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22,597 results for “Regulation”
Conserved regulation of RNA processing in somatic cell reprogramming
<p><strong>Data set 1. Transcript expression across human RNA-Seq samples: estimated read counts. </strong>The file contains estimated read counts, generated by kallisto (<a href="https://pachterlab.github.io/kallisto/">https://pachterlab.github.io/kallisto/</a>), for human transcripts and RNA-Seq samples used in this study (see Additional file 2 of the accompanying publication). The format is a compressed (GZIP) tab-separated transcript-by-sample matrix. Ensembl transcript identifiers and a combined Sequence Read Archive study/sample name identifier serve as row and column names, respectively.</p> <p><strong>Data set 2. Transcript expression across murine RNA-Seq samples: estimated read counts. </strong>As in Data set 1, but for mouse transcripts.</p> <p><strong>Data set 3. Transcript expression across simian RNA-Seq samples: estimated read counts. </strong>As in Data set 1, but for chimpanzee transcripts.</p> <p><strong>Data set 4. Transcript expression across across human RNA-Seq samples: estimated transcript abundances. </strong>As in Data set 1, but instead of read counts, transcript abundances in transcripts per million (TPM), as estimated by kallisto (<a href="https://pachterlab.github.io/kallisto/">https://pachterlab.github.io/kallisto/</a>), are listed. Format, column and row names as in Data set 1.</p> <p><strong>Data set 5. Transcript expression across murine RNA-Seq samples: estimated transcript abundances. </strong>As in Data set 4, but for mouse transcripts.</p> <p><strong>Data set 6. Transcript expression across simian RNA-Seq samples: estimated transcript abundances. </strong>As in Data set 4, but for chimpanzee transcripts.</p> <p><strong>Data set 7. Differential expression analyses across human RNA-Seq sample groups: log fold changes. </strong>The file contains log fold changes, inferred by edgeR (<a href="http://bioconductor.org/packages/release/bioc/html/edgeR.html">http://bioconductor.org/packages/release/bioc/html/edgeR.html</a>), for human genes and the RNA-Seq sample group contrasts listed in Additional file 3 of the accompanying publication in a compressed (GZIP) TSV gene-by-comparison matrix. Ensembl gene identifiers and a descriptive contrast identifier serve as row and column names, respectively.</p> <p><strong>Data set 8. Differential expression analyses across murine RNA-Seq sample groups: log fold changes. </strong>As in Data set 7, but for mouse genes.</p> <p><strong>Data set 9. Differential expression analyses across simian RNA-Seq sample groups: log fold changes. </strong>As in Data set 7, but for chimpanzee genes.</p> <p><strong>Data set 10. Differential expression analyses across human RNA-Seq sample groups: false discovery rates. </strong>The file contains false discovery rates (FDR) for the differential expression analyses summarized in Data set 7. Format, column and row names as in Data set 7.</p> <p><strong>Data set 11. Differential expression analyses across murine RNA-Seq sample groups: false discovery rates. </strong>As in Data set 10, but for mouse genes.</p> <p><strong>Data set 12. Differential expression analyses across simian RNA-Seq sample groups: false discovery rates. </strong>As in Data set 10, but for chimpanzee genes.</p> <p><strong>Data set 13. Quantification of alternative splicing events across human RNA-Seq samples. </strong>The file contains ‘percent spliced in’ (PSI) values computed by SUPPA (<a href="https://github.com/comprna/SUPPA">https://github.com/comprna/SUPPA</a>) for annotated alternative splicing events (inferred from the transcript annotation of the human genome, Ensembl release 84; <a href="http://www.ensembl.org/">http://www.ensembl.org/</a>). The format is a compressed (GZIP) tab-separated transcript-by-sample matrix. SUPPA-provided event identifiers and a combined Sequence Read Archive study/sample name identifier serve as row and column names, respectively.</p> <p><strong>Data set 14. Quantification of alternative splicing events across murine RNA-Seq samples. </strong>As in Data set 13, but for mouse alternative splicing events.</p> <p><strong>Data set 15. Differential splicing analyses across human RNA-Seq sample groups: differences in ‘percent spliced in’ (ΔPSI). </strong>The file contains ΔPSI values for human alternative splicing events (as in Data set 13). The RNA-Seq sample group contrasts are listed in Additional file 3 of the accompanying publication. Values were inferred by SUPPA’s diffSplice functionality (<a href="https://github.com/comprna/SUPPA">https://github.com/comprna/SUPPA</a>). The format is a compressed (GZIP) tab-separated gene-by-comparison matrix. SUPPA event identifiers and a descriptive contrast identifier serve as row and column names, respectively.