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111,247 results for “cells”
Feedback from retinal ganglion cells to the inner retina
<p>This repository contains the data files and codes used in the following paper:</p> <p>Feedback from retinal ganglion cells to the inner retina<br> Anastasiia Vlasiuk, Hiroki Asari<br> PLOS One (accepted)</p> <p>The preprint is also available from bioRxiv 2020.08.30.274514.</p>
Systematic Scoping Literature Review of Embryonic Stem Cells In Vitro Developmental Toxicity Tests: Included publications library
<p>The publications included in the systematic scoping review conducted according to the protocol:<a href="https://zenodo.org/record/2528920">https://zenodo.org/record/2528920</a></p> <p> </p>
Single-Cell Transcriptomics Reveals a Heterogeneous Cellular Response to BK Virus Infection
<p>The files are the Indrops count matrices for BKV and Mock samples corresponding to the 8 experiments and samples described in the Bioproject https://www.ncbi.nlm.nih.gov/bioproject/PRJNA715178</p> <p> </p>
VISION Invited lecture - Microfluidic technologies and their applications in cell biology
<p>Recording and presentation of the invited lecture that took place online on 27 April 2021 - <strong>Thorsten Knoll - Microfluidic technologies and their applications in cell biology.</strong></p> <p>In the past twenty years, microfluidic devices and systems have gained in importance in the field of bioanalytics and biomedicine, not only in research but also in the market. Lab-on-chip systems with microfluidic structures serve for medical tests with body fluids or extractions from fluids. Besides, microfluidic systems are also used for cell handling and culturing, for the mixing of liquids and for measuring quantitative amounts of components in liquid samples.</p> <p>Fraunhofer IBMT develops microfluidic systems for various applications in the field of life sciences. Different miniaturized approaches and solutions exist e.g. for transport studies or toxicological assays with single cells, 2D cell layers or 3D cell aggregates.</p> <p>The online lecture will cover some basic considerations regarding microfluidics and IBMT’s technological solutions for the fabrication of microfluidic devices and their use in different application scenarios. Furthermore, the presentation describes solutions for the integration of the microfluidic devices in a complete set-up comprising of peripheral fluidic components and optical or electrical measurement systems.</p>
MSVermet/Cell-Nuclei-Detection-And-Segmentation-CENet-UNet: Cell nuclei detection and segmentation
<p>Fully automated nuclei detection and segmentation with CE-Net and U-Net on the PanNuke dataset. Repository includes code and dataset.</p>
Supplementary data for: Transposon mutagenesis identifies cooperating genetic drivers during keratinocyte transformation and cutaneous squamous cell carcinoma progression
<p><strong>Supplementary Note 1:</strong></p> <ul> <li>S1 Text: Oncogenomic comparisons between SB candidate Trunk driver genes and their direct orthologs in human Cancer Gene Census; Pyrosequencing analysis of SB-driven keratinocyte cancer models; References.</li> </ul> <p><strong>Supplementary Figures 1-11:</strong></p> <ul> <li>S1 Fig: Overview of genetic crosses to generate SB|Trp53|Onc3 mouse model.</li> <li>S2 Fig: SB insertion patterns in activated and inactivated drivers.</li> <li>S3 Fig: Evaluating the reproducibility of SBCapSeq results from bulk cuSCC and normal skin specimens.</li> <li>S4 Fig. Hierarchical two-dimensional clustering of recurrent events in cuKA and cuSCC.</li> <li>S5 Fig. Curated biological pathways and processes enriched within SB-induced cuSCC.</li> <li>S7 Fig: ZMIZ1 metagene within the TCGA Head & Neck Squamous Cell Carcinoma (hnSCC) RNA-seq dataset.</li> <li>S8 Fig: Clonally selected SB insertions affect trunk driver proto-oncogene expression in SB-cuSCC genomes.</li> <li>S9 Fig: Clonally selected SB insertions affect trunk driver genes by inactivating expression in SB-cuSCC genomes.</li> <li>S10 Fig: CREBBP knockdown does not alter proliferation rate in cuSCC cell lines.</li> <li>S11 Fig: Gross photographs of cuSCC xenograft masses collected at necropsy showing robust TurboGFP expression.</li> <li>S12 Fig: SB T2/Onc3 TG.12740 allele donor position mapping and exclusion for SB Driver Analysis.</li> </ul> <p><strong>Supplementary Tables 1-20:</strong></p> <ul> <li>S1 Table: Tumor incidence and subgroup classifications by cohort.</li> <li>S2 Table: Specimen metafile data for projects sequenced using SBCapSeq protocol with Ion Torrent Proton sequencer.