Feature point based 3D tracking of multiple fish from multi-view images

Bringing Artificial Intelligence (AI) into Environmental Toxicology Studies: A Perspective of AI-Enabled Zebrafish High-Throughput Screening

The booming development of artificial intelligence (AI) has brought excitement to many research fields that could benefit from its big data analysis capability for causative relationship establishment and knowledge generation. In toxicology studies using zebrafish, the microscopic images and videos that illustrate the developmental stages, phenotypic morphologies, and animal behaviors possess great potential to facilitate rapid hazard assessment and dissection of the toxicity mechanism of environmental pollutants. However, the traditional manual observation approach is both labor-intensive and time-consuming. In this Perspective, we aim to summarize the current AI-enabled image and video analysis tools to realize the full potential of AI. For image analysis, AI-based tools allow fast and objective determination of morphological features and extraction of quantitative information from images of various sorts. The advantages of providing accurate and reproducible results while avoiding human intervention play a critical role in speeding up the screening process. For video analysis, AI-based tools enable the tracking of dynamic changes in both microscopic cellular events and macroscopic animal behaviors. The subtle changes revealed by video analysis could serve as sensitive indicators of adverse outcomes. With AI-based toxicity analysis in its infancy, exciting developments and applications are expected to appear in the years to come.

Unlocking the Potential of Zebrafish Research with Artificial Intelligence: Advancements in Tracking, Processing, and Visualization

Zebrafish have become a widely accepted model organism for biomedical research due to their strong cortisol stress response, behavioral strain differences, and sensitivity to both drug treatments and predators. However, experimental zebrafish studies generate substantial data that must be analyzed through objective, accurate, and repeatable analysis methods. Recently, advancements in artificial intelligence (AI) have enabled automated tracking, image recognition, and data analysis, leading to more efficient and insightful investigations. In this review, we examine key AI applications in zebrafish research, including behavior analysis, genomics, and neuroscience. With the development of deep learning technology, AI algorithms have been used to precisely analyze and identify images of zebrafish, enabling automated testing and analysis. By applying AI algorithms in genomics research, researchers have elucidated the relationship between genes and biology, providing a better basis for the development of disease treatments and gene therapies. Additionally, the development of more effective neuroscience tools could help researchers better understand the complex neural networks in the zebrafish brain. In the future, further advancements in AI technology are expected to enable more extensive and in-depth medical research applications in zebrafish, improving our understanding of this important animal model. This review highlights the potential of AI technology in achieving the full potential of zebrafish research by enabling researchers to efficiently track, process, and visualize the outcomes of their experiments.

Comparing behavioral performance and physiological responses of Sebastes schlegelii with different aggressiveness

In fish, aggression has significant individual differences, and different personalities exhibit distinct behavioral performances and physiological stress responses. Under intensive culture conditions, Sebastes schlegelii juveniles display severe aggression and cannibalism, causing damage to fish welfare and economic loss. Herein, we investigated the alterations in behavioral performance and physiological stress indicators of Sebastes schlegelii juveniles with different aggressiveness. The results revealed that latency to the first movement, distance to center point, mobile frequency, and immobile frequency were significantly lower in high-aggressive individuals than low-aggressive individuals. In contrast, the immobile time was significantly higher in high-aggressive individuals compared to low-aggressive individuals. PCA was performed to identify the key parameters of fish behavior. From the results of PCA, position, motion state, and physical status could be used as behavioral screening indicators for individuals with different aggressiveness. The 5-HIAA/5-HT ratio was significantly lower in high-aggressive individuals than in low-aggressive individuals. Moreover, cortisol levels were positively correlated with immobile time, and the ratio of 5-HIAA/5-HT was significantly and positively correlated with the distance to the central point. These results suggested that individuals with different aggressiveness can be effectively distinguished in a short period of time according to behavioral factors such as position, motion state, and physical status. For a single measure, the distance to center point associated with brain monoaminergic activity may be a more direct factor. The results could be a non-invasive method to measure fish aggression and fish welfare, and then build on to improve fish welfare and enhance aquaculture management.

