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668 results for “infectious”
A Twitter Dataset for Spatial Infectious Disease Surveillance
<p>Dengue is a mosquito-borne viral disease which infects millions of people every year, specially in developing countries. Some of the main challenges facing the disease are reporting risk indicators and rapidly detecting outbreaks. Traditional surveillance systems rely on passive reporting from health-care facilities, often ignoring human mobility and locating each individual by their home address. Yet, geolocated data are becoming commonplace in social media, which is widely used as means to discuss a large variety of health topics, including the users' health status. In this dataset paper, we make available two large collections of dengue related labeled Twitter data. One is a set of tweets available through the Streaming API using the keywords dengue and aedes from 2010 to 2016. The other is the set of all geolocated tweets in Brazil during the year of 2015 (available also through the Streaming API). We detail the process of collecting and labeling each tweet containing keywords related to dengue in one of 5 categories: personal experience, information, opinion, campaign, and joke. This dataset can be useful for the development of models for spatial disease surveillance, but also scenarios such as understanding health-related content in a language other than English, and studying human mobility.</p>
Dataset for: Infectious disease responses to human climate change adaptations
<p>Original and derived data products referenced in the original manuscript are provided in the data package.</p> <h3>Description of the data and file structure</h3> <p><em>Original data:</em></p> <p><code>Table_1_source_papers.csv</code>: Papers that met review criteria and which are summarized in Table 1 of the manuscript.</p> <ol> <li><strong>ID</strong>: The paper identification number</li> <li><strong>Topic</strong>: The broad topic (i.e., each row of Table 1)</li> <li><strong>Authors:</strong> The names of the authors of the paper</li> <li><strong>Article Title</strong>: The title of the paper</li> <li><strong>Source Title</strong>: The name of the journal in which the paper was published</li> <li><strong>Abstract</strong>: The paper's abstract, retrieved from the Web of Science search</li> <li><strong>study_type:</strong> Classification of the study methodology/approach. "A" = a designed study that shows effect ,"B" = a pre/post study, "C" = a comparison of health outcomes or pathogen risk relative to a 'control/comparison' area, "D" = some quantitative effect but no control, "E" = qualitative comments but little supporting evidence, and/or a qualitative review.</li> <li><strong>pathogen_broad</strong>: Broad classification of the type of pathogen discussed in the paper.</li> <li><strong>transmission_type</strong>: Categorization of indirect, direct, sexual, vector, or other transmission modes.</li> <li><strong>pathogen_type</strong>: Categorization of bacteria, helminth, virus, protozoa, fungi, or other pathogen types.</li> <li><strong>country:</strong> Country in which the study was performed or results discussed. When countries were not available, regions were used. NA values indicate papers in which a geographic region was not relevant to the study (i.e., a methods-based study).</li> </ol> <p><em>Derived data:</em></p> <p><code>change_livestock_country.csv:</code> A dataframe containing values used to generate Figure 4a in the manuscript.</p> <ol> <li><strong>County Name</strong>: The name of the county in Kenya</li> <li><strong>Sheep and goats 1980</strong>: The estimated number of sheep and goats in 1980</li> <li><strong>Sheep and goats 2016</strong>: The estimated number of sheep and goats in 2016</li> <li><strong>pct_change_shoat</strong>: The percent change in sheep and goat numbers from 1980 to 2016</li> <li><strong>Cattle 1980</strong>: The estimated number of cattle in 1980</li> <li><strong>Cattle 2016</strong>: The estimated number of cattle in 2016</li> <li><strong>pct_change_cattle</strong>: The percent change in cattle numbers from 1980 to 