Influenza virus causes periodic pandemics and thousands of deaths annually, but many of the details of the viral replication cycle are still poorly understood. This study develops a mathematical model of the dynamic transitions of a virus from the extracellular space through the initial intracellular replication processes. These stages include: binding, endocytosis, HA Acidification, Fusion, and Uncoating. Experimental data from the viral entry phases were fit to a system of differential equations, which represent the biological processes. The model parameters were estimated using optimization techniques that minimize the sum of squared residuals, thereby aligning model predictions with observations. An identifiability analysis was performed to see which parameters can be estimated with the given model and available data. We find that the model fits the experimental data well with identifiable parameters, allowing us to characterize the different stages of viral entry. The model can be used to compare different viral strains or treatment options, in addition to helping explain the kinetics of viral entry.

Syncytia are a type of multinucleated cell that can be formed by virus infection. Quantifying their growth is of particular interest for understanding virus infection within the body. One useful tool we have to understand the growth of these cells is ordinary differential equation (ODE) models. Current models neglect the regeneration of cells that form the syncytia. To account for regeneration, we will discuss a proposed modification of a basic model for cell-cell fusion, which will consider the addition of a logistic growth term. In addition, we will also consider a non-negligible death rate of syncytia. By making these modifications, we can better replicate syncytia dynamics. We present mathematical analysis of this model, which gives insight into the factors that generate long-term syncytia formation as well as the overall biological characteristics of such an infection.

When stars form from collapsing gas clouds, about half of them form in pairs (binary systems). However, identifying which stars in the Milky Way and other nearby galaxies are binaries is difficult; even nearby two-star systems look like a single point of light. Due to the distances of even the most nearby galaxies, a method to reliably identify these binary systems is needed. We will apply the Binary Information from Open Clusters Using SEDs (BINOCS) code to aid in separating the light emitted from each star. Open clusters have known ages, distances, and metallicities, so we can apply these parameters to the stars in the clusters to determine their masses and fit to their spectral energy distributions (SEDs). The BINOCS method has successfully been applied to some open clusters; we want to identify which globular clusters and nearby dwarf galaxies the method can be applied to. In order to reach these more distant objects, we need to use deep space-based data. The data we explore in this work is from stars in ~200 cluster or galaxy targets observed by the Hubble Space Telescope (HST), James Webb Space Telescope (JWST), and Spitzer Space Telescope. The fraction of binaries is a key factor in measuring the amount of dark matter in dwarf galaxies. One example system we plan to analyze is NGC 104, a globular cluster ~15 thousand light years away from Earth, with an age of ~13 billion years.

Graphene quantum dots (GQDs) are spherical nanoparticles comprised of stacked layers of graphene known in part for their biocompatibility and fluorescence, which leads to many potential uses in medicine as a diagnostic tool. Solutions of GQDs are known to fluoresce less when the GQDs are allowed to clump together, leading to processes such as sonication being used to break apart these clumps in research environments. Similarly, the addition of surfactants to a solution of GQDs has also been found to modify fluorescence response of the solution. This research explores the effect of introducing four different human blood proteins on the fluorescence response of reduced graphene quantum dots (rGQDs). Fibrinogen, transferrin, gamma globulin, and albumin were added to samples of rGQDs in increments around their respective concentrations in human blood. Generally, we found that the addition of any of the blood proteins lowered fluorescence response in the visible spectrum. In the near-infrared spectrum, smaller concentrations of blood proteins generally increased fluorescence response, while larger concentrations reduced fluorescence response below the control. This has implications for deep-tissue imaging relying on the near-infrared fluorescence of intravenous GQDs.

Smaller galaxies and old star clusters that have been devoured by our Galaxy, provide unique probes into the assembly history of the Milky Way. Previous studies have characterized these Galactic sub-structures using their kinematics and chemistry, but to fully understand these stellar populations, a record of when individual stars formed is required. To reconstruct this star formation history, we utilize the relationship between the ratio of the amount of carbon and nitrogen on the surface of a star and the age of that star. This [C/N]-Age relationship has been calibrated using both young and old star clusters by Spoo et al. (2022; 2025) allowing its use at a wide range of metallicities (-1.2 ≤ [Fe/H] ≤ +0.3 dex). We apply this “chemical clock” to the accreted sub-structures in order to measure the star formation history of each, so that we can better understand how the Milky Way formed and evolved using its accreted stellar populations.

