With the onset of the SARS-CoV-2 pandemic in the U.S. in early 2020, much of the early response in the U.S. was made on a state level with varying levels of effectiveness. To characterize the effects of early preventative measures by state legislatures we can use a SEIR model and data gathered to analyze the effectiveness of lockdown measures from state to state. Using the data collected we can model the effect of lockdown measures on the infection rate to characterize the effect preventative measures had on case numbers. We chiefly used 4 models to simulate the change in infection rate: instantaneous, linear, exponential, and logarithmic. Then using these models, we fit each model to the case data and compared the relative accuracy of each model to the data to determine which model most accurately represented the change in infection rate within the first months of the pandemic. Following this, we used the fits obtained to create a possible distribution for each parameter, which helps accurately predict the actual number of cases and how it was affected by preventative measures.

Several different vaccines have been introduced to combat the spread of SARS-CoV-2 infections. As the virus is capable of mutating to escape the protection given by the vaccine, using multiple vaccines is believed to help prevent the virus from mutating to escape all vaccines, helping to combat spread of the virus. We simulate the effect of using multiple vaccines on the virus using a mathematical model. With the model, we can better understand the effect of multiple types of vaccines in helping to control pandemics.

Oxidative stress, an imbalance of reactive oxygen species, has been shown to participate in a multitude of diseases from Alzheimer to cancer. Thus, there is a search for radical scavenging agents capable of circumventing oxidative stress. Due to their remarkable properties, quantum dots are known to be utilized in a variety of applications including binding of reactive oxygen species (ROS). However, the translation of nanomaterials to clinic is often hampered by their off target toxicity. Thus, the aim of our work is to develop and test fully biocompatible graphene quantum dots (GQDs) with a variety of dopants that will the tune radical scavenging activity (RSA) of the GQD. We have synthesized and tested over ten types of doped GQDs and accessed their radical scavenging ability via DPPH, KMnO4, and RHB assays. Among those, thulium and aluminum doped GQDs show superior scavenging.

Respiratory syncytial virus (RSV) can cause a severe respiratory illnesses particularly in young children and the elderly. Defective viral genomes (DVGs) have recently been found during RSV infections and are thought to be linked to the severity of the illness. In this study, we use mathematical models to simulate the spread of RSV using data from environments in which DVGs are detected early and late in order to estimate infection rates and other infection parameters in each setting. We find that the presence of DVGs is reflected in changes in the infection rate and viral clearance rate of infections.

During the first billion years after the Big Bang the first, faint, galaxies formed. With luminosities less than one millionth that of our Milky Way galaxy, they are too faint to be observed by even our most advanced telescopes. A fraction of these first galaxies are preserved as ultra-faint dwarf galaxies in the local universe. These ultra-faint dwarfs are the fossils of the first galaxies. Therefore, we can study the faintest satellites of the Milky Way and learn about the formation and evolution of the first galaxies using galactic paleontology. We know that the stellar properties of the faintest Milky Way satellites match the stellar properties of galaxies formed in high resolution hydrodynamic simulations of the first billion years. We also know that the semi-analytic model Galacticus can reproduce the stellar properties of the faintest Milky Way dwarfs in the modern epoch. In this work, we determine whether Galacticus is also able to match the high resolution simulations of the first billion years.

The creation and evolution of elements throughout time across the Milky Way disk provides a key constraint for galaxy evolution models. To provide these constraints, we are conducting an investigation of the zirconium, neodymium, cerium, and barium abundances created in supernovae explosions, for a large sample of open clusters. The stars in our study were identified as cluster members by the Open Cluster Chemical Abundance & Mapping (OCCAM) survey that culls member candidates by Doppler velocity, metallicity, and proper motion. We have obtained new data for the elemental abundances in these clusters using the Subaru Observatory 8-m telescope in Hawaii with the High Dispersion Spectrograph (HDS). Analyzing these neutron-capture abundances in star clusters will lead us to new insight on star formation processes and the chemical evolution of the Milky Way galaxy.

Cancer is a leading cause of death worldwide with around one in every six caused by cancer, but many cancers can be cured if treated properly. Mathematically programmed cancer cell models can be used by researchers to study the use of oncolytic viruses to treat tumors. With these models, we are able to help predict the viral characteristics needed in order for a virus to effectively kill a tumor. Our approach uses both cancerous and non cancerous cells in relationship to the tumor to determine the speed at which the cells replicate, however there are several models used to describe cancer growth, including the Exponential, Mendelsohn, Logistic, Linear, Surface, Gompertz, and Bertalanffy. We study how the choice of a particular model affects the predicted outcome of treatment.

The first galaxies formed 12.5 billion years ago during the first billion years after the Big Bang. However, these first, faint, galaxies remain too faint for direct detection, even by our most powerful telescopes. Therefore we study them using their fossils relics, ultra-faint dwarf galaxies orbiting the Milky Way. In this work, we look at the histories of star formation in simulated analogs to the ultra-faint dwarfs. These star formation histories will allow us to study the details of how and when star formation occurred during the first billion years of cosmic time. We are particularly interested in how massive the first galaxies were when they formed the majority of their stars.

Coinfection affects up to 60% of patients hospitalized influenza-like illnesses, however, the role of the innate immune response in coinfections is not understood. Interferons, part of the innate immune response, are a type of chemical released by infected cells that can help establish an antiviral state in cells by increasing resistance to infection and reducing production of viruses. Although the increased resistance to infection can help suppress both viruses, the reduction in the production of one virus may aid in increasing the growth of another virus during coinfection due to less competition. We will use a mathematical model to examine the interaction via interferons between respiratory syncytial virus (RSV) and influenza A virus (IAV) during coinfections. This model will measure viral titer, duration of the viral infection, and interferon production allowing us to understand how interferon production of one virus helps or hinders the secondary virus.

Due to the enormity of different forms of cancer and the increase in cancer rates globally, it is essential to continually develop more advanced methods of early and localized detection of cancer cells, as well as methods of targeted drug delivery. As a result, a vast amount of research has gone into the use of nano-materials such as graphene quantum dots (GQDs) as the basis for a wide variety of biomedical sensing and treatment applications. While many diagnostic biomarkers have been detected using modified GQDs, one biomarker that has not yet been successfully detected or targeted using GQDs is Transgelin-2. Transgelin-2 is a unique actin-binding protein that has been projected to be a useful biomarker and target of treatment for many different forms of cancer, as well as asthma and immune diseases such as lupus. Herein I review the structure of the Transgelin-2 protein, novel methods of GQD modification to sense cell membrane surface proteins, and ultimately determine the viability of GQDs as a method for detecting and targeting Transgelin-2. Furthermore, I develop a possible methodology by which these biophysical applications may be tested.