</li> <li>S3 Table: Discovery and progression SB Driver Analysis for cuSCC60_SBC.</li> <li>S4 Table: Trunk SB Driver Analysis for cuSCC60_SBC.</li> <li>S5 Table: Discovery and progression SB Driver Analysis for cuKA11_SBC.</li> <li>S6 Table: Trunk SB Driver Analysis for cuKA11_SBC.</li> <li>S7 Table: Discovery and progression SB Driver Analysis for cuSK32_SBC.</li> <li>S8 Table: SBCapSeq read depth and analysis for 4 cuSCC genomes selected for multi-region resequencing because they had intermixing of cuSCC and cuKA histologies.</li> <li>S9 Table: Enrichr gene set pathway enrichment analysis of cuSCC drivers.</li> <li>S10 Table: Summary of 7 cuSCC transcriptomes selected for whole transcriptome RNAseq analysis.</li> <li>S11 Table: BED file of SBfusion insertions in 7 cuSCC genomes by whole transcriptome RNAseq analysis.</li> <li>S12 Table: Venn diagram for overlap of genes with SBfusion reads detected by whole transcriptome RNAseq analysis and cuSCC60_SBC discovery driver.</li> <li>S13 Table: Venn diagram for overlap of genes with SBfusion reads detected by whole transcriptome RNAseq analysis and all cuSCC drivers.</li> <li>S14 Table: Transcripts per million (TPM) normalized whole transcriptome RNAseq values per gene from RNA isolated from cuSCC genomes with and without Zmiz1 insertions.</li> <li>S15 Table: Fragments Per Kilobase of Transcripts per Million (FPKM) normalized whole transcriptome RNAseq values per gene transcript from RNA isolated from cuSCC genomes with and without Zmiz1 insertions.</li> <li>S16 Table: Normalized microarray values per gene from RNA isolated from cuSCC genomes with and without <em>Zmiz1</em> insertions.</li> <li>S17 Table: Normalized microarray values per probe from RNA isolated from cuSCC genomes with and without <em>Zmiz1</em> insertions.</li> <li>S18 Table: All 289 genes with differential expression analysis from microarray data from RNA isolated from cuSCC genomes with and without Zmiz1 insertions with P<0.0001 and q<0.05.</li> <li>S19 Table: Lentiviral vectors containing shRNAs used in this study.</li> <li>S20 Table: TaqMan probes used in this study.</li> </ul> <p><strong>Supplementary Datasets 1-5:</strong></p> <ul> <li>S1 Data: BED file of SB insertions for cuSCC60_SBC.</li> <li>S2 Data: BED file of SB insertions for cuKA11_SBC.</li> <li>S3 Data: BED file of SB insertions for cuSK32_SBC</li> <li>S4 Data: BED file of SB insertions for 4 cuSCC genomes selected for multi-region resequencing because they had intermixing of cuSCC and cuKA histologies.</li> <li>S5 Data: Numerical data for graphs pertaining to Figure Panels Fig1A; Fig5A–E; Fig6A–B,D; Fig7C–G; Fig8A–B,D–F; Fig9A–I in the paper on the publicly availble <em>PLOS Genetics</em> Web site.</li> </ul>
Insights from Transient Absorption Spectroscopy into Electron Dynamics Along the Ga-Gradient in Cu(In,Ga)Se2 Solar Cells: Data
<p>Excel file with data to all figures published in our article found at https://doi.org/10.1002/aenm.202003446 (Advanced Energy Materials), Synopsis user manual, Matlab code to analyse and fit data</p> <p> </p>
Supplementary data and simulation code - "ADCY10 is a key regulator of cell cycle control"
<p>Experimental FACS data of cell cycle analysis: The folder raw_data_FACS contains the FACS data for different concentrations of bicarbonate (hco3-_rawdata_all2.csv) and for the KH7 experiments (kh7_rawdata_all.csv). The folder summarized_data_input4analysis contains the csv-files that where used for the Bayesian analysis of cell cycle control. Details are given in the headings of the files.</p> <p>We also set up a webpage, where simulations of the suggested mathematical model of the cell cycle can be performed:</p> <p><a href="https://cellcycle-simulation2.herokuapp.com/">https://cellcycle-simulation2.herokuapp.com/</a></p> <p>The results may be used for prediction or experimental design.</p> <p> </p>
Simulation data for "Phylogenetic analysis of migration, differentiation, and class switching in B cells"
<p>Simulation data for https://doi.org/10.1101/2020.05.30.124446.</p> <p>Scripts available at: https://bitbucket.org/kleinstein/projects/src/master/Hoehn2020/</p> <p>data_twostate.tsv: AIRR TSV file of data used for simulations</p> <p>trees_twostate.RData: Tree topologies used to simulate migration</p> <p>simulations.tar.gz: Files used in simulation analyses. File names show:</p> <p><rate (r)>_<rate A to B (r_ab)>_<pi_A>_<repetition></p> <p>laddersims.tar.gz: Files used in ladder tree simulation analyses. File names show:</p> <p><rate (r)>_<rate A to B (r_ab)>_<pi_A>_<number of tips>_<number of trees>_<repetition></p> <p> </p>
