Throughout history, it has been perceived that significant advancements in STEM have been a result of primarily white males’ accomplishments. To help correct this misconception on our campus, TCU Chemistry Club has initiated “TCU Jeopardy Game Night”. This is an initiative where students, staff, and organizations in STEM throughout the year get exposed to and educated on the accomplishments in the history of diverse groups underrepresented in STEM with an emphasis in chemistry through an engaging and interactive mechanism. This spring term, our organization will host a Jeopardy-style game night with trivia questions over the material presented and prizes purchased with the ACS DEIR Grant. This material serves as an incentive toward the ultimate goal of educating our campus population regarding diversity, equity, inclusion, and respect for scientists who have not been acknowledged for their exceptional work.

Project Overview:
2020 we partner with Once Upon a Room. We partnered with Cook Children's Hospital to decorate rooms for children who will have an extended stay. Due to COVID-19, we could not continue with Once Upon a Room. In the 2021-2022 we created Bags of Joy and continued to partner with Cook Childrens Hospital. We provided holiday-themed goodie bags as well as welcome hygiene bags.

Background of the Community:
The community in which we are working with those under the age of 18 with poor health.
Our community partner is Anne Stankus and Megan Hodges, Child life specialist at Cook Children

Need Statement:
Our community partners have communicated to us that there is a need for hygiene and essential items for the caregivers of a patient to receive when they are admitted to the hospital, so that they have to opportunity to stay with their child durning this time of need.
Another need that has been expressed to us has been for the hosting of events, like game nights.

Project description:
We had hoped to continue to give hygiene bags and make a switch from goodie bags to hosting a game night.
This year we were to supply 36 welcome bags for Cook, host a game night, and donate 400 easter eggs for their annual easter egg hunt.

Deliverables:
As this was our first year hosting a game night at Cook children, we struggled in the begging to find games and activities that would fit into the guidelines but in the end, we were able to make it a successful event and now we have ideas on ways to improve for next year.
We continue to have good feedback from our Cook Children's coordinator.
As two of our members are graduating we will still have one member who is an active student at TCU. Molly, our non-graduating member, will take over our EPIC grant.

We show that a discrete two-dimensional logistic predator-prey dynamical system with two parameters undergoes a Neimark-Sacker bifurcation under certain conditions. Our evidence includes numerical computations of orbits and bifurcation diagrams.

A Latin square is a nxn square that contains n different symbols, often numbers, and are arranged such that each symbol appears exactly once in each row and column. In this project, we look at the probability of a random arrangement of symbols being a Latin square. I start with n number of n symbols, for example a 3x3 square will contain the numbers 1,1,1,2,2,2,3,3,3 in a random assortment. Using counting methods and statistical estimation through Python, we discover the proportion of total squares that are Latin squares.

Geodesics are significant objects and a major topic in differential geometry. They are "straight" curves on surfaces that can locally represent the shortest path between two points. In this research, we employ the genetic algorithm, an optimization method in classical Artificial Intelligence, to construct a geodesic net on closed surfaces. A geodesic net is a network that connects multiple points with the shortest curves while ensuring that each point is ``balanced'' and stretched equally by its neighbors through those curves.

PREVALENCE AND ASSOCIATED FACTORS OF FOOD INSECURITY AMONG COLLEGE STUDENTS

Maddie Jacobs; Gina Hill, PhD, RD, LD; Kelly Fisher, DCN, RD, LD; Kristi Jarman, PhD

Background - The USDA defines food insecurity (FI) as when individuals lack the resources to obtain food in socially acceptable ways. According to the USDA, 10.2% of the U.S. population was food insecure in 2021. According to current literature, university campuses have an average of 36% FI. There are limited studies regarding FI at private universities, likely because FI is assumed to be low.

Objective – This study aimed to identify the rate and distribution of FI at a private university in North Texas and to analyze the demographic, socio-economic, and other factors associated with FI among college students.

Design – In this cross-sectional study, participants completed a one-time online survey.

Methods – The survey included sociodemographic questions and the validated USDA Adult Food Security Survey Module to measure FI status among current university students >18 years of age. Ordinal logistic regression, based on the Proportional Odds model, was conducted to determine the association between FI and sociodemographic variables.

Results – The majority of participants were white (82%, n=288), non-Hispanic (83%, n=293), and women (77%, n=271) with a mean age of 22.5±6.6. Of the 353 participants in the study, 22.4% (n=79) were classified as food insecure and 9.6% (n=34) were classified as having very low food security with evidence of reduced intake and disrupted eating patterns. Participants who were underclassmen (p=0.029), receiving more financial aid (p=0.016), international (p=0.081), Hispanic/Latinx (p=0.478), and older (p=0.283) were more likely to have greater FI. Among the food insecure participants, 30.4% (n=24) were aware of resources to obtain food on or near campus.

