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.

The research question at hand asks how block parties can be used as an empowerment tool to positively affect civic engagement for the Black community. The project lies within the contexts of outreach strategies for underrepresented minority communities; the power of civic engagement and identity; and the inherently political history of the Black community and public space. The primary methods include an in-depth literature review to adequately frame the topic; the interviewing of Black community leaders in applicable disciplines across the United States for their experience and insight; and a creative depiction of the block party re-envisioning into one that centers Black empowerment, learning, and civic amplification. The findings propose a potential solution to magnifying Black voices in civic processes by taking a contemporary approach to the traditional idea of a block party. The final product is the literature review and representation of the Bl*ck Empowerment Party through a virtual zine. These products aim to provide individuals of underrepresented identities with the framework and inspiration to implement the project through discovering innovative methods of public outreach and empowerment rooted in the histories of their own communities in hopes of encouraging engaged citizens. The Bl*ck Empowerment Party addresses the inequities within city structures and development processes through offering a creative solution that honors the rightful space for affected communities to be empowered stakeholders and catalysts.

Dry eye is a common disease of older adults that produces symptoms ranging from mild discomfort to visual disturbances. Treatments range from lifestyle changes, use of eye drops, to surgical interventions. A nonhuman primate model of severe dry eye disease has been developed using rhesus macaques and employs the use of the Schirmer 1 Tear Test (STT-1). Pigtail macaques would likely be similarly suited as a model for this disease, however, no STT-1 values have been published for this species. Our objectives are to determine the normal range of STT-1 values in apparently healthy pigtail macaques, while considering age, sex, and type of sedation. The data consists of STT-1 values in each eye on 218 pigtail macaques, along with who performed the test, type of sedative used, and the age and sex of each monkey. Analysis of these data using t-tests, ANOVA and linear regression methods indicate that STT-1 values vary significantly between left and right eyes, type of sedation, and staff conducting the test. STT-1 values do not vary significantly by sex but may vary by age. We used the observed relationships to determine an overall 95% baseline interval of STT-1 values for healthy pigtail macaques, as well as individual 95% baseline STT-1 intervals that depend on sedation type and age.

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).