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111,247 results for “cells”
Nanostructured La0.75Sr0.25Cr0.5Mn0.5O3–Ce0.8Sm0.2O2 Heterointerfaces as All-Ceramic Functional Layers for Solid Oxide Fuel Cell Applications
<p>Dataset for article "Nanostructured La<sub>0.75</sub>Sr<sub>0.25</sub>Cr<sub>0.5</sub>Mn<sub>0.5</sub>O<sub>3</sub>–Ce<sub>0.8</sub>Sm<sub>0.2</sub>O<sub>2</sub> Heterointerfaces as All-Ceramic Functional Layers for Solid Oxide Fuel Cell Applications" published in <em>ACS Appl. Mater. Interfaces</em> 2022.</p> <p>The data includes:</p> <ul> <li>Schematic on the nanostructures fabricated for the work (Figure 1)</li> <li>Top view AFM images of the nanostructures studied (Figure 3)</li> <li>TEM-EDX images of the nanostructures studied (Figure 4)</li> <li>ASTAR analysis of the nanostructures studied (Figure 5)</li> <li>X-Ray Diffraction data of thin films with composition: La<sub>0.75</sub>Sr<sub>0.25</sub>Cr<sub>0.5</sub>Mn<sub>0.5</sub>O<sub>3</sub> (LSCrMn), Ce<sub>0.8</sub>Sm<sub>0.2</sub>O<sub>2</sub> (SDC), and two La<sub>0.75</sub>Sr<sub>0.25</sub>Cr<sub>0.5</sub>Mn<sub>0.5</sub>O<sub>3</sub>–Ce<sub>0.8</sub>Sm<sub>0.2</sub>O<sub>2 </sub>(LSCrMn-SDC) nanostructures -bilayer (BL) and nanocomposite (NC)-</li> <li>Electrochemical Impedance Spectroscopy raw data for LSCrMn, SDC and LSCrMn-SDC thin films measured under air and wet hydrogen atmospheres at different temperatures (630-750 ºC)</li> <li>Arrhenius analysis of the area specific resistance (ASR) of the films under air and hydrogen atmospheres</li> <li>In-plane conductivity evolution with temperature data measured under air and 5% hydrogen atmospheres of the two LSCrMn-SDC nanostructures</li> <li>ASR evolution with time measured for over 400 h at 780 ºC</li> </ul>
Database of optical parameters for the simulation of perovskite/silicon solar cells
<p>This dataset contains a set of representative optical parameters, i.e. the wavelength dependent complex refractive index (n+ik), for common materials used in perovskite-silicon tandems. In particular: SnO2, Spiro-MEOTAD, CH3NH3PbI2, a-Si:H, undoped c-Si, MgF.<br> The data are stored in the ASCII file “<em>nk_MaterialParameters.txt</em>”, where for each material, we report three data columns: wavelength (in µm), refractive index <em>n</em> and extinction coefficient <em>k</em>.</p>
Flow manipulation in a Hele-Shaw cell with an electrically-controlled viscous obstruction
<p>The dataset named “Dataset: Flow manipulation in a Hele-Shaw cell with an electrically-controlled viscous obstruction” consists of Raw time-averaged images, which are generated by sequence of 100 frames extracted from experimental videos captured at various voltages (5V, 10V, 15V, 20V, and 50V), and saved as .tif files. These images were analysed to produce the data used in figure 2 and 3 of the article. The dataset also includes two Excel files named as “Figure 2_Experimental data.xlsx” and “Figure 3_Experimental data.xlsx”. These excel files contain the data used to create the experimental plots shown in Figure 2C, and Figure 3 of the research article respectively.</p> <p>In the “Figure 2C_Experimental Data.xlsx” excel file, each sheet corresponds to a different voltage value shown in the figure, and contains three columns: A, B, and C. which represents the X-location, Y-location, and orientation angle (in degrees) of the experimental plot (red rods in the figure) respectively. This plot is overlaid on the model data (black rods in the figure) and displayed in Figure 2C given in the article.</p> <p><span>The “Figure 3_Experimental data.xlsx” file contains three sheets for each voltage (5V, 10V, 15V, 20V, and 50V) and each of these three sheets provide data at three different X-locations (X=579, X= 1079, and X= 1779) as a function of Y-location as shown in the Figure 3 of the article. Each sheet has five columns: A, B, C, D, and E. These columns represent the X-location, Y-location, Orientation angle (in degrees), Coherency, and Error in the orientation angle (in degrees), respectively. These data points are used to create the experimental scatter plot shown in Figure 3 of the article.</span></p>
Datasets: Natural killer cells associate with malignant epithelial cells in the pancreatic ductal adenocarcinoma tumor microenvironment
