Enteric alpha defensins, such as human defensin 5 (HD5), are antimicrobial peptides secreted by Paneth cells in the lumen of the small intestine as part of the innate immune response. Defensin activity effectively inhibits many bacterial and viral pathogens during infection. However, not all pathogenic human viruses are neutralized in the presence of defensins. For example, among the seven human adenovirus (HAdV) species, infections of certain serotypes are enhanced by HD5, while other serotypes are inhibited. Our goal is to identify the molecular determinants that confer HAdV neutralization or enhancement by HD5. We have previously identified regions of the three major capsid proteins that form the outside of the virus, hexon, penton base, and fiber, as key determinants. We identified these determinants through rational design and the creation of chimeric viruses in which we swapped portions of the three major capsid proteins between two serotypes with opposite HD5-dependent phenotypes. From these data, we have created a model of the enhancement and neutralization mechanisms. To further test these models, I am creating a panel of chimeric HAdVs by swapping capsid components from additional HAdV species and serotypes. I will study the concentration-dependent effect of HD5 on each chimera’s infectivity compared to the wild type viruses, which will uncover new mechanistic detail and allow us to create a more generalized model.

Adolescence is a critical developmental period for many with regard to initiation of marijuana use. With increasing nationwide trends toward legalization of marijuana and known negative consequences associated with an earlier age of marijuana use onset, it is imperative to identify adolescent risk factors associated with motives for using marijuana. Previous research among adults has found that motives for marijuana use vary based on personality dimensions, with higher levels of neuroticism being significantly associated with greater coping motives. Despite these findings, little research has been done on this topic with late adolescents. The overarching aim of this study was to examine associations between personality risk factors and motives for marijuana use among late adolescents. The current study included 170 late adolescents (15-18 years old, M_age = 16.86, 50% female) recruited from Washington State with stratified sampling to enroll participants who ranged from never having used marijuana to those who report heavy, regular marijuana use. Participants completed online assessments that included the Mini-IPIP Big Five Factors of Personality Scale and the Comprehensive Marijuana Motives Questionnaire to assess personality and marijuana use motives, respectively. We examined associations between personality dimensions (extraversion, agreeableness, conscientiousness, neuroticism, imagination) and marijuana use motives (enjoyment, conformity, coping, celebration, perceptions, anxiety, risk, sleep) using correlational and regression analyses. We hypothesized several positive associations between personality and motives including between 1) neuroticism and using to cope with depression/anxiety, 2) imagination and using to alter perceptions, and 3) agreeableness and using to conform to peers. Further, we predicted that late adolescents high in neuroticism that use marijuana to cope would report more marijuana-related consequences. The results inform whether screening for personality dimensions in adolescents could help predict future motives for marijuana use and thus be beneficial in preventing negative marijuana consequences and providing early interventions for marijuana misuse.

Everyday environmental exposure can lead to the cell’s DNA to undergo double-strand breaks (DSB). If left unrepaired, these DSB are toxic to the cell’s survivability, since it can lead to the breakdown of the genetic code that is essential for the production of proteins vital to cellular life. As such, organisms have developed mechanisms to account for these common DSB situations. These mending processes are evolved to strongly combat these breaks, as failures or mistakes in the process can result in error-stricken genetic information, resulting in faulty proteins. Not only that, DSB and its repair are observed to be important to the contribution of genetic diversity, as it allows for chromosomal crossovers. This proves that the DNA repairing mechanisms to be a complex yet significantly important process for organismal life. In Escherichia coli, DSB repair processes are carried out by a trimeric protein complex, RecBCD. The mechanism behind RecBCD is not completely understood. Previous studies in the Smith lab have proposed a working model of the protein. Here, we combine both computational prediction and genetic assay to further understand the specifics of its structure-function relationship. Past experiments have identified a structural change that affects the protein’s function. Then, using a protein-protein docking algorithm, we have identified the possible conformation and interacting amino acid docking pair accounting for it. We then follow up with site-directed mutation of these pairs to test the validity of our prediction to elucidate the exact model. These experiments will allow us to further understand the conformational effects on the complex’s function. Proper understanding of RecBCD will enable us to generate new drugs to target bacterial infections whilst also reinforcing our understanding of DSB repair.

