Department of Information and Statistics, Chungbuk National University, Cheongju-si, Chungbuk, 28644, Republic of Korea.
Department of Environmental Science, Hankuk University of Foreign Studies, 81, Oe-daero, Mohyeon-myeon, Cheoin-gu, Yongin-si, Gyeonggi-do, 17035, South Korea.
Sci Rep. 2023 Mar 2;13(1):3530. doi: 10.1038/s41598-023-27554-y.
Daphnia magna is an important organism in ecotoxicity studies because it is sensitive to toxic substances and easy to culture in laboratory conditions. Its locomotory responses as a biomarker are highlighted in many studies. Over the last several years, multiple high-throughput video tracking systems have been developed to measure the locomotory responses of Daphnia magna. These high-throughput systems, used for high-speed analysis of multiple organisms, are essential for efficiently testing ecotoxicity. However, existing systems are lacking in speed and accuracy. Specifically, speed is affected in the biomarker detection stage. This study aimed to develop a faster and better high-throughput video tracking system using machine learning methods. The video tracking system consisted of a constant temperature module, natural pseudo-light, multi-flow cell, and an imaging camera for recording videos. To measure Daphnia magna movements, we developed a tracking algorithm for automatic background subtraction using k-means clustering, Daphnia classification using machine learning methods (random forest and support vector machine), and tracking each Daphnia magna location using the simple online real-time tracking algorithm. The proposed tracking system with random forest performed the best in terms of identification (ID) precision, ID recall, ID F1 measure, and ID switches, with scores of 79.64%, 80.63%, 78.73%, and 16, respectively. Moreover, it was faster than existing tracking systems such as Lolitrack and Ctrax. We conducted an experiment to observe the impact of toxicants on behavioral responses. Toxicity was measured manually in the laboratory and automatically using the high-throughput video tracking system. The median effective concentration of Potassium dichromate measured in the laboratory and using the device was 1.519 and 1.414, respectively. Both measurements conformed to the guideline provided by the Environmental Protection Agency of the United States; therefore, our method can be used for water quality monitoring. Finally, we observed Daphnia magna behavioral responses in different concentrations after 0, 12, 18, and 24 h and found that there was a difference in movement according to the concentration at all hours.
大型溞是生态毒性研究中的一种重要生物，因为它对有毒物质敏感，并且易于在实验室条件下培养。它的运动反应作为生物标志物在许多研究中得到了强调。在过去的几年中，已经开发出多种高通量视频跟踪系统来测量大型溞的运动反应。这些高通量系统用于对多个生物体进行高速分析，对于有效测试生态毒性至关重要。然而，现有的系统在速度和准确性方面存在不足。具体来说，速度在生物标志物检测阶段受到影响。本研究旨在使用机器学习方法开发更快更好的高通量视频跟踪系统。视频跟踪系统由恒温模块、自然伪光、多流池和用于记录视频的成像相机组成。为了测量大型溞的运动，我们开发了一种使用 K 均值聚类进行自动背景减除的跟踪算法、使用机器学习方法（随机森林和支持向量机）对大型溞进行分类以及使用简单在线实时跟踪算法跟踪每个大型溞的位置的跟踪算法。基于随机森林的提出的跟踪系统在识别 (ID) 精度、ID 召回率、ID F1 度量和 ID 切换方面表现最佳，得分分别为 79.64%、80.63%、78.73%和 16。此外，它比现有的跟踪系统（如 Lolitrack 和 Ctrax）更快。我们进行了一项实验，观察有毒物质对行为反应的影响。毒性在实验室中手动测量，并使用高通量视频跟踪系统自动测量。在实验室和设备中测量的重铬酸钾的半数有效浓度分别为 1.519 和 1.414。这两种测量都符合美国环境保护局提供的指南；因此，我们的方法可用于水质监测。最后，我们观察了大型溞在不同浓度下 0、12、18 和 24 小时后的行为反应，发现所有时间的运动都根据浓度而有所不同。
