In 2020 marijuana use among college students was at an all-time high with 44% reporting using in the past year. Research demonstrates individuals with ADHD are at risk for increased marijuana use later in life. Additionally, some studies demonstrate self-reported symptoms of ADHD among undiagnosed college students prospectively relate to increased marijuana use, which was mediated by changes in perceptions of typical peer marijuana use (i.e., descriptive norms). However, this is complicated by frequent marijuana use being linked to issues with attention, impulsivity, and memory; all captured by self-reported ADHD measures. This study will evaluate bidirectional relations between ADHD-related symptoms, marijuana descriptive norms, and marijuana use among college students. Data collection is in progress and will be completed in March 2022 as part of a larger study. Students completed assessments at baseline, 6 months, and 12 months. A cross-lagged panel model will be conducted in R using the lavaan package to evaluate the bidirectional associations over a 1-year period, as well as test whether marijuana norms at 6 months mediate the longitudinal associations between ADHD-related symptoms and marijuana use from baseline to 12 months.  We hypothesize those reporting more ADHD-related symptoms will use marijuana more frequently 6 months later. Additionally, those who use marijuana more frequently will report more ADHD-related symptoms after 6 months. We also hypothesize that those experiencing more ADHD-related symptoms at baseline will have increased marijuana norms at 6 months and subsequent greater marijuana use at 12 months. This study attempts to disentangle the bidirectional associations between ADHD-related symptoms, marijuana norms, and marijuana use over a one-year period. Conclusions and future directions for research will be presented. Additionally, this study may have implications for norms-based interventions to better address memory, impulsivity, and other ADHD-related symptoms in association with marijuana use and perceptions.

Although the best psychological treatments, such as cognitive behavioral therapy, for anxiety disorders already rely on fear extinction mechanisms, these treatments underutilize principles of vicarious extinction learning. Vicarious extinction learning is a form of social safety learning that involves learning safety from the experience of others and can potentially enhance treatment potency. Social anxiety disorder (SAD), which is characterized by persistent social avoidance due to fear of negative evaluation by others, may uniquely benefit from vicarious extinction learning. Broader social contexts, like cultural norms, can also have influence on social learning and the efficacy in treatments of social anxiety. Although culture is a broad and nuanced construct, interdependent and independent self-construals have been shown to capture two of the many facets of culture. In this study, we propose that cultural factors, such as interdependent and independent self-construals, may impact vicarious extinction learning in people with high levels of social anxiety symptoms. Twenty socially anxious adults and twenty healthy controls will complete the Self-Construal Scale (SCS) and undergo a vicarious extinction task while undergoing fMRI. We expect that those with interdependent self-construals will show enhanced safety learning due to their value for seeing themselves as similar to others, as compared to those with independent self-construals. This will be evidenced by lower skin conductance responses (SCR) and greater ventromedial prefrontal cortex activation during the reinstatement phase of the task. Our results will highlight a potentially important cultural moderator of social safety learning and advance our understanding of the boundaries and clinical implications of learning safety from others.

 Improper hand hygiene can lead to the transmission of various diseases including gastrointestinal infections, such as salmonellosis, and respiratory infections, such as influenza, colds and coronavirus. The area under the fingernails (subungual area) is a source of bacteria accumulation that is often overlooked during hand washing. This research evaluates the effect of fingernail length on the effectiveness of a 20 second hand wash on reducing subungual bacteria counts. We performed a randomized controlled trial to determine total bacteria counts of the subungual area right before and after a 20 second hand wash with antibacterial soap. Bacteria counts were determined on standard media after a 72 hour incubation. Subjects were grouped by fingernail lengths of 0mm, 0-1mm, 1-2mm, >2mm, with a control group of 0mm. The results suggest that regularly trimming fingernails and paying attention to the subungual area during hand washing may be an important tool in preventing infections and diseases caused by pathogenic bacteria and viruses. This is especially important for people with impaired immune systems or young children who are more susceptible to infections, as well as for people who can transmit diseases to others, like healthcare personnel and childcare providers.

Soldiers who are transitioning to civilian life have alarmingly high rates of suicide, alcohol abuse, and mental illness. There are multiple facets that may influence these poor outcomes for veterans. Organizational dissatisfaction is a risk factor for poor mental health and lack of access to mental health support for veterans, but this has yet to be studied in-depth. The aim of this project is to assess how demographic variables and organizational dynamics impact mental health during the transition process. First, we will use preliminary data from The Network Study, one of the first longitudinal studies to follow the mental health, alcohol use, and socioeconomic stressors of service members as they transition out of the Army. Multiple regression will be used to analyze the influence of interpersonal support and conflict within the Army on depressive and anxiety symptoms. Mental health symptoms were measured by the PHQ-9 and GAD-7, two common screening questionnaires used to briefly assess depressive and anxiety symptoms, respectively. We will additionally assess various demographic variables as moderators. Next, findings will be used to develop questions for qualitative interviews to be conducted with recent veterans. We predict there will be a positive association between organizational support and positive mental health outcomes, and qualitative interviews will show lower work-related stress and increased access to mental health supports as major themes for those with positive work relationships. In line with previous research, we predict these associations and themes will be strongest for women, racial and ethnic minorities, and those of lower rank. Support and intraunit conflict are potentially modifiable factors, so our research demonstrates a potential pathway for prevention of negative mental health outcomes for veterans.

