Oluwasen Adu, Integrative Biology
(Advisor: Michael Blouin)
Genome Wide Association Study of Biomphalaria glabrata snail and it's Schistosome Parasite
Schistosomiasis is a parasitic disease of humans that is caused by trematodes in the genus Schistosoma. Schistosomes require aquatic snails as an intermediate host to complete their lifecycle. Infected snails shed parasites into water, which then infect humans through their skin. With WHO’s goal to eliminate schistosomiasis as a global health problem by 2030, a renewed emphasis on snail control was enacted. Because different genomic regions are important in different snail-parasite population combinations, it is important to look at many combinations of Biomphalaria glabrata and Schistosoma mansoni populations to find more regions. Via genome-wide association studies (GWAS), genomic regions associated with variation in resistance of Biomphalaria glabrata population to infection by Schistosoma mansoni population will be identified. The goal is to identify new snail genomic regions or previously known regions that could confer resistance to schistosome infection. This work might point to new strategies for blocking transmission of schistosome parasites.
Vera Alenicheva, Chemistry
(Advisor: Vincent Remcho)
A Microfluidic Paper-Based Assay for the Quantification of CBD and THC
Rapid and accurate quantification of cannabinoids, including cannabidiol (CBD) and tetrahydrocannabinol (THC), is crucial for medical, legal, and regulatory applications. Differentiating these cannabinoids is particularly important due to their contrasting properties: CBD is non-psychoactive, while THC is psychoactive. This study introduces a novel microfluidic paper-based assay designed for point-of-care diagnostics, addressing the demand for portable, efficient testing methods. Using selective laser ablation, a paper-based device was developed featuring circular test zones (8.5 mm) optimized for UV-Vis spectroscopy, fluorescence analysis, and RGB-based colorimetric detection, which utilizes color changes captured through the Red, Green, and Blue channels for analyte evaluation. Cannabinoid quantification is achieved through a reaction with Fast Blue BB Salt (FBBB) under alkaline conditions, forming distinctive chromophores—orange for CBD and pink for THC—with a maximum absorption at 480 nm used for both chromophores, alongside a fluorophore specific to THC, with excitation at 480 nm and emission at 655 nm. ImageJ software enables analysis of RGB values extracted from the wells of the captured image of the array-based paper device, while optimized reagent concentrations ensure low detection limits and robust linearity. Bench-top UV-Vis and fluorescence analysis further validate the device's reliability. For all assays, 0.5M NaOH provided optimal conditions, enabling smoother color formation and higher signal intensity. THC fluorescence analysis achieved a 0.77 µg/mL limit of detection (LOD) at 10 mM FBBB, as higher concentrations quenched fluorophore formation. For THC colorimetric detection, 15 mM FBBB was optimal, with an LOD of 24.87 µg/mL. The CBD assay performed best with 10 mM FBBB, achieving an LOD of 48.45 µg/mL. The assay demonstrates significant advantages over traditional methods, offering rapid, accessible testing for field and clinical applications. Future work will focus on stabilizing FBBB with polymer supports, developing cannabinoid extraction protocols for biological samples, and enhancing detection limits with alternative substrates. Integration into a prototype device aims to expand point-of-care capabilities, establishing this portable assay as a transformative tool for on-site cannabinoid testing across diverse environments.
Lucas Allan, Chemistry
(Advisor: Tim Zuehlsdorff)
FC2DES: Modeling 2D Electronic Spectroscopy for Harmonic Hamiltonians
Franck-Condon spectroscopy has long been a ubiquitous method for modeling the linear optical properties of molecular chromophores. Its working principal, which requires the nuclear motion of the molecule to be represented as harmonic, leads to a robust approximation of the optical properties of the system and is freely accessible to researchers through standard computational chemistry software packages such as Gaussian. It is thus desirable to extend this Franck-Condon methodology to modern, more complex non-linear experiments. However, there previously were no Franck-Condon formulations for non-linear experiments that could be applied to systems of molecular scale. In my work, I present the FC2DES approach, providing the first, readily accessible formulation for the Franck-Condon simulation of non-linear spectroscopic experiments for systems of molecular size.
