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4,078 results for “SARS”
Supplementary Data - MORTALITY RATE DUE TO PULMONERY FIBROSIS ASSOCIATED WITH SARS- COV-2 INFECTION: SCOPE OF BEST FIT REGRESSION
<p>The dataset contains number of infected pateints - Death Frquencies - Mortality rate globally due to pulmonary fibrosis associated with SARS-COV-2 infection with effect from 21st Jan to 28 th April ,2020 . Data analysis report by best fit regression software Curve Expert V.1.4 supported with Spreadsheet ( Excel , Office 2007 ) are included for computation of statistical significance .</p>
SARS-CoV-2 NSP13; A Target Enabling Package
<p>To contribute towards the development of novel anti-viral therapeutics targeting the current and future emerging coronavirus threats, the Gileadi lab at the University of Oxford, together with the XChem team at Diamond Light Source, have teamed up to perform a crystallographic fragment screen against SARS-CoV-2 NSP13 helicase. NSP13 is believed to act in concert with the replication-transcription complex (NSP7/NSP8<sub>2</sub>/NSP12), possibly being involved in either disrupting downstream RNA secondary structures or template switching, and plays an essential role in the life cycle of SARS-CoV-2.</p> <p>This TEP includes expression clones and methods for producing the full length NSP13, and fluorescence-based activity assays suitable for compound screening. We provide a crystallisation system that produces reproducible crystals that diffract to high resolution, and have performed a crystallographic fragment screen revealing 63 fragment hits across 51 datasets. The fragment hits include several hits in pockets predicted to be of functional importance, including the nucleotide and nucleic acid binding sites, opening the way to development of novel antiviral agents.</p>
SARS-CoV-2 Nidoviral RNA Uridylate‐Specific Endoribonuclease (NSP15); A Target Enabling Package
<p>The non-structural protein 15 (NSP15, NendoU<sup>SARS-CoV-2</sup>) from severe acute respiratory syndrome 2 virus (SARS-CoV-2) is an uridylate-specific endoribonuclease, likely responsible in the viral immune evasion mechanism. This TEP provides a set of reagents for further interrogation of the molecular function of NSP15. We have established a purification protocol for the active protein for biochemical and structural studies. Moreover, we have crystallised the protein and performed a crystallographic fragment screen which yielded several hits. Data generated here will be used for the development of enzyme inhibitors that would illuminate the biological role of the gene product, and eventually point the way to new antiviral therapies.</p>
DATA ANALYSIS - SARS-COV-2 ( Del69-70 VARIANT ) – NEW UK MUTANTS
<p>The data for S - genome sequence analysis known as Del69-70 is under variant of concern ( VOC ) . It is also termed as variant of investigation ( VUI ) . The data for VUI is statistically analysed by datewise and regionwise . The software used for data analysis is CURVE FINDER V.1.4 . The reproducibility of correlation and standard error is reported here for analysis of scattered data an attempt to study the Rational Fit and Harris Fit .</p>
Discovery of South African plant-based biomarkers as potential flagships for SARS- CoV-2 receptor
<h1><span>Table S1: </span><span>Distinguished metabolites in the extracts of <em><span>Artemisia annua </span></em><span>and <em>Artemisia afra </em></span>using UPLC-MS/MS set in positive ionization mode.</span></h1> <h1><span>Table S2: Identified and docked compound-based biomarkers from <em><span>Artemisia annua </span></em><span>and <em>Artemisia afra </em></span>(ESI+ scan).</span></h1>
Identifying the interplay between protective measures and settings on the SARS-CoV-2 transmission using a Bayesian network [Dataset]
<p>data07B.csv: dataset for the study of the SARS-CoV-2 transmission.</p> <p>CPTNetica.txt: conditional probabilities tables of each variable given through Netica once the BN obtained in R code is loaded.</p> <p>code01.R: code to learn structure and parameters of the SARS-CoV-2 BN model.</p>
Dataset for: Pre-pandemic artificial MERS analog of polyfunctional SARS-CoV-2 S1/S2 furin cleavage site domain is unique among spike proteins of genus Betacoronavirus