Pandemics require quick decisions about how to distribute a limited number of vaccines, even when the disease is not fully understood and vaccine delivery is limited. We create a disease model that divides the population into groups based on how likely they are to be hospitalized and how likely they are to get infected, so we can test different group-based vaccination strategies. We compare vaccinating only one group, simple step-by-step priority policies that vaccinate groups for set time periods, and a sensitivity analysis to see which model factors most affect outcomes.

We find that vaccinating people who are both high-risk for hospitalization and highly likely to become infected leads to the biggest reductions in total hospitalized time and deaths, while vaccinating lower-risk groups gives little improvement in severe outcomes. A short step-by-step policy that quickly prioritizes high-risk groups can reduce infections and deaths within about 20 days. The sensitivity analysis shows that the death rate and the rate at which infected people move into hospitalization have the strongest influence on severe outcomes, showing that hospital and clinical processes matter a lot in addition to vaccination. Overall, these results support clear, practical, and easy-to-apply prioritization rules for reducing severe disease when vaccine supply is limited.

Graphene quantum dots (GQDs) have emerged in nanobiotechnology as useful tools for numerous biomedical applications, including non-invasive cellular imaging, drug delivery, and gene targeting. Several studies have shown the successful uptake of GQDs in healthy and cancerous cells. While some in vitro and in vivo studies highlight mechanisms underlying GQD internalization in cells, there is a gap in our understanding of GQD interactions with complex biological media, such as blood serum. Proteins in biofluid environments adsorb to the surface of nanoparticles such as carbon nanotubes, forming the “protein corona.” Graphene quantum dots have an abundance of charged surface functional groups, which are likely to interact with complementary charged regions in proteins. Herein, we investigate the interactions of individual proteins with negatively charged sodium citrate and reduced graphene oxide-derived GQDs, as well as positively charged nitrogen-doped GQDs. This study will advance our understanding of protein-GQD interactions in physiological environments, ultimately guiding the optimization of GQDs for biomedical applications.

Influenza can cause severe respiratory illness and spreads quickly, making prevention especially important for people at higher risk of complications. Because vaccines are not always fully protective, effective antivirals can provide an added layer of defense before infection begins. CD388 is a new antiviral being tested as a preventive treatment for influenza. In this project, we used a mathematical model to better understand how the drug changes the course of infection inside the body. Viral load data from a human challenge study were fit to a target-cell model with an eclipse phase, allowing us to estimate key infection parameters. Compared to placebo, CD388 lowered peak viral load and reduced overall viral burden by about 22%, largely by suppressing viral production. Bootstrap analysis was used to assess uncertainty in the parameter estimates. These results help explain how CD388 limits viral spread and supports its potential as a prophylactic therapy.

Favipiravir, an antiviral that inhibits viral RNA-dependent RNA polymerase, has demonstrated promise as a therapeutic for RNA viral infections such as SARS-CoV-2. Mathematical modeling of viral kinetics provides a tool for analyzing the progression of viral infections and the action of antiviral drugs. In the present investigation, the viral kinetics of SARS-CoV-2 infection in cynomolgus macaques treated with the antiviral drug favipiravir were analyzed using a target cell-limited mathematical model of viral infection. Parameters of the model representing the dynamics of viral infection and replication were estimated by fitting the model to the viral kinetics data. Statistical resampling techniques were applied to analyze the uncertainty of the parameter estimates and to compare viral kinetics between the different treatment regimens. The results demonstrate that antiviral treatment induces measurable effects on viral kinetic parameters, reflecting dose-response effects on viral infection dynamics.

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.

Research indicates that parental factors influence children’s neurodevelopmental outcomes. For example, parental diagnoses of related conditions or parental trauma history may increase the likelihood that a child develops attention-deficit/hyperactivity disorder (ADHD). Additionally, adverse childhood experiences (ACEs) are associated with greater probability of an ADHD diagnosis and may intensify the impact of parental ADHD or trauma history on ADHD risk. This project examines whether parental ADHD diagnosis predicts higher ADHD risk in children, whether parental trauma history is similarly associated with elevated ADHD symptoms or diagnoses, and whether a child’s ACE exposure amplifies the combined effects of parental ADHD and trauma history. Participants (expected N = 100) will be recruited through Texas Christian University’s SONA system and will complete self-report questionnaires assessing parental ADHD history, parental trauma exposure, personal ACE exposure, and the participant’s current ADHD symptoms and diagnoses. We hypothesize that both parental ADHD and parental trauma histories will correspond with increased ADHD symptoms and diagnosis, and that participant exposure to ACEs will strengthen these relationships. Clarifying how vulnerabilities interact with environmental adversity may improve understanding of pathways shaping ADHD risk. The findings carry clinical relevance as identifying individuals at elevated risk can guide prevention, early identification, and intervention strategies that support resilience and adaptive functioning among people with ADHD.

