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9,798 results for “Muscle”
Short example of Biceps Brachii muscle surface HDEMG decomposition using the DEMUSE Tool
<p>This dataset contains 4 examples of synthetic high density surface EMG signals of the Biceps Brachii muscle and results of their decomposition into separate motor unit activity. It is intended as a demonstration of the DEMUSE Tool software for sEMG decomposition and as a basis for practical example of dataset preparation for the HybridNeuro project webinar on Data management and ethics (<a href="https://www.hybridneuro.feri.um.si/results.html#webinars">https://www.hybridneuro.feri.um.si/results.html#webinars</a>). Two sets of data are included: the raw simulated sEMG signals and the results of decomposition of those signals with the DEMUSE Tool.</p>
A Dataset of sEMG and Self-Perceived Fatigue Levels for Muscle Fatigue Analysis
<p>Muscle fatigue is a risk factor for injuries in athletes and workers. This brings relevance to the study of this biochemical process to allow its identification and prevention.</p> <p>This dataset contains raw surface electromyographic (sEMG) data collected using the Delsys Trigno system, focusing on eight muscles, four per arm, from 13 healthy adult participants. Participants performed a series of 12 upper-body dynamic movements, consisting of 4 uni-articular and 2 complex/compound movements per arm. In addition to raw sEMG data, the dataset includes participants' self-reported fatigue levels. </p> <p><strong>Data Structure:</strong></p> <ul> <li><strong>sEMG Data.zip:</strong> Recorded in 1259 Hz, formatted as .csv.</li> <li><strong>self_perceived_fatigue_index.zip:</strong> Time-stamped fatigue ratings in 0-2 level, recorded at 50hz.</li> <li><strong>Protocol:</strong> Includes trial description, movements illustration and sampling frequencies.</li> <li><strong>Code</strong>: Jupyter Notebook file containing the base code to read and compute classic fatigue metrics such as Median Frequency and Mean Frequency.</li> <li><strong>Metadata:</strong> Includes participant anthropometrics, exercise habits and caffeine intake on the day of the trials.</li> </ul> <p>This dataset may contribute to the testing of new fatigue detection algorithms and analysis of the underlying mechanisms.</p>
Four lipidomics datasets (mouse liver, mouse pancreatic islets, mouse soleus muscle and mouse visceral adipose tissue), generated for the publication Mehl et al., "A multiorgan map of metabolic, signalling, and inflammatory pathways that coordinately control fasting glycemia in mice"
<p>Mehl, Thorens et al present a multiomics study aimiing to<span> identify the pathways that are coordinately regulated in pancreatic </span><span>b</span><span>-cells, muscle, liver, and fat to control fasting glycemia we fed C57Bl/6, DBA/2 and Balb/c mice a regular chow or a high fat diet for 3, 10 and 30 days. We measured fasted glycemia, insulinemia and whole-body insulin resistance. Transcriptomic and lipidomic analysis were used in a data fusion approach to identify organ-specific pathways related to the glycemic levels across all conditions investigated. In pancreatic islets, constant insulinemia despite higher glycemic levels were associated with reduced expression of mRNAs encoding hormone and neurotransmitter receptors as well as OXPHOS, cadherins, integrins and gap junction proteins. Higher glycemia and whole-body insulin resistance were associated, in muscle, with reduced expression of mRNAs encoding insulin signaling proteins and enzymes of the glycolysis, Krebs’ cycle and OXPHOS pathways, as well as endocytosis and exocytosis proteins; in hepatocytes, with lower expression of mRNAs of the insulin signaling pathway, of branched chain amino acid catabolism and of OXPHOS; in adipose tissue, with increased expression of mRNAs of innate immunity and lipid catabolism. These data provide a map of the pathways that are coordinately recruited in the investigated tissues to control fasting glycemia and a resource for further studies of interorgan communication in glucose homeostasis. </span></p>
Comparative profiling of skeletal muscle models reveals heterogeneity of transcriptome and metabolism
