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800 results for “Coronavirus”
Carbohydrate vitrification in aerosolized saliva is associated with the humidity-dependent infectious potential of airborne coronavirus - Datasets
<p>Data includes:</p> <p>- WIBS files for each individual chamber run</p> <p>- RT-qPCR results</p> <p>- TCID50 results</p> <p>- Spreadsheet calculations</p> <p>- MOUDI results</p> <p> </p>
Protein Structure Files and Galaxy Workflows for Conducting Molecular Dynamics Simulations of Coronavirus Helicases
<p>The files included here are a set of Galaxy workflows, starting structure files (PDB, mol2, and frcmod), and specialized force field files (ZAFF) for the simulation of coronavirus helicases in the apo and drug-bound state. The inhibitor molecules include those from virtual screening (FCID1 and thioguanine), as well as experimentally validated candidates (Lumacaftor and SSYA10-001).</p>
Supplementary data of the paper 'Adaptive trends of sequence compositional complexity over pandemic time in the SARS CoV 2 coronavirus'
<p>Supplement of the paper<br> "Adaptive trends of sequence compositional complexity over pandemic time in the SARS-CoV-2 coronavirus”<br> During the spread of the COVID-19 pandemic, the SARS-CoV-2 coronavirus underwent mutation and recombination events that altered its genome compositional structure, thus providing an unprecedented opportunity to check an evolutionary process in real time. The mutation rate is known to be lower than expected for neutral evolution, suggesting natural selection and convergent evolution. We begin by summarizing the compositional heterogeneity of each viral genome by computing its Sequence Compositional Complexity (SCC). To analyze the full range of SCC diversity, we select random samples of high quality coronavirus genomes covering the full span of the pandemic. We then search for evolutionary trends that could inform us on the adaptive process of the virus to its human host by computing the phylogenetic ridge regression of SCC against time (i.e., the collection date of each viral isolate). In early samples, we find no statistical support for any trend in SCC values, although the viral genome appears to evolve faster than Brownian Motion (BM) expectation. However, in samples taken after the emergence of high fitness variants, and despite the brief time span elapsed, a driven decreasing trend for SCC and an increasing one for its absolute evolutionary rate are detected, pointing to a role for selection in the evolution of SCC in the coronavirus. We conclude that the higher fitness of variant genomes may have leads to adaptive trends of SCC over pandemic time in the coronavirus.</p> <p>Supplementary files</p> <table> <tbody> <tr> <td> <p><strong>File</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>SupplementaryTables S1-S19.zip</p> </td> <td> <p>Excel supplementary tables: The strain name, the collection date, and the SCC values for each analyzed genome.</p> </td> </tr> <tr> <td>nextstrain_ncov_open_global_timetree.nwk</td> <td>ML phylodynamic tree for the Nextstrain sample</td> </tr> <tr> <td> <p>SupplementaryTable S20.pdf</p> </td> <td> <p>A complete list acknowledging the authors, originating and submitting laboratories of the genetic sequences we used for the analysis of the Nextstrain sample.</p> </td> </tr> <tr> <td>Nextstrain_sample_fasta_3059.zip</td> <td>Nextstrain sample (sequences in Fasta format)</td> </tr> <tr> <td> <p>PhylogeneticTimetrees_NewickFormat.zip</p> </td> <td> <p>Phylogenetic timetrees (Newick format).</p> </td> </tr> </tbody> </table> <p> </p>
Coronavirus Twitter Data: A collection of COVID-19 tweets with automated annotations
<p>This dataset contains tweets related to COVID-19. The dataset contains Twitter ids, from which you can download the original data directly from Twitter. Additionally, we include the date, keywords related to COVID-19 and the inferred geolocation. Check detailed information at <a href="http://twitterdata.covid19dataresources.org/index">http://twitterdata.covid19dataresources.org/index</a>.</p>
Protein Structure Files and Galaxy Workflows for Conducting Molecular Dynamics Simulations of Coronavirus Helicases -- Output Files
<p>These are the output files generated using the input files and Galaxy workflows for coronavirus helicase simulations, from: </p> <pre>https://doi.org/10.5281/zenodo.7492987</pre>
MHC class II genes mediate susceptibility and resistance to coronavirus infections in bats