Conclusions - More research is needed regarding FI at private universities. However, this study provides sufficient data to take action to address FI by means of advocacy, dissemination of resource information, and the addition of new resources, such as an on-campus food pantry.

Despite the significant prevalence of food intolerances in children and adolescents (2 to 18-year olds), food intolerance mechanisms and testing is severely misunderstood and under researched. A food intolerance is a non-immunological response that occurs after consuming a specific food particle causing gastrointestinal (GI) issues such as bloating, nausea, diarrhea, and abdominal pain. The lack of understanding of food intolerances is causing too many children to unnecessarily follow unsupervised elimination diets which increases the risk of developing nutrient deficiencies. The objectives of this study were to demonstrate the serious impact to the quality of life (QOL) that food intolerances have towards children and adolescents by analyzing available literature and utilizing a case study participant. Findings suggested that there must be more research done to understand food intolerance to improve the QOL in children and adolescents.

Background: Eating disorders (EDs) can lead to decreased quality of life (QOL), medical complications, and death, with the second highest mortality rate of all mental illnesses. ED treatment can include psychologists, registered dietitians (RD), and/or physicians. Insufficient research exists regarding RDs’ effects on ED treatment.
Objective: Describe the impact of RDs on ED treatment and QOL.
Design: A cross-sectional sample of participants with a history of ED completed a one-time, online survey.
Methods: Healthcare providers were emailed with recruitment materials for clients >18 years. Survey included demographic, validated Eating Disorder Quality of Life scale (EDQOL), and RD effects and helpfulness questions. In SPSS, paired t-test was used to assess QOL post-treatment for RD vs non-RD groups, plus effect size. Independent-samples t-tests were used to compare post-treatment QOL scores and mean differences in pre- and post-treatment QOL scores for RD vs non-RD groups. Using conventional qualitative analysis, narrative responses to the question “How has working with a registered dietitian (RD) affected your eating disorder recovery?” were coded by two researchers separately, then consensus was reached for final themes.
Results: Participants (n=70) were 87.1% (n=61) white, 90% (n=63) female, and RD treatment group (n=60). Most participants had positive perceptions of RD impact on ED recovery and described RDs as helpful, supportive educators. Over 62% of participants (n=35) reported that the RD helped reduce disordered eating behaviors a great deal/a lot. Statistically significant improvement in QOL after treatment existed for both RD treatment (-22.68, n=56, p < 0.001) and non-RD treatment groups (-14.9, n=10, p=0.008), without a significant difference between groups (p=0.193).
Conclusions: Results suggest RDs contribute to certain aspects of recovery. Participants reported that RDs helped decrease ED behaviors, shame, and meal skipping. Future research needs include the effects of RDs on ED treatment in larger, diverse samples.

Although there is an effective vaccine for SARS-CoV-2, or COVID-19, the virus is still spreading and affecting millions of people worldwide. SARS-CoV-2, along with many other viruses, is able to form large, multi-nucleated cells, known as syncytia. Syncytia formation, along with syncytia death, may affect the SARS-CoV-2 course of infection. We have been able to compute the death rate of syncytia using data from a study by Vanhulle et al. (2023) that used measurements of electrical impedence to study syncytia formation in cell-cell fusion assays. The death rate of syncytia was found using mathematical modeling. This knowledge can help further our understanding of syncytia and viral disease propagation.

Zinc Oxide (ZnO) nanoparticles are attractive candidates for application as antibacterial agents due to high biocompatibility with effectiveness against antibiotic-resistant strains of both Gram-positive and Gram-negative bacteria. Despite this potential, applications are limited by fundamental gaps in understanding of the underlying antibacterial pathways. ZnO nanoparticles are currently more widely used in antibacterial research compared to ZnO microparticles due to the potential for internalization into bacterial cells. Microparticles are nevertheless of interest as a research platform as the increased scale allows both the nonpolar and polar facets of the ZnO crystals to be distinguished. This in turn provides a useful platform to experiment on and study surface interactions with bacteria. In addition, because of their larger size, ZnO microparticles would not internalize inside typical bacteria, allowing for more targeted investigation of other, potentially more potent, antibacterial mechanisms.