Hallee Boyd, Chemistry
(Advisor: May Nyman)
Characterization of Trivalent Lanthanide Keggin Phosphomolybdate Sandwich Clusters
Lanthanide polyoxomolybdate (PMo) sandwich complexes offer a versatile platform for examining actinide chemistry by using non-radioactive surrogates to elucidate structural and chemical trends. By exploring molybdate-based systems, comparisons can be extended to tungsten analogues offering insights into how variations of the POM framework influence coordination behavior, solution behavior across the lanthanide series. Single Crystal X-Ray Diffraction reveals coordination geometries, metal–ligand bond lengths, and overall complex bonding angles that reflect periodic trends across the lanthanide series based on ionic radius. By investigating the complexes’ behavior in the solution state via 31P NMR and X-Ray scattering, the distribution of monomeric (1:1) and dimeric (2:1) POM:lanthanide species are examined. The chemical tunability, radiation resistance, high solubility, and compatibility of PMos with a wide range of characterization techniques support their use in microgram-scale studies, enabling new opportunities to investigate possible trends relevant to actinides while minimizing the challenges associated with handling radioactive materials.
Daniel Malone Buoy , Statistics
(Advisor: Claudio Fuentes, Sarah Emerson)
Representative Sampling Methods for K-Fold Cross Validation
K-fold cross validation is widely used for assessing the accuracy of predicative regression and classification models. We investigate improving representativeness of the training set and testing set for K-fold cross validation in the context of random forests, and in particular exploring the potential for using a mixture of stratified sampling and cluster sampling to reduce variance in estimation of model performance measured by residual standard error and apparent error rate. Simulations and applications to real-world data are considered to compare performance to competing methods for cross validation, namely to the standard random fold assignment.
Olivia Burleigh, Integrative Biology
(Advisor: Virginia Weis)
Transcription Factor-targeted ChIP-Seq for Smad3-mediated TGF-β Signaling in Heat-stressed Aiptasia
Reef-building corals rely on a crucial symbiosis with endosymbiotic dinoflagellates, yet the cellular and molecular mechanisms behind this relationship remain unclear. Recent studies suggest the host’s innate immune system plays a key role in establishing, maintaining, and breaking down the symbiosis. One signaling molecule involved is TGF-β, which promotes immune tolerance and symbiosis establishment. However, the downstream signaling pathways, particularly those leading to immunosuppression, are not fully understood. To investigate this, I adapted chromatin immunoprecipitation sequencing (ChIP-Seq) protocols for use with the Smad3 transcription factor in Exaiptasia diaphana—a model for coral symbiosis—where ChIP-Seq has only been applied to histones before. This approach enables genome-wide analysis of Smad3-DNA interactions and will help reveal how gene regulation differs depending on the symbiotic state or under acute heat stress, shedding light on the transcriptional mechanisms that support or disrupt symbiosis in response to environmental change.
Jun Cai, Integrative Biology
(Advisor: Virginia Weis)
Effect of Sphingolipid Metabolic Pathway Inhibition and Knockdown on Cnidarian-Algal Symbiosis
Climate change has accelerated coral bleaching and the destruction of coral reefs, threatening the survival of these crucial hotspots of ecological, cultural, and economic services for millions worldwide. Despite the threat of increased bleaching events, many aspects of the cellular mechanisms of coral-algal symbiosis remain under-studied. A greater understanding of these fundamental aspects of coral biology is essential for predicting how corals respond to ongoing environmental change and for developing successful conservation strategies. Sphingolipids are important lipid signalers that regulate many cellular processes related to host-microbe interactions. We investigated the functional role of the sphingolipid pathway during symbiosis establishment in Exaiptasia diaphana (commonly known as Aiptasia), a model system for coral-algal symbiosis studies. We used chemical inhibitors for two key sphingolipid enzymes during symbiont uptake and used RNAi to knock down the expression of these enzymes to assess their importance in symbiosis. We examine the effects of the inhibitors and RNAi on aposymbiotic, symbiotic, and recolonized polyps. Changes in symbiont density and photosynthetic efficiency were quantified, physiological changes were visualized with microscopy, and qPCR was performed to confirm the effects of gene knockdown.
