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54,099 results for “Expression”
Distinct tissue-dependent composition and gene expression of human fetal innate lymphoid cells
<p>Countmatrix and metadata for gene expression in bulk NK cells and CD304+ ILC3s isolated from human fetal liver, lung, intestine, and skin. </p>
Radio Science observations of Mars Express and Tianwen-1 spacecraft from the 2021 Maritan Solar Conjunction
<p>Phase scintillation dataset from the 2021 Martian solar conjunction is contained in the DATA.zip file. The Read Me pdf file contiants information useful to understanding the file naming conventions and their contents.</p>
Consensus molecular environment of schizophrenia risk genes in co-expression networks shifting across age and brain regions
<p>This is the online data repository accompanying the following manuscript:<br><strong>Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions</strong></p> <p><em>Giulio Pergola<sup>1,2,3,*</sup>, Madhur Parihar<sup>1</sup>, Leonardo Sportelli<sup>1,2</sup>, Rahul Bharadwaj<sup>1</sup>, Christopher Borcuk<sup>2</sup>, Eugenia Radulescu<sup>1</sup>, Loredana Bellantuono<sup>2,5</sup>, Giuseppe Blasi<sup>2,4</sup>, Qiang Chen<sup>1</sup>, Joel E. Kleinman<sup>1,3</sup>, Yanhong Wang<sup>1</sup>, Srinidhi Rao Sripathy<sup>1</sup>, Brady J. Maher<sup>1,3,7</sup>, Alfonso Monaco<sup>5,9</sup>, Fabiana Rossi<sup>1,2</sup>, Joo Heon Shin<sup>1</sup>, Thomas M. Hyde<sup>1,3,6</sup>, Alessandro Bertolino<sup>2,4,*</sup>, Daniel R. Weinberger<sup>1,7,8,*</sup></em></p> <p> </p> <p><strong>Affiliations:</strong></p> <p><em>1)Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (USA)<br>2)Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy<br>3)Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>4)Azienda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy<br>5)Istituto Nazionale di Fisica Nucleare (INFN), Bari, Italy<br>6)Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>7)Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>8)Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>9)Dipartimento Interateneo di fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy</em></p> <p> </p> <p><strong>Abstract:</strong></p> <p><em>Schizophrenia is a neurodevelopmental brain disorder whose genetic risk is associated with shifting clinical phenomena across the life span. We investigated the convergence of putative schizophrenia risk genes in brain coexpression networks in postmortem human prefrontal cortex (DLPFC), hippocampus, caudate nucleus, and dentate gyrus granule cells, parsed by specific age periods (total N = 833). The results support an early prefrontal involvement in the biology underlying schizophrenia and reveal a dynamic interplay of regions in which age parsing explains more variance in schizophrenia risk compared to lumping all age periods together. Across multiple data sources and publications, we identify 28 genes that are the most consistently found partners in modules enriched for schizophrenia risk genes in DLPFC; twenty-three are previously unidentified associations with schizophrenia. In iPSC-derived neurons, the relationship of these genes with schizophrenia risk genes is maintained. The genetic architecture of schizophrenia is embedded in shifting coexpression patterns across brain regions and time, potentially underwriting its shifting clinical presentation.</em></p> <p> </p> <p><strong>Citation:</strong> <em>Giulio Pergola et al. ,Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions.Sci. Adv.9, eade2812(2023).DOI:10.1126/sciadv.ade2812</em></p> <p> </p> <p><strong>Data Files:<br>DLPFC hit.genes_kb_200__online.version.zip: </strong><br>Interactive Sankey plot for age-parsed DLPFC networks with SCZ genes (200 kbp list) only. For Sankey plots, hover mouse over the links to see the list of genes. Also supports zoom, drag and selection.<br><strong>DLPFC hit.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed DLPFC networks with SCZ genes (200 kbp list) only. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>DLPFC all.genes_kb_200__online.version.zip:</strong><br>Interactive Sankey plot for age-parsed DLPFC networks with all genes<br><strong>DLPFC all.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed DLPFC networks with all genes. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>HP hit.genes_kb_200__online.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with SCZ genes (200 kbp list) only<br><strong>HP hit.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with SCZ genes (200 kbp list) only. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>HP all.genes_kb_200__online.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with all genes<br><strong>HP all.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with all genes. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>Modulewise SCZ enrichment(1.0).xlsx:</strong><br>Excel file contains module level SCZ enrichment results for all networks<br><strong>wide_form_test_slidingwindow_NC_SchizoNew(v1.4)_final.xlsx:</strong><br>Excel file contains WGCNA output for sliding window networks<br><strong>wide_form_WGCNA(v3.7.1)_final.xlsx:</strong><br>Excel file contains WGCNA output for our generated networks and from previously published networks<br><strong>libdnetworks(NC).preprocessed.exp.RData: </strong><br>Preprocessed ranknormalised expression assay for age-parsed/nonparsed NC networks (DLPFC, HP, CAUDATE, DENTATE). For fixed window and sliding window study.<br><strong>libdnetworks(SCZ).preprocessed.exp.RData: </strong><br>Preprocessed ranknormalised expression assay for nonparsed SCZ networks (DLPFC, HP, CAUDATE, DENTATE). For the sliding window study.<br><strong>sample_matched_HP_DG_qsva(NC).preprocessed.exp.RData:</strong><br>Preprocessed ranknormalised expression assay for the sample-matched HP-DG. QSVA removed pipeline. For Cell population enrichment study.<br><strong>sample_matched_HP_DG_noqsva(NC).preprocessed.exp.RData:</strong><br>Preprocessed ranknormalised expression assay for the sample-matched HP-DG. No QSVA removed pipeline. For Cell population enrichment study.<br><strong>stemcell.preprocessed.exp.RData:</strong><br>Preprocessed ranknormalised expression assay for the iPSC network. For replication in human iPSC data study. Neuronal samples averaged for each “RealGenome”.<br><strong>SCZ.ref.list.sciadv.ade2812.rds</strong>: List of All Biotypes/ Protein Coding Schizophrenia reference genelist for following bins: PGC3, 0 kbp, 20 kbp, 50 kbp, 100 kbp, 150 kbp, 200 kbp, 250 kbp, 500 kbp.</p> <p> </p> <p>Accompanying code can be found at: <a href="https://github.com/LieberInstitute/Brain_WGCNA">https://github.com/LieberInstitute/Brain_WGCNA</a><br>Data from this repository is also available at: <a href="https://nets.libd.org/age_wgcna/">https://nets.libd.org/age_wgcna/</a></p> <p> </p> <p>For any data inquiries please contact:<br><strong>Giulio Pergola: </strong><a href="mailto:Giulio.Pergola@libd.org"><strong>Giulio.Pergola@libd.org</strong></a></p> <p> </p>
Gene expression and splicing counts from 49 tissues from GTEx v6p genome build hg19 - non-strand specific
<p><strong>Dataset description:</strong></p> <p>49 folders, each corresponding to one tissue from GTEx v6p and containing the following files:</p> <ol> <li> <p>geneCounts: gene-level counts </p> </li> <li> <p>k_j: split counts spanning from one exon to another.</p> </li> <li> <p>k_theta: non-split counts covering a splice site</p> </li> <li> <p>n_psi3: total split counts from a given acceptor site</p> </li> <li> <p>n_psi5: total split counts from a given donor site</p> </li> <li> <p>n_theta: total split and non-split counts for a given splice site</p> </li> <li> <p>Sample annotation describing each sample from the dataset</p> </li> <li> <p>Description file with global information from the dataset</p> </li> </ol> <p>The gene counts were originated using the GTF file from <a href="http://www.gencodegenes.org/human/release_29lift37.html">release 29 of GENCODE</a>, and the split and non-split counts contain only the annotated junctions from the same release. Statistics are reported only for GENCODE-annotated introns and splice sites, in compliance with the regulations of the GTEx consortium. For a description of the samples, methods, and protocols, see the GTEx publication specified below.</p> <p><strong>Use: </strong>The count matrices are intended to help researchers that are interested in using RNA-Seq data with the purpose of diagnostics. Researchers can merge their own dataset with the downloaded ones, provided the tissue, genome build, strand, and paired-end specifications match. Afterwards, the <a href="https://github.com/gagneurlab/drop">Detection of RNA outliers Pipeline (DROP) </a> can be used to compute gene expression and splicing outliers.