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54 results for “particle tracking”
Expectation maximization based framework for joint localization and parameter estimation in single particle tracking from segmented images - Simulation Data
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Benchmark Data Set of "epiTracker - A framework for highly-reliable particle tracking for the quantitative analysis of fish movements in tanks"
<p>Data set containing five videos differing in fish species (zebrafish and medaka), number of individuals (3 to 10), lighting and type of tank for comparison of tracking algorithms. For each video a Matlab file (*.mat) exists, which contains the position data of the fish.</p> <p>Each of the five videos consists of ~10000 frames, which at a frame rate of 30FPS corresponds to a length of about 5.5min.</p>
Model hydrodynamic outputs and example codes for running different Lagrangian particle tracking packages
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Supplementary Files for "Evaluation of Particle Tracking Codes for Dispersing Particles in Porous Media"
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Data Repository: Fast Single-Particle Tracking of Membrane Proteins Combined with Super-Resolution Imaging of Actin Nanodomains
<p>This repository contains a collection of correlated 2D super-resolution images obtained through single molecule localization microscopy and 3D time series of single particle tracking data of membrane proteins. By utilizing high-speed fluorescent microscopy, we tracked a transmembrane protein – the high-affinity IgE receptor – and an outer-leaflet protein – GPI-anchored protein – at the frame rate of 490 Hz. Subsequently, actin structures of the same cells were captured using a super-resolution microscopy (dSTORM technique). Additionally, this dataset includes Technical Validation to support the transition from live-cell imaging to fixed-cell imaging when adding fixation buffers. </p> <p>The data was classified as “Class I” and “Class II” describing two categories of RBL-2H3 cells. In "Class I", the cells were untransfected. In “Class II”, the cells were transfected to express GFP-GPI-anchored fusion protein. The zip folder names that include “IgE Untreated” include image time series of fluorescently labeled IgE receptors in untreated RBL-2H3 cells and image series of corresponding super-resolution imaging of fluorescently labeled actin filaments of the same cell in “SRImage” folder.</p> <p>The data with folder names including with “IgE Treated” are image time series of fluorescently labeled IgE receptors in either phalloidin- or PMA-treated RBL-2H3 cells and corresponding image series of super-resolution imaging of fluorescently labeled actin filaments in the same cell.</p> <p>Similarly, the data with folder names starting with “GPI Untreated” are image time series of fluorescently labeled GPI-anchored proteins in untreated RBL-2H3 cells and corresponding image series of super-resolution imaging of fluorescently labeled actin filaments in the same cell.</p> <p>The data with folder names starting with “GPI Treated” are image time series of fluorescently labeled GPI-anchored proteins in phalloidin treated RBL-2H3 cells and corresponding image series of super-resolution imaging of fluorescently labeled actin filaments in the same cell.</p> <p>The number within each folder name represents an individual experiment and the corresponding data collected under the same conditions.</p> <p>For all experiments an IR movie was added to monitor the cell morphology during live-cell image and adding initial fixation buffer and it was saved in the tracking file. </p> <p>The HDF5 files of all data are available in the second version of this repository. For each sample, there are two files: "Tracking.h5" and "SuperResolution_actin.h5". The "Tracking" files include two groups: single-particle tracking data and IR images during tracking. The "SuperResolution_actin" files contain two groups: super-resolution images of actin filaments and IR reference images for image registeration and drift correction during data collection. </p> <p> </p> <p> </p> <p> </p>
TXLA simulation and particle tracking
<p>Output of the simulation and particle tracking in the TXLA model</p>
Data from: Automated single particle detection and tracking for large microscopy datasets
Recent advances in optical microscopy have enabled the acquisition of very large datasets from living cells with unprecedented spatial and temporal resolutions. Our ability to process these datasets now plays an essential role in order to understand many biological processes. In this paper, we present an automated particle detection algorithm capable of operating in low signal-to-noise fluorescence microscopy environments and handling large datasets. When combined with our particle linking framework, it can provide hitherto intractable quantitative measurements describing the dynamics of large cohorts of cellular components from organelles to single molecules. We begin with validating the performance of our method on synthetic image data, and then extend the validation to include experiment images with ground truth. Finally, we apply the algorithm to two single-particle-tracking photo-activated localization microscopy biological datasets, acquired from living primary cells with very high temporal rates. Our analysis of the dynamics of very large cohorts of 10 000 s of membrane-associated protein molecules show that they behave as if caged in nanodomains. We show that the robustness and efficiency of our method provides a tool for the examination of single-molecule behaviour with unprecedented spatial detail and high acquisition rates.