Giovanni Crestani, Integrative Biology
(Advisor: Molly Burke)
Genomics of Experimentally Evolved Postponed Reproduction in Drosophila Melanogaster
Evolve and resequence (E&R) experiments with model organisms can reveal the genetics underlying complex traits and adaptation dynamics. We sequenced genomes of Drosophila melanogaster populations evolved for postponed reproduction over 20 generations. These populations, maintained on a 70-day cycle, evolved distinct life-history phenotypes, including increased lifespan, compared to controls. Comparing populations with the same selection treatment but different evolutionary histories allowed us to test how past evolution shapes future adaptation. Our results demonstrate that allele frequencies align with selection treatment rather than recent evolutionary history, indicating evolutionary convergence within selection regimes. We also show that adaptation for postponed reproduction has a polygenic basis and occurs rapidly through shifts in standing genetic variation.
Ushasi Datta, Chemistry
(Advisor: Marilyn Mackiewicz)
Unveiling the Hidden Properties: How Nanomaterial Surface Chemistry and Biomimetic Systems Shape Reflectance and Contrast
Nanomaterials have great potential to enhance optical coherence tomography (OCT) imaging through their reflectance properties. However, understanding how their surface chemistry and physicochemical features—such as NP surface coatings, environmental pH, and salinity—impact reflectance remains a challenge. Most nanomaterials developed for OCT imaging function in the NIR-I window (650–950 nm) and exhibit smaller scattering cross sections in the NIR-II window (1000–1700 nm). Gold nanoparticles (AuNPs) and silver nanoparticles (AgNPs), with strong localized plasmon resonance (LSPR) bands, are promising candidates that can be tuned to match the operating wavelength of optical coherence tomography (OCT). However, there is a gap in knowledge regarding how the surface chemistry of nanomaterials, their biological exposure conditions, and environmental factors alter their reflectance properties and OCT contrast properties. Here we examine how varying physiological salt concentrations, pH levels, and biological media, including gelatin, intralipid, and melanin, change the reflectance behavior of nanomaterials. Gelatin mimics extracellular tissue, intralipid replicates retinal tissue, and melanin regulates light while protecting the retina. Hence, it is essential to understand how biological tissue mimics fluorescence reflectance. This knowledge is necessary for identifying nanomaterials for use as OCT contrast agents and will be beneficial to the biomedical imaging community.
Konstantin Drallios, Chemistry
(Advisor: Thomas Osborn Popp)
3D Printable Radiofrequency Coils
To perform nuclear magnetic resonance (NMR) spectroscopy, a perpendicular magnetic field must be applied to the sample. Experimentally, this condition is met by placing the sample in a radiofrequency (RF) coil. The fabrication of RF coils is generally performed using wire, but the range of possible coil geometries that can be wrapped from a strand of wire are limited, and it can be difficult to ensure dimensional accuracy and reproducibility during winding. Here, we present a new approach that integrates computer-aided design (CAD), 3D printing, and electrochemical copper plating to produce RF coils. By using a home-built Python program to compute 3D magnetic field distributions for CAD-modeled coils, we further enable our fabrication technique by optimization and prediction of coil magnetic field. As a validation we apply this technique to generate a solenoid coil with optimized magnetic field homogeneity and demonstrate its capabilities to perform NMR spectroscopy.
Rudranil Dutta, Chemistry
(Advisor: Claudia Maier)
Identification and Quantitation of Bioactive Alkaloids in Withania Somnifera
Withania somnifera (WS) has been widely used as a medicinal herb for a variety of health problems related to stress and aging. WS is rich in withanolides, withanolide glycosides, tropane alkaloids, and alkyl ferulates. Withanolides and withanolide glycosides have been traditionally associated with the bioactivity and health promoting effects of WS extracts. However, we have recently discovered that the tropane alkaloids found in WS extracts are a potential new class of bioactive compounds in WS. However, high resolution tandem mass spectrometry, mass spectral analysis, authentic standard synthesis and NMR verification are all required to elucidate alkaloid compositional differences in WS. Finally, developing a fast and reliable method for quantifying tropane alkaloids in plant material and commercial products is forthcoming using multiple reaction monitoring mass spectrometry to quantify these alkaloids reliably.