<br> <strong>Organism:</strong> Homo sapiens<br> <strong>Genome assembly:</strong> hg19<br> <strong>Gene annotation:</strong> gencode29<br> <strong>Strand specific: </strong>FALSE<br> <strong>Paired end: </strong>TRUE<br> <strong>Protocol: </strong>poly(A) enrichment</p> <p><strong>Contact:</strong> Vicente A. Yepez, yepez at in.tum.de; Christian Mertes, mertes at in.tum.de; Julien Gagneur, gagneur at in.tum.de</p> <p><strong>Citation:</strong> Write the following in the "Data availability" section of the manuscript or similar replacing the three citations by the ones from the References section below:</p> <blockquote> <p><strong>The count matrices for the GTEx samples <cite GTEx publication, see below> were downloaded from Zenodo (doi: 10.5281/zenodo.5596755) and were generated through DROP <cite DROP, see below> using the release 29 of the GENCODE annotation <cite GENCODE, see below>. </strong></p> </blockquote> <p>Also, write the following in the Acknowledgements section:<br> </p> <blockquote> <p><strong>The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The raw data used for the analyses described in this manuscript were obtained from the GTEx Portal on June 12, 2017, under accession number dbGaP phs00424.v6.p1.</strong></p> </blockquote> <p><br> </p>
Usability ratings on the MuLiMi platform expressed by Participants
<p>The database contains usability ratings for the MuLiMi platform for the screening of Dyslexia risk in multilingual children, expressed by the participants (children users). Ratings are expressed through an ad-hoc questionnaire with 12 items on a 1-to-5 point scale. NB ratings for question .1 are to be reversed in order to obtain a scale with increasing scores from the negative to the positive pole, like all other questions.</p>
usability ratings for the MuLiMi platform expressed by Examiners
<p>The database contains usability ratings for the MuLiMi web-platform for the screening of Dyslexia risk in multilingual children, expressed by Examiners on an ad-hoc Usability Questionnaire. Ratings for Questions 2 to 9 are expressed on a scale from 1 to 5, Questions from 10 to 46 are expressed on a scale from 1 to 9. NB ratings to Questions 2, 4, 7, 9 and 38 are to be reversed so as to obtain consistent ratings with increasing numbers correponding to increase from the negative to the positive pole. </p>
Genome-wide gene expression noise in Escherichia coli is condition-dependent and determined by propagation of noise through the regulatory network
<p>In this repository we provide raw and processed datasets for the article: “Genome-wide gene expression noise in <em>Escherichia coli </em>is condition-dependent and determined by propagation of noise through the regulatory network<strong>” </strong>by Arantxa Urchueguía, Luca Galbusera, Dany Chauvin, Gwendoline Bellement, Thomas Julou and Erik van Nimwegen.</p> <p>A preprint is available under the following DOI: <a href="https://doi.org/10.1101/795369">https://doi.org/10.1101/795369</a>. </p> <p>The repository consists of the following datasets: </p> <p><strong>1. preprocessed_datasets.zip(~22GB)</strong></p> <ul> <li>This dataset contains raw data from the flow cytometry experiments (FACS Canto II, BD Bioscience) in all measured conditions in RData format. Raw fcs files were processed with the tools described in the publication ''Using fluorescence flow cytometry data for single-cell gene expression analysis in bacteria" published here: <a href="https://doi.org/10.1371/journal.pone.0240233">https://doi.org/10.1371/journal.pone.0240233</a>. The tools themselves are available here: <a href="https://github.com/vanNimwegenLab/E-Flow">https://github.com/vanNimwegenLab/E-Flow</a>. Included in the files are the outputs of these processing tools together with all raw values that came directly from the flow cytometer. The file <em>directory_structure_in_preprocessed </em>contains information about how the files are organized.</li> </ul> <p><strong>2. info_files: </strong>This is a set of csv files containing detailed information about the experiments done to acquire the preprocessed_datasets as well as annotation files that we used to retrieve promoter information. </p> <p><strong>3. processed_datasets:</strong> These files correspond to the processed datasets from the raw Rdata files under 1 above. The processed data provide mean and variance estimates in fluorescence of E.coli promoters across the different growth conditions. Note that we discarded flow cytometry measurements from promoter/growth-condition combinations that contained abnormal fluorescence distributions (due to contamination) as well as measurements from reporters with annotation mismatches. The folder contains the following clean dataset files that were used in the paper:</p> <ul> <li><strong>FULL_dataset_mean_var_wreplicates:</strong> In