Repetitive transcranial magnetic stimulation (rTMS) is a psychiatric treatment which has shown promise for experimental treatment of memory loss in Alzheimer’s Disease. rTMS uses a coil and electric current which is able to create a magnetic field that can depolarize neurons noninvasively and induce synchronized activity of large populations of neurons, ultimately inducing, lasting changes through synaptic plasticity. Alzheimer’s disease patients show disruptions in the Default Mode Network (DMN), a network of brain regions which is typically active at rest. The DMN has an important role in memory consolidation and is disrupted in Alzheimer's Disease. We hypothesize that strengthening the default mode network through rTMS applied to area left 8AV of the frontal cortex will create improvements in patient memory. To answer this question, we are performing a single-blind, single-arm, randomized cross-over trial of rTMS on early-stage Alzheimer's disease patients. Region 8AV is located by using MRI scans obtained before patients receive either the sham or experimental procedure. This region was chosen due to its connection to the default mode network and previous promising TMS research. Our primary outcome measure is the speed of forgetting, a new, reliable index of memory function obtained by fitting a computational model of episodic memory to behavioral data from an adaptive memory test. Due to the frequent use of rTMS in mood disorder treatment, we are using depression and anxiety scales to track possible mood improvements as a secondary outcome measure. MRI scans will also be analyzed to see if the experimental treatment caused any structural differences in patient brains. Should our hypothesis be correct, we expect to see improvements in memory or cessation of memory decline in patients. Successful treatment would provide a novel target for Alzheimer’s Disease treatment using rTMS, and additional evidence for the continued investigation of rTMS for Alzheimer’s Disease.

With this project, researchers for the Harry Bridges Center for Labor Studies from Seattle, Tacoma, and Bothell campuses seek to gain a better understanding of the intricate relationship that exists between the UW undergraduate experience and employment, as well as inform students on existing resources and support services both on and off campus. To sufficiently measure unique student experiences, we have sought survey responses through canvassing, contacting departmental and Registered Student Organization (RSO) listervs, and visiting in-person classes. Over the course of the past 6 weeks, we have made 73 points of outreach at the Seattle campus, 62 at Bothell, and 44 at Tacoma, resulting in 430 survey responses as of February 7th, 2024. Our Qualtrics survey will run through the end of winter quarter. We will then also offer paid interviews for 15-20 students to supplement survey data through further examination of survey themes and trends. Modeling UCLA Labor Center’s Unseen Costs: The Experiences of Workers and Learners in Los Angeles County, we will then compile our survey results and interview stories into a cumulative research report about why UW students work while pursuing their undergraduate degrees, what fields they have worked in, and any associated workers’ rights violations they may have faced while in these positions. With this research, we aim to create a more supportive environment for our student workers, and to help educate our respective campuses to meet their student body needs.

Test anxiety is a prevalent issue among K-16 students, with approximately 20-40% reporting its adverse effects. This pilot study assessed the preliminary efficacy of a Short Mindfulness Activity to Reduce Test Stress (SMARTS) recording in mitigating test anxiety, indicated by heart rate (HR) physiological markers. Our research team’s undergraduate students assembled a student advisory panel to receive feedback on existing mindfulness recordings and narrow down the most effective traits of each one. From the feedback, we designed a 7-minute mindfulness recording to be administered to University of Washington Bothell Introductory Biology students before an exam. Using a randomized controlled trial design, 29 students were randomly assigned to the mindfulness recording group (n = 15) and to the control group (n = 14). The intervention group was exposed to the SMARTS recording before an exam while the control group studied in the hallway. We found that listening to the SMARTS recording reduced the increase in heart rate due to the exam by an average of 8 beats per minute. Additionally, 56% of participants expressed intentions to continue using the recording before future exams, suggesting its perceived efficacy in alleviating test anxiety. This research suggests promising implications for integrating mindfulness interventions into educational settings to support students in managing test anxiety. Further study is warranted to evaluate the intervention’s efficacy in a fully powered trial. In addition, we will explore heart rate variability to provide insight into the role of parasympathetic activity in stress management.

