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878 results for “human factors”
Global consensus map of human transcription factor footprints
<p>Vierstra, J. <em>et al.</em> <strong>Global reference mapping of human transcription factor footprints.</strong> <em>Nature</em><strong> </strong>583, 729–736 (2020). <a href="https://doi.org/10.1038/s41586-020-2528-x">https://doi.org/10.1038/s41586-020-2528-x</a></p> <p>Preprint @ bioRxiv: <a href="https://doi.org/10.1101/2020.01.31.927798">https://doi.org/10.1101/2020.01.31.927798</a></p> <p><strong>Contact:</strong> Jeff Vierstra (<a href="mailto:jvierstra@altius.org?subject=Consensus%20DNase%20I%20footprints">jvierstra@altius.org</a>)</p> <p>Genomic DNase I footprinting enables quantitative, nucleotide-resolution delineation of sites of transcription factor occupancy within native chromatin. We combined sampling of >67 billion uniquely mapping DNase I cleavages from >240 human cell types and states to index, with unprecedented accuracy and resolution, human genomic footprints and thereby the sequence elements that encode transcription factor recognition sites.</p> <p>Please see <a href="http://vierstra.org/resources/dgf">http://vierstra.org/resources/dgf </a>for additional information and a complete set of raw DNase I data for individual datasets. Additionally, raw data can also be accessed via the ENCODE data portal (<a href="http://encodeproject.org">http://encodeproject.org</a>) using the dataset accessions found in Supplementary Table 1.</p> <p>Code for footprint analysis and tutorials on how to access and manipulate digital genomic footprint data can be found at <a href="https://footprint-tools.readthedocs.io/en/latest/">https://footprint-tools.readthedocs.io/en/latest/</a>.</p> <p>All files herein correspond to human genome build version GRCh38 (UCSC hg38).</p> <p><strong>Dataset contents:</strong></p> <ul> <li><strong>Biosample metadata</strong> – Supplementary_Table_1.xlsx</li> <li><strong>Motif clustering metadata </strong>– Supplementary_Table_2.xlsx</li> <li><strong>ChIP-seq validation metadata </strong>–<strong> </strong>Supplementary_Table_3.xlsx</li> <li><strong>Consensus footprint coordinates and assigned motif archetypes</strong><br> TSV file (BED-format) with consensus footprint (posterior probability>0.99) coordinates and overlaps with matches to motif model clusters. The legend file contains column definitions in detail. <ul> <li>consensus_footprints_and_motifs_hg38.bed.gz</li> <li>consensus_footprints_and_motifs_legend.txt</li> </ul> </li> <li><strong>Motif archetype matches overlapping consensus footprints</strong><br> TSV file (BED-format) containing the coordinates for clustered motif model matches that overlap consensus footprints <ul> <li>collapsed_motifs_overlaping_consensus_footprints.bed.gz</li> <li>collapsed_motifs_overlaping_consensus_footprints_legend.txt</li> </ul> </li> <li><strong>Footprint occupancy matrix of consensus footprints</strong><br> Rows are same order as the consensus footprint file and columns are same order as in the metadata files. <ul> <li>consensus_index_matrix_full_hg38.txt.gz (Values are –log(1-posterior))</li> <li>consensus_index_matrix_binary_hg38.txt.gz (binary occupancy matrix, where footprints with posterior footprint probability >0.99 are considered occupied)</li> </ul> </li> <li><strong>Single nucleotide variants tested for allelic imbalance </strong><br> The legend file contains column definitions in detail. <ul> <li>genotypes.vcf.gz - Genotyping and allelic read depth for each biosample (see header for more information)</li> <li>tested_snvs_padj.bed.gz - SNVs tested for imbalance (TSV, BED-format)</li> <li>tested_snvs_padj_legend.txt</li> </ul> </li> </ul>
Human T-box transcription factor T (Brachyury); A Target Enabling Package
<p>Chordoma is a rare cancer occurring along the spinal cord (OMIM: <a href="https://www.omim.org/entry/215400">215400</a>). Chordoma is derived from an embryonic tissue, the notochord, and over-expresses the embryonic transcription factor T-box transcription factor T, the homologue of mouse Brachyury. Chordomas are “genomicaly silent” cancers that do not carry an extensive mutation load. Recent studies indicate that expression of TBXT is essential for persistence and growth of chordoma cells. As TBXT is not expressed in any post-embryonic tissues, it could be an excellent target for treatment of chordoma. The long-term aim of this project is to test whether TBXT can be targeted with small molecules with sufficient affinity and specificity to be therapeutically useful. In this TEP we have determined crystal structures of the DNA-binding domain (DBD) of TBXT with and without cognate DNA oligonucleotides. The DNA-free protein crystals were used in a high-throughput fragment screen to identify 29 fragments bound in 6 clusters. The crystal structures of the bound fragments provide starting points for development of stronger binders which could be used to disrupt TBXT activity or to induce the degradation of the protein through a Proteolysis-targeting chimeric molecule (PROTAC) approach.</p>