this dataset we include the processed means and variances (in both logarithmic and linear scale) of all promoters in each condition. Included as well are replicate measurements for some conditions.. We also include the name and Blattner number of the gene immediately downstream of each promoter, the DNA sequence of each promoter, and regulatory information (number of unique inputs for transcription factors sites and their names) which we obtained from RegulonDB v 10.5 (<a href="https://doi.org/10.1093/nar/gky1077">https://doi.org/10.1093/nar/gky1077</a>). </li> <li><strong>dataset_with_noise_estimates: </strong>In this dataset we provide noise estimates for all promoters expressed above an expression threshold (mean GFP fluorescence at least as large as autofluorescence). Note that the noise estimate correspond to the difference between the promoter’s variance in log-expression and the minimal variance as a function of its mean expression (i.e. the so called noise floor was subtracted). Apart from the mean, variance, noise and promoter features (sequence, name of gene downstream, number of unique regulatory inputs and name of the TFs binding), we also include the parameters used for fitting the minimal noise, i.e. noise floor, in each of the conditions. </li> <li><strong>time_course_data_SI</strong>: This dataset contains mean and variance measurements of one of the plates of the library measured at different time points during growth in Minimal media 0.4M NaCl: 0h (just after dilution), 1h, 2h, 3h, 5h, 6.5h, 8.5h, 10h and 11h. </li> <li><strong>growth_curves_SI</strong>: Growth data (OD<sub>600</sub> as a function of time) for a subset of the promoters from the library across different growth conditions.</li> <li><strong>singlecell_areas_SI: </strong>Single-cell areas estimated using agar patches of cells growing in each condition. Each row of the table contains data for a single-cell. </li> <li><strong>synthetic_promoters_dataset: </strong>This dataset contains mean, variance and noise measurements of a set of constitutive promoters from <a href="https://doi.org/10.7554/eLife.05856.001">https://doi.org/10.7554/eLife.05856.001</a> across different conditions.</li> <li><strong>MARA_results:</strong> All transcription factor activities results explaining measured noise levels in each condition. This data has been obtained after performing Motif Activity Response Analysis on the noise levels of all measured promoters in each condition.</li> </ul>
Immune disease variants modulate gene expression in regulatory CD4+ T cells
<p>We mapped genetic regulation (QTL) of gene expression and chromatin activity in Tregs and we identified 133 colocalizing loci with immune disease variants.<br> For the time being, the preprint DOI: <a href="https://doi.org/10.1101/654632">10.1101/654632</a></p>
Dataset Klussmeier et al., Secretin receptor as a target in gastrointestinal cancer: expression analysis and ligand development
<p>Numerical dataset for the manuscript by Klussmeier et al., Secretin receptor as a target in gastrointestinal cancer: expression analysis and ligand development.</p>
Predicted gene expression in ancestrally diverse populations leads to discovery of susceptibility loci for lifestyle and cardiometabolic traits
<p>Full summary statistics for the publication "Predicted gene expression in ancestrally diverse populations leads to discovery of susceptibility loci for lifestyle and cardiometabolic traits". </p> <p>The files, bmi.UKBBsummary.txt and height.UKBBsummary.txt, contain tissue specific associations with body mass index (BMI) and height respectively. The suffix UKBB450k indicates results from all ~450,000 European ancestry individuals in UK Biobank. The suffix UKBB50k corresponds to results from a subset of 50,000 Europeans in the UK Biobank. The suffix PAGE corresponds to results from ~50,000 individuals in the Population Architecture using Genomics and Epidemiology (PAGE) study. </p> <p>The file PAGE_PrediXcan_associations.txt includes trait~tissue specific GReX associations for 25 traits. The first field specifies the tissue.trait.gene of the association results.</p>
Summary Statistics from "Genetically regulated gene expression and proteins revealed discordant effects" (LWAS of biomarker)
