About one quarter of patients hospitalized with influenza-like illnesses are infected with more than one respiratory virus. Coinfections can lead to more serious outcomes for patients and are more complex to treat than infections with single viruses. Mathematical models can be used to help us understand the dynamics of viral coinfections and optimize treatment. Previous mathematical models of viral coinfections assume a cell-free transmission pathway with virus leaving one cell and traveling to nearby uninfected cells. However, viruses can also tunnel directly from one cell to another, which can affect how coinfecting viruses interact. This project analyzes a system of coupled ordinary differential equations that includes both cell-free and cell-to-cell transmission to better replicate actual viral spread. We measure coinfection duration for combinations of five common respiratory viruses as a function of the amount of cell-to-cell transmission. We find that coinfection duration depends nonlinearly on the cell-to-cell transmission rates, with differing patterns for different coinfecting viruses. This study highlights the importance of considering different transmission modes when modeling viral dynamics.

A key proponent to galaxy evolution is the multiphase gas that surrounds and permeates the galactic disk. Studying this complex gas network allows us to better understand how it regulates the metallicity, structure, and star formation within a galaxy. Within the disk there are small dust grains called polycyclic aromatic hydrocarbons (PAHs). These grains are effective tracers of cold molecular gas and H II regions, as well as production sites for molecular hydrogen which make PAHs excellent probes for studying star formation in galaxies. We use the James Webb Space Telescope (JWST) to study the infrared emission from PAHs throughout the disk of the giant low surface brightness galaxy Malin 1. Compared to high surface brightness galaxies, Malin 1 exhibits less structure and overall dust content. This is potentially hinting at a deficit of cold molecular gas, which is a necessary ingredient for star formation. By mapping out where the dust exists throughout the disk, we can trace areas of stellar formation and gain insight into the properties of this extreme galaxy.

Nanomaterial use continues to rise in biomedical applications, and the need for rapid, accurate, and non-invasive cytotoxicity measurements has become increasingly evident. Existing approaches for evaluating nanomaterial cytotoxicity are often indirect and typically depend on well-plate, salt-based fluorescence assays or complicated microscopy methods. To address these limitations, we introduce FluoAI, a two-stage neural network workflow that directly determines live/dead cells from the nanomaterial’s fluorescence, eliminating the need for additional labeling. The workflow uses two consecutive convolutional neural networks: Mask R-CNN first performs instance segmentation of individual cells from grayscale, single-channel fluorescence images, followed by a DenseNet-121 classifier that assigns alive or dead labels to each segmented cell, achieving performance values of up to 92.0%. In addition to viability classification, FluoAI also performs expert-level analyses of corrected total cell fluorescence (CTCF), mean fluorescence, and cell area, with results showing minimal to no significant differences compared with human measurements of Graphene Quantum Dot (GQD)- and fluorescein dye-treated model cells. Because the entire pipeline is automated, these quantitative fluorescence metrics are generated faster than manual analysis while maintaining comparable accuracy. Overall, this AI pipeline enables non-invasive cytotoxicity assessment and automated in vitro analysis using a conventional fluorescence microscopy setup. As its dataset continues to expand, FluoAI provides a strong foundation for reliable, high-throughput nano-cytotoxicity assays and automated data analysis, ultimately supporting the development of novel and safer nanomedicines.

Graphene quantum dots (GQDs) have gained interest within the bioimaging community due to their biocompatibility and their ability to exhibit near-infrared (NIR) fluorescence suitable for imaging and tracking within biological systems. Creating a simple and reproducible synthesis method for biocompatible NIR-fluorescent GQDs from a variety of precursors remains a critical task. Development of multiple NIR fluorescent GQD structures from a variety of precursors can facilitate their application in multiplex imaging, multianalyte sensing and combination therapy delivery. Herein, we demonstrate the synthesis of 11 distinct GQD structures capable of NIR fluorescence, achieved through a facile microwave-assisted bottom-up carbonization of 11 different materials: ascorbic acid, chitosan, citric acid - urea, dextran, glucose, glucosamine hydrochloride, hyaluronic acid, l-glutamic acid, polyethylene glycol (PEG), sodium cholate, or sodium citrate. All GQD structures exhibit substantial biocompatibility at concentrations up to 2 mg/mL. Internalization of GQDs is observed through their NIR fluorescence, allowing them to be successfully tracked in vitro in HEK-293 cells. This work provides a comprehensive study demonstrating how precursor selection enables versatile synthetic outcomes and NIR-emissive GQDs with distinct physical, chemical, and optical properties relevant to bioimaging. Expanding the precursor range democratizes the use of GQDs in biological applications by providing broader access to structures with tunable NIR emission and surface characteristics.

