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11,177 results for “identify”
Clay models and eDNA are useful tools for identifying predators of Salamanders
<p class="MsoNormal">Clay models are a popular technique for studying predation in nature due to their ease of deployment and minimal disruption of natural processes, but a drawback is the ambiguity of identifying predators based on bite marks. However, it is possible to amplify and sequence environmental DNA (eDNA) from these bite marks and to identify the predators responsible for attacking models. In this study, we sought to test the viability of eDNA from clay models as a means of identifying predators. We deployed molded clay models that resemble <em><span>Plethodon ventralis</span></em> Highton (Southern Zigzag Salamanders) into the field. We then extracted eDNA from visible bite marks, amplified and sequenced the 12S rRNA mitochondrial locus on an Illumina MiSeq, and used BLAST to determine the identity of representative sequences. We identified likely predators as <em><span>Procyon lotor</span></em> L. (American Raccoons), <em><span>Didelphis virginiana</span></em> Kerr (Virginia Opossums), <em><span>Turdus migratorius</span></em> L. (American Robins), and <em><span>Tamias striatus</span></em> L. (Eastern Chipmunks). We believe that this technique is helpful for adding a layer of specificity to clay model studies, albeit with a few potential pitfalls that we discuss.</p>
Genetic architecture of alcohol consumption identified by a genotype-stratified GWAS, and impact on esophageal cancer risk in Japanese
<p><span>An East Asian-specific variant on <em>aldehyde</em> <em>dehydrogenase</em> <em>2</em> (<em>ALDH2</em> rs671, G>A) is the major genetic determinant of alcohol consumption. We performed an rs671 genotype-stratified genome-wide association study (GWAS) meta-analysis in up to 175,672 Japanese individuals to uncover additional loci associated with alcohol consumption in an rs671-dependent manner. Three loci (<em>GCKR</em>, <em>KLB</em>, and <em>ADH1B</em>)</span> <span>satisfied the genome-wide significance threshold in wild-type homozygotes (GG), whereas six loci (<em>GCKR</em>, <em>ADH1B</em>, <em>ALDH1B1</em>, <em>ALDH1A1</em>, <em>ALDH2</em>, and <em>GOT2</em>) did so in heterozygotes (GA). Of these, five loci showed genome-wide significant interaction with rs671. Genetic correlation analyses revealed ancestry-specific genetic architecture in heterozygotes. Subsequent polygenic risk scoring depicted interactions highlighted by stratified GWAS. Further, most discovered loci showed significant effects on risk of esophageal cancer, a representative alcohol-related disease, and multiple other phenotypes. Our results identify the genotype-specific genetic architecture of alcohol consumption and reveal its potential impact on alcohol-related disease risk.</span></p>
POMFinder: Identifying polyoxometalate cluster structures from pair distribution function data using explainable machine learning
<p>Databases that were used to train the POMFinder ML model.</p>
Applying genomic approaches to identify historic population declines in European forest bats
<ol> <li>Anthropogenically-driven environmental changes over the past two centuries have led to severe biodiversity loss, most prominently in the form of loss of populations and individuals. Better tools are needed to assess the magnitude of these wildlife population declines. Anecdotal evidence suggests European bat populations have suffered substantial declines in the past few centuries. However, there is little empirical evidence of these declines that can be used to put more recent population changes into historic context and set appropriate targets for species recovery. </li> <li>This study is a collaboration between academics and conservation practitioners to develop molecular approaches capable of providing quantitative evidence of historic population changes and their drivers that can inform the assessment of conservation status and conservation management. We generated a genomic dataset for the Western barbastelle, <em>Barbastella barbastellus</em>, a globally Near Threatened and regionally Vulnerable bat species, including colonies from across the species' British and Iberian ranges. We used a combination of landscape genetics and approximate Bayesian computation model-based inference of demographic history to identify both evidence of population size changes and possible drivers of these changes. </li> <li>We found that levels of genetic diversity and inbreeding were related to broadleaf woodland cover around the colony location. Genetic connectivity was impeded by artificial lights and facilitated by the combination of rivers and broadleaf woodland cover. </li> <li>The demographic history analysis showed that both the northern and southern British barbastelle populations have declined by 99% over the past 330–548 years. These declines may have been triggered by loss of large oak trees and native woodlands due to shipbuilding during the early colonial period.