AIMC Topic: Animals

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Performance comparison of machine learning models used for predicting subclinical mastitis in dairy cows: Bagging, boosting, stacking, and super-learner ensembles versus single machine learning models.

Journal of dairy science
Mastitis has a substantial impact on the dairy industry across the world, causing dairy producers to suffer losses due to the reduced quality and quantity of produced milk. A further problem, related to this issue, is the excessive use of antibiotics...

Harnessing artificial intelligence for analysing the impacts of nectar and pollen feeding in conservation biological control.

Current opinion in insect science
Plant-derived foods, such as nectar and pollen, have garnered substantial research attention due to their potential to support natural enemies of pests. This review is a pioneering exploration of the potential for artificial intelligence approaches t...

Utilizing 3D Point Cloud Technology with Deep Learning for Automated Measurement and Analysis of Dairy Cows.

Sensors (Basel, Switzerland)
This paper introduces an approach to the automated measurement and analysis of dairy cows using 3D point cloud technology. The integration of advanced sensing techniques enables the collection of non-intrusive, precise data, facilitating comprehensiv...

Animal-robot interaction-an emerging field at the intersection of biology and robotics.

Bioinspiration & biomimetics
The field of animal-robot and organism-robot interaction systems (ARIS, ORIS) is a currently rapidly emerging field in biorobotics. In this special issue we aim for providing a comprehensive overview of the cutting-edge advancements and pioneering br...

Phase Aberration Correction for In Vivo Ultrasound Localization Microscopy Using a Spatiotemporal Complex-Valued Neural Network.

IEEE transactions on medical imaging
Ultrasound Localization Microscopy (ULM) can map microvessels at a resolution of a few micrometers ( [Formula: see text]). Transcranial ULM remains challenging in presence of aberrations caused by the skull, which lead to localization errors. Herein,...

Naturally occurring caffeic acid phenethyl ester from chestnut honey-based propolis and virtual screening towards DYRK1A.

Natural product research
Neurodegenerative diseases (NDDs) are disorders with dysfunction and ongoing loss of neurons, glial cells and the neural networks in the brain and spinal cord. DYRK1A protein was reported to modulate to the cytoskeletal fraction in human and mouse br...

Deep-Learning-Based Analysis Reveals a Social Behavior Deficit in Mice Exposed Prenatally to Nicotine.

Cells
Cigarette smoking during pregnancy is known to be associated with the incidence of attention-deficit/hyperactive disorder (ADHD). Recent developments in deep learning algorithms enable us to assess the behavioral phenotypes of animal models without c...

Deep Learning Algorithm Based on Molecular Fingerprint for Prediction of Drug-Induced Liver Injury.

Toxicology
Drug-induced liver injury (DILI) is one the rare adverse drug reaction (ADR) and multifactorial endpoints. Current preclinical animal models struggle to anticipate it, and in silico methods have emerged as a way with significant potential for doing s...

Early inner plexiform layer thinning and retinal nerve fiber layer thickening in excitotoxic retinal injury using deep learning-assisted optical coherence tomography.

Acta neuropathologica communications
Excitotoxicity from the impairment of glutamate uptake constitutes an important mechanism in neurodegenerative diseases such as Alzheimer's, multiple sclerosis, and Parkinson's disease. Within the eye, excitotoxicity is thought to play a critical rol...

Heterogeneous fusion of biometric and deep physiological features for accurate porcine cough recognition.

PloS one
Accurate identification of porcine cough plays a vital role in comprehensive respiratory health monitoring and diagnosis of pigs. It serves as a fundamental prerequisite for stress-free animal health management, reducing pig mortality rates, and impr...