2016</li> <li><strong>Camel 1980</strong>: The estimated number of camels in 1980</li> <li><strong>Camel 2016</strong>: The estimated number of camels in 2016</li> <li><strong>pct_change_camel</strong>: The percent change in camel numbers from 1980 to 2016</li> <li><strong>human_pop 1980</strong>: The estimated human population in the county in 1980</li> <li><strong>human_pop 2016</strong>: The estimated human population in the county in 1980</li> <li><strong>pct_change_human</strong>: The percent change in the human population from 1980 to 2016</li> <li><strong>area_sq_km</strong>: The land area of the county</li> <li><strong>change_ind_per_sq_km_shoat:</strong> Absolute change in number of sheep and goats from 1980 to 2016</li> <li><strong>change_ind_per_sq_km_cattle:</strong> Absolute change in number of cattle from 1980 to 2016</li> <li><strong>change_ind_per_sq_km_camel:</strong> Absolute change in number of camels from 1980 to 2016</li> </ol> <p><code>country_avg_schist_wormy_world.csv</code>: A dataframe containing values used to generate Figure 3 in the manuscript.</p> <ul> <li><strong>Country:</strong> The country in which the schistosome prevalence studies were performed.</li> <li><strong>Latitude:</strong> The latitute in decimal degrees</li> <li><strong>Longitude:</strong> The longitute in decimal degrees</li> <li><strong>Maximum.prevalence:</strong> The mean maximum schistosomiasis prevalence of studies conducted within each country.</li> </ul> <p><code>kenya_precip_change_1951_2020.csv</code>: A dataframe containing values used to generate Figure 4b in the manuscript.</p> <ul> <li><strong>Precipitation (mm):</strong> Binned annual precipitation values</li> <li><strong>1951-1980:</strong> The density of observations for each annual precipitation value for the 1951-1980 period</li> <li><strong>1971-2000:</strong> The density of observations for each annual precipitation value for the 1971-2000 period</li> <li><strong>1991-2020:</strong> The density of observations for each annual precipitation value for the 1991-2020 period</li> </ul> <h3>Sharing/Access information</h3> <p>Data were derived from the following sources:</p> <ul> <li> <p>Ogutu, J. O., Piepho, H.-P., Said, M. Y., Ojwang, G. O., Njino, L. W., Kifugo, S. C., & Wargute, P. W. (2016). Extreme wildlife declines and concurrent increase in livestock numbers in Kenya: What are the causes? <em>PloS ONE</em>, <em>11</em>(9), e0163249. https://doi.org/10.1371/journal.pone.0163249</p> </li> <li> <p>London Applied & Spatial Epidemiology Research Group (LASER). (2023). <em>Global Atlas of Helminth Infections: STH and Schistosomiasis</em> [dataset]. London School of Hygiene and Tropical Medicine. https://lshtm.maps.arcgis.com/apps/webappviewer/index.html?id=2e1bc70731114537a8504e3260b6fbc0</p> </li> <li> <p>World Bank Group. (2023). <em>Climate Data & Projections—Kenya</em>. Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org/country/kenya/climate-data-projections</p> </li> </ul>
Data associated with "Developing a standardized but extendable framework to increase the findability of infectious disease datasets"
<p><strong>Data associated with "Developing a standardized but extendable framework to increase the findability of infectious disease datasets"</strong></p> <p> </p> <p>Includes:</p> <ul> <li>NIAID Dataset schema</li> <li>NIAID ComputationalTool schema</li> <li>Crosswalk between NIAID schemas and common schemas</li> <li>Survey of Schema.org-compliant repositories</li> </ul> <p><br> The open access movement and scientific reproducibility concerns have led the biomedical research community to embrace efforts to make scientific datasets openly accessible. While many datasets are now available, there are still challenges in ensuring that they are Findable, Accessible, Interoperable, and Reusable (FAIR). To improve the FAIRness of datasets, we evaluated dataset repositories for compliance with Schema.org standards – a collection of standards developed to increase metadata searchability across the internet. Adoption of the Schema.org Dataset standard was highly variable in biomedical research datasets, and the standard omitted many desirable metadata fields. We customized the Schema.org Dataset standard to catalog datasets collected across a Systems Biology research consortium consisting of 15 Centers. We developed a reusable process for creating a schema which is interoperable with other standards, but still extendable and customizable to a particular context. Here, we describe our process along with the associated gains in FAIRness, and discuss ongoing challenges with dataset discoverability – the first step to ensure that the vast amount of open data published by the research community is reused to its maximum value.</p>
