Individuals exposed to chronic, severe, and/or inescapable stress may adopt dissociation as a coping mechanism to alleviate suffering. In dissociation, individuals report losing connection to their bodies, reality, memories, etc. Dissociative experiences are hallmark symptoms of psychiatric disorders such as Post-Traumatic Stress Disorders (PTSD) and Dissociative Identity Disorder (DID), which has high comorbidity with personality disorders. Maladaptive personality patterns can be thought of as chronic patterns of behavior that are resistant to change. Brain systems are known to underlie these sorts of habitual learning. The interactions between dissociative experiences and action-learning mechanisms are still poorly understood. In this project, we review the history of dissociation and action-learning research and synthesize a model of how dissociative mechanisms may interact with action-learning mechanisms to develop pathological behavioral patterns and even multiple personalities. We propose employing the PRISMA method to select relevant literature from PubMed or other medical databases systematically and conduct a comprehensive review across the spectrum of dissociative disorders. By utilizing the Dissociative Experiences Scale (DES) scores as a measure method, we aim to identify action-learning brain region activity (BOLD signal) and volume changes and explore how these changes correlate with dissociative symptoms; and we’ve found that DID, Dissociative Disorders, and PTSD has the highest DES mean scores. Beyond this, our findings show that in these patients with different kinds of dissociative disorders, for the patients with different kinds of dissociative disorders, their Hippocampus and Amygdala are smaller while their Pallidum Volumes in their brains become typically larger. Despite this relationship having elementary processes, combining with future findings and discoveries, the relationships between dissociative experiences and action-learning mechanisms will be better understood.

Every year, substance abuse, mental disorders, and psychotic behavior become more and more severe, especially in the local communities of Seattle. Our research explores the interconnection between psychology and the utilization of modern computer vision and emotion recognition tools to analyze human semantics/behavioral patterns in order to comprehend the underlying dynamics that lead to these behaviors and ultimately, spearhead efforts for prevention. As a computer science researcher, I employ advanced machine learning algorithms built upon open source programs to track and interpret gestures, facial expressions, and body language in video data. By filtering the data through a database and comparing emotions through statistical analysis in similar individuals, I am able to uncover nuanced patterns in behavior and emotion that a human researcher would otherwise not notice. While the study is ongoing, our current data illustrates correlations between specific gestures, facial expressions, and emotional states in hallucinogenic use. Though these individuals exhibit typical behavior of substance use, they have different backgrounds and contexts that lead to unique obscure changes in emotion and behavior, and our program allows us to identify and analyze these transformations that current medical knowledge overlooks. These findings hold promising implications for informing future research directions and intervention strategies across multiple domains, from understanding human behavior in social interactions to enhancing human-computer interfaces.

Individuals exposed to chronic, severe, and/or inescapable stress may adopt dissociation as a coping mechanism to alleviate suffering. In dissociation, individuals report losing connection to their bodies, reality, memories, and more. Dissociative experiences are hallmark symptoms of psychiatric disorders such as Post-Traumatic Stress Disorders (PTSD) and Dissociative Identity Disorder (DID), which have a high comorbidity with personality disorders. Maladaptive personality patterns can be thought of as chronic patterns of behavior that are resistant to change, which often are heavily present in brain systems that focus on habitual learning. However, the interactions between dissociative experiences and action-learning mechanisms are still poorly understood. In this project, we review the history of dissociation and action-learning research to synthesize a model of how dissociative mechanisms may interact with action-learning mechanisms as a cause for the development of pathological behavioral patterns and even multiple personalities. We propose employing the PRISMA method to select relevant literature from PubMed or other medical databases systematically and conduct a comprehensive review across the spectrum of dissociative disorders. By utilizing the Dissociative Experiences Scale (DES) scores as a measure method, we aim to identify action-learning brain region changes and explore how these changes correlate with dissociative symptoms; thus far, we’ve found that DID, dissociative disorders, and PTSD have the highest DES mean scores. Beyond this, our findings show that in these patients with different kinds of dissociative disorders, the hippocampus and amygdala are smaller and the volume of the palladium is typically larger. When combined with future findings and discoveries, the relationships between dissociative experiences and action-learning mechanisms will be better understood.

