Star clusters have long been used as chemical and dynamical tracers for our home galaxy, the Milky Way. Many of these clusters are the old, metal poor, and massive objects known as globular clusters. These globular clusters are ideal test-beds for studying stellar evolution, stellar dynamics, and Galactic evolution since all the included stars are born from the same gas cloud. In this work, we combine the positions and motions of stars on the sky, provided by the European Space Agency’s Gaia space telescope, with the high-resolution chemical abundances from the Apache Point Galactic Evolution Experiment (APOGEE) to create a catalog of globular clusters. By only using data from two sources this sample of clusters is less susceptible to systematic offsets induced by combining multiple literature datasets. Overall, our catalog includes nearly half of all known Milky Way globular clusters, and a total of 5000 likely stellar members with APOGEE chemical abundances. We use these data to explore the internal properties of globular clusters as well as the population of the clusters as a whole to paint a picture of what the Milky Way looked like when it was first forming.

Characterizing Galactic sub-structures is crucial to understanding the assembly history and evolution of the Milky Way. To accomplish this, we need to identify and analyze the accreted sub-structures. With ESA Gaia and SDSS-IV/APOGEE, studies have been done to analyze the kinematics and chemical abundances, respectively. However, one challenge that still remains is deriving reliable ages for these sub-structures. We utilize the new relationship between the carbon to nitrogen ratio and stellar age derived by the OCCAM team, which has recently been extended to the metal-poor regime, to probe stars within the sub-structures in the metallicity range -1.2 ≤ [Fe/H] ≤ +0.3 dex. This allows us to determine the ages of a greater number of stars within these sub-structures, which paints a more coherent picture of the original galaxies that have been disrupted to form the Milky Way’s halo. Using the sample of halo sub-structures in Horta et al. (2023), we apply the newly extended calibration to determine ages of stars within these sub-structures and compare them to previous age estimates.

The most common immunological models for analyzing viral infections assume even spatial distribution between virus particles and healthy target cells. However, throughout an infection, the spatial distribution of virus and cells changes. Initially, virus and infected cells are localized so that a target cell in an area with lower virus presence will be less likely to be infected than a cell close to a location of viral production. A density-dependent rate has the potential to improve models that treat cellular infection probability as constant. A Beddington-DeAngelis model was used to understand how density dependent parameters could impact the severity of an influenza infection. Parameter values were varied to understand implications of density constraints. For low density dependence, a steeper increase in number of virus and greater viral peak was predicted. Higher density dependence predicted a longer time to viral load maximum and a greater infection duration. Initial localization of infected cells likely slows the progression of infection. The model demonstrates that accounting for density dependence when analyzing influenza infection severity can result in an altered expectation for viral progression. A density-dependent infection rate may provide a more complete view of the interaction between infected and healthy cells.

Mathematical models of cancer cells 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 non-cancerous cells in addition to the tumor to determine when the virus will spread to non-cancerous cells. 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.

Galaxies are giant playgrounds in which stars, planets, and potentially sentient carbon-based lifeforms live out their lives. We live in the Milky Way galaxy, however, like all larger galaxies the Milky Way has a slight cannibalism problem. Larger, more massive galaxies are assembled from smaller galaxies where the surviving small galaxies are dwarf galaxies. The latest victims of our Milky Way’s cannibalism are the Large Magellanic Cloud (LMC) and the Small Magellanic Cloud (SMC), and we have no idea what happened to their dwarf galaxies. To further complicate things, we don’t know how many dwarf galaxies fell into the Milky Way with the LMC, or where they ended up. In addition, the dwarf satellites of the LMC should be extremely faint and difficult to detect. We use computer simulations in order to take a bite out of these questions. We send a perfectly innocent LMC and its satellites into the gravitational potential of a Milky Way galaxy, and see where the dwarf satellites are flung.

