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19,493 results for “Populism”
Figure 4c. from: A first integrative study of the identity and origins of the British Dwarf Pill Millipede populations, Trachysphaera cf. lobata (Diplopoda, Glomerida, Glomeridae) - Biodiversity Data Journal 3: e5176 (09 June 2015) https://doi.org/10.3897/BDJ.3.e5176
Figure 4c. - Habitus. Preserved Trachysphaera specimens from two locations, multi-layer photographs, showing high variation in colour and/or encrusted dirt. Not to scale; for further specimen information see Checklists.Figure 4a.TW15: T.lobata, male, Isle of WightFigure 4b.TW18: T.lobata, female, Isle of WightFigure 4c.TW20: T.lobata, male, Isle of WightFigure 4d.TW26: T.lobata, female, South WalesFigure 4e.TW31: T.lobata, female, South WalesFigure 4f.TW32: T.lobata, female, South Wales <br> TW31: T.lobata, female, South Wales
Figure 7c. from: A first integrative study of the identity and origins of the British Dwarf Pill Millipede populations, Trachysphaera cf. lobata (Diplopoda, Glomerida, Glomeridae) - Biodiversity Data Journal 3: e5176 (09 June 2015) https://doi.org/10.3897/BDJ.3.e5176
Figure 7c. - Telopods. SEM micrographs of Trachysphaeralobata male from South Wales. For TW# refer to Checklists. Abbreviations: fem = femur; pre = prefemur; syn = syncoxite; ta = tarsus; ti = tibia.Figure 7a.anterior view (MBiID 853010)Figure 7b.anterior view (MBiID 853007)Figure 7c.posterior (anal) view (MBiID 853009)Figure 7d.posterior (anal) view (MBiID 853008) <br> T.cf.drescoi, anterior view (MBiID 852849)
Figure 7a. from: A first integrative study of the identity and origins of the British Dwarf Pill Millipede populations, Trachysphaera cf. lobata (Diplopoda, Glomerida, Glomeridae) - Biodiversity Data Journal 3: e5176 (09 June 2015) https://doi.org/10.3897/BDJ.3.e5176
Figure 7a. - Telopods. SEM micrographs of Trachysphaeralobata male from South Wales. For TW# refer to Checklists. Abbreviations: fem = femur; pre = prefemur; syn = syncoxite; ta = tarsus; ti = tibia.Figure 7a.anterior view (MBiID 853010)Figure 7b.anterior view (MBiID 853007)Figure 7c.posterior (anal) view (MBiID 853009)Figure 7d.posterior (anal) view (MBiID 853008) <br> posterior (anal) view (MBiID 853009)
Figure 6a. from: A first integrative study of the identity and origins of the British Dwarf Pill Millipede populations, Trachysphaera cf. lobata (Diplopoda, Glomerida, Glomeridae) - Biodiversity Data Journal 3: e5176 (09 June 2015) https://doi.org/10.3897/BDJ.3.e5176
Figure 6a. - Telopods. SEM micrographs of Trachysphaeralobata males from Isle of Wight. For TW# refer to Checklists. Abbreviations: fem = femur; pre = prefemur; syn = syncoxite; ta = tarsus; ti = tibia.Figure 6a.anterior view (MBiID 852943)Figure 6b.anterior view (MBiID 852942)Figure 6c.posterior (anal) view (MBiID 852974)Figure 6d.anterior view (MBiID 852977) <br> posterior (anal) view (MBiID 852974)
Figure 1b. from: A first integrative study of the identity and origins of the British Dwarf Pill Millipede populations, Trachysphaera cf. lobata (Diplopoda, Glomerida, Glomeridae) - Biodiversity Data Journal 3: e5176 (09 June 2015) https://doi.org/10.3897/BDJ.3.e5176
Figure 1b. - Trachysphaera sp. habitus. Living animals, images by Paul Richards, Museum Sheffield.Figure 1a.Figure 1b. <br> same specimen, view of the opening between anal shield and thoracic shield, exposing head and collum (reduced first tergite) usually held inside the sphere when rolled up
Figure 5e. from: A first integrative study of the identity and origins of the British Dwarf Pill Millipede populations, Trachysphaera cf. lobata (Diplopoda, Glomerida, Glomeridae) - Biodiversity Data Journal 3: e5176 (09 June 2015) https://doi.org/10.3897/BDJ.3.e5176
Figure 5e. - Trachysphaera SEM characters. A+B: MBsID 852812, C-E: MBsID 852823. Abbreviations: AS = anal shield; Co = collum; Gr = groove, 'Ohrgrube'; h = head; T# = refers to number of tergite; th-sh = thoracic shield; c2 = collum, number of toothed ridges; c3 = thoracic shield anterior margin, number of rows of sclerotized nodules; c4 = thoracic shield, number of rows of large sclerotized protuberances; c5 = endotergum structure; c6 = endotergum, number of rows of setae; c7 = endotergum, number of rows of sclerotized nodules; c9 = female tergite 10, posterior margin, number of rows of large bacilli (with row counts 1, 2); c11 = female anal shield, shape; c13 = female anal shield, setae at posterior margin; c15 = female anal shield, large circular grooves (for more information on characters see Table 2).Figure 5a.TW1: T.lobata, female, Isle of Wight; anterior view; scale bar: 400 µmFigure 5b.TW1: T.lobata, female, Isle of Wight; anterior body part, lateral view; scale bar: 300 µmFigure 5c.TW29: T.lobata, female, South Wales; tergite 10 and anal shield, lateral view; scale bar: 300 µm; c13 points to Fig. 4eFigure 5d.TW29: T.lobata, female, South Wales; endotergum (underside of tergite); scale bar: 20 µmFigure 5e.TW29: T.lobata, female, South Wales; posterior margin of anal shield, detail; scale bar: 50 µm <br> anterior view (MBiID 852942)
Figure 6b. from: A first integrative study of the identity and origins of the British Dwarf Pill Millipede populations, Trachysphaera cf. lobata (Diplopoda, Glomerida, Glomeridae) - Biodiversity Data Journal 3: e5176 (09 June 2015) https://doi.org/10.3897/BDJ.3.e5176
Figure 6b. - Telopods. SEM micrographs of Trachysphaeralobata males from Isle of Wight. For TW# refer to Checklists. Abbreviations: fem = femur; pre = prefemur; syn = syncoxite; ta = tarsus; ti = tibia.Figure 6a.anterior view (MBiID 852943)Figure 6b.anterior view (MBiID 852942)Figure 6c.posterior (anal) view (MBiID 852974)Figure 6d.anterior view (MBiID 852977) <br> anterior view (MBiID 852977)
Raw Genotyping data from: Variation in recombination rate and its genetic determinism in sheep populations from combining multiple genomewide datasets
