AIMC Topic: Animals

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Upconversion and NIR-II luminescent rare earth nanoparticles combined with machine learning for cancer theranostics.

Nanoscale
How to develop contrast agents for cancer theranostics is a meaningful and challenging endeavor, and rare earth nanoparticles (RENPs) may provide a possible solution. In this study, we initially modified RENPs through the application of photodynamic ...

Applications of Artificial Intelligence for Heat Stress Management in Ruminant Livestock.

Sensors (Basel, Switzerland)
Heat stress impacts ruminant livestock production on varied levels in this alarming climate breakdown scenario. The drastic effects of the global climate change-associated heat stress in ruminant livestock demands constructive evaluation of animal pe...

A natural language processing system for the efficient extraction of cell markers.

Scientific reports
Single-cell RNA sequencing (scRNA-seq) has emerged as a pivotal tool for exploring cellular landscapes across diverse species and tissues. Precise annotation of cell types is essential for understanding these landscapes, relying heavily on empirical ...

Machine learning reveals the rules governing the efficacy of mesenchymal stromal cells in septic preclinical models.

Stem cell research & therapy
BACKGROUND: Mesenchymal Stromal Cells (MSCs) are the preferred candidates for therapeutics as they possess multi-directional differentiation potential, exhibit potent immunomodulatory activity, are anti-inflammatory, and can function like antimicrobi...

Preventing illegal seafood trade using machine-learning assisted microbiome analysis.

BMC biology
BACKGROUND: Seafood is increasingly traded worldwide, but its supply chain is particularly prone to frauds. To increase consumer confidence, prevent illegal trade, and provide independent validation for eco-labelling, accurate tools for seafood trace...

Oscillations in an artificial neural network convert competing inputs into a temporal code.

PLoS computational biology
The field of computer vision has long drawn inspiration from neuroscientific studies of the human and non-human primate visual system. The development of convolutional neural networks (CNNs), for example, was informed by the properties of simple and ...

Leveraging new approach methodologies: ecotoxicological modelling of endocrine disrupting chemicals to Danio rerio through machine learning and toxicity studies.

Toxicology mechanisms and methods
New approach methodologies (NAMs) offer information tailored to the intended application while reducing the use of animals. NAMs aim to develop quantitative structure-activity relationship (QSAR) and quantitive-Read-Across structure-activity relation...

Application of Machine Learning in a Rodent Malaria Model for Rapid, Accurate, and Consistent Parasite Counts.

The American journal of tropical medicine and hygiene
Rodent malaria models serve as important preclinical antimalarial and vaccine testing tools. Evaluating treatment outcomes in these models often requires manually counting parasite-infected red blood cells (iRBCs), a time-consuming process, which can...

Enzymes from Fishery and Aquaculture Waste: Research Trends in the Era of Artificial Intelligence and Circular Bio-Economy.

Marine drugs
In the era of the blue bio-economy, which promotes the sustainable utilization and exploitation of marine resources for economic growth and development, the fisheries and aquaculture industries still face huge sustainability issues. One of the major ...

ARViS: a bleed-free multi-site automated injection robot for accurate, fast, and dense delivery of virus to mouse and marmoset cerebral cortex.

Nature communications
Genetically encoded fluorescent sensors continue to be developed and improved. If they could be expressed across multiple cortical areas in non-human primates, it would be possible to measure a variety of spatiotemporal dynamics of primate-specific c...