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The authors develop a machine learning method to reduce misclassification of objects in safety-critical applications such as medical diagnosis.

Researching gun violence and mass shootings in the U.S. is difficult due to the lack of consistent data collection. Some studies have linked mass shootings to personal financial stress, but little formal research exists on the impact of broader economic conditions. This study hypothesized an inverse relationship between mass shootings and economic performance, using the S&P 500 and unemployment rate as indicators.

The authors computationally screened potential ALDH1 inhibitors, for use as potential cancer therapeutics.

The authors evaluated the effect of concentration and variant of N-Acetylcysteine in hydrolyzing mucus.

The authors used machine learning to predict the presence of anxiety and burnout in students based on facial expressions and demographic information.

The authors created a 1D model to diagnose hepatomegaly and cirrhosis via ultrasound of the liver.

The authors looked at the impact of different harvest and feeding treatments on Heterandria formosa over three generations as a model for changes in marine ecosystems.

The authors looked at the impacts of short-chain fatty acids on muscle fiber formation as well as insulin sensitivity using a model of mouse myoblasts.

Here the authors aimed to find the best preservative combinations to stop bacterial growth in food, using data modeling and biochemical experiments. They discovered that single preservatives are often not enough, with varying effectiveness against different bacteria, and suggest future research into combining preservatives for better results.

The authors looked at beach nourishment (a way to combat erosion on coasts) and resulting grain size distribution. Their work is important to understand the dynamics of erosion and it's relation to wave action and the implications this has for efforts to mitigate coastal erosion.