJf7ByZckhj4mDQpsWl2gL3C9eT260z/33H1au2Ml2fwRSXtGIdg0VfuGEE452L/9As1jgjBnLIRNv2O8Iuu7BYV+8lPHjx7tXtDQpQdEYEfazzz6L+ox41gafecyYMe7lc8EFF7ifbdu2lRc55bEOEld1uvXWW+X44493Sj7/jwWBfRPhjjvucMGMiT/Rt29fue2222SbYER3j2effVYuvvhimT17tjvw7LrrrnOn2CmEZjjvvPPkpZdecgo67tSkKVh4hmEYhmEYhmEYhmEYRupBrFmkpUiH1MXP+iLRNiQFBaHiuLnR4O+1PSZyvAf8GXVItL3pppvkiCOOcKIt/4+1ApCIaMuJbbha33333S5Oxs033yy77767iy/RJsK+BZT2ww47TK655hrZe++9XdBglP9vvvlGevfu7T7D9T744AMXb4MYG4Rx4JQ74lYQv8IwDMMwDMMwDMMwDMNIbRBQkyGi4tSJ+FueaGsYySaucx0J2qtxK/h/tNcff/yR0M0nTpwoxx13nBxzzDGy+eabO/GWWBPEr4jELbfc4kIynH322c5dmlgVnFp3O8fDesIu8T9wl0a0xUMYD96pU6cmlDbDMAzDMAzDMAzDMAzDMIxaK9rGoqCgwIUkSJR169bJ119/Lbt4R5ISK5ffP//884jf4X3/84Bnrv/57bffXl555RX5+++/nQv7pEmT3IFpsYIWG4ZhGIZhGIZhGIZhGIZh1LmDyDgljhixflDhq666ynm7ItzutNNOLtxB8+bN47rev//+606gIyCwD79zqlskiHsb6fO8rxATF+/ajh07SmZmphOC77vvPhkyZEjUtHDSHS9lxYoV7mdhYaF7GXUQyi4UcsI9P91+BsoyRcqTx9DHSqVHo71RZhpU3JVfyrRDnoHn0oDpoaL3ClOrQkItrpRax+KtU3Xo0RLG2lsdJYUrZU09WqJ2oTL3sP6tjmHtrU5S2fZWHTah4mwg48kU6ttS/NFKtbeanLsF01EVlIxPqub6RmoRitEmEmkjmYmEMjj44INLhSG45JJL3Al2hCq48MILnYDL52oSRNspU6Y4b9suXbrI5MmT5eSTT3YxbYNeugoxci+77LIy7y9atEjWrFlTDak2ks7atU6sXb5+vYTy8yWdRrF4cfjoyBSANQYeiRMsU+nR1haslcKVhbIuM3y8Z2FBoSxetFhWZqbAwwkLQxhnPbqU/y/m+ERJmQqpx7LW4kpJB7l8+XLXgbKol0KPljDW3uooKVwpa+rRErULFcHaWx3F2ludpLLtrTpsQsXZAMaTqTbBSe1HK25v69PWS2Go5uZu+fn5ru3iUMgruYskYQEOp0ORfHeWk2GUB/WQOonza1ZWVqm/5ebmStJF259++qmUIPvcc8/Jrrvu6sRa4JCycePGxS3atmrVSjIyMmTBggWl3uf3du3aRfwO78f6/OrVq+WCCy6QF198UYYPH+7e23LLLeXbb7+VG2+8Mapoe/7557sDzHxP206dOknr1q2lSZMmcT2PUcvIy5PCtDRJy8iQ1llZkk5PSVzmBg0kFcjLI5yISEZGuONPlUfLy8+T9NXpkl0ULX7turXSsnVLaZCVAg8neUURabK9QTexwhukToXUKP+1uL3RcTLQwr7HMxGrQ4+WMNbe6igpXClr6tEStQsVwdpbHcXaW52ksu2tOmxCxdkAxpOpNsFJ7Ucrbm8Z6Rmu7dTU3A1nO4QwdlvzSp5oi0CrIm1GGfHNMKKhO/85Hwy91Cf4eyzirs00AD2MDD755BMZMWJE8e9bbLGFzJs3L+4b04n2799f3n//fdl///3dezRyfj/llFMifmfQoEHu76eddlrxe++++657X1dXeAU7V8ThWO7HOTk57hWE69S+jtqIC8oN0TYtTdKLXu69FCnPoscrfqXKo9HeKDNdvXTllzLtMD3Q6acVvZeeWhUSanmlTKRe1bFHSwhrb3WUFK6UNfloVd3fWHuro1h7q5Mko73V3jHoBjKeTKG+LcUfrVR7q8l2E0xHsiEiQsn4xDxtjfKJ1SYSaSNxf3KjjTaSGTNmuP9z8Nh3333nDv1ScPltkOByEd6txJt9+OGH3bVPPPFEWbVqlRxzzDHu70ceeaTzglXw5H3rrbdkwoQJLu7t+PHj5auvvioWefGKHTp0qJx99tny4YcfyqxZs+Shhx6SRx55RA444ICE0mYYhmEYhmEYhmEYhmEYhlETxC3a4lWLh+ujjz4qxx13nAtJsN122xX/HfF0s802S+jmhx56qAtbQGzcrbbayoUxQJTVw8bmzJkj//zzT/HnEYmfeOIJuffee6Vv374uRMNLL70kvXv3Lv7MU089JQMHDpQjjjhCNt98c7n22mvdgWljxoxJKG2GYRiGYRiGYRiGYRiGYZQPjpVoe1XNQw89JM2aNdsgiiTu8AgIq3///beceuqpTrB97LHHXNgB5cknn5R99tkn4QTgJRstHALespHEYz8sQxDS9uCDDyacDsMwDMMwDMMwDMMwDCM1WLd+nRQUVvRgsvVFcaTT3EFkBQWFkhkqiBkeITM9U7IzNOZ0+VxzzTXywgsvuJ3k9evXd46K1113XSmHSOL1nnnmmc5Bce3atbL77rvLnXfeWezsOHv2bOnWrZtMmzatjGA6bNgw997NN98syYZ84DwpDXcKZ511lowdOzbp99qQiVu0pQIRZiAakyZNSlaaDMMwDMMwDMMwDMMwDKPCgu3Uv6bKyvyVlRBt1xTHtF2/PksyMrJiiraNshrJNh23iVu4/eijj+Tkk092u8ULCgrkggsukN12202mT58uDRs2dJ85/fTT5fXXX5dnn31WmjZt6pweDzzwQPn000+lttGoUSP3MpJHioS/NgzDMAzDMAzDMAzDMAxxHrYIttnp2dI4u3EFX428V+zPch/ul4hnL+FBjz76aNliiy1cCFC2/RMm9Ouvv3Z/X758udx///0yceJE2WmnnaR///5uZ/lnn30mU6ZMSaiY2cnuH9amL+4fiXXr1jmBuH379lKvXj3p0qWL8wyGrl27up+cHcU19PdgeASujSfu1Vdf7TyDCWlw+eWXO4Gas6hatGghHTt2LLVbXtO5bNmy4vcIpcp7eBVHQu/jQ3hXPI0Vwqv26dPHOaS2bNlSdtllF3emVsp42hqGYRiGYRiGYRiGYRhGXSEnM0fqZdaroKdtqMTTNi27XE9bhM7KgEgLiJmAeJufn+8ERqVnz57SuXNn+fzzz0udM1UehF7wz4yaMWOG7LXXXjJkyJCIn7/11lvllVdekWeeecbdb+7cue4FX375pbRp08aJrXvssUep0KlBPvjgAyfMTp482XkHjx492onO3PeLL76Qp59+Wk444QTZdddd3eeqgn/++UcOO+wwuf76653QnJubKx9//LELe1HbMdHWMAzDMAzDMAzDMAzDMGqIwsJC5x26ww47SO/evd178+fPl+zs7DKHbuG1yt+Comx6eunN9KtXry72fOU6nAEFixcvlmOPPVZGjRrlXpHA47dHjx4yePBgJ1Tjaau0bt3a/SRdes1oIEAjAJM2YvUinObl5blQEHD++efLtddeK5988omMHDlSqkq0LSgocGEl9Dnwuq0LmGhrGIZhGIZhGIZhGIZhGDUEsW1//PFHJ15WBDxWe/XqVeq9I444oszn8Nw96KCDnHh5yy23RL0eIQfwfkVoxZt27733dvF2E4XQD76YjOCsojTgpUu4goULF0pV0bdvX9l5552dUMtBbjzHwQcfLM2bN5fajsW0NQzDMAzDMAzDMAzDMIwagNixr732mkyaNKlUiAC8WAm54Md3hQULFpTxcO3UqZNssskmpV7Ebw1y4oknujAHHGyWmRndj3PrrbeWWbNmyRVXXOE8dg855BAndCZKVlZWqd/x2o30Hp7GoAKvH7ogPz8/5j34TjDUgf8dhOF3331X3nzzTdl8883ltttuc2I0z5cSnra4MsfLqaeeWpn0GIZhGIZhGIZhGIZhGEZKg9A4duxYefHFF90BXN26dSv1dw4eQ+B8//33nXcs/PLLLy50waBBgxK+HweaEaOWmLJ4t5ZHkyZN5NBDD3UvBFs8bpcsWeJCHpCu9euJ+5tcNPQCIQ2aF3nCchBZed/BS9mH7/jiMMIwoSd4XXLJJc7TmHw/44wzpM6LtjfddFOp3xctWuRiUGhcDVT/Bg0auEDEJtoahmEYhmEYhmEYhmEYRuyQCE888YS8/PLL0rhx4+I4tU2bNnVesvzk4C6ERYRSRFREXgTbRA4hg/fee0/OOeccueOOO6RVq1bF99L7RBJ427dvL/369XOerHjm4t2rOmDXrl2dmIwImpOTk7RQA3gI4zU8fvx4ueqqq2TmzJkyYcKEmN/Zaaed5IYbbpBHHnnE5c1jjz3mRFzSDhx4RloJi4Buye/omsFwEnU2PAIuw/oi0whkzElzKOy8+D+u07hNG4ZhGIZhGIZhGIZhGEZNs7ZgrawpWFPlL+6TKHfddZcsX75chg0b5gRSfRGf1neiJJ4snrZDhgxxwukLL7yQ8L2IlYtn7JgxY0rda9y4cRE/j4jMoWEDBgyQgQMHyuzZs+WNN94oDl+AkErIAQRWFUeTAd6xTz75pPz888+y5ZZbynXXXSdXXnllzO8Qp/biiy92ojRpzc3NlSOPPLL474jdkydPlr322ks23XRTueiii1z699xzT0m5g8jIiOeee87Ff1D4PxUJd+lIgY4NwzAMwzAMwzAMwzAMozrITM+URlmNZGX+ShcXNnHY+r/G/Y9wqevXZ0lGRpbbZh8N7sd94yUYhzUS9erVc96xvCKBx2u06xByQcFzlVe8HHfcce4VjX322ce9fIL3eOihh2KmSUEQ9sF79/vvvy/1nv+MHJLGy+eyyy5zr0jgUfvWW29JXSRh0Za4EgUFBWXeR7EnGLJhGIZhGIZhGIZhGIZh1BTZGdmyTcdtpKCwrH4Vv2i7kmioTjAsKKgnmZnZMUVbBFvuaxg1JtruvPPOcsIJJ8j//vc/FxIBvv76a3cC3S677JK0hBmGYRiGYRiGYRiGYRhGRUBArbiIimhbWCLappUv2hpGjcS09XnggQdcDA3iWhBsmNc222wjbdu2dUKuYRiGYRiGYRiGYRiGYRiGUY2etq1bt3bBhznBjcDA0LNnTxfM1zAMwzAMwzAMwzAMwzAMw6hm0TYY7Lh79+6SmVnhyxiGYRiGYRiGYRiGYRiGYRiVCY+Ql5cno0ePlgYNGsgWW2whc+bMce+PHTtWrr322kQvZxiGYRiGYRiGYRiGYRiGYVRGtD3//PPlu+++kw8//FDq1atX/D6HkD399NOJXs4wDMMwDMMwDMMwDMMwDMPwSDiuwUsvveTE2e22267UqXl43f7++++JXs4wDMMwDMMwDMMwDMMwDMOojKftokWLpE2bNmXeX7VqVSkR1zAMwzAMwzAMwzAMwzAMw6gG0XbAgAHy+uuvF/+uQu3//vc/GTRoUAWSYBiGYRiGYRiGYRiGYRhGXWX8+PGy1VZbVfl9HnroIWnWrJlsCCQs2l599dVywQUXyIknnigFBQVyyy23yG677SYPPvigXHXVVVWTSsMwDMMwDMMwDMMwDMOIm3UikleNL+5XMa699lrnFHnaaaeVen/NmjVy8sknS8uWLaVRo0Zy0EEHyYIFC4r/Pnv2bPe9b7/9tsw1hw0bVuZ6yYJ7Ej7V56yzzpL333+/Su63oZJwTNvBgwe7ykCF6tOnj7zzzjuy9dZby+eff+5+NwzDMAzDMAzDMAzDMIyaAwF1qoisrOD31yOZer9nFb1ihQVtJCLbiEh2Qnf68ssv5Z577pEtt9yyzN9OP/10t9v92WeflaZNm8opp5wiBx54oHz66adS20BU5mXUoKctdO/eXe677z6ZOnWqTJ8+XR577DETbA3DMAzDMAzDMAzDMIxaQEGRYIuA2riCr0beq7zPZhfdj/vGz8qVK+WII45wGlvz5s1L/W358uVy//33y8SJE2WnnXaS/v37u13un332mUyZMiWh+3z44YfOOzb4OvrooyN+ft26dU4gbt++vdSrV0+6dOki11xzjftb165d3c8DDjjAXUN/D4ZH4Nr777+/27Hftm1bF9Lg8ssvd7v2zz77bGnRooV07NjRPVMwncuWLSt+D8dR3sOrOBJ6Hx88jPE0Vp577jmnW9avX995Le+yyy7ubK6U8LRdsWJF3Bds0qRJZdJjGIZhGIZhGIZhGIZhGEkgR0TqVdDTNuT9nh2Hp23i4REIfTB8+HAnIl555ZWl/vb1119Lfn6++5vSs2dP6dy5s9vtvt1228V9n+23317++eef4t9nzJghe+21lwwZMiTi52+99VZ55ZVX5JlnnnH3mzt3rnupZ3CbNm2c2LrHHntIRkZG1Pt+8MEHTpidPHmy8w4ePXq0E5257xdffCFPP/20nHDCCbLrrru6z1UF//zzjxx22GFy/fXXO6E5NzdXPv74YwmF/PKtw6ItargeOBYNHpbPrF9PxTYMwzAMwzAMwzAMwzAMIxJPPfWUfPPNN04EjcT8+fMlOzu7zKFbeK3yt6Aom55eejP96tWriz1fuU67du3c/xcvXizHHnusjBo1yr0iMWfOHOnRo4cLkYrWh6et0rp1a/eTdOk1o4E3LQIwadtss82ccJqXl+fOyoLzzz/fhV/95JNPZOTIkVUm2hYUFLiwEvocdSW8a1yi7aRJk6o+JYZhGIZhGIZhGIZhGIaR4uC1Om7cOHn33Xdd+IHKgsdqr169Sr1H2IUgeO5ymBni5S233BL1eoQcwPsVoRVv2r333lt22223hNO1xRZblBKTEZx79+5d/DteuoQrWLhwoVQVffv2lZ133tkJtbvvvrt7joMPPrhMOIo6K9oOHTq06lNiGIZhGIZhGIZhGIZhGCkOoQ8QKrfeeuvi99i5ThiB22+/XdauXeu8WIktS3xX39t2wYIFZTxcO3XqJJtsskmp94jfGuTEE090gjFnVGVmRpcESdesWbPkzTfflPfee08OOeQQF6aB2LCJkJVFSIkS8NqN9F5hYaH7vwq8fuiC/Pz8mPfgO8FQB/53EIYRxwnL8M4778htt90mF154oQvP0K1bN0m5g8iWLl0qN954o4tFwWvChAmyZMmS5KfOMAzDMAzDMAzDMAzDMFIIPD9/+OEHd8iWvgYMGOC8Y/k/QiMHjyFwvv/++8Xf++WXX1zogkGDBiV8Tw40I0btyy+/7Lxb4zmz6tBDD3WHpOHJ+/zzzxdrf6SrKsKjaugFP/7ut99+W+53/M9H+g7C8A477CCXXXaZTJs2zYWLePHFFyUlPG19UP332Wcfadq0qatQQHwKToB79dVXowYxNgzDMAzDMAzDMAzDMIwNncaNG5cKEwANGzZ0Yqq+j+6Go+QZZ5zhYsMioo4dO9YJtokcQgZ4y55zzjlyxx13SKtWrYpj4uKNy30iCbzt27eXfv36OU/WZ5991nn3qsdv165dnZiMEJqTk5O0UAN4C+M1PH78eLnqqqtk5syZzlE0FjvttJPccMMN8sgjj7i8eeyxx+THH390aQc8akkrYRE4QI3fFy1aVCacREp42nKyHUo7btIvvPCCe/3xxx8uYDB/MwzDMAzDMAzDMAzDMIyaZ62IrKmGF/dJPjfddJOLJ0scWpwkEU7R4RKFg77wjB0zZowTY/VFXN1oojKHhuGsOXDgQJk9e7a88cYbxeELEFIJOYDAquJoMsCD98knn5Sff/5ZttxyS7nuuuvkyiuvjPkd4tRefPHFTpQmrbm5uXLkkUcW/x2xGwfUvfbaSzbddFO56KKLXPr33HNPSTlP299++83FsMBVW+H/KP+o2oZhGIZhGIZhGIZhGIZRs3JXIxFZKSLrKvD99UVirJJV9EqL8Z1GFZHZivnwww/LvMchZXjH8ooEHq/BeK6RrofnKq94Oe6449wrGuzA5+UTvMdDDz0UM00KgrAP3rvff/99qff8Z+SQNF4+hD3gFQk8at966y2piyTsaUsw4hkzZpR5n/c4kS1RqHhUMiritttu64IhxwKX7J49e7rPc/IbSn+ktOy7777OxRv3cpR2Yn4YhmEYhmEYhmEYhmEYqU62iGwjIkMq8doeCbHo545xfH6bovsaRnJIeAng1FNPde7TeNxqDI0pU6Y48fXaa68tpYbjyhwLAhnjoXv33Xc7wfbmm292bs0EVibORBBOejvssMPkmmuuce7hTzzxhOy///7yzTffFMf8+P3332Xw4MEu7gcqO27QP/30kxN5DcMwDMMwDMMwDMMwjA2B7EqIqHjaFhZ51uLlWa/oWrE8bQ2jhkVbRFMgVkSkv3EiG27L/CzvJDkCG+Nufcwxx7jfEW9ff/11eeCBB+S8884r8/lbbrlF9thjDzn77LPd71dccYWLoXH77be778KFF17o4lQQe0Pp3r17oo9pGIZhGIZhGIZhGIZhGIZRN0RbDiBLBuvWrZOvv/5azj///OL3CGi8yy67yOeffx7xO7yPZ64PnrkvvfSS+39hYaETfRGUeX/atGnSrVs3dw88cqOxdu1a91JWrFhRfD1eRh2EsguF3AICP4UXZZki5clj6GOl0qPR3igzjVfjyi9l2iHPwHNpLJ5Q0XuFqVUhoRZXSq1j8dapOvRoCWPtrY6SwpWyph4tUbtQmXtY/1bHsPZWJ6lse6sOm1BxNpDxZAr1bSn+aKXaW03O3YLpqApKxidVc30jtQjFaBOJtJGERdsuXbpIMvj333+dJ27btm1Lvc/vnBIXifnz50f8PO/DwoULZeXKlS5MA6fLccocwYYPPPBAmTRpkgwdOjTidQm3EClg8aJFi2TNGj/wtFFnWLvWibXL16+XUH6+pNMoFi8WWUkQ8roPaww8Es7sqfRoawvWSuHKQlmXGQ4UX1hQKIsXLZaVmSnwcO400UIvCD7/X1wUGD9FKuS6omerxZWSDnL58uWuA9WTT1Pk0RLG2lsdJYUrZU09WqJ2oSJYe6ujWHurk1S2vVWHTag4G8B4MtUmOKn9aMXtbX3aeikM1dzcLT8/37XdgoIC90ruIklYgAvvJM93u8oNozyoh9TJxYsXS1YWB9iVkJubK/FSoWPt5s2bJ5988okTSYMKMTFvawpNy3777Senn366+/9WW23lYuESPiGaaIsnru/Bi6dtp06dpHXr1i4mrlEHycuTwrQ0ScvIkNZZWZJOT9mypUiDBpIK5OXhmS6SkRHu+FPl0fLy8yR9dbpkZ4fjDq1dt1Zatm4pDbJS4OEkr+jsx2xv0N1SRBqkToUsKjc3Mq2llZJ+goEW9j2eiVgderSEsfZWR0nhSllTj5aoXagI1t7qKNbe6iSVbW/VYRMqzgYwnky1CU5qP1pxe8tIz3Btp6bmbjjbIYRlZma6V3LQeLYq0maUEd8MIxrUQ/qQli1bljljK5EztxKuzQ899JCccMIJrhPk5v4qA/+PV7Rt1aqVZGRkyIIFC0q9z+/t2rWL+B3ej/V5rknGbL755qU+06tXLycyRyMnJ8e9gpDBta+jNuKCckO0TUuT9KKXey9FyrPo8YpfqfJotDfKTO2KK7+UaYfpgU4/rei99NSqkFDLK2Ui9aqOPVpCWHuro6RwpazJR6vq/sbaWx3F2ludJBntrfaOQTeQ8WQK9W0p/mil2ltNtptgOpINERFKxifmaWuUT6w2kUgbSbg1XXzxxXLJJZe4LSOzZ892MW719ccff8R9HUTf/v37y/vvv1/8Hisz/D5o0KCI3+F9//PAQWT6ea45cOBA+eWXX0p9ZubMmUkL62AYhmEYhmEYhmEYhmEYhlGVJOxpm5eXJyNHjkzK6gkhCY466igZMGCAbLPNNnLzzTfLqlWr5JhjjnF/P/LII2WjjTZyMWdh3LhxLsTBhAkTZPjw4fLUU0/JV199Jffee2/xNc8++2w59NBDZciQIfKf//zHxbR99dVX5cMPP6x0eg3DMAzDMAzDMAzDMAzDMKqahJXX0aNHy7PPPpuUmyOu3njjjc5zl9iz3377rRNZ9bCxOXPmyD///FP8+e23316eeOIJJ9L27dtXnnvuOXnppZekd+/exZ854IADXPza66+/Xvr06SP/+9//5Pnnn5fBgwcnJc2GYRiGYRiGYRiGYRhGLYfTTQkqXF0vPU01BcERku3+y5Ytq9L7sKOf+6APGhXwtMXrde+993biKqJoMBDzxIkTE7reKaec4l6RiOQdO2LECPeKxahRo9zLMAzDMAzDMAzDMAzD2MBAQJ06VWTlygpeYD1HnIX/G+LXLJGMrJLgtpFo1Ehkm21KTlUth/Hjx8tll11W6r3NNttMfv755+Lfu3btKqeddpp7Bb+LE2NViJvDhg1zjpXshvedKHGqbNq0adLvZyRZtH377bddRYLgQWSGYRiGYRiGYRiGYRiGUWMUFIQFWwTUCAfPxyfaZnqibXZs0Xbt2vD9uG+coi1sscUW8t577xX/npmZsExXLXCGVLt27Wo6GRscCYdHIJ7sAw88IDNmzHCesJMmTSp+ffDBB1WTSsMwDMMwDMMwDMMwDMNIBATbevWq/lUhYTgs0iKG6qtVq1YVug5OlMEXXrrRuPPOO6VHjx5Sr149F6L04IMPdu8fffTR8tFHH8ktt9xSfB1CFgTDIzz00EPSrFkzee2115xTZ4MGDdw1OAfr4Ycfdvdu3ry5nHrqqbJ+/fpS6cRD2IfrcL1IPFR0Hx++7zuNfvfdd+5Mq8aNG0uTJk2kf//+7vyrVCBhCT8nJ0d22GGHqkmNYRiGYRiGYRiGYRiGYWwA/Prrr9KhQwcnng4aNMjtbu/cuXPC1/HPg1q1apXsscce7nqRQNBETH300Udd2IMlS5bIxx9/7P6GWDtz5kx3dtTll1/u3mvdurUTboMg0N56663y1FNPSW5urhx44IHunClE1jfeeEP++OMPOeigg5yGyJlWVcURRxwh/fr1k7vuuksyMjJcyIhgKNcNRrQdN26c3Hbbba5gDMMwDMMwDMMwDMMwDMNIjG233dZ5kuKpiuhKfNsdd9xRfvzxR+c1qpx77rly0UUXlfruunXrZPPNNy/+XUMXhEIhJ5QSe/aee+6JeN85c+ZIw4YN3XlV3KdLly5O9AS+RygEPGfLC4eQn5/vhNLu3bu73/G0RQhesGCBNGrUyKUPD1h25lelaDtnzhw5++yzpWfPnu53PIhThYRF26lTp7owCLhAE3sjqF6/8MILyUyfYRiGYRiGYRiGYRiGYaQUe+65Z/H/t9xySyfiIqA+88wzMnr06OK/IUgStsAHR8rJkyeXueYFF1wgn3/+ufOmrV+/fsT77rrrru4+G2+8sfPI5YWHLEJtIvB5FWyBMAuERUCw9d9buHChVCVnnHGGHHvssU4w3mWXXWTEiBGl0rVBxbTFzRmX56FDh7pYG6jw/sswDMMwDMMwDMMwDMMwjMT0tk033VR+++23Uu+jvW2yySalXi1atCjz/ccee0xuuukmefHFF2WjjTaKeh+8a7/55ht58sknpX379nLJJZdI3759i+PVxkvQiZM4s5HeKywsLPU73sBBj91opKenl/v58ePHy08//STDhw93TqZ4+JIHG6Sn7YMPPlg1KTEMwzAMwzAMwzAMwzCMDZCVK1fK77//Lv/9738T/i7etXibEhJhu+22i+sANLxSeV166aVOMEbwxEmT8Aj+4WHJhPi4fvxdYvoSGzfW53Nzc12cXkI6ADFrgyB28zr99NPlsMMOc9ol3sMbnGhrGIZhGIZhGIZhGIZhGEbFOeuss2SfffZxoQrmzZvnxFMO0kJ0TIT58+c7gXLkyJGy++67u9+BayF6BiHcKYeEDRkyRJo3b+4ODcMblti6QIiDL774wh0+RqiDSF69FWWnnXaS22+/3R2ShjBMvN5Yh4Ztu+22LgwDYR84PI10EQdYWb16tQsfQTzdbt26yV9//SVffvmli+u7QYZHgOeee04OOeQQp95vvfXWpV7GBk5BAb7qFfsurvh+TBZWdj78kFYY/Tuem32VEXDFd3zzjUhurlQbf/whcsIJImvXJpbuX38ViXDKo1FJWAlMcOuIYRhJYs4ckRUrLDuN1IQxVBV5thiGYRjGBglz6DVrqv6VyFy9CARGBFrEUjS2li1bypQpUyIKrbH4+eef3eFfDz/8sAt3oK+BAwdG/DxetZxHhYDaq1cvufvuu12oBM6tUjEZwZcwA6SFg76SxYQJE6RTp07uwLXDDz/c3StWLN0WLVq4sA8Iy3369HHpJByCQjoXL14sRx55pPO0JR+JFcyhbqlAwp62BDu+8MILXRDkl19+WY455hjnvo2SffLJJ1dNKo26w9NPi/z0k8jVV0f/zL//irz0ElGrRZYvFyHAdna2yCefiLz6qsjjj4vssIPIp5+GP//kk+GfbduKXHKJCK7069aJXH99+P2JE0WK3OSLWbSorKDL9fne8OEEUimbLozsqaeG/3/++Swv4XcvctddImwvOOaYsFD388/ECSFquMj++0d+Rp6LYNtNmoTTrUyZIsKJhs2ahdMzY4YIq2gZGeHnwKiS7k03DX+P50J81XgsnBh58MGS+dYHkt53nEiWZ9wQkQn4zbMh1t54Y8nfzjlHpDYE4mYiyrNGIxSStIL1ItlVMAn2V+/I02AdWLWqbD2KJqBfd134/5zGSXmxWMH1uSZiEid9+tenE8/hnpV8jldeCdcttstw3wkTws+WmSly4okiBJrn948/FtllFwIAlf4+f/voI6Lci7z7rsjhh0duCxsimo+VyQ/aNye40u43FHRRq6L5NmuWyBdfsO9J5D//KamzTz0lsvvuHF8b/l3fZ2GPPgJ7xgm0xOqi3IIsXixSb41Iw5JTd8tAu33iibC91S1kPA+eCe3bV+x5UolIdjJRsFOU3bRpInhulHe4xbp1kr52vUjjyIdmFHP33eFrDhsW7kOrIy/UzvvMmxeuu/77PDPjF8YxQRscDzfcIJKTI7LbbuH7Yq8NwzCMugP9RaSxiVH9UA7Mj1euDOsACcMi6prwfxnyrs8SySia80WD+yVQ/k8x5i0HvF0jgXCp4uWwYcPKxH2NxeDBg+VDHOSigPhJuAUfvG/9e6AJBg9H89Ok+F6x0KFDB3n77bdLvefH0g3eB/bff3/38jnuuOPcT0I5IOSmKglbkzvvvFPuvfdetxpA5p9zzjnuxDkCFy9ZsqRqUmnUTmhITE4ee0zks89K/w2vUCbgI0eWCGLPP88ySViY9Rk7Fh/58DUQOxC9VLDdeOOwSAYLFohce63I3Lmlv3/GGSKnnx6eeMPUqSL33SeyySb40pcWlDGg3J8JnxpbhMTvvgsLcMojj4jsvXfJe4gxiAQIXn/9FX7vzTfDwi2TK1CRmEkaIqmCQHvNNWHBje/stVdYOH700fDfv/oq/PysXgVXsEaMEHn22ZLfyZsHHpC0ApFWBW9LVmidrKnfXLLeeldk9Yqw0EzclptuKn0d0kz6KYeZM8OeokwoKwrp5DoIg5TLvfeG09q5c+lJuYqlTGx5/9xzw0IL4jQCKXlFmiivk06SzKuvk81//krmXHdB9M6QevfDDyLTp5fUr2ifo3OmfE45JTzxZRLcsmVYlOf+7A4gPRdfLLJ0aXixgb8D4jz5Wa9eyfUmTQrXI7+eR4PnRaDHK9tRIDKkpcgR+5UvcAS54oqw4PH+++HfO3YMp9cPUM9Kou/9TSesiyeIxiyUkH7QOsU1BwyInBa+w4IFAjQLKxURb/zOlutFOb20XF5+OVyW1LFkLhhQ90jTjz+K3HFHuG3uF718shfMlQbLWEHfJLw4xWJTjx4lZcdiDm17332lxmERijKj/SFCUld23LH0Z+izKaMicS5n4RKRLlJS//k79YzJBzaDNkeeHXxw2N5iz1ksUMg78hnbyb25rqZD7aSi98WmK888E/7JYgJ1lcUntbeHHMLoMizYwu+/l9RvRC4W80iT1tMLLxNpu1rkspOi5xHfoa/hRf1EAKRcb7stLBZjt2MtMqUylM2YMSIsxldUNFRhVaH8sNm+wEk5M7nh1a6dZJ11nvT6R2TOhV5/HIRBvl4XO8c1KyMuM6bAzlHHeXEt+gfqPn0uC6i33hquc/z067J6cHToIHLppeH2wZgEaAvbbBO+Ptfqv2X0hQu+r9f988/wT/pYYIE62kG/fp/B5/k/Y5/aBvabhRRbDNmwwc7S5nfdteYFLWxcZcYllbmvLZbXfRgXYauDDjGULzabuStj6Ios3NXEAnoQHI9ataq+9FcljNXpiymzCoFou5LMLVrArSeSmR07r7Fv3NcwkkTCPSZu0dtvv737f/369V1AYCBQMuESiE1h1EHwzGPC+/rr4Qk54ikTZgwcXiSIbn36hA0Ug29EO0THolgppWAiz2SfgRkCIiKXirAqOCEYIo4hluA59cEH4b9deWVY7GKizL35yT3PPDMsLvmrQf/3f+G/P/xwWKRUL0kmTcAES0VbJk9cp3nzErELsQXj+/33JeIsHoy8j0evvse1+V3FAsAjl8kbIgOiLJMRVqLIQ8QFH1aNVNjg/njvvvFG+Pe+fcOCsT7/sceGPfV00qfiWqdOIkceGRa+EVMIyP31W64vdVpx+6LWzCqcL9gecUS4jPDoZavDhReWFqZ79RLZZ59wp4wwjShDueBVTDnjAff33+HyQuBTT8SrrgpfgwkY+aD5RAePAE2+cG3ui1eqvzWBv1En9tgjLPJQfnxu7FhJKwx3qB3uf0YWHrSHNPt5hmTd+55In75hcZh0IgSrkE8dQ1jo16+0CEVdoDxYuTzqqPDfKGdeCvWEes9LueCC8M/evcN1kzyjPKgTKhBttVVY6OVeiEXRYPJfLNgWMflHkY06iWRlinRrItKh6H3STJ0iz2l7TCKY1CBycB2EKxWvQFdkscW6YKKCLQIDZcBqI+VJ/vqhHBDMv/wy/FwsGNCus73BB8J6MPQDnz/ttHAaEHB873F/8oPwQtuinlJ3dVJGm+e7lBX55w90+O5zz4UHVF26hL3kyQMEDPKDRR0lmmjLs/M9yr1Nm7IDqXfeCaeJevx//yfp//4rabRpPkcd1kD45FdQtC0slDafvSLrBu8k7V+8U0KLl0jWzDYiSxeW/pyGB8KGUu7YUYQYBr68h9cobZGFDrzssZ+0c+pWMgbFhJEhv6mX2BNsJbbJL0u8DLBV2IVXXpGs116RLXL/kayMLFnXuqWkzZ8vmTv+K7LjsLCIHQ3qVDRxHZjIYOv8cDfduoXzg7/5dRnGjQunHc9xwPsV/M9hN1TUZZHOt3Nnn13y/3btwjsgnMfsUpHXPhXZvKvI8++InD2k9H3pI+ivyBfaC3mHJzvwk3TRNshb+hrqN0KuCtLUFxYJEemxm7QnRF/6HZ5VbS/1DttYEVSUo45yPdot98ZuYadoi+WFasHusQCViAe4enRg/2OJtvSn9O/BHSyk0RdsgXrPi0VN+kHGEUX9WZCNHr9e5u8/RjLyuGbjEqEDux30CsEeYpNB2xKfx3bGs3viyislLRSSJqtXSxq2l7K++WaR886LnJcsGlLmLIQpjJGCi3jUKc/rIz3vIGmYP1fafvu7LDhsn5LP6cIFddpfuKVO0V7JLzxWqNvUVdLAIrCWO+I49UHHKP4CdBDqEOIy7bs6RTO14zxHJOFWBYe33gr3SfQZ8UD9o7w2pN0NtQX6Z+yTLmzHw9dfh9sN7YyFEhY66XMZN9BnMt+gTLG/2KyqXpQC7PXOO4fHH4nAgiTzlHhsDJ+lL6H/ow9hAXXo0LBdZ26jg3lsFnYABwjG/MD7jHvjzWfmO4zvmUuxgEpeV2aMwf15xWsvqkOUJj8ZI2rfHEsc0z5Ud9OxEBfJXpBuxpGMISPBGJf+kDqMbdbxCGPqUaNK8pgxn85RmPvFssdB6DNpA+XZP8YatBkWLBljkN/0izwr80ieJbhQnkj58BmcWShz+gvyhPeor8GdJcmCtkQe8qqKOkQdqbCIimhbmJhoaxhJJi2UiA+1c3zcWJ5//nnp16+fDBgwwLkkn3DCCfLOO++4oMep4G27YsUKadq0qSxfvlyapPpAkEEX7up0QnR8FY2jxqQUUQJDzmSdauV7YKmogYigk6ugCMBkNErMlVKiAMIoItKBB4aNOxP4IN27S+jDD2XJ7rtL85UrJZ1JJRMFPDrvvz88+WFSjYeogrDI+8DnGfSoR656zzDI1LADiGXRxAugo8bzRYUI8hdREK9JIN0MzBBwGaAi0BUF/nYwWcOjCO8/8s0jb0GuLD7mLFnRd0eZ329PGTQkSxo0yw6LDIAgxfORdoTN4LYLBn8MQJIFk1WEN9JcEYo8qlefPlZ+mPqqdH57ins7f32+tGvcXrLSA4NFREkVuhOFOsqkEGENARHxAEGS/KC8y8Of7DLApg0xYaf++oMPJh54oTOwQgR/9AGRNQghOtjJF7noZpF1GSWhPiKBiMo9aVfUdya/eNwiOOEFx3MwuaH+IlAXLao5MR0BSenfPyxm6YD0vfdKFgWwc+o1Tr0H6g9tNei1reIpkxwGv7yYiDOY0/bPoJX2FmmQT90766zwQoTf1oByQRChjTO4Qjj3YRCJVxrPhaCORxltVidfwFZpPCYVbBqLDAy2GXYNHSqrP/hAGqSnS1pwwMUzk37SxqTqlVck/9MvZH7Ro+W3aOsWD9q1D+vuSQNRBpGIwTYLOfGADaS/pVxZBPEXJEAXqGKQX1gg84tE24jtDUEQ73TdFcDgnXL27SYLc6SBe6mtSwTEejy5VVBCLMRuUh+ZhGDnCeWhIWKGDAmLzthkJqV8JuK2MhaA/ind3g49ReTpl8OTdOoDojAejDyXLpQB12dSpgtSyQBhjufAllOH2c7FBIu+MtIECHtEO2Gyjm1hMsnLPVpBuByo+7Rnyp72xostarQx6jaL6FyDfscP58M16Y+iTeS5PjbmhRfCv7Pwhq0jz+mzSDdpZtzwwAPhz5AWbJOK09QHdjTgtYwdoG/wFw2jkF8gxe1Nk9rm8F0k5+P3Sn+QyWqkcncNNRBXn/vTxyDQI3Rj9197LWyb2RF0331uCx6HWOCMUMYuAPVBRYJy2pWzHyzeIkBiq7DPs2eXam9Lh24rf+60tQxpuLk0GF+0COrDAjaTY2wpdi8REBAYS1G+tCf6Sp6J9qL9JmMEQotE2j3F9zQPWBBDHKb8NTwEdYC6wM4jRCFs9W8/iHSbI5KhYxUWEYeIrEkvGZcojGlog6SHMRUCtC5k+2MK/5wM7N3//hcW1uhPaR+MmzREkY7VeAbSe8st4X4Ae8piC/nASxf2eEbaCW2Gukm9DQoF9B0suNH+cGLQ+QB5Qj/LYhHlTB1igZv32U1FXtEOEZZYMNQdCUFxDw9s8pQFLfjll7BN4nfaHP0RbYj3WCDieVkMJy+oi9he0kSauTd9Me0wgd0Bq7/4Xr76LkvSt+hV/GiY2AZ/zgj3L+QPacReYuc1D1hEpw6RDvou0uDnHeMLxrOkGRtA+Bn6dcoAYSramRDB9kvdp55ybd7n+1ybcZu2Ea6FnaNOYRf42+zZsqZdK/nrhYekcOONZfFewyT/rz9lYLMtpP78f8vu+ANsM7Yfu9CnjxSuXi2LcnKkdY8ekq7PR52gn8LZgrqhnnvsDiHj6IsZc+uuLdJM/dTQa5GgLvHyQ5pFo3j3XYHIji1Ftuoj0nvjkvYWqh/+u+7KigTPQV+A4wT2hbKkPdFuGE9STzm4h3yk7tHXFI2fXBqxp5QDbZfv0sZ4j+8zXlWnGuY0WieCbYs84p6M8VjIpv2S94y7yH8WoWjr5B/5Sr1gjKuL+aRb2zt2hHkV7Rx7i42j7SB8MoZBJFcnEsYazAG5J44RPIOK5Thh0Mdir2jzPA/PzrOVVzbci2cmn+iP+S79DHNW2iljmn33lbx6LWTytMaSlZ3mHm1oh1+lfkFu2KaSF7rIhw2nPvM97Ct9CGnh2fzFcPLNH/8qtDXmB+oIAtgH8omfXA/bojCHIX8Zy2OvsGe6CEjbxanHnzeywEL+KW+8IWuy0uTDbmmSlZ4l+YX5MqTLEGnwXdHCNn0iDkLYFPpQykfrIs/NdXG0oSwY89G2Gb8kKg6vWiVrVq6UWUuWuIOo6iWyoBSnpy1jhYKCepKZmV16rKC7x6hLuvhbHvQR9EVVIYInE2wsdUnnoYq2ax07JLozTXfjUSe0/9KwIr4zUR1nzZo1MmvWrIh1MhHNMWHR9thjj3VBgznV7o477nCntO2www7y1VdfyYEHHij3YwzrOBuUaOuvNgPGmk5RPc9YAVfvqSBM5jGuEKlh0TnQ4TOoRWSrqsaHeErjxmAwYGJAv/HGUnjOObJm9mypn5EhafSODJoYpCCE6mRUwaNGB87lbVVXWDFnYMbEAAPDarl6zPAT4wbcm+35xx8fHvQyGaFDxXu4gtAn0G/r+NYNsolKwKCEgSQD2Eixevkg5aETbowjAzMNm4DowmCMLT3kB+XPoJsOnEGqQlmSz0zYGVghClCXmMRg3BFBGcj7MLggLXhWcX2FQVWRJ25ewWqZPPsjaT0/V9o8+4YbmLUdOEyyZ/4eHtTowJ8BAA+OkWdw6g9Q1UsWGJgg7FFXEWfKW73legzmEQQpUyYHXI9BMF4PDKwTqcfcj3RSP1fMF/nhcZF3fhLZdYDIo695LtIS9rpg4I8nFQNa6qlOXmiTTEJj3Tv4bJQtQhflhxgYRCc6kfC3RFNmXJv74xUaiD9UfC8GqTqBYbBKPUdUYRBOnWILme9xwKCUisxknb8FvfISEB/LwIQDUZmFBBUpaG8TJhSLM/UmTJB02jdCEhMYRI4IolJQRJpx8MWy7QEdpEHG2nBbo40wiWayTl1hUsXEgTpD3WbixiSECToe4fqcTGR56RbooO0MentQBgxqKU8WyIKLYj4qyDAQZvKDbdIte7Qj2vKWW0re8cfIjAeul4LttpFG3/8s/3ZoJtu06Sf1Fy8Plz95T50iDjmDMbVpTNSop0xyNZyIphFBkvrLZJp08p6K99QR2gLGiskR16CNxjNgpR7SnzCo88UPJtHkrXplkfcIJY2yRQ5sLfL8VyJf/xIWbf32piCO8ByIvzphQsRlUsFz8zzkJWlmfBOpvBS8TXUhh8UIbC1tItjfAGIIn+W6uuihHogsBgZDDvlwTSabpA2wE9EWSXywnUwmKH/qJosyTE6pF4hvRQdPuMkSiydFYmNE/LjzCvnI85A+8oz+CLvho96oTOjVA5lxFs8wfbrkNWojn3+TI52/eVEaT5/q3i6zSKJ9Bn00