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22,597 results for “Regulation”
Modelica Models and Jupyter Notebooks for System Analysis of Glucose Insulin Regulation
<p>This dataset contains source code of Modelica models of Glucose-Insulin regulation using different techniques.</p> <p>Accompanying Jupyter notebook is demo for system analysis (parameter estimation) of artificial data and to match model simulation able to be used in Teaching class.</p> <ul> <li><strong>ModelicaIdentification.ipynb</strong> - default notebook - code contains ellipsis which needs to be replaced as per instruction in text</li> <li><strong>ModelicaIdentificationResolution.ipynb - </strong>notebook - code with exemplar solution to default notebook</li> <li><strong>glucoseinsulin.mo - </strong>Modelica source code</li> <li><strong>PatientInsulinConcentration.csv</strong> - sample data to be fitted against model</li> <li><strong>seminar11hw.GIExperiment.fmu</strong> - FMU exported from Modelica in order to run simulation in Python and PyFMI library</li> </ul> <p>Thanks to the MYBINDER service, the Jupyter notebook can be viewed and executed as</p> <ul> <li><a href="https://mybinder.org/v2/zenodo/10.5281/zenodo.3633324/">https://mybinder.org/v2/zenodo/10.5281/zenodo.3633324/</a> note that you need to launch terminal first in Jupyter -> New -> Terminal and install pyfmi and matplotlib by:</li> </ul> <pre><code class="language-bash">conda install -c conda-forge pyfmi matplotlib</code></pre> <ul> <li>Most recent version with other models and notebooks <a href="https://mybinder.org/v2/gh/creative-connections/Bodylight-notebooks/master?filepath=Seminar11GlucoseInsulinIdentification/">https://mybinder.org/v2/gh/creative-connections/Bodylight-notebooks/master?filepath=Seminar11GlucoseInsulinIdentification/</a></li> </ul>
Dataset related to article "TNF-Stimulated Gene-6 Is a Key Regulator in Switching Stemness and Biological Properties of Mesenchymal Stem Cells."
<p>Mesenchymal stem cells (MSCs) are well established to have promising therapeutic properties. TNF-stimulated gene-6 (TSG-6), a potent tissue-protective and anti-inflammatory factor, has been demonstrated to be responsible for a significant part of the tissue-protecting properties mediated by MSCs. Nevertheless, current knowledge about the biological function of TSG-6 in MSCs is limited. Here, we demonstrated that TSG-6 is a crucial factor that influences many functional properties of MSCs. The transcriptomic sequencing analysis of wild-type (WT) and TSG-6<sup>-/-</sup> -MSCs shows that the loss of TSG-6 expression leads to the perturbation of several transcription factors, cytokines, and other key biological pathways. TSG-6<sup>-/-</sup> -MSCs appeared morphologically different with dissimilar cytoskeleton organization, significantly reduced size of extracellular vesicles, decreased cell proliferative rate, and loss of differentiation abilities compared with the WT cells. These cellular effects may be due to TSG-6-mediated changes in the extracellular matrix (ECM) environment. The supplementation of ECM with exogenous TSG-6, in fact, rescued cell proliferation and changes in morphology. Importantly, TSG-6-deficient MSCs displayed an increased capacity to release interleukin-6 conferring pro-inflammatory and pro-tumorigenic properties to the MSCs. Overall, our data provide strong evidence that TSG-6 is crucial for the maintenance of stemness and other biological properties of murine MSCs.</p> <p> </p> <p>Some dataset of this research are in prism format, to ensure open access we attach a pdf instruction about this format and a link where downoladed it</p>
Brazilian Federal legislations and Health Professional Councils regulations about telemedicine, according to historical phases and public policy purposes from 1990 to 2018.
