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.

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.

Through the use of large-scale surveys, astronomers are able to investigate Milky Way galaxy evolution, both dynamically and chemically; however, determining reliable stellar ages has been elusive.  Star clusters are the most reliable way to measure ages of stars, and new surveys are measuring detailed chemistry for cluster stars that may be able to be correlated with age. For our study, we are using carbon and nitrogen abundances within red giant stars as age indicators. Using the Open Cluster Chemical Abundances and Mapping (OCCAM) survey, we utilized stellar parameters and abundances, and created a uniform empirical relationship between stellar ages and carbon-to-nitrogen abundances using star clusters. This new calibration will allow us to determine reliable ages for over 100,000 stars across the Milky Way galaxy, allowing us to measure the chemical evolution of the Galaxy.

Antimicrobial action of micro- and nanoscale ZnO particles has been documented, but the fundamental physical mechanisms driving these actions are still not identified. We hypothesize that one of the key mechanisms behind the antibacterial action of ZnO is rooted in interactions between ZnO surfaces and extracellular material. An investigation was done of the biological components of that interaction using diffusion theory and more specifically Brownian motion computational models to look at the interaction of Zn+2 and O-2 ions with staphylococcus aureus bacteria. The analysis allowed us to find a correlation between the thickness of the staphylococcus aureus bacteria and the amount of the zinc and oxygen ions present in the solution.

Nano- and microscale zinc oxide (ZnO) have demonstrated potential for applications in electronic, pharmacological and chemical industries among others. At these scales, surface properties dominate, rendering surface defects highly influential. Consequently, understanding of defect- related phenomena are crucial to achieving impactful figures of merit. Many optoelectronic properties of ZnO relevant for applications have been linked to defect-related visible luminescence. Its fundamental origins are still being debated, with attributions to oxygen vacancies, zinc vacancies, oxygen antisites, donor-acceptor pairs, etc. In our studies, we contribute to this discussion by probing the relationship between crystal morphology and this luminescence. We conducted optoelectronic studies to characterize the effects of remote oxygen plasma treatment on hydrothermally-grown microscale ZnO samples with controlled morphology as a means to help elucidate the nature of the visible emission. We report on the observed changes in the photoluminescence spectra indicative of the relationship between surface defects, morphology, and electronic structure of ZnO.

With the development of personalized cancer medicine and moving away from a conventional biopsy, there is a need in creating a multifunctional platform for cancer diagnosis and treatment monitoring. Sonography offers many advantages over standard methods of therapeutic imaging due to its non-invasiveness, deep penetration, high spatial and temporal resolution, low cost, and portability. The benefits of the ultrasound method make contrast agents an ideal platform for the efficient strategy of cancer diagnostic and therapy. In this work, we developed metal-doped graphene quantum dots that demonstrate high-contrast properties in ultrasound brightness mode. The successful imaging enhancement was observed in tissue phantom and chicken breasts tissue. The relatively small size of the metal-doped graphene quantum dots makes them easily be internalized into the cells, while functional groups on their surface allow binding a cancer-targeted marker and therefore be used as a cancer-targeted delivery. By a combination of imaging and targeting capabilities, ultrasound contrast agents based on metal-doped graphene quantum dots enable desired cancer-focused nanotherapeutic and imaging approaches.

For billions of years, our Milky Way galaxy has churned out countless stars. However, the best star-forming days of our galaxy are long gone and our galaxy is in a midlife crisis! It’s running out of gas to make new stars, and extraneous resources are scarce. Worse yet, high stellar winds might eject some gaseous material, such as the Smith Cloud. After it was ejected, the Milky Way’s gravity caused this cloud to reverse course and fall back toward our Galaxy. The Smith Cloud is now only 40,000 light-years away and carries with it the equivalent of over 1 million Suns worth of material. As it makes the journey back to our Galactic Plane, it must endure heavy winds that have temperatures in excess of 1 million degrees Celsius from the Milky Way galaxy’s coronal gas. I have already measured the amounts of various ions in adjacent cloud fragments positioned on the side of the Smith Cloud using Hubble Space Telescope observations. These ions include C+, Si+, Si2+, Si3+ , and S+. I will then determine the effects that these high winds have on the adjacent fragments and the trailing wake of the Smith Cloud to better understand the perils that gas clouds must undergo to reach massive galaxies.

Dwarf galaxies are the building blocks larger galaxies. Their shallow gravitational potentials makes them extremely sensitive to explosions of stars. To understand how dwarf galaxies form and evolve, we must study their formation, evolution and fate in a range of environments. Located 13 million light years away, Centaurus A is the closest massive elliptical galaxy making it ideal for investigating the effects of local environment on the formation and evolution of dwarf galaxy populations. We model the dwarf satellite populations of Centaurus using a suite of high resolution N-body simulations and the semi-analytic model (SAM) Galacticus (Benson, 2010). We determine the best fit parameters for well studied Milky Way satellite, before applying those parameters to generate simulated satellites around our Centaurus A's analog. Given the relatively low computational cost of N-body simulations and SAMs, we are able to explore the effects of uncertainties in the Centaurus A system, including its relatively unconstrained mass. Here we present initial results from our study for the first Centaurus A like galaxy.