<p>Understanding the immunogenetic basis of coronavirus (CoV) susceptibility in major pathogen reservoirs, such as bats, is central to infer their zoonotic potential. Members of the cryptic <em>Hipposideros</em> bat species complex differ in CoV susceptibility, but the underlying mechanisms remain unclear. The genes of the major histocompatibility complex (MHC) are the best understood genetic basis of pathogen resistance, and differences in MHC diversity are one possible reason for asymmetrical infection patterns among closely related species. Here, we aimed to link asymmetries in observed CoV (CoV-229E, CoV-2B, and CoV-2Bbasal) susceptibility to immunogenetic differences amongst four <em>Hipposideros</em> bat species. From the 2,072 bats assigned to their respective species using the mtDNA cytochrome b gene, members of the most numerous and ubiquitous species, <em>Hipposideros caffer</em> D, were most infected with CoV-229E and SARS-related CoV-2B. Using a subset of 569 bats we determined that much of the existent allelic and functional (i.e., supertype) MHC DRB class II diversity originated from common ancestry. One MHC supertype shared amongst all species, ST12, was consistently linked to susceptibility with CoV-229E, which is closely related to the common cold agent HCoV-229E, and infected bats with ST12 had a lower body condition. The same MHC supertype was connected to resistance to CoV-2B, and bats with ST12 were less likely be co-infected with CoV-229E and CoV-2B. Our work suggests a role of immunogenetics in determining CoV susceptibility in bats. We advocate for the preservation of functional genetic and species diversity in reservoirs as means of mitigating the risk of disease spillover.</p>
SAFEty Study of Early Infusion of Vitamin C for Treatment of Novel Coronavirus Acute Lung Injury (SAFE EVICT CORONA-ALI)
ClinicalTrials.gov study NCT04344184. IPD Sharing: NO. Countries: 1. Publications: 10.
Treatment With CSL312 in Adults With Coronavirus Disease 2019 (COVID-19)
ClinicalTrials.gov study NCT04409509. IPD Sharing: YES. Countries: 1. Publications: 1.
Evaluation of Full Versus Fractional Doses of COVID-19 Vaccines Given as a Booster in Adults in Australia - Mongolia, Indonesia, Australia Coronavirus (MIACoV).
ClinicalTrials.gov study NCT05228730. IPD Sharing: YES. Countries: 1. Publications: 1.
Coronavirus Disease 2019 (COVID-19) Antibody Plasma Research Study in Hospitalized Patients
ClinicalTrials.gov study NCT04524507. IPD Sharing: YES. Countries: 1. Publications: 1.
The Safety and Preliminary Tolerability of Lyophilized Lucinactant in Adults With Coronavirus Disease 2019 (COVID-19)
ClinicalTrials.gov study NCT04389671. IPD Sharing: NO. Countries: 2. Publications: 2.
MHC class II genes mediate susceptibility and resistance to coronavirus infections in bats
Open the record for dataset details and reuse information.
Dataset with Curated Coronavirus-related R&D Outputs (1970 - March 2020)
<p><strong>The zip file includes metadata about R&D outputs related to all </strong><a href="https://www.niaid.nih.gov/diseases-conditions/coronaviruses"><strong>Coronaviruses</strong></a>.</p> <p>The two main data sources used for this work include:</p> <ul> <li> <p><a href="https://docs.microsoft.com/en-us/academic-services/project-academic-knowledge/introduction"><strong>Microsoft Academic Graph (MSA) API</strong></a>: Subset of 10.000+ coronavirus R&D outputs, with data from 1970, including patents and scientific publications.</p> </li> <li> <p><a href="https://www.grid.ac/"><strong>The Global Research Identifier Database (GRID)</strong></a><strong>:</strong> Used to enrich the organization data extracted from MSA.</p> </li> </ul> <p>After cleaning and enriching the data we extracted a total of <strong>1.100+ organization and</strong> <strong>26.700+ researchers</strong> spread across <strong>90+ countries</strong> and <strong>700+ cities</strong>.</p> <p>Inside the zip file you will find: </p> <ul> <li>documents.csv: Full list of documents</li> <li>topics.csv: list of topics connected to documents</li> <li>terms.csv: list of terms connected to documents</li> <li>people.csv: list of people (authors) connected to documents</li> <li>orgsplus.csv: list of organisations (incl their locations) connected to documents</li> </ul> <p>More information is available at <a href="https://app.gitbook.com/@dataverz/s/coronavirus-r-and-d/">https://app.gitbook.com/@dataverz/s/coronavirus-r-and-d/</a> </p>