<p>This dataset is a complement to the following publication: Ahmed M. Abdelmoez, Laura Sardón Puig, Jonathon AB. Smith, Brendan M. Gabriel, Mladen Savikj, Lucile Dollet, Alexander V. Chibalin, Anna Krook, Juleen R. Zierath, and Nicolas J. Pillon. <a href="https://doi.org/10.1152/ajpcell.00540.2019">Comparative profiling of skeletal muscle models reveals heterogeneity of transcriptome and metabolism. </a>Am J Physiol Cell Physiol. 2019 Dec 11.</p> <p>METHODS: Publicly available data from myotubes and skeletal muscle tissues were selected from the GEO database. Raw files were downloaded and robust multi array (RMA) normalization was performed in unison for all samples from the same platform. For each human ENSEMBL, the rat and mouse orthologs were found using the R package BioMart and the arrays were merged based on the human ENSEMBL annotation. The database was then aggregated according to the official human gene symbol. When multiple ENSEMBL were found for a single gene symbol, an average was calculated.</p>
3D Reconstruction of Shoulder Muscles in Hominoid Primates: Correlating Scapular Attachment Areas with Muscle Volume
<h2><strong>How To Cite:</strong></h2> <p>If you use this data or code in your research, please cite the associated open-access <strong>manuscript, </strong>which you can find here: <a href="https://doi.org/10.1111/joa.14199">https://doi.org/10.1111/joa.14199</a><br>and this <strong>zenodo repository</strong>.</p> <h2><strong>Online Visualization:</strong></h2> <p>You can access an interactive, web-based view of the notebooks and analyses <a title="Shoulder Muscle Reconstruction Code" href="https://juliavanbeesel.github.io/ShoulderMuscleReconstructions/intro.html" target="_blank" rel="noopener">here</a>.</p> <h2><strong>Repository Description:</strong></h2> <p>This repository contains two zip files related to the analysis and visualization of 3D reconstructed muscle volumes and lengths from various hominoid specimens.</p> <ol> <li> <p><strong>MeshFiles.zip:</strong></p> <ul> <li><strong>Contents:</strong> This zip file includes all <code>.obj</code> files for 3D reconstructed muscle volumes and associated anatomical structures. Specifically, it contains: <ul> <li><strong>Muscles:</strong> Supraspinatus, Infraspinatus, Subscapularis, Teres Major, Teres Minor</li> <li><strong>Bones:</strong> Scapula and Humerus</li> <li><strong>Attachment Sites</strong></li> </ul> </li> <li><strong>Organization:</strong> The files are organized into folders by specimen. There are 9 hominoid specimens from the following species: <ul> <li><em>Hylobates lar</em></li> <li><em>Symphalangus syndactylus</em></li> <li><em>Pongo pygmaeus</em></li> <li><em>Pongo abelii</em></li> <li><em>Gorilla gorilla</em></li> <li><em>Pan troglodytes</em></li> <li><em>Homo sapiens</em></li> </ul> </li> <li><strong>Surface Scans of Muscle Geometry: </strong>The specimens <em>Pongo</em> (ID 3) and <em>Symphalangus </em>(ID 122) also contain surface scans that depict the muscle geometry of the listed muscles. These surface scans can be used for training with the iterative polygonal modelling approach. The scans are stored as <code>.obj</code>, <code>.mtl</code> and <code>.png</code> files. To view textures on these meshes, keep all three files together in the same folder.</li> <li><strong>Additional Details:</strong> Muscle reconstructions were performed for different arm positions. Each folder contains multiple humerus files, with each file representing a humerus in a specific position aligned with the corresponding muscles. The humerus file names indicate the muscles the humerus is aligned with.<br><br></li> </ul> </li> <li> <p><strong>DataAndCode.zip:</strong></p> <ul> <li><strong>Contents:</strong> <ul> <li><strong>Excel File:</strong> The original data used for analysis, presented in Table 2 of the manuscript.</li> <li><strong>Jupyter Notebook Files: </strong>These notebooks provide the analyses and figures as described in the manuscript: <ul> <li><em>Accuracy_Muscle_Length_Reconstruction:</em> Analysis of muscle length measurement comparisons, detailed in Supplementary Information Section 3: <em>Accuracy of estimating Muscle Length from 3D reconstructions</em>.</li> <li><em>Accuracy_Muscle_Volume_Reconstruction:</em> Analysis of muscle volume measurement comparisons, detailed in Results Section 3.2: <em>Accuracy of Muscle Volume and Length Reconstruction</em>.</li> <li><em>Correlation_Analysis_SIS:</em> Correlation analysis of muscle origin area to volume for the supraspinatus, infraspinatus, and subscapularis muscles, detailed in Results Section 3.3:<em> Correlation Analysis</em>.