Advances in language models have made diverse applications in social and health sciences possible. With many models emerging daily, it is important to understand general principles behind how language models represent text data. The study aims to investigate the effects of text manipulation on the numerical representation of meaning, or embeddings, through language models. The research is focused on numerous text manipulations (ex. changing direction of sentiment, sentence incompleteness, object of reference, intensity of statement). The research employs SentenceTransformer for embedding generation and UMAP for the visualization of semantic shifts. A comprehensive analysis of over 50 sentence manipulations provides a preliminary basis for understanding the nuances of semantic space organization. The core hypothesis suggests that incomplete and intensity based modifications lead to greater shifts compared to negative and referencing modifications. This outcome is expected to be substantiated through measures of embedding displacement from original text, and generalized through statistical validation, with tests accounting for multiple comparisons. This research is positioned to scale towards publishable results, aiming to systematically quantify the effects of semantic modifications across a larger dataset. The anticipated contributions of this work are twofold. It offers insights into the organization of semantic space as influenced by sentence modification, providing implications for the development of more nuanced NLP (Natural Language Processing) applications. And crucially, it lays a foundational basis for understanding how large language models (LLMs) process psychological statements and terms, an area of increasing importance as these models become more integrated into applications involving psychological analysis. Overall, the outcomes of this research are poised to deepen the understanding of how semantic alterations impact sentence embeddings, with potential applications in enhancing NLP models' interpretability in processing complex linguistic constructs.

Advances in language models have opened up endless applications in the social and health sciences. A key to fulfilling this promise is the development of rigorous practices for gathering data used for model training. This research project reports the development of standardized protocols for building databases for self-reported psychological questionnaires that can be used to fine-tune language models. These questionnaires cover a spectrum from childhood experience to altered states of consciousness and beyond. We systematically review the literature on appropriate database-building strategies, use combined data collection techniques, and instill specific criteria into our collection process to help facilitate the research’s computational pipelines on evaluating the relationship between semantics and psychological factor membership. Using a 2D semantic map of the PsycTests database as a reference, we can further evaluate the effectiveness of our protocols by developing quantitative measures to assess comprehensiveness and objectivity in our database building. Our initial explorations involve measuring data variance and density of data distribution. This research provides a general toolkit for building text databases for appropriate and effective model training purposes across various disciplines.

The effective treatment of individuals with HIV relies on maintaining therapeutic drug concentrations, necessitating accurate measurement of antiretroviral (ARV) drug levels. Current methods, such as liquid chromatography tandem mass spectrometry (LC-MS/MS), are limited by cost and accessibility. Our research addresses this gap by developing the INTEGRase activITY (INTEGRITY) assay for measuring integrase strand transfer inhibitors (INSTIs), a leading class of ARV drugs. This 2-step assay quantifies INSTIs using a DNA strand transfer reaction and quantitative polymerase chain reaction (qPCR). The presence of INSTI drugs disrupts the strand transfer reaction, inhibiting full-length target DNA formation, which is then measured through real-time qPCR. My work focused on optimizing the limit of detection of INTEGRITY by altering the strand transfer reaction conditions and protocol. Specifically, I conducted experiments altering INSTI drug concentrations and optimizing pre-incubation times of integrase with the drug to enhance the LOD. I observed that preliminary incubation of integrase and INSTI drugs for 5 minutes at 37 degrees Celsius improved the LOD of INTEGRITY by an order of magnitude. The simplicity of the INTEGRITY assay, utilizing standard laboratory equipment, holds immense promise for broadening access to routine clinic-based ARV drug level monitoring. This advancement has the potential to significantly enhance HIV care on a global scale by offering a cost-effective and accessible solution for monitoring therapeutic drug concentrations.

