AIMC Topic: Animals

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Machine learning powered CN-coordinated cobalt nanoparticles embedded cellulosic nanofibers to assess meat quality via clenbuterol monitoring.

Biosensors & bioelectronics
The World Anti-Doping Agency (WADA) has prohibited the use of clenbuterol (CLN) because it induces anabolic muscle growth while potentially causing adverse effects such as palpitations, anxiety, and muscle tremors. Thus, it is vital to assess meat qu...

Estimation of invasive coronary perfusion pressure using electrocardiogram and Photoplethysmography in a porcine model of cardiac arrest.

Computer methods and programs in biomedicine
BACKGROUND: Coronary perfusion pressure (CPP) indicates spontaneous return of circulation and is recommended for high-quality cardiopulmonary resuscitation (CPR). This study aimed to investigate a method for non-invasive estimation of CPP using elect...

A surgical activity model of laparoscopic cholecystectomy for co-operation with collaborative robots.

Surgical endoscopy
BACKGROUND: Laparoscopic cholecystectomy is a very frequent surgical procedure. However, in an ageing society, less surgical staff will need to perform surgery on patients. Collaborative surgical robots (cobots) could address surgical staff shortages...

Analysis of Varroa Mite Colony Infestation Level Using New Open Software Based on Deep Learning Techniques.

Sensors (Basel, Switzerland)
Varroa mites, scientifically identified as , pose a significant threat to beekeeping and cause one of the most destructive diseases affecting honey bee populations. These parasites attach to bees, feeding on their fat tissue, weakening their immune s...

Artificial intelligence enables unified analysis of historical and landscape influences on genetic diversity.

Molecular phylogenetics and evolution
While genetic variation in any species is potentially shaped by a range of processes, phylogeography and landscape genetics are largely concerned with inferring how environmental conditions and landscape features impact neutral intraspecific diversit...

AI analysis of super-resolution microscopy: Biological discovery in the absence of ground truth.

The Journal of cell biology
Super-resolution microscopy, or nanoscopy, enables the use of fluorescent-based molecular localization tools to study molecular structure at the nanoscale level in the intact cell, bridging the mesoscale gap to classical structural biology methodolog...

Synthetically Feasible De Novo Molecular Design of Leads Based on a Reinforcement Learning Model: AI-Assisted Discovery of an Anti-IBD Lead Targeting CXCR4.

Journal of medicinal chemistry
Artificial intelligence (AI) de novo molecular generation provides leads with novel structures for drug discovery. However, the target affinity and synthesizability of the generated molecules present critical challenges for the successful application...

Machine Learning Applied to the Detection of Mycotoxin in Food: A Systematic Review.

Toxins
Mycotoxins, toxic secondary metabolites produced by certain fungi, pose significant threats to global food safety and public health. These compounds can contaminate a variety of crops, leading to economic losses and health risks to both humans and an...

Investigating the impact of initial parameters on autonomous robot swarm movement strategies for enhanced exploration efficiency: a comprehensive study.

Bioinspiration & biomimetics
Swarm robots are frequently preferred for the exploration of harsh environments and search and rescue operations. This study explores the factors that influence the movement strategies of autonomous robot swarms and their impact on swarm distribution...

Machine learning algorithms integrate bulk and single-cell RNA data to unveil oxidative stress following intracerebral hemorrhage.

International immunopharmacology
BACKGROUND: Increased oxidative stress (OS) activity following intracerebral hemorrhage (ICH) had significantly impacting patient prognosis. Identifying optimal genes associated with OS could enhance the understanding of OS after ICH.