Digital Commons Network^™

In Vitro Pharmacological Evaluation Of Novel Cmpi Derivatives As Selective Positive Allosteric Modulators Of The Alpha4 Beta2 Nicotinic Acetylcholine Receptor, Josue Gaona, Nataly Sanchez, Wilder Felix, Nathalia Menegasso, Brisa Cea, Ganesh Thakur, Ayman K. Hamouda

School of Medicine Student Publications and Presentations

Nicotinic acetylcholine receptors (nAChRs) contribute to the pathogenesis of neurodegenerative diseases such as Parkinson’s disease (PD), where degeneration of dopaminergic and cholinergic neurons disrupts neurotransmission. The (α4)₃(β2)₂ nAChR subtype is expressed on affected neuronal populations and represents a promising therapeutic target. Positive allosteric modulators (PAMs) may enhance receptor function while preserving endogenous signaling. We evaluated chemical derivatives of CMPI [3-(2-chlorophenyl)-5-(5-methyl-1-(piperidin-4-yl)-1H-pyrazol-4-yl)isoxazol] as selective PAMs of (α4)₃(β2)₂ nAChRs. Two-electrode voltage-clamp recordings were performed in Xenopus laevis oocytes expressing defined receptor stoichiometries to assess potentiation and generate dose–response curves. Cytotoxicity was assessed in HEK cells using the MTT assay. Substitutions at the pyrazole …

Argument-Based Consistency In Toxicity Explanations Of Llms, Ramaravind K. Mothilal, Joanna Roy, Syed Ishtiaque Ahmed, Shion Guha

Health Services and Informatics Research

The discourse around toxicity and LLMs in NLP largely revolves around detection tasks. This work shifts the focus to evaluating LLMs’ reasoning about toxicity—from their explanations that justify a stance—to enhance their trustworthiness in downstream tasks. Despite extensive research on explainability, it is not straightforward to adopt existing methods to evaluate free-form toxicity explanation due to their over-reliance on input text perturbations, among other challenges. To account for these, we propose a novel, theoretically-grounded multi-dimensional criterion, Argument-based Consistency (ArC), that measures the extent to which LLMs’ free-form toxicity explanations reflect an ideal and logical argumentation process. Based on uncertainty quantification, …

A Mineralogical And Geochemical Characterization Of The Amo Meteorite, Karinn Johnson '27, Kenneth Brown, Chad Byers, Thomas Grier

Annual Student Research Poster Session

Meteorites provide a wealth of information about solar system evolution, planetary formation and differentiation, and provide clues to the origins of water and life on Earth. On December 10th, 2024 (4:04 EST), a meteor fall was observed approximately 50 km west of Indianapolis, over the small town of Amo, Indiana (Hendricks County). Several pieces of the meteor ranging from about 1g to >60kg were recovered. A local Greencastle, IN resident collected a 1451.3g sample, of which a 7.5g piece was donated to DePauw University for detailed textural, mineralogical, and geochemical characterization using stereomicroscopy, scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), …

An Odd Protocol For An Agent-Based Model Of Hepatitis C Virus Transmission In A Two-Group Structural Heterogeneous Syringe-Sharing Network (M2), Seun Ale, Que Nguyen Dr., John D. Kelleher Prof., Elizabeth Hunter Dr.

Reports

The model described in this ODD is an agent-based model of hepatitis C virus (HCV) transmission among people who inject drugs (PWID). The model extended a baseline homogeneous model by incorporating structural heterogeneity via a two-group interaction framework. The syringe-sharing population in the model is divided into inner and outer circle groups representing individuals with differing levels of syringe-sharing interaction intensity. While all agents share the same syringe-sharing probability and epidemiological processes remain identical across agents, the number of daily interaction opportunities differs between the two groups. Interactions in the model are generated dynamically using proximity-based sampling at each timestep …

An Odd Protocol For An Agent-Based Model Of Hepatitis C Virus Transmission In A Three-Group Structural Heterogeneous Syringe-Sharing Network (M3), Seun Ale, Que Nguyen Dr., John D. Kelleher Prof., Elizabeth Hunter Dr.