The Washington Creative Districts program, managed by ArtsWA, is a place-based investment strategy that offers state certification to geographically defined areas focused on cultural and creative enterprises. ArtsWA provides technical and professional assistance, marketing, and funding to certified Creative Districts to encourage localized economic development through tourism and hubs of economic activity. My research examines Washington Creative Districts policy to determine what unintended consequences may be felt by communities pursuing state certification. I identify potential unintended consequences by reviewing enacted legislation in the Revised Code of Washington, Creative Districts guidelines, academic literature, and recent Washington state employment and housing data. I find the state’s interpretation of the creative economy includes professions with high social capital, such as writers and photographers, and professions with high economic capital, such as software developers and computer programmers of the knowledge economy. This broad framing of the creative economy could result in Creative Districts using social capital of traditional artistic professions for branding while primarily courting the mobile, economic capital of the knowledge economy. I expect to find that communities pursuing state certification, some of which are in economically distressed regions, may accelerate cycles of gentrification in urban areas or introduce gentrification to rural ones, resulting in long-term resident displacement. Additional research is needed to document market, demographic, and population change trends present in Washington Creative Districts and determine if their relationship to state certification is causal or a correlation. Additional work is also needed to identify local and state policy changes that could better equip Creative Districts communities to mitigate any unintended consequences and focus state support and investment in areas of the local creative economy where it is most needed.

Adoptive cell immunotherapy (ACT) with engineered T cells has shown impressive efficacy against some cancers, particularly CD19+ leukemias. However, ACT efficacy in solid tumors can be limited by restrictive tumor microenvironments (TMEs), with increased inhibitory signals, reduced T cell infiltration/accumulation, and inadequate metabolic substrates. We engineer T cells with novel switch receptors that combine an inhibitory ectodomain with a costimulatory signaling endodomain, to “replace a break with an accelerator”. FasL is a death receptor ligand that is overexpressed in the majority of human TMEs and can protect tumor cells from immunity by giving T cells inhibitory signals upon binding. We engineered a Fas-4-1BB switch receptor to convert the Fas death signal to a costimulatory 4-1BB signal and demonstrated enhanced Fas switch-receptor-T cell in vivo persistence and therapeutic efficacy in a murine pancreatic cancer model (KPC). We developed new switch receptor candidates by combining CD200R, an inhibitory signaling receptor, ectodomain with various co-stimulatory endodomains. Our aim is to screen for CD200R switch receptors that exhibit the best expression on T cells, best ability in tumor-lysis and proliferation with the least exhaustion in TMEs, and test the best candidates in KPC models. We are able to transduce mouse T cells to express our CD200R constructs, which is verified by flow cytometry, and ready to start in vitro killing assay to assess their ability in tumor-lysis. This T cell engineering strategy may help overcome the restrictive TMEs, catalyze an endogenous immune response, and greatly improve T cell anticancer efficacy.

Ocean conveyor belt circulation is conceptually foundational in oceanographic education. It is the idea that oceans slowly overturn through the movement of deep water toward the equator and surface water toward the poles. Introductory courses often spend the majority of a semester building the skills required to understand the basics of this theory. Ocean circulation is also typically covered through multiple courses to build upon its complexity through different study lenses. The multi-dimensional concept of ocean circulation is commonly conveyed through 2D diagrams and animations rather than presenting ideas in a 3D space that allows students to form connections between theory and physical space. Students were asked to interact with a 3D printed model that recreates circulation diagrams by connecting cross-sections of oceanographic data to specific locations on the model. Furthermore, students were asked to describe how perturbations to surface conditions could change ocean stratification and how water circulates based on its temperature and salinity. Previous 3D printing experience allowed us to expand students’ experiences while struggling to parse these interdisciplinary oceanographic topics. Here we show that using a physical model in teaching thermohaline circulation enhances the speed and depth at which students understand ocean circulation compared to the traditional 2D approach. Surveys were given to assess students’ understanding of the driving factors behind thermohaline circulation prior to and after interacting with the model. Preliminary results show that students can better connect oceanographic concepts taught in lectures to data and locations essential to ocean circulation after completing exercises that ask them to interact with the model. We anticipate students to exhibit further proficiency in concepts related to ocean circulation after interacting with this model. We also expect to find that students will express a desire to see the utilization of similar models in more of their core oceanography courses.

