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111,247 results for “cells”
Data from: A cerebellar granule cell–climbing fiber computation to learn to track long time intervals
<p>In classical cerebellar learning, Purkinje cells (PkCs) associate climbing fiber (CF) error signals with predictive granule cells (GrCs) active just prior (~150ms). Cerebellum also contributes to behaviors characterized by longer timescales. To investigate how GrC-CF-PkC circuits might learn seconds-long predictions, we imaged simultaneous GrC-CF activity over days of forelimb operant conditioning for delayed water reward. As mice learned reward timing, numerous GrCs developed anticipatory activity ramping at different rates until reward delivery, followed by widespread time-locked CF spiking. Relearning longer delays further lengthened GrC activations. We computed CF-dependent GrC→PkC plasticity rules, demonstrating that reward-evoked CF spikes sufficed to grade many GrC synapses by anticipatory timing. We predicted and confirmed that PkCs could thereby continuously ramp across seconds-long intervals from movement to reward. Learning thus leads to new GrC temporal bases linking predictors to remote CF reward signals—a strategy well-suited to learn to track long intervals common in cognitive domains.</p>
Graph 1 in Gastroesophageal tube of the Iguana iguana (Iguanidae): histological description, histochemical and immunohistochemical analysis of 5-HT and SS cells
Graph 1. Number of 5-HT and SS-cells/µm2 per oesophageal and gastric region in I. iguana. Values are averages with their standard deviatin shown by vertical bars (p = 0,05). Cervical oesophagus (CER): 4.6x10-2 ± 2.0 [5-HT cells]/µm2. Celomatic oesophagus (CEL): 4.0x10-2 ± 1.0 [5-HT cells]/µm2. Cranial and middle regions of the stomach (C/MS): 6.18x10-2 ± 3.2 [5-HT cells]/µm2. Caudal Stomach (CS): 0.6x10-2 ± 0.2 [5-HT cells]/µm2 and 1.4x 10-2 ± 0.9 [SS-cells]/µm2.
SI Figure 4: SEM images of either unwashed (left) or washed (right) E. antarcticus nematodes. A. Unwashed head region with arrows pointing to attached material and possible fungal hyphae. B. Washed head region with arrows pointing to the remaining attached material. C. Unwashed annules with arrows pointing to commonly attached foreign material. D. Washed annules with arrows pointing to remaining attached material. E. Unwashed somatic pore with arrows pointing to the common organic material. F. Washed vulva with an arrow pointing to remaining attached organic material. G. Unwashed cuticle with arrows showing a possible biofilm. H. Washed cuticle showing single attached cells indicated with arrows. I. Unwashed cuticle showing an off-axis line of attached material. J. Washed cuticle showing a similar off-axis line of material (as indicated with arrow) but reduced in quantity compared to the unwashed. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
SI Figure 4: SEM images of either unwashed (left) or washed (right) E. antarcticus nematodes. A. Unwashed head region with arrows pointing to attached material and possible fungal hyphae. B. Washed head region with arrows pointing to the remaining attached material. C. Unwashed annules with arrows pointing to commonly attached foreign material. D. Washed annules with arrows pointing to remaining attached material. E. Unwashed somatic pore with arrows pointing to the common organic material. F. Washed vulva with an arrow pointing to remaining attached organic material. G. Unwashed cuticle with arrows showing a possible biofilm. H. Washed cuticle showing single attached cells indicated with arrows. I. Unwashed cuticle showing an off-axis line of attached material. J. Washed cuticle showing a similar off-axis line of material (as indicated with arrow) but reduced in quantity compared to the unwashed.
