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A Micropositioning System with Real-Time Feature Extraction Capability for Quantifying C. elegans Locomotive Behavior

机译:具有实时特征提取能力的微定位系统,用于量化线虫的机车行为

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Tracking C. elegans and extracting locomotive features of the nematode allow the investigation of how genes control behavioral phenotypes. Existing systems require large storage space for image data recording and significant time for off-line processing. This paper presents a visually servoed micropositioning system capable of extracting locomotive features on line at a full 30Hz. The employment of Gaussian Pyramid Level-2 images significantly reduces the image size by 16 folds and permits real-time feature extraction, without sacrificing accuracy due to the cubic smoothing spline fitting. Enabled by the capability of the micropositioning system in revealing subtle differences in locomotive behavior across strains, the relationship between C. elegans locomotive behavior and the number of muscle arms, for the first time, was investigated. A total of 128 worms of four C. elegans strains with different numbers of muscle arms were continuously tracked for 3 minutes per sample, and locomotive features were extracted on line. The potential impact of this research extends beyond revealing subtle phenotypic differences in C. elegans locomotive behavior across strains by demonstrating how automation techniques can be used to provide valuable tools for genetic investigations of C. elegans.
机译:跟踪线虫和提取线虫的机车特征可以研究基因如何控制行为表型。现有系统需要用于图像数据记录的大存储空间以及用于离线处理的大量时间。本文提出了一种视觉伺服微定位系统,该系统能够以完整的30Hz在线提取机车特征。使用高斯金字塔Level-2图像可将图像大小显着减小16倍,并允许实时特征提取,而不会因三次平滑样条拟合而降低精度。借助微定位系统揭示各种机车行为细微差异的能力,首次对秀丽隐杆线虫机车行为与肌肉臂数之间的关系进行了研究。每个样本连续跟踪4个线虫的4种秀丽隐杆线虫菌株的128条蠕虫,每次采样持续3分钟,并在线提取机车特征。这项研究的潜在影响超出了通过展示自动化技术如何可用于为秀丽隐杆线虫的遗传研究提供有价值的工具而揭示线虫跨株的细微表型差异的范围。

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