Asian Americans are victims of anti-Asian racism, but recent studies have also shown that they have a different role in perpetuating anti-Blackness and White supremacy. Our research aimed to specifically examine whether White and Asian Americans perceive a difference between anti-Blackness and White supremacy. We ran a qualitative study where anti-Blackness and White supremacy were defined for Asian and White American participants, who were then asked to write about their racial group’s relationship with those phenomena. We defined anti-Blackness as the belief that Black people are inherently inferior to others, and the corresponding practice of them being given insufficient power. Similarly, we defined White supremacy as the belief that White people are inherently superior to others and the corresponding practice of them being given disproportionate power. A few research assistants and I rated the similarity between the responses on anti-Blackness and White supremacy on a Likert scale from 1 to 7, with 1 indicating “not similar at all,” and 7 indicating “very similar.” We then performed a two-sample t-test (a type of statistical data analysis in psychological research) on this data to compare the responses between the Asian and White American participants. As expected, we found that the Asian American participants rated anti-Blackness and White supremacy as being more distinct than the White Americans did. The p-value for this data analysis was a statistically significant 0.004. These findings establish a foundation for future studies on the Asian American role in anti-Blackness and White supremacy. I have also conducted data analysis and ran participants for two such studies, which examined whether reminders of anti-Blackness caused Asian Americans to take more responsibility for anti-Blackness and show more solidarity with African Americans more than reminders of White supremacy.

​​​​Bacteria evolve primarily through horizontal gene transfer, the movement of genetic material between organisms that are not directly related. This allows the rapid acquisition of traits like virulence and antibiotic resistance, an increasing public health concern. The mechanisms by which bacteria integrate and control these new traits is incompletely understood. Acquired virulence genes have allowed uropathogenic Escherichia coli (UPEC) to become the predominant cause of urinary tract infections (UTI) throughout the world. A critical step in the UPEC infectious process is the transition from a free-swimming, unicellular, “planktonic” form in the urinary tract to an stationary multi-cellular community, or “biofilm,” when it invades the bladder epithelium. The Fang lab has shown that the transcription factor, MprA, promotes the expression of a horizontally-acquired gene cluster encoding the enzymes required for the biosynthesis of polysaccharide capsule, an important UPEC virulence factor. We hypothesize that this capsule is associated with planktonic growth, and that by regulating capsule, MprA controls the switch between planktonic and biofilm-associated growth. We will test this by growing biofilms of wildtype, mprA, and capsule-deficient mutant strains in the laboratory. Additionally, we will assess their impact on virulence by infecting larvae of Galleria mellonella, the wax moth, with each strain. Understanding how factors like MprA control horizontally-acquired genes can inform the development of future antibacterial therapies.

Stigma against individuals with substance use disorders has been shown to negatively affect their health outcomes. Interpersonal stigma has been shown to further perpetuate intrapersonal stigma within this population. The prevalence of this phenomenon has implications in nearly every society, which manifests as structural, societal and interpersonal impacts. This literature review examines how stigma directed at drug users directly affects their health and well-being. The implications are great in that by affecting the health outcomes of drug users, the well-being of an entire population suffers. Data was procured and reviewed using four databases: JSTOR, PubMed, Science Direct and Elsevier. Parameters for the inclusion of data stated that the article must address stigma and how it affects the health and overall well-being of drug users. Additional articles were used to provide background information on the topic. Data suggests that stigma is not only a contributing factor to perpetuated use, but also exacerbates barriers to treatment, fosters a mistrust of healthcare professionals and discourages engagement with evidence-based interventions. Additionally, these factors lead to social isolation and a reduced sense of self-worth. This systematic review highlights some of the gaps in current knowledge pertaining to how stigma negatively affects those who use drugs. By addressing this issue, especially on the systemic level, within areas such as policy reform and health care professional education and training programs, outcomes for those who use drugs can be improved in such a way as to benefit all of society.

