Browse Articles

The authors employ computational protein design to identify a mini-protein with the potential to enhance binding of the tight junction protein, claudin-5, at the blood-blood barrier with therapeutic potential for neurodegenerative diseases.

The authors looked at the correlation between being able to speak more than one language (multilingualism) and visual imagery. They found multilingual individuals had higher visual imagery as measured by the VVIQ.

Authors examine the effectiveness of Large Language Models (LLMs) like BERT, MathBERT, and OpenAI GPT-3.5 in assisting middle school students with math word problems, particularly following the decline in math performance post-COVID-19.

Debates around legacy preferences, recruited athletes, and affirmative action in U.S. college admissions often focus on the belief that graduating from a more selective institution leads to higher future earnings. The study hypothesized a positive correlation between college selectivity and future income due to enhanced resources and opportunities.

People with Type One diabetes often rely on Continuous Blood Glucose Monitors (CGMs) to track their blood glucose and manage their condition. Researchers are now working to help people with Type One diabetes more easily monitor their health by developing models that will future blood glucose levels based on CGM readings. Jalla and Ghanta tackle this issue by exploring the use of AI models to forecast blood glucose levels with CGM data.

The authors compare neuroimaging datasets to identify potential new biomarkers for earlier detection of Alzheimer's disease.

The authors looked at how a student's own background influence their attitude towards integration of diverse cultures and ethnicities. While overall students viewed other groups positively, the authors found that groups still indicated they felt judged by their peers.

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.

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

Semantic segmentation - labelling each pixel in an image to a specific class- models require large amounts of manually labeled and collected data to train.