Over the past decade, we have seen major forest loss due to events like deforestation and tree die off. Previous studies have examined ways in which the distribution of plant types and how they function impact local and global climate. Local climate can be impacted because plants alter fluxes of water, energy, and momentum between the land surface and the atmosphere. Global climate can be impacted because local changes influence atmospheric response in clouds, humidity, and gradients in energy which drive changes in circulation. This project aims to identify if observed forest loss has a measurable effect on the noisy climate system. We have compiled spatial data of actual forest loss derived from satellite observations and test the climate impact of forest loss in simulation experiments using an Earth system model. We will assess differences between simulations with and without forest loss to identify how forest loss impacted the atmosphere and surface climate over land. We use the identified impacts as hypotheses for the expected climate response to forest loss and will subsequently analyze if these patterns can be seen in observed environmental conditions following forest loss. We expect to see some effect in the climate due to the observed forest loss. This project serves to advance our understanding of the effect of forest loss on global climate, atmospheric circulation, and energy balance, and thus will help to coordinate efforts to mitigate climate change by identifying potential unwanted impacts of forest change due to human actions.

Metal–organic frameworks (MOFs) are crystalline, porous extended solids that are formed through coordination between metal cations and bridging organic ligands. Since their discovery in the late 1990s, MOFs have been a topic of acute interest in the scientific community due to their intrinsic porosity, high surface area, and precise tunability. However, MOFs are typically insulating, which limits the scope of their applications. The recent development of electrically conductive MOFs has opened the door to exciting multifunctional applications in electrocatalysis, advanced electrochemical energy storage, chemical sensing, and much more. However, a molecular-level understanding of charge transport in MOFs remains lacking. My research aims to address this knowledge gap through the investigation of one-dimensional (1D) metal organic chains. These 1D chains can be thought of as the primary subunit of higher-dimensional MOFs; they allow for high synthetic and electronic tunability, making them ideal model materials for studying the genesis and tuning of electronic properties in conductive MOFs. Here, I will present the synthesis of a series of highly-tunable 1D metal–organic chains that exhibit delocalized π systems and high electrical conductivity along with our studies of how structural parameters such as metal identity, chain structure (linear vs. zig-zag), and metal/ligand oxidation state can influence the overall electrical and magnetic properties of the resulting chain.

A comparison of rates of osteoarthritis in the spine by occupation allows us to understand the impact of different jobs on spine health. This study will compare the rates of spinal osteoarthritis between non-manual labor workers (such as retail/service and information technology workers) and heavy-lifting occupations (such as agricultural, building, and manufacturing laborers). We will be evaluating the spines of decedent individuals with known occupation categories, available through the New Mexico Decedent Image Database, for spinal disk space narrowing and osteophytosis with a grading scheme of 0-3. For disk degeneration, 0 = normal disk spacing, 1 = mild disk space narrowing, 2 = moderate disk space narrowing, 3 = severe degeneration of the disk space. To assess osteophytosis, we will grade the anterior side of each vertebral body for the presence of osteophytes on the 0-3 scheme (0 = no osteophytes, 1 = mild osteophytosis, 2 = moderate osteophytosis, 3 = severe osteophytosis) with a score for each vertebral body and intervertebral space. We will calculate the prevalence of osteoarthritis and perform regression analyses between the occupation categories to determine the relationship between occupation and spinal osteoarthritis. As osteoarthritis is positively correlated with age, age categories will be established for the analyses. In addition, we anticipate that different trends in the development, location, and prevalence of osteoarthritis within the spinal column may exist between the occupation groups due to differences in biomechanical loading and areas of "wear and tear." We will perform regression analyses to determine if any such pattern is present in our sample. The results of this study will increase our understanding of the prevalence of osteoarthritis by occupation, aiding health care providers in developing preventative care and treatment plans for their patients and occupational health officials find effective ways to protect their employees.

The Washington Department of Natural Resources (DNR) publishes statewide predictions of various forest characteristics—diameter, density, height, age, species composition—in publicly-available spatial datasets called RS-FRIS (Remote Sensing Forest Resource Inventory System). Digital aerial photogrammetry is used to produce this model because of its relative ease of acquisition as compared to active remote sensing (LiDAR). However, the point clouds extracted with this method provide less detail and may be less representative of forest conditions in areas with more vertical variation. Additionally, the RS-FRIS model is an empirical model best suited to predict forest conditions reflected in field plots used in model development. I investigate how forest metrics reported by RS-FRIS compared to ground data in the Long-Term Ecosystem Productivity (LTEP) experiment in Sappho, WA. The purpose of my comparison is to evaluate if the RS-FRIS data can make reasonably accurate predictions of treatments in the LTEP experiment site and the feasibility of using this in other research sites with novel silviculture treatments. I expected the RS-FRIS to work well on the areas of our site that are likely represented in the DNR’s field plots, and my results do show more agreement between the datasets in metrics for tree diameters greater than 15.2 cm (6”) and broadly in the Douglas-fir treatment and unthinned control. I find that RS-FRIS does differ from ground measurements for predicted basal area, trees per acre, and species, particularly in the thinned treatments and where predictions account for hardwood. These results suggest that RS-FRIS is less suitable for inventory of areas that may not be well represented in the DNR’s land base. Thus, for remote sensing to be used in monitoring, additional models must be developed.

