McClanahan Patrick D, Golinelli Luca, Le Tuan Anh, Temmerman Liesbet
Animal Physiology and Neurobiology, Department of Biology, KU Leuven, Leuven, Belgium.
bioRxiv. 2023 Jul 15:2023.03.16.533066. doi: 10.1101/2023.03.16.533066.
Entomopathogenic nematodes including 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 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 , 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 dauers and 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 from high-density liquid cultures largely mirrors their development into dauers, and to quantify nictation in 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.
包括 属物种在内的昆虫病原线虫作为化学杀虫剂的生物替代品发挥着越来越重要的作用。这些线虫的感染性幼虫利用立尾行为（一种动物用尾巴站立的行为）作为寻找宿主的策略。自由生活线虫 发育上等效的 dauer 幼虫也会立尾，但这是作为一种搭便车前往新食物来源的手段。已经为 开发了先进的遗传和实验工具，但立尾行为耗时的人工评分减缓了对这种行为的理解，而立尾所需的有纹理的底物会使传统的机器视觉分割算法受挫。在这里，我们展示了一种基于 Mask R-CNN 的跟踪器，它能够在适合立尾的有纹理背景上分割 dauer 幼虫和 感染性幼虫，以及一种对立尾行为进行评分的机器学习管道。我们使用我们的系统表明，来自高密度液体培养物的 的立尾倾向在很大程度上反映了它们发育成 dauer 幼虫的情况，并在存在潜在宿主的情况下量化 感染性幼虫的立尾行为。该系统是对现有的基于强度的跟踪算法和人工评分的改进，有助于对立尾行为以及潜在的其他线虫行为进行大规模研究。