<p>The following are necessary data files for the manuscript "Natural killer cells associate with malignant epithelial cells in the pancreatic ductal adenocarcinoma tumor microenvironment":</p> <ul> <li>.zip files for TMA_1, TMA_2, TMA_3, and TMA_4 are .mcd files acquired from imaging mass cytometry (IMC) for each slide of the pancreas TMA slide series</li> <li>pancreas_TMA_sample_info.xlxs includes info on all samples of the TMA slide series that were imaged by IMC</li> <li>custom_gates_0.zip includes histoCAT-derived single cell data files from all IMC samples in the pancreas TMA to be used for single cell analyses in R</li> <li>PDAC_IMC.RDS is a Seurat object of the IMC-derived PDAC single cell data to use for single cell and spatial analyses</li> <li>PDAC_sce is a SingleCellExperiment object of IMC-derived PDAC single cell data to use for spatial analyses</li> <li>mat.RDS is a distance matrix of PDAC cell types to use in R to generate network graph (Figure 2)</li> </ul> <p> </p> <p> </p>
An annotated high-content fluorescence microscopy dataset with EGFP-Galectin-3-stained cells and manually labelled outlines
<p>Here we present a benchmarking dataset of fluorescence microscopy images with EGFP-Galectin-3-stained cells together with annotations of their outlines. Images were randomly selected from an RNA interference screen with a modified U2OS osteosarcoma cell line, acquired on a Thermo Fischer CX7 high-content imaging system at 20x magnification. </p> <p>The dataset contains 60 images showing over 2000 labelled nuclear objects in total, which is sufficiently large to train well-performing neural networks for instance or semantic segmentation. It is pre-split into training, development and test set, each in a zip file. The dataset should be referred to as Aitslab_bioimaging2.</p> <p>For most of the images, nuclear staining and annotations have been published previously in the dataset Aitslab_bioimaging1 (https://doi.org/10.5281/zenodo.6657260). The conversion script to produce the png images from the C01 images was published together with this dataset.</p>
Cardelino: Integrating whole exomes and single-cell transcriptomes to reveal phenotypic impact of somatic variants
<p>This dataset consists of the reference data files, metadata and processed results files for the paper "Cardelino: Integrating whole exomes and single-cell transcriptomes to reveal phenotypic impact of somatic variants," which investigates clonality in normal human dermal fibroblast cell populations in 32 cell lines from distinct donors, using bulk whole-exome sequencing and single-cell RNA-sequencing data.</p> <p>This dataset contains everything required to reproduce the results presented in the paper from processed data and results of our data processing workflows. Our analyses can be reproduced using the <a href="https://github.com/davismcc/fibroblast-clonality">source code</a> and instructions available at our <a href="https://davismcc.github.io/fibroblast-clonality/">project website</a>.</p> <p>The <em>entire</em> analysis workflow from raw data to final results is also reproducible but is substantially more complicated and computationally intensive. It also requires large datasets to be obtained from other repositories. Specifically, single-cell RNA-seq data have been deposited in the ArrayExpress database at EMBL-EBI under accession number E-MTAB-7167. Whole-exome sequencing data is available through the HipSci portal (www.hipsci.org). Combined with the dataset in this repository and following the instructions on the project website, it is possible to run our entire analysis pipeline.</p> <p> </p>
Cisplatin enhances cell stiffness and decreases invasiveness rate in prostate cancer cells by actin accumulation: Confocal and atomic force microscopy
<p><strong>Summary</strong></p> <p>Dataset of imaging data related to the publication Raudenska, M., Kratochvilova, M., Vicar, T., Gumulec, J., Balvan, J., Polanska, H. Pribyl, J. & Masarik, M.:Cisplatin enhances cell stiffness and decreases invasiveness rate in prostate cancer cells by actin accumulation. <em>Scientific Reports </em><strong>2019, </strong>9, 1660</p> <p>This dataset includes image data of <em>atomic force microcopy</em> (Young modulus) and <em>confocal microscopy</em>(staining of F-actin and β-tubulin) of prostate cell lines PNT1A, 22Rv1, and PC-3. </p> <p><strong>Materials and Methods</strong></p> <p><em>Cells, cell culture conditions</em></p> <p>Cells confluent up to 50–60% were washed with a FBS-free medium and treated with