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Here, recognizing a declining supply of rock phosphate, as well as its role in crop fertilization, the authors investigated a fungus that forms a symbiotic relationship with many crops. They found that symbiosis between the fungus and common bean plant increased the affinity of the plant towards absorbing nutrients as evidenced by lower root-to-shoot ratios in beans planted in soil with various concentrations of phosphorous and salinity.

In the battle against Alzheimer's disease, early detection is critical to mitigating symptoms in patients. Here, the authors use a collection of MRI scans, layering with deep learning computer modeling, to investigate early stages of AD which can be hard to catch by human eye. Their model is successful, able to outperform previous models, and detected regions of interest in the brain for further consideration.

This study investigated whether thiamine (vitamin B1) could mitigate the harmful effects of lead contamination on plant growth. Using garden cress (Lepidium sativum), we found that lead exposure reduced plant growth and biomass, while thiamine treatment partially alleviated these effects, improving shoot length and overall plant length. These findings highlight the potential of thiamine as a simple strategy to improve plant resilience to heavy metal stress, with implications for agricultural productivity and food security.

This study investigated how 18 commonly used cosmetic products affect the growth of two skin-resident bacteria, Staphylococcus epidermidis and Micrococcus luteus. At higher concentrations, half of the tested products inhibited at least one bacterial species, suggesting that some cosmetics may disrupt the skin microbiome and its natural balance.

The authors examined the relationships between S&P 500 Technology and Financials equity sectors and their corresponding investment-grade fixed-income markets from 1995 to 2025.

The authors used a particle-in-cell simulation to determine the effects on extensive and intensive metrics. They found that preliminary simulations could be run quickly with much lower particle counts before more technically demanding and comprehensive simulations are performed.

This study compares the performance of two deep learning models, U-Net and DeconvNet, for segmenting gliomas from MRI scans.

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.

The authors looked at different models of semantic segmentation to determine which may be best used in the future for segmentation of CT scans to help diagnose certain conditions.

Here, the authors investigated the most efficient way to position magnets to hold the most pieces of paper on the surface of a refrigerator. They used a regression model along with an artificial neural network to identify the most efficient positions of four magnets to be at the vertices of a rectangle.