Colorectal Cancer (CRC) incidence is rapidly rising in patients younger than fifty with no prior family history. Recent research has revealed that very low frequency somatic mutations accumulate in normal tissue with aging. Many of these mutations occur in common cancer genes and are positively selected, producing clonal expansions, that might be linked to cancer risk. I hypothesize that the normal colon of patients with early onset CRC might carry clonal expansions driven by mutations in CRC genes, which could be detected with ultra deep duplex sequencing. Duplex sequencing is an error correction method that improves sequencing accuracy through double stranded molecular tagging. I used duplex sequencing to perform ultra deep sequencing (~3000x) of the main driver genes of colorectal cancer (CRC) which includes: BRAF, APC, FBXW7, KRAS, PIK3CA, SMAD4, and TP53. First, I performed adjustments of gene probes by increasing or decreasing the proportion of each probe in each gene pool to achieve comparable depth of sequencing across genes. Then, I sequenced normal colon tissue of two patients with and without CRC. Preliminary data showed deleterious mutations in tumor suppressor genes: APC, FBXW7, and TP53, which occurred at higher frequency in the patients with cancer. The next steps of this research involve analyzing the normal colon from a larger cohort of patients with and without CRC. Our results have demonstrated that clonal expansions are visible in early onset CRC with these sequencing methods, and I anticipate that I will identify more clonal expansions in patients with early onset CRC than in those without CRC. In conclusion, duplex sequencing allows for the detection of cancer driver mutations in normal tissue, allowing the discovery of early somatic events in CRC and offering potential for early CRC detection, prevention, and prediction.

Continuous gravitational waves (CW) from sources such as a non-axisymmetric spinning neutron star have not yet been detected. If CW from neutron stars exist, the weak signals could be hidden within noise. The search for CW signals is impeded by the presence of narrow spectral artifacts (lines) caused by instrumentation or the environment at the Laser Interferometer Gravitational Wave Observatory (LIGO). Better identification of line noise would make a detection more likely, and a detection has the potential to expand our current knowledge of neutron stars. A non-machine learning (non-ML) line finding algorithm is currently used on a daily basis at the LIGO Hanford and Livingston Observatories. A different approach to line finding utilizes a machine learning (ML) algorithm. My research uses both the ML and non-ML line finding methods, comparing the accuracy and efficiency of these two methods applied to various data sets. Expected results are that the non-ML method is currently more accurate and efficient from a computing resources perspective, but that the ML approach has the potential for high accuracy and adaptability, and will eventually be more efficient in human hours. The impact of my research is to implement more accurate and efficient line finding algorithms. This work is important because automated line finders at LIGO Hanford and LIGO Livingston save researchers hundreds of hours of work and could be set up to alert researchers on changes in noise behaviors.

Light-front quantization was first introduced by Dirac in 1949 as a new form of relativistic quantum mechanics. It has become a useful tool in analyzing a variety of high energy experiments. This project is an application of light front dynamics to the deuteron, or two nucleons in a bound state, with an exponential form of the separable Yamaguchi potential.
The objective of this project is to develop a model for the deuteron wave function, incorporating Einstein's special relativity principles. The same model will be then used to predict scattering states, more specifically, calculating phase shifts as a function of energy. The Schrödinger equation is first formulated in the light front coordinates. Analytical derivation of the deuteron wave function then yields an expression containing transcendental functions. Numerical methods are implemented in Python to solve the transcendental functions and produce a model of the deuteron, which is then be used to calculate the T-matrix of the scattering states.This work will help contribute to the understanding of nucleon-nucleon interactions in the deuteron and produce a better treatment of the deuteron wave function that is consistent with Einstein’s special relativity.

The US education system is a tool used to push the dominant Anglo-American cultures among immigrants and other minoritized cultures within the United States. With their proximity to the US border and historical events, Mexicans living in the United States have faced subjugation and discrimination from the Texas Revolution to the anti-immigrant policies and rhetoric; these educational practices have targeted Mexicans for almost two centuries. This causes a conflict of cultural identity for Mexican youths living in the United States, as they grow up within multiple social spheres that consider them too American to be Mexican yet too Mexican to be American. This conflict of identity has caused multigenerational trauma and is only made worse by the discrimination from the media and the bias of the schools, as these students are forced to look elsewhere to discover their history, such as family or the community. My research study examines the value of ethnic studies and bilingual education practices and how they not only empower these students but gives them the motivation to succeed within an academic setting. This research is based on interviews that I have conducted with Mexican Americans who have experienced the education system during different years, ranging from the 60’s to the early 2000’s. Furthermore, I examine the autobiography of a prominent Mexican American scholar as I draw scholarship on ethnic studies and bilingual education, criticizing the current education system while offering solutions to address those critiques. Through the interviews I conducted, I found that the people who were most connected to their heritage and the Spanish language, not only experienced more success academically, but were also happier, indicating the need for ethnic and bilingual studies within the K-12 curricula.