Why does human rights law fall short in protecting people from human rights abuses made by governments acting in the name of self-sovereignty? In this project, I analyze these questions in the context of human rights abuses, such as extrajudicial killings, false imprisonments, rapes committed by law enforcement officers, and forced disappearances in Panjab, India. To do so, I use official reports from non-governmental organizations, an original interview I conducted with a survivor of Operation Blue Star, which was an attack on one of the holiest shrines for Sikhs, process-tracing, and human rights legal analysis. After developing the political and legal framework, I present my research in the form of three narrative case studies. I argue that the acts orchestrated by the Indian government and the tactics they employed to eradicate Sikhs constitute genocide under human rights law. Nonetheless, Sikhs struggle in gaining recognition due to extreme efforts by the Indian Government to cover-up and malign the names of Sikhs through their own governmental groups, and an international political environment that discourages foreign governments from condemning Indian government actions. With the research conducted in this project, links between other human rights violation in India can be found. The lack of accountability of the Indian government in multiple cases can be uncovered, which will prove how even with the existence of human rights law, governments have the most power.

Tumor budding, a feature of colorectal cancer and numerous other cancers, has gathered interest as an important prognostic marker and is defined as the presence of small clusters of up to four epithelial cells at the invasive tumor margin. However, current methods of evaluating tumor buds rely on 2D serial sectioning, and 3D reconstructions of this data demonstrate that many of the tumor buds identified in the cross-sectional views are not isolated buds, but extensions of a larger tumor. Advances in 3D microscopy, as well as tissue optical clearing and fluorescent labeling protocols, could provide a means to overcome these 2D limitations. One such method, open-top light sheet (OTLS) microscopy, has demonstrated its potential to visualize similar cancer features and generate rich 3D datasets for cancer risk assessment and analysis. We hypothesize that OTLS microscopy will provide further insight into the differences and distribution between tumor buds, poorly differentiated clusters (PDC) and the main tumor mass, which can be used to improve the diagnostic grading of cancer. Thus far, I have stained 12 colorectal and 4 lung cancer specimens with a fluorescent analog of hematoxylin and eosin (H&E) along with pan-cytokeratin antibodies, and then optically cleared, imaged, and processed them to generate false-colored volumetric atlases for analysis of the tumor invasive margin in 3D. These datasets are used in conjunction with deep learning-based tumor bud segmentation workflows for the calculation of volume, distance, and orientation metrics. The investigation into what proportion of tumor buds are actually extensions of the tumor mass could have implications for prognostic evaluation by pathologists.

Previous work has shown that short-term treatment of rapamycin (an mTOR inhibitor) in aged mice can increase lifespan, improve heart function, attenuate inflammation in gingiva and periodontal bone, and remodel the oral microbiome toward a more youthful state. Nevertheless, important questions arise of whether the beneficial effects of rapamycin persists. Here, we demonstrate that rapamycin continues attenuating periodontitis in old mice even following an 8-week cessation in treatment. Quantitative Real-Time PCR analysis was used to determine expression levels of inflammation markers attributed to periodontitis. Quantitation and analysis were completed by first standardizing target genes relative to ß-actin and GAPDH expression. Data were analyzed where appropriate using one-way analysis of variance (ANOVA) with post-hoc Tukey test for multiple comparisons, where p-values <0.05 were considered statistically significant. Statistical analysis was completed on GraphPad Prism 10.0. Our findings show elevated levels of inflammatory cytokines IL-1a and IL-1b and bone turnover markers NFATc1 and MMP13 in aged alveolar bone were decreased after rapamycin treatment, and the attenuation persisted. Additionally, PI3K-α and PI3K-γ expression, both upstream of mTOR, associated with periodontitis, and elevated in old mice, were inhibited to levels of young mice, and results persisted. These findings demonstrate that the potential therapeutic effect of rapamycin persists in attenuating periodontitis and suggest that a drug dosage regimen can potentially be optimized.