ec2EOui9rWmk7VFfy6HwjjtkzTHHhEVb+msENPp26iYTTURf+kx/wYL8YfzkT0J1wqYTGTyu+O769ZJ/4glOtF2+7+7S6vUPShZJth4QzgOdjGNTEDYVf2FLwRbTX5AmBGj6Oco3KH5Gg/aNcHHnnWGxhDYTDD1FWfI8seDZKI/PPxY5urfIoG3C78/9Q6TxziLnertsWDjATsYLz8Nz8vzxHPypu7wqAvYKm8QYEZGFskPU0PAwVETaM+MX2irpSdRTmbpPH0i+0+fRPyM+sTBKPY21uyEa9F14ZyMkIrwA4z0mhni1035JO+VNW6ePot1g65o3l/znX3b926L/niGrO28mK5cVyE4vniJZGVGmhgjUtG0WpIMwzsem6E6SaPgLLuQH/Sft5/LLSz6DXcD+VoJyFyWZvyA8UWci9P3lLuRUFMbZ9DfU60j1lbE1Qpe/6F7eouRRQ0U2HynywFNh20U5sRuAMRDXqeqDc6nPOBLEAnvJHIF6z9g6GrooyfiMPol6EE9b052LyYC6TNtRj1mFfKUdMbbC9jH+iSSaaqz1wO4LxpO/ZW8h//5nhLR+53HpHvq1rAOA9rvcS0OaRSNWO2HewLiJBWKEZOZxwfwijYjdjGXjsc26M42xLPMKbAvjkFtuce3tj2YhabBwqfy17zDp17C71HvRWxzBnjK28BdsNCSiDzaEuTzjR/pwxoz0c/Tj2A7KBPtBf8J4jLQwv8PuzJsna+bNk1mHHSbduneXer6Gg03nGtQl6paOb/2/M57Ueb/24YwxMtNFMleLrC2Q0L/LpaD1RpKZVSTaksfYVGy5D2PVaN7OioZCoh5RDr72oH0vaSIPeI+8U89y2jRpw9ZH2mGYTDSdjL2pc5Q7bSCS/dJn4TPUTY0hzLiI/zOuoK/m+7HGZvTJ5Z2BUEeoMdG2sLDQvTKLDCiBkz/77DPp0aOH87glCHBdZ4MSbQFDTiPjJ53QoEHhxkSnGunQLowkhqi8rUQIZ4gSHJ6lXnXVRV6eFH70kSz7/ntptnq1pCP2qbKJgWUCxGCATiuerU2RwLCSZ1w72mpyJBhQkd8V3S4bS7SNhgoAiDcVbaN0HIhGdJCaZww8qS+ROiaN8UuHTIfq54t6jjAp87bA5eXnyeQ/J0vj7MaSll8gqxfPl0H99pEG/oFrkWBQykBK70Hdo5wRyZNBeQJ+XNABMykvOhhp+o8it0wNi0jBSbreUycTCOiRtupWFkQzv3zVU9KfPAVhYs8quK62IxIwaGXgxsAQoQbhkkFrsFLyTAhfbO3yweucZ2RAgu1BtKTsEIGwQXgvkDbaDXnFxJPBH4M3GgGxg2mH1DU/5rAPCwWzZ0vh1Knyb69e0qpPn7BoG1zAIh0MTGkzxx0nq5u2ky8+L5Q2C3+UdS3ayfwOW0dub+W1fSb/Knr6q9M8K8+HV4UOqvHaQDxAsECc4vmYRETz9iDd1P/yBogKnmWNG0teWkFxe4Pcdblh74jy2ludwGtvq1aLFK4Q+b6pyLwl4QkAwgn2MLCLwQkflH+0wyqZUDIQxeZQRuXZBfVE17A9QAXyvWUUyg/vNPWaRpBh4MpkCptJ+WM38Ujn+3ge0gHQ1nR7JoNrxCeekTpXnvCB94vvdabiFeChhHigXs70+Tp4ByYKCH4MxCMJTuStHtqoIC7RzjUWdJS+DU06Iy9XVi1ZK4MXvyQ5/3dISfx58i9avpMffE4HxvwfG4qHFfZDPYa4PzcqKofCu+6SJe+8Iy0yMyWduhEJ2iviJvnAc/ge/XGw+ruvZcqK6ZLdZWNp+ONMafnIs2ER6aJLSsZIQY9F33ZirzV8VDRbwyINC1Uq4NG/YjtIO/0wdRsP2XjFXR/CR7GIr5Nsyr94olokIl0/TiQnS2TcjaUXSVg0o79n0QpBT0/ERvAmfaSLciFvfY89HxaCETj9OuiH3/Enywis9FXUXfIMIVbPDcDe+rENqWy+kID3P2MInTSriOS7f5NerosNoa/Ay476zqIY/RLjO+oIi4GIWaQDb93yOOmkcLvlXtgCxFH6VHUG0PirLKrQ/itJvmTK/LkFktayheRusZ00mvxGySIJz4R4hEhLWoLjAsQg0oowGAs+R5+vYzSEGOqnetAq6vGIHdTBLQte/F8Xi1mAp01Tl/3FMHbT0Jb5HGPc3r0l/6up8mv7bGm8ZJVkLV0uq5rUl45bDpacLfuF+1tfCMQ+YzPxLmTHwAcfSOHAgbLmzjulfsOGLnxKMYx/EbuoI3ooLOMzroF3OP1C8DBmBCbGLsF2y7PwHNgzFs6o27QTDYHD+FrTibBG/vA5uuiFr4hc8lRRaJHAomQwlirjKsqAeqkCK22ZfGBcTzkjMDI2YN7L2J7rsvhNG6VMqId6IBG2LximhvJkIYl2ETytPSgusmhHm+TZ1JuVcSXtn7zCPqnnJW2NNsl4k3LHPvBTwzbwu3/gEU4kLK4QhoPxHPWN9JMGFhF1xxDzQPJF2zn1m7IlX/z+mbRRDvR1kewu9lBDNGEDqKPqqMQ4gR07RQtK6gSg0TDabdpEsjq2DdsG0olQS19A/QnaOeoFQiZ9F+2O5wDuRZ5TVtR97FVwd4PCbjzGypEW/X0BFbtFOZBn7ARTD1vqFXkbXIylBuZkyvyFsyQ9LV0KQ4Xhvi2n6HDkZBJpN43HmqZNZda++0q39u2lHv0Mz0Udwcb7B5RTJygIDS0WM94t/WfY8zO0dKWsb9hUMlq0lDSuqSG1gHvprhS/f1ExWG0c6aKtBQ9MrwzcWz1eqQ/0FTrXoN+jHZU3/ydfGeeqpzDfJ390TKgLAeVBO/LHvEF8r23SShppI9RrtbWM18zTtnKi7YbABifaVhU0WFbR8bqs7kDmRaLtwpwcaZOdLelMdspVNusOCYu2dQRftE1pEcmRK/L2WpGWG0U/DKwqDheIBe1ED+FJBF9pKbXHskFsL3W8a5i8+QcDRgNhiArvL5BwnWDcO4RP3bINgWuz6Lhw4UJp06ZNadFWbRafZUDBCnD79gk/WsIwSGHwott0Na5yeSBqkAdBz9ME2ODaG9tH3Wy3BtByRuhiMKxbfWNtu4wWC8+vlAzQqbeRKiXtGcEXbzIG4SwCUbf5HhNiBA4djAdRD2TdvsZgnbbB55kAISzj0a6QDiaCiAC8jycMW8TjXUiI8GhV0t6CMJlaulQKW7SIbheSRLW2NzIOb5dodlW9hhFXsJmIEkwkmYDyXY2fizgSKT/0nIDiyX+EcCQ7HiQyZNewkJkoiPPYd+6N8MJLKwGTyODhURqvMh78RTO/P+Ea/g4ovFNZDKEvQEyl/0EgQTxh4ZD8inf7Le0DTzDaB7swNJa+Cs4IKQjNidhy8ghPaRWcNB4+AiL2hr6PcqK9cj/SwKKDR95lN8hXry+QTV8J2yHMUusz/iv1+vUqvYsCqBeIxSwKIAppI/X7dIXFSL5flQc5klj1SPPTos+Wt1wm//WpNK7XVDJyV8mynMKE2psbK/z1l7Tp0EHSdaEk3vEYn2MHGLuzqsR4FfVvoSLvvNfeE3ltrsgRR4XrJgJIVcMzIgSzYM9CfdBrkcURykZFaH4nHB+iMQJstB022glgfxC/EEFjPQ/XZp7JAj99XaJjZfrlYDum76V+seAQTx8Wa7E38Gg/3vqBZKYXyupGraXfkX2kQaMI9pUdkOQV3qnBMyQ0zbTBaLvw6PfZgTFxYrjN0l9jyxTeQ7jmrAfQA6Lp64MORdgSypg8YuHF372pNuy//5W8jdrIF1NflAbL8yRt6VLZalk9yT7xlHBZIkTjaIH3N4K/CuUalgoxGFuF1z07jbC3LFJgVxDssV30Z7pQwLPTb7FQgN3GGYrP/PmnrLniCpk1c6Z0bd9e6gc9tPkdu029ijb2AcRN9RB18xTCg4SF4rAumuHGCaU88H1RlGeh34hHsKZ/9GPZBwVNylh3G1IXaDcaVoG/0X/EI/5iI2lH0RxMSKsu9kbCX9AH+io/XIKmwV9AJa3ki3osk27f65+8re2hIZIAOzZmz55d/Z62b731ljRq1EgGs0rmwjveIffdd59svvnm7v/N1ZOoDmOibQpgom2dxESkOkpFlRa6H/XEriZiira1QUQCBqoMphH1EBFIAANswlaoWJEEYcnaWy2BiS8TWdoCnq2EF2Fwy+S7KiulHnCIJ7eGj8EDK4oXbLloSIUIAkq81Eh7q4BdSIn2lozDXpgwMxHO+1ck50uR174XeR9PdkLFsOW8ji0AMZknFjGCAIK2ioKIndhgvNyT4QTABBnRtSh0RrHncTJ2ApFeJsKRJsM0KI1Pmp4ueR03lckfFspGv0+Wda06yKJmPWTI0LSKPxppieYtXs1Utr1Vh01ImUXJZJKqXim17dFIADGMEUUr0l7VyaEoHJC2t1IxbctraypOqpDFuIfdRHq4tI8K/+hM0Ra3VqyQ9Q0byszPP5c23bpJS9/jFcHSH5vg+e3iL7ULPwtpQFDk2sFFwYJ1IssWiKxaI6FmjaQgs7FkrlkradgFrqkHqAXh+hqPmxd9JT+5D4KzvxhEXqj3bxDdgRBtEYw+RBcz+T8CKb+rOF58ankR3FsFbRVUIx0sD4wNyRMqLUK0ppHnjlRvuKeK7JHgXnw/1o6hFGPx4sWuL9l0000lI1CGiWiOCR+lQgzb64q2u/3www9yxhlnyJlnnimTJk1y/3+Qk0ANwzAMozzosKtRsK0zsEXQPxiLLZhG6qKe9kxGEj3BvDLgIeOfUk17rKhgCwhaRt0hGRMmnTzn4LBRT+SQncMvJyLVQZjIsv2clw8Teo3zngyYAMfaYZMIwUl+LBWIiTaLNApz+vR0WbF1UaiPyhZbrRM3DcOIaoPw/K8oFQ0t6BP0cEYQjCTYan/lj1ci0aSJIIk1695dFiKMtm4tDQhvon2dfwC3iooIxRoeQd8rc1D3epFG4QNeQ5npUkCY0AYNwteNGVbBS7t64fKMeMzyPf+7fEa9eyPZVRV+o6GCsP4fW++L1BoDl88gfmv4AV+s5fl5+XF93eOvLwlLo0QLk6ACdqzDzhF148m3Ok4oFJK8vDwn2DZr1qyMYJsoCYu2BNLFqxaef/552WeffeTqq6+Wb775RvYKxmQ0DMMwDMMwDMMwDMMwjCqkHd6zhH1mO3+sMAhxg5cqImWaE+IKC7MkPT0juQcUVhcIt+y48A8eRVBGQOdvFT3M04gKgq3WyWoVbTloDNUY3nvvPTmSE9Wd13kL5+JrGIZhGIZhGIZhGIZhGNUFYmr79u1daJP8pByEtpo4Vk42KyzMl8WLu0vLlu1rYdiUOMFLlsMbgbjJwXAQRtLIysqqtIdthUVbYtkSBmGHHXaQqVOnytNFJ3XPnDlTOpbntm4YhmEYhmEYhmEYhmEYVQBiWXIEMzxt17P3XwoL10tWVqY7UKrOirZ41V5/fdl46EatJuGSuv322yUzM1Oee+45ueuuu2Qjghm7gwrflD322KMq0mgYhmEYhmEYhmEYhmEYRmUwwbZOkbCnbefOneW1114r8/5NN92UrDQZhmEYhmEYhmEYhmEYhmFssCQs2kJhYaH89ttv7jQ0/u8zZMiQZKXNMAzDMAzDMAzDMAzDMAxjgyNh0XbKlCly+OGHy59//ulO0AsGfl6/npgfdRt9LjtYrQ6TlyeFeXmSu2aN1MvIkHQCkXNQXkGBpAKcBbhqVcnvqfJoefl5sip3leSnhwPHrytc59phQVYKPJxwgCOFpkHx11FyIlKQOhVSA/5zAmktrZQsNObm5sYdj6oOPVrCWHuro6RwpaypR0vULlQEa291FGtvdZLKtrfqsAkVZwMYTyop0rel+KMVtzcldeZufnsLh4EN24UVtdAuGHUN1RqDmmpSRNsxY8bIgAED5PXXX3cn8yHUpho0RujUqVNNJ8UwDMMwDMMwDMMwDMMwjBTTHps2bRrzM2mheKRdj4YNG8p3330nm2yyiaQqrK7OmzdPGjdunJKi9Ia0eoHwPnfuXGnSpElNJ8cwjFqA2QXDMMwuGIZhYwXDMGwOYdQUyLAIth06dCjXczthT9ttt93WxbNNZdGWTOvYsWNNJ8NIEgi2JtoahmF2wTAMGy8YhmFzCMMwTFswapryPGwrLNqOHTtWzjzzTJk/f7706dNHsrKySv19yy23TPSShmEYhmEYhmEYhmEYhmEYRkVF24MOOsj9HDVqVPF7hBDAvTdVDiIzDMMwDMMwDMMwDMMwDMOoM6LtrFmzqiYlhpFkcnJy5NJLL3U/DcMwzC4YhmHjBcMwbA5hGIZpC0ZdIeGDyAzDMAzDMAzDMAzDMAzDMIxa5GnbuXNnGTZsmAwdOtT97N69e9WkzDAMwzAMwzAMwzAMwzAMYwMkYU/bxx57TCZPniwffvih/Pbbb7LRRhs5AVdF3B49elRdag3DMAzDMAzDMAzDMAzDMFKcSoVH+Oeff+Sjjz6S1157TZ5++mkpLCy0g8gMwzAMwzAMwzAMwzAMwzAqQXpFvpSXlyfvvPOO3HbbbXLLLbfIc889J71795ZTTz21MmkxjHLp2rWrpKWllXmdfPLJ7u9r1qxx/2/ZsqU0atRIDjroIFmwYEGpa8yZM0eGDx8uDRo0kDZt2sjZZ58tBQUFlvuGUUdZv369XHzxxdKtWzepX7++C9tzxRVXiL8myf8vueQSad++vfvMLrvsIr/++mup6yxZskSOOOIIadKkiTRr1kxGjx4tK1eurIEnMgyjsuTm5sppp50mXbp0cW1+++23ly+//LL472YTDCO1YWfoPvvsIx06dHBzhZdeeqnU35NlA77//nvZcccdpV69etKpUye5/vrrq+X5DMNIvl144YUXZLfddnNaAn//9ttvy1zD9Aaj1ou2DHqpxOedd56rsPzE43batGly0003VU0qDaMIJlzUN329++677v0RI0a4n6effrq8+uqr8uyzzzov8Hnz5smBBx5YStxBsF23bp189tln8vDDD8tDDz3kBm2GYdRNrrvuOrnrrrvk9ttvlxkzZrjfmTSxsKjw+6233ip33323fPHFF9KwYUPZfffdXT+mMDH76aefnF1hBwkDu+OPP76GnsowjMpw7LHHurb86KOPyg8//OAmYYgyf//9t/u72QTDSG1WrVolffv2lTvuuCPi35NhA1asWOFsC4tDX3/9tdxwww0yfvx4uffee6vlGQ3DSK5d4O+DBw92c4lomN5gVDuhBGnevHmoZcuWocMOOyx0zz33hH755ZdEL2EYSWPcuHGh7t27hwoLC0PLli0LZWVlhZ599tniv8+YMQNXu9Dnn3/ufn/jjTdC6enpofnz5xd/5q677go1adIktHbtWisZw6iDDB8+PDRq1KhS7x144IGhI444wv0f+9CuXbvQDTfcUPx37EVOTk7oySefdL9Pnz7d2Yovv/yy+DNvvvlmKC0tLfT3339X27MYhlF58vLyQhkZGaHXXnut1Ptbb7116MILLzSbYBgbGPTvL774YvHvyRoX3HnnnW5u7M8hzj333NBmm21WTU9mGEay7ILPrFmz3N+nTZtW6n3TG4yaIGFP28WLF8sHH3wg2223nbz99tuyww47uMPIDj/8cLnvvvuqRlk2jAjgLcvBeKNGjXLbF1jhzs/Pd540Ss+ePaVz587y+eefu9/52adPH2nbtm3xZ1hVZ6WclXTDMOoe7AB5//33ZebMme737777Tj755BPZc8893e+zZs2S+fPnl7INTZs2lW233baUbWDr44ABA4o/w+fT09OdB45hGHUHQh6xs4btyj5sgcY2mE0wjA2bZNkAPjNkyBDJzs4uNa/45ZdfZOnSpdX6TIZhVD2mNxg1QcKiLeLYlltu6eLXEsv2zTfflF133dVtRx8zZkzVpNIwIkAMmmXLlsnRRx/tfmfwxaCJAZYPAi1/08/4gq3+Xf9mGEbdgzA9I0eOdIs0WVlZ0q9fPxfLkm2NftuO1PZ920CMa5/MzExp0aKF2QbDqGM0btxYBg0a5GJbEyYJAZdFXgQWQiuZTTCMDZtk2QCbVxjGhoXpDUZNkJnoF7755hv58MMP3QtvBQ56wHNx7NixMnTo0KpJpWFE4P7773eedAQSNwxjw+WZZ56Rxx9/XJ544gnZYost3KEBiLbYhqOOOqqmk2cYRg1ALFt24rAbLCMjQ7beems57LDDnJeMYRiGYRiGYaSkaLvNNts4LyYE2uOOO85tCWE7iWFUJ3/++ae899577oRHpV27di5kAt63vrftggUL3N/0M1OnTi11Lf6ufzMMo+5x9tlnF3vbAguJ2IhrrrnGibbatmnrnBKt8PtWW23l/s9nFi5cWGaLNSdHm20wjLpH9+7d3YGkHCpCCCTa/qGHHiobb7yx2QTD2MBJ1riAnzqP8K/h38MwjNTB9AajToRHoKP68ssv5cYbb5R99tnHBFujRnjwwQfdlqXhw4cXv9e/f3+3NZrYlgoxpebMmeO2SQI/OUXaH4RxImyTJk1k8803r+anMAwjGeTl5bkYcz541hUWFrr/d+vWzQ2yfNuAiENMOt82sODje+ERv51rEOPOMIy6CSfCI8oQX5KzGPbbbz+zCYaxgZOscQGfmTx5sjtTw59XbLbZZtK8efNqfSbDMKoe0xuMOuFpi7gFdGAzZsxw/0fsYtuZYVQHDJYQbfGgI7aUgsf36NGj5YwzznDxpqirhO1gQMXBebDbbru5+vrf//5Xrr/+eheX5qKLLpKTTz5ZcnJyrAANow7CAuJVV13lDh0kPMK0adNk4sSJbmu0xmInXMKVV14pPXr0cJO1iy++2IVP2H///d1nevXqJXvssYfbQXL33Xe7Cdgpp5zivHctBIth1D0QaDkcGvHkt99+cx75xL0+5phjzCYYxgbAypUrXdv3Dx8jfBJzBMYLyRgXcBD3ZZdd5uYf5557rvz4449yyy23yE033VRjz20YRsXtAg6KOHwRD18dwIBFHl6mNxg1QihBFixYEBo2bFgoLS0t1Lx5c/fi/zvttFNo4cKFiV7OMBLm7bffDlF1f/nllzJ/W716deikk05y9bJBgwahAw44IPTPP/+U+szs2bNDe+65Z6h+/fqhVq1ahc4888xQfn6+lYRh1FFWrFgRGjduXKhz586hevXqhTbeeOPQhRdeGFq7dm3xZwoLC0MXX3xxqG3btqGcnJzQzjvvXMaGLF68OHTYYYeFGjVqFGrSpEnomGOOCeXm5tbAExmGUVmefvppZwuys7ND7dq1C5188smhZcuWFf/dbIJhpDaTJk1y84Xg66ijjkqqDfjuu+9CgwcPdtfYaKONQtdee221PqdhGMmzCw8++GDEv1966aXF1zC9wahu0vgnEZGXeGB//PGHPPLII24FEqZPn+68HjfZZBN58sknq0pfNgzDMAzDMAzDMAzDMAzDSHkSFm1xCecAqIEDB5Z6n8Od2HpO7B/DMAzDMP6fvfMAj6ra2vAKnVAVqaKAioBKUbFgAUXsDXv7FVGxoSJ67YrX67V3QcHuxYYdO2JBQcWCioVqAVER6TXUZP7n3eMeTk5mkpn0mXzv8wwkkzNn9tl17W+vvbYQQgghhBBCCFFOB5ERT5TDnsLwnj/0RQghhBBCCCGEEEIIIUQ5iba9e/e2QYMGxYIzw59//mmDBw+2/fbbr5jJEEIIIYQQQgghhBBCCFGs8Ai///67HXHEETZlyhTbYostYu/tsMMO9vrrr1vr1q2Vs0IIIYQQQgghhBBCCFFeoi3wEeLaTp8+3f3OgWR9+vQpbhqEEEIIIYQQQgghhBBClES0FUIIIYQQQgghhBBCCFE21EjlYg4ae/LJJ+2VV16x2bNnW1ZWlrVr186OPfZYO/XUU93vQgghhBBCCCGEEEIIIcrB05bLDj/8cHv77beta9eu1rFjR/fetGnT7IcffnBxbkePHl2CpAghhBBCCCGEEEIIIYRI2tMWD9vx48fbBx98YPvuu2++v3344YfWt29fGzlypJ122mnKVSGEEEIIIYQQQgghhCgm1ZK98LnnnrOrr766gGALvXv3tiuvvNKeeeaZ4qZDCCGEEEKIcuP00093TgcVBaHFbr755qSuPfHEE+2uu+4q8zQJIYQQQog0DI/QokULGzNmjHXr1i3u37/99ls7+OCDbd68eaWdRiGEEEIIIZKmqHMWrr/+ehs8eLAL9dW4ceNyz9nvvvvOOT389ttvVr9+/SKv//HHH61nz542a9Ysa9SoUbmkUQghhBBCpEl4hMWLF1vz5s0T/p2/LVmypLTSJYQQQgghRLH466+/Yj8///zzNmTIEJsxY0bsPYTSZMTSsmLo0KF23HHHJZ2GHXbYwbbeemt7+umnbeDAgWWePiGEEEIIkUbhEXJzc61GjcQab/Xq1W3Dhg2llS4hhBBCCCGKBTvE/AvPVDxvg+8hlobDI+yzzz524YUX2sUXX2ybbLKJc0h45JFHbNWqVda/f39r0KCBbbPNNvbOO+8U8IJltxn35DOEPVi4cGGhNvVLL73kDvgN8uCDD1r79u2tTp067j7HHntsvr9z/ahRo1QjhBBCCCGqCEl72rJ9DOO2du3acf++du3a0kyXEEIIIYQQ5cr//vc/u/zyy+3LL790HrrnnXeevfrqq3bUUUe5sx3uueceJ8rOmTPHsrOzbenSpS7MwVlnneX+tnr1arviiivs+OOPdwf1xuP777+3ZcuWWffu3WPvTZo0yS666CJ76qmnbI899nA73CZMmJDvc7vuuqvddNNNzuZOZI8LIYQQQogqKNr269evyGtOO+20kqZHCCGEEEKICqFr16527bXXup+vuuoqu/XWW22zzTazAQMGuPcIszB8+HAnvO6+++42bNgw23HHHfMdKPb444/bFltsYTNnzrRtt922wHcQx5Ydas2aNYu9hwhcr149O+yww5xHb5s2bdx9g7Rq1crWrVvnzo/g70IIIYQQIrNJWrR94oknyjYlQgghhBBCVCBdunSJ/Yyw2qRJE+vcuXPsPX++w/z582MHio0bNy5ubNpffvklrmiLNy6essHD0vbff38nxG611VZ20EEHuRfevXjzeurWrev+z8nJKbXnFUIIIYQQGRDTVgghhBBCiEymZs2a+X5HWA2+54XWvLw89//KlStdrNnJkyfne/3000/Ws2fPuN+B5y7CK16zHrxrv/nmG3vuueesZcuWzqMXr1/CL3gImQBNmzYt5acWQgghhBCVEYm2QgghhBBCFIOddtrJpkyZYm3btnWHlAVfhDuIR7du3dz/U6dOzfc+B/726dPHbr/9dhd+Yfbs2fni4nLgWevWrZ3oK4QQQgghMh+JtkIIIYQQQhSDgQMHOg/Yk046yb766isXEuHdd9+1/v37W25ubtzP4CmL2PvJJ5/E3nvzzTft/vvvd166xLwdOXKk8+bt0KFD7BoOJjvggANUTkIIIYQQVQSJtkIIIYQQQhQDDgf79NNPnUCLoEr824svvtgaN25s1aolNrPPOusse+aZZ2K/c/0rr7xivXv3tk6dOtmIESNcqITtt9/e/X3NmjU2evTo2IFoQgghhBAi88mKRCKRik6EEEIIIYQQVQUOI8OL9vnnn7cePXoUef3w4cPt1VdftbFjx5ZL+oQQQgghRMUjT1shhBBCCCHKkbp167oQCAsXLkzqeg5DGzp0aJmnSwghhBBCVB7kaSuEEEIIIYQQQgghhBCVCHnaCiGEEEIIIYQQQgghRKaItmvXri29lAghhBBCCCGEEEIIIYRITbR95513rF+/frbVVlu52FrZ2dnWsGFD69Wrl9100002d+5cZakQQgghhBBCCCGEEEKUdUxbTqu94oorbMWKFXbIIYfYrrvuaq1atXKHKCxevNh+/PFHmzBhgk2cONFOP/10u/HGG61p06YlSZcQQgghhBBCCCGEEEJUSZISbXv06GHXXnutHXzwwVatWmLn3D///NOdbNu8eXMbPHhwaadVCCGEEEIIIYQQQgghMp6kRFshhBBCCCGEEEIIIYQQaXAQmRBCCCGEEEIIIYQQQohKKtq+9tprNnLkyNK6nRBCCCGEEEIIIYQQQlRJSi08QseOHe2nn36y3Nzc0ridEEIIIYQQQgghhBBCVEkU01YIIYQQQgghhBBCCCEqEYppK4QQQgghhBBCCCGEEJWIGql+YPz48YX+vWfPniVJjxBCCCGEEEIIIYQQQlRpUg6PUK1aQefcrKys2M+KaSuEEEIIIYQQQgghhBDlGB5hyZIl+V7z58+3MWPG2C677GJjx44tQVKEEEIIIYQQQgghhBBCpCzaNmrUKN9rs802s/33399uu+02u/zyy5WjQgiRgH322ce9UoXdDP/+97/TMl/btm1rp59+ulUGZs+e7fLyySeftEyCZ7rgggusKpc5ZUo+UMaVAZ6ffKjoeuyvvfPOO8skLSKz+qSKTk9xx8jCYOwM7ggsb37//XerU6eOffrpp7H36BtIU2XruysLo0ePjuUPr0mTJlkmUNHtS1Qem2L33XeXbiKEKP+DyJo3b24zZswordsJIUSF88svv9g555xjW221lZt0NWzY0Pbcc0+77777bPXq1VaV+eGHH+zYY4+1Nm3auLzZfPPN3QLe0KFDLZPYeeed7fzzzy/SsM+USWWyfPbZZ04MWbp0aZl+z7p161x723HHHV37a9y4sW2//fZ29tln2/Tp08v0u9OJt99+u0wXdubMmWPnnnuuE5tq165tzZo1s759++YToorDgw8+WCUEjGeffdbuvffeMv+eBQsW2KBBg6xjx45Wt25dV0677rqrXXHFFbZy5cpyT095kZOT4+r/Rx99ZJWN//znP7bbbrs52yHI3nvvbU899ZT169evgKgXfI4PPvjAzjjjDNt2220tOzvb2SNnnXWW/fXXXwW+C8E7KHb610EHHVTg2rVr17p60apVK1dXSON7771nFQH9SrD/6t69u8sb+vnicPfdd7vnfv/99xNe88gjj7hrXn/9dasKUKfi1Q1eJ554YkUnr8pB23vggQds3rx5FZ0UIUQmHkT2/fff5/udkLgYDrfeeqt169atNNMmhBAVxltvvWXHHXecEyhOO+0022GHHZyA9Mknn9hll11mU6ZMsYcffrhc0oJAXKNGyt11mQp2++67r2255ZY2YMAAa9GihfMm+vzzz53AduGFF8auZTEvXiz0dICx7dtvv3WTblGwDtxwww3OoxQhNUhplvkxxxxj77zzjp100kmurq1fv96JtW+++abtscceTpyCU0891U08aa+ZDgsl9Ak1a9bMJ9oyASwL4RZh9pBDDnE/IxZtt912bqKJ2IrwFG7zqYq27NiqLN74ZQUi6Y8//mgXX3xxkWVZXBYvXuzEruXLlzuRj7axaNEiZ7cPHz7czjvvPKtfv365pae8RVv6Iwh76l577bV25ZVXVki6ENH/97//uVcYxNf/+7//S0rcoWyxR9q3b2+//vqrDRs2zPWBkydPduNvkNatW9stt9yS7z2E2TC0uZdeesnVAe5Le6adjxs3zvbaay+rSHgG8mbDhg3FsrMYC7DTqOd9+vSJew1/a9KkiR188MFWXlSG9nXRRRe5kIZBympXSDpT1jbFkUce6RaiGQNlYwohiiJlFQBhllW58PlluPk//vjjqd5OCCEqHbNmzXLGGgb2hx9+aC1btoz9beDAgfbzzz87Ube8wJO1MnHTTTe58DhfffVVAcGOOOdB0llEQywk73v37l3RSUkrSqvMqV8IE9S3q6++Ot/fEC2CXr7Vq1d3r6oANlh59QmcXYBHPZ54iLdbb7117G+XXHKJHXjggU70wSMdEV1UXFk+9thjziOacgqXBUJurVq1yjU9lQUWPCtq0fPpp59233344YcX+x54jSKiBhfC8Jzt1auX6wf/+9//5ruesbkoMfjLL7+0UaNG2R133GH/+te/3Ht+cZpQdyzKpTOI1Cwsv/LKK27BIjwm/fnnnzZ+/HjnyVsSATUvL88t5ifbZipD+2KhjT49GRDNecZk+o5Mo6xtCtoz5TBy5Ei34FSRIVyEEJWfasURM1jl5X9ev/32m1vhZoD3Hi9CCJHO3H777W4rKZPgoGDr2WabbdwWVM8TTzzhhD22ojI5wBONiUIyrFmzxnnHsfURY57vO/roo11ohkQxbRPFy4wXu8/HzHvxxRdduhBfevTo4cIbwEMPPeSeh+/GQymZ+F2kjS3qYcEWyIOi4pvi+cWEk7TgUcOkkzwMxw/js4cddpjzbmaLL2nEOwkjNwheSEw8O3fu7DzJ8F7Ae+a7776zkoAwz8SPdKYCXoj9+/d3z0Z9oEzxqgjnLR4W5CPXMMlkQSAcbuCnn35y3qZ4U/H83JMFhWXLlhX43meeecY6dOjgrkNEY1IahskqXniENOJ7+f54C66EueBvbMfdZJNNnAcfnkm+nuHFBO3atYttsfTPF6/Mea7BgwfHttfzHIgECxcuTJiPvg2EtxUDkym8pAqLP8dkk7SStzwHZTl16tQC6fOfRexCiGzatKnVq1fPjjrqKOcpF+S1116zQw891N2T50DEvPHGGy03N9dShe/iGYKL4Hiskpb7778/9t7ff//t3vN9SjguIs+Cly0Et7yGwWON9JJuPK0QxYuC/oH6jLgTFGyBdoEHId8V9BTy+Un9I7wMz0ibpLwRgT2UAzsWPv7441iag16S2Jp4F2666aau/HAOiLdYhh16xBFHuDKj/6GevfvuuwW2mcMXX3zhBC+ELe5JPxQO8eD7URbnvCc519OmsXfjCXO0N/KDtNI+2Xng4ZlIN+n0z+n770QxLvEmP/74411d5L6062uuuabQsqK90C7IpzDkvxeLUk0PeUC/iiBMf8zPhMPxdY6xhPGP/Geh0/cT4fwsTsxIBLEhQ4a4/KUM+A5EJ7xBPXyefAIvfgTHzHjfjxhFu/XtgednYYiQAUGSHYMKi81K2AHv4VwcevbsWWDnAu9R16ZNmxb3MzxfMBxGGDxsqSvB8AM825lnnmkTJ07MV39LakOkMoaVJgjXfEe8PgPBmvHhlFNOcb8T85uFDvoqno/6Rh6F8XnBWOvHbhZ3qSeM8fHsO+ot/WBR7YuxmZAz/Ex9xqYJjyt4zuMB6kMFEVoDO6c04uQG458TOsW3DcZM3ychMlLvKEdsgnihJejT6Q+C9h02RritJzqrIZH9wOLgFlts4dJEfeMsHcowXvqTGeuK6mMT9U+UN30QfVGDBg2cPcAzF8cGJKQY/TAe80IIURgpLz1jkAkhRCbzxhtvuIlZsp5jiCkY8AgHeNXweeKgYlAixCUCg5wJITHrmMQgBK9YscLFlWPralgkKS4TJkxwxrVPC1sn+V48ahAOSStiCmI1gh7exUWNA0zsSCOeOanAxATxDGP4qquucobvo48+mtA7E+GEiQKTSSYoGP8Y9EyqyHMv7jA5RuBBRETkYiKJIMOEI97W0KJgGz7x8G6++eaUP8sEFSMeAY4JCN7HlCmihxdHmKwgMLB1k23LhBSgHjG5QETC+wfBAk9GhATuxaSX/MP7lEkMk0EPwtfzzz/vtj6Sl5Qr4hQeVb6MyBcEHT/xZLLCBIS8xRPPb5Um1h/3Id+pk0w8EdoRvE4++WS3qDBz5kx77rnn7J577nHb28ELJ2EQD5jkIDBQv3baaScn1lIn//jjj9jnE9kbTJARblP1lqN+UafxciMfmdzyP88TD/IYgfr66693kysmruQT+RqcyDGpRnDlf9oKohL5h7CZCuQJ+Udd8WVEW0Wg4X/KwL/nhZp4IAjMnTvX1THiQMYDIY2+hWspf/KFcqTtFOZpRl/GBJ3JbTxob3gBkg9s+w0ucJB3CAvUdV+/maD62IrkL3lOPvrJMosJvq7S/yKSkg+IKQjE9LGIKQjqsGrVKicQEMqEukob4VmDop6HNLKYQ99BGZPPfsGNPEaUC8Iz83z0l998843rpxCFEQs8eIFfd9117lpCRyDys+BBWRFahefn2RCPqOuUNxQm4tHWqBuUC6IafQaCLGXB9yWC9sKYEo6TGibV9AD3Je94LuoObZLypf/mfohf1KcRI0Y4cR5Rj7wrKbQr8t2HR6EOs5hKO6ZvY/cf/Y4P/0C9IB3QpUuXhPelrKhP9HGXXnqp69soZ/qoV199NeUxKNEYQn9Oukob+lRe8fpO+mbKhfGD9kS+0UcF2zl1k4VixL8gvg0gIiGQldSGSGUMK22oB+Q9/YGvEx7eo734BUFCvNC3UI9JM6Iu9gTpRJQLwrO98MILrv6T/9RzBGKenQVkRE0PbZY6XJTnM+2LfELgR3TE9rjrrrucDejrD/YkYxn1nvdwlGIRsbC2Hg/aUHixNJhm+kTGSPoebAn+xhhFXrFYQ6gR6hd5gMj88ssvx/pjxErsOxYN/HUIqKkufAdhDMCWo94wfhGWC0cxxnf6/XBs7mTGuuL2sb5vpawYB0gbfQ9jIG3K23fJ2IBAHwLYfMTtF0KIhERKia+++iry8ccfl9bthBCiQli2bBlub5Ejjzwy6c/k5OQUeO/AAw+MbLXVVvne69Wrl3t5Hn/8cfddd999d4HP5+XlxX7mmuuvvz72e79+/SJt2rQp8BmuCXfr/F67du3IrFmzYu899NBD7v0WLVpEli9fHnv/qquucu8Hr43H2LFjI9WrV3evHj16RC6//PLIu+++G1m3bl2Ba0kn6fVceOGFkaysrMi3334be2/RokWRTTfdtMB381neGz9+fOy9+fPnu+e59NJLY++tWbMmkpubm+97uQ/X/ec//8n3Hvd74oknIkXxwQcfJJUX3IvrGANhyZIl7vc77rgj4Wd4hlq1akUOOOCAfOkeNmyY+yz1Asgjfn/xxRcLTQPX8Jo0aVLsvd9++y1Sp06dyFFHHRV778wzz4y0bNkysnDhwnyfP/HEEyONGjWK1WPq/vbbb1/od/J8ifInXOZDhgxx177yyiuF1vN4f6O98NnmzZtHTjrppMgDDzzgni1ROfj0zJs3L1KjRo1I3759813373//210XTJ//bJ8+ffKlZ/Dgwa6OL126tNC2fs4550Sys7NdPSyqjYbrAd/74IMPut/5nmrVqkWOO+4497yeiy66yLUPn7Z49XjgwIEF2n7w2iZNmkQWL14ce/+1115z77/xxhuFprFx48aRrl27FnoN6eNe33//fb783HnnnfP1Cbfffrt7n+/2UM+CfaLn4osvdtdOmDAh9t6KFSsi7dq1i7Rt2zbWbu666y533ejRo2PXrV69OtKxY0f3/rhx49x75F379u1dvxwsY8qTe+6///4F+tEzzjgjX5poS+SjZ/bs2a5+3HTTTfmu++GHH1zdC75/6KGHxq0P8cqyZ8+ekQYNGhSo54W1FV/nmzZt6u7H85977rmRZ599Nl/9LU56qMu8d/PNN8feo5+rW7eu68tHjRoVe3/69OkFxqt441K8NhtvjNywYUNk7dq1+T7Hd9M+guWzYMGCAt+b6PsnT57sfj/rrLPyXfevf/3Lvf/hhx+mPAbF4+eff3afHTp0aJF9ZKrceOON7t6MU0HIE/q4l19+OTJy5MjIEUcc4a47/vjj811Hu+vdu3eB+06ZMsVdP2LEiFKxIZIdw5IdX1OFvpRxELsuXEdJa6J+nX5rhx12KJBHfI4+mnwKMmPGDPe34cOH53uf/Ke/Kqzv9u0raKvAjjvu6PpQD2XKdffee2/sPfpB0piMXUNf6G2F8It0+bQ1bNjQ1fEg++23X6Rz5875xjieaY899nD9arjf/uKLL2LvcS/si3BbT9Rew22Dul6vXr3IzJkz81135ZVXuv53zpw5KY91yfSx4f6J8YfxcMCAAQX6XZ7Pv5+MDRgEW/C8885L6lohRNWl1E6HYbsGq2tCCJHO4BUBbHtKlqAXAR5MeDHgGcDKfmFbAPFQwFMj3iE+pRnfar/99su3uo83h/cGCD6nf590FwZbuvC0xTMF70U8GfA8wAujqJOYx4wZ47ywggdX4snhtymGYTsmHhEevKrYxhZMI94gfvsoHitsIcRzjOvwkCsOHOzEd6d6QAd1gfhveBMGt4IHwYsGbx48W4PbXvGIwvPJb+f0Xkhs9Y63LTsIeeq9NgBvFLbj8VnyhDkS9Q1PHX6mjvoXZUc99XmFdyBeeMlsn08Gvrdr164xb5xk6zl/I/1sr8QDFs9ePL3wkDrhhBMKhJIIgvc63j54gAUp7MAsPG6C6aHekXd4h8Zr695jiesoH7ZbpgJ1GW8pH8YCbxu2LBN6Ak9TthV7Lzc8eUrSJ5Bf5GHw2ZJp6zxjUX2h/7vvOz3heJF4h+EtTdsqCq7B6y94IBJtmnviBe237NKf0O/QF3nwDKYtBcFzkPzEU5z+wdd9PHXpHymD4FZbOPfcc/P9Tp7xWf+cxMvkM3jZBtsT3oQc7BTP27co8NQlLXgr0oaDFFX+eFXSH5Nu+h68XnlevIMJBRA+iyJV8E710EfQv+JJF/TC5j3+VlS9Shbag4+nSV7jyUi7Zmt2Sfp2wFs+CB63EN5On8wYFA/qCgTbXWlA/WCXBvkejreOFzJe5HgWMi/DE5O2gFckB4V68IqPt7vFh9Dg76VhQ6QyhpUFeLjiNUpb9fjwHUGbI9iv03YYDynzeHUM2446EQSvZZ4dD3QPdZWdLHxPMn13vP4mWMfo6+hPg30b9kNhu7nigdc1Xp/BV/AwO8o0uGuG58C7mPrmxzxe1G9sB/pVvGB922I3T3DXAvdKZN8lA2E5yAvaUbCfZZcS43M4DFRRY11x+1jyCZsDr/9gOuijKHvf3ydjAwbxzyWEEIVRaqItE6TSMtKEEKKi8NsFMU6TBbEFA5IJLBNWjFR/cFJhoi3bsZj8lfUhKWHD1E+kgtsfg+8nY2gSJ4yJENeyXY+tauQZ20i9oBIPBDDikYWJ9168tHsjN5hGJvNs80UoYSKKEE4ZsAWuuHHzmLiHt0UmA9/Ptjkma4gofjsx2wY9XgSk7INg6BOWw/+dbZcIC2wP5pmYIBFHMt4z8exhmEgyUWaSwosJB1sVyZvgi9hrwUPkOK0cgYyJF/dlUhiO+5kK1PNUw2gE85Pt12xbJgQAwi2TQr89NRE+D8P1igWCRCJKuK7564J1jS2PiM+0FfoK8s9vfS1OXWNC6cMf8D9iFC/Sye8IhAhxQdGoOCTzbPFAkCmqL/R/D4u74TpJnSK2XzJxsym/cPuATp06xf7u/2cLcXiyHS53L4CztTVc/2lfbN8Ol19RecY9EUJ5zvA9qa/hQxmTwdvRxW0v5C/bddk2TEgKYiOTHoQaBL3igpgXDn9CGyBmYzjveT+ZMSRZCGNAqAPSQJgM0kH/XNy+nTqD2BWuIwhXjN/BRZpkx6DCKKlYHoSFIfof6gf1Nhm8GM1ioQdhKRy/F3zomPB29uLaEKmMYWUBIT3oS4NxlhlDWEQMhrYgDALjCnWM633IjXjpTBT2g7AgjJO+/iA2EiID8bw47Stcx7gv7ZtY3MnYTokg9j/2avAVPBwt/HyEB6EOEwYm3M+xQAC+ryON8WyReH15stDPIliHv5t0B7872X67uH2sH0NYKAmnZezYsbF0JGMDBiFvdQiZEKIoSk0pKE7MQCGEqGwgxNCfEa81WUEKLxQ85jjlmUkM4hseBwiJYe+t0iCRgZfoMKREJ+Amej+VSSbPioDLC5EQAZDJijfmS0oyaSTuLBMKPCfwKGPSxaQcT9bi5D+HbDI5TvYwuTB8Lx6txNnFw4i0EQMQb5VU45YR1474iXhMMTEgvif3wmsKwSRZfD4gMCaKgedjQCKMIfgwkWWyhKcscQsRfvDwqiiYsBL7GU8gJtwIt8SYLa1Fj6LqGqI3Xlb0ERy8hVjIZBdvLITu4tQ1PEmJIcxEEpEWcZb2zfv8Tl/EfUsq2ha3rVMXiNWHwJMo7jSLI3iAxZusVxZ82RB3OOjlHyQc17WoPOOelBWT83jXluTwqZJCuuiPebH4RNngBRj0lk2FkowhqY5X4UPe6P+InYkHOl7DfCd9YPCwzuKQrFBS3LbjD0osLQGbw8EOOOAAJ4xiXyS7G8gLq3hMBvtS7x0ZBLE/3pyuJOVfWmNYcaBfwkOUPpbdC8QURXxDRPPQz+Kpj7jGOEfe8Dliu4YP1YNE8VkZmzgEkXbGoj11lwW4ZATLRPlYEYSfz/edHIyG6B6PVIXjwgj3C3w/u7uInxwP+rjStmvj4fOBuLZBz2RP0A5JxQbErkgU118IITwlmumwIssWzyDhoPZCCJFucMAGHomEAGDbeWFwcAGCBmEBgiv8yWyNRfThABS8MQo7DCgMngPxtoaHPYTKGyYowYlfPNjajudGmHjvJQsHExGeJ+xJVlxjGC8uJsbBrdmpQtni4cSLSSJCEZNXJnL+gC2EUTxrPYynCMbegyToGcPr2muvdQdwcCAIW58JGxD2AgkfSINXjvfgYZLPhCh8/3jgNc42Q16ki+22HNCBRzVCZSqeIeRFsosgyUBbQWDmmf129DA+j6lXQc8htnQWV0RhuyOfx8M8eCgYZVZcvBjL1kvCUXB4C3B/Fg0QTyiLYOiLeJSVpw59If0gCzHxDtPBaxbRgzoVnuxTPsGwWRycRN9wyCGHFJluyo/2EcaHoPDly/949oe9lcL9iT/UERs1mfqfDNyT76V+hYWD4paP7w9Ks71wT8aMYL9cnp5d3tON/hhP1lTGK/p20k+bC6Y5vCiYyvNQZxBgqJ/ecxsQ9UhjaR24jD1AmyhJ/+Ch30GwxdZgZyPCYrJ4z8KgJyfjETYKnvzBeRv2iP97aZLMGFZWsDWf7+JAScqCusIWdw+LkoxpiGvBhSlE21RgsZgFEkRbvhOv2/AhWSWBekmZsXsm6G1bEtsplT6JcbeovpM0xrNF4vXl8exYbI2w/Ug/y9hRWv12cftYP4awcJRMWgqzAT0snPDMwX5ICCFKJTwCgwVbEum0mEjQ6QZfQgiR7rCiT/+GVxITuTB4+HDacHBVP7iKz5a6ZAx+PAYRnYYNG5aSVwDGIN+Bh5sHQzd86nVZwcQhXvp8rMDCPEvw1EAEIsakBw+gYCy4VKEMwulBZIrnSZQMPAcT5OJ4cDJG+i2mwfJCMPXbUTH48VBm63Iw3YjOlKsPy8CEmviNQZj44kUc3tpKngbj7+GVhWcTz0H+8KK+MUGNN1khfEI4FqOHtBLDj7SywAC0DygsrqyH72WLf7z6WVg9Z6KDZ1QYvpPnxeYIbyn14P1O+YW9peO1tWSJ19aZcOGdVVwQ/IjJilc+eetPM0fMpZ9BtGLbblF1MZXySAVO4Mbew8sxHAKLeo5nPfmBF3YYFr58fQHKgvrMluVguuOlGWGXsCuUs4f4s9yT2Jo+piT9Ce08GEubdOFZFwTRm3bIyewIAIXV/2RhIYM6gfd5uB7ze7Ad8ZzJbAmnPiPYP/744wXqflGeYghu5FEY8pG0BPvlZNNTGnixIxh7knQS9qA4bY7nDNYL8CJWMvXfLxqEBTV2ykBxwuLEA5GLhcxJkyaV6D7kFWmmnjM2JfJoZ7wIjwvkmxdGg16ShDFiAY/25OGz2C3E5wyHPSguqYxhZQV9Kn0GYhnCLbslgh6+1DGE3KCHJ4tReEmmCqEQWESiv+S+eN+WFpQf/Wmwb2PxgXATZQn9/z777GMPPfRQ3AX5YN9JPcWDmj4n+Pd49h39QjgeLfUx7GmLpzTtHVE9DO09XL/Kqo8l/1ngYGdXcFwL50MyNqDn66+/dv/vscceKT2DEKLqkfKMlIGICTvGN4MTgwWGBJ35rbfeWjapFEKIcgQDi21xeBmyAk6sMuJfIdDgJYIgyHY/QBRD1GIrFAIHggBGNYZuYR6nwH1HjhzpYr5h5CLUMEEj9hwHKHGQVDyYCLAdm9h2bDXESKRPxturuIezpAKHOfGdfD9hIXy+MCFicuRjpCYSxJk8sd2N+yAeEO8OryTE2+J4gOENyHZ1vhfj94cffnCThKAXa7JwAAtjHJ45xQHvVgRDJhoIS4htiJWI/34Cx6QBj1XEnoMOOshtzcQTBfGPMBPeo5GtdCySHnfcca5smZywNc8LsEGon0wqqA94C3khMRjOgDGaZ2NSzmEmpI88p85Q5/z2Weo03qtMdonJRnxOxE7EDL8l13t+Em+W50KgoA148TBsNyA+8hyEsOCzfBdCG/lMfMF4IPRykBIiH20DTybsDcQe4tsiuiTaCkm6Bw0a5DxbyF/ymfuxlR3v6+LUM+oWQjHhJchn7kF5lHTbJc82atQoJ2b4xe+ddtrJ5SX1iTwoCl8epIt6UFqCAVu8KTvKnjSxkEW9IT4foSnw8mIBK96kk37BtwVfv/FeDx4aRrrpuxCW2GJLv0nMQDyOiT1J2fNMlD3ljqccCw/+AD/6XOomnnOUNx6ItH0fo9GXM9fTz3A/QmvQVyCWU59oE0zG2TWR6jhBumnLiDxs4ad9kEbaPIemsaXYPyf9I309bZzQCbSXeLCYQz6R39wDYZ/7swMguNgVhrrIs9Mv832MS7RdxAnyw8dZTzU9JYX+hP79zDPPjIlZpIl+MN6iTLhvx8uWZ6IOkrf0GdTBoPiORyvv8Uz0ldQX+sR4cSvpb2jDCEQ+5AnjL/WLMizNQ5UZw+kjwx6tqYDXJumj76Q8eXkoN9IM9OO0A160JcYy6iEen9Qj6pOHMYD+mLpLLE6u5/mpZyWJfRwmlTEsWeh3aL8IzN4OKwz6APpQxDbAVghCvUKwZ4zgOvKDuS15ElwYTwbuRZ+JjUhfQ39WWlDOxJnHc5N+F9uLMdSP22XpPU9+0CcxRmE7YFth0yCmcmgpY6u37yhf8pL+mDGMdoYHbjgvGUs4fI16gD3IPRBmw7uj6DN4TvoCypu+CzsZO4+xiTqb6o6q4vSxtF+vffA5xlffh/E57CXGomRsQA87bOgbUw2bJYSogkRSZIsttoiMGzfO/dygQYPITz/95H4eOXJk5OCDD071dkIIUWmZOXNmZMCAAZG2bdtGatWq5fq8PffcMzJ06NDImjVrYte9/vrrkS5dukTq1Knjrr3tttsijz/+OEpOZNasWbHrevXq5V5BcnJyItdcc02kXbt2kZo1a0ZatGgROfbYYyO//PJL7Bruc/311+f73NixYyM77LCDS1eHDh0iTz/9tLsm3K3z+8CBA/O9R5p4/4477sj3Pn0777/44ouF5ss777wTOeOMMyIdO3aM1K9f36Vhm222iVx44YWRv//+O9+1bdq0ifTr1y/fe99++21k7733jtSuXTvSunXryC233BK5//773XfPmzcv32cPPfTQAt8fzkfK4tJLL420bNkyUrduXVdGEydOLHCdf+4nnngi4bO9+eabkaysrALPkQjuxT2/+uor9/vChQtdfpM39erVizRq1Ciy2267RV544YUCnx02bJi7jnJv3rx55LzzzossWbIk9vdff/3V5fPWW2/t6tamm24a2XfffSPvv/9+3DKmDrRv397l64477hgbq4PwXFzLWO7r23777Rd5+OGHY9c89NBDkZ49e0aaNGni7sX3X3bZZZFly5blu9eNN94Y2XzzzSPVqlXLV9fjlfmiRYsiF1xwgbue+kK5cw35lQjSeuutt7oypGxr1KgR2WSTTSK9e/eOvPTSS3HLIdjeNmzYELnuuuvcM1Iv+Ny0adPcc5177rkJyzDcHoL5+Omnn0Z23313d79WrVpFLr/88si7775b4DqejXxIhgceeMB9nvIP0qdPH/f+Bx98kO/9ePWYZ6X9NW3a1NVf3w8kauuJ+pVEcB/6wi233NLVm8022yxyxBFHRCZMmFDgWp+fH3/8ceTss892ZUY/ccopp7h6EIT2Thunb+UzwfZKH0hf2LhxY1f/d911V9c+w9BOuAdlwvPTF7z88svufp9//nmBvufoo4+O1W3K6Pjjj8+Xx74fXbBgQZF1DPiuvfbay7V3XrRp2tiMGTNi16xcuTJy8sknu2fhHr5uJOqTfvzxx8hRRx0Ve3b6eOpyYXz//feune60006ur6C90G6OO+64yDfffJPv2lTSQ13mucJQVttvv32B9+P1219//bXrB2n71KG77747bn6G++y8vLzIzTff7O7p+zXqQLz29dlnn0V23nln9x3Buh1vXFy/fn3khhtuiI279IdXXXVVvnE90bPES2dhfRjl8NRTTxW4b7iPTATXkv54r2Ae0A4oa2wQ6kx2drbLjxEjRrh8DLN69erIv/71L9c/kre77LJLZMyYMQWuK4kNkewYloh4fTP2F+/FS2sipkyZ4j7DcwbHWM9jjz0WGztpv3xvsvZUmPPPP99d9+yzzxb4WyrtK9730yfRbukvsS1OP/10NyZx3ahRowpNV1H2XWFjhe+PTzvtNFdfaDOM5YcddliBsZh+iLZBeXMNdgL5G27rubm5kSuuuMKNJdTVAw88MPLzzz/HbRsrVqxw7RM7k/bNZ/bYY4/InXfeGVm3bl2xxrqi+thE/T35SFrJfz5H3aYcJk2alJINyPPTP1977bVx81sIIYJk8U8qIi+rumz9YGWI7SWsgLPyx+o3K3Dxtp0JIYQQhcHBDezYYAypyEM58HBmO2twe5/IHPCsw5sVD0k84ETp4z3hiNHr41yXN3hhcygQXmB41ApRUeBhjPcdsZ897EghXv7QoUOdl3C8HQpVGbz08U5mBwI7coJ9CR6MeEVW1jGafgdvZXYjBGPPlhWEccAT/ZNPPomF16msYwJaAXVfRMsNz27CIKUSo1oIUTVJOaYtWyJ8UH22ZnB6M7CtLHjAgBBCCBEPtm0GId4iW+rYrlbRpyhzWEQwpIDInHoWjGNJjD6RmeVMPEEWgIj9KcFWVDQcmoboSJiCIAiSbK8m1JHID7F7yRsE2yD4GXEgZHkcYFYc6HsI/8SW/7IQbMN9HfFfEf7Zuh8MfyEqP7fddpsLHSLBVghRJjFtWSkj7gwxoIg5RgwsYrgQlNsH8RdCCCESgYcRohnxgonzhVcKXjXXXXddhWca8c1EZkB8Szx8OByFXUJ4IhEnlRibldUjSVixDgRj9xcLLhyuhWgyffr0Eh1uKERpQd0MH0xE3fQCXGkd+pVJ0D8T79PjD9EjbisxZysbpIm48MRYZRGaeK5lASI29QYbikOt2O3KeQLE68VjW6QP4cMUhRCiVEVbtn14OAEbw5jTDwnY3qVLl1RvJ4QQooqBiMbkhgMqmIThIYJwy4m+QpQW2CQcAnL77be7RQF/OFll9dISxYOD1zhkDCEMzzMOf8GLkYMkhaiMaNGocPCyZY6ZLhA2kAPjOHiMQ65YQCoLOKSRwzXffPNNtxDA3BtPWzw2hRBCZC4px7QVQgghhBBCCCGEEEIIUcExbfFYSJbff/+9QNwmIYQQQgghhBBCCCGEEKUo2g4fPtzFHmSL4bRp0wr8nRhiBI3nFES2uRLPRwghhBBCCCGEEEIIIUQZhkd4/fXXXdycDz/80OrVq+diw9WpU8eWLFli8+bNs80228xOP/10F/OWv6UzeXl5NnfuXGvQoIGLtyiEEEIIIYQQQgghhBAlARl2xYoV1qpVK6tWrVrpxrRduHChO4H5t99+cydYItbuuOOO7lXUl6ULf/zxh05zFUIIIYQQQgghhBBClDqEl23dunWh1+ggsjgQ7qFx48YuAxs2bFj6JSPKl5wcsxkzzDp0MMvOzqjH8uGj99wzox7Nctbn2Kdzog+355Z7WnbN7KQ85BcsWOBOHa7cC0g5Zubjfu9pZplUcGprVaGtpQ/JtbX06Tsyv61l8qOprWWWsZWW/UYVaWu+vc1YOMM6bNahSo5raUkGT2yCj9ajR56tWpXefUfVaGu+vc0wsw6Z1dZEpWD58uXOUXTp0qXWqFGjQq+tUW6pSiN8SAQEW4m2GUCNGmb161OgGWUA8Fj16kV/zrBHsxrra1i9BtGHow0mK9quWbPGXV+5jSC63X8KzlgUyqSCU1urCm0tfUiuraVP35H5bS2TH01tLbOMrbTsN6pIW/Ptrf7a+lV2XEtLMnhik//R8qxatfTuO6pGW/PtrX7mtTVRqUgmHGv69xRCCCGEEEIIIYQQQgiRQUi0FUIIIYQQQgghhBBCiEqERNsqDsfQde9u9vnnFZ0SIYQQQgghhBBCCCEESLSt4qxZE/3/118rOiVCCCGEEEIIIYQQQoikDyK75JJLks6tu+++WzmbRtSta3bUUWZLllR0SoQQQgghhBBCCCGEEEl72n777bdJvSZPnpxyrj7wwAPWtm1bq1Onju2222725ZdfJrx2ypQpdi+qIckAAQAASURBVMwxx7jrOWXt3nvvLfE9RVS4feIJsxUrlBtCCCGEEEIIIYQQQqSFp+24cePK5Muff/5558U7YsQIJ64iwh544IE2Y8YMa9asWYHrc3JybKuttrLjjjvOBg8eXCr3FGYnnWT27LNmf/1l1qCBckQIIYQQQlRefvvNbNo0s4MOquiUCCGEEEJkaExbQikMGDDA+vfvb9ttt50TWrOzs+3xxx+Pe/0uu+xid9xxh5144olWu3btUrmnMGvZ0myrrczmz1duCCGEEEKIygd2ar9+0Z/vuMPs2msrOkVCCCGEEJXA0zbMpEmT7IUXXrA5c+bYunXr8v3tlVdeSeoefO7rr7+2q666KvZetWrVrE+fPjZx4sTiJKtM7lmVhNu5cys6FUIIIYQQQhRkyBBCpWHvmy1bphwSQgghROaTsmg7atQoO+2001zIgbFjx9oBBxxgM2fOtL///tuO4kSrJFm4cKHl5uZa8+bN873P79OnT081WSW659q1a93Ls3z5cvd/Xl6ee1UFWrUy+/NPntkyDx4qEon+n0EP6B/L/5xBj+baXeSfh0u2HfrPVP42S/oigZ8re3pTQG2tSrS19CG5tpY+fUfmt7VMfjS1tZIzc2aW+3/x4oitWxf9OS/Pt/HyNbbSst+oIm0tXD7pXEZV0ob0P2dQueV/tPTvO6pGWwu2twxra6JSkEp7SVm0vfnmm+2ee+6xgQMHWoMGDey+++6zdu3a2TnnnGMtcddMQ2655Ra74YYbCry/YMECW7NmjVUFsrPr2KOP1rOuXZfZdtttsIwCQZ5yXLTIbOVKy6TH8m09wx7N1m5Ya3krow+3aMEiW1ljZVId37Jly5zhgId95YUFIt9JLzKzTCo4tbWq0NbSh+TaWvr0HZnf1jL50dTWSs5229W3CRNq2y+/LLU1a+rb+vU1bM6cRVanjpW7sZWW/UYVaWu+va1ZvsYWRarmuJaWZPDEJv+j5dmaNendd1SNtubb25rMa2uiUrBixYqyE21/+eUXO/TQQ93PtWrVslWrVllWVpY7GKx3795xxc94bLbZZla9enXnoRuE31u0aJFqskp0T8IpcHhZ0NN2iy22sKZNm1rDhg2tKnDkkWavvZZlzz3XxB56qIy9FsqbnBwUeLMmTVCnLZMey4/1GfZolrM+x6qtjj5ck6ZNLLtm0Q/HBIq+iHZbuY2gnEA48SYsmVjGoLZWJdpa+pBcW0ufviPz21omP5raWsmJRLKsZk2z6tWbWPXq0Z9r1WpmZXrOcAJjKy37jSrS1nx7W5C1wJpsVjXHtbQkgyc2+R8tz1atSu++o2q0Nd/eFmReWxOVgjoprDinLNpusskmMVV48803tx9//NE6d+5sS5cutRx6pCRB8N15553tgw8+sL59+8YMIH6/4IILUk1Wie7JoWbxDjajI82EzjQZttzS7OGHzY4/noXNLPNaNVElMIq33rroezz6qNkhh0RDLVQqKMOsrOj/GVSe/rH8zxn0aK7dMRlKtR3ymcrfbklbVuDnypzWFFFbqzJtLT1Ivq2lR9+R+W0tkx9Nba3krF4d/X/58izzDiorVmSVrd1ZiLGVdv1GFWlrwfaWzuVTZW1I/3MGlVv40TKpbmZuWwu2twxra6JSkEp7SVm07dmzp7333ntOqD3uuONs0KBB9uGHH7r39ttvv5TuhXdrv379rHv37rbrrrvavffe6zx3+/fv7/5O7FyEYcIX+IPGpk6dGvv5zz//tMmTJ1v9+vVtm222SeqeIjFbbRX9H3175Mjoz//3f4jhZp99VnjOLV1qNmJENF7PgAHKZSGEEEIIUXqsWhX9/5dfzJYsiToV6EAyIYQQQmQyKYu2w4YNi8V5veaaa6xmzZr22Wef2THHHGPXXnttSvc64YQTXNzYIUOG2Lx586xbt242ZsyY2EFic+bMyadAz50713bcccfY73feead79erVyz766KOk7ikKB0/Zt982Qxvfbrvoe+vXJ76eE3zRw2fMiP7+zjtmxx6LR3b+a6pXj76EEEIIIYRIFb+h77HHov+3axd1GhCiKrFhQ5ZlZUU0rxJCiCpCyqLtpptuGvsZQfXKK68sUQIIW5AodIEXYj1t27aNnXJd3HuKwrn88qhoe9pp0Z+hbt341xIj/sAD8783Z47Z6NFRIddz1FFmO+1kduONhX834u6sWWYdOqiUROaSm2t25pmd7f77p8bCkAghyoYXXmhhRxwxv2wPKhJClJtoe/75Zg8+aFavnrlYtvK0FVWNI47YybbddpXde29Fp0QIIUR5UKzgHMSJnTlzpn3yySc2fvz4fC+R3tSvHxVY4fbbo//jWI3Q5MHzlrAJXrD1Dtb+IIgHHsjvncu5cJMnR3/m5Mxff43/3S+9ZHbKKWYTJpT2UwlRubZ3/vhjffv553oVnRQhMhrWeG+/vZ1NmBDY+iGEKBPmz+dw4rLbUrVhQ3T83H//je27USN52oqq2dY++UTjmhBCVBVS9rT9/PPP7eSTT7bffvutgNcrQahzg+qeSEvwYNh9943CLR63v/22Mebtm29GDygDdHoO91y+3Oygg6LvX3KJ2V9/RQ83W7s2eh2/P/kkp+QR1iIqzh52mFn79hvjk919d/TnwYPNXn+9Eh5oJkQpbu9cvVoB7YUoS/6J5GRr1qitifRi7FizLbYw69TJ0sr7r2fPxbEF/9Lm66+jobc239zsrbei8WyffdZsAQd7CyGEEEJkKCnPZM4991x3yNePP/5oixcvtiVLlsRe/C7Snxo1zN54I+rx2rt39D2EVkCnf+45szPOMJs0KSrYAuEU8LTt2TP6+7Bh0VN+f/45ej//nr/PM8+YnXRSNMQC8XNPOCF/GrxnrgdR+P77o566VYlvvjH788/CRQkvAor0YPXq6PGxy5alvGYmhCjGoUXE/xMinbj6arMSRh8rd2hnkyY1KrP7Y5PutVf05HWOqSBaW4sWZvPmldlXCiGEqMJ8912WXXpp64pOhhCpi7Y//fST3XzzzdapUydr3LixNWrUKN9LZAYtW26MZYtYigctMWd/+ika3uDkkxN/FkF3/nyzvfc269fPbNddzb780qxLl4LXEmIBwRcwwMeMib6HZ2+QDz80GznSbOLEaMxdvIGrAmefbXbTTYn//tRTZlddZWkNwv0HH1iVgcUMWLq0ZkUnRYiMJicnKtbm5OgUTJF+BM7hTRvWratWZqERRo2KirZBEG0JwRWExf2wDSmESAxt5quvlENChHn55Rr28cf1lTGiwknZutptt93sZ9wnRZWhR4/oljSEUzw/MZobN058/ZFHErNz4+/bbx+dfDz+eNQ7F+EVEfeiizZeQxzcd98122yzqHjLycBTpmz8u/c2HTQoKmJyr7lzLaNBzATyCk/jeBBWAu/ndPZAZiJ2xRUF3x//dgtb8FedDBZt5WkrRFmycmX0f3m1i3QinaOMrVtXNl7teNNWrx51BggSz9P2u+/MjjnGbOHCMkmKEBkHoUfOO89MG2aFyE+dOvlDgQqRNqLthRdeaJdeeqk9+eST9vXXX9v333+f7yUyj6wss6OOioY0QDwlVm1hEG9sxx03/t62bf6/E4cMEffUU82efz4q5O62W/R74IILovFs8dL1W/8//dTs2GOjP/s4uYRbSJbVa7KcuJlOzJq18WeE2zAIuT5//vjD0gLCbvjySyRmel56pJ29PrKIypbG3n/ytBWibPF9yrJl8moXlYN1a6sVKcr6BVs8SLG70om8vLIRbcmTJk0Keh8TJoEwKH6BJrjIz66wadPMbr65TJIkRAHYEdi9e3TXYGnDbkccNcoCP4eYPbts7i9EuoJmAYnmrkJUWtH2mGOOsWnTptkZZ5xhu+yyi3Xr1s123HHH2P8iMyFWLcYv8Wzxhi2K++4ze+edqMesP+k3DCLt1lsXfL92bbOzztr4va+9ZjZzptk552yMfXvdddGDOi67LLEXqmfW7Czbe0AHe3xkem2RJcQETWqHHcxGjMj/tzlzoqETABF93Liox215k8x3YmgOGRIV42+4IepVzbN5fMzj33/PvxUS1q+vlrQ4E0/YThVCb5DWskSetkKUD2vWRAWk5cvl1S4qB7cM6mqjHoxj+ARgfGzYMDp23nNP5u8qKgzsPAQwL9qGqV8/erZC0NvW5xcCFAvFr7wiD0JRPnhb/dpri56bFGdXGnMg+gVP8OeS4NuMQiQIkR92eMCyZcoZkWai7axZswq8fv3119j/IjPhFGPEQyjK0xYwops2NevatXhx2Q4/3OzQQ6M/33hj9H9CNFx6adTrtlu36HuIlRyWVlhn+sln0QTM+7vyH0aDOO1DHTBx43C34483W7Fio0BKeImjj46ujFMWxB9+5BGzPfcs37SSnt13N/v448TXLF1qtsceUTH088+j73Ha8yGHRMNtcI8vvoi+j8i/ZEn0Z2/s5qxMTmwhtu8FF5S8fAm9QVq9p1NZHkQmT1shyhYOagSFR8h8GDvSIUzQor/r2OTPmhR+zaLotn/PRx9ZlT6QDQGMUAfxRFsW/2vViu7Y8vz110bRlr8B4bdE1WLOb1n26YcNy+37CNuGiOpDv+F1W5r88EP0/4ULN9q6pSUMew9b5hJCiI14D9tlyyq/hiAym5TltDZt2hT6EpkJhvHQoVHRdJ99yuf7Lr88GuuUlWUEPUAAxhOXqob3xBFHRN/fb7+NnpoIgcTU9Yb7fQ9Ghb+3xlSv1CESmHBywBsHt2GI4ZHKsyJKI9oedJDZHXeY3XLLxs/g9bzddtGfMRaDIRXKmsmTo7H3EDrffz/qER32CMKI9Zx+ev7fKd9ddjHzUVUQXjmEji2hy5ZGB8fZMxrYaSf/M+tKYlBdsaL4gyrP4if9eASXpadtrVp5imkrKg2P3trBvp5QuJCUjnivdnnaZjYsBLKjh7ETm6Cyi7e5uVlFiraM7U8+adauXf4zAiorfncMlGb++51dCxbEF22BcxaC28axBVnY5zAyDs/Fjpo6tfTSlGlUdHuhbNnJV9oMG1rDRtzRysoLf0Ayod+ID0v79c4KJYVFC5xU4PPPq1n16hHbZJP1NnduVqkIttz7xBOjv1fmeZIQFRXSrrQ954Uoc9H29ddfj/t644037L333nMetyIzadDA7KSTyu/76tUzO+64aAgEvHbD4GXKVnYvBBIyAQMJIRCBEG9dvFWJR3PJKX87UTPeicLr15feFiOMneLEveFzTDg9GHu816gRQdCj6WMixzZBDNyXX45uwzrllGgIBbZNAfn19NNWLhCPGDi44Morox7RfsJMeocPj06m+/aNGrOER+jSJRrG4IAD8t/LxzPms2wHZXBs1CRaKL/+mjgeMXHsmJT973/R36+5JnmvCtJI2r79Nvo7kz7SgQj+2Wfx4+yWBsQhbtVqrS1ZojibonLw/Reb2pgXtrBMDI+QnZ0r0TYDYdz2Y8bdd298n4VDdnFUBhi3GAP9ZM+PYxvWVytUKPNepexu+te/NnrYVWaCY2VpbiMl/IE/XCyRaNunj9mrr0aFWfKaBWVCa2HvzZgRFdHIQ4lR8esa50eUha2TDJTJaadFy6i0WVeOMSi9YHvGGVE70odyK63Ytq+/Hv2fxYinn65u9erlWufOK+3LL4uxlTEEDjBAOYDEqbKDOaofuzJtp8uox+NM1DMAYqaDPG1FRZNyb9+3b1876qij3P/h14EHHmjbbLON9erVy5b4fc5ClDEIgW++GfVGQRgEH0eXbfgMkPvtssL22yfPGVIYp4iiiL0PP2w2YEB0C7/3zI3ngck2/2S8ETCsCFOQ6KARIojcdVf0ELWguHvVVRt/Zlsk26qI9ztoUPS9M8+MiuZMhhCr8TRmQucDpLduHd1GCPfeG/U08WJkUQIi2wZTEa1J93vvRb1r+U7yGM4/P/o/4jP5STxjIMRDUJDGW/qaa8xGjzb797+jXjCEJGB7I5MH7vvTzGpWv+F6u/WpL2MxkoFJWXBygbducCFhxowaLt7vxt8LGqB81/jx0fwhxATlz8EReDlzIB7lQwwjtolxUnWielESIQnRli3bqS4WMPGUQS3KgjU5pRPzmz41mb6nPKCvaNGCBZIaFe5NVhaszCn5hD0deeEFsx49oj97D0p2oPAe4yeLiMTAL6+DQxAMg+MO4QwQQjCDEZTZLcM46Q8OhZkzCnrI8Xfi+TPmeIGSRUR+v/PO0k0z4nZpiph+XG7YcIP99VfpbSP149033yQ+TwF7gzAIeAtOmBC1i8hv4txiu3GILTt4EHM9LNJecknFe5kGIQyEXwwvCyhvdm0FIV+8XVRaYE9hYyUDC+Q4IkBph6Xy5yWUtSDN3MM7j2BP+sUGdscxN0HILWk9mz6dg8DNtt2WvibLNmzIsoMPXmAjRtSIhQEqLux2o+5hq+Mo4vuGr78umziemShaJgMOLiziBc/vyBS+/66avfVik3wHQmYKvv/47TeFRxAVS8oWP960HEDG/8uWLXMvft5tt93szTfftPHjx9uiRYvsX7gHCFFOMFFDvAW2JTGBY2LnabrJBrt88HonpiLEIZqyMo7A4Lce+ji6CHm8MFoQ1bgfnhwIisScRczDKOXFRIIJGp06htNDD0XvhfhHKAlEySlTooYboibxaTnMDdGY9PEdeMYiVAJGHoO6T6OHNBAiAk9WH+M3DHFuOURgq63MjjoqajwSNgLhN+hhzEQWQwyxFm8UBFQ8k/2kMRyamrAFPKuf4BG2wYvMm28e9XbGQMcwjQeGYDwvaoTmww6Lxij2ntTe4L337hpWr8EGy66fa81bRPMKJ348AbiGSfLAgRvj2BHv+PPPI7bTTuvs2GOz7Prro0IvnshMmDFCmZzwzKSXyRqfD4O3B2lh0ufLEs9f0hgUopgMIs5TdxCOEYfDUB8QERCng5DH7drlWOPGG+zHHxMbAYgAHKISpH//jUJ+slDGn3xiaQ/1trQFdLGRZONHFwV9Krsennii4nOXNtix4yqrXTvPfvihYg1u+k8mbaXFl5Oq2T7nbFthHnJlCeNvcPwOgvjh+33GKkQfxn4WagnhxMLppptGBQf66OC2/WRAOLr11oKCJuM0i6dh8YVJKiGCguM15u/NN2/cUUL62PUzYXw1q1Ezz7r3WmCvvFRwkeTnn6PCIv0+zwAs1oLfTVMavPVW1JYIjy8ljdVO2J/WrdcU2LKdahl4KANEWx+XNhjnNwgLwYzr9DmIsQi2Qa9cxF7yEVsmvHjrbZ/K4M2FVyYhsEoK9h0LF9TBYD1G0N5336iN6RcQvFCKnVMaO868/chZBbQVyj7c77GYQRrYJYWt5vH2X2nhnSfee7e6s73jhcggH7D1StKPclgefQ19j3ekAG8TI4oydyguzB/YPcCuOh8vNyenuvXsuTgWHqQkdYV8wU6EVq2idhZzGBa+br/dSg3ymnLALi4svzPVI94fwJyJjhd+gfSvUgjXkUjoDx5gXZ6sWpVlm26aa2+/nV6HmYvMI2XRdtCgQXb33XfbfvvtZw0aNHAvfr7jjjvssssusz333NPuvfdeJ+QKUZ6w0o1oycQKEC+ZPJ16Uq4z6jHiESjD4NXqtzIh7LFlnhdhFvDgQJgFYm7hjenFPDj77Oh3ILJiOAUHFQ7owthC7EW8JSwA4EELiIqIxHjG+uv/85/oqdEY0OGQEIRJYGLCSnsi2JYV9MjBIENwPOaYqLGO+IpxiZjr84J8wqBkwnvxxVGjyntdfPDBRi9XQjLgPYSx7Z+DySjeDBw8B/wd71s+48XoI48srNQKPqMXhL3n31PPrnWTCUI/eI8DJsl+UsCki8kYZdyv3+rYpDQ4QWOCSl6Hyx+jFAEAYZXJ/047Rd/3h+fxfBjLCKVMKJhsYFAyucJI4bv5ezzDlskLhgb5wETMT1wwXNna1r37Mps0aWMXzH0RKvw2OIR9YuuGvbZT8bYlf/g+yjEdoe1Rlog41FsWVtIF2lSvXtHFmtIU60oDvP+8h5Vn/bpqbqtsacCiBos7fltZRYGxTVvbZZfl9tVXpe+VGvScLArC/CDiJdqFkSrnXxxVB/74MzO8P8gX7zGGuISgGOznGAMQYoO7Nuhb2W4aHGPou4Nbkjkss7DvZDz3cdXZZUFc9ZdeMrv//o2hhujrGafZoo+w6mEM8jH+GQdYXA2OO0HbhHT/94aaVr1GxPY+aJ69/VZ1t7AbjCjmD+WEoFepH48Zy4vTl4TFOC+UEYe+tECEqVs3zzbbbL2LuRleUMZ28t9LP1PYGEb/gT1E2SL6MT7z2cLK0nteY8+xmOxDLvnP0A8j0npPU+/diWBFvUNArEhYaA/WS8a+Rx9N7R6Mk9h42HfYXthjwYNi/TPS/3uRjnxg9xaidrLxV7FVsC3iLaJi7wIew9R58j8YDos6zwILaWCXFJ7Q1A+ETxaqWfRLFcqQ5w73rUuWZFlWNbPbb63h7DEW/f2YxDPQz5APOGIUtbDN9Xhy0wY9OA9Qj+k/6DcQPINgW/vDyP7v/1IXnQiBRvkxf4DOnaM28n775dmeey6x2rUjduGF0RUR3y+Ex3VEefqzRJB2yt8vEnGwMZ6gfnGK8kkG2hULTl5wpc+iHyZ/iJlL2dB/e1Hfi88ssgQ9byd+Uc12GdTDXhifYIUmjfHlX9nswdJgyeJoh1saMZbjwTjNYpyvl/TlzJeD0H8kWuwNwpiSymZw7LwTTlhss2bh4Z5iwoVr8/TPmehgUN6kPIv55ZdfrCGqUgje+/Wf3rh9+/a2sLRmf0KkgDfUPQTWHzRwQz7DGJGO18SJUQOM7YdMqDEaESQbN97o2RGcCLJtMQgelvHASMPDgQEGIZTJBqIhRiziHfFXfcw9BnG8QTHO8T71YmFJwAB75pmoJzHGnvfcCG+LA0RgBjnyjXQxUcDw5H06WQ6C8wed4X3EcwETWp6nbt2C340BjsDWsWM0L8NlUhQIzAPO3WBH9ou6ByMII9wBoit5yaSM70agRzj3HjgdO26wMWMisTh4eEGTpjAY64i03rMDr99gmWPEk4f//e/G5wcmj4j5eFVxQIz3jmYyj9gcnIgy8QQMVvLKT1wIak+cze7dl9s771R3BgFrXNwX8RdDBCPWD3B4PHCvoDcv3sPJTAAQ2oGuGeHAnxCcaGKPAe/jJ8eL/1zeMLGj/fjQG/DiiyW/b3kYXoRqYYLIYg0e3JQfxjoTl4qE8qWfw+ucSZb3kGjSfI199GHpehIgWic76SvN58PA53uJ/ffbb3WtS5cVNnVq6Yi2lCGTYNoUE34m7ggfflEuHuQxfRGwIFeYJxGTWxbOkp3g/1lGk6TigukXFDaTgYUqBE8WPpl4+S25Qe808o1FNvB5yefos+gjgzCOEvvR44Ua+lrGOS9iIvTR1+MRx3jJLhgP4itCrV/o9DtGEB8RxOjT/W4NFhGBRWO/yMkYSRgjxho/TsDa1dWtzbbRPaQIMixG0jfwP9/vveaCnqJ+AZfFw7AdQt1jMStenvOc5BWLz1yDaMekFxOedDJuJRIrqYepeIRFRdtc22OPJW5cC46Z7HShLBnH6b/JC8YwL5z6tHqwPxD32A7uBWzSH7bLgrCt24dqIryRz1/KARAywfuTIBb5+KPYaV5ATMTbX25mE6c1trKAMgkKOTw/dg2iHWXky6OoRaJgbGf6EOoSTgW+PbHryx82BTwvYz3iIvkbz9eGtFEPqF/UFYRhbBUEWT4TDEFC+hKFJOFvf/4ZLRPaEl6j0LZtNI1eNAyLtoiptEHKFruLv3OfIN6O4+/BnWJLl2bZVbfMsSdGrouFD2NMwjueZxg8OPoeNmKwLvr0Bu0l7HrmCbRBfqYfoN1Tj9lx5Rf8w2A/evvVh+cg34PhVIJtgDzmu3kxL6AdALaht6VvuWW93XffdPdzv365ztbGvmTxCucE8svbOOzQw3YP2v+I3FzPmEUeeKcLL9r6nX/J2krUK5xC6Jv4n0UQ6jAhF3A8IDwJji18l5cP2LnGOIjDDGOo5+MJ0XF6zKQEsVCKoDJ76XpBvTLJI9QzL4QyDjFWUH/oG1PJy8VetE1iEZn7+nvTn+As5OMqx4PFIZxYgH6HNs5uScZ/f/g1dZm5J+2ExRXaaryQJLRdFpJYOEo2FA1ztjZtogNUMqJwpsNYj/2QbJgTxiJAY6GPY25N+w+OZ4xRlbntVhZSnsXsvPPOzqN2QWCE4+fLL7/chU2An376ybbYIvMONRGZBduYiKfqwWjEixWDBUGPCRiGHtsSGRAQCBHU8J7l73REiL/8zHt0SHwGIw2DiGuZFGLYYsDgmYpBw0o5hosXjzFkghPM0qBDh+jgRvgBvpNBjEmpF3DxIMUYRgAFBGyMUGBS5d/34jQiD1ueGRCZBBXm8VIa9Ds919rvsHHG2Lx5VIDEA5q8xEOIyTAT/fBEjnLkeRHGEW8pP64DJsZMGhB8C5sAkn+8ELARO/G4wjMA70Egb3kPg9hPBpjwYMAjMjJBRWhl0hScSLE6zMFq9evnWu/ei+z337Oc0RqMaYzHC5MADCgmbkA5Iu6D385JfaTMuA6DhvpG/Qvif8eopvwRPcKnNHtvGYwmDDUMfL4P8RyDiLqPeB324IjHCy9Xt4vu2MJ67Fu7RAdwMHgzYfITG9JBnWTLJ3leEliYoP6GJ2lFgYGCkZjMFlK+g0k4/QsTKvKO8qNfoAwqyjhh8u29zWnHTLLGjokqHDvvvdDuuyf1OMtB4k3Y/U6FVGCiSJ9TnDiA1HmEOO+Vfc01v9hWW+W4dlcUfF+4DQXBsERgoI3gOQVM3A8+OLqwEKxT3Mt7ftHm6JdY6KE/8mKUn8AFJ8Zcy6QEkcIv3FCX6IeCokrXztFK9PsflUu0RZALnkJemGFPXaOfoO/y9QQBxosyXggnL4O7Bfg9GN6G8SEM9Zv8YkEOoYa+knqBQEdfEpysgo9JyUIuEzvviehBRGdSCPRDfiylP8LLjnrjhRu8hJn8Mmb5hU2+c/jD6+y0wT85UXnCxLWxBSj6BjxuaZ/0vRD02kOswS5BWKRP9AItYxB1jz6ckFA8UzAuKOOlj1TGNXzej+3eM5X38KyjrHyoKKCeMp7Fg/GNSRf54cuI8Ah42vbps8j1A3gy0W8zdgB20TbbmN1228b7+PGN68gv8ph6EO4zkl3Mph6Rbh/nPrgQzrhKPWDhmTxk3CR/6KP95J0wWIkY8nR7u3B4pzLpu/3iKvbBzjtHF9SAxWkWK2gPLExQJ7FrKEMfIsTDIquPI0sbxD7wC8U4K9Af8nfe8wsC9EP0WYSXoj5Ql7HxggIKk3PqAW2afCXPgnhhExuS9NGmWBjxfRziCvA3wjvxHHhhYhdRF1gA87aYDyWFLQ3UB19/+Bk7B9HWCyeURVA4ZqEFu5zvp04t5NmarLf220ZcXfXP5b3jgbTQ1/KdPuYv9yEPERtJL3UlKCbTt4TDHSQK3eH7J9oztiJlRz0jPcEFaJ6BNkAe4yzh+x++n3wLtsVwe/hn6p1PzKbegB+TuCf3QpDD45gxxS+Ge/Ea6MOCNiKfR3Rm4Yu5DukP2wjYlx4Ecb/wxPN6T2//GfpZ7HPw41nQE3j5iixrtelam7ekdqx/pj9IxjOSsiQvgnF4KSfstrLe9ZOMuE0bpa570XbevKI7NupkOGRdaRyUzVhBXaCesTsTIZ9xyJcTi6H0BUG7nzHO7wwNs+SfRaeiPG0pf8qIF99Pf8JclH6Z+kD9oS/xdgN/8977nuDCJfWRuhF07ECw9fHNwwR3wrBYEK8/D4qJftEK0ZbFQ/rfVMYA6gX9USbFcaZMyEfsBdo4/UKiPAnufiC0I22Rvgk7z+8gII9YVA2XsygF0faxxx6zWbNmWevWrd2hY7z4efbs2fboP3t5Vq5cadeyxCZEGsNECcOKiQb4wy0wzoKCHz/zHpPyVL1KywsmLAiNCLhsgWPgRDz2EOIA45qJAJMYBFomhAzQHHiGmM0qPoYjhmVpeASnCl5LyeYv1/kVfcqHCQyTSwxXJnKpQN4gvCFiIrpwLwRQ8gkPKLwUMBCYCPE7Bi4CAZMS6oSPKUt6yGPEo549l7iYtl26RGKiKZ8hjdtvHx3QmFgyAQlvYcU4QUjncxikGFp4mHhvNUQH7oVBFA7dwIQer09EBsqXwZNJDyuf3jDkPt7AwCDi+5hMIVSEDUkEwOBg/fjIGvbZ9/Xc51MZAjCMvAGJgY5Bx4QJ48lPVpj4sD0QY5L66cHgpG6zcOK3OSeCtHqhIFGsNv/sTGqC3imkC+EED6146WfigQGD0Oa/g0kw+RycJHrjBQPeG65hDyomnKUdFp6wIueeu3HC5Png/Wicze27L3H54yfM1K1UvYIxnukb6GPoJ8