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13,587 results for “specification”
Cofactor specificity of the tetrameric 6-phosphogluconate dehydrogenase from Gluconobacter oxydans: Studies of wild-type and mutants forms.
<p>Here we present the results regarding the characterization of the kinetics parameters for NAD and NADP of the 6-phosphogluconate dehydrogenase from Gluconobacter oxydans at different concentrations of 6PG.</p> <p>The raw data of kinetic parameters for NAD or NADP at different 6PG concentrations are represented in kcat and KM, with the units s-1 and µM, respectively. Al the rest values as an efficient constant are represented in s-1µM-1. </p> <p>Also, we included the kinetic characterizations of the mutants Go6PGDH-R31L, Go6PGDH-R31V, and Go6PGDH-D30N. The CSV files contain four columns. 1. NAD or NADP concentration in µM; 2. enzyme activity in U/mg; 3. Standard deviation and 4. The number of independent experiments. </p> <p> </p> <p> </p> <p> </p>
Wikidata Subsets and Specification Files Created by WDumper
<p>WDumper is a third-party tool enables users to craete custom dump of Wikidata. Here we create some topical subset of Wikidata by WDumper. There are 4 usecases:</p> <ol> <li>politicians: People with occupation of politicians in Wikidata</li> <li>militPoliticians: People with occupation of politicians which are military and also have a military rank of General in Wikidata</li> <li>ukUniversities: All United Kingdom universities in Wikidata</li> <li>geneWiki: A subset from <a href="https://elifesciences.org/articles/52614/figures#fig1">this class-diagram</a></li> </ol> <p>The subsets are in .nt.gz format. For each usecase there are two types of subsets in terms of content, one with References and Qualifiers that has a "withRQFS" in the name, and one without this feature. Also for each use case, there are two extracted subsets one from 27 April 2015 and another from 13 November 2020. The corresponding JSON file for each use case is the WDumper specification file.</p>
Time spent in distinct life-history stages has sex-specific effects on reproductive fitness in wild Atlantic salmon
<p><span>In species with complex life cycles, life history theory predicts that fitness is affected by conditions encountered in previous life history stages. Here, we use a four-year pedigree to investigate if time spent in two distinct life history stages has sex-specific reproductive fitness consequences in anadromous Atlantic salmon (<i>Salmo salar</i>). We determined the amount of years spent in fresh water as juveniles (freshwater age, FW, measured in years), and years spent in the marine environment as adults (sea age, SW, measured in sea winters) on 264 sexually mature adults collected on a river spawning ground. We then estimated reproductive fitness as the number of offspring (reproductive success) and the number of mates (mating success) using genetic parentage analysis (>5000 offspring). Sea age is significantly and positively correlated with reproductive and mating success of both sexes whereby older and larger individuals gained the highest reproductive fitness benefits (females: 62.2% increase in offspring/SW and 34.8% increase in mate number/SW; males: 201.9% offspring/SW and 60.3% mates/SW). Younger freshwater age was significantly related to older sea age and thus increased reproductive fitness, but only among females (females: -33.9% offspring/FW and -32.4% mates/FW). This result implies that females can obtain higher reproductive fitness by transitioning to the marine environment earlier. In contrast, male mating and reproductive success was unaffected by freshwater age and more males returned at a younger age than females despite the reproductive fitness advantage of later sea age maturation. Our results show that the timing of transitions between juvenile and adult phases has a sex-specific consequence on female reproductive fitness, demonstrating a life-history trade-off between maturation and reproduction in wild Atlantic salmon.</span></p>
Data from: Species-specific effects of passive warming in an Antarctic moss system
Polar systems are experiencing rapid climate change and the high sensitivity of these Arctic and Antarctic ecosystems make them especially vulnerable to accelerated ecological transformation. In Antarctica, warming regions result in a mosaic of ice-free terrestrial habitats dominated by a diverse assemblage of cryptogamic plants (i.e. mosses and lichens). Although these plants provide key habitat for a wide array of microorganisms and invertebrates, we have little understanding of the interaction between trophic levels in this terrestrial ecosystem, and whether there are functional effects of plant species on higher trophic levels that may alter with warming. Here, we used Open Top Chambers (OTCs) on Fildes Peninsula, King George Island, Antarctica, to examine the effects of passive warming and moss species on the abiotic environment and ultimately on higher trophic levels. For the dominate mosses, Polytrichastrum alpinum and Sanionia georgicouncinata, we found species-specific effects on the abiotic environment, including moss canopy temperature and soil moisture. Additionally, we found distinct reproductive shifts in P. alpinum plants under warming compared to mosses without warming, and invertebrate communities in this moss species were strongly correlated with plant reproduction. Mosses under warming had substantially larger total invertebrate communities, and some invertebrate taxa were influenced differentially by moss species. However, warmed moss plants showed lower fungal biomass than control moss plants, and fungal biomass differed between moss species. Our results indicate that continued warming will differentially impact the reproductive output of Antarctic moss species, potentially altering terrestrial ecosystems dynamics from the bottom up. Understanding these effects requires clarifying the foundational, mechanistic role that individual plant species play in mediating complex interactions in Antarctica's terrestrial food-webs.