<p>Data supporting :</p> <p><strong>Variation in recombination rate and its genetic determinism in sheep populations from combining multiple genomewide datasets</strong></p> <p>Morgane Petit, Jean-Michel Astruc, Julien Sarry, Laurence Drouilhet, Stephane Fabre, Carole Moreno, Bertrand Servin</p> <p>http://doi.org/10.1534/genetics.117.300123</p> <p><strong>Abstract</strong></p> <p>Recombination is a complex biological process that results from a cascade of multiple events during meiosis. Understanding the genetic determinism of recombination can help to understand if and how these events are interacting. To tackle this question, we studied the patterns of recombination in sheep, using multiple approaches and datasets. We constructed male recombination maps in a dairy breed from the south of France (the Lacaune breed) at a fine scale by combining meiotic recombination rates from a large pedigree genotyped with a 50K SNP array and historical recombination rates from a sample of unrelated individuals genotyped with a 600K SNP array. This analysis revealed recombination patterns in sheep similar to other mammals but also genome regions that have likely been affected by directional and diversifying selection. We estimated the average recombination rate of Lacaune sheep at 1.5 cM/Mb, identified about 50,000 crossover hotspots on the genome and found a high correlation between historical and meiotic recombination rate estimates. A genome-wide association study revealed two major loci affecting inter-individual variation in recombination rate in Lacaune, including the <em>RNF212</em> and<em> HEI10</em> genes and possibly 2 other loci of smaller effects including the <em>KCNJ15</em> and <em>FSHR</em> genes. Finally, we compared our results to those obtained previously in a distantly related population of domestic sheep, the Soay. This comparison revealed that Soay and Lacaune males have a very similar distribution of recombination along the genome and that the two datasets can be combined to create more precise male meiotic recombination maps in sheep. Despite their similar recombination maps, we show that Soay and Lacaune males exhibit different heritabilities and QTL effects for inter-individual variation in genome-wide recombination rates.</p> <p> </p> <p>Data files are provided in Plink format ( https://www.cog-genomics.org/plink2 ).</p> <p> </p>
Mountain landscape connectivity and subspecies appurtenance shape genetic differentiation in natural plant populations of the snapdragon (Antirrhinum majus L.)
<p>This dataset provides the raw data for the population genetic analyses for the article: "Mountain landscape connectivity and subspecies appurtenance shape genetic differentiation in natural plant populations of the snapdragon (Antirrhinum majus L.)" by Benoit Pujol; Juliette Archambeau; Aurore Bontemps; Mylène Lascoste; Sara Marin; and Alexandre Meunier found in the journal "Botany Letters", Vol 164 pp. 111-119 (DOI: 10.1080/23818107.2017.1310056).</p> <p>Link to journal open access article: http://www.tandfonline.com/doi/pdf/10.1080/23818107.2017.1310056</p> <p>Link to Zenodo article reporsitory: https://zenodo.org/record/801169</p> <p>The datafile includes three data sheets:</p> <p>Data, which contains for each plant : the name of the population, the name of the sampled individual, the subspecies, the latitude of the population, the longitude of the population, the altitudinal elevation of the population in meters, and the microsatellite genotype of each plant. Genotype data is recorded by locus (two columns for the two alleles at one locus). Locus name is found as the title of the column. The record for each allele is its allele size.</p> <p>valleys 1 and valleys 2, which contains the association between populations and valleys following the two scenarios that we analyzed in the paper.</p> <p>Microsatelite loci were developed during previous work: see the following paper for more details: Debout, G., E. Lhuillier, P.-J. Malé, B. Pujol, and C. Thébaud. 2012. Development and characterization of 24 polymorphic microsatellite loci in two Antirrhinum majus subspecies (Plantaginaceae) using pyrosequencing technology. Conservation Genetics Resources 4:75-79.</p>
Effect of variable thresholds on calculating linkage disequilibrium and population structure, using Plink 1.9
<p>The figure presented here shows how the number of LD-independent SNPs and the apparent population structure can change drastically, depending on what input thresholds are used for the calculations. The population sample consists of 220 fruit-fly (Drosophila melanogaster). Most of the population structure plots indicate four subpopulations, which on further investigation using Fst indicate that this is caused by defined trans-centromeric regions, without evidence for genotyping error, and probably reflective of historic admixture. In the plots of population structure, the number in each box indicates the number of independent SNPs which were used in the IBD calculatations. Points are coloured by order in which each fly was sequenced, and some error is noticable for the beige points in the top-right plots.</p> <p>The Plink program provides a useful method for selecting single-nucleotide polymorphisms (SNPs) which are independent of linkage disequilibrium (LD), and also of visualising the genetic relatedness between individuals in a population sample, using identity-by-descent analysis (IBD). The selection of LD-independent SNPs requires three user-specfied paramaters, alongside the genotype data: i. Window-size, in kilobases (Kb) within which all pairwise comparisons between SNPs will be made, ii. Step-size, in number of SNPs, iii. r2 threshold between any two SNPs, below which they are considered to be independent (fixed here at 0.5).</p>
Statistical Data of Vienna's population
