FIReS Presentation Abstracts-2025

Infectious diseases are cited as one of the main causes of death worldwide. Many drugs to treat such illnesses were discovered in the mid-1900s, but this type of research has declined in recent years. At the same time, antimicrobial-resistant “superbug” infections are on the rise, including superbug biofilms present in the oral cavity, which are responsible for diseases such as tooth decay, gum disease, and implant failure. Therefore, we have been working to explore various plants from a medicinal garden on the Valparaiso University campus to screen extracts against common disease-causing oral bacteria with the goal of discovering novel compounds to fight these illnesses. Several of these extracts have shown promising antimicrobial effects specifically against Gram-positive bacteria, and the results will be presented in this poster. Future work will focus on separating and chemically characterizing several promising extracts to identify novel plant compounds relevant in dental health. This is a student-initiated project that pre-dental student Daileen Serrano received internal funding to work on via a Valpo CWR Guild Undergraduate Research Expense grant.

Abstract: According to the W.H.O., infectious diseases account for three of the top ten global causes of death. Antimicrobial drug discovery surged in the mid-twentieth century but has sharply declined in recent years. At the same time, antimicrobial-resistant “superbug” infections are on the rise. Plants produce a robust array of novel metabolic compounds including many antimicrobial agents. However, with the advent of modern antibiotic drugs, natural plant-derived antibiotic sources have largely been left unexplored. Therefore, our work focuses on screening underexplored medicinal plants in hopes of discovering novel antimicrobial drugs. To accomplish this, we have been testing extracts of plants found in the Valparaiso University medicinal garden for their effects against 12 microorganisms, both fungal and bacterial, of interest. To date, methanolic and hexane extracts of aerial, reproductive, and root portions of 16 unique plants have been screened, with five plants showing promising activity levels, including chokeberry, raspberry, lavender, yarrow, and calendula. Antimicrobial activities of the most active plant extracts are presented herein. Chokeberry methanol extracts have begun to be separated using column chromatography techniques to determine the specific antimicrobial compounds through analytical chemistry methods. These data highlight the importance of plants as an invaluable pharmaceutical resource at a time when antimicrobial drug discovery has plateaued.

In the colon, the Cl- driven fluid secretion is dependent on the Na+-K+-2Cl– cotransporter 1 (NKCC1). The effect of prolonged activation of PKC on NKCC1 expression is unknown; we used immunoblotting to test this effect. In preliminary experiments, prolonged activations of PKC on NKCC1 resulted in a decrease in expression. While ubiquitination is a known signal for degradation, we tested whether ubiquitination is a signal for NKCC1 internalization using fluorescence microscopy in Madin-Darby Canine Kidney cells expressing eGFP-NKCC1. Phorbol 12-myristate 13-acetate (PMA) was used to activate PKC, and PYR-41 to inhibit the ubiquitin ligase E1. We quantified the number of endocytosed vesicles using ImageJ software. One-way ANOVA shows a significant difference among our conditions (P < 0.001). A Tukey’s post-hoc test shows no significant difference between control (0.9 ± 0.2 vesicles/cell, n=89) and DMSO (1.4 ± 0.4, n=41, p=0.2). PMA (8.3 ± 1.1, n=27) significantly increases the number of vesicles/cells compared to control (P < 0.001). Unexpectedly, PYR (7.0 ± 0.7, n=77) significantly increased the number of vesicle counts (P < 0.001), and PYR + PMA (8.7 ± 1.5, n=83) was not statistically different from PYR or PMA (P=0.3 and 0.8, respectively). We can infer that inhibiting ubiquitination reveals a ubiquitin-independent mechanism of NKCC1 internalization. While degradation is present during PKC activation, it cannot be linked to ubiquitination due to inconclusive microscopy results.

Our research analyzes marketing in higher education by examining how generative AI is influencing the field, with a specific interest in marketing at Valparaiso University. Higher education in the United States operates in a market characterized by monopolistic competition. Demographics, price sensitivity, and substitutes such as public universities, community colleges, and online programs shape enrollment demand. Challenges include a declining number of graduates and rising price elasticity of tuition, creating enrollment pressures for private institutions like Valparaiso University.

Managerial economics can be used to analyze marketing in higher education through market structure, elasticity, supply constraints, and demand. Generative AI shifts staff roles toward curation and strategy, creating reliance on data and the need for governance to combat risks of authenticity and bias. Demand is downward sloping and elastic, with high cross-price elasticity from cheaper substitutes. Marketing, enhanced by generative AI, can shift the demand curve outward by increasing perceived value.