Flagellate grazing morphometrics, particle tracking, prey-handling behavior, clearance rates and forces
<p>Heterotrophic nanoflagellates are the main consumers of bacteria and picophytoplankton in the ocean. In their micro-scale world, viscosity impedes predator-prey contact, and the mechanisms that allow flagellates to daily clear a volume of water for prey corresponding to 10<sup>6</sup> times their own volume is unclear. It is also unclear what limits the observed maximum ingestion rates of about 10<sup>4</sup> bacterial prey per day. We used high-speed video-microscopy to describe feeding flows, flagellum kinematics, and prey searching, capture, and handling in four species with different foraging strategies. In three species, prey-handling times limit ingestion rates and account well for their reported maximum values. Similarly, observed feeding flows match reported clearance rates. Simple point-force models allowed us to estimate the forces required to generate the feeding flows, between 4-13 pN, and consistent with the force produced by the hairy (hispid) flagellum, as estimated using resistive force theory. Hispid flagella can produce a force that is much higher than the force produced by a naked flagellum with similar kinematics, and the hairy flagellum is therefore key to foraging in most nanoflagellates. Our findings provide a mechanistic underpinning of observed functional responses of prey ingestion rates in nanoflagellates.</p>
Particle tracking algorithm and additional data for "Optimized and Validated Settling Velocity Measurement for Small Microplastic Particles (10–400 µm)"
<p>This repository provides additional files for in the publication "Optimized and Validated Settling Velocity Measurement for Small Microplastic Particles (10–400 µm)" by Stefan Dittmar, Aki Sebastian Ruhl and Martin Jekel (DOI: <a href="https://www.doi.org/10.1021/acsestwater.3c00457">10.1021/acsestwater.3c00457</a>) </p><p>It contains:</p><p>- image processing routine for particle tracking written in Python (<strong>1_particle_tracking_algorithm.zip</strong>)<br>- single particle raw data from settling experiments (<strong>2_settling_data.zip</strong>)<br>- single particle data from appyling empirical model for interactions between settling particles (<strong>3_model_results_data.zip</strong>)<br>- additional video & animated graph referenced in publication or SI (<strong>4_videos.zip</strong>)</p>
Data from: Automated single particle detection and tracking for large microscopy datasets
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Flagellate grazing morphometrics, particle tracking, prey-handling behavior, clearance rates and forces
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Backward in time particle-tracking simulation in the Kuroshio Extension re-circulation gyre in 2019 using GLORYS12
<p>Tracers were released at the targeted mesoscale eddy in September 03 2019, and their surface transport was modeled for the previous three months. Units are expressed as the number of tracer particles in each glid of 1/12° horizontal resolution. The color shades indicate the number of particles. The particle number 50 indicates that the number of particles in a grid is 50 or more. </p>
Data belonging to "DNA-PAINT single-particle tracking (DNA-PAINT-SPT) enables extended single-molecule studies of membrane protein interactions"
<p>Source data for "DNA-PAINT single-particle tracking (DNA-PAINT-SPT) enables extended single-molecule studies of membrane protein interactions"</p>
The impact of DnaA titration on Escherichia coli DNA replication initiation elucidated through live-cell single-particle tracking
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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.