From 749cbc4b5d38b8a4dc510b5c7a9fc47fcb88b774 Mon Sep 17 00:00:00 2001 From: pauladkisson Date: Fri, 7 Jun 2024 12:25:51 -0700 Subject: [PATCH 1/4] initial setup --- .gitignore | 1 + 000971/lernerlab/seiler_2024/README.md | 0 .../stream_nwbfile.cpython-310.pyc | Bin 0 -> 1264 bytes 000971/lernerlab/seiler_2024/environment.yml | 11 + .../seiler_2024/example_notebook.ipynb | 333 ++++++++++++++++++ .../lernerlab/seiler_2024/stream_nwbfile.py | 35 ++ 6 files changed, 380 insertions(+) create mode 100644 000971/lernerlab/seiler_2024/README.md create mode 100644 000971/lernerlab/seiler_2024/__pycache__/stream_nwbfile.cpython-310.pyc create mode 100644 000971/lernerlab/seiler_2024/environment.yml create mode 100644 000971/lernerlab/seiler_2024/example_notebook.ipynb create mode 100644 000971/lernerlab/seiler_2024/stream_nwbfile.py diff --git a/.gitignore b/.gitignore index 763513e..a580852 100644 --- a/.gitignore +++ b/.gitignore @@ -1 +1,2 @@ .ipynb_checkpoints +.DS_Store \ No newline at end of file diff --git a/000971/lernerlab/seiler_2024/README.md b/000971/lernerlab/seiler_2024/README.md new file mode 100644 index 0000000..e69de29 diff --git a/000971/lernerlab/seiler_2024/__pycache__/stream_nwbfile.cpython-310.pyc b/000971/lernerlab/seiler_2024/__pycache__/stream_nwbfile.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d2e1f4881e29172d3b557088c373e4fe36dbf7d9 GIT binary patch literal 1264 zcmZ8hPj3`A6!&=k?CfRxVSI~ z_u=S!Kp0}4K$S;4&+=H{$|t_#eGyb42^}AZs2Y#~@F9!-MkHo~eKd*Rg9kM6`r-V^ z!(XS5?#v#8Fe*yH^-3FFffPIf34S-Hv}Walz1glPd2Mo=#ph-7jp6LV(XW6Qbd277 zv&J1h@jA%x6rbSEJN4Fn=O6kEGoJ;o@j7?{`2;9H$B=Oy-$eSmBdEjfKj4^vMaSMc z?81|<3)tWkuOkz65sTRntOsV$4Zza~__&Kt;#0I9cEc_%Fx0j|k359-jpDRQpuEIZ zl@v-=$@IZ|I!ma^m*sQ5JxX9XvPTuIxZz4W@y7XbqO;onvF%4w~!Lq>Cx z%%)CfBV`RW%m3ZmvnnJ1%ciU^=i;m#`Hh=a)tB;c`ub?$>bPs2*v>euP2zKjPEkq- zecsbccV>?hxj5vxNxml@zEmjVFa@DGGSsR$wtW`q6 zYeIb>8(ymbSY>Cp4`4#rWYlk5XmCF~?_nS1Zs1AioRdi3w!V5?KoRcNr>COE))_8k zKR9dBTcoam?%$W`?;2(*ZD=bfdsb>K>vSsfwt~ELx=Rfe@Y?3QRWgO9rBiAQ-Iew8 zl)s==BlwLP`mvDmnND|hc7FQtW-7R+rae20zum0*&C99? z^3h)biLZU>ZF%2*i2bo=cMMy74Ik!{AqkrmxNzu@sL;CMxr2@b%R9}=K}R+n@Y{~k irgR-~4qVL_$aQeq$M>M4t>8b~fzbBvVvIfo9^;QS#7-Xo literal 0 HcmV?d00001 diff --git a/000971/lernerlab/seiler_2024/environment.yml b/000971/lernerlab/seiler_2024/environment.yml new file mode 100644 index 0000000..9a11757 --- /dev/null +++ b/000971/lernerlab/seiler_2024/environment.yml @@ -0,0 +1,11 @@ +# run: conda env create --file environment.yaml +name: lerner_notebook_env +channels: + - conda-forge +dependencies: + - python==3.10 + - ipywidgets + - pip + - pip: + - matplotlib + - lerner-lab-to-nwb @ git+https://github.com/catalystneuro/lerner-lab-to-nwb.git@main \ No newline at end of file diff --git a/000971/lernerlab/seiler_2024/example_notebook.ipynb b/000971/lernerlab/seiler_2024/example_notebook.ipynb new file mode 100644 index 0000000..ab98ece --- /dev/null +++ b/000971/lernerlab/seiler_2024/example_notebook.ipynb @@ -0,0 +1,333 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from stream_nwbfile import stream_nwbfile\n", + "DANDISET_ID = '000971'\n", + "file_path = 'sub-112-283/sub-112-283_ses-FP-PS-2019-06-20T09-32-04_behavior.nwb'\n", + "nwbfile, io = stream_nwbfile(DANDISET_ID, file_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + "

root (NWBFile)

session_description: RI60 Training with concurrent fiber photometry, rewards delivered on left nose pokes
identifier: f1b1251a-82a5-4ea1-92bc-203a4979679b
session_start_time2019-06-20 09:32:04-05:51
timestamps_reference_time2019-06-20 09:32:04-05:51
file_create_date
02024-05-28 14:00:34.728285-07:00
experimenter('Seiler, Jillian L.', 'Cosme, Caitlin V.', 'Sherathiya, Venus N.', 'Schaid, Michael D.', 'Bianco, Joseph M.', 'Bridgemohan, Abigael S.', 'Lerner, Talia N.')
related_publications('https://doi.org/10.1016/j.cub.2022.01.055',)
acquisition
commanded_voltage_series_dls
starting_time: 0.0
rate: 6103.515625
resolution: -1.0
comments: no comments
description: The commanded voltage for the frequency-modulated DLS calcium signal and DLS isosbestic control.
conversion: 1.0
offset: 0.0
unit: volts
data
starting_time_unit: seconds
frequency__unit: hertz
commanded_voltage_series_dms
starting_time: 0.0
rate: 6103.515625
resolution: -1.0
comments: no comments
description: The commanded voltage for the frequency-modulated DMS calcium signal and DMS isosbestic control.
conversion: 1.0
offset: 0.0
unit: volts
data
starting_time_unit: seconds
frequency__unit: hertz
fiber_photometry_response_series
starting_time: 0.0
rate: 1017.2526245117188
resolution: -1.0
comments: no comments
description: The fluorescence from the DMS calcium signal, DMS isosbestic control, DLS calcium signal, and DLS isosbestic control.
conversion: 1.0
offset: 0.0
unit: a.u.
data
starting_time_unit: seconds
fiber_photometry_table_region
description: The region of the FiberPhotometryTable corresponding to the DMS calcium signal, DMS isosbestic control, DLS calcium signal, and DLS isosbestic control.
table
description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.
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locationindicatoroptical_fiberexcitation_sourcephotodetectordichroic_mirrorcoordinatescommanded_voltage_seriesemission_filterexcitation_filter
id
0DMSdms_green_fluorophore abc.Indicator at 0x5083247472\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 0.8 4.7]\\n injection_location: medial SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x5083243344\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_calcium_signal abc.ExcitationSource at 0x5083238832\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 465.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x5083236768\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x5083989568\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n[0.8, 1.5, 2.8]commanded_voltage_series_dms abc.CommandedVoltageSeries at 0x5084873152\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DMS calcium signal and DMS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x5083246800\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nexcitation_filter abc.BandOpticalFilter at 0x5083246656\\nFields:\\n bandwidth_in_nm: 30.0\\n center_wavelength_in_nm: 475.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n
1DMSdms_green_fluorophore abc.Indicator at 0x5083247472\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 0.8 4.7]\\n injection_location: medial SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x5083243344\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_isosbestic_control abc.ExcitationSource at 0x5083240416\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 405.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x5083236768\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x5083989568\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n[0.8, 1.5, 2.8]commanded_voltage_series_dms abc.CommandedVoltageSeries at 0x5084873152\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DMS calcium signal and DMS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x5083246800\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nisosbestic_excitation_filter abc.BandOpticalFilter at 0x5083234992\\nFields:\\n bandwidth_in_nm: 10.0\\n center_wavelength_in_nm: 405.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n
2DLSdls_green_fluorophore abc.Indicator at 0x5083244736\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 1.3 4.2]\\n injection_location: lateral SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x5083243344\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_calcium_signal abc.ExcitationSource at 0x5083238832\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 465.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x5083236768\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x5083989568\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n[0.1, 2.8, 3.5]commanded_voltage_series_dls abc.CommandedVoltageSeries at 0x5084885392\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DLS calcium signal and DLS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x5083246800\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nexcitation_filter abc.BandOpticalFilter at 0x5083246656\\nFields:\\n bandwidth_in_nm: 30.0\\n center_wavelength_in_nm: 475.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n
3DLSdls_green_fluorophore abc.Indicator at 0x5083244736\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 1.3 4.2]\\n injection_location: lateral SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x5083243344\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_isosbestic_control abc.ExcitationSource at 0x5083240416\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 405.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x5083236768\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x5083989568\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n[0.1, 2.8, 3.5]commanded_voltage_series_dls abc.CommandedVoltageSeries at 0x5084885392\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DLS calcium signal and DLS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x5083246800\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nisosbestic_excitation_filter abc.BandOpticalFilter at 0x5083234992\\nFields:\\n bandwidth_in_nm: 10.0\\n center_wavelength_in_nm: 405.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n
keywords
processing
behavior
description: Operant behavioral data from MedPC.\n", + "Box = 3\n", + "MSN = FOOD_RI 60 LEFT TTL
data_interfaces
behavioral_epochs
interval_series
reward_port_intervals
resolution: -1.0
comments: no comments
description: Interval of time spent in reward port (1 is entry, -1 is exit)
conversion: 1.0
offset: 0.0
unit: n/a
data
timestamps
timestamps_unit: seconds
interval: 1
left_nose_poke_times
description: Left nose poke times
timestamps
timestamps__unit: seconds
left_reward_times
description: Left Reward times
timestamps
timestamps__unit: seconds
right_nose_poke_times
description: Right nose poke times
timestamps
timestamps__unit: seconds
epoch_tagsset()
devices
dichroic_mirror
description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.
manufacturer: Doric Lenses
model: 4 ports Fluorescence Mini Cube - GCaMP
dls_green_fluorophore
description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.
manufacturer: Addgene
label: GCaMP7b
injection_location: lateral SNc
injection_coordinates_in_mm
[3.1 1.3 4.2]
dms_green_fluorophore
description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.
manufacturer: Addgene
label: GCaMP7b
injection_location: medial SNc
injection_coordinates_in_mm
[3.1 0.8 4.7]
emission_filter
description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.
manufacturer: Doric Lenses
center_wavelength_in_nm: 525.0
bandwidth_in_nm: 50.0
filter_type: Bandpass
model: 4 ports Fluorescence Mini Cube - GCaMP
excitation_filter
description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.
manufacturer: Doric Lenses
center_wavelength_in_nm: 475.0
bandwidth_in_nm: 30.0
filter_type: Bandpass
model: 4 ports Fluorescence Mini Cube - GCaMP
excitation_source_calcium_signal
description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.
manufacturer: Doric Lenses
model: Connectorized LED
illumination_type: LED
excitation_wavelength_in_nm: 465.0
excitation_source_isosbestic_control
description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.
manufacturer: Doric Lenses
model: Connectorized LED
illumination_type: LED
excitation_wavelength_in_nm: 405.0
isosbestic_excitation_filter
description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.
manufacturer: Doric Lenses
center_wavelength_in_nm: 405.0
bandwidth_in_nm: 10.0
filter_type: Bandpass
model: 4 ports Fluorescence Mini Cube - GCaMP
optical_fiber
description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.
manufacturer: Doric Lenses
model: Fiber Optic Implant
numerical_aperture: 0.48
core_diameter_in_um: 400.0
photodetector
description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.
manufacturer: Doric Lenses
model: Newport Visible Femtowatt Photoreceiver Module
detector_type: photodiode
detected_wavelength_in_nm: 525.0
gain: 10000000000.0
subject
age: P10W/
age__reference: birth
description: Male and female WT (C57BL/6J) and (DAT)::IRES-Cre knockin mice (JAX006660) were obtained from The Jackson Laboratory and crossed in house. Only heterozygote transgenic mice, obtained by backcrossing to C57BL/6J wildtypes, were used for experiments. Littermates of the same sex were randomly assigned to experimental groups (fiber photometry-14 males, 22 females; DMS excitatory optogenetics- 20 males, 19 females; DMS inhibitory optogenetics- 13 males, 13 females; DLS excitatory optogenetics- 18 males, 18 females). Adult mice at least 10 weeks of age were used in all experiments. Mice were group housed under a conventional 12 h light cycle (dark from 7:00pm to 7:00am) with ad libitum access to food and water prior to operant training. All experiments were approved by the Northwestern University Institutional Animal Care and Use Committee.
genotype: DAT-IRES-Cre: B6.SJLSlc6a3tm1.1(cre)Bkmn/J
sex: F
species: Mus musculus
subject_id: 112.283
strain: C57BL/6J
lab_meta_data
fiber_photometry
fiber_photometry_table
description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.
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", + " \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", + "
locationindicatoroptical_fiberexcitation_sourcephotodetectordichroic_mirrorcoordinatescommanded_voltage_seriesemission_filterexcitation_filter
id
0DMSdms_green_fluorophore abc.Indicator at 0x5083247472\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 0.8 4.7]\\n injection_location: medial SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x5083243344\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_calcium_signal abc.ExcitationSource at 0x5083238832\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 465.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x5083236768\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x5083989568\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n[0.8, 1.5, 2.8]commanded_voltage_series_dms abc.CommandedVoltageSeries at 0x5084873152\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DMS calcium signal and DMS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x5083246800\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nexcitation_filter abc.BandOpticalFilter at 0x5083246656\\nFields:\\n bandwidth_in_nm: 30.0\\n center_wavelength_in_nm: 475.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n
1DMSdms_green_fluorophore abc.Indicator at 0x5083247472\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 0.8 4.7]\\n injection_location: medial SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x5083243344\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_isosbestic_control abc.ExcitationSource at 0x5083240416\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 405.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x5083236768\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x5083989568\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n[0.8, 1.5, 2.8]commanded_voltage_series_dms abc.CommandedVoltageSeries at 0x5084873152\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DMS calcium signal and DMS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x5083246800\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nisosbestic_excitation_filter abc.BandOpticalFilter at 0x5083234992\\nFields:\\n bandwidth_in_nm: 10.0\\n center_wavelength_in_nm: 405.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n
2DLSdls_green_fluorophore abc.Indicator at 0x5083244736\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 1.3 4.2]\\n injection_location: lateral SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x5083243344\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_calcium_signal abc.ExcitationSource at 0x5083238832\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 465.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x5083236768\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x5083989568\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n[0.1, 2.8, 3.5]commanded_voltage_series_dls abc.CommandedVoltageSeries at 0x5084885392\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DLS calcium signal and DLS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x5083246800\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nexcitation_filter abc.BandOpticalFilter at 0x5083246656\\nFields:\\n bandwidth_in_nm: 30.0\\n center_wavelength_in_nm: 475.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n
3DLSdls_green_fluorophore abc.Indicator at 0x5083244736\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 1.3 4.2]\\n injection_location: lateral SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x5083243344\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_isosbestic_control abc.ExcitationSource at 0x5083240416\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 405.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x5083236768\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x5083989568\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n[0.1, 2.8, 3.5]commanded_voltage_series_dls abc.CommandedVoltageSeries at 0x5084885392\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DLS calcium signal and DLS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x5083246800\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nisosbestic_excitation_filter abc.BandOpticalFilter at 0x5083234992\\nFields:\\n bandwidth_in_nm: 10.0\\n center_wavelength_in_nm: 405.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n
experiment_description: Compulsive behavior is a defining feature of disorders such as substance use disorders. Current evidence suggests that corticostriatal circuits control the expression of established compulsions, but little is known about the mechanisms regulating the development of compulsions. We hypothesized that dopamine, a critical modulator of striatal synaptic plasticity, could control alterations in corticostriatal circuits leading to the development of compulsions (defined here as continued reward seeking in the face of punishment). We used dual-site fiber photometry to measure dopamine axon activity in the dorsomedial striatum (DMS) and the dorsolateral striatum (DLS) as compulsions emerged. Individual variability in the speed with which compulsions emerged was predicted by DMS dopamine axon activity. Amplifying this dopamine signal accelerated animals' transitions to compulsion, whereas inhibition delayed it. In contrast, amplifying DLS dopamine signaling had no effect on the emergence of compulsions. These results establish DMS dopamine signaling as a key controller of the development of compulsive reward seeking.
session_id: FP_PS_2019-06-20T09-32-04
lab: Lerner
institution: Northwestern Unitersity
notes: Hemisphere with DMS: Right\n", + "Experiment: Fiber Photometry\n", + "Behavior: RI60\n", + "Punishment Group: Punishment Sensitive\n", + "Did Not Learn: False\n", + "
surgery: GCaMP7b in DMS & DLS projecting SNc, fiber photometry probes in DMS & DLS
virus: AAV5-CAG-FLEX-jGCaMP7b-WPRE
" + ], + "text/plain": [ + "root pynwb.file.NWBFile at 0x5083991488\n", + "Fields:\n", + " acquisition: {\n", + " commanded_voltage_series_dls ,\n", + " commanded_voltage_series_dms ,\n", + " fiber_photometry_response_series \n", + " }\n", + " devices: {\n", + " dichroic_mirror ,\n", + " dls_green_fluorophore ,\n", + " dms_green_fluorophore ,\n", + " emission_filter ,\n", + " excitation_filter ,\n", + " excitation_source_calcium_signal ,\n", + " excitation_source_isosbestic_control ,\n", + " isosbestic_excitation_filter ,\n", + " optical_fiber ,\n", + " photodetector \n", + " }\n", + " experiment_description: Compulsive behavior is a defining feature of disorders such as substance use disorders. Current evidence suggests that corticostriatal circuits control the expression of established compulsions, but little is known about the mechanisms regulating the development of compulsions. We hypothesized that dopamine, a critical modulator of striatal synaptic plasticity, could control alterations in corticostriatal circuits leading to the development of compulsions (defined here as continued reward seeking in the face of punishment). We used dual-site fiber photometry to measure dopamine axon activity in the dorsomedial striatum (DMS) and the dorsolateral striatum (DLS) as compulsions emerged. Individual variability in the speed with which compulsions emerged was predicted by DMS dopamine axon activity. Amplifying this dopamine signal accelerated animals' transitions to compulsion, whereas inhibition delayed it. In contrast, amplifying DLS dopamine signaling had no effect on the emergence of compulsions. These results establish DMS dopamine signaling as a key controller of the development of compulsive reward seeking.\n", + " experimenter: ['Seiler, Jillian L.' 'Cosme, Caitlin V.' 'Sherathiya, Venus N.'\n", + " 'Schaid, Michael D.' 'Bianco, Joseph M.' 'Bridgemohan, Abigael S.'\n", + " 'Lerner, Talia N.']\n", + " file_create_date: [datetime.datetime(2024, 5, 28, 14, 0, 34, 728285, tzinfo=tzoffset(None, -25200))]\n", + " identifier: f1b1251a-82a5-4ea1-92bc-203a4979679b\n", + " institution: Northwestern Unitersity\n", + " keywords: \n", + " lab: Lerner\n", + " lab_meta_data: {\n", + " fiber_photometry \n", + " }\n", + " notes: Hemisphere with DMS: Right\n", + "Experiment: Fiber Photometry\n", + "Behavior: RI60\n", + "Punishment Group: Punishment Sensitive\n", + "Did Not Learn: False\n", + "\n", + " processing: {\n", + " behavior \n", + " }\n", + " related_publications: ['https://doi.org/10.1016/j.cub.2022.01.055']\n", + " session_description: RI60 Training with concurrent fiber photometry, rewards delivered on left nose pokes\n", + " session_id: FP_PS_2019-06-20T09-32-04\n", + " session_start_time: 2019-06-20 09:32:04-05:51\n", + " subject: subject pynwb.file.Subject at 0x5083234800\n", + "Fields:\n", + " age: P10W/\n", + " age__reference: birth\n", + " description: Male and female WT (C57BL/6J) and (DAT)::IRES-Cre knockin mice (JAX006660) were obtained from The Jackson Laboratory and crossed in house. Only heterozygote transgenic mice, obtained by backcrossing to C57BL/6J wildtypes, were used for experiments. Littermates of the same sex were randomly assigned to experimental groups (fiber photometry-14 males, 22 females; DMS excitatory optogenetics- 20 males, 19 females; DMS inhibitory optogenetics- 13 males, 13 females; DLS excitatory optogenetics- 18 males, 18 females). Adult mice at least 10 weeks of age were used in all experiments. Mice were group housed under a conventional 12 h light cycle (dark from 7:00pm to 7:00am) with ad libitum access to food and water prior to operant training. All experiments were approved by the Northwestern University Institutional Animal Care and Use Committee.\n", + " genotype: DAT-IRES-Cre: B6.SJLSlc6a3tm1.1(cre)Bkmn/J\n", + " sex: F\n", + " species: Mus musculus\n", + " strain: C57BL/6J\n", + " subject_id: 112.283\n", + "\n", + " surgery: GCaMP7b in DMS & DLS projecting SNc, fiber photometry probes in DMS & DLS\n", + " timestamps_reference_time: 2019-06-20 09:32:04-05:51\n", + " virus: AAV5-CAG-FLEX-jGCaMP7b-WPRE" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nwbfile" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "lerner_notebook_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": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/000971/lernerlab/seiler_2024/stream_nwbfile.py b/000971/lernerlab/seiler_2024/stream_nwbfile.py new file mode 100644 index 0000000..b5cff46 --- /dev/null +++ b/000971/lernerlab/seiler_2024/stream_nwbfile.py @@ -0,0 +1,35 @@ +from pynwb import NWBHDF5IO +from fsspec import filesystem +from h5py import File +from dandi.dandiapi import DandiAPIClient + +def stream_nwbfile(DANDISET_ID, file_path): + '''Stream NWB file from DANDI archive. + + Parameters + ---------- + DANDISET_ID : str + Dandiset ID + file_path : str + Path to NWB file in DANDI archive + + Returns + ------- + nwbfile : NWBFile + NWB file + io : NWBHDF5IO + NWB IO object (for closing) + + Notes + ----- + The io object must be closed after use. + ''' + with DandiAPIClient() as client: + asset = client.get_dandiset(DANDISET_ID, 'draft').get_asset_by_path(file_path) + s3_url = asset.get_content_url(follow_redirects=1, strip_query=True) + fs = filesystem("http") + file_system = fs.open(s3_url, "rb") + file = File(file_system, mode="r") + io = NWBHDF5IO(file=file, load_namespaces=True) + nwbfile = io.read() + return nwbfile, io \ No newline at end of file From d82d697f5730abaf981f669d5f8cb649e23eee7f Mon Sep 17 00:00:00 2001 From: pauladkisson Date: Mon, 10 Jun 2024 11:22:29 -0700 Subject: [PATCH 2/4] added fiber photometry example notebook --- .../seiler_2024/example_notebook.ipynb | 333 ----------- .../fiber_photometry_example_notebook.ipynb | 522 ++++++++++++++++++ 2 files changed, 522 insertions(+), 333 deletions(-) delete mode 100644 000971/lernerlab/seiler_2024/example_notebook.ipynb create mode 100644 000971/lernerlab/seiler_2024/fiber_photometry_example_notebook.ipynb diff --git a/000971/lernerlab/seiler_2024/example_notebook.ipynb b/000971/lernerlab/seiler_2024/example_notebook.ipynb deleted file mode 100644 index ab98ece..0000000 --- a/000971/lernerlab/seiler_2024/example_notebook.ipynb +++ /dev/null @@ -1,333 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from stream_nwbfile import stream_nwbfile\n", - "DANDISET_ID = '000971'\n", - "file_path = 'sub-112-283/sub-112-283_ses-FP-PS-2019-06-20T09-32-04_behavior.nwb'\n", - "nwbfile, io = stream_nwbfile(DANDISET_ID, file_path)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - "

root (NWBFile)

session_description: RI60 Training with concurrent fiber photometry, rewards delivered on left nose pokes
identifier: f1b1251a-82a5-4ea1-92bc-203a4979679b
session_start_time2019-06-20 09:32:04-05:51
timestamps_reference_time2019-06-20 09:32:04-05:51
file_create_date
02024-05-28 14:00:34.728285-07:00
experimenter('Seiler, Jillian L.', 'Cosme, Caitlin V.', 'Sherathiya, Venus N.', 'Schaid, Michael D.', 'Bianco, Joseph M.', 'Bridgemohan, Abigael S.', 'Lerner, Talia N.')
related_publications('https://doi.org/10.1016/j.cub.2022.01.055',)
acquisition
commanded_voltage_series_dls
starting_time: 0.0
rate: 6103.515625
resolution: -1.0
comments: no comments
description: The commanded voltage for the frequency-modulated DLS calcium signal and DLS isosbestic control.
conversion: 1.0
offset: 0.0
unit: volts
data
starting_time_unit: seconds
frequency__unit: hertz
commanded_voltage_series_dms
starting_time: 0.0
rate: 6103.515625
resolution: -1.0
comments: no comments
description: The commanded voltage for the frequency-modulated DMS calcium signal and DMS isosbestic control.
conversion: 1.0
offset: 0.0
unit: volts
data
starting_time_unit: seconds
frequency__unit: hertz
fiber_photometry_response_series
starting_time: 0.0
rate: 1017.2526245117188
resolution: -1.0
comments: no comments
description: The fluorescence from the DMS calcium signal, DMS isosbestic control, DLS calcium signal, and DLS isosbestic control.
conversion: 1.0
offset: 0.0
unit: a.u.
data
starting_time_unit: seconds
fiber_photometry_table_region
description: The region of the FiberPhotometryTable corresponding to the DMS calcium signal, DMS isosbestic control, DLS calcium signal, and DLS isosbestic control.
table
description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.
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locationindicatoroptical_fiberexcitation_sourcephotodetectordichroic_mirrorcoordinatescommanded_voltage_seriesemission_filterexcitation_filter
id
0DMSdms_green_fluorophore abc.Indicator at 0x5083247472\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 0.8 4.7]\\n injection_location: medial SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x5083243344\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_calcium_signal abc.ExcitationSource at 0x5083238832\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 465.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x5083236768\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x5083989568\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n[0.8, 1.5, 2.8]commanded_voltage_series_dms abc.CommandedVoltageSeries at 0x5084873152\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DMS calcium signal and DMS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x5083246800\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nexcitation_filter abc.BandOpticalFilter at 0x5083246656\\nFields:\\n bandwidth_in_nm: 30.0\\n center_wavelength_in_nm: 475.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n
1DMSdms_green_fluorophore abc.Indicator at 0x5083247472\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 0.8 4.7]\\n injection_location: medial SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x5083243344\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_isosbestic_control abc.ExcitationSource at 0x5083240416\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 405.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x5083236768\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x5083989568\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n[0.8, 1.5, 2.8]commanded_voltage_series_dms abc.CommandedVoltageSeries at 0x5084873152\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DMS calcium signal and DMS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x5083246800\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nisosbestic_excitation_filter abc.BandOpticalFilter at 0x5083234992\\nFields:\\n bandwidth_in_nm: 10.0\\n center_wavelength_in_nm: 405.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n
2DLSdls_green_fluorophore abc.Indicator at 0x5083244736\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 1.3 4.2]\\n injection_location: lateral SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x5083243344\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_calcium_signal abc.ExcitationSource at 0x5083238832\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 465.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x5083236768\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x5083989568\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n[0.1, 2.8, 3.5]commanded_voltage_series_dls abc.CommandedVoltageSeries at 0x5084885392\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DLS calcium signal and DLS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x5083246800\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nexcitation_filter abc.BandOpticalFilter at 0x5083246656\\nFields:\\n bandwidth_in_nm: 30.0\\n center_wavelength_in_nm: 475.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n
3DLSdls_green_fluorophore abc.Indicator at 0x5083244736\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 1.3 4.2]\\n injection_location: lateral SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x5083243344\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_isosbestic_control abc.ExcitationSource at 0x5083240416\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 405.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x5083236768\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x5083989568\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n[0.1, 2.8, 3.5]commanded_voltage_series_dls abc.CommandedVoltageSeries at 0x5084885392\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DLS calcium signal and DLS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x5083246800\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nisosbestic_excitation_filter abc.BandOpticalFilter at 0x5083234992\\nFields:\\n bandwidth_in_nm: 10.0\\n center_wavelength_in_nm: 405.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n
keywords
processing
behavior
description: Operant behavioral data from MedPC.\n", - "Box = 3\n", - "MSN = FOOD_RI 60 LEFT TTL
data_interfaces
behavioral_epochs
interval_series
reward_port_intervals
resolution: -1.0
comments: no comments
description: Interval of time spent in reward port (1 is entry, -1 is exit)
conversion: 1.0
offset: 0.0
unit: n/a
data
timestamps
timestamps_unit: seconds
interval: 1
left_nose_poke_times
description: Left nose poke times
timestamps
timestamps__unit: seconds
left_reward_times
description: Left Reward times
timestamps
timestamps__unit: seconds
right_nose_poke_times
description: Right nose poke times
timestamps
timestamps__unit: seconds
epoch_tagsset()
devices
dichroic_mirror
description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.
manufacturer: Doric Lenses
model: 4 ports Fluorescence Mini Cube - GCaMP
dls_green_fluorophore
description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.
manufacturer: Addgene
label: GCaMP7b
injection_location: lateral SNc
injection_coordinates_in_mm
[3.1 1.3 4.2]
dms_green_fluorophore
description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.
manufacturer: Addgene
label: GCaMP7b
injection_location: medial SNc
injection_coordinates_in_mm
[3.1 0.8 4.7]
emission_filter
description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.
manufacturer: Doric Lenses
center_wavelength_in_nm: 525.0
bandwidth_in_nm: 50.0
filter_type: Bandpass
model: 4 ports Fluorescence Mini Cube - GCaMP
excitation_filter
description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.
manufacturer: Doric Lenses
center_wavelength_in_nm: 475.0
bandwidth_in_nm: 30.0
filter_type: Bandpass
model: 4 ports Fluorescence Mini Cube - GCaMP
excitation_source_calcium_signal
description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.
manufacturer: Doric Lenses
model: Connectorized LED
illumination_type: LED
excitation_wavelength_in_nm: 465.0
excitation_source_isosbestic_control
description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.
manufacturer: Doric Lenses
model: Connectorized LED
illumination_type: LED
excitation_wavelength_in_nm: 405.0
isosbestic_excitation_filter
description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.
manufacturer: Doric Lenses
center_wavelength_in_nm: 405.0
bandwidth_in_nm: 10.0
filter_type: Bandpass
model: 4 ports Fluorescence Mini Cube - GCaMP
optical_fiber
description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.
manufacturer: Doric Lenses
model: Fiber Optic Implant
numerical_aperture: 0.48
core_diameter_in_um: 400.0
photodetector
description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.
manufacturer: Doric Lenses
model: Newport Visible Femtowatt Photoreceiver Module
detector_type: photodiode
detected_wavelength_in_nm: 525.0
gain: 10000000000.0
subject
age: P10W/
age__reference: birth
description: Male and female WT (C57BL/6J) and (DAT)::IRES-Cre knockin mice (JAX006660) were obtained from The Jackson Laboratory and crossed in house. Only heterozygote transgenic mice, obtained by backcrossing to C57BL/6J wildtypes, were used for experiments. Littermates of the same sex were randomly assigned to experimental groups (fiber photometry-14 males, 22 females; DMS excitatory optogenetics- 20 males, 19 females; DMS inhibitory optogenetics- 13 males, 13 females; DLS excitatory optogenetics- 18 males, 18 females). Adult mice at least 10 weeks of age were used in all experiments. Mice were group housed under a conventional 12 h light cycle (dark from 7:00pm to 7:00am) with ad libitum access to food and water prior to operant training. All experiments were approved by the Northwestern University Institutional Animal Care and Use Committee.
genotype: DAT-IRES-Cre: B6.SJLSlc6a3tm1.1(cre)Bkmn/J
sex: F
species: Mus musculus
subject_id: 112.283
strain: C57BL/6J
lab_meta_data
fiber_photometry
fiber_photometry_table
description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.
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", - " \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", - "
locationindicatoroptical_fiberexcitation_sourcephotodetectordichroic_mirrorcoordinatescommanded_voltage_seriesemission_filterexcitation_filter
id
0DMSdms_green_fluorophore abc.Indicator at 0x5083247472\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 0.8 4.7]\\n injection_location: medial SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x5083243344\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_calcium_signal abc.ExcitationSource at 0x5083238832\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 465.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x5083236768\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x5083989568\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n[0.8, 1.5, 2.8]commanded_voltage_series_dms abc.CommandedVoltageSeries at 0x5084873152\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DMS calcium signal and DMS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x5083246800\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nexcitation_filter abc.BandOpticalFilter at 0x5083246656\\nFields:\\n bandwidth_in_nm: 30.0\\n center_wavelength_in_nm: 475.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n
1DMSdms_green_fluorophore abc.Indicator at 0x5083247472\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 0.8 4.7]\\n injection_location: medial SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x5083243344\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_isosbestic_control abc.ExcitationSource at 0x5083240416\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 405.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x5083236768\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x5083989568\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n[0.8, 1.5, 2.8]commanded_voltage_series_dms abc.CommandedVoltageSeries at 0x5084873152\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DMS calcium signal and DMS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x5083246800\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nisosbestic_excitation_filter abc.BandOpticalFilter at 0x5083234992\\nFields:\\n bandwidth_in_nm: 10.0\\n center_wavelength_in_nm: 405.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n
2DLSdls_green_fluorophore abc.Indicator at 0x5083244736\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 1.3 4.2]\\n injection_location: lateral SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x5083243344\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_calcium_signal abc.ExcitationSource at 0x5083238832\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 465.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x5083236768\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x5083989568\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n[0.1, 2.8, 3.5]commanded_voltage_series_dls abc.CommandedVoltageSeries at 0x5084885392\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DLS calcium signal and DLS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x5083246800\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nexcitation_filter abc.BandOpticalFilter at 0x5083246656\\nFields:\\n bandwidth_in_nm: 30.0\\n center_wavelength_in_nm: 475.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n
3DLSdls_green_fluorophore abc.Indicator at 0x5083244736\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 1.3 4.2]\\n injection_location: lateral SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x5083243344\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_isosbestic_control abc.ExcitationSource at 0x5083240416\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 405.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x5083236768\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x5083989568\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n[0.1, 2.8, 3.5]commanded_voltage_series_dls abc.CommandedVoltageSeries at 0x5084885392\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DLS calcium signal and DLS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x5083246800\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nisosbestic_excitation_filter abc.BandOpticalFilter at 0x5083234992\\nFields:\\n bandwidth_in_nm: 10.0\\n center_wavelength_in_nm: 405.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n
experiment_description: Compulsive behavior is a defining feature of disorders such as substance use disorders. Current evidence suggests that corticostriatal circuits control the expression of established compulsions, but little is known about the mechanisms regulating the development of compulsions. We hypothesized that dopamine, a critical modulator of striatal synaptic plasticity, could control alterations in corticostriatal circuits leading to the development of compulsions (defined here as continued reward seeking in the face of punishment). We used dual-site fiber photometry to measure dopamine axon activity in the dorsomedial striatum (DMS) and the dorsolateral striatum (DLS) as compulsions emerged. Individual variability in the speed with which compulsions emerged was predicted by DMS dopamine axon activity. Amplifying this dopamine signal accelerated animals' transitions to compulsion, whereas inhibition delayed it. In contrast, amplifying DLS dopamine signaling had no effect on the emergence of compulsions. These results establish DMS dopamine signaling as a key controller of the development of compulsive reward seeking.
session_id: FP_PS_2019-06-20T09-32-04
lab: Lerner
institution: Northwestern Unitersity
notes: Hemisphere with DMS: Right\n", - "Experiment: Fiber Photometry\n", - "Behavior: RI60\n", - "Punishment Group: Punishment Sensitive\n", - "Did Not Learn: False\n", - "
surgery: GCaMP7b in DMS & DLS projecting SNc, fiber photometry probes in DMS & DLS
virus: AAV5-CAG-FLEX-jGCaMP7b-WPRE
" - ], - "text/plain": [ - "root pynwb.file.NWBFile at 0x5083991488\n", - "Fields:\n", - " acquisition: {\n", - " commanded_voltage_series_dls ,\n", - " commanded_voltage_series_dms ,\n", - " fiber_photometry_response_series \n", - " }\n", - " devices: {\n", - " dichroic_mirror ,\n", - " dls_green_fluorophore ,\n", - " dms_green_fluorophore ,\n", - " emission_filter ,\n", - " excitation_filter ,\n", - " excitation_source_calcium_signal ,\n", - " excitation_source_isosbestic_control ,\n", - " isosbestic_excitation_filter ,\n", - " optical_fiber ,\n", - " photodetector \n", - " }\n", - " experiment_description: Compulsive behavior is a defining feature of disorders such as substance use disorders. Current evidence suggests that corticostriatal circuits control the expression of established compulsions, but little is known about the mechanisms regulating the development of compulsions. We hypothesized that dopamine, a critical modulator of striatal synaptic plasticity, could control alterations in corticostriatal circuits leading to the development of compulsions (defined here as continued reward seeking in the face of punishment). We used dual-site fiber photometry to measure dopamine axon activity in the dorsomedial striatum (DMS) and the dorsolateral striatum (DLS) as compulsions emerged. Individual variability in the speed with which compulsions emerged was predicted by DMS dopamine axon activity. Amplifying this dopamine signal accelerated animals' transitions to compulsion, whereas inhibition delayed it. In contrast, amplifying DLS dopamine signaling had no effect on the emergence of compulsions. These results establish DMS dopamine signaling as a key controller of the development of compulsive reward seeking.\n", - " experimenter: ['Seiler, Jillian L.' 'Cosme, Caitlin V.' 'Sherathiya, Venus N.'\n", - " 'Schaid, Michael D.' 'Bianco, Joseph M.' 'Bridgemohan, Abigael S.'\n", - " 'Lerner, Talia N.']\n", - " file_create_date: [datetime.datetime(2024, 5, 28, 14, 0, 34, 728285, tzinfo=tzoffset(None, -25200))]\n", - " identifier: f1b1251a-82a5-4ea1-92bc-203a4979679b\n", - " institution: Northwestern Unitersity\n", - " keywords: \n", - " lab: Lerner\n", - " lab_meta_data: {\n", - " fiber_photometry \n", - " }\n", - " notes: Hemisphere with DMS: Right\n", - "Experiment: Fiber Photometry\n", - "Behavior: RI60\n", - "Punishment Group: Punishment Sensitive\n", - "Did Not Learn: False\n", - "\n", - " processing: {\n", - " behavior \n", - " }\n", - " related_publications: ['https://doi.org/10.1016/j.cub.2022.01.055']\n", - " session_description: RI60 Training with concurrent fiber photometry, rewards delivered on left nose pokes\n", - " session_id: FP_PS_2019-06-20T09-32-04\n", - " session_start_time: 2019-06-20 09:32:04-05:51\n", - " subject: subject pynwb.file.Subject at 0x5083234800\n", - "Fields:\n", - " age: P10W/\n", - " age__reference: birth\n", - " description: Male and female WT (C57BL/6J) and (DAT)::IRES-Cre knockin mice (JAX006660) were obtained from The Jackson Laboratory and crossed in house. Only heterozygote transgenic mice, obtained by backcrossing to C57BL/6J wildtypes, were used for experiments. Littermates of the same sex were randomly assigned to experimental groups (fiber photometry-14 males, 22 females; DMS excitatory optogenetics- 20 males, 19 females; DMS inhibitory optogenetics- 13 males, 13 females; DLS excitatory optogenetics- 18 males, 18 females). Adult mice at least 10 weeks of age were used in all experiments. Mice were group housed under a conventional 12 h light cycle (dark from 7:00pm to 7:00am) with ad libitum access to food and water prior to operant training. All experiments were approved by the Northwestern University Institutional Animal Care and Use Committee.\n", - " genotype: DAT-IRES-Cre: B6.SJLSlc6a3tm1.1(cre)Bkmn/J\n", - " sex: F\n", - " species: Mus musculus\n", - " strain: C57BL/6J\n", - " subject_id: 112.283\n", - "\n", - " surgery: GCaMP7b in DMS & DLS projecting SNc, fiber photometry probes in DMS & DLS\n", - " timestamps_reference_time: 2019-06-20 09:32:04-05:51\n", - " virus: AAV5-CAG-FLEX-jGCaMP7b-WPRE" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nwbfile" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "lerner_notebook_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": "3.10.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/000971/lernerlab/seiler_2024/fiber_photometry_example_notebook.ipynb b/000971/lernerlab/seiler_2024/fiber_photometry_example_notebook.ipynb new file mode 100644 index 0000000..9358ba7 --- /dev/null +++ b/000971/lernerlab/seiler_2024/fiber_photometry_example_notebook.ipynb @@ -0,0 +1,522 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fiber Photometry Example Session" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from stream_nwbfile import stream_nwbfile\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook showcases one example session from the 000971 dataset containing operant behavior and concurrent fiber photometry recordings." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + "

root (NWBFile)

session_description: RI60 Training with concurrent fiber photometry, rewards delivered on left nose pokes
identifier: f1b1251a-82a5-4ea1-92bc-203a4979679b
session_start_time2019-06-20 09:32:04-05:51
timestamps_reference_time2019-06-20 09:32:04-05:51
file_create_date
02024-05-28 14:00:34.728285-07:00
experimenter('Seiler, Jillian L.', 'Cosme, Caitlin V.', 'Sherathiya, Venus N.', 'Schaid, Michael D.', 'Bianco, Joseph M.', 'Bridgemohan, Abigael S.', 'Lerner, Talia N.')
related_publications('https://doi.org/10.1016/j.cub.2022.01.055',)
acquisition
commanded_voltage_series_dls
starting_time: 0.0
rate: 6103.515625
resolution: -1.0
comments: no comments
description: The commanded voltage for the frequency-modulated DLS calcium signal and DLS isosbestic control.
conversion: 1.0
offset: 0.0
unit: volts
data
starting_time_unit: seconds
frequency__unit: hertz
commanded_voltage_series_dms
starting_time: 0.0
rate: 6103.515625
resolution: -1.0
comments: no comments
description: The commanded voltage for the frequency-modulated DMS calcium signal and DMS isosbestic control.
conversion: 1.0
offset: 0.0
unit: volts
data
starting_time_unit: seconds
frequency__unit: hertz
fiber_photometry_response_series
starting_time: 0.0
rate: 1017.2526245117188
resolution: -1.0
comments: no comments
description: The fluorescence from the DMS calcium signal, DMS isosbestic control, DLS calcium signal, and DLS isosbestic control.
conversion: 1.0
offset: 0.0
unit: a.u.
data
starting_time_unit: seconds
fiber_photometry_table_region
description: The region of the FiberPhotometryTable corresponding to the DMS calcium signal, DMS isosbestic control, DLS calcium signal, and DLS isosbestic control.
table
description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.
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", + " \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", + "
locationindicatoroptical_fiberexcitation_sourcephotodetectordichroic_mirrorcoordinatescommanded_voltage_seriesemission_filterexcitation_filter
id
0DMSdms_green_fluorophore abc.Indicator at 0x4985524560\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 0.8 4.7]\\n injection_location: medial SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x4985528784\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_calcium_signal abc.ExcitationSource at 0x4985527200\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 465.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x4985530176\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x4985523120\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n[0.8, 1.5, 2.8]commanded_voltage_series_dms abc.CommandedVoltageSeries at 0x4983888848\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DMS calcium signal and DMS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x4985525616\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nexcitation_filter abc.BandOpticalFilter at 0x4985525760\\nFields:\\n bandwidth_in_nm: 30.0\\n center_wavelength_in_nm: 475.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n
1DMSdms_green_fluorophore abc.Indicator at 0x4985524560\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 0.8 4.7]\\n injection_location: medial SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x4985528784\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_isosbestic_control abc.ExcitationSource at 0x4985527344\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 405.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x4985530176\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x4985523120\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n[0.8, 1.5, 2.8]commanded_voltage_series_dms abc.CommandedVoltageSeries at 0x4983888848\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DMS calcium signal and DMS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x4985525616\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nisosbestic_excitation_filter abc.BandOpticalFilter at 0x4985527824\\nFields:\\n bandwidth_in_nm: 10.0\\n center_wavelength_in_nm: 405.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n
2DLSdls_green_fluorophore abc.Indicator at 0x4983897344\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 1.3 4.2]\\n injection_location: lateral SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x4985528784\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_calcium_signal abc.ExcitationSource at 0x4985527200\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 465.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x4985530176\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x4985523120\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n[0.1, 2.8, 3.5]commanded_voltage_series_dls abc.CommandedVoltageSeries at 0x4983887312\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DLS calcium signal and DLS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x4985525616\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nexcitation_filter abc.BandOpticalFilter at 0x4985525760\\nFields:\\n bandwidth_in_nm: 30.0\\n center_wavelength_in_nm: 475.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n
3DLSdls_green_fluorophore abc.Indicator at 0x4983897344\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 1.3 4.2]\\n injection_location: lateral SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x4985528784\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_isosbestic_control abc.ExcitationSource at 0x4985527344\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 405.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x4985530176\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x4985523120\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n[0.1, 2.8, 3.5]commanded_voltage_series_dls abc.CommandedVoltageSeries at 0x4983887312\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DLS calcium signal and DLS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x4985525616\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nisosbestic_excitation_filter abc.BandOpticalFilter at 0x4985527824\\nFields:\\n bandwidth_in_nm: 10.0\\n center_wavelength_in_nm: 405.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n
keywords
processing
behavior
description: Operant behavioral data from MedPC.\n", + "Box = 3\n", + "MSN = FOOD_RI 60 LEFT TTL
data_interfaces
behavioral_epochs
interval_series
reward_port_intervals
resolution: -1.0
comments: no comments
description: Interval of time spent in reward port (1 is entry, -1 is exit)
conversion: 1.0
offset: 0.0
unit: n/a
data
timestamps
timestamps_unit: seconds
interval: 1
left_nose_poke_times
description: Left nose poke times
timestamps
timestamps__unit: seconds
left_reward_times
description: Left Reward times
timestamps
timestamps__unit: seconds
right_nose_poke_times
description: Right nose poke times
timestamps
timestamps__unit: seconds
epoch_tagsset()
devices
dichroic_mirror
description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.
manufacturer: Doric Lenses
model: 4 ports Fluorescence Mini Cube - GCaMP
dls_green_fluorophore
description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.
manufacturer: Addgene
label: GCaMP7b
injection_location: lateral SNc
injection_coordinates_in_mm
[3.1 1.3 4.2]
dms_green_fluorophore
description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.
manufacturer: Addgene
label: GCaMP7b
injection_location: medial SNc
injection_coordinates_in_mm
[3.1 0.8 4.7]
emission_filter
description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.
manufacturer: Doric Lenses
center_wavelength_in_nm: 525.0
bandwidth_in_nm: 50.0
filter_type: Bandpass
model: 4 ports Fluorescence Mini Cube - GCaMP
excitation_filter
description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.
manufacturer: Doric Lenses
center_wavelength_in_nm: 475.0
bandwidth_in_nm: 30.0
filter_type: Bandpass
model: 4 ports Fluorescence Mini Cube - GCaMP
excitation_source_calcium_signal
description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.
manufacturer: Doric Lenses
model: Connectorized LED
illumination_type: LED
excitation_wavelength_in_nm: 465.0
excitation_source_isosbestic_control
description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.
manufacturer: Doric Lenses
model: Connectorized LED
illumination_type: LED
excitation_wavelength_in_nm: 405.0
isosbestic_excitation_filter
description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.
manufacturer: Doric Lenses
center_wavelength_in_nm: 405.0
bandwidth_in_nm: 10.0
filter_type: Bandpass
model: 4 ports Fluorescence Mini Cube - GCaMP
optical_fiber
description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.
manufacturer: Doric Lenses
model: Fiber Optic Implant
numerical_aperture: 0.48
core_diameter_in_um: 400.0
photodetector
description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.
manufacturer: Doric Lenses
model: Newport Visible Femtowatt Photoreceiver Module
detector_type: photodiode
detected_wavelength_in_nm: 525.0
gain: 10000000000.0
subject
age: P10W/
age__reference: birth
description: Male and female WT (C57BL/6J) and (DAT)::IRES-Cre knockin mice (JAX006660) were obtained from The Jackson Laboratory and crossed in house. Only heterozygote transgenic mice, obtained by backcrossing to C57BL/6J wildtypes, were used for experiments. Littermates of the same sex were randomly assigned to experimental groups (fiber photometry-14 males, 22 females; DMS excitatory optogenetics- 20 males, 19 females; DMS inhibitory optogenetics- 13 males, 13 females; DLS excitatory optogenetics- 18 males, 18 females). Adult mice at least 10 weeks of age were used in all experiments. Mice were group housed under a conventional 12 h light cycle (dark from 7:00pm to 7:00am) with ad libitum access to food and water prior to operant training. All experiments were approved by the Northwestern University Institutional Animal Care and Use Committee.
genotype: DAT-IRES-Cre: B6.SJLSlc6a3tm1.1(cre)Bkmn/J
sex: F
species: Mus musculus
subject_id: 112.283
strain: C57BL/6J
lab_meta_data
fiber_photometry
fiber_photometry_table
description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.
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locationindicatoroptical_fiberexcitation_sourcephotodetectordichroic_mirrorcoordinatescommanded_voltage_seriesemission_filterexcitation_filter
id
0DMSdms_green_fluorophore abc.Indicator at 0x4985524560\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 0.8 4.7]\\n injection_location: medial SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x4985528784\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_calcium_signal abc.ExcitationSource at 0x4985527200\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 465.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x4985530176\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x4985523120\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n[0.8, 1.5, 2.8]commanded_voltage_series_dms abc.CommandedVoltageSeries at 0x4983888848\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DMS calcium signal and DMS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x4985525616\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nexcitation_filter abc.BandOpticalFilter at 0x4985525760\\nFields:\\n bandwidth_in_nm: 30.0\\n center_wavelength_in_nm: 475.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n
1DMSdms_green_fluorophore abc.Indicator at 0x4985524560\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 0.8 4.7]\\n injection_location: medial SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x4985528784\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_isosbestic_control abc.ExcitationSource at 0x4985527344\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 405.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x4985530176\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x4985523120\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n[0.8, 1.5, 2.8]commanded_voltage_series_dms abc.CommandedVoltageSeries at 0x4983888848\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DMS calcium signal and DMS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x4985525616\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nisosbestic_excitation_filter abc.BandOpticalFilter at 0x4985527824\\nFields:\\n bandwidth_in_nm: 10.0\\n center_wavelength_in_nm: 405.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n
2DLSdls_green_fluorophore abc.Indicator at 0x4983897344\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 1.3 4.2]\\n injection_location: lateral SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x4985528784\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_calcium_signal abc.ExcitationSource at 0x4985527200\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 465.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x4985530176\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x4985523120\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n[0.1, 2.8, 3.5]commanded_voltage_series_dls abc.CommandedVoltageSeries at 0x4983887312\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DLS calcium signal and DLS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x4985525616\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nexcitation_filter abc.BandOpticalFilter at 0x4985525760\\nFields:\\n bandwidth_in_nm: 30.0\\n center_wavelength_in_nm: 475.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n
3DLSdls_green_fluorophore abc.Indicator at 0x4983897344\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 1.3 4.2]\\n injection_location: lateral SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x4985528784\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_isosbestic_control abc.ExcitationSource at 0x4985527344\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 405.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x4985530176\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x4985523120\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n[0.1, 2.8, 3.5]commanded_voltage_series_dls abc.CommandedVoltageSeries at 0x4983887312\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DLS calcium signal and DLS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x4985525616\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nisosbestic_excitation_filter abc.BandOpticalFilter at 0x4985527824\\nFields:\\n bandwidth_in_nm: 10.0\\n center_wavelength_in_nm: 405.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n
experiment_description: Compulsive behavior is a defining feature of disorders such as substance use disorders. Current evidence suggests that corticostriatal circuits control the expression of established compulsions, but little is known about the mechanisms regulating the development of compulsions. We hypothesized that dopamine, a critical modulator of striatal synaptic plasticity, could control alterations in corticostriatal circuits leading to the development of compulsions (defined here as continued reward seeking in the face of punishment). We used dual-site fiber photometry to measure dopamine axon activity in the dorsomedial striatum (DMS) and the dorsolateral striatum (DLS) as compulsions emerged. Individual variability in the speed with which compulsions emerged was predicted by DMS dopamine axon activity. Amplifying this dopamine signal accelerated animals' transitions to compulsion, whereas inhibition delayed it. In contrast, amplifying DLS dopamine signaling had no effect on the emergence of compulsions. These results establish DMS dopamine signaling as a key controller of the development of compulsive reward seeking.
session_id: FP_PS_2019-06-20T09-32-04
lab: Lerner
institution: Northwestern Unitersity
notes: Hemisphere with DMS: Right\n", + "Experiment: Fiber Photometry\n", + "Behavior: RI60\n", + "Punishment Group: Punishment Sensitive\n", + "Did Not Learn: False\n", + "
surgery: GCaMP7b in DMS & DLS projecting SNc, fiber photometry probes in DMS & DLS
virus: AAV5-CAG-FLEX-jGCaMP7b-WPRE
" + ], + "text/plain": [ + "root pynwb.file.NWBFile at 0x4983882704\n", + "Fields:\n", + " acquisition: {\n", + " commanded_voltage_series_dls ,\n", + " commanded_voltage_series_dms ,\n", + " fiber_photometry_response_series \n", + " }\n", + " devices: {\n", + " dichroic_mirror ,\n", + " dls_green_fluorophore ,\n", + " dms_green_fluorophore ,\n", + " emission_filter ,\n", + " excitation_filter ,\n", + " excitation_source_calcium_signal ,\n", + " excitation_source_isosbestic_control ,\n", + " isosbestic_excitation_filter ,\n", + " optical_fiber ,\n", + " photodetector \n", + " }\n", + " experiment_description: Compulsive behavior is a defining feature of disorders such as substance use disorders. Current evidence suggests that corticostriatal circuits control the expression of established compulsions, but little is known about the mechanisms regulating the development of compulsions. We hypothesized that dopamine, a critical modulator of striatal synaptic plasticity, could control alterations in corticostriatal circuits leading to the development of compulsions (defined here as continued reward seeking in the face of punishment). We used dual-site fiber photometry to measure dopamine axon activity in the dorsomedial striatum (DMS) and the dorsolateral striatum (DLS) as compulsions emerged. Individual variability in the speed with which compulsions emerged was predicted by DMS dopamine axon activity. Amplifying this dopamine signal accelerated animals' transitions to compulsion, whereas inhibition delayed it. In contrast, amplifying DLS dopamine signaling had no effect on the emergence of compulsions. These results establish DMS dopamine signaling as a key controller of the development of compulsive reward seeking.\n", + " experimenter: ['Seiler, Jillian L.' 'Cosme, Caitlin V.' 'Sherathiya, Venus N.'\n", + " 'Schaid, Michael D.' 'Bianco, Joseph M.' 'Bridgemohan, Abigael S.'\n", + " 'Lerner, Talia N.']\n", + " file_create_date: [datetime.datetime(2024, 5, 28, 14, 0, 34, 728285, tzinfo=tzoffset(None, -25200))]\n", + " identifier: f1b1251a-82a5-4ea1-92bc-203a4979679b\n", + " institution: Northwestern Unitersity\n", + " keywords: \n", + " lab: Lerner\n", + " lab_meta_data: {\n", + " fiber_photometry \n", + " }\n", + " notes: Hemisphere with DMS: Right\n", + "Experiment: Fiber Photometry\n", + "Behavior: RI60\n", + "Punishment Group: Punishment Sensitive\n", + "Did Not Learn: False\n", + "\n", + " processing: {\n", + " behavior \n", + " }\n", + " related_publications: ['https://doi.org/10.1016/j.cub.2022.01.055']\n", + " session_description: RI60 Training with concurrent fiber photometry, rewards delivered on left nose pokes\n", + " session_id: FP_PS_2019-06-20T09-32-04\n", + " session_start_time: 2019-06-20 09:32:04-05:51\n", + " subject: subject pynwb.file.Subject at 0x4985529744\n", + "Fields:\n", + " age: P10W/\n", + " age__reference: birth\n", + " description: Male and female WT (C57BL/6J) and (DAT)::IRES-Cre knockin mice (JAX006660) were obtained from The Jackson Laboratory and crossed in house. Only heterozygote transgenic mice, obtained by backcrossing to C57BL/6J wildtypes, were used for experiments. Littermates of the same sex were randomly assigned to experimental groups (fiber photometry-14 males, 22 females; DMS excitatory optogenetics- 20 males, 19 females; DMS inhibitory optogenetics- 13 males, 13 females; DLS excitatory optogenetics- 18 males, 18 females). Adult mice at least 10 weeks of age were used in all experiments. Mice were group housed under a conventional 12 h light cycle (dark from 7:00pm to 7:00am) with ad libitum access to food and water prior to operant training. All experiments were approved by the Northwestern University Institutional Animal Care and Use Committee.\n", + " genotype: DAT-IRES-Cre: B6.SJLSlc6a3tm1.1(cre)Bkmn/J\n", + " sex: F\n", + " species: Mus musculus\n", + " strain: C57BL/6J\n", + " subject_id: 112.283\n", + "\n", + " surgery: GCaMP7b in DMS & DLS projecting SNc, fiber photometry probes in DMS & DLS\n", + " timestamps_reference_time: 2019-06-20 09:32:04-05:51\n", + " virus: AAV5-CAG-FLEX-jGCaMP7b-WPRE" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "DANDISET_ID = '000971'\n", + "file_path = 'sub-112-283/sub-112-283_ses-FP-PS-2019-06-20T09-32-04_behavior.nwb'\n", + "nwbfile, io = stream_nwbfile(DANDISET_ID, file_path)\n", + "display(nwbfile)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Retrieve Photometry and Behavioral Data" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "# Photometry\n", + "fiber_photometry_responses = nwbfile.acquisition['fiber_photometry_response_series'].data[:]\n", + "dms_calcium_signal = fiber_photometry_responses[:, 0]\n", + "dms_isosbestic_control = fiber_photometry_responses[:, 1]\n", + "dls_calcium_signal = fiber_photometry_responses[:, 2]\n", + "dls_isosbestic_control = fiber_photometry_responses[:, 3]\n", + "fs = nwbfile.acquisition['fiber_photometry_response_series'].rate\n", + "timestamps = np.arange(0, len(dms_calcium_signal) / fs, 1/fs)\n", + "\n", + "# Behavior\n", + "left_nose_poke_times = nwbfile.processing['behavior'].data_interfaces['left_nose_poke_times'].timestamps[:]\n", + "left_reward_times = nwbfile.processing['behavior'].data_interfaces['left_reward_times'].timestamps[:]\n", + "reward_port_intervals = nwbfile.processing['behavior'].data_interfaces['behavioral_epochs'].interval_series['reward_port_intervals']\n", + "reward_port_interval_data = reward_port_intervals.data[:]\n", + "reward_port_interval_times = reward_port_intervals.timestamps[:]\n", + "reward_port_entry_times = reward_port_interval_times[reward_port_interval_data==1]\n", + "reward_port_exit_times = reward_port_interval_times[reward_port_interval_data==-1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "t_start = 2000\n", + "t_end = 2100\n", + "photometry_slice = slice(int(t_start * fs), int(t_end * fs))\n", + "left_nose_poke_mask = np.logical_and(left_nose_poke_times >= t_start, left_nose_poke_times < t_end)\n", + "left_reward_mask = np.logical_and(left_reward_times >= t_start, left_reward_times < t_end)\n", + "reward_port_interval_mask = np.logical_and(reward_port_entry_times >= t_start, reward_port_entry_times < t_end)\n", + "lineoffsets = 200\n", + "linelengths = 50\n", + "y = np.arange(170, 180, 0.1)\n", + "alpha = 0.3\n", + "ylim = [165, 235]\n", + "\n", + "fix, ax = plt.subplots(2, 1, figsize=(10, 10), sharex=True)\n", + "ax[0].plot(timestamps[photometry_slice], dms_calcium_signal[photometry_slice], label='Calcium Signal')\n", + "ax[0].plot(timestamps[photometry_slice], dms_isosbestic_control[photometry_slice], label='Isosbestic Control')\n", + "ax[0].eventplot(left_nose_poke_times[left_nose_poke_mask], lineoffsets=lineoffsets, linelengths=linelengths, color='r', label='Left Nose Poke')\n", + "ax[0].eventplot(left_reward_times[left_reward_mask], lineoffsets=lineoffsets, linelengths=linelengths, color='g', label='Left Reward')\n", + "for i, (reward_port_entry_time, reward_port_exit_time) in enumerate(zip(reward_port_entry_times[reward_port_interval_mask], reward_port_exit_times[reward_port_interval_mask])):\n", + " x1 = reward_port_entry_time*np.ones(len(y))\n", + " x2 = reward_port_exit_time*np.ones(len(y))\n", + " if i == 0:\n", + " ax[0].fill_betweenx(y, x1, x2, color='b', alpha=alpha, label='In Reward Port')\n", + " else:\n", + " ax[0].fill_betweenx(y, x1, x2, color='b', alpha=alpha)\n", + "ax[0].set_ylim(ylim)\n", + "ax[0].set_title('DMS')\n", + "ax[0].legend()\n", + "ax[0].set_ylabel('Fluorescence (a.u.)')\n", + "\n", + "ax[1].plot(timestamps[photometry_slice], dls_calcium_signal[photometry_slice], label='Calcium Signal')\n", + "ax[1].plot(timestamps[photometry_slice], dls_isosbestic_control[photometry_slice], label='Isosbestic Control')\n", + "ax[1].eventplot(left_nose_poke_times[left_nose_poke_mask], lineoffsets=lineoffsets, linelengths=linelengths, color='r', label='Left Nose Poke')\n", + "ax[1].eventplot(left_reward_times[left_reward_mask], lineoffsets=lineoffsets, linelengths=linelengths, color='g', label='Left Reward')\n", + "for i, (reward_port_entry_time, reward_port_exit_time) in enumerate(zip(reward_port_entry_times[reward_port_interval_mask], reward_port_exit_times[reward_port_interval_mask])):\n", + " x1 = reward_port_entry_time*np.ones(len(y))\n", + " x2 = reward_port_exit_time*np.ones(len(y))\n", + " if i == 0:\n", + " ax[1].fill_betweenx(y, x1, x2, color='b', alpha=alpha, label='In Reward Port')\n", + " else:\n", + " ax[1].fill_betweenx(y, x1, x2, color='b', alpha=alpha)\n", + "ax[1].set_ylim(ylim)\n", + "ax[1].set_title('DLS')\n", + "ax[1].legend()\n", + "ax[1].set_xlabel('Time (s)')\n", + "_ = ax[1].set_ylabel('Fluorescence (a.u.)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Zoom in for more detail" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "t_start = 2000\n", + "t_end = 2020\n", + "photometry_slice = slice(int(t_start * fs), int(t_end * fs))\n", + "left_nose_poke_mask = np.logical_and(left_nose_poke_times >= t_start, left_nose_poke_times < t_end)\n", + "left_reward_mask = np.logical_and(left_reward_times >= t_start, left_reward_times < t_end)\n", + "reward_port_interval_mask = np.logical_and(reward_port_entry_times >= t_start, reward_port_entry_times < t_end)\n", + "lineoffsets = 200\n", + "linelengths = 50\n", + "y = np.arange(170, 180, 0.1)\n", + "alpha = 0.3\n", + "ylim = [165, 235]\n", + "\n", + "fix, ax = plt.subplots(2, 1, figsize=(10, 10), sharex=True)\n", + "ax[0].plot(timestamps[photometry_slice], dms_calcium_signal[photometry_slice], label='Calcium Signal')\n", + "ax[0].plot(timestamps[photometry_slice], dms_isosbestic_control[photometry_slice], label='Isosbestic Control')\n", + "ax[0].eventplot(left_nose_poke_times[left_nose_poke_mask], lineoffsets=lineoffsets, linelengths=linelengths, color='r', label='Left Nose Poke')\n", + "ax[0].eventplot(left_reward_times[left_reward_mask], lineoffsets=lineoffsets, linelengths=linelengths, color='g', label='Left Reward')\n", + "for i, (reward_port_entry_time, reward_port_exit_time) in enumerate(zip(reward_port_entry_times[reward_port_interval_mask], reward_port_exit_times[reward_port_interval_mask])):\n", + " x1 = reward_port_entry_time*np.ones(len(y))\n", + " x2 = reward_port_exit_time*np.ones(len(y))\n", + " if i == 0:\n", + " ax[0].fill_betweenx(y, x1, x2, color='b', alpha=alpha, label='In Reward Port')\n", + " else:\n", + " ax[0].fill_betweenx(y, x1, x2, color='b', alpha=alpha)\n", + "ax[0].set_ylim(ylim)\n", + "ax[0].set_title('DMS')\n", + "ax[0].legend()\n", + "ax[0].set_ylabel('Fluorescence (a.u.)')\n", + "\n", + "ax[1].plot(timestamps[photometry_slice], dls_calcium_signal[photometry_slice], label='Calcium Signal')\n", + "ax[1].plot(timestamps[photometry_slice], dls_isosbestic_control[photometry_slice], label='Isosbestic Control')\n", + "ax[1].eventplot(left_nose_poke_times[left_nose_poke_mask], lineoffsets=lineoffsets, linelengths=linelengths, color='r', label='Left Nose Poke')\n", + "ax[1].eventplot(left_reward_times[left_reward_mask], lineoffsets=lineoffsets, linelengths=linelengths, color='g', label='Left Reward')\n", + "for i, (reward_port_entry_time, reward_port_exit_time) in enumerate(zip(reward_port_entry_times[reward_port_interval_mask], reward_port_exit_times[reward_port_interval_mask])):\n", + " x1 = reward_port_entry_time*np.ones(len(y))\n", + " x2 = reward_port_exit_time*np.ones(len(y))\n", + " if i == 0:\n", + " ax[1].fill_betweenx(y, x1, x2, color='b', alpha=alpha, label='In Reward Port')\n", + " else:\n", + " ax[1].fill_betweenx(y, x1, x2, color='b', alpha=alpha)\n", + "ax[1].set_ylim(ylim)\n", + "ax[1].set_title('DLS')\n", + "ax[1].legend()\n", + "ax[1].set_xlabel('Time (s)')\n", + "_ = ax[1].set_ylabel('Fluorescence (a.u.)')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "lerner_notebook_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": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 963bcd00631240609e43b0aee3bd1c3b4f6f6bb2 Mon Sep 17 00:00:00 2001 From: pauladkisson Date: Tue, 11 Jun 2024 11:51:39 -0700 Subject: [PATCH 3/4] added optogenetic example notebook --- .../optogenetics_example_notebook.ipynb | 299 ++++++++++++++++++ 1 file changed, 299 insertions(+) create mode 100644 000971/lernerlab/seiler_2024/optogenetics_example_notebook.ipynb diff --git a/000971/lernerlab/seiler_2024/optogenetics_example_notebook.ipynb b/000971/lernerlab/seiler_2024/optogenetics_example_notebook.ipynb new file mode 100644 index 0000000..99c454c --- /dev/null +++ b/000971/lernerlab/seiler_2024/optogenetics_example_notebook.ipynb @@ -0,0 +1,299 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Optogenetics Example Session" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from stream_nwbfile import stream_nwbfile\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook showcases one example session from the 000971 dataset containing operant behavior and concurrent excitatory optogenetic stimulation." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + "

root (NWBFile)

session_description: FR1 Training with optogenetic stimulation, rewards delivered on both left and right nose pokes, optogenetic stimulation delivered on all nose pokes
identifier: 0aa6698d-a238-45ca-9c8c-0d26389ba921
session_start_time2020-10-20 13:00:57-05:51
timestamps_reference_time2020-10-20 13:00:57-05:51
file_create_date
02024-05-28 14:04:04.197588-07:00
experimenter('Seiler, Jillian L.', 'Cosme, Caitlin V.', 'Sherathiya, Venus N.', 'Schaid, Michael D.', 'Bianco, Joseph M.', 'Bridgemohan, Abigael S.', 'Lerner, Talia N.')
related_publications('https://doi.org/10.1016/j.cub.2022.01.055',)
stimulus
OptogeneticSeries
resolution: -1.0
comments: no comments
description: During operant training (beginning with FR1), each rewarded nosepoke was paired with a train of blue light (460nm, 1 s, 20 Hz, 15 mW) generated by an LED light source and pulse generator (Prizmatix). A subset of mice (\"ChR2 Scrambled\") received the same train of light but paired with random nosepokes on a separate RI60 schedule.
conversion: 1.0
offset: 0.0
unit: watts
data
timestamps
timestamps_unit: seconds
interval: 1
site
device
description: Optogenetic stimulus pulses were generated from the Optogenetics-LED-Dual (Prizmatix) driven by the Optogenetics PulserPlus (Prizmatix). Engineered for scaling Optogenetics experiments, the Optogenetics-LED-Dual light source features two independent fiber-coupled LED channels each equipped with independent power and switching control. Optogenetics Pulser / PulserPlus are programmable TTL pulse train generators for pulsing LEDs, lasers and shutters used in Optogenetics activation in neurophysiology and behavioral research.
manufacturer: Prizmatix
description: Mice for DMS excitatory optogenetics experiments received 1 ml of AAV5-EF1a-DIO-hChR2(H134R)-EYFP (3.3e13 GC/mL, Addgene, lot v17652) or the control fluorophore-only virus AAV5-EF1a-DIO-EYFP (3.5e12 virus molecules/mL, UNC Vector Core, lot AV4310K) in medial (AP -3.1, ML 0.8, DV -4.7) and a single fiber optic implant (Prizmatix; 250mm core, 0.66 NA) over ipsilateral DMS (AP 0.8, ML 1.5, DV -2.8). Hemispheres were counterbalanced between mice.
excitation_lambda: 460.0
location: Injection location: medial SNc (AP -3.1, ML 0.8, DV -4.7) \n", + " Stimulation location: DMS (AP 0.8, ML 1.5, DV -2.8)
keywords
processing
behavior
description: Operant behavioral data from MedPC.\n", + "Box = 3\n", + "MSN = FR1_BOTH_WStim
data_interfaces
reward_port_entry_times
description: Reward port entry times
timestamps
timestamps__unit: seconds
right_nose_poke_times
description: Right nose poke times
timestamps
timestamps__unit: seconds
right_reward_times
description: Right Reward times
timestamps
timestamps__unit: seconds
epoch_tagsset()
devices
Optogenetics_LED_Dual
description: Optogenetic stimulus pulses were generated from the Optogenetics-LED-Dual (Prizmatix) driven by the Optogenetics PulserPlus (Prizmatix). Engineered for scaling Optogenetics experiments, the Optogenetics-LED-Dual light source features two independent fiber-coupled LED channels each equipped with independent power and switching control. Optogenetics Pulser / PulserPlus are programmable TTL pulse train generators for pulsing LEDs, lasers and shutters used in Optogenetics activation in neurophysiology and behavioral research.
manufacturer: Prizmatix
ogen_sites
OptogeneticStimulusSite
device
description: Optogenetic stimulus pulses were generated from the Optogenetics-LED-Dual (Prizmatix) driven by the Optogenetics PulserPlus (Prizmatix). Engineered for scaling Optogenetics experiments, the Optogenetics-LED-Dual light source features two independent fiber-coupled LED channels each equipped with independent power and switching control. Optogenetics Pulser / PulserPlus are programmable TTL pulse train generators for pulsing LEDs, lasers and shutters used in Optogenetics activation in neurophysiology and behavioral research.
manufacturer: Prizmatix
description: Mice for DMS excitatory optogenetics experiments received 1 ml of AAV5-EF1a-DIO-hChR2(H134R)-EYFP (3.3e13 GC/mL, Addgene, lot v17652) or the control fluorophore-only virus AAV5-EF1a-DIO-EYFP (3.5e12 virus molecules/mL, UNC Vector Core, lot AV4310K) in medial (AP -3.1, ML 0.8, DV -4.7) and a single fiber optic implant (Prizmatix; 250mm core, 0.66 NA) over ipsilateral DMS (AP 0.8, ML 1.5, DV -2.8). Hemispheres were counterbalanced between mice.
excitation_lambda: 460.0
location: Injection location: medial SNc (AP -3.1, ML 0.8, DV -4.7) \n", + " Stimulation location: DMS (AP 0.8, ML 1.5, DV -2.8)
subject
age: P10W/
age__reference: birth
description: Male and female WT (C57BL/6J) and (DAT)::IRES-Cre knockin mice (JAX006660) were obtained from The Jackson Laboratory and crossed in house. Only heterozygote transgenic mice, obtained by backcrossing to C57BL/6J wildtypes, were used for experiments. Littermates of the same sex were randomly assigned to experimental groups (fiber photometry-14 males, 22 females; DMS excitatory optogenetics- 20 males, 19 females; DMS inhibitory optogenetics- 13 males, 13 females; DLS excitatory optogenetics- 18 males, 18 females). Adult mice at least 10 weeks of age were used in all experiments. Mice were group housed under a conventional 12 h light cycle (dark from 7:00pm to 7:00am) with ad libitum access to food and water prior to operant training. All experiments were approved by the Northwestern University Institutional Animal Care and Use Committee.
genotype: DAT-IRES-Cre: B6.SJLSlc6a3tm1.1(cre)Bkmn/J
sex: F
species: Mus musculus
subject_id: 119.416
strain: C57BL/6J
experiment_description: Compulsive behavior is a defining feature of disorders such as substance use disorders. Current evidence suggests that corticostriatal circuits control the expression of established compulsions, but little is known about the mechanisms regulating the development of compulsions. We hypothesized that dopamine, a critical modulator of striatal synaptic plasticity, could control alterations in corticostriatal circuits leading to the development of compulsions (defined here as continued reward seeking in the face of punishment). We used dual-site fiber photometry to measure dopamine axon activity in the dorsomedial striatum (DMS) and the dorsolateral striatum (DLS) as compulsions emerged. Individual variability in the speed with which compulsions emerged was predicted by DMS dopamine axon activity. Amplifying this dopamine signal accelerated animals' transitions to compulsion, whereas inhibition delayed it. In contrast, amplifying DLS dopamine signaling had no effect on the emergence of compulsions. These results establish DMS dopamine signaling as a key controller of the development of compulsive reward seeking.
session_id: Opto-DMS-Excitatory-ChR2-2020-10-20T13-00-57
lab: Lerner
institution: Northwestern Unitersity
notes: Hemisphere with DMS: Right\n", + "Experiment: DMS Excitatory\n", + "Behavior: RI60\n", + "Punishment Group: nan\n", + "Did Not Learn: False\n", + "
surgery: ChR2 in DMS projecting SNc, probe in DMS
stimulus_notes: Excitatory stimulation on rewarded nosepokes
" + ], + "text/plain": [ + "root pynwb.file.NWBFile at 0x5072347984\n", + "Fields:\n", + " devices: {\n", + " Optogenetics_LED_Dual \n", + " }\n", + " experiment_description: Compulsive behavior is a defining feature of disorders such as substance use disorders. Current evidence suggests that corticostriatal circuits control the expression of established compulsions, but little is known about the mechanisms regulating the development of compulsions. We hypothesized that dopamine, a critical modulator of striatal synaptic plasticity, could control alterations in corticostriatal circuits leading to the development of compulsions (defined here as continued reward seeking in the face of punishment). We used dual-site fiber photometry to measure dopamine axon activity in the dorsomedial striatum (DMS) and the dorsolateral striatum (DLS) as compulsions emerged. Individual variability in the speed with which compulsions emerged was predicted by DMS dopamine axon activity. Amplifying this dopamine signal accelerated animals' transitions to compulsion, whereas inhibition delayed it. In contrast, amplifying DLS dopamine signaling had no effect on the emergence of compulsions. These results establish DMS dopamine signaling as a key controller of the development of compulsive reward seeking.\n", + " experimenter: ['Seiler, Jillian L.' 'Cosme, Caitlin V.' 'Sherathiya, Venus N.'\n", + " 'Schaid, Michael D.' 'Bianco, Joseph M.' 'Bridgemohan, Abigael S.'\n", + " 'Lerner, Talia N.']\n", + " file_create_date: [datetime.datetime(2024, 5, 28, 14, 4, 4, 197588, tzinfo=tzoffset(None, -25200))]\n", + " identifier: 0aa6698d-a238-45ca-9c8c-0d26389ba921\n", + " institution: Northwestern Unitersity\n", + " keywords: \n", + " lab: Lerner\n", + " notes: Hemisphere with DMS: Right\n", + "Experiment: DMS Excitatory\n", + "Behavior: RI60\n", + "Punishment Group: nan\n", + "Did Not Learn: False\n", + "\n", + " ogen_sites: {\n", + " OptogeneticStimulusSite \n", + " }\n", + " processing: {\n", + " behavior \n", + " }\n", + " related_publications: ['https://doi.org/10.1016/j.cub.2022.01.055']\n", + " session_description: FR1 Training with optogenetic stimulation, rewards delivered on both left and right nose pokes, optogenetic stimulation delivered on all nose pokes\n", + " session_id: Opto-DMS-Excitatory-ChR2-2020-10-20T13-00-57\n", + " session_start_time: 2020-10-20 13:00:57-05:51\n", + " stimulus: {\n", + " OptogeneticSeries \n", + " }\n", + " stimulus_notes: Excitatory stimulation on rewarded nosepokes\n", + " subject: subject pynwb.file.Subject at 0x5070799008\n", + "Fields:\n", + " age: P10W/\n", + " age__reference: birth\n", + " description: Male and female WT (C57BL/6J) and (DAT)::IRES-Cre knockin mice (JAX006660) were obtained from The Jackson Laboratory and crossed in house. Only heterozygote transgenic mice, obtained by backcrossing to C57BL/6J wildtypes, were used for experiments. Littermates of the same sex were randomly assigned to experimental groups (fiber photometry-14 males, 22 females; DMS excitatory optogenetics- 20 males, 19 females; DMS inhibitory optogenetics- 13 males, 13 females; DLS excitatory optogenetics- 18 males, 18 females). Adult mice at least 10 weeks of age were used in all experiments. Mice were group housed under a conventional 12 h light cycle (dark from 7:00pm to 7:00am) with ad libitum access to food and water prior to operant training. All experiments were approved by the Northwestern University Institutional Animal Care and Use Committee.\n", + " genotype: DAT-IRES-Cre: B6.SJLSlc6a3tm1.1(cre)Bkmn/J\n", + " sex: F\n", + " species: Mus musculus\n", + " strain: C57BL/6J\n", + " subject_id: 119.416\n", + "\n", + " surgery: ChR2 in DMS projecting SNc, probe in DMS\n", + " timestamps_reference_time: 2020-10-20 13:00:57-05:51" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "DANDISET_ID = '000971'\n", + "file_path = 'sub-119-416/sub-119-416_ses-Opto-DMS-Excitatory-ChR2-2020-10-20T13-00-57_ogen.nwb'\n", + "nwbfile, io = stream_nwbfile(DANDISET_ID, file_path)\n", + "display(nwbfile)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Retrieve Optogenetic and Behavioral Data" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "# Optogenetic Stimulation\n", + "optogenetic_stimulation_timestamps = nwbfile.stimulus['OptogeneticSeries'].timestamps[:]\n", + "optogenetic_stimulation_data = nwbfile.stimulus['OptogeneticSeries'].data[:]\n", + "opto_onset_times = optogenetic_stimulation_timestamps[optogenetic_stimulation_data == 0.015]\n", + "opto_offset_times = optogenetic_stimulation_timestamps[optogenetic_stimulation_data == 0]\n", + "\n", + "# Behavior\n", + "right_nose_poke_times = nwbfile.processing['behavior'].data_interfaces['right_nose_poke_times'].timestamps[:]\n", + "right_reward_times = nwbfile.processing['behavior'].data_interfaces['right_reward_times'].timestamps[:]\n", + "reward_port_entry_times = nwbfile.processing['behavior'].data_interfaces['reward_port_entry_times'].timestamps[:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "t_start = 1\n", + "t_end = 3600\n", + "right_nose_poke_mask = np.logical_and(right_nose_poke_times >= t_start, right_nose_poke_times < t_end)\n", + "right_reward_mask = np.logical_and(right_reward_times >= t_start, right_reward_times < t_end)\n", + "reward_port_entry_mask = np.logical_and(reward_port_entry_times >= t_start, reward_port_entry_times < t_end)\n", + "opto_onset_mask = np.logical_and(opto_onset_times >= t_start, opto_onset_times < t_end)\n", + "opto_offset_mask = np.logical_and(opto_offset_times >= t_start, opto_offset_times < t_end)\n", + "lineoffsets = 1\n", + "linelengths = 1\n", + "alpha = 0.3\n", + "ylim = [-2, 2]\n", + "y = np.arange(-1, 0, 0.1)\n", + "\n", + "fix, ax = plt.subplots(figsize=(10, 5))\n", + "ax.eventplot(right_nose_poke_times[right_nose_poke_mask], lineoffsets=lineoffsets, linelengths=linelengths, color='r', label='Right Nose Poke')\n", + "ax.eventplot(right_reward_times[right_reward_mask], lineoffsets=lineoffsets, linelengths=linelengths, color='g', label='Right Reward')\n", + "for i, (onset_time, offset_time) in enumerate(zip(opto_onset_times[opto_onset_mask], opto_offset_times[opto_offset_mask])):\n", + " x1 = onset_time*np.ones(len(y))\n", + " x2 = offset_time*np.ones(len(y))\n", + " if i == 0:\n", + " ax.fill_betweenx(y, x1, x2, color='b', alpha=alpha, label='Optogenetic Stimulation')\n", + " else:\n", + " ax.fill_betweenx(y, x1, x2, color='b', alpha=alpha)\n", + "ax.set_ylim(ylim)\n", + "ax.set_title('DMS Excitatory ChR2')\n", + "ax.yaxis.set_visible(False)\n", + "ax.set_xlabel('Time (s)')\n", + "_ = ax.legend(bbox_to_anchor=(1.01, 1), loc='upper left')" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "t_start = 2200\n", + "t_end = 2250\n", + "right_nose_poke_mask = np.logical_and(right_nose_poke_times >= t_start, right_nose_poke_times < t_end)\n", + "right_reward_mask = np.logical_and(right_reward_times >= t_start, right_reward_times < t_end)\n", + "reward_port_entry_mask = np.logical_and(reward_port_entry_times >= t_start, reward_port_entry_times < t_end)\n", + "opto_onset_mask = np.logical_and(opto_onset_times >= t_start, opto_onset_times < t_end)\n", + "opto_offset_mask = np.logical_and(opto_offset_times >= t_start, opto_offset_times < t_end)\n", + "lineoffsets = 1\n", + "linelengths = 1\n", + "alpha = 0.3\n", + "ylim = [-2, 2]\n", + "y = np.arange(-1, 0, 0.1)\n", + "\n", + "fix, ax = plt.subplots(figsize=(10, 5))\n", + "ax.eventplot(right_nose_poke_times[right_nose_poke_mask], lineoffsets=lineoffsets, linelengths=linelengths, color='r', label='Right Nose Poke')\n", + "ax.eventplot(right_reward_times[right_reward_mask], lineoffsets=lineoffsets, linelengths=linelengths, color='g', label='Right Reward')\n", + "for i, (onset_time, offset_time) in enumerate(zip(opto_onset_times[opto_onset_mask], opto_offset_times[opto_offset_mask])):\n", + " x1 = onset_time*np.ones(len(y))\n", + " x2 = offset_time*np.ones(len(y))\n", + " if i == 0:\n", + " ax.fill_betweenx(y, x1, x2, color='b', alpha=alpha, label='Optogenetic Stimulation')\n", + " else:\n", + " ax.fill_betweenx(y, x1, x2, color='b', alpha=alpha)\n", + "ax.set_ylim(ylim)\n", + "ax.set_title('DMS Excitatory ChR2')\n", + "ax.yaxis.set_visible(False)\n", + "ax.set_xlabel('Time (s)')\n", + "_ = ax.legend(bbox_to_anchor=(1.01, 1), loc='upper left')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "lerner_notebook_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": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 2b98e991f8353f3b1afd206c056c8cc113471f5a Mon Sep 17 00:00:00 2001 From: pauladkisson Date: Tue, 11 Jun 2024 13:32:26 -0700 Subject: [PATCH 4/4] added README --- 000971/lernerlab/seiler_2024/README.md | 8 + .../fiber_photometry_example_notebook.ipynb | 156 +++++++++--------- .../optogenetics_example_notebook.ipynb | 28 ++-- 3 files changed, 102 insertions(+), 90 deletions(-) diff --git a/000971/lernerlab/seiler_2024/README.md b/000971/lernerlab/seiler_2024/README.md index e69de29..5577e49 100644 --- a/000971/lernerlab/seiler_2024/README.md +++ b/000971/lernerlab/seiler_2024/README.md @@ -0,0 +1,8 @@ +# Example Sessions for Dandiset 000971 + +This submission provides 2 notebooks showcasing example sessions for the Dandiset 000971, which corresponds to the 2022 Current Biology paper: [Dopamine signaling in the dorsomedial striatum promotes compulsive behavior](https://doi.org/10.1016/j.cub.2022.01.055.) by Seiler et al. + +Each notebook provides an example of how to access the critical data and metadata from the 2 types of experiments in the dataset: + +- `fiber_photometry_example_notebook.ipynb` showcases one example session from the 000971 dataset containing operant behavior and concurrent fiber photometry recordings. +- `optogenetics_example_notebook.ipynb` showcases one example session from the 000971 dataset containing operant behavior and concurrent excitatory optogenetic stimulation. \ No newline at end of file diff --git a/000971/lernerlab/seiler_2024/fiber_photometry_example_notebook.ipynb b/000971/lernerlab/seiler_2024/fiber_photometry_example_notebook.ipynb index 9358ba7..3cd5da3 100644 --- a/000971/lernerlab/seiler_2024/fiber_photometry_example_notebook.ipynb +++ b/000971/lernerlab/seiler_2024/fiber_photometry_example_notebook.ipynb @@ -69,7 +69,7 @@ " });\n", " });\n", " \n", - "

root (NWBFile)

session_description: RI60 Training with concurrent fiber photometry, rewards delivered on left nose pokes
identifier: f1b1251a-82a5-4ea1-92bc-203a4979679b
session_start_time2019-06-20 09:32:04-05:51
timestamps_reference_time2019-06-20 09:32:04-05:51
file_create_date
02024-05-28 14:00:34.728285-07:00
experimenter('Seiler, Jillian L.', 'Cosme, Caitlin V.', 'Sherathiya, Venus N.', 'Schaid, Michael D.', 'Bianco, Joseph M.', 'Bridgemohan, Abigael S.', 'Lerner, Talia N.')
related_publications('https://doi.org/10.1016/j.cub.2022.01.055',)
acquisition
commanded_voltage_series_dls
starting_time: 0.0
rate: 6103.515625
resolution: -1.0
comments: no comments
description: The commanded voltage for the frequency-modulated DLS calcium signal and DLS isosbestic control.
conversion: 1.0
offset: 0.0
unit: volts
data
starting_time_unit: seconds
frequency__unit: hertz
commanded_voltage_series_dms
starting_time: 0.0
rate: 6103.515625
resolution: -1.0
comments: no comments
description: The commanded voltage for the frequency-modulated DMS calcium signal and DMS isosbestic control.
conversion: 1.0
offset: 0.0
unit: volts
data
starting_time_unit: seconds
frequency__unit: hertz
fiber_photometry_response_series
starting_time: 0.0
rate: 1017.2526245117188
resolution: -1.0
comments: no comments
description: The fluorescence from the DMS calcium signal, DMS isosbestic control, DLS calcium signal, and DLS isosbestic control.
conversion: 1.0
offset: 0.0
unit: a.u.
data
starting_time_unit: seconds
fiber_photometry_table_region
description: The region of the FiberPhotometryTable corresponding to the DMS calcium signal, DMS isosbestic control, DLS calcium signal, and DLS isosbestic control.
table
description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.
table\n", + "

root (NWBFile)