</p> <p><strong>Data set 16. Differential splicing analyses across murine RNA-Seq sample groups: differences in ‘percent spliced in’ (ΔPSI). </strong>As in Data set 15, but for mouse alternative splicing events.</p> <p><strong>Data set 17. Differential splicing analyses across human RNA-Seq sample groups: P values. </strong>The file contains P values for the differential splicing analysis of human alternative splicing events summarized in Data set 15. Format, column and row names as in Data set 15.</p> <p><strong>Data set 18. Differential splicing analyses across murine RNA-Seq sample groups: P values. </strong>The file contains P values for the differential splicing analysis of mouse alternative splicing events summarized in Data set 16. Format, column and row names as in Data set 15.</p> <p><strong>Data set 19. Transcript expression across murine RNA-Seq time course data: estimated read counts. </strong>As in Data set 2, but for the time course data generated for the accompanying publication.</p> <p><strong>Data set 20. Transcript expression across murine RNA-Seq time course data: estimated transcript abundances. </strong>As in Data set 5, but for the time course data generated for the accompanying publication.</p> <p><strong>Data set 21. Quantification of alternative splicing events across murine RNA-Seq time course data. </strong>As in Data set 14, but for the time course data generated for the accompanying publication.</p>
The influence of heart rate variability biofeedback on cardiac regulation and functional brain connectivity
Open the record for dataset details and reuse information.
Conserved mechanism of Xrn1 regulation by glycolytic flux and protein aggregation
<p>This dataset contains the raw and processed microscopy data that form the basis of our research article titled <em><a href="https://www.cell.com/heliyon/fulltext/S2405-8440(24)14817-7" target="_blank" rel="noopener">Conserved mechanism of Xrn1 regulation by glycolytic flux and protein aggregation</a></em> (DOI: <a href="https://kwnsfk27.r.eu-west-1.awstrack.me/L0/https:%2F%2Fdoi.org%2F10.1016%2Fj.heliyon.2024.e38786/1/010201924b2cde3a-4e9b4379-d805-432d-b94e-11d8b32354dc-000000/-WTssLbV_1JADo7ZwuFgWEmy9i0=394" target="_blank" rel="noopener noreferrer">doi.org/10.1016/j.heliyon.2024.e38786</a>) publlished in the <a href="https://www.cell.com/">CellPress</a> journal <a href="https://www.cell.com/heliyon/home">Heliyon</a> (<a title="Go to table of contents for this volume/issue" href="https://www.sciencedirect.com/journal/heliyon/vol/10/issue/19"><span><span>Volume 10, Issue 19</span></span></a>, 15 October 2024, e38786).<em> </em>The paper describes the mechanism underlying the binding of <a href="https://www.yeastgenome.org/locus/S000003141">yeast Xrn1</a> to the plasma membrane microdomain stabiliser <a href="https://doi.org/10.1016/j.cub.2017.11.073">eisosome</a> in a glucose-dependent manner. The images stored in the dataset were acquired with a <a href="https://www.iem.cas.cz/en/devices/zeiss-lsm-880-airyscan-en/">Zeiss LSM 880 confocal microscope</a> performed at the <a href="https://www.iem.cas.cz/en/department/microscopy-unit/">Microscopy Service Centre</a> of the <a href="https://www.iem.cas.cz/en/home-en/">Institute of Experimental Medicine CAS</a> supported by the MEYS CR (LM2023050 <a href="https://www.czech-bioimaging.cz/">Czech-Bioimaging</a>). Detailed step-by-step instructions for live microscopy sample preparation that we follow can be found at protocols.io: <a href="https://www.protocols.io/view/live-cell-microscopy-sample-preparation-yeast-cult-8epv5r23dg1b">https://www.protocols.io/view/live-cell-microscopy-sample-preparation-yeast-cult-8epv5r23dg1b</a>. The quantification of microscopy data was performed using our custom-developed Fiji and R scripts that can be found at <a href="https://github.com/jakubzahumensky/microscopy_analysis">https://github.com/jakubzahumensky/microscopy_analysis</a>. Their use is described in detail in the <a href="https://doi.org/10.1101/2024.03.28.587214">https://doi.org/10.1101/2024.03.28.587214</a>. For further information, please refer to the README file attached to the dataset.</p> <p>Note: This final version of datasets supplements the previous datasets of version 1 (DOI: <a href="https://doi.org/10.5281/zenodo.12748899">10.5281/zenodo.12748899</a>) and version 2 (DOI: <a href="https://doi.org/10.5281/zenodo.13772845">10.5281/zenodo.13772845</a>).</p>
Light-regulated gene expression and alternative splicing data from rice seedlings.