Preliminary studies indicate that hydrothermally grown ZnO microparticles exhibit comparable antibacterial activity to commercial ZnO nanoparticles further adding to their utility. The goal of this research is to validate the nature of these behaviors by investigating differences in surface cleanliness between “home-grown” microparticles which were synthesized in the lab through a bottom-up hydrothermal growth method and commercial nanoparticles. Such differences may influence cytotoxicity, skewing the results of antibacterial studies. To do so, both Scanning Electron Microscopy (SEM) and Fourier Transform Infrared (FTIR) spectroscopy were used to probe the quality and cleanliness of the ZnO crystalline free surface of the microparticles and nanoparticles.

In this work we detected similarities in the vibrational modes at the surface stemming from ZnO growth precursors. These are seen to be similar across all samples investigated, however, a weak O-H bending is found in the home-grown microparticles. We demonstrate that these results justifies our low-cost hydrothermally lab-grown specimen as a suitable platform for future surface-specific antibacterial studies.

Syncytia formation is the fusion of cells by a virus to create a multinucleated cell (syncytium) that shields the virus from outer factors in the extracellular space, such as antibodies. However, this process is much more energy intensive for a virus than tunneling between cells, which also shelters the virus. Why would a virus fuse cells together rather than save energy and tunnel? In order to determine what the benefits of syncytia formation are for viruses, a mathematical model including syncytia formation and antibodies was developed to simulate viral dynamics. Characteristics like viral duration, viral titer peak, and time of peak were measured while changing parameters such as fusion rate, which allowed comparison of infections with and without syncytia formation. Mathematically modeling and analyzing these comparisons and changes helps us understand whether syncytia formation helps protect viruses from the effect of antibodies.

To track drug delivery within the body, the vehicle must be biocompatible, soluble, and transparent in the human body. Being transparent in the human body means the vehicle exhibits fluorescence in the near-infrared (NIR) III biological transparency window (1500 – 1800 nm). These traits will respectively not oppose health defects in the subjects, will be stable within the blood and cells of the body, and be able to be found within the body through the means of infrared detectors. This is where graphene quantum dots (GQDs) come into the picture. GQDs prepared by a one-step hydrothermal method from glucosamine and ascorbic acid precursors are biocompatible and soluble in water. On their own, they do not demonstrate fluorescence in the NIR-III. To add this capability, we dope GQDs with erbium ions (Er-GQDs) as they demonstrate a fluorescence peak at 1550nm followed by excitation at 980nm laser. Fluorescence light coming from erbium ions at 1550 nm covers the NIR-III biological window, which is the last specification needed to have an eligible vehicle. In our work, we synthesized Er-GQDs at 200℃ for 8 h and 17 h in deuterium oxide. The fluorescence of erbium ions is known to be quenched by OH functional groups. The average size of Er-GQDs is growing from 3 to 5 nm after 8 h and 17 h treatment times, respectively, and exhibit fluorescence with 1550 nm emission peak in deuterium oxide. All aforementioned results make Er-GQDs a potential imaging agent for bioimaging.

A novel approach is presented that increases sensitivity and specificity for detecting minimal traces of DNA in liquid and on solid samples. Förster Resonance Energy Transfer (FRET) from YOYO to Ethidium Bromide (EtBr) substantially increases signal from DNA bound EtBr highly enhancing sensitivity and specificity for DNA detection. The long fluorescence lifetime of the EtBr acceptor, when bound to DNA, allows for multi-pulse pumping with time gated (MPPTG) detection, which highly increases the detectable signal of DNA bound EtBr. A straightforward spectra/image subtraction eliminates sample back-ground and allows for a huge increase in the overall detection sensitivity. Using a combination of FRET and MPPTG detection an amount as small as 10 pg of DNA in a microliter sample can be detected without any additional sample purification/manipulation or use of amplification technologies. This amount of DNA is comparable to the DNA content of a single human cell. Such a detection method based on simple optics opens the potential for robust, highly sensitive DNA detection/imaging in the field, quick evaluation/sorting (i.e., triaging) of collected DNA samples, and can support various diagnostic assays.

COVID-19 now has antiviral treatments to help prevent hospitalization. Paxlovid is the most prevalent and effective of these medications. Paxlovid consists of two medications taken twice daily for five days, however, there have been anecdotal reports of rebound infection after a course of Paxlovid. This project aims to use mathematical models to investigate the infection conditions that result in rebound cases. Stochastic modeling is used to simulate the time course of infections with different doses and durations of Paxlovid to determine when rebound will occur. These findings could help physicians develop more consistent treatment regimens for Paxlovid.

