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2,667 results for “Prevalence”
Prevalence and determinants of cardiovascular risk factors in Lesotho: a population-based survey
<p>These are pseudo-anonymised data from the ComBaCaL survey and belong to the manuscript "Prevalence and determinants of cardiovascular risk factors in Lesotho: a population-based survey" which can be found at <a href="https://doi.org/10.1093/inthealth/ihad058">https://doi.org/10.1093/inthealth/ihad058</a>. </p> <p>The data dictionary explains the critical data available in the dataset. Between November 2021 and August 2022 , 6061 participants over 18 years old were visited in their households in two districts of Lesotho. </p>
Data from: Spatial and host-related variation in prevalence and population density of wheat curl mite (Aceria tosichella) cryptic genotypes in agricultural landscapes
<p><strong>Filename: coord.csv</strong></p> <p>Names of the sampling locations and their geographic coordinates.</p> <ol> <li>Name - sampling locality identifier</li> <li>Lat - latitude</li> <li>Long - longitude</li> </ol> <p> </p> <p><strong>Filename: lineages.csv</strong></p> <ol> <li>id.sample - sample identifier</li> <li>host - host species (Arrela=<em>Arrhenantherum elatius</em>, Avesat=<em>Avena sativa</em>, Broine=<em>Bromus inermis</em>, Elyres=<em>Elymus repens</em>, Horvul=<em>Hordeum vulgaris</em>, Seccer=<em>Secale cereale</em>, Triaes=<em>Triticum aestivum</em>, Tririm=<em>Triticale rimpaui</em></li> <li>x, y - geodetic coordinates</li> <li>stems - no. of stems in a sample</li> <li>leaves - no. of leaves in a sample</li> <li>MT.01 to MT.27 - no. of mites belonging to each genetic lineage</li> </ol>
Prevalent trends in realized probability of occurrence of main European forest tree species for 2000–2020
<p>High resolution maps resulting from a trend analysis conducted for the period 2000–2020 on the probability of occurrence maps prepared by <a href="https://doi.org/10.7717/peerj.13728">Bonannella et al. (2022)</a>. For this analysis we selected the realized distribution time series layers at 30m spatial resolution for 6 out of 16 species described in the mentioned publication:</p> <ul> <li>Silver fir (<em>Abies alba </em>Mill.)</li> <li>European beech (<em>Fagus sylvatica </em>L.)</li> <li>Norway spruce (<em>Picea abies </em>L.)</li> <li>Black pine (<em>Pinus nigra </em>J. F. Arnold)</li> <li>Scots pine (<em>Pinus sylvestris </em>L.)</li> <li>Common oak (<em>Quercus robur </em>L.)</li> </ul> <p>The trend analysis was conducted per pixel on each of these species individually. We fitted simple OLS regression models with the probability of occurrence as the dependent variable and time as the independent variable. After the model fitting, we also calculated the t-test statistics to determine the presence of an increasing (positive) or decreasing (negative) trend or no trend at all.</p> <p>By combining the regression slope coefficient (<em>β</em>) and the <em>p</em>-value from the t-test statistics we assigned each pixel to one of three classes:</p> <ul> <li><em>positive</em>: <em>β</em> > 0.25 AND <em>p</em>-value < 0.05</li> <li><em>negative</em>: <em>β</em> < −0.25 AND <em>p</em>-value < 0.05</li> <li><em>no trend / stable</em>: −0.25 ≤ <em>β</em> ≥ 0.25 OR <em>p</em>-value > 0.05</li> </ul> <p>We then aggregated the resulting classes at 1km resolution maps to capture the prevalent trend in probability of occurrence over a certain area. Files are named according to the following naming convention, e.g.:</p> <ul> <li>veg_abies.alba_slope_30m_0..0cm_epsg3035_v1.0</li> </ul> <p>with the following fields:</p> <ul> <li>theme: e.g. <strong>veg</strong>,</li> <li>species code: e.g. <strong>abies.alba</strong>,</li> <li>variable name: e.g. <strong>slope</strong>,</li> <li>resolution in meters e.g. <strong>30m</strong>,</li> <li>reference depths (vertical dimension): e.g. <strong>0..0cm</strong>,</li> <li>coordinate system: e.g. <strong>epsg3035</strong>,</li> <li>data set version: e.g. <strong>v1.0</strong>.