<p>This data contains analyzed data from the experiment conducted on rice seedlings under dark and light conditions. Seeds of rice (Oryza sativa spp. japonica cv. Nipponbare) were sown in the dark and germinated on day 2 and continued to grow in the dark for another 6 days. 3 biological replicates of the dark-grown etiolated shoots were harvested on day 8 after sowing. The remaining dark-grown seedlings were exposed to continuous white light at 120 mol/m2/sec for 48 hours or another 2 days (Days 9 and 10 after sowing). Three replicates of the light-treated green-colored seedling samples were harvested at the end of day 10. Harvested samples were frozen in liquid nitrogen and stored at -80C until further processing.</p>
Emotion regulation in the Ageing Brain, University of Reading, BBSRC
Open the record for dataset details and reuse information.
Microbiome homeostasis on rice leaves is regulated by a precursor molecule of lignin biosynthesis
<p>A GWAS pipeline for identification of the loci associated with >3000 bacterial species (Selected from over 6000 bacterial species of rice Phyllosphere).</p><p> </p>
Simulations of focal and reentrant sources with Acetylcholine regulation in atrial fibrillation
<p><strong>Simulations of focal and reentrant sources with Acetylcholine regulation in atrial fibrillation</strong></p> <p>This contains focal and reentrant sources with Acetylcholine regulation in atrial fibrillation simulations used in the manuscript <strong>Detection of focal source and arrhythmogenic substrate from body surface potentials to guide atrial fibrillation ablation (</strong><a href="https://doi.org/10.1371/journal.pcbi.1009893">https://doi.org/10.1371/journal.pcbi.1009893</a><strong>)</strong>. <strong>Please cite our manuscript if you use our code</strong>.</p> <p>Detailed simulation files for focal and reentrant sources with Acetylcholine regulation in <a href="https://carpentry.medunigraz.at">CARPentry</a>. Tested with CARP GIT commit hash: 2e280733.<br> The formats of .elem, .lon, .pts, .dat and .igb used or produced by carp can be found in <a href="https://carpentry.medunigraz.at/getting-started/file-formats.html">the CARPentry website</a>.</p> <p><strong>Data (<code>data/</code>)</strong></p> <ul> <li><code>Mesh1(.elem, .lon, .pts)</code>: files (elements, fibres, nodes) of a mesh Mesh1 with basic tagging of atrial structures.</li> <li><code>Mesh1_FS_L22.vtx</code>: vertices of a focal site at (αLA=0.2,βLA=0.2\alpha_{LA} = 0.2, \beta_{LA} = 0.2αLA=0.2,βLA=0.2).</li> <li><code>Mesh1_L22_r0(.elem, .lon, .pts)</code>: files (elements, fibres, nodes) of a mesh Mesh1 with basic tagging of atrial structures and tagging of regions (Section 1 - 48) for reentrant sources.</li> <li><code>Mesh1_UAC*.dat</code>: plain text files where each row specifies a Universal Atrial Coordinate ( <code>Mesh1_UAC1.dat</code>: alpha, <code>Mesh1_UAC2.dat</code> : beta, <code>Mesh1_UAC3.dat</code> : LA or RA) of Mesh1 in Roney et al. 2019, which could be used to select vertices and tag elements. We annotated elements with tags of 1-4 and 11-28 for the Universal Atrial Coordinate.</li> <li><code>vest.pts</code>: a <code>.pts</code> file specifying the locations of 252 vest leads.</li> <li><code>Mesh1_ACh_islands.adj</code>: adjustment file specifying the node indices (first column) and concentration of the ACh (second column) for ACh islands.</li> </ul> <p><strong>Par files: parameter files for CARPentry software.</strong></p> <ul> <li><code>Focal_source.par</code>: to simulate a focal source with a CL of 180 ms on the left atrial focal site lasting for 3000 ms.</li> <li><code>Focal_source_ACh.par</code>: to simulate a focal source with a CL of 180 ms lasting for 3000 ms with ACh.</li> <li><code>Reentrant_source.par</code>: to simulate a reentrant source around a left atrial core of (αLA=0.2,βLA=0.2\alpha_{LA} = 0.2, \beta_{LA} = 0.2αLA=0.2,βLA=0.2). To run this file in CARP, the user is advised to compute the initial state files of each segment (<code>init/*.sv</code>) using <code>Reentrant_source_get_init_states.py</code>. This serves as initial states for regions with tags 100 - 147 for the left atral sections of a phase distribution method (Section 100 - 147 refers to the Section 1 - 48 in the main article Fig S1) in <code>Mesh1_L22_r0.elem</code>.</li> </ul> <p><strong>Ionic model</strong></p> <ul> <li><code>CRN_ACH.model</code>: an ionic model file with Acetylcholine introduction of Bayer et al. (2019), with Acetylcholine concentration 0 by default.