Click on this link to download the full high-definition interactive pdf for AJUR Volume 22 Issue 2 (June 2025). The Crossref link for this issue is https://doi.org/10.33697/ajur.2025.136
Links to individual manuscripts, abstracts, and keywords are provided below.
p.3. Understanding the Molecular Mechanisms That Govern Opioid Potency Through a Course-embedded Computational Research Project
Keegan Gunderson, Faith Oldenburg, Sudeep Bhattacharyay, & Sanchita Hati
https://doi.org/10.33697/ajur.2025.137
ABSTRACT: In recent decades awareness surrounding the class of drugs known as opioids has risen due to what many have termed the “opioid epidemic.” Rates in opioid-related drug overdoses have spiked due to increased opioid addiction and the illegal lacing of illicit drugs, such as marijuana, with opioid compounds like fentanyl, increasing their potency. Coexisting with the devastating realities of overdose and addiction, however, is the clinical demand for these drugs and their potency to manage extreme pain. Herein, we describe the results of a course-embedded computational research project to understand the factors responsible for opioid potency. In the present study, the chemical properties of commonly used opioids and their interactions with receptors are investigated using computational techniques. In particular, the molecular basis for the high potency of fentanyl is investigated. The drug molecules were constructed using a web-based tool, WebMO. The electronegativity and chemical hardness of nine opioids and one pain reliever were determined using a quantum chemistry software package named Q-Chem. The binding interactions between the same set of opioid molecules and their receptors were studied using AutoDock FR, interactions within the receptor’s binding pocket were analyzed using Visual Molecular Dynamics (VMD), and SwissADME was used to investigate the pharmacokinetic properties of each drug molecule. KEYWORDS: Noise Pollution; Transportation; Environmental Justice; Rural Communities; Minority Communities; Public Health
Brittany C. Yencho, Charles J. West, Andrew J. Brown, & Conor R. Flannigan
https://doi.org/10.33697/ajur.2025.138
ABSTRACT: Bacteriophages, or phages, are viruses that infect bacteria and are the most prevalent biological entities in the world. While phage research has continued to develop, the investigation of phages within ecological biology remains in the early stages and requires an extensive database of environmentally derived phages, such as from marine ecosystems. The objectives of this research were to a) determine the local beach bacterial composition and select locally isolated strains as potential bacteriophage hosts, b) determine the efficacy of local bacteria as marine bacteriophage hosts (Erythrobacter citreus Pensacola AB and Microbacterium oleivorans Pensacola AB), and c) compare the host efficacy between locally isolated bacterial strains and non-local Microbacterium isolates (Microbacterium sp. Casco Bay and Microbacterium foliorum NRRL B-24224). The results suggest that of the two locally isolated bacterial strains tested, Microbacterium oleivorans was not a successful host for the scope of this research. In contrast, Erythrobacter citreus Pensacola AB was an efficient host across several trials. Of the non-local bacteria, both Microbacterium sp. Casco Bay and Microbacterium foliorum NRRL B-24224 were successful hosts at isolating phage, although not at the same consistency as Erythrobacter citreus. This research concluded that local bacterial strains could be effective for phage hunting and that the overall success of finding local marine phage was not dependent on using local bacterial strains. Four phages, two M. sp. Casco Bay phages, one M. foliorum phage, and one E. citreus phage, were selected for further purification and characterization. The four phage genomes were sequenced to characterize the molecular nature of these marine phages. KEYWORDS: Microbiome; Bacteriophage; Marine phage; Plaque Assay; Genomic sequencing; Marine ecology; Zinc chloride (ZnCl2) precipitation; PEG (polyethylene glycol) precipitation
Kaitlyn A. Carr & Clare Conry-Murray
https://doi.org/10.33697/ajur.2025.139
ABSTRACT: The present study examined participants’ comprehension of consent form information based on the consent forms’ reading level, format, and level of interactivity. Our sample consisted of 228 adult English speakers who were randomly assigned to one of eight groups. Each group saw consent forms with a different combination of reading level, format, and interactivity of questions. All participants were asked to answer 12 multiple-choice questions about two consent forms (framed as two studies). Participants read and self-reported whether they read the form or not. Asking participants questions immediately after exposure to the information in the consent form improved comprehension compared to asking all comprehension questions after reading the entire form. Reading level and bulleted format did not improve comprehension significantly. Furthermore, participants were more likely to report that they read the consent form if they were given interactive questions about the form. Results suggest that interactive questions can be an effective method for improving participant comprehension of the purpose, risks, and benefits of the study or procedure in which they plan to participate. KEYWORDS: Comprehension; Informed Consent; Format; Reading Level; Interactivity; Participants; Ethics in Research; Consent Form
Matthew Whalen
https://doi.org/10.33697/ajur.2025.140