Astronomers determine chemical abundances of stars through spectroscopy, which provides clues as to where the stars were formed. We use the chemical composition of stars to infer their relative ages due to past enrichment. However, the surface abundance of stars is not always constant during its life and will change as the star evolves due to its internal processes. As a result, if we assume the chemical makeup of stars is constant within a star cluster, it can cause systematic errors when inferring stellar parameters. For example, in previous investigations, the star cluster M67 has been observed to have signatures of atomic diffusion: the combined effect of gravity pulling elements deeper into the star and radiation preventing elements from floating to the surface locks elements below the observable surface of a star which cannot be unlocked until the star evolves further, changing the measured abundance. When the star evolves, convection reaches into the interior of the star and carries these elements back to the surface where they can now be observed once again. This process can explain the elemental abundance variation found in main-sequence stars, like our Sun, and also evolving stars, which can also affect what apparent age we determine. Stars within a cluster tend to form from the same gas cloud at the same time, giving them the same age and initial chemical composition. Therefore, star clusters are ideal test-beds for investigating elemental abundance and the resulting apparent age variations. Data from the Apache Point Galactic Evolution Experiment survey provides the opportunity to investigate how abundance variation/diffusion is affected by age.

Abstract: Researchers hypothesize that the initial amount of virus will affect the severity of the disease. They also believe that this will affect the amount of antivirals needed. We used mathematical modeling to study the effect of the initial viral dose on the effectiveness of antivirals. We simulated Sars-Cov-2 infections starting with different amounts of virus and treated with different amounts of antivirals, then measured the duration of the infection. This mathematical model predicts little to no effect on the amount of antivirals needed when the starting dose of virus is changed.

With novel materials getting smaller and their size falling to the nanometer scale, it becomes harder to fully characterize them by only using the experimental apparatus at hand. Therefore, taking advantage of computational methods proves to be trustworthy in filling those gaps and in aiding our experimental data to get a better understanding of the nanomaterials’ structural and electronic properties. Graphene quantum dots (GQDs) have recently become one of the flagships of carbon nanotechnology due to their remarkable physical properties and, when functionalized, their ability to become water soluble, biocompatible, and capable of fluorescence in the visible and near-infrared. This makes them perspective carriers for therapeutic delivery and image-tracking. In order to assess the advantages of their utilization for a variety of bioapplications, we have investigated the optical properties of doped GQDs and their interactions with biomolecules using a variety of molecular simulation approaches. The true atomic ground state of the N-GQD is achieved by performing first-principle calculations based on density functional theory (DFT). DFT calculations also unrevealed the contributions of each functional group within the structure to HOMO–LUMO band edges. The adsorption of biomolecules and genes on the GQD surface has been further investigated with regard to the GQD structure, complementing experimental results that verify gene and drug complexation.

While silencing RNA (siRNA) technology has become a powerful tool that can enable cancer-specific gene therapy, its translation to the clinic is still hampered by several critical factors. These include the inability of cell transfection by the genes alone, poor siRNA stability in blood, and the lack of delivery tracking capabilities. Recently, graphene quantum dots (GQDs) have emerged as a novel platform allowing targeted drug delivery and fluorescence image-tracking in the visible and near-infrared. These capabilities can aid in overcoming primary obstacles to siRNA therapeutics. Here, for the first time, we utilize biocompatible nitrogen and neodymium-doped graphene quantum dots (NGQDs and Nd-NGQDs) for the delivery of Kirsten rat sarcoma virus (KRAS) and epidermal growth factor receptor (EGFR) siRNA effective against a variety of cancer types. The non-covalent loading of siRNA onto GQDs is evaluated and optimized by the electrophoretic mobility shift assay and zeta potential measurements. GQDs as a delivery platform facilitate successful gene transfection into HeLa cells confirmed by confocal fluorescence microscopy at biocompatible GQD concentrations of 375 µg/mL. While the NGQD platform provides visible fluorescence tracking, Nd doping enables deeper tissue near-infrared fluorescence imaging suitable for both in vitro and in vivo applications. The therapeutic efficacy of the GQDs/siRNA complex is verified by successful protein knockdown in HeLa cells at nanomolar siEGFR and siKRAS concentrations. A range of GQDs/siRNA loading ratios and payloads is tested to ultimately provide substantial inhibition of protein expression down to 31-45% comparable with conventional Lipofectamine-mediated delivery. This demonstrates the promising potential of GQDs for the non-toxic delivery of siRNA and genes in general, complemented by multiwavelength image-tracking.