Arpa Ebrahimi, Chemistry
(Advisor: Claudia Maier)
Characterizing the Lipidomic and Proteomic Profile of the 5xFAD Alzheimer’s Disease Mouse Model: A Comparative Study Using MALDI Imaging Mass Spectrometry
Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by amyloid-beta (Aβ) plaque accumulation, disrupting neuronal communication and leading to cognitive decline. Although plaque deposition is a hallmark of AD, early neurodegeneration can occur before visible plaques form. Using the 5xFAD mouse model, we investigated lipidomic alterations in the hippocampus of young (1 month old) and old (8–9 months old) 5xFAD mice compared to wild-type controls. Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging enabled spatial visualization of lipid distribution within the hippocampus. Our analysis revealed distinct changes in specific lipid classes, particularly phospholipids and sphingolipids, in young 5xFAD mice prior to detectable plaque formation. Comparative profiling across age groups highlighted progressive lipid dysregulation coinciding with disease development. These findings suggest that lipid metabolism alterations contribute to early AD pathology and may serve as potential biomarkers for pre-symptomatic detection. This study also establishes an optimized MALDI imaging workflow for future applications in brain lipidomics research.
Jessica Etter, Chemistry
(Advisor: Claudia Maier)
LC-QTOF and LC-TIMS-qQTOF MS Analysis of Fecal Inoculum Biotransformation Products and LC-MRM-MS Analysis of Human Withanolide Pharmacokinetics of an Ashwagandha Supplement
Withania somnifera (ashwagandha) and its withanolide constituents have been shown to exhibit a variety of effects in humans, including stress relief, anti-inflammatory activity, and neuroprotective properties. Despite ashwagandha’s growing popularity, there remains a significant lack of research into the metabolism of withanolides, particularly within the human gut microbiome. Methods include the use of in silico metabolism prediction software, in vitro fermentation using fecal inocula, and pharmacokinetic studies. This comprehensive approach contributes to understanding the interplay between gut microbiota and bioactive, bioavailable metabolites derived from ashwagandha, aiding both pharmacological development and insights into the role of gut health in these processes. Preliminary results indicate a variety of in vitro transformations of withanolides following incubation with gut microbiota from human fecal inocula. Additionally, data suggest that the bioactive compound sominone may be generated in vivo from precursor molecules found in ashwagandha, potentially through transformation in the gastrointestinal tract.
Caroline Hernandez, Microbiology
(Advisor: Maude David)
Whole-Cell Crosslinking Reveals Direct Lactobacillaceae and Rhizobiaceae Interactions with Host Duodenal Neuropods
Communication along the microbiota-gut-brain axis occurs through neuronal, endocrine, metabolic, and immune pathways. While most research has focused on indirect signaling via microbial metabolites and hormone secretion, a direct afferent pathway was recently discovered between neuropods—specialized enteroendocrine cells (EECs)—and vagal afferents. Neuropods, in contact with gut contents, may transmit signals to the brain within milliseconds. However, direct receptor-ligand interactions between microbiota and neuropods remain unexplored. In this study, we used Sulfo-SBED, a cell-impermeable crosslinker, to covalently capture direct interactions between murine duodenal EECs and gut microbes. 16S amplicon sequencing, metagenomics, and single-cell proteomics revealed 24 enriched bacterial taxa—exclusively from Lactobacillaceae, Rhizobiaceae, and Pseudomonadaceae—interacting with EECs. Notably, Ligilactobacillus animalis, L. murinus, and Agrobacterium spp. were consistently enriched. Proteomic analysis identified five EEC-associated proteins, including pro-glucagon and ghrelin, as significantly enriched in crosslinked samples. This work provides the first evidence of direct microbial interactions with neuropods, offering novel insight into gut-brain communication.