Social contact patterns relevant for infectious disease transmission in Cambodia
<p>Social contact data from a community-based survey conducted in Cambodia in 2012. </p>
A meta-analysis on global change drivers and the risk of infectious disease database and code
<p>Data and code associated with the manuscript "A Meta-analysis on Global change drivers and the risk of infectious disease".</p>
Identification of infectious agents in early marine Chinook and Coho salmon associated with cohort survival
<p>Recent decades have seen an increased appreciation for the role infectious diseases can play in mass mortality events across a diversity of marine taxa. At the same time many Pacific salmon populations have declined in abundance as a result of reduced marine survival. However, few studies have explicitly considered the potential role pathogens could play in these declines. Using a multi-year dataset spanning 59 pathogen taxa in Chinook and Coho salmon sampled along the British Columbia coast, we carried out an exploratory analysis to quantify evidence for associations between pathogen prevalence and cohort survival, and between pathogen load and body condition. While a variety of pathogens had moderate to strong negative correlations with body condition or survival for one host species in one season, we found that <em>Tenacibaculum maritimum</em> and Piscine orthoreovirus had consistently negative associations with body condition in both host species and seasons, and were negatively associated with survival for Chinook salmon collected in the fall and winter. Our analyses, which offer the most comprehensive examination of associations between pathogen prevalence and Pacific salmon survival to date, suggest that pathogens in Pacific salmon warrant further attention, especially those whose distribution and abundance may be influenced by anthropogenic stressors.</p>
Extended Data Fig. 7 in Bat coronaviruses related to SARS-CoV-2 and infectious for human cells
Extended Data Fig. 7 | Analysis of the inter-subunitshydrogen bondsat the interfaceof RBDandhACE2. Frequencyofformationofhydrogenbondsat theinterface of RBDandhACE2 intheknob (A), base (B), andtipregions (C). Theanalysisisperformedfor 9 different MDsimulations:3 replicatesof the SARS-CoV-2 (shadesof green),BANAL-236 (shadesof red), and BANAL-52/103 (shadesofblue) RBD–hACE2 complexes.
Extended Data Fig. 3 in Bat coronaviruses related to SARS-CoV-2 and infectious for human cells
Extended Data Fig. 3 | Nucleotideandamino-acidalignmentsof thefurin cleavagesiteregion. Completenucleotideandamino-acidspikesequencesof representativebat SARS-CoV-2-likecoronavirusesweredownloadedfrom GenBankand GISAIDandaligned with MAFFT (G-INS-I parameter) (A & C). Alignmentsweremanuallyeditedas proposedby Zhou2 andLytras47 with CLC Main Workbench (Qiagen) (B & D). Alignmentsof thefurincleavageregionare presentedatthenucleotide (A & B) andtheamino-acid (C & D) level, respectively.
Extended Data Fig. 1 in Bat coronaviruses related to SARS-CoV-2 and infectious for human cells
Extended Data Fig. 1 | Spike identity matricesatthe genus level of representativesarbecoviruses. Amino-acid (lower) andnucleotide (upper) identitymatricesof Laotianandrepresentativehuman,bat, andpangolin sarbecoviruses.Spike N-terminal (NTD), Receptor-binding (RBD) and S2 nucleotideandamino-acidsequenceswerealigned with MAFFT, andidentity matriceswereconstructedusing CLCMain Workbench 21.0.4 (Qiagen). Matriceswerecoloredaccordingtotheidentityscale,from 25% (red) to 100% (green) of nucleotideoramino-acididentity.