Oncolytic adenoviruses are promising cancer therapies because they can selectively infect and destroy tumor cells, however their replication in cancer cells is sometimes limited leading to incomplete tumor suppression. Recently, researchers have started to modify viruses to enhance their replication in cancer cells. In this study, we use a system of ordinary differential equations (ODEs) to model tumor growth and compare viral treatment dynamics of a modified oncolytic adenovirus ICVB-1042 and a wild-type adenovirus type 5 (Wt Ad5). The model was fit to experimental allowing us to estimate important model parameters for both viruses: infection rate, infected cell death rate, rate of cell protection by the immune response, rate of cell resistance loss, viral production rate, and viral clearance rate. We found differences in the viral production rates and the clearance rates between the two viruses, providing insight into how genetic modifications have altered viral dynamics. These findings highlight how viral properties determine the effectiveness of oncolytic virus therapy.

Psychological stress interacts with the immune system to increase inflammation, a physiological response involving the body’s defense against pathogens, which can promote biological and behavioral changes related to depression. However, research is needed to better understand factors that contribute to the inflammation–depression pathway. One such factor is repetitive thinking, defined as recurrent and intrusive thoughts about negative, positive, or neutral content (e.g., rumination, defined by negative thoughts focused on potential loss or failure, or worry, defined by thoughts of future danger). Higher rumination is related to greater inflammatory activity under acute stress conditions; however, the relationship between worry and inflammatory reactivity to stress is less clear. Further, there is limited literature demonstrating how reflection, defined as neutral or positive repetitive thinking, is associated with inflammatory activity under stress. Additionally, while past findings have focused on how trait-based personality characteristics related to repetitive thinking are associated with inflammatory reactivity under stress, the research on state-based repetitive thinking and inflammatory reactivity following an acute stressor is less clear. The purpose of the proposed study is to examine how state-based momentary changes in repetitive thinking under acute stress are related to inflammatory activity. To investigate the proposed study, 150 undergraduate participants will be randomly assigned to complete a laboratory-based stress induction or control task. Participants in the stress induction group will complete the Trier Social Stress Test, and the control task will approximate the physical demands of the stress induction without prompting social-evaluative threat. State-based repetitive thinking will be collected with self-report measures obtained before, during, and after the stress/control task. We will obtain whole blood samples from participants before the stress/control task and 55 minutes after the initiation of the task. Blood will be processed for serum, which will then be assayed for the inflammatory proteins - interleukin (IL)-6, IL-8, IL-10, C-reactive protein, and tumor necrosis factor-α. Multiple regression analyses will be conducted to test our hypotheses that (1) higher state-based repetitive negative thinking (i.e., rumination and worry) will increase following exposure to an acute stressor in comparison to those in the control task (2) higher state-based repetitive negative thinking will predict greater inflammatory reactivity under stress, and (3) higher state-based reflection will predict lower inflammatory reactivity under stress. Findings from this study may prompt future research to examine how other types of stressors impact the relationship between repetitive thought and inflammation.

Title: Evaluation of A Learning Intervention to Support Dementia Care over Two Months
Authors: Emily Q. Anderson, Morgan Shumaker, & Uma Tauber
The Micheal and Sally McCracken Annual Student Research Symposium: April 17, 2026

Abstract:
Research indicates that providing care for individuals with Alzheimer’s Disease and Related Dementia (ADRD) can be highly demanding. Caregivers, who are often relatives or friends, face elevated stress levels and may lack adequate knowledge or resources to effectively support their loved ones (Jorge et al., 2021). This gap in knowledge often leaves caregivers feeling incompetent and may also lead to a lack of confidence in their caregiving skills. We have previously found that our digital health education intervention improves caregivers’ knowledge of behavioral and psychological symptoms of dementia (BPSD). In this research, we extend on our prior work by establishing the impact of our intervention on caregivers’ long-term retention – 2 weeks and 2 months post-intervention. Caregivers were taught 12 categories of BPSD (e.g., anxiety, agitation) by reading information and then either rereading or taking practice tests with detailed, corrective feedback, which has been shown to enhance learning and retention (Ariel et al., 2023; Carpenter et al., 2022; Dunlosky et al., 2013). The study consisted of 3 sessions. Caregivers first completed a screening process to determine their eligibility to participate. Session 1 consisted of teaching caregivers about the BPSD via reading and then rereading or taking tests with feedback. Session 2 consisted of a second round of the learning intervention, as well as taking survey assessments and knowledge tests. Finally, Session 3 consisted of completing final tasks and knowledge assessments. Our goal was to have caregivers learn what physicians would want them to know for how to care for their loved one living with dementia. This study provides caregivers with the knowledge and in turn, confidence to manage BPSD. Data is being collected concurrently, with nearly half of the target participants enrolled so far; thus,, the current presentation reports preliminary observations from this initial cohort.

