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266 results for “selection analysis”
Criteria for prioritizing selection of Mexican maize landrace accessions for conservation in situ or ex situ based on phylogenetic analysis
<p>Data for processed SSR markers in maize accessions. A database in Structured Query Language (SQL) is provided. Please see the text file "READMEmaizeSSR.pdf".</p>
Figures S1-S7. SMR-HEIDI analysis results for 8q24.21 locus between BP and selected phenotypes.
<p><strong>Supplementary Figures S1-S7. </strong><strong>SMR-HEIDI analysis results for rs6651255 between BP and selected phenotypes.</strong></p> <p>This project contains the following figures:</p> <ul> <li> <p>Figure S1. SMR-HEIDI analysis results for rs6651255 between BP and LDH.</p> </li> <li> <p>Figure S2. SMR-HEIDI analysis results for rs6651255 between BP and <em>GSDMC</em> expression in skeletal muscle (GTEx v6).</p> </li> <li> <p>Figure S3. SMR-HEIDI analysis results for rs6651255 between BP and <em>FAM49B</em> expression in Brain anterior cingulate cortex BA24 (GTEx v6).</p> </li> <li> <p>Figure S4. SMR-HEIDI analysis results for rs6651255 between BP and <em>FAM49B</em> expression in CD8 cell line (CEDAR).</p> </li> <li> <p>Figure S5. SMR-HEIDI analysis results for rs6651255 between BP and heel bone mineral density (UKBB).</p> </li> <li> <p>Figure S6. SMR-HEIDI analysis results for rs6651255 between BP and disc problem phenotype (UKBB)</p> </li> <li> <p>Figure S7. SMR-HEIDI analysis results for rs6651255 between BP and height (UKBB).</p> </li> </ul> <p> </p> <p><strong>Figures legend:</strong></p> <p>Each figure consists of four parts (1 – top left; 2- top right; 3- bottom left; 4 – bottom right):</p> <ol> <li> <p>Regional association plots for GWAS-1 (in our case BP GWAS) and GWAS-2 (expression or complex trait). Blue triangles represent SNPs used to calculate HEIDI test. Crossed triangle is leading SNP for which SMR test was computed.</p> </li> <li> <p>Z-Z plot (GWAS-1 on y-axis and GWAS-2 on x-axis).</p> </li> <li> <p>Visualization of LD matrix for SNPs used in calculation of HEIDI test.</p> </li> <li> <p>Plot of SMR regression coefficient estimates. The plot visualizes the heterogeneity of SMR coefficient. Blue color represents SNPs used to calculate HEIDI test.</p> </li> </ol>
Kin selection explains the evolution of cooperation in the gut microbiota, by Simonet & McNally, 2020, Dataset S1 and codes for statistical analysis and figures production
<p>Dataset S1 contains all raw and processed material referred to in the published article "Kin selection explains the evolution of cooperation in the gut microbiota". R codes files provide all codes to replicate the analysis. Please refer to the README file for a description of all code files. The manifest files are those obtained by accessing the HMP portal on April 2020 under Project > HMP, Body Site > feces, Studies>WGS-PP1, File Type > WGS raw sequences set, File format > FASTQ.</p> <p>We also provide access to these data and codes at our GitHub (https://github.com/CamilleAnna/HamiltonRuleMicrobiome gitRepos.git) which can be cloned to directly re-run this analysis. </p> <p><strong>Legends for Dataset S1:</strong></p> <ul> <li>Sheet 1: Metagenomic samples used and access links.</li> <li>Sheet 2: Reference on bacterial cooperation retrieved from Web of Science search: TI¯((microb* OR bacter* OR microorganis* OR micro-organis*) AND (coop* OR social*)</li> <li>Sheet 3: Retained bacteria cooperation keywords</li> <li>Sheet 4: GOs identified by annotating all MIDAS database genomes (5944 genomes) with PANNZER2.</li> <li>Sheet 5: Full list of potential bacterial cooperation GO terms and description of manual curation decisions.</li> <li>Sheet 6: Final list of bacterial cooperation GO used for the analysis</li> <li>Sheet 7: Genomic diversity of the bacterial population within and across host. Computed from MIDAS snp_diversity.py pipeline.</li> <li>Sheet 8: final dataset for statistical analysis.</li> <li>Sheet 9: per-gene annotation of cooperation.</li> </ul>
Healthcare Expenditure and Demographic Trends: A Comparative Analysis of Selected Countries (2000-2024)
