跟踪蠕虫跟踪器。

Steven J Husson, Wagner Steuer Costa, Cornelia Schmitt, Alexander Gottschalk
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引用次数: 113

摘要

秀丽隐杆线虫由于其明确的神经系统而被广泛用作神经科学的模型系统。然而,至少与高等动物相比,这种神经系统在解剖结构和神经元连接方面看似简单,但却隐含着丰富多样的行为。蠕虫在全基因组诱变或RNAi筛选中的有用性,其中数千个菌株被评估表型,强调了对生成行为自动参数化的计算方法的需求。此外,行为可以根据外部线索进行调节,如温度、O(下标)2(/下标)和CO(下标)2(/下标)浓度、机械感觉和化学感觉输入。不同的机器视觉工具已经开发出来,以帮助研究人员努力盘点和表征已定义的行为“输出”。在这里,我们的目的是提供不同的蠕虫跟踪包或视频分析工具的概述,旨在量化运动的不同方面,如方向变化(转弯,ω弯曲),正弦形状的曲率(振幅,身体弯曲角度)和速度(速度,向后或向前运动)的发生。
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Keeping track of worm trackers.

C. elegans is used extensively as a model system in the neurosciences due to its well defined nervous system. However, the seeming simplicity of this nervous system in anatomical structure and neuronal connectivity, at least compared to higher animals, underlies a rich diversity of behaviors. The usefulness of the worm in genome-wide mutagenesis or RNAi screens, where thousands of strains are assessed for phenotype, emphasizes the need for computational methods for automated parameterization of generated behaviors. In addition, behaviors can be modulated upon external cues like temperature, O(subscript)2(/subscript) and CO(subscript)2(/subscript) concentrations, mechanosensory and chemosensory inputs. Different machine vision tools have been developed to aid researchers in their efforts to inventory and characterize defined behavioral "outputs". Here we aim at providing an overview of different worm-tracking packages or video analysis tools designed to quantify different aspects of locomotion such as the occurrence of directional changes (turns, omega bends), curvature of the sinusoidal shape (amplitude, body bend angles) and velocity (speed, backward or forward movement).

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