<table> <tbody> <tr> <th> </th> <td> <div> <h3><strong>Data File Descriptions and Methods</strong></h3> <ol> <li><strong>Data file 1 [betacov_matching_IPR042578.fasta]</strong>: Representative set of 2,465 betacoronavirus S protein overlapping homologous superfamily sequences retrieved in fasta format on 4 December 2022 from the InterPro repository at https://www.ebi.ac.uk/interpro/entry/InterPro/IPR042578/.<br><br></li> <li><strong>Data File 2 [betacov_matching_IPR042578_motif.fasta]</strong>: With Data File 1 as input, extracted 98,122 furin cleavage site (FCS) output motifs of 20 amino acids length, including overlapping and redundant sequences, produced with the FindFur algorithm with preset parameters as described by (Gu, 2020). FindFur as used was deposited on 15 December 2020 at the GitHub software repository at https://github.com/chwisteeng/FindFur.<br><br></li> <li><strong>Data File 3 [table_s1s2_hits_betacov_polyf.pdf]</strong>: Compiled summary table of sequence hits (PDF) of spike S1/S2 domains across genus <em>Betacoronavirus. </em>The compiled table of hits removed from Data File 2 sequences corresponding to spike protein fragments (incomplete length spike proteins as deposited at GenBank) and duplicates (redundant parts identically overlapping within the 20 amino acids motif windows), and then selected one sequence representative for multiple but identical sequences.<em> </em>Collection dates and geographical locations were retrieved from the NCBI Genbank protein database at https://www.ncbi.nlm.nih.gov/protein/. For SARS-CoV-2 spike variants, these data were also cross-validated with the SARS-CoV-2 lineage mutation tracker (Gangavarapu, 2023) available at https://outbreak.info which was based on extensive sequencing data from the global GISAID initiative (https://gisaid.org/). Solid lines (-) depict pat7 NLS, asterisks (*) O-glycosites, and circumflex (^) symbols FCS.<br><br></li> <li> <p><strong>Data File 4 [table_s1s2_hits_betacov_polyf.xlsx]</strong>: Compiled summary table of sequence hits (MS Excel) of spike S1/S2 domains across genus <em>Betacoronavirus. </em>The compiled table of hits removed from Data File 2 sequences corresponding to spike protein fragments (incomplete length spike proteins as deposited at GenBank) and duplicates (redundant parts identically overlapping within the 20 amino acids motif windows), and then selected one sequence representative for multiple but identical sequences.<em> </em>Collection dates and geographical locations were retrieved from the NCBI Genbank protein database at https://www.ncbi.nlm.nih.gov/protein/. For SARS-CoV-2 spike variants, these data were also cross-validated with the SARS-CoV-2 lineage mutation tracker (Gangavarapu, 2023) available at https://outbreak.info which was based on extensive sequencing data from the global GISAID initiative (https://gisaid.org/). Solid lines (-) depict pat7 NLS, asterisks (*) O-glycosites, and circumflex (^) symbols FCS.<br><br></p> </li> <li> <p><strong>Data File 5 [betacov_s1s2_nls_pat7_furin_psort.txt]: </strong>Nuclear localization signal (NLS) detection output for 5 representative betacoronavirus spike sequence domains, including the positive hits for pat7 in SARS-CoV-2 and for MERS-MA30 CoV. NLS predictions used the PSORT algorithm available as a webservice at https://wolfpsort.hgc.jp/ which is based on the work of Nakai and Horton (Nakai and Horton, 1999). Numbering refers to Data File 3 and Data File 4.<br><br></p> </li> <li> <p><strong>Data File 6 [betacov_s1s2_oglyc_netogly.txt]: </strong>Detection output for 5 representative betacoronavirus spike sequence domains tested for Thr/Ser O-glycosite residue pairs with the standard prediction software NetOGlyc4.0 (Steentoft et al., 2013) as available at https://services.healthtech.dtu.dk/services/NetOGlyc-4.0/. Positive hits have scores above 0.5. Numbering refers to Data File 3 and Data File 4.