Open-top light sheet (OTLS) microscopy enables the volumetric imaging of large tissue specimens for research and potential clinical assays. No destructive sectioning is required, allowing the tissue to be used for standard downstream assays (e.g. H&E and molecular analyses) after the 3D pathology process is completed. While our typical 3D pathology workflow uses many of the same reagents as standard formalin-fixed paraffin-embedded (FFPE) histology, including xylene and ethanol along with a food-grade cinnamon oil (ethyl cinnamate), we would like to show that our processes do not negatively impact the quality of molecular biomarkers in valuable archived clinical specimens (FFPE). In previous research, we qualitatively demonstrated that tissue morphology and immunohistochemistry markers were unchanged before and after our 3D pathology workflow. Here, we aim to quantitatively assess the effects of our processing methods on FFPE breast carcinoma tissues using standard ER and HER2 immunohistochemistry (IHC) and HER2 fluorescent in situ hybridization (FISH) analyses, as well as nucleic acid integrity metrics (RIN scores, RNA bulk yield, signal quality). We hypothesize that the molecular characteristics of our processed specimens are statistically equivalent to those of unprocessed specimens. To demonstrate this, I have obtained two adjacent 3-mm diameter punch biopsies from 26 archived (FFPE) breast specimens. For each specimen, one sample will undergo our standard 3D pathology workflow. I will then submit both samples to pathology labs for quantitative comparison of standard clinical biomarkers (e.g. HER2 and ER expression) and nucleic acid integrity metrics. We will demonstrate that our lab’s 3D pathology protocols do not negatively impact tissues in terms of molecular characteristics, which will be important for clinicians to allow our nondestructive 3D pathology methods to be performed on valuable archived tissue specimens, and for our methods to more-easily translate into standard clinical practice.

Periodontal disease is a prevalent oral disease most frequently found in older adults, and left untreated can lead to inflammation, bone loss, and patients may lose their teeth. Treatment of periodontitis includes repeated maintenance by a dentist and surgical procedures. These treatments can be invasive and time-consuming and do not target the underlying molecular changes that occur during the aging process. Previously, the mTOR pathway was shown to decrease inflammation and bone loss during periodontal disease in aged mice. Here, we investigate if targeting a marker upstream of mTOR, called colony-stimulating factor 1 receptor (CSF-1R), can recapitulate the effects of targeting mTOR. CSF1-R plays a role in inflammatory diseases by regulating myeloid cell lineages such as macrophages and osteoclasts. We hypothesize that inhibiting CSF-1R will decrease the expression of inflammation markers associated with periodontal disease in aged mice. Real-time quantitative PCR was used to identify relative expression levels of various inflammatory markers involved during aging and periodontal disease, including IL1-a and IL1a. Quantitation and analysis were completed by first standardizing target genes relative to ß-actin and GAPDH expression. Data was analyzed where appropriate using one-way analysis of variance (ANOVA) with post-hoc Tukey test for multiple comparisons, where p-values <0.05 were considered statistically significant. Statistical analysis was completed on GraphPad Prism 10.0. Our analysis demonstrates that CSF-1R inhibition decreases multiple cytokines involved during periodontal disease and major isoforms of the PI3K pathway. These findings indicate the importance of the CSF1R signaling along the mTOR pathway and possible implications for future research. This study was supported by the VitaDAO/Molecule Longevity Fund to Dr. Jonathan An, Oral Health Sciences