Bacterial metabolic engineering shows great promise for sustainable chemical production. Non-model microbes such as Pseudomonas putida, Rhodobacter sphaeroides, and Rhodopseudomonas palustris offer unique opportunities for metabolic engineering, given their tolerance to environmental stressors, their ability to grow on waste substrates, and their natural production of industrially relevant compounds. However, tools for engineering these bacteria are underdeveloped. Here we present genome engineering and gene regulation tools that are generalizable to multiple non-model microbes, offering improved versatility for metabolic engineering. Firstly, we employed a high-efficiency genome engineering tool using serine recombinases (SAGE) in R. sphaeroides and R. palustris. We evaluated integration efficiency for 10 different recombinases using a fluorescent reporter screen, revealing variation in recombinase performance across microbial hosts. We used BxbI, the top-performing recombinase, to integrate a heterologous metabolic pathway into the genome of R. palustris for the bioproduction of a biofuel precursor. In addition to genome engineering tools, we developed gene regulation tools using dCas13, a protein which regulates genes at the translational level. Genome-wide functional screens were conducted in P. putida using an inducible guide RNA system to study levels of gene regulation in native aromatic biosynthesis pathways. Overall, this work advances tools for genomic integrations and gene regulation in non-model microbes, offering new strategies for metabolic engineering and expanding the host range for synthetic biology applications. 

Ocean eddies contribute significantly to the transfer of heat and energy throughout the world’s oceans, playing a key role in regulating climate. Eddies are observed predominantly through Earth-orbiting satellites that collect data on sea surface height (SSH), a metric that can be used to estimate eddies on a global scale. Historically, satellites could only capture point-wise measurements, resulting in low-resolution SSH maps, which led to underestimations of small-scale eddy strength. Launched in 2022, NASA’s Surface Water and Ocean Topography (SWOT) satellite now provides groundbreaking 2D SSH imagery with higher resolution relative to existing SSH products. However, there are only two years of SWOT data available, unlike other satellites with decades-long records. Here, we considered how the recent SWOT data could be deployed to improve the spatial resolution of SSH products from the preceding 30 years. To achieve this, we trained an image-to-image U-Net neural network to predict the high-resolution SSH from an existing low-resolution product (NeurOST). We used SWOT high-resolution data as a ground truth to train this neural network and minimize the mean squared error of the SSH output with respect to the SWOT data. By evaluating the accuracy of the SSH output maps against an independent withheld satellite, we demonstrated that our method improves the spatial resolution of the SSH product compared to the NeurOST dataset. We next plan to test the accuracy of our method when applied to years before SWOT was launched. Overall, our research highlighted how leveraging deep learning and SWOT can enhance the spatial resolution of a decades-long eddy observation time series, enabling more accurate studies of eddies and their climate impact.

Microbial bioproduction supports the manufacturing of sustainable chemicals but requires accurate and easy-to-use tools for monitoring cell growth. A simple and effective tool for estimating cell concentration in aqueous systems is optical density (OD). However, commercially available OD measurement systems are expensive and require manual sampling, which is time-consuming and disrupts culture growth, particularly in anaerobic microbes. To address this, I developed a low-cost OD sensor for continuously monitoring anaerobic bacteria in culture tubes. The sensor design, based on Deutzmann et al. (2022), consists of a 3D-printed sample holder with an LED and a photosensor positioned on opposite sides. The photosensor generates a voltage, which a Python script processes to calculate optical density values for each bacterial species. Plotting these OD values provides researchers with insights into bacterial growth behavior and enables optimization of culture conditions. This device's advantage over commercial spectrophotometers is that it can measure optical density directly from sealed culture tubes, eliminating the need for manual sampling into cuvettes and saving researchers valuable time. It can be configured to run autonomously, further minimizing measurement time and disruptions to bacterial growth. Additionally, the design is fully open-source and customizable while costing less than $100 to reproduce, making it accessible for a wide variety of lab setups. Overall, this low-cost, open-source OD sensor offers a practical, efficient, and customizable solution for continuous monitoring of anaerobic bacterial growth, making it a valuable tool for research laboratories.