</li> </ul> <p>For each species here we provide the following layers:</p> <ul> <li>veg_abies.alba_<strong>slope</strong>:<strong> </strong>slope coefficient (scaling factor: 10000)</li> <li>veg_abies.alba_<strong>pvalue</strong>:<strong> </strong><em>p</em>-value (scaling factor: 1000)</li> <li>veg_abies.alba_<strong>pos.trends_30m</strong>: pixels classified as <em>positive </em>on the original maps at 30m resolution (boolean layer with range 0–100, only the two extremes values are present)</li> <li>veg_abies.alba_<strong>pos.trends_1km</strong>: proportion of pixels of the <em>positive </em>class over a 1×1 km area (range 0–100)</li> <li>veg_abies.alba_<strong>neg.trends_30m</strong>: pixels classified as <em>negative </em>on the original maps at 30m resolution (boolean layer with range 0–100, only the two extremes values are present)</li> <li>veg_abies.alba_<strong>neg.trends_1km</strong>: proportion of pixels of the <em>negative </em>class over a 1×1 km area (range 0–100)</li> <li>veg_abies.alba_<strong>no.trends_30m</strong>: (pixels classified as <em>no trend / stable </em>on the original maps at 30m resolution (boolean layer with range 0–100, only the two extremes values are present)</li> <li>veg_abies.alba_<strong>no.trends_1km</strong>:<strong> </strong>proportion of pixels of the <em>no trend / stable </em>class over a 1×1 km area (range 0–100)</li> </ul> <p>Files are provided as GeoTIFFs and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in <em>QML</em> format</p> <p>A publication describing, in detail, all processing steps is currently in review. See at:<br> <br> Bonannella, C., Parente, L., de Bruin, S. and Herold, M. (2023). Multi-decadal trend analysis and forest disturbance assessment of European tree species: concerning signs of a subtle shift, PREPRINT (Version 1) available at Research Square [<a href="https://doi.org/10.21203/rs.3.rs-3288937/v1">https://doi.org/10.21203/rs.3.rs-3288937/v1</a>]</p> <p> </p>
Prevalence of Creative Commons licenses in the Directory of Open Access Journals by discipline, author fees, number of journals per country and publisher
<p>An analysis on the prevalence of Creative Commons licenses in the Directory of Open Access Journals by discipline, author fees, country and publisher according to the number of journals.</p>
BeBOD estimates of incidence, prevalence, and years lived with disability for 57 cancer types, 2004-2021
<p><strong>Belgian National Burden of Disease Study</strong></p><p><strong>Estimates of the morbidity burden of disease for 57 cancer sites</strong></p><p><i>Incidence</i></p><p>Data on new cancer cases in Belgium are collected by the <a href="https://kankerregister.org/Annual%20Tables">Belgian Cancer Registry</a> (BCR). For the current study, we selected 80 ICD-10 (C00.0-96.9 and chronic myeloid neoplasms) codes resulting in 57 cancer sites. Data were extracted by year (from 2004 to 2021), age group (5-years), sex and region (N=3). We excluded "Respiratory system and intrathoracic organs, NOS (not otherwise specified)" from further analyses because of too few cases.</p><p><i>Prevalence</i></p><p>Prevalence estimates were estimated using the above-described incidence estimates and the survival estimates also provided by BCR, derived from linkage with the Belgian Crossroads Bank for Social Security. We used a 10-year prevalence perspective meaning that from the year 2013 onwards, we were able to define the prevalence in a given year as the sum of person-months spent in the different health states. Specifically, we used a microsimulation approach to simulate future health states for each year-, age-, sex-, region- and cancer-specific cohort of incident cases.</p><p>See for more details: <a href="https://doi.org/10.1186/s12885-021-09109-4">https://doi.org/10.1186/s12885-021-09109-4</a></p><p><i>Years Lived with Disability</i></p><p>Years Lived with Disability (YLDs) were calculated using both an incidence and prevalence perspective as a measure of morbidity. YLDs are calculated as the product of the number of prevalent cases with the disability weight (DW), averaged over the different health states of the disease. The DWs reflect the relative reduction in quality of life, on a scale from 0 (perfect health) to 1 (death). We calculate YLDs using the Global Burden of Disease DWs.</p>
BeBOD estimates of mortality, years of life lost, prevalence, years lived with disability, and disability-adjusted life years for 38 causes, 2013-2020