Reports

The model described in this ODD is an agent-based model of hepatitis C virus (HCV) transmission among people who inject drugs (PWID). The model incorporates structural heterogeneity through a three-group interaction framework. The syringe-sharing population in the model is divided into core, inner, and outer circle groups representing individuals with high, moderate, and low levels of syringe-sharing interaction intensity, respectively. While the syringe-sharing rate and all epidemiological processes remain identical across agents, the number of daily interaction opportunities differs by agent grouping, capturing variation in structural position within the syringe-sharing network. Interactions are generated dynamically using proximity-based sampling at each …

An Odd Protocol For An Agent-Based Model Of Hepatitis C Virus Transmission With Inter-Group Structural And Behavioural Heterogeneous Syringe-Sharing Networks (M4), Seun Ale, Que Nguyen Dr., John D. Kelleher Prof., Elizabeth Hunter Dr.

Reports

The model described in this ODD is an agent-based model of hepatitis C virus (HCV) transmission among people who inject drugs (PWID). The model incorporates structural heterogeneity through a three-group interaction framework and behavioural heterogeneity through group-specific syringe-sharing rates. The syringe-sharing population in the model is divided into core, inner, and outer circle groups representing individuals with high, moderate, and low levels of syringe-sharing interaction intensity, respectively. In addition to differences in the number of daily interaction opportunities across groups, agents in each group are assigned distinct syringe-sharing probabilities, reflecting variation in risk-taking behaviour across structural groups within the syringe-sharing …

Interpretable Machine Learning For Personalized Profiling Of Mild Cognitive Impairment From Daily Activities, Budhitama Subagdja, Ah-Hwee Tan, Kenneth Kwok, Iris Rawtaer

Research Collection School Of Computing and Information Systems

Continuous monitoring of individual daily activities is essential to detect mild cognitive impairment (MCI) wherein timely intervention can still be applied to prevent more severe mental decline. Recent approaches in predicting MCI are mostly considering digital biomarkers across individuals but often neglecting specific indicators from a single person over a long period of time. Making this personalized, dynamic, and highly noisy prediction model with irregular distribution of missing information to be explainable and actionable for clinical use, remains a challenge. This paper presents a study on a personalized MCI prediction and profiling from an in-home and mobile cognitive health monitoring …

Evaluating Carbon Risk And Benefit Under Improved Forest Management Prescriptions In A California Mixed Conifer Stand, Jonathan A. Garcia

Master's Theses

Improved forest management (IFM) prioritizes carbon accumulation through practices such as extended rotations and retention harvesting. These management strategies, increase fuel loads and elevate the potential fire intensity, may offset the benefits of carbon sequestration. Additionally, IFMs use spatially complex silvicultural treatments that may introduce prediction errors into the simulation processes used for long-term projections, and carbon accounting.

We evaluated the extent to which calibration in the Forest Vegetation Simulator (FVS) reduces error in aboveground carbon stock predictions and tested whether modeling approaches and calibration influence the magnitude and direction of basal area increment (BAI) prediction error using generalized mixed-effects …

The Modern Sisyphus Blows Leaves: Attitudes And Impacts Regarding Leaf Litter Removal On A Suburban Campus, A. C. Nony, M. R. Mcclung, M. L. Reid

Journal of the Arkansas Academy of Science

Leaf litter habitat disruption in suburban spaces stems from the prevalence of “lawn culture,” that is, the accepted societal norm that land immediately surrounding humans or meant for human recreational use should be clean, tidy, controlled, and aesthetically pleasing, above other considerations. When leaf litter on residential and recreational lawns is cleared, it removes vital habitat for organisms of several taxa, especially species that rely on litter as a moisture and temperature-controlled substrate for overwintering. We administered an opinion survey to students, staff, and faculty at Hendrix College, a highly residential and suburban campus in Conway, Arkansas, to assess views …

Time-Dependent Amplification Of Growth Rates In A Plankton-Oxygen Model, Rapha Coutin