Systemic racism is a widespread issue in America that has an adverse impact on the psychological well-being of ethnic minority populations. Although there is significant research on the importance of addressing racism in psychotherapy and adopting culturally humble interventions, limited studies exist on the frequency with which clinicians ask Clients of Color about experiences of race or racism. Thus, this study examines the percentage of community mental health clinicians who report intent to discuss issues of race or racism with Clients of Color. In this study, surveys were distributed to participants (N = 138) from a statewide Cognitive Behavioral Therapy training initiative in Washington state. Clinicians rated their level of agreement with 11 statements describing their intentions of discussing race/racism with clients on a 7-point Likert-type scale. We will employ descriptive statistics to assess the frequency with which clinicians intend to address issues of race or racism with Clients of Color. The existing limitations of this study include the predominant use of self-report questionnaires after receiving CBT+ (Cognitive Behavioral Therapy) training. Ultimately, our findings can highlight if clinicians initiate discussions surrounding race or racism with Clients of Color, which can inform researchers if more support is needed in the form of resources or additional training for clinicians to effectively approach discussions of race or racism.

Microplastics, plastic pieces less than 5 mm, are widespread in the environment and a concern for human and environmental health. In aquatic environments, microplastic particles are often mistaken for food by fish and other wildlife which remain in their digestive system and can cause starvation. Microplastics also absorb and concentrate endocrine disruptors such as PCBs (polychlorinated biphenyls) and POPs (persistent organic pollutants). These molecules can cause reproductive and developmental issues in both wildlife and humans. Many studies characterize the microplastic load and composition in various environments; fewer studies have documented the changing microplastic concentrations over time. Our study fills in this gap by collecting and characterizing the current microplastic load at various points in Lake Washington and comparing these findings to data from ten years prior. Samples were collected from multiple sites in and around Lake Washington using a manta net for surface tows. Microplastics were extracted using an acid digestion and inspected under a microscope to characterize and quantify the microplastic load. The results of our study help expand upon previously collected data about Lake Washington and the change in microplastic concentrations. This study will hopefully inform local policies to address and mitigate microplastic pollution and its consequences.

As a dancer, I know that emotion and movement have a reciprocal relationship. I've been dancing for more than half of my life, and while I've felt my mood change because of the choreography that I'm performing, I've also noticed instances when my feelings alter my movement. However, because the brain is so poorly understood in comparison to most other human organs, especially when it comes to complex subjective experiences such as emotion, the physiological nature of this relationship is very poorly understood. In this project, I will be investigating the reciprocal physiological relationship between movement and emotion in the human body. The first component of this project is primarily focused on writing a literature review. The existing relevant literature has covered so many otherwise disconnected areas of inquiry that tying them together in this foundation is crucial. The second part of the project is an exploration of applying my findings to movement. I will generate a series of improvisational scores, inspired and directed by my literature findings, that will aim to supplement the empirical with embodied research. The experience of dancing and observing these scores will be in dialogue with my scientific research to influence the direction and application of both. I'm expecting to find conscious and unconscious connections between emotion and memory in the association of bodily movements and states with mental states; however, I'm also expecting it to be difficult for a dancer to identify many of their associations because they are complex, multi-faceted, and not entirely conscious. The findings of this study can be used to make recommendations for future research by identifying areas of unknowns as well as inform how dance performers and teachers approach emotional investment onstage and in the classroom.

In a world where the pace of change is increasing drastically, it has become less feasible to complete tasks alone. The skill of being able to communicate and work in a team has become valuable, if not necessary. Working with others, however, can be challenging for many reasons. One reason, which affects project teams across all fields, is when one team member worries that the deadline is close (e.g., “the deadline is only a week away!”), while another team member believes the deadline is further out (“we still have an entire week to work on it!”). This disparity occurs because the team members have different Temporal Distance Perceptions (TDP) of the deadline. This project seeks to analyze how these individual differences in team members’ TDP affects the team’s performance. Evaluating divergent TDP can improve team project planning, helping them work better together. In this project, I contacted faculty members from University of Washington Bothell’s School of Business to gather data from 11 undergraduate classroom teams, working on a quarter-long team project in their courses. The duration of this task allows them to establish their own perception of the deadline’s temporal distance. I disseminated a survey, comprised of only validated scales, to the project teams one week before the project’s due date. The procedures used for data collection, which included a consent form that implied use of their performance on the project, were submitted for review and approved by the Institutional Review Board prior to survey dissemination. I compared an individual team member’s TDP with the rest of the team’s individual TDP to determine the team’s collective TDP. Then I compared this collective TDP with their performance, determined by the team’s grade earned. I expect to find a positive correlation between collective TDP and output performance.