<p>Statistical analysis of the population of Vienna by districts from 2001 to 2015.</p> <p>Input Data is taken from https://www.data.gv.at/katalog/dataset/stadt-wien_viebevlkerungseit1869wien/resource/f55512e6-81ef-4fa9-a01f-19f9c5f838c2.</p> <p>The source code for experiment which produces the data provided here can be found on https://bitbucket.org/BerwanY/dp3</p> <p>The csv-files of the districts population are containing the years as labels and the population for every year in the first row. The overview of vienna's population csv-file contains as first label 'YEAR', where the other label are the districts. So every row contains the population data of every district for one year.</p> <p>The csv-files are named as follows:</p> <ul> <li>For districts: 'district_<em>DISTRICTNUMBER</em>.csv'</li> <li>Overview: 'district_all.csv'</li> </ul> <p>, where DISTRICTNUMBER is replaced with the corresponding code of the district.</p>
Magellan/M2FS Spectroscopy of Galaxy Clusters: Stellar Population Model and Application to Abell 267
<p>supplementary data products, including all sky-subtracted spectra from individual galaxies, as well as random draws from posterior PDFs for model parameters (see included README file)</p>
Distinct Stromal Cell Populations Define the B-cell Acute Lymphoblastic Leukemia Microenvironment
<p>Processed single-cell RNA-seq from the study </p> <ul> <li>10X Genomics CellRanger output (barcodes.tsv, genes.tsv, matrix.mtx) for each each sample</li> <li>Metadata</li> <li>Seurat object of the integrated scRNAseq dataset</li> <li>Xenium object of the spatial transcriptomic data</li> </ul> <p>Distinct Stromal Cell Populations Define the B-cell Acute Lymphoblastic Leukemia Microenvironment</p> <p>Mauricio N. Ferrao Blanco<sup>1</sup>, Bexultan Kazybay<sup>1</sup>, Mirjam Belderbos<sup>1</sup>, Olaf Heidenreich<sup>1</sup>, Hermann Josef Vormoor<sup>1,2</sup></p> <p><sup>1 </sup>Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands</p> <p><sup>2 </sup>University Medical Center Utrecht, Utrecht, the Netherlands</p> <p><strong>Abstract</strong></p> <p>The bone marrow microenvironment plays a critical role in B-cell acute lymphoblastic leukemia (B-ALL) progression, yet its cellular heterogeneity remains poorly understood. Using single-cell RNA sequencing on patient-derived of bone marrow aspirates from pediatric B-ALL patients, we identified two distinct mesenchymal stromal cell (MSC) populations: early mesenchymal progenitors and adipogenic progenitors. Spatial transcriptomic analysis further revealed the localization of these cell types and identified a third stromal population, osteogenic-lineage cells, exclusively present in the bone biopsy. Functional <em>ex vivo</em> assays using sorted stromal populations derived from B-ALL patient bone marrow aspirates demonstrated that both early mesenchymal and adipogenic progenitors secrete key niche-supportive factors, including CXCL12 and Osteopontin, and support leukemic cell survival and chemoresistance. Transcriptomic profiling revealed that B-ALL cells interact differently with stromal subtypes. Notably, adipogenic progenitors, but not early mesenchymal progenitors, provide support to leukemic cells through interleukin-7 and VCAM1 signaling. Stromal cells from B-ALL patients exhibited an enhanced adipogenic differentiation capacity compared to healthy controls. Moreover, co-culture experiments showed that B-ALL cells induce adipogenic differentiation in healthy MSCs through a cell contact-dependent mechanism. Adipogenic progenitors were also enriched in relapse samples, implicating them in disease progression. These findings highlight the complexity of the B-ALL microenvironment and identify different specialized stromal niches with which the leukemic cells can engage.</p> <p> </p> <p> </p> <p> </p>
The chart of rapid population synthesis codes
<p>A simplified chart of the current state-of-the-art population synthesis codes. </p> <h3>Chart Interpretation</h3> <p><strong>The river</strong> of the maps indicates detailed stellar evolution codes and stellar evolution tracks:</p> <ul> <li>STARS (<a href="https://ui.adsabs.harvard.edu/abs/2011ascl.soft07008E/abstract">Eggleton et al., 2011, Astrophysics Source Code Library</a>)</li> <li>MESA (<a href="https://ui.adsabs.harvard.edu/abs/2011ApJS..192....3P/abstract">Paxton et al., 2011, ApJS, 192, 3</a>)</li> <li>PARSEC (<a href="https://ui.adsabs.harvard.edu/abs/2012MNRAS.427..127B/abstract">Bressan et al., 2012, MNRAS, 427, 127</a>)</li> <li>FRANEC (<a href="https://ui.adsabs.harvard.edu/abs/2008Ap%26SS.316...25D/abstract">Degl'Innocenti et al., 2008, Astrophys.Space Sci., 316, 25</a>)</li> <li>BASTI (<a href="https://ui.adsabs.harvard.edu/abs/2013A%26A...558A..46P/abstract">Pietrinferni et al., 2013, A&A, 558, 46</a>)</li> <li>GENEC (<a href="https://ui.adsabs.harvard.edu/abs/2008Ap%26SS.316...25D/abstracthttps://ui.adsabs.harvard.edu/abs/2008Ap%26SS.316...43E/abstract">Eggenberger et al., 2008, Astrophys.Space Sci., 316, 43</a>)</li> <li>BRUSSELS (<a href="https://ui.adsabs.harvard.edu/abs/2004NewAR..48..861D/abstract">De Donder & Vanbeveren, 2004, New Astron.Rev. 48, 861</a>)</li> <li>BEC (<a href="https://iopscience.iop.org/article/10.1086/308158">Heger et al., 2000, ApJ, 528, 368</a>)</li> </ul> <p><strong>The cities </strong>represent rapid population synthesis codes and their vicinity to a given (or multiple) rivers indicates that the stellar evolution recipes are taken from that stellar evolution code or track. </p> <p>In particular, the codes in the <strong>BSE island </strong>follow stellar evolution by fitting equations based on the <a href="https://ui.adsabs.harvard.edu/abs/1998MNRAS.298..525P/abstract">Pols+98 </a>stellar tracks made with the code STARS:</p> <ul> <li>BSE (<a href="https://ui.adsabs.harvard.edu/abs/2002MNRAS.329..897H/abstract">Hurley