Viral stability, replication, and host-virus interactions are all significantly impacted by temperature. Numerous experimental studies have demonstrated that SARS-CoV-2 grows differently at different temperatures, but it is still unknown which specific infection processes are impacted. In this work, we used a mathematical modeling approach to quantify the effects of temperature on the kinetic parameters controlling SARS-CoV-2 replication. Results from previously published experiments were used to determine the viral load from in vitro infections of Vero E6 and human nasal epithelial (hNEC) cells at 33 and 37 C. We fit a mathematical model of viral infections to estimate model parameters at the two temperatures. Vero E6 cells showed evidence of temperature dependence when parameter distributions were compared; the infection rate, eclipse phase transition rate, and infected cell death rate varied between 33 and 37 C. The parameter estimates in hNEC cells, on the other hand, revealed no statistically significant differences and showed a significant overlap in parameter estimates between temperatures. These results imply that the cellular environment has a significant impact on how temperature affects SARS-CoV-2 replication dynamics. The measurement of temperature-dependent variations in viral kinetic parameters sheds light on SARS-CoV-2 replication and could enhance forecasts of infection dynamics under various environmental and physiological circumstances.

Our Milky Way’s neighbor, the Large Magellanic Cloud (LMC), is a galaxy significantly shaped by powerful explosions from massive, dying stars that drive gas outflows. These explosions release gas and heavy elements, enriching the galaxy's outskirts and contributing to the formation of stars and planets. Understanding these processes is crucial for studying galactic evolution and the mechanisms that drive it. Our research uses observations from the Hubble Space Telescope to characterize the properties of the outflows from the LMC. Our observations are of light from background stars that pass through the LMC’s gas clouds. These clouds block some of the incoming light, and we analyze the missing features to study the physical properties of the outflows. To compare complex stellar spectra on a similar scale, we fit regions of the light that are free from major features blocking it with a best-fit polynomial. This process helps us differentiate components that either belong to the background star or the LMC’s outflowing gas. By examining the missing light, we gain a deeper understanding of how bursts of star formation impact the galactic environment and ultimately connect our existence to the explosive deaths of distant stars.

As stars begin to die, their surface chemistry changes over time. This is due to the combined effect of two competing processes: (1) gravitational settling that causes heavier elements to sink below the stellar surface and (2) radiative acceleration from photons that push gas upward. Although diffusion is a primary physical process in stellar interiors, its impact on surface chemical abundances is often overlooked in large-scale spectroscopic surveys, leading to systematic biases in stellar age estimates. This project investigates the onset (`turn-on') and suppression (`turn-off') signatures of atomic diffusion as dying stars transition into giants. Using high-resolution optical spectra, we will analyse open-cluster stars across various evolutionary stages to identify the age (or mass) threshold at which diffusion becomes detectable and shuts off. The resulting measurements will constrain the magnitude of diffusion-driven abundance changes, the stellar age (or mass) at which diffusion becomes observable, and the efficiency of abundance restoration during the first dredge-up. It will improve stellar age determinations and enhance the precision of Galactic archaeology and chemical-tagging studies.

ZnO is a wide-bandgap semiconductor with applications spanning optoelectronics, photovoltaics, pharmaceuticals, and related technologies. At the micro- and nanoscale, its functional properties are strongly governed by by surface structure, defect chemistry, and electronic states associated with the crystalline free surface. Targeted lattice doping therefore represents an effective strategy for tailoring surface energetics and enabling new functionalities. Fe incorporation has been proposed to stabilize ZnO nano- and microparticle surfaces by mitigating the internal surface dipoles and passivating dangling bonds. Such provides a controlled materials platform for probing the fundamental bactericidal mechanisms of ZnO. Although the origin of ZnO-induced cytotoxicity remains under debate, our recent findings indicate that surface-mediated interactions with bacteria and/or growth media components facilitate Zn²⁺ ion release from reactive surface defect sites. Surface stabilization through Fe doping is expected to reduce the density of these active sites, thereby limiting Zn²⁺ ion release. In this study, we systematically investigate the bulk and surface characteristics of hydrothermally synthesized Fe-doped ZnO across varying doping dopant concentrations. The antibacterial activity of both pure and Fe-doped ZnO is evaluated against Escherichia coli and Staphylococcus aureus assays. Structural and chemical analyses are performed using X-ray diffraction and X-ray photoelectron spectroscopy, whereas Raman spectroscopy is employed to probe dopant-induced modifications in lattice dynamics and bonding, providing further insight into the relationship between surface states and antibacterial performance.

