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In this study, the authors tested the ability and accuracy of a neural net to identify patterns in complex number matrices.

Parkinson’s disease (PD) is a prevalent neurodegenerative disorder in the U.S., second only to Alzheimer’s disease. Current diagnostic methods are often inefficient and dependent on clinical exams. This study explored using machine and deep learning to enhance PD diagnosis by analyzing spiral drawings affected by hand tremors, a common PD symptom.

The authors train a neural network to detect text-based emotions including joy, sadness, anger, fear, love, and surprise.

Playing video games may improve mental performance by encouraging practicing logical reasoning skills. Students who played video games in between two tests tended to perform better on the second test than those that did not play video games.

Investigation of the largest rigid shape that can be moved through a circular hallway of unit width with an arbitrary turn angle

Robot-assisted minimally invasive surgery (RMIS) benefits from increased precision and faster recovery, with force feedback from the surgical tool being critical for control. Researchers tested the use of neural networks for detecting the vanishing point of the tool, a key element for force feedback.

This study explores how to predict and minimize distortion in 3D printed parts, particularly when using affordable PLA filament. The researchers developed a model using a gradient boosting regressor trained on 3D printing data, aiming to predict the necessary CAD dimensions to counteract print distortion.

Kalirin is a guanine nucleotide exchange factor (GEF) for the GTPase RAC1, linked to schizophrenia and Alzheimer’s Disease. It plays a crucial role in synaptic plasticity by regulating dendritic spine formation and actin cytoskeleton remodeling, which are essential for creating new synapses. Authors developed two novel compounds targeting kalirin, confirming that predictive modeling can indicate biological activity.

Naturally occurring neuroactive alkaloids are often studied for their potential to treat Neurological diseases. This team of students study Rivastigmine, a potent cholinesterase inhibitor that is a synthetic analog of physostigmine, which comes from the Calabar bean plant Physostigma venenosum. By comparing the effects of optimized synthetic analogs to the naturally occurring alkaloid, they determine the most favorable analog for inhibition of acetylcholinesterase (AChE), the enzyme that breaks down the neurotransmitter acetylcholine (ACh) to terminate neuronal transmission and signaling between synapses.

In this article, Choi and Rossitto investigated the limitations of virtual learning by examining in-person dance learning compared to virtual dance learning while wearing EEG headsets. They found that in-person learners outperformed virtual learners and that virtual learners had higher mirror neuron activity as assessed by Mu rhythm power.