Extended Data Fig. 2 in Bat coronaviruses related to SARS-CoV-2 and infectious for human cells
Extended Data Fig. 2 | Alignmentofthespike RBDdomain. Protein alignmentof the Receptor Binding Domain (RBD) of Laotianandrepresentative human, batandpangolinsarbecoviruses.Sequenceswerealignedwith MAFFT in G-iNS-Imode. Residuesinteractingwithhuman ACE2 receptorare highlighted ingrey.Thedomainusedforinteractionsmodeling,basedonthe X-raystructure 6M0J (residues T333 to G526), ishighlightedbyablackline.
Fig. 2 in Bat coronaviruses related to SARS-CoV-2 and infectious for human cells
Fig. 2 | Recombinationevents in the evolutionaryhistoryof sarbecoviruses. Representationof the 15 recombinant fragmentsof relevant Sarbecovirus genomescomparedtothe SARS-CoV-2 humanprototypestrain (NC_045512). Thecoordinatesof thebreakpointsrefertothenucleotide positioninthealignment.Wherepossible,theclosestviralsequenceis indicatedforeachfragment.Inothercases,MULTindicatesagroupof multiple sequences.Theasteriskmarksunresolvedfragment phylogeny (fragment 13, from positions 27,344 to 27,800 inthealignment).Sequencesarecolouredas in Fig. 1. Thecomplete phylogeneticanalysesarepresentedin Supplementary Fig.2.
Fig. 1 in Bat coronaviruses related to SARS-CoV-2 and infectious for human cells
Fig. 1 | Genomicdescriptionofbat-bornesarbecovirusesidentifiedin Laos. a, Mapof samplingsites.All BANALisolateswerecollectedfromthesamesite (site 1). Themapwasdownloadedfrom DIVA-GIS (https://www.diva-gis.org/ gdata). b, Phylogeneticanalysisof theproteinsequenceof the RBDof Laotian andrepresentativehuman,bat,andpangolinsarbecoviruses.Sequenceswere alignedwith MAFFT (ref.49) inautomode, andmaximum-likelihood phylogeneticreconstructionwasperformedwith PhyMLimplemented throughthe NGPhylogenyportal50 withthe LG + Gsubstitutionmodel.Branch supportwasevaluatedwiththeaBayes parameter.Batspeciesarespecifiedin thename of thesequences.Sequencesarecolouredaccordingto Fig. 1c. c, Similarityplotanalysisof Laotianandrepresentativebatandpangolin sarbecovirusesbasedonthefull-lengthgenomesequenceof the SARS-CoV-2 humanprototypestrain (NC_045512, Wuhan-Hu-1) usedasareference. Theanalysiswasperformedwiththe SimPlotprogramversion 3.5.1 (ref.51) with the Kimuratwo-parametermodel, awindowsizeof 1,000 basepairs,astepsize of 100 basepairs,a transition/transversionrate (T/t) of 2.0, anda Gap/Strip parameter: on51. nsp,non-structural protein;RdRP, RNA-dependent RNA polymerase.d, Heatmapof identitiesattheproteinlevelof representative human,batandpangolinsarbecovirusescomparedtohuman SARS-CoV-2 lineage B (NC_045512). Spikeproteinhasbeendividedintofunctionaldomains, andthesequencesareorderedaccordingtopercentageof identityof the RBD domain.Theasteriskmarkstheabsenceof afunctional ORF10 in Thaibat RacCS203 (accessionnumber MW251308). Theheatmapwascreatedusingthe gplotspackagein R (version 3.6.3). AA, aminoacid.
Extended Data Fig. 5 in Bat coronaviruses related to SARS-CoV-2 and infectious for human cells
Extended Data Fig. 5 | Additionalanalysisof the MDsimulationsof the BANAL-52/103 RBD–hACE2 complex. Comparisonof thetimeseriesof interface RMSDduringthecourseof two MDsimulationsof the BANAL-52/103 RBD–hACE2 complexwithshort (BANAL-52/103-CoV.1) andlong (BANAL- 52/103-CoV.1*) equilibrationphase.Thelargefluctuationsof theinterface RMSDareduetotheflexibilityof the RBDloopbetweenresidues S443 and Y449 (insets, inyellow).Whentheseresidueswerenotincludedinthe calculationof theinterface RMSD, thetimeseriesdisplayedamorestable behavior (BANAL-52/103-CoV.1-Land BANAL-52/103-CoV.1*-L).
