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50 results for “High throughput phenotyping”
Image dataset for the evaluation of a low-cost high-throughput plant phenotyping system
<p>This dataset contains the raw and processed images from a low-cost high-throughput plant phenotyping (HTP) system, as well as the raw and processed images that were manually acquired for comparison. The HTP images were automatically and wirelessly acquired for entire benches of plants with a system composed of a Raspberry Pi and eight GoPro cameras. The entire file system of each GoPro camera was copied directly into a subfolder of finalGoProImages (numbered by camera). The raw HTP images were processed by correcting for lens distortion, computing the "greenness index" for each individual pixel, and filtering out extreme high and low values. These processed HTP images were then saved in the "greenness" subfolder of finalGoProImages. The manually acquired images in the finalDSLR folder each represent an individual plant from one of five time points during the same greenhouse experiment. The raw manually acquired images were processed in the same manner as the raw HTP images by computing the greenness index for each individual pixel and filtering out extreme high and low values. The two tab-delimited text files include the number of green pixels and mean greenness index for each HTP (greennessGoProTable2.txt) and manually acquired (greennessDSLRTable2.txt) image.</p>
Analysis scripts for the evaluation of a low-cost high-throughput plant phenotyping system
<p>Data analyses to complement "Image dataset for the evaluation of a low-cost high-throughput plant phenotyping system" (DOI: 10.5281/zenodo.5725224). "README_SetupAndAnalyses.pdf" contains instructions for setting up the high-throughput phenotyping (HTP) system and analyzing the resulting image datasets. The analyses are split into two parts. First, the automatically acquired HTP and manually acquired (DSLR) images are processed using the Python script labeled "finalGreennessAnalyses.py". The csv file labeled "labelTable.csv" is used to rename the DSLR images in terms of the date acquired and experimental conditions and must be included for the Python script to process the DSLR images. The output of the Python script includes "greennessGoProTable.txt" containing tab-delimited data regarding foliar size and greenness for each HTP image and "greennessDSLRTable.txt" containing tab-delimited data regarding foliar size and greenness for each DSLR image. The second step of the analyses includes inferential statistics (e.g., correlations and linear mixed effects modeling) and is based on the R script labeled "ghGoProAndDSLR_toPublish2.R". The csv file labeled "parAllBenches.csv" includes average solar daily light integral (solar DLI) data that were used as part of the linear mixed effects models in R.</p>
Data and scripts for: Genetic dissection of seasonal vegetation index dynamics in maize through aerial based high-throughput phenotyping
<p>Plant phenotyping under field conditions plays an important role in agricultural research. Efficient and accurate high-throughput phenotyping strategies enable a better connection between genotype and phenotype. Unmanned aerial vehicle-based high-throughput phenotyping platforms (UAV-HTPPs) provide novel opportunities for large-scale proximal measurement of plant traits with high efficiency, high resolution, and low cost. The objective of this study was to use time series normalized difference vegetation index (NDVI) extracted from UAV-based multispectral imagery to characterize its pattern across development and conduct genetic dissection of NDVI in a large maize population. The time series NDVI data from the multispectral sensor were obtained at 5 time points across the growing season for 1,752 diverse maize accessions with a UAV-HTPP. Cluster analysis of the acquired measurements classified 1,752 maize accessions into 2 groups with distinct NDVI developmental trends. To capture the dynamics underlying these static observations, penalized-splines (P-splines) model was used to obtain genotype-specific curve parameters. Genome-wide association study (GWAS) using static NDVI values and curve parameters as phenotypic traits detected signals significantly associated with the traits. Additionally, GWAS using the projected NDVI values from the P-splines models revealed the dynamic change of genetic effects, indicating the role of gene-environment interplay in controlling NDVI across the growing season. Our results demonstrated the utility of ultra-high spatial resolution multispectral imagery, as that acquired using a UAV-based remote sensing, for genetic dissection of NDVI.</p>
UV mutagenesis conjugated to high throughput screening as a tool to generate new phenotypic diversity in wine yeast