</li> </ul> <p><strong>Initial conditions for reentrant sources</strong></p> <ul> <li><code>Reentrant_source_get_init_states.py</code>: Python script that output Linux commands to call <code>bench</code> software in CARPentry to initiate the reentrant sources. The produced initial state files <code>init/*.sv</code> are to be used by <code>Reentrant_source.par</code>.</li> </ul> <p><strong>Tags for the atrial structures in the element file (specified in <code>.elem</code>)</strong></p> <ul> <li>1 - Right atrial body</li> <li>2 - Right atrial appendage</li> <li>3 - Sinoatrial node</li> <li>4 - Line of block</li> <li>5 - Coronary sinus</li> <li>6 - Superior vena cava</li> <li>7 - Inferior vena cava</li> <li>8 - Crist terminalis</li> <li>9 - Pectinate muscle</li> <li>10 - Bachman Bundle</li> <li>11- Left atrial body endocardial layer</li> <li>12 - Left atrial body epicardial layer</li> <li>13 - Left atrial appendage endocardial layer</li> <li>14 - Left atrial appendage epicardial layer</li> <li>21, 23, 25 & 27 - endocardial layer of four left atrial PVs</li> <li>22, 24, 26 & 28 - epicardial layer of four left atrial PVs</li> </ul> <p><strong>References</strong></p> <ul> <li>Feng Y, Roney CH, Bayer JD, Niederer SA, Hocini M, Vigmond EJ (2022) Detection of focal source and arrhythmogenic substrate from body surface potentials to guide atrial fibrillation ablation. PLoS Comput Biol 18(3): e1009893.<strong> </strong><a href="https://doi.org/10.1371/journal.pcbi.1009893">https://doi.org/10.1371/journal.pcbi.1009893</a></li> <li>Roney CH, Pashaei A, Meo M, Dubois R, Boyle PM, Trayanova NA, et al. Universal atrial coordinates applied to visualisation, registration and construction of patient specific meshes. Medical Image Analysis. 2019 Jul 1;55:65–75. <a href="https://10.1016/j.media.2019.04.004">https://10.1016/j.media.2019.04.004</a></li> <li>Bayer, et al. (2019). Acetylcholine Delays Atrial Activation to Facilitate Atrial Fibrillation. Frontiers in Physiology, 10, 1105. <a href="https://doi.org/10.3389/fphys.2019.01105">https://doi.org/10.3389/fphys.2019.01105</a></li> </ul>
Catching the audience in a job interview: Effects of emotion regulation strategies on subjective, physiological, and behavioural responses
<p>Dataset used in the publication: Santos, A. C., Arriaga, P., & Simões, C. (2021). Catching the audience in a job interview: Effects of emotion regulation strategies on subjective, physiological, and behavioural responses. Biological Psychology, 162, 108089. <a href="https://doi.org/10.1016/j.biopsycho.2021.108089" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.biopsycho.2021.108089</a></p> <p>Includes: data in SPSS and CSV and codebook. </p> <p>In the emotion regulation process more than one strategy is often used, though studies continue to rely on the manipulation of one strategy alone. This study compares the effects of Combined Cognitive Reappraisal (CCR: acceptance and reappraise via perspective-taking) and suppression using the Trier Social Stress Test (TSST). One hundred participants were randomly assigned to one of the two groups and subjective, physiological, and behavioural data were recorded. Continuous electrocardiography was recorded to measure heart rate variability (HRV) and stress levels. Affective ratings were provided before and after the TSST. Behavioural expressions were videotaped and analysed independently. Trait social anxiety/fear, age and gender entered as covariates. Although no group differences were found on affective ratings, the CCR group presented less physiological stress, higher HRV, their speech was better perceived, displayed more affiliative smile and hand gestures. Results suggested that CCR is more appropriate than suppression for managing social stress situations.</p>
Relief from nitrogen starvation entails quick unexpected down-regulation of glycolytic/lipid metabolism genes in enological Saccharomyces cerevisiae
<p>Data and code supporting the manuscript "Relief from nitrogen starvation entails quick unexpected down-regulation of glycolytic/lipid metabolism genes in enological Saccharomyces cerevisiae" by Tesnière et al. (2019) PLoS ONE 14(4): e0215870. https://doi.org/10.1371/journal.pone.0215870</p> <p>README.pdf or README.md files contain information about the files in this archive.</p>
Supplementary Data to "Disparate regulation of Smad3 phosphorylation and collagen gene transcription by full-length IL-33"
<p>These are Supplementary Figures for the article "Disparate regulation of Smad3 phosphorylation and collagen transcription by full-length IL-33"</p>
DNA Crookedness Regulates DNA Mechanical Properties at Short Length Scales
