Association of Accelerometer-Measured Physical Activity With College Student Success and Retention.

Background: This study examined the relationship between physical activity (PA) and academic performance and retention among college students using accelerometer data while controlling for sex and socioeconomic background.

Methods: Data were collected from 4643 first-year college students at a private university in the south-central United States who began their studies in the Fall semesters between 2015 and 2022. Daily step counts were collected using accelerometers worn as part of the institutions PA requirements.

The influence of chronotype on the body mass index of U.S. college students.

Objectives: The relationship between a college student's chronotype and body mass index (BMI) is important to understand for university decision makers who want to build healthy and inclusive academic communities. This study aimed to evaluate how a student's chronotype influences their BMI.

Material And Methods: Participants were college students from Oral Roberts University (n=384) with a mean age of 18.

Teaching Cheminformatics through a Collaborative Intercollegiate Online Chemistry Course (OLCC).

While cheminformatics skills necessary for dealing with an ever-increasing amount of chemical information are considered important for students pursuing STEM careers in the age of big data, many schools do not offer a cheminformatics course or alternative training opportunities. This paper presents the Cheminformatics Online Chemistry Course (OLCC), which is organized and run by the Committee on Computers in Chemical Education (CCCE) of the American Chemical Society (ACS)'s Division of Chemical Education (CHED). The Cheminformatics OLCC is a highly collaborative teaching project involving instructors at multiple schools who teamed up with external chemical information experts recruited across sectors, including government and industry.

Prediction of 1-octanol solubilities using data from the Open Notebook Science Challenge.

Background: 1-Octanol solubility is important in a variety of applications involving pharmacology and environmental chemistry. Current models are linear in nature and often require foreknowledge of either melting point or aqueous solubility. Here we extend the range of applicability of 1-octanol solubility models by creating a random forest model that can predict 1-octanol solubilities directly from structure.

Chemistry in second life.

This review will focus on the current level on chemistry research, education, and visualization possible within the multi-user virtual environment of Second Life. We discuss how Second Life has been used as a platform for the interactive and collaborative visualization of data from molecules and proteins to spectra and experimental data. We then review how these visualizations can be scripted for immersive educational activities and real-life collaborative research.