</li> <li><em>Correlation_Analysis_TT:</em> Correlation analysis of muscle origin area to volume for the teres major and minor muscles, detailed in Supplementary Information Section 1: <em>Correlation results of teres major and minor</em>.</li> </ul> </li> <li><strong>Requirements.txt:</strong> A file listing the necessary packages required to run the Jupyter notebooks.</li> </ul> </li> <li><strong>Purpose:</strong> The Python files include code for performing statistical analyses and generating figures as described in the manuscript.</li> </ul> </li> </ol> <h2><strong>Usage Instructions:</strong></h2> <ul> <li>For analyzing muscle volumes and lengths, refer to the Jupyter notebooks included in the <code>DataAndCode.zip</code>. Ensure all dependencies listed in the <code>requirements.txt</code> file are installed.</li> <li>The <code>MeshFiles.zip</code> contains the 3D models necessary for visualizing muscle and bone reconstructions, organized by specimen and arm position.</li> </ul>
Passenger kinematics and muscle responses from autonomous braking events from Olafsdottir et al. 2013
<p>This data was extracted by the "authors" of this dataset from the publication</p> <p>Olafsdottir, J. M., Östh, J., Davidsson, J., & Brolin, K. (2013). Passenger kinematics and muscle responses in autonomous braking events with standard and reversible pre‐tensioned restraints. In Ircobi conference proceedings 2013, IRC-13-70, pp. 602-617; <a href="http://www.ircobi.org/wordpress/downloads/irc13/pdf_files/70.pdf">http://www.ircobi.org/wordpress/downloads/irc13/pdf_files/70.pdf</a></p> <p>within the OSCCAR project and made publicly available for future validations of active Human Body Models.</p> <p>If you use this data, please cite the original paper.</p> <p>To use the data, a generic model of the vehicle environment is also openly available: <a href="https://openvt.eu/osccar/precrash_seat_models/safer-ahbm_2-3">https://openvt.eu/osccar/precrash_seat_models/safer-ahbm_2-3 </a></p> <p> </p>
MUSCLE: MUlti Session Classification Longterm Electromyography
<p>The <strong><em>MUSCLE</em></strong> dataset, acronym for <em><strong>MU</strong>lti <strong>S</strong>ession <strong>C</strong>lassification <strong>L</strong>ongterm <strong>E</strong>lectromyography</em>, represents a significant resource for evaluating the robustness of upper limb prosthesis control. It features 10 recordings of High-Density surface electromyography (HD-sEMG) data gathered over an extensive timeframe. Utilizing a novel HD-sEMG setup, the dataset comprises 64 EMG signals during 10 repetitions of 10 distinct gestures, mirroring the functionality of a 5 Degrees of Freedom upper limb prosthetic device. This comprehensive dataset, accompanied by the release of control model code, not only enhances transparency but also fosters advancements in prosthetics research. For a deeper understanding of the HD-sEMG acquisition setup and control method implementation, readers are encouraged to explore the related publication titled "Non-Invasive High-Density Electromyography for Real-Time Control of 5 DoFs Hand Prosthesis", D. Di Domenico*, M. Iacono* et al., (2024).</p>
Alterations in RNA editing in skeletal muscle following exercise training in individuals with Parkinson's disease
<p>Parkinson’s Disease (PD) is the second most common neurodegenerative disease behind Alzheimer’s Disease, currently affecting more than 10 million people worldwide. The progression of PD results in the loss of function due to neurodegeneration and neuroinflammation. The etiology of PD is multifactorial, including both genetic and environmental origins. We explored changes in RNA editing, specifically editing through the actions of the Adenosine Deaminases Acting on RNA (ADARs), in the progression of PD. Analysis of ADAR editing of skeletal muscle transcriptomes from PD patients and controls, including those that engaged in a rehabilitative exercise training program revealed significant differences in ADAR editing patterns based on age, disease status, and following rehabilitative exercise. Further, deleterious editing events in protein coding regions were identified in multiple genes with known associations to PD pathogenesis. Our findings of differential ADAR editing complement findings of changes in transcriptional network identified by a recent Lavin et al. 2020 (<a href="https://doi.org/10.3389/fphys.2020.00653">https://doi.org/10.3389/fphys.2020.00653)</a> study and offer insights into dynamic ADAR editing changes associated with PD pathogenesis. VCF files were generated using AIDD (Plonski et al., 2020) (<a href="https://doi.org/10.1186/s12859-020-03888-6">https://doi.org/10.1186/s12859-020-03888-6</a>).</p>