A standardized method for the construction of tracer specific PET and SPECT rat brain templates: validation and implementation of a toolbox
<p>Data set used in "A standardized method for the construction of tracer specific PET and SPECT rat brain templates: validation and implementation of a toolbox"</p>
Unbiased metagenomic sequencing complements specific routine diagnostic methods and increases chances to detect rare viral strains
<p>Raw Illumina MiSeq data in zipped FASTQ format.</p> <p>Files are named by sample type and time point (weeks after transplantation).</p>
Domain-specific model selection for structural identification of the Rab5-Rab7 dynamics in endocytosis - Additional Files
<p>Additional files from the manuscript titled "Domain-specific model selection for structural identification of the Rab5-Rab7 dynamics in endocytosis" published in BMC Systems Biology:</p> <ul> <li>Data containing delimited time points and measurements used for fitting the different model structures</li> <li>Supplementary material containing additional figures and tables</li> <li>Archive containing the complete library, the incomplete model and the task used for modeling the Rab5-Rab7 switch</li> </ul> <p> </p>
RPL: a domain-specific language for designing and implementing parallel C++ applications
<p>Parallelising sequential applications is usually a very hard job, due to many different ways in which an application can be parallelised and a large number of programming models (each with its own advantages and disadvantages) that can be used. In this paper, we describe a method to semi-automatically generate and evaluate different parallelisations of the same application, allowing programmers to find the best parallelisation without significant manual reengineering of the code. We describe a novel, high-level domain-specific language, Refactoring Pattern Language (RPL), that is used to represent the parallel structure of an application and to capture its extra-functional properties (such as service time). We then describe a set of RPL rewrite rules that can be used to generate alternative, but semantically equivalent, parallel structures (parallelisations) of the same application. We also describe the RPL Shell that can be used to evaluate these parallelisations, in terms of the desired extra-functional properties. Finally, we describe a set of C++ refactorings, targeting OpenMP, Intel TBB and FastFlow parallel programming models, that semi-automatically apply the desired parallelisation to the application's source code, therefore giving a parallel version of the code. We demonstrate how the RPL and the refactoring rules can be used to derive efficient parallelisations of two realistic C++ use cases (Image Convolution and Ant Colony Optimisation).</p>
Domain-Specific Language domain analysis and evaluation: a systematic literature review
<p>In order to successfully implement Domain-Specific Languages (DSLs), it is needed to systematically define and to support its development process; namely its Evaluation and the Domain Analysis phase. For that purpose, the studies were systematically selected from the most relevant venues that focus on the implementation of DSLs, in order to get insight if and how these development phases were performed. The special focus was given to the human-machine DSLs (excluding the machine-machine languages), the involvement of its end-users in the development process and the evaluation of DSLs usability, i.e. quality in use of DSLs. </p> <p>Preliminary results give us a notion that there is increased the state of practice of performing the evaluation of the DSLs, mostly including usability concerns, at least after its implementation. Generally, the quality of the reviewed studies was high. On another hand, rarely the assessments are done during domain analysis, which in general is not reporting inclusion of end-users or consideration of different use-cases, although the majority of studies refer to target non-programmers and to contribute easy in use. </p> <p>We did collect the valuable body of primary studies that are giving us answers to our research questions, however, to raise the credibility of the conclusions we should extend the analysis to other venues. Also, as we get insights into the categorization of practices we could specify more concretely answers that would give us means to perform more detailed meta-analysis. </p>