session_description: RI60 Training with concurrent fiber photometry, rewards delivered on left nose pokes
identifier: 64828a03-4c9b-4212-ad60-d5486717b6df
session_start_time2019-06-20 09:32:04-05:51
timestamps_reference_time2019-06-20 09:32:04-05:51
file_create_date
02024-06-11 13:06:38.624732-07:00
experimenter('Seiler, Jillian L.', 'Cosme, Caitlin V.', 'Sherathiya, Venus N.', 'Schaid, Michael D.', 'Bianco, Joseph M.', 'Bridgemohan, Abigael S.', 'Lerner, Talia N.')
related_publications('https://doi.org/10.1016/j.cub.2022.01.055',)
acquisition
commanded_voltage_series_dls
starting_time: 0.0
rate: 6103.515625
resolution: -1.0
comments: no comments
description: The commanded voltage for the frequency-modulated DLS calcium signal and DLS isosbestic control.
conversion: 1.0
offset: 0.0
unit: volts
data
starting_time_unit: seconds
frequency__unit: hertz
commanded_voltage_series_dms
starting_time: 0.0
rate: 6103.515625
resolution: -1.0
comments: no comments
description: The commanded voltage for the frequency-modulated DMS calcium signal and DMS isosbestic control.
conversion: 1.0
offset: 0.0
unit: volts
data
starting_time_unit: seconds
frequency__unit: hertz
fiber_photometry_response_series
starting_time: 0.0
rate: 1017.2526245117188
resolution: -1.0
comments: no comments
description: The fluorescence from the DMS calcium signal, DMS isosbestic control, DLS calcium signal, and DLS isosbestic control.
conversion: 1.0
offset: 0.0
unit: a.u.
data
starting_time_unit: seconds
fiber_photometry_table_region
description: The region of the FiberPhotometryTable corresponding to the DMS calcium signal, DMS isosbestic control, DLS calcium signal, and DLS isosbestic control.
table
description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.
table
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0DMSdms_green_fluorophore abc.Indicator at 0x4985524560\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 0.8 4.7]\\n injection_location: medial SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x4985528784\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_calcium_signal abc.ExcitationSource at 0x4985527200\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 465.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x4985530176\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x4985523120\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\ndms_green_fluorophore abc.Indicator at 0x4963995696\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 0.8 4.7]\\n injection_location: medial SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x4963999968\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_calcium_signal abc.ExcitationSource at 0x4963998384\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 465.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x4964001360\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x4963994496\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n[0.8, 1.5, 2.8]commanded_voltage_series_dms abc.CommandedVoltageSeries at 0x4983888848\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DMS calcium signal and DMS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x4985525616\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nexcitation_filter abc.BandOpticalFilter at 0x4985525760\\nFields:\\n bandwidth_in_nm: 30.0\\n center_wavelength_in_nm: 475.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\ncommanded_voltage_series_dms abc.CommandedVoltageSeries at 0x4962263024\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DMS calcium signal and DMS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x4963994736\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nexcitation_filter abc.BandOpticalFilter at 0x4963994208\\nFields:\\n bandwidth_in_nm: 30.0\\n center_wavelength_in_nm: 475.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n
1DMSdms_green_fluorophore abc.Indicator at 0x4985524560\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 0.8 4.7]\\n injection_location: medial SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x4985528784\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_isosbestic_control abc.ExcitationSource at 0x4985527344\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 405.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x4985530176\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x4985523120\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\ndms_green_fluorophore abc.Indicator at 0x4963995696\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 0.8 4.7]\\n injection_location: medial SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x4963999968\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_isosbestic_control abc.ExcitationSource at 0x4963998528\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 405.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x4964001360\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x4963994496\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n[0.8, 1.5, 2.8]commanded_voltage_series_dms abc.CommandedVoltageSeries at 0x4983888848\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DMS calcium signal and DMS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x4985525616\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nisosbestic_excitation_filter abc.BandOpticalFilter at 0x4985527824\\nFields:\\n bandwidth_in_nm: 10.0\\n center_wavelength_in_nm: 405.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\ncommanded_voltage_series_dms abc.CommandedVoltageSeries at 0x4962263024\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DMS calcium signal and DMS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x4963994736\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nisosbestic_excitation_filter abc.BandOpticalFilter at 0x4963999008\\nFields:\\n bandwidth_in_nm: 10.0\\n center_wavelength_in_nm: 405.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n
2DLSdls_green_fluorophore abc.Indicator at 0x4983897344\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 1.3 4.2]\\n injection_location: lateral SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x4985528784\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_calcium_signal abc.ExcitationSource at 0x4985527200\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 465.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x4985530176\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x4985523120\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\ndls_green_fluorophore abc.Indicator at 0x4962259616\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 1.3 4.2]\\n injection_location: lateral SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x4963999968\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_calcium_signal abc.ExcitationSource at 0x4963998384\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 465.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x4964001360\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x4963994496\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n[0.1, 2.8, 3.5]commanded_voltage_series_dls abc.CommandedVoltageSeries at 0x4983887312\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DLS calcium signal and DLS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x4985525616\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nexcitation_filter abc.BandOpticalFilter at 0x4985525760\\nFields:\\n bandwidth_in_nm: 30.0\\n center_wavelength_in_nm: 475.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\ncommanded_voltage_series_dls abc.CommandedVoltageSeries at 0x4962255920\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DLS calcium signal and DLS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x4963994736\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nexcitation_filter abc.BandOpticalFilter at 0x4963994208\\nFields:\\n bandwidth_in_nm: 30.0\\n center_wavelength_in_nm: 475.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n
3DLSdls_green_fluorophore abc.Indicator at 0x4983897344\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 1.3 4.2]\\n injection_location: lateral SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x4985528784\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_isosbestic_control abc.ExcitationSource at 0x4985527344\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 405.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x4985530176\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x4985523120\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\ndls_green_fluorophore abc.Indicator at 0x4962259616\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 1.3 4.2]\\n injection_location: lateral SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x4963999968\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_isosbestic_control abc.ExcitationSource at 0x4963998528\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 405.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x4964001360\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x4963994496\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n[0.1, 2.8, 3.5]commanded_voltage_series_dls abc.CommandedVoltageSeries at 0x4983887312\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DLS calcium signal and DLS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x4985525616\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nisosbestic_excitation_filter abc.BandOpticalFilter at 0x4985527824\\nFields:\\n bandwidth_in_nm: 10.0\\n center_wavelength_in_nm: 405.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\ncommanded_voltage_series_dls abc.CommandedVoltageSeries at 0x4962255920\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DLS calcium signal and DLS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x4963994736\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nisosbestic_excitation_filter abc.BandOpticalFilter at 0x4963999008\\nFields:\\n bandwidth_in_nm: 10.0\\n center_wavelength_in_nm: 405.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n
keywords
processing
behavior
description: Operant behavioral data from MedPC.\n", - "Box = 3\n", - "MSN = FOOD_RI 60 LEFT TTL
data_interfaces
behavioral_epochs
interval_series
reward_port_intervals
resolution: -1.0
comments: no comments
description: Interval of time spent in reward port (1 is entry, -1 is exit)
conversion: 1.0
offset: 0.0
unit: n/a
data
timestamps
timestamps_unit: seconds
interval: 1
left_nose_poke_times
description: Left nose poke times
timestamps
timestamps__unit: seconds
left_reward_times
description: Left Reward times
timestamps
timestamps__unit: seconds
right_nose_poke_times
description: Right nose poke times
timestamps
timestamps__unit: seconds
epoch_tagsset()
devices
dichroic_mirror
description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.
manufacturer: Doric Lenses
model: 4 ports Fluorescence Mini Cube - GCaMP
dls_green_fluorophore
description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.
manufacturer: Addgene
label: GCaMP7b
injection_location: lateral SNc
injection_coordinates_in_mm
[3.1 1.3 4.2]
dms_green_fluorophore
description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.
manufacturer: Addgene
label: GCaMP7b
injection_location: medial SNc
injection_coordinates_in_mm
[3.1 0.8 4.7]
emission_filter
description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.
manufacturer: Doric Lenses
center_wavelength_in_nm: 525.0
bandwidth_in_nm: 50.0
filter_type: Bandpass
model: 4 ports Fluorescence Mini Cube - GCaMP
excitation_filter
description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.
manufacturer: Doric Lenses
center_wavelength_in_nm: 475.0
bandwidth_in_nm: 30.0
filter_type: Bandpass
model: 4 ports Fluorescence Mini Cube - GCaMP
excitation_source_calcium_signal
description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.
manufacturer: Doric Lenses
model: Connectorized LED
illumination_type: LED
excitation_wavelength_in_nm: 465.0
excitation_source_isosbestic_control
description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.
manufacturer: Doric Lenses
model: Connectorized LED
illumination_type: LED
excitation_wavelength_in_nm: 405.0
isosbestic_excitation_filter
description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.
manufacturer: Doric Lenses
center_wavelength_in_nm: 405.0
bandwidth_in_nm: 10.0
filter_type: Bandpass
model: 4 ports Fluorescence Mini Cube - GCaMP
optical_fiber
description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.
manufacturer: Doric Lenses
model: Fiber Optic Implant
numerical_aperture: 0.48
core_diameter_in_um: 400.0
photodetector
description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.
manufacturer: Doric Lenses
model: Newport Visible Femtowatt Photoreceiver Module
detector_type: photodiode
detected_wavelength_in_nm: 525.0
gain: 10000000000.0
subject
age: P10W/
age__reference: birth
description: Male and female WT (C57BL/6J) and (DAT)::IRES-Cre knockin mice (JAX006660) were obtained from The Jackson Laboratory and crossed in house. Only heterozygote transgenic mice, obtained by backcrossing to C57BL/6J wildtypes, were used for experiments. Littermates of the same sex were randomly assigned to experimental groups (fiber photometry-14 males, 22 females; DMS excitatory optogenetics- 20 males, 19 females; DMS inhibitory optogenetics- 13 males, 13 females; DLS excitatory optogenetics- 18 males, 18 females). Adult mice at least 10 weeks of age were used in all experiments. Mice were group housed under a conventional 12 h light cycle (dark from 7:00pm to 7:00am) with ad libitum access to food and water prior to operant training. All experiments were approved by the Northwestern University Institutional Animal Care and Use Committee.
genotype: DAT-IRES-Cre: B6.SJLSlc6a3tm1.1(cre)Bkmn/J
sex: F
species: Mus musculus
subject_id: 112.283
strain: C57BL/6J
lab_meta_data
fiber_photometry
fiber_photometry_table
description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.
table\n", + "MSN = FOOD_RI 60 LEFT TTL \n", + "Box = 3
data_interfaces
behavioral_epochs
interval_series
reward_port_intervals
resolution: -1.0
comments: no comments
description: Interval of time spent in reward port (1 is entry, -1 is exit)
conversion: 1.0
offset: 0.0
unit: n/a
data
timestamps
timestamps_unit: seconds
interval: 1
left_nose_poke_times
description: Left nose poke times
timestamps
timestamps__unit: seconds
left_reward_times
description: Left reward times
timestamps
timestamps__unit: seconds
right_nose_poke_times
description: Right nose poke times
timestamps
timestamps__unit: seconds
epoch_tagsset()
devices
dichroic_mirror
description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.
manufacturer: Doric Lenses
model: 4 ports Fluorescence Mini Cube - GCaMP
dls_green_fluorophore
description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.
manufacturer: Addgene
label: GCaMP7b
injection_location: lateral SNc
injection_coordinates_in_mm
[3.1 1.3 4.2]
dms_green_fluorophore
description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.
manufacturer: Addgene
label: GCaMP7b
injection_location: medial SNc
injection_coordinates_in_mm
[3.1 0.8 4.7]
emission_filter
description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.
manufacturer: Doric Lenses
center_wavelength_in_nm: 525.0
bandwidth_in_nm: 50.0
filter_type: Bandpass
model: 4 ports Fluorescence Mini Cube - GCaMP
excitation_filter
description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.
manufacturer: Doric Lenses
center_wavelength_in_nm: 475.0
bandwidth_in_nm: 30.0
filter_type: Bandpass
model: 4 ports Fluorescence Mini Cube - GCaMP
excitation_source_calcium_signal
description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.
manufacturer: Doric Lenses
model: Connectorized LED
illumination_type: LED
excitation_wavelength_in_nm: 465.0
excitation_source_isosbestic_control
description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.
manufacturer: Doric Lenses
model: Connectorized LED
illumination_type: LED
excitation_wavelength_in_nm: 405.0
isosbestic_excitation_filter
description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.
manufacturer: Doric Lenses
center_wavelength_in_nm: 405.0
bandwidth_in_nm: 10.0
filter_type: Bandpass
model: 4 ports Fluorescence Mini Cube - GCaMP
optical_fiber
description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.
manufacturer: Doric Lenses
model: Fiber Optic Implant
numerical_aperture: 0.48
core_diameter_in_um: 400.0
photodetector
description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.
manufacturer: Doric Lenses
model: Newport Visible Femtowatt Photoreceiver Module
detector_type: photodiode
detected_wavelength_in_nm: 525.0
gain: 10000000000.0
subject
age: P10W/
age__reference: birth
description: Male and female WT (C57BL/6J) and (DAT)::IRES-Cre knockin mice (JAX006660) were obtained from The Jackson Laboratory and crossed in house. Only heterozygote transgenic mice, obtained by backcrossing to C57BL/6J wildtypes, were used for experiments. Littermates of the same sex were randomly assigned to experimental groups (fiber photometry-14 males, 22 females; DMS excitatory optogenetics- 20 males, 19 females; DMS inhibitory optogenetics- 13 males, 13 females; DLS excitatory optogenetics- 18 males, 18 females). Adult mice at least 10 weeks of age were used in all experiments. Mice were group housed under a conventional 12 h light cycle (dark from 7:00pm to 7:00am) with ad libitum access to food and water prior to operant training. All experiments were approved by the Northwestern University Institutional Animal Care and Use Committee.
genotype: DAT-IRES-Cre: B6.SJLSlc6a3tm1.1(cre)Bkmn/J
sex: F
species: Mus musculus
subject_id: 112.283
strain: C57BL/6J
lab_meta_data
fiber_photometry
fiber_photometry_table
description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.
table
\n", " \n", " \n", " \n", @@ -187,54 +187,54 @@ " \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", - " \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", "
0DMSdms_green_fluorophore abc.Indicator at 0x4985524560\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 0.8 4.7]\\n injection_location: medial SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x4985528784\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_calcium_signal abc.ExcitationSource at 0x4985527200\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 465.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x4985530176\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x4985523120\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\ndms_green_fluorophore abc.Indicator at 0x4963995696\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 0.8 4.7]\\n injection_location: medial SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x4963999968\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_calcium_signal abc.ExcitationSource at 0x4963998384\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 465.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x4964001360\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x4963994496\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n[0.8, 1.5, 2.8]commanded_voltage_series_dms abc.CommandedVoltageSeries at 0x4983888848\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DMS calcium signal and DMS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x4985525616\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nexcitation_filter abc.BandOpticalFilter at 0x4985525760\\nFields:\\n bandwidth_in_nm: 30.0\\n center_wavelength_in_nm: 475.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\ncommanded_voltage_series_dms abc.CommandedVoltageSeries at 0x4962263024\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DMS calcium signal and DMS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x4963994736\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nexcitation_filter abc.BandOpticalFilter at 0x4963994208\\nFields:\\n bandwidth_in_nm: 30.0\\n center_wavelength_in_nm: 475.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n
1DMSdms_green_fluorophore abc.Indicator at 0x4985524560\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 0.8 4.7]\\n injection_location: medial SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x4985528784\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_isosbestic_control abc.ExcitationSource at 0x4985527344\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 405.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x4985530176\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x4985523120\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\ndms_green_fluorophore abc.Indicator at 0x4963995696\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 0.8 4.7]\\n injection_location: medial SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x4963999968\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_isosbestic_control abc.ExcitationSource at 0x4963998528\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 405.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x4964001360\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x4963994496\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n[0.8, 1.5, 2.8]commanded_voltage_series_dms abc.CommandedVoltageSeries at 0x4983888848\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DMS calcium signal and DMS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x4985525616\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nisosbestic_excitation_filter abc.BandOpticalFilter at 0x4985527824\\nFields:\\n bandwidth_in_nm: 10.0\\n center_wavelength_in_nm: 405.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\ncommanded_voltage_series_dms abc.CommandedVoltageSeries at 0x4962263024\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DMS calcium signal and DMS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x4963994736\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nisosbestic_excitation_filter abc.BandOpticalFilter at 0x4963999008\\nFields:\\n bandwidth_in_nm: 10.0\\n center_wavelength_in_nm: 405.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n
2DLSdls_green_fluorophore abc.Indicator at 0x4983897344\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 1.3 4.2]\\n injection_location: lateral SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x4985528784\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_calcium_signal abc.ExcitationSource at 0x4985527200\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 465.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x4985530176\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x4985523120\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\ndls_green_fluorophore abc.Indicator at 0x4962259616\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 1.3 4.2]\\n injection_location: lateral SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x4963999968\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_calcium_signal abc.ExcitationSource at 0x4963998384\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 465.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x4964001360\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x4963994496\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n[0.1, 2.8, 3.5]commanded_voltage_series_dls abc.CommandedVoltageSeries at 0x4983887312\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DLS calcium signal and DLS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x4985525616\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nexcitation_filter abc.BandOpticalFilter at 0x4985525760\\nFields:\\n bandwidth_in_nm: 30.0\\n center_wavelength_in_nm: 475.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\ncommanded_voltage_series_dls abc.CommandedVoltageSeries at 0x4962255920\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DLS calcium signal and DLS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x4963994736\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nexcitation_filter abc.BandOpticalFilter at 0x4963994208\\nFields:\\n bandwidth_in_nm: 30.0\\n center_wavelength_in_nm: 475.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n
3DLSdls_green_fluorophore abc.Indicator at 0x4983897344\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 1.3 4.2]\\n injection_location: lateral SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x4985528784\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_isosbestic_control abc.ExcitationSource at 0x4985527344\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 405.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x4985530176\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x4985523120\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\ndls_green_fluorophore abc.Indicator at 0x4962259616\\nFields:\\n description: Mice for fiber photometry experiments received infusions of 1ml of AAV5-CAG-FLEX-jGCaMP7b-WPRE (1.02e13 vg/mL, Addgene, lot 18-429) into lateral SNc (AP 3.1, ML 1.3, DV 4.2) in one hemisphere and medial SNc (AP 3.1, ML 0.8, DV 4.7) in the other. Hemispheres were counterbalanced between mice.\\n injection_coordinates_in_mm: [3.1 1.3 4.2]\\n injection_location: lateral SNc\\n label: GCaMP7b\\n manufacturer: Addgene\\noptical_fiber abc.OpticalFiber at 0x4963999968\\nFields:\\n core_diameter_in_um: 400.0\\n description: Fiber optic implants (Doric Lenses; 400 um, 0.48 NA) were placed above DMS (AP 0.8, ML 1.5, DV 2.8) and DLS (AP 0.1, ML 2.8, DV 3.5). The DMS implant was placed in the hemisphere receiving a medial SNc viral injection, while the DLS implant was placed in the hemisphere receiving a lateral SNc viral injection. Calcium signals from dopamine terminals in DMS and DLS were recorded during RI30, on the first and last days of RI60/RR20 training as well as on both footshock probes for each mouse. All recordings were done using a fiber photometry rig with optical components from Doric lenses controlled by a real-time processor from Tucker Davis Technologies (TDT; RZ5P). TDT Synapse software was used for data acquisition.\\n manufacturer: Doric Lenses\\n model: Fiber Optic Implant\\n numerical_aperture: 0.48\\nexcitation_source_isosbestic_control abc.ExcitationSource at 0x4963998528\\nFields:\\n description: 465nm and 405nm LEDs were modulated at 211 Hz and 330 Hz, respectively, for DMS probes. 465nm and 405nm LEDs were modulated at 450 Hz and 270 Hz, respectively for DLS probes. LED currents were adjusted in order to return a voltage between 150-200mV for each signal, were offset by 5 mA, were demodulated using a 4 Hz lowpass frequency filter.\\n excitation_wavelength_in_nm: 405.0\\n illumination_type: LED\\n manufacturer: Doric Lenses\\n model: Connectorized LED\\nphotodetector abc.Photodetector at 0x4964001360\\nFields:\\n description: This battery-operated photoreceiver has high gain and detects CW light signals in the sub-picowatt to nanowatt range. When used in conjunction with a modulated light source and a lock-in amplifier to reduce the measurement bandwidth, it achieves sensitivity levels in the femtowatt range. Doric offer this Newport product with add-on fiber optic adapter that improves coupling efficiency between the large core, high NA optical fibers used in Fiber Photometry and relatively small detector area. Its output analog voltage (0-5 V) can be monitored with an oscilloscope or with a DAQ board to record the data with a computer.\\n detected_wavelength_in_nm: 525.0\\n detector_type: photodiode\\n gain: 10000000000.0\\n manufacturer: Doric Lenses\\n model: Newport Visible Femtowatt Photoreceiver Module\\ndichroic_mirror abc.DichroicMirror at 0x4963994496\\nFields:\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n[0.1, 2.8, 3.5]commanded_voltage_series_dls abc.CommandedVoltageSeries at 0x4983887312\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DLS calcium signal and DLS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x4985525616\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nisosbestic_excitation_filter abc.BandOpticalFilter at 0x4985527824\\nFields:\\n bandwidth_in_nm: 10.0\\n center_wavelength_in_nm: 405.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\ncommanded_voltage_series_dls abc.CommandedVoltageSeries at 0x4962255920\\nFields:\\n comments: no comments\\n conversion: 1.0\\n data: <HDF5 dataset \"data\": shape (22570496,), type \"<f4\">\\n description: The commanded voltage for the frequency-modulated DLS calcium signal and DLS isosbestic control.\\n frequency__unit: hertz\\n offset: 0.0\\n rate: 6103.515625\\n resolution: -1.0\\n starting_time: 0.0\\n starting_time_unit: seconds\\n unit: volts\\nemission_filter abc.BandOpticalFilter at 0x4963994736\\nFields:\\n bandwidth_in_nm: 50.0\\n center_wavelength_in_nm: 525.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\nisosbestic_excitation_filter abc.BandOpticalFilter at 0x4963999008\\nFields:\\n bandwidth_in_nm: 10.0\\n center_wavelength_in_nm: 405.0\\n description: Dual excitation band fiber photometry measurements use a Fluorescence Mini Cube with 4 ports: one port for the functional fluorescence excitation light, one for the isosbestic excitation, one for the fluorescence detection, and one for the sample. The cube has dichroic mirrors to combine isosbestic and fluorescence excitations and separate the fluorescence emission and narrow bandpass filters limiting the excitation fluorescence spectrum.\\n filter_type: Bandpass\\n manufacturer: Doric Lenses\\n model: 4 ports Fluorescence Mini Cube - GCaMP\\n
experiment_description: Compulsive behavior is a defining feature of disorders such as substance use disorders. Current evidence suggests that corticostriatal circuits control the expression of established compulsions, but little is known about the mechanisms regulating the development of compulsions. We hypothesized that dopamine, a critical modulator of striatal synaptic plasticity, could control alterations in corticostriatal circuits leading to the development of compulsions (defined here as continued reward seeking in the face of punishment). We used dual-site fiber photometry to measure dopamine axon activity in the dorsomedial striatum (DMS) and the dorsolateral striatum (DLS) as compulsions emerged. Individual variability in the speed with which compulsions emerged was predicted by DMS dopamine axon activity. Amplifying this dopamine signal accelerated animals' transitions to compulsion, whereas inhibition delayed it. In contrast, amplifying DLS dopamine signaling had no effect on the emergence of compulsions. These results establish DMS dopamine signaling as a key controller of the development of compulsive reward seeking.
session_id: FP_PS_2019-06-20T09-32-04
lab: Lerner
institution: Northwestern Unitersity
notes: Hemisphere with DMS: Right\n", @@ -242,10 +242,10 @@ "Behavior: RI60\n", "Punishment Group: Punishment Sensitive\n", "Did Not Learn: False\n", - "
surgery: GCaMP7b in DMS & DLS projecting SNc, fiber photometry probes in DMS & DLS
virus: AAV5-CAG-FLEX-jGCaMP7b-WPRE
" + "
source_script: Created using NeuroConv v0.4.11
source_script_file_name: /opt/anaconda3/envs/lerner_lab_to_nwb_env/lib/python3.12/site-packages/neuroconv/basedatainterface.py
surgery: GCaMP7b in DMS & DLS projecting SNc, fiber photometry probes in DMS & DLS
virus: AAV5-CAG-FLEX-jGCaMP7b-WPRE
" ], "text/plain": [ - "root pynwb.file.NWBFile at 0x4983882704\n", + "root pynwb.file.NWBFile at 0x4962263696\n", "Fields:\n", " acquisition: {\n", " commanded_voltage_series_dls ,\n", @@ -268,8 +268,8 @@ " experimenter: ['Seiler, Jillian L.' 'Cosme, Caitlin V.' 'Sherathiya, Venus N.'\n", " 'Schaid, Michael D.' 'Bianco, Joseph M.' 'Bridgemohan, Abigael S.'\n", " 'Lerner, Talia N.']\n", - " file_create_date: [datetime.datetime(2024, 5, 28, 14, 0, 34, 728285, tzinfo=tzoffset(None, -25200))]\n", - " identifier: f1b1251a-82a5-4ea1-92bc-203a4979679b\n", + " file_create_date: [datetime.datetime(2024, 6, 11, 13, 6, 38, 624732, tzinfo=tzoffset(None, -25200))]\n", + " identifier: 64828a03-4c9b-4212-ad60-d5486717b6df\n", " institution: Northwestern Unitersity\n", " keywords: \n", " lab: Lerner\n", @@ -289,7 +289,9 @@ " session_description: RI60 Training with concurrent fiber photometry, rewards delivered on left nose pokes\n", " session_id: FP_PS_2019-06-20T09-32-04\n", " session_start_time: 2019-06-20 09:32:04-05:51\n", - " subject: subject pynwb.file.Subject at 0x4985529744\n", + " source_script: Created using NeuroConv v0.4.11\n", + " source_script_file_name: /opt/anaconda3/envs/lerner_lab_to_nwb_env/lib/python3.12/site-packages/neuroconv/basedatainterface.py\n", + " subject: subject pynwb.file.Subject at 0x4963999104\n", "Fields:\n", " age: P10W/\n", " age__reference: birth\n", @@ -325,7 +327,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -357,12 +359,12 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { - "image/png": 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3IUEQBEEQBFFyNGjQALt378Ztt92G5557Dq1atcIdd9yBdevWYd68eQCkIhHvvvsuZsyYgVatWmHRokWqBWz16Nq1K5YvX45Vq1ahbdu26Natm8vcpfnz56OgoADt27fH2LFj8cYbb3j1dzK6d++O7du3495770WTJk0waNAgBAYGYt26dahatarud95991107NgRd955J7p3747OnTujefPmCAwMNH3eu+++G88++yxGjx6Ntm3bYuvWrZg4caK3flaZYXNYybQrYY4dO4bGjRtj3759aNWqlebzzMxMTJgwAT/++CMOHToEu92OSZMmYdWqVSoX6MmTJ+UHIy4uTnOc3Nxc5Obmyv+np6ejTp06SEtLq/BJbwRBEARBEKVNTk4OTp48ifr161sSsImKR2ZmJq677jrMmjULw4cPL+vmeIyrPpueno6IiAi3ukG5KTZRVFSEsWPHonPnzhol6qOPPkJoaChCQ0Px66+/Ij4+Hna7HQCQnJyMGjVqqPZn/ycnJ+uea/r06YiIiJD/6tSpUwK/iCAIgiAIgiAqNn///TeWLFmC48ePY/fu3RgyZAgAoF+/fmXcsrKn3ChSo0aNwv79+7F06VLNZ0OGDMHff/+NjRs3okmTJhg8eDBycnI8Ptf48eORlpYm/505c6Y4TScIgiAIgiCIa5aZM2fi+uuvR/fu3ZGZmYnNmzejWrVqZd2sMqdcrCM1evRorF69Gps2bULt2rU1nzPPUePGjXHzzTejSpUqWLlyJR544AHExMRoYk7Pnz8PQFq9WY+AgAAEBAR4/4cQBEEQBEEQxDVEXFycqn4BoVCmHimHw4HRo0dj5cqVWL9+PerXr2/qOw6HQ85x6tixI/bt26da8Cs+Ph7h4eFyOUeCIAiCIAiCIAhvUqYeqVGjRmHx4sX48ccfERYWJuc0RUREICgoCCdOnMCyZcvQo0cPVK9eHWfPnsVbb72FoKAg9OnTBwDQo0cPtGjRAg8//DDefvttJCcnY8KECRg1ahR5nQiCIAiCIAiCKBHK1CM1b948pKWloWvXrqhZs6b8t2zZMgBAYGAgNm/ejD59+qBRo0a47777EBYWhq1btyI6OhoA4Ovri9WrV8PX1xcdO3bEQw89hEceeQSvv/56Wf40giAIgiAIgiCuYcrUI+Wu8nqtWrXwyy+/uD1OvXr1TO1HEARBEARBEAThDcpN1T6CIAiCIAiCIIiKAilSBEEQBEEQBEEQFiFFiiAIgiAIgiBKGJvNhh9++KHSnLesGTZsGPr371+i5yBFiiAIgiAIgqiUlIawXVpMnjwZbdu21WxPSkpC7969i3XsDRs2oE+fPqhatSqCg4PRokULPPfcc/jvv/+KdVyeU6dOwWazYc+ePV47ZklDihRBEARBEARBXKPExMQUa0mgTz75BN27d0dMTAxWrFiBAwcO4OOPP0ZaWhpmzZrlxZaaIy8vr9TPaQQpUgRBEARBEIR3cTiAvMyy+XNTFdoV3333HVq3bo2goCBUrVoV3bt3R2ZmJgCgqKgIr7/+OmrXro2AgAC0bdsWa9askb+bl5eH0aNHo2bNmggMDES9evUwffp01fGZdygoKAgNGjTAd999p/r8zJkzGDx4MCIjIxEVFYV+/frh1KlT8ucJCQm48cYbERISgsjISHTu3BmnT5/GwoULMWXKFOzduxc2mw02mw0LFy4EoA3tO3v2LB544AFERUUhJCQEHTp0wJ9//ql7Pc6ePYsxY8ZgzJgxmD9/Prp27YrY2Fh06dIFn3/+OSZNmiTvu2LFCrRs2RIBAQGIjY3VKFmxsbGYNm0aHnvsMYSFhaFu3br49NNP5c/r168PAIiLi4PNZkPXrl0BKF7DN998E7Vq1ULTpk0BAPv27UO3bt3kezVixAhkZGQY3doSoUzLnxMEQRAEQRDXIPlZwLRaZXPuV84B9hDLX0tKSsIDDzyAt99+GwMGDMDVq1exefNmebmeOXPmYNasWfjkk08QFxeH+fPn4+6778a///6Lxo0bY+7cuVi1ahW+/fZb1K1bF2fOnMGZM2dU55g4cSLeeustzJkzB19//TXuv/9+7Nu3D82bN0d+fj569uyJjh07YvPmzfDz88Mbb7yBXr164Z9//oGPjw/69++PJ554AkuWLEFeXh7++usv2Gw23Hfffdi/fz/WrFmD33//HQAQERGh+Y0ZGRm49dZbcd1112HVqlWIiYnB7t27UVRUpHtNli9fjry8PLz44ou6n0dGRgIAdu3ahcGDB2Py5Mm47777sHXrVjz11FOoWrUqhg0bJu8/a9YsTJ06Fa+88gq+++47jBw5ErfeeiuaNm2Kv/76CzfeeCN+//13tGzZEna7Xf7eunXrEB4ejvj4eABAZmamfK127NiBCxcu4PHHH8fo0aNlBbI0IEWKIAiCIAiCqPQkJSWhoKAAAwcORL169QAArVu3lj+fOXMmXnrpJdx///0AgBkzZmDDhg2YPXs2PvzwQyQmJqJx48a45ZZbYLPZ5GPw3HvvvXj88ccBAFOnTkV8fDzef/99fPTRR1i2bBmKiorw+eefw2azAQAWLFiAyMhIJCQkoEOHDkhLS8Odd96Jhg0bAgCaN28uHzs0NBR+fn6IiYkx/I2LFy/GxYsXsWPHDkRFRQEAGjVqZLj/0aNHER4ejpo1a7q8du+++y5uv/12TJw4EQDQpEkTHDhwAO+8845KkerTpw+eeuopAMBLL72E9957Dxs2bEDTpk1RvXp1AEDVqlU1vyEkJASff/65rFx99tlnyMnJwVdffYWQEElp/uCDD3DXXXdhxowZqFGjhsv2egtSpAiCIAiCIAjv4h8seYbK6twecP311+P2229H69at0bNnT/To0QP33HMPqlSpgvT0dJw7dw6dO3dWfadz587Yu3cvACkE7Y477kDTpk3Rq1cv3HnnnejRo4dq/44dO2r+Z8UV9u7di2PHjiEsLEy1T05ODo4fP44ePXpg2LBh6NmzJ+644w50794dgwcPdqvk8OzZswdxcXGyEuUOh8MhK3WuOHjwIPr166fa1rlzZ8yePRuFhYXw9fUFALRp00b+3GazISYmBhcuXHB7/NatW6s8VAcPHsT1118vK1HsfEVFRTh8+HCpKVKUI0UQBEEQBEF4F5tNCq8riz8Tgr8evr6+iI+Px6+//ooWLVrg/fffR9OmTXHy5ElT32/Xrh1OnjyJqVOnIjs7G4MHD8Y999xj+vwZGRlo37499uzZo/o7cuQIHnzwQQCSh2rbtm3o1KkTli1bhiZNmmD79u2mzxEUFGR6X0DyLKWlpSEpKcnS94zw9/dX/W+z2QzDCnl4hak8QYoUQRAEQRAEQUAS7Dt37owpU6bg77//ht1ux8qVKxEeHo5atWphy5Ytqv23bNmCFi1ayP+Hh4fjvvvuw2effYZly5ZhxYoVSElJkT8XlZ7t27fL4Xnt2rXD0aNHER0djUaNGqn++HynuLg4jB8/Hlu3bkWrVq2wePFiAIDdbkdhYaHL39emTRvs2bNH1SZX3HPPPbDb7Xj77bd1P09NTQUghRjqXZsmTZrI3ih3MI+Tu9/Azrd37165EAg7n4+Pj1yMojQgRYogCIIgCIKo9Pz555+YNm0adu7cicTERHz//fe4ePGirOi88MILmDFjBpYtW4bDhw/j5Zdfxp49e/DMM88AkPKElixZgkOHDuHIkSNYvnw5YmJi5IIMgFS8Yf78+Thy5Ahee+01/PXXXxg9ejQAYMiQIahWrRr69euHzZs34+TJk0hISMCYMWNw9uxZnDx5EuPHj8e2bdtw+vRp/Pbbbzh69KjcvtjYWJw8eRJ79uzBpUuXkJubq/mNDzzwAGJiYtC/f39s2bIFJ06cwIoVK7Bt2zbda1KnTh289957mDNnDoYPH46NGzfi9OnT2LJlC5588klMnToVAPDcc89h3bp1mDp1Ko4cOYIvv/wSH3zwAZ5//nnT1z86OhpBQUFYs2YNzp8/j7S0NMN9hwwZgsDAQAwdOhT79+/Hhg0b8PTTT+Phhx8utbA+gBQpgiAIgiAIgkB4eDg2bdqEPn36oEmTJpgwYQJmzZolL2Y7ZswYjBs3Ds899xxat26NNWvWYNWqVWjcuDEAICwsDG+//TY6dOiAG264AadOncIvv/wCHx9F3J4yZQqWLl2KNm3a4KuvvsKSJUtkj1ZwcDA2bdqEunXrYuDAgWjevDmGDx+OnJwchIeHIzg4GIcOHcKgQYPQpEkTjBgxAqNGjcKTTz4JABg0aBB69eqF2267DdWrV8eSJUs0v9Fut+O3335DdHQ0+vTpg9atW+Ott95y6TV66qmn8Ntvv+G///7DgAED0KxZMzz++OMIDw+XFaV27drh22+/xdKlS9GqVStMmjQJr7/+uqrQhDv8/Pwwd+5cfPLJJ6hVq5Ym54onODgYa9euRUpKCm644Qbcc889uP322/HBBx+YPp83sDkcxSi2f42Qnp6OiIgIpKWlITw8vKybQxAEQRAEUaHIycnByZMnUb9+fQQGBpZ1cwjCLa76rFndgDxSBEEQBEEQBEEQFiFFiiAIgiAIgiAIwiKkSBEEQRAEQRAEQViEFCmCIAiCIAiCIAiLkCJVWcjMlBaos9mk9wRBVHgy8zJhm2KDbYoNmXn0XBOlCM0pBEEQpEgRBEEQBEEQBEFYhRQpgiAIgiAIgiAIi5AiRRAEQRAEQRAEYRFSpAiCIAiCIAiCICxCihRBEARBEARBeIDD4cCIESMQFRUFm82GPXv2lHWTygWxsbGYPXt2WTejxCFFiiAIgiAIgqiUDBs2DP379/f4+2vWrMHChQuxevVqJCUloVWrVrDZbPjhhx/cftdmsyEwMBCnT59Wbe/fvz+GDRvmcZuKy6lTp2Cz2eS/qlWrokePHvj777/LrE3lFVKkCIIgCIIgCMIDjh8/jpo1a6JTp06IiYmBn5+fpe/bbDZMmjSphFpXPH7//XckJSVh7dq1yMjIQO/evZGamlrWzSpXkCJFEARBEARBlAyZmaX752X279+P3r17IzQ0FDVq1MDDDz+MS5cuAZC8WU8//TQSExNhs9kQGxuL2NhYAMCAAQPkba4YPXo0vvnmG+zfv99wn9zcXIwZMwbR0dEIDAzELbfcgh07dsifX7lyBUOGDEH16tURFBSExo0bY8GCBfLnZ86cweDBgxEZGYmoqCj069cPp06dcvvbq1atipiYGHTo0AEzZ87E+fPn8eeffwIAVqxYgZYtWyIgIACxsbGYNWuWy2N9/vnniIyMxLp16wC4vq4VCVKkCIIgCIIgiJIhNLR0/7xIamoqunXrhri4OOzcuRNr1qzB+fPnMXjwYADAnDlz8Prrr6N27dpISkrCjh07ZAVnwYIF8jZXdO7cGXfeeSdefvllw31efPFFrFixAl9++SV2796NRo0aoWfPnkhJSQEATJw4EQcOHMCvv/6KgwcPYt68eahWrRoAID8/Hz179kRYWBg2b96MLVu2IDQ0FL169UJeXp7paxEUFAQAyMvLw65duzB48GDcf//92LdvHyZPnoyJEydi4cKFut99++238fLLL+O3337D7bff7va6ViSs+R8JgiAIgiAIohLwwQcfIC4uDtOmTZO3zZ8/H3Xq1MGRI0fQpEkThIWFwdfXFzExMarvRkZGarYZMX36dLRp0wabN2/G//3f/6k+y8zMxLx587Bw4UL07t0bAPDZZ58hPj4eX3zxBV544QUkJiYiLi4OHTp0AACVF2zZsmUoKirC559/DpvNBkBS8iIjI5GQkIAePXq4bV9qaiqmTp2K0NBQ3HjjjRg3bhxuv/12TJw4EQDQpEkTHDhwAO+8844mt+ull17C119/jY0bN6Jly5YAzF3XigIpUgRBEARBEETJkJFR1i3wmL1792LDhg0I1fF0HT9+3GsCf4sWLfDII4/g5ZdfxpYtWzTnyc/PR+fOneVt/v7+uPHGG3Hw4EEAwMiRIzFo0CDs3r0bPXr0QP/+/dGpUyf5Nxw7dgxhYWGq4+bk5OD48eMu29WpUyf4+PggMzMTDRo0wLJly1CjRg0cPHgQ/fr1U+3buXNnzJ49G4WFhfD19QUAzJo1C5mZmdi5cycaNGgg71ta17U0IEWKIAiCIAiCKBlCQsq6BR6TkZGBu+66CzNmzNB8VrNmTa+ea8qUKWjSpImpan8ivXv3xunTp/HLL78gPj4et99+O0aNGoWZM2ciIyMD7du3x6JFizTfq169usvjLlu2DC1atEDVqlURGRlpuV3/93//h59//hnffvutKnSxNK9rSUOKFEEQBEEQBEEItGvXDitWrEBsbKylanz+/v4oLCy0dK46depg9OjReOWVV9CwYUN5e8OGDWG327FlyxbUq1cPgJT3tGPHDowdO1ber3r16hg6dCiGDh2K//u//8MLL7yAmTNnol27dli2bBmio6MRHh5uuU18WxjNmzfXeM62bNmCJk2ayN4oALjxxhsxevRo9OrVC35+fnj++ecBeH5dyyNUbIIgCIIgCIKotKSlpWHPnj2qvzNnzmDUqFFISUnBAw88gB07duD48eNYu3YtHn30UZeKUmxsLNatW4fk5GRcuXLFdDvGjx+Pc+fO4ffff5e3hYSEYOTIkXjhhRewZs0aHDhwAE888QSysrIwfPhwAMCkSZPw448/4tixY/j333+xevVqNG/eHAAwZMgQVKtWDf369cPmzZtx8uRJJCQkYMyYMTh79qxH1+u5557DunXrMHXqVBw5cgRffvklPvjgA1lR4unUqRN++eUXTJkyRV6g19PrWh4hRYogCIIgCIKotCQkJCAuLk71N2XKFNSqVQtbtmxBYWEhevTogdatW2Ps2LGIjIyEj4+xCD1r1izEx8ejTp06iIuLM92OqKgovPTSS8jJyVFtf+uttzBo0CA8/PDDaNeuHY4dO4a1a9eiSpUqAAC73Y7x48ejTZs26NKlC3x9fbF06VIAQHBwMDZt2oS6deti4MCBaN68OYYPH46cnBzLHipGu3bt8O2332Lp0qVo1aoVJk2ahNdff91wEeFbbrkFP//8MyZMmID333/f4+taHrE5HA5HWTeirElPT0dERATS0tI87lTlnsxMpSxoRkaFjlkmCEIiMy8TodOl5zpjfAZC7PRcE6UEzSmEQE5ODk6ePIn69esjMDCwrJtDEG5x1WfN6gYVS+0jCIIgCIIgCIIoB5AiRRAEQRAEQRAEYRFSpAiCIAiCIAiCICxCihRBEARBEARBEIRFylSRmj59Om644QaEhYUhOjoa/fv3x+HDh+XPU1JS8PTTT6Np06YICgpC3bp1MWbMGKSlpamOk5iYiL59+yI4OBjR0dF44YUXUFBQUNo/hyAIgiAIgiCISkKZKlIbN27EqFGjsH37dsTHxyM/Px89evRAZmYmAODcuXM4d+4cZs6cif3792PhwoVYs2aNXDcfAAoLC9G3b1/k5eVh69at+PLLL7Fw4UJMmjSprH4WQRAEQRAEQRDXOOWq/PnFixcRHR2NjRs3okuXLrr7LF++HA899BAyMzPh5+eHX3/9FXfeeSfOnTuHGjVqAAA+/vhjvPTSS7h48SLsdrvb81L5c4IgKiJU/pwoM2hOIQSo/DlR0bjmyp+zkL2oqCiX+4SHh8PPzw8AsG3bNrRu3VpWogCgZ8+eSE9Px7///qt7jNzcXKSnp6v+CIIgCIIgCIIgzFJuFKmioiKMHTsWnTt3RqtWrXT3uXTpEqZOnYoRI0bI25KTk1VKFAD5/+TkZN3jTJ8+HREREfJfnTp1vPQrCIIgCIIgCIKoDJQbRWrUqFHYv38/li5dqvt5eno6+vbtixYtWmDy5MnFOtf48eORlpYm/505c6ZYxyMIgiAIgiAqHw6HAyNGjEBUVBRsNhv27NlT1k0qFU6dOlWpfq8R5UKRGj16NFavXo0NGzagdu3ams+vXr2KXr16ISwsDCtXroS/v7/8WUxMDM6fP6/an/0fExOje76AgACEh4er/giCIAiCIIjKxbBhw9C/f3+Pv79mzRosXLgQq1evRlJSElq1agWbzYYffvjB7XdtNpv8Fx4ejhtuuAE//vijx20hSp8yVaQcDgdGjx6NlStXYv369ahfv75mn/T0dPTo0QN2ux2rVq3SJIN17NgR+/btw4ULF+Rt8fHxCA8PR4sWLUr8NxAEQRAEQRCVk+PHj6NmzZro1KkTYmJi5Bx+syxYsABJSUnYuXMnOnfujHvuuQf79u0rodZaJy8vr6ybUK4pU0Vq1KhR+Oabb7B48WKEhYUhOTkZycnJyM7OBqAoUZmZmfjiiy+Qnp4u71NYWAgA6NGjB1q0aIGHH34Ye/fuxdq1azFhwgSMGjUKAQEBZfnzCIIgCIIgKjWZeZml+udt9u/fj969eyM0NBQ1atTAww8/jEuXLgGQvFlPP/00EhMTYbPZEBsbi9jYWADAgAED5G2uiIyMRExMDJo0aYKpU6eioKAAGzZskD8/c+YMBg8ejMjISERFRaFfv344deqU3DYfHx9cvHgRgLT+qo+PD+6//375+2+88QZuueUWANKSQcOHD0f9+vURFBSEpk2bYs6cOar2MA/dm2++iVq1aqFp06YAgL/++gtxcXEIDAxEhw4d8Pfff3t8Ta8lrKnNXmbevHkAgK5du6q2L1iwAMOGDcPu3bvx559/AgAaNWqk2ufkyZOIjY2Fr68vVq9ejZEjR6Jjx44ICQnB0KFD8frrr5fKbyAIgiAIgiD0YUs0lBaO17y3qk9qaiq6deuGxx9/HO+99x6ys7Px0ksvYfDgwVi/fj3mzJmDhg0b4tNPP8WOHTvg6+sLAIiOjsaCBQvQq1cveZs7CgoK8MUXXwCAvHRPfn4+evbsiY4dO2Lz5s3w8/PDG2+8gV69euGff/5By5YtUbVqVWzcuBH33HMPNm/eLP/P2LhxoyxnFxUVoXbt2li+fDmqVq2KrVu3YsSIEahZsyYGDx4sf2fdunUIDw9HfHw8ACAjIwN33nkn7rjjDnzzzTc4efIknnnmmWJf32uBMlWk3C1h1bVrV7f7AEC9evXwyy+/eKtZBEEQBEEQRCXngw8+QFxcHKZNmyZvmz9/PurUqYMjR46gSZMmCAsLg6+vryYvn3ma3PHAAw/A19cX2dnZKCoqQmxsrKzULFu2DEVFRfj8889hs9kASM6GyMhIJCQkoEePHujSpQsSEhJwzz33ICEhAY8++ig+//xzHDp0CA0bNsTWrVvx4osvAgD8/f0xZcoU+dz169fHtm3b8O2336oUqZCQEHz++eeyQvfpp5+iqKgIX3zxBQIDA9GyZUucPXsWI0eO9PDKXjuUqSJFEARBEARBXLtkjM8o6yZ4zN69e7FhwwaEhmq9asePH0eTJk2KfY733nsP3bt3x4kTJ/Dss89i7ty58nqqe/fuxbFjxxAWFqb6Tk5ODo4fPw4AuPXWW/Hpp58CkLxP06ZNw5EjR5CQkICUlBTk5+ejc+fO8nc//PBDzJ8/H4mJicjOzkZeXh7atm2rOn7r1q1lJQoADh48iDZt2qjqFHTs2LHYv/1agBQpgiAIgiAIokQIsYeUdRM8JiMjA3fddRdmzJih+axmzZpeOUdMTAwaNWqERo0aYcGCBejTpw8OHDiA6OhoZGRkoH379li0aJHme9WrVwcgRW+NHTsWR48exYEDB3DLLbfg0KFDSEhIwJUrV9ChQwcEBwcDAJYuXYrnn38es2bNQseOHREWFoZ33nlHTqNhhIRU3HtW2pAiRRAEQRAEQRAC7dq1w4oVKxAbG2upGp+/v79cFM0KN954I9q3b48333wTc+bMQbt27bBs2TJER0cbLtXTunVrVKlSBW+88Qbatm2L0NBQdO3aFTNmzMCVK1dUdQi2bNmCTp064amnnpK3Mc+WK5o3b46vv/4aOTk5sldq+/btln/ftUi5WEeKIAiCIAiCIMqCtLQ07NmzR/V35swZjBo1CikpKXjggQewY8cOHD9+HGvXrsWjjz7qUlGKjY3FunXrkJycjCtXrlhqy9ixY/HJJ5/gv//+w5AhQ1CtWjX069cPmzdvxsmTJ5GQkIAxY8bg7NmzAKS1qLp06YJFixbJSlObNm2Qm5uLdevW4dZbb5WP3bhxY+zcuRNr167FkSNHMHHiROzYscNtmx588EHYbDY88cQTOHDgAH755RfMnDnT0u+6ViFFiiAIgiAIgqi0JCQkIC4uTvU3ZcoU1KpVC1u2bEFhYSF69OiB1q1bY+zYsYiMjISPj7EIPWvWLMTHx6NOnTqIi4uz1JZevXqhfv36ePPNNxEcHIxNmzahbt26GDhwIJo3b47hw4cjJydH5aG69dZbUVhYKCtSPj4+6NKlC2w2myo/6sknn8TAgQNx33334aabbsLly5dV3ikjQkND8dNPP2Hfvn2Ii4vDq6++qhvuWBmxOcyUxbvGSU9PR0REBNLS0gxdpxWezEyAJUtmZAAU/0oQFZ7MvEy5tHDG+IwKnYtAVDBoTiEEcnJycPLkSdSvX19VlIAgyiuu+qxZ3YA8UgRBEARBEARBEBYhRYogCIIgCIIgCMIipEgRBEEQBEEQBEFYhBQpgiAIgiAIgiAIi5AiRRAEQRAEQRAEYRFSpAiCIAiCIAiCICxCihRBEARBEARBEIRFSJEiCIIgCIIgCIKwiF9ZN4AgCIIgCIK4NsnOBvLySu98djsQFFR65yMqN6RIEQRBEARBEF4nOxv48UfgypXSO2eVKkC/fqRMWWHYsGFITU3FDz/8UNZNqXBQaB9BEARBEAThdfLyJCUqKEhScEr6LyhIOp8VD9iwYcPQv3//Yv3OyZMnw2azwWazwdfXF3Xq1MGIESOQkpJSrOOWFxISEuTfZ7PZUKNGDQwaNAgnTpzwynFTU1O909AygDxSBEEQBEEQRIkRGAiEhJTOubKzS+c8Ii1btsTvv/+OwsJCHDx4EI899hjS0tKwbNmysmmQDvn5+fD39/f4+4cPH0ZYWBiOHj2KESNG4K677sI///wDX19fj9pyLUAeKYIgCIIgCIIA0LVrV4wZMwYvvvgioqKiEBMTg8mTJ7v9np+fH2JiYnDdddehe/fuuPfeexEfH6/a5/PPP0fz5s0RGBiIZs2a4aOPPpI/u+eeezB69Gj5/7Fjx8Jms+HQoUMAgLy8PISEhOD3338HAKxZswa33HILIiMjUbVqVdx55504fvy4/P1Tp07BZrNh2bJluPXWWxEYGIhFixahsLAQ48aNk7/34osvwuFwmLo20dHRqFmzJrp06YJJkybhwIEDOHbsGABg3rx5aNiwIex2O5o2bYqvv/5a9V2bzYZ58+bh7rvvRkhICJ544gncdtttAIAqVarAZrNh2LBhptpRniBFiiAIgiAIgiCcfPnllwgJCcGff/6Jt99+G6+//rpGKXLFqVOnsHbtWtjtdnnbokWLMGnSJLz55ps4ePAgpk2bhokTJ+LLL78EANx6661ISEiQ99+4cSOqVasmb9uxYwfy8/PRqVMnAEBmZibGjRuHnTt3Yt26dfDx8cGAAQNQVFSkasvLL7+MZ555BgcPHkTPnj0xa9YsLFy4EPPnz8cff/yBlJQUrFy50vI1CnImoeXl5WHlypV45pln8Nxzz2H//v148skn8eijj2LDhg2q70yePBkDBgzAvn37MGXKFKxYsQKA5OlKSkrCnDlzLLejrKHQPoIgCIIgCIJw0qZNG7z22msAgMaNG+ODDz7AunXrcMcddxh+Z9++fQgNDUVhYSFycnIAAO+++678+WuvvYZZs2Zh4MCBAID69evjwIED+OSTTzB06FB07doVzzzzDC5evAg/Pz8cOHAAEydOREJCAv73v/8hISEBN9xwA4KDgwEAgwYNUp1//vz5qF69Og4cOIBWrVrJ28eOHSufEwBmz56N8ePHy9s+/vhjrF271tL1SUpKwsyZM3HdddehadOm+N///odhw4bhqaeeAgCMGzcO27dvx8yZM2WvEwA8+OCDePTRR+X/T548CUDydEVGRlpqQ3mBPFIEQRAEQRAE4aRNmzaq/2vWrIkLFy64/E7Tpk2xZ88e7NixAy+99BJ69uyJp59+GoDkPTp+/DiGDx+O0NBQ+e+NN96Qw/FatWqFqKgobNy4EZs3b0ZcXBzuvPNObNy4EYDkoeratat8vqNHj+KBBx5AgwYNEB4ejtjYWABAYmKiql0dOnSQ36elpSEpKQk33XSTvM3Pz0+1jytq166NkJAQ1KpVC5mZmVixYgXsdjsOHjyIzp07q/bt3LkzDh48aNiWawXySBEEQRAEQRCEE7Egg81m04TMidjtdjRq1AgA8NZbb6Fv376YMmUKpk6dioyMDADAZ599plJiAMiFGmw2G7p06YKEhAQEBASga9euaNOmDXJzc7F//35s3boVzz//vPy9u+66C/Xq1cNnn32GWrVqoaioCK1atUKeULIwxItVPjZv3ozw8HBER0cjLCzM8ve92ZbyAnmkCIIgCIIgCMKLTJgwATNnzsS5c+dQo0YN1KpVCydOnECjRo1Uf/Xr15e/w/KkEhIS0LVrV/j4+KBLly545513kJubK3t9Ll++jMOHD2PChAm4/fbb0bx5c1wxsVhXREQEatasiT///FPeVlBQgF27dpn6TfXr10fDhg01SlTz5s2xZcsW1bYtW7agRYsWLo/HcsgKCwtNnb88Qh4pgiAIgiAIosRwpgxdM+cxQ8eOHdGmTRtMmzYNH3zwAaZMmYIxY8YgIiICvXr1Qm5uLnbu3IkrV65g3LhxAKSKgc8++yzsdjtuueUWedvzzz+PG264QfboVKlSBVWrVsWnn36KmjVrIjExES+//LKpdj3zzDN466230LhxYzRr1gzvvvtusddxeuGFFzB48GDExcWhe/fu+Omnn/D999/LFQaNqFevHmw2G1avXo0+ffogKCgIoaGhxWpLaUOKFEEQBEEQBOF17HZpodwrV0pvfacqVaTzlgeeffZZDBs2DC+99BIef/xxBAcH45133sELL7yAkJAQtG7dGmPHjpX3b926NSIjI9GkSRNZoejatSsKCwtV+VE+Pj5YunQpxowZg1atWqFp06aYO3euah8jnnvuOSQlJWHo0KHw8fHBY489hgEDBiAtLc3j39m/f3/MmTMHM2fOxDPPPIP69etjwYIFbttz3XXXYcqUKXj55Zfx6KOP4pFHHsHChQs9bkdZYHOYLR5/DZOeno6IiAikpaUhPDy8rJtTMmRmAkzLz8govZXxCIIoMTLzMhE6XXquM8ZnIMROzzVRStCcQgjk5OTg5MmTqF+/PgIDA+Xt2dmAkLZTotjtgLMyN0G4xKjPAuZ1A/JIEQRBEARBECVCUBApNsS1CxWbIAiCIAjCc47EAzObAsfXl3VLCIIgShXLHqnc3Fz8+eefOH36NLKyslC9enXExcWpqo4QBEEQBFFJ+PUFIO8CsHoc8Myesm4NQRBEqWFakdqyZQvmzJmDn376Cfn5+YiIiEBQUBBSUlKQm5uLBg0aYMSIEfjf//7nUW15giAIgiAqIBnnAbsNuHKyrFtCEARRqpgK7bv77rtx3333ITY2Fr/99huuXr2Ky5cv4+zZs8jKysLRo0cxYcIErFu3Dk2aNEF8fHxJt5sgCIIgCIIoZ1ANM6Ki4I2+asoj1bdvX6xYsUKz0jOjQYMGaNCgAYYOHYoDBw4gKSmp2A0jCIIgCIIgKgZMRszKykIQVZcgKgBZWVkAYKjfmMGUIvXkk0+aPmCLFi3crmRMEARBEMQ1gs0XQFFZt4IoY3x9fREZGYkLFy4AAIKDg2Gz2cq4VQShxeFwICsrCxcuXEBkZCR8fX09PhaVPycIgiAIwnN8SJEiJGJiYgBAVqYIojwTGRkp91lP8ZoiNXToUJw5cwbr11P5U4IgCIKoNPj4Asgv61YQ5QCbzYaaNWsiOjoa+fnUJ4jyi7+/f7E8UQyvKVLXXXcdfHxoWSqCIAiCqFTYii+MENcWvr6+XhFSCaK84zVFatq0ad46FEEQBEEQFQUfH4rsIwiiUkIuJIIgCIIgPIc8UgRBVFIse6Qee+wxl5/Pnz/f9LGmT5+O77//HocOHUJQUBA6deqEGTNmoGnTpvI+n376KRYvXozdu3fj6tWruHLlCiIjI1XHSUlJwdNPP42ffvoJPj4+GDRoEObMmYPQ0FBLv40gCIIgCIvYyCZLEETlxPLod+XKFdXfhQsXsH79enz//fdITU21dKyNGzdi1KhR2L59O+Lj45Gfn48ePXogMzNT3icrKwu9evXCK6+8YnicIUOG4N9//0V8fDxWr16NTZs2YcSIEVZ/GkEQBEEQVvEhjxRBEJUTyx6plStXarYVFRVh5MiRaNiwoaVjrVmzRvX/woULER0djV27dqFLly4AgLFjxwIAEhISdI9x8OBBrFmzBjt27ECHDh0AAO+//z769OmDmTNnolatWpbaRBAEQRCEFcgjRRBE5cQro5+Pjw/GjRuH9957r1jHSUtLAwBERUWZ/s62bdsQGRkpK1EA0L17d/j4+ODPP//U/U5ubi7S09NVfwRBEARBeACF9hEEUUnx2uh3/PhxFBQUePz9oqIijB07Fp07d0arVq1Mfy85ORnR0dGqbX5+foiKikJycrLud6ZPn46IiAj5r06dOh63myAIgiAqNaRIEQRRSbEc2jdu3DjV/w6HA0lJSfj5558xdOhQjxsyatQo7N+/H3/88YfHxzDL+PHjVb8jPT2dlCmCIAiC8ASbTXlfVCSVQycIgqgEWFak/v77b9X/Pj4+qF69OmbNmuW2op8Ro0ePlotE1K5d29J3Y2JicOHCBdW2goICpKSkICYmRvc7AQEBCAgI8KitBEEQ5ZbsK8DB1UDLAUAAVS0lSgneI1WUD/jQ/EoQROXAsiK1YcMGr53c4XDg6aefxsqVK5GQkID69etbPkbHjh2RmpqKXbt2oX379gCA9evXo6ioCDfddJPX2npN8dNY4Pq7geZ3lXVLCILwJqufBf5dCaQmAt1eLevWEJUF3iNVmA/4kSJFEETloEz976NGjcI333yDxYsXIywsDMnJyUhOTkZ2dra8T3JyMvbs2YNjx44BAPbt24c9e/YgJSUFANC8eXP06tULTzzxBP766y9s2bIFo0ePxv33308V+4z4Zxmw7KGybgVBEN7mX2dV1a1zy7YdROWC90gV5pVdOwiCIEoZrylSr7zyiuXQvnnz5iEtLQ1du3ZFzZo15b9ly5bJ+3z88ceIi4vDE088AQDo0qUL4uLisGrVKnmfRYsWoVmzZrj99tvRp08f3HLLLfj000+988MIgiAqGgU5Zd0CorJSmF/WLSAIgig1LIf2GfHff//hzJkzlr7jcDjc7jN58mRMnjzZ5T5RUVFYvHixpXMTABwOdUgGQRAVl6Kism4BUVkpKuTekyJFEETlwWuK1JdffumtQxGlRWEexbITxLVC9hX1/4X5gK9/2bSFqFzwSjyF9hEEUYmgGqWVmbxM488cDuDqeemVIIjyzy/Pqf/PvFg27SAqHw7OI0WhfQRBVCI88khlZmZi48aNSExMRF6e2vo0ZswYrzSMKAXyMoHgKP3P9i0Hvn8C6P0OcNOI0m0XQRDWOboWABeqm3EBCKeCO0Qp4ChQ3pMiRRBEJcKjdaT69OmDrKwsZGZmIioqCpcuXUJwcDCio6NJkapIuEpI/14q7oFfXyBFiiAqIuSRIkqLIl6RotA+giAqD5ZD+5599lncdddduHLlCoKCgrB9+3acPn0a7du3x8yZM0uijURJUZBb1i0gCKKkEHOmCKKkKKLQPoIgKieWFak9e/bgueeeg4+PD3x9fZGbm4s6derg7bffxiuvvFISbSRKikJSpAjimiUnraxbQFQWqGofQRCVFMuKlL+/P3x8pK9FR0cjMTERABAREWG5/DlRxhRQCAZBXLNkp5Z1C4jKgqrYBM0rBEFUHiznSMXFxWHHjh1o3Lgxbr31VkyaNAmXLl3C119/jVatWpVEG4mSgjxSBHHt0fYhYM83QE5qWbeEqCwUFQK+zvcU2kcQRCXCskdq2rRpqFmzJgDgzTffRJUqVTBy5EhcvHgRn376qdcbSJQg5JEiiGuLTmOAqFjpPYX2EaUF5UgRBFFJseyR6tChg/w+Ojoaa9as8WqDiFKEPFIEcW3h6w8ERkrvySNFlBaOQsil9ym0jyCISgQtyFuZoap9BHFt4cMrUuSRIsoA8kgRBFGJMKVI9erVC9u3b3e739WrVzFjxgx8+OGHxW4YUQqQ5ZAgri18/YDACOl9ThpQWAA4HGXbJqJyQVX7CIKoRJgK7bv33nsxaNAgRERE4K677kKHDh1Qq1YtBAYG4sqVKzhw4AD++OMP/PLLL+jbty/eeeedkm434Q3II0UQ1xY+foA9WHqfeQl4Pw4IrQE89hvgQwEIRClABjqCICoRphSp4cOH46GHHsLy5cuxbNkyfPrpp0hLk8JGbDYbWrRogZ49e2LHjh1o3rx5iTaY8CI04RHEtYWPP+AfJL1P/096TU0E0s4AVeqVXbuIygOF9hEEUYkwXWwiICAADz30EB566CEAQFpaGrKzs1G1alX4+/uXWAOJEoQ8UgRxbeHrD/gHa7ennSVFiigdSJEiCKIS4XGsR0REBGJiYkiJqsiYrdpHORYEUTHw8VM8UjzZV0q/LRUBvmw34R0o0oEgrk0KC4ClQ4B1r5d1S8oVFDRfmTG7jlRRQcm2gyAI7+Djp++Ryk0v/baUd3YuAKbXBo6tK+uWXFuQR4ogrk3+2wUcWg1snkVGKA5SpCozZj1SZGEkiIqBrz/gF6jdnkOKlIafnwPys4AfnirrllxbUNU+grg24WXG7NQya0Z5gxSpyoyRR0oM5SNFiiAqBnyxCR5aU0qLw2lRJY+7d6H5giCuTfhnOz+z7NpRziBFqjJj5JFyFAn7kYWRICoEvn6SV8pHyF3Nyyib9lQEbLaybsG1Bc0XREUkPxtYeCew+tmybkn5JS9L/30lxyNFKjU1FZ9//jnGjx+PlJQUAMDu3bvx33//ebVxRAlj5JESLbSeWBj/2wX8OFpay4YgKgsFecC5v8uuQAtToMQ8qYKc0m9LRYE8KN6FFCmiInLqD+DUZmDnfKmoQnmhqMj9PqVFXqb++0qOZUXqn3/+QZMmTTBjxgzMnDkTqampAIDvv/8e48eP93b7iJLESLjyhiK1/FHg76+BjW9b/y5BVFS2fwh82rXs+r2Pc0ULMbwvP7v021JRKE9C07UAKaZERYTvt+WhyqnDASzoA3zQofx4f/hwPgrtk7GsSI0bNw7Dhg3D0aNHERioJDX36dMHmzZt8mrjiBLGKLRPtCh6YmFMPS29/rvS+ncJoqKybqr0mjCtbM7va6BIkUeKKC3II0VURPgxsjwoCenngNNbgJTjQPI/Zd0aCV6hI+OcjGVFaseOHXjyySc126+77jokJyd7pVFEKZFv5JESylq6sjA6HEDidiD3qsHn5cgtTRAljaOMS8LKoX3kkTINjVHehar2ERURfowsD+NlTqryvrykSORzihR5nmUsK1IBAQFIT9eW0j1y5AiqV6/ulUYRpYTRYGEltO/flcD8nsDqcfqfUyI3QZQevgaKFHmkXEALjnsVErCIiggvD5WHUDq+0mp2Stm1g4fPiyowuXxOJcCyInX33Xfj9ddfR36+ZHWy2WxITEzESy+9hEGDBnm9gUQJYuS+Fi2KrkI19iySXvd96502EQThOSxHKiBMvd3I+0wQ3oZyzoiKCO9tKQ+hfSpFqhzkbAGCR4o8zwzLitSsWbOQkZGB6OhoZGdn49Zbb0WjRo0QFhaGN998syTaSJQU3vBIuRXQyCNFEKWGkSJVUA5CVcorZVVh8VqFPFJERYSXh8pDH+YXUS8PoYaA2lNnlGNfCfGz+oWIiAjEx8djy5Yt2Lt3LzIyMtCuXTt07969JNpHlCRG7msrOVLlYcAhCEKChfb5iTlS5JEyhhQpr0JzAlERKW/eFl5RKS+lxnlPXXm4RuUEy4oUo3PnzujcubM320KUNvkGipSVqn18cr3Doc2JstGazwRRaogL8TLII6WG90KRR8q7iBENBFERUHmkyoGSwD9H5dEjRTlSMpal3DFjxmDu3Lma7R988AHGjh3rjTYRpYU3Qvt87dr9+Bh5XwPBjiCI4qEXNy8/b4JyQB4pNaoxjhQpr0IeKaIiUt4q0hWWQ0Uqn0L79LCsSK1YsULXE9WpUyd89913XmkUUcIwL1F+lr41VqNIubDO8IpSbob0ylu/fXw9ayNBEMbsWQLMiAUW3qXezp43/2D1dqrap6Y8WJyvVcqDEEoQVnHnkcrPAQpKsW/zRb/KQ/ELQB1iSGOojGVF6vLly4iIiNBsDw8Px6VL5aTWPeEaORHdoS9gWcmRKuLWYGEPO2/9ptA+gvA+f7wnvZ7bqd7OQvuuv196rRUnvZZ2GEZREbDkAeNlEcoaXkih0D7vQlX7So8LB/XXcHQ4SNC1iqtiE7lXgblxwCf/p5Z5SpLyGNqXT6F9eliWchs1aoQ1a9Zotv/6669o0KCBVxpFlDABnCJ89Dft55ry5yaLTbD4Wf5ho3h5gvA+V07pb2ce4nqdgKd3A/culP4vbY/U5WPA4V+AnV+UHyGAp7CChPblZQLb5wEZF8u6JeYhj1TpcHYX8NHNwPJh2s9+GAm83RC4dLTUm1VhcRXad3obcPUccPEQkJZYOu3hFWGjfPbSJq+chT+WEywXmxg3bhxGjx6Nixcvolu3bgCAdevWYdasWZg9e7a320eUBH52IDBCWqfg5+eBFv3Un1sJ7dNzP6ssO6RIEYTX8QvQj1H34Yb0qg2BLOdCjo5C6Vn09bi+kEU45SQ7VbtAcFnDCwGOIsnK7FMOvedbPwASpgG7vgRGbS/r1rjG19knScAqHf79Xno99ru60FPuVWDvEuc+PwC3vlAmzatwuArtu3xMeZ95GagSW/Lt4eWw8rBAMCBU7aPnnGF5Vn3ssceQm5uLN998E1OnTgUAxMbGYt68eXjkkUe83kCiBPDxl5Sn3V8BmRe01fasFJvgBxwWP1vekjaJikdeJmDzBfwDy7ol5RO+yAuPjzCk+3HXryAH8A0tuTbx8ONCbjqAmqVzXrPoed19ymFf+/Nj6fXiwbJthxn8AyVFiqIQSgc+BD83XTKOAsDFw8r28uLJqAioImlcKFKltTgu/xyVlxxXPkeKQvtkPDLBjRw5EmfPnsX58+eRnp6OEydOkBJVkfD1A3rNUP7PTVd/LnqRTCtSzoGIf+jFAYkg3JGdCsxpC3x8izZfrzwjxs6XZNv9AvS3i1UyVYpUKU58/Jihl8NR1ogW5/I6TjkqUP9na5d503h2+Ffg1B/eO961BJ9/nJ2qvOfD+XK47YRrXIWtpZxQ3peWclreQvsK8tTXhXLwZIoVy1C9enWEhpaShZPwHj7+gD0YsDuLTmRcUH9uJbSPf7Dy9TxS9LARFrlwUPKUXj4KXD5e1q0xj5XcwuJiVCBBXEfKx0fxXpWmVVNlYMkovfOaxcoYV1LkZQHpSa738TVQmMsjzHvsrX6fcRFY+iCwsG/lLt9fVARcPa/dzof28goTL/TzChbhGlehfVmX9PcrSVTFJspB/xfHcSp/LmNZkTp//jwefvhh1KpVC35+fvD19VX9ERUAlicRVEV6FQdbK6F9enG8vAWaFCnCKrwinnam7NphFc1C1iWoSBl5UPSWG2BeqVJVpPgiNOWkdC9Pad4rI5YNAd5tDvy323gfI89jeSS4mvSak+4dwS/1tJS/BgDp/xX/eBWVrXOAWU2AfcLyMvzzzIebZXKFSXLSSrZt1xKuUhJ4Gam0vEMqRaoceKRERYpC+2Qs50gNGzYMiYmJmDhxImrWrAkbn1tDVAxY+E9QpFSBRnT/W8qR0hGYcrhQwaJ8bQ4WQbiCFxB4oaC8o/FIlaARQe/YNn/958wvAMhF2SlSueXQIyWOaaWtSBXkAsfXS++PrAWua6e/H58LV14LYjDCYpQiRuf3A7U7FO94V5OV92L4uR5n/gLWvQ70nAbUbFO8c5cnfp8svf7yPND6HmU7v6YRr0hlXeb2KQeejIqCK49UWShSqtC+clD5VBzHKf9dxvKo/Mcff2DRokUYOXIk+vfvj379+qn+rDB9+nTccMMNCAsLQ3R0NPr374/Dhw+r9snJycGoUaNQtWpVhIaGYtCgQTh/Xu3mTkxMRN++fREcHIzo6Gi88MILKCighFdDWPiP7JESkieLG9onTnqUfExYgZ80ckwIUOWFUvVI6TxTYqEJRpl4pCi0T5fk/cCxdWoPi6vFNvmct/KyKKcRPr5A7P9J7xO3Ff94fH81o4z/PA44tRlYeGfxz10eEYVpPrSKF/RZpU6gfHgyyhKza8QVFaqvp5gLlMdF2ZRWBT3eMFeYW3rrVxmx9EH1/+SRkrGsSNWpUwcOLy1guHHjRowaNQrbt29HfHw88vPz0aNHD2RmKhPGs88+i59++gnLly/Hxo0bce7cOQwcOFD+vLCwEH379kVeXh62bt2KL7/8EgsXLsSkSZO80sZrEsuKlCuPlE5onyj8kuWCsAIvQFWk0JTSVKTYuaJbKNuMSpuz8LCyKjZBoX0KH3cGvhkIHPpZ2ebKWMAXFCgPeRKu8PEDqjeT3qecLP7xrBYsSd7n3LcCjRlWEPso/zxnc8oT75Eq732mJDm1BZheW1o6wB0aJZWvOir0vYLSypESCs2U1nn1yM8BrgjPNMl1MpYVqdmzZ+Pll1/GqVOnin3yNWvWYNiwYWjZsiWuv/56LFy4EImJidi1axcAIC0tDV988QXeffdddOvWDe3bt8eCBQuwdetWbN8uranx22+/4cCBA/jmm2/Qtm1b9O7dG1OnTsWHH36IvDy60br4iDlSxVGkeI8Uy5EiRYooBvykVpGEotIM7WPn6vW2ss3QI+WsplZmOVLl0SNViorU7q+B/SvU2w7+pLx3FbbG96HyHqZl8wVCo6X3vGDvKeW9D5U2DsEjoVKkuDmcF/zLUvguazZMk/rNT2Pc7+tKkRINUN5STg//KnmojRDnj7II7ysskEJIU08r2257VXolj5SMZUXqvvvuQ0JCAho2bIiwsDBERUWp/opDWpokNLHj7Nq1C/n5+ejevbu8T7NmzVC3bl1s2yaFDmzbtg2tW7dGjRo15H169uyJ9PR0/Pvvv7rnyc3NRXp6uuqvUqEpNiEoUhprrckFedlkp/FIUWgfYQFVEnVqmTXDMqXl5SgqUoSqwDBlu6EiRR4pDZolHkpI6T2zA1g1GvjuMSDtrLKd99i48rry17G8K1I+fsZziie4EmYJ9TXJ4q43H6JWmT1S5/eZ31cMm1WF9omeQC8oNKe3AkvuB76623gf0dhT2opUUaHkQf+gA3DxkLStRmsl95EM5DKWi03Mnj27BJoBFBUVYezYsejcuTNatWoFAEhOTobdbkdkZKRq3xo1aiA5OVneh1ei2OfsMz2mT5+OKVOmePkXVCC8FdpXVKi2kuVVAI/U1WTgh5FA+2HSosTeJDsV+GcZcP0DQGC4d49dmeAn/4oU419qihR3ngBOkTJac6isq/aVy3WkSqnYxKlNyvv/dinvM7klJ1yF9pW3hHNX+Pgoc0qWlz1S5V2JLAsKDTxSfBGKyuyRslmoIi3mPblSpLyhnLK10bIuS3JDUKR2H1EOK+3nP+WkokAdWSu9RsUqSzLwivzVZOCr/kCzPsDtlS+txrIiNXTo0JJoB0aNGoX9+/fjjz9KfvG98ePHY9y4cfL/6enpqFOnTomft9zg6yVFShQcz+6QXjXFJtxYe09slCw0XZ7XLijqbba+L1XLOr4emOzlsLHfXwN2LQSOxgMPfed2d8IAXnkq78IjT2mF9vHHtXPr+BkVdWEeqdK0TquKTZRDj1RphfZdOKS8T03U38elR4oTVsq7MuHjBwQ4DUhmquy5Q6VImfBI+fiX/MLKDockNIbFlH0lWlXVPk5xrWweqYJcYOcCoGV/6b4wjDz0eogGO3788sQjlXkJOPCjVGUxMML1+TIu6CtSote8tJViPtfu5GbptVoTZT7h+9k/y4CLB6W/SqhIeVRL9fjx45gwYQIeeOABXLggWdZ+/fVXw1A6d4wePRqrV6/Ghg0bULt2bXl7TEwM8vLykJqaqtr//PnziImJkfcRq/ix/9k+IgEBAQgPD1f9VSpc5UhlpQBrX1HvbyQQigNM6mkp5lcT2udmclt8H7DxLeDfH1zv5w0uHna/j6fsWii9HosvuXNUBgquEY9USYUj8cIi8zYBxiG0Ze2RKo+KlJXw5eLAL+SZarAmmtkcqdI0KpxIAOZ1ViznZvDxkxZ6B7xT2cxqfpjeGmreZt93wLvNJINcWaO3jpTDod5eHqq9lTRb5gBrXgIW9FFvt6JIiWNUcT1Sm2dJVSTXjNf/nPfYGj3/ZR3ap1rP0WkEqtWOW+CdV+RTlfdeKkZXkbCsSG3cuBGtW7fGn3/+ie+//x4ZGVJezN69e/Haa69ZOpbD4cDo0aOxcuVKrF+/HvXr11d93r59e/j7+2PdunXytsOHDyMxMREdO3YEAHTs2BH79u2TFToAiI+PR3h4OFq0aAFCB41HKlX5bOPbSriev3NSNAzt0xHczu3WCe1zI6QwS8uJBNf7eQN/TvCshA98hYCfMCqSR6q0Qvt4hYkXFsQqT4wyz5Eqh4UCrBTUKQ6ZnCLF50jxuPJIFZSRR2rTTGktqO9HmP+OzVeZM7xhALHqkSoNVj8rvcZPLNt2AGqPwNVkaT7Tm2uv9fC+3V9LrynH1dutKNYajxTf9zzwSG3/SHrds0j/c96DaPT8a0L7StmoqHe+69rpe6TAyVIVyfjpJSwrUi+//DLeeOMNxMfHw25XFgvs1q2bXEnPLKNGjcI333yDxYsXIywsDMnJyUhOTkZ2ttRRIyIiMHz4cIwbNw4bNmzArl278Oijj6Jjx464+eabAQA9evRAixYt8PDDD2Pv3r1Yu3YtJkyYgFGjRiEgoAKtCl+ayB6pSOmV90gd/kV5z8KGzHqkACkfwtPy56Wx3hS/wGV5zN0gBI9UBRACUhOBuXHApnfU20vKy8EslT5+6vAih8H5/Muiah/XllObgUtHS+/cZigtpZe3PPN5UTwFOfqKgsMhVEUtxWfhlDOUh1/vyh0+foA9RHqfl1l8Q1V5zJHKK+U5w1WhJl7Az0mVQrEKdfrRtR7ex7ygIvzSAe76osYj5SK0zxvPIV8cxMgjJd770r6Polc5KAoIr6XvkeJlN08iEArygC1zgfMHrH+3HGBZkdq3bx8GDBig2R4dHY1Lly7pfMOYefPmIS0tDV27dkXNmjXlv2XLlsn7vPfee7jzzjsxaNAgdOnSBTExMfj+++/lz319fbF69Wr4+vqiY8eOeOihh/DII4/g9ddft/rTKg96VfvYQBPAhTmyRHZ3OVJ+gcANjzuPlaoMDGwgM6sgGSXLexO+OIY3KksRnpGaCPy9SF9QqGgeqW0fAikngOPr1NtLzCPFFCkhn1Asj8woa48UACx7uPTObQZNsYkSUnp5b1yGgSIF6BecKCqEytJbXpQJI3x8FI8UHMVvryth1gypZ6QIi0xrckm5wtU1FD+7eFgt3LJiC9e6R8qoqASvSLl7vkUvSlFJK1Jc/pGR4qEJ7Stjj1SUM2JMzyPF/wZPDNQHV0le3s9vt/7dcoDlYhORkZFISkrShOH9/fffuO666ywdy8zCvoGBgfjwww/x4YcfGu5Tr149/PLLL4afEwI2IbTPUSh1/sBwxaIIAAHMI8UNJJeOAVXqSeGBbLuPv+K9yk5RtgdXk6ywrgQ4PhzJSBD0JvwDn50i/RYrHN8AXDkFdHjUq82qdPw4Gji5UZos2g9Tf1bRPFJGVddKrGqfU/k0W5ilrHOkACkJ2RV5WZLSwdYhKmlKK7SPP65LRSoNCK0ufFdcv6aEn4WUE5JQanVMZPAeKUC6p8wb6gnF9UhtfAv4+xspd7efsfxQrhHnzsICxRDKrk/VxsDlo9JflVhpm4+/dO1z0699j5TNwB/Ah/blZwF+dv39AMX7EhAuXTOX5c+9cD350D6j51oco1YMl8aQjk8V//xGbPsQ2LccuH+JVpGqwhSpQKV9RYXSdeaVJ0/GKbZAeQUNC7Tskbr//vvx0ksvITk5GTabDUVFRdiyZQuef/55PPLIIyXRRsLbMAHMP0h5KJh3hp8IxdC+f1cCH7QHfp8s/c8LdOw4vLAQUs35fReKFP/QGeV4eBPeXZ152Xg/I1Y+CaweK1X9Izzn5Ebpde8y7Weqqn0eDqz/7TJO7vc2RgpNSVftM5tMXSYeKb1cDRfnXzwYmHO9en2lkqS0Qvt4oUvPM8DuoV6ehEaAK8H7l58DfHIrMKeNVvDOyzI3Ntt8JaGKlUcW1+axSnFzpP7+Rv1aERGFdr0qjjVaSq+Xjyuf+wVwBhQTgm1eZsUwWulhlAvF91l3v4311cBI6VXPG8pC2oqrmDoc6pBfo77N2mDnlrhYa1C8wlusfQU49zewd4nWUxbprGzNp0ewtvOKlCt5z4jytESOB1hWpKZNm4ZmzZqhTp06yMjIQIsWLdClSxd06tQJEyZMKIk2EiIZF4DDazyPQecFP7FyHx9vzDxErJNvc1r1tn2g3u7rrwhrLIzCHsoN5C4eEn6AKxWPFBdqk+WBIpXhrBDpakXykuDSUUmB9cb6LOUJPWsiP1F5Yv1LOQl8fkfphQkYTeQlvY6UZY9UKQpKer89/Zz+vkVFUk5OfpayXklJUxql6gsL3I9p4bWk15xUne+LVSCzJe/nT89IS0Z4k+wUJSRbzIt6p5FkDXcHew68VbnPatU+I3y9mStdyiXPRaGUCa5832IexMyLylzra1cKK7kT/AvygA9uBD68qeSMPyWJmfHXnUGO9dWgCO132TVhaQ9pidJc7Cp/zeW5MtTjj9G4zAzV/FqBQOl4GC8c1Cqf4c6IMz/ueSrUUaRcyXtG+HNyZ2kY1L2MZUXKbrfjs88+w4kTJ7B69Wp88803OHToEL7++mv4+pZC+VFCqhy05D6lMoxVeEu2qEjxyhl7zwYV8QFmA4yvnfNsOQV9/2CD6i4C/CBSGoM4P6BmGcTOOxz6Sir/gJd2JbJfXgD+eE+qplXR4e+z3losfJ/Iz7JuMEjcJoWrZpwvndLbRp6h0s6RMkL0SJ36A/ioI3B6m/fbxmC/vfndyjajXJXSzv3JvAxkXFRvK4l7ZeZ3MeFEzyMlWqrzc4B930rLLHx1t3b/4sArPaJSl58pRSO4ew5Zf/QPUb5XHCx7pLixhC/5zUdZFBcrJbW9gfi7WZ/i59QIp6cg44LaI8WEU3cGlJQTQPpZKQTSaK2z8oxRaF+BBYMckwtkj5ROaF8glz/+x3ueVxkWjaFGipGswAmKVEnldvPPt69dq3wyo4+PH+RnjSlNfMEMTzxSvAG/AhYB82gdKQCoU6cO+vTpg8GDB6Nx48bebBPhjkOrpdd9Hi76queRYpMnP3AzS48s+AoTKR9ixIQ15uWxh+hXdxEprvfBKrxgrSfYXToKTK8NrNMpVsJ/t7TL8Z7YIL3u+Lx0z1sSGOUUMcSJhfWLy8eBj29x3+95L4Cr0tLewijZucSq9rGQWrOhfUKO1A9PARcOSGGqJQX77bVvkNYeASSLuR78c1/SlTsL8oCPbgK2CzkzVhQps4q9u2P6B3Pjr5nQvmzgWAmFFPNKT66Bkcid8cjrHqli5EjxfY1ftLq4lLoiZTAW8vNPhHPtzcwLylzLh/a582DwBkVPojTKGn78VXkxLVS8ZHM7q2SsOo7zWosL617xMAw5W1CkjPq2kUeqpOY0/vn29dcaIVmOlM2mNZIX1yPFG0FKY872MpYVqUGDBmHGjBma7W+//TbuvfderzSKMImnibx8jCuzwDArCW9N6DjKuc35YIgCBB87zAZtdhx7CGcJdzGQ81aP0lBO+MFBb9LYtVAaUP54V/tZeagmVxol4ksavj/oWZ/E/sKu9eZ3geR97sOM+HtTGoOyUfhWufFIMUXK+Xylnla/lgT82BDiLKJgpEipxoASNqZkXtRvh1mld93rwLRaUh6BO9z9FnuoIpzplUDWqxbGh9V4cx08XukxEqbdCdlMyfDWWlKqxYjN9AvuevBrdnkzZNzMum3eROORylW/2nyBEGeBluxUpc/5BnDLHriZq/hzVMRKtrxHymjdNXd9UfZIuQjtExUpTxE9Uu4UKd4TBng+p/23C/ism7EnTbWobpEyjwZXA7q8CEQ3Vz5n4bKyR4pTwjwZw/lQR1HRrABYVqQ2bdqEPn36aLb37t0bmzZt8kqjCJMYubTdwQQrQDuRswej30dAtabSe3lQESZuPleDxWOzEuamQ/vKmUcq5YTxd3mrrV5OA48ZIcfhkEIp+H0vH5eSvnd/ZfCdihc/rIGfpPSuo6iksknOjPDK7w8YW9e9ibvlAbyNUY5UYBX9/cuyap+vv1KNzlCR4tpV0iGzRoKiWaV38yypf+360v2+7gxD9hAl78KMRyo/W20E86YxJ9/NuAiYV6TktaSKeS+L45HivQXezA304YX2UniejIxKbLtfgCJo56RzoX128x4p1eLZpRAK7W1UZc45oy8vnLstNuH83FWxiQBBofH0+RPHIKuhfZ4qUuvfkJSpVU/rf84fNy9TmUdvewXo9qo6DJ9VQGT9jX/WPTEg8vlmn3aV2lmBsCyJZ2RkqBbiZfj7+yM93U3IDuFdPPVOqDxS3CAMKA9GSDVFUGMPtMYjxQl0vHIGSOEdotVCj9L0SBXkqQfXq0naffjEVfH38lZbd5Y7M0L0n58As1srRTwAySOWtMd4sLsW4K8NbwVjaBQpJjQYlK8tyAOWPQSsf1P7/dIosGBlwWqvnM/53IthRg/qVEAEyrZqnxmPFH+PSrqNxVWkGLzHwwh3vyUgVBl/9TyzYr/Kz1YLjd4sFayqZmpwn/SeVR7WH5ngV1wjRoFFRYr3PP23W3nvzeR8PoysNJL+xblTzpHiQviYgJ+brvQ5Tz1SFbFyH3/f2fURZSO3xSbEqn28R8p5fTSKlMnnT5QjrHqkxGIpnipSrNKwmAf370pJFll0j7ItP1v5fXwhCIYvN6cUFggGcQ/GcLH4z6ZZ1o9RhlhWpFq3bq1aMJexdOlStGjRwiuNIlzAJ9F6qkjx4SH8IAyoq/4whUvPI1VUKBSbEB52/xBzHil+MvIkSdEKYvKzXhUxPlxKHCj5/08kSF4jFt5RJISPmPktW2ZLr7+9qmyrYJYYj+CvTW66+to5HNqJn/1vVH0rcStw8Cdg09vSBMBbVUtD2DH0SJVy1b6Y1vr7l4VHShbozIT2FXMStoKhImXC8MErG3Yd4UJzTHceqTBF6dDLG9QUm8hSCxze9N7lmzASuRPgmBGKRTmI+185LRU6MYuq6pobAd/hUN9DfhwtyPZeGKRKaC8FpcNdjpQv55GCQ/EaWsmRUilSFXAdH72iJHpGCFew3y3nSOmE9okyjtHzJ4Z8inJarvBcGCpSzvOK67qJ3y8OV5OB5cOAX19UG5bzs5RrpjfW+XGyYZ5gBPKo/LlwvzKSrR+jDLGcOTlx4kQMHDgQx48fR7du3QAA69atw5IlS7B8+XKvN5AQ4Dupp6FDvEBq5JHyC1AUqaICSdjlB4iCXGWw8fHXCrn2YHPFJkraGp19RWqHPUSb/Jx5AaoFDgG1xTcvU1hgkhPQC/Mkr1FwNaBZH61FpSAPcFd1V/SIZaUAp7co/zsckjtdvC5se0WFn6QcRZKAve19oM5NQOOeirASGCmF/jFBgFccCvOV/3nvwNWkMvBIGa0DUl5ypNi6PqUZ2scpe1ZypEramCIqCT5+0vhm5l7xgpMZwZw9twER+sJPQChnyNLzSImhfVlqj3lxiznw5JkIW3YXzsyeR9E4x1h0L3DpMDAiAagV575NVkL7igqhMvRdOKC8dxRJxxIFYU+wnLdVTPQqN/LbmcLk4y/NQewZ8+M8Uu6Uo8IKrkjxcy/rM3rPjivYs8Q8Uo4iZbFZcR0p+TsGYZCaZQty1XMXk7WCoqR8IKO+zSIPWt0DpP0H7F3s/L4XFSmjJSnys5W5U88jxefditfBk2ITorJ5/2LrxyhDLHuk7rrrLvzwww84duwYnnrqKTz33HM4e/Ysfv/9d/Tv378Emkio4AdWT/Nl+BApQ49UgPrhL8rXutD1FuRl8DlSLotN8EKvlyemq8nArObA/J7S/+yBD4iQBChHkbIulF4bRIuT3mB89i/pVbPApwcC4eqxwvmc10YUsnLSpIG1oiJeqx2fA1vfl8Lzfn9N2c4qmrHrzoeyGRUNybgoLOh7DYb2ebyOlJvnK3m/cX6MVXjhQ15iIVV/X1VYSAkvzCgqA6xUt5l7xfclM94gNlYHR+l/bg/hwuD0ik3oWNVLynvAH8tIUDMb2mfkkbp0WHo99ru5NqmUFjfPsXj/xOvprXFApdyVhUfKeU7e4GmzKQZR9vz6Bph/7vk+5U3lPP0csPEdaR4uSfTWG9OE9ulcg19eBL68SwpBZdEqzCPFH5fP91Qd0+D+i33RqG+G1jBuG6D8BnsIMGAecLOz+Jc3FClmCDJSBnmPlF5RM6ZUrn0V+Kq/+jNP5DhxrAuLsX6MMsSjWp59+/ZF3759vd0WQo/sVCD5HyD2/6QBk38oPa0axFvmDD1SdrUFpjBP60LXW5CXYQ9RJlZXQopKkfKyNfr0VmniSd4nKSNM+GG5CWlnpME+4jr9NoiDjN4kI4cSiLHsHvyWAz+q/89Nlzx7olCw7CHptw37GajX0fp5yhrx2iTtVd7La6PZFIFMHpg5i3NehjLp8X0o43wZKFKlHNpnlCNlhD9nPTTi0lHgk/8DqsQCY0wW9XAFr0gxK6+RR4O/R0YGiNPbgOpNjZUSM/y3W0q45rEHS94iM959VRUwE8KC7JEKk66D2B/8Ao29N4D2WuRlqcdZPSEoKwVY+wrQZjDQsJvr9uVmAD8/Jxki+IpcRoKaO4+URpEyyJk2+0xa8Ui5e9a8YaTTFDAog2ITmZel8Z9dQxYJEhAu3UfZI2XnPFIWlFBvKufr3wD2LAKS9wL3feO944rolTwXn+ffXpWewZtGSP/nZgB/fSK9P7pW65ECpOviH6hOd+AxulYaj5RwD9lzEVoduHjQWCEXDWZsvvNEkRI96AU5Uv8wVKSylXbrKVJs2/l92s88mffEiJ4KhmWP1JkzZ3D2rBJK89dff2Hs2LH49NNPvdowwslPYySryb8rpf+9kRjKh+FpPFJc7LUYSqVaRylHHWIkKlJ+geaS3HkBoiQ8Uoysy8rAZw8Bwmo69xFc2yqPlJgjpTPoMI+WaAHzhlLIPFGiR+rUZskb+efHxT9HWSBONBcPavfxD+LKKLNKVQZKLv8cZF5U3zexT10+7n3lqrxU7TPCjGX6yFrJQ5tywjv5JHxoHxOssw0EAHceqTN/AQt6SeNgcVjxuPZesT5m1SNlZqzSWxyVx8dPa8gqKtJawpmCkp/l3iO1/SNg7xLgm0Hu23d0LfDPUuBYPLB1rrLdUJEymyOlU4mQN/qZze0VFSlX/dLomKw4hDeeec0YXwZGmkM/SfmgzKvH5lh2zTMuSK+eeqS8qUjtWSS9HvzJe8fUQ0/h1hPMf31ReZ/ORXQk79fmSAHa51CUccx6pERZwIxHqqhIiQBiIdxGnl4ziAoTKwRj5Fl3V2xCLLzB44nsw1ft6zja+vfLGMuK1IMPPogNG6TFQZOTk9G9e3f89ddfePXVV/H66zqLmBLWSE8CVv5PKfXMvBR/Oq0nxSkJy+BD+0TroTxo2KWJkU1EhXlaQUJVtU8sNhHkvtjEd8PVC98W5mmLNhQHXgHJvqIMJvYQZZVuMUZYNamY8EhlpwJHfpPuGY/V0D49oZsNuEbVr8wK0uUNcaIRqwgBkhDgLwgCRmGX/PbsK8YeqVNbgPfbSVZ4byIK/1bCxTyhuOtI6cHn3nhD6FSF9kVK78XCIgz+/uk9N/tXSK/n93venqIiIOW4djtLpLbqkTIz9vJGKT0B1cePC+27KikKn98OfNBBeuZZv2JWct5KDOgfM+kf6dXM2klWQ/jMhvYFOb2GmReUz/g+ZTaSwp1lX7WvzrNm81X6njeMdJpQyzLwSF05pf6fzbEBQmifn10ZPy15pEoxj9Jb8AouGz90n2dOEeeNrEl7uGiVMLXMwx/LW6F9OYIixRTy/3YDG6ZJzz7/m9wVcTGD6E1mv9fT0D5Xa2oVZx2pO6YCPd5wvW85xLIitX//ftx4440AgG+//RatW7fG1q1bsWjRIixcuNDb7at8bHxLsiiKcadsYvSG9UjXI+V8OPkF/QCuYESOUBgiR20ZF4tN+Nrdlz/f/512mzeFT17Yzr6i/G8P5RQpIdfIqkcqJ1XK6zm+TjiOxd8hlkQFjD1SDG8kT5cFhkomV0DDPwjwE5Kl+b7PK5dixTGjBV53fCa9Mkuppxz5TQqtZIh9VhbOSypHiuUmmgztk6t3ZWn7JRNq+bLO3qgGx1tx5UnXoV90QVXmWueaecOzJ1aWYlhRej1VpPwC9I/v669WpDIvAed2S8LypcPKd9j103ikdAQ5K0VojATB4ob2VWssvV48rHiR+OtldoFcvXW0zO4LSCHc4hhSHDSehtLIkRLGyiyhWIrskXL2ETm0L1DxJLBrn5clCeuaMK8KXmxCFdrn/C3uvJ7870z8U+mT9hBttWI2X/nagcY99I9h1B6+TQzZIxWt/nzVGGDjDCBhutqj5usFj5RoBDGlSLnwSPGeOxGP1pFy/t5qjStkIS3LilR+fj4CAqSH9/fff8fdd98NAGjWrBmSkpJcfZVwx6Vj0jpCgHbSYtXkVBZJDwdyvRyp3KtSZ2YDip+gSInrpqhypHTKn/OhfXqCs5FV0pvhffxAl5epKCT2UCDcmReVLvRZV4KKkUeKrxDFcOeREgVGvmBC7RukV3eKlKc5cma5eARYPc7cmjlWMBKMY1op7/0CuRh/Z59QJfsblDjPTlXvx793t/aXGTIvAUvuAxb05ry4wu9hlR5LKrTPqkcqpJr0WpSvYzjQyfHziiIleKuZQOtu4Vm950YMMfYEI2+K3YIixfczM5Z7VQVUHaNHeC1pLGL78tcmJ01pk5wLaEKRsnJ9jARBsVpXZF3p1axHqmojaU7Iy1A8KCoDlSC8FRVJeazieGbJI6Xzu+2h2jGkOGgKGHDX/2oysGWOvkGsOIi/WTQI+IqKlE5oH2vnt48An92mzcW1UmbeLJr1r0qwGqeZ8uciqqgFrj/684qUWGzCDvR5B4h72Pk9N7lNeu0DtB4pdhyWb5S4Td1+MbTP3XOohyhP5hooUv9zVg3m+x1fuZhRUh4ps3NaOcOyItWyZUt8/PHH2Lx5M+Lj49GrVy8AwLlz51C1alWvN7BSIVYq4S1HTJFSCR156thSV/DH4pMmmUfKUaQWNNk+TIhZKBQXKchRJ71rFKkA5Rj/7QIStwvfNxhYvTng8oNEXpZU0Q2QBEszHil+gL1yWt96Y2Sldfc7RO9WFldtSQy31EtEB4wVLG+RMA3Y+YU6/NIbsD7cqLt6e3Uu4d0/iAvt08uR4kP7eGUpxTi0T0wW9oSLhxVjAwtJ1HikQvW3ewvLOVIBSrhVygn1Z3phk8VdSBVQCx+AogzoCQG88qT33KiqNVpsW8oJYPvHxqXX2bNmKrTPao4Ut87ebeOl97dPAu58T7Jsxz2iFlL4NuZncx6pSOfx8gQDgo4ixAv77nLd3AnNTXoB4w4BA5z5z2ZzpHz9leIV5/91nsuFInVwFfDxLTrh0VY8UkaKlDCGFAdX7dn0DhA/Cfj1peKfh8fdPMLC9MWcFT+7ogiz630sXnrdOd/4HN7ySGkEdw/nqj/ek6rruZJzVKF9zntkVLyAKXh6irV/CODjo4yrmtA+u1SMp90jzmMYeaTc5Ug5rwVbFqIgR71PQLjaqOCuGqYZNB4pZ58Qx1NRBvU1yO/UU6RqS5FqHlVeLbQYZVHOsKxIzZgxA5988gm6du2KBx54ANdffz0AYNWqVXLIH+EhAaHAg861uALC1Q+q7JESHkqzEwQ/0fBKj3+Q8qCyRFV+HyPhMz9HLSyJFld+HYvURKkE+WUuR8FIEPGmR0qlSGUo1rqQ6pxHysBCDygeqL+/Aea0Uar8tB0C3OZcRNfIOqQnRDscisVSFCaYR8o/ULu2jNEkVNKLl7ICJ/8s8+5x5ZCnQKD1vcr2qg2V96rQPqZIGeRIiV6nPANFihfIPS2zzZ+XFRoRvShyAYNieKRyrwIpJ/U/Y4IDn9fkDlZcRaNI5apfAS97pJgixUqg61js+euk+9xwoWBWlbyfnwfWvARseFP/c97b4w6rCwfzixJ3HC1Ze28ZB3R4DBiyHAipKn3G+qWhIsUJLbyAqqdYqNb6czOWulOk/IOA8JrKvTMb2gcA4bWlV7awJj9Pif1r8yzpdd+36u0agdRijlRAqLZgTXFw5SHb8bn0Kv6G4uJWkXIqioGCIuUboAjF4nqF4pynWkfKSx4p0ftvZAx0RcpJ4PfJ0rwrhs7z6OWNGylerB/qPe8BTgOYJrRPMAq5q4boaimUokLFq8h7pPg8YZuPogjafCTlDlArUlYLAhndD/E6sGedEVxVP9QuqoHyfvDXwL0LlbmcPFLu6dq1Ky5duoRLly5h/nzFsjFixAh8/HEFrSJWnqgpKabITReEdOeDY8VKx8M/zLwiZbNp46ttPsqkyGL4RTQ5Un7qidQvQPvdi4fV39c9bjGUg6JC4N8flHA90XrLCkuE11JKnqcnqRPg+QmfeY3YRM+o0VKpLGO0lpfe7zj0M/B2fSkERKNIOQVMvyB13gRgbIHydpXD0oIXslvdI72PaSMN2gxVaJ+7qn1isQn+HuoYIwDPlQWV58ZdaJ+HyprDIVWoe78dcGaH9nPZE2xh0glzTtpmPFJGcfNWENdeYeGFmZeBA6uADdOV38HfV73npjjhzEwAO77e2Y7qwMDPlc9DnHkKZhQ0lUcq270wozI0+Uuhq6JQYrMp/UWlSGUp/YoXklUhmDr3iT++u2vlTnlk8wRfdtlVMSB+/Gf5HywKgH9GNdda5zoWFSpjK/PwugrPcxvaVwKKVGksrcCeB6NQKl8jj1QAEOFUZi8fUwvS4nyiylEsKUXKA4/UxUPK+ws6lV0ZeuXPjTxSemHiDPYcsjGrSBif2HZ3yrnGAMD9z18H9ozAIS0/wci+oh++zfpAUT7w42hpLDWLxkOYLo0f/G9oO0QyzvFG8ehm+ser30UK4a3ZFmjWF2g5wFyVZiNEw1sFw7IiBQAOhwO7du3CJ598gqtXpY5ht9sRHKzjAiSswVdI4SdWJvhpVjo36Yrnvyd6j+SKPxeVz9mEbJRUWJCrrWbDH9cvUDv48wO4oSJVjIH88C/A8qHAIqdlRAztSzsjvY+sK1mDmOWHv856Himxqpx/sHSfXD30ekL01vel1/hJWmGCKVL+gdpFOpkVP0C4niXtkSopeAGzSU/g/iXAkO/U1jB2jQH34Wd8n8lKUYdN8s8H79nwVFngr7lY6ZJRXEUqK0Wq2ukoAvYt135uNbQPUKyfV06rt+t5pLwRMipacYOZInUR+PZhqajOwR/V+4rvxTYC+gVfrFArDqjVVvk/1BleY0ax5gV5R5H7hHY2Pvq5EQ7szuedXww5j8uHMgqv0fVIcW1yNze4UwSYR5iFFjqKXF8nXuhjRhEmUKs8UkL/sumIIbxwzOYnyx6pMO8WmxCF89IwZLFzGClSRh4pvwCgSn0p8qKoANizRPlMU1XOpEfK4TAfViYqUkZrirmCn3fZ3C1SVAiVIi57pIxC+3SiGxiyIiV6pNhz7JRv/IU+5XBIBipmZHB1fdnY6mtX31NeacxJ5aIOOOOEPVRp255vgD/e1f2Juoj348hvwPQ6wN9fS//3eAPo71zDkZdBWw7UP15AGDB6J/DEBiUyQiy3v2kmMLMJcF4nh1zEagGlcoZlRer06dNo3bo1+vXrh1GjRuHiRUkInTFjBp5//nmvN7DSwXdiPtSOCX5iGJG7ydDhkGKiz/ylbPMRbjtTltj5+IlfdPUyxHWkALWnS1Wtywkf1mOkBBQnKfjwGumVJW2qBOpMINU5GEfUloRQJlyyUAeHQ2v9zr2qFZjsIWpPnh56A/UlziMnCiQstM8vSBvax5SshrcJ5ygFi2hJwC/6bLMBzfpIHhO+r9mDuWRpZz4e7/0zCu3LvGCsMLlabNksKmXOeX8M1ybyMLQv9ZTyXq+Qid4k6w5m/bwihAu6Ky3vCbw3gU38LB+AN1pcdnrH3C3poGfccDik591qcn9gpNpTHuoMfzKjPIptc1tWmgvtc4XskeLLhWepvXpmFSkriwa7ewZYfpF/oGIk4y3bZ3fq7w9oveouPVI6oUN8n2BKgqvxTs8D4e1iE5o1nX42n6PsKfKizkaKlPO+iB4pX7sklLZyCsJJe5TPxDabXZty00zgrXrAiQR3rTauEmcFvsiRuEwJw0hpYWOvqKSz36qnWDODhtvQPuezWFQgnefPT4C5ccBvE9Tnls+pU8hHVoqcfZ9/lrJT9RULm00tH4ql8F0h3o/DP6vnU/64/FhTpZ7xMW02tSwpeqTWT5XC35kB2RVWCyiVMywrUs888ww6dOiAK1euIChIufgDBgzAunUu4lgJc/j4KQ8/iy8HlMnHqkfq9FZg9bPAiuHG+7BEdBZLzXuW+JW+eQpytAMMr0j5BmgHd74yXUl4pFRlr3PVgkLmJUWRYyEPYp6UJmwyUwqLEGEDjUtFSq/6GHd9XOZIiR4ppzWpaR+gXmfX5/AmVgR1Kxi58YOjlPf+wVwIRZZO9SoDRUpE7BPy9z1UpFQCIVvE2svlz3mhgQ+HZciTjhVFinmkTqm36+ZIFdPrwwsScmifjiKlF66sl7umt3bSgR+l6onfPmKtbYER6jGtelPpNS/D/Rp2Yh9055EQwxuN0Avt46uiGi3oqzf2q8Ik3YX2ufNIcYqRXrGQFY8L+3PCmDiGucqRMuuR0quaqrc/wx6iHkOKi6iAnN8PrJtS/OMCUvuP/a79jayPGUWGuPJIAUCEs+IiHzqWn6n+LWaLTWz/CIAD+OEp430Y3gjt478j5nUxjJQWpljXbAsMWaF8zvq8mdA+o4Wx+WcxP0sqlgIA2z/Ub5PKI8XWqwpVK0anNiv78BU7RcWizX3qc+ekAds+1FYfFmH3w2ghXf7Z5ZUqNm6bgfVF0dh/6Yj773oSZVGOsKxIbd68GRMmTIDdrhaCYmNj8d9/Bp2dMI/NpnRqlswOKMKNRpFyMxny690YwTwBbJE6XiFy5ZGSO7+f9nt6oX186IqRhbA4cef8tcm4oJ6UmEAaEK60i1XuY5OMRljPkkrSi7AB15UipbtuDPfMiMIqU/L0cqSY1T2kGjDsZ+Cx36T/S3rxRF5Q96blVVTAGZrQPi5UQOz3Kk+Ti+sgLiItby9BjxS7f54quioDwAXtfWYFBTwJ7RMFpZKo2sdfD9kj5Qz14sN15DWGrHikMiWFZ89i6f9Tm4Gj8Qbt0BGuAyOkfvXor8CjaxSjCuDeai6OTW4VKTY+BrjeT1akuPGxIFv9nOgtiunOI+W2cqgFRYopn8wjVZCrnp8A9fgvetVdeaR4RerbocD6N9XCK5+jZYRhsQnnb7hysvhjmG649tziHZOxfwXwzSBg8WD1dnYP2Vwlwu6R6LFifY5dO1ER4a+lq/LnV04Byx8FTm5S5igz5be9oUjxz6OhR8og3JI31jXuLoU58p+zcZBXLMRiE+LSEHIBLn9l3b28LLWB2OFwXbWPGRaY94sdky/G4ShUrrFoLOs+Gbj+Ael91mXgr8+Ata9If65g9yPSwMPEjy/8vGJJkTLKkTJRGKPQgyiLcoRlRaqoqAiFhdoE+7NnzyIszKAwAWENNvhf5RWpDP2H1J0wbWZtM+YJYIoUL+DylrBW9wBtH5LeF+RyJStZqXSd0D7e0uHKIyVWaPOEq9xgm3FeLZAyqwgvODW4VXr961NJmNPz9ul5pJigbNUjxYdM8teC/1/lkXJOPmwCC45SJ6eXdIw+P6h5UnXJCCOPVBDnkbL5qPuERsnNlJTcf5a7tlR7PbRPyJHiw9gYVkpq6yEK9OI6XkWeeKSi9bfreqSKmSPFj1HMoip7xLjQQnbd+P2L8qVrWlQk5YkVFWnDbZc/Ahxdq2xbdI/a4s7vK8Is9/U6AfU6SoIou47uhD1NaJ+b54+v2ucKVkzByCPla9d6HAD3HimzxSZY/pqInkeKCeB6yja/vziGiUaMrBSpMNDXA5V5BwAO/ABselvZ39fOKXEWFSl7mCIg7v5Kqt5YHPTCB30DtJ5MTwwou525KrxnAlCuA4ueEBEX5JW329XbRaWXV3RUXswc5fec2yPl8/77vbRYLMPMgqleUaR4g9JF/etqtGaT6OEQi46wZ5dVMwWUeVXMgeJzFQGnJ4nzdPJLGOSk6XikdEL7mNLGy0c+fspYIeerC8Yye4hSfj03Q1KkAOkeuYLdD6NQPV6R4scVI0O6HmKOFMPMAtyVzSPVo0cPzJ49W/7fZrMhIyMDr732Gvr06ePNtlVe9DxScECzICPgPmSBWU5cIYf2ORURI49U4zs4a3uO1v0s5kjZbEDfWVx5X97iKvwOlvRdHOWAd29fTVYLpEwRiKijbGv7kDTRXE2S8qr0hHW99WeY4GPVI8ULvlcFCxtftY9fJJkvmc7ukzxgWfROWkWlNKR6/7iigMnnrhTmKgYFPUUq9yrw4yjg+8eBXKeAxe4LD/98FHpDkRJyiXS9HpHOfbn7ceGQvrCvh9i2NKHYiSfx5EyREWG/hxdIih3ax3kTWAw9K8XMe6TYvdGz4G6dA3zaFfhjlrbYxMGftOfUu7Z6Y4n4zNpsSr9xJ+xpPFLucqRMCge6VfuyFU+dr10/JMetR8rNWMq+bzSO8TlP4mKgeoYVlUfKhSIFSDkl616Xqiqm6yz4zQuSctnnVP12AvreJr78OaCUKPcUvTE9qIq2H3iiNBh5yFnfjzBSpIxC+1ihEIN7y98/MRSrIAc4+jvw6a3K4r28AcSMTOHt0D5AW8YdcOGREjwcopDPxh7e08e8RGLflT1S3HzFK2a8QSXzkhuPFFOknOfgn7Hgqoq8xfq/nrGMjVd5GeaUWkAx1FZrrP85bwTh22tliQ0/wZPHMFOqvbLlSM2cORNbtmxBixYtkJOTgwcffFAO65sxY0ZJtLHywR4uvtgEID2Ersqf84vOMsxYx1x6pDgvQXBVtftWtCLwDyPbL24I8IgzhvjMdmVtHHFiZa53T2PZC/PViueVU9B1KfMeKf9AoEZr6f3l4/pKKlPGWKw54LlHir93vBUW4Kr28cUm0p1J585jsfukt8jkvz8Ab9YEts8zbpMVCvPVwrUnVZcMj20Q2sdPCjZfRQgqyNYP7TuzXb1NL/zFMLTPw34mlgkXhRBAURpy0qVJJD8b+Px2YF4nrYChh0aREj1STk+OlUnOUJESwlcA74X28feXt/wy8gwUqcJcYJNzyYH1b6ivsTi+MfTWp9JTNPQUEtnbkqp/bIZmDT8XY2vmZSUfTVysXIQpUqKXwK1HSk+RErwLrnCnSPHWcjG0T08w5i3zmmITQltzUoGU4zCEKWy+dteLOTN0PVKh+iGRnqJnNPEL0HrEi+u9v5oMfNVP8raze8gbAMXzA/rlzwHje5t5Cfj0NimUUsxLzM92vXagWKxKD/Y8hjnH5OJ6pAD98D6jUuNGHikxlFnPI2WkSPkaKFK8pzTzos54ZlBsAlA/Y0FRynPGjM56RhjmzcrNMKekOByKYtboDv2cRP458TQknVdWVc+KmzY6HFxxokqiSNWpUwd79+7Fq6++imeffRZxcXF466238PfffyM62iB8hLAGe7hEQXHnF9rBnBcIlz4IzLlevZCnGQ8PU5bYsfiJP6q+8j44iquklq21uIo5UowQLnTklxe07bprDpfD4aFHKuM8VA+s0STNLyQHcOtJndMJ3clWhEreIsiEGqseKf63iZMCC6fiQ/uyUxUFy8efG3xZUmeeEobBEoHXvGzcJiuIk5gnq6kb4aosdPVm0ut17dRV+0Srr14+Cz8psj6dl8nl4njokUr6Bzi9zXkMQRnTE65YOxyFUjuvnHZ6r/LMeaWMCpEwPAmDCIzQz9WRq1xxFv3iVu3T88SEREMTZ+zKI8WHF/L3zSjpXE/I1vVI6SlSbLFgN0qu2AeNPMBFRcAX3YHTf0j/mw3tE8/FF6vQ9UjphfZZqNrHvm9kpQ7g2iUqM3qCMX+/ZWGPFZuwOK6z8UYV2pdqvL9RjpRfCShS1ZsrfaYgR+tN8mis5J6NHV9IlfG+f1zp+8HVgNj/k9437avsywRg/0B1P5NzpwzSLY6vA87tlkIpxeU98rNcV4Qz45FiY1aVWOnVkzFF/I6eIiVW1BVzpHwEIy97Jth1ZUYvQLmn7JrJS87oKVJcaB/fLzMv6njJeI+U87lhz4fGIxWpHIdvPw/znOVnul+CAZDmE3Zdal4P3L8Y6PGmtHYjg5fXPC2SJCtSedbmV/56VYYcqfz8fDRs2BBHjx7FkCFD8Pbbb+Ojjz7C448/rqrgRxQTIwvmxhnayZOfzE9skB6uw78q20x5pIQ4WH7AqNbEORnZgKqN1R4pcbDiLeRG1uhEp3LIHuymfYD2w9RhXJ4gro8jLjzKiGml/p+17WqSVvDIy1QEAb5anlWPlLygLPfbjJJn/YKkda5sPtK5Wflrlh8FqPsHu478OhTeQOxnXs2RcpE7cuds4I7XgesfVFv9xH4sxvwD6n4mL+7r4K6/4E0yy5d3AQt6Ob2WYmifzqQTUk15JnLSgFSub15NAvYuAza/a1wlTlOIRBDw5fLnFhQpm01ZlJdHN7SvBDxSvn7aPC25gI6oSOUY/88rUr3eAm5wVo7TE171xhK9mH+mdLtTpMTxwUg5yLyoHn/Mhvapjp2r9Hm/AHMeqcICtWDFxhsjqzX7frtHJM88W2RcbhenSDFlhl0jUZHihTJAUfzysyQPKjuXv85v1YMJp77+Jj1SelX7wrQeKebN9QT2jARXBR53VijOy9LxtnkwVvLe+OR9ynt2D/0CgMFfAf/7Q4ryYOgV+OC3GylS/HIooiJYkGNssADMecIzBUXK3fzhcGjDM9n4UK2J9KrXJiPvj1g+XBby2Vygo0ixiA8x1NeVRyr3qnq8zLzoer0x2SPFik3wilQVziPFQlv1Qvu4Z4ifK4xy9bKc3i2/IOm7TXsDnUZLSpX8e7gQWE8XxZVlwxz1M+HumeOvV2XwSPn7+yMnp4QT3AnX4Qhizg6/KJy8zWRFMwYfvgeoB+fAcGD4b8BT26T3KvetUN6Xb4Oq4lM4cNP/pPeRzhA5WUhw7seHcXnCf7vU/1/UKbnpFwjU7ajeJitSyToeKS60r/YNzolsi/K5mXWkTmwEpl0H/D5ZrQyw0L4QQbj0D5Tuf1WnlZglH/MCIG9lZecxK6DokZUi5fDwiOEqXvVIuSgLXa8j0PkZKXxEDsfgcqTk9WyE9th8lDw7QBLa2XVisfW8wG42tK8wXxHqzv8reLWyDBSp6mrhj7f4Zl4CVo6Qyiaf+VP/nGxyZJOquFaSbMCwENoH6If3lURon1EOnNjXXXmkjP5nBohqTYCbRyrCo17/1Bv7WKgRj2mPlI7HWg9RETW7jpR4bFVoHzfWMI8A34dzM4B1k4X25gLbPwZmNZUKBzAcDuD4BmWeqNoYGPkH0G2i+vu8R4opwcyAISpSd84SfhP33bwM5drd9CQQ95D4a7VY9UjpFYIQc6SA4uX/8Z5W3iMhjiXFHSt5Tz2b7/0CJUE/prX62orzrLhdz9sJGBsaAUnpYWPmuEPKWmsMvdAwHodD8UixiBZXoX0OB/BZNyn0WTW+Op8jpkid+1v7XSPvj2jklQ21zGPl3I8fE1k/40P7HA5lX14uYvefX6IGkMZqV6F9okdKVdAlivNIXVK3n8c/SLkHqvD+7xUFedtHktxxeI1yrJBqaoWdX26ELyp2s7O8/S3jtOd2BV/+nH/O3M21Ko9UJVCkAGDUqFGYMWMGCgpKeCG6ygz/cLUcoP5MtMSz/Am+M/IGSCs5UnrnB4CabYDo5tJ7viS1bPVhnZ9XpASv2vX3S68sdpoJIexcouvdCicSgHinENDqHumVFXPgy8LWbKttF7PSZ5xXrpWczJmlXvehRT+1R8tojS1AGTx3fyWFeP3xnlrwYkKMaKVnwn+NltLrKWd4EK/s+vopLnAmoPBCtdUyv98+AszrCPy3W9s+hldzpEyWheb7BOsXfJiouC9/jfyDFKU95YR2sWWzAhW/n6NQfQ/zMhXlLCACuGkkcNsESTCWE+TT1GEyvDAg5j6J54x05kUYeaSsWu94oaFaU+lVTM4GXHukUhOBWc2kEtVGiOWCGaJXRc6R0kl2N/qfFZQRhR69NuspOnrVC80qUppiEwZjqyg0eqJI8YYqvwD1OMbaywso+1doF77Mz5Yq1WWcl9YSZBxfD3zdX/mfGSz8AqAKMbNz3gze4ARoPQw1BE+/X4C6GqLskQrSKtR6MGXEx19b6EIPwxwpYS7zmiLlvGaOQq3hobiKlN4i0yrPE6ccqfLSdDxSPr76ypQrpTTlpFRpzcdfGjM03lA3BQ7Sz0kCvs0XqNpI2uZKkcpNl8IMLx2WwqgZbMxnMtChX7TeDaPQPk3+NmeUA5RnN6gKULeT1L/rOY2s/Bpo/PH58Zbdf3H9puwUN6F9Qo4UbzQP4jxSiVu152TYbPqexhXDpdy6/Bzg99eka/D7a4oiJUdpOFGt+8bd41ueldbe6jpeew5X8H2U71/uooyMrnEFwrIitWPHDnz//feoW7cuevbsiYEDB6r+CC8gxs22GqT8z1e+A4BdC6SiFLywwZdjNuWREsJdREVK7zO9BXn5QU60ljOPiVhSlD18YslRs6QmSoMHIMWRd3tV/Tnvuo9prf1+KK9IOa+VLKhkchYknYHLTGifqny6ToiNqBiw6yGucaVRdrmwNwCqyc3qRH5qszRx8iVUS9IjZbYstMoj5fyd4mTA8AsU1qEKkryIgFTlrTAfqutvVqBSVf3LF0I2M9XCbu+3gFudOYCycJ6i9kjxYX56BRLYcQElwdwwtM9iPDn/LFzXXnplv8ds1b6/PpOs1ZveNt5HLBfMEJ8h9jtFwUMUuvj/WYVGMZ9BL3RIHPsa99T34ln1SIlhQiLi9fMoRypHfR15YZZZjwvzFAX4RIJxewH1b2MLiDLYc8aXdQbUArtGkXJTPIAX9nIz1BEIRoVPeJgg5mdXnvmsS4a7y31IbL8YbVEcRaqI83Lw59FUqNPpi/nZUmiwEXw0h17YNz8n64VcAur5SK8UvTuYss7mnPCaUmSAmJ/nLmokaa/0Gt1cmd9cebn5vsTmGd4TFPt/0jNQmAuknVF/18j7o1lElz2zuepXv0Bpbcax+5VxgLX5arK0SDKDH8/YM6MpGnXZtUeK9RV2LpVHMUK78LLRGG+0sG5+lmSgY+dMT1KuqSjn2bk+zMucfs61t/RymF3BXx/eGOBqeRJAeXZtvuarEJYzLCtSkZGRGDRoEHr27IlatWohIiJC9Ud4Ad5SEFwV6Puu8j8bpPm1P87uFNZi4RUpEx6pgHB1AqlLRcpFjpSrdXPYQyvmq4iKlNWkZJZzBQB9Z0rV/1STSKiU32TzUdZf4GGT+lUdRcpRpIQo6Ak7ZopNiGsMGZ2fwa4DGyiZpV0cBHnPIKAW+D0tVc7fP68kULs5jzvrE7sWRQXKRGxakQqWyvUDUp6ZXtimGfhJIPeqTtU+gwqEzOqecV6tPPFCkpGFnQl7rMKkYWifRevdjU9KxoSe0xXFXPZI8eEnLryPetZyEb1ywYBWoMsTjCoMUTjRS6hmAgcvrIuwsaZeZym3ZPCX+u21qkjJhhaDsUr0jrkaTwGD0L4c9XXkBSe+nzOhVs8jw/dVfk5QGbz8jZ9DfswLdypSGclSLoaZPDo75y1k7fQPUofgGsE84L4ByhiZfcXFwqzO388vIBpcVervvDfP09BxQFFaff2ke8KEXLHf6I2VKx4H3m8nhXrrofK66ilSnJDK9xde8OafL9UcyPUdV4UigpzXaeNb0mu4c/wRPVLuPAzn90uvMa2VNl06bJwrwytSzLjEXw97sOJJZrlXDFHmOPOn5FErFLz2fJEsQP1s+fioc5HY7z67A1hyv7Jd7x6IJdl5RYpda358ExUpXpkJitTO80bPppEiBaiXVsnPUgr3BAgyTMNu0mutdsbHsoKvP2SDLm8kdDfXVvA1pADAcomMBQsWlEQ7CB69Si41WktrHbEJ5uaRwPqp0vtMwSOlem9i4rDZpHMwpcFVuV7eIyUqQ64qyDALHrOiijlSsofFokeKDWRt7lNCAIKqKNv9g4EHlkqx5nwFQoZcLTBTco0DamGdPeSWPVI5ToHDjQVUXDncKEnYKPySCXO88GtFkeItofyk5O2SvjxGyocIb1BgE5A9RDIiiNZpvwD1NfILVDw66ee0gqY7KxmDVyjzMtSTYlGBcn9FpYF5f64mqz1SvJJgJLiLipR4P8VEarNUbyIpFIC0hg/A5RRwzy4rq6tnHTRT+ljMZ2OIfTr3qmQ91ShSgnCiB3v2+DVVjNrhF6jvjWaYDu3jFKmrScZjq+it4YUlPYxC+5ig7hsABHL3mhei8rOl66pnMOMVPd7jyOe4iPeYN/zwXpeQaOl7RQXSWGqmnDXvLeRD+0x5pFiOlL90vW2+UtvebS7lqV4+KuV13vqi5GVkY1ftG6SiBCHRyvnH/A18dJPUbrPPvR6ywOd81v2Dpd8mepb1FKlDq6XX/SuUheB5+HlPr1/xilFojLMIVIA6508VembgkQqrqb9uF+D0bnFjFatUq/FI5RiPD4BSICm6uXouXT0WuPt97f58X8rSUaRYfljaGW0FUz3j7Zd3A62dIf7yvRIiOIy85oD+ml02H7U3W/ZI6SlSnMyQkyp4pJy/j81V/PUJjJC8b3zlXSNjmSsvIx+G7yhU5hPRGBzTWpoP9Jam8ASbTeoruWnq61KUL10TI0Wpgq8hBXjgkWJcvHgRf/zxB/744w9cvGiwtgfhGXzhABaawAZGNugERkiVzQBJAFCFHPGDsgmPFCAUMzAZ2scmatmb5OJc4srZBcJ3xWRQszDBlA9b4sMd/IMlS4yeEgVorTSAJNi4WixWPo+eIuWcXI6vB34Y6d6CLwoVTHkQz6cpCMKFF/FrzgCucwlE+ImIP4a3E6hV59RJ3tWDz9lgQq5/kFRxSLOv4JEKDFeqUWZf0XqkzFam4wWvvCytkMMmRrG/sMnpv91qAZ0/r6Ei5dwn/Dr9/TwN7eMRF6jkBW1HobFnmLdoi0JMepJ6PTbx/op9OiMZeK+lItix50n0SOnBhDtxzRceXnh3hTtFqqhIWr+K3Xt5wWWD8U7sW2LBAxGj0D6jYhN+gepiB4D+7zc0ovHGExcVE3ml2ddPGavS/zOpSDl/V+YlpZiN1RwpvwCpHXxf/2cp8MNTkufk6G/O3+Hsi5F1gBEbpQJJjJCqyjk9XT+OP4dcwMDZr8R+IxaE4q+Vnvd1+TDX5cZhUwuhvn7AU9uBJxKMDRuqEEdekYrR7svQLFbto78dcB05wtZ6C79OMmYxZWHPEn2vFG+kY4oSu042571n85/RUhA8aYlKFADrN8xYYZRawKOn6DuEinjs+jKFgUUI8R4pdt11PVJR6u8B0rgSGi1VrmUYKR96VTwZmtBoIS+LJ6a1cd6xJzAFMVUIwXT13HlqGCxHWFakMjMz8dhjj6FmzZro0qULunTpglq1amH48OHIyirGIEUoBOqEcTDBh7eKyZXBREXKwDvlCpUiZcIjxa/tw7bd4PTo1NCx/voFQhaK+XLWcmifh1X72EDGVxbiwx3cWYMBbbiYf5Ba+LL56iuXehMMv07VP0vde4fE5HemULrzSPGhfWIhCCseKT7RX+WRKgehfTabch+2zJZe/QKATs9ohVN/UZGKVATe/Czt7zErUPEeqfxMrSDEJkbxt7AiJsfXGR/byMvHlDfmkRLLyXrDgseH6AJagdoop4G3yvLX0OFQFh1mHjhxHLG7sKICyv0z5ZFiihS3OKWImNPk7rxGitS6ycDMxooQx8YXoxAnsS16Hid3n6uKTQihffYQrYVd1yPH9VXeC+VpVUbWH9POmvNQM8Ht+yck4RaQDEV6BT9E2PGZgYIfp1JOKn2PVZ+Tc2L8pYJArFALQ7xeniCPW0JJbdFwJVbE44vKiOPOP8uAf1e6Pq89VOv98fF1LXjy+5tVpMT8HJabrTfPubqO/KKzNhsweqf0f1E+kPyPdn9+/mKRBnIxqiCnp8PA88zuuzgfsKq9YvVCuaS5C0XKx9c4hJwhKtFs3s+6orSJnZOdq6hIG9rHKzHs+eLvl2GOlIuxVHw2ZY9UMSr7moVdNzGXzVV/qYweqXHjxmHjxo346aefkJqaitTUVPz444/YuHEjnnvuuZJoY+WDH7jY4CYKA7yVMidNPdHwyojZxdV4L44pj1Su1iPV7hFpcd2HVmi/xycy52dqhRx2DKuhF2wQFoVohpmy4N2nqP/3C1B/L0BnIgPU18nHD4hqCHQc5f58PJqqfQaKlMYjxQSDHO3AaaXCnhiqxmATPruuJVJswo1HCtD2Rb8gyQp71xy1wu7jr+4Dvv7q5yjjgvo4ZvuZxiMlGCaYECX+FjFcIljH6md0TdnEF1pD8QDxwlpJeKTESo9GgjJvmeWvTX6W5KkoyJFyNgH3oX0irI/v1xk/RGSPFMsldOWRKqYitWUOVF4cfjFWPUSl3Z1HzChHyqjYhJ9dxyNlUOCAoVKkXIwPdW6SXvX6KxP0THukdO63f5DrkGgGH9oHAPW7KJ/xebHM4+8uXNjTYkY8RaJHynkPWL9hi4lfPqEOmeYNA2IfO73V/Xn1oib0YBXyNN/n+o5RGJfNV23o6P020LiH9N6qR4pdY2bEDI6S1osEpEgNEb4vMW+eaGiVQ3iFZ4uNW6xCK4MJ8qIixRZol/uLwRzU9kH97QzxmWaKVN5VdaVfQKnsmpuujJ9sDIlqqByD/Qb+ufEkR4r3iDocxjlSJQFTDEVjgkuPVMXPkbKsSK1YsQJffPEFevfujfDwcISHh6NPnz747LPP8N1335VEGysf/EPClAJRGPD1VybgvCzBI8UrUiZLYas8Ui5yV/hF10SPlK+/tLiu3sKfgNoqKOeWsJwg52+2mosjDtqAdY9Uu4eBhrcr//sFqb9nNGjxylWvt4Axu40nMyPEMBdZkRLOqcmR4u6DxpXvoSKlKqTgvK4sBl88Zvo5YPU44MJB8+diFHJeVXeIExbr820GA09w3p6ifLVVMqyWZFlk1zFTVKQ8qNqXn6UNPZU9UsJvEcND9PIi9BTeoiLlubKHKn2Z9zJ6IxRC9EiJITJGoY9GIcT89ZTDsoRrorcYrpXPeZhiwVuaxYVnZWONydC+nDRzC7bKxSaMqvYJz6OYBymiF3JTmMsJk4JHqiBP62HhvUyikgxAVdXTlRJ0y7PSc9T9Ne1nKo+UhdA+Hv9gc5W5ZEXK2U9vn6x8xudHsvfuvNyy4ukNj5QY2pcqvVZvKr3mpqkVJr7SrqhIuQtvBsx7EuIelpb44ItTAYJHymBu9gtUe5tb3aPcJ8seKTYnc+1u5Jxft38MzQLcKkWKhfYJhlZZ1hEVKeexIuoAnccq2+Wqj0wR477PG5eNZJ3bXgXuNShOA2g9YJF1FKMXW6JG9EixMHD/YEWeq91Bkh3u+0bpT/x44IlHShUa7XAd2udt2DOgqlYM14bLQi8YBssYy4pUVlYWatTQPozR0dEU2uctVGsLREqveh4p3ippqEiZ9EjxQoyrB072HGUqAp07i6v43U+7Akd+ld6z32U24VuEDay8B0nMkTID78r3C1B/z9X16D4ZaNJLWSfLzMTIsPnolD93Xg8xBlr0SPGClMYjZcF7ZLS2EgtpY6Eg4jG3fgDs/AJY6sZyp4dRVTc9xN/Nt4O/1iz5udcMoOVAafV2QBECRI+UWJXQCHFhQT52H+AUKWESqFJP/X99PUVK5z7x3mR7sNKX+eeiRDxSLK6fGTSMFCmuvxgqUqnSq2jtdacomfFUMFglNiZQOIq0wp1pj1Sk8t7Ms+MuR4q/dl1f8cwjBSh9wTdAWCdOT5FyCqODvgD6vCO955VhXnlx5bFu2hsY/59+hVNWzSztjElFSscAxQxUsf/n+ruih6l2e2DUDu1+7LeIhSCMzpufLQnyxzdYj34QLeeiRyowUvH4XDkpKV65GfoFZs7sABbdC1wSBE7dtpsUgKvUA57cqITYM/i5hN1DEb8A9fOsivCw6pHSmZPbPiT9n3lBKhTCoyo2cUl9fH9BkRI9G/x9v2MK0P5R9ed6ihjfdiOPlH8Q0LK/VPHT6HOeoCqKsZN5IEWPlJgfBUjP5c0jgeZ3KdtUHimD/uxqrBTXGmXXtDQUKaMKgK4U78rokerYsSNee+015OQonTE7OxtTpkxBx44dvdq4SgtLMgeUh05PkeIrVhkqUi5KkvPwA6crtzETXnlB1F0OAoMNPiqLkPN4cr5Xqtay7Ao2sOopn4B5ax6vSPkHCQsduhiAbnkWeHCZMviZ8bLw5xQHN7M5UrwgLAo1VkL7jApMMMGYrWeVk66+L399Kr2KLnxT52QhS2YUqUj1/3Vu1N+PCYo3/w+4d4Fyz5jQy8Id2HXLugwcXO2+r6muCRfax/qLrEgJE3JghDrmW6/dekI7L9z5BekbGLydI1VUqA05MRKUjcYZXpFi4VaiUUHswyK88aJRd6DtEOV/sWyz7JEKgextEdts1iPl66+ENYmGHI2yZFOEGHfrSPWcBnR9yfW5AfeKFruON42UnpnOY9Tr8hXkKsJI4zuU8ZsPB7WZ9EgBxgUMmFEl44I5RUpPcGMhg3e/D9wz3/0xeGMLG4t4mBFJVrzceaQygR2fSwsSrzFxb3hky7lBsQm/QCDSaUBJTQQ+6ABMv05ZLJ7fd9M7UqGMC//qn0s1H5tcB8oIfi4Tc8cYfoFKKJ9vgLoP6HqkXChSck6OsE5RtDP0MeWken/d0D4x9J8pQmKOlCCEi95fWRHjZSUd+cOI216VjDb9PhKOKxhoAyOVOYEpzmxMKcwFNs8CPnOWG3dnUOLlDSNDMB/5wueHA1pFihkRSyNHis8R53EV2lcZc6TmzJmDLVu2oHbt2rj99ttx++23o06dOti6dSvmzJlTEm2sfNS9WbKsP/itMqCJk62vvzJQ5WUZ50jpVbXRQ6x4ZoRGabKZV6T09pM9MJHSa1E+8NsEC+WpWRgUN+B4xSPFXW8rE5m7fflQvpBondA15yAqKrOadaRYpUSdYhOWQvt0wvkATpFyKvV8GVXA/D3Xw2yxCUA9Mfb7CGg92GBHg3Ah0SPFWwOXDdEuTioiXhP2nImKlN6E3MFpHW1+t7pPMnLTpVA+HtmaGyw9+7yBgSGH9hVHkeIUcd7YwvoZL7BcPg7s/15SOs14pIyuiVsBgnt2Gt4urJ0Uqd6X5RbabMYl0OUcThN9lS/cwyMWEvALcF3gArCe3G2zwbD/Asp97jUdeDlRWkxZ5ZHmBFF7qDbkTDqJ8pYfH3rNMNdGQHkWMy+aM9aIBij/EOW+RdVXLzRvBG9sCQjVCvWsHW5D+7jrtWeR9H73V+7Pz6PxSImKVIAyLlw5rV+Jj4UHH13r+lxVGyvvrUQ56MHnx1UxqF7rZ5fuR/fJwBNCHpOuR8rAiFBYoC22wGDhzpqqhlxfyr6izg/UC81TnU9QoDXLieh4pHhDnrsw09jOwMungbgh6u1ipERwVeXes3GR90ix5SYApZqsEfxcobeAOCCFildtJEXDtLlX/ZkYfcEUu+Iq5GaIMPB4msqRqkShfa1atcLRo0cxffp0tG3bFm3btsVbb72Fo0ePomXLlpaOtWnTJtx1112oVasWbDYbfvjhB9Xn58+fx7Bhw1CrVi0EBwejV69eOHpU7RbOycnBqFGjULVqVYSGhmLQoEE4f17QyCsaNptkWW/SU9kmDqZ+AYK7urihfZHKezMeKf57ZtaWAfSVGn6gZBaJbR8AOz4zd0y90D7VbzHpzg7hFalAmA7tE6naCLj1ZeD/DAqvVOWSS9naFLw3I4C3tHOIAgJfPbFYxSZ4jxQ3STHhLLS64g3gPShmhFMj3CX68nR4VPqtXV6QJjOjwdZowmFCAJu8RWFeL/mZJ19HQQB0PFI63rWe06TY934fGExiXCIwgylurP+59Eh5KUeKN7YwrxEvnH/7CPDdo8CRtWpLtMojxSkVckiaxRwp/hmMrKu+ZqIwx1thjUqgyzmcJkKPjXI0xQqY/sHuw5DZPbUUSsN5RsUCOew54atY8sUTWJv9Q6TngN1bvT5TVKRcp8fWAjc9ab6JLAz56nlzobH8/YuoK63nZPScGiH2ITE0TeORMlFswkxVSD34yoCAttKsX6AiJPNrx4mYqarKzxOulGwz8AVijKr2+QVKc8wtz0pVD3ms5Ejx/UKc73lFnEd8btP/U1ftA0woUs77rgmV11HErBQ7AvSVLeZdYwRHacc3MUeKISphIrwiZRRCGVQFeHqXFA3T+l5nQRrn90SPFOtvpeGRCozUX/T5GvdIeTQTBwcH44knnij2yTMzM3H99dfjsccew8CBA1WfORwO9O/fH/7+/vjxxx8RHh6Od999F927d8eBAwcQEiJ1imeffRY///wzli9fjoiICIwePRoDBw7Eli1bit2+coUoDPjalY4nlmXWKzbRtA+AZcbH58Mm9EIojNphJUFcT/hmx7PZ1ItBsnVH3KEb2ldF/70reI9UQJgQ2mfBkmOzAbeNl97nZQJn/pKuJ1uQMaiKZFH+b5cUhiPCvIHuLGX8OlJsrA2IkBKdvemRsodJE2l2ivO4Tg+VlRBGHlXFJBPHqN8FmODCMNKwm6QMGXmqRI+U0VopRvDXhF/DhCkcrjxSvv5K7LvDAUkgEkIJc9LUbco3UKR44Usu3VsMZdbQIyUoUg4HcH6/9P7UZmOPlN5EqRfa1/5RqS8d+FH9WcfR6mcwso46bFQcd1QLjoYCV6EVyKx4pNhzJx5DUxwgUBFYjATi4pYb9g9UBFKbr76XxZ+LRhCrhLFrxSvp7L7lZUDugzWvN1f4gcEEYb0KiXrwVeCuizMudOAKcYyIuE4dDqfxSBkpUiwUMlttgMjNMG9sKxQ8wWI0Ae+REhWpbhOAbR9Jfd9MHjD/LLgbo9zB8nz8gtSKrD1UMYC48nrpratkWGjFOQ7YfLTHtKJIWa3a59Yj5fy+qmKwh3MYIEVqBFfllkSI0pZMZ/1KNGy6k0l4w7SZdTVrXg+MPwMcWwcsfUC75hWjNHKkfHykcV68x2bKn1fgHCnLitRXX7l2hz/yiE6SqgG9e/dG7969dT87evQotm/fjv3798uernnz5iEmJgZLlizB448/jrS0NHzxxRdYvHgxunWT4k8XLFiA5s2bY/v27bj55ptNt6XcIw5Kvv6KIiUmUOp5pG5/DS4Vqdo3ANWbSw8/i/PWw9dPmohYeJElRUrHI6VKhL1OKVtqxuKpCiMwKDbhiSIVGGG8oKEVejvDZta+qmzzCwT6fQgc/EmxBvMKpN410rPw8AsYs4EoojZwIa0YOVJcv2HKWECodI+yU9QeKV4Yyc8GjqyRKkYZLXwsn48T2r0xcPZ+Gzi5CWg3VP9z0SPlZ5euJ7vmmReB8wckZaH1vVrBklcQWNUlm49yXFceKR6bTepH7gqDiPkFesUmXC0maRZmjS3I5e4Jl//D2sF7mooK9A02V07plyzXs/jeNVt6new8T0QdYOgqKeSID7OMqKN+7vzskhcqI1nybvCw/cTQPkseKecxNGuyCf8X5ZvwSHmgSN3whOSFb3AbcOmIst0eoq/sqCqgClW59BRH9pyz/ufjb10RZ9Zm9uz4BUHSYA3g75+eMG4GsY+LHpVcUZFyE9qXl6W+p6mngRomI2mMik3IbeUq34mKVFQDqd+YVaSCIqXlHc7vAxp0Ndc+I2p3AIZ8Jy2+ymMPUfqOK+9McJTUhuR9Ujj6xYPuS//76/RbpuSIHhNRkUr7jytIJIbmGawjxWQhzXIirCow1xeZUa04hiibDagVBxz7Xfo/oraOIuWUbcQwSDPrqFndlw85NqI0PFKAdB1ERcpVqoa7QjEVAMuK1DPPPKP6Pz8/H1lZWbDb7QgODrakSLkiN1easAMDlc7u4+ODgIAA/PHHH3j88cexa9cu5Ofno3v37vI+zZo1Q926dbFt2zZDRSo3N1c+PgCkp1ssuV0WaHKk7OrJQRSIi4ok64DZTuofBIza7nofRrWmilXQldIlojdw8WGEcQ8DCdOk96oSngbwQi4/qfHKk1he3Ah+3ZSAcPWgU1xLDn8s/yAgurn0J28LVgQCXaFJLySS5UhlKxaoiNrSffG0ah9TXh0OyWMGSAKQvF4Z95zwitTB1cD3j0sT5QtcFSqHQ7La8/eDD3Mobuw/AFRrLP0ZIXqkfAOkqlasWEZOOrB8qCS8+voDLQdI27NSpO/yFlBmPPAL4iycWeZ/iz3UfRimkUeKz3eRLarFuH58aB+fMyAufMm3ryBX32CzfBhw7m/jc+jRapCkfHV+RklQrneLpCTFtJaEN1X1qgCg09PA768Bd0xWH0sugV6MHKkAA4+UeL8K8oRy6UXa0GZPFKnbXpHCkloNAhbfp2w3OgbrH1vmKKX9mfKtN86y+8buZ2C4NW8U4LQ2V1XOFxgG4ILx/mYXgnWFOPaKuYZ5GVKxFFGgFmHXK/2sWrA9sta8IiWGIIn9yi8AgLPPZwjzV3A1a5Vpg6pI1RdPbwE6PGaufa7gox+a9Jaq5nYaA/zGjHxuiu489L30+5cPkxQpd6F9ev2WRbqk/afezp65yLqSApp+Vnke5eJLzn6QnQqknlGKZmhC+ww8Uv6B0niek6YYa4sruLd9UFKk6twsHV9UpIzkBr012kRufRnY962SZ2sGd+NNaeRIAerfF1TFmfdG5c9VXLlyRfWXkZGBw4cP45ZbbsGSJUu81jCmEI0fPx5XrlxBXl4eZsyYgbNnzyIpSYpxTk5Oht1uR2RkpOq7NWrUQHKysSA+ffp0REREyH916hhUsilPaDxSdrW7mh/Y8jOB9+OkDuyNNWdEmvVR3otWLlfoKQS8kN3lecnDAADpJuLY8w3CCKLqS96RmDbm13XiLT+RdYViE15UpPSEnNb3SK+1b1Bvj24hvdbrpP0Om2AKchXhiCV65lpRpDjFpqhAEhRPblK2VYlVJjVeQeO/d3Kj9Jp5US3Mbn0fmBGr9laoPFKlYIFiihS/kn2vt6Sy1ICUL8E8AGec5ZUvHgbeaSgJDXoCg1geHzD3W/iJTO+aAty6aM4+IxZBKCrkFDovhPblZwM7vpDe+wdqCzfwikR+ln5on54SBbhWpPrMBAZ/pRYSQ6oCY/8B7ncWA+CNLPZgoNNoYMIFbZECOUdKVHo88EjlpgMZF4G9S6VrI96fghzlnjiK9J810UNkhuAooOvLklGAH3uMiuXIRrSrilGAnU+vXxTmSYYN2dPsIg/WFbygGlzdeD9AGjvkfQ2ER74yox6icNjwNumVX6A3N939kgrsel0SSm+vm2K+sq14Dr017mTPptAvQjhFig8RNiKoClCvozQnFicfVY+750prJPH5ce6ugY+v1A553jHySOms68hgyk/qafV2pkhVdxoX0/7TVu1j/TXlODC7FXDA6b0WC++IOTp6ixFfcZ6/uIa8lgOlPMMHnHKvWJXUSHGpFef+2LeNl3IKxYWGXWF3oyiVmkeKuw5svNBTpBwOycBZGcuf69G4cWO89dZbGm9VcfD398f333+PI0eOICoqCsHBwdiwYQN69+4NH7PFDQwYP3480tLS5L8zZ854qdUliCZHyl89yYrx+ldOqQVib3bSTmOcyeDh0sKoZhEnhOrN1KF9Pr5As77S+6tJ2opmInIYgbDIo6+/tJ7Gk5vMK5DBUZISd9sESfnyRmgfw50i9X/PSQsZ93hTvb3XdCkH6I7Xtd9h/SE/S5mIIpz5S3qLkxohlnfOzwTOO72NVepLAoDskUrl9uMGRj5Ugw9pYWV/f5sk3csv7wY+da6nxAptlDRiTlRAmHTe2Fuk//nKWkzZOhovCckHV+lPAGJ5fMCkIsUJ1nJZeUHgYkox63MsZ4kJXwVe8uiFcvkuW2Y7j8f9LvZs8R4pvRBiV/3M1TUJjgJa9NP2AZtNeZb5544pCXp9hu338zh1ezzNkdrwBrDySWDDm1oPVWGudN2ZZ+SqTv5ecRfA5O+r0YLiegoW22ZUTp03uriqzOoKviCIOy9TeC3FSm8kPHYcLSlTtxqUIhefswa3AaN3AkNWKGNpTpr5YhN6hSZcFYbgEQsgiPfAHmIsrAZXVRSp9HMG+1RT719ShEZLaySpZAKT84U877jxSIkFUwAleiXzojpHkj1jrMBG+jkpjBDQD80DpIJUgPa+M68pg+/nLLw05bj6O55is0kVlpniIN6zkGqCUhcBPPIjUEcwmHoLVwZfm4/5CsbFhS/4wa653jy68wtgZmNg+zzp/8pWbEL3QH5+OHfOYIDwkPbt22PPnj1IS0tDXl4eqlevjptuugkdOnQAAMTExCAvLw+pqakqr9T58+cRE2M8yAcEBCAgwAthRaWJnkeKfzDY2i08vOXLm500MBz43xZJ2BTLEruCn+DrdgQeWKrdJ7QGAJtkpci65DpGWAyDErEausJb6HghSK90tRX4Y+kJdRG1gbt0lg5o0NU4Pj6Q82iwsC82UTmKJKu4GQVQtCzmZSmTIVM22O9XeSe4ifTiYeV9RjJQo4X6mFfPAWd3KJ4rwHzFpOIiKlJyeXm2xgcXEsueFz78UM967BeoFZjMKDX8xB1RG7h4SKtIyYs2OoUu1v9ZaKK4mKRYEcosgRFSSB1f0KEgRxsmx99zzXp1Wa5DNopr8VV58Fz0Zd5qe/mYEurpqUeKlcXe+r7x/uHXSYaF9P/UFbwK8hQLq6cWYL69RsqYnrLEzmfkqSzIURQJsyHPIrxHKirW9b42GzDsF+kZqtlGf58aLYD+HwGHf9X/XPz9NptyfwMjgAzn8g/uFCnxXrS6B0jaI/WXq0lClTwDxJBaPY+UUWEIPrQv/T/t52P+Bk79Aax62rl/CSpSepgtaOHWIyXkePIERyl5X/tXSIaUoiJljIlweqz40vDsWRDHcTYe6q0fFhCmhJ/y40atttIc9O9K57G9PAfphfaFVFMMjbXbFz/fzRWuDDd6OWslBb8OamQ9AJv1Fe9NM6VXVsyoMpU/X7Vqlervxx9/xMcff4yHHnoInTt3Lok2IiIiAtWrV8fRo0exc+dO9OvXD4CkaPn7+2PdunXyvocPH0ZiYuK1tziwJkcqQLK+iKur8/DrCXg7jCow3JoSBagFhKoN9b/v669YMfQmHB69NaS8BV9hqtiKFO+RMiHUmYF5KrKvKImdkfWUOGOzBSfE8vj5WVx4hrPdzOLG+pPDoc4z4EM1xHV3AEmxS/5Hva20Eks1ihQr4qBjkc90rgDPezXSzmr38wvUTlpWfw/zSBmV22ZCF7P6Z16QhA52v2y+xZ947pyt/h05qdqkbl7Ry81QC1C5V/XvN6O4yrKeR0qPW8Yp73nvgic5UmafG3b/xDGKT4b3WJHirpu70D4eM4oUy320EjLEw5duNrMOVHQzaS0edzS8HWh2p7ZojCsFmi9Z77Zqn3C9GvfQzz90BZtv/I08UqH6/bTbBOlZZecTc4QAyajBzw3uSmR7G7OKlDuPlDh3iHR9WXpl1yA/E7I3TG+x4ADB8MVgBU/0ioyoPLrc9276n/p3ulsI2ypiaJ9/kNpgUdI5Sq7Gm+KmJ1iB91SzcUav2IToHa5MHqn+/fur/rfZbKhevTq6deuGWbNmWTpWRkYGjh1TktNPnjyJPXv2ICoqCnXr1sXy5ctRvXp11K1bF/v27cMzzzyD/v37o0cPaQXuiIgIDB8+HOPGjUNUVBTCw8Px9NNPo2PHjtdWxT5Aaz2RkzDDJOFXXIQN4EKubKUTRuUOVey/i4c+so7k2bh0zHU8MR/a520iuEFdbx0NK7jzSHmCHG+fotz70OqScMFKlZ9NBuInSZ62FnfrH0fjkcrUevqqOD1dLLbcyBoJSNX77KFAkx7q7WI4S0kov3qI9471O70cESZQ8eFceqXkWeIyj1XrJlPOr5yWzucfDPzwFPDPUvXnIdGQPLQFkmVfzB0oDg1uBZ7eDcxqomwTywzzvz/3qlrx3jnf9TIFxRVUVIqUi/HCP1AK+TqxQT0OerKOlOEaQ87S9TeOkP6VFSmhX7Pr5hvgeTg1f92MfrfemMT29fVXV9djnN6ieNvEfEyztHtYKtDQ/hFzuR5m8bNLuXFXTgO7v1S2uwofDOSUX3ltICNFihtvYloD19+nPGti+KYRcrVMVsBAzyMlzLM3/U9aAw9w7ZEC1GWrxaIJJQUr8HBdB3P7u/NI5buZk1meEpNNmOHCxw8I01l2Rc77E8bXq8lOw5KOAs1fRz4NJLyWtNDxJWcEhSsZxBM0HqkQKaKAiWElrUj5+ErXXS9KoLTyowCg0R2SUbf2DUqfz8+S5tf4SUCb+/SNKxU4R8qyIlXkLm/FAjt37sRtt90m/z9unGRZHDp0KBYuXIikpCSMGzcO58+fR82aNfHII49g4sSJqmO899578PHxwaBBg5Cbm4uePXvio48+8lobyw2i4CS7vCOlQUksKQooFnZf/9Jz67pCJSC4EKSv6yCFgp3Zrl21m8ddaF9xqNpQGpyLCsyFfbhCpUh5qa3M+pWaCNmiFxKtrPmUkw6sfQX4bydw4YALRUrPIyWEZ7CkcZZP5GpNiH+WSX/PCB4oUXgorXhtsfx9u4elV71JjZU3d7cOl1+Q1ptq1SPFBOE93wBHf5Os1kywA5RcFF8/KTwk86JkXGDnKc4aKDzi2j5i1T5eyNQLc0zcanzs4hoN+HvkbhkDdj94D5olj5TzXEn/6H/ed6Z07Vs7xyMWvmLkkSqO4MILjUa/u3oz7Tb2TNls6pAixt6lABzSOnasOqVVal4P/D979x3eVPXGAfx705Xu0t3SDS0U2kJZCmWUPZUpyg8VEEUZIoKIqCBLcQAKbkWGE0REEZBNAdl7tcy2tNAFdO+R/P64ze1Nk45ASwt8P8/TB5rcJDfpzb3nPec973m9dP5KTjWWqDCUvFdfMNLu0CpPXrBFKgRRQYeG/Nqjee8VLeRckeKqRqT0BFLyc39lI1IA4PWYOILi2bbmvt9VGfQ1cOY3sahFddzriJQ037b0eyovfqIvQ6WikZSSQvH41letsXEPMW1aUw1UzsZdFkjV8IhU+c45Ewvtgit3W+DFEKZWZe0iC0dxeoRmX+4XaxexaBAAnPxJ/LcoT+x4O7labB/oWxvyAa7aV6d7Hh4eDnUlk5UnT56MyZMnV/ocSqUSX375Jb788sua3r36Rd6rqknrA8pOPvoCKU3Dp77U569OTysgVis68jUQV0U59soqBN0rpS0wZisAte6QvaHk79XQdMiKSA2s0u+PqZX4OWh6abMSxCAKEAOErGT9C2KW71mUz3vR9Ng1KF0bKvWaGHhVNi9GQ5P3rFFXI1K2HmLvbs4tYPB3ZT3cRibid0qeoqiZZ6gvvcvItOyirW9EqjoXKpVshED++JwU4J9y5zl56pWVq7j/WcmARenfvSYXV2wzVpz469RUt9iEvAqj5qJcXfd68TYyEeeyXNkOBA6ofFt5mhdQ2lutGUGoRoNJ830qv+YLIPZihz6v3bjVFHapaETqXv4+8v2t6Nzj0lwsUnP4a1mlR9lrWrvpXhPij4j/tnmh5ivB1RQz67JRkq5vVx5QaL5D8qp9FfVqy8/Bmkqu0vpj1QykisqNBuuMSFnpBlLyzgDN37L860nnWR9gWtT9mz8KiPNgNXNhq0Nav7CiQKqK0v/yoi4qlXbxE30p9JVVokuPK7t+yY/njq+Lt4c8rfsY+Zzrmr4GCYJu8CJfV/F+lB83syqbH2blXLYv92MxXn00n3FRLnBxk/h/zd9MUGiPHtaXdupdqFYgpRkpqo4lS5bc9c5QJbTy5mUnDc3JR99Qu2ZEqr5E+vIGQmXD6pqy31VVUyrf4K9pHq1r5nm0Fgu+xzRBDfMGYi+cZmK7JhVE06C8slN7+5snxEm81m5leeqA7hypwlzdANXOu+wCsfl1IGxK1ftX/m9XvuTt/eohEwSxzHbS+bIy8xpKGyBb1iAoztNf8hoQR/syS+dLGSt1L/rVGYFo2Bq4cbT0+apI3ZGPOFi7iBO0sxLLUnRr8qLc4z3Axk2cnyL1FJcGUOUXwDRETaQfDikt7V1VarLme5USJU4m95elllYnaJD3HJf33J+6DfoKU/tKP697mZOgNSJVQSAlCED32WI60bbSUv7yhqGNu1hMQU7zt5VPBq+PBn0jzqls+1Ll28lT+6oqNmHnLQZoObfL0tgqWjtMH1VJWcekJiAqH6Ar7aBT/c5Mz4iUFkFcL0ra/j6t9XO3NN9pfR0OQNWpfdKojFoMKDUdH0pb/ddG+VzW4KeAc+vKfk+/rj+939IR6F/BNBN5IFUb7QZLp7LgxchYe1TsfhQQkV+HrJzFbJTyt99PJrJASr5Ib1G+2H6RF0t62FP7Tp2qYJ2QcoT6kD72sJL3fmlNSi2fuvQ8cGmr2CshfaHrSaRf3dQ+zQlHM4lYYSyuVeMSpN2gkQKpGh6ir2lWLmWjHw18q96+OhRG4lwyTdU1zQVCnjImd3wFcHWH+P9Wo8SGM1DBiFS5ErZGxmJq07rRwOlfxQUdq5JebkmB8oHV/Uw18O6gfy0uTVqsXF6abmqfwkQMZjSBlIm5nrLq1Wg4hz4LREeIgay+OSq2XkD7CWLjWbMmGCCOSAFiap/mdWsyTURpWzaPQzNqVpgtLrRcUTW16qiJv3F153ZqGlwX/hR/eswtu686I1LWbmVVEDUjIhr6KtxpghGdxUVrILVP/tiqGl/yktnlR6QqIi9PXB/5hFWvQIWU2pcuW0+ogtEchREwdod4vtOMJkprPlWjwEhmgthppTAp+2zl1x2lnXhtUqvF65Vmf0wrSU/16QQ899eDVa1M856L9HTcAlWn9pkoy75n+Zllwb2Zje7nYGIpppJq9P5AHD2L2SdW/Uu/LisAUs1zjeZcKn8vNSloiLhsgmtplUr59b6yCsQ1RX68WckyUOoskCr9jAuytZeKyLlV9h3RqC8d/nehWnu+Z8+e2t4PqkpFI1LlU8V8OgOBTwK/DKvnqX2VNDyVtmXDvrmp4onzzxfFAODJZWXbVVZqtT4xUQLPrhcb6fc630rOzrsskNKMcMgb4IA4sTrpXFkQBYgpelIgVa58dmGO/pTJ5oOBo9+LE9blC+xWJCO+8vvr6sQup6+RuiRQNz3HykV7f82sSxtxpQUIgOqlTrgGARNl6arDVoqf/9YZYoOicXfg8fG6j9OkZGYllzXi7nYdoKpo3qe6BFhbxWKpVbmf6WPlRwiPfCP+qzCuXkNVoQD6fghc3AwM/BJY0qysWIO+9yFVXcwQRzTKLwp8L4GuPK2zqup68nWd5A11zf7pU9HiuA8aTaeCvKe7sl7t8oUryi8+XRnNiLqdZ1lwL7+eac6/giB+h6QAoZIRKROLByuIAmQjUhUVm6jGvGWljfg3+6GXmIIO6B+NmnBQ+zi2chbXW9Q0yNNkgVR12wDy56uNa9Bjr4ijo/69xd8dGol/97z0suCqNsmPN600xjpK7dN0xmrmpWlkJugWw6mJDIY6Uu3y59HR0ZXOZ6JaVn6OlIYm31vazkz3Il5fTtZaVfsqOfEpjGRV6W4DJ1aK/5dXcwJkvVH1oFFeFZ+wqud5GEpTTQ8oO2k26lZ2m5EZ0EnPJOKspLL/6yzIm1vxxTCgj/ivfDHFiuirfibvcaoPo4gVzT8pf4LXLD6tobTTXfjxbi7KQUOAliOA3guBJv2B8Jn6t5NGpJJ1F+ytadV5H9Zu4oiPvJRw2xeBqeUq+N3PUcfy57zyi6dWR5sXxA4Pa1ft3H29r2dd9pqZsmP9Xhe8BbTTgZyaVL6t1iKusuO5siIN93uNotqieb/y9EpDOg0NKTahqVhqp+ecC2ifz8xkQUH59Qjl35n63gGojzQiVdEcqWqMyGqCpizZ301fx0NF6baadcRunqw6lbA8eUfjvc591kdpIxYO0iy6a2wmzrUevblmO1ErIj/etEak6uhYq+g6Xz7VH6i/8zarodqBlL+/P27dKuv5efrpp5GcrKfAAdUO+YiUPIUy8End7cr37tSXESmthSaraLDJ10nSt9gw8OCk9tUW+YVGk34U0BsYf1CsxjRqI+DfU3eNEHnDo/yCroW5FU8Y1sw7KR9oAIBzc+3f5cGatE1g2f9Vep7jfqvuhdStRdm8PaBsFFj++d9LL3/oSGDEr/qLgQBl+5mXVrZe3L2ubVYRTQndyhgrgY5TgPaTym6zcdft8b+fPYzlz3maDoK7PTdUp2Gmr3JfTYxI+XQSC4D0nFd1OpC8sSRvJJYfmdZQ2t6/inC1TdPBIB/9rq1ASvM3lq91pDUfRfZ3kGeJyANqhUJ7ztv97GioKZrv9M3j+tfYqyq1D9D/3TCk48G3i3hNS7lQVkm2uiMu8vmB92utruqupVYT5AGTPOi/3+uSaVQ0Mq4vkKqpNTbrQLUDqfKjUVu2bEFObZQ/Jb3yIUtZkF8srF3E3mANU0vdk1J9WeisulX7AO11kuQjaiqVmDaRl14zFbIeZPITpbzh5NIcaPk/wOtx8XPu8hZgL+sNk48WaRqcmotbkZ51pDScmojzeDTkI6OuQdrblp+ED4jrhLi3Ev+vGd2qS1oXl0rmd7q31C5eoEmvs5VdlGtz3Rdp4dA0cZ4UoN1wq2lVfTc1jSn5PihtdZdYuJ8dHDqBlGa0+i6DuTZjxH99u1S8jaaRoGlQXtggpiED9zYiZWwKDFgChL1W9bY2bkCXGWIFP/l8jIoCqYclrQ8o+x5q5rMpjLXXDapK+UqPldEEW+WDAE1HZqgsDVYeSJXfXj4a+CAGUvIOnB3v6d5frdQ+PWl8ms9Jc66rrKPI0gHwKjfntbrz/qxdS48boWbXQasv5B2UjrL1AWtj9K06LOy126uepeu7pukbkXoEAimqW1suyPLAy/f0OviX/d/MRs+IVH0JpPRUG6yIvBdePmfl3DrgI1/gI++yxSUfxBSJmiBP7ZP/v7zwGcDkk0CfD8Xf9QVSmou/vqp9GoKgHTDJ52+4lBuR0jdqZeMOPLUSGPoDEDS04v29X+QBg740Es26XI17iKkaQcPEtCu/cPH+ti+JnRQBfWs3fVYeSGnmB1Q0elUjr6fnoiu/GGpGxzXz7Cp8zH0871RUDfNuG6vtJwFd3wWGLq94G02wknlTXH9q3Wggdr94m1kF+1Mbur4tVvCTB7IV9QTXVYOqNmi+A5o0TEPLhksjUtWYI1VUwUjL0B+ACYeBZoNkzytPAy53HGilAz+A1y032Tyfi5t176+k/HleYQnWHY9HnpGetGRNx0Ov98VzydM/Vb4fIcO1f6/uiIum6Mgr+7VLkz8s3FqW/V8+7aOqdfhqk6a8vp034KKpyPyIBlKCIOhU5WOVvvvnvytla7ioy6dmyHNvlTZi40EefNSXA9TCUWx4GptXPhkakDUeU7UnA59YKWukl46SPog9ezLnb2ag16d7sfZYFeXey3NqKl6YlXbV613TpF5l6gukSj/vwpzKy8rLRyGcA8WUN1MrsRiFvkVC5VyDxIAleFiFgUeJSo1fjlzHhQQ9Jchrmmtw2f8fe0X7PiMzYOIRYMr5st7OYT8Ak0+VpVv5dgKmRgHP/FrhSxSVqLDueDyib91DGXG9I1KuFW9/r+SNJQ35vB3N+cRa9h2u6wZ6RSNAd3vus3EDukyvPLVOE0hlxAO3yk2mrq1iINVlZAJ4PiaeH+SjqXWV4lMbyldTNDRwNyS1r6KUNWNT8TwobwvJK02Wn8so/548CHN7yzMyEc+JgFhwQvO5aFSSJbLqYCym/3EWhxOKde6Tgs/QkcCb0YBv58r3o8UzQPMh4v+bDjCsI8vOU/vc/zAJfRZo8T/xmmRkLHYGBfQBmvSru33q+zHQYgTw5OdlHQn6lrZ5gItNVPvoU6vVGD16NMzMxF6f/Px8vPLKK7C01D4Z/PnnnzW7hwQAyC8u6+EvKSrU/sNprTnjJp7UlbZiEALUnwNUaQOM2yMGeVX1xsnnSOWll90ed0h32/pQAe4e/PBfDC4nZ2PB5ij0C3aDkUKAhWk1vpqmlsCk42KPbHWKD2gavvGHgZSLgHNT5OblwALAHZUVHIDSY0ZTiU7P30g+D8bUsqyksKUj8MJWIGY/8Ptz+l+/cc8qd/HA1dt4Z8N5OFmb4dg7ParcXpNyXL5TJzkzHxamRrBW6m9crT0WhyPXXLGw2zyYuTcHGnUXR+U2vAwAyBEsoFQ2gJFCkF5HEAQcuHob1+/kYkQ7T/E1rSpP6fv9eDze2XAeQQ1tsOnVTlr3lajUGL3yKIwVApaPaiu9lg5NIFWUW3YBqs1Sul3fFlNEGrYCts8SCxc4NAZulRaT0DR65J0hlZXbvh8qGJG6nqmCeWY+nG1q4RwoBVI3datU1mR5+rv1/EaxgthOWQpWXQe8NcnYVHcBVEMYFEhlV/815Kn0xuVGyQwYkSoqUeF4bBpCveygNKnmMgD3YEdkMn48FIt5A4Pg61jJNdXOU3wfuXfEamzyTrxKRqS2XRA7gS5lKNC1/OVNnhpdnQ56YzMxu2HgF/VnDnh9YGYFDP667Pcu0+tuXzQc/YHBpVVUNeta6QukylegfoBUe0Rq1KhRcHZ2hq2tLWxtbfHss8/C3d1d+l3zQ7UjObMA60vEhthvQj/sjJQV+rDzBJ7+BRi1qezELe8RrS8jUoDYE6QZ3q2MvBe+qvK0D8CIlEqlxu3sAp3bXl97GhtOiROZs/KL8dgHu9B/2X8oLlHhakoWTsenS9vnFZZgyfZLOBWXVvYkFvaApSMS0vOwePslrDwQg+6LI/DPmQSckT0WgHYQ9K94gk3NEBsRBxJKgyd5KWF9n6v8OYyV4onb0hFxd3KRa2StM1cq39IDgCCmvlSWfljqeKwY/N/KKqiySmhxiQr/+/4Iui/ei7ScsoWFb6TlIuzD3Xj628N6H6dSqfH2hvP483QC1psNFlP3BAHwai9tk1xoht0XxRXij8WmotnsbXj3r3OY8MtJvL3hHP67elvvcwPAtVvZeOnH4zh07Q4OXhWXIDh/MxPFJdqV4K7fycH+K7ex59ItXE6upDFnZgO1pmCIptiEnqpsuYXFuJGWq3O7wez9xEZKh1eBCYeAF3dqByqac4udFxA8XOwZlqcXl7qbKq8Rl1Lw9+mbVW9YXgXzJGMz1PhuX7Thz1cNGaalo7MZN3QDqboekQLEVGqlDW7KT591meJTG2TBfLGREheTqjHfSUMTSBXn4e0/TiIrv0jvZiUqdcWpffpo5ktpUoDltEakKr8urz4YixHfH8bsv89X/ZrVdCe7ACmZ+kuXL9wShf1XbuObiGvSbSUqtf5zilNp4aDvwoF/ZHP5Kgmk8ovEzuBMtZ7rShUdQzfT83T+PlvPJ+Hrg0lQ16P1h1Ky8u97dev1J25gzVEDs1nqiqaDXF9V1Ad4/ma1j8CVK1fW5n5QKZVKjejbOWjkZKnVy379Tg5mF43GD8V9EXnVB7h6HKdn94SdhSl2Ribj3M0AjGjnBZPsArz713m8nWcKqalVbkTqTHwaWjStX6M4J+PSsPF0Al7r7o8GlqaARekFP/uW7poVmgX9NGop11wzCmGoK8lZ2HwuES909IW5iRFMjBT4aNtFfLs3Gl+PbIW+wWLvfWRiphREaeQWliDmdg4uJWdh9MpjuJVVgJ1TO6OxszU2n0vEst1XsWz3VbT2boD/tfPC0NYeUKvVmLLmNI7GllU3fPU3cRHtDRM6INSrAZZsv4SLiRn41swGQkEmELMP3+29ih75uYACSFWLDdGSrFswAqA2NoegbzFUWUpZsYklNhyPh7mpESb9egphjR3wy/8CtDb/tyAIg9/YA1g4Qq1W40pKNrzsLSruYZV93vlFKpibVtwTeyEhE4eixUDlgy1RsLcyxYTwxjgTn4FilRqRiZn47Wgc1GrgYlImhrfxRFBDWyRm5ouNIwDxskZCvmkDaL4pKiiw/UISejZzwff7opFXVIKfD5ddrGJu56CTv/ZoVH5RCW5lFWDRtkvYEZmMK8lZaORU1sC/mZ4Hb4ey7921W2XFemJv5yDQTbvx/eWeq7iSnIVpvZrAUmUBe0FsEaeqrWCvZ3L16JVHcTGxCP9M6ohm7jXUkNdUWpSndGoaoIIADP2+woc+v+IofnyhXbW/Q4XFKoz76QQKi1Vo5GSFoIYGdMxV8Br5MMXWC0l4d0A1Om8MNHXbHfwAoCQ9Hkble1jrOGA5fzMDbrZKOFiZIfJ2IaSyKKX7lZCeh10XU/BUa49qjXao1Wocv54GHwdLOFlXfy7S+ZsZ+OPEDbzSpRFcbbWvQ2q1Gu9vjgIAvNM/8O6mCth5AUlnAQCxWUCfz/bD31k8dhY/1QKKikZ5Aa1R/M3Hr8DZzgpTemifv7aeT8TkNadx0DkVjkD1AqlG3YCxO7XTYTVkI1KFCnOoi0tgZqz/8193XCxi8vvxG/h4WAu92xiiRKVG/2X/IbugGHveCNf5O0bfFs9HB6PLOonmb4rEqoOx+ObZ1ugTJOtEc24KXP9P/P+JVUCPucg3soJZYbZYtkfP55SVL6b0ZUL3vt1JZuhWQX2Ui0mZGLDsP/g4WmLH652l4+SNdWeQXVAMPydL9G6unep8/mYGrJXGWufb2rZ8fzQWbI7C1J4BmNxdt2PJEPlFJdh9MQWP+znA3rJsxC0tpxC/HYvD4NCGcLM1R25hMaatOwMAaORshZaedjAxqnx85M+TN3A1JRtv9GpS+fejNpRfesGpaVmmQ0Xl7h8A9SeUJwCQGtyzBjTD2I7iZMjsgmLczi4EYI5ItY+07ZyNF3D8ehpupIkVqn47GgcXGyXO3czAsyYm8Cw9P+eoTPDPsTg8U/q4nw7FoUVTD8TczsE/ZxLw3OPeYvBSy/KLSnApKQshHrY6F81F2y7h4LU7yC8qwYdDQ8oaIvqGgL3bA3euSb3A7/0bg/bhSWjt3cCgi3x5vx+Px6oDsRgd5oPoWznYfTEZJkYKrB/fAUoTI6TmFGLzuUQcib6Dab2aVJj+MG9TJPZfuY1v90ajsESFcZ398O1esVf8/S1RUiBV2Tyg/Vdu41aWGCyejs9AY2drRCaU9baeuJ6GE9fTMKRVQxy4ekcriJL78N+LKFGJjSAAGOe3Gt8nDAYAfPnvCfQ3E3v50iE2+AsykmEhAGlFxvh6cyTe6V/WAC0uUSEFDtD0Aa88mYH3M89K9x+4egerT9zBKNnr38o3wsg10fCyT0aAixXm/hOJJ1u4Y+kzLfU2nOSjNhl5RZUGUudln9+6E2KjIz2nCMEeZQ3wmX+ek/6/MzIZO6Z2wVnZSF1Cetl6KFfT1dCMpymgwuEYMUhLySpXIh5ASqZ4W/StbCSk5+O3o3HYfE577azYO7kwNS67qN1My4NCEHA7uwChXg1wTTZvau/lW+jRzEW6CF5NycIn28R5N/aWZhiptpYCqQhVS4TnFmldYAHg3M1MKKDEzqjkmgukNBxlDYPSNJys/CJcu5WDFqWfd3xqHuTLx+6/IqZB+lSWJiQTl5qDwmLx7x+VmGlYIFWBPJjCzFi3YZGVX4QT19MQ1thR+szVajVyCktgZVbx/D1N+mV+UQn2J5sCSsCoJB/qhNNadR+zjBpg79kEdPJ3gq35vRfdKCxWwcRIwPL9MfB3sUJ4k4p78I/HpmLYN4fQ1NUaW6d0RlyWbM9Kz6vvb47C5nOJiL2dg1myIFOtVqNYpYaJkQLFJSpEJWYh0M0aey7dwks/HoeDpSl8HC0xKLQhngvWDuZLVGq8+ttJGCkU+OzplsjMK8K4H48jISMf+y7fQlZBMcZ3aYQXSq9rFxIysfy/GADA0NYeUkfC/iu38M+ZBLzZpykcrfSfzyMupcDUSAGPEgfpmEsvEv9uV1KycSUlGy928kVzd1tcTcnCd/uiEeJhh2cfF0fF03MLYWdhKnYyFufDR0jCuRtl55OcgmKciU/HJ9suobBYhaTbqXBUoPrzmjTrCJWTBmtoQuwZG6/i+pHD+HNCGEpUahy8dhshHnbS8ZKcVdaBuDMyGR39He8pxS8pMx9JpaNRh6PvoGtTZ+lYL5Kdd/MKy/7/82GxKMB7G89rB1Ll58Nm3sRXJ4owtTQt/ExKEeb/fBAvdvJFnyA3lKjUSC59bfmIVJ5LKyy+EYgf1sfhfHAgLPV89w5du4NilRpXU7KRkJGPhnbmyMovQnaBGJhdKzf/9EZaLoZ8dRAWZkY49k4PncBCzN64jCdauFX6PTKUJvBdsuPyPQdSPx6KxQdbLqJHoAu+ebYV1ABMjBRYtP0SfjkSh/2Xb+O3cY8jIb3sGHnqm0N4soU7lo2oeL60SqXGjPVnUVSihq25CR73c0ALT7t72leDlE8t9nwMaDdOzHRR2kClUt//4K4GMJCqZ5bvFy8si7dfkgKp8zf1N7j/Oq1dYjolq0Bq9GWh7GT157k7+OBIpBRIFZaeNN/fHImdUSmIS83FoqcM6/HKKSjGcz8cgb+zNT4aVr0Vu9/fHIWfDl/HgkFB0gVN4+A1sdGqSaeShoBlKTPR6obIUFvgptNL6F/8JYTS+w7dLMLqn0/AyswYe6eHw6H04rv9QhL8Xawrz/eG2AAYu/oYIi6JaW1v/nFW6/6TcWmIuZ2DdzaUpVgcj03Dh0ODkZJVAB8HSzhbmyH6djaUJkbYX1oYJK80leFrWarErawCLN15BcPaeOBiUsXpXP+eL1uHSXOhSM3R06DPKsDWC3oWvy11JEY7wNoRnYc7ZtZwELLgKqTCDGIglaYWe2ctBPE1cmGG7/fHYGhrD6RkFuBkXBo+23kFShTgYmnHcmqObirMe5svY6iZElaCeILPVpvjwNU7OIA70jYbzyRge2QSnm7jiRc7+cHTvuxYzcgr0vq/phc7u6AYZsYKrUbvhQTdNJ4jMXfgYqO/8ZWQkY++S/cjLrVsFErTCQGII4SaQMpYKEF8ah4S0vOkBoDc2uPxMDFS4MdDsbgjSyss73Jy2UU+LjUXM/48i/jUPGye3BFXU8ruW3MsHhl5Rfj62da4kZaLHkv2SfdFXErBAFkv7hFVINySstC+kf6FVdNziyocTT17Ix3/nEnApK7+sLUQG2xXU7Lw/uYoTOrWGN4OljhxPQ3dmzrD2EiBwmIVFAJQ7NJaGq3TlNWd+vsZ7IhMxmdPt0RyZj4W/nsRseWmIt1Iy6t2ICUfoUvK0J9+dPDabbz66yncySlEoJsNVo9pWzb/6bFXgGM/AKqyYyhfbYpilW6qzeLtl7HqYCzGdvSVAolp687gr1M38dPYxxDW2BHz/olEzO1sLBsRitl/X8C+y7fw54QO8HawRPStHBTCBLfUtnASMiDkaqd6tv/iPLJhgeFtPHRGE0pUamw6m4Dm7jZo7Fz13MaLSZkY+MUBuNuZI6Z01ODSgj7SSEb0rWzYWZhKgfXJ0tTfi0lZyC4oRlJJWUAaX2iBwQt2lHbMiQ02eSD1ys8ncDQmFZsmd8LG0wn4aOtFvNbdXxpNuJNTiDs5hThxPQ1dG7aDZhBh67kkbI/LwpZz4nnraMwdJGeWna80ox3zNkVKgZQ8nfVSUpYUSL294RziU/OQW1iCz0eE6hzH125lY8yqY1CrgbFGJZhVGqfmqrW/93F3cqEQBDz5xX8oKlHjjxM30DfIFfuv3MaUtafRyssOf5ZmO/xtNhuvGW+THvvx1otYfaisspgFSo9HU0vsikrGb0fj8E7/ZjrXluyCYqw+GIu+Qa7wKx2NLipR4fqdXNzOLsC3m25gZWn/R1KROU7GpSM9txB7L9/Ca2tOo0egMz4f0QrmpkZQyN73iz8e1zqWNN9LYz2jDzsikxGXmovoW9lIziyAo5Upjl9Pg42yrLn36m+n0MDCBLunhePbfdH4Zm/ZNaqg9LqlCaoBMVNCrtA+APJunIiTF/DzvhJMLf0qLt4Tj+PX03DuZgYuLXBDSla+9FzJ6rLG9OGgeVh+XTyPf7LtEno1d0GHRo5a56/Y22Xnhb2XbmFACzcky84P6bmlnYG5hVi+PwYJ6XkoLFGhMFeF2Ns58HfR/o79dPg61p+8gfUnb+Do293hbKPE9Ts5OHjtDoa0aqh3hDAhPQ9LdlzG2I6+OpkDGimywPdOdoHUDpF/luWDup2RyThzIx0vd2kES1MjJGcW4N2/zmFnlNgO2hmVjFd+PoH9V27j06db4pcjYsfyoeg7uJmehz2a9lKpjWcS8PGwEChNjPReAxIz81FUIv4dFv4rjgRtmdwJzdxtkJFbhG0XktA7yBW25ibILihGVGIm2ng3qHC0+J8zCVh/8gY+HhoCJ2szHIq+g4VbLmJYaw+M6uADlUqNMzfSsT0yGRO7NsbtfEv4yJ/AzBpoOxaAeNxO+vUkZg1optM+rO8YSNUjuYXFUspRbmEJPvz3IqzMjLBo+2WDnie8iRMyr5U1TnOhfYHRNAw1X9Y/TohfhD2XUuBma47sgmK09hYn2idl5CM5M1+n12LXxRScjEvHybh0TO7hj4Z25ihRqRFzOwcbT9/EmDBfnVGun0p7txZsjsSQVg2lggoqWUNHpckv1oxIla7xcUdtjW4Fn4i37QVaBZSNjGiCgOyCYuy6mAIbpQnG/3ICajVgb2mKE+/2QEGxCvM2RSI8wAm9StMAjkTfwaqDsejd3FUKovSJTMjEmmPacyCSMvMxeuUxAICxQoCxkYD8Ij15v+UUFKvw6c7LWLb7ivS31kc+v+lKcjZ+OnxdJ3AGxF7/U3HpOrd/MDgYb284p3M7ACSp7eEgZMFNSIUSYoNK8xlqaBol/Zf9p7Wf+TBDlMoLgYo4HFHpr9KXAUtYlTY8MvSkcQBi2t7qQ9ex+tB1dGvqjDlPNIeXg4VWIJWQkYfZf4uFJ/ZeugVbCxN8/3wbvLj6OBramSOnULf6U+ydXCzbfVXvawLQCqIAcWTvwNXbaOPTANdkgU2RsQ1QIPbc6huRupVVgE93Gva9jLh0C/GpYuD20dZL2HdZ+5j793wSMvOLMH9TpNbt0bdzkCWbr3Za1QgJEVfh0cAc9nqmBq04EIM9l1KwZXInnRG99zZewKm4dBSVqDHnSbFk/dcR0dhz6Rb2XLqFJ1u4Y+OZBPRq5oImrtZYfTAWpsYKFBSpMMZmOl4PKsCthj1x4lwidpTO01x7LB7X74iNnZ+Ke+A54534qlhcX0feuKhKjKzBlFBBIPXToetS4BqVmIl1J25gYtfSMr99PkRJ+Nu4vLAjAhVigyMPZrgt+/slZ+bjlyNxWHUwFoBY6GXWgGbILijGnyfFNNufD19HC087rDgQI70/TQru+hM3MLVXE2w9L3Ze3FQ7wEnQ7ui6rGqI7NKOrN+P34BnAwu08m6AsMbiCM6OyGS8tuY03G2VOPBWN72NlKz8Iuy+mIIWHnb4OuIaCopVWp9PfGoeGjtbIfpWNvp8th9udkpEvBEOQRC0Avu3/zyHzFxzaFq9ay6ppSAKgFajsahEhW0XxL/p8v3R2HxWfI9Ld11BWGPdoL3np/sQVfr/138/jTzTsihaHkSVl5FbBEszI61Ohv+u3oaTtRlCPGyl78ims4lws1Xinf7NoFaL++1oZYq1x+KhuUzcUJeNiuVD+3oTn5aLuNRcqeGoUgMn49Lxa2lj9GRcOqJNXeGnEIM/59yrAMR1bjRBlCMyEKSIljqYYGqBJX9fxoWETFiYGuv0/v965Do+2XYJPx++joNvdUN+kQrLdl/B1xHXYKwQECQ7z2rOue9sOC+NZu+MSkHg7K14s08TaU6Rxu/Hb+DtfoFYuusKVh6IhZ+TJTZMCIOVmbE0UlpUosLEX05KnaWVScstQuj8HTq3ZxUUY+G/UXi6Tdk8TFNZABCVmInPdubhW9lj/tx/GhaCOAqTqzbDvqtiMF9QrEJqTqHWPMXT6kaIN/bG7UITjN2cDs1U/VUHY7HqYCwuzO2NUSuOIr+4BP2C3bQC2rc3nMOnOy9j4eCyqns3SzvDPt1xWWtbQLxOlw+k5B1Yc/65AH9naxy4ehvHr6chp6AYL3bSTcn84b8Y/HHiBvZevqVVBCm3sBi5hSWwtzBFjizYPHE9TWpnpOcW4pnvDiMzrwh/T+ooZc2oVGpMW3cGGXlF+Hz3VTzZwh1e9hZSu0xD8/uEX05q3R724W6d/QTETomMvCKM//kEQjzsMKqDjzSaqK96bL9l++HnaInW3g2w7sQNfLc/Gm62SmTmFeHMjQy82acJXKyV6NrUWScL4oMtUUjMyEe7D3Zp3X7uZgb+u3ob/125rdWh3MTZCl+p3NBIIR7vScVWOBeZjPAmTvh+XzQKilV4f3MUAym6ezkFJejd3EW6mMl7icoTBEA+p7GNdwMpfeup1p4wy/aAZhAgQ63d2krLLdQaygfEtLa3ZGlQk7o2xhu9m+D5FUdwOTkbP499DB39yy5a8oIH12/n4Gx8OiavOSVdtG6k5WHJ0y317nt+kQq9Pt2HnVO7QGlipNVQvZ1diBdXH8esHg0h/yrlqLW7undG5+P50qM3DWXvr/xoUmpOIcasOobiEjX+u3obvx6Jw+8vt8e8TRdw/qYYpMlHf/RZsDmq0vuLVWq9vd4adhYmUq+Zhjw4MTNWwNPeQusEL7czKhk7o5L13vdGaX40AMx5ohnm/BOJYa090KOZM97eoH9/EtX2aI7raCjclkakbkE7jUoTfOsL9sYWvgE34Q5OqgN07gPE1I2GgnjwZairHo3YfTEFlmbG6NjYAQdkRRw2nUnUGlHLKihG/2X7oVKL8400bJTGyMzXU1K3mkYuPwIjhYASlRpext0x0ngXznk9B0SJx0ZlAa8htl4oO87KB1EaIXO2671dQNk+XFF74NKV2+i7dD+2Tmmnd/uY2zk4fj0VTVytcSkpCzfS8vD9/mhEl476HLx2G7ezC7AzMhmHrpV95hvPiMH69shkbI/UPuaW3QqFmWkT7FpzHidlwXtqTqEU+HxS/DQuqH3wd4m4YGZlDery5IHsb0fj0MzdBgVFJfhk2yV8/WwrdGvqovV3B8Re7C3nErH0mVA0drbC+HVXMFY2Gp8PsYET9N42zOjbFH+evKHT8fDvuUQUyf7GF5OysEWWpin//q84EAsTI4UUrEeqvNFSITYSk9QN8L/Cd5BarlNi8Y7LsFEa49TsXkjPLZRSehMy8pGcWSA1NNJyC5FfWII2PvYYvfIYTlxPQ0WOxabixdXHEHtH7Bi4ficXiRn5cLcz10r32XgmAV6C2OVUoDbGj1e0O9VyCouRX1QCpYkR4mWdDOduZGidlw9cvYO7ZW1mjKyCsu9ni3m6x/gfJ27gj9L0XLnv98fgrb6BWFd6fZJf5wAgQRZI5UD7GpGQnq/z3b2akq2VUr2seAg+M/0KAKDMFYPlPFmDeJHJNwg3KjvHxmRCGgk/EnMHJSo11p+4gdY+DdDIyUpq9CZm5GPk8iM4HpsmBTXFKjVShbJjI0VtBwA6KcEA8PHWSzq3AcBzPxzFudIMlehbOWgxdzvCGjtg2TOhsDQzxtWU7GoFUVX5dm+0VqpjWm4hiktUKChWYczKY0jKLMEio6fwhsk6AIC1kAcrtXjc5ZTruB3343Gtv1kBTNEp+wMIUEOtp95Z8/fKRgY112i5W1kF0hxgALhRek44Xb64EoC/TyegtXcDnL2RgZ8OX8eM3k21OiTEEdSy8/KCzVFISM/Hm32aaKVRbi1tI9ySfSfUajWe+e4wLiZm4dvnW0tpyQBwODoVvZq74kpyFjacuilln3y37xqslSYY+ZgXUnMKtToON55JgPM9TE3QGPjlAdhbiue9Q9F3cCj6Dt7tH4hmbjbS+b+86Ns50qjx1ZRsrbaI/FhcNiIUcXdy8JifA/67chuJFXR4AZA62uQupWRjq3FbTFRsBAB8eSwDP+0/jv4hbtJxnVdUgi/3XC3rIHsAMJCqR5yszfDtc23g85buQnemRgqM7eQrpYmtGNUWH2+7hOm9A/CYrwOib+XgiS/+g6OVGboHOmPPxRbAnV8A6I4KpOUUSb04Gkt3XdH6/Ys9V/F8e2+p13DepgtwsVHi7X6BCHSzwSVZWtqNtDysOxEvBVEA8Oepm5jWuwka2umvTHQjLQ9n4tPF3qr92lW1NIHD1yZt0ddIHPUphPY8g+vqstzm4ioO4/KjTf/7/nClgU9VPO3N4WytxMSujfDpjivSCaC5uw2WjQjF0p1XpAYpAHw4JASv/HxC73O92z8QL3bywz9nEqSLg5WZMRytTPFG7yaY9OspvY/TZ1QHH3QKcEJDO3OdeSEbJ4Xht6NxSEjPRxfntsDxU/ARkmAiiI2GZLX25Hg7WzugXFvf2swYW17rhE4f79FqwJQnn0ycAUuYGivQJcBJ74lV458zCfjnjPaI2/HruvO+yv/ZTI0V+PnFx7DlXFKFHQ8vd/FDYnq+1t+kPE2Da27x82g1dCoszfyBqOPSPitNFGhgYVrhhaOBhQl2TO2CNgt2VvgaFfn06RZYuOWiVsO1oZ05vn62FZ784gAA4K+SMHQ2OoddJaFQlTY+sguKsfJgTIXPG30rB9/svaa3EZyWW4TF2y/ht6Pxeh5ZMc28LblLshStTFhiTUk36fdbWQXYczEFk387hXf6B+KZdmWzqDT58EUlKihKS8vLzfqrLJX2+30x6NbURe/nfyEhE6NWHIWFqRGupGRjmGwei2ZkNbugWOv55MaX6+mNuZ2j0yGjkV1QjMU7ykYivy8ZgP8Z7wEAXFZ5IFotBi2vdffH9/ujpZSozPxi/Hz4Ot7beEHr+Y7GpuLtP89J8z0A4LnHvSsNogAxTVr+GEBMldt/5ZZWYAwAcWoXjC58EwUw0Ur5BsTOuJvpeUjKyMcc2b4dr+L1q6tPc1cEuFhVOkpclUZvb5H+X36/bsrOQ7nlOttWHYyFX2nqnZutEokZ+TqdVX+pOqJXyXH0MzoK07xb+P1YPN5cX/a3lwdRAPD0ynNA6SwnAQJ2RCbjzfVnYa00xvTeTXBU1vGjSVeXi1M745fi7kiDFVJRvXmMZsYKuNoqcf1OrnStkTtw9Q5aL9gJjwbm6B9Sc8sQyPdfpRbTyH86fF2aZ/WT6XD4FCdjmNE+WCEP3b2NgSTdjlv9x5IANfSni1VHnmy07kx8Ot5afxZnbuh+Nn+cuIGiEhUiLt2SghbN6HlFVhyIgbudOAJzKSkL72w4hzRZR+gP/8UgMT0PxSo1zpa+5pJyWUMrDsQgLbdQp5jU96VTNy4mZeIxX91RXn3ZD+V18neUpg9UJLVcunlVncHVNfm3qtsjc59srnOek9tc8jgmGouB1IUCsYiRZvRbY+/lWw9UIFXt8ud0/wxvo12+pqmrNba81lG6KABA16bO+Pe1TujW1AWWZsYI9rDF+vHt8ddEsTCCslEYitRij8oVVUOt58suKJbS7DT0NVDkw7WXk7Ox/8ptPPH5f3jyi/+0TrI30/NwLFb3ZNnl4z1Sg0BfSdAFm6Mw/peTelPTAGB80RTp/56C2NPXyEn8DP4u6YizKl/MKXoeALBrWhe9z6GPJohq6qrdc/xyFz/0bOai7yESX0dLfP98G6wf3wHdmrqgtXdZANKtqTMaOVnppHv0CXLFuTm9yj8VAEhD2O187WFpaoT2fg44+14v7JoWjgEh7jj6dnd42msHo+/0C8S3z7WGtSznvXdzFwiCgEZOVlCaGEEQBLTzEXPRW3jYIsTDDguHhGD1C+1g4iimLgz2LGtUvPpkR60Lm7erE8rP+QxqaAtPewtsm6J/scSlz7TE0mdaaq2hY+fggjXjHteaSzCinRfGhzeCt4MFzCuZPH39TtWlvH0dLBHiYYe3+jbFaxVM8PVoYIEQWQGKYa09MKmCk7S9jTX8QzqgrY92YBngYo2I6eE4P7e3zmN6BLpgzxvhcLQyg13pvCNTIwWWPtMSw1p74KOhFS/+2C/YFYNDPTCus3Y6ycjHvdDY2Ur6G2xQdcSGZp8h74lvtLbTXJj1EdMW9Y8k3MoqMDiIuhspWfn4Zu81ZBUU460/z0kj2afi0hA0Zxve3nAOnT/eg0Zvb6kwnQ8Q5wSM+O6w1CP898QwrftvpufhSmkj2cq2rIGir0JYTXp/7CB8YfsGTqka47PiodLtj/s5aFVsBKC3cfHRvxd1AqLy52Z9yj8GEOcfzVh/Tit1T+OAEIpDKjGVs72fA34Y1QbupfMP41JzMX3dGenzuxt9ylVNA4CPh4Xgm+dao1+IG0yMDGs0v9s/sNIiHbNL53WlouwcrhR037emlz28iVggZf3JG1opWEDZyJAiJ1kriAJ0r1l5stGW5Kx87C0dWc7KL8bsvytuPJYR8E7xWCwqfroa24ouLeiLzZM7VbndjbQ8qaiRPv2D9QdZfk7V+450+HC31JH7zbOtcea9XsgVxMDcSsjDxMfF602RmR0a2pnj46Hac6d9HS3x7ONeuFvv9AvEqPb6U77kqffl5zD9fTpBCqI2n03U6vCtyILNUei+eC8m/HJSK4gCxEqGy/+LkdKDAUgBrrzjuHwQJbflXFKlwUZlugTorl04pYe/1lxHjR6BtbjeoB5e9hZ4pp3u0hwWshTzSLUPPix6Bl8XP4GTav3X7PFdGtXaPtYGjkjVQ3OebI4xYb4oLlHDwsxIuiA7WSvx39Xb6Buk/4TY2rtsEmdwE3+8qn4DjsUpSLQLxfhAe+DTsm1/+E9/A0wh6Pb6y8l7YTTKj2bJt914+iZaezfQmawKQKd3TVONpyxNS8CfJR0xxOg/HDJui6+HtUIbH3sM+foA4lOBJwvfBwAsf74NGjlZYd7A5tW8mAGdA5zw4wvt8Opvp6SREG97SzzV2gN+TpbYfDYRN9Ly8PGwEBy4eht/n07QqqSoIa+OJg+qghra4PzNTCmnuKKFYTXpAy42SpyY1RMKQdCqWuNso8TeN7ri7M0MDPv6IJ5o4Y6XShvdPQNdoAaw52IK2pRr+APApG6NsepgLN7pH6h9R2mZUcfcsmNg8GMBwF5bID8dACCYWmBgy4bYcOomTIwEhHjYYf4gsSHWxNUatuYmWmkJDe3M8WQLdwiCgP+2WqA0YxAfvzgAJrYNcFU2H+KDwUEQBAEz+jSFSqVG8JxtOo0bfdr52OtUJ7QxLzuFTenhD2OFoDViAAAeDczR3s8BWfnFaOpqLVVNjL2Tg01nEzGusx+ib+WgY2MHDA71gLGRAnYWpmjqai2lZLjYKGFmbAR9Bd1aeNiKFcAATO/dBPP+icTSZ0LRJ8gVA1uKnRhO1mbwbGABW3MTqYPiu+daS4F7/xA3/Hz4OoIa2qJHoAsGhLjB2EiBDo0c8d/V21BDgZ4Dn4OlqRHO3xErm+krtmGsEGBjboLUnMJKR+BqQ98gV7TyaoDhbT0RcSkFr605jajETK0iEoO/Ooizc3rhi91XkVtYIs1X0dD3N9bQlLq3NTdBC087mJsYafVMA4C5iRF8Pd2hmbyTpa58rR4/R0uEejXAhlM3Kj3vPfu4l1b5ewCY2LUROjR2RJOX3kTrBa2k2xvamaO1dwOtxkNFyqcqyq0c0xbvbjhf6TZyFaXtzBrQDCfj0qRe39UvtIOpsQJrjsUjISMfY0rnepYnCIC3vYWUPnh4ZndkFxQj4lIKFmyOgp9j2ejWkNYNseFKutbjg0urLjZ1tcGeN8Ix889zOj3pc59sDltzE8Sl5iIxIw9pOUXo2tQJQ1p5YGdUMg5H6x4Lr/cIwIAWbli8/RJszU2gbtACQtIZRNs+DlTQUT+wZUP8fvyG3jRdTSDVULgNMXgSYIF8zDfRXfJFPt9YrRZTUA3lYmOmN+XV3VaJhIx8hDV20OkAsTIzhpe9hTTH88jb3fFYuXkpGg3tzHWOmR6BzvhyZCsszC/SSR/WZLRUl0cDc+m85erkBNwBnE0KYFEsno8Cfb1x4H/iqHTsnRx8VRp87Z7WBcdi03S+R/rseSMcA5bt17ouvNTZD2q1GlGJWRWeI7wdLPDva52kUuQVkZ/ba1IbnwYY79MI78pGv7s3dcb7g4Px+EL9f69ezVzgaqvET4evw9VGib8nhWHVgVjcSMuTzuFv9ArAf1dvY0Q7L2luuVw7H3u42iqxYLP2lI/m7rY6c64A8TtkY26Muf9oz8f945X2EAQBQ78+qPMYH4eyc4FGz2YuuJKcheTMAnw8LAStvRvAzNgIj/naa6Xlv9m7CY5dF89B4zr7wcFqBt7fIv59ym+7/fXOCCg3r62+YyBVD1mYGuutDGNrboKlz1Rc2lLO0coM7017HYXFKixwsARy9J8oh7RqiH2XxfkSozv4YPaAZthw6qa0NkFlXu7sh2+rWOzyRloe/j59E6+tOV3l8w1t1RDhTZ0x5CvxS9zY2QqLU55CotoeZ52ewrelDWB/Z2tpQvKhmd3gZis2lgaHNpQCqaau1nilSyNMWav/dQOcxeC0hYetFEh52VugsbM1ZvYNxHOPeyM+NQ/tGzlgeBtPLBgUpDcY6hLgBGszY1iaGaOdb1kg+0qXRpj3T6RWRUNNY0wzv2167yZaz1VRaVuFQkBLTzscf7eHVmlmTcDVo4JRtM4BTuisp/dKU3VNqohoZAoojMTSpKWBFEwtMa93c/Ru7oLOAU46J295gZCVo9vCT7buWYx5c3TMj8AdtTUcbMSe6n4hbvj3fCI6+jtpTa5XKAT4u1jrzW/X8LQ3R5cAJ/Rq5ornS1O4TI0VSM8t0jrhCoKAvsGuWLLzstbFpHHpKN3rPbXndM0fGISn2niiY2NHabK2XDtfe+li6yZbA2dQS3etwh/+LmUjDyMf88bTbTx1qml1a1r2N5o/sDlSc4rQI9BF+izcbM0RMb2rzj5M7u6PozGpCPawlf72b/VtiqfbeqLrogitbS1MjbBxYieUqNTou3S/znMBwF8TwxCVmKlVFr66/nilPVxslFh7LB5f7BFTtfwcLaVe/0GhDaX1XJytxc/rmp4G2pLtl7Hviv45YsPaeFTYSNLwKq3y+FoPf/x58gZslCY4fj0NHwwOxpBWDaH875QUSHUIaoQN54BO/k4689Kcrc2w+41wAMDYjr64kJCBw9GpWH9SnKujNFFgwaBgpOYUYEyYr04DsHHpOcTBygyOVma4nV2A4W088E7/ZjA1VqCFp51O1Uy5Zm42iEwUG5+tvRvghTBfTPy1LM3wcV8HvNzFTzqnBTe0RVNXa6w7cQMmRgK6NXWW5tNWJsDFCsYKAZvPJiLQzUYqye/ZQP/6ex8PDcHl5CyE+TviWEwqvoq4hkA3G6mCpreDBcyMFejS0AJ4R3xMkLstlCYK5Bep4OdoCT8nK60Rf48GFmjkZCUFUstGhCK8iRNsKuhgAsSOrfKB1K5pXeDnKJ5rdk7rAnMTIwi5K4CEU5geNBTBkcm4lV2Iz3Zc1iq6EeJhi+XPt8GFhAwUFqvQobEjvOwtsOdSCnKOngbuAIOMDsIKeXixaDqGG0VgqJH2d6hAbYISGF5+vJ2vPRpYmGDbhWR0aOQAI4WgE0h9NDQYT7f1QnpuIWzNTfDDfzFYsDkKr8h65l8I88GcfyIxrrMfXGyU+ObZ1sgtLEZwQ1vYmJvgu33R6NXMBe187TFvUyRWHoiVHqspI2+jNNFpuLrbKvF8e2/8eOg63u0fiLjUXPwoK9owtWcAlsg6pyaEN5bOly39vYA7QO/GFmXXE6uya86LnfyQmJGP/sFuEAQBbbwb6A0U5RY/1QK+jpY4MasnUjILMPHXk3iuNHNDEAS8Eu6Ho6v0f680x5O7nikFnvbmUruhkbNVlYGUtdJYqlZZGfn1wMveAl2bao8CjergA1dbJUa19xarnD4Tip8OX8c/ZxLQwtMO3z7XGoIgYEQ7Lzhbm8HBygxv9mmKTWcTpECqo78TJnUTR2+u6Fm8vZGzlXhuHtceX0dcFZcr6OSrVRH3jV4BOBKTCiOFgEndGkMhAF9FXMOtrAK08LRDlwAntPGx13luQOyga9/IEbF3tM+BM/o0hZutEvlFJVpVCid2bYzcwkuY2jMAZsYKPObngMGtPDCirRfaN3LQWjj7rb5NMeTrg9I1+0ELogAGUg81TYBRmb5BblgyvKVWqcwhrRoiKTNf73wIjYvz++BMfHqVgdSuiynYJSvR6WqjxMKhwTq9oMYKAX2C3BDgYoV+wa5QmhiJJ9SZ2fik+Bm0VpSNuPi7WEll0l2syxq48kCnRKWWGjr6aO4L9bKTbgtwLdveo4EFPGQNjYpGlFxslNg/oysUCkEr2BgQ4o4BIe5a277eIwCNnKwwOLQhCotVBq95pRn1uGf2foCdN5BeerHUpOLJFxE1tYa10gR9Khj97NHMRUpdKH/hENq+gPmb03HH6TF8VnpMWZkZY+UY/YURmlQSSJkaKbB9SheYm4rlXL8a2Qq+jpZIyszHpjOJOsFRY2drbH61E1xszPDtvmgoTYy0LiZyDSxN9aZJaLTwsAMgfkYuNmXH2SdPtcCr3f0RezsHV1Ky0SNQO5DVV5JY7rn2PpXeL9fO1x77Z3TVqZbk62iJH0a1wdLd5xBf2gu/e1o43G2tUaJS682jb+Fhi5aedmjhYQtXGyVupOdVOG9IH19HSzhYmeGN3k1wMz0PJ+PSsOblx/HB5ijcySnU+iydy5WgD3SzwUudfDH19zNaKTFyXvYW6Bfshj9P3tA7EqGhKW//SpdGeKVLI+QWFuNqSjaCG5auT2duJ207rENz9BzcDgoBeGHVMThYmqGxsxW+iriKeQODpO2audugmbsNejZzQdemTuje1AUqtVprXZtQLzutNGR/Wenyd/o3xb7LtzH7ieZSwDvyMS+ciktD90AXfFhaalhu8+SOuJSchbg7uejZTAyqF223lCbDm5saoXdzV3yx+yr6Brli7sAgqNVqtPJuAG8HsThNdQKpJi7WCPVqgJzCYvSSdbq42yn1bt+hsQOGtxXTc0Ia2sLESIGhrcrSzU2MFOIxLOuca2Bpij/Hh8HESNCpkqYhL7/dp7mr1hpr+ng5aH9vuwQ4aaVLStc3i8aAo5iqqzlf9W7ugtErjkmBqoWpMbo2ddY5V418zBtw6gj8+DEAoIfRKfwwpDF8dl0AymU6ykejygcjGn6Olpjc3V/qwHurb1O80qURbmcX4HE/BzzZwh1fRVzT+W5q3ovmHP9CmC9CvRpIo3oA8Hx7H3T0d5JS/LXWdQK0Urvee6I5ujZxxvMrjgKA1vnj15ceR3JmPjqUVn2ztzLFuC5+eEm2FEVKZgG2XkhCJ39HvNKlEYIa2qCtjz0EQdDqzHN2FOeo2ZWkAvvXizc6lKVN21ua4lNZ0SmFQsBPLzyGW9kFmLcpUmduzH8zukrXXaWJEbwcLPDPqx21tmnjYw9rM2MoFALGdfbDpzsu4zE/exyLScOkbuJrdw5wQisvO62iOINDPbCsNHumubsNPBtY6J1X++zjXniyRUOxil25IlwaGyeFYWhp43/uk0FwsVVi+4VkDGvtARdrM63MHs21Y+7AIMx5srmYdu9rj7f7NUUDC1Op3VW+89xHtpiwvC0jv54FN7TFi518pddo52uPUK82OHczAyENbXFKdl1t3tBWCsY0Vo1pi9vZhTrXQU31Vg2liZHOVAhAvCYYKQSd9b/0deLamptIBcsCXW0wqKU7zIyN0LI0iKuscnJ9x0DqEfbxsBAph1Y+SiAIAsZ29MWvR+KgVqthb2WqVT2nb5CrdJKTm9ozAP2C3VBUosLHWy9ij54vRgNLU7TxbiD1ZD/7uBcmd/dHQZFKOkF8NbK1tL2m4lMjWR73EyHu+PHgdfQPcdNZvK2drz2OxqSie6ALAt1s0C/YFf+eT0L5KVqai32oZwM8394bjlZmUi+6oaob4DhYmWFMmG/VG9Y2QQAadRVXpAcApSaQkvVGmVUchAJiL1vsnRxM69lE574hbXzwc/E0jCjXaKnIyMe9sC0ySauy4Wvd/ZGSVYDxXRpJJbwFQUC/0lHJQDcbdK1gMUVNuuXb/QL13l9drWSpms1kFzkTIwUaOVmhkZMVugdWPqeuJsiDOLnugS5o6WUOx0Xi75o5JUYKAT+NfQzRt7JxJ6cQT31zSOt5BEFA16bOKFGppUDK1FiBkY954cLNTFiYGeHgtTuY0sMfNkoTzN8UiQAXa60ex0+fLut8+UzPKHn5ToIXwnwwsGVDrD0Wr9MANTNWYO/0rnCyNoORQsDK0e2QlV+Etzecw55Lt2BuYqQ1J6h8p4aFqTFCPOxkTyhrkNi4SZ/LulfESoJqtRovdvLV+721szDV6QDR+GRYCHZFpeCznVdgb2mKJrKGxeBQDwwO1Z7b6u1giXWvdICqtLJb+TlIgiCgqasNmrqW7e+kro0xbd0ZzHlCbBS72Chx5O3u0vlZ03MNiI2r5ftjENbYQWe+2zNtPZGYkQ8/J0tpna0J4drzApu7a1fqHBzaEE+0cNPqQHKwMtPprKhIVYtAhzV2xLLdV9HGu0GVQRQgdrppzHmiGfoZUEjB2VqJ6X2aYMzKYzpzjnXYaM8h7m6TAGTrFgfKhRne6tsU+UUleKVLI2w8nYA3159Fh0YOeKd/IDzsLGBrYQK1Wo2pv5+GSi2OBgLiiJDm3K9vTpJ8xBsQAw55qrjmtso6B8uTd2bIFzY2UghwtzNHoJsNom9lo1NjJ5gZa3c4vdW3KZq4WmNMmA9MjRVaI+paNN+16D1ltzUbVOl+KRQCXGyUcCzXOdSnuWuFxankbJQm2PJaJxgbCXCzNceE8EYQBEFrMVcrM2P8OSEMey6mYNKvJ/FW36Zo6dkAy3ZdQQMLEwwJ9YC5qRH8nCylwjLWZsZY9UI7hHraSc/zTDsvfL77qlaqpKmxAsENbbFmXHuYmxjB1sIEM/sGYmbfsuuNvaWpNFdRXolP3s6qqqO7ubsN3ugVAG8HS63gVWlihOfbe+PgtTv4YVSbsnX0SpkYKdDKSzx2/EoDnRKVGiF6Fjgvfw7QGB/eCCUqtVRRUmmi0Mq4aepqjaGtPPRmclSHQqF93XilSyMciU7VnYbwgBDU+qoAPGIyMzNha2uLjIwM2NhUr5LOAycnB7AST8KBr/8Be+cGOPBWt0ofkl9UArVaLPP64urjmNorAI5WZugZ6CKtEfXToVjYmJugf7CbTk/8sl1XtFICACCssQN+efHxau/25rOJ2HQ2AXMHNtcKdIpKVDBWCDprsJy/mYHN5xIxPryRNMyfU1CMN/84ixvpedL6TBfm9ta7ivoj4/SvwF/jxf+7tQBe3gf8OQ44u1a8rcccoOPr92131Go1ztzIwKAvD6BfsKtWMF2Xfjp8HQnpeZjaM0BnMcX6IKcwB1YLxe919sxsWJpqN9JUKjX8Sque9Q9xw5f/a6V1v6ZCqKmxApcX9AUg/i1yCkuki3d6bqFYwKaSwiDlqdVqdP5kD+JT8/Dx0BAMa+0BhUJs7Hy7LxqHo+/g/cFB2BGZjGZuNnjMT7eCVWGxChl5Rbh+Jwe/HomDQiHgeGwqVoxuKy12qtelf4HfSpcffzcFML73ksJyKVn5MDMykhY0ro6E9DysPRaPm+l5+OPEDYR62WHDhDC922YXFGs1nKpj2u9nsP7kDQxv44HXegTAzUap08lUnlqtxj9nE9HQTglBEBDqaVfhwpt6ya4pyM4GLKsuWnA5OQseDcz1zvMoL/Z2DrotjoC/szW2va6/wE1VEtLz4FrVZ1GUByz0AFSVp3FlWTeC9bSy1MsSlRo7o5LR2ruBVqACAP9duY2Y29kY+Zi3zmtfTMpEn8/K0gYtTY1wYlZPg75f1XEnuwCtS6uILn6qBYa21g4o84tKkJVfbHBmhJYrO4FfyoqswKcTMHpTtR76+a4r0nzW3dO6VP6dvgfybJurKVlwslZqFTKZvykSP/wXg4VDgqVOCrmfDl/HrL/OY3J3f1xLyUa/YLcqqyN2WLhLKp4T/UG/Kr+LtSnikpi9E15Bx2Nlhn9zCEdjU/Fqt8aY1qsJNp1NgJOVmd7z9b2qaBH5ulTd2OARbkk+ulp722FUz+ZVbqc5sYc3cUbU/D56G5KVpSlN7u6PASFu+PPkTWlORXUuoHL9Q/SftCpq1AY1tEVQuZ4XSzNjfDmyFfKLSrBkx2W08LB7tIMoAHApS2uSehUtZCXNTWvnolYRQRDnge1/s6s0F6M+eO4BWxiwPIVCQBMXa1xKzkK4njRGzYivPK2kfPrO3aSUCoKAfyZ1RG5hidZ8BYVCwPjwRhgfLs79qGyE1tRYASdrMzhZm1WYu69Xo27A4xMB58AaD6IA3NXItbudOV7vGYDb2QVwtzPHCD2VrTQMDaIA4P3BQegc4IiuTZ0rnXckJwgCnmyhf/Stthgy/8HH0RI7pna5p7V19M2V0WFiDjzzG/DrU5VuZm2t3ZAyUgjSnMDyOvo7aq27KNfU1QarxrSFt4MlbqTlwlppUuNBFAA0sDCV1jDUN1poaOeIXspyIxqWFadKl2cn64hoUFNp63rIG+eNnXWPv5l9m+KpNh5oUsGx+exjXujT3BWOVqbVbuhb6pnLXFfuJoDS+HhYCDafS8ToDj4AUOFofU2ob0GUIR7x1uSj6ecXH69W76Hc3fbG+zlZYVqvACmQyi28+4VT75XSxOie070eGk5Ny/5fVJq2YC9r1Jprp5XcLxXNZ6K7t+TpFjh3IwODQxvq3De5uz8Wbb9UK+Vm7SxMYVcXf05jM6DPB3XwwlVztDLD1GqmyhlCaWIkVYh8mJQvIV9rAnoB4W8DEbLjpvObwL6Py343rkZQVk2axq18WYiaplAIWPdye9zKLtBbvKpGyOYjAgCsqt9ol3eqVlbqvrYZGym00mvLEwTB4FG74W088f6WKPg4PNjXMx9HywdqPae6wkCKap0gCFLPd0WVoug+MzYFTCyAotyy+VCOsomoVrU/94fuj+buthXmwr/U2Q+jOvhUa84K0UNN3pFk5Qp0ewd47GXgk9JOhgewx9zfxbrC4h81QmdEquKF2svr2tQZnvbm6NbEuc5HbWras497I7ewBH2D9Y9Y0sOFgRTdFx8NC8Ffp27WSm8s3aVus4CDnwM95oq/uwQBitJTgnzEih5qDKKIIK2vB6Cs8pw8MFBVvdbdI+ceUvvsLU2x/83K52k/qMxNjfBaD/2LzdLDh4EU3Rf9gt2kimtUT7SfIP5oWDoCL+0G1CqttUCIiB568kBK38iKmoGUDmMzMeWxuDQ93PLu5+MQPajYFUlEZdxaAO7VW/SZiOihYelUNhLl20n3frXq/u7Pg0I+T8qAESmihwVHpIiIiOjRJgjA8B+B2ANAq1G699s92BU8a42ZDZBVurCuAXOkiB4WHJEiIiIicmkOPDYOMJJVkeswGRCMgMfH191+1WcKWQl1A6r2ET0sOCJFREREpE/PeUCXGWXVTUmbvAiHae2VcyeqrzgiRURERKSPIDCIqozXY3W9B0R1ioEUERERERmu7YuAtTvQb1Fd7wlRnWBqHxEREREZzq0FMC2qrveCqM5wRIqIiIiIiMhADKSIiIiIiIgMxECKiIiIiIjIQAykiIiIiIiIDMRAioiIiIiIyEB1Gkjt27cPTzzxBNzd3SEIAv766y+t+7OzszFp0iR4eHjA3NwczZo1wzfffKO1TX5+PiZOnAgHBwdYWVlh6NChSE5Ovo/vgoiIiIiIHjV1Gkjl5OSgRYsW+PLLL/XeP3XqVGzduhU///wzoqKiMGXKFEyaNAkbN26Utnn99dfxzz//YN26ddi7dy8SEhIwZMiQ+/UWiIiIiIjoEVSn60j17dsXffv2rfD+gwcPYtSoUQgPDwcAjBs3Dt9++y2OHj2KJ598EhkZGfjhhx/w66+/olu3bgCAlStXIjAwEIcPH8bjjz9+c/WftAABAABJREFUP94GERERERE9Yur1HKkOHTpg48aNuHnzJtRqNfbs2YPLly+jV69eAIATJ06gqKgIPXr0kB7TtGlTeHl54dChQxU+b0FBATIzM7V+6N7l5AAZGWU/OTnat2t+J6IHX07O3X2nNeeDlBSeF4iI6MFWpyNSVfn8888xbtw4eHh4wNjYGAqFAt9//z06d+4MAEhKSoKpqSns7Oy0Hufi4oKkpKQKn3fhwoWYO3dube76IycnB/jrL0Aek9rYAD17Ajt2iLfb2ACDBgGWlnW1l0RUEzTfd8Cw77TmcbduAZcvAwEBgJMTzwtERPRgqtcjUp9//jkOHz6MjRs34sSJE1i8eDEmTpyInTt33tPzzpw5ExkZGdJPfHx8De3xo6u4WAyWzM2BBg3EfzMzgfz8suAqM1PcjogebJrvu6Hfac3jVCogLQ0oKeF5gYiIHlz1dkQqLy8Pb7/9NjZs2ID+/fsDAEJCQnD69GksWrQIPXr0gKurKwoLC5Genq41KpWcnAxXV9cKn9vMzAxmZma1/RYeSUplWc9yXl7Z7WZmQEFB3ewTEdUvpqbivzwNExHRg6zejkgVFRWhqKgICoX2LhoZGUGlUgEAWrduDRMTE+zatUu6/9KlS4iLi0P79u3v6/4SEREREdGjo05HpLKzs3H16lXp95iYGJw+fRr29vbw8vJCly5dMH36dJibm8Pb2xt79+7Fjz/+iCVLlgAAbG1tMXbsWEydOhX29vawsbHBq6++ivbt27NiHxERERER1Zo6DaSOHz+Orl27Sr9PnToVADBq1CisWrUKa9aswcyZMzFy5EikpqbC29sb77//Pl555RXpMZ9++ikUCgWGDh2KgoIC9O7dG1999dV9fy9ERERERPToqNNAKjw8HGq1usL7XV1dsXLlykqfQ6lU4ssvv6xwUV8iIiIiIqKaVm/nSBEREREREdVXDKSIiIiIiIgMxECKiIiIiIjIQAykiIiIiIiIDMRAioiIiIiIyEAMpIiIiIiIiAzEQIqIiIiIiMhADKSIiIiIiIgMxECKiIiIiIjIQAykiIiIiIiIDMRAioiIiIiIyEAMpIiIiIiIiAzEQIqIiIiIiMhADKSIiIiIiIgMxECKiIiIiIjIQAykiIiIiIiIDMRAioiIiIiIyEAMpIiIiIiIiAxkXNc7QPeJpSWgVtf1XhBRDbI0tYT6PX6vqQ7wmkJExBEpIiIiIiIiQzGQIiIiIiIiMhADKSIiIiIiIgMxkCIiIiIiIjIQAykiIiIiIiIDMZAiIiIiIiIyEAMpIiIiIiIiAzGQIiIiIiIiMhADKSIiIiIiIgMxkCIiIiIiIjIQAykiIiIiIiIDMZAiIiIiIiIyEAMpIiIiIiIiAzGQIiIiIiIiMhADKSIiIiIiIgMxkCIiIiIiIjIQAykiIiIiIiIDMZAiIiIiIiIyEAMpIiIiIiIiAzGQIiIiIiIiMlCdBlL79u3DE088AXd3dwiCgL/++kvrfkEQ9P588skn0japqakYOXIkbGxsYGdnh7FjxyI7O/s+vxMiIiIiInqU1GkglZOTgxYtWuDLL7/Ue39iYqLWz4oVKyAIAoYOHSptM3LkSFy4cAE7duzApk2bsG/fPowbN+5+vQUiIiIiInoEGdfli/ft2xd9+/at8H5XV1et3//++2907doVfn5+AICoqChs3boVx44dQ5s2bQAAn3/+Ofr164dFixbB3d299naeiIiIiIgeWQ/MHKnk5GRs3rwZY8eOlW47dOgQ7OzspCAKAHr06AGFQoEjR45U+FwFBQXIzMzU+qGakZ8P5OSI/8oVFNTN/hBR/VNYKP7L8wIRET3IHphAavXq1bC2tsaQIUOk25KSkuDs7Ky1nbGxMezt7ZGUlFThcy1cuBC2trbSj6enZ63t96PC2BiwsQHy8oC0NPFfGxtAqRT/BcR/jet0DJSIaoLm+27od1rzOIUCaNAAMDLieYGIiB5cD8zla8WKFRg5ciSUSuU9P9fMmTMxdepU6ffMzEwGU/fI0hIYNAgoLi67zdhY+3bN70T0YNN8rzX/N/RxxcXiaJSZGc8LRET04HogAqn9+/fj0qVLWLt2rdbtrq6uSElJ0bqtuLgYqampOvOr5MzMzGBmZlYr+/ooq6gxxEYS0cPnbr/XPB8QEdHD4oFI7fvhhx/QunVrtGjRQuv29u3bIz09HSdOnJBu2717N1QqFR577LH7vZtERERERPSIqNMRqezsbFy9elX6PSYmBqdPn4a9vT28vLwAiGl369atw+LFi3UeHxgYiD59+uCll17CN998g6KiIkyaNAnPPPMMK/YREREREVGtqdMRqePHjyM0NBShoaEAgKlTpyI0NBSzZ8+WtlmzZg3UajVGjBih9zl++eUXNG3aFN27d0e/fv3QsWNHfPfdd/dl/4mIiIiI6NEkqNVqdV3vRF3LzMyEra0tMjIyYKMpMUdERERERI+c6sYGD8QcKSIiIiIiovqEgRQREREREZGBGEgREREREREZiIEUERERERGRgRhIERERERERGYiBFBERERERkYEYSBERERERERmIgRQREREREZGBGEgREREREREZiIEUERERERGRgRhIERERERERGYiBFBERERERkYEYSBERERERERmIgRQREREREZGBGEgREREREREZiIEUERERERGRgRhIERERERERGci4rnegPlCr1QCAzMzMOt4TIiIiIiKqS5qYQBMjVISBFICsrCwAgKenZx3vCRERERER1QdZWVmwtbWt8H5BXVWo9QhQqVRISEiAtbU1BEGo033JzMyEp6cn4uPjYWNjU6f7Qg8GHjNkKB4zZCgeM2QoHjNkqPp0zKjVamRlZcHd3R0KRcUzoTgiBUChUMDDw6Oud0OLjY1NnR9E9GDhMUOG4jFDhuIxQ4biMUOGqi/HTGUjURosNkFERERERGQgBlJEREREREQGYiBVz5iZmeG9996DmZlZXe8KPSB4zJCheMyQoXjMkKF4zJChHsRjhsUmiIiIiIiIDMQRKSIiIiIiIgMxkCIiIiIiIjIQAykiIiIiIiIDMZAiIiIiIiIyEAMpIiIiIiIiAzGQIiIiIiIiMhADKSIiIiIiIgMxkCIiIiIiIjIQAykiIiIiIiIDMZAiIiIiIiIyEAMpIiIiIiIiAzGQIiIiIiIiMhADKSIiIiIiIgMxkCIiIiIiIjIQAykiIiIiIiIDMZAiIiIiIiIyEAMpIiIiIiIiAzGQIiIiIiIiMhADKSIiIiIiIgMxkCIiIiIiIjIQAykiIiIiIiIDMZAiIiIiIiIyEAMpIiIiIiIiAzGQIiIiIiIiMhADKSIiIiIiIgMxkCIiIiIiIjIQAykiIiIiIiIDMZAiIiIiIiIyEAMpIiIiIiIiAzGQIiIiIiIiMhADKSIiIiIiIgMxkCIiIiIiIjIQAykiIiIiIiIDMZAiIiIiIiIyEAMpIiIiIiIiAzGQIiIiIiIiMhADKSIiIiIiIgMxkCIiIiIiIjIQAykiIiIiIiIDMZAiIiIiIiIyEAMpIiIiIiIiAzGQIiIiIiIiMhADKSIieuisWrUKgiBIP0qlEu7u7ujduzeWLVuGrKwsre3nzJkDQRBw+/btSp83NjYWY8aMQaNGjaBUKuHq6orOnTvjvffeq823Q0RE9ZBxXe8AERFRbZk3bx58fX1RVFSEpKQkREREYMqUKViyZAk2btyIkJCQaj/X1atX0bZtW5ibm+OFF16Aj48PEhMTcfLkSXz00UeYO3duLb4TIiKqbxhIERHRQ6tv375o06aN9PvMmTOxe/duDBgwAE8++SSioqJgbm5eref69NNPkZ2djdOnT8Pb21vrvpSUlBrdbyIiqv+Y2kdERI+Ubt26YdasWbh+/Tp+/vnnaj/u2rVr8PDw0AmiAMDZ2bkmd5GIiB4ADKSIiOiR89xzzwEAtm/fXu3HeHt7Iz4+Hrt3766t3SIiogcIAykiInrkeHh4wNbWFteuXav2YyZPngxTU1N0794doaGhmDJlCv7++2/k5ubW4p4SEVF9xUCKiIgeSVZWVjrV+yrTvHlznD59Gs8++yxiY2OxdOlSDBo0CC4uLvj+++9rcU+JiKg+YiBFRESPpOzsbFhbWxv0mICAAPz000+4ffs2zp49iw8++ADGxsYYN24cdu7cWUt7SkRE9REDKSIieuTcuHEDGRkZaNy48V093sjICMHBwZg5cyY2bNgAAPjll19qcheJiKieYyBFRESPnJ9++gkA0Lt373t+Lk159cTExHt+LiIienAwkCIiokfK7t27MX/+fPj6+mLkyJHVftz+/ftRVFSkc/uWLVsAAE2aNKmxfSQiovqPC/ISEdFD699//8XFixdRXFyM5ORk7N69Gzt27IC3tzc2btwIpVKptf2SJUtgYWGhdZtCocDbb7+Njz76CCdOnMCQIUMQEhICADh58iR+/PFH2NvbY8qUKffrbRERUT0gqNVqdV3vBBERUU1atWoVxowZI/1uamoKe3t7BAcHY8CAARgzZoxWoYk5c+Zg7ty5ep/LyMgIxcXFOHjwIH799Vfs3bsX8fHxyM3NhZubm7TAr5+fX62/LyIiqj8YSBERERERERmIc6SIiIiIiIgMxECKiIiIiIjIQAykiIiIiIiIDMRAioiIiIiIyEAMpIiIiIiIiAzEQIqIiIiIiMhAXJAXgEqlQkJCAqytrSEIQl3vDhERERER1RG1Wo2srCy4u7tDoah43ImBFICEhAR4enrW9W4QEREREVE9ER8fDw8PjwrvZyAFSKvbx8fHw8bGpo73hoiIiIiI6kpmZiY8PT2lGKEiDKQAKZ3PxsaGgRQREREREVU55YfFJoiIiIiIiAzEQIqIiIiIiMhADKSIiIiIiIgMxDlSRERERFQj1Go1iouLUVJSUte7QlQhIyMjGBsb3/OyRwykiIiIiOieFRYWIjExEbm5uXW9K0RVsrCwgJubG0xNTe/6ORhIEREREdE9UalUiImJgZGREdzd3WFqanrPvf1EtUGtVqOwsBC3bt1CTEwM/P39K110tzIMpIiIiIjonhQWFkKlUsHT0xMWFhZ1vTtElTI3N4eJiQmuX7+OwsJCKJXKu3oeFpsgIiIiohpxtz37RPdbTRyrPNqJiIiIiIgMxECKiIiIiIjIQAykiIiIiIjuwapVq2BnZ1ft7X18fPDZZ5/V2v7ci9GjR2PQoEH3/XXnzJmDli1b3vfXvRcMpIiIiIjokZWUlIRXX30Vfn5+MDMzg6enJ5544gns2rWr1l7z2LFjGDduXK09f2W+//57tGjRAlZWVrCzs0NoaCgWLlwo3b906VKsWrWqTvbtQcOqfURERET0SIqNjUVYWBjs7OzwySefIDg4GEVFRdi2bRsmTpyIixcv1srrOjk51crzVmXFihWYMmUKli1bhi5duqCgoABnz57F+fPnpW1sbW3rZN8eRByRIiIiIqIapVarkVtYXCc/arW62vs5YcIECIKAo0ePYujQoQgICEDz5s0xdepUHD58WNpuyZIlCA4OhqWlJTw9PTFhwgRkZ2dX+tz//PMP2rZtC6VSCUdHRwwePFi6T57aFxsbC0EQcPr0aen+9PR0CIKAiIgIAEBERAQEQcC2bdsQGhoKc3NzdOvWDSkpKfj3338RGBgIGxsb/O9//6t0QeSNGzdi+PDhGDt2LBo3bozmzZtjxIgReP/996Vtyqf2ZWVlYeTIkbC0tISbmxs+/fRThIeHY8qUKVrv54MPPsALL7wAa2treHl54bvvvtN67RkzZiAgIAAWFhbw8/PDrFmzUFRUVOlnWN9xRIqIiIiIalReUQmazd5WJ68dOa83LEyrbuKmpqZi69ateP/992Fpaalzv3zOk0KhwLJly+Dr64vo6GhMmDABb775Jr766iu9z71582YMHjwY77zzDn788UcUFhZiy5Ytd/2eNObMmYMvvvgCFhYWGD58OIYPHw4zMzP8+uuvyM7OxuDBg/H5559jxowZeh/v6uqKvXv34vr16/D29q7Wa06dOhUHDhzAxo0b4eLigtmzZ+PkyZM685kWL16M+fPn4+2338Yff/yB8ePHo0uXLmjSpAkAwNraGqtWrYK7uzvOnTuHl156CdbW1njzzTfv6TOpSwykiIiIiOiRc/XqVajVajRt2rTKbcuPvixYsACvvPJKhYHU+++/j2eeeQZz586VbmvRosU97/OCBQsQFhYGABg7dixmzpyJa9euwc/PDwAwbNgw7Nmzp8JA6r333sOQIUPg4+ODgIAAtG/fHv369cOwYcP0rquUlZWF1atX49dff0X37t0BACtXroS7u7vOtv369cOECRMAiKNPn376Kfbs2SMFUu+++660rY+PD9544w2sWbOGgRQRERERkYa5iREi5/Wus9euDkNSAHfu3ImFCxfi4sWLyMzMRHFxMfLz85GbmwsLCwud7U+fPo2XXnqp2s9fXSEhIdL/XVxcpDQ5+W1Hjx6t8PFubm44dOgQzp8/j3379uHgwYMYNWoUli9fjq1bt+oEU9HR0SgqKkK7du2k22xtbaXgqKJ9EwQBrq6uSElJkW5bu3Ytli1bhmvXriE7OxvFxcWwsbEx7AOoZzhHioiIiIhqlCAIsDA1rpMfQRCqtY/+/v4QBKHKghKxsbEYMGAAQkJCsH79epw4cQJffvklAKCwsFDvY8zNzav9WWmCF3lgV9HcIRMTE+n/giBo/a65TaVSVfmaQUFBmDBhAn7++Wfs2LEDO3bswN69e6u9z1XtW/l9OXToEEaOHIl+/fph06ZNOHXqFN55550KP78HRZ0GUgsXLkTbtm1hbW0NZ2dnDBo0CJcuXdLa5uWXX0ajRo1gbm4OJycnDBw4UOeAj4uLQ//+/WFhYQFnZ2dMnz4dxcXF9/OtEBEREdEDxN7eHr1798aXX36JnJwcnfvT09MBACdOnIBKpcLixYvx+OOPIyAgAAkJCZU+d0hISLXLp2sq+CUmJkq3yQtP1LZmzZoBgN7PwM/PDyYmJjh27Jh0W0ZGBi5fvmzQaxw8eBDe3t5455130KZNG/j7++P69ev3tuP1QJ0GUnv37sXEiRNx+PBh7NixA0VFRejVq5fWH7J169ZYuXIloqKisG3bNqjVavTq1QslJSUAgJKSEvTv3x+FhYU4ePAgVq9ejVWrVmH27Nl19baIiIiI6AHw5ZdfoqSkBO3atcP69etx5coVREVFYdmyZWjfvj0AoHHjxigqKsLnn3+O6Oho/PTTT/jmm28qfd733nsPv/32G9577z1ERUXh3Llz+Oijj/Rua25ujscffxwffvghoqKisHfvXq35RDVp/PjxmD9/Pg4cOIDr16/j8OHDeP755+Hk5CS9Xzlra2uMGjUK06dPx549e3DhwgWMHTsWCoWi2iN/gDj6FxcXhzVr1uDatWtYtmwZNmzYUJNvrU7UaSC1detWjB49Gs2bN0eLFi2watUqxMXF4cSJE9I248aNQ+fOneHj44NWrVphwYIFiI+PR2xsLABg+/btiIyMxM8//4yWLVuib9++mD9/Pr788ssHfriQiIiIiGqPn58fTp48ia5du2LatGkICgpCz549sWvXLnz99dcAxCIRS5YswUcffYSgoCD88ssvWgvY6hMeHo5169Zh48aNaNmyJbp161bp3KUVK1aguLgYrVu3xpQpU7BgwYIafZ8aPXr0wOHDh/HUU08hICAAQ4cOhVKpxK5du+Dg4KD3MUuWLEH79u0xYMAA9OjRA2FhYQgMDIRSqaz26z755JN4/fXXMWnSJLRs2RIHDx7ErFmzaupt1RlBbchMu1p29epV+Pv749y5cwgKCtK5PycnB++++y7+/vtvXLx4Eaamppg9ezY2btyoNQQaExMjfTFCQ0N1nqegoAAFBQXS75mZmfD09ERGRsYDP+mNiIiI6H7Lz89HTEwMfH19DWpg04MnJycHDRs2xOLFizF27Ni63p27Vtkxm5mZCVtb2ypjg3pTbEKlUmHKlCkICwvTCaK++uorWFlZwcrKCv/++y927NgBU1NTAEBSUhJcXFy0ttf8npSUpPe1Fi5cCFtbW+nH09OzFt4REREREdGD7dSpU/jtt99w7do1nDx5EiNHjgQADBw4sI73rO7Vm0Bq4sSJOH/+PNasWaNz38iRI3Hq1Cns3bsXAQEBGD58OPLz8+/6tWbOnImMjAzpJz4+/l52nYiIiIjoobVo0SK0aNECPXr0QE5ODvbv3w9HR8e63q06Vy/WkZo0aRI2bdqEffv2wcPDQ+d+zciRv78/Hn/8cTRo0AAbNmzAiBEj4OrqqpNzmpycDEBcvVkfMzMzmJmZ1fwbISIiIiJ6iISGhmrVL6AydToipVarMWnSJGzYsAG7d++Gr69vtR6jVqulOU7t27fHuXPntBb82rFjB2xsbKRyjkRERERERDWpTkekJk6ciF9//RV///03rK2tpTlNtra2MDc3R3R0NNauXYtevXrByckJN27cwIcffghzc3P069cPANCrVy80a9YMzz33HD7++GMkJSXh3XffxcSJEznqREREREREtaJOR6S+/vprZGRkIDw8HG5ubtLP2rVrAQBKpRL79+9Hv3790LhxYzz99NOwtrbGwYMH4ezsDAAwMjLCpk2bYGRkhPbt2+PZZ5/F888/j3nz5tXlWyMiIiIioodYnY5IVVV53d3dHVu2bKnyeby9vau1HRERERERUU2oN1X7iIiIiIiIHhQMpIiIiIiIiAzEQIqIiIiIqJYJgoC//vrrkXndujZ69GgMGjSoVl+DgRQRERERPZLuR2P7fpkzZw5atmypc3tiYiL69u17T8+9Z88e9OvXDw4ODrCwsECzZs0wbdo03Lx5856eVy42NhaCIOD06dM19py1jYEUEREREdFDytXV9Z6WBPr222/Ro0cPuLq6Yv369YiMjMQ333yDjIwMLF68uAb3tHoKCwvv+2tWhIEUEREREdUstRoozKmbnyqqQlfmjz/+QHBwMMzNzeHg4IAePXogJycHAKBSqTBv3jx4eHjAzMwMLVu2xNatW6XHFhYWYtKkSXBzc4NSqYS3tzcWLlyo9fya0SFzc3P4+fnhjz/+0Lo/Pj4ew4cPh52dHezt7TFw4EDExsZK90dERKBdu3awtLSEnZ0dwsLCcP36daxatQpz587FmTNnIAgCBEHAqlWrAOim9t24cQMjRoyAvb09LC0t0aZNGxw5ckTv53Hjxg1MnjwZkydPxooVKxAeHg4fHx907twZy5cvx+zZs6Vt169fj+bNm8PMzAw+Pj46QZaPjw8++OADvPDCC7C2toaXlxe+++476X5fX18AQGhoKARBQHh4OICyUcP3338f7u7uaNKkCQDg3Llz6Natm/S3GjduHLKzsyv609aKOi1/TkREREQPoaJc4AP3unnttxMAU0uDH5aYmIgRI0bg448/xuDBg5GVlYX9+/dLy/UsXboUixcvxrfffovQ0FCsWLECTz75JC5cuAB/f38sW7YMGzduxO+//w4vLy/Ex8cjPj5e6zVmzZqFDz/8EEuXLsVPP/2EZ555BufOnUNgYCCKiorQu3dvtG/fHvv374exsTEWLFiAPn364OzZs1AoFBg0aBBeeukl/PbbbygsLMTRo0chCAKefvppnD9/Hlu3bsXOnTsBALa2tjrvMTs7G126dEHDhg2xceNGuLq64uTJk1CpVHo/k3Xr1qGwsBBvvvmm3vvt7OwAACdOnMDw4cMxZ84cPP300zh48CAmTJgABwcHjB49Wtp+8eLFmD9/Pt5++2388ccfGD9+PLp06YImTZrg6NGjaNeuHXbu3InmzZvD1NRUetyuXbtgY2ODHTt2AABycnKkz+rYsWNISUnBiy++iEmTJkkB5P3AQIqIiIiIHnmJiYkoLi7GkCFD4O3tDQAIDg6W7l+0aBFmzJiBZ555BgDw0UcfYc+ePfjss8/w5ZdfIi4uDv7+/ujYsSMEQZCeQ+6pp57Ciy++CACYP38+duzYgc8//xxfffUV1q5dC5VKheXLl0MQBADAypUrYWdnh4iICLRp0wYZGRkYMGAAGjVqBAAIDAyUntvKygrGxsZwdXWt8D3++uuvuHXrFo4dOwZ7e3sAQOPGjSvc/sqVK7CxsYGbm1uln92SJUvQvXt3zJo1CwAQEBCAyMhIfPLJJ1qBVL9+/TBhwgQAwIwZM/Dpp59iz549aNKkCZycnAAADg4OOu/B0tISy5cvl4Kr77//Hvn5+fjxxx9haSkGzV988QWeeOIJfPTRR3Bxcal0f2sKAykiIiIiqlkmFuLIUF299l1o0aIFunfvjuDgYPTu3Ru9evXCsGHD0KBBA2RmZiIhIQFhYWFajwkLC8OZM2cAiCloPXv2RJMmTdCnTx8MGDAAvXr10tq+ffv2Or9riiucOXMGV69ehbW1tdY2+fn5uHbtGnr16oXRo0ejd+/e6NmzJ3r06IHhw4dXGeTInT59GqGhoVIQVRW1Wi0FdZWJiorCwIEDtW4LCwvDZ599hpKSEhgZGQEAQkJCpPsFQYCrqytSUlKqfP7g4GCtEaqoqCi0aNFCCqI0r6dSqXDp0qX7FkhxjhQRERER1SxBENPr6uKnGg1/fYyMjLBjxw78+++/aNasGT7//HM0adIEMTEx1Xp8q1atEBMTg/nz5yMvLw/Dhw/HsGHDqv362dnZaN26NU6fPq31c/nyZfzvf/8DII5QHTp0CB06dMDatWsREBCAw4cPV/s1zM3Nq70tII4sZWRkIDEx0aDHVcTExETrd0EQKkwrlJMHTPUJAykiIiIiIogN+7CwMMydOxenTp2CqakpNmzYABsbG7i7u+PAgQNa2x84cADNmjWTfrexscHTTz+N77//HmvXrsX69euRmpoq3V8+6Dl8+LCUnteqVStcuXIFzs7OaNy4sdaPfL5TaGgoZs6ciYMHDyIoKAi//vorAMDU1BQlJSWVvr+QkBCcPn1aa58qM2zYMJiamuLjjz/We396ejoAMcVQ32cTEBAgjUZVRTPiVNV70LzemTNnpEIgmtdTKBRSMYr7gYEUERERET3yjhw5gg8++ADHjx9HXFwc/vzzT9y6dUsKdKZPn46PPvoIa9euxaVLl/DWW2/h9OnTeO211wCI84R+++03XLx4EZcvX8a6devg6uoqFWQAxOINK1aswOXLl/Hee+/h6NGjmDRpEgBg5MiRcHR0xMCBA7F//37ExMQgIiICkydPxo0bNxATE4OZM2fi0KFDuH79OrZv344rV65I++fj44OYmBicPn0at2/fRkFBgc57HDFiBFxdXTFo0CAcOHAA0dHRWL9+PQ4dOqT3M/H09MSnn36KpUuXYuzYsdi7dy+uX7+OAwcO4OWXX8b8+fMBANOmTcOuXbswf/58XL58GatXr8YXX3yBN954o9qfv7OzM8zNzbF161YkJycjIyOjwm1HjhwJpVKJUaNG4fz589izZw9effVVPPfcc/ctrQ9gIEVEREREBBsbG+zbtw/9+vVDQEAA3n33XSxevFhazHby5MmYOnUqpk2bhuDgYGzduhUbN26Ev78/AMDa2hoff/wx2rRpg7Zt2yI2NhZbtmyBQlHW3J47dy7WrFmDkJAQ/Pjjj/jtt9+kES0LCwvs27cPXl5eGDJkCAIDAzF27Fjk5+fDxsYGFhYWuHjxIoYOHYqAgACMGzcOEydOxMsvvwwAGDp0KPr06YOuXbvCyckJv/32m857NDU1xfbt2+Hs7Ix+/fohODgYH374YaWjRhMmTMD27dtx8+ZNDB48GE2bNsWLL74IGxsbKVBq1aoVfv/9d6xZswZBQUGYPXs25s2bp1VooirGxsZYtmwZvv32W7i7u+vMuZKzsLDAtm3bkJqairZt22LYsGHo3r07vvjii2q/Xk0Q1Op7KLb/kMjMzIStrS0yMjJgY2NT17tDRERE9EDJz89HTEwMfH19oVQq63p3iKpU2TFb3diAI1JEREREREQGYiBFRERERERkIAZSREREREREBmIgRUREREREZCAGUo+KnBxxgTpBEP9PRA+8nMIcCHMFCHMF5BTye033Ea8pREQMpIiIiIiIiAzFQIqIiIiIiMhADKSIiIiIiIgMxECKiIiIiIjIQAykiIiIiIjuglqtxrhx42Bvbw9BEHD69Om63qV6wcfHB5999lld70atYyBFRERERI+k0aNHY9CgQXf9+K1bt2LVqlXYtGkTEhMTERQUBEEQ8Ndff1X5WEEQoFQqcf36da3bBw0ahNGjR9/1Pt2r2NhYCIIg/Tg4OKBXr144depUne1TfcVAioiIiIjoLly7dg1ubm7o0KEDXF1dYWxsbNDjBUHA7Nmza2nv7s3OnTuRmJiIbdu2ITs7G3379kV6enpd71a9wkCKiIiIiGpHTs79/alh58+fR9++fWFlZQUXFxc899xzuH37NgBxNOvVV19FXFwcBEGAj48PfHx8AACDBw+WbqvMpEmT8PPPP+P8+fMVblNQUIDJkyfD2dkZSqUSHTt2xLFjx6T709LSMHLkSDg5OcHc3Bz+/v5YuXKldH98fDyGDx8OOzs72NvbY+DAgYiNja3yvTs4OMDV1RVt2rTBokWLkJycjCNHjgAA1q9fj+bNm8PMzAw+Pj5YvHhxpc+1fPly2NnZYdeuXQAq/1wfJAykiIiIiKh2WFnd358alJ6ejm7duiE0NBTHjx/H1q1bkZycjOHDhwMAli5dinnz5sHDwwOJiYk4duyYFOCsXLlSuq0yYWFhGDBgAN56660Kt3nzzTexfv16rF69GidPnkTjxo3Ru3dvpKamAgBmzZqFyMhI/Pvvv4iKisLXX38NR0dHAEBRURF69+4Na2tr7N+/HwcOHICVlRX69OmDwsLCan8W5ubmAIDCwkKcOHECw4cPxzPPPINz585hzpw5mDVrFlatWqX3sR9//DHeeustbN++Hd27d6/yc32QGDb+SERERET0CPjiiy8QGhqKDz74QLptxYoV8PT0xOXLlxEQEABra2sYGRnB1dVV67F2dnY6t1Vk4cKFCAkJwf79+9GpUyet+3JycvD1119j1apV6Nu3LwDg+++/x44dO/DDDz9g+vTpiIuLQ2hoKNq0aQMAWqNga9euhUqlwvLlyyEIAgAxyLOzs0NERAR69epV5f6lp6dj/vz5sLKyQrt27TB16lR0794ds2bNAgAEBAQgMjISn3zyic7crhkzZuCnn37C3r170bx5cwDV+1wfFAykiIiIiKh2ZGfX9R7ctTNnzmDPnj2w0jPSde3atRpr8Ddr1gzPP/883nrrLRw4cEDndYqKihAWFibdZmJignbt2iEqKgoAMH78eAwdOhQnT55Er169MGjQIHTo0EF6D1evXoW1tbXW8+bn5+PatWuV7leHDh2gUCiQk5MDPz8/rF27Fi4uLoiKisLAgQO1tg0LC8Nnn32GkpISGBkZAQAWL16MnJwcHD9+HH5+ftK29+tzvR8YSBERERFR7bC0rOs9uGvZ2dl44okn8NFHH+nc5+bmVqOvNXfuXAQEBFSr2l95ffv2xfXr17Flyxbs2LED3bt3x8SJE7Fo0SJkZ2ejdevW+OWXX3Qe5+TkVOnzrl27Fs2aNYODgwPs7OwM3q9OnTph8+bN+P3337VSF+/n51rbGEgREREREZXTqlUrrF+/Hj4+PgZV4zMxMUFJSYlBr+Xp6YlJkybh7bffRqNGjaTbGzVqBFNTUxw4cADe3t4AxHlPx44dw5QpU6TtnJycMGrUKIwaNQqdOnXC9OnTsWjRIrRq1Qpr166Fs7MzbGxsDN4n+b5oBAYG6oycHThwAAEBAdJoFAC0a9cOkyZNQp8+fWBsbIw33ngDwN1/rvURi00QERER0SMrIyMDp0+f1vqJj4/HxIkTkZqaihEjRuDYsWO4du0atm3bhjFjxlQaKPn4+GDXrl1ISkpCWlpatfdj5syZSEhIwM6dO6XbLC0tMX78eEyfPh1bt25FZGQkXnrpJeTm5mLs2LEAgNmzZ+Pvv//G1atXceHCBWzatAmBgYEAgJEjR8LR0REDBw7E/v37ERMTg4iICEyePBk3bty4q89r2rRp2LVrF+bPn4/Lly9j9erV+OKLL6RASa5Dhw7YsmUL5s6dKy3Qe7efa33EQIqIiIiIHlkREREIDQ3V+pk7dy7c3d1x4MABlJSUoFevXggODsaUKVNgZ2cHhaLiJvTixYuxY8cOeHp6IjQ0tNr7YW9vjxkzZiA/P1/r9g8//BBDhw7Fc889h1atWuHq1avYtm0bGjRoAAAwNTXFzJkzERISgs6dO8PIyAhr1qwBAFhYWGDfvn3w8vLCkCFDEBgYiLFjxyI/P9/gESqNVq1a4ffff8eaNWsQFBSE2bNnY968eRUuItyxY0ds3rwZ7777Lj7//PO7/lzrI0GtVqvreifqWmZmJmxtbZGRkXHXB1W9l5NTVhY0O/uBzlkmIlFOYQ6sForf6+yZ2bA05fea7hNeU6ic/Px8xMTEwNfXF0qlsq53h6hKlR2z1Y0NHqywj4iIiIiIqB5gIEVERERERGQgBlJEREREREQGYiBFRERERERkoDoNpBYuXIi2bdvC2toazs7OGDRoEC5duiTdn5qaildffRVNmjSBubk5vLy8MHnyZGRkZGg9T1xcHPr37w8LCws4Oztj+vTpKC4uvt9vh4iIiIiIHhF1Gkjt3bsXEydOxOHDh7Fjxw4UFRWhV69eyMnJAQAkJCQgISEBixYtwvnz57Fq1Sps3bpVqpsPACUlJejfvz8KCwtx8OBBrF69GqtWrcLs2bPr6m0REREREdFDrl6VP7916xacnZ2xd+9edO7cWe8269atw7PPPoucnBwYGxvj33//xYABA5CQkAAXFxcAwDfffIMZM2bg1q1bMDU1rfJ1Wf6ciB5ELH9OdYbXFCqH5c/pQfPQlT/XpOzZ29tXuo2NjQ2MjY0BAIcOHUJwcLAURAFA7969kZmZiQsXLuh9joKCAmRmZmr9EBERERERVVe9CaRUKhWmTJmCsLAwBAUF6d3m9u3bmD9/PsaNGyfdlpSUpBVEAZB+T0pK0vs8CxcuhK2trfTj6elZQ++CiIiIiIgeBfUmkJo4cSLOnz+PNWvW6L0/MzMT/fv3R7NmzTBnzpx7eq2ZM2ciIyND+omPj7+n5yMiIiKiR49arca4ceNgb28PQRBw+vTput6l+yI2NvaRer8VqReB1KRJk7Bp0ybs2bMHHh4eOvdnZWWhT58+sLa2xoYNG2BiYiLd5+rqiuTkZK3tNb+7urrqfT0zMzPY2Nho/RARERHRo2X06NEYNGjQXT9+69atWLVqFTZt2oTExEQEBQVBEAT89ddfVT5WEATpx8bGBm3btsXff/991/tC91+dBlJqtRqTJk3Chg0bsHv3bvj6+upsk5mZiV69esHU1BQbN27UmQzWvn17nDt3DikpKdJtO3bsgI2NDZo1a1br74GIiIiIHk3Xrl2Dm5sbOnToAFdXV2kOf3WtXLkSiYmJOH78OMLCwjBs2DCcO3eulvbWcIWFhXW9C/VanQZSEydOxM8//4xff/0V1tbWSEpKQlJSEvLy8gCUBVE5OTn44YcfkJmZKW1TUlICAOjVqxeaNWuG5557DmfOnMG2bdvw7rvvYuLEiTAzM6vLt0dERET0SMspzLmvPzXt/Pnz6Nu3L6ysrODi4oLnnnsOt2/fBiCOZr366quIi4uDIAjw8fGBj48PAGDw4MHSbZWxs7ODq6srAgICMH/+fBQXF2PPnj3S/fHx8Rg+fDjs7Oxgb2+PgQMHIjY2Vto3hUKBW7duARDXX1UoFHjmmWekxy9YsAAdO3YEIC4ZNHbsWPj6+sLc3BxNmjTB0qVLtfZHM0L3/vvvw93dHU2aNAEAHD16FKGhoVAqlWjTpg1OnTp115/pw8SwsLmGff311wCA8PBwrdtXrlyJ0aNH4+TJkzhy5AgAoHHjxlrbxMTEwMfHB0ZGRti0aRPGjx+P9u3bw9LSEqNGjcK8efPuy3sgIiIiIv00SzTcL+r3am5Vn/T0dHTr1g0vvvgiPv30U+Tl5WHGjBkYPnw4du/ejaVLl6JRo0b47rvvcOzYMRgZGQEAnJ2dsXLlSvTp00e6rSrFxcX44YcfAEBauqeoqAi9e/dG+/btsX//fhgbG2PBggXo06cPzp49i+bNm8PBwQF79+7FsGHDsH//ful3jb1790rtbJVKBQ8PD6xbtw4ODg44ePAgxo0bBzc3NwwfPlx6zK5du2BjY4MdO3YAALKzszFgwAD07NkTP//8M2JiYvDaa6/d8+f7MKjTQKqqJazCw8Or3AYAvL29sWXLlpraLSIiIiJ6xH3xxRcIDQ3FBx98IN22YsUKeHp64vLlywgICIC1tTWMjIx05uVrRpqqMmLECBgZGSEvLw8qlQo+Pj5SULN27VqoVCosX74cgiAAEAcb7OzsEBERgV69eqFz586IiIjAsGHDEBERgTFjxmD58uW4ePEiGjVqhIMHD+LNN98EAJiYmGDu3LnSa/v6+uLQoUP4/ffftQIpS0tLLF++XArovvvuO6hUKvzwww9QKpVo3rw5bty4gfHjx9/lJ/vwqNNAioiIiIgeXtkzs+t6F+7amTNnsGfPHlhZ6Y6qXbt2DQEBAff8Gp9++il69OiB6OhovP7661i2bJm0nuqZM2dw9epVWFtbaz0mPz8f165dAwB06dIF3333HQBx9OmDDz7A5cuXERERgdTUVBQVFSEsLEx67JdffokVK1YgLi4OeXl5KCwsRMuWLbWePzg4WAqiACAqKgohISFadQrat29/z+/9YcBAioiIiIhqhaWpZV3vwl3Lzs7GE088gY8++kjnPjc3txp5DVdXVzRu3BiNGzfGypUr0a9fP0RGRsLZ2RnZ2dlo3bo1fvnlF53HOTk5ARCzt6ZMmYIrV64gMjISHTt2xMWLFxEREYG0tDS0adMGFhYWAIA1a9bgjTfewOLFi9G+fXtYW1vjk08+kabRaFhaPrh/s/uNgRQRERERUTmtWrXC+vXr4ePjY1A1PhMTE6komiHatWuH1q1b4/3338fSpUvRqlUrrF27Fs7OzhUu1RMcHIwGDRpgwYIFaNmyJaysrBAeHo6PPvoIaWlpWnUIDhw4gA4dOmDChAnSbZqRrcoEBgbip59+Qn5+vjQqdfjwYYPf38OoXqwjRURERERUFzIyMnD69Gmtn/j4eEycOBGpqakYMWIEjh07hmvXrmHbtm0YM2ZMpYGSj48Pdu3ahaSkJKSlpRm0L1OmTMG3336LmzdvYuTIkXB0dMTAgQOxf/9+xMTEICIiApMnT8aNGzcAiGtRde7cGb/88osUNIWEhKCgoAC7du1Cly5dpOf29/fH8ePHsW3bNly+fBmzZs3CsWPHqtyn//3vfxAEAS+99BIiIyOxZcsWLFq0yKD39bBiIEVEREREj6yIiAiEhoZq/cydOxfu7u44cOAASkpK0KtXLwQHB2PKlCmws7ODQlFxE3rx4sXYsWMHPD09ERoaatC+9OnTB76+vnj//fdhYWGBffv2wcvLC0OGDEFgYCDGjh2L/Px8rRGqLl26oKSkRAqkFAoFOnfuDEEQtOZHvfzyyxgyZAiefvppPPbYY7hz547W6FRFrKys8M8//+DcuXMIDQ3FO++8ozfd8VEkqKtTFu8hl5mZCVtbW2RkZFQ4dPrAy8kBNJMls7MB5r8SPfByCnOk0sLZM7Mf6LkI9IDhNYXKyc/PR0xMDHx9fbWKEhDVV5Uds9WNDTgiRUREREREZCAGUkRERERERAZiIEVERERERGQgBlJEREREREQGYiBFRERERERkIAZSREREREREBmIgRUREREREZCAGUkRERERERAYyrusdICIiIqKHU14eUFh4/17P1BQwN79/r0ePNgZSRERERFTj8vKAv/8G0tLu32s2aAAMHMhgyhCjR49Geno6/vrrr7relQcOU/uIiIiIqMYVFopBlLm5GODU9o+5ufh6hoyAjR49GoMGDbqn9zlnzhwIggBBEGBkZARPT0+MGzcOqamp9/S89UVERIT0/gRBgIuLC4YOHYro6Ogaed709PSa2dE6wBEpIiIiIqo1SiVgaXl/Xisv7/68TnnNmzfHzp07UVJSgqioKLzwwgvIyMjA2rVr62aH9CgqKoKJicldP/7SpUuwtrbGlStXMG7cODzxxBM4e/YsjIyM7mpfHgYckSIiIiIiAhAeHo7JkyfjzTffhL29PVxdXTFnzpwqH2dsbAxXV1c0bNgQPXr0wFNPPYUdO3ZobbN8+XIEBgZCqVSiadOm+Oqrr6T7hg0bhkmTJkm/T5kyBYIg4OLFiwCAwsJCWFpaYufOnQCArVu3omPHjrCzs4ODgwMGDBiAa9euSY+PjY2FIAhYu3YtunTpAqVSiV9++QUlJSWYOnWq9Lg333wTarW6Wp+Ns7Mz3Nzc0LlzZ8yePRuRkZG4evUqAODrr79Go0aNYGpqiiZNmuCnn37SeqwgCPj666/x5JNPwtLSEi+99BK6du0KAGjQoAEEQcDo0aOrtR/1CQMpIiIiIqJSq1evhqWlJY4cOYKPP/4Y8+bN0wmKKhMbG4tt27bB1NRUuu2XX37B7Nmz8f777yMqKgoffPABZs2ahdWrVwMAunTpgoiICGn7vXv3wtHRUbrt2LFjKCoqQocOHQAAOTk5mDp1Ko4fP45du3ZBoVBg8ODBUKlUWvvy1ltv4bXXXkNUVBR69+6NxYsXY9WqVVixYgX+++8/pKamYsOGDQZ/Rualk9AKCwuxYcMGvPbaa5g2bRrOnz+Pl19+GWPGjMGePXu0HjNnzhwMHjwY586dw9y5c7F+/XoA4khXYmIili5davB+1DWm9hERERERlQoJCcF7770HAPD398cXX3yBXbt2oWfPnhU+5ty5c7CyskJJSQny8/MBAEuWLJHuf++997B48WIMGTIEAODr64vIyEh8++23GDVqFMLDw/Haa6/h1q1bMDY2RmRkJGbNmoWIiAi88soriIiIQNu2bWFhYQEAGDp0qNbrr1ixAk5OToiMjERQUJB0+5QpU6TXBIDPPvsMM2fOlG775ptvsG3bNoM+n8TERCxatAgNGzZEkyZN8Morr2D06NGYMGECAGDq1Kk4fPgwFi1aJI06AcD//vc/jBkzRvo9JiYGgDjSZWdnZ9A+1BcckSIiIiIiKhUSEqL1u5ubG1JSUip9TJMmTXD69GkcO3YMM2bMQO/evfHqq68CEEePrl27hrFjx8LKykr6WbBggZSOFxQUBHt7e+zduxf79+9HaGgoBgwYgL179wIQR6jCw8Ol17ty5QpGjBgBPz8/2NjYwMfHBwAQFxentV9t2rSR/p+RkYHExEQ89thj0m3GxsZa21TGw8MDlpaWcHd3R05ODtavXw9TU1NERUUhLCxMa9uwsDBERUVVuC8PC45IERERERGVKl+QQRAEnZS58kxNTdG4cWMAwIcffoj+/ftj7ty5mD9/PrKzswEA33//vVYQA0Aq1CAIAjp37oyIiAiYmZkhPDwcISEhKCgowPnz53Hw4EG88cYb0uOeeOIJeHt74/vvv4e7uztUKhWCgoJQWK5koWUNVvnYv38/bGxs4OzsDGtra4MfX5P7Ul9wRIqIiIiIqAa9++67WLRoERISEuDi4gJ3d3dER0ejcePGWj++vr7SYzTzpCIiIhAeHg6FQoHOnTvjk08+QUFBgTTqc+fOHVy6dAnvvvsuunfvjsDAQKRVY7EuW1tbuLm54ciRI9JtxcXFOHHiRLXek6+vLxo1aqQTRAUGBuLAgQNatx04cADNmjWr9Pk0c8hKSkqq9fr1EUekiIiIiKjWlE4Zemhepzrat2+PkJAQfPDBB/jiiy8wd+5cTJ48Gba2tujTpw8KCgpw/PhxpKWlYerUqQDEioGvv/46TE1N0bFjR+m2N954A23btpVGdBo0aAAHBwd89913cHNzQ1xcHN56661q7ddrr72GDz/8EP7+/mjatCmWLFlyz+s4TZ8+HcOHD0doaCh69OiBf/75B3/++adUYbAi3t7eEAQBmzZtQr9+/WBubg4rK6t72pf7jYEUEREREdU4U1Nxody0tPu3vlODBuLr1gevv/46Ro8ejRkzZuDFF1+EhYUFPvnkE0yfPh2WlpYIDg7GlClTpO2Dg4NhZ2eHgIAAKaAIDw9HSUmJ1vwohUKBNWvWYPLkyQgKCkKTJk2wbNkyrW0qMm3aNCQmJmLUqFFQKBR44YUXMHjwYGRkZNz1+xw0aBCWLl2KRYsW4bXXXoOvry9WrlxZ5f40bNgQc+fOxVtvvYUxY8bg+eefx6pVq+56P+qCoK5u8fiHWGZmJmxtbZGRkQEbG5u63p3akZMDaKL87Oz7tzIeEdWanMIcWC0Uv9fZM7NhacrvNd0nvKZQOfn5+YiJiYGvry+USqV0e14eUG7aTq0yNQVKK3MTVaqiYxaofmzAESkiIiIiqhXm5gxs6OHFYhNEREREREQGMnhEqqCgAEeOHMH169eRm5sLJycnhIaGalUdISIiIiIiephVO5A6cOAAli5din/++QdFRUWwtbWFubk5UlNTUVBQAD8/P4wbNw6vvPLKXdWWJyIiIiIielBUK7XvySefxNNPPw0fHx9s374dWVlZuHPnDm7cuIHc3FxcuXIF7777Lnbt2oWAgADs2LGjtvebiIiIiOoZ1jCjB0VNHKvVGpHq378/1q9fr7PSs4afnx/8/PwwatQoREZGIjEx8Z53jIiIiIgeDJo2Ym5uLsxZXYIeALm5uQBQYXxTHdUKpF5++eVqP2GzZs2qXMmYiIiIiB4eRkZG+D971x0eRfVFz/b0hISEJPTeOxY6ItJsKPaCvWLF3iv2rqg/e0URFRVpIr1KDz10QgjpvW3//fHmzbx5+2ZLsglB53xfvs3uzs7Ozrx57557z703ISEB+fn5AICoqCgYDIaTfFQ6dPjC6/Wiuroa+fn5SEhIgMlkqvO+9PLnOnTo0KFDhw4dOuqN1NRUAJDJlA4dTRkJCQnymK0rwkakrrvuOhw7dgxLly4N1y516NChQ4cOHTp0nCIwGAxIS0tDSkoKnE7nyT4cHTo0YbFY6hWJoggbkWrZsiWMRr0tlQ4dOnTo0KFDx38ZJpMpLEaqDh1NHWEjUi+99FK4dqVDhw4dOnTo0KFDhw4dTRp6CEmHDh06dOjQoUOHDh06QkTIEakbb7zR7/tffPFF0Pt6+eWX8euvv2Lv3r2IjIzEkCFD8Oqrr6Jr167yNp988glmzpyJLVu2oKKiAiUlJUhISFDtp7i4GHfffTfmzp0Lo9GIyZMn491330VMTExIv02HDh06dOjQoUOHDh06gkHIEamSkhLVX35+PpYuXYpff/0VpaWlIe1rxYoVmDp1KtavX4/FixfD6XRi7NixqKqqkreprq7G+PHj8fjjj2vu5+qrr8auXbuwePFi/Pnnn1i5ciVuvfXWUH+aDh06dOjQoUOHDh06dASFkCNSc+bM8XnN4/HgjjvuQMeOHUPa18KFC1XPv/rqK6SkpGDz5s0YMWIEAOC+++4DACxfvly4jz179mDhwoXYuHEjBg0aBAB4//33MXHiRLzxxhtIT08P6Zh06NChQ4cOHTp06NChIxDCkiNlNBoxbdo0vP322/XaT1lZGQAgMTEx6M+sW7cOCQkJMokCgDFjxsBoNOKff/4RfsZut6O8vFz1p0OHDh06dOjQoUOHDh3BImzFJg4ePAiXy1Xnz3s8Htx3330YOnQoevXqFfTncnNzkZKSonrNbDYjMTERubm5ws+8/PLLiI+Pl/9at25d5+PWoUOHDh06dOjQoUPHfw8hS/umTZumeu71enHixAnMmzcP1113XZ0PZOrUqdi5cydWr15d530Ei8cee0z1O8rLy3UypUOHDh06dOjQoUOHjqARMpHaunWr6rnRaERycjLefPPNgBX9tHDXXXfJRSJatWoV0mdTU1ORn5+ves3lcqG4uBipqanCz9hsNthstjodqw4dOnTo0KFDhw4dOnSETKSWLVsWti/3er24++67MWfOHCxfvhzt27cPeR+DBw9GaWkpNm/ejIEDBwIAli5dCo/HgzPOOCNsx6pDhw4dOnTo0KFDhw4dFCETqXBi6tSpmDlzJn7//XfExsbKOU3x8fGIjIwEQHKgcnNzceDAAQDAjh07EBsbizZt2iAxMRHdu3fH+PHjccstt+Djjz+G0+nEXXfdhSuuuEKv2KdDhw4dOnTo0KFDh44GQdiKTTz++OMhS/s++ugjlJWVYdSoUUhLS5P/Zs2aJW/z8ccfo3///rjlllsAACNGjED//v3xxx9/yNt8//336NatG84++2xMnDgRw4YNwyeffBKeH6ZDhw4dOnTo0KFDhw4dHMIWkTp+/DiOHTsW0me8Xm/AbZ599lk8++yzfrdJTEzEzJkzQ/puHTp06NChQ4cOHTp06Kgrwkakvv7663DtSocOHTp06NChQ4cOHTqaNMIm7dOhQ4cOHTp06NChQ4eO/wrqFJGqqqrCihUrkJWVBYfDoXrvnnvuCcuB6dChQ4cOHTp06NChQ0dTRZ36SE2cOBHV1dWoqqpCYmIiCgsLERUVhZSUFJ1I6dChQ4cOHTp06NCh41+PkKV9999/P84//3yUlJQgMjIS69evx9GjRzFw4EC88cYbDXGMOnTo0KFDhw4dOnTo0NGkEDKR2rZtGx544AEYjUaYTCbY7Xa0bt0ar732Gh5//PGGOEYdOnTo0KFDhw4dOnToaFIImUhZLBYYjeRjKSkpyMrKAkCa6IZa/lyHDh06dOjQoUOHDh06TkWEnCPVv39/bNy4EZ07d8bIkSPx9NNPo7CwEN9++y169erVEMeoQ4cOHTp06NChQ4cOHU0KIUekXnrpJaSlpQEApk+fjmbNmuGOO+5AQUEBPvnkk7AfoA4dOnTo+I/D6yV/OnTo0KFDRxNCyBGpQYMGyf+npKRg4cKFYT0gHU0ExYeA/YuBAVMAS+TJPhodOnScanC7AHgBk6V++6ksAD4aDHSdCFzwXlgOTYcOHTp06AgH9Ia8OsRY9CSw4GFg2fSTfSQ6dOg41VBTArzRGfjx6vrva99CoKoA2PJ1/felQ4cOHTp0hBFBEanx48dj/fr1AberqKjAq6++ihkzZtT7wHScZGTOI48Hlpzc49ChQ8ephwNLgJpiYP+i+u/LUaX87/HUf386dOjQoUNHmBCUtO/SSy/F5MmTER8fj/PPPx+DBg1Ceno6IiIiUFJSgt27d2P16tWYP38+zj33XLz++usNfdw6Ggtm28k+Ah06dJxqcDuV/521gCWiHjtjcqNcNYA1uh770qFDhw4dOsKHoIjUTTfdhGuuuQazZ8/GrFmz8Mknn6CsrAwAYDAY0KNHD4wbNw4bN25E9+7dG/SAdTQyDLr6U4cOHSHCyCwtrnoSKY9b+d9RpRMpHTp06NDRZBB0sQmbzYZrrrkG11xzDQCgrKwMNTU1SEpKgsVSz2RiHU0XrGdZhw4dOkKF21G/z7PSPkclgJT67U+HDh06dOgIE0Ku2kcRHx+P+Pj4cB6LjqYIj+tkH4EOHTpONXgYB4zLXr99OSqY/6vrty8dOnTo0KEjjNB1Wzr8w1/vlvIcYN6DQMG+xjseHTp0NH2wkez6RqTsleHblw4dOnTo0BFG6ERKRwD4IVJLpwMbPwX+N6LxDkeHDh1NH55wEikmIqUTKR06dOjQ0YSgEykd/uEvInVQKo3uqmmcY9GhQ8epAXc4pX1MRKq++9KhQ4cOHTrCCJ1I6QgAP0RKL42uQ4cOEVTSvnoWrNGlfTp06NCho4miTkSqtLQUn332GR577DEUFxcDALZs2YLjx4+H9eB0nCSwUSh/ESkYGvxQdOjQcQpCJe0LY7GJfzuRWv8xsPkr9WsFmUCZvrbq0KFDR1NEyFX7tm/fjjFjxiA+Ph5HjhzBLbfcgsTERPz666/IysrCN9980xDHqaMxwfZt8ReR0ntM6dChQ4SGikj9m6V9tWXAwkfI/z0mAZEJQFUh8PFw0ofroYOASW81okOHDh1NCSFbwtOmTcP111+P/fv3IyJCabI4ceJErFy5MqwHp+MkgfUmez3a29WXSJVmAdmb6rcPHTp0ND2Ek0g5/iPSPra0e3UReczdTiJ6tWVA0cGTc1w6Tj1kLgRe7wzs//tkH4kOHf96hGwJb9y4EbfddpvP6y1btkRubm5YDkrHSQbbO8qftM9QT2nf52OBz84Gjm+p33506NDRtMASnnCWP/83R6TY8+SUSFUFs6aW6/K+/yy83gAyew5z7wGq8oG59zbcMenQoQNAHYiUzWZDeXm5z+v79u1DcnJyWA5Kx0mGqglvA0r7Kk6QxxydSOnQ8a8CO4fUh0i5XYCzKjz7aupgfxsljE6mIiqdL3X8t3AiA5ieCqx+K/jPVOaRx/LshjkmHTp0yAjZEr7gggvw/PPPw+kkcg2DwYCsrCw88sgjmDx5ctgPUMdJAJsj1VDFJjyMZNDt0t5Ohw4dpx5YOZ+njve31wv8bzi33/8IkaIEio3AletE6j+J5a8ArlpgyfMn+0h06NAhQMhE6s0330RlZSVSUlJQU1ODkSNHolOnToiNjcX06dMb4hh1NDaCNYL8SfuWPA/MulbtUWWhysNyi7fRoUPHqYlwSPtqSoD83erX/ivSPvo7XbXKa1UFjXs8OpoGHFWBt9GhQ8dJQ8hV++Lj47F48WKsWbMGGRkZqKysxIABAzBmzJiGOD4dJwNBy3I0iJSzBlj1Jvn/0FVA1wn+v6OuHmsdOnQ0TYRD2leZ7/taOCNS9grg6Dqg09mA0RS+/dYVLpZI1aofAaCmuHGPR0fTgN6v8dRDaRZxhjTvfLKPJPzI3wts/xE4/TYgLu1kH02TQMhEimLo0KEYOnRoOI9FR2OBJq5qRZRURpCfilvs59n9FR1QXq/WWPxVREqPSOnQ8a+CqmpfHR0lVQIiFc6I1Jr3gJWvAQOvB85/N3z7rSvcAYgUreT3b4GzhhQSidFzq/3CWGczTcfJgNcLfDGB5KdN2wPEpZ/sIwovljwPZM4jrRku/OBkH02TQMjSvnvuuQfvvfeez+sffPAB7rvvvnAck46GxudjgZmXab8fLJFiI1LsdoX7lP9rSjS+g83D0omUDh3/Kqga8tYxisRK2fpcUb99ibDyNfLIN8A9WVDJIaXzxxJHLadUU4GjWp37GgjfTALe6weU5zTUEf07oPcOO7XgrFaKfLC20L8FmfPIY8YPJ/c4mhBCJlK//PKLMBI1ZMgQ/Pzzz2E5KB0NjLydwP6/iLRFhLrIctjt2KTo2lKN7cPYZ0aHDh1NC6yjpK7kp7aMPHY9F0hoQ/7/r+RIUSLK5pg2ZWlfwT7gldbA/AeD297rBY6tJz3C9i1s2GM71WFkiFQoJdB1nBywzuP6pC04a4CDS5uuYkdPyZARMpEqKipCfHy8z+txcXEoLCwMy0HpaCRQQ4WHSnbnDG7yZo0AWnoV8FNsgvkOVr6iQ4eOUx8qIlVHRwmdnyLiAbNV2td/hUhJ8+OpEpHa+g055k2fB7c9+1uMesTFL9iIVDBrpU62Ti5YIsU22Q4Vq94Cvr0IWNaEirjpTm8hQiZSnTp1wsKFvh6kBQsWoEOHDmE5KB2NBLbRJQve06DlefCyJcyZG4xNEtea+FVE6l9sHOnQ8V8EK9f1hINIRZD//81zhSivjJ0/HZVNt1VEVYj5Ww5m7fk3l7QPB9gcKS3HJAv9fJ5csOTJWQ8iRaXHtHBXUwCvYqoPUfwXIeQsxmnTpuGuu+5CQUEBRo8eDQBYsmQJ3nzzTbzzzjvhPj4dDQmHBpHiF2u3Q6zT1sqDYJPEnTqR0qHjP4dwVO2rlRq/R8QBJqly2b95rmB/G51beUeUswow+SpCTjpCLYjAksZ/8zUNN4IhUny5dLcLMOkFKxoNrF30bytdzxOpmmLAGnVyjqUJIeS768Ybb4Tdbsf06dPxwgsvAADatWuHjz76CFOmTAn7AepoQAQTLQIkQyjadzu3BpFiI12uYKR9+kKqQ0fY4KwF/n4G6HAW0HX8yTmGsEv7/gNESlhsgpujHdXkfDQ1hEyk2AqFQZCD/zLYeykYaR9vvLtqAVNMeI9JhzZY2+bfRqR453t1MRDf6uQcSxNCyNI+ALjjjjuQnZ2NvLw8lJeX49ChQzqJOhWhZZT4ECkNOQlrIFXkAvsWEX02O9kHFZHSc6R06AgbMucB/3wM/HD5yTuGoCt/+oGdRqQYIvWvzpFiG6ELqvYB9ZMKNSTYPlzByA/Z6/hvJsfhABvhCGat5MeIvr42Ltjx/2+br/h0kKZcAKcRUa94b3Ky3v/hlIaW5IbPadDajn19wcOkGuC4l9QTdzARKV3TrUNHeLD7d2DDZ8pzl0Mp1NCYUOVP1rNqny1Oybn6NxvdrNFFjTFeytVUiZSqIEINYIr1vz1LGrXkal4vsOcPoEUvIKlj/Y/xVAW7Vmo5JlmIIlI6Gg+eMMlWDaam1xqGl/ZpVX7+jyHkiFReXh6uvfZapKenw2w2w2Qyqf50nELQlPZxN28whCtvJ3ncPouLSGkskm49IhUsHvt1Oy79eC2qHQ2TaF7tcKG02oEPlx9AZq4+MZ6yOLYB+GkKkLVWee1kNXENR0SKlfb9F3KkROXP+d/bVJO7DYwpEWpBBK35/+gaMp4/G6O5mx82ZOGKT9bhRNm/WB6oUm8E8Tt9IlL/4numKSJc+X8WJveosSoxetzAn9OAv58Tv+/QiZQIIUekrr/+emRlZeGpp55CWloaDAZD4A/pqBd+3JCF5Fgbzu7eok6fP1hQid9W78MD/BsuLYLES/s0DCHR6waj2mum9dnGyJFy1gDrZgC9LgYSmYqSR9cSqVDLgQ3zvWGE1+vFDxuOAQAW787Dhf1a1mtfP2/ORofkaAxsmwgAyC6pxti3V6LaQcjz3IwTWHDv8PofOIOtWSU4UlSFSf1aNun5Yn9eBZJibEiMPgkRnHDg8Arf16qLgLi0xj+WcBebEJUDPxXg9QKHlgMtBwTObXIFkSPlbKI5F6o8niCuEbuNFvHat4g81hRrRlbfWJSJoioHPlh6ANMn9SJy0KaYQ1YfsOe2LhGpYIithGWZ+Xhyzk5MO6cLJg/Uc1/qhHA0IwdIEQdKXJw1jVPU4fgWpYXBGbcDsZzN2VARqV2/AQsfAy77Gmh9enj22YgIOSK1evVqfP/997jjjjswadIkXHjhhaq/UPDyyy/jtNNOQ2xsLFJSUjBp0iRkZmaqtqmtrcXUqVORlJSEmJgYTJ48GXl5eaptsrKycO655yIqKgopKSl46KGH4HI10TKxIWLPiXI8+usO3PT1JlTZ6/abZiw9gM9WHfZ9w23HseJqPDQ7Qx2J4MmPprRPQJLcLvXirxnNaoSI1OavgKUvAJ+OVl4rOgh8ORH4YkLT7ssioaRaOceVdbz+FNuzy/DQz9sx+aN18ljamlUqkyiAjDdvmL1f9/64DffPysDSvfmBN9aA2+PFgfyKsBxbcZXvmDxWXI3x767CBR+shsdzivZhKT3q+5pWQ+yGhifc5c8bNkfqqzWHkVsW3DxUaXcFfy/u+hX4dhIw+4bA2wr7SEnHRMu/O6rxwp+7ccs3mxosQl0nhJrHo1WoSLVPZgyxvXkk1DrdKJLu5RNltcDyl4FX2wMHlgRzxKcOVBGOOlTtC8H58PXaIzheWoMHZmcE/RkdHNxhchKbGMdBY83j1Uwv2BKBzcjnSIWLSK1+G6jIAX69NTz7a2SETKRat24dNkNrxYoVmDp1KtavX4/FixfD6XRi7NixqKpSJoL7778fc+fOxezZs7FixQrk5OTg4osvlt93u90499xz4XA4sHbtWnz99df46quv8PTTT4flGE82dh5XmuYeKaqbN/LXrcfFb7js+GjFQczenI3bv9usvC6s2ieA6HUPT6ROYkRq63fkkV2Ec3cA8BKDLGdryLtcvDsPL/65Wzak8itqUVqtnIfPVh3CZR+vQ355eMhhHrMfQ9nxoKsALdmTh/HvrMSKfQU47/1VuPuHrdieXSq/v0MaVyJJTJGAaPhDblktHv45A39k5Pi853B5kFVMpCYbjvgSV6/Xiz+355Dj8Hg0f99bizMx5q2V+GTloZCOjcdHyw9iwAuL8fXaI6p5bOORYrg9XmSX1OB4qXJOXG5PvQksi3CTVBVKs31f++pcYP5DDfedWghQtc/j8aLE3zhzO5XoS0QCI+3Tvq+8Xi8KKuzy/1lF1TIpnr/jBO79cSuOFIrH17Nzd+OJOTu0j0eCw+XBmDdXYNTry1Fpd+FoURX25pZrf2DzV+TxYBDGvb+qfZEkglxYUoLPVx/G4t15WLw7DycTB/IUI2pHFiMhDYpIBVFsgpWoCSSqh5lrWV7jBFa8SnJKFj0BAE3DIVJdDPz9LJC3W3OT1fsL8e36o9rHG+payVdW83M9ahxu1DqVe3XbsVL5f9H96fee/TejNAtY8RpQEcQ9F678b/azAkdCg4BdfysFjs8wRKS8Xi8+X30Yaw4wpO3ENvJYJljDTgGETKTeeecdPProozhy5Ei9v3zhwoW4/vrr0bNnT/Tt2xdfffUVsrKysHkzMerLysrw+eef46233sLo0aMxcOBAfPnll1i7di3Wr18PAPjrr7+we/dufPfdd+jXrx8mTJiAF154ATNmzIDDcWrf9F6vF/vzlUlR5DF1uDyaE3BptQP/HPKTI+F2YPYmIhtjFyU+R6rWLpiIPW4Agu91OziDIIj8qoYiUtx+HS4Pvl64Rn6+MSMDn648FLRx63B5cPcPW/DZ6sP4cvVhlNc6cfabKzD27ZVwuj3wer14cd4ebDhSjO/WC6IDINc0FGOaEqlOhmxcvu584Icrg/rcR8sPYm9uBa77YgN2Hi/H3IwcLN6jTIy7cspR7XAhM9e3l9ieE2LD8K3F+/DCn7tV483r9eLO7zfjp03ZePCnDPy+7biK/LNjtrLWl5Cs2FeAu2ZuxYR3VwGzrgHe6g6UKOcuv6IWc7Zm41MpovrRioNB/X7ld5apfs+nqwgRe+aPXbjk43Xyb9mXp5yHE8wx3/rtZgx8YTH2nCiHx+PFZ6sOYeW+gpCOgeLO7zfj7DdXoKymgbrDay1CGz5pfEkcmyTNE4Q5d2DlN8+i/wuL8fs2DScPu0DbYpny59pz+nf/ZOG06X/jo+UHsWhXHka8vgxP/r4TLrcHj/26A79vy8FDP2t72pcEETHdl1eB3PJaFFba8d6S/Rj79kpMfHcVMiQD1Ov1ws3Ox6wBxF+DygJ1VNxfjlQUIVKHTijGx768k5efsD27FOd/oMylhwuUez6osRZMHglLCKTx8MvmbPy29TjeWJSJy/63Tn5701HmPBuMmD5vN06b/jf2n8RzBIBUz1z9NvDnfcK3vV4v7vh+M576bSdW7teYV1TFJoKISJVx95QGkap1unHWG8sx+o3lJLpXaUcpo4A4VKheG976KxP9X1iMnySbIRRk5lYgv6JxcqFrnW4sy8xXEcR6Y9ETwLLpwTXHFdg2dXKgsfdFTWnon68L2PElIm9hyJFaub8QL/y5G1d/9k+dVVZNDSETqcsvvxzLly9Hx44dERsbi8TERNVffVBWRiZjup/NmzfD6XRizBgl2bRbt25o06YN1q0jk+i6devQu3dvtGihaDnHjRuH8vJy7Nq1S/g9drsd5eXlqr+mgrzyWizPzMeyzHxMnblF5YHP4YhUfnktBr+8BGPfWYkahxt/bs/BG4syUe1wYefxMvR7fjEu/2S95nf9tP4gnG7lBi+jkygXkXp41ia1cQBoEyR+UdSS9YTaG6MuMKqLn+zNLUdNiRI1Wb1lJ6bP34PdGsSBx768CtQ6STWytQeLsD+vAhW1LuRX2HG4sAr5FcpvZwkwi49XHEKXJxdg4c7coL6T7nOUMQMmrws4vAJej0e47Vt/ZeKxX7fD5faoDQsJLAFYvDsXY95cgV+2+Brf136+AQcLyPFX2l2Y+U8W2j06D+8t2Y/PVx/G8n2KwZlfYceWrFIAgMPtwb0/bsN5769Gpd0Fu8uNo8UKQT9W4msELJOM19JqBynbXVsGZM6X358+bw/un5UBh8sjbedEjcONVfsLcM8PW3Egv1IZtxxySmtw0YdrcdGHa2RPKivr23y0RDYW2KgCjdK53B4s3ZsPu8uDHzdkYeX+Arw4bw+mfLEBOaWhJbe7PV7M35GLQ4VVWBTktWcxa2MWPlt1CB6PFz9tPIZ9eRU4VlyNS//HFJaoPEEeH9gH9L5UvYPSrJC/MxC2ZpXgwg9Wi8eyVrGJg0uBjJkYdeQdAF68tjCT/yQBlbJYoklFOHPgiNSn0lz53pL9+HkzGdcz/8nCvB0nZPK68UgJyqqdKK8Vjxne4CmstGPtgUL8sCELkz9aiwU7T8jvfbLyEOwuDzxe4M3F++D2eHHBB2tw9pvLlf07NKIqteXAjNOBDwcr5FBFOF0kv0oybFw2kveTU6AQrwMacwyL8lonquwuPD93N9o9Og/j31mJQwXanztcWIXPVh0KKBtcc0DtoLOAzeMhx+z1erWNWdVv1SJSjHPPWYVDBZV4YHYG7pu1DR8sO4AKgWOG7vvTVYdRVOXAjGUH/P4OHl6vFzuPl8HuIsd9pLAKf+/OC2wIV+QB318KbJupfv3wSvJ47B/l8DwK2c4rt8u/gyfGXq8Xry/ai5wSti9jEGtlyRH1c+YzhwoqMfbtFXh+7m7sOVGO3PJa5JTVYu3BQgx88W/1oRdWw+v1Ykd2GWocbtmZ9bHAmbXtWCm2ZImjJocKKjHh3ZUY+/bKsBvOC3eewGZurXt9USZu+HIjXp6/J3xftOcP8rjhf4G3VclW7cgtq8WwV5fh5q83qsdR5kLgwN++nxftR0Paump/ASZ/tBZ/hys6zUaBa0pQ63Tjj4wcOdIvEyeah1gHIrWXsbe2Z5ep32xqVQqDRMjFJt55550GOAzA4/Hgvvvuw9ChQ9GrVy8AQG5uLqxWKxISElTbtmjRArm5ufI2LImi79P3RHj55Zfx3HMaVUlOIg4XVuGsN5Zrvp9bVoNdOWWIsZnRNikaC3floqjKgaIqB/7IOI7n5u5GtcMNs8kgnOx4HC8sVT3PzKvA6e0TfchPSUUVsoqr0b4505RXU+7HLYonUdrnNRhBSxt43U7klNYiCsp3JRtKAZDoTM90JUHZ5fbghq82wuHy4JubTse2rFJ0bhGL3TnsBFCKFZkKMTmYX4lYmwmtDPnI9qbIRITFin0FeHXhXgCkEt/4Xqmq9yvtLkRbTfhk5SFU1Lpw35jOskTQw/g8dh8rQM+26jFfVuPEe0uJ0TC0U/OA52b9If/5YYt356HlkEiMe3ulSuoGEON0dLcW+HVLNqb9JPbw/7b1ON5evE8lE1y5rwD3z9qGPq3iMWVwO5iMBlRIC6sVYjnE79t85YJDXlmCxGgrDhZU4Y+MHMRFmLF42ki0iIuA1+vFTV9vQq3TDZvZKBOwbdmlGC44L2/+tQ8vTuqF/UxE6o9tOcgrr1UVdymqcmAdE91duDMXUwa3hdmkXBev14uHft6OGocb717RT/VeCSP/1DJS88pr4fJ40TIhUvV6blktHvmFyM6OFVfj63VHERdhxjVntsXO4+UA3dzrgddgwttri3EToqFKuS/PAZp3Fn4vADz6y3ZsOlqCH289E81jbJrbsfhk5SFkZJfhxXm7fcayprSPMQiiUYvjpeLiI2WlReT46YIt50hpR6SohLTG6caxYsUguPfHbart/tyRg5fn7cJOxo1ohAceGFFhdyEugpTy9nq9uPrTf5DJGLi80dY6MRLHimuwcl8B5mbkyJLZFZkFGNElGfGVioFz9EQ+HpuVhXE9U7F81TJ8SXuwlBwGkruqzpPL5YDJ7YBBivovz3JhDID8IuX7DxX4l/meKKvB2LdXwmgwyERyb24FXl+UiY+uUQrtFFTY8cDsDOSX12KvlCv7/T9ZmHHVAPRIj5O323asFBnHSnHpoFY4XqquDGeG4tzxumphAHDXzK1Yub8Ac+4cgk4pXDl0ds7Xmv9ZI81Z42t0acDD7M/h9qDK7kK0TW3qHCyoRMuESERY1M62udtP4J4ftmJCr1S8ekkf4oSpduLRCd1w+0g/Jdi3zwL2/0X++l2lvG6LU21md7kx4Z1VMJsM+OOuYaqoz69bjmNfXiUeGd8NybE27M+vxIxlB3G2tQLp0litqKyAt9Ypj1ERvCVHobqrGCL148Zj2JdXiX15lfhijZID88XqIz77OVJYhd+35eC+WdswpnsL1EikmB93FbVOXPHJOtQ6PVj18FlonaguirD5aAk8XuIE23C4GGd1S5Hfq3W6MX/HCQzvnIzk2ODmHYp9eRW4/bstAICdz43Dkj15mLHsgKwu+HrdUTx3Ya+Q9hkWqGwbB+Zm5OB4KZGMHymS7KjKfLnP349nr8EVw5XjdLo9cLm9iGRsqewTObC1ssvnqKDCjkkz1shr82uL9mJMD3ExslqnG5+vPoyRXZLRq6W4EEtxlQP3z9qGB6Jy0Ie+WFOC2Zuz8dRvO3FG+0R8ecNpKMkrQEsAruhUmGvL/BKpvbnlKKt24owOSarX2fGzJasEgzsy73vFTuKmjpCJ1HXXXdcQx4GpU6di586dWL16dYPsn8Vjjz2GadOmyc/Ly8vRunXrBv/eQGiXFIXmMVYUVoqNha1Zpfhk5SFYTUb8NnUoXlmwV37v5QV75aIB363PkiMn/mA1qElOZm45Tm+fCI/bpQpVWuBCblktR6Q0PEt8VaFgClU0UAK5w+UBnZr3ZefjRJkDMQyRijMQY4CPLvy9Jw+r9hMJzV0zt2Lx7jx0bRGL3q2USajK4ZaJCwDc8f0W3GSaj9W27/C48yb8VDAGDpcHVjM5k5mSzI6ipNoJu8sNm5ks5L9szsaDP2egfVI0DkkyS6MBKJYMcCeUBX/rvqOIjIpGXKQFiVFWGI0GlZzurpmh535N6JWKwR2T8PTvJIq7O6ccv2097kOiACKBOlZcjWf/EEd8AeDJ33YKX5+z9TjmbD0Ok9GAKYPbyQZvBHNdau1OeB1u2TDmUVLtVBXhKK91YdnefFxxehscLqwSFrU4mF+Jbqm+vW0W7MxFlcOt+p1L9uZjyd58vMzcX0v35quKcjz/527M2Xocv00dCpPRgMJKO1ZkFsiRkOuHtsNp7ZQIfWGl8vtKBBE0p9uDc99bjVqnGyseGoVomxkmowEWkxHrDilyrq/XHZV/M2/UA0CtKQ7vLTuEhKhK3Mi+wSYRM9h2rBRL9+Thx41ErvPXrjxcdUYb4bY8/jlMiEB2SY1qrAPwkyegmHhRsKMKatIIkMjS0oXL8IMVpGIfgH2FDnQBNI1uPvKxL197gX978X64HLVAhPKaBS7YYUV+uR2FFXYs2JmLfq0TVCRKhA+vGoiPVhzA/B25Kjnv3T9shcngwUEbYyiv34e1B6Ox9mAR+hvKQCcnb2UevEldYGR+2+Kd2fj6xEr8KD3Pd0UBZqCmRtnf0aJquD1e5JXXItpmRnwkMa6PFVdj3aEiFFU6hFGbBTtzMWtjFr5ZdxRndkhC8xibj1z1cGEVrvn8H9wxsiN25pRhaMfmeGnBHpRWO/GMdN+zV87EOEJu+3INrr+pP+btING779Zn4dkLeqoPgpn/K6uqEOH2qBwPAFQRqYqKMqw4FJyk1ulW1r75O3Lx1648/HDrmfL9uP5QEa74ZD0Gd0jCD7eeib9358FkMuCsrin4Trq/FuzMxQIm0vrHthzcPrKjPE/wzg4UKpFVr8eDrJIatGoWhZxKgLUsdh4vl+f3zUdLcLRImeP25lZgb24F1h8qwp2jOoEWODUxJPWrFXvw8+bVWHz/SNS63NiRXYbBHZLkbfecqED7kmxEAqjwRiLWUKO6Z7TI92omX+WSga3w8+ZsHCmqkp1Hf+9RRzyOFFbhw+UHcFF/UtmP2htTvtiARfeNwE+bjqFvqwT0bhWvKmS14Ugx2iRFYf2hIlw2qDXeW7IfHy4/iBFdkvHNjcFXa3O5PfhslaLWycytwAM/ZcDFKWcq7S7EMCS60u7Cnd9vQZeUGDx5Xg8AxLFld3rQrB7VWu0uNx79ZQf6tIrHpOoqNKNvuO2qvLONh4uJHVWhjK3P56/B6d3aoUNyDDweL859bxUKymuw1avcU78tXY05W7vg72kjYTAY8Of2HNWadbCgCl6vFwYDWYuyiqsxoA05it+3HcfmxT8gcdk29HziKxhsvuvg7E3HsGJfAQaYj6KPdLp2HTyCzyvIOf7ncDFGvb4cr9pz0NIIHKiNQzcAeYWFaO7xwmQ0wOv1orzWhfhICzKOlWLyR2vh8njxysW9ccXpyprCOpk3HC7G1LPUxzL+nZU4v286pp7VKYQrcHJRp4a8Bw8exJdffomDBw/i3XffRUpKChYsWIA2bdqgZ8+egXfA4a677sKff/6JlStXolUrpeRmamoqHA4HSktLVVGpvLw8pKamytts2LBBtT9a1Y9uw8Nms8FmC8370RgwGAwY0KYZ/tII0649SCY1p9uNc95eqXqP1TZToy0hyqJ6nYcN6vd+25YDi8mIg/N34AnmdQtcsr75aFEVZv6ThSk9bRAW4ualB3WISDndHkz7KQPd02Jx56jQbyav14sPlx/E2MIKdJbW5oMnipFTakIfg2LUxYEsYlnF1Xh/yX70SI/D2d1b4Ku1R+RtaEJ3Zl5FQKPqKQspbvGS5XPMrD0bBwsq0T2NGIIZTKEHii1HSxFjM6N3q3h8s+4IqZTM5Kq9t/QAoq2EQLHSmRU7DuL5FUVwuDxIirYiOdam8vKJ0LVFLKJsJlx9Rltkl1Rj0a48DGrbDN9Kxt/bl/dDhMWENolRuP7LjdiSVSJLNc7rk4aXLu4Nt9uLG77aiG3HSnH7d5tRzhhpY7qn4O89gXNMKObvOIEpg9vJuXlspPDjpbvwzl8Lg94XADz66w58tvqwptxp0a5cGJnS65EWk+xh1cp5YlUYLImi2HG8DB0fn49f7xyCO7/bglymMMiO7DLZcMsvr8UOxpteUOlLBg7kV8r37bLMAjw/dxc6JMdgzp1DsHKfmARRIsMi30W8wcftNoB1WmtUqHxodoZKhnpUUNDG6/Xit23H0SmZOBMO5Ffgg6UHVDLJA/mVqugFG5GqtdcqnIWZH2wGhzDN8vPVh9EXtNBEPEqrHZjyzXasNwHwurHtaCH6tW2OHdll2JNbjksGtFLneMJ/25XCSjviuLnPKhOpWnyw7IA812rh8kGtMaAtMRT7tkrA/B25PnJaq1ftRFq3NwtAdwBApEEZA3d++jcykzz4wFSIHvT43S4cOF4gk71yECdWJBxo3zwaOaU1sLs8WJ6Zj7tmbkVitBU/3nom3lq8D3M0igtd2C8dmZKxTiOcu3K0Zc3FVQ5Ml+RRosgwC3Z+ioATdzPOHGrw1TrdOJBfiZ7pcTAw5DorvwQz5+7Ci5N6AyBFIgwGwMAQqXfmZ2BOja833WoywuFWOw0LKtT3l8vjxesLM/HjrWfizcWZ+Gg5UWusO1SE1fsLcfM3mwAAd4/uJCyIAxDjr6TKgbFvrYAXwJpHRqsM78MFFWgv/b9w60HcMXsfrjitNc4qdCtEyuVQFfzZq5E3lF1Sg8fn7EAPae0wM+fW4LbjaFE1rv9ygzxGbx7WHr9uPQ6LyYC8cjs22CoQaQBKvDGESElSS7fHK7y/Wfx8+2AUVznw8+Zs7D5RjuxisYR5lKSc+WlTtoqoHC6swm3fbsKyzAIkx9qw4fGzVevmxsPFWLQrF4cKqlBa7cSH0rVYua8Abo8XRgOwfF8B+rSMR1KMDaXVxBnQOjEKf2TkIDnGhsEdk/Dygr34aZMiS88qrvIhUQCZl/q1TkBFrRNbs0rx9O87caSoGiv3FeC6Ie2weHceXpq/B0ajATNvPgMD2jRDUZUDybE2uNwebDhSjEFtEqCiWG4XYFKbzsv25stOwqiU47icvuGyY0++co9tOFKMy05rrYrMx6Ia7y89gMkDWuG3bSQqaYND5ejpbTiMNwqqcKy4Bm2SouSiORf1b4k5W4/D7fHieGkNIi0m3PndFmw4Uox3r+iH5jE2PPrrDqy0fI3WxgJUrXwf29rfitcWZaJ9UhT251fio6sHynnE7Dp89HgOjjBSv/wKO6KsZDzsqIhGNyOQX1iA9//YiYfGdsNPm45h+vw9eGR8N1TanfL1mLXpGM7p0QJXf/YPWidGqYj1P4eLsDunXJ73AHJfnNnh1GpzETKRWrFiBSZMmIChQ4di5cqVmD59OlJSUpCRkYHPP/8cP//8c9D78nq9uPvuuzFnzhwsX74c7du3V70/cOBAWCwWLFmyBJMnTwYAZGZmIisrC4MHDwYADB48GNOnT0d+fj5SUogxuXjxYsTFxaFHjx441dC7ZbyKSEVZTbj37M4q7ziPZlEWoZd7VJdk/CYtgFbe2wfgygEtYEzpgQiLCY/P2YHNR0uw+WgJbjA5VUbYS5bPsflQO6DfDXjjr32Ym5GDvGNuvCM6GD4nyu3AnK3Z8HqBiwcwfSn4HCmvF9SttuVoCeZm5GBuBnDNmW01ZQxerxdzth6HzWxCrdON8/qm4fm5u/Hz5mzYXR6cb2UX62LkVMTidCivdYxzAUVEUkExeUCrgLK3QW2bCXOQeFz7+T947oJeOLdPmtDAv/JTkr92w9B2yNCQrVRJBryVMfw+Lb8dn2ECXsS1srRzb4BGulOGtMXVZ7SVn983pgsKKuxYsicPI7smyxIXKnHMlvKZUuMi8NolfRBlJVNF97RYbDtW6mOAje+VhoFtE1HjcKFjSoyPnIri8kGtMWvTMWw+WoKHf86Qo6+RDMGNhjgPoG1SFM7u1kIlSWEhOsetmkUiu6QGG4+UYOMRcs16t4zHLSM64J4f1JE7owEIVOirRZwNRZUO1aJ98Ydrfbaji0Wt042J761WRaQycytQVu3ElC83wGoy4JXJfUixDQnfrj+K8loXth0rxZ4TFSHp34s8hEhRw1uGoOpZrdPtk8snigJuO1aK+2cRCee+Fyfg4xWH5HmFYveJchWR8nrccuyprLJasQmYZOZI6V5kI7Nujxe55bUYbpKOwxaHjOwylDkNoEHZKz9agR+nno17ftyKo0XVsJgMiDD7NoMf1LYZMrJL5TxQ9r61QR2pMUvPDxVWqSScIlhMBrwwqZccgVMRSAaRUBOpKIMdA7APr1o+xSZPF/n1JEM5DhVU4bilHD2kn2GBmxhTAGq9FtR4bdI+7RjQphlsZiP25lbgpq8JCaAlqzcIyDXFgDbNMK5nKu75YavQ6ATUDgYRzuyQiA2Hi33uE9bYtxkcKknv4t15KKiw46PlB/HFmsN44cKeuJZRIVjhxHfrs/DChb1QWOnA+e+vRqtmkfjJXiErI9yCRsRjuqfgwXFdMf6dVarXRSR6w5FifLTiIGYsU0ver/lcyV16n1EYUNw2sgO+WH0YdpcHf2TkyPPx5qMlspTK6fZgd1YB2ksHuyZjLwAjftx4DD3MBtnKqizNV60b+/MqNHP1AMi5u2YVSSXnlSX6n61Wz4d0mzJEAygAXHZ8sfowXlm4V5Y6P3BOF7y5eJ/qc4+M74ZB7RLlXK1A0lEKvqrpMknyXiDlDrO5X+zvf32ROj/ySFEV9ktyvQFtEvDz7UNw7nurkVteiw+vHiDP19Mv6oXPud9M5ycek2aswUdXD8Ad32/xeW/bsVJ8vvowuRc8Xnyw7AA6p8Tg01WH8eS53fHaokw4XB5cPaA5prMfrCkBYpKx83gZZm86hqlndVJdj+yictl+8jjtqmrL87afwB2jOqJ5SYEsvY4x1MgkjMLKzU89jUcAeDHi9WUY1LYZTEYyu57ePhEZx0pxqLAKw15dpvoMuwa3NpJrknsgA1cvIWOeFsgZ8bryOZZIxcP3+seCzN85nkTASJ5/tz4LC3fmwS7NG68u3Itzeyu9C7cdK8XszdlyxJWiTWIUsoqrMfG9VTgSof4en4hvE0fIxSYeffRRvPjii1i8eDGsVoWnjx49Wq6kFyymTp2K7777DjNnzkRsbCxyc3ORm5uLmhpyseLj43HTTTdh2rRpWLZsGTZv3owbbrgBgwcPxplnngkAGDt2LHr06IFrr70WGRkZWLRoEZ588klMnTq1SUadAuGSQa3QOSUGY7q3wBWntcZf94/AZYNaw8iInod3bo5OKTHo2iIWH1zVHxf0TRfuq38bOcCMTi2ifd6PNrpw47D2uOK01ujAyPZYKQEAJBkqMGTXM5g6cwvmSiWutx0JEH2QeiB43Q7cPysD037KkA3dgwWV+GE9U8ba6wE8Lvy8ORvfrj8qG/EAkFVUDbvLDbvLjbcX75OjBwt35uLuH7Zi2k8Z+PCHX4DfbsMbv6zG9/9kwS4tFqzXN7uwFDllNYhkJopEk6+3jRZfSI2LwJjuLTCiSzIW3Dtc1sd3SonBsxf0REKUtkYdIJywsNKBh3/OQMaxUtnAmX5RL0xmCSWAL9cc8fn85YPUUlN+Yr3ZvED4va9d0gd9WsUj1mZGlxYx8uvJgryX5Fgb1jw6Gi9f3Ef1WtskReP+4LiuMokCgG6pitF4VtdkpMdHIMpqwojOzXHHqI6YNrYrLuzXEu9c3g8juyTL2z4xsTseHt8Vz0/qiQ7No+F0e1UeRXYCZ/9ncc0ZbTGonTKmLx/UGs1j/MsxRBHNlFgbxnRPwfncfXPpQOWcX9A3HTazenoc3CEJX994Op69oCcGtm0Gf9ibS/pxvb90v4pEAcQI6/v8X8g4VoqNR0ow/h11dDmDkYLM3Z6DCrsLERYjLh4QuBlzqZdc8zKv+n4vyD+B+2dtw3VfbEB2CTFKWTkoxYKdubjnh6047/1VWLgzF5uPFmMj46U/XFglyxdZ7M4pB/bOB46QSm5VtQoZrqqpQUmVA/O2n0BWvmJw0HuRjSDQQgg0Wuy2xWF3TjkcjGfHBid+35Yjy6Lun5XhYygZDcB3N5+Bp85TnGmDOybJ8jcbJ2umEZUnf9vpN5oFAPGRVpWMkc2vZBFlUF/3SNjxTacV6Gw8jivNivGSiAr5d1GY4ZKP0RYZjSuHdiX7MDgwsG0zdGnhK8/xR6IAYnRN7J2G/dMn4N0r+uGdy/up3v/ftQOx54Xx+PKG0zT3cdmg1lj+4FlqGSeAOJuyQLVP8PXP3vzNJtkB8uK8PSqlAv3dOWW1+CMjB7nltdh0tASOasXoEs0JV53RBt1S43BB33QYmTXLYBBfQN5w18ItwxWH7iUDWqFdErmX2EIjX649jNcX7UV5rRPztp+Ayav8nuxcZW00M/P2pDfnYd52ZR97citwpFAsX6aIsZkRYVJ+TwRHzkWg55POAZsOnsDzf+6WSZTZaMBFzFzSt1U8XhpYgdsHku3bJEZBq296Gy7/icXNw9r7vDb6zRXIKyfXLsLi39xcc6AQczPI+dmSVYqjxdU4XloDt8eLVxlH8hNzFNl4pMXXgcJDRKIAIr9l5XHLMwvkghovztsjn695WzjHneSUem1RJr5edxQP/7IdSxg1htmgEN/jRaXweMlv79A8GjVON85+cwVe+lWxlaNRiyfM3+F585egIXoLM268BiOaG8qRCnJ/bzpaIqsR2iZGoUOyr23Hgt3XluP+x1sEM2clGHwdkzHSvHzCS9QW0QZy/gor7XK+MwBVawavF6o0FIAQpeuGtNM8jvRTjEiFHJHasWMHZs6c6fN6SkoKCgvFEhQtfPTRRwCAUaNGqV7/8ssvcf311wMA3n77bRiNRkyePBl2ux3jxo3Dhx9+KG9rMpnw559/4o477sDgwYMRHR2N6667Ds8//3xoP6yJIC0+EounjfR5fd49w/HBsgNIjYvAExO7w8gwK4vJKOdO2MxGmUi0TlQGY5RFcKklmY3RaMBT5/fADV9uBKD2gFHEuMtUi4BFsA0LpyUGFncxDF6PnMi99mAhOqXE4LNVh+DNKsSVDBcpKC3Hg1ITwPE9FUnmwz9vx4GCSvRuGS/nhCx/cBTu+H6zbPDcYZ6L80zrsX5XET7FU/JnWY/whv0nsM9rRiQTpYpwa3vcTmufiPev7C8/754Wh+uHtENMhBkxNjO2PT0Wm4+WoLzGibnbc4hhwziEFt8/EmPeWoEqhxsXzlDKBHdOiUV5jTi/7N6zO+O3bcdJoYlzOmOWVGY21maGRZCTdsfIDhjWORlXf/aPtO8YXDaoNS6VOtK/OG+PnHibEhfh83mAyEl5vH9lf/y+LQctEyIxmTPeBzDk/MFxXdEmMQpOtxeJnL58Uv+WGN65OYa+uhRWkxHXnNkWkZJM8ZEJ3XDbt0rfstHdUlDGNOKe3LsZLr5oPG7/bjNqHG68d2V/7M+vwJCOzWF3uTGmewskRVvx8sW9YTT2wbK9+bjhq42q7+/VMg7DOyfjskGt8OWaw6rIS2p8BKKsZrx/ZX/E2Mz4YQOpaHflGW3kc96/TQIu6t8Saw4U4v5zuqC81om0eHI/dUuNwzVntkVptQODXvwbLo8Xj03opooaZ2SXYdQby1U5EFpgK2fyoO0JuqfF4cVJvTCgTTP0a52A894X55JWSpkr1VBf71W7jmCOnXg8Z/6TBYvJiHeX7Bfug/YDU/WWk6DVM2nrrl3A5psBAFlTjyHG6ZTToSxeF0a8voyMa/MB3CdNRfT+LKiwo1WzKJRVO+WoYayUv1juicT+vAq4YYLLa4TZ4IEVLszcIG4vcG7vNBwpqsJ9Y7ogwmJCerwyB7aIi8Bp7RLx9548VYQXAKwGl0pmeNmgViiuciAu0oKD+ZWqiDHrIACAxGgrOiRHyx58q1TkZNrIVgDjW5wyMBkxu9TREwBoZiCEgZ1T+7eMwcLzTgM+AwzmCKQkkfsu2eZG716pGNi2GXLLajWlaCxuG9EBFpNRzhE0GAy4sB+5r19ZsFeWpNJzdVbXFEwZ3BbfSGvK4xO7obTaiaNF1ZjYOw0RFhPWPjoaeSeKgLfJd6TGmCHZWBjZPhavcUE91jmQEGVRyblpru4F769GHzkP1Quzq0oeQ9QpNuOqAfh1SzbyKmpxppTE/uwFPTG2cyzwp/J9P98+GJd8rJRH5zG8c3OU17pUxwWQ637riI6YvyMX3dPi0CklBh2TY7A/v1KlVFhzoAhrDhTJEa7PLcr8XFlRCiBV+m3KNU2A2jDlv1uE8/umIy7TAMojIxgHwC0dSpCSNQ/vuS5GBQjBMcAjE/BKI3GqrNmrdny0bBaJVs2icP+YLiitceCpAQ4YP70NKOoN3L4aERYT0uIi5CrB5/VJw5/S2j+qazIm9k7DFZ+sx9ndUuDyeLFCcm5eMqiVT3SMgjp+ad4ci8RoK4qrHHJ+LgVbeOsQJ93t3TIeL1/cGzmlNbj1W995SoSUWBtuGtYe/xwuVuXRDmzbDCaDQfNe4lMgUF0Er9crO3WXM0WnAPV97KglRGNCrzTcNKy9PG/HM2NhQHQBbnGSSrVfu8fioLelPD+5DWaYkrsC+bswKu4EfixXF25onRiFUV2JrJ6NJl9zZhvM234CJdVOFfm2GvxXTVRFpAzKOb97dCe8v/QAYiTidMJLjiMOYuknldteeXpr/LDBt1x+y2aROJ3JIebBOnNPBYRMpBISEnDixAkfGd7WrVvRsmVgjymLYGrrR0REYMaMGZgxY4bmNm3btsX8+fM13/83oHtaHGZcNUD43tndUnDL8PZokxSN5VKiPAC0SYzG/WO64NNVh/DguC6+H2TyFdiIVGqsGSJ1FSVEgNrLIcLxagvaSQ4omsi95kAhRnVJwQ8bjuEak5qIXfnRCtDs64W7lERMKm9gE+tHMRMsAJxnItbKmUa23KlXNSnQiYklV0Z7OSwmg2zIjuvZAot2EU9KH0F1m9R4tXFKoxJyfhJDpDqlxMiTD4suLWJI80gGBgPw7Y1nYFjn5rh9ZEd4vF5E28w4q2sylmUW4Ilzu6PFPxEAZ5w8MLotzLYonNauGTYeKcG9YzpL+yPWRwpTBSktXkykROjTKgF9WiUI3+vVMg7PX9gTNrNR0xNPkRRjw593D0Ok1SyTKAAY1zMVt47ogE9WHsK7V/SD0WDAT/uUaxXhrQWsJnzNJB/Tcx9lNeOz6wapvoefdNPiI/DjrYNl7f7vdw3FuoNFsgyKLZpy56iOOJhfifP7paNbaqxsBHdLjcPgjknyteWrfgFAQpQVH149AHnltbjmzLY4UVaLoiqHHLUNhkSxuGFoO5/oJJU+9kqPR5SVVOoDiIdTVFDG7iXeiaNedc6czaMcy6YjJXJ1uVDBSza7tohFSbUDprK9cvGE39bvxvVMhMBscMtFDyIYoyRCypEqqLBjbkYO7maklvTeLXZZcKiULOh2WGCGHTaDQ7OYzn1jOqMzE61paziBGZZ38K17LJJjB2Jg22YSkVLPX71To5DN2Hjn9UnHCIkwrd5fiB82ZmFAm2ZYvb8A957tW/3wtLaJMpH6674ROFxUhRHR2SoiNbh1BLDH7NNe4uyWbjx3FLAwRk6CzQDQKIfZBqOVjPGR7WOAaCsSo6346fbBKKiw441FmbIDoHmMFQ+N64qOyTG49vMNuHhASzw2sbvwXAFE+kqJVFqCMke0TVLukV7p8RjCVbxsHmNDUpoSnY63QSZS3ZIt6N0yHoWVdiy4dzhGvbFclaubV27H1iP5oG4qei2KqhyyLMwGJ8wG5RpHwIH7x3TBuX3ScG4fRTYEEEP8vB6JKiLVp1UCzu6WgiV78/HYhG7YlVMuOwjm3zMcHZKj8eGyAz5kZphUPW7No6Pl1zqmRAPadXUAqNfDaIMd8JKIuS1D+d03DEjAwJgOOLNDEqZ+v0XOuxzeublc3AgAHpvQDd+sO4rSagduH9kBMYchE6lYkxNxJjO6tIjF464XYTDvRjUi8LbrEvm8UbRr1RI47ksE7NK9Q9cLuTdS7g5ZYh8XaZGJ1HVD2slEqmNyDM7skIQVD41CekIk5u84gRX7CjC8c3N0TolV5aylx0cgwmJCanwEbhrWHp1SYhBhMeHCfumYIhVeGtAmAef3Tcdzc7UbFrM4v286RndLlotcRFnFEamkaCtMRoOqJcnrl/bFyC7JOKNDkopIDWiTgC4tYn2IlNEALHtwFKa88aPq9Tlrt+OZr3yjNWe0T8SGI8UqRzQd269f0gdmkxFPn9cDz/+5WyYkAHBzx3JA8sG1MhQSIiXNBQazFUjrA+TvwsuDPXhyyDj0emaR/NnU+Ahcc2ZbjOicjPSECFz12T84VFCFqWd1whWntcHmoyW4vEcEaB5GFOwY3S0F246VqnJcASAuwowoRnIbx0j7xvZIxftL9yOGSvskImUzOPHg6HZ4Y+kRn/MBAI9P7I6tWaU+qQdDOiahWxqdp3ke4EW75v6jbE0NIROpK664Ao888ghmz54Ng8EAj8eDNWvW4MEHH8SUKVMa4hh1BIDZZMQT5xIJi93pxpK9+YixmdG+eTTuHdMZd43uBFONwKjb+ycw53bggg/QuplijDaLMAqJVBRqUSl5vgIRqUqmphNN5F60K08mKrx80FlVAurFCwciDU4YGYkHXUzY6nAGVw3aJ5ixr4i8d+nA1vLxaZUJDQWTB7TCH6s2YobhdXzvPhtLoyciIcqqkuX0SIvDC5N6yaSMJRxvXtYPe3PLSVWmwngfImV2VgG2KHxy7SBkl9SoqgoCUEWJgi1pHQgGA6m2Fyx8yh5LeGxCN9w1uhPiIiyotLuQ0zYKoIasMzQC0ooZu8M6NccnUwaq5IhRVjNGd0tB75bxOFJYhXFMxLN1YhR+un2w/HzGVQOQXVKNMzsE1xNvLLMvWplsf56iBX/yXGLEvjhP3NPk3N5pmLfjBFLjInDHqI5CmSdACCyL3i3j5egNi9M6peHS6FaYvRm43vEwBht34TbzPMQZHXj3in6498dtKoPBZDTg6fN6oFfLOEz+SNuDL4LFZMD0i3qhsNKOr2cq8pnZa3fjRqtyf7NzBXv/0RygW7/d7COV7NzMBFQAf+4pQ6YUsXHAgmjYVSRoYu9U7Mopl0lrp5QY1X7a7PkUnU0b0MmQA2fCrejXOgE/bz6Ga9qlAjuU7V6/uBsGHolBtcON1PgIDO+sEIdhnZtjmPT8JoF8CSC5NJuOFmNklxS0ax5NDIEj6hwUuGp9mp0DQBtTEd69oh86LbcApdKLHqfi6LJEkj/A595IjrXhnB4tZCJ1xWltcPlppELWjmfHyrkUWmAdBIlRynzRupkyf/fUmAvZaLaZ6f9icjvw651DYDYaYDAYcPmg1vgf0w8RALYdKUB/6at9PP4Aru6XKBuXAJFFtvAnYWKKV0SZSVTwrcv6YfWBQpzTowX2nCjHX7tzcU6PVDmnbWTXZFXlVavJiBGdm/vsmh9TFOnxStSGlYpGowZGA/DiRb1gdCYBUrD93E42nNufzAdDOjaXK+Hdc3ZnmUg1i7LgtpEdcdUZbUgxoRgbwBjmfVIjsOnmc2AyGmB4npCPc4ybUWCKR0dDDt53XSRv27F1K4lIqQ1mH1kyW+zJWQNYozCuZyr25lbg/L7p6N0yHs1jbKh2uHBWV+KgoUT7gr7pSIuPRNcWsTAZDWiVGCk7FH66fbBqbgaANy/rS86FNO/dfXZndEqOwSsL9spKGopHzD9govEfXO18AtneZFzYLx3vXtFftU2H5Bg8OLYL3vhLfa91TY3FzFvOxEOzMzBbkiLT392nZTyuObMN/tqVh4Ftm+G2kR0RH2lBfoUdyTE2PPzLdgAkX7ZtUjSW33cm8JGy77W7DqHcra5sel6fNLx/ZX8szyxAmw3zAWm4Ww1O9GoZJ1ekvHFYe5TXOhG9nLFDmBYJLcxVgAO4b1RbYB1gNNuAtL5Axg8w5G5XFfYAiBoJANpIzsRZt54Jl8cLi8mItPhIYseUKhGhOEMVRnVNxvieqXjn73144twe6JpKoq5ujxclM16FpCBEnKEGJrjhhgmdW8SgdbQHVjcZi1TaBwCTk7PQ1/IS3nNdjO2mHvJ1TIy2IjbCggm90uT1cN49w/DPoWJcflprWExGnNsnDX/vVBfHGdE+zud3NnWEfLQvvfQSpk6ditatW8PtdqNHjx5wu9246qqr8OSTTzbEMeoIAVef0RYFFXac2SFJXkT9LqYZPwD9r4Wx3VBcMrAV5mw9joGt45QFnUEk7LBGJyA5xgZzvn9pH0uk/px6Bm75+bAsMwN85YMvmz/DVU7t8WMyGjC+V6pKXji0UxIu7t8KmKve9slzu2NEaxPwlfLa5L7NsXkbEGVQLyqdYx3YV0TOz2ntE3GzNNGd3j7E5tJsdUIDmdzaNY/GvMH7EbPhCF42fo7NVxPZYSvGSBneublmvk1itBVDOkoLu6hEvL0ciElGs2irsHTriC7JaBZlwdndWwQ0qBobBoNBLiISYzPjtsHpwK/Sm4LEcn9g8zWax1hVJIr9vtm3D4bb4xVGlyjO0ejFEQpuGd4BX687glcn90H3tDgcKaySidRD47qqcjVuGNoOF/RLR+eUGCTH2HBW12SsOVCEty/vhzf/ypQlLb1bJqi+46ZhHbDxyGZcP7gtXtimvN6uRTOkmUhkYbmnH0Z1iAGy56FfCzMM3dW/7aFxXXHD0Hby+bpsELn/bx/ZUZh4T9E9LQ7z7h4Gp8cjF3r5xaw4LaxwwcpEE6JN6siC8r9yz7AR5xuHtke7XAAVQLXXhhqpOSrMNsBdiZ9j38ZLNZNQ0ulivHZJX7jcHry1eB8u7JfuI1W17SOTQ1djNjxpcTAaDVjywCjg6DoVkYoxe3Hz8A6avzkQOiTHkP2ycNb4Phc1KC89RqR2bM0SN0OkzDbAEiXeJ4DereJlSfeF/ZS8P59y4gL0bRUvS7NYufjgjkno1TIOp7VLlPPK/IL9Xa4a2cADgPvP6YKBbZvBC+DumVvhcHtU5ClCIDc6v3u8mkgZHOjoj0gxZCDBSsZifJRFjl71bZ2AbU+PVfWNGtg2EV/dcBo6Jscgv6IWNrMJCVG+8+jYHqkY0SUH7ZKi8NiE7jj9pb9RUevCSxf3xpdrjmDFvgIuIlWL1NgIcg7Y88JUart1RAesOVCIkV2SMahtM1w2qBV+2pSNG4cSoh7LFlhiIphpUQC4/LREQzletHwJANjqITmhHoMJlkjixEqyebD03pEor3Vh9qZjvhFVdkzVlgHWKNw9uhMGtm2GgW2bIcJiwpJpI2F3u5ESq1Y2GAwG1VqZwIyVFhpycgB4/dI+uP+cLjJJzXxxAh77dQd+2JCFFyb1wpdrDuOOCnLvTjauxLvuybheI6fmrtGdkV9hl6WogFJw5OIBrTB7czYm9EqVjXOj0YAXJ/WWq0RS0HLbf+3Oxd978nGeNHYMXCXiGE7Odkb7RDx1Xg8YDAaiYDgYoRApuFS5t/R7jmdFAbRHOkOkbh6UgG6JPXBRx3JgHQCTDUiVcphPEILXv00CtmaVCs+FwWCAxcSt9YyToVN0Lfqf1gZWs5FUD2RgNhmQbFPfi3GoQt+uHRFhMWH+9Z2AzwGvLQ4tYpNRXW5DlMGO1DVPI820D72MR/BIx9/lYmnUkXv1mW2w7lAhxvZIRc/0eJWS5e3L+qFiQnvgXeU7P7z81CsSFzKRslqt+PTTT/H0009jx44dqKysRP/+/dG5s3azRx2Nh0irya+UQ4gKQk5eubg3njm/B2JXbxBu9stNfdCmcx9szy7F6x+Lt6Go8Vrh9JpgMbjRMtaEX+4Ygmf/2C0Xc+CJVDdTNt66qK+wwWuH5GjMvm0wkmJseOMSN/blVaCoyo7R3STDkCFS/dsk4Koz2iCqWq3FPr9nEjLMrdF8nxusg+6MVAPmHSH/x0da5N4SIYP1FBuV2yrGrkySlDAZjQZcP6Qdlmfmq3v2VBUBy18Guk4AOp2t3r9LkGQcoJFxi7gIbHrynCZHooRwVon/DxJ3j+6EX7ccx90C2RUF33yzoTB5YCtMHqgUFGmTGIUzOySiqNKBm4a1h92p9CBr1zwagxit+BfXnwa7y4MIiwlRNhNu+XoTOibHoCvXA2tczxZY9fBZaBbtUREpmKxowcg4e7ZrCWQDsQY7YDPL+QgAcOnAVirS+erkPnjugl6ItJpQUGGXe0sZDOQ30KjP2d1SYDQaYDOS8xlhMaF/q1hAKuQXZ3bBYvSCBp1tRjeaSyQxYrsyjoe1j8EfXN/wZ8/vgctPawObRKqrJb1gu6QoNItsDhQWoZkjB6+bPgSunQ4YiVH5vFbjTaavHUsUgm4cXh84uHGsFWmtLgTslepjUhGpCCYi5UukWsRFYOYtZ8Du8qhkjcHg0kGt8dfuPJ8oc2yEBX/ePTz4HflpZxFhMcmR2zvP6oj3luzH4HaxgJS6Y4Qbqx4cjuFvKPljvZPVZCHG5NSMDAFQGYtGjb6Eovt/lBRh4ZvIsoi2mVU9jr664XRkl1RjZJdkWM1GrNpfgPRYE6gSSlUYgz2W1e8AQ+4GQAp/bH92rBy1e2xCd0zonYaRndX5dwC4cyuNCSZnNtWgELReMRWAAzBaIsm4AXBBzyQgmZy7fq0TfPfPXi97OYA0mE1GWdoKEFKq7qcgxmntE7FFMvItfoh8lNXscz2nT+qFR8Z3RUKUFT3T44AvyOtnNK/FFxMGqYpn8bh/TBe0TIiU81SpP2VwxySseviskKTtj03sjh5pcUoxBKc2kRrTPQWfXccVZ2HmkjiLB1MGt1W9bTEZ0S6Wib5VKjlWXWKd6DKsPZBNZOgwWYHU3gAMQHk2UHIEd47qhHt+2Ipnzg/SVmGIYBLKfIi4CpwT87GzUjHsDEI4Y10kamqIaYH5dwyH5Z1mQGUuDIUkGtjMUImnzu0uEym75ABrHmPDj7cOhghWsxFJEWr7JMbYAHNxA6PO8bPWrVs3iSa2OsKAKnIjm01GxJqMPhp+ijax5Abs0yoBn1/bF/jBzz7NkXB6zLDADbPXhdgIC968rC96psfhpfl70CreAlQDK9x9MNK0Hc0MlTizXYJwV62bRUkSB0IU+4oWAwlz7hxK/ilTGxyxJjdevaQP8II0qVhjAEclJnS04s+8GNn7VGcIDBwAcnSKB5GBcT3XVr0BbPwU2PQF8OB+IJpJLBU1NuZ7dglwSpAoQH3+QoxIAcADY7ti2jldhMUzTjaMRgN+vHWw3DDxjA5JwNIDaJcUhSQukmgwGGSD76yuKVj72GhEW80+19FgMKB1YhSqeGPdZEULxmscH59A/nGQaPD5fdLw9bqjuKh/S58CJAaDQZaW3jy8PQ7kV2LqWZ0woG0zlNc4Mfw1kgTYPU0tMwSAkZ0SZCJ169BWMKxX5hCjx4lNT48BADgdCYBU42JYuxh8O+p0XPs5ccqM69kC10seebjIGKiRiNSgdolAeYL6S6vygdgAcmAVOWH6v/BOCK3G4fUBPyfU+EoxZZQeVR+D26EcozkCMEtEyiWeZwa2DTGCLqF1YhQW3j0EwoZeocAtMPYFuG9MF9w5qhOsv8+RiRQApMcYkYxSnG7ci0WeQTBzv3Nom2i5RL4Q7PUM4GCqL2ikBiASvX0vToD54xdkIhWNGkUazhL06kLiLJPmdZZoNIu2yrI5H7ByUDqmHOJ+eRd1NpN8LnOETKQCrhPsPaKx32Bx9eltsWpfoZzLGQqMRoMcEeyYoJC2lgmRaNvNv1KgWbQVt43siKV78/HP4WJVJWN/JFmEjskxmDa2q/ICH5Fi8puGdvKVgrL2k8lth08JxKoiYNccZv9cRBBQ5gKzlTQlbz8COLwCyJiFc0Y9gl3PjVM7hvyBvR9qSsiYNGmQYs7Zc1nPWIBW0KuQnMKxqeRejIgDKnNV27eOqMH5fdMxNyMHNw8LMsLPO7E05rimjJCJ1OTJk3H66afjkUceUb3+2muvYePGjZg9e3bYDk5HI6GSK2Uu0PEDUE241gBV+9KbJ8CZLy18zI1y47D2uPrMNqhdvAX4BzAmtIS3cicMXg9STeKKYKF4k2TwUQ2XHfB4lN8Q1xIozESyoQI/3XZO6PvnwRq0boeqL5byup8JDCDJvgDgdQP7FgL9r2Y+KzAOGthgaFSwE3iIOVIUTZFEsaDHN6RjEr6/+Qy0TYoKeMy8lCYgzDZVUZSU5hIZlwykxyZ2x7heqRgUwPDulBKLn+8YIj+PizDjjPaJKKy0Y0QXX+OhZwvFWJnQJR5YzxjmHhe594xGWJgmtenRQGK7RFmWRquwkeMlYyAiKhZRdhOR9SzncnVKjwFRScQRFCdoAeHhClI4KoBIyavdKESKm4PKfSuWySg5ojYo3HbFaA4QkaoXXHbgwzMBgwm4c53/+ckf/ESkeFjNRp9tTB4HnrF8g/NM6/Gi82rAcbHq/RhjgOvDzo8epzzewo7qYmLQdp1IJJeQJJTM+BmQasVVNFrAn4vig2oHmdcL5O0Ckrv5NHmVwY4LOjfyDhQJyZDIujlCPr6AREqVIxXYOecPbZKiMP/eECKZGog3KuM8LS74/N43Lu2LdQeLVOXd6w3u/J3fNQajzhmBdQcLcdUZAsLIXi+v27eB74KHtL+rplT6TumamKTf3uNCMu6yidMpaBLF7ouiukjbAUXHl8lG7il6PIAiQYyRSK1NEP0uO4YXLuyJq05vE3SesY/cuZ5j8GQg5Jlm5cqVmDhxos/rEyZMwMqVKwWf0NHkwWh0AWhGpFTysgCGR8f05oiIiBBuazObSJUnAMO7tYIhmnjijFX5GNKRLDJsCXS+Wh72/QV8NBTI+gc+oOJo3uBw29WejnhJeiVoVFon+Hwf/c3MhGf33zQXZYyLtoArTiCU9p16E44m6hmROpVgMBgwtFNzn0TssMBkRfe0OJzbOw23jeyAhASJONgJkYqwmDCkY3OfPkCBYDAYMOu2wVjywCh1/gYFO2eIiDBdLFWRg1pEWEx45/J+mHpWR1XDaEpCnpl8GjY+MYZ4+CMS1PuszAX+vB94qweQudD3Ox3c/cbef/z81RDSPn5OKM8RbwcQUsieG60cKXsl6dfFO7/qivw9QPEhoGg/UCguhx8UWGMoGLLn44W2y9VXr7CsFMgiA+yTnx815H31xrKXgNnXA0u49irMeBrTMUq5t/nj4Neb7bOAj4cCS57T/k723ircB/xvBHBMsPYBiuFrYch3KESqqUQDaGQGgNUdfJSsdWIULpMKGYQN3PlLj3Sha2osrh/aXjyP8sSAHwM7f9H+LjkiJe2DOjYSpLmxIs/3M4HAf39lHpC9Gfj2IuA4Vzqerr3UMVVbynxOij75JVLHkRBlxeCOScE7Nvm5uKmMwRAQ8mirrKxUNeKlsFgsKC8XRxR0NHHwCzyduI2ch4z3+vmB2RYFm1VMpFTfYbIAMZKkoTIP709MwYwL0vHKZCUR1KeCy7oPgLydwJxbfY+Tymd4Y9xZo14wmrUjjyVH/P6OoMEv9PS7QpFNsIS2Qh0y//dHpJjzZy8DVr3lXwrVVFF8GHi7F7D2/ZPz/WYbTEYDZlw9AI9N6A5YpcXOVaOWX4UbrLEn8pZTw4CdCyTP44TeaXhoXDe1USLdv5aIGKU4CC+TrcgFtn4LwEvmBB6MMQZAJpMAGicixc9BFQIiRclhbZmaXLjs4hwpRwXw45XAT9eF5xjZe4yT6YQED3fsgcDPZ8z1S2reQrlWBknVEIhI8fsLd+SOYuOn5PGfj7nvZ8aPSp1AS9hLayFPpFa8Rh7Xvqf9nbxj80QGsPAx8bbU8DVHMhGpANcjjBGpsIG9dwM5IBsa/DkJdDwCJ0HQoNePjmd6DRkbKWTwTobSLGD1W8DBpcD8h5XXPW61Ygfg5gfJeRNLiZSvxFvlDA4W/PlqqHu3AREykerduzdmzZrl8/qPP/6IHj1OvWobOqBNpKI4Cc/a94Hfp5KJIZAH1xyheFNE28pkzaSEmfN3I+mbUTh37eVIqDyEaxN2wmjw+jTAxOEV5JGSINbAeq09sOUbX6+4i5HKGC1AulRG9YRvcYs6gf8+urCyi6qGHAMAmcTYffAL7r89IsX/liXPAWveFW8bCLk7gHf6AJu/qvdhhYz1HwJlx4C/TlIFUxPn5LIxCd31zH/wC5H8iEWVtAhzESlN0H1YmKhday6pe+Nnyv+ie4snUuzv9yk20RDSPuk3RElSLlH0O1bKzbSXczlSbPlzhkhRZK31lS7WBew5qirU3i4QgsyRUrbn1gSGSDWLiVSuZ7Q09wfyUvPzY0M7mXhyw1471tCmx0G9+LxzKNC58nggzF+r1rhWNCJltinkLRQS2lTWFDYSYm/AeSsY8OckEJHiUyNCOac+0j5pPo+QSIu9DsEK/vtLjpDWNwBwfBO5d4oOqudtGpFipX0VfiJSraRiLOV1IVLcvdtUyHwICJlIPfXUU3jhhRdw3XXX4euvv8bXX3+NKVOmYPr06Xjqqaca4hh1NDQKM4knnYJOBFFJ6u0O/A1s/Q44sCQwkbJEKpOAMCIlfYfRrHhbdvxCohGVecCHZ+CF2pew8mKvuhIV6+U1R0h5T9z+/7hbQKRq1R7eFlKhh0Ku10sgeL1AQSaZfDxuYP3HwJcTgUPLue+TJkJ20vW3IPDHW811Wae/8eJPgbZD1a/9GyCaPP/5pG77Wv8xSd6fe2/9jqkuKD4UeJuGBPVgss/pfdiQRCqQtOu9/kDOVvWY9WdgUEPaypS87nsVcMbtQIdR5HkBUx+bJxoAUMsZHawR4iMFa0BpHz+PsoiTiFRtKVcYw67cE+ZINaGkqIt3mgd7TupipFGwxmNQRMrP3GUvV2SZMRKRCjUi1diEgB1PKsIujasoKV+Ej1LSiJsW2PuqJ5M3piW/p0SNqdoXWkSqiciqm1JEip4feh8HJFJ8RIobi7yDmoWPtE+au2n0x1Urdqr6A3+vlRxVP//7GeD9AcQBDQAwKFEnUUSKEilWutdZyjOvk/TwPyjtO//88/Hbb7/hwIEDuPPOO/HAAw8gOzsbf//9NyZNmtQAh6ijUTDrWuV/OkmzRgyL4oOBjXhzhDIJiGSAdKIwmpUbM2+Hz2at8lcoT2pKgNc7Ks+tMdoLpigixXp4qbSvMi807+WeucCM04FZVwPzHgAWPgIcXUNC5Szowq6KSPkxZHmSxXsu6f6iEhVPUFPxHoYDosmzrr8vN0xRxrqAjwiFgkVPAG92r5/cVPT9Vikq1ZCeXdaQ1oq87vjZv4woZytQJjVnFEWkrFHAhFeB3pf57ltkaPtI+9hIAffdDVlswp/hRCNSIscJmyPFE2RAOyoRCthzxBPPUKCS9tWXSFUoY5VGpAIZ+D7GWCPLnrUiUnTejqREijPCAxXEYAnT+e8AZz/tf3tZ2hdC1b6mJu0rywZ+vkF5frKJlLz2SvdxIIdDIGkfHSvnveP7WS1pHyujC/V8iCJSLNZ/SB4XPUEeLVFArBSRKlOa+frkSPWTimH1mqy8xs+5wYCXnP92J1krTiHUKSPv3HPPxZo1a1BVVYXCwkIsXboUI0eODPex6WhM5O1QCjXQybvdMGUhY+H1MpOFRkKhJVKR9ok8KLK0zwLE+ClhzHoaszerF1SDUdtTyXv+XDXqiFQEUwEsFANix0/kcf9fwOYvtbejvzlYIsUbn/xzuj+TLXjt+6kE0QLu9V8ZUhMmxuhsyLwg4XfXseoZQORNFTlAxo/1+H4BkaLyvoY0SNwBIlKAFMnWiEgVZAKfjga+Po9cM7qdyJlDvaUsaop9X/NHpHyKTTRg+XMajRCBypqrCtSvez3KfGGJ9K0ACqhlN3UFe47KjikFJ9yu0KR+IVTtI/sXONeSpB5w9grluCjRDBiRChAFaEh4vWoiqcrFk8aVHJHi5nWN9hgy2PNqjgQ6BVlh1hKhyMGqi5W1XQT2XDWFaACfX8qTz8YGHVvREpEKZC8Ekvb5i1Q7q8mYkdd7aT43mQGLNBfaQyQr9H60STaPpqNOGiPWKKB5F/I/Vey47Ipzl85ZbYcA9+8CLvoEiEwgr7GSzGDBz732cuKcPoUQMpE6duwYsrMVHeSGDRtw33334ZNP6ijD0XFyENWceB7u2qS8RhcvOnnHpQMPZAJdJnAf9jKGjkaTRFajLSqUwOZIUWmfCOykxMumnNXaE79PjhErlbGR77WFqDt21pCKWcFAjkgxi6q/HCl+seC3ZT1UwXoaTyWEcwFnCVh95Ep1gZEhUqFKMCj2LQQWPFK3xF1R5IIWnGhIg0Ql7ZOcGAaTuhBMwR61h5OO36z1JMrr9ZB7nCVFIskeNa5ZVAsKk4iI1PafSNU1Pi+0QRrycjlSIsi5M6W+71GDjd7vPu8LPhMq2HO06Qvgg0GkHPeCh4HXOwEHl4W+Ty3Sk78XyNtN/hcRV1nmWK4YbTRXw+3Qbssh2l9jOpn471ZJ+6T3aNl9HyLFSPuEucTMbzaatVUiPMxUdWEghre/6rSCAjAnFfv/Uj+3V/gngg0NOpaoU9le7j8/0Ufax0p2Xcr7WvNCbZlyTVjHGCXGoUaO6fc3a0Mei5jqnCJpqSUSaC45NYoOkmOmsj6jRRnLAKl+bDKri+aECtFcICpk0YQRMpG66qqrsGwZmVxzc3MxZswYbNiwAU888QSef/75AJ/W0WQw5Tfgnq3khqGDlnogqRffaCaEw8It5C6Hf48xELhqEJsj1XIAIXUJbYABU9TbqaQnfPJ4lXapbN7rosqRkowz+rsDGSTVxcSgeKuHb5Sk67lir6IckWKJVBARKTq5umq4vANmYg22P8ipBK0FPNhS6GXZJFdtzh3qhabRiRSzMIVCDtmFOWcrqQpGK3qFAr8RqUYuNmE0AyMfIbKmFr19P0MN7n/+p369lJItg5hExLfy/Z32Mt/oI3/ta8uBufeRZph8NLkxik2IQI0zUYVKO0ekeHVAOKpaigjcnj+BTZ8D8EqPIUI039srgM/GAJ+MJN8pOt+UILvtSoSO7Q/mLyrlQ6QacW708agLpH1yfg13D7Jrh79qlzAQGWAoRMoSCcS3Js/95QKz58pHEn8S8nBj09XPvZ6Tm7tFryF1+Ho9/p1SfP6aVsRPk0iVMo5TZp6j9krpUZKTHSy5pPvizysgVn1Yosm4sUQR0ld8SJ0fJYqOU4VPXaLkIgeCqLR6E0bIRGrnzp04/XRSoeOnn35C7969sXbtWnz//ff46quvwn18OhoKtlglREs9DNRrxZc/N3FebkdlYCJlCdAQkHpljGZCoO7NAG5fo3hoKVhjgRKRjqOlF7zaJIh6XeTKRSyRko4rWA/Pj1cD305SPOXJ3ZT32g8HogURNbedTHTswunPkKXvsTJHVRldpopPsEnEDQm3E8jeVPfKYdXFXM5KkJFFLSydTuQAGTNJDh+FvQL44x7SM6Oxva2hGCGihXnL16F/pzAiJRGpBi02wSzIcnVMEzDyYeCRw+rm0hR0/PI9cWiFP0uU9qI95Xfgki+AIXcrr/PEgne81Jb5NsnljyWcoMYflQRRWBkjQZYLlZJHCzOf0nmJOrLOeQFoOQhoOZA8D4dUU0TGWAVBXfKmRPN9yREyxt0OYphR44k9F+zcX5pFHtnoo18iVY+S0/UF/92OSknu5yZGN8DkSHH3YKC2AfxaLCo6IgKN5CZLEq2CTO1t2XmKvXY/3wS82ZVEJRoTIilsXca6x01ymut7n9DzY4tV1l5/kRcfIsXmoDFjmI3ssKgt85X2AQpZ+f0u4JsLldwmEUqOkjYcfz+r7Cu2BTRTMVhYowhpT+5KnhfsYfKjNNRD4ZT2AerUi1MAIRMpp9MJm40s1n///TcuuOACAEC3bt1w4oSfzu06mhZYCRJl/9TI4CdvM+f9ddUqg9+mJe2LDECkmD5SALlBI+KUBYeCTcKmCw0tXQ4oUbRm7YGHDgHtR5DndOGg+2MjUnSRCUYiV3qMlBqmaDccuHERMQCMFqDzWHGX8JKjwMrX1R4fdqF01gLzHyJSI0BZYKMSlXC7sB+JrWlEpP68H/jsbN9+KsGgtoxUCfp4uELEtIykYImUVhn7ooOEkBxcKneFb1CwRlUoTUG1FuZQy6jzTg+gcXKkWDkLHbesrE+Ua0nJc1JH9evU+8lHwlm0HUKSnMe+yHhDuTwpuqhTIllyGJpoUGkfR6RYA4rOvXQ+NFuVa8hHpPpdCdyyRKk4Go7G1SIiVc6s4946OErs5b73s0pZUCpePyKbKb+dSkCjkhQFgb+oxMmMSPGkzeOSihsxr2tJ+9jf5I9I0XWSd1xqGeN0jaBOP7bCJQ+3hqG/82dyTzV24r+oImGgaHp5ju/9sH0WMOsa4HtBcZpQIMvsbMFFXvjIuCriJ51fc6TvtZR7N5UyESlmDuRLoG/wk06TOZ/cQ6vfVvZliVIIjz9Qsp7cnTwWZCoVQkW2DqCuKhhsXrLLTirsiuZlvvl6E0fIRKpnz574+OOPsWrVKixevBjjx48HAOTk5CApyY+EQUfTApsUz1f1ChSRYvtIaeVIWdiqQX6KTfCyOD7cXSMgUrY43waHlkggOkmpxkcnP+rd4nOkAGXC8OfpzNupfj74LjIZ3b4SuG0lMQJFORuLHgOWTVe/xnoj9y8iE+Gvt0jHIC0C1mhxBIHtK0ENi5MZkdr6LXlke/kEixMZxIArOax4urSMwmAqk3m9igebB9u5vT59coKFqpiC4Pq4nSTPjv+9Wp7/te+HVlJdVOyCGuuNXWyCvbdFMhZ6fvjzRGVdwXrfqbOEr3xHjff4VuTRn1e+Iav2RXO/PbGd8j//G01MuXp6/Ly8kc4PWtG1UCAiUmxEt6aEXM8t3wDHNgbYmYEQZq9Hfd8BaiLFSvtYCY8tViFWciSnmUKoQ5L2NcLceHiVWubORg8clWqCEtVMeZ0FS6RE15Nfi40m9XigY5sHXSOok8JfJVBRbzeWuDR2sQd6PAltlCilP4l23i7gnd4k95EF7UPIOkLrAnp9zVYmF6hUe3u2kBYgjkhZIn3zP6mMtbbUf0SKwiiY6ynY9UQenxb/MmMKSvBoRCp/j1LWXCsixc5jwc5LO38hVY9FzsJgCF8TQshE6tVXX8X//vc/jBo1CldeeSX69u0LAPjjjz9kyZ+OUwCst5jeOA4uImWiESmOSLmDyJGyxfmPnLg5TxsFH9ZnKw7RCd0ardy41NCmk1KX8erPU48dX7UPCG6Bpv21UvsA1/wCdBlHnid2AFr0IP+zOn4KUYSBXUSp1x0gBM/JHJt8PaTtvV6u2IQt8HE3FupSgpk16KuLgW0zgTINIsQbx1r701rsWRISjuT8QFA1VRUY5xs+BX68Epj/oPp1f4ZCkYBIlRwhZWJzuZYBfotNNKS0j/FCsjlSFMIKVdL45QmeTKQEhSZEoHOGT0RKOqfU2BR5Puk80hBESisiNfhuck3OuN33N5qtigJAlvZx29Bj9le8JliIiBTbZ6a6mLTG+ONu4OvzgSo/EWKTFKEHfIvysPOho0rsiLPG+DrmIhMZh5e/iNRJqNr39XnAj1cpY8cSqRAYe4X6mCKkdYiPrLBzuF9pH5N7yRqtNAeKB13b4qUCA/4K14j6SLE9yuo6zuwVJJoVat4MPZ9jnlWcJP6cQNtmkvO0f5E6byhceVWyE9PGSNiCkPZRp4AoR8oSqb6mAFNCvNS3/DngW4BBNNdTqHJ1meMPhUjRyn1FB4AKKUqtVWHZbFMcZ8FGyvct0n7v3x6RGjVqFAoLC1FYWIgvvvhCfv3WW2/Fxx/XQeaj4+RAFZHiDHf2xgN8E7tZaZ8WkYqIVz4vLDbBeW0oeGmfx6kclxyRilW+V45ISYtLt3PVi4tMpOy+vWnkiJkfQkKNr46jgU5jxDkbzdpqf54Fu4iy+7FXqCdYntiyC3Jj5kgV7CPGegGXqMwuasFGDVSfZ85D8SHgtzu0tw1G2uePhLCe2HCUiw6EQBGpdR+Qx23fq1/3l4si6hb/93NkH7T3B4Wo2ASNiLDkPdwQEilmMRflPcjeb2k80XufEimtanU8go1IiUAjIg3SkLda/R0UbYcAjx0Dxr/iPyJFPbu8wSTPD/U0FD0esXOBRokBUo7/wGLyv6sGOLJKe39GC9DqNPI/H0Vlx7ezRiMiFeN7riLiFSLpjxzxRDh/T8MWV6E4sFgdkbIxagJ2HaW/i3X4sGX+geBypAA12eRziinovUPHvlzAhd+/h5PlCogUX5o/WKx4FfjlJmBxgN5XPITnzc+15Ek6hb+IjRZObAcyZqkJGVv4IRhpn4dzEmhFpHhQklZTqox1vxEpP82c2fFCz4/J6mtfiUDnl6RO5LH4kFLlNL6l+DMGg5LfGTSB9VMs498ekQIAr9eLzZs343//+x8qKsjEYLVaERVVB6NKR+g4sgY4vsX39YNLSWJ9MAnC7CQjS8mqiNF8XCqJThdwfiFXSfsYIsUmDkfE+89BYotNsIgSaL6pgUQnSTYiRT2krNHFRqXoQuOqVc4L1RrTycxfEQK6APkjSwnMezY/SZL2CqbhKPOd9nKNiBQlUsxEbLIq3sZweV1rSnwjGwDJ8dr2PfG6sqC/oa5giQ+v3T/nBeLZol58Xo63/Sdgzu1cZT4/iyxLpBqjsaMrAJHSIr/+PJwiQ2bXr+TxOCe3Enkp6fik8sfqYuDo2vCWFHYLjDH23hYt4DKRkq6l3FOJRpmDXE9oRHjPXPXrIRGpMEekvF71fKX6zhhieBgMgoiUzZcMm7lt6P7qK+2zl4eeA0X7TIlgMivXgif/KmO3kvHaM2sGH5GyxUv9c4LJkeKI8PoZJA9zx891L4gTLFgixcrkZUkYk1tTy5TO5n+PXyLFrtfMfZHez3cNBZT1MEFyKtrLxHMMn8dJj6mCIdPBqAJEWDeDPIZaNIctrMTndzprSRl91fYsGWXWAvY+8lc6n8VX5wJzblXnhbEyO97ZIwL9LnqfiogUf08D6hLi9DtFOVIU/iKFrKOWRp2DlfZRQtSsHVmLHZWkkiwgVt9Q0HEZbATTXw+1YAhfE0LIROro0aPo3bs3LrzwQkydOhUFBWRAvfrqq3jwwQcDfFpHvVGaRSQWn43xneC+vYhMWnQC8wdhRKoKmP+A8rpZIyKlkvbF+m4PqKV9IiOFTjYmbhEQ3UA1AiJFb1qRDIh6UgDFOHPWKpMxXbyDiUjRxUcrqRcAWvQijwYjkNZHe7sDi4G3exB5F7uI1papI1J8TgtrJDREH6mfrgM+HuabA0GbDxdxxhMrE6kLOWEXuzLOUzrkbuDRY8DpUu4YH5H69RYg4wfyJ9qfv+9qDLmPStonIE1a5ICPqlmigCH3kP9DiaSJik0kSPIeKtma9wDw5QQiiQkXtPpIUVgFv9tZK1W2lMYQvVfZvMdg0P9a8rj/LzWRpfduYgftz1K5TLgjUq5ayB5Xf4SQN6hMVt/fzUdpwiXtowZWKFFlf/l6RrNyDfnoJxuJYce6Vo4UoHil5WITIeRIASSq8stNwN65vu/VBVqOB9bQZiNSbG6KHE3wKueC/z1BR6QYYp7UieTq3vS3+nN0DFmjlTVVFJXi50R6DOz1q2tuqYikVxUBi58heU1akEmE1XctXPgI8OEZSpEmQC1PZdcj1rYIpg2G16tsR4kDoC42kdie/M/mEfKg10wmUoJiE6K5jc2/CkbaV12sXVGRJW8ykbL6bw5OQY/bbFXWDirhF5VQpwhGgsuCLyrC2ljBHGcTQshE6t5778WgQYNQUlKCyEhlMFx00UVYsmRJWA9OhwC5O0glOK/bt2wwRcDqZAZ1WJiV9h1eqbxODXZeYsNWJGIndbYyl9kaICIlWCAAcdlLShjpJGllJCBCIsVUAaMeFFetMknaQohI8Z8RoXkn4Mofgat+0tass1j4qG9/KdlTFcF4L0ulY5fOtcGkTjYOBzHweoHDK8j/u+YE9xnW2+yoDN7bR8FGkFhS1v184kmzxSieMzYHizVk2Iaq9BppFT6haIycMpW0T2DcsePU6yXRve8uIRWmWETEq6UeLPxFkkTFJmgBlvJsQhhoNGvTF77b1hXsGKBjW3QsLJxVQP5uxeCi+vtQc6RaDZKcPV5Fy88aRen9tb2f1Msb7ogUlUaxUQoAPuWHjUY1mTLbfPMDeE90uKR91MCKTBQ35hTBnyfeaFEUAFUF3JhgSAIbTVblSEWrn1PDiv5ef04bf9fvsB85YijQ+g6VtI+prMZK1CwRipODEvxgIlJyT0fm+qiieNGkimMCt+6wBjh9j3daAb5zFD2mKoZIFWYCmQtCi2BrtX7Y9Dmw5h1SwlsLbua8yURKGjObvyKPy19RtmcdyuzYYsdfMA4/lpCxlYrZYhO0AEPWP9rnw4dICYp5iCqSqqR9omITCdzxFitRVx6sbUDXW5NVLZnTivqwNl0iY0sZTP6dUqESKV451WU8yRu94ANxCkUTRshEatWqVXjyySdhtaqjFO3atcPx4/WU/OhQsO8vYMkLvkY+awzm7xF/NpBcgzdwWGmfajvpGvMGjZa077SbABiUEuT+GvLSz/NEymBQS+UAZYKTI1IxyoIll0pmjjGlO2SDhSZMuuzKhEuJSjDae1kOGKCvQdcJQOdzxBMkb6RENlMbQY4qtaeKTpjUgOa9U+HMkWJ/u6pUu5/JkE9c5iNCJ7b7rxLlEBCplB7AxZ8qr1OPFLtIqkqLM//ThYJ2Y9dCoxCpAOXP2fHudpDy8QcWA9kCiR41JPmCAP6MRpG0L6YFGTNeD2dMNbC0LxCRAoCPhpBHg1HpqSRL+4IkUgaDcs9Qg8tZrRg0MS2UUr48GkraR6N/CW0JWaIQyQzZ38kbO4BvRCpc0j6ZSDUDLnjf/7Y06u6PSJkskiTXQMaaqnUFG4EvVf5nI1DWGN9y6IDiFV/5hvY97C+iyBchqSsCESk2h6a2TDkmeh+w7wGCiJQgss6XPwfUTj0rp66gYMk5de4FFZGiOVJcRPGHK4gcOFjwRYjoWkVzQ3MEqQkUcqsPlphy50ZVBZKNSHG5ePLrQeTLlTP2KxstYQlxx7MJYSjaD+Ru1zh+SqQExSb4PG0WbESK73kJ+DpUKEQtSFjbgJJIk0WtINJaL1mbjt0mvZ//lhSytC9YIsVJTc02YMKrwIBrg/t8E0LIRMrj8cDt9vVAZ2dnIzb21OpG3KQx/wFg1RukwSgLdnLQ0qv7054CvuSFz8mhkKvbcTe9264sHjEtSA+niATS1+XuzcAVkuyKrdpXfJgshJQcaBWbAIArvidGdffzyXNRjhQ1LqjnV1XJqBVw9Wwid6CLl6uGVJ8BFK9KMJIRu3Sz+4tIsVBJdQykgeaNi4D0AcrLbqf6XDuq1D2u+OZ2vHdKbjIcBmLAeuqoB+/QCuAlpqS7yUp0/b9PJeSeJ1LsInUiA/jfCODTs7WJnigildxNbVCKcqRYT5dIFx9IV+1PwhkusORJ9PvZe9NRBWStE+/HYNImUv4kXSJpn8GgRGbZ3IdwwiMgkMEQKQpbrHJPU0IfLJEC1DkogLJIG81kbug5SeN7G0jaRx0JNLdyxEPk8WxB4j07d7GOFAp+7tGar0OFTKQSSMPkK2dpb5tGqvP6Lf5iNBM5FY0mqyq/Mfc8vTYGk9rJZItVG3rUmUIbEBcfBDZr5NvIlfMYI5A2bg9X2wOtKAudl0xW5p4t9XWAsUTq4DKlhQS/HxYi5QZrUFPiyRMp1uCN9xOR4skhHVOieUKLOIjAE+7qIpK/7c/BRsESFznnrELtVK4uVCJCqhYpzDhTVUQMhkgxKgdVD0cmZysiTlnL+SJMFB6eSLE5UlzlYBZsREpUUl/LmSsqIsSSNyolNVmBjmeR+3PQTWoHDVtZlF1He19K7lFbHHDe2+Lvpwg5IlWqfi4qlHSKIGQiNXbsWLzzzjvyc4PBgMrKSjzzzDOYOHFiOI/tvw2aGM4XlWBvECpjAbgwc4CwKN9gV2thpjcun9/gciiTgyUSuGUpMHUDWfiSOjKTOxORWvoisPQFYMVr5DVqtIsqz6T2BvpcpkSmivaT7akhzEr7qNHFT0ydzwFan6a8bq9UKvDRKFWgog1s/oaWN4gHu4B1GU8aaLY+Dbh+HnDRJ+T12jIuZ6CCk/YlKNsB6okc8B/pCxUskaLHwOYfAWRSz9kKbP2OkHu+MAV7/vYtAuAlC50W0Wd/O510+UVClvYxhhuvNc/dAfx4teIptcUAw6YRY2awQDriT8IZLqgiUgLDiyVajkrtynRGs2KU8QuO1kJlMKujHyzomGLPp4h0ieCoIvLD1e9obyNqosk7Sfw5eGxxvg4bvmy4P/De/lrGAWIwAAOvJ/NJ+xFqY5saO+GOSJUyESkAGPkocN8OMq/xYAmjJUqdH2CN9Z0jLSF6frXARqQA/95mSqT8kRK5ubok72OJFJ8TCkgOGuZ+0YpI9btaMV4PLhV/N71+bJ4FJVINHZGizkET02eopsS3+i0do2XHgZmXAes/VO9HmCNFlRvMGGBJFSXZfCSanVf8Sfv43+SsIr9HFDHyVxCHB0+kdsz2LTqhGV1kCCibI8XnOVUXE7uAlT6z6xnrOAsmR0qV+8vm1jIRR0CJkNJ7nIffHCkuItVGisin9VWTcEr8WKmrFpESFRsRSfvMNhJhevgQcN5b6nmHjTxFM/Nuq0HA/TuBabuVOUALoTp4+PH0XyJSb7zxBtasWYMePXqgtrYWV111lSzre/XVVxviGP97YDtD8zcJe4Owngj29UD6UjNnsMgadG6ykRdYQUTKyUSHohKBWEEZVlaCtlPS8VKNs4eTPYjQUlo8j29We5Ss0b5eWq2EaXoMXjeZ4CzRinc+kAfFUaXIJHl5jRbYiJTKIGK94l61x08rIkUNHbb6E90G4CbLCuDnm4BVbwV3nPLnmGtOCY6o50g+kxzMNylmF0S26SlL9LW2p/AhUrQ3UIniJFBJNcpIw8W9fyqeXWssMOYZ4KFDQLthvt/RKBGpAFX7WDLHXnceRjNDfjhDUMuA9rcQ0fPL9gjiHSpa2PMnkR/+/Yx2XoComz1/b7M9SPgm1mxLA3l7jeaPIlBHh0ykOEluTApw33bgurnqsUbHWdiJlOQIoxEpk1kxwHiwBo01ivSsoxDNj3y7irrCh0hxc2gcI0NM6UEeXTXiaw0oaxW9bnR9yt+rrvTJEilVawezOEfKZAYmvk7+z94gHoN0P+ycm96fPFYLemXVBSKpLqA4OtiIVG2pcv/TeZte/8VPicfbPx+TdgbsPCc7HAXGMqCMHYNBTZ7Y/5tLeT3HBOdOznWWzrvHRdbo6iLffN9QInv8trk7fbfRMrhVxTsYIsUb3mVZ0mvMb2IJUKjSvvzdzLGx+6HkR7rvYqRccK1qhnL5cz9V+6jTYvgDQNthxMnKSvv4CsOAkuvKQ2Tvsd9Jnc38XMI6lKhzGfCVH8elB2f/hBKR8np9r6fWGD8FEDKRat26NTIyMvDEE0/g/vvvR//+/fHKK69g69atSEkJYeHToQ1WysPfJKwhppJOBBi8hUx1F62+JOz+Jn+uGFq8geOyK9/ntyKVoCGvwUiq1tGy1/5unrR+5DFvtzLpGkxqTxWFljeVf715J+WcysUmNAxsSjIMpuArW7FGEV/pz2xT9sMaFnyxCbqAZS4gC4CPtE9wXnf/TsjqkudCK1fLevDoORZ5HtmxIcPgexxs9ETrOERjlSdS9Dx63YqRpPqeMuLlZEG92UajuPBEYxebEBlLLJlzVGlLpYxGdaNZVaNJDQPEHzGSidQR5liCJA9s4nlNCYk08tE9NrJAwRO7wVPJ46CbSGEWvgUDf83Y4jWBwPeboWNY5MVVSVqkyGe4pX3UI6/VwJIFO7dYY4CeFwFxUr+Wjmf5bq+V0xoqfIgUJ6Wk/ccAKe9Uglbzax8ilUeixh+eQQoWUNBrY7b6OhJEESmAkEuTjRwzlWizoPcadaAkd1McZuGKSGndL2xVNNYJxkv7UqU8M+FcKmHdB8CWb5TnIgk8G6ll7QOWPLHXst0wYjSXHwf+vB/46ymFDNNxz57rvX+SxzNuU8goEFrzdT4ixVd+BbQdAex5o1UgS7MEROq4r+yZrtket3ZZdOHxFqmrmFIyUFOiRPKoHRTJzMsUR9cBM68gtgrvfGXnfLk6r3TPdx4D3DAPSOmmjB17ufK7WIexJRK4/Htg7Ivqay0qpCFy0PHzMTtGkrsSNcfQe9VVj0NBKDlS9grfXP7/SkTK6XSiY8eO2L9/P66++mq89tpr+PDDD3HzzTerKvjpqAfKc4Bfb1ae8waLVrhaVeZZ4DljK/nxHgOZSEkGk9EC9L5EeZ8nEWxzW1FZY3m/XMUdgCzC85ky+f6a5sW3AmAgv5l6eK1SDxatBGyfY4hRkzXW88J6UL6/FHh/kFomwHqFgq0iwxI3UQlP6nWqYPXYleqIVOvTyTF7XMDu3/wUm2DGBlusQKsIiQgiIkUfb1igHG8ln2RuUHLNWIKiSgIWNEh1u8QeK97gZSN7dMyr5EECqQY7BkQetMaQ9gXqI6WKSFX6VuSjfcgG3agYTG4Hdw9pRaT8SPVERCroMrVMTmzGj8AHg0i+HAsREeGdJKdJBGrCq6RNwOA7lfdssb5zSV2IFD1PlNCLJLnscclEyo9MtqaESBu3fq+9DQ+5bUJC4G3Z322JInPIXRtJnuiE1323p2TD46yfvDdQRIo12KObK4aOVgU0WdonEamqAiBnm+92bDGRFj2lz0pjl82RYnM1zFZFoSCqVkuN5pYDgZuXAlN+Vz7vrAbm3FH/6n2a0j6WSDHyLFkSJv22gTcE9z0nmFwkUY5Un8vIGtDvGvXnVBEpZi6wRBB5OQBs/hJY+x5wdDV5Tse9NVpZi+la0m4YcPYzyn6CaY4ub8vN/aIy3VpVCqmBbWKq5BXu891n+XHf15a+ACx83NdpFqhqX/YG9XzorCF/H5yuvEbvU1Hu6pp3gX0L1M3l6Zzr4PYLiCXdbG6kPH9x62L380iLENaxVlNCSOXmr5XvEs0LPkXGmPXSEkXUHOc8X/eKeXJD3iAcPCJn7X+FSFksFtTWNoIx8l/GmneBQ8uV57wciTXE3EyuEj8J8GANKN4jxkv7eFIikvbRSdCiQWAAhUiIkiEp/HXnNtuURZlGsOix+Uj7NI7DYFA3oVMRKclYryokPWiK9gMHmRL+wZQ+9zlmQcEEFiLDylGlTkKNSlQKbRQd1C42wU6WbIRLS1InArvAUPkDq8+m3m9+zMSmKQYdO95ERBQg8szXOhAnQTDSPrMNcsRr3YfAO72B7M3MvgUTMTtOhUQqhJwSR3XdohQqaZ9grmSPwV7pu8Bf9jWJBg+4jizcdFxrFd1g4TciJY1hVtcfbISO/T4qo6RSXQpRCXyRB7TLOMbgZqI19ZX28dW96DjkCzcAakOBnhd/hGT7T0Ta+Pud2tvw8BcR48FuQ401azQxmNmokLwNE7Xhx0/uTmD2Db5NS0WghiCdp/mI1MDryDXsJTnVZLKq4d2nhjibIyXKTZGLiUQBPS8G+l8DXCFFA9iIFC8Xby0Ztcc2wAdyhTwr0GogiWRExCvFLDJmAl+fJz7uYKFFtul5NFshrLxGSWJUIjDuJd/P8w6DsizmOwU5UsldgUeOABd+oP6cijxx9xIvpaWFFdi1hb3/TDZSqbHjWcC1UluMqhCIFJ/XScdBDHNNhUSKOcdmG5DQjqypbjuQm6HetuyYb0QKIM2Y+ahlIGkflavTNdtZTWTqbDSe3neiirL7FpDHE9uU1yjhEuUhiwrpmMy+FRn560bBRnPcDtJfa+495BEIMiLFFbmpL0Jpy8CPD8C3p+gphJClfVOnTsWrr74Kl0tDJ62jfuAT/XljhydWsvHLDF7RTSSSQ1Dwky4vsREVmwgmIkUnEn+GfaDKXnQioR4terPGcROM3+NgPJtsUiWdSNhzwxqsIp1yIKgiUgIjSGTY2SsZHbY0oVFZY3kOE5Hi+nq5ahXPFFtxyF+JYp/vFhR+oAucLUY53/w+41sppJEdk+wEyXqm9s4n3tVdc8TeQd7gZDX/K14hEcm/n2WOO1BESnDNig8C7/YlHksWbqc6d6g8B3izK/DdZN99+IPXq5a48ca526kuMV9d5CuJazmARIOp4UQNadYbrCXp8ufRo5GuUsZIqwuREpVQBjSkfYHubcaossXWT9rHVvcClHEoclywBJmeF38eazYn0BOgtQRFKERKJd/x45iiMJqY38vdB6veIH3CFj6qvKblEAgk7es8FnggE5j8GXkuSwo1jFK+2ET5Cf9zkSWSGKUXziASJ/Y72P1Q0Op9tNiNs0YhzHJhB2bMGQzhbewZ6DyqpH2lvrmtgNqRR9HhLCJ3pcUx2PxZrRwpS6Rv5IB9zl/L2FT1czqfsNXhWMdFy4HKuRQV/gkErSbikz4CUqQopGgcsXOmyUYkzlT5wEc3y45ryzZ9iJRgvSjPUXK3CqXtqZTRWePbdNeftE+EUIkUoJ6vUntpFw/i57U9UtNpKgsVRqT8SPtCcRZrIZhiE/Qe+i9HpABg48aN+PXXX9GmTRuMGzcOF198sepPRz3B9xzijR1eniT3TWEGL++xLtwPZM7X/k7eE8w/58tKu2oYKVoQRMofAiUYUp07nRgpYeITL7WkfYB60qGJt4By7GwUQVW5h0akgjCGKFiiFC2ISIkMK7boACUP9LM1xUzSMpcjBSjvsVJBNgKYu0Md4eTBT/IeDxeREuTPAaTDOyWNdEx6veoFlCX3rHHANvSlEBFMPr+NHeOhRKTYanElR4jHkjWI5z0AvNtHaWx4eCW59odXhCYH5A0t3qHB38u8g8FoERTdCFAGnoWohxSFHHlhjinY4huqnmfMeNHq60UR6N5WRaTi1PewwSh2RGiBXu+i/SRfQSYyCb7bJjNzAF+kQgSW3Nj9bEfBJlIHFZFivdBBECnAlzgCJApFm2ofWkYef5oCvNUDqBDk5QSS9tHoODXQ2WazALB7rnp7er3p+c3bIZAEMxCtHTQ3DPCd4+l4qS4izqf/jQTe7knWNxfnbKLgx1CwRFgEraglW7VPJe3jJNmAmEhFJpAqahOkirbstRL1kdICK/fiDXU+V4/OJ6xsnK61AJGQUbBEKtimvGz7AdVxtPCNXHi9wPeXAR8NVcakwahEKJIokdoqHav028pztPNwWYcR4EvavF7gq/OAj4cSMkUr+raQiqo4a3z3Ta+jVlsKHn6JlIbdxNpMzdpr7/uM27Tf83o1IlJ+pH3BSJADgXe0ZPwIvNOHNC8GyPo6PQ3Y8q1OpBISEjB58mSMGzcO6enpiI+PV/3pqCf4wc7fELzxQwcta/CxBpvXC3zLEdzJX6ifByJSlgjSsPGM29WvG4z+yVJEQmBjKtD71JMmEynpZo1NU8vo/Hly2Qa/rBEl8gpt/0mRxchEKoT+aOx38V5AQINIVah7ZLHb1Zb5kiz2uGdfRwpNsJM69cZ7vcA3k4BvLlRX02OhmuSr1GRFRaQ4gyi5q29Eylmjjkw4NQxwEUQTuVnDaweIy22z45Y1XkTeNpYY0rK8a6WmpCyxDrRYsuClP7zhxRMpGkU0WYmU75LPffdJCXV1EETK6C8iJRjDwZbP1izGwvYgE1XtC7AwsvdHRLzauIhK8i/75UF/36HlwJfjgd1/kOeicTX4LjImxr2s3Gf2cm0jkR3TwVT/YpsBhyrtC5Y88jK7zIWkqAMLt5PMDVX5wL6FvvvgiRR/vfiIB5Xd0e/c8pX6fTqXp/Qg0YTaMt9G0yxE82/zTsDl3xE5Gb8OycVXSoGdv5ACFo5KYP9iJomf2yd/PoMpg62FYCJSlLg7mHLdLLmLb+27VtF5gt4P9jJlPRDlSGmBHb+8HaEVkWKlfayMjErLAeUcepzBnz+6BvGVKlVEShpHRQeB/YtI5PfwSul4mPmbEgraMLe5VAyhMk8597zDhI+E8vdtVaESccpcQPpcAkrjaUeVOrLS90rlf7airD9izs4tFHR9DYZI0bL1Ipx5Jyk8wVdWBIjTMqiIFHMMwcxTgcBHpBY8TKTk8x8gz5e/TMbQH3eJI5ah9B1sYghZlPjll182xHHooOANEt5o4m8QOkGocqSY/wsy1ZprAOg2Qf3cR8onICUDphDDi+2iHZ3i39gxGknZ2j/v97NNAGMplo9IRSufazUIOCIlEPvzqAy8jvQfOe0m9feJfmdlLvDpWUSDXhdpX2J7oM1gck7jBeWORcfJ5spQo19FpKhsRZoIjWZCYr0eYiDxRhLdl7NaMcBPbFeTSH5bgBjMcnVEIzFKZM8340GKSiKVxWSJjUT0eN0zuxAFMkBFE7m/vjYisAsDawTGpvoeW1U+kYeyuT30XLDHWlPiKyPVQqCIFO8EoUQqOgW44D3xPqm8jY0yUgJkjlDv01+OlIhMOquJ8RUouVgredhRqRgVQiIVYGFkpVt86etQolGAL1Gkc55oXLU5A3g0i/xu2SvuIR5oUU6SKiIXBJHimwEHPPb6EClpzK6f4bsNe6z89fF6fYlUoHHAfycv86Tzk8lCjMCiA+pqfTy05E2sEc9C9vCXKQY3QL5HSzLFO/pqS+vufdfMkSolj2aberyVCsag0UiaxVflA4ufJtskdSTv2WIJyXJWEWM4qaO4j5Qm/ESL+FwbvrUGdebs/FXq4cisXZZI5bhKjpJCMYFAz0l8a6D4EPnfYCLzBW9ws73BaA4nO5fx5bibdyFrT2W+Iq9LaAPklirb8OXXeekumyu6dy65Hkaz0nqAXQv7XkXkpxR0THk9ZCyKKsQamXwne4Uyz9JxqpWKoCJSGu0SAHIOu59Hqjzy/cGKD2pEpDjFAiudDkY9FAj0N9WWAetmKPMgXU9ZuaUoIuWv8FgTR8gRKYqCggKsXr0aq1evRkFBCDkZOrThcfuGk3N3ECJCpVFa1WjY19noFC0ykdpb+3vNVrXHS0tewkuHgkkGH3Sjom0X7jOAsUw9aTS5kj02dr/+8inaDgEe2g+MelT9utZC7qwmPSXqUmzCaAJuXAhc87NY3ywy7KoKldwZaqyIiBQ9VwaD/2gNHRPsWBLlsLDbAmRRpQsgrY7Ij4WJbwD37ya6dTYilfUP8O1F6m1VhRUCRKRE58XfbxSBX5zoPtsO9d2WeixFlS/ZYw2ldDJf1ctH2sc9pzJSfxFPmh9Ai60Aynnl72l/0j7hd2hIQHhoRqQYQ13krQ9EpNjCAm5n4KqLfvelcY9Ga8xRfAsEAHiru9jD7AzBIQCoZX3BVMBijRiRHFgEOTpUAfzziZpYUPgjgPYKhVwFa0Sx0h2P23/uq6jse+ex6ufBtpSgYKMOLEGryGWqoQWISImqfQYLLWkfdTKZrOqCATTfmXfEtRtKHFFjpwOn3UL+KOh6R9f7QH2kWPDlpFnwziC6NsjSPivQdjDweI5vEQt2u/8ND07eR+8BNqoS3Vyd30eJyuEVyjZylJ6Zy1i5JwAkSXnOziqFzPOkg8/n8hn/zDg4IRWxSB+g2BFuO1MgI0W9lpttSlSxulgcXTFalDnM61bGJ58LzYO9F0XRJn/bUxQdCK5qX3o/acxag2vTEAj0umZvABY9rn6dHzPCYhP/ISJVVVWFG2+8EWlpaRgxYgRGjBiB9PR03HTTTaiuDqEqlg5f1JRA9ip1v0B5fdMXwKo3yf+84UMlU6zR6rYrEzA1GKMCJG7zpTBFMJrU4WGRdE2E4Q9qLwSBiJRPUQnmOLtfQCInzdrXzaPir1JN4YG6RaQCQUQYaH6TwSiW9vHlzwH/x06JACtL0zIAeYJDxwudFHlyYo1RIkVsjtSKV9TGPuDfkDNwU4+IBIRaSYiXzIx/BWg3HBg+DXIFQAoqVWQNq+oict+wi2wohlcgIsVHpAr3kUd/xnN6P/J47B9lMaIGSOszFKIFBCh/rjGGg5H3aW2zbDqw+BlSslhE1IPxMHYYRYzfXpM5OWaoREpj+/iW4tcpDAbFwHfbNfrdhCBRBULLjwLUvXpYabA/UGN971xgwUPibVTVNDmJqlxpLkLjPhMQQFbaV3HCN8rFjm/eyXbVbODSr9RjVJS/5g9s1LL4iPJ6VYF2RIq/Bg0h7aOQc2gS1K8ndxdv3+MC4Nw31HMsXVMrc9XfGcy95M8pwhfu8JH2SceuVTktfYDyP5XYacHrVQxlVpVBxwQr7fN61eXsKZFi5wL+Ho5NVeZ6uubw9w1PpPh1TlQQod0w9fihUS1RxImVmYrk30aT9Dul+6joAPDzTYqKQysVgb0n/En7KDSJVBBV+2JTgZuXAHdv1i5qEQq0HPD2Mt8IFCWfrKMjHJUDTxJCPnvTpk3DihUrMHfuXJSWlqK0tBS///47VqxYgQceeKAhjvG/g6gk4LHjwL0ZwFlcZbFNkqRSXhylG1SW9vFFKaTnVNolkqywYCcLfwnPLMnS8vby6DYReLpI7DUOGJFKVz9nC1+0GghM3QDcsrRuvQ/85VUVHfCV24UDIuOBGiQ0CgQoBoCzWjkOdnHxV42KGgsqIqVhQPCv03KvdAz4q+DIRqRYeQaFP09+MMQ31ImVJ339rgKu/5NIQ3jnAP2d7ATvcRGjTFUSPgTDi2/YGShHikYh/RncbQYTI6o0S0mIlr2aUepyyqFK+9h9AcRoE0ZkNIjU3j+BNe8AmfM0yp8HYfxd9RNw3w5STZO9h8NFpBLaBf7sBYwHvmAvkUPPukapEsaXrA+EUIlU807AFT8ANy32fw1Z0N97ZI32NqriLNw45mV9PESOL9rjyVEhrt7IRlx5J1vbwWROYaOQdamox+YgUVTlK84mfs7gf4e/oiKB4K/XGKCMd36O73Zu8N/hE5GiZd2DuJe0SmUDZO1gFRw00s5K+/xhwqvK/zSfSAuOKmVNU0WkJHvBxkSkasvU+Uw0UsYeTxwn7YuIU0gZlejRIhH8fih4IiW6j9sNU9sjslNRYCfIBSeKNaIrVjKfUVK9+i11ywittY2dA/nfLYKQSB0KLkcKIDJNfxLCUKBJpCrUxaYARY7IFjj5LxGpX375BZ9//jkmTJiAuLg4xMXFYeLEifj000/x888/B96BDm0YDGSSadbOd1B5nETCQScEOpFQTz8tf0lBPRLUqxJo0WJvgqCJVJAyFH/7DVbaR8H/juad617i1mRRV0kc/oDi+Ss+qBjRDRWR4hd51mi3xUMmy7SKE+vNFfWoorIEOSLFLCaaRIpbYGguDl3sfAqPsJFLaYzykjV6nP48+cF4o7XGRpvB4tf9SYV4kkXvC/68lOeoz0kohpdWRMrjJp5XLYlc6zPErwPk/NNKX9SAYXX27GLr774NRKRqy0iFpc9G+5KiQP23Di0Xe+v9SQ3ZbWIE0XIq3wkWIiKV0jOwAwkgZdh7SaXuy7KBFa+S+XThY+Q1Va5fA0SkAOJson2SggGd/6mjjK/2CqiPmx/HtNiKljxbZNSwESk+LwNQe8HZ/ZpsikOGzXfhq8EGA9E5ZSut8sfdZZz6eX2kfVoNeSnovMcat10mhObkY9t95Gwj0V4gOGnfmXdIjxr9zm5YANwm5RRXF5M5SS7RHoBItRxAosdA4IgUHZMmmzoSRtcnViLKF4WQI6XM3BHdXL322eJ87/eU7sQZQduG0GOQiTdHnOjzZu3IupHSkxApo1FxEMoRKcG8yvaSEkWk6PFTR/DRter3tRw/7YaTx9Q+wTlVRPl+1YXBVe0LN3in6/hXyKO9Ql1VGFDWMla2GarUtwkhZCJVXV2NFi1a+LyekpKiS/vCCVF+yIKHlEozstFcScp3Hluv3pYaPzTEHSiJOVgixRqkofR50dpvoJByZDO1QR1qEro/GAxqXfnQe4FLpchf0QHF+AhrRIoxBHhdMquJNhqVSZLmIrCLi0iCQeUN9ZH2USJl1SJSbKVE6bpUct4mKsWghpzX6/v91mig39Xk/2HTxMem5aESlRAWHSsL3tCk9wVvYJbnhFHaZycRnq/PB97r55sATcFXw+RBiQY1OtiIFLsQiQo+UGhK+6RrdGQ1WexytpL8QBaBiFTebrG0L9QcNwAYeD2ZVwZcG9rnWMOk3XCSmymqgqgFasCW55CKcACQJRk/jUGkQgXvRb5wBpDUSf1atR9HyraZ5JGPYnSRChG1G+b7nawBzCbrU7Djj53bYlIUMkGrogGkME+oEJ1T9nv5MdfmTOC2lQoJqI+0j0acqcHHy+1E0r5gC9VQUOKx6XPgk5HAtu+l7wqCSPW9kki1zn5G/L7ZpvRR9Lq5QkZBOD1oRIktfCMCJbbxrbj1jhIpptgEv68aQUTKYFBHLqKSfElBdDJxRtA1hc5Z1NnLrz/0nm4zBLh/F3Dz38r1o+tOFedUZMFGpEREih4/vf58+xAtO4Y2QL7yR/H7WscBKPdyZb44X66hy4vz6y+VnXtcSsERipL/OJEaPHgwnnnmGdTWKoy3pqYGzz33HAYP1vAU6wgdgcKcdIJwVPgaPgDR43o8ivEWKHqkijT4GdDscYUjIhUIBgOQ1ld5zhsL9QaTBBkRDyRKFZSKDtWt2EQgsIss7w2+6GP1c+qxlYkUQyhFBgXtuyIkUhoGoE+OFE+kuEVERaSk60mbGVLQyZF6/Vx2xdDuczl57HkRMG46MPlzYIRGjofWeOGrOFH4k4PxshxKSnii5BORKlW/X1Oi9DOhKNxPepJs4toKOGtIz6yja0jRF9rPi72OA68P7HmM5okUkw/CVjbUaoIJECOBJZN0Aab7YgsH0NwtikB5VOXHxSSuLlKN894BHtzv20MoENhxmt4POO9t4qUOFtRQqzgBn/wglkgeWq7dSoCCjpmGJFL8+Uls70uKWG+/SsLqBvbOI/9TDz7FWY8D/a9RS7ko2IhUiYBIDblH+Z9ttsyuE8MfIGQqfYB2ZNkffM4pc61oA1ceaX0V4hmOiFSPScC924HxL6vfp8Y9a9yGmsTPX0NqcAYTTaCVbP1VO7VEKuv7jtnKvBjM/ul6VRWASFHZZ3wr9ZxMiRhLpPh9ya0+OGLHEqnYVKDHher35X1ztgudO51V6ki73CsxWqokyHyOnh8+X5gFVYRU5IrnXTrHs8fNwt/c0HF04NxOCnasUZKsVQSmoYkUbzeyeWu06BkFvc7s+amLfdhEEHL583fffRfjxo1Dq1at0LcvMXAzMjIQERGBRYsWhf0A/7MISKSYiJTIiMneAJzYpkwGkQFIjyoiJZg45OOqj7TPz3794aL/AX/cTbxyrU6r2z60kNqbJICmSBrrxPYADCRBkoafG0raFxFPJHz2MkIoeC8wnSSp0cIuSmfcAez5EyoiSI0rt4PI7VgipVW2mZIGWt6WFmGg44GfHFVESvqfT9CnEzZNJma/+8IZxJhK6kyMnt6XiI9L9N2iYwjmdcBXMy6q2gcQUuAvR2rO7aTc/OXfK00r139EyvDTUvzsd7IGLCUosWmKgZTSU/uYKag8hRqBbESKhVakjoL1Usa1JOOD7os1Boo476GWJJGiPCc8xUKAuuU6AmoyWhfPJpUQlwuMEDYitW8BsP8vUj5d5KkGGiaSzYOXgyZ2IDmB7Bhke4/RY3JUEQUDdWzwToy0PupSzyxojlTZMXIOWNy8FOgwSHkuknQBpKT3HX7yugLBp+eWQfmdwUhbaYW94kNkfu84Ovgxx8rgmrX1jSrQqI6qKXuICopYX7UPgOAiUsEiMpHc9/MfVH5DMDJcuRVDgCrNckSqtfraUwcYG9mk+6LtPCh4o5+1HaJTCGlv0ROYeQXJv6PkkR8D0c1ByLaXzPn0Pqf3tOge9imwJBhXKd3IY/5usQSdzkei4jHNu9R9nuPBEqmkzqR6p0jWZzRrFxIJF/jzFJNC5gxHhVJEhEdyN+X/YIuXNUGEHJHq1asX9u/fj5dffhn9+vVDv3798Morr2D//v3o2TMIo4DBypUrcf755yM9PR0GgwG//fab6v28vDxcf/31SE9PR1RUFMaPH4/9+9VGW21tLaZOnYqkpCTExMRg8uTJyMvjwqinIgJ5iOgE5ajUzuP4bAwhU0DgCZ010v3lHKmaZtYzInX208F9LrE9KRow5bfw63xHPES8lWNfIM8tkcqE3xARKT5H6vJvgNFPEi8nD3odaNI4S+jaDQUeOQyMeVZ5rRkzadvLA0ek3C6l0pbsbZQWNs0cKcY4pu/xkjbq/fW4yHsyWYsi1y+5a3BVgrQMcXMEcObUwJ9n4UOkJOOLv3cqTqhlIPz7tGcX20+Nj1DJ31mr/jwlTy16Kr2DWg7w/RwPvn+PVoWyYX76tQFQkW66T2pQsJE31qPp9QaW9nnd4m0C5T+GG827EoMslOR+CprLUH4cqvPkdvn+Nq8beLklsJArCEQRqJBDOGCLBdqPVJ5HJ5No7/XzFIO+iqlcVpkPHN8M/HIz8IVUpTAqKTTjis4JudsJEWPnxRZc9I+NxITzPLDzZ2Simsz46w9FP7f2fdLA94vxwHcXA7t/C/67+X5+vJEsS/uY3xvqGqlVMCKc6x6br0olzsFI+4KNSNEebgmtyRrW9yoSFew4mrzOlj+n+6JNdyl4Ytd1PHnsMIqMWYMB6DQGuH8ncDWTm887a21xSv/E7T8pr7MRKR78vCrahkZys/5RpO2iCqq8c+v894Apf/jur65gx5q/CHxjyOb4XmcRCco6U6aRV5faG7jgfeDiT0MvMNSEUKeah1FRUbjlllvw5ptv4s0338TNN9+MyMjQvY9VVVXo27cvZszw9YB5vV5MmjQJhw4dwu+//46tW7eibdu2GDNmDKqqFA/h/fffj7lz52L27NlYsWIFcnJycPHFF9flZ51aYLW/1FgbeIN6Gy8Tyg6U2MuGkv3lIamIVIjeNvaztywlkYmTjR4XkqphncYor9EGiRThjEhZucmiwyhC5kQGDW+A8BNNZDN15cS4lsr+a8vVUQZRbgC7oFIPsk/5c17ax0ogBPd88y7AsPuY761kFq0QI5JaHmZLFCGQd28BBt0U3L58qvYVSmV6pXuHzZFhzwsrBWKlIaz3lD+31HvMR6So0WKLBa6aBVzyhf8eaxRszyAg+FLP/kDPhxyRYkg3q+d31cJvo0+t/YqOr6Fx3R/A7WvUUuBgQZ0nfBGF6kLtIgOiJriAct81JJECgHOeI/P6qMeJYWk0kag2X4gCILkcn44GMucrrwVbdZWCv397+VlnWWdcMP0GgwVLlqKS1GuQv/HPkr7vL1HGOM0VCwZ8hbtmHJGir7PHGKpqgy9TThHOZqUiSWUwRE3OkQoQkaJOKnrdL/qI5KnR80Ln9eObgZWvk//5fDme2A28AbhuLnDJl+rXY1PVpItfM0xWki8JkLwzCupAElXu5dcK0bqVPoBE3OxlwDapXxhLmswCImWJAgZMCT1vzh/YOSaxvTYhbmynFkCcpdRm4YtNUEQ2I+ekz2WNd1wNgJBjfd98843f96dMmRL0viZMmIAJEyYI39u/fz/Wr1+PnTt3ypGujz76CKmpqfjhhx9w8803o6ysDJ9//jlmzpyJ0aOJt+PLL79E9+7dsX79epx55plBH8spB+rpYiNS/haSQBEpttCB1mQOqA3IUKvlsROSv9LjJxuJHZV8FiD0fif+wEZiAmmW+WIeouNgt4lrScaAo4JM8Gyui6jYhFxWPUIZO3zJV35hYidkfsE56wlg5MPKe85qMj7lMvIhEil/0j6zlRDe028lFZEGa1Sqohh6L7DsRaDrRGJMuqTO9ZQEJXcjkZjy49oRKTYxmpVP8JWnbHHEcHXViHMyLJFEjhIsZFlSAGlfKLDyRKpUeY8lUmx+lMGkds4I9xsduOlkQyE2te7ykNg08e8LVKHMXuk7rhuLSKX3Bx4+5CsTknM8NIqbUIRq5PO/Uyv/AyDH1GsyqX7Y54rQvscfVNK+RLUX3N/6p+UMO7KaVJwMhkjI0j7JWI1KIvcmvS/pXMlG44JpqspCyysfzojU2U8Dh1epy+MHc6/KRW8CRKRkeyRB/L7IQeYT3ePWRoMBaD8i4CH6zInmCKD3pcCCh0mejrOG/FY6r9U1IkVl6avfVvLM2PxtOkZYgmgwhk/SRxHXklRurS0F0vqTe0B0ffzlzTUk6Him0r6IBEX9YDCd0lEoFiETqXvvvVf13Ol0orq6GlarFVFRUSERKX+w28ngjIhQBoDRaITNZsPq1atx8803Y/PmzXA6nRgzRokmdOvWDW3atMG6des0iZTdbpf3DwDl5fVIQD1ZoIvgsX8UYzoinixa2wUVXwJFA2io2hLtv6ADa0CGasixE1KoRnVjgv39RnP4kyDT+hHJJZ8wy6PvlcA6pscNKx2gYJPOE9sTg6EcQOYCjkhVkKhDxo9EAhSVyBCcWMWwptdXi0ipcqS468/KUqhB7ahUvH9hi0gxx5DSDZi6Xrwdi8F3kvyDHpOAN7oQklNVoBCdlO7AoWVksdWKNrE6b/q/y+Er/7PFSkTKLpbdhnrfBCvtC4Ru55HeT70mKx5umhPESj9ZIkVJkckGjHoEWPI8MXpo1TbWkARIhIQSy1OpL4jJTIwSKkui0JKkUFTmCYiUVHnMn9QsXBAZZvReZtsfiBBqpIiPpvvrWwQQuY7bGV4jjpf2sZH8YCNSLJzVRKqY2icwWeEjUgYDWStytkjfIZ2ftkOIcRvbInQipWVo87Kp+qBFT+DGBcD/GGISzL1KI1JVhSQ6r3VMgRrZi9YBvgplMFLDYPZtthGHBnXslecQB5w/Z5RPREpjHeo6kRApClrsAVAK+7DRMlET4PrCZAFuX0Vk9NFJ2kSqLhVUwwE6N9I8/mZtgROl5P+oxPATy5OEkKV9JSUlqr/KykpkZmZi2LBh+OGHH8J2YJQQPfbYYygpKYHD4cCrr76K7OxsnDhBFv/c3FxYrVYkJCSoPtuiRQvk5uYK9krw8ssvIz4+Xv5r3TrEye5kw2hWvD1uB7DrV/J/RDzJ9aG9CFgEGrCtTyPNMa+b63/hYyfHUG8CdqGqa+GJxgAr7YtsFv6bffLnRBfsTxoDAKm9gKeLgQs/JFXIeCkJACR3IXKHGxeRSZsaDPukwi+0mIGjEvj7OWDho8A8qdw4S6S0Fg92ETGauWvoj0gxOnj2e0KBFuGoi0zBGk0kBBFxTBW8QuXY5LLAXNnY0mPAZ+cAS15QRycqcgmJonI9FvQecdWKmzWGSjB8iJRkBIS6OI5+kshcRj+pHMOqN4DszeqCIBV5RPbIfpclkiR437xEXa2MNR4AdZQinJHcxkCCYB0IFJHio5EF+5QqqqEa0eECjfZXC8oys6hv+wp/ygWAGNrh9oSrIlLN1NI+f7ms/kjWp6OBmZcH/m6aI8Uax+z4p3NeRBxw33bgxr+CywXlwcrMKcIp7QN8c7eCce5Q563X7dvwlkVdIlL82hZsY+pA+zZHSOXTpdQFKt3154ziX9NSz6T2URcBYR2wbDuILlJ+l+i6hgNRiYpTRMt501gRqf7XkMfel5JHfs1nI49s6fNTHHXKkeLRuXNnvPLKKz7RqvrAYrHg119/xb59+5CYmIioqCgsW7YMEyZMgLEukxODxx57DGVlZfLfsWOC5oJNAZqGZKQ4ohMRT26o4VxfHlEhAxG6jANaBcjZ6C/1d2kzJLh9sqDGGdDEiRQzITaEMdi8EzHqgy1p2/9qRectQq+LSb8UQDEYaLnRlv3Jo7Ma2Czpy3fNIY9+iZSg2ARvuPMLDCurop+3V9Q9IqVZna+eibPUIKgqUM5BdLI4589tJxUwV70BFOxl3vCS0ubVAvkUHTNej5hohUoE5cIQEtmhDZBDJWQp3QkhT+yg/q3bvld7S912xdBwMhIYk4WUV+40hswpo5/0NcjYXMuGlraFG6JIfMDmo5xB+fez5NEWJ67Y1Rig4yJQRCpUIsWvOaGW9g4HfIpNMOPPH7ELlOd6cInS/JbFgb+Bg0vJ/3Jpbub+TWScbqzRaDTVvUrauW8Bk7hWGOEussTLOoOZS0wWZd7wJ+8LlGogGnfNu3LfVdeIFE+kpP3QiFepFHH2S6SY9cUcoX0dLRHqOYP9XWyD8lGPAQOuI3NvQ0PrnDdWRGrEw6Qv5DlS8S7eucESZpHj6hRFWIgUAJjNZuTkaCSU1REDBw7Etm3bUFpaihMnTmDhwoUoKipChw5E4pSamgqHw4HS0lLV5/Ly8pCaqj3J22w2xMXFqf6aJEY9Jn7daBQvGtSAYyMDw6YBkz4K3zF1GkMqQ109uw4fZohUQ5firA9UMoMQEu2bAqjBQOVWCe18t6EGEFuVUEsXzi5MPrkY3Gd4aR9ADHR/FZL8QfXdjIykvt41uuBVF6rJZGpvZhuB7GnZdPXzsmzfiASgXjz4RoxA3aV9JUdJc1+qya+P5DS9v/o5LzuhkTSHIN/JbAMu+5oUSeE9oKqKZSHmUJ5ssM1iKTZLOcHxrUnVsTZDSOSeVvljibLHTXJuAJIvWFeven1BI8VaLQ8oQiW6Po6TABGphgBrKPLOD39J/MEURuJluLVlwHeTgW8vIoRZFJFKZghAuMo3N2sL9LtS7cQLZ/lzQK1eAIKfkwI15fV4Ale7NVuJXULft8b4VrcLphy7CFpEikrg/7ibHHuw0r5AcywbkWTHGPt70vsBF7zXOMSBHTOq69tIEalmbYExzyj3Iu88Zdt98D3sTmGEfHf+8Ye6dKPX68WJEyfwwQcfYOjQoWE7MBbx8WTy3L9/PzZt2oQXXiBsd+DAgbBYLFiyZAkmT54MAMjMzERWVta/oznw4KlA57HEE7jzF2CxVC7c7SKT9um3Ahs+Ubani0xcS6LjdjuA024mC6uzynf/dYHBIO56Hwz4EtRNFSaLUpSAdko/VcAvXiLjoraMRAdVOVLcgkEnQHZR8XKkUiXtM6gNZ+q9djBV++pTbCK+peJNrK93TRSRssURCQYtMpLSDThcAL9EuvSY2FNsi1X6ooj6EoUs7aNSwRrSJ4QiIl7cMyQYdBlPKk/lbCFkjze6a0qJTI+NSInAGrYGI9B+FCkxHZ3csA1pGwItBO07aN8hazRw7Rzl9Q4jgYwfFCJVXSyVzi8juUSn3dzwx6uFYAv5hGr480qQcFYzDRaqHk3N1ZI3f1Kh+FbAyEdJzzd6TdP6AicylG1qStRFmYoPqf8XRaQ6jyWVV1v0Cn/UKDJBcWiEe98AMfwp6QmWSMUkAwV7xA4kQKp4Ks2Z/u7/Mc+Qv8MrCTkzmZVehkDdiZTRRK4PvVb0d7GRkNVvBy/tC0SkWId2ZDNgzHNkjJ2s+1/VpzJBub4nLUeKk/a1OYPYrfl7SJTuX4KQidSkSZNUzw0GA5KTkzF69Gi8+eabIe2rsrISBw4ckJ8fPnwY27ZtQ2JiItq0aYPZs2cjOTkZbdq0wY4dO3Dvvfdi0qRJGDuW9MGIj4/HTTfdhGnTpiExMRFxcXG4++67MXjw4H9HxT6jSWn8NvRehUhR/e3E10mHeio/oTdRRBzxmsIbfIfsxkDPSaQEaahld08GLvgAyFpLCNWpBN64YaUvKT1I/oarhhjIKmmfRkSKTSjmk4vZxdcWp45YUYOnqlCpgheqtI+WpAZIs0FKpOodkZLOSWW+mkgNuokYw4dXAsMfJLlDIgdEbDop51qaJc77MlvJb7WXixsRhlqEQEteWp8EdJOZlKn/aQoximhEymQjES+2gSugbZyr+qJZgE5nk14prU+v+7GdLIiIFAVvaFLvc3URcGgF8M0FilHVosfJjbjzuYsU6f2JsdeiFzHQO57dqIcVFqgiUinEy2+OIMns6QF6sp31GFlPZ19Pnrc6TU2k+HxG2pAdII4FUUTKGgVM+T3EHxEkGjIiBZB5kPa2C9a5Q9fubd+T+4W/Z+i8YbIFN0+zlfhYh299ynVbo5miSdKaw46Nwv0hRKQC5PV2Px/Y+BnQchCZj4fdp27/0dhg15bIBKV4zskq/MOvj7Y4Yrf+yxDy3enxeAJvFCQ2bdqEs846S34+bRrJ7bnuuuvw1Vdf4cSJE5g2bRry8vKQlpaGKVOm4KmnnlLt4+2334bRaMTkyZNht9sxbtw4fPjhh2E7xiYJVn8blcgQKcaI7ngWmhzaDQeu/iW4JqQnG9FJZJI81cDrz6OSiBzzwN9ETvH7nSRSWV3kP0dK5E3kF3O2fDu/PZVSlB4F6SyP0IlUs3ak0pzJQozUg0uk462nJzxOImhsU11bLCFA5zyvvCYiUaOfBGAAlr5A8tBoSWC2ep3JRvZnLyd5VDxClVQ1VGSHGkVlx5WKZHHpxLiSpX00v03DOOcbTBsMwMBT1NMYmUByNQozgRa9gbwdyns+REqKvlYXK7mHVMbJy5QaG/yxjnqcXNc+l9Xd08/DUA8SXx9ExJM5wVEFpEmV9m5cSKLlwUgNqSQTAFqdToxgCp5I0TxTgESrZCLVSDIp1ihuiO9knWxBR6SkOePQcuCzMcCjx9ROA7liXx3mLGu0EukK1BrEHyzRAIqUfQJERTNgCrDlG5I7GGxEKpCKosMokurA53idLKhyCJl1pjEa8orAn79/SblzHic1UWXUqFHw8nIhBvfccw/uuecev/uIiIjAjBkzhE19/71gc42YhbGpV8kyGIDODVS5RgcBL2+JTiJliHN3EO/f4qdI1CUQkRI1cOYjIGwEipf90MqHR1YruSeh5vQYDMAV35P/5zHNm+tLpERa9UAypdNvJR7sPpcBO6UqmSWHId+LbBlks01QhjeSRAKBwM2xeZjMZH+s/K4uxV54UNLNkr24lhKRkjzLgXpWsQt3uIz0k4nLvwNytpLch08ZZxRPJNmIVGGm+r2TTaT4+yypI+l5E06crERxdk6g4PP9/CGlG2CLByLjiTyTBd/zjSVS9jIlylHXQgihQmUIN0BEQVXxMEgnFysHdVYDxQcJOWnRkzimqPqgLrJPNupd34gU/7/BQGT6W74hDYVpnqkwIhWCtA+oe6pDQ0AlfWWcqmwVwcYEu1YbLf+ONUKAoIgUjRQFg7feeqvOB6OjDlD19TlJTdd0NB2wcjiALJYRjNEQlSQRqWImKTjW11BkvaFJnYGi/erEanb/1UW+Hrku48kkWrBXqXZXn8gKGw2rZ9VOYVlqkaHS8yJS4XDMc2q5Bm2yWHxY+VxyV4VImay+hknr04HDK8j/fM+UYBARrxCp9iPCU0AmhotemqyK7LFGKp0dqOIie01Ppb5RWkjuQv4A4MH9wBtSMjlffpoaofsWCPZxkr3TPuWbw3hdznqSNLY+68nw7bMxERFPSpMbjMTYP/02YMP/yHt2jkgVHVT+d9YyMtdGGuesUdwQESnWqA02Ss4W5AGA7bPUvQ4p6hqREh1bffbDzlv0nmUdR4Gq9jXl6sIisOedXdM9ARqpNxTYCFRT7h1aTwRFpLZu3RrUzgz/kuZapxSadwGOrDrZR6GjqUAVkTL4Rm+oJGnxM0q0wRbnm+/EJjePeRZY+RowlqtaB5DqZGvfI/kH/PdM/kzqzyJFbfiSu6Gg+/nE49lxdN33QZHYQZ3YrIVz3yINrjtxuSRUHlRVQPpJAWrj2RrlK2E48w7g6FpSPrwujVojEhQJ78Q3fAlzXWCLU4rSAMRooAZVZT6QtV6JTGlK+xKU/xtL8tRYYBvW8ka2v6gi31urscF72cPpBR7xIJFIxaQA1dXh229jgr3/Jr5GJH3bZ/lGpNh2B84a5V5ojEbL/Pc0hDTLwDikgiWHrc8EmrVXcqvYHDMWdSJSbNnxcBEpZp+USLG9AkVzFvuZU02KplIIRAKdxwH7F5F79mSAJaKB8s1OYQRFpJYtW9bQx6EjGEQnE+ON9Y72mgxs+uLk3Sg6mhZUZfG9viXLqS6ezf+gnd/Z5yy6n0f+RDjtJvInQpdxhIQc+Fv93XVBu2HArSvUzZLrCmsUcO2vpEFx1lrtpPuoRKDreN/XZQPHSxKXASC5G7P/WF9PZvsRwAN76y5LZI0etultfWAwkDmFEjQbQ6RWv0X+KIKR9v0bIlI8aIGWlB7q1/2V0z5Z/aMoRE1JwwWD4eSUPW9I0HuSJcvVxeoecbWlyvPGqkapkvY1gJOiRY/A2/CwRgF3rgd+uQnY+ydpQC1CXeY5ds6sD5HSiihFxCvVVAFyX4jUDadyREpFviOJM7M8Ryla1tj4j0SkgtbIHDp0yG8+k45GwDBJYnn+u8pr7YYSCcq5uqRSBwLL3kQGYGSCfyJVH/T9f3v3HxxVdf9//LVJIAmEzQ8SEtP8UD9QCIoQASVgkR+BAEpBsSoFBijKBw34RTq2Q8eBqZ9ROgq1lYKoBZSKwAcrSKlSEYKgogIaMEDxFwgfJKDEJCSEJJDz/WO7y27YADfZzW6S52NmZ7P3nr17Lrz37n3fc+45Yy/+3dAkKLmH764QpvWRJv7DcU/MPcusvTcs/GJ/fvOfLhPuiVRIaK3JOcMc/75t4+s/t5B765kvr5K696Nv077u//u6rsI3t3ukahvyP1LmeOn233oud/8eXfuzixOVt4lv2GiKvlDX5Nrwznk/j3uL1Pe17nvb/Z8BRULCLj/xr0/rFXPxb39czb/xHseF2PtXWXtfq4iLLeLOUeFqq0+yWXsi3Poybt3Y3HtBhITWPc+SRz3cLgg1tbnwPIY/tzsegUqiJM/fqmZ8HLrqRKpTp076/vuLcwfcd999OnnSy2ST8J8+D0mPfSNl1prbKCohuCe4ReNKvczQ/14TqVjP7gy+PFHocqfUdZTU52HPrlLBIDTM0WWwPl113N9jC/W876l1VK0rcfZLWwatuu72K5epj6tOpOo4oag9/Hlz0ylbGrXIc34hyfPfKSJa6jvDMcTyHfMbt37e1G6Ramrdkxpb7RapM4XSxkc9yzgvZKT09s+cTt64D9hQ+35GX23/nmVSl3pM8XGlbtoNvUeqQQN6uB1ra7eSu//+1XXcj3IbUKMh3dEDwd/Jt1Uev4NBUB8/uepEqnZr1FtvvaXych9N8oqrY7Nd+oMO1JY91zGK3C//99J13hKpiBjPHxxfzj3WKkK6d4U0bJ7vthkM3E+kozo4rnZmjnfcs9hpiOfVN1/cU9F3hpR+m/Wrx1finty27VB3IlXXlVmPUZla0MUc9wtXETGOe+Sm5jkGKAm02iePTW1i5MZWu0Vq6/84Jp31pjEnaL+2v+N4ndY3+P4Pa0+zUVt9Ru1zT6Qa0g2s6yjHczsvXaDdLxLW9W/qnLqjrm0EM48J0oNgzAL35Kk5dv3+jxb0ywe0EOl9pRl7vK/zdlLfJs5zVJ/L3f8Bh9qJlORouXAKr9U3v6Ha/5c0+Z8N305t7sMZJ3WrO5Gqq5XSPaEI1BC7gXJdf+nIB9Kt/x3omniq3bWvGV8J9gnnxYDyU46ROAvWXVyX1M0xdYSTL7s9X0nb9tKjBxqvBcyKKyZSMda36ZFINWCKixvHOEaS7ehlqhX3411dx+XQMOmuFxwDaXjbRjALCXUMOFV6XEq9NdC18UyegiGx85OrTqRsNtslo/IxSh/QxHjrXuf80YtOlUqOST/p2ahVapLcW2i8JRnuJwLBPL9bj3HSgQ2OfvQ9J3nOnRMSJtWcd/x9ubmRek6S9rzsaDVrSX75v46R3NxPzoJB7RPS5njvmi85T6j/b5f0XA/Pden9aiVSMY1VK4dgndLkSgMHNbRrX31atJxaRUj96ph/1H7Nxb8vlwx2v9/xaIomrJPKf3BMWB0Mbpkq5b8m9b38nLBN2VUnUsYYTZo0SeHhjoPyuXPnNG3aNLVt69kf+4033vBtDQH4Tno/x6AlR3Y4Thx+0vPilaL7VzpGYeqUE9g6NgXurXbektPao0UFq/b/Jc3YffG1+4liVq7jpvvEGy9/0/WI+Y6Yig3waHWNrVVkcHZXcW+B8sUw+c1dXa0fdz7ruDfw4yUXlzVmi1Qw88c9Uu7/D/7qFWF3+z54m0+wOUjoHPi57NyNeEYa8kRwHit95KoTqYkTJ3q8Hj9+vM8rA8DPQls57qGqLHMMm/9Tt6Tpmu6OB64s8gotUldzU3Mwikp0XG0++4Oj69qQJ678ntBWLS+JCmbtrpGu6SGdyA++bofByFvrR1ik1HOy9NWWWmVjGqVKQe9KXfsuN89aXTpmO0br69DVfyfd1/3s4t+pt/jnM3CpZpxESRYSqeXLl/uzHgAaU3hU3d0fcGUeLVJeEin3LiRN6Z6zkFDpl2uk01/XPb8WgpvNJk3aKBUfq99cQS2NtxapmDTHv2PtERCb0kURf6p93930PdLRndKG6Y7X9Wm5i0mVHvnMv/f0Jd4gjV7iuIeK4xt8hMEmAMCqKyVS7qM9Nda8M76S0svxQNMV3o4k6mp5a5Fydtd1T6Sc88Hh0oED4jtKZW7T4dR3uHZfTTZ+OT3GXrkMYAGJFABY5X7PkLdubW3ipOg0x4SVdCEBgpe3iUJD/zNxdu2BOxhgq27JmVJChuO+y2C+LxTwMRIpALAqoYvjZCs03PuIdjabNOVf0pkTjhMMAMHJZnMMunN8jyNZqiyVek12rHNPshhG/vJat5FyP5JqzTkKNHckUgBgVUyq9MAWx020te+jcLInN05XFQANM/4NqeT/pLjrpJLjUsJ/Lo60duvK52ylgkOvKdLupY7pD9zRaocWhkQKAOojWObpANAwkTEXB5JIcGthdr8nKiS0MWsU/AbMdgze0O2eQNcECCgSKQAAgNrckycbiZSHqASp95RA1wIIuJBAVwAAACCohXLdGcClSKQAAAAuh6HPAXhBIgUAAODNLVMdz7f/JrD1ABCUaKsGAADwZtgfpH7/T4pOCXRNAAQhWqQAAAC8CQkliQJQJxIpAAAAALCIRAoAAAAALCKRAgAAAACLSKQAAAAAwCISKQAAAACwiEQKAAAAACwikQIAAAAAi0ikAAAAAMAiEikAAAAAsIhECgAAAAAsIpECAAAAAItIpAAAAADAIhIpAAAAALCIRAoAAAAALCKRAgAAAACLSKQAAAAAwCISKQAAAACwKKCJ1Pbt2zVy5EglJyfLZrNp/fr1HuvLyso0ffp0paSkKDIyUl27dtWSJUs8ypw7d065ublq3769oqKiNGbMGJ08ebIR9wIAAABASxPQRKq8vFzdu3fXokWLvK6fNWuWNm3apFdffVUHDx7UzJkzNX36dG3YsMFV5tFHH9U//vEPrV27Vu+9956+++473X333Y21CwAAAABaoLBAfvjw4cM1fPjwOtd/+OGHmjhxogYMGCBJmjp1ql544QV98skn+vnPf66SkhItXbpUr732mgYNGiRJWr58uTIyMvTRRx+pT58+jbEbAAAAAFqYoL5Hqm/fvtqwYYOOHz8uY4zy8vL0xRdfaOjQoZKkPXv2qLq6WtnZ2a73dOnSRWlpadq5c2ed262srFRpaanHAw1XXi6VlFx8lJd7Lne+BtD0lZfX7zvtPB6cOsVxAQDQtAW0RepKFi5cqKlTpyolJUVhYWEKCQnRSy+9pP79+0uSCgsL1bp1a8XExHi8LzExUYWFhXVud968efr973/vz6q3OOXl0vr1kntOardLQ4ZImzc7ltvt0ujRUtu2gaolAF9wft8la99p5/u+/1764gvppz+VEhI4LgAAmqagbpFauHChPvroI23YsEF79uzRggULlJubq3fffbdB2509e7ZKSkpcj2PHjvmoxi3X+fOOZCkyUoqNdTyXlkrnzl1MrkpLHeUANG3O77vV77TzfTU10o8/ShcucFwAADRdQdsiVVFRod/97ndat26d7rjjDknSTTfdpPz8fM2fP1/Z2dlKSkpSVVWViouLPVqlTp48qaSkpDq3HR4ervDwcH/vQosUEXHxynJFxcXl4eFSZWVg6gQguLRu7XjmMAwAaMqCtkWqurpa1dXVCgnxrGJoaKhqamokST179lSrVq20ZcsW1/pDhw7p6NGjysrKatT6AgAAAGg5AtoiVVZWpq+++sr1+vDhw8rPz1dcXJzS0tJ0++2367HHHlNkZKTS09P13nvvacWKFfrjH/8oSYqOjtaUKVM0a9YsxcXFyW63a8aMGcrKymLEPgAAAAB+E9BEavfu3Ro4cKDr9axZsyRJEydO1Msvv6zVq1dr9uzZGjdunIqKipSenq4nn3xS06ZNc73n2WefVUhIiMaMGaPKykrl5ORo8eLFjb4vAAAAAFqOgCZSAwYMkDGmzvVJSUlavnz5ZbcRERGhRYsW1TmpLwAAAAD4WtDeIwUAAAAAwYpECgAAAAAsIpECAAAAAItIpAAAAADAIhIpAAAAALCIRAoAAAAALCKRAgAAAACLSKQAAAAAwCISKQAAAACwiEQKAAAAACwikQIAAAAAi0ikAAAAAMAiEikAAAAAsIhECgAAAAAsIpECAAAAAItIpAAAAADAIhIpAAAAALCIRAoAAAAALAoLdAXQSNq2lYwJdC0A+FDb1m1l5vK9RgDwmwIAtEgBAAAAgFUkUgAAAABgEYkUAAAAAFhEIgUAAAAAFpFIAQAAAIBFJFIAAAAAYBGJFAAAAABYRCIFAAAAABaRSAEAAACARSRSAAAAAGARiRQAAAAAWEQiBQAAAAAWkUgBAAAAgEUkUgAAAABgEYkUAAAAAFhEIgUAAAAAFpFIAQAAAIBFJFIAAAAAYBGJFAAAAABYRCIFAAAAABYFNJHavn27Ro4cqeTkZNlsNq1fv95jvc1m8/p45plnXGWKioo0btw42e12xcTEaMqUKSorK2vkPQEAAADQkgQ0kSovL1f37t21aNEir+tPnDjh8Vi2bJlsNpvGjBnjKjNu3Djt379fmzdv1saNG7V9+3ZNnTq1sXYBAAAAQAsUFsgPHz58uIYPH17n+qSkJI/Xb775pgYOHKjrr79eknTw4EFt2rRJu3btUq9evSRJCxcu1IgRIzR//nwlJyf7r/IAAAAAWqwmc4/UyZMn9c9//lNTpkxxLdu5c6diYmJcSZQkZWdnKyQkRB9//HGd26qsrFRpaanHA75x7pxUXu54dldZGZj6AAg+VVWOZ44LAICmrMkkUq+88oratWunu+++27WssLBQHTp08CgXFhamuLg4FRYW1rmtefPmKTo62vVITU31W71birAwyW6XKiqkH390PNvtUkSE41lyPIcFtA0UgC84v+9Wv9PO94WESLGxUmgoxwUAQNPVZH6+li1bpnHjxikiIqLB25o9e7ZmzZrlel1aWkoy1UBt20qjR0vnz19cFhbmudz5GkDT5vxeO/+2+r7z5x2tUeHhHBcAAE1Xk0ikduzYoUOHDmnNmjUey5OSknTq1CmPZefPn1dRUdEl91e5Cw8PV3h4uF/q2pLVdTLESRLQ/NT3e83xAADQXDSJrn1Lly5Vz5491b17d4/lWVlZKi4u1p49e1zLtm7dqpqaGt16662NXU0AAAAALURAW6TKysr01VdfuV4fPnxY+fn5iouLU1pamiRHt7u1a9dqwYIFl7w/IyNDw4YN04MPPqglS5aourpa06dP1/3338+IfQAAAAD8JqAtUrt371ZmZqYyMzMlSbNmzVJmZqbmzJnjKrN69WoZYzR27Fiv21i5cqW6dOmiwYMHa8SIEbrtttv04osvNkr9AQAAALRMNmOMCXQlAq20tFTR0dEqKSmR3TnEHAAAAIAW52pzgyZxjxQAAAAABBMSKQAAAACwiEQKAAAAACwikQIAAAAAi0ikAAAAAMAiEikAAAAAsIhECgAAAAAsIpECAAAAAItIpAAAAADAIhIpAAAAALCIRAoAAAAALCKRAgAAAACLSKQAAAAAwCISKQAAAACwiEQKAAAAACwikQIAAAAAi0ikAAAAAMCisEBXIBgYYyRJpaWlAa4JAAAAgEBy5gTOHKEuJFKSzpw5I0lKTU0NcE0AAAAABIMzZ84oOjq6zvU2c6VUqwWoqanRd999p3bt2slmswW0LqWlpUpNTdWxY8dkt9sDWhc0DcQMrCJmYBUxA6uIGVgVTDFjjNGZM2eUnJyskJC674SiRUpSSEiIUlJSAl0ND3a7PeBBhKaFmIFVxAysImZgFTEDq4IlZi7XEuXEYBMAAAAAYBGJFAAAAABYRCIVZMLDwzV37lyFh4cHuipoIogZWEXMwCpiBlYRM7CqKcYMg00AAAAAgEW0SAEAAACARSRSAAAAAGARiRQAAAAAWEQiBQAAAAAWkUj5wbx589S7d2+1a9dOHTp00OjRo3Xo0CGPMufOnVNubq7at2+vqKgojRkzRidPnvQoc/ToUd1xxx1q06aNOnTooMcee0znz5/3KLNt2zbdfPPNCg8PV8eOHfXyyy/7e/fgY0h23JQAAA3ZSURBVL6Il71792rs2LFKTU1VZGSkMjIy9Oc///mSzyJemgdfHWOcTp8+rZSUFNlsNhUXF3usI2aaB1/GzMsvv6ybbrpJERER6tChg3Jzcz3W79u3Tz/72c8UERGh1NRUPf30037dN/iHr2Jm165dGjx4sGJiYhQbG6ucnBzt3bvXowwx0zxcTcy8+OKLGjBggOx2u9ffHEkqKirSuHHjZLfbFRMToylTpqisrMyjTNDEjIHP5eTkmOXLl5uCggKTn59vRowYYdLS0kxZWZmrzLRp00xqaqrZsmWL2b17t+nTp4/p27eva/358+fNjTfeaLKzs81nn31m3nrrLRMfH29mz57tKvPNN9+YNm3amFmzZpkDBw6YhQsXmtDQULNp06ZG3V80jC/iZenSpeaRRx4x27ZtM19//bX529/+ZiIjI83ChQtdZYiX5sMXMeNu1KhRZvjw4UaS+fHHH13LiZnmw1cxs2DBApOcnGxWrlxpvvrqK7N3717z5ptvutaXlJSYxMREM27cOFNQUGBWrVplIiMjzQsvvNBo+wrf8EXMnDlzxsTFxZlJkyaZf//736agoMCMGTPGJCYmmqqqKmMMMdOcXE3MPPvss2bevHlm3rx5l/zmOA0bNsx0797dfPTRR2bHjh2mY8eOZuzYsa71wRQzJFKN4NSpU0aSee+994wxxhQXF5tWrVqZtWvXusocPHjQSDI7d+40xhjz1ltvmZCQEFNYWOgq8/zzzxu73W4qKyuNMcb85je/MTfccIPHZ913330mJyfH37sEP6pPvHjz8MMPm4EDB7peEy/NV0NiZvHixeb22283W7ZsueRHjZhpvuoTM0VFRSYyMtK8++67dW538eLFJjY21vU7ZYwxv/3tb03nzp39tCdoLPWJmV27dhlJ5ujRo64y+/btM5LMl19+aYwhZpqz2jHjLi8vz2sideDAASPJ7Nq1y7Xs7bffNjabzRw/ftwYE1wxQ9e+RlBSUiJJiouLkyTt2bNH1dXVys7OdpXp0qWL0tLStHPnTknSzp071a1bNyUmJrrK5OTkqLS0VPv373eVcd+Gs4xzG2ia6hMvdW3HuQ2JeGnO6hszBw4c0BNPPKEVK1YoJOTSnwNipvmqT8xs3rxZNTU1On78uDIyMpSSkqJ7771Xx44dc71n586d6t+/v1q3bu1alpOTo0OHDunHH39sjF2Dn9QnZjp37qz27dtr6dKlqqqqUkVFhZYuXaqMjAxde+21koiZ5qx2zFyNnTt3KiYmRr169XIty87OVkhIiD7++GNXmWCJGRIpP6upqdHMmTPVr18/3XjjjZKkwsJCtW7dWjExMR5lExMTVVhY6CrjnkQ51zvXXa5MaWmpKioq/LE78LP6xkttH374odasWaOpU6e6lhEvzVN9Y6ayslJjx47VM888o7S0NK/bJmaap/rGzDfffKOamho99dRT+tOf/qTXX39dRUVFGjJkiKqqqlzbudJvF5qe+sZMu3bttG3bNr366quKjIxUVFSUNm3apLffflthYWGu7RAzzY+3mLkahYWF6tChg8eysLAwxcXFWTpHbixhjfppLVBubq4KCgr0/vvvB7oqaAJ8ES8FBQUaNWqU5s6dq6FDh/qwdghG9Y2Z2bNnKyMjQ+PHj/dTzRCs6hszNTU1qq6u1nPPPec6tqxatUpJSUnKy8tTTk6OP6qLIFDfmKmoqNCUKVPUr18/rVq1ShcuXND8+fN1xx13aNeuXYqMjPRTjRFoLeX8lxYpP5o+fbo2btyovLw8paSkuJYnJSWpqqrqkpFKTp48qaSkJFeZ2iPfOF9fqYzdbufg1AQ1JF6cDhw4oMGDB2vq1Kl6/PHHPdYRL81PQ2Jm69atWrt2rcLCwhQWFqbBgwdLkuLj4zV37lzXdoiZ5qUhMXPNNddIkrp27epan5CQoPj4eB09etS1nSv9dqFpaUjMvPbaazpy5IiWL1+u3r17q0+fPnrttdd0+PBhvfnmm67tEDPNS10xczWSkpJ06tQpj2Xnz59XUVGRpXPkxkIi5QfGGE2fPl3r1q3T1q1bdd1113ms79mzp1q1aqUtW7a4lh06dEhHjx5VVlaWJCkrK0uff/65RzBt3rxZdrvd9SOWlZXlsQ1nGec20DT4Il4kaf/+/Ro4cKAmTpyoJ5988pLPIV6aD1/EzN///nft3btX+fn5ys/P11//+ldJ0o4dO1zDWRMzzYcvYqZfv36u5U5FRUX64YcflJ6eLskRM9u3b1d1dbWrzObNm9W5c2fFxsb6bf/ge76ImbNnzyokJEQ2m81Vxvm6pqZGEjHTnFwpZq5GVlaWiouLtWfPHteyrVu3qqamRrfeequrTNDETKMPb9ECPPTQQyY6Otps27bNnDhxwvU4e/asq8y0adNMWlqa2bp1q9m9e7fJysoyWVlZrvXO4c+HDh1q8vPzzaZNm0xCQoLX4c8fe+wxc/DgQbNo0SKGJm6CfBEvn3/+uUlISDDjx4/32MapU6dcZYiX5sMXMVObtxGUiJnmw1cxM2rUKHPDDTeYDz74wHz++efmzjvvNF27dnUNZV1cXGwSExPNhAkTTEFBgVm9erVp06YNQ1k3Qb6ImYMHD5rw8HDz0EMPmQMHDpiCggIzfvx4Ex0dbb777jtjDDHTnFxNzJw4ccJ89tln5qWXXjKSzPbt281nn31mTp8+7SozbNgwk5mZaT7++GPz/vvvm06dOnkMfx5MMUMi5QeSvD6WL1/uKlNRUWEefvhhExsba9q0aWPuuusuc+LECY/tHDlyxAwfPtxERkaa+Ph48+tf/9pUV1d7lMnLyzM9evQwrVu3Ntdff73HZ6Bp8EW8zJ071+s20tPTPT6LeGkefHWMcVfXULTETPPgq5gpKSkxv/rVr0xMTIyJi4szd911l8fQ1sYYs3fvXnPbbbeZ8PBw85Of/MT84Q9/aIxdhI/5Kmbeeecd069fPxMdHW1iY2PNoEGDLpmGgZhpHq4mZuo6X3Evc/r0aTN27FgTFRVl7Ha7mTx5sjlz5ozHZwVLzNiMMcbnzVwAAAAA0IxxjxQAAAAAWEQiBQAAAAAWkUgBAAAAgEUkUgAAAABgEYkUAAAAAFhEIgUAAAAAFpFIAQAAAIBFJFIAAAAAYBGJFACgSZs0aZJGjx4dsM+fMGGCnnrqqasqe//992vBggV+rhEAoDHYjDEm0JUAAMAbm8122fVz587Vo48+KmOMYmJiGqdSbvbu3atBgwbp22+/VVRU1BXLFxQUqH///jp8+LCio6MboYYAAH8hkQIABK3CwkLX32vWrNGcOXN06NAh17KoqKirSmD85YEHHlBYWJiWLFly1e/p3bu3Jk2apNzcXD/WDADgb3TtAwAEraSkJNcjOjpaNpvNY1lUVNQlXfsGDBigGTNmaObMmYqNjVViYqJeeukllZeXa/LkyWrXrp06duyot99+2+OzCgoKNHz4cEVFRSkxMVETJkzQDz/8UGfdLly4oNdff10jR470WL548WJ16tRJERERSkxM1D333OOxfuTIkVq9enXD/3EAAAFFIgUAaHZeeeUVxcfH65NPPtGMGTP00EMP6Re/+IX69u2rTz/9VEOHDtWECRN09uxZSVJxcbEGDRqkzMxM7d69W5s2bdLJkyd177331vkZ+/btU0lJiXr16uVatnv3bj3yyCN64okndOjQIW3atEn9+/f3eN8tt9yiTz75RJWVlf7ZeQBAoyCRAgA0O927d9fjjz+uTp06afbs2YqIiFB8fLwefPBBderUSXPmzNHp06e1b98+SdJf/vIXZWZm6qmnnlKXLl2UmZmpZcuWKS8vT1988YXXz/j2228VGhqqDh06uJYdPXpUbdu21Z133qn09HRlZmbqkUce8XhfcnKyqqqqPLotAgCaHhIpAECzc9NNN7n+Dg0NVfv27dWtWzfXssTEREnSqVOnJDkGjcjLy3PdcxUVFaUuXbpIkr7++muvn1FRUaHw8HCPATGGDBmi9PR0XX/99ZowYYJWrlzpavVyioyMlKRLlgMAmhYSKQBAs9OqVSuP1zabzWOZM/mpqamRJJWVlWnkyJHKz8/3eHz55ZeXdM1zio+P19mzZ1VVVeVa1q5dO3366adatWqVrrnmGs2ZM0fdu3dXcXGxq0xRUZEkKSEhwSf7CgAIDBIpAECLd/PNN2v//v269tpr1bFjR49H27Ztvb6nR48ekqQDBw54LA8LC1N2draefvpp7du3T0eOHNHWrVtd6wsKCpSSkqL4+Hi/7Q8AwP9IpAAALV5ubq6Kioo0duxY7dq1S19//bX+9a9/afLkybpw4YLX9yQkJOjmm2/W+++/71q2ceNGPffcc8rPz9e3336rFStWqKamRp07d3aV2bFjh4YOHer3fQIA+BeJFACgxUtOTtYHH3ygCxcuaOjQoerWrZtmzpypmJgYhYTU/VP5wAMPaOXKla7XMTExeuONNzRo0CBlZGRoyZIlWrVqlW644QZJ0rlz57R+/Xo9+OCDft8nAIB/MSEvAAD1VFFRoc6dO2vNmjXKysq6Yvnnn39e69at0zvvvNMItQMA+BMtUgAA1FNkZKRWrFhx2Yl73bVq1UoLFy70c60AAI2BFikAAAAAsIgWKQAAAACwiEQKAAAAACwikQIAAAAAi0ikAAAAAMAiEikAAAAAsIhECgAAAAAsIpECAAAAAItIpAAAAADAIhIpAAAAALDo/wM2OJD/IAZdRgAAAABJRU5ErkJggg==", + "image/png": 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x4osvokOHDrjzzjuxceNGfPbZZwDsRSI++OADzJs3Dx06dMCyZctEDWzl6Nu3L3766SesWbMGN998M26//Xa3uUuLFy+G2WxGly5dMHnyZLz11lt+PU9Gv379sHPnTtx3331o1aoVRo4cicDAQGzcuBF169aV/cwHH3yAHj164K677kK/fv3Qq1cvtG3bFoGBgV5/7913340XXngBEydOxM0334zt27djxowZ/jqtKkNl8yXTroI5e/YsWrZsiSNHjqBDhw4u7xcVFWH69On47bffcPLkSej1esycORNr1qwRuUBTUlKEByMxMdHlOAaDAQaDQXidn5+P+Ph45OXl1fikN4IgCIIgiMqmtLQUKSkpaNq0qU8CNlHzKCoqQsOGDbFgwQKMHTu2qodTZtzds/n5+YiIiPCoG1SbYhNWqxWTJ09Gr169XJSo//3vfwgNDUVoaCj++usvbNiwAXq9HgCQnp6OBg0aiPZnr9PT02W/a+7cuYiIiBD+4uPjK+CMCIIgCIIgCKJmc+DAAfzwww84d+4c9u/fj4ceeggAMGzYsCoeWdVTbRSpCRMm4OjRo1ixYoXLew899BAOHDiALVu2oFWrVhg1ahRKS0vL/F3Tpk1DXl6e8Hfp0qXyDJ0gCIIgCIIgbljmz5+Pm266Cf369UNRURG2bduGevXqVfWwqpxq0Udq4sSJWLt2LbZu3YpGjRq5vM88Ry1btsQtt9yCOnXqYPXq1XjwwQcRExPjEnN67do1APbuzXIEBAQgICDA/ydCEARBEARBEDcQiYmJovoFhJMq9UjZbDZMnDgRq1evxqZNm9C0aVOvPmOz2YQcpx49euDIkSOihl8bNmxAeHi4UM6RIAiCIAiCIAjCn1SpR2rChAlYvnw5fvvtN4SFhQk5TREREQgKCsL58+excuVK9O/fH/Xr18fly5fx7rvvIigoCIMHDwYA9O/fH+3atcMjjzyC9957D+np6Zg+fTomTJhAXieCIAiCIAiCICqEKvVIffbZZ8jLy0Pfvn0RGxsr/K1cuRIAEBgYiG3btmHw4MFo0aIF7r//foSFhWH79u2Ijo4GAGg0GqxduxYajQY9evTAww8/jEcffRRvvvlmVZ4aQRAEQRAEQRA3MFXqkfJUeT0uLg5//vmnx+M0adLEq/0IgiAIgiAIgiD8QbWp2kcQBEEQBEEQBFFTIEWKIAiCIAiCIAjCR0iRIgiCIAiCIIgKRqVS4ddff60131vVjBkzBsOHD6/Q7yBFiiAIgiAIgqiVVIawXVnMmjULN998s8v2tLQ0DBo0qFzH3rx5MwYPHoy6desiODgY7dq1w4svvogrV66U67g8Fy5cgEqlwsGDB/12zIqGFCmCIAiCIAiCuEGJiYkpV0ugL774Av369UNMTAxWrVqF48eP4/PPP0deXh4WLFjgx5F6h9ForPTvVIIUKYIgCIIgCMK/2GyAsahq/jxUhXbHzz//jI4dOyIoKAh169ZFv379UFRUBACwWq1488030ahRIwQEBODmm2/GunXrhM8ajUZMnDgRsbGxCAwMRJMmTTB37lzR8Zl3KCgoCM2aNcPPP/8sev/SpUsYNWoUIiMjERUVhWHDhuHChQvC+0lJSejevTtCQkIQGRmJXr164eLFi1i6dClmz56NQ4cOQaVSQaVSYenSpQBcQ/suX76MBx98EFFRUQgJCUHXrl2xa9cu2etx+fJlTJo0CZMmTcLixYvRt29fJCQkoHfv3vj6668xc+ZMYd9Vq1ahffv2CAgIQEJCgouSlZCQgHfeeQdPPPEEwsLC0LhxY3z55ZfC+02bNgUAJCYmQqVSoW/fvgCcXsO3334bcXFxaN26NQDgyJEjuP3224Xfaty4cSgsLFT6aSuEKi1/ThAEQRAEQdyAmIqBd+Kq5rtfuwroQ3z+WFpaGh588EG89957GDFiBAoKCrBt2zahXc/ChQuxYMECfPHFF0hMTMTixYtx991349ixY2jZsiUWLVqENWvW4Mcff0Tjxo1x6dIlXLp0SfQdM2bMwLvvvouFCxfiu+++wwMPPIAjR46gbdu2MJlMGDBgAHr06IFt27ZBq9XirbfewsCBA3H48GGo1WoMHz4cTz31FH744QcYjUbs3r0bKpUK999/P44ePYp169bhn3/+AQBERES4nGNhYSH69OmDhg0bYs2aNYiJicH+/fthtVplr8lPP/0Eo9GIV155Rfb9yMhIAMC+ffswatQozJo1C/fffz+2b9+OZ599FnXr1sWYMWOE/RcsWIA5c+bgtddew88//4zx48ejT58+aN26NXbv3o3u3bvjn3/+Qfv27aHX64XPbdy4EeHh4diwYQMAoKioSLhWe/bsQUZGBp588klMnDhRUCArA1KkCIIgCIIgiFpPWloazGYz7rnnHjRp0gQA0LFjR+H9+fPnY+rUqXjggQcAAPPmzcPmzZvx0Ucf4dNPP0VqaipatmyJW2+9FSqVSjgGz3333Ycnn3wSADBnzhxs2LABH3/8Mf73v/9h5cqVsFqt+Prrr6FSqQAAS5YsQWRkJJKSktC1a1fk5eXhrrvuQvPmzQEAbdu2FY4dGhoKrVaLmJgYxXNcvnw5MjMzsWfPHkRFRQEAWrRoobj/mTNnEB4ejtjYWLfX7oMPPsAdd9yBGTNmAABatWqF48eP4/333xcpUoMHD8azzz4LAJg6dSo+/PBDbN68Ga1bt0b9+vUBAHXr1nU5h5CQEHz99deCcvXVV1+htLQU//d//4eQELvS/Mknn2Do0KGYN28eGjRo4Ha8/oIUKYIgCIIgCMK/6ILtnqGq+u4ycNNNN+GOO+5Ax44dMWDAAPTv3x/33nsv6tSpg/z8fFy9ehW9evUSfaZXr144dOgQAHsI2p133onWrVtj4MCBuOuuu9C/f3/R/j169HB5zYorHDp0CGfPnkVYWJhon9LSUpw7dw79+/fHmDFjMGDAANx5553o168fRo0a5VHJ4Tl48CASExMFJcoTNptNUOrcceLECQwbNky0rVevXvjoo49gsVig0WgAAJ06dRLeV6lUiImJQUZGhsfjd+zYUeShOnHiBG666SZBiWLfZ7VacerUqUpTpChHiiAIgiAIgvAvKpU9vK4q/rwQ/OXQaDTYsGED/vrrL7Rr1w4ff/wxWrdujZSUFK8+37lzZ6SkpGDOnDkoKSnBqFGjcO+993r9/YWFhejSpQsOHjwo+jt9+jRGjx4NwO6h2rFjB3r27ImVK1eiVatW2Llzp9ffERQU5PW+gN2zlJeXh7S0NJ8+p4ROpxO9VqlUimGFPLzCVJ0gRYogCIIgCIIgYBfse/XqhdmzZ+PAgQPQ6/VYvXo1wsPDERcXh+TkZNH+ycnJaNeunfA6PDwc999/P7766iusXLkSq1atQnZ2tvC+VOnZuXOnEJ7XuXNnnDlzBtHR0WjRooXoj893SkxMxLRp07B9+3Z06NABy5cvBwDo9XpYLBa359epUyccPHhQNCZ33HvvvdDr9Xjvvfdk38/NzQVgDzGUuzatWrUSvFGeYB4nT+fAvu/QoUNCIRD2fWq1WihGURmQIkUQBEEQBEHUenbt2oV33nkHe/fuRWpqKn755RdkZmYKis7LL7+MefPmYeXKlTh16hReffVVHDx4EM8//zwAe57QDz/8gJMnT+L06dP46aefEBMTIxRkAOzFGxYvXozTp0/jjTfewO7duzFx4kQAwEMPPYR69eph2LBh2LZtG1JSUpCUlIRJkybh8uXLSElJwbRp07Bjxw5cvHgRf//9N86cOSOMLyEhASkpKTh48CCysrJgMBhczvHBBx9ETEwMhg8fjuTkZJw/fx6rVq3Cjh07ZK9JfHw8PvzwQyxcuBBjx47Fli1bcPHiRSQnJ+Ppp5/GnDlzAAAvvvgiNm7ciDlz5uD06dP49ttv8cknn+Cll17y+vpHR0cjKCgI69atw7Vr15CXl6e470MPPYTAwEA89thjOHr0KDZv3oznnnsOjzzySKWF9QGkSBEEQRAEQRAEwsPDsXXrVgwePBitWrXC9OnTsWDBAqGZ7aRJkzBlyhS8+OKL6NixI9atW4c1a9agZcuWAICwsDC899576Nq1K7p164YLFy7gzz//hFrtFLdnz56NFStWoFOnTvi///s//PDDD4JHKzg4GFu3bkXjxo1xzz33oG3bthg7dixKS0sRHh6O4OBgnDx5EiNHjkSrVq0wbtw4TJgwAU8//TQAYOTIkRg4cCD++9//on79+vjhhx9czlGv1+Pvv/9GdHQ0Bg8ejI4dO+Ldd9916zV69tln8ffff+PKlSsYMWIE2rRpgyeffBLh4eGCotS5c2f8+OOPWLFiBTp06ICZM2fizTffFBWa8IRWq8WiRYvwxRdfIC4uziXniic4OBjr169HdnY2unXrhnvvvRd33HEHPvnkE6+/zx+obLZyFNu/QcjPz0dERATy8vIQHh5e1cMhCIIgCIKoUZSWliIlJQVNmzZFYGBgVQ+HIDzi7p71VjcgjxRBEARBEARBEISPkCJFEARBEARBEAThI6RIEQRBEARBEARB+AgpUgRBEARBEARBED5CilRtoajI3qBOpbL/nyCIGk9RTgZUs1VQzVahKMdzZ3iC8Bu0phAEQZAiRRAEQRAEQRAE4SukSBEEQRAEQRAEQfgIKVIEQRAEQRAEQRA+QooUQRAEQRAEQRCEj5AiRRAEQRAEQRBlwGazYdy4cYiKioJKpcLBgwerekjVgoSEBHz00UdVPYwKhxQpgiAIgiAIolYyZswYDB8+vMyfX7duHZYuXYq1a9ciLS0NHTp0gEqlwq+//urxsyqVCoGBgbh48aJo+/DhwzFmzJgyj6m8XLhwASqVSvirW7cu+vfvjwMHDlTZmKorpEgRBEEQBEEQRBk4d+4cYmNj0bNnT8TExECr1fr0eZVKhZkzZ1bQ6MrHP//8g7S0NKxfvx6FhYUYNGgQcnNzq3pY1QpSpAiCIAiCIIiKoaiocv/8zNGjRzFo0CCEhoaiQYMGeOSRR5CVlQXA7s167rnnkJqaCpVKhYSEBCQkJAAARowYIWxzx8SJE/H999/j6NGjivsYDAZMmjQJ0dHRCAwMxK233oo9e/YI7+fk5OChhx5C/fr1ERQUhJYtW2LJkiXC+5cuXcKoUaMQGRmJqKgoDBs2DBcuXPB47nXr1kVMTAy6du2K+fPn49q1a9i1axcAYNWqVWjfvj0CAgKQkJCABQsWuD3W119/jcjISGzcuBGA++takyBFiiAIgiAIgqgYQkMr98+P5Obm4vbbb0diYiL27t2LdevW4dq1axg1ahQAYOHChXjzzTfRqFEjpKWlYc+ePYKCs2TJEmGbO3r16oW77roLr776quI+r7zyClatWoVvv/0W+/fvR4sWLTBgwABkZ2cDAGbMmIHjx4/jr7/+wokTJ/DZZ5+hXr16AACTyYQBAwYgLCwM27ZtQ3JyMkJDQzFw4EAYjUavr0VQUBAAwGg0Yt++fRg1ahQeeOABHDlyBLNmzcKMGTOwdOlS2c++9957ePXVV/H333/jjjvu8HhdaxK++R8JgiAIgiAIohbwySefIDExEe+8846wbfHixYiPj8fp06fRqlUrhIWFQaPRICYmRvTZyMhIl21KzJ07F506dcK2bdtw2223id4rKirCZ599hqVLl2LQoEEAgK+++gobNmzAN998g5dffhmpqalITExE165dAUDkBVu5ciWsViu+/vprqFQqAHYlLzIyEklJSejfv7/H8eXm5mLOnDkIDQ1F9+7dMWXKFNxxxx2YMWMGAKBVq1Y4fvw43n//fZfcrqlTp+K7777Dli1b0L59ewDeXdeaAilSBEEQBEEQRMVQWFjVIygzhw4dwubNmxEq4+k6d+6c3wT+du3a4dFHH8Wrr76K5ORkl+8xmUzo1auXsE2n06F79+44ceIEAGD8+PEYOXIk9u/fj/79+2P48OHo2bOncA5nz55FWFiY6LilpaU4d+6c23H17NkTarUaRUVFaNasGVauXIkGDRrgxIkTGDZsmGjfXr164aOPPoLFYoFGowEALFiwAEVFRdi7dy+aNWsm7FtZ17UyIEWKIAiCIAiCqBhCQqp6BGWmsLAQQ4cOxbx581zei42N9et3zZ49G61atfKq2p+UQYMG4eLFi/jzzz+xYcMG3HHHHZgwYQLmz5+PwsJCdOnSBcuWLXP5XP369d0ed+XKlWjXrh3q1q2LyMhIn8d122234Y8//sCPP/4oCl2szOta0ZAiRRAEQRAEQRASOnfujFWrViEhIcGnanw6nQ4Wi8Wn74qPj8fEiRPx2muvoXnz5sL25s2bQ6/XIzk5GU2aNAFgz3vas2cPJk+eLOxXv359PPbYY3jsscdw22234eWXX8b8+fPRuXNnrFy5EtHR0QgPD/d5TPxYGG3btnXxnCUnJ6NVq1aCNwoAunfvjokTJ2LgwIHQarV46aWXAJT9ulZHqNgEQRAEQRAEUWvJy8vDwYMHRX+XLl3ChAkTkJ2djQcffBB79uzBuXPnsH79ejz++ONuFaWEhARs3LgR6enpyMnJ8Xoc06ZNw9WrV/HPP/8I20JCQjB+/Hi8/PLLWLduHY4fP46nnnoKxcXFGDt2LABg5syZ+O2333D27FkcO3YMa9euRdu2bQEADz30EOrVq4dhw4Zh27ZtSElJQVJSEiZNmoTLly+X6Xq9+OKL2LhxI+bMmYPTp0/j22+/xSeffCIoSjw9e/bEn3/+idmzZwsNest6XasjpEgRBEEQBEEQtZakpCQkJiaK/mbPno24uDgkJyfDYrGgf//+6NixIyZPnozIyEio1coi9IIFC7BhwwbEx8cjMTHR63FERUVh6tSpKC0tFW1/9913MXLkSDzyyCPo3Lkzzp49i/Xr16NOnToAAL1ej2nTpqFTp07o3bs3NBoNVqxYAQAIDg7G1q1b0bhxY9xzzz1o27Ytxo4di9LSUp89VIzOnTvjxx9/xIoVK9ChQwfMnDkTb775pmIT4VtvvRV//PEHpk+fjo8//rjM17U6orLZbLaqHkRVk5+fj4iICOTl5ZX5pqr2FBU5y4IWFtbomGWCIOwU5WQgdFEDAEDhpGsIqRNdxSMiag20phASSktLkZKSgqZNmyIwMLCqh0MQHnF3z3qrG9QstY8gCIIgCIIgCKIaQIoUQRAEQRAEQRCEj5AiRRAEQRAEQRAE4SOkSBEEQRAEQRAEQfhIlSpSc+fORbdu3RAWFobo6GgMHz4cp06dEt7Pzs7Gc889h9atWyMoKAiNGzfGpEmTkJeXJzpOamoqhgwZguDgYERHR+Pll1+G2Wyu7NMhCIIgCIIgCKKWUKWK1JYtWzBhwgTs3LkTGzZsgMlkQv/+/VFUVAQAuHr1Kq5evYr58+fj6NGjWLp0KdatWyfUzQcAi8WCIUOGwGg0Yvv27fj222+xdOlSzJw5s6pOiyAIgiAIgiCIG5xqVf48MzMT0dHR2LJlC3r37i27z08//YSHH34YRUVF0Gq1+Ouvv3DXXXfh6tWraNDAXgb4888/x9SpU5GZmQm9Xu/xe6n8OUEQNREqf05UGbSmEBKo/DlR07jhyp+zkL2oqCi3+4SHh0Or1QIAduzYgY4dOwpKFAAMGDAA+fn5OHbsmOwxDAYD8vPzRX8EQRAEQRAEQRDeUm0UKavVismTJ6NXr17o0KGD7D5ZWVmYM2cOxo0bJ2xLT08XKVEAhNfp6emyx5k7dy4iIiKEv/j4eD+dBUEQBEEQBEEQtYFqo0hNmDABR48exYoVK2Tfz8/Px5AhQ9CuXTvMmjWrXN81bdo05OXlCX+XLl0q1/EIgiAIgiCI2ofNZsO4ceMQFRUFlUqFgwcPVvWQKoULFy7UqvNVolooUhMnTsTatWuxefNmNGrUyOX9goICDBw4EGFhYVi9ejV0Op3wXkxMDK5duyban72OiYmR/b6AgACEh4eL/giCIAiCIIjaxZgxYzB8+PAyf37dunVYunQp1q5di7S0NHTo0AEqlQq//vqrx8+qVCrhLzw8HN26dcNvv/1W5rEQlU+VKlI2mw0TJ07E6tWrsWnTJjRt2tRln/z8fPTv3x96vR5r1qxxSQbr0aMHjhw5goyMDGHbhg0bEB4ejnbt2lX4ORAEQRAEQRC1k3PnziE2NhY9e/ZETEyMkMPvLUuWLEFaWhr27t2LXr164d5778WRI0cqaLS+YzQaq3oI1ZoqVaQmTJiA77//HsuXL0dYWBjS09ORnp6OkpISAE4lqqioCN988w3y8/OFfSwWCwCgf//+aNeuHR555BEcOnQI69evx/Tp0zFhwgQEBARU5ekRBEEQBEHUaoqMRZX652+OHj2KQYMGITQ0FA0aNMAjjzyCrKwsAHZv1nPPPYfU1FSoVCokJCQgISEBADBixAhhmzsiIyMRExODVq1aYc6cOTCbzdi8ebPw/qVLlzBq1ChERkYiKioKw4YNw4ULF4SxqdVqZGZmArD3X1Wr1XjggQeEz7/11lu49dZbAdhbBo0dOxZNmzZFUFAQWrdujYULF4rGwzx0b7/9NuLi4tC6dWsAwO7du5GYmIjAwEB07doVBw4cKPM1vZHwTW32M5999hkAoG/fvqLtS5YswZgxY7B//37s2rULANCiRQvRPikpKUhISIBGo8HatWsxfvx49OjRAyEhIXjsscfw5ptvVso5EARBEARBEPKEzg2t1O+zveG/rj65ubm4/fbb8eSTT+LDDz9ESUkJpk6dilGjRmHTpk1YuHAhmjdvji+//BJ79uyBRqMBAERHR2PJkiUYOHCgsM0TZrMZ33zzDQAIrXtMJhMGDBiAHj16YNu2bdBqtXjrrbcwcOBAHD58GO3bt0fdunWxZcsW3Hvvvdi2bZvwmrFlyxZBzrZarWjUqBF++ukn1K1bF9u3b8e4ceMQGxuLUaNGCZ/ZuHEjwsPDsWHDBgBAYWEh7rrrLtx55534/vvvkZKSgueff77c1/dGoEoVKU8trPr27etxHwBo0qQJ/vzzT38NiyAIgiAIgqjlfPLJJ0hMTMQ777wjbFu8eDHi4+Nx+vRptGrVCmFhYdBoNC55+czT5IkHH3wQGo0GJSUlsFqtSEhIEJSalStXwmq14uuvv4ZKpQJgdzZERkYiKSkJ/fv3R+/evZGUlIR7770XSUlJePzxx/H111/j5MmTaN68ObZv345XXnkFAKDT6TB79mzhu5s2bYodO3bgxx9/FClSISEh+PrrrwWF7ssvv4TVasU333yDwMBAtG/fHpcvX8b48ePLeGVvHKpUkSIIgiAIgiBuXAqnFVb1EMrMoUOHsHnzZoSGunrVzp07h1atWpX7Oz788EP069cP58+fxwsvvIBFixYJ/VQPHTqEs2fPIiwsTPSZ0tJSnDt3DgDQp08ffPnllwDs3qd33nkHp0+fRlJSErKzs2EymdCrVy/hs59++ikWL16M1NRUlJSUwGg04uabbxYdv2PHjoISBQAnTpxAp06dRHUKevToUe5zvxEgRYogCIIgCIKoEEL0IVU9hDJTWFiIoUOHYt68eS7vxcbG+uU7YmJi0KJFC7Ro0QJLlizB4MGDcfz4cURHR6OwsBBdunTBsmXLXD5Xv359APborcmTJ+PMmTM4fvw4br31Vpw8eRJJSUnIyclB165dERwcDABYsWIFXnrpJSxYsAA9evRAWFgY3n//fSGNhhESUnN/s8qGFCmCIAiCIAiCkNC5c2esWrUKCQkJPlXj0+l0QlE0X+jevTu6dOmCt99+GwsXLkTnzp2xcuVKREdHK7bq6dixI+rUqYO33noLN998M0JDQ9G3b1/MmzcPOTk5ojoEycnJ6NmzJ5599llhG/NsuaNt27b47rvvUFpaKnildu7c6fP53YhUiz5SBEEQBEEQBFEV5OXl4eDBg6K/S5cuYcKECcjOzsaDDz6IPXv24Ny5c1i/fj0ef/xxt4pSQkICNm7ciPT0dOTk5Pg0lsmTJ+OLL77AlStX8NBDD6FevXoYNmwYtm3bhpSUFCQlJWHSpEm4fPkyAHsvqt69e2PZsmWC0tSpUycYDAZs3LgRffr0EY7dsmVL7N27F+vXr8fp06cxY8YM7Nmzx+OYRo8eDZVKhaeeegrHjx/Hn3/+ifnz5/t0XjcqpEgRBEEQBEEQtZakpCQkJiaK/mbPno24uDgkJyfDYrGgf//+6NixIyZPnozIyEio1coi9IIFC7BhwwbEx8cjMTHRp7EMHDgQTZs2xdtvv43g4GBs3boVjRs3xj333IO2bdti7NixKC0tFXmo+vTpA4vFIihSarUavXv3hkqlEuVHPf3007jnnntw//334z//+Q+uX78u8k4pERoait9//x1HjhxBYmIiXn/9ddlwx9qIyuZNWbwbnPz8fERERCAvL0/RdVrjKSoCWLJkYSFA8a8EUeMpyslA6KIGAIDCSdcQUie6ikdE1BpoTSEklJaWIiUlBU2bNhUVJSCI6oq7e9Zb3YA8UgRBEARBEARBED5CihRBEARBEARBEISPkCJFEARBEARBEAThI6RIEQRBEARBEARB+AgpUgRBEARBEARBED5CihRBEARBEARBEISPkCJFEARBEARBEAThI6RIEQRBEARBEARB+Ii2qgdAEARBEARB3JiUlABGY+V9n14PBAVV3vcRtRtSpAiCIAiCIAi/U1IC/PYbkJNTed9Zpw4wbBgpU74wZswY5Obm4tdff63qodQ4KLSPIAiCIAiC8DtGo12JCgqyKzgV/RcUZP8+XzxgY8aMwfDhw8t1nrNmzYJKpYJKpYJGo0F8fDzGjRuH7Ozsch23upCUlCScn0qlQoMGDTBy5EicP3/eL8fNzc31z0CrAPJIEQRBEARBEBVGYCAQElI531VSUjnfI6V9+/b4559/YLFYcOLECTzxxBPIy8vDypUrq2ZAMphMJuh0ujJ//tSpUwgLC8OZM2cwbtw4DB06FIcPH4ZGoynTWG4EyCNFEARBEARBEAD69u2LSZMm4ZVXXkFUVBRiYmIwa9Ysj5/TarWIiYlBw4YN0a9fP9x3333YsGGDaJ+vv/4abdu2RWBgINq0aYP//e9/wnv33nsvJk6cKLyePHkyVCoVTp48CQAwGo0ICQnBP//8AwBYt24dbr31VkRGRqJu3bq46667cO7cOeHzFy5cgEqlwsqVK9GnTx8EBgZi2bJlsFgsmDJlivC5V155BTabzatrEx0djdjYWPTu3RszZ87E8ePHcfbsWQDAZ599hubNm0Ov16N169b47rvvRJ9VqVT47LPPcPfddyMkJARPPfUU/vvf/wIA6tSpA5VKhTFjxng1juoEKVIEQRAEQRAE4eDbb79FSEgIdu3ahffeew9vvvmmi1LkjgsXLmD9+vXQ6/XCtmXLlmHmzJl4++23ceLECbzzzjuYMWMGvv32WwBAnz59kJSUJOy/ZcsW1KtXT9i2Z88emEwm9OzZEwBQVFSEKVOmYO/evdi4cSPUajVGjBgBq9UqGsurr76K559/HidOnMCAAQOwYMECLF26FIsXL8a///6L7OxsrF692udrFORIQjMajVi9ejWef/55vPjiizh69CiefvppPP7449i8ebPoM7NmzcKIESNw5MgRzJ49G6tWrQJg93SlpaVh4cKFPo+jqqHQPoIgCIIgCIJw0KlTJ7zxxhsAgJYtW+KTTz7Bxo0bceeddyp+5siRIwgNDYXFYkFpaSkA4IMPPhDef+ONN7BgwQLcc889AICmTZvi+PHj+OKLL/DYY4+hb9++eP7555GZmQmtVovjx49jxowZSEpKwjPPPIOkpCR069YNwcHBAICRI0eKvn/x4sWoX78+jh8/jg4dOgjbJ0+eLHwnAHz00UeYNm2asO3zzz/H+vXrfbo+aWlpmD9/Pho2bIjWrVvjmWeewZgxY/Dss88CAKZMmYKdO3di/vz5gtcJAEaPHo3HH39ceJ2SkgLA7umKjIz0aQzVBfJIEQRBEARBEISDTp06iV7HxsYiIyPD7Wdat26NgwcPYs+ePZg6dSoGDBiA5557DoDde3Tu3DmMHTsWoaGhwt9bb70lhON16NABUVFR2LJlC7Zt24bExETcdddd2LJlCwC7h6pv377C9505cwYPPvggmjVrhvDwcCQkJAAAUlNTRePq2rWr8P+8vDykpaXhP//5j7BNq9WK9nFHo0aNEBISgri4OBQVFWHVqlXQ6/U4ceIEevXqJdq3V69eOHHihOJYbhTII0UQBEEQBEEQDqQFGVQqlUvInBS9Xo8WLVoAAN59910MGTIEs2fPxpw5c1BYWAgA+Oqrr0RKDAChUINKpULv3r2RlJSEgIAA9O3bF506dYLBYMDRo0exfft2vPTSS8Lnhg4diiZNmuCrr75CXFwcrFYrOnToAKOkZGGIH6t8bNu2DeHh4YiOjkZYWJjPn/fnWKoL5JEiCIIgCIIgCD8yffp0zJ8/H1evXkWDBg0QFxeH8+fPo0WLFqK/pk2bCp9heVJJSUno27cv1Go1evfujffffx8Gg0Hw+ly/fh2nTp3C9OnTcccdd6Bt27bI8aJZV0REBGJjY7Fr1y5hm9lsxr59+7w6p6ZNm6J58+YuSlTbtm2RnJws2pacnIx27dq5PR7LIbNYLF59f3WEPFIEQRAEQRBEheFIGbphvscbevTogU6dOuGdd97BJ598gtmzZ2PSpEmIiIjAwIEDYTAYsHfvXuTk5GDKlCkA7BUDX3jhBej1etx6663CtpdeegndunUTPDp16tRB3bp18eWXXyI2Nhapqal49dVXvRrX888/j3fffRctW7ZEmzZt8MEHH5S7j9PLL7+MUaNGITExEf369cPvv/+OX375RagwqESTJk2gUqmwdu1aDB48GEFBQQgNDS3XWCobUqQIgiAIgiAIv6PX2xvl5uRUXn+nOnXs31sdeOGFFzBmzBhMnToVTz75JIKDg/H+++/j5ZdfRkhICDp27IjJkycL+3fs2BGRkZFo1aqVoFD07dsXFotFlB+lVquxYsUKTJo0CR06dEDr1q2xaNEi0T5KvPjii0hLS8Njjz0GtVqNJ554AiNGjEBeXl6Zz3P48OFYuHAh5s+fj+effx5NmzbFkiVLPI6nYcOGmD17Nl599VU8/vjjePTRR7F06dIyj6MqUNm8LR5/A5Ofn4+IiAjk5eUhPDy8qodTMRQVAUzLLyysvM54BEFUGEU5GQhd1AAAUDjpGkLqRFfxiIhaA60phITS0lKkpKSgadOmCAwMFLaXlACStJ0KRa8HHJW5CcItSvcs4L1uQB4pgiAIgiAIokIICiLFhrhxoWITBEEQBEGUmc0nr6H72/9g25nMqh4KQRBEpeKzR8pgMGDXrl24ePEiiouLUb9+fSQmJoqqjhAEQRAEUTt4Y80xZBjUmP7rUWx5+b+eP0AQBHGD4LUilZycjIULF+L333+HyWRCREQEgoKCkJ2dDYPBgGbNmmHcuHF45plnylRbniAIgiCImkdmgRHQB+Li9eKqHgpBEESl4lVo39133437778fCQkJ+Pvvv1FQUIDr16/j8uXLKC4uxpkzZzB9+nRs3LgRrVq1woYNGyp63ARBEARBEEQ1g2qYETUFf9yrXnmkhgwZglWrVrl0emY0a9YMzZo1w2OPPYbjx48jLS2t3AMjCIIgCIIgagZMRiwuLkYQVZcgagDFxXYvupJ+4w1eKVJPP/201wds166dx07GBEEQBEHcGGjUqqoeAlEN0Gg0iIyMREZGBgAgODgYKhXdG0T1w2azobi4GBkZGYiMjIRGoynzsaj8OUEQBEEQZUZDwjLhICYmBgAEZYogqjORkZHCPVtW/KZIPfbYY7h06RI2bdrkr0MSBEEQBFHNIY8UwVCpVIiNjUV0dDRMJlNVD4cgFNHpdOXyRDH8pkg1bNgQajW1pSIIgiCI2oRGrQKovgDBodFo/CKkEkR1x2+K1DvvvOOvQxEEQRAEUUNQq0CKFEEQtRJyIREEQRAEUWbUFNpHEEQtxWeP1BNPPOH2/cWLF3t9rLlz5+KXX37ByZMnERQUhJ49e2LevHlo3bq1sM+XX36J5cuXY//+/SgoKEBOTg4iIyNFx8nOzsZzzz2H33//HWq1GiNHjsTChQsRGhrq07kRBEEQBOEbpEcRBFFb8dkjlZOTI/rLyMjApk2b8MsvvyA3N9enY23ZsgUTJkzAzp07sWHDBphMJvTv3x9FRUXCPsXFxRg4cCBee+01xeM89NBDOHbsGDZs2IC1a9di69atGDdunK+nRhAEQRCEj5BHiiCI2orPHqnVq1e7bLNarRg/fjyaN2/u07HWrVsner106VJER0dj37596N27NwBg8uTJAICkpCTZY5w4cQLr1q3Dnj170LVrVwDAxx9/jMGDB2P+/PmIi4vzaUwEQRAEQXgP6VEEQdRW/JIjpVarMWXKFHz44YflOk5eXh4AICoqyuvP7NixA5GRkYISBQD9+vWDWq3Grl27ZD9jMBiQn58v+iMIgiAIwnfU1EeKIIhait+KTZw7dw5ms7nMn7darZg8eTJ69eqFDh06eP259PR0REdHi7ZptVpERUUhPT1d9jNz585FRESE8BcfH1/mcRMEQRBEbYYUKYIgais+h/ZNmTJF9NpmsyEtLQ1//PEHHnvssTIPZMKECTh69Cj+/fffMh/DW6ZNmyY6j/z8fFKmCIIgCKIM8HqU1WqjnCmCIGoNPitSBw4cEL1Wq9WoX78+FixY4LGinxITJ04UikQ0atTIp8/GxMQgIyNDtM1sNiM7OxsxMTGynwkICEBAQECZxkoQBFFdySs2Yf2xdAzpFIuQAL+1CSQIt9g9UvZGUiarFQFqasRKEETtwOeVdvPmzX77cpvNhueeew6rV69GUlISmjZt6vMxevTogdzcXOzbtw9dunQBAGzatAlWqxX/+c9//DbWG4nXfjmC3okJGNhBXtEkCKJm8tqvR/DH4TRczinGlP6tPX+AIPwA74AyW2wgHZ4giNpClTbknTBhAr7//nssX74cYWFhSE9PR3p6OkpKSoR90tPTcfDgQZw9exYAcOTIERw8eBDZ2dkAgLZt22LgwIF46qmnsHv3biQnJ2PixIl44IEHqGKfAqsPXMEz3++r6mEQBOFn/jicBgD4Yuv5Kh4JUZvgc6RMFmsVjoQgCKJy8Zsi9dprr/kc2vfZZ58hLy8Pffv2RWxsrPC3cuVKYZ/PP/8ciYmJeOqppwAAvXv3RmJiItasWSPss2zZMrRp0wZ33HEHBg8ejFtvvRVffvmlf06MIAiihmEwkzBLVA1GUqQIgqhF+M0Bf+XKFVy6dMmnz9hsNo/7zJo1C7NmzXK7T1RUFJYvX+7TdxP266+iaksEcUNg9WI+JYiKwMLde2YL3YcEQdQe/KZIffvtt/46FFFJGC1WBGgpKZggbgRyS0yi1yaLFTpNlUZvE7UEXnmi0D6CIGoTtMrWYkqMFsX3bDYbMgpKvfIaEgRR9bz5+3HR6+uFxioaCVHb4L2hpEgRBFGbKJNHqqioCFu2bEFqaiqMRvFiPWnSJL8MjKh4iowWRAbLv/fbwauYvPIgZt/dHo/1TKjUcREE4TtbT2cC+kjhdVahATERgVU3IKLWYLbaANjDxE0U2kcQRC2iTH2kBg8ejOLiYhQVFSEqKgpZWVkIDg5GdHQ0KVI1iFKTskdq8sqDAIA31hwjRYogaiCZhYaqHgJRS7BYySNFEETtxOfQvhdeeAFDhw5FTk4OgoKCsHPnTly8eBFdunTB/PnzK2KMRAVhpMpeBHHDklds8rwTQfgBK7eUkCJFEERtwmdF6uDBg3jxxRehVquh0WhgMBgQHx+P9957D6+99lpFjJGoIEiRIogbl/xSUqSIykHskaLQPoIgag8+K1I6nQ5qtf1j0dHRSE1NBQBERET4XP6cqFqo3wdB3LiQR4qoLCi0jyCI2orPilRiYiL27NkDAOjTpw9mzpyJZcuWYfLkyejQoYPfB0hUHOSRIogbj/u6NAJAHimi8iBFiiCI2orPitQ777yD2NhYAMDbb7+NOnXqYPz48cjMzMSXX37p9wESFQcpUgRxY/F072ZoUtdeijO/xFzFoyFqCxYbhfYRBFE78blqX9euXYX/R0dHY926dX4dEFF5GEiRIogbCq1GhYggHQAgr4Q8UkTlQB4pgiBqK9SQtxZDOVIEcWOhVasR7lCkKLSPqApIkSIIojbhlSI1cOBA7Ny50+N+BQUFmDdvHj799NNyD4yoeCi0jyBuLHQaFcIDnYqU2WKFzUahVkTlQaF9BEHUJrwK7bvvvvswcuRIREREYOjQoejatSvi4uIQGBiInJwcHD9+HP/++y/+/PNPDBkyBO+//35Fj5vwA6RIEcSNhUatRpBeAwC4XmhE3/lJqB8WgFXP9IRarari0RG1AfJIEQRRm/BKkRo7diwefvhh/PTTT1i5ciW+/PJL5OXlAQBUKhXatWuHAQMGYM+ePWjbtm2FDpjwH0azpaqHQBCEH9FpVAjS2RWptLxSAMDlnBJcyS1BfFRwVQ6NqCWYyEBHEEQtwutiEwEBAXj44Yfx8MMPAwDy8vJQUlKCunXrQqfTVdgAiYqDcqQI4sZCq1YJHimeq6RIEZWE2UqhfQRB1B7KXGwiIiICMTExpETVYLwN7aMcC4KoGWg1asEjxZNLFfxksZDQ73fIQEcQNyZmixXj/m8v3l9/sqqHUq2gqn21GG8VKbIwEkTNQKtWIVBGkSoopZ5SUpbvSkWHN9Zj6+nMqh7KDYXJTOsFQdyIHLqci7+PX8Onm8+REYqDFKlajMFLyyElDxNEzUCrUSNQ5zqtF1ApdBdm/HYUJSYLXvrpUFUP5YbCbKX1giBuRPjeo9Sn0AkpUrUYJY+UNJSPLIwEUTPQaeQ9Uvkl5JGSwiyqZFn1LxTaRxA3Jnxrg2IjrSkMUqRqMUqKlFSuoIWRIGoGWrUaOo0aOo241DktesqoqCq8XyHDG1ETKTVZ8MCXO/D66iNVPZRqSwm3jpQYqeozo0yKVG5uLr7++mtMmzYN2dnZAID9+/fjypUrfh0cUbEoKVLS0IyyhPYdupSLqT8fxvVCQ5nGRhA1EaPZiiOX86qsQIvWoUBJvVKlJlr0lKB+ev6FQvuImsiO89ex83w2lu1KhbkaGY+t1chjXmRwriNFpEgJ+KxIHT58GK1atcK8efMwf/585ObmAgB++eUXTJs2zd/jIyoQg4IAIQ11KYsiNfGH/Vi59xI+3nS2TGMjiJrIN/+mYOgn/2LRxqq577WOprvSyn2lpuojGFQ3qJiOf6GcWqImwvc/qw5VTm02G0Z9sQN3fLCl2nh/ijmDHEU5OPFZkZoyZQrGjBmDM2fOIDAwUNg+ePBgbN261a+DIyoWJUssHwdrf+37wngpuwQAsPZwmu8DI4gayvy/TwEAPvzndJV8v1Zjn9KlvaRKqfk2UUkYKbSPqIHwhuXqoLik55did0o2UrKKcOxqXlUPB4A4tI+iHJz4rEjt2bMHTz/9tMv2hg0bIj093S+DIioHJeFK6pFytzDabDbsvZCNQoO8dYJ6UBG1iaouXKBzeKQCtRTa5y1WmqP8CoX2ETWREm6OLKkG8yVfFe96kbEKR+KkmFMwyWDixGdFKiAgAPn5+S7bT58+jfr16/tlUETloGR18SVH6o8jabj38x2YrpCgSYncBFF5MI9UoNQjRaF9ipAe5V8otI+oiZSKwtaqXpHiK63mFlcPRYqXGakImROfFam7774bb775Jkwmu7asUqmQmpqKqVOnYuTIkX4fIFFxKFldzD6E9v209zIA4NeDV/03MIIgyoTG4ZEKC9CKtpNHiqgspKHhBFET4JWE6pD/k895pHKLqz5nCxArmCYq0iPgsyK1YMECFBYWIjo6GiUlJejTpw9atGiBsLAwvP322xUxRqKCUPJIuYT2uVGkPAto5JIiiMqClT0PlSpStOgpQmK/fyGPFFET4Q3L1cEYUGBwKk/VIdQQkIT20XMuoPW8i5iIiAhs2LABycnJOHToEAoLC9G5c2f069evIsZHVCBK7mtpFSt3kwotmgRRfdCqHaF9OrGNzFBNFuJqSdXLTDcUtCYQNRGRIlUNDE/Galb8AgBKTE5PHT3nTnxWpBi9evVCr169/DkWopJR8iZJeyi4m1R4Hctms0ElSYpSk0OKICoNaSNeBoX2ieGL4NhIk/Ir1cGaTxC+UsopK9WhYApv0K6WHqlqoGxWF3wO7Zs0aRIWLVrksv2TTz7B5MmT/TEmopJQzJHyoY+UnhPcmKuXV8R0mjL1fCYIwgN5MnHzrNiEVJSlYhNi+DmOik34F7JUEzURXh4yVgNjAJ+rXl08UrwipdSHtDbis5S7atUqWU9Uz5498fPPP/tlUETFwrxEJSaLbHlyX3KkeEWJdb3m8zE05JIiCL+zat9l3PTm3xizZJdoO2vIGyyp2megPlIipAV1CP9BihRREynhjE1yUTilJkulemH456i4mnikeIWOnnMnPitS169fR0REhMv28PBwZGVl+WVQRMUSFqgDYLfEylkVfMmR4pUuVumGDyMiPYog/M9nW84BAA5fFrei0Do8xCMSGwEAOjWyz9WVbT20Wm148tu9mP6rfFuEqsbEhe6QSuVfSEmtPE5fK5Dt4Wiz2UjQ9RF3SkKhwYy+7ydhyKJtsFZSr0BetiqtNh4p571GoX1OfFakWrRogXXr1rls/+uvv9CsWTO/DIqoWMKCnNbqzSczXN53yZFyMyHz77GJqEQUa0yLKkH4m9TsYtntrNhE96ZRSHqpLz4d3RlA5StS57OK8M+Ja/h+Z2q1zM/ihf3q3DS82GjG4n9TkFVoqOqheA1V86ocDl7KRf8Pt2LCsv0u77340yF0nrMB5zILq2BkNZNSk7IitSclG+n5pTiTUYgruSWVMp7qmCNFHil5fC42MWXKFEycOBGZmZm4/fbbAQAbN27EggUL8NFHH/l7fEQFoFerER6oRn6pGTN+O4ZBHWNF7/uSI2UWeaQcoX3cQ0/WSYLwPwEataxFkC82kVAvRGjkaLHaYLZYhRyqisf53OeVmBCo07jZt/Lh5zSrze5BU1dD9/lXW1Pw4T+nsWJPKv5+oU9VD8cteq39niQBq3JYe8jeu3HL6UxRoadCgxm/7L8CAPjzcBqeu6NllY2xJuEuR+p8VpHw/+tFRsRHBVf4eEShfdXFI2WiYhNy+KxIPfHEEzAYDHj77bcxZ84cAEBCQgI+++wzPProo34fIOF/tBo1BneMxYo9l5BVaHCptueSI+XmgeHfK3K4fUvcWHYIwhuKjWaoVapqJ4BXF/RaNSDjpJDmJAZondfPYK48RYoPBy4oNaFBeGClfK+3SOclo8WKQHX1u9eWbk8BAJy+Vv09C4EORYqMZ5WDhfOkFhjMCHeE7J+5ViBsry6ejJqAKJJGMj+kZDmfP2acqmhEoX3V5HcsNlAfKTnKtKqOHz8ely9fxrVr15Cfn4/z58+TElWD0GpUeGNoe+F1gSTGWipkuMuRErmfBY8Ul7RJDxvhI3klJvR+LwmDF21zUeqrM9LY+Yocu14rP3VLq2QGcPtVZngf/9wXlLrmcFQ1UmG/uoYg16T7nxk9/Dnn/3P8Gnaev+63491IqDnjJ1/B83ym03uSV+Ja2ZOQx50B+OJ1Zyh1ZSk1vNxVHRQpo9kqUp6M5pozN1U05TJP1q9fH6Ghof4aC1FJaNVqBOk1CA2wOyQzC8Smbeni7W2OFHM/V7cO4UTN4vS1AmQVGnA+swgpXEhFdcdk9T63sLwopfVoJR4ptVoFvUO5qszKfdUxLIVH2iemKhpwlhgtuJZf6nYfvbb6ecmUYIqUv0J+sgoNGPfdXjzw5c5qIUhWFVarDRkFrvcJf515henidVKkygLvkZKG9l0vdHqhKsvLx3vFqkP7Cr7QBEAeKR6fFalr167hkUceQVxcHLRaLTQajeiPqP6wPIqIIHsogHSy9SlHSqbXQSFnga4Oje2ImgW/oF2tpMRefyD1clTkQqP0XMm1G2BeqcpcjHlrZZFMVbGqRmrgqQrP+bjv9uKWuRtx+HKu4j4BCp7H6khUiB6APcLBH4rPpexisKUoPc+9wnkj88XW8+j+9kb8dvCKaDtvGOHX8Kwip9CfXw29wdUVdx4p/vqWGCtnrqhuxSak1SGN1FJDwOccqTFjxiA1NRUzZsxAbGysKLeGqBmw8J/IYB2u5Ja4KFK+9JHi32M5UgWlzuOZLDaXHCyCcAcvhNWkamVSRaoivRxynl6dRiX7nAXo1CgwVJ1HqshY/YQ5uRypysRgtmDbGXu7kI0nMtCpUaTsfnwIZ3UtiMGIDgtAeKAW+aVmnEjLR2LjOuU6XgYXKeFNeOi+izl4f/1JzLirHdrHubZoqanMW3cSAPDGmmMYdnNDYTvvkcrlQvtyOEWqNnvyfEWkSJmVFSmpZ6ai4I1l1UGRKjKIx0DFJpz4bO76999/sWzZMowfPx7Dhw/HsGHDRH++MHfuXHTr1g1hYWGIjo7G8OHDcerUKdE+paWlmDBhAurWrYvQ0FCMHDkS165dE+2TmpqKIUOGIDg4GNHR0Xj55ZdhNle/xbu6oOEUKUAcXw3I5Ei5iYWVC+GRLnrVNf+AqJ7wDZ2rY36NElJhvCLDWqXJ0AAUhWxWcMJQiR4pkSJlqHohQIqrR6py5qgTafnYejpT5GFxJyTxoZrVpSmnElqNCrc0qwsA2Hshp9zH45UAuV5JUmb8ehQ7z2fjgS93lvu7qyMlkhBZfr7hBf1sUqQEvG1tYLHaRIoBP3+ZLFbR/Sf9HSoK3jBnNFsrrX+VEuO+2yt6TaF9TnxWpOLj4/3Wd2PLli2YMGECdu7ciQ0bNsBkMqF///4oKnLG+L7wwgv4/fff8dNPP2HLli24evUq7rnnHuF9i8WCIUOGwGg0Yvv27fj222+xdOlSzJw50y9jvBHRORbnyCB7KIa0Co0vOVJyoX28R8rT5wlCCr/459egGH+XvJsKvO9Njme0RbQzR1Wnlp/OWXhYVRWbqCwLri/40ivPnwxauA2PLt6Nv485jYHS+ZKHLyhQ3YVijVqNVg3CAAAXs8uf28grt94oUsfT7M2pa5LxxRek9yhvGMnh1nD+/9X9nqlIdp2/jg5vrMeK3ake95VeJxMnAxVK7qfSSvLsSw3QlfW9cpSaLKKCGwB5pHh8VqQ++ugjvPrqq7hw4UK5v3zdunUYM2YM2rdvj5tuuglLly5Famoq9u3bBwDIy8vDN998gw8++AC33347unTpgiVLlmD79u3YudNudfr7779x/PhxfP/997j55psxaNAgzJkzB59++imMxsopU1nT0LIcKYdHKrccOVJGGY+UNC7bnUeLIKQYuEVNWlGyOlOpOVKOY0+/q62wTaNR8Eg5igBUZmifUSQEVz9hTjrHVaRQ8OOeS/jd0fOHse5YuvB/d3ksfAGTym6q7CsaFVAv1G6cyykuvwFE7NWsOfNARSF1SCh5pHjBvzoUKagqPthwGkVGC1795YjHfaVeYT60TzqP++ua/nP8Gk44lH85pMaeyvKEScdgNFtxOcepRE25sxUAUqR4fM6Ruv/++1FcXIzmzZsjODgYOp1O9H52dnaZB5OXlwcAiIqKAgDs27cPJpMJ/fr1E/Zp06YNGjdujB07duCWW27Bjh070LFjRzRo0EDYZ8CAARg/fjyOHTuGxMREl+8xGAwwGJzx1/n5yjfzjYjWYbmOdBSbyJUser4IhGYZy7PUIiitZkYQ7uAXKmnYaXXGNbSvYu57q9UmCFWheucULldoAuA8UpVabIKbF6qhEOza4qFirs3+1By8suowAKBLE2fOEG/dded15cdV3b0LGrUakcF2Rcofzy1/7iS0uSLySHHhfMYadM9UJO6UFClSJcXdveePa7o7JRtP/t9eRIXosX/GnbL7mFw8UpX7DFisNgxauA2lZgteG2Q32LWNDUdi40gA1d+wU5n4rEh99NFHFTAMwGq1YvLkyejVqxc6dOgAAEhPT4der0dkZKRo3wYNGiA9PV3Yh1ei2PvsPTnmzp2L2bNn+/kMag5CaF+wfNU+i0uIkrxHycIJdIDTI1VoqL6hfRn5pXjxp0MY3b0xBnWM9eux80pMWL3/MkZ2aYSwQJ3nDxCy8AtVdc8L4XEtNlExnljeMBES4JzCrQoh11Ud2lc9i01UTo7UjnPOHkiHLuUK/+eLqLgLRePvoeouFGvUziiHHD80LeUF2Mr0ptYUDNwzxkeV8M95db9nKhIlw5Ic0hYN/HxQER4p1hstu8iIvBKTUEGZxyKZkyrbI3XxehHOZNgbEW88mQEAaBIVLLTT4K9LRn4pHv5mF+5s1wAvD2hTqeOsDvisSD322GMVMQ5MmDABR48exb///lshx+eZNm0apkyZIrzOz89HfHx8hX9vdUHLik0o5Ei5CBkKAphUQTpwyZ5g7FJswoOQsv1sFnalZGPi7S1cGor6m6+2nce2M1nYdiYLF94d4tdjv/vXSfywOxVJpzOx9PHufj12bYIPs6iKcIayUlmV4PjnKSTQOYVLF14GC+2rTKGquhebqKwcqTPXCoT/X86RL+Wf7yZHSuxdqD4GKTk0ajXCHfejP/KU+HXIGyOATqOq8KIhNpsNGQUGRIcFVHklWnHVPqPs9sr2YlQFBrMFy3elYkjHWESHBwrbNQo5o3K465EknRu8yVW6XmjAX0fTcffNcQiXMarya1xmgUFWkZLm3Fa2UswbQ5hBqHl0iFBJlL/PVh+4gtPXCnH6WmGtVKTKJLWeO3cO06dPx4MPPoiMDLum+tdff+HYsWNlGsTEiROxdu1abN68GY0aNRK2x8TEwGg0Ijc3V7T/tWvXEBMTI+wjreLHXrN9pAQEBCA8PFz0V5tgPR7lcqRyiox464/jov2VhAzp9kvZJTiRlu+yiHoSKJ/4dg8WbjyDP4+keTX+8sAsLBXBD46k1qRTmRX2HbUBXmCsSRZVF0WqgoQYXpHi+wwpVcesCo+UKLSvOnqkfGjxUB6ucyFXVxR6orn3SHFemUp8FpLPZmHgR1sFy7k3aNUqBOnsipQ/mjDzz5M39666EhSbNYeu4j/vbMRX285X+Hd5gvfSsfB8m80mulbVodpbRfPFlvOY/ftx3C+p1ihtTu4Od6F90sgCb57DTzefw/Rfj+LN34/Lvs8rvkrFZqRGgcpeC/l+WWzu6tQoUlaR4qOa/FWMribhsyK1ZcsWdOzYEbt27cIvv/yCwkK7YHro0CG88cYbPh3LZrNh4sSJWL16NTZt2oSmTZuK3u/SpQt0Oh02btwobDt16hRSU1PRo0cPAECPHj1w5MgRQaEDgA0bNiA8PBzt2rXz9fRqBdIcKf4hWLTpjBCuF8Q61SsIGXKepsOXc10mBk8eKSY4/+voq1KRBGqdTaNr4wNfE+AtftWhf4a3+FKkpTzwoX28sCCttslwKlKV6ZHyreJaZePikaogJTPbC0XKXY4UH75VmVW7Pt50BifTC/DCyoNef0ajViFYb59fS/ygPIsUqWoyD7y++igA4J0/T1bxSMSCbEaBATabTdYjV5XV3iqDlXsuAQBSssSVIssX2scXmxC/541neHFyCgDg532XZd/PKXI+80qGFOl8XtlroZwB7KZGkcJ6wsuF/Ehr0prtL3xWpF599VW89dZb2LBhA/R6vbD99ttvFyrpecuECRPw/fffY/ny5QgLC0N6ejrS09NRUmJfcCIiIjB27FhMmTIFmzdvxr59+/D444+jR48euOWWWwAA/fv3R7t27fDII4/g0KFDWL9+PaZPn44JEyYgICDA19OrFUir9vGJwf+ccHr3WP6Ftx4pwD4puBSb8FKgVBIE/YmOs+BXRwGPEFveaoJH6nJOMfq+vxkfbzor2l5RihQzTGjVKqig4rbLf1+gULWvanKkdp7PxrnMivMElwWXfLYKCgnjiwAoNZc2mK2ySq5dMK6a0L6d5+1Fo9K4flee4BWpYpOl3IYqo48eqcqgstcMpWcacPUIZBcZZY2e1T0ktLywe04KH9nn6V6U5uLyXiij2f8KTY7IIyV/T7mEFFby7yg9zzrBOsREBEKvcRjYufuPl93KEsptNFvx5dZzOJVe4HnnaojPitSRI0cwYsQIl+3R0dHIyvLNo/DZZ58hLy8Pffv2RWxsrPC3cuVKYZ8PP/wQd911F0aOHInevXsjJiYGv/zyi/C+RqPB2rVrodFo0KNHDzz88MN49NFH8eabb/p6arUGnTRHqsQkTDShAc5Y3bBApkjJT0IsPCZAq8YjtzQBYLeusomBGYS8VqQqwUPEJ+RLqxUSlcflnGL8tPeSrKDAV6OqCdatr7el4ML1Ymw9LQ7prDCPlOO4Wkm5cyU7RFVU7ZOe+/jv91Xad3tDZVVYLOIs3UqKFCAvTFmsNvBTYnU3KmjUKgQ5hFqbrfzKDy/MluVYV3JLsGjjGVx3c92rO+7OW/re2YxCkXDLPDLV/b4pL0qeJz7U05OhROpB5b3+rgqNfxUppWI80giHyl4LpeGOjeuGAIBsaB/fnqAsrQrWHUvHO3+exPBPk8sy1CrH52ITkZGRSEtLcwnDO3DgABo2bOjTsbyxWAUGBuLTTz/Fp59+qrhPkyZN8Oeff/r03bUZraRqn8VqQ6HBjLBAHUI4605IgP3//ERyPrMQ8VHB0GnUQjiMTqMWvFc5xSZBSIkKCUBWocFtrghvyaiMUG6+FHNusQnxUb59/t8zWUjNLsbo/zT288hqF1NXHUby2eswW214sLv4Woo8UjWg2ISSRdFYQV4OtsAqNeCVEqCtij5S4mf+9DX3HqkSowWFBjPqh1VOFIFUga+oHCl+7sssUBbo80tMqBcqPveK6l+jxMXrRVCrVIiPCi7T5+0eKadIUWy0CN7QsuBrjpSUhf+cxo97L+NyTjHeu/emMo+jKpGet9liFYpFsXurWf0QnM8swvmsIjSua//tdBoVArUaFBjMN7wipVT0Q8NtLzFZBAVADhbaFxagRYHBXOHlz/k+a0phq1JFatIPB5BZYMDYW5vK7u8Pvt52HmsOXcVXj3Z1CXds4pgXmGHObLXBYrVBo1aJPLVlUfj+dvTVqwmGUzl89kg98MADmDp1KtLT06FSqWC1WpGcnIyXXnoJjz76aEWMkfAzrJpNoE4jPBTMOxPMlVNmiyKbSP44nIbbF2zBe+vs8eGsqoxOoxKOw1td64bYPV7uhBR+UqqMpFh+crhe5Lul8oUfD+K11Uew7QwVlCgPyWftSeyr919xeU9Uta+ME+uhS7mKOSn+Rq+VX8grKu/GrOCRUiJAVwXlz2VKv7tT5J5Yuge939uMVK6/UkVSWfls/DnLKULMqCXXlNclyb0CFeFSkwV3LfoXt7232UVQLDFavAq71qhU0KhVgsBa3iIjYkXK93P/ce9l0b81Eel5G2WuSdsYe7GsC1lFwlqt16i5ap2e7+1iY81VuJSKSvARLp7OjckF4Y68cf7ZY/chK/tdXoOGzWYTFZtQmpfZPB/KyWRz1soXr/AXb/1xAocv52HV/ssua2/DOkEAIFJI2f3GN4EuS5Glmt4nzmdF6p133kGbNm0QHx+PwsJCtGvXDr1790bPnj0xffr0ihgjISGzwICNJ66VOQZdzwlg0l5SwTrXYgxsIvn6X3uloq+22RMpWeywVqMWhLXrhfYJIkSvQaDO1QUshX9Ylfrg+BNekSpLrxNmVfal2Z8/OJdZiHnrTrqUqq/pyBkTxTlSvk+wqdeLcc9n2ystTECpWljFhfY5nztvYEaOqip/zriWJ2+4sFpt2HH+OkpMFmw8eU12H3/jbYuH8mC2WD162WMi7OWa5QpOyHmkCkpNmPbLEWw/69/CPLnFJhQ4rMrSvKgub23ApBUHPB5D7RBonQUnyne/+StHyp0nwlcqu+K5dO1k4bn8vdUoyi7gZnLRH3qtWlh/PRWbMJqt6LdgC+78cEu16vnoLWoFRYq/dp7uRfY+K0MuLjZh/z9LdbiSW4J56066zV9zR5HRIpp/lOZllsfJK1Lu9vcnp9MLXK5ZrGOuklOkCjiPVFm8+3yeW2Xkyvsbn2cYvV6Pr776CufPn8fatWvx/fff4+TJk/juu++g0ZTdjU94z/Rfj2Dst3vxzb8pZfo8L4A5e0nZF3JemWH/ZQ+9VKhlHim9Ri2EDzHlJEivlY2llcJPCpUxifNWUqb0SbHZbLJKKv+AF1Zyb5xZa47hs6RzLgUNaiL87yynhJRKcqR8NRjsvpANi9WGzAJDpZTeVrKIVlixCeYJ9rIqlTO0z/65neevY8CHW7HnQnaFjA9wnvugDs4WFFkKHuDKLiSQXWR0yVeqiGIT3pwXE07kekm5KlIW/HrwKn7YnYrRX+/yzyAd8M+JVKkrNlrwx+E0j88hux+ZMa68JdBFfaS8MKjwUwkf3RCiUIygLPhSUtsfSO8h9pq/NxpG2hWprEKj8L5eqxaq7noSvC9eL8LVvFJcyi5R7HVWnVH6SUSNiT0ok8USRUrcR8p+L4VxPfs+SzqHf8tozOAL0ADKxkKWpxUaKFak8txU+SwP/POt06hdnt8YR48urVolPGsGR0XD8nqkgriQ4JpYBKzMppr4+HgMHjwYo0aNQsuWLf05JsID64/Zrba/H7paps/zi0GExCPFTyDM0sOEIulCyie9M6s3U6RCAjRORcptaF/lVmbiJ4fsIldF6lxmITq8sR7vrz8l89nyTRblYZujNPx3Oy9W6vdWBJ6adUoXPXZfpGQVYfDCbVjj4b7njQH5JRU/KStaRCsoR6qsHil2HV/++RBOXSvAlB8PVsj4AOczn9g4Ejc1igCgbLjgBb2KtkYazVb0/3CLixHKFyuqt4q9pzkiSKdBhMOQJXefSr1kpWaLS0ETf8HPi0rJ4kUeFCONI9KBFZwotyLF99DyMbSPV9r5vK3y4ktJbX8gVSDZdeC3x0U4FKkCg3AfB2g1zmqdHpRQvteZ3JpY3dGIikrI5zZ59EiZ7Pe8rEfKcZxwSdPc1OyyhSFLi1wp3dtsLpR6pNy1SygP/POt06qFa8Jo4ig2oVKphDBHIbTPUD7ZiDeCVNT5VSQ+K1IjR47EvHnzXLa/9957uO+++/wyKMI7AsqYyMs38WQTB1OA+IfgSUdSo5JlnYX26TRqTpGyPwRBOo3wsLmbyHkhqjKqinkK7VuxOxVFRgv+l3TO5b0SUchZ1cST10S3txR+4ZCzPknvA7YI/m/zWRxPy8ekH9yHGfG/jZyl398oydUVV/7ctxwpp0Blvy6XsktE/1YETNnTadSo6yiioFQ9jX+uKtqYcr3IgCwZhc7b3+r99SfRbuZ6HLmc53FfT+cSEqBFeJBdSJJryunikTJaRHO3P/vg8b9BtkL4sNSSLkUrhPZpHcf0Y46UF2sDfzmu5pZy2/13nbRcgZfKmIulQrbUI6VRq1DPUaAlr8QkXCdRaJ+HtUpcRr3mKVJ8VINRQfn2lGvrzJFyrVTM7sPwQLEiVVaVWip3KHmkzDKeMKDsa9qhS7kY9mkykhU8adKmumzdrRuix6TbW6BVg1DhfWm0EW98KUs+Ix8mWROrKfusSG3duhWDBw922T5o0CBs3brVL4MivKOsxjE915SWTQ7MS8AejPfv7YQW0aGibdL1iIUYadUqQVhji0uwXuMMKfKy2ERlVBXjvUpyAlVKlrKVibdqeXKve7N422w2XM4pFu2bklWEoR//i5V7UmU/cyMoUnwir9x1dEl2d7w+csWz8AqIf6fKCBNQ8mZUWLEJhap9zLssReqRqgz4ip71Qu1el+sKgjj/e1f076W0SHurSH26+RxKTBb8oPB88niyzIYEaIT5Vza0z6VamFUwTrHX/kJUhEfBc+jJW8GKGDGPVFn6yfDIFVbwFt5bUOrH+55fcytjvZI+s+xZERQmjVoQtPNLnRVz9Rq1sCZ7kyPFKK8XsSrgvRlOWcXmVR4SwyVHirsmbG6QKjRlLYTkqkjJH0fpe8saZTH/71M4dCkXU1cdln2f9wQVGSzCvfDCna0wpX9rUXVEaVPeQpEi5fvzxvf1G/rJvzh0KdfnY1QlPitShYWFoka8DJ1Oh/z8yk3Ar+2UVajmkwXZQ8osouzBqBuqF/pNsQnJBvnQPr1WLbKUAnarpDc5UpVpjTaaraLJNSPftdkkH/YoVYb4RcZT0Qdvci6Wbr+AW+dtFoUZrdidiiNX8jB11RGPn6+p8AKSnCIlXaDYQqOUNG40W/HMd/vwwd+nXD5fGZ5DJYWpsvtIffpQZ9n9nVX7Kr/YhJ7zSCmV/xaF91awV1pZkfJtLr3qRUVIT9c7RK8V5t9Cuap9kvunxGQRCTP+zP8rEeWOyv9OnoxHbO4Mc4QilaWfDA+/bnijNPLTNS+I+XMO4EP7KqNBqpwyDQBGR25KgM6pSBUazILX2b4me1e1j5+Py1sgpCrgf3cmQ7j0YDK6vwZszWCGDblCJ9IQO2+VTqkc4RraJz82Jt/pJSHcZfVIsfQAaR7cH4fTsDQ5BWOW7Ba2lZgswjUJkol84kP7zBar6BzKEtpnkvxen2yuWbngPitSHTt2FDXMZaxYsQLt2rXzy6AIZfgkWulk4S0B3IMZLihSYo+UXuPMcXLmSDmPYbHaROE70jDDIL3Gy2IT5XsAfUG6SEirUwFi4VQq0PMTZ/LZ61i5J1WY7KSl273Jufh8iz188K0/TgjbDtQwS0xZ4H/nglKT6NrZbDYXwaeUs77KsedCNtYdS8eiTWdRarKIfufKCBdVDH2tqD5SCjlS7RxlkKVIi01UBuz+12lVQhsERY8Up3AwAbGiUApd8mbu4e+rYC8KGHi63qEBWkE4k8sblI6p2GgRogDYa38hMhIpKEyeBDimZLBcEun+l7KLsfP8da/HxD9XnrwqNptN1ERVqkj5K7yPn+Yrw0jjGtrn8EhxZc6Z8G+zOb0dvoT2+RICVx3h11r2zJglc6+3oX3Mq8/fe+z/zCAl/YwUqYFbKqdJw3g9eaSkfd3kWiWUlYz8UkxYvh+zfj+Oa/lOA0qpySKMS26u42U7qee5LFX7pBUQM9z03KuO+JyFOWPGDNxzzz04d+4cbr/9dgDAxo0b8cMPP+Cnn37y+wAJMfxNKp0svEXH9b0JE0L7HB4pruoP80iZrTZYrTZRXwa7d8cZ2icVcoO9VqQq1iOVV2yCTmtvFFksidnPKjSIGhwC4njrIoNFlKjMC1JGixVTVx1BVEgA7mzXQLSIA45z9tBblJ+4AHsOwu6UbOG1zWaDSqVyWUzZ9poKv0hZbfbE8K+3paBz4zq4vU20IKxEBOmQV2IShCgd9zuZLFbhNd8v6lp+aaV7pJQWjooyDPhetc8//U98gZ27PbTPfY4U33S5oo0pUmuwVq2C2WrzynvIh694I5ez+SwsUCurKIUEaJzzr4z3RuolKzVZRB7zilKklDxPnjxSOocRKixQXjl8fOkenM0oxO8Tb0VHRwESd/gSmmWx2kS/yan0AuH/Vpv9GQ3Qlr96n0i5qxRFSt4jxbYH6OzRIDqNCiaLTQhXD9A6Q/s8KRG+FGWojvDGBWaIkc7JXitSDiOA1Qah2Sz7zXUSGUfJ4yqdS4xmq+iz7LmoE6xDTrFJuY+UYyG8+6Y4XM0txar99n5o/izGkC4TlQPY7wO27gbKKFLsWTKarSiSeMbL5JGSzHVfPdLF52NUJT57pIYOHYpff/0VZ8+exbPPPosXX3wRly9fxj///IPhw4dXwBAJHv6hK2toH7+gSBc9g0iR4qrhWK0SF7pFUOT0WrWLtSZYzxWbcGNNFIf2+XcSz8gvxX/m/oN7P9sBwBmzHxaohVatgtVm773Bw49BGjojF0qzPzUHgKtSW5bJ5PVfxeF8bNGUhv3kl5qRllfzytQypAvN9zsu4sut5/HM9/vw7l8nhe2sxxkTtHlvoahoCOfpyCo0iN6rDAurUab5LFAZfaR8U6Q8PV8n0vIVlR1f4YUPaWVQKSKPVAUrUtIxsHweb34rX3O52LlEhbiGwgP25uehgcrFJuRC+9zNT+WBF6CVBDVPihTLkRLyviT7n80oBABsOZ3h1ZjESov730cqiEkVU38ZEXwZkz+QetTZPcg33lWpVIJCfl2kSHlnQKmoHKn0vFJ8vPGMbAi9P+Fzbtm5Sj0cBpl1YNaaYxj91U4UGcxCaCtfmY/91uzekhqLldYWOUWKh3mU6juKhCj2kWL55gFaLBh1E8Y6in/5o4AS89Aq5TGWcJEdsqF9jjVlzh8n8PA34lYMZcqRkhiiox2l1msKZaoLOmTIEAwZMsTfYyFkyCsx4djVPPRoVhcqlUr0UPonR0pSbILLbRB7AGySijhWYV+tWuWSIxWk0wqKmDvhiJ/g/B2GtftCNkpNVhxPy0ehwSwIHqEBWoQH6nAltwRpeaWIdZSPBcSTgHSSkZs42Zg9TZ7e8OeRdNHrglITgvQaF6Ht6e/2Ys+FHKwYdwu6JUT5/D1VjfTaHL3qzK1cnGzPF1OpnAIZE7R5Rb7IYBash/zvkllgkHikqi60r8L7SHlZ/tybMsjnMgsxZNE2NI4KRtLL/y33GHnhg/1OiooUH96rcM32XMhGy+hQRAbLKyXecPhyLhb8fVq0LVivQUGp2ascKV6J8WauYvuHBmih16hdzi1AqxbyiWSrV8qE9vHzrJz3IKfIiDl/HMeIxIa4rWV9t+MrMpgx49ejyC42onWDMGG7UuiQtzlSzkqE8sfx1rhh9MH74ymcyGCyAEHyxVi8xaWAQaUUmxB/R3aREc98t0+4hqxwVFigFtlFRlx3lH3Xa9UI1IqrdSp/R8V42eb/fQo/77uMY1fz8XkFehhMVtf5QxpO99YfJ6DTqPFYzwQA9nt/6fYLAIBNJzNcPFKAff4O1GlE3nUe5ZA88XdLn2NmNKkXGoDT1woVi6EwZZBFHrCxlaXYhDS01WC2n5uSMabEZBHmODlFiinpJ9Jc6yKURfYpa3RVdcFnj9SlS5dw+fJl4fXu3bsxefJkfPnll34dGGFn2i+HMfqrXfjjSBoAyeJSxolcrthEvpvQPsCeUM8/dAaTVZSrIVWkAnVqr/pI8Yu2v0P7+NC5nCKjMFkG6zVoEG63Bl2T5EnxApK0fK+ctY55tFwnz/IvSMyqKhVIdp63N5xdmnyh3N9RFUjvh9PXClz2CdRquIaSLJxF3hrPC+JZhUaRgCld7FKyivwekqOcI1XBHilvQ/uEYhPK49l8MgNWG3DherFf8kl4jxRTiJUVKfceqX0Xc3Df5zvwwJc7yzWm51ccdPlNWOiuN7+VuOed53uIn0uDZMJjdGq1S0SAlQsz5EOn7d9vEf2GcvPR4uQU/LL/Ch5bvNvlPSmbTmbglwNXkHQqE19sPS9sV/JIeRLgWI5UmEwlQksZcntF5c/NVrf3pdQDIR2TPwwq0nFXRWjf+mPpWHcsHVsc/cTYusvuI9ZoWlS1zwcl1J8eqZ/32eXEdcfSPexZPuTK5MvNybN+Pyb8n4/oOJGW71K1z34M++8tGJclMo5SGKQno2qBxCMlp+harTYhxJ2lHoRLZDVfkP6uzHCj1Buu1MgVm5CZu8IClY0SZQvtc37mqdua+vz5qsZnRWr06NHYvHkzACA9PR39+vXD7t278frrr+PNN9/0+wBrG9fySzHlx4NCnxLmpWBCs9EP1iP5qn3iYhMBWjU0apWwEJksVrGl32wRVeaSxp8H6jTOEpkKD9akHw6IGt8aLVaXog3lgQ+Jyy02CcJ3SIBW8EJJC06IhXXlYhOMvBITNp/MwIs/HZIcx7fJRG7iZ+NXisX2NrSruiFVOqVVhAC7Ih4gSZZW8hbyz0FeiUmsSHG/567z1/Hf+UmY/uvRcp6BGOn9HSyEi1VusQklnMUmlOcLPjfQH0KnUGxCoxKEk0KDWfb5Nkg83VJY4/GT6a4Kt7dYrTakZBW5bGfKujel6kt99HTy3n05oUujUQmhfYWlZthsNoz4XzLuWLAFRQazcF8JnlejOLRPzrNzzOHd9WYaVRLIlBReT7kZTOGr4wjlzOTKqPPXzts53mR2b9kX7SvzrGnUznvPH94jqdW8MgrZSM9Z2gSWreVhAeLQPn0ZQ/uqqj9ieeB/F8EjJXM/8Hp4BmdkPXIlT8jzCQvQimQeQNzKgUfJsypdD6QFdJhHqn6oOLTv8OVcfLDhNIoMZpHS7lLEpQw5UtJnutixfhYryBaeQvvCA5WD2crUR8pxvq8NboPXBrf1+fNVjc+K1NGjR9G9e3cAwI8//oiOHTti+/btWLZsGZYuXerv8dU6PvrnDH7Zf8Ul7tTqmAX8kRjKx/pKi02wh4BN0Cw8z2C2upQp5sswS601eq3aY7GJNQ4BicefVnzea5FbYhSEb7tHyh6DK022dGfxLZFxg+eVmPDuXyex1WEhZPh6HnLNgZnVSCkfQ+oFrCko3Q98/YxAncYlWZoXXHjlkl/8c4uNKFYQeP9v50UATktpWdl8MkNUFESqBAuKVEUXm/C6Ia9TIZVee+Yp4Ms6+6OXE98agYV62Wyeq9PJ3RvS+PmyUKgQwhLsQ46UWOHzPPey+zVAp5GdD3RqlbNqn8GM60VGHLqch9TsYpzNKHQ2AuVCWMUec9cx+GJaUVo/fFWwGOwealbf3n/w7LUCwYvEP6PeTo3S38SdkC/3+4XoNQh0zJH+KKLg0iC5CjxS0mIpbA1gz5iz2ITGpY9UidGCw5dzXTx7IpmiBipSvBLNvDue5gx+bd93MUcwPAQHuKYk8Eah/7Z2hsuWKCio0nvRNbRP4pFyvD911REs2ngGH244LRo/G4+z55zv87P02fXkkSrmPFKBMopUhJsw2bKF9tk/06xeaI0spOWzJGYymRAQYL8B/vnnH9x9990AgDZt2iAtLc2/o6tlnM8sxA+77Y0epTc+sxjzC3hZLce8AB7O9aAwWazChMKULbnKaGwcovLnEqE+QKt2FpuQWeSU8rv8Gd7HT5ZFBosweYQGaBEX6VCkXDxSytY5OY9UfokJp2RC0zxNJtL3c4qcv3di40gAzglXSbAta/l7bzmbUYjpvx7xqmeOLygJrW258t2BOtfQPt6qXKQQvpdbbBJVgePDJvL80DH9eqEBY7/dg1Ff7OB6r4l/BxYuVuHFJtTeTd+s2IHJYnO539l9yI/VH0UMmDdB5/BWM2VOTkjnv1tO4eDPs6zXVOm3Dw5gv5XnZ6k8Him5HmgxEUEIYaGFZqvI0pxfanJVpIwWj3lD0n4s7lBSBEoluRGN6ti99x5zpBwCX9N6IdBr1CgyWnAp2z53GETCuvj+slptOH4132VNcFVclK+53H0TEqAVKo75Q+mRhg/ySkdGfim+2HJOVPjGH0gVdula4Azts98jQmif1jW0b/yyfbj7k2T8dVQcalcRHinp+laRPezk5g9PzzP/2/HrerBOw/XPFM+Neq0abw7rgPu7xgMQVxvlka7LnkL72DVn+UZ7LuaIxs/mP396pJgHTuqR+uv52wCIn9eQABmPlBtFqiwynK8FlKobPitS7du3x+eff45t27Zhw4YNGDhwIADg6tWrqFu3rt8HWJtoIKlUwluOmCIlst5arIqx4VL4Y+lkik1YbWJrF1v4mTIkzU8wmJ3frZPxSAVoNUIi7KFLudh7IVv0vpKi4c8Jly/LWWIyI8vRm6BuSABiIuzXWlr9Tim071J2sWJonxyeFCmphZRPEg6XeAmVkrbL2/DSEx/+cxrf70wVhV/6A7bY9WklToZv1SBU+L+46pQnj5Rze06xSbRI8v/31oPjjrMZhYKxgYUkKnmkKipHij133lft0wjhVheui8PbpH1pAP97pAC4LTjhqZkj/7sVK1SZUuLi9SIsSU4RBEwpzJBUETlSQv8ZrRov9GsFAHh5QGu8PaID/tu6Pu7vFo9gTkjh+2zZw/jEoX1Gi7hni5yXxcJZsj3lunnyPtzRNhq7XrsDH95/MwBv+kg5jW+tYuzP8ol0u3DIC+jSIj7rjqVj8KJtePHHg6Ltvnik5EK5QgK0QsEFpYR+X3AtR+885sebzmLuXycxm8vD8Qeewgf1khwpfjtThNm6lXTKHjWxbNdF0b6ihrx+UqRcPCBl7H30WdI5zFpzzK2cw7+n1EdK+r7cvRSs10DNtXIRcqTMzsI58VHBGNXNrkh5Hdonec3m13pCaJ9VNJ+EB2pFRgVpERe/KFIKHqnoMHHPFv4+4gmXyZHq7DAAl8kj5WMBpeqGz6OeN28evvjiC/Tt2xcPPvggbrrpJgDAmjVrhJA/omyEBGixZEw3APZYXf5BZd5Ol07nXt60/CLA5zMF6tTCg8oLG87QPvlbpNTkzJHSadQupUH5GO3LOSW49/MduMDlKCgJIv6MOy+ReKTY+dUN1SNWUKSUi02wBejHvZdw23ubhSo/93ZphCl32gUjXxQpm80mWCylvReYRypQK+5Uz/8rpaIbrP5x2O5hXn3gil+Py65NoE6NYTfHCdsT6oUI/w/Sa1wsqgaRR4pXkvkcKaO4/Dn3fw3n2ShrmW3+ezMdirlyjlTZf59Cgxmp14tl32MWT2+LTQBOI81FF0XKPkaDG0G3LBgt4oUxMsjuFZMLYTXJCEI8/GVUCtFTYsZvxzD79+P4YMNp2feFHBovBEhfe945q32p8ORtTfHX87fh2b7N8dB/mmDJ490RFaKHXuOcf/nS8yUmp8efz0fghSg5QY4XID2NscTo/v1AnT0EOtJD1UUGfz+yHFTWWLNUwQgCAJ9uPgsA+PWgONTbRXFxo7zKhvYFaIVEeX+E9rlT7L5zhA1Lz6G8eFLwA4SqfWLBVq9RO8PXJWtcWq68VxrwXx8paePrshhnUq8XY966k1i6/QK2nslU3M8kc89L+zoy2D0k97yHOLzTSh4ptj3IQ38ul2IT3GuL1SZcC8EjZbaI8oRVKpWgHKpVgFotDe0z+VwQSOqRZ2OQpitIq6JGBetlQ+2a1HU2Cf784c74dHRnDLu5IYAylj/3sYBSdcNnRapv377IyspCVlYWFi9eLGwfN24cPv/8c78OrjbSvqE9vKnAYBYtXOyxkYbJeTvx8Q8zH4anUqkENy1LVFWrnDd0qEJSocFsFcJItGo1tJxAwL6Dxf8zzjj6iADKYRrlUQ4sVhv+PJKGa468pyKJQM2UptiIQGGhv5ZfKkp+5idYNsn8z7HQM9rEhOGp25oJ3ymH3AL49/FrSJyzAV9sOecSPsUEzECdxqlIOax4Shaoykh2rgj4henum+yKVPu4cFG/HXHVPlevSbFCsYncYpP4NzTxipRzDGUNX+OvOfMUShfOEB/CxeSw2WwY/dVO9J2/WehTxuNrsQnAuWinZImVM3Y+okIe/gjt47zVgN14AdjLN687mmbPA3DsI/WySxEVWPBR0GO5i9vOZAEA6oXqsfCBm4X3mVXYG0Gv1Cy+5zwJM7wyqdOo0TY23EUoUalUguKdJSnOwK4hLySLvAcy14I/vKdr5cn7ECDxJuaXmNwWiuDz7Nj9xqIA+GsnvdZyl9FitQlza4heHOIrh9x9ExrgOoeUBxdFqjLKnzvOWSm5nxkwpe8H6NSIdYSvp2QViQRpqWdRHNrnnzVFmsulFFXhDr6a6+lrhYr7icqfe/BIsftA7t5n95lOKy42IRhEHM8DU86VQvuk8z5/ffl7n809NhtwPtNp4MorNjplK26OZ3KayWLDKz8fRrYPYaRSI0hBqb0dDP9739ulETRqcXRRSy5KhKdni3poVi8EHRtG4M52MRjSKdbrfoVysN9QV0Pzvss0apvNhn379uGLL75AQYH9Ztfr9QgODvbwScITvBv1OrewMsHPxSPl5QLBW5yl3iOX0qlatbDgKyUVGswWZzUbx8TDP4ABWrVLHC2vECg9bOVZ8DYcv4Znl+3HmCV7AIjjf4uNFlx1hPE1rBOE+mEBUKvsk1JWkdMSLFdsQlpVLlivtZd3dyPIylnWv3KUGJ7710kUSqz+zFMVqNMICiibdHMci5I0fKMi484rEr7a4+1tovHVo12x5PFuImtYkF6DAJfy5/ILEu+VzeGqMwJiYZKXg5SSbD3Bj4GFXkoFLKESXBk9UjnFJhy+nAerDVgjY+EWwiB8sN4xwfZSjkSRkgnt80fIqLSJJVOSswqNeOb7/Vi48YyQq+HJI2Xwo8W8Y8MIdGgYIbyu57gu3pwzL3RYbZ5zFFmemFx+FA973sXzvbMwSIBCeI2cMMgbdjwpSp7mWuYRZvO41eZeyeZDMKMczzIT4EShfZJjyKX68fcEUyTd9UOSK+wS4pinAf+ErLmWP694QxZ7PpVyUlhlU5fQPo0aTaKCERsRCLPVhp/3OwvsuOQvedmvy2azeV162x+K1GVurroiU9kVsN/vvCLOrpdSKCBTTOV+O5bbqpOE9vHrFeDqkbLZbLh4vUgwMrgrf87WLb3GWYQHAM5kOJXGvBITLDIemhC9RhjDT/suuxh43ZEr8RBuOpmBjrP+xsq9lwAArw9ui/n33SQ6PwAY2ikOcoQGaLHxxT74bUIvwYAibbPx6eaz6Pb2PzjlRbVVpvjqvMz7rW74POqLFy+iY8eOGDZsGCZMmIDMTLvFb968eXjppZf8PsDaBl8hhYUOAU4LvHQS9LRA2Gw2LNt1Efsv5grb1BIBjClLfA8KRqSSImWyCgsLu/l5T1eATuMSR8uH9Sh5nsqjHGw8cQ2AM2mTD/EqNpmFyTguMgg6jVoQLlnog81mkyRF2wtUSBfQYL3G4cnzrQTo2UynVU2a5JkteKTUCHWUsmV9pHId7/WWNNisjIW8IuD766hUKtzZrgGiwwJF95o9tM+ZI2W2WEVCoqiPFPc7ZxUaRKWf+XtA1C+ljMqCQcayrhTaV9bwwUtcieOT6a4ND9n9qPFh0WH3ujRcUAjt48Mmy6lI8d4EJpQw6ysfvsZCfaWNvqUYRcYN+9hsNhs2nrgmPBveEhGkE5rgAs6cAG9yOKTPtOf+PPb3PcX9s4IXWS6hfU6vXrBMLxc5pdKX5qqevLLs+QvUaQRlkLdsH5B4S/mQ8VBJWw1xfqN4XCqZWoNiRcp+LHdh7HJKbWiA1uteSt4gfZ7/PpbudY5yWWG/p1LfHrZWS98P0NqjRIY6PP5Hr+QJ70mvladKkIxPNp3FTbP/RvLZLI/jVsrJ8QU+7F4ags9QUlqMXGgcD3uG5c6TGTT0CqF9eofBmHmkzI6eb0u3X0Cf95Pw9p8nRN8tjMnC3/usBYtdKWIe5AOpucI+eSUmwUPDK1IqlUp4JgHXUvjukP4eG45fE62ngdz8witSjaKCFI+pUqlEsqTQZsNxP72//hQyCwz4kutRp0StKzbx/PPPo2vXrsjJyUFQkPMijxgxAhs3bvTr4GojWrVKePgzCpyTBxPapIKGJwvt7pRsvL76KCatOKC4D/MEsJA4PbcgKnmkSk0WlzwI3vKq16hdrGQiRUpBCSiPcsBPjgazRSQoXC80Cp6duEj7fcvC+646YsalE2CJ0YKUTHFOCeCcSOUSLhlyQjSvoEo9IiKPlEQIYdetX7todG8aJTrHiqSi4pWNFrGQzajDe6S4qn0lkkakAEQePXchNnwpdFEeUBk9G/z9ycrQSu+boHJW7eOFhrMZrvcfX+TFW1jPEuniK3ik3Ai6vsKfNwvVqOvwSPFeF7aM81UP5T1SvEHE/v+/jqZj7Ld7Mf77/T6NLTxIJ7Lut4i2h64UGS0e+xtJ5yZPc5XglfPgkWIhRdd5z7jJKtzzfAU2HjlhkP8dPY1PqXwzI0BmHeAFsuclawo/RqdX3dUjJQ3tk5tm+PAoto7ItZ9gyIX2BQdoPOaz+IJUATmZXuC3QjwmixVbTme6rOfsHlAyaLJrLldsAgAaOta687wRz2gRF2jwstjE4uQU2GzAS5KeiXLkKpTb9oUC7jPp+fIeKSVFink4OjaMwNLHuwnvs2dCTm5ihV/YusSuC/sNWPU8XtEoMVkEz/o3/6aIvpshF0kREqC1K0aOZ2znuevCPvmlZpe8LMaIxIai784vNeHrbecF2U0J5iEMC5A3/gZycxTfgJcZwLxBCO2T/CbnMpXDMhm+tvSobvisSG3btg3Tp0+HXi9OSktISMCVK/5NSq+N2K0O9htZ5JEyls0jxfe7UYJN0tccTep4z1JEsFJon9Wlehi/8AboXEP7+JheJeG3PKE7/ISVVSguOnDWkZ8VFqAVFCBWcOJ8VqHL5wH7NWfv8bCSxWE+lgDlJ0XXHClWbEIjTHaFjjAKNgnWDQnAynG3YNX4HgAq3iPF5zz40/KqtEhEcvdasKjYhFXmt5HvIyVFVApdxrPhKyKPlJAjJV44w3yoBCcHP7asQoPL+QnFJnxRpByeF+l8IZsjVU6PlEiRcowxypEjdTnXqcixkByxR8q9l6XEofCwXmA7zl/H5lMZHsfBCA/UIVCnwY9P98BPz/QQBE3Ac26YNLTMkyFD8Lx68kjpxT2AAHHDc51GLRJuhH3kFCmzRfb/cijleDB467dUkTKYLcgsEHsD+XVDWjCn1I3Hk88bm7BsPz74+5SzRyHXVDe/RPn3kQ3t4zxSqdeLyz2Hyd1PX3hhbfeG3w9dxWOLd+OJpXtE29k9x6rMSpGWP2dIq2VelXh0+F5ERjc5iJeyizFx+X5sP5clrFGeio4A9jwfnrIoUvx9Ii2YwZDOvWyu4KvA9W0dLRRHYM8M+5dXLJzFJsQVkqUVSHUalbA2lhgtopL3NpvNfWhfqbMFC+AMh+OVRovVJuS0aSRWhqmD2uCeznZlKqfYiO92XMRbf5zAnLXHZa8Pg/1mjaLk02/4+YU3oNYN0cvtLovTIyW+h7zJFHYWm6gloX1WqxUWi+sEfPnyZYSFhfllULUdNvlncIpUkdHe+d7oY3iJN73NWGlkp0fKdQEFgLtvisN9XRoBYIqUOA/CJUcqUCtajHlrtNQjJYRxlcPLwltlMvJLRYrUOYciFccJTj1b1AMAfLv9Aoxmq8uYio0WURIog/VVcNfdW06I5gUNaaIo8zoF6NSCR8qZI2V/r46jgg4TvCq6ah8/oZYlxl0Jk1m8MDF4RUrNhTEYzBYXobDIYMH5zEL8dvCKbFl6RjHXs0apWIUviItNmERhbAx2X7AcGV+ResukfbzYc1eW0D4pcqF9vlbGk8ILDizsl3nELnKhhRaba06ByWK/nlarDUcu58FqtYlDj4wWPLtsPzaddCpPjy/ZI2v1lJsbmYDfvWkUuiVE2UOgHPe5J2FPejxPhgypx14JNp/wYY98w3O91tW7Dyh4pHwoHMA+ryQsyXmkmDIj57UM4OZ6wSMlE9pX7BA+/zyShkcX70YGN2//cSQNizadFfbXadTO73aTnyNX2CVU76zat2LPJcz+3b2w6Qk5RUqvVbt4MssSKbByjz1XZcf566Lt7PeMVVKkdPLFJvQalt9m384bZQGIQmKlobXsfI5eycPcv05g7eE0TPvliLCPN+Ybf3ik+Hssq9Aoe12lyrFQtU8SKiaUwRdypOzHasBdV+YZllZ6NEoUKZVK5fR0Gi1CaC4g9iYxjDJGKqa0BWrFCgyTpVgrBOncEazX4oFujR3HsuBbRyXhtYfd93BlxtjGCqF6/Dj4eUVaxc8d7F6UGvu9qTBokhjlaxo+K1L9+/fHRx99JLxWqVQoLCzEG2+8gcGDB/tzbLUW5mblJz+bzX6D+9pdXZoPJYdLaJ9CjlTf1vUFId9gdo7F6ZESK1IqlQpzhnUQBBVeeZBOinxPhbLCW60yCgwiCzOz+DSs45xI7uvSCOGBWlzLN+BEWr7LmEpMZlG4DYMJCe6a0smFKPHWpXSJK16ual9BqV15ZpZApmgoVcfxVzNFBn+veZtk7Mtx9ZJJk6/yaDBbhcm9xGhxUXKLDGa88vNhPL/ioKDkhXjIIzHKhIj5irQYiZxwJZTU5r7vzLUCr0IcANf8LWkz7LIUm5D2B2Gwe4gXRMuqZDJMXKI0m39YKWa+cAu7X+UEjy+2nsfQT/7F/5LOuoT2rTsmbigKQNbgITeXSJ9ZlUolCDWe8qRcQ/vcXydpMR4lmGHkumR+5D1acjkyHnOkPAj0TGBSmsd4IxgT1FnBILlrJcqRclxTNu9Kr1Xf+Ul4f/0pbD2d6eItAYBMh1Kp0ziryrrzhJhlyl3zHinAWaK8rMgpa5FBOpfrXJaeSUqRJYIiFSkvACuVPw8UFCz535ZXbKQyhcFsRdKpDNz18b/484j9WeMNIN7IFNJiE2W5JlLPZUa+61osbUAt55ECAGlPQna9eQWVzQPSYk9ynuVAPuyc++2uFxpc+0hx15cdk30H/4zVCdELEUDMqCKnWDDDS6HB7JWhHHDKXs3qy1fh458Tfg6ResTcwa6P1MDrTaV2sySntqbh86jnz5+P5ORktGvXDqWlpRg9erQQ1jdv3ryKGGOtQy60D7A/ONLJXNR81GhxaTzpTYlsp0fKWbVPeI+zVtYJ0YsSCs2SXBexImXf776u8Vj25H8AAHsv5gjJ7tKHjbneyxrLbrJYhcUXsIckyD3AcZHOiTNQp0Hb2HAA9hKxcqF9zCrGhwAxZdLXHCleYJQuCqyPVJCkal+JySlQsd+C3R/8b/vnkTS0nbkOix1x2uXFZLGK7jW/eqQULPV8iI9G7UyALTW75kgVG83Ye1Gc7C4X/lKiENrnLt/CHdLqY3Kex/ph9nEwRbjUZMHwT5Mx6KNtLv085PDaI+VTaJ+8RdtpwfV/aB//+0qbjQPKlUiNZqvQW2j+36dF1zirwFWYAuT7U7nzSPG4axbMIzVcuPMIZxcZhXw0T6F9TDDihU+7R8o5v8p7pGTyyUQ5gR48Uo77TMmzzgtX0mtUYHC9VnxBjFBJCwdpoYi8EhNSslyVXwZT2PRatVe/j9x8Gxqgla12WFbkwgcDdGoXj3h5G1pn5Jfi4a934beDV4R7rmGkp9A+SflzrbjiopTrhUYM++RfTFi2XzZd4Fc3vQO9Ea7Z8xjjeO7Lck2k4ddyBSekv4lR6pESKso51kyhIa/93+gw3iNlv4YhEkVKLtcxSO+sBslXI75eZHRb/rzIRZFy3p91gnVc4S+jaPw87LNFBrNXSorNZhPkwr6t6svmJPIKXVmLJAVwEST8emLzENxnszmjOmpNH6n4+HgcOnQIr7/+Ol544QUkJibi3XffxYEDBxAdHV0RY6x1sIdeKih+vzPVddLjmiqO+24ver+3WVSZy5vwLyagMyWGnzAaczG1UcF6YeIWx/DLlz9nsB4yADBzzVH757kFf+49HYXQH3clbt2RWWAQTSpKi3RC3RDRaxbql55f6iJ4lBgtwmTKK2DM+ueuap/cZMRbtKUx3+x7AnVOoSmv2CR4o3QaleBxYdfWaHGGYXzzrz0R+E0PsdLeIhUOytJNXQmjm7LQLR3J/50aRYrCMaRCsdzCzCtSzDhQzPX7EecBeX+fHbuahz0Xsl2OUWy0yApXDcLt97LFakOR0YJL2cUoMto9uGe98EpJlbzsIvG1l1bL9IbwQK3s9RYsuNziX15B0CiZFwB7/yap9bRYEjrjHJNFNAaDm+eGIXd/yoUCyXl2mKdXakWXIvVIKc1VVqsN9/wvGbsc+ameik0wj5TouzjjgU6jllV25HOk3OebyX1eyUodwo1LqszIeRh4xVnwSJXKe6Q8wTzgfGifO0VKzltk90j5z8LNvBytGoQK90ypyeriGXSXy6UE/2h8v/Mi/j2bhedXHBTu/aiQANzSzF5o6M52DYR9mSAeqNOIFHYm1Ep7OTK2nM7Eoct5+ONImkt7jxKTxW1FOI0XbhCmSDH5oSxzivQzaXmuBSeknkhn+XNxHyal5u5srgac80AYp6gA8h4poT+Z0SK6L68XGtyH9jnuFWY8CRApUnrhXmeRMHIeGqboFRstin0seYqNzrmkQ8MIfPlIV0wf0hbt48KFfXiFrsyKlNaprLoLt5fCP7u+9EasTvg0apPJhObNm+PMmTN46KGH8N577+F///sfnnzySVEFP6J8BCgsvIs2nnFZkHgPzrYzWSg2WrDBUQYc8C5eWxoHy08YLaJDEaizl+lsVj/EaXUwiRvy8v8CYuGBt0bvvZDjGJf9Ye3XtgEe7N7Y6SovY7GJS5KJ/+J1+YWAeaCkY0vPK3UJ0SgxWQSB4T9N6wrbhdA+dx4pmd4c/PGloX2MQJ0GjeoEQ62yh8WccpS/juQ6jAfIuOHPcM0L/YGLcOBHj5S73JF37umIaYPa4N7OjZw9YIyuHimptxYAGnDWRWYcsNm4PlQKjXo9MfqrXbjv8x0OryUXZmZ09RAD9jBVpkTkl5hEvZsy8kux+sBl/C/prGKVOKlHStoDpCzFJlQqlWx4n+CRsooVxPIgTc4G7AuktAKUkIMg7Wvj5vVVTpiaeVc7PHJLEwDyipRcaJ9c9TM2/0nzOlyOJw2lVZhbs4oMuMDNPx5zpGRCUg0mqyg3w5vQPrPFKqosx+YdpRwF9gzc3y0ebWPD8eStTcXjCnBVpNi9KBVyeaEMcM6RJSa7sMd+C7ky7nKw31OnUXsV2icXYhvCVe1jeCN4Kn+H/bN1gvX49dleAOy/gXQuKShLGDSnnBxPc7Y8YPNNgFaNzx/ugj8n3SbkKbPtDN4rxbaHKngb+dL10ue91GRRLDcOeBfal+3wqMQzRcrD+mGz2Vzyndi4mte3Gz/ljChGs7z3R1oFjjcA8/vxcyKbB0IkRgBhvdK6KlIFBrNovs4qNLrci3ItM9h38L+fSJFiHim50D7OwMGnLyjl6mULFYHVCNZr0K9dAzx5WzN0iHP20+MVKU+GHyWcKQdiw6dSc2ThfW7tqRVV+3Q6HUpL3ZdZJMqPOytapiR0T26x5C3aXnmkJJX59KLJWYdV43ti/eTeCAvUiawOzhwA+/68C5ePlw8L1GFMzwQAQCNHjhITavleJUDZi00cupwres2q9PEEaNXomlBHtI1ZpDILDLLFJthEldg4Ep891Bl/PX+b8L67HCl2rO1ns9D+jfWYt+6k6PgsH00qXAbq7NXqmJV453m7VZsXAPlSpez3l7Nqe0tusdFFEZOGVfgzR0p63/B0S4jC032aQ61WCUm/fLEJdm9KFTu1ytlcFbBfV3ZvsWst6iPlZWifyWIVBLhT6fle5UjVDdWLrOi8xfd6kREvrDyE99adwj5JHx7huI7FVhBepR4prqKZL8gVnGDPoUkmjr+sGDlPCo/0XmeCkjvBQ/qaCVPN64fgiVubCsKjnKIv5wWRC/9kz5annlTeFpuQeju97SMlOjbX8FyvFXukhIphfJipwYz3JKW4DWYrliSnoPs7G0V9hGw2G/51GN0Au4Hsr+dvw0sDWos+z3sz2L3DDBjSe2TO8A6i17wSVmQ0C9duTM8EjOraCJ5gv6dOo/LSI6UQ2idR3MparZP/Dr3WWUWxxGSRMTqVb67k118W4hWgVSMyWI92ceGi34Uv8CFWpOzjC1FYFy4oGBoBu9LD5sxdr93hYoDxNO3YbDahLyIL2Xc3p9hsNgz/NBkDF26T7WfHWhQc5u5hhtQjxeZ4k6QKHF8Blt8vmjPysvuML/ZkL/ClnCOVUSAN0Xcf2iet2icK7QvRuSpSMlEHgTq18Bvwc9Dvh69inyOK6Zt/U9DhjfXYeOKaENZXNyRAFD4fGeKUKfiiYk/0shtUnu3b3OW73SFEypitovBwj7mkvEeqtlTtmzBhAubNmwez2X8WakIM/3AN6RQrek9qiWeJ6PzNKOr07VWOlNgjJVXk2sdFoFWDMNF7BrPFJemd/16pVWNkZ/viyVz+LGaeTfjSic4Xks9m4Z0/TwKwVxYEnB4ffnHp2DBCpOABzhjpjIJSYRIP4ar28BakQR1jRR4tb6r2rdhzCRarDZ8lnRMpiUyIqRcqufaO69Ymxn69dzqqOPG/kVbjrDTGBPvylCp/5vt9GPDRVhzmlFGplbIicqQCPAiY4tA++2fcVRjjq/4FOTx7AJByvcil2bK3oX38dbBYxYtCscHpKQsL1OLxXgl48c5WCNY7S+znl5hE3tIjl53CgDT3SRibUZyXJ/VImSRhK95Sn1NkmHAiF9rnLkfqck4x/vPOP/jgb+X+OUbh2RaPT5rHIa2KxXAJoeN+N/Zcywk9UuTmErm+KOy+8ZQj5Tou+XtIev287SMl/S5nURaxR4opfrwA//uhqy6NL0tNFsz+/TgyCwx4fbWz6tq2M1l4+JtdwmtmXbcXCOLGFeAcFxM4WR6tdD6QevpF1RBLzcK1CtRpvOpNI/JIBZY9tE8635fH22rm8m7YemWx2lx+77KE9vHkFLmeJx+FwCupvAGNv0fYs6dRq2TvL3fX8sL1IlhtdiW2fmiAy3Mr10CZJz2/FKUmKzRqFZo5vEkFbuaUAoMZhy7n4WxGIY5ddXrjmIwwuKNdBpI2kQVkyp+b2HzmMDYJVfvExSbYfpFBOnRPiEKD8AB0T7CHTvIVJ3kPryi0z3FNr0m8ZDnFJtfQPtmGvKxqH18h2WmA2+0IJZfz0KhUKtmQzedXHMTDX+9CqcmCeX+dhMliw7t/nRQ8UlEhUlmDN3Y7jze+b3MsfbwbJvdr5fId7uDvUf7+8hT9wcsrtcIjBQB79uzBL7/8gsaNG2PAgAG45557RH9E+eFv8KhgvdCdHHAtm718V6rdm8It6hZOo/EmtE/aK0q6+Mi9Z1/oxcUm+ElHmpAqLSnKJrIAwSPlTN70hcs5xXjoa7tQUDdEjxf7ix9+PqywnST8BBBbWoXmhw6lpdhodrEg8XhTtY/P1ZKLsJEKFWwyYtWEWNn2SMlvJI355vE1DG/n+WxYbeISqtLfwa85UkKohPtJkz9Hdp7SxcC5rxqRQeKGvonxkQCAv4+lw2Sxia6/tyGk/H4mi1XikXKWug3QavDG0PZ47o6WAJzPVE6x2CPFh/nlFMl7QNh3svy9HEnujsXqqjx7A/8s3NQoEoB8sQl3wuZ3Oy7iWr4BizadVdzHyHlSeKTNIFlpemmZeKlSxIcFMQGePaNhnNAjRfps3N4mWvaaOT1SnhQpZ5iV/bWSR0qiSHkQDuS8yXzVvgBJ+XMmbBktzl5+/57Nkhmvc3x82CJrIMpgzxlf1hkQz3ns3mGlyj15LVUqlaDkFhnMwnwfqFMrluLnyeOKTbBnXrr28bD7Vzp+abRFeQqpsPBXrUYt+h5pSKicR6rUZMEFN8U1+MlJLuybN0rwCi7vSeBzdkWl6N0Y/HjYPcYqYDYID4RarXIJK/UUNXL0il0ZahkdKvx27q47/+yydYb3BPVoXhd6rRpGs9XF+KSktAj52xKPFJvv2H4BOjVWjLsFyVNvF+bsuiH2+/NaQSm2nMoUjq2XCe2TNsLNKZYL7XN9DtmcwxvNw4O0LjKFkodGLtQXsK/bV3NLhPNLzy8V7kepDMGH2UpD+/q2jvY5xI9XNPm51JPxgsmNGrVK5DGrSfisSEVGRmLkyJEYMGAA4uLiEBERIfojyo+0JOZbXNgEEyR4y/zBS7ki9zEfK+tNaF9YgFYkYCjlaPHvGcwWF6uPO08Ie2iFfBUWquV4+ILcKAbuYDlXAPDmsA5oHBUsWXS06N40CmqVPRdACgtbyBApUvYJx2pzetBkFSk3OVLsWJ5i8qVChRDbHmA/NvNOKHkN2aLGXzdvGibKYTQrC9N+LX+uEPolhd0TZs7qq6RIuXik9Br8t429+M3J9AIXg4K35c/5UKBCTiC0v+cM7ZMKy0xBziw0iJQnPs4/T8FyzcJJWaUuaciZs1Ssb4vOYz0T0C42HDPuaicImHLlz93leLgTZhkGi7wiJRXolIpNSMNlzDLPEBMgmWVXrpkueza6N43Cn5Nuw/8e6iw73ggvc6SMkvlBaa6SjsWdYQoQC8aMUpNVpJDyghNv+GJWe7kEcf6e5z2O/Pqg06gUn0Pe88Gqr2UUGGCV8cLIfl7v9BaW+uiRYuucXuNUvHKLTW4as9rPv16YuMpsu7hwkRJanvYaziq1Kug5j5tUAZczZE364QD6zk/CdhmFVzouuaIK/JoWLFMEBBCvUdLQeoY74ws71sKNZwAAcRFBjs/Le5KVOOHI8WoXF44wxzp2NqNQcS3klXJ2LXm5JVivFbzp1yXzjzT/Zp+jMrBLHykht1ucI6XXaKBWq0Te/VjHvHsgNRdP/t9eYbu4ap9DkZLMVdlFRmE+Y9eafzbZXM7mEF6ZiQjSueRwKuXBylXxZPCKeKnJgkIDi7QRf+a2lvUBADc18o/crtOoBI82X0XVc2hf2ULVqxM+J1YsWbKkIsZBcPDWgShHScy2seE4kZYvCDlP3NoU7zti4rMKDRBXa3L+3xvFRKWyx6EzISnATY6WXEIhm7TdKQ1MKGZWVDZGvuqQt+PlYRahEYkNhTDIyGCdEIISpFPjm8e64XqhEY3runb1Zot0sdGCST8cACAW1tmEHCKjSEW4qdpnMFtgtdo8xuRLQ/uYR0oqdPLxzIDYMwiIQ2188R7xuXV8vLm0clxllD+Xwt+HTMgN1mtQN0TvsqAG6NQiRSpQp3ZWZMwrlal26d358AplkcEsFk6tNiFEUKo0sNy7jPxSkUeKt2BKQ/aE7zSIPVJSxbisXeBbRIfiT0eO3/vr7aGw0lAYwK6822w2WeugN14wdkxp2W+pMaKw1IyruSUu1e+k4TJysLBaaalinlKTc46R80YzfM2RigzS41q+QVEoL5SEjUrzdKQoeaTY76vXqkXXnReMS4wWhAZoZcvw8+Pjn23+tpGGafFzOO91qReqh1plv+ezigxezQdhXNglE74DdRqvPFJ81b7IIB00ahUsVhtumbsRfz1/G85nFuH0tQJMuqMlNGqVME8nxtdBWm4p6oUGCPfblpf/i/4fbkFWoRElpnJ4pCTzVpBOgwKD2aX0vtz8+/dxewGo3w9fFRrB8/ARAHL3Fa8YRYcFoHn9EARoNYKCC4hDfXljrMizGBYg27cLsAvy/FzFFAqpwdBgtirODwBwypFv27pBGOpw69brq4/g3ZGdXPbn7yV2LXmDVYBWjTohOlzJLXHx4svlxj341U4Mu9kexaOTVO0rkSpSMkZjpkDyqFXiuU/wSLmE9hkFD3togBZ5JSaJIsW8Q/Z1n28vEx6owy3t6ooq7yqtkUrVGAHx9bRYbULObbDEYNMuLhx/TrpNVLmwPLCQw4JSsyB/AXYZymSxKp6LtI1OTaTMI8/MzMS///6Lf//9F5mZmZ4/QHgNv/A6ewfZfyomMIQHaoW8o9xik6I3wRuPFCAuZuDOgsr3MCrlwjU8fRd/TiUmCxczLw7t89ViyB7YaG4y4K10wXotQgK0skoUIK8gBes1LkKgnAVIziPF1pZtZ7Lw0k+HXMKypEiFChYzLQ2DknqkAoTrZRE1RwZ880jxngg+xKpCy58reCyk8DkbbAEK1Gnw5G3NZPflQ/vCAnWC1yW32ORyb3qbI8ULOMVGi8v9yYRv6SLAKggeupwnsljzFZ6UekoxjwZrwim1eDOhuDwWPL5oDCBubGmx2hSfZb5il1SIuZZfipSsImfYjGQekRoHMgoM6PnuJkGwY8qRNFxGDqZMeBPaF+jhPvOUI2W12vuwMO8Pm1+8zZHyVKnOk0eKzxMC7Pe61IPvTWgjgw9xlWvGyuB/ay3nGUrPK/WqIAmbW68XGnH6mj1EOcjHHCm9Vg21WiUSYlcfuIKXfjqEhRvPYPPJDADOe7FhnSD8/tytWPVsT2H/qBC98J18uxBfEaplsnAxx+8qvW+kRh6lUv6A3dAyYfl+t+XGVSqx91mrUePvF/pgzcReihX0eCWYX7uiZfq5MaTrmdox+cq1+XC31rNeb7GRQWgYGSQYJlftvyxrbOWvD1OU2LOlVtnnObb+ST3icorUldwSXHIohOy6MWMFW9cMbhQpOUVfOmxBkSqwz1V1ufBTNiam7PDPGDMIsvPhI4signSoHxaAd0Z0FLYpzfHuPFLSuUDau4qnXVw46nrxPHoL+62v5IrvZ3cpG8J6VkPzo4AyKFJFRUV44oknEBsbi969e6N3796Ii4vD2LFjUVysPBkQ3iMK43As2swTxDdp5EvSKnmhvFWk+HARb0L7Ss28MmSfVB52lCKWJh6zzzGh2N4NXCxsldkj5ZjI+MZ6vCLlyRoMyCRh6jQii55GrZK9JnI5Unyfql8OXPGo1MhV7QNchU5prH8gJwhLrcO+KFL8JO8uT8avHinWR8qDBUqlUgnn+fmWcwDs99HTvZu5lDUO1IlD+yKCnBWQSkwWF8+g96F94pLpUuE5l0uK52GK/dbTykYmpVw2IUfKkScnLSdb1mITPHyIrv2Y4nlCSVDme8jwi6NQeeujrbjsCGV0Ce3zUFmSWWml4TJyhEk8UnKhZoIi5aEpq6c+UvPWn0S3t/8RhDj23HubI+VJkZL1SJkskmITzn1C9BpR1ThA/vcSKUXc71bWqozMQ3o1t8RjOWvA+dtMXnlQKIrkrUeqQKja56wExrh4vUg47wvX7fk8zpwYFdrGhosaqLPvBcre8B3gqo1KwsWkBpGL18W5UHxej/T7Vx+4gj+43FQ5QvRaF++PRhKOJoXfX5zrpnztIyTr2dCb7BEecgZDd+s0mzND9BqoVCpserEPAPu8dZwrJsHgQ4mZEsp7k1UqlRCWJp3H2VwoXQ9Y1V6heqHDWCHtDSW3rmvUKsUQcgZ7/ticwaoT5nLFJth3su+yWm0uoX3897BelbxhRdEj5SatQBqazYx35ans6y1MQbwi6U1W6iYcVFphsSbi88inTJmCLVu24Pfff0dubi5yc3Px22+/YcuWLXjxxRcrYoy1jnCZxGKpMGDvr+Eo/VtiFi00fBd5uYahckR46ZEKkPVI2bfd3y0ec+/piG+f6ObyOT6Rme8LxCYy9p6vVZWE5PMgV+UTAIK96Gz/6sA2otcBWrVo0mELghR+EtaqVWhaLwRjJb1YPKGcIyUJ7VPKkeJ6XTF8UXr4sCo+F4UJ82zC92uOlJceKcC1gmSgzh7TPveejiKFXatWiRQprSRJOrNAbMn0NrSP36/YaHYxTPBJ8TxSy69ctUGla8o8UtFhgYIlnleO/dEFng/RBVxzDZQEZavN9R4B7AJiWl4pDGYrDqTmAvCcIyWFGQt+P3TV4/jZb+uuap8zfNj9fRbh8GQqhfZ9seW8yIvjMUdK4u30pMjJlaguNYtzpHhhVs95pNhvIB/a6BwHr0i5mx+6NLG3h5C7X52KlHceKalXHbDP8+6qnTLYs6F3FKTp2dzZx4/Pi2WCrKdw4SB/KFKS/m3smCxElzUTT8kqEoVM8x5WaRjgbkfTZnfIeSzlaFYvRHa7XNEQKRq1ShSdMWtoO/y3tT3HVM5g6C5yhCk7TNmIDNajX1t7E+GtZ1wNS/xcw8p+8/2zAGdYmrTHHvNmsLYqDKa8ss+zZ4yFLXtag+7lenXJIX2mmQG10GB26RXFnuMCg1nwbDEZJYH7zViVWd5oUpYcKd4jagPvkfLuPioPbN6Q9vF065Hicg9rKj4rUqtWrcI333yDQYMGITw8HOHh4Rg8eDC++uor/PzzzxUxxlpHuIxHKlCi3Oi0amFyKDGKczd4AUfaZ0EJXhFxJ+DK5UgxQUWnUePB7o1F3iEefjFjky0LUWOCka/NDJmgy1t9w0WhfZ4nj1Hd4tG7VX3hdaBOI/qcUoUcXrmaObQdNr/UV3ExU8J7j5QktE/IkbK4CDW+XEO5xsGAU6FlMfhS4Ss9rxTTfz2C02VoBOxtsQnAdcFiCu7wxIb4dYIzfMdstYmskjERdiWECXNZkv5rZSl/Lh/a5xD4pB4piYIslxchJ9Ba+ealARrZHjpCcm45Fh7eIMIfk6EkKEv7aDH465mvoFxKq0ZJiQh2bwXmYYJEKJcjJW0866tHKr/U7FXD1khPoX0SJd1TKJs0dwGwPyO8MMnPB0az1aXKKe8F81RV0J0SNL5PcwTpNHhlYGuX9xpyHil35awZcgpAkF7tVWUuVkKcPVevcMYuXlBk/2eNWeV609m/1zFflqv8OQupFRv/2BzQsoFdkSooNYu8m0w54PdluMtHZij1gpIyqms8OjaMEBWnAsRriVxTbkBcrh4A7r65ofA7yQnt7jxSJYJHyvm5Pq3t6+uS5Asu+ar8/ZgthPaJI1YEj5TkvmPHalgnCM/0cfY9YvMlu77sXiw2mEVrnpKsM+XOVorFaQBXD1jDOkGC0YsVy5GG9jFDTZCjVyQAJMZHYuZd7fD5w12Ebfx1U8pJlTNSMDK4/CSbzXl9K8Mj1cLxDJyXVKh0ZyA31cbQvuLiYjRo0MBle3R0NIX2+YlASSUXwHXC1WucDUuLjRbR5MQv8HL9NeTgPR7uLGCBgufI2WNBquR5+uzdHyfjnxP22HY2UXrby0UKe0CDZK6Zfbt3k0eUJLSRP5676zF1YBvc0SYa9zjy1XwpGapWuVp+mRAktdq6hPYJVYisLp4NX7xHfMy+KB/IkZTNPCvSY3617Ty+35mKcVxVI28xcSFLnpB64vhx8J5Tm82u2L4xtB3u6hQrWECZUi3tv+atZZpfAEq50D62vuWVyHegj48S5+T14izqwrnI3Ot8aeFgvVOR4gUwC1cutqwohfaFuQmVs+/PFySRN94IQozk9+Vz2OTwxlPBkCpSVpur4uCtIsXPF97kAgo5Ul4oKi/0a1UmjxTgPB9psQmTxaYY2rfwgZvx5rD2AMRhUHzUjDtDS792DXBs9gDc362xy3usJcPVvBIUejHHsMqjPEE6+7ne0izK7WeNEg/TzfGR+GdKH5f92LkIPQ2VPFLc9TKarfj3TJbXLRAYbL1jczz7Xdn9HhGkE0LnUrOLYbLYG5NmFPCVOu377k/NweNLdgulxt0hl8crR3xUMH5/7lYhxJ7BGwJjZQopAPb5gA935g2rsqF9bkqgs+Pwxsj7ujRCsF6DrEIDzmcVivbnDUrXi+zztNRIy4wNUoHcWcFUjVcHtcHo/4jvW7ZGBHMeKX4OU1qDAnUaDO4Yi+5N5e9TacpARJAzJ5e1CGDPNZPN2BzOr+UqlQpP3NoUAzvECNt4xVcpzcJd6xX+fgOcyqm7AhX+grXVkOKVR6o2hfb16NEDb7zxBkpLnT9WSUkJZs+ejR49evh1cLUVtmABTk+Ei0dKo3ZaWSSTA79AyJXFlYMXJty5jdmDzU9ongQF536OeHfOIsSOx1vepZZld7Bz5S1EET56pABx9RypR8rdQja+b3N8M6abMEn5okhFhehdji14pCRCiGton8PCai5faJ9RIS+KVY6L5TxS/O/yfzsuAAAuXPfdeOIMq/CsCEjLwXZuXEd2P2bkfrxXU3wyurNwXdmCwxZodr9lFxmx/li6x3utROKRYs8Zi20XPFKS3z08UCcKVejcxHXccgqv6LnS8oqU06rNCw9lhS82YbHahJATVh1S0SPFLYj84sh7Ydg1kRp/PHmk+Ge4T6v6ovAaqdLIhMNgvUb47QsM4usphB57eCZ1GrXw/EpLoEu9TioVlyOlIEwyq/n0IW3xfL+Wbr8b8Bx6yIS9x3slQK9RY1zvZgh2KCSlRrtyzwxm/20TLVwb3ijFV+fzlN+kVMCAhYVlFhi8Cu2TCyWq66hS+t7Im/Dxg4kej8E/V/y6yGBznVIbAgYfOv7dzot4+JtdmP37MY/fzyMt0yzNkwnQahDvCM+6nFOCOxZsQfs31gvN4vl9P9l0FptPZeJkurxHn39WvA3tU4JvyNuwjpIipcF/HV4jVuCDIVdswm1on8HVuBmo06BlgzAArmFf/L10XcEjxe53aWif1ChXzyXfWeKRMorTINzlgwPAi3e2QligFu/fK642KPVI2RUpR46nwyPEh/Z9uvkshn2abN/Xg+edV3iUZKumXOSL1MuYKYm+YEZEOc+3v2miUNTLnSdY2kanJuLzSrxw4UIkJyejUaNGuOOOO3DHHXcgPj4e27dvx8KFCytijLWOrk3q4I2h7bB4TFdhQpMutjqNWrDuSXM3SkUlmr0M7eMm7jAZSyJDOvGoVJ4nI4bcpCBVpEwWG97584T3DVMF65d8bw1vFakobnIL0KpFY/XFkuPJesiH+dQLDXC5JmzClyqzSg15DSbXYhO+5UgphGo5BOMYh/BisdpE73vqjeMOkw+hfXW58vDv39sJw2+Ok91PaQpmXo4sR44UHyL59Hf7sE7SnFSKVLlkizA7Dst5kLNsju5ut44O6hDjksgN2AUIqySUjDcMqNUq2dA+/1Ttc4bo8mF9zGvECzcpWUVYe/gqbDZxNT9xKCinSJXIXxOpMUAKb4nt3aq+bBNaBhMe+ER0abim0J7BC0OPnMIKuHrI9Ro1V3JdKbTPEdrk5byhUjn7r8jBwtVm3tUOh2f1x03xkULUQokkRzJErxXWCt6LyR+fnx/eGNrOqzECTu/59UKjV3OM9PyD9c6KfY3rBosazSvBzxEhAVoXryXzSLHQPqUCDHxY+U97LwEAVuy55PH7ecySIi/SHCl7mW77NbqUUyxbia/E0Vx8k6PaoBJ8iHh55lpAnB+nJOjqtWoMvSkOUwe2wW8Teone86XYhNliFQxlUk+r0AuqUPyM8fdvbrFJ5Pl3Cc0zyof2MSFcWoHOxSPFzeF6jecw0/80q4vDb/THfV3jRdul63FUiF747ZlMwgwJBrNVaFUDuEaXSOHnOqU5vlfzemhWLwR3tInG8MSGovek0Rcs1NDb+ag8SIu8MNyH9tXCYhMdOnTAmTNnMHfuXNx88824+eab8e677+LMmTNo3769T8faunUrhg4diri4OKhUKvz666+i969du4YxY8YgLi4OwcHBGDhwIM6cOSPap7S0FBMmTEDdunURGhqKkSNH4tq1a76eVrVCpVLh8V5NcXsbZwilVBjQa6UeKfkwG29D+7z2SMlYYpQsmFKkVhz+eMF6jWDB/2pbiuDx8ISn0L7/Z+++w6OoujCAv7Mtmx4SEpJAIAQIvYTepEvv8KGICoig0kSsqCAIigWsYEXAAoKIKCJFBAJI79J7JzQhCQnp2e+Pm5md2ZLskoQEeX/Psw9k6+zu7Mw99557rqsHj0AfdSClHZFyJ5CKKumNZ9tWwojWFRzeXr6k9UQmSaKsr7rXVd3TrmYbdKiLTdim6rg3R0q931hPUnJqV0lfD2U0QD2Cklcvem7kfdKV0btHGpWFh0GHUW0q4n/1I5w2lJztg/LogTxHyvYEuOG44wUyZbcz7AMEwDqC6WxECgBez8l9f7dvLYf7kMUCJNlWE1QqLDlPeZWrHuZrREq1QKW6yIj8euqg5JkfdmHk/D1Ye+SqpgGlPs6o769OSVPLqwGhTnMtU8JTMw/AtgGtLtLi4yQdMVUpNpF3Q9TXSdEK21Q/L5NeGSVNcFKcIlmZk+B6A1g9MGr7ODkglSRJeS+eqjlSSarXExVGtSlngLXzIjvbouxzPz/dBIOaRrq8jXKnxtVbaS4VBVIHxqUDPBH7Yiu301FtO+nCbRpqtiNSeaX2pWZk2S347Cp1ZUBAvYi8tXCSvI9fuOl8pN6V9FF1EQIXppTlSl0gxtn8ZQ+DDka9Ds+0qmBXdddRGpmzVC11eqBt+ltwzmLJtvNVbX9zlxNSVaPJ9oGQWobNGkRBtusy2hSbUI9IuZo94ijYis4ZXZMFeJnsjm/eNnOkZLbznW2pA1dnI3/+XkasfaEVvhnUAN1rh8NksFZxtg2k5OOAq3Pt8sPPbHT4G88ttc+2Gua96I4+WS8vLwwdOjTfL56cnIzatWvjiSeeQO/evTW3WSwW9OzZE0ajEb/99hv8/PzwwQcfoF27djh06BC8vcWB5rnnnsMff/yBRYsWwd/fHyNHjkTv3r2xadOmfG9fcWKbnmLU65Qdz3aOlPrHJx/8H6xmP69NLVSVNuEsj9rRdtimXuXGUYNGfj5JkjQTveV1R/KS4iCQUjeWXd0+9YiUj9mgrdrnRiAlSRKeezAagDjo7zl3E6H+Zqw6KIJ7f08TakcEYN/5eCWVQk1u0OXVU6YUm8jMAlKtj72Vmum0rLYjzkak5P/7eIgqWzdvZ+BWaibCchZBv9NGvLpikivP0bRCSRyd0snp7Q9UKomNx6+jZ53SDm+XT0ryydt+rZTcX18dLNxItjaA5P3FWdU+QLw/OfddLGCpbTADolGl3ia5x1X+rQQ4GJGyrtOU/9S+9MxszWK88kk+KSdNzmKxKOlHW07+q00hdlCcRM2+2IQJjzQqi/jb6Vi+XzsSOPSB8pr02tIBnpoy0rbHDvXfcoeS7SiJ7TyL3CiBlM1z2BUHMOiVpSKczedURqTusOFiNuqVz1Ovkxw2MNRV+2yrhNmuOQhYU6WS0zOVfbBGaX+XCj/Igrw97J43N+rOg9oR/k4b8bmxPUaE+Zs16XDyd26dI5V7al9KepamoZecluny8V1ptMtzpGwCBQ+jXtmHL9iUf36hfTS++fs0bt7OsEsfdUTdqaDLZyQlz/MxG7Vz7bxNemVfza3ohaMCFbaLaMvk46XOQaaKvP/8axNI2RYuuZSQ4nREynZB5Uybc4n8GjIPB3Os5Iwdd9LwbYX5mxHobVLmH5XwMtqVTJf3K9uOTf88OpTUnYKuLAdTo7Q/9k9sjw3HrmPodzvt1ryybk/hp/bpdBJKeBlx3WbU0bV1pO7dESm3j/Tfffddrrc//vjjLj9Xp06d0KmT40bS8ePHsXXrVhw4cEAZ6fr8888RGhqKH3/8EU8++SQSEhLwzTffYP78+WjTpg0AYM6cOahatSq2bt2Kxo0bu7wtxZ3diJRepwxn26X2adacEde/1MG+CpNa3bIlEF3KB94eBrtSomoGvajuI/diu1Npy1EgZTsRVl5vxJUV6NVpBOoy5+oRqbwOWjLbFcY973BESm1id7HfTlGtVG426vB+31pYeeAyBjWLBABNepejUTtHPTzWBXmzlZST0gGeOHL5lnupfVmOG8ZyMOZtMsDXbMTN2xmanlR1PnNqRhbWHL6KmqX9nS58LFOPkBbESuYTu1fHlpP/4uEGEQ5vl/P75QO7PHFfDtr/TUrH0cu3cDguET3qhNs1LNUBgpz2pZOsz2udH5H7e5EkCT4mg12jITElE1BNn0qxGZFyVGxCbsTkJ+XHpErtk39Dkup9yT2/6jkJmTYL9cr7y/kbt/H7P/Yly016++2TF5uMfOUPAGKfnT+0EcoGemnSLMuU8NTMEzQZdAjx9cDVW2l26SPymip2I1LKgrx5f04+SqMn9zTZzOxsJbh11iCW50i5Myfh8Sbl8N2Ws3igUkllDRxAHNccBTtK8YT0LOW7kt+Do/1CKcGsrM/keG283Ph7GjW/nbwCVPVxM/gOF/20bezalu+WG6l5rU2nLsykbtiev3kbVULt1z10xDal1vZYrV4U3DaQKhfkjQAvkwik8likHRCfddUwPxyOS0QzBxU/3RFTtgTmDG6A6jYjTV4eBuX3nVvhnwAvE5pXLIlDcYko6WPCsStJea6h5uVg7St5tMh2Do9t4ZK4+FSl3aKUPzc5Tqe1jkSK15JHvWTyb0G9L8pp3vnpiJIkCTVL+2N9zjqB4QGediNNcueM7WflyoLUMlfWXAPE+8wrULobI1KA6IyzC6RcWEfqvhqRevbZZzV/Z2Rk4Pbt2zCZTPDy8nIrkMpNWpr4sZnN1gOnTqeDh4cH/v77bzz55JPYtWsXMjIy0K5dO+U+VapUQdmyZbFlyxangVRaWpry/ACQmJhYINtcmGx/9EaDpJlAm27TwMnOtkCnk6wVUfI4aJiNevz5nH1VJEcqhvgovYIRuQRd9q9hvw3qNMJ+9SPw4V/HAFgnbOYmxUkagb+qOpirJ3B1D6Cv2aAJzPJb7cbLZvJodClfTWqAp1GvNLAdNpocjuRZU1XkOCw8J5ByJXVEpqnal3Ows1gs2Hc+HoA4kMuNa3WjUp3PvOrgZTy7YC9K+piw8/UHlestFgsSUzI1wayjQiP5USHYBxWCfZzeLo/2yCdvk0GHRxuVxbdbzgIQBQqGz9uFk9eSYdTr0KWWWITyZnI6/DyNNksJyA1IvXJil/dBVyoQenvYB1K2vZW2qX3+NiNfgHV0wWTQAa5NgbSjniOlLkcv96LKI2Pq7UvPytakEMuByoj5u/HPhQT718ilod2tdjh+33cJT7eMQrmcNVgaRQWhdIAnqoX7IcDLpEkNk4ssvLPiCF7ppF33zUdZY0b72abZrHOXG2X5BdtA1+b7ScvMVvbnxJQM5TirdvsORqSeaxeNQG8TutUOx5PfWithOgvG5Pf05YZTymirfJxwdJxNswmkfM1Gt0ajALm32aS8Xl7rgqmPm7brqrnKdrTIdq5ccnoWsrKto9x5zZGKS0jRNGzXHrnqeiBlk0Zm+zmbDDrIm3s1UVs5Lcjb5HQeniP+Xia82aM6tp++gQE2lejuhLwmFAC0qxqCvw5fxVMtojDlj8MAxHpDufn2iYbIyMrGyPm7cwIpJ6l9NscvNaXqY7z2s5FHOMuU8MSFmymIS0ixdgrIJcFzfgeJKRm4GJ+idKak26b22Y5IqSos+pkNSEzNxMV4xwuGu6tvvTJYf+wa6pcrAbNRbz8i5eT372iNNlvPtq2E3/ZetKtCmJu8jjd3Y44UoF1kOMDLiPjbGS6NSBVEx2pRcXvLb968qbkkJSXh6NGjaN68OX788ccC2zA5IBo3bhxu3ryJ9PR0vPvuu7hw4QLi4sRK4JcvX4bJZEJAQIDmsaVKlcLly84nkU+dOhX+/v7KJSLCcW92cWI7ImXUWxeNVQ9Xy3+3mhaLhNsZSuMvPxPTbanTBKuFu3YSAhwHBOo0vJFtKmJizuTnywmpdve15SyNoFyQF2qW9kf1cD9NdZvcqHt+SpfwtCl/nr8DkLemepH9T657TgGFmLIBmusr5wRbjkqwKsUmMrOVxq68MvqdriOVmW1BemY2tpz8V7mubKCXUnxE3ahUp3ZsPiHufz0pXTMq8PXGU6j95p+aBVbVC0TfjQOnnN+vXsl+QrfqeK6dSL+8nJCKkzkliHefEwt9nrh6C/WmrMbI+bsdngDEgs25z2FzRN3wlOf/2KZhyq/naTsilRNIZWVblN90/lL7rHPsfth6DoBIs/W26flVB88p6VkOA29HQRSQe3A5uUd1fD6gLh5pZC3VHOhtwt8vt8ZXj9UDoF0rxdOkx5MPROHolE52RQqcjSbJx0RXUvvk7+ZWagauJ6Xhl90XcuYf2gRnmdnKd5JtcVzYRQ7o3EmlKeFtwph20agQ7KP5Xp2t/SIfS5PSMpVOAfn1HI5IZWXDYrEox4bc5sHmpqRqDopto9WWenTaWeMxr4VPvW1+Z80ridEZ9QK9SamZ1uptztaRyvm8Tl7Tpoy/t/Ko3RpqzqTZzK2xPZ95mQzKucL2dx3oY1LOdbaL8joS4GlEg8hAjGhd0eWquK6a2rsWPhtQFwNV8+NsF+S2pdeJ+XlyO8RZIGUt/mS/zaUD5IqGNlX7cj4ruXPxUs7C3oB6ORDx2Z2+noxm76zFygNxOdutbYTLo6YydcaLPJp5/oYYLXSl8ys3XWuF4eenm2DWwPoA7IvpOOtoqFnGP8/nfu7BaMS+2FpZpNcVebVT7kZqH6ANpOTfvaPUb4vFgmu30pQRqYJso95tBdKSqVSpEt555x270ar8MBqN+OWXX3Ds2DEEBgbCy8sL69atQ6dOnaDLZ3WPcePGISEhQbmcP+9e9Z6iYDs3yaTXNuZs8/XP3biNzSetE+nzGpFyx7AWUcpkcGdzUxyxPSFUCvHRHOj0Ogntq4s5JVdvpdpVNLOlFJqwSX8x6nX4fVRzLBvV3OW82wAvEyZ2q4bnH4xGVElvbWrfHTY6ZOoRKUeNnBGtK6J/w7J4vUtVzfUTulXDA5VKYpxNDzxgbRyqJ5vLc9scLU7qjG2ue0p6Fg7njDaWC/JCkI91REo90qUOMNTrVqhTWuSyv1OXH0Z2tgUDZm1Ftxl/AxDBb37WQXKVbZECbw8D9DpJWcdGPqkC1mAr9ug1ZFuAFQcu21WJAsR+bHvScqV3U/0YeU6i7eih3DCX09rkCcw3lYVHVSN6+Sj4UdLXOt/li/UnAWjL/jsakUpOs1n4OyMr1/0st88kwMuETjXD7PYBUcFOXKf+3cnBkqN9Rv5cX//1gGZ7XF1HCtDOkZr+5zGM/WkfPlh9zG4+UHpmtpgnlRNM2a7ZAljTm+60A0bdaeao88nZ9fKaec7er+h0kUek7mzb1I0kec0kZ0L9zMr9nTUehz4Qhb71ymB0W8dl4r1sPsPmFUtizfMtMXdwQ6WRnZiaoRpVdXxMkeczOcp0sE3DcybNJjC3q7hq0jsNfAO9TEpKaJyTTkJ1sGk7ulGQgn090LlmmKbzx5LnmJSgZEI4WVbFWvzJ/nOQpwxcT0rHiv1xyvXyKHBkzsj05YRUHIoTWUKOUvMAUZAKsC97L4+aytT7eUjO/no6Z+5lfkekJElC/chAJYAK9NaOlgZ5m2yCOgPmPdnI6RIe+ZVb5oxOcn4sKWjqfVeeF+ko8P5h2zk0eOsvzP5bfJf31Rwpp09kMODSJfs8+fyoV68e9u7di4SEBKSnpyM4OBiNGjVC/fqiByA0NBTp6emIj4/XjEpduXIFoaGhTp4V8PDwgIfHneVsFxVHI1LqH0Z8sv0oxA1Vz1dBLnbmazZixbMPINtin2qRG3Vw0iCyBGYNbGB3n2BfD0iSyJv9Nzk91xzh3A7aQN4FG2wNalZe+b96mNydhUIdyWtEKjzAE1N717S7vlnFkk7z4+UANDHFOndJXgQ22yJSXlxJSUyzOSHezshUqvc1Li96feXeQHUvqzqQOqHq5b16KxWVQ7UVjS4npmLP+ZvYdMI60pXfk5irbCtOyZ+J3EhXj8jJvxf1Z3LTwe/KbNTbnZRceT/qNW7CAzxx/GqSXeqYXBlQ7r2W93+5EpM6kDHpdbiz+mPiO40M8tKsA5aWmW1XAU/9nd+2HZHKyMo1ZSO/37F6/80tKFH32p66nqykeiqVv1wpNqGUNM9UymJ/teGU0/uH+ZuRkJKBuIRUZX0cQARacg+rs0Z1XtSdZs56kW0rogHW44yz95uWmY0rOSln7szTUFOXly4bmHtvuSRJWDisMW4kp6N6uONAqnKoL6b9rzb+OuS40q7tiJQkScr36+dpxLVbaUhMzchzkW8vm99r99rhOHAxAaeuJ+NKYqpLmQupNnMTbb8DLw+D0+I1gd4mpcHtKNti/YutsPXUv3h58X4AeVd2K2iuFrRQV4t15HYuFStLeJuUeV+//3MJnWqGiSqSOY+R17hSl4aXX8828JfTS9NtStLL93WUflqjtD82nfgXf/wjgriCPgfZfmfeHgYEepuUY3ediIB8z3fLTW4jTo7mrBUWdWVNOXh2NEdqxlpRgVueJnIvz5Fye09aunSp5vLbb7/hiy++wKOPPopmzZrl/QR3wN/fH8HBwTh+/Dh27tyJHj16ABCBltFoxJo1a5T7Hj16FOfOnfvPLQ5sNyKVs2ie3KBzlC6gLoNZ0Dupr9noVhAFaN9D+ZLeDh9v1OuUeU15pffllkaQX+pKRe6+T1vqBpUrE99dUUJVOUye2FmmhKcyPO5qep/tgs2307PsSsrLPUzy/mSxWDTzDNSjOo4qmWVbgEOXtPMQ71Y+tG2VPvk9OVob5UaS/W/oUrx9b7WHQWcXpLp7UpbnC9imhsmfn9x7LffoXU9KQ3ZO6iUgRmby24P3dq+amoZqQkqGMgKQ7CC1LylNW9QmKTXTaeU6IP9z4NSfcW6dAsNbWZcauKgaXbBt+LryWq4WapFHFOMStPuHegTzTo9LmhEpJ8GYo1Ened929n7TMrKw70I8AORaUCg36hLP3V1YB6pSKV80igrK834tooPRvlop9G+oTbPP7Xv3NVu/s7yWVLANelpXCc6z+qIt28DctjPF2YjUC+2jYdBby1NfcnBeKxfkrflOS3jn75zjLlcb2cpC8E6KTeQ2RwoAxuQsUC3Pk7qdkaVUkXS0BpFtx5dMLnjiqOy9Opj2UX0fg5uW1wS6BT1CYzuK6GnUazos7nQU2FW5ddzcrbQ+QDtNQu7kcpTaZzs6fC+vI+X2N9uzZ0/N35IkITg4GG3atMH06dPdeq6kpCScOHFC+fv06dPYu3cvAgMDUbZsWSxatAjBwcEoW7Ys9u/fj2effRY9e/ZE+/btAYgAa8iQIRg7diwCAwPh5+eHUaNGoUmTJv+pin2A/YiUfDD3MRuQkpFltzYDYG34SncpjSov6nKxuf3oS5fwxNVbaTh1PSnXfGL14qUFTb0CvKN1NNzhykrl7lLn28uFFIJ9POCrKlV+JTEeU5cfxuBmkehYI8zh89iOSKWoAin5ZFgmUJvbbvsYtTWHr8LbZEDrKiGa623TWQoj+HVETktUXtfoOFUEsM5DUjembYsPAOK3aPu87ubby42q8zduIyktE55GPV5ctA+/7Lmoub2kjwmSJOav3bidbjd3ID+aViyJdS+2QsO3rB1R3rmk9iWlZWpG8OZtO4fjuSxTkN99Xd3wyG1/MRv1Shl8deeRe6l94vO+nOi480YuXT+wiZjTFaYEUtr7K1XQctbluROaESkn79vRMUkeRTfqJU11Pdm20zeU0baYiDtLL+pXPwJrDl9F/4YRqFkm4I6ewxGTQYevHq+P8zdu48ft1lR72yp9akqBkFRrgO/sM1fvA9XC/NArpgx+2S1+a7Yl751RUvvkESkHc6Rsz7ODmkZiZBsRPMjH7DgHnTOAdr2nvOafFRS5wENMRIBL989zRCqPzk35+5R/p/LxxaCTNEuwyOSRaNvOgauJomMpM8s+pVP9OaoLwYT6mxEV7KNUxSzoc1AJm0DKy6RHsK8HDudkMea3aFVe9Dkd646yBO5WxT4AaFU5GBGBnoiJKKHs86kZWUhIycA7Kw6jZ53SDjtXDPfwiJTbn2529h2WiXJg586daN26tfL32LFjAQADBw7E3LlzERcXh7Fjx+LKlSsICwvD448/jvHjx2ue48MPP4ROp0OfPn2QlpaGDh064LPPPiuwbSwubBtO8sHcPye9wbakKABljQOjLu8VvO8G9WiMo9QUWZ2IAOw5F4+dZ26iRy5zsOTGXm7PdaciS3rDpNchMzsb5YNcK1jhjLryVkFtq5wmcuFmitKjV9LHA36expxAKgOTlx3G3vPxOHblltNAytGIlG0JbjmF59wNEUjlVsp0yZ6LWLLnIja+1FpzvX0gdXcO7LYTgB/KKZPuaN6bXE0rr9E8s0Fn15B1d0RKfvyiXRew7ug1vNA+WgmiAOsC0Qa9DkHeopzs1cQ0mAzSHb2eM7Zr+3irUtwAbSNTnqeltv3MDafPnd9ASp3OZ7uQsi3581SnSloX5HW92MTBS44LZ7zZowZMekk5HoX6iY4W21FzZX5UPn7n6k4zZ++7Uoh9pUr59ypJkialSPbL7guwWIDaEQFKdUp31Sjtj02viKVGkJyc+53vgLoxqtdJdgvwqskp14kpGS4XmwCgvHdnizA7oyy8K1dHtPmOvT30dilyPg72YWdzpOqXC4SPhwExZQPuWurztP/Vxi+7L2BE64ou3V8+h6tTjNXktHBnx3f1dwZAM2fPUeaHs5Te9KxsXEtKs1uQFxAN+eNXkxDpYCmOMH+zEkgVdBEPX5tt9TTpUTbQuv+ql3MoLN4eBiWQCvI24d+cY3ZhtJGcCfE1Y+NL4hjxU07HTUpGFuZtO4sft5/HL7svOlwbsiCnn9xtdy9MdaBVq1a5TlYePXo0Ro8enetzmM1mzJw5EzNnzizozStW1D96Oa0PsPZcX3UwiVb+ERWX3FNNJbxcftgNIwMxZ9MZ7MilkQYUbmqfn9mIn55uAovFYtfT5C51b1B+0wRlcuqX/PPxNunhadIrjYPLCWnYm1PC/ObtDFy9lepwQUzbE2JKRpbSqyinFcmB1Jnrt5GemZ3rvBjZ4ThtKp9tCtTdmvga7m9GSR8RiHz4UG2lDLNRr4PZqNOkqNxUAin7hpVJr1NGY0QpXZuUQRfej3qEQD3v7npSGl75Zb/mvup5P8G+ZhFI3UpV8vALsofx0cZl8cPWc6gU4qM8rzwqqa7C+K+DQCo3+f2OjXodutcOx7ojV9GhuvM5r4D185S/O3UapCsNJvn35ChlKSrYGw/Vj9A0bsMCnIxIpeXekHSFekTKtiNAViXUFyNaV8Dsv8+ojoOqYiZ+ZrtAatdZUZVyQKOyBd6ILCg+OesYXriZgrEPRucaUPgpI1LWYhPORobVqU1ROfOhfDzcDaS0xSZcGZFSd9gEKItdZ9o8TjxPRKAXtr7aNt/V5NzROCoIjV1IvZTJ+42zzjQ5JdjZOVkeRUxKz0R2trqKpOOpAj65pKRduHnb4aLbT7esgNSMbPSMse+EVafaFXS7QZIkbfBi1KNcoLUTNr9Fq1zh46HH9ZwkgZI+Hsq23M0RKTVPVYbDqoNiHqQ8eqyToFk82GgoHu3UO+HSpyuPFLnigw8+uOONIefUI1LqE6188HGUbiWPSBWXaijqg52z3H8AiM4pVnAxj2pKtiMnBa2Oi+kOeVFvX37TBGUBXiYY9ZLSIydXYZNLlccevaq5/77zCVh75DhK+XlgTE7pb8B+RColPVPVq5hzgi/hqZwgXv91P55qWQF5sa2EZfv33eohkyQJnw2oh8NxieheW3ti9TUbkZqhTgXLRmpGll0BCECk2MlzG8xGnd1J35UKbbUjArD7XDwA7aR9R9QjDiE56SFXE9OUPPKCTBN5qWMVhPqZ8VCDssp7V0ak0vIOmp1xZSQoLx8+VAdA3qnJcqP62JVb+OOfOLSuEqzajrz3tdwWkv7uiYZ2DXo5tc92REoOQPPz/airMZZwMiIlSRJe7FAFJbxMylpA6uNMKT8z9l/Ujq7JhUPC/e9sftTdMv1/tXHwUiIeb1Iu1/tp50jlntpXpoQXypTwxL9J6aiTs8yEPELgSiCVlW1ROlrkzgzb/crf02hX/U4zqurg2C9JwKSchdvFNhVp33aezKqF4B3Jq3NT/s4sFhFMyfukn6fBYVEnX9UoTo864fhtr7Wg2fkbKQ4LTgX5eGByzxoOX189f6cwsiKCfKyBlEGvQznVcSUwj1H1gqB+T8G+Hjh6RRRycGdx8IIkdzakZGTj3yRt2rVBr9O0P/7zc6T27Nnj0pMVh/Sx/yr1QVv9f9uD88MNIvDX4au4npRmTe0rJoGUugcvt+AnMOdEdStNnCANOgn7LyagSqifpkEjH0SLa++qLNjXQxn9KJdHpStX6XUSSgd4KlXX5J42+US1aNcFzf3nbTuL2KNiFfb+Dcsquep2VfsczJEy6HV4s0cNjJi/Gz/vuoC2VUshLxfjcw+k7tYcKUCsw+VoLS45LVYtPmd+mZpRLyHYz6wKpPR2AbErgVS/+hHYdOI6xrSLtlszDBCTrYc0L48S3kZNSpNc+OTqrVRlblZBTlz2MxuVeRzy/ILb6VlYdfAy1hxxXE3NFQUx6ujq3E7581j2TxyW/ROnWbTXtlCPI6V8zTAZxIldHhGROapwpywuajPSKjfK89NwUfce51W9LUi1rpO2vL7zQL0wS2sXhEZRQS4VqJC/84QU63qJzkaw9DoJvzzTFGmZ2coyEeq1w/JyOTEVGVkWGPWScuxU79/+nkaYDDpYLBYYdJKyPeoRFdvRxcZRgfhhSKNi09HpCmUdKSepfdZ0e+dFUuTf2a3UTCXFz9fDaPc5eJn0qFHaOkf69S7V0DgqCJtP/ovf913ChZu3revuuXisUReRKox2Q9da4fhg9TFUz1lfs5xqWkCwg4yQgqYOxNVBY1GPSCWnZeKq6lz7b3K63RzO4pI5dSdc+nTXrVtX2NtBedCMSBmdj3A0qRCEDjVCMXjODiWQMhWTHdTDxUDKz9OoDPvevJ2OLSf/xbML9qJ/wwhM7V1LuV9hpvYVJLNRj28HN0R8SgYiXVwg2BURgV6qQEqcpG3nFFQL88OhuEQliALEooZyYyDXqn028wq+3RKI7advaBbYdcZRtTu1u5Xal5tAB43UxlPX2DXeg308NKmoPh4G+HoYlAIEgGtzYqqG+eHP51oqf894JAZpGdmY9PtBJKZmomXlYDzRvLzd4+T1T67eSlNSEwurApT8W8rKtuCp73fl67nuZgeH7XFwzqactUlcrG6o00l4o1s1/HnwCt7vWwtN3lmrnOgdvY/QnMb4rdRMJKVl2lX983VQFdJV6kI3eS3IGagqSqCeTxWWy6iTOvi6l8mfsTrlNLfGWIhN4Qo5yEl2YdT1fM780NIBnsrxQX0Mkz9TSZLgZdIrIy3aoFi7T3iZDPdUEAWoq/Y5C6TyPif7mQ24npSOPp9tVoq7ODqerRrTQlOAItjXA/0bllWmMZy/kaJsh6ttAPXzFUa7YXCzSGRkZSvFliJLeiHAy4iElAwluCpM6hRW7ehbEY1I5byuPC9NdjkhxS6QcqW6anHl8q/41KlTLi/ySQXPdo6UrEKwtmHuYdDZDZEXl4O1dkTKeUNQr5OU3rsbyemYt+0cAGiqOQHq1L7inQ4BiF7WvOZ5uEvdyJJ7zVtEW9epMBl0GNnGfhLxFVVlMrs5UppiE9rPtV1VcXJYceByntvmqPqZeuXyuzn51RlnE/ltD/BlSnhpevr8PY3Q6SRNIHYn+2DXWuHoU68MxnethgerlVJKA9uS57Zdu5VmXbA3Hw313LjyPkr5eeCVTlU0pYQfa1wO215tq7nf3QykbBti1lLVrm/DgEbl8O0TDRHiZ9ZU/nJEDqYBbXqfdc7HnR+TIlW92BUdFJVQUy/iqt6fwwOc937f7TWKCos8d1X9+buTfSGn9rlS8l4OpCJUGQUlfa2fo/rcpg6i1ccNP7NRW367GBwD3SWP7jotf54TlObWseTnoEKmo46HCCfZGxVCxO9j34V4t7NS1B0M+Z377Iiv2Yjn21dWFt31MOix6KkmWDC0cYF2ojqjHpUOLsT5YK5y1mGqXjJFVhCp4EXF5S2vVKkSrl2z9mo/9NBDuHLlztM+yD3qESl1n1uHGtrGucmgs5sIX1yGTNUHu7x68OVGQfztDKWamq17JbWvsKgXxJQDqTZVSmHlmAcw7X+1Mf/JRmhVOdhukUh1IKUuZw3kjEhlOK6G2LqyCKRsAw1ATH5Xc1T8JFq1cKmj57jbXE1xql7aT/P+5PlR6hN9fnr5/1c/Al8/Xt9hMRBAW+o+Iee34O9ZOJ0HetXadM6YjXo83bIChj4QpVwX6m/WpM2I+929E6PtMU8pVX2HxwZXRkzlghPaQCpnzkc+AqnGUUF4tHFZjOtUJdcFyQFtr7N6HR5n86D8zIa7VhGusMn7mzqN2J1CDT5K1b68U/vkoiLqz9h2PopMHdCqAwSdTtIEscVhVN5d8u9p7/l4h1kHtoWKHHHUyeBOx0PTCiWhk8RCrnIlWVfnlqk7GJzNPyxorq6lVhDUAVOEqmKgs6I1hS3UyfIF8lIqavdyO87lo47taNTy5cuRXAjlT8kxD0saXjP8gMa6Q5oTYYivGY81tk7K9TIZ7Hp3iuMcqbx64+S5X/G30zWTELOzLUhMzUBCSgZSMvJfavhepj5Qqk/wVUL90LdeGdSPDISXyYBn20ajvKo37HKCNchJy+lZlHvXb2dkOi3iUTHER/M66v2wapg2bcHRiFSovxm1c9YFa1s1xO72u019csltemfN0v5opVoXS06LVJ+Ugwpx3onc+Iq/naHkmQf7FF6+fV6LN8qdOurGo5+n0W6ObFGm9tkunuquAY3KAgCaVXTeAJLT++QG5R//xGHzyesA8pfaZzLoMKVnTZeKupTyM2N020oY0bqCpmPFWdnwvIqc3Evk36HcKDPoJM26QXnxdaNqn1yN0bbB3ymnI/N/9awLCasL0djeXz0KUtxT0h1R/87eWXHE7nbbQkV5PYdyXc7nJAehuVW3DfQ2oUFkoN11rgjxNaOUnwckCahVOsClx9xLMlUdlBWCraPZdytotFXC26Tp3KhfTozUOR6Ruvd+D7LinxNFAADD0d8x1LAcQ7EcvQ3LNbdFqdL7fM0Gu8VCi0sgpW7U5FUGXN14VM9Z+W3fRbyw6B9kZVuUxu+9mCJRECJUqX1lAp3PiXi2XSU8264SZv99Gm8uO4Qrt+xHpPy9jLiVlulwQV6ZJEmoGuan9ACXKeGJU9dEZ4rtiJSjEadQfzMmda+OPefj0blGwaY53gn1+ysb6IWz/2p7yUr6eCA9Mwsto4MR5OOB7rXD8c+FeDSrKNInH2scidWHrqBldHChps/KjYuEFGsgJc+bKpzXE+Xi1dTl3+VcdvViqY5O1HfzuGM7IiW7017/Jx+Igp/ZiIcblnV6n9IB1oITBy8lYMT83cpttmvKFKaxD0bbXedocVMg7/W47iXyiJR8qHF3pE0ekXJljpSj6nAA8PHDMXju32RNlU118GQbSKnTge/F85Z6ns+fh+xTvK2fk4P3ln4bOLgEEYaSdjfJHQ+vda6Kt5cfxswBdXPdjl4xpbHttHV5FFcDBb1Owi/DmyHhdkaulTrvVTVL+ysLTas7T4tqRAoAGkUFYuPx64gI9ETlUF/sPHsT5+/XESlJkux6HFml7y46aS34YVs8Qp1762c2wtOo1wQfxSX3NNBblOw2G3W5ToYGRMMeEGsgqdeymb/tnNJIlwdJ74U5Urm6tBeY2RjY/Z1bD6tUygeB3ib4expRq0xAnveXG75XVKlI8oiU3MBKTsuyVkJycDJUN+Arl/JF5VK+8Dbp0aVWmMNFQtWqhvkhItAL3WuHOw88srOAnbOBuH/yfD/5VU01ijaoaaTmNpNBh7/GtsCmV9oovfif9I9B7IutlZGYJhWCsGVcW3z1WH3nL5KVAeyZB1w/ccfbGeCpHpES351tGl1BcjQpWl3GVz6eqBvrRT3vxllq0J02Vkv5mTGqbaVcU+vCVCNStpOpC6sYiKuMeh3qlSuBQG8TWle2loIv6u+pINlWU3Q3cLctEJIbOUCwzX4wGXSILuWraQupz722aweV8LY2+L2M9955y6jXKQsyp2Vm260nlWv5/+1fAr8Nx6Br0+xukn8v/6sfgd3jH0TTCvbBllqvuqXRNWdh5fbVSrnVkVU6wBPV7kLhh6LQr34E+tQtg68eqweDXofnH4xG2yoheLBa3pV2C8vE7tXRu25pvNu7ljJyaFvFFyg+7dQ74fIv2WKxYNCgQfDwEAev1NRUPP300/D21k6g++WXXwp2C0nItO54xkxtSqV67kmInwckSYKf2YCbt0Xud3GJ9H3NRvw2ormYh5FHA0cZkUpJR0KKNYd9x5mbdve9F1MkNLZ+Blw7DKx6HajWE9DpAVPeE1O9TAasGdsS2RaLSznicknknWdv4viVW6hUyhelU4/iZ9NMbJAG4AAqIf52eq4BqroB72nS45fhTZGakYUgHw/8/HRTbDl1HU//sNvucQDQKjrY4fUap2KBZc8BPqWAF47lfX95Y207dRLjxGdodnLC3P0d2p75G2+0H42oMqXQolJJ+HsaMeGnLRhh+A2eRj0CzO3FdyG/jiSJ7btxGqg3CJAkh6WxNfZ8L95PWG3gqQ3a27KzgB/6AHoj0H+B9bVsyJ0KKRlZygkor7kz+TH2wWhkZVtQu0wA3l5xGOH+nogK9sbxnGBB7j1W57+XKsQRMlc4SheqKZ3C07d3ALcqAr4FPwIqp8/FJaTalfvPT2pfQZn3ZCNkZGXjnRVHoEcWsqD/T41ImQw6uwVQ3WFdkDfvOVK3XUhZk6lT0W0rkQW6k9qXlQGc2wqUaQAYC790No4sB7Z/BXSZDgQ5TystHeCJQG8TbiSn48TVJNQsYy1Rbl2Q2sF7O7wMABCdtN3uJnXKqSsd9B4GPWY8Uhfv9c0sNhk3xYG3hwHT+9VW/h7V1nEBo7upQrAPPuhXB4CY1wY4niOVV5ZScebyHjhw4ECEhITA398f/v7+ePTRRxEeHq78LV+okNyyDqO/5TUfOLpC+bt0gCe+fKwefhzaWDlwq0/kxamsZLVwP1S2SQNzRJ4jlXA7I88c9nsiRSI7G0i6Zn/dL8OAfxaKv9MSgOlVgC8eALIygWtHgQuqEtTpt4G1bwEXdipXlfA2iZNQwgVg7RRg6xfAjAbAgcXARW35anUxgzeWHgQA9E5eiPq6Y3j2+psAgOuqUsKOGibqdC6zUQ9vD4N4/Run4W9IV+ZKlUQCZhg/Rk+/Y5AkoEvNMKdVmDTObRX/Jl2xBknOZGUC33YDZtQHblvTPBB/DvioBjC3s+PHZWcDv4+B9M9CDPbbgZbRwZAkCQ0iA/GRcSaeMfyOQZZfgWOrxP3PbgHeDgeWjQV+GggsGwOcymVJiOvHgR8fAU5vBE6tF9fF7RPbq3bjtHie438CVw87fTpfDwNa6P5Ba90ePJr2E+pIJxzPgclIFu89n8oFeWPGI3UxtEUUVo1pgSUjmmqOJ3LvcZkSnuhZJxxda4UhqqSD0cg7qfJ6/C9g/89uP8zRPMlXDfPR+fZvwOZP3d8OF5TzSEJN6RQuxqfYLR5e1CNSgPh9+pqNqJKyG4c9BmGU/hdldPO/Qj0qGmGIB64cdPmx8nfUKGsPsn8bBaQmOr5jdpbT1D5H/le/DACgeUX7URV1ipU5r/PW9q+Ab7sCy5/P8zVdlnxd05bQWD1eHI/+/tB6XXaWw2OKnH3QbcbfGPfLfuX627lV0s20ZkKYoS1GVDKvYj3x5+2/n8O/w2v7pzC6MS+u0N1y4bxV0Pb+COz69u6+5h2SR2Qd1Zoq7uvb5cblo/2cOXMKcztIlp0N/HsCKFlJ28t+45Ty37JnfwHO/gK8dBrwCgSOrkCHq3uBegPFgXLZc5iYeQtPYgiyobMfMr24G4h+4O68H1ed3wEc+Blo+TLgFYiAnB/V9aQ0u0Vj5QX9ZIU2IiWPQrjr6hHg0K9A42cAo5cYcVgzEdj0MdDvO6BaD3G/K/utQZQsIxm4cRK4egiY11cEFCO2A8GVxXNueE9cIhoB9QYDdfqL7Vw8FDi32fo8Pz8h/n1yDVCmPrD2LZS5cgAh5ofhnXYNk88/D8um4XggQzxGjywYkYmsW1cBmOBrzHa4GKo8IuWNFAQYM0XamtET+HkwUL4l/Pr+jDrSCXTSb0NX/TY0xTm89uo+UYzBYgGuHQFKRIrHOKL+vDNSAFMuwdflfcCZjeL/f44HvIOA5mNFAJmdCVzeD+yaK173ykEg5lEgvA6QeBGw5KSkxJ9Vni7InI2WOlVK4ZE/gCqdgS0zgIzbwM5vrLf9exKo0Ea7PRmp4vta8yZw9A8xylhSNX8l4RwQaK10h+uqEbcbJ4HQGtrn2zgduHoEujav4zvTO8rVL2ARLN6j7T+PBY8CNw8DT60HQms6/9zcII92q0ci5Z58SZLw0cMxdo/xQDqWmN4Avv8aeGyJ67+hzHRgwSNAVhoQVFF8Vy5y1IvdRH9I/OfwUqDDWy4/l6tqrR+C3z0O4PH48bhws4XmtqKckwBApAv7lwG8SyLm5kqYpCw8b/wZn3i9Im5PuAAcWwnUedS10Q6LRXRyBFUAfNwoFBO3Txwjmo8B/MLtn/PP18X/20+5o2Nt6QBPHLwkGtjvpb0JfH4GCK4iRoB7fgHonPcVy6WiXzP8AN2ei4B/aaDVK9o7HVoKLH4S9XxGYz1iXCps9EClYPwyvKmmjL1MPUfKR5cBZKYBBiejuXt+sP7bY2aer5un7CzRSZd2Cxi92/57/Dcn/fi0auR81avAti+Ah34AqnZTro4u5avMUfpx+zm80qkK/PQZ6JW5AqtQ3/HnpAqEgpCIiwgGYEE/fSyirqcCZfs43u4rB4EvWwCBFYAR26z7yZJngPRbQFAloGpX7WPi9gEevtrjbWHbMlN8Xq1fA1q+lL/nykgVv8/IB8R5TXb7hjin1XpI7K/pycCvT4vbSkaLc70+j5GdfQtEJ22b8bn+PgqDbWpxpRAfJdOhrCsdrcVU0XebkZbc4O4wFWgyXFyXdgtIvmZ/3xUvA+e3WnuMds0VKSxxe9EGwCnzOjRO/RRhFgOwe5v1cdu/FoHUvyfFyEWDJ0VAVtgyUkSAEF7X/qS59k1xAM+4DXT/VBmRunAzBV5IRSfddqzJjsEteKFhZEmcvp6Mi/EpqCmdQu21M4DM4SK4cOckb2vPD+Kk0ehpMapwbJU4KA1ZLRobyf8Ch5YAZzYBbV53nv6w8hXRs7fpY3GibDZa/B8QDQc5kIrb53xbTq4VjXJABAbBlUVgIDu/TVxqPyzSzdRBlNrqN0RQcX4rJACbI5NhOJMzSrJ6vOauP5jeRqPUI/jR0Bp99RuAVc9oG6BZmSgnXcFw/W94ybgQNw+WA3ZaAxGcXg+/f2bjV48JylWB6XHAL/8DAssDwVWBlS8DNfoCfWY5bjhlqdJsUuNzD6TUn9/enEbH7RtAuKpx//uz1v8fXSFOxJdUqYcJF5T/esWfACRVzr8cpDnqwZWvu34CSDgvfnuHftXe58YpQK9qJMWfBySd6OwoU18bSJ34C6jc2XoSvHZUBGQA4G3fsy3dvqE9wQLA5X8Ak068zwIKpGRRqgpQShpOaqL4nZTOmRh+8wwAC6pK51BNdxY4dVZ8BrmkCWncPC2CKEA0ntwIpNRMyEAX3VbrFQYHgUJqInB+OxDV0vqZWyxAepJogDmSnWVNv8xIgce1AwCADpbN+ORCdeVuZqQhxHAbOBArgm3PgDt6HxqZ6WI7t8wQv6NK7Zzf99xWYHYHoFQN4JlNCE+2VldTJuWvek3sr/+eAjq+bX2sxSKOF3qjGEG9sh8oVVOMmi7oD3iVFN9nrX5Atf7a183OEh0qOgPQ+2sgNUGMzCZeAE6uEeex5s+JDiZA7K9bZoj/1+5v7Ug4uVacl9pOBHycpAMf/wvQG9FKOooEKRF7LRVQPuuMuO3aEXFpMhIIqyV+S5s/EeedBkPEfW7fgNErEB4GHaJ1YnI+Lu2xPn9akjjurp0MZKVhdML7+ADzXc5+kNcRshUuXYceWQhAErr+2RL4pxrw5Grx2Z1eL7ZR3l9uxVkfeHQFENU6fyl+iZeAWzmLqZ/ZCFRqb93X1cfdDNXo6o5Z4t/lL9oEUj4YrF+BurrjeDljGOISUuD9zweYbJyDB3U74XO9IrDoDaDJCKBad/H+5NcGECjdwkVLMLqF38J7N74Gls4CqncAPByMbJ/eKPbJ60fF8TogQvx+00WamOY4Coj20Kx2IrX7heP2gUXCBZHZUaNP7r8jd8mB77q38h9Ibf9KnJ8rdwb6fQ/AIt7H2sliDvHJtcCgZUDCRetj5nQU59a+3zh9WmRnA7+NBLIzxH4W2RwoXS9/2+oG21Gn+pElMLBpJEJ8PUTWQ3b2XQ/uCgIDqeJmc86JZe0UayDlrMG9/yft30mXxUVlq3kUcBzAQdVYalZO+taq14BjK0QDqOdn7m1nWhLwfS/RwO8xw7XH/Pm6ODB3+cB6QpPJvWDH/gRg7bm4eDMFLxoWYrBhFZLhCRMycCViPF7IrI9mt5ZjgH4N/OJOAQs3AyZf4Nm91obnkT9E72ReDbnsLGD+Q8CJ1eLv30Zob7+wXfTWLXvOet25rUD3T8XnHRgl5vT8e0KMtMhpXxk5ecDqVImkq0Dsu0CdR4Arh5xv0+Gl1v/LJ4rk6/b3u3UZOPy78+c5+7fmTyWIcqCRTjS4+htytn/LDLGdty4DF3YAsVNREcBLOeelEiln7Z5Dv+pl+yc+vV5cZAd+Ft9N3cdEY6eEtXw/UuOt/0+Jt/Zip90SDWJ1o9dRQYqzmwDfMMdvMPEC8EWznAZ/DnXayhXRMN6bHYWaujPQx58VJ111g0a253tAbxInvNsOvhfZNVXK3s0zwNJRYhTsqY3aBsDu78T7feh7sU0zG1pvO/6n/fNePQSUdzKqnHLT+Wjqxd2ikdriBcAzp7F37aj4bT7wgtiXz28DojsCeoNowEs61AvzgA7ZyIbOukjskqfFyFvvr8VntHoCXjD00L5e/DnXA6nrx63/T7zk+D6nNwCLBovPvFRN4NGflflPg5pGYt62sxiMlRhn/NH6GHUjUbbuLdFp0niENZD49RkxQvzYEiCqFbBynPhN9/kGWP4CcGKNaPQGRll77wEMMKxBk8yD6ALxPH95vIjS3/wrbox51H40ITsLOLhEBLvBlfP+XK4cBL5uA/iVFiOXAPD6VetIxvUT4ruUA+vzOXNQrhwA0m7Bw2JNqYrMOAW8387aMbf9K20gtfBR4OxmMZ/vwM/AXxOBlq8AaTmjCbevi8v5bUBYU+vjDi0FLqwHDv0m/j67Wfu7kT+vla9YAyl1OuvVQ9ZA6vcx4jeSfhvoO9t+P75+XIzWw4JHADziAbyYMcz+c7t5WnRcfNVKNBz3zhedWCfXAr8MBco0xCzJOlcjS2+GEiatmSQ+GxveHgbg6ErRcdLhLft9O+2WeFzVHkDJnMXQszJEGm/yVXRZ0wUZxqZYmtUUxswkcW65fUNs0+IhQHQn8Z5NXmLbZT8+rN2Xcn6X0Dtowh1ZLo41/x4Xx27vksC5bYBZNfXi5ycAz0Bg1C7RybfpI+ttcgqeHFQD4rtQqRRkwmPG7wEAB7Mjodt7DYat4jzXQr8flr/fEZ28cXtFIHXrsvW5AARL8Wgl7cVr9SKB1QBgEUFClS5A+Rba45e8zwOiw6lGbzEHVpaSk9Z9+4YYFUq4INo4Kemiszikivbz2TEL2DdfXJ4/Ko4fN06JY0vt/o5HCBMuAOveBhoPt88ckKk73JKvazvA1B0UakdXiIC92bOAyUf8ZpaNFe0yADi6XPwmT60Den0pgihABMLx54Hjq7TPd+BnsY8YzY7PAYkXxW8BAFbndHg+/bc4FqXcFPPYqnYTQVbaLeDyAaBsY+ejxQcWi9TCHjNEG+jMRpEdUmcA0GiYCIwu7QaOLAMeeB6h6ecw1vATQnETJy3hyDKNxqPy8j1HlouOmA5v27cPizkGUsVJerI15SgjWYwmePiIoModFR+0BgWOyD94+ce6dx7QfYZosPmXFj+giEai9zXxkvhx2/ZaHFspTgIXtot0vIAI0UD49ySwf5E4WdqOcsm9W6teEyMpckGFbFXqXs77D/AyoptuMx7OXodmBpH37o2csttb3kC70lPwpPFr7fOn3xLbZfYHFj4GwAJ4BQEvnhQnh5XjgEoPioM1IEaWtn0hDhy5fV5x/9hX1Lt1CZiXk4qgMwA6o6YgiFOZqUDs28D6d63ftSPq+U3XjorPzjZwBsQB9sIO++u7fiTm8uTXFw/kvp13KjNFNDi2fwVU6gB0eleMWqXEW++TeFH0hPqEiBOoZwDw8I/Aj/1F2lJ6kv3z3jglUh+dUQdRgGgQnooFyjYRnzOAPdmV4GXUIzrrOHDmb8cjUklXxPfojhOrramEf00UvfRqh5eKXvyV47TXqxrtir8/EL85OBix2/qZGE19+m/7Eb0VL4n9JSsD6JzzOf39kfjtH/9T9Gge+Bmo0hUIqSq+H70J5TPTsDMsEl9W/AIdylpEg/noH+Lxu78TDUUAIw2/aV9PHlV1hfp9Jl5wfJ/tX1sD1yv7RS9wixcAAG90q4bn21WA73s2IyXq0fzEOGDXHPG7B4CtM0UgkXYL2JcTfO34RhzvtuZ0Lu353pqCu/dHoM1rInBQidJdRiPdEVggobT0r/WGPT8AAZFARAMRnAGicbR4COBXBnjugONGSmqi+D7CY0RHTGaqtkF58ywQHC2CqM+biCBr9B7xXOrA/vcx8E6xBqU1Ts3Sfh7q0bqsDNHgAUSD9OAS8f/17wDlW9pv42eNrf9f8hRgUr0PR50PspSbotNLHUidihW/8/C61t/IwV9ER0qHt0SjMPka4B0M7P4WgHaSxftG+6AHN8+K37vccLRkiyBzZ840hQvb8YBqgCkp+TaUUMNBEAXkzBtd+VbO6K+3fe//zjliJHnHN8BzB8Xozob3xHeoE82tnvrN2J5d1fqYZc9ZR7OPrQDeDgPaviHSu9T2/AA8OBlY/x6w7XOR0vbkX2JUSR4pzcoAFg20dpbmJuUG8F55++vTEkUjO+Zx63XqAODyAdT725oC+YRhJUK2LtA8hSR3JmamikwOm3mKz3quRO2sAzlBVI5tX4jLuIuiCE9miii+pP4ulo0BYqcC3T62Xhd/XvwbO9X+e7t1yT6QuqbqwFrxkhjhPRUrAr/0ZDGKZmvLZ6KddOIvbRGk9GQRZHoFif/Lzm+ztjNu3xBzeVPigWHrrFkz2dmiMyo1Htjwvjj2loi0tstk8t+LBmqv/8hJQHf1oHitnx4Xx49GT1lHE/89bn//L5qLVOqIxiKzY/Onoh2YEi+CoLZviM7JSu3tsyD+HC/O09NtOoTi9orP9NQ6TYdycEg1jDZYO5AXWboBR86IdtnmT8X+8ufrDKQoH9KTRQNGPpmpe4nsSNCcTCIaiwMBAMQMwIF4A2pcX+Hwkbj9r30v7Z7vgd9V8y4eeAFoOx74vrfoVX/sV6BCa+vtqoIHuHFK/OB+HmI9acWfA3p/6fj1M1PESXjEDtFzoh5FS74G/Ngf5VpNxGvGeQiV7Kv0AcCTF193/Ny2o0m3/wXm/U9s16lY0YgavEL0jMojfYeX2j2Nxp+v5X57dqamt82OZwnReFBTBycGMxBQTqQuOHJ0ubg48usz1v93ek+cGOoMECkBLgZSP2e1EKl8jhRGEGXr+CrRYRDVSnxHsgO/aEfU0hKBLx8QDaIE1UiS2V8EIHfqux6ApFfe60lLOB6IDAVOHhcN5oL6DNQjh7ZBlOwd5+sWaZxcC3zeHBjk5HlunBTHg5Dq4sQaf06Mdssn0tMbRPGTo8u1cyIO5BR6OLLMehzKEZj2D8b5rQQW53SiyG7/6zzwya1BbUs9IrVrruglzUgVPdX9vgOiO4g0SrW1k0VQ1+cbSMHR8F3q4AScngS8XQZ4cKKYH2Db8XDoN+3x8MpB68gKIOY9yLZ+LkYiHQTrc01OAvh1U8Q++tJpcRyQjzuJOaOdcirV7Rui0VG2iRhxOb/N8fMBIpX3x4esc2dvnhYNGv8ymnRV5fvMUeLMH9rnSU8Sn7HRLAIP2aU92uOyekTZXR5+1hEtAHg30v4+e+eJi60tM4AH3xRBxO+jtee5vCRcsP/tXj8qgiAHspJzAmCb0ReZAZnwv3XC+vizm0Tn4d75ote+ZCXRkQeI7+K77iJzQQ5qVOeIjjrV78c2JRgQI2KOfN9LNFIB8Vt+t5wIcvvOFiMa14+6FkTlZdPH2lTHlBsi1TMzFZj3PxhVaXohUnzuz7XgEbvvrHbWAef3n1ra+n9H2ThJV6xzgAHrMUHdJpHt/1l0Cl/aIzoj276h7bA59BsAm996wgWg7QTtPF65jaDuGLJYgLldxPHi4fnWtGRAdMBV6SLmS/+zUMl2wKaPxbGg/hNi1EqdgXHgZ8CnAKqLft0mJ7BLEiNEZzYC7d8Sx1Nny3D8e8L6uVw/qm2LqPfFPt+IY0255uJcnXgRTh39w+4q6ao2C6fHvqeAXbeA6qr9OuM2sGGa0kF2L5AslrtdYqT4SUxMhL+/PxISEuDnVwzWF5jooPqh3iR6SuQ0sUcWiZ6vtuOBcs3EQfWrVoB3CDBmP47+OhWVD35kfXy6BZiak1P8RhgwaiPwqWrRO7/S9j+K549aexqCq4oh8PZTxND2t92sDbDuM8RJ8NwW7ePHHMjpNXfyvgYtF72nmz91PKpSWHSG3AOfvASUE5/FA8+L4X75ABBaS5zQYt/RNmAe+kEMzzvS/i2g6UgxRC6fHEy+Ym5Am/FiqNtVb8SLg6F/hEhPmBRgvW3oOtE4TbyIt71fwT87YrHANAVJFjMeTn8dyzxEYHo0uwwq6y5gmXdvdE22WcrAww94eiPwcW24Re8BVGzn8MCaqxLlxUE7r+d+YqU4ITrreGj2rMglt2lUOnOqx6+I8kwRc0JkBk8xwursxOEZKIqCTKvo0mto9PpK9ACrG67+ESJ4+Lq188cBSK4zFD77pgMAktJ84G1SpQN1el8EQ44awT6lRPre7kKu9tR4uJjbsXiIOHbUU/WqyvnwWRkiTemjWs4DssgHxJyAadGOR7n8I8QIwbUj9rcVhZaviONahqqXutP7wIoXtffr841IZZPnewBizqo8eu+MyVf7GAAYsFj0wK950/GcWmdG7hT79YqX3fv81OeUcb7aESlZ1W7i3JHbKHFhCqoojony+a32I6JR7GA0O8ErEv7tXgCWjnT4VLMzO+IJw0rrFb7hYlR34aOAh784Fy8vhMafwSxGBPI6FgaUFQ3STR/nfr879dxBsV/KbRBzgCYQuFGmLX4+Y8Ywg5vH+YJQ93Hn6zDW/B9wfLXY1uq9RPpadobj+8o6vC1GYK4cFCOGcvogIOavJ14UbQh5ZDusjrUdIKv1kH0xKVm1HiIYsT0euKJCG9GRVlw5Os65q1wzYLCTzuO7yNXY4N6b1XU/iLFpdJeqIdJ0glRrAkS3B575W/TSeviIIdwn/gSGrgGMZoRWqOP8+dOTROqBmqMGonq49tphMUz7VUsRsKl7sRMu2AdRAPBJHWu+vqN4fdWrYvjZnSBKXQVNbaSD3ihn5CCqlM3QeLNngcpdcn9sYAWg/4/AkD/FZx/RyHpbdAfRK2mb7lG1G/CKTU+6TB7CLttU9ChGPgC8ck68nxq9RTAbUE77mPZTgIfmiZO3rEpXkdZTspLoXZYk8ZyASJcpXRfo/gnw6GKEBpfE1uxq+DB4MnqnT8IBSxRSOn+KJzJexNCM5zEyfRSWlHxKm6MPiEpYJSKBZxx814BoFPb5BmdM1u/odmBVYNAfQJCqelK9QWLSeYnyoqqhM3k1HAAxR6F0XeDBSaLx6khAWWtBBECM2D3gpMHjG46oWg+IHma1kCoidWqcg4Z+5c5iroFPsHXekd4kPo86A8RcOmeq9QBqPwQ0HaW9vv4TYv6M6js4XWMUbnf7XHu/7U5GfQExouFsJCHpSuEHUYBIi9z0sRiR+H20tdf4wk5gahkRRHxcG3gz0HkQBYhe1bldrUHUUJuGRMJ5JQg44Nu84N+HM48vxbWgBvbXRzYXv0U1R42LvybaB0R5BVGA/WMAMcq+dJTjIEqnSs2KfADov1CkFgIi/e3XEfkLQqt0tb+ux0zRiVS9p/b1XdHhbdFQd6bjO8p/e6a96fx+ci97xZyiAvvmO04JBuB/+4zTIAqANogCxGjiib/E/9MSCieIAoDXr4gOrLzEn8s9iKrW0/H1QZUcX2/rw+rWIOqhH4BXziJDshYP2Fn3HXyR2Q1HjVUB/7Kig1UtsAJQPx8pW+2nAA0dzIUDtEFUKZtCO/sXWQO+g0vyDqIA0TaZUV+k06mDKABYNU6MlMpBFGANovxVGQXOgihAdPzdabBRoa39da3GiQDPVnSnO3uNO1UiUttZJjPmvS6mRrMxBbE1dw0DqeKo03vA05uAYbGiQf3MJtGoqtJF9K489IPjx5VtJBqNAPyjmyPTIBqploByQLPntPfd6qScqm3j2VZ2pnbIHxA59M7uu3+R+L86f1hm24PjH6GdEKty1RgueugH/WEfWPRfIBotnaflvu1qFdqKz7WGquRqifJAuzdEQJXzOaLHTPGZA+JANXo3UMpaoQthtaz/j1A1vsNyRm28cnKKnS0MK6cP+IUBL54AHv1F9NLLOe++ocDovcCTa8VIWq2cRnfVrsDLZ4AJN8T7d9RYb/GCmH/UW5s3LpcZ/flWDRyziBFDU/3HsMvUEOcspbAsuwnMHibr+9YZRcDYRYx8oFQ1+0aOf4T4LGv2RabemhKR9PhqMT+kpCoo7/oR0G6iKAwy7qIIIF0hB4Zq6v2l1StAawcpnwHlRGDS8hWxD/X8TPQeV+8tbm86SgRDnd4Dhm8WE7i9AkVanMw3XIzyOaroFl7XOh+w7QTRg9x3NlCzr3ituo8Dj/wEDN8mAmPZw/OB/+UEM9V7iSIG1XuL4g1NR4sRlvLWstrlu70Mr7r9xUkmVLXfqekM1n3OxRG4AlO1u2jsvHwW6J0TDFw5oE3PnNVWpGFueF+M1uyao+3EcfQdy+QqiuYAMYfJURBu9MLOsP5YmtXEtW0OqihGKfI67jlqAD7wPBDVEsFPaOeHwL+s+L24sl/bpiqqDfhZ2zDLi6P5D4A4bqnLQz/6C1C5o/UYNa+vkyBW0paPHntYpGN3yJkbGKQafa1jMy8NEL30gDhejt4tRiZtdXpf7O+tXwPqDhSdTt0/FQ1mZ5UnW70qficmH8CvDLLDauOjzN6ID6ju+P6AqDIouVZxz3UWMcrvLmfFcOTA1tF8NA9f0UiVjc0l6HW0z0R3Avp9KzrpbEU2c/5cjgSUFcdLAMeCRKP+liEQ1zPMuAE/vF/mU+C5/aKYUPOx1seN2iWOia4Ytdv+99N0lDhG53aMKFFedDC3f8v5fQDtsb0glW0kimmpRXcUvx1nqnQFGgwFIImR0+ePic+thuqzavO66ADp843jku5lm4gRNNiMCsu/cVutXgU6vmt//ROrRJViRxy9buUu4nqjlzjnDfpDnCfL2XRmtXtDnOMA8T22V837t73v8K1ioOAewjlSxZHJ23FlGM8AUTbaFT7BMIzaCWSlQQqMApKTATjouavdX1SjSr4qyn53mCp6UuS1CXLTdLQoK5ub+HMiT3mxCz1RtfuLg8E3Ob2HJSsruboBwRHWsuHBVawTkp87JCZGAiLIkHsFS9UQAdEvQx2/VkjOZN/S9URaHSBOVMGVRU5+gyfFnIHyD4gRwi4fOA6GKrQVKW8mH6CcqvHWbIwoGqCuaFh/SM5aRDnz29pO0D6Xs7WVdDqgTD1RytXDT3s9AFR20utUsa242G5yTtW1i/FiboZJr4NeJ6GElxEJKaK3ztukF4Fpla7iOUw2PUoWVYGQRxaJClU5k+bNXj7IqQuCYP+ck2H1niKlJqq1dnK9Tie+z4u5jCgGlBM9ylW6AD/0Fr1bBpOYbyJ/j4B43mrdRUU29fzBktHis21tU8Shy3QgZoDYJp2DRla5pmJ+EaBdA6dmP23hD3XltfpPiEnattW0ojtY/995mpgPE93J+ln45xQLsNXyZVEBLTzGGsQ9OEkEZ+rUXECczJ6OzVkvxknj6Mm1okiDuiy8q55YJQL73d8DG3M6LeTUKUD8/uQGe04lPbvSxIBIhz3hZG5XzADnpfxlcoOy5ctizpPZX8zB6PoRULs/bsaew6z9mfgnOwoDanqh/JFZOekwNq/pEyoad4CokBr3j5jbsG++uM7gCXT9QMxlaPyMdh0xwNo54F1SpFQnXxXHivZvif2zdIxd1UyN0JrWJQ0iGonXWDTIenu5ZmLpBPmYFlZHHNf2/iA6N6I72M1jcyikigiwDy4RvfWGnFGEEuUc37/7DDE6FdVK7Ht/fyAeJ/8GAsuLEdewJsALNa3bZvAU81+DKorfnLrDKaCs+J3IRQj6fCMmmDvpOBPbF2kNnmUjd4rnlySRSmv0xEfJJuy/WAl+tcKBI7+L72HdVG3RjfAY0eF0eZ+oeBfVUjz/8T9xefsvCL1qP+Lzd1Z1+Em3UUvnwsh4bso1EyPVR5aJjhGdwX7uYPdPxW/69g1x3y0zxdxcdc984+FiDmzTUaLj7aEfRAdlWB3xOW7+VBwjyzUV555tqtFruYy82V80XNX7pV8Z0ZDf8bXYd2+eEf+XtX4t55iao/lY5Xh5sPxgnLiSgONRT8ASL+aWlZSXRwBEVdbEi6IRLUmis7F8y9zn3PX8QmQavHhCjGgvGiTOx4B4juZjgPlOjhHy/uRfxv62gHLWdkNwtPXY7oyHvxhpzIv6fFAiMiegUWk4TPx2Gg4Tlen6fC1GnQ8sFu2Ph34Q76veIHHc9C4pAo8Dv1g7w6LaAC1yRrCuOgiiS0aLfWLwcjFqePxP+4q4bV4Xv2dJLzpZJZ34bSddEdtRsZ19JoZMZxD7rmo9UwCiQ9QvXMydU1cpfGCsGPlt/ZoIrCKbi/ND3YHiedQLZz84SZSrl8/Z6nP6PYKB1H+ZHGDkpmp3oNcX2lKZtR8WB7+1k50/7rUrorJcXoHUsZXWCbiA6Nnv/klO+VoVnUE0goOrioDJ6AX0/FyZ56NZviOkirXsp7p3Tx3oZGfmXlpYThEsXV/1vNWs/w8oax2Vsn1uNb8w4Nl94sSiDjZq9BYXtdavitet1U9MCPYp5Xz7HCmgtb4ig7wQEeiJ8zdEtONrFocBfy8T8K84GXp7GMR7rtbd8ZNU7mRNXbDpPQpr2AtYsQm3S9aCl7xPefgCAxY5fq6Qqs4DKb1J9FCZvMQ++r9vRUPqVpw40bR6VXv/4MoiDcY3TMyZMno5bzB6BVpTfhwpXc/aoPBT7Wc9PxPrhPx7UjQ4c3pnrducx2G1oZPg3pFyTYFn/7FfSyqogkjPWj0V+Dcnzfbpv4GQSiKQimptbbTKwuuKgLx0XfE7TDgH/PG869sSVFFsR9vxYiTl/HYxz/HP10WjVR20y4GUrFRNMRdwyVPalBi1EpHit79vgX0DWk3+zTcfIy7pyaLiYngMIEnw9zTigiUYs7K64MGGjVG+x+ui0TC/n9j+kpVFA6KLagQ7tKa4VO4kGvjRHUVngXpdmzINtGnI6mpg7aeIQK3jO9aAt/4TIoUxuiPw1xv27+OpjaLs980zYh+SJGDNZGt1PpOX6MjYME38Dju/L34DEQ1EL/C1oy4GUtXEtqcnWSuJAaL325GolmI0ARCfqd4kzgkyvVHsw8mqLAOvQFEaXme0r5ImU3cCVe1uDeicUY/AAOK3qk6XzDm/RXmp1jmTj1dVuomqqnKgavIWxynbnu76T+BUQHec+LY7musPYnD6i5jxUE1s/vMnjPm3F5LhiVcMP+Jpw+/ax9kGI7KgiiLAlzvw2k0S+2jSNTGiULMvsPED+zku8nchH+MbDwciGlpH9QAR7ES1to4EqtZ1AqAtY9/pHbEf/5BzDvJSHT8GLhXHzw9zAl3vIBGwNx1p/cyTLoviOFGtRTAXVluMekiSZlQ+o2QVvJQxEg9klMTGdWK/LV9SdR70DtJmROh0onBV0hWRIidXhpSN2W897xo9RdD+lE3QVbax2JcknQgqY6eKYPXcFmuwUbEtUKahtihO7YdFtVxAjOgHlHM8r7b+EPE9RTSyFjmxNXQd8E17ABYxT84vTMy9qvOIOD5JOmtno3y86vy+GFGTJHFcf3Cy+L7lc6Rt57m6tH6wakqD+ncRVkcETPL5qVxT8Vu/tFf8dtXHq7A61s9HNuBn0fFgex6Uq7fKDJ6OR/GCKoi2j+36X446cT0DrAXLStUQAajBwxrE5VY5uZhjIHU/6zHTOpqhHiWQJPHj3DVXnLi9g7TVc6p2F/NwbE90rV8TedhZ6aLSi6P1b7yCxAFK7smuP0SceDJTrM/XT5XvLFd8Up9Aq/cWZZCr97JfvK1cM1FNKbqj+LFW65FTqthmjpbc61GmgThB+YQAvm4GNsp7cjHA8S4JNHZhpK+QSZKE5hWD8eN2keYhB1LKYp0AfDzyCgaeEr1Tre0rGupjHgEyU+ClHoXJTf0nRINQXdmw5SviZN5sjLWEtySJkS1AnHQqPej4+eSUIHX6wJ2IaGj9vzrvXm8U+2PJSiJFqrCpgzi1yh2BwKrAZ5Hib7k3VqcHHv9VVGhKviYWagSsIwqSJBqU2VnWQErvIb6HuH2i0Xl6g0iVNPuL+QLBVbTBXO+vrJ0vfVS91zLbhbEbPyNSRXd/b98ANZjFaJxPKbHtj/yUM6dqjDiGGL20c4JsOzVM3po5cPL+DACh/mbAM6dh90ROh47FIgr3OPrdegXad4DIeswUnUKx74jjmLrjpfZD4qIWGCVeMztblFa3nYMkSWLURj1y0+JFkQ3QKac4g18Y8PwR6/FZ7rkGREC4ZYbo4bed71b3cbF0RVAla1D7wFjtfWzTfmo9JNJz1R1I3iXtR3KdyWsR6KhWouhEROO8gyhAG+h1es/5HB9HfEuJCm3z+trPObYR6m9Gl4znUTbzKo5aymKPVyMM/de6D63Kqm8NpNpNEr3vzZ4VmRZLR4oe9vZvicJKniXE/rXkKdGQls8zPsHWY7+8vpSz9wqI85r6+CNfp25Q5/kZqDoz1L9HnV6M2JSqKdJBo1qLBq36fN5ukmg4N3pKfFdOjuW+ZnHO2HjcOvrXuaaT45X6ffiFiVL2alW7iRTxvJj9RWeZziiC6QeeF78L9WKuHr4isD/2pyjY1G6iWAR9/buiMFDth8VxpWQla6VfDz/g0cWic1V+nnoDRRqyOgVX7yGCfkWMiQABAABJREFUlMHLRbDnWUJksTyoyvjxCrLOVVR/D+p2Vl4d3aG1xChSYJQ2pdxoFm2W0xuARxbad1rpjaKzBRDvT65Iq16oXqaemqDW/DnRGS1XlDSaRZAmC6ku0nkdZXK4QqfTnjeajxHttvyes4sIA6n7lX9E7icYoxkYuUOcFM7mlNpt/Zo4IMs98P6lRZqSOUAENeqe+AGLxHoX6pQAQDRUzH7WlJq8dP9EjDy0UaXBhdcRud46B7tvh7fFj7/5c+JH3u87sXjwbyPEwVBen0luBOl02p7p+0S9ciWUQMpHCaSsjRsvUx6HhjL1xBomjpi8RQ+nq0rXFaWhL+4GZrURwa+rjbfCFFRBpP8lXNCW/i9ODE7SQQHRYFPntdsuBml7EuyUM9fRYhGjF/LJu3ovx2mnzhZpBESjRE6l6T5DpO3qdMDA34HNH4sUuq4filSX0Jra1EmTl7j0+05MEr9xSqzPo9Pn9Dq/5Px1od2PQ/3Njrf7TkZ3gyuLS62HRePS9vN0RqcTDbTd34tj0N55ogPHkTr9RXqkuuHk7HP2LSVGwwFR+XDffHFMb/mKaJjbdjLZimwuUuz8I0QPepn6uX+n+RXZTIwuqwO13EQ0FNsVXEU06N1V6UFRac43PNe7hQd4IkPniaPZYrse/UZbdn6PpRKeSn8O4f4mvNF8jPWGOo+IRnREI2vqHCA+w0d/ER2FFR109qgLFAEiLdy2U7IgqDMeHBXueHK1WD/NttMDEMc+F47BfmbteaJJVBAiAnMpIKTmrXrdkbscB5jOqD8veZ91tL9HtxcFguT7jNguPhfPAPF3zKPAlUNiznj7yfbBKyAa+X88Lzp8rx0VnXmS5Pi+Mr0qvTG3oim5kST7ESSZq20W75KiY0r+v6tCa4g5dbM7iVTreoPEdX3niM/P3Xl1eYlsDrx6qXCPP4WIgdT9KKIh0Hps3veTG0+V2gGvXXbccMgtTanlS2L0aN+P1jkVrhYWkFXvZZ2kqOasERNeR1zUPHzEQSEjRQR2pevZD0XfZ6qGWRtq8uhToLdJdV1BT8zOgySJ4OzZfc5TjoqCnJt/r9LpRKfB1UOOG3XyiK86rcQmfeeOgg5JEsVyMm5r5yvodKKTo3lO8ZvcRmgNJtHI8wlxnrvvwAPRJTGkeXlULuULD0Mh7Md3MnLtX0Y0TJOuif/XdVDZSuaooEleun4gUmnymnekJkmuT/4vKO7MfwiqYG343ilHc2VsmI16fP14fQye67x67KrsBqjlbfO56vTaIh5qFVo773wpVV2Uqg8sLzoaPPxFx2VB8wy0rmHoaLTQ6Ol8Xq6L/Dy15+EgHxdGGpXtC7D+v4DS1h1SN84dpfs/+KaYm6keYVarP0Rk4XgHu97QV7cv8urQKGyVcklfz0uPGaJjumFOR4az0fqCcI8GUQCr9t2fHv8NqNI57/upudr7aqtkRTE8LXNSevauMHqKoWNHgdl9plKItbGWmiFyuSODrD2JtifIu6ZE5J3va+RYry+Bbp+I1C1bLV8SqXVyYFOQvAJdasgWNA+DHuO7VkO/Bi6kCd1tPsFirqQr81fdYfQUQZGrQdS9omQl5/NTC1DrKiF4rp02bW50G+0Iibkgg/JK7USgWKGN6EAqDDodMHilGAV2VLyqAPjbnCc0hSbyou5UvdNRm4KgN4jg1llDXpJEZ447DX0528dRpbt7SVAFkTp5n3c854UjUlT4JMna8+1s4j/dVSaDDp5GPVIyspQRKWXCNoAQ30LoIaWiEVbLeS5801Git9GVOStE/2HlVB1JIb4eGNu+MgY2jUS9KTkpzPdih3lIFQBOin8UAD+zbSDlxnGkUnuR/hvdoehHbQpa/SFA+m3nxZroP4WBFN0d3T8Va0o5KE5AReOFDpXx9YZTeKWTONFWCfWFQSdaC5VKsQfqvsEgikgzt0euPBekGmHJznawqPx9zs9T24QMcmdEyjsIGPNPAW9RMWHyAlq9XNRbQXcJAym6O6r3tFZco2JhSPPyGNK8vPJ3kI8Hfh3RDBaLmykaRET3uLKqQMrR8S/LwkDKlodBD7NRp6SH87xB9yMGUkSkqFH6PzbHgojIBSV9TIgq6Y1T15PRuEKQ3e0ckHLM39OI1Iw0AG4WmyD6j2AgRURERPc1SZLw2aN1se3UDTzsoFBJRIn8Vbj7r/I1G3ElUQRSJb05IkX3n//YDD8iIiIi91UJ9cPAppEw6q1No6daREGvkzC4WflcHnn/0quq2ZX05YgU3X84IkVERETkwCudqmB020rw9mBzyRH13LE8F3In+g/iiBQRERGRA5IkMYjKRf1yJYp6E4iKFAMpIiIiInLbo43LIdTPjMk9qhf1phAVCXazEBEREZHbapT2x9ZX2xb1ZhAVGY5IERERERERuYmBFBERERERkZsYSBEREREREbmJgRQREREREZGbGEgRERERERG5qUgDqQ0bNqBbt24IDw+HJEn49ddfNbcnJSVh5MiRKFOmDDw9PVGtWjV88cUXmvukpqZixIgRCAoKgo+PD/r06YMrV67cxXdBRERERET3myINpJKTk1G7dm3MnDnT4e1jx47FypUr8cMPP+Dw4cMYM2YMRo4ciaVLlyr3ee655/D7779j0aJFWL9+PS5duoTevXvfrbdARERERET3oSJdR6pTp07o1KmT09s3b96MgQMHolWrVgCAYcOG4csvv8T27dvRvXt3JCQk4JtvvsH8+fPRpk0bAMCcOXNQtWpVbN26FY0bN74bb4OIiIiIiO4zxXqOVNOmTbF06VJcvHgRFosF69atw7Fjx9C+fXsAwK5du5CRkYF27dopj6lSpQrKli2LLVu2OH3etLQ0JCYmai6Uf8nJQEKC9ZKcrL1e/puI7n3JyXf2m5aPB1ev8rhARET3tiIdkcrLp59+imHDhqFMmTIwGAzQ6XT4+uuv0aJFCwDA5cuXYTKZEBAQoHlcqVKlcPnyZafPO3XqVEyaNKkwN/2+k5wM/PoroI5J/fyABx8EVq8W1/v5AT17At7eRbWVRFQQ5N874N5vWn7ctWvAsWNAdDQQHMzjAhER3ZuK9YjUp59+iq1bt2Lp0qXYtWsXpk+fjhEjRuCvv/7K1/OOGzcOCQkJyuX8+fMFtMX3r8xMESx5egIlSoh/ExOB1FRrcJWYKO5HRPc2+ffu7m9aflx2NnDzJpCVxeMCERHdu4rtiFRKSgpeffVVLFmyBF26dAEA1KpVC3v37sW0adPQrl07hIaGIj09HfHx8ZpRqStXriA0NNTpc3t4eMDDw6Ow38J9yWy29iynpFiv9/AA0tKKZpuIqHgxmcS/PAwTEdG9rNiOSGVkZCAjIwM6nXYT9Xo9srOzAQD16tWD0WjEmjVrlNuPHj2Kc+fOoUmTJnd1e4mIiIiI6P5RpCNSSUlJOHHihPL36dOnsXfvXgQGBqJs2bJo2bIlXnzxRXh6eqJcuXJYv349vvvuO3zwwQcAAH9/fwwZMgRjx45FYGAg/Pz8MGrUKDRp0oQV+4iIiIiIqNAUaSC1c+dOtG7dWvl77NixAICBAwdi7ty5WLBgAcaNG4cBAwbgxo0bKFeuHN566y08/fTTymM+/PBD6HQ69OnTB2lpaejQoQM+++yzu/5eiIiIiIjo/lGkgVSrVq1gsVic3h4aGoo5c+bk+hxmsxkzZ850uqgvERERERFRQSu2c6SIiIiIiIiKKwZSREREREREbmIgRURERERE5CYGUkRERERERG5iIEVEREREROQmBlJERERERERuYiBFRERERETkJgZSREREREREbmIgRURERERE5CYGUkRERERERG5iIEVEREREROQmBlJERERERERuYiBFRERERETkJgZSREREREREbmIgRURERERE5CYGUkRERERERG5iIEVEREREROQmBlJERERERERuMhT1BtBd4u0NWCxFvRVEVIC8S4TA8gZ/11QEeE4hIuKIFBERERERkbsYSBEREREREbmJgRQREREREZGbGEgRERERERG5iYEUERERERGRmxhIERERERERuYmBFBERERERkZsYSBEREREREbmJgRQREREREZGbGEgRERERERG5iYEUERERERGRmxhIERERERERuYmBFBERERERkZsYSBEREREREbmJgRQREREREZGbGEgRERERERG5iYEUERERERGRmxhIERERERERuYmBFBERERERkZsYSBEREREREbmpSAOpDRs2oFu3bggPD4ckSfj11181t0uS5PDy/vvvK/e5ceMGBgwYAD8/PwQEBGDIkCFISkq6y++EiIiIiIjuJ0UaSCUnJ6N27dqYOXOmw9vj4uI0l9mzZ0OSJPTp00e5z4ABA3Dw4EGsXr0ay5Ytw4YNGzBs2LC79RaIiIiIiOg+ZCjKF+/UqRM6derk9PbQ0FDN37/99htat26NqKgoAMDhw4excuVK7NixA/Xr1wcAfPrpp+jcuTOmTZuG8PDwwtt4IiIiIiK6b90zc6SuXLmCP/74A0OGDFGu27JlCwICApQgCgDatWsHnU6Hbdu2OX2utLQ0JCYmai5UMFJTgeRk8a9aWlrRbA8RFT/p6eJfHheIiOheds8EUt9++y18fX3Ru3dv5brLly8jJCREcz+DwYDAwEBcvnzZ6XNNnToV/v7+yiUiIqLQtvt+YTAAfn5ASgpw86b4188PMJvFv4D411CkY6BEVBDk37u7v2n5cTodUKIEoNfzuEBERPeue+b0NXv2bAwYMABmsznfzzVu3DiMHTtW+TsxMZHBVD55ewM9ewKZmdbrDAbt9fLfRHRvk3/X8v/dfVxmphiN8vDgcYGIiO5d90QgtXHjRhw9ehQLFy7UXB8aGoqrV69qrsvMzMSNGzfs5lepeXh4wMPDo1C29X7mrDHERhLRf8+d/q55PCAiov+KeyK175tvvkG9evVQu3ZtzfVNmjRBfHw8du3apVy3du1aZGdno1GjRnd7M4mIiIiI6D5RpCNSSUlJOHHihPL36dOnsXfvXgQGBqJs2bIARNrdokWLMH36dLvHV61aFR07dsTQoUPxxRdfICMjAyNHjsTDDz/Min1ERERERFRoinREaufOnYiJiUFMTAwAYOzYsYiJicGECROU+yxYsAAWiwX9+/d3+Bzz5s1DlSpV0LZtW3Tu3BnNmzfHV199dVe2n4iIiIiI7k+SxWKxFPVGFLXExET4+/sjISEBfnKJOSIiIiIiuu+4GhvcE3OkiIiIiIiIihMGUkRERERERG5iIEVEREREROQmBlJERERERERuYiBFRERERETkJgZSREREREREbmIgRURERERE5CYGUkRERERERG5iIEVEREREROQmBlJERERERERuYiBFRERERETkJgZSREREREREbmIgRURERERE5CYGUkRERERERG5iIEVEREREROQmBlJERERERERuYiBFRERERETkJkNRb0BxYLFYAACJiYlFvCVERERERFSU5JhAjhGcYSAF4NatWwCAiIiIIt4SIiIiIiIqDm7dugV/f3+nt0uWvEKt+0B2djYuXboEX19fSJJUpNuSmJiIiIgInD9/Hn5+fkW6LXRv4D5D7uI+Q+7iPkPu4j5D7ipO+4zFYsGtW7cQHh4Onc75TCiOSAHQ6XQoU6ZMUW+Ghp+fX5HvRHRv4T5D7uI+Q+7iPkPu4j5D7iou+0xuI1EyFpsgIiIiIiJyEwMpIiIiIiIiNzGQKmY8PDzwxhtvwMPDo6g3he4R3GfIXdxnyF3cZ8hd3GfIXffiPsNiE0RERERERG7iiBQREREREZGbGEgRERERERG5iYEUERERERGRmxhIERERERERuYmBFBERERERkZsYSBEREREREbmJgRQREREREZGbGEgRERERERG5iYEUERERERGRmxhIERERERERuYmBFBERERERkZsYSBEREREREbmJgRQREREREZGbGEgRERERERG5iYEUERERERGRmxhIERERERERuYmBFBERERERkZsYSBEREREREbmJgRQREREREZGbGEgRERERERG5iYEUERERERGRmxhIERERERERuYmBFBERERERkZsYSBEREREREbmJgRQREREREZGbGEgRERERERG5iYEUERERERGRmxhIERERERERuYmBFBERERERkZsYSBEREREREbmJgRQREREREZGbGEgRERERERG5iYEUERERERGRmxhIERERERERuYmBFBERERERkZsYSBEREREREbmJgRQREREREZGbGEgRERERERG5iYEUERERERGRmxhIERERERERuYmBFBERERERkZsYSBEREREREbmJgRQREf3nzJ07F5IkKRez2Yzw8HB06NABn3zyCW7duqW5/8SJEyFJEq5fv57r8545cwaDBw9GhQoVYDabERoaihYtWuCNN94ozLdDRETFkKGoN4CIiKiwvPnmmyhfvjwyMjJw+fJlxMbGYsyYMfjggw+wdOlS1KpVy+XnOnHiBBo0aABPT0888cQTiIyMRFxcHHbv3o13330XkyZNKsR3QkRExQ0DKSIi+s/q1KkT6tevr/w9btw4rF27Fl27dkX37t1x+PBheHp6uvRcH374IZKSkrB3716UK1dOc9vVq1cLdLuJiKj4Y2ofERHdV9q0aYPx48fj7Nmz+OGHH1x+3MmTJ1GmTBm7IAoAQkJCCnITiYjoHsBAioiI7juPPfYYAODPP/90+THlypXD+fPnsXbt2sLaLCIiuocwkCIiovtOmTJl4O/vj5MnT7r8mNGjR8NkMqFt27aIiYnBmDFj8Ntvv+H27duFuKVERFRcMZAiIqL7ko+Pj131vtxUr14de/fuxaOPPoozZ87g448/Rs+ePVGqVCl8/fXXhbilRERUHDGQIiKi+1JSUhJ8fX3dekx0dDS+//57XL9+Hf/88w/efvttGAwGDBs2DH/99VchbSkRERVHDKSIiOi+c+HCBSQkJKBixYp39Hi9Xo+aNWti3LhxWLJkCQBg3rx5BbmJRERUzDGQIiKi+873338PAOjQoUO+n0surx4XF5fv5yIionsHAykiIrqvrF27FpMnT0b58uUxYMAAlx+3ceNGZGRk2F2/fPlyAEDlypULbBuJiKj444K8RET0n7VixQocOXIEmZmZuHLlCtauXYvVq1ejXLlyWLp0Kcxms+b+H3zwAby8vDTX6XQ6vPrqq3j33Xexa9cu9O7dG7Vq1QIA7N69G9999x0CAwMxZsyYu/W2iIioGJAsFoulqDeCiIioIM2dOxeDBw9W/jaZTAgMDETNmjXRtWtXDB48WFNoYuLEiZg0aZLD59Lr9cjMzMTmzZsxf/58rF+/HufPn8ft27cRFhamLPAbFRVV6O+LiIiKDwZSREREREREbuIcKSIiIiIiIjcxkCIiIiIiInITAykiIiIiIiI3MZAiIiIiIiJyEwMpIiIiIiIiNzGQIiIiIiIichMX5AWQnZ2NS5cuwdfXF5IkFfXmEBERERFREbFYLLh16xbCw8Oh0zkfd2IgBeDSpUuIiIgo6s0gIiIiIqJi4vz58yhTpozT2xlIAcrq9ufPn4efn18Rbw0RERERERWVxMREREREKDGCMwykACWdz8/Pj4EUERERERHlOeWHxSaIiIiIiIjcxECKiIiIiIjITQykiIiIiIiI3MQ5UkRERERUICwWCzIzM5GVlVXUm0LklF6vh8FgyPeyRwykiIiIiCjf0tPTERcXh9u3bxf1phDlycvLC2FhYTCZTHf8HAykiIiIiChfsrOzcfr0aej1eoSHh8NkMuW7t5+oMFgsFqSnp+PatWs4ffo0KlWqlOuiu7lhIEVERERE+ZKeno7s7GxERETAy8urqDeHKFeenp4wGo04e/Ys0tPTYTab7+h5WGyCiIiIiArEnfbsE91tBbGvcm8nIiIiIiJyEwMpIiIiIiIiNzGQIiIiIiLKh7lz5yIgIMDl+0dGRuKjjz4qtO3Jj0GDBqFnz553/XUnTpyIOnXq3PXXzQ8GUkRERER037p8+TJGjRqFqKgoeHh4ICIiAt26dcOaNWsK7TV37NiBYcOGFdrz5+brr79G7dq14ePjg4CAAMTExGDq1KnK7R9//DHmzp1bJNt2r2HVPiIiIiK6L505cwbNmjVDQEAA3n//fdSsWRMZGRlYtWoVRowYgSNHjhTK6wYHBxfK8+Zl9uzZGDNmDD755BO0bNkSaWlp+Oeff3DgwAHlPv7+/kWybfcijkgRERERUYGyWCy4nZ5ZJBeLxeLydg4fPhySJGH79u3o06cPoqOjUb16dYwdOxZbt25V7vfBBx+gZs2a8Pb2RkREBIYPH46kpKRcn/v3339HgwYNYDabUbJkSfTq1Uu5TZ3ad+bMGUiShL179yq3x8fHQ5IkxMbGAgBiY2MhSRJWrVqFmJgYeHp6ok2bNrh69SpWrFiBqlWrws/PD4888kiuCyIvXboU/fr1w5AhQ1CxYkVUr14d/fv3x1tvvaXcxza179atWxgwYAC8vb0RFhaGDz/8EK1atcKYMWM07+ftt9/GE088AV9fX5QtWxZfffWV5rVffvllREdHw8vLC1FRURg/fjwyMjJy/QyLO45IEREREVGBSsnIQrUJq4rktQ+92QFeprybuDdu3MDKlSvx1ltvwdvb2+529ZwnnU6HTz75BOXLl8epU6cwfPhwvPTSS/jss88cPvcff/yBXr164bXXXsN3332H9PR0LF++/I7fk2zixImYMWMGvLy80K9fP/Tr1w8eHh6YP38+kpKS0KtXL3z66ad4+eWXHT4+NDQU69evx9mzZ1GuXDmXXnPs2LHYtGkTli5dilKlSmHChAnYvXu33Xym6dOnY/LkyXj11Vfx888/45lnnkHLli1RuXJlAICvry/mzp2L8PBw7N+/H0OHDoWvry9eeumlfH0mRYmBFBERERHdd06cOAGLxYIqVarkeV/b0ZcpU6bg6aefdhpIvfXWW3j44YcxadIk5bratWvne5unTJmCZs2aAQCGDBmCcePG4eTJk4iKigIA9O3bF+vWrXMaSL3xxhvo3bs3IiMjER0djSZNmqBz587o27evw3WVbt26hW+//Rbz589H27ZtAQBz5sxBeHi43X07d+6M4cOHAxCjTx9++CHWrVunBFKvv/66ct/IyEi88MILWLBgAQMpIiIiIiKZp1GPQ292KLLXdoU7KYB//fUXpk6diiNHjiAxMRGZmZlITU3F7du34eXlZXf/vXv3YujQoS4/v6tq1aql/L9UqVJKmpz6uu3btzt9fFhYGLZs2YIDBw5gw4YN2Lx5MwYOHIhZs2Zh5cqVdsHUqVOnkJGRgYYNGyrX+fv7K8GRs22TJAmhoaG4evWqct3ChQvxySef4OTJk0hKSkJmZib8/Pzc+wCKGc6RIiIiIqICJUkSvEyGIrlIkuTSNlaqVAmSJOVZUOLMmTPo2rUratWqhcWLF2PXrl2YOXMmACA9Pd3hYzw9PV3+rOTgRR3YOZs7ZDQalf9LkqT5W74uOzs7z9esUaMGhg8fjh9++AGrV6/G6tWrsX79epe3Oa9ts92WLVu2YMCAAejcuTOWLVuGPXv24LXXXnP6+d0rijSQmjp1Kho0aABfX1+EhISgZ8+eOHr0qOY+Tz31FCpUqABPT08EBwejR48edjv8uXPn0KVLF3h5eSEkJAQvvvgiMjMz7+ZbISIiIqJ7SGBgIDp06ICZM2ciOTnZ7vb4+HgAwK5du5CdnY3p06ejcePGiI6OxqVLl3J97lq1arlcPl2u4BcXF6dcpy48UdiqVasGAA4/g6ioKBiNRuzYsUO5LiEhAceOHXPrNTZv3oxy5crhtddeQ/369VGpUiWcPXs2fxteDBRpILV+/XqMGDECW7duxerVq5GRkYH27dtrvsh69ephzpw5OHz4MFatWgWLxYL27dsjKysLAJCVlYUuXbogPT0dmzdvxrfffou5c+diwoQJRfW2iIiIiOgeMHPmTGRlZaFhw4ZYvHgxjh8/jsOHD+OTTz5BkyZNAAAVK1ZERkYGPv30U5w6dQrff/89vvjii1yf94033sCPP/6IN954A4cPH8b+/fvx7rvvOryvp6cnGjdujHfeeQeHDx/G+vXrNfOJCtIzzzyDyZMnY9OmTTh79iy2bt2Kxx9/HMHBwcr7VfP19cXAgQPx4osvYt26dTh48CCGDBkCnU7n8sgfIEb/zp07hwULFuDkyZP45JNPsGTJkoJ8a0WiSAOplStXYtCgQahevTpq166NuXPn4ty5c9i1a5dyn2HDhqFFixaIjIxE3bp1MWXKFJw/fx5nzpwBAPz55584dOgQfvjhB9SpUwedOnXC5MmTMXPmzHt+uJCIiIiICk9UVBR2796N1q1b4/nnn0eNGjXw4IMPYs2aNfj8888BiCIRH3zwAd59913UqFED8+bN0yxg60irVq2waNEiLF26FHXq1EGbNm1ynbs0e/ZsZGZmol69ehgzZgymTJlSoO9T1q5dO2zduhX/+9//EB0djT59+sBsNmPNmjUICgpy+JgPPvgATZo0QdeuXdGuXTs0a9YMVatWhdlsdvl1u3fvjueeew4jR45EnTp1sHnzZowfP76g3laRkSzuzLQrZCdOnEClSpWwf/9+1KhRw+725ORkvP766/jtt99w5MgRmEwmTJgwAUuXLtUMgZ4+fVr5YcTExNg9T1paGtLS0pS/ExMTERERgYSEhHt+0hsRERHR3ZaamorTp0+jfPnybjWw6d6TnJyM0qVLY/r06RgyZEhRb84dy22fTUxMhL+/f56xQbEpNpGdnY0xY8agWbNmdkHUZ599Bh8fH/j4+GDFihVYvXo1TCYTAODy5csoVaqU5v7y35cvX3b4WlOnToW/v79yiYiIKIR3RERERER0b9uzZw9+/PFHnDx5Ert378aAAQMAAD169CjiLSt6xSaQGjFiBA4cOIAFCxbY3TZgwADs2bMH69evR3R0NPr164fU1NQ7fq1x48YhISFBuZw/fz4/m05ERERE9J81bdo01K5dG+3atUNycjI2btyIkiVLFvVmFblisY7UyJEjsWzZMmzYsAFlypSxu10eOapUqRIaN26MEiVKYMmSJejfvz9CQ0Ptck6vXLkCQKze7IiHhwc8PDwK/o0QEREREf2HxMTEaOoXkFWRjkhZLBaMHDkSS5Yswdq1a1G+fHmXHmOxWJQ5Tk2aNMH+/fs1C36tXr0afn5+SjlHIiIiIiKiglSkI1IjRozA/Pnz8dtvv8HX11eZ0+Tv7w9PT0+cOnUKCxcuRPv27REcHIwLFy7gnXfegaenJzp37gwAaN++PapVq4bHHnsM7733Hi5fvozXX38dI0aM4KgTEREREREViiIdkfr888+RkJCAVq1aISwsTLksXLgQAGA2m7Fx40Z07twZFStWxEMPPQRfX19s3rwZISEhAAC9Xo9ly5ZBr9ejSZMmePTRR/H444/jzTffLMq3RkRERERE/2FFOiKVV+X18PBwLF++PM/nKVeunEv3IyIiIiIiKgjFpmofERERERHRvYKBFBERERERkZsYSBERERERFTJJkvDrr7/eN69b1AYNGoSePXsW6mswkCIiIiKi+9LdaGzfLRMnTkSdOnXsro+Li0OnTp3y9dzr1q1D586dERQUBC8vL1SrVg3PP/88Ll68mK/nVTtz5gwkScLevXsL7DkLGwMpIiIiIqL/qNDQ0HwtCfTll1+iXbt2CA0NxeLFi3Ho0CF88cUXSEhIwPTp0wtwS12Tnp5+11/TGQZSRERERFSwLBYgPbloLnlUhc7Nzz//jJo1a8LT0xNBQUFo164dkpOTAQDZ2dl48803UaZMGXh4eKBOnTpYuXKl8tj09HSMHDkSYWFhMJvNKFeuHKZOnap5fnl0yNPTE1FRUfj55581t58/fx79+vVDQEAAAgMD0aNHD5w5c0a5PTY2Fg0bNoS3tzcCAgLQrFkznD17FnPnzsWkSZOwb98+SJIESZIwd+5cAPapfRcuXED//v0RGBgIb29v1K9fH9u2bXP4eVy4cAGjR4/G6NGjMXv2bLRq1QqRkZFo0aIFZs2ahQkTJij3Xbx4MapXrw4PDw9ERkbaBVmRkZF4++238cQTT8DX1xdly5bFV199pdxevnx5AEBMTAwkSUKrVq0AWEcN33rrLYSHh6Ny5coAgP3796NNmzbKdzVs2DAkJSU5+2oLRZGWPyciIiKi/6CM28Db4UXz2q9eAkzebj8sLi4O/fv3x3vvvYdevXrh1q1b2Lhxo7Jcz8cff4zp06fjyy+/RExMDGbPno3u3bvj4MGDqFSpEj755BMsXboUP/30E8qWLYvz58/j/PnzmtcYP3483nnnHXz88cf4/vvv8fDDD2P//v2oWrUqMjIy0KFDBzRp0gQbN26EwWDAlClT0LFjR/zzzz/Q6XTo2bMnhg4dih9//BHp6enYvn07JEnCQw89hAMHDmDlypX466+/AAD+/v527zEpKQktW7ZE6dKlsXTpUoSGhmL37t3Izs52+JksWrQI6enpeOmllxzeHhAQAADYtWsX+vXrh4kTJ+Khhx7C5s2bMXz4cAQFBWHQoEHK/adPn47Jkyfj1Vdfxc8//4xnnnkGLVu2ROXKlbF9+3Y0bNgQf/31F6pXrw6TyaQ8bs2aNfDz88Pq1asBAMnJycpntWPHDly9ehVPPvkkRo4cqQSQdwMDKSIiIiK678XFxSEzMxO9e/dGuXLlAAA1a9ZUbp82bRpefvllPPzwwwCAd999F+vWrcNHH32EmTNn4ty5c6hUqRKaN28OSZKU51D73//+hyeffBIAMHnyZKxevRqffvopPvvsMyxcuBDZ2dmYNWsWJEkCAMyZMwcBAQGIjY1F/fr1kZCQgK5du6JChQoAgKpVqyrP7ePjA4PBgNDQUKfvcf78+bh27Rp27NiBwMBAAEDFihWd3v/48ePw8/NDWFhYrp/dBx98gLZt22L8+PEAgOjoaBw6dAjvv/++JpDq3Lkzhg8fDgB4+eWX8eGHH2LdunWoXLkygoODAQBBQUF278Hb2xuzZs1Sgquvv/4aqamp+O677+DtLYLmGTNmoFu3bnj33XdRqlSpXLe3oDCQIiIiIqKCZfQSI0NF9dp3oHbt2mjbti1q1qyJDh06oH379ujbty9KlCiBxMREXLp0Cc2aNdM8plmzZti3bx8AkYL24IMPonLlyujYsSO6du2K9u3ba+7fpEkTu7/l4gr79u3DiRMn4Ovrq7lPamoqTp48ifbt22PQoEHo0KEDHnzwQbRr1w79+vXLM8hR27t3L2JiYpQgKi8Wi0UJ6nJz+PBh9OjRQ3Nds2bN8NFHHyErKwt6vR4AUKtWLeV2SZIQGhqKq1ev5vn8NWvW1IxQHT58GLVr11aCKPn1srOzcfTo0bsWSHGOFBEREREVLEkS6XVFcXGh4e+IXq/H6tWrsWLFClSrVg2ffvopKleujNOnT7v0+Lp16+L06dOYPHkyUlJS0K9fP/Tt29fl109KSkK9evWwd+9ezeXYsWN45JFHAIgRqi1btqBp06ZYuHAhoqOjsXXrVpdfw9PT0+X7AmJkKSEhAXFxcW49zhmj0aj5W5Ikp2mFauqAqThhIEVEREREBNGwb9asGSZNmoQ9e/bAZDJhyZIl8PPzQ3h4ODZt2qS5/6ZNm1CtWjXlbz8/Pzz00EP4+uuvsXDhQixevBg3btxQbrcNerZu3aqk59WtWxfHjx9HSEgIKlasqLmo5zvFxMRg3Lhx2Lx5M2rUqIH58+cDAEwmE7KysnJ9f7Vq1cLevXs125Sbvn37wmQy4b333nN4e3x8PACRYujos4mOjlZGo/Iijzjl9R7k19u3b59SCER+PZ1OpxSjuBsYSBERERHRfW/btm14++23sXPnTpw7dw6//PILrl27pgQ6L774It59910sXLgQR48exSuvvIK9e/fi2WefBSDmCf344484cuQIjh07hkWLFiE0NFQpyACI4g2zZ8/GsWPH8MYbb2D79u0YOXIkAGDAgAEoWbIkevTogY0bN+L06dOIjY3F6NGjceHCBZw+fRrjxo3Dli1bcPbsWfz55584fvy4sn2RkZE4ffo09u7di+vXryMtLc3uPfbv3x+hoaHo2bMnNm3ahFOnTmHx4sXYsmWLw88kIiICH374IT7++GMMGTIE69evx9mzZ7Fp0yY89dRTmDx5MgDg+eefx5o1azB58mQcO3YM3377LWbMmIEXXnjB5c8/JCQEnp6eWLlyJa5cuYKEhASn9x0wYADMZjMGDhyIAwcOYN26dRg1ahQee+yxu5bWBzCQIiIiIiKCn58fNmzYgM6dOyM6Ohqvv/46pk+frixmO3r0aIwdOxbPP/88atasiZUrV2Lp0qWoVKkSAMDX1xfvvfce6tevjwYNGuDMmTNYvnw5dDprc3vSpElYsGABatWqhe+++w4//vijMqLl5eWFDRs2oGzZsujduzeqVq2KIUOGIDU1FX5+fvDy8sKRI0fQp08fREdHY9iwYRgxYgSeeuopAECfPn3QsWNHtG7dGsHBwfjxxx/t3qPJZMKff/6JkJAQdO7cGTVr1sQ777yT66jR8OHD8eeff+LixYvo1asXqlSpgieffBJ+fn5KoFS3bl389NNPWLBgAWrUqIEJEybgzTff1BSayIvBYMAnn3yCL7/8EuHh4XZzrtS8vLywatUq3LhxAw0aNEDfvn3Rtm1bzJgxw+XXKwiSxZKPYvv/EYmJifD390dCQgL8/PyKenOIiIiI7impqak4ffo0ypcvD7PZXNSbQ5Sn3PZZV2MDjkgRERERERG5iYEUERERERGRmxhIERERERERuYmBFBERERERkZsYSN0vkpPFAnWSJP5PRPe85JtXIU2SIE2SkHwz75XhiQoMzylERAykiIiIiIiI3MVAioiIiIiIyE0MpIiIiIiIiNzEQIqIiIiIiMhNDKSIiIiIiO6AxWLBsGHDEBgYCEmSsHfv3qLepGIhMjISH330UVFvRqFjIEVERERE96VBgwahZ8+ed/z4lStXYu7cuVi2bBni4uJQo0YNSJKEX3/9Nc/HSpIEs9mMs2fPaq7v2bMnBg0adMfblF9nzpyBJEnKJSgoCO3bt8eePXuKbJuKKwZSRERERER34OTJkwgLC0PTpk0RGhoKg8Hg1uMlScKECRMKaevy56+//kJcXBxWrVqFpKQkdOrUCfHx8UW9WcUKAykiIiIiKhzJyXf3UsAOHDiATp06wcfHB6VKlcJjjz2G69evAxCjWaNGjcK5c+cgSRIiIyMRGRkJAOjVq5dyXW5GjhyJH374AQcOHHB6n7S0NIwePRohISEwm81o3rw5duzYodx+8+ZNDBgwAMHBwfD09ESlSpUwZ84c5fbz58+jX79+CAgIQGBgIHr06IEzZ87k+d6DgoIQGhqK+vXrY9q0abhy5Qq2bdsGAFi8eDGqV68ODw8PREZGYvr06bk+16xZsxAQEIA1a9YAyP1zvZcwkCIiIiKiwuHjc3cvBSg+Ph5t2rRBTEwMdu7ciZUrV+LKlSvo168fAODjjz/Gm2++iTJlyiAuLg47duxQApw5c+Yo1+WmWbNm6Nq1K1555RWn93nppZewePFifPvtt9i9ezcqVqyIDh064MaNGwCA8ePH49ChQ1ixYgUOHz6Mzz//HCVLlgQAZGRkoEOHDvD19cXGjRuxadMm+Pj4oGPHjkhPT3f5s/D09AQApKenY9euXejXrx8efvhh7N+/HxMnTsT48eMxd+5ch49977338Morr+DPP/9E27Zt8/xc7yXujT8SEREREd0HZsyYgZiYGLz99tvKdbNnz0ZERASOHTuG6Oho+Pr6Qq/XIzQ0VPPYgIAAu+ucmTp1KmrVqoWNGzfigQce0NyWnJyMzz//HHPnzkWnTp0AAF9//TVWr16Nb775Bi+++CLOnTuHmJgY1K9fHwA0o2ALFy5EdnY2Zs2aBUmSAIggLyAgALGxsWjfvn2e2xcfH4/JkyfDx8cHDRs2xNixY9G2bVuMHz8eABAdHY1Dhw7h/ffft5vb9fLLL+P777/H+vXrUb16dQCufa73CgZSRERERFQ4kpKKegvu2L59+7Bu3Tr4OBjpOnnyZIE1+KtVq4bHH38cr7zyCjZt2mT3OhkZGWjWrJlyndFoRMOGDXH48GEAwDPPPIM+ffpg9+7daN++PXr27ImmTZsq7+HEiRPw9fXVPG9qaipOnjyZ63Y1bdoUOp0OycnJiIqKwsKFC1GqVCkcPnwYPXr00Ny3WbNm+Oijj5CVlQW9Xg8AmD59OpKTk7Fz505ERUUp971bn+vdwECKiIiIiAqHt3dRb8EdS0pKQrdu3fDuu+/a3RYWFlagrzVp0iRER0e7VO3PVqdOnXD27FksX74cq1evRtu2bTFixAhMmzYNSUlJqFevHubNm2f3uODg4Fyfd+HChahWrRqCgoIQEBDg9nY98MAD+OOPP/DTTz9pUhfv5uda2BhIERERERHZqFu3LhYvXozIyEi3qvEZjUZkZWW59VoREREYOXIkXn31VVSoUEG5vkKFCjCZTNi0aRPKlSsHQMx72rFjB8aMGaPcLzg4GAMHDsTAgQPxwAMP4MUXX8S0adNQt25dLFy4ECEhIfDz83N7m9TbIqtatardyNmmTZsQHR2tjEYBQMOGDTFy5Eh07NgRBoMBL7zwAoA7/1yLIxabICIiIqL7VkJCAvbu3au5nD9/HiNGjMCNGzfQv39/7NixAydPnsSqVaswePDgXAOlyMhIrFmzBpcvX8bNmzdd3o5x48bh0qVL+Ouvv5TrvL298cwzz+DFF1/EypUrcejQIQwdOhS3b9/GkCFDAAATJkzAb7/9hhMnTuDgwYNYtmwZqlatCgAYMGAASpYsiR49emDjxo04ffo0YmNjMXr0aFy4cOGOPq/nn38ea9asweTJk3Hs2DF8++23mDFjhhIoqTVt2hTLly/HpEmTlAV67/RzLY4YSBERERHRfSs2NhYxMTGay6RJkxAeHo5NmzYhKysL7du3R82aNTFmzBgEBARAp3PehJ4+fTpWr16NiIgIxMTEuLwdgYGBePnll5Gamqq5/p133kGfPn3w2GOPoW7dujhx4gRWrVqFEiVKAABMJhPGjRuHWrVqoUWLFtDr9ViwYAEAwMvLCxs2bEDZsmXRu3dvVK1aFUOGDEFqaqrbI1SyunXr4qeffsKCBQtQo0YNTJgwAW+++abTRYSbN2+OP/74A6+//jo+/fTTO/5ciyPJYrFYinojilpiYiL8/f2RkJBwxztVsZecbC0LmpR0T+csE5GQfPMqfD4pBQBIGn0F3iVCiniL6L7BcwrZSE1NxenTp1G+fHmYzeai3hyiPOW2z7oaG9xbYR8REREREVExwECKiIiIiIjITQykiIiIiIiI3MRAioiIiIiIyE1FGkhNnToVDRo0gK+vL0JCQtCzZ08cPXpUuf3GjRsYNWoUKleuDE9PT5QtWxajR49GQkKC5nnOnTuHLl26wMvLCyEhIXjxxReRmZl5t98OERERERHdJ4o0kFq/fj1GjBiBrVu3YvXq1cjIyED79u2RnJwMALh06RIuXbqEadOm4cCBA5g7dy5Wrlyp1M0HgKysLHTp0gXp6enYvHkzvv32W8ydOxcTJkwoqrdFRERERET/ccWq/Pm1a9cQEhKC9evXo0WLFg7vs2jRIjz66KNITk6GwWDAihUr0LVrV1y6dAmlSokywF988QVefvllXLt2DSaTKc/XZflzIroXsfw5FRmeU8gGy5/TveY/V/5cTtkLDAzM9T5+fn4wGAwAgC1btqBmzZpKEAUAHTp0QGJiIg4ePOjwOdLS0pCYmKi5EBERERERuarYBFLZ2dkYM2YMmjVrhho1aji8z/Xr1zF58mQMGzZMue7y5cuaIAqA8vfly5cdPs/UqVPh7++vXCIiIgroXRARERER0f2g2ARSI0aMwIEDB7BgwQKHtycmJqJLly6oVq0aJk6cmK/XGjduHBISEpTL+fPn8/V8RERERHT/sVgsGDZsGAIDAyFJEvbu3VvUm3RXnDlz5r56v84Ui0Bq5MiRWLZsGdatW4cyZcrY3X7r1i107NgRvr6+WLJkCYxGo3JbaGgorly5orm//HdoaKjD1/Pw8ICfn5/mQkRERET3l0GDBqFnz553/PiVK1di7ty5WLZsGeLi4lCjRg1IkoRff/01z8dKkqRc/Pz80KBBA/z22293vC109xVpIGWxWDBy5EgsWbIEa9euRfny5e3uk5iYiPbt28NkMmHp0qV2k8GaNGmC/fv34+rVq8p1q1evhp+fH6pVq1bo74GIiIiI7k8nT55EWFgYmjZtitDQUGUOv6vmzJmDuLg47Ny5E82aNUPfvn2xf//+Qtpa96Wnpxf1JhRrRRpIjRgxAj/88APmz58PX19fXL58GZcvX0ZKSgoAaxCVnJyMb775BomJicp9srKyAADt27dHtWrV8Nhjj2Hfvn1YtWoVXn/9dYwYMQIeHh5F+faIiIiI7mvJ6cl39VLQDhw4gE6dOsHHxwelSpXCY489huvXrwMQo1mjRo3CuXPnIEkSIiMjERkZCQDo1auXcl1uAgICEBoaiujoaEyePBmZmZlYt26dcvv58+fRr18/BAQEIDAwED169MCZM2eUbdPpdLh27RoAsf6qTqfDww8/rDx+ypQpaN68OQCxZNCQIUNQvnx5eHp6onLlyvj444812yOP0L311lsIDw9H5cqVAQDbt29HTEwMzGYz6tevjz179tzxZ/pf4l7YXMA+//xzAECrVq0018+ZMweDBg3C7t27sW3bNgBAxYoVNfc5ffo0IiMjodfrsWzZMjzzzDNo0qQJvL29MXDgQLz55pt35T0QERERkWM+U33u6utZ3ii4VX3i4+PRpk0bPPnkk/jwww+RkpKCl19+Gf369cPatWvx8ccfo0KFCvjqq6+wY8cO6PV6AEBISAjmzJmDjh07KtflJTMzE9988w0AKEv3ZGRkoEOHDmjSpAk2btwIg8GAKVOmoGPHjvjnn39QvXp1BAUFYf369ejbty82btyo/C1bv3690s7Ozs5GmTJlsGjRIgQFBWHz5s0YNmwYwsLC0K9fP+Uxa9asgZ+fH1avXg0ASEpKQteuXfHggw/ihx9+wOnTp/Hss8/m+/P9LyjSQCqvJaxatWqV530AoFy5cli+fHlBbRYRERER3edmzJiBmJgYvP3228p1s2fPRkREBI4dO4bo6Gj4+vpCr9fbzcuXR5ry0r9/f+j1eqSkpCA7OxuRkZFKULNw4UJkZ2dj1qxZkCQJgBhsCAgIQGxsLNq3b48WLVogNjYWffv2RWxsLAYPHoxZs2bhyJEjqFChAjZv3oyXXnoJAGA0GjFp0iTltcuXL48tW7bgp59+0gRS3t7emDVrlhLQffXVV8jOzsY333wDs9mM6tWr48KFC3jmmWfu8JP97yjSQIqIiIiI/ruSxiUV9SbcsX379mHdunXw8bEfVTt58iSio6Pz/Roffvgh2rVrh1OnTuG5557DJ598oqynum/fPpw4cQK+vr6ax6SmpuLkyZMAgJYtW+Krr74CIEaf3n77bRw7dgyxsbG4ceMGMjIy0KxZM+WxM2fOxOzZs3Hu3DmkpKQgPT0dderU0Tx/zZo1lSAKAA4fPoxatWpp6hQ0adIk3+/9v4CBFBEREREVCm+Td1Fvwh1LSkpCt27d8O6779rdFhYWViCvERoaiooVK6JixYqYM2cOOnfujEOHDiEkJARJSUmoV68e5s2bZ/e44OBgACJ7a8yYMTh+/DgOHTqE5s2b48iRI4iNjcXNmzdRv359eHl5AQAWLFiAF154AdOnT0eTJk3g6+uL999/X5lGI/P2vne/s7uNgRQRERERkY26deti8eLFiIyMdKsan9FoVIqiuaNhw4aoV68e3nrrLXz88ceoW7cuFi5ciJCQEKdL9dSsWRMlSpTAlClTUKdOHfj4+KBVq1Z49913cfPmTU0dgk2bNqFp06YYPny4cp08spWbqlWr4vvvv0dqaqoyKrV161a3399/UbFYR4qIiIiIqCgkJCRg7969msv58+cxYsQI3LhxA/3798eOHTtw8uRJrFq1CoMHD841UIqMjMSaNWtw+fJl3Lx5061tGTNmDL788ktcvHgRAwYMQMmSJdGjRw9s3LgRp0+fRmxsLEaPHo0LFy4AEGtRtWjRAvPmzVOCplq1aiEtLQ1r1qxBy5YtleeuVKkSdu7ciVWrVuHYsWMYP348duzYkec2PfLII5AkCUOHDsWhQ4ewfPlyTJs2za339V/FQIqIiIiI7luxsbGIiYnRXCZNmoTw8HBs2rQJWVlZaN++PWrWrIkxY8YgICAAOp3zJvT06dOxevVqREREICYmxq1t6dixI8qXL4+33noLXl5e2LBhA8qWLYvevXujatWqGDJkCFJTUzUjVC1btkRWVpYSSOl0OrRo0QKSJGnmRz311FPo3bs3HnroITRq1Aj//vuvZnTKGR8fH/z+++/Yv38/YmJi8NprrzlMd7wfSRZXyuL9xyUmJsLf3x8JCQlOh07vecnJgDxZMikJYP4r0T0v+eZV+HxSCgCQNPoKvEuEFPEW0X2D5xSykZqaitOnT6N8+fKaogRExVVu+6yrsQFHpIiIiIiIiNzEQIqIiIiIiMhNDKSIiIiIiIjcxECKiIiIiIjITQykiIiIiIiI3MRAioiIiIiIyE0MpIiIiIiIiNzEQIqIiIiIiMhNhqLeACIiIiL6b0pJAdLT797rmUyAp+fdez26vzGQIiIiIqICl5IC/PYbcPPm3XvNEiWAHj0YTLlj0KBBiI+Px6+//lrUm3LPYWofERERERW49HQRRHl6igCnsC+enuL13BkBGzRoEHr27Jmv9zlx4kRIkgRJkqDX6xEREYFhw4bhxo0b+Xre4iI2NlZ5f5IkoVSpUujTpw9OnTpVIM8bHx9fMBtaBDgiRURERESFxmwGvL3vzmulpNyd17FVvXp1/PXXX8jKysLhw4fxxBNPICEhAQsXLiyaDXIgIyMDRqPxjh9/9OhR+Pr64vjx4xg2bBi6deuGf/75B3q9/o625b+AI1JERERERABatWqF0aNH46WXXkJgYCBCQ0MxceLEPB9nMBgQGhqK0qVLo127dvjf//6H1atXa+4za9YsVK1aFWazGVWqVMFnn32m3Na3b1+MHDlS+XvMmDGQJAlHjhwBAKSnp8Pb2xt//fUXAGDlypVo3rw5AgICEBQUhK5du+LkyZPK48+cOQNJkrBw4UK0bNkSZrMZ8+bNQ1ZWFsaOHas87qWXXoLFYnHpswkJCUFYWBhatGiBCRMm4NChQzhx4gQA4PPPP0eFChVgMplQuXJlfP/995rHSpKEzz//HN27d4e3tzeGDh2K1q1bAwBKlCgBSZIwaNAgl7ajOGEgRURERESU49tvv4W3tze2bduG9957D2+++aZdUJSbM2fOYNWqVTCZTMp18+bNw4QJE/DWW2/h8OHDePvttzF+/Hh8++23AICWLVsiNjZWuf/69etRsmRJ5bodO3YgIyMDTZs2BQAkJydj7Nix2LlzJ9asWQOdTodevXohOztbsy2vvPIKnn32WRw+fBgdOnTA9OnTMXfuXMyePRt///03bty4gSVLlrj9GXnmTEJLT0/HkiVL8Oyzz+L555/HgQMH8NRTT2Hw4MFYt26d5jETJ05Er169sH//fkyaNAmLFy8GIEa64uLi8PHHH7u9HUWNqX1ERERERDlq1aqFN954AwBQqVIlzJgxA2vWrMGDDz7o9DH79++Hj48PsrKykJqaCgD44IMPlNvfeOMNTJ8+Hb179wYAlC9fHocOHcKXX36JgQMHolWrVnj22Wdx7do1GAwGHDp0COPHj0dsbCyefvppxMbGokGDBvDy8gIA9OnTR/P6s2fPRnBwMA4dOoQaNWoo148ZM0Z5TQD46KOPMG7cOOW6L774AqtWrXLr84mLi8O0adNQunRpVK5cGU8//TQGDRqE4cOHAwDGjh2LrVu3Ytq0acqoEwA88sgjGDx4sPL36dOnAYiRroCAALe2objgiBQRERERUY5atWpp/g4LC8PVq1dzfUzlypWxd+9e7NixAy+//DI6dOiAUaNGARCjRydPnsSQIUPg4+OjXKZMmaKk49WoUQOBgYFYv349Nm7ciJiYGHTt2hXr168HIEaoWrVqpbze8ePH0b9/f0RFRcHPzw+RkZEAgHPnzmm2q379+sr/ExISEBcXh0aNGinXGQwGzX1yU6ZMGXh7eyM8PBzJyclYvHgxTCYTDh8+jGbNmmnu26xZMxw+fNjptvxXcESKiIiIiCiHbUEGSZLsUuZsmUwmVKxYEQDwzjvvoEuXLpg0aRImT56MpKQkAMDXX3+tCWIAKIUaJElCixYtEBsbCw8PD7Rq1Qq1atVCWloaDhw4gM2bN+OFF15QHtetWzeUK1cOX3/9NcLDw5GdnY0aNWog3aZkoXcBVvnYuHEj/Pz8EBISAl9fX7cfX5DbUlxwRIqIiIiIqAC9/vrrmDZtGi5duoRSpUohPDwcp06dQsWKFTWX8uXLK4+R50nFxsaiVatW0Ol0aNGiBd5//32kpaUpoz7//vsvjh49itdffx1t27ZF1apVcdOFxbr8/f0RFhaGbdu2KddlZmZi165dLr2n8uXLo0KFCnZBVNWqVbFp0ybNdZs2bUK1atVyfT55DllWVpZLr18ccUSKiIiIiApNzpSh/8zruKJJkyaoVasW3n77bcyYMQOTJk3C6NGj4e/vj44dOyItLQ07d+7EzZs3MXbsWACiYuBzzz0Hk8mE5s2bK9e98MILaNCggTKiU6JECQQFBeGrr75CWFjY/9m76igpjq97e3zd2IVdYHF3d0KQYBES4gYhHuLyi/sXIe5OiBIjJASSQHB3h8VtWVh3G+/vj5rqrq7pHlmBJel7Docd66np7qp6ct99yMzMxGOPPRbSuO677z68+uqraNeuHTp27Ii33nqr1n2cHnnkEVx55ZXo1asXRo8ejfnz52Pu3LmSwqAWWrRoAUEQsGDBAkyYMAERERGIjo6u1VjONHRHSocOHTp06NChQ0edw2IhjXKLi89cf6eEBPK9DQEPPPAApk6dikcffRS33HILIiMj8frrr+ORRx5BVFQUunXrhvvvv196f7du3RAfH4/27dtLDsWIESPg8XgU9VEGgwE//vgj7r33XnTt2hUdOnTAe++9p3iPFh566CFkZ2djypQpMBgMmDZtGi699FKUlpbW+HdOmjQJ7777Lt544w3cd999aNWqFWbNmhV0PE2bNsXzzz+Pxx57DDfddBNuvPFGfPXVVzUex9mAIIYqHv8vRllZGeLi4lBaWorY2NizPZz6QWUlQL38iooz1xlPhw4d9YbK4jxEv9cYAFBxby6iElLO8oh0/Geg7yk6ONjtdhw7dgytWrWCzWaTnq+uBriynXqFxQL4lLl16AgIrXsWCN030DNSOnTo0KFDhw4dOuoFERG6Y6Pj3wtdbEKHDh06dOjQoUOHDh06wkTYGSmHw4GNGzfixIkTqKqqQnJyMnr16qVQHdGhQ4cOHTp06NChQ4eOfzNCdqTWrl2Ld999F/Pnz4fL5UJcXBwiIiJQVFQEh8OB1q1b47bbbsMdd9xRI215HTp06NChQ4cOHTp06DhXEBK17+KLL8ZVV12Fli1b4p9//kF5eTkKCwuRlZWFqqoqHDp0CE899RSWLl2K9u3bY/HixfU9bh06dOjQoUOHDh0NDLqGmY5zBXVxr4aUkZo4cSJ+/fVXv07PFK1bt0br1q0xZcoUZGRkIDs7u9YD06FDhw4dOnTo0HFugNqIVVVViNDVJXScA6iqqgIATf8mFITkSN1+++0hH7Bz585BOxnr0KFDhw4dOnTo+PfAaDQiPj4eeXl5AIDIyEgIgnCWR6VDhz9EUURVVRXy8vIQHx8Po9FY42Pp8uc6dOjQoUOHDh06ao0mTZoAgORM6dDRkBEfHy/dszVFnTlSU6ZMwcmTJ7Fs2bK6OqQOHTp06NChQ4eOcwSCICA1NRUpKSlwuVxnezg6dGjCbDbXKhNFUWeOVNOmTWEw6G2pdOjQoUOHDh06/sswGo11YqTq0NHQUWeO1Msvv1xXh9KhQ4cOHTp06NChQ4eOBg09haRDhw4dOnTo0KFDhw4dYSLsjNS0adMCvv7ll1+GfKxXXnkFc+fOxf79+xEREYHBgwdjxowZ6NChg/Sezz77DLNnz8a2bdtQXl6O4uJixMfHK45TVFSEe+65B/Pnz4fBYMDkyZPx7rvvIjo6OqzfpkOHDh06dOjQoUOHDh2hIOyMVHFxseJfXl4eli1bhrlz56KkpCSsY61cuRLTp0/Hhg0bsHjxYrhcLlxwwQWorKyU3lNVVYVx48bhiSee0DzOddddh71792Lx4sVYsGABVq1ahdtuuy3cn6ZDhw4dOnTo0KFDhw4dISHsjNRvv/3m95zX68Wdd96JNm3ahHWshQsXKh5/9dVXSElJwdatWzF8+HAAwP333w8AWLFiheox9u3bh4ULF2Lz5s3o27cvAOD999/HhAkT8MYbbyAtLS2sMenQoUOHDh06dOjQoUNHMNRJjZTBYMCDDz6It99+u1bHKS0tBQAkJiaG/Jn169cjPj5ecqIAYPTo0TAYDNi4caPqZxwOB8rKyhT/dOjQoUOHDh06dOjQoSNU1JnYxJEjR+B2u2v8ea/Xi/vvvx9DhgxB165dQ/5cTk4OUlJSFM+ZTCYkJiYiJydH9TOvvPIK4uLipH/Nmzev8bh16NChQ4cOHTp06NDx30PY1L4HH3xQ8VgURWRnZ+PPP//ElClTajyQ6dOnY8+ePVizZk2NjxEqHn/8ccXvKCsr050pHTp06NChQ4cOHTp0hIywHant27crHhsMBiQnJ+PNN98MquinhbvvvlsSiWjWrFlYn23SpAny8vIUz7ndbhQVFaFJkyaqn7FarbBarTUaqw4dOnTo0KFDhw4dOnSE7UgtX768zr5cFEXcc889+O2337BixQq0atUq7GMMGjQIJSUl2Lp1K/r06QMAWLZsGbxeLwYMGFBnY9WhQ4cOHTp06NChQ4cOirAdqbrE9OnTMXv2bMybNw8xMTFSTVNcXBwiIiIAkBqonJwcHD58GACwe/duxMTEID09HYmJiejUqRPGjRuHW2+9FZ988glcLhfuvvtuXH311bpinw4dOnTo0KFDhw4dOuoFdSY28cQTT4RN7fv4449RWlqKESNGIDU1Vfr3008/Se/55JNP0KtXL9x6660AgOHDh6NXr174448/pPd8//336NixI0aNGoUJEyZg6NCh+Oyzz+rmh+nQoUOHDh06dOjQoUMHhzrLSJ06dQonT54M6zOiKAZ9z3PPPYfnnnsu4HsSExMxe/bssL5bhw4dOnTo0KFDhw4dOmqKOnOkvv7667o6lA4dOnTo0KFDhw4dOnQ0aNQZtU+HDh06dOjQoUOHDh06/iuoUUaqsrISK1euRGZmJpxOp+K1e++9t04GpkOHDh06dOjQoUOHDh0NFTXqIzVhwgRUVVWhsrISiYmJKCgoQGRkJFJSUnRHSocOHTp06NChQ4cOHf96hE3te+CBB3DRRRehuLgYERER2LBhA06cOIE+ffrgjTfeqI8x6tChQ4cOHTp06NChQ0eDQtiO1I4dO/DQQw/BYDDAaDTC4XCgefPmeO211/DEE0/Uxxh16NChQ4cOHTp06NCho0EhbEfKbDbDYCAfS0lJQWZmJgDSRDdc+XMdOnTo0KFDhw4dOnToOBcRdo1Ur169sHnzZrRr1w7nnXcennnmGRQUFODbb79F165d62OMOnTo0KFDhw4dOnTo0NGgEHZG6uWXX0ZqaioA4KWXXkJCQgLuvPNO5Ofn47PPPqvzAerQoUOHjv82RFEMqYG7Dh06dOjQcSYRdkaqb9++0t8pKSlYuHBhnQ5IR8PAicJKLN+fh6v7p8NmNp7t4ejQoeMcg9vjhQjAbKxdu8KCCgfGvbMKYzo3xiuXda+bwenQoUOHDh11AL0hrw5V/N+f+/Dc/Ay8tfjg2R6KDh06zjGUVrnQ76UluP3brbU+1rJ9eSiocOKHTXoNrg4dOnToaFgIyZEaN24cNmzYEPR95eXlmDFjBj788MNaD0zH2cXijFwAwMoD+Wd5JDp06DjXsOJgHoqrXFi2P6/Wx6p0uqW/vV6d3qdDhw4dOhoOQqL2XXHFFZg8eTLi4uJw0UUXoW/fvkhLS4PNZkNxcTEyMjKwZs0a/PXXX5g4cSJef/31+h63jjMEq1lPWurQoSM8uD2yw2N3eWpFD2ZLo+xuDyItYTPSdejQoUOHjnpBSDvSzTffjOuvvx6//PILfvrpJ3z22WcoLS0FAAiCgM6dO2Ps2LHYvHkzOnXqVK8D1nFmIQjC2R6CDh06zjGYjPK64XB7a+VIeRlPqtKhO1I6dOjQoaPhIOQdyWq14vrrr8f1118PACgtLUV1dTWSkpJgNpvrbYA6zi7cHu/ZHoIOHTrOYTjdtVtDKh0e6e8qpxuAtZYj0qFDhw4dOuoGNQ7txcXFIS4uri7HoqMBgqXo6NChQ0cocDHrhrOWwRi2RqrK6QnwTh06dOjQoePMQi+A0REQIrQdqZxSO56ZtweH8yrO4Ih06NDR0MFmsl21zEhVOGRHyqVnyHXo0KFDRwOC7kjpCIhAPTDf/OcAvll/Ahe+v/rMDUiHDh0NHi5v3WWkKuyyI1VbmqAOHTp06NBRl9AdKR0BEYjYt+oQkUa3u3TjRocOHTLYLFTta6R0R0qHDh06dDRM6I6UjoAQA6SkrKaaK3Hp0KHj3wu3l3GkapuRYhwph07t06FDhw4dDQg1cqRKSkrwxRdf4PHHH0dRUREAYNu2bTh16lSdDk7H2QHrPAXKSOnK6Dp06FCDQmyithkpRmyitvVWDR2z1h7DD5syFc8dzitHdmn1WRqRDh06dOgIhLBV+3bt2oXRo0cjLi4Ox48fx6233orExETMnTsXmZmZ+Oabb+pjnDrOIDxMfUMgT8qge1I6dOhQASsKUVvlT1b+vLbZrYaMMrsLz8/PAABM6JaKuAgzCiscmPDeGthMBmx9egzMRp1EokOHDh0NCWGvyg8++CCmTp2KQ4cOwWazSc9PmDABq1atqtPB6Tg7cDOOlDcAta+2flRWcRW2ZxbX7iA6dOhocGCdp9oq7VX8R2qkqhlp96JKJwAgI7sMTrcXZXY3jhdUnq2h6TjHsHRfLvr+3xKsOJB3toeiQ8e/HmE7Ups3b8btt9/u93zTpk2Rk5NTJ4PScXbBOlIBqX21/J7JH6/DpR+tw66skloeSYcOHQ0JrPNU6z5S/xFHiv1t1KnKLXNIz2WX2s/4mHQ0DIiiGLBemcdjc3ejoMKBJ+bursdR6dChA6iBI2W1WlFWVub3/MGDB5GcnFwng9JxduFhosmB1u7aUvuokbAzq7RWx9GhQ0fDgquOMlJuj1fRhPffTO1jf5vDTX6z3SX/9pwy3ZH6L2LPqVJ0fHohPlpxJOTP5JeTvfW07nzr0FHvCNuRuvjii/HCCy/A5XIBAARBQGZmJh599FFMnjy5zgeo48yDVdwK1JC3Nn6Ul8l6uf/FxpEOHf9FsGtITWukRFHExPfWKJ77N2ekWIeTtpRwML83VzeK/5N4Z8khONxevL7owNkeig4dOlQQtiP15ptvoqKiAikpKaiursZ5552Htm3bIiYmBi+99FJ9jFHHGYZb4eQEcKQCkPteX7Qfd363VRFRZeFiDC2FuIUOHTrOedQFta+02oUDueWK5/7NGSmXW14HaUaK/g8Ahb66KR3/LVS73MHfpEOHjrOGsFX74uLisHjxYqxduxY7d+5ERUUFevfujdGjR9fH+HScBbCOVCBajlZGyu7y4MPlhIYwuXcBRndu7Pce1nnSHSkdOv5dqAtqH6UnsajLjFSFw43Nx4owvH0yjIazr0Dq9MhOE81Esc3Oi6t0R+q/CIuu1HjOIau4Cg63F22So8/2UOoch3LLMXf7KUwd3BKNY23BP/AfQNiOFMWQIUMwZMiQuhyLjjMEURQBUYSg4Ql5FEZQaE6OyBzvaL6sLqW1+bPOmieMIlodOnQ0fLjrQP48v6J+HanPVh7Be8sO45r+6Xjlsm51dtyawslkpGgmn81IFf3LMlJ2lwcVDjcaRVvP9lAaNIwG3ZE6lyCKIq78ZD1Ol9qx4fFRaBL373I2Xlt0AIszclFU4cSMy7uf7eE0CIQ9Q++991689957fs9/8MEHuP/+++tiTDrqGZM/XodpX23WfJ2l3QXOSAnM+2Qj4Eh+hfR3abVL9bOss+bVM1I6dPyr4Aoxqx0IhRWy43BZr6YA6taRem/ZYQDwa4B7tqDWe8vBZKRKqtTX0oaCaqcnrLX8+i824rzXliNHr/0KCIvp7GdLdYSOapdHEvlgbaF/CxZn5AIAft2WdZZH0nAQtiP166+/qmaiBg8ejDlz5tTJoHTUL/Zll2P5gXxFfxYWnhoYQez7chl1KS1HinXWnLVs2KlDh46GBdagrmldU5mdrB1jOjdGs4SIWh3rXADrJFKxDjYj1ZCpfUfyytHtuUV45o89Ib1fFEVsOVGMSqcHS/fn1vPozm2YmIxUOBLoOs4O2ICHuxZBYrvLg9WH8hts6UNtftu/DWE7UoWFhYiLi/N7PjY2FgUFBXUyKB1nBmUaTo6bo/aFsnizjhRb26AlNsEuDqyxoEOHjnMfijpLd8023LJqEuiJtZlhMZGt6r+i2kfPH5uRKm7A1L4fN5+E2yviuw2hZfeKGWPTrFPXAsJklDNSjhDuf93ZOrtgHalqZ82FQj5afhg3zNyEtxY3HLXG2jZX/7ci7BWsbdu2WLhwod/zf//9N1q3bl0ng9JxZlAZQkYK0I48sAu2U9ORUp94rLPm0HiPDh06zk0o2ht4a5eRio0wwWoyAgjNkDxX4VSj9jG/t9LpabCtIoorw6MdKposN9Df1FDAOprVzuBBR/18nl2wKotVIVwvLVDqMRXuagjgbcZQ7sf/AsIWm3jwwQdx9913Iz8/HyNHjgQALF26FG+++Sbeeeeduh6fjnqEFrXPxRk+Lo8XZhXlIDY6wdZIsUXiWhkpdx1Qf3To0NEwURfzu9znSMX8RzJS7G+jayu/fla5PIhtgCpupjBVD12K5sP/3mta17CHwN7gjVu3xwtTA7xn/q1gbaHaOFINEeV2pc1YXOVEhCXiLI2m4SBsR2ratGlwOBx46aWX8OKLLwIAWrZsiY8//hg33nhjnQ9QR/1BawPjM1IutwhY/N+npO8wkVPGQbNrfgezkeoZKR066gx2lwev/r0fw9o1wqhO/q0HzgS8Yl1S+0yw+hypfzMNmDXAJGoft35WOz2ItZnP6LhCgSFsR8pfoVCHOth9VovhwaKSM94dbt2ROpNg7aeqWlD7GiIqnf6OVFq87kjVaHbdeeedyMrKQm5uLsrKynD06FHdiToHoRXd5eWK+QyV9Dzz+dwyO5btz4UoiorNP5SM1L/ZONKh40xjcUYuvlp3HDd/veWsjcHtqT21r1yi9skZqX9z9kKp2ucvNgE0XCqNj3kJACHRD9m95998TesC7PwJZa/k63J0R/XMQtGM/F92b/PUvoauJHqmUOM+UgCQnJxcV+PQcRag6UipUPtUP88YS8/+sRf7c8rx1MROioVb05FiPvtvW2x06Dhb+Ht3Nr5df0J67HR7JSfkTILtDVfTAuUyu5yRoof4NxvdSmofOX98BqKhUoVYZTm724voIBkQlu7p0NgjRFHEwj056JQai5aNoupmoOcgws1I8ffIv3nONEQo6r9rce6NBqHBKfbx1D7+8X8VYe+wubm5uOGGG5CWlgaTyQSj0aj4p+PcgdYk58UltKg5rMO1P6ccAPD7jlM1yEjpC31A/HEv8OU4wFkZ/L01gbMSqCoCVr8F5GbUz3foqHfsOFmMO7/fhk3Hi6TnzpZkNmsAOGtM7fNlpP4rNVIK1T6NjJSrYRouRobaF0rWjHWutfaIjceKcOf323DpR2u1D7T1K+CrC4HSUyGP9VyDJ0wapO5InV2wdlFt1qsIs2xPnyklRo9XxFO/78ZrC/ervl7pUN5bWnX2/zWEnZGaOnUqMjMz8fTTTyM1NVXRlFVHPWHr10B0Y6DDuJp9vuAQsO5Lv6edHg1pco7ap1Us7lJZJAyCoIiauTR6RHnCpCvUBHaXBzPXHMOF3VPRIkmOaG46VgSryYAezePr5XvrFKIIbPua/H3gb6Db5bU71o7ZQFJbIH0Aea4kE/hwIODyOWl75gJ3rqndmHlkbQEKjwDdrwQa8nqRtx+ISgaiks72SGqEdUeK/J4rqnSicaztjI/FXQcNeWm0M8Zm1qwZaugQRRFrDxeie/O4oLVNij5SKqp9QMPNSCmMxzCpfVpZluX78wAQqXTNzOrSF4GqAmD1GxAnvoVyh7tB1pDVBsqMVCiOVC2ofYcWAwseBM5/Auh5Teif0yHBVUcZqQiLUXJU7C4vIiz1n6jYmVUitTCYOqQlUmKUe0eFQ0nlq7DXDbXvr93ZeGF+Bj68rjf6tEiok2OeSYSdkVqzZg2+//573HnnnZg0aRIuueQSxb9w8Morr6Bfv36IiYlBSkoKJk2ahAMHlJr5drsd06dPR1JSEqKjozF58mTk5iob+GVmZmLixImIjIxESkoKHnnkEbjd/xJPOWcPMP9e4IerAEcNu2SvegPY8KHf0063Fyg+Afw+XZGJCJXa51JJO7s8ooKqofXZukp/B8LsjZl4fdEBXPKhHNE8VlCJqz5bjys+XY+SBtzgUkIVYxw7ymp3rNPbgHl3AV9eIN9LWZtlJwoAcncTh6su8evNwG+3AQcX1fwYXg+Qf6BuxlZZ6P9c8XHg48HA5yOAGtb0nG2cLqn2e06rIXZ9o67lz+WMVN07EtGoAjZ+CpSdDu0DjnLyLwQs2JWN62duxN2ztwd9r5oKKjWCqdhGldMDLHwC+OHa+stQ1wDuMLMmijoSrT2CuYdKqlXWapedOFEAUHoKby85hF4vLMaqg/khjvrcADt/6p3at/FToDQT+P2O0D+jQ4FwgwpasDD02DO1jhdVyPMss7DK7/UKLiPFC5vUFB+vOIKcMjse/HlHnRzvTCNsR6p58+Z1lmZcuXIlpk+fjg0bNmDx4sVwuVy44IILUFkpbxAPPPAA5s+fj19++QUrV67E6dOncdlll0mvezweTJw4EU6nE+vWrcPXX3+Nr776Cs8880ydjPGsI3un/HfR0ZodY9ePqk873V5gzdvAju+An66Xnvej9mk5UirPe7xexcKt9Vkl9ad+DNeft5wEoCyI3JddBlEk37krqzT8g+7/C1j0pGxIlecqnZ31HwJfjgfKc2ozdBnl2dKfpaUloasAHVgIfDQYOLwE+HQ4MGcacGqb/Hr2Dt9BVSgxlWE21i47DcybDuye4/+a20mcFADIXOf/uiiSLFjpKXi9ovbvW/4S8GF/YN174Y2Nx5q3gddbAxs/UzplmRsA0UMydKVMU1GPO2SjOSTUI0Ujp9Tfkbr6sw14dt6eevtOLbiDUfu8XuW84eDyeCWDkKX2BTQKRRGoyJP/LjomO8V7fwd+vYVkRjlMMf4D/P0/YMEDgX8UQO7nD/oD7/Um90XRUSB3r+bbf9hE7qVQjHuXKrWP/J8QSWRTheLjJCh24E+yFp1N5MlBzzGnP0EE7ABCU2Fl13yt9b+acchU+1QVHpb/tpdi+bJF8HhF/N+fvqBgAwiIlFQ5MWPhfhzICbCGHFkObP5Cc7zhNq/nBQG0atAAAM4qwMWsG6cYgRq1+Rlgzv6bkVVchfeWHkJeuT3oe+uq/pt1wlQDCfWAKuZeKWDa2FBU1EWNlCgC6z8Cjq6Qntp9ithiasHAcwFhO1LvvPMOHnvsMRw/frzWX75w4UJMnToVXbp0QY8ePfDVV18hMzMTW7duBQCUlpZi5syZeOuttzBy5Ej06dMHs2bNwrp167BhwwYAwD///IOMjAx899136NmzJ8aPH48XX3wRH374IZzOcyDjEAiiCOTvkx+rRUzdTu0No6oIOK7NL3e4vcD278iDItnAYBfuBJTBY/fPhHm8oqpN6PKIigVAi9rnOgM1Un7RILcTvZZei3fNHwAQYd39PbDu/dCNW7eTOCTrPwA2fALYS4EP+gEfDQI8LnKcRU8Qh2HzTPVjiGJ4xjTjkH23Yhdu/SZEJbY1bwN5e4HvJhNnfM+vhBpIkb2LRLTz9vl/Nne3+jGXv0yi4ez9JorAz1PIffT7ncSZYp3/MsZRU3NIDi8F5twEfDIEt3+3FQNfXoqTRUwkrDwX2PkTsO4D+XeFg+xdJKtLse598v/fjwBfjpV/S57GPPvxWuD1tuQYXi8Zx+Gl4Y2B4ucbgQ/6AtUlNft8EJxWcaQA4Ov1J864MqZXg9rn8njx0M87se3bR4HXWqk731Bu2NGM/HlAw2TLTOCNduQe2b8AeK8n8OeDxBmefx+w+xfi8HO43LiS/HHQv9G8H/L3AeWngco8YOUMMvc/GQqcInsWRJFkT31ggzj8NSiocKC4Ut6j1MQmqFMSH0noavG565VjOVs4tY1kb30YXjwXk42rAYRm7CvEJjTezzoEEqVoxw/Arp8JpW/WBPnNJzdgvvUpjDVshkEQSLDrjXaErnsW8eXa4/h4xRE88ZvGmkrXzz8fAo4sU31LuM3rT5cojX3N/dVVDbzfB3i/L/m7sgCoLpZfLzikfP+yl8icpTZDOMjNIGv5mYDLTiiKruBOT6h46c99eGvxQXwUQnNct1qQuAYBNHY9KD1D6nh2p/raRcHLn/NUv5BwZCmw6HHgm0tqzrJqYAjbkbrqqquwYsUKtGnTBjExMUhMTFT8qw1KS4lXSo+zdetWuFwujB49WnpPx44dkZ6ejvXryYayfv16dOvWDY0by/1Sxo4di7KyMuzdqx4pdDgcKCsrU/xrMCjLBg4tIQvBL1Nkww8AyrKU7y3PAd7qBHw0kESW9swlG4yzEji9gyx6X02AP0T0Fg5izPbpgJeZCL5FlC7csajESusD6PT7eIVxAGhnmvjol3ZGqv5rpIx8PU7uHqSWbMMlxnV4yvQdBux+FvjnKSBHY5Pjkb8PcPuM1WMrySbtKAUqckh0lM1C5Wts4GvfAV5MBvbND+07K+RjTjf+hpHH34Go5Tgve4kIU3jcwMkN/q8fYRyAA3+RyPrO2f7v+/ZSeRN1lANbZgHPxRHDccOHwOHF8nvLc4CsTeRvj5PQ+D4dTj7ndgDFx+T3FstqchIO+eh+1cVYmpGNMrsbizOYDfefJwkt0OOQ3gdnFTE65twM5B9Ubv4sSrOAL0aTfzSSWsXQ+k5uBAp9v5PNKtAsncdNxue2kzq1I8vIeL67jBw7HHg9QMY8cp+Eeu1ZbPuGOHFeL7DtW+L4FR8Hvp0kvSWvjJyjTU+OwiU90xQfzyquh0hf1hbgs/NVfw9Lb2H/Xn0oH79uy0KHo766vyXPqx6a0voiLUaYjQamj1QAQ5KulStfI7WAALB1FpDxO2AvIY8z15P7xV4KC8h3RAuMwcUbPBX5wNGVRNRg5gXkGrLf57YDopfMPa8H+GwEcZbtZC/TyqqU210Y89ZKjH1nlWQwsSqobo8XoihKDVhTbU4Y4UFCIZNVzlfS4FVhLyXGysLHyRz+eAhQcFj7/YVHSFY9GG3w2Eq/p3oIxMiU6GeiqMx2MAiljoQtbK9yesi4f78DmHsrsPoNsvZyuNy4kjhp6z/w1U69Gfh38BBFsne6fetN4RESgApiCOeV23HTrE2Ys1W5Lqw/QrL7W08wa5TXI++n5dny78jjhH5EEVj6AiaW/Sw9FUpDXkUgChzVsuAwqYld+DhZ88pPE7vi2Crg9TbKAxUd8Z2P7WTNXU+DWe/4f2nWVuDkZvUBFRwGPhkCfDSg7g3njD+Ak5uUzy19Afj+cmDx03X2NX/vIfvwV+uOB32vm5c/LzsNvNMNmH214j5aui8XKwNkql2KjJSKw+KqJnvSzAuUQdJagGWElFS7iDO6e46U6S9nlFQB/wxVSGD32tNKynNDUykMFWGLTbzzzjv1MAzA6/Xi/vvvx5AhQ9C1a1cAQE5ODiwWC+Lj4xXvbdy4MXJycqT3sE4UfZ2+poZXXnkFzz+vvoGfVRQeAd7vrf162WkSYbdGA4mtiQFTVUD+7ZkD/P0YqXcxmtUXOx/uNc3FndY/AT5Ln7cPaDFYMnzaCVmIFaqBikxitCXJC60W95d/PqQaqXpqyGsSRLxl/gh20Qy3exxMTHbkFhOz8OTsAlK7y489brIQe1zADXOJwZjckZx7itPbCW2OouAgvJZYOTLBR/MA8v4lz5G//7gX6HSR8nVHOWCJJvQ1exkw4nEFtQ8Abjb9DUfWDljTufukugRY9Rr5u/UIjTPC4PjqwK/v/xMYcAehB7JUN4A4Vu3HAjt/BH67Xf3zu34mGSxawwAQR27ubUBab6D/rYDBqMhSJaEU+UhQ3jO7f/E/9ttdgKhGQMFBct/b4oC7NgKxqWSjmn0VcXhNNtkBO7UVaH2+/7GW/R9w4dtKx3fPHHLeO4yXn6ssAI6vkh/vmw/0uxUwMkuoKJKMh7MSmDxT+RpLiXH5c88BkCCK1w3EN+eePw38cQ/5u+QEsOkz8pv73kyCAFbyklcEYoUqJG96A60F5f2RW2pHm+Ro9e8FyPEzNwJT/wSiQ2xrsfZdUne36Am/e5ndD1nVTxrljBJ814W/t3yoKiEbNxUOsPoaFQXMSFEKqatK/hsgzj2Lvb9BXPQU/rbEYoLzFZjAGJqOMnJuAXI9v7lYaeCe3Kg8VnwLck2OLCVZX0qZPbwEaDMKeWWyk+bI2Q/8/jTQ8SJY136Ae50d8bx7CjKLKtE2JUZx3wvuarjcXogikIYCvJ/3FE5ZYpFczASHCgM4RAAJCHw0iAi8UEcydw+w9Hngqm/l91XkAb/dAVTkktcBklG/8mugSTf5fVlbyTzqdR1QctLv60YYd6CfZz8crr7kiV+mEtraLYuB5A6K94bSR4rNSFU7PeReCwITPDC7mMy3x0GMdyt37xccAuKaA2ZOiGXPr+R+6XQxcMkHJAhTXQSMfh4Yer/m9/6+/RSWH8jH8gP5uLxPM+n5GJsZsahAGXzf73aQWkyDGbhthfIa7vyRrEOjnwOiU8jfq9/E9QBmCR1xRGwKoSIXsCfI96gKThWV43XTJ6hABJ5336g8v9u+IgHB/H3Aho/k59m/KQqPkPV37q1AhwnyulXI7W32MuCriWTNvW8XkNBC+frJjSTYUF0MnFgHtL9Afs1VTYITbUaS3xwO8vYBP99A/n48i9TgrnpDztRu+gyY8Hp4x6wDsEECp8dL7qnSk+Rf0VEgqQ3yyx24+estmGb8GwMmdoNtMLOPelyAx6WYI0L2TqCFQT5HFXnA56Pk9XPJ88r9SjEgO3GC240BUnuov6eyEPjtNqQbhwAgc7WkykXKPv58CGgxFLjuZ7QrWIJPzQswK2Y6NtitgVX7cvcSu6TlEOXzrG2UtQloNUx6eI76UeE7UlOmTKmPcWD69OnYs2cP1qypY8UwFTz++ON48MEHpcdlZWVo3rx5gE+cISS2JqphlRpRiqzNwNr3AJMVuGUpsPhZ+bXFz8iiAZtnypkTFdxpWgBART0tdy9xpHxqfqkCY/yVnVY4UnzTXgq+GFaL2qdIf9eiIDMQ0r0ncZmR3E+Vp3fDpCWRy2cXDvwFHF1O/v7lJlKPkNIZSOslv8dZITsuAPDzjcr0buEhQgU0kdoG5GYQmh1FdRHZVE0+K3jHD4Qal9RG3lwFgzKDQr/6yEpYI2PJZhqZBBgMsgEHEKpcuOh0MdBqOPDXw+Rxzm5g10/qhu7BhcRQ/ft/2sf780H153f9RP4ZjMSZYgze20x/Yq5nGEzOVBIBZY1hFtVF5B+FvRQ49A/QZwrZ/GmWi0X+AXINeez7gzg+pYxheHAh+beYqbM8uEgpyrHwMWL43LqM/JaKfGI87/ievD7gDqDFIPn9lXny32p1Bh4XyeS5qoH7dhCH2mAkQZFjjNO76TP5N3OR2NbCaVxoOwxh9Y+YZknB23hHeq2QoZApkLWV/NZt35DH+xcAfUO8f074aMMlmcp7HTQLJcIGp2J+hyTauO59dPrnKVxuvB07bRMBAJHF+9BX2I/tnk7qn+EzH2qUVYrlr0BwVaKNoRLfWl5BLJjrWp5LrmXG70Czvv5ZAh5Xfk2ohBnzlHTeOdMgCgb0cD+GdSCBwdgt75PI/7FVsAC4yXQCb7qvQH6ZA20bRUmGU1shC88duhXGLzvgTXMUocx5gQ6GMoD9mUVHSWajPJvcLxHx5Pni48DxNWQfUcnaYN8f5Hpv+hxoOYw4zmy2GiDZiG8uAYY+QKi6rc4jEf7qYkKLVUGyUIZfrC8AP74ATJlPziEAbPkSGD9D8V7qNFrgQpvq3YCnnzLwAGV03FORB5xagmBIEMrRQmTW+Yx5JCg0ZYE8H4+vIYZ/y2HA1AUkmm8wEUOTXsN9f5B/FHvmEEeKOpBcsONwXgXaCVk4KqZC9HohlBwH4lvg/OrF+NI2Ay+5rgUwkZxLur6f3EDq+Cjy9pJ/x1cDQx9UTJZRhm2o9Nhw1YZbgYwUYPomssef3g60HC6/N3cPmpVsxhUmEvT5wzMYDndP+TtUagQBKOpV0PM6so4VHZUDbge4erzCI8Cat4DuV5PH1N749lLgrg3A9m+Bpr3JnsnOocx1QGIrctxeN5Ds8Zq3gDajSNAyVHjccoYMIPP9t9tJIIqFoxywxigf/zwFSOkEjH2JPOesJHtxZC0YVW4HCUal9UZERUc8a/oaMz0T4HQ3IoFYihPrgKQ2yCu3o41wCs+YvwX+AdB+FNCoLWEcfDIMYkUO4r0v4zzDTjxj/gZxa6qA/e3JdRcEX10xszcXHCSBH0Eg61fxcaB5P/La7p+BZS/CtXwGnI+eRpSaquWO74DDSzASSwDMRjzK0ev4F8Ah37pwYg3wXm/cUFEIs9GFKM93yBEmoF/RJsDbm+xVokj2pYh4EnCZeQG5Hhe9R/ZmaayMI3ViHTDsIenh5caVwMcvA10uBYY/XPPrcYZRo4a8R44cwaxZs3DkyBG8++67SElJwd9//4309HR06dIl7OPdfffdWLBgAVatWoVmzeRoTpMmTeB0OlFSUqLISuXm5qJJkybSezZtUhoUVNWPvoeH1WqF1WoNe5z1DkEAmg8gxowajvki4k4XSZOzYOlN1GiLSNCmPalh9y+A0YLLlzyJPcYrEc3u2hU+ulXRUWDLLHg7Xqd6CJ6mpyaRDnB9pPiMlMdFFsXGXYFhGgZ5IIgisPpNfFr+ovSUN2ev0lhmUXwcWPk6ib52GCcbqwBxogCyGQQzqlh43WRxa0KMKNVo6slNJFKa1sv3naIyQrnqNWIgcbDs/QVY8wqJtkY2ItL4bJRPDSmdAUsU0HcaMXz3LSAS6Ju/IK9f9hlgjgASWgHfTyaRIkrZ63IZcNE7xGj7/gpSkPzTDRJ9CQDQfjxwMAx6QcY84kgxv/dW01+41fQXsBbkXziYfy+hJBVo0J32LyCLPYU5Uo6w8kakBDatokJ1yt4BvJAI3LyE1D+Vn1a+Rg238hxCF6Kgc4lF/gF53h76hzhqSe2AW5Zoj++EMujU23AIV4HQLmOceYrXNPtJzbtLmY1TE7QRRZJhTG5P7tX8AyTyyzr5BQcU2QuPR8QH5vdwnmEXHnG+CYBEJR0uL0wIQgdZT6LjM0yf4cqISUBVERJ/vBA/Wapxh+sBuDOTYUrvR4zI3L1Aj2tVDMQAoU3Gqe1v4O6X8mxCG6NrrRZ63QA070/OR9M+5H7m6LSC6MXr5k8xxPE+phoXIuGQfz3YAsuTaPldLpDUDpPc3dDUaMGdpnmI9FYD2dswWU3xOLENqT10+2pB5txE1oGpC4goy66f1Mfc7QpicObukTOcObvU3wuQ6/vPU+RvtcxwIPzCOOPU+XDZyb2W2kNypO4wzseDJXOAv/cDF75F3uf1AoIgKYKZ4cb5Sy8BnCoBCKOFUIp9aC3k4B3Hs8r3eN2E7jX1T2D5/8l1lsdXk4zZDz5nYPgj6oI4ADH+qooIjV4UgQf2KAzvPoV/4jXrDPzkHgHHLidsv98M9L4Ro0uII/KkeTac7g9hYQV/cjMU1G35fGUCC+5XzKeRxh0oRgwsnkpCl/5+snyPDrob2PkDORfl2WDJjOcbt8PuutR3HjzBBaumLSLXfcf3JJhWokLHBmTmzPbvAAvjqBQdAX66jqxh0Y2Bhw4o980T68neU3iI2CZrfNf8yFIyPsFAAlJpvQjroKqIZIkTWhKKWXRjksFY/IyyVqvomL8TBRDqd7M+JGuWtZkECouOku/rfytxov95ChCMwJQ/gGb9CYsiOoU4a5nr4G02ALGogBFeFCMWbo8XJr7h9MFFUpBwaMIANDdtRC/DIbzk/lDO8gKEWtz7BpRWudBVYJzoVa8BPa4h8yx/HwQAD5t+wtWmFfJ7Cg4SWyWxlVzT3v0q8r2ih9g35kiyF2WuAy77ggRJ/rgXAGAWnfhq+Tbc2qGazIfENmQ+XvmNoo44CaWYalqEsbm/K39jRQ6oCzawehXeNx9Bt9LjwF/lwKinyfX45ymSUXWUy9dj+7dAx4nA1xeT68jeD8fXAjm78bDpJww07ENr4TSQWwG0HOp/LRswwnakVq5cifHjx2PIkCFYtWoVXnrpJaSkpGDnzp2YOXMm5sxRLx5WgyiKuOeee/Dbb79hxYoVaNWqleL1Pn36wGw2Y+nSpZg8mUTzDxw4gMzMTAwaRIyUQYMG4aWXXkJeXh5SUkjac/HixYiNjUXnzioR6IaO1J5KR8ocBYx4VBkd5xGRqIzQU7QdQ6IRAFlkObzeeS4eST9IKFAL7icp+JMbYQXwnOkbzPaMlN578PAhtO8GQoXa8yuis3bgemMbHBObYK2XLPiDDHsxxfAPZnivxjExFYAv27TzJwAi0ONq6Xh8jZQoinJPspObSDp8z69Av5u1aQyiSBYRk5Vs0l0vIwbojtnEyGCRfwCoVGae5kdcgouq55FNiKLHtcFpb80Hqtcg8fh2EqEWdLlUvWbq6wvJ/wPu1KatOP055dYChmNMqZ152sphAMim0Xea/HjEY4QecGAh0HYUcaIAmeJY4ot2xaQRiovF14ercRfiSPEGWOeLiWPmrAIatQfm3qI+jl43kIU1cwOhwWllX9WQ0IrQF9RoKIC6ExWfTn5L5nryDyBzbPA9/pQvwUAoKIEQk0rGzG7aM0f7v4/ywF3VwCfDlBmpvAxiRHw3mczLi98nNQQUm78gTuqpLWQTDpH/3stwGI1EeR24xLAGdliwyNsfRWoZKVe1/32plgU8tZXUqQHAU/mE0kfXFYqc3QrDz+S140IjocGNr/4LAAm8VLs8SAQnOsJmZr0eySE1CiJibCbg9DYIrioYBeBzy1vAl2+RTOCcm4lRaTD7U7QAMk9PbZXrQEOZt4WHlRlANRjMwMS35AwcS3/j0FQoxHHbtZqvtzT4nOrCQxiJQxgZSguk5gPI/MvdQ9piAD7J6jvlLKEamvUHOl5I7ns1oxNQBhhUBzyMfEewecJSeg/8SdaaNW+TuTvhDTjchGZ7JTUUt8wEJr5J5tan5wHx6fDYiQHYQsiBTc2Jaj+eGHAfD5aeihU0xp65jhjtfM0UU2OIVSo0sCH3Ecfe7asVoevxyY0ylcrjwqScdwEAV5lWoGp3JHl+2zdIZQ5VWlKIZPb+y9+nDEbxYGp3+woHUGqQeyEqHH02M8NhlGE7Nri9wIaPCYOFUp3Pf4o4lSxGPwekD5SzuTyFTwtObj4f+of8X5FLAhxsdpj9/UtfUH6u6KhM12vWnzh1n55HggZXfiOv1xe+7d/Oha5PPL4YST77843+r53aSq6t1w3ATYJDyR3I+bzgJTI+jwPeHtfhb+tCRMKBEY63UFLtQqNoK8kubv+OZFSY69G8mKx7PQ1HIbrsSud172/A0AdQXVyNGebP5ecpU4OBwomieK8nWccMPtO9xWDyOwoPkzosFip7cOKxP4CNH8i/nx6TwQLrk2iMwAF4k+hCN8Nx8mDLTEJ1pzbXkueAzpPkN2dtIeeJZlwpElqS/eaTobib90TimuFcQthiE4899hj+7//+D4sXL4bFIhvnI0eOlJT0QsX06dPx3XffYfbs2YiJiUFOTg5ycnJQXU0yIXFxcbj55pvx4IMPYvny5di6dStuuukmDBo0CAMHDgQAXHDBBejcuTNuuOEG7Ny5E4sWLcJTTz2F6dOnN8ysUzD0vJbU43SYAPS+EZi+gRifAnOpWp8PNOpAsgyXz9Ju0tqsn/w3x1EvEyORJzQCBt4J9J5CmrQysAou3GSSKVKHd60lnPc9vwIAbJmr8H/mWfjW/CpSQSLTj5p+xDjjZtxrmiv1QGjmzSKL3G+3k+gQABQcQq8tj6GDQIx1r+jLUO2YTYxIasQDZKK5HeTf8ldkxbR984mC3m+3k3H9fgeq/3iI0Eh4JwqAofCgUkEOwFr09D9nVHwhJo1cgzajgDvWAkPuJ8836gBMeI1k+zgc9DaFUzTiRdd1AARiFMy7myxYJ3xG/IVvE2eNxcaP/cfR6wb/50LBJR+SaJ41FkhmaFDRjf3fG51CIqsXv6d8LoEJaIx6WnaiAJIlpGh3ARDbjDj7bUYSGtDIJ4HuVwCXfQ60ZRyMC/4PGPUsMZaS2hLjNlz1p343EyOSotcNhAobCENVMpoxTYgR1HWy8vmeTJa16+UkwMCi5TDg+l+B8a8px6GG3L3E0V/1utKJAogRNqMluS8y15M6FhZ0gwPIfHOUAaYIErEMgPFGZbH3u5aP8KnlHXQQMhGZv5PUp303WZ5frLoixb4/iIPy6XAyxzI3EvoFReFhmb7IImc3FmfkYuNRshbc4/5aemmi828SWd77GxLyNyNZKFF+ls3QcbWFSRavuhjM7jmykMlvt/kbSoIBuHEeMO4V+blWwwBbvPQww8vVcgA+SmoQon5EgoLGiCYadQcqeDL1M1zqeB6rPV2DvxnAGk8X9MRP+LUvc86b9yd7BI9AThRAjK4uk4CnC0i0+rLPla9f9T3wZDZwXYBgaK/rgXu2AUbl3nrU3Abfu0ehl/0TFMaqBDB/uFoOgPzzFFweLwR4lQZbaRa5ruWngZMbcJWT7DVthGz/4wGEgtq4C5mroWDZi8HfA5AMD0WPa2VKO0v12/AxMbTtpcDe32AV5T3HlKue5Uv+oC0xpCly9wbPEFlikCskwyR4Mda4JfB7VdDFcAIdjn9LAozUiTKYgB5XyW9K643T3e9CXndf76iEVlCl/gPE+NUCe94oPugj1/maIgIP9ugKyb5A1iYyv0szSbZlCZNlZNsUmCMDHxNQd6IAYj+w9LjDi2Wn9J8npfNl2vk9mgqFSBAqMNCwT1baXPI8YZLMu1tT8fPB0ldJ0MEUQfY9VxXwQV+M+nMYbEJwxbsCMRY9HZ/hPfck+cmTG2Q2QkIrwlwIEZPztJ1uilShCAYhzGKlyjxln0sFFVRUXj8AiEsH+mvUVwPnnCMVdkZq9+7dmD3bX+krJSUFBQUFKp/QxscfEwNyxIgRiudnzZqFqVOnAgDefvttGAwGTJ48GQ6HA2PHjsVHH8kRaaPRiAULFuDOO+/EoEGDEBUVhSlTpuCFF7hox7mCuKbA9I3+z9++mlBOYtKIUWpgHCujRaajmWyyI8EWfXILTraYKBehGgzAuFeJwIIGJoirAZWkh0EQcb1pMfZ709HTQNLNlxrX4nfzRBRUA39anpDffGwloQetex8tT83Hp+ZNGOEkNAtnaQ7Mv99J3scWrs+bThywtJ5yofc92wi1jDN4InZ9Cy1EHVVG9b9xj0GGS536CYDQsi7/Un7cpCsw4HbCt7bGAI8eJ5mz6hJg71wguSMmLmiFKFSjBDF4+v77gQ/7kQjm53JmD8kdtaOQ5z1GolIOn9DEdt/vscZKi9QRbyraGMjGJA55AEKbEaSWgR671/WyQ7DoSbnwNlrjt6oVrVz+JTFo4pv7G+/NGed85NNkY/W4gKgk5fu6X0mcq7e7EqOz782AxXcPjn6e0D8o2o2V6pp+9QzDrg4P4PkrB5HIpLMKuHwmyZy0Oo/c2x0mkNqwi94j9+7Bf4DZVyi/P7UH+f5eNwAbP1FmXmLTiHN4+ZfkWm79ijzf5yb5nDfrR2gTx1aSa2EvJXMTIMZbv5uJc/BGOxLRHPOCMmt8ehvwXi+laqEWvAE2VOpsNulKnNBm/UjtzqfDgx/Xh3tMv2P0oZ2A10fV3TKLrBkrX1X/wB6fIc30lpOgQW91ZyzA/Sv6oBIROPxwJ1whykEYEzzAuz0BRykmAfhHuFf54Yo8kjmsLpazhj60ELLVJay3zFIfe+dJxDgd8TjJUrEbckwqcSZ8m/wCzwB0NmhQl3pdTwqwI+JJNpvNGLMBAoDc+0nt5Ai+0Qp4HMgf8hwWrFwvBaS2dn8WP22JgVuMxt/eARhm3AM1ZHqT8W3KI7j0kktx/YebkWIzoTKxPRZ4BqBPRA5SO11MHPmy09pUNBaD7yXXu7GPdi8IJNgBkCwFpaTS+7vdGCKkstnnaI15kVyboqNA50tI9vqBvUDOYeAVkg36y3Yh3iglwc2d6Tdi5J7HlGNggwMRCXC6vWgp5MIsMFTwz0aQuhofpop/4BdhIFqxjtQVX5HaxPJsmf4z/jU421yA3N+eRHODL8M9bRFpb6CFNiN9Wd+tyud7XU+yUBl/kDmX3AFo1I6sHyxT4dhK8k9FFdBSEaKiJ//daug2GUt2leE6l3/9UGn6GMRlLlb5EIFTsMIiOjD0MDfGuOZkvo14Aqguxp5uj+LCD9ah88kt+Ou+YWTexDaVVYK7XEb2OICwXLpMIjVm7ceRtY+KLvW8Vjs71qgDqUuidXMsIpMInZDW51Kwwlu8sEpqTxIALM0iLSpCQXQTYNBdhE7G1tE2H0CofSHMpe6GIyS7L4oy5fqw9jUY7PbZLJ0vBgbeBXx2nt97Sm1NEWdXr9/+yTsKTRqn4oecUbglbisiK7nyhIQWZL4e/FuZTe57M3Ha1ZhKNcHwR9QztlqgdNveU4jiLY/45ooaYodohhMmxAi+PSqxdS0Ge+YRtiMVHx+P7OxsPxre9u3b0bRp07COFUpjX5vNhg8//BAffvih5ntatGiBv/76S/P1fwWadCWbiBrajyPRoMRWhDNPoyMJrchiue59YNQzAORiXTeMSllURkiiIKotLBWniGJfCJhu+sPvua+9T0hqYhKOriBGiG9itTTk4m7jbxhg2IeImUzUnpVTptFoVi2LUzbMEhuhmaDuxP/oHuGXIu9r/xgFiEOkA4DZLBuyHS+UaZWssARFrFJSGs37k/999UmuBX+iBD6+eHJ79cVH1ZESgBt+A9qcTzZx0Utqp9pdQGgSF/wfdq1fCOTtwzTn//Cp5S04RDN6D38cNqsVSB9EDNDzHvUdzuccxTBZKH7sgdC0t8KgUSC1JzDhDULFYpUO1RDVCLh9JVngLYwj3+lCQq1b9z6JjBsM0sb2nGsKBotx5P3X/+o/fksUcA1DxQT8F93YpqQeghYZ37qM0LUoDSqRkfkd+gBx1LtNJoamzwhG4y4kg0Frz3jVL4DUSFz5DTFo+91CVNKqCuSoaihOFIsBd/pnJyn1MbUH+e39fPQWU0RAQRkWFxo3ACwTK3ODUpwkHPB0yJTOQFURTGWZeMA0B//nvgGuzbP8NxZG9GCCUVnTiopccs7mTAOPZp6T6up0Wr99xONAipytOeVNhLQrRTcmc9bnSP3iGYEHTL/CLHhgb9wbtlzGWepyGaG8AqSOZtvXhG50dDlwnorISvpA2ZG6az1QdBTZ1t54fml7vOC+AU2FQtyeOgJGwz64vSL+9AzANcalyBKTUdRkKK7LJwGl6c57sdDbD33NyRgv+hQLzQbYLGbc7boPI9uk4MuoJOK8TfubOKFLX5ADAFHJZK1v1J4U/ve4GrggQCYmPl12pGIZp5OdU6k9gNacARidDDSRMwzbTD2lvw8kjMDI1J7k3r1jDVmv2Vrd8mz0P/klBpm4DFpVgUwLA2ATXJhumoeBBp/zPuIJQpPucqnyc1FJqOpwKUrxMprDN1/SepN98eBCEuTI3iUHCO5YQxzf1W/6OzOtz/dl6pksaKP2CAW5Yjwas9nWXjfAs/17LPT0RZJQjqTu49Auzksy2z9PkesuW58vixsBZLybPifnbMh9mL97La4DcWQqDLGItghAcidcWXIP5ohrZOOTwz9NbsOF2e/7v0Cl3UeQ/WLlcjK/MrLLZIq9LU52pPrfJjtSjdoTB/be7cQhy5hHHKnW55O9ja1Zi23mc8rSgIHTyZ5ojiQMmu8uI+9p1p8wAxY+GtI5RtfJJPBGM2rmKPX3RTYimTe2Bm3Sh8T+aDFU6Ug160fWMd6REgzAPVtJQMyHbsIxNNr4KvCzSh1ii6G+rLCICtGmbKtwyUdETGXcqyQ76MO37tFYm/Yoju/bimdN32CQUQ5UuUUDfhNGoXtaLObmJOGznr/i/uFpwCvMPI1tSvaENiPJ9fjmYpLVH/YQYTSd3ISC9lfiudffwAcW373QbiyhjfNCVrY47SBvx4mSLfOHZxAuNpKA1wLPAIw7/3yYVmkE5S74P0Lt40sPWg1XsFvWeLsiU0yRWVD/dkfq6quvxqOPPopffvkFgiDA6/Vi7dq1ePjhh3HjjRopVB31C6NJVqBx2cnmYYkhztGIR4n6SbWS7pYglOOfjFw8+PMOvDa5O0zxLaXXyixNsM2bhsuNQQquw8X+BX5CGg+bfUXMKrX8oWKC4xVMM/2N+01y1E4UDJhgfwn7xHSkCYUYbpQ3xgLEAgCqXIA3OR0GWrjZ63p5fKk9az4gih7XQFz3AQRq9MWkEuObpeU06UbqLahTxjockz4hC1DLYZh7qi++yjoOAJjsJNL9W1yAzQrg6tmkKJh3/iIbyX+HKy2rBUEg9VahgqOUShjzInE0bXGAoxw5zcbi22NxKEck6RkTDuLT5b9bjyDng6UjWqKIXHtqTxJV73Sh/FpCS2KQUlzxFaG+hVrs2nGi/PcEn4pj3n554xj7Mole/vOk+uc7TyJR2pg04tSp0TwBf9natJ5+2ZtbnQ/iorYJuCbxEFFhYmAXbLBd+gHhzbMGg2Akm3tqD+DLIIIlPAxmQlWtyAN+vgETjRuxxtsNERsJVfRF13XY6W2DOVYlO+BCI0cB//FaP6rkvtih6FS2BpcdeTowfafzJcRIpk4rd799scsFiVQS14wIQ+yYjdymFyB/Yzwecd2OHoYjGDrhbbQ7/TtR8IpNI4YJRZvzyT+ARLTVMOR+EuxpO5qsu0ltUOWjOYowIEtMht3lhdcXPCxFNC5yvgwA6C7G4brJXTFj0SH8WUCCE26vKInw2ExGRJiJ6kQ1PzeiU8g9SB2p3lOIAQUAj51UCqyogQ0QRDJZZZbNoCWZzGSzi8QYwNeXq9pjICIpBhN5T68bSEsHBiNOfQoEGJq923Ww7f5eUl0FoAj28XC6vVjs6YOuhuNYLfTFMJMFuPQTErzrMJE0Gd//J9BxglzT1na0UnnVaFFed4pGGmtYbDNFb8cfPSNxH92DBANw4du4t/Ra/JlBnMjXWnZHu77Nyeutz5PpT+c9KjtSEYkkmNbnJuKQRDXCMRzFL+7huNS4Bj8m3YVbbn8IMBhx4ImFOG5pjG7Ccb+hlYkR2Nd0srojRfcaH1h5dLvLiwiLkbBC8vYSxyWtJxCVQuZGuzHkjdTQ7TqZzJeUTuRei28hBxSm/a1cmwHgUt/6Rte98/5HnLMlz6pS8hXodgUw+Qvlc43aAiOfIrXbLBp3JsqRv0+X10K6xqT1Ihmb/X+SczHkfpJ5Ls8mwZY/fBRF0QsktsaB20/i8fdmYq71OWJHHFChGne5jDAcDi3GnHV78fz+Ztht89UopfaQFSkH3kmclRWvYKGnH552T0OvChf2i+m41vUEDlpug9lThfyhz+PqpZEojGiMrmlxmLvtFPac5hQIAaLqCpAgOkACiF43eT6uKZDWE46SauwQmbnTbgy5viteBcb+H3EiG7UHvB589O5LuKvsLf/fl9wJYmQyKivL8ZV7rORIbfZ2RJO40egLnyPFMqIikwBbLFmj6X54+2ribPa6gYyxy6UQ983Hx46LYBY8uAmL4GkxDEb+dzZwhF0j9fLLL6Njx45o3rw5Kioq0LlzZwwfPhyDBw/GU089VR9j1BEO+k4jVI7Lv5Q3UZXN9C03oXbM3XaKNAs0GAglTDBic9r1WOTpq378yCSFjPSLLn/6j11UVkyX3LJBWa9TEwhG/0hkq/OASZ+gDFFY7OkjPz/2ZZRNWY59YgsAAm50PYaszqQY1Q4LWP63K4opB04fRDJ7va4nFKAwwPaAMdDDJ7XBp91/QX/7h3jDdQXJXgBks6Fofb7fxiYhKolEbgRBtdeK1AwvMlE9g9ZmJNmYe14X3KA606BRTwCwxmBj37fxoWcSABVjMRjYepWoFKUTxX7ftEXAg/sC8/w7TgAG3hGiTrcGBt9Drscda4FB05X9PUZxojED7yS1KVPmE6O43QXEoLviK2XdIn99B00n/zMCIlu97XGg0RhF9jEr/RJ86x6N/0v4P6JIyWLk08DjJ4EBtxGhkF7XE+douLq8tYTG3YBnioEnTpFMTLsL4DZFIVUowlcWYphu9HbGd54x2CJ2xM9QEePgwWacB96F3VED5cc+uso6oRe2etthjmkiPAYLERu45EOScex3KzDtH7/r9uu+SrzuuhKfuicCTbqTXmN3b0Zmr4cAAL97h+J59xQ4TNHknJ73P3Iewr3+jdoCd29W1GRVc83Jq10e1XYQp4qrgW6XY7lJFhxxe7xS81Wr2SA7Ui6VuZHaU67n68ZQXI2m4L8jjck8s3TxlsOIATjgTllWPQA8zPpnd3uJkUS/+/wnSXDjqu9VRY/oXsSirN1l/l8SIDPkcHvxoecS3O28B08LvrkRkUD2DJOFONCPHlfStdMHANf9Cty3k9w7tyxRl8DuOJHUyva7FXgiG7D61q2L3lXQPFd6mAx9TBpgNKPaK++FpVUMhXfwPSRA0OkiMod6+fbRgT5H3RZLMvogTUofcd+BLo4vsdI2kvwe33p+SvSvEf3WPRoPuO+BNSIan7gvQpE5Fbh7K3DLMrJecFL0Duaeoo2wMfwR4Pq5hD5tjgDu3kSyUNRgpxAEslfSmmG2djgmFZqY9BEwfTMx6hNaAE/lAn2mktcmvqle9zPgDvVjDX+EXBsWlO1EBa46XSw7IQYDUYh8+ADppxadTO7X4Q8DvW8g6wpAnCOQ+3m32BrVov+9C4Bkosa+TM5F+wuwM34MyhGJPzw+6lpPzkYa9hB+bPYUHnWRMeeXkwyhCANWdJsBjHsVeZ2m4ojYFBajAV3SSOA347QvW8TWvvMQBNm58sHp9iJLTMEDzjvxreVKX7DlBuDBvWR+JHfwfc6Ev8wj/ew3tB0DmG04fs0KnOd4G4ctHYFG7VEuRmCptzceXc4IYrG0ZxrI7TuNrCfjXiUsloF3ygGcSz+D/Z4MbBE7Yr23C8Y5XkXFpG+0f18DRdgZKYvFgs8//xzPPPMMdu/ejYqKCvTq1Qvt2oVe8KajHmGJDEzlADDJ8Tx2mOXrleubyLjoPWDcqzi+4jQWe2PwuOtm7PC2xUXG9egpHEbzGz9F83bdgVPb4J05Fl87z8e3njFoKeTgBhOhDRaKMejj+FShVOWIbg7c/A/w96OymAOHAsSj0aUz1Bu8JrUDpi0kG8slH5G6n8oCkmUAgB//xF6xJZZ6emFUCxPQZyoqqgwAKO9YwPEON6OZtRq3b1duxmXJfZF80hf1jIiXM3thgjVwTIxBctAegzwk4APPpXiYOkwGA9kUDv2j6NlTVOnEO0sOYnSnxhjeXrlBqjUi1WpkKSE2FXjkcMNzolTAZqHCzkgBZDPd+ZM67YpCTdmtPtDzGvKPIqEV2Ugq84mh5LLLkfDENsSQorj2ZxLRM0cQ6fsfriYGJN8Dq+OFpPklbMBuOdpvNRnIdfehtMVYPH0wHp3EWGJI0HoEgBhvrNN58QfA+NfJGlKRK/eWguBTWPJlfdqPJfewwcfdNdtQmHoeGp8kEXav0YZ7XffBAWJ4/OwZiStjdxEnkcmUHW1+KVqfZIrvASLk0esGrPxpD3p7Z6OtwUc7S2yNZ9zP43BeBeAEHsa1OHr1RBho1GLiG6qXwu72Sg767cy85OeTVuPw2oAPCKg6QSA9viodbsWYnB45I2U1GUmWAFDSsSliU4Eb/yCUVIbWGBJ6XU8yI/04hS9bLHB76IwED0PTd/BjNNvkzO2wh4CVM/Bz6sNodPIfWODCZ+4JuPf+J2H6oKf0kcKk3rCIUYgXCFWhyhCNSK3sNog6rBsmLPAOQpRbY71Tm//tfIZfoOCKNVrZ4+j6OSRr3XYUcWqOLMNPtsux357u91H2mn6y8ghuHe7L5LQYDDyWKWftxrxIsjRtRvkdg7YKccAiXX+3737NFeMV7y2OaImni6chymLEALMBL7uvwcG2D+OtRr6gTLM+4MHuI+V2FxrH2ogT3pYZi4q4kipaDJLbZnAGvQKWKEL1YzHxbSJIFJlIBFx8aqg74kah58TbSW2oFs5/gtTd0DpV6sS3GkYcZZa2GgwXvEiylgOILWJ3eeCCCe+7J+F/Zk6ttP144NofFU+5fYrEj7hux0/COHzPMziMZqyOHIVSkNq/ggqH9NLuqIEYM7A9nJkki2k2GtA5LRaCAJwuteNkURWaD32QUKzHadDpONCWNL95h2G524wbTBoOIcje+6VnPO4y/YHXXFfh5vM7IakfCXRkO20oRBxax0YAd6zBpBkLkeUwAQVVeNV4NSYYN6Lb2JcgUFYPzUxFJ5PWDGowWeC0ycGL/WI67MZIaOg0N1iEnZGiaN68OSZMmIArr7xSd6LOMRwQ08FmZQqoI2U0AbZYeLwiAAE/eEZhn9gCr7mvxrWup1AR3ZK8r2lvrL1iO553T4ETZjztlqPiMSDR42KjTCtzeg1kY770YzL5DSbkxsiG4WfuiRjjeA2edEYCmkVCCyk6B0skiS5SJ0qCgJtdj5CooiXKz4ipMMYBl3yA9W5yr0b5DJMTLSYDLYaQup9awK5h/Bu0IsLjZ/gifDIX+INlh/HN+hO46avNfnLVaoYe37NLfQAN34kClEanlsEZECOfAu7fRQrDGxoMBrKR3LWBOEi003tia/m+phAEWYq+3RjggQzg5sX+11EQfPQr5f1lNhoUkWBDHBEZkZqbUqXC7lcR9UL+mJRaOuhuIrN77S8kkn/jPPl9tDcag5OtLodXFOAUjcjq/yTyvDI1Y4enFfDIIWDSh7ivxTxUiMSgzWh2FakNpOh4ITFeLJGocAGvuxllsfRBiItgDTNBYYBogTVk3cwc8ut3p9E4vDbg16ASNiPB4WRxlaJxscvjlcZoMxtgM5OtWtWRAkh2pVXoAiQSElrAfdtquHpNCf+zDFyKdhYBnNIRjwFP5mJt7ARMc/0P17uehB1WOKOVNZxVbuAF1w1Y5+mMx1y34JUmb8sS+SpQOqF17xQr0Lw/qfMRBHLOn8rHTMv1qIJNVoJM6+k3lsJKp3JdZ7N2kYlkvhv8TTIPc2/Shve0x9Y/XuJcLPf0wEfuizGnBWEF2cxGWE1kzQgWcGNfr3TUYO1l0ecm4oRc+E74nzUYpIygI17eFzNih6rs9xwifZTIFj5KNqvkmNDSr9lzQDRqR9RnfWszPT8zPROQLXIZy9Yj/D5O1xIHLNjgbu+XFS6qdOLPXbKAip3po1lW7VIcw2oyIMZmxuA2hHY7d9spwpp4/JSyyW0AsHOjpMoVMGhU7fTgDfeVGOl4Ax95LkZm+6mSYA/NnKXEWMlcZNrSfOK5GBc7XyLZT7rHqKk4qoAfj+Ya14ARdkZq8uTJ6N+/Px59VFkc+Nprr2Hz5s345ZcwG/fpOOvgDRK3hlGh2KygjDZ95x6F601L8bmHRB7XJVyMiQVfYp5nMLqxE2XgnUDfafhz4Qb03vQAzHGpeKfgclSJVhQYkqAi0h2eUIIPvBHjcHvh9YrSb0iNj8DhvArkCI2Am2ovVMJmUZwer7Ivlg8uj5cYuhrYl02U+TxeEUv35eIKyqdHDTNS5xBY50ky+sNFbeh4ZwJSlPQ84pgktg4+5hjVGaEJi8mgmC+mpNYAMlDp8J3TMS8SOlHzgeoHoEjuANzMFGTb4oiRUpmnGjXPSx6MC5wzUCFG4K02EyAul6l6bq8Ir1eEwSCgRIzEpc4X0EgoxciIDkB6qsyrbzlM+ky104PN3m6oikhDpLsUGHA7Yhcq16mskmokRFlQWOFEkzj/bIPXq1zHKh0exEWS+XcmMlJVnEGQW6ZdA5JZWKUYg9PtVWSkbIGofbWAw+3B2LdXwWAQsOj+4QHXp0Age4bgO2aQc2my+J1/h0cAWwlX4fBgrnc45nqJczgInCooB/Z4Lo98v9U1iiudWHekEKM7p0iOCowmyfB93HUz/q/pRnTzUTz533msoBKJUXJGQBRF7M8pR7uUaP8mr/T3ME4qXRvp/2u93TDQ/j4KEQcXTJjoTQWQDavJQLLTCG6YskGFWhuxia2ImEctUS5EY5unM5ob8rA/WoP6roZJH5GeTkzPytqCnhMHLLjC+Qxu7ujGTeOGEBXHPjf5vZ8N2Hi8ol8D32f/UJE/9oE6UvS+sfiu4fiuqVh7uBDbfJkqNYdbC/x8LK50IiVWnZ1R7fLACwOyjM0Atxel1XLwR3akyGejbf4Zx9MldiRNeIOclxDrjHl7k3UszxWEvWquWrUKEyZM8Ht+/PjxWLWqjsUJdJwR5JUrDRS2WS4LPmLK4nX3Vbjd+QDecZNI0PqUq/E/w0N40jXNP9prsqLY0gSTnC/ip7YzEB1NOMD5FS45qspKoMcq1SCX78/DuHdWYesJf2lPqgTJGxwOt1xzAABp8STqX6zWqLQG4L+Pnit2K5eMWQ2cLpXVlw7lKRvxqkVZHefggqMFu8KROvciUmFBEEgkky/ErgNYTAaihtR5EjDkPtjiiCNWQe89s43MsQD0DlUIAnDTn6QOyBbr97LL48VhsRlykKR6/agx6HR7cUhshvXeLsSAM0cAl31GKF9MvVeVy40q2LDpwn+Ahw8CqT24jBSQV+bAU7/twaBXl2LpvlzwqOAc8nKHbBTwxkV9ZDH4LHV2qbYjdaqkmnMG5IyU1STXSFU6PFickSsZNbXFodwKHC+swtH8ShzNr7nij0uRNQk+f/n9w+nxStH+MiEGVdxaGcyB5K9ffWWl3l5yENNnb8PrCw8ov8937XaKbTGn+RPS3OYdKX6/+W37KYx/dzVeX6Q8HgsPExA4kl+JC99fTeqafchBEly+mDg1xG1m2fkO5tiyY7Q3kOBcWbUL17iexHmOt1HgDtJ/ikVCC1L/E4hWGCbY85clpmCbpS8Rsxhwu+o66uICOPy9OH/nac3vojVqdH7QwEazBHIOeFstFPD3YF65AztOluCGmRux82SJ4jW6dqf6AlOsI0W/OzmGZIZjrP55mNOl1SQ72GpYyIHNf0NGKmxHqqKiQtGIl8JsNqOsrKxOBqXjzIKPlFJOtomL6PFRPxaliMYibz9pQTdao7DKNAQViFSN9srfYZAmZl65HTmjP0TZ+A9IvRaFRSk9/cWao9ifU44HftrpN0468fmshsPlUTge6YlkYcosqvIbW03g50j5zhW7iFYEcaTyyuRFkr8m6hmpc2/B0QK7eJbb3fhoxWFlcfY5gszCKgx5dRk+X3X0rHy/xWggNMArvwbGvIAo32Znd3kVkdK6BmvsVapkFGnUkZ0PUuSx8yVEhIMxSuiGbouIlAqTeZpsXrkdP205CVEEvljtLzVfVq28f1jakl9Gqh4MSN6hVMtIUeewrNqtWFOdbq90fqwmg1QjVeFw49ZvtmD699vqZIws3TCvPIhqWgC4mesfSqacf88Xq49ipns8nKIRn8Q9KK2VRt/aHsy44q9nfRlj36wnfce+Wndc+f3MfV3JXHe699HsUFGV0pF6fxmRHv80wHrh5gzzPafK8MJ89X5udP+zmo3SdwbbJ5SqfQ1jTymzuwEI8MCIcnsNGQp1BP6cVNgD70v8OhtOwJNePweXkaI2Uk0CKPzcyCquxscrDmP1oQJFdszDMHaaxPo7UgpqH4AYm4ojVRJaSw7F+LjzVddZ9zOBsB2pbt264aef/DX0f/zxR3TurNLRXEeDBx8ppUYRS0EAgM9WHcX/5uyEw+0JSoWxmY0wm8gmqBYdpN9hMgrSxDyQU4Exn+/DqCWpOFRuRkVMa4iCwa8B5trDpFieOkGsgdXzhcX4aXOm3+LHZqTMRgHdm8YDAHaf0uibECb46DNdkFjjLVCmxeMVFQsIXyP1b6f28en81xYewCerjtToWBmnyzDstWX4YVNmXQwtLMxccxSnSqrx0l/7zvh3A/LGSxFllWurKusx0+cOkpGg9GH2Pg5k4FFqbqRFHn+vFsqC92/Xy8101eigZdXK59hABr8m1UuNlO880HWUn9MA0DiWrH3ldpdiTE5FjZQsf06x6XiRH3WxJihjjMLCippn59k6nlACPPz+8fnqY/jCMxHdHV9gW+Qgaa1M8p27cB2p+l4beeeG/T2VKvdZiu8688GhYL/L6xWh1m5T7V4CGEfKZIBVqqsLIyPVQIxYNggSjMlR3+DvpWABUQ93b4RzL9LrR+8biy8jFeuj0ZUHceLUwM/Hk0VVWLSXZPB3nCyB0+3FsYJKhf0hZaRUAi3UqYtiMlK90+MBBM66a+E/mZF6+umn8eKLL2LKlCn4+uuv8fXXX+PGG2/ESy+9hKeffro+xqijnnE4rwKZhXJmxq3hSK08mI+ft2Rh1cECzToqCqvZKKWl1aK99PNGgyBNzD92nka53Y38cgfGvL0Kw/P/hy3jFyiUqNjaJ6vJQOqeuIn46K+7/ZwWB1dz0DGVFMMfCZPOIooiDueVw+n2wuMVMWvtMVz56XqsOaxsCEzHxG4CgRZgPgrDF6bT471zVU/0b0UoMGrO1bkKtcXzay7qGypmrT2Gk0XVeHyuSr+PesbxwrrJcNYUvCNlNRmlzbg+DRK2jkNNvv6811dgd1YpZ7Rp379VKo7U5b2bYerglhjalhSBs/RXq9lfVIU3OhSO1BmokaL3dEKkNs2osS/yW2Z3KWtQmYwUS9NikR+C2EYwsOeovBb3h9sb2nWl0HJc7bCiwuGWrlWjaLI3BItS88bqmaY9s9eOvc/o3pcQSfZSfl/SFCOin2fO64XdZREZ3pGjoPuGzWyATRKbOIM1UnUE1sEP5rjUN+i9Re2hYBky/t7mz39SlDatmgZ/6H1j9q3nNPvjcHvD3vf5uXGyWLlHvfr3fpz/xgr86As8CgKkGqqSADVSLGFpRAfSpzIvQB2oFlzu/2CN1EUXXYTff/8dhw8fxl133YWHHnoIWVlZWLJkCSZNmlQPQ9RxJnD7d1ulv2lEhTViWBwvqAzKQbeZDZIBp7bo043XxDhSVGyBogixmJ8tK8OUVrnQ+8XF0uNoq0kz2uNXI+XySBkpm9mA5gmktDm/3BEWRW7R3hyMfmsVbv92C56etwfPz8/ApmNF+GiFMntCFzs2Ul4VQBGJN3JLqtUzUglRFomb/K+i9tVhxm3v6bNHMa5psT4AvPRnBga+vBQna0E3tah8P81K1acjxUZheZEFij92nuKofcr37c4qRbavTpA6YxEWOeoZYTHiuYu74JKe/uIzfpLboPQgGRXM4zNRI0XnflKUttocdaSKKpVOn8vjlc4PKxzAojYZJAo2a1eTaDeFu5YZKRYVdrd0ryZFE6MzWG85/nhnem10aVC46X0V73OkeNqrMYggBjuvXr6sGx4Zqy0BD8gZDZvZKGWkwqqRagBG7OmSatw9e7v0uOIsU/ucnCMVzLFze/l7kVtrfI9fvrSb32e1MlLRTPYnXMeS/36+nOHLtYQWTVkUEWajtC6xVD2+RuryPkQM66IeaRKriF9zQ4GLO1+P/LIT83ac0nh3w0SNdv2JEydi7dq1qKysREFBAZYtW4bzzjuvrsem4wxiX3aZJNRAHZ+BrZPQKNo/eiJClDYOrYCazSRnpNQiKGyNFI1wqIH97PaTxQoHSRAEzUglv/HauYxULFO4Hg4H+/ftpFB0+YF8zN6oTR2TqH3MOAItgLyRyztd7MIa6gZ5LkEtEspTJEIFm5Wpz7og9e+uuVLY56uPIafMjl+3ZdXi+9UcKbIJ1ybjEAwKsQENo3flwXyO2if/fTivHJd8uAbXfLYBbo9Xut8jVTIxaopTxSr1dHyNVAUjNnFG+ki5aPAjUEaKGCCFlcrskleU1w6r2einAAoo6xdqCjbyf6q4GkfySZbP7fGiMIyMl6JGKgRjXG1PaJ1MeppVONzSuKhBF8zA93ekzty8F0VRcf+za7lDykiZ/V4DlFF9NbDn1WYyYkQH/wa8arCZjIjx0cGKKp3S3q6GhlYj9RlXL3a2M1L03go1I8Wzdfj5QAO6iSrrQrXLAyeTdaIBFJPRIAW2ww140GPRrJZWXTi9RSItRrTxzcXDvqy/w+2Rsp3UaerfKhHrHhuJt6+UhYBqsibxjKVyhxubjhWFfZyzibAdqZMnTyIrS97oN23ahPvvvx+fffZZnQ5MR/0iKcqCCLMRSx+SHWAaTaCqfalxNmx8YjRGd0pRfFYU5cUlyqKuoG81G5hiV5UaKY9cI0UjHGpgN5ITHG2q2unWXPh5DrnD5VFEeI0GQcrshOpI2V0eLFZRB1MD/c3sxhlI1pvv38FHLlk5VKk/SAOIHtYV6nID9zJGw5nehE0Bmr6GimX78/DcH3trVLirlpGi0cz6zEixDisNbhgNgkII5mBuBU4xv4le8y3HizD6rVXwioQayTpFESpZcep8sCiu8s/OlHEGR7ndjd+3n8L02duQw1FQ6kNsoto3h3mKNItkH3VNzQChBhPtIcWjThwp5hjfb8zEqDdXYn9OGZ6bvxd9X1qCNYcKAnxaHXaNbNCh3HIcyCkHoO64NpFojm7JaKO1Gk6PN2Bg5WxmpPhsJruW03HF+wxNP2ofMz/Uzglbe2YyCIjU2G95WM0GpCdGQhDIfa9VUwU0vIzUigN5iscVDndAR7C+Qc8PnavldlfA+kSegcPei26PV3K6Kd2TR5ndxaj2yfcHdYTCFd+gtghl4bDqnGoZUZvZiDbJRODneGEl3B4vCnzZb7NRQDxDVU6Lj4DJaGBEc2rgSKnQfGNUpNUbMsJ2pK699losX74cAJCTk4PRo0dj06ZNePLJJ/HCCy/U+QB11A++vXkAVj4yAm2SoyWHgkYgXVL9EnE4+PoDp1teDLTofzYTSy3w39ToYmM0COjeLA4RZiOaJUTg6n7NFe9jJyY/SatcHk0BBz7q4nB7pQWF/h66MAWb/CVVTqw5VIBBryz128zHdG6sGlVUq5EKVOxfyRlddpfScGAjVKGqMZ1L0HIKg1F6KE6XVOPKT9fjoZ93KjaaM634xG5MWgalGtiNeVdWKb5adxzvLzsU9vcHykjVqyPFUvucsiN176h2SIg0o1NqrN9nKJ2TV0CjzpYgQJXSlhYf4ecwltvdftlH/tqX29144rfd+HNXtl82uT7FJrQMJgBoFKMuQgDI46eBE54dUFpde2qfmjO2aE8uvtuQCVEEvttwQuVTgaE2lyscblz60Tpc9P4alFa7VM83zT453V5JnITtDxYo2OIMkgWoT/C/Ra0WL0GDFsbWSAVqGyAIxOmK0thvedh8vcfS4og6baBaYDbQqaU+eybRmMs4e8Wzq+RG93I6/7yif2sFFv6OFOOoMn9rBVhKq11+qn2A7FycLKrC2sMFITuXkhKfSq89teBEpMWIpvERiDAb4fKIOF5YJdVHJUdbVbPjsbXJSKkEENQUARsywnak9uzZg/79SYO0n3/+Gd26dcO6devw/fff46uvvqrr8emoJ8TYTBJFJt6XYqZRXUlRz2cUWjmjpdLpkSZ6lEovAYBENaixo5aRYmukmiVEYtX/zsff9w3zy06xxY7UERnWjhSbi6L2xKVRF7YpIZuRIueAKuEENjBv+2Yrrp+5UYqUt0uR5dgHtU6SCqJZON2kKS/rPAUyZOlrKczvZzNYTilCFTjTd6bg8nixPbO4xsphJVVOhVGh5XTwcsFaePOfg9h0rAi/bsvCsQLZaCi3u/H43F24YebGM05bCccIUduYf9h0MuzvDORIVQSo0astWDqLlJESiCO1/ZkLcEWfZn6foXVNbE8cACjwbdoRGpS2WJsZ390yAO9f0wu3DW8tPV/CrQV8gKTM7tIMvNRPjZRStY+CrXegr9F1jA1M8Rmpx8d3Qs/m8ejRPN73eu0dY/6cAYDTw7QicIRvGKmtS5mFVahwuOH0eHGisFIynthzwa591JluzNC+AxnTfEbxTK6N/HdX+jIoHq8IujzKYhPKa6aoLVRZA/i9WC1DqwZ6z7T17VWHub6ELLRU++79YTsGvLxEsZ6eCag5GDWpk/J4RSzck1NrVgI9P9E2k7T3Bgq+8n042YAnGxiM0xChKauWhWfYmttYn3Pxvzm7cN0XGzFzjX/LB4qTRaQNx4yF+6VjpcRYQ2rtFGExwWAQ0K4xuXcO5ZZLIhJa7KHaUPvU1t5/vSPlcrlgtZKTuWTJElx88cUAgI4dOyI7O7tuR6ej3mBiUsaUnkc3fjZbBPgbZ3aXLH/OyiuzsJkZClrAGinyHckxVsTYzFJRLkUJY0jTjaZ7M1mAglIWWiRFYtvTYzC4TRIAeeOgGxibkaKbjM0sO1laOFVSjU3HZb7uoNZJmHPnYERbTTAbBZzfMcUvggaQhez9ZYeVGyXrOLg8eHbeHvy+nRRVUocrIdIinXfW6HMxESpriI0W6xNP/bYHl360DrNqoKxXZndhxBsrMPG91ZIjppV5CrVh8t7T6jL2xwsr8cOmk1h9qEDuCl+PYKNr4ThSWhvzy2HKqKs5UtG+ORqs/0lt4FZR7WNpfUkqtZY0OtuqUZTieZqNUFOqo+jfKhEX9UjDExM6SQZGCed0U2ofjeJnBlBUrI/IOz0P/G9nGwvHWMnfdD20mAzSNeQzUpP7NMPv04egU5MYxfFrA14dFABySuXaqJrUKZbbXX5rKkuzJBkp//0jLtIs/fZTxcSRSoiySOt0oN/bkKh9bq/op64WL9VIKcfFOk/8a4AcoKCUYZ7aF69hjNM9or3PGD6YWx7S+Nnz9sfO0yiucuGPHdoNZOsDatnKYM5QTqnd7/74bfsp3PHdVkybtblW45Hrk40hZV4C1UjReWEzG/zKItgmuPQ76dwHmMCv71x8s147W7w4IxenSqrx8YojUmDEZjb6NTVXQ4RvvrVLIevMobwKSSE0WaOePVoSwAq9X6HD7cGstcdU1+VQxtmQELYj1aVLF3zyySdYvXo1Fi9ejHHjxgEATp8+jaSkpDofoI76AVvLwddQ0IgKdbZ448zp8UqGvRZnm1UNUjNSPB6ls0bBF2Cy9RJ0o4mxmaXIEDW0bSYjEqMsSE+MlMYIyBuNw82qYBmlMQKBKVj7OAW4W4a1QlyEGX/eOxQL7hmGVo2iVGs2XlyQgbcWH1Q8x2YElu/Pw9frT+D+n3YAkOspIi1GKSrNbh4Oj+xI2WhG6izSHX7aQrIlNaH+7DlVipIqF04UVklKQFqZgsIQHClRFJFVrF5PxHZuD1QnUFfgJax5uDxeLM7I9dv0tbILn606ihOFoUeEA9ZI1WMfKZdKRoqt/1BTrqP3Lx8QoNec752kBUqb4gUnqCJdWjyhNx3O147K14fYhJyRUv72FkmR0t8RFuX1shhltdMyjRopuuZqqSOGAzWD8Dhzv5VUEafop82ZQQMRgkDoT16R9KdhwQYKSpmIO5uRiraapMfUf4uPNMvrdKCM1FkQm1h/pFAhDMDOvUqHm3OkLNLzLNh1QM1R5DNSRoOgoLtS6h4Puke09AUpAimBsvsIrZFSUtLPLD2a7t3NEiKkDEggR2p/ThmGzliG6bO3KZ7/dCVR0mUDoTUaDxPEDCXzQoMitL5JTcwjQqWlAaXeldndiu+kiOWcCzYYzoPdT+jabDEZkBiAZkxBHbx2jBOeV6ZU7OPBZkpDXZfm78zG8/MzVHsu8r+1oSNsR2rGjBn49NNPMWLECFxzzTXo0aMHAOCPP/6QKH86Gj7YIsZIyZEiE8DFRcF444zUSFGxCXVjJ4ZJg6vXSFFnTXlstYwU5QLTxT2KcTao0WXzPR7ZUSmMQR0pu8ujkpEinwkU6Tzh24C6pMXi62n9peO3SIpCB19kWI17rCYDykYf2R4whHZIx2aUM4S+6yGKomKztkqGxdkvDA5H2YuCpWkUVzkxZ2uWQoSARSgZKbbvDA/WKKyL4vxgUGSkVIzzb9afwK3fEOl8FoFoWmrUmpNFVXj4l53IyFE6+oGpffUpfy7/1iqVjJSach2djzxth/LxtUQWeNA1g79XKC2NOlK8WA0gO2v16UjxfWNuHdYa0VYTpg5u6WdMqWWkbCble+jaV1UH15PP4gFKo7ukyoU7vtuKR3/djWs/3xAwGGE2GKR+MoszlKI87HpY5fBIewzrSEVZTH4Mh4RIi3SNAlH7zkaN1DWfb8Bt326R5rnNbJDu2UqHRzH/qQHOO1LsGq7msNB90sju18yeS+9tHnSPaOp7XWt9BZTrFN0LaYBLbcyhosLhxrwdp8Jed50+e+HRcR0ltcNA1L45W7Lg9opYtj9PUTdUV3VVao4U3+ybBXV+o1TalLD7PB9EptRWNtCgrJFSBq3VgmYUrJNFHWWL0SAFnQKB2oNUcOJYQaXUjDdFw5GymgxSrXiomfLl+/M0X/vXZ6RGjBiBgoICFBQU4Msvv5Sev+222/DJJ5/U6eB01B9YB4Y6Q9TQ5ycxb5w53F5p44rUqJGKtck0DbVNjaf2UfCF2S6PXGdEN5ooq0mKytK6LpqOvqBLE2nzYI/ncHvl3jRmmpHyUfsCRC8zfYb4sHbJOK99smrNBlXDCQbWkGWPU+GQ1QetZgMiad8f3+9lo/2WMyg2cTivAg//stOPX8/+jlA5+yxYg+FEYSUe/mWn5ntDySIFckJY4/nMOFL+AiEsvlh9FAAwZ6tS4jyQpK1at/gZC/djztYsvLFwv+J5tc2VGvL55bVv4KoFhfy5SkZKTXCBvo/eT9RokoIjoWakfJ/jaWp8RkoN0T7jpF7EJnz3eTS3RvZvlYjdz12AZy/q7Jd1s5gMUpCLOmJWPiNlNSperym8XlF1TrBGdE6ZHSsO5AMgRuCGo4WaxzMZBfROTwAAvywqe39XM9TwaMY4jLKa/OhOsTaTdI4CBY54R/hAbnm9iqtQrDiQL323xWSQrjWtB1N7noKV+QfUa6TU9kmWBZKiwoYA5DrgYI6U16uUbqfZBHatqGm/sveWHsJ9P+7Aq3+HR09m7Q+188aDXf/ZOVGTnn57T5fit+1ZCoeMHU9sCAJVvKKxmpiHWradVb6jezubfYy1hZ6RYp00GsQwGw0Bm4NTUHuQUq6PF1RKe1BavDq1TxBkRclQ1yUR2mvuv96RAkiEfOvWrfj0009RXk64txaLBZGRoRmUOmqHjUcLsSurxO/51Yfy8fjcXSH1GVBbmCudHhzOq5BoGVYNR8rp9qhmpFiDgWSkAtRIMfLnLNQmOo000wkaaTFJBjxd5FmjaxQj105T0Q63RzovlGtMPxOIIkc3oOaJ2sZY80T5vg9UJFnhcEsNR9nvLLe71TNS1LFlNls2I1VX9JXSKhcyVJrYfrDsEOZszcJt325RPJ9dA1luFmx08WCu0kl7YkJHGATZ+Ocdqd+3n8KDP+9Q3OOBDCY2un4mGjsq+yT531datTi8VDcLtazfgl2kHnXHSWVtmFpGqpnP0c/ydbQvqXJi07GiOpUUdgfLSKk4UvT+pdeS9pMrqpTFJkIBrS1YuDdH8Tw9p001Nn8AkmJpXYtNiKIoGaW8smmU1QRBECAIgl8gwmI0+F1D3qGkvbVqS+0rd7gRbgnUkQCiBSaDgCZxZL09XaJ0/tkIfqXTLTkIrOPEUvsAspaajAaZORCG2MTMNccw4o0V+GPn6RoL4oQKl5tSuQyyQqbTLY3JajQgNkLu5SbVhXK/R80A5WukAOX91K1pnF8wEpDvmaYJZN8qt7tV1xj+vqfOP81AAKEL/vCgQaNwRXPYnol8Nt3u8uAQV+/F/gZ2L2BZN6HW+l396QY88NNO/LFTrguTxyNI61hBpXZQin4XvU5qYh68EjIgO0qs2IQ1QEaK7zXJgr0jaNbZbBICKohS0DUpPTESBoHYhbuzyD7TRINKyn4uUJsXxRgDKF+EMs6GhLAdqRMnTqBbt2645JJLMH36dOTnk2jVjBkz8PDDD9f5AHUokVVchWu/2IhLP1rnR8u4YeYm/LDpJL5Yra3mQsFGayidosrhxjMM5UgrI6Wg9jEbn3LSy3VM6g15qWpfYGofIEeaJWqflaX2+RtdbPE629CRFmnSKCilzATi3lMDID5Ce2J39FH8DAKhAGphxYF8DHplGb5Zf1yxaZbbXVKdls1klK4HjbSxRgKbkaorFbq7Zm/FhPdW+9VA/O4rMj7KSeey0c2aOCdsrRjfK+nWYa2x+7mxuGFQCwD+m/j9P+3A3G2n8CuT0QnY6Jg5z2eCCqks3Pb/Pq0sC59VizAbcbtPkS6cTJq6I0U2v5NF5Fw/9fseXPnper+sWG2gUO3znXNW2lktc2l3eSCKonT9aHSdBkdCzXZe0Ze0TFh+IE+x1tCocYukKNXPAbJxUtd9pBxur9TgMtDv4Gl7VpPB7zk+oyVFfmuZcaGS66E6rADp86UFk1Furp7POf9sFpq915UZKaNiP6G07JqITQAkq3LvD9uxiHOwawqtwAMt5reYZAEBNiNlNhkkI1lkpLP9HCkVw9jDCT8BShZIq0ZRWHDvUMy9a7Dic3SdibSYpODkKZU6Up4tQvclNiNV09pSNd+lqNKJV//ej/05/oE7CjYDROcnXSOen5+BMW+vkkSaACU9lW06ztoWoexToihKn9+VJQeo2Ixjuq++8XgAJUMaJIi0BspI+a/TbP0Vm82kiOUcqeIqp6aiIvudVJnTYjQE7GlHQe9hi8kgBeEoS6CJirAWBbXJQqX2eTgWACucEkrmrCEhbEfqvvvuQ9++fVFcXIyICNk7vfTSS7F06dI6HZwOf2ScLoPHS6RVtxxXL/4NpSjYqJKRqnB4sO6ITN2gBruV29idHrnAlo0osupUisaxKpF5voiWgl8sAJm+VykJMsiRS7WMVEsVR4pkpMjn6eIsq/ZpG1FlUhZLO9PUOjkaX9zYFzOn9kPT+OBZ2RfmZygMiwqHW1p8bGaGh21XZqSMBkFRbFwXGSlRFLH2MLnmf+4KTXWTpZpVOj1hK3uxUUPWKRvXpQkEQUCU1SQt+EUMrYQ1ZHLK5I2ebrLBeqyE09eppgim2sca1aIoIru0GlNnbcJvjGEAALERJqnglqesBcokqdFZqABLdmk1XB6vlM36nuulVBuwvVPova3m1LGocnpwILdcMrioEU43bX7d0UKv5vGwGA0QRSDXJ9MriqI037s3i1Pt9QbI2em6rpGihqjFqFTn4oOwBoOgqAVj6zDkMXKOVB1R+0p8fagSIs2qjTnVUBggEm8yCFINRWGFQ7EusHOezSYraqS4jBQNXoVS48fXSLFYH4COGA60spZOJiNFHcMKVjDAl1Wj84E6+Han8niBaqRY5kYMV1fWsUksmnH0VTaoSbNSqo6UR3kP0X2ogHGED+dVYElGblgZbK3M+3cbTuCTlUfw6JxdQT/LUvvoXP5hE1mz3l0q99hjRWZYJ529/0KR8WcDVuzaJfV0MholJbstJ4o1z4dUI2XxZ7zIqn0q1L5IxpFSETDhBRiKq1w4/40VmLdDuXeQMfu3XTEbDYpjaDkrkVZ1W8poEBRCOTxCqWVkwV+TkR1TMHVwS7w2uXvAbFVDRNiO1OrVq/HUU0/BYlF6ti1btsSpU/4XVEfNsHx/Ht5YdMAv68DepAfz1CVNg613Zi4LRCd8tUu5kNPFhI9YOlxyRoqddNcPbAFBgCRBLjfkVVMvU1ftEwTBj0ZHHSkasWMpIHJhujyODo1jJIOlTXKUNGYauaLRQSpQESizwztfWhjduTHO75CiWiDP/8b4SLMialPl8EgLn81sVKT4AfgtqrY6pPaxx2A3nkBRJT6LxBsAe0+XBlSJYg0ieqwOjWPwztU9pedpap/NSLFcfpcKnaMN099LDfZ6VK2jCOZIsYEDp8eLr9Yex4oD+dieWaJ4n9VklCJ0fK+fQDQ0tQa2yTFWWE0GeEXltatLwpNbUevhT+3Twrh3VgMg2Vza8JJGwEPNSAmCIBkIdL5WuzySc5ccY0X7xjGqn6XrSF3XSNH7v1lihKJWTE1ljV1fVR0pq/KxFPmtZUaaOuhxkRa8elm3gO+lWfdA9TJmX8RbEEg2opiZu+x6UsrQ/KI4R4oVm6D3P82ofrj8sOZaHcgR5tUcawqte8TFUNGktdvuYjJS5PrL67pGRiogtY9xpLgsHuBPFWP3Q3rPqdVJaWWkCsqV1/mWb7Zg07HQ1e/4LBbd32gWfGdWqd9nKCSVOaMB0VZ1kQ5eBZKCzTzZFc5E8LnC0lHZvZB17Ia3b4QIsxFH8yuxV4UOT8avVDTWUu3jwWak1BvyqtsgfENzQHldK5gaKTZQ0TpZfb+MZMbWmnGkujaNC1i3KlP7QluXeMEOq8mI5y7ugiv7NQ/p8w0JYTtSXq8XHo//icrKykJMjPpmpSN8PD1vDz5Yfhi/blPSb9gJciRPPa0bzJnn65J41T4KGhHmJXqdHllsIjnaihZJkYiLMOOi7mlY9tAIfH5jXwBQNOTNLKzCh8sPS4uexytH8Xh8en1fvHNVT4zr0gQAQ+1jJMJp5I9SSNiFKS0+Al9O7Ye5dw2WDCy7y4OjvjR4Sx/VR6L2BchU8HVVwcAuNIIA9Gwej1/uGIQeTO8rl0dUnOtKp1wjFcH0eqDnil9U65Lax0bwvD4PfN3hAnR6ZqH0vMVogNcr4n9zduKNRQf8NmR2k9tzqhQXvr8Gkz5cqymGUalwpMjm1a5xtOLcqdVIsYYHazhRqqAaLZTFmchIKeTPVYw7NtJW5fBoSvMaDYIUkS/lDMFATq6a2IQgCFIdUW5Z/QhOuFQMj3CKvaOtJsmopmuDGv1FC1INim++0k3aZBAQYTZiQrdU9e+11U+NVKbPkaJCNPeMbAsAeGRsB7/3smuXzeTf6yWaz0jRmtZaUvuooxMfYcYVfZtj5pS+mu/t2pSsX4FoXiajAJNRllhm6WFssIUawUaDACMzH9h7AJDnc49m8QCIehjNSPCQjVf5XNLG7UUBsmjhQCvLQg1Hs8kgOX9qWQV6j5bZXVhzqAA/bT7JHSdQQ151g5oaxnwAhQ3o0YwUHwAD/O97ep1yy/0FbrQcBzUUcNTO4koXHp+7S5oXgeBQZKTkViDsfldYKav5sg47G6Rj18lQFEtzyuTzw84teT0TEGMzS30sj2i0U6DXjI5d6UjJtdA8YoM4UrzYBAV/rvnvrJAyUgKGtmuExCgLrh+YrnCqWGVRVtnvkp5pMBoExFhNeGlSV9XvpwiX2sdT1i0BxDMaOsJ2pC644AK888470mNBEFBRUYFnn30WEyZMqMux/adB++Ls4orJWeOULQhl08zB0qJmk3pGil/I6cIfYVZu5E63V0pXR1iMmDd9CBY/OBwJURa0ahQlbYZyHykP3vjnAF5fdADv+VLyfNNfFp3TYjGpV1MpEnk0vwIerygtQlFWk0RvoIsWnwk6v0MKeqcnSAtWpdMjNX5rk+JzpIKoQbH1G2qUQzWw4xjVMQW/Tx+C3ukJ+PG2QXj7KtIqoMzuUvbpcHgUjfpY9R7A3zANJOIRLtQ2njmc8+70eLHrVCl+3pKFD5Yf9hOmYM8fkaAlGx1fW0XBGlXUOeJpC1JvIMZwYykS1U4PMk6X4bZvtmCzL1IabTXirhFtEB9pxi1DW/l975mokQqm2ud0Kx1ovh6GwmgQFEYZC62In8kgKLIfLOg9xRrC1hAdnSqnG1NnbcInvr4sauD57oCy2BuAJr0OIIEKXpSB778UCHImwO37X6bkCoKAa/qno3liBAa3SVJ8j5yRqtt746RP2INm1+8b1Q5rHj0fk3o19XuvjRlPhMWoCAhEW01+a2S4BosW6H0l1yJpR5u7+mo/g1H7AFngh3Wk2HuWXhuzUYCLESmJshg5ah8Z1+V9mkmBqNWHClS/m16/eGYdGd4uGQAx4usCWvdImVSDIshBsCpWwpqcV3qPZpdWY9pXm/HlWmUts1rWxKWyTxoVThU5Ju9IsbRYqtyXpeJI8b+p2ulBabVLUSMk/c4wGnrz/f/m7TjlJzqhFQika6TFpKRK8nWkxVUueDjlSXY/Y48fiiPFZqQUjhRXrySL96iLLvnXSDH7Fkft698yEQDQtWmsdO+WVLkkW4ylBWv1VuLrzPnvpHXCFpMBbZKjse3pMfi/Sd0UAZzWyXLmie351ys9AWsfHYn1T4ySgilaoHZiyBkp7n6qicpiQ0HYI3/jjTewdu1adO7cGXa7Hddee61E65sxY0Z9jPE/B5YmY+SMEdYYZDcq1qgO5tfzkV6akuV7H9GIOG/gON1eaUGI9G38KSodr1mDn6rg0Igi/Y2BJDx7NI8HAOzIKlUY35EWo1+GSKtgmm4wHq8It1dEpMUoFUzS86BFkalyeqT6DT4qrAXWMGYNogiLHBUXRWXEr8rpVqj5xHFyznSzo7/FJlEmlRvFvT9sx0crDoc0TulzzDWn5zi7xD8aeYApDt6fo6SUshsWK5WeU+Z/HEDdAOSjbZTaV1LtkoIE7L1fbnfj01VH8E9GrtQcOMpiwv/GdcS2p8ZgYOskv++oK3GOQHAGofaxv6HK6dHMkpkMslFWzAluaG1UfICEBd2EqXIfELyGiWLR3hysOJCPV//er1kXwKr2UfA94tg1gm9iHc20NKBoFB26clMMJ0tMM1P0dyfHWLH6fyMx+9aBinuNOhF17UhRI4tmpExGuXCbB7t2RVqMCsEa3hml7wFq32CZri9ajlQa0x+vQxMyJrvLq9ifWFCDjnekDuWWK+oq6TUyGw2S4h1AzpGa2ITJaMBzF3cBQOp/1e5BWqfERtO7+ZwvtV5ZNYFWRooa8haTQTKG2ayCVTLAiUPz8l/7VTOgX607jpf+zFCsUx6VGikWdC8QBGWjXjag19ZHed6mUtcj1zqTa+/2ivhhUyaKKp2KNiJAeKITvNLovmz/bJZWRpVV7aPUvnKHv+rgqeJqlNtdilKGSoUjpa7mp4UDzL7GCiLR/YquT41i1BVlKej8kGukGLEJphYaAO46vw0GtErE21f2VARQ1UoK0hPV1w+1O0ONss+zFVi7ri1Di0/lVE6bxNn8BG/UEKkRkFeDKIp+EvL8fnEuIeyRN2/eHDt37sSTTz6JBx54AL169cKrr76K7du3IyUlJfgBdAQFWxPBTxJ2kdWK+KnhGJOG5jdMGvUoYI733jW9JEOLd6Qcbq/0fXy2ioUaBc0gCPh2/XEc8hncatEUChoBOZBTJtVHUbEF3rHRiqbyz7dOjpIydtSB1KqdoYuZ0UcPCgVsXQdfzGk1GaXjsM4KoS3IKX+6gS3Zl4tKrh8JPQ6g3Cj+2p2NP3aexmsLD4TUxFb6jUzBJ42IqkUe1XoQ0cQne33Z6KCWAaN2r9LsJwU9Tx6mzwnrdJTZXZi347TiM9QIMxgEhUFGUVcNGgMhWENevghY61oZBDkjVVLlUjaarIkj5XMeWGqNlmHIg62XKKly4Uh+hZ9TqlY/wjsBtwwjWcLrB6bjy6n9FK8TxTblHGsUHXpGihocNBhAqX1qdBh27aCUFtagrwvQWiKtPj8slI6UCRO7p0pUzKG+rAoLem+HKjOsBalGyhcw49e4RMaRbd9YNrS06k2osU+vW36FAxmnyzDm7VWKAAsN2FlNBj/6LztvWYpjl7Q4WEwGlFS5JIo2CzrvBrQiAZR2KdHSOayrGiktBgAV7TAbDYogGN+PsVMqcUYD9XP7fPUx/MjQF9VqpFgaFss+Yfc69u+BrUkWNrvUjid/34NX/tonGftSJo8J+lGVw6mDW0o0NsA/yxQIfC2d2jXTsllY6W8qXpJVXO1neJ8urfa7tjTz5PGKivU3WEaqqNKpUDGl61tplUuislPHiAb52ADX5uNFuOXrzTiQI4vn0HuZ3bfo33SujeiQgp9uH4R2jWOk+73c4ZbmJhswtpmN+PSGPnhyQieF01yu8tvUenfyGR82E94mORp3jWiD289rraiLCgfhZMorVFov/GeofS6XC23atMGhQ4dw3XXX4bXXXsNHH32EW265RaHgp6PmyCm14/4fd0iPeToSOynZVDcbcVGrTdnGFLPz0RkqGEEXeLNRwMU90qTX+aJvtrkt72SxUGumV+Fw4+l5e6XHgTJSafE2CAI5BzSSHmkxQhAEf0lgq/o4oixGxSbUhimwZPuT3DRrE0a+uULhCJRz9KBQwBb9qtXs0MWSzdawmQmbyYDe6QkwGQS4vSL+3J3tx7WXRDwYY5YVKzjI9dkIhApFM0O3NB4A+Pn2QdJ4C7iNURDkWjP2HmWdMJ5SU2Z3we3xqmaGeIOXbUJKzw27QKs14WUNcbXC3DMif87WSIWQkeJpe3Tc1w1MlygWTo9XsVlqGdCBNiLqqLKOVKiOpYdx4uZuP4VRb67E/zjVLbWMFL9xXz+wBb6c2hfPXtQFXdLiMI2hX0bbzH6GfDiOFK/uRc+r2n2g6G/lM0odATJSpVUuTJ21Cb9sCb0fDp0HoTSWZNfXSIsRNrMRSx86D+9c1RPP+zIxLKIYgYzaNOWmDgB12Pl1nqVWJkVbpfVHSwGNRpRpRqqg3IE9p/wpYkVMw+WOPueCOhvRVjYQpVSBpfS+rSf8VWmpQ9AzPR6/Tx+C728ZIK2/1S4PHvp5J9YzirQ1gVbWkhq9ZqOBk7D2sQx8v+26AekhfQ9bi6RWIzWpVxqsJgOu6NNM8TnWuFZmp4xSo+TZGzPx6aqj2OijQ1PnMNJilAIbdC8Z2DoJ/xvbUTpOOAE6Pot+TIXmrapS6PFKBrbFZEA7nwN/JL/CT700u6Ta73teX3QALy7I8NtjgsmfbztRrFgPq12Ebj/qrZXSc3R+xDP0TYpPVx7Bkn15iubydE9jHUa7lJHSFpsA5PWLDzCO7dIEtw5vrRAKKq1yIbu0Gj9uypT2SLV1gQ+0RXIBnP+N64jHx3eqsWKeJDYRwr7Cs5+A/xC1z2w2w25Xp+voqBt8svII1hyWeeA89Yc1xJwerzRh2EVAzWA8WSwvZPmcIg/NSFFjjafY8AaO0+2RjLlAjhTdoANF4AIpe1lNRsmYohksOlY/SWCNcQiCoKB7sI4U/V2FFU4sP5CPo/mVWHUwX3q9LETFPhY2ZrFKUunZoGZYVXIZqYQoC8Z2JUIbxwsq/SKbaqp9tNEvoE2pU4PSyfUonouyGqUoHH8NG8fYJMdFKyPFOjs7T5ag9wuLcd9PO1QNeJ7/bTUZpIzXzNXHMHTGMqlRNKCeNYvkmnvyCIfaV+301IjuxX5GLYJdzfH2+Ujpx9f1wXvX9MLV/dIRYZH7pbEy8FoblZrQBAWNbJ6sgSPFGgI/+wrk2YaVgLKPFIVfBNRsxMiOjaXnWapfDCc0AMgUmlDAq3vR+zCYI0PPS6Cm3L9tz8KKA/l4JIBkMw8aPdcqEGfB3vv0ekdaTJjUq6lq3xe2boI3EPdll+Hu2dv8mpaqgRqCNHPOr/PX9GsOi9EgBdVoJk8rI2WmNVJMRkptnspiIkZc1D0VV/Zths9u6OP32/hsXu8WxBnYrtLeQ1bOE9CzeTxSYm2Itcn1Zb9uy8I1n29QHXeo0MrgljDUPoXymktJ7YuPtOCpiZ38Ps9TWFkxH7UaqbYpMdj57AV47fLuis+xwSfeKebPJaVasnsLu35aTAZ0TI3B0HaN8O3N/QGER+3jA0TUtqBONqB+Hymaz5sMaJ4QCZvZAKfbi72nlU756VK7nxAPQJox8/2VglH7aF8rumdXOz04nFehEHKg92a8SkZqyb48AMBuJnBA7R+FkiAVlVKxV0xGg0LaPtpqQmOVkglAWRPv9Hjx/B8ZeGzubjw/nwSp1fYeniHA2kxqasPhgp6fUDJSatftP0Xtmz59OmbMmAG3u3a0Ah3qmMsV+vO0M94YpIuRIuqhYhQczddWy+GdEL4fD+9YOT0MtS+gI0UWnEBKYYGofYDcAI42wKOZp8ZcY7hAFEOWYsc6UvR3sYsuy+2WMlLW4MYQBRtpSgjLkVJGqrr5aI05pXY/rj3bR4ouqDlMDUIgiWK176agY6CNPqOtJqlglm+wmRZvU21ozNIvqhg5/cUZuSTDtitbNTrIO6ss5//dpYeQVVyNGQv3S6+rZaRYQ1Ctpu1YQSWGv7YcLy7IUDzv8ngVDkZOqR39X1qCqbM2+R0jEERRVFDceOPc5fEqZHWLK51+lLjuzeNwcY80yXCihjRLq9Gk9gVypHzXkS2QDlWsgL2+ahLKgLKPFEUw+fMUxqgiNVLKtSQpDLEJVt0LCOxIsWPlKYFqYGsCvSH2TKNBGK0CcRaskE2EJXjQxmgQpDWad8Q/WH4YC3Zl4wXmHtfMpEjnSJ3ad37HFGx8YhTe9bUliLKqfycFZRdQYzmn1B6QDkaFNV67vAdGdCBlAWwAhDW6AaCnT72Pit3YXXJGlzoE7H4iCEKdNvbUOo/UKLT4ZaTous1QqFRaNAxt2wjXD0yXVAZzmUCYVo2UzWz0yxwITCEAfy35/ZJml+j6YzYaFA5dz2bx0noitaKohSNF8eYVPSQpfbXMOuusWowGGAyCxHzgBTBOq2SkKHhFPbV7NqfULtVuUWEkWldnd3n8nDFqeyRwNcxakBwpVtCJ1kJr0LDjmPu1U2pMUPEgioU+OuaPvkCXmiPFB9pYal+oqsSBQM9PIKeVziG1AItaPei5grAdqc2bN2Pu3LlIT0/H2LFjcdlllyn+6agdeIWmQBkpQI52sIsS70gdya/A4oxcze+M9KPJKR/Hc5uR3eWVJmqg2qFQNrFA1D5A3gCO+BY6amzxhZd8fQULdtFhiyqprDsbBWOLoukCGE5Giv0utYwUn6oHSNE4q9oHyAZ0cZVTyjry8ueAvGCyWSg2ipZxugxrmQwnD5YyVu30wOsVpSL2SItJMtgKuIxUi6QoWfXQNz5RVCoosYY6axycLvXPmKkZvDz9QaH8pbJRq6mxAUq1uMyiKsxcc0xhED8zbw+GvbZcamy47kgByh1urD1cGFYWi3eK+M2MPxafOTQbBb8MhpoMvKbYRMCMlH9Pk1BpYVVc82gK1rBUEyAIRtVQOFI2ZUbKIEA1G6MF6jgfza/E5uNF0v2hdl+1Y9aAWEmkQnvzZ4NFag48D7aQOpSMFGvEBGsqLb2PozICRNSBNtWm6nZ3fb8Vg15ZplB4paA1jJLYBNfmwmoyICHKIhnsNOtH74FFe3IU76fXm66x+7LL/NYNFmr0piaMwAW/xtOsSlGVE5UONy58fw0Gv7IUR/IrpPuaP2YCR68O1RFWQ1CxCaNBCh4q5M+Z9bqtSu+euAgz/m9SN0lQI485Z2o1UloQGcIXfx5SOKeUOrjsGFOZHmcXdGks/c3uRaE25aWBBH7cyTFWRpRA3jemfbUZ495ZJTknBkHOUFBHimZ76B6ZU2rXrH/jAz68IyWKIq75fAPGv7sa+7LLcMIXSOvgc/KqXR6/Gl/qEMczQkiBQAMU7ByVRbrUbQrW1kpP1K5Vmjq4peZroqhO+eXXY5baFwoFORhoRope17nbsjDstWXYeoLQSOftOIXOzyzEz5tPqu7foYofNUSEPfL4+HhMnjwZY8eORVpaGuLi4hT/dNQOfHozUI0UIC8QCmUYxmATRRE3zlRG1t+/ppfiMb95849tZiNmTO7mN3kNQuDePbE2c9ANQE3+nAVV9zpWQCJMdAFqHGtTpKMDUQybM2pZrCOltpH/tv2URIsJtRkvC6rMRMfIQ82wqnS4pcWH/g5WzpnPSLHjnv79Nvy9O1sRHaMbuyiKuGHmRlz3xUYc1mjezNdIsbQxVkmNz0i1TYmWzj+9R+0ur8KZYA3+QBF/QMOR0pAGB9QzIKzByzqbatE29vdQWd4vVhM5YtZB0IqsqoEXl+AdKZ5KR51Li9GAa/o3x3tXK+clIBsxbC8cLUqe1aQ9l6JVzkGoMrXVTnUDko08ql2PYBHGFGZ+xHI1UolRlqBrAwtq5K85XIArPlmPv31GvlpG6JZhrRBjNeHpCztL84yof2koEjL3dEUIAg9sM2C1wAkPNiOllsVWg0yzI+NZui8XY95epXiPy+PFX7tzUFDhwDIf9YgFL3/OR6z5jEc0F3HmezrR69WhSQwsJgPK7G5sZ+i4PNSCcK2To/HJ9X3w7c39/YxNyYCtcmH+ztM4nFeBSqcHKw7k+wWiKPjzqVaYHyq0eo1JYhMmWWmzwuGWnAlFT6f4CH8VXN9x6X5RbndLwQuPRO0LbqqxZYq80ayVkWLruFj63zgftRyQ1yCXRwz5/NF7i90PAeJI0SAAvY+OFVRi2f487M8pxzpfHRtrVLdIIvs3DXK2bkT28PwKB0p9zg6/f/ANhfksSWGlU8o4LcnIxQlfa5ROPnXKKqdHoYp5WW+5bYGckXIGdMzjIvxrxKslJo/69aRKyYDc/0sN04a2wqc39PFTVgSIIx6K2ESkQlo9dBtHC7KaKPm9z/2xFyeLqvH074Ru+O6SQ3B5RPzv112q++q5XCMV9tmbNWtWfYxDhw8ebmLy9Bt+gtCbVlEoyXzmcF4FTpVUg51uozs3Bgu/migVp+SqfumodnoUXbQbRVsDGjsGg4DnL+mCJ3/bo/meYI4WpfbRRY86eUYD4cJvOEqiHYEiKlf3T8eqQwW4fmC6YrxqUaG8cgcu/mAtdjw7JuxmvADJ1PRrmYAoq0l1kVMz7CqZWhn6XVLzRpXIpskgwCAAXhFYuj8PS/crjSR6rGqXR4o87j1dhrYpMX7frey74ZVofQaBGAA008dG1RKjLLiweyoysmWKDeDvdCgbIgY22tUczHB52+z1ZI3AlBir39jyyx1oHGtTzDd6LtixllS5VB1iNbjcvCMVeO5Sw6BRtAWvXKasd6BI8tWbsGIf1U5Z9QyMvRAooqcWDKh2eSCKYtDi4mqXuvFU4XBLxq1ajVQwzjsbJTcZlUqLfCYhGHgqJ41Iq823Pi0Sseu5CyAIgnSPekWi7qaWBVMEBEIwJPlmwMHAri+hZuFiOCGfmWuO+b2nirmPeUdXFEVZ/txnvAW7DyTxIN9akFWipItTQ8hsNKBZfASOFlQq1Pp4aJ0b1ohnQYv8y+1uyeAGSJCN789DwbMiyqpdNY6+a9ZISdQ+o8IppgJJ7NpmMAiYNbUfCiqceOXvfcgqrpYyLpTeWuX0ILfMgVaNTFKNVCgZqUDg2w1QShxVqzQbDbimfzoW7MrGpT2bKqT6bWajNK6TRVXokhY8YE4zDk0TInDc56QYDQISIi1+GSm2Nxjtv8Y69alxyvW3TUo0MrLLkF/ukDJSzRIiFGs834SZz0ixVO5FGSTYYDIIUuuBapdH2gsn926G15l6NEq/84rkXlQTujIZBGleVzjc0jpL71OtUgSW2sc7oSwiLSaM7dIEX6w+6pd9O1ZQqU7t4/aHJIbKyTpwNQXdf8uqXfhi9VEpkEBtVFa5UU1sorb3+NlEjV3A/Px8rFmzBmvWrEF+fn7wD+gICo9X9OP8ZmSX4cnfdiPPF73mjTO6qbFGq52ZRFSlq3OavxFNYTEZFDdxlEbamZ+IPIddDdcNaCH1g1KDNUDWAZAjadQOYGmHPZsnSH8HUvjq3yoRW54ajftHt1c8r7WRV7s8OJhTUaOMlNEg4Jc7BuOrm/qr8pvVDLuCCifTDZ18l5yRYvuRkPEKghCweSa9J1jag5o8NftegERG6WYUZSFKhbyz+eIlXbDusZGE2sfIsG89UYQbZm5UvFdBCQvSzFEtIhboN6qBj/RSo2ZA60S/91IHk/39kiPFPKfFwVcDX0Phl03mMkmnfRtgoB5lrXxStKwSIzVAOjP9hgC58acaYlTEN0QxtKbOWrVUbLG4KwTVPh6s4+TyeJXUzDDmHKD++wAgWaMXFXUaWGd94CtLVSPMrCMZSmPPMqaHVSgKWCydJ1RHKopxpL5ed1zhWFCwNYq8A1jhcEvOFU/dDuU7PV4RuaXatD21veH8Dkop90D1tWpgHSDWQcstcyjEeljw5zOcprI8tDJSdJ+wmAwKwYC52whVmN8/BrROwsTuqXhqYifcOKgFbhzUUnqN7nd0v/f4vpPvJ6kGbwDaHR8Mos6fQxLpMKBfy0RkPD8WMy73D+pQJ3Lie2tCovdJjhQTTKRZZpkCRs7buiOyI0Vrfdm1LJULSFJ57iqnR3IieKejSEMWXe3xnlMkINi9WZwUuHK6vZKxnxxjVezlVpMsAlRc5VTNrpiMsrKwxytK92d1kNpytqF0M5VALI84FQeIOFL+azafce7WNA4WowEWo8GP+lkTUIdyW2YJ/u/PffLzFpPfPfNvy0iFPfLKykpMmzYNqampGD58OIYPH460tDTcfPPNqKrSFjTQERwlVU6pudx4Jir3/cZMfLj8MAB/Y4zNPlA43XJROxUeSAoiJcwaMVqT3GgQFJMx1Ml39/ltNaMNwbIOjbloFEs7HNe1CQwCSf3XJMoYKFp8tKBmjlQwxKoci1K8DIJ8HeTmfG5Vrn2gsVP6Bcvx1oqk81QNmvmgRhPvnERaTJKxIlP7PHhnySFJWZFCGclX3rf87aDmUIfrSPH37TMXdcHA1om4a0Rb8PYsrd1QyrUTh7aCkXcOpSaGwp/aF7i+kdb9BRJVoL3UtjLNNOlm3Cc9QaK9AMFqpNTnRyj0Pq33vPnPAbz69364PV4N1b7gxt/Qto1gMxtwUY80BR0zlAaQLLQcL7buQw2CIEgGvtPtxdEC/wxK+BkpWh8V2m+gwjKAkoYcCPT8LNqbg2f/2Kv6HtYB5us5qCFtNRlU55ma/xfNULJyy+x+WS52b+IdqVlT++HD63or1rBw12yT0SCt/6yMf2GFQ470+wVTlN8RznzmEUzFk/62OM4xbddYPYg5rmsqXrikq2LM9LzROil6js0hROsDBUX461FU5V8jBWhnkdl+UtkqNa4s2FrZpvHy/UzVHCMl0RKSEWfl7Kkjxa4Fadwcbhxrk/alQz7KOt/smpdq5/cfNcXAga2TFPsqrUtVq1tkG8ariU4YBcHXqoU8PlpQgXt/2C6xOLRKEdg5EYjaR6EWBNHKSJk56ndKrA2/TR+MZQ+fpylqEQ60AvDldpdfBoqukWygI9z9viEhbEfqwQcfxMqVKzF//nyUlJSgpKQE8+bNw8qVK/HQQw/Vxxj/M0iMsmDv82Ox+n/n44ExyuzJbB8fnRpjdILSjZ13sOhjGnlPDEKVYaPDWhMCUG5UofZ5GdO5MQ6/PEE1ahwsI9WEi6SxlJ+ezeOx+MHz8PtdQ2rU+yBQRPRofiXjSNWd8lMgFTGaBQJkA6Da5ZEMe3ZzCRRFphkVVmJUK5LOK+hRoQq62fEZKaVkqiw2wdIzKHipbxaBauvk44dL7VNez8v7NMOPtw1CWnyEn+NJfyfrSLm9IgorHIqxlocRweapPzyVj69t8oRQR9OvZQLMRgFZxdWS8cjW0z06Tu7zEkj+XMvRYMfk8nhVMzJaghv/ZOTik5VHsDgj14+SDARX5ASAmVP7Ys2jI9EmOVoxh8MNXmg5Xs0TgzsmbAT+UG4FDueV445vt0o9kMJ2pJiMVChonRyNz2/si1/vHBxywTW9nht91GY1sOPm72O+PoqHWuBLykg53arqjez9nsLJNvdrlYhIi0lxnULNhLFga5AoCiqc0tyzceePp56rFbmHimANrGkfN36Nv4Cj0gcCzRzR4BplEoQiDR0osGk1GRXMEBpko85hsGzAc0w/M1pPpIUqp1wjyDoDjXzjkxpK++rIWNoyZQCw8yA1XnkvxdhMkmN4sojch1QkgoJXGOT3H7V5PLB1kmKPLayke6H/2kLvXa2MlMVkgCDINXMfrTiiaBmhFQhlzZhgQSBAmcGiOFZQGVKNFEAaXfNOaE2h5RxWONxShpWCqsey9l24GeqGhLAdqV9//RUzZ87E+PHjERsbi9jYWEyYMAGff/455syZUx9j/M9AEEidQPPESL+J5vKI+HrdcWlBoE4MfbyQU1Cixg/lCidGBd60QslIAcoFIFiWy+87VLjEWjKgFDy3mzfA2yRHh1ygzcNsFBQb7fTz20hR9OOFlYqGvHUF1rjijRU2O0SaAJO/83zy8ezmopbFoPK19J5gqX1alBZ+g6H9oqjBw6shsvcGvRf4RZuOM5DYRCjRaK0IVb+WCarPRwaQwOcXebrR8hHqnDK74rlwDC8t1T6PV4QoipoOSZ8W6r+HjNskSfZTA0aOvpvQhJkfgeat1j1M663K7C4Mm7Eckz5a6+cUBctarTlcoBqtt4bgCLO94ljQgvJQoeZIdWwSExJVLiXGhot8vZJOlVTj3aWHsXBvDl6YTyTEgzWC5kFrpEJR7KMY07lxwPuABzUkaaBMrVZVqXKpHDdV8dOiZ6sJvbAiAaeKVRwpt3pGymKSM0lpjFEcbh0coO6cnmacOn4OjOyYonhcm4yUU4MeTUHXPdZBHN0pJawgX2PfeTteWIk9p0olNcxQsru0wfW0Ia1UX//59oH4896hAMjeIIqiKttBDd2bxWNoWyLPzvYsVANdWy0mJWWskW8u0kBtpdOjUJgF2HozZT9GdnzRNpPffO/QOAaf39hXyu7SMag53oBMK0z32Vodm8RgQOtEGAyCFMCjbJ6AGakqJ0qr/enfdLzUUdh0TBnw4HvmUQxqTc5xl7TYkIIqasGIokpnSKp9dQ3+Nz1zYWcAJFjLK9RmFhE2Blv/Fko9aUNF2Ge2qqoKjRv7R1hSUlJ0al8dQs0IefaPvZLoApVFrnR4sC+7DFu4bu/VXEYqITKw06PISAWQEo9UZKTC2wjVMl3BUspxEWaFsxXMIQwHgiAoeOW3n9cG71/TGwCJ6tBNN1yaUSCwxhUfQWT55AaDHM3K9Tk3bPZOTTaeRpboplFSHZzax28wUkbKop6RYhc7uuHw0sppvsWRFuuKouj3/ZEWIy7v0wwAcNeINqpj01pY26r0YgECOxIGzpih84J3lLJLlY5UOIYX70w43ERO/prPN+C811dIEU4eUwerGz4UjSTBCfJ5uWDZgCbMPaOWFaLQcqSosb3hSCFyyuzYlVWKA0zfJPb7tHAgp1xVtS+Q6qIWrumfjkbRFlzVr3lYn2Pn6MDWibhuQDre49RJA4E6pLlldsz3RY43HSfGT6WG/LsW5IxU3a0bPPgo8muTu0u1IxRsfR+fkfp1K6nf4Rt+ju5EHI8BrZP8vlNW7fNIQgos2HuAXduSo62SM9GxiVzXl54UfiRczTllv5e/5/q2TMSCe4ZKTkA4GWYeTq7lB+/cUOOfDRKFKlRDQZXzvtuQiQvfX4M5vr6SoShYTu7dDL/dNRj/G9dB9XWrySgFZTxeEWV2mTYeLKAJyPs97/zwoNnKpvERinWHZqRksQm3nzy+WkZKEASFwZ0UZfHL0CVFWzCmc2NpT6FrlmwncRkp37rXr2Ui1j8+Er/dNUTaX+n1pb9Tzemh9M3iSnVqHx0/vf58Q3utAA9tgPzFlL6qr/uPQz4ODToXVDigthUEYizUBXiHs2UjMr/dXlESHKGgQUE22xhIebmhI+wzO2jQIDz77LOw22Xjqbq6Gs8//zwGDRpUp4P7LyOYd06Nq0qn28/wAYB7f9gOr1eUIjOJQZwe9vu0ehwAyixBOH1eAPWMVDAIgiDViQBAqzAj1cHA1kDG2sxScf+xgkqUO0LvBRMq2E2Wjwa/eWUPxWMa9cpV4Y2rjYn2XaF0PXaBV2uCC/g7CnTzCCkjZZF797CglAS6WTncsiz6pb2IjOzE7ql4emJnvHdNL9wzsp3q2LQcIy3KQ6DMIV+/RBsv+2WkSu0KY5nP5JVWubCbawx5JL8CV3+2Ht9vPKF43u7y4nRpNTYdK0JmURXWHCKCAOx1vKZ/etDII1VXohFSO1OwzBqOpQGMRKvJqDDGaCSTZltymY2eb2YZrHFvdqldtY+UlsRvILx8aVdsfnJ0SJQ8Fqyx071ZPF66tBvaa9SmqIEaPDllDr/6IPb3rz1coNlKgCKcHlI1Bd9jqUVSpJ/RzjbmZmsUPF4R/2QQBgO7tgLAA2Pa48q+zfDcxZ39vpMVm6CUKha3DW8t/c1KabPqYNPPb4uOTWLQo1kc+rf0F4EJBt45Za+VxWRQDcx1bRoniRHURY3UhG6pWP2/86WIO4WZ1kgxAgA8xTEY+GtIDc5QaLJGg4Be6QkBa01sZqO01/+x45S0LoaS8WqkoiCqBpqtTIu3KR0p330gZzY9fsdyaGTI2POSEmPDBE7ZkY6N3zPovVfl9CgCTdSxirKSptBqTAu5Xtj/fFIHLa/coUrto04ZrzhIEah+cli75JBofYCS2ked5ByNGrb6bnjLn3u23pNVSQTk68z+znOZ2hd2yOzdd9/F2LFj0axZM/ToQQy/nTt3wmazYdGiRXU+wP8qghXeJTE0LrVo8LbMEuw5XSptpolB+OisIRIoMsDe7GFT+wI4aIHw9pU98eivu2AyCuiVHl+jY2ihc2osMrLL0MFndLVIioQgkA33RAGZ/HVL7WN7N5gRYzOh3O7Ge9f0wkAuCkydLioJy47jpiEtsSgjR+EIUuPK6fHC7vIoFngt+XFaf0XlbenmQa8V79Cz9yV9jXekqEFR6ZN9ZaOBr13eHdPPb4PWjaJhMAi42EepUoPWfagVZAg0Z3j6YaFE7fPPSFUEyEg9+PMOLN2fh09v6IOxXchmPmvtMWw4WiRJ8Uvf6fYoKFXUQWkSZ5MMpI5Nghv7cZLsMxmrltRzGy4jwYPNvjaJtaGkyiX1DWMzc8cLlNczWEYqp8yuGu2sSfFwTWodAaXhVZPvlWpTVIwQliK3ZF8elh/Ix65nL9Ck55TVg0gND54G2CIpCpP7NMP6o7J6HysBTa9vldONfdllUmDjnpFtFcfpkhaH1y5XBnQoaHDldEk1VhzIUxgP8+4egvatZeOWDRKxjclbNYrCwvuHh/ITVRHLScULkOdyoGbG9FpQ+vSJwkqcKKzCsHaNQr7nWBpc88RIP0q5WkYqWACThxbVsi6N4IRIM6pLPXh63l4pGBoKjYxmlAI1WQZkqmVaXISCgp7my54rMlK+gBZt50HBrydsxrlRtAW3DW+NjqmxuOXrzejXMlGa8zzrJSnKCkEgAdPCCofUu46KTajNYd6gV7NbqIDIgZwyWE3xfq/T8asFhNokR9V4nePBUvtaJ0dh3ZFCVaEJk0EIqc6uNuDPU3KMFdFWEyocbk2BEpZdEm72tiEh7DPbtWtXHDp0CK+88gp69uyJnj174tVXX8WhQ4fQpUuX4AdgsGrVKlx00UVIS0uDIAj4/fffFa/n5uZi6tSpSEtLQ2RkJMaNG4dDhw4p3mO32zF9+nQkJSUhOjoakydPRm5ubrg/q8EhGJ+VLlCVDrdmHcelH62TuoEHyx6xC1Ug7jpr3CaFmZHiN7pHxqpTEHikJ0Xih9sG4tubB9Q5z/eekW3RLCECT0zsBIAYYVQliG66dSk2wXL8jQYBn1zfBw9f0N4vwgbIPVCoIccaZgNaJ2HH0xcoxAaaJ8rRnXK7W6HaxyrRUbg9Xkm8RKq54OgM/EajzFySv/lsD43+ur0inB6vtGlFmI0wGw1omxITkkqQtsNkwM1DA9PhePhnpHzUPs5Ryi0LnJGiPbu+Wntceo7PUFE43F7F56lYRIfGMdK1ZNWwtEDnJr0fZWqf8vzcwmQE1MA63fT7abaFdbpzGaqmKIpBHSmPV/09NaH21QZtU6JhEMIr7qdo4oscZ5dVK86T2+P1y8h5vCK6PLsILy7IUD0WnXehCKrUFNFWE4a0lQMvjaItuLRXU/x420DJKC5kCu4LKhzYebIE9/6wA5M/Xg+A7AnhGFfUSN17mjhi7HrEZ//YTExdngd2/YyPNCucmUB1l3QN/2zVUSw/kIfLP1mPG7/chL9252h+hofkSPmcGp5eqVYjFe4eqWVM1qURzK5vUj2TMfhcpVkfvjk7D4nalxCBhCgLJvduhq5NYzGsHVHHlNQfmRqpFknKIBDv2I3yUU6Htm0Ek5EIOZzXPhlrHx2JWTf1k97Hs16ibSa09WVqft9xSnqe1kipOd+8I6VWktDV13piy4liSWGxJUNVpeNvk6z8Xa9e1g2zbx3od7yagu3/1CFABv5M1B/x9NNYm1m61tkq4jQACWTPmNwN71zVs05LKM40ajTyyMhI3HrrrbX+8srKSvTo0QPTpk3DZZddpnhNFEVMmjQJZrMZ8+bNQ2xsLN566y2MHj0aGRkZiIoiN+gDDzyAP//8E7/88gvi4uJw991347LLLsPatWtrPb6GDJqRqnS4JWPt2gHpmL1R7jbPprITg9RIsVzVQE4XG+0NV+SBjVjMmz4kYH+pM4Xx3VIxvluq4rlWjaIUqlR1GVmO5hblIW0bYYiPv8+DN0CirUpDIS7SrKhTS42LkCJA5XYld1uttoOVgE2OtuJEYZVMZ5BqpLQzUmqR/zbJUbjzvDbSfVjp8EjfrRXB10KERgbTZiZqddcPbIGZa47iuw2Zqu9THMtsVJyDwkoHRFGUsjyNY63ILXMgu7RaURNTzlGipL8Za5vPWpkMAtxeEQ6XVxHkoEZLtM2EL6f2Q06pHT1DmAMSpYrrGUc2fHkcsdbQHf4IqZcLORbrdFNxE4A4gyG0jZGPazbKGbMzTNWYfcsAFFe5/BS8QgGNlvMiCkWVTs3+QTPXHMPTF/pT4Oi8q2nj11Dx2LhOuPHLjZg6uBUEQYBRIMpjydFWnCqpViiXFVe5cMmHyj0x7BpXbv5e2D1V453KRrih9BsMFawjlRhpUdQ+BlJJVGTzZ22W/p6z9SQmBvgdLCi1jxrJzTl5aqtJJSMV5h6ppbwXivx5qOjXMtGviTsvja0GuUYqMLWPvk6vO09Zp3bAzpMl2HmyBABhUxxjMuG8I3Vt/3S0ahSFzqnK3nkpnOPJOz0WkwHXDUjHc/Mz8N2GTNw2nNTjVkprqEpGitvX1EoSejSLR9P4CJwqqcavvjq2NsnRUi2Q7EjJGZcIsxFX9WteZ9koQOm0pydFwWIyqKpLWs+CkIPBIBB10TL4iU1QxEeacVW/9DM8srpH2BbiN998E/D1G2+8MeRjjR8/HuPHj1d97dChQ9iwYQP27NkjZbo+/vhjNGnSBD/88ANuueUWlJaWYubMmZg9ezZGjhwJAJg1axY6deqEDRs2YODAuvP8GxpopKvC4Q5JJSohyKbJCh0E2vjYxmp81/hgYI3yhlxY2LJRJNYclh/XpUHEZmKCZdd4Q0dtHKziWWqcDbE26ki5FbUuan0zyhlZdWqEyPLn6hkpVpKcv4YPjmmPe0eReidqUFc63JIDEx1mjZwmtc9ihMVkQKtGUZgyqCU2HSsKmqG6fXhrvLn4IEZ3aowl+3Jhd3lR5fRITlD7xjHILXMgp9SuoCKyjhBbZM3SJ/ji6xibCcVVLtjdHtWajAizEf3CqA+hRiA9j3ZFRqpmNR+Rvo2VOj1sRoqNOLO0NqNBCChoAZB6AsmRClHKu66QEmvzM6xCReMYq+rvC9Yzp9Lh9psjwaTF6wrdmsVh29Nj/AwzGlHnJaB5BOpfpgY+YhyojkMQBFzUIw2L9uRIdZF1Aba2JD7SolA+DbT/abEKNhwtgsvjDYnpwNfvJEZZEGM1SZli6iCwzlDTEJqqstCKytdlRuqRcR2w/mihYm6Hkj3mRW+0QAO7WvumWs0Ry6YA/B0pQRAwuI16wJEFv2dYTQZc0rMpnpufgcyiKthdHtjMRkmtVC0jxQcItUSyLu6Zho9XHJEcl9bJUVi6X/5eQCmoYhBqTl3WQpM4G9LibCitdqFb0zjE2syq1yfcViJ1BXo/07qtuAiztD4aDcI5nYViEfavuO+++xSPXS4XqqqqYLFYEBkZGZYjFQgOB7kZbDZ5YzQYDLBarVizZg1uueUWbN26FS6XC6NHj5be07FjR6Snp2P9+vWajpTD4ZCODwBlZWV1MuYzCRrp2naiRFrgYiNMuKxXU8zdfsrv/YH444BcdBxpMUqCC2pgDchw08VKZcCGO4FYQQuTQahzp69b0zjsPlWKCd386XwsLuvdDJ+vPiY9bqGicsVysNOTIonBUGrHkn25UsNXgGRNSqtc+HVbFi7t1RQJURbJMI+xmSTji17fUDJSPAWC7QlBDepKp1vK8IR7zbXOO7vpt2scg38eOC/osW4e1gopsVZM6JaKfi8tgd3lRWGFU3J02qXEYPWhAmQWVSm4+qwjxBrVOT4JYKfb60cPjPY5Ug6XV1V2Ptx5E21VOlLKGqnQHamxXRpj0d5cXNQjTYpw05ogNlvHKkzR77KYDLhvVDu8vugAmidGSGIDrCEJEOOWRqTPpeJhk9GAJrE2v/5IwaSe88sdfvf1mcpIAeqGGZ03rGqfGhqFmSniDeDGcYE//85VPeG63FunjTbZrFNCpFnhYARSSdSU/3d5kHG6DJ3TYoM6U3zPJUEQ0Do5Cjt91F56H/RvlYi0OBuSY21hO1JahnYoqn2homOTWPx8+yBc+P4a6blQ5ioNsBb5GpdrjSlY/0W1miOeJmmtoePo70gZER9plgJ72aV2tGoUpdnAGQgtIwUAozs1xscrjkiPWzPZJ3puWKXdqiAU6ZrAbDTgr/uGweURkRhlQWyEScOROjtrMd27aB1/88QIlJ4i62NCpLnOHcuzhbDv1uLiYsW/iooKHDhwAEOHDsUPP/xQZwOjDtHjjz+O4uJiOJ1OzJgxA1lZWcjOzgYA5OTkwGKxID4+XvHZxo0bIydHm/v8yiuvIC4uTvrXvHl4UrtnGyZGGtvp8WLBLnI+Ym1mPD6hEwa29o92B7the6cn4MupfTH71oEBJx27IYU7CdjoYcN2pORFPb4eJvu7V/fEjMndcGF3baEFAOiUGosjL0/A65d3x0uXdlUtXG2bEo0Pru2FOXcMgtVklK7PMh91g4oZVDrcmLFoP15YkIGn5u0BIFPFoq0mlc2DHIfd9EwGQWFs8J9hlbpYZabKmlL7aiAqoYVIiwlX9UtHjM0sReILKh2SE9AmhQQP+IRLVkk1LvtoLd5YdEByngCi1uR0e1WN1Rgfxc7h9qgqOoXrYEg1Uhy1L9zz8NAFHXDdgHQ8fEF7iXb3wfLD2HGyRCFGklfukDLPNHIbYTbituGt8dtdg/H0RJnO1pqTomdVqs6EI1GXaJrgb/SeLgmckeKNlsN5FTiQS1T90sI0ousKdN4UVwaW+g67xpWbvylBxIaMBqHODTg265QQZVG0w4gJQG0NlK265MO1uPnrLUG/2yFJhcu/iTWe6TyNsZmx+tGR+PWOQSHVgvI4r32y33N1rbiWxLEdQgnu0OCtxysGdNJl1Ur19V41I6VRbxYu+HvU6muMS0sXqBBGoDXUT2xC49x0SYtV2DRsANrN9Bwb5etlpnZd6wLxkRbJydVac89URurKvkR+/pKexLbhM07sdQ5VmfBcQJ2c3Xbt2uHVV1/1y1bVBmazGXPnzsXBgweRmJiIyMhILF++HOPHj4chBCnQQHj88cdRWloq/Tt58mQdjbpuEciQVDNKYyPMSI6xYvr5SiWmid1C44CP7Ng4aM3GFX2J01kT6VrWRg2WITubYDNSgXj3NUXr5Ghc1S89JDqJ0SDgir7Ncd2AFprvubB7Gvr6rgcdb6aPq03FDKpdHqlu6U+f402diGibyS+SR68Pe534TYf/DFsoTR2wSocbVT4jPdw0vpbDURNZbRaNGDnxCl/GKCnKqlrP4HR7sS2zBB8sP4yDuTJVUhRJtqJQpV6ARsa9IlCsQq8K17iMtsnnEgDstJ9NmHOofeMYvPT/7d15eFTl3Tfw7yyZySSTyWSyErKwk7AaNgkouyxaBKUuFChYKg8KUk0f7UMXeWtfpa+KPpVCrZZFKwIXti61SkUWcQEhSIAIBVlDIQmSkISEbJD7/WNyTs6ZzJCcZCYzk3w/15UL5syZM2eSe865f/fyu+/pj9TocDgU8+/eOXBeXvMLcH5mKQlJZY3z3zCTM1FIRkoURveOxV39O+G/J/ZqVBlXBlLKidDBwHViOOB5bL/kissaMv9vq3NsT4TZ2Khy2FakclHSRI+U1rlLrvecWI2pvb1B2etkDwuBQzE88Wafp6l5rrtPfO82hf9nJ77H5999D0AZSDVcf7ookiRYFe9haEWWtP87vR9W3KeeV9Sc9OdauF7rmnNNCjHo5dfdbHhfwzpq7u+d7oaUuq4N2NJAqlGPVH0AIfV4SeufVdaq1wTzdAyzUe/x7xgaoh69oxyKr5xX+cQdvTBzWDKevae/ps/SEp4aDNoq8c9j43ri0THd8cs7ncm7rC7fO2VjsNbe2kDmtW+n0WjExYsXvXU4AMDgwYORk5ODkpIS5OfnY+vWrSgqKkK3bs7sVAkJCaipqUFJSYnqdYWFhUhI8Dxsymw2w2azqX4C0RN3uF9fR69zPylVagFSVmgfHdMdL97nPp1tS4zpFYtNC4arMuU0l3LSuq9TcbZGkrJlWsNE+0DgmubXXWVOKjs37ZGqD4TCFJUn1zZR14q8stxJAdi1moY5UlqHSCr3Vw4jMbfypiCl7S+uqFYNb1ROZI5x09r+0rYTqscXSirdLrKrHNLiLsOV1kBKamk/f+UafvT6XnlMvqeW0ubo75It8JpLZrpSRbpsQF3hMBsNWDVrEBaP69moBVSZIMXuxcWz24JysVjJxn3OxofOdgtu7xmDYV0cWDtviDyMVRko36gT2FuffjxrYq8WVwZbS/reVDSx/pfWHkPX8qbsgW4ryopiTLhZ1SMV72HNHqB5SR9ch+iWVdVi7tp9mLNmH65U1KC6fmiWWdG63zO+IQDwlChCq2RHGGYMTlL9fbzdI2U2GhDRzCVPlOSEE1fdB+l1dUJ1TXXHZNTj0THd5fcPNxkaNWK0dPFY12GD0nFS6udg/eJvR/D91eqGeaZNzJFqahSFMpmEMtuxMjDs1zkSy+8d0CaBg7LMKP++bTW0L9kRhqcmp8l1AdfGU2VmQdd7UDDTPL7qgw8+UD0WQiA/Px9//OMfMXLkSK+dmFJkpPMX/t133yE7Oxu/+93vADgDrZCQEGzfvh0zZswAABw/fhx5eXntYnHg+bd1w9jecQg3G/GPQxex/GNna+f1OoE4WyjmZqbijT0Ni4BKLUCdIkNhMuhRc6MOczJTnReLli/orqLT6Rqtd9RcNTe8P0bYF0IMejkpwQ/ru6qDhevNy13loqyqFkIIRRKIkEY3FOkCqKxAu8aUYSHKYZ7q5CMNi3fekCvjmnukFMfvFBmK/9RnVGvtTaGhVbVGDiYjQo0YlxaHL05eBgD0irfWZ/bzfJwLVyrd9ipGmI3yuijuFkfUOkdK+ptW1dbhq1MN6wTZLCGounmng0fj0+IwMCkSh/5Tiktl1apMhYAzkEqIDJXH9XuaJ6AcvqPXASO6R+O13acRYzX5dEFaX3C3ppc0nDLMZMBf598qbx/RIx9//+YCiut7fUqu1aCgrApXq67DajZiznDPPci+pvze3IzWir/rMDVvLgvRXK4Z8YyKAKPTTRKNJNot+Nn4nlj75Rn5b9qvsw25FxrmR5dcq1EFXNI6ggBwtqjC7dC+sb3jcFuPGKR3ivD60hzKifm+aHh0WE1yg1tze7djrGacKCz32CNVXnNdvmbe7Pv/1OQ0PDU5DV+duoxYqxlGg15eyxBQB6taGPQ6mI16+W8lBVbKBsU/7TrVkPnU3dA+N0t8eKLsBbWHmfA/U9Kw9oszfvv+u65TKf19/Z1sQjKkSxTmZqbiRGE5HhgaXFNqbkZzIDV9+nTVY51Oh9jYWIwbNw4rVqzQdKzy8nKcPNmQHu3MmTPIycmBw+FASkoKtmzZgtjYWKSkpODIkSP42c9+hunTp2PixIkAnAHW/PnzkZWVBYfDAZvNhsceewyZmZntImOfQa+TF377r9HdGwKp+vG3v53WD58cLZQnwUsXrojQEKydNxQCIqDGod7ZvxPe2pvntrU/0Dz/wwHYd6YYd7RgTRp/cq3cKIde9Y6PwPHCq6iqrUNl7Q1VEOF6Q5FurMqeINfJxaGKIXZWs1E1l0yq8Dh7feor4xoXZFa24HWLtSoCqdbdFKT5Ad9fbZgjZTUbMXt4KgqvVmHPqSIsHtsDOedLGvXUAM6kGgVlVfjPlUq3ra4mox7hJmcSBneBlNaeAE9DZFozAd1o0GPh6O54ZMM3KKqokYdfSulzpeE5UoUjzEPlXHluRoMeo3vF4vf39scglwVjg0FaJ88jE1wrVNLQyCsVNfjq5GX86C9fy5Wq3gkRfu1x91T5G5AUiUhLCPp0sqG0shajfDRnw5eU5S0mwozOdgvMRj1u1AkMSL55C/cTd/RCr/gILHr7GwBARnKUKpBync94rrhC9Vz19fpKvqKn0WIy4K2f3gpfUF4nvJlsQuIIN8kLgze3cUe6d79z4D9I6xTRqBdXmh9lMuqb1eClzMSnCqRaMeog3GxE9fX6ZTzqG4AGJNnl509fLve4qDmg/l001fg3uV8C/rr3HG5JtsOg12Hh6O5YOLp7i8+9tZRlxh4WIifP8VuyCZf7o9VsxG+n9fPLufiS5kCqrs79mhotkZ2djbFjx8qPs7KyAABz587F+vXrkZ+fj6ysLBQWFqJTp0748Y9/jN/85jeqY7z88svQ6/WYMWMGqqurMWnSJKxevdpr5xiIahV/g6gwkyKQavhz3taz6VShbS2zWzTWPzQUAxUXtUDlCDdhsptFcgOd65yVqHATRveKxWcnvsejY7vjyS2HUXOjDsUVNaogwrXyFekmA5bR5WauHH7h2vqYUj8W+nxxJaT4Smv682SHBRP7xCPEqEes1YzdJ5xzFVrbEi4tuLz+q7PyNmuoESajHkunpMvb3AVR/z2xF3Q6HV7413GcL74mZz5TZq8zGfWwhjofX3QTSGlNi+1p0nZrSeeeX1Ipj+nvFBmKc0XXUFo/96eiiRZr5d/dqNdBp9PhwWHBuS5IpCUEPeKsOHmpHOmdbDiW31DJdq2ISGvoFVfUYEP98D8p22GPWPV8j7bm+l1+YkIvdIoMxbSMxFYPi5X4omLfHLZQIyb2iUdl7Q30rc+0987CERAQqkWAPUlQZBoclGrHX/c2jOhwHdonLaANOAOpGjdzpHxJWSn2RUVYea9o7vGlQOqLk5dxz6qvcOT/TFQ1Gki9fS3pjXY2tNUvENyK37G6R8l57RzezYEHhyZj0/7zuHKt9uZZ+xTbmhraN7JHDDYtGN5ojpe/KH/vyvtMWyzI647r7881sGov/PqpxowZo1qXyNWSJUuwZMmSmx4jNDQUq1atwqpVq7x9egFL+StTXnB8kRjBm3Q6Hcb0jvP3abRrrpnCosNN+MODt+DoxTJkdo/Gcx8dQ2FZNa5U1DbMkQo1NrqRui4GDDQe2qPsgXKtWHWpn4S793QR0utb+sM0Du3T6XR47cdDAAC/eS9X3h6h8Tiu3I1Vb+rGPzczFRkpUZie0RkfHnbOBT1XfE0e7qhMg2wy6N2uvyUlcNAaSBkNeoSbDKo5Ly1J9uJKqkgpg70EmzOQknukpKF9ngIpRcDdVhVMX3p19mAcuVCC7rFW3P3HhgVsG/VI1f/urlyrwclL5arnpAyQ/uJaOewaG467B948Q6hW/poorrwmSLTMtegZH4GIUCMiLSEY6bIu0VWXpQrOKwKpsqrrjdaR8rVIxXXCF2uyKef0NHfYdbxiXlxl7Q2cLarA5v3n0TvBhvVfnZHXK2xJ449rkoeWUmYFlObq6nQ6/HBwEjbtP4/L9RlXAfcBRqiGoX0AWjzVwReUwbcyqcf1Jtb/8xXlvTrEoPNaQ06gaVZpl3qKmuOll15q8cmQdsoLgb+6bylwdHKZExUV7pyrMqKHs9IQFWZyBlLXalBevyCvs0dKfSlQXpC7xYbj9PcV6Omm1c0RbkJxRU2jFrkJ6XGIMBvx3aVyfFdf0WzNnBlloNaSlMJK7tJcu/vu3DWgE/55OB//MyVNNVwj1eGsKJ8ruiZ//7rHWRsCKWPjQGpwahS+POmc3+S6Zkpz2CwhciA1onu0VxLIuK4jZDLo5RZnaS0kqVfO07BM5d+0PVx/esRZ5bK8/1cTMPTZTwE0nqMiVUI/PXbJ7TH8ybVy6M1K+M/v6IUV207g5xN7ee2YbckWGoIvfjEOep2zZ3veiC5yz7Tr4tmnFevwVdfekL8LbdW67+seKeU8pOY27vRJVA/le/fgBdVah5KIFjTqqgOpln9e5bVK2Xgn9SIr14Zrao5UsC0Yq2xMV/4+m1pI3VeUv79AXvKmtZr1yQ4ePNisg7WXxbWCSfe4cOw5XdT0jtQhKHukdLrGvTdSS/ryj/8tZy5yLsjbcFONMBtVE6d/MTkNK3d8h1/dlQ5XWXf0wmu7T+OJCeqKlT3MhD/MvAXz38iWe1CbkznLk8n9ErD+q7O43QtDVrtEh6vG43vy7PR+uDejc6O5JNJCpEUV1Sgsc964esY1JCoIMxka/d4fGtEV+84U45Zke4vWV4q0hMhDeJ+Z1tcraxRFmI1yUhrA2ZIrtYJfLq9G9tliec6Dp5ZZX1f2/Ek5kbzcpZIddZOKZ/cAG9pn9uLfZfG4HnhgWDJirWbg2rWmXxCAlGX2/9zdF6WVtXj34IVGPVLKnsbKmulAgD4AACKWSURBVBvydyFSY4+yN87TF4tb6xX1teZ+d4ekOpAaHSbPrVLOMVNqWY9Uw2ta0+un7JFSfhekeY3KmMJdz1eYhqF9gca1YWtcWhx2/PsSHvRTYgfl7y9c4xzpYNKsT7Zz505fnwc1Q4zVhMvlNapUqFMHJGLD13l++6JQYIlVJPIQonHjhhTMKOd/RFpCVJm+XNNWT+qbgEl93c8Xmz08FbM9ZCgalxaPUT2d87OAxotAajG8WzQ+fOw2echga1hMBrz5k2F4futx7Dtb7HHSvT3MhPHpjZONSBUcIYDTl52t1sreunCzsdGikyN6RGPv0vEtnt+l/DsmeCmBjE6nQ7S1YY5luNkIe/1nW73rFFbvOiXv63loX8PnaQ9D+1xJCVp6u2T0u1mjQEt6HL3J4lJh8ebfRafTNWsuUjCRl4xQBMtXKmpQpEhtX1ZVKz9uq2yUdmUjhQ+GRLmW6eawmAz41+OjsGTjQXxytLDRsFZJy+ZIeWdon7JHSVmRt1lC5Gyq0nu4G90QagreHinXhq0/PHgLCkqr5KRlbU2ZjCnYfpdaNPuTnT59Gl27dmWvkx89MqYHfvfhUTynWNjt1m7R2P+rCaqLLnVcTQ17c1cBtIeZVC2e3lxI9d5BneVASrl4ZUv06+y9dSeGdHHg7YdvxafHLiGzu7Yx7majQe7RkoZM9FLcqIx6neoGbtTrYAkxaM5aqFSpSE/uzRtSjNUsB1KOcJPHIT6e5l8qK0ze7PkIFEvvTMNHR/KxZLx6Tb8oxfdoeDcHUh3h2Jx9HtHhJr8lYpC4Br3tuQLjDe4CqZPfqwOEDfWLmRv1Os0LGbeUslLs2jDjDXcPTMTe08Wa58+FhhjkHnEpK5wrm5tkRU3xViClHMamTKhh0OsQaQmRF9H21KilDMS0zmf1N+XvPSLUiIjQEL8sUyBRD+1rf/cHSbNLe8+ePZGfn4+4OGeygAceeACvvPIK4uODKz10MPvJyC64J6Nzo8pwMKQTp7YzJDUK2eeuuH0uyk0SCbtFvY6UNysKk/om4M7+CUiwWdqsAtJcRoO+xZkZIy0h8tBAg16nmncVZjKqhvZFhBpb3QA1okcMzhblteoY7ih7CaPCTB6DaHflBlDfuEP8HED4wpjecW4T5CgbriItIXh4VFf8u6AMC0b5L/WxxNOacOSeVNGUEqxcKqvCr949otpH+q5npNi9vl5UU+cFNJ7P6K3jr5yZ0aLXxjQxuqBFPVJm7wzt091kyGJUuEkOpNxlpgXU66tFB1ndShl8B8L3XtmgaA2ydQW1aHZpdc2u99FHH6GiosLD3uQLOp2uVfNMqGN4anIausWEY+28IY2ec1d+nEP7Gm44rgkrWiM0xIDVswbj6al9vHbMQKC8YcVYnb0Q9w9JQvfYcIzpHau6gbRkTpSrh2/vhlu7OvD6jxv/TVtD2QgTYzV7nP/haU6Q8mbt756YtmR0Sf3fIy4C7y++DXcN6OTHs3JynUDvbq0zauDaI/XiJ8dxotD9kLX7BrfdEPrM7tFwhJswrIsj4Ba3birAaEmZC/fSkLop9Y1jCW4WaFYOfffUyy4t3eHpGIFMGXwHwuVYWQ58kXkyUPAKS9TODOvqwI7/HuP2OXeV+qgwE64r1iZjsN405ZAPqaft+R82ZNJzHZvfWl1jwrH5vzJbfRxXynTGfRJtHocIe+pNVAYU/kqx6y8jukfj6zPFmDeyi79PRaXR0D4GUjclVT4vl1cjr+gaPjycLz/Xp5MNR5XzSdtwqJcj3ISv/mdcm/WAaeG6XqGrllzzlHP7WjMcberARFy5VoPRvRr3JMcrAiNPwanRoMdL9w9E7oUyjA6yRasNeh06RYYiv7QqIBZFV/YI6tvxtKBmX2F1Ol2j4SmcL0UUXNxViKXhWZ3tFlwoqQyKBZP9TRlsxrppnVW2xHmjR8pX7hucjI9zC9ArLgI/GpaiWoTUqNfJwdHNMtHNHJaCjfvy8PDt3Xx+voFk7byhKKusRVyAtVorA6kIs7Hdrt3iLdJ39WBeCUa9oE6sNayrQx1ItfF3OVAzYTaVOKglPWjKHqnW9KKGhhg8DrFNUIy2uNlnuHdQEu4d1OJT8Ku/zh+GovIa9E303pzi1pibmYp3DvwHD49qv/eHZpdWIQTmzZsHs9lZaaiqqsLChQsRHq6eQP73v//du2dIRF4zrKsDj47pjj2ni3AwrwQDk+1yg8if5wzGqe/LMS6NiyY3RTlnyF1wqkz1GmjDcpS6xIRjx8/HyI+VFcX5t3fFycJypHeyqZIruHpmWl88OqY7kh3+zVbX1kJDDAFZ0bWaG/6G3kiT3955StX97D39EBVmkteZAoIv+YCvKBd7daclySaUvVC+GhWhHLae1E6/Gz3iItAjgG7hv53WD0vvTA/Ia6W3NLu0z507V/V49uzZXj8ZIvKtEIMeT01OQ0X1dWz4+pwqaOrXOdKrmfHas6YCKWVFwBtD+9pKbIQZ0eEmFFXUYET3GCyd0njtMFchBn2HC6ICWbzNjP6dI3HkQmnADTsMRO6GkYWG6PGjYSlyxlFJIPcut6WmeqTsHpLT3Mzo3rEwG/VIS4jwWaVbmaE1IwCGvnUU7TmIAjQEUuvWrfPleRBRGwo3GwMiw1iwimpiaJ9yLL4jPHgqXwa9Dn+ZOwRniyowyguLH1Pb0+l02LhgOC5cqWzRWkEdjbthZElRYdDpdI0WZGUg5eSaDGLHz0cj++wVPPW3wwDQouVYOtst+OzJsT6d05eWYMOK+wY651D1DK75TxS4OAuViEgjZXDkbo6Mciy+u0ArkGWkRCEjha21wcxqNjKIaiZ3PVJSem/lfDNpPThqPD++W6wV31+tlh+3NF17ghczxnoyY3CSz9+DOhYGUkREGimHriRHNR7WFhUWIifvGJzqaMtTIyINwk2NgyNTfYKOcFUmudavB9eeDUiyo1e8FV2iwwN6XiiRtzGQIiLSqFd8BEwGPUxGPbrHhTd6XqfT4W+PjEBhWRX6J3HeGVGg0ul0GJhsx6HzJYgwG3G1+jp+NCwFABBmVqxtxDTyN2UxGfDJE6MbrTlK1N7xykBEpFFnuwXvLhqB0BADwkzuL6MJkaFtMlSFiFrnzZ8Mw8WSSqRGh+FiSRV6xDnT/Su/24G4npM/zR6egrf25mFmfdApYa8ddTQMpIiIWiBQ1ukgotaJtITIiSSkIAqAak6UgQGCyuMTeiEtwYa7b0n096kQ+RUDKSIiIiIXBr3O7f8JiLGaMXt4qr9Pg8jv2FdNREREdBNGAwMpImqMgRQRERHRTTD1ORG5w0CKiIiIyI25mc7ha0vG9/TzmRBRIOIcKSIiIiI3np7aF/81ujsS7RZ/nwoRBSD2SBERERG5YdDrGEQRkUcMpIiIiIiIiDRiIEVERERERKQRAykiIiIiIiKNGEgRERERERFpxECKiIiIiIhIIwZSREREREREGjGQIiIiIiIi0oiBFBERERERkUYMpIiIiIiIiDRiIEVERERERKQRAykiIiIiIiKNGEgRERERERFpxECKiIiIiIhIIwZSREREREREGjGQIiIiIiIi0oiBFBERERERkUYMpIiIiIiIiDTyayC1e/duTJ06FYmJidDpdHjvvfdUz5eXl2Px4sVISkqCxWJBnz598Oqrr6r2qaqqwqJFixAdHQ2r1YoZM2agsLCwDT8FERERERF1NH4NpCoqKjBw4ECsWrXK7fNZWVnYunUr3nrrLRw7dgyPP/44Fi9ejA8++EDe54knnsA//vEPbNmyBZ999hkuXryIe++9t60+AhERERERdUBGf775lClTMGXKFI/Pf/XVV5g7dy7GjBkDAFiwYAH+/Oc/Y9++fbj77rtRWlqKNWvW4O2338a4ceMAAOvWrUN6ejr27t2L4cOHt8XHICIiIiKiDiag50iNGDECH3zwAS5cuAAhBHbu3IkTJ05g4sSJAIADBw6gtrYWEyZMkF+TlpaGlJQU7Nmzx+Nxq6urUVZWpvqh1quoAEpLG34qKtTbpcdEFPwqKlr2nZauB5cu8bpARETBza89Uk1ZuXIlFixYgKSkJBiNRuj1erz++usYNWoUAKCgoAAmkwl2u131uvj4eBQUFHg87vLly/Hb3/7Wl6fe4VRUAO+9ByhjUpsNuOMOYNs253abDZg+HQgP99dZEpE3SN93QNt3Wnrd998DJ04AvXoBsbG8LhARUXAK6B6plStXYu/evfjggw9w4MABrFixAosWLcKnn37aquMuXboUpaWl8s/58+e9dMYd1/XrzmDJYgGiopz/lpUBVVUNwVVZmXM/Igpu0vdd63dael1dHXDlCnDjBq8LREQUvAK2R6qyshK//OUv8e677+Kuu+4CAAwYMAA5OTl48cUXMWHCBCQkJKCmpgYlJSWqXqnCwkIkJCR4PLbZbIbZbPb1R+iQQkMbWpYrKxu2m81AdbV/zomIAovJ5PyXl2EiIgpmAdsjVVtbi9raWuj16lM0GAyoq6sDAAwePBghISHYvn27/Pzx48eRl5eHzMzMNj1fIiIiIiLqOPzaI1VeXo6TJ0/Kj8+cOYOcnBw4HA6kpKRg9OjRePLJJ2GxWJCamorPPvsMb775Jl566SUAQGRkJObPn4+srCw4HA7YbDY89thjyMzMZMY+IiIiIiLyGb8GUtnZ2Rg7dqz8OCsrCwAwd+5crF+/Hps2bcLSpUsxa9YsFBcXIzU1Fc8++ywWLlwov+bll1+GXq/HjBkzUF1djUmTJmH16tVt/lmIiIiIiKjj8GsgNWbMGAghPD6fkJCAdevW3fQYoaGhWLVqlcdFfYmIiIiIiLwtYOdIERERERERBSoGUkRERERERBoxkCIiIiIiItKIgRQREREREZFGDKSIiIiIiIg0YiBFRERERESkEQMpIiIiIiIijRhIERERERERacRAioiIiIiISCMGUkRERERERBoxkCIiIiIiItKIgRQREREREZFGDKSIiIiIiIg0YiBFRERERESkEQMpIiIiIiIijRhIERERERERacRAioiIiIiISCMGUkRERERERBoZ/X0C1EbCwwEh/H0WRORF4VFxEMv4vSY/4D2FiIg9UkRERERERFoxkCIiIiIiItKIgRQREREREZFGDKSIiIiIiIg0YiBFRERERESkEQMpIiIiIiIijRhIERERERERacRAioiIiIiISCMGUkRERERERBoxkCIiIiIiItKIgRQREREREZFGDKSIiIiIiIg0YiBFRERERESkEQMpIiIiIiIijRhIERERERERacRAioiIiIiISCMGUkRERERERBoxkCIiIiIiItKIgRQREREREZFGDKSIiIiIiIg08msgtXv3bkydOhWJiYnQ6XR47733VM/rdDq3Py+88IK8T3FxMWbNmgWbzQa73Y758+ejvLy8jT8JERERERF1JH4NpCoqKjBw4ECsWrXK7fP5+fmqn7Vr10Kn02HGjBnyPrNmzcK3336Lbdu24cMPP8Tu3buxYMGCtvoIRERERETUARn9+eZTpkzBlClTPD6fkJCgevz+++9j7Nix6NatGwDg2LFj2Lp1K/bv348hQ4YAAFauXIk777wTL774IhITE3138kRERERE1GEFzRypwsJC/POf/8T8+fPlbXv27IHdbpeDKACYMGEC9Ho9vv76a4/Hqq6uRllZmeqHvKOqCqiocP6rVF3tn/MhosBTU+P8l9cFIiIKZkETSL3xxhuIiIjAvffeK28rKChAXFycaj+j0QiHw4GCggKPx1q+fDkiIyPln+TkZJ+dd0dhNAI2G1BZCVy54vzXZgNCQ53/As5/jX7tAyUib5C+71q/09Lr9HogKgowGHhdICKi4BU0t6+1a9di1qxZCA0NbfWxli5diqysLPlxWVkZg6lWCg8Hpk8Hrl9v2GY0qrdLj4kouEnfa+n/Wl93/bqzN8ps5nWBiIiCV1AEUp9//jmOHz+OzZs3q7YnJCTg0qVLqm3Xr19HcXFxo/lVSmazGWaz2Sfn2pF5qgyxkkTU/rT0e83rARERtRdBMbRvzZo1GDx4MAYOHKjanpmZiZKSEhw4cEDetmPHDtTV1eHWW29t69MkIiIiIqIOwq89UuXl5Th58qT8+MyZM8jJyYHD4UBKSgoA57C7LVu2YMWKFY1en56ejsmTJ+Phhx/Gq6++itraWixevBgPPvggM/YREREREZHP+LVHKjs7GxkZGcjIyAAAZGVlISMjA08//bS8z6ZNmyCEwMyZM90eY8OGDUhLS8P48eNx55134rbbbsNrr73WJudPREREREQdk04IIfx9Ev5WVlaGyMhIlJaWwialmCMiIiIiog6nubFBUMyRIiIiIiIiCiQMpIiIiIiIiDRiIEVERERERKQRAykiIiIiIiKNGEgRERERERFpxECKiIiIiIhIIwZSREREREREGjGQIiIiIiIi0oiBFBERERERkUYMpIiIiIiIiDRiIEVERERERKQRAykiIiIiIiKNGEgRERERERFpxECKiIiIiIhIIwZSREREREREGjGQIiIiIiIi0oiBFBERERERkUZGf59AIBBCAADKysr8fCZERERERORPUkwgxQieMJACcPXqVQBAcnKyn8+EiIiIiIgCwdWrVxEZGenxeZ1oKtTqAOrq6nDx4kVERERAp9P59VzKysqQnJyM8+fPw2az+fVcKDiwzJBWLDOkFcsMacUyQ1oFUpkRQuDq1atITEyEXu95JhR7pADo9XokJSX5+zRUbDab3wsRBReWGdKKZYa0YpkhrVhmSKtAKTM364mSMNkEERERERGRRgykiIiIiIiINGIgFWDMZjOWLVsGs9ns71OhIMEyQ1qxzJBWLDOkFcsMaRWMZYbJJoiIiIiIiDRijxQREREREZFGDKSIiIiIiIg0YiBFRERERESkEQMpIiIiIiIijRhI+cDy5csxdOhQREREIC4uDtOnT8fx48dV+1RVVWHRokWIjo6G1WrFjBkzUFhYqNonLy8Pd911F8LCwhAXF4cnn3wS169fV+2za9cuDBo0CGazGT169MD69et9/fHIy7xRXg4dOoSZM2ciOTkZFosF6enp+MMf/tDovVhe2gdvXWMkRUVFSEpKgk6nQ0lJieo5lpn2wZtlZv369RgwYABCQ0MRFxeHRYsWqZ4/fPgwbr/9doSGhiI5ORnPP/+8Tz8b+Ya3ysz+/fsxfvx42O12REVFYdKkSTh06JBqH5aZ9qE5Zea1117DmDFjYLPZ3N5zAKC4uBizZs2CzWaD3W7H/PnzUV5ertonYMqMIK+bNGmSWLduncjNzRU5OTnizjvvFCkpKaK8vFzeZ+HChSI5OVls375dZGdni+HDh4sRI0bIz1+/fl3069dPTJgwQRw8eFB89NFHIiYmRixdulTe5/Tp0yIsLExkZWWJo0ePipUrVwqDwSC2bt3app+XWscb5WXNmjViyZIlYteuXeLUqVPir3/9q7BYLGLlypXyPiwv7Yc3yozStGnTxJQpUwQAceXKFXk7y0z74a0ys2LFCpGYmCg2bNggTp48KQ4dOiTef/99+fnS0lIRHx8vZs2aJXJzc8XGjRuFxWIRf/7zn9vss5J3eKPMXL16VTgcDjFv3jzx73//W+Tm5ooZM2aI+Ph4UVNTI4RgmWlPmlNmXn75ZbF8+XKxfPnyRvccyeTJk8XAgQPF3r17xeeffy569OghZs6cKT8fSGWGgVQbuHTpkgAgPvvsMyGEECUlJSIkJERs2bJF3ufYsWMCgNizZ48QQoiPPvpI6PV6UVBQIO/zpz/9SdhsNlFdXS2EEOKpp54Sffv2Vb3XAw88ICZNmuTrj0Q+1JLy4s6jjz4qxo4dKz9meWm/WlNmVq9eLUaPHi22b9/e6KbGMtN+taTMFBcXC4vFIj799FOPx129erWIioqS71NCCPGLX/xC9O7d20efhNpKS8rM/v37BQCRl5cn73P48GEBQHz33XdCCJaZ9sy1zCjt3LnTbSB19OhRAUDs379f3vbxxx8LnU4nLly4IIQIrDLDoX1toLS0FADgcDgAAAcOHEBtbS0mTJgg75OWloaUlBTs2bMHALBnzx70798f8fHx8j6TJk1CWVkZvv32W3kf5TGkfaRjUHBqSXnxdBzpGADLS3vW0jJz9OhRPPPMM3jzzTeh1ze+HbDMtF8tKTPbtm1DXV0dLly4gPT0dCQlJeH+++/H+fPn5dfs2bMHo0aNgslkkrdNmjQJx48fx5UrV9rio5GPtKTM9O7dG9HR0VizZg1qampQWVmJNWvWID09HV26dAHAMtOeuZaZ5tizZw/sdjuGDBkib5swYQL0ej2+/vpreZ9AKTMMpHysrq4Ojz/+OEaOHIl+/foBAAoKCmAymWC321X7xsfHo6CgQN5HGURJz0vP3WyfsrIyVFZW+uLjkI+1tLy4+uqrr7B582YsWLBA3sby0j61tMxUV1dj5syZeOGFF5CSkuL22Cwz7VNLy8zp06dRV1eH5557Dv/7v/+Ld955B8XFxbjjjjtQU1MjH6epexcFn5aWmYiICOzatQtvvfUWLBYLrFYrtm7dio8//hhGo1E+DstM++OuzDRHQUEB4uLiVNuMRiMcDoemOnJbMbbpu3VAixYtQm5uLr744gt/nwoFAW+Ul9zcXEybNg3Lli3DxIkTvXh2FIhaWmaWLl2K9PR0zJ4920dnRoGqpWWmrq4OtbW1eOWVV+Rry8aNG5GQkICdO3di0qRJvjhdCgAtLTOVlZWYP38+Ro4ciY0bN+LGjRt48cUXcdddd2H//v2wWCw+OmPyt45S/2WPlA8tXrwYH374IXbu3ImkpCR5e0JCAmpqahplKiksLERCQoK8j2vmG+lxU/vYbDZenIJQa8qL5OjRoxg/fjwWLFiAX//616rnWF7an9aUmR07dmDLli0wGo0wGo0YP348ACAmJgbLli2Tj8My0760psx06tQJANCnTx/5+djYWMTExCAvL08+TlP3LgourSkzb7/9Ns6ePYt169Zh6NChGD58ON5++22cOXMG77//vnwclpn2xVOZaY6EhARcunRJte369esoLi7WVEduKwykfEAIgcWLF+Pdd9/Fjh070LVrV9XzgwcPRkhICLZv3y5vO378OPLy8pCZmQkAyMzMxJEjR1SFadu2bbDZbPJNLDMzU3UMaR/pGBQcvFFeAODbb7/F2LFjMXfuXDz77LON3oflpf3wRpn529/+hkOHDiEnJwc5OTn4y1/+AgD4/PPP5XTWLDPthzfKzMiRI+XtkuLiYly+fBmpqakAnGVm9+7dqK2tlffZtm0bevfujaioKJ99PvI+b5SZa9euQa/XQ6fTyftIj+vq6gCwzLQnTZWZ5sjMzERJSQkOHDggb9uxYwfq6upw6623yvsETJlp8/QWHcAjjzwiIiMjxa5du0R+fr78c+3aNXmfhQsXipSUFLFjxw6RnZ0tMjMzRWZmpvy8lP584sSJIicnR2zdulXExsa6TX/+5JNPimPHjolVq1YxNXEQ8kZ5OXLkiIiNjRWzZ89WHePSpUvyPiwv7Yc3yowrdxmUWGbaD2+VmWnTpom+ffuKL7/8Uhw5ckT84Ac/EH369JFTWZeUlIj4+HgxZ84ckZubKzZt2iTCwsKYyjoIeaPMHDt2TJjNZvHII4+Io0ePitzcXDF79mwRGRkpLl68KIRgmWlPmlNm8vPzxcGDB8Xrr78uAIjdu3eLgwcPiqKiInmfyZMni4yMDPH111+LL774QvTs2VOV/jyQygwDKR8A4PZn3bp18j6VlZXi0UcfFVFRUSIsLEzcc889Ij8/X3Wcs2fPiilTpgiLxSJiYmLEz3/+c1FbW6vaZ+fOneKWW24RJpNJdOvWTfUeFBy8UV6WLVvm9hipqamq92J5aR+8dY1R8pSKlmWmffBWmSktLRU/+clPhN1uFw6HQ9xzzz2q1NZCCHHo0CFx2223CbPZLDp37ix+//vft8VHJC/zVpn55JNPxMiRI0VkZKSIiooS48aNa7QMA8tM+9CcMuOpvqLcp6ioSMycOVNYrVZhs9nEQw89JK5evap6r0ApMzohhPB6NxcREREREVE7xjlSREREREREGjGQIiIiIiIi0oiBFBERERERkUYMpIiIiIiIiDRiIEVERERERKQRAykiIiIiIiKNGEgRERERERFpxECKiIiIiIhIIwZSREQU1ObNm4fp06f77f3nzJmD5557rln7Pvjgg1ixYoWPz4iIiNqCTggh/H0SRERE7uh0ups+v2zZMjzxxBMQQsBut7fNSSkcOnQI48aNw7lz52C1WpvcPzc3F6NGjcKZM2cQGRnZBmdIRES+wkCKiIgCVkFBgfz/zZs34+mnn8bx48flbVartVkBjK/89Kc/hdFoxKuvvtrs1wwdOhTz5s3DokWLfHhmRETkaxzaR0REASshIUH+iYyMhE6nU22zWq2NhvaNGTMGjz32GB5//HFERUUhPj4er7/+OioqKvDQQw8hIiICPXr0wMcff6x6r9zcXEyZMgVWqxXx8fGYM2cOLl++7PHcbty4gXfeeQdTp05VbV+9ejV69uyJ0NBQxMfH44c//KHq+alTp2LTpk2t/+UQEZFfMZAiIqJ254033kBMTAz27duHxx57DI888gjuu+8+jBgxAt988w0mTpyIOXPm4Nq1awCAkpISjBs3DhkZGcjOzsbWrVtRWFiI+++/3+N7HD58GKWlpRgyZIi8LTs7G0uWLMEzzzyD48ePY+vWrRg1apTqdcOGDcO+fftQXV3tmw9PRERtgoEUERG1OwMHDsSvf/1r9OzZE0uXLkVoaChiYmLw8MMPo2fPnnj66adRVFSEw4cPAwD++Mc/IiMjA8899xzS0tKQkZGBtWvXYufOnThx4oTb9zh37hwMBgPi4uLkbXl5eQgPD8cPfvADpKamIiMjA0uWLFG9LjExETU1Naphi0REFHwYSBERUbszYMAA+f8GgwHR0dHo37+/vC0+Ph4AcOnSJQDOpBE7d+6U51xZrVakpaUBAE6dOuX2PSorK2E2m1UJMe644w6kpqaiW7dumDNnDjZs2CD3ekksFgsANNpORETBhYEUERG1OyEhIarHOp1OtU0Kfurq6gAA5eXlmDp1KnJyclQ/3333XaOheZKYmBhcu3YNNTU18raIiAh888032LhxIzp16oSnn34aAwcORElJibxPcXExACA2NtYrn5WIiPyDgRQREXV4gwYNwrfffosuXbqgR48eqp/w8HC3r7nlllsAAEePHlVtNxqNmDBhAp5//nkcPnwYZ8+exY4dO+Tnc3NzkZSUhJiYGJ99HiIi8j0GUkRE1OEtWrQIxcXFmDlzJvbv349Tp07hX//6Fx566CHcuHHD7WtiY2MxaNAgfPHFF/K2Dz/8EK+88gpycnJw7tw5vPnmm6irq0Pv3r3lfT7//HNMnDjR55+JiIh8i4EUERF1eImJifjyyy9x48YNTJw4Ef3798fjjz8Ou90Ovd7zrfKnP/0pNmzYID+22+34+9//jnHjxiE9PR2vvvoqNm7ciL59+wIAqqqq8N577+Hhhx/2+WciIiLf4oK8RERELVRZWYnevXtj8+bNyMzMbHL/P/3pT3j33XfxySeftMHZERGRL7FHioiIqIUsFgvefPPNmy7cqxQSEoKVK1f6+KyIiKgtsEeKiIiIiIhII/ZIERERERERacRAioiIiIiISCMGUkRERERERBoxkCIiIiIiItKIgRQREREREZFGDKSIiIiIiIg0YiBFRERERESkEQMpIiIiIiIijRhIERERERERafT/AVhOGhP0FrNGAAAAAElFTkSuQmCC", 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" ] @@ -428,12 +430,12 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] diff --git a/000971/lernerlab/seiler_2024/optogenetics_example_notebook.ipynb b/000971/lernerlab/seiler_2024/optogenetics_example_notebook.ipynb index 99c454c..045fb92 100644 --- a/000971/lernerlab/seiler_2024/optogenetics_example_notebook.ipynb +++ b/000971/lernerlab/seiler_2024/optogenetics_example_notebook.ipynb @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -69,19 +69,19 @@ " });\n", " });\n", " \n", - "

