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54,099 results for “Expression”
Expression and purification of huntingtin interacting protein HMGB1 2017/07/07
<p>Huntingtin structure-function open lab notebook</p>
Expression and purification of three different constructs of the p53 protein 2017/07/15
<p>Huntingtin structure-function open lab notebook </p>
Expression and purification of huntingtin domain construct spanning P80-G428 2017/07/15
<p>Huntingtin structure-function open lab notebook project</p>
Expression and purification of huntingtin interacting protein HMGB1 - 2nd attempt (2017/07/20)
<p>Huntingtin structure-function open lab notebook project</p>
Developmental Changes in the Categorical Processing of Positive and Negative Facial Expressions
<p>Categorical biases in the processing of emotional facial expression have been the subject of much debate in the literature. Opposing views on this topic claim either that positive or negative facial expressions enjoy improved processing in the human brain. The developmental changes in the processing advantages of positive and negative facial expressions are also disputed, with studies using varying paradigms showing seemingly contradictory results. Therefore, to further investigate the development of categorical processing and extraction of emotional information from faces, we tested 6-, 9-, and 12-year-old children, as well as adults, on their ability to categorize various facial expressions as positive or negative as quickly as possible. This was a simplified paradigm designed to explicitly contrast the processing efficiency of positive and negative facial expressions on the broader level of those emotional valence categories, rather than specific single emotional expressions. Our results show an early age processing advantage for positive facial expressions, which disappears in adults who show no such differences in the case of response time measures. In the case of accuracy measures, the early advantage for positive facial expressions gradually disappears, and is reversed into a negativity advantage in adults. These findings demonstrate that category-based positive and negative processing advantages are strongly modulated by age over the course of development, and can exhibit opposite effects depending on the developmental stage of the participant. Here we archive the accuracy and and response time data used for our publication.</p>
Expression and purification of huntingtin domain constructs spanning aa. P80-G428 – 2017/08/09
<p>Huntingtin structure-function open lab notebook project</p>
Expression and purification of huntingtin interaction protein High mobility group protein B1 (HMGB1) – 2017/08/14
<p>Huntingtin structure-function open lab notebook project</p>
Supplementary Information for the manuscript: "Gene expression evolution is predicted by stronger selection at more pleiotropic genes"
<p>This repository contains the supplementary information for the manuscript "<em>Gene expression evolution is predicted by stronger selection at more pleiotropic genes</em>" (<a href="https://doi.org/10.1101/2024.07.22.604294">https://doi.org/10.1101/2024.07.22.604294</a>).</p> <p>The supplementary data "data.tar.gz" is related to the Github repository <a href="https://github.com/charlesrocabert/Koch-et-al-Gene-expression-evolution-is-predictable-and-driven-by-indirect-selection-pressures">https://github.com/charlesrocabert/Koch-et-al-Predictability-of-Gene-Expression</a>.</p> <h2>Content of the repository</h2> <ul> <li><strong>Script S1. </strong>BSFG estimates (50.9 MB).</li> <li><strong>Script S2.</strong> WGCNA analysis and results G1 (99.6 MB).</li> <li><strong>Data S1.</strong> Datasets resulting from the global genomics analysis of the output of the transcriptomics pipeline (5.9 MB).</li> <li><strong>Data S2.</strong> VCF file containing the 566,296 quality-checked SNPs (1.1 GB).</li> <li><strong>Data S3.</strong> VCF file containing the 358,142 SNPs with a call rate higher or equal to 50% (964.3 MB).</li> <li><strong>Data S4.</strong> VCF file containing the imputed genotypes (964.3 MB).</li> <li><strong>Data S5.</strong> Results of the imputation tests (479.5 kB).</li> <li><strong>Data S6.</strong> VCF file containing imputed genotypes where SNPs with a minor allele frequency lower than 0.05 have been filtered out (MAF $\leq$ 0.05) (176.0 MB).</li> <li><strong>Data S7.</strong> List of the 1,273 significant eQTL associations (for gene expression levels or relative fitness as phenotypes) (61.2 kB).</li> <li><strong>Data S8.</strong> List of allele frequency changes (AFCs) for every markers in HD environment, for lines L1, L2, L3, L5, L6, Mx1 and Mx2 (32.4 MB).</li> <li><strong>Data S9.</strong> List of all SNPs indicating if their AFC is significantly higher in each line and their degree of parallelism (2.4 MB).</li> <li><strong>Data S10.</strong> Excel file containing the results of the gene functional enrichment analysis of the hub and eQTL carrier genes (33.7 KB).</li> <li><strong>data.tar.gz.</strong> Dataset mandatory to re-run the genomics analysis (see <a href="https://github.com/charlesrocabert/Koch-et-al-Gene-expression-evolution-is-predictable-and-driven-by-indirect-selection-pressures">https://github.com/charlesrocabert/Koch-et-al-Predictability-of-Gene-Expression</a>) (7.8 GB).</li> </ul> <p> </p>