<p>Summary statistics of 92 blood protein levels. The corresponding publication is currently under revision.</p> <p> The zipped txt file is tab-delimited and contains the following columns:</p> <ul> <li>protein: protein name abbreviation</li> <li>cytoband: genomic region</li> <li>gene: gene name abbreviation</li> <li>setting: either "combined" (adj. for sex & age) or sex-stratified ("males", "females"; adj. for age)</li> <li>variant_id_hg19: SNP ID according to hg19</li> <li>variant_id_hg38: SNP ID according to hg19</li> <li>chr: chromosome</li> <li>pos_hg19: base position according to hg19</li> <li>pos_hg38: base position according to hg19</li> <li>effect_allele: also known as counted allele in additive model</li> <li>other_allele: not-counted allele</li> <li>eaf: effect allele frequency</li> <li>maf: minor allele frequency</li> <li>info: imputation info score</li> <li>n_samples: number of samples</li> <li>beta: effect estimate</li> <li>se: standard error</li> <li>zscore: Z-statistic</li> <li>pvalue: p-value</li> <li>FDR: FDR by gene and setting</li> <li>BBFDR: hierarchical FDR by setting</li> <li>hierFDR: TRUE if SNP is significant after hierarchical FDR</li> </ul>
Gene expression and splicing counts from 49 tissues from GTEx v8 genome build hg38 - non-strand specific
<p><strong>Dataset description:</strong></p> <p>49 folders, each corresponding to one tissue from GTEx v8 and containing the following files:</p> <ol> <li> <p>geneCounts: gene-level counts </p> </li> <li> <p>k_j: split counts spanning from one exon to another.</p> </li> <li> <p>k_theta: non-split counts covering a splice site</p> </li> <li> <p>n_psi3: total split counts from a given acceptor site</p> </li> <li> <p>n_psi5: total split counts from a given donor site</p> </li> <li> <p>n_theta: total split and non-split counts for a given splice site</p> </li> <li> <p>Sample annotation describing each sample from the dataset</p> </li> <li> <p>Description file with global information from the dataset</p> </li> </ol> <p>The gene counts were originated using the GTF file from <a href="https://www.gencodegenes.org/human/release_29.html">release 29 of GENCODE</a>, and the split and non-split counts contain only the annotated junctions from the same release. Statistics are reported only for GENCODE-annotated introns and splice sites, in compliance with the regulations of the GTEx consortium. For a description of the samples, methods, and protocols, see the GTEx publication specified below.</p> <p><strong>Use: </strong>The count matrices are intended to help researchers that are interested in using RNA-Seq data with the purpose of diagnostics. Researchers can merge their own dataset with the downloaded ones, provided the tissue, genome build, strand, and paired-end specifications match. Afterwards, the <a href="https://github.com/gagneurlab/drop">Detection of RNA outliers Pipeline (DROP) </a> can be used to compute gene expression and splicing outliers.<br> <strong>Organism:</strong> Homo sapiens<br> <strong>Genome assembly:</strong> hg38<br> <strong>Gene annotation:</strong> gencode29<br> <strong>Strand specific: </strong>FALSE<br> <strong>Paired end: </strong>TRUE<br> <strong>Protocol: </strong>poly(A) enrichment</p> <p><strong>Contact:</strong> Vicente A. Yepez, yepez at in.tum.de; Christian Mertes, mertes at in.tum.de; Julien Gagneur, gagneur at in.tum.de</p> <p><strong>Citation:</strong> Write the following in the "Data availability" section of the manuscript or similar replacing the three citations by the ones from the References section below:</p> <blockquote> <p><strong>The count matrices for the GTEx samples <cite GTEx publication, see below> were downloaded from Zenodo (doi: 10.5281/zenodo.6078397) and were generated through DROP <cite DROP, see below> using the release 29 of the GENCODE annotation <cite GENCODE, see below>. </strong></p> </blockquote> <p>Also, write the following in the Acknowledgements section:<br> </p> <blockquote> <p><strong>The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The raw data used for the analyses described in this manuscript were obtained from the GTEx Portal on June 12, 2017, under accession number dbGaP phs000424.v8.p2.</strong></p> </blockquote> <p> </p>
Mars Express Power Challenge dataset
<p>The Mars Express Power Challenge dataset is the official dataset of <strong>ESA's Kelvins</strong> <strong>competition for "Mars Express Power Challenge"</strong>. It consists of a training set of 3 Martian Year composed of context and electric current measurements data of ESA's Mars Express spacecraft.</p> <p>Goal of the competition was to predict the average electric current per hour. A test set of a fourth Martian year is provided for this purpose.</p> <p>Detailed information about the related competition can be found at <a href="https://kelvins.esa.int/mars-express-power-challenge/home/">https://kelvins.esa.int/mars-express-power-challenge/home/</a>.</p>