The mouthparts of the adult dragonfly Anax imperator (Insecta: Odonata), functional morphology and feeding kinematics

Insects evolved differently specialized mouthparts. We study the mouthparts of adult Anax imperator, one of the largest odonates found in Central Europe. Like all adult dragonflies, A. imperator possesses carnivorous-type of biting-chewing mouthparts. To gain insights into the feeding process, behavior and kinematics, living specimens were filmed during feeding using synchronized high-speed videography. Additionally, the maximum angles of movement were measured using a measuring microscope and combined with data from micro-computed tomography (µCT). The resulting visualizations of the 3D-geometry of each mouthpart were used to study their anatomy and complement the existing descriptive knowledge of muscles in A. imperator to date. Furthermore, CLSM-projections allow for estimation of differences in the material composition of the mouthparts' cuticle. By combining all methods, we analyze possible functions and underlying biomechanics of each mouthpart. We also analyzed the concerted movements of the mouthparts; unique behavior of the mouthparts during feeding is active participation by the labrum and distinct movement by the maxillary laciniae. We aim to elucidate the complex movements of the mouthparts and their functioning by combining detailed information on (1) in vivo movement behavior (supplemented with physiological angle approximations), (2) movement ability provided by morphology (morphological movement angles), (3) 3D-anatomy, and (4) cuticle composition estimates. This article is protected by copyright. All rights reserved.

Feature Point Extraction and Motion Tracking of Cardiac Color Ultrasound under Improved Lucas-Kanade Algorithm

The purpose of this research is to study the application effect of Lucas–Kanade algorithm in right ventricular color Doppler ultrasound feature point extraction and motion tracking under the condition of scale invariant feature transform (SIFT). This study took the right ventricle as an example to analyze the extraction effect and calculation rate of SIFT algorithm and improved Lucas–Kanade algorithm. It was found that the calculation time before and after noise removal by the SIFT algorithm was 0.49 s and 0.46 s, respectively, and the number of extracted feature points was 703 and 698, respectively. The number of feature points extracted by the SIFT algorithm and the calculation time were significantly better than those of other algorithms (P < 0.01). The mean logarithm of the matching points of the SIFT algorithm for order matching and reverse order matching was 20.54 and 20.46, respectively. The calculation time and the number of feature points for the SIFT speckle tracking method were 1198.85 s and 81, respectively, and those of the optical flow method were 3274.19 s and 80, respectively. The calculation time of the SIFT speckle tracking method was significantly lower than that of the optical flow method (P < 0.05), and there was no statistical difference in the number of feature points between the SIFT speckle tracking method and the optical flow method (P > 0.05). In conclusion, the improved Lucas–Kanade algorithm based on SIFT significantly improves the accuracy of feature extraction and motion tracking of color Doppler ultrasound, which shows the value of the algorithm in the clinical application of color Doppler ultrasound.

epiTracker: A Framework for Highly Reliable Particle Tracking for the Quantitative Analysis of Fish Movements in Tanks

Behavioral analysis of moving animals relies on a faithful recording and track analysis to extract relevant parameters of movement. To study group behavior and social interactions, often simultaneous analyses of individuals are required. To detect social interactions, for example to identify the leader of a group as opposed to followers, one needs an error-free segmentation of individual tracks throughout time. While automated tracking algorithms exist that are quick and easy to use, inevitable errors will occur during tracking. To solve this problem, we introduce a robust algorithm called epiTracker for segmentation and tracking of multiple animals in two-dimensional (2D) videos along with an easy-to-use correction method that allows one to obtain error-free segmentation. We have implemented two graphical user interfaces to allow user-friendly control of the functions. Using six labeled 2D datasets, the effort to obtain accurate labels is quantified and compared to alternative available software solutions. Both the labeled datasets and the software are publicly available.

