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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Main Authors: Jason Henry, Alvaro Rodriguez, Donald Wlodkowic
Format: Article
Language:English
Published: PeerJ Inc. 2019-08-01
Series:PeerJ
Subjects:
Online Access:https://peerj.com/articles/7367.pdf
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author Jason Henry
Alvaro Rodriguez
Donald Wlodkowic
author_facet Jason Henry
Alvaro Rodriguez
Donald Wlodkowic
author_sort Jason Henry
collection DOAJ
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 doaj.art-669286fc99e84de499e7b84f220303ce2023-12-03T09:46:04ZengPeerJ Inc.PeerJ2167-83592019-08-017e736710.7717/peerj.7367Impact of digital video analytics on accuracy of chemobehavioural phenotyping in aquatic toxicologyJason Henry0Alvaro Rodriguez1Donald Wlodkowic2School of Science, RMIT University, Melbourne, VIC, AustraliaBiomedical Research Institute A Coruña (INIBIC), University Hospital Complex of A Coruña, Coruña, SpainSchool of Science, RMIT University, Melbourne, VIC, AustraliaChemobehavioural 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.https://peerj.com/articles/7367.pdfAnimalBehaviourTrackingVideoToxicityPhenomics
spellingShingle Jason Henry
Alvaro Rodriguez
Donald Wlodkowic
Impact of digital video analytics on accuracy of chemobehavioural phenotyping in aquatic toxicology
PeerJ
Animal
Behaviour
Tracking
Video
Toxicity
Phenomics
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
Behaviour
Tracking
Video
Toxicity
Phenomics
url https://peerj.com/articles/7367.pdf
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AT alvarorodriguez impactofdigitalvideoanalyticsonaccuracyofchemobehaviouralphenotypinginaquatictoxicology
AT donaldwlodkowic impactofdigitalvideoanalyticsonaccuracyofchemobehaviouralphenotypinginaquatictoxicology