The 2019 Novel Coronavirus (COVID-19) pandemic has necessitated the implementation of various infectious disease control measures, including the closure of non-essential businesses, social distancing, and the virtualization of schools and universities. As final year nursing students at the University of Washington (UW) adjust to virtual learning and social distancing, certain students working in healthcare may also face the threat of contracting the virus. Little is known about the psychological implications of the COVID-19 pandemic on this population, and there is a need to fill this knowledge gap. This study first aims to capture the perceived stress levels of final year nursing students at the UW amid the COVID-19 pandemic. It secondly aims to explore associations between perceived stress and factors such as COVID-19 testing history, living situation, and healthcare work history. We administered an online survey to final year UW nursing students that inquires about their COVID-19 testing history, living situation, and healthcare work history since March 1, 2020. The survey also includes the 10-item Perceived Stress Scale, which questions students’ feelings and thoughts over the past month. We will analyze the data for associations between scores on the Perceived Stress Scale and students’ testing history, living situation, and work history. Overall, we expect to find moderate to high perceived stress levels among nursing students. We also anticipate that several factors may be associated with higher stress levels among nursing students, including having a history of COVID-19 testing, living with more individuals, and working more hours in healthcare positions. The results of this study may indicate a need for increased psychosocial support in final year nursing students at the UW, as they complete the nursing program and join the nursing workforce to help combat the COVID-19 pandemic.

Rhabdomyosarcoma (RMS) is a devastating pediatric soft tissue cancer. Currently, the standard treatment regimen for RMS patients remains relatively unchanged. Conventional treatment of RMS includes a combination of chemotherapy, radiation, and surgical tumor resection. Unfortunately, with the heterogeneity of cancer between patients, these therapies are not always effective and can cause undesired health issues for the patient due to their non-specific effects. Targeted drug therapy can help patients live more normal lives. The angiotensin II receptor type 1 (AGTR1) and 2 (AGTR2) are potential targeted therapy targets that can inhibit RMS cancer growth and cause less side effects compared to conventional therapies. AGTR1/2 are the main effectors in the renin angiotensin system regulating cardiovascular health. While there are Federal Drug Administration (FDA) drugs blocking these receptors to treat high blood pressure (Irbesartan), AGTR1 and AGTR2 have yet to be investigated for their role in RMS. Tumor propagating cells (TPC), which function as tumor stem cells in RMS, are proposed to drive tumor metastasis and relapse through a process called self-renewal. Preliminary disruption of AGTR1 and AGTR2 in the two major subtypes of RMS (embryonal and alveolar) with the CRISPR/Cas9 gene editing system resulted in decreased tumor cell growth and self-renewal capabilities. RMS cell lines treated with Irbesartan also decreased in viability compared to untreated cells. Based on our preliminary results, I propose that AGTR1/2 plays a role in regulating RMS cell growth and self-renewal. Further functional characterization of AGTR1/2 and investigation of the cellular mechanism by which AGTR1/2 regulates RMS tumor cell growth and self-renewal can provide a strong rationale for prioritizing AGTR1/2 as targets for drug therapies to slow the progression of RMS without greatly compromising more of the patient's health.

Embryonal rhabdomyosarcoma (ERMS) is a common pediatric cancer that has poor prognosis for patients with relapsed disease. ERMS typically harbors mutations in one of 3 RAS proteins. Mutations in NRAS, a member of the RAS family, have been shown to be a driver for many different cancers, including ERMS. The Chen lab previously demonstrated that genetic disruption of the NRAS gene by the CRISPR/Cas9 technique successfully reduced ERMS tumor growth in a human xenograft mouse model. However, these mice also experienced disease relapse. I was able to confirm successful targeting of at least one copy of NRAS in ERMS cells. I have subsequently isolated clones of ERMS cells that continued to grow despite the presence of NRAS gene disruption. In my investigation of candidate genes and pathways that might be responsible for driving continued ERMS tumor cell growth, I saw an increase in the level of YAP1 being produced in NRAS-targeted ERMS cells when compared to the control cells. Based on my preliminary findings, once NRAS is successfully disrupted in ERMS cells, tumor relapse is then driven instead by YAP1. This study could provide novel insight into the mechanisms underlying cancer relapse in response to NRAS targeting and promise alternative treatment plans for ERMS patients.