DHEcQQ915jRcGkmPYT3NoLmBjkbVBsox4ioJFu8tLHfKYc9tgjzzbffK116rTS/vgjy6Ul6E3HRI566hcHaH+0IdopkxeuJx3+4BAMS1/H+Hswbi8LRIg/GKB8njqJWMJCEZMMvI9oLwgx5A/CIJMUxAEWEfwzBb2c+CyTLIQW+hwvYADvb7vlWps+M7nOmPqMoMUrOOmOFyuOSUpxJhm+3oPfPUBfEyR4UAmCbbCfoG+mjyKPu3aN5j+CAAIQ6WdyQD+EZxx9HP0qzxNvXKBfRozy4Yioz37CxqTZiyI+HA1QJvTrCIyUO+IQL7/bgsU1f0o83pyIQN4LljTg4U09x1stHp27RKx7z4Wx6xlP/GIp9QLw6kVcC9+DXSt43TE+sGCAcOsn0HyGtsUzkd8+hFE8b20WE8lb2qn3ECZPWbBjQZC6Sz77dhteOKFukGY/RjE+cA3tjR0kjRpF1Qs8mbxAzDhD3iK6kX/0i3h30q6oZ76foU+mDnkRHsHd21/JwvWJFmT9QbVefCePGVOC4Qm851Yi7nugRqyvK06MdfKBfsoLs4DNwFhPnaWP9u1n+PDoxDgY/oOxnzRyHWM5dRB7DvsEsBfoJ7BHsFexXSgzn1Z+57vxdkWcoyy4ljrRqVO0viOg+NBK2AZhGJP9QjP2B2JhUNSkDBAGqUfUFV8etGEf3gSb1Hs/e3xYC8ZRxBzS7wWuYFgw+ljygPHAPxcCDHYQbcAvzEDDxhtVA/qNoD1FOBLygYUP8pk8x+sPodqPyYgOPCP9Le2PaxAXKEMWZ3jGYMiERFCW4fqFaE2+US/9wb1AHwL0bTgKxDt/IQh/R6Bi4YZ2Rx77hQjuhRDL2BYUUHyZ0Fd4r2dgYcOL+tyDNo4jAsIujgukH1uH5/e7DRHumQN5r3bAs5Y2hYCLowX2DPYontWUPe97hwfGcGwt5h/TZ2bZEbvPt/nLatnb71Zz30+fwHOFF9qD4xhhw/ge8LsdfN/DYg7PlUw4I/pM6i7PR1/o+7d4YBNSflzLGE4awuIV/RvzLoRk8on+m3ZAWvr128SNS5Rdop019I30DZRpvJjMqUDdZ6zgefxOQKDN88zg+xFgUYE+xuMdWLC3aPPBNC9ekmVbbrXWZv1azeVJojBQfvwMtkkWU4E+BVuZ8Z52Rjppj+QX8yfGA/LP7wDxC4D+4HEfo93Hlaa9kg7ynfrFfbz4z/jDGBqO6+5Dx1COXEu+Y7s1apRr//3veuctT3vAjklmJwp1m/7IL4TQd9Gn0Wa95z3fRR9PmynqkLqKPjiT9Pm+HxsAu5t+gTrMc1C+vp3RPulb/YIKthUODx76AzytvROYt7exwamT8XYHV3VSPnmkQ4cONnXqVBs7dqzN/Gepkff2339/q/aPktM3vBQrhKg0+BN1CwPDGeM+kyip0IygkGhrPkaon4RhzPhJlveKRSDnZ4za007LtU03jSoiw4evsz33jBYIkzDSiKeFjy3HRJ0XwgNiIZNrrsGIY7Bk0Mczq3376OST+4cPKsPI8d6DDKx+C2Fwxdx7ZSAGYJQx8Q+eto2wy8SSCSLilDe2vVGK0YqAsnCRWcN6uXbXfetswEV13USF5/KiCunAsMVwx1jC6MLAwjADBvug8AN8F+IGkzAm2aSFCUhUAI8O+uBXbcMeSEF8PGo8zREvEUcwyrgX6eTZyFuMCTyL/JZVJrFecMSwYIKLQcIkGyOG6zHMAMMSMPb9wgHPhLjDBBZhAqGIdHoDH4MODysPz8KEAMOayRDX8TuGTNBTLVkwtoPGMpNhygwhd9Tz1WzD+mrWrsNKO/rYXPv3v6u7eu69Z4L5icFIWvz24zAY8SxU0MeQTibSGGHkt/e4YiKHEY1nF8I8bQORAU9D6iFl4j1vEDCZ8AAGMtuFgXTTP1FX/UQx3la3q6+OtrOGDXNt//3z7LTTqscEgKCnVFZWljOi/cIFaWDy4CeFCBvByT4iHpNX2hyTdibAvg5Qh73HW3CCTn1gRwX3oR4GJ7jABIJ0YdCzMER78wceeiGECSzlyBZaJrf9919pT7zXyPqM25hPQXgeH/qF7/U7BchrysD3K0wMaVMIB/5wSdLixb9kQBzwHpJMerxhT7lR1xB/2P7P4gr9CBM1H+aA9PHiGvpOhFv6NTxUvPcFxj/XUGd4eYoS9ZhYIN7yjLRV6hqTeMqWvgUPuER9BhM7YBKKOEQ/Rn9B2cTz7uXvwYOIkoEJCgscye5eYSKKwEr/RD1EuGIiGPQMps35HR60C/oeJp5MNCljH/effh4xKhgbkLrr6y/wvNQHriHmb1AgR4Bkss81nTrVsAYNct3f//3v9a4f8Z9nMgq0dS/aIUj5w19JH23D1we+D6Ep1bwsCsYdypR2wcIfaaVvp19lBwRtgT4HEYC6R9kQdsELHFs2XW1Pj6pvF18eHWenTs2yI47IdnWMzzFeInr4BSv6Q7xouTcT22DMWL6fDYmIhj5OPyCg0RYZ56m31E/6EfoP+lHGKcqaMmLcCHpF0s978d/DQjULmwik1LGgV6tPJ3WZ7+PztFH6JgRA75VH26Ys+E7e8yIJ6SddlBfjOs+ELeR3sPnvwjZgzGPM9SEs4kF5YKOQV74vQWzDLuHZ6b+oc9TpYH+PLYFtQHroa6hXCHw99syzutn5VQ7yhzZDH+jHMd6jv/d2DenGG5YXYgNlS7vzHtzBnRLJwndg/3Av8pv+lbzzYjd1g7qALcSzUf+TXbBgvOVFuXhxk/6Ses6YwfMypmOj0v4Qif1By9TvMH7hh/EBWwfbh/JgbKct+76FMQ/hlv+pA9SncF+ayDOTHWbUCxYAqF+x8Ssvy3r3XeRCkfz7pvr5XMv4Di9+Y3fyfQiIfNaHoKAeU9cQienzaDv+GbA9yHvsGPI9GBaCz4RjpPp+kLInP0mr3z1CewjbqnhEQzAPWJj08X6pc6SVXQoHHRTtSB95JMstTtOX+/jHHuwgRC7qhReTC7Nvi8LbyixWIGZSJxEg/XMg1lO36fcYL7F7WJCgDlH2tHEItj/qNHnyx5ws69Alxya83ci+nxxth1zPOE+b9zv5fJ7Qz1D/g6FsqEs+rjvXeBuJ9CErkQ+ki/4UOygY355+kz6AMqNcsZFor4z39A+Iz0GRGduN9kGZU0+w+3kWb/Nwn+BiTOPGudasWZ5LIza8jzPv6zj9NHWMeWIwhrfv8+njmbPRX/u5BLYifRrjj/eS5xo/PmOjMWZ5KAcWHOkfvchOvWUMIwwJfRh9L+VBPuFwxZhL+2LeQn4E63yqsPDgFzf9zgUcEeizvX0PpJl26BfkGd8pO76b8YM+h/GTvA/LhT7/KQsW/f1BrCJKViSiKBJhli9fbo0aNbJly5bFjd8r0gx6RnpK3AhK0mNVMoLiAEZ8Bj2a5azPsfG/RR+uZ5uell2z6IfLy8uz+fPnW7NmzWILSBUFYh8DnI97CwxWCKFbbskypJ8V97Tu3bPdwBX0OMOoxCPQx3mLh99+HZzY8r0YDBgVGJjeuAyC8R4U/hh4EZQRVzA+gicRMzr4SToGqTfaPBg6+WI85eXauHu/twbdO9h+h2fn26aGER08JC8M6WX7Efdj8sjAj4gaLkqEl+ChLeBFheCg7z3YAI9IDBi/jZBJRnjrL39LtDXeex1hWJMe0oVnDnnivS2ZDGCUMtmFRMY1hqOfuCJSIjx6McULmH6bKAY0AhX5gYgAfCceoj7fqTdM1DDuMT7x6iS0B2WHQYdXr4c8YVLo85Rn2mvvXMutudRue/or69q4px1+UP62hjiEQeYPZsIzgjrkjW3qDQYioiATEv73W82pn+F4o0Eok+CW7CDkK8/K5IDJI30dHjQ8G4atFzO8AU7dxBBkskBZMMns02djWxs/fh+75JLA9gLbOCF46qmIXXzxEnviiU1s552z3Ge9Bxdl4AV5X648F/kbXACjvQa3V/s6xqJL0HsWKCffzkhvuK4gTjNp95MSDGQWbIKTFNrac9dNs5Nu7GRWrborf4RsRFom43zGi02UC3U76OWaDIho1HUmBfRRGN9MrlhAQWRm4kA95Du9AEQ5UY8R+LzQGmyL8WDy5id05AftjTwn76mrmGF43iRaLEgG7/3BOImXO/ct7yhexRnX4kGboc+mrwt6IJJvCI9eXAIvACaC6/1BrCyyIKb6CTl1N16cV+o1Ahd/R2SPTjhz7YADZtvNN/9keXk97f33s51YQp+eqNwIeYGo6L8bEYdrfQy8soL2x+Q27KGNaBJeTHHk5brG89YNX9uhd/Ry7Q123TVin366wWrWpCPYeDPGYNpA0Is7XN+Di2jsIvIhEsLQz2A3sHhH+TJBRsikvTC+e9GW9p3qTi8/ued/Fne9Dcm4FPSwTTSW8Tm8UFkAY3Id3sVREqgLCDj0AUHPaW/jAPUEQS1cv7w32prcHJu2YJp1atqpyLbmx2XyNbjVuixBSPHiC3WAxVrv+V04+W1Is+wC5eJtHfpTv9iZLIxv2KteZMEewR7jnrQd5hsIfsFwDHjRM54nCwtziMOMH9Q1vyh69aVr7ehNxtn9r7WxkV9s69oadQAxjDGOcYF6zu/YB+QX4xvtlmfF/mKHCmOl3zVE+infsMiKfY4t47fTexDF6DOxh7Gf/a4TFtbj7T6irftDND2IewiJwb6Yekm/yat+/Yjl5m7sO3gmBGjsZGxB+k6eBxCYqeOIldgS/syOVMFODoqNvm0zRjCGM76H5wsIuN4uxt5hHMY+8t6m2BncY/Zvufbfh6bYtedsb9WyEg/UjAeJQqf5ts0Ckw+HhB1U2OGE9BPYtNioYTuDBcFwWDfaN04uOA1gE3hbnHGUMYFFc8qC+s7vyAb77LPattoKN+dOtmFDdpELrIxp2KCUJWVOviYKx4aYzA4gFqUQM+nfyU8v0FKPWMBGHCXd3rb2ZYKN5XcjIFojUPvPYjdhh/p6RD75sF4e5hL0dwjFwYVfb1tjZ/t46rQVxi0/v/RgTwU9tIOw8MTcjPzFYYj5JvMI7ssinw93x1jJWEbeIfBy/9LebZgJmmNSou39999vZ599ttWpU8f9XBgX+SMh0xiJthmGRNu0I91F21QM7rlzs51HTfhQt+ISFFrjgRGBUYi3HxMuH88M0QtRMtEEG89LvN4w5PF2IM0YCRjyCHNMJPfaebW1WjrVWToffZkdd9BFhMSwwGPIb2PDGEQM8gZjYcUXnpBgiGIAYKgwAcIoZALqPS/C+BV6VrGDHnse8sBvQ8PQwGBCbMZAZYJIvsVLHwIKBgqiK4JconiQ3uBiksLqOKv5fpWavMdA4nnwFvKewYCRST7Fi9MVxm8dDx92wmfD9eytd9fY+pYTrE52bqytYWQxOUi0nRARz0+cvUHo4X1/YGFQYCpMpOX52YrmBXDvOYVHEJ4KGJV8DkOYa308Tv7Hi4C6Ez/syca2Fon0tPfey3ZlQ94woSMvMIb32iti69dHJ1DDhmW5yRzCFffEyOcarqfcCztwjPqMAU4dIOwBkxCErUT1mckPeYLh7L3X8ATku2nD/J3Jg1+84dkRRTF6x7+/1gbs/qOdeX9X+37qRvUYIYOJbDCESDCfaR9sZSfPEF5JM5OieIcQFYb3/qHdkz5ENya+8TwjeA5EXBYUqIN4wdCmyFcmf0wYfZ/FpJg+hrrHe3jqkMbCYpCnC6Ul2hYFCynkM5POwuK0Bq9HePB5jAcSLybMlB0TRTxB6feZBDKR995A9JXUx7Fjc23gwKnWv/+fcYWkRBAvlPsjyFR0eCk8lv2EnDYRE3n+EW2/um+i9bppf8tZEx0kJ07E8yrHJkyoZx06ZLm2lWirvBcf6N95TsYHv4iCEOX7tFRhPC+JuRPPPGaMpQ9DRGCxJt5hrh7EKvoOhJbydBygb8UGKazO0N6SFW0rChZFWFxLbWdb4aIt0G9iP7BAHw5DUVLoo0kv4xZ2D2MjXqGplj/ehyzCAp6oeNtnrY56o6xZV81+abW3ddyxbswmxa4JxxL2YwXQvmhnQTGIvsnnLeM+i7FBpwVEVX8AJQuP7JzhhVDL2IbISV+K2BQ8DDi40EPb8XadF/yx1YJxbhHuEKlJCzIJou2VVy6xr77axA45JMuJziy2hkVGxCzswaDd6z3fk2kjjPHYw9gyPA91wS8mJbPQQt30cdZ5Vh8qiPQginrv1WNPWGf7n/KDDTi8W0y0xXahfiCk+zAEjDHkbyKwr8hP7AN/cF1R/Rv5HG8HKeMadQzPVm9L+/z0YIMEQ9Vg42OTea/paPuhgrE6RkFnx3bIYa+SNyy4+TYWTzhmJwb9qLfvccBgbhHs89npxWID3rLem9fDOO53Pfj6yrwHW9Hb1h7SQh76Ou5jdntYcGdexnhPvvo2Fd5ZxRyJscWnOZg/wYUID/Y7DjXkud85GV7IiTc3pd6zGOLDdlBewcO/qwLLS1u0bdeunU2aNMmaNGnifk54s6ws+7W0ImZXIBJtMwyJtmlHVRJtk53cljZ4OiAolcR7rcCkMdDWInWznRHLKiyGC2IpAl8wviuTVowojJdChpa43wnhYsZIYDUXWDVmGx+iQ9DDIug1xOiHkYFR5D0f8UrBiOBRvBCINxoeIXhOMFEh7xCqvXCB6MBKeXFh0uO3ZmLk8Bx8v1/59kIz6WUyxoQEIxFvDDwwMegQUsiXoNDrRVTun8hbqrC2hjHMhAVPFG8QMjEKGrmA1w1GIPmE+Oi9jYHJD+WL4cqLSRTCCPnI73gC4y3As+FZxcTE18mgiOLzCYHQb2/kc3golbStTZ6cZ48/nmOdO2fbmWdWiztJYDJFPhcmYJQE7k2ZI0QkgvxAJHN17Z+2tnzzTtb7sOyYiOphksCiDO8zWaO++vAIhXmsMDEjTxFTKftEsemARRzvAc7Pxdk2XIWG7HITbcsSysZP+IKsX59jNWtW/LhWUuiH/GSVbc2IGkf0ib+tydscdeo0s8aNoxlCm6Fvoy3iEUc7ZIHQhykJtmUWLxBxKlKsztS2li6ibTrbkOW9hZDFYMRTLySxmIfNgfDDoiDjnBeAEfxY9E3kARi0ucBvq0/k9MD4jx3LGIrYyTUsfPoY1B5CILBYjvewD/HBYiXX+UdDOD766DxbvXrjfMV7tCKwBb1hgzslgjsTEu2gwOsSmwvPfEJZsKiCnUR+YAtif5GPeHUjJJdk+zm2MzszCLtwweAc+331NLMF21mDunWdFza2nPcKxnsfYRXHinghOcoa+mTqTfAsFy82Ysd7Bwe8srFnWUjDFoo6OuQXbQsjKK4HBWLeZ+ch+YBtRdmRfyykY89RR6h/XiwF5kssalCX/M4yhFXmB96LHI0PBwvqo/9ewMEhuADAHCK8g8CLwXj7Ui+oz6SB7/Mh88J4h4rCnp96zNiJB3ylnopnqmhb1ZBom2FkqFWq8Aj5kWhbCajgtuZFRrbx4G0RNC6YRGPgJ/JoKsxjKVFbwwuBn0sjig7rnRj8Qa8YJiZ4teTbFl8EeGsghOLVgRDNCM8r4bMlISTheYuBx4QBw47V9iBMIMg/Jglsy/Iib2l4gyGIY1AzIYsXt7U0Jrfp03fEb2tzFma7rf5MQPD0RaBHvPf1NF6YlHiEJ6yUG78zucCTCi8mH5cXMZh6iXcI2/aocz6WZSk+WiYN2Rkh2iam6glJadlvVJG2BhJtM2tiw3iEKMnYU54e2x4E0LDoV5g9xziMDeaFu+Cj7bVXnq1cubHvwLmBRWzGWdLCbhicA7wXb/h8AoQ+HAwQyPAQjh4EGd0ZxEGe3tOXHVvey9WHgygL0r2t4SWKqBv0wkXojTpuJC/aAnUUW6g4gjjzFAR15gEsOGCT4SxBOXr7Chvfh6PC49YfushOGHZRIoqzUwI7DUHXh45gkRDvYARfxHS/04N6Gt4xB1zLDirEZ2w9HG7KOnRRVWR5CqJtygeRhdmwYYOtWbPG6hc3yIoQQghRCmCoIuzFE/cw9Asz9osz58abobRgq5M/+MiDEZmKYAt4cwXj2DIBKKknlz+V2c+jMCIRCdnaycSAvOOFoBvPc7skeoaPdSoS471/8UIOH0QIyR7oFK4nvtyYBHivcggeHoFRL4QQQpQljEelKdhCYedGhElWsE1kzxUGu1u89yQg+MV7Vuwhwh/5AyyDYaLwoEV8BOwytpnjvICATHizwkJ2VXXixbePH3KraAiTU1wo82C8c2yycBg3bHx2gODkEdzhR8xj6gQ78CDsoMLiOnWEFwsgHBZLPQLsOwRfDx7t2JV8Bo/1VNqJKDuSFm3feOMNW7RokZ0eCKBx00032Y033uiE2969e9vzzz9vmxS2t08IIYQQaQtGpA9JEDbkMiHuqBBCCCFEZQSvSH8oGrvJEGwhGO4Bj1ofcowQRsnuthHpAaJ+OCQbixpesC0Krg3Gdka8JTQHDhmEh/COACxUpLJYIcqWpP1f7r77bluFr/g/fPbZZzZkyBC77rrr7IUXXrDff//dCbhCCCGEEEIIIYQQovQWzgl/xIFebIkHYpJy8Bd89FHBMyIk2IqiwBmDUAonnKC8SntP2ylTpjjh1vPSSy/Z/vvvb9dw9J5T4+vYoEGD8l0jhBBCCCGEEEIIIUoOO5s44DB6WFY03u306RsP/xJCVFFP2xUrVliTJk1iv3/yySe2X+DElO23397mcrSdEEIIIYQQQgghhCgTEGyBM4xSPQNBCJGBou3mm29u0zhi1DhxbqV99913toePgm2ccrjIsjPt6FEhhBBCCCGEEEIIIYSorKLtcccdZxdffLE99dRTNmDAAGvRooXtvvvusb9PmjTJOnTokHICHnjgAWvbtq0Lr7DbbrvZl19+Wej1L774onXs2NFd37lzZ3v77bfz/R1B+YILLrDWrVtb3bp1bbvttrMRI0aknC4hhBBCCCGEEEIIIYSo1KIth47tsssudtFFF9nkyZPt6aefturVq8f+/txzz9nhhx+e0pc///zzdskll9j1119v33zzjXXt2tUOPPBAmz9/ftzrOfzspJNOsjPPPNO+/fZb69u3r3v9+OOPsWu435gxY1z68AxGaEbEff3111NKmxBCCCGEEEIIIYQQQlRq0Rav1ZEjR9qSJUucGLr33nvn+/u4cePsiiuuSOnLObQMr93+/fvHPGIJsfA40bTjcN9999lBBx1kl112mXXq1MluvPFG22mnnWzYsGH5hN1+/frZPvvs4zx4zz77bCcGF+XBK4QQQgghhBBCCCGEEJWBGhX1xevWrbOvv/7arrrqqth71apVsz59+tjEiRPjfob38aQNgmfu6NGjY78TZxev2jPOOMNatWplH330kc2cOdPuueeehGlZu3ate3mWL1/u/s/Ly3MvkeZQhpFI9P8MKk//WP7nDHo01+4i/zxcsu3Qf6byt1nSFwn8XNnTmwJqa1WiraUPybW19Ok7Mr+tZfKjqa1llrGVlv1GFWlr4fJJ5zKqkjak/zmDyi3/o6V/31E12lqwvWVYWxOVglTaS4WJtgsXLrTc3Fxr3rx5vvf5ffr06XE/M2/evLjX875n6NChzruWmLY1atRwQvAjjzxiPXv2TJiWW265xW644YYC7y9YsMDWrFlTjKcTlQoEecpx0SKCHlsmPZZv6xn2aLZ2w1rLWxl9uEULFtnKGiuT6viWLVvmDAfafeWFBSLfSS8iErdlDGprVaKtpQ/JtbX06Tsyv61l8qOprWWWsZWW/UYVaWu+va1ZvsYWRarmuJaWZPDEJv+j5dmaNendd1SNtubb25rMa2uiUrBixYrKL9qWFYi2n3/+ufO2bdOmjY0fP94GDhzovG7x4o0H3r5BD148bbfYYgtr2rSpNWzYsBxTL8qEnBwUeLMmTcyyszPqsfxYn2GPZjnrc6za6ujDNWnaxLJrFv1wTKCysrJcu63cRlBOIDJNEzPLpIJTW6sKbS19SK6tpU/fkfltLZMfTW0ts4yttOw3qkhb8+1tQdYCa7JZ1RzX0pIMntjkf7Q8W7UqvfuOqtHWfHtbkHltTVQK6tSpU/lF280228wdZPb333/ne5/fW7RoEfczvF/Y9atXr7arr77aXn31VTv00EPde126dHEHp915550JRdvatWu7Vxg60kzoTKs8lGFWVnS0zKDy9I/lf86gR3PtjslQqu2Qz1T+dkvasgI/V+a0pojaWpVpa+lB8m0tPfqOzG9rmfxoamuZZ2ylXb9RRdpasL2lc/lUWRvS/5xB5RZ+tEyqm5nb1oLtLcPamqgUpNJeKqz21apVy3beeWf74IMPYu+xas3vPXr0iPsZ3g9eD++9917s+vXr17tXOAMQhzMvxooQQgghhBBCCCGEECITScrT9v7770/6hhdddFHS1xKSoF+/fta9e3fbdddd7d5777VVq1ZZ//793d9PO+0023zzzV3MWRg0aJD16tXL7rrrLudJO2rUKJs0aZI9/PDD7u+EMuDvl112mdWtW9eFR/j4449t5MiRdvfddyedLiGEEEIIIYQQQgghhKjUou0999xT4ICunJwca9y4sft96dKllp2dbc2aNUtJtD3hhBPcvYYMGeIOE+vWrZuNGTMmdtjYnDlz8nnN7rHHHvbss8/atdde68IgtG/f3kaPHm077LBD7BqEXGLUnnLKKbZ48WIn3N5000127rnnJp0uIYQQQgghhBBCCCGEqCiSEm1nzZoV+xnR9MEHH7THHnvMOnTo4N6bMWOGDRgwwM4555yUE3DBBRe4Vzw++uijAu8dd9xx7pUI4ts+8cQTKadDCCGEEEIIIYQQQgghKgMpx7S97rrrbOjQoTHBFvgZb1w8YIUQQgghhBBCCCGEEEKUo2j7119/2YYNGwq8n5uba3///XcJkiKEEEIIIYQQQgghhBAiZdF2v/32c2EQvvnmm9h7X3/9tZ133nnWp08f5agQQgghhBBCCCGEEEKUp2j7+OOPu7ix3bt3t9q1a7vXrrvu6g4Pe/TRR0uSFiGEEEIIIYQQQgghhKjyJHUQWZCmTZva22+/bTNnzrTp06e79zp27Gjbbrttlc9MIYQQQgghhBBCCCGEKHfR1tO2bVuLRCK29dZbW40axb6NEEIIIYQQQgghhBBCiJKER8jJybEzzzzTsrOzbfvtt7c5c+a49y+88EK79dZbU72dEEIIIYQQQgghhBBCiJKItldddZV999139tFHH1mdOnVi73MI2fPPP5/q7YQQQgghhBBCCCGEEEIESDmuwejRo504u/vuu1tWVlbsfbxuf/nll1RvJ4QQQgghhBBCCCGEEKIknrYLFiywZs2aFXh/1apV+URcIYQQQgghhBBCCCGEEOUg2nbv3t3eeuut2O9eqH300UetR48exUiCEEIIIYQQQgghhBBCiGKHR7j55pvt4IMPtqlTp9qGDRvsvvvucz9/9tln9vHHH6d6OyGEEEIIIYQQQgghhBAl8bTda6+9bPLkyU6w7dy5s40dO9aFS5g4caLtvPPOqd5OCCGEEEIIIYQQQgghREk8bWHrrbe2Rx55pDgfFUIIIYQQQgghhBBCCFFS0Xb58uWWLA0bNkz6WiGEEEIIIYQQQgghhBDFEG0bN24cO3AsEZFIxF2Tm5ubzC2FEEIIIYQQQgghhBBCFFe0HTduXDKXCSGEEEIIIYQQQgghhCgP0bZXr14l/R4hhBBCCCGEEEIIIYQQZXUQ2ZIlS+yxxx6zadOmud+3224769+/v2266abFuZ0QQgghhBBCCCGEEEKIf6hmKTJ+/Hhr27at3X///U685cXP7dq1c38TQgghhBBCCCGEEEIIUY6etgMHDrQTTjjBhg8fbtWrV3fvcfjY+eef7/72ww8/lCA5QgghhBBCCCGEEEIIUbVJ2dP2559/tksvvTQm2AI/X3LJJe5vQgghhBBCCCGEEEIIIcpRtN1pp51isWyD8F7Xrl1LkBQhhBBCCCGEEEIIIYQQKYdHuOiii2zQoEHOq3b33Xd3733++ef2wAMP2K233mrff/997NouXbooh4UQQgghhBBCCCGEEKIsRduTTjrJ/X/55ZfH/VtWVpZFIhH3P7FuhRBCCCGEEEIIIYQQQpShaDtr1qxUPyKEEEIIIYQQQgghhBCirETbNm3apPoRIYQQQgghhBBCCCGEEGV1EBnMnTvXXnjhBRs2bJjdf//9+V6pQizctm3bWp06dWy33XazL7/8stDrX3zxRevYsaO7vnPnzvb222/HPRTtiCOOsEaNGlm9evVsl112sTlz5qScNiGEEEIIIYQQQgghhKj0nrZPPvmknXPOOVarVi1r0qSJi13r4WcOKkuW559/3i655BIbMWKEE2zvvfdeO/DAA23GjBnWrFmzAtd/9tlnLm7uLbfcYocddpg9++yz1rdvX/vmm29shx12cNf88ssvttdee9mZZ55pN9xwgzVs2NCmTJniRF4hhBBCCCGEEEIIIYTIOE/b6667zoYMGWLLli2z2bNnuxi3/vXrr7+mdK+7777bBgwYYP3797ftttvOibfZ2dn2+OOPx73+vvvus4MOOsguu+wy69Spk91444220047OY9fzzXXXGOHHHKI3X777bbjjjva1ltv7bxu44nAQgghhBBCCCGEEEIIkfaetjk5OXbiiSdatWrFiqwQY926dfb111/bVVddFXuPe/bp08cmTpwY9zO8j2duEDxzR48e7X7Oy8uzt956yy6//HL3/rfffmvt2rVz34FHbiLWrl3rXp7ly5fH7sdLpDmUYSQS/T+DytM/lv85gx7NtbvIPw+XbDv0n6n8bZb0RQI/V/b0poDaWpVoa+lDcm0tffqOzG9rmfxoamuZZWylZb9RRdpauHzSuYyqpA3pf86gcsv/aOnfd1SNthZsbxnW1kSlIJX2krJoS9gB4speeeWVVhIWLlxoubm51rx583zv8/v06dPjfmbevHlxr+d9mD9/vq1cudJuvfVW++9//2u33XabjRkzxo4++mgbN26c9erVK+59CbdAKIUwCxYssDVr1pTgKUWlAEGecly0yGzlSsukx/JtPcMezdZuWGt5K6MPt2jBIltZY2VSHR87ADAcSrqoVLawQOQ76UVmlkkFp7ZWFdpa+pBcW0ufviPz21omP5raWmYZW2nZb1SRtubb25rla2xRpGqOa2lJBk9s8j9anq1Zk959R9Voa769rcm8tiYqBStWrCg70dbHk0UM5SCwmjVrFgh5UNFq9ZFHHmmDBw92P3fr1s3FwiX0QiLRFk/coAcvnrZbbLGFNW3a1MXEFWlOTg4KvFmTJmbZ2ZZJj+XH+gx7NMtZn2PVVkcfrknTJpZdMzup9k9cbdpt5TaCcgKRaZqYWSYVnNpaVWhr6UNybS19+o7Mb2uZ/Ghqa5llbKVlv1FF2ppvbwuyFliTzarmuJaWZPDEJv+j5dmqVendd1SNtubb24LMa2uiUpDKmVvFEm3fffdd69Chg/s9fBBZsmy22WZWvXp1+/vvv/O9z+8tWrSI+xneL+x67lmjRg0XHzcI8W8/+eSThGmpXbu2e4WhI82EzrTKQxlSN/k/g8rTP5b/OYMezbU735+k0g75TOVvt6QtK/BzZU5riqitVZm2lh4k39bSo+/I/LaWyY+mtpZ5xlba9RtVpK0F21s6l0+VtSH9zxlUbuFHy6S6mbltLdjeMqytiUpBKu0lZdH2rrvucgeFnX766VYSatWqZTvvvLN98MEHsXizrFrz+wUXXBD3Mz169HB/v/jii2Pvvffee+59f89ddtnFZsyYke9zM2fOtDZt2pQovUIIIYQQQgghhBBCCFEepCza4pG65557lsqXE5KgX79+1r17d9t1113t3nvvtVWrVln//v3d30877TTbfPPNnXcvDBo0yIU4QDg+9NBDbdSoUTZp0iR7+OGHY/e87LLL7IQTTrCePXvavvvu68I4vPHGG/bRRx+VSpqFEEIIIYQQQgghhBCiLEnZzxvhdOjQoaXy5Yird955pw0ZMsTFnp08ebITWf1hY3PmzLG//vordv0ee+xhzz77rBNpu3btai+99JKNHj3adthhh9g1Rx11lItfe/vtt7uYu48++qi9/PLLttdee5VKmoUQQgghhBBCCCGEEKJSedp++eWX9uGHH9qbb75p22+/fYGDyF555ZWU7kcohEThEOJ5xx533HHuVRhnnHGGewkhhBBCCCGEEEIIIUTGi7aNGze2o48+umxSI4QQQgghhBBCCCGEEFWclEXbJ554omxSItKfmTPNttzSrE6dik6JEEIIIYQQQgghhBBVJ6atqGK8/77Z668n/vuSJWZTppj99pvZXXeZffxxwWvmzDFbtSr6c16e2YoVZZdeIYQQQgghhBBCCCGqmqctcADYCy+84A4KW7duXb6/ffPNN6WVNlFRRCJmf/8dFWRffDH63hFHbPzbLbeYtWtnNnt29AVdu0b/J6bx0qWcMhf9fflys5tuMuvY0Wz77c1efnnj/fr0Mfv9d7PXXjM76KDovZo0Mdt5Z7NQrGT3vYsWmW22WeI0Z2VZxoHIXU1rK0IIIYQQQgghhBBViZRF2/vvv9+uueYaO/300+21116z/v372y+//GJfffWVDRw4sGxSKcoWhNXRo6Pesh06mH3wQf6/I4ZOmsQpdGbffRd9j2uD8P7uu5t9/rnZhx+atW8fFWAJmQDTp0dfHrx3gx68/jogBMfhh5vtu2/0O6dN2/i9xFPG+5c0n3JK9DsRfXmvdWuz/v3NWrWKCp0IubNmmdWubWkLh/T17Wt2wAHJXb9wYVT8rlfP7OefzTbf3GynnQoXhVeuNMvONqtRrDWcwkGUp1wyUVAXQgghhBBCCCGEKCNSVmkefPBBe/jhh+2kk06yJ5980i6//HLbaqutbMiQIbZ48eKySaUoWy67bOPPf/wR/R8xFCEP8fTaa80eeWTjNcccY7bXXtG/r19vVr262a+/mrVta3b66Wa33mr20EMbr99nH7OJE8322y96TfPmZmvXmt17r1nLltH3f/rJrGHDqOA4b57ZG29EX2Hw5PU884zZJ59sFJBJ+403Rn8++OCogIy38IYN0f+bNjXr0iXqsbvjjmYzZkS/F6680qxFC7M//4ze97zzooImfyfNW2wRvS4312zNmqjH8IEHRq/h9zZton/nO3fdNXUBlJAR9etHf/YCJ+nk+375Jf5nFi2yNq8+ZdXWr7WaYxaarV4e/zrSjnh64onR9J15phltlfxAoA+K9CedZPb999Fn2HbbaHk0amQ2dmzUW5qyDAuw5AHhL559NvoMvL7+eqOgjnj/r3+Zbbpp9HkoD/Jn7lyz1avNPv006lmN9/bIke7zWaecYNYgjve096jmc8uWRUV5PLtzcqL37N7dDO//WrWiwn08T2XqLPnBc1K/qYv33292/PFm8+dH0y2EEEKIigXbArsB+1AIIYQQogqSsmhLSIQ99tjD/Vy3bl1b8U980lNPPdV23313GzZsWOmnUpQdCFae//wn6qmJlyZCpefcc80mT46GN8ATNyhI+jAG22yz8b3Bg80GDYqKuVdfHfV8RQwMc889G38mJEIQRDVCbSAsbredWePGUcHuhx+igiMiKeIwgi3et3iijh8f9cr99luzd96J3ofvRfxEiEUsRKgDxNkg3CvIddfl/50JA98fjMeL2BiP//0v+v8550QFxk02iaaL2L+EgcDj+L33LCF4CyMcfvFF9Hfy/vnnoz8fd5zZq686EbXmBrP6f/3zGbRNimWrraJezoSRIP8RQclLGDUq/rMFee656P+kNQzp4B541D/wQNQDmDT+5RMRAsEWyP+rrrIimTAh+v/KlVZj+EPWYvumNu+wff4RyldEhd9CyIpErPaKFZZFPQiKveefH603PrYyddzj6wncdlv0/3ffNRsyRN7BQgiRSbCwy1jAoi74BcTg3xk7K/owVXYSsZh+xRXRMb0qw7jMeH7DDdGxG3sK20aIqsaPP0YdFQ7fr2zuj0NDODSdEEKI9BRtW7Ro4Txq27RpY1tuuaV9/vnn1rVrV5s1a5ZFELVEekHoAAQ4vBPxnMULNgwel7yShQnPiBHRexY3NAHeod671YPhjqesn2z17h2dYOH1y9969Yq+EAmZ9OC5CXhhIkLzO16ZdetGxWrqK5/HaxMhE2OFOLsIxYideGjilYqXMfcDPsv1QQjjMG5c9OeePaPeuQiZQW9jj/fsLQzCQyCGTp0a9f584YVoyAn46quNwnGLlraiVhPLyttgkQ41zY47smCekV6ESvKIEBOkCe9TBGV+5xn79YvmBWlm0eXQQ6NesRhweDbz/s03Rw+ZQ9CkvgAhNTzNmkUnmeQTojoiNyJpgwbRv1MfgAkyZecFaLxtEd2ZLOM1jaf2mDGWW6eGNXn0fmvy+fdWs8GLZtXidFUsIOApu2CBu2fkjz+sNiIszxzkwQfz/075Ivpj/FJXgEUGPI75nTSNGRMVzskjypQ8pM5Qfk8+Gf0MAvyee0afPQhe50wqKQvv5Uuek8c8e95qs1AS44JwTv3k/l6ExuuavKV8+I5gLOfyiH/M9+GNHK+fEEKIisDv8mJxl4VZ+l7sD8YDxjcv8rF7h8VHv4jHGMfuDHb70I/6xdtLLomOL0Ho9xhrWLwOwxjDwvXeexccf7zYQj++yT+7aRLB7hEWq72Nge1BiCTCHJU2jOWMJT16RH/HNiLvGO/KGhbbsbdY0AdsGsqLXTlhOF8BGIvJZxaLvegeD3ZVUR+23rrwNPjdOezKKYpEcxsWibkPfyf/qH8edmAReirROQxhWJwmDBnPi+2BQI29QV3gmV96KWq3UMfKAveMWUWfEUE58TfsIep6aQjoqeZVRVHRZ2fgCOH6rZVmvWuaNS6iPwHKCRveO+Jgy1KGtBPmRG+9FXWawZkEp4z//jdqIyfTHorKC59f4cUxIeLhDypnd6fIHJhXowX4sVaLQ8UmK5Ki0nrWWWfZFltsYddff7098MADdtlll9mee+5pkyZNsqOPPtoee+wxS3eWL19ujRo1smXLlllDbclKfzCq8XTt1CkqTCcDIRoQxuhkMHKYsHkR2BtCiKrdukWNEh8mwd8fT15EQcRWBC4mgAhqfA7hGeOUz/kXMFjxfRddFP0dw4lwD/wdIRBP1M8+i/7toYfcY+FUCsyzkno0DDXSkeygSOdKF+E7W56TyRMTVyZ9eEHjCZSs8QYYcBj6hXwmZ32OTXviDmv24RfWokFLq7ll22h4BxYEEFV3263AZ/LWrbNF331nTfLyrBp/5zspI0IfIDrHmxDGMz4JBYFx7MVlJvVMpgqDCejtt0fjNHsBPyEbzHZvZFa9mtnXOWZrcqMe6QiyW24ZFRwQkL2nMtAP+YUDT1AA9xx5ZHTyx3NTx/gcYT8oR0R2JugcEsg1PDvp5m+UL5NOyhdPewR6yoi/4cVOG8A7nDIPgwcU4v4hh0SFCQQRYlQjxHMvYk/zfW++GW1HeIIzgO+//8Z853vwbub7SQee2aSb5/MhSYLiNfA+XuQcjHjaadE6yfOwUMT3APcjpjIwGUZ0IR/YPcAEm3bK9/Cs4UkF+UM/0LKla2ufvb3Umk56x9oPOsSym9ZLPAnxdYrJFd9J/QmK6ZQj+R305uM5Waghb3jOoiaH/J0+jeu5LlFa/EGhvv3S/3AtD0QYk6ws19bG/xbtSHq22M2ya9ePTuQQCz76KFr+LKykMkFDpCLfU5mE83lelFG8RQHaPWUVzEu+i7ZCfaLeUQf5fCyOd46Z/dNJGmLUP51kaBKZl5dn8+fPt2bNmlm18MJHWU7UExmvyRi1xRnXaDM8XyWPMZ7w0XzoG9olgx+LmuAXDOPB9X789bHxk4VwQPRHtEnGBRg+PDoOU9+o54ghxI/30K5YJGXhj91LLLpyHc913ZU2dcxTtqhHN+tZY2urW6d+1M6gbjO+syjroT/zO15Ix2GHbVwE9WIeu4pIGwIl34v9zWIooYzIK8YBbBAOfmWc5jP0S5S/3+mEnUGb8XBwrG+3fqGRsYnvRjzmnAG+h2djBxDppK2339ys2if52xr1jXGAsQzhkf7WL+rHgzSSVgRLfyAtonVwwZz84rsTtQ8W3NkFVZiXMu3L35e+jfTzmTPOiI4FXtSnHNnZxIIy9Qgx9dhjo3Vt+nSLPPecrV692up06WLVfB0g/6i8Tz8d/X3AgOgYQD5SxtyL/CYvGetZeA7afclAn8yiMeMrZUS/zj14Lup5orzh2rvvjtYn+sisLFv97XRbeeM91ngTs5qHHhity5T3v/8drQfkE3YEeUNZMiYEdyvh8ED+8XyI8YnsQdoRIcko17ffjqaZXZv06exOY/yL5+iQDJQH5eYFZeod7a1BHZu2cLp1atLBsleti+YX7YFnZ/wnDXwWe4F6HHQyCU6NuSdtFFsHaF8XXrhxDMFeIjzYHXdsDOPBAgxtMhVRG/ucz/vPkM+UAWmhzbDg5NhgdvRWZgfuvLGtYZthh1H+fB5HBMqCnWaA/ex323mwN6nnQQgdh1MH/StCLo4s9C/0Z9RrfzYJY/T1128cT0g7+Ul6yRfsbvovFmbIO8AJhmeiP6B+ER4vTLEmNsUYq7ENqSe0lfA8oIycE3KWb7Dxn0XrzF575dnKlQlsjoqkmPbO6mk/2NQGa6xT8+0tu2Z24jzn5dsN/QxzURYq/SHlwJjGfI38ZzGHOTPjVLA/pw+lnrPzwtuO/O6/o4g5ZmpgR04zs04bbUi+j/rDs/j6wzwl3qJtWeCddPifvoZ8SIQfx/24QDrRJ3if9LMYmIqDXap1hP4EWGwN7oJmHBKWiuaYsmjL5IZXjX8a3ahRo+yzzz6z9u3b2znnnGO1klm1ruRItM0wijO5rUgQyJhsYegHO0Y6ZwwzJg6bbFIi26ay44WkmouXWY9uh1l2rXpFfiah8JLqAEP+42kFeLUw0cJoxcBggoUwxD0xSL3XcRAMdSYEPj40RmxwkoPBbT6kBEZrIR4ITHCIDxyka9eC4oMfvCsaL1YwMS8LzwoGNGJJ0zaYlCfDxRdH42cXBZMcxi8EGQwgGhSCC3P8DWbz/imyFi3NasZ7NCbnCDgIGiyyhEEw79zZ7OGHNxotPAf1CKM1NiH7B+os4gTPjDiCgYhI5UWEYJ4gXHvjkbqBsJQMjRvbmj772HcLp1ikejXb+b0pVjOeVzv1GbEEjx2EEwR3BCPqNsagFyww/IIhd+iYeE5/MCR12ffDfIYY2xiQ/MykPgzPwSSRMkHQOvvsaJvAYKd9JRJ/8K5kEaxjG7PmM83qYJf0NIvUjU7SL788KngzUZ83zyKzZ9uCo46yzRBfEJZYeKEs+RmRgnrBs2CQIzjgjUR