The stress and uncertainty posed by infectious disease outbreaks generates intergroup social conflict. In communities with disparate distributions of power, one population may blame another for spreading the disease. This social scapegoating subsequently exacerbates existing economic inequalities, medical discrimination, and harmful stereotypes. Amidst power struggles, political elites can mobilize scapegoating as a tool to unify their support base around a common enemy. In the United States, Asian Americans frequently fell victim to epidemic scapegoating, from outbreaks of smallpox in the nineteenth century to the more recent SARS outbreak of the twenty-first century. During these previous health emergencies, Asian Americans became associated with disease and cultural backwardness. The negative perceptions of Asian Americans then translated into restrictive immigration and citizenship policies. The Coronavirus Pandemic renewed anti-Asian American xenophobia. In 2020 alone, reported hate crimes against Asian Americans increased 150 percent. Racist and Sinophobic language proliferated the internet and media. President Donald Trump and other prominent politicians began utilizing location-based labels for the coronavirus, such as China Virus, Chinese Flu, and Kung Flu. This research assesses whether these location-based labels perpetuates anti-Asian xenophobia. To do so, this research analyzes the transcripts of 163 public appearances made by Donald Trump in 2020. I examined the frequency and context of Trump’s location-based terms throughout the year and compared this with the percentage of hate crimes considered anti-Asian. The data suggests that Trump’s utilization of location-based terms correlated with a rise in anti-Asian hate crimes in the early months of the pandemic. Additionally, the data shows support for the fact that the upcoming election motivated Trump’s usage of these location-based terms. By illuminating the mechanisms through which the President incited anti-Asian xenophobia during the coronavirus pandemic, this research provides insight into how to prevent social scapegoating in future epidemics.

The conditional knockout (KO) of Ndufs4 in only GABAergic interneurons leads to a severe epilepsy phenotype, suggesting GABAergic interneurons drive the severe and often fatal epilepsy phenotype commonly reported in Leigh Syndrome (LS) patients. Dysfunctions or loss of parvalbumin (PV) interneurons, a subtype of GABAergic interneurons, have been shown to play a key role in the mechanisms of various forms of epilepsy both in human and animal models. The present study aims to target PV interneurons. We hypothesized that KO of Ndufs4 in PV interneurons will cause dysfunctions or loss of PV neurons leading to epilepsy in our cell-specific model of LS. Experimental mice models with Ndufs4^flx/flx/PVCre^flx/+ genotype for the mutants, and Ndufs4^flx/flx/PVCre^+/+ genotype for the controls were used. For imaging experiments, Ndufs4^flx/flx/Ai14^flx/+/PVCre^flx/+ were used for mutants and Ndufs4^+/+/Ai14^flx/+/PVCre^flx/+ were used for controls. Seizure susceptibility was assessed by recording occurrence, frequency and duration of seizures and epileptiform events. Mice susceptibility to provoked seizures was examined by the pentylenetetrazol (PTZ) challenge. Assessment of cell loss was tested in imaging studies. Ai14-labeled PV interneurons in key areas associated with epilepsy were counted between the two groups. Finally, to assess motor dysfunctions comorbid to epilepsy, I tracked the movement of mice of both genotypes. Our results showed PV mutants had an increase in the frequency of spontaneous myoclonic seizures and interictal spikes on electroencephalograms (EEGs). There was no difference in seizure susceptibility to PTZ seizures between mutants and controls, nor any major impairments in locomotor activity or anxiety like behavior in PV mutants. Finally, no cell loss changes in PV mutants were detected. In conclusion, PV mutants display a mild seizure phenotype with no cognitive or motor abnormalities, suggesting targeted Ndufs4 KO in PV interneurons drives a small portion of the severe epilepsy phenotype observed in LS.