Zinc oxide (ZnO) is a versatile, inexpensive semiconductor material with unique characteristics. ZnO is particularly known for its inhibitory effects on bacterial growth. ZnO can reduce bacterial growth through mechanisms such as oxidative stress, the deterioration of crucial proteins in the bacterial cell, and the release of Zn²⁺ ions that affect bacterial cell function. The exact mechanism behind ZnO’s antibacterial properties remains unclear. It has been seen that changing the surface and morphology of the particles changes their effectiveness for bacterial inhibition. An additional lesser explored branch of ethanol-based synthesis is solution pH pertaining to ZnO morphology. Our research aims to explore this by doing a wholistic investigation of an ethanol-based synthesis, especially pertaining to how pH affects particle morphology. To produce these materials, we used ethanol-based solvothermal synthesis to create ZnO micro- and nanocrystals. We performed a thorough characterization of these materials to observe changes to the ZnO lattice. This was done by employing scanning electron microscopy (SEM), energy-dispersive X-ray (EDX) spectroscopy, and X-ray diffraction (XRD) spectroscopy.

Graphene quantum dots GQDs possess broad potential in bioimaging and optoelectronics due to their unique optical properties, tunable structure, aqueous solubility, and minimal in vivo and in vitro toxicity. However, despite their solubility, GQD fluorescence may be quenched through interactions with water molecules and aggregation via non radiative decay pathways that reduce emission efficiency. Inspired by the ability of surfactants to prevent quenching interactions for single walled carbon nanotubes, we investigate their utility in preserving GQD fluorescence. Five structurally distinct surfactants, sodium dodecyl sulfate SDS, sodium dodecylbenzene sulfonate SDBS, sodium deoxycholate SDC, sodium cholate SC, and Pluronic F127, are tested across a range of concentrations for preserving fluorescence of top down and bottom up synthesized GQDs to determine optimal conditions. This work reveals that surfactant structure and concentration can non-linearly affect GQD emission in the visible and near-infrared, with SC and SDC providing maximum concentration dependent fluorescence increase. Zeta potential and dynamic light scattering measurements are conducted for each surfactant and GQD system to quantify interfacial charge, colloidal stability, and aggregate size distributions. The present study provides mechanistic understanding of how surfactants influence GQD photophysics, offering strategies to optimize GQD based probes for biomedical imaging and photonic applications establishing a structure-to-function framework that links solution phase organization to fluorescence emission.

Graphene quantum dots (GQDs) have gained significant attention due to their unique optical properties, biocompatibility, and potential applications in bioimaging, biosensing, and optoelectronics. The breakdown of single-walled carbon nanotubes provides an alternative method of producing GQDs that has the potential to be more efficient than current methods. We will investigate the effectiveness of various methods to break down single-walled carbon nanotubes, including through UV-light irradiation. Solutions of carbon nanotubes with sodium hypochlorite are placed under 254nm UV-light for two hours, and fluorescence in the visible spectrum is measured before and after UV-light irradiation to observe the production of GQDs. The use of surfactants in these solutions can affect the resulting fluorescence, so solutions of sodium dodecyl sulfate (SDS) and sodium dodecylbenzene sulfonate (SDBS) are also UV-light irradiated and observed. We will perform transmission electron microscopy (TEM) analysis on the samples to characterize the resulting GQDs and determine their size distribution. The findings from this study will contribute to the broader scientific community by improving an avenue of production for GQDs through conversion of carbon nanotubes into smaller, more functional materials while reducing the toxicity associated with carbon nanotubes.

Viral entry in a host cell is mediated by interacting viral fusion proteins and cell receptors. After entry, newly translated viral fusion proteins can end up on the surface of the infected cell. If the infected cell comes into contact with a cell expressing the associated receptor, the interaction can result in membrane fusion. The result of this fusion is a multi-nucleated cell, called a syncytium. Syncytia can cause an increase in severity and duration of an infection, as well as cause damage to the surrounding tissue. Syncytia formation is heavily dependent on spatial interactions and some models are not able to represent this component whatsoever. Agent-based models (ABMs) can accurately represent the temporal and spatial components of syncytia formation by simulating interactions between individual cells. We developed an ABM that can model syncytia formation for up to one million cells at a time. Implementing this model computationally, we have begun fitting to cell-cell fusion experimental data. This model allows us to get new spatial parameters that have never been looked into before. By investigating the spatial aspects, we will develop a better understanding of the role of syncytia during viral infections.