The Piraeus AIS data set expressed under the vesselAI ontology
<p>See also <a href="https://github.com/gsantipantakis/dataExtraction/releases/">https://github.com/gsantipantakis/dataExtraction/releases/</a> for data extraction to tab separated values (TSV) files.</p>
CRISPR-Cas12a-integrated transgenes in genomic safe harbors retain high expression in human hematopoietic iPSC-derived lineages and primary cells
<p>We identified and characterized potential integration safe harbor sites (SHS) in human cells. Using the CRISPR-MAD7 system, we integrated transgenes at these genomic sites in iPSC, primary T and NK cells, and Jurkat cell line, and demonstrated efficient and stable expression at these loci. Subsequently, we validated the differentiation capabilities of engineered iPSC towards CD34+ hematopoietic stem and progenitor cells (HSPC), lymphoid progenitors (LPC), and natural killer (NK) cells, and showed that transgene expression was retained in these lineages. </p>
Comparison between Parma Polyhedra Library and ParetoLib for computing the validity domain of a parametric Signal Temporal Logic (STL) expression
<p>Comparison between Parma Polyhedra Library and ParetoLib for computing the validity domain of a parametric Signal Temporal Logic (STL) expression. The code and dataset in this folder is related to the example 5 in section 2 of paper "Mining of Extended Signal Temporal Logic Specifications with ParetoLib 2.0" in the journal Formal Methods and System Design.</p>
Stage-specific expression patterns and co-targeting relationships among miRNAs in the developing mouse cortex
<p>Analysis of expression patterns and co-targeting relationships between miRNAs in the embryonic mouse cortex. The following supplementary data are uploaded:</p><p>Supplementary_table_1.xlsx: Differentially expressed miRNAs between E14, E17 and P0 cortical samples as well as in NPCs isolated from the mouse cortex and differentiated into neurons in vitro.</p><p>Supplementary_table_2.xlsx: Weighted co-expression gene network analysis of miRNAs in the embryonic mouse cortex.</p><p>Supplementary_table_3.xlsx: Gene ontology terms of miRNA targets of the black and green modules from the WCGNA analysis.</p><p>Supplementary_table_4.xlsx: Significant co-targeting relationships between miRNAs in the embryonic mouse cortex.</p><p> </p><p> </p>
Data and code for Fitzgerald et al: MDD seeded co-expression networks
<p>Below is a decription of the data and code supplied within this repository related to Fitzgerald et al "Astrocyte fatty acid metabolism as a driver of risk for major depressive disorder"</p> <table> <tbody> <tr> <td>Generated data </td> </tr> <tr> <td>Data</td> <td>About</td> <td> </td> </tr> <tr> <td>All_GTEx_DLPFC_networks.RData</td> <td>Non-thresholded coexpression summary statistics for MDD risk genes in GTEx frontal cortex</td> <td> </td> </tr> <tr> <td>my_big_negative_GTEx_DLPFC_list.RData</td> <td>"All_GTEx_DLPFC_networks.Rdata" data filtered to those genes with R < -0.5 and FDR < 0.05</td> <td> </td> </tr> <tr> <td>my_big_positive_GTEx_DLPFC_list.RData</td> <td>"All_GTEx_DLPFC_networks.Rdata" data filtered to those genes with R > 0.5 and FDR < 0.05</td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>Generated code</td> </tr> <tr> <td>File</td> <td>About</td> <td>Related figure</td> </tr> <tr> <td>Chang_bootstrap.R</td> <td>Bootstrapping of coexpression networks in the Chang et all data for comparing FADS1 coexpressed genes across disease states</td> <td>Fig 4G</td> </tr> <tr> <td>CMC_QC.R</td> <td>Quality control for the common mind consortium data for validation of coexpression networks</td> <td>Supp</td> </tr> <tr> <td>Cont_vs_MDD_modscores.R</td> <td>Generating module scores in snRNA-seq data</td> <td>Fig 4E</td> </tr> <tr> <td>FADS1_clustering.R</td> <td>Clustering of snRNA-seq data using genes coexpressed with FADS1</td> <td>Fig 5</td> </tr> <tr> <td>gene_analysis.sh</td> <td>Annotation of GWAS summary statistics using Hi-C data</td> <td>Fig 2A</td> </tr> <tr> <td>gene_set_analysis.sh</td> <td>GWAS enrichment analysis using MAGMA</td> <td>Fig 6C</td> </tr> <tr> <td>GTEx_coexp_networks.R</td> <td>Generating seeded coexpression