In this article, the authors investigate the RNA expression differences between groups of chronic cocaine abusers and drug-free subjects.

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

In this study, the authors hypothesized that closed-circuit television images could be stored with improved resolution by using enhanced deep residual (EDSR) networks.

In this article the authors look at the lung microbiome in patients with cystic fibrosis to determine what the major bacterial species present are.

To address whether odor sensory circuits are organized topographically, the authors investigate whether the neuronal responses to similar odors amongst different mice mapped similarly in brain.

Sound waves can be amazingly powerful, especially when they work together. Here the authors create an “acoustic lens” that focuses sound waves on a single location. This makes the sound waves very powerful, capable of causing damage at a precise point. In the future, acoustic lenses like this could potentially be used to treat cancer by killing small tumors without surgery.

To best identify tuberculosis and pneumonia diagnoses in chest x-rays, the authors compare different deep learning convolution neural networks.

In this article, the authors looked at developing a strategy that would allow for earlier diagnosis of Diabetes as that improves long-term outcomes. They were able to find that BMI, tricep skin fold thickness, and blood pressure are the risk factors with the highest accuracy in predicting diabetes risk.

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

Here, in an effort to develop a model to predict future groundwater levels, the authors tested a tree-based automated artificial intelligence (AI) model against other methods. Through their analysis they found that groundwater levels in Texas aquifers are down significantly, and found that tree-based AI models most accurately predicted future levels.