AIMC Topic: Animals

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Assessing foraging landscape quality in Quebec's commercial beekeeping through remote sensing, machine learning, and survival analysis.

Journal of environmental management
Honey bees (Apis mellifera) play an important role in our agricultural systems. In recent years, beekeepers have reported high colony mortality rates in several parts of the world. Inadequate foraging landscapes are often cited as a major factor dete...

Robotic Fast Dual-Arm Patch Clamp System for Mechanosensitive Excitability Research of Neurons.

IEEE transactions on bio-medical engineering
OBJECTIVE: A robotic fast dual-arm patch clamp system with controllable mechanical stimulation is proposed in this paper for mechanosensitive excitability research of neurons in brain slice.

iDIA-QC: AI-empowered data-independent acquisition mass spectrometry-based quality control.

Nature communications
Quality control (QC) in mass spectrometry (MS)-based proteomics is mainly based on data-dependent acquisition (DDA) analysis of standard samples. Here, we collect 2754 files acquired by data independent acquisition (DIA) and paired 2638 DDA files fro...

Genomic and algorithm-based predictive risk assessment models for benzene exposure.

Frontiers in public health
AIM: In this research, we leveraged bioinformatics and machine learning to pinpoint key risk genes associated with occupational benzene exposure and to construct genomic and algorithm-based predictive risk assessment models.

Using machine learning models to predict the dose-effect curve of municipal wastewater for zebrafish embryo toxicity.

Journal of hazardous materials
Municipal wastewater substantially contributes to aquatic ecological risks. Assessing the toxicity of municipal wastewater through dose-effect curves is challenging owing to the time-consuming, labor-intensive, and costly nature of biological assays....

Sulfonic acid functionalized β-amyloid peptide aggregation inhibitors and antioxidant agents for the treatment of Alzheimer's disease: Combining machine learning, computational, in vitro and in vivo approaches.

International journal of biological macromolecules
Alzheimer's disease (AD) is characterized as a neurodegenerative disorder that is caused by plaque formation by accumulating β-amyloid (Aβ), leading to neurocognitive function and impaired mental development. Thus, targeting Aβ represents a promising...

MCTASmRNA: A deep learning framework for alternative splicing events classification.

International journal of biological macromolecules
Alternative splicing (AS) plays crucial post-transcriptional gene function regulation roles in eukaryotic. Despite progress in studying AS at the RNA level, existing methods for AS event identification face challenges such as inefficiency, lengthy pr...

Biocomputing at the crossroad between emulating artificial intelligence and cellular supremacy.

Current opinion in biotechnology
Biocomputation aims to create sophisticated biological systems capable of addressing important problems in (bio)medicine with a machine-like precision. At present, computational gene networks engineered by single- or multi-layered assembly of DNA-, R...

Demystifying artificial intelligence for veterinary professionals: practical applications and future potential.

American journal of veterinary research
The field of veterinary medicine, like many others, is expected to undergo a significant transformation due to artificial intelligence (AI), although the full extent remains unclear. Artificial intelligence is already becoming prominent throughout da...

Classification of chicken Eimeria species through deep transfer learning models: A comparative study on model efficacy.

Veterinary parasitology
Eimeria is a protozoan parasite that causes coccidiosis in various animal species, especially in chickens, resulting in infections characterized by intestinal damage, hemorrhagic diarrhea, lethargy, and high mortality rates in the absence of effectiv...