In order for galaxies to sustain current star-formation rates, including our Milky Way, they need to replenish their reservoirs of gas. High-velocity clouds (HVCs) entering our galaxy, like the Smith Cloud, present a possible source of gas for future star formation. Although the chemistry of the Smith Cloud has been previously studied, it is unclear whether there is variation in the chemistry of the Smith Cloud. With the Hubble Space Telescope, we measure the absorption of various elements along the tail and an adjacent fragment of the Smith Cloud. For the tail, we used existing observations, and for the fragments, we observed two new sightlines with Hubble. We additionally use radio emission-line observations from the Green Bank Telescope and from the Galactic All-Sky Survey (GASS) to understand the neutral hydrogen gas. Using observations in conjunction with the Cloudy simulations, we provide constraints on the chemistry of all five sightlines. Our new sulfur abundances for the adjacent fragment of the Cloud are higher than those downstream in the trailing wake. By quantifying the properties of gas clouds traveling through the Galactic halo, we can assess how they are impacted by their environments and better understand how the star-formation gas reservoirs of large galaxies are replenished.

There is currently a mismatch between the chemical properties of a typical star and those within star clusters across the Milky Way galaxy. Star clusters are groups of stars bound by gravity, many of which are found in the disk of the Milky Way. Studying these star clusters reveals essential information about the rich history of our Galaxy, as we can measure their age and their chemical composition independently. While some clusters interact with their environment, causing them to dissolve, other clusters remain bound for billions of years. In order to investigate these disruption events, we will study the evolution of star clusters throughout cosmic time via simulations. With the use of cosmological simulations, such as the Feedback In Realistic Environment (FIRE) simulation, we are able to learn why clusters move from their original place of formation and how far they go. Additionally, FIRE allows us to trace star clusters through their different stages of their evolution, and study how they survive as they interact with other components of the galaxy. In this study, we will investigate the Galactic chemical gradient mismatch for the Milky Way, as we compare the FIRE simulations to the observed star cluster distribution and properties measured from Gaia satellite and the Sloan Digital Sky Survey.

Dark Matter (DM) is hypothesized to be an exotic particle that is invisible to human observation. But thankfully, its existence is proven through its gravitational interaction with luminous matter (such as stars and galaxies), and it is responsible for the formation of the humongous structures across our universe. The leading interpretation of DM is what we call Cold Dark Matter (CDM), where the DM particles have relatively low velocities and low energies. This causes structures to form quite quickly and easily in the early universe. While CDM can explain many observed properties of the universe, it is not without its flaws (specifically on the scale of low-mass dwarf galaxies). The hypothesis of Warm Dark Matter (WDM) poses a viable solution to the shortcomings of CDM. In WDM, the DM particles are of higher energy and have higher velocities. This would cause the formation of the first gravitationally bound structures in the Universe to be delayed when compared to CDM. Using a model to approximate varying temperatures of DM, we compare the rates and characteristics of early structure formation for the current CDM hypothesis, and that of many other types/temperatures of WDM. We expect that the differences between CDM and WDM will be most apparent during the first billion years after the Big Bang, just as the first stars in the Universe ignite. These results may be indicative of the true nature of dark matter, and finally bring our understanding into the light.

This report presents a novel approach to increase the detection sensitivity of trace amounts of DNA in a sample by employing Förster Resonance Energy Transfer (FRET) between intercalating dyes. Two intercalators that present efficient FRET were used to enhance sensitivity and improve specificity in detecting minute amounts of DNA. Comparison of steady-state acceptor emission spectra with and without the donor allows for simple and specific detection of DNA (acceptor bound to DNA) down to 100 pg/ul. When utilizing as an acceptor a dye with a significantly longer lifetime (e.g., Ethidium Bromide bound to DNA), multi-pulse pumping and time-gated detection enable imaging/visualization of picograms of DNA present in a microliter of an unprocessed sample or DNA collected on a swab or other substrate materials.