Fig. 4 in Bat coronaviruses related to SARS-CoV-2 and infectious for human cells
Fig. 4 | BANAL-236 entryandpropagationinhumancells. a, Resultsof spike-pseudotyped BANAL-236 (squares) and SARS-CoV-2 (strain BetaCoV/ France/IDF0372/2020, GISAIDaccessionnumber EPI_ISL_406596,diamonds) pseudovirusentryassayin HEK-293T cellsexpressing (purple lines) ornot (grey lines) thehACE2 receptor,showninrelativeluminescenceunits (RLUs) producedbythefireflyluciferasepresentinthelentiviralbackbone andthe Bright-Gloluciferasesubstrate.Asingleexperimentperformedintriplicate representativeof twoexperiments isshown.Centrevaluesrepresentthe averageof thethreereplicatesanderrorbarsindicates.d. b, Resultsof spike-pseudotyped BANAL-236 (black) and SARS-CoV-2 (strain BetaCoV/ France/IDF0372/2020, grey) neutralizationassayexpressedas apercentageof neutralizationofluciferaseactivityintheabsenceof serum.Seraneutralizing SARS-CoV-2 werefrompatientswithconfirmedinfectionswhereas non-neutralizingserasampleswerecollectedbeforethespreadof SARS-CoV-2 Laos.Thedashedlinemarkstheneutralizationthreshold.Asingleexperiment representativeof threeindependentexperimentsisshown. c, Humancelllines expressingendogenous ACE2,Calu-3 (blue lines) and Caco-2 (green lines),were infectedatan MOIof 0.01 withthe BANAL-236 virus (squares) andthevirusfirst detectedin Wuhan (diamonds).VeroE6 cellswereinfectedatan MOI of 0.0001 with BANAL-236 virus (squares) and SARS-CoV-2 (strain BetaCoV/France/ IDF0372/2020, diamonds) pre-incubatedwith (grey lines) orwithout (pink lines) solublehACE2 (sACE2) at 25 Μgml−1 for 30 min. Genomecopynumberwas determinedbyquantitative RT-PCRinthesupernatantsrecovered 3 and 4 days post-infection. Asingleexperimentperformedintriplicateisshown.Centre valuesrepresenttheaverageof thethreereplicatesanderrorbarsindicates.d.
Fig. 3 in Bat coronaviruses related to SARS-CoV-2 and infectious for human cells
Fig. 3 | Dynamicsof thebindingof hACE2 to bat-sarbecovirus-borne RBDs andinsightintothestructureofthecomplex. a, Biolayerinterferometry (BLI) bindinganalysis of thehACE2 peptidasedomaintoimmobilized BANAL-52/103,BANAL-236 or SARS-CoV-2 RBDs.Black linescorrespondto aglobalfitof thedatausinga 1:1 bindingmodel.b, Frequencyofformationof saltbridgesclosetothe RBD–ACE2 interface (fromlefttoright:D30/K417, E35/ K493, D38/K493, K31/E35 and D38/K353) duringthecourseof the MD simulations.Theanalysiswasperformedforninedifferent MDsimulations (threereplicatesforeachcomplex) ofhACE2 incomplexwith RBDs from SARS-CoV-2 (SARS-CoV-2.1,SARS-CoV-2.2 and SARS-CoV-2.3, shadesof green), BANAL-236 (BANAL 236-CoV.1, BANAL 236-CoV.2 and BANAL 236-CoV.3,shadesof red) and BANAL-52/103 (BANAL 52/103-CoV.1, BANAL 52/103-CoV.2 and BANAL 52/103-CoV.3, shadesofblue). c, Ribbon representationsof thecrystalstructuresof thehACE2 peptidase domain (cyan) incomplexwith SARS-CoV-2 (PDB 6M0J) or BANAL-236 (this study,PDB 7PKI) RBDs (pink). Blackarrowsintheoverallstructuresindicatethestructural differencebetweenthetwocomplexesatthelevelofhelix H4. The magnificationsshowthemaininteractionsinthe ACE2–RBD interfaces. Residuesinthereceptor-bindingmotif alteredbetween SARS-CoV-2 and BANAL-236 arehighlighted withcolouredoutlines.Numbers 1, 2 and 3 indicate thethreemainclustersof interactionsbetweenhACE2 and RBDs.