</li> <li> <em>Synthesis and applications</em>. Genomic approaches can be applied to provide a better understanding of the conservation status of threatened species, within historic and contemporary context, and inform their conservation management. This study shows how we can bridge the implementation gap and promote the application of genomics in conservation management through co-designing studies with conservation practitioners and co-developing applied management targets and recommendations.</li> </ol>
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Lily Hart, <a href="https://orcid.org/0000-0002-2906-1849">https://orcid.org/0000-0002-2906-1849</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Joint host-pathogen genomic analysis identifies hepatitis B virus mutations associated with human NTCP and HLA class I variation
<p>Summary statistics for "Joint host-pathogen genomic analysis identifies hepatitis B virus mutations associated with human NTCP and HLA class I variation" </p><p>Files are organized in the following directory structure:</p><p><strong>G2G/</strong> - Summary statistics of G2G associations (SNPs, HLA, and gene-level analysis). </p><p><strong>HLA/ </strong>- Peptide binding prediction results</p><p><strong>preS1_haplotypes/ - </strong>Resolved intra-host haplotypes of the preS1 binding region. </p><p><strong>DnDs/</strong> - Calculation of intra-host positive selection, within the preS1 binding region. </p><p> </p>
ProTInSeq: transposon insertion tracking by ultra-deep DNA sequencing applied to identify small and large translated ORFs
<p>ProTInSeq is a novel -omics technique designed to characterize proteomes by using DNA ultra-deep sequencing. The technique is based on transposons engineered to have a positive or negative protein selection marker expressed when the transposon is inserted in-frame into a protein-coding gene. In the genome-reduced bacterium Mycoplasma pneumoniae, ProTInSeq identifies 80% of known expressed proteins, as well as 5 new open reading frames (ORFs; >100 amino acids); and 153 novel small ORF-encoded proteins (SEPs; ≤100 aa) that represent up to 18% of this bacterium’s proteome. ProTInSeq can be used to detect translational noise, for protein quantification and to provide insight into functional protein aspects such as relative half-life, stability, and membrane topology. Herein, we describe a methodology that can be easily implemented in any living system and allows the deep understanding of proteomes and more importantly the identification of small proteins by DNA ultra-sequencing.</p> <p>We include the following files:</p> <p>- processed_inscalling.zip: output obtain after running FASTQINS transposon calling tool over the datasets found at <a href="https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-10380?key=5f54209d-ce59-490a-9bcd-7084e9c619ee">https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-10380?key=5f54209d-ce59-490a-9bcd-7084e9c619ee</a>. This include every genome position in <em>M. pneumoniae</em>, the number of times an insertion has been mapped to that position and the total read count value.</p> <p>- separated_library_metrics.zip: insertion and read count processed from processed_inscalling files associated to every ORF and intergenic region in <em>M. penumoniae. </em>Columns include frame measured (0 - whole gene, 1 - in-frame, 2 and 3 for following positions) and metric. Metrics account for number insertions in-frame (<em>I</em>), read count (<em>R</em>), linear density from non-coding regions used in the Poisson evaluation (<em>rNC</em>), probability measured (<em>sfNC</em>) and a binary for prediction (<em>pred</em>; 0 - no significant, 1 - significant).</p> <p>- allmetrics.xlsx: merged table with the combination of results from separated_library_metrics.zip tab-delimited files.