Master's Theses

Near-bottom hypoxia occurs when dissolved oxygen levels drop to a level that is harmful to marine biology, creating biological dead zones along the ocean floor. Recent years have seen a dramatic increase in the percentage of coastal, near-bottom, hypoxic water, with the average in 2021 nearly double that of the average from 2009 to 2018 and about twenty-eight times the average from 1950 to 1980. Recent literature has linked this increase in oceanic hypoxia to the increase in upwelling-favorable winds caused by climate change. Upwelling brings low-oxygen, nutrient-rich water up to the surface, leading to plankton blooms and mass consumption …

Optimizing And Fortifying Ai Software Through The Lens Of Artifact Synthesis, Jieke Shi

Dissertations and Theses Collection (Open Access)

Artificial Intelligence (AI) has transformed the software landscape, ushering in a new era of intelligent systems that increasingly shape our daily lives. This transformation is evident in various domains, including Software Engineering (SE), where Large Language Models (LLMs) support many development tools, and control systems, where self-driving cars and autonomous drones rely on deep learning models for real-time decision-making. These AI systems are collectively referred to as AI software, with the former categorized as AI4SE software (AI for Software Engineering) and the latter as AI4Control software (AI for Control). As AI software becomes central to modern computing infrastructure, its reliability …

Does Genetic Testing Influence Outcomes: Early Experience In Melanoma Case Series, Ryan Reyes, George Skenteris, Stella Coker Watson Self Ph.D., Ms, Christine Marie-Gilligan Schammel, Steven D. Trocha

Faculty Publications

Background:

The treatment and prognosis of melanoma have historically been based on histologic stage and Breslow depth; however, due to the increase in surveillance, melanomas are being identified at an earlier stage and lower Breslow depth. Advances in genetic testing, such as Decision Dx®, mean that melanoma diagnostic decisions and prognosis can now be directed by genetics. The purpose of this project was to assess the influence of a Decision Dx® high-grade (Class 2A/B) classification on the treatment of melanoma patients at a regional medical center, particularly those not deemed high risk by conventional classification methods, including Breslow depth and …

Navigation Beyond Wayfinding: Robots Collaborating With Visually Impaired Users For Environmental Interactions, Shaojun Cai, Nuwan Janaka, Ashwin Ram, Janidu Shehan, Yingjia Wan, Kotaro Hara, David Hsu

Research Collection School Of Computing and Information Systems

Robotic guidance systems have shown promise in supporting blind and visually impaired (BVI) individuals with wayfinding and obstacle avoidance. However, most existing systems assume a clear path and do not support a critical aspect of navigation—environmental interactions that require manipulating objects to enable movement. These interactions are challenging for a human–robot pair because they demand (i) precise localization and manipulation of interaction targets (e.g., pressing elevator buttons) and (ii) dynamic coordination between the user’s and robot’s movements (e.g., pulling out a chair to sit). We present a collaborative human–robot approach that combines our robotic guide dog’s precise sensing and localization …

Molecular Lung Imaging Following Exposure To Radiation Predicts Long-Term Survival In Rats, Anne V. Clough, Kathrina Mpala, Taheri Pardis, Laura Norwood Toro, Andreas M. Beyer, Tracy Gasperetti, Ming Zhao, Sarah Kerns, Heather A. Himburg, Said H. Audi

Mathematical and Statistical Science Faculty Research and Publications

Delayed effects of acute radiation exposure (DEARE), including radiation pneumonitis (lung-DEARE), develop weeks to months after radiation exposure. Pathway-targeted biomarkers that capture early oxidative stress and cell death could improve risk stratification and provide objective measures of mitigator efficacy. The objective was to test whether molecular lung imaging predicts long-term survival and mitigator response after irradiation. Rats received 13.5 Gy leg-out partial-body irradiation with a subset treated with the radiation-injury mitigator lisinopril. Rats underwent lung imaging at weeks 2 and 4 post-irradiation with ^99mTc-duramycin (cell death) and ^99mTc-HMPAO (oxidative stress). Plasma mitochondrial damage-associated molecular patterns (mtDAMPs) were also …