Raw data for Kierdorf et al, "Muscle function and homeostasis require cytokine inhibition of AKT activity in Drosophila"
<p>This upload contains the raw data corresponding to the publication "Muscle function and homeostasis require cytokine inhibition of AKT activity in Drosophila" by Katrin Kierdorf et al., eLife 2020.</p>
Modular organization of the murine locomotor pattern in the presence and absence of sensory feedback from muscle spindles
<p>In this study, we made use of non-negative matrix factorization (NMF) to extract muscle synergies from electromyographic (EMG) data. We implemented the NMF algorithm in R version 3.5.1 (R Foundation for Statistical Computing, R Core Team, Vienna, Austria), a programming language available in a free software environment. However, even if the software does not require a paid license, often researchers are either not confident with or prefer not to spend time writing the code required to perform NMF. We make available, as we recently did with human data (Santuz <em>et al.</em>, 2018), an example open access data set of EMG and muscle synergy data for murine walking and swimming. The data presented in this supplementary information part is available in three formats: 1) the raw EMG of two example trials (one recorded during walking and the other during swimming in a wild type animal, six muscles), unprocessed together with the touchdown and lift-off timings of the recorded limb for walking and the cycle timings for swimming; 2) the filtered and time-normalized EMG and 3) the muscle synergies extracted via NMF. Moreover, we provide the R code for obtaining the results described in the previous three points. We do not report any metadata, since trials are relative to a single representative animal. The R code is profusely commented.</p>
Figure 4 in The muscles, body wall and valve-opening mechanism of extant craniid (inarticulated) brachiopods
Figure 4. Line drawings and digital photographs illustrating cilia current flow and valve features of two species. (A) Diagram showing flow directions of dorsal mantle cilia currents. (B) Diagram of craniid valves and body (after Williams et al. 1997). (C–E) Novocrania californica. C. Cutaway drawing down midline of valves and muscle raising structures. (D, E) OU 44518a-b. Oblique photographs of dorsal and ventral valve interiors. (F–H) Novocrania japonica. (F, G) TUM 951117a. Oblique photographs of dorsal and ventral valve interiors. (H) Cutaway drawing down midline of valves and muscle raising structures. Abbreviations: a, anus; aasc, anterior adductor muscle scar; dv, dorsal valve; msp, median spike; pasc, posterior adductor muscle scar; raams, raised anterior adductor muscle scar; rp, raised pedestal; tve, thin valve extension; vmd, ventral mound; vv, ventral valve.
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>
Ontogeny of hepatic metabolism in two broiler lines divergently selected for the ultimate pH of the Pectoralis major muscle
<p>Nutrient availability during early stages of development (embryogenesis and the first week of life) can have long-term effects on physiological functions and bird metabolism. The embryo develops in a closed structure and depends entirely on the nutrients and energy available in the egg. The aim of this study was to describe the ontogeny of pathways governing hepatic metabolism that mediates many physiological functions in the pHu+ and pHu- chicken lines, which are divergently selected for the ultimate pH of meat, a proxy for muscle glycogen stores, and which differ in the nutrient content and composition of eggs. Our study provides the first detailed description of the evolution of different hepatic metabolic pathways during the early development of embryos and post-hatching chicks. We found a metabolic orientation for the pHu+ line towards proteolysis, glycogen degradation, ATP synthesis and autophagy, likely in response to a higher energy requirement compared with pHu- embryos. The metabolic orientations specific to the pHu+ and pHu- lines are established very early, probably in relation with their different genetic background and available nutrients.</p>
