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

The authors test the antioxidant content of Taiwanese high mountain teas grown in or processed under differing conditions.

The authors investigated the impact of light pollution on astrophotography.

The main goal of this study is to determine what demographics are related to tuberculosis incidence in the United States populations, particularly if changing demographics are related to differences in tuberculosis risk over two discrete time periods. The major finding is that in the two studied time periods, tuberculosis risk factors were somewhat consistent and may be influenced by things such as immigration, healthcare access, and race or ethnicity, although the top predictor did change.

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.

Here the authors investigate the potential of the bioluminescent fungus Panellus stipticus to serve as a sustainable bioindicator for environmental lead contamination. Their findings demonstrate that higher lead concentrations cause a measurable decrease in fungal bioluminescence intensity over time suggesting that the fungus could be an effective tool for detecting lead in an environment.

This study examined how social determinants of health (SDH) relate to vision loss by analyzing publicly available data from 18 northern and southern U.S. states and using Bayesian correlation analysis.

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

Textile waste from the fashion industry is a major environmental pollutant, but recycling waste into novel building material is a strategy to reduce the negative effects. This manuscript characterized five different binders that can be used to repurpose textile waste into bricks for construction purposes. Water-based glue, cement, white cement, plaster of Paris, and epoxy resin were mixed with shredded textile waste, and the mechanical characteristics and thermal insulation of each brick type were measured. Bricks with increased mechanical strength had the poorest thermal resistance, and the contrasting properties would suit different building purposes. This work provides a first step in generating recycled textile bricks for construction in a circular economy framework.

This manuscript investigates the urban heat island (UHI) effect by utilizing two satellite datasets: Landsat (high spatial resolution, lower temporal resolution) and MODIS (lower spatial resolution, high temporal resolution). The authors hypothesized that Landsat would provide better spatial detail, while MODIS would better capture temporal variations. Their analysis in the Washington D.C.–Baltimore region supports these hypotheses, demonstrating that Landsat offers finer spatial details, whereas MODIS provides more consistent seasonal patterns and better detects heatwave frequencies.