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1,661 results for “Throughput”
Figure 3 in Biased heteroplasmy within the mitogenomic sequences of Gigantometra gigas revealed by sanger and high-throughput methods
Figure 3. The different nucleotides in the ITS-1 and ITS-2 regions are shown. The result shows the different nucleotides at nucleotide position np 1897 (G nucleotide and T nucleotide) and np 2790 (C nucleotide and T nucleotide) obtained by Sanger and HTS methods.
Figure 1. Gigantometra gigas. A. Female, dorsal view. B. Male, dorsal view. C in Biased heteroplasmy within the mitogenomic sequences of Gigantometra gigas revealed by sanger and high-throughput methods
Figure 1. Gigantometra gigas. A. Female, dorsal view. B. Male, dorsal view. C. The narrow distribution of G. gigas.
A reductionist paradigm for high-throughput behavioural fingerprinting in Drosophila melanogaster - DATASET 1 of 2
<p>Dataset associated with "A reductionist paradigm for high-throughput behavioural fingerprinting in <em>Drosophila </em><em>melanogaster" </em>by Jones et al "A reductionist paradigm for high-throughput behavioural fingerprinting in Drosophila melanogaster". </p> <p>See http://lab.gilest.ro/coccinella for more information</p> <p>This is archive 1 of 2</p> <p> </p> <p> </p>
A high-throughput multispectral imaging system for museum specimens
<p>We present an economical imaging system with integrated hardware and software to capture multispectral images of Lepidoptera with high efficiency. This method facilitates the comparison of colors and shapes among species at fine and broad taxonomic scales and may be adapted for other insect orders with greater three-dimensionality. Our system can image both the dorsal and ventral sides of pinned specimens. Together with our processing pipeline, the descriptive data can be used to systematically investigate multispectral colors and shapes based on full-wing reconstruction and a universally applicable ground plan that objectively quantifies wing patterns for species with different wing shapes (including tails) and venation systems. Basic morphological measurements, such as body length, thorax width, and antenna size are automatically generated. This system can increase exponentially the amount and quality of trait data extracted from museum specimens.</p>
Data analysis of LiP-MS data for high-throughput applications
<p>Proteins regulate biological processes by changing their structure or abundance to accomplish a specific function. In response to any perturbation or stimulus, protein structure may be altered by a variety of molecular events, such as post translational modifications, protein-protein interactions, aggregation, allostery, or binding to other molecules. The ability to probe these structural changes in thousands of proteins simultaneously in cells or tissues can provide valuable information about the functional state of a variety of biological processes and pathways. Here we present an updated protocol for LiP-MS, a proteomics technique combining limited proteolysis with mass spectrometry, to detect protein structural alterations in complex backgrounds and on a proteome-wide scale (Cappelletti et al., 2021; Piazza et al., 2020; Schopper et al., 2017). We describe advances in the throughput and robustness of the LiP-MS workflow and implementation of data-independent acquisition (DIA) based mass spectrometry, which together achieve high reproducibility and sensitivity, even on large sample sizes. In addition, we introduce MSstatsLiP, an R package dedicated to the analysis of LiP-MS data for the identification of structurally altered peptides and differentially abundant proteins. Altogether, the newly proposed improvements expand the adaptability of the method and allow for its wide use in systematic functional proteomic studies and translational applications. </p>
Data for: High-throughput profiling of sequence recognition by tyrosine kinases and SH2 domains using bacterial peptide display
<p>Tyrosine kinases and SH2 (phosphotyrosine recognition) domains have binding specificities that depend on the amino acid sequence surrounding the target (phospho)tyrosine residue. Although the preferred recognition motifs of many kinases and SH2 domains are known, we lack a quantitative description of sequence specificity that could guide predictions about signaling pathways or be used to design sequences for biomedical applications. Here, we present a platform that combines genetically-encoded peptide libraries and deep sequencing to profile sequence recognition by tyrosine kinases and SH2 domains. We screened several tyrosine kinases against a million-peptide random library and used the resulting profiles to design high-activity sequences. We also screened several kinases against a library containing thousands of human proteome-derived peptides and their naturally-occurring variants. These screens recapitulated independently measured phosphorylation rates and revealed hundreds of phosphosite-proximal mutations that impact phosphosite recognition by tyrosine kinases. We extended this platform to the analysis of SH2 domains and showed that screens could predict relative binding affinities. Finally, we expanded our method to assess the impact of non-canonical and post-translationally modified amino acids on sequence recognition. This specificity profiling platform will shed new light on phosphotyrosine signaling and could readily be adapted to other protein modification/recognition domains.</p>