ABSTRACT: This study introduces a regular five-point finite difference method for approximating the two-dimensional Poisson equation with Dirichlet boundary condition for convex polygonal domains. The Poisson equation frequently emerges in many fields of science and engineering, such as field potentials and heat transfer. As exact solutions are rarely possible, numerical approaches are important to develop efficient and practical modeling. This introductory paper addresses the uniqueness of the problem, finite difference discretization, consistency of the problem, and maximum norm error analysis. We also provide numerical results that not only validate theoretical results but also demonstrate the method’s efficiency. Principally, this paper intends to serve as a compilation of the research which underpins the finite difference methods in a way that is unified, consistent, and accessible to undergraduates. Additionally, we have made the MATLAB code for these results publicly available at the end of this paper for reference and practical implementation. KEYWORDS: Dirichlet; Poisson; Convex; Finite Differences; Norm; Computer Simulation; Numerical Methods
p. 49. A Comparison of Zero-Inflated Models for Modern Biomedical Data
Max Beveridge, Zach Goldstein, & Hee Cheol Chung
https://doi.org/10.33697/ajur.2025.141
ABSTRACT: There has been a growing number of datasets exhibiting an excess of zero values that cannot be adequately modeled using standard probability distributions. For example, microbiome data and single-cell RNA sequencing data consist of count measurements in which the proportion of zeros exceeds what can be captured by standard distributions such as the Poisson or negative binomial, while also requiring appropriate modeling of the nonzero counts. Several models have been proposed to address zero-inflated datasets including the zero-inflated negative binomial, hurdle negative binomial model, and the truncated latent Gaussian copula model. This study aims to compare various models and determine which one performs optimally under different conditions using both simulation studies and real data analyses. We are particularly interested in investigating how dependence among the variables, level of zeroinflation or deflation, and variance of the data affects model selection. KEYWORDS: Zero-InflatedModels; HurdleModels; Truncated Latent Gaussian CopulaModel; Microbiome Data; Gene-Sequencing Data; Zero-Inflation, Negative Binomial; Zero-Deflation
Erin Koster & Michelle Stuhlmacher
https://doi.org/10.33697/ajur.2025.142
ABSTRACT: Exposure to noise pollution has been linked to a variety of negative health effects including heart disease and hypertension. Exposure to noise is not distributed equally; minority communities are often adjacent to highways and airports and have been found to have disproportionately high levels of transportation noise exposure. What is not yet understood, however, is if transportation noise exposure is increasing or decreasing over time and for whom. In this research we examine the change in transportation noise between 2016 and 2018 in the U.S. with an emphasis on the types of communities impacted. We utilize modeled transportation noise data from the U.S. Department of Transportation and conduct bi-variate regressions with demographic data at the census tract level. Our results show that transportation noise pollution is increasing nationwide, with minority and rural communities disproportionately affected by this increase. We close with a discussion of the policy recommendations for combating the growing inequality in transportation noise exposure. KEYWORDS: Noise Pollution; Transportation; Environmental Justice; Rural Communities; Minority Communities; Public Health; Geographic Information Systems; Spatial Data; Geography; Data Science
p.81. Utilizing a Large Language Model for Training Students in Personal Care Product Formulation
McKinnley Bilbao, Caitlin West, Tomas Carmona, Morgan Covarrubia, Alex Goslin, Katherine Judge, Garland Munn, Hazel Ticas, Abe Tonioli, Collin Tuttle, & Daniel Scott
https://doi.org/10.33697/ajur.2025.143
ABSTRACT: This study examines the use of a large language model (LLM), specifically ChatGPT 3.5, to train novice formulators in the development of personal care products. The aim is to assess the LLM’s ability to guide students as they formulate a 10-minute hydrating face mask. The research explores how effectively students can rely on the LLM for ingredient substitutions and recipe adjustments during an iterative formulation process, with the goal of producing a high-quality or improved product. Results indicate that while ChatGPT 3.5 demonstrates above-average chemistry knowledge and can provide useful suggestions when prompted clearly, it has significant limitations.1 These include unreliable memory in extended conversations and difficulty with precise mathematical calculations, particularly for ingredient adjustments. For example, the LLM’s limited memory hindered its ability to incorporate information from earlier iterations, often resulting in redundant or inconsistent recommendations. To address these calculation errors, the team developed in-house code to ensure formulation accuracy. Additionally, the LLM’s contribution to cost optimization was minimal, and it struggled to identify complex formulation components that trained formulators would typically recognize. Although the LLM supported rapid initial product development, it was less effective in more advanced stages, including cost optimization and refining complex components.
KEYWORDS: Machine Learning; ChatGPT; Cosmetics; Formulation; Novice; Formulators; Face; Mask; Education; LLMs