AIMC Topic: Animals

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Hybrid neural networks for continual learning inspired by corticohippocampal circuits.

Nature communications
Current artificial systems suffer from catastrophic forgetting during continual learning, a limitation absent in biological systems. Biological mechanisms leverage the dual representation of specific and generalized memories within corticohippocampal...

Targeted nanoliposomes for precision rheumatoid arthritis therapy: a review on mechanisms and potential.

Drug delivery
Rheumatoid arthritis (RA) is an inflammatory immune-triggered disease that causes synovitis, cartilage degradation, and joint injury. In nanotechnology, conventional liposomes were extensively investigated for RA. However, they frequently undergo rap...

Identifying bee species origins of Philippine honey using X-ray fluorescence elemental analysis coupled with machine learning.

Food chemistry
Stingless bee honey is emerging as a superfood, given its enhanced health and therapeutic benefits. In this paper, we used handheld X-ray fluorescence spectroscopy (hXRF) with machine learning techniques to classify Philippine honey based on its ento...

Deep learning assisted prediction of osteogenic capability of orthopedic implant surfaces based on early cell morphology.

Acta biomaterialia
The surface modification of titanium (Ti) and its alloys is crucial for improving their osteogenic capability, as their bio-inert nature limits effective osseointegration despite their prevalent use in orthopedic implants. However, these modification...

Temporal pavlovian conditioning of a model spiking neural network for discrimination sequences of short time intervals.

Journal of computational neuroscience
The brain's ability to learn and distinguish rapid sequences of events is essential for timing-dependent tasks, such as those in sports and music. However, the mechanisms underlying this ability remain an active area of research. Here, we present a P...

Automated and explainable machine learning for monitoring lipid and protein oxidative damage in mutton using hyperspectral imaging.

Food research international (Ottawa, Ont.)
Current detection methods for lipid and protein oxidation using hyperspectral imaging (HSI) in conjunction with machine learning (ML) necessitate the involvement of data scientists and domain experts to adjust the model architecture and tune hyperpar...

Cardiac Heterogeneity Prediction by Cardio-Neural Network Simulation.

Neuroinformatics
The bidirectional interactions between brain and heart through autonomic nervous system is the prime focus of neuro-cardiology community. The computer models designed to analyze brain and heart signals are either complex in terms of molecular and cel...

Identification of hub biomarkers in liver post-metabolic and bariatric surgery using comprehensive machine learning (experimental studies).

International journal of surgery (London, England)
BACKGROUND: The global prevalence of non-alcoholic fatty liver disease (NAFLD) is approximately 30%, and the condition can progress to non-alcoholic steatohepatitis, cirrhosis, and hepatocellular carcinoma. Metabolic and bariatric surgery (MBS) has b...

Machine Learning and Experiments Revealed Key Genes Related to PANoptosis Linked to Drug Prediction and Immune Landscape in Spinal Cord Injury.

Molecular neurobiology
Spinal cord injury (SCI) is a severe central nervous system injury without effective therapies. PANoptosis is involved in the development of many diseases, including brain and spinal cord injuries. However, the biological functions and molecular mech...

Machine Learning-Powered Optimization of a CHO Cell Cultivation Process.

Biotechnology and bioengineering
Chinese Hamster Ovary (CHO) cells are the most widely used cell lines to produce recombinant therapeutic proteins such as monoclonal antibodies (mAbs). However, the optimization of the CHO cell culture process is very complex and influenced by variou...