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Dataset results
468 results for “Spikes”
Hydroxypropyl-β-cyclodextrin spikes local inflammation that induces Th2 and Tfh responses to the coadministered antigen
GEO Series GSE63332. Mus musculus. 30 samples. Type: Expression profiling by array.
Single-cell RNA-seq with spike-in cells enables accurate quantification of cell-specific drug effects in pancreatic islets [10X]
GEO Series GSE142465. Mus musculus; Homo sapiens. 35 samples. Type: Expression profiling by high throughput sequencing.
Class switch towards non-inflammatory, spike-specific IgG4 antibodies after repeated SARS-CoV-2 mRNA vaccination
GEO Series GSE221320. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
The SARS-CoV-2 Spike protein modifies the transcriptional profile of human cardiac pericytes
GEO Series GSE218644. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Spike-in Experiment for ChIP-chip Simulation
GEO Series GSE9732. Homo sapiens. 3 samples. Type: Genome binding/occupancy profiling by genome tiling array.
ChIP-seq Analysis of Estrogen Receptor-alpha on Treatment with Fulvestrant with internal and spike-in controls
GEO Series GSE102882. Homo sapiens. 39 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Comparative Transcriptomic and Molecular Pathway Analyses of HL-CZ Human Pro-Monocytic Cells Expressing SARS-CoV-2 Spike S1, S2, NP, NSP15 and NSP16 Genes
GEO Series GSE171080. Homo sapiens. 21 samples. Type: Expression profiling by high throughput sequencing.
Preferred analysis methods for Affymetrix GeneChips. II. An expanded, balanced, wholly-defined spike-in dataset
GEO Series GSE21344. Drosophila melanogaster. 18 samples. Type: Other.
Single-cell RNA-seq with spike-in cells enables accurate quantification of cell-specific drug effects in pancreatic islets [Drop-seq]
GEO Series GSE147202. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
Cytotoxic T cells targeting spike glycoprotein are associated with hybrid immunity to SARS-CoV-2
GEO Series GSE223236. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Molecular Docking and Virtual Screening of 716 Candidate Natural Bioactive Compounds Against Spike Receptor-Binding Domain of Coronavirus reveals Octahydroeuclein as possible inhibitor
<p>the data of study shows a possible drug candidate for COVID-19</p>
Immunoblot test results for commercially available antibodies against SARS-CoV-2 spike protein and host proteins relevant to Coronavirus research.
<p>Attached is a description and results of the tested SARS-CoV-2 spike protein and host proteins.</p>
Data from: Mirrored STDP implements autoencoder learning in a network of spiking neurons
The autoencoder algorithm is a simple but powerful unsupervised method for training neural networks. Autoencoder networks can learn sparse distributed codes similar to those seen in cortical sensory areas such as visual area V1, but they can also be stacked to learn increasingly abstract representations. Several computational neuroscience models of sensory areas, including Olshausen & Field's Sparse Coding algorithm, can be seen as autoencoder variants, and autoencoders have seen extensive use in the machine learning community. Despite their power and versatility, autoencoders have been difficult to implement in a biologically realistic fashion. The challenges include their need to calculate differences between two neuronal activities and their requirement for learning rules which lead to identical changes at feedforward and feedback connections. Here, we study a biologically realistic network of integrate-and-fire neurons with anatomical connectivity and synaptic plasticity that closely matches that observed in cortical sensory areas. Our choice of synaptic plasticity rules is inspired by recent experimental and theoretical results suggesting that learning at feedback connections may have a different form from learning at feedforward connections, and our results depend critically on this novel choice of plasticity rules. Specifically, we propose that plasticity rules at feedforward versus feedback connections are temporally opposed versions of spike-timing dependent plasticity (STDP), leading to a symmetric combined rule we call Mirrored STDP (mSTDP). We show that with mSTDP, our network follows a learning rule that approximately minimizes an autoencoder loss function. When trained with whitened natural image patches, the learned synaptic weights resemble the receptive fields seen in V1. Our results use realistic synaptic plasticity rules to show that the powerful autoencoder learning algorithm could be within the reach of real biological networks.
Dataset related to article "ECoG spiking activity and signal dimension are early predictive measures of epileptogenesis in a translational mouse model of traumatic brain injury"
<p>Excel folder with raw data related to the paper 10.1016/j.nbd.2023.106251</p>
Targe Shield (With Spike)
A Scottish Targe Shield I modelled and textured for my College project. It was a great learning experience. Source: Objaverse 1.0 / Sketchfab
Figure 1 from: Bozhanov S, Smerikarova M, Maslarska V (2024) Simultaneous HPLC determination of remdesivir and dexamethasone in the presence of metformin, sitagliptin, and glimepiride in a synthetic mixture and spiked human plasma. Pharmacia 71: 1-10. https://doi.org/10.3897/pharmacia.71.e120463
Figure 1 Chemical structures of RDV, DXM, MTF, STG and GLM.
Figure 2 from: Bozhanov S, Smerikarova M, Maslarska V (2024) Simultaneous HPLC determination of remdesivir and dexamethasone in the presence of metformin, sitagliptin, and glimepiride in a synthetic mixture and spiked human plasma. Pharmacia 71: 1-10. https://doi.org/10.3897/pharmacia.71.e120463
Figure 2 UV spectra of RDV, DXM, MTF, STG and GLM.
Structural basis of Omicron immune evasion: A comparative computational study of Spike protein-Antibody interaction
<p>The COVID-19 pandemic has caused more than 424 million infections and 5.9 million deaths so far. The vaccines used against SARS-COV-2 by now have been able to develop some neutralising antibodies in the vaccinated human population and slow down the infection rate. The effectiveness of the vaccines has been challenged by the emergence of the new strains with numerous mutations in the spike (S) protein of SARS-CoV-2. Since S protein is the major immunogenic protein of the virus and also contains Receptor Binding Domain (RBD) that interacts with the human Angiotensin-Converting Enzyme 2 (ACE2) receptors, any mutations in this region should affect the neutralisation potential of the antibodies leading to the immune evasion. Several variants of concern (VOC) of the virus have emerged so far. Among them, the most critical are Delta (B.1.617.2), and recently reported Omicron (B. 1.1.529) which have acquired a lot of mutations in the spike protein. We have mapped those mutations on the modelled RBD and evaluated the binding affinities of various human antibodies with it. Docking and molecular dynamics simulation studies have been used to explore the effect of the mutations on the structure of the RBD and the RBD-antibody interaction. The analysis shows that the mutations mostly at the interface of a nearby region lower the binding affinity of the antibody by ten to forty per cent, with a downfall in the number of interactions formed as a whole and therefore, it implies the generation of immune escape variants. Notable mutations and their effect was characterised by performing various analyses that explain the structural basis of antibody efficacy in Delta and a compromised neutralisation effect for the Omicron variant. Our results pave the way for robust vaccine design that can be effective for many variants.</p>
Guerrero Copper Spike and Sheathing (2019)
A copper spike sits alongside a wad of crumpled, algae-covered copper sheeting at shipwreck site 2-04-8MO02343, thought to be that of the 1827 pirate slave ship *Guerrero*. The spike is one of many found acros the site. Copper sheeting is also common at the shipwreck, though this piece is bigger than average. Scale = 10 centimeters. Source: Objaverse 1.0 / Sketchfab
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>
ScienceDex guides
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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.