<p>Data presented in the annual conference DPG conference 2019 (<a href="https://regensburg19.dpg-tagungen.de/">https://regensburg19.dpg-tagungen.de/</a>). </p> <p>Here we discuss how DNA sequence allow us to modulate its structure and mechanical properties, thus proving for the first time a one to one map between sequence and mechanical code. This is intended to provide an overview of the results published in the following peer-reviewed freely available papers:<br> Phys. Rev. Lett. 122, 048102 (2019) [DOI: 10.1103/PhysRevLett.122.048102 or https://doi.org/10.1101/283648]<br> Nanoscale Nanoscale 11, 21471 (2019) [DOI: 10.1039/C9NR07516J]<br> PNAS 114, 7049 (2017) [DOI: 10.1073/pnas.1705642114]<br> Nucleic Acids Research, gkaa225 [DOI: 10.1093/nar/gkaa225]<br> (Please cite them, if you found this information useful.)</p>
Simulated river flow and temperature in regulated river systems in the southeastern United States
<p>Streamflow and stream temperature are important water resources variables, and regional-scale simulations are essential for water resources management and multi-sector assessment for large regions, e.g., regional ecological assessment and power system planning. In large-scale stream temperature modeling practices, reservoir thermal stratification is mostly ignored. We have synthesized a process-based modeling approach, consisting of a series of established models, to simulate streamflow and stream temperature for a complicated river-reservoir system, which explicitly considers thermal stratification. This approach consists of a large-scale, spatially-distributed hydrological model (Variable Infiltration Capacity or VIC; Liang et al., 1994; Hamman et al., 2018), a river routing model (Model for Scale Adaptive River Transport or MOSART; Li et al., 2013), coupled to a spatially-distributed water management model (WM; Voisin et al., 2013, 2017), and a stream temperature model (River Basin Model or RBM; Yearsley, 2009; 2012) that includes a two-layer reservoir thermal stratification module (2L; Niemeyer et al., 2018). To generate this dataset, we applied this modeling approach at a temporal resolution of 1 day and a spatial resolution of 1/8º to river systems in the southeastern United States that include 271 major reservoirs. We used an ensemble of downscaled meteorological forcing data from 20 global climate models (GCM) based on RCP8.5 to simulate potential climate change impacts. This dataset includes simulated river flow and temperatures for both historical (1980-2009; 1980s) and future periods (2070-2099; 2080s). The simulations for the 1980s are based on the gridMet data set (Abatzolglou, 2013), which also forms the basis for the statistical downscaling that is applied to each of the climate models. All simulations for the 2080s are based on downscaled climate model outputs. This dataset includes streamflow and stream temperature using both unregulated and regulated model setups to quantify the impacts of reservoir regulations. The unregulated setup does not account for withdrawals and impoundments in the river system. The stream temperature in the unregulated model setups is constant in each river cross section. In the regulated setup, we explicitly considered reservoir regulation, thermal stratification, and water withdrawal. For a more detailed description of the model configuration, please see Cheng et al. (2020).</p> <p> </p> <p>File structure and filenames: The archive includes two directories, named “streamflow/” and “stream_temperature/”, which contain model output for streamflow and stream temperature, respectively. Within each directory, subdirectories named “regulated/” and “unregulated/” contain model output for the regulated and unregulated model setups, respectively. All data files are in netCDF format and provide model outputs at a temporal resolution of 1 day and a spatial resolution of 1/8º. The unit for streamflow is m<sup>3</sup>/s and the unit for stream temperature is °C.</p> <p> </p> <p>Files are constructed as follows:</p> <p>SERC.<climate simulation>.RCP85.<model setup>.<variable>.nc</p> <p>where</p> <ul> <li><climate simulation> is either ‘historical’ for the simulation that represents the 1980s or an abbreviation that indicates the climate model for the simulations that represent the 2080s. The abbreviations for the climate models are shown in column 1 in the Table below.</li> <li><model setup> is either ‘regulated’ or ‘unregulated’ for the regulated and unregulated model setups, respectively.</li> <li><variable> is either ‘streamflow’ or ‘stream_temperature’ for streamflow and stream temperature, respectively.</li> </ul> <p>More details please see README.pdf</p>
Build-in-Wood Regulation Analysis – Fire Safety