Esteban Hernandez, Chemistry
(Advisor: Jennifer Field)
Experimental pKa Values of Substituted and Unsubstituted Perfluoroalkyl Sulfonamides via 19F NMR
Perfluoroalkyl sulfonamide-containing substances (FASAs), such as perfluorooctane sulfonamide (FOSA) and perfluorobutane sulfonamide (FBSA), are a class of per- and polyfluoroalkyl substances (PFAS) found throughout the environment in water, soil, and organisms. Exposure to FASAs leads to negative health effects like nephrotoxicity and hepatotoxicity. Despite their prevalence and toxicity, relatively little information exists on how FASAs partition among phases in the environment. One characteristic of a molecules that determines its transport through the environment is the acid dissociation constant, pKa. The pKa informs when a molecule is predominantly charged or neutral, which has implications on partition coefficients, aqueous solubility, sorption to sediment, and partitioning into biota. In this work, five FASAs were analyzed by 19F NMR to determine experimental pKa and the effect of chain length and substitution on the nitrogen on the pKa.
Lucas Kolanz, Physics
(Advisor: Davide Lazzati)
Cosmic Dust Bunnies
Cosmic dust is an important part of many astrophysical processes, however, its formation mechanism in the early universe is still unknown. We use Soft Sphere Discrete Element Method simulations of dust formation to study the structures that arise in an astrophysical environment consistent with a supernova remnant. We find that temperature plays an important role in the resulting structure of coalescing dust grains.
Weiqi ‘Grace’ Li, Statistics
(Advisor: Yuan Jiang)
Reframing Spatial Transcriptomics Prediction: From Regression to Classification
Spatial transcriptomics (ST) is an emerging technology that sequences gene expression from intact tissue while preserving spatial context, promising unprecedented insights into tissue structure and disease mechanisms. However, its high cost and technical complexity hinder widespread clinical adoption. To address this, recent methods leverage deep learning to predict spatial gene expression from widely available histology (H&E) images. Although these models demonstrate modest correlation with true expression levels, their ability to distinguish between expressed and non-expressed genes, or between low and high expression, remains limited (AUC ≈ 0.6). This project is, to our knowledge, the first to reframe the prediction task as classification rather than regression. Applied to real-world, subcellular-resolution ST data, our classification-based model significantly improves performance—achieving AUCs of approximately 0.8 (binary decision) and 0.9 (probability-based). This significant boost in discriminative power provides a foundation for broader applications of gene expression reconstruction in disease screening and treatment outcome prediction.
Sarah Louie, Biochemistry & Biophysics
(Advisor: Richard Cooley/Ryan Mehl)
Optimizing genetic code expansion technology to access post-translationally modified proteins
Genetic code expansion (GCE) enables the incorporation of non-canonical amino acids (ncAAs) into proteins at UAG stop codons using an orthogonal tRNA–aminoacyl-tRNA synthetase (aaRS) pair. This allows site-specific chemical modifications for studying protein structure, function, and interactions. A major application of GCE is encoding post-translational modifications (PTMs) such as phosphorylation. Protein phosphorylation on serine (pSer) and threonine (pThr) regulates key processes including cell growth and signaling. While efficient GCE systems exist for pSer, current pThr systems lack the efficiency to produce functional proteins. To improve phospho-threonine encoding, we are modifying key GCE components. We screened orthologs of the L-Thr kinase pduX to increase intracellular pThr availability. Additionally, we are evolving a more efficient pThr aaRS by generating a mutation library targeting its active site. Enhancing these components will improve system fidelity and accessibility, enabling better tools to study the role of pThr in protein and cellular function.
Praveeni Mathangadeera, Mathematics
(Advisor: Malgorzata Peszynska)
Computational Modeling of the Nonlinear Heat Equation in Frozen Soil and Snow
Heat conduction in snow is modeled by a nonlinear parabolic partial differential equation which modifies the Stefan problem of liquid/ice phase transition with a free boundary. In soil, heat conduction is a nonlinear parabolic equation which is smoother than the Stefan problem. We discuss and compare three time stepping formulations for discretization of these models: the fully implicit, sequential and semi-implicit approaches. For the soil problem we also compare the use of different primary unknowns. Next, we explore the pointwise snow model which solves a single algebraic equation instead of a PDE and thus approximates the snow PDE model. This pointwise model involves n=13 environmental parameters including the albedo of surface. We analyze the sensitivity of the solutions and set up a ML regression model: that allow us to assess the robustness of our computational model as well as to understand the uncertainty associated with the parameters and the model itself. Our simulations and analyses contribute to the understanding of the response of the soils in the Arctic to the weather data and help to assess the reliability of the model.
