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800 results for “Coronavirus”
Profiling of linear B-cell epitopes against human coronaviruses in pooled sera sampled early in the COVID-19 pandemic
<p>Background: Antibodies play a key role in the immune defence against infectious pathogens. Understanding the underlying process of B cell recognition is not only of fundamental interest; it supports important applications within diagnostics and therapeutics. Whereas the nature of conformational B cell epitope recognition is inherently complicated, linear B cell epitopes offer a straightforward approach that potentially can be reduced to one of peptide recognition.</p> <p>Methods: Using an overlapping peptide approach representing the entire proteomes of the seven main coronaviruses known to infect humans, we analysed sera pooled from eight PCR-confirmed COVID-19 convalescents and eight pre-pandemic controls. Using a high-density peptide microarray platform, 13-mer peptides overlapping by 11 amino acids were in situ synthesised and incubated with the pooled primary serum samples, followed by development with secondary fluorochrome-labelled anti-IgG and -IgA antibodies. Interactions were detected by fluorescence detection. Strong Ig interactions encompassing consecutive peptides were considered to represent "high-fidelity regions" (HFRs). These were mapped to the coronavirus proteomes using a 60% homology threshold for clustering.</p> <p>Results: We identified 333 human coronavirus derived HFRs. Among these, 98 (29%) mapped to SARS-CoV-2, 144 (44%) mapped to one or more of the four circulating common cold coronaviruses (CCC), and 54 (16%) cross-mapped to both SARS-CoV-2 and CCCs. The remaining 37 (11%) mapped to either SARS-CoV or MERS-CoV. Notably, the COVID-19 serum was skewed towards recognising SARS-CoV-2-mapped HFRs, whereas the pre-pandemic was skewed towards recognising CCC-mapped HFRs. In terms of absolute numbers of linear B cell epitopes, the primary targets are the ORF1ab protein (60%), the spike protein (21%), and the nucleoprotein (15%) in that order; however, in terms of epitope density, the order would be reversed.</p> <p>Conclusion: We identified linear B cell epitopes across coronaviruses, highlighting pan-, alpha-, beta-, or SARS-CoV-2-corona-specific B cell recognition patterns. These findings could be pivotal in deciphering past and current exposures to epidemic and endemic coronavirus. Moreover, our results suggest that pre-pandemic anti-CCC antibodies may cross-react against SARS-CoV-2, which could explain the highly variable outcome of COVID-19. Finally, the methodology used here offers a rapid and comprehensive approach to high-resolution linear B-cell epitope mapping, which could be vital for future studies of emerging infectious diseases.</p>
Incidencia Coronavirus Dataset (23/10/2021 a 27/10/2021)
<p>Este dataset ha sido generado mediante Web Scraping de https://www.worldometers.info/coronavirus/ conteniendo información sobre los casos, las defunciones... de coronavirus a nivel mundial.</p> <p>Ejercicio realizado dentro de la asignatura M2.851 - Tipología y ciclo de vida de los datos de la UOC.</p>
A HKU5-related Coronavirus identified and assembled from Short-Read sequencing data of Gossypium Barbadense
<p>This is the complete annotated genome of the Merbecovirus identified from Gossypium Barbadense sequencing data, <a href="https://trace.ncbi.nlm.nih.gov/Traces/sra/?run=SRR5885860">SRR5885860</a></p>
Population-based age-stratified seroepidemiological investigation protocol for coronavirus 2019 (COVID-19) infection in the Federation of Bosnia and Herzegovina
<p>Results of population-based age stratified seroepidemiological investigation in the Federation of Bosnia and Herzegovina</p>
Scapegoating Mechanisms in Hungarian Commentary-Folklore During the Coronavirus Crisis – Database
<p>Database of commentary-folklore in Hungarian language collected during the first wave of coronavirus disease-19. </p> <p> </p>
Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.