Current physics education research has demonstrated that, when not taught directly, students have a wide range of conceptual resources regarding the use of variables upon entering introductory physics. There is a growing body of work that characterizes students’ use of variables and how students connect variables to their physical meaning (Brahmia 2019). We build on this work by seeking to better understand how students are making sense of variables in introductory physics labs. Data was collected from students’ responses to lab curriculum on the online lab platform, Pivot Interactives, from the 2020-2021 academic year. By examining students’ variable choice when graphing experimental data over the course of a quarter, we are able to identify emerging commonalities in variable use and how the variables students choose correlates with the students’ broader understanding of quantitative reasoning. Preliminary data from student graphs of position versus time show a prevalence of students using math-like variables, such as y and x, instead of variables traditionally used to represent these quantities in physics, such as x to represent position and t to represent time. Use of math-like variables in a physical context suggests that these students’ may have not yet formed a strong association between the variable itself and the meaning of the physical quantity it represents. Insights into student variable use and its relationship to the students’ overall quantitative reasoning can help instructors consider effective methods that adapt curriculum to directly address the use and meaning of variables within physics. By doing so, instructors may have an opportunity to directly impact their students’ quantitative reasoning – a skill valued across all STEM disciplines.

Global climate change is likely to have an effect on plants, pollinators, and the interactions between the two ecological groups. Recent studies have suggested that climate change may affect the growth and development of these organisms in the long-term, but variations in temperature could also change the short-term behavior of pollinators when they visit plants. For instance, pollinators with certain physical traits might be better suited to forage in areas with higher or lower temperatures. In this study, I explored this idea with the assumption that the foraging decisions of pollinators are indicative of the temperatures that they are flying at. I addressed the questions of 1) how variation in temperature affects pollinator traits? and 2) are certain pollinator traits correlated with differences in the identities and traits of the plants that they are captured visiting? To answer these questions, I measured the size of bees collected in the Rocky Mountains during the summer of 2021, and I compared these sizes to the temperatures at the sites at which the bees were caught. I predicted that 1) pollinator traits would correlate with temperature, and 2) the interactions in my data analysis would show trait matching between the pollinators and the plants that they were visiting when caught. The results of this study should help illustrate how the impacts of climate change might affect plant-pollinator communities, which could help to identify potentially advantageous traits as well as interactions that may be flexible or persistent during changing climatic conditions.

Coronavirus disease 2019 (COVID-19) is caused by SARS-CoV-2 infection. COVID-19 is associated with higher risk of new-onset diabetes and metabolic complications of diabetes, which may occur via injury to insulin-producing islet β cells due to direct SARS-CoV-2 entry. While the canonical pathway of viral entry via human angiotensin converting enzyme 2 (ACE2) has been established as a major route of SARS-CoV-2 infection in the lung, expression of ACE2 has not been unequivocally demonstrated in beta cells. It is therefore important to understand how other proteins known to be highly expressed in pancreatic endocrine cells may be involved in SARS-CoV-2 entry. Glucose regulatory protein 78 (GRP78), also known as binding immunoglobulin protein (BiP), is an endoplasmic reticulum (ER) chaperone. Under conditions of ER stress, as induced by SARS-CoV-2 infection, GRP78 translocates from the ER membrane to the plasma membrane, where it has been shown to bind to SARS-CoV-2-spike in vitro. Previous studies have found GRP78 to aid the entry of other coronaviruses. As GRP78 is expressed in β cells, we investigate GRP78-mediated SARS-CoV-2 entry into β cells using a mouse β cell line, wherein mouse ACE2 is unable to bind SARS-CoV-2-spike. To promote GRP78 translocation to the plasma membrane, ER stress is induced using thapsigargin or cytokine treatment. Verification studies involve validating the upregulation of GRP78 at the mRNA level and in plasma membrane fractions. A cell fractionation protocol has been adapted to isolate plasma membrane protein from other cellular fractions. Further experiments will involve the use of a pseudovirus system to investigate SARS-CoV-2 entry under conditions of GRP78 overexpression and knockdown. Studying SARS-CoV-2 entry into β cells may illuminate possible therapeutic strategies to protect islets from the deleterious effects of COVID-19, including the development of diabetes.

Prediction of response to preoperative or ‘neoadjuvant’ chemotherapy (NAC) can help guide treatment strategies for patients with triple-negative breast cancer (TNBC), a highly aggressive subtype of breast cancer. Breast magnetic resonance imaging (MRI) can provide noninvasive measurements of the microenvironment across a tumor volume. We hypothesize that pre-treatment measurements from dynamic contrast-enhanced (DCE-) MRI reflecting tumor perfusion and vascular function are predictive of NAC response for TNBC patients. Women with TNBC who underwent pre-treatment MRI and NAC at our institution (2005-2019) were retrospectively identified. DCE-MRI was acquired at 2, 5, and 8 minutes after contrast injection. From DCE-MRI, whole tumor contrast kinetics measures including functional tumor volume (FTV), percent enhancement (PE) at 2 mins post-contrast and signal enhancement ratio (SER) were calculated, and hotspot measures of peak PE and peak SER (representing the highest mean PE and SER, respectively, for 3?3 voxel subregions) were determined. Imaging measurements were compared between those with complete pathologic response (pCR; no residual cancer present in the breast at surgery) and non-pCR patients with a two-tailed Student’s t-Test (p<0.05 considered significant). 95 women (median age: 49, range: 30-79 years) with TNBC were evaluated, of which 29 (31%) achieved pCR. FTV was significantly higher in non-pCR patients (21.1±28.1 cc) compared to pCR patients (8.6±11.3 cc, p<0.01). Peak SER was also higher in non-pCR patients (1.8±0.3) compared to pCR patients (1.7±0.3), trending toward significance (p=0.06). No significant differences between groups were observed in peak PE measures. Patients with lower pre-treatment tumor FTV and peak SER on DCE-MRI were more likely to achieve pCR after standard NAC. These findings indicate that baseline DCE-MRI measurements may help predict response and assist in optimizing treatment plans for TNBC patients, such as selecting more aggressive regimens incorporating immune checkpoint inhibitors or other novel agents in predicted non-responders.