Key recommendations are to adopt AI in measurable ways before scaling, allowing staff to refine strategies for its use. Generative AI can refine financial aid strategies, reduce marginal content costs, and redirect staff time. It can also support differentiation through community, location, and mission. Oversight structures must monitor authenticity and ensure alignment with institutional goals.

Generative AI provides opportunities for efficiency and personalization, but proper strategy, adoption, and governance are necessary to sustain competitiveness in higher education.

This research is a collaborative effort between Pelagos Pharmaceuticals and Valparaiso University. The overall goal is to develop drugs that will halt and/or reverse congestive heart failure and muscle disorders by targeting two nuclear receptors: the Rev-Erb (REV-ERB) receptors and the estrogen-related receptors (ERRs). The work reported here is focused on the treatment of mouse myoblasts (skeletal muscle cell precursors) with potential ERR agonists and then looking for protective changes in gene expression. Previous studies have shown that activation of ERR can enhance mitochondrial function and certain agonists are effective in enhancing muscle function and reducing fibrosis. Additionally, ERR agonists are effective in maintaining oxidative metabolism which helps protect against induced heart failure in mice. These experiments aim to investigate the effects of SLU-PP-332, an ERR agonist, on the gene expression in both differentiated and undifferentiated myoblasts. Changes in gene expression will be assessed using RNA sequencing. Analysis of the gene expression results will be carried out in collaboration with the Valpo U. Dept of Mathematics. The goal of future work will be to characterize changes in gene expression in response to multiple REV-ERB and ERR agonists in order to identify suitable clinical development candidates for treatment of human disease.

Microplastics are particles of plastic ranging from 1µm to 5mm and nanoplastics are particles smaller than 1µm. Micro and nanoplastics have been linked to negative health effects such as cancer, general inflammation and reproductive harm. It is vital for research to be done on the formation of microplastics due to fragmentation, as they permeate everything as well as being present in humans and animals. This research has focused on understanding the fragmentation of oxidized real world plastics using techniques previously used on pure lab plastic; these were determined to release small microplastics and nanoplastics in water in the presence of other liquids. Real world plastic has differences from pure plastic; chemicals are added that can for example, soften plastic and absorb UV light. Real world plastics in the environment in conditions that can oxidize the surface, a process where plastic combines with oxygen. Pieces of plastic were solubilized in 15mL of solution (pure water or a mix of H₂O and H₂O₂), using 20µL of the chemical, n-dodecane, that was used to facilitate the fragmentation of lab plastics, with some exposed to UV radiation. Each set of experiments involved pieces of plastic, each piece underwent solubilization a total of 5 times for 24 hours per solubilization. Infrared spectroscopy was used to determine the measure of oxidation of the plastic after each solubilization. There are changes in surface from solubilization. Further research must be done to obtain more data to better understand the oxidation and fragmentation process.

In communities located near industrial zones, indoor air quality plays a critical role in shaping health outcomes due to the compounding effects of exposure to airborne pollutants. Volatile and semi-volatile organic compounds (VOCs and sVOCs) are of particular concern, as they accumulate indoors and originate from both industrial emissions and everyday consumer products and materials. These compounds have been associated with a range of health effects, including respiratory illness, neurological disorders, endocrine disruption, and various cancers. As part of the Northern Lake County Environmental Partnership, this ongoing study investigates personal exposure to VOCs using passive sampling methods. Two approaches were evaluated: silicone wristbands worn by residents and personal samplers and solid-phase microextraction (SPME) fibers coated with polydimethylsiloxane and divinylbenzene deployed indoors for one week. After exposure, compounds captured on both media were analyzed using gas chromatography-mass spectrometry (GC-MS). Preliminary analysis detected a broad spectrum of indoor pollutants, including hydrocarbons (e.g., n-hexadecane, n-octane, toluene), phthalates (dihexylphthalate) and alcohols (e.g., 2-n-propyl-1-heptanol). Method development was also extended to include polycyclic aromatic hydrocarbons (PAHs), with six out of thirteen target PAHs successfully recovered from the standard solutions. Future work will incorporate phthalate standards, analyze the full set of wristband samples, and explore strategies to enhance the efficiency, reliability, and non-invasiveness of passive air sampling methods.