With media consumption constantly evolving, it is crucial to understand how information is perceived based on the medium used to deliver it. The current study surveyed Dallas-Fort Worth community members (N = 40) to determine how textual versus audiovisual news may influence an audience’s perception of crime dangerousness and community safety concerns within their area. Using a randomized, between-subjects design, participants were assigned to one of two conditions - either an audiovisual broadcast about a recent local news clip or a textual transcript of the same news story. Subsequently, participants completed standardized measures assessing perceptions of crime severity, community safety, social media use frequency, and demographic characteristics. We hypothesize that the audiovisual news media format will predict stronger emotions and heightened judgments of crime severity and community safety. Such findings carry important implications for journalism, politics, and criminal justice, as the format through which news is delivered may shape public perception of crime beyond what the facts alone convey. Both media producers and consumers should be aware of these effects to avoid forming distorted or amplified reactions to crime reports, and ensure more accurate evaluations of potential danger. 

Vulnerable populations face elevated risks for diminished well-being, prompting growing interest in interventions as a form of social support. Evaluating the effectiveness of such interventions is critical for understanding their impact on well-being outcomes. The current study utilizes data collected as part of ACTION, a NIDA-funded R01 study examining the feasibility of two interventions: a mobile health unit and a patient navigator. Specifically, the current study investigates changes in well-being scores from baseline to a 6-month follow-up. A moderated regression analysis was conducted to assess whether the number of interventionist visits moderated changes in well-being scores over this period. Results revealed significant main effects for three well-being domains (anxiety, depression, and sociality) while the interaction effect was nonsignificant. These findings indicate that well-being scores changed significantly over the 6-month period. However, the number of interventionist visits did not significantly moderate this change. Future research should explore additional factors that may moderate improvements in well-being among vulnerable populations.

Widespread use of hormonal birth control (HBC) calls for a deeper understanding of its systemic side effects. Emerging research suggests HBC may deplete essential micronutrients (Palmery et al., 2013), including B vitamins, magnesium, and zinc, which serve as critical cofactors for neurotransmitter synthesis and energy regulation (Muscaritoli, 2021). These deficiencies are hypothesized to interfere with physiological systems, leading to mood instability, chronic fatigue, and functional somatic symptoms that can significantly impair daily functioning (Kennedy, 2016). This study addresses a virtual literature gap regarding the efficacy of targeted, non-pharmacological interventions for these issues. Utilizing a longitudinal, double-blind design, we investigated whether taking a daily supplement for 28 days, designed to combat nutritional deficiencies in HBC users, increases happiness levels, decreases mood instability, increases energy, and improves digestive health. By identifying safe interventions to mitigate these symptoms, this research contributes to personalized health strategies and improved well-being for women. Data are forthcoming.

As trauma-informed care continues to flourish, professionals across diverse roles interact with children in different ways that may shape how intervention principles are applied. Trust-Based Relational Intervention is an attachment-based, trauma-informed approach that equips professionals working with children and youth with evidence-based relational strategies through Practitioner Training. Although TBRI is implemented across a range of child-serving contexts, limited research has examined the specific roles individuals report playing in their work with youth and how these roles have evolved over time among TBRI practitioners. This exploratory study examines the expansion of the roles TBRI Practitioners play in working with youths. Data from 2,185 participants from across five years of training sessions (2021-2025) was analyzed to assess trends in role representation across 6 professional categories: (1) I work in leadership, (2) I work directly with youth, (3) I supervise staff, (4) I train clients or staff, (5) I am support staff or direct care or leadership, (6) Other (specific). This study contributes to the understanding of trends in professional roles within TBRI training and elucidates the importance in understanding relational contexts when applying and implementing this training.

The zebra finch (Taeniopygia guttata) is a commonly studied model organism for understanding the neural basis of motor learning due to its ability to learn its characteristic song. Researchers investigating the neuronal mechanisms that produce zebra finch song are often interested in measuring the internal pressure of the zebra finch's air sac, which physiologically produces the observed song. Unfortunately, this is an often difficult and intrusive signal to measure. Previous work has used biophysical modeling to demonstrate that air sac pressure can be reconstructed from a bird's song using a dynamical system with parameters fine tuned to a specific bird. In this project, we aim to develop a machine learning model that can infer internal air sac pressure from acoustic data across multiple birds. Specifically, we train a variational recurrent neural network to translate observed acoustic data into a low-dimensional set of latent variables that can be decoded into air sac pressure.