et al., 2002, MNRAS, 329, 897</a>)</li> <li>MOBSE (<a href="http://dx.doi.org/10.1093/mnras/sty1999">Giacobbo N. & Mapelli M., 2018, MNRAS, 480, 2011</a>)</li> <li>BSEEMPH (<a href="https://arxiv.org/pdf/2110.10846">Tanikawa, A., 2022, AJ, 926, 83</a>)</li> <li>STAR TRACK (<a href="https://ui.adsabs.harvard.edu/abs/2008ApJS..174..223B/abstract">Belczynski K., et al., 2008, ApJS, 174, 223</a>)</li> <li>COSMIC (<a href="http://dx.doi.org/10.3847/1538-4357/ab9d85">Breivik K. et al., 2020, ApJ, 898, 71</a>)</li> <li>BINARY_C (<a href="http://dx.doi.org/10.1051/0004-6361:20066129">Izzard R. G., 2006, A&A, 460, 565</a>)</li> <li>COMPAS (<a href="http://dx.doi.org/10.3847/1538-4365/ac416c">Riley J. et al., 2022, ApJS, 258, 34</a>)</li> <li>SEBA (<a href="https://ui.adsabs.harvard.edu/abs/1996A%26A...309..179P/abstract">Portegies Zwart S. F. & Verbunt F., 1996, A&A, 309, 179</a>)</li> <li>BSELEVELC (<a href="https://academic.oup.com/mnras/article/511/3/4060/6484809">Kamlah A. W. H. et al., 2022, MNRAS, 511, 4060</a>)</li> </ul> <p>Within the BSE island there are the <strong>Triple Peaks mountains</strong> referring to code implementing also formalisms for triple or multiple system evolutions. The one I am aware of all uses the BSE-like formalism for stellar and binary evolution and they are represtend by <strong>towers</strong> in the mountains:</p> <ul> <li>TSE (<a href="https://ui.adsabs.harvard.edu/abs/2022MNRAS.516.1406S/abstract">Stegmann et al., 2022, MNRAS, 516, 1406</a>)</li> <li>TRES (<a href="https://ui.adsabs.harvard.edu/abs/2022MNRAS.516.1406S/abstract">Toonen et al., 2017, AAS, 229, 326</a>)</li> <li>MSE (<a href="https://ui.adsabs.harvard.edu/abs/2021MNRAS.502.4479H/abstract">Hamers et al., 2021, MNRAS, 502, 4479</a>)</li> <li>TRIPLE_C (<a href="https://ui.adsabs.harvard.edu/abs/2013MNRAS.430.2262H/abstract">Hamers et al., 2013, MNRAS, 430, 2262</a>)</li> </ul> <p>The cities on the <strong>Interpolator lakes </strong>are codes that include stellar evolution by interpolating a set of stellar tracks, so in principle, they are not connected to any specific stellar evolution codes. The cities are anyway arranged to reflect stellar evolution models that have been mostly used in connection with the code (e.g. PARSEC and MESA for SEVN, MESA for MIST, BRUSSELS for COMBINE):</p> <ul> <li>SEVN (<a href="https://ui.adsabs.harvard.edu/abs/2023MNRAS.524..426I/abstract">Iorio et al., 2023, MNRAS, 524, 426</a>)</li> <li>COMBINE (<a href="http://dx.doi.org/10.1093/mnras/sty2190">Kruckow M. et al., 2018, MNRAS, 481, 1908</a>)</li> <li>METISSE (<a href="http://dx.doi.org/10.1093/mnras/staa2264">Agrawal P. et al., 2020, MNRAS, 497, 4549</a>)</li> <li>MINT (<a href="https://doi.org/10.1093/mnras/stad2048">Mirouh et al., 2023, MNRAS, 524, 3978</a>)</li> <li>TRILEGAL (<a href="https://ui.adsabs.harvard.edu/abs/2005A%26A...436..895G/abstract">Girardi L., et al., 2005, 436, 895</a>)</li> </ul> <p>The other cities present on the map indicate the codes:</p> <ul> <li>STARBUST99 (<a href="https://ui.adsabs.harvard.edu/abs/1999ApJS..123....3L/abstract">Leitherer, C. et al., 1999, ApJS, 123, 3</a>)</li> <li>IBIS (<a href="http://dx.doi.org/10.1093/mnras/280.4.1035">Tutukov A. & Yungelson L., 1996, MNRAS, 280, 1035</a>)</li> <li>SCENARIO_MACHINE (<a href="https://ui.adsabs.harvard.edu/abs/1996A%26A...310..489L/abstract">Lipunov V. M. et al., 1996, A&A, 310, 489</a>)</li> </ul> <p>The STARBUST99 code focuses mostly on modelling the spectroscopic properties of galaxies and uses the Geneva stellar evolution models. For the the last two codes, I was not able to find a direct connection to any of the known stellar evolution codes, therefore I put them somewhere close to a Glacier and I put a Dragon to refer to the famous sentence "hic sunt dracones" to indicate my ignorance about the details of the two codes.</p> <p><strong>The three codes represented by ships</strong> are population synthesis codes that hybrid between detailed stellar and binary evolution models and population synthesis. They are located on the rivers related to the detailed codes they are based on:</p> <ul> <li>BRUSSELS pop synth (<a href="https://ui.adsabs.harvard.edu/abs/2004NewAR..48..861D/abstract">Donder & Vanbeveren, 2004, New Astron.Rev. 48, 861</a>)</li> <li>BPASS (<a href="http://dx.doi.org/10.1017/pasa.2017.51">Eldridge et al., 2017, PASA, 34, e058</a>)</li> <li>POSYDON (<a href="http://dx.doi.org/10.3847/1538-4365/ac90c1">Fragos T. et al., 2023, ApJS, 264, 45</a>)</li> </ul> <h3>Disclaimer</h3> <p>The map represents an oversimplification for the sake of visualisation. The order of names, their fontsizes and relative locations, and possible Typos and errors do not have any specific meaning and do not "hide" any personal opinion of the authors. </p> <p>If you want to suggest changes, additions or ask to remove a given code please send me an email at giuliano.iorio.astro@gmail.com</p>
Population-scale skeletal muscle single-nucleus multi-omic profiling reveals extensive context specific genetic regulation
