Browse Articles

People with Type One diabetes often rely on Continuous Blood Glucose Monitors (CGMs) to track their blood glucose and manage their condition. Researchers are now working to help people with Type One diabetes more easily monitor their health by developing models that will future blood glucose levels based on CGM readings. Jalla and Ghanta tackle this issue by exploring the use of AI models to forecast blood glucose levels with CGM data.

The impact of age on mental health is a crucial yet understudied aspect of public health. While mental health is gaining recognition as a vital component of overall well-being, its correlation with age remains largely unexplored. In Canada, where the median age has risen significantly over the past half-century, understanding this relationship becomes increasingly pertinent. Researchers hypothesized that older adults would exhibit lower rates of mental health disorders and report better perceived mental health due to increased emotional stability and maturity.

Wolbachia pipientis (Wolbachia) is a maternally inherited endosymbiotic bacterium that infects over 50% of arthropods, including pillbugs, and acts as a reproductive parasite in the host. In the common terrestrial pillbug Armadillidium vulgare (A. vulgare), Wolbachia alters the sex ratio of offspring through a phenomenon called feminization, where genetic males develop into reproductive females. Previous studies have focused on the presence or absence of Wolbachia as a sex ratio distorter in laboratory cultured and natural populations mainly from sites in Europe and Japan. Our three-year study is the first to evaluate the effects of the Wolbachia sex ratio distorter in cultured A. vulgare offspring in North America. We asked whether Wolbachia bacteria feminize A. vulgare isopod male offspring from infected mothers and if this effect can be detected in F1 offspring by comparing the male/female offspring ratios. If so, the F1 offspring ratio should show a higher number of females than males compared to the offspring of uninfected mothers. Over three years, pillbug offspring were cultured from pregnant A. vulgare females and developed into adults. We determined the Wolbachia status of mothers and counted the ratios of male and female F1 progeny to determine feminization effects. In each year sampled, significantly more female offspring were born to Wolbachia-infected mothers than those from uninfected mothers. These ratio differences suggest that the Wolbachia infection status of mothers directly impacts the A. vulgare population through the production of reproductive feminized males, which in turn provides an advantage for further Wolbachia transmission.

Chess engines, or computer programs built to play chess, outperform even the best human players. Kaushikan and Park investigate the inner workings of these chess engines by studying popular chess engines' evaluations of which side of a chess match is most likely to win, and how this is affected by the number of pieces and controlled squares on each side.

The authors looked at phytoremediation, the process by which plants are used to remove pollutants from our environment, and the ability of Lemna minor to perform phytoremediation in various simulated polluted environments. The authors found that L. minor could remove pollutants from the environment and that the addition of bacteria increased this removal.

Many common respiratory illnesses like bronchitis, asthma, and chronic obstructive pulmonary disease (COPD) lead to bronchial inflammation and, subsequently, a blockage. However, there are many difficulties in measuring the severity of the blockage. A numeric metric to determine the degree of the blockage severity is necessary. To tackle this demand, we aimed to develop a novel human respiratory model and design a deep-learning program that can constantly monitor and report bronchial blockage by recording breath sounds in a non-intrusive way.

Breast cancer is the most common cancer in women, with approximately 300,000 diagnosed with breast cancer in 2023. It ranks second in cancer-related deaths for women, after lung cancer with nearly 50,000 deaths. Scientists have identified important genetic mutations in genes like BRCA1 and BRCA2 that lead to the development of breast cancer, but previous studies were limited as they focused on specific populations. To overcome limitations, diverse populations and powerful statistical methods like genome-wide association studies and whole-genome sequencing are needed. Explainable artificial intelligence (XAI) can be used in oncology and breast cancer research to overcome these limitations of specificity as it can analyze datasets of diagnosed patients by providing interpretable explanations for identified patterns and predictions. This project aims to achieve technological and medicinal goals by using advanced algorithms to identify breast cancer subtypes for faster diagnoses. Multiple methods were utilized to develop an efficient algorithm. We hypothesized that an XAI approach would be best as it can assign scores to genes, specifically with a 90% success rate. To test that, we ran multiple trials utilizing XAI methods through the identification of class-specific and patient-specific key genes. We found that the study demonstrated a pipeline that combines multiple XAI techniques to identify potential biomarker genes for breast cancer with a 95% success rate.

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.

With increased screen time and exposure to blue light, an increasing number of people have sleep deprivation. Blue light suppresses the release of melatonin and hinders sleep at night. We hypothesized that people could get a greater amount of sleep by controlling the blue light exposure from screen time before bedtime. BBG’s effect on reducing time to fall asleep was significant within the teenage group, but not significant in the adult group. This indicated that BBG could improve the time taken to sleep for young teenagers post screen time in the evening.

The authors looked at differences in water quality between Chinatown and Bayside. They wanted to look at the racial and economic demographics of each region and how that correlated to access to clean drinking water. Ultimately they did not find any significant differences in water quality, but identified important future directions for this work.