AIMC Topic: Animals

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Unbiased analysis of spatial learning strategies in a modified Barnes maze using convolutional neural networks.

Scientific reports
Assessment of spatial learning abilities is central to behavioral neuroscience and a useful tool for animal model validation and drug development. However, biases introduced by the apparatus, environment, or experimentalist represent a critical chall...

Automatic wild bird repellent system that is based on deep-learning-based wild bird detection and integrated with a laser rotation mechanism.

Scientific reports
Wild bird repulsion is critical in agriculture because it helps avoid agricultural food losses and mitigates the risk of avian influenza. Wild birds transmit avian influenza in poultry farms and thus cause large economic losses. In this study, we dev...

Bio-inspired design of hard-bodied mobile robots based on arthropod morphologies: a 10 year systematic review and bibliometric analysis.

Bioinspiration & biomimetics
This research presents a 10-year systematic review based on bibliometric analysis of the bio-inspired design of hard-bodied mobile robot mechatronic systems considering the anatomy of arthropods. These are the most diverse group of animals whose flex...

Vaccine design and development: Exploring the interface with computational biology and AI.

International reviews of immunology
Computational biology involves applying computer science and informatics techniques in biology to understand complex biological data. It allows us to collect, connect, and analyze biological data at a large scale and build predictive models. In the t...

Deep Learning Used with a Colorimetric Sensor Array to Detect Indole for Nondestructive Monitoring of Shrimp Freshness.

ACS applied materials & interfaces
Intelligent colorimetric freshness indicator is a low-cost way to intuitively monitor the freshness of fresh food. A colorimetric strip sensor array was prepared by -dimethylaminocinnamaldehyde (PDL)-doped poly(vinyl alcohol) (PVA) and chitosan (Chit...

Varroa Mite Counting Based on Hyperspectral Imaging.

Sensors (Basel, Switzerland)
Varroa mite infestation poses a severe threat to honeybee colonies globally. This study investigates the feasibility of utilizing the HS-Cam and machine learning techniques for Varroa mite counting. The methodology involves image acquisition, dimensi...

Application of machine learning models for property prediction to targeted protein degraders.

Nature communications
Machine learning (ML) systems can model quantitative structure-property relationships (QSPR) using existing experimental data and make property predictions for new molecules. With the advent of modalities such as targeted protein degraders (TPD), the...

Flexible multitask computation in recurrent networks utilizes shared dynamical motifs.

Nature neuroscience
Flexible computation is a hallmark of intelligent behavior. However, little is known about how neural networks contextually reconfigure for different computations. In the present work, we identified an algorithmic neural substrate for modular computa...

Multi-Task ADME/PK prediction at industrial scale: leveraging large and diverse experimental datasets.

Molecular informatics
ADME (Absorption, Distribution, Metabolism, Excretion) properties are key parameters to judge whether a drug candidate exhibits a desired pharmacokinetic (PK) profile. In this study, we tested multi-task machine learning (ML) models to predict ADME a...

Multi-Omics Analysis by Machine Learning Identified Lysophosphatidic Acid as a Biomarker and Therapeutic Target for Porcine Reproductive and Respiratory Syndrome.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
As a significant infectious disease in livestock, porcine reproductive and respiratory syndrome (PRRS) imposes substantial economic losses on the swine industry. Identification of diagnostic markers and therapeutic targets has been a focal challenge ...