<p>The file contains one excel file with two datasheets, one with legislations from Brazilian Federal Government and other from Health Professional Councils, from 1990 to 2019. Each spreadsheet has the original database, the number ID of the normative, what institution the document is from, its publication date, the abstract (in Portuguese), its public URL, historical phases and the purpose of it.</p>
Can digital platforms be regulated? A Dialogue Between The European Union and México
<p>Can digital platforms be regulated? A Dialogue Between The European Union and México</p> <p>This video presentation was produced as a Digital Knowledge Product for the Horizon 2020 MSCA RISE project, Promoting Research on Digitalisation in Emerging Powers and Europe towards Sustainable Development (PRODIGEES, GA #873119).</p> <p>The original publishing agency is Instituto Mora EN VIVO 1. The original publishing platform is YouTube. The link: https://www.youtube.com/watch?v=VY5A8GdIjy4</p>
Data from: ZmIBH1-1 regulates plant architecture in maize
<p>Leaf angle (LA) is a critical agronomic trait which affects grain yield through planting density in maize. Much research has been conducted in recent years to investigate the genes responsible for LA variation and a few genes were identified through map-based cloning. Here we cloned the <i>ZmIBH1-1</i> gene, which is a bHLH transcription factor with both a basic binding region and a Helix-Loop-Helix domain; and qRT-PCR results showed that <i>ZmIBH1-1</i> is a negative regulator of LA in maize. Histological analysis showed that the change in LA was mainly caused by differential cell wall lignification and cell elongation in the ligular region. To reveal the regulatory framework of <i>ZmIBH1-1</i>, we conducted RNA-Seq and DAP-Seq analysis. Overlay of the RNA-Seq and DAP-Seq results revealed 59 ZmIBH1-1 modulated target genes with annotation, and they were mainly cell wall related, cell development or hormone related genes. We have built a new regulatory model of <i>ZmIBH1-1 </i>gene controlling plant architecture in maize.</p>
Multiple sequence alignments of sensor histidine kinases and response regulators
<p>The two FASTA files contain multiple sequence alignments of sensor histidine kinase and response regulator sequences. The source sequences were obtained by BLAST, clustered with usearch and aligned with muscle. More details to be found in Multamäki et al. 2021.</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>
Supplementary tables for publication "A reference-free algorithm discovers regulation in the plant transcriptome"
<p>Supplementary tables for publication "A reference-free algorithm discovers regulation in the plant transcriptome" (doi: https://doi.org/10.1101/2024.05.23.595613)</p> <p>Table A: complete list of significant anchors and associated genes from analysis of sorghum dataset</p> <p>Table B: complete list of significant anchors and associated genes from analysis of maize dataset</p> <p>Table C: complete list of significant anchors and associated genes from analysis of Arabidopsis P/Fe dataset</p> <p>Table D: complete list of significant anchors and associated genes from analysis of Arabidopsis FLOE1 dataset</p> <p>arabidopsis_floe1_ALL_anchors_satc_truncated.txt: data from the Arabidopsis FLOE1 dataset used to generate figures in the paper. Columns are sample ID, anchor, target, and counts of that anchor/target combination in that particular sample. </p> <p>arabidopsis_pfe_ALL_anchors_satc_truncated.txt: data from the Arabidopsis P/Fe dataset used to generate figures in the paper. Columns are sample ID, anchor, target, and counts of that anchor/target combination in that particular sample. </p> <p>maize_pollen_ALL_anchors_satc_truncated.txt: data from the maize dataset used to generate figures in the paper. Columns are sample ID, anchor, target, and counts of that anchor/target combination in that particular sample. </p> <p>sorghum_drought_ALL_anchors_satc_truncated.txt: data from the sorghum dataset used to generate figures in the paper. Columns are sample ID, anchor, target, and counts of that anchor/target combination in that particular sample.</p> <p>cryptic_splicing_anchors.tsv: list of anchors described in Supplementary Information section of the article that are examples of cryptic splicing. Columns are dataset name, gene name/ID, anchor sequence, target 1 sequence, and target 2 sequence. </p>
Western blots supporting "Phosphatases modified by LH signaling in ovarian follicles: testing their role in regulating the NPR2 guanylyl cyclase"
<p>Western blots used to generate figures 2-6 and S4 of the publication, Egbert JR, Silbern I, Uliasz TF, Lowther KM, Yee S-P, Urlaub H, and Jaffe LA. 2023. Phosphatases modified by LH signaling in ovarian follicles: testing their role in regulating the NPR2 guanylyl cyclase. <i>Biology of Reproduction</i>, ioad130. <a href="https://doi.org/10.1093/biolre/ioad130">https://doi.org/10.1093/biolre/ioad130</a></p>