Accompanying data for the paper "Accounting for the mechanical response of the cell membrane during the uptake of random nanoparticles"
<h2>Contributions</h2> <ul> <li><strong>Iaquinta Sarah</strong> did contribute to the first draft edition, the development of the theoretical background of the algorithms and their implementation</li> <li><strong>Khazaie Sharam</strong> did contribute to the revision and edition of the draft, and to the development of the theoretical background of the algorithms</li> <li><strong>Jacquemin Frédéric</strong> did contribute to the project management and to the revision of the article.</li> <li><strong>Fréour Sylvain</strong> did contribute to the project management and to the revision of the article.</li> </ul> <h2>Funding sources</h2> <p>i-Site NExT : Grant/Award Number: ANR-16-IDEX-0007, Région Pays de la Loire and CNRS (French National Centre for Scientific Research).</p> <h2>Data structure and information</h2> <ul> <li>code - <code>np_uptake source and data directory</code> <ul> <li>workflow - <code>scripts to reproduce figures</code></li> <li>np_uptake - <code>source code producing results and figures</code> <ul> <li>figures - <code>utility module to produce figures</code></li> <li>model - <code>see detailed description below</code></li> <li>metamodel_implementation - <code>see detailed description below</code></li> <li>sensitivity_analysis - <code>see detailed description below</code></li> </ul> </li> </ul> </li> </ul> <h3>Detailed description</h3> <h4>Abstract</h4> <p>In order to improve the efficiency of the delivery of cancer treatments to cancer cells, the cellular uptake of nanoparticles (NPs), used as drug delivery systems, is numerically investigated through a mechanical approach. The objective is to optimize the NP's mechanical and geometrical properties to enhance their entry into cancer cells while avoiding benign ones. In previous studies, these properties are modeled as constant during the process of cellular uptake. However, recent observations of the displacement of the membrane's constituents towards the region in the cell membrane where the uptake of the NPs takes place show that the mechanical properties of the membrane vary during this process. Reason for writing The important contribution of adhesion to the wrapping process is already well documented in literature. It is therefore crucial to model this parameter properly as the conclusions made with a constant adhesion model may not be accurate compared to reality. Methodology Based on the existing knowledge on the reaction of membrane constituents to interaction with NPs, a 3-parameter sigmoidal function, accounting for the delay, amplitude, and speed of the reaction, has been used to model the evolution of adhesion. A variance-based sensitivity analysis has then been performed in order to quantify the influence of these parameters on the outputs of the model. Results It was found that the introduction of a variable adhesion tends to alter the predictions of endocytosis of NPs. The contribution of the amplitude and delay is respectively 0.32 and 0.43 times as important as that of the NP's aspect ratio, which is the prominent parameter. The influence of the slope of the transition is the least important parameter and does not appear to contribute to endocytosis. Implications Hence, models of the cellular uptake of NPs should use a variable, instead of constant, adhesion in order a representative as possible of the behavior of the cell membrane. The predictions are different from those obtained using a model with constant adhesion.</p> <h4>Code</h4> <p>This repository is divided into 4 folders:</p> <ul> <li> <p><em>model</em>: contains the code used to compute the total variation of energy of the interface between a circular NP and a membrane by accounting for the mechanical accommodation of the latter. This folder also contains the routine to determine the final wrapping phase of the system.</p> </li> <li> <p><em>metamodel_implementation</em>: contains a script to check for the representativeness of the dataset used to create a metamodel, a script to create Kriging and PCE metamodels using the Openturns opensource library, and a routine to validate the metamodel that has just been created;</p> </li> <li> <p><em>sensitivity_analysis</em>: contains a script that allows to create samples based on the Kriging metamodels that have been created and exported as .pkl files in the metamodel folder. These samples are then used to the apply sensitivity algorithms. The user can choose among the various sensitivity algorithms provided by Openturns. For PCE metamodels, a routine is implemented to directly get the Sobol indices from the coefficients of the PCE metamodel. The indices can be plotted through plot routines;</p> </li> <li> <p><em>figures</em>: contains a utils script to display the graphs and save them as PNG files with consistency.</p> </li> </ul>