Identification Of Microplastics And Additives From The Lake Erie Watershed And The Cuyahoga River Via Maldi-Ms And Dart-Ms, Chrys Wesdemiotis, Calum Bochenek, Luciana Rivera Molina, Robert Brand

University Research

Micro(nano)plastics have received increased attention as environmental contaminants due to their harmful effects on ecosystems and human health. Conventional extraction methods for microplastic analysis are often lengthy and complicated, resulting in the loss of important chemical components such as additives. In this study, we provide a complementary approach, utilizing matrix-assisted laser desorption/ionization (MALDI) and direct analysis in real time (DART) mass spectrometry (MS) techniques, with minimal extraction and sample preparation to preserve both polymer and additive information. MALDI-MS was applied to aqueous samples collected along the shores of Lake Erie and the Cuyahoga River, successfully identifying the base polymer(s) in …

Draft Final Quarterly Operations And Maintenance Report: Butte Treatment Lagoon System – Fourth Quarter 2025, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Environmental Impact Of Preventative Fire-Retardant Applications On Soil Water Chemistry, Patrick C. Michelsen

Master's Theses

Between 1992 and 2012, 84% of all documented wildfires were started by humans. These fires often start around high-risk locations such as power lines and roadsides where fuel loads are near these potential human-caused ignition sources. Point source ignition sites that are surrounded by shrubs or grasslands are especially important because fires that occur within these biomes are responsible for far more destruction of property than forest fires. One strategy that is effective at mitigating the spread of fires from ignition sites that are within grassland or shrub biomes are preventative fire-retardant products (PFRPs). These PFRPs differ from traditional fire-retardant …

Deconstructing Digital Disinformation: Social Media Data Preparation And Analysis For Healthcare Research, Russell W. Cantrell, Matt Campbell

Shelby Hall Graduate Research Forum Posters

The spread of medical misinformation poses significant threats to public health, healthcare system stability, and the quality of patient care. Our research examines misinformation targeting the U.S. healthcare system. It uses a mixed-methods approach that includes social media data analysis, surveys of practicing nurses, and agent-based simulation. This poster focuses on the initial phase, which attempts to detect potential misinformation and disinformation by analyzing patterns in social media posts and user account behaviors. A detailed account of the data preparation process lays the groundwork for examining how disinformation operates online. This phase draws on the Pushshift repository, which offers historical …

Brain Computer Interfaces: Enhancing Low-Cost Eeg Performance Through Deep, Anwar Rassoul

Shelby Hall Graduate Research Forum Posters

The field of Brain Computer Interfacing (BCI) has traditionally been confined to clinical and research environments due to the high cost and complexity of medical-grade EEG systems. However, the emergence of low-cost hardware exemplified has catalyzed a shift toward accessible, portable BCI applications. While these devices lower the barrier to entry for developers and researchers, they often suffer from a lower signal-to-noise ratio (SNR). This increased noise makes it difficult to extract the clean neural signatures required for high-accuracy control, particularly when operating in non-shielded, real-world environments.

This research focuses on Steady-State Visually Evoked Potentials (SSVEP), a robust BCI paradigm …

Trophic Structure And Mercury Bioaccumulation In Walleye And Yellow Perch In The Upper And Lower Red Lake Basins, Marissa Pribyl

Biology Graduate Theses

Mercury is a persistent global contaminant that biomagnifies through aquatic food webs, with dietary and environmental factors serving as the primary drivers of accumulation in fishes. Trophic structure and methylmercury dynamics in Walleye (ogaa; Sander vitreus) and Yellow Perch (asaawens; Perca flavescens) were investigated in the Upper and Lower Red Lake basins in Red Lake, Minnesota, during 2024-2025. Diets of Walleye and Yellow Perch were assessed through stomach dissections, and tissue samples from both species were analyzed for total mercury concentrations. Additional analyses included shiners (gigoozens; Notropis spp., Hudsonius spp.) along with a variety of freshwater fish and …

Wayne E. Sabbe Arkansas Soil Fertility Studies 2025, Nathan A. Slaton

Arkansas Agricultural Experiment Station Research Series

Rapid technological changes in crop management and production require that the research efforts be presented in an expeditious manner. The contributions of soil fertility and fertilizers are major production factors in all Arkansas crops. The studies described within will allow producers to compare their practices with the university’s research efforts. Additionally, soil-test data and fertilizer sales are presented to allow comparisons among years, crops, and other areas within Arkansas.

