In this study, the authors sample water at different points closer and closer to two different airports to determine if these airports may be contributing to water pollution, specifically by measuring metals, nitrates, and pH.

Research relating to freshwater acidification is minimal, so the impact of aquatic plants, Anubias barteri var. congensis and Anubias barteri var. nana, on minimizing changes in pH was explored in an ecosystem in Northern California. Creek water samples, with and without the aquatic plants, were exposed to dry ice to simulate carbon emissions and the pH was monitored over an eight-hour period. There was a 25% difference in the observed pH based on molar hydrogen ion concentration between the water samples with plants and those without plants, suggesting that aquatic plants have the potential to limit acidification to some extent. These findings can guide future research to explore the viable partial solution of aquatic plants in combating freshwater acidification.

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.

In this article, the authors investigate whether stock selection across various sectors is efficient enough to outperform an overall market. Stocks from 2006 to 2020 were selected across sectors to calculate beta values using the Capital Asset Pricing Model.

Phage therapy has been suggested as an alternative to antibiotics because bacteria resistant to antibiotics may still be susceptible to phages. However, phages may have limited effectiveness in combating bacteria since bacteria possess several antiviral defense mechanisms and can quickly develop resistance to phages. In this study, Wu and Pinta compare the effectiveness and specificity of antibiotics and phages in combating bacteria. They found that T4 phages are more specific and effective in fighting or inhibiting both antibiotic-resistant and sensitive bacteria than antibiotics, suggesting that phage therapy can be developed as an efficient tool to combat antibiotic-resistant bacteria.

Here, recognizing the effects of the COVID-19 pandemic on young peoples' mental health and wellbeing the authors used an online survey which included the short General Health Questionnaire (GHQ-12) to probe 102 young adults. Overall they found that young adults perceived the pandemic to be detrimental to many areas of their wellbeing, with females and those aged 18-19 and 22-23 reporting to be the most significantly impacted.

Nonalcoholic Fatty Liver Disease (NAFLD) is a condition where a surplus of triglycerides or fat are present in the liver. In this study, ezetimibe, a cholesterol lowering drug, was used to treat flies modeling NAFLD. Compared to the coconut oil fed flies that were transferred to the control medium, the flies transferred to the control medium treated with ezetimibe showed a decrease in their triglyceride and alanine transaminase level.

Disruptions to the microbiome, specifically the imbalance in the two major phyla, the Firmicutes and the Bacteroidetes, have been linked to the development of obesity. This study explored whether or not Fage plain total 0% Greek yogurt, which contains live and active bacterial cultures belonging to the Firmicute phylum, could decrease the numbers of Bacteroides thetaiotaomicron, an organism found in the human gut that belongs to the Bacteroidetes phylum.

In this study E. coli bacteria was exposed to small UV lights currently used in school laboratories to see the effect on colony growth. This project explores how UV radiation methods could be applied in common households to inhibit bacterial growth.

Every year, around 40% of undergraduate students in the United States discontinue their studies, resulting in a loss of valuable education for students and a loss of money for colleges. Even so, colleges across the nation struggle to discover the underlying causes of these high dropout rates. In this paper, the authors discuss the use of machine learning to find correlations between the built environment factors and the retention rates of colleges. They hypothesized that one way for colleges to improve their retention rates could be to improve the physical characteristics of their campus to be more pleasing. The authors used image classification techniques to look at images of colleges and correlate certain features like colors, cars, and people to higher or lower retention rates. With three possible options of high, medium, and low retention rates, the probability that their models reached the right conclusion if they simply chose randomly was 33%. After finding that this 33%, or 0.33 mark, always fell outside of the 99% confidence intervals built around their models’ accuracies, the authors concluded that their machine learning techniques can be used to find correlations between certain environmental factors and retention rates.