Supplemental Data for Architector for high-throughput cross-periodic table 3D complex building
<p>This repository contains all of the data presented in either the main text or the SI for the manuscript "<strong><em>Architector</em> for high-throughput cross-periodic table 3D complex building</strong>".</p>
High-throughput crystallography for rapid early-stage fragment growth from crude arrays by low-cost robotics
<p>Data to support the paper - <em>High-throughput crystallography for rapid early-stage fragment growth from crude arrays by low-cost robotics</em>. Data includes a summary of X-ray and LCMS results for the reactions executed on the OpenTrons, output reports and summaries from MSCheck (semi-automated LCMS analyzer tool) and the Python scripts used to execute single and multistep chemistry on the OpenTrons.</p> <p><strong>Abstract</strong></p> <p>We demonstrate that a simple workflow of array synthesis, combining low-cost robotics with analytic techniques to deconvolute crude reaction mixtures, is an effective way to collect structural data on a binding site. Starting from the high information content of the crystallographic fragment screens on PHIP(2) (second bromodomain of the pleckstrin homology domain interacting protein), a collection of more than 1800 compounds was enumerated. Several thousand <em>Crude Reaction Mixtures</em> (CRMs) were synthesized on one robotic platform, an OpenTrons OT-1 liquid handler, using reaction sequences of up to 5 chemical steps. Analysis via MScheck, an algorithm-based system for finding a m/z in a CRM, significantly shortened product identification protocol times. 957 usable X-ray diffraction datasets were acquired, which resolved as 22 reaction products binding to the protein, 19 with conserved poses relative to the original fragment and 3 with a new, unexpected binding pose. The 22 crystallographic hit compounds were subsequently tested with peptide displacement alpha-screen assay and time-resolved grating-coupled interferometry-based biosensor assays, which confirmed one molecule with an IC<sub>50</sub> = 34 μM and K<sub>D</sub> = 50 μM, from an inactive fragment. The procedures described are entirely formulaic and engineerable and the method is eminently scalable. We anticipate that this cheap, low solvent-use approach will yield vast amounts of data, enabling rapid SAR landscape exploration around fragments, leading to faster fragment to lead times.</p>
Data from: Accelerated high-throughput imaging and phenotyping system for small organisms
<p>Studying the complex web of interactions in biological communities requires large multifactorial experiments with sufficient statistical power. Automation tools reduce the time and labor associated with setup, data collection, and analysis in experiments that untangle these webs. We developed tools for high-throughput experimentation (HTE) in duckweeds, small aquatic plants that are amenable to autonomous experimental preparation and image-based phenotyping. We showcase the abilities of our HTE system in a study with 6,000 experimental units grown across 2,000 treatments. These automated tools facilitated the collection and analysis of time-resolved growth data, which revealed finer dynamics of plant-microbe interactions across environmental gradients. Altogether, our HTE system can run experiments with up to 11,520 experimental units and can be adapted for other small organisms.</p>
Nanomaterial genotoxicity evaluation using the high-throughput p53-binding protein 1 (53BP1) assay - data from the article published in PlosOne
<p>Data that have been used in the manuscript "Nanomaterial genotoxicity evaluation using the high-throughput p53-binding protein 1 (53BP1) assay" by M. Fontaine et al., published in PlosOne in 2023.</p>
Raw, processed and merged Data for Swiss Cat+ East A1 project related to the automated and high-throughput Bayesian Optimization of CO2 hydrogenation heterogeneous catalysts