</p> <p>- selective_metrics_allannotations.xlsx: this table includes all the available information about the 30,112 sequences that could encode for a coding sequence in <em>M. pneumoniae</em>. For each identifier (column B), we include coordinates information and nucleotide and amino acid length information (columns C-H). Column I includes the gene name when the entry is found annotated in <em>M. pneumoniae</em>. Localization and function are described in columns J and K. Column L includes the operon number in which the annotation would be expressed. We also included transcription-related information average expression (column M; as log2(gene read count/gene length) and estimated average RNA copies per cell (column N) considering 4 RNA sequencing samples covering different growth times (6, 24 and 48 hours, ArrayExpress identifier E‐MTAB‐6203). Column O accounts for the number of mass spectrometry experiments detecting that entry (to a maximum of 116) and column P accounts for the total number of unique tryptic peptides detected. This comes <a href="https://paperpile.com/c/BImj5N/eljz">[5]</a>, available for 12,426 sequences that present an amino acid length ≥19 (from 116 mass spectrometry experiments, ID PRIDE: PXD008243). Columns Q to T recapitulate protein copies per cell under different conditions (overall, extracting with urea, extracting with SDS and mean, respectively). Column U includes half-lives of the proteins. Columns V and W describe the reference density of insertion and essentiality assigned in previous studies. Column X-AA includes the predicted RanSEPs score, ribosome binding site presence, homology into seven groups: 0—no hits passed the thresholds defined; 1—conserved with an annotated function; 2—conserved as an annotated SEP in NCBI but no associated function; 3—conserved in a different species but target and homologous sequence not found in NCBI; 4—sequence is completely or partially (> 75%) repeated ≥ 3 times in the reference genome; 5—potential pseudogene; and 6—to depict those annotations that are found in the reference NCBI annotation file. , and function expected by homology, respectively. Columns AB to AD cover the output provided by Phobius, including the number of transmembrane segments, presence of signal peptide and transmembrane topology predicted by TM-HMM. Column AE includes the complex information where 1 implies that entry is functional as a monomer, 2 as dimer, and so on. Finally, columns AF-AH will be 1 if the protein is a Lon protease target, a lipoprotein, and/or a truncated gene or pseudogene, respectively, 0 otherwise. Following columns include for every sample presenting selective insertion rates in-frame using the following identifiers separated by underscores: marker (BarnB, Cm or Ery), type (control-AC or selection-BD, antibiotic concentration, sample replicate, frame measured, metric. Metrics account for number insertions in-frame (<em>I</em>), read count (<em>R</em>), linear density from non-coding regions used in the Poisson evaluation (<em>rNC</em>), probability measured (<em>sfNC</em>) and a binary for prediction (<em>pred</em>; 0 - no significant, 1 - significant). Last columns combine the number of samples each annotation has been identified. Notice for barnase library the results need to be interpreted considering it is a negative selection marker inverting the 0 and 1 meaning.</p>
Identifying Spatial Co-occurrence in Healthy and InflAmed tissues (ISCHIA)
<p>Integrated IBD scRNASeq reference: Seurat object and cell type markers. </p><p>Molecular Cartography (Resolve) data data of human colon samples. 6 ulcerative colitis colon resections: 3 inflamed, 3 non-inflamed areas (4 patients in total). Segmented object (Seurat), and imaging data used for figures: B2-1 and C1-2 (healthy samples), C2-1 and C1-4 (inflamed samples) </p><p>Visium (10X) spatial transcriptomics data of human colon samples. 4 ulcerative colitis colon resections: 2 inflamed, 2 non-inflamed areas (3 patients in total). </p><p>Supplementary Table 1: positively co-occurring ligands and receptors in composition class 5.</p><p> </p><p> </p>
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Juan-P. Durango, <a href="https://orcid.org/0000-0003-3705-9834">https://orcid.org/0000-0003-3705-9834</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Kennet Lundin, <a href="https://orcid.org/0000-0002-0083-3817">https://orcid.org/0000-0002-0083-3817</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Ana Tronholm, <a href="https://orcid.org/0000-0002-0151-2804">https://orcid.org/0000-0002-0151-2804</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Data from: Mouse gingival single cell transcriptomic atlas identified a novel fibroblast subpopulation activated to guide oral barrier immunity in periodontitis
