In this study, the authors develop a new hydrogel using photochemical crosslinking with bovine serum albumin and methylene blue. They find that this new hydrogel has some useful applications!

In this study, the authors investigate the disinfectant potential of many household spices and minerals. More specifically, they test whether these compounds can be used to disinfect mobile phones after daily use with the hope of identifying environmentally-friendly cleaning options.

Ant colony optimization algorithms simulate ants moving from point to point on a graph and coordinate their actions, similar to ants laying down pheromones to strengthen a path as it is used more frequently. These ACO algorithms can be applied to the classic traveling salesman problem, which aims to determine the lowest-cost path through a given set of points on a graph. In this study, a novel multiple-colony system was developed that uses multiple simulated ant colonies to generate improved solutions to the traveling salesman problem.

Previous studies have found that how a food item is labeled may influence people's liking of it. This study used a cookie taste test to investigate whether people's liking of a dessert item would be swayed by the use of different labels.

Machine learning and deep learning techniques can be used to predict the early onset of breast cancer. The main objective of this analysis was to determine whether machine learning algorithms can be used to predict the onset of breast cancer with more than 90% accuracy. Based on research with supervised machine learning algorithms, Gaussian Naïve Bayes, K Nearest Algorithm, Random Forest, and Logistic Regression were considered because they offer a wide variety of classification methods and also provide high accuracy and performance. We hypothesized that all these algorithms would provide accurate results, and Random Forest and Logistic Regression would provide better accuracy and performance than Naïve Bayes and K Nearest Neighbor.

In this study, the authors examined the effects of commonly used pesticides (metolachlor, glyphosate, chlorpyrifos, and atrazine) on population size, motor function, and learning in Drosophila melanogaster.

Enzyme chemotaxis is a thermodynamic phenomenon in which enzymes move along a substrate concentration gradient towards regions with higher substrate concentrations and can be used to steer nanovehicles towards targets along natural substrate concentrations. In patients with Alzheimer’s disease, a gradient of tau protein forms in the bloodstream. Tau protein is a substrate of the enzyme CDK5, which catalyzes the phosphorylation of tau protein and can travel using chemotaxis along tau protein gradients to increasing concentrations of tau and amyloid-beta proteins. The authors hypothesized that CDK5 would be able to overcome these barriers of Brownian motion and developed a quantitative model using Michaelis-Menten kinetics to define the necessary parameters to confirm and characterize CDK5’s chemotactic behavior to establish its utility in drug delivery and other applications.

In this study, the authors develop an architecture to implement in a cloud-based database used by law firms to ensure confidentiality, availability, and integrity of attorney documents while maintaining greater efficiency than traditional encryption algorithms. They assessed whether the architecture satisfies necessary criteria and tested the overall file sizes the architecture could process. The authors found that their system was able to handle larger file sizes and fit engineering criteria. This study presents a valuable new tool that can be used to ensure law firms have adequate security as they shift to using cloud-based storage systems for their files.

In this study, the authors developed a model named DNA Sequence Embedding Network (DNA-SEnet) to classify DNA-asthma associations using their genomic patterns.

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.