<p>Data accompanying the manuscript "Population-scale skeletal muscle single-nucleus multi-omic profiling reveals extensive context specific genetic regulation".</p> <p>Note: For ATAC fragment files, e,caQTL full cis scan summary files, clustering objects, please see the CMDGA portal (https://cmdga.org/search/?searchTerm=stephen-parker%3AVarshney2024)<br>For raw data including fastq files, please see dbGaP repo phs001048.v3.p1</p> <p>Data in this repository includes:</p> <p>Filename: Description</p> <p>1. list of 8,666 genes for which exon-only counts were considered. See methods section "Adjusting RNA counts for overlapping gene annotations" in the manuscript.</p> <p>2. nucleus_sample_cluster_map.tsv: nucleus-sample-cluster map with other QC info. <br># index: nucleus identified syntax <modality>.<batch>.NM.<10X channel>.<barcode> <br># UMAP_1, UMAP_2: UMAP coordinates for visualization<br># modality: rna or atac<br># batch: processing batch identifier<br># hqaa_umi: high quality autosomal alignments (HQAA) for atac nuclei, unique molecular identifier (UMI) for tna <br># fraction_mitochondrial: fraction of reads mapping to the mitochondrial genome<br># cohort: sample cohort<br># tss_enrichment: TSS enrichment for atac nuclei<br># coarse_cluster_name: cluster name</p> <p>3. peaks.tar.gz: snATAC peak features including:<br># consensus-summits.bed: consensus summits along with the cell type that the summits was highest in.<br># narrow peaks in clusters<br># consensus summit feature (summit +- 150bp) identified in each cluster - these were used in GWAS enrichments.</p> <p>4. snrna-cell-type-specific-genes.tsv: Normalized expression scores for genes in each cell-type cluster</p> <p>5. eqtl_permute.tar.gz: Permutation scan eQTL in each cell-type cluster. Columns: <br># variant: syntax <chrom>:<hg38 pos>:<ref>:<alt><br># effect_allele: effect allele (was the alt allele)<br># other_allele: non-effect allele<br># feature: gene name<br># featureCoordinates_tss: gene TSS<br># p-value: nominal p value<br># beta: slope/beta of the linear regression. Keyed on the alt allele<br># se: standard error of the slope<br># snp: SNP ID<br># strand: gene strand<br># n_variants_tested: number of variants tested for the gene<br># distance_var_pheno: distance of the variant with the gene TSS<br># n_effective_tests: number of effective tests<br># p_beta: beta distribution adjusted p value<br># qvalue: qvalue (Storey)</p> <p>6. caqtl_permute.tar.gz: # Permutation scan caQTL in each cell-type cluster. Columns: <br># variant: syntax <chrom>:<hg38 pos>:<ref>:<alt><br># effect_allele: effect allele (was the alt allele)<br># other_allele: non-effect allele<br># feature: peak feature coordinates<br># p-value: nominal p value<br># beta: slope/beta of the linear regression. Keyed on the alt allele<br># se: standard error of the slope<br># snp: SNP ID<br># n_variants_tested: number of variants tested for the gene<br># distance_var_pheno: distance of the variant with the gene TSS<br># n_effective_tests: number of effective tests<br># p_beta: beta distribution adjusted p value<br># qvalue: qvalue (Storey)</p> <p>7. eqtl_credible_sets.tar.gz: # eQTL credible set. The file name denotes the egene and the signal hit id. Bed file columns: <br># 1: snp chromosome<br># 2: snp start<br># 3: snp end<br># 4: snp chrom_pos_ref_alt<br># 5: Bayes Factor <br># 6: PIP<br># 7: SNP rsid</p> <p>8. caqtl_credible_sets.tar.gz: # caqtl credible set. The file name denotes the capeak and the signal hit id. Bed file columns: <br># 1: snp chromosome<br># 2: snp start<br># 3: snp end<br># 4: snp chrom_pos_ref_alt<br># 5: Bayes Factor <br># 6: PIP<br># 7: SNP rsid</p> <p>9. cicero_all.tar.gz # Cicero coaccessibility results. Columns<br># Peak 1: Macs2 narrowpeak coordinate for peak 1<br># Peak 2: Macs2 narrowpeak coordinate for peak 2<br># coaccess: Cicero coaccessibility score</p> <p>10. cicero_gene_tss.tar.gz: Cicero coaccessibility results between peak and genes. Macs2 narrow peaks in the TSS+1kb upstream region are assigned that gene name. Columns<br># Cicero coaccessibility results between peak and genes. Macs2 narrow peaks in the TSS+1kb upstream region are assigned that gene name.Columns<br># Peak 1: Macs2 narrowpeak coordinate for peak 1<br># gene_name: Assigned gene<br># Peak 2: Macs2 narrowpeak coordinate for peak 2<br># coaccess: Cicero coaccessibility score<br>## Peak1 is the narrowpeak in the TSS region, peak2 is the distal peak</p> <p>11. mash.tar.gz Mashr results for e/caQTL - lfsr, posterior means and posterior SD for each tested eSNP-eGene, caSNP-caPeak pair. </p> <p>12. cellregmap.tar.gz: Cellregmap results for endothelial nucleus-level eQTL scans.<br>## Persistent genetic effect beta_g was calculated in a simple association model. <br>## An interaction model was fit to test for GxC effect. columns:<br># rho1, g2, e1, and eps2 are variance component measures outputs from CellRegMap corresponding to interaction, genetic, environment and residual variance components. <br># p_nominal: nominal p from cellRegMap<br># kind: model kind in CellRegMap - simple association or interaction<br># beta_g: Persistent genetic effect<br># gene_name: gene name for eQTL or peak feature name for caQTL<br># context: context used either factors (continuous) or subclusters (discrete)<br># snp: index snp for which model is fit. This is the most significant identified snp from our standard e,caQTL scans. chrom-hg38pos-rsid</p> <p><br>13. coloc-eqtl-caqtl.tsv: # Summary of eQTL-caQTL coloc in each cluster. Columns:<br># nsnps: Number of SNPs in the region<br># eqtl_hit: SNP with the highest Bayes factor in the SuSiE eQTL credible set<br># caqtl_hit: SNP with the highest Bayes factor in the SuSiE caQTL credible set<br># PP.H0.abf: Coloc posterior probability for no signal<br># PP.H1.abf: Coloc posterior probability for signal in dataset 1<br># PP.H2.abf: Coloc posterior probability for signal in dataset 2<br># PP.H3.abf: Coloc posterior probability for different signals in datasets 1 and 2<br># PP.H4.abf: Coloc posterior probability for shared signal in datasets 1 and 2<br># idx1: Index of the SuSiE credible set for dataset 1<br># idx2: Index of the SuSiE credible set for dataset 2<br># cluster: cluster name<br># egene: eGene name<br># capeak: caPeak coordinates</p> <p>14. cit-mrs-summary.tsv: Summary from CIT and MR Steiger directionality tests. Columns:<br># cluster: cluster name<br># egene: eGene name<br># capeak: caPeak coordinates<br># eqhit: SNP with the highest Bayes factor in the SuSiE eQTL credible set<br># cahit: SNP with the highest Bayes factor in the SuSiE caQTL credible set<br># p.cit_c_c-e: P value for CIT causal cahit-ca-to-e model<br># q.cit_c_c-e: q value for CIT causal cahit-ca-to-e model<br># p.cit_rc_c-e: P value for CIT reverse-causal eqhit-ca-to-e model <br># q.cit_rc_c-e: value for CIT reverse-causal eqhit-ca-to-e model <br># p.cit_c_e-c: P value for CIT causal eqhit-e-to-ca model<br># q.cit_c_e-c: q value for CIT causal eqhit-e-to-ca model<br># p.cit_rc_e-c: P value for CIT reverse-causal cahit-e-to-ca model <br># q.cit_rc_e-c: q value for CIT reverse-causal cahit-e-to-ca model <br># cit_direction: Direction inferred from CIT <br># correct_causal_direction--ca-to-e: MR Steiger directionality test - is ca-to-e direction correct?