Browse Articles

The authors access a panel of leaf traits across ten deciduous tree species to explore adaptive strategies between large and small trees.

Here the authors investigated the potential of plant-derived smoke water to mitigate the negative impacts of abiotic stressors, such as heat, salinity, and water fluctuations, on the germination and growth of various agricultural crops. Their findings suggest that smoke water, specifically from buckwheat, pea, and radish, significantly improves plant resilience against drought and excessive water stress, offering a viable strategy to bolster food security in the face of climate change.

The authors used developed a machine learning tool for studying social media discourse surrounding use of tobacco and cannabis.

The authors assessed the correlation between the stock, commodity, and consumer markets with the housing market.

The authors looked at the potential of different phenolic compounds to reduce oxidative stress (i.e., act as antioxidants).

The authors use computational methods to compare tau acetylation to the better studied tau phosphorylation in Alzheimer's disease and then design and computationally test a new drug to prevent abnormal post-translational modifications of tau.

The authors used RNA-seq datasets to assess the effect of CDK7 inhibition on transcriptional pathways in castration-resistant prostate cancer cells.

Here the authors investigated a combination therapy to target the Kirsten rat sarcoma viral oncogene homolog mutation in lung cancer, by analyzing publicly available data. Their findings indicate that the combination therapy of CA170 and Kvax enhances helper T cell function and improves cytotoxic T lymphocyte infiltration, while Kvax alone drives plasma and memory B cell proliferation.

This manuscript explores the performance of five different machine learning models in classifying brain tumors from a dataset of MRI scans. The authors find that several of the models showed >90% accuracy. Thus, the authors suggest that machine learning models demonstrate potential for effective implementation in clinical settings, including as a diagnostic tool that can be used to complement the expertise of neuroradiologists.

The authors looked at using computer vision to evaluate the degree of exotropia in individuals with strabismus.