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14 results for “bla”
Dominant Rural Technological Trajectories (TTs) dataset at municipality level of the Brazilian Legal Amazon (BLA)
<p>This dataset contains the dominant technological trajectories (TTs) of the Brazilian Legal Amazon (BLA) municipalities for the years of 1995, 2006 and 2017. The dominant trajectory is the one, among the six identified by Costa (2021), that is economically most important in the municipality. The relative share of the Gross Value of Rural Production of the trajectory in the total Gross Value of Rural Production in the municipality was taken as a proxy of economic importance. From the tabulation of the datasets in Costa (2022), the dominant technological trajectory was identified, it means, considering the methodology used, which of the six TTs was responsible for over 50% of the municipal Gross Value of Rural Production. The dominant TTs were calculated using the official municipal grid for the year the agricultural census was carried out.</p> <p>The dataset is organized as a .csv table, for each year (1995, 2006 and 2017), with a geographic key for each municipality (6-digit municipality code, 2-digit state code), that can be easily linked with municipality available shapefiles and other datasets. </p> <p>References:</p> <p>Costa, F. A. Structural diversity and change in rural Amazonia: a comparative assessment of the technological trajectories based on agricultural censuses (1995, 2006 and 2017). <strong>Nova econ</strong>. 31 (02), May-Aug 2021, doi:10.1590/0103-6351/6373.</p> <p>Costa, F. A, (2022). Database of Rural Technological Trajectories of the Legal Amazon delimited by the Method of Differentiation and Structural Signification of Rural Production (M-DASTRU). <strong>Zenodo.</strong> DOI: 10.5281/zenodo.7035753</p>
1p_BLA_sync_permutation_social_valence
<div> <h1>Genetically- and spatially-defined basolateral amygdala neurons control food consumption and social interaction</h1> <a href="https://github.com/limserenahansol/1p_BLA_sync_permutation_social_valence#genetically--and-spatially-defined-basolateral-amygdala-neurons-control-food-consumption-and-social-interaction"></a></div> <p>Highlights:</p> <ol> <li>Classification of molecularly-defined glutamatergic neuron types in mouse BLA with distinct spatial expression patterns.</li> <li>BLALypd1 neurons are positive-valence neurons innately responding to food and promoting normal feeding.</li> <li>BLAEtv1 neurons innately respond to aversive and social stimuli.</li> <li>BLAEtv1 neurons promote fear learning and social interactions.</li> </ol> <p> </p> <div> <h2>"Step by step" for codes for BLA neuron calcium imaging analysis</h2> <a href="https://github.com/limserenahansol/1p_BLA_sync_permutation_social_valence#step-by-step-for-codes-for-bla-neuron-calcium-imaging-analysis"></a></div> <ol> <li>Either using our demo files or your calcium imaging and behavior CSV files need to be used: Demo lists: 1. Figure4_food, 2. Figure5_social, 3. Figure5_fear_conditioning, 4. suppl.fig10_longitudinal_footshock_social.</li> </ol> <div> <p>Note</p> <p>Demo files are already preprocessed H5 files, so you can skip 2-3steps.</p> </div> <ol> <li>you need to download "core_codes" <strong>(logger.py, preprocessing.py, util.py)</strong> to synchronize the extracted behavior statistics with calcium traces (The TTL emission-reception delay is negligible (less than 30ms), therefore the behavioral statistics time series can be synchronized with calcium traces by the emission/receival time on both devices) and it would generate combined one H5 file (behavior+calcium data)</li> <li>Run <strong>Synchrnoize_h5generation.py</strong> code to apply core-codes (preprocessing) to your data:</li> </ol> <div> <pre><code>import core.preprocessing as prep </code></pre> <div> </div> </div> <ol> <li>You can run each code (5 codes) described in the paper to generate the results: <strong>1. Fear Conditioning, 2.social, 3. food, 4. permutation, 5, suppl.10 social_footshock.py</strong> code number 4 is the percentage comparison with shuffling of data in Figure4-5 as bar graph you can reproduce using the code: Figure4_5_permutation_bargraph.