<br># correct_causal_direction--e-to-ca: MR Steiger directionality test - is e-to-ca direction correct?<br># sensitivity_ratio--ca-to-e: MR Steiger Sensitivity ratio for ca-to-e model <br># sensitivity_ratio--e-to-ca: MR Steiger Sensitivity ratio for e-to-ca model<br># steiger_test--ca-to-e: MR Steiger directionality test P value for ca-to-e model<br># steiger_test--e-to-ca: MR Steiger directionality test P value for e-to-ca model<br># steiger_q--ca-to-e: MR Steiger directionality test q value for ca-to-e model<br># steiger_q--e-to-ca: MR Steiger directionality test q value for e-to-ca model<br># mrs_direction: Direction inferred from MR Steiger<br># direction: Direction inferred requiring consistent results between CIT and MR Steiger directionality test</p> <p>15. coloc-gwas-eqtl.tsv and<br>16. coloc-gwas-caqtl.tsv # Summary of e/caQTL coloc with GWAS in each cluster. Columns:<br># nsnps: Number of SNPs in the region<br># gwas_hit: SNP with the highest bayes factor in the SuSiE GWAS credible set<br># eqtl_hit: SNP with the highest bayes factor in the SuSiE eQTL credible set<br># caqtl_hit: SNP with the highest bayes factor in the SuSiE caQTL credible set<br># PP.H0.abf: Coloc posterior probability for no signal<br># PP.H1.abf: Coloc posterior probability for signal in dataset 1<br># PP.H2.abf: Coloc posterior probability for signal in dataset 2<br># PP.H3.abf: Coloc posterior probability for different signal in datasets 1 and 2<br># PP.H4.abf: Coloc posterior probability for shared signal in datasets 1 and 2<br># idx1: Index of the SuSiE credible set for dataset 1<br># idx2: Index of the SuSiE credible set for dataset 2<br># cluster: cluster name<br># egene: eGene name<br># capeak: caPeak coordinates<br># p12min: Min prior p12 where the PP H4 > 0.5. Lower this value, more robust is the colocalization<br># trait: GWAS trait name<br># gwas_locus: GWAS locus name for the coloc test - a 250kb left and right flanking genomic window on this SNP was considered for testing coloc between all pairs of GWAS/QTL signals identified in this region <br># traitname: Expanded GWAS trait name<br># variable_type: GWAS type <br># source: Source of GWAS - either UKBB or other study</p> <p>17. supplementary_tables.xlsx: Supplementary tables from the manuscript.<br>Information included in sheets:<br>1. "marker_genes": Marker genes known from literature used to annotate clusters<br>2. "n_nuclei": n pass-QC nuclei per modality-sample-cluster</p> <p>2. "snrna_GO_enrichment": GO term enrichment: matrix of cluster vs top 2 GO terms</p> <p>3. "qtl_scan_info": e/caQTL scan info<br>cluster: cluster<br>ntested_eqtl: N genes tested for eQTL<br>nsig_eqtl: N significant (5% FDR) eGenes<br>n_pheno_pcs_eqtl: N phenotype PCs considered for eQTL<br>ratio_eqtl: Ratio of N eGenes/N genes tested<br>nsig_caqtl: N peaks tested for caQTL<br>ntested_caqtl: N significant (5% FDR) caPeaks<br>n_pheno_pcs_caqtl: N phenotype PCs considered for caQTL<br>ratio_caqtl: Ratio of N caPeaks/N peaks tested<br>nsamples_eqtl: N samples for eQTL<br>nsamples_caqtl: N samples for caQTL</p> <p>4. "gwas_trait_list": GWAS trait info<br>trait: GWAS trait ID<br>traitname: GWAS trait description<br>variable_type: GWAS type. case/control (cc), continuous_irnt=continuous inverse-normal transformed<br>source: GWAS source<br>doi: GWAS study DOI</p> <p>5. "traits_in_ldsc_baseline" - list of annotations included in the baseline model for LDSC</p> <p>6. "gwas_enrichment_in_peaks" GWAS enrichment in cluster peaks (S-LDSC)</p> <p>7. "gwas_enrichment_in_qtl_peaks" GWAS enrichment in QTL peaks (fGWAS) # fGWAS results comparing GWAS enrichment in type 1 annotations<br>CI_lower_ln, estimate_ln, CI_upper_ln: natural log of lower confidence interval, estimate, and upper confidence interval<br>trait: trait id<br>traitname: trait name<br>annotation: annotation<br>sig: 1 if CIs don't overlap 0, otherwise 0</p> <p>8. t2d_gwas_caqtl_coloc and<br>9. t2d_gwas_eqtl_coloc:<br>Summary of e,caQTL coloc with T2D GWAS in each cluster, along with target gene nominations. Columns:<br>nsnps: Number of SNPs in the region<br>gwas_hit: SNP with the highest bayes factor in the SuSiE GWAS credible set<br>eqtl_hit: SNP with the highest bayes factor in the SuSiE eQTL credible set<br>caqtl_hit: SNP with the highest bayes factor in the SuSiE caQTL credible set<br>PP.H0.abf: Coloc posterior probability for no signal<br>PP.H1.abf: Coloc posterior probability for signal in dataset 1<br>PP.H2.abf: Coloc posterior probability for signal in dataset 2<br>PP.H3.abf: Coloc posterior probability for different signal in datasets 1 and 2<br>PP.H4.abf: Coloc posterior probability for shared signal in datasets 1 and 2<br>idx1: Index of the SuSiE credible set for dataset 1<br>idx2: Index of the SuSiE credible set for dataset 2<br>cluster: cluster name<br>egene: eGene name<br>capeak: caPeak coordinates<br>p12min: Min prior p12 where the PP H4 > 0.5. Lower this value, more robust is the colocalization<br>trait: GWAS trait id<br>diamante_gwas_locus: GWAS signal from the DIAMANTE 2018 study. Some signals that our SuSiE runs identified were not present in the original study in which case this column is NA<br>traitname: Expanded GWAS trait name<br>capeak_in_tss: caPeak in TSS + 1kb upstream region of a gene<br>gene_target_standard_cicero: caPeak coaccessible with TSS peak of a gene considering nuclei from all samples for co-accessibility<br>gene_target_allelic_cicero: caPeak coaccessible with TSS peak of a gene considering nuclei from samples homozygous for the caSNP allele associated with increased accessibility<br>gwashit_nominal_egene: gwas_hit nominally associated with these genes nominated in the columns capeak_in_tss, gene_target_standard_cicero, and gene_target_allelic_cicero</p> <p>10. MPRA results for the C2CD4A locus</p>