<br><br></p> </li> <li> <p><strong>Data File 7 [betacov_s1s2_nls_pat7_furin_blastp.txt]</strong>: Comprehensive sequence database searches using were performed using the NCBI protein BLAST (blastp) algorithm with webservice available at https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE=Proteins. The following blastp search parameters and settings were used: Word size=2; Expect value=200000; Hitlist size=500; Gapcosts=9,1; Matrix=PAM30; Filter string=F; Genetic Code=1;Window Size=40; Threshold=11; Composition-based stats=0; Database Posted date=Jan 19, 2023 2:59 AM; Number of letters=17,117,563; Number of sequences=10,766; Entrez query: Includes: Betacoronavirus (taxid:694002); Excludes: SARS-CoV-2 (taxid:2697049). The six polyfunctional input query consensus motif sequences were TXXPR(K/H/R)XRSX and TXXPRX(K/H/R)RSX.</p> </li> </ol> <h3><strong>References</strong></h3> <p>Gu, C., 2020. FindFur: A Tool for Predicting Furin Cleavage Sites of Viral Envelope Substrates. Master’s Thesis, San Jose State University, CA, USA. doi: <a href="https://doi.org/10.31979/etd.4ahv-9jya">10.31979/etd.4ahv-9jya</a> </p> <p>Gangavarapu K, Latif AA, Mullen JL, Alkuzweny M, Hufbauer E, Tsueng G, Haag E, Zeller M, Aceves CM, Zaiets K, Cano M, Zhou X, Qian Z, Sattler R, Matteson NL, Levy JI, Lee RTC, Freitas L, Maurer-Stroh S; GISAID Core and Curation Team; Suchard MA, Wu C, Su AI, Andersen KG, Hughes LD. Outbreak.info genomic reports: scalable and dynamic surveillance of SARS-CoV-2 variants and mutations. Nat Methods. 2023. 20(4):512-522. doi: <a href="https://doi.org/10.1038/s41592-023-01769-3">10.1038/s41592-023-01769-3</a>.</p> <p>Nakai, K., Horton, P., 1999. PSORT: a program for detecting sorting signals in proteins and predicting their subcellular localization. Trends Biochem Sci 24, 34–36. doi: <a href="https://doi.org/10.1016/s0968-0004(98)01336-x">10.1016/s0968-0004(98)01336-x</a></p> <p>Steentoft, C., Vakhrushev, S.Y., Joshi, H.J., Kong, Y., Vester-Christensen, M.B., Schjoldager, K.T.-B.G., Lavrsen, K., Dabelsteen, S., Pedersen, N.B., Marcos-Silva, L., Gupta, R., Bennett, E.P., Mandel, U., Brunak, S., Wandall, H.H., Levery, S.B., Clausen, H., 2013. Precision mapping of the human O-GalNAc glycoproteome through SimpleCell technology. EMBO J 32, 1478–1488. doi: <a href="https://doi.org/10.1038/emboj.2013.79">10.1038/emboj.2013.79</a></p> </div> </td> </tr> </tbody> </table>
SARS-CoV-2 wastewater surveillance data and metadata in the Open Data Model format. Part 1: Québec City
<p>SARS-CoV-2 wastewater surveillance data and metadata in the Open Data Model format. Part 1: Québec City Authors</p> <ul> <li>Therrien, J-D<sup>1</sup></li> <li>Maere, T.<sup>1</sup></li> <li>Sanchez-Quete, F.<sup>2</sup></li> <li>Tsitouras, A.<sup>2</sup></li> <li>Goitom, E.<sup>3</sup></li> <li>Cloutier, F.<sup>4</sup></li> <li>Dufour, D.<sup>4</sup></li> <li>Proulx, F. <sup>4</sup></li> <li>Nicolaï, N.<sup>1</sup></li> <li>Philippe, R.<sup>1</sup></li> <li>Tohidi, M.<sup>1</sup></li> <li>Dorner, S.<sup>3</sup></li> <li>Frigon, D.<sup>2</sup></li> <li>Vanrolleghem, P.A.<sup>1</sup></li> </ul> <p>Affiliations</p> <ul> <li><sup>1</sup> model<em>EAU</em>, Département de génie civil et de génie des eaux, Université Laval</li> <li><sup>2</sup> Microbial Community Engineering Lab (MiCEL), Department of Civil Engineering, McGill University</li> <li><sup>3</sup> Polytechnique Montréal</li> <li><sup>4</sup> Ville de Québec</li> </ul> <p>General Remarks</p> <p>Wastewater-based surveillance of SARS-CoV-2 virus can detect between 1 and 30 infected individuals per 100,000 (including asymptomatic ones) by analyzing the population's sewage. As such, this method is very attractive since it costs only a fraction of clinical testing (as low as 1%). Human faeces may contain the virus a few days before a person becomes ill. Thus, this approach allows for detection of outbreaks 2-7 days before the increase in reported cases stemming from clinical screening tests (Bibby et al., 2021). Wastewater-based surveillance complements clinical testing by geolocating outbreaks, which may help targeting intensive screening programs. Moreover, it provides a quick indication of whether new public health measures (e.g., masks, social distancing, confinement, and curfew) are effective.