Mosquito-borne diseases are a major issue across the world, and the objective for this project was to determine the characteristics that make some communities more susceptible to these diseases than others. The authors identified and studied characteristics that make communities susceptible to mosquito-borne diseases, including water in square miles, average temperature, population, population density, and poverty rates per county. They found that the population of a county is the best indicator of the prevalence of mosquito-borne diseases.
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.
The authors looked at using genetic algorithms to look at the Indian labor market and what features might best explain any variation seen. They found that features such as economic growth and household consumption, among others, best explained variation.
Evidence suggests certain food preservatives may be genotoxic due to their ability to impair normal cellular pathways. The authors investigated the genotoxic potential and effects of commonly used synthetic food preservatives, specifically sodium nitrite, potassium sulfate, and hydrogen peroxide.
The misfolding of proteins leads to numerous diseases including Akzheimer’s, Parkinson’s and Type II Diabetes. Understanding of exactly how proteins fold is crucial for many medical advancements. Chenna and Englander addressed this problem by measuring the rate of hydrogen-deuterium exchange within proteins exposed to deuterium oxide in order to further elucidate the process of protein folding. Here, mass spectrometry was used to measure exchange in Cytochrome c and was compared to archived 1H NMR data.
Poor air quality is associated with negative effects on human health but can be difficult to measure in an accurate and cost-effective manner. The authors design and test a monitor for measuring indoor air quality using low-cost components.
We know relatively little about how vegan diets and non-vegan diets compare when it comes to the gut microbiome. Gollamudi and Gollamudi tackle this challenge by investigating how changes in a participant's diet affected the diversity of their intestinal microbiome.
The purpose of the study was to determine whether graph-based machine learning techniques, which have increased prevalence in the last few years, can accurately classify data into one of many clusters, while requiring less labeled training data and parameter tuning as opposed to traditional machine learning algorithms. The results determined that the accuracy of graph-based and traditional classification algorithms depends directly upon the number of features of each dataset, the number of classes in each dataset, and the amount of labeled training data used.
Habitat loss and global warming remain present-day issues that continue to place pressures on various ecosystems and their species. The authors of this paper performed studies over two years to understand whether birds feed more from wooded or exposed areas.
Alzheimer's disease is one of the leading causes of death in the United States and is characterized by neurodegeneration. Mishra et al. wanted to understand the role of two transport proteins, LRP1 and AQP4, in the neurodegeneration of Alzheimer's disease. They used a model organism for Alzheimer's disease, the nematode C. elegans, and genetic engineering to look at whether they would see a decrease in neurodegeneration if they increased the amount of these two transport proteins. They found that the best improvements were caused by increased expression of both transport proteins, with smaller improvements when just one of the proteins is overly expressed. Their work has important implications for how we understand neurodegeneration in Alzheimer's disease and what we can do to slow or prevent the progression of the disease.