<p>This dataset contains an analysis of selected EU Member State building regulations covering fire safety in residential multi-storey wood buildings. The data has been collected as part of the Build-in-Wood project (<a href="https://www.build-in-wood.eu/)">https://www.build-in-wood.eu/)</a> which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 862820.</p> <p>Disclaimer: The presented data might be outdated, flawed, or otherwise incomplete. Users are responsible for checking the correctness of the presented data.</p>
Build-in-Wood Regulation Analysis – Overview
<p>This dataset contains an overview of selected EU Member State building regulations relevant for construction of multi-storey wood buildings. The data has been collected as part of the Build-in-Wood project (<a href="https://www.build-in-wood.eu/)">https://www.build-in-wood.eu/)</a> which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 862820.</p> <p>Disclaimer: The presented data might be outdated, flawed, or otherwise incomplete. Users are responsible for checking the correctness of the presented data.</p>
Mapping of FoodEx2 Exposure Hierarchy with the food categories of Annex II (part D) of Regulation (EC) No 1333/2008 on food additives
<p>FoodEx2 is a comprehensive food classification and description system aimed at covering the need to describe food in data collections across different food safety domains. All foods, including beverages and food supplements reported in the EFSA Comprehensive Food Consumption Database are coded with the FoodEx2 Exposure Hierarchy. EFSA developed a mapping of all FoodEx2 basic terms reported in the Comprehensive Database with the food categories of Annex II (part D) of Regulation (EC) No 1333/2008 on food additives in order to facilitate the assessment of exposure to food additives. In particular, this mapping has been used in the Food Additives Intake Model 2.0 (FAIM), which allows the estimation of chronic dietary exposure to food additives based on use levels proposed for food categories as presented in Annex II (part D) of Regulation (EC) No 1333/2008 on food additives.</p> <p>Some of the food categories, restrictions and/or exceptions presented in the Regulation could have not been mapped with FoodEx2 basic terms and the original food descriptors and/or FoodEx2 facets might have been used to correctly map all eating events. This information might not be available in this table.</p>
Molecular Details Underlying Dynamic Structures and Regulation of the Human 26S Proteasome
<p>The 26S proteasome is the macromolecular machine responsible for ATP/ubiquitin dependent degradation. As aberration in proteasomal degradation has been implicated in many human diseases, structural analysis of the human 26S proteasome complex is essential to advance our understanding of its action and regulation mechanisms. In recent years, cross-linking mass spectrometry (XL-MS) has emerged as a powerful tool for elucidating structural topologies of large protein assemblies, with its unique capability of studying protein complexes in cells. To facilitate the identification of cross-linked peptides, we have previously developed a robust amine reactive sulfoxide-containing MS-cleavable cross-linker, disuccinimidyl sulfoxide (DSSO). To better understand the structure and regulation of the human 26S proteasome, we have established new DSSO-based in vivo and in vitro XL-MS workflows by coupling with HB-tag based affinity purification to comprehensively examine protein-protein interactions within the 26S proteasome. In total, we have identified 447 unique lysine-to-lysine linkages delineating 67 inter-protein and 26 intra-protein interactions, representing the largest cross-link dataset for proteasome complexes. In combination with EM maps and computational modeling, the architecture of the 26S proteasome was determined to infer its structural dynamics. In particular, three proteasome subunits Rpn1, Rpn6 and Rpt6 displayed multiple conformations that have not been previously reported. Additionally, cross-links between proteasome subunits and 15 proteasome interacting proteins including 9 known and 6 novel ones have been determined to demonstrate their physical interactions at the amino-acid level. Our results have provided new insights on the dynamics of the 26S human proteasome and the methodologies presented here can be applied to study other protein complexes.</p> <p>For more information about how to reproduce this modeling, see https://salilab.org/26S-PIPs or the README file.</p>
Greed is good? Of equilibrium impacts in environmental regulation - Dataset
<p>This dataset contains data and codes required to replicate the results in the article "Greed is good? Of equilibrium impacts in environmental regulation" to be published in Journal of Environmental Economics and Management. See the enclosed Readme for further instructions.</p>