Specific conductance and major ion composition in United States lakes and their natural and human drivers
<p>This dataset includes specific conductance and major ion data and their natural and human drivers for lakes across the conterminous US. The data were collected for and analyzed in the paper: </p> <p>Sun, X., Cheruvelil, K.S., Hanly, P.J., Webster, K.E., & Soranno, P.A. Natural and human drivers of specific conductance and major ion composition in United States lakes. (<em>Under Review)</em></p> <p>The <strong>conductivity data all factors trans.csv</strong> dataset contains specific conductance (as a proxy for salinity) and natural and human factor data for 9,785 lakes.</p> <p>The<strong> ion cluster data all factors trans.csv</strong> dataset contains major ion concentrations (in ion equivalent concentration, ueq/L), the cluster and ion group each lake was assigned to, and natural and human factor data for 1,218 lakes.</p> <p>Detailed explanations of each variable and data sources can be found in the <strong>metadata file</strong> and the supplementary Table S1 of the manuscript. Some natural and human factors were natural log transformed, and information about the transformation applied can be found in the supplementary Table S1 of the manuscript.</p> <p>The <strong>process.R</strong> file contains R code used for filtering and pre-processing the SC and ion concentration data, and the <strong>analysis_code.R</strong> file contains R code applied to analyze the data.</p> <p>Please contact Xinyu Sun (sunxiny9@msu.edu) for any questions and/or further clarification/explanation.</p>
Data and code for the publication of a surge-specific DEM workflow on ASTER DEMs.
<p>This repository is associated to the publication submitted with the title "Glacier surge monitoring from temporally dense elevation time series: application to an ASTER dataset over the Karakoram region".<br>It contains elevation change maps produced by the workflow, the Python script of the workflow, and vector outlines used in the study.</p> <p>_______________________________<br>Content of the data repository:</p> <p>1) Elevation change maps (raster <em>**.tif</em> files)<br> Regional maps of the elevation changes over 3 years periods, interpolated results in metre (m).<br> Name: <em>dh_[date1]_[date2].tif</em><br> <br>2) Surge-affected areas (vector <em>**.gpkg </em>files)<br> Surge-affected areas drawn manually of four selected glacier surges, divided into reservoir and receiving areas.<br> <br>3) Python script <em>dem_processing_publi.py</em><br> Script with the implementation of the workflow presented in this study.</p>
Data on the taxon and morpho-specific year-round diet and endozoochorous seed dispersal of the world's largest grouse, the Capercaillie Tetrao urogallus
<p><span>Here we present the quantitative data from our original high-resolution taxon- and morpho-specific dietary study based on cuticle microhistological analyses of food remains from the feces of Western Capercaillies <em>Tetrao urogallus</em>. By providing integrative quantitative dietary data based on the functional classification of different plant parts representing 49 kinds of plant food items from four major food categories (</span><span>leaves, buds, inflorescences, and fruits</span><span>), and intact seeds, arthropods, and mineral particles (grit), our dataset has potential applications in dietary studies, dispersal capabilities, and the reintroduction biology of gallinaceous birds. </span><span><span> </span></span></p>
Comprehensive epigenomic profiling reveals the extent of disease-specific chromatin states and informs target discovery in ankylosing spondylitis
<p>We performed comprehensive epigenetic profiling in immune cell samples from patients with ankylosing spondylitis and healthy controls. <br><br><em>Note: Due to Zenodo updating their maximum file limit to 100 files, version 4 </em>(v4) <em>of this archive has been split into 5 compressed archive (tar.gz) files containing all previous and additional files. <br><br></em>Version 4 of this archive (updated 03/06/2025) adds 4 files to the archive that were omitted in previous versions which have now been made available. These were: <em><br></em></p> <ul> <li>RNA_CD8_raw_counts.txt.gz</li> <li>RNA_CD8_normalised_counts.txt.gz</li> <li>RNA_CD14_raw_counts.txt.gz</li> <li>RNA_CD14_normalised_counts.txt.gz </li> </ul> <p><em>--------------------------------------------------------------------------------------------------------------------------</em></p> <p><strong>RNA-seq/ATAC-seq/ChIPm/eRNA: </strong>Raw and normalised count data for each gene or epigenetic peak in CD4+ T cells, CD8+ T cells, and CD14+ monocytes from AS patients and healthy controls. File name is in the format: "modality_cell-type_raw/normalised_counts.txt.gz". Table S2 shows which experiments were performed on which samples. </p> <p>This data can be found in the "Raw_Counts.tar.gz" and "Normalised_Counts.tar.gz" archives. </p> <p>--------------------------------------------------------------------------------------------------------------------------</p> <p><strong>ChromHMM: </strong>We used ChromHMM to integrate epigenomic data into a 14-emission state model detailing chromatin functionality in AS patients and healthy controls. ChromHMM filenames are in the format: "ChromHMM_sampleID_celltype_n.bed.gz" where n is the number of states in the ChromHMM emission model.</p> <p>This data can be found in the "ChromHMM_AS_HV.tar.gz" archive. </p> <p>--------------------------------------------------------------------------------------------------------------------------</p> <p><strong>Capture-C: </strong>We performed Capture-C to detect chromosome looping interactions between gene promoters and SNPs associated with ankylosing spondylitis. Capture-C count data are shown in the format: "CaptureC_celltype_gene_Pro/SNP_normalised.unionbdg". We used PeakY to calculate a score for each interaction. PeakY scores are given in the following format: "PeakY_AS/HV_celltype_tier_chrloc_gene_Pro/SNP.txt". gene_Pro and gene_SNP relate to the baitsets given in Table S8.</p> <p>This data can be found in the "CapC_Count_Data.tar.gz" and "PeakY_regions.tar.gz" archives. </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>
Energy and specific heat for J=7/2 magnetic crystals
<p>Complementary data for the manuscript "Specific heat of Gd3+ and Eu2+ -based magnetic compounds" by D.J. Garcia, J. Sereni and A.A. Aligia[1].</p> <p>We include the energy and specific heat for many magnetic lattices. The magnetic moment is S=7/2. Interactions are taken as Heisenberg-like</p> <p>H = Sum_{i,\delta} J_\delta S_i S_{i+\delta}</p> <p>and \delta is taken to consider first, second, and possibly more nearest neighbors.</p> <p>Interactions are either all FM or AF to keep the system non-frustrated. We take all |J_\delta|=1K unless otherwise stated.</p> <p>Computations are performed using quantum Monte Carlo (QMC) simulations from the ALPS libraries (“dirloop_sse” package) [2, 3]. Most calculations are done using 20^3 conventional unit cells.</p> <p><strong>Directory name</strong> convention is as follows:</p> <p>fig*data/{Lattice Name}( {effective z}) , J_2 = ... , J_3 =... , K=... , L={size of the linear length} )</p> <p>fig*data refers to data of the corresponding figure in reference [1].</p> <p><em>Lattice names</em> are BCC, Diamond, FCC, HCP, and SC with their classic meaning of Body-Centered Cubic, Face-centered cubic, Hexagonal close-packed, and simple cubic.</p> <p>LHc corresponds to a layered honeycomb lattice with three NN within the plane of the honeycomb layer and one above or below the layer in alternating sites.</p> <p><em>magnetic order</em> is either ferromagnetic (FM) or antiferromagnetic (AF).</p> <p><em>effective z </em>agree<em>s </em>with the<em> number of neighbors</em> when all J_\delta are equal. It is computed by increasing the considered neighbor radius. See [1].</p> <p>The file<strong> SpecificHeatAndEnergy.tgz</strong> contains the raw data for all lattices in txt format. This version updates results with larger statistics. An ALPS example directory is also included.</p> <p>Finite size effects are almost negligible in these cases except for the antiferromagnetic simple cubic lattice ( SC_AF(6) ).</p> <p> </p> <p>[1] D. J. Garcia, J. G. Sereni, A. A. Aligia, "Specific heat of Gd3+ and Eu2+-based magnetic compounds", arXiv:2410.23519</p> <p>[2] A. Albuquerque, F. Alet, P. Corboz, P. Dayal, A. Feiguin, S. Fuchs, L. Gamper, E. Gull, S. Gürtler, A. Honecker, R. Igarashi, M. Körner et al., The ALPS project release 1.3: Open- source software for strongly correlated systems, Journal of Magnetism and Magnetic Materials 310(2, Part 2), 1187 (2007), doi:https://doi.org/10.1016/j.jmmm.2006.10.304, Proceedings of the 17th International Conference on Magnetism. </p> <p>[3] B. Bauer, L. D. Carr, H. G. Evertz, A. Feiguin, J. Freire, S. Fuchs, L. Gamper, J. Gukelberger, E. Gull, S. Guertler, A. Hehn, R. Igarashi et al., The ALPS project release 2.0: open source software for strongly correlated systems, Journal of Statistical Mechanics: Theory and Experiment 2011(05), P05001 (2011), doi:10.1088/1742-5468/2011/05/P05001.</p>