Dataset: Brainstorm Cell Therapeutics Inc. (BCLI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Data from: Live-cell analysis of IMPDH protein levels during yeast colony growth provides insights into the regulation of GTP synthesis
<p>Here we present real-time, live-cell analysis of accumulation of the Imd2 isoform of IMPDH in <em>Saccharomyces cervisiae</em> yeast cells forming a monolayer colony in a microfluidic device over a 50-hour time course. We observe two distinct phases of increased Imd2 accumulation: a guanine-insensitive phase early in outgrowth and a guanine-sensitive phase later, when cells become crowded. We show that the IMPDH inhibitor mycophenolic acid enhances both phases of increase. Deletion of a transcription attenuator upstream of the mRNA start site that decreases Imd2 mRNA synthesis in the presence of high GTP increases the baseline level of Imd2 protein ten-fold and abolishes guanine-sensitive but not guanine-insensitive induction. Our results suggest that at least two mechanisms of yeast Imd2 regulation exist, the known GTP-dependent attenuation of RNA polymerase II elongation and a GTP concentration-independent pathway that may be controlled by cell growth state.</p>
Lattice Boltzmann simulation of liquid water transport in gas diffusion layers of proton exchange membrane fuel cells: Impact of gas diffusion layer and microporous layer degradation on effective transport properties
<p><span>Underlying data to publication Sarkezi-Selsky et al., <em>J. Pow. Sour.</em> 556 (2023) 232415,<span> https://doi.org/10.1016/j.jpowsour.2022.232415</span> <br><br>Polymer Electrolyte Membrane Fuel Cells (PEMFCs) represent a promising technology for clean drivetrain solutions, in particular for heavy-duty applications. However, lifetime requirements demand high durability of each cell component.<br></span><span>In this work, transport of liquid water through pristine and degraded gas diffusion layers (GDL) was simulated with a 3D Color-Gradient Lattice Boltzmann model. The GDL microstructure was reconstructed </span><span>from high-resolution X-ray micro-computed tomography (</span><span>μ</span><span>-CT) of an impregnated Freudenberg H14. The </span><span>effect of a microporous layer (MPL) was considered by reconstruction of an impregnated and MPL-coated H14. Aged microstructures were generated artificially, assuming loss of polytetrafluoroethylene (PTFE) within the GDL and increase of MPL macroporosity as main degradation mechanisms. Liquid water transport within aged microstructures was simulated by imposing a liquid phase flow rate until breakthrough was reached. Subsequently, the GDL microstructures were analyzed for their breakthrough characteristics by means of saturation and effective gas transport properties. When the MPL was pristine, no distinct GDL degradation effect was observable, this was attributed to the MPL dominating capillary transport. MPL aging, however, led to increased saturations and thus to a deterioration of the effective gas transport. With a partially degraded MPL, aging of the GDL then appeared to affect the breakthrough characteristics.</span></p>
Figures 98–106. Stricosus and Thalassiosira. Figs 98–103. Stricosus harrisonii. Figs 98, 99. Living cells showing plastids with pyrenoids. Figs 100, 101 in Marine benthic diatoms of Guam: new records, Dictyoneis apapae sp. nov., and updates to the checklist
Figures 98–106. Stricosus and Thalassiosira. Figs 98–103. Stricosus harrisonii. Figs 98, 99. Living cells showing plastids with pyrenoids. Figs 100, 101. Acid cleaned valves in LM and SEM. Fig. 102. External apex showing triangular hyaline area, two spines, rimoportula opening, and ocellulimbus with at least 7 rows of pores (contrast Fig. 62, Hyalosynedra). Fig. 103. Apex internal view showing symmetrical rimoportula (arrow). Figs 104–106. Thalassiosira nanolineata. Fig. 104. Valve in LM at two focal planes. Fig. 105, 106. External and internal aspects in SEM, showing central fultoportulae (F) and rimoportula (R) (arrows). Scale bars: Figs 98, 100, 101 = 20 µm, Fig 104 = 10 µm, Figs 99, 103, 105, 106 = 5 µm, Fig. 102 = 2 µm.