Raw data to: "Persistent RNA virus infection is short-lived at the single cell level but leaves transcriptomic footprints"
<p>Raw data underlying the publication by Reuther and Martin et al. entitled "Persistent RNA virus infection is short-lived at the single cell level but leaves transcriptomic footprints"</p>
Dopant Engineering for Spiro-OMeTAD Hole-Transporting Materials towards Efficient Perovskite Solar Cells
<p>Optoelectronic, photovoltaic, and supplementary characterization data for “Dopant Engineering for Spiro-OMeTAD Hole-Transporting Materials towards Efficient Perovskite Solar Cells”, DOI:10.1002/adfm.202102124</p> <ul> <li>CV.zip: Data (cyclic voltammograms) described in Figures S3–S5 in Origin (*opj) file format.</li> <li>EPR.zip: Data (EPR spectra) described in Figure 5, Figure S2, and Table S2 in Origin (*opj) file format.</li> <li>Optical.zip: Data (UV-vis, PL, and TRPL spectra) described in Figure 4 and Table S1 in Origin (*opj) file format.</li> <li>PV.zip: Data (photovoltaic characteristics) described in the Figures 2–3, Table 1, Figure S1, and Figure S6 in Origin (*opj) file format.</li> </ul>
Cancer cell migration followed with TrackMate
<p>Cancer cells migration followed with TrackMate.</p> <p>For more details see https://imagej.net/plugins/trackmate/trackmate-stardist</p> <p> </p>
Cell migration with ERK signalling
<p>Movie following cells expressing ERK and a nuclei staining, tracked with TrackMate and later analyzed with MATLAB.</p> <p>See https://imagej.net/plugins/trackmate/trackmate-stardist for more details.</p> <p> </p>
Tracking cells in Xenopus tissue with TrackMate-MorphoLibJ
<p>Tracking cells in Xenopus tissue with TrackMate-MorphoLibJ.</p> <p>For more details see https://imagej.net/plugins/trackmate/trackmate-morpholibj</p> <p>Image courtesy of Jakub Sedzinski.</p> <p> </p> <p> </p>
Assessment of 3D MINFLUX data for quantitative structural biology in cells
<p>Reanalysed data for "Assessment of 3D MINFLUX data for quantitative structural biology in cells"</p> <p>https://www.biorxiv.org/content/10.1101/2021.08.10.455294v1</p> <p>Contact Hell lab for the raw data</p> <p>https://www.mpibpc.mpg.de/hell</p>
Tracking cell migration with the TrackMate threshold detector
<p>Migrating cells tracked with TrackMate, using the threshold detector.</p> <p>For more information see https://imagej.net/plugins/trackmate/trackmate-thresholding-detector</p> <p> </p>
Tracking Data I/II of the publication "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"
<p>DATA belonging to the paper<br> "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"<br> Katharina Löffler, Tim Scherr, Ralf Mikut<br> doi: https://doi.org/10.1101/2021.03.16.435631</p> <p>-----------------------------</p> <p>To investigate the influence of different segmentation errors on the tracking performance we simulate errorneous segmentation data:<br> - under-segmentation (referred to as "merge" in the folders), over-segmentation("split"), False Negatives ("remove"), combination of the aforementioned errors ("mixed")<br> - percentages: 1,2,5,10,20 of errorneous masks per dataset<br> - runs: 5 randomly initialized runs per combination<br> - datasets: Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ each with two image sequences<br> ---> in total 4 (error types) * 5 (percentage) * 5 (runs) * 2 (data sets) * 2 (image sequences) = 400 datasets</p> <p>The datasets can be recreated by running our code https://git.scc.kit.edu/KIT-Sch-GE/2021-cell-tracking<br> ----------------------------</p> <p>RESULTS<br> We evuated the four tracking algorithms KIT-Sch-GE(1), KTH-SE, MU-Lux-CZ and our proposed algorithm on the aforementioned datasets and compare their performance using the CTC metrics DET, SEG and TRA.<br> This repository contains all metrics as xls files and all tracking results as image sequences.</p> <p><br> <strong>xls files</strong><br> -----------<br> compare_all_trackers_on_synt_bm.csv<br> Comparing the tracking algorithms MU-Lux-CZ, KTH-SE, KIT-Sch-GE(1) and the proposed tracking algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets).<br> Reported scores are DET, SEG and TRA from the Cell Tracking Challenge<br> (Fig8 and Fig9 and Supplementary Figures 3 and 4 are created from this data)</p> <p><br> compare_postprocessing_on_synth_bm.csv<br> Comparing the different post-processing strategies of the proposed tracking algorithm algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets).