Using DeepLabCut for markerless cardiac physiology and toxicity estimation in water fleas (Daphnia magna).

Daphnia magna is one species of water flea that has been used for a long time for ecotoxicity studies. In addition, Daphnia has a myogenic heart that is very useful for cardiotoxicity studies. Previous attempts to calculate the cardiac parameter endpoints in Daphnia suffer from the drawback of tedious operation and high variation due to manual counting errors. Even the previous method that utilized deep learning to help the process suffer from either overestimation of parameters or the need for specialized equipment to perform the analysis. In this study, we utilized DeepLabCut software previously used for animal pose tracking and demonstrated that ResNet_152 was the best fit for training the network. The trained network also showed comparable results with ImageJ and Kymograph, which was mostly done manually. In addition to that, several macro scripts in either Excel or Python format were developed to help summarize the data for faster analysis. The trained network was then challenged to analyze the potential cardiotoxicity of imidacloprid and pendimethalin in D. magna, and it showed that both pesticides cause alteration in their cardiac performance. Overall, this method provides a simple and automatic method to analyze the cardiac performance of Daphnia by utilizing DeepLabCut. The method proposed in this paper can contribute greatly to scientists conducting fast and accurate cardiotoxicity measurements when using Daphnia as a model.

Using crayfish behavior assay as a simple and sensitive model to evaluate potential adverse effects of water pollution: Emphasis on antidepressants.

The freshwater crayfish, Procambarus clarkii is an excellent aquatic animal model that is highly adaptable and tolerant. P. clarkii is widely used as a toxicity model to study various pharmaceutical exposure. This animal model has complex behavioral traits and is considered sensitive to environmental changes, making it an excellent candidate to study psychoactive drugs based on a behavioral approach. However, up to now, most behavioral studies on crayfish use manual observation and scoring that require panelists. In this study, we aim to develop an automation pipeline to analyze crayfish behavior automatically. We use a deep-learning approach to label body parts in multiple crayfish, and based on the trajectory results, the intra- or inter-individual crayfish were calculated. Reliable and fast results of several behavior endpoints in multiple crayfish were retrieved. We then validated the detection performance of numerous crayfish in specific gender groups (male-male and female-female). Based on the result, the male crayfish displayed significantly higher aggression than females. We also tested the antidepressant exposure on this animal model to evaluate the psychoactive effects of this drug. As male crayfish display more distinct agonistic behavior than females, we exposed them to sertraline (SRT) 1 ppb for 7 and 14 days. It was revealed that sertraline was able to alter several behavioral endpoints in crayfish. Significant increases in extend claw ratio, total distance moved, average speed, and rapid movement were displayed in sertraline-exposed crayfish but decreased interaction time and longest interaction time. In addition, SRT 14 days exposure could atler the aggressiveness and bold behavior In the present method, DeepLabCut (DLC) has been utilized to analyze the locomotion behavior of multiple crayfish. This established method provides rapid and accurate ecotoxicity measurements using freshwater crayfish, which beneficient and applicable for environmental research.

Deep Learning-Based Automatic Duckweed Counting Using StarDist and Its Application on Measuring Growth Inhibition Potential of Rare Earth Elements as Contaminants of Emerging Concerns.

In recent years, there have been efforts to utilize surface water as a power source, material, and food. However, these efforts are impeded due to the vast amounts of contaminants and emerging contaminants introduced by anthropogenic activities. Herbicides such as Glyphosate and Glufosinate are commonly known to contaminate surface water through agricultural industries. In contrast, some emerging contaminants, such as rare earth elements, have started to enter the surface water from the production and waste of electronic products. Duckweeds are angiosperms from the Lemnaceae family and have been used for toxicity tests in aquatic environments, mainly those from the genus Lemna, and have been approved by OECD. In this study, we used duckweed from the genus Wolffia, which is smaller and considered a good indicator of metal pollutants in the aquatic environment. The growth rate of duckweed is the most common endpoint in observing pollutant toxicity. In order to observe and mark the fronds automatically, we used StarDist, a machine learning-based tool. StarDist is available as a plugin in ImageJ, simplifying and assisting the counting process. Python also helps arrange, manage, and calculate the inhibition percentage after duckweeds are exposed to contaminants. The toxicity test results showed Dysprosium to be the most toxic, with an IC50 value of 14.6 ppm, and Samarium as the least toxic, with an IC50 value of 279.4 ppm. In summary, we can provide a workflow for automatic frond counting using StarDist integrated with ImageJ and Python to simplify the detection, counting, data management, and calculation process.