Browse
Submit Data
Databases
API
Help

Dataset Information

19 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

MARGO (Massively Automated Real-time GUI for Object-tracking), a platform for high-throughput ethology.

ABSTRACT: Fast object tracking in real time allows convenient tracking of very large numbers of animals and closed-loop experiments that control stimuli for many animals in parallel. We developed MARGO, a MATLAB-based, real-time animal tracking suite for custom behavioral experiments. We demonstrated that MARGO can rapidly and accurately track large numbers of animals in parallel over very long timescales, typically when spatially separated such as in multiwell plates. We incorporated control of peripheral hardware, and implemented a flexible software architecture for defining new experimental routines. These features enable closed-loop delivery of stimuli to many individuals simultaneously. We highlight MARGO's ability to coordinate tracking and hardware control with two custom behavioral assays (measuring phototaxis and optomotor response) and one optogenetic operant conditioning assay. There are currently several open source animal trackers. MARGO's strengths are 1) fast and accurate tracking, 2) high throughput, 3) an accessible interface and data output and 4) real-time closed-loop hardware control for for sensory and optogenetic stimuli, all of which are optimized for large-scale experiments.

SUBMITTER: Werkhoven Z

PROVIDER: S-EPMC6876843 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Real-Time Object Tracking via Adaptive Correlation Filters.

Project description:Although correlation filter-based trackers (CFTs) have made great achievements on both robustness and accuracy, the performance of trackers can still be improved, because most of the existing trackers use either a sole filter template or fixed features fusion weight to represent a target. Herein, a real-time dual-template CFT for various challenge scenarios is proposed in this work. First, the color histograms, histogram of oriented gradient (HOG), and color naming (CN) features are extracted from the target image patch. Then, the dual-template is utilized based on the target response confidence. Meanwhile, in order to solve the various appearance variations in complicated challenge scenarios, the schemes of discriminative appearance model, multi-peaks target re-detection, and scale adaptive are integrated into the proposed tracker. Furthermore, the problem that the filter model may drift or even corrupt is solved by using high confidence template updating technique. In the experiment, 27 existing competitors, including 16 handcrafted features-based trackers (HFTs) and 11 deep features-based trackers (DFTs), are introduced for the comprehensive contrastive analysis on four benchmark databases. The experimental results demonstrate that the proposed tracker performs favorably against state-of-the-art HFTs and is comparable with the DFTs.

| S-EPMC7435421 | biostudies-literature

Advances in real-time object tracking: Extensions for robust object tracking with a Monte Carlo particle filter.

Project description:The huge amount of literature on real-time object tracking continuously reports good results with respect to accuracy and robustness. However, when it comes to the applicability of these approaches to real-world problems, often no clear statements about the tracking situation can be made. This paper addresses this issue and relies on three novel extensions to Monte Carlo particle filtering. The first, confidence dependent variation, together with the second, iterative particle filtering, leads to faster convergence and a more accurate pose estimation. The third, fixed particle poses removes jitter and ensures convergence. These extensions significantly increase robustness and accuracy, and further provide a basis for an algorithm we found to be essential for tracking systems performing in the real world: tracking state detection. Relying on the extensions above, it reports qualitative states of tracking as follows. Convergence indicates if the pose has already been found. Quality gives a statement about the confidence of the currently tracked pose. Loss detects when the algorithm fails. Occlusion determines the degree of occlusion if only parts of the object are visible. Building on tracking state detection, a model completeness scheme is proposed as a measure of which views of the object have already been learned and which areas require further inspection. To the best of our knowledge, this is the first tracking system that explicitly addresses the issue of estimating the tracking state. Our open-source framework is available online, serving as an easy-access interface for usage in practice.

| S-EPMC7089693 | biostudies-literature

Platform for Automated Real-Time High Performance Analytics on Medical Image Data.