Integrating differential expression and weighted correlation network analysis for identifying genes controlling shoot development in Sorghum bicolor
<p>Supplementery materials of journal article "Integrating differential expression and weighted correlation network analysis for identifying genes controlling shoot development in <em>Sorghum bicolor</em>"</p>
Beethoven's Indications of Tempo and Expression
<p><strong>Database: Beethoven’s Indications of Tempo and Expression</strong></p> <p>This data set was created during 2012-2016 as a tool for my doctorate research on Beethoven’s tempo indications at the University of Manchester. It provides an unprecedented overview of Beethoven’s entire published oeuvre, both with opus numbers and without, by documenting the time signature, tempo indication, indicators of expression, and metronome marks for each individual section of each piece. This data set has been used for my doctoral project and its spin-off publications to achieve the following objectives:</p> <p>The first objective was to create an understanding of how Beethoven thought about tempo through the consideration of the surviving documentary evidence. This was achieved by combining the theoretical principles of tempo that Beethoven was familiar with and subscribed to earlier in his life and observing how these principles played out in the context of his entire oeuvre. Here, Beethoven’s own metronome marks served as benchmarks of the indicative speeds, with the later editorial speeds by Czerny, Moscheles, and Holz also being considered relevant in the absence of authoritative indications. (Admittedly, subsequent research has somewhat complicated the connection between these later editorial indications and Beethoven's practices.) The result is not only a detailed overview of how Beethoven used different combinations of note values, metres, and tempo indications to indicate the desired speed — or, in a small number of cases, how he used this system inconsistently or erroneously — but also exposed a number of longstanding errors in the literature. A relevant example of the latter is the metronome mark for the <em>Schreckensfanfare</em> that opens the last movement of the Ninth Symphony. The sources for this metronome mark show a discrepancy that in all literature had hitherto been resolved in favour of dotted minim = 66, but using the database I have been able to show that a reading of dotted minim = 96 is actually more plausible in the context of Beethoven's use.</p> <p> The second objective was to provide some overview of how Beethoven used expressive indications, and what they may have intended to convey. This was achieved by studying the definitions of the different expressive indications that Beethoven used (<em>dolce</em>, <em>espressivo</em>, <em>cantabile</em>, etc.), as well as the specific contexts in which these expressions occur within Beethoven’s oeuvre. This was originally planned to be the secondary focus of my doctoral project, but it was ultimately excised from my thesis for reasons of space, and has since been the focus of an independent study. </p> <p> </p> <p><strong>About the database</strong></p> <p>The database consists of four different layers, which in the file represented as individual worksheets. <strong>Opus numbers</strong> contains all of the opus numbers and WoO numbers of the compositions included in the database. In order to not overcomplicate the data, the same variable <em>Opus</em> has been used for Opus numbers and WoO numbers, with the former using 1-138 and the latter 1001-1158. Not included are arrangements made of Beethoven’s works without his involvement or works that are clearly spurious, such as the arrangements opp. 63-4 and the canon WoO 162, respectively. In cases of opus number being used multiple times, such opus 81 or 121, rather than use letters to indicate the different versions as is most common, this dataset represents these as a decimal number (i.e. 72,1; 81,2, etc.). Also included are the place and publisher of the first edition used to obtain the indications in the rest of the dataset, as well as any relevant comments on the publication.