<p>Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.</p>
Rapid Creation of a Data Product for the World's Specimens of Horseshoe Bats and Relatives, a Known Reservoir for Coronaviruses
<p>This repository is associated with NSF DBI 2033973, RAPID Grant: Rapid Creation of a Data Product for the World's Specimens of Horseshoe Bats and Relatives, a Known Reservoir for Coronaviruses (<a href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=2033973">https://www.nsf.gov/awardsearch/showAward?AWD_ID=2033973</a>). Specifically, this repository contains <strong>(1) </strong>raw data from iDigBio (<a href="http://portal.idigbio.org">http://portal.idigbio.org</a>) and GBIF (<a href="https://www.gbif.org">https://www.gbif.org</a>), <strong>(2) </strong>R code for reproducible data wrangling and improvement, <strong>(3)</strong> protocols associated with data enhancements, and <strong>(4)</strong> enhanced versions of the dataset published at various project milestones. Additional code associated with this grant can be found in the BIOSPEX repository (<a href="https://github.com/iDigBio/Biospex">https://github.com/iDigBio/Biospex</a>). Long-term data management of the enhanced specimen data created by this project is expected to be accomplished by the natural history collections curating the physical specimens, a list of which can be found in this Zenodo resource.</p> <p><strong>Grant abstract:</strong> "The award to Florida State University will support research contributing to the development of georeferenced, vetted, and versioned data products of the world's specimens of horseshoe bats and their relatives for use by researchers studying the origins and spread of SARS-like coronaviruses, including the causative agent of COVID-19. Horseshoe bats and other closely related species are reported to be reservoirs of several SARS-like coronaviruses. Species of these bats are primarily distributed in regions where these viruses have been introduced to populations of humans. Currently, data associated with specimens of these bats are housed in natural history collections that are widely distributed both nationally and globally. Additionally, information tying these specimens to localities are mostly vague, or in many instances missing. This decreases the utility of the specimens for understanding the source, emergence, and distribution of SARS-COV-2 and similar viruses. This project will provide quality georeferenced data products through the consolidation of ancillary information linked to each bat specimen, using the extended specimen model. The resulting product will serve as a model of how data in biodiversity collections might be used to address emerging diseases of zoonotic origin. Results from the project will be disseminated widely in opensource journals, at scientific meetings, and via websites associated with the participating organizations and institutions. Support of this project provides a quality resource optimized to inform research relevant to improving our understanding of the biology and spread of SARS-CoV-2. The overall objectives are to deliver versioned data products, in formats used by the wider research and biodiversity collections communities, through an open-access repository; project protocols and code via GitHub and described in a peer-reviewed paper, and; sustained engagement with biodiversity collections throughout the project for reintegration of improved data into their local specimen data management systems improving long-term curation.</p> <p>This RAPID award will produce and deliver a georeferenced, vetted and consolidated data product for horseshoe bats and related species to facilitate understanding of the sources, distribution, and spread of SARS-CoV-2 and related viruses, a timely response to the ongoing global pandemic caused by SARS-CoV-2 and an important contribution to the global effort to consolidate and provide quality data that are relevant to understanding emergent and other properties the current pandemic. This RAPID award is made by the Division of Biological Infrastructure (DBI) using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.</p> <p>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria."</p> <p><strong>Files included in this resource</strong></p> <ul> <li><em>9d4b9069-48c4-4212-90d8-4dd6f4b7f2a5.zip</em>: Raw data from iDigBio, DwC-A format</li> <li><em>0067804-200613084148143.zip</em>: Raw data from GBIF, DwC-A format</li> <li><em>0067806-200613084148143.zip</em>: Raw data from GBIF, DwC-A format</li> <li><em>1623690110.zip</em>: Full export of this project's data (enhanced and raw) from BIOSPEX, CSV format</li> <li><em>bionomia-datasets-attributions.zip</em>: Directory containing 103 Frictionless Data packages for datasets that have attributions made containing Rhinolophids or Hipposiderids, each package also containing a CSV file for mismatches in person date of birth/death and specimen eventDate. File bionomia-datasets-attributions-key_2021-02-25.csv included in this directory provides a key between dataset identifier (how the Frictionless Data package files are named) and dataset name.