<p><strong>Belgian National Burden of Disease Study</strong></p> <p><strong>Estimates of the burden of disease</strong></p> <p><em>Causes of death</em></p> <p>Our estimates are based on the official causes of death database compiled by <a href="https://statbel.fgov.be/en/themes/population/mortality-life-expectancy-and-causes-death/causes-death">Statbel</a>. We first map the ICD-10 codes of the underlying causes of death to the Global Burden of Disease cause list, consisting of 131 unique causes of deaths. Next, we perform a probabilistic redistribution of ill-defined deaths to specific causes, to obtain a specific cause of death for each deceased person.</p> <p><em>Years of Life Lost</em></p> <p>In addition to counting the number of deaths, we also calculate Years of Life Lost (YLLs) as a measure of premature mortality. YLLs correspond to the life expectancy at the age of death, and therefore give a higher weight to deaths occurring at younger ages. We calculate YLLs using the Global Burden of Disease reference life table, which represents the theoretical maximum number of years that people can expect to live.</p> <p><em>Prevalence</em></p> <p>Our estimates are based on the GBD cause list for morbidity by <a href="https://www.healthdata.org/">IHME</a>. We first select for each of the 38 causes, the most suitable local data source as described in the <a href="https://www.sciensano.be/en/biblio/belgian-national-burden-disease-study-guidelines-calculation-dalys-belgium-2">protocol</a>. Next, we calculate the prevalence by year, region, age, and sex, to obtain a prevalence for each of the included diseases.</p> <p><em>Years Lived with Disability</em></p> <p>In addition to calculating the number of prevalent cases, we also calculate Years Lived with Disability (YLDs) as a measure of morbidity. YLDs are calculated as the product of the number of prevalent cases with the disability weight (DW), averaged over the different health states of the disease. The DWs reflect the relative reduction in quality of life, on a scale from 0 (perfect health) to 1 (death). We calculate YLDs using the Global Burden of Disease DWs.</p> <p><em>Disability-Adjusted Life Years</em></p> <p>Disability-Adjusted Life Years (DALYs) are a measure of overall disease burden, representing the healthy life years lost due to morbidity and mortality. DALYs are calculated as the sum of YLLs and YLDs for each of the considered diseases.</p>
Estimated prevalence of chronic hepatitis B in Denmark on December 31, 2016 – an update based on nationwide registers
<p>Anonymised dataset analysed in the study "Estimated prevalence of chronic hepatitis B in Denmark on December 31, 2016 – an update based on nationwide registers". </p>
Prevalence and Characterization of Asymptomatic Thyroid Nodules in Assin North District, Ghana
<p><strong>Study design and sampling </strong></p> <p>The study was cross-sectional involving six (6) communities in the Assin North District of the Central Region of Ghana. Ethical approval was obtained from the Institutional Review Board of the University of Cape Coast, Ghana, with this reference number: UCCIRB/EXT/2017/18. All community entry protocols with local authorities were observed before the study commenced. Further, protocol involving informed consent was also duly observed during and after this study. Both verbal and written consent were obtained from each participant prior to participation. Participation in the study was strictly on voluntary basis.</p> <p>Each of these six (6) communities was considered a stratum in which all households were listed to constitute a sampling frame from which the respective number of households in each community were sampled using systematic random sampling technique. The sampling interval (<em>K</em><sup>th</sup>) in each community was determined by dividing the total number of listed households (N) by the number of households respectively require (n) (based on proportion-to-population size) as shown in Table 1. The simple random sampling technique was then employed to select the first household (<em>i</em><<em>K</em>) in each community, from which every <em>K</em><sup>th</sup> household was selected until the expected number of households in each community was met. One eligible consenting participant in each selected household was then randomly selected for the study. In a few instances where there were no consenting or eligible participant, the next household on the roll was considered.