Single cell T-cell Atlas (V3)
<p>The attached datasets comprised of the merging of 21 high quality single cell T cell based dataset that had both the TCR-seq and GEx. The object contains ~1.3 paired TCR-seq with GEx in the Seurat Object (<a target="_blank" rel="noopener noreferrer">supercluster_added_ID-240531.rds</a>).</p> <p>We also included the original identifiers in the <a target="_blank" rel="noopener noreferrer">Sup_Update_labels.csv</a> a. See our <a href="https://stegor.readthedocs.io/en/latest/" target="_blank" rel="noopener">https://stegor.readthedocs.io/en/latest/</a> for how we processed the 12 datasets (V2) and decided on the current 47 T cell annotation models using scGate (TcellFunction). Additionally, based on collaborator recommendataion, we have also now included a simpler T cell annotion model in STEGO.R process (Tsimplefunctions). </p> <p>This is the accompanying data set for the paper entitled ‘T cell receptor-centric approach to streamline multimodal single-cell data analysis.’, which is currently available as a preprint (<a href="https://www.biorxiv.org/content/10.1101/2023.09.27.559702v2" target="_blank" rel="noopener">https://www.biorxiv.org/content/10.1101/2023.09.27.559702v2</a>). Details on the origin of the datasets, and processing steps can be found there.</p> <p>The purpose of this atlas both the full dataset and down sampling version is to aid in improving the interpretability of other T cell based datasets. This can be done by adding in the down sampled object that contains up to 500 cells per annotation model. This dataset aims to improve the capacity to identify TCR-specific signature by ensuring a well covered background, which will improve the robustness of the FindMarker Function in Seurat package.</p> <p> </p>
Single-cell proteo-transcriptomic profiling reveals altered characteristics of stem and progenitor cells in patients receiving cytoreductive hydroxyurea in early-phase chronic myeloid leukemia
<p>This repository contains CITE-seq data generated from CML stem and progenitor cells before and after hydroxyurea treatment using the BD Rhapsody Single-Cell Analysis System. </p> <p><strong><br>File descriptions:</strong></p> <p>1. RSEC-adjusted UMI count files generated using the BD Rhapsody Targeted Analysis Pipeline (v. 1.10.1):</p> <ul> <li>CartridgeS1_RSEC_MolsPerCell.csv</li> </ul> <p>2. RSEC-adjusted UMI counts for cells remaining after cell quality filtering using SeqGeq software (genes expressed vs library size):</p> <ul> <li>CartridgeS1_RSEC_MolsPerCell_postQC.csv</li> </ul> <p>3. Sample tag (sample of origin) calls for each putative cell, outputted by the BD Rhapsody Targeted Analysis Pipeline (v. 1.10.1). </p> <ul> <li>CartridgeS1_Sample_Tag_Calls.csv</li> </ul> <p> </p>
Annotation of the non-canonical translatome reveals that CHO cell microproteins are a new class of therapeutic antibody drug product impurity
<p>3,681 novel Chinese hamster proteoforms (derived from uORFs, ouORFs and ORFs encoded on NCBI annotated non-coding RNAs).</p>
Microcore cell counting
<p>This dataset shows the raw cell countings values that we have done from anatomical cross sections of peatland trees microcores. The first column corresponds to the tree ID, the second to the Date in the format DD.MM.YY hh:mm, the third column corresponds to the type of cell counting: 3EN corresponds to the number of enlarging cells, 2TH corresponds to the number of thickening cells, ans 1MA correpsonds to the number of mature cells. </p>
Figure 5 in Quantitative phosphoproteomic analysis of chicken DF-1 cells infected with Eimeria tenella, using tandem mass tag (TMT) and parallel reaction monitoring (PRM) mass spectrometry
Figure 5. KEGG pathways of the differentially expressed phosphorylated proteins. The abscissa indicates the first 10 significantly enriched KEGG pathways and the ordinate indicates the significance of enriched KEGG pathways, the more left, the more significant.
Figure 4 in Quantitative phosphoproteomic analysis of chicken DF-1 cells infected with Eimeria tenella, using tandem mass tag (TMT) and parallel reaction monitoring (PRM) mass spectrometry
Figure 4. Gene ontology annotations of the differentially expressed phosphorylated proteins. The abscissa indicates the enriched GO functional classification, including biological process (A), cellular component (B), and molecular function (C). The ordinate indicates the size of the significance of corresponding to each entry, the more left, the more significant.