Project description:Biomedical data are quickly growing in volume and in variety, providing clinicians an opportunity for better clinical decision support. Here, we demonstrate a robust platform that uses software automation and high performance computing (HPC) resources to achieve real-time analytics of clinical data, specifically magnetic resonance imaging (MRI) data. We used the Agave application programming interface to facilitate communication, data transfer, and job control between an MRI scanner and an off-site HPC resource. In this use case, Agave executed the graphical pipeline tool GRAphical Pipeline Environment (GRAPE) to perform automated, real-time, quantitative analysis of MRI scans. Same-session image processing will open the door for adaptive scanning and real-time quality control, potentially accelerating the discovery of pathologies and minimizing patient callbacks. We envision this platform can be adapted to other medical instruments, HPC resources, and analytics tools.

| S-EPMC5858700 | biostudies-literature

Subtyping of Swine Influenza Viruses Using a High-Throughput Real-Time PCR Platform.

Project description:Influenza A viruses (IAVs) are important human and animal pathogens with high impact on human and animal health. In Denmark, a passive surveillance program for IAV in pigs has been performed since 2011, where screening tests and subsequent subtyping are performed by reverse transcription quantitative real-time PCR (RT-qPCR). A disadvantage of the current subtyping system is that several assays are needed to cover the wide range of circulating subtypes, which makes the system expensive and time-consuming. Therefore, the aim of the present study was to develop a high-throughput method, which could improve surveillance of swine influenza viruses (swIAVs) and lower the costs of virus subtyping. Twelve qPCR assays specific for various hemagglutinin and neuraminidase gene lineages relevant for swIAV and six assays specific for the internal genes of IAV were developed and optimized for the high-throughput qPCR platform BioMark (Fluidigm). The qPCR assays were validated and optimized to run under the same reaction conditions using a 48.48 dynamic array (48.48DA). The sensitivity and specificity was assessed by testing virus isolates and field samples with known subtypes. The results revealed a performance of the swIAV 48.48DA similar to conventional real-time analysis, and furthermore, the specificity of swIAV 48.48DA was very high and without cross reactions between the assays. This high-throughput system provides a cost-effective alternative for subtyping of swIAVs.

| S-EPMC5972299 | biostudies-literature

Automated high-throughput high-content autophagy and mitophagy analysis platform.

Project description:Autophagic processes play a central role in cellular homeostasis. In pathological conditions, the flow of autophagy can be affected at multiple and distinct steps of the pathway. Current analyses tools do not deliver the required detail for dissecting pathway intermediates. The development of new tools to analyze autophagic processes qualitatively and quantitatively in a more straightforward manner is required. Defining all autophagy pathway intermediates in a high-throughput manner is technologically challenging and has not been addressed yet. Here, we overcome those requirements and limitations by the developed of stable autophagy and mitophagy reporter-iPSC and the establishment of a novel high-throughput phenotyping platform utilizing automated high-content image analysis to assess autophagy and mitophagy pathway intermediates.

| S-EPMC6603000 | biostudies-literature

Automated melt electrowritting platform with real-time process monitoring

Project description:Graphical abstract Melt electrowriting (MEW) is an additive manufacturing (AM) technology with the ability to fabricate complex designs with high-resolution. The utility of MEW is studied in many fields including tissue engineering and soft robotics. However, current MEW hardware offers only basic functionality and is often designed and built in-house. This affects results replication across different MEW devices and slows down the technological advancement. To address these issues, we present an automated MEW platform with real-time process parameter monitoring and control. We validate the developed platform by demonstrating the ability to accurately print polymer structures and successfully measure and adjust parameters during the printing process. The platform enables the collection of large volumes of data that can be subsequently used for further analysis of the system. Ultimately, the concept will help MEW to become more accessible for both research laboratories and industry and allow advancing the technology by leveraging the process monitoring, control and data collection.

| S-EPMC9123438 | biostudies-literature

Inertial Microfluidic Cell Stretcher (iMCS): Fully Automated, High-Throughput, and Near Real-Time Cell Mechanotyping.