</p> <p> The worksheet <strong>Movements</strong> breaks the opus numbers down into individual pieces, and adds the variables <em>Number</em>, <em>Work title</em>, and <em>Movement</em>, as well as another layer of commentary. The most useful worksheet is <strong>Sections</strong>, which includes the <em>Time Signature</em>, <em>Tempo Markings</em>, <em>Metronome Mark</em>, and localised <em>Indicators of Tempo and Expression</em>. (Should the note values in the metronome marks not display correctly on your computer, please install the Bach Font.) Using Microsoft Excel’s Filter and Sort feature, it is possible to get a complete overview of Beethoven’s uses of these elements for all works included in the database. There is also a <em>Sorting number</em> which can be used to bring the records back into the original order.</p> <p> </p> <p> </p> <p>This database has been used as a research tool in the preparation of the following publications:</p> <p> </p> <p>Noorduin, Marten, <em>Beethoven’s Tempo Indications</em>, PhD thesis, University of Manchester, 2016. <a href="https://www.escholar.manchester.ac.uk/uk-ac-man-scw:302884">https://www.escholar.manchester.ac.uk/uk-ac-man-scw:302884</a></p> <p> </p> <p>——————— , ‘Re-examining Czerny’s and Moscheles’s Metronome Marks for Beethoven’s Piano Sonatas’, <em>Nineteenth-Century Music Review</em>, 15/2 (August 2018), 209–235. <a href="https://doi.org/10.1017/s1479409817000027">https://doi.org/10.1017/s1479409817000027</a></p> <p> </p> <p>——————— , ‘The Metronome Marks for Beethoven’s Ninth Symphony in Context’, <em>Early Music</em>, 49/1 (February 2021), 129–145. <a href="https://doi.org/10.1093/em/caab005">https://doi.org/10.1093/em/caab005</a></p> <p> </p> <p>——————— , ‘Beethoven’s Indicators of Expression in his Piano Works’, in <em>Beethoven and the Piano</em> (Musikforschung der Hochschule der Künste Bern), ed. Leonardi Miucci, Claudio Bacciagaluppi, Daniel Allenbach and Martin Skamletz (Edition Argus, 2023), pp. 118-136. <a href="https://doi.org/10.26045/kp64-6180-006">https://doi.org/10.26045/kp64-6180-006</a></p> <p> </p> <p>——————— , ‘Transcending Slowness in Beethoven’s Late Works’, in <em>Manchester Beethoven Studies</em>, ed. Barry Cooper and Matthew Pilcher (Manchester University Press, 2023), pp. 214-43. <a href="https://doi.org/10.7765/9781526155696.00015" target="_blank" rel="noopener">https://doi.org/10.7765/9781526155696.00015</a></p> <p> </p> <p>The above list is continuously updated. Users of this database are kindly invited to notify me at martennoorduin@gmail.com with comments, questions, or to add their publications to the above list.</p> <p> </p> <p> </p> <p>Marten Noorduin, Berlin, August 2023.</p>
Maximum performance expression is affected by octopamine and antennae removal in Acheta domesticus
<p>Animals in nature seldom use their maximum performance abilities, likely in part due to context-dependent differences in performance motivation. Despite interest in the factors affecting performance expression, the physiological mechanisms underlying variation in performance motivation are poorly understood. We manipulated levels of the biogenic amine octopamine (OA) to test the hypothesis that OA drives motivation to express maximum bite force in male house crickets. We also tested the effect of antenna removal on bite force given prior evidence of potential links among antennaectomy, aggression, and OA. We found that administration of an OA antagonist, epinastine, significantly decreases realized maximum bite force, as does antenna removal. In addition, the performance decrement induced by antennaectomy is abolished by administration of excess OA, and that rescue effect is itself nullified by the simultaneous administration of epinastine. These data show that OA is an important mediator of performance, and thus of performance motivation, in insects, and potentially a promising candidate for the short term manipulation of performance as well.</p>
Host and pathogen gene expression profiles in Necrotizing Soft Tissue Infections
<p>Data underlying the article</p> <p><strong>Analysis of host-pathogen gene association networks reveals patient-specific response to streptococcal and poly-microbial necrotizing soft tissue infections</strong></p> <p>Sanjeevan Jahagirdar<sup>1</sup>, Lorna Morris<sup>2</sup>, Nirupama Benis<sup>3</sup>, Oddvar Oppegaard<sup>4</sup>, Mattias Svenson<sup>5</sup>,<sup> </sup>Ole Hyldegaard<sup>6</sup>, Steinar Skrede<sup>4,7</sup>, Anna Norrby-Teglund<sup>5</sup>, INFECT Study group, Vitor A. P. Martins dos Santos<sup>1,2</sup>, Edoardo Saccenti<sup>1*</sup></p> <p><sup>Contains</sup></p> <p>Data described in 4.3.2 Sample Selection and in Figure 6:</p> <p>INFECT_DualRNASeq_norm_counts_Human.txt Normalised counts from Kallisto mapping to GRCh38 (release 91) for 81 NSTI patients</p> <p>INFECT_DualRNASeq_relative_abundance_Bacteria.txt Relative abundance for all species from HumanN2 mapping for 81 NSTI patients</p> <p>BStrep_filtered_D1 - INFECT_DualRNASeq_relative_abundance_Bacteria.txt filtered for samples classified as Streptococcal from Thänert <em>et al </em>(2019) taken on the day of admission (day 1).