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111,247 results for “cells”
ROS-specific Huntingtin Interactions: GFP reactivation assay WT vs HD mouse striatal cells second attempt
<p>Optimization step in the lead up to phenotypic analysis of ROS-dependent huntingtin interacting proteins.</p>
ROS-specific Huntingtin Interactions: GFP reactivation assay WT vs HD mouse striatal cells
<p>Optimization step in the lead up to phenotypic analysis of ROS-dependent huntingtin interacting proteins.</p>
ROS-specific Huntingtin Interactions: GFP reactivation assay optimization in HEK 293 cells
<p>Optimization step in the lead up to phenotypic analysis of ROS-dependent huntingtin interacting proteins.</p>
Analysis of Optical Losses in a Photoelectrochemical Cell: A Tool for Precise Absorptance Estimation
<p>-optical constants of all layers</p> <p>-SCOUT optical model file</p>
Dataset: Quantifying cell densities and biovolumes of phytoplankton communities and functional groups using scanning flow cytometry, machine learning and unsupervised clustering
<p>This dataset contains all relevant data for the manuscript (in submission) "<em>Quantifying cell densities and biovolumes of phytoplankton communities and functional groups using scanning flow cytometry, machine learning and unsupervised clustering</em>".</p> <p>Code written to analyse this dataset (which may be adapted for other flow cytometry datasets) is found at https://zenodo.org/record/999747</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Naming convention for raw flow cytometry data files (located in /Script 3. Generating raw data subset/input/):</p> <p>[Allparameters] _ [Year] - [Month] - [Date] [Hour] [u] [Minute] _ [Depth]</p> <p>e.g: Allparameters_2014-07-31 08u08_1.0m</p> <p>The date, time and depth indicate the location and time at which the measurement was taken.</p>
Red blood cell cytoskeleton tomography data
<p>1) WT-14_MinorClean.mat - Three-dimensional image of a red cell cytoskeleton obtained by cryoelectron tomography. Edges were removed by running the following commands in Matlab:</p> <p>load WT-7_MinorClean.mat;<br> [M, N, P] = size(v);<br> [NN, MM, PP] = meshgrid(1:N,1:M,1:P);<br> v( MM > 790) = 0;<br> v( PP < 12) = 0;<br> v( PP > 70) = 0;</p> <p>To binarize the image, we used a threshold of -40, i.e. each voxel containing an intensity value less than -40 was considered to be a part of the cytoskeleton.</p> <p>2) ActinFilamentMASKED_BIN.mat - Structure of actin protofilament from Protein Data Bank after masking and binning.</p> <p>3) coordsWT-14sk.mat - Positions of actin protofilaments identified inside the tomogram after template matching.</p>
ROS-Specific Huntingtin Interactions: ROS Source Optimization in Mouse Striatal Cells
<p>Optimization step in the lead up to mass spec identification of ROS-specific huntingtin protein-protein interactions. </p>
Data supplementing the article "Avoiding quantification bias in metabarcoding: application of a cell biovolume correction factor in diatom molecular biomonitoring" V. Vasselon, A. Bouchez, F. Rimet, S. Jacquet, R. Trobajo, M. Corniquel, K. Tapolczai, I. Domaizon submitted to Methods in Ecology and Evolution journal
<p>These data supplement the article "Avoiding quantification bias in metabarcoding: application of a cell biovolume correction factor in diatom molecular biomonitoring" V. Vasselon, A. Bouchez, F. Rimet, S. Jacquet, R. Trobajo, M. Corniquel, K. Tapolczai, I. Domaizon submitted to Methods in Ecology and Evolution journal</p> <p>The directory contains the following files:</p> <p>1<strong>5 fastq files raw reads (5 mock communities, 3 replicates)</strong><strong>.rar </strong>- contains the 15 fastq files provided by the sequencing platform with demultiplexed DNA reads (raw data prior any bioinformatics treatments).</p> <p><strong>15 fastq files information.xlsx</strong> :</p> <p>- contains the information relative to the 15 fastq files corresponding to the PGM raw data of the 5 mock communities (sequenced with 3 replicates), including: the ID of the fastq files, the mock community name, the replicate number, the final sample Id and the number of raw reads per fastq file.</p> <p>- contains the information of the proportion of the 8 diatoms species (%) used to create the 5 mock communities (estimated from microscopy).</p>
Generation of fluorescently labelled OP9 stromal cells for growing patient leukemic cells
<p>We added fluorescent labels to OP9 stromal cells to allow us to easily distinguish stromal cells from primary leukemia cells in co-culture growth conditions.</p>