Raw data: Patterned apoptosis has an instructive role for local growth and tissue shape regulation in a fast-growing epithelium
<p>What regulates organ size and shape remains one fundamental mystery of modern biology. Research in this area has primarily focused on deciphering the regulation in time and space of growth and cell division, while the contribution of cell death has been overall neglected. This includes studies of the <i>Drosophila</i> wing, one of the best characterised systems for the study of growth and patterning, undergoing massive growth during larval stage and important morphogenetic remodeling during pupal stage. So far, it has been assumed that cell death was relatively neglectable in this tissue both during larval and pupal stage and as a result the pattern of growth was usually attributed to the distribution of cell division. Here, using systematic mapping and registration combined with quantitative assessment of clone size and disappearance as well as live imaging, we outline a persistent pattern of cell death and clone elimination emerging in the larval wing disc and persisting during pupal wing morphogenesis. Local variation of cell death is associated with local variation of clone size, pointing to an impact of cell death on local growth which is not fully compensated by proliferation. Using morphometric analyses of adult wing shape and genetic perturbations, we provide evidence that patterned death affects locally and globally adult wing shape and size. This study describes a roadmap for precise assessment of the contribution of cell death to tissue shape, and outlines an important instructive role of cell death in modulating quantitatively local growth and morphogenesis of a fast-growing tissue.</p><p>This repository contains the raw data (local projection images, ROI and quantificatiosn) of the Current Biology article "<strong>Patterned apoptosis has an instructive role for local growth and tissue shape regulation in a fast-growing epithelium".</strong></p>
Getting the Most Out of Every Training Day – A Diary Study on the Influence of Instructors on Self-Regulated Learning During Firefighter Leadership Courses
<p>This is the online supplement for a diary study on self-regulated learning in firefighter leadership courses and how it is susceptible to instructor behavior.</p> <p><em>Abstract: </em>Leadership courses in the fire services are highly challenging, up to the point that they can seriously exhaust the trainees and hamper their self-regulated learning efforts (e.g., setting goals, focusing attention, seeking feedback). We theorize that experiences of failure or overload can curtail trainees’ available energy resources on subsequent training days, which in turn should affect trainees’ learning efforts. Given the central role of instructors in leadership courses, we hypothesize that supportive and humble instructor behaviors decrease experiences of failure and overload, and thus increase self-regulated learning. Moreover, we argue that supportive instructor behavior fosters the learning-promoting effect of high levels of energy resources among trainees, while humble instructor behavior mitigates hampering effects of low levels of resources. We conducted a preregistered diary study with 118 firefighters who participated in two-week leadership courses at a German fire academy. The participants completed short questionnaires before and after classes each day. Multilevel analyses confirmed that perceived daily supportive and humble instructor behavior predict trainees’ reports of daily self-regulated learning activity. Notably, this effect was independent of positive effects of trainees’ reported resources in the morning. However, supportive and humble behavior did not moderate the effect of energy resources. Our results suggest that instructors can elicit effective learning despite challenging training conditions. Furthermore, this study offers implications for leaders in the fire services who themselves often conduct trainings with their subordinates.</p> <p>The study was approved by the ethics committee of the Faculty of Psychology & Sports Science of [institution anonymized for review] and pre-registered with Aspredicted.org (see https://aspredicted.org/DM2_78R).</p> <p><br>This online supplement includes</p> <ul> <li>a codebook describing all instructions and items</li> <li>raw data (anonymised) and analysis script (Note: The raw data contains only the information of persons who were included in the analysis and have agreed to it.)</li> <li>supplemental analyzes</li> </ul>
Functional antagonism between STAT3 and SMAD4 regulates EMT