Defective interfering particles (DIPs) are virions missing the viral genome that allows them to replicate on their own, so they require coinfection with a standard virion to enable replication, interfering with the production of standard virus in the process. DIPs may also stimulate an interferon (IFN) response that further suppresses standard virus replication. Our aim was to evaluate the impact of DIPs and IFN on viral replication. We used Python programming to simulate a mathematical model evaluating the effects of DIPs and IFN on viral replication. Features of the viral titer curve were measured, including peak viral load and area under the viral curve, as functions of IFN parameters and DIP production rates. We examined a range of parameter values for DIP production rate and IFN response strength to assess the effects of DIPs and IFN independently and together. DIP production rate over a range of values resulted in no change in DIP or standard virus population dynamics. However, decreased IFN response resulted in an increase in standard virus and DIP population, while increased IFN response resulted in decreased standard virus and DIP population. DIP production in isolation did not impact viral replication, while IFN demonstrated an inverse relationship to viral replication and DIP production. Increased IFN and DIP production rate led to a reduction in infection intensity. IFN is essential to the antiviral effects of DIPs.

The lining of the human respiratory tract (HRT) has a layer of ciliated cells known as an epithelium. When exposed to virus, these cells actively push virus into mucous layers lining the epithelium and then funnel this mucous up and out of the human respiratory tract. This process is called mucociliary clearance (MCC) and is the first line of defense against a viral infection. We know that MCC plays a role in preventing respiratory infections, but we know little else. We hypothesize that, under the right conditions, MCC prevents infection by limiting the ability for virus to enter the lower respiratory tract. To test this, we constructed a compartmental model that uses a system of diffusion-driven partial differential equations to describe the virus propagation in the HRT as a travelling wave front with an advection term included to approximate MCC.  Our model shows that MCC can change the waveform of the virus propagation, and suggests that there exists a critical advection speed that prevents virus from entering the lower respiratory tract.

Galaxy simulations are an effective way to study the evolution of galaxies across
cosmic time. They have provided insights into the structural and chemical evolution
of galaxies, gas and star formation, and how LCDM models predict the large scale
structure of universe. Nevertheless, two primary issues have persisted using LCDM -
the core-cusp problem and the diversity of rotation curves for dwarf galaxies of similar
masses. To determine the effect of AGN on these issues, we utilize FIRE-2, which only
includes stellar feedback. We chose this particular galaxy at redshift 0 and compared
the curve to 8 previous observations, and we find that the innermost regions of the
curve are better matched to the data, but diversity still remains a problem. Thus, we
conclude that AGN feedback prescriptions may be removing too much mass from the
center of the galaxy, causing this discrepancy. Hence, more work is necessary to identify
the cause of this issue and potentially resolve it.

Indole derivatives are known to exhibit diverse luminescent behavior that is strongly affected by molecular structure and the surrounding environment. In this work, we investigate a series of regioisomeric indole-based compounds embedded in poly(vinyl alcohol) (PVA) films. By combining absorption and steady-state fluorescence measurements with room-temperature phosphorescence (RTP), fluorescence and phosphorescence anisotropy, and time-resolved emission decays under UV excitation, we examine how small changes in the position of substitution on the indole scaffold determine the luminescent properties of the studied compounds. Although structurally similar, the regioisomers exhibit distinct absorption and emission maxima, visibly different emission colors, and significantly varied excited-state lifetimes. Immobilization in the PVA matrix selectively enhances RTP for certain compounds, while others remain predominantly fluorescent, indicating a substitution-dependent balance between intersystem crossing and nonradiative decay pathways. Overall, the results indicate that even minor structural modifications in indole-based luminophores result in significant changes in their luminescent properties, and that regioisomerism can be used to control luminescent behavior in polymer matrices.

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

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

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

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

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

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

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

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

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

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

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

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

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

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