</p> <p>Sampling</p> <p>The reported dataset contains open data collected in the province of Québec as part of the SARS-CoV-2 wastewater-based surveillance program <a href="https://www.centreau.ulaval.ca/en/covid/">CentrEau</a>-COVID. Four of the largest cities in the province (Montréal, Laval, Québec City, and Trois-Rivières), as well as the municipalities of four rural regions (Mauricie, Centre-du-Québec, Bas-St-Laurent, and Gaspésie) participated in the program. The entire dataset includes 31 sampling sites covering approximately half the population of the province of Québec (population size of 8.5 million). The timeframe covered by the dataset varies for each site. The earliest surveillance program was launched in March 2020, others followed soon after. Samples were collected using various methods, such as 24h composite samples, grab samples, and passive sampling using variations on the Moore swab method (Schang et al., 2020)</p> <p>Analysis</p> <p>Prior to the analysis of the samples for SARS-CoV-2, physiochemical parameters such as total suspended solids (TSS), turbidity, conductivity, ammonium concentration, and pH were measured. The samples were subsequently concentred by filtration using a MEC filter (0.45 um), followed by total RNA extraction using the Qiagen AllPrep PowerViral DNA/RNA Kit (Qiagen, USA) with some modifications (beta-mercaptoethanol concentration raised to 10% and lysis performed at 55 °C for 30 minutes) (Ahmed et al., 2020). SARS-CoV-2 viral RNA was detected by a one-step RT-qPCR. To assess the RNA recovery rate of the procedure, samples were spiked before extraction with a known concentration of Bovine Respiratory Syncytial Virus (BRSV) using the Zoetis INFORCE 3 vaccine (Zoetis, USA). In addition to SARS-CoV-2, samples were assessed for Pepper Mild Mottle Virus (PMMoV), the daily load of which is hypothesized to represent the fecal load contributions to the samples at a given site and time. PCR conditions and primer used to collect viral data are described in the files <code>primers.md</code> and <code>PCR conditions.md</code>.</p> <p>Compilation</p> <p>The measurements on wastewater samples carried out by the participating laboratories of this study are found in the <code>WWMeasure</code> table. The values provided by municipalities come from laboratories accredited by the Centre d'expertise en analyse environnementale du Québec (CEAEQ), in compliance with the latter's quality assurance protocols. The COVID-19-related public health data found in the <code>CPHD</code> table were collected from the Institut National de Santé Publique du Québec (INSPQ)'s public reports. Wastewater data taken in-situ at the sampling sites (e.g., the flow at pumping stations or water resource recovery facilities (WRRFs)) are found in the <code>SiteMeasure</code> table and were taken by the institutions responsible for managing the sites. All of the data, stemming from multiple sources, were combined into the <a href="https://github.com/Big-Life-Lab/ODM">Open Data Model (ODM)</a> standard format using the <a href="https://github.com/modelEAU/ODM-Import">ODM-Import python package</a> (see also Structure).</p> <p>Validation</p> <p>Wastewater and sample data were manually assessed for quality by our research collaborators. Data points for which the quality appeared to be uncertain were tagged with the value <code>True</code> in the <code>qualityFlag</code> column. Conversely, data deemed of good quality have a quality flag of <code>False</code>. Data that were not checked have a quality flag of <code>NA</code>. Textual comments describing the issues with the data points in more detail are also included in the dataset using the <code>notes</code> column of the relevant tables. Note that data validation was carried out by the data custodians responsible for each city in the dataset according to available resources. As the project continues and data validation is undertaken on more sections of the dataset, data may be re-analyzed, flagged, or commented as needed. Revisions to the dataset will be reported to the best of our ability.