Regulation of Dye-decolorizing Peroxidases Gene Expression in Pleurotus ostreatus Grown on Glycerol as the Carbon Source
<p>This dataset contains the raw data and code necessary to reproduce the results of: Regulation of dye peroxidas gene expression in Pleurotus ostreatus grown on glycerol as the carbon source.</p> <p> </p> <p>These data are also available at github: <a href="https://github.com/JLuisCuamatzi/Pleurotus_ostreatus_CarbonSources">JLuisCuamatzi/Pleurotus_ostreatus_CarbonSources: Data and scripts to reproduce the analysis performed at Regulation of dye peroxidas gene expression in Pleurotus ostreatus grown on glycerol as the carbon source (github.com)</a></p>
Amino Acids Modulate Liquid-Liquid Phase Separation in vitro and in vivo by Regulating Protein-Protein Interactions
<p>The metadata, plots and microscopy images for the manuscript "Amino Acids Modulate Liquid-Liquid Phase Separation in vitro and in vivo by Regulating Protein-Protein Interactions".</p>
Dataset for the article "Frequency regulation with storage: On losses and profits"
<p>This dataset complements the article <em>"Frequency regulation with storage: On losses and profits"</em> by Dirk Lauinger, François Vuille, and Daniel Kuhn, available at <a href="https://doi.org/10.1016/j.ejor.2024.03.022">https://doi.org/10.1016/j.ejor.2024.03.022</a> and at <a href="https://arxiv.org/pdf/2306.02987v2.pdf">https://arxiv.org/pdf/2306.02987v2.pdf</a>. </p> <p>The dataset contains the following files:</p> <p>1. <strong>Case_study.ipynb</strong>, which relies on the datafiles <strong>delta_10s.h5</strong>, <strong>pa.h5</strong>, <strong>pb.h5</strong>, <strong>pd.h5</strong>, and on the excel files <strong>conso_mix_RTE_2019.xls</strong> and <strong>ReserveAjustement_2019.xlsx</strong> to construct all figures in the article. The jupyter notebook is also available at <a href="https://github.com/lauinger/cost-of-frequency-regulation-through-electricity-storage">https://github.com/lauinger/cost-of-frequency-regulation-through-electricity-storage</a>.</p> <p>2. <strong>delta_10s.h5</strong>, which contains normalized frequency deviations with a 10s resolution from 1 January 2015 through 31 December 2019. This dataset is constructed from the raw frequency measurement data in <strong>build_delta_10s.rar</strong>. The frequency measurements are taken from the website of the French transmission system operator RTE: <a href="https://www.services-rte.com/en/download-data-published-by-rte.html?category=public_transmission_system&type=network_frequencies">https://www.services-rte.com/en/download-data-published-by-rte.html?category=public_transmission_system&type=network_frequencies</a> (link live as of 7 June 2023).</p> <p>3. <strong>pa.h5</strong>, which contains availability prices for delivering frequency regulation to RTE from 1 January 2015 through 31 December 2019. This dataset is constructed from the raw price data in <strong>build_price_data.rar</strong>. The availability price data are taken from the website of the French transmission system operator RTE:<br><a href="https://www.services-rte.com/en/download-data-published-by-rte.html?activation_key%3D291fafe6-4f21-4603-810f-0c96b0ea126f%26activation_type%3Dpublic=true&category=market&type=balancing_capacity&subType=procured_reserves">https://www.services-rte.com/en/download-data-published-by-rte.html?activation_key%3D291fafe6-4f21-4603-810f-0c96b0ea126f%26activation_type%3Dpublic=true&category=market&type=balancing_capacity&subType=procured_reserves</a> (link live as of 7 June 2023).</p> <p>4. <strong>pd.h5</strong>, which contains delivery prices for delivering frequency regulation to RTE from 1 January 2015 through 31 December 2019. This dataset is constructed from the raw price data in <strong>build_price_data.rar</strong>. The delivery price data are taken from the website of the French transmission system operator RTE:<br><a href="https://www.services-rte.com/en/download-data-published-by-rte.html?activation_key%3D291fafe6-4f21-4603-810f-0c96b0ea126f%26activation_type%3Dpublic=true&category=market&type=balancing_capacity&subType=actived_offers">https://www.services-rte.com/en/download-data-published-by-rte.html?activation_key%3D291fafe6-4f21-4603-810f-0c96b0ea126f%26activation_type%3Dpublic=true&category=market&type=balancing_capacity&subType=actived_offers</a> (link live as of 7 June 2023).