Cellular senescence is one hallmark in the biology of aging in which cells stop growing and accumulate over time. These cells secrete factors called SASP, or senescence-associated secretory phenotype, which may be involved in inflammation and cancer. A cancer that most often occurs with age is cancer of the tongue. Investigating tongue cancer has been primarily in animal models that have induced cancer, thereby losing the opportunity to study the impact of aging on cancer. This project evaluated cellular senescence in aged male and female mice tongues and tested the effect of inhibiting mTOR via rapamycin, an intervention that improves the health and extends lifespan in animal models. RNA from tongue specimens was isolated and Real-Time PCR was used to quantify the levels of SASP expression. Histology was completed to visualize the tongue structure. The results show differences in aged male and female mice in senescence markers p16 (Cdkn2a) and p21 (Cdkn1a). There were also significant increases in major markers such as IL-1A, IL-1B, and TNF-a. Treatment with rapamycin decreased SASP expressions in older mice. Further research is needed to test how these SASP markers relate to aging and oral cancer. Future experiments include conducting protein assays and overexpressing these SASPs to determine if they increase the risk for oral cancer in mice. This project is important because these markers can become valuable tools for clinicians to diagnose oral cancer in older adults before its onset. This will provide more time for them to implement preventative measures and treatments, increasing the survival rates in the fight against cancer and age-related diseases.

Our research aimed to investigate the microbial diversity in Pieris rapae larvae obtained from three distinct settings: wild-collected larvae, larvae fed with leaves in a laboratory setting, and larvae fed with a standard artificial diet. Additionally, this investigation sought to identify the factors contributing to developmental delays in artificial diet-fed larvae, while also investigating the underlying cause of increased cases of infection and mortality affecting the laboratory colony. Initial observations indicated that wild larvae exhibited the fastest development and appeared healthiest, followed by those fed with leaves, both settings showing the lowest instances of disease and mortality. Conversely, the artificial diet group experienced developmental delays and frequently succumbed to disease before pupation. Based on these observations, we hypothesized that variations in microbial composition within different diets and environments contributed to the disparate outcomes. Furthermore, we speculated the possible identification of bacterial strains with known associations with promoting or hindering larval success. To analyze the larval gut microbiome, a specialized contraption was designed for sterile frass (feces) collection. Frass, skin, artificial diet, and leaf surfaces were swabbed onto TSA media, enabling the isolation of distinct colonies and species. Morphological characteristics, gram staining, and Sanger sequencing data of the 16S rRNA gene were used to identify the bacterial colonies at the species level. The identification of six bacteria at the species level yielded intriguing results, with particular interest surrounding Bacillus subtilis and Bacillus thuringiensis. The presence of B. subtilis exclusively in the artificial diet group suggests its potential involvement in the developmental delay observed in the larval population fed by artificial diet. Conversely, the identification of B. thuringiensis implicates a potential bacterial infection, which could explain symptoms experienced by the colony. These findings have potential implications for enhancing larval outcomes in colonies or, in contrast, pesticidal applications in an agricultural setting. 

Rodents are a common model for ischemic stroke research; however, their brains are mostly grey matter while approximately half of tissue affected by stroke in humans is white matter. To study stroke in white matter, we model ischemia in the mouse optic nerve (MON), a pure white matter tract. We observe impaired axonal function and conductance in the MON after ischemia that is improved by ischemic preconditioning (IPC), a phenomenon in which a brief ischemic stimulus protects against subsequent prolonged ischemia. Our prior work demonstrates microglia are required for IPC-mediated axonal protection. Several models of injury and disease report elongation of the nodes of Ranvier (NoR) leading to reduced axonal conductance, but the role of microglia in protecting axons at the NoR is unknown. Here we investigate how NoR are affected by ischemia and microglial depletion. Based on our previous work, we hypothesize that IPC will preserve NoR lengths after exposure to ischemia and this protection will be lost when microglia are absent. Microglia were depleted with PLX5622, a colony stimulating factor 1 receptor antagonist. After treatment, a subset of animals were collected to assess baseline average NoR lengths after microglial depletion alone. Another cohort (N=5) received an in vivo IPC stimulus (15-minute transient common carotid artery occlusion) and 72 hours later experienced ex vivo oxygen-glucose deprivation (ischemic stroke) for 45 minutes. MONs were fixed overnight in paraformaldehyde and prepared for immunohistochemistry using fluorescent antibodies against Nav1.6 (nodes) and Caspr (paranodes) to identify NoRs with confocal microscopy. Nodes are measured using FIJI and the distance between Caspr+ paranodes flanking a Nav1.6+ node is calculated using MATLAB. Microglial depletion alone was found to be associated with increased NoR lengths. Our ongoing work is focusing on the impact of ischemia on NoR lengths and how this may be modulated after IPC.
 

