The authors develop a new method for training machine learning algorithms to differentiate between hate speech and cultural speech in online platforms.

Dental resins can be a source of reactive oxygen species (ROS) which in unruly amounts can be toxic to cellular and overall health. In this report, the authors test whether the consumption of antioxidant rich foods like avocado and asparagus can protect against the effect of dental resin-derived ROS. However, rather than testing humans, they use fruit flies and their climbing abilities as an experimental readout.

The authors found that SARIMAX-GARCH is more accurate than SARIMAX for load forecasting with respect to energy consumption.

The authors develop and test a machine learning algorithm for predicting diabetes diagnoses.

In this study, the authors developed and assessed the accuracy of a machine learning algorithm to identify skin cancers using images of biopsies.

In this study, the authors investigated the biological mechanism underlying the actions of a traditional medicinal plant, Astragalus membranaceus. Using C. elegans as an experimental model, they tested the effects of AM root on heat stress responses. Their results suggest that AM root extract may enhance the activity of endogenous pathways that mediate cellular responses to heat stress.

The authors compare the predicted versus actual geyser eruption times for the Old Faithful and Beehive Geysers at Yellowstone National Park.

The authors use pictures of candy wrappers and neural networks to improve nutritional accuracy of diet-tracking apps.

In this study, the authors characterized the trends of cigarette use amongst people who do and don't use electronic nicotine delivery systems (or ENDS). This was done to help determine if the use of ENDS is aiding in helping smokers quit, as the data on this has been controversial. They found that use of ENDS among people either with or without previous cigarette usage were more likely to continue using cigarettes in the future. This is important information contributing to our understanding of ways to effectively (and not effectively) reduce cigarette use.

The authors explore how diversity in data sets contributes to bias in artificial intelligence.