<p> All files generated during the fully digitalized automated and high-throughput experimentally-guided Bayesian Optimization project, which led to the synthesis of 144 heterogeneous catalysts with a Chemspeed unit (6 generations of 24) and their testing under CO2 hydrogenation conditions with Avantium fixed bed units. Below are some indication to understand the naming of the files.</p> <ul> <li>A1 stands for the internal project number.</li> <li>G1 to G5 stands for the catalyst generation number and G2NC for the alternative second generation suggested by the Bayesian Optimizer without considering the cost of catalyst as an objective (No_Cost).</li> <li>Three fixed bed units have been used, named XDB4x (a 4 parallel reactors unit), XDC4x (another 4 parallel reactors unit) and XR16x (a 16 parallel reactors unit).</li> <li>Individual fixed bed testing raw files (FB_RawData) generated by each unit are then processed to extract the mean and standard deviation (std) values (e.g conversion, selectivity) and to compute reactions rates.</li> <li>Then the processed files for each individual reactor (XDB, XDC, XR) are combined into one file (All_FBData), and finally aggregated with the synthesis details, viathe catalyst barcodes (AllData_Processed).</li> <li>Finally, the processed file for each generation are merged together (AllGen_Merged) and a condensed file is generated for a given reaction temperature (AllGen_275CDataProcessed_Merged)</li> </ul>
Supplement data for : Intratumoral drug-releasing microdevices allow in situ high throughput pharmaco phenotyping in patients with gliomas
<p>Transcriptomic and metabolomic data associated with the manuscript: Intratumoral drug-releasing microdevices allow in situ high throughput pharmaco phenotyping in patients with gliomas.</p> <p> </p>
Spectrophotometric and Fluorimetric High-Throughput Assays for Phenolic Acid Decarboxylase
<p>Biocatalytic decarboxylation of hydroxycinnamic acids yields phenolic styrenes, which are important precursors for antioxidants, epoxy coatings, adhesives and other polymeric materials. <em>Bacillus subtilis</em> decarboxylase (<em>Bs</em>PAD) is a cofactor-independent enzyme that catalyzes the cleavage of carbon dioxide from <em>p</em>-coumaric-, caffeic-, and ferulic acid with high catalytic efficiency. Real-time spectroscopic assays for decarboxylase reactions remove the necessity of extensive sample workup, which is required for HPLC, mass spectrometry, gas chromatography, or NMR methods. This work presents two robust and sensitive assays based on photometry and fluorimetry that allow decarboxylation reactions to be followed with high sensitivity while avoiding product extraction and long analysis times. Optimized assay procedures were used to measure <em>Bs</em>PAD activity in cell lysates and to determine the kinetic constants (<em>K</em><sub>M</sub> and <em>V</em><sub>max</sub>) of the purified enzyme for <em>p</em>-coumaric-, caffeic- and ferulic acid. Substrate inhibition was shown for caffeic acid.</p>
Dataset - Speeding up high-throughput characterization of materials libraries by active learning: autonomous electrical resistance measurements
<p>With the trend towards multinary materials and the associated increase in measurement time, there is a clear need for increasing the efficiency of measurement procedures. In systems requiring long materials characterization times, the implementation of active learning can help decreasing the measurement duration significantly. This dataset is part of the publication in Digital Discovery under the same title and holds the algorithm as well as the data used to test its performance. The algorithm leverages an active learning approach with a Gaussian process model capable of selecting the next measurement area of a library of materials based on the highest uncertainty. Ten materials libraries were manufactured by magnetron sputtering, the composition was measured with EDX and the electrical resistance was measured using the described test stand. The code can also be found on <a href="https://gitlab.ruhr-uni-bochum.de/fthelen/auto-resist-meas">Gitlab</a>.</p>
Data for: High-throughput profiling of sequence recognition by tyrosine kinases and SH2 domains using bacterial peptide display
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MCount: An automated colony counting tool for high-throughput microbiology
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Data for: Tools and methods for high-throughput single-cell imaging with the mother machine
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Data from: Towards drift-free high-throughput nanoscopy through adaptive intersection maximization
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A high-throughput multispectral imaging system for museum specimens
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Data and scripts for: Genetic dissection of seasonal vegetation index dynamics in maize through aerial based high-throughput phenotyping
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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.