Although there is an effective vaccine for SARS-CoV-2, or COVID-19, the virus is still spreading and affecting millions of people worldwide. SARS-CoV-2, along with many other viruses, is able to form large, multi-nucleated cells, known as syncytia. Syncytia formation, along with syncytia death, may affect the SARS-CoV-2 course of infection. We have been able to compute the death rate of syncytia using data from a study by Vanhulle et al. (2023) that used measurements of electrical impedence to study syncytia formation in cell-cell fusion assays. The death rate of syncytia was found using mathematical modeling. This knowledge can help further our understanding of syncytia and viral disease propagation.

Zinc Oxide (ZnO) nanoparticles are attractive candidates for application as antibacterial agents due to high biocompatibility with effectiveness against antibiotic-resistant strains of both Gram-positive and Gram-negative bacteria. Despite this potential, applications are limited by fundamental gaps in understanding of the underlying antibacterial pathways. ZnO nanoparticles are currently more widely used in antibacterial research compared to ZnO microparticles due to the potential for internalization into bacterial cells. Microparticles are nevertheless of interest as a research platform as the increased scale allows both the nonpolar and polar facets of the ZnO crystals to be distinguished. This in turn provides a useful platform to experiment on and study surface interactions with bacteria. In addition, because of their larger size, ZnO microparticles would not internalize inside typical bacteria, allowing for more targeted investigation of other, potentially more potent, antibacterial mechanisms.

Preliminary studies indicate that hydrothermally grown ZnO microparticles exhibit comparable antibacterial activity to commercial ZnO nanoparticles further adding to their utility. The goal of this research is to validate the nature of these behaviors by investigating differences in surface cleanliness between “home-grown” microparticles which were synthesized in the lab through a bottom-up hydrothermal growth method and commercial nanoparticles. Such differences may influence cytotoxicity, skewing the results of antibacterial studies. To do so, both Scanning Electron Microscopy (SEM) and Fourier Transform Infrared (FTIR) spectroscopy were used to probe the quality and cleanliness of the ZnO crystalline free surface of the microparticles and nanoparticles.

In this work we detected similarities in the vibrational modes at the surface stemming from ZnO growth precursors. These are seen to be similar across all samples investigated, however, a weak O-H bending is found in the home-grown microparticles. We demonstrate that these results justifies our low-cost hydrothermally lab-grown specimen as a suitable platform for future surface-specific antibacterial studies.

Syncytia formation is the fusion of cells by a virus to create a multinucleated cell (syncytium) that shields the virus from outer factors in the extracellular space, such as antibodies. However, this process is much more energy intensive for a virus than tunneling between cells, which also shelters the virus. Why would a virus fuse cells together rather than save energy and tunnel? In order to determine what the benefits of syncytia formation are for viruses, a mathematical model including syncytia formation and antibodies was developed to simulate viral dynamics. Characteristics like viral duration, viral titer peak, and time of peak were measured while changing parameters such as fusion rate, which allowed comparison of infections with and without syncytia formation. Mathematically modeling and analyzing these comparisons and changes helps us understand whether syncytia formation helps protect viruses from the effect of antibodies.

To track drug delivery within the body, the vehicle must be biocompatible, soluble, and transparent in the human body. Being transparent in the human body means the vehicle exhibits fluorescence in the near-infrared (NIR) III biological transparency window (1500 – 1800 nm). These traits will respectively not oppose health defects in the subjects, will be stable within the blood and cells of the body, and be able to be found within the body through the means of infrared detectors. This is where graphene quantum dots (GQDs) come into the picture. GQDs prepared by a one-step hydrothermal method from glucosamine and ascorbic acid precursors are biocompatible and soluble in water. On their own, they do not demonstrate fluorescence in the NIR-III. To add this capability, we dope GQDs with erbium ions (Er-GQDs) as they demonstrate a fluorescence peak at 1550nm followed by excitation at 980nm laser. Fluorescence light coming from erbium ions at 1550 nm covers the NIR-III biological window, which is the last specification needed to have an eligible vehicle. In our work, we synthesized Er-GQDs at 200℃ for 8 h and 17 h in deuterium oxide. The fluorescence of erbium ions is known to be quenched by OH functional groups. The average size of Er-GQDs is growing from 3 to 5 nm after 8 h and 17 h treatment times, respectively, and exhibit fluorescence with 1550 nm emission peak in deuterium oxide. All aforementioned results make Er-GQDs a potential imaging agent for bioimaging.