Human Capital-weighted population estimates for 185 countries from 1970 to 2100
<p>We provide a novel dataset of human capital-weighted population size (HCWP) for 185 countries from 1970 to 2100. HCWP summarizes a population's productive capacity and human capital heterogeneity in a single metric, enabling comparisons across countries and over time. The weights are derived from Mincerian earnings functions applied to multi-country census data on educational attainment. The model used to compute the returns to schooling accounts for the diminishing positive relative relationship between education and wages as the overall education of populations rises. The population weights are adjusted by a skills assessment factor representing differences in education quality across countries and years. HCWP is calculated by applying these adjusted human capital weights to population estimates and projections disaggregated by age, sex and education, spanning the period 1970-2020 and 2020-2100 for five Shared Socioeconomic Pathway scenarios. Validation analyses demonstrate the utility of the new HCWP data in explaining national income trends. As a more comprehensive population measure than basic size and age-sex indicators, HCWP enhances the power of statistical models aimed at the assessment of socioeconomic change impacts and forecasting.</p>
GSTRIDE: A database of frailty and functional assessments with inertial gait data from elderly fallers and non-fallers populations
<p>The GSTRIDE database contains relevant metrics and motion data of elder people for the assessment of their health status. The data correspond to 163 patients, 45 men and 118 women, between 70 and 98 years old with an average Body Mass Index (BMI) of 26.1±5.0 kg/m<sup>2</sup> and a cognitive deterioration status index between 1 and 7, according to the Global Deterioration Scale (GDS) scale. In this way, we ensure variability among the volunteers in terms of socio-demographic and anatomic parameters and their functional and cognitive capacities. The database files are stored in CSV format to ease their usability with common data processing software.</p> <p>We provide socio-demographic data, anatomical, functional and cognitive variables, and the outcome measurements from test commonly performed for the evaluation of elder people. The evaluation tests carried out to obtain these data are the Gait Speed Test (4-metre), the Hand Grip Strength, the Short Physical Performance Battery (SPPB), the Timed up and go (TUG) and the Short Falls Efficacy Scale International (FES-I). We also include the outcomes of the GDS questionnaire, the Frailty assessment and the information about falls during the last year prior to the tests.</p> <p>These data are complemented with the gait parameters of a walking test recorded by an Inertial Measurement Unit (IMU) placed on the foot. Inertial data from foot-mounted IMUs (acceleration (m/s2), angular velocity (rad/s) and timestamps (s)) are included in the database in .csv files for each participant.</p> <p>The current version includes a new gait analysis processing conducted following the methodology described in [1].</p> <p>The complete gait analysis is included for each participant and trial in .csv files, including the gait parameters estimated for all individual steps and the gait segmentation events. The gait parameters included are: cycle duration (CD) (s), cadence (steps/min), stride length (SL) (m), path length 3D (%SL), path length 2D (%SL), stride velocity (m/s), percentage of swing (%CD), percentage of stance (%CD), percentage of stance subphases (loading, foot-flat, and pushing) (%stance), heel strike pitch (degrees), toe-off pitch (degrees), peak angle velocity (degrees/s), turning angle (degrees), and heel range of motion (ROM) (degrees). </p> <p>GSTRIDE is specially focused on, but not limited to, the study of faller and non-faller elder people. The main aim of this dataset is the availability of study these different populations. By including the results of the health evaluation tests and questionnaires and the inertial and spatio-temporal data, researchers can analyze different techniques for the identification of fallers. Moreover, this database allows the analysis of cognitive deterioration and frailty parameters of patients by the research community.</p> <p>[1] L. Ruiz-Ruiz, J. J. García-Domínguez and A. R. Jiménez, "A Novel Foot-Forward Segmentation Algorithm for Improving IMU-Based Gait Analysis," in <em>IEEE Transactions on Instrumentation and Measurement</em>, vol. 73, pp. 1-13, 2024, Art no. 4010513, doi: 10.1109/TIM.2024.3449951.</p>
An observed population of intermediate-mass helium stars that have been stripped in binaries - theoretical, computational and observational data