+ePwkj7+TVvInOFlHZCOuO2LBDjtE+xgmKQhFfvJKPaBcqQse6jrfQ90JG9WJxjXSgkhI2XBfyondG4wbiAv0ZQh7PgwA9YoyYwGEOuR31QT7UModbwmMe76TZ4g34HANC1B+kSzY93pBid997Hd+jzNhjftoXgRAsEIgoD6wAMXCCGUUDwSnYMgu0oQ45uPqkxbqH2IHZYyQxvMjwCSzK8ZDuVNWLEyFF9ICrM/bYPNWRDsStxgZr635GPQ+DFG4fw22/UTsskt+Abi0CAowYXiUyJ9mB+xktuleZs8EJuGlBe3ah5+iv6Yt+xj0lCOeuAiDHjynKVc8b2lH9F20I9p1MDxXYQuhwHs+DBRt6p+xjekTom3dZs0sC3EhEfTjLEomwgvSiIH/93/ResTviGp8NxNshAvswn8WAOIu5HpY9MRWxCZA7EcYYaGDsYFDewMwti1dYlHRNtkhG1HFL4wC4yVtEvwuI/oXBED6dfKLRQ+/s6koaIM0fvoMbCfGh1NPjToJeLxYzdjonRnitLdfjtjbtn59Qvy2FoR+kbzCOYM+8dJLC7+e8YH+gv7Lh/byiwD+7A0EacY/yrEwA52xjEV/ngeRlIULnovxAbuC+uDrH+L52LfM/phk1mdHs8a7m9WoX1CQjTcexwMhm/EIGOP8pKK4UC+pz95DHns4XtugL6aMGe9YCKGO4ARDXSecHfehf2fcRGgnX6kLOFBQD2jnfA/CPwsYtGff9hkjuMafaULeYTdTJ+kTgmHuOL+kKHuJ3ZTckz6EOszzYOfRrvidfhkhmvrDGEn9ZWzhOhaR/hG6aWtzF9a2P/bvb11P72qr/vrFms6YYdVoJ+QTC2P/LDi49kUeIlYyXnJv6lpZHKxNvhKGhjIInk1EXcRWoB0+9VS0/yGNtBUEOMqQedHtt7u2NrtnF9u812GWveXW+dspdgdtxM+JCuPkk6PjbyKwoSh3H54QW8T3iZSRX1zFtqbtJAvjGvUjqGGRduzthrXMOlYza71P1IakffvvSQRtGPuSc2EQVck72hdpZQGFukHZ0vaouyxqkpe0CWxTHIB4RuYv2AP0sdQjysjvBqYsqJMslrGAgmNNsC/0dhhCKc4qnN3DM1F3881J/9k5Sl9AW2GM8w4U5LdfLOWZ6d+xLc46K1oXsWPJaxylaAsskHtnNJ6NNIS/y8Pn+Www1CSLYaSBfPAONxnO8rIUbasCEm0zjHQTbZOkKoi20LNNz8QrtwEK9ZZLFSY3TOwxuosSfRnI+D4EKUJLYCzG89YDF5Ovttmit8z+WmxWZw+ztp2inmIMehg2FCQTUow/vheDiUGfwdeHHGEyxkDONVQEX/jcn8krBiDGKtcwQUVowhuVdoBhizFLe0CMQ5zwgyq/M8HgWiZKCD4Y0RiQGM1MaLyog+GLOMn3MNgTDxmjjHvjbcTgTdr4mXQz4cYYYKLJ9XfdlX/SiUcuBjjpwYhmsoHnMAM3hntQzAqC5xBiDpNUbxQirPP54CF7/n0aC8aLhzLDszQRNWrY+jUbbO78Gvb7IefarnNftVoc+hc0/MNQVhj0CIwvvmilJowEwXjiWh+zOtnP+QMG//HCjSskMUlhYo6ox+QUozueMOaFA4xHnx98DnEVYbO8YHGFtlogr/9ZIGnfyuywgWYPPhF3EhsTX+rWtaxUvTMQWiiLcBxwJhbkdXiBJRnPfdombTWIjyFO3cJjAhGG7ex4nzAhTUbMSwXEAdqFn3yHQURiIkF+Ug9YdAgKOcE2x9+ZUCQp2qzr1NWmdj7BOmy+0uquWRLtW+KJ+h6EO/os6iLtjj6JciSfmAAQFoEQCEVtmQ/DZ5lkkce8aAOII9Q3+mTaHBNu+ijvwc6km7QysWIizMSNfrNePVv/yAgbf/zu1uaZN63N6tpWc8edo/XWh+zxYR1YZEJwpl9n4sSiDPf20D6pczwP7Z/+PSxqkh4mVtRBJoyPPhp9n0kUfTqTcyZk3vuGfpv+GaE4KHwCE/dg22KSySSeRSjqIPernme2aEr8xUjyh76Eeu0XNxCzfN/B5I5JLM/y+OMbD4z1fRvegowZhKAgT8OLYkzOg4fl4v3pD6VNBM/K4izjHB6eQbGAsZ/v43tIJyI46WLc5XmZ4Pbta3nVq2+0OahvjF2MawhBCFAs/gTFNBbdEBkoG+opO1Luu29jmpOZpJIG+ppSOj9kfc1s+3m3U6ztHq2s7t+zo4IBfQzhGBg/EZwRer2xSVvj3AVsGsoseNYA+ZaovwjC4jf1IVinEDkQKooC8YB2Rx3yCxOUFf0q4x9lgPDSooWtn/2rLV29xBrX3cRq7rtfdByjzLEZ4vWxYYKCkBfDaYvxhEgErpAg7tqoF+FoZ7TF4DjB4iFtCRuvKBDJ6MNoL2uXmF10Sv62Rl/BuMvYjg2HMOPFWOowfQvppmypf5QntiILjIzV9FMsPHmHBb6H9Af7XdoUwhb5hjjkxzGeDbuKNuU9zLHd6Ps9tHXShPDEwoS/LskFklLH7zj0iw1h6JcZY0uRfIv/LSK2fn0xbA76i1RirofnL8HfWQyId+B3sgRsTuzIWFurXTdap1nUCJ4fg71A26ef9WO0nxvRh2Mve095hEjGPPo7xEycQYLQT1F//RgRDxZe6CeYOyDoMmZTV8k/5lGMtcwZ6DN830O9py1xXUxkxo5kfOxk9ldAgGT8ZHwmDbSrZBelKgP069Rv2nHYYaQ0YC7p+w76Z/pN6gJjBCIwfWVRu1KrSEz/5WUp2o4ZM8bq169ve7GyZNh3D9gjjzxi2223nft5k8JctNMEibYZhkTbtKPCRdsyJcGW7YrExzkraf/N5J3tdkxyk1lF4HsZ0JOBoQqPA+6L0Yc4zsQgGEcwbJQyMWFSjsGGgRkOU8AkxoctwWjjdy9K4+nCBKZOHctZsMomfFHLIjVqblwg8QcjYoQguiCc8PykiUmTB+MVw5NnxcAjHaSPCTtiARMjJkPkvU8fAihpwbBBjMPrjkkiHpNM3hD4vScn98R7N7jNMbhN0/eBGK9B4/2f0BOrZ0yxqV+8aY2/mWotr7/DsluE4mIH8xUjGQMaY49nRiz0YIj5FXnA4GUCSVkxOUIkYhJMOqhrxB/H0MVg9nHJ/XPzXXyORQyeg0keEzm8HoJw6CWiaRDym0ko4vHSBWZzvsw/uWVyjaDCpIw8W7bM8iZOtFV//231ly61LCYaGIte9KNMvLcEYhZ/R4gNi7EIMogIpDf8N+ofLy9wUIl4dibKiCNMZJmgHnVU9D3vNc37TKyC2++D5ceEk3oTnNgykeAZaSvUf4xe6jH1hUk2i0zxJjqItJSrFwCDUG58H/WOxRi8aePBRAoRgHygXpJvYcgfxIU774x/j8K8//Cs9sIpHrN431JGaXIAbnHGtRg+nj2x/MPQLhGqfF+GZxoCTHjHCSQjEMRbqPTbif3260Tj2qdTzEZONbvz3mhbZuJW3HGFtkyfEuzLWLhAQI0H/aiPMU9eMVmn7SAkIgIwIfdnGeDBFN7KSugHPk//Hcy74Ao5C4v/pCeuzRGO38kzIFbhLR/sLz30a/SBqcYSpg0zEJEWyoUyoS3w/X4Bh3TQFyHas7hAHiCA8/z/bAvPWZ2VnE9DosXrcBgB8pp+kr6EMZb+iH7MC/ZsiQ16Cwa3qfv8YqxjkYdxgWsZP/AWoy+Mt/XWh8MJ1XHGttmzvrU2PQ+37OxGBcuGPKQPZQxBQEDM4bPYAix48D73pM7R/9OmeD5sDh/OAs8w+kTSz84chG7CL/Eeoj9nIPjxPxH0+fSJPqwA+cczcWYCYxQ2EuNljByzVWPN5sw3+5OF2wXRfoGFnzCpxpOlvfKstAHaD30tC+/J7qINOhHEg3ZJPtOf8T9tggUlFofpx6gniEjUAR+6gMUhvBsR/BhjsCW4nr9hGwF9DAubLPhgJ7G4ggjIQo8P8eZDN7DYRXulTBBBWYTAnvELbz7WtT+LAmGdfCEvEaLpR0gTbc73vdRbxibaG30W7RrvWK4jrcuWWc6StfbJdw2s2eev2/Z/f+BE2zo332zVGCexA7iX55hjoo4g/I2+A8cFIO2I/YkgzXyGBQofoovdPNiqCHTYZbQpbHTfT1DnWYyh72NhgHznswhtQN/kd6f4BQbaI/fB3jvrLMtplG1zXxlpW67Islo/TI1eQ32krMhP6nc8AY70ck1RobR82C2/uwrHDsqKxUMW3BhraH/YdeQR7S4Zz96iwFmlNruXPjT7YZFZ222i9ZBFEPrecJ9IX8Z7LEhRt7H3cYrB3sPmxyObusZcgXqCbUs/x+981u8qY3yiPgwdGr0vdZu6RJvBBiff+J0+FXva29fUa/oL+ly/wxDbk36X8Y3v9ovEQbANqePck3zlHnwXdcSHPaMekib6YxafaGcsIDMXo73yPIjrQWcc8op2kKhM6Q+8fUBfjP1JnnEv5jqVPJRXpRdtO3fubLfddpsdcsgh9sMPP1j37t3t0ksvtXHjxlnHjh3tCQoxzZFom2FItE07JNqmKWprFUcx45WWSEgqa+IJBf69pA4zyDHbMM7s4+/NNu1l1q1H3PyJiS81a1o1JiHJ5iFpYXKI4fnPQnZMbGXSHp6M4InBRC+80FDUs2CgMwnEqKaMMYqZvGLoY3zzHmnw2+WC92XCVFR85PDfSTeCBhPgRJ/Fw4ZJCJ/nezDcw56CpBtFyHuuhP/uzU+u43sQldq1s5yFObbg3mesZZ0lVqtXj+gEgglJWDhIRYisBFTqtlaai5GRvc2yklyMKwl4p1InmPhS7wuL1V+S2NQJtjWlz0JxBg3Z9BWIN/Q5/8TlLfRshAXTrFPTTqXf1hAv6IfDB9EmGrPou+lTWRT2Xn4INAg7wT4b2DVE3xsv5mtlXfgvLTJ4C2Hw0VxM28V/WLPWrQvvO4JjI2IdwiQCJWEeGFMRZekDEdr538ddThZCyiSKu4yIRj1NIrZvvrZm/9gyRdpnZQhCKSEDCMPEggcOEAiBfhcMCyLYY4jTiIaIsCzmhyUx15bx+J1itqG9Wa2Q7VZcKNdE5zeU5jkEZXkmA/kXdDYJfmca2WXppDmmvO9g1qxZzqsWXn75ZTv88MPt5ptvtm+++cYJuUIIIYQoZ0rjFO/KRjyjz7+X7ISgRnWz/XY0s24bT0dPBAZoKoYm14a953gPBQTC3iPxDkNM5lkwivHw5eXx2+WZ1PIKT/yTua9PbxjuFe9+QfCG4FVUuvEiK+q7fd31h31mZ9vCowZY005mtQqbs2tSUDkpr3Jhi2ey36e6khnQpyBoVjR4ocXbpp5ozGJsCXqcI9zipRYPPHpF5pOM97IXxPDWZYEWcRZR3x+kGA92/SDucvAbXrfYHSwWsFjsD+tisRlv28LsxuLukKhIsdaD7RWMCY/nfDIkGicoh9I8s4n7JXPQXUnt+rIc9xLZsxpry4yURVsOGsv5Z7Lw/vvv22nEEXM2/qZOLRZCCCGEEEKIMkOTQyFEVerv2K7PQjEH1wELp+ymwWOUre2EIUBcJMwB4GkaD8K8CCEyW7Qllu0ll1xie+65p3355Zf2PHH/jF1yM611FTnpTQghhBBCCCGEEKJcIGYrMV3TNCSLEKJ4pNzihw0bZjVq1LCXXnrJhg8fbpv/s13vnXfesYM4UVUIIYQQQgghhBBClB4SbIWocqTsabvlllvam3FO8LyH0xWFEEIIIYQQQgghhBBClK9o609M/fnnn92pqfwcpCenPQohhBBCCCGEEEIIIYQoH9H2888/t5NPPtl+++03i0Qi+f6WlZVlubm5lu7459LBahkCB+etXEmBmm3YYJn0WKtWRX/OsEeznPU5tmrFqlg73FCz6IdjAWnFihVWp04dq1aptw5xkOM/BWcc3phJBae2VhXaWvqQXFtLn74j89taJj+a2lpmGVtp2W9Ukbbm29vKFSttee2qOa6lJRk8scn/aHm2alV69x1Vo6359rYy89qaqBR4rTGsqZaKaHvuueda9+7d7a233rKWLVs6oTbTwAiDLbbYoqKTIoQQQgghhBBCCCGEyDDtsVGjRoVekxVJRtoNUK9ePfvuu+9sm222sUyF1fO5c+dagwYNMlKUFiJTV6tYaPn999+tYcOGFZ0cIUSaoL5DCKF+Qwghm0MIUV4gwyLYtmrVqkiv+5Q9bXfbbTcXzzaTRVsyrXXr1hWdDCFEMUCwlWgrhFDfIYQoa2RzCCHUdwghikNRHrbFFm0vvPBCu/TSS23evHnWuXNnq1mzZr6/d+nSJdVbCiGEEEIIIYQQQgghhCiuaHvMMce4/88444zYe4QQwL03Uw4iE0IIIYQQQgghhBBCiLQRbWfNmlU2KRFCiBJQu3Ztu/76693/QgihvkMIUVbI5hBCqO8QQpQHKR9EJoQQQgghhBBCCCGEEKISedpuueWWts8++1ivXr3c/1tvvXXZpEwIIYQQQgghhBBCCCGqICl72j799NM2fvx4++ijj+znn3+2zTff3Am4XsRt37592aVWCCGEEEIIIYQQQgghMpwShUf466+/7OOPP7Y333zTnn/+ecvLy9NBZEIIIYQQQgghhBBCCFECqhXnQzk5OTZ27FgbOnSo3XffffbSSy/ZDjvsYBdddFFJ0iKEEPnAq//www+3Vq1aWVZWlo0ePTrf31lzGjJkiLVs2dLq1q1rffr0sZ9++infNYsXL7ZTTjnFGjZsaI0bN7YzzzzTVq5cqZwWIkO55ZZbbJdddrEGDRpYs2bNrG/fvjZjxox816xZs8YGDhxoTZo0sfr169sxxxxjf//9d75r5syZY4ceeqhlZ2e7+1x22WW2YcOGcn4aIUR5MXz4cOvSpYuzF3j16NHD3nnnndjf1W8IIYri1ltvdXOWiy++WH2HEKJiRNs99tjDTXKuvPJKZ7zwPx633377rd1zzz2lkyohhDCzVatWWdeuXe2BBx6Imx+333673X///TZixAj74osvrF69enbggQe6vsmDYDtlyhR777333K4AhOCzzz5b+StEhsIOIATZzz//3LX79evX2wEHHOD6E8/gwYPtjTfesBdffNFdP3fuXDv66KNjf8/NzXWC7bp16+yzzz6z//3vf/bkk0+6RSIhRGbSunVrJ7h8/fXXNmnSJOvdu7cdeeSRzoYA9RtCiML46quv7KGHHnKLP0HUdwghSkQkRTbZZJNIkyZNIieddFLkoYceisyYMSPVWwghRMrQXb366qux3/Py8iItWrSI3HHHHbH3li5dGqldu3bkueeec79PnTrVfe6rr76KXfPOO+9EsrKyIn/++adKQYgqwPz5810/8PHHH8f6iZo1a0ZefPHF2DXTpk1z10ycONH9/vbbb0eqVasWmTdvXuya4cOHRxo2bBhZu3ZtBTyFEKIiYN7z6KOPqt8QQhTKihUrIu3bt4+89957kV69ekUGDRrk3pfNIYQoKSl72i5atMg+/PBD23333e3dd9+1Pffc0x1GdvLJJ9sjjzxSMgVZCCGSZNasWTZv3jwXEsHTqFEj22233WzixInud/4nJEL37t1j13B9tWrVnGeuECLzWbZsmft/0003df/jRYf3bbDv6Nixo2255Zb5+o7OnTtb8+bNY9fgxb98+fKY150QInPB237UqFHOQ58wCeo3hBCFwQ4fdugEbQtQ3yGEKCk1Uv0AMVpw+ed14YUXuo5o2LBh9swzz7jDyAYMGFDiRAkhRFEg2EJQVPG/+7/xP7Eog9SoUcOJN/4aIUTmwgGpxJVjgZnY+0Dbr1WrllvQKazviNe3+L8JITKTH374wYm0hFki3vWrr75q2223nU2ePFn9hhAiLizwfPPNNy48QhjZHEKIchdt6ZA++ugj9/rkk09sxYoVzhsFAbdXr14lTpAQQgghRGl5vvz444/OXhFCiKLo0KGDE2jx0Oeg5X79+rm410IIEY/ff//dBg0a5GLo16lTR5kkhKh40XbXXXe1HXfc0Qm0eNX27NnTbUkWQojypEWLFu5/Tnxv2bJl7H1+79atW+ya+fPn5/scp78vXrw49nkhRGZywQUXxA4f5IAhD22fA8aWLl2az9uWvsP3C/z/5Zdf5rsff/d/E0JkJnjhb7PNNu7nnXfe2XnO3XfffXbCCSeo3xBCFIBdx8w1dtppp3zhVbA92I1MOEnZHEKIkpByTFvEDgyYO++80w4//HAJtkKICqFdu3ZOPPnggw9i7xFvkli1bG0E/keYwaDyEJObLdPEvhVCZB6cW4hgy7Zm2jt9RRCEmJo1a+brO2bMmGFz5szJ13ewTTq46IMXTcOGDd1WaSFE1QB7Ye3ateo3hBBx2W+//Zy9gIe+f3GWximnnBL7WTaHEKJcPW2ZsAAiyLRp09zPTGCCq0tCCFEarFy50n7++ed8h49hABGTlkODiFX53//+19q3b++Emeuuu85atWplffv2ddd36tTJDjroILcrYMSIEe7wIcScE0880V0nhMjMkAjPPvusvfbaa9agQYNYDFp2BdWtW9f9f+aZZ9oll1zi+hLsGkI8IdRyyCoccMABzrY59dRT7fbbb3f3uPbaa929a9euXcFPKIQoC6666io7+OCDnX1B+Df6EcLB4SmnfkMIEQ/sDB8z31OvXj1r0qRJ7H3ZHEKIEhFJkb///juyzz77RLKysiKbbLKJe/Fz7969I/Pnz0/1dkIIkZBx48ZF6KbCr379+rm/5+XlRa677rpI8+bNI7Vr147st99+kRkzZuS7x6JFiyInnXRSpH79+pGGDRtG+vfvH1mxYoVyXYgMJV6fweuJJ56IXbN69erI+eef72yY7OzsyFFHHRX566+/8t1n9uzZkYMPPjhSt27dyGabbRa59NJLI+vXr6+AJxJClAdnnHFGpE2bNpFatWpFmjZt6myKsWPHxv6ufkMIkQy9evWKDBo0SH2HEKJUyOKfVEReYjr9+uuvNnLkSOfFBlOnTnWB+okB9dxzz5VMRRZCCCGEEEIIIYQQQogqTMqiLduD3n//fdtll13yvc+BHWwnJH6kEEIIIYQQQgghhBBCiHI6iIyA/ATTDsN7/E0IIYQQQgghhBBCCCFEOYq2vXv3tkGDBtncuXNj7/355582ePBgd3qiEEIIIYQQQgghhBBCiHIMj/D777/bEUccYVOmTLEtttgi9h6nI77++uvWunXrEiRHCCGEEEIIIYQQQgghqjYpi7bAR4hrO336dPc7B5L16dOnLNInhBBCCCGEEEIIIYQQVYpiibZCCCGEEEIIIYQQQgghyoYaqVzMQWNPPvmkvfLKKzZ79mzLysqydu3a2bHHHmunnnqq+10IIYQQQgghhBBCCCFEOXjactnhhx9ub7/9tnXt2tU6duzo3ps2bZr98MMPLs7t6NGjS5AUIYQQQgghhBBCCCGEEEl72uJhO378ePvggw9s3333zfe3Dz/80Pr27WsjR4600047TbkqhBBCCCGEEEIIIYQQxaRashc+99xzdvXVVxcQbKF379525ZVX2jPPPFPcdAghhBBCCFFunH766c7poKIgtNjNN9+c1LUnnnii3XXXXWWeJiGEEEIIkYbhEVq0aGFjxoyxbt26xf37t99+awcffLDNmzevtNMohBBCCCFE0hR1zsL1119vgwcPdqG+GjduXO45+9133zmnh99++83q169f5PU//vij9ezZ02bNmmWNGjUqlzQKIYQQQog0CY+wePFia968ecK/87clS5aUVrqEEEIIIYQoFn/99Vfs5+eff96GDBliM2bMiL2HUJqMWFpWDB061I477rik07DDDjvY1ltvbU8//bQNHDiwzNMnhBBCCCHSKDxCbm6u1aiRWOOtXr26bdiwobTSJYQQQgghRLFgh5h/4ZmK523wPcTScHiEffbZxy688EK7+OKLbZNNNnEOCY888oitWrXK+vfvbw0aNLBtttnG3nnnnQJesOw24558hrAHCxcuLNSmfumll9wBv0EefPBBa9++vdWpU8fd59hjj833d64fNWqUaoQQQgghRBUhaU9bto9h3NauXTvu39euXVua6RJCCCGEEKJc+d///meXX365ffnll85D97zzzrNXX33VjjrqKHe2wz333ONE2Tlz5lh2drYtXbrUhTk466yz3N9Wr15tV1xxhR1//PHuoN54fP/997Zs2TLr3r177L1JkybZRRddZE899ZTtsccebofbhAkT8n1u1113tZtuusnZ3InscSGEEEIIUQVF2379+hV5zWmnnVbS9AghhBBCCFEhdO3a1a699lr381VXXWW33nqrbbbZZjZgwAD3HmEWhg8f7oTX3Xff3YYNG2Y77rhjvgPFHn/8cdtiiy1s5syZtu222xb4DuLYskOtWbNmsfcQgevVq2eHHXaY8+ht06aNu2+QVq1a2bp169z5EfxdCCGEEEJkNkmLtk888UTZpkQIIYQQQogKpEuXLrGfEVabNGlinTt3jr3nz3eYP39+7ECxcePGxY1N+8svv8QVbfHGxVM2eFja/vvv74TYrbbayg466CD3wrsXb15P3bp13f85OTml9rxCCCGEECIDYtoKIYQQQgiRydSsWTPf7wirwfe80JqXl+f+X7lypYs1O3ny5Hyvn376yXr27Bn3O/DcRXjFa9aDd+0333xjzz33nLVs2dJ59OL1S/gFDyEToGnTpqX81EIIIYQQojIi0VYIIYQQQohisNNOO9mUKVOsbdu27pCy4ItwB/Ho1q2b+3/q1Kn53ufA3z59+tjtt9/uwi/Mnj07X1xcDjxr3bq1E32FEEIIIUTmI9FWCCGEEEKIYjBw4EDnAXvSSSfZV1995UIivPvuu9a/f3/Lzc2N+xk8ZRF7P/nkk9h7b775pt1///3OS5eYtyNHjnTevB06dIhdw8FkBxxwgMpJCCGEEKKKINFWCCGEEEKIYsDhYJ9++qkTaBFUiX978cUXW+PGja1atcRm9llnnWXPPPNM7Heuf+WVV6x3797WqVMnGzFihAuVsP3227u/r1mzxkaPHh07EE0IIYQQQmQ+WZFIJFLRiRBCCCGEEKKqwGFkeNE+//zz1qNHjyKvHz58uL366qs2duzYckmfEEIIIYSoeORpK4QQQgghRDlSt25dFwJh4cKFSV3PYWhDhw4t83QJIYQQQojKgzxthRBCCCGEEEIIIYQQohIhT1shhBBCCCGEEEIIIYTIFNF27dq1pZcSIYQQQgghhBBCCCGEEKmJtu+8847169fPttpqKxdbKzs72xo2bGi9evWym266yebOnassFUIIIYQQQgghhBBCiLKOactptVdccYWtWLHCDjnkENt1112tVatW7hCFxYsX248//mgTJkywiRMn2umnn2433nijNW3atCTpEkIIIYQQQgghhBBCiCpJUqJtjx497Nprr7WDDz7YqlVL7Jz7559/upNtmzdvboMHDy7ttAohhBBCCCGEEEIIIUTGk5RoK4QQQgghhBBCCCGEECINDiITQgghhBBCCCGEEEIIUUlF29dee81GjhxZWrcTQgghhBBCCCGEEEKIKkmphUfo2LGj/fTTT5abm1satxNCCCGEEEIIIYQQQogqiWLaCiGEEEIIIYQQQgghRCVCMW2FEEIIIYQQQgghhBCiElEj1Q+MHz++0L/37NmzJOkRQgghhBBCCCGEEEKIKk3K4RGqVSvonJuVlRX7WTFthRBCCCGEEEIIIYQQohzDIyxZsiTfa/78+TZmzBjbZZddbOzYsSVIihBCCCGEEEIIIYQQQoiURdtGjRrle2222Wa2//7722233WaXX365clQIIcqYffbZx71ShV0R//73vy0dadu2rZ1++ulWGZg9e7bLyyeffNIyCZ7pggsusKpc5pQp+UAZVwZ4fvKhouuxv/bOO+8sk7SIzOqTKjo9xR0jC4OxM7izsLz5/fffrU6dOvbpp5/G3qNvIE2Voe/u27dvLC077LCDZQoVXe6iYtu9Z+rUqVajRg378ccfVSRCiPQ9iKx58+Y2Y8aM0rqdEEJkDL/88oudc845ttVWW7lJV8OGDW3PPfe0++67z1avXm1VmR9++MGOPfZYa9OmjcubzTff3C0EDh061DKJnXfe2c4///wixcJJkyZZVeKzzz5zk+KlS5eW6fesW7fOtbcdd9zRtb/GjRvb9ttvb2effbZNnz69TL87nXj77bfLdGFnzpw5du655zqxqXbt2tasWTMn9gSFqOLw4IMPVhrBsix59tln7d577y3z71mwYIENGjTIOnbsaHXr1nXltOuuu9oVV1xhK1euLPf0lBc5OTmu/n/00UdW2fjPf/5ju+22m7Mdguy999721FNPWb9+/QqI5oU9x4ABA9w1hx12WMpp4b7hxa3Bgwe7dFBnikOXLl1syy23tMKi9vHszDc3bNhgVQEW7bwQHn6xy1WUH9ttt50deuihNmTIEGW7EKLyH0T2/fff5/udwfWvv/6yW2+91bp161aaaRNCiLTnrbfesuOOO84JFKeddprzQEFA+uSTT+yyyy6zKVOm2MMPP1wuaUEgxlOgMgl2++67r5uoMYFs0aKF8yb6/PPPncB24YUXxq5lUTBeTPV0gDHy22+/dZNuUbAO3HDDDW5yipAapDTL/JhjjrF33nnHTjrpJFfX1q9f78TaN9980/bYY4+Y0HDqqafaiSee6NprpsNCCX1CzZo184m2DzzwQJkItwizhxxyiPv5rLPOcpPgefPmObEV4Snc5lMVbdn5VVm88csKRFI8vS6++OIiy7K4LF682Lp3727Lly+3M844w7WNRYsWOft/+PDhdt5551n9+vXLLT3lLdrSH0HYY+/aa6+1K6+8skLShYj+v//9z73CsBj8f//3fyndj8VB2h0LpaVFr1693P+PPvqoLVy4MOXPn3LKKS5/J0yYEPdQawTiiRMnOo/i8rRjKrLcgbGIPA3TtWvXCklPZaaswzSy4MgYhiPG1ltvXabfJYQQQVIe9RBmWeELr4Tuvvvu9vjjj6d6OyGEyFhmzZrlBCAmsB9++KG1bNky9reBAwfazz//7ETd8qI0J2ilwU033eTC7Hz11VcFBDvipQdJZxENsZC87927d0UnJa0orTKnfiHOUt+uvvrqfH8bNmxYPi/f6tWru1dVAFuuvPoEzkDAox6vTcTb4IT3kksusQMPPNAJf3ikI6KLiivLxx57zHlEU07hskDIrVWrVrmmp7KAUFhRi55PP/20++7DDz+8xPdi/nbRRRe5ReQPPvjAKgsnn3yyXXXVVW4hIJ5o+9xzz7m0I+6WhFWrVlm9evXSotz996ciyqf6fJlEMn1TSejTp49tsskmbvFEi/BCiPKkWnFEiF9//dX9z+u3335zK9N4yxR3S4wQQmQit99+u9tKyiQ4KNh6ttlmG7cF1fPEE084YY+tqAhWeKLh2ZQMa9ascd5x2267rZss831HH3208whIFNM2UbzMeDHcfMy8F1980aUL8aVHjx4uvAE89NBD7nn4bjyUkokJStrYoh4WbIE8KCq+KZ5fePeQltatW9t///tfl4fhbZt8li2geDezxZc04p00cuTIAh5m//rXv6xz587Ok4xt9AcffLB99913VhIQ5vEoJp2pgBdi//793bNRHyjTI488skDe4mVIPnJNq1at3IJAONzATz/95LxN8Wbm+bknCwrLli0r8L3PPPOMdejQwV2HiDZ+/PgC1/z555/OC4+tqnwv3x9v4ZYwF/wtOzvbTXbw4GNS7usZ3ubQrl272LZP/3zxypznYhuu317PcyA+FObZ5dtAeFsxINA2adKk0Ji2eXl5Lq3kLc9BWRLfLpw+/1nELoTIpk2busnzUUcd5Tzlgrz22mtuqyX35DkQMW+88UbLzc21VOG7eIbgYjoeq6Tl/vvvj733999/u/d8nxKOO8qz4GULwW24YdgZQHpJN4fQIooXBf0D9fmOO+4o4KFEu2ASzHcFJ8I+P6l/hJfhGWmTlDcisIdyYMfCxx9/HEtz0EsSm5XdDptuuqkrP5wM4i2WYc8eccQRrszof6hn7777btxt5l988YUddNBBbtGJe9IPhUM8+H6UxTnvSc71tGns5njCHO2N/CCttE92Hnh4JtJNOv1z+v47UQxZvMmPP/54Vxe5L+36mmuuKbSsaC+0C/IpDPnvxdhU00Me0K8iCNMf8zPhcHydYyxh/CP/Wej0/UQ4P4sTh5rdLWxrJn8pA74D7+5x48bFruHz5BPgbeufyY+Z8b6fbfq0W98eeH4WhtauXZvvumTHoESMHj3ahUbwHs4lgRAGeEeziJWIUaNGubxq0KCBK3PGRDzhy5ItttjCibUvvfSS2wkRhvpAPpMP1DnCDVGfqdf0DbTxcB3wdYO+getp14wZlDvvv/rqq3G/h7/h1VuUPUS5sHvKj4HxQhbQdzDuUeakn76wtOLk+vswHiF6M8butddeSfcp4T6d66ifeDuH48Mmamc+VEZ59ZGkz9sT1Jegd228mLa0xeuvv97Zp5QT9YwzeMJt9L333nN5RxpoZ9St8CIvOwe4P+O3EEKUJykvHWJICSGEKJo33njDTcyS9RxDTMHwRzjAu4LPM9FANEKISwRCDxNCvGYwyhGCV6xY4YxQJmeltY0LQ/7111+PpeWWW25x34sBjHBIWhFTEKsR9PAuLmo8YWJEGlM9uATREPEMgx/vHCbhbCFM5J3JpABPvzPPPNPF/kNgZJLAhIY89+IOkzAmf4iIiFxMsJhsMClCYEsVJp/vv/++3XzzzSl/FpEVMQoBjkk/3seUKaKHF0eY9CAw4AHCtmVCClCPENKYIDHJQLDAk5FJCvdCuCX/8D5FBGWS5GFy+/zzzztPLPKScmXi9eWXX8bKiHxB0PETV4QOvInJWzzx/FbpRx55xN2HfKdOsrCA0M5kjgkmiwozZ850HlT33HOP294OXjgJwwIIQsu0adNc/dppp52cWEud/OOPP2KfT2S3IEYj3KbqNUX9ok7j5UY+IuLzP88TD/KYCSUTRSa4xPwkn8jX4ASYiSGCK//TVhCVyD+EzVQgT8g/6oovI9oqoSX4nzLw70E8LzZAGJ07d66rYwg78UDQoG/hWsqffKEcaTuFbYWnL0O0QECMB+2NCTP5wLb64AIHecdEmrru6zeijRcLyF/ynHz0giSLCb6u0v8iAJAPiDsIxPSxiEMI6t47DcGQUCbUVdoIzxoU9TykkcUc+g7KmHz2C27kMaJCEJ6Z56O//Oabb1w/hXjEAb4eBLTrrrvOXUvoCER+FjwoK0Kr8Pw8G4ss1HXKGwoT8Whr1A3KhdjN9BkIspRFYYId7YUxJRwnNUyq6QHuS97xXNQd2iTlS//N/fCipD6NGDHCifMsDJJ3JYV2Rb778CjUYRZTacf0bewipN/x4R+oF6TDx1pNBGVFfaKPu/TSS13fRjnTR4UFwWTGoERjCP056SopPDdxiRGjqOPxoP2TT/vtt1+sjvI8jCfBReaygPKnrrJYEoy1i6CPneDjiZIfOAth7yDC0s9SdghqjNWIekGwTShfPk9b5zrEO+qf7wM8vIfNRN0rDAT4V155xd0bcZsFMsZsxme/EEjbZfxkwZVxmvrPwlSiMS4R4UVJ2nRw3MZmad++vbMz/OJdMn0K0A7oz+knGbvpy+kfEXnJo+JQFn0k+ccYQDrJQ7xqaW981wEHHBA3HdjOPAtlRb3q1KmTq0v0V9ge2HvA2El9o61zb2wf2mu8WOs8E6ItfQoLGkIIUS5ESomvvvoq8vHHH5fW7YQQIq1ZtmwZlnPkyCOPTPozOTk5Bd478MADI1tttVW+93r16uVenscff9x91913313g83l5ebGfueb666+P/d6vX79ImzZtCnyGa8LDA7/Xrl07MmvWrNh7Dz30kHu/RYsWkeXLl8fev+qqq9z7wWvjMXbs2Ej16tXdq0ePHpHLL7888u6770bWrVtX4FrSSXo9F154YSQrKyvy7bffxt5btGhRZNNNNy3w3XyW98aPHx97b/78+e55Lr300th7a9asieTm5ub7Xu7Ddf/5z3/yvcf9nnjiiUhRfPDBB0nlBffiOsZSWLJkifv9jjvuSPgZnqFWrVqRAw44IF+6hw0b5j5LvQDyiN9ffPHFQtPANbwmTZoUe++3336L1KlTJ3LUUUfF3jvzzDMjLVu2jCxcuDDf50888cRIo0aNYvWYur/99tsX+p08X6L8CZf5kCFD3LWvvPJKofU83t9oL3y2efPmkZNOOinywAMPuGdLVA4+PfPmzYvUqFEj0rdv33zX/fvf/3bXBdPnP9unT5986Rk8eLCr40uXLi20rZ9zzjmR7OxsVw+LaqPhesD3Pvjgg+53vqdatWqR4447zj2v56KLLnLtw6ctXj0eOHBggbYfvLZJkyaRxYsXx95/7bXX3PtvvPFGoWls3LhxpGvXroVeQ/q41/fff58vP3feeed8fcLtt9/u3ue7PdSzYJ/oufjii921EyZMiL23YsWKSLt27SJt27aNtZu77rrLXTd69OjYdatXr4507NjRvT9u3Dj3HnnXvn171y8Hy5jy5J77779/gX70jDPOyJcm2hL56Jk9e7arHzfddFO+63744QdX94LvH3rooXHrQ7yy7NmzZ6RBgwYF6nlhbcXX+aZNm7r78fznnntu5Nlnn81Xf4uTHuoy7918882x9+jn6tat6/ryUaNGxd6fPn16gfEq3rgUr83GGyM3bNgQWbt2bb7P8d20j2D5LFiwoMD3Jvr+yZMnu9/POuusfNf961//cu9/+OGHKY9B8fj555/dZ4cOHVpkH1kUpI166vsYPk8ZBhk0aFCkYcOGLs+KA/leVL+fCPoW8oQ+OsiVV17p8mDGjBkJ+8+JEye6a0aOHFmgbuy1114Fngc7he8K1mvKhDZXVL3jd8Zeysbz3XffFSinww8/3PXpf/75Z+y9n376yX1HMlNw32bCL1+3fdrC+ZVsn0K/2qxZs0i3bt3ytY+HH3443/cE8zI8VtM3lnUfSZ4xpvF+2EYLfke43T/11FPuc8H+H0aMGOG+99NPP3W/33PPPe532n9R0Bdy7RdffFHktUIIUVqU2qkuHN6B15MQQoioZw/ggZEsQe8yPJjwrsDLE8+HeNvYPS+//LLzMox3iE9pbMHz4HkTDKfANkXAuyT4nP590l0Y+++/v/O0xRMC70U8r/B8Ysss3pOFwTZEPGGCB2DiGZIo3h0hHfA68+Dpwva3YBrxrvAHX+ERw+E7fpsc3h/FgYOd+O54YSiKqgt4kuBNGNwKHgQPXrxo8Y4JHtiFJxkeIH4LuPfIwXsp3pbDIOQpniQeDokjJAOfJU+Yr1Lf8DrlZ+qof1F21FOfV3jy4IWXzPb5ZOB7OXwl7BlVVD3nb6Sf8Bl4wOLZi7c4HoUnnHBCgVASQfBeZws03lRBCjswC4+eYHqod+Qd3qHx2jreb+Qf11E+bGlPBeoy4al8GAu8g9jeTugJPE0JjQF4OOHNWpI+gfwiD4PPlkxb5xmL6gv9333fGczPoBcvHod4S9O2ioJr8OoKbhmmTXNPvPPwyvP9Cf0OfZEHz2DaUpDJkye7/MRTnP7B13289+gfKQO8u8KH1wQhz/isf0689fgM3mbB9oQnJN5z8bx9iwKvOtKCRzptOEhR5Y+XMv0x6abvweuV58XzjVAA4TMtUgWvPw99BP0rnrZBL2ze429F1atkoT34eJfkNaFwaNdsWy9J3w54ywfB4xbCITiSGYPiQV2BYLsrDngWEuIAT/7C4oWT79RnPG7LG56Rg54Y/0kDUN8I10BZEf4p3H/iiUwesf2dtMcrT9pxOFY5ntzsPsHj3sNuCOpFMjFk2d0S3MWElybjri9P+nzG6L59++bbpUM68UJNFvohyiL4uuuuuwrtY5LtUziQjh08fD4YDxYP8KAnbyqURR+JRyyfwVM6fDhpYf0Z4bzwrmV8DOaDP1/A54P3OsaDNpy2ML4dFuewPSGEKC6lJtoysSkt40oIIdIdv20KsSJZEFuYCDCBxYhkUudjahUm2rLllclfWR+WEZ78e6M+vIXOv59IbAxCTEwmGFzLNlW2opNnbCP1gko8EMCY/ISJ9168tHvjO5hGjHW2zTGpYVKLEE4ZsM24sPwvDCbuxC5NFb6frYGEHUBE8duJiQvq8SIgZR+EyRdhOfzf2XaIsMCWQ54JcZU4kvGeiWcPw0QZMREhiBciJzHwyJvgizh0wUPk2IaLQIZoxn0RSuNtN0wW6nmqYTSC+cn2a7b5EgIA4ZYQDy+88ILbnp0In4fhesUCQSIRJVzX/HXBusZ2TMRn2gp9BfnnhYLi1DUmuT78Af8jcPAinfzO5BchLigaFYdkni2RIFtUX+j/HhZ3w3WSOsV242TiZlN+4fYBTOT93/3/CDBhASBc7l4AZ3t7uP7TvhCBwuVXVJ5xT4QpnjN8T+pr+FDGZPD2eHHbC/nLdnPCRRCSgq3ffns526mLCwJUeGs4bYAt7uG85/1kxpBkIYwBwhppYPs66aB/Lm7fTp1BQArXEYQxxu/gIk2yY1BhlFQsJ7QBW8tZZC0MFqjo8xEWKReE/3ixWssKFl4R+HzcUMIg0NaDC7KEUKEuYnsEx2rGpnjlGS/EBkIe9gfhEDz8zLiQyI5IpTxpt6QzFTslHojN2IXBV3BhNd7zJdun+Doa7mNZJMOGKA5l0Ucy9tPWWPhINS2MteF0ePHf5wOLkYROYkEJe4uwG9gG8QRc3w5L0yFCCCGKotRm+MWJ9SeEEJkKQgz9InHYkgGjFC8EJhJ33323m4wgvuHNg5BY1Op/cUhkdCY6DCnsqVLU+6lMMnlWJlC8MKgRAPGSIB5aaZBMGokHRww4Jql4lCF4MVHAk7U4+c9hnXhNJnuYXBi+F49WvEzwFCVtxHwjhtuOO+6Y0r3wzMF7hokwB3cQ35N7ff75525iniw+HxAYE8W79DEgEcYQfIidy6QfT1li5DLZJj5dRYEgxaQM8YJYkkzOiDFbWoseRdU1hAU86OkjiJ+HWIiQhIcYQndx6hqepMQQRqxDpEWcpX3zPr/TF3Hfkoq2xW3r1AXiKDJhT+Tlx+IIYkG8hYPKgi8bvBWDXv5BwnFdi8oz7klZsUAT79rSOHyquJAu+mNeLD5RNghbQW/ZVCjJGJLqeBU+wIj+D69HPNDxGuY76QODh3UWh2TFm+K2HR8ftSQCNmMGfTALpMHFDrxKERZ5j/GOPom8wVuSMYc6yYt4pHimInyXNcQWRbAnpjTemvxP3tFnB3c6kCbGSHaHcD3lwDXx+s9Eh4DyTIjZ7Aihb2I8HDZsWFLpLA27p7QIP19Z9CnJtr+y6COLC2nhED