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The authors combine fine needle aspiration biopsy and machine learning algorithms to develop a breast cancer detection method suitable for resource-constrained regions that lack access to mammograms.

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 studied the chemoreception of moon jellyfish in response to food, and developed an AI tool to identify track and quantify the pulsation of swimming jellyfish.

Here the authors hypothesized that reducing folliculin (FLCN) might affect p62 protein levels in the dorsal hippocampus of mice, given their potential functional connection and p62's role in neurodegenerative diseases. Their study, using western blots and a two-way ANOVA on young wild-type mice, found that p62 levels correlated with FLCN expression, but ultimately concluded there's no evidence of a functional connection between FLCN and p62 in this specific model.

Toxic particulates in the atmosphere pose significant health risks, and while modern masks can help reduce inhalation of these pollutants, their availability may be limited during health crises. This study evaluated the effectiveness of household fabrics (cotton, fleece, wool, and rayon) as particulate filters, finding that cotton outperformed the others in filtration efficiency, while rayon was the least effective. The findings suggest that cotton is a preferable alternative for filtration purposes, while rayon should be avoided.

This study examined the impact of erosion on the performance of a triangular aerofoil at a low Reynolds number (Re = 10,000), relevant for harsh conditions like those on Mars.

This study used an improved CMS-seq method to profile 5hmC in ormalin-fixed and paraffin-embedded (FFPE) samples from HNC tumors and adjacent normal tissues, identifying three genes (PRKD2, HADHA, and AIPL1) with promising potential as biomarkers for Head and neck cancer (HNC) diagnosis.

The authors use machine learning to analyze electroencephalogram data and identify slowing patterns that can indicate undetected disorders like epilepsy or dementia

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.

Droughts have a wide range of effects, from ecosystems failing and crops dying, to increased illness and decreased water quality. Drought prediction is important because it can help communities, businesses, and governments plan and prepare for these detrimental effects. This study predicts drought conditions by using predictable weather patterns in machine learning models.