</p> <p>Exclusion criteria included participants with anterior neck swelling or clinical evidence of thyroid disease, smokers, persons on lithium, phenytoin, oral contraceptive drugs, and women during menstruation, pregnant women or women who had delivered within the last 12 months and persons with any systemic disorder</p> <p><strong>Data collection</strong></p> <p>Data collection was conducted in July, 2019 in two phases. The first phase consisted of face-to-face interviews with participants using a structured interview guide to elicit socio-demographic information such as age, sex, marital status, and highest level of education; history dietary salt intake (often intake- at least 5g or one teaspoon of iodized salt per day and not often intake- less than 5g or one teaspoon of iodized salt per day or not at all); and history of alcohol intake (yes- consumed alcohol regardless of quantity and no- had not consume alcohol before). Anthropometric measurements of body weight (kg) and height (cm) were measured using standard anthropometric techniques and further computed to generate measures of body surface area (BSA) and body mass index (BMI). Data collection in the phase was conducted by six (6) trained research assistants from UCCSMS and duly supervised by key investigators (listed authors) of the study.</p> <p>The second phase of the data collection mainly focused on diagnostic imaging of the thyroid gland by a specialist radiologist with over five (5) working experience in thyroid examination using various imaging technologies. A screening center was staged in each of the study communities on different days while ensuring that such days did not conflict with market days or other important community events. Participants who were interviewed at the household level were given an identification chit to present with to the screening stage for easy synchronization of their interview data with thyroid data. Given that ultrasound has been recognized as the initial imaging modality of choice for the early detection of thyroid nodules [2,9,22,23], a real-time ultrasound scanner (MEDISON SA8000SE-MAI, 1003 Dachi-Dong, Gangnam-Gu, Seoul Korea) with a 7.5 MHz, 50 mm linear transducer was used in examining the thyroid gland of study participants.</p> <p>Participants were examined while in a supine position with hyperextended cervical spine. Ultrasound gel was applied over the thyroid area with the transducer directly placed on the skin over the thyroid gland. Longitudinal and transverse scans were performed, to obtain length and width in centimeters, of each thyroid nodule. If there were multiple nodules in a single thyroid lobe only the dimensions of the largest were recorded. Documented characteristics of thyroid nodules included the location of nodules in the thyroid lobe; number of nodules (solitary or multiple), nodule composition (cyst, solid or mixed), calcifications, and nodule size (length and width in centimeters). Out of the 343 participants interviewed in the initial phase of the data collection, 23 participants failed showed up for the thyroid screening in the second phase despite countless attempts to contact them. Hence the current study is based on 320 participants who were successfully interviewed and screened.</p> <p><strong>Statistical analysis</strong></p> <p> The data was captured using SPSS and later exported to STATA 11.0 for further management and analysis. A protocol was designed from the outset for imputing, ensuring data quality and preserving of data for reuse. Descriptive statistics including frequencies, percentages, means and standard deviation were used to summarize participants’ socio-demographic and thyroid characteristics. Bivariate and multivariate logistic regression analyses were conducted to determine factors associated with ATN. Odds ratios and corresponding confidence intervals were reported with statistical significance at p < 0.05</p>
COMPREHENSIVE LIVESTOCK HEALTH PROGRAM: TARGETED TREATMENT AND HOLISTIC INTERVENTIONS FOR MAJOR PREVALENT DISEASES IN THE LIVESTOCK FARMING COMMUNITY OF DAYNILE DISTRICT, MOGADISHU, SOMALIA.