<p><strong><em><span>This research journal investigates the interplay between healthcare expenditure, life expectancy, and demographic characteristics across selected countries from 2000 to 2024. Utilizing quantitative analysis, the study examines healthcare spending as a percentage of GDP, average life expectancy, and the age distribution of populations. For instance, the USA's healthcare expenditure is projected to reach 19.2% of GDP by 2024, with an average life expectancy of 81.9 years. In contrast, Bangladesh's healthcare expenditure is anticipated to be 7.0% of GDP, with a life expectancy of 70.0 years. The findings reveal critical insights regarding the effectiveness and sustainability of healthcare systems, emphasizing the need for policy interventions that prioritize healthcare funding, especially in aging populations where the percentage of individuals aged 65 and older is expected to rise significantly—projected at 16.0% for the USA and 7.0% for Bangladesh in 2024</span></em></strong><strong><span>.</span></strong><strong><span> </span></strong></p>
Selective Attention VR and PC : Data And Analysis
<p><strong>Data and Analysis Repository for</strong></p> <p><strong>Developing Virtual Reality and Computer Screen Experiments One to One Using Selective Attention as a Case Study</strong></p> <p>June 2023, Rasmus Ahmt Hansen and Marta Topor</p> <p>The current repository holds all data and analysis scripts used in the report named above. Data files are saved in .csv format and analysis scripts were written using R and R Markdown.</p> <p>The report preprint can be accessed at:</p> <p>The study aimed to develop a reliable PC control condition for a VR experiment assessing selective attention in grade 0 children.<br> The selective attention task we developed and implemented can be accessed here:</p> <ul> <li>PC : <a href="https://doi.org/10.5281/zenodo.7844487">https://doi.org/10.5281/zenodo.7844487</a></li> <li>VR : <a href="https://doi.org/10.5281/zenodo.7844593">https://doi.org/10.5281/zenodo.7844593</a></li> </ul> <p><strong>Participants</strong></p> <p>73 grade 0 children from Danish primary schools completed the selective attention test in both VR and PC environments. Performance quality was low and thus we only included 19 participants in final analyses. All data, included and excluded, are openly available in this repository.</p> <p><strong>Data</strong></p> <ul> <li>Raw data from the PC condition can be found in the RAW PC folder</li> <li>Raw data from the VR condition can be found in the RAW VR folder</li> <li>Demographic data, anonymised, can be found in the demographics.csv file</li> <li>The final data from the 19 participants included in statistical analyses can be found in the final_data_set.csv file</li> </ul> <p><strong>Analysis</strong></p> <ul> <li>Demographic analyses can be found in the demographics.R file</li> <li>Data processing, quality control and statistical analyses can be found in the full_analysis_script.Rmd</li> <li>The plots folder holds plots used in the study report</li> </ul>
VESPA: Analysis of selected CPTAC datasets
<p>This is a supplemental dataset to the VESPA manuscript "<a href="https://doi.org/10.1101/2023.02.15.528736">Network-based elucidation of colon cancer drug resistance by phosphoproteomic time-series analysis</a>". VESPA was used to reconstruct signaling networks and to conduct kinase/phosphatase activity inference for selected subsets of the following CPTAC datasets:</p> <ul> <li><a href="https://pubmed.ncbi.nlm.nih.gov/31031003/">CPTAC S037 (COAD)</a></li> <li><a href="https://pubmed.ncbi.nlm.nih.gov/31675502/">CPTAC S044 (ccRCC)</a></li> <li><a href="https://pubmed.ncbi.nlm.nih.gov/32649874/">CPTAC S046 (LUAD)</a></li> <li><a href="https://pubmed.ncbi.nlm.nih.gov/33242424/">CPTAC S047 (PBT)</a></li> <li><a href="https://pubmed.ncbi.nlm.nih.gov/31585088/">CPTAC S049 (HBV-HCC)</a></li> <li><a href="https://pubmed.ncbi.nlm.nih.gov/34358469/">CPTAC S058 (LSCC)</a></li> <li><a href="https://pubmed.ncbi.nlm.nih.gov/34534465/">CPTAC S061 (PDAC)</a></li> </ul>
Data from: Genome-wide selection components analysis in a fish with male pregnancy
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PanDDA analysis of SP100 screened against selection of Maybridge Fragment Library (HTML Summary)
<p>Interactive summary page for "PanDDA analysis of SP100 screened against selection of Maybridge Fragment Library".</p> <p><strong>Please click on "0_index.html" in the "Files" section to open the interactive summary.</strong></p> <p>All datasets are also available as combined zip files from https://zenodo.org/record/48771 .</p>
Proteomic data sets after selecting mitochondrial proteins from the scaffold software for Ingenuity Pathway analysis (IPA Qiagen)