Graphene quantum dots (GQDs) are a frontier of research in the interdisciplinary world of biology and medicine. They have been hallmarked for their remarkable applications, from cellular imaging to drug delivery. Due to their unique physicochemical and optical properties, there is a strong desire to bring them to clinical application. However, prior to any therapeutic and bioimaging studies comprehensive analysis of GQDs cytotoxicity has to be done in vitro. In our research, we assess the biocompatibility of a variety GQDs synthesized from different carbon-based precursors in non-cancerous cells through cell viability assay. Our results show that GQDs prepared from chitosan and glucosamine demonstrate 80% cell availability at 1.2 and 2.2 mg/mL concentrations, respectively, making them the most promising candidates for further therapeutic applications among over 15 GQD candidates tested.

The SARS-CoV-2 virus, which induced a global pandemic in 2020, is a serious pathogen that can cause acute respiratory distress in infected individuals. In order to garner a greater understanding of the SARS-CoV-2 virus and attenuate its effects, researchers have aimed to estimate key viral kinetic parameters. In this study, data from a previously published challenge study on the impacts of SARS-CoV-2 on young adults, including viral load, upsit score, and symptom score, was used to calibrate a system of ordinary differential equations, generating pathogenic parameters. In addition, Pearson covariance values and the Lyapunov exponents were calculated for each participant from the challenge study. For a majority of participants, the Lyapunov exponents were positive and finite, indicating chaotic behavior in vector space. Similarly, for most participants, there was a weak positive correlation between upsit/symptom scores and viral load. Future research will consist of implementing a newer system of ordinary differential equations that may be a better fit for the data

Galaxies, like our Milky Way, harbor stars and planets that are created out of gas. We utilize observations from Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) and Hubble Space Telescope (HST) to inspect the gas in and outside of galaxies. We then use these data to compare against the rate at which these galaxies are forming stars. We use ratios of spectral emission and absorption lines from MaNGA to determine whether a particular point in the galaxy best resembles a star-forming region, an active galactic nucleus, or something in between. We will further assess the star-formation activity in the galaxies based on their ionized gas and stellar spectral indices. We will use HST observations of the same galaxies to quantify the amount and properties of the gas surrounding them to better understand how the environments of galaxies impact the activity occurring within them. Through this work, we will contribute to our understanding of the galactic gas cycle and its connection with star formation within these galaxies.

When massive stars in a galaxy die, they explode and create clouds of gaseous debris. If these clouds of debris break out of the galaxy, they can create galactic winds. The nearby Large Magellanic Cloud (LMC) galaxy is ideal for studying galactic winds as it is oriented face-on and is driving out 85 million Sun’s worth of gas. Using observations from the Hubble Space Telescope, we are studying chemical absorption fingerprints from the light that passes through the LMC’s galactic winds. These chemical fingerprints enable us to assess the physical properties of the winds. We are using the light from 150 young, massive stars in the LMC to probe through the LMC’s galactic winds. In order to determine where the LMC’s disk ends and the winds begin, we utilize the Galactic All-Sky Survey observations to trace the motions of the neutral hydrogen gas. Together, these observations will allow us to measure how fast the winds are moving, how much gas they contain, and their ionization states. Exploring the LMC’s galactic outflows will contribute to our understanding of the relationship between a galaxy’s environment and its evolutionary progression.

The antimicrobial properties of ZnO are well documented. Demonstrated effectiveness against various strains of both Gram-positive and Gram-negative bacteria in addition to being an abundant and inexpensive material leave it well positioned for application as an antibacterial agent. ZnO based antibacterial agents see current usage in biomedical, water treatment, food storage and various other industries. Despite the significant promise and proven application, realization of both novel and efficient, targeted applications is hindered by a lack of understanding in the fundamental mechanisms responsible for the antimicrobial properties of ZnO. In particular the role and nature of components of the local bacterial environment in mediating/hindering these antibacterial interactions. Phosphate-rich environments in particular have been observed to inhibit antimicrobial behavior in ZnO though the manner in which this occurs has not been adequately described. To elucidate the environmental interactions relevant to the antimicrobial action of ZnO we investigated the effects of interactions with both bacteria and the bacterial environments on the physicochemical and optoelectronic properties of the free crystalline surface of ZnO microparticles (MPs). This involves exposing hydrothermally grown ZnO MPs to phosphate-buffered saline (PBS) media both with and without the presence of Newman strain S. aureus bacteria. Changes in the surface electronic structure and charge dynamics due to these exposures are monitored via both time and energy dependent surface photovoltage (SPV) conducted prior to and following biological assays. In doing so we demonstrate significant changes in the characteristic timescales of long-lived processes in the SPV transients after exposure to phosphate-rich environments. Such findings point to significant phosphate adsorption at the free crystalline surface. This is further supported by suppression of oxygen rich defect centers after exposure to PBS media. We also comment on the interaction of bacteria as the presence of S. aureus suppresses this adsorption and influences charge transfer processes at short and intermediate timescales.