Understanding human mobility based on location data from smartphones and other digital accessories has become a fundamental part of urban and environmental planning in cities. Through collection of these geo-traces, it has become possible for the scientific community and policy-makers to model citizens’ daily mobility patterns. Because of privacy issues, collection of location based data is rare, sparse and infrequent. To this end generative mobility models are used to create synthetic data. Because this data can be used in big decisions such as resource allocation and planning, if the data is skewed towards privileged groups, existing social and structural inequities can be exacerbated. Therefore, a critical concern that arises is the extent to which such synthetic data is fair and inclusive. Our goal is to create tools that measure whether socioeconomic conditions and geographical location affect individual and group trajectory predictions made by generative models. Using Gravity, Deep Gravity, and other models to generate mobility flow dataframes, we layered census data onto the flow dataframes by census tract to analyze the high income areas against lower income areas. We used this analysis to establish and implement a set of fairness metrics for mobility datasets and models in a python package that’s distribution could identify skewed modality data and hopefully influence other researchers to measure fairness in their generative models.

Though Seattle’s reputation is for economic and technological prosperity, some Seattle high schools do not nurture students with the academic and professional rigor needed to partake in their local economy. This project examines the relationship between poverty and income in the City of Seattle and secondary education outcomes in Seattle Public Schools (SPS). The project puts forward the concept and practice of community mapping and GIS (Geographic Information Systems) for representation and visualization. Conducting a mixed-methods research using both qualitative and secondary research analyses, we uncover education narratives and outcomes in SPS while considering socioeconomic and political influences. SPS and state government online databases provided relevant information such as school graduation rate, college-readiness index, census tract poverty percentage, average annual income, and income inequality. We also conducted an online survey on SPS counselors, inquiring about their perception and input behind local socioeconomic contributions to the SPS curriculum and school life. In visualizing our research data, we utilized ArcGIS Pro, sectioning Seattle by SPS school zones and census tract, organizing our data by poverty percentage and income level through bivariate analysis, and juxtaposing them with education outcomes in SPS. Altogether, employing a social community investigation in conjunction with our mapping process allows for a deeper geospatial analysis that can be used as a tool to highlight socio-economic issues. In light of the socio-economic problems in Seattle, SPS counselors infer that other variables such as rich community sentiment, domestic and scholastic support, extracurricular involvement, and an overall meaningful life balance can contribute to successful education. Though our work establishes the relationship between high poverty percentage, low income, and poor education outcomes, we found that specific education metrics such as graduation rates do not necessarily entail success beyond secondary education. We proposed to use other metrics, such as the college readiness index, to infer education outcomes more accurately.

Invasive species have spread around the world, purposefully or inadvertently. Many species do not survive in new environments, but of the ones that do they can thrive. These thriving invasives may crowd out native plants, creating ecosystems of a single plant type, which causes the system to lose ecological functions. Despite the issues caused by invasives, not much is known about the causes of their spread. This project aims to determine which parameters influence the spread of invasive English Holly (Ilex Aquifolium), Cherry Laurel (Prunus Laurocerasus), and Portuguese Laurel (Prunus Laurocerasus). I plotted species locations in Saint Edward State Park which is comprised of a mixed decidous and coniferous forest. As I plotted their locations, I also measured the diameters and height. Using the size data, I will be able to determine the age of the plants which will provide more information into time frames that the plants took root. Additionally, I gathered light and temperature data over a year which I will use to find average lux and temperature in each month as well as each season to create predictive temperature maps of the entire study area. Furthermore, I took soil samples, near the invasive plants and in areas where the plants were absent which I will analyze to see if there is a correlation between soil nutrient loads and locations of the invader’s growth. Finally, using a drone I took pictures that will be used to determine tree species, tree heights, and hillslopes in the area. I will then create maps that use all the gathered data to predict environments that the plants will grow in. The information from this project can be used Park wide to determine areas that these invasive species grow so that park managers can find and remove invasive plants quickly and efficiently.