Coronavirus Open Citations Dataset
<p>The Coronavirus Open Citations Dataset curated by OpenCitations currently contains (as of 16 May 2020) information about 189,697 citations and about the 49,719 citing or cited articles involved in these citations. A subset of these data (stored in the "_partial.json" files introduced below) is used for creating the visualization available at <a href="https://opencitations.github.io/coronavirus/">https://opencitations.github.io/coronavirus/</a>.</p> <p>Each item in both the JSON files storing citations ('citations_full.json' and 'citations_partial.json') contains the following fields:</p> <ul> <li>"id", a numeric identifier of the citation;</li> <li>"source", the citing entity;</li> <li>"target", the cited entity.</li> </ul> <p>Each item in both the JSON files storing article metadata ('metadata_full.json' and 'metadata_partial.json') contains the following fields:</p> <ul> <li>"id", the DOI of the article;</li> <li>"author", the surname of all the authors of the article;</li> <li>"year", the year of publication;</li> <li>"title", the title of the article;</li> <li>"source_title", the title of the venue where the article has been published.</li> </ul> <p>In addition, any item in the file 'metadata_partial.json' contains another field:</p> <ul> <li>"count": the overall number of citations that the article received.</li> </ul>
Datasets to "Piecewise quadratic growth during the 2019 novel coronavirus epidemic"
<pre>This directory contains an index.html file with links to the run directories for Figs.8-11 and idl plotting routines with secondary data for the other figures for the paper "Piecewise quadratic growth during the 2019 novel coronavirus epidemic" by Axel Brandenburg (Nordita) with the URL https://arxiv.org/abs/2002.03638 </pre>
Coronavirus testing indicates transmission risk increases along wildlife supply chains for human consumption in Viet Nam, 2013-2014
<p>Outbreaks of emerging coronaviruses in the past two decades and the current pandemic of a novel coronavirus (SARS-CoV-2) that emerged in China highlight the importance of this viral family as a zoonotic public health threat. To gain a better understanding of coronavirus presence and diversity in wildlife at wildlife-human interfaces in three southern provinces in Viet Nam 2013-2014, we used consensus Polymerase Chain Reactions to detect coronavirus sequences. In comparison to previous studies, we observed high proportions of positive samples among field rats (34.0%, 239/702) destined for human consumption and insectivorous bats in guano farms (74.8%, 234/313) adjacent to human dwellings. Most notably among field rats, the odds of coronavirus RNA detection significantly increased along the supply chain from field rats sold by traders (reference group; 20.7% positivity, 39/188) by a factor of 2.2 for field rats sold in large markets (32.0%, 116/363) and 10.0 for field rats sold and served in restaurants (55.6%, 84/151). Coronaviruses were also detected in rodents on the majority of wildlife farms sampled (60.7%, 17/28). These coronaviruses were found in the Malayan porcupines (6.0%, 20/331) and bamboo rats (6.3%, 6/96) that are raised on wildlife farms for human consumption as food. We identified six known coronaviruses in bats and rodents, clustered in three <em>Coronaviridae</em> genera, including the <em>Alpha-</em>, <em>Beta-</em>, and <em>Gammacoronaviruses</em>. Our analysis also suggested either mixing of animal excreta in the environment or interspecies transmission of coronaviruses, as both bat and avian coronaviruses were detected in rodent feces on wildlife farms. The mixing of multiple coronaviruses, and their apparent amplification along the wildlife supply chain into restaurants, suggests maximal risk for end consumers and likely underpins the mechanisms of zoonotic spillover to people.</p>
Pandemic-related Attitudes, Stressors and Work Outcomes among Medical Assistants during the SARS-CoV-2 ("Coronavirus") Pandemic in Germany: a cross-sectional Study
<p>File type: SPSS file (.sav)</p> <p>Study type: Cross-sectional study</p> <p>Population: Medical assistants in Germany</p> <p>Study period: April 7th-April 14th, 2020</p> <p>Number of participants: 2150</p> <p>Research question: Investigation of pandemic-related attitudes, stressors and work outcomes among medical assistants during the SARS-CoV-2 (“Coronavirus”) pandemic</p> <p>Missing values: None (due to online survey) </p> <p>Original variables: v_982, v_1, v_2, v_3, v_5, v_6, v_7, v_13, v_14, v_21, v_22, v_23, v_24, v_26, v_27, v_28, v_29, v_31, v_32, v_33, v_40, v_41, v_42, v_43, v_46, v_47, v_48 v_49, v_52, v_57, Beruf_MFA</p> <p>All other variables were calculated from the original variables either by rescaling or dichotomization. </p>