Extended Data Fig. 4 in Bat coronaviruses related to SARS-CoV-2 and infectious for human cells
Extended Data Fig. 4 | Analysisof thestability and conformational heterogeneityofRBD–hACE2 complexes. Timeseries (leftcolumn) and violinplots (rightcolumn) ofbackbone Root Mean Square Deviation (RMSD) fromtheinitial,energy-minimizedmodelcalculatedontheresiduesin RBD (A), hACE2 (B), attheinterface of RBDandhACE2 (C), andontheentirecomplex (D). Intheviolinplots,thewhitecirclecorrespondstothemedianvalue,the blackrectangleextendsfromthefirsttothethirdquantiles,andthethinblack linerepresentsthe 95% confidenceintervals.Populationof the 3 most significantclustersvisitedduringthecourseof the MDsimulations (E). Theanalysisisperformedfor 9 different MDsimulations:3 replicatesof the SARS-CoV-2 (shadesof green),BANAL-236 (shadesof red), and BANAL-52/103 (shadesofblue) RBD–hACE2 complexes.
Extended Data Fig. 8 in Bat coronaviruses related to SARS-CoV-2 and infectious for human cells
Extended Data Fig. 8 | Stick representation. Stick representation of segments D364-S375 of BANAL-236 (left panel) and SARS-CoV-2 (right panel) RBDs.A 2Fo-Fc composite omit map (contoured at 3σ) is shown for this region in BANAL-236 RBD.
Extended Data Fig. 6 in Bat coronaviruses related to SARS-CoV-2 and infectious for human cells
Extended Data Fig. 6 | Estimationof RBD–hACE2 bindingenergy. Time series (leftcolumn) andviolinplots (rightcolumn) of the RBD–hACE2 binding energyestimatedusing ROSETTA (A) and FoldX (B). Intheviolinplots,the whitecirclecorresponds tothemedianvalue,theblackrectangleextendsfrom thefirsttothethirdquantiles,andthethinblacklinerepresentsthe 95% confidenceintervals.Theanalysisisperformedfor 9 different MDsimulations: 3 replicatesof the SARS-CoV-2 (shadesof green), BANAL-236 (shadesof red), and BANAL-52/103 (shadesofblue) RBD–hACE2 complexes.
Extended Data Fig. 9 in Bat coronaviruses related to SARS-CoV-2 and infectious for human cells
Extended Data Fig. 9 | Isolationof BANAL-236 on VeroE6 cells. (A) CPE observedon VeroE64 daysafterinoculationatan MOIof 10-4 fromthe C1. (B) Uninfected VeroE6 cellslayer.(C) Plaqueassayperformedfromthe C2 stock on VeroE6 cells.(D) Comparative CPEobservedon VeroE6 cellsinfectedwith SARS-CoV-2 ('Wuhan', toppanel) or BANAL-236 (bottom panel) inabsence (left) orinpresence (right) of soluble ACE2. Photosweretakenwitha EVOSXLCore microscopeatx10. Asingleexperimentperformedintriplicateisshownfor eachcellline.
Fig. 2 Infected R in Isolation of infectious Theileria parva sporozoites secreted by infected Rhipicephalus appendiculatus ticks into an in vitro tick feeding system
Fig. 2 Infected R. appendiculatus adult ticks feeding on a silicone membrane. a A representation of adult ticks attached to a silicone membrane. b Detection of tick salivary gland infected with T. parva via p104 PCR. Amplicons were visualized in 2% agarose gel
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.