AIMC Topic: Animals
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Nature's All-in-One: Multitasking Robots Inspired by Dung Beetles.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Oct 30, 2024
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...
Comprehensive multi-omics analysis identifies chromatin regulator-related signatures and TFF1 as a therapeutic target in lung adenocarcinoma through a 429-combination machine learning approach.
Frontiers in immunology
Oct 30, 2024
INTRODUCTION: Lung cancer is a leading cause of cancer-related deaths, with its incidence continuing to rise. Chromatin remodeling, a crucial process in gene expression regulation, plays a significant role in the development and progression of malign...
MosquitoSong+: A noise-robust deep learning model for mosquito classification from wingbeat sounds.
PloS one
Oct 30, 2024
In order to assess risk of mosquito-vector borne disease and to effectively target and monitor vector control efforts, accurate information about mosquito vector population densities is needed. The traditional and still most common approach to this i...
Predicting egg production rate and egg weight of broiler breeders based on machine learning and Shapley additive explanations.
Poultry science
Oct 29, 2024
Egg production rate and egg weight are core indicators for evaluating the production performance of broiler breeders. The accurate prediction of these indicators can significantly enhance farm economic efficiency and can provide a basis for future pr...
Deep learning-assisted detection of psychoactive water pollutants using behavioral profiling of zebrafish embryos.
Journal of hazardous materials
Oct 29, 2024
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...
Machine learning approach to assess brain metastatic burden in preclinical models.
Methods in cell biology
Oct 29, 2024
Brain metastases (BrM) occur when malignant cells spread from a primary tumor located in other parts of the body to the brain. BrM is a deadly complication for cancer patients and severely lacks effective therapies. Due to the limited access to patie...
Characterization of Brain Abnormalities in Lactational Neurodevelopmental Poly I:C Rat Model of Schizophrenia and Depression Using Machine-Learning and Quantitative MRI.
Journal of magnetic resonance imaging : JMRI
Oct 28, 2024
BACKGROUND: A recent neurodevelopmental rat model, utilizing lactational exposure to polyriboinosinic-polyribocytidilic acid (Poly I:C) leads to mimics of behavioral phenotypes resembling schizophrenia-like symptoms in male offspring and depression-l...
The phobic brain: Morphometric features correctly classify individuals with small animal phobia.
Psychophysiology
Oct 28, 2024
Specific phobia represents an anxiety disorder category characterized by intense fear generated by specific stimuli. Among specific phobias, small animal phobia (SAP) denotes a particular condition that has been poorly investigated in the neuroscient...
Using the super-learner to predict the chemical acute toxicity on rats.
Journal of hazardous materials
Oct 28, 2024
With the rapid increase in the number of commercial chemicals, testing methods regarding on median lethal dose (LD) relying animal experiments face challenges such as high costs and ethical concerns. Classical quantitative structure-activity relation...
High-throughput prediction of oral acute toxicity in Rat and Mouse of over 100,000 polychlorinated persistent organic pollutants (PC-POPs) by interpretable data fusion-driven machine learning global models.
Journal of hazardous materials
Oct 28, 2024
This study utilized available oral acute toxicity data in Rat and Mouse for polychlorinated persistent organic pollutants (PC-POPs) to construct data fusion-driven machine learning (ML) global models. Based on atom-centered fragments (ACFs), the coll...
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