AIMC Topic: Animals

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Insights into the contribution of multiple factors on Ixodes ricinus abundance across Europe spanning 20 years using different machine learning algorithms.

Ticks and tick-borne diseases
The interplay of biotic and abiotic factors driving Ixodes ricinus abundance trends are not fully understood. Machine learning (ML) approaches are being increasingly used to explore this and predict future abundance patterns of this species, however,...

Nanobody screening and machine learning guided identification of cross-variant anti-SARS-CoV-2 neutralizing heavy-chain only antibodies.

PLoS pathogens
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) continues to persist, demonstrating the risks posed by emerging infectious diseases to national security, public health, and the economy. Development of new vaccines and antibodies for emer...

Feature selection enhances peptide binding predictions for TCR-specific interactions.

Frontiers in immunology
INTRODUCTION: T-cell receptors (TCRs) play a critical role in the immune response by recognizing specific ligand peptides presented by major histocompatibility complex (MHC) molecules. Accurate prediction of peptide binding to TCRs is essential for a...

An Open-Source, User-Friendly Machine-Learning Method for Automated Segmentation and Analysis of Peripheral Nerve Cross-Sections.

Plastic and reconstructive surgery
BACKGROUND: Quantitative neuromorphometric analysis of the peripheral nerve is paramount to nerve regeneration research. However, this technique relies upon accurate segmentation and determination of myelin and axonal area. Manual histologic analysis...

Deep learning-based image classification reveals heterogeneous execution of cell death fates during viral infection.

Molecular biology of the cell
Cell fate decisions, such as proliferation, differentiation, and death, are driven by complex molecular interactions and signaling cascades. While significant progress has been made in understanding the molecular determinants of these processes, hist...

Self-supervised learning of scale-invariant neural representations of space and time.

Journal of computational neuroscience
Hippocampal representations of space and time seem to share a common coding scheme characterized by neurons with bell-shaped tuning curves called place and time cells. The properties of the tuning curves are consistent with Weber's law, such that, in...

Machine learning-driven Raman spectroscopy: A novel approach to lipid profiling in diabetic kidney disease.

Nanomedicine : nanotechnology, biology, and medicine
Diabetes mellitus is a chronic metabolic disease that increasingly affects people every year. It is known that with its progression and poor management, metabolic changes can lead to organ dysfunctions, including kidneys. The study aimed to combine R...

Deep proximal gradient network for absorption coefficient recovery in photoacoustic tomography.

Physics in medicine and biology
The optical absorption properties of biological tissues in photoacoustic (PA) tomography are typically quantified by inverting acoustic measurements. Conventional approaches to solving the inverse problem of forward optical models often involve itera...

Neuromorphic computing at scale.

Nature
Neuromorphic computing is a brain-inspired approach to hardware and algorithm design that efficiently realizes artificial neural networks. Neuromorphic designers apply the principles of biointelligence discovered by neuroscientists to design efficien...

I2Bot: an open-source tool for multi-modal and embodied simulation of insect navigation.

Journal of the Royal Society, Interface
Achieving a comprehensive understanding of animal intelligence demands an integrative approach that acknowledges the interplay between an organism's brain, body and environment. Insects, despite their limited computational resources, demonstrate rema...