<p>Periodontitis, one of the most common non-communicable diseases, is characterized by chronic oral inflammation and uncontrolled tooth supporting alveolar bone resorption. Its underlying mechanism to initiate aberrant oral barrier immunity has yet to be delineated. Here, we report a unique fibroblast subpopulation activated to guide oral inflammation (AG fibroblasts) identified in a single-cell RNA sequencing gingival cell atlas constructed from the mouse periodontitis models. AG fibroblasts localized beneath the gingival epithelium and in the cervical periodontal ligament responded to the ligature placement and to the discrete topical application of Toll-like receptor stimulants to mouse maxillary tissue. The upregulated chemokines and ligands of AG fibroblasts linked to the putative receptors of neutrophils in the early stages of periodontitis. In the established chronic inflammation, neutrophils together with AG fibroblasts appeared to induce type 3 innate lymphoid cells (ILC3s) that were the primary source of interleukin-17 cytokines. The comparative analysis of <em>Rag2-/-</em> and <em>Rag2gc-/-</em> mice suggested that ILC3 contributed to the cervical alveolar bone resorption interfacing the gingival inflammation. We propose that the AG fibroblast–neutrophil–ILC3 axis as a previously unrecognized mechanism which could be involved in the complex interplay between oral barrier immune cells contributing to pathological inflammation in periodontitis.</p>
Exploring Data-Driven Chemical SMILES Tokenization Approaches to Identify Key Protein-Ligand Binding Moieties
<p>This repository contains materials for the paper, "Exploring Data-Driven Chemical SMILES Tokenization Approaches to Identify Key Protein-Ligand Binding Moieties", published in <a href="https://onlinelibrary.wiley.com/doi/10.1002/minf.202300249">Molecular Informatics.</a></p> <p>`data.zip` contains vocabulary and dataset files for identifying chemical vocabularies and key chemical words associated with protein ligand binding. </p> <p>`results.zip` comprises outputs specific to vocabularies and datasets, as well as various related statistics.</p> <p> </p> <p> </p>
Supplemental Movie Files for "Agent-Based Modeling of a Nuclear Chromosome Ensemble Identifies Determinants of Homolog Pairing During Meiosis" by Chriss et al.
<p>This set of Supplemental Information contains two movies made from the simulations from the model developed in the manuscript "<strong>Agent-Based Modeling of a Nuclear Chromosome Ensemble Identifies Determinants of Homolog Pairing During Meiosis</strong>" by A. Chriss, G. V. Börner, and S. D. Ryan. </p> <p> </p> <p>Supplemental Movie S1: <strong>WT Chromosome Trajectories during Prophase I. </strong>The first file "movie_WT..." contains the file for the results of simulations for the wild-type chromosomes and the exact parameter values can be found in Table 1 of the manuscript. The movie shows one realization of the agent-based model. The simulation movie covers the homology search process from <em>t = 3h </em>to <em>t = 9h</em>. Matching colors correspond to homologous pairs. True chromosome lengths are incorporated and scale the relevant interaction radii. The radius represents the attractive and non-homologous repulsive region.</p> <p> </p> <p>Supplemental Movie S2: <strong>WT Chromosome Trajectories during Prophase I with active dumbbell model. </strong> The second file "movie<em>WT</em>_activedumbbell..." contains the file for the results of the simulations for the modeling of chromosomes as active dumbbells (from polymers) to allow for the study of the effects of elongation, orientation, and flexibility. The movie shows one realization of the agent-based active dumbbell model which is closer to modeling a chromosome as a polymer. The simulation movie covers the homology search process from <em>t = 3h </em>to <em>t = 9h</em>. Matching colors correspond to homologous pairs. True chromosome lengths are incorporated and scale the relevant interaction radii, but are allowed to change in time as the two beads expand and contract. The radius represents the attractive and non-homologous repulsive region.