Microplastics can be found everywhere in the environment due to the extensive production and use of plastic materials. These particles are less than 5 mm in diameter and studies have shown that they have negative effects on human and environmental health. Therefore, studies have been conducted to address small plastic particle contamination and methods in which to remove them from environmental samples. This study used a methodology to remove microplastics from water by using a non-toxic additive, n-hexadecane, that agglomerates the microplastics to make them removable and recoverable from water. A small volume of n-hexadecane was added to water containing microplastics and then stirred using a jar tester. A variety of microplastics were tested using this methodology including lab-grade polyethylene, plastics from municipal solid waste and plastics collected from the Lake Michigan shoreline. In addition, this methodology tested the fate of other dissolved contaminants in the water, such as sulfamethoxazole and benzophenone. Using liquid chromatography, the concentrations of the dissolved substances were determined before and after the agglomeration which overall showed a lower presence of contaminants after the agglomeration. The lab grade polyethylene had a percent agglomerated within a range of 85%-95% repeatedly and for the real world plastics, the percent agglomerated was approximately 70%. Future work will attempt to expand on n-hexadecane’s ability to facilitate the removal of microplastics from water, by increasing the amount of mixed plastics in successful removal, examining the sizing limitations of method applicability and testing utilization of varying plastic forms.

The prevalence of Inflammatory Bowel Diseases (IBD), including Crohn’s disease and ulcerative colitis (UC), is continually increasing, especially in areas such as the US and Europe. IBD are chronic inflammatory diseases for which there is no cure. Additionally, available treatments have significant adverse effects, especially first generation anti-inflammatory small molecules like sulfasalazine (SSZ), and many have low efficacy rates. Therefore, an area of interest for IBD treatment research is identifying agents to be used in conjunction with current drugs to decrease side effects while maintaining or improving efficacy. One of these natural compounds we evaluated is carnosic acid (CA), a diterpene from Salvia rosmarinus, which was found to modulate the expression of the inflammatory-linked protein neutrophil gelatinase-associated lipocalin 2 (NGAL) in UC patient-derived colon epithelial organoids. Using CA as a tool to evaluate the pathway affecting NGAL expression, we investigated the effect on cell viability and modulation of NGAL protein expression by CA and SSZ co-treatment in sodium butyrate differentiated HT-29 colon cancer cells. The cell viability was tested using an MTT assay and NGAL expression levels by western blot. The results showed that CA and SSZ co-treatment was not toxic at lower SSZ concentrations of 0.5mM and 0.25mM. To understand the physical interaction between small molecules like CA and NGAL, and to find other small molecules that directly target NGAL, we expressed the active NGAL form in E. coli cells. In the future, we will use the purified recombinant protein in assays including surface plasmon resonance.

The competitiveness in collegiate swimming has been increasing throughout the years; the amount of world-class talent that congregates in the United States has made the NCAA the most competitive swimming league in the world. This makes athletes and coaches focus on the small details persistently, as the smallest improvement could take you from not making finals to an NCAA champion. This research investigates how stroke count relates to race performance in competitive swimming and builds models that recommend specific target stroke counts depending on your event and distance. The data was created manually by using public race videos and official results from collegiate and professional competitions. It was then assembled as a dataset that pairs race times with counted strokes across events. Preliminary modeling with linear regression shows a strong relationship between stroke count and race time in exemplar events (e.g, men’s 100 backstroke: R² = 0.716). The study further expands the dataset and compares multiple regularized and polynomial regressions with cross-validation to reduce overfitting and to control for event distance and swimming styles. The research suggests that swimmers who race with fewer strokes tend to post faster times – a strong correlation observed in every event. This suggests elite performance relies heavily on maintaining a higher effectiveness per-stroke (e.g, maximizing effective propulsion and underwater speed to reach the 15-yard mark), rather than focusing solely on raw fast motion. The results aim to give coaches and swimmers a user experience through quantitative guidance for technique optimization and training goals.

This project studied NBA player injuries to find what factors increase the risk of getting hurt and to predict when injuries might happen. Two datasets from Kaggle were combined: one listed injury reports from 2010 to 2020, and the other contained player statistics from 2013 to 2023. The data were cleaned and merged to show patterns such as which injuries are most common and which are more serious. The analysis showed that ankle and knee injuries happen most often, while shoulder injuries are most likely to end a season. Logistic regression and decision tree models were used to predict injury chances. A logistic regression model reached 73 percent accuracy, while a bagging model reached about 94 percent accuracy. Distance traveled during games was found to be the strongest factor linked to injury risk. These results can help teams manage player workload and prevent injuries. Adding more years of data and including information about physical strength and movement could make future models even more accurate.

