The availability, portion sizes, and consumption of highly palatable food has been linked adverse health outcomes. McBurnett and O’Donnell sought to assess the relationship between reward-based eating drive, consumption, cravings, and knowledge of the effects of sugary foods. In this study population, reward-based eating drive was related to both consumption and cravings. Further, for females, the knowledge of sugar’s effects was significantly and inversely associated with its consumption.

In this study, the authors investigate situations in which people make sports bets that seem to go against their better judgement. Using surveys, individuals were asked to bet on which team would win in scenarios when their home team was involved and others when they were not to determine whether fandom for a team can overshadow fans’ judgment. They found that fans bet much more on their home teams than neutral teams when their team was facing a large deficit.

Seeking to investigate the effects of ambient pollutants on human respiratory health, here the authors used machine learning to examine asthma in Lost Angeles County, an area with substantial pollution. By using machine learning models and classification techniques, the authors identified that nitrogen dioxide and ozone levels were significantly correlated with asthma hospitalizations. Based on an identified seasonal surge in asthma hospitalizations, the authors suggest future directions to improve machine learning modeling to investigate these relationships.

In the United States, there are currently 17.8 million affected by atopic dermatitis (AD), commonly known as eczema. It is characterized by itching and skin inflammation. AD patients are at higher risk for infections, depression, cancer, and suicide. Genetics, environment, and stress are some of the causes of the disease. With the rise of personalized medicine and the acceptance of gene-editing technologies, AD-related variations need to be identified for treatment. Genome-wide association studies (GWAS) have associated the Filaggrin (FLG) gene with AD but have not identified specific problematic single nucleotide polymorphisms (SNPs). This research aimed to refine known SNPs of FLG for gene editing technologies to establish a causal link between specific SNPs and the diseases and to target the polymorphisms. The research utilized R and its Bioconductor packages to refine data from the National Center for Biotechnology Information's (NCBI's) Variation Viewer. The algorithm filtered the dataset by coding regions and conserved domains. The algorithm also removed synonymous variations and treated non-synonymous, frameshift, and nonsense separately. The non-synonymous variations were refined and ordered by the BLOSUM62 substitution matrix. Overall, the analysis removed 96.65% of data, which was redundant or not the focus of the research and ordered the remaining relevant data by impact. The code for the project can also be repurposed as a tool for other diseases. The research can help solve GWAS's imprecise identification challenge. This research is the first step in providing the refined databases required for gene-editing treatment.

Due to the susceptibility of adolescent age groups to opioid misuse, here the authors sought to determine if there was a difference in the perception and knowledge between 9^th and 12^th graders regarding the opioid crisis. An educational intervention trial was done with the 9^th graders and surveys were used to identify its effects. Although the authors acknowledge a small sample size, their results suggest that their are gaps within the knowledge of adolescents in regards to opioid misuse and its long-term effects that could be addressed with further education.

Triggered largely by the warming and pollution of oceans, corals are experiencing bleaching and a variety of diseases caused by the spread of bacteria, fungi, and viruses. Identification of bleached/diseased corals enables implementation of measures to halt or retard disease. Benthic cover analysis, a standard metric used in large databases to assess live coral cover, as a standalone measure of reef health is insufficient for identification of coral bleaching/disease. Proposed herein is a solution that couples machine learning with crowd-sourced data – images from government archives, citizen science projects, and personal images collected by tourists – to build a model capable of identifying healthy, bleached, and/or diseased coral.

As the COVID-19 pandemic continues, the authors noticed a change in the physical activity of many people, as well as a change in the type of physical activity they practice. Here, the authors used a cross-sectional survey of 150 participants from the province of Basra in Iraq. They found an overall decrease in the number of days of physical activity for participants along with an increasing proportion of at-home exercises compared to other activities that are performed inside sports clubs during the pandemic.

The authors conduct a survey to investigate the perceptions both sighted and blind people have on blindness. The results reveal both groups perceive the most challenging adversities to be the lack of awareness and understanding of their circumstances.

Here the authors sought to use three machine learning models to predict poverty levels in Cambodia based on available household data. They found teat multilayer perceptron outperformed the other models, with an accuracy of 87 %. They suggest that data-driven approaches such as these could be used more effectively target and alleviate poverty.

Genetically modified organisms (GMOs) are crops or animals that have been genetically engineered to express a certain physical or biological characteristic and have various benefits that have made them become increasingly popular. However, the public has had mixed reactions to the use of GMOs, with some skeptical of their safety. The purpose of this study was to evaluate how opinions on genetically modified foods can change from exposure to small amounts of information