Constructing Lentiviral NSD3-Short-3xFLAG Constructs to Transduce AML Cell Lines
<p>SGC Open Lab Notebook Project - Understanding the Role of NSD3 in Cancer </p> <p>Funding Acknowledgment: The SGC is a registered charity (number 1097737) that receives funds from AbbVie, Bayer Pharma AG, Boehringer Ingelheim, Canada Foundation for Innovation, Eshelman Institute for Innovation, Genome Canada through Ontario Genomics Institute [OGI-055], Innovative Medicines Initiative (EU/EFPIA) [ULTRA-DD grant no. 115766], Janssen, Merck KGaA, Darmstadt, Germany, MSD, Novartis Pharma AG, Ontario Ministry of Research, Innovation and Science (MRIS), Pfizer, São Paulo Research Foundation-FAPESP, Takeda, and Wellcome.</p>
Membrane Fluctuation Model to Understand the Effect of the Receptor Nanoclustering on the Activation of Natural Killer Cells through Biomechanical Feedback
<p>Data for all the plots from original paper "Membrane Fluctuation Model to Understand the Effect of the Receptor Nanoclustering on the Activation of Natural Killer Cells through Biomechanical Feedback".</p>
MR1-restricted T-cell clonotypes are associated with 'resistance' to Mycobacterium Tuberculosis infection - TRA/D immunoSEQ
<p><strong>Summary</strong></p> <ul> <li>Number of files: 39</li> <li>Data format: tsv</li> <li>Data type: TCRA/D immunoSEQ (Adaptive Biotechnologies)</li> <li>immunoSEQ version: v2</li> <li>Sample type: peripheral blood mononuclear cells (PBMCs)</li> <li>Genomic DNA extraction protocol: QIAGEN DNeasy Blood and Tissue kit</li> </ul> <p><strong>Sample description</strong></p> <p>Peripheral blood samples were collected from participants enrolled in a longitudinal cohort study based in Uganda. Donors were classified as either "resistors" (RSTRs, n = 19, defined as concordantly negative for tuberculin skin test and IFNg release assay despite high environmental exposure) or latently infected with M. Tuberculosis (LTBI, n = 20, concordantly as longitudinally positive for tuberculin skin test and IFNg release assay). Genomic DNA (gDNA) was extracted from cryopreserved PBMCs without stimulation or enrichment of T-cells using the Quiagen DNeasy Blood and Tissue kit. Protocol can be found at: https://www.qiagen.com/us/products/discovery-and-translational-research/dna-rna-purification/dna-purification/genomic-dna/dneasy-blood-and-tissue-kit </p>
Vimentin supports cell polarization by enhancing centrosome function and microtubule acetylation
<p>Cell polarity is important for controlling cell shape, motility, and cell division processes. Vimentin intermediate filaments are important for cell migration and cell polarization in mesenchymal cells and assembly of vimentin and microtubule networks is dynamically coordinated, but the precise details of how vimentin mediates cell polarity remain unclear. Here, we characterize the effects of vimentin on the structure and function of the centrosome and the stability of microtubule filaments in wild-type and vimentin-null mouse embryonic fibroblasts (mEFs). We find that vimentin mediates the structure of the pericentrosomal material, promotes centrosome-mediated microtubule regrowth, and increases the level of stable acetylated microtubules in the cell. Loss of vimentin also impairs centrosome repositioning during cell polarization and migration processes that occur during wound closure. Our results suggest that vimentin modulates centrosome structure and function as well as microtubule network stability, which has important implications for how cells establish proper cell polarization and persistent migration.</p>
Supplementary information for: Macrophage- and CD4+ T cell-derived SIV differ in glycosylation, infectivity, and neutralization sensitivity
<p>The human immunodeficiency virus (HIV) envelope protein (Env) mediates viral entry into host cells and is the primary target for the humoral immune response. Env is extensively glycosylated, and these glycans shield underlying epitopes from neutralizing antibodies. The glycosylation of Env is influenced by the type of host cell in which the virus is produced. Thus, HIV is distinctly glycosylated by CD4<sup>+</sup> T cells, the major target cells, and macrophages. However, the specific differences in glycosylation between viruses produced in these cell types have not been explored at the molecular level. Moreover, it remains unclear whether the production of HIV in CD4<sup>+</sup> T cells or macrophages affects the efficiency of viral spread and resistance to neutralization. To address these questions, we employed the simian immunodeficiency virus (SIV) model. Glycan analysis