Project description:Mechanical biomarkers associated with cytoskeletal structures have been reported as powerful label-free cell state identifiers. In order to measure cell mechanical properties, traditional biophysical (e.g., atomic force microscopy, micropipette aspiration, optical stretchers) and microfluidic approaches were mainly employed; however, they critically suffer from low-throughput, low-sensitivity, and/or time-consuming and labor-intensive processes, not allowing techniques to be practically used for cell biology research applications. Here, a novel inertial microfluidic cell stretcher (iMCS) capable of characterizing large populations of single-cell deformability near real-time is presented. The platform inertially controls cell positions in microchannels and deforms cells upon collision at a T-junction with large strain. The cell elongation motions are recorded, and thousands of cell deformability information is visualized near real-time similar to traditional flow cytometry. With a full automation, the entire cell mechanotyping process runs without any human intervention, realizing a user friendly and robust operation. Through iMCS, distinct cell stiffness changes in breast cancer progression and epithelial mesenchymal transition are reported, and the use of the platform for rapid cancer drug discovery is shown as well. The platform returns large populations of single-cell quantitative mechanical properties (e.g., shear modulus) on-the-fly with high statistical significances, enabling actual usages in clinical and biophysical studies.

| S-EPMC5565626 | biostudies-literature

High-throughput platform for real-time monitoring of biological processes by multicolor single-molecule fluorescence.

Project description:Zero-mode waveguides provide a powerful technology for studying single-molecule real-time dynamics of biological systems at physiological ligand concentrations. We customized a commercial zero-mode waveguide-based DNA sequencer for use as a versatile instrument for single-molecule fluorescence detection and showed that the system provides long fluorophore lifetimes with good signal to noise and low spectral cross-talk. We then used a ribosomal translation assay to show real-time fluidic delivery during data acquisition, showing it is possible to follow the conformation and composition of thousands of single biomolecules simultaneously through four spectral channels. This instrument allows high-throughput multiplexed dynamics of single-molecule biological processes over long timescales. The instrumentation presented here has broad applications to single-molecule studies of biological systems and is easily accessible to the biophysical community.

| S-EPMC3896158 | biostudies-literature

A Robust Real-Time Detecting and Tracking Framework for Multiple Kinds of Unmarked Object.

Project description:A rodent real-time tracking framework is proposed to automatically detect and track multi-objects in real time and output the coordinates of each object, which combines deep learning (YOLO v3: You Only Look Once, v3), the Kalman Filter, improved Hungarian algorithm, and the nine-point position correction algorithm. A model of a Rat-YOLO is trained in our experiment. The Kalman Filter model is established in an acceleration model to predict the position of the rat in the next frame. The predicted data is used to fill the losing position of rats if the Rat-YOLO doesn't work in the current frame, and to associate the ID between the last frame and current frame. The Hungarian assigned algorithm is used to show the relationship between the objects of the last frame and the objects of the current frame and match the ID of the objects. The nine-point position correction algorithm is presented to adjust the correctness of the Rat-YOLO result and the predicted results. As the training of deep learning needs more datasets than our experiment, and it is time-consuming to process manual marking, automatic software for generating labeled datasets is proposed under a fixed scene and the labeled datasets are manually verified in term of their correctness. Besides this, in an off-line experiment, a mask is presented to remove the highlight. In this experiment, we select the 500 frames of the data as the training datasets and label these images with the automatic label generating software. A video (of 2892 frames) is tested by the trained Rat model and the accuracy of detecting all the three rats is around 72.545%, however, the Rat-YOLO combining the Kalman Filter and nine-point position correction arithmetic improved the accuracy to 95.194%.

| S-EPMC6982905 | biostudies-literature

Real-Time Occlusion-Robust Deformable Linear Object Tracking With Model-Based Gaussian Mixture Model.

Project description:Tracking and manipulating deformable linear objects (DLOs) has great potential in the industrial world. However, estimating the object's state is crucial and challenging, especially when dealing with heavy occlusion situations and physical properties of different objects. To address these problems, we introduce a novel tracking algorithm to observe and estimate the states of DLO. The proposed tracking algorithm is based on the Coherent Point Drift (CPD), which registers the observed point cloud, and the finite element method (FEM) model encodes physical properties. The Gaussian mixture model with CPD regularization generates constraints to deform a given FEM model into desired shapes. The FEM model encodes the local structure, the global topology, and the material property to better approximate the deformation process in the real world without using simulation software. A series of simulations and real data tracking experiments have been conducted on deformable objects, such as rope and iron wire, to demonstrate the robustness and accuracy of our method in the presence of occlusion.

| S-EPMC9136076 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data