<p><strong>The current global changes, societal and climatic, strongly challenge the wine industry. Multiple methods are applied in the development of new strains for the industry, but many are based on the existing phenotypic and genetic diversities. UV mutagenesis, as an untargeted strategy, has been successfully used for years, with significant examples on wine. Here we developed and validated a UV-mutant generation strategy coupled with a high throughput screening in wine-like conditions. This strategy led to the production of a 502 mutant’s library for which concentrations of eight primary metabolites after fermentation were assessed. This data paper presents the resulting data.</strong></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>
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>
Data and scripts for: Genetic dissection of seasonal vegetation index dynamics in maize through aerial based high-throughput phenotyping
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Data from: Accelerated high-throughput imaging and phenotyping system for small organisms
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Development of a high-throughput small molecule screening assay for phenotypical characterization of lysosomal storage disorder-affected cells, with infantile cystinosis as a proof of principle
<p>Together with the Pivot Park Screening Centre we performed a drug screen on CTNS-/- proximal tubule cells. For this we developed an assay to evaluate LC3-II positive puncta, and which may be applied for any disease in which autophagy plays an important role. The screen was optimized by the hotel for a 384 well format, making it useful for high throughput screening. The screen was performed with 1280 compounds from the Prestwick library.</p>
Applying RGB- and Thermal-Based Vegetation Indices from UAVs for High-Throughput Field Phenotyping of Drought Tolerance in Forage Grasses
<p>Basic data from publication <a href="https://doi.org/10.3390/rs13010147">https://doi.org/10.3390/rs13010147</a></p> <p><strong>All_TDRdata.csv</strong> contains the data from 48 TDR sensors (30 cm) installed in the three rainout shelters.</p> <ul> <li>Sensors 1 - 18 were installed vertically to obtain soil moisture content averaged over the 10 - 40 cm profile, on 6 locations per shelter</li> <li>Sensors 19-21 were installed diagonally to obtain soil moisture content averaged over the 20 - 40 cm profile on one location per shelter</li> <li>Sensors 22-24 were installed diagonally to obtain soil moisture content averaged over the 40 - 60 cm profile on one location per shelter</li> <li>Sensors 25-27 were installed horizontally to obtain soil moisture content at 10 cm depth on one location per shelter</li> <li>Sensors 28-30 were installed horizontally to obtain soil moisture content at 20 cm depth on one location per shelter</li> <li>Sensors 31-33 were installed horizontally to obtain soil moisture content at 30 cm depth on one location per shelter</li> <li>Sensors 34-36 were installed horizontally to obtain soil moisture content at 40 cm depth on one location per shelter</li> <li>Sensors 37-39 were installed horizontally to obtain soil moisture content at 50 cm depth on one location per shelter</li> <li>Sensors 40-42 were installed horizontally to obtain soil moisture content at 60 cm depth on one location per shelter</li> <li>Sensors 43-45 were installed horizontally to obtain soil moisture content at 70 cm depth on one location per shelter</li> <li>Sensors 46-48 were installed horizontally to obtain soil moisture content at 80 cm depth on one location per shelter</li> </ul> <p>Climate.txt contains the daily averaged microclimatic data</p> <p>PhenotypingData.csv contains the phenotypic data from the UAV flights and the breeder scores</p> <p> </p>
A high-throughput assay for quantifying phenotypic traits of microalgae
<p>High-throughput methods for phenotyping microalgae are in demand across a variety of research and commercial purposes. Many microalgae can be readily cultivated in multi-well plates for experimental studies which can reduce overall costs, while measuring traits from low volume samples can reduce handling. Here we develop a high-throughput quantitative phenotypic assay (QPA) that can be used to phenotype microalgae grown in multi-well plates. The QPA integrates 10 low-volume, relatively high-throughput trait measurements (growth rate, cell size, granularity, chlorophyll a, neutral lipid content, silicification, reactive oxygen species accumulation, and photophysiology parameters: ETRmax, Ik, and alpha) into one workflow. We demonstrate the utility of the QPA on Thalassiosira spp., a cosmopolitan marine diatom, phenotyping six strains in a standard nutrient rich environment (f/2 media) using