root (NWBFile)

session_description: FR1 Training with optogenetic stimulation, rewards delivered on both left and right nose pokes, optogenetic stimulation delivered on all nose pokes
identifier: 0aa6698d-a238-45ca-9c8c-0d26389ba921
session_start_time2020-10-20 13:00:57-05:51
timestamps_reference_time2020-10-20 13:00:57-05:51
file_create_date
02024-05-28 14:04:04.197588-07:00
experimenter('Seiler, Jillian L.', 'Cosme, Caitlin V.', 'Sherathiya, Venus N.', 'Schaid, Michael D.', 'Bianco, Joseph M.', 'Bridgemohan, Abigael S.', 'Lerner, Talia N.')
related_publications('https://doi.org/10.1016/j.cub.2022.01.055',)
stimulus
OptogeneticSeries
resolution: -1.0
comments: no comments
description: During operant training (beginning with FR1), each rewarded nosepoke was paired with a train of blue light (460nm, 1 s, 20 Hz, 15 mW) generated by an LED light source and pulse generator (Prizmatix). A subset of mice (\"ChR2 Scrambled\") received the same train of light but paired with random nosepokes on a separate RI60 schedule.
conversion: 1.0
offset: 0.0
unit: watts
data
timestamps
timestamps_unit: seconds
interval: 1
site
device
description: Optogenetic stimulus pulses were generated from the Optogenetics-LED-Dual (Prizmatix) driven by the Optogenetics PulserPlus (Prizmatix). Engineered for scaling Optogenetics experiments, the Optogenetics-LED-Dual light source features two independent fiber-coupled LED channels each equipped with independent power and switching control. Optogenetics Pulser / PulserPlus are programmable TTL pulse train generators for pulsing LEDs, lasers and shutters used in Optogenetics activation in neurophysiology and behavioral research.
manufacturer: Prizmatix
description: Mice for DMS excitatory optogenetics experiments received 1 ml of AAV5-EF1a-DIO-hChR2(H134R)-EYFP (3.3e13 GC/mL, Addgene, lot v17652) or the control fluorophore-only virus AAV5-EF1a-DIO-EYFP (3.5e12 virus molecules/mL, UNC Vector Core, lot AV4310K) in medial (AP -3.1, ML 0.8, DV -4.7) and a single fiber optic implant (Prizmatix; 250mm core, 0.66 NA) over ipsilateral DMS (AP 0.8, ML 1.5, DV -2.8). Hemispheres were counterbalanced between mice.
excitation_lambda: 460.0
location: Injection location: medial SNc (AP -3.1, ML 0.8, DV -4.7) \n", + "

