This thesis includes three separate documents: a) a comprehensive document detailing the methods and analysis of the creative factors tied to series success, b) an hour long pilot script based on this data, and c) an industry-standard pitch deck for a TV show created with data insights. In a larger sense, the aim of this study is to take the first steps in remedying information asymmetry between streaming services and content creators. If streaming services were more transparent with their data and communicated to their creators what has been proven to work in the past, showrunners and staff writers could have a new tool to increase the competitiveness of their series and aid in show renewal each year.

For my project, I delve into the relationships of Victor and the Monster as well as the relationships Victor shares with other characters that were underdeveloped within the original novel by Mary Shelley in the novel Franeknstein. I examine their relationships in two components. The first through my own interpretation of Victor and the Monster’s relationship within a creative writing piece that extends the novel as if Victor had lived rather than died in the arctic in order to explore the possibilities of a more complex set of relationships between Victor and the Monster than simply creator-creation. My writing focuses on the development of their relationship once all they have left is each other. The second part of my project focuses on an analytical component. I analyze and cite the reasoning for my creative take on Victor and the Monster as well as their relationship within the novel and Mary Shelley’s intentions.

This paper explores the well-known Atkins Diet, as it also places a strong regulation on macromolecule consumption, specifically carbohydrates, in order to assist with the weight loss process. A review of available literature will be used to investigate: the history of the diet, necessity of macromolecule consumption, the impact this has on the individual biochemical pathways (glycolysis/gluconeogenesis) and the microbiome as a whole, as well as overall success rates and long-term health complications/benefits. Additionally personal statements from various individuals who have experience with the diet, myself included, will be incorporated into a holistic analysis of the effectiveness and longevity of the Atkins weight-loss strategy.

Lyme disease is a common tick-borne illness caused by the Gram-negative bacterium Borrelia burgdorferi. An outer membrane protein of Borrelia burgdorferi, P66, has been suggested as a possible target for Lyme disease treatments. However, a lack of structural information available for P66 has hindered attempts to design medications to target the protein. Therefore, this study attempted to find methods for expressing and purifying P66 in quantities that can be used for structural studies. It was found that by using the PelB signal sequence, His-tagged P66 could be directed to the outer membrane of Escherichia coli, as confirmed by an anti-His Western blot. Further attempts to optimize P66 expression in the outer membrane were made, pending verification via Western blotting. The ability to direct P66 to the outer membrane using the PelB signal sequence is a promising first step in determining the overall structure of P66, but further work is needed before P66 is ready for large-scale purification for structural studies.

The field of biomedical research relies on the knowledge of binding interactions between various proteins of interest to create novel molecular targets for therapeutic purposes. While many of these interactions remain a mystery, knowledge of these properties and interactions could have significant medical applications in terms of understanding cell signaling and immunological defenses. Furthermore, there is evidence that machine learning and peptide microarrays can be used to make reliable predictions of where proteins could interact with each other without the definitive knowledge of the interactions. In this case, a neural network was used to predict the unknown binding interactions of TNFR2 onto LT-ɑ and TRAF2, and PD-L1 onto CD80, based off of the binding data from a sampling of protein-peptide interactions on a microarray. The accuracy and reliability of these predictions would rely on future research to confirm the interactions of these proteins, but the knowledge from these methods and predictions could have a future impact with regards to rational and structure-based drug design.