<p>The general objective of this project was to intervene with the most common livestock diseases in Dayniile district by carrying out a comprehensive campaign for treatment and control. The specific objectives consisted of a treatment campaign, improving infrastructure for establishing disinfectant foot dips and hand washing points, providing disinfectant tools, and finalising community engagement and education by doing training at the farm level.<br>The team visited different donors and added their contribution. After collecting sufficient funds from various sources, the team began the procurement of the necessary materials. This included purchasing veterinary drugs and supplies from local pharmacies and other essentials like stationery. The first activity was treatment campaigns, which were a central aspect of the project. Over 290 animals were treated for various diseases and conditions. The farm manager was informed of the diagnoses, and upon receiving their permission, the appropriate treatments were administered. The second intervention action was a vaccination campaign. The team vaccinated a total of 70 animals against clostridial bacteria, which is one of the most common camel diseases encountered in the area. The third intervention was the establishment of biosecurity facilities at select livestock farms. Among all the farms involved in the project, five were chosen for the provision of enhanced biosecurity measures. These measures included the installation of foot dips and teat dips. The fourth activity was educating livestock farmers on strategies for controlling and preventing livestock diseases. The training was held at Beder Camel Dairy Farm and attended by approximately 10 individuals, comprising 3 females and 7 males. The content of the training was three modules: the first was general farm biosecurity, the second was operational biosecurity, and the third was concern for vaccination. Recommendation: We recommend that each farm hire livestock health specialists to easily implement disease prevention steps and promptly solve each new case.<br> We recommend the livestock association, veterinary clinics, and other institutions working on livestock do routine campaigns that facilitate the determination of prevalent diseases and the treatment of those cases</p>
Population size, HIV prevalence, and antiretroviral therapy coverage among key populations in sub-Saharan Africa: collation and synthesis of survey data 2010-2023
<p>This dataset contains surveillance study estimates for population size, HIV prevalence, and ART coverage among female sex workers (FSW), men who have sex with men (MSM), people who inject drugs (PWID), and transgender men and women (TGM/W) from 2010-2023. It was created to support the UNAIDS Estimates Key Population Workbook for use by HIV estimates teams in sub-Saharan Africa. Key population surveillance reports, including Ministry of Health-led biobehavioural surveys, mapping studies, and academic studies were used to populate the database.</p> <p>The dataset was populated using existing key population size estimate databases including:</p> <ul> <li>UNAIDS Key Population Atlas</li> <li>US Centers for Disease Control and Prevention surveillance database</li> <li>Global Fund against HIV/AIDS, TB, and Malaria surveillance database</li> <li>Global.HIV database</li> <li>Systematic review databases among MSM (<a href="https://pubmed.ncbi.nlm.nih.gov/31601542/" target="_blank" rel="noopener">Stannah et al, 2019</a> and <a href="https://pubmed.ncbi.nlm.nih.gov/37453439/" target="_blank" rel="noopener">Stannah et al., 2023</a>) and PWID (<a href="https://pubmed.ncbi.nlm.nih.gov/36996857/" target="_blank" rel="noopener">Degenhardt et al., 2023</a>)</li> </ul> <p><br>and was additionally supplemented by a literature review of peer-reviewed and grey literature sources.</p> <p>The data can be <a href="https://shiny.dide.ic.ac.uk/kp-data/" target="_blank" rel="noopener">explored in this web application</a> and the <a href="https://www.medrxiv.org/content/10.1101/2022.07.27.22278071v3" target="_blank" rel="noopener">accompanying manuscript can be found here</a></p>
Supplementary dataset for the publication "Prevalence of tick-borne bacterial pathogens in Germany – has the situation changed after a decade?"
<p>The dataset supplements the journal article "Prevalence of tick-borne bacterial pathogens in Germany – has the situation changed after a decade?" published by Katja Mertens-Scholz, Bernd Hoffmann, Jörn M. Gethmann, Hanka Brangsch, Mathias W. Pletz and Christine Klaus in the journal mdpi microorganisms (DOI: <a href="https://doi.org/10.3389/fcimb.2024.1429667">https://doi.org/10.3389/fcimb.2024.1429667 </a><a>)</a></p> <p>The file "Tick samples" contains all data regarding time, location and detected pathogens". The files "Rickettsia sequences" and Borrelia sequences" contains all sequence information of positive samples. The file "Rickettsia reference genes" contains information of used reference genes for phylogenetic analysis.</p>