A novel approach is presented that increases sensitivity and specificity for detecting minimal traces of DNA in liquid and on solid samples. Förster Resonance Energy Transfer (FRET) from YOYO to Ethidium Bromide (EtBr) substantially increases signal from DNA bound EtBr highly enhancing sensitivity and specificity for DNA detection. The long fluorescence lifetime of the EtBr acceptor, when bound to DNA, allows for multi-pulse pumping with time gated (MPPTG) detection, which highly increases the detectable signal of DNA bound EtBr. A straightforward spectra/image subtraction eliminates sample back-ground and allows for a huge increase in the overall detection sensitivity. Using a combination of FRET and MPPTG detection an amount as small as 10 pg of DNA in a microliter sample can be detected without any additional sample purification/manipulation or use of amplification technologies. This amount of DNA is comparable to the DNA content of a single human cell. Such a detection method based on simple optics opens the potential for robust, highly sensitive DNA detection/imaging in the field, quick evaluation/sorting (i.e., triaging) of collected DNA samples, and can support various diagnostic assays.

COVID-19 now has antiviral treatments to help prevent hospitalization. Paxlovid is the most prevalent and effective of these medications. Paxlovid consists of two medications taken twice daily for five days, however, there have been anecdotal reports of rebound infection after a course of Paxlovid. This project aims to use mathematical models to investigate the infection conditions that result in rebound cases. Stochastic modeling is used to simulate the time course of infections with different doses and durations of Paxlovid to determine when rebound will occur. These findings could help physicians develop more consistent treatment regimens for Paxlovid.

Graphene quantum dots (GQDs) are a frontier of research in the interdisciplinary world of biology and medicine. They have been hallmarked for their remarkable applications, from cellular imaging to drug delivery. Due to their unique physicochemical and optical properties, there is a strong desire to bring them to clinical application. However, prior to any therapeutic and bioimaging studies comprehensive analysis of GQDs cytotoxicity has to be done in vitro. In our research, we assess the biocompatibility of a variety GQDs synthesized from different carbon-based precursors in non-cancerous cells through cell viability assay. Our results show that GQDs prepared from chitosan and glucosamine demonstrate 80% cell availability at 1.2 and 2.2 mg/mL concentrations, respectively, making them the most promising candidates for further therapeutic applications among over 15 GQD candidates tested.

The SARS-CoV-2 virus, which induced a global pandemic in 2020, is a serious pathogen that can cause acute respiratory distress in infected individuals. In order to garner a greater understanding of the SARS-CoV-2 virus and attenuate its effects, researchers have aimed to estimate key viral kinetic parameters. In this study, data from a previously published challenge study on the impacts of SARS-CoV-2 on young adults, including viral load, upsit score, and symptom score, was used to calibrate a system of ordinary differential equations, generating pathogenic parameters. In addition, Pearson covariance values and the Lyapunov exponents were calculated for each participant from the challenge study. For a majority of participants, the Lyapunov exponents were positive and finite, indicating chaotic behavior in vector space. Similarly, for most participants, there was a weak positive correlation between upsit/symptom scores and viral load. Future research will consist of implementing a newer system of ordinary differential equations that may be a better fit for the data

Galaxies, like our Milky Way, harbor stars and planets that are created out of gas. We utilize observations from Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) and Hubble Space Telescope (HST) to inspect the gas in and outside of galaxies. We then use these data to compare against the rate at which these galaxies are forming stars. We use ratios of spectral emission and absorption lines from MaNGA to determine whether a particular point in the galaxy best resembles a star-forming region, an active galactic nucleus, or something in between. We will further assess the star-formation activity in the galaxies based on their ionized gas and stellar spectral indices. We will use HST observations of the same galaxies to quantify the amount and properties of the gas surrounding them to better understand how the environments of galaxies impact the activity occurring within them. Through this work, we will contribute to our understanding of the galactic gas cycle and its connection with star formation within these galaxies.