For people living with epilepsy (PWE), anti-seizure medicines (ASMs) are the primary treatment option. However, 30% of PWE are unable to control their seizures with ASMs because they have drug-resistant epilepsy (DRE). Pathological mechanisms that contribute to DRE are not currently understood. Nevertheless, both clinical and preclinical data indicate potential involvement of changes in the architecture of neuronal networks. I used a clinically relevant rat model of temporal lobe epilepsy, and novel medication in food delivery system to confirm DRE, or failure to reduce their baseline seizure frequency by 50% with two or more clinically used ASMs. I hypothesized that the DRE animals would have lowered neuronal cell density compared to the those with drug-sensitive epilepsy (DSE). All rats were euthanized at the end of a 6-week treatment period to process the brains for immunohistochemical labeling of mature neurons with the antibody, NeuN. Total percent staining area was quantified in the hippocampus, piriform cortex, and somatosensory cortex of brains. No differences in NeuN immunoreactivity were observed between DSE and DRE animals in any brain region. However, NeuN levels in animals with epilepsy, regardless of treatment outcome, trended lower than naïve animals without epilepsy in the CA1 and dentate gyrus regions of the hippocampus. Together, these data suggest that neuron density may not be driving pharmacoresistance. However, it is possible that the ratio between excitatory and inhibitory neurons may be disrupted in DRE. This underscores the need for future studies to quantify neuronal subtypes, providing a more nuanced understanding of the underlying mechanisms of pharmacoresistance. These studies play a crucial role in guiding future research into novel treatments designed for DRE.

Alpine areas are host to diverse plant communities that support ecosystems with their structure and floral resources and existing through specialized adaptations to harsh high-elevation conditions. An ongoing question in these plant communities is whether composition is shaped by stochastic processes (e.g., dispersal limitations) or by deterministic processes (e.g., climate) and if those processes select for common phylogenetic clades across space. This study evaluates the drivers of dissimilarity in vascular plant communities of alpine areas of 32 peaks in the Cascade Mountain Range of Washington State and the effects of incorporating phylogenetic relatedness to these conclusions. Observing an average of 54 species per peak, an inventory of 315 vascular total plant taxa were compiled to construct a phylogenetic tree relating each taxa to one another. We used multivariate techniques to quantify the phylogenetic and taxonomic differences between alpine plant communities and to relate those differences to each peak’s climate, geology, and topography. Our models indicate that each peak’s elevation, geologic parent material, and precipitation seasonality had the largest role in shaping alpine plant communities relative to the baseline effects of distance between peaks. Despite phylogeny contributing to lower overall dissimilarity, it conforms to the same trends between peaks and does not change the relationships to space, geology, and climate seen in taxonomic distance at the mountain-range scale. These results support the existence of deterministic spatial patterns of geology and climate driving community composition but fail to explain any evolutionary processes influencing colonization and survival in alpine environments.