BeBOD estimates of mortality, years of life lost, prevalence, years lived with disability, and disability-adjusted life years for 38 causes, 2013-2021
<p><strong>Belgian National Burden of Disease Study</strong></p> <p><strong>Estimates of the burden of disease</strong></p> <p><em>Causes of death</em></p> <p>Our estimates are based on the official causes of death database compiled by <a href="https://statbel.fgov.be/en/themes/population/mortality-life-expectancy-and-causes-death/causes-death">Statbel</a>. We first map the ICD-10 codes of the underlying causes of death to the Global Burden of Disease cause list, consisting of 131 unique causes of deaths. Next, we perform a probabilistic redistribution of ill-defined deaths to specific causes, to obtain a specific cause of death for each deceased person.</p> <p><em>Years of Life Lost</em></p> <p>In addition to counting the number of deaths, we also calculate Years of Life Lost (YLLs) as a measure of premature mortality. YLLs correspond to the life expectancy at the age of death, and therefore give a higher weight to deaths occurring at younger ages. We calculate YLLs using the Global Burden of Disease reference life table, which represents the theoretical maximum number of years that people can expect to live.</p> <p><em>Prevalence</em></p> <p>Our estimates are based on the GBD cause list for morbidity by <a href="https://www.healthdata.org/">IHME</a>. We first select for each of the 38 causes, the most suitable local data source as described in the <a href="https://www.sciensano.be/en/biblio/belgian-national-burden-disease-study-guidelines-calculation-dalys-belgium-2">protocol</a>. Next, we calculate the prevalence by year, region, age, and sex, to obtain a prevalence for each of the included diseases.</p> <p><em>Years Lived with Disability</em></p> <p>In addition to calculating the number of prevalent cases, we also calculate Years Lived with Disability (YLDs) as a measure of morbidity. YLDs are calculated as the product of the number of prevalent cases with the disability weight (DW), averaged over the different health states of the disease. The DWs reflect the relative reduction in quality of life, on a scale from 0 (perfect health) to 1 (death). We calculate YLDs using the Global Burden of Disease DWs.</p> <p><em>Disability-Adjusted Life Years</em></p> <p>Disability-Adjusted Life Years (DALYs) are a measure of overall disease burden, representing the healthy life years lost due to morbidity and mortality. DALYs are calculated as the sum of YLLs and YLDs for each of the considered diseases.</p>
Prevalence of Multimorbidity among Urban–Rural Older Adults in Mongolia: A Cross-Sectional Study
<p>A face-to-face, questionnaire-based cross-sectional study was conducted with 800 valid participants aged ≥60 years in Mongolia from June to September 2023.</p>
Tissue heterogeneity is prevalent in gene expression studies
<p>This archive contains results associated with the publication</p> <p><em>Tissue heterogeneity is prevalent in gene expression studies. Gregor Sturm, Markus List and Jitao David Zhang.</em></p> <p> </p> <ul> <li>expr.tissuemark.affy.roche.symbols.gmt: The tissue signatures from the BioQC publication used in this study</li> <li>gtex_v6_gini_solid.gmt: The cross-platform cross-species validated tissue signatures produced in this study</li> <li>heterogeneity_results.tsv.gz: Signature scores and heterogeneity calls for each tested signature</li> <li>heterogeneity_fractions.tsv: Fraction of heterogeneous and severely heterogeneous samples per tissue</li> </ul>
Field data for: Enterovirus sequence data obtained from primate samples in Central Africa suggest a high prevalence of enteroviruses with possible zoonotic potential
<p>Enteroviruses infect humans and animals, can cause disease, and some may be transmitted across species barriers. We collected different types of samples from various species of Central African wildlife, including data on sampling location and tested the samples for the presence of Enterovirus RNA using a family level PCR. Specimen collection was approved by an Institutional Animal Care and Use Committee (IACUC) of the University of California Davis, and the Governments of Cameroon and the Democratic Republic of the Congo. Enterovirus RNA was detected in samples from 17 primates and 2 rodents. Some sequences were very similar while others were dissimilar to known species, highlighting the unexplored enterovirus diversity in wildlife.</p> <p>The samples and filed data were collected by field ecologists as part of the USAID funded PREDICT project (https://ohi.vetmed.ucdavis.edu/programs-projects/predict-project) and screened for enterovirus RNA using consensus PCR. Maps were generated using basic maps from Paintmaps (http://www.paintmaps.com), a free tool for educational and academic use. The dataset contains the metadata on enterovirus screening among wildlife in Cameroon and the Democratic Republic of the Congo from 2003-2014 as part of the USAID funded PREDICT project. Please refer to the article for more information on methods and references.</p>