</p> <p>5. <strong>pb.h5</strong>, which contains utility prices for a subscribed apparent power of 9kVA from the state regulated <em>"tarif bleu"</em> of EDF, the largest electricity provider in France. This dataset is constructed from the raw price data in <strong>build_price_data.rar</strong>. Current price data (including taxes and transportation fees) is available at <a href="https://particulier.edf.fr/content/dam/2-Actifs/Documents/Offres/Grille_prix_Tarif_Bleu.pdf">https://particulier.edf.fr/content/dam/2-Actifs/Documents/Offres/Grille_prix_Tarif_Bleu.pdf</a>. The French Energy Department publishes the electricity prices in the official French Government journal. The corresponding legal texts are accessible under the following links:<br>01/11/2014 - 31/07/2015: <a href="https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000033172637">https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000033172637</a><br>01/08/2015 - 31/07/2016: <a href="https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000030954456">https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000030954456</a><br>01/08/2016 - 31/07/2017: <a href="https://www.edf.fr/sites/default/files/contrib/collectivite/electricite-et-gaz/CGV%2018avril/jo_du_29_juillet_2016_trv.pdf">https://www.edf.fr/sites/default/files/contrib/collectivite/electricite-et-gaz/CGV%2018avril/jo_du_29_juillet_2016_trv.pdf</a><br>01/08/2017 - 31/01/2018: <a href="https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000035297675">https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000035297675</a><br>01/02/2018 - 31/07/2018: <a href="https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000036559814">https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000036559814</a><br>01/08/2018 - 31/05/2019: <a href="https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000037262170">https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000037262170</a><br>01/06/2019 - 01/08/2019: <a href="https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000038528381">https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000038528381</a><br>01/08/2019 - 31/12/2019: <a href="https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000038850867">https://www.legifrance.gouv.fr/jo_pdf.do?id=JORFTEXT000038850867</a><br>These prices exclude taxes and transportation fees. The <em>"Option Base"</em> offers a flat price throughout the day. The <em>"Option Heures Creuses"</em> has a higher price for peak (6:00-22:00) than off-peak (22:00-6:00) hours. EDF refers to peak hours as <em>"Heures Pleines (HP)"</em> and to off-peak hours as <em>"Heures Creuses (HC)"</em>. The prices of these options only change, when EDF changes its electricity tariffs, which is up to three times per year. Conversely, the <em>"Option Tempo"</em> is a pricing scheme in which each day is either a high-price (<em>"Rouge"</em>), medium-price (<em>"Blanc"</em>) or low-price (<em>"Bleu"</em>) day. The price level of each day is announced by 10:30 am on the previous day. RTE, the French transmission system operator, keeps track of the daily price levels (<a href="https://www.services-rte.com/en/view-data-published-by-rte/schedule-of-Tempo-type-supply-offerings.html">https://www.services-rte.com/en/view-data-published-by-rte/schedule-of-Tempo-type-supply-offerings.html</a>). The price data can be downloaded from RTE's eco2mix platform (<a href="https://www.rte-france.com/eco2mix/telecharger-les-indicateurs">https://www.rte-france.com/eco2mix/telecharger-les-indicateurs</a>).</p> <p>6. <strong>conso_mix_RTE_2019.xls</strong>, which contains total French electricity demand throughout the year 2019 with 15 minute resolution. This data is provided by RTE, the French electricity system operator, at <a href="https://www.services-rte.com/en/download-data-published-by-rte.html?category=consumption&type=short_term">https://www.services-rte.com/en/download-data-published-by-rte.html?category=consumption&type=short_term</a> (link live as of 27 March 2024).</p> <p>7. <strong>ReserveAjustement_2019.xlsx</strong>, which contains the quantities and availability price for four reserve products: "réserve primaire" (primary frequency regulation, which we use in this article), "réserve secondaire", "réserve rapide", and "réserve complémentaire", with 30 minute resolution throughout the year 2019. This data is provided RTE, the French electricity system operator, at <a href="https://www.services-rte.com/en/download-data-published-by-rte.html?category=market&type=balancing_capacity&subType=procured_reserves">https://www.services-rte.com/en/download-data-published-by-rte.html?category=market&type=balancing_capacity&subType=procured_reserves</a> (link live as of 27 March 2024).</p>
ScienceDex guides
Understand access before you commit
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