</p> <p>Structure</p> <p>The data contained in this dataset has been structured according to the <a href="https://github.com/Big-Life-Lab/ODM">Open Data Model (ODM) for Wastewater-Based Surveillance</a>. This model provides a standardized dictionary to collect and share data and metadata stemming from wastewater-based surveillance programs. By convention, it splits all data into 10+ thematic tables with each record representing a unique measurement, i.e., long format. For convenience, the <code>wide</code> folder presents the data found in all the other tables in a wide format, i.e., multiple measurements are aligned by <code>timestamp</code>, with each column representing a different parameter.</p> <p>Acknowledgements</p> <p>The authors would like to acknowledge that this dataset was collected thanks to the financial support of the Fonds de Recherche du Québec, the Molson Foundation, the Trottier Family Foundation, CentrEau and NSERC. The authors would also like to acknowledge the efforts of Douglas Manuel (Ottawa Hospital) and Howard Swerdfeger (Public Health Agency of Canada) for their original idea for the Open Data Model and continued development.</p> <p>References</p> <ol> <li> <p>Ahmed, W., Bertsch, P.M., Bivins, A., Bibby, K., Farkas, K., Gathercole, A., Haramoto, E., Gyawali, P., Korajkic, A., McMinn, B.R., Mueller, J.F., Simpson, S.L., Smith, W.J.M., Symonds, E.M., Thomas, K. v., Verhagen, R., Kitajima, M., 2020. Comparison of virus concentration methods for the RT-qPCR-based recovery of murine hepatitis virus, a surrogate for SARS-CoV-2 from untreated wastewater. Science of the Total Environment 739. <a href="https://doi.org/10.1016/j.scitotenv.2020.139960">https://doi.org/10.1016/j.scitotenv.2020.139960</a></p> </li> <li> <p>Bibby, K., Bivins, A., Wu, Z., North, D., 2021. Making waves: Plausible lead time for wastewater based epidemiology as an early warning system for COVID-19. Water Research 202, 117438. <a href="https://doi.org/10.1016/j.watres.2021.117438">https://doi.org/10.1016/j.watres.2021.117438</a></p> </li> <li> <p>Schang, C., Crosbie, N., Nolan, M., Poon, R., Wang, M., Jex, A., Scales, P., Schmidt, J., Thorley, B.R., Henry, R., Kolotelo, P., Langeveld, J., Schilperoort, R., Shi, B., Einsiedel, S., Thomas, M., Black, J., Wilson, S., McCarthy, D.T., 2020. Passive sampling of viruses for wastewater-based epidemiology: a case-study of SARS-CoV-2 [WWW Document]. URL <a href="https://www.researchgate.net/publication/347103410\_Passive\_sampling\_of\_viruses\_for\_wastewater-based\_epidemiology\_a\_case-study\_of\_SARS-CoV-2?channel=doi&linkId=5fd800f392851c13fe892393&showFulltext=true">https://www.researchgate.net/publication/347103410\_Passive\_sampling\_of\_viruses\_for\_wastewater-based\_epidemiology\_a\_case-study\_of\_SARS-CoV-2?channel=doi&linkId=5fd800f392851c13fe892393&showFulltext=true</a> (accessed 1.18.21).</p> </li> </ol>
Dry trajectories of SARS-CoV-2 RBD from accelerated molecular dynamics simulation
<p>These are supplementary files to the preprint/paper "SARS-CoV-2 spike protein unlikely to bind to integrins via the Arg-Gly-Asp (RGD) motif of the Receptor Binding Domain: evidence from structural analysis and microscale accelerated molecular dynamics" (http://dx.doi.org/10.1101/2021.05.24.445335).</p> <p>The attached code in Jupyter notebook can be run after installing the virtual environment using the `environment.yml `</p> <p>The file `data.zip` needs to be extracted to the same path where the notebook is run from</p>
SARS-CoV-2 genomics resources for Galaxy
<p>Reference and custom annotation data expected as input by Galaxy SARS-CoV-2 variation analysis workflows developed by covid19.galaxyproject.org</p>
Diet and SARS-Cov-2 Infection Risk: A Retrospective Observational Study
<p>Dataset, Analysis, Regression and Description.</p> <p>From mid-summer 2020 to January 2022, based on phase 2 to 3 of a self-reported questionnaire survey, we asked 15851 families across Iran about their diet and their COVID-19 disease. The results showed that some diets increased the risk of SARS-Cov-2 Apparent Infection Risk and some reduced it.</p> <p>The results show that the risk of reporting SARS-CoV-2 apparent infection in the second group was 12 times higher than the Third group. <strong>The two-tailed P value is less than 0.0001</strong>. Also, the risk of reporting SARS-CoV-2 apparent infection in the first group was 9 times higher than the Third group. <strong>The two-tailed P value is less than 0.0001</strong>. By conventional criteria, these differences are considered to be extremely statistically significant.</p>
