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.

In this study, the authors test the longevity of a anti-mite compound, amitraz, in commercially-sold strips and the age-dependent efficacy of these strips in preventing honey bee colony collapse by ectoparasitic mite Varroa destructor.

The cause of insect colony collapse disorder (CCD) is still a mystery. In this study, the authors aimed to test the effects of two environmental factors, water vapor and smoke levels, on the social behavior and physical condition of insects. Their findings could help shed light on how changing environmental factors can contribute to CCD.

In an extensive study of gene mutations, and their resulting effect on protein-protein interactions, Desai and Stork found that HTT-PRPF40B-MECP2 interactions are weakened with progression of Lopes-Maciel-Rodan syndrome.