<p>List of fold change proteomic data sets of dFCM- 39 vs. 12Day and105 vs. 12Day, cFCM- 40 vs. 12Day and115 vs. 12Day , mouse heart 90 vs. 1 day after selecting mitochondrial proteins from the scaffold software for Ingenuity Pathway Analysis (IPA Qiagen)</p>
Text-fig. 3. Phylogenetic relationship of Peignecyon felinoides n. gen. et n. sp., within some selected Amphicyonidae, and some extinct caniform carnivorans. Paramiacis exilis is the outgroup. Searches were performed by means of the Branch and Bound and a Bootstrap analysis through 1,000 replicates. One tree is obtained (length 73 steps, consistency index (CI) = 0.6301, retention index (RI) = 0.7000). The numbers below nodes are Bremer indices, and the numbers above nodes are Bootstrap support percentages (only shown ≥ 50). in A New Thaumastocyoninae (Amphicyonidae, Carnivora) From The Early Miocene Of Tuchořice, The Czech Republic
Text-fig. 3. Phylogenetic relationship of Peignecyon felinoides n. gen. et n. sp., within some selected Amphicyonidae, and some extinct caniform carnivorans. Paramiacis exilis is the outgroup. Searches were performed by means of the Branch and Bound and a Bootstrap analysis through 1,000 replicates. One tree is obtained (length 73 steps, consistency index (CI) = 0.6301, retention index (RI) = 0.7000). The numbers below nodes are Bremer indices, and the numbers above nodes are Bootstrap support percentages (only shown ≥ 50).
Data for Integrated Step Selection Analysis of translocated female greater sage-grouse in the 60 days post-release, North Dakota 2018-2020
<p>The data include used and random available steps at 11-hour resolution generated for 26 female greater sage-grouse in the 60 days post-translocation to North Dakota, with associated environmental predictors and individual information. The code fits individual habitat selection models in an Integrated Step Selection Analysis framework.</p> <p>Data used to fit the models described in:</p> <p>Picardi, S., Ranc, N., Smith, B.J., Coates, P.S., Mathews, S.R., Dahlgren, D.K. <i>Individual variation in temporal dynamics of post-release habitat selection</i>. Frontiers in Conservation Science (in review)</p> <p>Code used to implement the analysis is available on GitHub: https://github.com/picardis/picardi-et-al_2021_sage-grouse_frontiers-in-conservation</p>
Meta-analysis of major histocompatibility complex (MHC) class IIA reveals polymorphism and positive selection in many vertebrate species
<p>Pathogen-mediated selection and sexual selection are important drivers of evolution. Both processes are known to target genes of the major histocompatibility complex (MHC), a gene family encoding cell-surface proteins that display pathogen peptides to the immune system. The MHC is also a model for understanding processes such as gene duplication and trans-species allele sharing. The class II MHC protein is a heterodimer whose peptide-binding groove is encoded by an MHC-IIA gene and an MHC-IIB gene. However, our literature review found that class II MHC papers on infectious disease or sexual selection included IIA data only 18% and 9% of the time, respectively. To assess whether greater emphasis on MHC-IIA is warranted, we analyzed MHC-IIA sequence data from 50 species of vertebrates (fish, amphibians, birds, mammals) to test for polymorphism and positive selection. We found that the number of MHC-IIA alleles within a species was often high, and covaried with sample size and number of MHC-IIA genes assayed. While MHC-IIA variability tended to be lower than that of MHC-IIB, the difference was only ~25%, with ~3 fewer IIA alleles than IIB. Furthermore, the unexpectedly high MHC-IIA variability showed clear signatures of positive selection in most species, and positive selection on MHC-IIA was stronger in fish than in other surveyed vertebrate groups. Our findings underscore that MHC-IIA can be an important target of selection. Future work should therefore expand the characterization of MHC-IIA at both allelic and genomic scales, and incorporate MHC-IIA into models of fitness consequences of MHC variation.</p>
Scripts of data selection and analysis: role of community size in driving spatial variation in riverine fish metacommunities around the world