Anshika Nagar, Chemistry
(Advisor: Marilyn Mackiewicz)
Shielded Nanoparticles: Advancing X-Ray Fluorescence Microscopy with Oxidant-Resistant Nickel and Cobalt
Synchrotron-based X-ray fluorescence microscopy (XFM) is a powerful tool for imaging the distribution of transition metals in biological specimens, yielding insights into metal-related physiological and pathological processes. However, conventional organic fluorophores are unsuitable for XFM due to their inability to emit detectable X-rays, rapid degradation, and non-specific mapping. Alternative methods like Fourier Transform Infrared Microscopy (FTIRM) and immunofluorescence show promise, but ptychography with XFM faces challenges in identifying organelles. We propose using functionalized cobalt (CoNPs) and nickel (NiNPs) nanoparticles to enhance cellular visualization in XFM scans. Their distinct K-edges enable effective metal mapping without interference from other biological metals. Nevertheless, these nanoparticles must overcome issues such as surface oxidation and aggregation. Our presentation will focus on strategies to stabilize NiNPs and CoNPs, improving their water solubility and preventing oxidation. Ultimately, these nanoprobes aim to facilitate the mapping of metal ion pools in cells and clarify their roles in diseases.
Luke Nearhood, Physics
(Advisor: Patti Hamerski)
Computing the Tension
Computational Physics is a key part of the landscape of 21st century physics. However there has been limited research on effective teaching techniques in computational physics. Especially in upper division lab courses, where practicing physics can look very different to introductory courses, as students join the physics community and develop their physics identity. We conducted interviews of students in a one-credit junior level computational physics lab course at a large public research university. We analyzed these interviews through the framework of Activity Theory. In so doing we sought to identify tensions in the activities of the course and thus where improvements might be made, and to understand the general dynamics and character of the course as it currently exists. We identified tensions between group work expectations, building proficiency at computing, and other aspects of the course. The specific contextual features of this computational physics lab add nuance to our findings.
Joline Nguyen, Biochemistry & Biophysics
(Advisor: Sarah Clark)
Isolation of Scarce Membrane Protein Complexes from C. elegans
Membrane proteins serve critical roles in many biological processes and have been identified as potential targets for the development of therapeutic treatments. Biochemical and structural studies of many membrane proteins have been hindered by challenges associated with recombinant expression in heterologous cell lines and low abundance in native tissue. To overcome these challenges, we have developed a strategy that enables isolation of sufficient amounts of endogenous membrane proteins from transgenic Caenorhabditis elegans for biochemical, biophysical, and structural analyses. We detail a step-by-step workflow from large-scale growth of genetically modified C. elegans to protein purification utilizing standard laboratory equipment. We also describe how a protein’s quantity and quality can be assessed in small scale via fluorescent detection size exclusion (FSEC) analysis, and conditions for protein isolation in small-scale experiments can be adjusted and optimized to improve the yield and purity of the protein of interest. To demonstrate the feasibility and utility of this strategy, we describe herein a successful isolation of a low-abundance, multi-subunit “protein complex A” that cannot be reconstituted in cell culture. Our isolation strategy can help facilitate the studies of challenging, biomedically relevant membrane proteins.
Victory Chiamaka Obieke, Mathematics
(Advisor: Vrushali Bokil)
Compatible Energy Preserving Discretizations for Nonlinear Optical Wave Propagation: The Maxwell-Duffing Approach
This poster presents the modeling and numerical simulation of electromagnetic wave propagation in nonlinear optical media using the Maxwell-Duffing model. The material’s nonlinear response is described by a cubic Duffing oscillator coupled with Maxwell’s equations in a non-magnetic, non-conductive setting. We derive the associated energy relations for the one-dimensional system to guide the development of stable numerical methods. A high-order spatial discretization is introduced using a fully discrete leap-frog finite-difference time-domain (FDTD) scheme combined with operator splitting. Emphasis is placed on second-order time and higher-order space accuracy for resolving traveling wave solutions. We establish the energy stability of the proposed Yee-type FDTD schemes and support our theoretical findings with numerical experiments. This work addresses key challenges in simulating nonlinear photonic phenomena and provides robust tools for advancing research in nonlinear wave propagation.