Literature Datasets for the publication "Systematic Review: Prevalence and Practices of Immunofluorescent Cell Image Processing"
<p>This dataset contains the CSV files returned from PubMed searches used to complete a Systematic Review of Image Processing Publication Practices for methods applied to immunofluorescent images of all CNS cells. <br> <br> The file names are organized "date_supplementarytablenumber" followed by the appropriate search terms. </p>
Extended catalogue of infant and adult gut phageome shows high prevalence of lysogeny
<p>Leveraging metagenomes from the Finnish HELMi birth cohort, a large collection of 6,186 MAGs from infant and adult gut microbiota was obtained and screened for integrated prophages, allowing the identification of 7,165 proviral sequences longer than 10kb. Strikingly, more than 70% of the near-complete MAGs were identified as lysogens. The prevalence of prophages in MAGs varied across bacterial families, with a lower prevalence observed in Coriobacteriaceae, Eggerthellaceae, Veillonellaceae and Burkholderiaceae, while a very high prevalence of lysogen MAGs was observed for Oscillospiraceae, Enterococcaceae, Enterobacteriaceae. Interestingly for several bacterial families such as Bifidobacteriaceae and Bacteroidaceae, the prevalence of proviruses in MAGs was higher in early infant time point (3 weeks and 3 months) than in later sampling points (6 and 12 months) and in adults. The proviral sequences were clustered into 5,616 species-like vOTUs, 77% of which were novel.</p> <p>This repository contains the fasta files for the MAGs collection and the proviral sequences retrieved in this study.</p>
Bird plumage brightness scores and blood parasite prevalence values of North American passerine species
<p>Dataset with bird plumage brightness scores and blood parasite prevalence values for 114 North American passerine host species. One file contains the data table. One file contains a table with descriptions of the columns in the data table.</p> <p>Note: These data were reconstructed from files used in Read & Harvey 1989 (<a href="https://doi.org/10.1038/339618a0">https://doi.org/10.1038/339618a0</a>) with column headings inferred with the help of Read 1991 (<a href="https://doi.org/10.1086/285225">https://doi.org/10.1086/285225</a>).</p>
MCR LTER: Nitrogen source drives differential impacts of nutrients on coral bleaching prevalence, duration, and mortality
Data are from an 18-month field experiment on the fore reef of Moorea, testing how different forms of nitrogen (nitrate vs. urea) impact coral bleaching and mortality during two mild thermal stress events in the Austral summers of 2016 and 2017. These data are associated with a manuscript currently in review at Ecosystems. Tentative mansucript title and author list are: Nitrogen source drives differential impacts of nutrients on coral bleaching prevalence, duration, and mortality Deron E. Burkepile, Andrew A. Shantz, Thomas C. Adam, Katrina S. Munsterman, Kelly E. Speare, Mark C. Ladd, Mallory M. Rice, Shelby McIlroy, Andrew J. Brooks, Russell J. Schmitt, and Sally J. Holbrook These data are part of the NSF project: RAPID: How does nutrient availability alter coral bleaching, mortality, and recovery on Moorea coral reefs? (funded wholly or part by NSF Awards OCE-1619697).
WorldCOM Deliverable 1: Prevalence of ESBL subtypes in bacterial pathogens and a sequence database of selected alleles
<p><strong>OHEJP Project: WorldCOM, Deliverable 1, Work Package 1.</strong></p> <p>This dataset is connected to Work Package 1, Task1 of the WorldCOM consortium grant within the One Health EJP group. The aim was to analyse publicly available sequences for antimicrobial resistance genes associated with <em>Salmonella</em>, <em>Campylobacter</em> and <em>E. coli</em>. For the initial phase of this work package, we have focused on ESBL-related AMR genes. As these genes are absent from <em>Campylobacter</em>, we have not included this bacterium in these analyses, and have used the important pathogens <em>Klebsiella</em> and <em>Acinetobacter</em>. All types and subtypes of Extended Spectrum β-Lactamases (ESBLs) and plasmid-mediated colistin resistance genes have been analysed for frequency among reported and extracted sequences. High frequency resistant genes subtypes have been highlighted for further sequence analysis to illustrate geographic distribution and geographic-specific single nucleotide polymorphisms (SNPs). The data shown are work in progress. </p>
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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.