<p>Oncogenic mutations in KRAS are among the most common in cancer. Classical models suggest that loss of epithelial characteristics and the acquisition of mesenchymal traits are associated with cancer aggressiveness and therapy resistance. We identify STAT3 as a genetic modifier of TGF-beta-induced epithelial to mesenchymal transition in mutant KRAS tumors. RNA sequencing was performed with murine cells expressing mutant KRAS either overexpressing hyperactive STAT3Y640, or CRISPR-mediated knockout of STAT3, SMAD4, or KRAS. Excel files of differential expression compared to control mutant RAS cells or fpkm files are provided.</p>
Image quantification data for: Activity-dependent mitochondrial ROS signaling regulates recruitment of glutamate receptors to synapses
<p>Our understanding of mitochondrial signaling in the nervous system has been limited by the technical challenge of analyzing mitochondrial function <em>in vivo</em>. In the transparent genetic model <em>Caenorhabditis elegans, </em>we were able to manipulate and measure mitochondrial ROS (reactive oxygen species) signaling of individual mitochondria as well as neuronal activity of single neurons <em>in vivo</em>. Using this approach, we provide evidence supporting a novel role for mitochondrial ROS signaling in dendrites of excitatory glutamatergic <em>C. elegans</em> interneurons. Specifically, we show that following neuronal activity, dendritic mitochondria take up calcium (Ca<sup>2+</sup>) via the mitochondrial Ca<sup>2+</sup> uniporter MCU-1 which results in an upregulation of mitochondrial ROS production. We also observed that mitochondria are positioned in close proximity to synaptic clusters of GLR-1, the <em>C. elegans</em> ortholog of the AMPA subtype of glutamate receptors that mediate neuronal excitation. We show that synaptic recruitment of GLR-1 is upregulated when MCU-1 function is pharmacologically or genetically impaired but is downregulated by mitoROS signaling. Thus, signaling from postsynaptic mitochondria may regulate excitatory synapse function to maintain neuronal homeostasis by preventing excitotoxicity and energy depletion.</p>
Figure 1 in Are fisheries regulations influencing the biology and reproduction of the surmullet Mullus surmuletus Linnaeus, 1758 on the south-eastern coasts of France (NW Mediterranean)?
Figure 1. – Sampling sites in West, AMA (Adjacent Marine Protected Area of Hyères Bay) and East zones on the south-eastern coast of France, NW Mediterranean Sea.
Figure 4 in Are fisheries regulations influencing the biology and reproduction of the surmullet Mullus surmuletus Linnaeus, 1758 on the south-eastern coasts of France (NW Mediterranean)?
Figure 4. – Percentages of both sexes and females:males sex-ratio of Mullus surmuletus by zone (A) and season (B).
Figure 3 in Are fisheries regulations influencing the biology and reproduction of the surmullet Mullus surmuletus Linnaeus, 1758 on the south-eastern coasts of France (NW Mediterranean)?
Figure 3. – Mean (± SE) total length (TL, cm) of Mullus surmuletus in West, AMA and East zones. N: number of analyzed individuals per sex in zones. Values with the same post-hoc letters (red for females, blue for males) are not significantly different (p> 0.05).
Figure 2 in Are fisheries regulations influencing the biology and reproduction of the surmullet Mullus surmuletus Linnaeus, 1758 on the south-eastern coasts of France (NW Mediterranean)?
Figure 2. – Percentage of individuals of Mullus surmuletus analyzed by two-cm size class (total length in cm) and by sex. N: number of individuals, F: females, M: males, Undet.: unidentified group includes immature individuals and those whose sex could not be identified.
Figure 8 in Are fisheries regulations influencing the biology and reproduction of the surmullet Mullus surmuletus Linnaeus, 1758 on the south-eastern coasts of France (NW Mediterranean)?
Figure 8. – Mean percentages of gonadal development stages (GDS) of Mullus surmuletus. A: By zone for females; B: By zone for males; C: By season for females; D: By season for males. N: number of individuals.
Figure 6 in Are fisheries regulations influencing the biology and reproduction of the surmullet Mullus surmuletus Linnaeus, 1758 on the south-eastern coasts of France (NW Mediterranean)?
Figure 6. – Mean gonado-somatic index (GSI, %) of males and females of Mullus surmuletus by (A) zone and (B) season. Values with the same post-hoc let-letters (red for females, blue for males) are not significantly different (p> 0.05).
Figure 7 in Are fisheries regulations influencing the biology and reproduction of the surmullet Mullus surmuletus Linnaeus, 1758 on the south-eastern coasts of France (NW Mediterranean)?
Figure 7. – Mean percentage of gonadal development stages of Mullus surmuletus by 2-cm size class (TL, cm) and sex for (A) females and (B) males. N: number of individuals.
ScienceDex guides
Understand access before you commit
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.