Data from: Selectivity of Guanine Nucleotide Exchange Factor-mediated Cdc42 activation in primary human endothelial cells
<p>Data that was reported in "Selectivity of Guanine Nucleotide Exchange Factor-mediated Cdc42 activation in primary human endothelial cells" by </p> <p>Nathalie R. Reinhard<sup>1</sup>, Sanne van der Niet<sup>1</sup>, Anna Chertkova<sup>1</sup>, Marten Postma<sup>1</sup>, Theodorus W.J. Gadella Jr.<sup>1</sup>, Peter L. Hordijk<sup>1,2</sup>, and Joachim Goedhart<sup>1*</sup><br> </p> <p><strong>Affiliations:</strong></p> <p><sup>1 </sup>University of Amsterdam, Molecular Cytology, Swammerdam Institute for Life Sciences, van Leeuwenhoek Centre for Advanced Microscopy, Amsterdam, the Netherlands</p> <p><sup>2 </sup>Department of Physiology, Free University Medical Center, Amsterdam, The Netherlands</p> <p> </p> <p>*Correspondence to: j.goedhart@uva.nl</p>
Dateset on 'Disentangling associations of human wellbeing with green infrastructure, degree of urbanity, and social factors around an Asian megacity'
<p>The data was collected a part of the baseline survey on household socio-economics among the Bengalurian along the rural-urban interface. </p>
TF-Marker: A comprehensive manually curated database for transcription factors and related markers in specific cell and tissue types in human.
<p>Here, we developed the TF-Marker database (TF-Marker, http://bio.liclab.net/TF-Marker/) which is committed to a comprehensive manual curation of TFs and related markers with experimental evidence in specific cell and tissue types in human. Currently, through reviewing <strong>2,091</strong> published literature, we have manually classified TFs and related markers into five types according to their functions: 1) <strong>TF</strong>: TFs, which regulate the expression of markers; 2) <strong>T Marker</strong>: markers, which are regulated by TFs (TF and T Marker pairs can identify cell types more specifically); 3) <strong>I Marker</strong>: markers, which influence the activity of TFs (I Markers can also influence the development of specific cells and tissues); 4) <strong>TFMarker</strong>: TFs, which play roles as markers (TFMarkers are cell/tissue-specific TFs used as cell markers in biology experiments); and 5) <strong>TF Pmarker</strong>: TFs, which play roles as potential markers. By curating thousands of published literature, <strong>5,905</strong> entries including <strong>1,316</strong> TFs, <strong>1,092</strong> T Markers, <strong>473</strong> I Markers, <strong>1,600</strong> TFMarkers and <strong>1,424</strong> TF Pmarkers, were annotated in <strong>383</strong> cell types and <strong>95</strong> tissue types in human. Moreover, TF-Marker divided markers into disease markers and tissue/cell-specific markers. TF-Marker is an elaborate database, which provides TFs and related markers supported by experimental evidence. We believe TF-Marker will provide strong support for research into cell/tissue-specific TFs and related markers.</p>
CollecTRI Data for Investigation of SETBP1 gene expression and transcription factor activity across human tissues
<p>Here we provide the human CollecTRI prior (accessed May 2023) for inference of TF activity across 31 GTEx tissues using multivariate linear modeling method decoupleR.<br> <br> The `human_prior_tri.csv` includes 1,178 unique TFs (referred to as the source) that target 6,627 unique genes (referred to as targets) to give us 42,595 interactions in the CollecTRI prior input. Interactions are represented as a + or - 1 (mor).</p>
Supporting Data for Human Factors in Developing Automated Vehicles:A Requirements Engineering Perspective
<p>This data set complements our manuscript in submission with the title:</p> <p>"Human Factors in Developing Automated Vehicles: A Requirements Engineering Perspective"</p> <p>We provide two files:</p> <p>a) the interview guide</p> <p>b) an overview that maps from themes to example quotes and codes derived from particular interview subjects</p>
Mammalian Evolution of Human cis-regulatory Elements and Transcription Factor Binding Sites
<p>Code and data associated with the manuscript entitled "Mammalian Evolution of Human cis-regulatory Elements and Transcription Factor Binding Sites "</p>
DS-8201a Versus T-DM1 for Human Epidermal Growth Factor Receptor 2 (HER2)-Positive, Unresectable and/or Metastatic Breast Cancer Previously Treated With Trastuzumab and Taxane [DESTINY-Breast03]
ClinicalTrials.gov study NCT03529110. IPD Sharing: YES. Countries: 15. Publications: 9.
