Body dysmorphic disorder (BDD) is a relatively common debilitating psychiatric disorder, marked by a preoccupation with one's physical appearance and associated with significant morbidity and mortality. This preoccupation is due to one's perceived flaws that are not noticeable to others, causing extreme distress and functional impairment. Previous neuroimaging studies have shown how aberration in visual processing is central to BDD. BDD participants often exhibit an unusual focus on high-detail image information (high spatial frequency processing) versus broad image information (low spatial frequency processing), leading to perceptual disturbances. Other research has shown hypoactivation of early cortices, such as V1 and V2, can be found in BDD participants compared to healthy controls when viewing low spatial frequency information. Hypoactivation in visual cortices extends across multiple stimulus categories, indicating a fundamental deficit in integrating low-spatial frequency information. This study aims to isolate and examine low-level visual processing using fMRI and visual mapping techniques to precisely localize the stage of the visual hierarchy where visual processing is altered in BDD. We aim to recruit n=30 participants (n=15 with BDD, n=15 healthy controls), all of whom will undergo clinical diagnostic interviews and behavioral assessments characterizing spatial frequency processing, ending with a spatial frequency processing task involving both low-level and high-level spatial frequency processing. We hypothesize that the results of our research will show that those with BDD will display reduced sensitivity to low spatial frequency stimuli in early visual cortices. If our hypothesis is confirmed, these findings will reveal potential new biomarkers of perceptual dysfunction in BDD, informing intervention efforts to address more fundamental perceptual deficits and identify potential risk markers for early detection of this disorder.

Members of the gut microbiota produce an array of bioactive metabolites that impact many aspects of host metabolism, immunity, and behavior. However, the mechanisms by which these metabolites are generated remain poorly understood and the biosynthetic enzymes are largely understudied. Recently, our laboratory discovered a family of gut microbial amidases that were found to affect hunger-related biological pathways in malnourished children. These amidases hydrolyze N-acyl ethanolamines (NAEs), lipid messengers with known roles in satiety, visceral pain, and inflammation. Using one of these family members as a model amidase, I am exploring and defining the catalytic mechanisms that are responsible for NAE hydrolysis and the production of a new class of gut microbial metabolites, N-acyl amino acids (NAAAs). I used computational models to predict specific residues that might be important for the amidase activity. I then cloned mutant enzymes into an E. coli expression vector, induced recombinant protein expression, and tested the ability of the purified mutant enzymes to hydrolyze labeled NAEs using liquid chromatography - tandem mass spectrometry (LC-MS/MS). These analyses have pinpointed residues that are important for substrate recognition and binding. My work is advancing our understanding of the selectivity of these intriguing gut microbial enzymes and the regulation of NAAAs in the gut lumen. These efforts are expected to generate the knowledge required to engineer more selective enzymes that produce metabolites of known bioactivity.

Alternative-technology (alt-tech) platforms like Telegram, Truth Social, and Rumble have emerged as self-proclaimed free speech hubs, appealing to distrust in governments and tech companies. While alt-tech platforms remain niche, they play a growing role in political communication and have contributed to real-world events, such as the January 6 Capitol attack. Despite their influence, research on these platforms remains limited compared to mainstream social media studies. This study develops a typology of five right-wing alt-tech platforms—Rumble, Truth Social, Gab, Gettr, and Telegram—analyzing their ownership structures, monetization models, and content moderation policies. By examining how these platforms’ designs and financial incentives influence content production and user behavior, the study aims to provide insights into their role in shaping online information ecosystems. Prior research has examined individual alt-tech platforms, such as Bitchute, and broader comparisons between mainstream and alt-tech platforms. However, this study offers a comparative analysis focused exclusively on alt-tech platforms, identifying patterns in governance, incentive structures, and policy enforcement. Findings from this research could inform policymakers on potential regulatory approaches, including ownership transparency, antitrust interventions, and content accountability measures. Additionally, technologists and platform developers may leverage these insights to design more transparent governance frameworks for alternative digital spaces. By addressing gaps in current social media research, this study enhances public understanding of alt-tech platforms and their growing influence in digital discourse and political communication.

With the rising popularity of TikTok and its role in widespread information sharing, and concerns about political misinformation on the platform, studying political discourse through immersive ethnographic methods leading up to the 2024 US Presidential Election was imperative. This study, conducted over 12 weeks in the summer of 2024, aimed to uncover the rhetorical and ideological topics and trends salient to Black Republican and Democratic TikTok creators through content analysis. To capture the distinct feeds that a person interested in right-leaning or left-leaning content may see, two partisan personas were created on separate phones. The personas were developed from a seed list of known partisan creators and snowball sampling. A quantitative content analysis was conducted using LabelStudio software on a sample of 120 acquired videos from Black creators across the two research phones. The videos were coded for style, topic, and person of interest, alongside other inductive attributes that emerged during the coding process. Thematic analysis revealed key discursive themes around harm and blame, along with different tactics of evidence used by creators to further their points. We find a divide between left- and right-leaning creators with regard to the institutions and politicians they hold accountable for harms, and the ideologies they perceive being pushed by the oppositional party. This study shows how the affordances of the TikTok platform allowed for, and algorithmically rewarded, infighting within the Black community leading up to the election. Future studies may apply these methods of persona-enabled ethnographic data collection for conducting bipartisan investigation on other online communities, including but not limited to racial minority groups, in gaining a better understanding of prevalent issues within these communities, political or otherwise.