PSSH2 - database of protein sequence-to-structure homologies (including Sars-CoV-2 structures)
<p><strong>Protein sequence and structure data</strong></p> <p>This data set contains data from Uniprot (in the files called protein_sequence, protein_synonyms, protein_names, organism_synonyms) and PDB (in the files called PDB and PDB_chain) as used by the <a href="https://github.com/ODonoghueLab/Aquaria">Aquaria web resource</a> at the time of download (2022-02-08).</p> <p> </p> <p><strong>The PSSH2 data set</strong><br> <br> PSSH2 is a database of protein sequence-to-structure homologies based on HHblits, an alignment method employing iterative comparisons of hidden Markov models (HMMs). To ensure the highest possible final alignment quality for matches in Aquaria using HHblits, we first calculate HMM profiles for each unique PDB sequence (PDB_full) and also for each unique Swiss-Prot sequence. We generated PSSH2 using HHblits to find similarities between HMMs from PDB and HMMs from UniProt sequences.</p> <p> </p> <p><strong>Calculating PSSH2</strong></p> <p>The Swissprot and PDB data was downloaded in November 2021.<br> Generating PSSH2: We used <a href="https://gwdu111.gwdg.de/~compbiol/uniclust/2021_03/UniRef30_2021_03.tar.gz">UniRef30_2021_03</a> (originally called UniRef30_2021_06) from HH-suite, a database of non-redundant UniProt sequence clusters in which the highest pairwise sequence identity between clusters was 30%. The HHblits code and the code for running the calculations was retrieved from git (https://github.com/soedinglab/hh-suite.git and https://github.com/aschafu/PSSH2.git respectively) at the respective time of calculation in the timeframe until December 2021. <br> </p> <p><strong>PDB based sequence-to-structure alignments</strong></p> <p>In addition to the PSSH2 data, new PDB structures were retrieved based on the primary accession of the proteins, by querying for all chains in all PDB entries with exact matches using the sequence cross references records given in PDB. Sequence-to-structure alignments were then created, again based on information provided in each PDB entry. These are contained in the PDBchain data.</p> <p>This data covers sequences and PDB structures in the timeframe until February 2022. </p> <p> </p> <p><strong>Evaluating PSSH2</strong></p> <p>The resulting alignment data was analysed using CATH domain assignments downloaded from /cath/releases/all-releases/v4_2_0/cath-classification-data/ to define correct hits and false hits: </p> <ul> <li>The set of query sequences is defined by the CATH non-redundant S40_overlap_60 dataset (ftp://orengoftp.biochem.ucl.ac.uk/cath/releases/all-releases/v4_2_0/non-redundant-data-sets/)</li> <li>The set of all expected hits are all pdb structures containing a domain with the same CATH code if contained in the set of processed sequences (-> all) or only if also contained in the set of non redundant sequences (-> nr40).</li> <li>The set of true positives is defined by sharing the same CATH code up to the level of homology ("CATH") or up to the level of topology ("CAT").</li> </ul> <p>The data was evaluated with respect to false discovery rate (FDR) and recall (true positive rate TPR) by cumulatively considering all hits with an E-value below the threshold ("C") or in bins with an E-value between the threshold and one tenth of the threshold ("B"). This evaluation was carried out for the data obtained in November 2021 (202111) as well as previous data from October 2020 (202010), February 2020 (202002) and September 2017 (201709). The results are collected in <a href="https://zenodo.org/api/files/445add84-fcf1-4dfe-b8a1-63dc55f378ee/PSSH%20CATH%20validation.csv?versionId=a5df7473-6efd-442b-b422-3944e9452003">PSSH CATH validation.csv</a>. </p> <p> </p> <p><strong>Known errors</strong></p> <p>Due to processing error, the profile of pdb structure 5fia A / B (sequence md5 052667679fc644184f40063c7602c9e1) is incomplete in the pdb_full hhblits database which led to further errors in generating sequence based alignments for sequences for 1vtm P (sequence md5 c844aff103449363cb8489c78c58ebf1) and 434t A / B (sequence md5 d67aa1c3a36492c719cb48b5e7ecc624).<br> <br> </p>