</li> </ol> <div> <h3>required</h3> <a href="https://github.com/limserenahansol/1p_BLA_sync_permutation_social_valence#required"></a></div> <div> <p>Note</p> <p>-System requirements Python (3.10.8): we used a Python IDE for professional developers by JetBrains, Pycharm. Packages: suite2p : <a href="https://github.com/MouseLand/suite2p">https://github.com/MouseLand/suite2p</a></p> </div> <div> <pre><code>pip install git+https://github.com/MouseLand/suite2p.git </code></pre> <div> </div> </div> <blockquote> <p>matplotlib</p> </blockquote> <div> <pre><code>pip install matplotlib pip install PyQt5 </code></pre> <div> </div> </div> <blockquote> <p>numpy</p> </blockquote> <div> <p>Warning</p> <p>our code included : matplotlib Qt5Agg An Error is happening because Google Colab and Jupyter run on virtual environments which do not support GUI outputs as you cannot open new windows through a browser. Running it locally on a code editor(Spyder, or even IDLE) ensures that it can open a new window for the GUI to initialize.</p> </div> <div> <p>Tip</p> <ul> <li>"our analysis pipeline is based on basic python packages:"</li> </ul> </div> <div> <pre><code>> import numpy as np >import h5py as h5 >import pandas as pd </code></pre> <div> </div> </div> <p>Installation guide above</p> <ul> <li>Demo -Demo_data.zip</li> </ul> <div> <h3>Acknowledgement</h3> <a href="https://github.com/limserenahansol/1p_BLA_sync_permutation_social_valence#acknowledgement"></a></div> <p>Yue Zhang</p> <p> </p> <p>https://github.com/limserenahansol/1p_BLA_sync_permutation_social_valence</p> <blockquote> <p><a href="https://github.com/limserenahansol/1p_BLA_sync_permutation_social_valence">Github link for this </a></p> </blockquote>
Adolescent alcohol exposure, pain, and synaptic function at BLA inputs onto prelimbic neurons
Open the record for dataset details and reuse information.
FIGURE 4 in Fossil calibrations for the cockroach phylogeny (Insecta, Dictyoptera, Blattodea), comments on the use of wings for their identification, and a redescription of the oldest Bla
FIGURE 4. "Gyna" obesa (Piton, 1940) with ventral perspective. 1, Illustration of holotype. Hashed line on pronotum denotes two possible paths delimiting the posterior margin of the pronotum. Broken lines elsewhere denote damage or incomplete preservation. Scale bar equals 10 mm. 2, Photo of holotype MNHN.F.R06689. Photo is a composite of two images taken with polarized lighting at different angles. Scale 10 mm total. 3, Higher magnification of posterior end showing stout, segmented cerci and details of abdominal termination. Scale 3 mm total.
FIGURE 1 in Fossil calibrations for the cockroach phylogeny (Insecta, Dictyoptera, Blattodea), comments on the use of wings for their identification, and a redescription of the oldest Bla
FIGURE 1. Cladogram of Blattodea phylogeny synthesized from eight previous studies (figure 2 in Maekawa et al., 2003; figure 4 in Klass and Meier, 2006; figure 1 in Inward et al., 2007; figure 3 in Pellens et al., 2007; figure 2 in Murienne, 2009; figure 2 in Djernaes et al., 2012; figure 3 in Djernaes et al., 2015; figures 4, 5, and 6 in Legendre et al., 2015). Support for a clade is represented by circles, each colored to indicate the studies in which that clade was recovered. Inward et al. (2007) and Pellens et al. (2007) do not report all support values for nodes on their trees so support from the topology of these studies may be over-represented here. The numbering next to Anaplectidae represents different possible positions of this group, or the potential that this group is polyphyletic. Legendre et al. (2015) supports Blattoidea and Blattoidea + Corydioidea but without Anaplectidae – 1, yet will still note their support for these clades with this one exception. Single terminals are shown for other taxa even though some may prove paraphyletic or polyphyletic with further study (e.g., Pseudophyllodromiinae and Epilamprinae). We did not find any reliably supported relationships within most of the Blaberidae, and thus its phylogeny is largely a polytomy. In some cases, a clade was recovered but the study had insufficient taxon sampling to support that clade (e.g., Maekawa et al., 2003 recovered Blattodea as monophyletic by necessity because it did not include Mantodea or other outgroup taxa).