Figure 3 in Quantitative phosphoproteomic analysis of chicken DF-1 cells infected with Eimeria tenella, using tandem mass tag (TMT) and parallel reaction monitoring (PRM) mass spectrometry
Figure 3. Clustering heatmap of different expression phosphorylated peptides. Each row represents a phosphorylated peptide segment, each column represents a group of samples. The logarithmic value (logarithmic transformation based on 2) of the significantly differentially expressed phosphorylated peptides in different samples is displayed in the clustering heatmap in different colors. Red represents significant upregulation of phosphorylated peptides; blue represents significant down-regulation of phosphorylated peptides.
Figure 2 in Quantitative phosphoproteomic analysis of chicken DF-1 cells infected with Eimeria tenella, using tandem mass tag (TMT) and parallel reaction monitoring (PRM) mass spectrometry
Figure 2. Volcano plots from different group comparisons. The abscissa indicates difference multiple (logarithmic transformation based on 2), the ordinate indicates the significant of difference (logarithmic transformation based on 10). The red point is significantly upregulated phosphorylated peptide segment, the blue point is significantly downregulated phosphorylated peptide segment and the gray point is a phosphorylated peptide segment with no significant difference.
Discrete Flow Cytometry of Underway Samples From TN428 Using a BD Influx Cell Sorter
<p>The dataset consists of BD Influx-based analysis of phytoplankton populations from discrete flow cytometry data collected underway during a transit cruise (TN428) from American Samoa to Australia. The data consists of cell abundance, cell size (equivalent spherical diameter), carbon quota, and carbon biomass for picophytoplankton populations, namely the cyanobacteria Prochlorococcus, Synechococcus and Crocosphaera, and small eukaryotic phytoplankton (<5 μm ESD). Time is in UTC format, latitude and longitude are in decimal degrees, and depth is in meters. Further information can be found here: [https://github.com/fribalet/FCSplankton](https://github.com/fribalet/FCSplankton)</p>
High-resolution AI image dataset for diagnosing oral submucous fibrosis and squamous cell carcinoma
<p>This deposition contains only training dataset of ORCHID database. The validation and test dataset related to the same study can be found at DOI: <strong>10.5281/zenodo.12646943.</strong></p>
FIB-SEM tomography datasets of entire single-cell human monocyte derived macrophages
<p>FIB-SEM tomography was used to determine the absolute number of internalized gold nanoparticles in GM-CSF and M-CSF human monocyte-derived macrophages at the single-cell level. All experiments were performed using a Thermo Scientific Scios 2 Dual Beam microscope (Thermo Fisher Scientific, Waltham, MA, USA). A selected cell was protected with a platinum (Pt) layer of 1 µm thickness (30 kV and current of 1 nA). Then, a trench of 5 µm in depth was milled on the front face and each side of the cell of interest using the ion beam at 7 nA current. Finally, the front face of the volume of interest was polished with an ion beam current of 1 nA until the beginning of the cell was visible to optimize the focus and contrast. FIB-SEM tomography of the whole cell was performed using the FEI Slice and View software (Thermo Fisher Scientific, Waltham, MA, USA, version 4.1). The electron beam acceleration voltage was set to 5 kV, the current to 0.4 nA, the resolution to 1536 × 1024 pixels, and the dwell time to 30 µs. Images were acquired in immersion mode with the backscattered electron detector, yielding clear signals from AuNPs due to the detector's sensitivity to backscattered electrons, which correlates with the atomic number of Au (Z = 79). The ion beam operating with a current of 1 nA current at 30 kV was used to slice through the cell of interest at an interval of 18 nm and a depth of 5 µm. The datasets available on the repository have been aligned and cropped.</p>
FIGURE 5 in Cells and soft tissues in fossil bone: A review of preservation mechanisms, with corrections of misconceptions
FIGURE 5. Simplified overview of factors that influence the preservation and destruction of the cellular, soft tissue, and mineral content of bone. Diagenesis of these materials is more complex than is shown here. Additional factors also have influence, and multiple levels and modes of preservation and destruction may occur in different regions of a single bone (see text for details).
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.