</p> <p>BPoly_filtered_D1 - INFECT_DualRNASeq_relative_abundance_Bacteria.txt filtered for samples classified as polymicrobial from Thänert <em>et al </em>(2019) taken on the day of admission (day 1).</p> <p>HStrep_filtered_D1 - INFECT_DualRNASeq_norm_counts_Human.txt filtered for samples classified as Streptococcal from Thänert <em>et al </em>(2019) taken on the day of admission (day 1).</p> <p>HPoly_filtered_D1 - INFECT_DualRNASeq_norm_counts_Human.txt filtered for samples classified as polymicrobial from Thänert <em>et al </em>(2019) taken on the day of admission (day 1).</p>
Gene expression in male and female sticklebacks from populations with convergent and divergent throat coloration
<p class="MsoNormal">Understanding of genetic mechanisms underlying variation in sexual dichromatism remains limited, especially for carotenoid-based colors. We addressed this knowledge gap in a gene expression study with threespine stickleback. We compared male and female throat tissues across five populations, including two in which female red coloration has evolved convergently. We found that the expression of individual genes, gene ontologies, and coexpression networks associated with red female color within a population differed between California and British Columbia populations, suggesting differences in underlying mechanisms. Comparing females from each of these populations to females from populations dominated by dull females, we again found extensive expression differences. For each population, genes and networks associated with female red color showed the same patterns for males only inconsistently. The functional roles of genes showing correlated expression with female color are unclear within populations, whereas genes highlighted through inter-population comparisons include some previously suggested to function in carotenoid pathways. Among these, the most consistent patterns involved <em>TTC39B</em> (Tetratricopeptide Repeat Domain 39B), which is within a known red coloration QTL in stickleback and implicated in red coloration in other taxa.</p>
Gene expression plasticity, genetic variation and fatty acid remodelling in divergent populations of a tropical bivalve species: lipid profiles
<p><span>Ocean warming challenges marine organisms' resilience, especially for species experiencing temperatures close to their upper thermal limits. A potential increase in thermal tolerance might significantly reduce the risk of population decline, which is intrinsically linked to variability in local habitat temperatures.</span></p> <p><span>Our goal was to assess the plastic and genetic potential of response to elevated temperatures in a tropical bivalve model, <em>Pinctada margaritifera</em>. We benefit from two ecotypes for which local environmental conditions are characterized by either large diurnal variations in the tide-pools (Marquesas archipelago) or lower mean temperature with stable to moderate seasonal variations (Gambier archipelago).</span><br><br><span>We explored the physiological basis of individual responses to elevated temperature<em>, </em>genetic divergence as well as plasticity and acclimation by combining lipidomic and transcriptomic approaches.</span><br><br><span>We show that <em>P. margaritifera</em> has certain capacities to adjust to long-term elevated temperatures that was thus far largely underestimated. Genetic variation across populations overlaps with gene expression and involves the mitochondrial respiration machinery, a central physiological process that contributes to species thermal sensitivity and their distribution ranges.</span><br><br><span>Our results present evidence for acclimation potential in <em>P. margaritifera</em> and urge for longer term studies to assess populations resilience in face of climate change.</span></p>
SCADIE: simultaneous estimation of cell type proportions and cell type-specific gene expressions using SCAD-based iterative estimating procedure
<p>This repository contains the source code and data used in the paper: SCADIE: simultaneous estimation of cell type proportions and cell type-specific gene expressions using SCAD-based iterative estimating procedure.1.</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.