a fresh medium with FBS and required antineoplastic drug concentration (IC50 concentration for the particular cell line). The cells were treated with 93 µM (PC-3), 38 µM (PNT1A), and 24 µM (22Rv1) of cisplatin (Sigma-Aldrich, St. Louis, Missouri), respectively. IC50 concentrations used for treatment with docetaxel (Sigma-Aldrich, St. Louis, Missouri) were 200nM for PC-3, 70nM for PNT1A, and 150nM for 22Rv1. </p> <p><em>Long-term zinc (II) treatment of cell cultures</em></p> <p>Cells were cultivated in the constant presence of zinc(II) ions. Concentrations of zinc(II) sulphate in the medium were increased gradually by small changes of 25 or 50 µM. The cells were cultivated at each concentration no less than one week before harvesting and their viability was checked before adding more zinc. This process was used to select zinc resistant cells naturally and to ensure better accumulation of zinc within the cells (accumulation of zinc is usually poor during the short-term treatment of prostate cancer cells). Total time of the cultivation of cell lines in the zinc(II)-containing media exceeded one year. Resulting concentrations of zinc(II) in the media (IC50 for the particular cell line) were 50 µM for the PC-3 cell line, 150 µM for the PNT1A cell line, and 400 µM for the 22Rv1 cell line. The concentrations of zinc(II) in the media and FBS were taken into account. </p> <p><em>Actin and tubulin staining</em></p> <p>β-tubulin was labeled with anti- β tubulin antibody [EPR1330] (ab108342) at a working dilution of 1/300. The secondary antibody used was Alexa Fluor® 555 donkey anti-rabbit (ab150074) at a dilution of 1/1000. Actin was labeled with Alexa Fluor™ 488 Phalloidin (A12379, Invitrogen); 1 unit per slide. For mounting Duolink® In Situ Mounting Medium with DAPI (DUO82040) was used. The cells were fixed in 3.7% paraformaldehyde and permeabilized using 0.1% Triton X-100. </p> <p><em>Confocal microscopy</em></p> <p>The microscopy of samples was performed at the Institute of Biophysics, Czech Academy of Sciences, Brno, Czech Republic. Leica DM RXA microscope (equipped with DMSTC motorized stage, Piezzo z-movement, MicroMax CCD camera, CSU-10 confocal unit and 488, 562, and 714 nm laser diodes with AOTF) was used for acquiring detailed cell images (100× oil immersion Plan Fluotar lens, NA 1.3). Total 50 Z slices was captured with Z step size 0.3 μm.</p> <p><em>Atomic force microscopy</em></p> <p>We used the bioAFM microscope JPK NanoWizard 3 (JPK, Berlin, Germany) placed on the inverted optical microscope Olympus IX‑81 (Olympus, Tokyo, Japan) equipped with the fluorescence and confocal module, thus allowing a combined experiment (AFM‑optical combined images). The maximal scanning range of the AFM microscope in X‑Y‑Z range was 100‑100‑15 µm. The typical approach/retract settings were identical with a 15 μm extend/retract length, Setpoint value of 1 nN, a pixel rate of 2048 Hz and a speed of 30 µm/s. The system operated under closed-loop control. After reaching the selected contact force, the cantilever was retracted. The retraction length of 15 μm was sufficient to overcome any adhesion between the tip and the sample and to make sure that the cantilever had been completely retracted from the sample surface. Force‑distance (FD) curve was recorded at each point of the cantilever approach/retract movement. AFM measurements were obtained at 37°C (Petri dish heater, JPK) with force measurements recorded at a pulling speed of 30 µm/s (extension time 0.5 sec).</p> <p>The Young's modulus (E) was calculated by fitting the Hertzian‑Sneddon model on the FD curves measured as force maps (64x64 points) of the region containing either a single cell or multiple cells. JPK data evaluation software was used for the batch processing of measured data. The adjustment of the cantilever position above the sample was carried out under the microscope by controlling the position of the AFM‑head by motorized stage equipped with Petri dish heater (JPK) allowing precise positioning of the sample together with a constant elevated temperature of the sample for the whole period of the experiment. Soft uncoated AFM probes HYDRA-2R-100N (Applied NanoStructures, Mountain View, CA, USA), i.e. silicon nitride cantilevers with silicon tips are used for stiffness studies because they are maximally gentle to living cells (not causing mechanical stimulation). Moreover, as compared with coated cantilevers, these probes are very stable under elevated temperatures in liquids – thus allowing long-time measurements without nonspecific changes in the measured signal.