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Automated scoring of nematode nictation on a textured background

Entomopathogenic nematodes, including Steinernema spp., play an increasingly important role as biological alternatives to chemical pesticides. The infective juveniles of these worms use nictation–a behavior in which animals stand on their tails–as a host-seeking strategy. The developmentally-equival...

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Autores principales: McClanahan, Patrick D., Golinelli, Luca, Le, Tuan Anh, Temmerman, Liesbet
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10393159/
https://www.ncbi.nlm.nih.gov/pubmed/37527261
http://dx.doi.org/10.1371/journal.pone.0289326
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author McClanahan, Patrick D.
Golinelli, Luca
Le, Tuan Anh
Temmerman, Liesbet
author_facet McClanahan, Patrick D.
Golinelli, Luca
Le, Tuan Anh
Temmerman, Liesbet
author_sort McClanahan, Patrick D.
collection PubMed
description Entomopathogenic nematodes, including Steinernema spp., play an increasingly important role as biological alternatives to chemical pesticides. The infective juveniles of these worms use nictation–a behavior in which animals stand on their tails–as a host-seeking strategy. The developmentally-equivalent dauer larvae of the free-living nematode Caenorhabditis elegans also nictate, but as a means of phoresy or "hitching a ride" to a new food source. Advanced genetic and experimental tools have been developed for C. elegans, but time-consuming manual scoring of nictation slows efforts to understand this behavior, and the textured substrates required for nictation can frustrate traditional machine vision segmentation algorithms. Here we present a Mask R-CNN-based tracker capable of segmenting C. elegans dauers and S. carpocapsae infective juveniles on a textured background suitable for nictation, and a machine learning pipeline that scores nictation behavior. We use our system to show that the nictation propensity of C. elegans from high-density liquid cultures largely mirrors their development into dauers, and to quantify nictation in S. carpocapsae infective juveniles in the presence of a potential host. This system is an improvement upon existing intensity-based tracking algorithms and human scoring which can facilitate large-scale studies of nictation and potentially other nematode behaviors.
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spelling pubmed-103931592023-08-02 Automated scoring of nematode nictation on a textured background McClanahan, Patrick D. Golinelli, Luca Le, Tuan Anh Temmerman, Liesbet PLoS One Research Article Entomopathogenic nematodes, including Steinernema spp., play an increasingly important role as biological alternatives to chemical pesticides. The infective juveniles of these worms use nictation–a behavior in which animals stand on their tails–as a host-seeking strategy. The developmentally-equivalent dauer larvae of the free-living nematode Caenorhabditis elegans also nictate, but as a means of phoresy or "hitching a ride" to a new food source. Advanced genetic and experimental tools have been developed for C. elegans, but time-consuming manual scoring of nictation slows efforts to understand this behavior, and the textured substrates required for nictation can frustrate traditional machine vision segmentation algorithms. Here we present a Mask R-CNN-based tracker capable of segmenting C. elegans dauers and S. carpocapsae infective juveniles on a textured background suitable for nictation, and a machine learning pipeline that scores nictation behavior. We use our system to show that the nictation propensity of C. elegans from high-density liquid cultures largely mirrors their development into dauers, and to quantify nictation in S. carpocapsae infective juveniles in the presence of a potential host. This system is an improvement upon existing intensity-based tracking algorithms and human scoring which can facilitate large-scale studies of nictation and potentially other nematode behaviors. Public Library of Science 2023-08-01 /pmc/articles/PMC10393159/ /pubmed/37527261 http://dx.doi.org/10.1371/journal.pone.0289326 Text en © 2023 McClanahan 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, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
McClanahan, Patrick D.
Golinelli, Luca
Le, Tuan Anh
Temmerman, Liesbet
Automated scoring of nematode nictation on a textured background
title Automated scoring of nematode nictation on a textured background
title_full Automated scoring of nematode nictation on a textured background
title_fullStr Automated scoring of nematode nictation on a textured background
title_full_unstemmed Automated scoring of nematode nictation on a textured background
title_short Automated scoring of nematode nictation on a textured background
title_sort automated scoring of nematode nictation on a textured background
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10393159/
https://www.ncbi.nlm.nih.gov/pubmed/37527261
http://dx.doi.org/10.1371/journal.pone.0289326
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