When massive stars in a galaxy die, they explode and create clouds of gaseous debris. If these clouds of debris break out of the galaxy, they can create galactic winds. The nearby Large Magellanic Cloud (LMC) galaxy is ideal for studying galactic winds as it is oriented face-on and is driving out 85 million Sun’s worth of gas. Using observations from the Hubble Space Telescope, we are studying chemical absorption fingerprints from the light that passes through the LMC’s galactic winds. These chemical fingerprints enable us to assess the physical properties of the winds. We are using the light from 150 young, massive stars in the LMC to probe through the LMC’s galactic winds. In order to determine where the LMC’s disk ends and the winds begin, we utilize the Galactic All-Sky Survey observations to trace the motions of the neutral hydrogen gas. Together, these observations will allow us to measure how fast the winds are moving, how much gas they contain, and their ionization states. Exploring the LMC’s galactic outflows will contribute to our understanding of the relationship between a galaxy’s environment and its evolutionary progression.

The antimicrobial properties of ZnO are well documented. Demonstrated effectiveness against various strains of both Gram-positive and Gram-negative bacteria in addition to being an abundant and inexpensive material leave it well positioned for application as an antibacterial agent. ZnO based antibacterial agents see current usage in biomedical, water treatment, food storage and various other industries. Despite the significant promise and proven application, realization of both novel and efficient, targeted applications is hindered by a lack of understanding in the fundamental mechanisms responsible for the antimicrobial properties of ZnO. In particular the role and nature of components of the local bacterial environment in mediating/hindering these antibacterial interactions. Phosphate-rich environments in particular have been observed to inhibit antimicrobial behavior in ZnO though the manner in which this occurs has not been adequately described. To elucidate the environmental interactions relevant to the antimicrobial action of ZnO we investigated the effects of interactions with both bacteria and the bacterial environments on the physicochemical and optoelectronic properties of the free crystalline surface of ZnO microparticles (MPs). This involves exposing hydrothermally grown ZnO MPs to phosphate-buffered saline (PBS) media both with and without the presence of Newman strain S. aureus bacteria. Changes in the surface electronic structure and charge dynamics due to these exposures are monitored via both time and energy dependent surface photovoltage (SPV) conducted prior to and following biological assays. In doing so we demonstrate significant changes in the characteristic timescales of long-lived processes in the SPV transients after exposure to phosphate-rich environments. Such findings point to significant phosphate adsorption at the free crystalline surface. This is further supported by suppression of oxygen rich defect centers after exposure to PBS media. We also comment on the interaction of bacteria as the presence of S. aureus suppresses this adsorption and influences charge transfer processes at short and intermediate timescales.

Excitation and emission (observation) conditions heavily impact fluorescence measurements. Both observed spectra and intensity decay (fluorescence lifetimes), when incorrectly measured, may lead to incorrect data interpretations. The necessity of using so-called total fluorescence intensity or intensity measured under magic angle (MA) conditions is demonstrated for both steady-state and time-resolved fluorescence measurements. Rhodamine 6G (R6G) in two solvents - ethanol and glycerol have been used in order to demonstrate the general importance of Magic Angle observation.

The SARS-CoV-2 pandemic initially made landfall in the United States in early 2020, and at that point in the pandemic, few developed treatments left the initial prevention of the disease largely up to preventative measures like mask mandates, quarantines for infected individuals, and social distancing policies. As a result, we must understand how preventative measures affect the transmission of infectious diseases to prepare us to fight the future spread of similar diseases. To accomplish this, we used a SEIR model with a variable transmission rate and fit SARS-CoV-2 case data to it. Principally, we used four models for the change in transmission rate: instant, linear, exponential, and logistic. Then using these models for the decay of transmission rate, we obtained SSR and parameter values that allowed us to compare models for each state. After comparing models between the four states we fit, there was no evident best-fit model for the decay in transmission. These results may suggest that regional differences like behavior, socioeconomic status, and exact preventative measures enforced could be responsible for the disparity in how the transmission rate decayed.

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.