<p>This Zenodo repository contains the observational and computational data presented in the manuscript "An observed population of intermediate-mass helium stars stripped in binaries" by Drout, Götberg, Ludwig, Groh, de Mink, O'Grady and Smith.</p><p>We organize the data as follows:</p><ul><li>The stacked spectra presented in Figures S16-S21 are located in stacked_spectra.tar.gz, which contains a text file for each star. The text files have three columns that correspond to wavelength in Angstrom, normalized counts, and errors, respectively.<br> </li><li>The spectral model grid computed based on binary evolutionary model output and presented in detail in the Supplementary information section S1.2.1, is labeled with names starting S121. The file S121_evol_based_006_absolute_magnitudes.txt contains the absolute AB magnitudes for the models in UV and optical filters. The .tar.gz S121_evol_based_006_spectra.tar.gz contains files with the full spectral energy distribution and normalized spectra of each model. The .tar.gz S121_evol_based_006_complete_models.tar.gz contains the full CMFGEN models.<br> </li><li>For the stellar atmosphere model grid presented in Supplementary material section S1.2.2, we refer to the Zenodo repository 10.5281/zenodo.7976200, which is made available in association with the second paper in our series. We note that we used a subset of that grid in the article associated with this Zenodo repository. We refer to section S1.2.2 for more details.<br> </li><li>The spectral models demonstrating the mass loss rate variations in Supplementary information section S1.2.3 are presented here with names starting with S123. There is one file containing the absolute magnitudes (S123_mdot_variation_absolute_magnitudes.txt), the S123_mdot_variation_spectra.tar.gz contains the spectral energy distributions and normalized spectra for each of the models, and the S123_mdot_variation_complete_models.tar.gz contains the full CMFGEN models.<br> </li><li>The spectral model grid computed based on main-sequence evolutionary model output and presented in detail in the Supplementary information section S1.3.1, is labeled with names starting S131. The file S131_MS_evol_based_006_absolute_magnitudes.tar.gz contains three files with the absolute AB magnitudes for the models in the UV and optical filters, each file corresponding to either 20%, 60%, or 90% through the main-sequence evolution and labeled f20, f60, and f90, respectively. S131_MS_evol_based_006_spectra.tar.gz contains three folders labeled f20, f60 and f90, which each contains the SEDs (in Flambda and ABmag) and normalized spectra for the corresponding models. The files S131_MS_evol_based_006_complete_models_fX0.tar.gz contain the complete CMFGEN models.<br> </li><li>The custom index files we use for astrometry.net in section S3.1.1 are located in the zip file called S311_astrometry_index_files.zip. This information was used to recalculate the astrometry on the Swift UVOT images of the Magellanic Clouds.<br> </li><li>To make Figure 2B, we calculated the equivalent widths for a set of models assuming a signal-to-noise ratio of 35. This procedure is described in Section S3.7.2. In Figure2B_Model_EWs.zip, we provide three files that each contain these modeled equivalent widths for (1) stripped star models, (2) OB star models, and (3) composite models. <br> </li><li>To make Figure S7 (see also Sections S1.2.3 and S2.2), which is similar to Figure 2B, but presents the effects of varying the wind mass loss of stripped stars, we used a similar set of modeled equivalent widths as when we produced Figure 2B. These modeled equivalent widths are provided in FigureS7_Model_EWs.zip. <br> </li><li>To make Figure 3, we calculated equivalent widths for the model grid described in Section S1.2.2 and the TLUSTY OB star grids (see Section S1.3.2) assuming a signal-to-noise ratio of 100. These model equivalent widths are provided in the file called Figure3_Model_EWs.zip. </li></ul>
Georeferenced data for the study Environmental suitability throughout the late Quaternary explains population genetic diversity
<p>Data filtered from GBIF (datasetKey: 50c9509d-22c7-4a22-a47d-8c48425ef4a7) Contains 150 records of the <i>Sciurus aberti </i>squirrel filtered in latitudinal windows of 5 degrees from 20 to 45 degrees N. </p>
Data for: Multi-generational fitness effects of natural immigration indicate strong heterosis and epistatic breakdown in a wild bird population
<p><span>The fitness of immigrants and their descendants produced within recipient populations fundamentally underpins the genetic </span><span>and population dynamic</span><span> consequences of immigration. </span><span>I</span><span>mmigrants can </span><span>in principle </span><span>induce contrasting genetic effects on fitness across generations, reflecting multi-faceted additive, dominance, and epistatic effects. Y</span><span>et, full multi-generational and sex-specific fitness effects of regular immigration have not been quantified within naturally structured systems, precluding inference on underlying genetic architectures </span><span>and population outcomes</span><span>. We used four decades of song sparrow </span><span>(<em>Melospiza melodia</em>)</span> <span>life-history and pedigree data to quantify fitness of natural immigrants, natives, and their F1, F2, and backcross descendants, and test for evidence of non-additive genetic effects. Values of key fitness components (including adult lifetime reproductive success and zygote survival) of F1 offspring of immigrant-native matings substantially exceeded their parent mean, indicating strong heterosis. Meanwhile, F2 offspring of F1-F1 matings had notably low values, indicating surprisingly strong epistatic breakdown. Further, magnitudes of effects varied among fitness components, and</span> <span>differed between female</span><span>s</span><span> and male</span><span>s</span><span> descendants. These results demonstrate that strong non-additive genetic effects on fitness can arise within </span><span>weakly </span><span>structured </span><span>and fragmented </span><span>populations </span><span>experiencing </span><span>frequent </span><span>natural </span><span>immigration. </span><span>Such effects will substantially affect the net </span><span>degree of effective gene flow and resulting local genetic introgression and adaptation.</span></p>
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.