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 this study, the authors created a survey to assess misconceptions and knowledge deficits regarding cardiovascular diseases exist among US adults and minors.

In this study, potential physiological effects of Red 40 food dye, found in many different food products, are tested using Daphnia magna, a small freshwater crustacean.

In healthy ecosystems, organisms interact in a relationship that helps maintain one another's existence. Stress can disrupt this interaction, compromising the survival of some of the members of such relationships. Here, the authors investigate the effect of stress on the interaction between anemones and their microbiome. Their study suggests that stress changes the composition of the surface microbiome of the anemone D. lineata, which is accompanied by an increase in mucus secretion. Future research into the composition of this stress-induced mucus might reveal useful antimicrobial properties.

The authors investigate whether amylase or yeast had a more prominent role in determining the bioethanol concentration and bioethanol yield of banana samples. They hypothesized that amylase would have the most significant impact on the bioethanol yield and concentration of the samples. They found that while yeast is an essential component for producing bioethanol, the proportion of amylase supplied through a joint amylase-yeast mixture has a more significant impact on the bioethanol yield. This study provides a greater understanding of the mechanisms and implications involved in enzyme-based biofuel production, specifically of those pertaining to amylase and yeast.

Fossil fuels are a limited resource; thus, it is important to explore new sources of energy. The authors examine the ability of switchgrass to produce ethanol and test the effects of pretreatment and grinding on ethanol yield.

Coronary heart disease (CHD) is a global disease that causes fatal buildup of plaque in the arteries. Currently stents are placed in the artery for many patients with CHD to support proper blood flow. Here, the authors build a system to explore how the shape of the stent affects blood flow rate, a finding that can help optimize stents for patients.

Chemicals used in fertilizers and pesticides often end up in nearby bodies of water due to runoff and may have negative impacts on these important ecosystems. In this study, the authors use water containing different nitrogen levels to investigate the effect on the growth of the English daisy.

Here, the authors chose to investigate the efficacy of zinc oxide nanoparticles (ZnO NPs) and cisplatin or zinc ions in inducing cancer apoptosis. While both treatments were found to reduce the proliferation of lung cancer cells, the authors suggest that further studies to identify the mechanism are necessary.

This study hypothesized that a machine learning model could accurately predict the severity of California wildfires and determine the most influential meteorological factors. It utilized a custom dataset with information from the World Weather Online API and a Kaggle dataset of wildfires in California from 2013-2020. The developed algorithms classified fires into seven categories with promising accuracy (around 55 percent). They found that higher temperatures, lower humidity, lower dew point, higher wind gusts, and higher wind speeds are the most significant contributors to the spread of a wildfire. This tool could vastly improve the efficiency and preparedness of firefighters as they deal with wildfires.