Designing binders for single beta strand and beta-hairpin peptides could be useful for a wide range of biomedical applications. This project aims to design peptide binders that force unstructured peptides into a beta strand conformation. The computationally designed peptide-binder pairs were expressed and purified utilizing Immobilized Metal Affinity Chromatography (IMAC) and characterized via Size-Exclusion Chromatography (SEC) and biolayer interferometry. Successful binders were redesigned to bind pathogenic fragments of Amyloid-beta, alpha-synuclein and tau. These fragments are known to form beta strand mediated fibrils in the brain that are associated with the development of neurodegenerative diseases like Alzheimer’s and dementia. I am currently characterizing the redesigned binders for Amyloid-beta and expect higher-affinity binding with these designs. Success of this project could provide a new tool in studying the fibril formations and allow for future drug therapies.

Long term exposure to air pollution can be detrimental to health, leading to respiratory effects, cardiovascular effects, and total mortality. In the 1930s, a practice called redlining reduced the accessibility of mortgage financing for racial minorities and immigrants, and continues to affect the infrastructure and demographics of neighborhoods today. Understanding characteristics of air pollutants is important because people of color and low income communities have historically been exposed to higher concentrations of air pollution as a result of discriminatory practices. Communities exposed to high levels of multiple pollutants at once may be more at risk for health effects. In this project I aimed to define pollutant mixtures of black carbon (BC), nitrogen dioxide (NO₂), carbon dioxide (CO₂), and ultrafine particles (UFPs) at measured locations in Seattle, then predicted the mixtures at other locations to evaluate exposure disparities due to redlining. Annual average air pollution estimates for BC, NO₂, CO₂, and UFPs were obtained from mobile monitoring measurements of 309 residential locations throughout urban Seattle in 2019 and 2020. Distances to roadways, airports, and other pollution sources were obtained using GIS software, and land use, population density, and greenspace were also characterized. Initial pollutant mixtures were defined at the mobile monitoring locations using principal component analysis. The first PC is a weighted average of pollutants, representing the contrast between high and low exposure locations, and the second PC is a contrast between UFPs and other pollutants. I then predicted the mixtures in redlined neighborhoods using geographic covariates. These predictions provide insight into how the patterns of UFP exposure differ from other pollutants, giving us a better understanding of the pollutant mixtures in our community as a result of urban planning. These insights can lead to further research on the toxicity of these mixtures and to equitably work toward cleaner air.

Women with dense breasts have increased amounts of fibroglandular tissue (FGT) and are at higher risk of developing breast cancer. Quantitative measurement of FGT from magnetic resonance imaging (MRI) could provide more robust measurement of density, supplanting conventional qualitative radiologist assessments. Current quantitative methods involve manual selection of a signal intensity threshold, which can be time consuming and subjective. Fuzzy c-means (FCM) clustering is an automated approach to tissue segmentation, offering a reproducible process for quantifying FGT volume. The aim of this study is to evaluate the efficacy of the FCM clustering in identifying FGT compared to manual thresholding. Women (N=10) who underwent screening breast MRIs at our institution were evaluated in this preliminary study. Fat-suppressed T₁-weighted pre-contrast images acquired as part of their clinical breast MRI exams were used for FGT segmentation. Prior to segmentation, I cropped the images to include only the breast. FGT was then segmented two ways, 1) manually, using a signal intensity threshold that I chose and adjusted and 2) automatically, using existing lab software for FCM clustering. The Sørensen-Dice similarity coefficient was calculated between the manual and automatic segmentations for each patient to determine the degree of overlap. The concordance correlation coefficient (CCC) was calculated between automatic and manual segmentation volumes across the whole data set. Across the 10 patients, an average (± standard deviation) Dice coefficient of 0.81±0.04 was observed, indicating good spatial agreement between the manual and automatic segmentations. The CCC between the FGT volume from manual and automated segmentation was 0.89, demonstrating high correlation in volume estimates between the two methods. Fuzzy c-means clustering was determined to be an effective and efficient method of FGT segmentation in breast MRI data. Future work will evaluate the application of this technique in assessment of background parenchymal enhancement, a clinical marker of cancer risk.

