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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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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. |
format | Online Article Text |
id | pubmed-10393159 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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