VAM (volumetric additive manufacturing) is a method of 3D printing that produces 3D geometries using projected light and resin. Using VAM produces layerless prints in minutes. Current methods only use a UV projector to produce a geometry. Our method of VAM aims to use projection methods of much lower power to produce comparable final objects at a much more accessible cost. Due to the nonlinear nature of VAM resin, we attempt to use an ambient light source to reduce the power output of the projection. The resin cures only in regions that pass a light dose threshold. Similarly, SLS (selective light sintering) uses just enough laser light to melt powder in the outline of an object. SLS uses a heated chamber just below the melting point. This allows the laser required to create the final object to have relatively lower power requirements than without. This same principle enables a lower power projector to be used in VAM, lowering the cost of such a device as well as our modular setup. Our experimentation required the creation of a modular optical setup and open-source software. This was done using modified consumer electronics to achieve 405 nm projection and a consumer pegboard for optical alignment. An adjustable 405 nm LED light source with a custom lens is used to create evenly disturbed ambient light. Using Python for the backend and an XAML frontend, a functional slicer was achieved. Together, this allows for controllable printing conditions. Lower printing times and lower required projection intensity in printing were achieved.

Objective: To evaluate whether adalimumab or surgical intervention is more effective in treating moderate to severe hidradtentis suppurativa (HS), and to determine whether a combined therapeutic approach offers superior outcomes.

Methods: A comprehensive literature search was conducted using PubMed and Medline databases to identify relevant studies on treatment outcomes for moderate to severe HS. Search terms included “adults”, “moderate to severe”, “hidradentitis treatment”, “biological therapy”, and “surgical intervention”. PubMed returned 80 results, and Medline yielded 33. Studies were included if they examined adult patients 18 years or older with moderate to severe HS, utilized biologic therapy (adalimumab) either alone or in combination, and included surgical intervention as a comparator. Articles were required to report outcomes related to HS flare frequency or severity and be published from 2020 onward. Studies were excluded if they focused on pediatric or mild HS populations, lacked direct comparison between biologic and surgical therapies, or failed to measure flare outcomes. Primary endpoints were a reduction in flare frequency/severity and overall disease improvement.

Results: Adalimumab was found to reduce inflammation, decrease flare frequency, and improve quality of life, but did not significantly impact surgical outcomes such as recurrence or lesion size. Emerging evidence supports that combination therapy, with adalimumab followed by surgery, provides superior disease control compared to either modality alone. However, variability in study design, recurrence definitions, and follow-up duration limits generalizability and long-term conclusions.

Conclusion: While initially focused on comparing adalimumab and surgery, current evidence supports a multimodal approach for optimal HS management. Adalimumab offers clinical benefits, but surgical interventions remain critical in severe cases. A combination of both may yield the best outcomes. Further high-quality, long-term studies are needed to guide standardized treatment protocols. Until then, clinical judgment and a multidisciplinary strategy remain essential

Keywords: Hidradentits supporativa, adalimumab, surgical intervention, biologic therapy, flare reduction, combination treatment

The aim of the Neutron Electric Dipole Moment (nEDM) experiment at Los Alamos National Laboratory (LANL) is to measure the nEDM with a sensitivity of 2×10-27 e*cm. A nonzero nEDM would indicate CP violation while testing the validity of the Standard Model of particle physics. To achieve this sensitivity, the experiment must be carried out in a stable magnetic field with field gradients less than 0.3 nT/m. This project focused on searching for small scale sources of magnetic field inhomogeneities, such as magnetic dipole contaminations. To ensure magnetic cleanliness of the experimental apparatus, a magnetic impurity scanner has been designed and built at LANL to scan components of the magnetic field mapper before it is used to characterize the magnetic field inside the magnetically shielded room where the experiment will be carried out. While scanning components, we reviewed magnetometer data to search for discrepancies and cleaned any parts which showed signs of magnetic dipole contamination. Impurity scanner design, measurement results, and scanner upgrade effectiveness will be presented.