</p> <p><em>Image analysis</em></p> <p>Fluorescence microscopy data were analyzed in ImageJ 1.52h and Python 3.7.1 as follows: cells were manually segmented using actin fluorescence channel, two regions were created for analysis: whole cell and cell periphery, lining a 4 μm thick region around cell border and including most of periphery actin cytoskeleton. In these two regions following parameters were measured for both actin and tubulin fluorescence: Integrated intensity, median intensity, and following regions were measured to describe cell morphology: Cell area, Maximum caliper (max feret diameter), roundness, and aspect ratio. Moreover, stress fibers were manually segmented in every cell and following parameters were measured: number of fibers per cell, feret angle of fiber, integrated intensity, fiber length, mean intensity. Next, a standard deviation of feret angles of individual fibers was calculated relatively to mean of feret angle using a circstd function from scipy package for Python.</p> <p><strong>Identification of files</strong></p> <p><em>Microscopy data</em></p> <p>Files are separated into individual zip files. The dataset of <em>confocal microscopy </em>is separated based on treatments: untreated control, docetaxel-treated cells, cisplatin-treated cells, zinc-treated cells. Filenames actin_tubulin_Zstack_cisplatin.zip, actin_tubulin_Zstack_untreated_control.zip, actin_tubulin_Zstack_zinc.zip, actin_tubulin_Zstack_docetaxel.zip. Files included in these ZIP archives are named as follows: "cellline_treatment_FOV". Files are 3-layer 16bit tiff files with layer sequence as follows: F-Actin (Phalloidin)/b-tubulin/Hoechst 33342. The dataset contains 242 FOVs of three cell line types/three treatments + one control, files are Z-stacks made of 50 slices.</p> <p>The dataset of <em>atomic force microscopy </em>(AFM) is included in one ZIP archive "AFM_YoungModulus_SetpointHeight.zip", which includes data on Young modulus and Setpoint Height of cell lines 22Rv1, PNT1A and PC-3 and treatments zinc, docetaxel, cisplatin (+control), i.e. identical like for confocal microscopy. The file naming is as follows: "AFM_cellline_treatment_FOV_Youngmodulus.tif" for Young modulus and "AFM_cellline_treatment_FOV_setpointheight.tif" for setpoint height. The data are filtered 32-bit tiff images, where the pixel value correspond to cell stiffness (young modulus) in Pa or setpoint height in m.</p> <p><em>Confocal microscopy analysis files</em></p> <p>Following files are csv tables including image analysis of actin/tubulin staining captured by confocal microscope:</p> <p>Cytoskeleton_fluo_analysis_Cell_Cell_periphery_morphology.csv: table includes analyzed data for actin and tubulin staining in following cellular regions: cell, cell periphery. Standard ImageJ parameters regarding intensity and morphology included.</p> <p>Cytoskeleton_fluo_analysis_Fibers.csv: table includes results of manual segmentation and consequent analysis of actin stres fibers in the cells. Apart from standard ImageJ parameters, also number of stress fibers per cell and standard deviation of fiber angle relative to the cell mean angle (for details see methods) are included.</p>
RNA datasets to derive predictors for immune checkpoint inhibitor therapy of non-small cell lung cancer
<p>Nanostring nCounter datasets and corresponding clinical data of tumor samples of patients with advanced NSCLC who received anti-PD-1 immuntherapy. Prospectively divided into a discovery and a validation cohort.</p> <p>Please cite the corresponding publication in Annals of Oncology (10.1093/annonc/mdz049)</p>
Potential of the three-terminal heterojunction bipolar transistor solar cell for space applications
<p>In this video we repeat the presentation we gave at the European Space Power Conference (2019), in JeanLes Pins, describing the potential of the three-terminal heterojunction bipolar transistor solar cell for space applications. There is a paper published in the proceedings of the Conference were you can find more details.</p>
Proteins required for stereocilia elongation during mammalian hair cell development ensure precise and steady heights during adult life