DS-8201a in Human Epidermal Growth Factor Receptor 2 (HER2)-Expressing Gastric Cancer [DESTINY-Gastric01]
ClinicalTrials.gov study NCT03329690. IPD Sharing: YES. Countries: 2. Publications: 2.
DS-8201a in Human Epidermal Growth Factor Receptor 2 (HER2)-Expressing or -Mutated Non-Small Cell Lung Cancer
ClinicalTrials.gov study NCT03505710. IPD Sharing: YES. Countries: 5. Publications: 3.
DS-8201a in Human Epidermal Growth Factor Receptor2 (HER2)-Expressing Colorectal Cancer (DESTINY-CRC01)
ClinicalTrials.gov study NCT03384940. IPD Sharing: YES. Countries: 5. Publications: 3.
Phase I/II Study of U3-1402 in Subjects With Human Epidermal Growth Factor Receptor 3 (HER3) Positive Metastatic Breast Cancer
ClinicalTrials.gov study NCT02980341. IPD Sharing: YES. Countries: 2. Publications: 1.
Footprint of the host restriction factors APOBEC3 on the genome of human viruses
<p><span><span>APOBEC3 enzymes are innate immune effectors that introduce mutations into viral genomes. These enzymes are cytidine deaminases which transform cytosine into uracil. They preferentially mutate cytidine preceded by thymidine making the 5'TC motif their favored target. Viruses have evolved different strategies to evade APOBEC3 restriction. Certain viruses actively encode viral proteins antagonizing the APOBEC3s, others passively face the APOBEC3 selection pressure thanks to a depleted genome for APOBEC3-targeted motifs. Hence, the APOBEC3s left on the genome of certain viruses an evolutionary footprint.</span></span></p> <p><span><span>The aim of our study is the identification of these viruses having a genome shaped by the APOBEC3s. We analyzed the genome of 33,400 human viruses for the depletion of APOBEC3-favored motifs. We demonstrate that the APOBEC3 selection pressure impacts at least 22% of all currently annotated human viral species. The <i>papillomaviridae</i> and <i>polyomaviridae</i> are the most intensively footprinted families; evidencing a selection pressure acting genome-wide and on both strands. Members of the <i>parvoviridae</i> family are differentially targeted in term of both magnitude and localization of the footprint. Interestingly, a massive APOBEC3 footprint is present on both strands of the B19 erythroparvovirus; making this viral genome one of the most cleaned sequences for APOBEC3-favored motifs. We also identified the endemic <i>coronaviridae</i> as significantly footprinted. Interestingly, no such footprint has been detected on the zoonotic MERS-CoV, SARS-CoV-1 and SARS-CoV-2 coronaviruses. In addition to viruses that are footprinted genome-wide, certain viruses are footprinted only on very short sections of their genome. That is the case for the <i>gamma-herpesviridae</i> and <i>adenoviridae</i> where the footprint is localized on the lytic origins of replication. A mild footprint can also be detected on the negative strand of the reverse transcribing HIV-1, HIV-2, HTLV-1 and HBV viruses.</span></span></p> <p><span><span>Together, our data illustrate the extent of the APOBEC3 selection pressure on the human viruses and identify new putatively APOBEC3-targeted viruses.</span></span></p>
Journal Selection Factors and Opinions about Open Access of Austrian Researchers in the Humanities and Social Sciences
<p>Data from the Study of Open Access Publishing Data (SOAP, http://bit.ly/ejuvKO) has been selected to explain why the uptake of Open Access publishing in the humanities and social sciences in Austria is reluctant compared to other fields. Two questions from the survey seemed to be especially relevant in this context: factors to choose a journal for publishing and general opinions about Open Access.</p>
An activity-specificity trade-off encoded in human transcription factors
<p>Data repository for the publication <strong>"An activity-specificity trade-off encoded in human transcription factors"</strong>. </p> <p>Imaging datasets with larger sizes can be downloaded here: https://owww.molgen.mpg.de/~TFsuboptimization/</p>
Human seminal extracellular vesicles enhance endometrial receptivity through leukemia inhibitory factor