Human adenoviruses (AdVs) infect and cause disease in multiple organ systems, and certain human AdV serotypes are associated with particular diseases; however, the basis for AdV tissue tropism is unknown. To better understand this problem, I am using mouse adenoviruses (MAdV). Like human AdVs, MAdV serotypes are associated with distinct tissue tropisms: MAdV-1 infects macrophages whereas MAdV-2 infects intestinal epithelial cells. By swapping genes between MAdV-1 and MAdV-2, I hope to uncover the genetic basis of tissue tropism in MAdVs. Importantly, these studies will be aided by the availability of a cell culture system that recapitulates the cellularity of the intestinal epithelium and supports MAdV-2 replication, but not MAdV-1 replication. Due to genetic conservation, principles of MAdV tissue tropism are likely to apply to HAdVs. A major determinant of viral cell tropism is receptor usage, which may also play a primary role in tissue tropism. For both MAdV species, the trimeric fiber protein that extends from the icosahedral capsid is thought to be the viral attachment protein that binds to a host receptor. Although the MAdV receptors are not completely known, they are distinct for MAdV-1 and MAdV-2. To gain insight into tissue tropism, I have created a chimeric MAdV-1-M2f virus, where the fiber gene of MAdV-2 has been inserted in place of the native fiber gene in the MAdV-1 genome. A prior student in the Smith lab created the inverse chimera. If replication is fiber dependent, the chimeric MAdV-1-M2f virus should be capable of infection. By studying these chimeric viruses, I will uncover whether the fiber/receptor interaction plays a central role in determining tissue tropism.

Despite widespread implementation of solar energy, there are still issues with efficiency in varying conditions. For example, photovoltaic (PV) arrays function optimally at a specific temperature and have decreasing efficiencies at higher temperatures. Other factors that also impact the power production of a PV array include the amount of direct sunlight, the distribution of incident light, and the intensity of incident light. As solar energy installations continue to increase worldwide, proper modeling of PV arrays is critical for potential asset owners and power system operators. Effective simulations of a PV array’s power production require models that effectively consider the uncertain external factors that vary by geographical region and climate. In this research project, a realistic PV cell model is developed in the programming language Python. This model explores the sensitivity of a PV cell’s power production to different external variables, including ambient temperature, solar irradiance, and other weather conditions. Additionally, this PV cell model is extensible, allowing power production from PV modules and arrays to easily be considered. This model can be seamlessly integrated with other energy asset models, including those for energy storage and flexible demand resources, allowing for complex scenarios to be simulated. To display this functionality, a cost-minimizing consumer with a behind-the-meter PV array and battery is simulated.

Wireless power through magnetic resonance between coils of wire has enabled a new charging paradigm in a variety of domains, from robotics to biomedical implants. As wireless power systems move from simplistic to more perfomant architectures comprising of many coils, the design complexity scales very quickly. This is due to the difficulty in simulating and modeling the magnetic fields that form the backbone of the wireless power transfer, as in the multi-coil case the computational complexity quickly exceeds the capacity of even high end servers. To enable the development of next generation wireless power devices, we developed the Mostly Printed Field Characterization System (MPFCS), a robotic scanner that collects high-fidelity, high-resolution magnetic field data. However, while the system creates useful visualizations for wireless power, it does not provide a mathematical model that would allow for the precise optimization and rigorous understanding of the fields that engineers often need. Addressing that, we present physics-driven machine learning methods that combine electromagnetic theory with data collected from the MPFCS to build simplified mathematical models for these magnetic fields. We provide, for the first time, a characterization of fields for systems that were previously too complex to analyze effectively by hand or through computation. Preliminary evaluation of the data shows that there is very little error compared to simulated values. Based on the algorithm's performance on similar problems, this suggests promising final results. This work provides a deeper understanding and design tool to build and iterate on next generation devices, leading to both accelerated prototyping and novel research directions.