Automated scoring of nematode nictation on a textured background
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Abstract
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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The authors have successfully developed a computational pipeline to automatically score nictation, a specific motility behavior in nematodes, and demonstrated its utility with data, such as the detection of an increase in nictation after nematode exposure to G. mellonella. To enhance the manuscript's clarity and the utility of the work, I propose the following suggestions:
Please consider linking the Github page (https://github.com/TemmermanLab/nictation-tracking-and-scoring) directly in the manuscript to allow easier access to the code for readers.
Additional information in the Github readme to assist users would be beneficial. More explicit instructions on how to use the computational pipeline, as well as improved visibility of key documents like the 'manual_scoring_instructions.docx', would enhance user navigation.
To provide better …
The authors have successfully developed a computational pipeline to automatically score nictation, a specific motility behavior in nematodes, and demonstrated its utility with data, such as the detection of an increase in nictation after nematode exposure to G. mellonella. To enhance the manuscript's clarity and the utility of the work, I propose the following suggestions:
Please consider linking the Github page (https://github.com/TemmermanLab/nictation-tracking-and-scoring) directly in the manuscript to allow easier access to the code for readers.
Additional information in the Github readme to assist users would be beneficial. More explicit instructions on how to use the computational pipeline, as well as improved visibility of key documents like the 'manual_scoring_instructions.docx', would enhance user navigation.
To provide better comprehension of the nictation behavior, consider including a visual representation or graphic. This addition would help readers, like myself, who still find the concept of nictation somewhat unclear.
I used ChatGPT to help craft the language in this feedback, but verify that the content is accurate.
-Galo
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