<p>Seminal extracellular vesicles (EVs) contain different subgroups that have diverse effects on sperm function. However, the effect of seminal EVs—especially its subgroups, on the endometrial receptivity was largely unknown. Here, we found that the seminal EVs could be divided into high-density EV (EV-H), medium density EV (EV-M), and low-density EV (EV-L), after purification using iodixanol. Then we demonstrated that EV-H could promote the expression and secretion of leukemia inhibitor factor (LIF) in human endometrial cells. In EV-H-treated endometrial cells, we identified 1274 differentially expressed genes (DEGs). DEGs were enriched in cell adhesion and AKT, STAT3 pathways. Therefore, we illustrated that EV-H enhanced the adhesion of human choriocarcinoma JAr cell spheroids to endometrial cells through the LIF-STAT3 pathway. Collectively, our findings indicated that seminal EV-H could regulate endometrial receptivity through the LIF pathway, which would provide novel insights into male fertility.</p>
TG 111: Factors Governing the Individual Response of Humans to Ionising Radiation
<p>The current system of radiological protection, that aims to avoid tissue reactions (deterministic effects) and minimize risk of stochastic effects (cancers and hereditary effects), through justification and optimisation of practices and limitation of exposures, is based on estimations of risk to a notional average person. The use of an age- and sex-averaged approach is not a reflection of the reality of population structures, but is a pragmatic approach. However, we know that there are variations in radiosensitivity between individuals within the population. The underlying reasons for this variation can be genetic (for example, the radiation-sensitive syndromes such as ataxia telangiectasia and Gorlin syndrome), epigenetic and also environmental or behavioural (for example, the difference in lung cancer risks in smokers and non-smokers). A better understanding of the factors that underlie inter-individual variation in response to radiation, and the magnitude of the variation could impact on the approaches adopted to radiation protection in the occupational, medical and public sectors. One example where this may impact is in radiotherapy for cancer, where severe normal tissue complications can be observed in a sub-set of the individuals treated. Understanding the subpopulations for which such severe complications occur, and the underlying mechanistic basis could inform radiotherapy treatment decision making, improving tumour cure rates while avoiding the severe complications. In 2018, the International Commission on Radiological Protection established a Task Group, TG111 to consider Factors Governing the Individual Response of Humans to Ionising Radiation. The aim of this presentation is to review the current state of knowledge on variation in individual response to radiation and to provide an update on progress with the work of the ICRP Task Group.</p>
Supporting Data for Human Factors in Developing Automated Vehicles:A Requirements Engineering Perspective
<p>This data set complements our manuscript in submission with the title:</p> <p>"Human Factors in Developing Automated Vehicles: A Requirements Engineering Perspective"</p> <p>We provide two files:</p> <p>a) the interview guide</p> <p>b) an overview that maps from themes to example quotes and codes derived from particular interview subjects</p>
Global analysis of environmental and socioeconomic factors associated with human burden of environmentally mediated pathogens
<p>This repository contains four datasets that support repeatability of the analyses in the Sokolow et al. paper published in <em>Lancet Planetary Health</em>. Descriptions of the four datasets are included in the metadata document. This study found that 80% of pathogen species known to infect humans are environmentally mediated, causing about 40% of contemporary infectious-disease burden (global loss of 130 million years of healthy life annually). More than 91% of this environmentally-mediated disease burden occurs in tropical countries, and the poorest countries carry the highest burdens across all latitudes. There were weak associations between disease burden and biodiversity or agricultural land use at the global scale. In contrast, the proportion of people with rural poor livelihoods in a country was a strong proximate indicator of environmentally mediated infectious disease burden there. Political stability and wealth were associated with improved sanitation, better health care, and lower proportions of rural poverty, indirectly resulting in lower burdens of environmentally mediated infections."</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)
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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.