American crows (Corvus brachyrhynchos) make a wide variety of calls, however, the scientific understanding of these vocalizations remains subjective and anthropomorphized. Here, we classify crow calls based on behavioral context using less-biased means. In this study, we recorded crows in pre-roost aggregations and during mobbing events throughout Western Washington. Pre-roost aggregations are daily occurrences where crows gather together for protection starting in the late afternoon. We simulated mobbing events by placing an owl model near a group of at least five crows and playing a recording of a crow in distress. We used a bioacoustics analysis program, Raven Pro, to pull out acoustic variables from our recordings for further analysis (e.g. frequency variables, call duration variables, etc.). We are now using these variables to compare the classification efficacy of two machine learning algorithms, random forest and artificial neural network. Based on preliminary results, our first random forest showed over 90% classification accuracy. We are currently exploring the mean decrease in accuracy per variable in our random forest analysis to identify the most important acoustic attributes when classifying calls with this model. We are in the early stages of using an artificial neural network to predict the contexts of calls based on acoustic variables. Machine learning is becoming increasingly common in biological research, but the techniques used by researchers still vary greatly. Comparing machine learning algorithms and establishing consistent techniques for doing so will improve reproducibility across studies and help us resolve outstanding scientific questions like "what do all those crow calls mean?".

The intersection of data visualization and qualitative research has seen a lack of importance within the scientific field. Its potential to advance the field is diminished due to the challenges researchers face when working with qualitative data and the lack of appropriate visualization methods. WordStream is a qualitative data visualization type that aids researchers in visualizing nuanced perspectives of qualitative data. Within the user study, we purposefully recruited participants who have a background in STEM education research along with qualitative methods. We present a user study of two WordStream-based platforms, Journal Data Dashboard and WordStream Maker, that surveyed participants in STEM education research who use qualitative methods to garner insights. A mixed-method study allows us to examine visualizations and a questionnaire to explore our topic. Our initial study consists of 4 respondents, with a plan to increase to 10 respondents. We apply a Likert scale to our questionnaire to investigate and measure the ease of use and usefulness. Additionally, our method analyzes open-ended questions regarding trust, envision use, and desired features. Through quantitative and qualitative analysis, we report user preferences between the two qualitative visual analytic platforms. Two themes emerged: (1) Users prioritize functions that increased their visual control to explore the data in context; (2) Users’ perspectives reflect a strong context of use in evaluating the WordStream functions related to their areas of expertise. Preliminary findings from four participants show that users prefer the Journal Data Dashboard to the WordStream Maker for ease of use and usefulness. The study is ongoing, and we will present additional data and the implications of the findings for improving Wordstream and designing other qualitative visual analytics.

Lake Washington is home to a complicated metapopulation of Oncorhynchus nerka. There is a potentially native population of sockeye spawning in the Sammamish River and tributaries with at least two history variations - typical sockeye that migrate to the ocean and return as adults, and residual sockeye that remain in freshwater and return at a smaller size. Historically, there was also a native, genetically distinct, freshwater-only population of O. nerka called kokanee. Thought to have been eliminated from Lake Washington in the 20th-Century, recent genetic evidence indicates that a small population of native kokanee remains with a single cohort spawning every third year. We are comparing spawning ages for sockeye, residual sockeye, and native kokanee by extracting otoliths (mineralized structures in the ear containing annual rings similar to tree trunks). Otoliths are mounted on slides, viewed under a light microscope, and aged by counting the number of rings present. We expect ocean going sockeye to be age 4 (typical for sockeye); kokanee to be age 3, reflecting the 3-year cycle in kokanee abundance; and residual sockeye to be younger than typical sockeye. Lack of information prevents a clear spawn-age expectation for residuals. A large run of kokanee returned in 2020 and another was expected in the fall of 2023. Surprisingly, a sizable run was present in 2022. We hypothesize that the 2022 run was composed primarily of early-returning 2-year-olds while the 2023 run were typical 3-year-olds. We are comparing the age distributions of kokanee from runs in 2022 and 2023 to test this. Kokanee are remnants of a once abundant population. It is important to have a better understanding of kokanee relative to sockeye and residuals as a basis of conservation; much remains unknown. This study will also help interpret potential differences in spawning ages between similar life history variants.