We studied room temperature phosphorescence of tryptophan (TRP) embedded in poly (vinyl alcohol) [PVA] films. With UV (285 nm) excitation, the phosphorescence spectrum of TRP appears at about 460 nm. We also observed the TRP phosphorescence with blue light excitation at 410 nm, well outside of the S0→S1 absorption. This excitation reaches the triplet state of TRP directly without the involvement of the singlet excited state. The phosphorescence lifetime of TRP is in the sub-millisecond range. The long-wavelength direct excitation to the triplet state results in high phosphorescence anisotropy which can be useful in macromolecule dynamics study via time-resolved phosphorescence.

Graphene Quantum Dots (GQDs) are highly perspective bioimaging agents due to a plethora of advantageous properties making them superior to conventional fluorophores. Those properties include stability to photobleaching, large Stokes shifts circumventing biological autofluorescence, and a capability of functionalization for drug delivery. In this work, a variety of GQD structures are imaged via visible fluorescence microscopy in order to evaluate the optimal GQD structures for bioimaging and bioengineering in vitro.

Infections deriving from the highly pathogenic avian H5N1 influenza virus often result in severe respiratory diseases with a high mortality rate. Although rarely transmissible to humans, recent events such as the SARS-CoV-2 pandemic have shown that a proper understanding of the life cycles of deadly viruses like H5N1 and any variables that affect its terminality are vital. One such variable could be the method of entry, and its impact on the progression of H5N1 is the focus of the study. Utilizing previous data on cynomolgus macaques subject to samples of H5N1, we study how entry via a combined intrabronchial, oral, and nasal pathway affect disease progression. We fit the data using a viral kinetics model, which allows us to estimate parameters describing the H5N1 life cycle. This allows us to better understand the life cycle of H5N1 in vivo.

Everyone gets sick and illness negatively affects all aspects of life. One major cause of illness is viral infections. Some viral infections can last for weeks; others, like influenza (the flu), can resolve quickly. During infections, healthy cells can grow in order to replenish the cells that have died from the virus. Past viral models, especially those for short-lived infections like influenza, tend to ignore cellular regeneration – since many think that uncomplicated influenza resolves much faster than cells regenerate. This research accounts for cellular regeneration, using an agent-based framework, and varies the regeneration rate in order to understand how cell regeneration affects viral infections. The model used represents virus infections and spread in a two-dimensional layer of cells in order to generate graphs of virus over time for corresponding regeneration rates. We find that the effect of cell regeneration depends on the mode of transmission of the infection.

The first stars in the Universe, Pop III stars, formed out of the primordial hydrogen and helium sometime during the first billion years of cosmic time. Their formation ended the Cosmic Dark Ages. Despite their critical role in kick starting the formation of all “metals,” (ie. the carbon in our bodies and the oxygen we breathe), we do not know how massive these first stars were, and when and how the era of the first stars ended. While Pop III stars are too faint for a direct detection, their deaths are potentially visible by James Webb Space Telescope (JWST). A subset of Pop III stars end their lives as Pair Instability Supernova (PISN), explosions 100 times more powerful than a typical supernova. However, determining the astrophysics of the first stars will require combining the detection of PISN with theoretical work on the mass distribution of Pop III stars. In this theoretical work, we need to fully explore the range of mass distribution of Pop III stars to determine how dependent the PISN rate is on the masses of Pop III stars. In this work, we present results from a new model which explores the distribution of Population III masses with a set free parameters. We find an order magnitude difference in the PISN rates for various Pop III mass distribution. In addition, we find that PISN rates may provide one of the first independent probes of the maximum mass of Pop III stars.

