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.