Planetary nebulae occur towards the end of the life cycles of stars with a mass less than about eight times the mass of the Sun. As the star expands at the end of its life, it ejects its outer layers, and the high temperature of the leftover core causes the ejected gas to ionize and glow. At the centers of some of these planetary nebulae, there can be two stars present instead of one. These binary stars can be detected through a variety of ways, with the most common being photometric variability, or a change in the brightness of the system. For the system presented here, PHR J1040-5417, this was accomplished through observations of the system’s brightness as observed from Earth. This data can then be used to construct a light curve, or a plot of how the brightness changes over time, which can then be used to obtain the period of the binary. In this work, we will explain our process in determining the orbital period of PHR J1040-5417 to very high precision.

A planetary nebula is one of the many fascinating and important processes in space that can occur in binary systems consisting of a white dwarf and a companion star in a very close orbit. Because of this, understanding this type of system is important to our overall understanding of our universe. These binary systems are born within planetary nebulae. However, only 26 such systems within planetary nebulae have been fully modeled to determine the physical parameters of the system. This is not a large enough number to result in meaningful statistical data. In order to work towards increasing this number, I used computational modeling to determine the physical characteristics of the binary system inside the planetary nebula Pa 164, consisting of a white dwarf star and a main sequence star. The modeled characteristics, or parameters, consist of the temperature, mass, and radius of both stars, secondary albedo, and system inclination. I present here the modeling results.

The Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Laboratory provides a unique environment to study the spin structure of the proton through polarized proton-proton collisions. One of the major goals of the STAR experiment at RHIC is to understand how gluons contribute to the proton’s intrinsic spin of ½ ?. One important measurement in this effort is the longitudinal double-spin asymmetry, ALL, of neutral pion (??0) production in polarized proton collisions which can be related to the gluon’s contribution. Using data from the 2013 proton-proton collisions at ?s = 510 GeV we aim to extract the ALL. Our work, in particular, focuses on two main components: determining the number of ??0s and calculating relative luminosities. The ??0s decay into two photons whose energies and positions are recorded by the Endcap Electromagnetic Calorimeter (EEMC), which covers a pseudorapidity range 1.1

The Longitudinally-segmented Forward Hadronic Calorimeter (LFHCal) will be part of the ePIC detector at the Electron/Ion Collider (EIC). The LFHCal is made up of smaller modules that consist of alternating layers of steel and scintillating tiles read out by Silicon Photomultipliers (SiPMs). In September 2024 a prototype module was tested at the CERN Proton-Synchrotron T9 beam line with a wide range of energies and beam types. In one test beam readout mode, each SiPM reads out through both a high gain and a low gain path. Analysis of the test beam data revealed a discrepancy in the comparison between the high gain and low gain values for different beam types. My analysis of the test beam data has included studying minimum ionizing events (MIPs) in hadron and muon test beam runs to help us understand the HG/LG discrepancy. I will describe the LFHCal, the 2024 test beam and its analysis, and present the results of my MIP study.

White dwarfs are the final stage in a sun-like star’s life cycle. They are the remnants of the star’s core and are typically made of carbon and oxygen. Sometimes, these white dwarfs can be in orbit with another star, which are referred to as binary systems. We can understand more about white dwarf binaries by using computational modelling. We modelled the two close binary systems of Gaia DR2 3150 and GD 803, which have a white dwarf and a cool main star. This was done by adjusting the values within modeling software for the temperature, mass, and radius of both the white dwarf and sun-like star, along with the inclination of the system and secondary albedo of the sun-like star. The output model was then visually compared to light curves of Gaia DR2 3150 and for light curves and radial velocity curves of GD 803. Here we present our results for each of the two systems and discuss difficulties and possible next steps.

Interest in close binary star systems has led to increased efforts to find more. One method of doing so involves taking 2 spectra of many objects of interest and comparing the radial velocities, or speed toward and away from us, derived from them in search of binarity. Statistical tests are then applied to determine if the measured difference in radial velocities is significant. A significant difference would indicate a binary, while an insignificant difference would not. An issue with this, which can be resolved by increasing the number of observations taken, is that a binary may go undetected if the spectra happen to be taken at similar points in its orbit. While the increase in number of observations would help, time and resources are valuable. As such, it is important to understand the level at which additional observations will improve the rate, or percentage, of binary detection. Here, a Monte Carlo simulation was run to create many theoretical binary systems of different inclinations, object masses, and orbital periods. These were then used to determine what fraction of systems would be detected as binaries using different numbers of observations. The simulations demonstrated increased binary detection rates for runs with 3 and 4 observations taken. Here we present the full method, results, and implications for scheduling observations that search for these types of binaries.