networks for MDD risk genes in the GTEx dataset</td> <td>Fig 2B</td> </tr> <tr> <td>GTEx_QC_1.R</td> <td>Filtering of the GTEx dataset</td> <td>NA</td> </tr> <tr> <td>GTEx_QC_2.R</td> <td>Normalisation and regression of technical covariates from the GTEx data</td> <td>NA</td> </tr> <tr> <td>Labonte_et_al_QC.R</td> <td>Quality control, filtering and regression of technical covariates from the Labonte et al dataset</td> <td>Fig 4F</td> </tr> <tr> <td>Milo_analysis.R</td> <td>Neighbourhood based analysis for differentially abundant nuclei between control and MDD nuclei</td> <td>Fig 5H</td> </tr> <tr> <td>Nagy_et_al_astro_subsetting.R</td> <td>Subsetting astrocytes from the full Nagy et al snRNA-seq dataset </td> <td>NA</td> </tr> <tr> <td>Network_analysis.R</td> <td>To generate and analyse a graph of coexpression networks</td> <td>Fig 3E</td> </tr> <tr> <td>NicheNet.R</td> <td>For a NicheNet analysis to infer patterns of cell-cell communication</td> <td>Fig 6F</td> </tr> <tr> <td>Vizium_analysis.R</td> <td>Processing spatial RNA-seq data and generating cell scores for spatial inference of identified cell states</td> <td>Fig 5F</td> </tr> </tbody> </table>
The prenatal hypoxic pathology associated with maternal stress predisposes to dysregulated expression of the chrna7 gene and the subsequent development of nicotine addiction in adult offspring (Supplementary)
Open the record for dataset details and reuse information.
The Significance of Emotional Facial Expression in Understanding Tears: Threat, Sincerity, and Cluster A Personality
Open the record for dataset details and reuse information.
Transcription Factor Co-Expression Mediates Lineage Priming for Embryonic and Extra-Embryonic Differentiation
<p>In early mammalian development, cleavage stage blastomeres and inner cell mass (ICM) cells co-express embryonic and extra-embryonic transcriptional determinants. Using a double protein-based reporter we identify an embryonic stem cell (ESC)population that co-expresses the extra-embryonic factor GATA6 alongside the embryonic factor SOX2. Based on single cell transcriptomics, we find this population resembles the unsegregated ICM, exhibiting enhanced differentiation potential for endoderm while maintaining epiblast competence. To relate transcription factor binding in these to future fate, we describe a complete enhancer set in both ESCs and naïve extra-embryonic endoderm stem cells and assess SOX2 and GATA6 binding at these elements in the ICM-like ESC sub-population. Both factors support cooperative recognition in these lineages, with GATA6 bound alongside SOX2 on a fraction of pluripotency enhancers and SOX2 alongside GATA6 more extensively on endoderm enhancers, suggesting that cooperative binding between these antagonistic factors both supports self-renewal and prepares progenitor cells for later differentiation.</p>
Average Annual Atmospheric CO2 (ppm) with Average Regional Surface Downward Mass Flux of Carbon Dioxide Expressed as Carbon (molC_m²_yr)
<p>The fluctuation in both atmospheric CO2 and regional surface downward mass flux of carbon dioxide expressed as carbon. The annual data for both variable is taken from average monthly measured data.</p>
Genetic variability among and within domestic Old and New World Camels at the α-lactalbumin gene (LALBA) reveals new alleles and polymorphisms responsible for differential expression
<p><strong>Supplementary Figure 1</strong>. Schematic representation of the <em>LALBA</em> gene in camelids. Boxes indicate the exons (black for the un-translated, white for the signal peptide and grey for the translated). The line shows introns, 5’- and 3’-flanking regions.</p> <p><strong>Supplementary Figure 2</strong><strong>. </strong>Alignment of the α-LA calcium-binding site (red rectangle) of 23 terrestrial species available in the UniProt database (<a href="http://www.uniprot.org/uniprot/">http://www.uniprot.org/uniprot/</a>). The calcium-binding site in the <em>Camelus dromedarius</em> is indicated by a yellow rectangle.</p>
AUTH - Multimodal facial expressions dataset
<p>This is a dataset suitable for active and static single modality and multimodal facial expression recognition methods as well as other active perception tasks.. It contains (i) sequences of facial 3D models, (ii) Webots simulation environments and (ii) videos, along with synchronized audio (speech), captured from a grid of virtual cameras at 2 lighting levels. Each part of the dataset can be utilized in a standalone manner, i.e., it can be downloaded and utilized separately from the rest.</p>
time course gene expression of atrial cardiomyocyte differentiation
<p>atrial cardiomyocyte protocol was similar to that described by Cyganek et al. 2018</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.