Understanding and predicting changes in primary productivity depend on both upper ocean warming and nutrient supply from the ocean interior. Fronts, where distinct water masses converge, are hotspots for these vertical exchanges, transporting nutrients upward and supporting diverse ecosystems. These fronts create sharp gradients in temperature and salinity, generating strong vertical velocities that upwell nutrients and biomass. However, the exact dynamics of frontogenesis (the formation of fronts) remain poorly understood. Additionally, these processes occur at scales too fine to be resolved in global climate models and are only marginally captured by high-resolution ocean simulations. This underscores the need for observational studies to characterize frontogenesis and test existing theoretical frameworks. In this study, we diagnose frontal dynamics using Petterson’s frontogenesis function, which quantifies the roles of divergence and strain. Using NcCut, a GUI developed by our group, we compiled a unique dataset capturing the full life cycle of numerous ocean fronts in front-following coordinates from a state-of-the-art ocean simulation. Our results indicate that for mesoscale (~100 km) fronts, strain dominates over divergence, aligning with classical theories. In contrast, submesoscale (~10 km) fronts exhibit shorter life cycles and no clear dominant driver of frontogenesis within the Petterson framework. We also identified key limitations in conventional diagnostics and improved our analysis by masking the front from its surrounding environment before diagnosing its drivers. This enhancement provides a more accurate representation of frontogenesis dynamics. In the future, we plan to apply our method to satellite observations to study real-world ocean fronts, validate ocean models, and improve predictions of primary productivity changes. Our findings highlight the importance of refining frontogenesis diagnostics to better capture the small-scale dynamics critical to ocean biogeochemistry and climate predictions.

Protein Kinase Inhibitors (PKIs) are a family of heat stable, high-affinity inhibitors of the catalytic subunit of Protein Kinase A (PKAc). In the presence of Mg-ATP, the three isoforms—PKIα, PKIβ, and PKIγ—bind to PKAc with very low dissociation constants: 0.758nm, 1.875nm, and 0.4142nm respectively. In vitro studies have shown that PKIs can translocate PKAc from the nucleus to the cytoplasm, suggesting a role for PKIs in terminating nuclear cAMP-driven PKA activity. Previous research, including studies from our lab, has found that dysregulated PKAc mutants play a significant role in Cushing’s syndrome, a rare and potentially fatal metabolic disorder caused by excessive cortisol production. Building on these findings, we hypothesized that increasing PKI expression could counteract the hyperactivity of PKAc mutants and reduce cortisol production. To test this, we expressed each PKI isoform in adrenal cell lines and assessed their steroidogenic capacity using biochemical assays such as western blots, RNA-seq, qPCR, and ELISA-based cortisol assays. We observed that PKIα and PKIγ led to a general suppression of steroidogenic associated proteins such as StAR, Cyp11a1 and SF1. This altered proteome was accompanied by significantly suppressed cortisol synthesis only in the PKIα and PKIγ expressing cells. The difference between PKIα/γ and PKIβ was surprising given that all PKI isoforms are postulated to potently inhibit PKAc. Thus, we questioned whether PKIα/γ effects are mediated through PKAc. To answer this, we have cloned mutant PKI isoforms that do not bind PKAc, and confirmed the mutant PKIs do not inhibit PKAc through kinase assays. Our next step is to express the mutant PKI isoforms in adrenal cells and assess their effect on steroidogenic capacity of the cells. Our findings suggest that PKIα and PKIγ play key roles in cortisol regulation and may have broader implications for gene regulation in adrenal cells.