The Strand Lab Benzene Team quanitified the removal of benzene by a genetically modified plant (2E1 pothos ivy) under concentrations similar to home environments. The objective is to make this houseplant available to the public and develop a practical home biofilter that uses a genetically modified (GM) houseplant. The development of a genetically modified pothos ivy with the 2E1 gene provides means to degrade volatile organic compounds (VOC), for example, benzene. My partner developed extraction procedures while I developed the analysis procedures for influent and effluent samples concentrating benzene so that analysis could be done by injection of the concentrated extracts on gas chromatography with flame ionization detection (GC-FID). I created calibration curves with external standards to help quantify the concentration of benzene within a sample. GC-FID was used to measure benzene area peaks for both the influent and effluent samples from which benzene concentrations in conjunction with the standard curves could be calculated. Our findings reveal a 65% removal of benzene by the A9 transformant of pothos ivy containing the cytochrome P450 2E1. The wild-type plant showed no significant benzene removal. The development of this GM houseplant offers a promising solution for indoor air purification, potentially mitigating health risks associated with the exposure of benzene and other VOCs. Furthermore, the potential commercialization of GM houseplants could influence the biotech industry to expand the application of biofilters beyond the home environment such as office spaces, schools, and hospitals. 

North American Beavers (Castor canadensis) are a semi-aquatic species in the family Rodentia and agents of biogeomorphic change in watersheds. Research in the last 20 years has demonstrated that beaver activity can reduce downstream flooding and significantly improve water quality. Beavers have inhabited the North Creek Wetlands (NCW) at the University of Washington Bothell (UWB) since 2008. In Autumn 2023, they constructed several dams that inundated the northern third of the wetlands, creating a new opportunity for studying the impacts of the dams on surface water dynamics in the NCW. I want to estimate how much water the beaver dams are holding back in the NCW and how that impoundment affects sediment movement and water quality above and below the dams. To address these questions, I have estimated water storage in the northern third of the NCW using both aerial drone photography and previous site documentation. Sedimentation is being investigated with sediment traps placed throughout the affected area. Water temperature, turbidity, and dissolved oxygen are being measured at several sampling sites that have been used for many years for water quality testing, providing significant baseline data against which to compare final results. I anticipate that results will indicate the beaver dams have increased NCW water storage, increased sedimentation in the northern third of the NCW, and are improving water quality in the North Creek before it discharges into the Sammamish River. Understanding the impacts of beaver activity on the NCW will help the UWB and local stakeholders to better balance the risk of flooding near campus with the beaver’s benefits to water quality and reducing downstream flooding and may inform future actions by the UWB Facilities Services and Campus Operations Department.

Almost every form of cardiac disease is characterized by fibrosis, or the accumulation of collagen, an extracellular matrix (ECM) protein, secreted by the cardiac fibroblast. The buildup of fibrosis is a major clinical burden, as it contributes to diastolic dysfunction, or the heart’s inability to relax, and arrythmias, or an irregular heartbeat. In previous studies, the Davis lab has found that in chronic injury, the heart likely undergoes minor offenses along with periods of rest which accrue over a lifetime. Even when exposed to repeat injury stimuli, the heart is able to recover, and the cardiac fibroblasts can transcriptionally regress. Yet, what remains unclear is when the heart experiences repetitive stress, which is common with hypertension, will these once-activated cardiac fibroblasts have a more aggressive response? And if so, are the activation cues stored in the primed external environment, or are they intrinsic to the cell? To address this, we developed a fibroblast isolation and injection protocol that will ultimately allow us to isolate discrete populations of fibroblasts and study them in hearts void of injury. Our results found that fibroblasts from donor hearts that were subjected to a myocardial infarction injury were detectable at 4 and 14 days post cardiac injection but had little proliferation. However, there was an increase in host fibroblasts recruited to the graft site, many of which were proliferating, and fibrosis was found within these same regions. These results demonstrate that cardiac fibroblasts from the same strain can be isolated and adoptively transferred to other hearts, without exogenous ECM. We can apply this baseline protocol to further examine fibroblast memory in vivo in a model of intermittent hypertension.

