AI Medical Compendium Topic

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Behavior, Animal

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Deep learning algorithms reveal increased social activity in rats at the onset of the dark phase of the light/dark cycle.

PloS one
The rapid decrease of light intensity is a potent stimulus of rats' activity. The nature of this activity, including the character of social behavior and the composition of concomitant ultrasonic vocalizations (USVs), is unknown. Using deep learning ...

Automated analysis of a novel object recognition test in mice using image processing and machine learning.

Behavioural brain research
The novel object recognition test (NORT) is one of the most commonly employed behavioral tests in experimental animals designed to evaluate an animal's interest in and recognition of novelty. However, manual procedures, which rely on researchers' obs...

The Use of Triaxial Accelerometers and Machine Learning Algorithms for Behavioural Identification in Domestic Dogs (): A Validation Study.

Sensors (Basel, Switzerland)
Assessing the behaviour and physical attributes of domesticated dogs is critical for predicting the suitability of animals for companionship or specific roles such as hunting, military or service. Common methods of behavioural assessment can be time ...

Artificial intelligence-based analysis of behavior and brain images in cocaine-self-administered marmosets.

Journal of neuroscience methods
BACKGROUND: The sophisticated behavioral and cognitive repertoires of non-human primates (NHPs) make them suitable subjects for studies involving cocaine self-administration (SA) schedules. However, ethical considerations, adherence to the 3Rs princi...

Deep learning dives: Predicting anxiety in zebrafish through novel tank assay analysis.

Physiology & behavior
Behavior is fundamental to neuroscience research, providing insights into the mechanisms underlying thoughts, actions and responses. Various model organisms, including mice, flies, and fish, are employed to understand these mechanisms. Zebrafish, in ...

Enhancing eco-sensing in aquatic environments: Fish jumping behavior automatic recognition using YOLOv5.

Aquatic toxicology (Amsterdam, Netherlands)
Contemporary research on ichthyological behavior predominantly investigates underwater environments. However, the intricate nature of aquatic ecosystems often hampers subaqueous observations of fish behavior due to interference. Transitioning the obs...

Machine learning reveals prominent spontaneous behavioral changes and treatment efficacy in humanized and transgenic Alzheimer's disease models.

Cell reports
Computer-vision and machine-learning (ML) approaches are being developed to provide scalable, unbiased, and sensitive methods to assess mouse behavior. Here, we used the ML-based variational animal motion embedding (VAME) segmentation platform to ass...

Deep learning-assisted detection of psychoactive water pollutants using behavioral profiling of zebrafish embryos.

Journal of hazardous materials
Water pollution poses a significant risk to the environment and human health, necessitating the development of innovative detection methods. In this study, a series of representative psychoactive compounds were selected as model pollutants, and a new...

Nature's All-in-One: Multitasking Robots Inspired by Dung Beetles.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Dung beetles impressively coordinate their 6 legs to effectively roll large dung balls. They can also roll dung balls varying in the weight on different terrains. The mechanisms underlying how their motor commands are adapted to walk and simultaneous...

Development and validation of machine-learning models for monitoring individual behaviors in group-housed broiler chickens.

Poultry science
Animals' individual behavior is commonly monitored by live or video observation by a person. This can be labor intensive and inconsistent. An alternative is the use of machine learning-based computer vision systems. The objectives of this study were ...