In this essay, I set out to explore and analyze how Shirley Jackson’s The Haunting of Hill House, and the character of Eleanor Vance in particular, disrupts and redefines the traditional conventions of the Female Gothic within the context of the 20th century. I utilize Tania Modleski's gendering of Freud's theory of psychoanalysis in her exploration of the ‘Female Uncanny,’ arguing that the source of the Uncanny in the Female Gothic can be found in the "fear of being lost in the mother." I argue that Jackson's complex personal life, including her fraught relationship with her mother and her difficult marriage with literary critic Edgar Hyman, color her fiction and the primary motivations of her protagonist, Eleanor Vance. I also outline the traditional structure of the Gothic novel and the heroine's journey. With the necessary context provided, I then explain how Eleanor Vance’s character rejects these Gothic traditions and ushers in a new era of Female Gothic fiction.

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.