Emily Palmer, Statistics
(Advisor: Yuan Jiang)
A Group Penalization Framework for Detecting Time-Lagged Microbiota-Host Associations
We present a framework to identify time-lagged associations between abundances of longitudinally sampled microbiota and a stationary response (final health outcome, disease status, etc). We introduce a definition of the time lag by imposing a particular grouping structure on the association pattern of longitudinal microbial measurements. Using group regularization methods, we identify these time- lagged associations including their strengths, signs, and timespans. Simulations accurately identify both time lags and signal strengths. We apply this framework to find specific gut microbial taxa and their lagged effects associated with increased parasite worm burden in zebrafish.
Madison Phelps, Mathematics
(Advisor: Malgorzata Peszynska)
Nonlinear Solvers in Permafrost Applications
We study the nonlinear heat equation with phase change between liquid and ice where the energy to change phase is given by the enthalpy-temperature relationship, a(q). For Stefan problems, a(q) is usually a multivalued graph where the phase transition resembles a shifted Heaviside function. For permafrost problems, a(q) is continuous but its inverse requires a nonlinear solver. For soil with trapped air and large pores, we propose a model denoted (R*) where a(q) is a multivalued graph with piecewise continuous properties. We show how to extend analytical solutions for the Stefan problem to (R*). Also, the robustness of our numerical algorithm will be shown, where we use various local nonlinear solvers such as Newton-Anderson, to study the performance and convergence of our solver. We also describe progress towards a coupled Salinity model.
Kevin Rice, Microbiology
(Advisor: Maude David, Kenton Hokanson)
Electrical Characterization of Primary Enteroendocrine Cells: Developing Tools to Screen Novel Microbial Neuroactive Compounds
The vagus nerve plays a central role in gut-brain communication, relaying microbial signals sensed by enteroendocrine cells (EECs) to the brainstem. A subset of EECs, known as neuropods, synapse onto vagal afferents and release cholecystokinin (CCK), providing a rapid, direct gut-to-brain connection. However, the electrophysiological properties of CCK-EECs remain poorly characterized. Here, we performed whole-cell patch clamp recordings on primary mouse EECs labeled with CCK-GFP and isolated via FACS. Using a co-culture with vagal nodose neurons, we cultured stable, electrically active EECs and measured intrinsic properties, including resting potential, input resistance, and capacitance. Most cells fired action potentials upon stimulation and exhibited voltage-dependent Nav and Kv currents. This work establishes a foundational characterization of murine CCK-EECs. Additionally, by combining co-culture, whole-cell crosslinking, and single-cell proteomics, we identified microbial taxa and EEC-associated proteins, such as pro-glucagon and ghrelin, that can now be tested for their effects on gut-brain signaling.
Casey Rummelhart, Chemistry
(Advisor: Addison Desnoyer)
Frustrated Lewis Pairs Ligand for the Transformation of Carbon Dioxide to Chemical Feedstocks
With the deleterious role of carbon oxides on climate change, scientists are desperately seeking to discover methods to efficiently sequester and functionalize these gases. While this idea has been explored via frustrated Lewis pairs stoichiometrically, catalytic examples remain scarce. It is proposed that a rationally designed, bimetallic ligand will alleviate these challenges and allow an understanding of the mechanism of transformation. This design makes use of a modular bis-phosphine NacNac system, which will allow for tunability of both electronic and steric parameters. Additionally, this design takes advantage of inverted charge density compared to typical FLP examples, displaying a concentrated site of reactivity within the bimetallic pair.