As information processing machines approach the nanoscale level, DNA has emerged as a powerful tool in molecular engineering systems. The specificity and programmability of its hybridization interactions offer flexible and fine-tuned control over reacting species. Among the DNA computing techniques used today, strand displacement circuits are highly popular, with potential applications ranging from disease diagnostics to DNA-based artificial neural networks. The fundamental mechanism of these circuits is the hybridization of a single-stranded DNA input strand to a double-stranded complex which triggers the release of a prehybridized output strand. When released, this output can be detected and used to characterize circuit behavior. The output strands of strand displacement circuits are typically read out using fluorescence spectroscopy. However, due to spectral overlap of traditional reporters (e.g. FAM, TAMRA, Cy5), the number of outputs that can be detected in parallel is severely limited. To address this, we present the use of nanopore sensing technology as an alternative readout device that enables highly scalable, real-time detection and quantification of DNA strand displacement circuits. We demonstrate dynamic sensing of an operating circuit within the flow cell of a commercially-available high-throughput nanopore sensor array (Oxford Nanopore Technologies’ MinION device) and show that strand capture frequency can be correlated to concentration, allowing for direct quantification of desired circuit elements. To investigate this reporter strategy’s multiplexing potential, we present a collection of ten orthogonal circuit output sequences (barcodes) that can be classified at the single-molecule level from raw nanopore signal data using machine learning, with the potential to scale to larger barcode sets. We conclude that nanopore-based detection of strand displacement circuits holds key advantages over fluorescence-based methods for real-time, multiplexed circuit readout on an inexpensive, portable sensor device.

Planning for water resources management (WRM) requires the best available predictions of streamflow. We want to provide actionable predictions that improve WRM as climate change alters streamflows. However, the computer models of these systems result in imperfect predictions despite our best efforts to match observations. These systematic errors reduce the usefulness of model outputs. To improve our ability to plan for WRM, we apply statistical correction techniques to model outputs so that they agree better with observations. The Columbia River Basin, a major river basin in the Northwestern United States, is heavily regulated for a large number of competing uses. In this project, we focus on the Yakima river basin in central Washington, a subbasin of the Columbia River, and use it as a case study for evaluating multiple statistical correction techniques. By comparing streamflow observations to simulations for the same periods we can develop statistical corrections. The application of these statistical corrections is often referred to as “post-processing”. Post-processing adjusts predictions based on previous knowledge of the region as well as the historical observations.These post-processing techniques can then be applied at locations and times without observations. As part of this project, we developed a toolkit for the evaluation of different post-processing techniques. We explore which measures and visualization techniques are adequate at describing key aspects of the streamflow simulations. Our toolkit builds on open source technologies that will allow researchers to reliably measure the statistical performance of these post-processing techniques. Evaluation is performed through exploring different statistical metrics and building a suite of summarizing plotting capabilities in a standalone, open source Python package.

Secondary scholarship on life in Iraq during the period of direct rule by the Ottoman Empire from the mid-19th century to World War I is minimal. A few primary historical texts have survived - these “forgotten texts” are largely individual accounts of daily life and business, which illuminate the events of a period of which little has been written. A study of such texts can prove valuable, allowing us to get to know individuals dwelling in Iraq and their lives. In this project, I explore the social networks of Joseph Mathia Svoboda, a British steamship purser living in Baghdad, through a collection of his diaries written between 1865-1908. Due to his family ties, profession, and vibrant social life, Joseph interacts with a wide variety of groups, from family, friends, religious and political leaders, to individuals of diverse backgrounds who he encounters throughout his travels; thus, his writings provide a fascinating viewpoint from which to study the Ottoman Empire. I conduct text and social network analyses of Joseph’s diaries, which involve visually mapping ties between people and analyzing the dynamics of the resulting structures. In my presentation, I will review the use of network analysis and entity detection methods in various contexts, such as literature, history, and the social sciences, and explore how these techniques can be applied to automate the extraction of persons mentioned from the diaries, and then subsequently visualize this information. In particular, I focus on Diary 47 of Joseph Svoboda’s diaries as a case study. In the future, the insights gained from this could be applied to the rest of the collection. As the diaries were written from Joseph’s young adulthood to old age, his narratives provide a unique opportunity to study societal relations in Ottoman Iraq.