I-sharing is the belief in an identical shared subjective experience between two individuals. Shared subjective experiences play an important role in interpersonal contact and it can lead to increased liking between subjects. Interestingly, the liking that I-sharing builds can override in-group bias, leading individuals to prefer I-sharing dissimilar partners over non I-sharing similar partners. While increased liking from I-sharing has been explored, intergroup perceptions, such as sexist beliefs, after I-sharing have not been explored. Unlike I-sharing, quality and quantity of time spent with women have both been linked to a reduction in sexist beliefs in men, which may suggest that I-sharing could induce a similar effect (Vasquez, 2020). Since sexist beliefs are connected to violent behavior and acceptance of violent behavior towards women in men, any reduction in sexist beliefs is significant (Gutierrez, 2023). This study seeks to explore the relationship between men I-sharing with women and sexist beliefs.

Humans produce complex and learned behaviors like speech, playing musical instruments, and sports through exceptional motor abilities. These learned actions need specific motor planning and preparation. Researchers use songbirds in part because they produce a stereotyped motor sequence whenever they engage in singing behavior. Further, Zebra Finches learn their song through vocal production learning, similar to human speech acquisition; they mimic their adult male tutor's song and reproduce a similar version in adulthood. This motor learning process leads to the generation and execution of a highly skilled and stereotyped motor program production. Before the song, Zebra finches sing a sequence of introductory notes that are short-duration, non-stereotyped sounds. Previous work has speculated that these introductory notes are a form of motor preparation, but an experimental test of this hypothesis has not been conducted. This study casually examines the role of introductory notes as a motor preparation phase to help transition to executing the main song motor sequence. To distinguish motor preparation from song execution, we reasoned that presenting an external stimulus would delay preparation. We used air pressure recordings to identify introductory notes and triggered white-noise playback during their performance in twelve birds. We found that any white-noise playback, regardless of amplitude (e.g., high and low) or duration (e.g., short and long), led to abnormal pauses or breath holding before song execution, which is considered an interruption of introductory notes. Whereas low amplitude with short and long durations has ended with interruption in introductory notes, it does not cause termination or interruption of the syllables of the motif when white-noise playback is triggered in the motif part of the song, which is considered the continuation of the motif’s syllables. Moreover, high amplitude with short and long duration causes more termination or interruption of the motif’s syllables when the white-noise playback is triggered in the motif part of the song. However, another important subject in the most stereotyped part of the song (motif) regarding its termination or continuation by white-noise playback is the specific location of the triggered playback within the motif, that is which syllable of which motif of the song is targeted by the playback. Our findings suggest that, in addition to introductory notes, which are flexible and modifiable by external stimuli and are consistent with the hypothesis that they function as a preparatory motor gesture for the upcoming stereotyped song, high-amplitude white-noise stimuli, when triggered on the last syllables of the second or final motif of the bout, can cause termination in the execution of the song pattern in zebra finches.
Understanding motor planning can provide insight into neurological, behavioral, speech, and motor disorders that are characterized by deficits in neuromuscular preparation.

Social media has become deeply ingrained in young people’s daily lives. Coincidentally, mental health problems have also risen sharply over the past decade. This project examines the bidirectional relationship between social media use and mental health outcomes among TCU college students, who face unique challenges as they transition to a new phase of adult life. The causal relationships between undergraduate social media use and mental health remain unclear, with mixed findings in the literature. This is largely due to the overreliance on cross-sectional data and crude measures of social media use that focus solely on total screen time, which fail to capture fluctuations across the day and week, given the ever-changing contexts and diverse nature of social media use. Moreover, when studies consider mental health as a trait or categorical diagnosis, they fail to capture the fluctuating and co-occurring nature of symptoms. To address these limitations, we propose an Ecological Momentary Assessment (EMA) study leveraging Dr. Shi’s previously developed MediaHealth app for data collection.

Over 7 days, participants will receive push notifications through the MediaHealth app, prompting them to upload daily phone-use screenshots (from the Screen Time settings on their devices) and complete a questionnaire each day to report on their mental health states and social media use. These real-time assessments will capture daily stability and variations in social media use and mental health symptoms, helping to clarify potential causal pathways and inform strategies to support healthier digital behaviors among TCU students. This study aims to find how social media use among college students affects mental health, specifically looking at daily stress. It is expected that students who use social media more often will show an increase in their daily stress than those who use it less often. It can also be expected that stress can lead to increased use of social media among college students.