Pavel Sengupta, Chemistry
(Advisor: Dipankar Koley)
Quantifying Dissolved Oxygen in Biofilms with Non-invasive Flexible Amperometric Oxygen Sensors
Dissolved oxygen (DO) is one of the most frequent and important parameters for measurement in numerous scientific fields such as healthcare, environmental, food industries, energy, to name a few. Traditional oxygen sensors such as the optical, fluorescence, colorimetric and electrochemical oxygen sensors such as galvanic or polarographic types might be bulky, cause chemical leakage or degradation, and lack the flexibility to use in biological systems such as biofilms, human cells and tissues, and in small volume or miniaturized measurements. Furthermore, traditional methods of oxygen detection are often obtained as an end-result quantification and often not performed in real-time. Fabricated a unique flexible amperometric (electrochemical) oxygen sensor which addresses some of the challenges of traditional electrochemical oxygen sensors widely used. A system has been proposed with supported data to measure DO in bacterial specimen. This will be applied in quantifying the oxygen concentrations in oral biofilms non-invasively in real-time.
Michael Sieler, Microbiology
(Advisor: Thomas Sharpton)
Modeling the zebrafish gut microbiome’s resistance and sensitivity to climate change and parasite infection
As global water temperatures rise due to climate change, parasitic infections are expected to spread and intensify. Yet, the gut microbiome may buffer hosts against infection, particularly if it remains stable under environmental stress. Using zebrafish (Danio rerio), we tested whether the gut microbiome is resistant or resilient to elevated temperature, parasite exposure, and their combination—and whether these responses relate to infection outcomes. Adult zebrafish were exposed to the intestinal parasite Pseudocapillaria tomentosa at 28°C, 32°C, or 35°C, and fecal microbiomes were sampled across 42 days. We found that temperature and parasite exposure independently affected gut microbiome diversity, while temperature moderated parasite-microbiome associations. Surprisingly, higher temperatures reduced infection burdens. Our results suggest temperature alters the microbiome’s response to parasitic stress and may mitigate infection severity. This work highlights how climate change can yield unexpected effects on host health.
Gavin Tovar, Statistics
(Advisor: Robert Trangucci, Sarah Emerson)
Sequential Approach to K-Fold Cross-validation---Computational Reduction Technique
Cross-validation (CV) and variants of it are widely used for predictive error estimation and model selection criterion. We investigated using sequentially developed CV folds to reduce computational time, inspired by the common one standard error rule found in the CV literature. Simulations and applications to real world data were used to compare the performance to competing CV methods.
Hao Yue, Chemistry
(Advisor: Marilyn Mackiewicz)
Targeted X-ray Imaging Agents for Visualizing Triple-Negative Breast Cancer
Triple-negative breast cancer (TNBC) is a highly aggressive subtype lacking estrogen, progesterone, and HER2 receptors, limiting treatment to chemotherapy and surgery. Its poor prognosis and resistance to conventional imaging highlight the need for improved diagnostic tools. We developed hybrid-lipid-coated silver nanoparticles (AgNPs) functionalized with anti-mouse IgG secondary antibodies for targeted TNBC imaging. These nanoparticles bind PI-selective primary antibodies that recognize phosphatidylinositol lipids overexpressed on TNBC cells, enabling indirect labeling with enhanced specificity and signal amplification. The lipid coating improves stability and biocompatibility. To assess performance in a tumor-like context, we are generating 3D TNBC spheroids to evaluate nanoparticle uptake, fluorescence distribution, and cytocompatibility, supporting future diagnostic development.
Sima Ziyaee, Chemistry
(Advisor: Claudia Maier)
Exploring Cellular Heterogeneity through Single-cell Proteomics
Single-cell proteomics provides a powerful approach to explore cellular heterogeneity and capture subtle biological changes often masked in bulk analyses. Here, we present two studies that highlight its critical importance. First, MDA-MB-231 breast cancer cell lines stably overexpressing Bcl-2 were generated through electroporation and clonal selection in G418-containing media. High Bcl-2 expression clones (MDA-MB-231/Bcl-2) were identified by Western blotting and subjected to chemical library screening alongside vector controls. Single-cell analysis revealed specific protein expression changes, including differential expression of BCL2L1, that were not detectable at the bulk population level. The second study investigates PC12 cell differentiation into sympathetic ganglion neurons induced by nerve growth factor (NGF). Using single-cell proteomic profiling, we not only monitor differentiation progression and identify protein and peptide markers but also unveil cellular heterogeneity within the differentiated population, which remains obscured in traditional bulk proteomics. Together, these studies underscore the vital role of single-cell analysis in revealing hidden molecular diversity.