</li> <li><em>bionomia-problem-dates-all-datasets_2021-02-25.csv</em>: List of 21 Hipposiderid or Rhinolophid records whose eventDate or dateIdentified mismatches a wikidata recipient’s date of birth or death across all datasets.</li> <li><em>flagEventDate.txt</em>: file containing term definition to reference in DwC-A</li> <li><em>flagExclude.txt</em>: file containing term definition to reference in DwC-A</li> <li><em>flagGeoreference.txt</em>: file containing term definition to reference in DwC-A</li> <li><em>flagTaxonomy.txt</em>: file containing term definition to reference in DwC-A</li> <li><em>georeferencedByID.txt</em>: file containing term definition to reference in DwC-A</li> <li><em>identifiedByNames.txt</em>: file containing term definition to reference in DwC-A</li> <li><em>instructions-to-get-people-data-from-bionomia-via-datasetKey</em>: instructions given to data providers</li> <li><em>RAPID-code_collection-date.R</em>: code associated with enhancing collection dates</li> <li><em>RAPID-code_compile-deduplicate.R</em>: code associated with compiling and deduplicating raw data</li> <li><em>RAPID-code_external-linkages-bold.R</em>: code associated with enhancing external linkages</li> <li><em>RAPID-code_external-linkages-genbank.R</em>: code associated with enhancing external linkages</li> <li><em>RAPID-code_external-linkages-standardize.R</em>: code associated with enhancing external linkages</li> <li><em>RAPID-code_people.R</em>: code associated with enhancing data about people</li> <li><em>RAPID-code_standardize-country.R</em>: code associated with standardizing country data</li> <li><em>RAPID-data-dictionary.pdf</em>: metadata about terms included in this project’s data, in PDF format</li> <li><em>RAPID-data-dictionary.xlsx</em>: metadata about terms included in this project’s data, in spreadsheet format</li> <li><em>rapid-data-providers_2021-05-03.csv</em>: list of data providers and number of records provided to rapid-joined-records_country-cleanup_2020-09-23.csv</li> <li><strong><em>rapid-final-data-product_2021-06-29.zip</em>: Enhanced data from BIOSPEX, DwC-A format</strong></li> <li><em>rapid-final-gazetteer.zip</em>: Gazetteer providing georeference data and metadata for 10,341 localities assessed as part of this project</li> <li><em>rapid-joined-records_country-cleanup_2020-09-23.csv</em>: data product initial version where raw data has been compiled and deduplicated, and country data has been standardized</li> <li><em>RAPID-protocol_collection-date.pdf</em>: protocol associated with enhancing collection dates</li> <li><em>RAPID-protocol_compile-deduplicate.pdf</em>: protocol associated with compiling and deduplicating raw data</li> <li><em>RAPID-protocol_external-linkages.pdf</em>: protocol associated with enhancing external linkages</li> <li><em>RAPID-protocol_georeference.pdf</em>: protocol associated with georeferencing</li> <li><em>RAPID-protocol_people.pdf</em>: protocol associated with enhancing data about people</li> <li><em>RAPID-protocol_standardize-country.pdf</em>: protocol associated with standardizing country data</li> <li><em>RAPID-protocol_taxonomic-names.pdf</em>: protocol associated with enhancing taxonomic name data</li> <li><em>RAPIDAgentStrings1_archivedCopy_30March2021.ods</em>: resource used in conjunction with RAPID people protocol</li> <li><em>recordedByNames.txt</em>: file containing term definition to reference in DwC-A</li> <li><em>Rhinolophid-HipposideridAgentStrings_and_People2_archivedCopy_30March2021.ods</em>: resource used in conjunction with RAPID people protocol</li> <li><em>wikidata-notes-for-bat-collectors_leachman_2020</em>: please see <a href="https://zenodo.org/record/4724139">https://zenodo.org/record/4724139</a> for this resource</li> </ul>
Looking in the medicine cabinet: methods for using real-world data to assess the impact of measles, mumps and rubella (MMR) and recombinant adjuvanted varicella-zoster vaccines on coronavirus disease 2019 (COVID-19) prevention and case fatality
<p>Supplementary File S1. 20210712_vx_off_target_pubdraft_S1 (Tables, Graphs and scripts associated with publication)</p> <p>Data file 1. Basic_Analysis.R (descriptive analysis script in R, for use with cleaned data files 3, 4 and 6)<br> Data file 2. Cleaning (Script demonstrating how Cerner data was cleaned upon download)<br> Data file 3. COVID_all_cleaned (CSV file with all COVID+ subjects in Cerner institutions)<br> Data file 4. COVID_mmr_data_cleaned (CSV file with COVID+ patients between 25-64 years old, including institution id, age category, gender, whether patient is in emergency department or inpatient, flu vaccine history, MMR vaccine history and mortality outcomes)<br> Data file 5. COVID_mmr_data_matched (CSV file matching MMR vaccine-exposed cases to controls based on propensity scores)<br> Data file 6. COVID_zoster_data_cleaned (CSV file with COVID+ patients above 50 years old, including institution id, age category, gender, whether patient is in emergency department or inpatient, flu vaccine history, zoster vaccine history and mortality outcomes)<br> Data file 7. COVID_zoster_data_matched (CSV file matching zoster vaccine-exposed cases to controls based on propensity scores)<br> Data file 8. General_25_64_data_cleaned (CSV file, all patients in Cerner institutions between 25 – 64 years old, including institution id, age category, gender, whether patient is in emergency department or inpatient, flu vaccine history, MMR vaccine history, SARS-CoV-2 infection and COVID-19 mortality outcomes ).