Efficient phenotypic sex classification of zebrafish using machine learning methods

Sex, drugs, and zebrafish: Acute exposure to anxiety-modulating compounds in a modified novel tank dive test

This study investigated the effects of anxiogenic and anxiolytic drugs on zebrafish (Danio rerio) behaviour using a modified novel tank dive test with higher walls and a narrower depth. Zebrafish were administered chondroitin sulfate, beta-carboline, delta-9-tetrahydrocannabinol (THC), ethanol, and beta-caryophyllene, and their behaviours were evaluated for geotaxis, swimming velocity, and immobility. Both anxiogenic and anxiolytic compounds generally increased bottom-dwelling behaviour, suggesting that the tank's modified dimensions significantly influence zebrafish responses. EC50 values for ethanol showed a lower threshold for velocity reduction compared to zone preference. Chondroitin sulfate uniquely caused a sex-specific increase in male swimming velocity, whereas no other sex-differences were observed with any compound. Interestingly, the presence of drug-treated fish did not alter the behaviour of observer fish, suggesting limited social buffering effects. The findings underscore the complexity of zebrafish behavioural phenotypes and highlight the need for considering tank dimensions and multiple behavioural parameters to accurately assess the effects of anxiety-modulating drugs. This study demonstrates the utility of the modified novel tank dive test in providing nuanced insights into the behavioural effects of different pharmacological agents in zebrafish.

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.

Classifying high-dimensional phenotypes with ensemble learning

Classification is a fundamental task in biology used to assign members to a class. While linear discriminant functions have long been effective, advances in phenotypic data collection are yielding increasingly high-dimensional datasets with more classes, unequal class covariances, and non-linear distributions. Numerous studies have deployed machine learning techniques to classify such distributions, but they are often restricted to a particular organism, a limited set of algorithms, and/or a specific classification task. In addition, the utility of ensemble learning or the strategic combination of models has not been fully explored.We performed a meta-analysis of 33 algorithms across 20 datasets containing over 20,000 high-dimensional shape phenotypes using an ensemble learning framework. Both binary (e.g., sex, environment) and multi-class (e.g., species, genotype, population) classification tasks were considered. The ensemble workflow contains functions for preprocessing, training individual learners and ensembles, and model evaluation. We evaluated algorithm performance within and among datasets. Furthermore, we quantified the extent to which various dataset and phenotypic properties impact performance.We found that discriminant analysis variants and neural networks were the most accurate base learners on average. However, their performance varied substantially between datasets. Ensemble models achieved the highest performance on average, both within and among datasets, increasing average accuracy by up to 3% over the top base learner. Higher class R2 values, mean class shape distances, and between- vs. within-class variances were positively associated with performance, whereas higher class covariance distances were negatively associated. Class balance and total sample size were not predictive.Learning-based classification is a complex task driven by many hyperparameters. We demonstrate that selecting and optimizing an algorithm based on the results of another study is a flawed strategy. Ensemble models instead offer a flexible approach that is data agnostic and exceptionally accurate. By assessing the impact of various dataset and phenotypic properties on classification performance, we also offer potential explanations for variation in performance. Researchers interested in maximizing performance stand to benefit from the simplicity and effectiveness of our approach made accessible via the R package pheble.

The critical impact of sex on preclinical alcohol research - Insights from zebrafish

Sex is an important biological variable that is widely recognized in studies of alcohol-related effects. Complementing clinical and preclinical rodent research, the zebrafish (Danio rerio) is the second most used laboratory species, and a powerful model organism in biomedicine. Like clinical and rodent models, zebrafish demonstrate overt sex differences in alcohol-related responses. Collectively, this evidence shows that the zebrafish becomes a sensitive model species to further probe in-depth sex differences commonly reported in alcohol research.

Improving zebrafish laboratory welfare and scientific research through understanding their natural history

Globally, millions of zebrafish (Danio rerio) are used for scientific laboratory experiments for which researchers have a duty of care, with legal obligations to consider their welfare. Considering the growing use of the zebrafish as a vertebrate model for addressing a diverse range of scientific questions, optimising their laboratory conditions is of major importance for both welfare and improving scientific research. However, most guidelines for the care and breeding of zebrafish for research are concerned primarily with maximising production and minimising costs and pay little attention to the effects on welfare of the environments in which the fish are maintained, or how those conditions affect their scientific research. Here we review the physical and social conditions in which laboratory zebrafish are kept, identifying and drawing attention to factors likely to affect their welfare and experimental science. We also identify a fundamental lack knowledge of how zebrafish interact with many biotic and abiotic features in their natural environment to support ways to optimise zebrafish health and well-being in the laboratory, and in turn the quality of scientific data produced. We advocate that the conditions under which zebrafish are maintained need to become a more integral part of research and that we understand more fully how they influence experimental outcome and in turn interpretations of the data generated.

In Silico Prediction of Transcription Factor Collaborations Underlying Phenotypic Sexual Dimorphism in Zebrafish (Danio rerio)

The transcriptional regulation of gene expression in higher organisms is essential for different cellular and biological processes. These processes are controlled by transcription factors and their combinatorial interplay, which are crucial for complex genetic programs and transcriptional machinery. The regulation of sex-biased gene expression plays a major role in phenotypic sexual dimorphism in many species, causing dimorphic gene expression patterns between two different sexes. The role of transcription factor (TF) in gene regulatory mechanisms so far has not been studied for sex determination and sex-associated colour patterning in zebrafish with respect to phenotypic sexual dimorphism. To address this open biological issue, we applied bioinformatics approaches for identifying the predicted TF pairs based on their binding sites for sex and colour genes in zebrafish. In this study, we identified 25 (e.g., STAT6-GATA4; JUN-GATA4; SOX9-JUN) and 14 (e.g., IRF-STAT6; SOX9-JUN; STAT6-GATA4) potentially cooperating TFs based on their binding patterns in promoter regions for sex determination and colour pattern genes in zebrafish, respectively. The comparison between identified TFs for sex and colour genes revealed several predicted TF pairs (e.g., STAT6-GATA4; JUN-SOX9) are common for both phenotypes, which may play a pivotal role in phenotypic sexual dimorphism in zebrafish.

Intelligent fish farm-the future of aquaculture

With the continuous expansion of aquaculture scale and density, contemporary aquaculture methods have been forced to overproduce resulting in the accelerated imbalance rate of water environment, the frequent occurrence of fish diseases, and the decline of aquatic product quality. Moreover, due to the fact that the average age profile of agricultural workers in many parts of the world are on the higher side, fishery production will face the dilemma of shortage of labor, and aquaculture methods are in urgent need of change. Modern information technology has gradually penetrated into various fields of agriculture, and the concept of intelligent fish farm has also begun to take shape. The intelligent fish farm tries to deal with the precise work of increasing oxygen, optimizing feeding, reducing disease incidences, and accurately harvesting through the idea of "replacing human with machine," so as to liberate the manpower completely and realize the green and sustainable aquaculture. This paper reviews the application of fishery intelligent equipment, IoT, edge computing, 5G, and artificial intelligence algorithms in modern aquaculture, and analyzes the existing problems and future development prospects. Meanwhile, based on different business requirements, the design frameworks for key functional modules in the construction of intelligent fish farm are proposed.