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The authors analyze political endorsement patterns and impacts from the 2018 and 2020 midterm elections and find that such endorsements may be predictable based on the ideological and demographic factors of the endorser.

This article helps in understanding the effect of various social determinants on maternal mortality in the United States. It explains the relationship between maternal mortality rates and factors like race, income, education, and health insurance access.

The use of gamification in cybersecurity education, particularly through capture-the-flag competitions, involves scoring challenges based on their difficulty and the number of teams that solve them. The study investigated how changing the scoring formulas affects competition outcomes, predicting that different formulas would alter score distributions.

Organic and non-organic contaminants in tap water have been linked to adverse health effects. Tap water is a major source of lead, which is neurotoxic and poses a major health risk, particularly to children and pregnant women. Using publicly available annual water quality reports data for the state of Missouri, the authors show that communities with lower median household income and lower per capita incomes had significantly higher lead levels in their tap water.

Trihalomethanes, probable human carcinogens, are commonly found disinfection by-products (DBPs) in public water systems (PWS). The authors investigated the correlation between trihalomethane concentrations and socioeconomic factors in New York State, finding a negative correlation between median household income and trihalomethane concentrations. The inverse association between trihalomethanes and household income may indicate socioeconomic disparity regarding drinking water quality and the need for improved efforts to assist small- and medium-sized community water systems to lower DBP levels in New York State.

Utilization of neural network to analyze Free Word Association to predict accurately age, gender, first language, and current country.

The authors look at using publicly available data and machine learning to see if they can develop a criminal activity index for counties within the state of California.

Authors emphasize the challenges of manual tumor segmentation and the potential of deep learning models to enhance accuracy by automatically analyzing MRI scans.

Here, the authors investigated if dietary Omega-3 fatty acids could reduce the potential for cerebral cavernous malformations, which are brain lesions that occur due to a genetic mutation where high membrane permeability occurs between endothelial cell junctions. In a bovine-based study where some cows were fed an Omega-3 diet, the authors found the membranes of bovine blood cells increased in thickness with Omega-3 supplementation. As a result, they suggest that dietary Omega-3 could be considered as a possible preventative measure for cerebral cavernous malformations.

This study aimed to obtain an optimal non-antibiotic method to suppress the growth of pathogenic bacteria within the body. The two-fold purpose of this project was to determine which combination of bacteria would result in the most biofilm formation and then to assess the effect of ayurvedic plant extracts on the biofilm. The results show that the addition of a plant extract can affect the biofilm growth of a bacteria combination. The applications of this study can be used to design probiotic supplements with added beneficial plant extracts.