Task-driven neural network models predict neural dynamics of proprioception: Synthetic muscle spindle datasets
<p>#############</p> <p>Task-driven neural network models predict neural dynamics of proprioception, Cell 2024</p> <p>#############</p> <p>Authors: Marin Vargas, Alessandro (orcid=0000-0001-7073-4120) and Bisi, Axel (orcid=0009-0006-8602-7555) and Chiappa, Alberto Silvio (orcid=0009-0001-2764-6552) and Versteeg, Christopher (orcid=0000-0002-4269-5109) and Miller, Lee E. (orcid=0000-0001-8675-7140) and Mathis, Alexander (orcid=0000-0002-3777-2202)</p> <p>Affiliation: EPFL</p> <p>Date: January, 2024</p> <p>Link to the Cell article: </p> <p><a href="https://www.cell.com/cell/pdf/S0092-8674(24)00239-3.pdf">https://www.cell.com/cell/pdf/S0092-8674(24)00239-3.pdf</a></p> <p>--------------------------------</p> <p>Here we provide the synthetic spindle datasets of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the synthetic generated training dataset of simulated muscle spindles during arm passive movements generated with either character writing (PCR) or with 3D target reaching using reinforcement learning (RL).</p> <p>The overall structure of the data is:</p> <p>└── spindle_datasets<br> ├── pcr_dataset - Contains PCR synthetic training dataset<br> └── rl_dataset - Contains RL-generated synthetic training dataset</p> <p>The code to generate the PCR synthetic spindle dataset is available at: <a href="https://github.com/amathislab/Task-driven-Proprioception/tree/master/PCR-data-generation">https://github.com/amathislab/Task-driven-Proprioception/tree/master/PCR-data-generation</a></p> <p>The code to generate the RL-generated synthetic spindle dataset is available at: <a href="https://github.com/amathislab/Task-driven-Proprioception/tree/master/RL-data-generation">https://github.com/amathislab/Task-driven-Proprioception/tree/master/RL-data-generation</a></p> <p>--------------------------------</p> <p>The datasets, weights, activations and predictions are released with Creative Commons Attribution 4.0 license.</p> <p>The code is released under the MIT license, see <a href="https://github.com/amathislab/Task-driven-Proprioception">https://github.com/amathislab/Task-driven-Proprioception</a></p> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@article{vargas2024task,<br> title={Task-driven neural network models predict neural dynamics of proprioception},<br> author={{Marin Vargas}, Alessandro and Bisi, Axel and Chiappa, Alberto S and Versteeg, Chris and Miller, Lee E and Mathis, Alexander},<br> journal={Cell},<br> year={2024},<br> publisher={Elsevier}<br>}</p>
РИС. 3. Раковины Зрелых глохидиев Beringiana beringiana в раЗных ракурсах: А–С – Закрытые раковины, вид со стороны створки (А), вентрального угла (В) и лигамента (С); D–F – полностью открытые раковины, вид иЗнутри (D), снаруЖи (E) и боковых краев створок (F); G, H – приоткрытые раковины, стрелка укаЗывает на остатки волокон мускулаЗамыкателЯ. МасштабнаЯ линейка 100 мкм. СканируюЩаЯ ЭлектроннаЯ микроскопиЯ. FIG. 3. Mature glochidial shells of Beringiana beringiana from different angles of view: A–C – closed shells, view from the valve side (A), ventral angle (B), and ligament (C); D–F – open shells, interior view (D), exterior view (E), and from lateral margins of valves (F); G, H – ajar shells, the arrows indicate the remains of the adductor muscle fibers. Scale bar 100 µm. Scanning electron microscopy. in Первые данные о морфологии глохидиев двустворчатых моллюсков Beringiana beringiana (Bivalvia, Unionidae) оЗера Дальнее, Камчатка
РИС. 3. Раковины Зрелых глохидиев Beringiana beringiana в раЗных ракурсах: А–С – Закрытые раковины, вид со стороны створки (А), вентрального угла (В) и лигамента (С); D–F – полностью открытые раковины, вид иЗнутри (D), снаруЖи (E) и боковых краев створок (F); G, H – приоткрытые раковины, стрелка укаЗывает на остатки волокон мускулаЗамыкателЯ. МасштабнаЯ линейка 100 мкм. СканируюЩаЯ ЭлектроннаЯ микроскопиЯ. FIG. 3. Mature glochidial shells of Beringiana beringiana from different angles of view: A–C – closed shells, view from the valve side (A), ventral angle (B), and ligament (C); D–F – open shells, interior view (D), exterior view (E), and from lateral margins of valves (F); G, H – ajar shells, the arrows indicate the remains of the adductor muscle fibers. Scale bar 100 µm. Scanning electron microscopy.
