Loss of periodontal ligament tissue (PDL) and attachment is a serious complication of periodontal diseases - the most prevalent dental health problems. PDL-degeneration leads to alveolar bone degeneration, infection, gingivitis, and eventual tooth loss. There is currently no product that can cure PDL-degeneration as regeneration requires the combinatorial process of regenerating cementum, signaling the existing relevant cells to proliferate and form PDL, and its integration into a functional system. Current restorative treatments utilize cell-based tissue regeneration, synthetic scaffolds, tissue grafts with limited, temporary success. A market product, e.g., claims to restore periodontium using harvested fetal swine periodontal tissue with highly variable clinical outcomes. Although these traditional procedures are well-established and show some success, their efficacy is limited due to the lack of structural and functional integration of a deposited layer with the underlying tooth, specifically integration into the remineralized cementomimetic layer. GEMSEC labs have developed a proprietary technology dubbed “peptide-guided remineralization” which facilitates new mineral formation using protein-derived peptides and have successfully restored dental hard tissues via several case studies including enamel, cementum, dentin under in-vitro and in-vivo conditions. Translating this technology into a daily-use product, we propose a PDL-regenerating chimeric construct which includes a biomineralizing peptide, ADP5, derived from the key enamel protein, amelogenin, with cell signaling moieties. Herein, we aim to use established bioinformatics, machine-learning tools, and high-throughput experimentation to identify peptides from proteins involved in PDL development cell-signaling towards controlled biomineralization, bioadhesion, and cell-signaling functionalities necessary for PDL regeneration. Addressing current treatment protocol limitations, the interdisciplinary approaches developed in this project are designed for the regeneration and formation of fully functional PDL. 

In recent decades, partisanship between the Democratic and Republican parties in the US has grown, resulting in congressional gridlock and economic stagnation. Untangling the relationship between partisanship and major economic factors such as household income, homeownership, population, and poverty has the potential to solve these problems. Our hypothesis is that a state’s political leaning is not associated with these factors. Our data contained the political leaning of all 50 states and DC in the 2008 presidential election, alongside 71 economic variables in the aforementioned four categories of annual household income (1984-2018), home ownerships (1986-2014), population (2000-2005), and poverty rate (1990, 2000). We used principal component analysis (PCA) to condense the 71 dimensions into two dimensions for visualization, principal components (PCs) 1 and 2. They explained 48% and 13% of total variance. In the plot between the two PCs, dots represent states (and DC) colored by political leaning. The visualization revealed that a state’s political leaning is strongly tied to the 4 economic categories. Thus, we rejected our hypothesis. Political party and economic status are related, but determining how they are related is a limitation of our research. Further work must be done to determine whether it is political leaning that determines economic status or economic status that determines political leaning, or both. Unraveling the relationship between politics and economics can provide insights into the symptoms, causes, and possible solutions to the US’s growing polarization.

Keywords: partisanship, household income, homeownership, poverty, population, education

Against the backdrop of Coronavirus spreading on a worldwide scale, public health policy became a priority among universities worldwide. Statistical modeling emerged as a solid tool to visualize the role of mask-wearing and social gathering in the spread of this global pandemic. This research aims to simulate the social network of college students living on-campus and how that will impact the spread of Coronavirus within the college campus. We design a simple network model that approximates the type of social interactions occurring on a college campus, which relies on assumptions about people’s living situations (e.g., how many roommates they have, how many people have in-person work). We simulate the disease spreading through the network model by allowing individuals to move between Susceptible, Exposed, Infected, and Recovered states. The transition probabilities between states are determined by an individual’s social interactions and mask-wearing habits. We created an interactive data visualization tool where the users will be able to adjust the parameters to explore the impact of each parameter on the dynamics of the disease. The visualization tool indicates that even if a large percentage of people wear masks, a social gathering such as a party can lead to increased transmission of the disease. The result of this research will be able to approximate the pandemic trend within the campus to inform individual students and policymakers at the university and shape further decisions (e.g., Whether or not to attend social gatherings or initiate university re-opening).

Statistical models are fundamental to identify and understand cancerous tendencies and properties in our bodies. Much of the current research focuses on the relationships between binary gene expressions and cancer incidence, which often leads to uninterpretable models due to complex relationships between gene expressions. Instead, using knot identification and analysis in nonlinear modeling creates more interpretable trends. Using data by age, sex, and race from the National Cancer Institute, we analyze leukemia incidence in the period 1975-2017 using regression splines, a technique that partitions the model into several piecewise functions at various knots in the covariate space. Knot locations are chosen to provide interpretable results and minimize the least squared error, which allows for inference based on techniques from linear regression. After ANOVA forward selection for the polynomial regression model and general cross-validation for the natural cubic spline, the knot points converged on an interval between 1985 to 1986. This suggests that the female cancer incidence rate developed an exponential cancer growth in an interval of 1 year. Therefore, to oppose future exponential incidence increases in female rate, conducting medical research for genomic or environmental causation factors will be more explicit and accelerated due to the specificity of the 1985 to 1986 time inverval.

Extrasolar planets (planets orbiting another star) were discovered in the early 1990’s and since then over 4,350 exoplanets were confirmed to exist as of February 2021, according to the NASA Exoplanet Archive. Our research focuses on looking for trends between the physical/orbital properties and the atmospheric properties of exoplanets. To search for these trends, we use Python and the Habitable Zone Gallery, which is a website that is dedicated to tracking the orbits of exoplanets in relation to their habitable zones, to select exoplanets that are within our desired scope. We have written software that extracts particular physical and orbital data on planets from the Habitable Zone Gallery, but currently, both the download and the transfer to our Google Drive are done manually. These manual tasks are inefficient, among other things, due to the fact that they do not account easily for updates. I will present the software we have developed and our latest developments to resolve these issues, such as modifications to automatically download and upload all of the necessary data. This increases efficiency and in turn, ensures everyone on our team is using the same set of data. Additionally, we are designing a PostgreSQL database that would hold all of our collected data. To increase accessibility of the database, we would utilize interfaces that allow individuals from various academic backgrounds to perform searches on our data. This helps guarantee that all of our data is in one place, accessible, and up to date. In the future, we intend for our software and results to be published for the scientific community. This allows for all of our research to be accessible to individuals who wish to learn more about the relation between the physical/orbital and atmospheric properties of exoplanets.