FIGURE 3 in Fossil calibrations for the cockroach phylogeny (Insecta, Dictyoptera, Blattodea), comments on the use of wings for their identification, and a redescription of the oldest Bla
FIGURE 3. Illustrations of calibrating fossils. All illustrations reproduced with permission. Scale bars equal 1 mm. 1, Cretaholocompsa montsecana Martinez-Delclos, 1993 (in Martinez-Delclos, 1993, figure 8). 2, Cariblattoides labandeirai Vršanský, Vidlička, Čiampor, Jr. and Marsh 2012 (in Vršanský et al., 2012, figure 12). 3, Ectobius kohlsi Vršanský, Vidlička, and Labandeira 2014 (in Vršanský et al., 2014, figure 3).
FIGURE 2 in Fossil calibrations for the cockroach phylogeny (Insecta, Dictyoptera, Blattodea), comments on the use of wings for their identification, and a redescription of the oldest Bla
FIGURE 2. Simplified topology of Blattodea with fossil calibrations. Tree topology is simplified from Figure 1. Fossils are shown in grey with dashed lines to represent uncertain position within containing clade. Red symbol indicates the nodes each fossil calibrates with roman numerals indicating the fossil that corresponds to each node calibration. The minimum dates for each of the calibration points are: i – 125.45 Ma; ii – 52 Ma; iii – 48.14 Ma; iv – 48.14 Ma. Corydiidae s.s. consists of Holocompsinae, Tiviinae, and Euthyrrhaphinae. Corydiidae s.l. consists of Corydiidae s.s., Latindiinae and Nocticolinae. Note that no Ectobiinae other than Ectobius have been used in molecular phylogenetic studies to date.
Vesical Imaging-Reporting and Data System (VI-RADS) Followed by Photodynamic Trans-urethral Resection of Bladder Tumours (PDD-TURBT) to Avoid Secondary Resections (Re-TURBT) in Non-Muscle Invasive Bla
ClinicalTrials.gov study NCT05962541. IPD Sharing: NO. Countries: 1. Publications: 21.
PPARγ-bla dataset curated and enriched using the Enalos tools and Enalos KNIME nodes for machine learning analysis (SCENARIOS project)
<p>A curated and enriched dataset for PPARγ-bla, intended for in silico model development, was obtained from PubChem BioAssay under the numeric identifier AID 743194 using Enalos tools and Enalos KNIME nodes. This dataset specifically utilizes compounds from the Tox21 10K chemical library that underwent screening against the PPARγ-bla HEK293H cell line. The cell line contains a beta-lactamase reporter gene, and all the information was extracted from PubChem Bioassay ID 743194 using Enalos tools and Enalos KNIME nodes. The original bioassay, consisting of 6587 compounds, assessed the antagonist activity of small molecules and classified them as 'active', 'inactive' or 'inconsistent' based on their AC50 (potency) score. The curated PPARγ dataset comprises 1230 compounds selected from the original bioassay and enriched with 777 molecular descriptors extracted from their 2D structure using EnalosMold2 KNIME nodes.</p>
Molecular profiling of prelimbic projections to NAc, BLA, and VTA using vTRAP
GEO Series GSE104943. Mus musculus. 16 samples. Type: Expression profiling by high throughput sequencing.
LCI-GU-BLA-SPEC-001: Aurora Kinase Expression in Muscle-Invasive Bladder Cancer
ClinicalTrials.gov study NCT02164942. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Cell type-specific RNA-seq to profile transcriptional changes in sorted BLA projection neurons during conditioned taste aversion learning
GEO Series GSE138522. Mus musculus. 16 samples. Type: Expression profiling by high throughput sequencing.
Chronic Stress Reinforces the BLA-mPFC-LHb Neural Circuit Leading to Depression
GEO Series GSE274487. Mus musculus. 7 samples. Type: Expression profiling by high throughput sequencing.
Analysis of changes in the BLA and vmPFC brain region of control mice and global cerebral ischemia mice after acute stress
GEO Series GSE248200. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.