Human Immunodeficiency Virus(HIV) Type-1 has been studied heavily for decades, yet one of the main areas that has yet to be thoroughly researched is that of the cell-cell fusion. This cell-cell fusion creates multi-nucleated cells called syncytia. Cell-cell fusion of HIV can be regulated via cytosine arabinoside(AraC), a chemotherapy agent. Previous work has shown that syncytia and their formation can be modeled via ordinary differential equations, with an Erlang time distribution measuring the fusion of the cells, though this has not been applied to studying drug-treated systems. By applying the mathematical model to the spread of syncytia under drug treatment, we can gain novel information about the formation of syncytia and its regulation by AraC. We find that AraC affects both the syncytia formation rate and the fusion rate, and requires inclusion of the density of syncytia in the mathematical model. This information is much needed for explaining the full workings of HIV in vitro, and will further help the push to develop full models in regards to HIV type-1
Syncytia are the multinucleated cells that can occur due to virus infection of cells. Mathematical models in the form of ordinary differential equations can be used to simulate the growth of these infections. Several ODE models can explain syncytia growth. Before employing these models on actual data, it is essential to analyze their structural and practical identifiability. Structural identifiability is an inherent property of each model and its parameters, referring to our ability to determine parameter values for the model. Practical Identifiability analysis of a model is concerned with accurately determining parameter values given experimental error. Obtaining accurate parameter values allows us to make conclusions about our data within the context of our model that can provide insight into the nature of the spread of syncytia. These two techniques allow us to determine whether or not the parameters of a model are identifiable with the data we plan to collect. Consequentially, we can plan experiments adequately to truly parameterize the data in the contexts of our model and make accurate conclusions.
This study addresses the escalating concern over the interaction of multiple respiratory viruses by introducing a mathematical model to analyze triple infection dynamics involving influenza (IAV), respiratory syncytial virus (RSV), and SARS-CoV-2. With the ongoing COVID-19 pandemic and the resurgence of RSV, understanding the dynamics of triple infections is critical for public health preparedness. Comprehending the interactions among these viruses is crucial for improving our capacity to forecast and curb disease outbreaks. The central question addressed in this study is how variations in infection rates influence the duration and maximum population size of each virus in a triple infection scenario. Prior research has explored coinfections involving two respiratory viruses, yet triple infections, especially among adults, remain infrequent and poorly elucidated. The urgency to address these questions arises from the potential for overwhelming hospitals and exacerbating disease burden, especially in vulnerable populations. By developing a mathematical model to analyze triple infections, this research aims to provide insights that can inform public health strategies and mitigate the impact of respiratory virus outbreaks. Through extensive simulations, the study evaluates how variations in infection rates influence the duration and maximum population size of each virus. The findings unveil intriguing patterns: while SARS-CoV-2 demonstrates remarkable resilience across various infection rates, influenza and RSV display more nuanced responses, exhibiting sensitivity to changes in transmission rates.
Author(s): Ugur C. Topkiran Physics & Astronomy Giridhar Akkaraju Biology William Burnett Chemistry & Biochemistry Jeffrey Coffer Chemistry & Biochemistry Abby Dorsky Physics & Astronomy Himish Paul Physics & Astronomy Olivia Sottile Physics & Astronomy Alina Valimukhametova Physics & Astronomy Diya Vashani Physics & Astronomy
Advisor(s): Anton Naumov Physics & Astronomy
Graphene quantum dots (GQDs) have emerged as a forerunner of carbon nano-biotechnology due to their multifunctional delivery and imaging capabilities as they exhibit fluorescence in the visible and near-infrared, high biocompatibility, and water solubility. These properties put GQDs forward as a compelling drug delivery platform that has already been utilized in a variety of applications including the delivery of chemotherapeutics, antibiotics as well as siRNA and CRISPR-based gene therapy. However, cellular entry pathways of this nanomaterial still remain largely undefined. In a number of studies describing GQD cellular internalization different and, often, conflicting results have been presented due to surveying only few endocytosis inhibitors and disregarding their potential off-target pathways. Understanding the cell internalization routes of GQDs is crucial while delivering drugs in different types of cell lines. Herein, we performed a holistic approach to cell uptake studies on GQDs of different charges by the comparative study of their preferred endocytosis paths in non-cancerous (HEK-293) and cancerous (HeLa) cell lines. The concentration and cell viability of GQDs were determined by MTT assays, while their endocytosis paths were investigated through confocal fluorescence microscopy on cells treated for up to 24 hours. The potential for GQD interactions with the cell membrane was also examined via zeta (ζ) potential measurements. Our findings provide insights into the internalization mechanisms of the GQDs into cell membranes of healthy and cancer cells. The optimization of these mechanisms can serve for the enhancement of a variety of novel GQD applications in biomedicine including therapeutic delivery, disease detection through sensing as well as diagnostic imaging.