implied higher relative levels of oligomannose-type <em>N</em>-glycans in SIV from CD4<sup>+</sup> T cells (T-SIV) compared to SIV from macrophages (M-SIV), and the complex-type <em>N</em>-glycans profiles seem to differ between the two viruses. Notably, M-SIV demonstrated greater infectivity than T-SIV, even when accounting for Env incorporation, suggesting that host cell-dependent factors influence infectivity. Further, M-SIV was more efficiently disseminated by HIV-binding cellular lectins. We also evaluated the influence of cell type-dependent differences on SIV's vulnerability to carbohydrate-binding agents (CBAs) and neutralizing antibodies. T-SIV demonstrated greater susceptibility to mannose-specific CBAs, possibly due to its elevated expression of oligomannose-type <em>N</em>-glycans. In contrast, M-SIV exhibited higher susceptibility to neutralizing sera in comparison to T-SIV. These findings underscore the importance of host cell-dependent attributes of SIV, such as glycosylation, in shaping both infectivity and the potential effectiveness of intervention strategies.</p>
2D Cell Instance Segmentation Dataset of Master Thesis "Enhancing Cell Instance Segmentation in 3D Microscopy using Self-Supervised ViTs"
<p>This is the 2D cell instance segmentation dataset of master thesis "Enhancing Cell Instance Segmentation in 3D Microscopy using Self-Supervised ViTs". The data is originally from the BBBC038 dataset for the Kaggle 2018 Data Science Bowl. We keep the images with annotations and the final dataset comprises 670 images in the training set and 171 images in the test set. We also converted the dataset to MSCOCO format for convenience.</p>
Fig. 7 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images
Fig. 7 Confusion matrix of predictions by YOLOv4 models. YOLO,You Only Look Once (model)
3D Cell Instance Segmentation Dataset of Master Thesis "Enhancing Cell Instance Segmentation in 3D Microscopy using Self-Supervised ViTs"
<p>This is the 3D cell instance segmentation dataset of master thesis "Enhancing Cell Instance Segmentation in 3D Microscopy using Self-Supervised ViTs". The data is originally from the BBBC027 dataset comprising 30 sets of 3D image sets with high SNR level of quality. Because only the 4 to 100 slides of every 3D image set are annotated and the main difference between annotated and unannoated slides are the brightness, to avoiding misunderstanding by the algorithms, we removed the unannotated slides and separate every slide as individual images. We also converted the dataset to MSCOCO format for convenience.</p>
Supplementary materials: "Synthesizing Particle-in-Cell Simulations Through Learning and GPU Computing for Hybrid Particle Accelerator Beamlines"
<p>Data archive for stage 3 of manuscript for PASC24. See Readme stage 3.txt for more details.</p> <p> </p> <p>This work was supported by the Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory under U.S. Department of Energy Contract No. DE-AC02-05CH11231 and by LLNL under Contract DE-AC52-07NA27344. This material is based upon work supported by the U.S. Department of Energy, OFfice of Science, Office of High Energy Physics, General Accelerator R&D (GARD), under contract number DE-AC02-05CH11231. This material is based upon work supported by the CAMPA collaboration, a project of the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research and Office of High Energy Physics, Scientific Discovery through Advanced Computing (SciDAC) program. This research was supported by the Exascale Computing Project (17-SC-20-SC), a joint project of the U.S. Department of Energy's Office of Science and National Nuclear Security Administration, responsible for delivering a capable exascale ecosystem, including software, applications, and hardware technology, to support the nation's exascale computing imperative.This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 using NERSC award HEP-ERCAP0023719.</p>
A broadband pulse amplifier for Joule heating experiments in diamond anvil cells
<p>Voltage, current, and resistance timeseries for the submitted manuscript. The python script shows a simple example of how to load and plot data stored in the "..._processed.json" files. The spreadsheet describes the meanings of paramaters in the raw and processed data files.</p>
H&E cervix & endometrial plasma cell nuclei dataset
<p>H&E plasma cell nuclei dataset (from cervix & endometrial tissues, plasma cell/other, 224x224 pixels at 40x magnification)</p> <p>This description will be updated soon...</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.