Excitation and emission (observation) conditions heavily impact fluorescence measurements. Both observed spectra and intensity decay (fluorescence lifetimes), when incorrectly measured, may lead to incorrect data interpretations. The necessity of using so-called total fluorescence intensity or intensity measured under magic angle (MA) conditions is demonstrated for both steady-state and time-resolved fluorescence measurements. Rhodamine 6G (R6G) in two solvents - ethanol and glycerol have been used in order to demonstrate the general importance of Magic Angle observation.

The SARS-CoV-2 pandemic initially made landfall in the United States in early 2020, and at that point in the pandemic, few developed treatments left the initial prevention of the disease largely up to preventative measures like mask mandates, quarantines for infected individuals, and social distancing policies. As a result, we must understand how preventative measures affect the transmission of infectious diseases to prepare us to fight the future spread of similar diseases. To accomplish this, we used a SEIR model with a variable transmission rate and fit SARS-CoV-2 case data to it. Principally, we used four models for the change in transmission rate: instant, linear, exponential, and logistic. Then using these models for the decay of transmission rate, we obtained SSR and parameter values that allowed us to compare models for each state. After comparing models between the four states we fit, there was no evident best-fit model for the decay in transmission. These results may suggest that regional differences like behavior, socioeconomic status, and exact preventative measures enforced could be responsible for the disparity in how the transmission rate decayed.

Star clusters have long been used as chemical and dynamical tracers for our home galaxy, the Milky Way. Many of these clusters are the old, metal poor, and massive objects known as globular clusters. These globular clusters are ideal test-beds for studying stellar evolution, stellar dynamics, and Galactic evolution since all the included stars are born from the same gas cloud. In this work, we combine the positions and motions of stars on the sky, provided by the European Space Agency’s Gaia space telescope, with the high-resolution chemical abundances from the Apache Point Galactic Evolution Experiment (APOGEE) to create a catalog of globular clusters. By only using data from two sources this sample of clusters is less susceptible to systematic offsets induced by combining multiple literature datasets. Overall, our catalog includes nearly half of all known Milky Way globular clusters, and a total of 5000 likely stellar members with APOGEE chemical abundances. We use these data to explore the internal properties of globular clusters as well as the population of the clusters as a whole to paint a picture of what the Milky Way looked like when it was first forming.

Characterizing Galactic sub-structures is crucial to understanding the assembly history and evolution of the Milky Way. To accomplish this, we need to identify and analyze the accreted sub-structures. With ESA Gaia and SDSS-IV/APOGEE, studies have been done to analyze the kinematics and chemical abundances, respectively. However, one challenge that still remains is deriving reliable ages for these sub-structures. We utilize the new relationship between the carbon to nitrogen ratio and stellar age derived by the OCCAM team, which has recently been extended to the metal-poor regime, to probe stars within the sub-structures in the metallicity range -1.2 ≤ [Fe/H] ≤ +0.3 dex. This allows us to determine the ages of a greater number of stars within these sub-structures, which paints a more coherent picture of the original galaxies that have been disrupted to form the Milky Way’s halo. Using the sample of halo sub-structures in Horta et al. (2023), we apply the newly extended calibration to determine ages of stars within these sub-structures and compare them to previous age estimates.

The most common immunological models for analyzing viral infections assume even spatial distribution between virus particles and healthy target cells. However, throughout an infection, the spatial distribution of virus and cells changes. Initially, virus and infected cells are localized so that a target cell in an area with lower virus presence will be less likely to be infected than a cell close to a location of viral production. A density-dependent rate has the potential to improve models that treat cellular infection probability as constant. A Beddington-DeAngelis model was used to understand how density dependent parameters could impact the severity of an influenza infection. Parameter values were varied to understand implications of density constraints. For low density dependence, a steeper increase in number of virus and greater viral peak was predicted. Higher density dependence predicted a longer time to viral load maximum and a greater infection duration. Initial localization of infected cells likely slows the progression of infection. The model demonstrates that accounting for density dependence when analyzing influenza infection severity can result in an altered expectation for viral progression. A density-dependent infection rate may provide a more complete view of the interaction between infected and healthy cells.

Mathematical models of cancer cells can be used by researchers to study the use of oncolytic viruses to treat tumors. With these models, we are able to help predict the viral characteristics needed in order for a virus to effectively kill a tumor. Our approach uses non-cancerous cells in addition to the tumor to determine when the virus will spread to non-cancerous cells. However, there are several models used to describe cancer growth, including the exponential, Mendelsohn, logistic, linear, surface, Gompertz, and Bertalanffy. We study how the choice of a particular model affects the predicted outcome of treatment.