Brain-computer interfaces (BCIs) decode neural signals from the motor cortex to enable direct control of external devices. While existing BCI designs often combine signals from the premotor (PMd) and primary motor (M1) cortices, these regions have distinct functional roles and anatomical organizations. Prior research demonstrates that PMd and M1 play distinct roles in movement preparation and execution, with information generally flowing from PMd to M1 (Cisek & Kalaska, 2005). Additionally, cortical processing is known to occur in a layer-dependent manner (Bastos et al., 2012), suggesting that different depths within these motor areas may encode distinct aspects of task-related information, highlighting the need for depth-specific analyses. My hypothesis is that task-related information flows directionally from deeper layers of PMd to superficial layers of M1 as behavior transitions from movement preparation to execution. To investigate this, I used Neuropixel probes, which provide high-resolution sampling of neural activity across cortical depths, and performed simultaneous PMd and M1 recordings in two male rhesus macaques as they performed an arm reaching (center-out) task. Preliminary analyses provide evidence that (1) different cortical depths in PMd and M1 encode distinct movement-related and planning information, (2) neural activity in deep PMd exhibits stronger coherence with superficial M1 compared to other depth pairings within and across regions, particularly during movement-related periods, and (3) information flow between PMd and M1 is depth and directionally organized, with information flowing from deep layers of PMd to superficial layers of M1. These findings suggest that the spatial and temporal dynamics of task-related information across cortical depths are important for motor control. Revealing how task-related signals are organized and transmitted across motor cortical layers can inform the development of BCIs that target recordings to leverage these functional dynamics.

Asthma is a chronic respiratory disease characterized by airway inflammation and remodeling. One key feature of airway remodeling is the thickening of the subepithelial basement membrane zone (BMZ) beneath the airway epithelium, which has been identified in severe asthma relative to milder severity asthma and other airway diseases. We aim to characterize the relationship between BMZ thickness, airway physiology, and airway immune cell populations. I am utilizing design-based stereology to precisely measure BMZ thickness in endobronchial biopsies obtained from 30 individuals with asthma and 10 healthy individuals. These individuals underwent extensive characterization for asthma airway physiology, profiling of airway immune cell populations, and airway inflammatory gene expression. Stereology provides unbiased thickness estimates that have greater reproducibility and overcome the limitations of two-dimensional measurements. I am measuring BMZ thickness using the orthogonal intercept method, which involves averaging the lengths of lines extended perpendicularly from the epithelial surface across the thickness of the BMZ at systematically sampled points. I am correlating BMZ thickness with clinical characteristics (allergic sensitization), airway physiology (baseline lung function, measurements of airway hyperresponsiveness), densities of both mast cells and eosinophils in the airway wall, and gene expression profiles obtained from airway epithelial brushings. I hypothesize that individuals with asthma patients will have more BMZ thickening in comparison to healthy controls. I also anticipate that there will be a positive correlation between the thickness of the BMZ and the expression of type-2 (T2) inflammatory genes (IL4, IL5, IL13). Finally, I hypothesize that there will be a positive correlation between BMZ thickness and the density of mast cells in the airway epithelial compartment. This research study provides new insights into the potential mechanisms responsible for airway remodeling in individuals with asthma and how they connect with airway inflammatory endotypes, which may guide further development of targeted therapeutics. 

Gender disparities persist in male-dominated fields, with women often underrepresented in STEM fields such as computer science. We examine “sense of mattering”– the perception of one's contributions and work being valued and recognized by others– as a factor that may help explain women’s underrepresentation in male-dominated fields. We investigate whether manipulating sense of mattering in a hypothetical computer science class influences actual participation, interest, and anticipated performance in group tasks. Participants (n=200) recruited from the University of Washington’s Psychology Research Pool will be randomly assigned to either a high or low peer recognition condition via an online survey wherein participants engage in a group chat with peers to complete computer science tasks. Participants will contribute to this chat using both prewritten and open-response options. Participation will be analyzed for language content and response length and perceived interest and anticipated performance in computer science will be assessed through a self-report measure. We hypothesize that heightened peer recognition will lead to greater participation, interest, and anticipated performance outcomes for all participants, with a stronger effect for women than men. Future directions for this study include exploring other channels through which sense of mattering could be influenced (e.g., teacher behaviors) and investigating its relevance in disciplines beyond computer science. Examining the potential significance of mattering may pave the way for interventions that foster environments that better appreciate women's contributions.

