Objectives/Goals: A summer research program for medical students was implemented using real-world evidence (RWE) – electronic health record (EHR) databases to develop and answer research questions. Medical students were trained in capabilities of traversing the large RWE-EHR so they may query, extract, and analyze data as well as refine their research questions. Methods/Study Population: Ten medical students and 9 non-data scientist mentors underwent training in how to use the IU School of Medicine-Evansville RWEdataLab (CRC/Sidus Insights) national real-world cardiology and psychiatry deidentified EHR databases. The program began with students attending introductory training teaching database, spreadsheet, and statistical program usage. During the remainder of the program, a weekly best practices meeting took place among mentors and a weekly cohort meeting of students and mentors discussed student presented findings. At the end of the program, students generated abstracts and poster or podium presentations to share their findings at local symposia. A survey was also distributed to students to assess the impact of the tools, trainings, and program. Results/Anticipated Results: All students were able to define a question of interest, query and extract data related to their research question, and analyze multiple aspects of their data. Projects were well received at local symposia, with 2 receiving special honors, and 2 projects have been presented at regional/national conferences. Students rated the program highly and were likely to recommend the program to other students. They self-rated improvements in asking scientific questions, using excel, data presentations, and problem-solving. Students valued weekly “check-in” meetings and interactions with mentors more than lectures or technical “help desk” support. Discussion/Significance of Impact: The program provides mechanisms for non-data scientists and medical trainees, to learn and access RWE-EHR databases to address research questions. The cohort interactions fostered discussion among mentors and students promoting research question refinement and clarity findings. The program also introduces a new tool for potential patient care.

A simple procedure for testing heterogeneity of variance is developed which generalizes readily to complex, multi-factor experimental designs. Monte Carlo Studies indicate that the Z-variance test statistic presented here yields results equivalent to other familiar tests for heterogeneity of variance in simple one-way designs where comparisons are feasible. The primary advantage of the Z-variance test is in the analysis of factorial effects on sample variances in more complex designs. An example involving a three-way factorial design is presented.

A method of computing ΣX, ΣX2, ΣXY and higher moments on IBM equipment is described. The basic method is that of successively summary punching, collating a variable number of blank cards behind these summary cards, gang-punching the data from the summary cards into the blank cards, and totalling the entries on these summary cards. The method appears to have several advantages over those previously described, especially if coded data are used.

The Students Participating as Ambassadors for Research in Kentucky (SPARK) program provides novel health equity research training and targeted mentorship for undergraduates, particularly those from groups underrepresented in the biomedical and behavioral research and workforce. SPARK aims to address inadequate diversity in the medical and scientific research fields by providing comprehensive research mentorship and skill-building. Unlike most existing research training programs that are brief, focus on laboratory research, or are limited to graduate students and junior faculty, SPARK delivers a 16-month intensive behavioral and population health science training, equipping students with needed tools to conceptualize, plan, execute, and analyze their own health equity research study. Trainees complete didactic coursework on health equity, study design and proposal development, data analysis, and ethics. Students receive a stipend and research expenses, and multiple mentors guide them in creating original research projects for which they serve as Principal Investigator. Students disseminate their findings annually at an academic research conference as a capstone. Evaluation data from the first three cohorts suggest SPARK has been pivotal in preparing students for graduate studies and research careers in health equity and behavioral and population health sciences, providing strong support for further investments in similar undergraduate research training models.

The expensive-tissue hypothesis (ETH) posited a brain–gut trade-off to explain how humans evolved large, costly brains. Versions of the ETH interrogating gut or other body tissues have been tested in non-human animals, but not humans. We collected brain and body composition data in 70 South Asian women and used structural equation modelling with instrumental variables, an approach that handles threats to causal inference including measurement error, unmeasured confounding and reverse causality. We tested a negative, causal effect of the latent construct ‘nutritional investment in brain tissues’ (MRI-derived brain volumes) on the construct ‘nutritional investment in lean body tissues’ (organ volume and skeletal muscle). We also predicted a negative causal effect of the brain latent on fat mass. We found negative causal estimates for both brain and lean tissue (−0.41, 95% CI, −1.13, 0.23) and brain and fat (−0.56, 95% CI, −2.46, 2.28). These results, although inconclusive, are consistent with theory and prior evidence of the brain trading off with lean and fat tissues, and they are an important step in assessing empirical evidence for the ETH in humans. Analyses using larger datasets, genetic data and causal modelling are required to build on these findings and expand the evidence base.

The washing of synthetic materials has been named as the largest contributor of microplastic pollution to our oceans. With the consumption of petrochemical-based synthetic materials expected to grow, due to an increased demand, the release of microplastic fibres to our environments is expected to also accelerate. To combat microplastic fibre release, this study explores source-directed interventions within the design and manufacturing process of textiles to reduce the amount of pollution released from the surface and the edges of the fabric structure. Using standardised wash tests and polyester fabric swatches that were created in-house with systematic structural adjustments, single jersey knit fabrics were shown to release over three times more microplastic pollution than twill woven fabric. This illustrates that increasing the tightness of a fabric could be implemented within the design of fabrics for environmental benefits. Additionally, the laser cutting technique reduced microplastic fibres released by over a third compared to scissor cutting and overlock serging, showing that the edge of the fabric is a significant source of microplastic pollution released during laundering. This research highlights the adaptable and innovative eco-design approaches to clothing production which is necessary to help the sector reach international sustainability targets and regulations.

People often perform poorly on stock-flow reasoning tasks, with many (but not all) participants appearing to erroneously match the accumulation of the stock to the inflow – a response pattern attributed to the use of a “correlation heuristic”. Efforts to improve understanding of stock-flow systems have been limited by the lack of a principled approach to identifying and measuring individual differences in reasoning strategies. We present a principled inferential method known as Hierarchical Bayesian Latent Mixture Models (HBLMMs) to analyze stock-flow reasoning. HBLMMs use Bayesian inference to classify different patterns of responding as coming from multiple latent populations. We demonstrate the usefulness of this approach using a dataset from a stock-flow drawing task which compared performance in a problem presented in a climate change context, a problem in a financial context, and a problem in which the financial context was used as an analogy to assist understanding in the climate problem. The hierarchical Bayesian model showed that the proportion of responses consistent with the “correlation heuristic” was lower in the financial context and financial analogy context than in the pure climate context. We discuss the benefits of HBLMMs and implications for the role of contexts and analogy in improving stock-flow reasoning.