<br> Data file 9. General_over50_data_cleaned (CSV file, all patients in Cerner institutions above 50 years old, including institution id, age category, gender, whether patient is in emergency department or inpatient, flu vaccine history, zoster vaccine history, SARS-CoV-2 infection and COVID-19 mortality outcomes).<br> Data file 10. Included_tenants (CSV file, institution IDs whose contributed cases comprise at least 0.5% of the aggregate sample size).<br> Data file 11. MMR_ps (R script to run for MMR-related files analysis)<br> Data file 12. Zoster_ps (R script to run for zoster-related files analysis)</p>
#Coronavirus on TikTok: User engagement with misinformation as a potential threat to public health behavior
<p><strong>Background:</strong> COVID-related misinformation is prevalent online, including on social media. The purpose of this study was to explore factors associated with user engagement with COVID-related misinformation on the social media platform, TikTok.</p> <p><strong>Methods:</strong> A sample of TikTok videos associated with the hashtag #coronavirus were downloaded on September 20, 2020. Misinformation was evaluated on a scale (low, medium, high) using a codebook developed by experts in infectious diseases. Multivariable modeling was used to evaluate factors associated with number of views and presence of user comments indicating intention to change behavior.</p> <p><strong>Results:</strong> 166 TikTok videos were identified. Moderate misinformation was present in 36 (22%) videos, and high-level misinformation was present in 11 (7%). After controlling for characteristics and content, videos containing moderate misinformation were less likely to generate a user response indicating intended behavior change. By contrast, videos containing high-level misinformation were less likely to be viewed but demonstrated a non-significant trend towards higher engagement among viewers.</p> <p><strong>Conclusions:</strong> COVID-related misinformation is less frequently viewed on TikTok but more likely to engage viewers. Public health authorities can combat misinformation on social media by posting content of their own. </p>
Pandemic Triage Score in Patients With Known or Suspected Severe Acute Respiratory Syndrome (SARS) CoronaVirus (CoV) 2 Infection
ClinicalTrials.gov study NCT04371471. IPD Sharing: YES. Countries: 1. Publications: 7.
Prevention of Infection and Incidence of COVID-19 in Medical Personnel Assisting Patients With New Coronavirus Disease
ClinicalTrials.gov study NCT04405999. IPD Sharing: NO. Countries: 1. Publications: 1.
Study to Evaluate the Safety and Antiviral Activity of Remdesivir (GS-5734™) in Participants With Moderate Coronavirus Disease (COVID-19) Compared to Standard of Care Treatment
ClinicalTrials.gov study NCT04292730. IPD Sharing: YES. Countries: 15. Publications: 3.
A Study to Evaluate the Efficacy and Safety of Sirukumab in Confirmed Severe or Critical Confirmed Coronavirus Disease (COVID)-19
ClinicalTrials.gov study NCT04380961. IPD Sharing: YES. Countries: 1. Publications: 1.
Dornase Alfa for ARDS in Patients With Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2)
ClinicalTrials.gov study NCT04402970. IPD Sharing: NO. Countries: 1. Publications: 6.
Study of Oral Ibrutinib Capsules to Assess Respiratory Failure in Adult Participants With Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) and Pulmonary Injury
ClinicalTrials.gov study NCT04375397. IPD Sharing: YES. Countries: 1. Publications: 1.
Coronavirus Induced Acute Kidney Injury: Prevention Using Urine Alkalinization
ClinicalTrials.gov study NCT04530448. IPD Sharing: NO. Countries: 1. Publications: 23.
PATCH 2&3:Prevention & Treatment of COVID-19 (Severe Acute Respiratory Syndrome Coronavirus 2) With Hydroxychloroquine
ClinicalTrials.gov study NCT04353037. IPD Sharing: YES. Countries: 1. Publications: 17.
Factors Affecting Mortality in Critical Patients Admitted to Intensive Care Unit Due to Coronavirus Disease 2019
ClinicalTrials.gov study NCT04659876. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Hydroxychloroquine Post Exposure Prophylaxis for Coronavirus Disease (COVID-19)
ClinicalTrials.gov study NCT04318444. IPD Sharing: NO. Countries: 1. Publications: 1.
Pre-exposure Prophylaxis for SARS-Coronavirus-2
ClinicalTrials.gov study NCT04328467. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
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
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DANDI Archive for NWB datasets
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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.