1s6nh4hwPyDw9gPG9ZyKGt4HWNRy72UjB9vh0miqEvhBBlQYlmKBzCwdbMIArKLYQQGycfeCQSAqCoQy04HIZJA9sCg14HyWyNRfThQAa2CRZ2GFAYvBnibQ0PewiVN3gIAl5eiWBrOwdFhIn3XrKwTZIwP2FPMvKoOAY6xj+TyeDW7FShbNluywuvESZBCLCIEP6ALYTRoFcM4zKCMR45QZi88Lr22mud9xKeJWx9JmxA2EsmvK2Wg128hxyekEzUwvePB17jeLHwIl0c7sMBKXhUI1Sm4q1CXiS7CJIMtBUEZp7Zbx0N4/OYehX0ZmLrZnFFFEJe8HkElOChYJRZcfFiLFtnCUdx5ZVXut+5P4sGiLaURdhDK0xZeQ/RF9IPshATb+sxohHiMnUqLEBQPsHwWxxIR9/ANuqi0k350T7C+BAUvnz5H89+RILgvcL9id8Oja2bTP1PBu7J91K/vAdYScvH9wel2V7+v73zAI+q2trwSggkhN67oIAgICgiKmLHdu3Yr78FFcv12hXF3q+9Ye8XG169ysVeKSoiIqiINBXEQu8Q0ud/3nPYyckwk8wkkzLhe59nCJk5OXPO2Xvtvfa31157u+228/qMYLtcldFmLvqO9tgtZ461v6Jt5/qxueA1h08KxnM/1BlEIeqni9wGUpJwjYnauBl/AJuoSPvA5ljgNlcLwqaU1D0mh90mkkyiMmHIi3sk+pZNOZk4jCdKtDwwqcNKm9GjR3vPkjYD8SzYPlOeTBoG0wQwJi0t1U0kEHlZhcLKC8Rr+gT6qkSA+E0fl2g/JZFtiquj1GGXMgDwJalvpCOKZH9Bwu2vstpI6iFtdDQhONrfscKEgIiybJsJeo7jhcjLJD6rc/DBg/fBc+HYstpqIYSo1vQILJFkKSGdEQMAGvHgSwghhM+IESO8dpKoJAYf4RDhQ445cDP5wcgClpARTVIWRAwiOkWKECktUgGHlu8gws3BgDx81+vKAmc40vW5XIGRljU7WOKPCEREkIM8hcGljvFCGYRfDwNGBrXlgftgV+PyRHDS1zIIDS8vBFPEfWAgweCapcvB60Z0plxdWgaWxhNRFQTxloGHO5eDZxrMCfj777970bncB8+HF/WNqNlIghDpE8JzMTq4VpY3cq0MCgH7gFgG23wvA7BI9bO0es6A1IkWQfhO7hffJdpu3gzgKL/waOlYo7EiEcnWEbSJQi4vDM7JyYrwwrNFkHdiLu0MIgfLfsuqi/GURzywOzl+I1GO4am0qOdE1vM83O7wQZj4cvUFKAvqczAvJNcd6ZoRdkm7Qjk7WHrNOckz7Zbb0p5g58Fc2lwX0ctBEL2xw3vvvdcTj0ur/7GCkEadIPo8vB7ze9COuM9YlvNTnxHsn3vuuS3qflnRa0wAunyiQXiOXEuwXY71ehKBE4Nc7mbgOmOJ/oxkc9xnsF4Ak1Ox1n83afDggw+WeN9F9ZUnLU4kEBKZyCT/aHlBkKPdDH9RTzg3/0egjdRu00+41RPh/UVlQSoEbJ52A5sKz1Ufqa8eNWpUTFHXQZiMpR1hEhTf4ZBDDklYBKVLa8BKGVLyBAVbImArk1jbFMqeOsDkbTAIi5Un4TYQyf543rSlld1GEiFPPWRlSngkdWntGTl9adfD23FApHftHL5jOE4cDq/z3377rbdCp7w5f4UQojzEPZLE4WagjdPM5mPkxaNBZAb2zjvvLNdFCCFEbQTHlaV9RG4QicNSPHIM4hwT6YggyJJNQBRz0S0MVHB2cTQROkqLOAXOS1QKESMMrBFqcEbZBIMIGTaSihZlwnJscmuyXB6hkLadCILybs4SDyxx5Dv5ftJCuOfCsjQEFZcjNZogzkCLzcw4D+IB+dKISsIBL08EGNGADAr4XnL/zZw50xvIlSe3GwMC+koGQ+WB6FYEQwYdCEuIbQysEf/dMlEGW0SsMjBjsMkmSkQVIv6RZsJFNLI0lsnW448/3itbBK8XX3yxSIANQv1EwKI+EPHkhMRgOgP6eu6NDefY4IXr45lTZ6hzbgBEnSY6CgGRPHHk0kPsRMxweUtd5CcRLdwXAgU24MTDcP8D8ZH7IIUFf8t3IbTxnINRQUEQellmy+Ac22AZMH4LYg+DaUSXaMszuW6WzxLRxfPlOXM+Bt0M7stTz6hbCMVEivGcOQflUdGloNwbG/YgyLtJ9P79+3vPkvrEMygLVx5cF/UgfFlyeWGJN2VH2XNNTGRRb8jRjECAkMEEFs8mHNoFZwuufhO9Htw0jOum7SJqnEhA2k2EKiKOiaKj7Lknyp5yJ1qKiQe3qQ1tLnXz5JNP9sqbFBrYPpFy4MqZ42lnOB8Dd9oKxHLqEzZBdBmrJuLtJ7hubJmIYwQK7INrxObZNO2KK64ouk/aR9p6bJxlxk5sC4fJHJ4Tz5tzIOxzflYABCe7wqEucu+0y3wf/RK2iwDM83B51uO9nopCe0L7ftZZZ3ltAXWTa6IdjDQpE962E2XLPVEHeba0GdTBoLBERCvvcU+0ldQX2sRIuYFpb7BhRCuX8oT+l/pFGSZyc2b6cNpIJuDKs6KR5xYpByuRtbRxXK8D26RdxX5InUMkJYIoIlYwojge9t13X5s4cWLMbRzPku9mwpAyCY8QpjyppwhnlBfiO32PSyURD/hPRPYCaZESyU033eQtr6cPZANFRE7aGepTaTZYUWJtU+hvOY72j/LGV+UYggXC/R7aOyb+OCf1A9ugvwmfEK6MNpI2nfpP+dDPUR/wT1hVwioS0pxEAp2C9EdsdMZ3Uw6UASsteJ8UIAjX+H2I0bQNRB+T65Z+hjoYXCnFRAL1OHxjUiGEqHRCcdKpU6fQ+PHjvf83atQoNH/+fO//o0ePDh166KHxnk4IIWo98+bNCw0fPjzUpUuXUL169by2c8899wyNGjUqlJ2dXXTcuHHjQn379g1lZGR4x951112h5557jlFOaMGCBUXH7bPPPt4rSFZWVujaa68NbbvttqG6deuG2rZtGzruuONCv/zyS9ExnOfGG28s8XcfffRRqE+fPt519ejRI/TSSy95x4R3D/x+wQUXlHiPa+L9e+65p8T79BG8//rrr5f6XN5///3QmWeeGerZs2eoYcOG3jV069YtdOGFF4aWLl1a4tjOnTuHTj/99BLvzZgxI7TXXnuF0tPTQx07dgz961//Cj388MPedy9ZsqTE3x522GFbfH/4c6QsLr/88lC7du1C9evX98roq6++2uI4d9/PP/981Ht75513QikpKVvcRzQ4F+f85ptvvN9XrFjhPW+eTYMGDUJNmjQJ7bbbbqH//Oc/W/ztI4884h1Hubdp0yZ0/vnnh1avXl30+a+//uo9565du3p1q3nz5qH99tsv9Mknn0QsY+pA9+7dvee68847F/X5QbgvjsUncPXtgAMOCD311FNFxzz55JOhvffeO9SiRQvvXHz/lVdeGVq7dm2Jc916662hDh06hFJTU0vU9UhlvnLlytA///lP73jqC+XOMTyvaHCtd955p1eGlG1aWlqoWbNmof333z/0xhtvRCyHoL3l5+eHrr/+eu8eqRf83ezZs737Ou+886KWYbg9BJ/jl19+Gdp9992987Vv3z40YsSI0IcffrjFcdwbzyEWHn30Ue/vKf8gQ4YM8d7/9NNPS7wfqR5zr9hfq1atvPrr2oFoth6tXYkG56Et3Gabbbx607Jly9CRRx4Z+vzzz7c41j3PiRMnhs455xyvzGgnTjnlFK8eBMHesXHaVv4maK+0gbSFTZs29er/wIEDPfsMBzvhHJQJ909b8N///tc735QpU7Zoe4YOHVpUtymjE044ocQzdu3o8uXLy6xjwHcNHjzYs3de2DQ2Nnfu3KJjNmzYEPr73//u3QvncHUjWpv0448/ho455piie6eNpy6Xxg8//ODZaf/+/b22AnvBbo4//vjQ9OnTSxwbz/VQl7mvcCir3r17b/F+pHb722+/9dpBbJ86dP/990d8nuFtdmFhYeiOO+7wzunaNepAJPuaPHlyaJdddvG+I1i3I/WLeXl5oZtvvrmo36U9HDlyZIl+Pdq9RLrO0towyuHFF1/c4rzhbWQ8RLou2sSDDjoo1Lp166LnfO6554YWL14c0zkjlSfPk/YzHqiDPG/sKhz6t2HDhnntB23CwQcfHJozZ84WzyNamxwkJyfHa1voYzdt2rTF57H6Q9HKgzaB+sazpA985plnvLYFeyyLaDZTVhsTT5sCjz32mFeHsY0BAwaEJk2aFLFu0pbSn3AcvsY111wT+vjjj7fotyqrjcQf5llyPsqM6+P7HZGuOTc31/OlqZPu76iP2K3zRbimo446yuuLKSd+nnzyyZ7vHu6zcl1O+xBCiKoihX/iEXmZRSenDDO2zEAxc83O0MzMEd0RaSmEEEIIURUQOcTKD/qiaNGTVQGRGCxnJfJK1D6IrCOalSglIoBE4iEClygtoqlcnuuqhijsSy+91NuoiGgxIaoLIoyJmCf3s4MVKeTLJxKWiNRIKxSqivXr13tLyYkKJmWGS5/D+0RlYksXXHCB1TSIFCVakwjx8Hz2lQWRr7NmzYqYQ76mQHS0y8EuisuNVRdVlUJMCCHKndOW5RIuGT7LWVleACx1CG4MIIQQQlQmpCAIQp42lkyynK06BVtgKWkwpYCoPfUsmMfSDWxF7StnctoyAdS9e3cJtqLaYdM0JjC+/PLLEu+zRJ0UEaQ6qk5Yis51kOIoCMvOmfAglU5NhJyz5FklTUJVtCsIteS7V9+RXJAi5p133kl4Cg0hhIiFuCNt2eSCwTC5wcgfxMyk21SE5PvkAhNCCCGqQhhl4EOePXK9EiVDjtJPP/3U24RHiERFfPJi4yFWG33xxRdenlRybJITT9SOSFtyMLKKjHaFSEFyZhMNR37XWPIBC1HVIOA6UbBTp06lbt5Z2bChKblAgXaS/Kc1GTai45oR4chPXll5/MmPzd4FBD2RH5jc20Qkz5gxw5sQqqko0lYIIZJ4IzKWiTnYFZNk3uykSJJwt7uoEEIIUdkgorG5ERvBsGSNDXcQbiXYikSCb8NGcHfffbe3EZDbnIzUCKL2wMZrbKCDSMtmNWxwRBQjm/MIURNhY6WaQrKNARFPmZhhkoYJosqCzSuZ5GPTRTbPIp3FHXfcUaMFWyGEEEkeaSuEEEIIIYQQQgghhBCimnPaEmkQK7///vsW+ZaEEEIIIYQQQgghhBBCJFC0ZQkJOQNZGkgi7nDI/UVSdXJ+sTyVzWCEEEIIIYQQQgghhBBCVGJ6hHHjxtmoUaPss88+swYNGng53TIyMmz16tVenh6SuJNonZy3fJbMFBYWepvZNGrUyMuTKIQQQgghhBBCCCGEEBUBGXb9+vXWvn17S01NTWxO2xUrVng7J7MDJjuWItbuvPPO3qusL0sW/vjjD28XViGEEEIIIYQQQgghhEgkpJft2LFjqcdoI7IIkO6hadOm3gNs3LhxQgtFJIisLDNyJ9erZ5aeHvmYnByz3Fy21zXLzKxw9PXy5cutVatWlT45EcutJfj2qpSsvCz7ctGXVi+1nqWnRb7BnPwcyy3MtT232dMy6ybRzVmWmZHTu56ZlVJ4lmNmuez9bGZJdH9VbHdVaZ+yO9ldjaWG2l0ibFN2V9vtLkn7uiS3u9hurdBSU5dbZmYrS0lJrS235iE/M0ntrpZ3CLE3KYXWo8dy69ix8secVWlzoP5OJDOFCdaD1q1b5wWKrlmzxpo0aVLqsWkV/rZaiEuJgGAr0baGkpZm1qCBWaNGZhkZkY/JzjZbv56CTIhom52d7dWHyu5AY7m1BN9elZKWl2YNGjWwRvUaWUZa5BvMzs+29bnrveedXINYmtQGZtbIzEopPMs2s/W0MsnlUFex3VWlfcruZHc1lhpqd4mwTdldbbe7JO3rktzuYrm1zMxCS0/3bTOSaJukt+YhPzNJ7a6WdwixNinr1hVao0ZVM+asSpsD9XcimSmsJD0olnSsydESCCGEEEIIIYQQQgghxFaCRFshhBBCCCGEEEIIIYSoQUi0FcLM/u//zO69V49CCCGSkWnTGtuuu/a11avrVPelCCGEEEIIIURCkGgraiVZ2Sl2+aguFgrFdvycOWZjx1b2VQkhhKgMxo1rbaFQik2c2FAPWAghhBBCCFEriGkjsssuuyzmE95///0VuR5RC/n4Y7MuXcy6d6+67/z1j3Sb+F0TW31sXWu++b3PPzfbcUezpk0j/w0bkQohhEg+Nmzw3Zlx45ra0Uevre7LEUIIIYQQYqvnyivNLrjA14NEJYq2M2bMSNjOZyJ5yckxmzjR7KCDYv+b7783GznSrG9fs+eei/87ly0za906/r/Ly/fr4sfTW9iJh/mC7KWX+p9Nm1by2MLC4p+XXGI2bJhZv37xf6cQjqys1GTa0FaIpGfDhjrWrl2u/fBDfbv99rZ27bVLqvuShBBCCCGE2KoZP95syRKz0aPRC6v7amqxaDueJy22ej75xOzGG83228+sbt3YHsfy5f7PbbYpn2D7t7+ZPfmk2S67RBdn9zi1p330yHxr3qSg6P3PpjXyft7z323tvUX17LY7/febNNnyHGecUfz/L74wy8sze/TR+K9XCPjppwZ22mk72oQJ861hw80zAkKISiUvL8UuvHCxPfRQR3vrraZ25JFrbNiwLvb113M8BzFVyaCESDjZ2alen9e/f7H/JYQQQojkg7SSBLHVCWwPMWaM2WefmT31VPnOyd/C7Nlmq1ebNXdLoEVcaBgjYubPP4vF1Fh5803/56pV8T3ojRt9wRbOPdfstdcif+/rn7Xwfi5aUq/E+38sLf591uwUmzWrpGhLxPCLL5oNGIDIVvKcHEODlZ0d3zULAfPnN/B+Tp3q/xRCVD45OamWkVFo//vfz97vCLaw2249bdCgHkUrKoQQiWP06PZ2zjndbN68dD1WIYQQIol5/XX85pLvsVH79OnlO99XX5mNGFH8+1plL6ta0XbatGk2YsQIO+mkk2zo0KElXqL2wmZdQfG2LDZtMlu50o+y5Wc8hB9/zz3FIm6Qdyb70zUfftW4xPvtWubZ/x1crPJed51ZWprZmjX+75dfbvbQQ8XHN9y8dw1L2j/6yOyZZ8wGD/ZnnKoLiQzJyZIl/uB1xIgONn16/eq+HCG2CnJzU61evUKvnQ8nPz/F9tyzh915Z4fquDQhai1//JHh/TzvvHIspxJCCFFryM6qY6Nu6WLZm7T+PVkhrSW4gLZ49ZtwLrzQ//nCC/5Pp8OIKhBtx4wZY4MGDbLZs2fbW2+9ZXl5eTZr1iz77LPPrEmkteciKZg61U8NUBrO0P7xj9jOedVVZr/8Ynb00WYrVsR3PbFG5u7SY4P3c+5vGfbkf1sWvZ+VnWoNMkqGVlE9161jAF/yHMcf7+damTDB7K23/PfcEoC//91s/Xqrct55x+yaa6r+e0Vi8tn27bvR+/8553S2o47qah9+WHJSQYjqIDcnxebObFCLRVt/lm3KFH+G8ZprltiHH8637t1zvPQJb7zR0tatC6z5EkJUiBUr6llqasizq40btXhPCCG2VlYtT7fvpjaxhT/7k3miZvPhh/7mYI699vLfg3nzzN57z+zgg8u/YbsLPrvlFrM+fcx22MFPjyDKR9we1h133GEPPPCAvf3221avXj176KGHbM6cOXbCCSfYNuVJXCqqFQzwhht8IZZNuEqbUUG0Rfhs4WckKJPJk/2fO+3kG+nbb5u9/HLsom2PHn4elHr1SkbvBlm93g+r+uHn+vb02Ja2Zr0/IN+Uk2r10wvt0X/8ZB+MzfHe2247/292393/ycZon39u3o6G5Dwk2jb83ubP9xuu6ohqJh8wDd7ixYk/P+I1s18PPJD4c2/tbNpUx3bcMcvatcvzfv/zz7p27bXt7euvM23SpIY2Zkyz6r5EsZUyc2pzu3tkN9u4oWTXTztz+4ht7M/fMpI6PUJ6uu8hEm07efJcGzp0jbVoUWAvvLDQRoxY6n3255/Je49C1DRWrKhrt9/+m/f///ynma1bJ+FWCCG2RjZt9Mfga1bFtGVS0vHDDw1t9OhW1XoNxx0X+4rnsrj2WrOvv/b/X1BQUmO57TZfHwpPXRkPTlMaMsT/2axZ/OkyRTFxe1e//PKLHXbYYd7/EW03btxoKSkpdumll9pT5c1QLKoNNhRjJsUxcuSWxzCgf/xxX0Qkv2ysu/612tyudevmn+Pmm0uKhKQe2OAHym4BIm/LlmaNG5ec2WEWKJiyANH2mH2LY+3/83GzItE2M6PAduu51jvP88+b3Xprye/o0sWsfv0tN6i5++6Sv3PNCLfxzjBVBJeu4ZVX/FwwiU6VQLoI8swgohMR/Z//lP03CxaUPwl5OF9+2MYmvBej+l8JkMs4mGMn0RuzNGmSb2+//YuNH1+s+F9wwTZ2551t7d5729iAAf0q58uFKIU1K/3UHX8tKpl/ctXyNJszM9Mmf5a8EwpE0rpIWwj+Pz09ZCecsNo6d862tWtr52BCiOpg48Y61qZNnu2333p79NFWNnKkUpAIIcTWSPYmX7RdX0v9rEsu2cEefri9rV9fPZOTf/xhtnBhYoLJgroCaSEJ5nKgmwS59FI/GOKOO8pelR1k0SKzNm2Kg+/S0/30k6J8xF3rmjVrZus3rxfv0KGD/fjjj97/16xZY1lZWeW8DFFdIMAOG+aLg8y4kGg6PI8rAuqzz/ozMDvv7M+chM+U3H+/2amnFv/+119mjRr5eWMRRiNBlOu++0b+DIGYGZlonzlWrK1re/f3ld/mjQtsxtxM7//ZuSmWERi077ij3wgRubvttmYXX+wLwpHYf3+zcePI3ezntSXq9f/+L8WOOKJF0Q6IlY1L4TBpkv/zZ39vnYThoqDh0099obqsDeYQ7hFteXYI2NQZxE9e8fLaE9vZi491LPHez3My7Nd5pUfBMRPI9/3rX1ZuXP2urLIk0pYNkaBRo0KbNm2OXXCBX2mXLSt2ZNasqZ1Ojai5/PGrnxph6eK63s/8PLNbr+hsl57Rzfv9z4XJHWkbFGoj0aRJga1bJ7sTIpF2x6TIHnv4IThff93ABgzoaQMH9rQPPmhsY8aEjf6EEELUSrKzfP/q94W1b2NKxo7Of9xvv+29fq6q955xY/fwNI8V2acISMfodB0C+Qhq6tzZrGdP/7NTTvGDyUglGesKXeRBAv06dSp+j/QIpMskUpiUCeHBcGPGVE9Kylor2u6999728ccfe/8//vjj7eKLL7bhw4fbySefbAcccEBlXKOoZMhnsv32xXlLwvONkJfW0bGjH6E6a1bJYxDwZs8uFtaOPNLs11/N2rbdMjIXbR/BFuOHmTNLfs4MEjMxrkHZZZeSOVWCou3yNWnWulme/ffuX+3y/1tqS1b6Deq6DXWsYf2CLe4VoZadEYMCcyTat/d/PvhgyfevvnrLMOMPPvCFxOxsSxgsQeBZu6UK4c+oorhUEUEQqkvDiZxffomI7Qv15VkyETx24kfFebBvvaKL3Xixv+N7NN591//53/9auXFzS839PewSzqZNqVa/fsme/O9/L57lYNl227a5NmTIrvbcc8qvKaqOuT/49rZssT/tTXTtvFnFs2ozv21sM76svgj4ijjTbiOyskTb667rblOmaAm3qDl196fvNi+tSUKc3R199Bp7+OHfi1YuMZi97rr2du+9HWzAgD2q+zKFqFW8/XYru//+9vbTT8k70Spqb3qE8e833SIA7J7rO9n9N3W0JX8Gch4mEWgP6BnbbVc82GdyEvH2vvta2zXXtLczz+xmf/5ZeYL1kiX+z1dfrfi5li7101A6fv/dTxHZurUfvMY4+6WXzIYO9T9v2rRY41m71tc9Djqo5DkpcxfMdcYZ/nucz3HmmX6aTQLS0ByCEcMEBN57r9knn1T83morcY9cHnnkETvppJO8/1977bV22WWX2dKlS+3YY4+1ZwnHFElLZqZZgwYIk8XiGLBBFyDSETXLTIkTETGyb78tPvbRR4t3CoR27fyfGKczRJbiE2pPjlsg0jfYuLs0BkR0wpNPmt1+u388xk8Ur9tsbN3GNGvdPN86t8u1vt2z7Pel9WzFmjq2al2atWzi5xRNBMw49emTVzR7RMi/w+2w+PDDCfs6L20EjSbstpvZ3Lll/014B1lWWgw48ECzE07wBcyMMnw/GmwXVYsgHySe/DrLlqZYWl1fXHnmgXZFu4w2bpIfcQaRDsLNZlIXgImD8oDATvS3y9Gcl7gqUiLSNjOzpHhEJNKkSXO9F9GA227rd/qPPaaoP1E1YENZG9Jsj/1W248zGnh2d9e1xXno++26wQ46erk9f+/2NmF8/KImqzSqC9oM2j/srDT8tiTFxozRZImoGcydVd/uu66r/bHAXyWUrJG2iLWDBm20qVPn2L/+taVDkIjIICGEz803d7NXXmllp53WxW6/ve0W9rViRZqXf1OIqmRTVh1vY0ogCGfJn5tXdeWb/TCtgc34uqFde+4OVR6hmgg2bEix+vULbMyYuXbffX9Yx47FA8hXX21uH33U2H74oYEddVT/SruGeDd1L+tcpLEkRaITbV1qxtICnkjR4Px9onODZRlpk7FwbYF0CS4oL3g/Eyf6P9GhRGTiHpk1b97c2m8OQ0xNTbWrr77axo0bZ/fdd5+XOkEkN0RBYow33lgskBFBetZZRFn7v++6q7+J12WX+RGwhL937Wp2xBF+7tipU0sKwUCVcbM0jzyy5fcGo2ddXpV+YWk/EYBJz0DDAm9NbuP9bNbYj6ht28L3Wj6a0thWr69jLTaLgBWFTdouvzxk997rXxizR8w8uYbKCX/uusoLogOzVyxD4CczYCxRYCaLfLKlgYhKuTArVlYqBb4HEZh0B4jhfAe5aogqdoJ4EGbbiKxG5KTML7qo5OeUKzl24LvvzN55p/TvX7o0xZq3zrH7X/TDtR+61U+TkLFZ6HSzsFkb6tjg3dM90frss4s3kSNamudT3ly+b75ZnBojkR1gaZG2kJkZ8l4uD6BIDMzY/vCDnmZZMMFWWJBiu++72ub/VN/GPFs8/f3vd+fYZTf9Ydv38VPNzJ0Tn2tAapVzzimOAqhqXBtcVqRtnz6+17l8eYyJ2YVIEMFJ1Veebu2lAmJAe/uVnb33Jr6zeYY7iWASpKAgZQu7O/DA9Z54+8UXc23yZL9xPvXU5IyuErXXHj99u6Xl5iRnX9CsWbFg9NZbTW333XvaP/7RySZPbmC33NLODjmkt5155o7eOAU7FaKq0iPsdZC/snDB/Ay78uyulpebYmtXlwxQWbsq+foDtBBS3zFBuc8+G+zFFxfYuedGHkSWd4xaFugjpCwoK8gqFu66y18hffzx/qpoxvGlCabBVAboHS6CNhi4F9QQtimOCSkBf+cC0YLaz5Qp/k+lR4hOudYIFhYW2rx58+yLL76wSZMmlXiJ5IYIWyfWPv20H1lJlKwTXMGJt8HirlPHj9h0EHQdKVl1MKQfQRdxGMNGdCRakzQL/I4I7ATfIB06mC1e7P9/1qKG1qZ5cSvCsoW2LfLs/pfbeA5Zs0aJEW0J50cs5PxHH1088tpvP9/xI90CVHQ3xw8/NCPDyG/+RszeEgJy8SJW01mU1gk4wRjhdXMgfJk5cdggzi1lJJ0FRMpVQ/7Y00/3y5hy4dg99/SjVsl9w7WR8wYQV2+6qfR0CUTaNmuZY02a5Xsvov4+HtfMVi2va3XrheyXuf5y7Rfu277obxDlcD6Jtub+mM2rSAptZhOp05Wxi2Uwp200/vnPxZ7THeumfiI6TCScfXblPEhWGTARUhtYszrFMhvmW59d1lv3Xpvs03ebeT+feWuu1w7w6rWTn0xq+rfxuQZu0qa6RNucHP9nWTltzzprqe2661qbOzelqB8R1Q8rV2rz5hRvPtfFzj6in5cOiDzS77/Z3ItCYkDryMtNvpQdzu4iRbjTnmRkhIpscv58dXbJAD4t7XltiIz+9vOW9vPsyBHs69bUsVee7GDffl69O8GXl7y8VDvttGV2883FKsnUqQ3soos62bhxxWnHBg5Mt0suqaaLFBFhzBTc9Kk2kZ1Vx+o3KPCCABxnHtXDCxSAmx9aaK3b5diS36NsdpMEoq2DPUuGD1/h7VviXp9+6u/z9O9/p1VKNDGCJukNg2JnRXBjYHz3//2vdDH48sv9ldFubIQuAuefX5xCkaC2Xr383xGCe/c2O/TQkudhfyEXXBb0w50egeaQiPQPtZG4vcQpU6ZYt27dbIcddvDy2+67775Fr/1QsURSQ9g6OWSD0bIQLFrExNGj/f+zkRgGSUoE0ib885++mEeUbCTjDy5rx+iJzmVjMETbf//bT8GAIEf0brQZGvKwQHZuHfu/g0q2XAN2KFby6tWNI19AjCBOskEZ0Z8Iky+84L9/881+4xNPioKgIEkDTBRoELeBGxu68f9oIgOpKr76qvh3xNVoILS7tBPBwHii5IClEkBnQwMc7HSYrefciJ2kGKDhpTyuv75498lIeZAjRdo2a+mL7Tfc5yvUox9v40U8DD5grZcyYe7MBjbnu8BMged8+teD4MoOlK4exILLsxNMP0HunnjOUdFI2yD9+2+0//53hnddzEwG022I2HEDS9fZJwJnw9Q1Jp+YTCEHNi/qTaJg8otVDbR9M2b4/6cukOPpyiu3TNBfUTZsJJrdD7n5+9l+xb/4uj8sPaO40eL/Z46Ya7N+TIn5+zmO1QhQ1maGlQHt8LJlviBUt4w2n/brjjv8JFo33OC3uy63epDytOOi/NB/8WISsLZBXZrwth9FS982bXKjEp8zcXnWpYts+hctE27zlY273vT00vu7xo3zbbvtQhVeKYAPwAqhYGSPqPgqhWC9+/5736djcF7WCq+azr/v727/urK73XdDxy0ElDWr/Mi/NSvrJV37z/VlZaXaccettMMOW+eJRUS2Dx26xvv8lFNW2XPPzS86PnwPElG9sLqRFIEIcDW9rpUnPUL9zALbebcN9vBLxcs+H72zg/dzu+2zbdnidHv8ll528AHpFQq+qWqys1MsI6OgzH0TYPToOlF1jIpAnUFHoT+sCK7Nd0EprKaGSMFyDrQe9kByugUaAHvvwB13+PXapYxk35u//93XdVghHSS4/08wMjeYy/a++8p/b7WZuIe65513ng0YMMB+/PFHW7Vqla1evbroxe+idoAIQqQskY1EbzJjEoSZFCJjEDHJQbvH5n0mmGXBkMvKpeq+A5g1wjl0IiW5ToKJq4NwHcwI0dH9sSLdOrQqOcq56dyqCZ8aNao4hy/ss4/v/MYr6uBIEskbnIlCEA3Pj0sD+/LLW/49gxdmud54o/j5R9tgi86RqMRIUBZch1tmzOCIBpifZeESkbto2/79S0/RQKRt05Z+iE7rdnl299O/2lkX++V2yDF+G3L3SH83+8bFAQNFHH20H+GzeT/EmAjfJA5xmqjh0q6TzpHnFe/SMiJtyxJtoVEj/8RELpNuI577ET733FNcf9evTynKv10RiBYnxUekmWwXURqvjbuItOB72CPpRIgcHz7cnzhBGMbRQUhM9EBrU1aKZWzenLHbDtn24vtzrEmzLSt3v91XWaPGZadZcQRXWCRq9j8eeHannFLPW6IdS+R6s2b5du+9eZ5QTruLQB4Um++80081g6gLtIlMjOF8isrB5UcjuoKc8Q7aRCaBkxmX423wgatKDF7PumSxXXfPb/bIKz/bTrv5YVdPP7lljnMmT2tqVJYb+JU1WXLFFQu8yf7ywhLOTz8tnkhjWadIDE8/3chbqeLaQNJgOW67LXmfclAM+u6bhl7arWD08Pq1dSKKtq+95rf/zp+tDCoagYfdkZs9c/MkrPOBrrlmiSfgXnrpMuvbN6uELxt8HvFsHCwSS7AOMtl92mm+r5Ws/RwrN4PCM+kRiLSFZi3ybfR7c+y8K31l7u/D/Ubm/KsXFtVDghdqav8WaRxZ1gRlEHzMoD+TCNAYWI3MM6tI2hPkOvzl7t393xkPuyCxsnApEtF6xozx/0/aQQdaAtH9gwdH/vtjj/W/l4kLNqWn/rg6FGm1r6iAaDt//ny74447vEjbpk2bWpMmTUq8RO2BSFmW5kczvJ12ii4QxgOiLQ55MI9JNNGW9Ahe/ta90u3XJZlbiLbw6u0L7MNRxbPMlQEOUlBoJQ8MgnSkJNylER5di+hEpHM4bA5H8u+g4/X11yWFBPIMI6AgnESK2HHpKk4+OfLujDTAzHoFnXagU8WJPeaYyPdAw+/y4BCVSERrpNQYrsN5/71Ua96qWMVq1zHX9j1krScite+Ua387tnjyZ8e+JTvIb74pngl86imLmU2biv9PlDjROsxW8kyjwUQCkcnk9I3VyabjiSXS1j23Xr2KvZ3asgz/x18zbdX6wOxMJeJEVJyXCRPq2YgRKV5Z4wiUB+c4MHC74gp/FQCrCRyl1ZdokGaGdCIsJXJ26ZxU6lWwbgVToASj5xMBDnL6ZtG2rHq5fp0/mDjqqNInorif4DNxDlxVQGod7NPtPssu9rHSq1dhxHpE+bsJMKI+icRHuCDqjIm66to8w8u/OKO5HTOyp/f70pVptnhF7dnEkBzjrLoBBDlnB7TVTFYykRK++WWysHFDitVJC9mpF/xul95YbCz7HrzWevTxO6bMhgXWtfc6e/XlOltEXjF56jbPrImRR7FMljRtml+hHH9MZt19d/Hv+IpjxyZ2sM/kTNBPCGfN+jr21/Kq6deqCurazJn1ipa+0r4RNEEQggvEmD+/pMhUnonL6mDFct/ubhrlJ0+86pztbNgRPe3UQ/2X24RzzYp07zkg0iKyuIlgVo4lIlIOoYdAk6C/SjAEQS/4GOXBia7hG96G066d72czTkMMwbfHP6d88dVF1RNpHw1WatLPVcekd0VgRS4CHsEHwUjbYL2kb9hz/3Xe+O7Qof7YbsDgtda+c5YdMKTQC9Zi0+3KygGbSAi+KCv1XSQ/9bDDIq/oKg/0fS5XbPhYPR5I60i754LnBg2KfbWcO5bxETqRC3Cgf2Alslt5Fw0ib5mgx+fjmXIfru8N38tIVFC03W233eznWENwhIgAeWvdEn1ApMT5JqLEES1at7O/Z0cR7VpsqU523ybHWjSt/Mz7RBgDOYDpmJiziHUQQWOJsORSCiAQ0NhF27lx++39GVqcSDe75pxAhF/+tm9f/7mxqVikZf8ucgDnPJijOBjFjIOOWMn90CFzXffe64uc115beloNIOUAzj5LJyIt+3EbRjFIjcbJZy+zg4/xe46rr8nznFs6PWbv3OCQZ8/9xkpQ7CZKHCfWpeWIhrtWBovk6Y114EcERCyiLbzwQq4XOURZElmJ6EUn9thjfrmWVyj68kt/UJtogjOi0T4/4/budt2/u3vXzWzrLbdYpeEmC6gXubkpReI3M7jlmYUOXyxCfSayG9GOaGgca1YeUC6xiKrkZnI2jv0y645T5JytJ57wfwYFCaCuMwmTyKVjWRuLI23LYsiBhUV5uon8jZS+g3txzhu50BG3SeHCBgNEjzNxQ3tIPUQIZWfYRC3/5jx8J8+3PIRPCrrNMYmoDUK7w/c4DjnEqoVp01Ptqud72O/L0m3AqT3tsEu62RGX+qsRkh3aCfor0iu5fPqkWUKoc9AmsmmmOz4INlLZG1dQ/90qlHhh0qN+Zr7XL/fffYPd/ugCu+jaLRPgn33VHC99R6RNFRF9aDeYVGVw4wZc1Q12GEvkUdOmebZwYYpnZ8HUBpR7WZuBBkVC2mNA3GAyBV8yUXC+vfaK/FlefooN+Ud3O/KyrnbmzZ1tU3ZKjXj+FcVFgRE4AexdgO+Af+QG3m5VGRNk+ITvv29JAQEUjZrkWqdts+3ZsXOt0eYNi7c4bkU9W7I4xeunWbURhLpa0ahU+ozDD/d9vOCSZn4nSKM8Ew+bNqVYnTqhMiPcs7NTi/zzt94yGzKk+DP2KhFVD+2d2wg5fAUr/geRq+Ers2oitL2ujWAlhCN7o5/TtiyufvB7u/m2PG9ZPbCfS1VR1jim9PQIZfd3N9zws91wQ7636pQ9aujnnF5QEZy4SfuMVhBvkFh4PXRRtsD5WPXn0iSUBv0FE04u7QFBXRBJVygN7IB7YZzh2lnGdG5CSRtMJ0C0vfDCC+3yyy+3F154wb799lv74YcfSryEKAsEyOCAjAE0y9UZ3Lg8uNFyVLr3W7cyG3nCr5YZ56xXokEsdZGxNIIuN2xZMOuGsAQ07NEii4P3zXEuBYLbWIDcukEhgeNwBIhIZeYSoRYBMJg3Jho9evidAsItjTDn4rpiyReKsOtEZM7DQNptjhbEDXx79Ct9ivCQY5fboIOWenl3iehmJjo4e0fENc2Nuy86YMTdaAPrSBE0dFhElQQjSRwMChHnHIjXsTi5TmSLdTaWZ8vMoptd5DkycECwY7kUkdREZdDh8/3holI0EDjcIJS/4broGBl4xZPFhvQfwQmA2+5Ks10v3sPm/x45W/3Cv/yonanzmtjAvdM94S7ac8MGKhopRbvhUnj88ktakU1CefIVU2fJ0URUOfUO8RShgEgAomWB50mdYVk9QmzQtsjJTSQL9ZHPiNJ2GwsC7cPf/rblfQdXM7DMyLUjbtPHROCJRw1i213mxlvy7D//8SP3uT+ihRGugs+UdsjBoNClXjnlFP9vaKN4btRDUg4wWeRE3ljAlsmvGEl8531wkSlDhsTfD2ALDNQPPNAXm4kYprxxIlmW7yaiwA3msZ1YnX2OZTBz1VUVF9jOvzhyhN+SJI22RUBwER1OtGMgEJ5PjfaRtgy/gP8jemATwb6FvoEypJ5UdJd0N5ij73Spj7BjJmycoIxwFc/kw0YmSwKD2G22y7FdB2+pMjdoVGCHHV5QtAqGttvlzGdVDRNHTIbwPtHI5Yn6TzQ8h7I2/3ORtojt3E8wepFJkbImQvBhqBe060TnPP988Wf0+fg6icBNTCFk0oa7dvuwG/rbHucWzxD/8HN922t4D3vo1da2bkN8Q6iRI/1zVzQfYWnlwfNlAEx7X1Y9ffrpFOvQId+uu65kGbo+1eUopL9y7XtZIjsQrVrd9XPNmhRr2MTv7+qlh+yx1+Zbg83BAseettzbKOmCaxbakt8z7fihJVMkBDdFrEj9or8LrlQ5+ODiSSf3szybd1K+pEYoK8L99tvn2zXX5BftVeFgE2LalOAEGG1ycAjPvhSVvdcCq2QiTUwvWVXPps8tZRv7KPCs3SRgTc2VysQV9sSEPe242ziasRz9GgEy5JSuzkho+tGy0rXh6zrIYerYRHqEQNqOsqA/c/4u/nJF4DrwNcvytxh78LwrKz3CkUcutyOPLPAmn10fRpnjX7rxQRD8T95nZRF+abRxrBuTksKAsXG8GUn5HsaWLmVb+AZhCLGxpETEF6PeMhEdnHxo397ihvEW9d5bCZju7zuBD069CJ/UEOUQbY899libPXu2nXnmmbbrrrvaTjvtZDvvvHPRTyHKg1uKRTQWM/2lQSTds4/l2LGDK2EXqQpSlpPMIJ5GOZjnxkUylAWbvTmc0x9p7z8ETWYvmblElKCRZrBXlgOKQEtjjvga3DAuFliu4UTlevWi54tFZNlt90JLL0PUbNw03046P3oYLE4ncF8MqhEAcIIQVxhsucG36xydaMuzcXTq5M/q4byG40QBlu44QZWI0bLyE919ty+uxLuEho6KehHuADnY1ZPvZ+DunBLKivqEoBaOc+a5b/4GoYOl7gy8XA7i0pbB4LSdd54/QCYSGOjkv/vB7zJOvqmHf45VJXdI/eCrxrZT9y1DU8KdFEQ9RE+W/TKwYYCA04LTxrE4LYh/1JfSrhMnHSeD5/fpp+neey6SiwhZyjaeZdUMNKn7iDSIjMH8TthHeJ47Uo1QBxnwgIv8YtLD7azqCG78RxvmUsvgoGMzbhUBToxzhoCI3kRE1hGdk17GJgrB+oOIdtxx/u9EV/FMEFkQyIJ5p1z+bRy2WETZWKNImLRg1j/cuaZuuN1qAUfv6qvz7OGHA+EeMYAQRBngfBKxQH0BBriUFfnciYAmShghhH6J7wpO8lD3sRMXqeugncXOmJwk9Y9LuRAJHPXgJo6R6Ll95MKfOquBt2w7Z3OUeUVgQEL5JmKzPSa5gvmlFy6sYwMHpngTbwymEWIZnAIDbCIuXL572jMmNHkuTFrRtyCY8uxd1DMDSgZ6LD1kxQp9APWEV3kjYoHyIiqOATNtH6sfnB0jONI2cw/YY7yRtrEwcPfCov6ItjtYr8InoejreC8YCV7VxDqIbds2x2vjgv0Lk4jBwSLPOrgBLs+bPiEo7GCvO+7o1wtnr5FWANEmBVduxYKbpHGTUWyEQpu7dI3fr8A5xxQrli+939z2P397G3BWP9uwqU6R38BEcDjOF3FCCEuKK2PBIt9P/4cowcqHYGRlpGtiMuSUUzZ6/Q2TH0xU8Gzd5jIuAtdNhGIf+CJBH53zOOER+6DMSC8U3qZRV2gvXT/m52W1SmPN6hRr2LhkY/DE6/O9ZdpHn7zS2yip327Fa7JdxB/9eXAVF/ntgxAJGRQz8U94Lxzuz40HSO/i/K5wkdY923igbQjms43GwIFrbejQAm/ikPaWCVQm0l3AiPOVKBPaZEQlJlEQ5E480e/zAXEIsR675f9OLKIvx/6CeVqB51PaWMPVAVbx8D3hXPTEDnbO3d3s1z+23CQuEnw/10ad5/rpx/B5g/XLfScrgRgj0I5XVbQ818GzZvzgxhD0d4hfXCs2x3jETdgxvnv33Yr36eWFSXEmmKL5asE9Qhj3Bev/xvVp3gRkPNCX07YHV1WQcoHJH/r+8HaCsVKksTZ1HF+Lyb1o8HduwoJJqXiEz3jTI9COYN/YEWNiNymCIEmfRz2kvXSpoei3GKPhe3Btrn66KFQ3ZsAXKku0pV1yqcMcfA9BXPhmfB4MTKgIjBfQbsLHPLHggqfo44MrJxljlBXMtjUSt2i7YMGCLV6//vpr0U8hygM5WxnoIb7QIJVGz55+rt2ahhO3iMRDcKBRDA5CgJ0XaZQZkNJRIciUdb8OHOsXX/QbMyIvcbIjzWyFp5AIUtrMPJ/hQBFtG8uGPqXhNowL7ywRHP/8o+LOCIILnQ/gbOL4AA4RnTyimXtRBnR6iHEMOBzcI2JMpGXuLsKEATHlSrQo4i0DOvKeBQfkOIsIDXS+H33kN6mxRCdHuic3AMWhxBHC8cV5CoJTwjJGHHEGUdwvjgFOKhEibjkOg9Zo5YjzQMRncIdOHF+eFedH/HDpOoj85N6INP1tUfEJWab9t4