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513 results for “prone”
SUMO-targeted ubiquitin ligases (STUbLs) reduce the toxicity and abnormal transcriptional activity associated with a mutant, aggregation-prone fragment of huntingtin
GEO Series GSE115990. Saccharomyces cerevisiae. 11 samples. Type: Expression profiling by high throughput sequencing.
Genome-wide linkage analysis in Spanish melanoma-prone families [Round1]
GEO Series GSE109206. Homo sapiens. 53 samples. Type: Genome variation profiling by SNP array; SNP genotyping by SNP array.
Change-proneness datasets
<p>I performed pre-processing methods on refactoring datasets proposed in (Empirical evaluation of software maintainability based on a manually validated refactoring dataset) by <a href="https://www.sciencedirect.com/science/article/pii/S0950584916303561#!">PéterHegedűs</a> et al. The new version of these datasets support Change-proneness study.</p>
Validation Test of Landslide Prone Areas from the Weighting Results of Slope and Rainfall Parameters in Sragen Regency
<p>This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.</p>
Spike-in and real-world proteomics data sets used in publication of PRONE
<p>Spike-in and real-world data sets used in the evaluation study by Arend et al. (see reference), and some utilized in the vignettes of PRONE, an R package designed for preprocessing, normalization, and performance evaluation of normalization methods of proteomics data. </p> <h3>Overview of the Data Sets</h3> <p>Due to the unavailability of proteomics quantification data from all original publications, data were extracted from alternative sources, which are also listed in the table below. Please refer to the paper's supplementary material and GitHub repository (https://github.com/lisiarend/PRONE.Evaluation) for more comprehensive information on the data sets. A processed metadata file and the protein quantification data file are provided for all data sets. The original quantification data used to generate these two files for each data set are consistently provided in the `original_data` directory of each data set.</p> <p> </p> <div> <table> <tbody> <tr> <td> <p>Data Set</p> </td> <td> <p>Type</p> </td> <td> <p>Quantification Type</p> </td> <td> <p>Raw Data (ID)</p> </td> <td> <p>Quantification Data</p> </td> </tr> <tr> <td> <p>dS1</p> </td> <td> <p>UPS1 spike-in </p> <p>(4 levels)</p> </td> <td> <p>LFQ</p> </td> <td> <p>Tabb et al. <a href="https://www.zotero.org/google-docs/?gkmf6j">[1]</a></p> </td> <td> <p>Välikangas et al. <a href="https://www.zotero.org/google-docs/?Tt81W8">[10]</a> </p> </td> </tr> <tr> <td> <p>dS2</p> </td> <td> <p>UPS1 spike-in </p> <p>(6 levels)</p> </td> <td> <p>LFQ</p> </td> <td> <p>Ramus et al. <a href="https://www.zotero.org/google-docs/?HP8b9X">[2]</a> (PXD001819)</p> </td> <td> <p>Graw et al. <a href="https://www.zotero.org/google-docs/?aiEVfL">[11]</a></p> </td> </tr> <tr> <td> <p>dS3</p> </td> <td> <p>E.coli spike-in </p> <p>(5 levels)</p> </td> <td> <p>LFQ</p> </td> <td> <p>Shen et al. <a href="https://www.zotero.org/google-docs/?zHIDZy">[3]</a></p> <p>(PXD003881)</p> </td> <td> <p>Sticker et al. <a href="https://www.zotero.org/google-docs/?aZ7oZu">[12]</a></p> </td> </tr> <tr> <td> <p>dS4</p> </td> <td> <p>E.coli spike-in </p> <p>(2 levels)</p> </td> <td> <p>LFQ</p> </td> <td> <p>Cox et al. <a href="https://www.zotero.org/google-docs/?scM19M">[4]</a></p> <p>(PXD00279)</p> </td> <td> </td> </tr> <tr> <td> <p>dS5</p> </td> <td> <p>E.coli spike-in </p> <p>(3 levels)</p> </td> <td> <p>TMT 10-plex (1)</p> </td> <td> <p>Zhu et al. <a href="https://www.zotero.org/google-docs/?qAz08E">[5]</a></p> <p>(PXD013277)</p> </td> <td> <p>Phil Wilmarth <a href="https://www.zotero.org/google-docs/?i7xmgP">[13]</a></p> </td> </tr> <tr> <td> <p>dS6</p> </td> <td> <p>yeast spike-in </p> <p>(3 levels)</p> </td> <td> <p>TMT 11-plex (1)</p> </td> <td> <p>O’Connell et al. <a href="https://www.zotero.org/google-docs/?C2XoaJ">[6]</a></p> <p>(PXD007683)</p> </td> <td> <p>Ammar et al. <a href="https://www.zotero.org/google-docs/?UWcyly">[14]</a></p> </td> </tr> <tr> <td> <p>dR1</p> </td> <td> <p>Osteogenic differentiation of hPCLSCs (4 time points)</p> </td> <td> <p>TMT 6-plex (3)</p> </td> <td> <p>Li et al. <a href="https://www.zotero.org/google-docs/?BouGSs">[8]</a> (PXD020908)</p> </td> <td> <p>MaxQuant executed in-house</p> </td> </tr> <tr> <td> <p>dR2</p> </td> <td> <p>Prospective Ovarian JHU Proteome</p> </td> <td> <p>TMT 10-plex (13)</p> </td> <td> <p>Hu et al. <a href="https://www.zotero.org/google-docs/?kVn4pf">[9]</a></p> <p>(PDC000110)</p> </td> <td> <p>MaxQuant executed in-house</p> </td> </tr> <tr> <td> <p>dR3</p> </td> <td> <p>AROM+ transgenic vs. wild-type mice</p> </td> <td> <p>LFQ</p> </td> <td> <p>Vehmas et al. <a href="https://www.zotero.org/google-docs/?qwYM2A">[7]</a></p> <p>(PXD002025)</p> </td> <td> </td> </tr> <tr> <td> <p>dR4</p> </td> <td> <p>Mycobacterium tuberculosis</p> <p>(healthy, disease vs. treated)</p> </td> <td> <p>TMT 10-plex (2)</p> </td> <td> <p>Schmidt et al. <a href="https://www.zotero.org/google-docs/?m1VHIG">[15]</a></p> <p>(PXD030883)</p> </td> <td> </td> </tr> </tbody> </table> </div> <h3>References</h3> <p>[1] D. L. Tabb et al., ‘Repeatability and Reproducibility in Proteomic Identifications by Liquid Chromatography−Tandem Mass Spectrometry’, J. Proteome Res., vol. 9, no. 2, pp. 761–776, Feb. 2010, doi: 10.1021/pr9006365.<br>[2] C. Ramus et al., ‘Spiked proteomic standard dataset for testing label-free quantitative software and statistical methods’, Data Brief, vol. 6, pp. 286–294, Mar. 2016, doi: 10.1016/j.dib.2015.11.063.<br>[3] X. Shen et al., ‘IonStar enables high-precision, low-missing-data proteomics quantification in large biological cohorts’, Proc. Natl. Acad. Sci., vol. 115, no. 21, pp. E4767–E4776, May 2018, doi: 10.1073/pnas.1800541115.<br>[4] J. Cox, M. Y. Hein, C. A. Luber, I. Paron, N. Nagaraj, and M. Mann, ‘Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ *’, Mol. Cell. Proteomics, vol. 13, no. 9, pp. 2513–2526, Sep. 2014, doi: 10.1074/mcp.M113.031591.<br>[5] Y. Zhu et al., ‘DEqMS: A Method for Accurate Variance Estimation in Differential Protein Expression Analysis *’, Mol. Cell. Proteomics, vol. 19, no. 6, pp. 1047–1057, Jun. 2020, doi: 10.1074/mcp.TIR119.001646.<br>[6] J. D. O’Connell, J. A. Paulo, J. J. O’Brien, and S. P. Gygi, ‘Proteome-Wide Evaluation of Two Common Protein Quantification Methods’, J. Proteome Res., vol. 17, no. 5, pp. 1934–1942, May 2018, doi: 10.1021/acs.jproteome.8b00016.<br>[7] A. P. Vehmas et al., ‘Liver lipid metabolism is altered by increased circulating estrogen to androgen ratio in male mouse’, J. Proteomics, vol. 133, pp. 66–75, Feb. 2016, doi: 10.1016/j.jprot.2015.12.009.<br>[8] J. Li et al., ‘Dynamic proteomic profiling of human periodontal ligament stem cells during osteogenic differentiation’, Stem Cell Res. Ther., vol. 12, no. 1, p. 98, Feb. 2021, doi: 10.1186/s13287-020-02123-6.<br>[9] Y. Hu et al., ‘Integrated Proteomic and Glycoproteomic Characterization of Human High-Grade Serous Ovarian Carcinoma’, Cell Rep., vol. 33, no. 3, p. 108276, Oct. 2020, doi: 10.1016/j.celrep.2020.108276.<br>[10] T. Välikangas, T. Suomi, and L. L. Elo, ‘A systematic evaluation of normalization methods in quantitative label-free proteomics’, Brief. Bioinform., vol. 19, no. 1, pp. 1–11, Jan. 2018, doi: 10.1093/bib/bbw095.<br>[11] S. Graw et al., ‘proteiNorm – A User-Friendly Tool for Normalization and Analysis of TMT and Label-Free Protein Quantification’, ACS Omega, vol. 5, no. 40, pp. 25625–25633, Oct. 2020, doi: 10.1021/acsomega.0c02564.<br>[12] A. Sticker, L. Goeminne, L. Martens, and L. Clement, ‘Robust Summarization and Inference in Proteome-wide Label-free Quantification’, Mol. Cell. Proteomics, vol. 19, no. 7, pp. 1209–1219, Jul. 2020, doi: 10.1074/mcp.RA119.001624.<br>[13] ‘understanding_IRS’. Accessed: Mar. 07, 2024. [Online]. Available: https://pwilmart.github.io/IRS_normalization/understanding_IRS.html<br>[14] C. Ammar, M. Gruber, G. Csaba, and R. Zimmer, ‘MS-EmpiRe Utilizes Peptide-level Noise Distributions for Ultra-sensitive Detection of Differentially Expressed Proteins[S]’, Mol. Cell. Proteomics, vol. 18, no. 9, pp. 1880–1892, Sep. 2019, doi: 10.1074/mcp.RA119.001509.<br>[15] F. Biadglegne et al., ‘Mycobacterium tuberculosis Affects Protein and Lipid Content of Circulating Exosomes in Infected Patients Depending on Tuberculosis Disease State’, Biomedicines, vol. 10, no. 4, p. 783, Mar. 2022, doi: 10.3390/biomedicines10040783.</p>
3D ground reaction force data of walking individuals crossing the vibration-prone experimental pedestrian bridge HUMVIB, Darmstadt, Germany