the full 10-trait assay. The multivariate phenotypes of strains can be simplified into two dimensions using principal component analysis, generating a trait-scape. We determine that traits show a consistent pattern when grown in small volume compared to more typical large volumes. The QPA can thus be used for quantifying traits across different growth environments without requiring exhaustive large-scale culturing experiments, which facilitates experiments on trait plasticity. We confirm that this assay can be used to phenotype newly isolated diatom strains within 4 weeks of isolation. The QPA described here is highly amenable to customisation for other traits or unicellular taxa and provides a framework for designing high-throughput experiments. This method will have applications in experimental evolution, modelling, and for commercial applications where screening of phytoplankton traits is of high importance.</p>
High-throughput behavioural phenotyping of 25 C. elegans disease models including patient-specific mutations
<p>This repository contains: all code, phenomic data, extracted features, calculated stats, normalised z-scores and timerseries data for all of the disease mutant phenologs and data in our paper: High-throughput behavioural phenotyping of 25 C. elegans disease models including patient-specific mutations.</p>
Data and Results for: Cost-effective, high-throughput fruit phenotyping system for three-dimensional reconstruction of fruit form
<p>The dataset contains the raw data used for generating the results in the paper, and the reconstructed results at 1mm and 2mm voxel resolution.</p> <p><strong>Raw data</strong>: Samples are gathered into groups of five, DataObjects1-5.zip, ... , DataObjects56-59.zip.</p> <p>Once uncompressed, each directory holds the images and configuration information used for each sample. For instance, '1_obj' is for the first sample, and the 'data' folder has the image files by camera. In this case one camera was used, 'cam2', and there are 62 images. 'pattern_square_mm_external.txt', 'pattern_square_mm_internal.txt', and 'rotate_specification_file.txt' are all configuration files needed for the calibration step.</p> <p> </p> <p><strong>Results</strong>: Reconstructions results are in compressed folders 'reconstruction-results-1mm.zip' and 'reconstruction-results-2mm.zip' for 1 mm and 2 mm voxel resolutions, respectively. These results are three-dimensional model files than can be viewed with a variety of software, we have used the free MeshLab: https://www.meshlab.net/ .</p> <p> </p> <p> </p> <p> </p>
Unidimensional Phenotypes using Concurrent Imaging Collected Via a High-Throughput Imaging System
<p>SIPID and SIMID are dataset repositories used to compute Single-Aspect Phenotypes. SAPP has 28 samples and SAPM has 12 samples. Each dataset contains:</p> <ol> <li>Two species physically different. Buckwheat is a thin plant with a variety sizes of leaves and Sunflower is a bushy plant that contains flowering;</li> <li>the most commonly used induced environments in plant phenotyping such as a control and drought-induced; </li> <li>a temporal resolution that begins with the plants vegetative stage and ends with the plant fully matured;</li> <li>modalities (infrared, visible, near infrared) that are commonly used in plant phenotyping analysis; and </li> <li>multiple perspectives that are becoming widely acquired in plant phenotyping analysis due to its potential for three dimensional analysis.</li> </ol> <p>We thank Vincent Stoeger for acquiring the dataset using LemnaTec at the University of Nebraska-Lincoln.</p> <p>If you use this dataset, please cite this paper:</p>
Phenomic data-driven biological prediction of maize through field-based high throughput phenotyping integration with genomic data
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Data from: High-throughput mRNA sequencing-based DEGs: HNF4A mitigates sepsis-associated lung injury by upregulating NCOR2/GR/STAB1 axis and promoting macrophage polarization towards M2 phenotype
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Data from: High-throughput classification and quantification of skinning phenotype in sweet potatoes
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A high-throughput assay for quantifying phenotypic traits of microalgae
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Data from: Bacterial and fungal growth on fungal necromass and its diverse components: shared profiles and divergent constraints revealed by high-throughput phenotyping
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Deciphering salt stress responses in Solanum pimpinellifolium through high-throughput phenotyping
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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.