Fifty percent of stars in the night sky are actually binary star systems, but finding and characterizing them requires significant data, time, and analysis. Studying the brighter star of the pair is fairly straightforward, but the secondary is commonly hidden. Using the infrared spectroscopy data from the Sloan Digital Sky Survey combined with the WIYN Open Cluster Survey, we create a longer baseline with which we can better characterize these stars. The Joker, a new Monte Carlo analysis technique, will help us reveal the hidden binary stars by producing solutions for the orbits of the systems. By finding new binary stars, we can better understand the demographics and composition of our chosen star cluster, NGC 6819, and also learn more about each individual companion of the systems.

Nostalgia, a sentimental longing for the past, increases prosocial behavior. Research has demonstrated that when a former competitive athlete reflects on their time participating in their sport, they experience feelings of nostalgia. Applying the prosocial nature of nostalgia to an athletic domain, it was hypothesized in the current study that sport-specific nostalgia would predict greater sportsmanship attitudes among athletes. To test this, we primed former competitive athletes with sport nostalgia by instructing them to write about a memory from playing their sport. Then they completed items about their sportsmanship attitudes, such as respecting opponents and officials. Results showed that nostalgia-primed participants reported greater sportsmanship attitudes compared to a control group. This is consistent with research showing that nostalgic reflection increases prosocial attitudes and behavior. In future work we plan to examine these findings in current competitive athletes to give further insight into the role that nostalgia plays in sport settings.

Background: Reward loss is accompanied by a stress response affecting emotion and health. Problem: A comprehensive map of brain activity, or connectome, during an episode involving reward loss remains to be worked out. A connectome is being developed using the protein c-Fos expressed in recently activated neurons. Method: Experimental animals were exposed to reward loss (high-to-low sucrose and pellet downshift), whereas control animals had access only to the small or only to the large reward. c-Fos expression was measured in brain slices obtained after the reward loss event using immunohistochemistry. Brain activity levels in experimental and control animals were determined based on c-Fos expression in several key brain areas. Results: c-Fos expression was found to be higher in areas involved in negative emotion and lower in areas involved in reward processing in downshifted vs. unshifted groups. Contribution: This novel approach will continue to help identify the brain connectome underlying reward loss, that is the set of excited and inhibited areas when the organism is experiencing a loss.

Research suggests that cannabidiol (CBD) can act as an anxiolytic when injected (Blessing et al, 2015). We investigated whether these findings could be replicated in rats using chronic (12 day) voluntary oral consumption of non-pharmaceutical grade CBD oil at 20mg/kg. A control group consumed distilled water. An elevated plus maze (Test 1), open field (center vs. outer, light vs. dark, Test 2 and 3), and running wheel (Test 4) were used to examine the anxiolytic effect of CBD beginning on day 12 of administration and two hours after consumption. One test occurred each day. It was hypothesized that CBD rats would spend more time in the open arms of the elevated plus maze than the control group, and more time in the center and lit areas of the open field compared to the control rats. For the running wheel, we expected the CBD rats to turn the wheel more times than the control group. Results revealed that in the open field, the CBD group spent more time in the center compared to the control group, as was expected. There were no other differences between groups. These results are discussed with respect to administration route, timing of test, and type of test.

Varied practice, or studying many different examples of a given topic, can be a more effective method for learning a concept compared to studying one example repeatedly, as demonstrated in motor learning (e.g., Kerr & Booth, 1978) and category learning (e.g., Wahlheim et al., 2012). The present study examined how varied practice affects learning vocabulary from examples used in sentences. Although potentially beneficial for long-term understanding, varied practice can make initial vocabulary learning challenging because the example sentences for a given term may vary greatly. The current study presented participants with a sentence and asked them to select the correct vocabulary word that completed the sentence. For half of the vocabulary terms, participants were tested on that vocabulary word in the same sentence repeatedly (constant practice); for the other half of the words, participants were tested on that vocabulary word in different sentences (varied practice). Participants were also asked to answer questions about their attention during the task. After a short delay, participants took a final test to investigate how well they could identify the studied vocabulary words in novel sentences. The results will be discussed in terms of desirable difficulties, the distinction between learning and performance, and whether the type of practice may influence attention.

Learning and correctly remembering health information is important at all ages, and it can be particularly important in later adulthood (65+ years old). Thus, interventions focused on identifying methods to improve young and older adults’ health knowledge and memory for medication information are valuable. We developed a cognitive intervention relying on methods that have been identified to be effective for enhancing learning. Specifically, prior research has established that retrieval practice (recalling information from memory) can be a powerful tool for learning other kinds of information. Our goal was to evaluate the degree to which a retrieval practice intervention would improve younger and older adults’ self-regulated learning of medication side effects. Younger adults from TCU and older adults from the community were recruited to participate. Participants who received the intervention were given information about repeated retrieval practice that emphasized the effectiveness of this strategy for improving memory. Specifically, the intervention indicated that they should recall each medication’s side effects correctly 3 times during learning, and they should continue to space their retrieval practice until they met this goal. All participants learned medication names paired with a side-effect. They made decisions about when to study, engage in retrieval practice, and stop learning the list of medication-side effect pairs. Younger and older adults’ who received the intervention made better study decisions relative to those who did not. Further, the intervention enhanced both younger and older adults’ memory for medication side effects relative to control conditions. These outcomes suggest that our evidence-based intervention can help young and older adults learn and remember critical health information, which may assist them in monitoring for adverse outcomes during medication usage.