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Impact of digital video analytics on accuracy of chemobehavioural phenotyping in aquatic toxicology

Chemobehavioural phenotypic analysis using small aquatic model organisms is becoming an important toolbox in aquatic ecotoxicology and neuroactive drug discovery. The analysis of the organisms’ behavior is usually performed by combining digital video recording with animal tracking software. This sof...

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Detalles Bibliográficos
Autores principales: Henry, Jason, Rodriguez, Alvaro, Wlodkowic, Donald
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6686839/
https://www.ncbi.nlm.nih.gov/pubmed/31404436
http://dx.doi.org/10.7717/peerj.7367
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author Henry, Jason
Rodriguez, Alvaro
Wlodkowic, Donald
author_facet Henry, Jason
Rodriguez, Alvaro
Wlodkowic, Donald
author_sort Henry, Jason
collection PubMed
description Chemobehavioural phenotypic analysis using small aquatic model organisms is becoming an important toolbox in aquatic ecotoxicology and neuroactive drug discovery. The analysis of the organisms’ behavior is usually performed by combining digital video recording with animal tracking software. This software detects the organisms in the video frames, and reconstructs their movement trajectory using image processing algorithms. In this work we investigated the impact of video file characteristics, video optimization techniques and differences in animal tracking algorithms on the accuracy of quantitative neurobehavioural endpoints. We employed larval stages of a free-swimming euryhaline crustacean Artemia franciscana,commonly used for marine ecotoxicity testing, as a proxy modelto assess the effects of video analytics on quantitative behavioural parameters. We evaluated parameters such as data processing speed, tracking precision, capability to perform high-throughput batch processing of video files. Using a model toxicant the software algorithms were also finally benchmarked against one another. Our data indicates that variability in video file parameters; such as resolution, frame rate, file containers types, codecs and compression levels, can be a source of experimental biases in behavioural analysis. Similarly, the variability in data outputs between different tracking algorithms should be taken into account when designing standardized behavioral experiments and conducting chemobehavioural phenotyping.
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spelling pubmed-66868392019-08-11 Impact of digital video analytics on accuracy of chemobehavioural phenotyping in aquatic toxicology Henry, Jason Rodriguez, Alvaro Wlodkowic, Donald PeerJ Animal Behavior Chemobehavioural phenotypic analysis using small aquatic model organisms is becoming an important toolbox in aquatic ecotoxicology and neuroactive drug discovery. The analysis of the organisms’ behavior is usually performed by combining digital video recording with animal tracking software. This software detects the organisms in the video frames, and reconstructs their movement trajectory using image processing algorithms. In this work we investigated the impact of video file characteristics, video optimization techniques and differences in animal tracking algorithms on the accuracy of quantitative neurobehavioural endpoints. We employed larval stages of a free-swimming euryhaline crustacean Artemia franciscana,commonly used for marine ecotoxicity testing, as a proxy modelto assess the effects of video analytics on quantitative behavioural parameters. We evaluated parameters such as data processing speed, tracking precision, capability to perform high-throughput batch processing of video files. Using a model toxicant the software algorithms were also finally benchmarked against one another. Our data indicates that variability in video file parameters; such as resolution, frame rate, file containers types, codecs and compression levels, can be a source of experimental biases in behavioural analysis. Similarly, the variability in data outputs between different tracking algorithms should be taken into account when designing standardized behavioral experiments and conducting chemobehavioural phenotyping. PeerJ Inc. 2019-08-05 /pmc/articles/PMC6686839/ /pubmed/31404436 http://dx.doi.org/10.7717/peerj.7367 Text en ©2019 Henry et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Animal Behavior
Henry, Jason
Rodriguez, Alvaro
Wlodkowic, Donald
Impact of digital video analytics on accuracy of chemobehavioural phenotyping in aquatic toxicology
title Impact of digital video analytics on accuracy of chemobehavioural phenotyping in aquatic toxicology
title_full Impact of digital video analytics on accuracy of chemobehavioural phenotyping in aquatic toxicology
title_fullStr Impact of digital video analytics on accuracy of chemobehavioural phenotyping in aquatic toxicology
title_full_unstemmed Impact of digital video analytics on accuracy of chemobehavioural phenotyping in aquatic toxicology
title_short Impact of digital video analytics on accuracy of chemobehavioural phenotyping in aquatic toxicology
title_sort impact of digital video analytics on accuracy of chemobehavioural phenotyping in aquatic toxicology
topic Animal Behavior
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6686839/
https://www.ncbi.nlm.nih.gov/pubmed/31404436
http://dx.doi.org/10.7717/peerj.7367
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