Surface EMG recordings of the biceps brachii muscle during voluntary isometric contractions - pilot study on fatigue
<p>This publication contains the data recorded during a study on muscle fatigue and its evaluation using surface electromyography (EMG). There were a total of 16 participants. Each directory, with an alphanumeric value contains data from a single participant (.mat and .json files). The README.pdf file in the root directory provides details on the experimental set-up and protocol, and data in each file. </p> <p>The EMG data are provided in matlab files and in addition to those, there are recordings of the <a href="https://doi.org/10.1016/j.jelekin.2019.05.012">perceived fatigue of the participants using a modified Borg CR-10 scale</a> in the json file, in which further participant and experimental information are provided.<br>For each participant information on gender, age (in aggregate form) and height (in aggregate form) are provided.</p> <p> </p>
Total mercury (Hg) concentrations in beaks and muscle of the giant warty squid Moroteuthopsis longimana (Southern Ocean)
<p>In this study we used beaks and buccal masses (muscle) of the giant warty squid <em>Moroteuthopsis</em> <em>longimana</em> to evaluate if squid beaks can be used as a proxy for mercury (Hg) concentrations in the muscle. Beaks and respective buccal masses were collected from the diet of Antarctic toothfish <em>Dissostichus mawsoni </em>captured at the South Sandwich Islands (Atlantic Sector of the Southern Ocean; CCAMLR Subarea 48.4) in 2019 (ML1 to ML12) and 2020 (ML13 to ML21).</p> <p>We analysed Total Hg concentrations in the wing of the lower beak, hood tip of the upper beak, both upper and lower beaks, and in muscle sampled from buccal masses.</p> <p>This dataset includes the size of the beaks, the estimated mantle length, the estimated mass, and total Hg concentrations analysed in beaks and buccle mass in 21 individuals of <em>M. longimana</em>.</p> <p>This dataset is associated with the article: Lopes-Santos S, Xavier JC, Seco J, Coelho JP, Hollyman PR, Pereira E, Phillips RA, Queirós JP (2025) Squid beaks as a proxy for mercury concentrations in muscle of the giant warty squid <em>Moroteuthopsis longimana</em>. Marine Environmental Research 204:106841. Doi 10.1016/j.marenvres.2024.106841</p>
Cranial muscle reconstructions quantify adaptation for high bite forces in Oviraptorosauria
<p>This dataset contains 3D models, data, and python scripts for the cranial and mandibular retrodeformations of oviraptorosaurian theropod species <em>Incisivosaurus gautheri</em>, <em>Citipati osmolskae</em>, <em>Khaan mckennai</em>, and <em>Conchoraptor gracilis (</em>along with reconstructed cranial musculature and gape analyses) supporting the paper ‘Cranial muscle reconstructions quantify adaptation for high bite forces in Oviraptorosauria' published in Scientific Reports (<a href="https://www.nature.com/articles/s41598-022-06910-4">Cranial muscle reconstructions quantify adaptation for high bite forces in Oviraptorosauria | Scientific Reports (nature.com)</a>.</p> <p>A single ZIP compressed folder contains two Blender (<a href="https://www.blender.org/">https://www.blender.org/</a>) .blend files for each species. The Blender files named '[Genus]_cranial_muscles.blend' contain the cranial and mandibular retrodeformed models and final volumetric muscle reconstructions (along with the curve muscle origin-insertion paths and shrinkwrapped curves the final muscle volumes were derived from). The Blender files named '[Genus]_gape_analysis.blend' contain the cranial and mandibular retrodeformed models and the muscle origin-insertion cylinder connections (attached to an animated armature) used to estimate optimal and maximum gape angle. The .txt files named '[Genus]_gape_script.txt' are python scripts used to run the gape analyses for each species. The .txt files names '[Genus]_strain_values.txt' are the output strain values of each muscle cylinder during the gape analyses for each species. [Unzipped total size 1.74GB].</p>
Dataset related to article "SMA-miRs (miR-181a-5p, -324-5p, and -451a) are overexpressed in spinal muscular atrophy skeletal muscle and serum samples"
<p>mice survival after treatment with anti-miR-181a-5p; mice weight after treatment with anti-miR-324-5p</p>
The effects of robotic assistance on upper limb spatial muscle synergies in healthy people during planar upper-limb training
<p>This is the minimal dataset underlying the paper:</p> <p>"The effects of robotic assistance on upper limb spatial muscle synergies in healthy people during planar upper-limb training"</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.