<br> Reported scores are DET, SEG and TRA from the Cell Tracking Challenge<br> (Fig7 and Fig8 and Supplementary Figures 1 and 2 are created from this data)</p> <p><strong>PLEASE NOTE: the folder compare_postprocessing_synth_bm is provided in the repository 10.5281/zenodo.5227610 due to size restrictions.</strong></p> <p><strong>folders </strong>(decompressed approximately 90GB of data!)<br> -----------<br> tracking_data<br> compare_all_synth_bm<br> Contains all tracking results for each tracking algorithm on the synthetically degraded datasets ()</p> <p> compare_all_synth_bm_no_error<br> Contains the tracking results for each tracking algorithm provided with the perfect ground truth segmentation data</p> <p> compare_postprocessing_synth_bm [<strong>will be stored in 10.5281/zenodo.5227610 due to size restrictions</strong>]<br> Contains all tracking resuls for each postprocessing configuration of the proposed cell tracking algorithm<br> the leaf folders are names run_xPOSTPROCESSING where x is the run number and POSTPROCESSING the postprocessing key<br> Postprocessing keys: ("no untangle" or "no masks" is indicated by an overline in the paper)<br> all ("untangle + masks" in the paper)<br> nd ("no untangle + masks")<br> nd_ns-l ("no untangle + no masks")<br> ns-l ("untangle + no masks")</p>
Raw data for the article: Human Amnion Epithelial Cells Impair T Cell Proliferation: The Role of HLA-G and HLA-E Molecules
<p>The immunoprivilege status characteristic of human amnion epithelial cells (hAECs) has been recently highlighted in the context of xenogenic transplantation. However, the mechanism(s) involved in such regulatory functions have been so far only partially been clarified. Here, we have analyzed the expression of HLA-Ib molecules in isolated hAEC obtained from full term placentae. Moreover, we asked whether these molecules are involved in the immunoregulatory functions of hAEC. Human amnion-derived cells expressed surface HLA-G and HLA-F at high levels, whereas the commonly expressed HLA-E molecule has been measured at a very low level or null on freshly isolated cells. HLA-Ib molecules can be expressed as membrane-bound and soluble forms, and in all hAEC batches analyzed we measured high levels of sHLA-G and sHLA-E when hAEC were maintained in culture, and such a release was time-dependent. Moreover, HLA-G was present in extracellular vesicles (EVs) released by hAEC. hAEC suppressed T cell proliferation in vitro at different hAEC:T cell ratios, as previously reported. Moreover, inhibition of T cell proliferation was partially reverted by pretreating hAEC with anti-HLA-G, anti-HLA-E and anti-β2 microglobulin, thus suggesting that HLA-G and -E molecules are involved in hAEC-mediated suppression of T cell proliferation. Finally, either large-size EV (lsEV) or small-size EV (ssEV) derived from hAEC significantly modulated T-cell proliferation. In conclusion, we have here characterized one of the mechanism(s) underlying immunomodulatory functions of hAEC, related to the expression and release of HLA-Ib molecules.</p>
Fig. 3. Within the 15 occupied cells, 6 cells had a in Factors limiting the current distribution of the introduced Java sparrow (Lonchura oryzivora) in Bangkok, Thailand
Fig. 3. Within the 15 occupied cells, 6 cells had a percentage of paddyfield cover <1%, 6 cells had 1–20% paddyfield cover and 3 cells had 20–40% paddyfield cover. For the 26 cells with no detections, 16 had a percentage of paddyfield cover <1%, 7 cells had 1–20% paddyfield cover and 3 cells had 20–40% paddyfield cover.
Fig. 2. Forty-one grid cells randomly set within a 10 in Factors limiting the current distribution of the introduced Java sparrow (Lonchura oryzivora) in Bangkok, Thailand
Fig. 2. Forty-one grid cells randomly set within a 10-km radius around Don Muang airport, Bangkok, Thailand, used for surveying Java sparrows. Don Muang was the site where Java sparrows were first recorded in Thailand in 1924. The study area includes three provinces; Bangkok, Pathum Thani, and Nonthaburi. The primary land use was urban, while paddyfields were mostly located on the eastern side of the study area. Different symbols (circles, squares and triangles) represent the number of occasions (out of 5 possible visits to a location) on which the sparrow was detected. Size of symbols of Java sparrow detection points was related to the number of birds detected; smallest size indicated only 1 bird was detected, medium size indicated 2–10 birds were detected, and largest size indicated a group of more than 40 birds was detected.
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.