Hydrogels with tunable stiffnesses are a versatile method to study the interactions of human cells in vitro. These systems recreate human extracellular matrix (ECM) and capture the stiffness changes associated with a variety of biological processes and diseases, like cancer and cirrhosis. Photoresponsive chemistries allow light to be used to modulate the stiffness in these materials with high resolution. However, when creating more complex patterned gels with photomasks, bulk property analysis cannot capture the variation. To circumvent this and measure the stiffness of these complex gels, I performed rheology and fluorescence recovery after photobleaching (FRAP) to establish a correlation between diffusivity and stiffness in flood-illuminated gels. By finding and using the correlation, I am able to calculate the stiffness of the more complex patterned gels based off of their FRAP-derived diffusivity measurements. This method allows for better fine tuning of gels for use as a platform to study human cell growth through a range of stiffening events in multiple different parts of the body.

Childhood liver transplants have several risks, including rejection, homeostatic complications, and lifelong immunosuppression. Precisely controlling cell identity would enable the generation of transplantable tissue from a patient's cells through cell reprogramming, minimizing these risks and expanding transplant access. Cell identity is partly maintained by heterochromatin states that block transcription factor binding and restrict gene activation. Work from the McCarthy Lab has shown that diverse heterochromatin-associated proteins repress lineage-specific genes, and depleting these proteins can de-repress heterochromatin domains, enabling transcription factor binding, gene activation, and cell reprogramming. However, which proteins regulate distinct heterochromatin domains is poorly understood. My goal is to understand the connection between chromatin state and gene activation permissibility and investigate the roles of specific proteins in maintaining specific chromatin states. We hypothesize that we could utilize an enzymatically dead Cas9 (dCas9) fused with a transcriptional activation domain (VP64) as a programmable transcription factor proxy to investigate specific heterochromatin domains and the function of proteins that maintain them. I identified target genes in H3K27me3, H3K9me3, and unmarked heterochromatin domains in human fibroblasts, focusing on genes only expressed or elevated in the liver. I designed guide RNAs to target dCas9-VP64 to sites 75 to 150 base pairs upstream of gene transcription start sites. I transfected guide RNA plasmids for 14 genes into dCas9-VP64 expressing human fibroblasts and assayed gene activation and transfection efficiency by RT-qPCR. Like transcription factors, dCas9-VP64 could activate unmarked genes and weakly activate genes in H3K27me3 but failed to activate genes in H3K9me3. Knocking down heterochromatin protein ERH using siRNA enabled dCas9-VP64 to activate H3K9me3-marked genes. Future work will investigate connections between additional heterochromatin domains and regulatory proteins. Understanding distinct protein roles in maintaining heterochromatin and repressing genes will improve our ability to control cell identity to reprogram patient cells.

Hypoxic ischemic encephalopathy (HIE) is a neurological condition resulting from reduced blood and oxygen flow to the brain and is a leading cause of morbidity and mortality in neonates. Limited treatment options necessitate accessible and scalable interventions to improve outcomes in newborns impacted by HIE. Extracellular vesicles (EVs) have been previously shown to attenuate oxidative stress and inflammation in the brain. Further research suggests that EVs secreted by astrocytes, a brain cell type involved with the inflammatory and injury response, may elicit neurotrophic or neuroprotective properties. In this study, I isolated, characterized, and evaluated the therapeutic potential of astrocyte-derived EVs (AEVs) in an ex vivo model of hypoxic-ischemic (HI) brain injury. AEV characterization via protein assays and nanoparticle tracking analysis showed that we were able to produce AEV particles about 100 nm in size at concentrations up to 10^11 particles/mL. To assess their therapeutic efficacy, I administered AEVs at varying doses (5, 12.5, 25, and 50 µg) to neonatal rat brain slices exposed to oxygen-glucose deprivation (OGD), an ex vivo model for HI injury. Following 24h of exposure, I evaluated cell viability. Our results indicate that AEVs decrease cytotoxicity in a dose-dependent manner. To further elucidate AEVs’ mechanisms of action, we conjugate AEVs with quantum dots to track AEV localization and cell-type specific uptake in brain tissues. Understanding AEV interactions with neural cells provides insight into both the roles of AEVs and different brain cells in modulating inflammatory responses and promoting neuroprotection. By characterizing AEVs and their therapeutic potential, these findings contribute to the growing body of research on EV-based therapeutics and lay a foundation for developing reliable and scalable therapies with the potential to advance treatments for neurodevelopmental disorders and aid brain injury recovery.