4.6 Article

Reducing Results Variance in Lifespan Machines: An Analysis of the Influence of Vibrotaxis on Wild-Type Caenorhabditis elegans for the Death Criterion

期刊

SENSORS
卷 20, 期 21, 页码 -

出版社

MDPI
DOI: 10.3390/s20215981

关键词

image processing; lifespan automation; C. elegans

资金

  1. Universitat Politecnica de Valencia [20170020-UPV]
  2. Plan Nacional de I+D [RTI2018-094312-B-I00]
  3. European FEDER funds
  4. ADM Nutrition
  5. Biopolis SL
  6. Archer Daniels Midland

向作者/读者索取更多资源

Nowadays, various artificial vision-based machines automate the lifespan assays of C. elegans. These automated machines present wider variability in results than manual assays because in the latter worms can be poked one by one to determine whether they are alive or not. Lifespan machines normally use a dead or alive criterion based on nematode position or pose changes, without poking worms. However, worms barely move on their last days of life, even though they are still alive. Therefore, a long monitoring period is necessary to observe motility in order to guarantee worms are actually dead, or a stimulus to prompt worm movement is required to reduce the lifespan variability measure. Here, a new automated vibrotaxis-based method for lifespan machines is proposed as a solution to prompt a motion response in all worms cultured on standard Petri plates in order to better distinguish between live and dead individuals. This simple automated method allows the stimulation of all animals through the whole plate at the same time and intensity, increasing the experiment throughput. The experimental results exhibited improved live-worm detection using this method, and most live nematodes (>93%) reacted to the vibration stimulus. This method increased machine sensitivity by decreasing results variance by approximately one half (from +/- 1 individual error per plate to +/- 0.6) and error in lifespan curve was reduced as well (from 2.6% to 1.2%).

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据