This research investigates the impact of ethnic diversity on corporate boards in relation to the frequency of security class actions faced by firms. Security class action lawsuits hold significant concern for today's corporations and society at large due to their implications for accountability, investor protection, and the reinforcement of ethical standards. In this study, our team focuses on ethnic diversity as a vital contributor to enhanced monitoring and ethical practices by integrating varied cultural insights and ethical standards, thereby strengthening corporate governance and ethical oversight. We hypothesize that firms with greater ethnic diversity among their board members are less likely to encounter security class action lawsuits. To test this hypothesis, we sourced data on security class action lawsuits from the Stanford Securities Class Action Clearinghouse, and derived information regarding the ethnicity of board members from the Wharton Research Data Services Institutional Shareholders Services (WRDS ISS) database supplemented by various web sources. Employing a linear regression model to analyze the data, the empirical results substantiate the hypothesis, revealing that S&P1,500 firms with at least one director of minority ethnicity during the sample period of 2009 to 2015 are significantly less likely to face security class action lawsuits. This finding suggests that ethnic diversity on corporate boards is not only a marker of inclusivity but also serves as a protective measure against legal challenges. The study's implication is clear: promoting ethnic diversity within corporate boards is a strategic move for firms and shareholders, potentially reducing litigation risks and associated costs. This research contributes to the broader discourse on corporate governance, emphasizing the practical benefits of ethnic diversity in high-level decision-making bodies.

In my research, I aim to analyze the impact certain tax global policies have on economic development depending on the countries' initial industrialization level. Using the development indicators published by the World Bank, among other sources, I will assess both a countries industrialization level and the impact of the tax policies. My research methods will combine theoretical economic frameworks with global, macroeconomic, and survey data. My unique approach will consist of statistical and historical data, looking into a variety of expert fields like health and education to assess the country. Thus, this paper will take an interdisciplinary approach to assessing economic conditions and policy impacts. Participating in this research will contribute to my transformative education by analyzing real world data and deriving policy recommendations. My work aims to advance our understanding of how tax policies impact both local and global development, with the ultimate goal of contributing to the design of more effective targeted tax policy. In this research, I expect one of two findings. Global tax policies that work well in industrialized nations work similarly or equally well in developing countries. Global tax policies that work well in industrialized nations fail to achieve the same results in developing nations. This concludes that tax policy recommendations have to be adjusted according to the industrialization level of the country.
 

Quasars are the most luminous type of Active Galactic Nuclei. Both consist of a supermassive blackhole at the center with a surrounding luminous accretion disk. Quasars can expel clouds of gas outwards, which are called outflows. Historically quasar outflows have been detected by the presence of absorption lines in quasar spectra, with the most typical ion detected being CIV (Carbon ionized 3 times, or C3+). Some of these outflows reach relativistic speeds beyond 10% of the speed of light. Our team has built the largest database of these outflows, called extremely high velocity outflows. All outflows have been found to be variable between observations, but the exact cause of this variability is unknown. The most prominent theories are variability due to motion in and out of our line-of-sight, and the recombination/ionization of the outflow gas. While carrying out our study of quasar outflow variability, we found that several cases have a common minimum flux value but vary almost everywhere else within their absorption trough; we call this ‘strange’ variability. Its importance is that it can help us constrain the cause of variability since it sets restrictions on several absorption parameters: coverage fraction, how much the source of light is covered, and the optical depth. With this data, theoretical astrophysicists can further narrow down the causes of quasar outflows. We will present (1) the results of our analysis of how the observational data helps constrain these parameters in the current sample, including cases with multiple observational epochs, and (2) our search for more of cases of strange variability in a larger sample of quasar spectra in the Sloan Digital Sky Survey. We have developed automated tools in Python to aid our search, which we plan to present as well as the simulation process used to develop them.