Author(s): Alina Valimukhametova Physics & Astronomy Jeffery Coffer Chemistry & Biochemistry Abby Dorsky Physics & Astronomy Olivia Fannon Physics & Astronomy Olivia Sottile Physics & Astronomy Ugur Topkiran Physics & Astronomy
Advisor(s): Anton Naumov Physics & Astronomy
Due to high tissue penetration depth and low autofluorescence backgrounds, near-infrared (NIR) fluorescence imaging has recently become an advantageous diagnostic technique used in a variety of fields. However, most of the NIR fluorophores do not have therapeutic delivery capabilities, exhibit low photostabilities, and raise toxicity concerns. To address these issues, we developed and tested five types of biocompatible graphene quantum dots (GQDs) exhibiting spectrally-separated fluorescence in the NIR range of 928–1053 nm with NIR excitation. Their optical properties in the NIR are attributed to either rare-earth metal dopants (Ho-NGQDs, Yb-NGQDs, Nd-NGQDs) or defect-states (nitrogen doped GQDS (NGQDs), reduced graphene oxides) as verified by Hartree-Fock calculations. Moderate up to 1.34% quantum yields of these GQDs are well-compensated by their remarkable >4 h photostability. At the biocompatible concentrations of up to 0.5–2 mg ml−1 GQDs successfully internalize into HEK-293 cells and enable in vitro imaging in the visible and NIR. Tested all together in HEK-293 cells five GQD types enable simultaneous multiplex imaging in the NIR-I and NIR-II shown for the first time in this work for GQD platforms. Substantial photostability, spectrally-separated NIR emission, and high biocompatibility of five GQD types developed here suggest their promising potential in multianalyte testing and multiwavelength bioimaging of combination therapies.
Author(s): Ava Amidei Chemistry & Biochemistry Hana Dobrovolny Physics & Astronomy
Advisor(s): Hana Dobrovolny Physics & Astronomy
Location: Third Floor, Table 10, Position 2, 1:45-3:45
COVID-19, also known as SARS-Cov-2, has caused a worldwide crisis. SARS-CoV-2 is able to form syncytia cells, which are large multi-nucleated cells. Syncytia formation allows the virus to propagate without leaving the host cell. Currently, not much is known about syncytia cells, including the rate at which they form. Data from a study by Rajah et al. (2021) was used to estimate the rate of synctia formation for each variant of SARS-CoV-2. This includes the Alpha, Beta, D61G, and Wuhan Variants. The rates of syncytia formation were found by using mathematical modeling. This information can better our understanding of syncytia formation.
Author(s): Vivek Athipatla Physics & Astronomy Dustin Johnson Physics & Astronomy Yuri Strzhemechny Physics & Astronomy
Advisor(s): Yuri Strzhemechny Physics & Astronomy
Location: Third Floor, Table 1, Position 1, 11:30-1:30
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.
Author(s): Luca Ceresa Physics & Astronomy Bruce Budowle Physics & Astronomy Magdalena M Bus Physics & Astronomy Jose Chavez Physics & Astronomy Ignacy Gryczynski Physics & Astronomy Zygmunt Gryczynski Physics & Astronomy Joseph Kimball Physics & Astronomy Emma Kitchner Physics & Astronomy
Advisor(s): Zygmunt Gryczynski Physics & Astronomy
Location: First Floor, Table 4, Position 2, 11:30-1:30
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.
Author(s): Abby Dorsky Physics & Astronomy Olivia Sottile Biology Alina Valimukhametova Physics & Astronomy
Advisor(s): Anton Naumov Physics & Astronomy
Location: Second Floor, Table 1, Position 2, 1:45-3:45
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
Author(s): Andrew Glaze Physics & Astronomy Kat Barger Physics & Astronomy Bart Wakker Physics & Astronomy
Advisor(s): Kat Barger Physics & Astronomy
Location: First Floor, Table 6, Position 2, 1:45-3:45
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.
Author(s): April Horton Physics & Astronomy Francie Cashman Physics & Astronomy Andrew Fox Physics & Astronomy Suraj Poudel Physics & Astronomy Jo Vazquez Physics & Astronomy
Advisor(s): Kat Barger Physics & Astronomy
Location: First Floor, Table 1, Position 1, 11:30-1:30
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.
Author(s): Dustin Johnson Physics & Astronomy Alexander Caron Biology Rishi Manihar Physics & Astronomy John Reeks Physics & Astronomy
Advisor(s): Yuri Strzhemechny Physics & Astronomy Shauna McGillivray Biology
Location: Basement, Table 6, Position 2, 11:30-1:30
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.
Author(s): Emma Alexander Physics & Astronomy Luca Ceresa Physics & Astronomy Jose Chavez Physics & Astronomy Joe Kimball Physics & Astronomy Michael Seung Physics & Astronomy
Advisor(s): Zygmunt Gryczynski Physics & Astronomy Ignacy Gryczynski Physics & Astronomy
Location: Second Floor, Table 2, Position 3, 11:30-1:30
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.
Author(s): Natalie Myers Physics & Astronomy John Donor Physics & Astronomy Taylor Spoo Physics & Astronomy
Advisor(s): Peter Frinchaboy Physics & Astronomy
Location: Third Floor, Table 3, Position 2, 1:45-3:45
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.
Large galaxy simulations offer an avenue to investigate observed properties of star clusters and classify them according to their shape in a controlled, known environment. In this work, we present a machine learning (ML) algorithm applied to images of simulated Milky Way-like galaxies from the FIRE-2 galaxy simulations. This set of galaxy simulations are able to show individual star clusters in Milky Way-like galaxies at “present day”, with cluster masses as small as ~40,000 solar masses. The spatial resolution within the galactic disks provides new opportunities to explore how star clusters can be identified. In this study, we compare human-classification with ML-classification of star clusters in these simulations. Recent advances in synthetic imaging allow for mock Hubble Space Telescope images to be produced across a large range of wavelengths. Here, we present first results comparing the classifications determined by humans with the performance of the ML algorithm developed for this project. We discuss how the lack of ability to resolve the smallest features of the galaxy in these images affects the performance of the ML algorithm as well as how to improve the accuracy of the classifications, relative to human performance.
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.
Author(s): Iver Sneva Physics & Astronomy Mia Bovill Physics & Astronomy Sachi Weerasooriya Physics & Astronomy
Advisor(s): Mia Bovill Physics & Astronomy
Location: Second Floor, Table 3, Position 2, 11:30-1:30
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.