u72cDTe9ofS+va2g2p9uqHze3CY/+y924pub03wrFzvFjK7CYRKA/qhXPcWe7OIMkts2eAwXUGnznX4gRunj/XeMYZW3rffB/2RrRorBEjDHpdvqhIsIQb+3WRsW53bepgaUIO4kswMoGyweFigsQ9CyJbKXNXdxAD3WQNERk4/TwX2hXaSgTF0gYdON5BkRkbyIgjAsIN2oM2AwicblNEyiEo1BKdXBaI1GVBebnNqYK5cpm4cM8eUYc6ioDAsqxBg8qX4MttwOBwy/ApByKF3QQW7TTPg4Gmi+hE2OcauBb3HvU2KCpDcOMkJtEYADuHGkfdiXNOBMZeHExozJ2fYg+fN9smP/GDPXDZH/bmPb/YoL4b7dn/tfSWbe95Vg/76sdGpdYH2qhIebPd31D3aDcS4caR48zla6Teff755gj8qcXCFaI4tkw0SjDyHCGJgWCw7aIsGNQ6gYNBDfWD9jl88MYEQ3k28WNAxuCBZ+DSldDOBiHPMwQHQ/TFQYEC0TiYa46ctrHaXd9+hd7zj7YJUp8+/gsBiDpEv4dwHSx32oZIq0cqA1LSkNO2LCi/L7/MKVp1Qf/CJCL3Agwiedb0e9QX2sagYB2eNqp37+IoHI51/QF9C74VL9eHI45Sz8oanIcLFNjDYUN95+yJK3+x0TcvtOHHrLBPHptvQwaWjJSe83sDe/r5Ot4KDXbNxsbDgxLcElLXRrgIu0SAr0Xb4aKDnZ0zEA7WDcQ+Zy/UH+6ZSFugbWPyJOi7ICyR9svlLGSSEkhvxvdxLu6LiQ7qrbMPCAowPHvabSZYOZ7+nn7D+XDRQMQqb57PNWttC9E2HMYbDmwKUcStMqNtQcgN/34Ed0QYRGn+j1DEz3DhMhi1ygRLUBQOgl/gNnOiHGMR84nebxDDEnQH4gf3haBJ+4r90e/RT+FfBNsL6kUw7QgT0kyQ8Lccy3NyAjR9ExNclD11gf9T50gnxrnxVcL7E3w6rsX1ifg04bBfCfzrhbbec/lzWV1vcjJS/8b3IRa5ieSgnbv/U4/4TtoBggHYCIm+qKryM9PuMdZwE0KMd4NQ37gHxrW0i872/ve/DDvkkJSIkxvxrirB5wz6oLG0hdGi6oP+NNdL/82z/ddtaZazqU5RRHs84AcgttJ2cz4mOpj8wW8JBstQn/DBI6V/czZYWpQ//qeL6OWZUJdLm1QPT48Qz0ZkQRivY0dMpCF60+dRDx0u6AkYF3BttAXUeXJQU3b4RC6QAj2AFHrBSWraD8qBSQDGTfiqDteuUNcYh2Ob4RH4FcEJrvGCsM14gr4j3sjhrZG4pYXOnTuX+hKiPBCFkeyzKi4ikwE3jSmNJoOQ4AAyODjFcXLRRfFA3lqEBBcNEY7Ly4Zz5kwSp9JFxJUGM+0QS16beERbHCuW/sMuAxITXuGWcTD4Dwr64REelAHOZiQHjZl5nDeeDdGfdHgMmN2g2+WeQhynI2Vgw4Aehx7hgL+nHN3SWf9vYouqigQOEE4LdQiboJOmY0fo4DqD0bjBxPZcM8+YaGMcGp4D146Y4MqUSQJ+p16493CSuFfOh9PIs6JDB8RLHIfwJcsjjtusVAY4+oqudsD521tWdqr17ZZlrZrk2lWX5RddI06XKy8X5RG+CZ+DgScOTlBAw8HHdpwzybNAtEGwY0AfTLcCPEPn3ABlj/NT1pI5nL3SRFsnGCAEu+XTDNCpH24QgrAYfGacj2dLhBgDIGe33GNQnGIAGYxGw2kNpuhAzGOmnHaFQTQiL5HWDILctVCHeSY4cpQjtkCdRlh6/906VjcGgSWcYHQ6TiDfRftAOYbXDRwvvgsRlcEps+/YJ+VBHWZwT51iEIPjHz5gYyBCOTEo5u9c6hnaDuouf8cx3JsTfCoK9YcyoY3kektbZeAWEyHQ43gHN7zDIWdZPYNC99xoV4goB54BgyCECgbAPEu3BJJy4/lglwiTTPyxlJZnhcDBM9m+w0arVzdke+28wbZpm2dH77vG/gxsjHThA9vZzIVRkqJvzr1OfUIwwmF34g2TPwgoTuRKxEDWTUQyeN9vvxR76aXiihJsK4HyPP74ss/pVlfQRoanu3FtCwMS7A8B0A3wOX8wMigSCDPRhE4GS+GTr7T/XAN/w6QGAoVrm2gPGJgx0OZ7/Ujb2AaxLpIm2IchzLpJauyeCYLwttNNHmH7HOPENUCACRckOZ4JrWgExZHg/8OjmGPNaQu0c+GTzJRXeFQUA1QncNG+EG0VKXctbQ0Dd0RF+jwG4qw8Cvo52CeR7sDgPCi2Iwi4Os9gn2NpA3i2CN+wbLNgN6DnBuu1XbZ3D00bFdidF/5p4+7/xfr39DuUh/7X2Z58tnh5BDaOPdMvUR5BQQ+7dtAfxGpv1CVEafpx6p3zUahntKu0SfRfRGAFhT/EKie+4K9g65Qp/Sypf4J9TiRce0i/h4/u+nQmOFxbB/SJzj+lLwz6veHRpIh9QF1mcjHaYB1xK3wJb6ysDaRHKI2rH/zOrrsxr8ju3PcRhUbfzX24FEjB3Nn4XIitzv8MT0HmIq5p75w4HFwth//onhn1DjGKScBYxHzanlj3TYiGmxQhaMGJO0w40JZynU7gwwdzflOwHcSvcCI9k+3UK4IOCB5wgjW+CvdI30e/QzvkUrsFJ7ld+VNvb7zN79NOPGCFzZibabue1tOOuryrNzl54shtbcyHxUuWWGEy8oa6RRO7znfD1wL6Wco0XCR10FdwjbGkjysLJtWDGzATAODaHvpbbIe2LdqeLdQ3xhb4+y711jPPNPCeDb4e9s6EIm0u/jTjDtoPbJt2jPdLW5XGMbHmAHebgkUSbelPiQSmXcCuXcAMvPuOX9EblmMM5MatBCaFXyd+kUu/54JYwoOCqKP4iKwwdeKl6xeDuDIJgj8ZS8oj2uCMGFerhcOYjL4FGwuupKO95rppJ8I3HKUeuLy1PBP8ZaeTuOAFl8qFNp12JHz1rvOpXbvC91On8HlqguYS9OPdhIWITkooFN8CgXFREhSmpJCgOcO6detm27LDTxKzbt06a9Kkia1du9YaO9VG1CxooVGwWL/sEuFG83LxIsLVhTgpLCy0ZcuWWevWrS21lDBKnOdIOarodOioXQQP1Sra8r1Ybq2s26ODRzjBIUZEiXUmMVHQ8dIBEfHBoMA5p2wC9sSzWTbpt0nWqF4jy0iLfIPZ+dm2Pne97d15b8usG73scJoRIogCJfqD+3ZLAukIg9EtCOSRomrp3CNBmYXvgsnAzomOQVgyzvEFBZusc+fPrX59ph1LKTxDRWAEQNLS+Oomjq3rgHGauE8X2UfTi6OCGBjcrTgIA3oGfTiJpeV9RXxgFpROv2iJemGBTb3tY9tYt6lN+7WZXflQR2vcoMDWbSwOWZn29HclKiaDaJx2J0o4B93lhnQROpgVg4VoO6XjpHCfDBQpY7eUDAdlt90Kbd685TZ6dGv76KOUokEi4gqRaMDAnoEIDhr10y3HdeaME0M94nzRNkKMZnf0ooiw1HPqOPBsiRpzS0wdfA+DrrLEYQeDcgY4blfroLNTVrqOEucJFVjH3n/YVbf9EbfdUQfc0qp4U6cEYSASvoSKskfApd1koIs44Ab8REWEb+BAfd8ygpxRJTMajcpld5RfLKsLStjCZhCxwwUwBmcIzZwT0Q6xpEIUFtg3t39sKY2LOwXqBVHucNDu6+yjyY2sU/ON9uQrjaxV58yi+6EeBnM/O1wbEa39oy5T5tRV6hp2i/0GRR4mUnh2CGfUdaItgu2TT8jy8vKtY8c0+8c/Ujx7xG6cEEA7xPPjHKX1d1wH9ooQ5oQVxBBEP/4uPIKN/pPrddGHkZYhcw20B06kI5Ke+s1gHiGCVQauvvG8p0zxhftIG1bxfQha/G3QTT7jrBz7es4iO+/yZTHZXfb6TE9gRIDkfAzMETv4fvYDcNDvBSOn8RmCObDJLY9QEpxUcGIvf8ffI7LQlgbTwDDoZdDOBCATY/gQ1HHKn+tCpHYTOR99lG2vvrrCnn+eh5wRk81RZxhoMgBlUMrAkSg5hGk3sQvUs0jLzsNBHCHCNQjPCYGWe6GMEVRcvleXn5ooeq4FwcqJBAg8TjinTn79VYFnwNOe/zFq5Xzslab23Nstvdmfy66o4w08nTgTDgIYwkp4H4cQG9wPgftmksINqrn2SKlnKHPsOzjpwX3hW9KvBSP0sB3XBtAf09du3BiyO+5Yao0bt7aUlNSIfR0TWfiPPEPXplD/I+0T4L6H+oNPxHVwDp4JdZN7pFxo13lGnNvBBBYCBm0/doiw60Rhl5om0vXxHQhvwLOgLtO+sIltTqM5dtwp68rtZ1J3nE1hOzxvlm0juoQLzYiwTiwE2gH8iHAfEpvEN6fdQ2R3aT5o1/BLXFuFLxse+ewYNy7HPvhgqT322O8x210k6M9p0+h7sYFwv4f6SvtDuTl/Ml6/w0FdZmUX98n38lzwjWjb8N3ph6i79HXY3NRnf7SB50ZeunPLuX9Z9sZ8a1l3nV326gCzVN//5DzYlpvUdsI59QrxizrEcmyun/7GiZNEHdNP8PyZxC1rJSd+TFB4A+oJ9YW2n8kpfFX6DsqScR/NB6JaLOM7fIazzw5Z/fr5Vq8eX1TsnCB6Byfzw8FGWf4frDf42G4Mhp25DR2jQZ9KWdE3BlOsuPEV56Kfd3XC2fxVVxfYhtwN9vy7P0a1udLszvUxwfE0ZUpbAkzMIWq7cQ736SZE6DsQ7rnmoEhP387YjzpAXcPHx+YIPKC9dwIndSboQ3As38XxiP/0+3fdlWuZmT/bhReurpDdAX4T7RpibtDuaLMRcGkng0EBLl0f/hNlwN9js0wUEO0fPjbFnpg8oQ+kzyMQgqhlAhSc/xStXa1KuA83TmeFcmljr5pCrHpQZWiOcYu2XCACbfifuff4OXjwYBs7dqw1CybxSyIk2iYBNVS0ZVBPB4YzhiNNJx0+8MBJCu/wEy3aVjcIU5EiJFgye+SxiRNtI0EHy4AP54mOF+ef/zPTHKnoIpVRabnGgiIH0TQIasUDroqJR7HCNTAAw/kl+onll9wH4gwDIoTSaPlFceBw0vmcgZsTsnGMcDSpnwyCw6PAqdup2VmW8nlx5Vy1to4XeTR7QYa9+0UT675Njh2zx5KIFTP43HCog2Imn+EUMXh3AheiMbPiDHJKy/tGGTRp4ttns2atraAgtYQ9cK84f5FACMUBJyKQQSjOMt+Lc1TT7I4ulyVVLmKN68aeSouuwGFlcELk4bz5BXbubZOtT+/USrG7WIkmEobjxDLqOM4c0TAMEoLLyIqpGrsDHP3g8nW3NN45nkx0hC+Pxml2KT5YkeE2UgOE0eBqAUAsdPkEGbScemzkToFd7FetS7MOrfPs7U/q283PdvBHnZsHsYgr4Y5/EKJkXE5UBtPU+2iTPQ5ni9Q7J5Y4EHdchHuxSOSLttddh6CV6rU72BttFS9WZcTb3/GKNKmG4IOogtjJz3DRgMEzYiYRfgzaOCYolrFE0y1hdXlII/UZwTrMAJZnEi2txOB98iy/8Tw7ediahNodUVA8N+pbrEvtOd6lFnJ1lGdOHXGCXPDe6EcRDJk4oZ9BiKLNIQ0K7fcHHyRGPAoO2qn7CMZE0UZbTRSOu2YEDQbk3CPiMqKTy4+HuMfkEANxBvfhKTSYTArPTT2gf4Fde9j3dswhm0r1M595vYmtbLG9XXW9P6NBfWBlBBNSCKSIAQjkCAzecu8/fUGc5+lwAkRwEEs/yWopBvDhq0miQdvk+jvKDzGCfiBo604c6NOn0M44Y1mpoi02x4RpMEUF1++iorB5bIu2gP7TtQ+Iskzk0K/T9iGOM3HK3/JcuLdg1DHnQFBDSGB1AfWb/p8xLGXmNqBD0EVcYwmwE1xoP2lH3HPjfm+4Kc9a9Zlphxy+qUJ2xwQD9TEI34ewhbBPW047hJCCYIT/zrNBJKT+Be8xHCfGMwnCc3Yr8Vz7Tx1ixU04Y8bk2Dff/GX33fdXQuwuGvh8tKUIP9gQIhH+ovMN6QMQ8mhLWBVDvaN8mXBAPHUitIMoSeoL72Nv9A+YlVtB5lFYPFHy+eyWdun9URqBwpDt2nWVLczrYI8+UccTDWkL8YsQorh2bMFFVlJe1C/Al0B0dJvrhcOzx+a4L9f+0/5RN/FP6bOp30RGU+fChUKuw0Ub0w9i+9Rb6n2sQ1dSfg0enGVHH13fbr01PmHIpTXjmTC2oexos/ndRZ+6ySqWptMPI5ZSZoiUTNJhb/gPlCntEvfN2MBtrkVwggvGcBxwYIH9vqT8oi3wHAnIcH0V0FbSTzPRwcQw4xvXxjHuob7xk/pElH+kvV6CBIM1KA9WG1GvXb5X7pFzuBQUjNd5H9G2Xbt5ds45ayvV7oom5QcW+4z0C9QpysxN3gYnfYK+J20WfQ51ILipomubaR85V7QI9KrGrUQpTRepSRQmk2j76aef2rXXXmu33367Ddxco6ZOnWrXX3+9XXfddd4Xn3vuubbbbrvZsy7JZ5Ih0TYJqKGibbRLdcu7uVQieMo6PtlF2+DMMwSd16y8yhVty9M50mlQLjgsOCoMHkubbee5MxgMz7VXVeIRojjOSzDS00WDMVgoayY90v0Eo60qw+5wgnEUcYbCo0ZxIojgIPoUW8FBIsKDr+CecJbdLDKDUPJWIpbhdHPOsuyTmWtmmYlwCeabDML3MOjEWU0mu2MAwsAO0ZnBEINdIpgYLAcj72qK3VEPKAfKkIFPMI0FQhxOJxMP8S2VqjrRFmjLiezAYQ6KyAxiGPCFtwu0JwzQ3cYPbkBEhA/VlfvleTCwRAxjkOTsxROvN5Vtd1lrcu2sW7vY/HWti0TbcPjTSKkCOC3LxYlyIAqztFQ6RHYwKA6uYogEAwQ2vtljj0Lr3Tu6bZanv0NcLisy2gl5DFqDE1gIcwzKGMy4yKfgBFAssQakk3FLgxkAYoPBwRPihOvniXA/Yvj3dtDfKiYelQYCpYtIJyc4ghZtKIMgJvQQ7Ji0ZJKOekj6AepocNM5BngIKgwKmRSKbclwgQ0e/Ls9+OCfCRvEept/LY1v52gnfgWFGSZrXa506gBLet2GLw4njhG5hZAbzHEK65ZkWaMZk0pEuMfaIZQVvc+EMn6Ri/IGhAmuGZEsEnxGSpxgvlTg0piQ4B7CbTcoAgN1g0k8+rvbby+09PSyRdtIfR0+E8/L3SPHET3tBIWg+M/18azDl+NybfTrwWXlQVi9QjmGi0P4BsHoaq6Neu9Fam5eVfO/t/Ntl8On25575VXY7ty90Ddx3+ETh27in+sM5vUNn5yOBBNH+D3BCMMg+EuIbUxIMJ7nGU+dmmtLly6yW25ZWuniUXlhgoQIQOpHMN0L7RFiO8+UNgnxlLoBHNuxXb6lr11uY+741as4pEBYuirNGmQUWsPMQluxJs1Ll4Boi8C798GZdv+o6HnmCB7BX6aOBU2YZ4qdEbmI3RCxH54+C7GVekb7QlkS4UqQRrR0PvimwYkYoGx5Fi4VQ6wu9Lp1hdar1zLr0KG17b57qnetToBGaKUPRmxFMGZyy+Wsx9cNn6ilPeczJtyCewXEA/WaCGX393Tl4WmDvp+1yb78dbonMpZXtEUcJz1UuI0F2xN8i0iT/5QNbTliK8dESgHEtYVLUy6i3kVfI3hGWlTet2++7bvvT3baaeurxO6oN5Qd/Tj+MBMi4UFfwedAG+JSCwFtPL4I9Ya2NJjKQmwlom2fPn3sqaeeskFhYVxffvmlnXPOOTZr1iz75JNP7Mwzz7RFse7+UsOQaJsEJJFoGy+1RbSlZcFRIkojOHipKeJR5VC14lGy2Z23GVbGloPjWECUZDAfaXl+rPbJ3zvHhcEyqSLcEjmi/xC8GWQls91Fo6baHUIZ879EgMSykVlttDsGGbyi5peMw+4KB+9t2amZ/hLhq32BkcExkT4ubzKnYLkz4jHRNeFR9QgDiEq4cE74j5TaAlEZMYEll0SqOAGKqCuXu7Ys26ys/g5hCxGaQQ8TA9FyhxLlhlgZb355ng/Rmm5wSYQyQiliPt/J0lxE659mF9jJl0+zQYPzK83uGJwhPGBHLvdvuGjIoJu64GAwRzkTdRwOUZ/haUAiU2Dnnjvbhg+v/Mijsoi0ZBkRlPcR23hGCEaIH5Q5/6fseD9ql1FFfiaTbuGR4cG0DYC4TOQgEcPYM8IMEZ9EwJWVm5BIUAQW/o7IcEQoLnnq1PKLtmWBiImYybNFMAqm9gjHTVCFw0QSGxAy3GRShCJwm1ICE1oIgNgv9T4oZjNZMvzmr6xv35QK2x2iImJItBU/TOI5wRh4xqz4IvItVnhW1IGy8u77FNgJJ/xsI0asqLGibfhkOOVGG4lwiYDJfTL5RhvE6gYmNqnv3Tv4NlfqRAmbSH2ZbiMe6WTDL6xv514YPTEz9oJo7PZqCMLqCEQ+2kkmqag/tNll5UCPBH9HHXDSCPcaXMXhVgnFK9p26tTaPvvMX6GCwM1kViR7D7b3TNoTBQwEnzBByYQxYmRwMo7VJk7AJHKViQ8H0bZMBIan+0OwJsCCSedwfSkWH7O8/R0TNUxyuVWEtGHYvcu9zLWGr+7ELmmHeC4I3JQHwrvLBxuEtjE8ZSHnJ4ikeKPRAjv88AV2001VP1nCZB3tZHgwA34GeWpJ68PEXfl9aFErRdv69evbN99844m3QWbOnOlF3m7atMl+++0322GHHSwrht7n0UcftXvuuceWLFli/fr1s1GjRhVF8Ebi9ddf96J6Fy5caN27d7e77rrL/hZYo7dhwwa7+uqrvfQMK1eu9PLrXnTRRXae24o7BiTaJgESbSUe1UiSWzyqaXZXGZ2oW4rnxCiX15dbqg2TJckm2iYG2V1VVEwnXgADVJejPRglg0eJeObMsLpE20h4y6ZvKBmZwmC7suZhifC69vo8O+S8CdameWa12l34UkoEZqJt+UkEVvjAl6XPtI1EdDHZhrjCORAfGCgj6r7/fo4dddR4S0kprb9L0r6uCvs7xAWi5xAmEK/YiJRVB4gInJb3mXjg9NgYIkK09EeR4G+YHAumM+HWJk6sPNE2XvguxBS37BkByaWM8DcAKilwE8lOSieuzy2HRrglkphUBRuzCmzkkxOsfeuMCtsdbRqTxqU9A/Jtck3R9k6IpYwQolhBhdDJd3L/tFdbrg4qsOHD59i5565JGrtz5URdckKbg/vlcyIdY+0QvEj667rZ1Ol1LbVhYu8Pe6QMibQtDQRB+jpEaae1sLSfyVIEfiZF3SaxLtdteUTb8ghDtNmk7mByyom5XC/PDfugrrmNqp3gy14X2A/3jaBOneQ9PmOSCEGQyM5ol1OZom0iKG31A8+cFYCsxuAYUsm4NFgI20yQ/fFHgb3wwlfWp09K0tidqF2ibdwZJHbZZRe78sorbfTo0daKqQvPMV1uI0aMsF03r8GZP3++dWJ6tAxee+01u+yyy+yJJ57w0ik8+OCDdvDBB9vcuXO9hxHO5MmT7eSTT7Z//etfdvjhh9srr7xiRx99tE2fPr1IROZ8n332mb300kvWpUsX++ijj+wf//iHtW/f3o4M36lCCCHEVkUwZQC4JeuxRbgIsfVCtB+RiiyRjJQ3Lt5o1aoG0YpoYQbMVXGtuMgPjsqzSb+Vb8fpRMIS2kiRgi5XIWOP4PgDFzzohpNuxS0Nd0syjz66+u+rNsA4jXzyvIJpB9yqkOCSe+ptPIKt+5vw/NM1DQQslvEiFnG9RAe7FTlBccvl3HZ11aV14pm41BAITKvW59jUpXllTJ7HBmVRlmhN3l1SgCG6lgfu2bVJ7t64JyaWWJpPblnEQYQnhrKtWsWw3X0NgnIiXYInzIbB/UZ6vzS6dsi2aQ9/ZZYa5tAlyB6ZEAiKtqQv4n38RCLA6QfdypigzoIg6iIzmXwhMpb0R2VFwyca6mF4O8HvkdoOJ2SSWoZJTAf10W2sy0qdZKe0dDW0MZRTMI1CMCKZl1kOoYGbg3KEqHrilojJU7tgwQLr2LGjdevWzXvxfyJfn9mc8ZhoV/LblsX9999vw4cPt2HDhlmvXr088TYzM9OeC9/FYzMPPfSQHXLIIZ5oTCTvrbfeav3797dHArsHIOyefvrptu+++3qiLSkbiOAl764QQgghhCgfDK7L2uijJsMywpouLlc1wcjoso6LdSNBUf6yKCtfc21uWxDCuP9oIlf45AIgNJGbNEhpUfuVBZHriS47ouCJkET0ZKk70ZovvJBrRx21eYlQEkFcV2lpMmoS1DEmuYiA5ycTcKw6IOc5C3dd7uxIBOsAKxf4+2TZZEkIUXOJuxnp0aOH/fTTT14E6zwSbGx+78ADDywKEyb6tSxyc3Pt22+/tZEk1toMfz9kyBD7KsraEt4nkjYIkbmkQnCQa3fcuHFeTl2iaydMmOBd5wNsnxiFnJwc7xUMVXYh0LxEDcStH3KvSLjPOLaC5Ug9IJNIVdSHWG4twbdXpbhn6V6RcJ8lnw1yraHAKxruc45PovurYrurSvuU3cnuaiw11O4SYZuyu9pud0na1yW53cV2a84XK0zqW3ObeAWvsTb5mQh+wcvr1YsId667FtpdDesQEGcTefrYm5SqG3MmilhsLpnsbqvr70S16EHxnKdccz+Iq0S88iovK1assIKCAmvj1qZuht/nkFk5AuS9jXQ87zvIiUt0LdG/aWlp3rU+/fTTtnf4mtgApFu4ma31wiDtQ3Z5MpKLygeRnYpOYp5o8BnHsG1wcJvkchoV+UYw1MreiCyWW0vw7VUpOfk5Vrih0HLTcs2ibEiVW5BrhfmFtnL5StuQlkQ35y2foQEua+la7ubjVm5ebpMkVLHdVaV9yu5kdzWWGmp3ibBN2V1tt7sk7euS3O5iuTWEoYKCtZadHYqY0zZJb81DfmaS2l0t7xBibVKwzXXr1tqyZZU/5qxKmwP1dyKZKUywHrSehN6JFG0ffvhhTwjNyMjw/l8abPpVnSDaTpkyxYu27dy5s02aNMkuuOACL+qWKN5IEO0bjOAl0pacvOTsLSspsKgmSCyEsbitsEvrIVu0qPAuChhpSkqKVycquwON9dYSeHtVCsnqUzelWr169aInq883y8nNsRatWiThhkjUD5JdZcQw4G2RXMnqq9juqtI+ZXeyuxpLDbW7RNim7G5rsLsk7OuS3O5iubWUlEKrUyfFMjJaRRRtk/TWPORnJqnd1fIOIdbby84utMaNU6x168ofc1apzYH6O5HEFCZYD0JbTahoS2qBU045xTtxaWkGuIlYRduWLVtanTp1bKnbunsz/N6WzN0R4P3Sjt+0aZNdc8019tZbb9lhmzP49+3b17777ju79957o4q26enp3iscCiNZGsutDsqFxEHuFQn3WaQkWOWA+l0VdSKWW6uE26syeH48S/eKhPss+WyQa00JvKLhPuf4JLq/arC7WKlofZHdye5qLDXY7ipqm7K72m53SdrX1QK7i+3WqHv4ZKm15dY85Gcmqd3V8g4hniYl2fqEWGwO1N+JZCclgbYZzzliEm3ZeCzS/ysCMzG77LKLffrpp0U5cFGv+f2f//xnxL/ZY489vM8vueSSovc+/vhj733Iy8vzXuEPAHE4ufKmCCGEEEIIIYQQQgghtlYqvJ9hfn6+l/e1IdtmxgkpCU4//XQbMGCADRw40B588EHbuHGjDRs2zPv8tNNOsw4dOng5Z+Hiiy+2ffbZx+677z4vknbMmDE2bdo0e+qpp7zPSWXA51deeaXVr1/fS48wceJEGz16tN1///0VvVUhhBBCCCGEEEIIIYSodGKOyX377bfthRdeKPHe7bff7om1TZs2tYMOOshWr14d15efeOKJXtqCG264wXbaaScvjcEHH3xQtNnYokWLbPHixUXHDxo0yF555RVPpO3Xr5+98cYbNnbsWOvTp0/RMQi5u+66q5fOoVevXnbnnXd613neeefFdW1CCCGEEEIIIYQQQghRoyNtiVQ97rjjin6fPHmyJ7becssttsMOO9i1115rt956a9wRraRCiJYOYcKECVu8d/zxx3uvaJDf9vnnn4/rGoQQQgghhBBCCCGEECLpIm1nzZrlRbo6iHI98MADPbF26NChXsoConGFEEIIIYQQQgghhBBCVIFou379emvRokXR71988YUdcMABRb/37t3b/vrrrwpcihBCCCGEEEIIIYQQQoiYRVs2BJs9e7b3/w0bNtj3339fIvJ25cqVlpmZqScqhBBCCCGEEEIIIYQQVSHakkf2kksusRdffNGGDx/u5Y7dfffdiz6fNm2a9ejRoyLXIoQQQgghhBBCCCGEEFs9MW9ExqZjf/75p1100UWeYPvSSy9ZnTp1ij5/9dVX7YgjjtjqH6gQQgghhBBCCCGEEEJUiWhbv359Gz16dNTPx48fX6ELEUIIIYQQQgghhBBCCBFHegQhhBBCCCGEEEIIIYQQlY9EWyGEEEIIIYQQQgghhKhBSLQVQgghhBBCCCGEEEKIGoREWyGEEEIIIYQQQgghhKhBSLQVQgghhBBCCCGEEEKIGkRaLAc9/PDDMZ/woosuqsj1CCGEEEIIIYQQQgghxFZNTKLtAw88UOL35cuXW1ZWljVt2tT7fc2aNZaZmWmtW7eWaCuEEEIIIYQQQgghhBCVnR5hwYIFRa/bb7/ddtppJ5s9e7atWrXKe/H//v3726233lqRaxFCCCGEEEIIIYQQQoitnrhz2l5//fU2atQo69GjR9F7/J9o3Ouuu26rf6BCCCGEEEIIIYQQQghRpaLt4sWLLT8/f4v3CwoKbOnSpRW6GCGEEEIIIYQQQgghhNjaiVu0PeCAA+zcc8+16dOnF7337bff2vnnn29DhgxJ9PUJIYQQQgghhBBCCCHEVkXcou1zzz1nbdu2tQEDBlh6err3GjhwoLVp08aeeeaZyrlKIYQQQgghhBBCCCGE2EpIi/cPWrVqZe+9957NmzfP5syZ473Xs2dP23777Svj+oQQQgghhBBCCCGEEGKrIm7R1tGlSxcLhULWtWtXS0sr92mEEEIIIYQQQgghhBBCVCQ9QlZWlp111lmWmZlpvXv3tkWLFnnvX3jhhXbnnXfGezohhBBCCCGEEEIIIYQQFRFtR44cad9//71NmDDBMjIyit5nE7LXXnst3tMJIYQQQgghhBBCCCGECBB3XoOxY8d64uzuu+9uKSkpRe8TdfvLL7/EezohhBBCCCGEEEIIIYQQFYm0Xb58ubVu3XqL9zdu3FhCxBVCCCGEEEIIIYQQQghRBaLtgAED7N133y363Qm1zzzzjO2xxx7luAQhhBBCCCGEEEIIIYQQ5U6PcMcdd9ihhx5qP/30k+Xn59tDDz3k/X/y5Mk2ceLEeE8nhBBCCCGEEEIIIYQQoiKRtoMHD7bvvvvOE2x33HFH++ijj7x0CV999ZXtsssu8Z5OCCGEEEIIIYQQQgghREUibaFr16729NNPl+dPhRBCCCGEEEIIIYQQQlRUtF23bp3FSuPGjWM+VgghhBBCCCGEEEIIIUQ5RNumTZsWbTgWjVAo5B1TUFAQyymFEEIIIYQQQgghhBBClFe0HT9+fCyHCSGEEEIIIYQQQgghhKgK0Xafffap6PcIIYQQQgghhBBCCCGEiIFUKwerV6+2e++918466yzvdd9999mqVavKcyp79NFHrUuXLpaRkWG77babTZ06tdTjX3/9devZs6d3/I477mjvvffeFsfMnj3bjjzySGvSpIk1aNDAdt11V1u0aFG5rk8IIYQQQgghhBBCCCFqtGg7adIkT2R9+OGHPfGWF//fdtttvc/i4bXXXrPLLrvMbrzxRps+fbr169fPDj74YFu2bFnE4ydPnmwnn3yyJxTPmDHDjj76aO/1448/Fh3zyy+/2ODBgz1hd8KECfbDDz/Y9ddf74m8QgghhBBCCCGEEEIIUetE2wsuuMBOPPFEW7Bggb355pve69dff7WTTjrJ+ywe7r//fhs+fLgNGzbMevXqZU888YRlZmbac889F/H4hx56yA455BC78sorbYcddrBbb73V+vfvb4888kjRMddee6397W9/s7vvvtt23nln69q1qxd127p163hvVQghhBBCCCGEEEIIIWq+aPvzzz/b5ZdfbnXq1Cl6j/8TMctnsZKbm2vffvutDRkypPhiUlO937/66quIf8P7weOByFx3fGFhob377ru2/fbbe+8j1JJyYezYsfHephBCCCGEEEIIIYQQQtTcjciCENlKztgePXqUeJ/3SG8QKytWrLCCggJr06ZNiff5fc6cORH/ZsmSJRGP530grcKGDRvszjvvtNtuu83uuusu++CDD2zo0KE2fvz4qBuq5eTkeC/HunXrikRgXqIGQrmEQsWvSLjPOLaC5Ug9CIVCVVIfYrm1BN9eleKepXtFwn2WfDbItYYCr2i4zzk+ie6viu2uKu1Tdie7q7HUULtLhG3K7mq73SVpX5fkdhfbrTlfrLA23ZqH/Mwktbta3iHE3qRU3ZizKm0O1N+JZKYwwXpQPOeJW7S96KKL7OKLL/aianfffXfvvSlTpngbiiGWkkPW0bdvX6tK3I0fddRRdumll3r/32mnnbxcuKReiCba/utf/7Kbb755i/eXL19u2dnZlXzVolwgslPeubnRj+Ezjlm50mzDhgrXrbVr13qGSkR4dd9agm+vSsnJz7HCDYWWm5ZrVhywX4LcglwrzC+0lctX2oa0JLo5Y/KHdqiMwvM+57iVZpZE91fFdleV9im7k93VWGqo3SXCNmV3td3ukrSvS3K7i+XWEIYKCtZadnbIUlJSa8utecjPTFK7q+UdQqxNCra5bt1aW7as8secVWlzoP5OJDOFCdaD1q9fX3miLRuBwYgRIyJ+lpKS4t0IP4mkjUbLli29tApLly4t8T6/t23bNuLf8H5px3POtLQ0Lz9uEPLffvHFF1GvZeTIkV56h2CkbadOnaxVq1bWuHHjqH8nqpGsLPJpmNWrZ1baJnP0kC1amGVmVthIqdPUicruQGO9tQTeXpWSlZdlqZtSrV69epaRFuUG881ycnOsRasWllk3iW7OsjZnnalnZhkxDHhbmFkS3V8V211V2qfsTnZXY6mhdpcI25TdbQ12l4R9XZLbXSy3lpJSaHXqpFhGRquIom2S3pqH/Mwktbta3iHEenvZ2YXWuHGKtW5d+WPOKrU5UH8nkpjCBOtBGWW1cxURbdmALBFg1Lvssot9+umndvTRRxc9CH7/5z//GfFv9thjD+/zSy65pOi9jz/+2HvfnXPXXXe1uXPnlvi7efPmWefOnaNeS3p6uvcKh8JIlsZyq4NySUkpfkXCfcaxCShHjLQq6kQst1YJt1dl8Px4lu4VCfdZ8tkg15oSeEXDfc7xSXR/1WB3sVLR+iK7k93VWGqw3VXUNmV3td3ukrSvqwV2F9utUffwyVJry615yM9MUrur5R1CPE1KsvUJsdgcqL8TyU5KAm0znnPELdqWJn7GC9Gtp59+ug0YMMAGDhxoDz74oG3cuNGGDRvmfX7aaadZhw4dvPQFQFoGUhzcd999dthhh9mYMWNs2rRp9tRTTxWd88orr7QTTzzR9t57b9tvv/28nLZvv/22TZgwIWHXLYQQQgghhBBCCCGEEJVF3KIt/PXXX166ATb+Ck+gS87bWEFcJW/sDTfc4G0mRv5ZRFa32diiRYtKKNCDBg2yV155xa677jq75pprrHv37jZ27Fjr06dP0THHHHOMl78WoZdrYcO0//73vzZ48ODy3KoQQgghhBBCCCGEEELUbNH2hRdesHPPPddLRdCiRYsSIfD8Px7RFkiFEC0dQqTo2OOPP957lcaZZ57pvYQQQgghhBBCCCGEEKLWi7bXX3+9FxnL5l3JkmdFCCGEEEIIIYQQQgghkoW4VdesrCw76aSTJNgKIYQQQgghhBBCCCFETRBtzzrrLHv99dcr41qEEEIIIYQQQgghhBBiqyfu9Ahs8HX44Yd7G4btuOOOVrdu3RKf33///Vv9QxVCCCGEEEIIIYQQQogqFW0//PBD69Gjh/d7+EZkQgghhBBCCCGEEEIIIapQtL3vvvvsueeeszPOOEPPXQghhBBCCCGEEEIIIao7p216errtueeeib4OIYQQQgghhBBCCCGEEOURbS+++GIbNWqUHp4QQgghhBBCCCGEEELUhPQIU6dOtc8++8zeeecd69279xYbkb355puJvD4hhBBCCCGEEEIIIYTYqohbtG3atKkNHTq0cq5GCCGEEEIIIYQQQgghtnLiFm2ff/75yrkSIYQQQgghhBBCCCGEEPHntBVCCCGEEEIIIYQQQghRgyJt4Y033rD//Oc/tmjRIsvNzS3x2fTp0xN1baKqCYX8nykpJd9fscLsr7/MevQwW7XKrG3bLY8RQgghhBBCCCGEEEJUT6Ttww8/bMOGDbM2bdrYjBkzbODAgdaiRQv79ddf7dBDD03MVYmq5/ffzc47z38hxH/3ndmECWZff2127bVmjz5qdtFFZjfdZPbtt2Wfb9Yss4KC6J9nZZktXGj2228JvQ0hhBBCCCGEEEIIIba6SNvHHnvMnnrqKTv55JPthRdesBEjRth2221nN9xwg60iClMkH+vWmd12W/HvF15Y+vFvvWX28su+8Bpk2DCzDh3Mxo41+/FHswEDzI4+2qxVq+JIXhehe+mlxX83YoRZ165m8+aZ3XefGeJ/ly5mO+1ktnq1WbNmCbtVkeQUFvqTAXXrVveViK2dtWvNUtPN0tO33pUHwTZdCCGEEEIIIUT1irakRBg0aJD3//r169v69eu9/5966qm2++672yOPPJLYKxSVjxNsTzrJbMwY//+HH26Wl2e2fLnZPvvxW4U6AAAzD0lEQVSY1alj9sknZgcdZHb33ZHPE75J3bRp/gvhdrfd/HN//73Z+eeXPI7z3X+/2c8/+7+//77/c/Bgsy++MBs50hdxhRg92o/+fvzx8gm+H3zg16vGjfUsRblpPPcbq/v6M8U96HHHmQ0ZYvbLL2bZ2Wbduplt2uSvSkDcPfBAcgeZvfqq35517uxPer37ri/6Hnmk2YYNZq+9ZnbssX79nDPHT0lDH/vrr2YzZph9803xRVxwgd8ufvaZ/5PvpQ1u0sT/m759zXbf3ezzz8123NFS2ra0OnnZZvUaxS7IMmmWkUFn79vdzjv7bTp22L27fw/XXGP2xx/+REpmpr+CYtkyswMOMEvb/ICYxPvzT/8ZQWqqf78NG/qTMJznvffMttnGbJdd/O+mz+Gc+fnF56FPCk7YcJwnHJPah5+VmCZICCGEEEIIIWq6aNu2bVsvorZz5862zTbb2JQpU6xfv362YMECC7nBjkguNm40228//8WAnwH2IYdsGc3IIB3OOccfdHfq5EfB8n9EgjvvNFu50hcQlizxRQQg8paXwwluTz7pH//gg2aXXbbldSHYAudq184XLVq0KL6OSOTkmNWrF3nAjQDN9ToBABGPa68NkNKC+0FgSVJBImVTtoXS6xWVSUp+hPQaCFnc59tvmx1xROz3iYB2ySX+///3P/996tz22yf2JhCgUmMUj3JyzeogSMVxfgQxhC1Xb8k13b59ea926wDhkefFT8TQ5s39+kFZ0V6MGuXZTZ36Ta1JVk9rXLDasvc71FI3rLPCBo2K6pD7vf7COdbqw2fM2pkvUiI4vvGG/4rGRx8V//9f/9rycwRLR1CYLQ1S1kQCkRhIccMLZs2ytMJ862rrbPkFZ5i1amNpy1Za3ZWrrdEXU23t/nta/bm/2LK9+lu3UaOtTr8FZt9u/ttoE3Pz5xeLx5F4883Y3gvn3/8u/fOOHX2RmHa+KKd+vpktNnt8pFlhvtmy1WbtN6/wcCD+/v6X2U+zzb7/2uyQI/xVIDwjJikpZ0Thf/7TjInpvfc2a9TIrGXLsq9ZiESDT0X9xuepbZQ3Qt/rX1NrpP8iagkffujbHCsFayP0nU2b+hOmQgR1AAIIGB8zducnbS3jLf7PRDz+UrS2F1+6vMNpfHPG9/vvr/KIhaVL/bKoLfqFqDzRdv/997dx48bZzjvv7OW2vfTSS72NyaZNm2ZDhw6N93SiumFQgAhENCz07++/SoNIqHAQQ+64o/h3hAxE2VNOMbv++uL3ibQi3+2JJ/q/4xwhvj37rP87nQaNP/Tu7R+LUBAUCzivc/y5fsRYUnMg5j7wgP8ZUWaIyohciNBOYIAnnjD74QdyfZhdd52f0oHGj2OJgIPwjonBfA0bwKTk51nj+T9Y3defKrbk228vKTIgVj7zjO+gUUaIRzxjchdT7rzP4JDnh/jnhHp3TXTYRANOmmR2112J6SQorwh0uvE+y+6+rS0b/nfv9143PWp1u35hds11/gFEYtO5w8cfF4u2iPtENJLWg86MqEDSbOBkIGJTR8InlPidY1xdCr7P9bVuHfnaFy/26yepPICOk++j7hEZeN9DZr9MNrvitMiTC1nZZvVZTk9ZjTHrttRsyGFm//mP//fURaIysR/qLI4M9Tccohu//NJs4kTfXhGdXnnFF5ooUyI2sS3yUEeqW67MXX0vbZDMM3ETHbGC04fAzvm5DqIqndOHEFZW+gvum8EFz4R7ol7yO9dN2bjrJnqVsu/Z0zJmzrQU2gvOQZszZYo/AAtnjz38ySJXhptJzTfruPgr76s2pORaky/fi/pIaA3yrxhpdXfo4j+f2bPNHnrILz8iSh1O1AVWKTChxaQB9RROOMEv+2CqGFLPBM/hYLUCKWj4PsqO6ycCl//z6tnTbNdd/TrPMTxz1x66x1uvrnW+5ymrk1KnxPsZPy/0fjaY8KXlFeRZ6jfTzFI3lznlx/NynHmm3zZzX1zDiy/6bcmiRVYlUA8gbBNUj/PvNbPN93bcAWZ77Ww2cbrZh1+ZbdxE6dGYm6e4B21/u+38e8KeYPJk/wX33FOcAoO6RhmuWePXQWwfkZxBcDiUAUIxfxev/YSfh3Y8luPcsdS78Pejtd2kR6JdDbYFtHHcc/D7w8+5OYK76N55Dkz6YiDR7pdIdI6n3rDqhgj1TSlmoeb0aKXeXkpOtjWeO9NSG2DDO5v99JPfZ9EXUA7Oh+Ha6K+Z/MAPYIUQfUXwmjiGtgThnxd9C/WpVy9/kpj7p+yCbbh7DjyX4LMMtq8cQ3vNJDPPArtp7rd3dTZkmTUNm1QtLLS05ausTlrIrP7mv+e58Hy4bujTx8/9j/1PnWp28cW+vXOt2DjXzXfR9tH2Uw6klgJ3z6X5F0z8Uf5MYNJu8x2cm3aEZ0G9oI+lzeIYtyFtmzZmn75n1n6x2YAIPmG