root (NWBFile)

session_description: FR1 Training with optogenetic stimulation, rewards delivered on both left and right nose pokes, optogenetic stimulation delivered on all rewarded nose pokes
identifier: fc40e324-a189-49ed-8e65-81e994c0d698
session_start_time2020-10-20 13:00:57-05:51
timestamps_reference_time2020-10-20 13:00:57-05:51
file_create_date
02024-06-11 13:11:05.817671-07:00
experimenter('Seiler, Jillian L.', 'Cosme, Caitlin V.', 'Sherathiya, Venus N.', 'Schaid, Michael D.', 'Bianco, Joseph M.', 'Bridgemohan, Abigael S.', 'Lerner, Talia N.')
related_publications('https://doi.org/10.1016/j.cub.2022.01.055',)
stimulus
OptogeneticSeries
resolution: -1.0
comments: no comments
description: During operant training (beginning with FR1), each rewarded nosepoke was paired with a train of blue light (460nm, 1 s, 20 Hz, 15 mW) generated by an LED light source and pulse generator (Prizmatix). A subset of mice (\"ChR2 Scrambled\") received the same train of light but paired with random nosepokes on a separate RI60 schedule.
conversion: 1.0
offset: 0.0
unit: watts
data
timestamps
timestamps_unit: seconds
interval: 1
site
device
description: Optogenetic stimulus pulses were generated from the Optogenetics-LED-Dual (Prizmatix) driven by the Optogenetics PulserPlus (Prizmatix). Engineered for scaling Optogenetics experiments, the Optogenetics-LED-Dual light source features two independent fiber-coupled LED channels each equipped with independent power and switching control. Optogenetics Pulser / PulserPlus are programmable TTL pulse train generators for pulsing LEDs, lasers and shutters used in Optogenetics activation in neurophysiology and behavioral research.
manufacturer: Prizmatix
description: Mice for DMS excitatory optogenetics experiments received 1 ml of AAV5-EF1a-DIO-hChR2(H134R)-EYFP (3.3e13 GC/mL, Addgene, lot v17652) or the control fluorophore-only virus AAV5-EF1a-DIO-EYFP (3.5e12 virus molecules/mL, UNC Vector Core, lot AV4310K) in medial (AP -3.1, ML 0.8, DV -4.7) and a single fiber optic implant (Prizmatix; 250mm core, 0.66 NA) over ipsilateral DMS (AP 0.8, ML 1.5, DV -2.8). Hemispheres were counterbalanced between mice.
excitation_lambda: 460.0
location: Injection location: medial SNc (AP -3.1, ML 0.8, DV -4.7) \n", " Stimulation location: DMS (AP 0.8, ML 1.5, DV -2.8)
keywords
processing
behavior
description: Operant behavioral data from MedPC.\n", - "Box = 3\n", - "MSN = FR1_BOTH_WStim
data_interfaces
reward_port_entry_times
description: Reward port entry times
timestamps
timestamps__unit: seconds
right_nose_poke_times
description: Right nose poke times
timestamps
timestamps__unit: seconds
right_reward_times
description: Right Reward times
timestamps
timestamps__unit: seconds
epoch_tagsset()
devices
Optogenetics_LED_Dual
description: Optogenetic stimulus pulses were generated from the Optogenetics-LED-Dual (Prizmatix) driven by the Optogenetics PulserPlus (Prizmatix). Engineered for scaling Optogenetics experiments, the Optogenetics-LED-Dual light source features two independent fiber-coupled LED channels each equipped with independent power and switching control. Optogenetics Pulser / PulserPlus are programmable TTL pulse train generators for pulsing LEDs, lasers and shutters used in Optogenetics activation in neurophysiology and behavioral research.
manufacturer: Prizmatix
ogen_sites
OptogeneticStimulusSite
device
description: Optogenetic stimulus pulses were generated from the Optogenetics-LED-Dual (Prizmatix) driven by the Optogenetics PulserPlus (Prizmatix). Engineered for scaling Optogenetics experiments, the Optogenetics-LED-Dual light source features two independent fiber-coupled LED channels each equipped with independent power and switching control. Optogenetics Pulser / PulserPlus are programmable TTL pulse train generators for pulsing LEDs, lasers and shutters used in Optogenetics activation in neurophysiology and behavioral research.
manufacturer: Prizmatix
description: Mice for DMS excitatory optogenetics experiments received 1 ml of AAV5-EF1a-DIO-hChR2(H134R)-EYFP (3.3e13 GC/mL, Addgene, lot v17652) or the control fluorophore-only virus AAV5-EF1a-DIO-EYFP (3.5e12 virus molecules/mL, UNC Vector Core, lot AV4310K) in medial (AP -3.1, ML 0.8, DV -4.7) and a single fiber optic implant (Prizmatix; 250mm core, 0.66 NA) over ipsilateral DMS (AP 0.8, ML 1.5, DV -2.8). Hemispheres were counterbalanced between mice.
excitation_lambda: 460.0
location: Injection location: medial SNc (AP -3.1, ML 0.8, DV -4.7) \n", + "MSN = FR1_BOTH_WStim \n", + "Box = 3
data_interfaces
reward_port_entry_times
description: Reward port entry times
timestamps
timestamps__unit: seconds
right_nose_poke_times
description: Right nose poke times
timestamps
timestamps__unit: seconds
right_reward_times
description: Right reward times
timestamps
timestamps__unit: seconds
epoch_tagsset()
devices
Optogenetics_LED_Dual
description: Optogenetic stimulus pulses were generated from the Optogenetics-LED-Dual (Prizmatix) driven by the Optogenetics PulserPlus (Prizmatix). Engineered for scaling Optogenetics experiments, the Optogenetics-LED-Dual light source features two independent fiber-coupled LED channels each equipped with independent power and switching control. Optogenetics Pulser / PulserPlus are programmable TTL pulse train generators for pulsing LEDs, lasers and shutters used in Optogenetics activation in neurophysiology and behavioral research.
manufacturer: Prizmatix
ogen_sites
OptogeneticStimulusSite
device
description: Optogenetic stimulus pulses were generated from the Optogenetics-LED-Dual (Prizmatix) driven by the Optogenetics PulserPlus (Prizmatix). Engineered for scaling Optogenetics experiments, the Optogenetics-LED-Dual light source features two independent fiber-coupled LED channels each equipped with independent power and switching control. Optogenetics Pulser / PulserPlus are programmable TTL pulse train generators for pulsing LEDs, lasers and shutters used in Optogenetics activation in neurophysiology and behavioral research.
manufacturer: Prizmatix
description: Mice for DMS excitatory optogenetics experiments received 1 ml of AAV5-EF1a-DIO-hChR2(H134R)-EYFP (3.3e13 GC/mL, Addgene, lot v17652) or the control fluorophore-only virus AAV5-EF1a-DIO-EYFP (3.5e12 virus molecules/mL, UNC Vector Core, lot AV4310K) in medial (AP -3.1, ML 0.8, DV -4.7) and a single fiber optic implant (Prizmatix; 250mm core, 0.66 NA) over ipsilateral DMS (AP 0.8, ML 1.5, DV -2.8). Hemispheres were counterbalanced between mice.
excitation_lambda: 460.0
location: Injection location: medial SNc (AP -3.1, ML 0.8, DV -4.7) \n", " Stimulation location: DMS (AP 0.8, ML 1.5, DV -2.8)
subject
age: P10W/
age__reference: birth
description: Male and female WT (C57BL/6J) and (DAT)::IRES-Cre knockin mice (JAX006660) were obtained from The Jackson Laboratory and crossed in house. Only heterozygote transgenic mice, obtained by backcrossing to C57BL/6J wildtypes, were used for experiments. Littermates of the same sex were randomly assigned to experimental groups (fiber photometry-14 males, 22 females; DMS excitatory optogenetics- 20 males, 19 females; DMS inhibitory optogenetics- 13 males, 13 females; DLS excitatory optogenetics- 18 males, 18 females). Adult mice at least 10 weeks of age were used in all experiments. Mice were group housed under a conventional 12 h light cycle (dark from 7:00pm to 7:00am) with ad libitum access to food and water prior to operant training. All experiments were approved by the Northwestern University Institutional Animal Care and Use Committee.
genotype: DAT-IRES-Cre: B6.SJLSlc6a3tm1.1(cre)Bkmn/J
sex: F
species: Mus musculus
subject_id: 119.416
strain: C57BL/6J
experiment_description: Compulsive behavior is a defining feature of disorders such as substance use disorders. Current evidence suggests that corticostriatal circuits control the expression of established compulsions, but little is known about the mechanisms regulating the development of compulsions. We hypothesized that dopamine, a critical modulator of striatal synaptic plasticity, could control alterations in corticostriatal circuits leading to the development of compulsions (defined here as continued reward seeking in the face of punishment). We used dual-site fiber photometry to measure dopamine axon activity in the dorsomedial striatum (DMS) and the dorsolateral striatum (DLS) as compulsions emerged. Individual variability in the speed with which compulsions emerged was predicted by DMS dopamine axon activity. Amplifying this dopamine signal accelerated animals' transitions to compulsion, whereas inhibition delayed it. In contrast, amplifying DLS dopamine signaling had no effect on the emergence of compulsions. These results establish DMS dopamine signaling as a key controller of the development of compulsive reward seeking.
session_id: Opto-DMS-Excitatory-ChR2-2020-10-20T13-00-57
lab: Lerner
institution: Northwestern Unitersity
notes: Hemisphere with DMS: Right\n", "Experiment: DMS Excitatory\n", "Behavior: RI60\n", "Punishment Group: nan\n", "Did Not Learn: False\n", - "
surgery: ChR2 in DMS projecting SNc, probe in DMS
stimulus_notes: Excitatory stimulation on rewarded nosepokes
" + "
source_script: Created using NeuroConv v0.4.11
source_script_file_name: /opt/anaconda3/envs/lerner_lab_to_nwb_env/lib/python3.12/site-packages/neuroconv/basedatainterface.py
surgery: ChR2 in DMS projecting SNc, probe in DMS
stimulus_notes: Excitatory stimulation on rewarded nosepokes
" ], "text/plain": [ - "root pynwb.file.NWBFile at 0x5072347984\n", + "root pynwb.file.NWBFile at 0x5099719552\n", "Fields:\n", " devices: {\n", " Optogenetics_LED_Dual \n", @@ -90,8 +90,8 @@ " experimenter: ['Seiler, Jillian L.' 'Cosme, Caitlin V.' 'Sherathiya, Venus N.'\n", " 'Schaid, Michael D.' 'Bianco, Joseph M.' 'Bridgemohan, Abigael S.'\n", " 'Lerner, Talia N.']\n", - " file_create_date: [datetime.datetime(2024, 5, 28, 14, 4, 4, 197588, tzinfo=tzoffset(None, -25200))]\n", - " identifier: 0aa6698d-a238-45ca-9c8c-0d26389ba921\n", + " file_create_date: [datetime.datetime(2024, 6, 11, 13, 11, 5, 817671, tzinfo=tzoffset(None, -25200))]\n", + " identifier: fc40e324-a189-49ed-8e65-81e994c0d698\n", " institution: Northwestern Unitersity\n", " keywords: \n", " lab: Lerner\n", @@ -108,14 +108,16 @@ " behavior \n", " }\n", " related_publications: ['https://doi.org/10.1016/j.cub.2022.01.055']\n", - " session_description: FR1 Training with optogenetic stimulation, rewards delivered on both left and right nose pokes, optogenetic stimulation delivered on all nose pokes\n", + " session_description: FR1 Training with optogenetic stimulation, rewards delivered on both left and right nose pokes, optogenetic stimulation delivered on all rewarded nose pokes\n", " session_id: Opto-DMS-Excitatory-ChR2-2020-10-20T13-00-57\n", " session_start_time: 2020-10-20 13:00:57-05:51\n", + " source_script: Created using NeuroConv v0.4.11\n", + " source_script_file_name: /opt/anaconda3/envs/lerner_lab_to_nwb_env/lib/python3.12/site-packages/neuroconv/basedatainterface.py\n", " stimulus: {\n", " OptogeneticSeries \n", " }\n", " stimulus_notes: Excitatory stimulation on rewarded nosepokes\n", - " subject: subject pynwb.file.Subject at 0x5070799008\n", + " subject: subject pynwb.file.Subject at 0x5099724016\n", "Fields:\n", " age: P10W/\n", " age__reference: birth\n", @@ -150,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -175,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -222,7 +224,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 5, "metadata": {}, "outputs": [ {