<p>This data set is obtained within the framework of the DFG research project HUMVIB (Project number 446124066) to investigate human-induced vibrations and human-structure interaction on pedestrian bridges. In total, a set of data from 26 subjects for different walking step frequencies was investigated. Each condition was performed on the vibration-prone HUMVIB Bridge at the Lichtwiese campus of the Technical University of Darmstadt with a vertical natural frequency of 2.02 Hz. Each condition was also performed for the system configuration with the optional center support and consequently much higher frequency of 7.72 Hz. Thereby, each condition was repeated for 10 crossings and with each run 5 single steps were recorded by portable Ground Reaction Force Plates (GRFPs). In this case, the sensor plan <a href="https://zenodo.org/api/records/14049724/draft/files/02_HUMVIB_Bridge_with_GRFP.pdf/content" target="_blank" rel="noopener noreferrer">02_HUMVIB_Bridge_with_GRFP.pdf</a> shows that FP1, FP2, FP4, FP5 and FP6 are hit when crossing a bridge. Therefore, under these walking conditions, FP3 only records the mass inertia due to the bridge acceleration or noisy zero signals as a reference.</p> <p>The measured 3D ground reaction forces have been time-synchronized and measured with a sampling rate of 1,000 Hz. Complementary sensors on the structural response (accelerometers, strain gauges, ...) as well as on the subjects themselves (EMG sensors and MoCap) are less relevant for the analysis of the GRFs and will be published as a supplement after the analysis is completed. However, since FP3 is not hidden by a step of the walking subjects, it can also serve as a structural response sensor and thus not only provide the mass inertia but also the structural acceleration of the bridge. The dataset is well suited for the analysis of GRFs as a function of the tuning or structural acceleration between excitation and natural frequencies of walking individuals on vibration-prone structures. Furthermore, based on the given personal characteristics (height, weight, gender, age), an estimation of the influence of the personal parameters is possible. Finally, based on the size of the sample, the inter- and intra-variability of the persons walking can be investigated for verticals but also horizontal GRFs in general.</p>
Pulmonary Function During Prone and Supine Positioning in NICU Infants Requiring Assisted Ventilation
ClinicalTrials.gov study NCT00749762. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Minimally Invasive Esophagectomy (MIE) in Prone Versus Left Decubitus Position
ClinicalTrials.gov study NCT01144325. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Impairment in Glucose Homeostasis Among Obese Adolescents in High Risk Diabetes Prone Population - Jisr Az-Zarqa Village
ClinicalTrials.gov study NCT01535105. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Prone Position and Respiratory Outcomes in Non-Intubated COVID-19 PatiEnts The "PRONE" Study
ClinicalTrials.gov study NCT04517123. IPD Sharing: NO. Countries: 1. Publications: 0.
The Effect of Pressure Controlled Ventilation on the Pulmonary Mechanics in Prone Position Using the Wilson Frame: A Comparison With Volume Controlled Ventilation
ClinicalTrials.gov study NCT01272700. IPD Sharing: Not stated. Countries: 1. Publications: 0.
SuPr Study (Supine Versus Prone Treatment Position in Breast Radiotherapy)
ClinicalTrials.gov study NCT01001728. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Prone Position Assessed by 3D EIT
ClinicalTrials.gov study NCT07083973. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Treatment of Hypoxemic Respiratory Failure and ARDS With Protection, Paralysis, and Proning (TheraPPP) Pathway
ClinicalTrials.gov study NCT04070053. IPD Sharing: NO. Countries: 1. Publications: 0.
The Prone XLIF. A Pilot Study
ClinicalTrials.gov study NCT03509389. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Evaluating the Adequacy of Giro-Couch to Support Prone Breast Boost Irradiation Using IMRT Technique With IGRT.
ClinicalTrials.gov study NCT01016574. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Comparison of Thoracic Epidural Pressure in the Prone and Lateral Decubitus Position
ClinicalTrials.gov study NCT03128788. IPD Sharing: NO. Countries: 1. Publications: 0.
Prone Plank Exercises for Diastasis Rectus Abdominis in Postpartum Women
ClinicalTrials.gov study NCT06259240. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Prone Versus Prone-Flexed Position For Percutaneous Nephrolithotomy (PCNL)
ClinicalTrials.gov study NCT03147950. IPD Sharing: YES. Countries: 1. Publications: 0.
Are Diabetics Type 2 More Prone to Dental Caries?
ClinicalTrials.gov study NCT04883086. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.