In plant physiology, germination is a crucial process through which a seed transforms into a seedling. Physiological dormancy is a natural state in seeds where germination is internally inhibited until specific biochemical changes occur, ensuring that favorable conditions for plant development are met. To promote germination in Thalictrum occidentale, Western Meadow-Rue, I used Gibberellic Acid (GA) to break the physiological dormancy of the seeds. The SER-UW Native Plant Nursery had not successfully germinated Thalictrum occidentale but wished to grow and distribute this species to ecological restoration sites and the public, which motivated my investigation. GA has been used to successfully germinate other species in this genus as it is an essential plant hormone required for breaking seed dormancy and promoting germination. 900 seeds were divided into three groups, seeds received GA treatments applied either before or after 54 days of cold stratification in peat moss, alongside a control group. These were then sown in greenhouse flats. GA washes involved imbibition for 24 hours in 1000 ppm GA. This experiment was repeated in a growth chamber, placing seeds on blotter paper in petri dishes to more easily detect germination and to compare 6- vs. 10-week stratification periods. Seeds treated with GA before stratification produced higher and earlier germination rates in both greenhouse and growth chamber settings than other treatment groups. These findings support the hypothesis that applying GA treatments to Thalictrum occidentale seeds breaks their physiological dormancy and promotes germination. This study addresses limited research on this species’ physiological dormancy, offering insights into GA’s role in promoting germination. Additionally, it encourages further investigation into factors such as GA concentration and growing media selection to optimize germination outcomes. By deepening our understanding of Thalictrum occidentale’s germination process, this research contributes to plant physiology and ecological restoration efforts.

Gun violence is a prevalent and rising issue in the United States. However, limited research assesses the connection between gun prevalence and public health outcomes. Previous research indicates healthcare professionals are hesitant to have a role in addressing the rising gun violence statistics. Another previously drawn implication is that gun violence is associated with worse behavioral and physical health. These findings led us to pursue our research. This project aims to acquire quantitative insights into the relationship between neighborhood-level gun prevalence and violence and neighborhood-level utilization of primary healthcare. Within the context of this study, neighborhood gun prevalence and violence are defined as instances of violence encompassing firearm-related fatalities or the mean quantity of firearms within households. We define utilization of primary healthcare as a composite of a variety of healthcare variables, including, but not limited to, the percentage of dental check-ups, the percentage of the population with established primary care providers, health indicator screenings, and vaccination rates. We are using data from the Seattle & King County Public Health Database to examine the association between our variables of interest at the neighborhood level. We will conduct a linear regression model to statistically examine the correlation between the utilization of primary health care and neighborhood gun violence statistics. We anticipate that the findings of this study will elucidate a discernible correlation between indicators of primary care access and firearm-related violence at the neighborhood level in the Puget Sound region. We intend to employ this data to aid local public health officials in understanding and addressing the correlation between healthcare disparities and gun violence. Subsequently, this information can serve as a foundation for their efforts in formulating population-level legislation aimed at mitigating healthcare disparities to alleviate firearm violence at the neighborhood level.

Influenza is a deadly virus that remains a worldwide problem despite vaccinations, modern medicine, and public awareness. After infection, antiviral drugs are the primary treatment option. One common antiviral strategy is to prevent viral entry by targeting the hemagglutinin protein (HA) which sits on the surface of the virus. There are currently 18 known subtypes of HA, resulting in the efficacy of many antiviral drugs being limited. The goal of this project is to computationally design disulfide-closed cyclic peptides that target conserved binding regions on HA and test for broad spectrum activity against multiple HA subtypes. To start, I used RFdiffusion to generate peptides that are docked against the H1 subtype of HA. Then I used ProteinMPNN to optimize the amino acid sequence of the peptides. Afterwards, I used a custom written program named Protein Transform to take the peptides and dock them on the different HA subtypes. I then ran a custom program that uses RoseTTAFold2 (RF2) to predict the root mean square error (rmsd) between the target binding spot and the spot RF2 believes the peptide will stay and score the interface predicted align error (ipae), which is a confidence metric for the binding interaction. Next, I ran a Rosetta program to calculate the interaction energy (ddG) between the protein-peptide complex. The designs were then filtered based on ipae, rmsd, and ddG scores using our lab’s standard filtering thresholds. Our lab then used yeast surface display screening to test the peptides and measure the binding affinity (KD) against the H1 subtype. Then we verified the hits using surface plasmon resonance. We anticipate to see KD values in the range of 10 to 1000 nM. Identifying a lead compound with broad spectrum HA binding is an important step to creating a drug to treat any strain of Influenza.