</p> <p> </p> <p>Supplemental Movie S3: <strong><em>spo11</em> hypomorph (30% WT DSB levels) Chromosome Trajectories during Prophase I (parameters from Fig 7B)</strong>. The third file "movie_spo11..." contains the file for the results of the simulations for the spo-11 hypomorph and the associated parameter values can be found in Table 1 of the manuscript. The movie depicts one realization of the agent-based model for the {\it spo11} hypomorphic mutant. The simulation movie covers the homology search process from <em>t = 3h</em> to <em>t = 9h</em> where mutant <em>spo11</em> is associated with a weaker attractive and repulsive force (e.g., reduction to 77% of WT values). True chromosome lengths are incorporated and scale the relevant interaction radii. Matching colors correspond to homologous pairs. The radii represent the homologous attractive and the non-homologous repulsive region. Note that the reduction in interaction strength delays homologous pairing consistent with experimental observations in [13]. </p> <p> </p> <p> </p> <p>The codes that generated these movies were written in Matlab and freely available via GitHub: <a href="https://github.com/sdryan/ChromosomeDynamicsProphase1">https://github.com/sdryan/ChromosomeDynamicsProphase1</a></p> <p> </p> <p>For questions please contact the corresponding authors: G. Valentin Börner <a href="mailto:g.boerner@csuohio.edu">g.boerner@csuohio.edu</a> (Biology) or Shawn D. Ryan <a href="mailto:s.d.ryan@csuohio.edu">s.d.ryan@csuohio.edu</a> (Math).</p>
INFLAMeR: a machine learning algorithm based on large-scale perturbation screening identified new lncRNAs regulating differentiation and survival of leukaemia cells
Open the record for dataset details and reuse information.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Shade T. Shutters, <a href="https://orcid.org/0000-0002-8072-4134">https://orcid.org/0000-0002-8072-4134</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Zachary R. Dankowicz, <a href="https://orcid.org/0000-0002-0556-2067">https://orcid.org/0000-0002-0556-2067</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Identifying climate impacts from different stratospheric aerosol injection strategies in UKESM1
<p>Data to plot figures in Identifying climate impacts from different stratospheric aerosol injection strategies in UKESM1 - Wells et al., 2023</p>
Identifying the drivers of vegetation changes in Inner Mongolia based on residual analysis and Hasse diagram technique
<p><a>Exploring the effect of climate change and human activities on vegetation</a> is a key requisite for the reconstruction of regional ecological environments. Therefore, based on long-term vegetation GIMMS NDVI data, climate data, and statistical data, the present study applied the Hasse diagram technique and combined the multivariate regression residual analysis to quantitatively analyze the impact of human activities and climate change on vegetation in Inner Mongolia from detail human activities with some innovations. The results showed that (1) NDVI showed an overall increasing trend over the last 39 years, with an abrupt change in 2000; moreover, vegetation growth was better before the abrupt change (PⅠ: 1982–2000) than after it (PⅡ: 2001–2020), with significant downward trends in Xilin Gol and Hulunbuir. (2) Human activities can promote as well as inhibit vegetation, and the promotion effect was larger during 1982–2000 than during 2001–2020, whereas the inhibition effect was larger during 2001–2020. In addition, during PI, vegetation in Inner Mongolia generally experienced promotion by human activities and climate change, while during PII, climate-driven promotion had the strongest effect, followed by human-driven inhibition mainly distributed in Xilin Gol. (3) The result of the Hasse diagram analysis showed that the dominant pathways of human activities affecting most of the cities were economic factors and urbanization during PⅠ and economization during PII.</p>
Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer's disease
<p>Connectome, traits and behavior data for APOE234 mice.</p> <ul> <li>1. connectome.zip: mouse brain structural connectivity matrices from diffusion MRI.</li> <li>2. FAGNN_Phenotype.csv: a sheet of trait information of mice used in the study.</li> </ul> <p>columns: winding numbers, total distance, normalized NE time, normalized NE distance, normalized NW time, normalized NW distance, normalized SE time, normalized SE distance, normlaized SW time, normalized SW distance, island latency to first entry, island entries, normalized thigmataxis time, and normalized thigmotaxis distance</p> <div>rows: 4 trials for each day from day 1 to day 5 with 1 probing test each at day 5 and day 8</div> <ul> <li>3. mouse_anatomy.csv: brain region information regarding the connectivity matrix.</li> <li>4. behavior.zip: behavioral data for each mouse from Morris Water Maze experiments.</li> </ul>
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.