SARS-CoV-2 RNA levels in Scotland's wastewater
<p>Nationwide, wastewater-based monitoring was newly established in Scotland to track the levels of SARS-CoV-2 viral RNA shed into the sewage network, during the COVID-19 pandemic. We present a curated, reference data set produced by this national programme, from May 2020 to February 2022.</p> <p>Viral levels were analysed by RT-qPCR assays of the N1 gene, on RNA extracted from wastewater sampled at 122 locations. Locations were sampled up to four times per week, typically once or twice per week, and in response to local needs.</p> <p>These wastewater data are contributing to estimates of disease prevalence and the viral reproduction number (R) in Scotland and in the UK.</p> <p>We report sampling site locations with geographical coordinates, the total population in the catchment for each site, and the information necessary for data normalisation, such as the incoming wastewater flow values and ammonia concentration, when these were available. The methodology for viral quantification and data analysis is briefly described, with links to detailed protocols online. Check the README for details and the project <a href="https://biordm.github.io/COVID-Wastewater-Scotland/">COVID-WW Website</a></p>
Effects of Mouthrinsing and Gargling to CT Values of SARS CoV-2 DATASET
<p><strong>Background:</strong> Coronavirus disease 2019 can spread rapidly. Surgery in the oral cavity poses a high risk of transmission of severe acute respiratory syndrome coronavirus 2. The American Dental Association and the Centers for Disease Control and Prevention recommend the use of mouthwash containing 1.5% hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) or 0.2% povidone iodine (PI) to reduce the viral load in the upper respiratory tract and decrease the risk of transmission. The aim of the present study was to analyze the effect of mouthrinsing and gargling with mouthwash containing 1% PI, 0.5% PI, 3% H<sub>2</sub>O<sub>2</sub>, or 1.5% H<sub>2</sub>O<sub>2</sub> and water on the cycle threshold (CT) value.</p> <p><strong>Methods:</strong> In total, 69 subjects recruited from Persahabatan General Hospital who met the inclusion criteria were randomly assigned to one of four treatment groups or the control group. The subjects were instructed to gargle with 15 mL of mouthwash for 30 s in the oral cavity followed by 30 s in the back of the throat three times per day for 5 days. CT values were collected on postprocedural days 1, 3, and 5.</p> <p><strong>Results:</strong> The results of the Friedman test significantly differed among the groups. The CT values increased from baseline (day 0) to postprocedural days 1, 3, and 5.</p> <p><strong>Conclusions:</strong> Mouthrinsing and Gargling with mouthwash containing 1% PI, 0.5% PI, 3% H<sub>2</sub>O<sub>2</sub>, or 1.5% H<sub>2</sub>O<sub>2</sub> and water increased the CT value.</p> <p><strong>Background:</strong> Coronavirus disease 2019 can spread rapidly. Surgery in the oral cavity poses a high risk of transmission of severe acute respiratory syndrome coronavirus 2. The American Dental Association and the Centers for Disease Control and Prevention recommend the use of mouthwash containing 1.5% hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) or 0.2% povidone iodine (PI) to reduce the viral load in the upper respiratory tract and decrease the risk of transmission. The aim of the present study was to analyze the effect of mouthrinsing and gargling with mouthwash containing 1% PI, 0.5% PI, 3% H<sub>2</sub>O<sub>2</sub>, or 1.5% H<sub>2</sub>O<sub>2</sub> and water on the cycle threshold (CT) value.</p> <p><strong>Methods:</strong> In total, 69 subjects recruited from Persahabatan General Hospital who met the inclusion criteria were randomly assigned to one of four treatment groups or the control group. The subjects were instructed to gargle with 15 mL of mouthwash for 30 s in the oral cavity followed by 30 s in the back of the throat three times per day for 5 days. CT values were collected on postprocedural days 1, 3, and 5.</p> <p><strong>Results:</strong> The results of the Friedman test significantly differed among the groups. The CT values increased from baseline (day 0) to postprocedural days 1, 3, and 5.</p> <p><strong>Conclusions:</strong> Mouthrinsing and Gargling with mouthwash containing 1% PI, 0.5% PI, 3% H<sub>2</sub>O<sub>2</sub>, or 1.5% H<sub>2</sub>O<sub>2</sub> and water increased the CT value.</p>