Antagonistic effects in zebrafish (Danio rerio) behavior and oxidative stress induced by toxic metals and deltamethrin acute exposure

In natural environments, the aquatic organisms are exposed to complex mixtures of chemicals which may originate from natural sources or from anthropogenic activities. In this context, the aim of the study was to assess the potential effects that might occur when aquatic organisms are simultaneously exposed to multiple chemicals. For that, we have studied the acute effects of cadmium (0.2 μg L^-1), nickel (10 μg L^-1) and deltamethrin (2 μg L^-1) as individual toxicants and as mixture on the behavioral responses, oxidative stress (SOD and GPx), body electrolytes and trace metals profiles of zebrafish (Danio rerio). So far the scientific literature did not report about the combined effects of pesticides and toxic metals on zebrafish behavior using a 3D tracking system. Compared with other studies, in the present paper we investigated the acute effects of two heavy metals associated with a pesticide on zebrafish, in the range of environmentally relevant concentrations. Thus, the environmental concentrations of cadmium and nickel in three rivers affected by urban activities and one river with protected areas as background control were measured. The observations that resulted in our study demonstrated that deltamethrin toxicity was significantly decreased in some of the behavioral variables and oxidative stress when combined with CdNi mixture. Consequently, our study supports previous works concerning the combined toxicity of environmental chemicals since their simultaneous presence in the aqueous environment may lead to higher or lower toxicological effects on biota than those reported from a single pollutant. Therefore, the evaluation of toxic effects of a single contaminant does not offer a realistic estimate of its impact against aqueous ecosystems. This study also supports the idea that the interactions between different chemical compounds which do not exceed the maximum permitted limits in environment may have benefits for aquatic life forms or be more toxic.

The zebrafish subcortical social brain as a model for studying social behavior disorders

Social behaviors are essential for the survival and reproduction of social species. Many, if not most, neuropsychiatric disorders in humans are either associated with underlying social deficits or are accompanied by social dysfunctions. Traditionally, rodent models have been used to model these behavioral impairments. However, rodent assays are often difficult to scale up and adapt to high-throughput formats, which severely limits their use for systems-level science. In recent years, an increasing number of studies have used zebrafish (Danio rerio) as a model system to study social behavior. These studies have demonstrated clear potential in overcoming some of the limitations of rodent models. In this Review, we explore the evolutionary conservation of a subcortical social brain between teleosts and mammals as the biological basis for using zebrafish to model human social behavior disorders, while summarizing relevant experimental tools and assays. We then discuss the recent advances gleaned from zebrafish social behavior assays, the applications of these assays to studying related disorders, and the opportunities and challenges that lie ahead.

Automatic multiple zebrafish tracking based on improved HOG features

As an excellent model organism, zebrafish have been widely applied in many fields. The accurate identification and tracking of individuals are crucial for zebrafish shoaling behaviour analysis. However, multi-zebrafish tracking still faces many challenges. It is difficult to keep identified for a long time due to fish overlapping caused by the crossings. Here we proposed an improved Histogram of Oriented Gradient (HOG) algorithm to calculate the stable back texture feature map of zebrafish, then tracked multi-zebrafish in a fully automated fashion with low sample size, high tracking accuracy and wide applicability. The performance of the tracking algorithm was evaluated in 11 videos with different numbers and different sizes of zebrafish. In the Right-tailed hypothesis test of Wilcoxon, our method performed better than idTracker, with significant higher tracking accuracy. Throughout the video of 16 zebrafish, the training sample of each fish had only 200–500 image samples, one-fifth of the idTracker’s sample size. Furthermore, we applied the tracking algorithm to analyse the depression and hypoactivity behaviour of zebrafish shoaling. We achieved correct identification of depressed zebrafish among the fish shoal based on the accurate tracking results that could not be identified by a human.