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.