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7 results for “statistical reconstruction”
Statistical analysis and dataset for: Three-dimensional body reconstruction enables quantification of liquid consumption in small invertebrates
<p>Linked to the journal article published in bioRxiv (https://doi.org/10.1101/2024.06.14.599002).</p> <p><em><strong>Abstract</strong></em></p> <p>Quantifying feeding patterns provides valuable insights into animal behaviour. However, small invertebrates often consume incredibly small amounts of food. This renders traditional methods, such as weighing individuals before and after food acquisition, either inaccurate or prohibitively expensive. Here, we present a non-invasive method to quantify food consumption of small invertebrates whose body expands during feeding. Using the markerless pose estimation software DeepLabCut, we three-dimensionally track the body of Argentine ants, <em>Linepithema humile</em>. Using these extracted markers, we developed an algorithm which computationally reconstructs the ant’s body, directly measuring volumetric change over time. Moreover, we provide measures of accuracy and quantify the ant’s feeding response to a range of sucrose concentrations, as well as a gradient of caffeine-laced sucrose solutions. Small invertebrates are often prolific invasive species and disease vectors, causing significant ecological and economical damage. Understanding their feeding behaviour could be an important step towards effective control strategies.</p> <p> </p> <ul> <li><strong>VolEst_C1_volume_calculation_multiprocessing.py</strong>: Takes as input H5 3D DeepLabCut files, calculates the gaster volume at every frame using seven different methods and outputs these as CSV files.</li> <li><strong>VolEst_C2_interactive_GUI.py</strong>: Given a folder with Volume CSV files, interactively plots the volume over time, 3D coordinates tracked by DeepLabCut and the frame of interest for both cameras.</li> <li><strong>VolEst_C3_linear_regression.py</strong>: Applies a linear regression to each feeding event tracked and provides measures of interest such as crop load and consumption rate.</li> <li><strong>VolEst_C4_statistical_analysis</strong>: Complete statistical analysis and code for the manuscript.</li> <li><strong>VolEst_D1_sucrose_density.csv</strong>: Data obtained to quantify the density of sucrose solutions of varying molarity.</li> <li><strong>VolEst_D2_accuracy_weight_metadata.csv</strong>: Manually collected metadata pertaining to experimental conditions, subjects, and treatments for the weight-volume accuracy measurements.</li> <li><strong>VolEst_D3_accuracy_weight.zip</strong>: Folder containing the raw points tracked using DeepLabCut, all relevant data obtained from the algorithms created, and a sample video of the experiment for the weight-volume accuracy measurements.</li> <li><strong>VolEst_D4_accuracy_nanoliter_metadata.csv</strong>: Manually collected metadata pertaining to experimental conditions, subjects, and treatments for the volume-volume accuracy measurements.</li> <li><strong>VolEst_D5_accuracy_nanoliter.zip</strong>: Folder containing the raw points tracked using DeepLabCut, all relevant data obtained from the algorithms created, and a sample video of the experiment for the volume-volume accuracy measurements.</li> <li><strong>VolEst_D6_sucrose_caffeine_consumption_metadata.csv</strong>: Manually collected metadata pertaining to experimental conditions, subjects, and treatments for the sucrose and caffeine dilutions application measurements.</li> <li><strong>VolEst_D7_sucrose_caffeine_consumption.zip</strong>: Folder containing the raw points tracked using DeepLabCut, all relevant data obtained from the algorithms created, and a sample video of the experiment for the sucrose and caffeine dilutions application measurements.</li> <li><strong>VolEst_Camera_A-Henrique-2023-09-20.zip</strong>: DeepLabCut labels and trained network for camera A.</li> <li><strong>VolEst_Camera_B-Henrique-2023-09-20.zip</strong>: DeepLabCut labels and trained network for camera B.</li> <li><strong>VolEst_base.stl</strong>: 3D file for the resin platform used in the experimental validation of the setup.</li> <li><strong>VolEst_platform.stl</strong>: 3D file for the resin platform used in the experimental validation of the setup.</li> </ul>
Data from: A 4-lineage statistical suite to evaluate the support of large-scale retrotransposon insertion data to reconstruct evolutionary trees
Open the record for dataset details and reuse information.
Data from: Complementarity of statistical treatments to reconstruct worldwide routes of invasion: the case of the Asian ladybird Harmonia axyridis
Inferences about introduction histories of invasive species remain challenging because of the stochastic demographic processes involved. Approximate Bayesian computation (ABC) can help to overcome these problems, but such method requires a prior understanding of population structure over the study area, necessitating the use of alternative methods and an intense sampling design. In this study, we made inferences about the worldwide invasion history of the ladybird Harmonia axyridis by various population genetics statistical methods, using a large set of sampling sites distributed over most of the species' native and invaded areas. We evaluated the complementarity of the statistical methods and the consequences of using different sets of site samples for ABC inferences. We found that the H. axyridis invasion has involved two bridgehead invasive populations in North America, which have served as the source populations for at least six independent introductions into other continents. We also identified several situations of genetic admixture between differentiated sources. Our results highlight the importance of coupling ABC methods with more traditional statistical approaches. We found that the choice of site samples could affect the conclusions of ABC analyses comparing possible scenarios. Approaches involving independent ABC analyses on several sample sets constitute a sensible solution, complementary to standard quality controls based on the analysis of pseudo-observed datasets, to minimize erroneous conclusions. This study provides biologists without expertise in this area with detailed methodological and conceptual guidelines for making inferences about invasion routes when dealing with a large number of sampling sites and complex population genetic structures.
Data from: Complementarity of statistical treatments to reconstruct worldwide routes of invasion: the case of the Asian ladybird Harmonia axyridis
Open the record for dataset details and reuse information.
Post EVAR Endoleak Detection : Model-based Iterative Reconstruction (MBIR) vs Adaptive Statistical Iterative Reconstruction (ASIR) CTA; a Prospective Study
ClinicalTrials.gov study NCT02082834. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Virtual Anatomical Reconstruction of Mandibular Bone Defects Using a Statistical Shape Model
ClinicalTrials.gov study NCT04475237. IPD Sharing: NO. Countries: 1. Publications: 0.
Model-based Iterative Reconstruction (MB-IR VEOTM) in Ultra Low-dose Abdominal CT Versus Adaptative Statistical Iterative Reconstruction (ASIR): A Prospective Study for Acute Renal Colic
ClinicalTrials.gov study NCT02076737. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.