Image-based & machine learning-guided multiplexed serology test for SARS-CoV-2
<p>Single-cell extracted imaging features created in project "Image-based & machine learning-guided multiplexed serology test for SARS-CoV-2". The dataset includes train (with annotations) and test features used in the manuscript. Four SARS-CoV-2 antigens (S, N, R, M) were imaged separately with serum samples presenting IgG, IgA and IgM antibodies.</p>
Zellige example dataset: primary culture of human bronchial cells infected by SARS-CoV-2
<p>A human bronchial epithelium was infected by SARS-CoV-2. The specimen was imaged four days post-infection. The z-stack image encompasses the very irregular epithelium surface. It was acquired with a point scanning microscope (Zeiss LSM700) equipped with Zeiss Plan-Apochromat 63x lens (NA=1.4). Pixel size 0.110µm, z step 0.4µm. This dataset contains both the ground-truth height map and the height map generated with Zellige. The Zellige parameters used are: <span class="math-tex">\(T_{A}=23, T_{otsu}=16, S_{min}=5, \sigma_{xy}=4, \sigma_{z}=1, T_{OSE1}=0.9, R_{1}=5, C_{1}=0.9, T_{OSE2}=0.1, R_{2}=5, C_{2}=0.8.\)</span>.</p> <p>Nota: to compare the ground truth height map with the Zellige height map, one first needs to substrat 1 to all values of the Zellige height map.</p> <p>See the related paper:<br> <a href="https://hal-pasteur.archives-ouvertes.fr/pasteur-03319522">https://hal-pasteur.archives-ouvertes.fr/pasteur-03319522</a></p> <p>See the accompanying paper: Extracting multiple surfaces from 3D microscopy images in complex biological tissues with the Zellige software tool. Trébeau <em>et al.</em> 2022: <a href="https://doi.org/10.1101/2022.04.05.485876">https://doi.org/10.1101/2022.04.05.485876</a></p> <p> </p>
data set to bioRxiv preprint 'Persistent cross-species SARS-CoV-2 variant infectivity predicted via comparative molecular dynamics simulation
<p>This is supporting data and software code for the following preprint in bioRxiv</p> <p><strong>Persistent cross-species SARS-CoV-2 variant infectivity predicted via comparative molecular dynamics simulation</strong></p> <p>https://www.biorxiv.org/content/10.1101/2022.04.18.488629v1</p>
Analysis of the interacting residues between wild type SARS-CoV-2 spike protein and natural ligand hACE2, as well as three engineered alternative ligands
<p>The analysis of residue interactions between the SARS-CoV-2 spike protein and its natural (hACE2 <sup>1</sup>) and engineered binders P17 Fab <sup>2</sup>, Ty1 VHH <sup>3</sup> and LCB1 peptide <sup>4</sup> reveals that glutamine, serine and especially tyrosine residues on the ligand side are more frequent and influence spike binding efficiency, and that spike residues Glu484, Phe486, Tyr489 and Gln493 are more recurrent targets for interactions with ligands. The list of residues establishing contacts between the wild type structure of the SARS-CoV-2 spike protein and the binders defined above are described in Table 1. In Figure 1, the frequency and type of amino acids that interact with each spike residue is illustrated.</p>
SAR Stack of Pichincha volcano in Ecuador, from Sentinel-1
<p>A stack of Coregistered SLCs on Pichincha volcano, Ecuador</p> <p>Sensor: Sentinel-1 Descending track 142</p> <p>Time: 2016.04.19 - 2018.12.28, 46 acquisitions</p> <p>Processor: ISCE/topsStack</p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MiaplPy">MiaplPy</a>.</p>
Identifying and profiling structural similarities between Spike of SARS-CoV-2 and other viral or host proteins with Machaon - Pre-computed features for replication
<p>Machaon's computed features that were used in the structural comparisons with Spike protein.</p> <p>DATA_PDBS_vir_whole_1-3.zip files are parts of a single folder.</p> <p> </p>
ScienceDex guides
Understand access before you commit
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.