<p>This dataset contains all source data for Hartig <em>et al </em>2024, PNAS, including:</p> <p>Data files</p> <p>Raw images and TDT ABR/DPOAE files</p> <p>ROIS and raw measurements from quantifications in ImageJ</p> <p>R scripts for data visualization and statistics</p> <p>Reports of statistical analyses including diagnostic qq plots and distributions</p>
Underlying data for: "Capturing the mechanosensitivity of cell proliferation in models of epithelium"
<p>For our publication "Capturing the mechanosensitivity of cell proliferation in models of epithelium" (available as a preprint at <a title="BioRXiv Link" href="https://doi.org/10.1101/2023.01.31.526438" target="_blank" rel="noopener">DOI: 10.1101/2023.01.31.526438 )</a> we here provide the raw data for the included plots and the code used to generate the Delayed Fisher Kolmogorov (DFK) data referenced in the main publication</p> <p>The archive '<em>underlying_data.zip</em>' contains raw data underlying the plots in the publication. <br>The archive '<em>puls_proliferation_rate-1.0.zip</em>' contains the code for generating DFK trajectories referenced in the publication and its SI. <br>The archive '<em>ddesolver-1.0.zip</em>' contains the python code for solving delayed differential equations used by the puls_proliferation_rate project. It is included to ensure completeness and reproducibility of the simulations. </p>
Energy recovery by an unbiased gas phase photofuel cell with a nickel foam supported WO3 photoanode decorated with plasmonic gold clusters
<p>Dataset for the article titled "Energy recovery by an unbiased gas phase photofuel cell with a nickel foam supported WO3 photoanode decorated with plasmonic gold clusters".</p> <p>This research was conducted at Antwerp Engineering, Photoelectrochemistry and Sensing (A-PECS) group, University of Antwerp, Belgium.</p>
Backpain exercise therapy remodels human epigenetic profiles in buccal and human peripheral blood mononuclear cells: An exploratory study in young male participants
<pre><strong>###### Files description #####</strong><br> <strong>Notes</strong>. 1) "BT" refers to before therapy and "AT" to after therapy. 2) 0 refers to FALSE and 1 to TRUE for binary variables. The provided files have tab-separated columns except the .RDS which is and R output of the mixOmics DIABLO integration analysis. <strong># Questionnaire</strong> > participants_categories.tsv: per participant (rows), output of the clustering with the participant ("ID") category ("category") per class<br> ("class") > questionnaire_agility_metrics.tsv: questionnaire and agility metrics per participant (rows) for the participants ("ID") with at least one paired AT+BT data in one type of biological sample (indicated in the columns "swab", "PBMC", and "plasma") <strong># PTMs</strong> Samples´ names are encoded as PBMC_AT_8_batch1, i.e. cells origin_time upon therapy_ID_batch (we removed _batch column suffix for the <br>processed files). NA indicates an undetected intensity. > raw_PBMC_light_labelled_intensities.tsv: raw intensity of light/endogenous peptides (row) by precursor per sample (column) from PBMC > raw_swab_light_labelled_intensities.tsv: idem from buccal cells > raw_PBMC_heavy_labelled_intensities.tsv: raw intensity of light/endogenous peptides (row) by precursor per sample (column) from PBMC > raw_swab_heavy_labelled_intensities.tsv: idem from buccal cells > raw_PBMC_heavynormalized_intensities.tsv: raw intensity of light peptides normalized by heavy peptides intensity (row) by precursor per <br>sample (column) > raw_swab_heavynormalized_labelled_intensities.tsv: idem from buccal cells > processed_cleaned_PBMC_log2intensities.tsv: processed (heavy normalized, imputed, batch-corrected) intensity of peptides aggregated by modification (PTM, row) by precursor per sample (column) after log2-transformation. The relative abundances are computed from this file. Rows without me/ac suffix represents the amount of unmodified peptide for the considered site. > processed_cleaned_swab_log2intensities.tsv: idem from buccal cells > rel_abundance_PTM_PBMC.tsv: relative abundance computed per precursor, e.g. for a given sample, the H3_K4+H3_K4me1+H3_K4me2+H3_K4me3 <br>relative abundance values must sum to 100, with the relative abundance of H3_K4 representing the absence of modified K4. > rel_abundance_PTM_swab.tsv: idem from buccal cells > tests_from_rel_abundance_PTM_swab_PBMC.tsv: per type of samples ("Sample.origin", i.e.swab of PBMC) and per PTM (rows, "PTM"), report <br>the output of classic (p-values, adjusted with Benjamini-Hochberg (BH), or Benjamini-Yekutieli procedure (BY), from raw and arcsin square <br>root transformed percentage) and PLS-DA tests (VIP - Variable Importance score - and its 95% confidence interval). The percentage of change<br>of each PTM after therapy relative tobefore therapy is reported in "perc_change.AT.over.BT" column. The "is_candidate" indicates if the PTM has been considered as a hit in the swab or PBMC. <strong># Plasma</strong> Samples´ names are encoded as PLASMA_AT_8_batch1, i.e. cells origin_time upon therapy_ID_batch. NA indicates an undetected intensity. > raw_plasma_maxquant_log2ibaq_intensities.tsv: raw data from protein group MaxQuant file. The iBAQ columns are used in later steps. > processed_cleaned_plasma_log2intensities.tsv: processed (imputed, batch-corrected) intensity of protein groups after log2-transformation. > tests_from_intens_plasma.tsv: per protein group ("Proteins.ID"), report the output of classic (p-values, adjusted Benjamini-Hochberg (BH),<br>or Benjamini-Yekutieli procedure (BY), from log2-transformed intensities) and PLS-DA tests (VIP and its 95% confidence interval). The log2 <br>fold change after therapy relative to before therapy is reported in "log2FC.AT.over.BT" column. The "is_candidate" indicates if the protein group has been considered as a hit. <strong># Integration</strong> > circos_input: output of DIABLO analysis with correlation threshold set to 0.7. Use the readRDS R function to open.</pre> <p> </p>
Subcellular behavior model enables highly precise temporal super-resolved live-cell imaging
<div> <div>This repository contains the preprocessed dataset for [SuB-VFI](https://github.com/sduzzx857/SuB-VFI), including the real datasets we collected and the simulated testing and training datasets. You can refer to the Github repository for details.</div> <div> </div> <div>The simulated testing datasets can be downloaded from [the 2014 ISBI Particle Tracking Challenge](http://bioimageanalysis.org/track/).</div> <div>The EB1 datasets can be downloaded from the paper [The dynamic behavior of the APC-binding protein EB1 on the distal ends of microtubules](https://www.cell.com/current-biology/fulltext/S0960-9822(00)00600-X?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS096098220000600X%3Fshowall%3Dtrue). We used *Movie2* from the Supplementary data.</div> <br> <div>The CCR5 datasets can be downloaded from the paper [Tracking receptor motions at the plasma membrane reveals distinct effects of ligands on CCR5 dynamics depending on its dimerization status](https://elifesciences.org/articles/76281). We used *Video4* in the Results section. </div> <div> </div> <div>The Lysosome datasets can be downloaded from [Content-Aware Frame Interpolation Microscopy Datasets](https://zenodo.org/records/10076346). We used data from the `Zproject` folder within the compressed file `Source_Data_Lysosomes_z-proj_Fig_5.zip`</div> </div>
Identification and characterization of the cell division protein MapZ of Streptococcus suis
<p>Supplementary data and code related to the manuscript "Identification and characterization of the cell division protein MapZ of <em>Streptococcus suis</em>".</p>
GTEx analysis for the paper entitled: The histone variant H2A.J is enriched in luminal epithelial gland cells
<p>H2A.J is a poorly studied mammalian-specific variant of histone H2A. We used immunohistochemistry to study its localization in various human and mouse tissues. H2A.J showed cell-type specific expression with a striking enrichment in luminal epithelial cells of multiple glands including those of breast, prostate, pancreas, thyroid, stomach, and salivary glands. H2A.J was also highly expressed in many carcinoma cell lines and in particular, those derived from luminal breast and prostate cancer. H2A.J thus appears to be a novel marker for luminal epithelial cancers. Knocking-out the H2AFJ gene in T47D luminal breast cancer cells reduced the expression of several estrogen-responsive genes which may explain its putative tumorigenic role in luminal-B breast cancer.</p>
Oral Squamous Cell Carcinoma - Mass Spectrometry Imaging