<p>Here we describe how we obtained and analyzed data for the manuscript: High compositional dissimilarity among small communities is decoupled from environmental variation, accepted for publication in Oikos. (10.1111/oik.09802). A preprint is also available: https://doi.org/10.32942/osf.io/vngse</p> <p>We investigated the role of community size in mediate the strength of ecological drift and environmental selection in driving community spatial variation in metacommunities. </p>
Supplementary tables for: Dependent variable selection in phylogenetic generalized least squares regression analysis under Pagel's lambda model
<p class="MsoNormal"><span>Phylogenetic generalized least squares (PGLS) regression is widely used to detect evolutionary correlations. In contrast to the equal treatment of analyzed traits in conventional correlation methods such as Pearson and Spearman's rank tests, we must designate one trait as the independent variable and the other as the dependent variable. However, in our PGLS regression analyses (using Pagel's <em>λ</em> model) of both empirical and simulated datasets, switching independent and dependent variables yielded many conflicting results. A serious problem with PGLS regression that has not been noticed before is that selecting an inappropriate trait as the dependent variable will often result in an error. To assess correlations in simulated data, we established a gold standard by analyzing changes in traits along phylogenetic branches. Next, we tested seven potential criteria for dependent variable selection: log-likelihood, Akaike information criterion, <em>R</em><sup>2</sup>, <em>p</em>-value, Pagel's <em>λ</em>, Blomberg et al.'s <em>K</em>, and the estimated <em>λ</em> in <a name="_Hlk136010442"></a>Pagel's <em>λ</em> model. We determined that the last three criteria performed equally well in selecting the dependent variable and were superior to the other four. For practicality, we suggest using the trait with a higher <em>λ</em></span><span> or <em>K</em> </span><span>value as the dependent variable in future PGLS regressions. In analyzing the evolutionary relationship between two traits, we should designate the trait with a stronger phylogenetic signal as the dependent variable even if it could logically assume the cause in the relationship.</span></p>
Elk resource selection and indicator species analysis
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Data for Integrated Step Selection Analysis of translocated female greater sage-grouse in the 60 days post-release, North Dakota 2018-2020
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Supplementary tables for: Dependent variable selection in phylogenetic generalized least squares regression analysis under Pagel’s lambda model
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Meta-analysis of major histocompatibility complex (MHC) class IIA reveals polymorphism and positive selection in many vertebrate species
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Variant call file for mountain yellow-legged frog (MYLF) selection analysis
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Diversification and Management Practices in Selected European Regions. A Data-analysis of Arable Crops Production and soil organic carbon
<p>This data set contains a data-mining performed to assess the impact of intercropping, tillage and fertilizer type on soil organic carbon and crop yield in arable crops from four selected European pedoclimatic regions and typical cropping systems in the Atlantic, Boreal, Mediterranean North, and Mediterranean South regions. A further meta-analysis was performed with these data. </p> <p>These data correspond to the open-access articles:</p> <p>- Diversified Arable Cropping Systems and Management Schemes in Selected European Regions Have Positive Effects on Soil Organic Carbon Content. Agriculture 2019, 9, 261. https://www.mdpi.com/2077-0472/9/12/261?type=check_update&version=2</p> <p>- Diversification and Management Practices in Selected European Regions. A Data-analysis of Arable Crops Production. Agronomy 2020, 10, 297; doi:10.3390/agronomy10020297. https://www.mdpi.com/2073-4395/10/2/297</p> <p>- Deficit Drip Irrigation in Processing Tomato Production in the Mediterranean Basin: A Data Analysis for Italy. Agriculture 2019, 9, 79; doi:10.3390/agriculture9040079. https://www.mdpi.com/2077-0472/9/4/79?type=check_update&version=2</p> <p>The research and publications have been funded by he European Commission Horizon 2020 project Diverfarming [grant agreement 728003]. </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.