Coronavirus prevalence in Brazilian Amazon and Sao Paulo city
<p>SARS-CoV-2 spread rapidly in the Brazilian Amazon. Mortality was elevated, despite the young population, with the health services and cemeteries overwhelmed. The attack rate in this region is an estimate of the final epidemic size in an unmitigated epidemic. Here we show that by June, one month after the epidemic peak in Manaus, capital of the Amazonas state, 44% of the population had detectable IgG antibodies. This equates to a cumulative incidence of 52% after correcting for the false-negative rate of the test. Further correcting for the effect of antibody waning we estimate that the final attack rate was 66%. This is higher than seen in other settings, but lower than the predicted final size for an unmitigated epidemic in a homogeneously mixed population. This discrepancy may be accounted for by population structure as well as some limited physical distancing and non-pharmaceutical measures adopted in the city.</p>
Deep repertoire mining uncovers ultra-broad coronavirus neutralizing antibodies targeting multiple spike epitopes
<p><strong>Abstract:</strong> Development of vaccines and therapeutics that are broadly effective against known and emergent coronaviruses is an urgent priority. We screened the circulating B cell repertoires of COVID-19 survivors and vaccinees to isolate over 9,000 SARS-CoV-2-specific monoclonal Abs (<strong>mAbs</strong>), providing an expansive view of the SARS-CoV-2-specific Ab repertoire. Among the recovered antibodies was TXG-0078, an NTD-specific neutralizing mAb that recognizes diverse alpha- and beta-coronaviruses. TXG-0078 achieves its exceptional binding breadth while utilizing the same VH1-24 variable gene signature and heavy chain-dominant binding pattern seen in other NTD supersite-specific neutralizing Abs with much narrower specificity. We also report the discovery of CC24.2, a pan-sarbecovirus neutralizing antibody that targets a novel RBD epitope and shows similar neutralization potency against all tested SARS-CoV-2 variants, including BQ.1.1 and XBB.1.5. A cocktail of TXG-0078 and CC24.2 protects <i>in vivo</i>, suggesting potential use in variant-resistant therapeutic Ab cocktails and as templates for pan-coronavirus vaccine design.</p><p><strong>Datasets: </strong>This repository contains the 10x Genomic cellranger outputs (matrix and vdj contig files) as well as complied functional characterization dataset used to generate figures on the publication "Deep repertoire mining uncovers ultra-broad coronavirus neutralizing antibodies targeting multiple spike epitopes". </p><p>Post-vaccination samples for donors CC10, CC25, CC31, CC66 were processed in single 10x Genomic reactions. The timepoints samples consist of multiplexing donors CC10, CC25, CC31, CC66 into one 10x Genomic reaction. Similarly, donors CC26, CC42, CC62, CC67 were multiplexed into a single 10x Genomic reaction.</p><p><strong>Files:</strong></p><p>feature names.csv - csv file with sort bait/antigen barcode key </p><p>feature_reference.csv - csv file with cell hash and antigen barcode reference</p><p>filtered_contig<i>_</i>annotations.csv - High-level annotations of each high-confidence contigs from cell-associated barcodes. This is a subset of all_contig_annotations.csv.</p><p>filtered_contig.fasta - filtered antibody fasta</p><p>filtered_matrix.mtx.gz - 10x Genomic matrix file for filtered cells. Contains counts data for feature and gene expression library.</p><p>raw_matrix.mtx.gz - 10x Genomic matrix file for unfiltered cells. Contains counts data for feature and gene expression library.</p><p><strong>Code: </strong>All code used to generate analysis and figures is available under the MIT license on Github<br> </p>
Custom database for coronavirus prevalence analysis
<p>These coronavirus genomes in this dataset were obtained from the NCBI Refseq and are contained in the custom database utilised for both pan-coronavirus primer design, and homology search resulting in Figure 4c of the associated manuscript.</p>
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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.