0uka9GTfO//3GG6NPOlJXaFcJIOA+KBP6M2eTPCN8PK6D50baLtp72lmukedGG8XqP2yWiRgmfDY/d68MqSf0Mdw3DX+4/xMPL73ktw+U0//9n9830SZyD9QxymX4cL/toHycn+u+N/w7qZ+UZ3j9Auebhds09/HOO75dUlbO7qkjfEcixG6eabAtAO6F60H4YQ8MrhvfujJTm7mgkPBABe4Z/75T2/jOxzOinNzEInUmfMIuWEb4M/hobMxMerdIcDx+CPbLSkYmJKkf9Je0Lzwvt7Ez9kVdTjT4rOM+MTv3XP/3W2/1fzJZDYwPDjvMvxZskzaTsQJ2T91xZb01pEJi023a0vCAJeoa9Zt+JvgMGCswbuTv8CupQxzj2mLaoCD0SS06Ef4W/7XhE95yi+/jUS+5TuoQfQ11zK1Oov896yz/d+oy14YPTd2mnlKmvE9doy6y+pAgmFhh7MiKK57Jvff6bTLjjWnTLK19W+tkKy130G5W2LvXln+bl29WN9APU+emTPLbbkc04RYbor2P5sdQPtR12oPwic7y1F3Ox9+Ut85/O8Ps21l+/0X/St2grPAf8IF4j/EQYx3KBt8Cf+Oqq/y6dNRRkdtPntkNN/hjB1azia2KlFCc4bGFhYXeK22zMzhmzBibPHmyde/e3c4991yrR+VLctatW2dNmjSxtWvXWmMto04MDIpw7BgYh4PjjOBLR7Dvvr4zxPJiQND49NOSxzMDTidFA03nSkcVVV3J8xvKWAfNCEqbU354IIScc46FLr3UNm3a5KUESXGNON+NA8T3hDt3bOzGd/MT55mGlkEI18ryfKK7gkI0Di6d0YcfWnaXnjZ5aVdL79jKMjJTLXXDWkv/4xcrrN/QQnXrWfrvP1vd5X9a/emTva9o265kP+g16FzTww+XvJeyoEM/4wx/+TPCUjg4c3Q2PM8FC/zBHWIh94YIT1Ny9dW+qHbFFcV/N3eun/6CzjQ72/IK8+27QdtZ7gH7WkZahtc51lmzzjrcWRw5WBAq8MSjto3aWV0nHgXhGtgUD6cZx6E80LG7iQaunfpJmVA2dJ5nn+2LYv/9ry9UMQBz6UPCwenF4V1HXu/FVA6zy083a9rI7N0vzLp1MvvsG7O/lpt17WjWvqnZ59N88Sj+ubP4wXnEScNh4P8MZBl8MYmCsMCAgXtmgMlSfer2V18VR2oefLAvDvMMGBBhq26w5EQSbAzbQOzCEXR2R/kE60OQEtGSFl9d5ZlTx71LCG1pn0FwoBE4wm2de8Y5ysmx3Nf/Z0vG/2T10kJRmxTAtOYOHWm7Ht9li7FrRKKJZkFH0rVltCPYFgMp2hYG+og4tIvlAZGE+0xJsay8LJs65b/W84k3ikTbTb22t3V7DfT+n7Z6rTV9bayt7dDStltXx+oOPc5szz3NGjSI3eklYphr5cG4SObwh0Q5MLBxD5k2iu/g+dCWcM9uksnB83F/QzvKOajH3BvXtnC22Tcvm02YXSzaRiQg2mJ33NMOO/jR1/36RY9ejgbtISIMdZxyQ8wiBQZR/fzErrh/2ne+g0kzxAAGH0GRqLRVH4iStDvUDWwzKFLw3Txz7AgRs6z+jkEabRliHAMCbDXYpvGemxijvXARHVEm2kqFNB97722FK1famkmTrNn48RFtk3HcksVmG/Y61Dbte6i1ePsFy5wz3dbueahlLJxrobS6lvHb3KLufIv+rrT+OxI8fwbasYCgiKhJ3aOsqYeROO00fxCLuMnxsHkATX+3ZP1iq1unrmUP2MkyZ87xVpGsOPkoa/nyW0X93fqWja2LNbG62XlbTvjUWDZHuLdoaXbuCWar1pr9scysUabZLjuYfT7DLK3QLHed2WFXmJ0XYUUVbbibIHC8/nqxaJ1IsDvaGiYUYwFRhDLnb/A7KWP8N+oCZYM9Y7eIMfH4mbFCm8REINdL38v1hMNkIZOUtPUI026yKRzEHs7BBMYvv1jW4SfYxImF1nDTbGtSr4HVXzDHctt2smafvmFLhl1joXrp3u1uXJltg3faYJm/z/XbN0ThWHCiAm0bAjttOqJ5JDHEDUMRmPgORPfgZzNn+r4YAgjiBn5KcEUGz4l+kvKlz1m2zLO7L4YdYPU6dvb9zMD5UnJyrdnbH9vyvXaxVY3TbO8W/S3z6kBwSRDSw+H7OpGTPTf4DtISOU4/3T8mGL1KY4U4FjwuGqxyHD/et3meD20U/RI/Eat4Vjwb6iJjkq+/MNu7sdmQvVGto5wUUXu92bmv+30d7Th1yfl05QHxj/EMguFTT/ltLX40/Q/tHc+G8mECg/af+8ImsGX8Avo72l7KlLJy9sLv2BBiNv07Zc1EamkrB6mcfD/X5J4bdYd+kr6Qvgw7ZfIEG+XcXLeLJg0KffSxjCkdlCOTgHvt5dcrAnzCcVGopXHyyf5EEvexuV9w/d2y068sHuc68vOt+YevWk5Khi3eZlfbeebj1mTd6sg+bQ0k2Nf9eee1Vmf9Rmvw7Q/W9MOJxXOEV5xrG5o3tE0rFts+j7+/5fiO9GH4fvQBtFUuaIVxWBDaW8YABK/QHgTBVmmbg2I05Uk540i4CR/adXxO2hvqG9/FmD0IPmFRu05/PAlHo3S727jc7KYJZutimGyPFa6B+/rHP3x7Yex2/PElj+F9fDZsINgXcc/YJM8L+8T2osHnaDBM2tCGYWPYV1mreIPfxTVeGaF+Y+O01dF0QjdxVsMpLCy0ZcuWWevWrS01AYFs8WiOcYu2WwMSbWsQGDGNN4IRTjKOQtCZDhdtaRCIEkFQxFGk0SEdAw4LA1WiRctBmaKQW/aMo0Ajj5NZGqUMLl2nXpoeDUWD2F7Nre51V/lCXHiHAzhSDDpjWZocCRol/p7OLVZYYuyiVoORhIGOfcUZJ1i7/31mddYXi2k5221j6b8u8gaxK3p1se7b72Hpn4wv/mMcRKJML7/cF4qZ4QUaOgYtgFNG9AIdMTOWOIBEwOBo47ThxDnoWHhm0cTYuMk3u+1As+tejU88IuKIAnX5pekgqcfUewRSKoKrdzgWbgDBgMJFBwZBJC2vmF3q7W22OzYSpHPHznCQy5osicXBDUI5I3yRcsCJUThY2HG44NKxoxUefLCtWbTImm6/vaXikFHnqPcMsIOCAE49dodxhUV40bwQUN7ppw+t9Rdv2tJTr7Ccbbpbxi+zrLB+A8tt3yXiWCFZQLSdtHCitVy6wVI6d7aU3DwL1S/piGXnZ9v63PW2d+e9LbNuEt2cc6ZXFpr9sdYsM8Ps3hf9j24936xeXX8CZfI3ZmuWmf2NSYTMYkcSkdzBBAV2hVBCu0IkBC8noLnUQfFEp0QD+6BORxIDicwiIo+JMGd3CCFMgGITDJ6D0eLlmaSsDHg2gZUpUftOrrNlS8v7aZ4tWZhTZn+3rktf21CvufWyn6zu6mX+my7iCEc/fOAW7yQQ/RV9BdB3MNmY4IFseIR7xEnKvfbx20tWKSH4cF30v9QBVlMEcVHwPNeBA31/iRcCAXUXwacsECOpO/SD9PGkO2HSlnPwXBHxEaVpv/n+YDt+2BCztNlm//u2lP5uc19Xf1uzTfm+EEWKLSYc3P4IRIZx/QwWEQXJnw309cEVXIgg/D2rpRAAw/0szkGfFAnErljFWkc0P5Pn7YZOzu7wPZkwJ90XfRSiG0IVxzlbZWKXKLzKBHE2hhQ7+JmLF4esTuomq1ePMO8t/doN7be3tF/nlT5REg7iGMEZ3C8+THnaSaL5mJAPD9iI0+7+yl5uK4afYs1+/NkK62dYw2k/WJ2160vY3cyrhtm+o94uKR4RzY4YHi/kkKd9Jto73FYrY7KkaTOzPfqb9exitniFWYsmZjt2M1u+2uzF/5nN+3XLwACE9EjPlQAMxiXYfmVNFiEyMZ4LgoDE97nVPEGbo23CfhCamUBmUsKJOtg+fl5193fhK0RL44QTLGf+Ils2bkpRk7LiqDMtNXuTNfniHauzcX1Rk5KbF7JmTTdZw4aBfpMAA/x92k5gooC2hdVHTMi4CUMHQQk4rJHGhTwzNwnEWIGLYdzhJlRop3i2vE+7jKjJZDmrwRwIh/RN9DV77WWbvplss6d/ZO0//y5qX8cYLz8/1+r8utDv7w75m3/dBMnQH4TfQ42ClVNLzO6+2KxRc7NsVl0uMlu2yuyNT7cc2x1xTPGqQWASgMlwJjFcGdJeMpCIt28Ct1qMciQ9ZbCcKTfKLLjK2MGzJvjG2Rm+Bv0pgQecJxK0D7Tr2Cn+spvQpq4w8cqkEtHgaCCshAHaE7dqK6h3hK8IBvwKt98ROk2k1Ws1hKQSbT/44ANr2LChDWYw7E1CPGpPP/209erVy/t/s8pcDlNFSLStoVBVaehwyomyYmYUcc7NAIXP4CMq4TyTtoNGkb+n8yHiyS1Z5T0GNnRONDosP6ETYpaYRmnzoAIzWXrTTda6USNL5bwMljgWITgIDRHnCXfkcF4RnZzY5oQ1OlWc2kD0QmmibUHDxrbqb6dadqdutskytxSP6CBo/HmDqKxwgZlBGZ/RkOKUMsBzoiB5h93mbwgX/D3v0yHw3IkWo0HByaJDZxadMihr6S5l9fe/+57I+vWW1aa5rTz7lBKD2A0Dd7I1f9vfCjPrbykepdX3l4wTJUYnxDURxTZhgn9+ysEtkYxlRppnwMO9+eYty4pBH2KJ2/SJZ+lmYxlI4Fwy+HPLb5jV5IU4zUC/I8t8J5m98pXZxM0Dyo5tzFJTzIbsZvbjz2ZTZ5kNHWw2gEFnY7NX3yrufKmL1ONtt7WYcPdMGVBvg9AhP/20X/8oZ54dZcmkAoI3HSn2wKCcdptzcAx1wy3fZ3aae2dCggjIY47xO+jwyumi8tz/SfFAZ4azzXXQSfPMODeDGu6P6+Uz7Iz3cS5xyHmWpaVtcE48zj8RRvXrJ6QTdaItfkZGGpHDYUvkNpPUou1vk6xRvUYlI49qk2gbjICY9pMf1d6s8ZbRR0aE0ub7o14zEEH8I8WKS5cTDsIM77t6z+9EBeHAUiFwMmkbWGnABBL1msmioPhHW0vbEi/YCiJIpE6BQTjtMrZMP4PgjM1i01Rq7JzBFu0Y7YybZKHfZFBMW8/f8zdcKxNdVHLadkQnVl4wMOQ5cV8InG5wR7/Acwtvd+kfuM/PP/f6zrX9+lnjM8+01PA2ytndxJDt8NYdlrnCT7GxZv+h3uqS3FYdrO6qpbZ6/6G2MaNlsd3VD/ntZPBZUB5ERRHdwSCJsnCpbojGoW3nBAx68RsQAVzfFcmQeQZE5nDP3C/9HqmheJ+JPwZgbtDCqg+gT6T/ANrcTZssq16KTZ79kTVftckarM+xjTv1tmbjPrL0Pxbb8v8bavmtWlh23iZr/Oqb1nXYZZbZZvMS/tKgHedFe1lWe8dz4sWgnmvl+nk2lB0+UGnpaiC8X2Vgxjm8JeGb7W4xkYczzSZNN9thW7PZC4qP79nRbOkSs9V8T1rJtCQMHKNFt9O2M5Bkoo06FmsuW+6VSsU14nO4PIy8WD3ywgv+d9Ln8AywUz6jL+I7eE4u9QSDYOoQg1n6JPzJoOBFnaC9YDBL2ceylDzavgv8DTZGP4rtcu3UK9oVlgxji9gy18AzoS1h0163KgqfFZ8Ikdsty6fvd/4BbRV+Lr7Uscda7suv25IJP1n2tp2syaoV3sRkKK2eFWY2tPRF80vo0a2P3N3SWzbyz4FwTpuATeBL05dzjVwbzxMbp4/fvArGY/Mqq4SB0MGeBNwnogSRkAj/tJHUlUGDbNPq5bbiustinyzZ7wB/koxJAa4XAYHnR5kwIUTwB+0BwQ/4T3zmyhtfMpLYxLNCWMNWKQPqUHhaB+or56U8+T9+OXXW9Ru8T11kogN/lFRyRxxk9jYp5eqWPVmCeHTH3f4ECGINor4b9uPrU38pQ1cfXWoll5qDfoE6z3Oh3nFdtH8IfbQF9B0cS7tKO8txnK+8QSLRJkoctNsItvitpU1SUjddO0DkH20/1x2cNMHeeR70GUy6UHdcmQQ3zMYOXYQiYwb6Uq6Puu76VjeRgw3QpnI8wj12ie/L9zdu7F3S5HdXW/ePHrHMlZvFvABL/+9yq//Fx7YpN8U6r55iDW672VJpB8NTNkQieC3hq3fcijjaN8ap5RXFXPQlbVRYG+Z8zI7T5lubd/xgmzUH7WMFzZpYqE6qpa1ZZ03f+6zI7lofe5qlH7+5vwTaWsaZQSgbnjOpwdz3Uw+xe+oJn9PHu3Qwrk0k4IX6TvlTfrTP2BL3zrNkRQJtP3UXm8N/YnzOc6OMKSjq2HPPbTlZEqvdPbk57SM2xD3EMjYJpqYCrp/xDpGyLp0PY1H6MFIp4H8G05clG7Sr3CNpGN3qGiaJsfMaSmEyibY77rij3XXXXfa3v/3NZs6caQMGDLDLL7/cxo8fbz179rTng0u+kxSJtklACYUlSth+edQVOlwadDo+x2OPWeGOO9qy7t1LGinXQMQhg2YcBDeDzMAOBwuCO587EEfpWGisOBeeMZ0DnWidOpa1cpP9+MwUy9trf8tIj+78J1Q8cjn+Smswog1EuEdEPQbKLG1hQEykgsPdZ6Bj//azl63bS+/ZuiMPsQ2DdtnivGWKRwyI6dzJHYdTXB64X4Rz6g8ze7Eu/4hVPMpJNWNjtYgExKP8etUfKRArrs4zWYHzgFDNpAjli6NDXaAeV7GqmXDRNo5VecnCVifaRiSCaAsM5NyEVLw4AS3W9gMRgErG37gJQ/4WMdVtPIXo4Ta2Q4RxeboRS4i2dZvNMcghAiKeilmVuQnXrLHQiBH+hGfbthFtM2h3LeZ8aZu69/U3AEwmu+PiopT/Vml3a1hSmeFHuGNz65aYXfme2cjrfVEnCIPOYGQoFYFInZqwJDi8U8B2EGLo+5hERpyq7oqJyODyWcZ5a6RHSE9fZo0bt7aUlGLbbPD9ZMvaYRdr8PFY2xBqYDtccZhlNoizPBDKiKxELCmt/QnajguicJt1ElHNpAniDJPBrn2MwV/C7qZNeMWarc21Ok2aWYPpM23dXrtZbpeORaJlhxG3+uLRUX+39JNOKXldCJUIs8GxQDRoyxEzKQM3kYVAW2n1IcssNNFs5MtmBeQszjM7el+z7+f5Eyad2pj17WL27sSS4lF1QbmxYoSxFcI4E1uIhzwnxE23pBtx0tkczxKRimOoI+GTO/Ql1BsEe34GN86if4wWQBbvZswIYgncDDLYpLSe9p41nfg/W7vX4d4rdeM6K2zYxLvddesKrVevZdapU2KEoaqgqK9La2DbPPemrdtvD8vuUZzvue7iZdbugadt+f672boGadb36HMtMz1sQzwnmlI/8G9Y2VSdfQETwYzREXxTNpk9PdJsGqv9Au1tRrpfr1o1MztwJ7Pn2ey6vdkTgRVcFYWJClbTujYxGPEMpL2iT3LtERoDtsOEIraEOM1PbI0VndH26WHVJzZJMBltHz5nONgiwjlp8JioiQSTjC6wCkGd/Qtox8M1QsbvTN6GT/CxWpfc7DWQwmQSbYmy/fHHH61Lly520003ef9nI7Lp06d7Qu6S8MjDJESi7VYs2lbESBFemSVj1q8sQy5l4JyU4hGOKvfuNlLg/0QeuLxSW/sgNlbxqKbjj/T8irk5V2pNqJgSbctGdpdkdkf7yeCTZdbO7hj0RhuM1qgOIXbbTMr+Lg5kd5ttbt1OZo3abNlnUKhM9jKIROhjIjAhE6jJ52dWJaWJttV+a6SA4stZCVCJdlfw5x+2tl6h7dnr4FroZ2ab3fCo2ZqmZg9HyMlaU4lmc4hEBIAA0bGsdCCyNAntLpYmJalF21JsrvaP77L9VUZN9jPbLsIePhUhGD3NKhDEVyKK3SaDsYIoiy9JGiLG54imrC6hUrLaMgj9MnbGcZHGe1wT430qM+I2EfbYp1stGh7xTYQ0AQiRpEfSFRKgwOpZQJiOtsnjViraxh3exUZjWZvz3XzyySd2GmHM3kqE5t4XC7HVEk9qkJoQRZJIwpfasLwj1iX+Irmg7kZa3imESBws3QvmXcbeIqQXECIp8AYjEfoMBopEEAkRTKlVBeS1aWkFuXFs1ptsXH2CWcEeVitgNSMpC9ymSIlMtyFEItmZDbgqYfwbFAhZFRZMNRQPzo90KQgibRDvKC1QwF2TOx/R7qwEi3bNQEpIInVdOhEmaoE0Im7jMvLqk/+dVWiiYqItuWwvu+wy23PPPW3q1Kn22uaNgObNm2cdWdoghBBCCCGEEEKIqocUJV5UoNUe4VYIkdwEU/m46PmguEvKhPKmPazlxB3X+8gjj1haWpqXEuHxxx+3DiQxN9JnvG+HkHtECCGEEEIIIYQQQgghSqiQqbFt0CbKF2m7zTbb2DuBne4dDzzwQLynEkIIIYQQQgghhBBCCBFGWnmT8P78889eIl7+H2RvkoILIYQQQgghhBBCCCGEqBrRdsqUKfb3v//dfvvtNwuF7f6WkpJiBewel+S4+9LGajUYNsPbuNHf2ZBNryLBjoa5uRSkWX5+hb6OyYn169dbRkZGpe/kGcutJfj2qnyH0Y3rN1peap6lp0W+wZz8HMstzPVsML9ufpLtLrqRLS7Yja2U43LMLJdWxsyS6P6q2O6q0j5ld7K7GksNtbtE2KbsrrbbXZL2dUlud7HcWlZWoWVnr7e8vAxLSUmtLbfmIT8zSe2ulncIsTcpfr+5bl3ljzmr0uZA/Z1IZgoTrAc5rTFcU02IaHveeefZgAED7N1337V27dp5Qm1tg8KATuzOJ4QQQgghhBBCCCGEEAnUHps0aVLqMSmhWKTdAA0aNLDvv//eunXrZrVZRf/rr7+sUaNGtVKUFuWbCUHE//33361x48Z6hELUIGSfQtRMZJtC1Exkm0LUTGSbQmwdthkKhTzBtn379mVG7sYdabvbbrt5+Wxrs2jLQ+vYsWN1X4aogWCgEm2FqJnIPoWomcg2haiZyDaFqJnINoWo/bbZpIwI23KLthdeeKFdfvnltmTJEttxxx2tbt26JT7v27dvvKcUQgghhBBCCCGEEEIIUV7R9thjj/V+nnnmmUXvkUKA8N7ashGZEEIIIYQQQgghhBBCJI1ou2DBgsq5EiFqMOnp6XbjjTd6P4UQNQvZpxA1E9mmEDUT2aYQNRPZphA1k/Rq1IPi3ohMCCGEEEIIIYQQQgghRA2KtN1mm21s3333tX322cf72bVr18q5MiGEEEIIIYQQQgghhNgKiTvS9qWXXrJJkybZhAkT7Oeff7YOHTp4Aq4Tcbt37155VyuEEEIIIYQQQgghhBC1nAqlR1i8eLFNnDjR3nnnHXvttdessLBQG5EJIYQQQgghhBBCCCFEVaZHgKysLPviiy+8aNvx48fbjBkzrE+fPl6krRBCCCGEEEIIIYQQQojykxrvHwwaNMhatGhhV199tWVnZ3s/ibhFuH3ggQcqcClCVC6k9TjiiCOsffv2lpKSYmPHji3x+U033WQ9e/a0Bg0aWLNmzWzIkCH29ddflzhm1apVdsopp1jjxo2tadOmdtZZZ9mGDRtKHPPDDz/YXnvtZRkZGdapUye7++67VbRCVNA+g5x33nneMQ8++KDsU4hqts0zzjjDez/4OuSQQ2SbQlSzbcLs2bPtyCOPtCZNmnj+7a677mqLFi0q+pyx3AUXXOCN7Ro2bGjHHnusLV26tMQ5OP6www6zzMxMa926tV155ZWWn5+v8hWinLYZ3me61z333FN0jMacQlS9bW7YsMH++c9/WseOHa1+/frWq1cve+KJJ0ocUx39Ztyi7Zw5c7xOH3GL1w477OAJXELUdDZu3Gj9+vWzRx99NOLn22+/vT3yyCM2c+ZML5K8S5cudtBBB9ny5cuLjkGwnTVrln388cdeWhAM/5xzzin6fN26dd7fdO7c2b799luv80UMfuqpp6rkHoWorfbpeOutt2zKlCleZxuO7FOI6rFNRFom8N3r1VdflW0KUc22+csvv9jgwYO98RqrIwkquP76672gAsell15qb7/9tr3++uteyru//vrLhg4dWvR5QUGBN/DMzc21yZMn27///W974YUX7IYbblD5ClFO2wz2l7yee+45T0BC/HHIpxWi6m3zsssusw8++MDbx4tJz0suucQTcceNG1e9/WYoTgoLC0Pff/996KGHHgoNHTo01LJly1D79u1DJ598cuipp56K93RCVAtU/bfeeqvUY9auXesd98knn3i///TTT97v33zzTdEx77//figlJSX0559/er8/9thjoWbNmoVycnKKjrnqqqtCPXr0qLR7EWJrsc8//vgj1KFDh9CPP/4Y6ty5c+iBBx4o+kz2KUT12Obpp58eOuqoo6L+jWxTiOqxzRNPPDH0f//3f1H/Zs2aNaG6deuGXn/99aL3Zs+e7Z3rq6++8n5/7733QqmpqaElS5YUHfP444+HGjduXMLXFULEbpvh0Ifuv//+Rb+r3xSiemyzd+/eoVtuuaXEe/379w9de+211dpvxh1pyyxQ37597aKLLrI33njD3n//fTvwwAM9pZklq0LUBpgZITqW5WTMxsBXX33lpUQYMGBA0XGkUEhNTS1Ko8Axe++9t9WrV6/omIMPPtjmzp1rq1evroY7EaJ2wEaXp556qre8pHfv3lt8LvsUovogio/lXz169LDzzz/fVq5cWfSZbFOI6ukz3333XW8VGX4o9rnbbruVWArKirC8vDzPl3UQlbvNNtt4dgv83HHHHa1NmzZFx3A+Vpax8kwIUTFYVo2tknLPoX5TiOph0KBBXlTtn3/+SXCrt3/XvHnzvJXU1dlvxi3aTp8+3e6//34vPxJ5HPbYYw9vuc2FF15ob775ZrkuQoiaAikPyE3C0jFyNJMGoWXLlt5nS5Ys8ZzeIGlpada8eXPvM3dM0EDB/e6OEULEz1133eXZGxOGkZB9ClE9kBph9OjR9umnn3p2ylKxQw891FseJtsUonpYtmyZl5vvzjvv9Gz0o48+smOOOcZbwomNOtskyICAhHC/VX6tEFUDS6cbNWpUYnm1fFohqodRo0Z5eWzJaUv/SP9JKgWC8qqz30yL9w8GDhxoO++8s+2zzz42fPhw7waIRhSiNrDffvvZd999ZytWrLCnn37aTjjhBC+KNlysFUJUHcxqPvTQQ96kIas9hBA1h5NOOqno/0QWsBqra9euXvTtAQccUK3XJsTWHGkLRx11lJd/D3baaScvvx6bqjCOE0JUP+SzJX9tMNe0EKL6RNspU6Z40bbsUcT+RWw6xl4qwejaqiZu0ZadDBs3blw5VyNENcMme926dfNeu+++u3Xv3t2effZZGzlypLVt29aLXAjCLoDYBJ8BP8N3D3S/u2OEEPHx+eefe7bH0hMHUXyXX365Pfjgg7Zw4ULZpxA1hO22285bofLzzz97oq36TiGqHmyQ1SlEDAVhA2k22wVsk3Rga9asKRE1hN8a9GunTp1a4hzya4VInH9LCr3XXnutxPvqN4WoejZt2mTXXHONt+k1G4kBgQgE9N17772eaFtd/Wbc6RGcYEvkE7uq8SL6SYjaGqmQk5Pj/Z9UIBgodd/x2WefeceQJ8wdw4wMuU4cpFggz1+zZs2q4Q6ESH7IZUsaHjpN92LGk/y2H374oXeM7FOImsEff/zh5bRt166d97tsU4iqh+Wbu+66qycIBSE3H9FDsMsuu1jdunW91CYOjl+0aJFnt8DPmTNnlghawK9lPBguCAsh4oPAIOzQ7Z/iUL8pRNWTl5fnvdivKEidOnWKVq9UW78Z785lS5cuDe27776hlJSUULNmzbwX/2fHw2XLlpVrNzQhqoL169eHZsyY4b2o+vfff7/3/99++y20YcOG0MiRI71d/xYuXBiaNm1aaNiwYaH09HRvp3rHIYccEtp5551DX3/9deiLL74Ide/ePXTyyScXfc6Ogm3atAmdeuqp3t+NGTMmlJmZGXryySdVyEKU0z4j0blz59ADDzxQ4j3ZpxBVa5t8dsUVV3h954IFC0KffPKJt8sufWN2drZsU4hq7DfffPNNb5frp556KjR//vzQqFGjQnXq1Al9/vnnRec477zzQttss03os88+83zfPfbYw3s58vPzQ3369AkddNBBoe+++y70wQcfhFq1auX5zEKI8tkmrF271hsjsqt8JOTTClH1trnPPvuEevfuHRo/fnzo119/DT3//POhjIyM0GOPPVat/Wbcou0JJ5wQGjBgQOinn34qem/WrFneeyeddFK5L0SIygbjwzjDX6effnpo06ZNoWOOOSbUvn37UL169ULt2rULHXnkkaGpU6eWOMfKlSs9kbZhw4ahxo0be8Iuxh/k+++/Dw0ePNgTfDt06BC68847VbhCVMA+YxVtZZ9CVK1tZmVleU4pzijiEHY5fPjw0JIlS2SbQtSAfvPZZ58NdevWzRt09uvXLzR27NgS58D//cc//uEF4SAg4QsvXry4xDEEMxx66KGh+vXrh1q2bBm6/PLLQ3l5eSpfISpgmwT0YFME/ERCPq0QVW+bixcvDp1xxhmeJkS/2aNHj9B9990XKiwsrNZ+M4V/4onMZdOxTz75xFtyE4S8DQcddJC3fFwIIYQQQgghhBBCCCGEVU1OW/I5kMchHN5zuR6EEEIIIYQQQgghhBBCVJFou//++9vFF19sf/31V9F7f/75p1166aXeLsFCCCGEEEIIIYQQQgghyk/c6RF+//13O/LII23WrFnWqVOnovf69Olj48aNs44dO1bgcoQQQgghhBBCCCGEEGLrJm7RFvgT8trOmTPH+32HHXawIUOGVMb1CSGEEEIIIYQQQgghxFZFuURbIYQQQgghhBBCCCGEEJVDWjwHs9HYCy+8YG+++aYtXLjQUlJSbNttt7XjjjvOTj31VO93IYQQQgghhBBCCCGEEFUQacthRxxxhL333nvWr18/69mzp/fe7NmzbebMmV6e27Fjx1bgUoQQQgghhBBCCCGEEELEHGlLhO2kSZPs008/tf3226/EZ5999pkdffTRNnr0aDvttNP0VIUQQgghhBBCCCGEEKKcpMZ64KuvvmrXXHPNFoIt7L///nb11Vfbyy+/XN7rEEIIIYQQoso444wzvKCD6oLUYnfccUdMx5500kl23333Vfo1CSGEEEKIJEyP0LZtW/vggw9sp512ivj5jBkz7NBDD7UlS5Yk+hqFEEIIIYSImbL2Wbjxxhvt0ksv9VJ9NW3atMqf7Pfff+8FPfz222/WsGHDMo//8ccfbe+997YFCxZYkyZNquQahRBCCCFEkqRHWLVqlbVp0ybq53y2evXqRF2XEEIIIYQQ5WLx4sVF/3/ttdfshhtusLlz5xa9h1Aai1haWYwaNcqOP/74mK+hT58+1rVrV3vppZfsggsuqPTrE0IIIYQQSZQeoaCgwNLSomu8derUsfz8/ERdlxBCCCGEEOWCFWLuRWQqkbfB9xBLw9Mj7LvvvnbhhRfaJZdcYs2aNfMCEp5++mnbuHGjDRs2zBo1amTdunWz999/f4soWFabcU7+hrQHK1asKNWnfuONN7wNfoM89thj1r17d8vIyPDOc9xxx5X4nOPHjBmjGiGEEEIIsZUQc6Qty8dwbtPT0yN+npOTk8jrEkIIIYQQokr597//bSNGjLCpU6d6Ebrnn3++vfXWW3bMMcd4ezs88MADnii7aNEiy8zMtDVr1nhpDs4++2zvs02bNtlVV11lJ5xwgrdRbyR++OEHW7t2rQ0YMKDovWnTptlFF11kL774og0aNMhb4fb555+X+LuBAwfa7bff7vnc0fxxIYQQQgixFYq2p59+epnHnHbaaRW9HiGEEEIIIaqFfv362XXXXef9f+TIkXbnnXday5Ytbfjw4d57pFl4/PHHPeF19913t0ceecR23nnnEhuKPffcc9apUyebN2+ebb/99lt8B3lsWaHWunXrovcQgRs0aGCHH364F9HbuXNn77xB2rdvb7m5ud7+EXwuhBBCCCFqNzGLts8//3zlXokQQgghhBDVSN++fYv+j7DaokUL23HHHYvec/s7LFu2rGhDsfHjx0fMTfvLL79EFG2JxiVSNrhZ2oEHHugJsdttt50dcsgh3ovoXqJ5HfXr1/d+ZmVlJex+hRBCCCFELchpK4QQQgghRG2mbt26JX5HWA2+54TWwsJC7+eGDRu8XLPfffddidf8+fNt7733jvgdRO4ivBI16yC6dvr06fbqq69au3btvIheon5Jv+AgZQK0atUqwXcthBBCCCFqIhJthRBCCCGEKAf9+/e3WbNmWZcuXbxNyoIv0h1EYqeddvJ+/vTTTyXeZ8PfIUOG2N133+2lX1i4cGGJvLhseNaxY0dP9BVCCCGEELUfibZCCCGEEEKUgwsuuMCLgD355JPtm2++8VIifPjhhzZs2DArKCiI+DdEyiL2fvHFF0XvvfPOO/bwww97UbrkvB09erQXzdujR4+iY9iY7KCDDlI5CSGEEEJsJUi0FUIIIYQQohywOdiXX37pCbQIquS/veSSS6xp06aWmhrdzT777LPt5ZdfLvqd4998803bf//9bYcddrAnnnjCS5XQu3dv7/Ps7GwbO3Zs0YZoQgghhBCi9pMSCoVC1X0RQgghhBBCbC2wGRlRtK+99prtscceZR7/+OOP21tvvWUfffRRlVyfEEIIIYSofhRpK4QQQgghRBVSv359LwXCihUrYjqezdBGjRpV6dclhBBCCCFqDoq0FUIIIYQQQgghhBBCiBqEIm2FEEIIIYQQQgghhBCiBiHRVgghhBBCCCGEEEIIIWoQEm2FEEIIIYQQQgghhBCiBiHRVgghhBBCCCGEEEIIIWoQEm2FEEIIIYQQQgghhBCiBiHRVgghhBBCCCGEEEIIIWoQEm2FEEIIIYQQQgghhBCiBiHRVgghhBBCCCGEEEIIIWoQEm2FEEIIIYQQQgghhBDCag7/D0uXU1S8ayLDAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot optogenetic stimulation and demodulated signal together\n", + "fig, axs = plt.subplots(3, 1, figsize=(14, 6))\n", + "# Plot demodulated calcium signal\n", + "processed_fp = nwbfile_freq.processing[\"ophys\"]\n", + "for name, fp in processed_fp.items():\n", + " if \"downsampled_calcium\" in name:\n", + " calcium_signal = fp\n", + " elif \"downsampled_isosbestic\" in name:\n", + " isosbestic_signal = fp\n", + "\n", + "calcium_data_freq = calcium_signal.data[:]\n", + "isosbestic_data_freq = isosbestic_signal.data[:]\n", + "timestamps_freq = calcium_signal.get_timestamps()\n", + "\n", + "# Plot first trial\n", + "for i in range(3):\n", + " trial_start_time = trials_freq_df.iloc[i][\"start_time\"]\n", + " trial_end_time = trials_freq_df.iloc[i][\"stop_time\"]\n", + " tag = trials_freq_df.iloc[i][\"tags\"]\n", + " timestamps = timestamps_freq[(timestamps_freq >= trial_start_time) & (timestamps_freq <= trial_end_time)]\n", + " calcium_data = calcium_data_freq[(timestamps_freq >= trial_start_time) & (timestamps_freq <= trial_end_time)]\n", + " isosbestic_data = isosbestic_data_freq[(timestamps_freq >= trial_start_time) & (timestamps_freq <= trial_end_time)]\n", + " # Plot demodulated calcium signal\n", + " axs[i].plot(timestamps, calcium_data, color=\"blue\", linewidth=0.8, alpha=0.8, label=\"Calcium Signal\")\n", + " axs[i].plot(timestamps, isosbestic_data, color=\"red\", linewidth=0.8, alpha=0.6, label=\"Isosbestic Signal\")\n", + " axs[i].set_xlabel(\"Time (s)\")\n", + " axs[i].set_ylabel(\"Downsampled Signal (a.u.)\")\n", + " axs[i].set_title(f\"Calcium Signal / Isosbestic Signal with Optogenetic Stimulation ({tag}, Varying Frequencies)\")\n", + " axs[i].grid(True, alpha=0.3)\n", + "\n", + " # Add stimulus intervals as faded boxes over the signal\n", + " frequency_colors = {5.0: \"red\", 10.0: \"blue\", 20.0: \"green\", 40.0: \"yellow\"}\n", + " frequency_labels = {5.0: \"5Hz stimulus\", 10.0: \"10Hz stimulus\", 20.0: \"20Hz stimulus\", 40.0: \"40Hz stimulus\"}\n", + "\n", + " for r, row in ogen_df.iterrows():\n", + " # select rows within the trial time\n", + " if not (row[\"start_time\"] >= trial_start_time and row[\"stop_time\"] <= trial_end_time):\n", + " continue\n", + " frequency = row[\"stimulus_frequency\"]\n", + " color = frequency_colors.get(frequency, \"gray\")\n", + " axs[i].axvspan(\n", + " row[\"start_time\"],\n", + " row[\"stop_time\"],\n", + " color=color,\n", + " alpha=0.2,\n", + " label=frequency_labels.get(frequency, f\"{frequency}Hz\"),\n", + " )\n", + "\n", + " handles, labels = axs[i].get_legend_handles_labels()\n", + " by_label = dict(zip(labels, handles))\n", + " axs[0].legend(by_label.values(), by_label.keys())\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "hnasko-lab-to-nwb-env", + "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": "3.13.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/001528/HnaskoLab/Lotfi_2025/README.md b/001528/HnaskoLab/Lotfi_2025/README.md new file mode 100644 index 0000000..0a4e3d9 --- /dev/null +++ b/001528/HnaskoLab/Lotfi_2025/README.md @@ -0,0 +1,18 @@ +# **Fiber photometry of SNc/SNr dopamine and GABA neuron responses to optogenetic STN and PPN stimulation in Parkinson's disease** + +This tutorial shows how to access and process data from [DANDI:001528](https://dandiarchive.org/dandiset/001528/draft) for the study detailed in [*"Parkinson's Disease-vulnerable and -resilient dopamine neurons display opposite responses to excitatory input"*](https://www.biorxiv.org/content/10.1101/2025.06.03.657460v1) + +## Installing the dependencies + +```bash +git clone https://github.com/dandi/example-notebooks +cd example-notebooks/001528/HnaskoLab +conda env create --file environment.yml +conda activate hnaskolab_001528_demo +``` + +## Running the notebook + +```bash +jupyter notebook 001528_demo.ipynb +``` diff --git a/001528/HnaskoLab/Lotfi_2025/environment.yml b/001528/HnaskoLab/Lotfi_2025/environment.yml new file mode 100644 index 0000000..ebbf90b --- /dev/null +++ b/001528/HnaskoLab/Lotfi_2025/environment.yml @@ -0,0 +1,13 @@ +name: hnaskolab_001528_demo +channels: + - conda-forge +dependencies: + - python==3.13 + - ipykernel + - matplotlib + - dandi + - networkx + - pip + - pip: + - remfile + - hnasko-lab-to-nwb @ git+https://github.com/catalystneuro/hnasko-lab-to-nwb.git@main