<p>The dataset was first featured in <a href="https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/abs/10.1002/pmic.201500458">Widlak, Piotr, et al. "Detection of molecular signatures of oral squamous cell carcinoma and normal epithelium–application of a novel methodology for unsupervised segmentation of imaging mass spectrometry data." <em>Proteomics</em> 16.11-12 (2016): 1613-1621</a>. For the tissue sample's biochemical preparation details, please refer to the original publication.</p> <p>The biological material was collected from five patients who underwent surgery due to Oral Squamous Cell Carcinoma (OSCC). Tissue samples contained both tumor and surrounding healthy tissue.</p> <p>Each specimen was cut into 10 µm sections in a cryostat. During the sample preparation for the MS imaging, a high-resolution optical scan of each section was captured.</p> <p>Tissue sections were subjected to peptide imaging with the use of a MALDI ToF mass spectrometer. Spectra were recorded within <em>m/z</em> range of 800-4,000. A raster width of 100 µm was applied, and 400 shots were collected from each ablation point. The obtained dataset consisted of 45,738 raw spectra with 109,568 mass channels.</p> <p>An experienced pathologist analyzed the optical scan obtained during the data acquisition process, and tissue regions were annotated. For the highest confidence of the results obtained in this work, we will focus on the two tissue samples out of the entire dataset (8,005 and 11,869 spectra), which have the highest confidence labels, as explained by the pathologist.</p> <p>The preprocessing of the spectra was conducted in MATLAB. Standard preprocessing steps were applied to the spectra. Spectra were resampled to unify the <em>m/z</em> axis across the dataset. The baseline was removed with MATLAB procedure <em>msbackadj()</em> from the Bioinformatics Toolbox. Peaks were aligned using Fast Fourier Transform-based spectral alignment. The TIC normalization ensured a similar intensity level for all spectra. Finally, a GMM approach was used to model the spectra. GMM locates the peak but also estimates the peak area instead of a raw magnitude provided by most methods. Note that the peaks in MSI spectra are right-skewed, so the neighboring GMM components resulting from that phenomenon were identified and merged to better correspond to actual chemical compounds. The resulting dataset is characterized by 3,714 GMM components corresponding to MSI spectrum peaks.</p>
Synthetic memory circuits for stable cell reprogramming in plants
<p>The data supporting the publication: Synthetic memory circuits for stable cell reprogramming in plants</p>
Merging Bioactivity Predictions from Cell Morphology and Chemical Fingerprint Models Using Similarity to Training Data
<p>The applicability domain of machine learning models trained on structural fingerprints for the prediction of biological endpoints is often limited by the lack of diversity of chemical space of the training data. In this work, we developed “similarity-based merger models” which combined the output of individual models trained on cell morphology (based on Cell Painting) and chemical structure (based on chemical fingerprints) and the structural and morphological similarities of the test compounds to training compounds. We applied these similarity-based merger models using logistic equations to weigh individual features and predicted assay hit calls of 177 assays from ChEMBL, PubChem and the Broad Institute, where the required Cell Painting annotations were available. We found that the similarity-based merger models outperformed other models with an additional 20% assays (79 out of 177 assays) with an AUC>0.70 compared with 65 out of 177 assays using structural models and 50 out of 177 assays using Cell Painting models. Our results demonstrate that similarity-based merger models combining structure and cell morphology models can more accurately predict a wide range of biological assay outcomes and expand the applicability domain by better extrapolating to new structural and morphology spaces.</p>
Spectral decompositions dataset for the paper "Random walk informed heterogeneities detection reveals how the lymph node conduits network influences T-cells collective exploration behavior"
<p>This file contains the left and right approximated eigenvectors, as well as the approximated eigenvalues of the networks analyzed in the paper : Random walk informed heterogeneities detection reveals how<br> the lymph node conduits network influences T-cells collective<br> exploration behavior</p>
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.