Comparison Of Analytical Techniques From The Extraction Of Bioactive Compounds From Kratom, Curry, Ginger, And Turmeric, Riley Bruno

Honors Program: Senior Projects (Public)

Plants, herbs, and spices have been used as medicines for thousands of years. Early civilizations often attributed healing properties of plants to magical or divine forces. However, as chemistry and analytical technology advanced between the 16th and 18th centuries, scientists began to understand that bioactive compounds within the plants actually caused these effects. Today, natural products remain extremely important in drug discovery. As the number of newly developed synthetic drugs declines, there has been renewed interest in identifying biologically active molecules from plants. Bioactive molecules are a group of diverse chemical compounds that stimulate a response in living tissues. The …

Vaccination Games Of Boundedly Rational Parents Toward New Childhood Immunization, Wei Yin, Martial L. Ndeffo-Mbah, Tamer Oraby

School of Mathematical & Statistical Sciences Faculty Publications

Infectious diseases harm societies through disease-induced morbidity, mortality, loss of productivity, and inequality. Thus, controlling and preventing them is critical for public health and societal well-being. However, societies can hinder efforts to control the spread of diseases by failing to adhere to public health recommendations, such as through vaccine hesitancy. Various disease-transmission models have been utilized to help policymakers respond to (re)emerging outbreaks. The usefulness of such models in assessing the effectiveness of public health policies is significantly dependent on human behavior. This paper introduces a new model of parental behavior toward a new childhood immunization. The model incorporates societal …

Hybrid Deep Learning For Anti-Money Laundering: Unsupervised Detection Of Emerging Schemes Via Feature Fusion And Explainable Artificial Intelligence, Cosmas Ochieng Kungu, Kennedy Senagi, Evans Omondi

All Peer-Reviewed Publications

Traditional rule-based anti-money laundering (AML) transaction monitoring systems suffer from high false-positive rates and rigidity in detecting complex emerging risk. This limitation has prompted changes to the Financial Action Task Force (FATF) recommendation 16, mandating the use of advanced systems for detecting money laundering schemes in cross-border payments. This study developed a hybrid framework integrating VAE-learned behavioural latent factors, GNN-captured relational network signals, and rule-based heuristics for enhanced anomaly detection. The model was evaluated on 54,258 real-world cross-border transaction records from an East African commercial bank. The One-Class SVM, optimised via a rigorous grid search proved superior compared to Isolation …

Automated Machine Learning For High-Resolution Daily And Hourly Methane Emission Mapping For Rice Paddies Over South Korea: Integrating Modis, Era5-Land, And Soil Data, Jiah Jang, Seung Hee Kim, Menas Kafatos, Jaeil Cho, Gayoung Yoo, Sujong Jeong, Yangwon Lee

Institute for ECHO Articles and Research

Agriculture is a major global source of methane (CH4), and accurate emission estimates are essential for refining national greenhouse gas inventories and supporting climate-resilient policies. This study develops a high-resolution estimation framework for CH4 emissions from Korean rice paddies by integrating multi-source datasets, including Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis Version 5 (ERA5)-Land meteorological variables, and Harmonized World Soil Database (HWSD) soil properties. Using CH4 flux observations from four global rice ecosystems (Italy, Japan, South Korea, and USA), we constructed parallel daily and hourly machine learning models using an automated machine …

Diagnostic Trends And Service Enhancements In Deaf Mental Health: A Longitudinal Analysis, Kent Schafer, Steve Hamerdinger, Frank Wu, Rie Sakai Bizmark, Charlene Crump