It has been well established that ZnO is a versatile material with multiple existing and potential applications owing to its numerous and unique properties. ZnO in the nano- and microscale forms has been a focus of attention in recent years due to demonstrated utilities in pharmaceutics, bioengineering and medical diagnostics. Of particular interest is the utilization of ZnO as an antibacterial agent. With growth inhibition observed for both gram-positive and gram-negative bacteria as well as antibiotic strains, the antibacterial action of ZnO is well documented. Yet, there exists much debate over the fundamental mechanisms underlying the antibacterial action of ZnO. Commonly proposed mechanisms include the generation of various reactive oxygen species, release of Zn ions, surface-to-surface interactions, etc. In this work, we investigate the surface and near-surface optoelectronic properties of ZnO microcrystals as they relate to the antibacterial figures of merit. As microscale ZnO particles exhibit comparable antibacterial action to those at the nanoscale, while minimizing effects related to internalization, they are well-suited to serve as a platform to investigate the role of the crystalline free surfaces in this behavior. A bottom-up hydrothermal growth method was employed to synthesize ZnO microcrystals with tunable morphology and a well-controlled relative abundance of polar and non-polar surfaces. The quality of the crystalline lattice and free surfaces as well as the predominant morphology of these samples were confirmed by scanning electron microscopy, energy-dispersive X-ray spectroscopy, and surface photovoltage spectroscopy. The antibacterial efficacy of these particles was characterized via minimum inhibitory concentration assays, performed using Staphylococcus Aureus in a Mueller Hinton broth media. We performed a series of optoelectronic experiments including temperature dependent photoluminescence spectroscopy as well as spectroscopic and transient surface photovoltage as a means to observe changes occurring at the ZnO surface during these assays. We detected significant spectral changes due to interactions with bacteria and growth media. In particular, we showed that interactions with s.aureus resulted in considerable modifications of the excitonic luminescence.

Photothermal Therapy (PTT) provides a promising new method of radiative therapy cancer, using infrared wavelengths. In my project, the ability of these materials to heat up when shone with near infrared light, or the photothermal effect, of various nanomaterials—including reduced graphene oxide, reduced graphene quantum dots , and copper sulfide nanoparticles—is characterized by irradiation of the aqueous materials with near-infrared radiation.

CRISPR Cas9 is a programmable single guided RNA (sgRNA) ribonucleic protein (RNP) that has demonstrated their ease and practical use as a gene editing tool for in vitro and ex vivo applications. For in vivo applications of the Cas9 RNP, physiological barriers must be overcome and gene editing to occur transiently, demonstrating the need to develop biocompatible imaging agents to protect and locate Cas9 RNP in vivo. Graphene quantum dots (GQDs) are biocompatible carbon-based nanomaterials that have served as delivery and imaging agents for drug and gene medicine due to their ease in synthesis and repertoire of complexation capabilities arising from the choice of precursor materials. In this work, we have synthesized visible and near infrared emitting GQDs with glucosamine HCl and polyethylenimine (PEI) using a bottom-up approach to use them as non-viral delivery vehicles for the Cas9 RNP. PEI increases the net positive charge of GQDs allowing their electrostatic complexation with the net negatively charged RNP. We further demonstrate their complexation with gel retardation assay and TEM. The GQDs+PEI+RNP in vitro editing capability is shown by targeting the TP53 414delC frameshift mutation locus present in PC3 cancer cell line for prostate cancer. This form of editing serves as a guide for future cancer therapy using GQDs as non-viral delivery of Cas9 RNP to mutant TP53 genes overexpressed in about 50% of cancers.

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.

One of the large unanswered questions in astronomy is: How does the Milky Way galaxy evolve, chemically and dynamically? Of all the objects that we could use to probe this question, groups of stars which were all born from the same gas cloud, known as open clusters, are the most reliable. This makes open clusters ideal for exploring the evolution of our Galaxy because we can determine not only the distance, position, velocity, and chemistry of the cluster, but we can also pin a reliable age to the cluster as well. Historically, assembling a statistically significant dataset of open clusters has proved to be challenging without inducing large systematic uncertainties by collecting data from multiple sources. The Open Cluster Chemical Abundance and Mapping (OCCAM) survey is a uniform dataset of star clusters that uses dynamical data from the Gaia space telescope and 16 different chemical abundances from the APOGEE survey, which is a part of the Sloan Digital Sky Survey. This new update to OCCAM includes uniformly measured data for 153 open clusters and a total of 2061 member stars, which we use to investigate the chemical evolution of the Milky Way.

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.