Avoidable prolonged hospital stays can decrease bed availability for new patients and place patients at risk for adverse events, and be costly for hospital finances. There are few published studies that cover data of patients throughout an entire hospital; most only cover data from a specific service line. Our study aims to review patient data throughout an entire hospital to assess common barriers leading to these avoidable prolonged stays. In our institution, the care management department meets on a weekly basis to review patients who meet the crieteria of having avoidable days. We retrospectively reviewed notes from these weekly meetings and extracted common barriers, which we then organized into categories for comparison. Our study included patients discharged from August 2019 to February 2020, with any avoidable days and 21 or more hospital days. There were 180 encounters that met our criteria. The most common service lines represented are Medicine and Surgery, while the most common discharge locations are SNF and Home. The median for the total legnth of stay was 42 days, while the median for avoidable days was 17.5 days. We identified 21 common barriers. The most common discharge barriers identified are Funding, Long Term Care, COPES, Guardianship, Substance Use, Homelessness, and Care Needs too Great for SNF. In our hospital-wide study, we identified that avoidable days are found across inpatient services and represent a heterogeneous mix of discharge barriers. Further studies should investigate relationships between discharge barriers and whether any patterns exist in patients with avoidable days in service lines. 

Ever since the COVID-19 outbreak, hate crimes and violence towards minoritized groups such as Black and Asian people have surged. Consequently, Black, Indigenous, and People of Color (BIPOC) continue to face several mental health challenges as well as inaccessibility to mental health care. The literature suggests that culturally responsive psychotherapy can improve BIPOC clients’ clinical outcomes. Thus, this study examines the actions community mental health (CMH) clinicians have taken to better serve BIPOC clients. Data came from a Washington State-funded cognitive-behavioral therapy training initiative (CBT+) in 2020 - 2021. CMH clinicians and supervisors attended a virtual CBT training followed by six months of expert consultation and completed pre-training and post-consultation surveys. In the surveys, CMH clinicians reported on the frequency of performing certain actions to better serve their BIPOC clients (e.g., discuss issues of race or racism with BIPOC clients) using a 5-point Likert Scale ranging from 0 (Never) to 5 (Always). We conducted descriptive statistics to summarize clinicians’ responses. Our analyses showed that clinicians most frequently incorporated the following actions when working with BIPOC clients: “consider the client's ethnicity” and “the use of bilingual staff or interpreters for those whose English is not their first language.” On the other hand, clinicians appeared to rarely collaborate with natural community healers, spiritual healers, clergy, etc., as part of their service delivery. Our findings suggest that CMH clinicians have taken several actions to thoughtfully consider and incorporate BIPOC clients’ backgrounds and context into treatment delivery. However, certain CMH clinicians' actions to better serve BIPOC clients occur more frequently than others. Future research should better understand which culturally responsive actions are most helpful in helping BIPOC clients’ experiences in CMH.

The genetic makeup of beer-brewing yeast plays an essential role in determining the flavor profile during production of beer. With functional copies of genes on the MAL locus, beer yeasts, namely Saccharomyces cerevisiae, can utilize maltose and maltotriose as their carbon source. The three genes in this locus are responsible for the regulation (MALx3), transport across membrane (MALx1), and breakdown of sugars (MALx2) in brewers’ wort. If a strain lacks a functional copy of any of these three genes, it cannot digest these alternative sugars. Alternatively, some strains have more than one MAL locus, but which loci are functional, remains unknown. Apart from previous studies that have investigated a handful of MAL alleles, the function of the genes in these duplicated loci (or paralogs) cannot be determined based on sequences alone. To address this problem, I experimentally tested the function of alleles from 1,011 natural isolates. I focused on MALx3 because the reference strain lacks a functional MALx3 allele, preventing its growth in maltose. Therefore, introduction of any functional MALx3 alleles should permit growth in maltose. To test this, I cloned ~250 MALx3 alleles from three different loci (MAL1, MAL3, MAL7) and transformed the reference strain to generate three yeast libraries. The library with the MALx3 gene of the MAL3 locus, or MAL33, successfully grew to saturation after ~2.5 days of incubation in 2% maltose, showing this approach can be used to determine the function of MAL33 alleles. Looking forward, I will use DNA barcodes to track the growth of alleles in maltose to pinpoint which are functional. With the MAL loci serving as a great candidate for understanding paralog differences, by identifying the functional paralogs, we can better understand the evolutionary history of MAL genes and what role these loci play in the brewery and across all natural isolates.