JADARA

A longitudinal study investigated mental health diagnostic trends and patterns among Deaf and Hearing individuals using a comprehensive dataset from Alabama Department of Mental Health spanning 2004-2024 (Deaf=4,547; Hearing=2,056,188). Chi-square analysis revealed significant proportional differences in diagnoses between the groups. Deaf individuals showed significantly higher proportions of psychotic (12.93% vs. 8.33% Hearing), mood (20.76% vs. 18.84% Hearing), and personality (0.58% vs. 0.43% Hearing) disorders. These findings suggest that increased access to specialized and competent mental health services for individuals who are Deaf, particularly following the increase of staffing patterns for the Office of Deaf Services in 2015,contributed to more accurate …

N-Dqn: Neutrosophic Deep Q-Network For Uncertainty-Aware Forecasting And Decision Optimization, Rania Lutfi

Neutrosophic Systems with Applications

Uncertainty remains a critical challenge in dynamic spatiotemporal forecasting. This study proposes the Neutrosophic Deep Q-Network (N-DQN), a framework that integrates neutrosophic logic with deep reinforcement learning to enhance decision optimization under uncertainty. Features are modeled through truth, indeterminacy, and falsity membership functions, enabling robust handling of ambiguous data. The framework incorporates attention-guided preprocessing and horizon-aware optimization to adapt predictions across short- and long-term intervals. Experiments on benchmark traffic datasets (METR-LA and PEMS-BAY) demonstrate improved forecasting accuracy and reduced error rates compared with established baselines. The results highlight the scalability and resilience of N-DQN, positioning it as a promising approach …

An Electrochemiluminescence-Based Arsenic (Iii) Sensor Using Luminol On Screen-Printed Gold Electrodes, Harmesa Harmesa, Isnaini Rahmawati, Andrea Fiorani, Yasuaki Einaga, Eny Kusrini, A’An Johan Wahyudi, Asep Saefumillah, Tribidasari A Ivandini

Journal of Electrochemistry

Electrochemiluminescence (ECL) of luminol has been studied on a screen-printed gold electrode for a simple and sensitive detection of arsenic ions (As(III)). Cyclic voltammetry (CV) was applied as the proposed technique to study luminol’s electrochemical behavior and to evaluate the arsenic’s effect in the ECL system, while hydrogen peroxide (H₂O₂) served as a co-reactant to enhance luminol’s light emission under alkaline conditions. To achieve optimal electrode performance, key parameters including pH, scan rate, and the concentrations of H₂O₂ and luminol were carefully optimized. The presence of As(III) induced a quenching effect on the …

An Uncertainty-Aware Entropy-Oreste Framework For Big Data Platform Selection In Complex Multi-Sector Environments, Ahmed M. Ali, Ibrahim Alrashdi, Karam M. Sallam

Neutrosophic Systems with Applications

The increasing reliance on Big Data platforms across various industries has necessitated the development of systematic decision-support frameworks to guide their evaluation and selection. Given the diversity of available platforms, each offering different capabilities, scalability, and computational efficiency, choosing the optimal solution remains a complex challenge. This research proposes a novel analytical framework that integrates Spherical Fuzzy Sets (SFS) with the Entropy and ORESTE methods to address uncertainty and enhance the accuracy and robustness of Big Data platform evaluation. This hybrid integration, not previously applied to Big Data platform selection, enables objective criteria weighting through the Entropy method and comprehensive …

Client Feedback Effects On Enjoyment, Motivation And Self-Efficacy In A Project-Based Learning Course, Krista Stacey, David M. Bourrie

University Faculty and Staff Publications

This paper reports a mixed-methods evaluation of how feedback/project source (faculty-led versus client-led) shapes student outcomes in a two-course undergraduate game and simulation development sequence (N = 29 across two academic years). Quantitative measures (enjoyment, intrinsic motivation, self-efficacy) were collected with a six-point Likert survey and analyzed, but due to small sample sizes were not used to empirically evaluate the constructs. Instead, qualitative data comprised of de-identified focus-group transcripts and open-ended survey responses were analyzed with a keyword-assisted codebook and manual validation. Year 1 (faculty feedback) exhibited more consistent post-course gains, especially in self-efficacy, while Year 2 (client feedback) produced …