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The authors investigate whether air quality impacts solar production.

AI analysis of brain scans offers promise for helping doctors diagnose brain tumors. Haider and Drosis explore this field by developing machine learning models that classify brain scans as "cancer" or "non-cancer" diagnoses.

The authors test whether soaking maize seeds in a solution of melatonin improves seed germination and drought tolerance.

Semantic segmentation - labelling each pixel in an image to a specific class- models require large amounts of manually labeled and collected data to train.

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 study, the authors investigate the combinatorial effects of CO₂ plus other environmental factors including salinity, temperature, acidity, and drought on how effectively plants can transport water and carbon through their stomata.

Psoriasis is a heritable autoimmune disorder characterized by abnormal red and itchy skin patches. The authors study the family of a man with psoriasis. They explore whether the man's children, who do not show any symptoms of psoriasis, demonstrate gene expression consistent with the disease.

This study used multi-omic analysis to investigate gene expression and DNA methylation patterns associated with hypertrophic cardiomyopathy (HCM). The researchers found significant downregulation of mitochondrial and energy-related pathways, highlighting mitochondrial dysfunction—particularly altered PINK1 expression—as a potential contributor and therapeutic target in HCM.

The authors studied the accuracy of two commonly used explainable AI techniques for detecting pneumonia in chest x-rays.

This study developed an effective method for extracting valuable information from social media data and employed computer-assisted human coding alongside the AI-powered tool to analyze social media posts. The findings provide a deep understanding of the major challenges that home health care workers are experiencing and offer important implications regarding how to improve home healthcare workers' well-being, and provide suggestions on optimizing the home health care service experience.