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The authors identify disinfectant-resistant bacterial strains of infection-causing bacteria from samples collected at a hospital setting.

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.

The authors looked at using machine learning to identify skills needed to apply for certain jobs, specifically looking at different techniques to parse apart the text. They found that Bidirectional Encoder Representation of Transforms (BERT) performed best.

The authors use MoralBERT, a BERT-based machine learning model, to analyze the prevalence and evolution of moral language in art critique journals from 1960 to 2015. They draw on Moral Foundations Theory to examine changes in five virtue and five vice foundations over time. They discover slight increases in degradation and care and a decrease in purity among otherwise stable patterns. The study is a novel application of computational moral analysis in a domain not typically associated with such tools. Although the moral signals were relatively faint, the study offers insights into shifting art world discourses and models an exciting method for future inquiry.

This study follows the process of single-cloning and the growth of a homogeneous cell population in a superficial environment over the course of six weeks with the end goal of showing which of five tumor growth models commonly used to predict heterogeneous cancer cell population growth (Exponential, Logistic, Gompertz, Linear, and Bertalanffy) would also best exemplify that of homogeneous cell populations.