Human pan-body age- and sex-specific molecular phenomena inferred from public transcriptome data using machine learning - Data
<p>Expression data used in manuscript <i>Human pan-body age- and sex-specific molecular phenomena inferred from public transcriptome data using machine learning</i></p>
A Novel Laboratory Technique for Measuring Grain Size Specific Transport Characteristics of Bed Load Pulses
<p>We present a novel, time-efficient and non-destructive laboratory technique to investigate grain size specific transport characteristics of bed load pulses. The method consists of a through-water, high-resolution image acquisition followed by the application of a supervised color classification algorithm (Gaussian Maximum Likelihood Classification). Quality assessment based on a confusion matrix approach and basic random sampling showed a high classification performance. By statistically analyzing the temporal and spatial color distribution of the experimental reach, characteristic parameters to describe the propagation behavior were determined. The analyzed bed load pulse consisted of five different grain size classes of dyed quartz sand and gravel, each having a unique color. The initial experimental bed was uni-colored and contained the same size fractions as the augmented pulse.</p>
The supplemental data for the paper: "Methodology of generation of CFD meshes and 4D shape reconstruction of coronary arteries from patient-specific dynamic CT"
<p>The supplemental data for the paper: "Methodology of generation of CFD meshes and 4D shape reconstruction of coronary arteries from patient-specific dynamic CT"</p><p>A video file (minimum play resolution is HD to see the mesh) showing the movement of the LCA throughout the heart cycle and .STL files for 10--100% (increment of 10\%) of the heart cycle phase.</p>
Taxon-specific or universal? Using target capture to study the evolutionary history of a rapid radiation
<p>Target capture emerged as an important tool for phylogenetics and population genetics in non-model taxa. Whereas developing taxon-specific capture probes requires sustained efforts, available universal kits may have a lower power to reconstruct relationships at shallow phylogenetic scales and within rapidly radiating clades. We present here a newly-developed target capture set for Bromeliaceae, a large and ecologically-diverse plant family with highly variable diversification rates. The set targets 1,776 coding regions, including genes putatively involved in key innovations, with the aim to empower testing of a wide range of evolutionary hypotheses. We compare the relative power of this taxon-specific set, Bromeliad1776, to the universal Angiosperms353 kit. The taxon-specific set results in higher enrichment success across the entire family, however, the overall performance of both kits to reconstruct phylogenetic trees is relatively comparable, highlighting the vast potential of universal kits for resolving evolutionary relationships. For more detailed phylogenetic or population genetic analyses, e.g. the exploration of gene tree concordance, nucleotide diversity or population structure, the taxon-specific capture set presents clear benefits. We discuss the potential lessons that this comparative study provides for future phylogenetic and population genetic investigations, in particular for the study of evolutionary radiations.</p>
Supplementary File 7 from: Rapier-Sharman N et. al., Secondary Transcriptomic Analysis of Triple-Negative Breast Cancer Reveals Reliable Universal and Subtype-Specific Mechanistic Markers, 2024
<p>Supplementary Materials File 7. Please note that though the order of the supplementary materials has changed since initial upload (File S7 was previously File S9 or S10), the contents of this zipped folder remain the same.</p>
ScienceDex guides
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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.